{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Banderazo por el mar - 10/03/2018\n",
    "Vamos a probar de dibujar una bandera del mar, pero que en su parte superior siga el perfil de elevación de la ruta del banderazo (carretera El Alto / Apacheta - Oruro).\n",
    "\n",
    "## Digitalización de la ruta\n",
    "Primero, digitalizamos la ruta con QGis: colocamos un fondo de OpenStreetMap, creamos una nueva capa vectorial de tipo Shapefile, creamos un nuevo objeto Línea, digitalizando desde Apacheta (barrio a la salida de El Alto) hasta el Casco Minero en la entrada de la ciudad de Oruro, siguiendo la doble vía La Paz - Oruro.\n",
    "\n",
    "![Digitalización de la ruta del banderazo en QGis](digitalizacion_qgis.png \"Digitalización en QGis\")\n",
    "\n",
    "Exportamos el trazo en el archivo [ruta_banderazo.geojson](ruta_banderazo.geojson). Lo cargamos y visualizamos usando [geopandas](http://geopandas.org/):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f29bf0b9630>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "ruta_banderazo = gpd.read_file('ruta_banderazo.geojson')\n",
    "ruta_banderazo.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La capa usa la proyección \"pseudo-Mercator\" / EPSG:3857. Para lo que sigue, la reproyectamos en WGS84 / EPSG:4326."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f29bb5a38d0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f29fc06ae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ruta_banderazo = ruta_banderazo.to_crs({'init': 'epsg:4326'})\n",
    "ruta_banderazo.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elevación\n",
    "\n",
    "Ahora queremos calcular la elevación de la ruta, o sea para cada punto de la línea, encontrar la altura en metros, a partir de un archivo raster de elevación.\n",
    "\n",
    "Lo más simple es llamar a un servicio que hará el cálculo. Para eso usamos la librería [geocoder](https://geocoder.readthedocs.io/providers/Google.html#elevation) y en particular el servicio de Google. Para que funcione, es necesario tener la variable `GOOGLE_API_KEY` establecida con el valor de la clave, en el entorno de bash.\n",
    "\n",
    "Para saber como manipular los datos, en este caso LineString, ver la librería [shapely](https://shapely.readthedocs.io/en/latest/).\n",
    "\n",
    "Hacemos la prueba del servicio de Google para encontrar la elevación del primer punto (Apacheta, salida de El Alto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El primer punto (Apacheta) se encuentra a 3961.9 msnm\n"
     ]
    }
   ],
   "source": [
    "# sudo python3 -m pip install geocoder\n",
    "import geocoder\n",
    "# Hacemos la prueba con el primer punto, Apacheta\n",
    "# Nota: hay que manipular un poco el orden y la forma de presentar latitud y longitud\n",
    "punto1 = [ruta_banderazo.geometry[0].coords[0][1], ruta_banderazo.geometry[0].coords[0][0]]\n",
    "g = geocoder.google(punto1, method='elevation')\n",
    "print(f'El primer punto (Apacheta) se encuentra a {g.meters} msnm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es correcto, aproximadamente 4.000 metros sobre el nivel del mar.\n",
    "\n",
    "## Distancia\n",
    "\n",
    "Para poder generar el perfil de elevación, es necesario también calcular la distancia en kilometros entre dos puntos. Hay dos formas de hacer: proyectar en metros y calcular la distancia euclidiana, o calcular la distancia \"geográfica\" con cálculos sobre la esfera. Usaremos la segunda forma, gracias al algoritmo de Vincenty. Para eso tenemos que importar la librería [GeoPy](https://geopy.readthedocs.io/en/latest/index.html#module-geopy.distance).\n",
    "\n",
    "Para hacer la prueba, calculamos la distancia entre los dos primeros puntos, que están dentro del mismo barrio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La distancia entre los dos primeros puntos es de 335.5501109716255 metros\n"
     ]
    }
   ],
   "source": [
    "# sudo python3 -m pip install geopy\n",
    "from geopy.distance import vincenty\n",
    "punto1 = ruta_banderazo.geometry[0].coords[0]\n",
    "punto2 = ruta_banderazo.geometry[0].coords[1]\n",
    "print(f'La distancia entre los dos primeros puntos es de {vincenty(punto1, punto2).m} metros')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfecto, unos 300 metros entre los dos primeros puntos, es correcto.\n",
    "\n",
    "## Perfil de elevación\n",
    "\n",
    "Ahora, vamos a construir el perfil de elevación, poniendo en `x` la distancia en kilometros del punto desde el primer punto, y en `y` la altura en metros. Hay 144 puntos, las llamadas a la API de Google toman un cierto tiempo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "distancia = []\n",
    "elevacion = []\n",
    "punto_anterior = ruta_banderazo.geometry[0].coords[0]\n",
    "\n",
    "for i in range(0, len(ruta_banderazo.geometry[0].coords)):\n",
    "#for i in range(0, 20):\n",
    "    punto = ruta_banderazo.geometry[0].coords[i]\n",
    "    distancia.append(vincenty(punto_anterior, punto).km)\n",
    "    \n",
    "    # hay que probar varias veces, porque en mi caso, un 25% de las consultas falla\n",
    "    pruebas = 0\n",
    "    altura = None\n",
    "    while altura is None and pruebas < 10:\n",
    "        altura = geocoder.google([punto[1], punto[0]], method='elevation').meters\n",
    "        pruebas += 1\n",
    "    elevacion.append(altura)\n",
    "    punto_anterior = punto\n",
    "\n",
    "# Hay que sumar las distancias de manera cumulativa\n",
    "import numpy as np\n",
    "distancia_acumulada = np.cumsum(distancia)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mostramos el perfil usando [matplotlib](https://matplotlib.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f29b9737198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ax1 = plt.subplot()\n",
    "ax1.plot(distancia_acumulada, elevacion, 'k')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dibujo de la bandera\n",
    "\n",
    "Primero vamos a dibujar solamente un rectángulo azul, con la parte superior que sigue el perfil de elevación. Y luego aplicaremos este rectángulo como [patch](https://matplotlib.org/api/_as_gen/matplotlib.patches.Polygon.html#matplotlib.patches.Polygon) sobre una imagen de la bandera maritima."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f29d2316e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Polygon\n",
    "\n",
    "elevacion_min = 3500\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(distancia_acumulada, elevacion, 'w', linewidth=2)\n",
    "plt.ylim(ymin=elevacion_min)\n",
    "\n",
    "# Make the shaded region\n",
    "verts = [(0, elevacion_min)] + list(zip(distancia_acumulada, elevacion)) + [(distancia_acumulada[len(distancia_acumulada)-1], elevacion_min)]\n",
    "poly = Polygon(verts, facecolor='#003f82', edgecolor='0.5')\n",
    "ax.add_patch(poly)\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora mostramos la imagen de la bandera, y le aplicamos el perfil."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f29bb272160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = plt.imread('bandera.png')\n",
    "px_alto, px_largo, z = image.shape\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(40, 20))\n",
    "im = ax.imshow(image)\n",
    "\n",
    "# Ponemos el pefil de elevación a la escala de la imagen\n",
    "distancia_px = distancia_acumulada / max(distancia_acumulada) * px_largo\n",
    "elevacion_px = [(4200 - x) for x in elevacion]\n",
    "verts = [(0, px_alto)] + list(zip(distancia_px, elevacion_px)) + [(max(distancia_px), px_alto)]\n",
    "poly = Polygon(verts, edgecolor='0.5', transform=ax.transData)\n",
    "\n",
    "# Aplicamos la mascara sobre la imagen\n",
    "im.set_clip_path(poly)\n",
    "ax.axis('off')\n",
    "\n",
    "plt.savefig('BanderazoPorElMar.png', bbox_inches='tight')"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}