"
]
},
"metadata": {},
@@ -1048,7 +1185,8 @@
}
],
"source": [
- "sb.pairplot(iris_data_clean, hue='class')"
+ "sb.pairplot(iris_data_clean, hue='class')\n",
+ ";"
]
},
{
@@ -1061,7 +1199,7 @@
"\n",
"* Make sure your data is encoded properly\n",
"\n",
- "* Make sure your data falls within the expected range, and use domain knowledge whenever possible\n",
+ "* Make sure your data falls within the expected range, and use domain knowledge whenever possible to define that expected range\n",
"\n",
"* Deal with missing data in one way or another: replace it if you can or drop it\n",
"\n",
@@ -1076,49 +1214,27 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "##Bonus: Test our data\n",
+ "## Bonus: Testing our data\n",
+ "\n",
+ "[[ go back to the top ]](#Table-of-contents)\n",
"\n",
"At SciPy 2015, I was exposed to a great idea: We should test our data. Just how we use unit tests to verify our expectations from code, we can similarly set up unit tests to verify our expectations about a data set.\n",
"\n",
- "We can quickly test our data using `assert` statements: We assert that something must be true, and if it is, then nothing happens and the notebook continues running. However, if our assertion is wrong, then the notebook stops running and brings it to our attention. For example:"
+ "We can quickly test our data using `assert` statements: We assert that something must be true, and if it is, then nothing happens and the notebook continues running. However, if our assertion is wrong, then the notebook stops running and brings it to our attention. For example,\n",
+ "\n",
+ "```Python\n",
+ "assert 1 == 2\n",
+ "```\n",
+ "\n",
+ "will raise an `AssertionError` and stop execution of the notebook because the assertion failed.\n",
+ "\n",
+ "Let's test a few things that we know about our data set now."
]
},
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "ename": "AssertionError",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;31mAssertionError\u001b[0m: "
- ]
- }
- ],
- "source": [
- "assert 1 == 2"
- ]
- },
- {
- "cell_type": "markdown",
"metadata": {},
- "source": [
- "Let's test a few things that we know about our data set now."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "collapsed": false
- },
"outputs": [],
"source": [
"# We know that we should only have three classes\n",
@@ -1127,10 +1243,8 @@
},
{
"cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 17,
+ "metadata": {},
"outputs": [],
"source": [
"# We know that sepal lengths for 'Iris-versicolor' should never be below 2.5 cm\n",
@@ -1139,10 +1253,8 @@
},
{
"cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 18,
+ "metadata": {},
"outputs": [],
"source": [
"# We know that our data set should have no missing measurements\n",
@@ -1163,7 +1275,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "##Step 4: Exploratory analysis\n",
+ "## Step 4: Exploratory analysis\n",
+ "\n",
+ "[[ go back to the top ]](#Table-of-contents)\n",
"\n",
"Now after spending entirely too much time tidying our data, we can start analyzing it!\n",
"\n",
@@ -1182,26 +1296,24 @@
},
{
"cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 19,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ "''"
]
},
- "execution_count": 21,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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KmUpWD3Svm9PpxHPbjwqD6YOVDaLluiOhnasePK9atQr19fXIzMyEzu1k5uCZ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\nigZspxQN2E4pnEI+eN65cydOnDiBNWvWoKGhAe3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Tp/ofzKGkpARarRZvvvkmDh48iGef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\nAAAASUVORK5CYII=\n",
+ "image/png": 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\n",
"text/plain": [
- ""
+ "
"
]
},
"metadata": {},
@@ -1209,7 +1321,8 @@
}
],
"source": [
- "sb.pairplot(iris_data_clean)"
+ "sb.pairplot(iris_data_clean)\n",
+ ";"
]
},
{
@@ -1218,31 +1331,29 @@
"source": [
"Our data is normally distributed for the most part, which is great news if we plan on using any modeling methods that assume the data is normally distributed.\n",
"\n",
- "There's something strange going with the petal measurements. Maybe it's something to do with the different `Iris` types. Let's color code the data by the class again to see if that clears things up."
+ "There's something strange going on with the petal measurements. Maybe it's something to do with the different `Iris` types. Let's color code the data by the class again to see if that clears things up."
]
},
{
"cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 20,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ "''"
]
},
- "execution_count": 22,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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K/eW93zELRiqSU1htV6RUoq1Oh8YN7/FvV6lgTR3H+o6o3fud2/Cx3tivkp7z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L59B/2qrPOd60/U4kY/gtPcxbK0txmw/+nfiWXtP3AQWSue8zl4iYYAOa7dTkSh4k/SPsBzjKLz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\n",
+ "image/png": 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dUsqwY08CoLW1Ve7evdvJh9SsGj/yPob9C7vOmv749IL233f9voL2J1Oi0gdQsngth8EB7USnt9gEtJNcTovNcCKM9i3t2PXursy2mWfOxANzH0BSTSLkCyGSiEAIoH1LR95+a69Yy5WRhzBma43VOFrwJFR/PaKpWF7bWFWqiCQiCPqC6H2vF4/sewTPvfEcljYtxbV/cm1mTHVsrfh4Yry6neU8vgGAGEol84VKvu5LOBHGulfWYe7kuZg6fip6T/Ri88HNWPTBRYaYtTqnOBDbFY9XKp6jNS/pi5SvWv2/EGKtlLLdyeckohKymRdtlcfs9/gz75Q1+BugSpU5+zT2WI2jugatxbGi/TGW/UeZIhSEfCG0rGtBUiYz2x966SEsvmhx5v85nmhElvN4PaC31i7Tei9BbxAPvfQQ7t97f2abV3ix+KLFefsxtsmK0wX7I/lImZ+PiKCldenv6ha0KJjeYjP7HTs9LzrrZBdNRrFkxpK8d9MGU4NQ1SSCvnpEE2FACDSf0Wx4N635jGYMpgaRUlOZd5OD3iA8isfJl4CotFRV69SU+y62qgLxdK3L0deAbXcDLz+ttaodHNDGUTwC1RfM+wRGrw/Y9e4uzDtnHm6cfiOmjp+KSCKSGStLZiwx/CGo1w/wk0zKsJrHEzGtlnGkT15MYlsVGNU5xepckRuzw+0HwPC8+v5c9HLs4G+XqMbZ7atvyqzFpkledMBTl5effPW0qxFPxtG+dZn2vFuXIZlK5eXy33vZvQjHw+znT9XLsjYsZVrrgs+uA1qu07YPs95LwBNA95xuLG1aio7mDmw+uBnvhN8xjJW2aW1Y2rQ0r2aGKMMXBNoeNc7jn1un1b2MtM6LSWyrgydGfU4JeCxqWXJrXiz2k1Ianrc/3s+1j8YgR2teRnwyIV6UUl5c7OOw5oUKUPH81krnYxedO2z1jnL2c8QH0G6Se79q1ip86oefMmy7/4rvQIXMvEsmpTTN219z+Ro0+MuTyuAyYz5mq84wNS3YeM2I28NLf432F7tNx2jQG8zUtnztkq9h9c7Vefvdd/l9CPlCiCaiCPlC5X7XmfHqdoMDwI4HgA9+Gjj9Au0TwLrxwA+XjFjPaBbb4WV70b7zjlGdU+yej6z2yz2n/PivfoxVO1YVciwVj1cqXrnTxhg0RGVWdO6w3uoVsMyLDlrk3p/dcHbetjpvIPPHVb2v3rIOhv38qWoMU9NiZ7vVei96+ote2zJ1/FTT/ep99WhZ14I9i/YwXYby+UPAti7ghbuGtq3ss7fOi0lsB8dPHvU5xe75yGq/3HPK2Q1nszZmDCr3LHdfmZ+PaMyz21e/IDlrv0QTYdPneHvg7fznTRibDw63pgVRVYhHtBqWm3ZofxTetGOopsVsLY2c7VbrvehjVB/DvSd6Tfc7GT9Z/Jim2pG7Nlc8nB+f/e/aW+dFr5fJEj1xcNTnFLvnI6v9cs8pbw+87fz5jVzP0YsXIcT5QohHhBA/FUJs0b/0/5dSft/J53PalNiTBX0RVQOzNSOKyos3yYEOwpO/XsXsboz3j8/Z1mX6DptZ33++c0ZVwxfUalh+0jlU29JyHXDkNeAz38mvGfPXG2rJgq88Y7neCzA0hjcf3IzbL709b6zsemcXa11IY1Z/BeTHp816RrP9goFTR31OsXs+stpvfJ3xnDK+bryz5zeqCk6v8/ISgO9CW5gyU20rpbS1UKVdpcpvnfLVHxe0/4FvfWrknbKw5qUiKp6q6IZ87FF3GzNjlt/f3gP13ZcRPXcOgnWnIDp4EsHfbQPO/wSiMpHpNhb0BqEo+dmqKTWFaDLKbmMaxmy1sap5+fNurbvYnFu1WoN4GPA3DHUhy6olM+s2lj1G9TEc8AQMYyXgDWAwNVjJDkuMVzexmJ/xo478+LzmB2XvNgbYPx+Z7QfkP6/ZtmGOpeLxSsVzuuYlKaV80OHHJKIiKULJFC8W3EI198Rllt9/2geg9vVCpi84pOKB6g/B6/WjXmhdZOr947SHMzkheRRPpjh/jBbpUzWzqnk5/QKtLfLLT2vvWn/9yNC6GulaMlWqiCoCAQBmbyZmjxcAEEJk1kwSQrDGhYws5mfT+PQF8tPEzJjVPdrs5mX1xlTu+cjqgsbsvGV3G9UuRy5ehBCN6X/+SAhxE4AfAhjU/19K2efE81S7fW8crPQhEBVGT0F46gbtZDd5FrDgX/PWDEjGIzh+9gws37oMPYd60HxGM7pmd+G0VAJeb93Qw6XbNndu68zs1z2nG42BRv4RRtXLah2No68Zv4+HgbpxmU36eHjq9afw6amfxspfrjSMi9PqTsPxweN548VqO8cRmcbi8TfN43OwH9j410Nz+9WPAaGJ5p++ZLE7j6fUFPpifVi+fbnhvNAYaDR8ss7zAhXKqajYA2A3gOsB/D2AX6a36duJqBolItqFy4HtgJrUbn/1ENBmzIGOKcDy7cux691dSMokdr27C8u3L0dMTRgeLpqMonNbp2G/zm2dLK6k6mZWP9D2KPDKs0Pf/+UDgDCmQurjYe7kuVj5y5Wm48JqvHAckSmzWAydZhKfj2lzefbc/tQN2pw/ArvxF01GTc8LZvsxnqkQjnzyIqU8BwCEEAEpZSz7/4QQAfN7EZHrmaUgbOsC5nxFWw8gnUoWsmiVnNvuuOi2zURupCjaO9aZMREG9j6prasx5yvaJzCbbweuethwN308WLVAHm5ccRyRqbxYjAwV4Ru2BbW5PJtZq2QTdudxnheoVJyuefklgNxFKM22EZEL5eUdSwF59fcQnToHobpTEBk8iWDvNniSg4b76e2OsxcK09sdCyEyj6cIBUtmLMHcyXMxdfxU9J7oxeaDmxFNRpmrTNUpuyYMAKTUvl79EfBc59B+U2ZrfzRmrZWU2wLZbPzo4+XcU89FOBFGyKvVDnTP6cYtP7vFsD/HEVnKrVvR23XnppLlxKiZaDJqOY8DMCxAXMx5YTA1CFWqzjSaoZriSBQIIc4UQrQACAohmoUQF6e/LgPAleaIqoCed9y+pR0t61rQvqUdEajomzILHT+7BS3rWtDxs1vQN2UWUjJlaMUZgJLX7rh7djdSMmV4vHAijIUXLMTqnavRur4Vq3euRtu0NgQ8dSMfIJHbmLWlDR8BfveCeYtkk3ecrVog337p7fjde79D27Q2rN65Gi3rWrBs6zK8E34H619dj6ZJTbjnY/ewPSwZmcVk5Ii2PZsvqKU25qY6+kaOoYAnkInL7HlcSmmY71Myhe7Z3SOeF+KpOK6edrXh8a6edjXiqbhhv75YH1SbjQKotjnSKlkIcT2AvwHQCmONSz+A70spNxX9JFmqtVUyVo0vcP8The1PZireFrFa2niGE2G0b2k3vEv28wU/x9+98HeGbTPPnIk1l92LhtWTh+48ZTaS1z6NmJrIdJXxKB4s3bw0776rZq3Cp374KcO2tZevQT27jOkYs9ViNC2Sc+S2QA76guh9rxeP7HsEN06/Eat3rs4bQ1+75GtYvXM11ly+BiFfqNLvSjNe3cQqJhduNH6iMjgA7HhAS208/QIttfGVZ4FZN434yYvZucJqbr9/7v1Iqalhzws//qsfY9WOVfbOFVesLfbTxYrHKxXPqZqXxwE8LoRok1I+7cRjElF5meUdj/OPM89Z9o8zbMPBHfB6fGhIdxbTW7ma3ffshrPztgVzF0YjqgajaZGcI7sdbMgXQsu6FiRlEgCw+qOrTceQXiMT8oUM9yeyjMncWhZ/SKt5eeGuoW2KF/jYrSM+hVWNitncXuepg+LVYt/qvHB2w9n2zxX8dJHgfM3LB4QQt+RsOwFgj5Ryr8PPRUQmi4eZvrub7rUf9IUQTffaV5T8zke5eccD8QHTXORIfABi6a8RnDAN0WO/RfCVZ6BY5PPn5ju/PfC24Xmbz2hGNN3hJvv4IIRzC2sSlUJ2W9oL24Y+aYmd0L5/+WlgznLtXe66hvRilAFEkzEE0nGdvf6FPgbv33s/AFjWwvQn+rFkxhJEEpHMukiOLkRL1Sse0WIu9xOV3FqWeARq2/fyFhZWErERF64saG7PqcMyu+/bA2+bPl5frA+b5m8asQ5Gf1zG/tjh9G+3FcCXAJyd/loM4DIAjwghOoe5HxEVymZus5rutd++tUPLHd7aoeUOqynDfmZ5zF7Fa5rbrChetL/YjZb1rWh/sRt9M78ANSdXWs/nN+Q7z+nGeP/4vBzopJoyHF9/fCCv/ob5zuQ6elvay24D5q4EftIJ3DkJ+MF12vefXQe0XAdsvAa4YyLU/VvQFzuOda+uxzvhd9CRjvmOrR2ZWpa2aW1Y2rQUXuHF5oOb82rJbr/0dmz8v41arZhXa+ZpVq/G8TJG+YJazOmx+JNO7fuc+Vn1BdB3ziy0p+sZ2392C/rOmQU1p57R7JxS56nLi0t9/Zbc+T73kxKz88L4uvF52+697F4ICFt1MP3xfsb+GONIzUvmwYR4HkCblHIg/X0DgKcA/BW0T18+6MTzsOaFClDx/NaS5WPbzG0OxwfQvrUjL584t87ELI+52Fxks3eDIaXhUyBFKFi65WZbz+tAvnM1qN2YrUWqCsQHtAuU3LG44EnD9vBX30T7z27J1KyMVMsSSUQQ8AYQS8YMtTDPvfGcVnt2+Ro0+BssaxDKNF4Yr25S7HnhY/ei/luTh73vQHwA619dn/eJ/LV/cq2hi5jVJyCm5wUYPz0BYLuupsDamIrHKxXP6bSxyQDiWd8nAHxAShkVQgxa3IeIRsNmbnPQak0IG732i81Fzs7Hz5xIBDIXTfUF5kAz35lcR29BazYWc7YH604Zdl2X3FoWPSUstxZG319fL4PrZFBGseeFuvx6xtz7hnwhPPTSQ5n0RgDwCi8WX7Q4c7Ey3EWz6Xkh69/1vvqCaiZZGzP2OJ029iSAXwkhviGE+AaAXwDYIISoB/CKw89FNLaoqvaumkzfxsNavn02vU9/lmh6DZZs2XUmmf3SucjZ9Fzk3Pta5TaPhtnxvT3wNpbMWIJN8zdh76K92DR/E5bMWMIVl8l99HG54jBw0w6t1gXQxuLggFZ/AEC9sA2RRBi7F+1Gf6LfdFzpNS7RZBSqVBFOhKFKNbPeS+7+kfQYNhu7xYxJqmJ6zctNO4CVfdrtnOX2zwuD/cbHMzmn6PGYOz9Hcs4pxdDrv3Kfw+zc4+T5iKqDoxcvUso7oNW5vAetUP9LUsrbpZRhKeVfO/lcRGOKWX2LmtTy7e2sJZG3BkuXrVzkxkCjvdzm2V0IegKj+tHMjq8x0GhaaxMY5XMQlYQ+LjdeM1RfMHelVgPzme8Av/ou0HId1M+uQ98nVqFj6zK0rmvFxv/baFrLsvngZnTP6UbAEzDUsHRs7TDUwujjUB/DVvVlfPd5DLJZ8xL0BszPC4pvxHNKwGu+zoteg+UEq7VkzOpqxteNZ+yPMY7WvACAEMID4AxkpaRJKQ86+RyseaECVDy/1ZF4tcpjvuYHI3aGAex1GwPyc5EBYN0r6/JymxdN/QwQO2HsNvbhkdcHsJJ7fBCikjn8lVYbMTsWWI3Lzz0B/ORWrdvYlNkIX7MB7VuXGeJ5adNSXPsn1xq6jcVSsczYM4v/7FqYoDcIT9YYrmC3McarmxSwzou6f0t+t7HzLgcghj2nlKPGarjnAFBMt7GKxysVz9GaFyFEO4BvADgEIAUtSCSAi5x8HqIxxyqP2RcYWj9imAsHRfEY6kwA6z92cvOOTXObp98I5Z//WNsPABQv1NlfQTQ+MOIFkp3js8p35rtpVBFWLcmtxmUg/UbVTdqaL0EhMvE875x5uHH6jZg6fipiqRgAZGpb6hVt7FnVsOg1LkIICGH8G8yqjoBqXG5sWsSk6gsiGu9H0FePaCKMoC8I5ekvoF7V6qj0edywJpHFOSXoDWJScJKhjfFj+x5zdH4ero7LrK6GsT+2OF2wvwzABVLKYw4/Ltkw/fHpBe2/7/p9JToSclz2ehI6PRd5FJ926K1VO7d1oudQD5rPaEb3nG40BhoN71hZ9fOPnjiI7FOE+rHl6IsdR+f2rMdLp5fZvYDJZvm8OWsGEJWcnhr21A3aH4WTZ2mpNKGJ2h+NZuOC3dDyAAAgAElEQVTyxFta+th/3gwc3IFo+240n9GM04Ono6O5Ayt/uXJU4+73/b/HZ/7jM5b3ozHGLDYX/GteTFrOz23fg/Lvi4Yez+Y5ZTA1iI6LO7DiFysyj3fnR+7EYGrQsQsYngNoOE7Pem9Bq3UhIifp60mMkItsVzQZRee2Tux6dxeSMold7+5C57bOvCLHoAS6L73DmE/8kbsQrDvVcCzRD38JndtzHm/78lEXTTKHn1wjEdH+ODywXaszO7Bd+z4RMR+XbY8CnoB24ZK+T3DzHei+9A7c3HQzVv5y5cjjziT+7/zInfjO3u8Mez8aY8xi81cPaTFoZ36edvmozimqVLHiFysMj7fiFyscXVuF5wAajtOfvPQCeEEI8WMAmdbIUsp7HH4eorFFUbR3ehduHLG+xQ67rVUVXxCNP/oy1s7pHKpveX4llL/6LvDn3ZkVnIP+BlvtmO1ShILGQCPWXrGWqyZTZQ3XelaYjcsgIIThPsrLT6NReNB41UP2xp1J/N+x4w4898Zzw96Pxhiz2NzWBcy5xeb8XD+qc0o5WnPzHEDDcfri5WD6y5/+skUIcSqARwFcCK1G5v9JKXcMfy+isUWFRFQAQSB9K0f90antj+QHB6BMmIb69Dtq9VIFJkzT3vHLfrz4AJbMWJJX2B9NRAwLYRZCkUC9qjUUqVelNjOw1JLKbaSUTX2dF0C7VVNa0XTufU6flmmVnL3YpN7yOOQNQklEM39AZtewAMDh6GHDYTGFpsZZ1Vlls4rN4weBB4ba6Ee/etB8vk9EgNxziprS2vDXNWhx7K8HclJ/9TbGefO9w/HIOi6y4nSr5H+UUv4jgLv1f6e/H8l9AP5LSvnHAGYAeNXJ4yKqdqqa0lqnbu1Ay7oWtG/tQF+sD6qaGtXj2f5I3h8yabt5PZBKGrYFhNfZ1plmraEjR7TtROXkC+al4aDt0bzWswC0C5fwEa1F8me+k7mPevlt6Jv5hUyr5NU7V6OjuQNLm5bi9ktvx/pX16Nv4G2ov3rANM6ZQjPG2J3/rNKJQ6cZtgWFYtoWOammcs4px6HGTmqtv++YqN2Gj2hxncWqjTFb2VO5ONoqWQgxC8BjABqklJOFEDMALJFS3jTMfU4B8BKAqdLmwbBVsjkW7Juq+Hv1TsRrOD6A9q0d+W0jL18z6k82bLVWtWq7+RdrgLVDC5yFl+1F+847nGudabfdZ22qiZitGYMDwI4HgA9+OpOGg1eeBWaZtAbX//A7sF1brHLOrcDpFyCcjOS1Sp555kzcc9k9+Oavv4nn3nhOGy8Xd6L+x7eaxnkFWyGPhPHqtELmP7NPaIC8bSrS8ZPuNqYID5ZuuTl/zr7k66i/r8n4vAueBAKnZDaVo1VyCVU8Xql4TqeNfRvAlQCeAQAp5UtCiDkj3GcqgCMAvpe+2NkDYJmUMpy9kxBiMbQFMDF58mSHD5vIWU7Ha9AXcrSmBLD5kbxVvv9pHzAe3/jJzuZAD1dnQCXBOdaCP6TVEbxw19A2xQt87Nb8fesahuL25ae1L8WL4NePmI6Pcb5xmTqWnkM9CE6YZhnnTKExqul4LWT+y01b1OVsU6Cg3j8OAFDvH2fdjn58zmt5cEfeBVM5al6IhuP42zZSyrdyNo2U1+IFcDGAB6WUzQDCAL5q8rgPSylbpZStEydOdOZgiUrE6XiNJiJoPqPZsC2Ts1xKek51tsmzgONvGo/vxEEsmbEEm+Zvwt5Fe7Fp/iYsmbHEvBuSqmrvLMr0rVkqmNXzxkv8845hnGMtWMWiWQzrtS45+0YTYdPx23uiF/POmYdN8zdh96LdiMT7oX5sOePchpqO1zLMf5bnlBM5a4rrsZ5933TNi635nqgEnP7k5S0hxKUApBDCD6ADI9ev/B7A76WUv05//xRMLl7IeUwzqx5BbxDds7vQuX25oU9/yd/p0nOqc9e48Pi1dIL0tkDodLRNa8PyrOPrmt2FgKfO+HjDrZmRXYxq9bxFfNJENCpmsdj2qFbXsq3LGMP+eu3/nv6iYV+z8ds1uwt7D+/NX/dldhcafUHn31mk6lGG+S/oDZicU7oRhMcwt6PtUS2uswQ8dfbme6IScbrm5XRoxfcfh5ZX+FNoKWDDLlophNgO4ItSyteEEKsA1Esp/95qf9a8mCv0YqRQVXrxUvH8VqfiVVVT6ZzlwlewL/KJR8ypDgvYq8kpNpd7lK2hq0zNxGzNyI7FwQHtwiU7jSw7hs26NSWiUH/1AKIfnJ9pOR448jqi512BDrNxUx21AzrGaymUev4bHIC6fwui585BsO4URAdPIvi7bVDO/wSQig/bbawUNZhlVPF4peI5+smLlPIogL8exV3bAfxr+tOaXgBfcPK4iGqBongyJwZHThAmJ8dUuqgz5KtHJBFG0BuEEAqiitDaaSoCQaHl32fnVAet8qdz3yl0Ipe7xFRVIpJIIeT3IBJPIeTzQFF4vrNS06+XPkZ8QS1FTDfnFq2Af9vdWl1LdgwrnqHiZv3WH4Lysy7Ub9UueOoBQPEiZFELw9qB0quZuLXR2tiUPwTl6S+gXk0CGIpJfP0I4Et3Dcsq0s82XA3mQHwAIV8IkfQbbB6H32BzceMKKiNHfuNCiLVCiDVWXyPdX0q5N527epGU8i+llMedOC4ismDSijOVCKMvdhwdW5ehZV0LOrYuw/HYe+iP96N9S7vWTnNLu9aiOWclZds1OS6vZVFViWPhOG58fDfOv+053Pj4bhwLx6Gqzn1CXUtq+vXSx8iOB4ATbxnbx574vdZxbO5KravYSDFsEfcRi3ETSbpjPNQq18et3VbJemvuEVobmypiLraa7wcSA+hIt17uSLfzT42ynb8ZVarakgEjnI+o9jl1ubobWpcwqy8icpNERMunPrAdUJPAge2IQsXy7cux691dSMokdr27C53bO3Fi8IRx27bOvMJMPac/dx2BvHeQrdYlcEktSySRQseGHuzoPYakKrGj9xg6NvQgknDuBFxLavr10sfIBz8N/OfNhrGC/7x5aPvlK0aOYYu4V4SCOz9yp2Hc3PmRO6Gw4qWkXB+3JvMznrohb3FgxMNafVX2fk9/Uds+kiLmYvP5vhtPvvqk4VyxfPtyR4v4o8koOrd1jng+otrnSNqYlPJxO/sJIdZKKdudeE4iKoJJ+lbIP840FeDshrPztuVelCiKB42BRqy9fM3wNTmKohU2L9zoylqWkN+DXQf6DNt2HehDyF+G2qIqVNOvlz5GTr/APNVR3944BZAYPoYt4j4ggDU71uBrl3wts1L5mhfX4Juzv1nKn2zMc33c2k2vzW7Nnb2fnTTbIuZis/k+4A3ioZceMuzXc6gHIUebDLBFM2nK/RfDR8r8fEQE7eP2cCI8dJuI5aUMROL9pqkAbw+8nbctmoyatIoVCKoSAkBQlbCsi9RrWfS6mTJduKiqxMBgEqpM35qkiETiKcyc0mjYNnNKIyLx0b8ja+d5q5GqSoQHk5avV+7PnUqp7nsdhmvbrafVHH1taKxc2AbctEOrC4iHgRWH021k5ciPmRP3qgAiiQgORw/jqmeuQtO6Jlz1zFU4HD2MSKlboI8xubEYGTQf57FEKi9GKzJ+4xFgznIt1lb2abdzTFpoDw6Y75cc1BZMlap2m65ryVPEXKzXYCpCQb2/AdFk1DwFMhExnnuKSPGyeg5+8jL2uOPtTiIqGdM84VQE6ufWGVIGglJBl0kqwHj/+Px0MFU15GPLyBGI2AkoGxdC3DFRuw0fgWq2hksF2M1xD/k8WLOwGbOmToBXEZg1dQLWLGxGyDe6d2Rdn1s/SvrP9b2fv4GutovyXq+gVzH83P+yvdd9r8NIdQV6Ws0rzwKf+Q5w2W1ajctPOrNqX97SOo/pdQY2axX0MbnjDzvyxlzX7C4EvIEKvCC1yWwMplQVaxY2GeL2u9dejPBgMidGB9EfS5Q/bn1BoOU6LdbunKTdtlynbc/mD+Xv9+G/BWIncupgjlpfwDgk6A2anj9SMuVYjUrQG0T3nG7jc8zp5icvY5CjrZJHfDIhXpRSXlzs47BVsjm2SjZV8fYxlW7jGU6E0b6lPb+t5aV3oT56XEt/Ofoa8MqzSH10GaJqfKjbmPBC/HKtocVr8JVnoFx4NbA26x2wKbOBv1iTt01dsAFKYFwZf1pzA+k/Snb0DnVtnzV1Ah65vhUNdcbsWSe7EBXyvFlcH7PZP9f8GX+EpZefh/MmNSAST6Le70UkkTL83M9/eQ5WPfObQl+H0rLTtju721g8rP0hmLv/n3drfzQueFJ7B9tGK/DsMfnVmV/Fp879FE7xn4JIIoKANwCvUqHXZHRcHa9WY/Cxv2mFKpEZ55DAjU/k77f6qum47O4XDNtKHrd2W8qb7bf8APBvi/Lvu+BJy+5hTkml2/nr3cY8igdLNy91tBW4A93GKh6vVLxyz5AMGqIys8wTrp8E/PMfD21UvPB87FY0eLWFxhr847S0A5MWr5j9FeOTHNwBnPaBvG2izh1rVRSS464oIvOHSbF/oLg+t36Usn+uZ176A5556Q/wKgKv3zUPihB5P/d5kxrc9zrYqSvIbdc9XO2Lvp+NWoXsMfmtXd/Ct3Z9C17hxZ5Fe9j21WFWYzDg80AR2p8kDXVeqFKa7vf+xlDetpLHrd2aF7P9AuNHXwdTJI/iQUO6jX+DvwGqVQv9Ij4pUYSSufCporWQyGHlniXvK/PzEY15lnnCJw4ad5w8C0jEjPn6gwNIXf09DHz1TajfOI6Br76J1NXfA46/mX9fk21y0EbXmzIoRS2Lm5+31Eb6uXL/f//hAfe9DlatYrPrVHLrV8z212tiBgds1SqoUkUkEcHuRbuxaf4mzDtnHoDK5u7n1cTVUOtZu2PQar+3+iJ520oet5ZtjMPGeIyH8/eLnbCO61zD1Xw5IJqMontON7Yv2I6XrnsJ2xdsR/ecbtaoUNGcWuflR0KIZ6y+9P2klN934vnGiumPTy/oi8hM0BMwb2McONXYJvPz64B4v3HtF8WLvimz0PGzW7Te/T+7BX1TZiE17kzDfWXbY5DB03K2PQr43ZGL7HQti9uft9RG+rly///5l9/BfQua3PU6mLWK/cx3tBqWSLqGJbt+5XcvAG2P5u//yrPadn9oxFoFvdalY2sHWte1YvXO1eho7sDSpqUVy92v9bUz7I5B8/2acFrIV/64tWpjrCaN9VRqMn8/xZsfp3p8ZrO7lkwR6jx1aJrUhFte0M4ft7xwC5omNaHOU+fYc9DY5EjNixDiY8P9v5TyZ0U/SZaxUvMy/ZzJhT1+ibHmZXQqXfOCwQGov3ogv25l1s3aO256m0xIYMNCQ670wNcOouOFv8vLWV5z2bfRcOL3Q/Uy498P7HhQW/siq4YGH/5SyfOs7arUitqjeN6qiNmRfq7c/w96FUSTqrtWNFdVIJ5elfzoa8C2u4GXnx6qEciucblphxbTl3wRCJ4KDPYD/gbtne43fg6cd4W23zC1Clb1Z2suX4OQL1SRlDHLmjj7dQmuj1e7Y9BsPwAVmTcy9Vb6/CwU4MnP5cfWNT8wzuNCAL/dDJzzUS1Oo+9p8XnuZca52G5dTREG4tqilWbxrqeXVUDF45WK59Q6L45enBCRg/whKGZ1K3Nu1U6IgHaykqrttV9C/gbggazUhG8cB7Z1AS/cNbRN8QIfu7UEP9DoOFnLUg3PW2oj/Vxm/9/gUSz3rwi9puWOicZuTHqNQPZ4OP0CYNtsYM5X8vdXvFr7ZP2+2bJqFazqzyp14TLcMdVSBye7Y9Bqv4qM39x6K5P5GQd3AL5A/jz+9Bes41Nnt66mCCFfyDLeiYrh6GwphJgmhHhKCPGKEKJX/3LyOYgoi1nOspoy9vhPxs3z8HPrW0zyp63WfonE+43HUUiedVE/rnvWTHHTsbhB9uvRH0sgparV8bpk1xfo67jo67esOKR9/8kuLcZXHLaOdb0eYcVh7T4XtmX9n1Y34cZ1Ktx4TOWQSqnojyWG4jXlsjS53Lk9EbM3j1vVZuXOxZZ1Nc6tMRRJRCzXfiEqhtNv9XwPwIMAkgAuB/AEgHUOPwcRAeY5y4mwtuZEdo//RBRoud6Yh3/JjXn1LWb500Hkr/3SNbsLQSg286yd6wbjpjVT3HQsbpD7eix+Yg/ePh7LrO/i6tdFry/IXsflzknpdVx+DxzZD3zoL4EfXKdt3/lofqzr9QgbrxkaY3NXao959WPac8Cd61S48ZhKLZVScSwcx+In9mTi9Vg47p4LGLO5Pd6vzdsjzeMyBbQ9NnLNi1VdjYOfipit/dI1u6umY4vKw9F1XoQQe6SULUKIfVLK6elt26WUsx17ErDmpVJY8zI6Jat5sdvjv70H+FHHyNvM8qd9IaQgDb37g94gPBDGfGxfCIDU3n2ua9COzV8PKM4Vto5yzZSSKPGxVF3MWr0eq+Z/CKue+U1l13OxQ699MVvH5XNPaBcu2dsvu02r56prGL4eYcGTWl1M1srlDqxT4bgij6nq4rU/lsDiJ/bkxevD17VgXMBXikMsjFU9Su5aWlbz+FWPArG+kesPc+tqfCFDrDohd+2XoDcIj4PnhVGoeLxS8Zw+m8SEEAqA3wohbgbwNoBJDj8HEQH2e/yf9gF728zypwF4AEPv/ozsfGxAOxHq9xUKij1HpFIqIokU6uu8CA8mUV/ntb1WiN0C3dznCPk88HhGPnnX6voto2X1eujru4T8HiSTKqLJwl/rstDrC8zGRPDU/O3burR6LqEMX49Q1wDkvD/oxnUq3HhMpWQ1l9TXeaGq0jBX5MZt0OuB11viuLWqR8ldS8tqHm+YCNxzwdC2CtYf5q79QuQEp0fglwGEAHQAaAGwCMD1Dj8HEQHmOctm+fjH37S3rZh8Z4fbbpqldQzEkrbWa7Cb0lVM6kitrt8yWpFB89dDX9/l4LEI+iJxfO/nb7gzTQewrgGIvjdyDYHVffsOON5+looXHjSfS/pjScNckUyq6IsY54i+SBzJZIl/n1bxlLuWltU8brYOV27NSxlaJROViqMXL1LKXVLKAQAnAXRIKa+SUv7KyecgojSznGWz2pPgafn7hUy2FZPvnIgAT92gpS+oSe32qRu07aMQSaSwbONe7Og9hqQqsaP3GL7/izdw38KR1wqJJFLo2NBjuG/Hhh5EEqkRn2PZxr15+5mp1fVbRktRgH/67EWG16Or7SI8//I76Gq7CPf89+tYtnEvrrzwrIJf67KxWvfljZ+PXM9ldd+tdxY1Dqg0fIrIW3fovgVNSKZShrkimjSfI6LJEsetVT1K6DR783jOmlum9YcOz9lE5eRo2pgQohVa0f649PcnAPw/KeUeJ5+HiKD9xRiaqPXlz85Z9oW0XPvs2hOI/P2A/G2jzXd2uO2mWVrHmi37sfSK8/DI9a3DpoPZTekaLnVkJIoiMKHeP+KxjBUBnwd3P/8aVs3/EM6b1IBIPImgz4MrLzwLd//0NTzz0h/gVQTOmzSUNmL3tS6b3PGkj53Q6Vo6Ze6Yys7bz9x3gzaOjr4GbL5dWzNG8TrafpaK5/d58Nzut/DgtRfjlKAPJ6MJ/Ofet3Hth6cY5opi5oiiWM3tgM1tcvh4BcrSKpmoVJwegf8C4CYp5XYAEEJ8FNrFzEUOPw8RAflrAej0wszsAk2z/cy2jYae5pBdOKqnoY3isfW0juyCWj0tSy+otSoAj8RT6LjiPFx54Vk4b1ID9h8ewPMvv4NIPGW4j9VzhAeTtop2a3X9ltGIxFM4dHIQV357GwDg+S/PwfMvv4MrLzwL936+CUsvPw/Pv/wO9h8eSl0p5LUum+zxpI+d3O+tFl1V0nVeT3zGsXFApRGJp3AiksChk4MYF/Dh0MlBnIgkMmmO+lxR7BxRFKu53e62keLV4TmbqJycrnnp1y9cAEBK+XMA/cPsT0S1wOG2myGfxzStw05aVtCrYMElk7Hqmd/gghXPYdUzv8GCSyYjmFNkW8xzkFFuGl3vkX7T30Hvkf7afq3L0H6Wimc1R/Qe6Tekfwa95nNE0FsDcctYpSrmdKvke6EV7G+A1mPl8wCOA3gaAKSULzrxPGyVXBlslTw6JWuV7DYOt90cbSewQtoYj/Y5SqwqYza7w1t4MGnZitZlr7XzytB+1mWqLl6t5oiHr2tBvd9b+W5j5TL2YhVwQbxS8ZzOdWhK334jZ/ul0C5mrnD4+YjILazSHEbJ41EwLv3HbSEpGoW0MR7tc1C+7DS64WoFFCFq+7V2eByQ86zmCD0+s3m9CsZ5a3SOYKxSlXK629jlw3zxwoXI7VRVK/CU6dsKts1UVYmBwSRUmb5Vpem2XKVoY2zneUmjqtKyFW3VtZJ20Xig0csdv1atvasuPgvBWKYa4nS3sTMAfBPAH0kp5wkhPghglpTyMSefxzUKTAMjcjW97/9TN2hdZybPSrfnnFj2VAJ9rZaODT3YdaAPM6c0Ys3CJvg9Cr60/sWsbc2YUO83pHno9RfG+46+jbH5seQ/Lw29VrsPHMN9C5qwbOPezGt234ImBKopTcxF44FGz2z8fvfai3HfwiYs21DF8VkIxjLVGKej9vsAngfwR+nvX4e2cCURuZ2L+v6br9WyF8cjiRHXb8luY/z6XfPwyPWtRV1o2F03hoZeq6kTx2HjzoNYNf9DeO3OeVg1/0PYuPNg6dfHcJKLxgONntn4/dL6FyElqjs+C8FYphrjdM3L6VLKHwghvgYAUsqkEKJGZwOiGuOivv9WOenvbwzlbTOrZXGyjXEhNTRjnf5anTepAZ/ash/3/M9vM//nVQRunjutgkdXIBeNBxo9q/F7ekMd/vSbmzPbqi4+C8FYphrj9MVLWAgxAVpxPoQQHwZwwuHnIKJC2ekqU8G+/9mdqiLxFCBhulbLW33Gdwqz12QY7vFCPg9UVY6qa5BeQ2O25sxYX98ll/5a7T88kPn9nTuxHgODSZwS8GldxvweRBNqJo0v9/fkSCqeE12UuA5GTbAavydjCexd+WeZRSp3/O4owoNJADB0xAOQ15FQCFGauB3JaOOasUw1xukz7y0AngFwrhDiFwAmArja4ecYM/a9cbCg/d3WWplcwm6+s973P3e/Evf9N8tJf+S6Fiy4ZHJezUTI78GsqROGzVO3erxIPJX3eI0h/4gXME7X0NSygEfBfQuasOfNPiy4ZDI27jyIv2x+H5Y//b+Z166r7SL8R8/vcf2lUxBPqejIqjtwpJbIqfz+Co0HcpbZ+LWaD3yKwBe+v3v4/RY2oc5G7Z3jiolrxjLVGKfXefkstJqX9wNoA/CnAL7u1PouOtes8xK4xvFjKEapL164zsvoVHydl8EBYMMC47tuU2YDCzfmv+tWgb7/ZmsuvHDrZfjapn156zDc87kZOBlLGj6N+cJHzzG0MDV7vL0r/wx/u/5F03Ud7LQ/Nfskp4R/qFRtzPbHEvjez9/AFz56DhY/sQer5n8Iq575Td7rvmr+h1DnVUx/x2br8RSkkHgfydhcB6NQro/X3PErpTRdh+jBay9G0+3/ndlmNW+svmo6Lrv7BcO2ouN2JMXGNWNZV/F4peI5PdK+LqX8dyHEaQA+DuCfATwI7SJmWEKIAwD6AaQAJKWUrQ4fG5E7FXNSsXPfQvKdi+j7b/cP/Nz9gj4FZ5xSh+e/PCdzUfK+04KmeeoTG+oQ9HsgBHDGKXUYH/Khvs6LgXQ6UiSeMs1xPyXos1zXwQ4na2hqjf77DPq0uLvp8vMQTaSw/ot/img8hTNOqTPsr9fE6P/O/b+ia4mGi/e88RIEElHr8cN1MGpC7vhVpcybcx58YT9OCfoM26zmDbPau6BPMc5Dxb7BoaaAeFiLu8EB7baYuhXGMtUQpy+79eL8TwH4rpTyPwH4C7j/5VLKJl640JihpwJsWADcMVG7jRyx14Pf7n31fOdser6zYz+Glqp14+O7cf5tz+HGx3fjWDietx6K2X79sSQ6P/nHWPXMb3DBiuew6pnfoC8cR8cV5xnuu3ZhE/oicfzt+hdx/m3P4W/Xv4h5F56FwUTK8HgDsfw1Rk5GE6brOug57jQ6+u/zX7b34u3jMXzv52/gD+/FsPiJPbhgxXO48YnduPXKCzB/xh9l7jNzSiOOR+J4qy9SmrU2LOM9nD9ewkeAHQ8UPvaoqsXiKdx65QWGOefWKy9ANJ4ybDObS2ZOacThkzHDto4rzrM1/9mmprTY3HiNFpsbrwFi/SWfx4mqhdNpY88CeBvapy4tAKIAdkopZ9i47wEArVLKoyPtayelodAUsHIodZoZ08ZMVfwj4mHjtZhUALv3LUOPf7NULbNUikJSxL67qAVfWrcnk1f+0HUtWGKS6vHdRS2Y8Y8/zWy75ePTsOBPJxvWcCim5qUC3B2zWfTfp54eZpUmtvqq6fj4PT/DzCmN+KfPXoTTQn4kU2p5a17844AnP5c/Xv68G3hg1tD3o0kvG9uqJl51/bGEadrYPZ+bgVnf2pLZdsvHp+XV3unx+8XHh2pjHlrUgiXr8h9v1KlksZPaBUt2rF52G9ByPfA012opUsXjlYrndP7D5wB8EsDdUsr3hBBnAfh7m/eVAH4qhJAAHpJSPpz9n0KIxQAWA8DkySxMJ3ezHa/FtLC0e19F0U5wCzeWLN/Zbjths/3e3xgyvW9DnRer5n8ok8LRUOc13W9cwDiNrdmyH0uvOA+PXN9qSOEIeD14+LqWgruNjRWjmWOzWyNn32bbdaAPkyeE8Nqd87D/8ADufv413PP5JkBqsZH7eyq6lsgq3gXMx8vpFxi/Z/vYqlDM3wT1FnPJpFMChm1rtuzHTZefZ5iH9Pg1xK3T7dTNUsS2dQFzvlLSeZyoWjga9VLKiJRyk5Tyt+nv35FS/nSk+6V9REp5MYB5AJYKIebkPPbDUspWKWXrxHdTXQsAACAASURBVIkTnTxsIsfZjtdiUrqGS48ZHACkqt2qKlQBhBUBFelbh9970tuRZps5pRHxRAr9sQRUKdEfSyBmsp9V+tBbfRFc+e1tOPcffoIrv73NMvXrZDSRty2aUNFQ54UitFx3RRHwehWMC/igCIFxAR8vXHKMZo7Nbo2cfZtt5pRG/PbQQOb3eOjkIMKDSSiKyNQiZP+eHKHn94v0raJYj5ejrxm/LzINR5Uqwomw4ZacV0i8plKqYR4KD5qng5m1Yv/98ahhHjp0cjDTJl2PW6v5b9QpkIMD1nN7blyXAWOa3MY1Z28p5R/St4cB/BDAJZU9IqIy0FtYTpkNKF7t1m4LS6v7qklDXr86eAJ9sT60b2lHy7oWtG9pR1+sz9ETUNCrtcidNXUCvIrQUiaua8HJWBKLn9iD8297Douf2IOBeBLfvfZiw36nhXxYs9B43zULm3BqyGfY5lVE3nPct6DJ8P/afdnGuFz0NrTPv/wOutouytzm/o6ef/kdw/dBbwV+P74g0Paocby0PQq88mzhY8+CKtWSjzUqTCql4lg4bpiHUqrEfTlzzn0LmzA+6Mubh04L5W7Ln1/0ceDYPOQPmcdqBT4VZEyTGzla8zLqgxCiHoAipexP//u/Adwupfwvs/1Z82KONS+mKp7fOmK8OtltTCh5ef3hZXvRvvMO7Hp3V2bbzDNnYu0Va1Hvqx/tj2UwMJjEv2zvNSwqedapAdMalYeva4EQwpAqBOQvViilzFscTt/PFQvGlU7FD76QGoLsbmP66x9JpFDv9+K3hwew43dHMevc03HepAZE4hVM1xsc0IrzP/hpLVXs6GvAkf3AuZdp72I7kIYTToTRvqW9pGPNhVwdr1b1LY9e3wpVSq1bYSyJF9/sw6aeP+CWPzsfkyeEhp2b7HRRLGoeGhwA9m8BzvkoEDwViL4HvPFz4Lwryl6PVYMxXfF4peK5pefnGQB+KIQAtGN60urChajmFNPCMve+Us3LlQ6On4yeQz2GbT2HehD0Bkd7xHlCfg/WbNmPe/7nt5ltvav/3LI9saKNdUMxa34rYoFx6QUos9diMdvGNsaVk92Gtr5O4PzbnsPrd83D+bc9h2RWtyWvIvD6XfMyv/uy84e0uoEX7hrapniBrx8ZSsMpUtAbLPlYo8JY1bcE/R5Iibw4/cm+d/D6XfNGmJvyOdpO3R8Cnv6C9il65gnSsVpmjGlyI1ec6aWUvQBG7EhGRCPQ8/qzPnmJnjiI5jOaDe+cNZ/RjGgy6tg7Z5F4CmsXNmHWuafjlKAPJ6MJRNN54NnveOrtie0sDJlKqXmfsng8rsl0JRN6HIQHk5kC/fu37sczL/0h87vP/j2WdfFPk7GRqXFx6N3saDJa8rFGhdHrW3LnoYGYdmHw+l3zMBDT4vJQfyxzcT2QjtWKfIpbhli1izFNbuSKixdyxr43Dlb6EKjS9DqYrDaxwcCp6J7Tjc5tneg51IPmM5rRPafb0XfOAh4FLR9oxN+uf3GoFfHCJjx47cXGbTbrHfQ89dzWxhPq/byAcTE9DhY/MdTiuqvtIpw3sR4LLpmMn//2CFo+0IgJ9X4IIXAsHEfHhh5nWyVbMRkbxda45Ap6gyUfa1SYoNeD+xY05c0ligBuzIrTB6+9GB5Fwd/9m8Otu0ejDLFqF2Oa3MgVNS+FYs1Lhaw6UekjGI2K57cWugZB0UxqaFShvYMW9AYzt4pw7iLAKq98zcIm+DxK5tOYHb87itnnTxoxrcLq8R6+rsXWpzZVrmpj1ur39tCiFmx68fdY9aNXDHVPdtYGclQx9WV2n0KqJR1rLuTqeB0YTGL764cNnwr7PQpusLneVEnjcThliFXbh1JbMV3xeKXi8ZMXolpjUkOjAJmP+EvxUb9VXnljfR3O/YefZLbpdQ/9scSw6WBWj1fPmhbXUlU57O9t1Y9eMXyv/zt331GvjWFHMfVldp9CKCUda1SYkN+D9g17DXUtZvV4VutNlTQeh1OGWLV9KIxpcpmqvXQmIvcoZN2EgZz2ycfCcaRSxrabVo8XHkyC3EdVJY6F45Zr8ew/PGD4PjyYdH5tDCITZnHWH8ufX6zWm2I8ErkP38YsoymxJwva321pZtMfn17Q/lXaWplGIeQzyStf2IQ6j4JZUycYtn3/F29kUjN29B7Dso17tXSwrE9fTB9vQRPXb3GpSCKFjg09uPJDZ5j+3jbuPAivIjBzSiP+6bMXwSMEAum1MXJrXvg7JieFTOLMqwj802cvwt//+/9mtjUEPLhvYROWbdjLeCRyOV68EFHRPB4FE+r9ePi6lrw1WB65vjXTTSroU7Bmy37Dfc3Swawej8X67hTye7DrQF/movTBay/GKUEfIoMpPP3iW7jywrOw9Ipp2H94AHc//xru+XwTFCEwod5viI8aWKOHXEZR8uMs6Pfgth++hlXzP5RZl+rOZ1/FP39uBh65rhWhOsYjkZvx4oWIHOHxKHlrsKiqsSFIJJ5CxxXnGRazfP7ld0zbJwshkF77yfDvXGVtt0umr3ckkd8WGwAkJE5EErjy29sy22ZNnYBIPIWGOq+za2MQWciNs/5YAlNPN9ZuTD29HpF4ynKeISL34NmCiEpCr4PITtd45LoWLLhkcl5aUW77ZLP7mrUttbsfOcPq9W4M+bBmYTN2HziW3zJ7QRMAYM2W/UzFIVcIej2m85BHCNzw+G7OJUQux1bJLua6mpdzJlf6EAxs1tRU/KxT9lbJLjEwmMxrhWu3HanZfYvZr8q4NmaHe71DPg/C8aRlq+T6Oi8i8STq/V7+MVhbKv7LLHSOtWrp/eC1F6Pp9v82bKvyuYTyVTxeqXhMICeiktDrILLZbUdqdt9i9iNnDPd6K4qwbJXcEPDighXPob6OFy5UeVZxekrQl7eNcwmR+/DihYhKwqxFqd12pHbb6LLdbnmN9HpbtbjWWyjz90JuMFyc5m5jzBK5Dy9eiKgk9Bals6ZOgFcRmDV1Ak4L+bBmYZNhm1kNhNl9i9mPnDHS6623uM7+//sWNGHH747y90KuEfSax6n+b84lRO7GmhcXY83L8Fjz4n5mnakA2OoOZreLWA12G6v4wQ8XsyO93qmUikgilWlxHfR5EEuqtfB7IXMV/6WOZo5NJlVEk1lx6tXis8bmEsrHX2gNYBUaEZWMVStcO+1x7bbRZbvd8hrp9TZrmd3A9XnIZbxeBeO8xjgF7M1NRFRZPKMQUcFUVWJgMAlVpm9VabrN7n2p+qVSKvpjCahSoj+WQDKl8vdLrsV4JapefGuBiApittbHd6+9GPGUio4Ne7kuyxiUSqk4Fo7nrZux580+tE6ZwN8vuQrjlai68ZMXIipIJJFCx4Ye7Og9hqQqsaP3GI5HEujYsNewrWNDDyKJ1Ij3NduPqkskkcKyjcbf/7KNezHr3NP5+yXXYbwSVTdevBBRQcqxfgtVl+HWzeDvl9yG8UpU3XjxQkQFKcf6LVRduL4LVRPGK1F148UL2bbvjYMFfVFtKsf6LVRduL4LVRPGK1F1Y8E+ERVEUQQm1PvxyPWteeu35G7LLXq1ui+LY6ubx6NgQr0fD1/XYljfZfb5k/j7JddhvBJVN168EFHByrF+C1UXru9C1YTxSlS9OFKJiIiIiKgq8OKFiIiIiIiqAi9eiIiIiIioKvDihYiIiIiIqoKQUlb6GAomhDgC4M1KH0eFnQ7gaKUPwkWsXo+jUspPlvtgshUQr7XyO+XPUZxqilmnuT123H58QPmP0Y3xWg2/J7tq5Wdxy89R8Xil4lXlxQsBQojdUsrWSh+HW9TC61ELPwPAn4NGz+2vuduPD6iOYyy1WnoNauVnqZWfg9yBaWNERERERFQVePFCRERERERVgRcv1evhSh+Ay9TC61ELPwPAn4NGz+2vuduPD6iOYyy1WnoNauVnqZWfg1yANS9ERERERFQV+MkLERERERFVBV68EBERERFRVeDFCxERERERVQVevBARERERUVXgxQsREREREVUFXrwQEREREVFV4MULERERERFVBV68EBERERFRVeDFCxERERERVQVevBARERERUVXgxQsREREREVUFXrwQEREREVFV4MULERERERFVBV68EBERERFRVeDFCxERERERVYWqvHj55Cc/KQHwi192viqO8cqvAr8qjjHLrwK+Ko7xyq8CvqgGlOXiRQjhEUL0CCGeNfm/vxFCHBFC7E1/fXGkxzt69GhpDpSoBBivVG0Ys1RNGK9EY4u3TM+zDMCrAE6x+P9/k1LeXKZjISIiIiKiKlTyT16EEO8D8CkAj5b6uYiIiIiIqHaVI23s2wA6AajD7NMmhPhfIcRTQoj3l+GYiIiIiIioypT04kUI8WkAh6WUe4bZ7UcApkgpLwLwPwAet3isxUKI3UKI3UeOHCnB0RI5h/FK1YYxS9WE8Uo0dpX6k5ePAJgvhDgAYCOAK4QQ67N3kFIek1IOpr99BECL2QNJKR+WUrZKKVsnTpxYymMmKhrjlaoNY5aqCeOVaOwq6cWLlPJrUsr3SSmnAFgAYIuU8trsfYQQZ2V9Ox9aYf+YoqoSA4NJqDJ9q7KbHxFVN85rVAsYx0TuU65uYwZCiNsB7JZSPgOgQwgxH0ASQB+Av6nEMVWKqkocC8fRsaEHuw70YeaURqxZ2IwJ9X4oiqj04RHZNv3x6QXtv+/6fSU6Eqo0zmtUCxjHRO5UtkUqpZQvSCk/nf73yvSFi/7pzIeklDOklJdLKf+vXMfkBpFECh0berCj9xiSqsSO3mPo2NCDSCJV6UMjIhoVzmtUCxjHRO5UtosXMhfye7DrQJ9h264DfQj5PRU6IiKi4nBeo1rAOCZyJ168VFgknsLMKY2GbTOnNCIS5zs7RFSdOK9RLWAcE7kTL14qLOTzYM3CZsyaOgFeRWDW1AlYs7AZIR/f2SGi6sR5jWoB45jInSpSsE9DFEVgQr0fj1zfipDfg0g8hZDPw2JAIqpanNeoFjCOidyJFy8uoCgCDXXar0K/JSKqZpzXqBYwjonch2ljRERERERUFXjxQkREREREVYEXL0REREREVBV48VImqioxMJiEKtO3qqz0IREROY5zHVUDxilR9WL1WRmoqsSxcBwdG3qw60AfZk5pxJqFzZhQ72fXEiKqGZzrqBowTomqGz95KYNIIoWODT3Y0XsMSVViR+8xdGzoQSTBha6IqHZwrqNqwDglqm68eCmDkN+DXQf6DNt2HehDyM+FroiodnCuo2rAOCWqbrx4KYNIPIWZUxoN22ZOaUQkznd5iKh2cK6jasA4JapuvHgpkp2iv5DPgzULmzFr6gR4FYFZUydgzcJmhHx8l4eIaof5XNcERYAF0VQxuefpoFfhOZmoirFgvwh2i/4URWBCvR+PXN+KkN+DSDyFkM/DwkAiqimZue66VoTqPDh4LIK7fvwqDp0cZEE0VYTVebox5OM5mahK8ZOXIhRS9KcoAg11XigifctJkohqkKIIQAB//civcdndL+A/9v6BBdFUMVbn6WhS5TmZqErx4qUILPojIsrHuZHcgrFIVHt48VIEFv0REeXj3EhuwVgkqj28eCkCC/GJiPJxbiS3YCwS1Z6yFOwLITwAdgN4W0r56Zz/qwPwxP9n79zjoyjv/f95Zi/ZSwIkIQJFEalc5ZIIWFFDUc5pta3UFi/gjfZ4vGBLELWhHjwef9Tf8SRVkejxTk8FK7SKrXjl16oI9lDLJUEUrVAKKHJPCMneZ+f5/TE7m93Zmd3ZzV5mN9/365XXZmeeefbZ7DefmWdnPp8BMBnACQDXcM735WNcvSVXRnxJ4vCGwmQkJAjC9OjpFYWUEGZArxYBoDsgUn0SRBGSr7SxhQA+BdBPY91NADo452czxuYAaAJwTZ7G1WsUIz6A6GNvMJpgRhAEUWhS6VU2tZEgMkVdi7SfJYjiJueXjTHGTgfwXQDP6TT5PoDnI7+/DGAmY6zPqkc6CWYEQRCFhPSKKEaobgmiuMmH5+VRAI0AJJ31QwF8AQCccxFAJ4BqdSPG2C2Msa2Msa3Hjh3L1VgLDiWjlAZ9pV6J0iGTmiW9IgpFbzSW6pYgipucTl4YY98DcJRzvi1ZM41lCbdi5pw/wzmfwjmfUlNTk7Uxmg1KRikN+kq9EqVDJjVLekUUit5oLNUtQRQ3ub4Q+UIAsxhj3wHgANCPMfYC5/z6mDZfAjgDwJeMMSuA/gDaE7sqPsJhCd5QGO4yKzwBES6bBRZL8vmikoyivhaXklGIUmPC8xPSar9z3s4cjYTIlFi9GtSvDHf80ygMq3bBGwhDknicf4CCSAizkM5+NpP9OEEQuSWnkxfO+T0A7gEAxtgMAHerJi4AsA7APACbAVwJ4F3OecKZl2IjHJZwwhPEwjVtUXFcPqcW1W57UuGjlB6CIIoFRa9W/GgKPAERDavbNA3QZJAmzITR/Wym+3GCIHJLQf77GGNLGWOzIk9XAKhmjO0BcCeAnxdiTNnGGwpj4Zq2OEPgwjVthgyBSjKKwCKPtHMnCMKkCAKDxIGG1W26BmgySBNmw8h+tjf7cYIgckfe8is55xsAbIj8fl/Mcj+Aq/I1jnzhLrNqGgLdFBlKEESJkcoATQZpohih/ThBmBM675kjPAFR0xDoCYgFGhFBEERuSGWAJoM0UYzQfpwgzInhyQtjbABjrIEx9ghjrEX5yeXgihmXzYLlc2oxbUQ1rALDtBHVWD6nloz3BEGUHIoBOlbvYg3QqdYThBmh/ThBmJN0zn2+CeAvAHZC/54tfQJRlOATe9JHnFYLrNb4eaDFIqDabcczN05OmVJCKTyJSFyCT/TBaXVGHwUmpFxHEET+0TJAO60CvKEwnDYB3mAY1eWyHsbqHAB0B0TSvjxAmhqP1n6Xc56QLFblit+PO62UNpYORmurL9YgkTnpTF4cnPM7czaSIkEUJbR7E9NHqlx2zQlMRUTkKhw2zf4ohScRiUto97ejcWMjWo+0om5QHZqnN6PKIZ++11tHQkcQhUMxQAPyN9YnPEGs/nA/rqg7HYvXfhTVt6bZE/GH1i8x74LhCIYl3YQyInuQpsajvd+thc0iYP4L26PLnrz+XISoRjMmWd3F1pbRdgShkE5VrGKM3cwYG8IYq1J+cjYyk+ITtdNHfGJm125TCk8iPtGHxo2N2HJ4C0QuYsvhLWjc2Aif6Eu6jiAIc6Do2rfHD8HitR/F6dvitR/h2+OHoMMbSppQRmQP0tR4tPe7bTjpDcUtO0k12iuM1lZfrEGid6Rz5iUI4JcAlgBQ7sPCAYzI9qDMTLbTRyiFJxGn1YnWI61xy1qPtMJpdUZ/11tHEEThUXTt7NPKNfXt7NPKo7+r1/Vl7csVpKnx6O13z6hyxS07o8pFNdoLUtVduu0IQiGdMy93Ajibcz6cc35W5KdPTVyA7KePUApPIj7Rh7pBdXHL6gbVRb8l1FtHEIQ5UHRtz9FuTX3bc7QbX7R7SfvyBGlqPHr73S/avXHLqEZ7h9Ha6os1SPSOdCYvnwDwpmxV4jit2ukjTqsFksTRHRAhcfkxHJbinksST+iPUngScVqdaJ7ejKmDp8LKrJg6eCqapzfDaXUmXUcQhDlw2Sx49sbJGNzfgd/c/A1suHsGrqj9GqaNqEbT7IlY//EhVLpsaJlbS9qXB0hT43HZLHjq+nOx4e4Z+Pt/fgcb7p6Bp64/FwNctrh6HKBZo5Q2ZhSjtdUXa5DoHYzzxANqzYaM/R7AOQDeAxBQlnPOG3IzNH2mTJnCt27dmu+XBRBJKAmKECWOfk4bTvlCsAoMTpsF7d5QnAFw+ZxarPnrAbS8uyep0Y/SxhLJYjJOwf+QhazXfDLh+Qk57X/nvJ057d9EFH3NagWbtMythdtuRZlNgC8kRQ8ASfvyQw7Txgr+gaVbr7JhPxBnxH/oqkk42uXD2adVxCWLeYIiOrwhnFHlwhftXlS6bKhw2KhODWLCtDH64EqAdIwaf4j89Gm8oTBuXrkNm/eeiC6bNqIaz9w4OWoABBA18t8/6xw88qfdUaPfs/OmRBN5FGJTetTr+ioCE+C2uQEg+mhkHUEQhSc22ARA1BD9zI2TYREElJf1HJSQ9uUH0tQeZMN+fH3e/dIO3D/rHNyychuenTcFFQ4bugMibnthe8L+Xms/TmhjtLb6Wg0SvSOd/76XAfg552EAYIxZAJTlZFQmRs/op2fkV4ypynMy+hEEUepkO9iEILKJ3n5cCZhQ9tMUqEMQ5iSdc3LvAIi9ANEJ4E/ZHY750TP66Rn59xztjntORj+CIEqdbAebEEQ20duPKwETyn6aAnUIwpykM3lxcM6jR+KR311J2pckyQz26uXL59Ri/ceHyIxKEESfIlmwCUEUGq39tRIkEbufpkAdgjAn6ZzD9zDGzuWcbwcAxthkAEWVY5epMT5uu1AYVS4bnp03JaGfarc9brnTKuBf6kfgpzNH9ikzah6NdwRB5JlUOipJHP6whCqXHc/cODnO/Gy1kg7kGtLf1AgCQ6XTFl+fNgv+pX5EXD1r7df7yn68mKCa73ukM3m5A8BLjLGvIs+HALgm+0PKDXK6SDAuDUwv/cvwdozFmfa0jPflFiHueakjcQnt/nY0bmxE65FW1A2qQ/P0ZlQ5qkhMCKLISaWjmeoskR1If40RDiem4S2fU6tZpxSoY26o5vsmhj9ZzvkWAGMAzAdwO4CxnPNtynrG2D9nf3jZQ04XkdPARIlH07+8oeTXrma6XV/FJ/rQuLERWw5vgchFbDm8BY0bG+lmUwRRAqTSQ9LLwkL6awxvqCcNT6nThWvaqE6LEKr5vkla01LOeYhz/jHnfCfnPKRa3ZTFcWWdTFNDKG0kPZxWJ1qPtMYtaz3SSjebIogSIJUekl4WFtJfY1AaXulANd83yeY5NVNfE5BpagiljaSHT/ShblBd3LK6QXX0LQhBlACp9JD0srCQ/hqD0vBKB6r5vgnjnGenI8a2c87PVS1zANgI+X4wVgAvc87/Q9XmRwB+CeBgZNHjnPPnkr1WJnd/7o3nxRsUIUoc/Zw2nPKFYLcICHMOd5kV/mAYEudwlVmTmvkyDQsoNkx4/WnB/8i9vVt5oZjw/IRCDyGOnfN2FnoI+cK0NZtMRzmXNc5dZkW3X4TLZkF3UESFQ9ZGdwqNJHpPgfS34B9muhobDkvwhcJx+3WrwOCwWuATw1ETv8tmgcVi7O/WV/bxZiODmqcPpQTI9TnSAIBLOOfdjDEbgA8YY29xzv+iavdbzvlPczmQTFNDOOfwBsMJxr5t+9ux/pMjuPvbo/Gzlz5KOiHqSyZWgQmoclThsUseo+QPgigx9HSUc1nj1Dp58KQX1W4HFq9NrpFEdiD9NQbnSNivP3n9uWj3BbFwdaKJP9UEpi/t480G1XzfJJuf7j71Ai6j3BvGFvnJzqmeDFBSQ5SUMCOiomfsm/b1gZg/42z87KWPUppT+5qJVWAC3DZ33CNBEKWBlo7q6eTXayqweG1qjSSyB+lvanxiYr2e9IawcHVmJv6+to83G1TzfY+0zrwwxi4AMDx2O875ysjjD3W2sQDYBuBsAP/NOf9Qo9lsxth0AJ8DWMQ5/0Kjn1sA3AIAw4YNS2fYvULP2NfPaUOFw2bInEom1r5HoeqVIDKlNzWrp5PlDu3lpH1Eb8l2vZ5R5crYxE/7eILIL4anp4yxVQAeAnARgKmRnymptuOchznntQBOB3AeY2y8qslrAIZzzicC+BOA53X6eYZzPoVzPqWmpsbosHuNnrHvlC+EPUe7DZlTycTa9yhUvRJEpvSmZvV0stuvvZy0j+gt2a7XL9q9GZv4aR9PEPnFsGGfMfYpgHG8Fw5/xth/APBwzh/SWW8B0M4575+sn0wN0MkMdeGwFDWbxhr1wmFJ81pum1VAP4cNnoAIXyiMgeVl+KLdi0qXfEZGEFi0T5fdIvcRcy1txtfDShIQ8gJ2FxD0AjYXIAgJd5h1WBzwh/10DagJzHlk2M8OZNjPH5kYoBN0cm4t+pVZ0RUQ0RCnfbVwl1nhsMrfSpecyVlHo9Pupng0veAfWLr1KooSuoMiTnpDOKPKhS/avRhYbpcvf1R5Xqpc9pQm/pLyvGSpfrMyFNX/gBJ/rF6W5v9BkX0ghBbpXDb2MYDBAA4Z3YAxVgMgxDk/yRhzAvgnqO4HwxgbwjlX+pwF4NM0xmSYVCk5WhOUarcdgHxK+Mnrz0U/pw1dfhEWBty8clu07S+vmoi7fteGI6cCaJlbCyBxZ95wydl4+obJKHf0InFHkgDvMeDlm4ADm4Fh04ArV0ByDUR7oCMubaOpvglrd6/F0zueNkPiF0EQJYzFIqDabcczN06Opo1JnOPHv96KQf3K8OAPJ2BYtQsnugMAB57buBfzLhiOYFhSTWyK9IBPQUej4apJ6wBQK0GJND27BMMS7nllZ9xk2yYgWsOnfCE4rALavdrHBrETmEwDgUxHluo3K0PRSRGzCTYs2rDILGmmRIFI+Wkzxl5jjK0DMBDALsbYesbYOuUnxeZDALzHGPsIwBYAf+Scv84YW8oYmxVp08AY+4QxtgNAA4AfZf529ElmqEt2t11vKIybV25D7dI/YsQ9b+Jwpx83r9wW1/ZnL32E+TPOjvTZptnnI3/ajVtXbYMnIBoOC0gg5JVFZd8mQBLlx5dv0rzD7OJNizFz2Ey64yxBEHlBOZi77tkPIXGO+S9sx+a9J/CHtq8w46ENuO7ZD2GzCGhY04Zvjx+CDm8IDSqDdNGbnHU0GiFvWt2QpucWnxhONOevboPVYsEtK7fhlC+E2qV/hF+UdI8N1GQSCGQ6slS/2UDrf6BxYyM6A50Jy+j/oO9h5MyL5iVeRuCcfwSgTmP5fTG/3wPgnkxfwyipDHXJjHqx684+rVyz7dmnlafVZ0bYn+rNIgAAIABJREFUXfK3IbEc2AynzaV5h9kR/UfEPac7zhIEkUsUI3Q/p3aYibI8Vi/VbYra5Kyj0bC70upG767hpOnZQS9gIrZ+AejWca/242YmS/WbDfT+B4aWD01YRv8HfY+UZ1445+9zzt8H8B3l99hluR9idkhmqEt2t131Oj2T/p6j3Yb7zJigVz6NG8uwafCFvJp3mN3buTfuOX07QRBELlF075QvpBt0ouilnkG6qE3OOhqNYPpnXkjTc0ey/bNSpwB067hX+3Ezk6X6zQZ6/wMHuw8mLKP/g75HOhcJ/rPGssuyNZBc47JZ0DK3DtNGVMMqMEwbUY2WuXVw2Sxw2SxYPqc2bt3yObWa69Z/fCih7S+vmognN+wx3GfG2Fzy9afD6wHBKj9euQJOqxPN05sxdfBUWJkVUwdPRVN9E9458E70efP0Zvp2giCInKLo3ua/H9fUv81/P46m2ROx/uNDqHTZ0DK3VlOTixYdjYYt/TMvpOm5w2nV3j8f6/Jj+ZxavNp2MLou6/txM5Ol+s0GWv8DzdOb0b+sf8Iy+j/oe6RMG2OMzQdwO4ARAP4es6oCwJ8559fnbnja5DNtDJCTSWLTRpxWS9xzC2NwpNlnxhhMGyuzlMEv+uGyueANeeG0OmARUp/qDkth+ERfzHZOWISiFuqCX3hMaWPZgdLG8kdvalatl4reOm0W+FT6yxjrA2ljTiDkSzu9SS9tzGFxaGq0VjJTnkzMBf/AMqlXvf26TWCw23rqkXOesB8vybpVkMJA0AOUlQOBbsDuBrJ8DGC0Vo2mjXHO0zluKZEPqm9j5MLNFwG8BeBBAD+PWd7FOW/X3sScKIY6ANFHBYtFQEVkYlHhsEWXSxJHhy8Ul1K2fE4t1vz1AFre3RNNyHHYLIb77OWbkEUF6HlEzx1mAcBpccgpHZsW9yRy1DfJiRxJRCgshdHub8fimO2aItsV+QSGyAM7/3EgrfYTzqKbd5YaWnrZMrcOVS4b2r2Jy6vddl1NLlpiNdrmyji9KVbTlUcHHJoaXemoxMnAyYRkJkph0kavTmOT7nrqkcXtx0sqFlmNJAHe4zlNG9NLEdOqVa3/gdjf3TY3Hbf0UYxUowXAKQA/AdAV8wPGWFWS7UoCrZSyhZG0HLMm5PhEHxo3LY5P5Ni0OOV1oT7Rh8Wq7RYb2I4gCAJInuqot7ykyXJ6k55G+0W/ZjITabc2vanHkq7lPKSN6aWIZVqrdNzSNzHyVdc2ABzyqbZhADoivw8AcADAWTkbnQnQSylT0nKU52ZKyNFLH3OmuG7VpbOdqwDXuxIEUXzo6aVeupOZdDMnZDm9KZlGa2o+eQE0SZU+mqttTU8e0sb0UsQyrVU6bumbpJy8cM7PAgDG2FMA1nHO34w8vwzyTSdLGiWlbPPeE9FlseliynNvMGyayx6U9LEth7dEl9UNqoMv5IXbXq67nVdnO2/Ii/Ik2xHFgek8LGleZkaYHz29VFKc1MvNpJs5QUlv2repZ5mS3lSWvqYm02hNzRd9cZfbEDJ6dWqkHnuzrenJcr1qoaSIZatW6bilb5LORYxTlYkLAHDO3wLwzewPqXdIEkd3QL67c3dAhCRx3XWiKKHLH4LEObr8IYTDUkJ/ckqZKm1kbi3Wf3woPwk5kiSb5njkUUocoxqn1Ynm+qZoIsdPan+CRy9+FE6bC55gN8KSCE/IA4lL0Udlu6aY7aYOnormejnJwxPywCf6eraRwrrjiu03tn+CIEoPRVfDkgQB0EgQq4WFMfzm5m9gw90zcEXt10ojWUyPWM1mQk9604SrgAWtwLx1AHhUM9V6GZbCuvqpp9EWwYLnvvUcPpjzAVpvaMUbP3gDy2YsozMvOmjt11vmGksRS5Zcamq0jiXUy2xOw2ljkhSGJ9gt12mwWz4m0HpZVX07LA7NFDGtWhUlEd2R1+gOdkOUEiOqtf4nmuqbqPZLnJRpY9GGjK0HsAnAC5AvI7sewHTO+bdzNzxt9JJFkhnpACSsUxvvl8+pRbXbHpcIJknyxKbDG8IZVS580e5FpcsGm1WAw5bjpBFJytjsKUVSwxxWJzr8HWjc1BhnZlu7ey2e3vF0glkuNm2sO9SNFz99MdrugQsfQMv2Fhz1HZUDALb8D4T3m+LGJTEYNuPliYI7KM2SNpbrMy85P5Nyf2du+zcPRVOziuau/nA/rqg7HX9o/RJzzxsGTzAc1csBLhtW/u++mICTWrjLrHBYSyihSUFLs69eJR8ccjFByyXXQLQHOqJ6eeukWzF75Ow483GsfkpcgjfkRZiHUWGvQHeoG1ziWPT+omj7pRcsxet7X8eVo67Ml+4W/ENMV2NFUUJ3UMTJmP36AJcN5XYrrFYDSXBJkktNiVZdXrMKCAc1ji8GpkzHkyIm+VShQHrm/MqySvjD/qRpY6IkosPfoRlOYVUlp6aZkmriD4owSjqqNhdADYDfA/gDgNMiy0xDMiOdEeP9wjVtCaY7byiM217YjhkPbcDX/+1NzHhoA257YTskDghMTi/LmWj1wjwnCBa47eXwiz40bmpMMLPNHDZT0yxnESwot5fDJ/pwx3t34L/b/jva7t4/34ubJtzUEwAwblbCuLJtxiMIwrwouvrt8UOweO1H+Pb4IWhY0xanl/Nf2K4KOGmT9dPMB3uZoqXZv7sBYExTy9V6OXPYzATzcax++kQfGt5rwEVrLsKklZNwxHMEi95fFNf+vv+9DzOHzSTdTYJPDGO+ar8+/4Xt8InGTPdKcmnOjwGyhVZdejt0ji988iViLJKap/FFqdFQIL3jAX/YD7fNHU0T05pg+0W/bjiFGuW4RWACyu3llDLWBzB8gWYkFnlhDsfSa1IZ6YwY792qa1YLas7LgnlOz7w/ov+IuOfqU6x6pjplu9YjrXBWj0wYlzOyTr0dncIliNJD0cezTyuPe4zF7AEnWUVPs50DNJer9XlE/xFJ9VOty3rtleWku9roBUio9/8lg1ZdVp6Z8fGF0VCg3pjzyYhPJCPlmRfG2KORx9cYY+vUP7kfonEUI10sipFOb53aeO8JxF9TmazPnKOY52JRzHMGUcz7sdQNqsPezr1xz7W+MUm2Xd2gOvhO7E4Yl9529A0gQZQeij7uOdod9xiLXsBJSaKn2b6TmsvV+ry3c29S/VTrq157ZTnprjZKgEQsWvv/kkGrLjv2Z3x8oXdc4VNdFdKb4wGvzmt4sxjbTBQvRi4bWxV5fAjAwxo/piGZkU5r3fI58cb75XMSDXsFNefZXIbNc3qozfuKme2dA+8kNcs5rc4EU90DFz6AFTtXREyiTXDuWpcwLq3t9Mx4BEEUN4o+rv/4EJpmT4w+JtPZojA3Z4qWZs9+DvjHB8D3H0/QcrVevnPgnURDfox+Gmm/9IKleOfAO6S7SXBaLVg+pzahTp3WPlSXrsqMjy+0jiuaNUzyvTkecFgdmkZ8h9WR8Z+BKB3SMexfAuAvnPOCT3uTmfOSGenCYQneUBjuMis8ARFOqwU+UX7uD4YhcQ5XmRXeYBgOixBd5wmIsDAGRyHMeZIkX6+axDyXirAkwif6o2Y2h9WBQDigaZaTJFFeZnMjIPohQYLT6oJX9EKAAIfVEe3DGvJrjkviUrRfPTNeHin4xchk2M8SZNjPG+nUrKK5Tpsg62NEJxXtVJ47bRb4QrJ+xoailAyxWh3oBuxu2T9gc/REzSrLg57o+rDNoanPDosjzoTssDrgj2nntDrhD/vhsDii5ufY5XnUXdPXq3rf77JZwDni9vFOqwX+sGR+832mSGG57mLrEFxVmy55ImMA9XGF0+qARWNbreMBAIaOEURJjKt5h9UBgQm9Pb4owQ+375HOJ/4jAG2Msc2MsWbG2OWMscocjStj9Ix0ksTR7g3hlpXbMGrJW7hl5TZ0+EJw260ABzzBMG6OrNv0+VG0e4NxbbsDIqQwz785TxBSmueSIXEJHYGTaHivAZNXTUbDew04GTgZ/YePNctJkoh2fwcWvLcQk1dNxk/e/Sl8ITke2Rfy4afv/jTaR4e/A2GbQ3Ncsf3qmfEIgigNFM21CAIqHDZYBAFOqwXHuwK4ZeU2jL73bdyychvaPUF8sPsY2r2huAj7kkBJc1o9B/hFDbDmWsB7XI6e9Z6QnyvLPceAvzwFPHAapL88gQ5/R4I+OywOdAR6lr/w6QsJ7ToCHdFUJUVnFbMy6W4P4bCEE574/fkJTxB+MRy37KtOP361aS9OeIIlWp/H4+swcArwqGvzuDzJSdWdxnFFR+Ck5m0R1McDgJxIuuDdBZi8ajIWvLsA7f72hG0lLuGkxrFLV7Ar5bZE6WNY3TjnN3LORwGYDeBLAP8N4FiuBpZt0kkim/b1gVi4pi0hmcxoEomZSCf9Sy9BxC/6NFM/fBqpHwRBED4xrKmh074+MKq7JYVeMmTQk7h87b8C474HSCJ842Ylam5En9NJISP08Ya0a1H5XXmMpuX1lfr0dgBrNWoz6EnZXW9SRY1uq9euM9BJ/weE8bQxxtj1AOoBTABwHMDjkO/7UhSkk0TWz2krmSSSdNI+nDa3TrqH3nJK/SAIIhG9NCdFW0subUwvZaysXHv5wNEAAGf1SF1tTSeFjNAnWS2qlylpeX2iPvXSxspS35W+NyliRrfVaze0fGhGr0uUFumcV34UQC2AZwE0cM6bOeebU2xjGtJJIjvlC5VMEkk6aR++kEcn3UNvecHtTwRBmBC9NCdFW0subUwvZSzQrb38+N8AAL4Tu3W1NZ0UMkKfZLWoXqak5fWJ+tRLGwt0IxW9SREzuq1eu4PdBzN6XaK0MGzYBwDG2DkApgO4CMBIAH/jnN+QpL0DwEYAZZDP8rzMOf8PVZsyACsBTAZwAsA1nPN9ycaRiWFfuRN0w+pWbNnXHrnTcx2q3XYAQJc/hI7I3XbbPQHYLALmv7A92nb5nFpUue3wi2kY+pKZ7WPMczzkk9uWucEDspFTUMzvkTvHOm0u+BTTZozZXgBDmdUBX8SoKahuzqR1h9tlM5bBwiw9JjjBBqvFBkkMoiscQGewE0PLh+Jg90H0t/eH2+7WvNNtlaMSFsGaYMiLNZCmY6iL7ScQDkDiUjZM/wU355FhP0uQYT9vGKlZtdY6rQJ8ohQ17rvLrDhwwotH//Q5jpwKYPmcWmzb344pw6tQ7S4rTlO0ouk2p8r87AICXfKlOJXDIob88p427fsg7fkjfKO+BeeAM+ENdsNpcyEo+hHkYXQGOnF6xenwhDxwWV3Rg7GG9xrQeqQVt066FbNHzo7T4ObpzXBZXXEhKorR34j+ZjFYpeAfZLJ6VTwvC9e0xe3Py8usONoVwBlVLnzR7kWFw4q//uMELjy7BuUOa/7DeTJF6zgDUC1zys8lEXD0B/ydgKVMrtm1N8lnXIZNA2avgOSuhk8d6MPj+5NsTnhFH8I8jAp7BbqCXdFjCnUNaR0fdAQ64o5Jmqc3o7KsMq5uHRYHukPd6AzEHI+U9QcALNqwKG7bKkdVOrVr8g+UMEI6aWP9AFwI4JuQLx8bCDl9bF6SbRgAN+e8mzFmA/ABgIWc87/EtLkdwETO+W2MsTkAfsA5vybZWPSEKtkERZnAaE1swmEJJ7xBLFwdI25za+G2W+G0W+AJiPhg9zEsWN2W0KcuioHz5RhhuHIF4KqBnBBwTL6+tGIwMPM+4A+3R9vx2SvA3XK7dn87GlWThrW71+LpHU+jblAdHrjwAbRsb8FR31E01zfJ/8QaE5jYSYEn6EHjpsa4PisP7oAQ9KL9rAvi1v3nRf+JR7c9ipnDZuL8r50Pt80dlyyinhzp7WRTiUtsP6c5T0PDuQ2498/39kagFAouVDR5yRI0eckbqWpWrbUNl5yNOecNw5q/HsAVdadj8dqPYjS4Fi67FWUWAV+e9KHSZUOFw2b+g0I1iqZvfR6YdDXw6k/jDvpgtQMfPiuv2/G7uDbSNxejfeqP47R86QVL8emJT1F3Wm3C8tf3vo7ZI2ejwl4Bu8WOrmAX3DY3PCEPKuwV8IpehMNhLHp/UZyOf9X9Fb5W/rWU+qv1pVapamw4LMEXCkOUOPo5bTjlC8FhFXAqIMbt85+8/lyEwhIaYpYZ2tcXEr3jDIsd+O0NPcuuWQWIwYSJCmwu+Vik8kygYz8kdw3aeSCxLmCBENOfeN3L6Ah1JXyhWemohDUmcUyvzhwWB9r97XFfkloES3SyXjeoDi0Xt8Av+uOOR5rrmzHAMUA3LdUgJv0wiXRIZ/LyEeTJxwcANnLOv0zrhRhzRbadzzn/MGb5egD3c843M8asAA4DqOFJBqYnVN0BETc/vxWb956ILps2ohrPzpuC8iR+lS6/nEKm3u7J68/FkVMB3L/uk7T7RKBbTp7ZF2MLGl4PzF0DcElO9ti3Cbh9M/BmY0I7ac5q+ASGBe81YMvhLdFVUwdPxT3n3YMfrvthwvOpg6fisYtb4LbrX7PaHexGg0afLRcvB5PCWPD+nbqvN3XwVDx2yWPRxBAA8IQ8WPDugug2r8x6BQ/+9cGEPtTbqYntJ9M+dCi4UNHkJUvQ5CVvpKpZtdauv2M67l/3Ce6fdY6mXt4/6xx8+9GN0ecp9dOMKJr+nWZNzcblLUDYL69TtfH85EMs2N6coGmPzHgEd27Q1twH//ogWi5u0dFr7eXLL16Ohe8tTKmdat3Wa2cQU9er1v697b5/xvwXtsct23D3DNzzys709/WFRO844/IW4LGYS64WtAKvNaRs51nYhgV//UViXZz373Avr40u6/63L9GgUWctF7egPOb4Q6/O7p92P777++/GLVs2YxkuWnNRdNkHcz7Aog2LUr5GBhS8XoneY/g/knM+Mdl6xthjnPMFGsstALYBOBvAf8dOXCIMBfBF5DVExlgngGrIoQCx/dwC4BYAGDZsmOYYUpny9Uhm6KtwaJv3Uxr69AycdlfP74Bs3NRox8rccAKahrUR/UdoPm890gpnChO92giqbOeK7LCSvZ4RU12mxtLYfkrBnGqkXgnCTKRTs2qtVYzOymMsyvLY50VpiFY0XUezUXlmz++qNnrG/H72frqaq2XcV9brLXfrhKsYNUOXosZq7d+1QnnOqHJltq8vJHrHGUotKuiZ81XtnP2HaddF//i/r9EQn3RM9xX2irhlFfYKCgoidMlmEPyFWgs552HOeS2A0wGcxxgbr2qiNQtOOOvCOX+Gcz6Fcz6lpqZGcwDJTPnJSGboUwx86fapa+AMeuNNnMf/ptmOBzzwqUybgGxO29u5V/N53aA6+FKY6NVGUGU7b8gDX+BU0tczYqrL1Fga208pmFON1CtBmIl0alattYpO6unlnqPdcc+L0hCtaLqOZqNjf886VRs9Y/6poL7mahn3lfV6yz064SpGzdClqLFa+3etUJ4v2r2Z7esLid5xRsf++GV65nxVO1/nAe266Iw/q240xCcd031XsCtuWVewi4KCCF3ydhcrzvlJABsAXKpa9SWAMwAgctlYfwDtyACXzYKWuXWYNqIaVoFh2ohqtMytg8uW/JsTl82C5XNq47ZbPqcWm/9+HE9u2INfXjUx7T5hc8nXng6vl+9YO7xefm5zyXe2nf2cvGzTI8AVT8S147NXAHY3nFYnmuubMHXwVFiZFVMHT0VTfRPeOfBO9PkDFz6AFTtXYOrgqWiub0r5zZnD6kCTRp+O/R/C+feNCa8X1//0Zs1v8JqnN0e3eefAOwn9a22nJrafFTtX4IELH0i7D4Ig8oNaa9d/fAjL59Ri/ceH0DR7YoKWrv/4UHr6aUYUTd/1OvD9x+O1ffYKwFXZs07VxrlrXYK2Lr1gKbYc2pKgl0svWBrVUV291lm+9+ReQ/qr1u1S1lit/btVYAnLBrhsaJlbm/6+vpDoHWe4KuOXuSrlGlXXrDO+nbNsgHZdOAbEtXMwm25dxqJXZ/3t/RO2tTBL3DILs2i+RinWKJE+aaWNJe2Ise2c83NVy2oAhDjnJxljTgD/D0AT5/z1mDY/ATAhxrD/Q8751cleK5U5zxuSk248AREumwUWS+o5mno7p9UCf1iCy26BPxSGJAGuMkt6CSRJ08ZEeVlZObgYAMKhaHINt7ujpnvNtDHRH30uMKEnbQwCBJsjmgbii02c4Sy6TrTJffSkjVlhtdiBQDckuwu+2P4FC8osZWml1qjTbgQmpOxD3Q+ljWUf8rwUDUVRs0bSxtRaatoEp2RardUuoqUoKwdCPjk9MjZ5LOiVv6SKSSSL1VZvJB3SL/riNF1ZrmieRbAgHNkHRPU6kipWZimL13GdtDG99LG+kjYGaB8XcC7fTDVapzYLgmEps319IdFMG5N66lOpScDQMokJiXWhShuDzQURUkL9xZr1o8PTqDOJJ24raLwu5zyu9pX/iV5i8g+UMEI2XWhaBTEEwPMR34sA4Hec89cZY0sBbOWcrwOwAsAqxtgeyGdc5mQ6AEniaPeGdNPGkmGxCKiITHIqHPLNq8qt8nOXvefPlJZxTxB6bvgUe+MnSQK8J+SEkIrBYKq0MaakkgkCBMESNeC7rS7AewzuSLKIe9g0+azN396Ce/Rl0T60km2aL/gFql67H0LXIVivXIFyVw3AgXLlDtCR1xauXAG3qwZgQpzxP5mJU2BCdH30UXBHRctook1sP7HfrmRgICWKgOH+F9Nqvy83wyAyRBBYVA+VRxdj+omPjJnT+JwsGVI9gREE+eBQaa+RFonZzwHbVgIbm+KeCxubZM1W9Ffo0VjlsVz1CAAWQY6gTZYOprRXDh7dQo+OJttOrdulitb+HQDcAsOJ7sR6ddkt5qxVLdTHGZIIeI7LaaaxNekYIAcFxSaQhYMJdS+4ahLrgiHuNcJSWOf2CVUJkwutOout2XKN44zY19VqRxDZvGxsuXoB5/wjznkd53wi53w853xpZPl9kYkLOOd+zvlVnPOzOefncc73qvsxijcURsPqVmzeewKixLF57wk0rG6FN2Sya1ZDXlkw9m0C6u+Ud3z7Nsmis2+TvE7rus7Y7ZS2f7gdmHBVXB++cbPQuGkxthzeApGL2HJ4Cxr/99/hm35XfP9a/em9dgYoE5e4cWxsLKrrqgmCME7RaHAs6epgKv1e+6/AuO/pP89AYzPVUtLg5BRlvaYi6JVrTl2T4WD8Mm9Hxvt/n+jDYtUxxuJNi6muiLyR8qsFxthr0DDQK3DOZ0Uef529YWVGpmljeSc2IUQvucaukaihlyziHGAo2cZZPTKxf6OvnQGlkGhDEIRxikaDY0mVDJmsvZ5+Dxyd/HmaGpuplpIGJ6co6zUVZeXaNVmmOnOhl0BmoDaTpeARRD4wcublIQAPJ/kxDZmmjeWd2IQQveSaoMa3H3rJIr6ThpJtfCd2x/efLBEtC5RCog1BEMYpGg2OJV0dNKLfx/+W/HmaGpuplpIGJ6co6zUVsWmmCsOmyctj0UsgM1CbyVLwCCIfZM2wn0/0zHnquz6b9g65koRwyAMfJLjsFfCGPHCKIiyOfvJdbstr4BOERCOlJAGBTvl0b+SOuHBWAo4KSP4u+AIn4ew/DD7PUfCyiugdbDsCHXBZXXBYHfAGu+CEAAuzANayyLWxOtd6GzWx6r1NjbvrLpuxDFbBmg2TqFEK/sEXq2E/1wb8tD0v//Xd1I1Kg6Kt2aLR4FiMeF6iRn1njwm/fR+w549AjN9Q1/PCAal8YCR8xS1rvtUFf9ifYLx3Wh2wqIzPEpfgDXkR5mFU2CvQFeyChcleGLV+qg3SoiRi0YZFSX2HvTTvF/yD7VP1qiZhP+0A/F2AT3WcYHcBnQd7lpXXQJJE+PyR44bOA3A6BkAo6yeHUCTZ74elMLqD3egMdmJo+VAc7D6I/vb+cNvdCIQDKU33jLGEegNgqAazEDRRJB8skQzDkxfG2EgADwIYByCah8c5H6G7UY5IJlTqBBwzpoWEpTDa/e2JZredvwfzn0w02ys7G47EnewPnoL02RtoP2cWGv+8BK1HWnHrpFsxe+TsuP4fuPABtGxvwVHfUfm19m2G5eUfA9MXA+ffCpRVxAtVOibWJKgTxDwhj2EDf5Yo+IdPkxdtaPKiS1HXbDFocAJJkyEjWrj1eWDS1cCrP43XxLL+sp9ASWwSrEDXYfkgMdANQIK0dxPaz5oWp+tLL1iKT098itrTajWMz5VxExjdfYbKIK31hVHz9Ga4bW7dxEe9bdLQ5YJ/uH2uXhW09tPXrALEYPyXkrNXAFY78Nsbosuka1ahnYfRuCnmc69vRhWzQIhpp7Xf1/tiMiSFEurIxmxY9H7P5HnZN5chxEOGttWaZPeyVgET1CvRe9I5YvwfAE8CEAFcDGAlgFW5GFRvUBJwlGQbM4qQrtlt0tXaZnvFYKllLP39bfBNugaNf14S3WbmsJkJ/d/753tx04Sbel5rxHS5jw3/F1hzXU9coiJQWTLzK0kjSjQnmUcJorQpBg1OQElsYkK8DgI9Wjjue/LERa2Jkgg4+snbMgF48WrgsTpgaRXQdRD47Q3wfX16gq7f97/3YeqQqTrGZ3/c8IwapPUM+hKX4rTYyDZ9RZeLsl4VtPbT3g554hJn2L9JXh6zzOc/icZNqs99UyN8/pMp9/taNdMZ6NSso85gZ3y7YGI7vW2N1ndfqVWih3QmL07O+TuQz9bs55zfD+CS3AyrtNE1u5X10zfbW526xlJnWb+4bUb0H6HZx4j+I+JeK7aPBJNeuiZWA5B5lCCIokPRQiPhKmrdjGyj1mhA1r5+du3lauOzUYN0JhpLulzEaO2n9Yz4lWfGLXL2H6b9ufcflritPXWdDS0fqtnf0PKhKdvpbat1c1WqVQJIb/LiZ4wJAHYzxn7KGPsBgNNyNK6SRtfsFjilb7YXfbrGUl/gVNw2ezv3avaxt3Nv3GvF9pFg0suBmZ/MowSCZR8tAAAgAElEQVRBFB2KFhoJV1HrZmQbtUYDsvadCmovVxufjRqkM9FY0uUiRms/rWfE79gft8jXeUD7c+9UXS6ssd/XqpmD3Qc1+zvYfTBlO71ttc68UK0SQHqTlzsAuAA0AJgM4AYA83IxqKJCkuTrmnnkUZJSbuK0OtFU34Spg6fCyqyYOngqmuqb4NzxOzh3rcOyby7DGz94A203tOGNH7yBZTOWyd8s2Fzy9afD6+XrqofXA1c8AeeO36L5gl9E+3vnwDsJ/T9w4QNYsXNFz2vt3djTx5UrInfljUHrtbTapYHT6kTz9Oa4cTVPb6ZvTQiCMCdSWNb2eesARxVwxZOJmqiEqQCJurnrdWD2c3D+fSOaVZrcPL0ZjLNEvf+mHGoicQmekAcSl/T3GRrfTKersaTLJsXIsYXNJXtcFrQC97XLj+U1sscltk5nrwBclXHLnI4BiTVZ3wSnY0DK/b5WzfQv649lMxKPXfrb+8e3s/fX3NZIDVKtEgppp40xxvoB4JzzrtwMKTVmMUBnbGpXp40Fu+FkVlhsDoTFADpCngQTXaVjgGzgjDOWeuQdp80JKeSDjyGawME510wbi6bUQIBgcyRPEetl2pjmW+99Uki6FPwCZrPUKxn2iwaqWTMghQHPsfg7lV+zKuKLqZDTxjb8p2zO10tpDHRH9VOyu+AT/XDaXPiy60s80fYEhvcfjitHXpmg9y/vfhlP73g6zpCsldqkvps5kJnG9tW0MdNi9NhCz7APxKeSuirlyUj3sbhlUllFtCZ9kZoSwFLu9yUuoSvYhc5AT9pYlaMKftGfUMsDHANUaXqUNkb0nnTSxqZANu1XRBZ1AvgXzvm2HI1NF9MIVaAbWD1HNrUpDK8H5q5JvCGUwe26GUfDewux5fCW6Kqpg6ei5eLlKLdXaHSWiCfkwYJ3FyT0cc959+CH636IqYOn4rFLHoPb5jb8VouYgguVWeqVJi9FA9WsGfCfAtZcm6jTV68Efndj+rqPRG1+ZdYrePCvD+pqtfLc5HpN9ZptjB5baLVb0Aq81pC47eUtcpBEsv4MonWM8cYP3sD9m+/XOHZpQbk9/dfIIQWvV6L3WFM3ifIrALdzzjcBAGPsIsiTmYm5GFhRkKmpPcl2LkDHmGl8x6Vnaos17NNpVoIgiCTo3ancOSDjMBO1NqcKV1Gek173MYweW/TCsN+bAJ50DPvqUAmCyAbpnGvrUiYuAMA5/wBAwS4dMwWZmtqTbOcNeXSMmR7Dw9IztcUa9sngRhAEkQS9O5X7TmYcZqLW5lThKspz0us+htFji14Y9nsTwJOOYV8dKkEQ2SCdyctfGWNPM8ZmMMa+yRh7AsAGxti5jLFzczVAU5OpqT3JdkaNmcnQMrXFGvbJ4EYQBJECuxuY/ZzK+Pwc8I8PgO8/nlGYiVqbtcJVmuqb8M6Bd8iQ3Jcxemyh1c5Vqb2tyrDfmwAePcN+c31zQi07rI7UHRJEmqTjeXkvyWrOOc/bPV9MdX2rUVN7QjsnEPLFGO8tQMRAH7Y5IsZMN7whT8SYmXiFXzLjmnqdwATdOyybkSwa+wt+fatZ6pU8L0UD1Wy+SKXfUljWZ7tbfiwrjzPho6y8ZzvA0L5ArW1llrI4Q7PD6kAgHEjQvnyGnaT5WlSvuUCpvWjNuQGNgAZIYk8tKrUJIaEWJciBD3HmfK3+jA5Po0YkLiXUslXj2KU3r5GFmi94vRK9x3AVcM4vTvLTd29WmezOzApKIsjqOcAvauRH73F558Yhi8yLV8vrNj8Bi+c4yl+cC+EXNSh/cS4s3hMJMYkSl9Dub8eCdxdg8qrJWPDuArT72yFxuV3s3ZTdNnf0n17rDstmI9V7IwiC6DWaunwsXmsFC2Avl/V6zbVyuzXXAt4T8nJF94HUfSldqrTZKlhRbi+HwASU28thFaxx65WJS740kfTXBEiSRs0dT6wnKQx4VO08xwHwuOMSiQHtgQ4seK9B/kzfa0B7oKNXn6m6jgUmaNZyxn8CqkMiCcazEBkbxBhbwRh7K/J8HGPsptwNrYQIeeUow32b5G9J9m2Sn4e8ievGfU+O5tRqG4NP9KFxYyO2HN4CkYvYcngLGjc2lsS10aX83giCMAnJdDnddkb7ypB8aiLprwkwWk9BT+Lxwtp/lZfHUIyfaTGOmcgf6XwF/2sA6wF8LfL8c8g3riRSkSw5RL1u4GhDKSN6iWKlcG10Kb83giBMQm8SndTtMk2eNEg+NZH01wQYrSe9RDxV/HExfqbFOGYif6RzTm8g5/x3jLF7AIBzLjLGwjkaV2mhJILE5q7HJn3Erjv+N/22MYKkpH3EZqorqTQmvh+AIUr5vRUSs3lYck3aHp95O3M0EsKUJNPl2IM/I+2M9pUh+dRE0l8TYLSelEQ8dbtAN+DoF11UjJ9pMY6ZyB/pnHnxMMaqIbs0wBg7H/KNKnVhjJ3BGHuPMfYpY+wTxthCjTYzGGOdjLG2yM99ab2DHCFxCZ6QJ+6xZ6UkiwOPPGpc1xyHXnIIEwCbE9LVq+BZ2AbpvnZ4nJWQrl6VMhVEK+0jNpVGksLwBLvl8Qe7IUnG5plJ33eeSPXeCIIgkmJEo40kOkmSrNOp2sX0JU24WtbzeevgEViihkbGFpZEdEc0ujvYjXASjdbTRMUPk02tJv01Abq16ZBvnsol+dHu0k7Es7vi6t9pcWh/phaHoWMZreMCo8cKmR5TOK1OLJuxDG/84A203dCGN37wBpbNWEZ1SABIL23sXACPARgP4GMANQCu5Jx/lGSbIQCGcM63M8YqAGwDcAXnfFdMmxkA7uacf8/ooHOdLKIYxRo3NqL1SCvqBtWheXozqhxVEDhkI+bLN8mnZ4dNi8QQ1mib9YHIzqoT8HbIN4rq2A84K4G/PgvpxG60f3spGv+8JP61LC4IkfQxo6k10VQaKSyPf9Pinj7rm+TxJ0kXSfq+82zyp7SxHHB//5x2n+szL+mmjRXxmReq2d6gGPGNaHSytDGln63PA1PmAQFPj367KoGy/qpkMgmS6Ed72KuvoZE+wwc+RPvwaVgco9FNEY226Gi0WhNFScSiDYtyotWUNmYCEmrTIZvx1/5rT13Pfg5wD0xMG/OeSKh/yTUQvrC/5zO1OCB4j6f8P9E6Llg2YxlCUijlsUJvjilyeDxS8Holek86FfB1AJcBuACy92U3Ulx2xjk/xDnfHvm9C8CnAIZmNtT8kdQolokxM+QFfnsD8FgdsLRKfvzdDcC478E3/S40/nlJ4msxnjzBDNppH9Hxb1oc3+emxSmNbmYyyOm9N4IgiKSko9HJ0iKVfsZ9T36M1e/f3pDYnyDAx3hyDY306RsxHYtVGr04hUartXDRhkU502rSXxOgrs2gV8ec75UvEWOC/Bjya9a/EPLFf6Yhn6H/E63jgs5Ap6Fjhd4cU5jpeIQwH+ko0r9zzk8BqATwTwCeAfCk0Y0ZY8MB1AH4UGP1NMbYDsbYW4yxc3S2v4UxtpUxtvXYsWNpDDt9khrFMjFm6m0zcDSc1SOzbkpz2lzafaa4IRUZ5LJHPuuVILJBydRstszzSj8GQ1QAAxoa6dNV1k+zncvgTQNJq0uoXo1i0Jyf1SAKaNfa0PKhhuqvN3VKNU4kI53Ji3JB7ncBPMU5fxWA3ciGjLFyAGsB3BGZAMWyHcCZnPNJkC9L+4NWH5zzZzjnUzjnU2pqatIYdvooRrFYFKNY1EgXS6z5Xgu9bY7/Db4Tu/VfK9Pxh7zafaaI7Uz6vom0yGe9EkQ2KJmazUSjk/WjhKgY6C+lhkb69AZOabbzGoxWJq0uoXo1imLOj0Ux58ditP4NttOqtYPdBw3VX2/qlGqcSEY6k5eDjLGnAVwN4E3GWJmR7RljNsgTl99wzl9Rr+ecn+Kcd0d+fxOAjTE2MI1xZZ2khkUjJk81WtvMfg7Y9TqcGx9G8wW/yKo50ml1orm+Kb7P+qaUfZJRkyCIoicTjU7Wz67Xge8/bqi/lBoa6dO5dyOaVBrdZECjDb8OUXrY3TrmfFXyltH6N9hOq9b6l/U3VH+9qVOqcSIZ6Rj2XQAuBbCTc747YsafwDn/f0m2YQCeB9DOOde8JwxjbDCAI5xzzhg7D8DLkM/E6A4sH+Y8SQrLpjabC76QVzYsKkbKZCZP3Q7FRFNdyA/YXZBCfvgYz4Y53dj4k22XPaO8WSi4Oc80ZtIiN+xXjP15Wu27Pv2vtNqnGwiQQ6hme4ueRqfS7tj1gW75wDDo6WmveA+SaH5KDY28RtjmgE/0w2VzwRvRaD2zfkavkz+oXnOBFJZrL3rM4AbAE48jtO5ib/QYxWA7rVoDYKj+elOnOarxgtcr0XsM3+eFc+4F8ErM80MADqXY7EIANwDYyRhriyz7NwDDIn08BeBKAPMZYyIAH4A5ySYueUGSIHiPwx1J4XCrUzgUIx1gLMNfCuukhNQATIBgd0H57iRb+eWCYIHbLo9NeTS0XcTMl82xEARB5BUtjU6VQqa1/vuPAzt+J6eNuWp6zNPJXjqVhkbGZgFQHtHm8jQ02vDrEMWLFAY8x1THDCsAq10Oi0iVomf0GMVgO71aM1J/valTqnFCj5x+TcM5/4BzzjjnEznntZGfNznnT0UmLuCcP845P4dzPolzfj7n/H9zOSZDZJIoloygRyclxJPdcRMEQRDapNJ1rfWv/rQnbSxT/SeIdNE8ZrhJvt1Cto5LCKKIKerrgXJGttJqFIymhBAEQRC5IZWuJ0mF7JX+E0S66B0zVJ6ZuIzqkuiDGL5srE+hpHDs29SzTEnhyGTCoaSEqPsLdMu57ARBpCRdDwtBxJFK1/XWK2ljmeo/QaSL3jFDx/74dlSXRB+FJi9aKCkcyrXP0xcD59/aY+I0YtCPRUkJUXte1Ckh2SSTUAGCIIhSxeYCrlklX3pTeaZ8IOiq7ElXUut+rOflyhWy34VL+npKmktkC7sbuHoV4IupVWelbDUfXh/vedFK0dMy+6cRBkEQZocmL1oIgmyCm7sGsDlls/2a61Kb5HRhsnhcswpw9Af8nZGEkByFXqQyphIEQfRFwkHgtYZ4XVSI1f3YtLFp82WPwYtX6+spaS6RVRggadSqa2BPfepOorXM/pGAIJrAECUCqaoeSgpHyCcb5Xpjkgt5gdVzgabhwP+plB9Xz82d0S7bgQMEQRDFjhFdVHSfCfIlvYIFAJMTnpJtR5pLZBPdevL11GdZufbEmAKCiD4ATV5SkQ3zfrYDAMz2egRBEGYnU100sh1pLpFNelNPFBBE9AFo8pIKxcQZi2KSy2cf6ZDv1yMIgjA7meqike1Ic4ls0pt6Usz+6m0D3dkbH0EUGJq8pEIxcQ6vl30qw+v1TXK57CPfYyYIgiglMtVFI9uR5hLZpDf1pAQExW6b64AggsgzZNhPhdrEmUmKTDb6iCBJYfhEH5w2F3whL5xWJwTBEp90E/IaM/YRBEH0FTLVYUGQ9XTOi6r0JkHVRsPsH9J/DYlLspZbndFHgZFGEzBWc7rbWmRzfsK2iWZ93eMJgjA5NHkxgmLiBDK/bjQLfUhSGO3+djRuWozWI62oG1SH5vomVDkqIXhPaCfdMIGudS1V7u9f6BEQRHGRiQ5LEuA9njpJTBDkiYondeqYxCVZyzc29mj59GZUOapoAkMYrzk9BEvPPeR07iWnfzxRRRMYwvSQShYRPtGHxk2LseXwFohcxJbDW9C4aTF8oo+SbgiCIHJBOkliBtv6RB8aNzbGa/nGRlnLCSIP6XVJjycIwuTQ5KWIcNpcaD3SGres9UgrnDY3Jd0QBEHkgnSSnwy2dVqd2lpudWZjxESxk4f0Ov3jCTpuIMwPTV6KCF/Ii7pBdXHL6gbVwRfyUNINQRBELkgn+clgW5/o09Zy+tabAPKSXqd/PEHHDYT5oclLEeG0OtFc34Spg6fCyqyYOngqmuub5G/rKOmGIAgi+6ST/GSwrdPqRPP05ngtn95MZ14ImTyk1yU9niAIk0OG/SJCECyoclThsYtbEtNBspRmRhAEQcSQTkqZwbYCE2Qtv+QxShsjEsliQqn+SyQ5niAIk0OTlyJDECxw2+WUHOUxsqL3iWgEQRBEIunoq8G2AhPgtsn33lAeCSJKHvbpuscTBGFyaPJCECZhwvMT0mq/M0fjIAiCIAiCMCt0jpogCIIgCIIgiKIgp5MXxtgZjLH3GGOfMsY+YYwt1GjDGGMtjLE9jLGPGGPn5nJMGSFJ8l1qeeRRkgo9IoIgCKK3kLYTZoVqkyB0yfWZFxHAXZzzsQDOB/ATxtg4VZvLAIyM/NwC4Mkcjyk9JAnwHgNWzwF+USM/eo+RkBAEQRQzpO2EWaHaJIik5NTzwjk/BOBQ5PcuxtinAIYC2BXT7PsAVnLOOYC/MMYGMMaGRLYtPLF3ugV67nQ7dw0Z44mssvMfBwo9BILoO5C2E2aFapMgkpI3zwtjbDiAOgAfqlYNBfBFzPMvI8vU29/CGNvKGNt67NixXA0zkTzc6ZYoPQpWrwSRIX2uZknbi5qSrleqTYJISl4mL4yxcgBrAdzBOT+lXq2xCU9YwPkznPMpnPMpNTU1uRimNnm40y1RehSsXgkiQ/pczZK2FzUlXa9UmwSRlJxPXhhjNsgTl99wzl/RaPIlgDNinp8O4Ktcj8swebjTLUEQBJFnSNsJs0K1SRBJyannhTHGAKwA8Cnn/BGdZusA/JQxtgbANwB0msbvAuTlTrcEQRSe4T9/I6f97/uv7+a0fyJNSNsJs0K1mXe2bdt2mtVqfQ7AeNBtRMyCBOBjURT/dfLkyUdjV+T6JpUXArgBwE7GWFtk2b8BGAYAnPOnALwJ4DsA9gDwAvhxjseUPnT3eoIgiNKDtJ0wK1SbecVqtT43ePDgsTU1NR2CICRYF4j8I0kSO3bs2LjDhw8/B2BW7Lpcp419AG1PS2wbDuAnuRwHQRAEQRAEQegwniYu5kIQBF5TU9N5+PDh8ep1uT7zQhBEkTLc/2Khh0AQBEEQ+UCgiYv5iHwmCZfx0XV9BEEQBEEQBEEUBTR5IQiCIAiCIIgi4c477/zafffdN6jQ4ygUNHkhCIIgCIIgCKIoKErPy7Zt244zxvYXehwFZiCA44UehInQ+3u8zTm/NN+DiSWNejXZZ/q9TDc02fvImKy+D9ZkuGkx1Wy2MXvtmH18QP7HaMZ6LYbPySil8l7M8j4KXq+Z8Pjjj1e3tLQMYoxh7NixvhEjRgSUdQ8//PDA//mf/6kJhUJs+PDhgZdffvkfFRUV0q9+9avKBx988GuCIPCKiorw1q1b/7Z161bHj3/847NCoRCTJAlr1679+4QJEwLJXtuMMDnsiyg2GGNbOedTCj0Os1AKf49SeA8AvQ8ic8z+Nzf7+IDiGGOuKaW/Qam8F7O/jx07duybNGmSGSZXCWzdutVx5ZVXnr158+bPhgwZIh45csTS1NQ0qLy8PLx06dIjhw8ftgwePDgMAA0NDV8bNGiQuGTJkqOjRo0at379+t1nnXVW6Pjx45aBAweG582bd8b555/vmT9/frvf72eiKKK8vNzUE4EdO3YMnDRp0vDYZXTZGEEQBEEQBEGYkPXr1/e7/PLLO4YMGSICwKBBg8Kx67dt2+acPHny6FGjRo1bu3Zt9SeffOIAgClTpnRfd911wx9++OGBoigCAKZNm+Z5+OGHhyxZsmTw7t277WafuOhBkxeCIAiCIAiCMCGcczDGdCcZt9xyy1mPP/74gc8//3zX4sWLvwoEAgIAvPjiiwceeOCBr7744gt7bW3tOYcPH7bcdttt7a+++uoep9MpXXbZZaPWrVtXkb93kj1o8lK8PFPoAZiMUvh7lMJ7AOh9EJlj9r+52ccHFMcYc00p/Q1K5b2UyvvIO5deeumpdevWVR0+fNgCAEeOHLHErvd6vcKwYcNCgUCArVmzpkpZ/sknn5RdcsklnkcfffSryspKce/evfZdu3bZx44dG7j33nuPfutb3zrZ1tbmzPf7yQZFadgnAM45CUEMpfD3KIX3AND7IDLH7H9zs48PKI4x5ppS+huUynsplfdRCKZMmeK/6667DtXX148RBIGPHz/ee+aZZwaV9T//+c+/Ou+888YOHTo0OHbsWG93d7cFABYtWnT6vn37yjjn7KKLLjp1/vnn+5YsWTL4pZdeqrZarbympib04IMPflW4d5Y5ZNgnCIIgCIIg+ixmNuz3dciwTxAEQRAEQRBE0UKTF4IgCIIgCIIgigKavBAEQRAEQRAEURTQ5IUgCIIgCIIgiKKAJi8EQRAEQRAEQRQFNHkhCIIgCIIgCKIooMkLQRAEQRAEQRQQl8tVp7eurq5uTK5e9+c///ngXPWdK2jyQhAEQRAEQRAGkSRe1R0QJ0icT+4OiBMkiVel3ip9RFEEALS2tn6Wi/4BoKWlZUiu+s4VNHkhCIIgCIIgCANIEq864QmcefPzW+2jlryFm5/faj/hCZyZrQnM66+/XvGNb3xj1OWXX37W6NGjzwF6zsrs37/fNmXKlNFjxowZN3LkyHPefvvtcvX2W7dudUyYMGHsmDFjxo0aNWrczp07ywDgiSeeqFKWX3vttWeKoojbb799aCAQEMaMGTNu1qxZZwHA/fffP2jkyJHnjBw58pylS5eeBgCnTp0SZsyYcfbo0aPHjRw58pxnn322EgDuvvvuIePHjx87cuTIc+bOnXumJEnZ+BOkxJqXVyEIgiAIgiCIIscbCg9tWN0mbN57AgCwee8JNKxuE56dN2VoeZm1PRuv8dFHH7lbW1s/GTNmTDB2+a9+9auqmTNndjY1NR0WRRFdXV0JJyEee+yxmttvv/3I/Pnz2/1+PxNFEdu3b3e8/PLLVVu3bv2srKyMX3/99cOeeuqp6ieeeOLgr3/969M+++yzXQCwadMm14svvli9bdu2TznnmDx58tiZM2d27d69u2zw4MGhDRs27AGAEydOWADgZz/72dGHHnroEABcccUVZ61Zs6b/tdde25mNv0EyaPJCEARBEARBEAZw2S32Lfvi5yhb9rXDZbfYs/UaEydO9KgnLgBw/vnne2699dbhoVBIuPLKKzsuuOACn7rNtGnTPA899NCQL7/80j5nzpyOCRMmBN5+++2Kjz/+2DVp0qSxAOD3+4XTTjtNVG+7YcOG8u985zsn+/XrJwHAd7/73Y733nuvYtasWZ1Lliw5Y/78+UO///3vd1566aXdAPDWW29VPPLII4P9fr9w8uRJ67hx43wAcj55ocvGCIIgCIIgCMIA3mA4OHV4/BViU4dXwRsMJ0w2MsXlcmlef3XZZZd1b9y48W9Dhw4N/uhHPzrr8ccfr165cuWAMWPGjBszZsy4jRs3um677bb2V199dY/T6ZQuu+yyUevWravgnLOrrrrqxGeffbbrs88+27Vv376PH3nkka/U/XPONcczceLEwPbt23dNmDDBt2TJkqF33333EK/Xy+66664zX3nllb9//vnnu66//vrjfr8/L/MKmrwQBEEQBEEQhAFcNsvBlrm10rQR1bAKDNNGVKNlbq3kslkO5vq1P//8c/vQoUNDd9111/Hrr7/++Pbt21033njjSWVSMn36dO+uXbvsY8eODdx7771Hv/Wtb51sa2tzXnrppadef/31yoMHD1oB4MiRI5bPP//cDgBWq5UHAgEGAJdcckn3m2++OaCrq0s4deqU8Oabb1ZefPHFXfv27bNVVFRIt99+e/sdd9xxpK2tzeX1egUAGDx4sNjZ2Sm89tprlbl+/wp02RhBEARBEARBGEAQWHu1uwzPzpsy1GW32L3BcNBlsxwUBJYVv0sy1q9fX9HS0jLYarVyl8sV/s1vfvMPdZtVq1ZVvfTSS9VWq5XX1NSEHnzwwa8GDRoUvvfeew/OnDlzlCRJsNlsvKWl5cCoUaOC11133bGxY8eOGz9+vHfdunX/uPbaa0+ce+65YwHghhtuOHbhhRf61q5d2++ee+45XRAEWK1W/sQTT+wfOHBg+Lrrrjs2bty4c04//fTgpEmTPLl+/wpM7xSRmbn00kv522+/XehhEMUBK/QAqF6JNKGaJYoJqleimNCs1x07duybNGnS8XwPhkjNjh07Bk6aNGl47LKivGzs+HGqL6J4oHolig2qWaKYoHoliL5FUU5eCIIgCIIgCILoe9DkhSAIgiAIgiCIosAUkxfG2GjGWFvMzynG2B2FHhdBEARBEARBEObBFGljnPO/AagFAMaYBcBBAL8v6KAIgiAIgiAIgjAVpjjzomImgL9zzvcXeiAEQRAEQRAEQZgHM05e5gBYXehBEIVF4hI8IU/cI5Fd6G9MEARBmJW+to9yuVx1euvq6urG5HMsWnzzm988+/jx45Z0t7vzzju/dt999w3K5lhMNXlhjNkBzALwksa6WxhjWxljW48dO5b/wRF5Q+IS2v3tWPDuAkxeNRkL3l2Adn97UQmX2eu1FP7GRHYxe80SRCxUr6WN6fdRklSFQNcEcGkyAl0TIElVuXgZURQBAK2trZ/lon81oVBId93777+/Z+DAgeFCjkHBVJMXAJcB2M45P6JewTl/hnM+hXM+paampgBDI/KFT/ShcWMjthzeApGL2HJ4Cxo3NsIn+go9NMOYvV5L4W9MZBez1yxBxEL1WtqYeh8lSVXwHjsTq+fa8YsaYPVcO7zHzszWBOb111+v+MY3vjHq8ssvP2v06NHnAD1nZfbv32+bMmXK6DFjxowbOXLkOW+//Xa5evuJEyeO2bp1q0N5ft55543etGmT69SpU8JVV101fPz48WPHjh077oUXXhgAAC0tLdWXXXbZiEsuueTs+vr6UXqvMXTo0AmHDv1/9u49Porq7h/45+wtu5uES5CLQhFQAVFuEsCgoGj7VItaBapiAe1PwWol3vpAtZamVFGwFacTZVwAACAASURBVA1qi0r7CCraAvXGoz6tgFBFIdxEARERqQpySYBc9r7n98dkNjs7s8kku5vdzX7erxevkNm5JTlzZuac8/2egzYAePLJJzv17dt3QL9+/QZcffXVvQFgz549jpKSkr59+/YdUFJS0vfzzz93xJ7bBx984Bo8eHD/vn37DvjBD35wxpEjR6zqOd5xxx3dhw8f3u/BBx9sspcm015eJoFDxnKey+bC1u+2apZt/W4rXDZXms6o7eHvmIiIMlVG36MCtd2x/GYL9q8HwkFg/3pg+c0WBGq7J+sQH3/8cf6jjz76zRdffPFp9PK//OUvRZdeeumJ3bt379y1a9enI0eOrIvddsKECZUvvvhiEaC87Bw+fNg+evTouvvvv//UsWPHnvzkk092rV+//rMHHnigx8mTJy0AsGXLloJly5Z9+eGHH+5p6hgVFRXOP/zhD6e+9957ez777LOdixYtOgAAP//5z3vecMMNx/bs2bPzuuuuO3bbbbd9L/bcbrrppt5z5879es+ePTvPOeccz6xZs05TPzt+/Lh106ZNn/3ud7/TdWDEypiXFyGEG8APAKxM97m0hlwby9kcnqAHQ7tqh34O7To0M1pc2ojm/I7bSlltKz8HEVG2MFvvxq6X0c8BjnwHDmzQLjuwQVmeJIMGDart37+/P3b5+eefX7ts2bJT7rnnntM2btzo6tixo+4XOnXq1KrXX3+9IwAsWbKk45VXXlkFAGvXrm23YMGCU/v37z/gwgsv7Ofz+cTevXsdADB69OiTXbt2DZk5xjvvvNPuyiuvrDr11FODAKBut3Xr1vzp06dXAsBtt91WuXnzZk2v0LFjx6zV1dXWcePG1QDAtGnTjn344YeRdSZNmlRp9veTMS8vUso6KWUnKeWJdJ9LqmX8WM40c1qdmDd6HoZ3Gw6bsGF4t+GYN3oenFZn0xuTKS6bC/PHzNf8juePma9r1WorZbWt/BxERNnCbL1rtF4wHDR1j0oLf60fPUu0y3qWKMuTxO12G96cLr/88pp169Z91r17d/9NN93U+8knn+y0ZMmSDv379x/Qv3//AevWrXP37t070KFDh+BHH33kWrlyZdGUKVMqAUBKieXLl+/dvXv3zt27d+88ePDgjvPOO88bezyjY0Sfg5QSQgiZrJ9VVVhYaPqGnDEvL7kko8dyppHa4uINebHi8xW4b8R9qJhcgftG3IcVn6+AN+RN9ym2GRZhQZGzCAsvWYjNUzZj4SULUeQsgkVoq4TmlNVM7tngNUdE1Loaq3dje1li17t77d3It+c3eY9KC3v+N5i4OIxeowGLDeg1Gpi4OAx7/jepPvSePXsc3bt3D9x7771HJ0+efHTLli3uqVOnHldfSMaMGVMHABMnTqycO3dut+rqauuIESM8ADB27NiTf/zjH7uGw8q9+f333zd8EzQ6RvTnl1122cnXX3+96NChQ1YA+O6776wAMHTo0NrnnnuuIwAsWrSoqLi4uCZ6u06dOoXatWsXUmNoFi9e3KmkpESzjlkZMUllrsnosZxpora8zFw3E8/+4Fks2r4IT217KvK5TdgwfdD0NJ5h22MRFuTb8wEg8jWW2bIa/ffb+t1WDO06FPPHzM+Ymw2vOSKi1tVYvXvL/90SuVc891/PGa6XZ82L3D/i3aPSwmKphLszMGlZdzjyHfDX+mHP/wYWi+lhTy31zjvvFJaXl3ez2WzS7XaHXnzxxS+N1ps8eXLVb37zm5533nnnt+qyRx555Nvp06f37N+//wAppejRo4dvzZo1e5t7jOLiYu+99957cPTo0f0tFos899xz61asWLH/T3/604Ebb7yx1xNPPNGtU6dOwSVLluyP3fdf//rXL2+77bbTS0tLLT179vQtW7ZMt44ZQsqk9/ykXHFxsayoqEj3abRYbaAWM1bPwKZDmyLLhncbjoWXLMysC7QVRf9OVl61Eg9vfDhZvx+R1BNtgWwur2bLaqaX6Uw/vxhZUWZ7/WpVs/a5/5FxiZwSZa6sKK/U+uLVu2UlZRj3j4b6YNU1q1C2oay16mfD8rp9+/b9gwcPPprsg1Hitm/ffsrgwYN7RS9Lf5NoDjIbb5BLoltont3xLOaMmsPfTwYwW1YzvWeD1xwRUeuKV+8+ve1pzXpPb3ua9TM1C4eNpUF0vIHL5oIn6IHL5sqI4TXpomYW2XRoE9768i0AQFlJGXoU9uDvJ43MltXov59KzQyTCT0bvOaIiFqXUb1rERYc9hzWrHfYczgS38L6mcxgyUgTNd4g+msui22hOeo5CrddiRGL/f1kcmB4W2SmrLJng4iImuKwOAzvFXnWPD4TkWnseaGMYLZlPNMDw3NVpvdssNwQEbWuePVux7yOGXuvoOzA0kIp1ZxeEjMt/Ex5mxmM/q6Z3JvIckNElDxm7u3x6l1vyJux9wrKDiwxlDKpmBgw0wPDc0E2TvjIckNElBxm7wGsdylV+PJCKZOK1m41MDyaGhhOrSMbezFYboiIksPsPYD1bvO43e6h8T4bOnRo/0T3/+KLL7a///77uzV3OzPHvu66607fvHmzs2Vn1nx8eaGUSUWrCwPD0y8bW9NYboiIksPsPaAt17thGS6qDdQODMvwsPqvRak4TjAYBABs3bp1d6L7+ulPf3pi7ty5h2KXBwKBRrczc+xXXnnlq2HDhnkTOL1m4csLpUwqWl2iA8M3T9mMhZcsZNB1K8vG1jSWGyKi5DB7D2ir9W5YhosqvZWnz1g9w1E/bM5R6a08PVkvMG+++WbhyJEj+1555ZW9+/Xrdw7Q0Cvz1Vdf2YuLi/v1799/wFlnnXXO22+/XRC7/aBBg/pXVFREekFGjBjRb/369e7y8vJOU6dO7QkAEyZM6HXLLbf0GDlyZN/bb7+9x7fffmsbNWrUWQMGDDj7hhtuOP20004bePDgQVv0sd98883CESNG9Lvsssv69O7d+5yrrrqqdzgcjhxj3bp1bgBYvnx5uwEDBpzdr1+/ASUlJX0BYM2aNe6hQ4f2P/vsswcMHTq0//bt2/MS+R1ldwmijKQG8LlsLjw+9nH8YsgvIq0uCy5eoFknXpxEY8GAmRwY3hbF/i2cVqdha5rT6kxLCmuzSSFYboiIEhevR8XoHpBIvWu2bm/t6RM8QU/3metmWmKGzVk8QU/3ZB3j448/zn/00Ue/+eKLLz6NXv6Xv/yl6NJLLz2xe/funbt27fp05MiRdbHbTpgwofLFF18sApSXncOHD9tHjx6tW++LL75wvv/++3ueffbZr3/1q1+ddtFFF1Xv3Llz1/jx46sOHjzoMDqvXbt2uZ566qn/7N2799MDBw7k/fOf/9S8PH377be2O+64o9fKlSu/+Oyzz3a++uqrXwDA4MGDvRs3bty9a9eunb/97W+/mTlzZo9Efj9MlUxJZZgacfR8TBs4Df6wH7WBWty99u5G09UyrW3mMJvq0ml1ospX1ep/M5YVIqLWZZQaP9n3ALN1ezruAS6byxFn2JzhA39LDBo0qLZ///7+2OXnn39+7a233torEAhYJk6cWDVq1CjdkIepU6dWff/73++7YMGCb5csWdLxyiuvrDI6xvjx46tsNuU1YOPGjQWvvvrqXgCYOHHiyXbt2oWMthk4cGDtGWecEQCAc845p+6LL77Q/Mxr167NHzFiRLV67l27dg0BQGVlpfW6667rvX//fqcQQgYCAdGsX0iMjLi7CyE6CCGWCyF2CyF2CSFK0n1O2S5dEzl6gh4s37Mc9424DxWTK3DfiPuw/PPl8Ia8CMuw6SC/bAsIb6vMprr0hrym/2ZGZbOl5ZVlhYgo/ZpzDzCjOUkBWvse4Al6/HGGzeleNlrK7XYb3gQvv/zymnXr1n3WvXt3/0033dT7ySef7LRkyZIO/fv3H9C/f/8B69atc/fu3TvQoUOH4EcffeRauXJl0ZQpUyqN9lVQUBA5hpTS1Hnl5eVFVrRarQgGg5qXECklhNC/l8yaNav7RRddVP35559/+sYbb+z1+/0JvX9kxMsLgCcAvC2l7A9gMIBdaT6frJbOVLZOqxNX9LkCD298GMUvFOPhjQ/jij5XwGl1NivIL9sCwtuqZP/N4pXNan91i8orywoRUesyqseTXRdn8vOCy+b6Zv6Y+eGYYXNhl831TcoOWm/Pnj2O7t27B+69996jkydPPrplyxb31KlTj+/evXvn7t27d44ZM6YOACZOnFg5d+7cbtXV1dYRI0Y0+SY3YsSImqVLlxYBwMqVK9udPHnS2pLzGzt2bO1HH31UuHv3bgcAfPfdd1YAOHnypLVHjx5+AFi0aNEpLdl3tLS/vAgh2gEYA2AxAEgp/VLK4+k9q+yWrJaIlow39QQ9mP3BbM2xZ38wG56gx3SQXzYGhLdVyf6bGfbM7VmOE74TLSqvLCtERK3L6Bnj6+qvTdfFZie4vHXwrVh51Upsm7INK69aiVsH36rbX12gzvC4dQFdiEfSWISlsshZ9NXCSxb66xMR+IucRV9ZhMWwhyOZ3nnnncIBAwacc/bZZw947bXXOs6cOfM7o/UmT55ctWrVqqIf//jHps7pkUce+Xb16tXtBgwYcPaqVavad+7cOdChQwfDoWONOe2004Ll5eX7r7nmmjP79es34JprrukDALNmzTpUVlbW47zzzusfCjV7tzrCbFdRqgghhgB4BsBOKL0umwHcKaWsjVlvOoDpANCzZ89hX331VWufatYIyzCGLR2GoAxGltmEDZunbDY9BrSl4003T9kc99gA0jGGNaFxlS3VVsqr2b9FKBxCpbcSs9bPiqw3b/Q8FDmLYLVYNesdrD2I2R/Mjqw3Z9QcdMvvhqFLG25AZstrG415yYoy2+tXq5q1//2PjGvxuVFGy4rySslj9Iwxrvc4/HL4L5N2f0/0nnJq/qma9aIYltft27fvHzx48NHEfzvZyePxCJvNJu12O/71r3/l33HHHafv3r17Z7rPCwC2b99+yuDBg3tFL8uEl5diAB8CuEBK+ZEQ4gkAJ6WUv4m3TXFxsayoqGi1c8w2tYFazFg9A5sObYosG95tOBZeshD59vxID4kaaOeyuSKVhvoZgEb3Ee9YK69aiXcPvItLe16KPu37YN+JfXj3wLuYMmBKk8eOZnY9E9JyY42WCeU1kd+nmW1rA7XYW7UXfTr0Qb49H7WBWuw7vg9ndjxTU15q/DUoXVOqK1cLLl6AC1++ULMstqyl4mfLUFlRZvnyQvWyorySOWbre6Png6cufQphGU7KtoD5Z5ClO5fGfeYwwJcXAzt27Mi79tprzwiHw7Db7fKpp5766qKLLkpd91UzGL28ZMId/msAX0spP6r/fjmA89J4PlmvsYmhGouHif7MaXW2aLzpxoMbMeGsCZqYlwlnTYDTqqQcN5s2kWltkyfRGCgzf4s8ax5OKzgNd665E8OWDsOda+7EaQWnIc+qTeXutrsNy1Who7DFE5mxrBARJc7svcJpdWLe6HmaOnve6HlwWBxN1sVGMSpdXF0iLzXNjaFx2VyY2Hei5pljYt+JjHtspoEDB/p27dq187PPPtv5ySef7MqUF5d40n6Xl1IeAvAfIUS/+kWXQhlCRi3U2MRQjcXDeIIebP1uKx67+DEIIfDa1a/h8t6XR/arjl+NHqMaG3Mw4tQRmLV+lmb/s9bPgjfUahOvUozWyMbiDXqN/+5B7d89XoyKL+RD+dhybJ6yGeVjy9EhrwNfQoiIWpHZe4U35MWKz1doYhdXfL7C1H3eKJbl3uJ7WxxDYxEWdMzrqLl/dMzr2JL7RzgcDqe9F5G06v8mupbWTHk6mAHgRSHExwCGAJib5vPJevFaoxtrzciz5mFIlyG4Z+09GLZ0GMo2lOGu8+7CuN7jMLzbcDx4wYP4/Ybfa1pjYnt5+nTow+xPGaY1srHE61Fx292aZUYtdgsuWoBafy1K15Ri2NJhKF1TiipvFULhxIP6iIjInOb0dizavgjjXx+PIUuHYPzr47Fo+yJT9xSn1akbnSGEQBdXF816//7634a9O+ooDlVYhlHlq9LeP3xVLcmu+smRI0fa8wUmc4TDYXHkyJH2AD6J/SwjJqmUUm4DUJzu88gFast39DhStTVDShlpPQeATYc24YH3H8DCSxbimOcYHt/yON768i0AwMx1MyNjT6Mnq1Izfxjt30z8AiVfY3/zZP1N4v3d6wJ1KHA0TMAb3WKnjk8WFoGZ783UlLtZ62ehfGy5ZlsiIkods/eKRO4p3pBX95wxc91MlJWUYdWXDXF0F/a4UHevWPH5CiWWxaI9F7XXJnp/ZmMmVcFg8JZDhw49d+jQoXOROQ37uS4M4JNgMHhL7AcZ8fJCLdNYYF0oHIIn6IHb7kZdoA4WYYHT5oSUEuVjy1G6plST6UNtMYnX6vLjV3+sySwS3Rqj9u4ASgv8/DHzdZlE2POSPmrvWOzfxGl1ojZQ22Sge2xZctlcEEJoyp7TpvSoxGaGcdq0x3BanVi0fRGe2vZUZP/bp26P22tj5vyIiKhxZgLx490rjHpeWnqfd9lc6OLqgpVXrYy8lCzesRjdC7pj1TWr0L2gO76p+QbdC7rr7hU2YcMtA29Bjb8mcj+K1+vf3GeOYcOGHQZwVbM2orRJ+suLEKIDgKkAekXvX0pZmuxj5bLG0g1KKXUpBh+84EGUbyjHYc9hzBs9D09f+jQcVoemEqvx18RtPTfbyhIdb8OHzsxg9DdxWp2o8lW1OF2ly+bSvQB3dCrjjtWbitPmxHHfce0xRs/HrYNv1dyQqv3VhuWrJlCDu9bc1ZZSIBMRtTqz6Ymbc/+2W+woKymLvGzYLXZT5+IL+VB6XikeeP+ByLk8OuZRVPmqULahrNF7xfwx81HlrdLcjx4f+zhHe+SgVDwF/C+UF5cdUOZsUf9REjUVeB8dPH2K6xQEw0HMHT03ElgXDAd1+7RarHjwggc1Y0wfvOBBWCwWzB9tnL3MCLM/ZZ7Yv4k35DUVmBlbltQhXSEZ0m0bW6Z8IZ/+GOtn4oazb9CUJauw6sY2zx89Hy/teimlSQaIiHJBc5K2mLl/e4IebDy4Ee3y2kEIgXZ57bDx4EZT9XNYhvHA+w9ozqU2UGvqXnH+aefr7kcv7XqpWc8n1DakYtiYU0p5Twr2S1GaCqxTP7u89+UoHVqqm8DJZXNh2NJhmhaYPGseyreUa8aYlm8px0MXPoQlny/B42MfR4G9gL0pbYDZwMzGUhtH+/73vo9qf7WmRey5/3rOcNsCe4GuZc9lc2l6bdSA0KbOj4iIGpfspC3RyX2ie+RjU+ObPZfuBd1N3yti11u0fRGmDZzG0R45JhV/3aVCiGlCiFOFEEXqvxQcJ6fFppy9vPfleO3q1wAowdO3Dr4VADBt4DTM/mC2pqVi9gezUResM+yxOew5rMkgcthzGPtO7MO+E/tw3Hs8LT8rJV+8lMWxLWfqkMHY9ar91Zpl484Yp2sRU4eDxW5bF6jTtexZLVYUOApgERYUOArgDXl16TRvHXwre16IiJrJbH1vltnU+EbU55Pour3SWxk3hX7sceP9HBztkVtS0fPiB/AogF8DkPXLJIA+KThWzooOmOvi6qIbQzpv9DwAQJ/2xqmLo8eCRrfAxAbhzRk1B+/95z1d7w3jD7JbcwIzjQLxrcKK4d2GR5YVOgp15Szfnh83iL8pajpN3bbWprclygQDnx/YrPV33LgjRWdCuS6RAHsjZlPjG3HajOv2BRcvwN1r744sW3DxgshwsuhzXnDRAtz93t2abdkjn3uElLLptZqzQyG+ADBSSnk0qTuOUlxcLCsqKlK1+6yhZg8BgBmrZ2gC1oZ3G47yseUQQhh+dt+I+zD+9fGR79W0gtEZSb6u/hpPbnsS0wZOw8MbH9bto7mpCNMk7TnbM7W8GmWfAaBbJqVsMtuYlBKla0o1ZeTf1/8by3Yvw6U9L40MQ3z3wLuYfPZkCCEa7eJXZ1vO0jKXqKwos71+tarRz2Ptf2RcIqeUdXLo5SUrymuuM5NtzKwaf42uvo9+5mjsGGa3BYyfaxZeshBSSs39yGqxNuf0015eKXGpaDb/FEBdCvZLMdQu0njjWd12N/KseYYTPb174F3D4LbYtMdHPUfj9t6wtSO7xQZmAkCltxIzVs/AsKXDIpORCiE0Q7qsFqtuW7WHJrqcFToKDScyc9vdumPETijWGhNrEhHlimQm0jGq79XRHk3V7Y312sTeU+LdA2LvR5R7UjFsLARgmxBiDYDIgEWmSk6dpiaejJ3o6duabzH57MmYPmh6pOVCrciiW2dcNheeuvQpeENepiLMAYlM9mW1WHWpkv0hv2G5+br66yaP0RoTaxIRUfNZLVYUOYs09b3NYsPt796uq9ufuvQphGU40qNiERZTdbvZiY8pN6Wi5+VVAA8B+ABMldwq1PGsRqkC3Xa3pvX72R3PopOrE0rXlGLY0mEoXVOKKl8VwjIcyQUf3XJSG6iF0+qMu39qOxLp7QiFQ6jyVmnKVbW/GuVjy3Xl5ultTzd5jMbKNBERpVdskhWH1aG7f3RxdYkMAY5+plhw8YIm63Z14uPY3h0zMZPU9qWi52U5AK+UMgQAQggrgKbz51GLNTaxVOzEk9HZxwBtyzeAuC3vnHiy7UuktyN6PhgAkewz5WPLNeXGIiw47Dms2dboGJzslIgoexjdP24fcnvcZ4qm6nZv0KsbNbLi8xWYfPZk9rxQSl5e3gXwfQA19d+7APwfgFEpOFabFAwH4Q164ba74Q16EUYYbptbucitTlgCHsDhBvx1gN0NWCyauAU18L42UAu33Y3Hxz6Ol3a9hEXbF6FPh8bjVxhnkLtcNhcWXLwAJ3wnIrMmt89rjzxrHmr8NVEBkk5YLdqqw213o4urC1ZetTJyo1m8YzHcdrcmHafD4jCd9Sa2TBMRUfIYBvFLAIE63TNGU9s6rU7d/aNHYY+4zxTqy0q8ul0dNfLUtqciy2zChumDpqM2UMtGrRyXir+4U0qpvrig/v9N588jAMqLizr85v719+O47zhKV5dqA+A+fBr4fWdg2fVA3REgrA2Iix3+ddeauzDxrImomFwBTyB+vvfGcsEbBXLHBuJRdpNSIhAKoGxDGYpfKEbZhjIEQgHU+ms1w8EqvVUIhYOabb1BL0rPK8XDGx9G8QvFeHjjwyg9rxTeoFdTbqp8VXBanSgrKUPF5AqUlZTBbrGn6ScmIspNRsPEK72VCPtOKM8WzXjGUOt2SGjuHzWBmhbPLxPveSR2GBqfRXJTKl5eaoUQ56nfCCGGAWiypAoh9gshdgghtgkhcirnodpLEpZhzeRPNw+8GQ+8/4BmIqiZ62ehbuR0hB84jNpxf0B48/NKK0kUT9CD5XuW474R96FicgXuG3Efln++HN6QF1aLFQ9e8KBmHOmDFzwIi7DEjTOwCEuk6zd2YktqPdHlRP1qVigcQo2/BmEZRo2/BqFwSLeOJ+jBzPUxf+f1M3HCf0I3GZknZjIyCakrqw+8/wDqgnW6clPprcS4f4zDkKVDMO4f43D32rtZloiIWlF0ghbNfd17HNi/HggHla/LbwYCtYCvBpBhwFcTd9vYe8VLu17Sxa3MvXCuqZ4Si7Dg0TGPYtU1q7BtyjasumYVHh3zKLxBL59FKCXDxu4C8HchxLf1358K4DqT245N5fwwmUhtwVCH0WyesjnSzRo3RXFeIYYtLVaG3JSUocju0ryFOq1OXNHnCs2kknNGzYHT6oQQAuVbyjXjSMu3lGPu6LmwSKAIViwc8Ru42veE58QBuGAFrHkcTpZmseWkOROFhsIhVHordZOCFTmLNGkm46Ww7F7QXbcsdjKyeMH+HfM6mtofyxIRUeuJm6ClfU/tigc2AI584PmrlP/3LIHrxtdN3SsWbV+EaQOnoaykLDKUzCZscFgcTZ6fw+LA8dBxlG0oi9y3HrrgIXR2d9afM+8fOSfpPS9Syk0A+gO4DcDtAM6WUkayjQkhfpDsY2YjtfUcUFICnuI6BUEZxEn/yUhX6b4T+wy7Tfcd34egDOIU1ynKhDpCaFriPUFPJChfbZ2Y/cHsyNCww57Dmrk3DnsOKy0XgTpYXpmC/CeGwDKnSPn6yhR46lMWxp4HWztaT9xWMpPd72pvnrb3RLttXZy/8zc13+iW1cX29jVjW6NlLEtERK0nXn1fF6jRrtizBKjcr+mN8Rz/ynR9f8RzBL6QMmuGL+TD8s+Xm75v/fr9X2vuW79+/9eR56boY/D+kXtSEuUkpQxIKT+RUu6QUgZiPp4XbzMA/yeE2CyEmB77oRBiuhCiQghRceTIkaSfc2uKHS9atqEMpUNLcXnvy7Hqi1WRbtbFOxbrhnjNGTUHz+54Fpf3vhylQ0tRtqFMN/azsUmgGk1B63ArLSvRDmxQtonp+p0/eh5cVqYsjCfZ5TWRNMaNlYdoRqkp54+ej/aO9k2mqzTedh7a57VvehlTIGeEtlTHUtvH8poYl82FOaPm6J4vXPYCoNdowGJTvk5cDKydq9k27+gXpu4V88fMh03YNLGQV/S5IqH7VqGjkPcPgpBStu4BhdgqpRxqsPw0KeW3QoguAP4JYIaUcp3RPoqLi2VFRfaGxagBZ9EpBYd3G477RtyH8a+Px2MXPYaS00oaso3Vv5DUBeqw4dsN6NW+F87ocAa+rv4aT257Em99+VZkHwsvWQgpJUrXlOr2Xz62HAWOAuMMI8KijGlddr3SwqLqNRqYtAxhXy08YT9c7brDc+xzuHa+Dsv5twN5GZ+yUKT7BJJRXuOVGTMTSNb4axotD9HHWLpzKS7teWlkSOG7B97FpH6TIIVEO0c7nPSfxKaDmzCq+yjNcWv9Nfjg2w0YfupwzXoXnlaC8Imv4ep0VqTcYFQpPDIIl90NjzpJKmdJjpYV5dE0zwAAIABJREFUZbbXr1Y1a5/7HxmXyCml3cDnB6Z0/ztu3JHS/adQVpTXnBcOa7KI1Qpg6a4XdPX9lLMnI1+iIduYsAAvXat5Lqi57wA2HNyoq+8v6H6B5nnFIiy4Y/UdTd57jDR2zwOQSLaxtJdXSlw68ssZvi1JKb+t/3oYwD8AjGjNk2pN8VrR+7Tvg+HdhmNo16Fw292wSMDtq0bBS9fD8vvOcO1djSFdhuDhjQ/remzUfTitTryw6wVdq8icUXMirRNqCtrorwCUlIgTF2tbXa5+GnjzblhW3oJ8GYZl5TTkPzUSlvfmKZUbtYpEJm10xZnsyxXTe+KyuTQTmo5/fTz2n9iPgAzgnrX3YNjSYbhn7T04u9PZcFpje15cOLvT2br1HDanUl7mFClfj+6BxXcC+fVlOv+l62GpO6rLZkNEREkSDitZw6KyiLlCAUzsc5WmV2Ri7x8p95S8AuWlJa8AsDl1zwUue4FhfZ9nzdNMXOm0OU31+htp7J5n+PxCOSUVAfvNJoTIB2CRUlbX//+/AMxJ82mlTLzJAL0hb2TiJouwAP4aJdNHfYuHt3Nf3USAsz+YjftG3Ie3vnxLiYc5sS+SF/2xix9Dob0Q+07sw5v73lRaVBpr7bBYAPcpwPUvKZWW9wSw/RVgx9+Vz1+7A/jRfOCTFco4WF+Nsl4jueApORKZtNEa8KJo/waUX/QY3HntUOc7Cde+dbCecYmm58zsJGOzP5iN8rFPoECKSOucR0A3+ensD2aj/OLHUXD7BuCUfsDRzwBnkaZMR7LZTFoGQDQ5twAREUWJ6VExrDsDdbp61/LKFBRduxQLL3oMrrx28PhOwvXFOljO6KC8uETvz90ZmPRyQ30fFVcLRN8XtD0qiUx8zImKqTHpeHnZb7CsK4B/CCEA5ZxeklK+3Zon1ZrUFgWjifo0F2ZMDIqr01mN9tjMGTUH5VvLAdRn+Rg0DcVqVjKDlnadcBioO6pUcvVZRfDjJ4GvNyovLAfqH0J7jQYmPAd8+Gdg3TxlvYmLlQqOD5wp0+JJG+1uWHuORMHLk4EDG1Cg/r0MMobFlst4k4y57drsM+442WfcjgLgf2c2lKeJi4HCbtrzM8hmw/JEmWbHlweatf7A3j2bXokoEWqPSvQ926juNIpnLewGS9iP/L8p2+b3LFHu60IoPTSx+1MbuvIK4JZhUz0qjT3rmMGJiimelLy8CCFGAegVvX8p5ZL6r+Nj15dS7gMwOBXnkolMtyj465TKo761xHPs87g9NmUlZSjfWh6JfxnadSg8gTpsnrK5oUXlzEsaj1ExaJ3R9bYE6pSemQ//DKx9qGG95TcrLTOZHwOTeywWXcuZUeucUblUs4jpWs4CtciPKidq9hndese/0qyH5TcDV5Y39OYB2mw20euxPBERxWd0zzaqO301mmcJAMDF9+u3XXGLUj83sb+6OPeFukCdpueFvSeUKkkvQUKIpQD+AOBCAMPr/xUn+zjZLm7cSbSYGBTXztcNsn49Aqc1D26bE0c9RxuWl5TB/fqdsPyuI/IfOR2WFT9riFEJhxsmnPLXAb5q5f9xso1FelsmLgbs+Uoltm6efj3GwGQui0U7jjlOj0ZsuXTZnMaZ5mJazlyr5xqPT16tzVKDAxuAol5NZrNheSIiakK8e7bDrZlUEo58pZ6dsRWYXal87Xi68bYdTzfeXxSzcZSAyWcdomZKRc9LMYABsrXTmLVFMS3mFn8diuyuhlYMfzVcHy6C5b15KLpoFhZevAAuR6GSwWnD07B8sqJhXz1LGlrc1W7mwm7ApbOBV29XKqhfbNK3zqi9LZNebmitN2rFUffPlvI2xRLwoOjLDfpx0d3O1a7X6SwUyZgJTqUVlk5naXfYswQ4eVDpzVPjYMISqD6kX4/liYgovpjRGQDq41GrgZd/2jD0a9LLQCgAvFHasGzCc8CYWQ0jKNRtq77SHsOgLrZabChydkT52PJIZjGXzQmrJSPCqCkHpKKkfQKgG4CDKdh3q4mbTrhZO2kkkE7zWS0grIDdqV9PbTEHgLwCWMIh5IdCQLgW+ct+Gqm0LGseQv6X64BJy5APACOmAV+ui6qoFgN2lzYJwO0blBcXteJb86CSXUx9mYnERuRrW+rVHqHYcbYmMohQAswEZsbbtKXlWVhh6XEe8uvjZfLVsmRzNrTeVX0F5HeC5eWfRoaI5QNKr8r1LwL71+nLSVCZtAxWJ5CXH6c8uepbDRnET0SkY3QvnrAY+HCRduiX9wTwj5/rh4hdF1M/T3hOud9H1+3ujgjbXfAEajX3D6vFFhki1lTaY6JkS9rLixDiDShpkAsB7BRCbATgUz+XUl6VrGOlmjqJZGyQWZGzyPwLTGOBdID+s6ufBt6do7RAxwtWDoeA2iNKpTP1NeMuX7sbWPJjZR83/E15IfJVK5XZunnAA4cbtjuln3Yfn6xQhhVNWqZ0M8d7YDQZQ0FJZDYw02jTRMqzLQ+w2pVx0FEvKjh5UNuKd+PrxuUxrzCmnLiUpBDR205crGS5M1qvBT8vEVFOMLwXu/TDugu7GdfPzkJt3W7PB4J1mvo5fN1SVPqqEnseIkqyZJa8PwD4I4AyAFcDmFv/vfova3iCnkh62KAMYtOhTZi5biY8QY/5nUQH0oWDQEEXwFerTI/krwEqnm/4bP96pbdj9D0NAXKBOv0+/bXKi8v+9cpwm54l2s97lijL969X9h8OKstrjwFHdyvfR2939DOl2/j2Dco42Ns3AP2vAsyM+DMZQ0FJElue1HIS9GrHNhvMl9JoeW5iW/hrleMsHArMKVK++qqBV2/Tnov3hHF59NVolwV9xj9HwKMtTwFPnPUMrgsiIlIEvfr7evUh4/q5cr+2bq89oqt3Pd7jiT8PESVZ0p44pZTvSSnfA/Aj9f/Ry5J1nNYQbxJJs+n9AGgD6c6doMSWvFGqTBD18g3A4GuV5So1MF79v1Gwcl5Bwz7X/UFJYxwd+PzjJ5Xl505Q9v/yDcrx3ihVjn/uBO12X64Hhk1VUtk+2EX52nOEkkmsfiIr1B3hBIKZIE6qS/irNROPGf29Gi3PTWyrKXORZe0MlhUal0dHvvYY/uo4qZJjyntjgahERGQ4+ST8Ncqw8ej7ut2tDCdrKlGKQRC/q33PxJ+HiJIsFc3lPzBYdnkKjpMy6sRK0dSJlUxTA+kAYMwvlZTD0a3Ir92hLFepvSbq//0GLcxqoDygDPF6d47S5fubI8D455Tl458FfvQHYPvf9Me78gnlmN9+rKQ7HnJDQ0+Out6KW4ABV7C1O9NElydVdKrLRv5eccvz8a+a7tkwOm7VV/qWvZqjSpn70XxlaOKP5ivf+2v0x7j4fu3+jMq70XHjXRdERLkobo+8T1sXb3xWGVERvczu1idKqfpKV+96ThxI/HmIKMmS9vIihLhNCLEDQD8hxMdR/74E8HGyjtMa1ImVdGlfm9PSEJ3mODa2BGjoaVFbQa5+Glj/WFRKYoMWZke+ElCntp7UHFZaomVYGY722h1KS8vfphr37NjdDb0rjnzlX7zziv6erd3pF5M2G71GKymHTfROxC3PRmmMY//WRsct7KbvsRMCKP6Zdtnga5UyFnsMo1TJseXd6LhMCkFE1CBej7xF6OviglOAp0uUIWJPlwDbXtI+T/QaDbg66updl7ND4s9DREmWzGxjLwF4C8DDAH4VtbxaSlmZxOOkXFImVooOpAvESWfor1V6TdRsY+OfaTz43WIF8jsrvSZ5BQ352wOehh4UQD+5pHo8NR5mxS3KeanLY89L7QGKnCdT1qadUWCmv9ZUymrD8iwFLNUxCQGN/tZGxw0H9eVtxc3ANYu0KZC3/w0YONHgGLVNJ3tgUggiosYZpUo2mnzytTuA65Zqt931BnDe1JjnCTcAi6betdjdKBLgRJOUUZIZ83JCSrkfwC8AVEf9gxDCnqzjtJakTKykBrXb8/WtyNctVVqrAcBibwiul2EoSdvqRU8o6asBIABnOyWw2dlOeaFpbHLJ2HgY9TOH27h1e8JzwM432dqdiWKTJBiVqzh/L115tjnjbOvSB/HHHtcoDubABqDdqUrqY0D5OnIa4O5ocIx8c8kemBSCiCg+u1t5loiefDJej7yzvf5eb7Ur9StQ/9ViWO9yoknKNKmY52ULgO8BqIIymKkDgINCiMMApkkpN6fgmJktthU54FUCl1+ZApx9JTDgx0pLdnSu9fzOAIS59LjxJqpSe3Yq9yvxMdG9MGoLu1GaxZLbgYt+ydbuTJdI70S8FJtm0hPHm6TUVx0zCdpiJa0ye0+IiFIj5NfWu9e/GP95ILaXpe4Y09FTVkpFCX0bSsaxU6SUnaAE6/8NwO0Ank7B8bJDdGuGDDd06w78CbB5iTaQbvOS+qFkUNIrF3RpPKg6XnyAo0DpxMnLV+JjjFrnda0sVrZ2Z5NEeidit42Xnjg2HbPVocRoRZe3q58G/J6Y5A83N7wkszwREZkXO+rCKOunUcC+3wNM/Ku2N2biXxtGa6hfA16mo6eslYqel2Ip5c/Vb6SU/yeEmCulvEcIkZeC42Wf6GFezvZKMN1rdzS0fqgpZn/fueF7QOk5MQqqbqoFnrEDZEZj6ZhjW+c+e0sb3/LuHCXmJdqBDYyVIiJqLrOTEhvV2QWn6CcRvvpPgLtIux7T0VMWS8UTbKUQYpYQ4vT6fzMBVAkhrADiThgihLAKIbYKId5MwTmlXzgEeE82tKKo6Qj9NcZplP212pSzalrlMbMaWmP8dcpQHRlWWkvsbuMWbsYOtA1GLXFmWufMbhtdLlXx0jGf+QNt5prqQ0qazWhGk1QSEVHj4qVANpPK3mgS4VdvU54pmtqW6egpS6TiKfYGAD0AvArgNQA965dZAVzbyHZ3AtiVgvNJv3BImblWnTTywz83pCh0FBq3fuQVaNMcntIXuPjXSoral28AVk5X4hOWTeKEkrnAaDKyuiOA70TTE00abes7oV8mQ0oLnZl0zEbpjvPyjSepJCIi88z2ithd+nTHzvbxnyk02zIdPWWvpA8bk1IeBTAjzsd7jRYKIXoAGAfgIQD3JPucUipc3+vR2JAsf602tezah5Sv1y4BAnHS3R7Zre2Juf4l4PyfKy8u+9crEwO+ers2HeLym5XhYRyq0/ZEt8QBDX/vK8ubLgNG29ZVKcMKope9MgX4yfNKuXR1ADzHlVY8w+D8Gu1QRGEBPnhSnyq55HaWRyKi5oiXFCXgUSabVOtdyIaYWbXePXkwfp0tLNpnFQ4ppyyV9FIqhOgrhHhGCPF/QojV6r8mNnscwEw0PqxsuhCiQghRceTIkaSec4vFaw2Pbfk2Si27bp7SQvLm3frg5+i0xkBDq0n0fuJNfMnxqhkh6eU1Xktcx9P1y2LLgNG2HU833p+7ozLJ6e87K19lWN8bo/aoRA9FtDmB4hu1E6MV38hWvCySkXUsURxturw68pV6NrZXxF+jfd5w5CvPEtFDeP/1W32Pilpnxz6rABxSTlkpFSX17wC2AngAwH9H/TMkhLgCwOGmUihLKZ+RUhZLKYs7d+6czPNtObPjUo1iCXqWAN4TSpCzEMrX3xxReli2/60hrbG6rq9GO0b16Gccr5rBkl5e441PNoozCXibjmWp+sp4f5X7teX5lSnKPETR2fC2/01pAYwWnTTiN0eUr0y5mVUyso4liqNNl9eAR6lno+vdUBCoeF67zHtSX49XHwLCUl9n+04ysxi1Gal4sghKKf8kpdwopdys/mtk/QsAXCWE2A/gZQCXCCFeSMF5JZ/ZcakOt35c6oTngI//prSWLDgXeGJQ/br5SoyLUatJ9BjV9Y/pe2w4XrXtijc+OXYSyOuWKtnBmoplyYszweXaudrjHtgAFHYx16PCxBBERImzu/U92YXdlGeD6GV5hfoemqv/BNjztOsNm6o8b0TjSA3KYqlIlfyGEOJ2AP8A4FMXSikrjVaWUt4H4D4AEEJcDOCXUsrJKTiv5Is7OWRdTMyBVz8udfMSYMAV+u2AhhYXo9iB2MkuJy1TXmw4XrVti5cOG9Aug1R6S2JjWcY/py1TFc8Do+6IiVsRSqtdNKP4FpYzIqLUMarv/bUNmUkB5WvVV8CO5dq6fdsypW6PnpDS6gB2vaE9htGzClGWSMXLy431X6OHikkAfVJwrPRSW8Njc7HHtko73MCxPdplx/YolY3Fpt+u+Mb4+1Rbt9X9qlgBtX3Rf/vov3f0MhlWWuhu39BwM1v/mNJ7svIWbZmyORteQvIKlKx4E55Tkkuo6014Tnk5tlj1xyUiotbhyNeP9Fg7F/jhQ/rnhei63dlOicM186xClCVSkW2sdwLbrgWwNmknk2pNTQ6pCniBS2cr2cEik0Y9rcxc/psjnFSSkideWQt4my5TFiuQ31nbYhf94kJERKlnNEnlhMXKPG9qtlJA6Sl3FJqo200+qxBliVRkG3MLIR4QQjxT//1Z9UH5bZOZcf4y1JDWODJp1O1KS3dL90lkJF5ZkyFzZcpiVVrqhEX5Gu/FxezkmERE1DxGyYBW3Aycf6s+TtHm1NftRvUznyuoDUnFsLG/AtgMYFT9919DyUD2ZgqOlR2MunsPbFCW/75zQxcuszNRohora8li1CrI8ktElBzxkgHlmehlYf1MOSAVJfkMKeV8AAEAkFJ6AIgUHKf1JNrKHC/N7dHPlFaVgi6Ar1b5LbEVO/cksxcjXlkzSqHd0uOaTRFORETN11g93lTvCetnygGp6HnxCyFcUIL0IYQ4A1FZx7JOMloxjAL7f/wk8O4c4NwJSozCa3ewlSQXJbuVzGwSiUSOazZFOBGZNvD5gc3eZseNO1JwJpR2dqdx8hS7s+ltWT9TDkjF0/FvAbwN4HtCiBcBvAtgZgqO0zqS0YoRO4Ff9ESUY37ZkP6QrSS5J9mtZGYni0zkuM3p3SEioubx1zVMr6BONLl5ibk6lvUz5YBUZBv7pxBiC4DzoQyEulNKeTTZx2k1yWrF0KQ4LlDSIe9fp6SzZStJ7kpFK1m8lMrJOq7Z3h0iImq+vAJg3TxtZjGLDbjol01vy/qZckDSXl6EEOfFLDpY/7WnEKKnlHJLso7VKsLh+lZoqaQnHHBFw7wZO99MbHKn6NbxgMmJLqltMjvRaTxqOY2duDJ2WWzPSyLHZdpNIqLU8dUYP3f4apQskI1h/Uw5IJk9L39s5DMJ4JIkHiu1ouMBzr4SGDbVYOypK7FjqK3jnDwqtyXSShYvbsXqAF6Z0ngsS6Ktc2Z6d4iIqPkcbuPnDrM98qyfqY1L2suLlHKsmfWEED+QUv4zWcdNieh4gB/NVyoQtYV6/3rl+0kvJ6dSYCtJbkvk7x9dToGGuJUry/XLYssryx0RUWby1xk/d1z/UtM9L0Q5IB1PKvPScMzmiY4HaI2YFE4eldta+vePF7fS8XT9MqPyynJHRJR58grizPPCXhQiID0vL5k/50t0to6jnzFzB2WmeFllqr7SL2N5JSLKDr4a47rdV5Oe8yHKMOl4eZFpOGbzqPEAvUYD6x8Drn5a+b/FpnxlTAplguhyGl023R1ZXomIspUjX4lxia7HJzynLCeilExSmf1i4wECXmDSMqXiYGwAZYp4cSsAY1mIiLKVxQrkd1ZiXPIKlB4XR76ynIjS0vOyP3aBEMIphNgohNguhPhUCPG7NJyXVnQ8gMMN5BU2xAYASmUiw8rXcDi950q5yyhuxWhZOMwyS0SULSxWJThfWJSvFivrcaJ6yZznZXxjn0spV9Z/NVrPB+ASKWWNEMIO4N9CiLeklB8m6/ySJl56WqNZzIkyAcssEVF2Yz1OFJHMEn9lI/+uaGxDqVAj0ez1/zIzNiY6PW042JCKNsCAaMpQLLNERNmN9ThRRDLneflZItsLIawANgM4E8BTUsqPYj6fDmA6APTs2TORQyUmXnraZKZOpqyXMeUVYJklUzKqzBI1IefKK+txooiU9DUKIcYJIWYKIWar/5raRkoZklIOAdADwAghxLkxnz8jpSyWUhZ37tw5FadtTrz0tExFS1EyprwCLLNkSkaVWaIm5Fx5ZT1OFJH0bGNCiD8DcAMYC+A5ABMBbDS7vZTyuBBiLYDLAHyS7PNLmJqeNnbcKVPRUqZimSVqdTu+PNCs9Qf2zoHeA2o51uNEEalIlTxKSjlICPGxlPJ3Qog/AljZ2AZCiM4AAvUvLi4A3wcwLwXnlrh46WkZMEeZimWWiCi7sR4nikjFy4un/mudEOI0AMcA9G5im1MBPF8f92IB8Dcp5ZspOLfksESlTFa/EmUyllkiouzGepwIQGpeXt4UQnQA8CiALVCyhj3X2AZSyo8BDE3BuRARUQbo9atVzVp//yPjUnQmRESUzVLx8jJfSukDsEII8SYAJwBvCo5DREREREQ5JBWDJSO5/KSUPinliehlRERERERELZG0nhchRDcA3QG4hBBDAYj6j9pByT5GRERERETUYskcNvZDADdBmaflsajlJwHcn8TjEBERERFRDkray4uU8nkoGcMmSClXJGu/REREREREQGpiXt4XQiwWQrwFAEKIAUKIm1NwHCIiIiIiyiGpeHn5K4B3AJxW//0eAHel4DhERERERJRDUpEq+RQp5d+EEPcBgJQyKIQIpeA4REREOWvg8wObtf6OG3ek6EyIiFpPKnpeaoUQnaBMTgkhxPkATqTgOERERERElENS0fNyD4DXAfQRQrwPoDOAiSk4DhERERER5ZBUvLzsBPAPAHUAqgG8CiXuhYiIiIiIqMVSMWxsCYD+AOYCWAjgLABLU3AcIiIiIiLKIanoeeknpRwc9f0aIcT2FByHiIiIiIhySCp6XrbWB+kDAIQQIwG8H29lIcT3hBBrhBC7hBCfCiHuTME5ERERERFRlktFz8tIAFOFEAfqv+8JYJcQYgcAKaUcFLN+EMC9UsotQohCAJuFEP+UUu5MwbkREREREVGWSsXLy2XNWVlKeRDAwfr/VwshdgHoDiXwv9WEwxJ1gRDcDivq/CG47VZYLCLhdYmoZZJ9nfG6JSKV2fqA9QZR5kn6y4uU8quWbiuE6AVgKICPDD6bDmA6APTs2bOlhzAUDkscq/WjdNlWbNpfieG9ilA+aSg65Tt0lVRz1qXclcrymguSfZ3xum0ayyxlk0TKq9n6gPUGUWZKRcxLiwghCgCsAHCXlPJk7OdSymeklMVSyuLOnTsn9dh1gRBKl23Fhn3HEAxLbNh3DKXLtqIuEEpoXcpdqSyvuSDZ1xmv26axzFI2SaS8mq0PWG8QZaZUDBtrNiGEHcqLy4tSypWtfXy3w4pN+ys1yzbtr4TbYU1oXSJqmWRfZ7xuSaesfbrPgNLEbH3AeoMoM6W950UIIQAsBrBLSvlYOs6hzh/C8F5FmmXDexWhzm/Q89KMdYmoZZJ9nfG6JSKV2fqA9QZRZkr7ywuACwBMAXCJEGJb/b8fteYJuO1WlE8aipI+nWCzCJT06YTySUPhthv0vNitKJ80JGbdIYbrhkJhVHsDCEuJam8AoVDY8PjhsESNL4iwrP8alkn/GYkyWew14LJZTF9nZsS7bl02C689ohxjtj6IVw+x3iBKr7QPG5NS/htAWiPfLBaBTvkOPHtjsamMIg6rBQ+PH4jvFbnxn8o6OKz6d8BQKIxjtX7c+fK2SKDfE9cPQad8B6xR6zMgkHKd8TUwBC67tcnrrDmMrtsaXxA/f2ELrz2iHGOuPtDXQy67FZV1fpQu28Z6gyhNMqHnJW2iW3u9gRCkVFpPpJSR/wNAMNjQg1LrD+JYrQ++oNKL4guG8fwH+w0D/e58eZsm0O/Ol7cxIJDaLKMeRDPLvEGja2Abav0h3XXmDYZa1OJZFwjh+Q/26/ZXVRfgtUeUY+oCIRyr9aFTgQNCAJ0KHDhW6zOoD/T1UK0/hNJl23T1RkvrJiJqvrT3vKRLdGtv13Z5+OUP++G///6xrpdESqCyrqEHpfSSM3H9iJ6aHpV5EwbBZde+B+bn2QwD/fLztL9yBgRSW2DUe/LnyefBHwrHtFAOgcNq0bVudm2Xp9lf13Z5sAig7PVPG9a7fghqfcEWtXi67BZcPbQHZq34WHPdntbBqVmP1x5R2+e0WlCQZ8f0JZs19/wit0OznmE9FKe+amndRETNl1MvL9GTTdX6g5HW3nfuGoP//vvH2LDvGABgw75j2PxVJS48qzMERKQHBQB+eO6pmu837DuGWSs+xrNTi2ERYYSlhDvPhlpfEAsnDUGfzoU4s0sB9h6uwTufHEStL4hCpz1yTmpAoLo/oCEgsCAvp/48lMWiexAB5bqoqgtg18ET+NPk89DOZcdJTwAbvjiKs09tr1mvdNk2PDx+IF7d9m1kf3d9vy8q9ldqtg3Uvwhpt92KZ28s1l0rwWAYnmAI+fXXolUIzFrxse66/dPk8zTbxbv2EpmojpPcEaVXbH0gALy88QDKrjoncn9+eeMB/OzC3njnrjGRZe2cNiz7SLveso8O4K7v99XVV2brJiJKXM5cVbEtw589eHmkx+PMLgWa3o+yKwdg2OlFmL5kM164ZaTms9h1AaW11mm34NBJr6735uWNBzBu9d7I9y6btlXXZbPgieuH6GJjXLacHtFHWcaoB7FHBxfcDitui+plMWrd3LS/Ej07uVHSp1NkvR4d9du+OG2kqV7KYDCs6S2N11q6aX8l2rnsmuMaJepIJC6NMW1E6WVUH7xwywjDnli3w6rpZYm3XveOTk290bOTmyMoiFpRm3t5idfKWRcIoWL/sUhLbo03iNJLzsRj//ocew/XoPSSM/HDc0/FmV0KUOsL4n/e/xIb9h3TfVbtDUS2U6mttbG9N3e+vA1lV52Dx/71OToX5qHOH8IphQI1vmDkvDzBsGEL0P8b3QcFCQYoE6VK7HUBbXe4AAAgAElEQVQGCc11svdwDXyhsK6X8s6Xt+G5G4s1rZvvfHIQXn8Iz0wdFmkZ9Qf12x44VmfYS1nrC0IIETkXKaVuW6PeHXXbphJ1GPUqlS7bisU3FSMs0aJt2SJL1Do8wZCuPqj1hfDq1q81991Xt36Nn13YW7PMGwgb9tg+M3WYpt6o83EEBVFralNXVWOtnE6bBcNOL9K1AgPAh/uOGsax7D1Siw1f6D9Ttyuv71GZN2EQCpzGMS5ndinAVYNPwy//q5+m9UY9L7fDivLVezUvQzaLwB2XntV6vziiZjC6zhbfWKy7TuKNDa/zBzWtm3+afF79MM7Gt338X3tQPmmIZj21tXTycxub7KGJ7d1Rt7ValEaCeA8ZRr1KZse4M6aNKL2M4k/z86ymel7i9djm59lgEcp1XpBnQzgsUT5pqO7Zo6Wp3YmocW2qab+xzF2eONm/fnZhb0w473u6z2at+Bi/GHsmSs44xXC7qaN64bMHL0fZVefg1a1f46QnYDiZ1d7DNfjF2DMjrTex58VJsCjbGF1n/qheluhMPXd9v69m2+ix4ep6x+sCBtl79Nt+d9IHt8OGsqvO0Vx71d6gZtsab9Dwmqr2BnXbegLGcy9pfl6Da9To5zDKVMbrmyi9an36+qDOH9Ldk2et+Bi1vlCT9ZDaYxsterqFPQ9djmdvLObQUKIUalM9L26HFZed21UT5Pvatm8irZyNZf+K12sS77NCpx39HngLw3sV4dGfDMJr277Boz8ZZBjz8otLzkLXdnmaoTJ/WrtXOS8JtthQVjHqTWjnspvq7TAaG/69IuPx4rHbPnH9EORZLejaLg9CKL0fN47qpcvgt+VApWEcmVWXNWhoZLK5xoZ+qZPYRl+jZse4G23L65vSZeDzA5u1/o4bd6ToTFqPy2bV1QfxsoEWOG26ZUb1UGzsKqC8wKi9txwqRpRabeoK8wdCuPzcU3VDw/yBEAJhaTgm9aQnEPm/fryqkpUk3md7Hroc353wIiwlJp/fC4dOePDYtYPRtb0Tdb4QAImrhnSHLxDSpWJ+9CeD4A2E4HbYmjVBJlG6GWXIq67v7dBdJ76gJpalzqdf7z+VxrEsx2p8moaIylofAOheSsLQtoIueu9L/M9NxZrjumzKNRV9nblsFlTWBZoMpjeaxNbsGPfmToBLRMnlDYXxzfE6LJoyDAVOG2q8wUhvTLzngehltb6gph6yWQSvX6I0a1PDxgJhaTjEKxCWcNuV1peSPp1gswiU9OmEJ64fgle3foNXt35j+Nm/Pz8CTyCER38ySPPZoz8ZhDp/CPe8sg0hKfHLv3+Mfg+8hV/+/WPYrBZAAhDAtCWbcfEf1uJYrT8SzK+e13///WOE60esqC02FtHQcsPJrihTqb0J0deEzSIMrxOLECh02jVfY9crcFrxxCTt9Vc+aQjsVgtue2EL+v76Ldz2whZ0LnQaXt/R56BsOxR2m1VzXJvNorvOPMGw8TBTv36yudht3Q797yBej0rstnzwIWo9brsVPTrm49alm9H312/h1qWbYbcIw3t+bF3y6E8GIRAMY8icf6LPff+LIXP+iWlLNnMiW6I0a1M9L41NDGkRSgtodGvsyi1fo+yNnZF11dYVtaV2dN8ucDusuOeVbZoMJH945zM8dt0QPHbdEHj9ITw7dRjceTZNq2r00JrTOriMh5jk6R90mFqVMp1Rb4LLYcWv//GZ4XUSzemw4g8x6z345i788drBut4JKaXmem3s+m5Jz0bcYPo8K3767EfN7o1hjwpR5jG6Vh12K96q+I9uiPlPR56uq8P+eK22DmPCDaL0a1MvL/G6gtWJIa1WCwrr0w8LIfD2J99F1it7Yyfe+fQ7PHtjcWQSyQKbBdXeAL476cMPH18XWbekT6fIPt1RQ0Sih4tED63Ze7im0SEm8SbPBJhalTJT7PjuGl/Q8DqJHUZV28h6kesusr6IXK+FTjuqvYFGr2/ttk2LN0HsgWN1pq4/jnEnyk61viDe/uQ7/Pb1hsbLkj6dcFHfLrq66T+VdZptmQKZKP3a1LCxeEPDjIZyGA19MRr24XZYMW+CdpiLmlKxqXNR9/+ntXt1Q2XUY6k9LdOer0DfX78Ft8O4dZktPZTJ1MlWY6+92MlWW3o9Ac27vs0wrgOG4PF/7dGsx+uPKHvF3mOnPV8RCeLX1CWThqCj266rD/TLmHCDKN0youlACPEXAFcAOCylPLel+7FaLbqhYW67Fdb61lvNxHqBEIrc9iaHfXgCYcPJrP7f6D4oyIv/7hfbVe0NhPDs1GK487THqvFpe1qa6qUhykRmJ1tt6fUExL++hRBNZgwzYjScxCKUlMzReP0RZS+jiWJr/EFdEP8XR6rRr2s73TMBAA4PJcowmXI3/h8ATwJYkuiOooeGqUNJgCZiSaIC5WO57VZMGnl6i1KdRg8rcTuMh5fFjrt/as1ezJswSDehJVt6KJOZnWw1kesJ0F/ficaIxQ794mRzRG2LUWzbtv9UYcCp7XHr0s2azIUOqwU2m37SWg4PJcosGXElSinXCSF6pfIYRq0vZmJJUh2YGzvu/vXt3+LMzvmR1mW29FA2iBc/kurUwS29ruNhIH4WK2uf7jOgDGRUN/XqVBC/p9jWpkbTE7VJWXOVCiGmCyEqhBAVR44cafb2cTMLmRjLnspUp0bj7ieNPB35DqZWzWaJltdsYzaGDEju9ZTIdR1PrqY2zrUyS9nNbHk1qpt6dnKjfPVe/PDxdTjj/v/FDx9fh/LVexnbRpQlMqLnxQwp5TMAngGA4uLiZk98YrZluLWxpbdtSrS8Zpt0leNMva6zUa6VWcpuZstrIpPMElFmypqel0Q1p2W4teVqSy+1Lekox5l8XRNRZkhkklkiyjw508TAHg6itofXNRE1F+sNouyWET0vQohlADYA6CeE+FoIcXMqjsMeDqK2h9c1ETUX6w2i7JURPS9SyknpPgciIiIiIspsGdHzQkRERERE1BS+vBARERERUVbgywsREREREWUFvrwQEREREVFWEFJm31xkQogjAL5K93mk2SkAjqb7JDJIvN/HUSnlZa19MtGaUV7byt+UP0disqnMJluml51MPz+g9c8xE8trNvydzGorP0um/BxpL6+UuKx8eSFACFEhpSxO93lkirbw+2gLPwPAn4NaLtN/55l+fkB2nGOqtaXfQVv5WdrKz0GZgcPGiIiIiIgoK/DlhYiIiIiIsgJfXrLXM+k+gQzTFn4fbeFnAPhzUMtl+u88088PyI5zTLW29DtoKz9LW/k5KAMw5oWIiIiIiLICe16IiIiIiCgr8OWFiIiIiIiyAl9eiIiIiIgoK/DlhYiIiIiIsgJfXoiIiIiIKCvw5YWIiIiIiLICX16IiIiIiCgr8OWFiIiIiIiyAl9eiIiIiIgoK/DlhYiIiIiIsgJfXoiIiIiIKCvw5YWIiIiIiLICX16IiIiIiCgr8OWFiIiIiIiyAl9eiIiIiIgoK9jSfQItcdlll8m333473adB2UGk+wRYXqmZWGYpm2RNee31q1XN2u/+R8a19JQoc6W9vFLisrLn5ejRo+k+BSLTWF4p27DMUjZheSXKLVn58kJERERERLmHLy9ERERERJQV+PJCRERERERZgS8vRERERESUFVL68iKE+J4QYo0QYpcQ4lMhxJ0G61wshDghhNhW/292Ks+JiIiIiIiyU6p7XoIA7pVSng3gfAC/EEIMMFhvvZRySP2/OSk+p5wQlmHUBmo1X1O5XbL3QbklFA6hxl+DsAyjxl+DUDhkeluWN8ol8cq7+n30tcTrgYjaopS+vEgpD0opt9T/vxrALgDdU3lMUm5ild5KzFg9A8OWDsOM1TNQ6a1s8ibW0u2SvQ/KLaFwCJXeSpSuKcWwpcNQuqYUld5KUy8wLG+US+KVd/UaWrpzKQ7WHoxcS7weiKgtarWYFyFELwBDAXxk8HGJEGK7EOItIcQ5rXVObZUn6MHMdTOx6dAmBGUQmw5twsx1M+EJelKyXbL3QbnFE/Rg1vpZmjIza/0sU2WG5Y1ySWPlfea6mbi056WY/cFsXg9E1Ka1ysuLEKIAwAoAd0kpT8Z8vAXA6VLKwQAWAng1zj6mCyEqhBAVR44cSe0JZzmXzYWt323VLNv63Va4bK6UbJfsfbQFLK/mue1uwzLjtrub3JblLXlYZjNfvPKuXkN92vfJmeuB5ZUod6X85UUIYYfy4vKilHJl7OdSypNSypr6//8vALsQ4hSD9Z6RUhZLKYs7d+6c6tPOap6gB0O7DtUsG9p1qKmel5Zsl+x9tAUsr+bVBeoMy0xdoK7JbVnekodlNvPFK+/qNbTvxL6cuR5YXolyV6qzjQkAiwHsklI+FmedbvXrQQgxov6cjqXyvDJdogHILpsLCy5egFXXrMK2Kduw6ppVWHDxAlM9L/PHzMfwbsNhEzYM7zYc88fMb3bPS6L7oMySSHk0s63L5sK80fM0ZWbe6Hlw2pymtmV5o2xm9voKhUMQEHHL+/wx8/HugXcxZ9QcXg9E1KbZUrz/CwBMAbBDCLGtftn9AHoCgJTyzwAmArhNCBEE4AFwvZRSpvi8MpYakDlz3Uxs/W4rhnYdivlj5qPIWQSLMP+uGQgHULahTLMPM+wWO8pKytC9oDu+qfkGdou9WedvERYUOYuw8JKFcNlc8AQ9cNlczTp3yhyJlEez21otVhQ5i1A+thxuuxt1gTo4bU4c9x1vcluWN8pmZq8RNSB/1vpZ6OLqgrKSMvQo7KEp70XOIkwZMAVOqzNyLfF6IKK2SGTje0JxcbGsqKhI92mkRG2gFjNWz8CmQ5siy4Z3G46FlyxEvj0/pftIxrEzkEj3CWRzeU2kTKRr2zaAZTZHmC3nNf4alK4p1a1XPrYcBY6CVj1nA1lTXnv9alWz9rv/kXEtPSXKXGkvr5Q4NsdkmHQGzTP4mWIlUibStS1RtjBbzhNJakFE1Nbw5SXDpDNonsHPFCuRMpGubYmyhdlynkhSCyKitoYvLxkmnUHzDH6mWImUiXRtS5QtzJbzeEkteD0QUS5izEsGCstwJNDSbMBl7DZ51jx4g95IALTNYoPD6mhyf6FwCJ6gJ7Kd1WJFnjUPnqAHAgJOmxN1gTq4bC5YLdZU/PiG55HA8dI+vjUTyqtRmQJgqpyZLY9GfzP1GNHLJKSmbDptTtgs+twhSSwD2YZlNgdEX1fqdRB7XajfG9XpTpsTvpAPTqsT3pA3cn3Gft+SgP1m3oOyprwy5oWQAeWVEseelwxkERbk2/M1XxujZqyZsXoGhi0dhhmrZ6DKW4UXdr2AYUuHoXRNKSq9lbh//f2YsXoGKr2Vhuk4wzKMKl8VSteURrar8lZFtjvuO477198f2V8oHErJz69m1ok+j1Qer60zKh+V3kpU+6v/f3vvHidHVeb/v5/unpmeS0gyEJQNxBBAd1nQBBI0YJCLF+SyqKAkrFz2i+KNRFndxAvLDxEvybILBFcQiAJBEhAQkbCyCGQTMEgCCQTBXWIIIYASMpNJZqbn0tPn90dV91TXpbu6p3v6Ms/79epXd506derpPs9zuk/X+TzlKfPzizD+6NdnnX2ddA92Z5V1D3azu2+3p14ylfTY7PHF/s6C04YrSjXijsl0HKTH7DteuoPOvmH/T4+/zbHmTJzNumMWy19c7hvby19cnjeuw9pWTBuKoijlRCcvdUAimWDhmoWs/8t6kibJ+r+sZ9HaRZw85eTM9mVPXsZFR17E+r+sZ+Gahb7aAb92nMc5Xy9au6hs+oNEMsGitYs870f1DsXh168L1yykq7/LU1bsZ+zXZwvXes/R1d/FwrVeX+1L9oWyWX1AqQfyjdknTznZG0+2/zuP86vnHvsLjRuNPUVRqh2dvNQBQRlrpo2f5rsdlLUpXzvu1+XKdKOZdUpLUL9ObpvsKSt2DX1Qn7nPMbltcqi+1WxjSj2Tb6ydNn5aYJw4y4Pqucf+0chWqSiKMlro5KUOCMpYs7Vrq+92UNamfO24X5cr041m1iktQf36evfrnrJi/10N6jP3OV7vfj1U32q2MaWeyTfWbu3aGhgnzvKgeu6xfzSyVSqKoowWOnmpA/wy1iyes5hHtz+atX3w+INZ9clVXHPCNUQkQsqk6Bnsyaxl9mvnquOuYtnmZZ7X5cx0o5l1SktQRqPxTeNDZfNy+onTX9zncPfZkjnec4xvGs+SOV5fjcfioWweiQ+EeR+KMhrkG7Mf3f6oN56OX0I0EuWWj97Cqk+u4rSDT/Ot5x77w8SNMyYiEtFMf4qiVDWabaxOcGeHcWac6R7s5s6X7uSnz/2UGe+YwZI5S7jn5XuGt49fQnu8PTOhcbYTkcioZxtLmRS9g70MmSHGNY5j78BeohKlpaGl4Kw5VEFmkWrw12KzjaXFuwvXLGTjXzd6/MVZz6/PmmPNnsxHKZMKlW2smKx7ud5/mPdRJajPjgEKzTaWTCW5dPWlWf5rjOGRbY/wwQM/yIHjDiwq25hfbFxzwjXEIjHNNqbZxuqRivurMnJ08lLn9Az2MP+x+az/y/pM2ax3zuJbx3yLTz3wqcz29SddT2tDa6XMzCLI5iJtrPhAVcv+GrYvStxnJafa7XOhPqtkUc5xvASxUTP+qpMXhSrwV2XkeP/uVOqKsGL+aloSoILR6iFsX1R7n1W7fYqSi3KO4xob9cuRtx1ZUP3NF2wukyWKUlqqbr2EUlrCivmrSYypgtHqIWxfVHufVbt9ipKLco7jGhuKotQaOnmpc3yFz3OWBAo63aLmRDKRWZsdJHYulRA6fXw8GveIulUwWhnCCuebY80sPXEpT8x9gufOf44n5j7B0hOXEo/GQ/lGucX05UgAoCjlxBkLxhiuOeEaj/9OaJrAaQefltlO6xYLaV/HW0VRag1dNjYGaIg0cMXsK5jcNpnXu1+nIdrABX9/ARe/9+IsMaafcPOq465i9fbVnDDlBC578jKP2BkoiRDafe4vvO8LXHvitbQ1tI1YrK2MDI//RBo8ddIT3EVrFw2Lfj90DZ39naHE/uUW00ckQnu8netPur4kCQAUpZz4xcTiOYtZPGcx7fF2KwW5gXtfvpdvzPoGxhiuXn81byXeChU7Ot4qilLL6OhU5ySSCS5dfSmn/eo0pi+fzmm/Oo1LV1+aSYnZ2tCa+ZLyu7PyZU9exqmHnMplT17me8flUt2N2d3Of276T772+NdIJBNZNiqjS5D/uPu3L9nnudN310BXKN8YrTt6O/1dfUqpZvxiYtHaRezu352Jw4VrF3LylJNZuGYhu/t3s+qVVaFjR8dbRVFqGb3yUucUIsYMqrtP4z452yiF2FNFo9VJ2H5x3/kbYHLb5LoQ+yvKaBNWoD9t/LSihPsac4qi1DL690qdU4gYM6junoE9gW2USuypotHqJGy/uO/8DfB69+t1IfZXlNEmrEB/a9fWooT7GnOKotQyOnmpcwoRKvvVveq4q3jozw9x1XFX+bZRKiG0Cqqrk7D9Eo/FPXf6Ht84PrTYX/teUYbxi4nFcxZnJVq58tgreXT7o57yMLGjMacoSi1T1ptUishBwO3AO4EUcJMx5jpXHQGuA04FeoELjTHP5mq3Hm6gNpQaIpFMZO6mHIvEaIw2lkUs6b5TeVO0KetOzs2xZqKRqG/diERoijbRP9RPyqR8xc6luhN62HYKPF/Fb0hVDf46kj5KppKeO3/HIt4Vp371IhLxnBcIVVbqdfel8tNRQH22jnH6YXr8TSQTxGPxTPwkkgni0Th9Q32Zes796eP6hvqy6hUybjqPyzW+h6Bm/HWs3aRS7/PiS8X9VRk55f7mTgJfN8b8HfAB4CsicrirzseBw+zHxcANZbap4gylhujo62DB4ws4evnRLHh8AXsH9rLypZXMf2w+HX0dJU0V6xQox6NxOvs6s87d0dfBUGrIU7e1oTXzJdYcaw4UO5dKCB2mnXSWnPmPzefo5UeX5fOqN0bymQ2lhjz+0tnXmfEX5zl29+/Oqre7fzdAVp8Cvra465Vj4qJ+o1Qatx8ueHwBb/a8ybo31mXF2fzH5tPZ35kZf9sa24hFYrQ1tmW2o5EorQ2tmedCx810+wA9gz0aG4qi1AxlnbwYY95MX0UxxuwFXgImu6qdCdxuLJ4CJojIAeW0q9KkU8q6M8mcesipZcu0lO/ctbLWebQyU9UTI/nMwvpL2HNUqv/Ub5RqwM8PL//95cw6YJYnzkrpn7n8X2NDUZRaY9SyjYnIVGAG8AfXrsnAa47tHXbZm67jL8a6MsOUKVPKZeao4JeZKZ3VK/26XGuPg87d0tBSlvOVmlrJklNN/jqSzyysv4Q9R6X6r1b8ppJUk8/WK8VmdCzXeUuZMXK0UX/Nz+ZXtlfaBEUpC6Oy4FtE2oB7ga8ZY/a4d/sc4hHiGGNuMsbMNMbMnDRpUjnMHDX8MjOls3qlX5frX6+gc/cO9pblfKWmVrLkVJO/juQzC+svYc9Rqf6rFb+pJNXks/VKMRkdy3neUmaMHG3UXxVl7FLQ5EVEJorIe0XkqPQjxDENWBOXXxhj7vOpsgM4yLF9IPBGIXbVGs2xZk9mpsVzFvPQnx9i1jtncc0J12CMIWVSdA90e/QFhZIyKXoGe0iZFNFIlCVzvFlsRutfNqct6edC0Cw5hVPIZ5ZMJeke6M74XjwW55oPXcOqT65i03mbWPXJVVzzoWuKzhhWqf5Tv1GqAV8/nLMEY4yvf0YkUvAY6TfG5vJ/jQ1FUWqN0NnGROR7wIXAnxm+MmKMMSflOEaA24AOY8zXAuqcBlyClW3s/cBSY8wxuWyph0w4QdnG+of66RnoYeHahWz860ZmvGMGi+cspj3enskIVghpoebCNcPtXXPCNUQl6pttrJz42bLk+CW0x9sLEmhrtrHCCfOZJVNJOvs6WbR20XD/zFlCQ7SBS1dfmrfPypQtrmRotrHwVIPP1itOP+xL9tGb7GXhmoXs37w/X57+ZQ4cdyBv9b7Ftc9cy1uJtwoaI3ONsRCc0W+EsVEz/jrWso1xxfgC63eVx47qouL+qoycQr65PwMcYow5wRhzov0InLjYHAecB5wkIpvsx6ki8kUR+aJd5yFgK7AFuBn4cqFvohaJRqJZmWPSaWWHUkMsXLuwZIJ6PzHmpasvRUSystaMBqUShpYqu9lYIsxn1pfs84qG1y6kq78rVJ+F7ZdK9Z/6jVINOP0vZVKZMXHVK6s47Ven8bn//hx7B/ay6pVVBY+RucbYXP6vsaEoSi1RiGD/BWAC8FbYA4wxT5BnlmusSz9fKcCOuqbUgvpqEipXky2KlyDfm9w22VOmfaYoIyco5qaNn5a1HTbedIxVFGUsUMjfKz8ENorIwyLyQPpRLsPGKqUW1FeTGLOabFG8BPne692ve8q0zxRl5ATF3NaurVnbhVx50TFWUZR6p5DJy23AYuBHwL87HkoJCRLzF/vPWTWJMavJFsVLPBb3+N6SOUsY3zRe+0xRykDQeP/o9keLijcdYxVFGQsUItj/H2PMh8psTyjqXUzqFPP3JfswJkVzQwuJwV7isTh9Q/0ZYWVEIjRFmzyvm6JN9CX7Mm2kTCoj0I/H4vQ72iihcNO3Dadd8WicvqG+0RRNV1ycVw3+GrZfk6lkxm/SviJIVnKJ9A8hd5nBeI6NSMRzXoyxXts+3RxrJjJKuqsaQX22DhlKJUk44iMdE/FY3BM3zvHZOZY797vH0vR2PBrPxOYojbk1468q2M9XXwX7Sm1QyAj2jIj8UERmF5IqWSmctJgfY+gd7GH+4ws4evnRzH98AR19nSx/cbm1/dh8Ovs6+fbab/u+vuOlO/j22m+zu383C+w2Fjy+gE5XGx19HaRMKpOpZv5j8z37wuLXRpZd/Z2ZL08Vho4OYfs1ZVIeX+kZ7KGzvzPbf/o76R7oziobTA3S2dfp8bPewV7PefcOdLt8uoPUCNOBK0o1M5RK0uGKjzd73mTdG+s8cbO7f3dmjIxH475xtaVzS1ZML39xeWZ75h0zWWDHVTwap7O/c0RjuqIoSrVRyC/HGcAHgB8wvGTs6nIYpVgkkgkWurI/LVq7iJOnnJzZvuzJy7joyIs8r9P1LjryIi578rKcbaSz0ZQiG5hfG067iskupoyMsP3qV6+rv8v32K6B7Axkg6lBT6ayRWsXMWSG8h67cATZ9BSlFkj4ZPK7/PeXM+uAWd4Mf47YTCQTvnE1bcK0rPKTp5wc2E4pMjwqiqJUE6GzjRljTiynIYqX5pCZaNLbuV7nayO9FGikmWqCst04bdH116NL2AxEfvUmt00OlYGstaHVt964xnF5j9341400F5lNT1FqgaCsYvs07pMzNoOOc8fbtPHTArNUavYxRVHqjfB3oRL5gYhMcGxPFJGrymOWApAImYkmve33emvX1tDZbEqRqSaoDadd+q/f6BK2X/3qvd79eqgMZD2DPb719g7szXvsjHfMIFFkNj1FqQWCsortGdiTMzaDjnPHW9A4H3S8jsGKotQyhSwb+7gxZnd6wxjTCZxaepOqg5RJ0TPYk/U82jTHmlmSJxPNVcddxbLNyzyv0/WWbV7GVcddFSqbTSky1fi14bRLM9+MPmH71a/e+KbxvseOb8zOQNYQafDNmhSVaN5jl4wgm56iVBt+3x3NPpn8rjz2Sta/ud6b4c8Rm0HZyLbu3ppV/uj2RwPb0exjiqLUG4VkG3semGWM6be3m4ENxpi/L6N9vpQ7E05a4LxwzUI2/nUjM94xgyXHL6E93j7qAvOUnXlspNnGDMaTlcaTfSaVItXfRaJvN83jp5Do2k5zfAKRpvEQCf++c2UbG4XsYm4qnlmk0pmbUibF3oG9dPV3MbltMq93v874pvGMaxzn6Qu/rGSAp8zYGcOGMyfFMUNJ+tLfBdMAACAASURBVMwgLQ2t9A72EJcGItEGEi5f02xjeRnzPlur5PruMCblzTY22E18+3r69zuE5gnv8h0jndknw2Qbc4/rpcggmYea8VfNNpavvmYbU2qD0JoX4A7gURH5OWCA/4d175e6wylyBDIix+tPup7WhtZRtSUSidLa2AYw/Byxus1pi9/rNrt+i0NPkN7XGmn1HMdgL5G7zqN121prH8DUOTBvJTS1hbfZziSWy0Zl9EgkE1y6+tKMPwPMeucsX3/O13eZMhn2r7bGNujvhhVzabN9pw0yvtNq+47zWLdPK0o9kO+7IxMzBrj9zEy8xACmzqF13kpwTSwy2ScZjrlY+jvANY77jetBMa0oilKrFCLYX2Jfffkw1sz1e8aYh8tmWQUJK3CuOxpbYPu67LLt66xypWYZFX9W31GU8LGm8aIoilI0BV07Nsb81hjzDWPM190TFxFZF3RcrVEK4XpNMtALU2Znl02ZbZUrNcuo+LP6jqKEjzWNF0VRlKIp5cLXeAnbqihjVuTY0AJnL7OW+0Ri1vPZy6xypWYZFX9W31GU8LGm8aIoilI0hWhe8hFO+V8DRCRCe7yd60+6vpwix+JIDcFAj6VB6e+GxlbwEzunUjDYay1DGOi1vhTzie4jEWiZZGlcCjnOferyC0SVAhixP4fxpUiEVMt+JM5d6RLij+AcULgPK0oFiUiE9qaJXH/iUkccxIkM+PhxeqxtaB4e0wvwcx1nFUUZq+hIF0Ba5Oh8rjipIejZCSvPhe9Nsp57dlrlWfVS0LsTVsy16q2Ya22nQqR7jkSsL1Gxn4uYuHT0dTD/sfkcvfxo5j82n46+joqkmlaGKdqfQ/pSyqTo6O9k/uMLrH5/fAEd/Z3h+t3vHP1dxfuwolSKVIpI79u03jmXyPcmWc89b8O6n3j9OBKxJiq9bw+P6SH9XMdZRVHGMqX8Ra7p58rNQA/c+znYthZSSev53s9Z5U4Ge+Gei7Lr3XORVV5mnNl2kiaZybZT93qheiWkL42o3/3O0dtZMR9WlKLx8+V7PweHn+7vx0WO1TrOKooylinl5OW8Eral+NHU5p+hxp3GuIKZbMZsprZ6JaQvjajf/c4x8V2ajUmpPYLiZb/3ZG+n/bjIsVrHWUVRxjKhJy8i8ikReVlEukRkj4jsFZE96f3GmBfKY6KSob/bP0NNf3d2WQUz2YzZTG31SkhfGlG/+52j81XNxqTUHkHx8vb/Zm+n/bjIsVrHWUVRxjKFCPaXAGcYY14Ke4CI/Aw4HXjLGHOEz/4TgF8Dr9hF9xljrizApuoml9A5rKDeWS/aSOozy0n076Z5/BQSXdtpbppApLE1ux6G1Ll3k0gN0tw0jkT/XpqJEGlotiY6Dc0wmCjMrpD2prPtuO8wrf8IjpyUfaftou5MX0wCB4CGFlLnLCfR5/C5+IRhX7Lba24I6PdoE/TtyU4wgbhsabYyLd1zkfWv85TZ0DLRW6bZmJRqISie0lnEnH77meVW3cs7rEl52yQwKSueBeLn/5rEYDctDW2O+Mp/5UXHWUVRxiqFTF7+WsjExeZW4MfA7TnqrDXGnF5gu9VPWoTs/vHVMsnaH7TP+YPS1UbqrJ/TcfCxLHz6e8NfWHOW0I4h0vu2q95sFq5d5Ki3mPb1dxJJdMHR51vrsEPbtZ8lKs1nL1Weqa2GSaWG6Ojr8PZpvD3/BCaXL+aZwKQwdJik1+f69xC567xMe5FzltNuolx/zL8OT3KkiUjPLrjXcd6zbrEmMCvmeX3MneUORpz5TlFKTs6xPQWRRjhjqbX0sXsnDA3Ar76QFQOp7U/TcdB07nnlIU6fdjqX//7yrElIu+ReFqHjrKIoY5m8I529XOxTwAYRuUtE5qXL7PJAjDFrgI5SGVtT5BJihhVpuuolDjmehWtdIs21tkjTU2+Rq94iEu87xxKOukX/+ewa6ClIVFqVmdpqnEQy4d+nxQriQ4rf/c+7kETfbo/APnL3ebReN53Ile3Wc89Oa+LiFi+nX2fZkvBmuRth5jtFKQu54mmgF+4+D66fAVe2Q1+HbwwkDjmeheuu4OQpJ3P57y8vSniv46yiKGOVMFdeznC87gU+6tg2wH0jtGG2iDwHvAF8wxjzR79KInIxcDHAlClTRnjKUSCfEDOMSNPVRnPTPv4izYbWcPWa9oH9xhVuV1CiABVPB1Jqf21uaAno+xB9MIIEDoHnHe96T34C+yDRfXx8UbYo5aXmxthKUcjYvt97fOumx+hp46ep8L5I1F8VZeyS968aY8w/GWP+Cbgl/dpRtmyE538WeJcx5n3A9cD9Oey4yRgz0xgzc9KkSSM87SiQS4gZVqTpqpfo3+Mv0hzsCVevf48lHC3UrqBEASqeDqTU/poY7A3o+xB9MIIEDoHn7dqeXdFPYB8kuu/rKsoWpbzU3BhbKXLFk3usDBhv02P01q6tKrwvEvVXRRm7FHKd+fqQZaExxuwxxnTbrx8CGkRkv5G0OeqkUtYXlrGf0zcXSws3p86BSMx6TguOc+1z4qrX/Oc1LJmzmFnvnEVMYnxl+le49sRraW5opefcFaQ+vdy33qx3zmLJnMU0P3cXvPigpTtIn/uE78DcX1j/GkrEa9dZyyyNwtxfWHWd9krE+76VstAca/bv0zD/0Ab4W6qhmZ7BHlImlXl2+3NzLO5z3iU0xydkt9cy0RImz99oCZPnb4TWSZb/uP0p/TqX7ytKNeA3vgeO33GINgyPr0d+GprGwwUPWPFw5KczdZtThiXHXsWj2x/lymOvzI6vAOG9M04z8aooijIGEWNM7gois4Fjga8B1zh27QN80r5qkuv4qcCDAdnG3omVCMCIyDHAPVhXYnIaNXPmTLNhw4acdo8K+YTQpc42NtBLqiFOItlHPNZMZ18nC9cudAm4JxIZ7MvUszJT9dAsDURijcPZnQYT1nPP29mC6nRmnHHvhP698IebYM1iW2i6DFr3s/ankuAQbIcVgFeAit88tVT+WspsY6mGZjr6Oz3ZitqJZgnxOWc5KfBmG4vELDHyxHdZV1jS/pIlzl8GTeNg71+G67VMtH7wOY9tmWj9yKs+36kUdeOzNU2+pCtZ43fcHks/B393Bkw/d1gb4zw2ZeCRy2DvX6wsftEG4rbgvqWhJVB4nzIpK2GHO17j7dWgdakZf536zVUFtbvtR6cVa1J1cMX4/HWy6nflr+PgyNuOLKj+5gs2F1S/TFTcX5WRE2bUawTasPQx4xyPPcDZuQ4UkRXAOuA9IrJDRC4SkS+KyBftKmcDL9ial6XA3HwTl6oinxA6l+A4rBjZVS8SidHa2EZfstdHvG8LuB31IhKhtXEckYa441xR63kw4RWT3n0e9HdZyx3u+iys/r5DaGoLqxFr4qJ3Px9VIpGoo0/bwk9crIOz/Cgx1Od/h24/If5dLiH+XedZk4+0KPn6GTDU7yNMvsgqd9bzO/au89R3lOoj1/juHr8HeocTofzXQuh6zf/Yvg7Y/EvYttaKKwPRSJS2dFwHCO8TyYR/vOryMkVRxiB5BfvGmP8B/kdEbjXGvFpI48aYeXn2/xgrlXJtUsk72Te0Bov3w5LvbtAjTTigVC2Bd+gOI8Tfvs4qdxIfH16c7z5WfUepRgoZ391JTQKE+pmxNVdbPgTGqwr7FUUZg4RJlfwbEXkAuF5EHnA/RsHG6qWSd7If7AkW74cl192gixH2q+i6Zgi8Q3cYIf6U2Va5k76u8OJ897HqO0o1Usg4F1Koz9v/m78tHwLjVa+8KIoyBgmzbOxq4N+BV4AEcLP96AZeKJ9pNUBY4X0ZGJGAO42f/WfdYon61/4HfOInI0s4oFQt6Tt0e4TCfkJ8v75umZhdFollJ4JI+5KfON99rPqOUo0UMs41tmb7vzsxinNsLcLvA+NVr7woijIGySvYz1QUWWOMOT5f2WhQVWLS0ML7Ietmj01t1r906fpNbZDsh6HB7H2R/LfgSaWStoC71RJwE7G0LU5RfmOLdV6JWqJST+IAH7sG+4afzZD1xVxswoHKU3FxXln91dN/rZamyVPP218pISMQzgiFjfG2hxn21bSPQMiyiNdPoFZ8p1LUt89WI0HjWbo8PXY2tnrjI9lnxaGzbKAne4x3j/lF+H3KpLzxWnmxPtSQv6pgP199FewrtUEhI98kEZmW3hCRgwFNrh5GeJ8agp6dsPJc+N4keOpGKzPNynPh6ZutpTXpfSvPtfalkrnPm0oR6d1F653ziNx3Ma29HUTu/IzVxoq51vnW/cTenge9b8N9F1v7endaX8qplFXuPHfvLutLVSLWl23TuJElHFDKh9uvVp5rbaeGXPXsrEkr5g77R+9OIobsO3QbvP7Qvwd6drnOsQv69np9djDh9SXw+on6jlJNBMQHqZTlm+lMYk/daAnxnT6+5w1rIuIs63pteIx/6sbseGgs3u+dgv4gYb+iKMpYoJDR71JgtYisFpHVwONY6ZOVfAz0DGeiSSXh8NOHt4/8dPa+bWut7XxroZ2ZcOb8M9z/ZW8bh58+vH3/l616zow5+bKlKdWN268yvuPSPYXtZ796vZ3+WcQSnd7zpl+rLym1RL74SGcSO/x0+PUl2fXu/5KVItxZ9utLhsd45xis8aAoilIS8q9NsjHG/FZEDgP+1i76kzGmvzxm1Rm5MtE0T/DPStPUlrtNZyacsJltnFnENGtY7eP2K/D3nbBZk/zqhc02FpRZTH1JqXbyxUc6zoLGWb9YSNctMruYoiiKEkyYbGMn2c+fAk4DDrEfp9llSj5yZaJJ7PbPStPfnbtNZyacsJlt0tuaNaw+cPsV+PtO2H72qxc221hQZjH1JaXayRcf6TgLGmf9YiFdt8jsYoqiKEowYZaNfch+PsPncXqZ7KovGlutO9fP3wiXd0C83br7+NQ51g3L/LLSRBvApKBvj1fDAJYu5Ry7zf3enT+zzSd+YmUQ06xh9YM7w1G63xtd9/oJ7Oe45V9pP2uIe+u1TBz21cw5lkHzRB+fbRr28fkbLf9UX1KqnVzjYCpllZ2z3Bq33fU+cYOlC3QfG2+3xvwXH7SWBs/fCBc8ABirTUVRFKVoQmcbqyZqLhNOWhB6z0XW0oEps60vQ4kOZ/IySWvZTV8XSAyeugHWLLbqnnULtE7KziLlbvP4RfCBL1hfpAVlG6uZrGHFUvHMItWZbcwWId/7uWGfPOsWaNoH9v7FWgrT+ao1eYnEoHtndlnaz5yZlHp3Zfv42cugZVK9+dNoUN8+W434jYNgjbEbboPp8yx9y7h3woe/C/scYI3VTeOg+21obLZed2yD1T+wYujsZVY89e+p97ioGX/VbGP56mu2MaU2CK15EZE/A08Ba4E1xpgXy2ZVveEUhIL1fNd5MG+lnaFp3vA+sP69O3UJrP7+sBh67p0Q3ye4zdXfh21rrDbTmofM87jh49x6iHTmJ799SvUTiQ77hdM/PPVc/dy3Z1jsD8N+ds5yuN5xM7ypc+CMpd6yeSuzz9vf7fXxey7K9kdFqVb8xsG0T5+6xJq4pH17zj/DbV/wjtnuOLnnImvc1rhQFEUpKYX89XM48FNgX+BqEdkqIr8qj1l1Ri5BaNA+t9CzWBG2ovgRJPb3E937CZLDiP3VH5VaJu3TbqF+IcL9oDjTuFAURSmaQiYvQ8Cg/ZwC/gq8VQ6j6o5cgtCgfW6hZ7EibEXxI0js7ye69xMkhxH7qz8qtUzap91C/UKE+0FxpnGhKIpSNIVMXvYA1wKvABcYY2YbY75QHrPqjFyCUL99brF9QSJs/UdPCUGQ2D/92ulTLRPz+5n6o1JvpH36xQfhzB8P+/aLD3pjJyhOGls1LhRFUUpMaMG+iJwJfBA4BhgAfo+lfXm0fOb5U3ExaTEid4+wugUG+4afzdCweD8trC9YhF13YvtSUHFx3qj7q6/42HiF/eBTJv7C5TB+pv5YKsaez5aakfpi+viGZitGGluHY8WTECVPnNR/XNSMv6pgP199FewrtUHoEdQY82tjzL8AXwAeAi4EHiyTXdVLOsvXirnwvUnWc+/O3OkvUynofRtWnmsds/JcK9PTup9Y23d+xvpyM1hfjpGYJYKWiPXsN3GBYZGp2M/19YWoFIOff/Z3Wf6W5X87rfpuP/PzqbB+pv6oVAPFjNFBx1+1vxUvXa/BUzdacdPQYsVK2DjRuFAURSkpoUdREbnXzjh2HdAKnA9MLJdhVYszy1cqOZw9ZjDHGma/Y+79HBx+evg2FCUMfr7W2wn3+vjfQE+lrVWU0lPMGJ3v+F9fYo3XOk4riqJUnNCpkoEfAc8aY3zumAgi8hFjzCOlMauKKSarUtiMYpqBRhkpfr428V3+/qepWpV6ZKSZ73KN1zpOK4qiVJxClo2tD5q42CwugT3VTzFZlcJmFNMMNMpI8fO1zlf9/c+dwU5R6oGRZr7LNV7rOK0oilJxCrnykg+PCEpEfgacDrxljDnCZ79gLUM7FegFLjTGPFtCm4rHKbJMDsDQgL1uWaxsMe47JufKHtPQYt38r7dz+C7lzRPh6Zstfcvxi+ADX7DO1d/tLwZNr5Ouf/Hn2CNsnwbdBTyrrNnra22T4Kxl1tKxtM+edcuwvxUjzleUaiWdJcxvjPZLnJKVHMVObnHBA9CxDVb/APb+xco29tzdVjsiYFIaH4qiKBWilJMXv7RltwI/Bm4POObjwGH24/3ADfZzZUkLNu+5CP7uDDj8TEsjkP4i/MxymLfCkR0sxBfY0AD8ZkH2l+mxl8DxX7fF1P+Y/cPymdthzeLhui2TrHbSdjnbaZmkX6C1itPXcvWpX71zllt+5T420pjta2ctg9Z9rbt9O3+k9e7yHhtthLvOU/9SapdIxPLZeSu9Gfd6dmaP5c6x9vhFcPT52fvPXgaNbRCLw4x/tNq/8xyND0VRlApS1hHXGLMG6MhR5UzgdmPxFDBBRA4op02hcAo2j/y09WXmFG/efR4YEz57TJCA1KSsKyx+Ymo/Mf9IhahK9RG2T4OE+H7HJjpd/nSR9QPOmVlssM//2N5O9S+l9vHL8DXQ4x3LnWPt4ad7999zkXW15vYzrax9Ov4qiqJUnFJOXrYVccxk4DXH9g67zIOIXCwiG0Rkw86dO4s4VQE4BZvNE0Yuds4lIC1EzD9SIaoyaoT217B9WogQf+K7vGVufw06r9+x6l9jglEdYytBU1vusTYtyHfvTx8XtF/joyLUvb8qihJI3smLiHwq1yNdzxjzqVztBDXvU+Z710xjzE3GmJnGmJmTJk0q4lQF4BRsJnaPXOycS0BaiJh/pEJUZdQI7a9h+7QQIX7nq94yt78GndfvWPWvMcGojrGVoL8791ibFuS796ePC9qv8VER6t5fFUUJJMyVlzNyPE4f4fl3AAc5tg8E3hhhmyMnLficOgc2/9JaFz11jiWunzrHFju3Ftdeuo20gNRv31m3wIsPhqubL1mAUt2E7VO/ei0T/Y9tnpjfX4PO2zJR/UupTxpb/cfy9Fj74oPe/Wcvs447e5m1/8wfa3woiqJUGDHG90JH6U4gMhV4MCDb2GnAJVjZxt4PLDXGHJOvzZkzZ5oNGzaU2FIXQdnG+rutL7Ogu96HaS9vBjHNNlZC/K7ujSp5/bWk2cZsYXJWRqUAfw3bnvrXaFP9PlurhMk2Ntjn9f90rDQ0Dx+v8ZGmZvx16jdXFdTuth+dVqxJ1cEV4wus31VQ9SNvO7Kg+psv2FwN7VfcX5WRU1C2MXuy8fdAPF1mjLkyR/0VwAnAfiKyA/j/gAb7uBuBh7AmLluwUiX/U2Hml5G04BOgIW49wBI7j7Q9t/7Ab18hdZXaJmyfBtXzK0v7aS5/LaQ9RakHIlFvbLif/fzfGSvueoqiKMqoEnryIiI3Ai3AicAtwNnA07mOMcbMy7PfAF8Ja4OiKIqiKIqiKGOXQq53H2uMOR/oNMZ8F5hNtl5FURRFURRFURSlbBQyeUnYz70i8jfAIHBw6U1SFEVRFEVRFEXxUojm5UERmQD8G/AsVkrjW8pilaIoiqIoiqIoiotCJi9LjDH9wL0i8iCWaL+vPGYpiqIoiqIoiqJkU8iyscythY0x/caYLmeZoiiKoiiKoihKOcl75UVE3glMBppFZAbDObL3wco+piiKoiiKoiiKUnbCLBv7GHAhcCDwH47yPcC3y2CToiiKoiiKoiiKh7yTF2PMbcBtInKWMebeUbBJURRFURRFURTFQyGalydFZJmI/BeAiBwuIheVyS5FURRFURRFUZQsCpm8/Bx4GPgbe/v/gK+V3CJFURRFURRFURQfCpm87GeMuRtIARhjksBQWaxSFEVRFEVRFEVxUcjkpUdE9sW6OSUi8gGgqyxWKYqiKIqiKIqiuCjkJpX/DDwATBORJ4FJwNllsUpRFEVRFEVRFMVFIZOXF4FfAb3AXuB+LN1L3ZNKGXoHh2hpjNI7MERLQ5RIRPIfqChKSdFYrD20zxRFUZRSUsjk5Xase7v8wN6eBywHPl1qo6qJVMqwq2eABSs2sn5bB7OmtrN03gz2bW3UL2BFGUU0FmsP7TNFURSl1BSieXmPMeZzxpjH7cfFwLvLZVi10Ds4xIIVG1m3dRfJlGHd1l0sWLGR3kHNVaAoo4nGYu2hfaYoiqKUmkImLxttkT4AIvJ+4MnSm1RdtDRGWb+tI6ts/bYOWhqjFbJIUcYmGou1h/aZoiiKUmoKmby8H/i9iGwTkW3AOuBDIrJZRJ4vi3VVQO/AELOmtmeVzZraTu+A/nOoKKOJxmLtoX2mKIqilJpCJi+nAAcDH7IfBwOnAqcDZwQdJCKniMj/isgWEfmmz/4LRWSniGyyH58r7C2Ul5aGKEvnzWD2tH2JRYTZ0/Zl6bzpRARSxtDdnySVMpU2U1FqmlTKjqUcMeUfizNoadB/8auVnOOnjpuKoihKEYQW7BtjXi20cRGJAv8JfATYAawXkQeMMS+6qt5ljLmk0PZHg0hE2Le1kZsvmGlly+kfYiiV4qJbN6gAVVFKQFhRtycWNXNV1ZPps/Nn0tIUZfuuXr6/6iX+uqdfx01FGWMceduRlTZBqRMKufJSDMcAW4wxW40xA8BK4Mwyn7PkRCJCW1OMiAgIfPGOZ1WAqiglohBRtzMW25pi+sO3BohErHHzH2/+AydcvZr7N72h46aiKIpSNOWevEwGXnNs77DL3JwlIs+LyD0icpBfQyJysYhsEJENO3fuLIetoVABqhKGavHXWkBjqjoop89qHyulRsdYRRm7lHvy4ve3qHuh82+AqcaY9wK/A27za8gYc5MxZqYxZuakSZNKbGZ4VICqhKFa/LUW0JiqDsrps9rHSqnRMVZRxi6F3KSyGHYAzispBwJvOCsYY3Y5Nm8GFpfZphGRFqC61+eraFhRikNjqv7RPlZqkanfXFVQ/W0/Oq1MlthcMb687StKjVDuyct64DARORh4HZgLnOusICIHGGPetDf/AXipzDYVTCpl6B0cskTCg0NMbG7gpvOPprUpRk9/kuaYv2g4mUyRSA5l1YvFvBe7stpXEbIyxggS4htj2NuXzMRPS0OUaDTcxWK/mAI8ZcZY9Yo5R1jGSny732c8Ghke/waStLda46a7T7r7k3X/2SiKoiilo6yTF2NMUkQuAR4GosDPjDF/FJErgQ3GmAeABSLyD0AS6AAuLKdNheLOhLTgpEOZe8wUvrpyU+YfxOvmTmff1sasHz3JZIqO3gFPvfaWxqwJTNhMS4pSz6SF+ABtTTGGhlLs6vHGjzvO/PCLqRs/exQDQykWrNjkiLPpNEQjfOmOZws+R1jGSny73+f186Zz9Lvas/pv8Vnv5f6NO/jEjAO5f+MOLjh2qk+f1N9noyiKopSWcmteMMY8ZIx5tzHmEGPM9+2yy+2JC8aYbxlj/t4Y8z5jzInGmD+V26ZCcGdC+tgRB/DVlZuyMiN9deUmT9acRHLIt14iOZSzfc3CoyhWXISJs6Bj3THV2TvIghWbXHG2id29g0Wdo5D3MRbi2/0+Zx+yn6f/Ft37PB874oDMs3+f1N9noyiKopSWci8bq3ncWXIO3b/NN2tOa1P2R9naFAtVT7PwKIqXsPHjh19MHdTe4tveQe0tRZ0jLGMlvt3vc5/mBt/3nR4/D92/LVPmrlNvn42iBKIaFkUpirJfeal13FlytrzV7Zs1p6c/mVXW058MVU+z8CiKl7Dx44dfTL3W0evb3msdvUWdIyxjJb7d73NPYtD3fafHzy1vdQf2Sb19NoqiKEppGVNXXnIJZ4P2tTREufGzR9HZO8hB7S283d3PdfOm89UV2Wvx3VlzmmNRrps73bNmvzmWXc/d/msdvUxsadAsPErNUUphektDcPzs7RvMKbD3y2w1saWBpfOm+2peZk/bN2csj4SxkmXLOY4dOLGZxOAQv/j8+9m+q5drf/d//HVPf0bzsnTedFIGWhujPn1Sf5+NoiiKUlrGzOQll3AWyLlvYCjFt+7bnNl3w2eP4sbzjmZcPMaexCAxnx9osViE9pZGT1Yyv2xj7vaXzpte3g9DUUpMqYXpIkJLY5QbPnsU+zQ3sCcxSDwW8U2C4RbY+2Uvi0cjmIEkP/zUkZk/CRqiEdoaY1kxWupsY0GZ1OpRkD4wlOKBTa/ziRkHsuje57PGs5bGGPGGCJ+eeRDfX/USf93Tz9J5VgKTsfDZKEotcORtR1baBEUJxZhZNpZLOJt/X7ao9Et3PMtfuvqY9q2HmH7lI3z+9md8RaaxWIRx8QYiIoyLN/hOXPzaX7CitKJhRSk3pRam9w4O8fnbn2H6lY9k4qwvmQot4k9nL4uI9ZxIDvGlO57lhKtXc8i3H+KEq1fzpTueJZEcyorRUqdJ9rOlHn+cp8extCDfPZ7t6Ezw2VueZk9fkvs3vZEpTyRTdf/Z/pDveAAAFutJREFUKIqiKKWl7q68BC7/yiOcLXRfWnCa3vYT+YZZRjNWBL1KfeHx7Rx+XMx9PFoao5xyxDuyrrwEicDDxN5IEgAo+Un3f1BCE7dQP12u45yiKIpSKHV15SW9dOXzt23g3d/5Lz5/2wZ29QxYP2RyCGd7+/33/bWrj5f/6i/Q3/JWd9a2W+SbyxYnY0XQq9QPfr7d3ecvsO/uS+aNAT8GBof4+BEH8KU7nuXd3/kvvnTHs6FF/H72jSQBgJKf9DgWlNDEKdR3lus4pyiKohRKXU1eci1dSQtnZ0/bl1hEmD1t34w4NBKBf/v0e7P2/dun30vKGP7z8S0sPit733Vzp/PwC29mbbtFpmGX0eSyS1GqET/fvvXJV7hu3vTsOJk3nVuffKWopWSDKeNZIvbEyzu5bu50TyyGib2wxyrFkR7HHn7hTc94ufis9/LwC296xk0d5xRFUZRiEGPy/wtabcycOdNs2LDBU54yhuXrtnHm9MmZpSa/3vQ6582eSkSEZDJFIjmUJaBPb+9NDCL22vTuviTPbu9gzmH7c8i3H2LV/OOYsm9r5jjBYJDMdkNEaGyI0jcwRMoYWppi9PYPce+zr/GBaftx6P5t7O7tJxaJMq7ZKwx2L3FpjkVIJFMqYi0NFf/ggvy1VkkZw7u/818kHVdQYhHhT1ee4omvb9zzHF864VAO3b+NLW91c8PqLVx99vs89SIRyY6Bhgh/enNPVtxt39XDe96xj+dYEWvCkhHdN0b5+t3hzuunQwubNa2U2dVcVLXPut93PBohkRzKjKfOcbXHuWQwM75Z+1saoohIuT5DZfSoeIeFHWOnfnNVWe3Y9qPTCjugyu7zcuTBUyptwojYfMHmMNUq7q/KyKmrBd/OpSbObEQDg0PEIv6ZilY+vZ2tb/fwjY+9h3/55fNZ+3b39nPrhTOZNC7Oxbc/4zlu6WNbMtsvvrKLQ/cf52lj5dPbeaqlgY8fcQCX3PmMb5aktKAXrH8wS5m1SVFKTXqJ0LqtuzJl18+b7o2vedP519MP55I7h335lguO9tS74bNHMTiU8qQxnjyxJTvu5k2neyDpie+WxmhWvRs+exQLT/lbvn73c5myf//M+zzH+sVV2Kxppc6uViu43/eCkw5l7jFTeH13L5MntPDMqx0c/a72rP5Np0j+xIwDuX/jDuYeM4Xu/kH2bW1iwNPv9f8ZKoqiKCOjrpaN+S01+erKTQymDInkkO++jx1xAF864VD+5ZfPe/bFolGOsr+I/Y5zbh81pd23jY8dcQBnTp8cOktSqbM2KUqp8Vvq+MHDJnl9fMUm9vYls8qGUnjq7e4d9M24t7t30NOep2zlpsxrZ3tfv/u5rLKv3/2c51i/uAobf2M1Tt3v+2NHHMBXV27ikEnj+OrKTcw+ZD9P/y669/lMFrJ0/Unj4nT69nv9f4aKoijKyKirKy/5Mgrlyhrmt29cPP9x6e22uP+5D92/DRH/NvwyHWn2MaXa8b13SYDfHtTeklXmFycHtbeEOjaobJ/mhqLbc8dV2Pgbq3Hqft/pLGLpfg3KCOfMNpYe+1oa/cfMev8MFaVa2fzK9oLq1/oyM6V2qasrL7kyCgXt2/JWd2CGnD2JwZzHObeDsi1teaubPYnB0JmONPuYUgu4710SFCevdfRmlfnFyWsdvaGODSrbkxgsuj13XIWNv7Eap+73nR470/0aNNY5s42lx76gfqr3z1BRFEUZGXU1eWlpiAZmFGqO+e97+IU3uWH1Fk+2sZvPP5qIfZdvTxalgGxjQe3/etPrOTMdpVKG7v4kKWOICCx1nU+z8ijVjm98zZvO/uOa2HT5R9j6w1PZdPlHaIqKp96ElgYfn5/OhJYGT3uesrnTM6/ztTfRdaxfXAVl/2uORTIx2t2fpDkWGZNZAt2fTzqL2J937uW6udNZ9+e3Pf2bzjbmzDq2c28fE337qf4/Q0VRFGVk1FW2MYChoVR25iFHVq9c2cYSA0MkU4Zx8Rh9A0N09yczotMFJx3KhccdTFvce1xPf5InXt7J/BWbsuq5s4YNDA4xmDIeu/yEvzd+9iiikQgtTZqBpwRU/IOrt2xjfgwNpUgMWjGUzvQXj0XY05f0JMlob2nMm22spSFKytaqOesB+bONBWSxAorKItYci9DRO+gR57e3NJQrK2BV+2z6vlktTVG27+pl265uph80kbbGmJVtrCFKwpX9rad/iNamKImBIeKxKH1DqYL6RKlqKt5hmm2sMlTbsjHNNjZ2qCvNC0A0GmGcPVkZF89eCx+LRRgXy943zv5H9fO3bchkT9p0+UcyolOA//jdy6zb2sFN5x/tOe7i25/x1Lv5gpmZ7GFtti3xxhhx2w6nXU4BLMC6rbv44h3PcvMFMzNLchSl2ukdHMqKBfDGUVpgnxVHjljIxIz9HImIJ14B/zKfmHe3F1Tmxpn9r60pRnd/0hOjC1ZszI7zMRSnkYiAwD/e/Ies/p49bV9uOv9ozlv2ND897+jA/bFYhDZHiuqx+BkqiqIoxVNXy8aKxS1CDRKdugX2pRDtjlXhr1Jf+CXLCBtH1Y7GqJegzyTtB0EJTGqt7xVFUZTqQycveEWoYQX2pRDtjlXhr1Jf+An2C0lUUc1ojHoJ+kzSfhCUwKTW+l5RFEWpPso+eRGRU0Tkf0Vki4h802d/k4jcZe//g4hMLbdNbtwiVD/RqVNgH3RcMYLTUrShKJXGL2FFLOIV5/vFUbWjMeol12eydN6MjIDf3fdp3ZKiKIqiFEtZBfsiEgX+D/gIsANYD8wzxrzoqPNl4L3GmC+KyFzgk8aYc3K1Ww4BtFukG49GsoTBTuF/ruOKEZyWog0lkIp/kGNBsA/+yTLAK6b3i6NqZ5RjtCZ8NugzSZe7x9DmWJRYrPb6XslLTfgrqGC/1KhgX6kU5V6AfAywxRizFUBEVgJnAi866pwJXGG/vgf4sYiIGeU0aG6RLvgLg8McV4pzK0qtEZQsIyiBRi2hMeol6DNxlocZQxVFURSlEMr9N9hk4DXH9g67zLeOMSYJdAH7ltkuRVEURVEURVFqjHL/heh3ec59RSVMHUTkYuBigClTqutSpaK4UX9Vag31WaWWqEZ/LXRZ2rZ4/jrVzOZXthdUv9qWmSm1S7mvvOwADnJsHwi8EVRHRGLAeKDDVQdjzE3GmJnGmJmTJk0qk7mKUhrUX5VaQ31WqSXUXxVl7FLuyct64DAROVhEGoG5wAOuOg8AF9ivzwYeG229i6IoiqIoiqIo1U9Zl40ZY5IicgnwMBAFfmaM+aOIXAlsMMY8ACwDlovIFqwrLnPLaZOiKIqiKIqiKLVJWVMllwsR2Qm8Wmk7Ksx+wNuVNqKKCPo83jbGnDLaxjgpwF/rpU/1fYyMWvLZUlPtvlPt9sHo21iN/loL/RSWenkv1fI+Ku6vysipycmLAiKywRgzs9J2VAv18HnUw3sAfR9K8VT7Z17t9kFt2Fhu6ukzqJf3Ui/vQ6kO9I5hiqIoiqIoiqLUBDp5URRFURRFURSlJtDJS+1yU6UNqDLq4fOoh/cA+j6U4qn2z7za7YPasLHc1NNnUC/vpV7eh1IFqOZFURRFURRFUZSaQK+8KIqiKIqiKIpSE+jkRVEURVEURVGUmkAnLzWGiEwQkXtE5E8i8pKIzK60TZVGRC4VkT+KyAsiskJE4pW2KSwicpCIPG735R9F5KuVtqlYRCQuIk+LyHP2e/lupW0qFhGJishGEXmw0rbUO7UUA9XuF/r9ACJyioj8r4hsEZFvVtqeYhGRn4nIWyLyQqVtKZZaim2lttDJS+1xHfBbY8zfAu8DXqqwPRVFRCYDC4CZxpgjgCgwt7JWFUQS+Lox5u+ADwBfEZHDK2xTsfQDJxlj3gdMB04RkQ9U2KZi+SpjPLZGkVqKgWr3izH9/SAiUeA/gY8DhwPzqtiX8nErUOs3U6yl2FZqCJ281BAisg9wPLAMwBgzYIzZXVmrqoIY0CwiMaAFeKPC9oTGGPOmMeZZ+/VerB8bkytrVXEYi257s8F+1FxGEBE5EDgNuKXStowFaiUGqt0v9PsBgGOALcaYrcaYAWAlcGaFbSoKY8waoKPSdoyEWoltpfbQyUttMQ3YCfzcXrpwi4i0VtqoSmKMeR24GtgOvAl0GWP+u7JWFYeITAVmAH+orCXFYy+r2QS8BTxijKnF93ItsBBIVdqQsUaVx0C1+4V+P1g/jF9zbO9AfyxXBVUe20qNoZOX2iIGHAXcYIyZAfQANbumtxSIyESsf9YOBv4GaBWRz1bWqsIRkTbgXuBrxpg9lbanWIwxQ8aY6cCBwDEickSlbSoEETkdeMsY80ylbRlrVHMM1Ihf6PcDiE9ZzV39rTeqObaV2kQnL7XFDmCH49/se7C+rMYyHwZeMcbsNMYMAvcBx1bYpoIQkQasgf0Xxpj7Km1PKbCXq6ym9tZsHwf8g4hsw1pycpKI3FFZk+qfGoiBWvAL/X6wPoODHNsHUkPLiOuRGohtpQbRyUsNYYz5C/CaiLzHLjoZeLGCJlUD24EPiEiLiAjWZ1IzIlXb5mXAS8aY/6i0PSNBRCaJyAT7dTPWxPJPlbWqMIwx3zLGHGiMmYqV+OExY0zNXcmrJWohBmrBL/T7AYD1wGEicrCINGL11QMVtmnMUguxrdQmOnmpPeYDvxCR57EyOv2gwvZUFPtfxnuAZ4HNWD59U0WNKozjgPOw/sndZD9OrbRRRXIA8Ljtm+uxNC9VmVJWqSrqKQYqzZj+fjDGJIFLgIex/sS62xjzx8paVRwisgJYB7xHRHaIyEWVtqkINLaVsiDG6HJQRVEURVEURVGqH73yoiiKoiiKoihKTaCTF0VRFEVRFEVRagKdvCiKoiiKoiiKUhPo5EVRFEVRFEVRlJpAJy+KoiiKoiiKotQEOnlRFEVRFEVRFKUm0MlLhRGRE0Qk8F4YInKhiPy4DOe9UET+xrG9TUT2K/V5lPoln++GOH6miCwN2LdNRPYTkQki8uVSnVOpH9xjWI56t4rI2Tn2rxaRmSW2Tf1WCaRUvhvi+CtF5MM+5Rl/tF8fW6pzKspooJOXscuFQN7BU1HKhTFmgzFmQZ5qE4Av56mjjE0upHrHMPVbJRcXMgq+a4y53BjzuzzVTgCOzVNHUaoKnbyEQERaRWSViDwnIi+IyDkicrSI/I+IPCMiD4vIAXbd1SJyrYj83q57jF1+jF220X5+TxF2TBKRe0Vkvf04zi6/QkR+Zp97q4gscBzzryLyJxF5RERWiMg37H9VZmLdiXmTiDTb1eeLyLMisllE/jaHHW0i8nO73vMicpZd3i0ii+3P5Hf2e07b9A+Fvl9l5FTSd23/mCAWu0TkfLt8uYh82PXv374i8t/2OX4KiN3Mj4BDbD/9N7usTUTusf36FyIi3rNnbJhl2/yciDwtIuPsfz3vF5HfiMgrInKJiPyzfe6nRKS9uE9bGQkiMtXu09vsceUeEWnx81e/MUxELrfHxRdE5KZcfpHDho+KyDp7HPyliLTZ5dtE5Lvu8dEekx+xy38qIq+KdQVb/XYMUQnftcfl++zXZ4pIQkQaRSQuIlvt8sxVFBE5xbbxCeBTabuBLwKX2rbMsZs/3va/rZLnKoyILLRj4jkR+ZFdtlpErhGRNSLyku3P94nIyyJyVTGfsaJkYYzRR54HcBZws2N7PPB7YJK9fQ7wM/v16nRd4HjgBfv1PkDMfv1h4F779QnAgznOfSHwY/v1ncAH7ddTgJfs11fY9jQB+wG7gAasAXIT0AyMA14GvuGwc6bjPNuA+fbrLwO35LBpMXCtY3ui/WyAj9uvfwX8t23H+4BNle7HsfiosO/eCJwGHAGsd7T9MtDmPB5YClxuvz7N9qX9gKlpOxzn7AIOxPrzZV06JnzO3whsBWY534cdU1vsmJhkt/dFu841wNcq3W9j8WH3tQGOs7d/BvxLHn91jmHtjtfLgTPs17cCZ+c472qssXI/YA3QapcvcvjkNnzGR+DHwLfs16eo347NRyV81/aJV+zXV2ONsccBHwJWOI8H4sBrwGFYfwzdzfDYewX27wLHMb+0/fRwYEuO9/1x+z22ON+H/f4W26+/CrwBHID1G2UHsG+l+0wftf2IoYRhM3C1iCwGHgQ6sX6QPWL/QRIF3nTUXwFgjFkjIvuIyASsL5zbROQwrEGuoQg7Pgwc7vhTZh8RGWe/XmWM6Qf6ReQt4B3AB4FfG2MSACLymzzt32c/P4P9z0wOO+amN4wxnfbLAeC39uvNQL8xZlBENmMN7sroU0nfXYs1CXoVuAG4WEQmAx3GmG7Xn4vHY/ucMWaViHS6G3PwtDFmB4CIbMLyrSd86r0HeNMYs95ud499DMDjxpi9wF4R6QLSsbEZeG/I96eUnteMMU/ar+8Avk1uf3VyoogsBFqAduCPDPdrGD6A9WPtSftcjViTjDR+4+MHgU8CGGN+q347phlV3zXGJEVki4j8HXAM8B9Y42gUa+x18rdYE52XAUTkDuDiHM3fb4xJAS+KyDty1Psw8HNjTK9tU4dj3wP282bgj8aYN+1zbwUOwvqTVVGKQicvITDG/J+IHA2cCvwQeAQrGGcHHeKz/T2sL55P2pdqVxdhSgSYnZ6MpLEHxn5H0RBW3xa6bCLdRvr4IATvewQYNMaky1Pp9owxKRFRX6sAFfbdNcBXsK4SfgfrR97ZeL9Yg84dhJ+v+xHkp+42Uo7tVI72lPLj7q+95PZXAEQkDvwE69/s10TkCqx/mwtBgEeMMfMC9vuNj4WMseq39U0lfHct1tWPQeB3WFdNosA3QtiXC6ef5fLxML7q9NP0tvqqMiJU8xICsbKC9Bpj7sC6PPt+YJKIzLb3N4jI3zsOOccu/yDQZYzpwlqu87q9/8IiTflv4BKHXdPz1H8COMNeA9uGtRwnzV6sf9RLYcfEIttRykwlfdcY8xrWEprDjDFbsfzxG/hPXtYA/2if++NA2qdG4qd/Av5GRGbZ7Y7TSXTVMyXtm8A84CmC/dXpG+kfe2/bY10x2ZKeAo4TkUPtc7WIyLvzHPME8Bm7/kdRvx3LVMJ31wBfA9YZY3YC+2JdZfmjq96fgINF5BCHfWlG+lvg/4lIC4Co7koZJXTyEo4jgaftS/3fAS7HGmAWi8hzWLoSZ7aOThH5Pdaa/4vssiXAD0XkSax/RophATBTLEHgi1hCu0DsZQcPAM9hLXnYgLVOGqx/aG6UbMF+WK4CJoolLnwOOLHA45XRo9K++wfg/+zXa4HJ+C+V+S6WSPRZ4KPAdgBjzC6sZTwvyLDwORTGmAGsydj19nt9hML/jVdGl5eAC0TkeazlM9cT7K+3Yo9hWP/s3oy1ROV+rPX/BWH/+LsQWGGf/ymsH4K5+C7wUdtvP461LGiv+u2YpBK++wesJeJr7O3ngecdKyAAMMb0YS0TWyWWYP9Vx+7fAJ+UbMF+KIwxv8X6jbHBfi9+V3wUpeSIy8eVESIiq7HEbxsqbQtYmcFsfUEL1gB3sTHm2UrbpVQf1ea7ytjCXpL4oDHmiAqbEhoRaQKGbP3BbOAGY0y+K+JKnVGLvqsotYxeiq5/bhKRw7H+ubtNJy6KoiglYwpwt4hEsBKWfL7C9iiKotQ9euWlShCRf8JKKejkSWPMVyphD1SnTUr1UQ1+IiK/Ag52FS8yxjw8WjYo1U01+kg12qRUH5X2ExE5EiuFs5N+Y8z7R+P8iuJGJy+KoiiKoiiKotQEKthXFEVRFEVRFKUm0MmLoiiKoiiKoig1gU5eFEVRFEVRFEWpCXTyoiiKoiiKoihKTfD/AzQYfHVF7Dw0AAAAAElFTkSuQmCC\n",
"text/plain": [
- ""
+ "
"
]
},
"metadata": {},
@@ -1305,11 +1415,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "##Step 5: Classification\n",
+ "## Step 5: Classification\n",
+ "\n",
+ "[[ go back to the top ]](#Table-of-contents)\n",
"\n",
"Wow, all this work and we *still* haven't modeled the data!\n",
"\n",
- "As tiresome as it can be, tidying and exploring your data is a vital component to any data analysis. If we had jumped straight to the modeling step, we would have created a faulty classification model.\n",
+ "As tiresome as it can be, tidying and exploring our data is a vital component to any data analysis. If we had jumped straight to the modeling step, we would have created a faulty classification model.\n",
"\n",
"Remember: **Bad data leads to bad models.** Always check your data first.\n",
"\n",
@@ -1330,10 +1442,8 @@
},
{
"cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 22,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1345,7 +1455,7 @@
" [ 5. , 3.6, 1.4, 0.2]])"
]
},
- "execution_count": 24,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -1364,11 +1474,11 @@
"all_inputs = iris_data_clean[['sepal_length_cm', 'sepal_width_cm',\n",
" 'petal_length_cm', 'petal_width_cm']].values\n",
"\n",
- "# Similarly, we can extract the classes\n",
- "all_classes = iris_data_clean['class'].values\n",
+ "# Similarly, we can extract the class labels\n",
+ "all_labels = iris_data_clean['class'].values\n",
"\n",
"# Make sure that you don't mix up the order of the entries\n",
- "# all_inputs[5] inputs should correspond to the class in all_outputs[5]\n",
+ "# all_inputs[5] inputs should correspond to the class in all_labels[5]\n",
"\n",
"# Here's what a subset of our inputs looks like:\n",
"all_inputs[:5]"
@@ -1383,35 +1493,41 @@
},
{
"cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 23,
+ "metadata": {},
"outputs": [],
"source": [
- "from sklearn.cross_validation import train_test_split\n",
+ "from sklearn.model_selection import train_test_split\n",
"\n",
"(training_inputs,\n",
" testing_inputs,\n",
" training_classes,\n",
- " testing_classes) = train_test_split(all_inputs, all_classes, train_size=0.75, random_state=1)"
+ " testing_classes) = train_test_split(all_inputs, all_labels, test_size=0.25, random_state=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "With our data split, we can start fitting models to our data. Our head of data is all about random forest classifiers, so let's start with one of those.\n",
+ "With our data split, we can start fitting models to our data. Our company's Head of Data is all about decision tree classifiers, so let's start with one of those.\n",
"\n",
- "There are several decision tree classifier [parameters](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) that we can tune. For now, let's use a basic decision tree classifier."
+ "Decision tree classifiers are incredibly simple in theory. In their simplest form, decision tree classifiers ask a series of Yes/No questions about the data — each time getting closer to finding out the class of each entry — until they either classify the data set perfectly or simply can't differentiate a set of entries. Think of it like a game of [Twenty Questions](https://en.wikipedia.org/wiki/Twenty_Questions), except the computer is *much*, *much* better at it.\n",
+ "\n",
+ "Here's an example decision tree classifier:\n",
+ "\n",
+ "\n",
+ "\n",
+ "Notice how the classifier asks Yes/No questions about the data — whether a certain feature is <= 1.75, for example — so it can differentiate the records. This is the essence of every decision tree.\n",
+ "\n",
+ "The nice part about decision tree classifiers is that they are **scale-invariant**, i.e., the scale of the features does not affect their performance, unlike many Machine Learning models. In other words, it doesn't matter if our features range from 0 to 1 or 0 to 1,000; decision tree classifiers will work with them just the same.\n",
+ "\n",
+ "There are several [parameters](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) that we can tune for decision tree classifiers, but for now let's use a basic decision tree classifier."
]
},
{
"cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 24,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1419,7 +1535,7 @@
"0.97368421052631582"
]
},
- "execution_count": 26,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
@@ -1448,26 +1564,24 @@
},
{
"cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 25,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ "''"
]
},
- "execution_count": 27,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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WIPngsDYRheXomdCEF1ckOJwpfFNyDTDp1ahv6YUkcQnJVMJwJqIxBUURdY3d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\n",
+ "image/png": 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1Hk+vzSEO91hM/rmc9QWLKV0EWcPoAsY5fO+Cz+sWGfdjwJO0zxO0vrXASa19OvA4R7iQe6zXyehiz6+39mvbL0yA1/H9F2Of4Ni9GLuaOucO1sXoAuBXgVNnXdMq6jwdeFlr/wnw/tY+Ffhy+/1d29rHXJ2rrPG4eW2O1TLP4S/GXs73X4z93LSey5k/EFN8gC8DvsjoCO69re/9wFvHxrwPuO6Q7X4CeKj9Aj4EbJ11Laupk9GFrX9u9dwPvHls2/e27R4DLp11LdOok9G53kda/xeAn591Laus820t4L4IfOhg8LV1v8Hoovpe4JpZ1zLpGo/D1+bHGJ06/B9GR+lbgXcA72jrw+iPNH2p1bNpWs+ln4yVpM71eo5ektQY9JLUOYNekjpn0EtS5wx6SeqcQS9JnTPoJalzBr0kde7/AC40cbqbQG9XAAAAAElFTkSuQmCC\n",
"text/plain": [
- ""
+ "
"
]
},
"metadata": {},
@@ -1481,14 +1595,15 @@
" (training_inputs,\n",
" testing_inputs,\n",
" training_classes,\n",
- " testing_classes) = train_test_split(all_inputs, all_classes, train_size=0.75)\n",
+ " testing_classes) = train_test_split(all_inputs, all_labels, test_size=0.25)\n",
" \n",
" decision_tree_classifier = DecisionTreeClassifier()\n",
" decision_tree_classifier.fit(training_inputs, training_classes)\n",
" classifier_accuracy = decision_tree_classifier.score(testing_inputs, testing_classes)\n",
" model_accuracies.append(classifier_accuracy)\n",
" \n",
- "sb.distplot(model_accuracies)"
+ "plt.hist(model_accuracies)\n",
+ ";"
]
},
{
@@ -1497,7 +1612,9 @@
"source": [
"It's obviously a problem that our model performs quite differently depending on the subset of the data it's trained on. This phenomenon is known as **overfitting**: The model is learning to classify the training set so well that it doesn't generalize and perform well on data it hasn't seen before.\n",
"\n",
- "###Cross-validation\n",
+ "### Cross-validation\n",
+ "\n",
+ "[[ go back to the top ]](#Table-of-contents)\n",
"\n",
"This problem is the main reason that most data scientists perform ***k*-fold cross-validation** on their models: Split the original data set into *k* subsets, use one of the subsets as the testing set, and the rest of the subsets are used as the training set. This process is then repeated *k* times such that each subset is used as the testing set exactly once.\n",
"\n",
@@ -1508,16 +1625,14 @@
},
{
"cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 26,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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+ "image/png": "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\n",
"text/plain": [
- ""
+ "
"
]
},
"metadata": {},
@@ -1526,52 +1641,52 @@
],
"source": [
"import numpy as np\n",
- "from sklearn.cross_validation import StratifiedKFold\n",
+ "from sklearn.model_selection import StratifiedKFold\n",
"\n",
- "def plot_cv(cv, n_samples):\n",
+ "def plot_cv(cv, features, labels):\n",
" masks = []\n",
- " for train, test in cv:\n",
- " mask = np.zeros(n_samples, dtype=bool)\n",
+ " for train, test in cv.split(features, labels):\n",
+ " mask = np.zeros(len(labels), dtype=bool)\n",
" mask[test] = 1\n",
" masks.append(mask)\n",
- " \n",
+ " \n",
" plt.figure(figsize=(15, 15))\n",
- " plt.imshow(masks, interpolation='none')\n",
+ " plt.imshow(masks, interpolation='none', cmap='gray_r')\n",
" plt.ylabel('Fold')\n",
" plt.xlabel('Row #')\n",
"\n",
- "plot_cv(StratifiedKFold(all_classes, n_folds=10), len(all_classes))"
+ "plot_cv(StratifiedKFold(n_splits=10), all_inputs, all_labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
+ "You'll notice that we used **Stratified *k*-fold cross-validation** in the code above. Stratified *k*-fold keeps the class proportions the same across all of the folds, which is vital for maintaining a representative subset of our data set. (e.g., so we don't have 100% `Iris setosa` entries in one of the folds.)\n",
+ "\n",
"We can perform 10-fold cross-validation on our model with the following code:"
]
},
{
"cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 27,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ "''"
]
},
- "execution_count": 29,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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TQ0JCgtlRpAtq8x5zY2MjLpcLgPj4eHw+H8Fg8IT3qaiob1+6EJCWFhe24w/nsUP7xl9X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\nXvsnEhcXd9T/jdfrJT4+PqRe+8dzrLE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+ "image/png": "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\n",
"text/plain": [
- ""
+ "
"
]
},
"metadata": {},
@@ -1579,15 +1694,16 @@
}
],
"source": [
- "from sklearn.cross_validation import cross_val_score\n",
+ "from sklearn.model_selection import cross_val_score\n",
"\n",
"decision_tree_classifier = DecisionTreeClassifier()\n",
"\n",
"# cross_val_score returns a list of the scores, which we can visualize\n",
"# to get a reasonable estimate of our classifier's performance\n",
- "cv_scores = cross_val_score(decision_tree_classifier, all_inputs, all_classes, cv=10)\n",
- "sb.distplot(cv_scores)\n",
- "plt.title('Average score: {}'.format(np.mean(cv_scores)))"
+ "cv_scores = cross_val_score(decision_tree_classifier, all_inputs, all_labels, cv=10)\n",
+ "plt.hist(cv_scores)\n",
+ "plt.title('Average score: {}'.format(np.mean(cv_scores)))\n",
+ ";"
]
},
{
@@ -1596,33 +1712,33 @@
"source": [
"Now we have a much more consistent rating of our classifier's general classification accuracy.\n",
"\n",
- "###Parameter tuning\n",
+ "### Parameter tuning\n",
+ "\n",
+ "[[ go back to the top ]](#Table-of-contents)\n",
"\n",
"Every Machine Learning model comes with a variety of parameters to tune, and these parameters can be vitally important to the performance of our classifier. For example, if we severely limit the depth of our decision tree classifier:"
]
},
{
"cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 28,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ "''"
]
},
- "execution_count": 30,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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MAIAjCGUAABxBKAMA4AhCGQAARxDKAAA4glAGAMARhDIAAI4glAEAcAShDACA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\nDACAI/4f8siT/mmbi5QAAAAASUVORK5CYII=\n",
+ "image/png": "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\n",
"text/plain": [
- ""
+ "
"
]
},
"metadata": {},
@@ -1881,10 +1983,11 @@
}
],
"source": [
- "rf_scores = cross_val_score(decision_tree_classifier, all_inputs, all_classes, cv=10)\n",
+ "dt_scores = cross_val_score(decision_tree_classifier, all_inputs, all_labels, cv=10)\n",
"\n",
- "sb.boxplot(rf_scores)\n",
- "sb.stripplot(rf_scores, jitter=True, color='white')"
+ "sb.boxplot(dt_scores)\n",
+ "sb.stripplot(dt_scores, jitter=True, color='black')\n",
+ ";"
]
},
{
@@ -1893,9 +1996,11 @@
"source": [
"Hmmm... that's a little boring by itself though. How about we compare another classifier to see how they perform?\n",
"\n",
- "We already know from previous projects that Random Forest classifiers usually work better than individual decision trees. A common problem that decision trees face is that they're prone to overfitting: They complexify to the point that they classify the training set near-perfectly, but fail to generalize.\n",
+ "We already know from previous projects that Random Forest classifiers usually work better than individual decision trees. A common problem that decision trees face is that they're prone to overfitting: They complexify to the point that they classify the training set near-perfectly, but fail to generalize to data they have not seen before.\n",
+ "\n",
+ "**Random Forest classifiers** work around that limitation by creating a whole bunch of decision trees (hence \"forest\") — each trained on random subsets of training samples (drawn with replacement) and features (drawn without replacement) — and have the decision trees work together to make a more accurate classification.\n",
"\n",
- "**Random Forests** work around that limitation by creating a whole bunch of decision trees (hence \"forest\") — each with a subset of the features — and have the decision trees work together to make a better classification. Let that be a lesson to you: **Even in Machine Learning, you get better results when you work together!**\n",
+ "Let that be a lesson for us: **Even in Machine Learning, we get better results when we work together!**\n",
"\n",
"Let's see if a Random Forest classifier works better here.\n",
"\n",
@@ -1904,30 +2009,30 @@
},
{
"cell_type": "code",
- "execution_count": 40,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 35,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Best score: 0.9731543624161074\n",
- "Best parameters: {'n_estimators': 5, 'max_features': 3, 'warm_start': True, 'criterion': 'gini'}\n"
+ "Best score: 0.9664429530201343\n",
+ "Best parameters: {'criterion': 'gini', 'max_features': 1, 'n_estimators': 25}\n"
]
},
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
- " max_depth=None, max_features=3, max_leaf_nodes=None,\n",
+ " max_depth=None, max_features=1, max_leaf_nodes=None,\n",
+ " min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
- " min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=1,\n",
- " oob_score=False, random_state=None, verbose=0, warm_start=True)"
+ " min_weight_fraction_leaf=0.0, n_estimators=25, n_jobs=1,\n",
+ " oob_score=False, random_state=None, verbose=0,\n",
+ " warm_start=False)"
]
},
- "execution_count": 40,
+ "execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
@@ -1937,18 +2042,17 @@
"\n",
"random_forest_classifier = RandomForestClassifier()\n",
"\n",
- "parameter_grid = {'n_estimators': [5, 10, 25, 50],\n",
+ "parameter_grid = {'n_estimators': [10, 25, 50, 100],\n",
" 'criterion': ['gini', 'entropy'],\n",
- " 'max_features': [1, 2, 3, 4],\n",
- " 'warm_start': [True, False]}\n",
+ " 'max_features': [1, 2, 3, 4]}\n",
"\n",
- "cross_validation = StratifiedKFold(all_classes, n_folds=10)\n",
+ "cross_validation = StratifiedKFold(n_splits=10)\n",
"\n",
"grid_search = GridSearchCV(random_forest_classifier,\n",
" param_grid=parameter_grid,\n",
" cv=cross_validation)\n",
"\n",
- "grid_search.fit(all_inputs, all_classes)\n",
+ "grid_search.fit(all_inputs, all_labels)\n",
"print('Best score: {}'.format(grid_search.best_score_))\n",
"print('Best parameters: {}'.format(grid_search.best_params_))\n",
"\n",
@@ -1964,26 +2068,24 @@
},
{
"cell_type": "code",
- "execution_count": 42,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 36,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ "''"
]
},
- "execution_count": 42,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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HIiKC27qMZmP8wS7aYuw1Geput1tvvfWW/vGPf6ikpETDhg3TjTfeqPfff1/p6elaunTp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\nBECvAAAAAElFTkSuQmCC\n",
+ "image/png": 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\n",
"text/plain": [
- ""
+ "
"
]
},
"metadata": {},
@@ -1993,14 +2095,15 @@
"source": [
"random_forest_classifier = grid_search.best_estimator_\n",
"\n",
- "rf_df = pd.DataFrame({'accuracy': cross_val_score(random_forest_classifier, all_inputs, all_classes, cv=10),\n",
+ "rf_df = pd.DataFrame({'accuracy': cross_val_score(random_forest_classifier, all_inputs, all_labels, cv=10),\n",
" 'classifier': ['Random Forest'] * 10})\n",
- "dt_df = pd.DataFrame({'accuracy': cross_val_score(decision_tree_classifier, all_inputs, all_classes, cv=10),\n",
+ "dt_df = pd.DataFrame({'accuracy': cross_val_score(decision_tree_classifier, all_inputs, all_labels, cv=10),\n",
" 'classifier': ['Decision Tree'] * 10})\n",
"both_df = rf_df.append(dt_df)\n",
"\n",
"sb.boxplot(x='classifier', y='accuracy', data=both_df)\n",
- "sb.stripplot(x='classifier', y='accuracy', data=both_df, jitter=True, color='white')"
+ "sb.stripplot(x='classifier', y='accuracy', data=both_df, jitter=True, color='black')\n",
+ ";"
]
},
{
@@ -2014,11 +2117,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "##Step 6: Reproducibility\n",
+ "## Step 6: Reproducibility\n",
+ "\n",
+ "[[ go back to the top ]](#Table-of-contents)\n",
"\n",
- "Ensuring that your work is reproducible is the last and — arguably — most important step in any analysis. **As a rule, you shouldn't place much weight on a discovery that can't be reproduced**. As such, if your analysis isn't reproducible, you might as well not have done it.\n",
+ "Ensuring that our work is reproducible is the last and — arguably — most important step in any analysis. **As a rule, we shouldn't place much weight on a discovery that can't be reproduced**. As such, if our analysis isn't reproducible, we might as well not have done it.\n",
"\n",
- "Notebooks like this one go a long way toward making your work reproducible. Since we documented every step as we moved along, we have a written record of what we did and why we did it — both in text and code.\n",
+ "Notebooks like this one go a long way toward making our work reproducible. Since we documented every step as we moved along, we have a written record of what we did and why we did it — both in text and code.\n",
"\n",
"Beyond recording what we did, we should also document what software and hardware we used to perform our analysis. This typically goes at the top of our notebooks so our readers know what tools to use.\n",
"\n",
@@ -2027,60 +2132,27 @@
},
{
"cell_type": "code",
- "execution_count": 43,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Installed watermark.py. To use it, type:\n",
- " %load_ext watermark\n"
- ]
- }
- ],
- "source": [
- "%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%load_ext watermark"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 38,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Randal S. Olson Fri Aug 21 2015 \n",
+ "Randal S. Olson Thu Jul 12 2018 \n",
"\n",
- "CPython 3.4.3\n",
- "IPython 3.2.1\n",
+ "CPython 3.6.6\n",
+ "IPython 6.4.0\n",
"\n",
- "numpy 1.9.2\n",
- "pandas 0.16.2\n",
- "scikit-learn 0.16.1\n",
- "matplotlib 1.4.3\n",
- "Seaborn 0.6.0\n",
+ "numpy 1.12.1\n",
+ "pandas 0.23.1\n",
+ "sklearn 0.19.1\n",
+ "matplotlib 2.2.2\n",
+ "seaborn 0.8.1\n",
"\n",
- "compiler : GCC 4.2.1 (Apple Inc. build 5577)\n",
+ "compiler : GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)\n",
"system : Darwin\n",
- "release : 14.5.0\n",
+ "release : 17.6.0\n",
"machine : x86_64\n",
"processor : i386\n",
"CPU cores : 8\n",
@@ -2089,7 +2161,7 @@
}
],
"source": [
- "%watermark -a 'Randal S. Olson' -nmv --packages numpy,pandas,scikit-learn,matplotlib,Seaborn"
+ "%watermark -a 'Randal S. Olson' -nmv --packages numpy,pandas,sklearn,matplotlib,seaborn"
]
},
{
@@ -2101,32 +2173,30 @@
},
{
"cell_type": "code",
- "execution_count": 46,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 39,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[ 4.6 3.6 1. 0.2]\t-->\tIris-setosa\t(Actual: Iris-setosa)\n",
- "[ 5.2 2.7 3.9 1.4]\t-->\tIris-versicolor\t(Actual: Iris-versicolor)\n",
- "[ 7.1 3. 5.9 2.1]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
- "[ 6.3 3.3 4.7 1.6]\t-->\tIris-versicolor\t(Actual: Iris-versicolor)\n",
- "[ 6.7 3.3 5.7 2.5]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
+ "[ 7.2 3.6 6.1 2.5]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
+ "[ 7.4 2.8 6.1 1.9]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
+ "[ 5.8 2.7 5.1 1.9]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
+ "[ 6.1 2.6 5.6 1.4]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
"[ 6.9 3.1 5.4 2.1]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
- "[ 5.1 3.3 1.7 0.5]\t-->\tIris-setosa\t(Actual: Iris-setosa)\n",
- "[ 6.3 2.8 5.1 1.5]\t-->\tIris-versicolor\t(Actual: Iris-virginica)\n",
- "[ 5.2 3.4 1.4 0.2]\t-->\tIris-setosa\t(Actual: Iris-setosa)\n",
- "[ 6.1 2.6 5.6 1.4]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n"
+ "[ 4.9 3. 1.4 0.2]\t-->\tIris-setosa\t(Actual: Iris-setosa)\n",
+ "[ 5.1 3.7 1.5 0.4]\t-->\tIris-setosa\t(Actual: Iris-setosa)\n",
+ "[ 5. 3.2 1.2 0.2]\t-->\tIris-setosa\t(Actual: Iris-setosa)\n",
+ "[ 5.8 2.6 4. 1.2]\t-->\tIris-versicolor\t(Actual: Iris-versicolor)\n",
+ "[ 5.5 2.6 4.4 1.2]\t-->\tIris-versicolor\t(Actual: Iris-versicolor)\n"
]
},
{
"data": {
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+ "image/png": "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\n",
"text/plain": [
- ""
+ "
"
]
},
"metadata": {},
@@ -2138,8 +2208,7 @@
"import pandas as pd\n",
"import seaborn as sb\n",
"from sklearn.ensemble import RandomForestClassifier\n",
- "from sklearn.cross_validation import train_test_split\n",
- "from sklearn.cross_validation import cross_val_score\n",
+ "from sklearn.model_selection import train_test_split, cross_val_score\n",
"\n",
"# We can jump directly to working with the clean data because we saved our cleaned data set\n",
"iris_data_clean = pd.read_csv('iris-data-clean.csv')\n",
@@ -2161,25 +2230,21 @@
"all_inputs = iris_data_clean[['sepal_length_cm', 'sepal_width_cm',\n",
" 'petal_length_cm', 'petal_width_cm']].values\n",
"\n",
- "all_classes = iris_data_clean['class'].values\n",
+ "all_labels = iris_data_clean['class'].values\n",
"\n",
"# This is the classifier that came out of Grid Search\n",
- "random_forest_classifier = RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
- " max_depth=None, max_features=3, max_leaf_nodes=None,\n",
- " min_samples_leaf=1, min_samples_split=2,\n",
- " min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=1,\n",
- " oob_score=False, random_state=None, verbose=0, warm_start=True)\n",
+ "random_forest_classifier = RandomForestClassifier(criterion='gini', max_features=3, n_estimators=50)\n",
"\n",
"# All that's left to do now is plot the cross-validation scores\n",
- "rf_classifier_scores = cross_val_score(random_forest_classifier, all_inputs, all_classes, cv=10)\n",
+ "rf_classifier_scores = cross_val_score(random_forest_classifier, all_inputs, all_labels, cv=10)\n",
"sb.boxplot(rf_classifier_scores)\n",
- "sb.stripplot(rf_classifier_scores, jitter=True, color='white')\n",
+ "sb.stripplot(rf_classifier_scores, jitter=True, color='black')\n",
"\n",
"# ...and show some of the predictions from the classifier\n",
"(training_inputs,\n",
" testing_inputs,\n",
" training_classes,\n",
- " testing_classes) = train_test_split(all_inputs, all_classes, train_size=0.75)\n",
+ " testing_classes) = train_test_split(all_inputs, all_labels, test_size=0.25)\n",
"\n",
"random_forest_classifier.fit(training_inputs, training_classes)\n",
"\n",
@@ -2193,11 +2258,16 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "There you have it: We have a complete and reproducible Machine Learning pipeline to demo to our head of data. We've met the success criteria that we set from the beginning (>90% accuracy), and our pipeline is flexible enough to handle new inputs or flowers when that data set is ready. Not bad for our first week on the job!\n",
- "\n",
- "---\n",
+ "There we have it: We have a complete and reproducible Machine Learning pipeline to demo to our company's Head of Data. We've met the success criteria that we set from the beginning (>90% accuracy), and our pipeline is flexible enough to handle new inputs or flowers when that data set is ready. Not bad for our first week on the job!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Conclusions\n",
"\n",
- "##Conclusions\n",
+ "[[ go back to the top ]](#Table-of-contents)\n",
"\n",
"I hope you found this example notebook useful for your own work and learned at least one new trick by reading through it.\n",
"\n",
@@ -2209,17 +2279,24 @@
"\n",
"* [Submit an issue](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/issues) on GitHub\n",
"\n",
- "* Fork the [notebook repository](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/), make the fix/addition yourself, then send over a pull request\n",
+ "* Fork the [notebook repository](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/), make the fix/addition yourself, then send over a pull request"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Further reading\n",
"\n",
- "##Further reading\n",
+ "[[ go back to the top ]](#Table-of-contents)\n",
"\n",
"This notebook covers a broad variety of topics but skips over many of the specifics. If you're looking to dive deeper into a particular topic, here's some recommended reading.\n",
"\n",
"**Data Science**: William Chen compiled a [list of free books](http://www.wzchen.com/data-science-books/) for newcomers to Data Science, ranging from the basics of R & Python to Machine Learning to interviews and advice from prominent data scientists.\n",
"\n",
- "**Machine Learning**: /r/MachineLearning has a useful [Wiki page](https://www.reddit.com/r/MachineLearning/wiki/index) containing links to online courses, books, data sets, etc. for Machine Learning.\n",
+ "**Machine Learning**: /r/MachineLearning has a useful [Wiki page](https://www.reddit.com/r/MachineLearning/wiki/index) containing links to online courses, books, data sets, etc. for Machine Learning. There's also a [curated list](https://github.com/josephmisiti/awesome-machine-learning) of Machine Learning frameworks, libraries, and software sorted by language.\n",
"\n",
- "**Unit testing**: Dive Into Python 3 has a [great walkthrough](http://www.diveintopython3.net/unit-testing.html) of unit testing in Python, how it works, and how it should be used\n",
+ "**Unit testing**: Dive Into Python 3 has a [great walkthrough](https://www.diveinto.org/python3/unit-testing.html) of unit testing in Python, how it works, and how it should be used\n",
"\n",
"**pandas** has [several tutorials](http://pandas.pydata.org/pandas-docs/stable/tutorials.html) covering its myriad features.\n",
"\n",
@@ -2227,9 +2304,16 @@
"\n",
"**matplotlib** has many [books, videos, and tutorials](http://matplotlib.org/resources/index.html) to teach plotting in Python.\n",
"\n",
- "**Seaborn** has a [basic tutorial](http://stanford.edu/~mwaskom/software/seaborn/tutorial.html) covering most of the statistical plotting features.\n",
+ "**Seaborn** has a [basic tutorial](http://stanford.edu/~mwaskom/software/seaborn/tutorial.html) covering most of the statistical plotting features."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Acknowledgements\n",
"\n",
- "##Acknowledgements\n",
+ "[[ go back to the top ]](#Table-of-contents)\n",
"\n",
"Many thanks to [Andreas Mueller](http://amueller.github.io/) for some of his [examples](https://github.com/amueller/scipy_2015_sklearn_tutorial) in the Machine Learning section. I drew inspiration from several of his excellent examples.\n",
"\n",
@@ -2255,9 +2339,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.4.3"
+ "version": "3.6.6"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/example-data-science-notebook/iris_dtc.dot b/example-data-science-notebook/iris_dtc.dot
index 48f3903..6df575d 100644
--- a/example-data-science-notebook/iris_dtc.dot
+++ b/example-data-science-notebook/iris_dtc.dot
@@ -1,19 +1,20 @@
digraph Tree {
-0 [label="X[2] <= 2.4500\ngini = 0.666636637989\nsamples = 149", shape="box"] ;
-1 [label="gini = 0.0000\nsamples = 49\nvalue = [ 49. 0. 0.]", shape="box"] ;
-0 -> 1 ;
-2 [label="X[3] <= 1.7500\ngini = 0.5\nsamples = 100", shape="box"] ;
-0 -> 2 ;
-3 [label="X[2] <= 4.9500\ngini = 0.168038408779\nsamples = 54", shape="box"] ;
+node [shape=box] ;
+0 [label="X[2] <= 2.45\nentropy = 1.585\nsamples = 149\nvalue = [49, 50, 50]"] ;
+1 [label="entropy = 0.0\nsamples = 49\nvalue = [49, 0, 0]"] ;
+0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
+2 [label="X[3] <= 1.75\nentropy = 1.0\nsamples = 100\nvalue = [0, 50, 50]"] ;
+0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
+3 [label="X[3] <= 1.35\nentropy = 0.445\nsamples = 54\nvalue = [0, 49, 5]"] ;
2 -> 3 ;
-4 [label="gini = 0.0408\nsamples = 48\nvalue = [ 0. 47. 1.]", shape="box"] ;
+4 [label="entropy = 0.0\nsamples = 28\nvalue = [0, 28, 0]"] ;
3 -> 4 ;
-5 [label="gini = 0.4444\nsamples = 6\nvalue = [ 0. 2. 4.]", shape="box"] ;
+5 [label="entropy = 0.706\nsamples = 26\nvalue = [0, 21, 5]"] ;
3 -> 5 ;
-6 [label="X[2] <= 4.8500\ngini = 0.0425330812854\nsamples = 46", shape="box"] ;
+6 [label="X[2] <= 4.85\nentropy = 0.151\nsamples = 46\nvalue = [0, 1, 45]"] ;
2 -> 6 ;
-7 [label="gini = 0.4444\nsamples = 3\nvalue = [ 0. 1. 2.]", shape="box"] ;
+7 [label="entropy = 0.918\nsamples = 3\nvalue = [0, 1, 2]"] ;
6 -> 7 ;
-8 [label="gini = 0.0000\nsamples = 43\nvalue = [ 0. 0. 43.]", shape="box"] ;
+8 [label="entropy = 0.0\nsamples = 43\nvalue = [0, 0, 43]"] ;
6 -> 8 ;
}
\ No newline at end of file
diff --git a/follower-factory/Twitter Follower Factory.ipynb b/follower-factory/Twitter Follower Factory.ipynb
new file mode 100644
index 0000000..e092d44
--- /dev/null
+++ b/follower-factory/Twitter Follower Factory.ipynb
@@ -0,0 +1,196 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Twitter Follower Factory analysis script\n",
+ "\n",
+ "This Python script was inspired by the New York Times' [Follower Factory article](https://www.nytimes.com/interactive/2018/01/27/technology/social-media-bots.html), which showed that Twitter accounts can be analyzed to discover whether someone has purchased fake followers for that account. This script enables you to analyze any Twitter account and generate a similar scatter plot visualization of their followers. You can typically tell when someone purchased fake followers for an account when there are solid lines of followers that were created around the same time, as shown in the New York Times article.\n",
+ "\n",
+ "**Note that you can [execute the code in this notebook on Binder](https://mybinder.org/v2/gh/rhiever/Data-Analysis-and-Machine-Learning-Projects/master?filepath=follower-factory%2FTwitter%2520Follower%2520Factory.ipynb) - no local installation required.**\n",
+ "\n",
+ "## Dependencies\n",
+ "\n",
+ "You will need to install Python's [python-twitter](https://github.com/sixohsix/twitter/) library:\n",
+ "\n",
+ " pip install twitter\n",
+ "\n",
+ "You will also need to create an app account on https://dev.twitter.com/apps\n",
+ "\n",
+ "1. Sign in with your Twitter account\n",
+ "2. Create a new app account\n",
+ "3. Modify the settings for that app account to allow read & write\n",
+ "4. Generate a new OAuth token with those permissions\n",
+ "\n",
+ "Following these steps will create 4 tokens that you will need to place in the script, as discussed below.\n",
+ "\n",
+ "## Usage\n",
+ "\n",
+ "Before you can run this script, you need to fill in the following 5 variables in the file:\n",
+ "\n",
+ "```Python\n",
+ "USER_TO_ANALYZE = ''\n",
+ "OAUTH_TOKEN = ''\n",
+ "OAUTH_SECRET = ''\n",
+ "CONSUMER_KEY = ''\n",
+ "CONSUMER_SECRET = ''\n",
+ "```\n",
+ "\n",
+ "`USER_TO_ANALYZE` is straightforward: If you want to analyze my Twitter account, you would enter `randal_olson` in between the two single quotes.\n",
+ "\n",
+ "Once you've created the Twitter app account as described under the Dependencies section, you should be able to find the OAUTH and CONSUMER keys on the \"Key and Access Tokens\" section, as shown in the image below.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Once you've filled out those 5 variables, the script below will handle the rest.\n",
+ "\n",
+ "If you're analyzing an account with 100,000s or more of followers, running the script may take a while as the script follows the Twitter API's usage restrictions. A progress bar will keep you updated on the progress of the analysis.\n",
+ "\n",
+ "Once the script finishes, it will save an image to the directory that you ran the script in. That image will contain the scatter plot visualization for the account you targeted.\n",
+ "\n",
+ "## Security concerns\n",
+ "\n",
+ "Although you should be fine with this notebook, you should beware of placing private API keys and security-related information into scripts like this one. If you're particularly paranoid about the security of your Twitter account, you are welcome to review the code below and all of the open source libraries that it relies upon.\n",
+ "\n",
+ "If it looks like anything fishy is going on with the API keys, please don't hesitate to [file an issue on this repository](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/issues/new) and raise your concerns.\n",
+ "\n",
+ "## Questions and Comments\n",
+ "\n",
+ "If you have any questions or comments, please [file an issue on this repository](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/issues/new) and I'll get back to you as soon as I can. If you'd like to submit a pull request to improve this script in any way, please file an issue first to discuss your change(s). I'm generally open to accepting pull requests on this repository."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "from __future__ import print_function\n",
+ "import time\n",
+ "from datetime import datetime\n",
+ "import os\n",
+ "\n",
+ "from twitter import Twitter, OAuth, TwitterHTTPError\n",
+ "from tqdm import tqdm_notebook as tqdm\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "USER_TO_ANALYZE = ''\n",
+ "OAUTH_TOKEN = ''\n",
+ "OAUTH_SECRET = ''\n",
+ "CONSUMER_KEY = ''\n",
+ "CONSUMER_SECRET = ''\n",
+ "\n",
+ "twitter_connection = Twitter(auth=OAuth(OAUTH_TOKEN, OAUTH_SECRET, CONSUMER_KEY, CONSUMER_SECRET))\n",
+ "\n",
+ "pbar = tqdm()\n",
+ "pbar.write('Collecting list of Twitter followers for @{}'.format(USER_TO_ANALYZE))\n",
+ "\n",
+ "rl_status = twitter_connection.application.rate_limit_status()\n",
+ "if rl_status['resources']['followers']['/followers/ids']['remaining'] <= 0:\n",
+ " sleep_until = rl_status['resources']['followers']['/followers/ids']['reset']\n",
+ " sleep_for = int(sleep_until - time.time()) + 10 # wait a little extra time just in case\n",
+ " if sleep_for > 0:\n",
+ " pbar.write('Sleeping for {} seconds...'.format(sleep_for))\n",
+ " time.sleep(sleep_for)\n",
+ " pbar.write('Awake!')\n",
+ "\n",
+ "followers_status = twitter_connection.followers.ids(screen_name=USER_TO_ANALYZE)\n",
+ "followers = followers_status['ids']\n",
+ "next_cursor = followers_status['next_cursor']\n",
+ "pbar.update(len(followers))\n",
+ "\n",
+ "while next_cursor != 0:\n",
+ " rl_status = twitter_connection.application.rate_limit_status()\n",
+ " if rl_status['resources']['followers']['/followers/ids']['remaining'] <= 0:\n",
+ " sleep_until = rl_status['resources']['followers']['/followers/ids']['reset']\n",
+ " sleep_for = int(sleep_until - time.time()) + 10 # wait a little extra time just in case\n",
+ " if sleep_for > 0:\n",
+ " pbar.write('Sleeping for {} seconds...'.format(sleep_for))\n",
+ " time.sleep(sleep_for)\n",
+ " pbar.write('Awake!')\n",
+ "\n",
+ " followers_status = twitter_connection.followers.ids(screen_name=USER_TO_ANALYZE, cursor=next_cursor)\n",
+ " # Prevent duplicate Twitter user IDs\n",
+ " more_followers = [follower for follower in followers_status['ids'] if follower not in followers]\n",
+ " followers += more_followers\n",
+ " next_cursor = followers_status['next_cursor']\n",
+ "\n",
+ " pbar.update(len(more_followers))\n",
+ "\n",
+ "pbar.close()\n",
+ "\n",
+ "pbar = tqdm(total=len(followers))\n",
+ "pbar.write('Collecting join dates of Twitter followers for @{}'.format(USER_TO_ANALYZE))\n",
+ "followers_created = list()\n",
+ "\n",
+ "rl_status = twitter_connection.application.rate_limit_status()\n",
+ "remaining_calls = rl_status['resources']['users']['/users/lookup']['remaining']\n",
+ "\n",
+ "for base_index in range(0, len(followers), 100):\n",
+ " if remaining_calls == 50:\n",
+ " # Update the remaining calls count just in case\n",
+ " rl_status = twitter_connection.application.rate_limit_status()\n",
+ " remaining_calls = rl_status['resources']['users']['/users/lookup']['remaining']\n",
+ "\n",
+ " if remaining_calls <= 0:\n",
+ " sleep_until = rl_status['resources']['users']['/users/lookup']['reset']\n",
+ " sleep_for = int(sleep_until - time.time()) + 10 # wait a little extra time just in case\n",
+ " if sleep_for > 0:\n",
+ " pbar.write('Sleeping for {} seconds...'.format(sleep_for))\n",
+ " time.sleep(sleep_for)\n",
+ " pbar.write('Awake!')\n",
+ " rl_status = twitter_connection.application.rate_limit_status()\n",
+ " remaining_calls = rl_status['resources']['users']['/users/lookup']['remaining']\n",
+ "\n",
+ " remaining_calls -= 1\n",
+ "\n",
+ " # 100 users per request\n",
+ " user_info = twitter_connection.users.lookup(user_id=list(followers[base_index:base_index + 100]))\n",
+ " followers_created += [x['created_at'] for x in user_info]\n",
+ "\n",
+ " pbar.update(len(followers[base_index:base_index + 100]))\n",
+ "\n",
+ "pbar.close()\n",
+ "print('Creating Follower Factory visualization for @{}'.format(USER_TO_ANALYZE))\n",
+ "\n",
+ "days_since_2006 = [(x.year - 2006) * 365 + x.dayofyear for x in pd.to_datetime(followers_created)]\n",
+ "\n",
+ "mpl_style_url = 'https://gist.githubusercontent.com/rhiever/d0a7332fe0beebfdc3d5/raw/1b807615235ff6f4c919b5b70b01a609619e1e9c/tableau10.mplstyle'\n",
+ "alpha = 0.1 * min(9, 80000. / len(days_since_2006))\n",
+ "with plt.style.context(mpl_style_url):\n",
+ " plt.figure(figsize=(9, 12))\n",
+ " plt.scatter(x=range(len(days_since_2006)), y=days_since_2006[::-1], s=2, alpha=alpha)\n",
+ " plt.yticks(range(0, 365 * (datetime.today().year + 1 - 2006), 365), range(2006, datetime.today().year + 1))\n",
+ " plt.xlabel('Follower count for @{}'.format(USER_TO_ANALYZE))\n",
+ " plt.ylabel('Date follower joined Twitter')\n",
+ " plt.savefig('{}-follower-factory.png'.format(USER_TO_ANALYZE))\n",
+ "\n",
+ "print('Follower Factory visualization saved to {}'.format(os.getcwd()))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/follower-factory/images/twitter-app-example.png b/follower-factory/images/twitter-app-example.png
new file mode 100644
index 0000000..1554f46
Binary files /dev/null and b/follower-factory/images/twitter-app-example.png differ
diff --git a/optimal-road-trip/Computing the optimal road trip across the U.S..ipynb b/optimal-road-trip/Computing the optimal road trip across the U.S..ipynb
index 08b9f12..2854ff0 100644
--- a/optimal-road-trip/Computing the optimal road trip across the U.S..ipynb
+++ b/optimal-road-trip/Computing the optimal road trip across the U.S..ipynb
@@ -15,9 +15,9 @@
"\n",
"### Notebook by [Randal S. Olson](http://www.randalolson.com)\n",
"\n",
- "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is widely useable and shareable as possible.\n",
+ "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely useable and shareable as possible.\n",
"\n",
- "The code in this notebook is also available as a single Python script [here](OptimalRoadTripHtmlSaveAndDisplay.py) courtesy of Andrew Liesinger."
+ "The code in this notebook is also available as a single Python script [here](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/optimal-road-trip/OptimalRoadTripHtmlSaveAndDisplay.py) courtesy of [Andrew Liesinger](https://github.com/AndrewLiesinger)."
]
},
{
@@ -31,15 +31,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "If you don't have Python on your computer, you can use the [Anaconda Python distribution](http://continuum.io/downloads) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\n",
+ "If you don't have Python on your computer, you can use the [Anaconda Python distribution](https://www.anaconda.com/download/) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\n",
"\n",
"This code uses base Python libraries except for `googlemaps` and `pandas` packages. You can install these packages using `pip` by typing the following commands into your command line:\n",
"\n",
"> pip install pandas\n",
"\n",
- "> pip install googlemaps\n",
- "\n",
- "If you're on a Mac, Linux, or Unix machine, you may need to type `sudo` before the command to install the package with administrator privileges."
+ "> pip install googlemaps"
]
},
{
@@ -65,9 +63,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"all_waypoints = [\"USS Alabama, Battleship Parkway, Mobile, AL\",\n",
@@ -126,7 +122,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Next you'll have to register this script with the Google Maps API so they know who's hitting their servers with hundreds of Google Maps requests.\n",
+ "Next you'll have to register this script with the Google Maps API so they know who's hitting their servers with hundreds of Google Maps routing requests.\n",
"\n",
"1) Enable the Google Maps Distance Matrix API on your Google account. Google explains how to do that [here](https://github.com/googlemaps/google-maps-services-python#api-keys).\n",
"\n",
@@ -136,9 +132,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import googlemaps\n",
@@ -154,7 +148,7 @@
"\n",
"This is equivalent to doing Google Maps directions lookups on the Google Maps site, except now we're performing hundreds of lookups automatically using code.\n",
"\n",
- "If you get an error on this part, that most likely means one of the waypoints you entered couldn't be found on Google Maps. Another possible reason for an error here is if it's not possible to drive between the points, e.g., finding the driving directions between Hawaii and Florida will return an error."
+ "If you get an error on this part, that most likely means one of the waypoints you entered couldn't be found on Google Maps. Another possible reason for an error here is if it's not possible to drive between the points, e.g., finding the driving directions between Hawaii and Florida will return an error until we invent flying cars."
]
},
{
@@ -167,9 +161,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"from itertools import combinations\n",
@@ -203,18 +195,16 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now that we have the shortest routes between all of our waypoints, let's save them to a text file so we don't have to bother Google about them again."
+ "Now that we have the routes between all of our waypoints, let's save them to a text file so we don't have to bother Google about them again."
]
},
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
- "with open(\"my-waypoints-dist-dur.tsv\", \"wb\") as out_file:\n",
+ "with open(\"my-waypoints-dist-dur.tsv\", \"w\") as out_file:\n",
" out_file.write(\"\\t\".join([\"waypoint1\",\n",
" \"waypoint2\",\n",
" \"distance_m\",\n",
@@ -239,15 +229,15 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Instead of exhaustively looking at every possible solution, genetic algorithms start with a handful of random solutions and continually tinkers with these solutions — always trying something slightly different from the current solutions and keeping the best ones — until they can’t find a better solution any more."
+ "Instead of exhaustively looking at every possible solution, genetic algorithms start with a handful of random solutions and continually tinkers with these solutions — always trying something slightly different from the current solutions and keeping the best ones — until they can’t find a better solution any more.\n",
+ "\n",
+ "Below, all you need to do is make sure that the file name above matches the file name below (both currently `my-waypoints-dist-dur.tsv`) and run the code. The code will read in your route information and use a genetic algorithm to discover an optimized driving route."
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
@@ -268,9 +258,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import random\n",
@@ -356,8 +344,13 @@
"def run_genetic_algorithm(generations=5000, population_size=100):\n",
" \"\"\"\n",
" The core of the Genetic Algorithm.\n",
+ " \n",
+ " `generations` and `population_size` must be a multiple of 10.\n",
" \"\"\"\n",
" \n",
+ " population_subset_size = int(population_size / 10.)\n",
+ " generations_10pct = int(generations / 10.)\n",
+ " \n",
" # Create a random population of `population_size` number of solutions.\n",
" population = generate_random_population(population_size)\n",
"\n",
@@ -373,17 +366,19 @@
"\n",
" population_fitness[agent_genome] = compute_fitness(agent_genome)\n",
"\n",
- " # Take the 10 shortest road trips and produce 10 offspring each from them\n",
+ " # Take the top 10% shortest road trips and produce offspring each from them\n",
" new_population = []\n",
- " for rank, agent_genome in enumerate(sorted(population_fitness, key=population_fitness.get)[:10]):\n",
- " if (generation % 1000 == 0 or generation == generations - 1) and rank == 0:\n",
+ " for rank, agent_genome in enumerate(sorted(population_fitness,\n",
+ " key=population_fitness.get)[:population_subset_size]):\n",
+ " \n",
+ " if (generation % generations_10pct == 0 or generation == generations - 1) and rank == 0:\n",
" print(\"Generation %d best: %d | Unique genomes: %d\" % (generation,\n",
" population_fitness[agent_genome],\n",
" len(population_fitness)))\n",
" print(agent_genome)\n",
" print(\"\")\n",
"\n",
- " # Create 1 exact copy of each of the top 10 road trips\n",
+ " # Create 1 exact copy of each of the top road trips\n",
" new_population.append(agent_genome)\n",
"\n",
" # Create 2 offspring with 1-3 point mutations\n",
@@ -411,9 +406,7 @@
{
"cell_type": "code",
"execution_count": 18,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -455,94 +448,29 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "**Warning: This part requires a little bit of programming experience.**\n",
- "\n",
"Now that we have an ordered list of the waypoints, we should put them on a Google map so we can see the trip from a high level and make any extra adjustments.\n",
"\n",
- "There's no easy way to make this visualization in Python, but the Google team provides a nice JavaScript library for visualizing routes on a Google Map.\n",
+ "There's no easy way to make this visualization in Python, but the Google Maps team provides a nice JavaScript library for visualizing routes on a Google Map.\n",
"\n",
- "Here's an example map with the route between 50 waypoints visualized: [link](https://github.com/rhiever/optimal-roadtrip-usa/blob/gh-pages/major-landmarks.html)\n",
+ "Here's an example map with the route between 50 waypoints visualized: [link](http://rhiever.github.io/optimal-roadtrip-usa/major-landmarks.html)\n",
"\n",
"The tricky part here is that the JavaScript library only plots routes with a maximum of 10 waypoints. If we want to plot a route with >10 waypoints, we need to call the route plotting function multiple times.\n",
"\n",
- "The code below generates the series of route plotting calls that you'll need. Replace the current `calcRoute` calls (at [line 117](https://github.com/rhiever/optimal-roadtrip-usa/blob/gh-pages/major-landmarks.html#L117) and beyond) with the calls you generate below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "calcRoute(\"Graceland, Elvis Presley Boulevard, Memphis, TN\", \"Shelburne Farms, Harbor Road, Shelburne, VT\", [\"Vicksburg National Military Park, Clay Street, Vicksburg, MS\", \"French Quarter, New Orleans, LA\", \"USS Alabama, Battleship Parkway, Mobile, AL\", \"Cape Canaveral, FL\", \"Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\", \"Fort Sumter National Monument, Sullivan's Island, SC\", \"Wright Brothers National Memorial Visitor Center, Manteo, NC\", \"Congress Hall, Congress Place, Cape May, NJ 08204\"]);\n",
- "\n",
- "calcRoute(\"Shelburne Farms, Harbor Road, Shelburne, VT\", \"Maryland State House, 100 State Cir, Annapolis, MD 21401\", [\"Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\", \"Acadia National Park, Maine\", \"USS Constitution, Boston, MA\", \"The Breakers, Ochre Point Avenue, Newport, RI\", \"The Mark Twain House & Museum, Farmington Avenue, Hartford, CT\", \"Statue of Liberty, Liberty Island, NYC, NY\", \"Liberty Bell, 6th Street, Philadelphia, PA\", \"New Castle Historic District, Delaware\"]);\n",
- "\n",
- "calcRoute(\"Maryland State House, 100 State Cir, Annapolis, MD 21401\", \"Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\", [\"White House, Pennsylvania Avenue Northwest, Washington, DC\", \"Mount Vernon, Fairfax County, Virginia\", \"Lost World Caverns, Lewisburg, WV\", \"Olympia Entertainment, Woodward Avenue, Detroit, MI\", \"Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\", \"Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\", \"West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\", \"Gateway Arch, Washington Avenue, St Louis, MO\"]);\n",
- "\n",
- "calcRoute(\"Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\", \"Yellowstone National Park, WY 82190\", [\"Taliesin, County Road C, Spring Green, Wisconsin\", \"Fort Snelling, Tower Avenue, Saint Paul, MN\", \"Terrace Hill, Grand Avenue, Des Moines, IA\", \"C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\", \"Ashfall Fossil Bed, Royal, NE\", \"Mount Rushmore National Memorial, South Dakota 244, Keystone, SD\", \"Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\", \"Glacier National Park, West Glacier, MT\"]);\n",
- "\n",
- "calcRoute(\"Yellowstone National Park, WY 82190\", \"Pikes Peak, Colorado\", [\"Craters of the Moon National Monument & Preserve, Arco, ID\", \"Hanford Site, Benton County, WA\", \"Columbia River Gorge National Scenic Area, Oregon\", \"Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\", \"San Andreas Fault, San Benito County, CA\", \"Hoover Dam, NV\", \"Grand Canyon National Park, Arizona\", \"Bryce Canyon National Park, Hwy 63, Bryce, UT\"]);\n",
- "\n",
- "calcRoute(\"Pikes Peak, Colorado\", \"Graceland, Elvis Presley Boulevard, Memphis, TN\", [\"Carlsbad Caverns National Park, Carlsbad, NM\", \"The Alamo, Alamo Plaza, San Antonio, TX\", \"Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\", \"Toltec Mounds, Scott, AR\"]);\n",
- "\n"
- ]
- }
- ],
- "source": [
- "# Paste your optimal route after the equal sign (=) here\n",
- "optimal_route = ('Graceland, Elvis Presley Boulevard, Memphis, TN', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'French Quarter, New Orleans, LA', 'USS Alabama, Battleship Parkway, Mobile, AL', 'Cape Canaveral, FL', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', \"Fort Sumter National Monument, Sullivan's Island, SC\", 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Acadia National Park, Maine', 'USS Constitution, Boston, MA', 'The Breakers, Ochre Point Avenue, Newport, RI', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'Statue of Liberty, Liberty Island, NYC, NY', 'Liberty Bell, 6th Street, Philadelphia, PA', 'New Castle Historic District, Delaware', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Gateway Arch, Washington Avenue, St Louis, MO', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Fort Snelling, Tower Avenue, Saint Paul, MN', 'Terrace Hill, Grand Avenue, Des Moines, IA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Ashfall Fossil Bed, Royal, NE', 'Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Glacier National Park, West Glacier, MT', 'Yellowstone National Park, WY 82190', 'Craters of the Moon National Monument & Preserve, Arco, ID', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'San Andreas Fault, San Benito County, CA', 'Hoover Dam, NV', 'Grand Canyon National Park, Arizona', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'Pikes Peak, Colorado', 'Carlsbad Caverns National Park, Carlsbad, NM', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Toltec Mounds, Scott, AR')\n",
- "\n",
- "optimal_route = list(optimal_route)\n",
- "optimal_route += [optimal_route[0]]\n",
- "subset = 0\n",
- " \n",
- "while subset < len(optimal_route):\n",
- " \n",
- " waypoint_subset = optimal_route[subset:subset + 10]\n",
- " output = \"calcRoute(\\\"%s\\\", \\\"%s\\\", [\" % (waypoint_subset[0], waypoint_subset[-1])\n",
- " for waypoint in waypoint_subset[1:-1]:\n",
- " output += \"\\\"%s\\\", \" % (waypoint)\n",
- " \n",
- " if len(waypoint_subset[1:-1]) > 0:\n",
- " output = output[:-2]\n",
- " \n",
- " output += \"]);\"\n",
- " print(output)\n",
- " print(\"\")\n",
- " subset += 9"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**This last part is very hacky. Sorry.**\n",
- "\n",
- "Lastly, you'll need to fix the `switch` statements at [line 54](https://github.com/rhiever/optimal-roadtrip-usa/blob/gh-pages/major-landmarks.html#L54) and [line 91](https://github.com/rhiever/optimal-roadtrip-usa/blob/gh-pages/major-landmarks.html#L91) so the `case` statements match the first waypoints specified in the `calcRoute` calls.\n",
- "\n",
- "For example, from the `calcRoute` calls above we'll want the case statements to match on:\n",
+ "Thanks to some optimizations by [Nicholas Clarke](https://github.com/nicholasgodfreyclarke) to my original map, this is a simple operation:\n",
"\n",
- "* \"Graceland, Elvis Presley Boulevard, Memphis, TN\"\n",
+ "1) Copy the final route generated by the genetic algorithm above.\n",
"\n",
- "* \"Shelburne Farms, Harbor Road, Shelburne, VT\"\n",
+ "2) Place brackets (`[` & `]`) around the route, e.g.,\n",
"\n",
- "* \"Maryland State House, 100 State Cir, Annapolis, MD 21401\"\n",
+ " ['Graceland, Elvis Presley Boulevard, Memphis, TN', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'French Quarter, New Orleans, LA', 'USS Alabama, Battleship Parkway, Mobile, AL', 'Cape Canaveral, FL', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', \"Fort Sumter National Monument, Sullivan's Island, SC\", 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Acadia National Park, Maine', 'USS Constitution, Boston, MA', 'The Breakers, Ochre Point Avenue, Newport, RI', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'Statue of Liberty, Liberty Island, NYC, NY', 'Liberty Bell, 6th Street, Philadelphia, PA', 'New Castle Historic District, Delaware', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Gateway Arch, Washington Avenue, St Louis, MO', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Fort Snelling, Tower Avenue, Saint Paul, MN', 'Terrace Hill, Grand Avenue, Des Moines, IA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Ashfall Fossil Bed, Royal, NE', 'Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Glacier National Park, West Glacier, MT', 'Yellowstone National Park, WY 82190', 'Craters of the Moon National Monument & Preserve, Arco, ID', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'San Andreas Fault, San Benito County, CA', 'Hoover Dam, NV', 'Grand Canyon National Park, Arizona', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'Pikes Peak, Colorado', 'Carlsbad Caverns National Park, Carlsbad, NM', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Toltec Mounds, Scott, AR']\n",
"\n",
- "* \"Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\"\n",
+ "3) Paste the final route with brackets into [line 93](https://github.com/rhiever/optimal-roadtrip-usa/blob/gh-pages/major-landmarks.html#L93) of my road trip map code. It should look like this:\n",
"\n",
- "* \"Yellowstone National Park, WY 82190\"\n",
- "\n",
- "* \"Pikes Peak, Colorado\"\n",
- "\n",
- "If you have more than 6 `calcRoute` calls, you'll have to add more `directionsDisplay` variables and integrate them with the rest of the code.\n",
+ " optimal_route = [ ... ]\n",
+ " \n",
+ "where `...` is your optimized road trip.\n",
"\n",
- "If you'd like to clean the Google map visualization code up so it's easier to use, please submit a pull request to the [repository](https://github.com/rhiever/optimal-roadtrip-usa). "
+ "That's all it takes! Now you have your own optimized road trip ready to show off to the world."
]
},
{
@@ -600,9 +528,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.4.3"
+ "version": "3.6.5"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/optimal-road-trip/OptimalRoadTripHtmlSaveAndDisplay.py b/optimal-road-trip/OptimalRoadTripHtmlSaveAndDisplay.py
index ea71fc4..d348306 100644
--- a/optimal-road-trip/OptimalRoadTripHtmlSaveAndDisplay.py
+++ b/optimal-road-trip/OptimalRoadTripHtmlSaveAndDisplay.py
@@ -4,6 +4,7 @@
2: Dynamically create and open an HTML file showing the route when a shorter route is found
3: Make it easier to tinker with the Generation / Population parameters
"""
+from __future__ import print_function
from itertools import combinations
import googlemaps
import pandas as pd
@@ -76,59 +77,124 @@
"Taliesin, County Road C, Spring Green, Wisconsin",
"Yellowstone National Park, WY 82190"]
-def CreateOptimalRouteHtmlFile(optimal_route, distance, display=1):
+def CreateOptimalRouteHtmlFile(optimal_route, distance, display=True):
optimal_route = list(optimal_route)
optimal_route += [optimal_route[0]]
-
- Page_1 = 'The optimal road trip across the U.S. according to machine learning '
-
- subset = 0
- subsetCounter = 0
- StatementC1 = ''
- StatementC2 = ''
- StatementCalcRoutes = ''
-
- while subset < len(optimal_route):
- subsetCounter += 1
-
- waypoint_subset = optimal_route[subset:subset + 10]
- output = "calcRoute(\"%s\", \"%s\", [" % (waypoint_subset[0], waypoint_subset[-1])
- StatementC1 = StatementC1 + ' case "' + waypoint_subset[0] + '": directionsDisplay' + str(subsetCounter) + ' = new google.maps.DirectionsRenderer(directionsDisplayOptions); break; '
- StatementC2 = StatementC2 + ' case "' + waypoint_subset[0] + '": directionsDisplay' + str(subsetCounter) + '.setDirections(response); break; '
-
- for waypoint in waypoint_subset[1:-1]:
- output += "\"%s\", " % (waypoint)
+ Page_1 = """
+
+
+
+
+
+
+
- if len(waypoint_subset[1:-1]) > 0:
- output = output[:-2]
-
- output += "]);"
- StatementCalcRoutes = StatementCalcRoutes + ' ' + output
- #print(output)
- #print("")
- subset += 9
+ The optimal road trip across the U.S. according to machine learning
+
+
+
+
+
+
+
+
+ """
- #write the output to file
localoutput_file = output_file.replace('.html', '_' + str(distance) + '.html')
- fs = open(localoutput_file, 'w')
- fs.write(Page_1)
- fs.write(Page_2)
- fs.write(StatementC1)
- fs.write(Page_3)
- fs.write(StatementC2)
- fs.write(Page_4)
- fs.write(StatementCalcRoutes)
- fs.write(Page_5)
- fs.close()
-
- #Show the result
- if display ==1:
+ with open(localoutput_file, 'w') as fs:
+ fs.write(Page_1)
+ fs.write("\t\t\toptimal_route = {0}".format(str(optimal_route)))
+ fs.write(Page_2)
+
+ if display:
webbrowser.open_new_tab(localoutput_file)
@@ -213,8 +279,13 @@ def generate_random_population(pop_size):
def run_genetic_algorithm(generations=5000, population_size=100):
"""
The core of the Genetic Algorithm.
+
+ `generations` and `population_size` must be a multiple of 10.
"""
- current_best_distance =-1
+
+ current_best_distance = -1
+ population_subset_size = int(population_size / 10.)
+ generations_10pct = int(generations / 10.)
# Create a random population of `population_size` number of solutions.
population = generate_random_population(population_size)
@@ -231,10 +302,11 @@ def run_genetic_algorithm(generations=5000, population_size=100):
population_fitness[agent_genome] = compute_fitness(agent_genome)
- # Take the 10 shortest road trips and produce 10 offspring each from them
+ # Take the top 10% shortest road trips and produce offspring each from them
new_population = []
- for rank, agent_genome in enumerate(sorted(population_fitness, key=population_fitness.get)[:10]):
- if (generation % 1000 == 0 or generation == generations - 1) and rank == 0:
+ for rank, agent_genome in enumerate(sorted(population_fitness,
+ key=population_fitness.get)[:population_subset_size]):
+ if (generation % generations_10pct == 0 or generation == generations - 1) and rank == 0:
current_best_genome = agent_genome
print("Generation %d best: %d | Unique genomes: %d" % (generation,
population_fitness[agent_genome],
@@ -242,13 +314,14 @@ def run_genetic_algorithm(generations=5000, population_size=100):
print(agent_genome)
print("")
- #if this is the first route found, or it is shorter than the best route we know, create a html output and display it
+ # If this is the first route found, or it is shorter than the best route we know,
+ # create a html output and display it
if population_fitness[agent_genome] < current_best_distance or current_best_distance < 0:
current_best_distance = population_fitness[agent_genome]
- CreateOptimalRouteHtmlFile(agent_genome,current_best_distance, 1)
+ CreateOptimalRouteHtmlFile(agent_genome, current_best_distance, False)
- # Create 1 exact copy of each of the top 10 road trips
+ # Create 1 exact copy of each of the top road trips
new_population.append(agent_genome)
# Create 2 offspring with 1-3 point mutations
@@ -267,73 +340,70 @@ def run_genetic_algorithm(generations=5000, population_size=100):
return current_best_genome
-
-# if this file exists, read the data stored in it - if not then collect data by asking google
-print "Begin finding shortest route"
-file_path = waypoints_file
-if os.path.exists(file_path):
- print "Waypoints exist"
- #file exists used saved results
- waypoint_distances = {}
- waypoint_durations = {}
- all_waypoints = set()
-
- waypoint_data = pd.read_csv(file_path, sep="\t")
-
- for i, row in waypoint_data.iterrows():
- waypoint_distances[frozenset([row.waypoint1, row.waypoint2])] = row.distance_m
- waypoint_durations[frozenset([row.waypoint1, row.waypoint2])] = row.duration_s
- all_waypoints.update([row.waypoint1, row.waypoint2])
-
-else:
- #file does not exist - compute results
- print "Collecting Waypoints"
- waypoint_distances = {}
- waypoint_durations = {}
-
-
- gmaps = googlemaps.Client(GOOGLE_MAPS_API_KEY)
- for (waypoint1, waypoint2) in combinations(all_waypoints, 2):
- try:
- route = gmaps.distance_matrix(origins=[waypoint1],
- destinations=[waypoint2],
- mode="driving", # Change to "walking" for walking directions,
- # "bicycling" for biking directions, etc.
- language="English",
- units="metric")
-
- # "distance" is in meters
- distance = route["rows"][0]["elements"][0]["distance"]["value"]
-
- # "duration" is in seconds
- duration = route["rows"][0]["elements"][0]["duration"]["value"]
-
- waypoint_distances[frozenset([waypoint1, waypoint2])] = distance
- waypoint_durations[frozenset([waypoint1, waypoint2])] = duration
-
- except Exception as e:
- print("Error with finding the route between %s and %s." % (waypoint1, waypoint2))
-
- print "Saving Waypoints"
- with open(waypoints_file, "wb") as out_file:
- out_file.write("\t".join(["waypoint1",
- "waypoint2",
- "distance_m",
- "duration_s"]))
-
- for (waypoint1, waypoint2) in waypoint_distances.keys():
- out_file.write("\n" +
- "\t".join([waypoint1,
- waypoint2,
- str(waypoint_distances[frozenset([waypoint1, waypoint2])]),
- str(waypoint_durations[frozenset([waypoint1, waypoint2])])]))
-
-
-#optimal_route = run_genetic_algorithm(generations=100, population_size=100)
-print "Search for optimal route"
-optimal_route = run_genetic_algorithm(generations=thisRunGenerations, population_size=thisRunPopulation_size)
-
-#this is probably redundant now that the files are created in run_genetic_algorithm but leaving it active to ensure
-#the final result is not lost
-CreateOptimalRouteHtmlFile(optimal_route, 1)
-
+if __name__ == '__main__':
+ # If this file exists, read the data stored in it - if not then collect data by asking google
+ print("Begin finding shortest route")
+ file_path = waypoints_file
+ if os.path.exists(file_path):
+ print("Waypoints exist")
+ #file exists used saved results
+ waypoint_distances = {}
+ waypoint_durations = {}
+ all_waypoints = set()
+
+ waypoint_data = pd.read_csv(file_path, sep="\t")
+
+ for i, row in waypoint_data.iterrows():
+ waypoint_distances[frozenset([row.waypoint1, row.waypoint2])] = row.distance_m
+ waypoint_durations[frozenset([row.waypoint1, row.waypoint2])] = row.duration_s
+ all_waypoints.update([row.waypoint1, row.waypoint2])
+
+ else:
+ # File does not exist - compute results
+ print("Collecting Waypoints")
+ waypoint_distances = {}
+ waypoint_durations = {}
+
+
+ gmaps = googlemaps.Client(GOOGLE_MAPS_API_KEY)
+ for (waypoint1, waypoint2) in combinations(all_waypoints, 2):
+ try:
+ route = gmaps.distance_matrix(origins=[waypoint1],
+ destinations=[waypoint2],
+ mode="driving", # Change to "walking" for walking directions,
+ # "bicycling" for biking directions, etc.
+ language="English",
+ units="metric")
+
+ # "distance" is in meters
+ distance = route["rows"][0]["elements"][0]["distance"]["value"]
+
+ # "duration" is in seconds
+ duration = route["rows"][0]["elements"][0]["duration"]["value"]
+
+ waypoint_distances[frozenset([waypoint1, waypoint2])] = distance
+ waypoint_durations[frozenset([waypoint1, waypoint2])] = duration
+
+ except Exception as e:
+ print("Error with finding the route between %s and %s." % (waypoint1, waypoint2))
+
+ print("Saving Waypoints")
+ with open(waypoints_file, "w") as out_file:
+ out_file.write("\t".join(["waypoint1",
+ "waypoint2",
+ "distance_m",
+ "duration_s"]))
+
+ for (waypoint1, waypoint2) in waypoint_distances.keys():
+ out_file.write("\n" +
+ "\t".join([waypoint1,
+ waypoint2,
+ str(waypoint_distances[frozenset([waypoint1, waypoint2])]),
+ str(waypoint_durations[frozenset([waypoint1, waypoint2])])]))
+
+ print("Search for optimal route")
+ optimal_route = run_genetic_algorithm(generations=thisRunGenerations, population_size=thisRunPopulation_size)
+
+ # This is probably redundant now that the files are created in run_genetic_algorithm,
+ # but leaving it active to ensure the final result is not lost
+ CreateOptimalRouteHtmlFile(optimal_route, 1, True)
diff --git a/pareto-optimized-road-trip/my-waypoints-dist-dur.tsv b/pareto-optimized-road-trip/my-waypoints-dist-dur.tsv
new file mode 100644
index 0000000..6c51cfc
--- /dev/null
+++ b/pareto-optimized-road-trip/my-waypoints-dist-dur.tsv
@@ -0,0 +1,1129 @@
+waypoint1 waypoint2 distance_m duration_s
+2 E Main St, Madison, WI 53703 Wyoming State Capitol, Cheyenne, WY 82001 1484756 13.459166666666667
+L St & 10th St, Sacramento, CA 95814 Virginia State Capitol, Richmond, VA 23219 4496033 40.22388888888889
+Michigan State Capitol, Lansing, MI 48933 North Dakota State Capitol, Bismarck, ND 58501 1702226 15.2425
+Michigan State Capitol, Lansing, MI 48933 Oklahoma State Capitol, Oklahoma City, OK 73105 1577431 14.1075
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+402 S Monroe St, Tallahassee, FL 32301 Vermont State House, 115 State Street, Montpelier, VT 05633 2270947 20.70388888888889
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Virginia State Capitol, Richmond, VA 23219 3620386 32.61944444444445
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 4310134 38.85166666666667
+Nevada State Capitol, Carson City, NV 89701 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 4417580 39.431666666666665
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Missouri State Capitol, Jefferson City, MO 65101 1654361 15.761111111111111
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+Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 3965649 35.45805555555555
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Michigan State Capitol, Lansing, MI 48933 3125553 28.08
+300 SW 10th Ave, Topeka, KS 66612 Virginia State Capitol, Richmond, VA 23219 1817890 16.451666666666668
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 207363 2.6302777777777777
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Oklahoma State Capitol, Oklahoma City, OK 73105 873294 7.731944444444444
+New Jersey State House, Trenton, NJ 08608 Vermont State House, 115 State Street, Montpelier, VT 05633 609928 5.969722222222222
+Minnesota State Capitol, St Paul, MN 55155 North Dakota State Capitol, Bismarck, ND 58501 701087 6.226111111111111
+700 W Jefferson St, Boise, ID 83720 Rhode Island State House, 82 Smith Street, Providence, RI 02903 4262023 38.382222222222225
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+Maryland State House, 100 State Cir, Annapolis, MD 21401 Michigan State Capitol, Lansing, MI 48933 984795 9.165
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+New York State Capitol, State St. and Washington Ave, Albany, NY 12224 Wyoming State Capitol, Cheyenne, WY 82001 2836858 25.635833333333334
+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 Vermont State House, 115 State Street, Montpelier, VT 05633 3858914 35.9
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+Minnesota State Capitol, St Paul, MN 55155 Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 1215946 11.09361111111111
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 1177643 10.660277777777777
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Indiana State Capitol, Indianapolis, IN 46204 2734360 24.705277777777777
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+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 500 E Capitol Ave, Pierre, SD 57501 2545226 23.341944444444444
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+L St & 10th St, Sacramento, CA 95814 Nevada State Capitol, Carson City, NV 89701 209218 2.620277777777778
+L St & 10th St, Sacramento, CA 95814 Indiana State Capitol, Indianapolis, IN 46204 3518613 31.3025
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+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 300 SW 10th Ave, Topeka, KS 66612 1939553 18.27861111111111
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+402 S Monroe St, Tallahassee, FL 32301 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 1991155 17.946944444444444
+700 W Jefferson St, Boise, ID 83720 Massachusetts State House, Boston, MA 02108 4283333 38.500277777777775
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+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Massachusetts State House, Boston, MA 02108 611507 6.089722222222222
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+L St & 10th St, Sacramento, CA 95814 New Jersey State House, Trenton, NJ 08608 4526276 40.489444444444445
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+Georgia State Capitol, Atlanta, GA 30334 Nevada State Capitol, Carson City, NV 89701 3827390 34.82694444444444
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+Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 West Virginia State Capitol, Charleston, WV 25317 265168 2.6933333333333334
+200 E Colfax Ave, Denver, CO 80203 Vermont State House, 115 State Street, Montpelier, VT 05633 3117244 28.791666666666668
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 2180753 20.04
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+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 Utah State Capitol, Salt Lake City, UT 84103 774035 6.633333333333334
+300 SW 10th Ave, Topeka, KS 66612 Maine State House, Augusta, ME 04330 2649936 24.170833333333334
+Michigan State Capitol, Lansing, MI 48933 West Virginia State Capitol, Charleston, WV 25317 679517 6.6338888888888885
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+400-498 N West St, Jackson, MS 39201 2 E Main St, Madison, WI 53703 1365734 12.152222222222223
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+Massachusetts State House, Boston, MA 02108 Oklahoma State Capitol, Oklahoma City, OK 73105 2710778 24.665277777777778
+Illinois State Capitol, Springfield, IL 62756 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 1213082 11.370833333333334
+200 E Colfax Ave, Denver, CO 80203 West Virginia State Capitol, Charleston, WV 25317 2179520 19.378888888888888
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+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Louisiana State Capitol, Baton Rouge, LA 70802 2386413 21.665555555555557
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 North Dakota State Capitol, Bismarck, ND 58501 2555394 22.523611111111112
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+Georgia State Capitol, Atlanta, GA 30334 2 E Main St, Madison, WI 53703 1396771 12.522777777777778
+Vermont State House, 115 State Street, Montpelier, VT 05633 2 E Main St, Madison, WI 53703 1777622 17.235833333333332
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 4722133 43.030277777777776
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+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 North Carolina State Capitol, Raleigh, NC 27601 1779142 16.510277777777777
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+L St & 10th St, Sacramento, CA 95814 State House, 107 North Main Street, Concord, NH 03303 4819339 44.10638888888889
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+L St & 10th St, Sacramento, CA 95814 Maryland State House, 100 State Cir, Annapolis, MD 21401 4434054 39.71194444444444
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+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Nevada State Capitol, Carson City, NV 89701 4507409 40.7575
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+200 E Colfax Ave, Denver, CO 80203 North Dakota State Capitol, Bismarck, ND 58501 1115101 10.553333333333333
+Nevada State Capitol, Carson City, NV 89701 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 832440 8.6625
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 4892402 44.316944444444445
+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 New Mexico State Capitol, Santa Fe, NM 87501 1899972 16.586111111111112
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+300 SW 10th Ave, Topeka, KS 66612 New Mexico State Capitol, Santa Fe, NM 87501 1113558 10.723333333333333
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Minnesota State Capitol, St Paul, MN 55155 1917773 17.89361111111111
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+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Maine State House, Augusta, ME 04330 2338330 21.378055555555555
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Oklahoma State Capitol, Oklahoma City, OK 73105 2249555 20.97888888888889
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 993281 9.273055555555555
+200 E Colfax Ave, Denver, CO 80203 Rhode Island State House, 82 Smith Street, Providence, RI 02903 3149582 28.52111111111111
+L St & 10th St, Sacramento, CA 95814 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 4330179 38.74888888888889
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+L St & 10th St, Sacramento, CA 95814 North Carolina State Capitol, Raleigh, NC 27601 4499451 40.45944444444444
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+Oklahoma State Capitol, Oklahoma City, OK 73105 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 1092426 9.8425
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 1369160 12.65638888888889
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Massachusetts State House, Boston, MA 02108 1995567 18.72
+300 SW 10th Ave, Topeka, KS 66612 North Carolina State Capitol, Raleigh, NC 27601 1821308 16.68722222222222
+200 E Colfax Ave, Denver, CO 80203 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 2072303 19.064722222222223
+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 905401 8.324722222222222
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+200 E Colfax Ave, Denver, CO 80203 South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 2564693 22.948333333333334
+South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 West Virginia State Capitol, Charleston, WV 25317 569053 5.368888888888889
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 400-498 N West St, Jackson, MS 39201 399927 4.009444444444444
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+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 Vermont State House, 115 State Street, Montpelier, VT 05633 1492754 14.696666666666667
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+New York State Capitol, State St. and Washington Ave, Albany, NY 12224 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 473386 4.486944444444444
+State House, 107 North Main Street, Concord, NH 03303 500 E Capitol Ave, Pierre, SD 57501 2843784 26.504166666666666
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Minnesota State Capitol, St Paul, MN 55155 1857595 16.41888888888889
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+Indiana State Capitol, Indianapolis, IN 46204 Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 267015 2.671388888888889
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+200 E Colfax Ave, Denver, CO 80203 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 1857910 16.49972222222222
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+300 SW 10th Ave, Topeka, KS 66612 Oklahoma State Capitol, Oklahoma City, OK 73105 473488 4.159722222222222
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+700 W Jefferson St, Boise, ID 83720 Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 782531 7.341388888888889
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+Illinois State Capitol, Springfield, IL 62756 500 E Capitol Ave, Pierre, SD 57501 1322600 11.902777777777779
+300 SW 10th Ave, Topeka, KS 66612 Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 1992901 18.201944444444443
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+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Michigan State Capitol, Lansing, MI 48933 1314610 11.880555555555556
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+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Missouri State Capitol, Jefferson City, MO 65101 427240 4.4225
+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 State House, 107 North Main Street, Concord, NH 03303 3879141 36.28
+Louisiana State Capitol, Baton Rouge, LA 70802 Utah State Capitol, Salt Lake City, UT 84103 2687549 25.205277777777777
+New York State Capitol, State St. and Washington Ave, Albany, NY 12224 Utah State Capitol, Salt Lake City, UT 84103 3544096 32.043055555555554
+Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 1319361 11.98638888888889
+700 W Jefferson St, Boise, ID 83720 Maryland State House, 100 State Cir, Annapolis, MD 21401 3864627 34.63861111111111
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Virginia State Capitol, Richmond, VA 23219 1547668 14.019444444444444
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Georgia State Capitol, Atlanta, GA 30334 2974348 26.195833333333333
+Missouri State Capitol, Jefferson City, MO 65101 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 705688 6.440555555555555
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 West Virginia State Capitol, Charleston, WV 25317 3161299 28.406388888888888
+L St & 10th St, Sacramento, CA 95814 500 E Capitol Ave, Pierre, SD 57501 2363213 21.469722222222224
+Minnesota State Capitol, St Paul, MN 55155 New Jersey State House, Trenton, NJ 08608 1904120 17.467777777777776
+300 SW 10th Ave, Topeka, KS 66612 New Jersey State House, Trenton, NJ 08608 1943570 17.935555555555556
+State House, 107 North Main Street, Concord, NH 03303 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 5020691 46.1925
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 402 S Monroe St, Tallahassee, FL 32301 1549915 14.0425
+Indiana State Capitol, Indianapolis, IN 46204 South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 1001496 9.178055555555556
+Illinois State Capitol, Springfield, IL 62756 2 E Main St, Madison, WI 53703 425309 3.9405555555555556
+North Carolina State Capitol, Raleigh, NC 27601 South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 363112 3.348611111111111
+Nevada State Capitol, Carson City, NV 89701 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 4159337 37.14194444444445
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 1772295 16.54138888888889
+New Mexico State Capitol, Santa Fe, NM 87501 Wyoming State Capitol, Cheyenne, WY 82001 792157 7.053333333333334
+Georgia State Capitol, Atlanta, GA 30334 700 W Jefferson St, Boise, ID 83720 3498032 31.39
+Missouri State Capitol, Jefferson City, MO 65101 500 E Capitol Ave, Pierre, SD 57501 1190637 10.608055555555556
+Massachusetts State House, Boston, MA 02108 North Carolina State Capitol, Raleigh, NC 27601 1157235 11.136111111111111
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 400-498 N West St, Jackson, MS 39201 1736549 16.05638888888889
+Illinois State Capitol, Springfield, IL 62756 North Dakota State Capitol, Bismarck, ND 58501 1530029 13.591944444444444
+Nevada State Capitol, Carson City, NV 89701 Vermont State House, 115 State Street, Montpelier, VT 05633 4628271 42.11944444444445
+402 S Monroe St, Tallahassee, FL 32301 Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 1994047 18.71527777777778
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Rhode Island State House, 82 Smith Street, Providence, RI 02903 561475 5.556666666666667
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 1278074 11.519166666666667
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Wyoming State Capitol, Cheyenne, WY 82001 2783745 25.468333333333334
+200 E Colfax Ave, Denver, CO 80203 Nevada State Capitol, Carson City, NV 89701 1595181 15.340833333333334
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 State House, 107 North Main Street, Concord, NH 03303 2057414 19.745833333333334
+North Carolina State Capitol, Raleigh, NC 27601 West Virginia State Capitol, Charleston, WV 25317 509177 4.908055555555555
+Maine State House, Augusta, ME 04330 Massachusetts State House, Boston, MA 02108 260531 2.618888888888889
+Georgia State Capitol, Atlanta, GA 30334 Michigan State Capitol, Lansing, MI 48933 1252890 11.448333333333334
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Georgia State Capitol, Atlanta, GA 30334 1585527 14.938055555555556
+Indiana State Capitol, Indianapolis, IN 46204 500 E Capitol Ave, Pierre, SD 57501 1545420 13.984722222222222
+Michigan State Capitol, Lansing, MI 48933 Virginia State Capitol, Richmond, VA 23219 1107187 10.313055555555556
+Illinois State Capitol, Springfield, IL 62756 Nevada State Capitol, Carson City, NV 89701 2967815 26.966666666666665
+L St & 10th St, Sacramento, CA 95814 Minnesota State Capitol, St Paul, MN 55155 3086551 27.525277777777777
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+700 W Jefferson St, Boise, ID 83720 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 4018994 35.965
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 2 E Main St, Madison, WI 53703 1681120 15.633611111111112
+Oregon State Capitol, 900 Court St NE, Salem, OR 97301 Utah State Capitol, Salt Lake City, UT 84103 1303708 11.963611111111112
+Michigan State Capitol, Lansing, MI 48933 Utah State Capitol, Salt Lake City, UT 84103 2577637 23.119722222222222
+Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 2 E Main St, Madison, WI 53703 1902526 17.63888888888889
+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 1827872 15.870833333333334
+State House, 107 North Main Street, Concord, NH 03303 2 E Main St, Madison, WI 53703 1809409 17.50888888888889
+North Dakota State Capitol, Bismarck, ND 58501 Utah State Capitol, Salt Lake City, UT 84103 1494479 14.052777777777777
+300 SW 10th Ave, Topeka, KS 66612 Minnesota State Capitol, St Paul, MN 55155 804823 7.222777777777778
+Maryland State House, 100 State Cir, Annapolis, MD 21401 Oklahoma State Capitol, Oklahoma City, OK 73105 2150915 19.857222222222223
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Virginia State Capitol, Richmond, VA 23219 329515 3.4183333333333334
+Missouri State Capitol, Jefferson City, MO 65101 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 1462312 13.661666666666667
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Georgia State Capitol, Atlanta, GA 30334 1183433 11.198888888888888
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+400-498 N West St, Jackson, MS 39201 Utah State Capitol, Salt Lake City, UT 84103 2652406 24.896944444444443
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+Missouri State Capitol, Jefferson City, MO 65101 Vermont State House, 115 State Street, Montpelier, VT 05633 2063633 19.625555555555554
+Georgia State Capitol, Atlanta, GA 30334 Utah State Capitol, Salt Lake City, UT 84103 3025080 27.3475
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+Indiana State Capitol, Indianapolis, IN 46204 300 SW 10th Ave, Topeka, KS 66612 875152 8.04888888888889
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+200 E Colfax Ave, Denver, CO 80203 300 SW 10th Ave, Topeka, KS 66612 868171 7.5925
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+Georgia State Capitol, Atlanta, GA 30334 300 SW 10th Ave, Topeka, KS 66612 1388708 12.551666666666666
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+402 S Monroe St, Tallahassee, FL 32301 Louisiana State Capitol, Baton Rouge, LA 70802 713508 6.373333333333333
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+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 402 S Monroe St, Tallahassee, FL 32301 1952010 17.781666666666666
+200 E Colfax Ave, Denver, CO 80203 Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 1079887 9.487222222222222
+200 E Colfax Ave, Denver, CO 80203 Minnesota State Capitol, St Paul, MN 55155 1472312 12.963055555555556
+Maine State House, Augusta, ME 04330 Maryland State House, 100 State Cir, Annapolis, MD 21401 936447 8.990833333333333
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+Massachusetts State House, Boston, MA 02108 2 E Main St, Madison, WI 53703 1828313 16.995833333333334
+South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 2 E Main St, Madison, WI 53703 1530438 14.185
+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 State House, 107 North Main Street, Concord, NH 03303 1512981 15.07638888888889
+Missouri State Capitol, Jefferson City, MO 65101 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 1852941 16.93777777777778
+L St & 10th St, Sacramento, CA 95814 300 SW 10th Ave, Topeka, KS 66612 2735540 24.093055555555555
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+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 1165605 10.690833333333334
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+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 4831698 43.48416666666667
+L St & 10th St, Sacramento, CA 95814 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 4588421 41.03861111111111
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+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 3864119 35.05833333333333
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+300 SW 10th Ave, Topeka, KS 66612 Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 1157547 10.576666666666666
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+Rhode Island State House, 82 Smith Street, Providence, RI 02903 South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 1433934 13.457222222222223
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+402 S Monroe St, Tallahassee, FL 32301 State House, 107 North Main Street, Concord, NH 03303 2204336 20.1675
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+Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 500 E Capitol Ave, Pierre, SD 57501 2341717 21.20138888888889
+New York State Capitol, State St. and Washington Ave, Albany, NY 12224 2 E Main St, Madison, WI 53703 1563965 14.514444444444445
+Minnesota State Capitol, St Paul, MN 55155 Nevada State Capitol, Carson City, NV 89701 2864288 26.09138888888889
+New Jersey State House, Trenton, NJ 08608 Rhode Island State House, 82 Smith Street, Providence, RI 02903 398312 4.088888888888889
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+200 E Colfax Ave, Denver, CO 80203 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 1478566 13.878333333333334
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+Maine State House, Augusta, ME 04330 Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 1494741 13.688055555555556
+Massachusetts State House, Boston, MA 02108 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 5039595 45.67916666666667
+State House, 107 North Main Street, Concord, NH 03303 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 257015 3.1944444444444446
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Rhode Island State House, 82 Smith Street, Providence, RI 02903 4180240 38.32611111111111
+700 W Jefferson St, Boise, ID 83720 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 3099564 27.6725
+New York State Capitol, State St. and Washington Ave, Albany, NY 12224 Oklahoma State Capitol, Oklahoma City, OK 73105 2446430 22.183611111111112
+L St & 10th St, Sacramento, CA 95814 Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 4705821 42.25222222222222
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+402 S Monroe St, Tallahassee, FL 32301 700 W Jefferson St, Boise, ID 83720 3890868 35.385
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+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 2326097 21.381944444444443
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+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 2078837 19.298055555555557
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+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 Virginia State Capitol, Richmond, VA 23219 3617475 33.053333333333335
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+Maryland State House, 100 State Cir, Annapolis, MD 21401 North Dakota State Capitol, Bismarck, ND 58501 2516932 22.876666666666665
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+L St & 10th St, Sacramento, CA 95814 Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 1630833 14.535833333333333
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Vermont State House, 115 State Street, Montpelier, VT 05633 773092 7.4375
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+Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 3473890 32.7875
+500 E Capitol Ave, Pierre, SD 57501 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 1828065 16.27888888888889
+300 SW 10th Ave, Topeka, KS 66612 Utah State Capitol, Salt Lake City, UT 84103 1694356 15.054722222222223
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+402 S Monroe St, Tallahassee, FL 32301 North Carolina State Capitol, Raleigh, NC 27601 992856 8.732222222222223
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+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 1496159 13.354166666666666
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+L St & 10th St, Sacramento, CA 95814 402 S Monroe St, Tallahassee, FL 32301 4246543 37.35444444444445
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+New York State Capitol, State St. and Washington Ave, Albany, NY 12224 North Carolina State Capitol, Raleigh, NC 27601 1038859 9.794444444444444
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+300 SW 10th Ave, Topeka, KS 66612 Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 269993 2.882222222222222
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+Minnesota State Capitol, St Paul, MN 55155 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 2871909 25.724444444444444
+Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 Virginia State Capitol, Richmond, VA 23219 2029907 18.575277777777778
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+200 E Colfax Ave, Denver, CO 80203 Illinois State Capitol, Springfield, IL 62756 1465413 13.14
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+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Maryland State House, 100 State Cir, Annapolis, MD 21401 530919 5.235
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+L St & 10th St, Sacramento, CA 95814 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 1115233 10.574444444444444
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+402 S Monroe St, Tallahassee, FL 32301 Maryland State House, 100 State Cir, Annapolis, MD 21401 1454024 12.885277777777778
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+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 L St & 10th St, Sacramento, CA 95814 1212739 10.910555555555556
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+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 Louisiana State Capitol, Baton Rouge, LA 70802 1282198 11.503333333333334
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Minnesota State Capitol, St Paul, MN 55155 1381467 12.380555555555556
+402 S Monroe St, Tallahassee, FL 32301 Illinois State Capitol, Springfield, IL 62756 1392053 13.165277777777778
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 North Dakota State Capitol, Bismarck, ND 58501 2785840 25.285
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Louisiana State Capitol, Baton Rouge, LA 70802 2327122 20.377777777777776
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+700 W Jefferson St, Boise, ID 83720 500 E Capitol Ave, Pierre, SD 57501 1700787 16.372222222222224
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+Missouri State Capitol, Jefferson City, MO 65101 Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 872387 8.043611111111112
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+Georgia State Capitol, Atlanta, GA 30334 Maine State House, Augusta, ME 04330 1984970 18.6275
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 1894156 17.088055555555556
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 North Dakota State Capitol, Bismarck, ND 58501 1074658 9.473333333333333
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+Maryland State House, 100 State Cir, Annapolis, MD 21401 New Mexico State Capitol, Santa Fe, NM 87501 3015312 27.569444444444443
+L St & 10th St, Sacramento, CA 95814 Illinois State Capitol, Springfield, IL 62756 3161763 28.34111111111111
+Maryland State House, 100 State Cir, Annapolis, MD 21401 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 567996 5.439444444444445
+L St & 10th St, Sacramento, CA 95814 Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 3810136 33.81611111111111
+Illinois State Capitol, Springfield, IL 62756 Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 2406938 21.828611111111112
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Minnesota State Capitol, St Paul, MN 55155 2088042 19.18027777777778
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 1838597 17.153055555555557
+Nevada State Capitol, Carson City, NV 89701 Virginia State Capitol, Richmond, VA 23219 4325191 38.61694444444444
+Michigan State Capitol, Lansing, MI 48933 Vermont State House, 115 State Street, Montpelier, VT 05633 1228009 11.509444444444444
+300 SW 10th Ave, Topeka, KS 66612 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 990848 8.973888888888888
+Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 Nevada State Capitol, Carson City, NV 89701 2266886 20.369722222222222
+L St & 10th St, Sacramento, CA 95814 Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 2761755 24.421666666666667
+Louisiana State Capitol, Baton Rouge, LA 70802 Virginia State Capitol, Richmond, VA 23219 1701633 15.176944444444445
+Minnesota State Capitol, St Paul, MN 55155 Virginia State Capitol, Richmond, VA 23219 1935517 17.858055555555556
+700 W Jefferson St, Boise, ID 83720 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 3760751 33.675555555555555
+Missouri State Capitol, Jefferson City, MO 65101 West Virginia State Capitol, Charleston, WV 25317 1027298 9.319722222222222
+Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 4457921 40.05694444444445
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Virginia State Capitol, Richmond, VA 23219 731609 7.1575
+Louisiana State Capitol, Baton Rouge, LA 70802 South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 1183500 10.326944444444445
+Maryland State House, 100 State Cir, Annapolis, MD 21401 Missouri State Capitol, Jefferson City, MO 65101 1555721 14.639444444444445
+Maryland State House, 100 State Cir, Annapolis, MD 21401 Vermont State House, 115 State Street, Montpelier, VT 05633 847788 8.196388888888889
+North Carolina State Capitol, Raleigh, NC 27601 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 625512 5.963055555555556
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 New Jersey State House, Trenton, NJ 08608 1572177 14.505277777777778
+New Jersey State House, Trenton, NJ 08608 Utah State Capitol, Salt Lake City, UT 84103 3481284 31.463055555555556
+Indiana State Capitol, Indianapolis, IN 46204 New Jersey State House, Trenton, NJ 08608 1069303 10.036111111111111
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Michigan State Capitol, Lansing, MI 48933 1083435 10.286666666666667
+700 W Jefferson St, Boise, ID 83720 State House, 107 North Main Street, Concord, NH 03303 4249912 39.033055555555556
+Massachusetts State House, Boston, MA 02108 New Jersey State House, Trenton, NJ 08608 449801 4.581388888888889
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 2845972 25.66027777777778
+402 S Monroe St, Tallahassee, FL 32301 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 4701521 42.861666666666665
+Illinois State Capitol, Springfield, IL 62756 West Virginia State Capitol, Charleston, WV 25317 845239 7.972777777777778
+Maryland State House, 100 State Cir, Annapolis, MD 21401 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 179018 1.898611111111111
+Louisiana State Capitol, Baton Rouge, LA 70802 Maryland State House, 100 State Cir, Annapolis, MD 21401 1901317 17.226111111111113
+Illinois State Capitol, Springfield, IL 62756 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 3405261 30.89027777777778
+Minnesota State Capitol, St Paul, MN 55155 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 1966265 18.016666666666666
+400-498 N West St, Jackson, MS 39201 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 887636 8.437777777777777
+Georgia State Capitol, Atlanta, GA 30334 State House, 107 North Main Street, Concord, NH 03303 1832449 17.274444444444445
+Maine State House, Augusta, ME 04330 2 E Main St, Madison, WI 53703 2076842 19.205
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Missouri State Capitol, Jefferson City, MO 65101 1157419 10.383055555555556
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Louisiana State Capitol, Baton Rouge, LA 70802 591309 5.184444444444445
+200 E Colfax Ave, Denver, CO 80203 State House, 107 North Main Street, Concord, NH 03303 3137471 29.171666666666667
+300 SW 10th Ave, Topeka, KS 66612 North Dakota State Capitol, Bismarck, ND 58501 1239183 11.00388888888889
+L St & 10th St, Sacramento, CA 95814 Missouri State Capitol, Jefferson City, MO 65101 3030263 27.0325
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 1760444 15.849444444444444
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 2633957 23.764166666666668
+700 W Jefferson St, Boise, ID 83720 300 SW 10th Ave, Topeka, KS 66612 2166113 19.01972222222222
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 1232677 11.606111111111112
+State House, 107 North Main Street, Concord, NH 03303 New Mexico State Capitol, Santa Fe, NM 87501 3556272 32.89055555555556
+Oklahoma State Capitol, Oklahoma City, OK 73105 500 E Capitol Ave, Pierre, SD 57501 1174982 11.439166666666667
+North Dakota State Capitol, Bismarck, ND 58501 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 2174863 19.649722222222223
+Virginia State Capitol, Richmond, VA 23219 West Virginia State Capitol, Charleston, WV 25317 507154 4.714166666666666
+New Mexico State Capitol, Santa Fe, NM 87501 Oklahoma State Capitol, Oklahoma City, OK 73105 866244 7.8213888888888885
+Georgia State Capitol, Atlanta, GA 30334 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 1151965 10.569722222222222
+Massachusetts State House, Boston, MA 02108 New Mexico State Capitol, Santa Fe, NM 87501 3575175 32.37722222222222
+200 E Colfax Ave, Denver, CO 80203 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 2614534 23.720833333333335
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Vermont State House, 115 State Street, Montpelier, VT 05633 2358711 22.27027777777778
+402 S Monroe St, Tallahassee, FL 32301 400-498 N West St, Jackson, MS 39201 694704 6.720555555555555
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 1620221 14.949444444444444
+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 West Virginia State Capitol, Charleston, WV 25317 322220 3.0175
+State House, 107 North Main Street, Concord, NH 03303 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 4959988 45.359722222222224
+402 S Monroe St, Tallahassee, FL 32301 North Dakota State Capitol, Bismarck, ND 58501 2894327 26.169444444444444
+Rhode Island State House, 82 Smith Street, Providence, RI 02903 Utah State Capitol, Salt Lake City, UT 84103 3788124 34.424166666666665
+Illinois State Capitol, Springfield, IL 62756 400-498 N West St, Jackson, MS 39201 945911 8.484166666666667
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Massachusetts State House, Boston, MA 02108 2337722 21.607222222222223
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 North Dakota State Capitol, Bismarck, ND 58501 1948713 17.15972222222222
+Michigan State Capitol, Lansing, MI 48933 Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 2686056 24.39666666666667
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Maryland State House, 100 State Cir, Annapolis, MD 21401 105428 1.3294444444444444
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 2702432 24.357777777777777
+Michigan State Capitol, Lansing, MI 48933 State House, 107 North Main Street, Concord, NH 03303 1218614 12.2075
+State House, 107 North Main Street, Concord, NH 03303 North Carolina State Capitol, Raleigh, NC 27601 1250547 11.9375
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 2892479 26.149722222222223
+Oklahoma State Capitol, Oklahoma City, OK 73105 Utah State Capitol, Salt Lake City, UT 84103 1910174 16.99861111111111
+300 SW 10th Ave, Topeka, KS 66612 Massachusetts State House, Boston, MA 02108 2402441 22.00611111111111
+400-498 N West St, Jackson, MS 39201 Rhode Island State House, 82 Smith Street, Providence, RI 02903 2232241 20.464444444444446
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 State House, 107 North Main Street, Concord, NH 03303 4228062 39.20194444444444
+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 Maine State House, Augusta, ME 04330 1772326 16.46388888888889
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 2088428 18.80666666666667
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 307125 2.8447222222222224
+New York State Capitol, State St. and Washington Ave, Albany, NY 12224 Vermont State House, 115 State Street, Montpelier, VT 05633 254720 3.1572222222222224
+Georgia State Capitol, Atlanta, GA 30334 500 E Capitol Ave, Pierre, SD 57501 2226023 19.879166666666666
+400-498 N West St, Jackson, MS 39201 State House, 107 North Main Street, Concord, NH 03303 2377027 21.84777777777778
+Georgia State Capitol, Atlanta, GA 30334 Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 913039 8.318055555555556
+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 Rhode Island State House, 82 Smith Street, Providence, RI 02903 3891251 35.629444444444445
+Georgia State Capitol, Atlanta, GA 30334 Oklahoma State Capitol, Oklahoma City, OK 73105 1366266 12.402777777777779
+Massachusetts State House, Boston, MA 02108 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 3156474 28.857222222222223
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 200 E Colfax Ave, Denver, CO 80203 2283660 20.398611111111112
+New York State Capitol, State St. and Washington Ave, Albany, NY 12224 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 1591468 14.635
+300 SW 10th Ave, Topeka, KS 66612 500 E Capitol Ave, Pierre, SD 57501 908563 8.276111111111112
+L St & 10th St, Sacramento, CA 95814 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 2834432 25.199166666666667
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Vermont State House, 115 State Street, Montpelier, VT 05633 2037187 19.36611111111111
+Oklahoma State Capitol, Oklahoma City, OK 73105 West Virginia State Capitol, Charleston, WV 25317 1611885 14.410277777777777
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 1873972 17.040555555555557
+Indiana State Capitol, Indianapolis, IN 46204 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 3686093 32.94722222222222
+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 2750503 24.789166666666667
+Nevada State Capitol, Carson City, NV 89701 North Dakota State Capitol, Bismarck, ND 58501 2234268 20.314166666666665
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+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Louisiana State Capitol, Baton Rouge, LA 70802 1605426 14.594722222222222
+South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 711401 6.652777777777778
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 2255678 21.27111111111111
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 1568253 14.388333333333334
+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 North Dakota State Capitol, Bismarck, ND 58501 1903795 17.210833333333333
+Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 2 E Main St, Madison, WI 53703 3161453 28.25111111111111
+Virginia State Capitol, Richmond, VA 23219 2 E Main St, Madison, WI 53703 1533610 14.348055555555556
+500 E Capitol Ave, Pierre, SD 57501 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 2125997 19.844722222222224
+North Carolina State Capitol, Raleigh, NC 27601 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 2187972 19.727777777777778
+Oklahoma State Capitol, Oklahoma City, OK 73105 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 625486 5.748888888888889
+Indiana State Capitol, Indianapolis, IN 46204 Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 1064665 9.761111111111111
+Illinois State Capitol, Springfield, IL 62756 New Mexico State Capitol, Santa Fe, NM 87501 1727925 16.314444444444444
+500 E Capitol Ave, Pierre, SD 57501 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 1798030 17.05472222222222
+700 W Jefferson St, Boise, ID 83720 Illinois State Capitol, Springfield, IL 62756 2592335 23.267777777777777
+Maryland State House, 100 State Cir, Annapolis, MD 21401 Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 3500762 32.03055555555556
+State House, 107 North Main Street, Concord, NH 03303 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 1870532 17.2975
+New York State Capitol, State St. and Washington Ave, Albany, NY 12224 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 4714543 42.365
+Michigan State Capitol, Lansing, MI 48933 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 879693 8.182222222222222
+South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 Virginia State Capitol, Richmond, VA 23219 596292 5.276666666666666
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 1039311 9.755833333333333
+300 SW 10th Ave, Topeka, KS 66612 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 1747472 16.19472222222222
+Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 New Jersey State House, Trenton, NJ 08608 2071197 18.904444444444444
+New York State Capitol, State St. and Washington Ave, Albany, NY 12224 500 E Capitol Ave, Pierre, SD 57501 2598339 23.509444444444444
+Massachusetts State House, Boston, MA 02108 Utah State Capitol, Salt Lake City, UT 84103 3808444 34.52444444444444
+Missouri State Capitol, Jefferson City, MO 65101 State House, 107 North Main Street, Concord, NH 03303 2083860 20.005555555555556
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+Nevada State Capitol, Carson City, NV 89701 Wyoming State Capitol, Cheyenne, WY 82001 1584035 14.022777777777778
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 1622824 14.474444444444444
+Missouri State Capitol, Jefferson City, MO 65101 Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 2274975 20.53388888888889
+Illinois State Capitol, Springfield, IL 62756 Michigan State Capitol, Lansing, MI 48933 628469 5.753611111111111
+Louisiana State Capitol, Baton Rouge, LA 70802 North Dakota State Capitol, Bismarck, ND 58501 2494502 23.158333333333335
+300 SW 10th Ave, Topeka, KS 66612 Vermont State House, 115 State Street, Montpelier, VT 05633 2348793 22.15888888888889
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 North Carolina State Capitol, Raleigh, NC 27601 910884 8.335
+New Jersey State House, Trenton, NJ 08608 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 1356014 12.425555555555556
+L St & 10th St, Sacramento, CA 95814 Vermont State House, 115 State Street, Montpelier, VT 05633 4799113 43.72638888888889
+New Jersey State House, Trenton, NJ 08608 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 4651731 41.78527777777778
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+402 S Monroe St, Tallahassee, FL 32301 300 SW 10th Ave, Topeka, KS 66612 1781545 16.546666666666667
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+Maine State House, Augusta, ME 04330 500 E Capitol Ave, Pierre, SD 57501 3111216 28.2
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+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 Oklahoma State Capitol, Oklahoma City, OK 73105 1296244 11.693888888888889
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 400-498 N West St, Jackson, MS 39201 2124093 19.399722222222223
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Massachusetts State House, Boston, MA 02108 2090835 19.213333333333335
+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 Oklahoma State Capitol, Oklahoma City, OK 73105 2340718 20.198055555555555
+State House, 107 North Main Street, Concord, NH 03303 New Jersey State House, Trenton, NJ 08608 543114 5.3825
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 2 E Main St, Madison, WI 53703 1135904 10.694166666666666
+Maryland State House, 100 State Cir, Annapolis, MD 21401 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 1134038 10.484444444444444
+200 E Colfax Ave, Denver, CO 80203 Louisiana State Capitol, Baton Rouge, LA 70802 1966426 18.144166666666667
+L St & 10th St, Sacramento, CA 95814 200 E Colfax Ave, Denver, CO 80203 1881205 16.975555555555555
+Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 591688 5.702222222222222
+Maryland State House, 100 State Cir, Annapolis, MD 21401 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 4562790 41.075833333333335
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Utah State Capitol, Salt Lake City, UT 84103 2380471 21.142222222222223
+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 North Carolina State Capitol, Raleigh, NC 27601 831070 7.851944444444444
+300 SW 10th Ave, Topeka, KS 66612 Michigan State Capitol, Lansing, MI 48933 1223735 11.0925
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 1942564 17.828055555555554
+North Dakota State Capitol, Bismarck, ND 58501 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 2102664 18.569444444444443
+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 New Jersey State House, Trenton, NJ 08608 3586077 32.66305555555556
+Maine State House, Augusta, ME 04330 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 520684 4.886388888888889
+Oregon State Capitol, 900 Court St NE, Salem, OR 97301 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 3859065 34.80777777777778
+Oregon State Capitol, 900 Court St NE, Salem, OR 97301 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 4520252 40.81055555555555
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 L St & 10th St, Sacramento, CA 95814 3151067 28.136666666666667
+Rhode Island State House, 82 Smith Street, Providence, RI 02903 Virginia State Capitol, Richmond, VA 23219 838637 8.217222222222222
+Georgia State Capitol, Atlanta, GA 30334 Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 1448964 13.422222222222222
+South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 Wyoming State Capitol, Cheyenne, WY 82001 2626841 23.81972222222222
+L St & 10th St, Sacramento, CA 95814 Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 4532569 40.880833333333335
+300 SW 10th Ave, Topeka, KS 66612 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 3022102 26.950277777777778
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Georgia State Capitol, Atlanta, GA 30334 834382 7.7091666666666665
+Georgia State Capitol, Atlanta, GA 30334 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 402403 3.7736111111111112
+300 SW 10th Ave, Topeka, KS 66612 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 1096352 9.781944444444445
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+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 300 SW 10th Ave, Topeka, KS 66612 1849376 17.73027777777778
+L St & 10th St, Sacramento, CA 95814 400-498 N West St, Jackson, MS 39201 3427740 30.878611111111113
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+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 4362587 39.215833333333336
+402 S Monroe St, Tallahassee, FL 32301 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 1577371 14.174444444444445
+Michigan State Capitol, Lansing, MI 48933 Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 1165493 10.542222222222222
+700 W Jefferson St, Boise, ID 83720 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 2619542 24.863888888888887
+State House, 107 North Main Street, Concord, NH 03303 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 720814 6.955
+Maine State House, Augusta, ME 04330 Nevada State Capitol, Carson City, NV 89701 4903131 44.32888888888889
+Michigan State Capitol, Lansing, MI 48933 South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 1247121 11.903611111111111
+Louisiana State Capitol, Baton Rouge, LA 70802 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 692703 6.487777777777778
+300 SW 10th Ave, Topeka, KS 66612 Maryland State House, 100 State Cir, Annapolis, MD 21401 1837235 17.09138888888889
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Vermont State House, 115 State Street, Montpelier, VT 05633 321861 3.0213888888888887
+Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 609426 5.7011111111111115
+402 S Monroe St, Tallahassee, FL 32301 New Mexico State Capitol, Santa Fe, NM 87501 2364174 21.85611111111111
+Oregon State Capitol, 900 Court St NE, Salem, OR 97301 500 E Capitol Ave, Pierre, SD 57501 2250703 21.171944444444446
+Rhode Island State House, 82 Smith Street, Providence, RI 02903 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 3108475 28.349722222222223
+New Mexico State Capitol, Santa Fe, NM 87501 North Dakota State Capitol, Bismarck, ND 58501 1744158 16.00611111111111
+L St & 10th St, Sacramento, CA 95814 Wyoming State Capitol, Cheyenne, WY 82001 1754877 15.629722222222222
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 785111 7.170277777777778
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 2249106 20.584722222222222
+Oklahoma State Capitol, Oklahoma City, OK 73105 Virginia State Capitol, Richmond, VA 23219 2078855 18.6975
+Maine State House, Augusta, ME 04330 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 5227420 47.05555555555556
+Rhode Island State House, 82 Smith Street, Providence, RI 02903 West Virginia State Capitol, Charleston, WV 25317 1161764 10.8725
+Nevada State Capitol, Carson City, NV 89701 Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 3639294 32.20916666666667
+L St & 10th St, Sacramento, CA 95814 Rhode Island State House, 82 Smith Street, Providence, RI 02903 4831450 43.455555555555556
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Oklahoma State Capitol, Oklahoma City, OK 73105 547112 4.926666666666667
+Missouri State Capitol, Jefferson City, MO 65101 Wyoming State Capitol, Cheyenne, WY 82001 1282456 11.669444444444444
+Oklahoma State Capitol, Oklahoma City, OK 73105 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 2057100 18.861666666666668
+Maine State House, Augusta, ME 04330 Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 4165392 38.010555555555555
+200 E Colfax Ave, Denver, CO 80203 700 W Jefferson St, Boise, ID 83720 1314104 11.952222222222222
+Minnesota State Capitol, St Paul, MN 55155 500 E Capitol Ave, Pierre, SD 57501 637790 6.874722222222222
+Massachusetts State House, Boston, MA 02108 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 272155 2.6772222222222224
+700 W Jefferson St, Boise, ID 83720 Vermont State House, 115 State Street, Montpelier, VT 05633 4229685 38.653055555555554
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 700 W Jefferson St, Boise, ID 83720 4134203 37.20444444444445
+500 E Capitol Ave, Pierre, SD 57501 Utah State Capitol, Salt Lake City, UT 84103 1321211 12.447777777777778
+700 W Jefferson St, Boise, ID 83720 Minnesota State Capitol, St Paul, MN 55155 2349986 21.033333333333335
+700 W Jefferson St, Boise, ID 83720 Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 2192328 19.348333333333333
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 783537 7.793333333333333
+Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 New Mexico State Capitol, Santa Fe, NM 87501 1234237 12.029722222222222
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 1051234 9.8125
+Illinois State Capitol, Springfield, IL 62756 Massachusetts State House, Boston, MA 02108 1860373 17.20277777777778
+L St & 10th St, Sacramento, CA 95814 North Dakota State Capitol, Bismarck, ND 58501 2405110 21.92111111111111
+Utah State Capitol, Salt Lake City, UT 84103 Virginia State Capitol, Richmond, VA 23219 3454401 31.113333333333333
+North Dakota State Capitol, Bismarck, ND 58501 Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 1913954 17.184166666666666
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 455898 4.509444444444444
+500 E Capitol Ave, Pierre, SD 57501 Wyoming State Capitol, Cheyenne, WY 82001 683179 6.741666666666666
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 1048210 9.455833333333333
+Indiana State Capitol, Indianapolis, IN 46204 Michigan State Capitol, Lansing, MI 48933 410164 3.794722222222222
+Indiana State Capitol, Indianapolis, IN 46204 West Virginia State Capitol, Charleston, WV 25317 505363 4.8975
+New York State Capitol, State St. and Washington Ave, Albany, NY 12224 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 4775247 43.19777777777778
+400-498 N West St, Jackson, MS 39201 Virginia State Capitol, Richmond, VA 23219 1467102 13.298055555555555
+Oregon State Capitol, 900 Court St NE, Salem, OR 97301 West Virginia State Capitol, Charleston, WV 25317 4180675 37.68666666666667
+Illinois State Capitol, Springfield, IL 62756 Minnesota State Capitol, St Paul, MN 55155 832231 7.4875
+Illinois State Capitol, Springfield, IL 62756 Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 541419 4.966944444444445
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Georgia State Capitol, Atlanta, GA 30334 259130 2.347777777777778
+Maine State House, Augusta, ME 04330 Vermont State House, 115 State Street, Montpelier, VT 05633 289475 3.777222222222222
+New York State Capitol, State St. and Washington Ave, Albany, NY 12224 Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 981864 8.9975
+Indiana State Capitol, Indianapolis, IN 46204 New Mexico State Capitol, Santa Fe, NM 87501 2049550 18.461388888888887
+402 S Monroe St, Tallahassee, FL 32301 Michigan State Capitol, Lansing, MI 48933 1653544 15.526666666666667
+Nevada State Capitol, Carson City, NV 89701 South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 4204933 37.5875
+L St & 10th St, Sacramento, CA 95814 2 E Main St, Madison, WI 53703 3236161 28.86722222222222
+North Dakota State Capitol, Bismarck, ND 58501 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 2049899 18.350555555555555
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 987798 8.910277777777777
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+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 New Mexico State Capitol, Santa Fe, NM 87501 2130566 19.22222222222222
+200 E Colfax Ave, Denver, CO 80203 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 2906553 26.10388888888889
+Maryland State House, 100 State Cir, Annapolis, MD 21401 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 4623494 41.908611111111114
+New Mexico State Capitol, Santa Fe, NM 87501 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 1106403 10.647777777777778
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+L St & 10th St, Sacramento, CA 95814 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 3668991 32.74611111111111
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+700 W Jefferson St, Boise, ID 83720 Nevada State Capitol, Carson City, NV 89701 704765 6.965
+Minnesota State Capitol, St Paul, MN 55155 Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 1684917 15.38
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 North Dakota State Capitol, Bismarck, ND 58501 2376757 22.615833333333335
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 Illinois State Capitol, Springfield, IL 62756 1404632 13.471388888888889
+Oklahoma State Capitol, Oklahoma City, OK 73105 Vermont State House, 115 State Street, Montpelier, VT 05633 2658420 24.825555555555557
+200 E Colfax Ave, Denver, CO 80203 New Jersey State House, Trenton, NJ 08608 2810632 25.461666666666666
+North Carolina State Capitol, Raleigh, NC 27601 Wyoming State Capitol, Cheyenne, WY 82001 2749593 25.06388888888889
+Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 Vermont State House, 115 State Street, Montpelier, VT 05633 3178782 29.566111111111113
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 1718292 15.6325
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+North Dakota State Capitol, Bismarck, ND 58501 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 2406031 21.8175
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+Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 Vermont State House, 115 State Street, Montpelier, VT 05633 1803150 17.30361111111111
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Wyoming State Capitol, Cheyenne, WY 82001 2371746 21.289722222222224
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Minnesota State Capitol, St Paul, MN 55155 393856 3.536388888888889
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+Indiana State Capitol, Indianapolis, IN 46204 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 463791 4.415555555555556
+Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 Wyoming State Capitol, Cheyenne, WY 82001 1637527 15.243333333333334
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+402 S Monroe St, Tallahassee, FL 32301 Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 1841801 17.4175
+Illinois State Capitol, Springfield, IL 62756 Maryland State House, 100 State Cir, Annapolis, MD 21401 1302845 12.267222222222221
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 3708217 33.0375
+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 Utah State Capitol, Salt Lake City, UT 84103 2632968 23.90361111111111
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 1025967 9.347222222222221
+400-498 N West St, Jackson, MS 39201 Missouri State Capitol, Jefferson City, MO 65101 874641 8.697777777777778
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+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 300 SW 10th Ave, Topeka, KS 66612 2218253 20.36777777777778
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+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 4078845 37.13194444444444
+Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 Oklahoma State Capitol, Oklahoma City, OK 73105 696496 6.364444444444445
+Massachusetts State House, Boston, MA 02108 Minnesota State Capitol, St Paul, MN 55155 2235235 20.5425
+North Dakota State Capitol, Bismarck, ND 58501 500 E Capitol Ave, Pierre, SD 57501 336454 3.381388888888889
+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 500 E Capitol Ave, Pierre, SD 57501 1127755 10.738055555555556
+Illinois State Capitol, Springfield, IL 62756 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 1482225 14.017222222222221
+402 S Monroe St, Tallahassee, FL 32301 Massachusetts State House, Boston, MA 02108 2109362 19.355833333333333
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+402 S Monroe St, Tallahassee, FL 32301 Nevada State Capitol, Carson City, NV 89701 3969361 36.64666666666667
+L St & 10th St, Sacramento, CA 95814 South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 4312359 38.533055555555556
+200 E Colfax Ave, Denver, CO 80203 Maine State House, Augusta, ME 04330 3418388 30.80361111111111
+Indiana State Capitol, Indianapolis, IN 46204 Oklahoma State Capitol, Oklahoma City, OK 73105 1185153 10.749166666666667
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 New Mexico State Capitol, Santa Fe, NM 87501 3113952 28.691111111111113
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+State House, 107 North Main Street, Concord, NH 03303 Virginia State Capitol, Richmond, VA 23219 981920 9.505555555555556
+402 S Monroe St, Tallahassee, FL 32301 Minnesota State Capitol, St Paul, MN 55155 2196529 20.065
+Minnesota State Capitol, St Paul, MN 55155 West Virginia State Capitol, Charleston, WV 25317 1438029 13.313888888888888
+Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 2748473 24.67
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+Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 Wyoming State Capitol, Cheyenne, WY 82001 1922417 17.38083333333333
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Rhode Island State House, 82 Smith Street, Providence, RI 02903 1945535 18.18722222222222
+New York State Capitol, State St. and Washington Ave, Albany, NY 12224 Virginia State Capitol, Richmond, VA 23219 770231 7.3625
+Missouri State Capitol, Jefferson City, MO 65101 Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 565824 5.4494444444444445
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Indiana State Capitol, Indianapolis, IN 46204 1344056 12.471111111111112
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Oklahoma State Capitol, Oklahoma City, OK 73105 1274629 11.588611111111112
+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 3389980 30.9225
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 700 W Jefferson St, Boise, ID 83720 3551935 31.739166666666666
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+402 S Monroe St, Tallahassee, FL 32301 Rhode Island State House, 82 Smith Street, Providence, RI 02903 2059331 18.823055555555555
+Minnesota State Capitol, St Paul, MN 55155 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 2746945 24.42527777777778
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+Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 1316090 12.180277777777778
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+L St & 10th St, Sacramento, CA 95814 Michigan State Capitol, Lansing, MI 48933 3620857 32.124722222222225
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 700 W Jefferson St, Boise, ID 83720 3963934 35.91777777777778
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+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 L St & 10th St, Sacramento, CA 95814 3878584 34.79833333333333
+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 1138460 10.850833333333334
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 North Carolina State Capitol, Raleigh, NC 27601 598142 5.8502777777777775
+Rhode Island State House, 82 Smith Street, Providence, RI 02903 Vermont State House, 115 State Street, Montpelier, VT 05633 367228 3.4922222222222223
+Maryland State House, 100 State Cir, Annapolis, MD 21401 Minnesota State Capitol, St Paul, MN 55155 1819133 16.771944444444443
+402 S Monroe St, Tallahassee, FL 32301 500 E Capitol Ave, Pierre, SD 57501 2618860 23.874166666666667
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+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 737764 6.850555555555555
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 743518 6.871666666666667
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+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 1013497 9.551666666666666
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+700 W Jefferson St, Boise, ID 83720 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 861910 8.105
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+L St & 10th St, Sacramento, CA 95814 Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 2461787 21.753611111111113
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+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 402 S Monroe St, Tallahassee, FL 32301 1080808 10.475833333333334
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+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 New Mexico State Capitol, Santa Fe, NM 87501 1403049 12.560555555555556
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Maine State House, Augusta, ME 04330 2585218 23.77166666666667
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+State House, 107 North Main Street, Concord, NH 03303 West Virginia State Capitol, Charleston, WV 25317 1303076 12.18138888888889
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+Nevada State Capitol, Carson City, NV 89701 500 E Capitol Ave, Pierre, SD 57501 2192372 19.86277777777778
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+Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 Virginia State Capitol, Richmond, VA 23219 2367739 21.315277777777776
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+500 E Capitol Ave, Pierre, SD 57501 Virginia State Capitol, Richmond, VA 23219 2562885 23.285555555555554
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+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 Nevada State Capitol, Carson City, NV 89701 1439435 12.973055555555556
+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 2912212 26.12638888888889
+Indiana State Capitol, Indianapolis, IN 46204 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 1760623 15.893611111111111
+300 SW 10th Ave, Topeka, KS 66612 Missouri State Capitol, Jefferson City, MO 65101 352120 3.2605555555555554
+Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 Wyoming State Capitol, Cheyenne, WY 82001 2060174 18.44611111111111
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 500 E Capitol Ave, Pierre, SD 57501 809818 7.162222222222222
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 451917 4.066666666666666
+Georgia State Capitol, Atlanta, GA 30334 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 1534179 13.697777777777778
+New York State Capitol, State St. and Washington Ave, Albany, NY 12224 South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 1365529 12.602222222222222
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Rhode Island State House, 82 Smith Street, Providence, RI 02903 140938 1.5366666666666666
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 2 E Main St, Madison, WI 53703 474235 4.506666666666667
+Oklahoma State Capitol, Oklahoma City, OK 73105 Wyoming State Capitol, Cheyenne, WY 82001 1232122 10.689444444444444
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Oklahoma State Capitol, Oklahoma City, OK 73105 2528255 23.06833333333333
+Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 3998563 36.00805555555556
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Wyoming State Capitol, Cheyenne, WY 82001 1702419 14.832777777777778
+Vermont State House, 115 State Street, Montpelier, VT 05633 Virginia State Capitol, Richmond, VA 23219 1050640 10.086944444444445
+400-498 N West St, Jackson, MS 39201 Oklahoma State Capitol, Oklahoma City, OK 73105 980722 8.903333333333334
+Illinois State Capitol, Springfield, IL 62756 Indiana State Capitol, Indianapolis, IN 46204 331633 3.2391666666666667
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 Illinois State Capitol, Springfield, IL 62756 1683332 15.560555555555556
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 1832197 16.561944444444446
+State House, 107 North Main Street, Concord, NH 03303 Utah State Capitol, Salt Lake City, UT 84103 3789540 35.0375
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 1994652 18.935
+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 Wyoming State Capitol, Cheyenne, WY 82001 1925731 17.496388888888887
+200 E Colfax Ave, Denver, CO 80203 Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 2802811 25.78611111111111
+Maine State House, Augusta, ME 04330 Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 2644828 24.156111111111112
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 New Jersey State House, Trenton, NJ 08608 1916564 17.464166666666667
+Minnesota State Capitol, St Paul, MN 55155 Missouri State Capitol, Jefferson City, MO 65101 816553 7.780833333333334
+Oregon State Capitol, 900 Court St NE, Salem, OR 97301 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 257602 2.6061111111111113
+Illinois State Capitol, Springfield, IL 62756 State House, 107 North Main Street, Concord, NH 03303 1826951 17.735277777777778
+Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 North Carolina State Capitol, Raleigh, NC 27601 2033325 18.810833333333335
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Rhode Island State House, 82 Smith Street, Providence, RI 02903 2287910 21.03527777777778
+200 E Colfax Ave, Denver, CO 80203 402 S Monroe St, Tallahassee, FL 32301 2618682 23.936944444444446
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 1791330 16.66583333333333
+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 1282062 12.008611111111112
+New Mexico State Capitol, Santa Fe, NM 87501 Virginia State Capitol, Richmond, VA 23219 2934829 26.29083333333333
+Minnesota State Capitol, St Paul, MN 55155 Vermont State House, 115 State Street, Montpelier, VT 05633 2176956 20.704722222222223
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 West Virginia State Capitol, Charleston, WV 25317 1270293 11.675833333333333
+Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 1202866 11.0975
+Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 2897903 26.289166666666667
+200 E Colfax Ave, Denver, CO 80203 Michigan State Capitol, Lansing, MI 48933 1938989 17.19
+Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 1381842 12.518888888888888
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 Michigan State Capitol, Lansing, MI 48933 858932 7.764444444444444
+New York State Capitol, State St. and Washington Ave, Albany, NY 12224 Rhode Island State House, 82 Smith Street, Providence, RI 02903 261924 2.602222222222222
+Massachusetts State House, Boston, MA 02108 400-498 N West St, Jackson, MS 39201 2281091 20.94638888888889
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 3997144 36.134166666666665
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 3611622 32.29694444444444
+Oklahoma State Capitol, Oklahoma City, OK 73105 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 3237920 28.89388888888889
+Michigan State Capitol, Lansing, MI 48933 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 987696 9.139722222222222
+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 500 E Capitol Ave, Pierre, SD 57501 1811441 16.343611111111112
+State House, 107 North Main Street, Concord, NH 03303 North Dakota State Capitol, Bismarck, ND 58501 2914129 27.160555555555554
+L St & 10th St, Sacramento, CA 95814 Louisiana State Capitol, Baton Rouge, LA 70802 3538731 31.1025
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 402 S Monroe St, Tallahassee, FL 32301 3034934 26.629444444444445
+State House, 107 North Main Street, Concord, NH 03303 Vermont State House, 115 State Street, Montpelier, VT 05633 179010 1.801111111111111
+L St & 10th St, Sacramento, CA 95814 Oklahoma State Capitol, Oklahoma City, OK 73105 2613800 23.4225
+Oregon State Capitol, 900 Court St NE, Salem, OR 97301 Wyoming State Capitol, Cheyenne, WY 82001 1944950 17.691388888888888
+402 S Monroe St, Tallahassee, FL 32301 Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215 1332540 12.580833333333333
+Nevada State Capitol, Carson City, NV 89701 Rhode Island State House, 82 Smith Street, Providence, RI 02903 4660609 41.84861111111111
+Indiana State Capitol, Indianapolis, IN 46204 Utah State Capitol, Salt Lake City, UT 84103 2476810 22.338333333333335
+Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243 Virginia State Capitol, Richmond, VA 23219 988289 9.033333333333333
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 West Virginia State Capitol, Charleston, WV 25317 1052764 9.833333333333334
+Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 2148020 19.5825
+Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 2 E Main St, Madison, WI 53703 1307343 12.206388888888888
+Massachusetts State House, Boston, MA 02108 North Dakota State Capitol, Bismarck, ND 58501 2933033 26.647222222222222
+West Virginia State Capitol, Charleston, WV 25317 Wyoming State Capitol, Cheyenne, WY 82001 2241251 20.226388888888888
+402 S Monroe St, Tallahassee, FL 32301 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 1400273 12.708055555555555
+Michigan State Capitol, Lansing, MI 48933 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 2165494 19.401666666666667
+L St & 10th St, Sacramento, CA 95814 Maine State House, Augusta, ME 04330 5100256 45.73833333333334
+700 W Jefferson St, Boise, ID 83720 South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 3806347 34.12111111111111
+North Carolina State Capitol, Raleigh, NC 27601 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 4687982 42.62555555555556
+Oregon State Capitol, 900 Court St NE, Salem, OR 97301 2 E Main St, Madison, WI 53703 3286034 29.595555555555556
+Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106 State House, 107 North Main Street, Concord, NH 03303 258007 2.5094444444444446
+700 W Jefferson St, Boise, ID 83720 Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509 1892359 16.68
+Illinois State Capitol, Springfield, IL 62756 Oklahoma State Capitol, Oklahoma City, OK 73105 952777 8.616666666666667
+400-498 N West St, Jackson, MS 39201 Oregon State Capitol, 900 Court St NE, Salem, OR 97301 4016847 36.48111111111111
+Louisiana State Capitol, Baton Rouge, LA 70802 Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601 3232764 29.059722222222224
+State House, 107 North Main Street, Concord, NH 03303 Oklahoma State Capitol, Oklahoma City, OK 73105 2691874 25.178333333333335
+New Jersey State House, Trenton, NJ 08608 New York State Capitol, State St. and Washington Ave, Albany, NY 12224 330136 3.2127777777777777
+300 SW 10th Ave, Topeka, KS 66612 Nevada State Capitol, Carson City, NV 89701 2462119 22.726666666666667
+Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 Michigan State Capitol, Lansing, MI 48933 663503 6.138055555555556
+Oregon State Capitol, 900 Court St NE, Salem, OR 97301 Rhode Island State House, 82 Smith Street, Providence, RI 02903 5021523 45.5175
+Maryland State House, 100 State Cir, Annapolis, MD 21401 South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 826173 7.536666666666667
+Virginia State Capitol, Richmond, VA 23219 Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504 4684188 42.19861111111111
+Michigan State Capitol, Lansing, MI 48933 Nevada State Capitol, Carson City, NV 89701 3423796 30.715
+New Mexico State Capitol, Santa Fe, NM 87501 Utah State Capitol, Salt Lake City, UT 84103 1009968 10.115833333333333
+Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130 500 E Capitol Ave, Pierre, SD 57501 2279926 20.22833333333333
+State House, 107 North Main Street, Concord, NH 03303 Texas Capitol, 1100 Congress Avenue, Austin, TX 78701 3249787 29.65861111111111
+Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319 New Mexico State Capitol, Santa Fe, NM 87501 1539423 14.454444444444444
+Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120 Vermont State House, 115 State Street, Montpelier, VT 05633 725358 7.509444444444444
+402 S Monroe St, Tallahassee, FL 32301 Wyoming State Capitol, Cheyenne, WY 82001 2710679 24.935555555555556
+Georgia State Capitol, Atlanta, GA 30334 Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601 659468 6.027222222222222
+Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007 Nevada State Capitol, Carson City, NV 89701 1149381 11.2775
+L St & 10th St, Sacramento, CA 95814 Utah State Capitol, Salt Lake City, UT 84103 1051681 9.350555555555555
+Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901 South Carolina State House, 1100 Gervais Street, Columbia, SC 29201 924813 8.658333333333333
+Illinois State Capitol, Springfield, IL 62756 Rhode Island State House, 82 Smith Street, Providence, RI 02903 1786807 16.83888888888889
\ No newline at end of file
diff --git a/pareto-optimized-road-trip/optimized-state-capitols-trip.ipynb b/pareto-optimized-road-trip/optimized-state-capitols-trip.ipynb
new file mode 100644
index 0000000..7482b40
--- /dev/null
+++ b/pareto-optimized-road-trip/optimized-state-capitols-trip.ipynb
@@ -0,0 +1,714 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Computing optimal road trips on a limited budget\n",
+ "\n",
+ "This notebook provides the methodology and code used in the blog post, [Computing optimal road trips on a limited budget](http://www.randalolson.com/2016/06/05/computing-optimal-road-trips-on-a-limited-budget/).\n",
+ "\n",
+ "### Notebook by [Randal S. Olson](http://www.randalolson.com)\n",
+ "\n",
+ "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely useable and shareable as possible.\n",
+ "\n",
+ "### Required Python libraries\n",
+ "\n",
+ "If you don't have Python on your computer, you can use the [Anaconda Python distribution](http://continuum.io/downloads) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\n",
+ "\n",
+ "This code uses base Python libraries except for the `googlemaps`, `pandas`, `deap`, and `tqdm` packages. You can install these packages using `pip` by typing the following commands into your command line:\n",
+ "\n",
+ "> pip install googlemaps pandas deap tqdm\n",
+ "\n",
+ "### Construct a list of road trip waypoints\n",
+ "\n",
+ "The first step is to decide where you want to stop on your road trip.\n",
+ "\n",
+ "Make sure you look all of the locations up on [Google Maps](http://maps.google.com) first so you have the correct address, city, state, etc. If the text you use to look up the location doesn't work on Google Maps, then it won't work here either.\n",
+ "\n",
+ "Add all of your waypoints to the list below. Make sure they're formatted the same way as in the example below.\n",
+ "\n",
+ "*Technical note: Due to daily usage limitations of the Google Maps API, you can only have a maximum of 70 waypoints. You will have to pay Google for an increased API limit if you want to add more waypoints.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# https://en.wikipedia.org/wiki/List_of_state_capitols_in_the_United_States\n",
+ "\n",
+ "all_waypoints = ['Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130',\n",
+ " #'Alaska State Capitol, Juneau, AK',\n",
+ " 'Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007',\n",
+ " 'Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201',\n",
+ " 'L St & 10th St, Sacramento, CA 95814',\n",
+ " '200 E Colfax Ave, Denver, CO 80203',\n",
+ " 'Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106',\n",
+ " 'Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901',\n",
+ " '402 S Monroe St, Tallahassee, FL 32301',\n",
+ " 'Georgia State Capitol, Atlanta, GA 30334',\n",
+ " #'Hawaii State Capitol, 415 S Beretania St, Honolulu, HI 96813'\n",
+ " '700 W Jefferson St, Boise, ID 83720',\n",
+ " 'Illinois State Capitol, Springfield, IL 62756',\n",
+ " 'Indiana State Capitol, Indianapolis, IN 46204',\n",
+ " 'Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319',\n",
+ " '300 SW 10th Ave, Topeka, KS 66612',\n",
+ " 'Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601',\n",
+ " 'Louisiana State Capitol, Baton Rouge, LA 70802',\n",
+ " 'Maine State House, Augusta, ME 04330',\n",
+ " 'Maryland State House, 100 State Cir, Annapolis, MD 21401',\n",
+ " 'Massachusetts State House, Boston, MA 02108',\n",
+ " 'Michigan State Capitol, Lansing, MI 48933',\n",
+ " 'Minnesota State Capitol, St Paul, MN 55155',\n",
+ " '400-498 N West St, Jackson, MS 39201',\n",
+ " 'Missouri State Capitol, Jefferson City, MO 65101',\n",
+ " 'Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601',\n",
+ " 'Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509',\n",
+ " 'Nevada State Capitol, Carson City, NV 89701',\n",
+ " 'State House, 107 North Main Street, Concord, NH 03303',\n",
+ " 'New Jersey State House, Trenton, NJ 08608',\n",
+ " 'New Mexico State Capitol, Santa Fe, NM 87501',\n",
+ " 'New York State Capitol, State St. and Washington Ave, Albany, NY 12224',\n",
+ " 'North Carolina State Capitol, Raleigh, NC 27601',\n",
+ " 'North Dakota State Capitol, Bismarck, ND 58501',\n",
+ " 'Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215',\n",
+ " 'Oklahoma State Capitol, Oklahoma City, OK 73105',\n",
+ " 'Oregon State Capitol, 900 Court St NE, Salem, OR 97301',\n",
+ " 'Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120',\n",
+ " 'Rhode Island State House, 82 Smith Street, Providence, RI 02903',\n",
+ " 'South Carolina State House, 1100 Gervais Street, Columbia, SC 29201',\n",
+ " '500 E Capitol Ave, Pierre, SD 57501',\n",
+ " 'Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243',\n",
+ " 'Texas Capitol, 1100 Congress Avenue, Austin, TX 78701',\n",
+ " 'Utah State Capitol, Salt Lake City, UT 84103',\n",
+ " 'Vermont State House, 115 State Street, Montpelier, VT 05633',\n",
+ " 'Virginia State Capitol, Richmond, VA 23219',\n",
+ " 'Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504',\n",
+ " 'West Virginia State Capitol, Charleston, WV 25317',\n",
+ " '2 E Main St, Madison, WI 53703',\n",
+ " 'Wyoming State Capitol, Cheyenne, WY 82001']\n",
+ "\n",
+ "len(all_waypoints)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next you'll have to register this script with the Google Maps API so they know who's hitting their servers with hundreds of Google Maps routing requests.\n",
+ "\n",
+ "1) Enable the Google Maps Distance Matrix API on your Google account. Google explains how to do that [here](https://github.com/googlemaps/google-maps-services-python#api-keys).\n",
+ "\n",
+ "2) Copy and paste the API key they had you create into the code below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import googlemaps\n",
+ "\n",
+ "gmaps = googlemaps.Client(key='ENTER YOUR GOOGLE MAPS KEY HERE')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we're going to query the Google Maps API for the shortest route between all of the waypoints.\n",
+ "\n",
+ "This is equivalent to doing Google Maps directions lookups on the Google Maps site, except now we're performing hundreds of lookups automatically using code.\n",
+ "\n",
+ "If you get an error on this part, that most likely means one of the waypoints you entered couldn't be found on Google Maps. Another possible reason for an error here is if it's not possible to drive between the points, e.g., finding the driving directions between Hawaii and Florida will return an error until we invent flying cars.\n",
+ "\n",
+ "### Gather the distance traveled on the shortest route between all waypoints"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from itertools import combinations\n",
+ "\n",
+ "waypoint_distances = {}\n",
+ "waypoint_durations = {}\n",
+ "\n",
+ "for (waypoint1, waypoint2) in combinations(all_waypoints, 2):\n",
+ " try:\n",
+ " route = gmaps.distance_matrix(origins=[waypoint1],\n",
+ " destinations=[waypoint2],\n",
+ " mode='driving', # Change this to 'walking' for walking directions,\n",
+ " # 'bicycling' for biking directions, etc.\n",
+ " language='English',\n",
+ " units='metric')\n",
+ "\n",
+ " # 'distance' is in meters\n",
+ " distance = route['rows'][0]['elements'][0]['distance']['value']\n",
+ "\n",
+ " # 'duration' is in seconds\n",
+ " duration = route['rows'][0]['elements'][0]['duration']['value']\n",
+ "\n",
+ " waypoint_distances[frozenset([waypoint1, waypoint2])] = distance\n",
+ " waypoint_durations[frozenset([waypoint1, waypoint2])] = duration\n",
+ " \n",
+ " except Exception as e:\n",
+ " print('Error with finding the route between {} and {}.'.format(waypoint1, waypoint2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have the routes between all of our waypoints, let's save them to a text file so we don't have to bother Google about them again."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open('my-waypoints-dist-dur.tsv', 'w') as out_file:\n",
+ " out_file.write('\\t'.join(['waypoint1',\n",
+ " 'waypoint2',\n",
+ " 'distance_m',\n",
+ " 'duration_s']))\n",
+ " \n",
+ " for (waypoint1, waypoint2) in waypoint_distances.keys():\n",
+ " out_file.write('\\n' +\n",
+ " '\\t'.join([waypoint1,\n",
+ " waypoint2,\n",
+ " str(waypoint_distances[frozenset([waypoint1, waypoint2])]),\n",
+ " str(waypoint_durations[frozenset([waypoint1, waypoint2])])]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Use a genetic algorithm to optimize the order to visit the waypoints in\n",
+ "\n",
+ "Instead of exhaustively looking at every possible solution, genetic algorithms start with a handful of random solutions and continually tinkers with these solutions — always trying something slightly different from the current solutions and keeping the best ones — until they can’t find a better solution any more.\n",
+ "\n",
+ "Below, all you need to do is make sure that the file name above matches the file name below (both currently `my-waypoints-dist-dur.tsv`) and run the code. The code will read in your route information and use a genetic algorithm to discover an optimized driving route."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "waypoint_distances = {}\n",
+ "waypoint_durations = {}\n",
+ "all_waypoints = set()\n",
+ "\n",
+ "waypoint_data = pd.read_csv('my-waypoints-dist-dur.tsv', sep='\\t')\n",
+ "\n",
+ "for i, row in waypoint_data.iterrows():\n",
+ " # Distance = meters\n",
+ " waypoint_distances[frozenset([row.waypoint1, row.waypoint2])] = row.distance_m\n",
+ " \n",
+ " # Duration = hours\n",
+ " waypoint_durations[frozenset([row.waypoint1, row.waypoint2])] = row.duration_s / (60. * 60.)\n",
+ " all_waypoints.update([row.waypoint1, row.waypoint2])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "import random\n",
+ "import numpy as np\n",
+ "import copy\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "from deap import algorithms\n",
+ "from deap import base\n",
+ "from deap import creator\n",
+ "from deap import tools\n",
+ "\n",
+ "creator.create('FitnessMulti', base.Fitness, weights=(1.0, -1.0))\n",
+ "creator.create('Individual', list, fitness=creator.FitnessMulti)\n",
+ "\n",
+ "toolbox = base.Toolbox()\n",
+ "toolbox.register('waypoints', random.sample, all_waypoints, random.randint(2, 20))\n",
+ "toolbox.register('individual', tools.initIterate, creator.Individual, toolbox.waypoints)\n",
+ "toolbox.register('population', tools.initRepeat, list, toolbox.individual)\n",
+ "\n",
+ "def eval_capitol_trip(individual):\n",
+ " \"\"\"\n",
+ " This function returns the total distance traveled on the current road trip\n",
+ " as well as the number of waypoints visited in the trip.\n",
+ " \n",
+ " The genetic algorithm will favor road trips that have shorter\n",
+ " total distances traveled and more waypoints visited.\n",
+ " \"\"\"\n",
+ " trip_length = 0.\n",
+ " individual = list(individual)\n",
+ " \n",
+ " # Adding the starting point to the end of the trip forces it to be a round-trip\n",
+ " individual += [individual[0]]\n",
+ " \n",
+ " for index in range(1, len(individual)):\n",
+ " waypoint1 = individual[index - 1]\n",
+ " waypoint2 = individual[index]\n",
+ " trip_length += waypoint_distances[frozenset([waypoint1, waypoint2])]\n",
+ " \n",
+ " return len(set(individual)), trip_length\n",
+ "\n",
+ "def pareto_selection_operator(individuals, k):\n",
+ " \"\"\"\n",
+ " This function chooses what road trips get copied into the next generation.\n",
+ " \n",
+ " The genetic algorithm will favor road trips that have shorter\n",
+ " total distances traveled and more waypoints visited.\n",
+ " \"\"\"\n",
+ " return tools.selNSGA2(individuals, int(k / 5.)) * 5\n",
+ "\n",
+ "def mutation_operator(individual):\n",
+ " \"\"\"\n",
+ " This function applies a random change to one road trip:\n",
+ " \n",
+ " - Insert: Adds one new waypoint to the road trip\n",
+ " - Delete: Removes one waypoint from the road trip\n",
+ " - Point: Replaces one waypoint with another different one\n",
+ " - Swap: Swaps the places of two waypoints in the road trip\n",
+ " \"\"\"\n",
+ " possible_mutations = ['swap']\n",
+ " \n",
+ " if len(individual) < len(all_waypoints):\n",
+ " possible_mutations.append('insert')\n",
+ " possible_mutations.append('point')\n",
+ " if len(individual) > 2:\n",
+ " possible_mutations.append('delete')\n",
+ " \n",
+ " mutation_type = random.sample(possible_mutations, 1)[0]\n",
+ " \n",
+ " # Insert mutation\n",
+ " if mutation_type == 'insert':\n",
+ " waypoint_to_add = individual[0]\n",
+ " while waypoint_to_add in individual:\n",
+ " waypoint_to_add = random.sample(all_waypoints, 1)[0]\n",
+ " \n",
+ " index_to_insert = random.randint(0, len(individual) - 1)\n",
+ " individual.insert(index_to_insert, waypoint_to_add)\n",
+ " \n",
+ " # Delete mutation\n",
+ " elif mutation_type == 'delete':\n",
+ " index_to_delete = random.randint(0, len(individual) - 1)\n",
+ " del individual[index_to_delete]\n",
+ " \n",
+ " # Point mutation\n",
+ " elif mutation_type == 'point':\n",
+ " waypoint_to_add = individual[0]\n",
+ " while waypoint_to_add in individual:\n",
+ " waypoint_to_add = random.sample(all_waypoints, 1)[0]\n",
+ " \n",
+ " index_to_replace = random.randint(0, len(individual) - 1)\n",
+ " individual[index_to_replace] = waypoint_to_add\n",
+ " \n",
+ " # Swap mutation\n",
+ " elif mutation_type == 'swap':\n",
+ " index1 = random.randint(0, len(individual) - 1)\n",
+ " index2 = index1\n",
+ " while index2 == index1:\n",
+ " index2 = random.randint(0, len(individual) - 1)\n",
+ " \n",
+ " individual[index1], individual[index2] = individual[index2], individual[index1]\n",
+ " \n",
+ " return individual,\n",
+ "\n",
+ "\n",
+ "toolbox.register('evaluate', eval_capitol_trip)\n",
+ "toolbox.register('mutate', mutation_operator)\n",
+ "toolbox.register('select', pareto_selection_operator)\n",
+ "\n",
+ "def pareto_eq(ind1, ind2):\n",
+ " return np.all(ind1.fitness.values == ind2.fitness.values)\n",
+ "\n",
+ "pop = toolbox.population(n=1000)\n",
+ "hof = tools.ParetoFront(similar=pareto_eq)\n",
+ "stats = tools.Statistics(lambda ind: (int(ind.fitness.values[0]), round(ind.fitness.values[1], 2)))\n",
+ "stats.register('Minimum', np.min, axis=0)\n",
+ "stats.register('Maximum', np.max, axis=0)\n",
+ "# This stores a copy of the Pareto front for every generation of the genetic algorithm\n",
+ "stats.register('ParetoFront', lambda x: copy.deepcopy(hof))\n",
+ "# This is a hack to make the tqdm progress bar work\n",
+ "stats.register('Progress', lambda x: pbar.update())\n",
+ "\n",
+ "# How many iterations of the genetic algorithm to run\n",
+ "# The more iterations you allow it to run, the better the solutions it will find\n",
+ "total_gens = 5000\n",
+ "\n",
+ "pbar = tqdm(total=total_gens)\n",
+ "pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0., mutpb=1.0, ngen=total_gens, \n",
+ " stats=stats, halloffame=hof, verbose=False)\n",
+ "pbar.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Animated road trip map\n",
+ "\n",
+ "Now that we've optimized the road trip, let's visualize it!\n",
+ "\n",
+ "The function below will take the results of the genetic algorithm and generate an animated map showing the Pareto optimized road trips."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_animated_road_trip_map(optimized_routes):\n",
+ " \"\"\"\n",
+ " This function takes a list of optimized road trips and generates\n",
+ " an animated map of them using the Google Maps API.\n",
+ " \"\"\"\n",
+ " \n",
+ " # This line makes the road trips round trips\n",
+ " optimized_routes = [list(route) + [route[0]] for route in optimized_routes]\n",
+ "\n",
+ " Page_1 = \"\"\"\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " An optimized road trip across the U.S. according to machine learning\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \"\"\"\n",
+ "\n",
+ " with open('us-state-capitols-animated-map.html', 'w') as output_file:\n",
+ " output_file.write(Page_1)\n",
+ " for route in optimized_routes:\n",
+ " output_file.write('allRoutes.push({});'.format(str(route)))\n",
+ " output_file.write(Page_2)\n",
+ "\n",
+ "create_animated_road_trip_map(reversed(hof))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!open us-state-capitols-animated-map.html"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Individual road trip maps\n",
+ "\n",
+ "We can also visualize single trips at a time instead of"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_individual_road_trip_maps(optimized_routes):\n",
+ " \"\"\"\n",
+ " This function takes a list of optimized road trips and generates\n",
+ " individual maps of them using the Google Maps API.\n",
+ " \"\"\"\n",
+ " \n",
+ " # This line makes the road trips round trips\n",
+ " optimized_routes = [list(route) + [route[0]] for route in optimized_routes]\n",
+ "\n",
+ " for route_num, route in enumerate(optimized_routes):\n",
+ " Page_1 = \"\"\"\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ " An optimized road trip across the U.S. according to machine learning\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \"\"\"\n",
+ "\n",
+ " with open('optimized-us-capitol-trip-{}-states.html'.format(route_num + 2), 'w') as output_file:\n",
+ " output_file.write(Page_1)\n",
+ " output_file.write('optimized_route = {};'.format(str(route)))\n",
+ " output_file.write(Page_2)\n",
+ "\n",
+ "create_individual_road_trip_maps(reversed(hof))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "!open optimized-us-capitol-trip-48-states.html"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Some technical notes\n",
+ "\n",
+ "As I mentioned in the [original article](http://www.randalolson.com/2015/03/08/computing-the-optimal-road-trip-across-the-u-s/), by the end of 5,000 generations, the genetic algorithm will very likely find a *good* but probably not the *absolute best* solution to the optimal routing problem. It is in the nature of genetic algorithms that we never know if we found the absolute best solution.\n",
+ "\n",
+ "However, there exist some brilliant analytical solutions to the optimal routing problem such as the [Concorde TSP solver](http://en.wikipedia.org/wiki/Concorde_TSP_Solver). If you're interested in learning more about Concorde and how it's possible to find a perfect solution to the routing problem, I advise you check out [Nathan Brixius' article](https://nathanbrixius.wordpress.com/2016/06/16/finding-optimal-state-capitol-tours-on-the-cloud-with-neos/) on the topic.\n",
+ "\n",
+ "### If you have any questions\n",
+ "\n",
+ "Please feel free to:\n",
+ "\n",
+ "* [Email me](http://www.randalolson.com/contact/),\n",
+ "\n",
+ "* [Tweet](https://twitter.com/randal_olson) at me, or\n",
+ "\n",
+ "* comment on the [blog post](http://www.randalolson.com/2016/06/05/computing-optimal-road-trips-on-a-limited-budget/)\n",
+ "\n",
+ "I'm usually pretty good about getting back to people within a day or two."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git "a/pct-bachelors-degrees-conferred-women-usa/Percentage of Bachelor\342\200\231s degrees conferred to women in the US, by major.ipynb" "b/pct-bachelors-degrees-conferred-women-usa/Percentage of Bachelor\342\200\231s degrees conferred to women in the US, by major.ipynb"
new file mode 100644
index 0000000..2c7e6b4
--- /dev/null
+++ "b/pct-bachelors-degrees-conferred-women-usa/Percentage of Bachelor\342\200\231s degrees conferred to women in the US, by major.ipynb"
@@ -0,0 +1,242 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Percentage of Bachelor’s degrees conferred to women in the U.S., by major\n",
+ "\n",
+ "This notebook provides the methodology and code used in the blog post, [Percentage of Bachelor’s degrees conferred to women in the U.S., by major (1970-2014)](http://www.randalolson.com/2014/06/14/percentage-of-bachelors-degrees-conferred-to-women-by-major-1970-2012/).\n",
+ "\n",
+ "### Notebook by [Randal S. Olson](http://www.randalolson.com)\n",
+ "\n",
+ "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely useable and shareable as possible.\n",
+ "\n",
+ "### Required Python libraries\n",
+ "\n",
+ "If you don't have Python on your computer, you can use the [Anaconda Python distribution](http://continuum.io/downloads) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\n",
+ "\n",
+ "This code uses base Python libraries except for the `BeautifulSoup`, `pandas`, and `matplotlib` packages. You can install these packages using `pip` by typing the following command into your command line:\n",
+ "\n",
+ "> pip install beautifulsoup4 pandas matplotlib \n",
+ "\n",
+ "If you're on a Mac, Linux, or Unix machine, you may need to type `sudo` before the command to install the package with administrator privileges.\n",
+ "\n",
+ "### Scraping the NCES database\n",
+ "\n",
+ "To acquire the data used in the blog post, we need to scrape the [NCES database](http://nces.ed.gov/programs/digest/current_tables.asp). The NCES database is available for download as Excel files, but we didn't want to deal with a bunch of Excel files. Instead, let's scrape the NCES database web pages directly using [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/).\n",
+ "\n",
+ "Running the code below will create a file called `gender_degree_data.tsv` that contains all data about the gender breakdowns of various degree majors in the US."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "from bs4 import BeautifulSoup\n",
+ "import requests\n",
+ "\n",
+ "with open('gender_degree_data.tsv', 'w') as out_file:\n",
+ "\n",
+ " out_file.write('\\t'.join(['Year', 'Degree_Major',\n",
+ " 'Total_Bachelors',\n",
+ " 'Percent_change_Bachelors',\n",
+ " 'Male_Bachelors', 'Female_Bachelors', 'Female_percent_Bachelors',\n",
+ " 'Total_Masters', 'Male_Masters', 'Female_Masters',\n",
+ " 'Total_Doctorates', 'Male_Doctorates', 'Female_Doctorates']) + '\\n')\n",
+ "\n",
+ " table_list_response = requests.get('http://nces.ed.gov/programs/digest/current_tables.asp')\n",
+ " table_list_response = BeautifulSoup(table_list_response.text, 'lxml')\n",
+ "\n",
+ " for link in table_list_response.find_all('a', href=True):\n",
+ " # We only want the tables that stratify the data by degree and gender, which are in table group 325\n",
+ " if 'dt15_325' in link['href'] and int(link.text.split('.')[1]) % 5 == 0:\n",
+ " url = 'http://nces.ed.gov/programs/digest/%7B%7D'.format(link['href'])\n",
+ " url_response = requests.get(url)\n",
+ " url_response = BeautifulSoup(url_response.text, 'lxml')\n",
+ " degree_major = url_response.find('title').text.split('Degrees in')[1].split('conferred')[0].strip()\n",
+ "\n",
+ " all_trs = url_response.find_all('tr')\n",
+ " for tr in all_trs:\n",
+ " # We only want to parse entries that correspond to a certain year\n",
+ " year_header = tr.find('th')\n",
+ " if year_header is None:\n",
+ " continue\n",
+ "\n",
+ " # Stop parsing after all of the years are listed\n",
+ " if 'Percent change' in year_header.text:\n",
+ " break\n",
+ "\n",
+ " # Years always have a dash (-) in them\n",
+ " if '-' not in year_header.text:\n",
+ " continue\n",
+ "\n",
+ " year = str(int(year_header.text.split('-')[0]) + 1)\n",
+ " year_vals = [x.text.replace(',', '').replace('†', '0').replace('#', '0') for x in tr.find_all('td')]\n",
+ "\n",
+ " out_text = '\\t'.join([year, degree_major] + year_vals) + '\\n'\n",
+ " out_file.write(out_text)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Visualizing the gender breakdowns for the various degree programs\n",
+ "\n",
+ "Next, let's use [matplotlib](http://matplotlib.org) to visualize the trends in the gender breakdowns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/randal_olson/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
+ " warnings.warn(self.msg_depr % (key, alt_key))\n",
+ "/Users/randal_olson/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
+ " warnings.warn(self.msg_depr % (key, alt_key))\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "''"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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cY8aMEYKT+fPnCw8+6urq8M0332DatGkICAhAYGCg8Pfss88iLS1NaNr36aef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\niWTW6KoWxeY1z8fchIahNcGS84VjUMUlI1KlJkM4olDd4qa5x0cokvq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p7fMTVcDli7Cv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gDLdjXh8QVRCDB7tJVIozxR7C2bazbz4MoHaXBJtru5Ubm8ePaLRxUU3oCX3Q272Vq3la21W9le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BSLvrTpGcCOOSFJbobHLRXNFN1bZ2GvbZ5G8oYOY5Ocy5KA+VenxM9xQEQRDGFpGgnGy8PbDz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\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hF3pID2drxbs31GAsttG95U+oB3sMVF9w6B/CYPfk5HgPQggxIZVKEQwGczMix5NQ7PF4\nKCsrO+5KUeLMKigowOl00tPTw9DQEKqq0tNzYmviT3a2Ip1OT6mmFY1Gj/ozZ7VacTqduaDkXA6E\nrdZq1lz4G7q6vkV7+3+SSPgwGguw25sOBiNZy5Y9Suv+f2XrtlsBKCy8jPnN956SMVRXf4Tx8X10\ndT2MXm+jvv4zeUn0TY1f5O19X2Drtlsxmbw0Nn6OSKQj7xxNjV9kf9u/0//q5ZjNpVx6ySa83su5\nYPljdHb9Fz29j6MoemzWulwSe9bM3iD1eq9gcPAp2ju+QSYTxWQqxlt4GfPqPpn7OWts/CxGo5uu\nrodpSXwJk7GIsvL3THstvd7CqpU/oq396+ze8ynS6THMplIKCi7GaJx50aCqyg+iU0z09D5Ge/t/\nYjB48pprXrD8cdo7vsHb+/6JVGoEk7EIt2cV5eb3zvga5yvpg3IOCAZHeemljdgdfQBk0nexYcNd\nuX+Uyf5xhv5rG6hgrHRQ8ncXkMmkeOsnT5JJJnAUlbDsxr9AURSSiTRbft+KpkFxlZumVZWzeWtC\niFmmaRqxWIzR0VFGR0cZHx8/6v6zlVAsTo9QKERHRweJROKUnne6fA+9Xn/Uni3TURQFu92eC0gc\nDocEwKfQ5H4mQpxNZAblLBcKhXj6f++houJgcJK5LC840TIaoz9vBRXQKRTc3ISiVxjcvYtMMvsf\nTtUFq3L7D/cEmQhJS+fJ7IkQ5yNVVQmFQgSDQUZHR6d9SJzuAXPiIVPMHW63m+XLlx+zK/3hpusl\nkkqlcq9PzhmZKb1ej9Vqzft5s9vt8jMnxHlIApSzWDgc5le/+mcqq7JdTjXNy/orH857p3Ls5QOk\n+rNrc51XVGGqcJBJp+jfna1RbyvwUlhTd/B4DV/3oeR4Z6Ekxwtxvkgmk4yOjhIMBgmFQtMuobFa\nrXg8ntwSIJkVOT/odLoTqrDmdOYnF6dSqVywMjln6Ug/a4dX0zKbzfIzd8bJ91ucnSRAOUuFw2F+\n85u7qarejKKApulYueIhjMZDpelSw1HCz3UDYCi24roqWynDt28vqXg2H6Vq+aHZk5A/IsnxQpwn\nNE0jEonklm5NrrA1QVEUXC5XLig52ys5ibOb0WjE7Xbjdh9awz+R8B6NRslkMrlg5FxJYp/rLr1k\n02wPQYhpSYByFgqHQzzz7B2UV2wBQNPMXHDBtyksPFStQ1O1bNWutAYK2aVdRh1qOk3frmzJQIvL\nTVHdoVKBE6WFJTleiLkpk8kQCoVyMyWTl9xMMBqNuYDE7XbL8hlxWk0k0UvwK4Q4HhKgnGVCoQDP\nP38rRUUtAGiakwvXfB+Xa2nefpE3B0h2ZZuh2S8uxzwv+46Vr3UvyWh2yVfV8lUoB9+lSsbTBAay\n+xdVuDCa5KFEiLkgHo/nApJwODxtHoHNZqOgoICCggLsdrvMngohhDirSYByFgkGB3jppVtwe3oB\nUNViLln7E+z2/Hrc6WCC0DNdAOg9ZtzvmJfdP5PhwI6tAFicLkoa5+eOGeqdnBwvneOFOFdpmsbY\n2FguKInFYlP20el0uN1uPB4PHo/nnC7DKoQQ4vwjAcpZIhDo4PU3bsHu8AOQydSy7vKfYrEU5e2n\naRrBX7ehJbIdWwve04jOnP1r9LW+fWj25IJDsyfZzvHZ3idWpxlnoXR1FuJco6oqIyMj9Pf3TxuU\nmEwmCgoK8Hg8uN1uWeMvhBDinCUBylnA79/FW1v+CoslW44xnVrMVVf9CKNxalWV2I5h4vsCANhW\nlmCZXwhMzJ5kc1bMThfFk2ZPQsMREtHsWvSyWo8s7xDiHKKqKkNDQ/T3908pB+xwOHJBic1mk3/b\nQggh5gQJUGbZwODL7Nr1MYzGOADJ5Fo2XPM/GAymKftmxpMEf9MOgM5hxH1Dfe61of1vk4xkG6xV\nLV+FTncox8R3sHO8TpLjhThnpNNpfD4fg4ODecnuRqOR8vJyiouLpWGdEEKIOUkClFnU2/sU+1r+\nEb0+u1wrHn8H77juoSNW1Qk+3YEaTQPgubEBvT37cKJmMhzYfnD2xOHMyz3JJsdnZ2a8h0UBlQAA\nIABJREFUlS4MkhwvxFktlUoxMDCAz+cjk8nktpvNZioqKiguLpblW0IIIeY0CVBmSXvHY3R2fg2d\nDjRNIRb7S25451eP+OAR2ztCbMcwAJZFXqxLD+WmDO3fR2Ly7MmkAGeoR5LjhTgXJBIJBgYGGBoa\nymtsZ7PZqKiowOv1yhIuIYQQ5wUJUE5SLBbjwIEDGI1GzGYzFosFs9mM2WyediZE0zT2tXyN/v7H\nURTIZAzEon/Nu9/9+SMGJ2o8TfDXbQAoFj0FNzXkHlRU9VDuicnuoKRpQd61JpZ32ZxmnAWSHC/E\n2WSimaLP58Pv9+eVCHY4HFRWVuLxSN6YEEKI84sEKCdI0zR27tzJs88+O21FHciuFZ8IWCwWC1ar\nRqH3N5hMewFIJi3EY3dw442fOuqSjdDvOsmEs8mxnhvq0bsOlQwd3t9CYjy7hOvw2ZPJyfHSOV6I\n2aeqKmNjY3kfk2dLANxuN5WVlTidTvk3K4QQ4rwkAcoJCIVCPP3007S1tR11v1QqRSqVYmxsDLs9\nQFn5S5hM2WAiGnWSTHyMm26644g5J5qmEX6uh8ibgwCYG9zYVpfmXlfVDL2TZk9KmxfmHT94sLSw\nTq9QXO0+sZsVQpywdDrN2NgY4XCYsbExIpHItI0UAQoLC6moqMDhcJzhUQohhBBnFwlQjoOqqmzd\nupU//OEPuXKfdruda6+9FqfTSTweJ5FI5P0Zj8eB13A4fo+iyya4h0LzsFk/wTuv33jU4CT0207G\nX+kDslW7Cm5uzntHdbitlcRYtjt81bKVebMnyXiKwODB5PgKFwajJMcLcbqlUilCoVAuIDnS7CqA\nxWLB6XTicrlwuVzSTFEIIYQ4SAKUGQoEAvzmN7+hq6srt2358uVcd9112Gy2aY/JZBK0tn6Z/oFf\nHNyio6H+76mt/SiKcuQlXZqabcY4MXOid5spun0JhkLLpH1UDmx/CwCTzT5l9mSoJwgH36gtk+R4\nIU4LTdOIxWK5ru5jY2NH3Ndut+cCEofDgck0tZS4EEIIISRAOSZVVXnjjTd44YUXSKezMyAul4t3\nv/vdNDU1HfG4WKyHXbs+ydj4HgCMRi9LljxIYcHao15Py6gEftZKbHu2Ypfea6H49qUYCix5+w23\ntxI/OHtSuWwlOsOhv8pscnwQAJvLjEOS44U4ZVRVJRwO54KSRCIxZR9FUXA6nXkfR5otFUIIIUQ+\nCVCOYmhoiKeeeoq+vr7cttWrV3PNNddgsViOeJzf/wJ79v496XQ2gHC7V7FkyUNYzGVHvZ6WVhn5\n4T7ie0cAMJTaKP7IUvSu/HdaNVWl9+DsidFmo2z+orzXg0OTkuNrJTlezB2appFOp0kmkyQSiSmd\n1WdCURT0ej0GgyHvT71ej06nm/bfSzKZJBgMMjo6SigUmpLYDtklWxNd3Z1Op/QqEUIIIU6QBCjT\nyGQyvPLKK7z00ku5RmmFhYXceOONzJs374jHaVqGjo5v0tX9SG5bdfWHaWz4LDrd0Ts+q8kMI9/f\nS2J/dubDWOmg6MNLcs0YJxvu2E88HAKgamn+7AlM6hwvyfHiFFNVlXQ6TSaTyX1M/vrw1w4PBo72\nuV6vR9M0kslkLgCZCEImfz5dcHCqTIx38vgymQyRSGTafZ1OZy4osVplplIIIYQ4FSRAmSQejzM4\nOMgzzzyDz+cDsg8hF198MevXrz/qmvFk0s/uPZ9hdPQ1APR6OwsX/gelJe885nXVeBr/E3tIdmVn\nXEzzXBT9zWJ0lql/PZNzT4xWG6ULFuePI5FmVJLjxSkwUYFuIuE7Go0esQLVXDExQzOxnPNwBoMB\nj8dDQUEBbrcbg0F+hQohhBCn2nnzv+tE/4FQKJT7CAaDeV8fvpa8uLiYjRs3UlVVddRzB0Nb2L37\nLhKJbFK73d7E0iXfwm6vP+a4MpEU/v/ZTaov2wne3OTB+1eL0JmmDyz8nW3EQtlZlsplK9Af9oDk\n7w0d6hxfK8nxYmY0TSORSOQFJNkKdMfv8BmRiVmVEw1uDAYDZrMZk8mUa4I68bnJZDruJYyqqh5z\n9mfy55DNO/N4PDgcDlkyKYQQQpxmczpAee655+jt7c2V/TzW0hCXa4jaedtRUHB73LjdbnxDr+Ib\nOvp1QqEtaFr2Hdey0o0sWPAV9PrpK3tNlgknGX58F2lfFADLIi/eDy5AMUy/dl1TVXq3HZw9sVgp\nO2z2RNM0fD3Z4MViN+EslCUn54vjffifqD41OSBJpVLT7qsoCna7HYfDgdFoPOLyrImvp3uA1zQt\nL1g50jIxYEogIsnlQgghxPllTgcoAwMDdHd3H/F1i8WC2+3OfTicbaTTvz/46iCh0MyvpShGmpu+\nRGXlB2f0Dmt6NM7wY7vIjGTfpbZdUEzBXzaj6I+cWOvvbCcWyuaXZGdP8vNTxoNxYmPZWaCSGo+8\n0zuHJZPJvG7k0+VInCidTpdXfcrhcJx0kKAoCoqiSGldIYQQQhzTnA5QysvLSSaTeDyevEBk4uPw\nSlyjwT/T0fHGcV/HaPRQW/sx3K7lM9o/NRzF/9huMqFsMGG/sAzPTY0ouiMHFJqm0bv9z9nrWayU\nLVgyZZ+hg7MnACWSHD9nTCy/mpjpOJnlV9MxGAy5/hxOpxO73S7BrRBCCCFmzZwOUK655prj2r/A\ns4ZVK390mkaTlegOM/L9vajj2eU0jssrcb+z7pgPhCOdbcSC2dmTiqUXoDfmz55k0ir+A9kpn4JS\nBybr0auGibOXpmlEo9G8gORYy69OpM+GyWTC6XRisVgkIBFCCCHEWWNOByhnEy2jMfZiD+EXeuBg\nKozrmhqcV9cc8+EwO3uSzT0xWCyUL5w6exIYCJNJZ09cUuM5tYMXZ0QymWRoaAifz3fEgESv1+Nw\nOHKzHQ6HQ/ptCCGEEGJOkQDlDEgH4gR+vI9kT7b8L3oFz7vqcaytmNHxI10dREcDAFQuuQC9ceo6\n/onkeINJT0GZ89QMXJx2mqYRDofx+XyMjo5OSXY3Go25XBCXy4XNZpPZDiGEEELMaRKgnEaaphHd\nPkzw121oiWyFIkOpjcIPLMBUbp/ZOVQ1l3tiMJspW7h0yj7xSJKwP1sJrLjaje4ouSzi7JBOp/H7\n/fh8PmKxWN5rdrudkpISXC6XLL8SQgghxHlHApTTRI2lGf11G7Edw7ltjksqcF8/D+U4mif2bP0T\n0cAIABVLLsAwTRWkvOR4Wd51VotGo/h8PoaHh/PKXiuKgtfrpbS0VHptCCGEEOK8JgHKaZDoDBH4\nSQuZYLZKl85hpOAvmrEuKDyu8wR6ujiwYysAtoJCKpZMrRKmaRpDvdkAxeGxYHdZpuwjZpeqqgQC\nAXw+H2NjY3mvmc1mSktLKS4uxmiUwgZCCCGEEBKgnEJaRiX8fA9jL/bCwVQCy/wCCv6iGb3z+Po/\nxMfCtG5+DgC90ciCq6+f0vcEIDQcIRnLNokskc7xZ5V0Oo3P52NwcHBK0rvH46G0tBSPR/rVCCGE\nEEJMJgHKKZL2xxj5SQup3oPvkBsUPO+sx762/LgfQNVMhn0vPEsmmZ2Babz8Kqzu6ZduTSTH63QK\nRZWuE78BccqkUikGBwcZHBzMdUeHbL+R4uJiSktLp/TgEUIIIYQQWRKgnCRN04huGSL4m3a0ZPZh\n1Fhmo/CWBRhLZ5YIf7jON14m4s/mrpQvXkZRXeO0+6WSGQID2YDIW+HCcBy5LeLUSyQSDAwMMDQ0\nlJdfYrVaKS8vp6ioSEoCCyGEEEIcgwQoh8mEEqR8UdR4GjWeRotnsp/HJn0ez6AdfF2NZ9AOLrEC\ncFxagfsddSjGE3sQHWprYXDfHgCcxaXMW3PJEff1Hwihqdm1ZJIcP3tisRj9/f34/f68MsEOh4OK\nigoKCgpkGZcQQgghxAxJgDJJajiK7/9uhYx27J0Po3MaKfzL+ViaTzwPJDo6QvurmwAwmC3Mv+o6\ndEfpDj5RvctsM+Iqsp3wdcWJiUQi9PX1EQgE8ra73W4qKipwuVwSmAghhBBCHCcJUCaJ7fJPH5wo\noJgN6Cx6dBYDijX7p85iQLHo0bvN2FeXonccXyL8ZOlkkn3PP4uazs7GNF+5AbPjyA0Xx4MxIqE4\nkJ09kQfhM0PTNMbGxujr6yMUCuW9VlBQQGVlJQ6HY5ZGJ4QQQghx7pMAZZJER/aBU++1UPRXi1As\n2aBEMelRTmPzQ03TaH91E7FQdkakesUaCqpqjnrMUM+hh+OSalnedSak02na2toIBoN524uKiqio\nqMBmk1ksIYQQQoiTJQHKQVpaJdEVBsDS6MFYdmIJ7idi8O3d+Dv2A+CpqKb6gtVH3V/NqPgPZB+S\nPcV2zDbpn3G6jY+P09raSjKZBLKNFUtKSigvL5eKXEIIIYQQp5AEKAcle8KQzlZeMjecuRmJsWEf\nnX96BQCTzU7zlRtQjlHpKTA4RjqVHWtJrcyenE6apjE0NERXV1cuAb6wsJB58+ZhMp34kj4hhBBC\nCDE9CVAOircfWjJlrnefkWum4nFann8WTVVRFB3zr7oOo9V6zOMmkuMNRh2FZUfOUxEnJ5PJ0NnZ\nid/vz22rra2lrKxMcn6EEEIIIU4TCVAOSrQffOgvtZ1UsvtMaZrG/s1/JBEZB2DehZfgKi0/5nGJ\naIrgUASAoio3Or301TgdYrEY+/fvJxqNAmA0GmlqasLlkmaYQgghhBCnkwQogJrMkDzYAd5yhpZ3\nHdixhdEDPQB45zVQvnjZjI4b6j2UoC29T06PQCBAe3t7rgu8y+WisbFRlnQJIYQQQpwBEqAAye5w\nrrywueH0L+8K9vfSs/VNACwuN42XXzWjJUOapuWWd9ndFhyeYy8HEzOnaRo9PT0MDAzktlVUVFBd\nXS1LuoQQQgghzpDjWh/08ssvs3HjRqqqqtDpdDz55JNT9vnyl79MZWUlNpuN9evXs3fv3rzX77nn\nHrxeL7W1tfzwhz/Me+3pp59m3bp1J3AbJycxkX+igLnu9AYo0eAorS/+ETQNnd7AgquvxzDDd+bD\n/iiJaAqQ2ZNTLZlMsnfv3lxwotfraW5upqamRoITIYQQQogz6LgClPHxcZYuXcpDDz00bc+H+++/\nn29+85s8/PDDvPXWW5SUlLBhwwYikWzOxNNPP82Pf/xjnnvuOe6//35uv/32XBfu8fFx7rnnHr7z\nne+cgts6PomO7KyEsdyO7jSV7NVUlQM7t7Lj1z8hFY8B0HDpFdgLvTM+x8TsiaJTKKqSXIhTJRwO\ns2vXLsbGssv8bDYbS5cupbCwcJZHJoQQQghx/jmuAOX666/nK1/5Cu9973unfVf5wQcf5Atf+AI3\n3XQTixYt4nvf+x5jY2O5mZJ9+/Zx5ZVXsmLFCj7wgQ/gcrno7OwE4Itf/CK33XYb8+fPPwW3NXNq\nIk3yQPbB9HSVF44ERtj59C/o/vPrqAfzGqqWr6KkacGMz5FOZRjpz/ZpKSx3YjTJ6ryTpWkafX19\n7N27l1QqOzNVXFzMkiVLpLeJEEIIIcQsOWVPuZ2dnQwODrJhw4bcNovFwrp163jttde44447WL58\nOd/5zncIBoO0t7cTj8dpbGzkjTfeYNOmTWzduvVUDWfGEp1hyLYUOeUBiprJcGDHFg7s2IKmZi9i\ncXtoumw9rrKK4zqXvy+MqmbzZEpleddJ0TSNQCBAb28v8XgcyDZerKuro6SkZJZHJ4QQQghxfjtl\nAcrg4CCKolBaWpq3vbS0lP7+fgCuvfZabr31VtasWYPNZuPJJ5/Ebrdz55138u1vf5vHH3+cBx98\nELvdzkMPPcTatWtP1fCOaGJ5Fzowzzt1y6bGhn20vfwC0dHsEjYUhcqlK6hesQa94fi/7UPdowCY\nrAbcxWeuy/1cEwqF6OnpyS07BDCbzTQ3N2O3y/dVCCGEEGK2nfF1Qvfeey/33ntv7uuvfvWrXHrp\npbhcLu677z527tzJjh07eN/73kdnZyeGE3iYPx4TCfKmSic6y8lfK5NO07P1T/Tv3gEHO4/bCr00\nXX4VjqITe3c+Eo4zHsy+019S7ZGk7RMwPj5Ob28vodChhpwGg4HKykpKS0vR6aSfjBBCCCHE2eCU\nPf2XlZWhaRo+n4+qqqrcdp/PR1lZ2bTHtLa28t3vfpdt27bxxBNPcMUVV+QS6xOJBC0tLSxevDjv\nmJGRkbzO3hOKiorweqcmnB9t/wKri1R/tlHiRHnhkzn/2JCP7q1/Ijk+htthx+NyUX3BaiqXrUSn\n15/w+be/9jZDfdkHa0dNhljL6And7/m4fywWo7e3N1eMIRQKEQ6HKS4upri4mHA4TDgcPmvHL/vL\n/rK/7C/7n9v7n+ncWiHmAkXTDr7Nf5ycTicPP/wwt912W25bRUUFd911F5///OcBiMfjlJaW8sAD\nD3D77bdPOcf69eu5++672bhxIw8++CCbN2/ml7/8JZqmUVhYyOb/v707j4uq3P8A/mGGVTYFREAR\nUBFRGGZAFMUNdzMjt2xzuy797Kbeutp2K7Vudbs3S1sszVIz85q3m+ZSlua+C6LmigsgCCj7DsPM\n9/cHcS4joKggCJ/36+WrmTPPec5zzvMMzfecZ9m9GxpNzRYwvBuFp9ORvrpsGmSXKQGw9m1xV/mU\nlpQg/thBpJz9Xdlm19IVvr37o1mLms/SVRWD3oCo7RdRWmKAg0szBIR731N+TUVJSQkSExNx/fp1\nZVt5F8TWrVvDwqJuZmsjIiIiontzR09Q8vPzcfHiRYgIjEYjEhIScOLECTg5OcHT0xN/+ctf8O67\n78LPzw++vr74+9//Dnt7ezzxxBOV8lq+fDmcnJwQGRkJAOjVqxfmzZuH/fv3IyYmBpaWlnV+16H4\n0h/jT9RmsPS6s/EnIoKCzHTkpFxD0snjKM4vexKjUpujbUh3eHTRwKwWug1dPpmC0pKymb9aed1d\nANWUlJaW4tq1a0hJSYHxj4kJgLI7Xm3atOHsXEREREQN3B09Qdm9ezciIiIqjYGYOHEivvrqKwDA\nm2++iaVLlyIzMxPdu3fHp59+is6dO5ukv379OsLCwnDgwAGT7l/vvfceFi5cCAcHB3z22WcmM4LV\nhdRFUdCnFMDS2wGu/xd0y7RGgwF5adeRk5KMnNRryElNgaGk2CSNg3trdOjVDzYOtTPL1vWELFw8\nXjbBgGNLW3TuwUUDqyMiSElJQWJiIgx/TOUMAM2bN4enpycHwBMRERE9IO66i9eDzpBXguS/HwYA\n2A9oC8dBXiafl5aUIPd68h8BSTLybqQqa5jczMLGBp66bnDr1KXWAojCvGKc2HUZRoPAwkqNoH7t\nYVkLg/gbo+zsbMTFxaGwsFDZZmdnh7Zt28LBgQtaEhERET1Imuwv3uLL/5vNyaqdI0oK8pGTmqw8\nIcnPSFdm4bqZlb0DHFq5l/1z84CNY+3OrGU0GHHhWBKMhrLj+wa3ZnBSheLiYiQkJCA9PV3ZZm1t\njbZt26JFixZ82kRERET0AGqSv3pFBPlnywZPi0pw+thmFOVlV5u+mZMzHFp5/BGQuMPK1q5Oyxd/\n5jrys8umFfbo4IzmrnV7vAeN0WhEcnIykpKSlHEmKpUKrVu3hru7O6cMJiIiInqANYkARYxG5GeU\nDWjPSS17QtLyrCcsYI0iqzyT4MRMpYJdy1b/e0LSyg3mVvdvYHVGci6SL5dNiWvX3Bpt/bmyeUVZ\nWVmIi4tTVoAHACcnJ3h5ecHKyqoeS0ZEREREtaFRByhJp44jKykRudeTYdDrle3qUnNY6MuCjhK7\nIjRv0/aPYMQD9i1doarjxSGrU1yoVwbFq81V6Ni1DVQqdlMCyqasjo+PR2ZmprLNxsYG3t7ecHR0\nrMeSEREREVFtatQBSnr8FeSmJptss7BpBif530KS7Uf1h7VP7cy6dS9EBLFRSSjVlw3Eb691h7Wt\nZT2Xqv4ZjUYkJSXh2rVrKJ/PQa1Wo02bNlwBnoiIiKgRatQBiqObO/SFBXBw81AGtFvbOyDz+1gU\nIBVmlipYeTaMWZ4Sz6chJ70AAODq1RwurflUIDMzE3FxcSgu/t90zi4uLmjbti0sLRm8ERERETVG\njTpAaRvcHV5de1TaXj6Dl6W3I8zM6/8OfHZaPq6evwEAsLGzhE+A2232aPxSUlIQFxenvG/WrBl8\nfHxgb29ff4UiIiIiojrXqAOUqlZyL80sgiGjbIC1dfv6f0qhLylFbFQSAEClMkPH0DZQN4CgqT5d\nu3YNCQkJAMq6c3l6eqJVq1acNpiIiIioCWjUAUpVii9VXP+kfseeiAguHr+GkqJSAIB3QCvYOty/\nGcMaGhFBYmIikpLKAjZzc3N06tQJdnacZpmIiIioqWh6AcrlLACAmZUaFh71+8M35UomMlPyAABO\n7vZo5d2iXstTn0QE8fHxSElJAQBYWFjA398fzZo1q+eSEREREdH91KQCFBFB8aWyAMWqnSPM1PXX\nZSgvqxBxp1MBAJY2Fuig9WiyXZhEBFeuXMH162WLZ1paWqJz586wtm66T5OIiIiImqomFaAY0otg\nyC4BUL/duwylRlw4lgQxCmAGdOzaGuaW6norT30yGo24dOkS0tPTAQDW1tbw9/fnootERERETVST\nClCK/ujeBQBW9ThA/vLJZBTllwVKbTu1hINT0+zGZDQaERsbqyy+2KxZM3Tq1IlTCBMRERE1YU0q\nQCkfIK9qZg4LN9t6KUNqfCZuXC0rh6OLLVr7utRLOeqbwWDA+fPnkZOTAwCwtbWFv78/zM2bVJMk\nIiIiops0mV+DJuNPfBxhprr/4z0yU3Nx6UTZyvbmlmr4hjTNcSelpaU4d+4c8vLKJgiwt7eHn58f\ngxMiIiIiajoBSun1Ahjz9AAAqw73f/xJXmYhzh9NBKRsvRP/7p6wtLa47+Wob3q9HmfPnkVBQQEA\nwNHRER07doRa3TTH4BARERGRqSYToJSvHg+UzeB1PxXll+Ds4QQYDQIA8O3aGvZNcNxJSUkJzp49\ni8LCQgCAk5MTOnToAFUVC2oSERERUdPUdAKUP7p3qewsYO56/4IDfXEpzhxMgL7YAADw0bjB2d3h\nvh2/oSguLsaZM2dQXFwMAHBxcUH79u2bZBc3IiIiIqpekwhQxCjKExSr9s3v249iQ6kRZw9fVWbs\nau3rDHcfp/ty7IZERBAbG6sEJ61atYK3tzeDEyIiIiKqpEn0rdGn5MNYUArg/nXvEqPgQlQi8jLL\nujO1bOOItv6u9+XYDU1iYqIyIN7V1ZXBCRERERFVq0kEKCbjT9rX/QB5EcHlUynITCn7Ue7Y0hbt\ndU1zxq7s7GwkJSUBKFvnhMEJEREREd1K0whQ/hh/ona0hLmzdZ0fL+lCGlLj/lh80MEKfqFtoKqH\naY3rm16vx8WLFwEAKpUKvr6+HBBPRERERLfU6H8tilFQfOWP8Sft6n78yfWELCScuwEAsLSxQOew\ntjC3aHpT6IoILl26BL2+bGpnb29v2NjY1HOpiIiIiKiha/QBiv5aHqSobAatuu7elXU9D5dirgEA\n1BYqdA5rC0ubprfWCQAkJycjK6vsyZWzszNatmxZzyUiIiIiogdBow9Qyrt3AYBV+7obIJ+XVYhz\nRxIhApj9sRBjMwerOjteQ5aXl4erV68CAKysrODj48NxJ0RERERUI40+QCm6VNa9S+1kDfMWdTP+\npKigBGcPXYXRYAQA+Ia0hoOzbZ0cq6ErLS1FbGwsRARmZmbw9fWFuXmTmM2aiIiIiGpBow5QxGBE\nSVz5+JO6eXqiLynF2YMJ0BeXTWPsE9AKLh5NbyFGoGzcyZUrV5T1Tjw9PWFnZ1fPpSIiIiKiB0mj\nvrVdkpgHKSl7qmFdB+NPivJLcP5oIgrzyhZi9OjgDPf2zrV+nAfFjRs3kJ6eDgBwdHSEu7t7PZeI\niIiIiB40jTpAqcvxJxkpuYiNToJBXxYAubR2gFfnprkQIwAUFhYiLi4OAGBhYYEOHTpw3AkRERER\n3bEmEaCYt7SBupYGrIsIEs7eQFJsmrLNvZ0TvLq0arI/yI1GI2JjY2E0lgVrHTp0gIVF05y9jIiI\niIjuTaMOUCxa2aI0q7jWxp+UFJci9lgSstPyAQAqtQoddB5wad00x5yUi4+PR0FBAQDAw8MDjo51\nN1saERERETVuZiIi9V2IuialRpiZ39t8ADkZBbhwNBElRWWD4W3srdAptA1s7JvmVMLlMjIycOHC\nBQCAnZ0dOnfuzNXiiYiIiOiuNeonKOXuJTgREaRczkDc6VSUh3IubRzRPsgd6nsMeh50xcXFuHTp\nEgBArVajQ4cODE6IiIiI6J40iQDlbhn0BlyMSUb6tRwAgJkZ4BPohlbeLZrseJNyIoKLFy/CYDAA\nANq1awdr67pZZ4aIiIiImg4GKNUoyCnG+aNXlSmELW3M4de1DeydmtVzyeqfiCA+Ph65ubkAAFdX\nVzg7N93plYmIiIio9jBAqcKNxGxcirkGo6GsT1fzlrbwDWkNCytertLSUly8eBFZWWUzpNnY2MDb\n27t+C0VEREREjQZ/cVdgNArifk9BypVMZVsbPxd4+rVs8l26ACA/Px8XLlxQVoq3srJCx44dOe6E\niIiIiGoNA5QKKgYn5hZq+Ia0RotWdvVcqobh+vXruHLlCsonfWvRogXat28Pc3M2ISIiIiKqPfx1\nWUEbXxekX8uBpY0F/ELbwLqZZX0Xqd4ZjUZcuXIFN27cULa1bdsW7u7ufKpERERERLWuSayDcicK\ncophbWsBlZrdloqKinDhwgVlEUYLCwv4+vrCwaFpL0xJRERERHWHAQpVKSMjA5cuXVKmEba3t4ev\nry8sLflUiYiIiIjqDrt4kQkRwdWrV3Ht2jVlm7u7Ozw9PTkYnoiIiIjqHAMUUpSUlODixYvIySlb\nmFKtVqN9+/ZwcnKq55IRERERUVPBAIUAADk5OYiNjYVerwcANGvWDL6+vrCxsalv4L9pAAAgAElE\nQVTnkhERERFRU8IApYkrLS3F1atXkZqaqmxzcXGBj48P1Gp1PZaMiIiIiJoiBihNlIjgxo0bSEhI\nQGlpKQDAzMwM3t7ecHV15RTCRERERFQvGKA0QXl5ebhy5Qry8/OVbQ4ODvD29kazZs3qsWRERERE\n1NQxQGlC9Ho9EhISTBZdtLS0hJeXF5ycnPjUhIiIiIjqHQOUJkBEkJqaiqtXryrrmpiZmcHd3R2t\nW7fmWBMiIiIiajAYoDRyOTk5iIuLU1aDB4DmzZvDy8uLM3QRERERUYPDAKWRKikpQUJCAtLS0pRt\nVlZW8Pb2RvPmzdmdi4iIiIgaJAYojYiIIC8vD2lpabhx4waMRiMAQKVSwcPDAx4eHlwNnoiIiIga\nNAYojUBhYSHS0tKQlpaG4uJik8+cnJzg5eUFKyureiodEREREVHNMUB5QJWUlCA9PR1paWkm0wWX\nc3R0hLu7O5o3b14PpSMiIiIiujsMUB4gpaWlyMzMRFpaGrKzsyt9bmtrCxcXFzg7O8PS0rIeSkhE\nREREdG8YoDRwJSUlyM3NRUZGBjIyMiAiJp9bWVnBxcUFLi4unJWLiIiIiB54DFAaEBFBUVERcnNz\nkZOTg9zc3EpjSgDA3Nwczs7OcHFxgZ2dHWfkIiIiIqJGgwFKPRIR5OfnIzc3V/mn1+urTKtSqdCi\nRQu4uLjA0dGRs3ERERERUaPEAOU+MBqNKCkpQXFxMUpKSlBUVIS8vDzk5uYqUwHfTK1Ww87ODg4O\nDrC3t4ednR2DEiIiIiJq9Big3CMRgcFgUIKP4uJi5V/5++qeilRkYWEBe3t7JSBp1qwZu24RERER\nUZPDAOU2RAQlJSXVBh8lJSUwGAx3nK+1tTXs7e2VoMTKyooBCRERERE1eU0qQCl/2lFaWgqDwaD8\nq/i+tLQUer3eJBC5U2ZmZrC0tISVlZXy34qvLS0toVar6+AMiYiIiIgebI06QLl8+TJyc3OVwKO6\n8R53Sq1WVwo6Kr62sLDg0xAiIiIiortQJ6OulyxZgnbt2sHGxgZdu3bFvn37lM/ef/99tGrVCm5u\nbvjggw9M9jt+/Dj8/f2rnFr3bhQXF6OwsBAlJSU1Dk7Kn37Y29vDxcUFHh4e8PHxgZ+fHzQaDUJD\nQxEaGgqNRgM/Pz/4+PjAw8MDzs7OsLe3h6WlJYMTIiIiIqK7ZCY3r/x3j9atW4fx48fj888/R3h4\nOD799FOsWLECZ8+eRWZmJsLCwrB161YYjUYMHz4cR48eRZcuXWA0GtG9e3f885//RERERK2UJSkp\nCXl5eTA3N4darYZarVZeV9xW8T1nyiIiIiIiqj+1HqCEhYVBq9Xi888/V7Z17NgRY8eOhVarxYcf\nfogDBw4oaefOnYvRo0dj4cKFOH36NL766qvaLA4RERERET1AavVxgV6vR1RUFAYNGmSyffDgwThw\n4AACAwNx4cIFJCYmIj4+HrGxsQgMDMSVK1ewZMkSLFy4sDaLQ0RERERED5haDVDS0tJgMBjQqlUr\nk+2tWrVCSkoKOnXqhLfffhsDBw7E0KFD8Y9//AMdO3bEs88+i7fffht79uxBUFAQNBoNNm7cWJtF\nIyIiIiKiB8B9n8XrmWeewTPPPKO8X7NmDczMzDBgwAB07NgRhw8fRmlpKcLDwxEbGwsXF5f7XUQi\nIiIiIqontRqguLi4QK1WIzU11WR7amoq3NzcKqVPT0/H66+/jl27duHQoUPo2LEjOnbsCADw9fXF\n4cOHMXz48Er7pKWlVXlsZ2fnKo/B9EzP9EzP9EzP9ExfH+n9/PwqbSOiW7svg+T9/PwwduxY/P3v\nfzdJO2nSJOh0OsyePRsbN27EggULEB0dDQDQarV488038cgjj9Rm8YiIiIiIqAGr9S5eL7zwAiZM\nmIDQ0FCEh4fjs88+Q3Jyskm3LgDYvn07zp49ixUrVgAAQkNDcf78eWzatAlGoxEXLlxAt27dart4\nRERERETUgNV6gPLYY48hIyMDb7/9NpKTkxEQEICffvoJnp6eSpqioiLMnDkT69atUxY19PDwwOef\nf44ZM2YAAJYtW1ZltzAiIiIiImq8ar2LFxERERER0d3isulERERERNRgMEAhIiIiIqIGgwEKERER\nERE1GAxQiIiIiIiowWCAQkREREREDQYDFCIiIiIiajAYoBARERERUYPR4AOUvXv3IjIyEm3atIFK\npcLXX39t8vn169cxadIktG7dGra2tnjooYdw8eJF5fP4+HioVCqo1WqoVCqTfwsXLlTSZWVlYfz4\n8WjevDmaN2+OCRMmIDs7+76dJ5W51/oGgNTUVIwfPx7u7u6wtbWFVqvFt99+a5KG9d0w1EZ9X758\nGaNGjYKrqyscHR3x+OOP4/r16yZpWN8Nw7vvvotu3brB0dERrq6ueOSRR3D69OlK6ebPn4/WrVuj\nWbNmiIiIwJkzZ0w+LykpwcyZM9GyZUvY2dkhMjISSUlJJmlY5/Wvtur7iy++QP/+/dGiRQuoVCok\nJCRUyoP1TdS4NPgAJS8vD4GBgfjoo4/QrFmzSp9HRkbi0qVL+PHHHxETE4O2bdti4MCBKCwsBAC0\nbdsWKSkpSE5ORkpKClJSUrBkyRKoVCqMGTNGyeeJJ55ATEwMfvnlF2zbtg3R0dGYMGHCfTtPKnOv\n9Q0A48ePx/nz57Fp0yacPn0aEyZMwPjx47Fv3z4lDeu7YbjX+i4oKMDgwYMBALt27cKBAwdQXFyM\nESNGmOTD+m4Y9uzZg+eeew4HDx7Ezp07YW5ujoEDByIrK0tJ89577+HDDz/Ep59+imPHjsHV1RWD\nBg1Cfn6+kmb27Nn44YcfsG7dOuzbtw85OTl4+OGHUXHdYdZ5/aut+i4oKMCQIUOwYMECmJmZVXks\n1jdRIyMPEDs7O1m1apXy/sKFC2JmZianTp1SthmNRnF1dZUvv/yy2nwGDhwoQ4YMUd6fPXtWzMzM\n5ODBg8q2ffv2iZmZmVy4cKGWz4Jq6m7r287OTlauXGmSl5eXlyxcuFBERM6cOcP6boDupr63bdsm\narVasrOzlTTZ2dmiUqlkx44dIsL6bsjy8vJErVbL5s2blW3u7u7y7rvvKu8LCwvF3t5eli1bJiJl\n9WtpaSlr165V0ly9elVUKpX88ssvIsI6b6jupr4rOnbsmKhUKomPjzfZzv+HEzU+Df4Jyq0UFxfD\nzMwMVlZWyrby9xXvlld0+fJl/Pbbb3jmmWeUbQcPHoS9vT3CwsKUbeHh4bC1tcWBAwfq7gTojtS0\nvnv37o3vvvsOGRkZEBFs3LgRaWlpGDRoEADg0KFDrO8HQE3qu6SkpFIaKysrqFQqJQ3ru+HKycmB\n0WhEixYtAABXrlxBSkqK8l0FAGtra/Tp00epq2PHjqG0tNQkTZs2beDv76+kYZ03THdT3zXB/4cT\nNT4PdIDSqVMneHp64tVXX0VmZiZKSkrw3nvvITExEcnJyVXus3z5cqUvbLmUlBS0bNmyUlpXV1ek\npKTUWfnpztS0vtetWwcAcHFxgZWVFcaPH4+1a9ciMDAQAOv7QVGT+g4LC4OdnR3mzJmDgoIC5Ofn\nY86cOTAajUoa1nfDNXv2bAQHB6NHjx4AyurKzMwMrVq1MknXqlUrpa5SU1OhVqvh7OxcbRrWecN0\nN/VdE6xvosbngQ5QzM3N8cMPP+DSpUtwdnaGnZ0ddu/ejYceeggqVeVTMxgMWLlyJSZNmgS1Wl0P\nJaZ7UdP6/tvf/ob09HT89ttviIqKwty5czF+/HicOnWqHktPd6om9e3i4oL169fj559/hr29PVq0\naIGcnBzodLoq/wZQw/HCCy/gwIED+P7776sdV0CNB+ubiO6EeX0X4F7pdDpER0cjNzcXJSUlcHZ2\nRlhYGEJDQyul/fHHH5GamoopU6aYbHdzc8ONGzcqpb9+/Trc3NzqrOx0525X35cvX8Ynn3yCkydP\nIiAgAAAQGBiIPXv24OOPP8ayZctY3w+Qmny/Bw4ciNjYWGRkZMDc3BwODg5wd3dHu3btAPD73RA9\n//zz+O6777Br1y54eXkp293c3CAiSE1NRZs2bZTtqampSl25ubnBYDAgPT3d5ClKamoq+vTpo6Rh\nnTcc91LfNcH6Jmp8Gs0tRnt7ezg7OyM2NhbHjh3Do48+WinN8uXL0bdvX3To0MFke48ePZCXl4dD\nhw4p2w4cOICCggL07NmzzstOd666+i4oKICZmVmlu+dqtRpGoxEA6/tBVJPvt5OTExwcHPDbb7/h\nxo0bSjdO1nfDMnv2bKxbtw47d+6Er6+vyWc+Pj5wc3PDr7/+qmwrKirC3r17ER4eDgAICQmBubm5\nSZrExEScPXtWScM6bzjutb5rgvVN1AjV8yD928rLy5OYmBg5fvy4NGvWTN566y2JiYmRhIQEERFZ\nv3697Ny5Uy5fviwbNmwQb29vGTt2bKV84uPjRa1Wm8z8UtGwYcNEo9HIwYMH5cCBAxIYGCiRkZF1\nem5U2b3Wt16vF19fX+nbt68cOXJELl26JO+//76o1WrZsmWLko713TDUxvd7xYoVcvDgQbl06ZKs\nXr1anJ2dZe7cuSZpWN8Nw7PPPisODg6yc+dOSUlJUf7l5eUpad577z1p3ry5/Pe//5VTp07JuHHj\npHXr1iZpZsyYIZ6enrJ9+3aJjo6WiIgICQ4OFqPRqKRhnde/2qrvlJQUiYmJkTVr1oiZmZls3bpV\nYmJiJCMjQ0nD+iZqXBp8gLJr1y4xMzMTlUpl8m/y5MkiIvLRRx+Jp6enWFlZibe3t8ybN0/0en2l\nfObNmycuLi5SXFxc5XGysrJk/Pjx4ujoKI6OjjJhwgSTqUvp/qiN+r548aKMGTNG3NzcxM7OTrRa\nraxevdokDeu7YaiN+n755ZfFzc1NrKysxM/PTxYtWlTpOKzvhqGqulapVLJgwQKTdAsWLBAPDw+x\nsbGRfv36yenTp00+LykpkVmzZomLi4vY2tpKZGSkJCYmmqRhnde/2qrv+fPnV5lXxWnJWd9EjYuZ\nSIWVrYiIiIiIiOpRoxmDQkREREREDz4GKERERERE1GAwQCEiIiIiogaDAQoRERERETUYDFCIiIiI\niKjBYIBCREREREQNBgMUIiIiIiJqMBigEBERERFRg8EAhYiIiIiIGgwGKERERERE1GAwQCEiIiIi\nogaDAQoRERERETUYDFCIiIiIiKjBYIBCREREREQNBgMUIiIiIiJqMBigEBERERFRg8EAhYiIiIiI\nGgwGKERERERE1GAwQCEiIiIiogaDAQoRERERETUYDFCIiIiIiKjBYIBCREREREQNRq0HKGq1GsHB\nwQgICIBOp8MHH3wAEbnlPvHx8Vi7dm1tF4UqiI+PR2BgYJ0fZ926dXj33Xfr/Dj3Q0pKCoYMGVJp\n++TJk/Hf//4XADBt2jScO3eu1o5Z3Xeha9eu0Ov1tXacu3XixAn89NNPt0yze/duHDx48LZ5LViw\nAB988EGl7ferrd6tAQMG4OrVq9DpdAgODoa7uzvatGmjvC8tLa1yP4PBgBYtWlT52fjx4/Hjjz/e\nU7kOHToErVYLnU4HnU6n5JeXl4fhw4fD398fgYGBeO2115R9vvzyS7i6uiI4OBjBwcFYtWpVtfkv\nWLDA5P3AgQORm5t7T2UmIiKqSq0HKLa2toiOjsbvv/+OX3/9FT/99FOl/7Hd7MqVK/j2229ruyh3\nzGAw1HcR6pSZmVmdH+Onn37C0KFDa5S2Nq53XdbZzz//fNtz+eKLL9CpU6daO2ZV34W4uDi0adMG\nFhYW95T3zTcKjEbjHecRExODrVu33jLNrl27cODAgTvOu6L70VbvxtatW6HVauHp6Ynjx48jOjoa\nM2bMwAsvvKC8Nzc3r3b/ujwvrVaL6OhoHD9+HFu3bsX06dOVY7788ss4e/YsoqKisHPnTuzYsUPZ\n7+mnn0Z0dDSio6MxceLESvn+/PPPeOONN1BQUIAvvvgCH3/8MQDgqaeewmeffVZn50NERE1XnXbx\ncnFxwbJly/DJJ58AKLsz2qdPH3Tt2hVdu3bFoUOHAACvvPIK9u3bh+DgYCxevLjadBUVFBTg4Ycf\nhk6ng0ajwfr16wEAO3bsQHBwMIKCgjB16lTlrrOPjw8yMjIAAFFRUYiIiABQdldwwoQJ6NWrFyZM\nmACj0Yg5c+YgMDAQWq0Wn376KQAgOjoa/fr1Q2hoKIYNG4bU1NRbnvuqVaswevRoDBs2DH5+fnjp\npZeUz37++WeEhIRAp9Nh0KBByvlMmTIFYWFhCAkJwaZNmwAAZ86cQffu3REcHAytVotLly5VOlZ1\nZYuKilLuqJafR1XeeustdOrUCX369MGTTz6p3NWOiYlBjx49oNVqMXr0aGRnZ+P8+fPo3r27sm98\nfDw0Go3y/sSJE9DpdMp17dmzJ/z8/LB8+XIAZXfX+/Tpg8jISHTp0gUA8MEHHyAwMBAajQaLFy++\nbbkiIiLw/PPPo1u3bvjoo4+wefNm5boNHjwYN27cUOp20qRJ6NOnD3x8fPDDDz/gpZdegkajwUMP\nPaQENy+//DICAgKg1Wrx4osvmtTTsGHDAADPPfcc/P39MXjwYFy/fl1JExERgejoaABld6P9/PwQ\nFhaG6dOnY9asWQCAtLQ0jBkzBt27d0f37t2Vpwu7d+9W7rqHhIQgPz+/0nehvBzlgVJ529FqtUrb\nuflJRGBgIBISEhAfH49OnTph4sSJCAwMxNWrV2Fvb485c+ZAp9Ph0KFD1badiIgIvPzyy+jevTs6\ndeqE/fv3Q6/X44033sB3332H4OBg5TtXUXx8PD7//HMsWrQIwcHB2L9/f7X1U97Gbm4jFRmNRrz4\n4ovo3r07tFotvvjiCwBlT7f69u2L4OBgaDQa7N+/v9K+y5cvR7du3aDT6TB27FgUFRUBANavX4/A\nwEDodDr069cPQNn39dFHH0VERAT8/Pzw5ptvVsoPANasWYPIyEiTbVU9IX7kkUcQGhqKwMBAfPnl\nlyZpZ8+ejYCAAAwZMgSZmZmV9j127JhSJ8OHD1eu14cffoguXbpAq9ViwoQJlfaztraGSlX2J72g\noABmZmYQEdja2qJ3794AAEtLS+h0OiQmJt6y/BUNHToU/fv3x6JFi5CXl4eZM2cq59gQbiwREVEj\nJLXM3t6+0rYWLVrI9evXpbCwUIqLi0VEJDY2Vrp27SoiIrt27ZIRI0Yo6atLV9H3338v06dPV97n\n5ORIUVGReHp6ysWLF0VEZMKECbJ48WIREfHx8ZH09HQRETl27JhERESIiMj8+fOla9euyvE+++wz\nGTt2rBiNRhERyczMFL1eLz179pS0tDQREVm3bp386U9/EhGRzz//XJYuXVqpfCtXrpT27dtLbm6u\nFBUViZeXlyQmJsqNGzfE09NT4uPjlfxFRF599VVZs2aNiIhkZWVJx44dpaCgQGbOnCnffvutiIjo\n9XopKioyOc6tyqbRaGTfvn0iIjJ37lwJDAysVM6jR4+KTqeTkpISyc3NFV9fX1m4cKGy/969e0VE\n5I033pDnn39eRER0Op3ExcWJiMh7770nb7/9toiIREdHy8SJE5XrqtVqpbi4WNLS0sTT01OSk5Nl\n165dYmdnp5x/VFSUaDQaKSwslLy8POnSpYvExMTcslz9+vWTP//5z8o5ZGVlKa+XL18uc+bMUcrQ\nu3dvMRgMcuLECWnWrJls27ZNRERGjhwpGzdulPT0dPHz81P2z87OFhERg8EgOp1ORMra2uDBg0VE\n5Nq1a9K8eXP5/vvvlbJERUXJtWvXxNvbW7KysqS0tFR69+4tM2fOFBGRJ598Uvbv3y8iIgkJCeLv\n7y8iIiNGjJADBw6IiEh+fr4YDIZK3wURkcjISLly5Uq1bWf+/PnKtRERCQwMlPj4eImLixO1Wi1H\njhxRPjMzM5P//Oc/InLrttOvXz/lOm7dulUGDhwoImXtuvy8qnNzeW5VP1W1kbi4OKWtLlu2TGlf\nxcXF0rVrV4mLi5OFCxfKO++8IyIiRqNR8vLyKpUjIyNDef3aa6/JJ598olyfa9euicj/6nvlypXi\n4eEhmZmZUlhYKAEBARIVFVUpTy8vr0rHuvl8Rf5XNwUFBdK5c2elXZiZmcn69etFxPQ79fTTT8vG\njRuluLhYevbsqfytWrNmjfJ3zt3dXfR6vUm5b3bgwAHp0qWL2Nvby6ZNm6q8Jj4+PpKQkCAiZfXh\n4eEhQUFBMm7cOElKSqq0z88//yyvv/66vPTSS7Js2TLlOoqItG/fvtqyEBER3a3q+yLUbhAEACgp\nKcFzzz2HmJgYqNVqxMbGVpm+JukCAwMxZ84cvPLKKxg+fDh69eqFkydPol27dmjfvj0AYOLEiViy\nZAlmzZp1y7uEjzzyCCwtLQEA27dvx4wZM5SuGM2bN8fp06fx+++/Y9CgQRARGI1GeHh4AACeeeaZ\navMdMGAA7OzsAABdunRBfHw8MjIy0LdvX7Rt21bJHwB++eUXbNq0Cf/617+Ua5CQkIAePXrg7bff\nRmJiIkaOHIkOHTqYHOP8+fNVli07OxvZ2dkIDw8HUNbH/eeff65Uxv379yMyMhIWFhawsLDAiBEj\nAAA5OTnIzs5Gr169lGv52GOPAQDGjh2LdevW4cUXX8S6devw3XffATB94gAAkZGRsLS0hLOzM/r3\n748jR47A0dER3bp1U85/3759GDlyJKytrQEAo0ePxp49e2A0GqssV7lx48Ypr69evYrHHnsMycnJ\n0Ov18PHxUT4bNmwYVCoVAgMDYTQaMXjwYABl7ScuLg7Dhw+HjY0Npk6diuHDh+Phhx8GABw+fFh5\nUrR371488cQTAAB3d3f079+/0nU8cuQI+vXrB0dHR+Ualbfb7du34+zZs0obzMvLQ0FBAcLDw/H8\n88/jqaeewqhRo9C6detK+er1eiQlJcHb2xubN2+usu3crGJb9/LyQmhoqPLe3Nwco0aNAlB92ylX\nni4kJATx8fFVHqsmblU/VbWRoKAg5fNffvkFp06dUp7W5OTkIDY2FqGhofjTn/4EvV6PyMhIk33K\nnTp1Cq+99hqysrKQn5+vjCfq1auX0p7LzxEABg0apFzTUaNGKU+yKsrMzIStre1tz3nhwoXKU9Ck\npCRcunQJQUFBsLCwwJgxYwCUda166qmnTPY7e/YsTp8+jYEDByp14unpCQAICAjAU089hcjISDz6\n6KNVHrdHjx74/fffcfbsWUyePBlDhw5VupyVlpbi8ccfx9y5c5U8R44ciQkTJsDCwgJLlizB5MmT\nsW3bNpM8hwwZgiFDhuDNN9/EtGnTTD5zcXFBcnIyHBwcbntNiIiIaqrOA5TLly/D3NwcLVu2xIIF\nC+Dm5oaTJ0/CYDDAxsamyn0+/PDD26bz9fVFdHQ0tm7ditdffx0DBgzAI488Um0gYm5urvS5L+/q\nUe52PzhEBAEBAVV2I7kVKysr5bVKpVIGz1ZXxu+//x6+vr4m28q7DG3evBkPPfQQli1bpnRLuVXZ\nsrOz76isVamunOPGjcPYsWMxcuRIqFQqJSD85ZdflMHjgGl/exFR3t/qeldMd6ugsmIeM2fOxJw5\nczB8+HDs3r3bZMxTeR2YmZmZjOEorw+1Wo0jR45gx44dWL9+PT755BPs2LHjjsbSVCx7ddsPHz5c\naQzJSy+9hIcffhhbtmxBeHg4fvnll0r77t27VwkSqztGxbYNmLbvm6+1tbW1yfW9Vbsuv3Zqtbra\ngd81cav6qa6NVNz28ccfK93ZKtq7dy+2bNmCSZMm4a9//Suefvppk88nTZqEH3/8EQEBAVi1ahV2\n794NAFiyZAmOHj2KzZs3IyQkROmid/OxqxovcqvxJeV27NiBffv24ciRI7C0tETv3r0r/c2p7hgi\ngqCgIKWsFW3btg27d+/Gxo0b8c477+DUqVPVjmnx9/eHlZUVzpw5o3TBnDJlCjQaDWbMmKGkc3Jy\nUl5Pnz4dr7/+erXn9cYbb1TaVlRUVO3fcSIiortV62NQKv6AunHjBmbMmKH0Wc7Ozoa7uzsA4Ouv\nv1bGANjb25vMBlNduoqSk5NhY2ODJ598EnPmzEF0dDT8/PwQHx+Py5cvAwBWr16t/Jj38fFBVFQU\ngLJAoDqDBg3C0qVLlWNmZmbCz88PN27cUMbClJaW4syZM3d+cQCEhYVh7969yh3p8j7oQ4YMwUcf\nfaSki4mJAVA2aNrHxwczZ85EZGQkTp48aZJfdWVzdHRE8+bNlcHKa9asqbI84eHh2LRpE4qLi5GX\nl4fNmzcDABwcHODk5KT8eF29ejX69u0LAGjXrh3UajXeeust5UlGTk5OpVmKNm7ciJKSEqSnp2P3\n7t0md/LL9e7dGxs2bEBRURHy8/Pxww8/oHfv3ggPD8fmzZsrlasqOTk5yp3/W81CVNWP+4KCAmRl\nZWHo0KH44IMPlOu7Y8cODBw4EADQp08frFu3DkajEcnJydi5c2elfEJDQ7Fnzx5kZ2ejtLTUpI0N\nHjzYZGzNiRMnAJQF7126dMGLL76I0NBQnDt3Dvb29sjJyVHSVnwqVV3b8fb2Vn5kR0dH48qVK9We\nc8X3d9Kuy/e7uXxVuTnNrerndm1kyJAhWLJkiRIgxcbGoqCgAAkJCXB1dcWUKVMwdepU5fwrysvL\ng5ubG/R6vUn7v3z5MkJDQ7FgwQK4urri6tWrAIBff/0VWVlZKCwsxIYNG5SnjxX5+fkpf1+qk52d\nDScnJ1haWuL06dM4evSo8pler1eC+G+//dYk+ASAzp07IykpSdlHr9fjzJkzMBqNuHr1Kvr164f3\n3nsP6enpKCgoMNk3Li5OCVSvXLmCixcvwsvLC0DZOKvi4mLlCW25lJQU5fUPP/ygjAurCaPRiPT0\ndOWJHhERUW2p9QClqKhImWZ48ODBGDp0qHLn7dlnn8XKlSuh0+lw4cIF5QLBLb8AABJYSURBVO6u\nRqOBSqWCTqfD4sWL8ec//7nKdBWdOnVKGQD75ptv4rXXXoOVlRVWrFiBMWPGICgoCGq1WumC9cYb\nb2DWrFno1q3bLe+CTp06FZ6entBoNNDpdFi7di0sLCzwn//8By+99JIy6Lx8oPPSpUuxbNmy216X\n8jud5RMHjBw5EjqdDo8//jgA4LXXXoNer4dGo0FAQIByzb777jtlyubTp08rg2OHDx+OlJSUW5bt\nq6++wrPPPlupm0pycrLSlalr16545JFHEBQUhOHDh0Oj0SjdlFauXIk5c+ZAq9XixIkTJndQx40b\nhzVr1ijdvn799VflB305jUaDfv36oWfPnnjjjTfg5uZW6brodDpMmjQJoaGh6NGjB6ZPn46goKBb\nluvmu8bz5s3DmDFjEBoaipYtW962DirKycnBww8/jKCgIPTp0wcffvgh0tLSYGNjo7S78q51Xbp0\nwaRJk9CzZ89KeXp4eODVV19Ft27d0Lt3b/j4+CjlXbx4MY4dO4agoCAEBARg6dKlAIBFixYpkzFY\nWlpi2LBh0Gg0UKvV0Ol0WLRoEXbv3q0EhtW1ndGjRyM9PR2BgYFYsmQJ/Pz8qj3niu9v1Xaq2y8i\nIgJnzpypdpA8AIwYMQI//PCDMkh+/vz51dbP7drI1KlT0blzZwQHByMwMBD/93//B4PBgF27diEo\nKAjBwcH47rvvMHv2bABl0z6XBytvvfWWUh/+/v5KnnPnzoVGo4FGo0F4eLjyhKFbt24YNWoUtFot\nxo4dW+l7A5R976oKUG9Ok5+fr3yPw8LClM+aN2+OvXv3Kk+uyqf8Lb++lpaW+M9//oMXXnhBOb8j\nR46gtLQUTz75JLRaLbp27Yq5c+dW+ru4e/duaDQaBAcH47HHHsOyZcvg6OiI+Ph4/Otf/8Lvv/+u\nTMpQHiiWT1Ch0+mwbNkyZUC/0WhEt27dbnmeR48erRRgERER1QYzuVU/GmoS8vPzYWtri8LCQvTp\n0wdffPEFtFrtHeUxffp0TJ06VflRs2DBAtjb2+OFF16o13LdjTVr1iApKclkRq+aKC+vwWDAyJEj\nMWXKlEozPt2JpKQkTJ8+HVu2bLnrPKhmVq1ahaioKJOnmFVJSUnBxIkTK43TaIqee+45jBs3Tpkh\njIiIqLbcl0Hy1LBNnz4dZ86cQXFxMSZNmnRXQUBNniLVR7nuxs0Dl2tq/vz52L59O4qLizF48OB7\nCk4AoHXr1gxOGhg3NzdMmzYNeXl5ygQYTVVwcDCDEyIiqhN8gkJEd2XlypVYvHixSXew8PBwZSE/\nIiIiorvBAIWIiIiIiBqMOl1JnoiIiIiI6E7USYCyYcMGqFQqXLhwoUbpFy9ebLJOgL29fV0UC0DZ\nmg7lMwJFRkbedsrUmoqPj0dgYOAd7bNv3z50794d/v7+6Ny5M7744gvlswULFuCDDz6olbJVZ8CA\nAcjLy6uTvH18fJCRkXHPaeqyLdwvM2bMUGbHutn333+PQYMGISgoCP369cPq1avrtCyrVq1Spv2+\nlzRVqe02q9fr0bdvX5M1XoiIiKjxq5MA5d///jd69+6NtWvX1ij9okWLkJ+fr7yvbvGxmqhqzZSK\nbG1tER0djVOnTqFFixb49NNP7/pYN7uTcqempuKpp57CsmXLcPbsWezbtw9Lly7FTz/9VGvluZWt\nW7dCq9VWOdC3Nnr91eRa1Faahu7w4cMmU82We+WVV7BhwwZ89dVXOHHiBDZs2ICoqKhqZz67Xduu\nqQflultYWGDgwIH497//Xd9FISIiovuo1gOU/Px87N+/H19++aVJgLJ7926MGDFCeT9z5kx8/fXX\n+Pjjj3Ht2jX0798fAwYMAFD2A/m1116DVqtFz549cePGDQBlTykGDBgArVaLQYMGITExEQAwefJk\nzJgxA2FhYXjppZdqXNYePXogKSlJKffAgQPRtWtXBAUF4ccff1SO2blzZ0yfPh0BAQEYOnQoiouL\nAQBRUVHK+hEVA52+ffuaLKjYu3dvnDp1yuTYn376KSZPnoygoCAAZSs6//Of/8S7775bqZwfffQR\nunTpAq1WiyeffBJA2SJ9I0eORFBQEHr27Inff/8dQNld7ClTpiAiIgIdOnSodsDymjVrlFmm4uPj\n0alTJ0ycOBGBgYFITEzEs88+i27duiEwMNBk5W8fHx/Mnz8fISEhCAoKUp6SZWRkYMiQIQgMDMS0\nadNMgpyRI0ciNDQUgYGBWL58ubK9Ypry9Rg0Go3JooblUlJS0LdvXwQHB0Oj0SgLSK5du1ZZ0+Ll\nl19W0tvb21fZhioyGo2YO3eushZJeR3u2LEDwcHBCAoKwtSpU6HX65Vzf/XVV6HT6dCtWzccP34c\nQ4cOha+vr7K2yc3OnTuHjh07VvrBv3v3biQkJGD16tXw9PQEULZGxqJFi5Cenq4sKnpz2z569Ch6\n9uyJkJAQ9OrVC7GxsQDKnnqMHj0aw4YNg5+fn8n3YMWKFfDz80NYWJjJqvGbN29GWFgYQkJCMHjw\n4CqvUVWq+x5WVFttNjIystpFRomIiKiRklq2Zs0amTp1qoiIhIeHS3R0tIiI7Nq1S0aMGKGke+65\n52TVqlUiIuLt7S0ZGRnKZ2ZmZrJlyxYREXnxxRfl7bffFhGRESNGyOrVq0VE5KuvvpJHH31UREQm\nTZpkkvexY8dk2rRpVZbPzs5ORERKS0tl7Nixsm3bNhERMRgMkpubKyIiaWlp0qFDBxERiYuLEwsL\nCzl58qSIiDz22GOyZs0aERHRaDSyb98+ERGZO3euBAYGiojIqlWr5C9/+YuIiFy4cEFCQ0MrlWPU\nqFHy448/mmzLzs4WZ2dnERGZP3++LFy4UEREPDw8pKSkREkjIjJz5kx58803RUTkt99+E61Wq+wX\nHh4uer1e0tLSxNnZWUpLSysd38vLS/Ly8pRzVKvVcuTIEeXzzMxM5br069dPTp06JSJldfXpp5+K\niMiSJUuU6zxr1ix56623RERky5YtolKpJD093SSvwsJCCQgIUOra29tb0tPTJSoqSjQajRQWFkpe\nXp506dJFYmJiRETE3t5eREQWLlwo77zzjoiIGI1GycvLk2vXrknbtm0lPT1dDAaD9O/fXzZu3Cgi\n1behij777DMZO3asGI1GpZxFRUXi6ekpFy9eFBGRCRMmyOLFi5XyLl26VEREnn/+eQkKCpL8/Hy5\nceOGtGrVqlL+IiIffPCBrFixotL2J598UmJjY6WgoECeeOIJ6d69u/ztb3+T999/X06cOCGzZs0S\nkcptOzc3VwwGg4iIbN++XUaPHi0iIitXrpT27dtLbm6uFBUViZeXlyQmJkpycrJyjfR6vYSHh8vM\nmTNFRCQrK0vJd/ny5fLXv/5Vyas8TVWq+x7WRZs1GAzSsmXLastCREREjU+tP0FZu3atssL1uHHj\n8O2339Y0UFJeW1lZ4aGHHgIAhISEIC4uDgBw8OBBPPHEEwCA8ePHm9wNHjt2rPI6JCSk2nU5CgsL\nERwcDHd3d1y/fh2DBg0CUHY3/ZVXXkFQUBAGDhyIa9eu4fr16wDK7pyXjy8pL092djays7MRHh6u\nlKdiWbZs2QKDwYCvvvoKkyZNqtE1qE5QUBCefPJJrFmzBmq1GkDZ+JXyY0ZERCAjI0MZTzJ8+HCY\nm5vD2dkZrVq1QmpqaqU8MzMzTVai9vLyQmhoqPL+3//+N0JCQqDT6XDmzBmcOXNG+WzkyJEm1wIA\n9uzZg6effhoA8NBDD6FFixZK+kWLFkGr1SIsLAyJiYnKXf/ypwr79u3DyJEjYW1tDVtbW4waNQp7\n9+4F8L92ERoaihUrVuDNN9/EyZMnYWtri6NHjyIiIgJOTk5QqVR46qmnsGfPHgBlK3JX1YYq2r59\nO5555hmlHM2bN8f58+fRrl07tG/fHgAwceJEJU8AylPAwMBAdO/eHc2aNYOLiwusra2rHM+0bds2\nDB06tNL2pKQkdOjQAV988QXCwsJw6NAh5ObmIj8/Hx07dsTly5eVtBXbdlZWFsaMGYPAwEA8//zz\nJvUyYMAA2NnZwcrKCl26dEF8fDwOHz6sXCNzc3OMGzdOSX/16lUMGTIEGo0G77//vklet3Kr72G5\n2mqzKpUKVlZWJl1AiYiIqHGr1QAlMzMTv/32G6ZOnYp27drh/fffx/r16wEA5ubmJoNdKw6Kv5mF\nhYXyWq1Wo7S0FMCt+8VX/LF9K82aNUN0dDQSEhIgIkq3njVr1iAtLQ3Hjx/H8ePH4erqqpTRysqq\nyvJINWM1bGxsMGjQIGzYsAHr16+vcuG/zp0749ixYybbjh07hi5dulRKu2XLFjz33HOIjo5GaGjo\nbcciVCyvSqVSyluRubnpGp0Vr19cXBwWLlyInTt34sSJE3jooYdM6qs8/4rX4mbl12b37t347bff\ncPjwYcTExECr1d6y7m9WXue9e/fGnj170Lp1a0yePBnffPONyXFuVl0bqonq8gT+d+7lP5wrlvPm\nYxQWFiI7Oxtubm6V8lGpyr56586dUwKYYcOGAQCuX78OV1dXJW3Funn99dfRv39/nDp1Cps2baqy\nXsrzv107nTlzJmbNmoWTJ0/i888/r3G91GR8Sm222eLiYlhbW9eobERERPTgq9UAZf369ZgwYQKu\nXLmCy5cvIz4+Hj4+Pti3bx+8vLxw5swZ6PV6ZGVlYceOHcp+Dg4OJnefq/tB1bNnT2VcyzfffHNX\nqxiX521tbY3Fixfj/fffh9FoRHZ2NlxdXaFSqbBz507Ex8ffsjyOjo5o0aIFDhw4AACV+slPmTIF\ns2bNQrdu3eDo6Fhp/z//+c9YtWoVTpw4AQBIT0/Hyy+/XOUYmoSEBPTt2xf/+Mc/kJOTg/z8fPTp\n00f5kb5r1y64uLjc0crWfn5+JnfpK55jTk4O7OzsYG9vj9TU1BoN3O/Tp49yDX766SdkZWUBALKz\ns9GiRQtYWVnh3LlzOHToUKVj9u7dGxs2bEBRURHy8/Pxww8/oE+fPiZpEhIS4OrqiilTpmDKlCmI\njo5Gt27dsGfPHmRkZMBgMGDt2rXo169fja/BoEGDsHTpUuXHc2ZmJvz8/BAfH69cm9WrV99RnhXt\n3LkTERERVX7WqlUrxMXFoVOnTti2bRuAsqcter0ef//735WnUTfLyclB69atAZSNLbmd7t27Y8+e\nPcjMzIRer1duGJTn5eHhAaBsDEtN1eR7WFttNiMjAy4uLspTGCIiImr8ajVAWbdundL9p9yoUaOw\ndu1atGnTBmPHjkVAQAAef/xxBAcHK2mmTZuGoUOHKoPkq7tD+9FHH2HFihXQarVYs2aNMpj65vRR\nUVGYPn16lXlUTKvVahEUFIS1a9fiqaeewtGjRxEUFIRvvvkG/v7+Ve5T0VdffYVnn33W5FzKBQcH\nw8HBAZMnT65yXzc3N3zzzTeYNm0a/P390atXL0ydOlXpllSutLQUTz/9NIKCghASEoLZs2fDwcEB\n8+bNQ1RUFIKCgvDqq6/i66+/vu35VjR8+HDs3LmzynQajQZarRb+/v54+umn0atXr9vmN2/ePOzZ\nsweBgYHYsGED2rZtCwAYOnQo9Ho9unTpgldffRU9evSolJdOp8OkSZMQGhqKHj16YPr06dBoNCZp\ndu3ahaCgIAQHB+O7777D7Nmz4ebmhn/84x/o168fdDodunbtiocffviW5dy0aRPmz58PAJg6dSo8\nPT2h0Wig0+mwdu1aWFlZYcWKFRgzZgyCgoKgVqvxzDPP3DLP6j776aefquzeBZQNfp83bx6mTp2K\n/fv3IywsDPb29ti5cyf69eunBDY35zt37ly8/PLLCAkJueX0u+X7ubm5Yf78+QgLC0Pv3r3RuXNn\nJc28efMwZswYhIaGomXLltXmdbPqvoflarPN7ty5E8OHD69x2YiIiOjBx5Xk60j5zGTnzp2r76JU\nKSUlBRMnTlTu3lPt69q1Kw4fPlzt3f/Zs2ejtLQUb731FpycnJCbm4v169fjscceu6OnYY3Z6NGj\n8d5776FDhw71XRQiIiK6T7iSfB1YvXo1evTogXfeeae+i1ItNzc3TJs2rc4WaqSyMUW36pq0ePFi\n9OzZE6NHj4ZWq8Xw4cOhVqsZnPxBr9dj5MiRDE6IiIiaGD5BIaIqvfPOO1i/fj3MzMwgIjAzM8PY\nsWPxyiuv1HfRiIiIqBFjgEJERERERA0Gu3gREREREVGDwQCFiIiIiIgaDAYoRERERETUYDBAISIi\nIiKiBoMBChERERERNRj/D6kSljdXPIoEAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "\n",
+ "# This is my custom style that does most of the plot formatting\n",
+ "plt.style.use('https://gist.githubusercontent.com/rhiever/a4fb39bfab4b33af0018/raw/b25b4ba478c2e163dd54fd5600e80ca7453459da/tableau20.mplstyle')\n",
+ "\n",
+ "degree_gender_data = pd.read_csv('gender_degree_data.tsv', sep='\\t')\n",
+ "degree_gender_data = degree_gender_data[degree_gender_data['Year'] >= 1970]\n",
+ "degree_gender_data.set_index('Year', inplace=True)\n",
+ "\n",
+ "# Create a list of the degree majors ranked by their last value in the time series\n",
+ "# We'll use this list to determine what colors the degree majors are assigned\n",
+ "degree_major_order = degree_gender_data.groupby('Degree_Major')['Female_percent_Bachelors'].last()\n",
+ "degree_major_order = degree_major_order.sort_values(ascending=False).index.values\n",
+ "degree_major_order_dict = dict(zip(degree_major_order, range(len(degree_major_order))))\n",
+ "\n",
+ "degree_gender_data['Degree_Major_Order'] = degree_gender_data[\n",
+ " 'Degree_Major'].apply(lambda major: degree_major_order_dict[major])\n",
+ "\n",
+ "degree_gender_data.groupby('Degree_Major_Order')['Female_percent_Bachelors'].plot(figsize=(10, 12))\n",
+ "\n",
+ "plt.xlabel('')\n",
+ "plt.yticks(range(0, 91, 10), ['{}%'.format(x) for x in range(0, 91, 10)])\n",
+ "\n",
+ "plt.xlim(1969, 2014)\n",
+ "plt.ylim(-1, 90)\n",
+ "\n",
+ "plt.title('Percentage of Bachelor\\'s degrees conferred to women'\n",
+ " 'in the U.S.A., by major (1970-2014)\\n', fontsize=14)\n",
+ "plt.grid(False, axis='x')\n",
+ "\n",
+ "degree_major_pcts = dict(degree_gender_data.groupby(\n",
+ " 'Degree_Major')['Female_percent_Bachelors'].last().iteritems())\n",
+ "\n",
+ "degree_major_color_map = [x['color'] for x in plt.rcParams['axes.prop_cycle']]\n",
+ "\n",
+ "# We use this dictionary to rename the degree majors to shorter names\n",
+ "degree_major_name_map = {\n",
+ " 'the social sciences and history': 'Social Sciences and History',\n",
+ " 'the health professions and related programs': 'Health Professions',\n",
+ " 'visual and performing arts': 'Art and Performance',\n",
+ " 'foreign languages and literatures': 'Foreign Languages',\n",
+ " 'engineering and engineering technologies': 'Engineering',\n",
+ " 'the biological and biomedical sciences': 'Biology',\n",
+ " 'mathematics and statistics': 'Math and Statistics',\n",
+ " 'agriculture and natural resources': 'Agriculture',\n",
+ " 'the physical sciences and science technologies': 'Physical Sciences',\n",
+ " 'communication, journalism, and related '\n",
+ " 'programs and in communications technologies': 'Communications\\nand Journalism',\n",
+ " 'public administration and social services': 'Public Administration',\n",
+ " 'psychology': 'Psychology',\n",
+ " 'English language and literature/letters': 'English',\n",
+ " 'computer and information sciences': 'Computer Science',\n",
+ " 'education': 'Education',\n",
+ " 'business': 'Business',\n",
+ " 'architecture and related services': 'Architecture',\n",
+ "}\n",
+ "\n",
+ "# We use these offsets to prevent the degree major labels from overlapping\n",
+ "degree_major_offset_map = {\n",
+ " 'foreign languages and literatures': 1.0,\n",
+ " 'English language and literature/letters': -0.5,\n",
+ " 'agriculture and natural resources': 0.5,\n",
+ " 'business': -0.5,\n",
+ " 'architecture and related services': 0.75,\n",
+ " 'mathematics and statistics': -0.75,\n",
+ " 'engineering and engineering technologies': 0.75,\n",
+ " 'computer and information sciences': -0.75,\n",
+ "}\n",
+ "\n",
+ "# Draw the degree major labels at the end of the time series lines\n",
+ "for degree_major in degree_major_pcts:\n",
+ " plt.text(2014.5, degree_major_pcts[degree_major] - 0.5 + degree_major_offset_map.get(degree_major, 0),\n",
+ " degree_major_name_map[degree_major],\n",
+ " color=degree_major_color_map[degree_major_order_dict[degree_major]])\n",
+ "\n",
+ "plt.text(1967, -9,\n",
+ " '\\nData source: nces.ed.gov/programs/digest/current_tables.asp (Tables 325.*)\\n'\n",
+ " 'Author: Randy Olson (randalolson.com / @randal_olson)',\n",
+ " ha='left', fontsize=10)\n",
+ "\n",
+ "plt.savefig('pct-bachelors-degrees-women-usa-1970-2014.png')\n",
+ ";"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pct-bachelors-degrees-conferred-women-usa/gender_degree_data.tsv b/pct-bachelors-degrees-conferred-women-usa/gender_degree_data.tsv
new file mode 100644
index 0000000..7ed29a8
--- /dev/null
+++ b/pct-bachelors-degrees-conferred-women-usa/gender_degree_data.tsv
@@ -0,0 +1,796 @@
+Year Degree_Major Total_Bachelors Percent_change_Bachelors Male_Bachelors Female_Bachelors Female_percent_Bachelors Total_Masters Male_Masters Female_Masters Total_Doctorates Male_Doctorates Female_Doctorates
+1971 agriculture and natural resources 12672 0 12136 536 4.2 2457 2313 144 1086 1055 31
+1972 agriculture and natural resources 13516 6.7 12779 737 5.5 2680 2490 190 971 945 26
+1973 agriculture and natural resources 14756 9.2 13661 1095 7.4 2807 2588 219 1059 1031 28
+1974 agriculture and natural resources 16253 10.1 14684 1569 9.7 2928 2640 288 930 897 33
+1975 agriculture and natural resources 17528 7.8 15061 2467 14.1 3067 2703 364 991 958 33
+1976 agriculture and natural resources 19402 10.7 15845 3557 18.3 3340 2862 478 928 867 61
+1977 agriculture and natural resources 21467 10.6 16690 4777 22.3 3724 3177 547 893 831 62
+1978 agriculture and natural resources 22650 5.5 17069 5581 24.6 4023 3268 755 971 909 62
+1979 agriculture and natural resources 23134 2.1 16854 6280 27.1 3994 3187 807 950 877 73
+1980 agriculture and natural resources 22802 -1.4 16045 6757 29.6 3976 3082 894 991 879 112
+1981 agriculture and natural resources 21886 -4.0 15154 6732 30.8 4003 3061 942 1067 940 127
+1982 agriculture and natural resources 21029 -3.9 14443 6586 31.3 4163 3114 1049 1079 925 154
+1983 agriculture and natural resources 20909 -0.6 14085 6824 32.6 4254 3129 1125 1149 1004 145
+1984 agriculture and natural resources 19317 -7.6 13206 6111 31.6 4178 2989 1189 1172 1001 171
+1985 agriculture and natural resources 18107 -6.3 12477 5630 31.1 3928 2846 1082 1213 1036 177
+1986 agriculture and natural resources 16823 -7.1 11544 5279 31.4 3801 2701 1100 1158 966 192
+1987 agriculture and natural resources 14991 -10.9 10314 4677 31.2 3522 2460 1062 1049 871 178
+1988 agriculture and natural resources 14222 -5.1 9744 4478 31.5 3479 2427 1052 1142 926 216
+1989 agriculture and natural resources 13492 -5.1 9298 4194 31.1 3245 2231 1014 1183 950 233
+1990 agriculture and natural resources 12900 -4.4 8822 4078 31.6 3382 2239 1143 1295 1038 257
+1991 agriculture and natural resources 13124 1.7 8832 4292 32.7 3295 2160 1135 1185 953 232
+1992 agriculture and natural resources 15113 15.2 9867 5246 34.7 3730 2409 1321 1205 955 250
+1993 agriculture and natural resources 16769 11.0 11079 5690 33.9 3959 2474 1485 1159 869 290
+1994 agriculture and natural resources 18056 7.7 11746 6310 34.9 4110 2512 1598 1262 969 293
+1995 agriculture and natural resources 19832 9.8 12686 7146 36.0 4234 2541 1693 1256 955 301
+1996 agriculture and natural resources 21425 8.0 13531 7894 36.8 4551 2642 1909 1259 926 333
+1997 agriculture and natural resources 22597 5.5 13791 8806 39.0 4505 2601 1904 1202 875 327
+1998 agriculture and natural resources 23276 3.0 13806 9470 40.7 4464 2545 1919 1290 924 366
+1999 agriculture and natural resources 24179 3.9 14045 10134 41.9 4376 2360 2016 1249 869 380
+2000 agriculture and natural resources 24238 0.2 13843 10395 42.9 4360 2356 2004 1168 803 365
+2001 agriculture and natural resources 23370 -3.6 12840 10530 45.1 4272 2251 2021 1127 741 386
+2002 agriculture and natural resources 23331 -0.2 12630 10701 45.9 4503 2340 2163 1148 760 388
+2003 agriculture and natural resources 23348 0.1 12343 11005 47.1 4492 2232 2260 1229 790 439
+2004 agriculture and natural resources 22835 -2.2 11889 10946 47.9 4783 2306 2477 1185 758 427
+2005 agriculture and natural resources 23002 0.7 11987 11015 47.9 4746 2288 2458 1173 763 410
+2006 agriculture and natural resources 23053 0.2 12063 10990 47.7 4640 2280 2360 1194 710 484
+2007 agriculture and natural resources 23133 0.3 12309 10824 46.8 4623 2174 2449 1272 768 504
+2008 agriculture and natural resources 24113 4.2 12634 11479 47.6 4684 2180 2504 1257 742 515
+2009 agriculture and natural resources 24982 3.6 13096 11886 47.6 4878 2328 2550 1328 741 587
+2010 agriculture and natural resources 26343 5.4 13524 12819 48.7 5215 2512 2703 1149 626 523
+2011 agriculture and natural resources 28630 8.7 14678 13952 48.7 5766 2746 3020 1246 675 571
+2012 agriculture and natural resources 30972 8.2 15485 15487 50.0 6390 3026 3364 1333 721 612
+2013 agriculture and natural resources 33592 8.5 16618 16974 50.5 6336 2912 3424 1411 767 644
+2014 agriculture and natural resources 35116 4.5 17249 17867 50.9 6544 2966 3578 1407 739 668
+1950 architecture and related services 2563 0 2441 122 4.8 166 159 7 1 1 0
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+1968 architecture and related services 3057 0 2931 126 4.1 1021 953 68 15 15 0
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+1971 architecture and related services 5570 35.7 4906 664 11.9 1705 1469 236 36 33 3
+1972 architecture and related services 6440 15.6 5667 773 12.0 1899 1626 273 50 43 7
+1973 architecture and related services 6962 8.1 6042 920 13.2 2307 1943 364 58 54 4
+1974 architecture and related services 7822 12.4 6665 1157 14.8 2702 2208 494 69 65 4
+1975 architecture and related services 8226 5.2 6791 1435 17.4 2938 2343 595 69 58 11
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+1977 architecture and related services 9222 0.8 7249 1973 21.4 3213 2489 724 73 62 11
+1978 architecture and related services 9250 0.3 7054 2196 23.7 3115 2304 811 73 57 16
+1979 architecture and related services 9273 0.2 6876 2397 25.8 3113 2226 887 96 74 22
+1980 architecture and related services 9132 -1.5 6596 2536 27.8 3139 2245 894 79 66 13
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+1982 architecture and related services 9728 2.9 6825 2903 29.8 3327 2242 1085 80 58 22
+1983 architecture and related services 9823 1.0 6403 3420 34.8 3357 2224 1133 97 74 23
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+2000 architecture and related services 8462 2.6 5193 3269 38.6 4268 2508 1760 129 85 44
+2001 architecture and related services 8480 0.2 5086 3394 40.0 4302 2515 1787 153 83 70
+2002 architecture and related services 8808 3.9 5224 3584 40.7 4566 2606 1960 183 117 66
+2003 architecture and related services 9056 2.8 5331 3725 41.1 4925 2832 2093 152 83 69
+2004 architecture and related services 8838 -2.4 5059 3779 42.8 5424 3049 2375 173 94 79
+2005 architecture and related services 9237 4.5 5222 4015 43.5 5674 3180 2494 179 110 69
+2006 architecture and related services 9515 3.0 5414 4101 43.1 5743 3165 2578 201 108 93
+2007 architecture and related services 9717 2.1 5393 4324 44.5 5951 3304 2647 178 104 74
+2008 architecture and related services 9805 0.9 5579 4226 43.1 6065 3252 2813 199 103 96
+2009 architecture and related services 10119 3.2 5797 4322 42.7 6587 3657 2930 212 113 99
+2010 architecture and related services 10051 -0.7 5694 4357 43.3 7280 4012 3268 210 116 94
+2011 architecture and related services 9831 -2.2 5698 4133 42.0 7788 4265 3523 205 110 95
+2012 architecture and related services 9727 -1.1 5566 4161 42.8 8448 4504 3944 255 147 108
+2013 architecture and related services 9757 0.3 5581 4176 42.8 8095 4261 3834 247 134 113
+2014 architecture and related services 9144 -6.3 5173 3971 43.4 8048 4122 3926 247 134 113
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+1954 the biological and biomedical sciences 9279 0 6710 2569 27.7 1610 1287 323 1077 977 100
+1956 the biological and biomedical sciences 12423 0 9515 2908 23.4 1759 1379 380 1025 908 117
+1958 the biological and biomedical sciences 14308 0 11159 3149 22.0 1852 1448 404 1125 987 138
+1960 the biological and biomedical sciences 15576 0 11654 3922 25.2 2154 1668 486 1205 1086 119
+1962 the biological and biomedical sciences 16915 0 12136 4779 28.3 2642 1982 660 1338 1179 159
+1964 the biological and biomedical sciences 22723 0 16321 6402 28.2 3296 2348 948 1625 1432 193
+1966 the biological and biomedical sciences 26916 0 19368 7548 28.0 4232 3085 1147 2097 1792 305
+1968 the biological and biomedical sciences 31826 0 22986 8840 27.8 5506 3959 1547 2784 2345 439
+1970 the biological and biomedical sciences 34034 0 23919 10115 29.7 5800 3975 1825 3289 2820 469
+1971 the biological and biomedical sciences 35705 4.9 25319 10386 29.1 5625 3782 1843 3603 3018 585
+1972 the biological and biomedical sciences 37269 4.4 26314 10955 29.4 5989 4056 1933 3587 2981 606
+1973 the biological and biomedical sciences 42207 13.2 29625 12582 29.8 6156 4317 1839 3583 2892 691
+1974 the biological and biomedical sciences 48244 14.3 33217 15027 31.1 6408 4512 1896 3358 2684 674
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+1979 the biological and biomedical sciences 48713 -5.2 29173 19540 40.1 6638 4198 2440 3459 2593 866
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diff --git a/python-baseball-simulator/Simulating baseball in Python.ipynb b/python-baseball-simulator/Simulating baseball in Python.ipynb
new file mode 100644
index 0000000..6a6542d
--- /dev/null
+++ b/python-baseball-simulator/Simulating baseball in Python.ipynb
@@ -0,0 +1,1291 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Simulating baseball in Python\n",
+ "\n",
+ "This notebook provides the methodology and code used in the blog post, [How much does batting order matter in Major League Baseball? A simulation approach](http://www.randalolson.com/2018/07/04/does-batting-order-matter-in-major-league-baseball-a-simulation-approach).\n",
+ "\n",
+ "### Notebook by [Randal S. Olson](http://www.randalolson.com)\n",
+ "\n",
+ "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely useable and shareable as possible.\n",
+ "\n",
+ "### Required Python libraries\n",
+ "\n",
+ "If you don't have Python on your computer, you can use the [Anaconda Python distribution](https://www.anaconda.com/download/) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\n",
+ "\n",
+ "This code uses base Python libraries except for `seaborn`, `tqdm`, and `joblib` packages. You can install these packages using `pip` by typing the following commands into your command line:\n",
+ "\n",
+ "> pip install seaborn tqdm joblib\n",
+ "\n",
+ "### Using the baseball simulator\n",
+ "\n",
+ "Below is the Python code used to simulate baseball in my blog post, run the simulations, and generate the data visualizations shown in my blog post. When I get more time, I plan to clean up and comment this code better than it currently is.\n",
+ "\n",
+ "For the data visualizations, you will need to place [this tableau10.mplstyle](https://gist.github.com/rhiever/d0a7332fe0beebfdc3d5) in your `~/.matplotlib/stylelib/` directory for the visualizations to show up as they do in my blog post. Otherwise, you will have to use [other matplotlib styles](https://matplotlib.org/users/style_sheets.html).\n",
+ "\n",
+ "If you have any comments or questions about this project, I prefer that you [file an issue](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/issues/new) on this GitHub repository. If you don't feel comfortable with GitHub, feel free to [contact me by email](http://www.randalolson.com/contact/)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 160,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sb\n",
+ "import numpy as np\n",
+ "from tqdm import tqdm_notebook as tqdm\n",
+ "from joblib import Parallel, delayed\n",
+ "import time\n",
+ "\n",
+ "# ParallelExecutor code taken and modified from https://gist.github.com/MInner/12f9cf961059aed1a60e72c5531a697f\n",
+ "def text_progessbar(seq, total=None):\n",
+ " step = 1\n",
+ " tick = time.time()\n",
+ " while True:\n",
+ " time_diff = time.time() - tick\n",
+ " avg_speed = time_diff / step\n",
+ " total_str = 'of {}'.format(total if total else '')\n",
+ " print('step', step, '{}'.format(round(time_diff, 2)), 'avg: {} iter/sec'.format(round(avg_speed)), total_str)\n",
+ " step += 1\n",
+ " yield next(seq)\n",
+ "\n",
+ "all_bar_funcs = {\n",
+ " 'tqdm': lambda args: lambda x: tqdm(x, **args),\n",
+ " 'txt': lambda args: lambda x: text_progessbar(x, **args),\n",
+ " 'False': lambda args: iter,\n",
+ " 'None': lambda args: iter,\n",
+ "}\n",
+ "\n",
+ "def ParallelExecutor(use_bar='tqdm', **joblib_args):\n",
+ " def aprun(bar=use_bar, **tq_args):\n",
+ " def tmp(op_iter):\n",
+ " if str(bar) in all_bar_funcs.keys():\n",
+ " bar_func = all_bar_funcs[str(bar)](tq_args)\n",
+ " else:\n",
+ " raise ValueError('Value {} not supported as bar type'.format(bar))\n",
+ " return Parallel(**joblib_args)(bar_func(op_iter))\n",
+ " return tmp\n",
+ " return aprun\n",
+ "\n",
+ "def simulate_game(batters, return_stats=False):\n",
+ " '''Simulates the batting side of a Major League Baseball game\n",
+ " \n",
+ " This is a simplified simulation of a baseball game, where each batter performs randomly\n",
+ " according to their corresponding batting average. This simulation incorporates\n",
+ " different types of hits, such as singles, doubles, triples, and home runs, and uses\n",
+ " 2017-2018 Major League averages for the probabilities of those hit types occurring.\n",
+ " This simulation leaves out other aspects of the game, such as individual-level hit type\n",
+ " tendencies, double plays, stolen bases, errors, and so forth.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " batters: list\n",
+ " A list of batting averages for 9 batters in the desired batting order\n",
+ " Example input: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25]\n",
+ "\n",
+ " Returns\n",
+ " ----------\n",
+ " runs_scored: int\n",
+ " The number of runs scored by the batters in one simulated game\n",
+ " batting_stats: dict\n",
+ " Dictionary containing batting statistics for each batter\n",
+ " '''\n",
+ " runs_scored = 0\n",
+ " batter_num = 0\n",
+ "\n",
+ " # NOTE: Earned Bases is the total number of bases that the batter advanced themselves\n",
+ " # AND their teammates through batting\n",
+ " batting_stats = {}\n",
+ " for batter in range(len(batters)):\n",
+ " batting_stats[batter] = {\n",
+ " 'At Bat': 0, 'Single': 0, 'Double': 0, 'Triple': 0, 'Home Run': 0, 'Out': 0,\n",
+ " 'RBI': 0, 'Earned Bases': 0, 'Players On Base': 0, 'Bases Loaded': 0, 'Grand Slam': 0\n",
+ " }\n",
+ "\n",
+ " # Assume the game lasts for only 9 innings (no extra innings)\n",
+ " for inning in range(9):\n",
+ " bases = [\n",
+ " False, # First base, index 0\n",
+ " False, # Second base, index 1\n",
+ " False # Third base, index 2\n",
+ " ]\n",
+ " batters_out = 0\n",
+ "\n",
+ " while batters_out < 3:\n",
+ " batting_stats[batter_num]['At Bat'] += 1\n",
+ " if bases[2] and bases[1] and bases[0]:\n",
+ " batting_stats[batter_num]['Bases Loaded'] += 1\n",
+ "\n",
+ " if bases[2]:\n",
+ " batting_stats[batter_num]['Players On Base'] += 1\n",
+ " if bases[1]:\n",
+ " batting_stats[batter_num]['Players On Base'] += 1\n",
+ " if bases[0]:\n",
+ " batting_stats[batter_num]['Players On Base'] += 1\n",
+ "\n",
+ " if np.random.random() < batters[batter_num]:\n",
+ " # Batting estimates from MLB.com statistics in 2017/2018 seasons:\n",
+ " # Single base hit: 64% of hits\n",
+ " # Double base hit: 20% of hits\n",
+ " # Triple base hit: 2% of hits\n",
+ " # Home run: 14% of hits\n",
+ " hit_type = np.random.choice(['Single', 'Double', 'Triple', 'Home Run'], p=[.64, .2, .02, .14])\n",
+ "\n",
+ " if hit_type == 'Single':\n",
+ " batting_stats[batter_num]['Single'] += 1\n",
+ "\n",
+ " # All base runners advance 1 base\n",
+ " if bases[2]:\n",
+ " runs_scored += 1\n",
+ " batting_stats[batter_num]['RBI'] += 1\n",
+ " batting_stats[batter_num]['Earned Bases'] += 1\n",
+ " bases[2] = False\n",
+ " if bases[1]:\n",
+ " bases[2] = True\n",
+ " bases[1] = False\n",
+ " batting_stats[batter_num]['Earned Bases'] += 1\n",
+ " if bases[0]:\n",
+ " bases[1] = True\n",
+ " batting_stats[batter_num]['Earned Bases'] += 1\n",
+ "\n",
+ " bases[0] = True\n",
+ " batting_stats[batter_num]['Earned Bases'] += 1\n",
+ "\n",
+ " elif hit_type == 'Double':\n",
+ " batting_stats[batter_num]['Double'] += 1\n",
+ "\n",
+ " # All base runners advance 2 bases\n",
+ " if bases[2]:\n",
+ " runs_scored += 1\n",
+ " batting_stats[batter_num]['RBI'] += 1\n",
+ " batting_stats[batter_num]['Earned Bases'] += 1\n",
+ " bases[2] = False\n",
+ " if bases[1]:\n",
+ " runs_scored += 1\n",
+ " batting_stats[batter_num]['RBI'] += 1\n",
+ " batting_stats[batter_num]['Earned Bases'] += 2\n",
+ " bases[1] = False\n",
+ " if bases[0]:\n",
+ " bases[2] = True\n",
+ " batting_stats[batter_num]['Earned Bases'] += 2\n",
+ " bases[0] = False\n",
+ "\n",
+ " bases[1] = True\n",
+ " batting_stats[batter_num]['Earned Bases'] += 2\n",
+ "\n",
+ " elif hit_type == 'Triple':\n",
+ " batting_stats[batter_num]['Triple'] += 1\n",
+ "\n",
+ " # All base runners advance 3 bases\n",
+ " if bases[2]:\n",
+ " runs_scored += 1\n",
+ " batting_stats[batter_num]['RBI'] += 1\n",
+ " batting_stats[batter_num]['Earned Bases'] += 1\n",
+ " bases[2] = False\n",
+ " if bases[1]:\n",
+ " runs_scored += 1\n",
+ " batting_stats[batter_num]['RBI'] += 1\n",
+ " batting_stats[batter_num]['Earned Bases'] += 2\n",
+ " bases[1] = False\n",
+ " if bases[0]:\n",
+ " runs_scored += 1\n",
+ " batting_stats[batter_num]['RBI'] += 1\n",
+ " batting_stats[batter_num]['Earned Bases'] += 3\n",
+ " bases[0] = False\n",
+ "\n",
+ " bases[2] = True\n",
+ " batting_stats[batter_num]['Earned Bases'] += 3\n",
+ "\n",
+ " elif hit_type == 'Home Run':\n",
+ " batting_stats[batter_num]['Home Run'] += 1\n",
+ "\n",
+ " # Check if a Grand Slam was scored\n",
+ " if bases[0] and bases[1] and bases[2]:\n",
+ " batting_stats[batter_num]['Grand Slam'] += 1\n",
+ "\n",
+ " # All base runners and the hitter score a run\n",
+ " if bases[2]:\n",
+ " runs_scored += 1\n",
+ " batting_stats[batter_num]['RBI'] += 1\n",
+ " batting_stats[batter_num]['Earned Bases'] += 1\n",
+ " bases[2] = False\n",
+ " if bases[1]:\n",
+ " runs_scored += 1\n",
+ " batting_stats[batter_num]['RBI'] += 1\n",
+ " batting_stats[batter_num]['Earned Bases'] += 2\n",
+ " bases[1] = False\n",
+ " if bases[0]:\n",
+ " runs_scored += 1\n",
+ " batting_stats[batter_num]['RBI'] += 1\n",
+ " batting_stats[batter_num]['Earned Bases'] += 3\n",
+ " bases[0] = False\n",
+ "\n",
+ " runs_scored += 1\n",
+ " batting_stats[batter_num]['RBI'] += 1\n",
+ " batting_stats[batter_num]['Earned Bases'] += 4\n",
+ "\n",
+ " else:\n",
+ " # Batter struck out, flew out, or grounded out\n",
+ " batters_out += 1\n",
+ " batting_stats[batter_num]['Out'] += 1\n",
+ "\n",
+ " batter_num = (batter_num + 1) % len(batters)\n",
+ "\n",
+ " return runs_scored, batting_stats"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a48d044db65b42529e0cff1ac86648fa",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "designated_hitter_spot_scores = {}\n",
+ "num_simulated_games = 1000000\n",
+ "for team_avg in tqdm([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\n",
+ " for designated_hitter_spot in range(9):\n",
+ " batters = [team_avg] * 9\n",
+ " batters[designated_hitter_spot] = 0.35\n",
+ " aprun = ParallelExecutor(n_jobs=-1, use_bar=False)\n",
+ " designated_hitter_spot_scores[(team_avg, designated_hitter_spot)] = [runs_scored for runs_scored, _ in aprun(total=num_simulated_games)(delayed(simulate_game)(batters) for _ in range(num_simulated_games))]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "''"
+ ]
+ },
+ "execution_count": 64,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dh_batting_avgs = []\n",
+ "for team_avg in reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\n",
+ " dh_spot_avgs = []\n",
+ " for designated_hitter_spot in range(9):\n",
+ " dh_spot_avgs.append(np.mean(designated_hitter_spot_scores[(team_avg, designated_hitter_spot)]))\n",
+ " dh_spot_avgs = np.array(dh_spot_avgs) / np.mean(dh_spot_avgs)\n",
+ " dh_batting_avgs.append(dh_spot_avgs)\n",
+ "\n",
+ "plt.figure(figsize=(9, 9))\n",
+ "sb.heatmap(dh_batting_avgs, cmap='PuOr', center=1., annot=True, fmt='.3f', cbar=False)\n",
+ "plt.xticks([x + 0.5 for x in range(9)], [str(x) for x in range(1, 10)], fontsize=12)\n",
+ "plt.xlabel('DH Batting Position (BA=0.35)', fontsize=14)\n",
+ "plt.yticks([y + 0.5 for y in range(6)], reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]), fontsize=12, va='center')\n",
+ "plt.ylabel('Team Batting Average (BA)', fontsize=14)\n",
+ "plt.title('Batting order matters when one player is much\\nbetter than their teammates\\n\\n', fontsize=20)\n",
+ "plt.text(4.5, -0.1, 'Measured: Relative runs scored based on the DH batting position\\n>1 means more runs scored, <1 means fewer runs scored', fontsize=12, ha='center')\n",
+ "plt.text(-0.7, 6.8, 'Data source: League averages & custom baseball simulations\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\n",
+ "plt.savefig('mlb-batting-order-dh.png', bbox_inches='tight')\n",
+ ";"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "sig diff: team avg=0.1, dh pos=1 vs. dh pos=3 [p=1.993213882431106e-34]\n",
+ "sig diff: team avg=0.1, dh pos=1 vs. dh pos=4 [p=1.812699459453188e-33]\n",
+ "sig diff: team avg=0.1, dh pos=1 vs. dh pos=5 [p=4.841408611226961e-105]\n",
+ "sig diff: team avg=0.1, dh pos=1 vs. dh pos=6 [p=2.1989383913301317e-231]\n",
+ "sig diff: team avg=0.1, dh pos=1 vs. dh pos=7 [p=9.216188457618672e-207]\n",
+ "sig diff: team avg=0.1, dh pos=1 vs. dh pos=8 [p=3.667389122454236e-218]\n",
+ "sig diff: team avg=0.1, dh pos=1 vs. dh pos=9 [p=3.4321976744637874e-290]\n",
+ "sig diff: team avg=0.1, dh pos=2 vs. dh pos=3 [p=8.683361888508758e-21]\n",
+ "sig diff: team avg=0.1, dh pos=2 vs. dh pos=4 [p=4.275214713813377e-20]\n",
+ "sig diff: team avg=0.1, dh pos=2 vs. dh pos=5 [p=2.307786280302862e-79]\n",
+ "sig diff: team avg=0.1, dh pos=2 vs. dh pos=6 [p=6.56752502068817e-192]\n",
+ "sig diff: team avg=0.1, dh pos=2 vs. dh pos=7 [p=1.2862878707847538e-169]\n",
+ "sig diff: team avg=0.1, dh pos=2 vs. dh pos=8 [p=6.758757959239213e-180]\n",
+ "sig diff: team avg=0.1, dh pos=2 vs. dh pos=9 [p=1.7225195850226612e-245]\n",
+ "sig diff: team avg=0.1, dh pos=3 vs. dh pos=5 [p=1.8386948714134494e-21]\n",
+ "sig diff: team avg=0.1, dh pos=3 vs. dh pos=6 [p=1.6438493001481296e-90]\n",
+ "sig diff: team avg=0.1, dh pos=3 vs. dh pos=7 [p=1.8420283773808343e-75]\n",
+ "sig diff: team avg=0.1, dh pos=3 vs. dh pos=8 [p=2.922048694446345e-82]\n",
+ "sig diff: team avg=0.1, dh pos=3 vs. dh pos=9 [p=8.729968564474104e-128]\n",
+ "sig diff: team avg=0.1, dh pos=4 vs. dh pos=5 [p=7.157137876264341e-22]\n",
+ "sig diff: team avg=0.1, dh pos=4 vs. dh pos=6 [p=4.583023030013289e-91]\n",
+ "sig diff: team avg=0.1, dh pos=4 vs. dh pos=7 [p=4.8140938524114977e-76]\n",
+ "sig diff: team avg=0.1, dh pos=4 vs. dh pos=8 [p=7.650725116291236e-83]\n",
+ "sig diff: team avg=0.1, dh pos=4 vs. dh pos=9 [p=2.4714887218395744e-128]\n",
+ "sig diff: team avg=0.1, dh pos=5 vs. dh pos=6 [p=2.198816819980043e-26]\n",
+ "sig diff: team avg=0.1, dh pos=5 vs. dh pos=7 [p=1.0330372720533307e-18]\n",
+ "sig diff: team avg=0.1, dh pos=5 vs. dh pos=8 [p=4.768588761976132e-22]\n",
+ "sig diff: team avg=0.1, dh pos=5 vs. dh pos=9 [p=2.1756207162931975e-47]\n",
+ "sig diff: team avg=0.1, dh pos=7 vs. dh pos=9 [p=1.8820378340149334e-08]\n",
+ "sig diff: team avg=0.1, dh pos=8 vs. dh pos=9 [p=1.532389060082728e-06]\n",
+ "sig diff: team avg=0.15, dh pos=1 vs. dh pos=3 [p=3.6557297985692933e-06]\n",
+ "sig diff: team avg=0.15, dh pos=1 vs. dh pos=5 [p=1.4410639275113839e-18]\n",
+ "sig diff: team avg=0.15, dh pos=1 vs. dh pos=6 [p=2.813016210280305e-69]\n",
+ "sig diff: team avg=0.15, dh pos=1 vs. dh pos=7 [p=2.1148242931535942e-89]\n",
+ "sig diff: team avg=0.15, dh pos=1 vs. dh pos=8 [p=3.304295742713236e-120]\n",
+ "sig diff: team avg=0.15, dh pos=1 vs. dh pos=9 [p=2.137620551842133e-178]\n",
+ "sig diff: team avg=0.15, dh pos=2 vs. dh pos=3 [p=9.408419520887769e-10]\n",
+ "sig diff: team avg=0.15, dh pos=2 vs. dh pos=5 [p=9.070076759034137e-25]\n",
+ "sig diff: team avg=0.15, dh pos=2 vs. dh pos=6 [p=4.013501898130506e-81]\n",
+ "sig diff: team avg=0.15, dh pos=2 vs. dh pos=7 [p=7.944112339150383e-103]\n",
+ "sig diff: team avg=0.15, dh pos=2 vs. dh pos=8 [p=8.693675681304036e-136]\n",
+ "sig diff: team avg=0.15, dh pos=2 vs. dh pos=9 [p=2.4995069467551577e-197]\n",
+ "sig diff: team avg=0.15, dh pos=3 vs. dh pos=6 [p=2.1216224794592715e-38]\n",
+ "sig diff: team avg=0.15, dh pos=3 vs. dh pos=7 [p=1.3510161214298604e-53]\n",
+ "sig diff: team avg=0.15, dh pos=3 vs. dh pos=8 [p=9.038350203657884e-78]\n",
+ "sig diff: team avg=0.15, dh pos=3 vs. dh pos=9 [p=1.6883407647869588e-125]\n",
+ "sig diff: team avg=0.15, dh pos=4 vs. dh pos=5 [p=9.32637132490468e-16]\n",
+ "sig diff: team avg=0.15, dh pos=4 vs. dh pos=6 [p=2.516207750135432e-63]\n",
+ "sig diff: team avg=0.15, dh pos=4 vs. dh pos=7 [p=1.513573427948481e-82]\n",
+ "sig diff: team avg=0.15, dh pos=4 vs. dh pos=8 [p=4.0313322280118733e-112]\n",
+ "sig diff: team avg=0.15, dh pos=4 vs. dh pos=9 [p=2.808888831509607e-168]\n",
+ "sig diff: team avg=0.15, dh pos=5 vs. dh pos=6 [p=2.2369635603850538e-18]\n",
+ "sig diff: team avg=0.15, dh pos=5 vs. dh pos=7 [p=4.441855163805096e-29]\n",
+ "sig diff: team avg=0.15, dh pos=5 vs. dh pos=8 [p=3.653180674858543e-47]\n",
+ "sig diff: team avg=0.15, dh pos=5 vs. dh pos=9 [p=3.159014991272495e-85]\n",
+ "sig diff: team avg=0.15, dh pos=6 vs. dh pos=8 [p=1.504718632116115e-08]\n",
+ "sig diff: team avg=0.15, dh pos=6 vs. dh pos=9 [p=3.424427465554104e-27]\n",
+ "sig diff: team avg=0.15, dh pos=7 vs. dh pos=9 [p=7.239966785374588e-17]\n",
+ "sig diff: team avg=0.15, dh pos=8 vs. dh pos=9 [p=2.632610106337814e-07]\n",
+ "sig diff: team avg=0.2, dh pos=1 vs. dh pos=3 [p=4.975863769272197e-06]\n",
+ "sig diff: team avg=0.2, dh pos=1 vs. dh pos=6 [p=2.65483444031005e-28]\n",
+ "sig diff: team avg=0.2, dh pos=1 vs. dh pos=7 [p=1.8135853092597406e-47]\n",
+ "sig diff: team avg=0.2, dh pos=1 vs. dh pos=8 [p=1.150233689898291e-57]\n",
+ "sig diff: team avg=0.2, dh pos=1 vs. dh pos=9 [p=7.223424302190309e-92]\n",
+ "sig diff: team avg=0.2, dh pos=2 vs. dh pos=6 [p=1.0411706910861108e-19]\n",
+ "sig diff: team avg=0.2, dh pos=2 vs. dh pos=7 [p=5.1747610796208696e-36]\n",
+ "sig diff: team avg=0.2, dh pos=2 vs. dh pos=8 [p=6.258135807210273e-45]\n",
+ "sig diff: team avg=0.2, dh pos=2 vs. dh pos=9 [p=1.5616448083207673e-75]\n",
+ "sig diff: team avg=0.2, dh pos=3 vs. dh pos=6 [p=9.158553336775526e-11]\n",
+ "sig diff: team avg=0.2, dh pos=3 vs. dh pos=7 [p=3.301865172490029e-23]\n",
+ "sig diff: team avg=0.2, dh pos=3 vs. dh pos=8 [p=2.1319152939525667e-30]\n",
+ "sig diff: team avg=0.2, dh pos=3 vs. dh pos=9 [p=4.16454555014728e-56]\n",
+ "sig diff: team avg=0.2, dh pos=4 vs. dh pos=6 [p=7.091753492774976e-27]\n",
+ "sig diff: team avg=0.2, dh pos=4 vs. dh pos=7 [p=1.3363453468048125e-45]\n",
+ "sig diff: team avg=0.2, dh pos=4 vs. dh pos=8 [p=1.3048721867324426e-55]\n",
+ "sig diff: team avg=0.2, dh pos=4 vs. dh pos=9 [p=2.650185348750165e-89]\n",
+ "sig diff: team avg=0.2, dh pos=5 vs. dh pos=6 [p=1.4343465725467364e-11]\n",
+ "sig diff: team avg=0.2, dh pos=5 vs. dh pos=7 [p=2.2522684790558515e-24]\n",
+ "sig diff: team avg=0.2, dh pos=5 vs. dh pos=8 [p=1.0047752339155035e-31]\n",
+ "sig diff: team avg=0.2, dh pos=5 vs. dh pos=9 [p=7.322521942273125e-58]\n",
+ "sig diff: team avg=0.2, dh pos=6 vs. dh pos=8 [p=6.891044968687213e-07]\n",
+ "sig diff: team avg=0.2, dh pos=6 vs. dh pos=9 [p=1.8501164693310642e-20]\n",
+ "sig diff: team avg=0.2, dh pos=7 vs. dh pos=9 [p=5.55812122235324e-09]\n",
+ "sig diff: team avg=0.25, dh pos=1 vs. dh pos=6 [p=1.3831070831091773e-08]\n",
+ "sig diff: team avg=0.25, dh pos=1 vs. dh pos=7 [p=3.31894013383353e-18]\n",
+ "sig diff: team avg=0.25, dh pos=1 vs. dh pos=8 [p=1.7330019936918686e-26]\n",
+ "sig diff: team avg=0.25, dh pos=1 vs. dh pos=9 [p=2.313896122609819e-28]\n",
+ "sig diff: team avg=0.25, dh pos=2 vs. dh pos=7 [p=2.165570151778953e-08]\n",
+ "sig diff: team avg=0.25, dh pos=2 vs. dh pos=8 [p=4.208549321541679e-14]\n",
+ "sig diff: team avg=0.25, dh pos=2 vs. dh pos=9 [p=1.871896887582137e-15]\n",
+ "sig diff: team avg=0.25, dh pos=3 vs. dh pos=7 [p=3.2875112864625612e-06]\n",
+ "sig diff: team avg=0.25, dh pos=3 vs. dh pos=8 [p=3.877797203112571e-11]\n",
+ "sig diff: team avg=0.25, dh pos=3 vs. dh pos=9 [p=2.481145930009218e-12]\n",
+ "sig diff: team avg=0.25, dh pos=4 vs. dh pos=7 [p=1.1449304045033806e-12]\n",
+ "sig diff: team avg=0.25, dh pos=4 vs. dh pos=8 [p=1.226266128442354e-19]\n",
+ "sig diff: team avg=0.25, dh pos=4 vs. dh pos=9 [p=3.0013494742914022e-21]\n",
+ "sig diff: team avg=0.25, dh pos=5 vs. dh pos=7 [p=3.756596993806079e-07]\n",
+ "sig diff: team avg=0.25, dh pos=5 vs. dh pos=8 [p=1.9972127491978356e-12]\n",
+ "sig diff: team avg=0.25, dh pos=5 vs. dh pos=9 [p=1.0843693706190971e-13]\n",
+ "sig diff: team avg=0.25, dh pos=6 vs. dh pos=8 [p=6.095539057877671e-07]\n",
+ "sig diff: team avg=0.25, dh pos=6 vs. dh pos=9 [p=7.246783482517967e-08]\n",
+ "sig diff: team avg=0.3, dh pos=1 vs. dh pos=7 [p=3.590625549191536e-06]\n",
+ "sig diff: team avg=0.3, dh pos=1 vs. dh pos=8 [p=4.0136192312912695e-11]\n",
+ "sig diff: team avg=0.3, dh pos=1 vs. dh pos=9 [p=1.7985941375427346e-10]\n",
+ "sig diff: team avg=0.3, dh pos=2 vs. dh pos=8 [p=7.586368508725617e-10]\n",
+ "sig diff: team avg=0.3, dh pos=2 vs. dh pos=9 [p=3.0993959675870173e-09]\n",
+ "sig diff: team avg=0.3, dh pos=3 vs. dh pos=8 [p=7.627671059317768e-08]\n",
+ "sig diff: team avg=0.3, dh pos=3 vs. dh pos=9 [p=2.632896126595547e-07]\n",
+ "sig diff: team avg=0.3, dh pos=4 vs. dh pos=7 [p=5.161961870945482e-07]\n",
+ "sig diff: team avg=0.3, dh pos=4 vs. dh pos=8 [p=2.699565677716188e-12]\n",
+ "sig diff: team avg=0.3, dh pos=4 vs. dh pos=9 [p=1.3251648748904703e-11]\n",
+ "sig diff: team avg=0.3, dh pos=5 vs. dh pos=8 [p=7.733403379191462e-07]\n",
+ "sig diff: team avg=0.3, dh pos=5 vs. dh pos=9 [p=2.435910255012577e-06]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from scipy.stats import ranksums\n",
+ "from itertools import product\n",
+ "\n",
+ "for team_avg1, designated_hitter_spot1, team_avg2, designated_hitter_spot2 in product([0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9), [0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9)):\n",
+ " if team_avg1 != team_avg2:\n",
+ " continue\n",
+ " if designated_hitter_spot1 > designated_hitter_spot2:\n",
+ " continue\n",
+ " if team_avg1 == team_avg2 and designated_hitter_spot1 == designated_hitter_spot2:\n",
+ " continue\n",
+ " statistic, pval = ranksums(designated_hitter_spot_scores[(team_avg1, designated_hitter_spot1)], designated_hitter_spot_scores[(team_avg2, designated_hitter_spot2)])\n",
+ " if pval < 1e-5:\n",
+ " print('sig diff: team avg={}, dh pos={} vs. dh pos={} [p={}]'.format(team_avg1, designated_hitter_spot1 + 1, designated_hitter_spot2 + 1, pval))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "7090098ae5344608824b5159a98774c9",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "pitcher_spot_scores = {}\n",
+ "num_simulated_games = 1000000\n",
+ "for team_avg in tqdm([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\n",
+ " for pitcher_spot in range(9):\n",
+ " batters = [team_avg] * 9\n",
+ " batters[pitcher_spot] = 0.1\n",
+ " aprun = ParallelExecutor(n_jobs=-1, use_bar=False)\n",
+ " pitcher_spot_scores[(team_avg, pitcher_spot)] = [runs_scored for runs_scored, _ in aprun(total=num_simulated_games)(delayed(simulate_game)(batters) for _ in range(num_simulated_games))]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "''"
+ ]
+ },
+ "execution_count": 65,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "p_batting_avgs = []\n",
+ "for team_avg in reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\n",
+ " p_spot_avgs = []\n",
+ " for pitcher_spot in range(9):\n",
+ " p_spot_avgs.append(np.mean(pitcher_spot_scores[(team_avg, pitcher_spot)]))\n",
+ " p_spot_avgs = np.array(p_spot_avgs) / np.mean(p_spot_avgs)\n",
+ " p_batting_avgs.append(p_spot_avgs)\n",
+ "\n",
+ "plt.figure(figsize=(9, 9))\n",
+ "sb.heatmap(p_batting_avgs, cmap='PuOr', center=1., annot=True, fmt='.3f', cbar=False)\n",
+ "plt.xticks([x + 0.5 for x in range(9)], [str(x) for x in range(1, 10)], fontsize=12)\n",
+ "plt.xlabel('Pitcher Batting Position (BA=0.1)', fontsize=14)\n",
+ "plt.yticks([y + 0.5 for y in range(6)], reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]), fontsize=12, va='center')\n",
+ "plt.ylabel('Team Batting Average (BA)', fontsize=14)\n",
+ "plt.title('Batting order matters when one player is much\\nworse than their teammates\\n\\n', fontsize=20)\n",
+ "plt.text(4.5, -0.1, 'Measured: Relative runs scored based on the Pitcher batting position\\n>1 means more runs scored, <1 means fewer runs scored', fontsize=12, ha='center')\n",
+ "plt.text(-0.7, 6.8, 'Data source: League averages & custom baseball simulations\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\n",
+ "plt.savefig('mlb-batting-order-pitcher.png', bbox_inches='tight')\n",
+ ";"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "sig diff: team avg=0.15, pitcher pos=1 vs. pitcher pos=5 [p=3.8851625783791845e-06]\n",
+ "sig diff: team avg=0.15, pitcher pos=1 vs. pitcher pos=6 [p=1.6567387490416848e-09]\n",
+ "sig diff: team avg=0.15, pitcher pos=1 vs. pitcher pos=7 [p=3.521864556215601e-13]\n",
+ "sig diff: team avg=0.15, pitcher pos=1 vs. pitcher pos=8 [p=2.2218326239175095e-14]\n",
+ "sig diff: team avg=0.15, pitcher pos=1 vs. pitcher pos=9 [p=7.785499313865129e-19]\n",
+ "sig diff: team avg=0.15, pitcher pos=2 vs. pitcher pos=6 [p=2.0124828886376083e-06]\n",
+ "sig diff: team avg=0.15, pitcher pos=2 vs. pitcher pos=7 [p=2.0015496834873097e-09]\n",
+ "sig diff: team avg=0.15, pitcher pos=2 vs. pitcher pos=8 [p=1.995996488992889e-10]\n",
+ "sig diff: team avg=0.15, pitcher pos=2 vs. pitcher pos=9 [p=3.2618519915831955e-14]\n",
+ "sig diff: team avg=0.15, pitcher pos=3 vs. pitcher pos=7 [p=1.0796284981609147e-06]\n",
+ "sig diff: team avg=0.15, pitcher pos=3 vs. pitcher pos=8 [p=1.6098095825185125e-07]\n",
+ "sig diff: team avg=0.15, pitcher pos=3 vs. pitcher pos=9 [p=1.0132397815959589e-10]\n",
+ "sig diff: team avg=0.15, pitcher pos=4 vs. pitcher pos=6 [p=7.396386504689107e-07]\n",
+ "sig diff: team avg=0.15, pitcher pos=4 vs. pitcher pos=7 [p=5.706121239186367e-10]\n",
+ "sig diff: team avg=0.15, pitcher pos=4 vs. pitcher pos=8 [p=5.3022601322627294e-11]\n",
+ "sig diff: team avg=0.15, pitcher pos=4 vs. pitcher pos=9 [p=6.745193973135644e-15]\n",
+ "sig diff: team avg=0.2, pitcher pos=1 vs. pitcher pos=6 [p=3.736793590540399e-21]\n",
+ "sig diff: team avg=0.2, pitcher pos=1 vs. pitcher pos=7 [p=2.524976319110312e-18]\n",
+ "sig diff: team avg=0.2, pitcher pos=1 vs. pitcher pos=8 [p=6.746491316929479e-35]\n",
+ "sig diff: team avg=0.2, pitcher pos=1 vs. pitcher pos=9 [p=6.403222171714512e-45]\n",
+ "sig diff: team avg=0.2, pitcher pos=2 vs. pitcher pos=6 [p=2.745377273205046e-20]\n",
+ "sig diff: team avg=0.2, pitcher pos=2 vs. pitcher pos=7 [p=1.592850476032973e-17]\n",
+ "sig diff: team avg=0.2, pitcher pos=2 vs. pitcher pos=8 [p=9.402831597164582e-34]\n",
+ "sig diff: team avg=0.2, pitcher pos=2 vs. pitcher pos=9 [p=1.3256041606779473e-43]\n",
+ "sig diff: team avg=0.2, pitcher pos=3 vs. pitcher pos=6 [p=3.052850586857199e-19]\n",
+ "sig diff: team avg=0.2, pitcher pos=3 vs. pitcher pos=7 [p=1.4702483430295165e-16]\n",
+ "sig diff: team avg=0.2, pitcher pos=3 vs. pitcher pos=8 [p=2.2604947523265107e-32]\n",
+ "sig diff: team avg=0.2, pitcher pos=3 vs. pitcher pos=9 [p=5.097574771619022e-42]\n",
+ "sig diff: team avg=0.2, pitcher pos=4 vs. pitcher pos=6 [p=3.0892533399419725e-19]\n",
+ "sig diff: team avg=0.2, pitcher pos=4 vs. pitcher pos=7 [p=1.5077781232512707e-16]\n",
+ "sig diff: team avg=0.2, pitcher pos=4 vs. pitcher pos=8 [p=2.1263766982882966e-32]\n",
+ "sig diff: team avg=0.2, pitcher pos=4 vs. pitcher pos=9 [p=4.537418406485668e-42]\n",
+ "sig diff: team avg=0.2, pitcher pos=5 vs. pitcher pos=6 [p=1.8903311286109317e-12]\n",
+ "sig diff: team avg=0.2, pitcher pos=5 vs. pitcher pos=7 [p=2.4052778093024993e-10]\n",
+ "sig diff: team avg=0.2, pitcher pos=5 vs. pitcher pos=8 [p=2.987994710899591e-23]\n",
+ "sig diff: team avg=0.2, pitcher pos=5 vs. pitcher pos=9 [p=1.6689098012995852e-31]\n",
+ "sig diff: team avg=0.2, pitcher pos=6 vs. pitcher pos=9 [p=3.2940442949580448e-06]\n",
+ "sig diff: team avg=0.2, pitcher pos=7 vs. pitcher pos=9 [p=8.444971822549267e-08]\n",
+ "sig diff: team avg=0.25, pitcher pos=1 vs. pitcher pos=6 [p=1.263607488343873e-23]\n",
+ "sig diff: team avg=0.25, pitcher pos=1 vs. pitcher pos=7 [p=4.516058690648763e-49]\n",
+ "sig diff: team avg=0.25, pitcher pos=1 vs. pitcher pos=8 [p=1.868709972805515e-46]\n",
+ "sig diff: team avg=0.25, pitcher pos=1 vs. pitcher pos=9 [p=9.124875977854823e-82]\n",
+ "sig diff: team avg=0.25, pitcher pos=2 vs. pitcher pos=6 [p=9.121582635686449e-20]\n",
+ "sig diff: team avg=0.25, pitcher pos=2 vs. pitcher pos=7 [p=2.658571220387695e-43]\n",
+ "sig diff: team avg=0.25, pitcher pos=2 vs. pitcher pos=8 [p=7.532218740635377e-41]\n",
+ "sig diff: team avg=0.25, pitcher pos=2 vs. pitcher pos=9 [p=3.792046725058025e-74]\n",
+ "sig diff: team avg=0.25, pitcher pos=3 vs. pitcher pos=6 [p=8.383966192001412e-09]\n",
+ "sig diff: team avg=0.25, pitcher pos=3 vs. pitcher pos=7 [p=1.4330997662660238e-25]\n",
+ "sig diff: team avg=0.25, pitcher pos=3 vs. pitcher pos=8 [p=1.0181059424978879e-23]\n",
+ "sig diff: team avg=0.25, pitcher pos=3 vs. pitcher pos=9 [p=5.150512722206992e-50]\n",
+ "sig diff: team avg=0.25, pitcher pos=4 vs. pitcher pos=6 [p=2.4556456675838104e-22]\n",
+ "sig diff: team avg=0.25, pitcher pos=4 vs. pitcher pos=7 [p=3.8858858801144415e-47]\n",
+ "sig diff: team avg=0.25, pitcher pos=4 vs. pitcher pos=8 [p=1.405260832926898e-44]\n",
+ "sig diff: team avg=0.25, pitcher pos=4 vs. pitcher pos=9 [p=3.7388299589130853e-79]\n",
+ "sig diff: team avg=0.25, pitcher pos=5 vs. pitcher pos=6 [p=1.0041981665787318e-16]\n",
+ "sig diff: team avg=0.25, pitcher pos=5 vs. pitcher pos=7 [p=1.1186354391340145e-38]\n",
+ "sig diff: team avg=0.25, pitcher pos=5 vs. pitcher pos=8 [p=2.2474110418558506e-36]\n",
+ "sig diff: team avg=0.25, pitcher pos=5 vs. pitcher pos=9 [p=4.689603775373094e-68]\n",
+ "sig diff: team avg=0.25, pitcher pos=6 vs. pitcher pos=7 [p=2.6120598419790005e-06]\n",
+ "sig diff: team avg=0.25, pitcher pos=6 vs. pitcher pos=9 [p=6.531623482124316e-20]\n",
+ "sig diff: team avg=0.25, pitcher pos=7 vs. pitcher pos=9 [p=8.779138829657236e-06]\n",
+ "sig diff: team avg=0.25, pitcher pos=8 vs. pitcher pos=9 [p=1.194392607523925e-06]\n",
+ "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=2 [p=8.353757123256407e-08]\n",
+ "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=3 [p=1.6153351773311131e-22]\n",
+ "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=4 [p=2.6074603335403655e-06]\n",
+ "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=5 [p=6.0048940289536404e-15]\n",
+ "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=6 [p=1.7188273321673662e-52]\n",
+ "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=7 [p=6.956087996395689e-82]\n",
+ "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=8 [p=3.4107621024146694e-109]\n",
+ "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=9 [p=4.971738054338718e-173]\n",
+ "sig diff: team avg=0.3, pitcher pos=2 vs. pitcher pos=3 [p=9.959224425730982e-06]\n",
+ "sig diff: team avg=0.3, pitcher pos=2 vs. pitcher pos=6 [p=4.3000326686945074e-23]\n",
+ "sig diff: team avg=0.3, pitcher pos=2 vs. pitcher pos=7 [p=2.0450594381375726e-43]\n",
+ "sig diff: team avg=0.3, pitcher pos=2 vs. pitcher pos=8 [p=1.025315766940147e-63]\n",
+ "sig diff: team avg=0.3, pitcher pos=2 vs. pitcher pos=9 [p=6.1794625367623775e-114]\n",
+ "sig diff: team avg=0.3, pitcher pos=3 vs. pitcher pos=4 [p=4.4498404478852373e-07]\n",
+ "sig diff: team avg=0.3, pitcher pos=3 vs. pitcher pos=6 [p=4.320334730364867e-08]\n",
+ "sig diff: team avg=0.3, pitcher pos=3 vs. pitcher pos=7 [p=6.30824649646141e-21]\n",
+ "sig diff: team avg=0.3, pitcher pos=3 vs. pitcher pos=8 [p=2.0209047768680628e-35]\n",
+ "sig diff: team avg=0.3, pitcher pos=3 vs. pitcher pos=9 [p=2.6572390367461014e-74]\n",
+ "sig diff: team avg=0.3, pitcher pos=4 vs. pitcher pos=6 [p=6.026023750905654e-26]\n",
+ "sig diff: team avg=0.3, pitcher pos=4 vs. pitcher pos=7 [p=2.881407355644203e-47]\n",
+ "sig diff: team avg=0.3, pitcher pos=4 vs. pitcher pos=8 [p=2.215982775474632e-68]\n",
+ "sig diff: team avg=0.3, pitcher pos=4 vs. pitcher pos=9 [p=4.880235566606631e-120]\n",
+ "sig diff: team avg=0.3, pitcher pos=5 vs. pitcher pos=6 [p=1.1352532168880723e-13]\n",
+ "sig diff: team avg=0.3, pitcher pos=5 vs. pitcher pos=7 [p=9.404064042439993e-30]\n",
+ "sig diff: team avg=0.3, pitcher pos=5 vs. pitcher pos=8 [p=8.688559264804496e-47]\n",
+ "sig diff: team avg=0.3, pitcher pos=5 vs. pitcher pos=9 [p=1.391575735005446e-90]\n",
+ "sig diff: team avg=0.3, pitcher pos=6 vs. pitcher pos=8 [p=3.578976447437263e-12]\n",
+ "sig diff: team avg=0.3, pitcher pos=6 vs. pitcher pos=9 [p=2.2812745629023015e-37]\n",
+ "sig diff: team avg=0.3, pitcher pos=7 vs. pitcher pos=9 [p=8.020784190788164e-19]\n",
+ "sig diff: team avg=0.3, pitcher pos=8 vs. pitcher pos=9 [p=6.172240291460689e-09]\n",
+ "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=2 [p=3.813780983491385e-15]\n",
+ "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=3 [p=1.3830330447057497e-36]\n",
+ "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=4 [p=2.5780721611446785e-27]\n",
+ "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=5 [p=1.1216694099222578e-27]\n",
+ "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=6 [p=3.872729624040197e-90]\n",
+ "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=7 [p=2.626956916522825e-176]\n",
+ "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=8 [p=9.715274614129921e-230]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=9 [p=3.220267321795051e-281]\n",
+ "sig diff: team avg=0.35, pitcher pos=2 vs. pitcher pos=3 [p=1.6139670212100365e-06]\n",
+ "sig diff: team avg=0.35, pitcher pos=2 vs. pitcher pos=6 [p=8.299306585791126e-35]\n",
+ "sig diff: team avg=0.35, pitcher pos=2 vs. pitcher pos=7 [p=1.8586225058566005e-93]\n",
+ "sig diff: team avg=0.35, pitcher pos=2 vs. pitcher pos=8 [p=2.603919487805058e-133]\n",
+ "sig diff: team avg=0.35, pitcher pos=2 vs. pitcher pos=9 [p=4.022886588831997e-173]\n",
+ "sig diff: team avg=0.35, pitcher pos=3 vs. pitcher pos=6 [p=6.614034101392958e-14]\n",
+ "sig diff: team avg=0.35, pitcher pos=3 vs. pitcher pos=7 [p=1.7637445840810704e-55]\n",
+ "sig diff: team avg=0.35, pitcher pos=3 vs. pitcher pos=8 [p=7.739828991638169e-87]\n",
+ "sig diff: team avg=0.35, pitcher pos=3 vs. pitcher pos=9 [p=2.1719000592863943e-119]\n",
+ "sig diff: team avg=0.35, pitcher pos=4 vs. pitcher pos=6 [p=1.2942527134733691e-20]\n",
+ "sig diff: team avg=0.35, pitcher pos=4 vs. pitcher pos=7 [p=1.2835707549227605e-68]\n",
+ "sig diff: team avg=0.35, pitcher pos=4 vs. pitcher pos=8 [p=3.6300281863188245e-103]\n",
+ "sig diff: team avg=0.35, pitcher pos=4 vs. pitcher pos=9 [p=1.8965909045113902e-138]\n",
+ "sig diff: team avg=0.35, pitcher pos=5 vs. pitcher pos=6 [p=2.730228869895967e-20]\n",
+ "sig diff: team avg=0.35, pitcher pos=5 vs. pitcher pos=7 [p=5.1291556746202074e-68]\n",
+ "sig diff: team avg=0.35, pitcher pos=5 vs. pitcher pos=8 [p=2.0013757721704826e-102]\n",
+ "sig diff: team avg=0.35, pitcher pos=5 vs. pitcher pos=9 [p=1.3596282572447242e-137]\n",
+ "sig diff: team avg=0.35, pitcher pos=6 vs. pitcher pos=7 [p=2.1711619378971672e-16]\n",
+ "sig diff: team avg=0.35, pitcher pos=6 vs. pitcher pos=8 [p=1.0883133428675732e-34]\n",
+ "sig diff: team avg=0.35, pitcher pos=6 vs. pitcher pos=9 [p=4.59793707847946e-56]\n",
+ "sig diff: team avg=0.35, pitcher pos=7 vs. pitcher pos=9 [p=3.626139910490934e-14]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from scipy.stats import ranksums\n",
+ "from itertools import product\n",
+ "\n",
+ "for team_avg1, pitcher_spot1, team_avg2, pitcher_spot2 in product([0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9), [0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9)):\n",
+ " if team_avg1 != team_avg2:\n",
+ " continue\n",
+ " if pitcher_spot1 > pitcher_spot2:\n",
+ " continue\n",
+ " if team_avg1 == team_avg2 and pitcher_spot1 == pitcher_spot2:\n",
+ " continue\n",
+ " statistic, pval = ranksums(pitcher_spot_scores[(team_avg1, pitcher_spot1)], pitcher_spot_scores[(team_avg2, pitcher_spot2)])\n",
+ " if pval < 1e-5:\n",
+ " print('sig diff: team avg={}, pitcher pos={} vs. pitcher pos={} [p={}]'.format(team_avg1, pitcher_spot1 + 1, pitcher_spot2 + 1, pval))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "96d6c555d55e45c59744678efa8bf796",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "hitter_spot_scores = {}\n",
+ "num_simulated_games = 1000000\n",
+ "for hitter_ba in tqdm([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\n",
+ " for hitter_spot in range(9):\n",
+ " batters = [0.25] * 9\n",
+ " batters[hitter_spot] = hitter_ba\n",
+ " aprun = ParallelExecutor(n_jobs=-1, use_bar=False)\n",
+ " hitter_spot_scores[(hitter_ba, hitter_spot)] = [runs_scored for runs_scored, _ in aprun(total=num_simulated_games)(delayed(simulate_game)(batters) for _ in range(num_simulated_games))]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "''"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
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