system_assignation/presentation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-03-15T18:21:49.991853Z",
     "start_time": "2024-03-15T18:21:40.308604500Z"
    }
   },
   "outputs": [],
   "source": [
    "from geojson_creator import process_geojson\n",
    "from pathlib import Path\n",
    "from scripts.ep_workflow import energy_plus_workflow\n",
    "from hub.imports.geometry_factory import GeometryFactory\n",
    "from hub.helpers.dictionaries import Dictionaries\n",
    "from hub.imports.construction_factory import ConstructionFactory\n",
    "from hub.imports.usage_factory import UsageFactory\n",
    "from hub.imports.weather_factory import WeatherFactory\n",
    "from hub.imports.energy_systems_factory import EnergySystemsFactory\n",
    "from scripts.random_assignation import call_random, residential_systems_percentage, residential_new_systems_percentage\n",
    "import hub.helpers.constants as cte\n",
    "from hub.imports.energy_systems.energy_system_sizing import SystemSizing\n",
    "from system_simulation import SystemSimulation\n",
    "from hub.helpers.monthly_values import MonthlyValues\n",
    "from DistrictHeatingNetworkCreator import DistrictHeatingNetworkCreator, plot_network_graph\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "coordinate = [45.533721353550895, -73.60218001215149]\n",
    "geojson_file = process_geojson(x=coordinate[1], y=coordinate[0], diff=0.005)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T22:13:27.902869800Z",
     "start_time": "2024-03-15T22:12:59.575900800Z"
    }
   },
   "id": "af279e5953d738a3"
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "file_path = \"./input_files/output_buildings.geojson\""
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T04:22:17.955290700Z",
     "start_time": "2024-03-07T04:22:17.860146900Z"
    }
   },
   "id": "2151290f994c5e55"
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "city created from ./input_files/output_buildings.geojson\n"
     ]
    }
   ],
   "source": [
    "city = GeometryFactory('geojson',\n",
    "                       path=file_path,\n",
    "                       height_field='height',\n",
    "                       year_of_construction_field='year_of_construction',\n",
    "                       function_field='function',\n",
    "                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city\n",
    "print(f'city created from {file_path}')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T04:22:20.686337700Z",
     "start_time": "2024-03-07T04:22:19.684778200Z"
    }
   },
   "id": "76a49a4fa4b7e7b7"
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enrich constructions... done\n"
     ]
    }
   ],
   "source": [
    "ConstructionFactory('nrcan', city).enrich()\n",
    "print('enrich constructions... done')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T04:22:35.143609Z",
     "start_time": "2024-03-07T04:22:33.950471100Z"
    }
   },
   "id": "7dde0f5014c7c7c0"
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enrich usage... done\n"
     ]
    }
   ],
   "source": [
    "UsageFactory('nrcan', city).enrich()\n",
    "print('enrich usage... done')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T04:22:44.160020700Z",
     "start_time": "2024-03-07T04:22:40.519589100Z"
    }
   },
   "id": "6921c8feddc04a1c"
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enrich weather... done\n"
     ]
    }
   ],
   "source": [
    "WeatherFactory('epw', city).enrich()\n",
    "print('enrich weather... done')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T04:22:49.527124100Z",
     "start_time": "2024-03-07T04:22:48.593307Z"
    }
   },
   "id": "3c93617f2675095d"
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exporting:\n",
      "    idf exported...\n",
      "\r\n",
      "C:/EnergyPlusV9-5-0\\energyplus.exe --weather \\\\Mac\\Home\\Main\\Concordia\\Repositories\\system_assignation\\hub\\data\\weather\\epw\\CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw --output-directory \\\\Mac\\Home\\Main\\Concordia\\Repositories\\system_assignation\\out_files --idd \\\\Mac\\Home\\Main\\Concordia\\Repositories\\system_assignation\\hub\\exports\\building_energy\\idf_files\\Energy+.idd --expandobjects --readvars --output-prefix Mont-Royal_ \\\\Mac\\Home\\Main\\Concordia\\Repositories\\system_assignation\\out_files\\Mont-Royal_025e05.idf\r\n"
     ]
    }
   ],
   "source": [
    "energy_plus_workflow(city)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T04:24:41.598482900Z",
     "start_time": "2024-03-07T04:22:52.781718100Z"
    }
   },
   "id": "bcd081bd30056dfd"
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [],
   "source": [
    "roads_file = './input_files/roads/geobase_mtl.shp'"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T04:28:48.003487500Z",
     "start_time": "2024-03-07T04:28:47.785414600Z"
    }
   },
   "id": "6bc1cb99e3f23c4a"
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [],
   "source": [
    "central_plant = [45.50189527625893, -73.64679453009131]\n",
    "\n",
    "central_plant_longitude = central_plant[1]\n",
    "central_plant_latitude = central_plant[0]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T04:28:51.401333Z",
     "start_time": "2024-03-07T04:28:51.162465600Z"
    }
   },
   "id": "c116d3ce1e950a9a"
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [],
   "source": [
    "network_creator = DistrictHeatingNetworkCreator(\n",
    "    file_path,\n",
    "    roads_file,\n",
    "    central_plant_longitude,\n",
    "    central_plant_latitude\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-06T22:44:28.856784800Z",
     "start_time": "2024-03-06T22:44:28.650112800Z"
    }
   },
   "id": "737f42696366245c"
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [],
   "source": [
    "network_graph = network_creator.run()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T04:33:20.344805700Z",
     "start_time": "2024-03-07T04:28:56.838736300Z"
    }
   },
   "id": "9423327f25d546ec"
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x1200 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_network_graph(network_graph, central_plant_id=1):\n",
    "    plt.figure(figsize=(12, 12))\n",
    "    pos = {node: (node[0], node[1]) for node in network_graph.nodes()}\n",
    "\n",
    "    # Node colors based on type\n",
    "    node_colors = ['red' if data.get('building_id') == str(central_plant_id) else 'green' if data.get(\n",
    "        'type') == 'centroid' else 'blue' for node, data in network_graph.nodes(data=True)]\n",
    "\n",
    "    # Node sizes, larger for central plant\n",
    "    node_sizes = [100 if data.get('building_id') == str(central_plant_id) else 50 for node, data in\n",
    "                  network_graph.nodes(data=True)]\n",
    "\n",
    "    nx.draw_networkx_nodes(network_graph, pos, node_color=node_colors, node_size=node_sizes)\n",
    "    nx.draw_networkx_edges(network_graph, pos, edge_color='gray', width=1)\n",
    "\n",
    "    plt.title('District Heating Network Graph')\n",
    "    plt.axis('off')\n",
    "    plt.savefig('network_graph_visualization.png', format='png', dpi=300)  # Save as PNG with high dpi for clarity\n",
    "    plt.show()\n",
    "plot_network_graph(network_graph)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T12:37:25.242171200Z",
     "start_time": "2024-03-08T12:37:23.473287500Z"
    }
   },
   "id": "e32bf9d2b7684f81"
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [],
   "source": [
    "def enrich_graph(graph, city):\n",
    "    \"\"\"\n",
    "    Enrich the graph nodes with hourly building demand data.\n",
    "\n",
    "    :param graph: The networkx graph of the district heating network.\n",
    "    :param buildings: A list of building objects, each with a 'name' and 'heating_demand' attribute.\n",
    "    \"\"\"\n",
    "    for node in graph.nodes:\n",
    "        node_data = graph.nodes[node]\n",
    "        # Check if the node has a 'building_id' attribute before comparing\n",
    "        if 'building_id' in node_data:\n",
    "            for building in city.buildings:\n",
    "                if node_data['building_id'] == building.name:\n",
    "                    # Assuming `building.heating_demand` is properly structured for direct assignment\n",
    "                    graph.nodes[node][\"Demand\"] = building.heating_demand[cte.HOUR]\n",
    "                    graph.nodes[node][\"Peack_Demand\"] = building.heating_peak_load[cte.YEAR]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T05:02:30.262648Z",
     "start_time": "2024-03-07T05:02:29.981569600Z"
    }
   },
   "id": "aec54c50f6873ba2"
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [],
   "source": [
    "enrich_graph(network_graph, city)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T05:02:33.598079600Z",
     "start_time": "2024-03-07T05:02:33.535428200Z"
    }
   },
   "id": "714a25542a73d656"
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [
    {
     "data": {
      "text/plain": "                                  Start Node  \\\n0    (293268.92232946283, 5040206.601044122)   \n1     (293162.4584459267, 5040298.134698394)   \n2     (293169.9422966335, 5040254.972466738)   \n3     (293161.9162166267, 5040243.155704052)   \n4    (293243.96496123145, 5040244.083651768)   \n5     (293208.27353312843, 5040191.91032603)   \n6     (293179.0032363437, 5040271.258029018)   \n7     (293228.2223414392, 5040216.721843737)   \n8    (293212.70831835904, 5040249.072585436)   \n9      (293201.0374294459, 5040234.62735603)   \n10   (293177.91918802116, 5040196.235110773)   \n11   (293168.61618659477, 5040180.519320131)   \n12   (293211.21706085204, 5040312.731630573)   \n13  (293249.93628141866, 5040178.0515293395)   \n14    (293221.9359037339, 5040305.265169374)   \n15   (293247.62836591585, 5040286.946975689)   \n16    (293162.5326243373, 5040344.244190009)   \n17   (293274.85871539445, 5040270.178231545)   \n18    (293221.1012564584, 5040259.169113723)   \n19    (293258.2672356481, 5040190.475701797)   \n20    (293237.5724254025, 5040294.591521784)   \n21    (293221.0543914453, 5040205.772851501)   \n22    (293263.9478932525, 5040277.498572837)   \n23     (293193.935307871, 5040223.606890397)   \n24   (293186.26851059805, 5040282.301174366)   \n25  (293185.13387576357, 5040207.2769374205)   \n26   (293237.28612816025, 5040233.025292898)   \n27   (293193.65639875695, 5040323.624066331)   \n28   (293173.44689440983, 5040336.995190537)   \n29      (293276.818447755, 5040218.71502952)   \n30    (293202.12567603646, 5040180.27465022)   \n31   (293328.35617741547, 5040229.762923518)   \n32   (293251.93105040304, 5040217.926054459)   \n33   (293251.93105040304, 5040217.926054459)   \n34    (293171.90206806763, 5040312.19278805)   \n35    (293171.90206806763, 5040312.19278805)   \n36   (293186.20481946884, 5040244.159450427)   \n37   (293186.20481946884, 5040244.159450427)   \n38   (293178.29597659543, 5040232.264736303)   \n39    (293253.3895766028, 5040258.911978145)   \n40    (293253.3895766028, 5040258.911978145)   \n41   (293226.4958520454, 5040179.7648042375)   \n42    (293196.49133223895, 5040259.63012429)   \n43    (293244.0825581944, 5040206.150707917)   \n44   (293196.59516456636, 5040259.786285674)   \n45   (293186.35704876215, 5040244.388399713)   \n46   (293161.56838219793, 5040207.106826794)   \n47    (293152.78058224276, 5040194.16586493)   \n48   (293198.74294034916, 5040294.162240376)   \n49    (293232.9154049299, 5040189.396266925)   \n\n                                   End Node     Length  \n0   (293251.93105040304, 5040217.926054459)  20.419584  \n1    (293171.90206806763, 5040312.19278805)  16.935521  \n2   (293186.20481946884, 5040244.159450427)  19.529234  \n3   (293178.29597659543, 5040232.264736303)  19.670021  \n4    (293253.3895766028, 5040258.911978145)  17.569936  \n5   (293226.4958520454, 5040179.7648042375)  21.899009  \n6    (293196.49133223895, 5040259.63012429)  21.000992  \n7    (293244.0825581944, 5040206.150707917)  19.060309  \n8   (293196.59516456636, 5040259.786285674)  19.349860  \n9   (293186.35704876215, 5040244.388399713)  17.629281  \n10  (293161.56838219793, 5040207.106826794)  19.635250  \n11   (293152.78058224276, 5040194.16586493)  20.904415  \n12  (293198.74294034916, 5040294.162240376)  22.370202  \n13   (293232.9154049299, 5040189.396266925)  20.455154  \n14  (293209.58491002093, 5040286.879069806)  22.149395  \n15  (293236.59445823065, 5040269.586650846)  20.570075  \n16   (293150.67694876884, 5040326.42836015)  21.400020  \n17   (293263.5810838301, 5040252.434440949)  21.024440  \n18   (293230.2752063758, 5040273.603052741)  17.102630  \n19   (293241.2117340568, 5040201.843517671)  20.496765  \n20   (293225.9712000135, 5040276.338601143)  21.627703  \n21   (293236.8247611614, 5040195.261600439)  18.952334  \n22    (293252.495689268, 5040259.480116727)  21.349888  \n23   (293179.0986062601, 5040233.471872488)  17.817003  \n24   (293195.6375827925, 5040296.248286495)  16.801829  \n25   (293168.8711824724, 5040218.090067068)  19.529438  \n26   (293254.3945879081, 5040221.622179406)  20.560408  \n27     (293181.600750976, 5040305.67762853)  21.619743  \n28   (293161.4860233397, 5040319.189840496)  21.449777  \n29    (293259.9504738339, 5040229.95785464)  20.271400  \n30  (293219.23492961464, 5040168.871007625)  20.561362  \n31   (293319.5070828843, 5040216.163684101)  16.224851  \n32                (293271.554, 5040247.367)  35.381202  \n33  (293226.4958520454, 5040179.7648042375)  45.860989  \n34       (293213.057996733, 5040284.545999)  49.579788  \n35  (293198.74294034916, 5040294.162240376)  32.334704  \n36       (293213.057996733, 5040284.545999)  48.499138  \n37  (293178.29597659543, 5040232.264736303)  14.284048  \n38   (293196.49133223895, 5040259.63012429)  32.862371  \n39                (293271.554, 5040247.367)  21.522844  \n40  (293236.59445823065, 5040269.586650846)  19.900368  \n41   (293244.0825581944, 5040206.150707917)  31.709748  \n42  (293196.59516456636, 5040259.786285674)   0.187530  \n43   (293232.9154049299, 5040189.396266925)  20.134960  \n44  (293186.35704876215, 5040244.388399713)  18.490914  \n45  (293161.56838219793, 5040207.106826794)  44.770455  \n46   (293152.78058224276, 5040194.16586493)  15.642695  \n47   (293179.0986062601, 5040233.471872488)  47.303283  \n48  (293209.58491002093, 5040286.879069806)  13.061121  \n49   (293241.2117340568, 5040201.843517671)  14.958714  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Start Node</th>\n      <th>End Node</th>\n      <th>Length</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>(293268.92232946283, 5040206.601044122)</td>\n      <td>(293251.93105040304, 5040217.926054459)</td>\n      <td>20.419584</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>(293162.4584459267, 5040298.134698394)</td>\n      <td>(293171.90206806763, 5040312.19278805)</td>\n      <td>16.935521</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>(293169.9422966335, 5040254.972466738)</td>\n      <td>(293186.20481946884, 5040244.159450427)</td>\n      <td>19.529234</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>(293161.9162166267, 5040243.155704052)</td>\n      <td>(293178.29597659543, 5040232.264736303)</td>\n      <td>19.670021</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>(293243.96496123145, 5040244.083651768)</td>\n      <td>(293253.3895766028, 5040258.911978145)</td>\n      <td>17.569936</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>(293208.27353312843, 5040191.91032603)</td>\n      <td>(293226.4958520454, 5040179.7648042375)</td>\n      <td>21.899009</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>(293179.0032363437, 5040271.258029018)</td>\n      <td>(293196.49133223895, 5040259.63012429)</td>\n      <td>21.000992</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>(293228.2223414392, 5040216.721843737)</td>\n      <td>(293244.0825581944, 5040206.150707917)</td>\n      <td>19.060309</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>(293212.70831835904, 5040249.072585436)</td>\n      <td>(293196.59516456636, 5040259.786285674)</td>\n      <td>19.349860</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>(293201.0374294459, 5040234.62735603)</td>\n      <td>(293186.35704876215, 5040244.388399713)</td>\n      <td>17.629281</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>(293177.91918802116, 5040196.235110773)</td>\n      <td>(293161.56838219793, 5040207.106826794)</td>\n      <td>19.635250</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>(293168.61618659477, 5040180.519320131)</td>\n      <td>(293152.78058224276, 5040194.16586493)</td>\n      <td>20.904415</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>(293211.21706085204, 5040312.731630573)</td>\n      <td>(293198.74294034916, 5040294.162240376)</td>\n      <td>22.370202</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>(293249.93628141866, 5040178.0515293395)</td>\n      <td>(293232.9154049299, 5040189.396266925)</td>\n      <td>20.455154</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>(293221.9359037339, 5040305.265169374)</td>\n      <td>(293209.58491002093, 5040286.879069806)</td>\n      <td>22.149395</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>(293247.62836591585, 5040286.946975689)</td>\n      <td>(293236.59445823065, 5040269.586650846)</td>\n      <td>20.570075</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>(293162.5326243373, 5040344.244190009)</td>\n      <td>(293150.67694876884, 5040326.42836015)</td>\n      <td>21.400020</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>(293274.85871539445, 5040270.178231545)</td>\n      <td>(293263.5810838301, 5040252.434440949)</td>\n      <td>21.024440</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>(293221.1012564584, 5040259.169113723)</td>\n      <td>(293230.2752063758, 5040273.603052741)</td>\n      <td>17.102630</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>(293258.2672356481, 5040190.475701797)</td>\n      <td>(293241.2117340568, 5040201.843517671)</td>\n      <td>20.496765</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>(293237.5724254025, 5040294.591521784)</td>\n      <td>(293225.9712000135, 5040276.338601143)</td>\n      <td>21.627703</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>(293221.0543914453, 5040205.772851501)</td>\n      <td>(293236.8247611614, 5040195.261600439)</td>\n      <td>18.952334</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>(293263.9478932525, 5040277.498572837)</td>\n      <td>(293252.495689268, 5040259.480116727)</td>\n      <td>21.349888</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>(293193.935307871, 5040223.606890397)</td>\n      <td>(293179.0986062601, 5040233.471872488)</td>\n      <td>17.817003</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>(293186.26851059805, 5040282.301174366)</td>\n      <td>(293195.6375827925, 5040296.248286495)</td>\n      <td>16.801829</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>(293185.13387576357, 5040207.2769374205)</td>\n      <td>(293168.8711824724, 5040218.090067068)</td>\n      <td>19.529438</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>(293237.28612816025, 5040233.025292898)</td>\n      <td>(293254.3945879081, 5040221.622179406)</td>\n      <td>20.560408</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>(293193.65639875695, 5040323.624066331)</td>\n      <td>(293181.600750976, 5040305.67762853)</td>\n      <td>21.619743</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>(293173.44689440983, 5040336.995190537)</td>\n      <td>(293161.4860233397, 5040319.189840496)</td>\n      <td>21.449777</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>(293276.818447755, 5040218.71502952)</td>\n      <td>(293259.9504738339, 5040229.95785464)</td>\n      <td>20.271400</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>(293202.12567603646, 5040180.27465022)</td>\n      <td>(293219.23492961464, 5040168.871007625)</td>\n      <td>20.561362</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>(293328.35617741547, 5040229.762923518)</td>\n      <td>(293319.5070828843, 5040216.163684101)</td>\n      <td>16.224851</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>(293251.93105040304, 5040217.926054459)</td>\n      <td>(293271.554, 5040247.367)</td>\n      <td>35.381202</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>(293251.93105040304, 5040217.926054459)</td>\n      <td>(293226.4958520454, 5040179.7648042375)</td>\n      <td>45.860989</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>(293171.90206806763, 5040312.19278805)</td>\n      <td>(293213.057996733, 5040284.545999)</td>\n      <td>49.579788</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>(293171.90206806763, 5040312.19278805)</td>\n      <td>(293198.74294034916, 5040294.162240376)</td>\n      <td>32.334704</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>(293186.20481946884, 5040244.159450427)</td>\n      <td>(293213.057996733, 5040284.545999)</td>\n      <td>48.499138</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>(293186.20481946884, 5040244.159450427)</td>\n      <td>(293178.29597659543, 5040232.264736303)</td>\n      <td>14.284048</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>(293178.29597659543, 5040232.264736303)</td>\n      <td>(293196.49133223895, 5040259.63012429)</td>\n      <td>32.862371</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>(293253.3895766028, 5040258.911978145)</td>\n      <td>(293271.554, 5040247.367)</td>\n      <td>21.522844</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>(293253.3895766028, 5040258.911978145)</td>\n      <td>(293236.59445823065, 5040269.586650846)</td>\n      <td>19.900368</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>(293226.4958520454, 5040179.7648042375)</td>\n      <td>(293244.0825581944, 5040206.150707917)</td>\n      <td>31.709748</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>(293196.49133223895, 5040259.63012429)</td>\n      <td>(293196.59516456636, 5040259.786285674)</td>\n      <td>0.187530</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>(293244.0825581944, 5040206.150707917)</td>\n      <td>(293232.9154049299, 5040189.396266925)</td>\n      <td>20.134960</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>(293196.59516456636, 5040259.786285674)</td>\n      <td>(293186.35704876215, 5040244.388399713)</td>\n      <td>18.490914</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>(293186.35704876215, 5040244.388399713)</td>\n      <td>(293161.56838219793, 5040207.106826794)</td>\n      <td>44.770455</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>(293161.56838219793, 5040207.106826794)</td>\n      <td>(293152.78058224276, 5040194.16586493)</td>\n      <td>15.642695</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>(293152.78058224276, 5040194.16586493)</td>\n      <td>(293179.0986062601, 5040233.471872488)</td>\n      <td>47.303283</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>(293198.74294034916, 5040294.162240376)</td>\n      <td>(293209.58491002093, 5040286.879069806)</td>\n      <td>13.061121</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>(293232.9154049299, 5040189.396266925)</td>\n      <td>(293241.2117340568, 5040201.843517671)</td>\n      <td>14.958714</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_data = [(u, v, attrs['weight']) for u, v, attrs in network_graph.edges(data=True)]\n",
    "\n",
    "# Creating a DataFrame\n",
    "edges_df = pd.DataFrame(edges_data, columns=['Start Node', 'End Node', 'Length'])\n",
    "\n",
    "edges_df.head(50)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T05:08:59.648101700Z",
     "start_time": "2024-03-07T05:08:59.425424800Z"
    }
   },
   "id": "2a9aff199c7e0afb"
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "outputs": [
    {
     "data": {
      "text/plain": "67"
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network_graph.number_of_nodes()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T05:36:47.803538200Z",
     "start_time": "2024-03-07T05:36:47.772283Z"
    }
   },
   "id": "45264a34288ee52a"
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "outputs": [
    {
     "data": {
      "text/plain": "66"
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network_graph.number_of_edges()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T05:36:44.355784700Z",
     "start_time": "2024-03-07T05:36:44.073615700Z"
    }
   },
   "id": "8e39fa467979f701"
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n",
      "<class 'NoneType'>\n"
     ]
    }
   ],
   "source": [
    "for node, data in network_graph.nodes(data=True):\n",
    "        print(type(data.get('building_id')))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T05:50:22.435138200Z",
     "start_time": "2024-03-07T05:50:22.084297200Z"
    }
   },
   "id": "a99dc335bee49b62"
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "from collections import deque\n",
    "\n",
    "def label_graph_elements(network_graph, central_plant_id=\"1\"):\n",
    "    # Initialize all nodes and edges with solver_name '0' as placeholder\n",
    "    nx.set_node_attributes(network_graph, '0', 'solver_name')\n",
    "    nx.set_edge_attributes(network_graph, '0', 'solver_name')\n",
    "\n",
    "    # Find the central plant node\n",
    "    central_plant_node = None\n",
    "    for node, data in network_graph.nodes(data=True):\n",
    "        if data.get('building_id') == central_plant_id:\n",
    "            central_plant_node = node\n",
    "            break\n",
    "\n",
    "    if central_plant_node is None:\n",
    "        print(\"Central plant node not found.\")\n",
    "        return\n",
    "\n",
    "    # BFS to label nodes and edges\n",
    "    queue = deque([central_plant_node])\n",
    "    visited = {central_plant_node}\n",
    "    network_graph.nodes[central_plant_node]['solver_name'] = '1'  # Central plant labeled as '1'\n",
    "    solver_name_counter = 2  # Start labeling from '2' for other nodes\n",
    "\n",
    "    while queue:\n",
    "        current_node = queue.popleft()\n",
    "        for neighbor in network_graph.neighbors(current_node):\n",
    "            if neighbor not in visited:\n",
    "                network_graph.nodes[neighbor]['solver_name'] = str(solver_name_counter)\n",
    "                visited.add(neighbor)\n",
    "                queue.append(neighbor)\n",
    "                # Label the edge connecting these nodes\n",
    "                network_graph[current_node][neighbor]['solver_name'] = str(solver_name_counter)\n",
    "                solver_name_counter += 1\n",
    "\n",
    "# Assume network_graph is your network graph object\n",
    "label_graph_elements(network_graph)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T05:50:56.983642800Z",
     "start_time": "2024-03-07T05:50:56.686763400Z"
    }
   },
   "id": "195ea4ca176687c5"
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_network_with_labels(network_graph):\n",
    "    pos = {node: (node[0], node[1]) for node in network_graph.nodes()}\n",
    "    node_labels = nx.get_node_attributes(network_graph, 'solver_name')\n",
    "    edge_labels = nx.get_edge_attributes(network_graph, 'solver_name')\n",
    "\n",
    "    nx.draw(network_graph, pos, with_labels=False, node_size=500)\n",
    "    nx.draw_networkx_labels(network_graph, pos, labels=node_labels)\n",
    "    nx.draw_networkx_edge_labels(network_graph, pos, edge_labels=edge_labels)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "# After labeling the graph\n",
    "plot_network_with_labels(network_graph)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T05:52:16.833966500Z",
     "start_time": "2024-03-07T05:52:16.369710200Z"
    }
   },
   "id": "a465404806ec0c74"
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[0.        ],\n       [0.        ],\n       [0.        ],\n       [0.        ],\n       [0.        ],\n       [0.        ],\n       [0.1783932 ],\n       [0.        ],\n       [0.21754281],\n       [0.        ],\n       [0.16469595],\n       [0.        ],\n       [0.19665275],\n       [0.        ],\n       [0.16435554],\n       [0.        ],\n       [0.17640576],\n       [0.        ],\n       [0.22657272],\n       [0.        ],\n       [0.22139321],\n       [0.        ],\n       [0.19046172],\n       [0.        ],\n       [0.22496429],\n       [0.        ],\n       [0.16040858],\n       [0.        ],\n       [0.17852813],\n       [0.        ],\n       [0.        ],\n       [0.        ],\n       [0.16579267],\n       [0.        ],\n       [0.15764918],\n       [0.        ],\n       [0.20921297],\n       [0.        ],\n       [0.21256793],\n       [0.        ],\n       [0.17568882],\n       [0.        ],\n       [0.1769251 ],\n       [0.17564162],\n       [0.        ],\n       [0.16034261],\n       [0.        ],\n       [0.16133044],\n       [0.        ],\n       [0.18052214],\n       [0.        ],\n       [0.16037667],\n       [0.        ],\n       [0.16050263],\n       [0.        ],\n       [0.22199958],\n       [0.        ],\n       [0.16063176],\n       [0.        ],\n       [0.17139641],\n       [0.        ],\n       [0.26400758],\n       [0.        ],\n       [0.        ],\n       [0.16062107],\n       [0.16900195],\n       [0.15725743]])"
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Assuming your network_graph object is ready and has the necessary attributes\n",
    "\n",
    "# Sort nodes by their solver_name attribute to ensure iteration is in order\n",
    "sorted_nodes = sorted(network_graph.nodes(data=True), key=lambda x: int(x[1]['solver_name']))\n",
    "\n",
    "# Create the demand matrix\n",
    "num_nodes = len(sorted_nodes)\n",
    "demand_matrix = np.zeros((num_nodes, 1))\n",
    "\n",
    "# Fill the matrix with peak demand values if available, else leave as zero\n",
    "for idx, (node, data) in enumerate(sorted_nodes):\n",
    "    demand_matrix[idx] = data.get(\"Peack_Demand\", 0)\n",
    "    demand_matrix[idx] = demand_matrix[idx] / 4200 / 25\n",
    "# The demand_matrix is now populated based on your specifications\n",
    "demand_matrix"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T06:06:03.399775200Z",
     "start_time": "2024-03-07T06:06:03.207057500Z"
    }
   },
   "id": "da78ad2e3a41fc96"
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import networkx as nx\n",
    "\n",
    "# Assuming network_graph is your undirected graph from the DistrictHeatingNetworkCreator class\n",
    "# Convert the undirected graph to a directed graph\n",
    "directed_graph = network_graph\n",
    "\n",
    "# Sort nodes by their 'solver_name'\n",
    "sorted_nodes = sorted(directed_graph.nodes(data=True), key=lambda x: int(x[1]['solver_name']))\n",
    "\n",
    "# Sort edges based on the 'solver_name' of the nodes they connect\n",
    "# This approach assumes that all nodes have a 'solver_name' attribute and it is unique and sequential\n",
    "sorted_edges = sorted(directed_graph.edges(data=False), key=lambda x: (int(directed_graph.nodes[x[0]]['solver_name']), int(directed_graph.nodes[x[1]]['solver_name'])))\n",
    "\n",
    "# Initialize the incidence matrix\n",
    "num_nodes = len(sorted_nodes)\n",
    "num_edges = len(sorted_edges)\n",
    "incidence_matrix = np.zeros((num_nodes, num_edges))\n",
    "\n",
    "# Populate the incidence matrix\n",
    "for edge_idx, (u, v) in enumerate(sorted_edges):\n",
    "    u_idx = next(idx for idx, (node_id, _) in enumerate(sorted_nodes) if node_id == u)\n",
    "    v_idx = next(idx for idx, (node_id, _) in enumerate(sorted_nodes) if node_id == v)\n",
    "    \n",
    "    incidence_matrix[u_idx, edge_idx] = -1  # Source node\n",
    "    incidence_matrix[v_idx, edge_idx] = 1   # Target node\n",
    "\n",
    "# Now incidence_matrix is ready and populated as per your requirements\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T06:17:51.118962800Z",
     "start_time": "2024-03-07T06:17:50.962826900Z"
    }
   },
   "id": "d6f3251012a48716"
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [
    {
     "data": {
      "text/plain": "66"
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directed_graph.number_of_edges()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T06:17:54.519856Z",
     "start_time": "2024-03-07T06:17:54.251735500Z"
    }
   },
   "id": "b0dcdf2183910b29"
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[-1.,  0.,  0., ...,  0.,  0.,  0.],\n       [ 1., -1.,  0., ...,  0.,  0.,  0.],\n       [ 0.,  1.,  1., ...,  0.,  0.,  0.],\n       ...,\n       [ 0.,  0.,  0., ..., -1.,  0.,  0.],\n       [ 0.,  0.,  0., ...,  0., -1.,  0.],\n       [ 0.,  0.,  0., ...,  0.,  0., -1.]])"
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incidence_matrix"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T06:17:56.606008500Z",
     "start_time": "2024-03-07T06:17:56.542750400Z"
    }
   },
   "id": "cf9b2c4967e3f2fb"
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 5.70184321],\n       [ 5.70184321],\n       [-3.93235405],\n       [ 3.75396085],\n       [-1.76948917],\n       [ 1.55194636],\n       [ 3.5892649 ],\n       [-0.1783932 ],\n       [ 1.35529361],\n       [-0.21754281],\n       [ 3.42490936],\n       [-0.16469595],\n       [ 1.17888785],\n       [-0.19665275],\n       [ 3.19833664],\n       [-0.16435554],\n       [ 0.95749464],\n       [-0.17640576],\n       [ 3.00787491],\n       [-0.22657272],\n       [ 0.73253034],\n       [-0.22139321],\n       [ 2.84746634],\n       [-0.19046172],\n       [ 0.55400221],\n       [-0.22496429],\n       [-0.16040858],\n       [ 0.38820955],\n       [-0.17852813],\n       [-1.57722322],\n       [ 1.41957404],\n       [-1.27024312],\n       [ 1.06103015],\n       [ 0.17564162],\n       [-0.16579267],\n       [ 1.24388522],\n       [-0.15764918],\n       [ 0.88410506],\n       [-0.20921297],\n       [-0.21256793],\n       [ 1.08354261],\n       [-0.17568882],\n       [ 0.72277462],\n       [-0.1769251 ],\n       [-0.17564162],\n       [ 0.90302046],\n       [-0.16034261],\n       [ 0.56239794],\n       [-0.16133044],\n       [ 0.74251784],\n       [-0.18052214],\n       [ 0.34039836],\n       [-0.16037667],\n       [ 0.58188607],\n       [-0.16050263],\n       [ 0.16900195],\n       [-0.22199958],\n       [ 0.31787849],\n       [-0.16063176],\n       [-0.17139641],\n       [ 0.15725743],\n       [-0.26400758],\n       [-0.16900195],\n       [-0.16062107],\n       [-0.16900195],\n       [-0.15725743]])"
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Deleting the first row from both the demand matrix and the incidence matrix\n",
    "modified_demand_matrix_after_deletion = np.delete(demand_matrix, 0, axis=0)\n",
    "modified_incidence_matrix_after_deletion = np.delete(incidence_matrix, 0, axis=0)\n",
    "\n",
    "# Attempting to solve the equation modified_incidence_matrix * [m] = modified_demand_matrix\n",
    "m_solution, residuals, rank, s = np.linalg.lstsq(modified_incidence_matrix_after_deletion, modified_demand_matrix_after_deletion, rcond=None)\n",
    "\n",
    "m_solution"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T06:23:18.773672Z",
     "start_time": "2024-03-07T06:23:18.164639900Z"
    }
   },
   "id": "f91e518a7ddaf46f"
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\majidr\\AppData\\Local\\Temp\\ipykernel_8776\\646369358.py:23: MatplotlibDeprecationWarning: Unable to determine Axes to steal space for Colorbar. Using gca(), but will raise in the future. Either provide the *cax* argument to use as the Axes for the Colorbar, provide the *ax* argument to steal space from it, or add *mappable* to an Axes.\n",
      "  plt.colorbar(plt.cm.ScalarMappable(cmap=plt.cm.viridis), label='Mass Flow Rate Peak')\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assigning the 'mass_flow_rate_peak' attribute to edges based on the values from m_solution\n",
    "for idx, (u, v, data) in enumerate(sorted(network_graph.edges(data=True), key=lambda x: int(x[2]['solver_name']))):\n",
    "    network_graph[u][v]['mass_flow_rate_peak'] = abs(m_solution[idx][0])\n",
    "\n",
    "# Plotting the network_graph with a contour of colors based on the 'mass_flow_rate_peak' values\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "\n",
    "pos = {node: (node[0], node[1]) for node in network_graph.nodes()}\n",
    "\n",
    "# Edges are colored based on their 'mass_flow_rate_peak' attribute\n",
    "edge_colors = [network_graph[u][v]['mass_flow_rate_peak'] for u, v in network_graph.edges()]\n",
    "\n",
    "# Drawing nodes\n",
    "nx.draw_networkx_nodes(network_graph, pos, node_size=50, node_color='skyblue', alpha=0.6)\n",
    "\n",
    "# Drawing edges with mass flow rate as colors\n",
    "nx.draw_networkx_edges(network_graph, pos, edge_color=edge_colors, edge_cmap=plt.cm.viridis, width=2)\n",
    "\n",
    "# Drawing labels\n",
    "#nx.draw_networkx_labels(network_graph, pos, font_size=12, font_family=\"sans-serif\")\n",
    "\n",
    "plt.colorbar(plt.cm.ScalarMappable(cmap=plt.cm.viridis), label='Mass Flow Rate Peak')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T06:28:41.711114200Z",
     "start_time": "2024-03-07T06:28:41.125355100Z"
    }
   },
   "id": "3c652682edecdd66"
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Constants for the calculation\n",
    "rho = 997  # Density of water at room temperature in kg/m^3\n",
    "v = 1.5    # Design velocity in m/s\n",
    "a = np.pi * rho * v\n",
    "\n",
    "# Iterate over each edge in the network_graph, ordered by 'solver_name'\n",
    "for idx, (u, v, data) in enumerate(sorted(network_graph.edges(data=True), key=lambda x: int(network_graph[x[0]][x[1]]['solver_name']))):\n",
    "    # Convert mass_flow_rate_peak to float to ensure it's a scalar numerical value\n",
    "    mass_flow_rate_peak = abs(float(data['mass_flow_rate_peak']))\n",
    "\n",
    "    # Calculate the diameter of the pipe based on the mass flow rate as a scalar operation\n",
    "    diameter = (4 * mass_flow_rate_peak / a) ** 0.5\n",
    "\n",
    "    # Assign the calculated diameter back to the edge in the network_graph\n",
    "    network_graph[u][v]['diameter'] = diameter * 1000"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T14:25:45.080072Z",
     "start_time": "2024-03-07T14:25:44.689426300Z"
    }
   },
   "id": "69950bc72be9e177"
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "outputs": [
    {
     "data": {
      "text/plain": "   Edge Solver Name Start Node Solver Name End Node Solver Name  Diameter (m)  \\\n0                 2                      1                    2     69.673798   \n1                 3                      2                    3     69.673798   \n2                 4                      4                    3     57.861296   \n3                 5                      5                    3     56.533610   \n4                 6                      4                    6     38.813768   \n..              ...                    ...                  ...           ...   \n61               63                     63                   59     14.992362   \n62               64                     61                   64     11.995206   \n63               65                     65                   61     11.694000   \n64               66                     66                   63     11.995206   \n65               67                     67                   64     11.570908   \n\n    Length (m)  \n0    16.224851  \n1    57.211407  \n2    21.522844  \n3    35.381202  \n4    19.900368  \n..         ...  \n61    4.300275  \n62   18.471603  \n63   17.817003  \n64   21.400020  \n65   19.529438  \n\n[66 rows x 5 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Edge Solver Name</th>\n      <th>Start Node Solver Name</th>\n      <th>End Node Solver Name</th>\n      <th>Diameter (m)</th>\n      <th>Length (m)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>69.673798</td>\n      <td>16.224851</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3</td>\n      <td>2</td>\n      <td>3</td>\n      <td>69.673798</td>\n      <td>57.211407</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4</td>\n      <td>4</td>\n      <td>3</td>\n      <td>57.861296</td>\n      <td>21.522844</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5</td>\n      <td>5</td>\n      <td>3</td>\n      <td>56.533610</td>\n      <td>35.381202</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>4</td>\n      <td>6</td>\n      <td>38.813768</td>\n      <td>19.900368</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>63</td>\n      <td>63</td>\n      <td>59</td>\n      <td>14.992362</td>\n      <td>4.300275</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>64</td>\n      <td>61</td>\n      <td>64</td>\n      <td>11.995206</td>\n      <td>18.471603</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>65</td>\n      <td>65</td>\n      <td>61</td>\n      <td>11.694000</td>\n      <td>17.817003</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>66</td>\n      <td>66</td>\n      <td>63</td>\n      <td>11.995206</td>\n      <td>21.400020</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>67</td>\n      <td>67</td>\n      <td>64</td>\n      <td>11.570908</td>\n      <td>19.529438</td>\n    </tr>\n  </tbody>\n</table>\n<p>66 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Placeholder for edges data\n",
    "edges_data = []\n",
    "\n",
    "# Iterate over each edge in the network_graph, assuming each has a 'solver_name', 'diameter', and 'length'\n",
    "for (u, v, data) in sorted(network_graph.edges(data=True), key=lambda x: int(network_graph.edges[x[0], x[1]]['solver_name'])):\n",
    "    # Assuming 'solver_name' for nodes u and v can be accessed directly or computed\n",
    "    u_solver_name = network_graph.nodes[u]['solver_name']\n",
    "    v_solver_name = network_graph.nodes[v]['solver_name']\n",
    "    \n",
    "    # Append edge data\n",
    "    edges_data.append({\n",
    "        'Edge Solver Name': data['solver_name'],\n",
    "        'Start Node Solver Name': u_solver_name,\n",
    "        'End Node Solver Name': v_solver_name,\n",
    "        'Diameter (m)': data['diameter'],\n",
    "        'Length (m)': data['weight']\n",
    "    })\n",
    "\n",
    "# Create DataFrame\n",
    "edges_df = pd.DataFrame(edges_data)\n",
    "\n",
    "# Display the DataFrame\n",
    "edges_df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T14:25:46.847223200Z",
     "start_time": "2024-03-07T14:25:46.548312300Z"
    }
   },
   "id": "84bf3b6bf721543e"
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllrr}\n",
      "\\toprule\n",
      "Edge Solver Name & Start Node Solver Name & End Node Solver Name & Diameter (m) & Length (m) \\\\\n",
      "\\midrule\n",
      "2 & 1 & 2 & 69.673798 & 16.224851 \\\\\n",
      "3 & 2 & 3 & 69.673798 & 57.211407 \\\\\n",
      "4 & 4 & 3 & 57.861296 & 21.522844 \\\\\n",
      "5 & 5 & 3 & 56.533610 & 35.381202 \\\\\n",
      "6 & 4 & 6 & 38.813768 & 19.900368 \\\\\n",
      "7 & 7 & 4 & 36.349648 & 17.569936 \\\\\n",
      "8 & 5 & 8 & 55.279564 & 45.860989 \\\\\n",
      "9 & 9 & 5 & 12.323980 & 20.419584 \\\\\n",
      "10 & 6 & 10 & 33.968670 & 31.976183 \\\\\n",
      "11 & 11 & 6 & 13.609251 & 20.570075 \\\\\n",
      "12 & 8 & 12 & 53.999084 & 31.709748 \\\\\n",
      "13 & 13 & 8 & 11.841407 & 21.899009 \\\\\n",
      "14 & 10 & 14 & 31.680943 & 39.463801 \\\\\n",
      "15 & 15 & 10 & 12.939332 & 21.024440 \\\\\n",
      "16 & 12 & 16 & 52.182387 & 20.134960 \\\\\n",
      "17 & 17 & 12 & 11.829163 & 19.060309 \\\\\n",
      "18 & 14 & 18 & 28.551572 & 5.099774 \\\\\n",
      "19 & 19 & 14 & 12.255139 & 17.102630 \\\\\n",
      "20 & 16 & 20 & 50.604803 & 14.958714 \\\\\n",
      "21 & 21 & 16 & 13.888830 & 20.455154 \\\\\n",
      "22 & 18 & 22 & 24.973227 & 31.428602 \\\\\n",
      "23 & 23 & 18 & 13.729161 & 21.627703 \\\\\n",
      "24 & 20 & 24 & 49.236951 & 7.909941 \\\\\n",
      "25 & 25 & 20 & 12.734025 & 20.496765 \\\\\n",
      "26 & 22 & 26 & 21.717889 & 46.729327 \\\\\n",
      "27 & 27 & 22 & 13.839444 & 21.349888 \\\\\n",
      "28 & 24 & 28 & 11.686263 & 31.679314 \\\\\n",
      "29 & 29 & 24 & 18.180045 & 18.952334 \\\\\n",
      "30 & 30 & 26 & 12.328640 & 48.499138 \\\\\n",
      "31 & 31 & 26 & 36.644469 & 49.579788 \\\\\n",
      "32 & 28 & 32 & 34.764891 & 10.017552 \\\\\n",
      "33 & 33 & 28 & 32.885563 & 20.560408 \\\\\n",
      "34 & 30 & 34 & 30.055622 & 14.284048 \\\\\n",
      "35 & 35 & 30 & 12.228567 & 19.529234 \\\\\n",
      "36 & 31 & 36 & 11.880768 & 32.334704 \\\\\n",
      "37 & 37 & 31 & 32.542582 & 16.935521 \\\\\n",
      "38 & 32 & 38 & 11.585311 & 73.412250 \\\\\n",
      "39 & 39 & 32 & 27.435557 & 20.271400 \\\\\n",
      "40 & 34 & 40 & 13.346154 & 32.862371 \\\\\n",
      "41 & 41 & 34 & 13.452739 & 19.670021 \\\\\n",
      "42 & 36 & 42 & 30.372801 & 13.061121 \\\\\n",
      "43 & 43 & 36 & 12.230210 & 22.370202 \\\\\n",
      "44 & 44 & 38 & 24.806375 & 20.561362 \\\\\n",
      "45 & 40 & 45 & 12.273165 & 0.187530 \\\\\n",
      "46 & 46 & 40 & 12.228567 & 21.000992 \\\\\n",
      "47 & 42 & 47 & 27.727496 & 16.802088 \\\\\n",
      "48 & 48 & 42 & 11.683860 & 22.149395 \\\\\n",
      "49 & 45 & 49 & 21.881834 & 18.490914 \\\\\n",
      "50 & 50 & 45 & 11.719795 & 19.349860 \\\\\n",
      "51 & 47 & 51 & 25.142896 & 16.909912 \\\\\n",
      "52 & 52 & 47 & 12.397299 & 16.801829 \\\\\n",
      "53 & 49 & 53 & 17.023764 & 44.770455 \\\\\n",
      "54 & 54 & 49 & 11.685101 & 17.629281 \\\\\n",
      "55 & 51 & 55 & 22.257728 & 24.231841 \\\\\n",
      "56 & 56 & 51 & 11.689688 & 21.619743 \\\\\n",
      "57 & 53 & 57 & 11.995206 & 15.642695 \\\\\n",
      "58 & 58 & 53 & 13.747949 & 19.635250 \\\\\n",
      "59 & 55 & 59 & 16.451005 & 8.708682 \\\\\n",
      "60 & 60 & 55 & 11.694390 & 21.449777 \\\\\n",
      "61 & 57 & 61 & 12.079882 & 47.303283 \\\\\n",
      "62 & 62 & 57 & 11.570908 & 20.904415 \\\\\n",
      "63 & 63 & 59 & 14.992362 & 4.300275 \\\\\n",
      "64 & 61 & 64 & 11.995206 & 18.471603 \\\\\n",
      "65 & 65 & 61 & 11.694000 & 17.817003 \\\\\n",
      "66 & 66 & 63 & 11.995206 & 21.400020 \\\\\n",
      "67 & 67 & 64 & 11.570908 & 19.529438 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n"
     ]
    }
   ],
   "source": [
    "latex_code = edges_df.to_latex(index=False)\n",
    "print(latex_code)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T14:29:30.575556900Z",
     "start_time": "2024-03-07T14:29:29.834086300Z"
    }
   },
   "id": "980e1ba9f4a748e4"
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "outputs": [],
   "source": [
    "for idx, (u, v, data) in enumerate(sorted(network_graph.edges(data=True), key=lambda x: int(x[2]['solver_name']))):\n",
    "    network_graph[u][v]['mass_flow_rate_actual'] = m_solution[idx][0]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-07T16:07:33.942409500Z",
     "start_time": "2024-03-07T16:07:33.155887200Z"
    }
   },
   "id": "d33ad2a2eb78fd0"
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temperatures at the final time step: [78.18664027 78.1865898  78.1853651  78.18432742 78.18383629 78.18319726\n",
      " 78.18266043 78.18364069 78.18290756 78.18392728 78.18268013 78.18152717\n",
      " 78.18037209 78.17937684 78.17865192 78.17816613 78.1775758  78.17671086\n",
      " 78.17610327 78.17563732 78.1750894  78.1748886  78.17359073 78.17300582\n",
      " 78.17118745 78.17058708 78.16995702 78.16851666 78.16752476 78.16741675\n",
      " 78.16668729 80.         78.17469718 78.18097176 78.18417283 78.18730318\n",
      " 78.17541928 78.18602646 78.18868036 78.18222945 78.18687169 78.18549436\n",
      " 78.18745297 78.18331928 78.18201243 78.18078221 78.17804919 78.18177658\n",
      " 78.17576141 78.17874528 78.17766322 78.17795258 78.17734928 78.17747959\n",
      " 78.1741951  78.18008809 78.17616829 78.17995149 78.17727187 78.17215487\n",
      " 78.17066253 78.17596763 78.18353447 78.17598846 78.18339893 78.17228927\n",
      " 78.17720437]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import networkx as nx\n",
    "\n",
    "# Example Directed Graph Construction\n",
    "# Replace this with your actual graph construction logic\n",
    "di_graph = nx.DiGraph(network_graph)\n",
    "\n",
    "# Parameters\n",
    "T_initial = 80  # Initial temperature in Celsius\n",
    "Tg = 3  # Ground temperature in Celsius\n",
    "cp = 4200  # Specific heat capacity of water in J/(kg*K)\n",
    "rho = 980  # Density of water in kg/m3\n",
    "dx = 20  # Number of segments for each pipe\n",
    "delta_t = 60  # Time step in seconds\n",
    "\n",
    "n = di_graph.number_of_nodes()  # Number of nodes\n",
    "m = di_graph.number_of_edges()  # Number of pipes\n",
    "U = np.array([0.5 for _ in range(m)])\n",
    "lengths_list = []\n",
    "diameters_list = []\n",
    "for u, v, data in di_graph.edges(data=True):\n",
    "    lengths_list.append(data['weight'])\n",
    "    diameters_list.append(data['diameter'])\n",
    "\n",
    "# Convert lists to numpy arrays\n",
    "lengths = np.array(lengths_list)\n",
    "diameters = np.array(diameters_list)\n",
    "\n",
    "# Calculate cross-sectional area (A) for each pipe\n",
    "A = np.pi * (diameters ** 2) / 4\n",
    "\n",
    "delta_x = lengths / dx\n",
    "\n",
    "# Creating an array of mass flow rates 'G'\n",
    "G = np.array([data['mass_flow_rate_actual'] for u, v, data in di_graph.edges(data=True)])\n",
    "\n",
    "\n",
    "# Initialize temperature distribution along pipes\n",
    "T_deltat = {edge: np.full((dx+1), T_initial) for edge in di_graph.edges()}  # Temperature distribution along pipes\n",
    "node_index_map = {node: idx for idx, node in enumerate(di_graph.nodes())}\n",
    "\n",
    "# Simulation parameters (adjust based on your setup)\n",
    "steps = 50  # Number of time steps to simulate\n",
    "\n",
    "T_t = np.zeros(dx + 1)\n",
    "T_input = np.full(n, T_initial)\n",
    "T_output = np.zeros((steps, m))\n",
    "T_final_outlet = np.zeros(n)\n",
    "results = np.zeros((steps, n))\n",
    "\n",
    "# Main simulation loop\n",
    "for i in range(steps):\n",
    "    for j, (u, v, data) in enumerate(di_graph.edges(data=True)):\n",
    "        # If the flow is negative, reverse the edge direction (this is conceptual; adjust based on your graph structure)\n",
    "        if data['mass_flow_rate_actual'] < 0:\n",
    "            di_graph.add_edge(v, u, **data)\n",
    "            di_graph.remove_edge(u, v)\n",
    "            mass_flow_rate = abs(data['mass_flow_rate_actual'])\n",
    "        else:\n",
    "            mass_flow_rate = data['mass_flow_rate_actual']\n",
    "\n",
    "        C1 = 2 * delta_t * U[j] / (A[j] * rho * cp)\n",
    "        C2 = 2 * abs(mass_flow_rate) * delta_t / (rho * A[j] * delta_x[j])\n",
    "        C = 1 / (1 + C1 + C2)\n",
    "        # Inside your loop\n",
    "        T_t[0] = T_input[node_index_map[u]]  # Use the mapped index to access the correct initial temperature\n",
    "\n",
    "        for z in range(60):  # Assuming additional iterations for convergence\n",
    "            for k in range(dx):\n",
    "                # Corrected to access T_deltat using edge (u, v) tuple\n",
    "                T_t[k+1] = C * (T_deltat[(u, v)][k+1] + C1 * Tg + C2 * T_t[k])\n",
    "\n",
    "        # Corrected to store updated temperatures back in T_deltat using edge (u, v) tuple\n",
    "        T_deltat[(u, v)] = T_t\n",
    "        \n",
    "        # Corrected to store temperatures in T_output with edge index 'j'\n",
    "        T_output[i, j] = T_t[dx]\n",
    "        \n",
    "    for node in di_graph.nodes():\n",
    "        T_tot = 0.0\n",
    "        G_tot = 0.0\n",
    "        incoming_edges = di_graph.in_edges(node, data=True)\n",
    "        Count = len(incoming_edges)\n",
    "    \n",
    "        temp_incoming = []\n",
    "        flow_incoming = []\n",
    "    \n",
    "        for u, v, data in incoming_edges:\n",
    "            edge_index = list(di_graph.edges()).index((u, v))\n",
    "            temp_incoming.append(T_output[i, edge_index])\n",
    "            flow_incoming.append(abs(G[edge_index]))\n",
    "    \n",
    "        if Count > 1:\n",
    "            T_tot = np.dot(temp_incoming, flow_incoming)\n",
    "            G_tot = sum(flow_incoming)\n",
    "            T_final_outlet[node_index_map[node]] = T_tot / G_tot if G_tot else T_initial\n",
    "        elif Count == 1:\n",
    "            T_final_outlet[node_index_map[node]] = temp_incoming[0]\n",
    "        else:\n",
    "            T_final_outlet[node_index_map[node]] = T_initial\n",
    "    \n",
    "    # At the end of the step, adjust for source nodes and copy T_final_outlet to T_input\n",
    "    for node, data in di_graph.nodes(data=True):\n",
    "        if data.get('solver_name') == '1':\n",
    "            T_final_outlet[node_index_map[node]] = 80  # Reset source node temperature\n",
    "\n",
    "    T_input = T_final_outlet.copy()\n",
    "    results[i, :] = T_final_outlet\n",
    "    \n",
    "\n",
    "    \n",
    "print(\"Temperatures at the final time step:\", results[-1, :])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T11:19:49.631650300Z",
     "start_time": "2024-03-08T11:19:31.847446800Z"
    }
   },
   "id": "971b616575eeeae7"
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "outputs": [
    {
     "data": {
      "text/plain": "    Time Step  (293268.92232946283, 5040206.601044122)  \\\n0           0                                79.999991   \n1           1                                79.984571   \n2           2                                79.938864   \n3           3                                79.893189   \n4           4                                79.847931   \n5           5                                79.806133   \n6           6                                79.771978   \n7           7                                79.741512   \n8           8                                79.706654   \n9           9                                79.666174   \n10         10                                79.623977   \n11         11                                79.584010   \n12         12                                79.547721   \n13         13                                79.513287   \n14         14                                79.477642   \n15         15                                79.439476   \n16         16                                79.399958   \n17         17                                79.361138   \n18         18                                79.324050   \n19         19                                79.288091   \n20         20                                79.251873   \n21         21                                79.214569   \n22         22                                79.176483   \n23         23                                79.138538   \n24         24                                79.101367   \n25         25                                79.064843   \n26         26                                79.028360   \n27         27                                78.991446   \n28         28                                78.954128   \n29         29                                78.916800   \n30         30                                78.879808   \n31         31                                78.843173   \n32         32                                78.806643   \n33         33                                78.769969   \n34         34                                78.733109   \n35         35                                78.696222   \n36         36                                78.659484   \n37         37                                78.622941   \n38         38                                78.586497   \n39         39                                78.550028   \n40         40                                78.513490   \n41         41                                78.476941   \n42         42                                78.440465   \n43         43                                78.404102   \n44         44                                78.367817   \n45         45                                78.331553   \n46         46                                78.295278   \n47         47                                78.259011   \n48         48                                78.222790   \n49         49                                78.186640   \n\n    (293162.4584459267, 5040298.134698394)  \\\n0                                79.999999   \n1                                79.984490   \n2                                79.938783   \n3                                79.893108   \n4                                79.847855   \n5                                79.806077   \n6                                79.771946   \n7                                79.741477   \n8                                79.706599   \n9                                79.666105   \n10                               79.623909   \n11                               79.583955   \n12                               79.547676   \n13                               79.513243   \n14                               79.477590   \n15                               79.439416   \n16                               79.399898   \n17                               79.361082   \n18                               79.324000   \n19                               79.288042   \n20                               79.251821   \n21                               79.214514   \n22                               79.176426   \n23                               79.138484   \n24                               79.101315   \n25                               79.064793   \n26                               79.028309   \n27                               78.991393   \n28                               78.954074   \n29                               78.916747   \n30                               78.879756   \n31                               78.843121   \n32                               78.806591   \n33                               78.769916   \n34                               78.733057   \n35                               78.696170   \n36                               78.659432   \n37                               78.622890   \n38                               78.586446   \n39                               78.549976   \n40                               78.513438   \n41                               78.476889   \n42                               78.440414   \n43                               78.404051   \n44                               78.367767   \n45                               78.331502   \n46                               78.295227   \n47                               78.258960   \n48                               78.222739   \n49                               78.186590   \n\n    (293169.9422966335, 5040254.972466738)  \\\n0                                79.999990   \n1                                79.983150   \n2                                79.937444   \n3                                79.891771   \n4                                79.846530   \n5                                79.804813   \n6                                79.770755   \n7                                79.740277   \n8                                79.705337   \n9                                79.664804   \n10                               79.622614   \n11                               79.582695   \n12                               79.546448   \n13                               79.512018   \n14                               79.476342   \n15                               79.438147   \n16                               79.398626   \n17                               79.359826   \n18                               79.322761   \n19                               79.286807   \n20                               79.250578   \n21                               79.213260   \n22                               79.175170   \n23                               79.137234   \n24                               79.100073   \n25                               79.063555   \n26                               79.027069   \n27                               78.990148   \n28                               78.952828   \n29                               78.915503   \n30                               78.878517   \n31                               78.841885   \n32                               78.805355   \n33                               78.768678   \n34                               78.731818   \n35                               78.694932   \n36                               78.658197   \n37                               78.621657   \n38                               78.585213   \n39                               78.548744   \n40                               78.512206   \n41                               78.475657   \n42                               78.439184   \n43                               78.402822   \n44                               78.366538   \n45                               78.330274   \n46                               78.294000   \n47                               78.257733   \n48                               78.221514   \n49                               78.185365   \n\n    (293161.9162166267, 5040243.155704052)  \\\n0                                79.999992   \n1                                79.982018   \n2                                79.936312   \n3                                79.890640   \n4                                79.845410   \n5                                79.803743   \n6                                79.769745   \n7                                79.739258   \n8                                79.704267   \n9                                79.663703   \n10                               79.621519   \n11                               79.581628   \n12                               79.545408   \n13                               79.510979   \n14                               79.475285   \n15                               79.437072   \n16                               79.397549   \n17                               79.358762   \n18                               79.321710   \n19                               79.285760   \n20                               79.249525   \n21                               79.212198   \n22                               79.174106   \n23                               79.136175   \n24                               79.099022   \n25                               79.062506   \n26                               79.026019   \n27                               78.989094   \n28                               78.951772   \n29                               78.914449   \n30                               78.877467   \n31                               78.840838   \n32                               78.804307   \n33                               78.767630   \n34                               78.730769   \n35                               78.693884   \n36                               78.657151   \n37                               78.620612   \n38                               78.584169   \n39                               78.547699   \n40                               78.511161   \n41                               78.474613   \n42                               78.438141   \n43                               78.401780   \n44                               78.365498   \n45                               78.329234   \n46                               78.292960   \n47                               78.256694   \n48                               78.220475   \n49                               78.184327   \n\n    (293243.96496123145, 5040244.083651768)  \\\n0                                 79.999999   \n1                                 79.981472   \n2                                 79.935766   \n3                                 79.890094   \n4                                 79.844871   \n5                                 79.803235   \n6                                 79.769274   \n7                                 79.738782   \n8                                 79.703759   \n9                                 79.663176   \n10                                79.620995   \n11                                79.581122   \n12                                79.544917   \n13                                79.510489   \n14                                79.474784   \n15                                79.436560   \n16                                79.397037   \n17                                79.358257   \n18                                79.321213   \n19                                79.285266   \n20                                79.249026   \n21                                79.211694   \n22                                79.173600   \n23                                79.135672   \n24                                79.098523   \n25                                79.062010   \n26                                79.025521   \n27                                78.988595   \n28                                78.951272   \n29                                78.913950   \n30                                78.876970   \n31                                78.840342   \n32                                78.803811   \n33                                78.767133   \n34                                78.730271   \n35                                78.693387   \n36                                78.656655   \n37                                78.620117   \n38                                78.583675   \n39                                78.547205   \n40                                78.510666   \n41                                78.474119   \n42                                78.437647   \n43                                78.401287   \n44                                78.365005   \n45                                78.328741   \n46                                78.292467   \n47                                78.256201   \n48                                78.219983   \n49                                78.183836   \n\n    (293208.27353312843, 5040191.91032603)  \\\n0                                79.999990   \n1                                79.980720   \n2                                79.935016   \n3                                79.889344   \n4                                79.844136   \n5                                79.802570   \n6                                79.768690   \n7                                79.738185   \n8                                79.703091   \n9                                79.662465   \n10                               79.620291   \n11                               79.580458   \n12                               79.544289   \n13                               79.509863   \n14                               79.474131   \n15                               79.435883   \n16                               79.396357   \n17                               79.357594   \n18                               79.320569   \n19                               79.284626   \n20                               79.248377   \n21                               79.211033   \n22                               79.172935   \n23                               79.135014   \n24                               79.097875   \n25                               79.061365   \n26                               79.024873   \n27                               78.987941   \n28                               78.950616   \n29                               78.913297   \n30                               78.876321   \n31                               78.839696   \n32                               78.803165   \n33                               78.766484   \n34                               78.729622   \n35                               78.692738   \n36                               78.656009   \n37                               78.619473   \n38                               78.583031   \n39                               78.546560   \n40                               78.510021   \n41                               78.473474   \n42                               78.437004   \n43                               78.400645   \n44                               78.364364   \n45                               78.328100   \n46                               78.291826   \n47                               78.255561   \n48                               78.219343   \n49                               78.183197   \n\n    (293179.0032363437, 5040271.258029018)  \\\n0                                79.999990   \n1                                79.980147   \n2                                79.934443   \n3                                79.888772   \n4                                79.843567   \n5                                79.802019   \n6                                79.768159   \n7                                79.737650   \n8                                79.702539   \n9                                79.661903   \n10                               79.619731   \n11                               79.579908   \n12                               79.543748   \n13                               79.509322   \n14                               79.473584   \n15                               79.435330   \n16                               79.395804   \n17                               79.357045   \n18                               79.320025   \n19                               79.284083   \n20                               79.247832   \n21                               79.210485   \n22                               79.172387   \n23                               79.134468   \n24                               79.097331   \n25                               79.060823   \n26                               79.024330   \n27                               78.987397   \n28                               78.950071   \n29                               78.912753   \n30                               78.875778   \n31                               78.839154   \n32                               78.802624   \n33                               78.765942   \n34                               78.729079   \n35                               78.692196   \n36                               78.655468   \n37                               78.618932   \n38                               78.582491   \n39                               78.546020   \n40                               78.509481   \n41                               78.472935   \n42                               78.436465   \n43                               78.400107   \n44                               78.363825   \n45                               78.327562   \n46                               78.291288   \n47                               78.255023   \n48                               78.218806   \n49                               78.182660   \n\n    (293228.2223414392, 5040216.721843737)  \\\n0                                79.999990   \n1                                79.978423   \n2                                79.932720   \n3                                79.887076   \n4                                79.842472   \n5                                79.803126   \n6                                79.771225   \n7                                79.739893   \n8                                79.702796   \n9                                79.661183   \n10                               79.619406   \n11                               79.580739   \n12                               79.545439   \n13                               79.510883   \n14                               79.474354   \n15                               79.435483   \n16                               79.395992   \n17                               79.357746   \n18                               79.321189   \n19                               79.285288   \n20                               79.248720   \n21                               79.211050   \n22                               79.172901   \n23                               79.135189   \n24                               79.098291   \n25                               79.061852   \n26                               79.025244   \n27                               78.988153   \n28                               78.950775   \n29                               78.913535   \n30                               78.876680   \n31                               78.840112   \n32                               78.803547   \n33                               78.766795   \n34                               78.729898   \n35                               78.693042   \n36                               78.656372   \n37                               78.619874   \n38                               78.583428   \n39                               78.546929   \n40                               78.510373   \n41                               78.473836   \n42                               78.437395   \n43                               78.401061   \n44                               78.364785   \n45                               78.328513   \n46                               78.292233   \n47                               78.255972   \n48                               78.219771   \n49                               78.183641   \n\n    (293212.70831835904, 5040249.072585436)  ...  \\\n0                                 79.999990  ...   \n1                                 79.977620  ...   \n2                                 79.931917  ...   \n3                                 79.886275  ...   \n4                                 79.841687  ...   \n5                                 79.802387  ...   \n6                                 79.770517  ...   \n7                                 79.739163  ...   \n8                                 79.702029  ...   \n9                                 79.660402  ...   \n10                                79.618636  ...   \n11                                79.579993  ...   \n12                                79.544707  ...   \n13                                79.510146  ...   \n14                                79.473601  ...   \n15                                79.434720  ...   \n16                                79.395233  ...   \n17                                79.356998  ...   \n18                                79.320449  ...   \n19                                79.284547  ...   \n20                                79.247973  ...   \n21                                79.210298  ...   \n22                                79.172149  ...   \n23                                79.134442  ...   \n24                                79.097549  ...   \n25                                79.061111  ...   \n26                                79.024500  ...   \n27                                78.987407  ...   \n28                                78.950029  ...   \n29                                78.912792  ...   \n30                                78.875939  ...   \n31                                78.839372  ...   \n32                                78.802806  ...   \n33                                78.766053  ...   \n34                                78.729156  ...   \n35                                78.692302  ...   \n36                                78.655633  ...   \n37                                78.619136  ...   \n38                                78.582690  ...   \n39                                78.546191  ...   \n40                                78.509635  ...   \n41                                78.473099  ...   \n42                                78.436659  ...   \n43                                78.400325  ...   \n44                                78.364050  ...   \n45                                78.327778  ...   \n46                                78.291498  ...   \n47                                78.255238  ...   \n48                                78.219037  ...   \n49                                78.182908  ...   \n\n    (293168.8711824724, 5040218.090067068)  \\\n0                                79.999990   \n1                                79.974141   \n2                                79.928441   \n3                                79.882880   \n4                                79.838847   \n5                                79.800173   \n6                                79.767843   \n7                                79.735720   \n8                                79.698448   \n9                                79.657107   \n10                               79.615728   \n11                               79.577290   \n12                               79.541866   \n13                               79.507011   \n14                               79.470314   \n15                               79.431518   \n16                               79.392226   \n17                               79.354114   \n18                               79.317530   \n19                               79.281503   \n20                               79.244839   \n21                               79.207181   \n22                               79.169119   \n23                               79.131482   \n24                               79.094590   \n25                               79.058101   \n26                               79.021443   \n27                               78.984348   \n28                               78.947007   \n29                               78.909807   \n30                               78.872964   \n31                               78.836378   \n32                               78.799790   \n33                               78.763032   \n34                               78.726150   \n35                               78.689317   \n36                               78.652656   \n37                               78.616155   \n38                               78.579699   \n39                               78.543197   \n40                               78.506647   \n41                               78.470122   \n42                               78.433689   \n43                               78.397356   \n44                               78.361078   \n45                               78.324805   \n46                               78.288528   \n47                               78.252275   \n48                               78.216079   \n49                               78.179951   \n\n    (293254.3945879081, 5040221.622179406)  \\\n0                                79.999998   \n1                                79.970902   \n2                                79.925205   \n3                                79.879673   \n4                                79.835860   \n5                                79.797623   \n6                                79.765460   \n7                                79.733066   \n8                                79.695489   \n9                                79.654073   \n10                               79.612834   \n11                               79.574601   \n12                               79.539268   \n13                               79.504333   \n14                               79.467496   \n15                               79.428633   \n16                               79.389389   \n17                               79.351375   \n18                               79.314850   \n19                               79.278802   \n20                               79.242076   \n21                               79.204378   \n22                               79.166329   \n23                               79.128737   \n24                               79.091880   \n25                               79.055390   \n26                               79.018707   \n27                               78.981591   \n28                               78.944252   \n29                               78.907072   \n30                               78.870249   \n31                               78.833667   \n32                               78.797070   \n33                               78.760303   \n34                               78.723421   \n35                               78.686597   \n36                               78.649948   \n37                               78.613451   \n38                               78.576994   \n39                               78.540488   \n40                               78.503939   \n41                               78.467419   \n42                               78.430992   \n43                               78.394664   \n44                               78.358386   \n45                               78.322113   \n46                               78.285837   \n47                               78.249587   \n48                               78.213396   \n49                               78.177272   \n\n    (293181.600750976, 5040305.67762853)  \\\n0                              79.999997   \n1                              79.963269   \n2                              79.917576   \n3                              79.872113   \n4                              79.829077   \n5                              79.792906   \n6                              79.762035   \n7                              79.728595   \n8                              79.689387   \n9                              79.647354   \n10                             79.606631   \n11                             79.569419   \n12                             79.534691   \n13                             79.499502   \n14                             79.461964   \n15                             79.422663   \n16                             79.383552   \n17                             79.346008   \n18                             79.309830   \n19                             79.273750   \n20                             79.236731   \n21                             79.198790   \n22                             79.160748   \n23                             79.123358   \n24                             79.086692   \n25                             79.050231   \n26                             79.013435   \n27                             78.976196   \n28                             78.938838   \n29                             78.901742   \n30                             78.865019   \n31                             78.828475   \n32                             78.791840   \n33                             78.755017   \n34                             78.718119   \n35                             78.681329   \n36                             78.644732   \n37                             78.608265   \n38                             78.571802   \n39                             78.535274   \n40                             78.498716   \n41                             78.462212   \n42                             78.425814   \n43                             78.389507   \n44                             78.353234   \n45                             78.316956   \n46                             78.280679   \n47                             78.244438   \n48                             78.208263   \n49                             78.172155   \n\n    (293161.4860233397, 5040319.189840496)  \\\n0                                79.999995   \n1                                79.961862   \n2                                79.916170   \n3                                79.870716   \n4                                79.827692   \n5                                79.791424   \n6                                79.760404   \n7                                79.726971   \n8                                79.687874   \n9                                79.645915   \n10                               79.605195   \n11                               79.567926   \n12                               79.533139   \n13                               79.497940   \n14                               79.460441   \n15                               79.421183   \n16                               79.382082   \n17                               79.344516   \n18                               79.308309   \n19                               79.272220   \n20                               79.235214   \n21                               79.197294   \n22                               79.159262   \n23                               79.121865   \n24                               79.085185   \n25                               79.048717   \n26                               79.011925   \n27                               78.974697   \n28                               78.937344   \n29                               78.900247   \n30                               78.863518   \n31                               78.826970   \n32                               78.790337   \n33                               78.753518   \n34                               78.716623   \n35                               78.679834   \n36                               78.643235   \n37                               78.606766   \n38                               78.570303   \n39                               78.533778   \n40                               78.497222   \n41                               78.460718   \n42                               78.424319   \n43                               78.388011   \n44                               78.351739   \n45                               78.315462   \n46                               78.279186   \n47                               78.242945   \n48                               78.206771   \n49                               78.170663   \n\n    (293259.9504738339, 5040229.95785464)  \\\n0                               79.999998   \n1                               79.968989   \n2                               79.923293   \n3                               79.877773   \n4                               79.834126   \n5                               79.796380   \n6                               79.764566   \n7                               79.731945   \n8                               79.693964   \n9                               79.652379   \n10                              79.611251   \n11                              79.573266   \n12                              79.538092   \n13                              79.503107   \n14                              79.466101   \n15                              79.427124   \n16                              79.387904   \n17                              79.350002   \n18                              79.313566   \n19                              79.277516   \n20                              79.240721   \n21                              79.202961   \n22                              79.164909   \n23                              79.127365   \n24                              79.090556   \n25                              79.054076   \n26                              79.017367   \n27                              78.980220   \n28                              78.942874   \n29                              78.905714   \n30                              78.868916   \n31                              78.832345   \n32                              78.795739   \n33                              78.758958   \n34                              78.722071   \n35                              78.685255   \n36                              78.648619   \n37                              78.612130   \n38                              78.575672   \n39                              78.539161   \n40                              78.502609   \n41                              78.466092   \n42                              78.429673   \n43                              78.393350   \n44                              78.357074   \n45                              78.320800   \n46                              78.284523   \n47                              78.248275   \n48                              78.212088   \n49                              78.175968   \n\n    (293219.23492961464, 5040168.871007625)  \\\n0                                 79.999996   \n1                                 79.980098   \n2                                 79.934395   \n3                                 79.888767   \n4                                 79.843980   \n5                                 79.803319   \n6                                 79.769774   \n7                                 79.738678   \n8                                 79.702963   \n9                                 79.662199   \n10                                79.620315   \n11                                79.580899   \n12                                79.544905   \n13                                79.510304   \n14                                79.474280   \n15                                79.435899   \n16                                79.396473   \n17                                79.357910   \n18                                79.320997   \n19                                79.285006   \n20                                79.248626   \n21                                79.211199   \n22                                79.173127   \n23                                79.135296   \n24                                79.098223   \n25                                79.061708   \n26                                79.025161   \n27                                78.988183   \n28                                78.950860   \n29                                78.913580   \n30                                78.876641   \n31                                78.840022   \n32                                78.803471   \n33                                78.766767   \n34                                78.729901   \n35                                78.693034   \n36                                78.656325   \n37                                78.619796   \n38                                78.583348   \n39                                78.546867   \n40                                78.510326   \n41                                78.473786   \n42                                78.437326   \n43                                78.400974   \n44                                78.364691   \n45                                78.328424   \n46                                78.292149   \n47                                78.255887   \n48                                78.219676   \n49                                78.183534   \n\n    (293319.5070828843, 5040216.163684101)  \\\n0                                80.000000   \n1                                79.969750   \n2                                79.924053   \n3                                79.878528   \n4                                79.834700   \n5                                79.796320   \n6                                79.764007   \n7                                79.731663   \n8                                79.694220   \n9                                79.652875   \n10                               79.611617   \n11                               79.573309   \n12                               79.537915   \n13                               79.502985   \n14                               79.466200   \n15                               79.427381   \n16                               79.388139   \n17                               79.350093   \n18                               79.313535   \n19                               79.277483   \n20                               79.240778   \n21                               79.203103   \n22                               79.165060   \n23                               79.127455   \n24                               79.090582   \n25                               79.054087   \n26                               79.017412   \n27                               78.980307   \n28                               78.942973   \n29                               78.905790   \n30                               78.868959   \n31                               78.832374   \n32                               78.795779   \n33                               78.759018   \n34                               78.722139   \n35                               78.685313   \n36                               78.648661   \n37                               78.612163   \n38                               78.575706   \n39                               78.539203   \n40                               78.502656   \n41                               78.466135   \n42                               78.429708   \n43                               78.393378   \n44                               78.357101   \n45                               78.320829   \n46                               78.284554   \n47                               78.248304   \n48                               78.212113   \n49                               78.175988   \n\n    (293213.057996733, 5040284.545999)  (293154.256991112, 5040324.04599453)  \\\n0                            79.999998                             79.999994   \n1                            79.979534                             79.962874   \n2                            79.933831                             79.917182   \n3                            79.888207                             79.871723   \n4                            79.843516                             79.828812   \n5                            79.803201                             79.793083   \n6                            79.769957                             79.762582   \n7                            79.738731                             79.728962   \n8                            79.702708                             79.689367   \n9                            79.661792                             79.647152   \n10                           79.619971                             79.606515   \n11                           79.580736                             79.569531   \n12                           79.544875                             79.534967   \n13                           79.510254                             79.499745   \n14                           79.474106                             79.462050   \n15                           79.435629                             79.422632   \n16                           79.396210                             79.383533   \n17                           79.357727                             79.346091   \n18                           79.320886                             79.310002   \n19                           79.284901                             79.273926   \n20                           79.248472                             79.236843   \n21                           79.210995                             79.198841   \n22                           79.172915                             79.160791   \n23                           79.135116                             79.123443   \n24                           79.098081                             79.086823   \n25                           79.061577                             79.050375   \n26                           79.025012                             79.013554   \n27                           78.988010                             78.976285   \n28                           78.950679                             78.938918   \n29                           78.913411                             78.901838   \n30                           78.876491                             78.865139   \n31                           78.839881                             78.828604   \n32                           78.803324                             78.791963   \n33                           78.766609                             78.755126   \n34                           78.729738                             78.718221   \n35                           78.692876                             78.681437   \n36                           78.656175                             78.644852   \n37                           78.619653                             78.608392   \n38                           78.583204                             78.571927   \n39                           78.546719                             78.535394   \n40                           78.510175                             78.498833   \n41                           78.473637                             78.462331   \n42                           78.437182                             78.425939   \n43                           78.400833                             78.389636   \n44                           78.364552                             78.353364   \n45                           78.328283                             78.317085   \n46                           78.292008                             78.280806   \n47                           78.255747                             78.244566   \n48                           78.219538                             78.208395   \n49                           78.183399                             78.172289   \n\n    (293271.554, 5040247.367)  \n0                   80.000000  \n1                   79.971599  \n2                   79.925901  \n3                   79.880343  \n4                   79.836247  \n5                   79.797323  \n6                   79.764807  \n7                   79.732808  \n8                   79.695744  \n9                   79.654488  \n10                  79.613055  \n11                  79.574493  \n12                  79.538990  \n13                  79.504162  \n14                  79.467553  \n15                  79.428817  \n16                  79.389514  \n17                  79.351346  \n18                  79.314719  \n19                  79.278694  \n20                  79.242068  \n21                  79.204443  \n22                  79.166384  \n23                  79.128725  \n24                  79.091810  \n25                  79.055317  \n26                  79.018674  \n27                  78.981597  \n28                  78.944261  \n29                  78.907053  \n30                  78.870199  \n31                  78.833610  \n32                  78.797027  \n33                  78.760278  \n34                  78.723401  \n35                  78.686565  \n36                  78.649900  \n37                  78.613396  \n38                  78.576943  \n39                  78.540445  \n40                  78.503898  \n41                  78.467373  \n42                  78.430938  \n43                  78.394604  \n44                  78.358326  \n45                  78.322056  \n46                  78.285781  \n47                  78.249528  \n48                  78.213332  \n49                  78.177204  \n\n[50 rows x 68 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Time Step</th>\n      <th>(293268.92232946283, 5040206.601044122)</th>\n      <th>(293162.4584459267, 5040298.134698394)</th>\n      <th>(293169.9422966335, 5040254.972466738)</th>\n      <th>(293161.9162166267, 5040243.155704052)</th>\n      <th>(293243.96496123145, 5040244.083651768)</th>\n      <th>(293208.27353312843, 5040191.91032603)</th>\n      <th>(293179.0032363437, 5040271.258029018)</th>\n      <th>(293228.2223414392, 5040216.721843737)</th>\n      <th>(293212.70831835904, 5040249.072585436)</th>\n      <th>...</th>\n      <th>(293168.8711824724, 5040218.090067068)</th>\n      <th>(293254.3945879081, 5040221.622179406)</th>\n      <th>(293181.600750976, 5040305.67762853)</th>\n      <th>(293161.4860233397, 5040319.189840496)</th>\n      <th>(293259.9504738339, 5040229.95785464)</th>\n      <th>(293219.23492961464, 5040168.871007625)</th>\n      <th>(293319.5070828843, 5040216.163684101)</th>\n      <th>(293213.057996733, 5040284.545999)</th>\n      <th>(293154.256991112, 5040324.04599453)</th>\n      <th>(293271.554, 5040247.367)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>79.999991</td>\n      <td>79.999999</td>\n      <td>79.999990</td>\n      <td>79.999992</td>\n      <td>79.999999</td>\n      <td>79.999990</td>\n      <td>79.999990</td>\n      <td>79.999990</td>\n      <td>79.999990</td>\n      <td>...</td>\n      <td>79.999990</td>\n      <td>79.999998</td>\n      <td>79.999997</td>\n      <td>79.999995</td>\n      <td>79.999998</td>\n      <td>79.999996</td>\n      <td>80.000000</td>\n      <td>79.999998</td>\n      <td>79.999994</td>\n      <td>80.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>79.984571</td>\n      <td>79.984490</td>\n      <td>79.983150</td>\n      <td>79.982018</td>\n      <td>79.981472</td>\n      <td>79.980720</td>\n      <td>79.980147</td>\n      <td>79.978423</td>\n      <td>79.977620</td>\n      <td>...</td>\n      <td>79.974141</td>\n      <td>79.970902</td>\n      <td>79.963269</td>\n      <td>79.961862</td>\n      <td>79.968989</td>\n      <td>79.980098</td>\n      <td>79.969750</td>\n      <td>79.979534</td>\n      <td>79.962874</td>\n      <td>79.971599</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>79.938864</td>\n      <td>79.938783</td>\n      <td>79.937444</td>\n      <td>79.936312</td>\n      <td>79.935766</td>\n      <td>79.935016</td>\n      <td>79.934443</td>\n      <td>79.932720</td>\n      <td>79.931917</td>\n      <td>...</td>\n      <td>79.928441</td>\n      <td>79.925205</td>\n      <td>79.917576</td>\n      <td>79.916170</td>\n      <td>79.923293</td>\n      <td>79.934395</td>\n      <td>79.924053</td>\n      <td>79.933831</td>\n      <td>79.917182</td>\n      <td>79.925901</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>79.893189</td>\n      <td>79.893108</td>\n      <td>79.891771</td>\n      <td>79.890640</td>\n      <td>79.890094</td>\n      <td>79.889344</td>\n      <td>79.888772</td>\n      <td>79.887076</td>\n      <td>79.886275</td>\n      <td>...</td>\n      <td>79.882880</td>\n      <td>79.879673</td>\n      <td>79.872113</td>\n      <td>79.870716</td>\n      <td>79.877773</td>\n      <td>79.888767</td>\n      <td>79.878528</td>\n      <td>79.888207</td>\n      <td>79.871723</td>\n      <td>79.880343</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>79.847931</td>\n      <td>79.847855</td>\n      <td>79.846530</td>\n      <td>79.845410</td>\n      <td>79.844871</td>\n      <td>79.844136</td>\n      <td>79.843567</td>\n      <td>79.842472</td>\n      <td>79.841687</td>\n      <td>...</td>\n      <td>79.838847</td>\n      <td>79.835860</td>\n      <td>79.829077</td>\n      <td>79.827692</td>\n      <td>79.834126</td>\n      <td>79.843980</td>\n      <td>79.834700</td>\n      <td>79.843516</td>\n      <td>79.828812</td>\n      <td>79.836247</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>79.806133</td>\n      <td>79.806077</td>\n      <td>79.804813</td>\n      <td>79.803743</td>\n      <td>79.803235</td>\n      <td>79.802570</td>\n      <td>79.802019</td>\n      <td>79.803126</td>\n      <td>79.802387</td>\n      <td>...</td>\n      <td>79.800173</td>\n      <td>79.797623</td>\n      <td>79.792906</td>\n      <td>79.791424</td>\n      <td>79.796380</td>\n      <td>79.803319</td>\n      <td>79.796320</td>\n      <td>79.803201</td>\n      <td>79.793083</td>\n      <td>79.797323</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6</td>\n      <td>79.771978</td>\n      <td>79.771946</td>\n      <td>79.770755</td>\n      <td>79.769745</td>\n      <td>79.769274</td>\n      <td>79.768690</td>\n      <td>79.768159</td>\n      <td>79.771225</td>\n      <td>79.770517</td>\n      <td>...</td>\n      <td>79.767843</td>\n      <td>79.765460</td>\n      <td>79.762035</td>\n      <td>79.760404</td>\n      <td>79.764566</td>\n      <td>79.769774</td>\n      <td>79.764007</td>\n      <td>79.769957</td>\n      <td>79.762582</td>\n      <td>79.764807</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>7</td>\n      <td>79.741512</td>\n      <td>79.741477</td>\n      <td>79.740277</td>\n      <td>79.739258</td>\n      <td>79.738782</td>\n      <td>79.738185</td>\n      <td>79.737650</td>\n      <td>79.739893</td>\n      <td>79.739163</td>\n      <td>...</td>\n      <td>79.735720</td>\n      <td>79.733066</td>\n      <td>79.728595</td>\n      <td>79.726971</td>\n      <td>79.731945</td>\n      <td>79.738678</td>\n      <td>79.731663</td>\n      <td>79.738731</td>\n      <td>79.728962</td>\n      <td>79.732808</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>8</td>\n      <td>79.706654</td>\n      <td>79.706599</td>\n      <td>79.705337</td>\n      <td>79.704267</td>\n      <td>79.703759</td>\n      <td>79.703091</td>\n      <td>79.702539</td>\n      <td>79.702796</td>\n      <td>79.702029</td>\n      <td>...</td>\n      <td>79.698448</td>\n      <td>79.695489</td>\n      <td>79.689387</td>\n      <td>79.687874</td>\n      <td>79.693964</td>\n      <td>79.702963</td>\n      <td>79.694220</td>\n      <td>79.702708</td>\n      <td>79.689367</td>\n      <td>79.695744</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>9</td>\n      <td>79.666174</td>\n      <td>79.666105</td>\n      <td>79.664804</td>\n      <td>79.663703</td>\n      <td>79.663176</td>\n      <td>79.662465</td>\n      <td>79.661903</td>\n      <td>79.661183</td>\n      <td>79.660402</td>\n      <td>...</td>\n      <td>79.657107</td>\n      <td>79.654073</td>\n      <td>79.647354</td>\n      <td>79.645915</td>\n      <td>79.652379</td>\n      <td>79.662199</td>\n      <td>79.652875</td>\n      <td>79.661792</td>\n      <td>79.647152</td>\n      <td>79.654488</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>10</td>\n      <td>79.623977</td>\n      <td>79.623909</td>\n      <td>79.622614</td>\n      <td>79.621519</td>\n      <td>79.620995</td>\n      <td>79.620291</td>\n      <td>79.619731</td>\n      <td>79.619406</td>\n      <td>79.618636</td>\n      <td>...</td>\n      <td>79.615728</td>\n      <td>79.612834</td>\n      <td>79.606631</td>\n      <td>79.605195</td>\n      <td>79.611251</td>\n      <td>79.620315</td>\n      <td>79.611617</td>\n      <td>79.619971</td>\n      <td>79.606515</td>\n      <td>79.613055</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>11</td>\n      <td>79.584010</td>\n      <td>79.583955</td>\n      <td>79.582695</td>\n      <td>79.581628</td>\n      <td>79.581122</td>\n      <td>79.580458</td>\n      <td>79.579908</td>\n      <td>79.580739</td>\n      <td>79.579993</td>\n      <td>...</td>\n      <td>79.577290</td>\n      <td>79.574601</td>\n      <td>79.569419</td>\n      <td>79.567926</td>\n      <td>79.573266</td>\n      <td>79.580899</td>\n      <td>79.573309</td>\n      <td>79.580736</td>\n      <td>79.569531</td>\n      <td>79.574493</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>12</td>\n      <td>79.547721</td>\n      <td>79.547676</td>\n      <td>79.546448</td>\n      <td>79.545408</td>\n      <td>79.544917</td>\n      <td>79.544289</td>\n      <td>79.543748</td>\n      <td>79.545439</td>\n      <td>79.544707</td>\n      <td>...</td>\n      <td>79.541866</td>\n      <td>79.539268</td>\n      <td>79.534691</td>\n      <td>79.533139</td>\n      <td>79.538092</td>\n      <td>79.544905</td>\n      <td>79.537915</td>\n      <td>79.544875</td>\n      <td>79.534967</td>\n      <td>79.538990</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>13</td>\n      <td>79.513287</td>\n      <td>79.513243</td>\n      <td>79.512018</td>\n      <td>79.510979</td>\n      <td>79.510489</td>\n      <td>79.509863</td>\n      <td>79.509322</td>\n      <td>79.510883</td>\n      <td>79.510146</td>\n      <td>...</td>\n      <td>79.507011</td>\n      <td>79.504333</td>\n      <td>79.499502</td>\n      <td>79.497940</td>\n      <td>79.503107</td>\n      <td>79.510304</td>\n      <td>79.502985</td>\n      <td>79.510254</td>\n      <td>79.499745</td>\n      <td>79.504162</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>14</td>\n      <td>79.477642</td>\n      <td>79.477590</td>\n      <td>79.476342</td>\n      <td>79.475285</td>\n      <td>79.474784</td>\n      <td>79.474131</td>\n      <td>79.473584</td>\n      <td>79.474354</td>\n      <td>79.473601</td>\n      <td>...</td>\n      <td>79.470314</td>\n      <td>79.467496</td>\n      <td>79.461964</td>\n      <td>79.460441</td>\n      <td>79.466101</td>\n      <td>79.474280</td>\n      <td>79.466200</td>\n      <td>79.474106</td>\n      <td>79.462050</td>\n      <td>79.467553</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>15</td>\n      <td>79.439476</td>\n      <td>79.439416</td>\n      <td>79.438147</td>\n      <td>79.437072</td>\n      <td>79.436560</td>\n      <td>79.435883</td>\n      <td>79.435330</td>\n      <td>79.435483</td>\n      <td>79.434720</td>\n      <td>...</td>\n      <td>79.431518</td>\n      <td>79.428633</td>\n      <td>79.422663</td>\n      <td>79.421183</td>\n      <td>79.427124</td>\n      <td>79.435899</td>\n      <td>79.427381</td>\n      <td>79.435629</td>\n      <td>79.422632</td>\n      <td>79.428817</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>16</td>\n      <td>79.399958</td>\n      <td>79.399898</td>\n      <td>79.398626</td>\n      <td>79.397549</td>\n      <td>79.397037</td>\n      <td>79.396357</td>\n      <td>79.395804</td>\n      <td>79.395992</td>\n      <td>79.395233</td>\n      <td>...</td>\n      <td>79.392226</td>\n      <td>79.389389</td>\n      <td>79.383552</td>\n      <td>79.382082</td>\n      <td>79.387904</td>\n      <td>79.396473</td>\n      <td>79.388139</td>\n      <td>79.396210</td>\n      <td>79.383533</td>\n      <td>79.389514</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>17</td>\n      <td>79.361138</td>\n      <td>79.361082</td>\n      <td>79.359826</td>\n      <td>79.358762</td>\n      <td>79.358257</td>\n      <td>79.357594</td>\n      <td>79.357045</td>\n      <td>79.357746</td>\n      <td>79.356998</td>\n      <td>...</td>\n      <td>79.354114</td>\n      <td>79.351375</td>\n      <td>79.346008</td>\n      <td>79.344516</td>\n      <td>79.350002</td>\n      <td>79.357910</td>\n      <td>79.350093</td>\n      <td>79.357727</td>\n      <td>79.346091</td>\n      <td>79.351346</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>18</td>\n      <td>79.324050</td>\n      <td>79.324000</td>\n      <td>79.322761</td>\n      <td>79.321710</td>\n      <td>79.321213</td>\n      <td>79.320569</td>\n      <td>79.320025</td>\n      <td>79.321189</td>\n      <td>79.320449</td>\n      <td>...</td>\n      <td>79.317530</td>\n      <td>79.314850</td>\n      <td>79.309830</td>\n      <td>79.308309</td>\n      <td>79.313566</td>\n      <td>79.320997</td>\n      <td>79.313535</td>\n      <td>79.320886</td>\n      <td>79.310002</td>\n      <td>79.314719</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>19</td>\n      <td>79.288091</td>\n      <td>79.288042</td>\n      <td>79.286807</td>\n      <td>79.285760</td>\n      <td>79.285266</td>\n      <td>79.284626</td>\n      <td>79.284083</td>\n      <td>79.285288</td>\n      <td>79.284547</td>\n      <td>...</td>\n      <td>79.281503</td>\n      <td>79.278802</td>\n      <td>79.273750</td>\n      <td>79.272220</td>\n      <td>79.277516</td>\n      <td>79.285006</td>\n      <td>79.277483</td>\n      <td>79.284901</td>\n      <td>79.273926</td>\n      <td>79.278694</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>20</td>\n      <td>79.251873</td>\n      <td>79.251821</td>\n      <td>79.250578</td>\n      <td>79.249525</td>\n      <td>79.249026</td>\n      <td>79.248377</td>\n      <td>79.247832</td>\n      <td>79.248720</td>\n      <td>79.247973</td>\n      <td>...</td>\n      <td>79.244839</td>\n      <td>79.242076</td>\n      <td>79.236731</td>\n      <td>79.235214</td>\n      <td>79.240721</td>\n      <td>79.248626</td>\n      <td>79.240778</td>\n      <td>79.248472</td>\n      <td>79.236843</td>\n      <td>79.242068</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>21</td>\n      <td>79.214569</td>\n      <td>79.214514</td>\n      <td>79.213260</td>\n      <td>79.212198</td>\n      <td>79.211694</td>\n      <td>79.211033</td>\n      <td>79.210485</td>\n      <td>79.211050</td>\n      <td>79.210298</td>\n      <td>...</td>\n      <td>79.207181</td>\n      <td>79.204378</td>\n      <td>79.198790</td>\n      <td>79.197294</td>\n      <td>79.202961</td>\n      <td>79.211199</td>\n      <td>79.203103</td>\n      <td>79.210995</td>\n      <td>79.198841</td>\n      <td>79.204443</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>22</td>\n      <td>79.176483</td>\n      <td>79.176426</td>\n      <td>79.175170</td>\n      <td>79.174106</td>\n      <td>79.173600</td>\n      <td>79.172935</td>\n      <td>79.172387</td>\n      <td>79.172901</td>\n      <td>79.172149</td>\n      <td>...</td>\n      <td>79.169119</td>\n      <td>79.166329</td>\n      <td>79.160748</td>\n      <td>79.159262</td>\n      <td>79.164909</td>\n      <td>79.173127</td>\n      <td>79.165060</td>\n      <td>79.172915</td>\n      <td>79.160791</td>\n      <td>79.166384</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>23</td>\n      <td>79.138538</td>\n      <td>79.138484</td>\n      <td>79.137234</td>\n      <td>79.136175</td>\n      <td>79.135672</td>\n      <td>79.135014</td>\n      <td>79.134468</td>\n      <td>79.135189</td>\n      <td>79.134442</td>\n      <td>...</td>\n      <td>79.131482</td>\n      <td>79.128737</td>\n      <td>79.123358</td>\n      <td>79.121865</td>\n      <td>79.127365</td>\n      <td>79.135296</td>\n      <td>79.127455</td>\n      <td>79.135116</td>\n      <td>79.123443</td>\n      <td>79.128725</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>24</td>\n      <td>79.101367</td>\n      <td>79.101315</td>\n      <td>79.100073</td>\n      <td>79.099022</td>\n      <td>79.098523</td>\n      <td>79.097875</td>\n      <td>79.097331</td>\n      <td>79.098291</td>\n      <td>79.097549</td>\n      <td>...</td>\n      <td>79.094590</td>\n      <td>79.091880</td>\n      <td>79.086692</td>\n      <td>79.085185</td>\n      <td>79.090556</td>\n      <td>79.098223</td>\n      <td>79.090582</td>\n      <td>79.098081</td>\n      <td>79.086823</td>\n      <td>79.091810</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>25</td>\n      <td>79.064843</td>\n      <td>79.064793</td>\n      <td>79.063555</td>\n      <td>79.062506</td>\n      <td>79.062010</td>\n      <td>79.061365</td>\n      <td>79.060823</td>\n      <td>79.061852</td>\n      <td>79.061111</td>\n      <td>...</td>\n      <td>79.058101</td>\n      <td>79.055390</td>\n      <td>79.050231</td>\n      <td>79.048717</td>\n      <td>79.054076</td>\n      <td>79.061708</td>\n      <td>79.054087</td>\n      <td>79.061577</td>\n      <td>79.050375</td>\n      <td>79.055317</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>26</td>\n      <td>79.028360</td>\n      <td>79.028309</td>\n      <td>79.027069</td>\n      <td>79.026019</td>\n      <td>79.025521</td>\n      <td>79.024873</td>\n      <td>79.024330</td>\n      <td>79.025244</td>\n      <td>79.024500</td>\n      <td>...</td>\n      <td>79.021443</td>\n      <td>79.018707</td>\n      <td>79.013435</td>\n      <td>79.011925</td>\n      <td>79.017367</td>\n      <td>79.025161</td>\n      <td>79.017412</td>\n      <td>79.025012</td>\n      <td>79.013554</td>\n      <td>79.018674</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>27</td>\n      <td>78.991446</td>\n      <td>78.991393</td>\n      <td>78.990148</td>\n      <td>78.989094</td>\n      <td>78.988595</td>\n      <td>78.987941</td>\n      <td>78.987397</td>\n      <td>78.988153</td>\n      <td>78.987407</td>\n      <td>...</td>\n      <td>78.984348</td>\n      <td>78.981591</td>\n      <td>78.976196</td>\n      <td>78.974697</td>\n      <td>78.980220</td>\n      <td>78.988183</td>\n      <td>78.980307</td>\n      <td>78.988010</td>\n      <td>78.976285</td>\n      <td>78.981597</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>28</td>\n      <td>78.954128</td>\n      <td>78.954074</td>\n      <td>78.952828</td>\n      <td>78.951772</td>\n      <td>78.951272</td>\n      <td>78.950616</td>\n      <td>78.950071</td>\n      <td>78.950775</td>\n      <td>78.950029</td>\n      <td>...</td>\n      <td>78.947007</td>\n      <td>78.944252</td>\n      <td>78.938838</td>\n      <td>78.937344</td>\n      <td>78.942874</td>\n      <td>78.950860</td>\n      <td>78.942973</td>\n      <td>78.950679</td>\n      <td>78.938918</td>\n      <td>78.944261</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>29</td>\n      <td>78.916800</td>\n      <td>78.916747</td>\n      <td>78.915503</td>\n      <td>78.914449</td>\n      <td>78.913950</td>\n      <td>78.913297</td>\n      <td>78.912753</td>\n      <td>78.913535</td>\n      <td>78.912792</td>\n      <td>...</td>\n      <td>78.909807</td>\n      <td>78.907072</td>\n      <td>78.901742</td>\n      <td>78.900247</td>\n      <td>78.905714</td>\n      <td>78.913580</td>\n      <td>78.905790</td>\n      <td>78.913411</td>\n      <td>78.901838</td>\n      <td>78.907053</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>30</td>\n      <td>78.879808</td>\n      <td>78.879756</td>\n      <td>78.878517</td>\n      <td>78.877467</td>\n      <td>78.876970</td>\n      <td>78.876321</td>\n      <td>78.875778</td>\n      <td>78.876680</td>\n      <td>78.875939</td>\n      <td>...</td>\n      <td>78.872964</td>\n      <td>78.870249</td>\n      <td>78.865019</td>\n      <td>78.863518</td>\n      <td>78.868916</td>\n      <td>78.876641</td>\n      <td>78.868959</td>\n      <td>78.876491</td>\n      <td>78.865139</td>\n      <td>78.870199</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>31</td>\n      <td>78.843173</td>\n      <td>78.843121</td>\n      <td>78.841885</td>\n      <td>78.840838</td>\n      <td>78.840342</td>\n      <td>78.839696</td>\n      <td>78.839154</td>\n      <td>78.840112</td>\n      <td>78.839372</td>\n      <td>...</td>\n      <td>78.836378</td>\n      <td>78.833667</td>\n      <td>78.828475</td>\n      <td>78.826970</td>\n      <td>78.832345</td>\n      <td>78.840022</td>\n      <td>78.832374</td>\n      <td>78.839881</td>\n      <td>78.828604</td>\n      <td>78.833610</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>32</td>\n      <td>78.806643</td>\n      <td>78.806591</td>\n      <td>78.805355</td>\n      <td>78.804307</td>\n      <td>78.803811</td>\n      <td>78.803165</td>\n      <td>78.802624</td>\n      <td>78.803547</td>\n      <td>78.802806</td>\n      <td>...</td>\n      <td>78.799790</td>\n      <td>78.797070</td>\n      <td>78.791840</td>\n      <td>78.790337</td>\n      <td>78.795739</td>\n      <td>78.803471</td>\n      <td>78.795779</td>\n      <td>78.803324</td>\n      <td>78.791963</td>\n      <td>78.797027</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>33</td>\n      <td>78.769969</td>\n      <td>78.769916</td>\n      <td>78.768678</td>\n      <td>78.767630</td>\n      <td>78.767133</td>\n      <td>78.766484</td>\n      <td>78.765942</td>\n      <td>78.766795</td>\n      <td>78.766053</td>\n      <td>...</td>\n      <td>78.763032</td>\n      <td>78.760303</td>\n      <td>78.755017</td>\n      <td>78.753518</td>\n      <td>78.758958</td>\n      <td>78.766767</td>\n      <td>78.759018</td>\n      <td>78.766609</td>\n      <td>78.755126</td>\n      <td>78.760278</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>34</td>\n      <td>78.733109</td>\n      <td>78.733057</td>\n      <td>78.731818</td>\n      <td>78.730769</td>\n      <td>78.730271</td>\n      <td>78.729622</td>\n      <td>78.729079</td>\n      <td>78.729898</td>\n      <td>78.729156</td>\n      <td>...</td>\n      <td>78.726150</td>\n      <td>78.723421</td>\n      <td>78.718119</td>\n      <td>78.716623</td>\n      <td>78.722071</td>\n      <td>78.729901</td>\n      <td>78.722139</td>\n      <td>78.729738</td>\n      <td>78.718221</td>\n      <td>78.723401</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>35</td>\n      <td>78.696222</td>\n      <td>78.696170</td>\n      <td>78.694932</td>\n      <td>78.693884</td>\n      <td>78.693387</td>\n      <td>78.692738</td>\n      <td>78.692196</td>\n      <td>78.693042</td>\n      <td>78.692302</td>\n      <td>...</td>\n      <td>78.689317</td>\n      <td>78.686597</td>\n      <td>78.681329</td>\n      <td>78.679834</td>\n      <td>78.685255</td>\n      <td>78.693034</td>\n      <td>78.685313</td>\n      <td>78.692876</td>\n      <td>78.681437</td>\n      <td>78.686565</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>36</td>\n      <td>78.659484</td>\n      <td>78.659432</td>\n      <td>78.658197</td>\n      <td>78.657151</td>\n      <td>78.656655</td>\n      <td>78.656009</td>\n      <td>78.655468</td>\n      <td>78.656372</td>\n      <td>78.655633</td>\n      <td>...</td>\n      <td>78.652656</td>\n      <td>78.649948</td>\n      <td>78.644732</td>\n      <td>78.643235</td>\n      <td>78.648619</td>\n      <td>78.656325</td>\n      <td>78.648661</td>\n      <td>78.656175</td>\n      <td>78.644852</td>\n      <td>78.649900</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>37</td>\n      <td>78.622941</td>\n      <td>78.622890</td>\n      <td>78.621657</td>\n      <td>78.620612</td>\n      <td>78.620117</td>\n      <td>78.619473</td>\n      <td>78.618932</td>\n      <td>78.619874</td>\n      <td>78.619136</td>\n      <td>...</td>\n      <td>78.616155</td>\n      <td>78.613451</td>\n      <td>78.608265</td>\n      <td>78.606766</td>\n      <td>78.612130</td>\n      <td>78.619796</td>\n      <td>78.612163</td>\n      <td>78.619653</td>\n      <td>78.608392</td>\n      <td>78.613396</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>38</td>\n      <td>78.586497</td>\n      <td>78.586446</td>\n      <td>78.585213</td>\n      <td>78.584169</td>\n      <td>78.583675</td>\n      <td>78.583031</td>\n      <td>78.582491</td>\n      <td>78.583428</td>\n      <td>78.582690</td>\n      <td>...</td>\n      <td>78.579699</td>\n      <td>78.576994</td>\n      <td>78.571802</td>\n      <td>78.570303</td>\n      <td>78.575672</td>\n      <td>78.583348</td>\n      <td>78.575706</td>\n      <td>78.583204</td>\n      <td>78.571927</td>\n      <td>78.576943</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>39</td>\n      <td>78.550028</td>\n      <td>78.549976</td>\n      <td>78.548744</td>\n      <td>78.547699</td>\n      <td>78.547205</td>\n      <td>78.546560</td>\n      <td>78.546020</td>\n      <td>78.546929</td>\n      <td>78.546191</td>\n      <td>...</td>\n      <td>78.543197</td>\n      <td>78.540488</td>\n      <td>78.535274</td>\n      <td>78.533778</td>\n      <td>78.539161</td>\n      <td>78.546867</td>\n      <td>78.539203</td>\n      <td>78.546719</td>\n      <td>78.535394</td>\n      <td>78.540445</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>40</td>\n      <td>78.513490</td>\n      <td>78.513438</td>\n      <td>78.512206</td>\n      <td>78.511161</td>\n      <td>78.510666</td>\n      <td>78.510021</td>\n      <td>78.509481</td>\n      <td>78.510373</td>\n      <td>78.509635</td>\n      <td>...</td>\n      <td>78.506647</td>\n      <td>78.503939</td>\n      <td>78.498716</td>\n      <td>78.497222</td>\n      <td>78.502609</td>\n      <td>78.510326</td>\n      <td>78.502656</td>\n      <td>78.510175</td>\n      <td>78.498833</td>\n      <td>78.503898</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>41</td>\n      <td>78.476941</td>\n      <td>78.476889</td>\n      <td>78.475657</td>\n      <td>78.474613</td>\n      <td>78.474119</td>\n      <td>78.473474</td>\n      <td>78.472935</td>\n      <td>78.473836</td>\n      <td>78.473099</td>\n      <td>...</td>\n      <td>78.470122</td>\n      <td>78.467419</td>\n      <td>78.462212</td>\n      <td>78.460718</td>\n      <td>78.466092</td>\n      <td>78.473786</td>\n      <td>78.466135</td>\n      <td>78.473637</td>\n      <td>78.462331</td>\n      <td>78.467373</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>42</td>\n      <td>78.440465</td>\n      <td>78.440414</td>\n      <td>78.439184</td>\n      <td>78.438141</td>\n      <td>78.437647</td>\n      <td>78.437004</td>\n      <td>78.436465</td>\n      <td>78.437395</td>\n      <td>78.436659</td>\n      <td>...</td>\n      <td>78.433689</td>\n      <td>78.430992</td>\n      <td>78.425814</td>\n      <td>78.424319</td>\n      <td>78.429673</td>\n      <td>78.437326</td>\n      <td>78.429708</td>\n      <td>78.437182</td>\n      <td>78.425939</td>\n      <td>78.430938</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>43</td>\n      <td>78.404102</td>\n      <td>78.404051</td>\n      <td>78.402822</td>\n      <td>78.401780</td>\n      <td>78.401287</td>\n      <td>78.400645</td>\n      <td>78.400107</td>\n      <td>78.401061</td>\n      <td>78.400325</td>\n      <td>...</td>\n      <td>78.397356</td>\n      <td>78.394664</td>\n      <td>78.389507</td>\n      <td>78.388011</td>\n      <td>78.393350</td>\n      <td>78.400974</td>\n      <td>78.393378</td>\n      <td>78.400833</td>\n      <td>78.389636</td>\n      <td>78.394604</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>44</td>\n      <td>78.367817</td>\n      <td>78.367767</td>\n      <td>78.366538</td>\n      <td>78.365498</td>\n      <td>78.365005</td>\n      <td>78.364364</td>\n      <td>78.363825</td>\n      <td>78.364785</td>\n      <td>78.364050</td>\n      <td>...</td>\n      <td>78.361078</td>\n      <td>78.358386</td>\n      <td>78.353234</td>\n      <td>78.351739</td>\n      <td>78.357074</td>\n      <td>78.364691</td>\n      <td>78.357101</td>\n      <td>78.364552</td>\n      <td>78.353364</td>\n      <td>78.358326</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>45</td>\n      <td>78.331553</td>\n      <td>78.331502</td>\n      <td>78.330274</td>\n      <td>78.329234</td>\n      <td>78.328741</td>\n      <td>78.328100</td>\n      <td>78.327562</td>\n      <td>78.328513</td>\n      <td>78.327778</td>\n      <td>...</td>\n      <td>78.324805</td>\n      <td>78.322113</td>\n      <td>78.316956</td>\n      <td>78.315462</td>\n      <td>78.320800</td>\n      <td>78.328424</td>\n      <td>78.320829</td>\n      <td>78.328283</td>\n      <td>78.317085</td>\n      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<td>78.242945</td>\n      <td>78.248275</td>\n      <td>78.255887</td>\n      <td>78.248304</td>\n      <td>78.255747</td>\n      <td>78.244566</td>\n      <td>78.249528</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>48</td>\n      <td>78.222790</td>\n      <td>78.222739</td>\n      <td>78.221514</td>\n      <td>78.220475</td>\n      <td>78.219983</td>\n      <td>78.219343</td>\n      <td>78.218806</td>\n      <td>78.219771</td>\n      <td>78.219037</td>\n      <td>...</td>\n      <td>78.216079</td>\n      <td>78.213396</td>\n      <td>78.208263</td>\n      <td>78.206771</td>\n      <td>78.212088</td>\n      <td>78.219676</td>\n      <td>78.212113</td>\n      <td>78.219538</td>\n      <td>78.208395</td>\n      <td>78.213332</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>49</td>\n      <td>78.186640</td>\n      <td>78.186590</td>\n      <td>78.185365</td>\n      <td>78.184327</td>\n      <td>78.183836</td>\n      <td>78.183197</td>\n      <td>78.182660</td>\n      <td>78.183641</td>\n      <td>78.182908</td>\n      <td>...</td>\n      <td>78.179951</td>\n      <td>78.177272</td>\n      <td>78.172155</td>\n      <td>78.170663</td>\n      <td>78.175968</td>\n      <td>78.183534</td>\n      <td>78.175988</td>\n      <td>78.183399</td>\n      <td>78.172289</td>\n      <td>78.177204</td>\n    </tr>\n  </tbody>\n</table>\n<p>50 rows × 68 columns</p>\n</div>"
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_labels = [node for node in di_graph.nodes()]\n",
    "\n",
    "# Convert the results array to a pandas DataFrame\n",
    "results_df = pd.DataFrame(results, columns=node_labels)\n",
    "\n",
    "# Optional: If you want to add a column or index that indicates the time step\n",
    "results_df.index.name = 'Time Step'\n",
    "results_df.reset_index(inplace=True)\n",
    "\n",
    "# Showing the DataFrame\n",
    "results_df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T11:20:03.482299200Z",
     "start_time": "2024-03-08T11:20:02.899695700Z"
    }
   },
   "id": "eb38aad026338932"
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "outputs": [],
   "source": [
    "for node in di_graph.nodes():\n",
    "    if 'temperature_history' not in di_graph.nodes[node]:\n",
    "        di_graph.nodes[node]['temperature_history'] = []"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T11:24:19.494596600Z",
     "start_time": "2024-03-08T11:24:18.463102Z"
    }
   },
   "id": "ce01a2ebcf719a1f"
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "outputs": [],
   "source": [
    "last_temperatures = results_df.iloc[-1]\n",
    "\n",
    "# Skipping the first column if it's \"Time Step\", or adjust according to your DataFrame structure\n",
    "for node in di_graph.nodes():\n",
    "    # Ensure the node label matches the DataFrame's column for correct data appending\n",
    "    temperature = last_temperatures[node] if node in last_temperatures else None\n",
    "    if temperature is not None:\n",
    "        di_graph.nodes[node]['temperature_history'].append(temperature)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T11:24:33.186227700Z",
     "start_time": "2024-03-08T11:24:32.711855200Z"
    }
   },
   "id": "1a5fa74ffcc61119"
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "outputs": [
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                       {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('2be4ba45-cda8-44c2-8272-772cebc372e3');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "edge_x = []\n",
    "edge_y = []\n",
    "for edge in di_graph.edges():\n",
    "    x0, y0 = pos[edge[0]]\n",
    "    x1, y1 = pos[edge[1]]\n",
    "    edge_x.append(x0)\n",
    "    edge_x.append(x1)\n",
    "    edge_x.append(None)  # Prevents drawing a line to the next edge\n",
    "    edge_y.append(y0)\n",
    "    edge_y.append(y1)\n",
    "    edge_y.append(None)\n",
    "\n",
    "edge_trace = go.Scatter(x=edge_x, y=edge_y, line=dict(width=0.5, color='#888'), hoverinfo='none', mode='lines')\n",
    "\n",
    "node_x = []\n",
    "node_y = []\n",
    "for node in di_graph.nodes():\n",
    "    x, y = pos[node]\n",
    "    node_x.append(x)\n",
    "    node_y.append(y)\n",
    "\n",
    "node_trace = go.Scatter(x=node_x, y=node_y, mode='markers', hoverinfo='text', marker=dict(showscale=True, colorscale='YlGnBu', color=[], size=10, colorbar=dict(thickness=15, title='Mass Flow Rate', xanchor='left', titleside='right')))\n",
    "\n",
    "# Initialize an empty list for colors\n",
    "colors = []\n",
    "\n",
    "node_text = []\n",
    "for node, data in di_graph.nodes(data=True):\n",
    "    node_info = f\"Temperature: {data.get('temperature_history', 'N/A')}<br>Peak Demand: {data.get('Peack_Demand', 'N/A')}\"\n",
    "    last_temp = data.get('temperature_history', [T_initial])[-1] if 'temperature_history' in data else T_initial\n",
    "    colors.append(last_temp)  # Append the last known temperature to the colors list\n",
    "    node_text.append(node_info)\n",
    "\n",
    "# Make sure to set node_trace.marker.color using the colors list\n",
    "node_trace = go.Scatter(\n",
    "    x=node_x, \n",
    "    y=node_y, \n",
    "    mode='markers',\n",
    "    hoverinfo='text',\n",
    "    marker=dict(\n",
    "        showscale=True,\n",
    "        # Use the colors list for node colors\n",
    "        color=colors,  \n",
    "        size=10,\n",
    "        colorbar=dict(thickness=15, title='Node Temperature', xanchor='left', titleside='right'),\n",
    "        colorscale='YlGnBu'\n",
    "    ),\n",
    "    text=node_text\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=[edge_trace, node_trace], layout=go.Layout(title='<br>Network flow visualization', titlefont_size=16, showlegend=False, hovermode='closest', margin=dict(b=20, l=5, r=5, t=40), xaxis=dict(showgrid=False, zeroline=False, showticklabels=False), yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)))\n",
    "\n",
    "fig.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T11:38:25.454409200Z",
     "start_time": "2024-03-08T11:38:25.076630Z"
    }
   },
   "id": "8aa3a462ab1e885"
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "outputs": [],
   "source": [
    "from pyproj import Transformer\n",
    "\n",
    "# Initialize a transformer to convert from your current CRS to EPSG:4326\n",
    "# Replace 'EPSG:XXXX' with your current coordinate system\n",
    "transformer = Transformer.from_crs(\"EPSG:32188\", \"EPSG:4326\", always_xy=True)\n",
    "\n",
    "def convert_coordinates(geom):\n",
    "    return transformer.transform(geom.x, geom.y)\n",
    "\n",
    "for node in di_graph.nodes():\n",
    "    # node here is a tuple representing coordinates (x, y)\n",
    "    x, y = node  # Unpack the tuple\n",
    "    lon, lat = transformer.transform(x, y)  # Convert to geographic coordinates\n",
    "    \n",
    "    # Update the node's data with 'lat' and 'lon'\n",
    "    di_graph.nodes[node]['lat'] = lat\n",
    "    di_graph.nodes[node]['lon'] = lon"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T12:03:59.410046400Z",
     "start_time": "2024-03-08T12:03:58.831359900Z"
    }
   },
   "id": "bd8e6654e1c793e"
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CRS of the uploaded Shapefile: EPSG:32188\n"
     ]
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "# Load the Shapefile\n",
    "gdf = gpd.read_file(roads_file)\n",
    "\n",
    "# Print the CRS\n",
    "print(\"CRS of the uploaded Shapefile:\", gdf.crs)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T11:49:35.006423500Z",
     "start_time": "2024-03-08T11:49:19.156474Z"
    }
   },
   "id": "2de2020791b7c47a"
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "outputs": [
    {
     "data": {
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marker_49dcc6bf4ef72db02b3c7cefbafb5c03.bindPopup(popup_5be8388ac65ae4b823bc4da0ab74a1e1)\n        ;\n\n        \n    \n    \n            var marker_647674af1ff84005eac063b2c5962c80 = L.marker(\n                [45.502639925899615, -73.6482954316946],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_b54c46a9a73695656f8422fb625ca398 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_647674af1ff84005eac063b2c5962c80.setIcon(icon_b54c46a9a73695656f8422fb625ca398);\n        \n    \n        var popup_4601f931fe77746ad6ee15eca6e00926 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_8a0276233b233d889f11123ec510dc50 = $(`&lt;div id=&quot;html_8a0276233b233d889f11123ec510dc50&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Temperature: [78.18037209408065], Peak Demand: [18577.135108146904]&lt;/div&gt;`)[0];\n                popup_4601f931fe77746ad6ee15eca6e00926.setContent(html_8a0276233b233d889f11123ec510dc50);\n            \n        \n\n        marker_647674af1ff84005eac063b2c5962c80.bindPopup(popup_4601f931fe77746ad6ee15eca6e00926)\n        ;\n\n        \n    \n    \n            var marker_49e0ca3f69d40922816b449a5c301ba8 = L.marker(\n                [45.50142866237129, -73.64779679331274],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_692a19300a00b06b6bf77508243e93c6 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n  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marker_98fa4c276b3beda8bbaaf34835e66932.bindPopup(popup_9411985cff04d2a9f02952295012f558)\n        ;\n\n        \n    \n    \n            var marker_e7bbc4fe0b26e99762fcd16f47d78630 = L.marker(\n                [45.50240850916333, -73.64782889011876],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_e7c78583ccf6967453b95183b47bf3b5 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_e7bbc4fe0b26e99762fcd16f47d78630.setIcon(icon_e7c78583ccf6967453b95183b47bf3b5);\n        \n    \n        var popup_68f00b8a4e9eee91a1a98c6afdc90d17 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_364d916c159c526d9851bd16a8b8e08b = $(`&lt;div id=&quot;html_364d916c159c526d9851bd16a8b8e08b&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Temperature: [78.1781661297445], Peak Demand: [17293.074545317442]&lt;/div&gt;`)[0];\n                popup_68f00b8a4e9eee91a1a98c6afdc90d17.setContent(html_364d916c159c526d9851bd16a8b8e08b);\n            \n        \n\n        marker_e7bbc4fe0b26e99762fcd16f47d78630.bindPopup(popup_68f00b8a4e9eee91a1a98c6afdc90d17)\n        ;\n\n        \n    \n    \n            var marker_220d710956e6ca6a8dfb714ebe6cbdec = L.marker(\n                [45.50292267805605, -73.64891916575966],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_f02f8d35dcd0a50369ff92aa875624e5 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n   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marker_af578811a9f0bcdd4f8121e72225ed3e.bindPopup(popup_7c4ac3ae48d7324f9c852390c81d80e9)\n        ;\n\n        \n    \n    \n            var marker_794edeef71d629db28e2c3eee5d81f36 = L.marker(\n                [45.50215811301751, -73.6481676851036],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_01567fbab032a368c4aa2732491f0f89 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_794edeef71d629db28e2c3eee5d81f36.setIcon(icon_01567fbab032a368c4aa2732491f0f89);\n        \n    \n        var popup_c7c3d3e871854e9da48176da10d1db2e = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_8e30eee7dcd46ead0e8e0425287a9470 = $(`&lt;div id=&quot;html_8e30eee7dcd46ead0e8e0425287a9470&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Temperature: [78.17610326509721], Peak Demand: [23790.135607014832]&lt;/div&gt;`)[0];\n                popup_c7c3d3e871854e9da48176da10d1db2e.setContent(html_8e30eee7dcd46ead0e8e0425287a9470);\n            \n        \n\n        marker_794edeef71d629db28e2c3eee5d81f36.bindPopup(popup_c7c3d3e871854e9da48176da10d1db2e)\n        ;\n\n        \n    \n    \n            var marker_62419f910896eb266db97ad4ff54b4f2 = L.marker(\n                [45.50154059795872, -73.64769048150941],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_0d9b83d82212b45742d0c871c2442194 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n  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marker_836156d2e42808cd383ee6ff62d9b008.bindPopup(popup_39f602240b89a20148ac9841c4a003dd)\n        ;\n\n        \n    \n    \n            var marker_3f45ac18309c2d2ce0a7021141b430c4 = L.marker(\n                [45.501677631212225, -73.64816702447985],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_52c3f2dd46c0d1a1e4b2038cf9dab331 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_3f45ac18309c2d2ce0a7021141b430c4.setIcon(icon_52c3f2dd46c0d1a1e4b2038cf9dab331);\n        \n    \n        var popup_01b1a86f6d2acd9d1a89b21f56a85651 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_169fb1feeda9ce99fd5835c324ad6fcd = $(`&lt;div id=&quot;html_169fb1feeda9ce99fd5835c324ad6fcd&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Temperature: [78.17488860454799], Peak Demand: [18745.45363768308]&lt;/div&gt;`)[0];\n                popup_01b1a86f6d2acd9d1a89b21f56a85651.setContent(html_169fb1feeda9ce99fd5835c324ad6fcd);\n            \n        \n\n        marker_3f45ac18309c2d2ce0a7021141b430c4.bindPopup(popup_01b1a86f6d2acd9d1a89b21f56a85651)\n        ;\n\n        \n    \n    \n            var marker_f6f65d7b6eabb3308abe3b3e4507de13 = L.marker(\n                [45.5023237587039, -73.64761983725388],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_f0fc67aa1d2feb86be4186d30f993d1d = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: 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popup_e0aa1d4fc2bdf7c6378236e35f5b5655.setContent(html_4df345dcaee8f81b326ac80033a69658);\n            \n        \n\n        marker_47dc7fa9bf5befb616dd9fd8772267c0.bindPopup(popup_e0aa1d4fc2bdf7c6378236e35f5b5655)\n        ;\n\n        \n    \n    \n            var marker_016893428c87aac1e3ad7e28ef631cd0 = L.marker(\n                [45.50236568555619, -73.64861396337744],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_c562e8d40f48cd0b02a80097e77a56af = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_016893428c87aac1e3ad7e28ef631cd0.setIcon(icon_c562e8d40f48cd0b02a80097e77a56af);\n        \n    \n        var popup_99a23e2b2d1150d5573056bf2b8886fd = 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popup_91f8d3c7c36fed1630b5c32f77f0f8a0 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_a223056c8795b47a38f6ec7119701c6d = $(`&lt;div id=&quot;html_a223056c8795b47a38f6ec7119701c6d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Temperature: [78.17576140623375], Peak Demand: N/A&lt;/div&gt;`)[0];\n                popup_91f8d3c7c36fed1630b5c32f77f0f8a0.setContent(html_a223056c8795b47a38f6ec7119701c6d);\n            \n        \n\n        marker_74f4d50dc5cd93964a6db66b0183a937.bindPopup(popup_91f8d3c7c36fed1630b5c32f77f0f8a0)\n        ;\n\n        \n    \n    \n            var marker_acac71acdd999ef3b23fc9124a49763d = L.marker(\n                [45.502098215512106, -73.64762394164492],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_781a1801dbaa1362b1c9e5e8470d12c4 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: 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marker_d16352fa73a41fbc7a70ee23f77e938b = L.marker(\n                [45.50228814760426, -73.64805063281078],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_85b45b7b4053482670c7cc6626d176b4 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_d16352fa73a41fbc7a70ee23f77e938b.setIcon(icon_85b45b7b4053482670c7cc6626d176b4);\n        \n    \n        var popup_49b4bb359908c3a72362746114f22fce = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_a439416de99a17f58258fb681cc89e33 = $(`&lt;div id=&quot;html_a439416de99a17f58258fb681cc89e33&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Temperature: [78.17766322018711], 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popup_50fdbfc5750bb210cceed021dc64e99a = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_fc0486fdd50090c1d4e56293cc826a84 = $(`&lt;div id=&quot;html_fc0486fdd50090c1d4e56293cc826a84&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Temperature: [78.17795257834484], Peak Demand: N/A&lt;/div&gt;`)[0];\n                popup_50fdbfc5750bb210cceed021dc64e99a.setContent(html_fc0486fdd50090c1d4e56293cc826a84);\n            \n        \n\n        marker_f760453d8ffaba19b0c447c1f63133a6.bindPopup(popup_50fdbfc5750bb210cceed021dc64e99a)\n        ;\n\n        \n    \n    \n            var marker_c9fd1a0ab655ba36977345128efddcb4 = L.marker(\n                [45.502312691771785, -73.64810577288215],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_3fa30585ca914b888c44c47ec1623def = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: 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marker_ddc636f052044f58955e259cc8819f35 = L.marker(\n                [45.501583308319844, -73.64796497588617],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_04df68935091dd5cedba4d88ec1620d4 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_ddc636f052044f58955e259cc8819f35.setIcon(icon_04df68935091dd5cedba4d88ec1620d4);\n        \n    \n        var popup_31de8050d814d2dd69d456a527893e51 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_2edc6b866afec664ddf3240522b1976b = $(`&lt;div id=&quot;html_2edc6b866afec664ddf3240522b1976b&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Temperature: [78.17747959271962], 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popup_11bef2def1314f2cdd7b031b847db676 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_62f609be8cd16614338b7145351a77ef = $(`&lt;div id=&quot;html_62f609be8cd16614338b7145351a77ef&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Temperature: [78.1741950959567], Peak Demand: N/A&lt;/div&gt;`)[0];\n                popup_11bef2def1314f2cdd7b031b847db676.setContent(html_62f609be8cd16614338b7145351a77ef);\n            \n        \n\n        marker_d05e59638bd3cebe045980e161420d5b.bindPopup(popup_11bef2def1314f2cdd7b031b847db676)\n        ;\n\n        \n    \n    \n            var marker_d14af34591ec9c3171b6314ff2a25e31 = L.marker(\n                [45.50192618050006, -73.64870455531816],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_9c7f57cee333c64d0b514df22a5b98f9 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: 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marker_fe5a577840fe405993f340e0133c1edf = L.marker(\n                [45.50249134317708, -73.64849440330781],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_99df4d20f963408be785556f4365d2d0 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_fe5a577840fe405993f340e0133c1edf.setIcon(icon_99df4d20f963408be785556f4365d2d0);\n        \n    \n        var popup_5a99f0a39312391831999704d1019a18 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_d9827150fd3afbb8532aa07afbdd2615 = $(`&lt;div id=&quot;html_d9827150fd3afbb8532aa07afbdd2615&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Temperature: [78.17616828944418], Peak Demand: N/A&lt;/div&gt;`)[0];\n                popup_5a99f0a39312391831999704d1019a18.setContent(html_d9827150fd3afbb8532aa07afbdd2615);\n            \n        \n\n        marker_fe5a577840fe405993f340e0133c1edf.bindPopup(popup_5a99f0a39312391831999704d1019a18)\n        ;\n\n        \n    \n    \n            var marker_e5b78bc42c896926e6dc05aecc5cf4aa = L.marker(\n                [45.501787598391644, -73.64883506337482],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_ca06178c703c418a77de17fcb4200745 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_e5b78bc42c896926e6dc05aecc5cf4aa.setIcon(icon_ca06178c703c418a77de17fcb4200745);\n        \n    \n        var popup_7760c4dbdaa213b931ef4b0ab5235c9a = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_0b121babf66c91ab12d5a5151d888fe1 = $(`&lt;div id=&quot;html_0b121babf66c91ab12d5a5151d888fe1&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Temperature: [78.17995149429179], Peak Demand: N/A&lt;/div&gt;`)[0];\n                popup_7760c4dbdaa213b931ef4b0ab5235c9a.setContent(html_0b121babf66c91ab12d5a5151d888fe1);\n            \n        \n\n        marker_e5b78bc42c896926e6dc05aecc5cf4aa.bindPopup(popup_7760c4dbdaa213b931ef4b0ab5235c9a)\n        ;\n\n        \n    \n    \n            var marker_1175afbbc29e5213fd8219c1c23fff2a = L.marker(\n                [45.501820802417896, -73.64774076960333],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_be82b6376ae3a6292d7172831a5d3537 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: 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marker_6f0c958d7ef1e725bb97d5a994d8ec0b = L.marker(\n                [45.50257595853584, -73.64867424735355],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_640539b5dfb39748925087ff3328a8d5 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_6f0c958d7ef1e725bb97d5a994d8ec0b.setIcon(icon_640539b5dfb39748925087ff3328a8d5);\n        \n    \n        var popup_c122deed99a150673887efe1a4a336a1 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_09e8f581963fb645250dfe133910a3c3 = $(`&lt;div id=&quot;html_09e8f581963fb645250dfe133910a3c3&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Temperature: [78.17215486689207], 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popup_1a437b20d006ba6289d31d6a1e6a907e = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_f336edf0c55dac83022b5adcd7111be0 = $(`&lt;div id=&quot;html_f336edf0c55dac83022b5adcd7111be0&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Temperature: [78.17066253150507], Peak Demand: N/A&lt;/div&gt;`)[0];\n                popup_1a437b20d006ba6289d31d6a1e6a907e.setContent(html_f336edf0c55dac83022b5adcd7111be0);\n            \n        \n\n        marker_73886fcb1e4db769fe6d66d100c02bc4.bindPopup(popup_1a437b20d006ba6289d31d6a1e6a907e)\n        ;\n\n        \n    \n    \n            var marker_44270d4a82d26151c131345845f5402c = L.marker(\n                [45.50189590210437, -73.64766987127078],\n                {}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            var icon_8d637e8d2da0c098b15d1d2b9ec2d5b3 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: 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{&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_9f07ec5f326fe479ef7ecfb4fc943f02 = L.polyline(\n                [[45.50142866237129, -73.64779679331274], [45.50153046487892, -73.64801486241835]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_9f07ec5f326fe479ef7ecfb4fc943f02.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.22657272006680707\n                 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`&lt;div&gt;\n                     Mass flow rate: 0.16900195413424132\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_96e7df040dd8719927a778ecb7703275 = L.polyline(\n                [[45.502258067676415, -73.64748004658586], [45.502098215512106, -73.64762394164492]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            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&quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_e5a1d4464b1126ac675a5b75ec1bc979.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 1.2702431200525297\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_9f074b90d9f746edc04ffb59a2ca2773 = L.polyline(\n                [[45.50273764845309, -73.64852040301233], [45.50257595853584, -73.64867424735355]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: 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&quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_707c325e523241ed08647837660c6777.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.16063176478719737\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_1f9f36dffb88888ce6d06d92015ba8b4 = L.polyline(\n                [[45.50179501346945, -73.64745376034288], [45.50189590210437, -73.64766987127078]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_1f9f36dffb88888ce6d06d92015ba8b4.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.8841050585512209\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_2a66925ef427b28c40a7381f55317e56 = L.polyline(\n                [[45.501447873706205, -73.64840863732971], [45.50134554336541, -73.64818943556061]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_2a66925ef427b28c40a7381f55317e56.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.7227746161682795\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_508d3c9eb95e9190830a7c83aba54d80 = L.polyline(\n                [[45.50189527625893, -73.64679453009131], [45.50177275925696, -73.64690744707929]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, 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{&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_299a30426b42900b9ca4997ad41a2666.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 1.243885221488725\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_cd72b1f44926c37708cc2bec323a42f7 = L.polyline(\n                [[45.50263442295338, -73.64879851006545], [45.50238633086665, 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[[45.50202246995832, -73.64861387540223], [45.50211949889247, -73.64882223154281]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_1cb2eb718bfa2348060bf516d6a2b61e.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.17564161841978687\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_43e9a0ac0139a2672ab79f9dd92ef15a = L.polyline(\n                [[45.50202246995832, -73.64861387540223], [45.50238633086665, -73.64827120855466]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_43e9a0ac0139a2672ab79f9dd92ef15a.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.17852812988269506\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var 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`&lt;div&gt;\n                     Mass flow rate: 3.589264898851123\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_2152ddd08d3c39abe32e41ab152a7967 = L.polyline(\n                [[45.50144369062979, -73.64809678087796], [45.50168141315254, -73.64787236030689]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_2152ddd08d3c39abe32e41ab152a7967.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 3.424909357938174\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_b15676fd29f7c77a471a6fe5db0c49ca = L.polyline(\n                [[45.50144369062979, -73.64809678087796], [45.501552678380094, -73.6483302433662]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        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&quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_7efa385487ca95afd4f54fe015679388.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.17692509626806585\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_fb1230914054cc986751a1a34cf31b4c = L.polyline(\n                [[45.50168141315254, -73.64787236030689], [45.50144369062979, -73.64809678087796]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_fb1230914054cc986751a1a34cf31b4c.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 3.424909357938174\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_2387a509f52703b740cd180d06026e20 = L.polyline(\n                [[45.50168141315254, -73.64787236030689], [45.50153046487892, -73.64801486241835]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, 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&quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_d74b01bf44bd78a316093c7ccdb7f42d.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.562397941704163\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_005751da134aae886ded6a4c6002001f = L.polyline(\n                [[45.50216325939631, -73.64848128726508], [45.5020671210454, -73.6482748452624]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: 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&quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_38494970ebe54ebd62a2aa96e7ae6449.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.562397941704163\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_f530fde1250494b13ba2daaec440826d = L.polyline(\n                [[45.50202453267021, -73.64861193285377], [45.5016886449866, -73.64892825109119]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_f530fde1250494b13ba2daaec440826d.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.34039836284852976\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_8e361b27325d13a73826e00bd8046dbd = L.polyline(\n                [[45.50202453267021, -73.64861193285377], [45.50193694311846, -73.64842384779672]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_8e361b27325d13a73826e00bd8046dbd.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.16037667446411685\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_7de8613b5c995eda631a5e3ba98985cf = L.polyline(\n                [[45.5016886449866, -73.64892825109119], [45.50202453267021, -73.64861193285377]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_7de8613b5c995eda631a5e3ba98985cf.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.34039836284852976\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_6cac1320286e1a9e5a31ae0a6d363adc = L.polyline(\n                [[45.5016886449866, -73.64892825109119], [45.50157205034684, -73.64904039432656]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, 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{&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_0b209dfcfa54180e370f87cba05af3ee.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.15725742584196678\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_a74b5e9226fb3aa3026de44dfb44dd13 = L.polyline(\n                [[45.50157205034684, -73.64904039432656], [45.50192618050006, 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[[45.50247262374752, -73.64845461661997], [45.502639925899615, -73.6482954316946]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_1e3ffd6d0eb4a5195e8986a79fddce1b.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.17568882028569888\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_68a5f4f86b97a6203b568930f042703a = 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`&lt;div&gt;\n                     Mass flow rate: 1.3552936113506107\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_448af2ec97b691cd09f2d8b1c174c6a3 = L.polyline(\n                [[45.50225211118332, -73.64796967483917], [45.50240850916333, -73.64782889011876]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            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&quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_f46d34818412bc17f7d8cdea354e91ed.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 1.3552936113506107\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_054f59c6b13fd8a445c2844bb50e2a79 = L.polyline(\n                [[45.502098215512106, -73.64762394164492], [45.50228814760426, -73.64805063281078]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_054f59c6b13fd8a445c2844bb50e2a79.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 1.178887849192821\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_cb3c45f9592b015d3a9f9e5e9eebac5c = L.polyline(\n                [[45.502098215512106, -73.64762394164492], [45.502258067676415, -73.64748004658586]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_cb3c45f9592b015d3a9f9e5e9eebac5c.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.19665274692968038\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_1b6e77730ad7b40516c32e89586af25b = L.polyline(\n                [[45.50228814760426, -73.64805063281078], [45.502098215512106, -73.64762394164492]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_1b6e77730ad7b40516c32e89586af25b.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 1.178887849192821\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_097dddc5221bdd8c6a5f54f0afebe3aa = L.polyline(\n                [[45.50228814760426, -73.64805063281078], [45.502312691771785, -73.64810577288215]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_097dddc5221bdd8c6a5f54f0afebe3aa.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.957494639108599\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_7fb8c4411819546a48ee4989f2183319 = L.polyline(\n                [[45.50228814760426, -73.64805063281078], [45.50215811301751, -73.6481676851036]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_7fb8c4411819546a48ee4989f2183319.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.17640576215779158\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_aab9fd848cb330f01ef41ee0e99e9792 = L.polyline(\n                [[45.50164260776102, -73.64790899446989], [45.50153046487892, -73.64801486241835]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_aab9fd848cb330f01ef41ee0e99e9792.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 3.0078749136926235\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_c75794355c1cbb690a4e3f4cbbb33728 = L.polyline(\n                [[45.50164260776102, -73.64790899446989], [45.501583308319844, -73.64796497588617]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_c75794355c1cbb690a4e3f4cbbb33728.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 2.8474663370455087\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_f0726c2363b70ffd6d3fe66d2a3e9b22 = L.polyline(\n                [[45.50164260776102, -73.64790899446989], [45.50154059795872, -73.64769048150941]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_f0726c2363b70ffd6d3fe66d2a3e9b22.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.19046172417874768\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_9d424cc3031a538f77e55c20640281a2 = L.polyline(\n                [[45.502312691771785, -73.64810577288215], [45.50228814760426, -73.64805063281078]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_9d424cc3031a538f77e55c20640281a2.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.957494639108599\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_1e87587ebc5a96e6c67ac1cbd9f0cf6a = L.polyline(\n                [[45.502312691771785, -73.64810577288215], [45.50216143192533, -73.6477659595015]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_1e87587ebc5a96e6c67ac1cbd9f0cf6a.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.7325303447663111\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_7d64755cca2440478c2b28a3ff6b500a = L.polyline(\n                [[45.502312691771785, -73.64810577288215], [45.50247713125586, -73.64795774981344]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_7d64755cca2440478c2b28a3ff6b500a.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.22139321008422175\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_043e91b812f4446f5a28fa708ce7e1b5 = L.polyline(\n                [[45.501583308319844, -73.64796497588617], [45.50164260776102, -73.64790899446989]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_043e91b812f4446f5a28fa708ce7e1b5.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 2.8474663370455087\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_86e39238442f12b49a7634d3c95b6429 = L.polyline(\n                [[45.501583308319844, -73.64796497588617], [45.501677631212225, -73.64816702447985]],\n                {&quot;bubblingMouseEvents&quot;: true, 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{&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_307a2365169895b9374e3a55bdeba302.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.16040857664711594\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_dd38b3661df37fcb1b22ed7770c34193 = L.polyline(\n                [[45.50216143192533, -73.6477659595015], [45.502312691771785, 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[45.50238633086665, -73.64827120855466]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_76f3432f757e8b09ab1e2605766670d0.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.5540022148836161\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_04db0f73bef604d6286a23d6b13e3834 = L.polyline(\n                [[45.50216143192533, 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[[45.50192618050006, -73.64870455531816], [45.501837658420186, -73.64851446766693]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_2c1a7238402a9e90ac1634d940efcbd8.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.16062106635671114\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_16501fc5e4343cfbce5544085b55417a = L.polyline(\n                [[45.50192618050006, -73.64870455531816], [45.50157205034684, -73.64904039432656]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_16501fc5e4343cfbce5544085b55417a.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.17139640871428952\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var 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{&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_e1ebb36580fb6473aa6fe2806b76ce2a = L.polyline(\n                [[45.50249134317708, -73.64849440330781], [45.50236568555619, -73.64861396337744]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_e1ebb36580fb6473aa6fe2806b76ce2a.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.18052214444865236\n                 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`&lt;div&gt;\n                     Mass flow rate: 1.419574041774423\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_e7395963e34af4fadbb31909bdcd3a65 = L.polyline(\n                [[45.501820802417896, -73.64774076960333], [45.5019231288619, -73.64795996223775]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_e7395963e34af4fadbb31909bdcd3a65.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 1.2702431200525297\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_1468f4b4c7b71ee99975ac1930a6573d = L.polyline(\n                [[45.501820802417896, -73.64774076960333], [45.501583308319844, -73.64796497588617]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n     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&quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_8fdb86c7e18948ad2b6c48186154b4c9.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.1605026250130703\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_6ea384d72f0f618674c5a02328ad0b17 = L.polyline(\n                [[45.502697211532364, -73.64893196413891], [45.50257595853584, -73.64867424735355]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_6ea384d72f0f618674c5a02328ad0b17.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.5818860726746358\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_43fa0e718a0249f7e8f95498343147ef = L.polyline(\n                [[45.502697211532364, -73.64893196413891], [45.50274078850537, -73.64902458524783]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, 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&quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_7001b9bd0a7fe28fa4b29a69ba7dee39.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 1.419574041774423\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_c5d5c5899874c047888242303df20527 = L.polyline(\n                [[45.50189590210437, -73.64766987127078], [45.50179501346945, -73.64745376034288]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_c5d5c5899874c047888242303df20527.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.8841050585512209\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_756281c2c36e9ecdf51a8e3e08bc1566 = L.polyline(\n                [[45.50189590210437, -73.64766987127078], [45.50134554336541, -73.64818943556061]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: 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0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_8523e55cb0a1b46475383a569e3eeb0e.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.7227746161682795\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_3409cf62f8cacae9a52da9c663b1996c = L.polyline(\n                [[45.50134554336541, -73.64818943556061], [45.50189590210437, -73.64766987127078]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_3409cf62f8cacae9a52da9c663b1996c.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.15764917521855848\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_b876b4351d2641dc6c029f389ec04865 = L.polyline(\n                [[45.50177275925696, -73.64690744707929], [45.50205274844478, -73.6475217987503]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_b876b4351d2641dc6c029f389ec04865.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 5.7018432148172185\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_9985ad3e29fc01603bf3e7c9c8833069 = L.polyline(\n                [[45.50177275925696, -73.64690744707929], [45.50189527625893, -73.64679453009131]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_9985ad3e29fc01603bf3e7c9c8833069.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 5.7018432148172185\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_c145d785db6fa65b5a6c4ffc81602465 = L.polyline(\n                [[45.50238633086665, -73.64827120855466], [45.50216143192533, -73.6477659595015]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_c145d785db6fa65b5a6c4ffc81602465.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.5540022148836161\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_8d2d81030bf73b81ae3bed66977b69da = L.polyline(\n                [[45.50238633086665, -73.64827120855466], [45.50263442295338, -73.64879851006545]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, 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{&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_30be30456c02173586f77613091bb307.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 0.3178784921986756\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_cf7e33de249b48f9df3e1971263c9ada = L.polyline(\n                [[45.50274078850537, -73.64902458524783], [45.502762166209756, 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[[45.50205274844478, -73.6475217987503], [45.50177275925696, -73.64690744707929]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_bdd4dae610837dba96e4a32c8ed9bf18.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 5.7018432148172185\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var poly_line_ffa199c1ede5152eb02d454e415d7f3d = L.polyline(\n                [[45.50205274844478, -73.6475217987503], [45.502156334382086, -73.64775450766791]],\n                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;noClip&quot;: false, &quot;opacity&quot;: 1.0, &quot;smoothFactor&quot;: 1.0, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n            ).addTo(map_bd9ebef9bfbe392bf2340372d38a09dd);\n        \n    \n            poly_line_ffa199c1ede5152eb02d454e415d7f3d.bindTooltip(\n                `&lt;div&gt;\n                     Mass flow rate: 3.932354046610043\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n&lt;/script&gt;\n&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "\n",
    "# Initialize the map centered around the first node (adjust as necessary)\n",
    "first_node = list(di_graph.nodes(data=True))[0]\n",
    "center_lat, center_lon = di_graph.nodes[first_node[0]]['lat'], di_graph.nodes[first_node[0]]['lon']\n",
    "m = folium.Map(location=[center_lat, center_lon], zoom_start=15)\n",
    "\n",
    "# Add nodes to the map\n",
    "for node, data in di_graph.nodes(data=True):\n",
    "    folium.Marker(\n",
    "        location=[data['lat'], data['lon']],\n",
    "        popup=f\"Temperature: {data.get('temperature_history', 'N/A')}, Peak Demand: {data.get('Peack_Demand', 'N/A')}\",\n",
    "        icon=folium.Icon(color=\"red\", icon=\"info-sign\"),\n",
    "    ).add_to(m)\n",
    "\n",
    "# Add edges to the map\n",
    "for u, v, data in di_graph.edges(data=True):\n",
    "    u_lat, u_lon = di_graph.nodes[u]['lat'], di_graph.nodes[u]['lon']\n",
    "    v_lat, v_lon = di_graph.nodes[v]['lat'], di_graph.nodes[v]['lon']\n",
    "    folium.PolyLine(\n",
    "        locations=[[u_lat, u_lon], [v_lat, v_lon]],\n",
    "        color=\"blue\",\n",
    "        weight=3,\n",
    "        tooltip=f\"Mass flow rate: {data['mass_flow_rate_actual']}\",\n",
    "    ).add_to(m)\n",
    "\n",
    "# Display the map\n",
    "m"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T12:04:07.033537700Z",
     "start_time": "2024-03-08T12:04:06.052123300Z"
    }
   },
   "id": "a72386d5f2bc8e60"
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "outputs": [],
   "source": [
    "# Assuming 'm' is your Folium map object\n",
    "m.save(\"map.html\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T12:11:41.406508800Z",
     "start_time": "2024-03-08T12:11:40.559166300Z"
    }
   },
   "id": "e41182f9b4ca22d8"
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\majidr\\AppData\\Local\\Temp\\ipykernel_8776\\1404096142.py:23: MatplotlibDeprecationWarning:\n",
      "\n",
      "Unable to determine Axes to steal space for Colorbar. Using gca(), but will raise in the future. Either provide the *cax* argument to use as the Axes for the Colorbar, provide the *ax* argument to steal space from it, or add *mappable* to an Axes.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x800 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "\n",
    "pos = {node: (node[0], node[1]) for node in network_graph.nodes()}\n",
    "\n",
    "# Edges are colored based on their 'mass_flow_rate_peak' attribute\n",
    "edge_colors = [network_graph[u][v]['mass_flow_rate_peak'] for u, v in network_graph.edges()]\n",
    "\n",
    "# Set up figure\n",
    "plt.figure(figsize=(10, 8))  # Adjust figure size as needed\n",
    "\n",
    "# Drawing nodes with increased size for better visibility\n",
    "nx.draw_networkx_nodes(network_graph, pos, node_size=100, node_color='skyblue', alpha=0.7)\n",
    "\n",
    "# Drawing edges with mass flow rate as colors and increased width for visibility\n",
    "nx.draw_networkx_edges(network_graph, pos, edge_color=edge_colors, edge_cmap=plt.cm.plasma, width=3)\n",
    "\n",
    "# Optionally drawing labels with a smaller font size to prevent clutter\n",
    "# nx.draw_networkx_labels(network_graph, pos, font_size=10, font_family=\"sans-serif\")\n",
    "\n",
    "# Adding a color bar to indicate the mass flow rate peak values\n",
    "sm = plt.cm.ScalarMappable(cmap=plt.cm.plasma, norm=plt.Normalize(vmin=min(edge_colors), vmax=max(edge_colors)))\n",
    "plt.colorbar(sm, label='Mass Flow Rate Peak')\n",
    "\n",
    "plt.title('Network Graph Visualization')  # Add a title to the plot\n",
    "plt.axis('off')  # Turn off the axis\n",
    "\n",
    "# Save the figure\n",
    "plt.savefig('network_graph_visualization.png', format='png', dpi=300)  # Save as PNG with high dpi for clarity\n",
    "\n",
    "plt.show()  # Show the plot\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T12:15:37.033575700Z",
     "start_time": "2024-03-08T12:15:35.378548100Z"
    }
   },
   "id": "7a94aa3a7057cda0"
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "outputs": [
    {
     "data": {
      "text/plain": "0        01/01  01:00:00\n1        01/01  02:00:00\n2        01/01  03:00:00\n3        01/01  04:00:00\n4        01/01  05:00:00\n              ...       \n8755     12/31  20:00:00\n8756     12/31  21:00:00\n8757     12/31  22:00:00\n8758     12/31  23:00:00\n8759     12/31  00:00:00\nName: Date/Time, Length: 8760, dtype: object"
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Step 1: Read the CSV file\n",
    "df = pd.read_csv('./out_files/Mont-Royal_out.csv')\n",
    "df[\"Date/Time\"] = df[\"Date/Time\"].str.replace(\"24:00:00\", \"00:00:00\")\n",
    "df['Date/Time']\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T13:18:51.240886Z",
     "start_time": "2024-03-08T13:18:50.011359200Z"
    }
   },
   "id": "8c01118fa71e594b"
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "outputs": [
    {
     "data": {
      "text/plain": "0       2024/01/0101:00:00\n1       2024/01/0102:00:00\n2       2024/01/0103:00:00\n3       2024/01/0104:00:00\n4       2024/01/0105:00:00\n               ...        \n8755    2024/12/3120:00:00\n8756    2024/12/3121:00:00\n8757    2024/12/3122:00:00\n8758    2024/12/3123:00:00\n8759    2024/12/3100:00:00\nName: Date/Time, Length: 8760, dtype: object"
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date/Time'] = '2024/' + df['Date/Time']\n",
    "df['Date/Time'] = df['Date/Time'].str.replace(' ', '')\n",
    "df['Date/Time']"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T13:18:56.403897100Z",
     "start_time": "2024-03-08T13:18:55.717607Z"
    }
   },
   "id": "13216ced51acded9"
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "outputs": [
    {
     "data": {
      "text/plain": "0      2024-01-01 01:00:00\n1      2024-01-01 02:00:00\n2      2024-01-01 03:00:00\n3      2024-01-01 04:00:00\n4      2024-01-01 05:00:00\n               ...        \n8755   2024-12-31 20:00:00\n8756   2024-12-31 21:00:00\n8757   2024-12-31 22:00:00\n8758   2024-12-31 23:00:00\n8759   2024-12-31 00:00:00\nName: Date/Time, Length: 8760, dtype: datetime64[ns]"
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date/Time'] = pd.to_datetime(df['Date/Time'], format='%Y/%m/%d%H:%M:%S')\n",
    "df[\"Date/Time\"]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T13:19:00.519383Z",
     "start_time": "2024-03-08T13:19:00.224258100Z"
    }
   },
   "id": "986a986ce504635"
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T13:24:51.724024500Z",
     "start_time": "2024-03-08T13:24:50.790106800Z"
    }
   },
   "id": "321d797a408f54cd"
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly_demand = df.resample('M').sum()[[\n",
    "    '195043 IDEAL LOADS AIR SYSTEM:Zone Ideal Loads Supply Air Total Heating Energy [J](Hourly)',\n",
    "    '195047 IDEAL LOADS AIR SYSTEM:Zone Ideal Loads Supply Air Total Heating Energy [J](Hourly)',\n",
    "    '195066 IDEAL LOADS AIR SYSTEM:Zone Ideal Loads Supply Air Total Heating Energy [J](Hourly)'\n",
    "]]\n",
    "\n",
    "# Plotting with seaborn style\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "\n",
    "# Plotting\n",
    "ax = monthly_demand.plot(kind='bar', colormap='Set3')\n",
    "ax.set_title('Monthly Demand of Three Buildings', fontsize=18, fontweight='bold', color='navy')  # Set title properties\n",
    "ax.set_xlabel('Month', fontsize=14, fontweight='bold', color='navy')  # Set xlabel properties\n",
    "ax.set_ylabel('Demand (Joules)', fontsize=14, fontweight='bold', color='navy')  # Set ylabel properties\n",
    "ax.set_xticklabels([x.strftime('%b') for x in monthly_demand.index], rotation=45)  # Format xticklabels\n",
    "\n",
    "# Set legend labels and position\n",
    "ax.legend([\"Building One\", \"Building Two\", \"Building Three\"], loc='upper right', fontsize=12)\n",
    "\n",
    "# Adjust background color and grid color\n",
    "ax.set_facecolor('#f7f7f7')  # Light gray background\n",
    "ax.grid(color='white', linestyle='-', linewidth=1)  # White grid lines\n",
    "\n",
    "# Save the figure\n",
    "plt.savefig('monthly_demand_three_buildings.png', bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T13:28:56.676265500Z",
     "start_time": "2024-03-08T13:28:55.522179900Z"
    }
   },
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