Ejecución modelo de demanda GDL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Ejecución del modelo de demanda GDL"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 1) Generación y atracción de viajes"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "#NOTA: En este caso solo haremos una lectura y visualización de los viajes generados ya que ya se cuenta\n",
    "# con la matriz de generación y atración de viajes\n",
    "\n",
    "import geopandas as gpd\n",
    "import pandas as pd"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SQLalchemy is not installed. No support for SQL output.\n"
     ]
    }
   ],
   "source": [
    "import sys # for automation and parallelization: set manual to false when run by a launcher\n",
    "manual, scenario = (True, 'base') if 'ipykernel' in sys.argv[0] else (False, sys.argv[1])\n",
    "\n",
    "sys.path.insert(0, r'C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Quetzal Dev/quetzal-master')\n",
    "\n",
    "from quetzal.model import stepmodel\n",
    "import numpy as np"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "training_folder ='C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Modelo de Demanda GDL/Modelo GDL basado en paris/'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 1.1) Lectura del SM"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "zones: 100%|██████████| 29/29 [00:17<00:00,  1.63it/s]                    \n"
     ]
    }
   ],
   "source": [
    "sm = stepmodel.read_zip(training_folder + 'model/base/aon_pathfinder.zip'.format(scen=scenario))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 1.2) Lecture de la zonificación de viajes atraidos y generados"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "['Origen',\n 'Destino',\n 'Caminando',\n 'Transporte Colectivo',\n 'Taxi',\n 'Bicicleta',\n 'Motocicleta',\n 'Auto (Conductor)',\n 'Auto (Pasajero)',\n 'Camioneta (Conductor)',\n 'Camioneta (Pasajero)',\n 'Otros',\n 'Total']"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matriz_OD = pd.read_csv('C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Modelo de Demanda GDL/Data/inputs/data/OD2007.csv', encoding='latin', sep=';')\n",
    "## Renombramiento de columnas\n",
    "Matriz_OD.rename(columns={'ï»¿Origen': 'Origen'}, inplace=True)\n",
    "\n",
    "# Visualizacion de los nombres de las columas\n",
    "list(Matriz_OD.columns)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Viajes totales del DF tomando como referencia que estos datos corresponden al estudio de 2014"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "Matriz_OD.Total.sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Población total al momento del estudio (2014)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "POB2014=4636514\n",
    "print(POB2014)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Viajes al día  por persona"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "Viajes_per_capita= Matriz_OD.Total.sum() / POB2014\n",
    "print(Viajes_per_capita)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Agrupamiento de total de viajes generados por zona"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "Generados = Matriz_OD.groupby(['Origen'])['Total'].sum()\n",
    "Generados= Generados.to_frame() # Lo convertimos a DF porque lo genera orignalmente como serie\n",
    "Generados['ID_2'] = Generados.index # Le damos la columna\n",
    "Generados.head(4)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Agrupamiento del total de viajes atraidos por zona"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "Atraidos = Matriz_OD.groupby(['Destino'])['Total'].sum()\n",
    "Atraidos= Atraidos.to_frame() # Lo convertimos a DF porque lo genera orignalmente como serie\n",
    "Atraidos['ID_2'] = Atraidos.index # Le damos la columna\n",
    "Atraidos.head(4)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Lectura del layer de Zonificación\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "zones = gpd.read_file(r'C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Modelo de Demanda GDL/Modelo GDL basado en paris/inputs/zones/Zonificacion.shp')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Vinculación de viajes totales atraidos y generados con el GEO DF de la zonificación de estudio"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Merging DF con un GEODF de los viajes generados\n",
    "zones = zones.merge(Generados, on='ID_2') # el primer elemento es el espacial\n",
    "\n",
    "#Renombre\n",
    "zones.rename(columns={'Total': 'Total_Generados'}, inplace=True)\n",
    "\n",
    "### NOTA: EL merge lo hace bien pero no coiciden del todo los datos de generación con los que tiene\n",
    "#el archivo de zonificacion (zonas)     VERLO CON EL IMEPLAN\n",
    "#Info: https://geopandas.org/docs/user_guide/mergingdata.html"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Merge con el GEO DATA FRAME con el DF cde los viajes atraidos\n",
    "zones = zones.merge(Atraidos, on='ID_2') # el primer elemento es el espacial\n",
    "\n",
    "#Renombre\n",
    "zones.rename(columns={'Total': 'Total_Atraidos'}, inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Eliminación de columnas inecesarias"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "del zones [\"POB2005\"]\n",
    "del zones [\"POB2000\"]\n",
    "del zones [\"VIAJES_AUT\"]\n",
    "del zones [\"VIAJES_TP\"]\n",
    "del zones [\"Viaje_tot\"]\n",
    "del zones [\"trip_perso\"]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualiuzación de viajes generados por zona"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "zones.plot(column= 'Total_Generados',ax=ax, legend=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualización de viajes atraidos por zona"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "zones.plot(column= 'Total_Atraidos',ax=ax, legend=True)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Exportación de los DF de viajes atraidos y generados\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "##### Union de ambos DF en uno solo\n",
    "Viajes_totales = pd.merge(Generados,Atraidos, on=\"ID_2\")\n",
    "\n",
    "# Cambio de nombre de las columnas\n",
    "Viajes_totales = Viajes_totales.rename(columns={'Total_x': 'Generados', 'Total_y': 'Atraidos'})\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "print(Viajes_totales)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "Viajes_totales.to_csv(r'C:/Users/orlan/Desktop/Resultados preliminales del modelo de demanda\\Viajes totales.csv', index = False)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 2) Distribución\n",
    "###### Solo se tomara los viajes generados y atraidos en hora punta"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "### RECORDAR, leer el SM que ahora contiene :\n",
    "# zonificacion (contiene goemetria, ID de poligonos, viajes generados y atriados, pobacion)\n",
    "# road_links y road_nodos viales\n",
    "# links y nodes de TP ya vinculados a las vialidades\n",
    "# lines\n",
    "# car_los que son los tiempos de viaje en auto\n",
    "# pt_los que son los tiempos de viaje en TP"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### El modelo de distribución se basa en método gravitatorio. Recordar que el modelo gravitatorio toma como referencia la matriz de impedancia que está constituida normalmente por el tiempo o el costo de cada viaje"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SQLalchemy is not installed. No support for SQL output.\n"
     ]
    }
   ],
   "source": [
    "import sys # for automation and parallelization: set manual to false when run by a launcher\n",
    "manual, scenario = (True, 'base') if 'ipykernel' in sys.argv[0] else (False, sys.argv[1])\n",
    "\n",
    "sys.path.insert(0, r'C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Quetzal Dev/quetzal-master')\n",
    "\n",
    "from quetzal.model import stepmodel\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "training_folder ='C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Modelo de Demanda GDL/Modelo GDL basado en paris/'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "zones: 100%|██████████| 29/29 [00:19<00:00,  1.51it/s]                    \n"
     ]
    }
   ],
   "source": [
    "sm = stepmodel.read_zip(training_folder + 'model/base/aon_pathfinder.zip'.format(scen=scenario))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "##### Generar columnas igual al indice que  es lo que contiene el nombre de las zonas\n",
    "sm.zones['index'] = sm.zones.index"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Exportacion\n",
    "sm.zones.to_file('C:/Users/orlan/Documents/UPC/PhD/Redación Tesis/Resultados/zones.shp', driver='ESRI Shapefile')\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Tiempos medios de desplazamiento en auto"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "37.58710210745968"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.car_los[\"time\"].mean() /60   # /60 para pasarlo a minutos"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Tiempos medios de desplazamiento en Transporte público"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "57.090646312835155"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.pt_los [\"gtime\"].mean()/60  # Da 57 minutos"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Media de transferencia en Transporte público"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "7.904207989126973"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.pt_los [\"ntransfers\"].mean() # esto realmente esta mal, o es irrealista"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualización de viajes TOTALES generados y atraidos a lo largo del día"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "9904182"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.zones.Total_Atraidos.sum()  # ATRAIDOS, 9 millones"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "9903180"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.zones.Total_Generados.sum()   # GENERADOS, 9 millones"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Modificacion para viajes en hora pico (se toma el 10 % de los viajes totales)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "### AQUI para seguir con la misma nomenclatura del codigo se hará lo siguiente, se crea 2 nuevas columnas emissions y atraction (generados y atraidos)\n",
    "sm.zones['emission'] =  sm.zones['Total_Generados'].astype(int) / 10  # POR ESO esta division, para representar la hora pico\n",
    "sm.zones['attraction'] =  sm.zones['Total_Atraidos'].astype(int) / 10\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 2.2) Viajes totales en auto"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "### Primero se asigna un grado de motorización general\n",
    "motor_rate = 0.35  # 35 % de grado de motorizacion\n",
    "car = sm.copy()  # se hace una copia del sm que contiene el TOTAL de viajes generados y atraidos en hora pico\n",
    "\n",
    "# IMPORTANTE: Aqui se hace esta asumsión que influirá mucho en la distribución modal,\n",
    "# se integra el grado de motorización y se asumne que las personas que tienen auto lo van a utilizar de manera mucho más frecuente que las que no.\n",
    "# Esto se puede dejar con este método, o se puede integrar el grado de motorización por zona, o no incluir ningun grado de motorización y seguir con el modelo de los 4 pasos de manera clásica"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Se multiplica el total de viajes por zona por el grado de motorizacion que se tiene"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "car.zones['emission'] *= motor_rate"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 2.3)  Matriz de impedancia en autos"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "## IMPORTANTE: aqui se extrae la matriz de tiempos de desplazamiento de zona a zona. Estos datos ya  los contiene el SM  \"car\"\n",
    "# dentro del DF de \"car_los\", al final esta es la matriz de  impedancia (generadas en el codigo \"all_or_nothing_patfinder\", el DF de car_los)\n",
    "# REVISAR: En donde se generó la matriz de \"car_los\"\n",
    "\n",
    "imp_matrix_car = car.car_los[\n",
    "    ['origin', 'destination', 'time']\n",
    "].set_index(['origin','destination']).unstack()\n",
    "\n",
    "imp_matrix_car = imp_matrix_car.replace(0, 120)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 2.4) Ejecución del modelo gravitatorio"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Ejecución de la función de distribución de volumenes de viajes (aplicada a autos)\n",
    "###### La funcion step_distribution es la que contiene el modelo gravitatorio, esta funcion toma como input la matriz de impedancia (matriz de tiempos de viaje OD) y genera la distribución de volumenes de viaje. Las unidades de volumes son PERSONAS"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Distribución de viaje en auto\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "### Ditribucion de viajes basado en modelo gravitatorio\n",
    "car.step_distribution(deterrence_matrix=imp_matrix_car)\n",
    "\n",
    "## Se crea el DF \"volumes\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualización de la distribución de volumenes de viaje en auto"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "          origin destination    volume\n0         zone_0      zone_0  0.004131\n1         zone_0      zone_1  0.002615\n2         zone_0      zone_2  0.242071\n3         zone_0      zone_3  0.032596\n4         zone_0      zone_4  0.061390\n...          ...         ...       ...\n188351  zone_433    zone_429  0.345618\n188352  zone_433    zone_430  0.296179\n188353  zone_433    zone_431  0.223152\n188354  zone_433    zone_432  0.258303\n188355  zone_433    zone_433  0.082709\n\n[188356 rows x 3 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>origin</th>\n      <th>destination</th>\n      <th>volume</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>zone_0</td>\n      <td>zone_0</td>\n      <td>0.004131</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>zone_0</td>\n      <td>zone_1</td>\n      <td>0.002615</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>zone_0</td>\n      <td>zone_2</td>\n      <td>0.242071</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>zone_0</td>\n      <td>zone_3</td>\n      <td>0.032596</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>zone_0</td>\n      <td>zone_4</td>\n      <td>0.061390</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>188351</th>\n      <td>zone_433</td>\n      <td>zone_429</td>\n      <td>0.345618</td>\n    </tr>\n    <tr>\n      <th>188352</th>\n      <td>zone_433</td>\n      <td>zone_430</td>\n      <td>0.296179</td>\n    </tr>\n    <tr>\n      <th>188353</th>\n      <td>zone_433</td>\n      <td>zone_431</td>\n      <td>0.223152</td>\n    </tr>\n    <tr>\n      <th>188354</th>\n      <td>zone_433</td>\n      <td>zone_432</td>\n      <td>0.258303</td>\n    </tr>\n    <tr>\n      <th>188355</th>\n      <td>zone_433</td>\n      <td>zone_433</td>\n      <td>0.082709</td>\n    </tr>\n  </tbody>\n</table>\n<p>188356 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car.volumes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Viajes totales en auto en hora pico"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "346611.3"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car.volumes.volume.sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#####  Viajes totales en TP"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "## En este caso se simplifica y se dice que será la diferencia al % de autos ya establecido (es decir que el resto de los viaje que no se hace en auto se hacen en TP)\n",
    "pt = sm.copy()\n",
    "pt.zones['emission'] *= (1-motor_rate)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 2.5) Matriz de impedancia en TP"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "imp_matrix_pt = pt.pt_los[\n",
    "    ['origin', 'destination', 'gtime']\n",
    "].set_index(['origin','destination']).unstack()\n",
    "imp_matrix_pt = imp_matrix_pt.replace(0, 120)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Ejecución de la función de distribución de volumenes de viajes (aplicada a TP)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "pt.step_distribution(deterrence_matrix=imp_matrix_pt)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualización de la distribución de volumenes de viaje para TP\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "          origin destination    volume\n0         zone_0      zone_0  0.005632\n1         zone_0      zone_1  0.009078\n2         zone_0      zone_2  0.401442\n3         zone_0      zone_3  0.073305\n4         zone_0      zone_4  0.143701\n...          ...         ...       ...\n188351  zone_433    zone_429  1.183654\n188352  zone_433    zone_430  0.756884\n188353  zone_433    zone_431  0.784890\n188354  zone_433    zone_432  0.597552\n188355  zone_433    zone_433  0.110315\n\n[188356 rows x 3 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>origin</th>\n      <th>destination</th>\n      <th>volume</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>zone_0</td>\n      <td>zone_0</td>\n      <td>0.005632</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>zone_0</td>\n      <td>zone_1</td>\n      <td>0.009078</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>zone_0</td>\n      <td>zone_2</td>\n      <td>0.401442</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>zone_0</td>\n      <td>zone_3</td>\n      <td>0.073305</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>zone_0</td>\n      <td>zone_4</td>\n      <td>0.143701</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>188351</th>\n      <td>zone_433</td>\n      <td>zone_429</td>\n      <td>1.183654</td>\n    </tr>\n    <tr>\n      <th>188352</th>\n      <td>zone_433</td>\n      <td>zone_430</td>\n      <td>0.756884</td>\n    </tr>\n    <tr>\n      <th>188353</th>\n      <td>zone_433</td>\n      <td>zone_431</td>\n      <td>0.784890</td>\n    </tr>\n    <tr>\n      <th>188354</th>\n      <td>zone_433</td>\n      <td>zone_432</td>\n      <td>0.597552</td>\n    </tr>\n    <tr>\n      <th>188355</th>\n      <td>zone_433</td>\n      <td>zone_433</td>\n      <td>0.110315</td>\n    </tr>\n  </tbody>\n</table>\n<p>188356 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt.volumes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Total de viajes en TP en hora pico"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "643706.7000000001"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt.volumes.volume.sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 2.6) Combinación de los DF en un mismo SM"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "sm.volumes = pd.merge(\n",
    "    car.volumes,\n",
    "    pt.volumes,\n",
    "    on=['origin', 'destination'],\n",
    "    suffixes=[ '_car_owner', '_pt_captive']\n",
    ").rename(columns={'volume_car_owner': 'car_owner', 'volume_pt_captive': 'pt_captive'})\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "###  Aqui sumare las de auto y TP para crear una sola columna de total de viajes por zona\n",
    "\n",
    "sm.volumes[\"trips\"] = sm.volumes.car_owner + sm.volumes.pt_captive"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualización de volumenes"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "          origin destination  car_owner  pt_captive     trips\n0         zone_0      zone_0   0.004131    0.005632  0.009762\n1         zone_0      zone_1   0.002615    0.009078  0.011693\n2         zone_0      zone_2   0.242071    0.401442  0.643513\n3         zone_0      zone_3   0.032596    0.073305  0.105902\n4         zone_0      zone_4   0.061390    0.143701  0.205091\n...          ...         ...        ...         ...       ...\n188351  zone_433    zone_429   0.345618    1.183654  1.529272\n188352  zone_433    zone_430   0.296179    0.756884  1.053063\n188353  zone_433    zone_431   0.223152    0.784890  1.008042\n188354  zone_433    zone_432   0.258303    0.597552  0.855855\n188355  zone_433    zone_433   0.082709    0.110315  0.193024\n\n[188356 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>origin</th>\n      <th>destination</th>\n      <th>car_owner</th>\n      <th>pt_captive</th>\n      <th>trips</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>zone_0</td>\n      <td>zone_0</td>\n      <td>0.004131</td>\n      <td>0.005632</td>\n      <td>0.009762</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>zone_0</td>\n      <td>zone_1</td>\n      <td>0.002615</td>\n      <td>0.009078</td>\n      <td>0.011693</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>zone_0</td>\n      <td>zone_2</td>\n      <td>0.242071</td>\n      <td>0.401442</td>\n      <td>0.643513</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>zone_0</td>\n      <td>zone_3</td>\n      <td>0.032596</td>\n      <td>0.073305</td>\n      <td>0.105902</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>zone_0</td>\n      <td>zone_4</td>\n      <td>0.061390</td>\n      <td>0.143701</td>\n      <td>0.205091</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>188351</th>\n      <td>zone_433</td>\n      <td>zone_429</td>\n      <td>0.345618</td>\n      <td>1.183654</td>\n      <td>1.529272</td>\n    </tr>\n    <tr>\n      <th>188352</th>\n      <td>zone_433</td>\n      <td>zone_430</td>\n      <td>0.296179</td>\n      <td>0.756884</td>\n      <td>1.053063</td>\n    </tr>\n    <tr>\n      <th>188353</th>\n      <td>zone_433</td>\n      <td>zone_431</td>\n      <td>0.223152</td>\n      <td>0.784890</td>\n      <td>1.008042</td>\n    </tr>\n    <tr>\n      <th>188354</th>\n      <td>zone_433</td>\n      <td>zone_432</td>\n      <td>0.258303</td>\n      <td>0.597552</td>\n      <td>0.855855</td>\n    </tr>\n    <tr>\n      <th>188355</th>\n      <td>zone_433</td>\n      <td>zone_433</td>\n      <td>0.082709</td>\n      <td>0.110315</td>\n      <td>0.193024</td>\n    </tr>\n  </tbody>\n</table>\n<p>188356 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.volumes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Sumatoria de viajes totales del DF de volumes\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "990318.0"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.volumes.car_owner.sum() + sm.volumes.pt_captive.sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Exportaciones SM, matriz de impedancia y distribución de viajes"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "##### Exportación del SM\n",
    "sm.to_zip(\n",
    "    training_folder + 'model/base/distribution.zip'.format(scen=scenario),\n",
    "    only_attributes=['volumes', 'epsg', 'coordinates_unit']\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [],
   "source": [
    "##### Exportacion de la matriz de  impedancia\n",
    "# Matriz de impedancia del auto\n",
    "imp_matrix_car.to_csv(r'C:/Users/orlan/Documents/UPC/PhD/Redación Tesis/Resultados/matriz_impedancia_car.csv', index = True)\n",
    "\n",
    "# Matriz de impedancia del TP\n",
    "imp_matrix_pt.to_csv(r'C:/Users/orlan/Documents/UPC/PhD/Redación Tesis/Resultados/matriz_impedancia_pt.csv', index = True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "### Exportación de la distribución de viajes final \"volumens\" (la cual está interna en el SM)\n",
    "sm.volumes.to_excel(r'C:/Users/orlan/Documents/UPC/PhD/Redación Tesis/Resultados/distribucion_vol.xlsx', index = True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 2.7) Visualización de los resultados de tiempos de desplazamiento (en segundos)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5,1,'Car distribution')"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 720x360 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "od = pd.merge(sm.volumes, sm.pt_los, on=['origin', 'destination'])\n",
    "od = pd.merge(od, sm.car_los, on=['origin', 'destination'])\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(1, 2, figsize=[10, 5])\n",
    "\n",
    "plot = od[['gtime']].plot(kind='hist', weights=od['pt_captive'], bins=50, alpha=0.5, ax=axes[0])\n",
    "plot.set_title('PT distribution')\n",
    "\n",
    "plot = od[['time']].plot(kind='hist', weights=od['car_owner'], bins=50, alpha=0.5, ax=axes[1])\n",
    "plot.set_title('Car distribution')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 3) Road pathfinder\n",
    "#### Probar este codigo en una copia y eliminar totalmente  esta secccion.\n",
    "\n",
    "###### El código de pathfinder esta vinculado a la afectación del tráfico por los volumenes de flujo que se tienene en los links viales  ¿ y para TP?"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import sys # for automation and parallelization: set manual to false when run by a launcher\n",
    "manual, scenario = (True, 'base') if 'ipykernel' in sys.argv[0] else (False, sys.argv[1])\n",
    "import numpy as np"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "sys.path.insert(0, r'C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Quetzal Dev/quetzal-master')\n",
    "from quetzal.model import stepmodel\n",
    "import pandas as pd"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "training_folder ='C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Modelo de Demanda GDL/Modelo GDL basado en paris/'\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 3.1) Lectura del SM aon_pathfinder  y ditribution\n",
    "###### Recordemos que el SM \"aonpatfinder\" es el que integró los tiempos de desplazamiento de cada par OD (en car_los  y en pt_los, pero el otro SM tambien los tiene)  . Mientras que el SM \"distribution\" contiene la matriz de disrtribución de viajes generado por el modelo gravitatorio  (seccion 2 del codigo) en el DF de \"volumnes\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "road = stepmodel.read_zip(training_folder + r'model/{scen}/aon_pathfinder.zip'.format(scen=scenario))\n",
    "distribution = stepmodel.read_zip(training_folder + r'model/{scen}/distribution.zip'.format(scen=scenario))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 3.2) Generación de un solo SM con los datos de tiempos de desplazamiento y volumenes de viajes para cada OD\n",
    "###### Estos SM (road y distribution) aparetementemnente solo tienen la diferencia del que road tiene el DF de footpaths, todo lo demás apratmente tienen el mismo contenido"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "sm = road.copy()\n",
    "sm.volumes = distribution.volumes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 3.3) Dar capacidades máximas de flujo a las vialidades\n",
    "###### Esto se puede mejorar si en la etapa de \"Data preparation\" es posible dejar el tipo de vialidad de cada link y a cada tipo de vialidad asignarle capacidades diferenciadas"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "##### SIMPLIFICACION DE LAS CAPACIDADES DE LOS LINKS viales\n",
    "### En este caso se esta simplificando y se deja que todas tendrán una capacidad de 2,000\n",
    "sm.road_links['capacity'] = 2000"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Creación de la columna \"volumne_car\" que es igual a la \"car_owener\"\n",
    "###### Ya anteriormente había apuntado a que ese nombre de la columna (car_owner) que tiene los datos de volumene me parecía inadecuado, ahora lo está modificando"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "### Aqui simplemente hauy un cambio de nomenclatura (que debio ser asi desde el inicio)\n",
    "\n",
    "# 'volume_car' is used for road pathfinder\n",
    "sm.volumes['volume_car'] = sm.volumes['car_owner']\n",
    "sm.road_links  # esto es inencesario\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Este codigo es el de asginacion, de tráfico , es parte o todo el ultima etapa, esta en mi opinion mal colocado aqui"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    " ###############   AQUI, creo que abajo se comenzará a hacer el all or nothing y el de ewquilibrio de wardrop"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "### COPIAS DEL SM\n",
    "constrained = sm.copy()\n",
    "free = sm.copy()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#   ¿¿¿  Qué hace está función????\n",
    "constrained.step_road_pathfinder(maxiters=5, log=True, speedup=True, tolerance=0.01)\n",
    "#### HECHO: Esto genera un chingo de columnas nuevas en el DF de car_los\n",
    "### VER lo que le hace al DF de Road_links\n",
    "\n",
    "##### Esto ¡APARETMENETE! es el codigo asociado al equilibrium de Wardrop."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#### Nuevamente uso de la función  \"step_road_pathfinder\"  CON UNA TORELANCIA MENOR\n",
    "free.step_road_pathfinder(maxiters=1, log=True, speedup=False, tolerance=0.001)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 3.4) Exportación del SM con \"contrianed\" vialidades"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "###### HAY QUE SOLUCIONAR UN PROBLEMA DE TAMAÑO DE LA BASE DE DATOS O ALGO SIMILAR:\n",
    "###### https://es.stackoverflow.com/questions/355497/python-int-too-large-to-convert-to-c-long-al-intentar-hacer-un-update-en-mysq"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n",
    "#######################    POR LO PRONTO,  NO LO VAMOS A EXPORTAR, SEGUIREMOS CON ESTE SM nuevo internamente en Python para usar en el proximo codigo diractamente desde aqui\n",
    "\n",
    "\n",
    "constrained.to_zip(training_folder + 'model/base/constrained_road_pathfinder.zip'.format(scen=scenario))\n",
    "\n",
    "### DA UN ERROR POR EL VOLUMEN  Y TAMAÑO DEL ARCHIVO"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#################  Intento de solucionar el problema de la exportacion\n",
    "# Creo que: # este error tiene que ver con la cantidad de numeros enteros que contiene este objeto, aparetement los numeros enteros (int) consumen una\n",
    "# mayor memoria, hay que pasarlos a float.\n",
    "\n",
    "sys.maxsize\n",
    "\n",
    "constrained = np.zeros((1, 3))\n",
    "\n",
    "constrained.to_zip(training_folder + 'model/base/constrained_road_pathfinder.zip'.format(scen=scenario))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 3.5) Visualización de las restricciones viales por volumenes"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "### SE pasa a un DF\n",
    "road_links_constrained = constrained.road_links\n",
    "road_links_free = free.road_links"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "###### Modificacion , hacerlo más pro. Con barra titulo, fondo ETC\n",
    "road_links_constrained.plot(column='flow',legend=True);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "road_links_free.plot(column='flow',legend=True);\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 4) Modelo Nested Logit (distribución modal) aplicado a GDL"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "import sys # for automation and parallelization\n",
    "manual, scenario = (True, 'base') if 'ipykernel' in sys.argv[0] else (False, sys.argv[1])\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, r'C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Quetzal Dev/quetzal-master')\n",
    "\n",
    "from quetzal.model import stepmodel\n",
    "from quetzal.io import excel\n",
    "import pandas as pd\n",
    "if manual:\n",
    "    %matplotlib inline\n",
    "\n",
    "training_folder ='C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Modelo de Demanda GDL/Modelo GDL basado en paris/'\n",
    "\n",
    "from syspy.skims import skims\n",
    "import geopandas as gpd"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Lectura de los SM"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "volumes: 100%|██████████| 2/2 [00:00<00:00, 52.62it/s]\n",
      "zones: 100%|██████████| 28/28 [00:18<00:00,  1.51it/s]                    \n",
      "zones: 100%|██████████| 29/29 [00:17<00:00,  1.65it/s]                    \n"
     ]
    }
   ],
   "source": [
    "distribution_gdl = stepmodel.read_zip('C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Modelo de Demanda GDL/Modelo GDL basado en paris/model/base/distribution.zip'.format(scen=scenario))\n",
    "pt_gdl = stepmodel.read_zip(training_folder + 'model/base/pruning_pt_pathfinder.zip'.format(scen=scenario))\n",
    "sm_gdl = stepmodel.read_zip(training_folder + 'model/base/aon_pathfinder.zip'.format(scen=scenario))\n",
    "\n",
    "# NOTAS: El SM de distribution contiene el DF de \"volumes\" el cual es el output del paso 2 que es trip distribution generaado a partir del modelo gravitatorio\n",
    "# el SM del archivo llamdo aon_patfinder ya contiene los car_los, pt_ los y netwokcaster"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Unir los pt_los, car_los y networkcaster que estan en el SM \"sm\" con el SM de \"distribution\" dado que ese contiene el DF de  volumenes"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "sm_gdl.volumes = distribution_gdl.volumes\n",
    "sm_gdl.pt_los = pt_gdl.pt_los"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [],
   "source": [
    "# each segment will be processed independently in the logit\n",
    "sm_gdl.segments = ['car_owner', 'pt_captive']\n",
    "\n",
    "# se crea este serie que contiene los segmentos  de distribucion modal"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Lectura de las variables relacionadas a la operación de las modalidades (costos, etc)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "category  parameter        \ngeneral   parent               base\n          bus_speed_factor      0.8\n          boarding_cost_acf     300\n          boarding_cost_wor     300\n          routes_code          base\nName: base, dtype: object"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var= excel.read_var( file= 'C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Modelo de Demanda GDL/Data/inputs/parameters_gdl_paris.xlsx')\n",
    "var.head()\n",
    "\n",
    "########## IMPORTANTE, en este caso estamos leyendo las mismas que se aplicaron a PARIS porque no entiendo  muy bien las unidades y lo que expresan cada una, entonces dejaramos las mismas de PAris por lo pronto hasta que pueda reslver esta duda con Quentin"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "{'time': -0.00055,\n 'price': -1,\n 'transfers': -0.25,\n 'mode': 1,\n 'pt_mode': 0.5,\n 'pt_path': 0.1}"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kwargs = var['preparation_logit'].to_dict()\n",
    "kwargs\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Preperación para el modelo Nested Logit\n",
    "###### IMPORATNTE: A continuacion agrega varios DF al  SM como lo son: \"ulity values\" el cual capta ciertos datos que estan en el excel de parameters. No estoy 100 % seguro que represetan. Creo que tambien se agrega otro DF llamado \"logit scales\", \"mode utility\" y \"mode_nestests\" en este se dan de alta los nidos del modelo Nested logit"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [],
   "source": [
    "######## Funcion \"Prepration_logit\"\n",
    "sm_gdl.preparation_logit(\n",
    "    segments=['car_owner', 'pt_captive'],\n",
    "    **kwargs\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualización de los nuevos DF"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "value          time  price  ntransfers  mode_utility\nsegment                                             \nroot       -0.00055   -1.0       -0.25           1.0\ncar_owner  -0.00055   -1.0       -0.25           1.0\npt_captive -0.00055   -1.0       -0.25           1.0",
      "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>value</th>\n      <th>time</th>\n      <th>price</th>\n      <th>ntransfers</th>\n      <th>mode_utility</th>\n    </tr>\n    <tr>\n      <th>segment</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>root</th>\n      <td>-0.00055</td>\n      <td>-1.0</td>\n      <td>-0.25</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>car_owner</th>\n      <td>-0.00055</td>\n      <td>-1.0</td>\n      <td>-0.25</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>pt_captive</th>\n      <td>-0.00055</td>\n      <td>-1.0</td>\n      <td>-0.25</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_gdl.utility_values.T"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "route_type  root  car  walk  bus   pt\nsegment                              \nroot         1.0  0.0   0.0  0.1  0.5\ncar_owner    1.0  0.0   0.0  0.1  0.5\npt_captive   1.0  0.0   0.0  0.1  0.5",
      "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>route_type</th>\n      <th>root</th>\n      <th>car</th>\n      <th>walk</th>\n      <th>bus</th>\n      <th>pt</th>\n    </tr>\n    <tr>\n      <th>segment</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>root</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.1</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>car_owner</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.1</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>pt_captive</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.1</td>\n      <td>0.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_gdl.logit_scales.T"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "route_type root   car  walk bus    pt\nsegment                              \nroot        NaN  root  root  pt  root\ncar_owner   NaN  root  root  pt  root\npt_captive  NaN  root  root  pt  root",
      "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>route_type</th>\n      <th>root</th>\n      <th>car</th>\n      <th>walk</th>\n      <th>bus</th>\n      <th>pt</th>\n    </tr>\n    <tr>\n      <th>segment</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>root</th>\n      <td>NaN</td>\n      <td>root</td>\n      <td>root</td>\n      <td>pt</td>\n      <td>root</td>\n    </tr>\n    <tr>\n      <th>car_owner</th>\n      <td>NaN</td>\n      <td>root</td>\n      <td>root</td>\n      <td>pt</td>\n      <td>root</td>\n    </tr>\n    <tr>\n      <th>pt_captive</th>\n      <td>NaN</td>\n      <td>root</td>\n      <td>root</td>\n      <td>pt</td>\n      <td>root</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_gdl.mode_nests.T # nested structure can be set for each segment"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "route_type  root  car  walk  bus\nsegment                         \nroot           0    0     0    0\ncar_owner      0    0     0    0\npt_captive     0    0     0    0",
      "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>route_type</th>\n      <th>root</th>\n      <th>car</th>\n      <th>walk</th>\n      <th>bus</th>\n    </tr>\n    <tr>\n      <th>segment</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>root</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>car_owner</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>pt_captive</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_gdl.mode_utility.T"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "route_type  root  car  walk  bus\nsegment                         \nroot           0    0     0    0\ncar_owner      0    0     0    0\npt_captive     0 -100     0    0",
      "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>route_type</th>\n      <th>root</th>\n      <th>car</th>\n      <th>walk</th>\n      <th>bus</th>\n    </tr>\n    <tr>\n      <th>segment</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>root</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>car_owner</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>pt_captive</th>\n      <td>0</td>\n      <td>-100</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Aqui hace una modificacion a los valores en el DF de \"mode utility\"\n",
    "sm_gdl.mode_utility.loc[ 'car', 'pt_captive'] = -100 # aqui a la fraccion de las persona que solo pueden usar TP (captivos TP) se le asgina una utlidad al auto muy mala (-100) esto hará que dentro del modelo logit esa fraccion de las personas tengan una muy baja predisposición a usar el auto\n",
    "sm_gdl.mode_utility.T\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Evaluación de la utilidad"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [],
   "source": [
    "#### Agrega varias nuevas columnas a los DF de pt_los y car_los, aque les da valores de 0\n",
    "\n",
    "sm_gdl.pt_los['ntransfers'] = sm_gdl.pt_los['alighting_links'].apply(lambda x: max(len(x)-1, 0))   ## Este es redudandate porque el pt_los ya contiene esta columna con los mismos valores\n",
    "sm_gdl.pt_los['price'] = 0 # sm.analysis_pt_fare\n",
    "sm_gdl.car_los['ntransfers'] = 0\n",
    "sm_gdl.car_los['price'] = 0"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### IMPORTANTE: Cálculo de la utilidad\n",
    "###### Esto realmente no hace el calculo de la utilidad, más bien genera columnas de etqapas de tiempo en el viaje (waiting time, acces time, time in vehile)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "# sm_gdl.pt_los['time'] = sm_gdl.pt_los['gtime'] # creo que no es necesario\n",
    "\n",
    "### IMPORANTE: Esto genera 10 nuevas columnas en el pt_los vinculadas a tiempos y distancia (access_length, footpath_length, length, acces time, footpath_time, in vehicle time, waiting time, y time)\n",
    "sm_gdl.analysis_pt_time(walk_on_road=True) #############   IMPORTANTISMO, el walk_on_road= true"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [],
   "source": [
    "# builds 'route_type' in pt_los based on 'route_types'\n",
    "sm_gdl.analysis_pt_route_type(hierarchy=[ 'car','bus', 'walk'])\n",
    "sm_gdl.analysis_car_route_type() # add route_type = 'car' in car_los"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Creación del DF LOS, \"LOS\" contiene todos los viajes OD possibles bajo todas las modalidades y contiene la concatenacion de links (los links utilzados para ese viaje)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [],
   "source": [
    "# VER bien esto.\n",
    "# concatenate pt_los and car_los (uno de los PT_los y el car_los en uno solo que es LOS)\n",
    "sm_gdl.los = pd.concat([sm_gdl.pt_los, sm_gdl.car_los]).reset_index(drop=True)\n",
    "sm_gdl.los['path'] = sm_gdl.los['path'].apply(lambda p: tuple(p))\n",
    "\n",
    "# CREA UN NUEVO DF: llamado Los que contien la combinacion de pt_los y car_los"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "###### Generación de los valores de utilidad para cada viaje para todas las modalidades"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:00<00:00,  4.54it/s]\n"
     ]
    }
   ],
   "source": [
    "# Genera columnas de utilidad para cada modalidad en el DF de LOS\n",
    "sm_gdl.analysis_mode_utility()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "   origin destination         gtime  \\\n0  zone_0      zone_0      0.000000   \n1  zone_0      zone_1   1524.280407   \n2  zone_0     zone_10  13196.533922   \n\n                                                path reversed  \\\n0                                          (zone_0,)    False   \n1  (zone_0, 4738717039, 4738717043, 4738717041, 4...    False   \n2  (zone_0, 4738717034, 4738717037, 4738717022, 4...    False   \n\n  pathfinder_session broken_modes boardings alightings  \\\n0       mode_breaker        {bus}        []         []   \n1       mode_breaker        {bus}        []         []   \n2       mode_breaker        {bus}        []         []   \n\n                                           node_path  ... footpath_time  \\\n0                                                 []  ...      0.000000   \n1  [4738717039, 4738717043, 4738717041, 473871703...  ...   1196.048271   \n2  [4738717034, 4738717037, 4738717022, 473871642...  ...  11045.291148   \n\n  in_vehicle_time waiting_time boarding_time          time route_types  \\\n0             0.0          0.0           0.0      0.000000          ()   \n1             0.0          0.0           0.0   1367.990563          ()   \n2             0.0          0.0           0.0  13009.587489          ()   \n\n  route_type  mode_utility  (car_owner, utility)  (pt_captive, utility)  \n0       walk             0              0.000000               0.000000  \n1       walk             0             -0.752395              -0.752395  \n2       walk             0             -7.155273              -7.155273  \n\n[3 rows x 32 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>origin</th>\n      <th>destination</th>\n      <th>gtime</th>\n      <th>path</th>\n      <th>reversed</th>\n      <th>pathfinder_session</th>\n      <th>broken_modes</th>\n      <th>boardings</th>\n      <th>alightings</th>\n      <th>node_path</th>\n      <th>...</th>\n      <th>footpath_time</th>\n      <th>in_vehicle_time</th>\n      <th>waiting_time</th>\n      <th>boarding_time</th>\n      <th>time</th>\n      <th>route_types</th>\n      <th>route_type</th>\n      <th>mode_utility</th>\n      <th>(car_owner, utility)</th>\n      <th>(pt_captive, utility)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>zone_0</td>\n      <td>zone_0</td>\n      <td>0.000000</td>\n      <td>(zone_0,)</td>\n      <td>False</td>\n      <td>mode_breaker</td>\n      <td>{bus}</td>\n      <td>[]</td>\n      <td>[]</td>\n      <td>[]</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>()</td>\n      <td>walk</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>zone_0</td>\n      <td>zone_1</td>\n      <td>1524.280407</td>\n      <td>(zone_0, 4738717039, 4738717043, 4738717041, 4...</td>\n      <td>False</td>\n      <td>mode_breaker</td>\n      <td>{bus}</td>\n      <td>[]</td>\n      <td>[]</td>\n      <td>[4738717039, 4738717043, 4738717041, 473871703...</td>\n      <td>...</td>\n      <td>1196.048271</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1367.990563</td>\n      <td>()</td>\n      <td>walk</td>\n      <td>0</td>\n      <td>-0.752395</td>\n      <td>-0.752395</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>zone_0</td>\n      <td>zone_10</td>\n      <td>13196.533922</td>\n      <td>(zone_0, 4738717034, 4738717037, 4738717022, 4...</td>\n      <td>False</td>\n      <td>mode_breaker</td>\n      <td>{bus}</td>\n      <td>[]</td>\n      <td>[]</td>\n      <td>[4738717034, 4738717037, 4738717022, 473871642...</td>\n      <td>...</td>\n      <td>11045.291148</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>13009.587489</td>\n      <td>()</td>\n      <td>walk</td>\n      <td>0</td>\n      <td>-7.155273</td>\n      <td>-7.155273</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows × 32 columns</p>\n</div>"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_gdl.los.head(3)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Generación del DF de utilidades y probab ilidad"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:04<00:00,  1.05it/s]\n"
     ]
    }
   ],
   "source": [
    "sm_gdl.initialize_logit()\n",
    "sm_gdl.step_logit(nchunks=5)\n",
    "\n",
    "# sm_gdl.step_logit(keep_od_tables=True)  ANTERIOR\n",
    "### SE GENERA, LA MATRIZ DE UTILIDAD. en el DF llamado utilities.\n",
    "## tambien se genera el  DF de PROBABILITIES."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [],
   "source": [
    "###### Generación de una matriz mejor estructurada\n",
    "sm_gdl.od_probabilities = sm_gdl.probabilities.set_index(\n",
    "    ['origin', 'destination', 'segment']\n",
    ").unstack('segment').swaplevel(axis='columns')\n",
    "\n",
    "sm_gdl.od_utilities = sm_gdl.utilities.set_index(\n",
    "    ['origin', 'destination', 'segment']\n",
    ").unstack('segment').swaplevel(axis='columns')\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualización de la matriz de probabilidades"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "segment             car_owner pt_captive car_owner pt_captive car_owner  \\\nroute_type               root       root      walk       walk       car   \norigin  destination                                                       \nzone_0  zone_0              1          1  0.500000   1.000000  0.500000   \n        zone_1              1          1  0.391394   1.000000  0.608606   \n        zone_10             1          1  0.008507   0.275264  0.969096   \n        zone_100            1          1  0.140421   0.503434  0.721074   \n        zone_101            1          1  0.146917   0.415731  0.646605   \n...                       ...        ...       ...        ...       ...   \nzone_99 zone_95             1          1  0.418832   1.000000  0.581168   \n        zone_96             1          1  0.273882   0.546817  0.499133   \n        zone_97             1          1  0.302679   0.537401  0.436772   \n        zone_98             1          1  0.235424   0.444924  0.470866   \n        zone_99             1          1  0.500000   1.000000  0.500000   \n\nsegment                pt_captive car_owner pt_captive car_owner pt_captive  \nroute_type                    car        pt         pt       bus        bus  \norigin  destination                                                          \nzone_0  zone_0       3.720076e-44  0.000000   0.000000  0.000000   0.000000  \n        zone_1       5.784598e-44  0.000000   0.000000  0.000000   0.000000  \n        zone_10      1.166554e-42  0.022397   0.724736  0.022397   0.724736  \n        zone_100     9.617049e-44  0.138505   0.496566  0.138505   0.496566  \n        zone_101     6.806612e-44  0.206478   0.584269  0.206478   0.584269  \n...                           ...       ...        ...       ...        ...  \nzone_99 zone_95      5.161956e-44  0.000000   0.000000  0.000000   0.000000  \n        zone_96      3.707195e-44  0.226985   0.453183  0.226985   0.453183  \n        zone_97      2.884849e-44  0.260548   0.462599  0.260548   0.462599  \n        zone_98      3.310429e-44  0.293709   0.555076  0.293709   0.555076  \n        zone_99      3.720076e-44  0.000000   0.000000  0.000000   0.000000  \n\n[188356 rows x 10 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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th>segment</th>\n      <th>car_owner</th>\n      <th>pt_captive</th>\n      <th>car_owner</th>\n      <th>pt_captive</th>\n      <th>car_owner</th>\n      <th>pt_captive</th>\n      <th>car_owner</th>\n      <th>pt_captive</th>\n      <th>car_owner</th>\n      <th>pt_captive</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>route_type</th>\n      <th>root</th>\n      <th>root</th>\n      <th>walk</th>\n      <th>walk</th>\n      <th>car</th>\n      <th>car</th>\n      <th>pt</th>\n      <th>pt</th>\n      <th>bus</th>\n      <th>bus</th>\n    </tr>\n    <tr>\n      <th>origin</th>\n      <th>destination</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">zone_0</th>\n      <th>zone_0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0.500000</td>\n      <td>1.000000</td>\n      <td>0.500000</td>\n      <td>3.720076e-44</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>zone_1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0.391394</td>\n      <td>1.000000</td>\n      <td>0.608606</td>\n      <td>5.784598e-44</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>zone_10</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0.008507</td>\n      <td>0.275264</td>\n      <td>0.969096</td>\n      <td>1.166554e-42</td>\n      <td>0.022397</td>\n      <td>0.724736</td>\n      <td>0.022397</td>\n      <td>0.724736</td>\n    </tr>\n    <tr>\n      <th>zone_100</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0.140421</td>\n      <td>0.503434</td>\n      <td>0.721074</td>\n      <td>9.617049e-44</td>\n      <td>0.138505</td>\n      <td>0.496566</td>\n      <td>0.138505</td>\n      <td>0.496566</td>\n    </tr>\n    <tr>\n      <th>zone_101</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0.146917</td>\n      <td>0.415731</td>\n      <td>0.646605</td>\n      <td>6.806612e-44</td>\n      <td>0.206478</td>\n      <td>0.584269</td>\n      <td>0.206478</td>\n      <td>0.584269</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">zone_99</th>\n      <th>zone_95</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0.418832</td>\n      <td>1.000000</td>\n      <td>0.581168</td>\n      <td>5.161956e-44</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>zone_96</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0.273882</td>\n      <td>0.546817</td>\n      <td>0.499133</td>\n      <td>3.707195e-44</td>\n      <td>0.226985</td>\n      <td>0.453183</td>\n      <td>0.226985</td>\n      <td>0.453183</td>\n    </tr>\n    <tr>\n      <th>zone_97</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0.302679</td>\n      <td>0.537401</td>\n      <td>0.436772</td>\n      <td>2.884849e-44</td>\n      <td>0.260548</td>\n      <td>0.462599</td>\n      <td>0.260548</td>\n      <td>0.462599</td>\n    </tr>\n    <tr>\n      <th>zone_98</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0.235424</td>\n      <td>0.444924</td>\n      <td>0.470866</td>\n      <td>3.310429e-44</td>\n      <td>0.293709</td>\n      <td>0.555076</td>\n      <td>0.293709</td>\n      <td>0.555076</td>\n    </tr>\n    <tr>\n      <th>zone_99</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0.500000</td>\n      <td>1.000000</td>\n      <td>0.500000</td>\n      <td>3.720076e-44</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>188356 rows × 10 columns</p>\n</div>"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_gdl.od_probabilities"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "t = sm_gdl.los['path'].apply(tuple)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [],
   "source": [
    "if manual:\n",
    "    %matplotlib inline"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [],
   "source": [
    "sm_gdl.od = sm_gdl.volumes.set_index(['origin', 'destination'])[['car_owner', 'pt_captive']]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Visualizción de la matriz de probabilidades"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [
    {
     "data": {
      "text/plain": "route_type    origin destination     segment  root      walk           car  \\\n0           zone_123      zone_0   car_owner     1  0.154477  6.294760e-01   \n1           zone_123      zone_0  pt_captive     1  0.416914  6.319963e-44   \n2           zone_123      zone_1   car_owner     1  0.127477  7.119148e-01   \n3           zone_123      zone_1  pt_captive     1  0.442496  9.193032e-44   \n4           zone_123     zone_10   car_owner     1  0.014653  9.529017e-01   \n...              ...         ...         ...   ...       ...           ...   \n74643        zone_97     zone_97  pt_captive     1  1.000000  3.720076e-44   \n74644        zone_97     zone_98   car_owner     1  0.216080  4.642085e-01   \n74645        zone_97     zone_98  pt_captive     1  0.403292  3.223065e-44   \n74646        zone_97     zone_99   car_owner     1  0.302320  4.374402e-01   \n74647        zone_97     zone_99  pt_captive     1  0.537401  2.892689e-44   \n\nroute_type        pt       bus  \n0           0.216047  0.216047  \n1           0.583086  0.583086  \n2           0.160609  0.160609  \n3           0.557504  0.557504  \n4           0.032445  0.032445  \n...              ...       ...  \n74643       0.000000  0.000000  \n74644       0.319711  0.319711  \n74645       0.596708  0.596708  \n74646       0.260240  0.260240  \n74647       0.462599  0.462599  \n\n[376712 rows x 8 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>route_type</th>\n      <th>origin</th>\n      <th>destination</th>\n      <th>segment</th>\n      <th>root</th>\n      <th>walk</th>\n      <th>car</th>\n      <th>pt</th>\n      <th>bus</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>zone_123</td>\n      <td>zone_0</td>\n      <td>car_owner</td>\n      <td>1</td>\n      <td>0.154477</td>\n      <td>6.294760e-01</td>\n      <td>0.216047</td>\n      <td>0.216047</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>zone_123</td>\n      <td>zone_0</td>\n      <td>pt_captive</td>\n      <td>1</td>\n      <td>0.416914</td>\n      <td>6.319963e-44</td>\n      <td>0.583086</td>\n      <td>0.583086</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>zone_123</td>\n      <td>zone_1</td>\n      <td>car_owner</td>\n      <td>1</td>\n      <td>0.127477</td>\n      <td>7.119148e-01</td>\n      <td>0.160609</td>\n      <td>0.160609</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>zone_123</td>\n      <td>zone_1</td>\n      <td>pt_captive</td>\n      <td>1</td>\n      <td>0.442496</td>\n      <td>9.193032e-44</td>\n      <td>0.557504</td>\n      <td>0.557504</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>zone_123</td>\n      <td>zone_10</td>\n      <td>car_owner</td>\n      <td>1</td>\n      <td>0.014653</td>\n      <td>9.529017e-01</td>\n      <td>0.032445</td>\n      <td>0.032445</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>74643</th>\n      <td>zone_97</td>\n      <td>zone_97</td>\n      <td>pt_captive</td>\n      <td>1</td>\n      <td>1.000000</td>\n      <td>3.720076e-44</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>74644</th>\n      <td>zone_97</td>\n      <td>zone_98</td>\n      <td>car_owner</td>\n      <td>1</td>\n      <td>0.216080</td>\n      <td>4.642085e-01</td>\n      <td>0.319711</td>\n      <td>0.319711</td>\n    </tr>\n    <tr>\n      <th>74645</th>\n      <td>zone_97</td>\n      <td>zone_98</td>\n      <td>pt_captive</td>\n      <td>1</td>\n      <td>0.403292</td>\n      <td>3.223065e-44</td>\n      <td>0.596708</td>\n      <td>0.596708</td>\n    </tr>\n    <tr>\n      <th>74646</th>\n      <td>zone_97</td>\n      <td>zone_99</td>\n      <td>car_owner</td>\n      <td>1</td>\n      <td>0.302320</td>\n      <td>4.374402e-01</td>\n      <td>0.260240</td>\n      <td>0.260240</td>\n    </tr>\n    <tr>\n      <th>74647</th>\n      <td>zone_97</td>\n      <td>zone_99</td>\n      <td>pt_captive</td>\n      <td>1</td>\n      <td>0.537401</td>\n      <td>2.892689e-44</td>\n      <td>0.462599</td>\n      <td>0.462599</td>\n    </tr>\n  </tbody>\n</table>\n<p>376712 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_gdl.probabilities\n",
    "### NOTESE que hubo un cambio en la estructura del DF de probabilidades"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [],
   "source": [
    "a = sm_gdl.od_probabilities.copy()\n",
    "a.columns = pd.MultiIndex.from_tuples(a.columns)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [],
   "source": [
    "voy = a['car_owner'].apply(lambda c: c * sm_gdl.od['car_owner']) + a['pt_captive'].apply(lambda c: c * sm_gdl.od['pt_captive'])\n",
    "\n",
    "### VOY, creo que expresa la distribucion de VIAJES totales por modalidad de acuerdo a las probabilidad\n",
    "# dentro de VOY esta la columna root que realmente es la sumatoria de car y walk, pt seria la sumatoria de todos los modos de  TP pero en este c aso como solo hay bus pt es igual a bus"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualización de distreibucion de viajes"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 360x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = voy.sum()[sorted(set(sm_gdl.mode_nests.index) - set(sm_gdl.mode_nests['root']))]\n",
    "plot = s.plot(\n",
    "    kind='pie',\n",
    "    autopct='%1.0f%%',\n",
    "    shadow=False,\n",
    "    startangle=90, figsize=[5, 5])\n",
    "plot.set_aspect('equal')\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Viajes totales por modalidad"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "data": {
      "text/plain": "bus     422628.328848\ncar     257667.854291\nwalk    310021.816861\ndtype: float64"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Exportacion de las matrices de utlidad, matrices de probabilidad de distribucion modal y distribucion de viajes por modalidad"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [],
   "source": [
    "sm_gdl.utilities.to_excel(r'C:/Users/orlan/Documents/UPC/PhD/Redación Tesis/Resultados/matriz_utilidades.xlsx', index = True)\n",
    "\n",
    "sm_gdl.probabilities.to_excel(r'C:/Users/orlan/Documents/UPC/PhD/Redación Tesis/Resultados/matriz_probabilidades.xlsx', index = True)\n",
    "\n",
    "voy.to_excel (r'C:/Users/orlan/Documents/UPC/PhD/Redación Tesis/Resultados/distribucion_viajes_modalidad.xlsx', index = True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Exportacion del nuevo SM con la distribucion modal"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "sm_gdl.to_zip(training_folder + 'model/base/sm_gdl_modal_split.zip'.format(scen=scenario))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 5) Asignación a la red "
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SQLalchemy is not installed. No support for SQL output.\n"
     ]
    }
   ],
   "source": [
    "import sys # for automation and parallelization\n",
    "manual, scenario = (True, 'base') if 'ipykernel' in sys.argv[0] else (False, sys.argv[1])\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, r'C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Quetzal Dev/quetzal-master')\n",
    "\n",
    "from quetzal.model import stepmodel\n",
    "from quetzal.io import excel\n",
    "import pandas as pd\n",
    "if manual:\n",
    "    %matplotlib inline\n",
    "\n",
    "import geopandas as gpd\n",
    "\n",
    "training_folder ='C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Modelo de Demanda GDL/Modelo GDL basado en paris/'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Lectura del SM con la distribucion modal"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "zones: 100%|██████████| 37/37 [00:44<00:00,  1.22s/it]                    \n"
     ]
    }
   ],
   "source": [
    "sm_gdl = stepmodel.read_zip('C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Modelo de Demanda GDL/Modelo GDL basado en paris/model/base/sm_gdl_modal_split.zip'.format(scen=scenario))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "sm_gdl.car_los['path'] = sm_gdl.car_los['path'].apply(tuple)\n",
    "sm_gdl.pt_los['path'] = sm_gdl.pt_los['path'].apply(tuple)\n",
    "sm_gdl.los['path'] = sm_gdl.los['path'].apply(tuple)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Funcion de asignación"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "sm_gdl.step_assignment(boardings=True, alightings=True, transfers=True, road=True, segmented=False, boarding_links= True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualización de las caracterisitcas de los road_links"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "index                                                        53982\na                                                       1920779582\nb                                                       1920779584\nlength                                                      50.746\ngeometry         LINESTRING (-11521212.33264352 2366758.4017226...\ntime                                                       6.81714\nwalk_time                                                  20.4514\n(volume, car)                                                  NaN\n(volume, pt)                                                   NaN\nName: 53982, dtype: object"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_gdl.road_links.loc[53982]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "links = sm_gdl.links\n",
    "links = sm_gdl.links.loc[sm_gdl.links['volume'] > 0]\n",
    "links['label'] = ''"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "30321.439613117167"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Inspeccion de la capacidad maxima de volumen por links\n",
    "links['volume'].max()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "sm_gdl.road_links['volume'] = sm_gdl.road_links[('volume', 'pt')]\n",
    "\n",
    "df = sm_gdl.links\n",
    "df = df.loc[df['volume'] > 0] # toma solo los links que tienen un flujo mayor a cero\n",
    "\n",
    "### IMPORTANTE: aqui lo que hace es que toma el DF que contiene el flujo por links"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1224x1080 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1224x1080 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if manual:\n",
    "    import matplotlib.pyplot as plt\n",
    "    from syspy.syspy_utils import data_visualization as dv\n",
    "    spectral = list(reversed(\n",
    "        ['#9e0142','#d53e4f','#f46d43','#fdae61','#fee08b',\n",
    "         '#e6f598','#abdda4','#66c2a5','#3288bd','#5e4fa2']))\n",
    "\n",
    "\n",
    "### Esto es la gráfica de asignación de viajes en auto a la red vial\n",
    "    fig, ax = plt.subplots(figsize=[17, 15])\n",
    "    lv = (5000,3000, 2000, 1000)\n",
    "    df = sm_gdl.road_links\n",
    "    df['volume'] = df[('volume', 'car')]\n",
    "    df = df.dropna(subset=['volume'])\n",
    "\n",
    "    plot = dv.bandwidth(\n",
    "        df, value_column='volume', power=0.5, max_value=lv[0], legend_values=lv,\n",
    "        legend_length=0.5, label_column='label', max_linewidth_meters=100,\n",
    "        cmap=spectral,\n",
    "        label_kwargs={'size':14, 'color':'black'}, ax=ax\n",
    "    )\n",
    "\n",
    "\n",
    "\n",
    "#### Esto es la asginación a la red de transport público\n",
    "    lv = (18000,12000,10000, 7000,5000, 2000, 1000)   # esto son la categorizacion del color\n",
    "    fig, ax = plt.subplots(figsize=[17, 15])\n",
    "    infra_links = sm_gdl.links.loc[sm_gdl.links.road_length.isnull()]\n",
    "\n",
    "    sm_gdl.road_links['volume'] = sm_gdl.road_links[('volume', 'pt')]\n",
    "    df = pd.concat([infra_links, sm_gdl.road_links])\n",
    "    df['volume'] = df['volume'].fillna(0)\n",
    "    df = df.loc[df['volume'] > 0]\n",
    "\n",
    "    plot = dv.bandwidth(\n",
    "        df, value_column='volume', power=0.5, max_value=lv[0],legend_values=lv,\n",
    "        legend_length=0.5, label_column='label', max_linewidth_meters=200,\n",
    "        #geographical_bounds=list(raster_montreal.bounds),\n",
    "        cmap=spectral,\n",
    "        label_kwargs={'size':14, 'color':'black'}, ax=ax\n",
    "    )\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualización del DF con los  volumenes de viajes por link"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "   index           a           b   length  \\\n0      0  6347554816  1639281613   46.602   \n1      1  6347554816  6347554804   43.200   \n2      2  6347554816  6347554799  113.432   \n3      3  6347554817  6347554804   57.049   \n4      4  6347554817  1643880278  153.992   \n\n                                            geometry       time  walk_time  \\\n0  LINESTRING (-11495824.754 2348897.003, -114958...   6.260444  18.781332   \n1  LINESTRING (-11495824.754 2348897.003, -114958...   5.803424  17.410273   \n2  LINESTRING (-11495824.754 2348897.003, -114957...  15.238288  45.714863   \n3  LINESTRING (-11495897.234 2348871.309, -114958...   7.663879  22.991636   \n4  LINESTRING (-11495897.234 2348871.309, -114960...  20.687058  62.061175   \n\n   (volume, car)  (volume, pt)  volume  \n0            NaN           NaN     NaN  \n1            NaN           NaN     NaN  \n2            NaN           NaN     NaN  \n3            NaN           NaN     NaN  \n4            NaN           NaN     NaN  ",
      "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>index</th>\n      <th>a</th>\n      <th>b</th>\n      <th>length</th>\n      <th>geometry</th>\n      <th>time</th>\n      <th>walk_time</th>\n      <th>(volume, car)</th>\n      <th>(volume, pt)</th>\n      <th>volume</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>6347554816</td>\n      <td>1639281613</td>\n      <td>46.602</td>\n      <td>LINESTRING (-11495824.754 2348897.003, -114958...</td>\n      <td>6.260444</td>\n      <td>18.781332</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>6347554816</td>\n      <td>6347554804</td>\n      <td>43.200</td>\n      <td>LINESTRING (-11495824.754 2348897.003, -114958...</td>\n      <td>5.803424</td>\n      <td>17.410273</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>6347554816</td>\n      <td>6347554799</td>\n      <td>113.432</td>\n      <td>LINESTRING (-11495824.754 2348897.003, -114957...</td>\n      <td>15.238288</td>\n      <td>45.714863</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>6347554817</td>\n      <td>6347554804</td>\n      <td>57.049</td>\n      <td>LINESTRING (-11495897.234 2348871.309, -114958...</td>\n      <td>7.663879</td>\n      <td>22.991636</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>6347554817</td>\n      <td>1643880278</td>\n      <td>153.992</td>\n      <td>LINESTRING (-11495897.234 2348871.309, -114960...</td>\n      <td>20.687058</td>\n      <td>62.061175</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Road_ links contiene los volumenes de viaje por tipo de modalidad en cada link\n",
    "sm_gdl.road_links.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualización del DF de links"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "        index    a          trip_id  link_sequence  departure_time    b  \\\nlink_0      0   28  BUSES_GDL001001              1            62.0   28   \nlink_1      1   28  BUSES_GDL001001              2           124.0   28   \nlink_2      2   28  BUSES_GDL001001              3           186.0  130   \nlink_3      3  130  BUSES_GDL001001              4           248.0  130   \nlink_4      4  130  BUSES_GDL001001              5           310.0  130   \n\n        arrival_time  time      route_id  direction_id  ...  \\\nlink_0         124.0  62.0  BUSES_GDL001             1  ...   \nlink_1         186.0  62.0  BUSES_GDL001             1  ...   \nlink_2         248.0  62.0  BUSES_GDL001             1  ...   \nlink_3         310.0  62.0  BUSES_GDL001             1  ...   \nlink_4         372.0  62.0  BUSES_GDL001             1  ...   \n\n                                           road_node_list  \\\nlink_0  [1920779980, 2129996075, 2339995706, 298824068...   \nlink_1  [2988240678, 1993270560, 1870646419, 199327992...   \nlink_2  [1993279920, 6542850229, 7313230932, 187064638...   \nlink_3  [4712038824, 8386341496, 6135406360, 613540635...   \nlink_4  [3670859704, 3670859632, 1993047919, 471203882...   \n\n                                           road_link_list road_length  \\\nlink_0      [54742, 84554, 110820, 66929, 457806, 342280]     487.318   \nlink_1                   [457805, 209333, 273654, 218961]     326.371   \nlink_2  [218953, 284459, 219980, 273613, 282918, 45684...    1560.435   \nlink_3  [954, 456842, 456839, 208146, 478684, 371728, ...     822.072   \nlink_4  [61465, 200712, 287613, 966, 968, 977, 428140,...    1343.229   \n\n                                                 geometry disaggregated_a  \\\nlink_0  LINESTRING (-11519018.893 2367943.302, -115191...    node_1000000   \nlink_1  LINESTRING (-11519015.955 2367605.476, -115190...    node_1001089   \nlink_2  LINESTRING (-11519038.063 2367275.991, -115190...    node_1001200   \nlink_3  LINESTRING (-11518533.062 2366715.024, -115186...    node_1001306   \nlink_4  LINESTRING (-11518231.052 2366475.870, -115185...    node_1001417   \n\n       disaggregated_b headway    length       volume    boardings  \nlink_0    node_1001089     180   487.318          NaN          NaN  \nlink_1    node_1001200     180   326.371          NaN          NaN  \nlink_2    node_1001306     180  1560.435  3477.474358  3477.474358  \nlink_3    node_1001417     180   822.072          NaN          NaN  \nlink_4    node_1001526     180  1343.229          NaN          NaN  \n\n[5 rows x 30 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>index</th>\n      <th>a</th>\n      <th>trip_id</th>\n      <th>link_sequence</th>\n      <th>departure_time</th>\n      <th>b</th>\n      <th>arrival_time</th>\n      <th>time</th>\n      <th>route_id</th>\n      <th>direction_id</th>\n      <th>...</th>\n      <th>road_node_list</th>\n      <th>road_link_list</th>\n      <th>road_length</th>\n      <th>geometry</th>\n      <th>disaggregated_a</th>\n      <th>disaggregated_b</th>\n      <th>headway</th>\n      <th>length</th>\n      <th>volume</th>\n      <th>boardings</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>link_0</th>\n      <td>0</td>\n      <td>28</td>\n      <td>BUSES_GDL001001</td>\n      <td>1</td>\n      <td>62.0</td>\n      <td>28</td>\n      <td>124.0</td>\n      <td>62.0</td>\n      <td>BUSES_GDL001</td>\n      <td>1</td>\n      <td>...</td>\n      <td>[1920779980, 2129996075, 2339995706, 298824068...</td>\n      <td>[54742, 84554, 110820, 66929, 457806, 342280]</td>\n      <td>487.318</td>\n      <td>LINESTRING (-11519018.893 2367943.302, -115191...</td>\n      <td>node_1000000</td>\n      <td>node_1001089</td>\n      <td>180</td>\n      <td>487.318</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>link_1</th>\n      <td>1</td>\n      <td>28</td>\n      <td>BUSES_GDL001001</td>\n      <td>2</td>\n      <td>124.0</td>\n      <td>28</td>\n      <td>186.0</td>\n      <td>62.0</td>\n      <td>BUSES_GDL001</td>\n      <td>1</td>\n      <td>...</td>\n      <td>[2988240678, 1993270560, 1870646419, 199327992...</td>\n      <td>[457805, 209333, 273654, 218961]</td>\n      <td>326.371</td>\n      <td>LINESTRING (-11519015.955 2367605.476, -115190...</td>\n      <td>node_1001089</td>\n      <td>node_1001200</td>\n      <td>180</td>\n      <td>326.371</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>link_2</th>\n      <td>2</td>\n      <td>28</td>\n      <td>BUSES_GDL001001</td>\n      <td>3</td>\n      <td>186.0</td>\n      <td>130</td>\n      <td>248.0</td>\n      <td>62.0</td>\n      <td>BUSES_GDL001</td>\n      <td>1</td>\n      <td>...</td>\n      <td>[1993279920, 6542850229, 7313230932, 187064638...</td>\n      <td>[218953, 284459, 219980, 273613, 282918, 45684...</td>\n      <td>1560.435</td>\n      <td>LINESTRING (-11519038.063 2367275.991, -115190...</td>\n      <td>node_1001200</td>\n      <td>node_1001306</td>\n      <td>180</td>\n      <td>1560.435</td>\n      <td>3477.474358</td>\n      <td>3477.474358</td>\n    </tr>\n    <tr>\n      <th>link_3</th>\n      <td>3</td>\n      <td>130</td>\n      <td>BUSES_GDL001001</td>\n      <td>4</td>\n      <td>248.0</td>\n      <td>130</td>\n      <td>310.0</td>\n      <td>62.0</td>\n      <td>BUSES_GDL001</td>\n      <td>1</td>\n      <td>...</td>\n      <td>[4712038824, 8386341496, 6135406360, 613540635...</td>\n      <td>[954, 456842, 456839, 208146, 478684, 371728, ...</td>\n      <td>822.072</td>\n      <td>LINESTRING (-11518533.062 2366715.024, -115186...</td>\n      <td>node_1001306</td>\n      <td>node_1001417</td>\n      <td>180</td>\n      <td>822.072</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>link_4</th>\n      <td>4</td>\n      <td>130</td>\n      <td>BUSES_GDL001001</td>\n      <td>5</td>\n      <td>310.0</td>\n      <td>130</td>\n      <td>372.0</td>\n      <td>62.0</td>\n      <td>BUSES_GDL001</td>\n      <td>1</td>\n      <td>...</td>\n      <td>[3670859704, 3670859632, 1993047919, 471203882...</td>\n      <td>[61465, 200712, 287613, 966, 968, 977, 428140,...</td>\n      <td>1343.229</td>\n      <td>LINESTRING (-11518231.052 2366475.870, -115185...</td>\n      <td>node_1001417</td>\n      <td>node_1001526</td>\n      <td>180</td>\n      <td>1343.229</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 30 columns</p>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dentro de Links se encuentra la frecuencia de cada link para todos los viajes  y tambien se encuinetra a que ruta corresponde cada link en particular\n",
    "sm_gdl.links.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "6411244.5464611435"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_gdl.links.volume.sum() # 6 millones"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "########## PRIMERA APARICION DE VOLUMEN_ROUTE\n",
    "### Sumatoria agregada con las columnas Route ID y volumen\n",
    "volumen_route = sm_gdl.links.groupby(['route_id'])[\"volume\"].apply(lambda x : x.astype(float).sum())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "# Convertirlo a DF\n",
    "volumen_route= volumen_route.to_frame()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "# generar la columna del route ID\n",
    "volumen_route['Route'] = volumen_route.index"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "6411244.546461145"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volumen_route.volume.sum() # Duda para quentin, esto es el totla de viajes a lo largo de todo el DIA?"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualizacion del DF de volumenes por ruta"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "                     volume         Route\nroute_id                                 \nBUSES_GDL001  105122.177333  BUSES_GDL001\nBUSES_GDL002  232666.482138  BUSES_GDL002\nBUSES_GDL003  101309.278027  BUSES_GDL003\nBUSES_GDL004  113206.900892  BUSES_GDL004\nBUSES_GDL005   81824.013920  BUSES_GDL005",
      "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>volume</th>\n      <th>Route</th>\n    </tr>\n    <tr>\n      <th>route_id</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>BUSES_GDL001</th>\n      <td>105122.177333</td>\n      <td>BUSES_GDL001</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL002</th>\n      <td>232666.482138</td>\n      <td>BUSES_GDL002</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL003</th>\n      <td>101309.278027</td>\n      <td>BUSES_GDL003</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL004</th>\n      <td>113206.900892</td>\n      <td>BUSES_GDL004</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL005</th>\n      <td>81824.013920</td>\n      <td>BUSES_GDL005</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volumen_route.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "####  Resumen de métricas en el TP"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "import sys\n",
    "from quetzal.model import stepmodel\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from rasterio import plot\n",
    "import rasterio\n",
    "from shapely import geometry\n",
    "# import visualization\n",
    "\n",
    "\n",
    "from syspy.skims import skims\n",
    "from quetzal.io import excel\n",
    "\n",
    "manual, scenario = (True, 'base') if 'ipykernel' in sys.argv[0] else (False, sys.argv[1])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "category  parameter        \ngeneral   parent               base\n          bus_speed_factor      0.8\n          boarding_cost_acf     300\n          boarding_cost_wor     300\n          routes_code          base\nName: base, dtype: object"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var= excel.read_var( file= 'C:/Users/orlan/Documents/UPC/PhD/Papers/Paper 4/Modelo de Demanda GDL/Data/inputs/parameters_gdl_paris.xlsx')\n",
    "var.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['index', 'a', 'trip_id', 'link_sequence', 'departure_time', 'b',\n       'arrival_time', 'time', 'route_id', 'direction_id', 'service_id',\n       'trip_headsign', 'trip_short_name', 'shape_id', 'agency_id',\n       'route_short_name', 'route_long_name', 'route_type', 'road_a', 'road_b',\n       'road_node_list', 'road_link_list', 'road_length', 'geometry',\n       'disaggregated_a', 'disaggregated_b', 'headway', 'length', 'volume',\n       'boardings'],\n      dtype='object')"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizacion de las columnas del DF links\n",
    "sm_gdl.links.columns\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "link_0       bus\nlink_1       bus\nlink_2       bus\nlink_3       bus\nlink_4       bus\n            ... \nlink_4583    bus\nlink_4584    bus\nlink_4585    bus\nlink_4586    bus\nlink_4587    bus\nName: route_type, Length: 4588, dtype: object"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizacion del tipo de modos en TP (recordar que el DF de links es solo para TP)\n",
    "sm_gdl.links['route_type']\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "try:\n",
    "    sm_gdl.links['mode'] = sm_gdl.links['mode_' + var['general']['routes_code']] #verify\n",
    "except KeyError:\n",
    "    sm_gdl.links['mode'] = sm_gdl.links['route_type']\n",
    "\n",
    "from syspy.assignment.raw import fast_assign as assign\n",
    "rl = sm_gdl.road_links[['length']]\n",
    "modes = list(set(sm_gdl.links['mode']))\n",
    "\n",
    "for mode in modes:\n",
    "    l = sm_gdl.links.loc[sm_gdl.links['mode'] == mode].copy()\n",
    "    l['dummy'] = 1\n",
    "    rl[mode] = np.clip(assign(l['dummy'], l['road_link_list']), 0, 1)\n",
    "    rl[mode] = rl[mode] * rl['length']\n",
    "\n",
    "routes = list(set(sm_gdl.links['route_id']))\n",
    "for route in routes:\n",
    "    l = sm_gdl.links.loc[sm_gdl.links['route_id'] == route].copy()\n",
    "    l['dummy'] = 1\n",
    "    rl[route] = np.clip(assign(l['dummy'], l['road_link_list']), 0, 1)\n",
    "    rl[route] = rl[route] * rl['length']\n",
    "\n",
    "route_sum = rl[routes].sum() ## Que indica esto ?\n",
    "mode_sum = rl[modes].sum()   ## Que indica esto?\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Stacks"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "model_to_day = var['post_parameters']['model_to_day']\n",
    "model_duration = var['post_parameters']['model_duration'] # hour (supongo que se refiere solo a la hora pico, dado que el valor en el DF de parametes es 1)\n",
    "vehicle_disponibility = var['post_parameters']['vehicle_disponibility']\n",
    "mobility_rate = var['post_parameters']['mobility_rate']\n",
    "vehicle_capacities = var['vehicle_capacities'].to_dict()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "sm_gdl.links['per_hour'] = 3600 / sm_gdl.links['headway']  # estos SON LOS vehiculos por hora que pasan por detemrinado link\n",
    "sm_gdl.links['vehicle_capacity'] = sm_gdl.links['mode'].apply(vehicle_capacities.get)  # aqui une las capacidades de vehiculos al DF de links\n",
    "sm_gdl.links['capacity'] = (sm_gdl.links['vehicle_capacity'] * sm_gdl.links['per_hour']).astype(int)  # IMPORANTE: aqui lo que hace es que le asgina al LINk una capacidad, dado la frecuencia de paso de vehiculos por hora y la capacidad de los vehiculos"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "sm_gdl.links['vehicles'] = sm_gdl.links['time'] / sm_gdl.links['headway']  # Cantidad de vehiculos que pasan por link por hora\n",
    "sm_gdl.links['km'] = sm_gdl.links['length'] / 1000  # Distancia en km\n",
    "\n",
    "sm_gdl.links['pt'] = sm_gdl.links.volume"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [],
   "source": [
    "sm_gdl.links['vehicle_km'] = model_duration * sm_gdl.links['per_hour'] * sm_gdl.links['km'] # esto son la cantidad de kilometros que recorre las rutas en cada link por hora  (esto podria servir para hacer estimaciones de emisiones por zona)\n",
    "\n",
    "sm_gdl.links['seat_km'] = model_duration * sm_gdl.links['per_hour'] * sm_gdl.links['km']* sm_gdl.links['capacity']\n",
    "sm_gdl.links['voy_km'] = model_duration * sm_gdl.links['per_hour'] * sm_gdl.links['km']* sm_gdl.links['volume']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "summary = sm_gdl.links.groupby(['route_id']).agg(\n",
    "    {\n",
    "        'boardings': 'sum',\n",
    "        'pt': 'max',\n",
    "        'vehicles': 'sum',\n",
    "        'length': 'sum',\n",
    "        'time': 'sum',\n",
    "        'vehicle_km': 'sum',\n",
    "        'seat_km': 'sum',\n",
    "        'voy_km': 'sum',\n",
    "        'headway': 'max',\n",
    "        'capacity': 'max',\n",
    "        'mode': 'first',\n",
    "\n",
    "    }\n",
    ")\n",
    "sm_gdl.stack_routes = summary.stack()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualizacion del nuevo DF, resumen de caracteristicas de las rutas"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "                 boardings            pt   vehicles     length    time  \\\nroute_id                                                                 \nBUSES_GDL001  78614.303968   6604.888551  52.355556  71944.758  9424.0   \nBUSES_GDL002  95875.072294  11216.526637  37.200000  55800.746  6696.0   \nBUSES_GDL003  39611.877389   5275.548946  38.577778  63325.790  6944.0   \nBUSES_GDL004  48901.925827  11051.464710  26.177778  35284.331  4712.0   \nBUSES_GDL005  43399.217867   7256.655311  44.777778  59659.236  8060.0   \n\n              vehicle_km      seat_km        voy_km  headway  capacity mode  \nroute_id                                                                     \nBUSES_GDL001  1438.89516  3021679.836  1.210989e+06      180      2100  bus  \nBUSES_GDL002  1116.01492  2343631.332  3.070902e+06      180      2100  bus  \nBUSES_GDL003  1266.51580  2659683.180  1.416119e+06      180      2100  bus  \nBUSES_GDL004   705.68662  1481941.902  1.818178e+06      180      2100  bus  \nBUSES_GDL005  1193.18472  2505687.912  9.946018e+05      180      2100  bus  ",
      "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>boardings</th>\n      <th>pt</th>\n      <th>vehicles</th>\n      <th>length</th>\n      <th>time</th>\n      <th>vehicle_km</th>\n      <th>seat_km</th>\n      <th>voy_km</th>\n      <th>headway</th>\n      <th>capacity</th>\n      <th>mode</th>\n    </tr>\n    <tr>\n      <th>route_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>BUSES_GDL001</th>\n      <td>78614.303968</td>\n      <td>6604.888551</td>\n      <td>52.355556</td>\n      <td>71944.758</td>\n      <td>9424.0</td>\n      <td>1438.89516</td>\n      <td>3021679.836</td>\n      <td>1.210989e+06</td>\n      <td>180</td>\n      <td>2100</td>\n      <td>bus</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL002</th>\n      <td>95875.072294</td>\n      <td>11216.526637</td>\n      <td>37.200000</td>\n      <td>55800.746</td>\n      <td>6696.0</td>\n      <td>1116.01492</td>\n      <td>2343631.332</td>\n      <td>3.070902e+06</td>\n      <td>180</td>\n      <td>2100</td>\n      <td>bus</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL003</th>\n      <td>39611.877389</td>\n      <td>5275.548946</td>\n      <td>38.577778</td>\n      <td>63325.790</td>\n      <td>6944.0</td>\n      <td>1266.51580</td>\n      <td>2659683.180</td>\n      <td>1.416119e+06</td>\n      <td>180</td>\n      <td>2100</td>\n      <td>bus</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL004</th>\n      <td>48901.925827</td>\n      <td>11051.464710</td>\n      <td>26.177778</td>\n      <td>35284.331</td>\n      <td>4712.0</td>\n      <td>705.68662</td>\n      <td>1481941.902</td>\n      <td>1.818178e+06</td>\n      <td>180</td>\n      <td>2100</td>\n      <td>bus</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL005</th>\n      <td>43399.217867</td>\n      <td>7256.655311</td>\n      <td>44.777778</td>\n      <td>59659.236</td>\n      <td>8060.0</td>\n      <td>1193.18472</td>\n      <td>2505687.912</td>\n      <td>9.946018e+05</td>\n      <td>180</td>\n      <td>2100</td>\n      <td>bus</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### ES POR HORA!\n",
    "summary.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [],
   "source": [
    "sm_gdl.stack_routes.index.names = ['route_id', 'indicator']\n",
    "sm_gdl.stack_routes.name = 'aggregated'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "lines = summary.copy()\n",
    "lines['design'] = lines['voy_km'] / (lines['seat_km'] * (lines['pt'] / lines['capacity']))\n",
    "lines['empty_seats'] = np.round(1 - lines['design'], 2)\n",
    "lines['boardings'] = np.round(summary['boardings'] * model_to_day, -3).astype(int)\n",
    "lines['vehicle_km'] = np.round(summary['vehicle_km']).astype(int) # la original lo tenia * model day que vale 5, por que lo quiere multiplicar por eso?, yo lo quite y represntará los KM recorridos por hora por todas las unidades de cada ruta\n",
    "        # lines['vehicle_km'] = np.round(summary['vehicle_km'] * model_to_day, -2).astype(int),   ES COMO ESTABA ORIGINAL\n",
    "lines['seat_km'] = np.round(summary['seat_km'] * model_to_day, -3).astype(int)\n",
    "lines['voy_km'] = np.round(summary['voy_km'] * model_to_day, -3).astype(int)\n",
    "lines['max_load'] = np.round(summary['pt'].fillna(0), -2).astype(int) # cantidad total de personas por hora en cada ruta redondeada hacia arriba\n",
    "lines['vehicles'] = np.floor(lines['vehicles'] / vehicle_disponibility).astype(int) + 1\n",
    "lines['minutes'] = np.round(lines['time'] / (2*60)).astype(int)  # Duracion de la ruta\n",
    "\n",
    "## IMPORTANTE: la columna de capacity indica la cantidad total de personal que pueden transitar por esa linea, esto se generar derivado de la cantidad de vehiculos que pasa por esa ruta y la capacidad de estos\n",
    "# Por otra parte, max load  es la cantidad TOTAL de personas que abordan una linea en particuar redondeada, su magnitud como se podrá observar es muy similar a pt simplemente que redonbdeada hacia arriba"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "# Cálculo de las capacidades  y costos por unidad recorrida de cada linea (el costo por unidad recorrida esta vinculado casi exclusivamente a los gastos del energético que se use como propulsión, no toma en consideración la amortización de la compra de la unidad, salarios entre otros, es solo el costo opertaivo de las unidades necesarias para cada linea\n",
    "vehicle_capacities = var['vehicle_capacities']\n",
    "vehicle_capacities = int(vehicle_capacities) # hay que convertirla a int para poder usarla como constante en la multiplicacion para la capacidad\n",
    "\n",
    "\n",
    "####### REVISAR, ESTA INCORRECTO.ESTA ERA MI VERSSION\n",
    "#  lines['capacity'] =  lines['vehicles'] * vehicle_capacities   # capacidad real de la linea (pasajeros)\n",
    "\n",
    "# costo de operacion por hora (USD/hora)\n",
    "unit_distance_cost = var['post_parameters'] ['unit_distance_cost']\n",
    "lines['distance_cost'] =  lines['vehicle_km'] *  unit_distance_cost   # costo por operacion de la flotilla, solo aspecto energético (USD/hora)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [],
   "source": [
    "### Emisiones por operación (kg de CO2 /hora)\n",
    "emision_km = var['post_parameters'] ['emision_km']\n",
    "lines['emisions'] =  (lines['vehicle_km'] * emision_km) /1000 # emisiones de CO2 por hora de operacion de cada linea\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [],
   "source": [
    "## Calculo de la saturacion por linea (si es igual o menor a 1 la linea aguanta bien, si es mayor a 1 hay un deficit de capacidad en la linea)\n",
    "lines['km'] = np.round(route_sum/ (2*1000)).astype(int) # esto es la longitud de cada linea\n",
    "lines['saturation'] = np.round( lines['max_load'] / lines['capacity'], 1 )\n",
    "# Saturación entre más peor, para que la capacidad de la línea sea la indicadqa el valor de saturation tiene que ser = 1. Mayor a esto es la cantidad de veces que está saturada la línea en relación a su capacidad total"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Abordajes completos ¿¿??"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "outputs": [
    {
     "data": {
      "text/plain": "13877000"
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines.boardings.sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Emisiones totales de la red  (kg CO2/hora)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "outputs": [
    {
     "data": {
      "text/plain": "28399.409000000003"
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines.emisions.sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Costo de operación por hora de la red (USD/hora)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "outputs": [
    {
     "data": {
      "text/plain": "19974.114"
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines.distance_cost.sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Capacidad total de la red"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [
    {
     "data": {
      "text/plain": "81900"
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines.capacity.sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualización del nuevo DF LINES\n",
    "###### el DF de lines ahora contiene un resumen de una gran cantidad de datos referentes a la red de TP"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "              boardings            pt  vehicles     length    time  \\\nroute_id                                                             \nBUSES_GDL001     393000   6604.888551        66  71944.758  9424.0   \nBUSES_GDL002     479000  11216.526637        47  55800.746  6696.0   \nBUSES_GDL003     198000   5275.548946        49  63325.790  6944.0   \nBUSES_GDL004     245000  11051.464710        33  35284.331  4712.0   \nBUSES_GDL005     217000   7256.655311        56  59659.236  8060.0   \n\n              vehicle_km   seat_km    voy_km  headway  capacity mode  \\\nroute_id                                                               \nBUSES_GDL001        1439  15108000   6055000      180      6930  bus   \nBUSES_GDL002        1116  11718000  15355000      180      4935  bus   \nBUSES_GDL003        1267  13298000   7081000      180      5145  bus   \nBUSES_GDL004         706   7410000   9091000      180      3465  bus   \nBUSES_GDL005        1193  12528000   4973000      180      5880  bus   \n\n                design  empty_seats  max_load  minutes  alv  \nroute_id                                                     \nBUSES_GDL001  0.127422         0.87      6600       79  NaN  \nBUSES_GDL002  0.245323         0.75     11200       56  NaN  \nBUSES_GDL003  0.211944         0.79      5300       58  NaN  \nBUSES_GDL004  0.233134         0.77     11100       39  NaN  \nBUSES_GDL005  0.114870         0.89      7300       67  NaN  ",
      "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>boardings</th>\n      <th>pt</th>\n      <th>vehicles</th>\n      <th>length</th>\n      <th>time</th>\n      <th>vehicle_km</th>\n      <th>seat_km</th>\n      <th>voy_km</th>\n      <th>headway</th>\n      <th>capacity</th>\n      <th>mode</th>\n      <th>design</th>\n      <th>empty_seats</th>\n      <th>max_load</th>\n      <th>minutes</th>\n      <th>alv</th>\n    </tr>\n    <tr>\n      <th>route_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>BUSES_GDL001</th>\n      <td>393000</td>\n      <td>6604.888551</td>\n      <td>66</td>\n      <td>71944.758</td>\n      <td>9424.0</td>\n      <td>1439</td>\n      <td>15108000</td>\n      <td>6055000</td>\n      <td>180</td>\n      <td>6930</td>\n      <td>bus</td>\n      <td>0.127422</td>\n      <td>0.87</td>\n      <td>6600</td>\n      <td>79</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL002</th>\n      <td>479000</td>\n      <td>11216.526637</td>\n      <td>47</td>\n      <td>55800.746</td>\n      <td>6696.0</td>\n      <td>1116</td>\n      <td>11718000</td>\n      <td>15355000</td>\n      <td>180</td>\n      <td>4935</td>\n      <td>bus</td>\n      <td>0.245323</td>\n      <td>0.75</td>\n      <td>11200</td>\n      <td>56</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL003</th>\n      <td>198000</td>\n      <td>5275.548946</td>\n      <td>49</td>\n      <td>63325.790</td>\n      <td>6944.0</td>\n      <td>1267</td>\n      <td>13298000</td>\n      <td>7081000</td>\n      <td>180</td>\n      <td>5145</td>\n      <td>bus</td>\n      <td>0.211944</td>\n      <td>0.79</td>\n      <td>5300</td>\n      <td>58</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL004</th>\n      <td>245000</td>\n      <td>11051.464710</td>\n      <td>33</td>\n      <td>35284.331</td>\n      <td>4712.0</td>\n      <td>706</td>\n      <td>7410000</td>\n      <td>9091000</td>\n      <td>180</td>\n      <td>3465</td>\n      <td>bus</td>\n      <td>0.233134</td>\n      <td>0.77</td>\n      <td>11100</td>\n      <td>39</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL005</th>\n      <td>217000</td>\n      <td>7256.655311</td>\n      <td>56</td>\n      <td>59659.236</td>\n      <td>8060.0</td>\n      <td>1193</td>\n      <td>12528000</td>\n      <td>4973000</td>\n      <td>180</td>\n      <td>5880</td>\n      <td>bus</td>\n      <td>0.114870</td>\n      <td>0.89</td>\n      <td>7300</td>\n      <td>67</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines.head() # Gran parte de los datos esta representado para una hora de operacion de cad ruta\n",
    "# vehicules es el numero de unidades que se requiere por ruta, length es la longitud total en metros de recorrido en una por los vehiculos de esa ruta\n",
    "# time es el tiempo del recorrido de todas las unidades por una hora etc..."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Total de uniades de buses requeridas para la frecuencia y lineas totales propuestas\n",
    "###### Esto no significa que es el número final de buses requeridos, dado las capacidaddes su cnatidad y la demanda pueden que sean menos buses los requeridos o más"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "outputs": [
    {
     "data": {
      "text/plain": "1992"
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines.vehicles.sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Km recorridos por toda la flota por hora"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "outputs": [
    {
     "data": {
      "text/plain": "41099"
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines.vehicle_km.sum()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualización del DF LINES"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "data": {
      "text/plain": "              boardings            pt  vehicles     length    time  \\\nroute_id                                                             \nBUSES_GDL001     393000   6604.888551        66  71944.758  9424.0   \nBUSES_GDL002     479000  11216.526637        47  55800.746  6696.0   \nBUSES_GDL003     198000   5275.548946        49  63325.790  6944.0   \nBUSES_GDL004     245000  11051.464710        33  35284.331  4712.0   \nBUSES_GDL005     217000   7256.655311        56  59659.236  8060.0   \n\n              vehicle_km   seat_km    voy_km  headway  capacity mode  \\\nroute_id                                                               \nBUSES_GDL001        1439  15108000   6055000      180      6930  bus   \nBUSES_GDL002        1116  11718000  15355000      180      4935  bus   \nBUSES_GDL003        1267  13298000   7081000      180      5145  bus   \nBUSES_GDL004         706   7410000   9091000      180      3465  bus   \nBUSES_GDL005        1193  12528000   4973000      180      5880  bus   \n\n                design  empty_seats  max_load  minutes  alv  km  saturation  \nroute_id                                                                     \nBUSES_GDL001  0.127422         0.87      6600       79  NaN  32         1.0  \nBUSES_GDL002  0.245323         0.75     11200       56  NaN  27         2.3  \nBUSES_GDL003  0.211944         0.79      5300       58  NaN  30         1.0  \nBUSES_GDL004  0.233134         0.77     11100       39  NaN  16         3.2  \nBUSES_GDL005  0.114870         0.89      7300       67  NaN  27         1.2  ",
      "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>boardings</th>\n      <th>pt</th>\n      <th>vehicles</th>\n      <th>length</th>\n      <th>time</th>\n      <th>vehicle_km</th>\n      <th>seat_km</th>\n      <th>voy_km</th>\n      <th>headway</th>\n      <th>capacity</th>\n      <th>mode</th>\n      <th>design</th>\n      <th>empty_seats</th>\n      <th>max_load</th>\n      <th>minutes</th>\n      <th>alv</th>\n      <th>km</th>\n      <th>saturation</th>\n    </tr>\n    <tr>\n      <th>route_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>BUSES_GDL001</th>\n      <td>393000</td>\n      <td>6604.888551</td>\n      <td>66</td>\n      <td>71944.758</td>\n      <td>9424.0</td>\n      <td>1439</td>\n      <td>15108000</td>\n      <td>6055000</td>\n      <td>180</td>\n      <td>6930</td>\n      <td>bus</td>\n      <td>0.127422</td>\n      <td>0.87</td>\n      <td>6600</td>\n      <td>79</td>\n      <td>NaN</td>\n      <td>32</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL002</th>\n      <td>479000</td>\n      <td>11216.526637</td>\n      <td>47</td>\n      <td>55800.746</td>\n      <td>6696.0</td>\n      <td>1116</td>\n      <td>11718000</td>\n      <td>15355000</td>\n      <td>180</td>\n      <td>4935</td>\n      <td>bus</td>\n      <td>0.245323</td>\n      <td>0.75</td>\n      <td>11200</td>\n      <td>56</td>\n      <td>NaN</td>\n      <td>27</td>\n      <td>2.3</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL003</th>\n      <td>198000</td>\n      <td>5275.548946</td>\n      <td>49</td>\n      <td>63325.790</td>\n      <td>6944.0</td>\n      <td>1267</td>\n      <td>13298000</td>\n      <td>7081000</td>\n      <td>180</td>\n      <td>5145</td>\n      <td>bus</td>\n      <td>0.211944</td>\n      <td>0.79</td>\n      <td>5300</td>\n      <td>58</td>\n      <td>NaN</td>\n      <td>30</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL004</th>\n      <td>245000</td>\n      <td>11051.464710</td>\n      <td>33</td>\n      <td>35284.331</td>\n      <td>4712.0</td>\n      <td>706</td>\n      <td>7410000</td>\n      <td>9091000</td>\n      <td>180</td>\n      <td>3465</td>\n      <td>bus</td>\n      <td>0.233134</td>\n      <td>0.77</td>\n      <td>11100</td>\n      <td>39</td>\n      <td>NaN</td>\n      <td>16</td>\n      <td>3.2</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL005</th>\n      <td>217000</td>\n      <td>7256.655311</td>\n      <td>56</td>\n      <td>59659.236</td>\n      <td>8060.0</td>\n      <td>1193</td>\n      <td>12528000</td>\n      <td>4973000</td>\n      <td>180</td>\n      <td>5880</td>\n      <td>bus</td>\n      <td>0.114870</td>\n      <td>0.89</td>\n      <td>7300</td>\n      <td>67</td>\n      <td>NaN</td>\n      <td>27</td>\n      <td>1.2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### el DF de lines sigue acumulando variables, visualización\n",
    "lines.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Abordajes totales en todas las lineas"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "outputs": [
    {
     "data": {
      "text/plain": "13877000"
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines.boardings.sum() # 14 millones de abordajes, esto dado que el algoritmo de Dijstrtra no se esta ejecutando de una manera realista ya que hay viajes que requiren 7, 10 o más transferencias no nos podemos fiar de este numerp en concreto"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#####  Viajes totales en TP en hora pico"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "outputs": [
    {
     "data": {
      "text/plain": "394069.09079648816"
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines.pt.sum()\n",
    "# el DF de lines dentro de la columna de PT contiene los viajes en hora pico de cada ruta"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "try:\n",
    "    lines.index = [int(i) for i in lines.index]\n",
    "except ValueError: # base\n",
    "    pass\n",
    "lines.sort_index(inplace=True)\n",
    "lines.index.name = 'line'\n",
    "lines['voy_to_seat_km'] = lines['voy_km'] / lines['seat_km'] # Agrega una nueva columna\n",
    "data = lines[\n",
    "    [\n",
    "        'mode', 'km', 'minutes',  'vehicles','vehicle_km',\n",
    "        'voy_km', 'seat_km', 'empty_seats',\n",
    "        'headway', 'capacity',\n",
    "        'max_load', 'saturation', 'boardings'\n",
    "    ]\n",
    "].set_index('mode', append=True).swaplevel().loc[modes]\n",
    "\n",
    "\n",
    "total_boardings = data['boardings'].sum() # crea esta nueva variable que es el total de abordajes\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Visualizacion del DF de lines, agrupa los resultados más relevantes para el TP\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "outputs": [
    {
     "data": {
      "text/plain": "              boardings            pt  vehicles     length    time  \\\nline                                                                 \nBUSES_GDL001     393000   6604.888551        66  71944.758  9424.0   \nBUSES_GDL002     479000  11216.526637        47  55800.746  6696.0   \nBUSES_GDL003     198000   5275.548946        49  63325.790  6944.0   \nBUSES_GDL004     245000  11051.464710        33  35284.331  4712.0   \nBUSES_GDL005     217000   7256.655311        56  59659.236  8060.0   \n\n              vehicle_km   seat_km    voy_km  headway  capacity mode  \\\nline                                                                   \nBUSES_GDL001        1439  15108000   6055000      180      2100  bus   \nBUSES_GDL002        1116  11718000  15355000      180      2100  bus   \nBUSES_GDL003        1267  13298000   7081000      180      2100  bus   \nBUSES_GDL004         706   7410000   9091000      180      2100  bus   \nBUSES_GDL005        1193  12528000   4973000      180      2100  bus   \n\n                design  empty_seats  max_load  minutes  distance_cost  \\\nline                                                                    \nBUSES_GDL001  0.127422         0.87      6600       79        699.354   \nBUSES_GDL002  0.245323         0.75     11200       56        542.376   \nBUSES_GDL003  0.211944         0.79      5300       58        615.762   \nBUSES_GDL004  0.233134         0.77     11100       39        343.116   \nBUSES_GDL005  0.114870         0.89      7300       67        579.798   \n\n              emisions  km  saturation  voy_to_seat_km  \nline                                                    \nBUSES_GDL001   994.349  32         3.1        0.400781  \nBUSES_GDL002   771.156  27         5.3        1.310377  \nBUSES_GDL003   875.497  30         2.5        0.532486  \nBUSES_GDL004   487.846  16         5.3        1.226856  \nBUSES_GDL005   824.363  27         3.5        0.396951  ",
      "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>boardings</th>\n      <th>pt</th>\n      <th>vehicles</th>\n      <th>length</th>\n      <th>time</th>\n      <th>vehicle_km</th>\n      <th>seat_km</th>\n      <th>voy_km</th>\n      <th>headway</th>\n      <th>capacity</th>\n      <th>mode</th>\n      <th>design</th>\n      <th>empty_seats</th>\n      <th>max_load</th>\n      <th>minutes</th>\n      <th>distance_cost</th>\n      <th>emisions</th>\n      <th>km</th>\n      <th>saturation</th>\n      <th>voy_to_seat_km</th>\n    </tr>\n    <tr>\n      <th>line</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>BUSES_GDL001</th>\n      <td>393000</td>\n      <td>6604.888551</td>\n      <td>66</td>\n      <td>71944.758</td>\n      <td>9424.0</td>\n      <td>1439</td>\n      <td>15108000</td>\n      <td>6055000</td>\n      <td>180</td>\n      <td>2100</td>\n      <td>bus</td>\n      <td>0.127422</td>\n      <td>0.87</td>\n      <td>6600</td>\n      <td>79</td>\n      <td>699.354</td>\n      <td>994.349</td>\n      <td>32</td>\n      <td>3.1</td>\n      <td>0.400781</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL002</th>\n      <td>479000</td>\n      <td>11216.526637</td>\n      <td>47</td>\n      <td>55800.746</td>\n      <td>6696.0</td>\n      <td>1116</td>\n      <td>11718000</td>\n      <td>15355000</td>\n      <td>180</td>\n      <td>2100</td>\n      <td>bus</td>\n      <td>0.245323</td>\n      <td>0.75</td>\n      <td>11200</td>\n      <td>56</td>\n      <td>542.376</td>\n      <td>771.156</td>\n      <td>27</td>\n      <td>5.3</td>\n      <td>1.310377</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL003</th>\n      <td>198000</td>\n      <td>5275.548946</td>\n      <td>49</td>\n      <td>63325.790</td>\n      <td>6944.0</td>\n      <td>1267</td>\n      <td>13298000</td>\n      <td>7081000</td>\n      <td>180</td>\n      <td>2100</td>\n      <td>bus</td>\n      <td>0.211944</td>\n      <td>0.79</td>\n      <td>5300</td>\n      <td>58</td>\n      <td>615.762</td>\n      <td>875.497</td>\n      <td>30</td>\n      <td>2.5</td>\n      <td>0.532486</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL004</th>\n      <td>245000</td>\n      <td>11051.464710</td>\n      <td>33</td>\n      <td>35284.331</td>\n      <td>4712.0</td>\n      <td>706</td>\n      <td>7410000</td>\n      <td>9091000</td>\n      <td>180</td>\n      <td>2100</td>\n      <td>bus</td>\n      <td>0.233134</td>\n      <td>0.77</td>\n      <td>11100</td>\n      <td>39</td>\n      <td>343.116</td>\n      <td>487.846</td>\n      <td>16</td>\n      <td>5.3</td>\n      <td>1.226856</td>\n    </tr>\n    <tr>\n      <th>BUSES_GDL005</th>\n      <td>217000</td>\n      <td>7256.655311</td>\n      <td>56</td>\n      <td>59659.236</td>\n      <td>8060.0</td>\n      <td>1193</td>\n      <td>12528000</td>\n      <td>4973000</td>\n      <td>180</td>\n      <td>2100</td>\n      <td>bus</td>\n      <td>0.114870</td>\n      <td>0.89</td>\n      <td>7300</td>\n      <td>67</td>\n      <td>579.798</td>\n      <td>824.363</td>\n      <td>27</td>\n      <td>3.5</td>\n      <td>0.396951</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lines['route_id'] = lines.index  por si quiero agregar una columna extra igual a la del inidice que contiene el route_id\n",
    "lines.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Exportacion del SM con la asignación a la red y del DF que contiene los volumenes por link"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "stack_routes: 100%|██████████| 36/36 [00:38<00:00,  1.06s/it]            \n"
     ]
    }
   ],
   "source": [
    "sm_gdl.to_zip(training_folder + 'model/base/asigned.zip', omitted_attributes=['pt_los', 'car_los'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [],
   "source": [
    "lines.to_excel (r'C:/Users/orlan/Documents/UPC/PhD/Redación Tesis/Resultados/resumen_metricas_rutas_quentin.xlsx', index = True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### FALTA: Visualización espacial, emisiones por zonas, costos operativos de cada linea, calculo del punto de equilibrio de la linea de transsporte\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
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}