{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Pandapower Networks Without Geographical Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a network does not have geographic coordinates, you can create generic coordinates for plotting with the create_generic_coordinates function.\n",
    "\n",
    "###### You need to install the igraph package for this functionality: http://igraph.org/python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T23:29:00.724161Z",
     "start_time": "2024-03-30T23:28:58.111187Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightSimBackend import error: No module named 'grid2op'\n",
      "PhysicalLawChecker import error: No module named 'grid2op'\n",
      "TimeSerie import error: cannot import name 'TimeSerie' from 'lightsim2grid.timeSerie' (C:\\ProgramData\\miniconda3\\envs\\py3.11\\Lib\\site-packages\\lightsim2grid\\timeSerie.py)\n",
      "ContingencyAnalysis import error: cannot import name 'ContingencyAnalysis' from 'lightsim2grid.contingencyAnalysis' (C:\\ProgramData\\miniconda3\\envs\\py3.11\\Lib\\site-packages\\lightsim2grid\\contingencyAnalysis.py)\n",
      "rewards import error: No module named 'grid2op'\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandapower as pp\n",
    "import pandapower.networks as nw\n",
    "import pandapower.plotting as plot\n",
    "%matplotlib inline\n",
    "try:\n",
    "    import seaborn\n",
    "    colors = seaborn.color_palette()\n",
    "except:\n",
    "    colors = [\"b\", \"g\", \"r\", \"c\", \"y\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We delete the geocoordinates from the network and create generic ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T23:29:03.831141Z",
     "start_time": "2024-03-30T23:29:00.725221Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "This pandapower network includes the following parameter tables:\n   - bus (179 elements)\n   - load (147 elements)\n   - sgen (153 elements)\n   - switch (322 elements)\n   - ext_grid (2 elements)\n   - line (181 elements)\n   - trafo (2 elements)\n and the following results tables:\n   - res_bus (179 elements)\n   - res_line (181 elements)\n   - res_trafo (2 elements)\n   - res_ext_grid (2 elements)\n   - res_load (147 elements)\n   - res_sgen (153 elements)\n   - res_switch (322 elements)"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = nw.mv_oberrhein()\n",
    "# net.bus.geo.drop(net.bus.geo.index, inplace=True)\n",
    "net.bus.geo = np.nan\n",
    "# net.line.geo.drop(net.line.geo.index, inplace=True)\n",
    "net.line.geo = np.nan\n",
    "plot.create_generic_coordinates(net, respect_switches=False) #create artificial coordinates with the igraph package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the table bus_geodata has been created and we can now plot as before. Since the function only creates bus geodata, we can only use the direct line plotting. Furthermore it creates a distance between high- and low voltage bus of a transformer, which is why we also need a transformer collection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T23:29:04.061739Z",
     "start_time": "2024-03-30T23:29:03.832150Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Could not plot lines Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,  10,\n",
      "       ...\n",
      "       184, 185, 186, 187, 188, 189, 190, 191, 192, 193],\n",
      "      dtype='int64', length=181). Bus geodata is missing for those lines!\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sizes = plot.get_collection_sizes(net)\n",
    "bc = plot.create_bus_collection(net, net.bus.index, size=sizes['bus'], color='b', zorder=10)\n",
    "tlc, tpc = plot.create_trafo_collection(net, net.trafo.index, color=\"g\", size=sizes['trafo'])\n",
    "lcd = plot.create_line_collection(net, net.line.index, color=\"grey\", linewidths=0.5, use_bus_geodata=True)\n",
    "sc = plot.create_bus_collection(net, net.ext_grid.bus.values, patch_type=\"rect\", size=sizes['ext_grid'], color=\"y\", zorder=11)\n",
    "plot.draw_collections([lcd, bc, tlc, tpc, sc], figsize=(8,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The create_generic_coordinates function treats transformers as edges, which means the HV and LV side of the transformer are shown by seperate buses with a trafo symbol as connection (consisting of a line collection for the connections and a patch collection for the circles).\n",
    "\n",
    "If you do not want to plot the transformers you can use the fuse_geodata function. It fuses the geocoordinates of all buses that are geographically in one place (HV/LV bus of a transformer or buses and buses that are connected by a bus-bus switch):"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<pandas.core.indexing._LocIndexer at 0x237b4307840>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geojson\n",
    "net.bus.geo.loc"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-30T23:29:04.069647Z",
     "start_time": "2024-03-30T23:29:04.063750Z"
    }
   },
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T23:29:04.301951Z",
     "start_time": "2024-03-30T23:29:04.072215Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The number of given colors (1) is smaller than the number of nodes (179) to draw! The colors will be repeated to fit.\n",
      "Could not plot lines Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,  10,\n",
      "       ...\n",
      "       184, 185, 186, 187, 188, 189, 190, 191, 192, 193],\n",
      "      dtype='int64', length=181). Bus geodata is missing for those lines!\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.fuse_geodata(net)\n",
    "sizes = plot.get_collection_sizes(net)\n",
    "bc = plot.create_bus_collection(net, net.bus.index, size=sizes['bus'], color=colors[0], zorder=10)\n",
    "tlc, tpc = plot.create_trafo_collection(net, net.trafo.index, color=\"g\", size=sizes['trafo'])\n",
    "lcd = plot.create_line_collection(net, net.line.index, color=\"grey\", linewidths=0.5, use_bus_geodata=True)\n",
    "sc = plot.create_bus_collection(net, net.ext_grid.bus.values, patch_type=\"rect\", size=sizes['bus'], color=\"y\", zorder=11)\n",
    "plot.draw_collections([lcd, bc, tlc, tpc, sc], figsize=(8,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Structural Plans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "To plot a structural plan of the network instead of a geographical one, call the generic coordinates function with respect_switches=True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T23:29:04.605836Z",
     "start_time": "2024-03-30T23:29:04.303962Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "This pandapower network includes the following parameter tables:\n   - bus (179 elements)\n   - load (147 elements)\n   - sgen (153 elements)\n   - switch (322 elements)\n   - ext_grid (2 elements)\n   - line (181 elements)\n   - trafo (2 elements)\n and the following results tables:\n   - res_bus (179 elements)\n   - res_line (181 elements)\n   - res_trafo (2 elements)\n   - res_ext_grid (2 elements)\n   - res_load (147 elements)\n   - res_sgen (153 elements)\n   - res_switch (322 elements)"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = nw.mv_oberrhein()\n",
    "net.bus.geo = np.nan\n",
    "net.line.geo = np.nan\n",
    "plot.create_generic_coordinates(net, respect_switches=True) #create artificial coordinates with the igraph package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In that way, the algorithm seperates buses which are seperated by an open switch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T23:29:04.828361Z",
     "start_time": "2024-03-30T23:29:04.606846Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The number of given colors (1) is smaller than the number of nodes (179) to draw! The colors will be repeated to fit.\n",
      "Could not plot lines Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,  10,\n",
      "       ...\n",
      "       184, 185, 186, 187, 188, 189, 190, 191, 192, 193],\n",
      "      dtype='int64', length=181). Bus geodata is missing for those lines!\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.fuse_geodata(net)\n",
    "bc = plot.create_bus_collection(net, net.bus.index, size=.2, color=colors[0], zorder=10)\n",
    "tlc, tpc = plot.create_trafo_collection(net, net.trafo.index, color=\"g\")\n",
    "lcd = plot.create_line_collection(net, net.line.index, color=\"grey\", linewidths=0.5, use_bus_geodata=True)\n",
    "sc = plot.create_bus_collection(net, net.ext_grid.bus.values, patch_type=\"rect\", size=.5, color=\"y\", zorder=11)\n",
    "plot.draw_collections([lcd, bc, tlc, tpc, sc], figsize=(8,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a clearer arrangement, it might be useful to only plot the lines without an open switch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T23:29:04.983041Z",
     "start_time": "2024-03-30T23:29:04.830370Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Could not plot lines Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,  10,\n",
      "       ...\n",
      "       184, 185, 186, 187, 188, 189, 190, 191, 192, 193],\n",
      "      dtype='int64', length=181). Bus geodata is missing for those lines!\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": 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H9bOf/cyBhICzXnRb1ePSMd9tVdPTH+v8+dUOp4eXNDW9q0ikybH1gsESioiLfLNNa3BwUHV1dRQRAJ5QUFCg9vZ2rVy5UkePHlU8Htf69etf6tacTF1HLpjPtirASZFIk0pK3rSOgRfkizKSyWR07tw5vf3229ZRAGBOysrKtGfPHvX39yuZTGrz5s2qqamZ8zpMXUc25dvdqgC4xxdl5OrVq1q6dKkWLFhgHQUA5iwQCGjVqlWqr6/X8ePH1dfXp82bN6uoqOiF12DqOpzG3aoAZIMvysjf//537dy50zoGALyUSCSinTt36sqVK+ru7taGDRteuGAwdR3zxbYqAJY8X0Zu3rypiooKPpEB4Bv19fWqra3VyZMn1d/fr23btmnhwoXP/P1MXcfzsK0KQK7yfBk5e/Yst7EE4DtFRUXq7OzUV199pYMHD6qlpUUtLS1PvQ0wU9chsa0KgDd5uowMDw8rEomopKTEOgoAuCIejyuRSOjMmTN67733tH37dpWXl3/v9zB1Pb+wrQqAn3i6jJw+fVq/+MUvrGMAgKuCwaA6Ojo0MTGhI0eOqK6uTuvWrVNBQQFT132KbVUA8oVny8jY2JiCweCPPiEEAL+qqKjQ3r179cknnyiZTGrr1q0KBoOKxWI/2r6VSg1pdnba8Qzp9LhCoZcf0Mia326runfvnsbHr+ru3YAmJ6d09+5dSRkVFhaqpKRUZWVlWrp0kVaujGnBguIfrPBA0pd68OBLPXjgblYvrZlKDTi6HrzH6e8Bhh66y7MT2P/7v/9b69evVywWs44CAFl39+5dHTt2THfu3NH69eu1YsWKJ7+WSg2pt3fFT/xpAMBctLUNUkhc4smNpJOTk3r06BFFBEDeikaj2rVrl2ZmZnT69GldvXr1ya+5cUUEAPIZr6vu8WQZOX36tNavX28dAwBMpVIpVVZW6p133tHFixf1wQcf6P79+9axAAB4YZ47M3L37l3dvXtXS5cutY4CAKYeT10Ph8Pq6urSzZs3tX//frW0/PBsAQAAuclzV0bOnDmj9vZ26xgAYO7y5ctqaPj3Huba2lolEglNTk7ahQIAYA48VUbu37+vsbExvfLKK9ZRAMDUs6auFxYWavXq1UapAACYG0+Vkb///e9qa2uzjgEA5pi6DgDwA8+UkYcPH+qLL77Qz372M+soAGDu0qVL37udLwAAXuSZMnLu3DmtWbPmR4O9ACDfMHUdAOAXnribVjqd1pUrV7idL+BjbkwM98rE6Lmuefv2bVVVTenu3X8+9deZQA0A8ApPlJF//vOfam1t5aoI4FNMDJ+7cFg6f946BQAALyfny8js7KwuXryoffv2WUcB4BIm2wLIV01N7yoSaXJsvcdXWtPptP7yl5PaunWbo+s6JZUa0MAA7+2QpTIyMXFc6fTovP7stWvXtGJFRrdudX/v34dCVaqocOYJBgAAYCESaVJJyZuurH3//hXX1gac4noZmZg4rn/9q/Ol1xl4yhbolpZjFBIAAICniEajmp6eVklJiXUU4Jlcv5vWfK+IWK8NAADgZTU1Nfryyy+tYwA/yTO39gUAAMCLi8fjlBHkPMoIAACAD1VVVWl0lF0kyG2UEQAAAB8KBoPKZDLKZDLWUYBnoowAAAD4VEVFhcbHx61jAM9EGQEAAPApDrEj1+X80EMgF6VSQ44P6nN6oJSX1kylnnLvbgDAS6upqdHZs2f1xhtvWEcBnooyAsxRKjWk3t4V1jEAAHiu8vJyTU5OWsf4kWDQW7NPvJbXSygjwBw5fUUE8JqmpncViTRJkjKZjAYHL+nKlc+1bl2bKisXz3k9r1zBc2NNt9a1WPPIkf+rX/6yy9E15yOVGtDAwD5H1/SyQCCggoICzc7OKhgMWsd5IhJpUFvboCd2GQSDJYpEGhxdE/9GGQEAzEkk0qSSkjef/POaNau1cuVdHTt2TKWlBdq4caNCoZBhQmTbzMyMpCvf+75A7qiurtbIyIji8bh1lO/hDT4kDrADABwQjUa1a9cu1dbWKplM6urVq9aRkEWjo6OqqqqyjoFn4BA7chllBADgmMbGRu3du1cXL17UBx98oPv371tHQhaMjIyourraOgaegUnsyGWUEQCAo8LhsLq6utTa2qr9+/fr008/Zeiazw0PD2vJkiXWMfAM0WhU9+7ds44BPBVlBADgitraWiUSCY2NjenAgQO6c+eOdSS4ZGpqSosWLbKOgZ8QDof14MED6xjAj1BGAACuKSws1MaNG7Vp0yYdPnxYvb29XCXxmcePZyAQME6CnxKPx/X1119bxwB+hDICAHBdLBZTIpFQIBBQMpnUrVu3rCPBIVwV8QYOsSNXUUYAAFkRCAS0du1a7dixQydPntTJkyf16NEj61h4SZwX8YalS5dyZQQ5iTkj8LVUasjxgUqp1ICj6wH5prS0VLt379bAwICSyaQ2bdqkV155xToW5mlkZET19fXWMfAcRUVFSqfT1jGAH6GMwLdSqSH19q6wjgHgKQKBgJqbm7Vs2TKdOHFCfX192rp1q8LhsHU0zNHo6Kjeeust6xhPBIMl1hHmJJt5o9GopqenVVLirb8j+BtlBL7l9BUReFNT07uKRJocWy+dHlcoVOnYes9ac3BwUJK0YsXcC3UqNaCBgX2OZHNbcXGxduzYoWvXrqmnp0ft7e1qbGy0joU5SKfTKioqso7xRCTSoLa2Qcd/Brjx3A8GS7I6hfzxvJGVK1dm7WsCz+N6GQmF3JvI6ubaAPwhEmlSScmb1jHm7Nq1q+rq6lIkErGOkhV1dXWqqanRRx99pP7+fm3fvl3RaNQ6Fp5jZmZGoVDIOsaPZPMNvpfU1NTos88+o4wgp7heRioqtqml5ZjS6dF5/fl0+pHOnj2rjo4OffeugaFQlSoqtjmUEgByx+zsrB4+fJg3ReSxUCikLVu2aGRkRIcOHVJzc7NaW1u5ZWwOGx0dVVUVHwx6RVVVlU6ePGkdA/ierGzTetnSUFW1TNPTS9XQwCcdAPzv+vXrevXVV61jmKmurlYikVBvb696enrU2dmpykpnt8fAGSMjI6qurraOgRcUDAaVyWSUyWQo+cgZnri179q1axmUBSBvXLp0aV5nRfykoKBA7e3t6urq0okTJ/TXv/5Vs7Oz1rHwA9zW13sqKio0Pj5uHQN4whNlpLi4WNXV1bp+/bp1FABwVSaT0djYmBYvXmwdJSeUlZVpz549KisrUzKZZGhbjmHgofcw/BC5xhNlRJLeeustnT171joGALhqbGxMsViMLRTfEQgEtGrVKr399ts6d+6cjhw5opmZGetYee/xbgW+V72FMoJc45kysnDhQi1atIgnEABfY4vWs0UiEe3cuVMNDQ3q7u7W5cuXrSPltampKZWVlVnHwByVl5drcnLSOgbwhKfmjGzYsEHHjh3T7t27raPABU5PS2dSOrzoxo0bOTVALhfV19ertrZWJ0+eVH9/v7Zt26aFCxdax8o7w8PDHF73oEAgoIKCAs3OzioYDFrHAbxVRkpLSxUOh7l7hw8xLR2QUqmUwuHwS79BcHuicy5MuC4qKlJnZ6e++uorHTx4UC0tLWppaWHLUBaNjIyovr7eOgbmobq6WiMjI4rH49ZRAG+VEenbqyN/+ctf9Otf/9o6ChzEtPRveXVa+Mvy0sRwNw0NDWn58uUvvY5bE6il7E+Mfp54PK5EIqEzZ87ovffe0/bt21VeXm4dKy+Mjo5yFc+jHp8boYwgF3iujFRUVEiSJiYmnvx/wC+8Oi0czrh8+bK6urocWSuXCoPbgsGgOjo6NDExoSNHjqiurk7r1q1TQYFnjkV6UjqdVlFRkXUMzEM8Htdnn32mtWvXWkcBvHOA/bs2bNigU6dOWccAAMfk69R1J1VUVGjv3r1asGCBksmkhoeHrSP51szMjEKhkHUMzFM0GtW9e/esYwCSPFpGqqqq9ODBA01NTVlHAQBH5PvUdacEAgG1trbqV7/6lU6fPq0TJ04onU5bx/Kd0dFRVVVVWcfASwiHw3rw4IF1DMCbZUSS1q9fr9OnT1vHAABHcEtfZ0WjUe3atUu1tbXq7u7W1atXrSP5CjeS8b54PK6vv/7aOgbg3TJSU1OjqakpLjMC8DymrrunsbFRv/3tb3Xx4kV98MEHun//vnUkXxgeHtaSJUusY+AlMPwQucKzZUSS2tvbdebMGesYAPBSmLrurnA4rK6uLrW2tmr//v369NNPn0wPx/xMTU1p0aJF1jHwEpYuXcqVEeQET5eRV199VaOjo3zSBcDT2KKVHbW1tUokEhobG9OBAwd0584d60ie9LjIUZ69raioiPNUyAmeLiOBQEBr165Vb2+vdRQAmLcbN25weD1LCgsLtXHjRm3atEmHDx9Wb28vV0nmaGpqSmVlZdYx4IBoNKrpaeZ8wZbn5oz80PLly3X27FnNzMxwv/NnSKWGHB9+5vTgu1RqwLG1AC9xauo65iYWiymRSOjcuXNKJpPq7OxULBazjuUJw8PDHF73iXg8ri+//FIrV660joI85vkyEggEtHr1ap0/f55JsE+RSg2pt5ftH8htwWCJJ9d2wtDQkBoa8mdAYS55fHW9sbFRR48eVSwWU0dHhwoLPf+j0VUjIyOqr6+3jgEH1NTU6LPPPqOMwJQvXnGbmpr0+9//XmvXruWHyA84fUUEcEMk0qC2tkHHv1+DwZKcn0Tu5NR1zE9paal2796tgYEBJZNJbdq0Sa+88op1rJw1OjrKh38+UVVVpZMnT1rHQJ7zxTv3QCCgN954Q5988onWrFljHQfAPOR6aXADU9dzRyAQUHNzs5YtW6YTJ06or69PW7duVTgcto6Wc9LpNNuifSIYDCqTySiTyXBDApjx9AH273r99df12WefaXZ21joKALwQpq7nnuLiYu3YsUOvvfaaenp6dOnSJetIOWVmZkahUMg6BhxUUVGh8fFx6xjIY74pI8FgUM3Nzerv77eOAgAvhFv65q66ujolEgl98cUXev/993X37l3rSDlhdHRUVVVV1jHgIIYfwppvyogk/cd//IcuXLjAbRoB5Dymrue+UCikLVu2qKOjQ4cOHdLHH3+c9z9fRkZGuJOWz1BGYM1XZaSwsFANDQ0aGOA2sQByG1PXvaO6ulqJREIzMzPq6enJ6y0tw8PDWrJkiXUMOKi8vFyTk5PWMZDHfFVGJD25zW++f3oFILexRctbCgoK1N7erq6uLp04cUJ//etf8/KM4tTUlBYtWmQdAw4KBAIqKCjIy+9n5AbflZGioiK98sorunz5snUUAHgmpq57U1lZmfbs2aOysjIlk8m82t7y+EM+rub5T3V1tUZGRqxjIE/5roxIUltbm3p7e7k6AiAnMXXd2wKBgFatWqW3335b586d05EjRzQzM2Mdy3VTU1MqKyuzjgEXcG4ElnxZRoqLi1VdXa3r169bRwHmJNenhcMZTF33h0gkop07d6qhoUHd3d2+vyI/PDzM4XWfisfjlBGY8cXQw6d56623dPjwYdXV1VlHgU81Nb2rSKTJsfW8MC0czmDqur/U19ertrZWJ0+eVH9/v7Zt26aFCxdax3LcyMiI6uvrrWPABdFoVPfu3bOOgTzl2zKycOFCLVq0SF9++aVqamqs48CHIpEmlZS8aR0DHsPUdX8qKipSZ2envvrqKx08eFAtLS1qaWnx1fmK0dFRvfXWW9Yx4JJwOKwHDx5owYIF1lGQZ3y5TeuxDRs26MyZM9YxAOAJpq77WzweVyKR0PT0tN577z3dvn3bOpJj0um0ioqKrGPAJfF4XF9//bV1DOQhX5eR0tJShcNh7hABIGdwS1//CwaD6ujoUGdnp44cOaIzZ87om2++sY71UmZmZhQKhaxjwEUcYocV327TemzDhg36y1/+ol//+tfWUZ4rlRrS7Oy0w2syABLIFUxdzy8VFRXau3evPvnkEyWTSW3dutWzAwNHR0dVVVVlHQMuWrp0qXp7e61jIA/5voxUVFRIkiYmJp78/1yUSg2pt5dPSwE/Y+p6/gkEAmptbVVDQ4OOHTum0tJSbdy40XNXGUZGRjxbpPBiioqKlE6nrWMgD/l6m9ZjGzZs0KlTp6xj/CSnr4gAyD1s0cpf0WhUu3btUm1trbq7u3X16lXrSHPCbX3zQzQa1fQ070eQXXlRRqqqqvTgwQNNTU1ZRwGQx5i6jsbGRv32t7/VxYsX9cEHH+j+/fvWkV7I1NSUFi1aZB0DLmPeCCzkRRmRpPXr1+v06dPWMQDkKaau47FwOKyuri61trZq//79+vTTT5XJZKxjPdPjbGwv9D8OscOC78+MPFZTU6O//vWvunfvni+HUQHIbUxdfzY3bt7hhSGitbW1SiQSOnXqlA4cOKDt27ertLTUOtaPTE1NqayszDoGsqCqqkonT560joE8kzdlRJLa29t15swZbd261ToKfCAYLLGOAA9h6vrTuXnzjra2wZwvJIWFhdq4caNu3bqlw4cPq6GhQWvXrs2pqxCcF8kfwWBQmUxGmUwmp74H4W95VUZeffVVnT59Wvfv31dxcbF1nKxqanpXkUiTY+ul0+MKhSodWy+VGtDAwD7H1vsup//bJW986orcwdT1Z3Pz5h1eujFILBZTIpHQuXPnlEwm1dnZqVgsZh1L0rd30qqvr7eOgSypqKjQ+Pg4tyBH1uRVGQkEAlq7dq3+8Y9/6P/8n/9jHSerIpEmlZS8aR3DRD7/tyM3MHUdL+Lxz6jGxkYdPXpUsVhMHR0dKiy0/VE9Ojqqt956yzQDsufxuRHKCLIlbw6wP7Z8+XJdv35dMzMz1lEA5Alu6Yu5KC0t1e7du1VVVaVkMqkbN26Y5kmn0yoqKjLNgOzhEDuyLS+ujPzwcGRLS7H+8Y+kWlpa5r2mG9uUAPgPU9cxH4FAQM3NzVq2bJlOnDihvr4+bd26VeFwOKs5ZmZmKCJ5pry8XJOTk9YxkEd8X0Z+6nDk+fNZDgMg7zB1HS+juLhYO3bs0LVr19TT06P29nY1NjZm7euPjo7mzNkVZEcgEFBBQYFmZ2e5FTmywvfbtLx0gBGA/7BFC06oq6tTIpHQF198offff193797NytcdGRnRkiVLsvK1kDuqq6s1MjJiHQN5wvdlBAAsMXUdTgmFQtqyZYs6Ojp06NAhffzxx64PS+S2vvmJcyPIJsoIALiEqetwQ3V1tRKJhGZmZtTT06Px8XHXvtbU1JQWLVrk2vrITfF4nDKCrPH9mREAsMLUdbiloKBA7e3tWrlypY4ePap4PK7169c7WnwfX3XhvFP+iUajunfvnnUM5AnKSJ5w+m5dXrqbmBtrO/3fn+9rurWu9ZpXr/5ZGzas1/T0x46t+aK88jh56bmfi8NOy8rKtGfPHvX39yuZTGrz5s2qqalxZO2pqSmVlZU5sha8JxwO68GDB1qwYIF1FPhcIOP2hlNj09Mf6/z51dYxAAA+0NY2mHOF5LFUKqXjx48rHA5r8+bNL31L3osXL+revXtavZqfofno9OnTWrp0qZYtW2YdBT7HmREAAF5QLt+hMRKJaOfOnWpoaFB3d7cuX778UuuNjIxweD2PcYgd2UIZAQDAR+rr6/XOO+/o888/16FDh+a99390dFRVVVUOp4NXLF26VF9//bV1DOQByggAAD5TVFSkzs5OtbW16eDBg7pw4cKcbwOcTqeZvp7HioqKlE6nrWMgD1BGAADwqXg8rkQioenpab333nu6ffv2C/25mZkZigi0cOFCTU/n7tZE+ANlBAAAHwsGg+ro6FBnZ6eOHDmiM2fO6JtvvvnJPzM6OqpYLJalhMhVnBtBNlBGAADIAxUVFdq7d68WLFigZDKp4eHhZ/7ekZERLVmyJIvpkIsoI8gGyggAAHkiEAiotbVVv/rVr3T69GmdOHHiqecChoeHuZMWVFVVpdHRUesY8DnKCAAAeSYajWrXrl2qra1Vd3e3rl69+r1fn5qa0qJFi4zSIVcEg0FlMpk53/wAmAsmsAMAYCiVGnJ8fsmLTotvbGxUXV2dPvzwQ/X19Wnbtm1PJm4HAgFHM8GbKioqND4+rsWLF1tHgU9RRgAAMJJKDam3d4Ura7/otPhwOKyuri7dvHlT+/fv18qVK7kqgicenxuhjMAtbNMCAMCImxPd57p2bW2tEomEbt68qS+//FJ37txxKRm8hEPscBtlBAAASJIKCwu1ePFirV+/XocPH1Zvby/nBfJceXm5JicnrWPAxygjAADgidHRUa1YsUKJREKBQEDJZFK3bt2yjgUjgUBABQUFmp2dtY4Cn6KMAACAJ9LptIqKihQIBLR27Vrt2LFDJ0+e1MmTJ/Xo0SPreDBQXV2tkZER6xjwKcoIAACQJM3MzKioqOh7/660tFS7d+9WVVWVksmkbty4YZQOVjg3Ajf5vowEgyXWEQAA8ITR0VHFYrEf/ftAIKDm5mb95je/0YULF/SnP/1JDx8+NEgIC/F4nDIC1/j+1r6RSIPa2gYdv2NJOj2uUKjSsfVSqQENDOxzbD0AAOZqZGRES5YseeavFxcXa8eOHbp27Zp6enrU3t6uxsbGLCaEhWg0qnv37lnHgE/5voxIeqH7rAMAkO+Gh4e1YcOG5/6+uro61dTU6KOPPlJ/f7+2b9+uaDSahYSwEg6H9eDBgydDMQGn+H6bFgAAeDFTU1MvPPAwFAppy5Yt6ujo0KFDh/Txxx9zG2Afi8fj+vrrr61jwIfy4soIAABOSKUGcnq9l/G4SAQCgTn9uerqaiUSCfX29qqnp0ednZ2qrHRuGzNyQ01NjW7evKlly5ZZR4HPUEYAAHhBfj7bNzU1pbKysnn92YKCArW3t2vlypU6evSo4vG41q9fr2Aw6GxImFm6dKl6e3utY8CHKCN5oqnpXUUiTY6t5/QBfi+t6da6+bymW+tmY81Tp06publZ5eXljq3pBK88Tty8I3cMDw+rurr6pdYoKyvTnj171N/fr2Qyqc2bN6umpsahhLBUVFSkdDptHQM+RBnJE5FIk0pK3rSOAfhKJpPRrVufqrZ285y3tgC5ZmRkRPX19S+9TiAQ0KpVq1RfX6/jx4+rr69Pmzdv/tH8EnjPwoULNT09rZISxibAORxgB4B5unXrlmKxGEUEvjA6OqqqqirH1otEItq5c6caGhrU3d2ty5cvO7Y2bDD8EG6gjADAPA0ODmrFihXWMQBHpNNpV65e1NfX65133tHnn3+uQ4cOMa/CwygjcANlJEe4PSmeSfSA827cuKFXX33VOgbw0mZmZlzdRlVUVKTOzk61tbXp4MGDunDhArcB9qCqqiqNjo5ax4DPcGYkR7g1KV76togw+BFwViqVUjgc5m5B8IXR0VHFYjHXv048HlcikdCZM2f0hz/8QZ2dnS918wdkVzAYVCaTUSaTYXsqHEMZySEUBsA7hoaG1NDAcxb+MDIyoiVLlmTlawWDQXV0dGhiYkJHjhxRXV2d1q1bp4ICNmt4QUVFhcbHx7V48WLrKPAJnvkAMA9DQ0Navny5dQzAEU7c1neuKioqtHfvXi1YsEDJZFLDw8NZ/fqYH86NwGlcGQHmIZUacnxLHdvpvGN2dlYzMzOKRCLWUYBnmst091TqggoKlmh6+tlbb9yYXRMMlqi1tVUNDQ06duyYSktLtXHjRoVCIUe/DpxTU1Ojs2fP6o033rCOAp+gjABzlEoNqbfXnTsotbUNUkg84Pr16xxcdwg313DPXIZJxmLSxx//fy6meba2tkFFow3atWuXLl26pO7ubv385z/XsmXLTPLgp5WXl2tyctI6BnyEMgLMkRs3GcjG2nDOpUuXtGbNGusYvuDGzTuY6u4t333sGxsbVVdXpw8//FB9fX3atm2biouLDdPhhwKBgAoKCjQ7O8sNPOAIyggAzEEmk9HY2BiHNx3E1UB8VzgcVldXl27evKn9+/dr9erVam5u5u5NOaS6ulojIyOKx+PWUeADHGAHgDlg6jqQHbW1tUokEhobG9OBAwd0584d60j4Xxxih5MoIwAwB0xdB7KnsLBQGzdu1KZNm3T48GH19vYyLDEHxONxyggcQxkBgDlg6jqQfbFYTIlEQgUFBUomk7p165Z1pLwWjUZ179496xjwCc6MAMALYuo6YCcQCGjNmjVasWKFjh49qlgspo6ODhUW8lbGQjgc1oMHD7RgwQLrKPA4rowAwAti6jpgr7S0VLt371ZVVZWSyaRu3LhhHSkvxeNxff3119Yx4AOUEQB4QUxdB3JDIBBQc3OzfvOb3+jChQv605/+pIcPH1rHyiscYodTKCMA8AKYug7knuLiYu3YsUOvvfaaenp6dOnSJetIeWPp0qVcGYEj8mKjZSo15PgwuXR6XKFQZc6v6da6+bxmKjXg6Hr4lhvP02CwxLEZFkxdB3JXXV2dampq9NFHH6m/v1/bt29XNBq1juVrRUVFmpmZsY4BH/B9GUmlhtTby204gVzm5vO0rW3QkULC1HXvCAZLrCPAQCgU0pYtWzQyMqJDhw6publZra2tzARyUTQa1fT0tEpKeM5h/nxfRpz+pBWA89x8njqxNlPXvSUSaVBb26AnroinUgMaGNjn6Jr5rrq6WolEQr29verp6VFnZ6cqK53fdYB/nxtZuXKldRR4mO/LCAC8LKaue49T2/PgTQUFBWpvb9fKlSt19OhRxeNxrV+/nttyO6ympkafffYZZQQvhQPsAPAcTF0HvKmsrEx79uxRWVmZkskkd39yWFVVlUZHR61jwOMoIwDwHExdB7wrEAho1apVevvtt3Xu3DkdPXqUg9cOCQaDymQyymQy1lHgYZQRAPgJTF0H/CESiWjnzp1avny5uru7dfnyZetIvlBRUaHx8XHrGPAwyggA/ASmrgP+Ul9fr3feeUeff/65Dh06pHv37llH8jSGH+JlUUYA4CcwdR3wn6KiInV2dqqtrU0HDx7UhQsX2Go0T5QRvCzKCAA8A1PXAX+Lx+NKJBKanp7WH/7wB92+fds6kueUl5drcnLSOgY8jFv7AsAzMHUd8L9gMKiOjg5NTEzoyJEjqqur07p161RQwOe1LyIQCKigoECzs7OcrcO8UEaAHJJKDTi6nhtD2twa/JaLmLruXanUkGeGHuY7N/4O5vNYhULSf/3XCg0OXtL+/X/WunVtqqz896BTr7yeurHm89atrp7S9esnFIvF5rRmMFjCTCBQRoBcwiTm3MHUde9KpYbU28tcGK/Ixde96mrp2rVv/4fnCwSkGze+/d9ctbUNUkjyHNcgAeApmLruXU5fEQHgHp6voIwAwFMwdR0AAPdRRgDgKZi6DgCA+ygjAPADTF0HACA7KCMA8ANMXQcAIDsoIwDwA0xdBwAgOygjAPAdTF0HACB7KCMA8B1MXQcAIHt8P/RwdvaRdQQAhuY63Xlw8JSam5s1Pf3xU3/dKxOTLaYw58qaTDUHAO/wdRl59OiRTpw4LQYoA/lrrtOdFyyQPv/cpTAAAOB7fFtGHj16pPfff1+rV/8/isf/y/EJn174dNDNdb2w5uTkbV2+/LGGh++rqqpKDQ0NKi8vf+l1U6mBOb/BBQAAwI/5soz8u4is5o44eaykRKqt3aJMJqMvvvhCfX19mpj4TPX19Xr99ddVUlJiHREAACCv+a6MUETwQ4FAQLW1taqtrdXs7KyuXLmi48ePa2ZmRitXrlRTU5PC4bB1TAAAgLzjqzJCEcHzBINBrVixQitWrNDDhw918eJFHTp0SIWFhXr99de1fPlypm4DAABkSSCTyWSsQziBIoKXcffuXfX39+vy5csqKytTS0uLamtrFQgEfvR7p6c/1vnzqw1SAgDgL6tXn1dJyZvWMWDIF2WEIgInjY2Nqa+vTzdv3lRNTY1aWloUi8We/DplBAAAZ1BG4PltWhQROG3x4sX6xS9+8eTg+z/+8Q9NTEw8OfgOAAAAZ3i6jFBE4KZnHXz/5ptLzK4BAABwgGfLCEUE2fTdg+/j42fV12edCAAAwPuyUkYmJo4rnR51bL3Z2VmdPXtJq1f/vxQRZF1RUZF1BAAAAF9wvYxMTBzXv/7V6fi6FRVSRcUmSZQRZFcwyLBEAACcwM9UuF5GnLwiks21gWeJRBrU1jao2dnpp/56JpPR6OiIhoYu686dO6qpqVFDw3JFIgt/ct10elyhUKWjWb2yZio1oIGBfY6uCbihqeldRSJNjq7pleepV9Z0a12LNTOZjAYHL+nKlc+1bl2bKiuff2DRS3+nwWCJIpEGR9eE93j2zAhg6XkvnqWl0vLlenLw/dSpfj18+IWampqY+A54WCTSxG1IkVVr1qzWypV3dezYMZWWFmjjxo0KhULWsQDHUEYAFz1t4vsf//hHhUIhJr4DAF5INBrVrl27dOnSJXV3d+vnP/+5li1bZh0LcARlBMiScDisN954Q2+88caTie/d3d3PnfgOAIAkNTY2qq6uTh9++KH6+vq0bds2FRcXW8cCXgplBDAQjUbV3t6u9vb2JxPfT548+dSJ7wAAPBYOh9XV1aWbN29q//79Wr16tZqbm/kwC55FGQGMPWvi+89+9jOtWrVKJSXcaQQA8H21tbX63e9+p7/97W86cOCAtm/frtLSUutYwJxRRoAc8ayJ7w8fPuTgOwDgR4LBoDZu3Khbt27p8OHDamho0Nq1a7lKAk8psA4A4MceH3zftWuXdu3apUAgoD/+8Y96//33denSJc3OzlpHBADkiFgspkQioYKCAiWTSd26dcs6EvDCuDIC5DgOvgMAnicQCGjNmjVasWKFjh49qlgspo6ODhUW8lYPuY3vUMBDOPgOAPgppaWl2r17twYGBpRMJrVp0ya98sor1rGAZ6KMAB71tIPv4+Pjqq+v99zB92DQO1kBINcFAgE1Nzdr2bJlOnHihPr6+rR161bOHSInUUYAj/PDwfdIpEFtbYOanZ12dN10elyhUKVj66VSAxoY2OfYet/V1PSuIpEmx9Zz+r/dzXW99DgBXlJcXKwdO3bo2rVr6unpUXt7uxobG61jAd9DGQF8xMsT3yORBusIpiKRJpWUvGkdA4AP1dXVqaamRh999JH6+/u1fft2RaNR61iAJMoI4FscfAcAPBYKhbRlyxaNjIzo0KFDam5uVmtrKz8HYI5b+wJ54PHB93379qm9vV1XrlzR73//e/35z3/mFpAAkEeqq6uVSCQ0MzOjnp4ejY+PW0dCnuPKCJBn/HTwHQAwdwUFBWpvb9fKlSt19OhRxeNxrV+/Pme38cLfKCNAnvLDwXcAwPyVlZVpz5496u/vVzKZ1ObNm1VTU2MdC3mGMgLA0wffAQDzFwgEtGrVKtXX1+v48ePq7+/XL37xCxUVFVlHQ56gjAD4Hg6+A0D+iUQi2rlzp65cuaLu7m5t2LBBy5cvt46FPEAZmadUasjxmQjBYEne397UK9x4/HN1fsNrrxXptdeaNTl5W5cv/1+dOXNFixf/TA0NDSovL3coae7+939XKjXg2FqA1/BzLz/U19ertrZWJ0+eVH9/v7Zt26aFCxdax4KPuV5GQqEq19a+c6dA1dWuLf9MqdSQentXuLJ2W9sgL8w5zs3HP9cFAnrynPv8c9sswLMEg+7dhMHNtXMZP/fyS1FRkTo7O/XVV1/p4MGDamlpUUtLC1fF4QrXy0hFxTa1tBxTOj3q6LqZzCIdP35bCxd+pXg87ujaz+P0J0PZWhvO4DECclsk0qC2tkE+xXcQP/fyUzweVyKR0JkzZ/SHP/xBnZ2djl4RB6QsbdOqqNjmyrq7d9/XgQMHtGXLlqwXEgBA7srX0gA4LRgMqqOjQxMTEzpy5Ijq6uq0bt06FRQwqg7O8PR3UnFxsXbv3q0///nP+uqrr6zjAAAA+FJFRYX27t2rBQsWKJlManh42DoSfMLTZUSikAAAAGRDIBBQa2urfvWrX+n06dM6ceKE0um0dSx4nOfLiEQhAQAAyJZoNKpdu3aptrZW3d3dunr1qnUkeJgvyohEIQEAAMimxsZG/fa3v9XFixf1wQcf6P79+9aR4EG+KSMShQQAACCbwuGwurq61Nraqv379+vTTz9VJpOxjgUP8VUZkSgkAAAA2VZbW6vf/e53Ghsb04EDB3Tnzh3rSPAI35URiUICAACQbcFgUBs3btSmTZt0+PBh9fb2cpUEz5WVOSMWHheSAwcO6Oc/X6bKyohja6dSA46tlQ2p1JDjQ6XS6XGFQpV5uabXHn94g9PfV25877u1bj6v6da6Tq/J6x7mIhaLKZFI6Pz580omk+rs7FQsFvvR73Pj/Uk+Dyf1qkDG55X19u0+XbjQYh3jha1efV4lJW86tl4qNaTe3hWOrQcAgJOc/rmH3HLnzh0dPXpUsVhMHR0dKiz89nNwN9+ftLUNUkg8xJfbtL6rsDC/73/t9CcOAAAAL6q0tFS7d+9WVVWVksmkbty4Icnd9ye89/EW327TAgAAgL1AIKDm5mYtW7ZMJ06cUF9fn9rbnd/yCG/y/ZURAAAA2CsuLtaOHTv02muv6dixY9ZxkCMoIwAAAMiauro6dXX90joGcgRlBAAAAFkVDHJSAN+ijAAAAAAwQRkBAAAAYIIyAgAAAMAEG/ZyjNNTbpmaCwDIZW78nPLCVHsvrenGurw/wWOUkRwzMLDPOgIAAFnDzz0gv1FGgBzS1PSuIpEmx9bL50/d3FgzlRrgjRMAAA6ijAA5JBJpUknJm9YxAAAAsoID7AAAAABMUEYAAAAAmKCMAAAAADBBGQEAAABggjICAAAAwARlBAAAAIAJyggAAAAAE5QRAAAAACZ8X0aCwRLrCAB8gtcTAMh96fS4dQTMge8nsEciDWprG9Ts7LRja6ZSAxoY2OfYel7V1PSuIpEmx9ZLp8cVClU6tp5ba/L45y83Xk8k73zvu7VuPq/p1rpOr8nrHrzEjecp3OP7MiJ9+wYCzotEmlRS8qZ1DCCreD0BAMA5vt+mBQAAACA3UUYAAAAAmKCMAAAAADBBGQEAAABggjICAAAAwARlBAAAAIAJyggAAAAAE5QRAAAAACYoI/MQDJZYR4AhNx9/vrcA5CJemwC4JS8msDstEmlQW9ugZmenHV03nR5XKFTp6Jqp1IAGBvY5uma+c+vxDwZLmO4NICd56eeeW+vm85purMv7EzxGGZkn3jTmNx5/APmG1z0AbmCbFgAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGXE55gWDgAAcg3vT/BYIJPJZKxDwF2p1BDTwgEAQE7h/QkkyggAAAAAI2zTAgAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADABGUEAAAAgAnKCAAAAAATlBEAAAAAJigjAAAAAExQRgAAAACYoIwAAAAAMEEZAQAAAGCCMgIAAADAxP8P5RgCsPKND9gAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "closed_lines = set(net.line.index) - set(net.switch[(net.switch.et==\"l\") & (net.switch.closed==False)].element.values)\n",
    "lcd = plot.create_line_collection(net, closed_lines, color=\"grey\", linewidths=0.5, use_bus_geodata=True)\n",
    "plot.draw_collections([lcd, bc, tlc, tpc, sc], figsize=(8,6))"
   ]
  }
 ],
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