{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from mgo import mgo\n", "\n", "from IPython.display import display, Markdown\n", "import folium\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "file_dir = 'mgo/uploads'\n", "village = 'Nakiu'\n", "\n", "min_area = 70 # exclude any buildings with area (in m2) below this value\n", "max_length = 10000 # The furthest a single building can be (in total) from the PV point\n", "\n", "cost_wire = 25 # per metre\n", "cost_connection = 150 # per node\n", "opex_ratio = 0.02 # % of the above per year\n", "years = 20 # years over which to amortize (and maintain)\n", "tariff = 0.25 # USD/kWh\n", "demand = 6 # 6kWh/person/month = MTF Tier 2\n", "gen_cost = 7000 # USD/kW\n", "discount_rate = 0.08" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Skipping field relations: invalid type 5\n" ] }, { "data": { "text/markdown": [ "### Please click on the desired location for the PV point." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Then click on the marker that appears and copy the values in below (with all decimals values!)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "buildings = mgo.load_buildings(village=village,\n", " file_dir=file_dir,\n", " min_area=min_area)\n", "\n", "x_mean = buildings.geometry.centroid.x.mean()\n", "y_mean = buildings.geometry.centroid.y.mean()\n", "village_map = folium.Map([y_mean, x_mean], zoom_start=15, control_scale=True)\n", "\n", "popup_html = '

Latitude: \" + lat + \"

Longitude: \" + lng + \"

Copy these values into the variables below.

'\n", "folium.ClickForMarker(popup=popup_html).add_to(village_map)\n", "display(Markdown('### Please click on the desired location for the PV point.'))\n", "display(Markdown('### Then click on the marker that appears and copy the values in below (with all decimals values!).'))\n", "display(village_map)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "get_lat = -9.6266\n", "gen_lng = 39.1824" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "### A small graph is shown below, with the PV point in green" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAI1CAYAAADVQv5HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3XmczfUex/HXb2aYsSSUEpkoa6Iw6CBLIWlQQkSJkEJXSU1GctMMR2mTFoWSW1mSZRIhEk4YKZW9BVnKvq9nfvePbzORZbYz8zvL+/l4nMec353fnPOZ6s6857t9LNu2EREREQlVYU4XICIiIuIkhSEREREJaQpDIiIiEtIUhkRERCSkKQyJiIhISFMYEhERkZCmMCQiIiIhTWFIREREQprCkIiIiIQ0hSEREREJaRGZufnyyy+3S5cunUOliIiIiPjOypUrd9u2XSy9+zIVhkqXLk1ycnLWqxIRERHJJZZlbc7IfZomExERkZCmMCQiIiIhTWFIREREQprCkIiIiIQ0hSEREREJaQpDIiIiEtIUhkRERCSkKQyJiIhISFMYEhERkZCmMCQiIiIhTWFIREREQprCkIiIiIQ0hSEREREJaQpDIiIiEtIUhkRERCSkKQyJiIhISFMYEhERkZCmMCQiIiIhTWFIREREQprCkIiIiIQ0hSERkQDl9UJiIrRoYT6mpDhdkUhginC6ABERyRq3G9zxBzhOFElJkQAMGOBwUSIBSGFIRCRAeTzwBC+Tl5MMYCgej9MViQQmTZOJiAQolwtu5SsW0CjtWkQyT2FIRCRAxfU5Qu28qyjUrC4JCRAX53RFIoFJ02QiIgEqbOli8tauzpQvCjhdikhA08iQiEig+uoruPVWp6sQCXgKQyIigUphSMQnFIZERALRvn2wbh3Uru10JSIBT2FIRCQQLVoEdepAZKTTlYgEPIUhEZFApCkyEZ9RGBIRCSCpLTg2j5nHuM23qgWHiA9oa72ISABxu2FS/Hc8wxpi3urKusXXc/3dFaFSJahYESpUgHz5nC5TJKAoDImIBBCPB36gOj9QlTd5lPLhBbk+ZR1MmWIWVP/yCxQv/k84qnhGUCpWzOnyRfySwpCISABxuSApCT6lDdezlqNtX4Uzm7OePg2//WaC0dq18O23MG6ceR4RcXY4Sn1+zTUQHu7Y9yTiNMu27QzfHBMTYycnJ+dgOSIicjEpKTBsGOyYs5ohP7ai0K5fCQu30v9C24a//jKhKDUopX7cvRvKlTs7KFWqBOXLnzXl5vWaaTqPx4SyuDgI08pT8WOWZa20bTsm3fsUhkREApBtw7XXwowZUKVK9l7r8GFYv96EozOD0r+m3Gb9Vomh0yqylkrs4XISEmDAgPRfXsQpGQ1DmiYTEQlElgUtW8L06dkPQwULQo0a5nGm1Cm3v8NRnikehjOWouylIuvxeLL3tiL+It0BTsuyeliWlWxZVvKuXbtyoyYREcmIVq1MGMopERFm+qxlS3jqKVb0HEsdPFRkPWCmykSCgabJREQC1alTZhpr9WooWTLH3y51vZLWDEmg0DSZiEiwy5MH7rgDZs6Enj1z/O3CwrRGSIKTMr2ISCDL6akykRCgMCQiEshuvx2WLIFDh5yuRCRgKQyJiASyQoVM9/rZs52uRCRgKQyJiAS6Vq3MeUMikiUKQyIiga5FC5g1y+wuE5FMUxgSEQl0V18NZcrA4sVOVyISkBSGRESCgXaViWSZwpCISDBIXTeUiYN0RcRQGBIRCQZVqpgg9NNPTlciEnAUhkREgoFlaapMJIsUhkREgkVqF3sRyRSFIRGRYHHLLfDrr7Btm9OViAQUhSERkWBxZuNWEckwhSERkWCidUMimaYwJCISTJo1U+NWkUxSGBIRCSaXXGIat86Z43QlIgFDYUhEJNhoqkwkUxSGRESCTcuWatwqkgkKQyIiwaZkSbj2WjVuFckghSERkWDUsqXpVSYi6VIYEhEJRqnrhtS4VSRdCkMiIsFIjVtFMkxhSEQkGKlxq0iGKQyJiAQpb2wrtr89nRYtIDERUlKcrkjEP0U4XYCIiOSMFz316L7tV77bto2kpJIADBjgcFEifkhhSEQkSC1ZnocS3Mk2rsbCxuNxuiIR/6RpMhGRIOVyQRfGnXUtIufSyJCISJCKiwMIh3hISEi9FpF/s+xMnEERExNjJycn52A5IiLic5al84YkJFmWtdK27Zj07tM0mYiIiIQ0hSEREREJaQpDIiIiEtIUhkRERCSkKQyJiIhISFMYEhEJUl4vDH3BC6gdh8jFKAyJiAQptxtKPdsZgPh4GDbM4YJE/JTCkIgIZhQlMZGgamr6w6IDxJKUdq12HCLnpzAkIoIZRfkqfh5zk44HzSjKA/km8xW3YmEOXFQ7DpHzUxgSEQE8S226MpbptCIfR4NiFKX57g/wduxMbKzacYhcjHqTiYgArjoWD3w+nnF0IYlYVlafARR0uqys++UXrPXraTv/DtrmdboYEf+mkSEREcyoyfMJEUy5832K1ijDk/ObwcGDTpeVdePHQ4cOkFdJSCQ9GhkSEQHCwmDAAIBwSHkXeveGJk1g9mwoUsTp8jInJcWEoU8/dboSkYCgkSERkX8LC4NRo8yK49tugz17nK4oc775BgoWhGrVnK5EJCAoDImInI9lwSuvQNOm0KgR/PWX0xVl3AcfwIMPmu9BRNKlMCQiciGWBUOHQuvW0LAh7NjhdEXpO3IEPvsMOnZ0uhKRgKEwJCJyMZYFgwdDp07QoAFs3ep0RRfk9cKMB6eyIm8dEscWD4qDI0Vyg8KQiEhGDBgAPXuaQPT7705Xc16vP7eHOlP68ulfdYPm4EiR3KAwJCKSUU88Af36mUC0aZPT1fzjwAF47jkeGlaWIxSgH68wmTbsnP2905WJBASFIRGRzOjVCwYONGuI1q1ztpbDh00jtbJlYfNmJvRdSWm2UJrfWUodEr5vDi1bwooVztYp4ud0zpCISGZ1724OM7z1VvjyS7jhhtx9/6NH4c034cUXTQ2LF0OFCvRMgf1FweMpwOWuJyjwn0dg3BizALxyZXj2WahbN3drFQkACkMiIlnRuTNERkLjxvDFF7lzps+JEzB6tNnh5nLB/PlnBbF/Do5Mlc8cHtm9u9lu36kTlCljQlHDhtp6L/I3TZOJiGRV+/ZmhKZZs5ydijp5Et55B8qVMyNRn39uTpfO6IhUZCT06AEbNsADD8DDD8Mtt8CcOWDbOVe3SIBQGBIRyY7WrWHMGLjzTli61Levffo0vP8+VKxows+kSTBzZtZHofLkMYcxrlkDjz4Kjz8ON9+Md9pMEhNsWrQwS5C0JV9CjWVn4q+CmJgYOzk5OQfLEREJUF9+aaahJk82u82yw+uFiRPhv/+F4sVhyBCoX983dZ4pJQWmTuXP3s8T/uc2mvIlq6hBQsK/p9tEApNlWStt245J7z6NDImI+ELTpibAtG0L8+Zl7TVSUswIUNWqMHKk6Y+2cGHOBCEwi4zatKF7zPcsoR4P8CEAHk/OvJ2Iv1IYEhHxlUaNYOpUuO8+mDUr419n22b6q0YNM0/14otmyq1x41xZ5HxznTB68jYPMJ6r2I7LleNvKeJXtJtMRMSX6tUzwaZlS7Po+a67LnyvbZvptUGDzHb5IUOgVatc3+UVFwdwFV+P6cKMK4ZSPW5krr6/iNMUhkREfK12bbPdvnlzsxOsXbtz71m40Gxx373b9D5r29ZMWzkgbUt+t6ehUiXY9hSUKuVILSJOUBgSEckJ1aubUZ9mzUg5fpJhf3TC44E2JZbywKZnsX7/3YSg++6D8HCnqzWuuMKcSZSYCG+95XQ1IrlGYUhEJKdUrQrz53O4dmMuOfQtj/ArlfmZWXc9y53rOput7v7mySehQgV4+mkoXdrpakRyhRZQi4jkpEqVeOamL+jNKJZRi/Js4O3T3fwzCAFcfjk88gi88ILTlYjkGoUhEZEcVrJZVWZxBz9RhZNE+v9urX79YNo0+OUXpysRyRXphiHLsnpYlpVsWVbyrl27cqMmEZGgEhcH+ZvW54Ey35CQkLp7y48VKQJ9+pjdbSIhQCdQi4jkhqVLTdPU775zupKMOXAAypaFJUugfHmnqxHJEp1ALSLiT2JiTKPUAwecriRjLr0U+vY1LUFEgpzCkIhIbsibF2rW9H0z15z02GOmtciaNU5XIpKjFIZERHLLLbfAN984XUXGXXKJWUyt0SEJcgpDIiK5wOuFj/6oz89vLyIx0fRkDQi9esGiRbB6tdOViOQYHbooIuIrtg3798OWLec8ti/dQv0tmynJNqrHHweiTAsMf1egADz1lDkte+pUp6sRyREKQyIiGXXyJGzbdt6wk/YIC4Po6LMfVavyxuZoPtkSzW4u4yRReDxOfzOZ0LMnvPSS2QlXvbrT1Yj4nMKQiAiYUZ29ey8edHbvhquuOjvoVKtmOs1HR5vmppdeet6Xv3QzbDkjAPn9wYtnypePlKfi2NRxMP3KzsDlMmclOdRXVsTnFIZEJCh5veB2g8djgkdc3+OEbf/j4mEnKurcUZ0aNf55ftVVWW6qmnrQYlo9/n7w4r+8uL87ndYN4+i6ucQnNQEIjGk+kQxQGBKRoOR2w7fx0xlLN7xJ4aQM2kdYdMmzg06tWtCmzT+jOpdckmP1hIUFdnhYnBxFA0oyh2aMoyubPu8Mz9QFy3K6NJFsUxgSkaDk8YCHOuThJLHMoXizmkxPytqojpjRrHxJx2nPx1zHb/Tf2APKnoAHHjCPMmWcLlEkyzTjKyJByeWCPRRjPJ2J5XNq11EQyiqvFy7fvppiEfv5uXwbCr3wNEV3/AyffGLWUdWqBQ0akPLeWF4adJAWLQis4wMk5GlkSESCUuqanI1zupHwfXMKPvkc+pGXNW435Bn1IR/QkXUbwrAsCAu3zInaNWvCiBHw+edsjP+AbmufYDmjiU9qBwT21KCEDv1kEJGglLZGZ0BVqF0SvpwNsbFOlxWQPB4oTFWWUTvt+ix588Ldd/Pk2LvxrN2NhX3++0T8lKbJRCT4de8O773ndBUBy+WCCdzPRsqnXV/ovj1czm6KXfQ+EX+jkSERCX7t20P//rBjh9keL5mS0WMBAv34AAldlm3bGb45JibGTk5OzsFyRERySPfuZseTFrGIhAzLslbath2T3n2aJhOR0NC9O4wZoy1OInIOhSERCQ01a5qmowsWOF2JiPgZhSERCQ2WpYXUInJeCkMiEjo6dYIvvjAHBYqI/E1hSERCR5Ei5qyhCROcrkRE/IjCkIiElu7d4d13IRM7aUUkuCkMiUhoqV8fTp3S8cgikkZhSERCi2VBt25aSC0iaRSGRCT0dO4MU6fCwYNOVyIifkBhSERCz5VXwm23wccfO12JiPgBhSERCU2pC6lFJOQpDIlIaGrSBHbtglWrnK5ERBymMCQioSk8HLp21UJqEVEYEpEQ1qULfPIJHD3qdCUi4iCFIREJXdHRULs2TJnidCUi4iCFIREJbVpILRLyFIZEJLTFxsLGjbBundOViIhDFIZEJLTlyQMPPqiF1CIhTGFIROShh2D8eDh50ulKRMQBCkMiIuXKQeXKMH2605WIiAMUhkREwCyk1lSZSEhSGBIRAWjdGlauhN9/d7oSEcllCkMiIgBRUdCxI4wZ43QlIpLLFIZERFJ16wbjxsHp005XIiK5SGFIJER5vZCYCC1amI8pKU5X5AeqVIGrr4bZs52uRERykcKQSIhyD01hV/wrfJV0hPh4GDbM6Yr8RLduWkgtEmIUhkRC1Iqlp7iRH1hGbSqyFo/H6Yr8RPv28PXXsGOH05WISC5RGBIJUTXrRdKFcbzMEyyiPg/l/8jpkvxDwYLQti28/77TlYhILlEYEglRcXGQkGCxK7Yr03vPo9WqwfDII3D8uNOlOS91qkwLqURCgsKQcPz0cUYsHUGlUZUokFiASqMqMWLpCI6f1i/FYBYWBgMGwMyZ0G3kjVjJybB7N9StC7/+6nR5zqpZ04wQLVzodCUikgsUhkLc8dPHafphU56c+yTrdq/j6KmjrNu9jifnPknTD5sqEIWSQoVg0iTo3BluvhmmTXO6IudYlhkdevddpysRkVygMBTiRi0fxTdbvgEg3Hv2577Z8g2jlo9yoCpxjGXBY4+Z4aK+feHJJ+HUKaerckanTvDFF2a0TESCmsJQiHtv1d9biG1YOwpK7T/782NW6TTekFS7tmlNsXYtNGwIf/zhdEW5r0gRcwjThAlOVyIiOUxhKMRtObDFPLFgYWm49+cLfF5Cz2WXmRGiFi3MGpovv3S6otyXOlVm205XIiI5SGEoxEVfGp32/OMboMOPF/68hKCwMLPt7JNPoEsXGDTIHF0dKurXN9OE337rdCUikoMUhkJct2rd0p5/XRqKH4byZyyReKjaQ7lek/ihBg3MtNnixXD77fDnn05XlDu0kFokJCgMhbhetXpxS/QtAKSEwaTK/4wO1b+mPr1q9XKwOvErxYvD3LngckGNGrBokdMV5Qpvp84c/3gq7ZodPKeHm/q7iQSHCKcLEGdFRUTx5f1fMmr5KMasGsO0ar8xbmoKlyQk0Kt2b6IiopwuUfxJeDgMGWLOImrXDh5/HPr3N9NpQcr9/pVUPN6Im+YMZeacllT4aTv31NkB27fz05wd1PhuOxMZTlLSjYA5u0lEAovCkBAVEUW/Ov3oV6efWSj6ZVn65bsVFITkQpo1gxUrTCBavBg++ACKFnW6qhyxYslJbmcLLZnJbXxFylcloPBVUKIEX5+uxxeUYDPXAKi/m0iAUhiSs1mWaVT58cdQvbrT1Yg/K1XKNDSNizPTZpMmmV1nweTAAV5e34YfKUF9vuYoBUl4DFx/j/4cDoPZq/+53eVypkwRyR6FITlXhw5wxx3gdgf19If4QN688PLLUK8e3Hmn2W3Wq5cJ1YHujz+geXNKN72FT0q8zq3LwnG5TPZLlfrc4+Gcz4lI4LDsTJyfERMTYycnJ+dgOeI3qlSBUaPM1mKRjPjlF9PtvVw5s/uqUCGnK8o0r9f8DbBjzg8k/BBLwQGPEdb/yeAIdyIhyLKslbZtx6R3n/7sl/Pr0MFMlYlk1HXXwdKlULgwds2avPvY6oDbZeV2w/744Ty/qAHdD7zEsNP9FYREQoDCkJxfhw4wZUro9qWSrImKgnfeYcaNz9J5ZAzepM+Jj4dhw5wuLGO+XZpCd0YzjDgmca8WRIuECIUhOb8yZaBsWZg3z+lKJABN3duII+TnS5oCgbPL6oFLp3OQSxnO04AWRIuECoUhuTBNlUkWtS+YxCzuxEseIEBCRUoK9/z8X37pNJjYWIuEBC2IFgkV2k0mF9auHTz3HBw7BvnyOV2NBJBmp2YyvV1HYo8G0C6r6dOxwsNpNz6WdlomJBJS0g1DlmX1AHoAREeraWdIKV7cnB/z+efQpo3T1UigOHoU65tF3LX5Q+4q4nQxGZSSAv/9rzldWwumRUJOutNktm2Ptm07xrbtmGLFiuVGTeJPNFUmmeD1wqSH57M6bw0S3yoSMLvImD7dtBqJjXW6EhFxgNYMycW1bm0WUR844HQlEgDcboie8AJf7KkZOLvIUkeFBg/WqJBIiFIYkosrUgQaNoRp05yuRAKAZ6lNNVbxKG8ymTYcmzHX/w8Z0qiQSMhTGJL0aapMMqhV6R/YwjWUZDvzaEzvzf2hfHkYPhx27XK6vHNpVEhEUBiSjGjRAr791j9/mYlf6XrVLPa7mtMgthDRCT0p9scq+N//YN06E4o6dICFCyETbYBylEaFRASFIcmIAgWgeXOYPNnpSsTPhX0xi5rPNWfmTBgwAMLCLahdG8aOhd9+gzp1oHdvqFQJXnkF9u51rliNConI3xSGJGM0VSbp2bMHVq+GBg3O//nChaFPH/jxRxgzBr77zvQzu/9+WLIk90eLNCokIn9TGJKMuf12WLMGtmxxuhLxV19+aRbbR0Vd/D7Lgrp14cMPYdMmqF4dHnoIqlSBkSNh//6cr1WjQiJyBoUhyZi8ec02+4kTna5E/NWsWWY6NTMuuwwefxzWroU33jBd78uUga5dYdmynBst0qiQiJxBYUgyTlNlciFeL8yenfkwlMqyzKjSxx/D+vVQsSJ07AjVqsFbb8HBg76rVaNCIvIvCkOScQ0awM6d5peVyN+8Xni/1wp+P1GcxAnR2T9W6Ior4KmnYMMGeOklmD8frrkGevSAlSuzX7BGhUTkXxSGJOPCw03zVo0OyRncbtjyziwmHmru21Onw8KgcWOYMsVMo5UuDffcAzVrwnvvweHDmX9NjQqJyHkoDEnmpE6V+cs5MeI4jwcKcIQZtEy79rnixc1e/V9+geefh6QkiI6GXr3MDraM0qiQiJyHwpBkTq1acPo0rFrldCXiJ1wueJIRLKVu2nWOCQ+HO+4w7WFWrzZTanfead70/ffh6NELf61GhUTkAhSGJHMsC9q311SZpImLg4QEM9iSkGCuc8XVV8Nzz5nDHAcMMIeCRkfDf/5jjoH4N40KicgFWHYmpjtiYmLs5OTkHCxHAsJPP5m/zjdvNus6RPzF5s1mPdGYMVC2LDz8MN677sH9Sl7av1id5JZDaPNBC/1nKxIiLMtaadt2THr36UeCZN4NN5hu9kuWOF2JyNmuuQaGDDGhqG9fGD+eE1eUos6zjTh58Cj3Toj13QJvEQkaCkOSNTpzSPxZnjzmkNA5c/hP7W+5ih2kEAFYObPAW0QCmsKQZE379mbL86lTTlciclFlGl9HVX4kLydpzNycXeAtIgEpwukCJECVKWOabM6fD82aOV2NyAWZBd2RfPLZCD7a0pfL+n0P5HG4KhHxJxoZkqzTVJkEgLAws9ls4PKWFLuxJGHvvOV0SSLiZxSGJOvatYMZM+DYMacrEUmfZcGrr5oF1rt2OV2NiPgRhSHJuuLFoUYN+PxzpysRyZjrr4f77oNBg5yuRET8iMKQZI+myiTQDB4MU6fCDz84XYmI+AmFIcme1q1h3jw4cMDpSkQypkgR05bjscfUY09EAIUhya4iRaBhQ9MrSiRQdO+OvX8/U++bQosWkJhoWpeJSGjS1nrJvg4dTJPMzp2drkQkY8LD+V+t17n1vQ68zuV8llSQK7eF89DDERDx9yM8/J/nF7v+V9NXrxfcbvB4TP/YuDh1rRHxdwpDkn0tWkDPnmaHTrFiTlcjkiETdzagKpezkFtZQQxFJ5yGb7xw+vQ/D28GrsPCzgpHJ09H0O1YBNWoQfOkLwCztV9E/JfCkGRfgQLQvLnpGv7oo05XI5IhLhfcmLQaG4tarCDh6SyEFts282tnhKOubU8z/8vT2JgRI7X/EPF/GrwV39CuMgkwcXGQkGCeJySknlSdSZZlRoQiI80fBYUKUaVBUXZxBbsxo6Rq/yHi/yw7E7spYmJi7OTk5BwsRwLWyZNw1VWwahVERztdjUjGWZZPd5WlpMCwYVozJOIPLMtaadt2THr3aZpMfCNvXrPNfuJE6N/f6WpEHJPa/kNEAof+XhHf0VSZiIgEIIUh8Z0GDWDnTli/3ulKREREMkxhSHwnPNw0b9XokIiIBBCFIfGt1KkytTkQEZEAoTAkvlWrljlvZdUqpysRERHJEIUh8S3L0kJqEREJKApD4nsdOsAnn6jzpYiIBASFIfG9ypVNN/slS5yuREREJF0KQ5IzNFUmIiIBQmFIckb79jBlCpw65XQlIiIiF6UwJDmjTBm47jqYP9/pSkTOy+uFxETzPDFRS9xEQpnCkOQcTZWJH3O7YWf86wDEx5vmqiISmhSGJOe0awczZsCxY05XInIOjwfqsuSsaxEJTQpDknOKF4caNWDWLKcrETmHywU7uIonGJF2LSKhSWFIcpamysRPxcVB1dtLUO/aHSQkmGsRCU0RThcgQa51a3jiCTh4EAoVcroakTRhYXBrx6tg9g8wwOlqRMRJGhmSnFWkCDRsCNOmOV2JyFm8XvhoYQlWz9mu3WQiIU5hSHKepsrED7nd8MLY4kTt+UO7yURCnMKQ5LwWLcxWnV27nK5EJI3HA0cowHX8mnYtIqFJYUhyXoEC0Lw5TJ7sdCUiaVwuOE0edlI87VpEQpPCkOQOTZWJn4mLg4GPH8XKn1+7yURCnMKQ5I7bb4c1a2DLFqcrEQHMbrJHOh+lRNn8DBhgrkUkNOn//pI78uY12+wnTnS6EpF/HD0K+fM7XYWIOExhSHKPpsrE3xw7BvnyOV2FiDhMYUhyT4MGsHMnrF/vdCUihkaGRASFIclN4eGmeatGh8QPeL3w2f+Osvi7/Dp0USTEKQxJ7rrvPhOGbNvpSiTEud2w5pPvSdmxQ4cuioQ4hSHJXTVrmj/JV61yuhIJcR4PFOIQW4hOuxaR0KQwJLnLsqB9e02VieNcLijKXubSNO1aREKTwpDkvg4d4JNPtEhDHBUXB7eWWM/ldSvq0EWREBfhdAESgipXNt3slyyBW25xuhoJUWGWzVUH1zMiqQIUdroaEXGSRobEGTpzSDLJ64XERNP31ye7v7ZvN9vqCysJiYS6dEeGLMvqAfQAiI6OzvGCJES0bw+1a8Nrr0GePE5XIwHA7Yb4ePM8Kcl8HDAgGy+4fj1UrJjtukQk8KU7MmTb9mjbtmNs244pVqxYbtQkIcAbXYY/oq7juXrzdcaLZEjqbi8b66zrLFu3DipUyOaLiEgw0JohcYTbDfu33k2rrc/TYHljICJ7f+VL0HO5/hkRSr3OFo0MicjftGZIHOHxwFc0ojh/8hM3YE/9TAcxykXFxUFCgnnuk91fGhkSkb8pDIkjXC5YSU3Ksom+vEq3bYOhbl1YvNjp0sRPhYX9s0ZowABznS3r1ysMiQigaTJxSOpf9R6PhcvVjGL9m8AnH0HHjnDTTTB0KFx/vbNFSvA6ehT+/BNKl3a6EhHxAwpD4ogz/8o3wuH++6FtWxg1Cho2hJYtYfBguPpqZ4qU4LVxI1x7LUToR6CIaJpM/E1UFPTrBxs2wOWXw403wjPPwP79TlcmwUSLp0XkDApD4p8KFzZtxH/4Af76C8qXh5dfhhMnnK5MgoEWT4uwoSzWAAAgAElEQVTIGRSGxL9dfTWMGQMLFsDCheYX2Icf6mAiyR4tnhaRMygMSWCoXBlmzDBB6M03oXp1mD1b2/El07xe2LFwPf3erZjugZ8+bwEiIn5JqwclsNxyCyxdCp99Bv/5jxk5crshJsbpyiSHeb3mX/UAwP38Cfr3OkrYiWNmZ9jRo3Ds2NkfL/D8h8VHqbZ9JVW2v8GhJRNY/kUUNzeIhMhIs2Yt8p/nM7+IZMXESH7iJpKSygDZbAEiIn5JYUgCj2VB69Zmt9mYMeZj/frmJL7rrnO6Oskhbjcsip/NAODx5y7h5ND8RBXJb5qt5stnPqb3vGhRlh2N5CZgObWJIIUCe05wc+Rxsx7t4EHz8bi5LvrNCR7gBCfJy++UyX4LEBHxSwpDErgiIuDhh6FTJ3jlFdP4tUMHePZZuOIKp6sTH/N4YC5NAIjkJLGNYebMzL/OvuNwNP5pxvMgRyhIQifMcNN5LE78pzks+KAFiIj4Ja0ZksBXoAAMHAhr15oDjCpVguefh8OHna5MfMjlghTCsbDTrrMiLg7C8kXSqunxdNt6pLYAiY31UQsQEfFLlp2JBagxMTF2cnJyDpYj4gO//mrC0cKFZpSoWzfIk8fpqiSbUlLMaQsejwlCcXHZaMlRogSsWAElS/q0RhHxL5ZlrbRtO91FpRoZkuBz7bXw0UemxfnUqWYn2pQp2nkW4FJPLZ850we9yaKidGaViKRRGJLgVb06zJ0Lb7xh5jhcLli0yOmqxB9ERioMiUgahSEJfk2bwsqV0KcPdO5sFoD89JPTVYmTIiPNjjERERSGJFSEhUHHjqYNQ+PGcNtt0KULbN3qdGXiBE2TicgZFIYktERGQt++phFsiRJw003w1FOwb5/TlUlu0jSZiJxBYUhC06WXmnVEP/4I+/ebRrAvvqipkxDg9cKv2yMZ9PQJtdgQEUBhSEJdiRIwerRZWL10qWne+cEH5jemBCW3G/7cdJDDy37k9fidDE84dc496kkmElp0zpDImZYsMdNmhw6ZQ23uuMO0/5Cg0aIFfJxUkCPkxwKKWvuIKJgPLrss7fHzzss4uPo3xnM/M2hFr4SS6kkmEoB0zpBIVtStC4sXw5Ah0K8fNGoEy5c7XZX4UMsyP7KfwpRgB1fyF8OfPwFbtsC8efDmm9C3L7PDmrOVUjRjNj9wI50Ty0OPHvDxx7BjR67W6/XC0Be8GqUSyUHqTSbyb5YFrVrBnXfC+++bprAul/lNVK6c09VJNnULH8fShg/SvGD43ydZh0FYYShcOK3R74lVcO/3nQCwSGF0lx/pVnYhTJwIvXqZ3neNGplHgwZw5ZU5Vq/bDaWfvZ/ltCU+6W4AjVKJ+JimyUTSc/QovPYajBgB7drBc8/l6C8/yUEnT8LVV5v1YWXLXvC2i7b+8Hph9WpYsMC0fPnmG7P2rFEjaNjQPC6/3Gcld71tMy9+VZ0y/MYhChEbm7UGtSKhSNNkIr6SPz8884w5oygqCq6/3gSiQ4ecrkwyKynJ/Pu7SBCCdFp/hIdDtWrwxBMwYwbs3m0W3ZcuDePGmdGlqlXhscfgs89g795slfzwidcYS1cOUQjIeoNaEbkwjQyJZNbvv5sGsPPmmYaw3btD3rxOVyUZERtrRvceeCDn3uP0afjuu39GjpYsMf3yUkeO6teHIkUy9lr792Nfey1vdPuBL9eWyn6DWpEQk9GRIYUhkaz6/nvzm2nTJrOeqG1b7TzzZ9u3ww03mFPHCxTIvfc9dQqSk00wWrDAzL2VL2+CUaNGcMst5tyr83nxRfPf2f/+l3v1igQRTZOJ5LSbboLZs+Gdd8wq11q1zC878U/jx0ObNrkbhADy5DFzW888A19+CXv2wOuvm9GhV181a5hq1jRHOsyaBYcO4fXCsCGn2DXodcYU7qcdZCI5TCNDIr6QkgKTJkF8PJQvjzfRjfuLqudfgCu5z7ahYkWztufmm52u5mwnTsCyZf+MHK1YwbaiN/Db1nAKc4Aq/ERCgnaQiWSFRoZEclNYGLRvD2vXQvPmHKvflLrx9dmUtIb4eLMzSZzh9cL4nkvZuj2MxPm1/W+UJTLSrCMaNMiEod27eaPkMPJyklJsYQYt2DNrmdNVigQ1hSERX8qbF/r0oWvdDeTlJKPojUUKHo/ThYUutxvyjh7J64e7Ej/Q8v9gGhXFJS0aUpsVXMlffMEdDPq5HTRpAl9/bUa5RMSnFIZEcsBN9QtRn28Ix8tAXtB2aActW3Ka1kxlObUAAiKYxsWZPsJNYqO4OuFRLtmxEe67D7p1M6NIc+YoFIn4kE6gFskBcXEAeXhvwURGLY+h4I01gTscrio01a4bwYhZT9CCmSyiQUAE09Rzjv6RF7p0MUcCTJpkWsVEReEdMBD3mpZ4loVpbZpINmgBtUhOW7wY7rkHvv0WypRxupqQk5ICo5/cwL1v1ued+K08FZ8n8ANDSgrMmMGOXi+we/sJ+vMic2imhdYi/6IF1CL+ol49s8vsnnvg2DGnqwk5YWHQ8+XyFIkpS9yNXwR+EALzTd11Fz2qraA/L3L670H+QJgCFPFHwfBjQcT/9eljtnY/+qjWejilSxcYO9bpKnzKVcdiDs2YT2NzHQBTgCL+SNNkIrnlyBFzxk2fPtCjh9PVhJ5DhyA6GtavN13ng8BFG8qKSIanybSAWiS3FCgAn35qps2qVTOnDkvuueQSaNUKJkwwTVaDwLkLrUUkK/Q3hEhuKl8eRo82bSF273a6mtCTOlWmqUoROYPCkEhuu+suc2ZMhw7meGTJPfXrm0Xsmu4XkTMoDIk4YcgQMzoxaJDTlYQWyzKjQ+PGOV2JiPgRhSERJ0REwMcfm/Ur06c7XU1o6dwZJk7UMQcikkZhSMQpxYrB5MnQvTts3Oh0NaGjVCmoUQOmTXO6EhHxEwpDIk6qVctMmbVubbbeS+7o2lVTZSKSRmFIxGk9epiRih49tMspt9x1F6xcCVu2nPMprxcSE6FFC/MxJcWB+kQkVykMiTjNsuCtt2DNGhg1yulqQkNUFNx7L3zwwTmfGvHCMVbGf0qzpF7Ex5tDDUUkuOnQRRF/kC+fOZDR5YLq1aFOHacrCnrezl052Pxeui99grtLLOO+Eguxvl5In6Xf0YRylOQPejNK/b5EQoBGhkT8xbXXmnUs7drBzp1OVxP03PNqkLJ3P+NnX06ZsQNZ+s1pGDiQN+J30ozZgAWo35dIKNDIkIg/ad4cunUzUzjz5kGePE5XFLQ831rMZyKrqMY+LiP2EpjZFPo1Bm9EJEWf20fCEJu4OMvpUkUkh2lkSMTfDBpk+pg984zTlQQ1lwu+ojH7uCztGky/r7hn8xBxSX4G9DqgxqciIUAjQyL+JizMHMZYo4bpct+mjdMVBaW4OPPxzI7vZ7nsMtizBwoXzvXaRCR3KQyJ+KOiRc2C6mbNoHJlqFTJ6YqCTrod31PD0HXX5VpNIuIMDQCL+Kvq1cHtNgcyHjrkdDWhJzUMiUjQUxgS8WdduphO61276kDGXOT1wk87L2PEgD06eFEkBCgMifi711+HzZvh5ZedriRkuN2w84edFPr+ax28KBICFIZE/F1kJEyZAi++CAsXOl1NSMg7+X9UZTVTuRtABy+KBDmFIZFAEB0NH34I990H27Y5XU1w++wzem56kkYsZDbNAR28KBLstJtMJFA0aQK9e5sTqhcsgLx5na4oQ7xeM+105hZ2vz2754svoGdP8i+cTcc5lS+87V5EgorCkEggiYuDZcvgySfNWqIAMDzhFJc+15d5vEhSUn4gnS3tTlmwADp3hunTCatRjQE1nC5IRHKLv/59JiLnExZmOq1/8QX8739OV5O+OXO478WbuJeJjKIX4Kfrb5YuNSNukyZpTkwkBCkMiQSawoVh6lTo2xdWr3a6mvNbs8b0WevdmxWtEriOTdTBQzfe9b+s8d13cNddZk1Ww4ZOVyMiDtA0mUggqlIFXn0V7rkHVqzwn5YRu3bB4MEwebKZC5s2jdYRedlwPbw6dzqvrriFfLdcD9R1ulLjp59MaBs92pz2LSIhSSNDIoGqY0fzC/yBB5w/FfDECXjpJbj+eoiIgLVrzchV3rxpbS/eXlCBApPeJ6x9O/jjD2frBdiwAW6/HV55xYwMiUjISjcMWZbVw7KsZMuyknft2pUbNYlIRo0YAbt3O3cqoG2bM5Cuvx4WLYLFi+G110wri/Np3hz69IG774Zjx3K31jP9/rvZnTdkCHTo4FwdIuIXLDsTR/zHxMTYycnJOViOiGTatm1Qs6ZZWN2kSY6+1Znb5O8ulUyXHx/HOnjQhLLGjTP2IrZtAkjevKZmy/JJPRnetr9tm2lx8vjj5qgCEQlalmWttG07Jr37NE0mEuhKloSPPoL774ctW3L0rdxueDP+D9omPUCzt1oyq9iDZgFyRoMQmPAzZgz8+KNZ95TNeuLjbaKSJmWsbcZff5lae/RQEBKRNApDIsGgYUPo398sqD5+PMfexuOBh3mHrZSiAut5+9RDEB6e+RcqUACmTYPhw2HevCzXk7z4OBPoyP/oxPt05sev91745r17zchZu3bw9NNZfk8RCT4KQyLB4oknoHRp+M9/cuwtXC4YxBAGksBhLsneNvlrroGPP4ZOneDXXzP8ZV4vJCbCg43/IHFJA/Jyimg2c5BCjPbcAJ9+eu4XHTxoFps3aWJ2u4mInEFb60WChWXB2LFQq5b52LWrz98itS2Fz9pUNGwIAwdCq1bmRQsWTPdL3G5YE/8hb9CHYcTxXeOnqRllccg1kgL17oUe3UzIeuMNKF4cjhyBO++EmBjT7DYba5REJDhpAbVIsFm71iwQnjMHqld3upr02TZ07w779pnzidJZAd2iBVRL+i/V+Y67mU5sLMycecYNx4/D889jv/ces+omUGLxJMJLleSG5WMJi9BguEgo0QJqkVBVqRK8+aZZP7T3Imto/IVlwahRsGMHJCSke7vLBdFsZQ7N0q7PEhUFiYmMbTub6tOeJWX3XqqtGsOw4fpxJyLnp58OIsGobVsThjp2dP5AxoyIjIRPP8UePZrJ98+gRQuzLuh8pffvD3Uu/ZnthSvTpAk89dT5X3LaluqUZwMNWUAK4f7ZE01E/ILCkEiwGjbMHGz4/PNOV5IxV13Fx03GETuhHSeS5lxwq/yLw23KHUjm2/3lmTvXbEg7H5cLDlOIwxRKuxYROR8toBYJVhERMHGiWThcs6ZZROzPvv2W2z7pziya8+XfU2DE//04w4C/P/7JVVjYFxzx8flibxEJWhoZEglmV15pAlHXrpnavp6rUlLMFrFWrfjm7pdpw1QsbCxsEhNss8D6jMcb/TezlauxMJs/LjTik9oTbeZM8zHdk6lFJGRpZEgk2NWpY7avt24NS5dC/vxOV/SPP/80J2cfPQorVtD66mgSKl94NMfrhagjezgceRkVSpsjijTiIyLZpb+VREJB795QuTI88ogZYfEHc+dCtWpQuzYsXAjR0emO5rjdsODNnzl1IoX1683nNeIjItmlHyMiocCyYPRo00fsnXecreXUKTOc06ULTJhgOsdHZGyQ2uOBWJLYytVp1yIi2aVpMpFQUaAATJ0Kdev+MyKTS1K7y/86/zcGb7yPkjcUxVq1CooVy9TrNK7yJ82S5lCJtYB2iImIb2hkSCSUlCuH9513OXB7Wzo23XXBs3x8ze2G5fHTSPyqNiO2tmNonZmZDkIAj6W8yq+176NmbHESErReSER8QyNDIiHGvbYVVx+YwtC5Nbhm7hbArM/JSR4PnCaS5sxiJTHELsvCi+zfj/XuaGqsXMnM0r6uUERCmUaGREKMxwN9GElh9lGYfbmy7sblgtncwUpi0q4zKrVL/fib3+TH6DtJiS6dM0WKSMjSyJBIiHG5ICmpMPNowl1Mo5yrS46/Z3YOQHS7YXj8PjYxggYsouOwnB/JEpHQojAkEmJSg8jGT+9l4KFxlInL+TCUumU+KzweaMJc8nOMTkxgxeLngCif1icioU3TZCIhJjWYPL0oluv+9BC2d7fTJV2UywVTaEcZfqM8G3hnZQ1YvtzpskQkiCgMiYSqAgWgWTOz3d6PxcVBQgLUir2S9S9M4bJXB0HLlvDMM3D8uNPliUgQsOxMnEYbExNjJycn52A5IpKrpk6FUaNg/nynK8mcP/+ERx+FdevwvjcO94JaZ61H0qnUIgJgWdZK27Zj0rtPa4ZEQtkdd8BDD5lwceWVTleTcVdeCVOmwKRJHGvakojD9/MNA0hKKgJogbWIZI7+fhIJZfnywZ13mmARaCwL7r2XR1w/cDefMZemgFp0iEjmKQyJhLp774WJE52uIstaFF9BXk4Sy0xALTpEJPM0TSYS6po2hc6dYds2KFnS6WoyJLXX2foF2xn1bTc+7TaFWjuLZ/oMIxERUBgSkchIaNUKJk+Gvn2driZD3G54Lv4Ef1CNkXTBLlOPme86XZWIBCpNk4lIwE2VeTzQj5cpyGG6MJbLPhqJ99hJEhOhRQsu2oA2tb1HeveJSOjQyJCIwG23QadOsHkzXHON09Wkq1nF32mX9DI1WEleTjL11DMcLPkKP+5L4HPuJSnJ/J13vl1lbjfEx5vnSUlc8D4RCR0aGRIRyJMH7r4bJk3y2Uvm2AiMbfPo9z34qWk/ysVWpH1CVa5d+znDK4zlP7xGCuHYWAyIt8yOs389BsRb2JgHaPeZiCgMiUgqH0+Vud3wcvxuvkvaRnw8DBvmoxd+/32svXto9PmTzJxpRnXCwuCSFg1x4SE16iQm2GCf+0hMsNPuAe0+ExFNk4nI37y3NOT4uq08fusmSjcum+2TnD0e6MpYarOMNnzqmxGY7dvh6adh7lyIOPvHl9lFZp11EvX5pP7v6d0nIqFDYUhEAHCPiKDSERe3LoinwwIzQpSdtTQuFwxJ6kNv3qAOS3C56mavQNs2LTh69oQbbzzn06kNaNOT0ftEJHQoDIkIYEZKinAVBTicdp0dZsQlH59OGsLEff0p8fQS+HudTpZMmgQbNwbUrjcRCQxaMyQigBnJuZQDzKNJ2nV2pI7APL6yE1cXOUrYtKlZeh2vF0Y+vZV9DzzG+/XHkpInMnuFiYj8i8KQiABmJKdRiQ1cUbc8CQk+XEsTHg7Dh5sXPHky01/uHprC7cNvZdnJm+jydm3fLcQWEfmbpslEBIAwy+aqwxt5aXo5uMzHL960KXaZa5l7zzuMpE/awuWMLNC+dvxz/MWVtGUyoK3wIuJ7CkMiYuzaZXZoXebrJGSMv2YgHd9ryGCqEZ9UD7jwQubU3mPhn06i89YPqcpyDlMI0FZ4EfE9hSERMTZsgHLlcua1Dx2i5qfPsIHyfEhnGrIQj6fUBW93u2FfvJuBJNCARdzU5AoiIzO2FT41SJ25dT47RwSISPDTjwgRMTZuzJkwdOgQ3HEHJ8pW5gZ+5k0eZQGNaFpp6wW/JOWz6fyX/5KHU7TnE/76/WiG387thnnx83kr6Wo2xI/jtUF7fPFdiEgQUxgSEWPDBihf3mcv5/XCS88dYs01d7DqZGWqLH6LFxLC2BDbj9/veITeUxvB1n8FItuGhAQe29CLBnzNtfxGaX5jycZinEyanaGTrFcv2s/bPMLn3EkLkujhvhaaNIG33oKdO332/YlI8FAYEhHDxyND7mE21Z6/i2/2VabGircY/lIYAwbAzJnQeFY/rEcegUZnBKKjR+G++2D6dAr+vJy7E2pSM7Y4gytMZAjPMoH76cBHF19A7fXywm/3MZtm9OQd2vApo+J3wCOPwJIlUKkS1KsHr7wCv/+ere8vx3qviUiu05ohETF8PDLk+dZiEiNYTVVsws4NMf36kZIC+29qxAuVPqL/b49SvEEFrK+/JixfvrTF1YmJEB8fxyyaM51W7Di4GrwJZsv+v8XHc13J4xztNILY5WbN0JNx+SGsNbRubbb2z58PU6fC0KFQqhTcc4/5XMWKmfr+3hi4kxuGdacRu6mTZL45nWwtEpgUhkTEDGts2uTTkSGXC5KSbjrr+t+GnerHqb37eWlJbQYyhIKV4xmQ7+xTqv/pJVaV6VWX858lbaFVK/joIyhU6J8bP/kEJk7EWrGCuMvznL+ovHnhjjvM4623YPFi+PRTaNwYLr3UhKJ77jHtPqwLnJa9cycMH07XN95nAu2owk/cx//weDpm5h+PiPgRTZOJhDivF0bGbWNvyqUkjrzEZ9M9cXGQkACxsVzwEEePB6bQjj8oxVAG4vn23ACSepL1zJnQN6EY1ty5EB0NN99spvYAVq2CPn1g2jS4/PKMFRgRAQ0bwsiRsGULjB0LJ06YMHTddfDkk+Dx4D2VQmIi3N9kJ8vqPYF9/fWQksK4J37iUd6mJTN4lb60Kv1D1v9hiYijNDIkEoLO3H5+4gQUm7uQW7iC+HjzeV9M92SkIarLBXmSNvI9N6VdpytPHnjzTXj7bex69fjstjeoO60/S1uNolWVG7P2F15YGNSubR5uN6xebabSunfn6JZ9ND9UjEfYzAd0ZtnTP/HYsBL0ToHDhcDjqcKSAiN5aFZr+O8KKFo0KxWIiIMs27YzfHNMTIydnJycg+WI+I7Omznbmf88Th07Tfn5o2jPJxRjN6XYSjheBjOYtXf0Y+qsqFypKSUFFjYfzoGNf7L2oRGZ/nc0ofvX3PNeMybThs58SEKC79ft9Gy0nqYL42jNNCxsYmPNKNU5+vWDNWsgKen865lEJNdZlrXStu2Y9O4L4V8NEuzcboiPt/kzaXmGtmQHO7cb3onfTK2kZxkz/xru50N+4Ebu5jMKcITybKAGKxmzrDLMmGG2ueewsDC4tdRG7n6yLAMGZD6sTtzZgDos4SHGADnTqiO6SQXu4bO06wuOXrnd2MeOs7jxYO0wEwkwmiaToOXxQBzDaMtkarEcjye0/3P3eKANUyjEQZryJWuonPa5Jk0gMrIM61xTuavGXOj7mFlg/OqrUKFCzha2aRPce2+WvtTlgvik6mdd+1raWqf4C699AiAigtfqTKTL0Ou4n7XcmzQF0A4zkUAQ2r8dJKh1yzeBm3gbFx68RIR8TysTHPqlXZsAdL4pxCZmzczIkeZMni5dYODAs3du+dLGjVC2bJa+9J+dZhlr1ZEVaWuf4tMPNvNXF+MuitKE+fTlFTxL+wIX2JUmIn5DYUiC0/z5tPy6H+8+9hU1fi2RY78oA8n5gsMFp6Xy5IEnnjCHID7zjDmscNgw6NjRtwuvjh6FPXvMeT9ZkJFF2tmVutZqAGbq62L/3NqWWMIJoriJr/mcWI7t/Q28r2gNkYif0wJqCT6rV5tzYyZPhgYNnK4mOCxbBn36YIdHMLb6G0zbUt03i9J//NFMka1Z47NSfc0c+gg2Fhb2RRdp2/e2Z+6ROoy0HqPhTft5Yuk9WAULmjORChTI3cJFJMMLqDUyJEHD64U3n9lK+5GxLG41kla3NNAOAV+pXRu+/ZZZbcZxz5uNiCWK65J+AQpmb2Rm06YsT5Hlln8vyr7gIu3t27G+nEPT39+h6aUAheHkF9C9u2k7MnMmXHllDlcrIlmh3xUSNF57bi/3v1iFV473pPXEe0N+95jPhYUxZ3cNTpGXLUSzimrsmbUsyy/n9cJX72zks5/L+fXOq9S1Zhb2WdfneOcd6NDBnGSdKm9eeP99aN7cfOHatTlaq4hkjUaGJGgs+L4IFahLGX4FcmabdUhbt46E75vTlTeZQlta8ynjv28Jgx8xC6wjzI+TC57vdPw47N1rHvv2MfWdvZSfM4EVNPPpYY++lqFF2idPwujRpu/Zv1kWDB4MpUubE68nT4b69XOuYBHJNIUhCRquOhbtP/+EH7iR5nyOy3Wn0yUFj19/hSZNKPDaUKrtaMtxD9Rw3UO+B1zQrSvUrYv3gwm4p5Zj/Hi4ef04xvIUO5Ou5MiwfVxyaq8Z+rnsMihSBIoWpdTGolzKfh7mbZZTG4/nHqe/y/O62CLt1OCXZ8qntM1/PdEVr7/wcPuDD8LVV0ObNvDaa2YUSUT8g23bGX7UqFHDFvFXXq9tJyTY9tOur+2Dl5SwvX/ucrqk4PDHH7Zdpoxtjxp1/s+npNj2yJH2kfyX20MYYH9KK3sDZe3/8LJ9A6vtzrdtte0jR8x9Z0hIsG2w7Vp8a2+grP199Qdt+8CBdMs5fdp8bWys+ej1+uKbzBrzPaTYy6lh38VUOyEhA1+0erWdEh1tf9U00Y69M8VOSLDtkyf953sSCSZAsp2BfKMwJMHpiSdsu23bc34BSyb9+adtV6xo2253urf2bLDGPkmE/Q0uO5JjtjnC2r5gQEgNr7Gxtj180CE7pVt3E7oWL77o+6SGqFJsvujr54bYWNuuz0L7BHnscE7asbEZ+7rXnt5mf8dN9mAG2WDbTZrYaf+8nP6eRIJJRsOQFlBLcEpIgJ9/hk8+cbqSgOT1wsvP7uPXck1ZfFVbUp58Kt2vKdW0EtVYSQU2cjm7qVDh4ic2n9mNvv9/C2K9OxpeecV0jX/2WTh16rxf5/FAFVaznFrk46ija8NcLmjA17zJI3jJk+GDPef+XIL6LOI5nsfG4su5FjbmAVrvJpLbFIYkOEVFwfjx0LcvbNvmdDV+zes1Z+mk9dPau58vW7/FQy+U5puDVbllwX8ztDMvLg7uS6jKvHKPMr9Sb9b8bGe+31irVvD997BypVmHtHbD2bWlgOtmm5H0YTCDOUZ+R08Wj4uD3ldOYUedthdv1fEvLhcc5hJSI1DTJnba89TPi0ju0QJqCV41akCvXtCtG8yaZXb1yDncbhgYn0Jn3qdU0lecHJLEJUWa8Div8AEPAFaGRirSFhr3GwA33QTTpppRnswqXhw+/xzeeosTMXU5ebQXSQwiKcmkqrhrJ/HXVQfZUb07CXWcPVk8bEs4TcwAACAASURBVNMGrrB24f6mTqb+tPz3DrWnnoLhw3O2rYiIXJhOoJbgduoU1KljAtHDDztdjV9q0QJ2JXlYRAPiGMqfTTtTucHladvdgYueunxeixebk6V//hkKF85ybY82+Jlhi1x8SCd6M4p7mh1lys+VzInO9epl+XV9ZuhQ+OMPGDXK6UpE5Dx0ArUImB5b48ebc10aN4brrnO6Ir/jckHJpLcZxPO8Qj8SGvigAWq9eiZlxcXB229nubarb69MmUW/MZ/bGMozXLo8nJ9K1ef6OvX8Y45/yhR46SWnqxCRbEp3ZMiyrB5AD4Do6Ogamzdvzo26RHzr1VfNL66vv1bTzH9J2bqNExWq0OWWX6jaoEj2+439zbv3AEfKVCah8kdcGls/S6+bkmL6w84Yt4exm+pRms2UYyO9Eko6f0Djb7+ZNiXbt/+/vTuPs7H8/zj+OiPGNpasLSRCZM1YjkTWFKm+tMq+lGTLNhnaBydSloiQ5VfIkjgUZWwxZGyTrb4iUsmSym6W+/fH9R1Mhjkzc87cZ+a8n4/HeZijc5/zcfcw83Zdn+u6Lm84KSL+xdORoRS/NVmWNcWyrFDLskKLFCninepEMlrv3maU6N137a7E7wR9MJ5cXZ9j7oqCqW94vgHXh/kZ9s8Ahka1IDzcStPxKIl9SEXuLkQbFjCGfvzGbf6x2mrhQnjsMQUhkSzAL0aaRXwuKAg+/hhGjTInpYtx5gxMnWpW3XlZVBRM5gWyEUcwF9MVYJxO2Ms9DCPi8nPbLVxodpMWkUxPYUgCR6lSZulU+/bmLKkAFx8PK5+ezrc5GjJ8bmmvH5TqdMJFcvIj5alCTLoCTFiYaeJu2fLGexdlmCNH4McfzWn0IpLpaXxXAkunTrB4Mbz5Jrz9tt3V2Gpi2GE6LRtCK75gtQ8OSk0MLMc+DOXN6ltpFlYrze91o/PBbLFoEbRqZaZeRSTTUxiSwOJwmNPFq1Uzwwx16thdkT3Wr+fZ8U+zkNaswYxueLsP53KAKVgDoqOz1jj0ggVmcyARyRKy0rcnEc8ULw4TJkCHDnDunN3VZCzLgnHjoE0bVj4znY7MJPFUHp/14dSoYXaUziqOHjV9Z02b2l2JiHiJRoYkMLVpY6bLwsJMOAgE586ZjSe//x6ioniqVGkOls2AXY+rVDH9NRcumGNSMrvPP4cWLSA42O5KRMRLNDIkgWv8ePODbdUquyvxmcRzx7o2OsAfZepiJViwcSOULp3koFRvLqm/Rs6cUL48xMT46AMyRuK93DlsAQus1l5vOBcR+ygMSeAqWBCmTYPOneHvv+2uxidcLvg2fBkTVlfk7aOdGVFxNuTOna73vOZgV09CQY3/9Q1lYi4XvBd+nDtObqXdp83TtG+SiPgnhSEJbM2amSmPPn3srsQntn17jtEM4G2GMoHeRG1K/2G1Lhd8F76Y/e69hIfjWSjIAn1DUVFQjR0U4G8ukMs/Nn4UEa9QGBIZNcocLLp4sd2VeF348b5sowYRmLXz3miSjoqCvJwhkkbUINqjUBBfrQZ/LN+autEkP+N0wjc0TfJcRLIGNVCL5MkDM2eapuq6daFoUbsr8o5586j212pWvbqNltscXmuSdjoh3P0cpwlhOQ+zsthnwAM3vObdr+6h79E9rHefwu0uCPjZvkEeuHzvwv1k40cR8ZoUD2q9WmhoqBWdyef9Ra4rLMyselq40OxHlEnFx8PkwQd4dnwdFnX9ko7ja3i1OTrx8NSoKHi62GqeXfIUjmnTTBPRdTzyCLzhvpc1NKA/79GypWnczpQcDrNFgYj4Pa8d1CoSMN54A/bvh9mz7a4kXVwuaP2uk+mX2tJlYg2vN/pevQqt7dSGONxurG7dWNJm1nWnwZxOeJjlPMcnVGO7pphExK9omkwkUXCwCUJNm5ozp0qUsLuiNImKgge5nfk8cfm5T9WqxYzHl/Dsh/fj5jzh7ueBpNNgZkqpOJ9+5mL58a4UG7AZffsREX+hkSGRq1Wtak5w79Qpc3b5YkZhLBzEkuPyc5+6eJHqC4Ywnyf4iG7AtQEscTSp7/aOFL+7IKsffS9TN1OLSNaiMCTyb4MGwdmzMHGi3ZWkSVgYlCgeR937b/J9o29cHDz7LMHFb6YDMyGloz0cDiZVm0zDrwaz2f2H50vzRUR8SOPUIv92000wa5b5id6sGZQrZ3dFqRIUBMVujmXcxJugkmfXxMfDqOGxbN5kUfO+HISF3XhH6vh4cI20CJ38PHcFn6HsjiW8NTabR0d7fPljGZ6i4OUz0bRfj4jYTWFIJDlly5qG6vbtzR5EN2Wyvypxcamq2eWCXa/O5w1G0mX5NKDmDZe+u96OpdHr9bCAykQSPjbY46XyTif8485HCKc5QRE1U4uI7TRNJnI9PXpASAi8847dlaReXBxkz+7xy6OiYA7P4GIwblpSftogc7Brco4fp/mEFtRgK4t5jHPkSdXoTlgY5CkWQov6p7Vfj4j4BYUhkesJCoLp0+H992HHDrurSZ1UjgyZ0RkHn9KWynzP3Xl/Mc3ka9cmfWFkJFSvzoWKNbiXbQxgDHezN1WjO0FBULRMCOMjTvv2gFjSeI6aiAScTDb2L5LBSpSAd9+Fdu3MQaPBwXZX5JnY2FSFocTRGdPzU5QKYXPAvQTatiWh5SO8VyiC2+a/R4uj08jz2QzqNGvGMyNh3twI1hx/hiJ9NwE5Pfqs+Hg4dCwvs/qfJsejpNiflB6ukRZ3DX2KcYzD7S4OZL6dr0XE9zQyJJKS554zPUSvvmp3JZ5L5TTZ1RspXh6tadUKdu1iZ3QcfYcX4c7/rqDs6W2M3Nbs8utf2tmNYnXLEDTE87kulwt+3X+Wk9/t9/lqshNfbuFetvEHxQA1a4tI8hSGRFLicMDkyWaF2YYNdlfjmVROk11XgQIsDWrFEW6jLhv4g+JJA4XDAR99hLVoEfM6LPdoOmp/5GGqEsMpCgC+DSjPxX7MDDoC5ngVNWuLSHI0TSbiiSJF4MMPoUMH0z+UN6/dFd1YKqfJkpO4fL51zOv0ZSwJmJGmawLFzTfzyYOzaTH1MUazgnB3LeA601EJCbx2oAMjeIVPaJf8+3nL+fNU3/8ZGwbuoOXelJf8i0jgUhgS8dSjj8LixTBwIEyaZHc1N5bKabLkuFwQNdTNw8TzBY9SvrzZaSC5QDHvaANuohlf0Zz+jCFqYwcSR2OSGDOGkrfFka/zIFpu9nFAWbwYR40a9HqnBL189BEikjUoDImkxvvvQ5UqsGIFPPig3dVcnxemyaKi4DQhvMwYLIIoW/b6zcdOJzzjnkdlYviEtmT/eSmcnAKFCl15UUwMuFw4vvuOV+7Mlq7aPPLxx+ZYFRGRFKhnSCQ18uc3P2S7dIFTp+yuJnmWZcJQtvQFDqcT1vIAq2l0+fn1hIVBRATc0bIKy1/fQvlmpczS/BUrzAsuXIC2bWH0aLjzznTV5ZHDh2HrVnjsMa+9ZeIyfdAyfZGsxmFZlscvDg0NtaKjo31Yjkgm0acPnDgBn3xidyXXioszWwDEx6frbRISzEqvq4/YSNUS+MhIrI4d2Xrbo/z2u0W5vEcpt3M+QdmSmT7ztrffht9+8+r5csOHQ3i4OQTXgUVEhJbpi/g7h8Ox1bKs0JRep5EhkbQYMcLsOzR/vt2VXMsL/UJwneX2qdGoEe912En8pu9ocWgi9+2ezEiXb4NQfDwMj7D4bcQMPqaTV0dv/r3qTcv0RbIOhSGRtMidG2bPhl694OhRu6tJylvL6r1g05ZsFOMPXmICf1LI5wHC5YKYoXM5cy6IzpNCvbqHUeI0oQMryXMRyfwUhkTSqlYt6NbNPFIx3exL8fEwxhXLmYs3+UVfyyt/DmQVjfmQFwHfB4ioKOjATL6nMuDwavhK7Itq2RKdqSaSxfjHPx9FMqthw6BOHXOGWZcuaX6b+HgzqrFt/Vlq1snGwJ7nCLp4Hs7/73HuXPJf/+v59nXnKLb5FNm4SHi4eW/b+lpWrKDaH18ROSyGltszZp8fpxNudf9Od6Zcfu4tidOGIpL1KAyJpEeOHGZn6oYNoVGjNK+UcrngYPhHzKc7l77KwcWRuclVMBfkymWm5HJ58HXhwmw9k4vt3MRv3ArY2Nfy11/QtSuOjz+mf5P89M+gjw3rdpLYNw9yS+N7ibhPozci4hmFIZH0qlQJBg0ye9pERqa+0/jiRe6d3JvWrKYKO9lFFVo2MY3LqXUyDiaHX3luW19L377mqPgmTTL0Y4O+XUdww/tYvCz9DeQiEjgUhkS84eWXYckSGDsW+vXz/LrDh6FNG0qFlKAm0ZwmH5D2EJP09HmbRkaWLIH162Hnzoz/7DVr4IEHMv5zRSRTUxgS8YZs2WDmTKhd2+xMXbFiytesXGnOtxgwgHL9+hPmcqQ7xNje13LyJPToAXPm2HN+25o1MGVKxn+uiGRqCkMi3lK6tFlm1L69GZq53l4/CQlmB7+JE2HePGjQgCCySHNur17wxBNQv36Gfmx8PIx77STP7znIuJX3MqhmGvZFEpGApW8XIt7UrRsULXrl3IZ/O3XKHPj65Zdm08YGDTK2Pl9auNAcgXG9P7sPuVxwKGIW38fdzSuvZvfq/kIikvUpDIl4k8MBU6eaUZ9/H12zYweEhkKZMmY659ZbbSnRJ44dg549YcYMs7Itg51Y/h2v8hbrMOFSu0OLSGooDIl42623mkbq9u3N/j9gQkLTpmYa7f33vXJcht+wLHjxRejQwZ7la2vX8taOlrRnFoMYBWh3aBFJHfUMifjC00+TsOhzohuF8edvF6h5bi0FI9cQVPkeuytLtcQNIf99YGvi72efP4eOv+2l0Kz/y/h/XS1fDh07kmvxXOp+1wiHnavoRCTTUhgS8ZGx5SbSe35RfuNWmrKYNkvvYUhlu6tKPZcLvg1fTjNWEuI+zQ9zTlPh9tP8tvcMrQ6d5B720pmplH0/Z8Y2gc+fDy+9BEuWEFSnDkMydksjEclCFIZEfCRyZyHWsICaRPMlD3Pk/epQrTc0b56pljpFRcF5cnGIOzhNCCdyhFDhpbx8EBHCikMhPMhXuHiF+XNj4ZXnTd+Ur82YYZbfrVgB1ar5/vNEJEvLPN+RRTIZpxOW8DjDiOAODvFb/adh6FAoX970FP39t90lesTphDU05H36MY2uxLd+Clq0IF/L+uygOi5e4T428OQfE8z5bBcu+LagCRPg1Vdh9WoFIRHxCo0MifhI0t2gc9IirCM4OsDGjTBuHLzxBrRta6Z6ype3tdYbud6u1kl/vyyFXtoE3bpAvXqwaBGULOn9YkaMgGnTYN06KFXK++8vIgHJYVmWxy8ODQ21ov+9XFhE0ubIEfjwQ/joI6heHXpnvim0a1gWjBmDNWoUc5rPYs7JZkmartP1vkOGmKM+vv46a21LICI+43A4tlqWFZri6xSGRGx24YLZiXrcOKx//uHr8r2YGteRavXzpT9E2OSTLpE8M70xB7mTSBpz+6OhPDQsFCpXhhw5UvdmCQkmKG7aBF99BYUL+6ZoEclyPA1DmfDbrEgWkzOn2aMnOppZjWby57KNjFhRnWHh8Zl2J+W5xxrxN/nowSS2U53sWzdBx45QoADUqmX2JZo+HWJiIC7u+m8UFwedO5tDX1etUhASEZ9Qz5CIv3A4WPBbXdzUJRfnSCBbpt1JuX6Ns+RyX+RrmvE1Dm7vAU2GAGfPmp24t2yByEh45x0zXVi1qtmd+3+P+LvKM2pEHPdPepZb8p6m1LavCArJ49Uar7d/kogEHoUhET/idILbDefJffl5ZtT/qSP8Nf52WtZ1JN0EMU8euO8+80j099+wfbsJSG43vP46cUeO0fdSLJuoTQVW8tr4YK/vYeRywdvhZ8nNOdzuIkAWOSxXRFJNYUjEj1xv5VZmE/TbEW6uUoKlSz14cf788MAD5vE/nR48yasrnYylN5cI9skI2a8rdrGN1pThALuoxJ+TqkDOqmaUqkoVKFLk8ms1iiSStSkMifiRoKAsMjrxyy9w++1pvrxSg0KsXNmcUhwGvDxCdvYsvPkmo6Kn0583mc1zVGQvr4XGwMGd8MUXpkcpd24TiqpWxX2wCl/OL8F31MLtzglkkf9PIgIoDImILxw5AiVKpPnysDBYueluyu3cSZHnvThCtmQJ9OoF999Pzv9+T4kZxWkYBU5nLR4Kq3VlSYllmUC3cyfExFDo28WsxM1UutCbCZm2l0tEkqcwJCLXlZbpofh4iFnyC5vOV+VU3rRNKQUFQfN+FeD1eTT3xgjMoUNmef6+fWYVW+PGBHGD0R2Hw2waWbIkPPIIf2+dRsznh+nDOCDz9nKJSPI06y0i1zXq7YuUC2/NdvcvhIfj0VJ/lwtObfmRlbuKe3xNsu6+G/buTePFJpSNfCuWjyu8w7mKNUgIrWmW8jdunLo3OnGChzcMYVfPD2nRMoiIiMzbyyUiydPIkIgkb80a2o7qwSUusoIHaUwkUVHFk7wkPh5cIy1i1v3FE4Ui+U++b+g4+xtu5hfG0Rsg7VNKxYvDpUtw8iQUKpS6a8+f56tnP+GlxX3YQi0qs5ku2cowJDgNdYSF4Xj6abqMrU6XNFwuIv5PI0MiktSxY9C+PbRvz+bHRnAX+5nLM6yjPg/efSjJS0cNj+XZoaWYtvJ2cs2ZxqrDZVnWZSG5OM8XPA6kY0rJ4TCjQ/v2eX7Nr79CeDjccQdFNy6mJx/QiFUcoEzaQtmGDfDll/DWW2m4WEQyC40MiYiRkGDOSRs2zOyIvWcP/8mdl4iKEBU1jPpWPnrOux+6rDQhxbKoOaUr/6UsFdjLBXLTMht88T4cL+6l7QESw9DV+xIlZ/NmGDvWHNfx3HOwYQNfzy/LrPArL0l1KIuNhR49YMwYyJcv1aWLSOahMCQSoK5ujn70jh102doDR1AQfPONWVIO/2oy7gMz8mM1bMj01svJ/9U8Kp7+kVBWceGqTSK9uj1AhQrX7xuKjYWFC00IOnrUNEhPmmT2LcILezaNG2em6p58Mu31i0imoDAkEqDeGR7Hold30JuxPMhKlj8+nBYLOt146VfHjiz6Oh9tP3ByigJUYjf1muYmONhHm0TefTesX5/0906cgClTYOJEKFsWBg+GRx6BbNmSvCxdoeyXX2DECJOkHI40vomIZBYKQyJZUJIl8bUTCHv2MEF7dsGuK4+Xv/+RdtzMcQpTiV3UiS1CCw+6CNccuYsHycZLTOBPChEcjGc7Taflz1GuAn9H7aPDI9Cq9C66nh2LY+ECePxxc3RHtWq++eC+faFnTxO2RCTLUxgSyYJcLqgXXp+H+Zsy7gOcGZWffM5KULkyNG0K/foxdmkFBr+R+/I1KfXUxMfD6Lcv0GtTW17iAxbRxqPr0uOd+Xcy4M+f6eduyN38wLrGPWjwww9QtKhPPi8+HhZ0Xs79X+1kdpVPGJigYzdEAoHCkEgW9OuKXVRiF08yj62EUq9+wWtGbwZUh7gcnvfUuFwQ/Ho431OOmXSgfHmz6MyXe+5EbQ4iluycoDClWMGDuXLQwDc5CDDbBDSZ9Tqd+ICVr+ckPruO3RAJBCmGIYfD0R3oDlCyZEmfFyQi6dfz1FuM4BVW0RRIfvQmtT01mzfG8zgneZ7JgIOyZX0fFOrcl41qy3eykbrcxq84nXf69POiNjkYwSrOEGKe69gNkYCQYhiyLGsKMAUgNDTU8nlFIpI+u3ZR4dhair42nZZbvdfYXLtuNjotm3H5eUYcSWHqLsfS2YNYfakrJQd/A/iuodnpBLc7JMlzEcn6HJbleb4JDQ21oqOjfViOiKTbU09BaCgMHOjVt01IMEdrpOacMq+JizMf2r07dOvms4+x9c8oIl7ncDi2WpYVmuLrFIbEn6TlYNDMIq2Hnqbqml27oEkT+OknyJPHq/Xb7vvvoVEj2LYNSpSwuxoRyQQ8DUNqoBb/cP48bNzIprcica7dyGusxO3ODmSdBlaXC34Jn0RfFnDGnZc9n4VQqU5eCAmBvFf9etXXM+eH8OmkYIJIwO2uAjhufD/eegv69896QQjMSrheveCFF8yyeu3/IyJeojAk9rh0CbZsgchI89iyBapW5cixhkxmGLHkMK8L/98jC7g6wzzKYuo7TlOp6mk4cwZOn4YjR8yvic/PnKH+jtM8wClu5wixZOf4yNKw5S4oU8Y87jJfx99WkukD99Lmi7VMrTCd/ll1SXhYGNSsCf/3f9Cund3ViEgWoTAkGSM+HrZvN8Fn9WpzAGbZsmbaY9AgqFcPQkL4aTisDgcHZvo2IiLrjAwNH27OEE1U+wmgx42v+eyqa0L4h/fa/0SXB36C/fvNdNH8+bB/P9Zvf9AhPoG1NGDQG3mIzZF17lsSOXLA9Onw0ENmv6Tixe2uSESyAIUh8Q3Lgt27r4z8rF0Lt95qwk/37vDJJ3Dzzddclu7zpPxYWv5sSa/JR6ew6hBU/ZrXPfnwBQp+OZu3GUYXphIV1dWLlfuZGjWga1d48UVzNpmmy0QkndRALd5hWWa0YvXqK6M/ISEm/DRsaB76V7zPJI463cV/+Zqm/PfhPjRd1s/usnwm/uwFTt1Znam3v05Cm6eyVKO9iHiPGqjF93755crIT2SkWZfcuDE0b266he+4w+4KA8aVEaSyuCuso+fiJvDmaRg2LEuOnLjG5mT38XA+Pt6OotubAQWz5rSgiGQIhSHx3LFjV0Z+IiPhr7/MiE+jRmZYomzZLPmDNzNIupt0Sei/3vTU/PMPjBqV5f6/HFh1EBdhDGAUf1NQO0WLSLooDMll1+xp8/wpgtavvRKAfvkFGjQw4adnT6hUSXMT/qpYMVizxjQav/ACTJwI2bLZXZV3/P477+xoyjBeYSI9Ae0ULSLpo54huWx4hMW6oStoRCSNiKRyjh8IfqCuCT+NGkH16nCT8nOmcvo0tGplmtdnzIDs2e2uKH3+/BMaNCDhyacYmW1oltycU0S8RztQS6o98gg8536KPVQkkkYUfqgWny8PtrssSa/z56F1a7Msfe5cyJnT7orS5swZM/VXty6MHp3lpv5ExPs8DUP6t5Rc5nTC08zjTV7jW+6nZj0FoSwhVy5YvNiMCj3yCJw9a3dFqXfxIvznP1ChgoKQiHid5jzksqy8x0/Ay5ED5swxh5w2awbLlkGBAnZX5Zm4OGjb1mzVMGWKgpCIeJ2myUQCSUIC9O2L9e23jH1oBatiivh3z41lmQ0WDx8255EFa7RSRDynaTIRuVZQEIwdy8Z8D9FseAO2uX8lPBxGjrS7sGRYFgwcCHv2wOefKwiJiM8oDIkEGoeDkSERzKI9oxkA4J/79IwYAStWmCm9vHntrkZEsjD1DIkEIKcTwt1hZOfS5ed+ZdIkcyDr+vXJnmEnIuJNCkMiAehKs3wO/2uW//RTc9jaunVwyy12VyMiAUBhSCQAJT2+w48sWwYvvwyrVsGdd9pdjYgECIUhEbFV4jEw/7jXMSymE7m+XkrQPffYXZaIBBA1UIuIrVwjEggOf5lXox6k09nxjFxd2+6SRCTAaGRIROwRGwtz5tD2nXdI4Az7uYsP6cHR0eMg+D/mCJFSpeyuUkQCgEaGRCRjnT0LY8fCXXfBjBlE/Wc0pTlIVb6nOEeJaTUM9u2DWrWgRg3TTL1vn91Vi0gWppEhEckYJ07A+PEwcSLUrw/z50OtWjyZAAfKJR4Dk4Mnw5pDUHOzvH79eli0CBo3NseHtG5tziirWlXHcoiI1+g4DhHxrZ9/hnffhU8+MWFmwAAoXz5175GQAJs3m2C0cKEJQq1bm0fNmn56loiI2E3HcYiIvWJi4LnnzFRX7tywezd89FHqgxCYsON0wqhR8NNPsGCBOZ6jUycoWRJ694a1a83SNBGRVNLIkIh4RXw8uIbHc969ig5/vU+Z0ztw9OkDL7wA+fP77oP37r0yYvTrr/Doo8Q/1ppR0Q3ZsCWHfx9EKyI+5enIkMKQiHjF8LcTqD+sPhXZw2Bc3PV6Owa/ljNjizhwABYt4sj4ReQ9vIejFGcW7bm3bUXavHYPlC4N2bJlbE0iYhtPw5AaqEUk/RISqDmlKwkEcQc/c4Z8tIyGwRldR+nSMGAAPdYO4MDhXbRnNgX4izu+ngobdsMff0C5cnDPPeZRsaL5VSFJJKApDIlI+iQkQNeulLvpABX5knPkAew9/NXpBLe7EmG4AIjoAzWHAGfOmGX6u3ebx9Sp5tcUQlLiLtlmxZum3USyGoUhEUm1xHCwaWMCb/3elSohByixcxnh4/MkCQx2uXIQ7b9qyZsXQkPN42ophKS93EPRnbm4m/Ksd1filiOF6TSwMBQubN5Ty/xFMjX1DIlIqg0fDq+GX2I+T1GQU2x5bRkDX89jd1ned/Ys7N3Le113U27nPG4iDosgyuQ/QdkCJ+DkSbh40YSiwoWhUCHPvg4JUYASyQDqGRIRn/njq+38xGPcyq/cxq/U3pqHgXYX5Qt58kBoKOefDKXlzg6XfztiEAwZ8r8nFy6YUHTypNlYMvFx8iQcPAhbtiT9b1cHqKtDkgKUiG0UhkTEc3/9BcOGMXzbZ/QhgnqsYzQDOVJnJpB1f1Bfd9oNIGdOuO028/CUtwJUSmFKAUrEIwpDIpIyy4LZs2HwYGjVilwH91D6o0K41z/DpG21KFR8BtDJ7ip9JijoqpEgb/CXAJVcmFKAkgCkniERubGYGOjZE86fN+eK1aqV9L/v2QMNGsDq1VCpkj01SvISA1RiOLo6KF3v69QEqMSvFaDET6lnSETS559/4LXXzJlib74J3bolvxdPxYowejQ88YQZncibN+NrleSlZwQquaB0oxGo1PQ/KUCJn1EYEpGkLAvmzIGBA+Ghh8wS8yJFbnxNhw6wZg306AGzZumHXGZmV4DypBdKAUp8RGFIRK7YzWU12QAACPpJREFUvdtMif3zjzkMNTU7J06YYKbQPv4YOnf2XY3if7wdoH7+GaKjr+2PunQpddN3HgQobagpoDAk4tcy7Bv16dPwxhswcya8/ro5XDW1x1PkyQPz50P9+lCzJlSu7INCJcvwkwC1bGNh1i8ozBoewO3OBXi5WV4yBYUhET/mcsGq8G+wcBDubgx4+Ru1ZcFnn0H//tCkCezaBcWKpf39KlaEd9+FJ59U/5B4nw8CVL6N0fThJFuoyQVyERXlu/LFfykMifixqI0W79OPOzjETqqy9bMu0OcJMwqTXvv2wUsvwfHjMHcu1KuX/vcE9Q+Jf0khQG0sBuHhV57beaae2EczoyJ+rH3BpWQjnmIcZQwv0+LCAihRArp3h82bzchOap09a+bb7r8fWrWCrVu9F4QSTZgA27aZ/iERPxYWBhER0LKl+dXOM/XEPhoZEvFXFy/SZlN/5nacQJMTuanpfJwyYY/D77+a3p62bc2/ert0gXbtTA/EjVgWLFoE/fqZfYFiYuCWW3xTu/qHJJPw+oaakilp00URfzV6tJlucruT/++WBevWwbRpsGQJNG1qglHTptc2P//4I/TqBb/+Ch98YMJQRpg5E0aOVP+QiNjC000XFYZE/NGxY6YZecMGKF8+5df//bfZG2jaNDh6lIT2HZl0sTOr9xSj3/nh1P3+QxxDhphAlD277+u/SkLHTuzeGceQ22bhrOvQ0mURyTCehiF9SxLxR0OHQvv2ngUhgPz5zXL4LVtg2TK2rv6HZ9+tztwv83FozQHGd42Bl1/O8CAE8G6pCQTt2EaBZf9HeLgZKBIR8SfqGRLxNzt2mGmvffvSdn2VKrxZaCy/0IFltKQtn9JyN/T2bpUeW7c1D5P5gr8oAKClyyLidzQyJOIl8fEwPMLikUdg+HBISEjDm1gW9O1rNj4sUCDNtTidEEwsRyhx+bldnE74ibs4SWHbaxERSY5GhkS8xOWCQkNfoDihhLu7AWlYpbJoEfz5J3Ttmq5awsKg1P7T5FySl4iX7V0unPjZV++iLSLiTxSGRLwkKgr2MYAltKIKMXyz4T1S9VfswgVzOOrUqXBT+v5qBgXBs63OwMm8VLV52bCWLouIv9M0mYiXOJ2wn7LUYRNl+In3f2huRnk89d57ULUqNGrknYLOnNFydhERD2hkSMRLrkwH5Wdn7aU89FcY1K5tmqErVLjxxb//bs702rzZK7XEx8PK+WdgRwjbh+skbhGRG1EYEvGSpNNB2YBRULmS2eBwxgx4+OHrXzxkiNkwsUwZr9TicsGpJacpRt7L5y5pqkpEJHkKQyK+1KEDlCsHbdqYVWIDBlx7cGl0NKxYkfal9MmIioJIXuQm4i4/FxGR5GngXMTXnE7YtMmcDN+hg2mUTpS4lP6ttyBfPq9+5Dny8A/5Lz8XEZHkaWRIJCOUKAHr10OnTtCwoVlCf8stMG8enDsHHTt69eO0nF1ExHMKQyIZJXduMzr09ttYtWszq/kcHvq/waxoO5u2jmxeHabVcnYREc9pmkwkIzkcMGwYi+qN4ZmPGnLsfF7aT62v87pERGykMCSS0c6epfjquRygFP0ZDajBWUTETpomE8lIhw7Bo4+S99bqVD36PZcIBtTgLCJiJ4UhkYyyfj08+SQMHkzlXn14zeVQg7OIiB9IMQw5HI7uQHeAkiVL+rwgkSzpo49g6FCYPRuaNSMINTiLiPiLFMOQZVlTgCkAoaGhls8rEslKYmPh5Zfh66/NyFC5cnZXJCIi/6JpMhFfOXnSTIsFB5szx/Lnt7siERFJhlaTifjC7t1QqxbUqAFLlyoIiYj4MY0MiXjb0qXQuTOMGQPt2tldjYiIpEBhSCSd4uPNKfFRGy1ejh3JA7s/wOF2Q+3adpcmIiIeUBgSSSeXC6aEH2QW7cjJJcYP2kzv2rfZXZaIiHhIYUgkneI/X8JWOpKHcxTkT5rsyU1vu4sSERGPqYFaJK0OHoRWrXjx50E8yWccpiTV2KndpEVEMhmFIZHUungRIiKgZk1wOil4eCeNI5rwbYXuTK7+oXaTFhHJZDRNJuKBxCbpc0u+od9PPbnZeTeO6GgoVerKbtLdO0LZsvDXn3DzzTZXLCIintLIkIgHXCMtbgnvRNfNXel0YhQj6nwBpUolfVHhwvDwwzBrli01iohI2igMiXggapOD2bSjIntYSiuioq7zwhdegMmTwdLJNSIimYXCkIgHnE5YTSPOk/vy82TVqwcOB6xbl3HFiYhIuqhnSMQDiU3RUVEmCF23SdrhuDI61KBBhtUnIiJp57BSMZwfGhpqRUdH+7AckSzg1Cm4807473+hSBG7qxERCVgOh2OrZVmhKb1O02Qi3lawIDz+OMyYYXclIiLiAYUhEV94/nmYMgUSEuyuREREUqAwJOILtWtDnjwQGWl3JSIikgKFIRFfcDjM6NDkyXZXIiIiKVAYEvGVtm3hm2/g6FG7KxERkRtQGBLxkfg8+dh+1xPMemA6w4erfUhExF8pDIn4iMsFXaOfp+4P0wkPtxg50u6KREQkOQpDIj4SFQXbqEFdNgKO6x/hISIitlIYEvGRxCM7jlM0yXMREfEvOo5DxEc8PsJDRERspTAk4iNBQTBkiN1ViIhISjRNJiIiIgFNYUhEREQCmsKQiIiIBDSFIREREQloCkMiIiIS0BSGREREJKApDImIiEhAUxgSERGRgKYwJCIiIgFNYUhEREQCmsKQiIiIBDSFIREREQloCkMiIiIS0BSGREREJKApDImIiEhAUxgSERGRgKYwJCIiIgFNYUhEREQCmsKQiIiIBDSFIREREQloDsuyPH+xw3EcOOS7cjxWGDhhdxE20z3QPQDdA9A9SKT7oHsAugeQ9B7cYVlWkZQuSFUY8hcOhyPasqxQu+uwk+6B7gHoHoDuQSLdB90D0D2AtN0DTZOJiIhIQFMYEhERkYCWWcPQFLsL8AO6B7oHoHsAugeJdB90D0D3ANJwDzJlz5CIiIiIt2TWkSERERERr1AYEhERkYCmMCQiIiIBTWFIREREAprCkIiIiAS0/we24eImM95nQgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "network, nodes = mgo.create_network(buildings,\n", " gen_lat=get_lat,\n", " gen_lng=gen_lng)\n", "\n", "xs = [n[1] for n in network]\n", "ys = [n[2] for n in network]\n", "xe = [n[3] for n in network]\n", "ye = [n[4] for n in network]\n", "\n", "fig = plt.figure(figsize=(10, 10))\n", "plt.scatter([n[1] for n in nodes[1:]], [n[2] for n in nodes[1:]], s=20, lw=0, c='blue')\n", "plt.scatter(nodes[0][1], nodes[0][2], s=80, lw=0, c='green')\n", "plt.plot([xs, xe], [ys, ye], c='red', lw=1)\n", "display(Markdown('### A small graph is shown below, with the PV point in green'))\n", "\n", "fig.axes[0].tick_params(labelbottom=False, labelleft=False)\n", "plt.axis('equal')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "Total houses connected: 141 out of 150" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "Generator installation size: 110 kW" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "Total length of lines is 5683m" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "CAPEX: $933568" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "Annual OPEX: $18671" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "Annual Income: $39617" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "**NPV over 20 years is $-732413**" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results, network, nodes = mgo.run_model(network, nodes,\n", " demand=demand,\n", " tariff=tariff,\n", " gen_cost=gen_cost,\n", " cost_wire=cost_wire,\n", " cost_connection=cost_connection,\n", " opex_ratio=opex_ratio,\n", " years=years,\n", " discount_rate=discount_rate)\n", "\n", "display(Markdown(f'Total houses connected: {results[\"connected\"]} out of {len(nodes)-1}'))\n", "display(Markdown(f'Generator installation size: {results[\"gen_size\"]:.0f} kW'))\n", "display(Markdown(f'Total length of lines is {results[\"length\"]:.0f}m'))\n", "display(Markdown(f'CAPEX: ${results[\"capex\"]:.0f}'))\n", "display(Markdown(f'Annual OPEX: ${results[\"opex\"]:.0f}'))\n", "display(Markdown(f'Annual Income: ${results[\"income\"]:.0f}'))\n", "display(Markdown(f'**NPV over {years} years is ${results[\"npv\"]:.0f}**'))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "network_gdf, buildings_gdf = mgo.network_to_spatial(buildings, network, nodes)\n", "\n", "village_map = folium.Map([y_mean, x_mean], zoom_start=16, control_scale=True)\n", "\n", "folium.GeoJson(network_gdf).add_to(village_map)\n", "\n", "icon = folium.Icon(icon='bolt', color='green', prefix='fa')\n", "folium.Marker([get_lat, gen_lng], icon=icon, popup='PV plant location').add_to(village_map)\n", "\n", "def highlight_function(feature):\n", " return {\n", " 'fillColor': '#b2e2e2',\n", " 'fillOpacity': 0.5,\n", " 'color': 'black',\n", " 'weight': 3,\n", " }\n", "\n", "styles = []\n", "max_area = buildings_gdf['area'].max()\n", "for index, row in buildings_gdf.iterrows():\n", " if row['area'] > max_area*0.8: fill_color = '#006d2c'\n", " elif row['area'] > max_area*0.6: fill_color = '#2ca25f'\n", " elif row['area'] > max_area*0.4: fill_color = '#66c2a4'\n", " elif row['area'] > max_area*0.2: fill_color = '#b2e2e2'\n", " else: fill_color = '#edf8fb'\n", " styles.append({'fillColor': fill_color, 'weight': 1, 'color': 'black', 'fillOpacity': 1})\n", " \n", "buildings_gdf['style'] = styles\n", "folium.GeoJson(buildings_gdf, highlight_function=highlight_function).add_to(village_map)\n", "\n", "display(village_map)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(150, 141)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(buildings), len(buildings_gdf)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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147way606344173{'building': 'yes'}{}POLYGON ((39.1874251 -9.628617999999706, 39.18...80.6008271481463787-114520136.0000001413.6761751{'fillColor': '#edf8fb', 'weight': 1, 'color':...
148way606344174{'building': 'yes'}{}POLYGON ((39.1874123 -9.628928700000186, 39.18...91.4289681491463787-114523753.0377221377.6761751{'fillColor': '#b2e2e2', 'weight': 1, 'color':...
149way606344177{'building': 'yes'}{}POLYGON ((39.1880085 -9.629304200000259, 39.18...74.9542871501463841-114527036.4005491289.6241691{'fillColor': '#edf8fb', 'weight': 1, 'color':...
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141 rows × 13 columns

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" ], "text/plain": [ " type id tags meta \\\n", "1 way 543041768 {'building': 'yes'} {} \n", "2 way 543041770 {'building': 'yes'} {} \n", "3 way 543041775 {'building': 'yes'} {} \n", "4 way 543041777 {'building': 'yes'} {} \n", "5 way 543041782 {'building': 'yes'} {} \n", "6 way 543041783 {'building': 'yes'} {} \n", "7 way 543041785 {'building': 'yes'} {} \n", "8 way 543041789 {'building': 'yes'} {} \n", "9 way 543041794 {'building': 'yes'} {} \n", "10 way 543044727 {'building': 'yes'} {} \n", "11 way 543044728 {'building': 'yes'} {} \n", "12 way 543044732 {'building': 'yes'} {} \n", "13 way 543049551 {'building': 'yes'} {} \n", "14 way 543049553 {'building': 'yes'} {} \n", "15 way 543049554 {'building': 'yes'} {} \n", "16 way 543049555 {'building': 'yes'} {} \n", "17 way 543049556 {'building': 'yes'} {} \n", "18 way 543049557 {'building': 'yes'} {} \n", "19 way 543049571 {'building': 'yes'} {} \n", "20 way 543049608 {'building': 'yes'} {} \n", "21 way 543049626 {'building': 'yes'} {} \n", "22 way 543058863 {'building': 'yes'} {} \n", "23 way 543068153 {'building': 'yes'} {} \n", "24 way 543068164 {'building': 'yes'} {} \n", "25 way 543068333 {'building': 'yes'} {} \n", "26 way 543068351 {'building': 'yes'} {} \n", "27 way 543068373 {'building': 'yes'} {} \n", "28 way 543068388 {'building': 'yes'} {} \n", "29 way 543068451 {'building': 'yes'} {} \n", "30 way 543068459 {'building': 'yes'} {} \n", ".. ... ... ... ... \n", "120 way 606324581 {'building': 'yes'} {} \n", "121 way 606324582 {'building': 'yes'} {} \n", "122 way 606324589 {'building': 'yes'} {} \n", "123 way 606324608 {'building': 'yes'} {} \n", "124 way 606324609 {'building': 'yes'} {} \n", "125 way 606324616 {'building': 'yes'} {} \n", "126 way 606324623 {'building': 'yes'} {} \n", "127 way 606324624 {'building': 'yes'} {} \n", "128 way 606324629 {'building': 'yes'} {} \n", "129 way 606324630 {'building': 'yes'} {} \n", "130 way 606324634 {'building': 'yes'} {} \n", "131 way 606324635 {'building': 'yes'} {} \n", "132 way 606324642 {'building': 'yes'} {} \n", "133 way 606324644 {'building': 'yes'} {} \n", "134 way 606324646 {'building': 'yes'} {} \n", "135 way 606324648 {'building': 'construction'} {} \n", "136 way 606331254 {'building': 'yes'} {} \n", "137 way 606344131 {'building': 'yes'} {} \n", "138 way 606344134 {'building': 'yes'} {} \n", "139 way 606344135 {'building': 'yes'} {} \n", "140 way 606344136 {'building': 'yes'} {} \n", "141 way 606344140 {'building': 'yes'} {} \n", "142 way 606344148 {'building': 'yes'} {} \n", "143 way 606344155 {'building': 'yes'} {} \n", "144 way 606344161 {'building': 'yes'} {} \n", "145 way 606344163 {'building': 'yes'} {} \n", "146 way 606344167 {'building': 'yes'} {} \n", "147 way 606344173 {'building': 'yes'} {} \n", "148 way 606344174 {'building': 'yes'} {} \n", "149 way 606344177 {'building': 'yes'} {} \n", "\n", " geometry area index \\\n", "1 POLYGON ((39.1904398 -9.625552500000355, 39.19... 234.564190 2 \n", "2 POLYGON ((39.1901281 -9.62556700000016, 39.190... 250.245536 3 \n", "3 POLYGON ((39.1887618 -9.625144900000011, 39.18... 127.014864 4 \n", "4 POLYGON ((39.1894811 -9.624799500000131, 39.18... 247.794520 5 \n", "5 POLYGON ((39.1896567 -9.624462300000044, 39.18... 239.726859 6 \n", "6 POLYGON ((39.189641 -9.624392399999982, 39.189... 264.799264 7 \n", "7 POLYGON ((39.190197 -9.623478100000101, 39.190... 262.447767 8 \n", "8 POLYGON ((39.189415 -9.623191799999955, 39.189... 80.418789 9 \n", "9 POLYGON ((39.1893828 -9.623356299999898, 39.18... 71.573659 10 \n", "10 POLYGON ((39.1882158 -9.626891599999935, 39.18... 86.439146 11 \n", "11 POLYGON ((39.1883645 -9.626769099999883, 39.18... 73.276014 12 \n", "12 POLYGON ((39.18848490000001 -9.626659499999995... 75.952526 13 \n", "13 POLYGON ((39.1883947 -9.632379300000153, 39.18... 99.896645 14 \n", "14 POLYGON ((39.188415 -9.631569400000007, 39.188... 218.245874 15 \n", "15 POLYGON ((39.1886512 -9.631751899999648, 39.18... 114.917663 16 \n", "16 POLYGON ((39.1882995 -9.63165709999998, 39.188... 82.678650 17 \n", "17 POLYGON ((39.1883913 -9.6318752, 39.1883832999... 184.098757 18 \n", "18 POLYGON ((39.1884414 -9.631976500000127, 39.18... 283.181884 19 \n", "19 POLYGON ((39.1869165 -9.630327799999966, 39.18... 80.190846 20 \n", "20 POLYGON ((39.18925049999999 -9.629519999999983... 102.253511 21 \n", "21 POLYGON ((39.1883393 -9.629003500000392, 39.18... 71.330257 22 \n", "22 POLYGON ((39.1874013 -9.633807399999739, 39.18... 147.914821 23 \n", "23 POLYGON ((39.1858343 -9.632513400000116, 39.18... 86.284967 24 \n", "24 POLYGON ((39.1849595 -9.633267400000117, 39.18... 72.972538 25 \n", "25 POLYGON ((39.1850399 -9.633892300000037, 39.18... 88.950194 26 \n", "26 POLYGON ((39.18551 -9.633962400000065, 39.1855... 72.789441 27 \n", "27 POLYGON ((39.1853722 -9.633427400000052, 39.18... 92.436770 28 \n", "28 POLYGON ((39.1864329 -9.633610700000085, 39.18... 87.470835 29 \n", "29 POLYGON ((39.18678600000001 -9.633339200000044... 76.819090 30 \n", "30 POLYGON ((39.1864505 -9.633800399999796, 39.18... 88.425838 31 \n", ".. ... ... ... \n", "120 POLYGON ((39.1867733 -9.631876599999758, 39.18... 71.403833 121 \n", "121 POLYGON ((39.1866881 -9.631908299999571, 39.18... 93.201070 122 \n", "122 POLYGON ((39.1869047 -9.632155599999988, 39.18... 75.593758 123 \n", "123 POLYGON ((39.1876397 -9.633126700000037, 39.18... 71.475632 124 \n", "124 POLYGON ((39.1877308 -9.632835799999867, 39.18... 92.261592 125 \n", "125 POLYGON ((39.1877925 -9.632669199999922, 39.18... 78.378389 126 \n", "126 POLYGON ((39.1870023 -9.632486000000307, 39.18... 73.210570 127 \n", "127 POLYGON ((39.1865 -9.631856699999785, 39.18651... 109.414335 128 \n", "128 POLYGON ((39.1861336 -9.632004800000264, 39.18... 252.792031 129 \n", "129 POLYGON ((39.186303 -9.631981199999812, 39.186... 73.597710 130 \n", "130 POLYGON ((39.186086 -9.632194599999545, 39.186... 80.844751 131 \n", "131 POLYGON ((39.1858868 -9.632371500000048, 39.18... 165.499792 132 \n", "132 POLYGON ((39.1869973 -9.632704600000062, 39.18... 72.795404 133 \n", "133 POLYGON ((39.1869255 -9.632912900000081, 39.18... 71.997158 134 \n", "134 POLYGON ((39.1865089 -9.632623599999844, 39.18... 166.286644 135 \n", "135 POLYGON ((39.1865829 -9.63320939999986, 39.186... 130.312453 136 \n", "136 POLYGON ((39.1874527 -9.63069170000005, 39.187... 84.508380 137 \n", "137 POLYGON ((39.1875887 -9.631038299999881, 39.18... 82.610928 138 \n", "138 POLYGON ((39.18819489999999 -9.630165000000083... 92.793881 139 \n", "139 POLYGON ((39.1881815 -9.629884700000046, 39.18... 91.926587 140 \n", "140 POLYGON ((39.18800439999999 -9.630016899999802... 100.920074 141 \n", "141 POLYGON ((39.1877979 -9.629574000000353, 39.18... 100.779492 142 \n", "142 POLYGON ((39.1880018 -9.627636899999711, 39.18... 83.694390 143 \n", "143 POLYGON ((39.18799909999999 -9.628074600000188... 70.483770 144 \n", "144 POLYGON ((39.1880125 -9.628451399999722, 39.18... 94.243862 145 \n", "145 POLYGON ((39.1880085 -9.62824119999968, 39.187... 98.554492 146 \n", "146 POLYGON ((39.187936 -9.62891150000009, 39.1879... 73.467663 147 \n", "147 POLYGON ((39.1874251 -9.628617999999706, 39.18... 80.600827 148 \n", "148 POLYGON ((39.1874123 -9.628928700000186, 39.18... 91.428968 149 \n", "149 POLYGON ((39.1880085 -9.629304200000259, 39.18... 74.954287 150 \n", "\n", " X Y marg_dist tot_dist connected \\\n", "1 1464108 -1144842 28.792360 2125.881186 1 \n", "2 1464077 -1144843 11.704700 2108.793525 1 \n", "3 1463929 -1144795 25.709920 1895.387391 1 \n", "4 1464009 -1144754 89.894382 1985.281773 1 \n", "5 1464022 -1144719 37.336309 2022.618083 1 \n", "6 1464022 -1144688 31.000000 2053.618083 1 \n", "7 1464085 -1144599 96.176920 2258.606767 1 \n", "8 1463998 -1144553 32.015621 2194.445468 1 \n", "9 1463990 -1144584 108.811764 2162.429847 1 \n", "10 1463863 -1144987 76.026311 1586.242421 1 \n", "11 1463879 -1144973 21.260292 1607.502712 1 \n", "12 1463899 -1144971 20.099751 1627.602464 1 \n", "13 1463885 -1145632 51.546096 1168.159130 1 \n", "14 1463897 -1145538 22.360680 1064.174409 1 \n", "15 1463912 -1145557 24.207437 1088.381846 1 \n", "16 1463877 -1145548 61.983869 1041.813729 1 \n", "17 1463877 -1145567 19.000000 1060.813729 1 \n", "18 1463901 -1145583 28.231188 1116.613034 1 \n", "19 1463730 -1145389 54.037024 1117.774252 1 \n", "20 1463967 -1145297 102.107786 1418.805928 1 \n", "21 1463875 -1145236 35.014283 1359.652735 1 \n", "22 1463776 -1145797 38.470768 1102.870384 1 \n", "23 1463620 -1145645 18.601075 902.326608 1 \n", "24 1463528 -1145734 45.607017 1061.978451 1 \n", "25 1463541 -1145814 41.048752 1249.577966 1 \n", "26 1463582 -1145816 29.274562 1208.529214 1 \n", "27 1463570 -1145752 45.694639 1107.673090 1 \n", "28 1463678 -1145775 21.931712 1092.465283 1 \n", "29 1463721 -1145749 24.207437 1014.389617 1 \n", "30 1463687 -1145800 12.206556 1082.740126 1 \n", ".. ... ... ... ... ... \n", "120 1463720 -1145578 9.433981 828.505088 1 \n", "121 1463712 -1145583 24.413111 819.071107 1 \n", "122 1463726 -1145605 26.076810 845.147917 1 \n", "123 1463808 -1145723 52.345009 1116.744625 1 \n", "124 1463814 -1145699 24.738634 1141.483259 1 \n", "125 1463825 -1145670 31.016125 1172.499384 1 \n", "126 1463742 -1145654 16.643317 897.484370 1 \n", "127 1463692 -1145569 39.446166 794.657996 1 \n", "128 1463659 -1145595 13.928388 837.586384 1 \n", "129 1463672 -1145590 29.000000 823.657996 1 \n", "130 1463650 -1145615 21.931712 859.518096 1 \n", "131 1463631 -1145630 24.207437 883.725533 1 \n", "132 1463743 -1145674 20.024984 917.509355 1 \n", "133 1463729 -1145694 24.413111 941.922466 1 \n", "134 1463696 -1145662 22.203603 988.539180 1 \n", "135 1463702 -1145734 48.259714 990.182180 1 \n", "136 1463790 -1145444 21.023796 1023.698823 1 \n", "137 1463805 -1145479 13.601471 973.830616 1 \n", "138 1463869 -1145376 66.483081 1152.045802 1 \n", "139 1463867 -1145349 27.073973 1179.119775 1 \n", "140 1463847 -1145354 20.615528 1199.735303 1 \n", "141 1463828 -1145304 53.488316 1253.223620 1 \n", "142 1463847 -1145086 51.000000 1474.369213 1 \n", "143 1463847 -1145137 20.099751 1423.369213 1 \n", "144 1463848 -1145183 52.611786 1377.250238 1 \n", "145 1463849 -1145157 26.019224 1403.269462 1 \n", "146 1463840 -1145235 35.014283 1324.638452 1 \n", "147 1463787 -1145201 36.000000 1413.676175 1 \n", "148 1463787 -1145237 53.037722 1377.676175 1 \n", "149 1463841 -1145270 36.400549 1289.624169 1 \n", "\n", " style \n", "1 {'fillColor': '#66c2a4', 'weight': 1, 'color':... \n", "2 {'fillColor': '#2ca25f', 'weight': 1, 'color':... \n", "3 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "4 {'fillColor': '#2ca25f', 'weight': 1, 'color':... \n", "5 {'fillColor': '#66c2a4', 'weight': 1, 'color':... \n", "6 {'fillColor': '#2ca25f', 'weight': 1, 'color':... \n", "7 {'fillColor': '#2ca25f', 'weight': 1, 'color':... \n", "8 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "9 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "10 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "11 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "12 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "13 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "14 {'fillColor': '#66c2a4', 'weight': 1, 'color':... \n", "15 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "16 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "17 {'fillColor': '#66c2a4', 'weight': 1, 'color':... \n", "18 {'fillColor': '#2ca25f', 'weight': 1, 'color':... \n", "19 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "20 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "21 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "22 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "23 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "24 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "25 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "26 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "27 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "28 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "29 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "30 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", ".. ... \n", "120 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "121 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "122 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "123 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "124 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "125 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "126 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "127 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "128 {'fillColor': '#2ca25f', 'weight': 1, 'color':... \n", "129 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "130 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "131 {'fillColor': '#66c2a4', 'weight': 1, 'color':... \n", "132 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "133 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "134 {'fillColor': '#66c2a4', 'weight': 1, 'color':... \n", "135 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "136 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "137 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "138 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "139 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "140 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "141 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "142 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "143 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "144 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "145 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "146 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "147 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "148 {'fillColor': '#b2e2e2', 'weight': 1, 'color':... \n", "149 {'fillColor': '#edf8fb', 'weight': 1, 'color':... \n", "\n", "[141 rows x 13 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "buildings_gdf" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "buildings_gdf.to_csv('before_change.csv')" ] } ], "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }