{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JepNylgTObAo"
   },
   "source": [
    "TO-DO\n",
    "\n",
    "\n",
    "\n",
    "*   Crear Análisis G-R\n",
    "*   Leer Seismic Gap Hypothesis' Ten Years After\" by Y. Y. Kagan and D. D. Jackson y leer  https://sci-hub.se/https://doi.org/10.1038/ngeo1073\n",
    "*   https://sci-hub.se/https://doi.org/10.1038/ngeo1073\n",
    "\n",
    "*   Buscar lagunas Sísmicas (confirmar las conocidas y buscar \"nuevas en la base de datos\")\n",
    "*   Estimar tiempos de retorno\n",
    "*   Calcular Riesgo Sísmico en las distintas latitudes (distribución de Poisson)\n",
    "*   Mapear Riesgo Sísmico\n",
    "*   Compararlo con el historial de los últimos años\n",
    "*   Y si se puede, ocupar machine learning para predecir sismos / Mapear \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "R5Aiq1LYOBGJ"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from cartopy import config\n",
    "import cartopy.crs as ccrs\n",
    "from scipy import stats\n",
    "from datetime import datetime\n",
    "import copy\n",
    "from jupyterthemes import jtplot\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "from matplotlib.colors import ListedColormap\n",
    "jtplot.style(context='paper', fscale=1.4, spines=False, gridlines='--',figsize=(6, 4.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "jtplot.style(theme='gruvboxd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "cbAtrmnUV-Go"
   },
   "outputs": [],
   "source": [
    "def obtenerUrlSismo(a,b,magmin=6,startime=\"1960-01-01\",endtime=\"2020-11-11\"):\n",
    "    #[latitud,longitud]\n",
    "    minlatitude=str(min(a[0],b[0]))\n",
    "    minlongitude=str(min(a[1],b[1]))\n",
    "    maxlatitude=str(max(a[0],b[0]))\n",
    "    maxlongitude=str(max(a[1],b[1]))\n",
    "\n",
    "    standard_url = 'https://earthquake.usgs.gov/fdsnws/event/1/query?format=csv&orderby=magnitude&minmagnitude='+str(magmin)+'&starttime='+ str(startime)+'&endtime='+ str(endtime)\n",
    "\n",
    "    url = standard_url + '&minlatitude=' + minlatitude + '&minlongitude=' + minlongitude + '&maxlatitude=' + maxlatitude + '&maxlongitude=' + maxlongitude+ '&starttime=1971-01-01' \n",
    "    return url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "n_g736rdWCUs"
   },
   "outputs": [],
   "source": [
    "##Crea un pd desde la url del usgs\n",
    "earthquakes = pd.read_csv(obtenerUrlSismo([-14.796358, -73.850519],[-41.897113, -68.597967]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 774
    },
    "id": "vs6YBZPFX9nD",
    "outputId": "7958f3f3-e0eb-41a8-d6f3-fac37371bbad",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>depth</th>\n",
       "      <th>mag</th>\n",
       "      <th>magType</th>\n",
       "      <th>nst</th>\n",
       "      <th>gap</th>\n",
       "      <th>dmin</th>\n",
       "      <th>rms</th>\n",
       "      <th>...</th>\n",
       "      <th>updated</th>\n",
       "      <th>place</th>\n",
       "      <th>type</th>\n",
       "      <th>horizontalError</th>\n",
       "      <th>depthError</th>\n",
       "      <th>magError</th>\n",
       "      <th>magNst</th>\n",
       "      <th>status</th>\n",
       "      <th>locationSource</th>\n",
       "      <th>magSource</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-02-27T06:34:11.530Z</td>\n",
       "      <td>-36.1220</td>\n",
       "      <td>-72.8980</td>\n",
       "      <td>22.90</td>\n",
       "      <td>8.8</td>\n",
       "      <td>mww</td>\n",
       "      <td>454.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.09</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-08-14T21:38:33.360Z</td>\n",
       "      <td>offshore Bio-Bio, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>official</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-06-23T20:33:14.130Z</td>\n",
       "      <td>-16.2650</td>\n",
       "      <td>-73.6410</td>\n",
       "      <td>33.00</td>\n",
       "      <td>8.4</td>\n",
       "      <td>mww</td>\n",
       "      <td>518.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.05</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-08-14T20:50:43.254Z</td>\n",
       "      <td>near the coast of southern Peru</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>official</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-16T22:54:32.860Z</td>\n",
       "      <td>-31.5729</td>\n",
       "      <td>-71.6744</td>\n",
       "      <td>22.44</td>\n",
       "      <td>8.3</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.684</td>\n",
       "      <td>1.02</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-12-18T19:30:04.835Z</td>\n",
       "      <td>48km W of Illapel, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-04-01T23:46:47.260Z</td>\n",
       "      <td>-19.6097</td>\n",
       "      <td>-70.7691</td>\n",
       "      <td>25.00</td>\n",
       "      <td>8.2</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.66</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-11-13T19:14:26.579Z</td>\n",
       "      <td>94km NW of Iquique, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995-07-30T05:11:23.630Z</td>\n",
       "      <td>-23.3400</td>\n",
       "      <td>-70.2940</td>\n",
       "      <td>45.60</td>\n",
       "      <td>8.0</td>\n",
       "      <td>mw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.10</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-08-14T11:41:21.455Z</td>\n",
       "      <td>Antofagasta, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>hrv</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>2016-02-22T06:36:59.400Z</td>\n",
       "      <td>-30.4242</td>\n",
       "      <td>-72.2967</td>\n",
       "      <td>12.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.622</td>\n",
       "      <td>1.37</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-11-10T22:13:35.520Z</td>\n",
       "      <td>105km WSW of Coquimbo, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>2016-10-27T20:32:55.790Z</td>\n",
       "      <td>-33.7771</td>\n",
       "      <td>-72.5321</td>\n",
       "      <td>12.57</td>\n",
       "      <td>6.0</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.774</td>\n",
       "      <td>1.05</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-01-20T00:31:10.040Z</td>\n",
       "      <td>86km WSW of San Antonio, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>2016-11-08T04:55:45.900Z</td>\n",
       "      <td>-36.5776</td>\n",
       "      <td>-73.5603</td>\n",
       "      <td>20.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.82</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-07-10T15:13:00.804Z</td>\n",
       "      <td>42km WNW of Talcahuano, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>2017-04-23T02:36:07.830Z</td>\n",
       "      <td>-33.0354</td>\n",
       "      <td>-72.0296</td>\n",
       "      <td>21.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.329</td>\n",
       "      <td>0.92</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-07-10T15:27:39.435Z</td>\n",
       "      <td>37km W of Valparaiso, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.050</td>\n",
       "      <td>39.0</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>2019-12-03T08:46:35.780Z</td>\n",
       "      <td>-18.5042</td>\n",
       "      <td>-70.5760</td>\n",
       "      <td>38.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.258</td>\n",
       "      <td>1.38</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-07-10T17:55:35.215Z</td>\n",
       "      <td>28km W of Arica, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.071</td>\n",
       "      <td>19.0</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>280 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         time  latitude  longitude  depth  mag magType    nst  \\\n",
       "0    2010-02-27T06:34:11.530Z  -36.1220   -72.8980  22.90  8.8     mww  454.0   \n",
       "1    2001-06-23T20:33:14.130Z  -16.2650   -73.6410  33.00  8.4     mww  518.0   \n",
       "2    2015-09-16T22:54:32.860Z  -31.5729   -71.6744  22.44  8.3     mww    NaN   \n",
       "3    2014-04-01T23:46:47.260Z  -19.6097   -70.7691  25.00  8.2     mww    NaN   \n",
       "4    1995-07-30T05:11:23.630Z  -23.3400   -70.2940  45.60  8.0      mw    NaN   \n",
       "..                        ...       ...        ...    ...  ...     ...    ...   \n",
       "275  2016-02-22T06:36:59.400Z  -30.4242   -72.2967  12.00  6.0     mww    NaN   \n",
       "276  2016-10-27T20:32:55.790Z  -33.7771   -72.5321  12.57  6.0     mww    NaN   \n",
       "277  2016-11-08T04:55:45.900Z  -36.5776   -73.5603  20.00  6.0     mww    NaN   \n",
       "278  2017-04-23T02:36:07.830Z  -33.0354   -72.0296  21.00  6.0     mww    NaN   \n",
       "279  2019-12-03T08:46:35.780Z  -18.5042   -70.5760  38.00  6.0     mww    NaN   \n",
       "\n",
       "      gap   dmin   rms  ...                   updated  \\\n",
       "0    17.8    NaN  1.09  ...  2020-08-14T21:38:33.360Z   \n",
       "1     NaN    NaN  1.05  ...  2020-08-14T20:50:43.254Z   \n",
       "2    19.0  0.684  1.02  ...  2020-12-18T19:30:04.835Z   \n",
       "3    23.0  0.609  0.66  ...  2020-11-13T19:14:26.579Z   \n",
       "4     NaN    NaN  1.10  ...  2020-08-14T11:41:21.455Z   \n",
       "..    ...    ...   ...  ...                       ...   \n",
       "275  39.0  0.622  1.37  ...  2016-11-10T22:13:35.520Z   \n",
       "276  78.0  0.774  1.05  ...  2017-01-20T00:31:10.040Z   \n",
       "277  54.0  0.342  0.82  ...  2020-07-10T15:13:00.804Z   \n",
       "278  40.0  0.329  0.92  ...  2020-07-10T15:27:39.435Z   \n",
       "279  82.0  0.258  1.38  ...  2020-07-10T17:55:35.215Z   \n",
       "\n",
       "                               place        type horizontalError depthError  \\\n",
       "0            offshore Bio-Bio, Chile  earthquake             NaN        9.2   \n",
       "1    near the coast of southern Peru  earthquake             NaN        NaN   \n",
       "2           48km W of Illapel, Chile  earthquake             4.7        3.2   \n",
       "3          94km NW of Iquique, Chile  earthquake             NaN        1.8   \n",
       "4                 Antofagasta, Chile  earthquake             NaN        NaN   \n",
       "..                               ...         ...             ...        ...   \n",
       "275     105km WSW of Coquimbo, Chile  earthquake             4.7        1.7   \n",
       "276   86km WSW of San Antonio, Chile  earthquake             4.2        2.7   \n",
       "277    42km WNW of Talcahuano, Chile  earthquake             4.1        1.8   \n",
       "278      37km W of Valparaiso, Chile  earthquake             3.7        1.7   \n",
       "279           28km W of Arica, Chile  earthquake             5.5        1.9   \n",
       "\n",
       "     magError  magNst    status  locationSource magSource  \n",
       "0         NaN     NaN  reviewed              us  official  \n",
       "1         NaN     NaN  reviewed              us  official  \n",
       "2         NaN     NaN  reviewed              us        us  \n",
       "3         NaN     NaN  reviewed              us        us  \n",
       "4         NaN     NaN  reviewed              us       hrv  \n",
       "..        ...     ...       ...             ...       ...  \n",
       "275       NaN     NaN  reviewed              us        us  \n",
       "276       NaN     NaN  reviewed              us        us  \n",
       "277       NaN     NaN  reviewed              us        us  \n",
       "278     0.050    39.0  reviewed              us        us  \n",
       "279     0.071    19.0  reviewed              us        us  \n",
       "\n",
       "[280 rows x 22 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "earthquakes"
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  },
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    "scrolled": false
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cartopy\\mpl\\geoaxes.py:387: MatplotlibDeprecationWarning: \n",
      "The 'inframe' parameter of draw() was deprecated in Matplotlib 3.3 and will be removed two minor releases later. Use Axes.redraw_in_frame() instead. If any parameter follows 'inframe', they should be passed as keyword, not positionally.\n",
      "  return matplotlib.axes.Axes.draw(self, renderer=renderer,\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,15))\n",
    "ax = plt.axes(projection=ccrs.Robinson(-46.0))\n",
    "lat = earthquakes[\"latitude\"]\n",
    "lon= earthquakes[\"longitude\"]\n",
    "ax.set_extent([lon.min(),lon.max(),lat.min(),lat.max()],crs=ccrs.PlateCarree())\n",
    "ax.coastlines()\n",
    "ax.stock_img()\n",
    "ax.gridlines()\n",
    "plt.scatter(earthquakes['longitude'],earthquakes['latitude'],marker='o',transform=ccrs.PlateCarree(),color=\"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "A42vSh-V5M_S"
   },
   "outputs": [],
   "source": [
    "limites = np.linspace(-14.796358,-41.897113,27) # array con los límites de los intervalos de las latitudes,\n",
    "# si se cambia la cantidad de puntos, cambia el ancho del intervalo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-14.796358  , -15.83869473, -16.88103146, -17.92336819,\n",
       "       -18.96570492, -20.00804165, -21.05037838, -22.09271512,\n",
       "       -23.13505185, -24.17738858, -25.21972531, -26.26206204,\n",
       "       -27.30439877, -28.3467355 , -29.38907223, -30.43140896,\n",
       "       -31.47374569, -32.51608242, -33.55841915, -34.60075588,\n",
       "       -35.64309262, -36.68542935, -37.72776608, -38.77010281,\n",
       "       -39.81243954, -40.85477627, -41.897113  ])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-8cb0d3ec2c42>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  intervalo_iesimo[\"year\"][k] = float(intervalo_iesimo[\"time\"][k][:4])  #crea una columna con el valor float del año de cada terremoto\n"
     ]
    }
   ],
   "source": [
    "intervalos = []  #lista de los datasets de los sismos en cada intervalo\n",
    "i = 0\n",
    "while i <(len(limites)-1):  #añade recursivamente el dataset de cada intervalo\n",
    "    intervalo_iesimo =pd.read_csv(obtenerUrlSismo([limites[i], -73.850519],[limites[i+1], -68.597967]))\n",
    "    intervalo_iesimo[\"year\"]=intervalo_iesimo[\"time\"]\n",
    "    k=0\n",
    "    while k <len(intervalo_iesimo.index):\n",
    "        intervalo_iesimo[\"year\"][k] = float(intervalo_iesimo[\"time\"][k][:4])  #crea una columna con el valor float del año de cada terremoto\n",
    "        k+=1\n",
    "    intervalos.append(intervalo_iesimo)\n",
    "    i+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>depth</th>\n",
       "      <th>mag</th>\n",
       "      <th>magType</th>\n",
       "      <th>nst</th>\n",
       "      <th>gap</th>\n",
       "      <th>dmin</th>\n",
       "      <th>rms</th>\n",
       "      <th>...</th>\n",
       "      <th>place</th>\n",
       "      <th>type</th>\n",
       "      <th>horizontalError</th>\n",
       "      <th>depthError</th>\n",
       "      <th>magError</th>\n",
       "      <th>magNst</th>\n",
       "      <th>status</th>\n",
       "      <th>locationSource</th>\n",
       "      <th>magSource</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1984-06-18T11:20:17.940Z</td>\n",
       "      <td>-15.7050</td>\n",
       "      <td>-72.4910</td>\n",
       "      <td>116.8</td>\n",
       "      <td>6.5</td>\n",
       "      <td>mw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>southern Peru</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>hrv</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1989-11-29T01:00:14.850Z</td>\n",
       "      <td>-15.8080</td>\n",
       "      <td>-73.2420</td>\n",
       "      <td>70.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>mw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.10</td>\n",
       "      <td>...</td>\n",
       "      <td>southern Peru</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>hrv</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-12-01T22:40:26.610Z</td>\n",
       "      <td>-15.3122</td>\n",
       "      <td>-70.8270</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.455</td>\n",
       "      <td>1.11</td>\n",
       "      <td>...</td>\n",
       "      <td>44km NE of Huarichancara, Peru</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>us</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-05-31T05:09:38.593Z</td>\n",
       "      <td>-15.3256</td>\n",
       "      <td>-70.7731</td>\n",
       "      <td>186.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2.570</td>\n",
       "      <td>0.77</td>\n",
       "      <td>...</td>\n",
       "      <td>43 km W of Lampa, Peru</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.039</td>\n",
       "      <td>63.0</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>us</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-12T06:12:47.330Z</td>\n",
       "      <td>-15.0410</td>\n",
       "      <td>-70.4450</td>\n",
       "      <td>198.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>mwb</td>\n",
       "      <td>335.0</td>\n",
       "      <td>32.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.07</td>\n",
       "      <td>...</td>\n",
       "      <td>southern Peru</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>us</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-07-17T02:37:43.180Z</td>\n",
       "      <td>-15.6570</td>\n",
       "      <td>-71.7400</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>mww</td>\n",
       "      <td>506.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.19</td>\n",
       "      <td>...</td>\n",
       "      <td>18km W of Chivay, Peru</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>us</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006-09-30T16:26:56.120Z</td>\n",
       "      <td>-15.5880</td>\n",
       "      <td>-73.1600</td>\n",
       "      <td>107.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>mwb</td>\n",
       "      <td>338.0</td>\n",
       "      <td>50.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.81</td>\n",
       "      <td>...</td>\n",
       "      <td>southern Peru</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>us</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1987-07-13T19:14:57.920Z</td>\n",
       "      <td>-15.3320</td>\n",
       "      <td>-70.0610</td>\n",
       "      <td>241.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>mw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.10</td>\n",
       "      <td>...</td>\n",
       "      <td>southern Peru</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>hrv</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1994-06-16T18:41:28.280Z</td>\n",
       "      <td>-15.2500</td>\n",
       "      <td>-70.2940</td>\n",
       "      <td>199.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>mwb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>southern Peru</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>us</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1979-05-21T22:22:23.600Z</td>\n",
       "      <td>-15.2500</td>\n",
       "      <td>-70.0890</td>\n",
       "      <td>208.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>southern Peru</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>us</td>\n",
       "      <td>1979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       time  latitude  longitude  depth  mag magType    nst  \\\n",
       "0  1984-06-18T11:20:17.940Z  -15.7050   -72.4910  116.8  6.5      mw    NaN   \n",
       "1  1989-11-29T01:00:14.850Z  -15.8080   -73.2420   70.8  6.3      mw    NaN   \n",
       "2  2016-12-01T22:40:26.610Z  -15.3122   -70.8270   12.0  6.2     mww    NaN   \n",
       "3  2020-05-31T05:09:38.593Z  -15.3256   -70.7731  186.0  6.1     mww    NaN   \n",
       "4  2009-07-12T06:12:47.330Z  -15.0410   -70.4450  198.9  6.1     mwb  335.0   \n",
       "5  2013-07-17T02:37:43.180Z  -15.6570   -71.7400    7.0  6.0     mww  506.0   \n",
       "6  2006-09-30T16:26:56.120Z  -15.5880   -73.1600  107.0  6.0     mwb  338.0   \n",
       "7  1987-07-13T19:14:57.920Z  -15.3320   -70.0610  241.4  6.0      mw    NaN   \n",
       "8  1994-06-16T18:41:28.280Z  -15.2500   -70.2940  199.5  6.0     mwb    NaN   \n",
       "9  1979-05-21T22:22:23.600Z  -15.2500   -70.0890  208.0  6.0      mb    NaN   \n",
       "\n",
       "    gap   dmin   rms  ...                           place        type  \\\n",
       "0   NaN    NaN  1.00  ...                   southern Peru  earthquake   \n",
       "1   NaN    NaN  1.10  ...                   southern Peru  earthquake   \n",
       "2  43.0  2.455  1.11  ...  44km NE of Huarichancara, Peru  earthquake   \n",
       "3  73.0  2.570  0.77  ...          43 km W of Lampa, Peru  earthquake   \n",
       "4  32.1    NaN  1.07  ...                   southern Peru  earthquake   \n",
       "5  37.0    NaN  1.19  ...          18km W of Chivay, Peru  earthquake   \n",
       "6  50.6    NaN  0.81  ...                   southern Peru  earthquake   \n",
       "7   NaN    NaN  1.10  ...                   southern Peru  earthquake   \n",
       "8   NaN    NaN  1.00  ...                   southern Peru  earthquake   \n",
       "9   NaN    NaN   NaN  ...                   southern Peru  earthquake   \n",
       "\n",
       "  horizontalError depthError magError  magNst    status  locationSource  \\\n",
       "0             NaN        NaN      NaN     NaN  reviewed              us   \n",
       "1             NaN        NaN      NaN     NaN  reviewed              us   \n",
       "2             6.2        1.7      NaN     NaN  reviewed              us   \n",
       "3             5.1        1.8    0.039    63.0  reviewed              us   \n",
       "4             NaN        NaN      NaN     NaN  reviewed              us   \n",
       "5             NaN        NaN      NaN     NaN  reviewed              us   \n",
       "6             NaN        NaN      NaN     NaN  reviewed              us   \n",
       "7             NaN        NaN      NaN     NaN  reviewed              us   \n",
       "8             NaN        NaN      NaN     NaN  reviewed              us   \n",
       "9             NaN        NaN      NaN     NaN  reviewed              us   \n",
       "\n",
       "   magSource  year  \n",
       "0        hrv  1984  \n",
       "1        hrv  1989  \n",
       "2         us  2016  \n",
       "3         us  2020  \n",
       "4         us  2009  \n",
       "5         us  2013  \n",
       "6         us  2006  \n",
       "7        hrv  1987  \n",
       "8         us  1994  \n",
       "9         us  1979  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intervalos[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-d37764fc9fdd>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  earthquakes[\"year\"][i] = float(earthquakes[\"time\"][i][:4])  #crea una columna con el valor float del año de cada terremoto\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "earthquakes[\"year\"]=earthquakes[\"time\"]\n",
    "while i <earthquakes[\"mag\"].count():\n",
    "    earthquakes[\"year\"][i] = float(earthquakes[\"time\"][i][:4])  #crea una columna con el valor float del año de cada terremoto\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-02-27T06:34:11.530Z</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-06-23T20:33:14.130Z</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-16T22:54:32.860Z</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-04-01T23:46:47.260Z</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995-07-30T05:11:23.630Z</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>2016-02-22T06:36:59.400Z</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>2016-10-27T20:32:55.790Z</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>2016-11-08T04:55:45.900Z</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>2017-04-23T02:36:07.830Z</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>2019-12-03T08:46:35.780Z</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>280 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         time  year\n",
       "0    2010-02-27T06:34:11.530Z  2010\n",
       "1    2001-06-23T20:33:14.130Z  2001\n",
       "2    2015-09-16T22:54:32.860Z  2015\n",
       "3    2014-04-01T23:46:47.260Z  2014\n",
       "4    1995-07-30T05:11:23.630Z  1995\n",
       "..                        ...   ...\n",
       "275  2016-02-22T06:36:59.400Z  2016\n",
       "276  2016-10-27T20:32:55.790Z  2016\n",
       "277  2016-11-08T04:55:45.900Z  2016\n",
       "278  2017-04-23T02:36:07.830Z  2017\n",
       "279  2019-12-03T08:46:35.780Z  2019\n",
       "\n",
       "[280 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthquakes[[\"time\",\"year\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      280.0\n",
       "unique      49.0\n",
       "top       2010.0\n",
       "freq        29.0\n",
       "Name: year, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthquakes[\"year\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "earthquakes[\"year2\"]= earthquakes[\"year\"]-1970"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "earthquakes70s= earthquakes[(earthquakes[\"year\"]>=1970)&(earthquakes[\"year\"]<1980)]\n",
    "earthquakes80s= earthquakes[(earthquakes[\"year\"]>=1980)&(earthquakes[\"year\"]<1990)]\n",
    "earthquakes90s= earthquakes[(earthquakes[\"year\"]>=1990)&(earthquakes[\"year\"]<2000)]\n",
    "earthquakes2ks= earthquakes[(earthquakes[\"year\"]>=2000)&(earthquakes[\"year\"]<2010)]\n",
    "earthquakes2k10s= earthquakes[(earthquakes[\"year\"]>=2010)&(earthquakes[\"year\"]<2020)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "decadas=[]\n",
    "decadas.append(earthquakes70s)\n",
    "decadas.append(earthquakes80s)\n",
    "decadas.append(earthquakes90s)\n",
    "decadas.append(earthquakes2ks)\n",
    "decadas.append(earthquakes2k10s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def separaDecadas(earthquakes):  #separa el catálogo de terremotos en una lista con los catálogos de cada decada\n",
    "    decadas=[]\n",
    "    earthquakes70s= earthquakes[(earthquakes[\"year\"]>=1970)&(earthquakes[\"year\"]<1980)]\n",
    "    earthquakes80s= earthquakes[(earthquakes[\"year\"]>=1980)&(earthquakes[\"year\"]<1990)]\n",
    "    earthquakes90s= earthquakes[(earthquakes[\"year\"]>=1990)&(earthquakes[\"year\"]<2000)]\n",
    "    earthquakes2ks= earthquakes[(earthquakes[\"year\"]>=2000)&(earthquakes[\"year\"]<2010)]\n",
    "    earthquakes2k10s= earthquakes[(earthquakes[\"year\"]>=2010)&(earthquakes[\"year\"]<=2020)]\n",
    "    decadas.append(earthquakes70s)\n",
    "    decadas.append(earthquakes80s)\n",
    "    decadas.append(earthquakes90s)\n",
    "    decadas.append(earthquakes2ks)\n",
    "    decadas.append(earthquakes2k10s)\n",
    "    return decadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FrecuenciaProm(decadas,n_decadas=5):  # Calcula el promedio de las frecuencias de terremotos por década\n",
    "    i=0\n",
    "    suma=0\n",
    "    while i <n_decadas:\n",
    "        x = decadas[i][\"mag\"].count()\n",
    "        i+=1\n",
    "        suma+=x\n",
    "    return suma/n_decadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "frecuencia_intervalos=[] # Crea un arreglo con las frecuencias promedio en cada intervalo de latitud\n",
    "i=0\n",
    "while i <(len(limites)-1):\n",
    "    decadas_intervalos= separaDecadas(intervalos[i])\n",
    "    frecuencia_intervalos.append(FrecuenciaProm(decadas_intervalos))\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.0,\n",
       " 1.6,\n",
       " 2.2,\n",
       " 2.0,\n",
       " 2.6,\n",
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       " 2.8,\n",
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       " 1.4,\n",
       " 1.6,\n",
       " 0.4,\n",
       " 0.2,\n",
       " 0.0]"
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     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frecuencia_intervalos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-22-1de5cb23f1b8>:3: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  Periodos_de_retorno.append(i**(-1))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5,\n",
       " 0.625,\n",
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       " 5.0,\n",
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       " 0.5,\n",
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       " 0.20833333333333334,\n",
       " 0.20833333333333334,\n",
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       " 0.7142857142857143,\n",
       " 0.625,\n",
       " 2.5,\n",
       " 5.0,\n",
       " inf]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Periodos_de_retorno=[] # Crea un arreglo con los periodos de retorno en cada intervalo de latitud\n",
    "for i in frecuencia_intervalos:\n",
    "    Periodos_de_retorno.append(i**(-1))\n",
    "Periodos_de_retorno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8646647167633873,\n",
       " 0.7981034820053446,\n",
       " 0.8891968416376661,\n",
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       " 0.8891968416376661,\n",
       " 0.18126924692201818,\n",
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       " 0.8347011117784134,\n",
       " 0.5506710358827784,\n",
       " 0.8891968416376661,\n",
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       " 0.7534030360583935,\n",
       " 0.9726762775527075,\n",
       " 0.9899481642553665,\n",
       " 0.99177025295098,\n",
       " 0.99177025295098,\n",
       " 0.9666267300396739,\n",
       " 0.9092820467105875,\n",
       " 0.9592377960216338,\n",
       " 0.7534030360583935,\n",
       " 0.7981034820053446,\n",
       " 0.3296799539643607,\n",
       " 0.18126924692201818,\n",
       " 0.0]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Peligro_sismico=[] #arreglo con las probabilidades de ocurrencia de un terremoto en los distintos intevalos de latitud\n",
    "for i in Periodos_de_retorno:\n",
    "    T=1  #Periodo de tiempo que se está estudiando, en decadas  ej: T=1, probablilidad de un terremoto en un plazo de 10 años\n",
    "    Peligro_sismico.append(1-(np.exp(-T/i))) #formula obtenida a partir de considerar los terremotos como una distribución de Poisson\n",
    "Peligro_sismico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Prob_sismos(mag=6,n_intervalos=27,periodo=1):\n",
    "    def obtenerUrlSismo(a,b,magmin=mag,startime=\"1960-01-01\",endtime=\"2020-11-11\"):\n",
    "        #[latitud,longitud]\n",
    "        minlatitude=str(min(a[0],b[0]))\n",
    "        minlongitude=str(min(a[1],b[1]))\n",
    "        maxlatitude=str(max(a[0],b[0]))\n",
    "        maxlongitude=str(max(a[1],b[1]))\n",
    "\n",
    "        standard_url = 'https://earthquake.usgs.gov/fdsnws/event/1/query?format=csv&orderby=magnitude&minmagnitude='+str(magmin)+'&starttime='+ str(startime)+'&endtime='+ str(endtime)\n",
    "\n",
    "        url = standard_url + '&minlatitude=' + minlatitude + '&minlongitude=' + minlongitude + '&maxlatitude=' + maxlatitude + '&maxlongitude=' + maxlongitude+ '&starttime=1971-01-01' \n",
    "        return url\n",
    "#se definen los intervalos    \n",
    "    limites = np.linspace(-14.796358,-41.897113,n_intervalos)\n",
    "    intervalos = []  #lista de los datasets de los sismos en cada intervalo\n",
    "    i = 0\n",
    "    while i <(len(limites)-1):  #añade recursivamente el dataset de cada intervalo\n",
    "        intervalo_iesimo =pd.read_csv(obtenerUrlSismo([limites[i], -73.850519],[limites[i+1], -68.597967]))\n",
    "        intervalo_iesimo[\"year\"]=intervalo_iesimo[\"time\"]\n",
    "        k=0\n",
    "        while k <len(intervalo_iesimo.index):\n",
    "            intervalo_iesimo[\"year\"][k] = float(intervalo_iesimo[\"time\"][k][:4])  #crea una columna con el valor float del año de cada terremoto\n",
    "            k+=1\n",
    "        intervalos.append(intervalo_iesimo)\n",
    "        i+=1\n",
    "#se separan en decadas y calcula las frecuencias promedio\n",
    "    frecuencia_intervalos=[]\n",
    "    i=0\n",
    "    while i <(len(limites)-1):\n",
    "        decadas_intervalos= separaDecadas(intervalos[i])\n",
    "        frecuencia_intervalos.append(FrecuenciaProm(decadas_intervalos))\n",
    "        i+=1\n",
    "#se calculan los periodos de retorno\n",
    "    Periodos_de_retorno=[] # Crea un arreglo con los periodos de retorno en cada intervalo de latitud\n",
    "    for i in frecuencia_intervalos:\n",
    "        Periodos_de_retorno.append(i**(-1))\n",
    "\n",
    "#se calcula el peligro sísmico\n",
    "    Peligro_sismico=[] #arreglo con las probabilidades de ocurrencia de un terremoto en los distintos intevalos de latitud\n",
    "    T=periodo  #Periodo de tiempo que se está estudiando, en decadas  ej: T=1, probablilidad de un terremoto en un plazo de 10 años\n",
    "    for i in Periodos_de_retorno:\n",
    "       Peligro_sismico.append(1-(np.exp(-T/i)))\n",
    "    return Peligro_sismico\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-24-9c6b161cfdcd>:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  intervalo_iesimo[\"year\"][k] = float(intervalo_iesimo[\"time\"][k][:4])  #crea una columna con el valor float del año de cada terremoto\n",
      "<ipython-input-24-9c6b161cfdcd>:36: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  Periodos_de_retorno.append(i**(-1))\n"
     ]
    }
   ],
   "source": [
    "g = Prob_sismos(mag=7,n_intervalos=27,periodo=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 0.18126924692201818,\n",
       " 0.3296799539643607,\n",
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       " 0.18126924692201818,\n",
       " 0.0,\n",
       " 0.18126924692201818,\n",
       " 0.3296799539643607,\n",
       " 0.5506710358827784,\n",
       " 0.0,\n",
       " 0.18126924692201818,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.18126924692201818,\n",
       " 0.3296799539643607,\n",
       " 0.5506710358827784,\n",
       " 0.3296799539643607,\n",
       " 0.18126924692201818,\n",
       " 0.18126924692201818,\n",
       " 0.0,\n",
       " 0.4511883639059735,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def frecuencia_prom(frecuencias,n_decadas):\n",
    "    return np.mean(frecuencias[:n_decadas])\n",
    "def es_laguna(decadas):\n",
    "    laguna = []\n",
    "    frecuencias = []\n",
    "    i=0\n",
    "    while i <5:\n",
    "        frecuencias.append(decadas[i][\"mag\"].count())\n",
    "        i+=1\n",
    "    promedio = frecuencia_prom(frecuencias,3)\n",
    "    k = 0\n",
    "    while k+3<5:\n",
    "        if frecuencias[3+k]<promedio:\n",
    "            laguna.append(True)\n",
    "        else:\n",
    "            laguna.append(False)\n",
    "        k+=1\n",
    "    if all(laguna):\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lagunas_en_chile(mag=6,n_intervalos=27):\n",
    "    def obtenerUrlSismo(a,b,magmin=mag,startime=\"1960-01-01\",endtime=\"2020-11-11\"):\n",
    "        #[latitud,longitud]\n",
    "        minlatitude=str(min(a[0],b[0]))\n",
    "        minlongitude=str(min(a[1],b[1]))\n",
    "        maxlatitude=str(max(a[0],b[0]))\n",
    "        maxlongitude=str(max(a[1],b[1]))\n",
    "\n",
    "        standard_url = 'https://earthquake.usgs.gov/fdsnws/event/1/query?format=csv&orderby=magnitude&minmagnitude='+str(magmin)+'&starttime='+ str(startime)+'&endtime='+ str(endtime)\n",
    "\n",
    "        url = standard_url + '&minlatitude=' + minlatitude + '&minlongitude=' + minlongitude + '&maxlatitude=' + maxlatitude + '&maxlongitude=' + maxlongitude+ '&starttime=1971-01-01' \n",
    "        return url\n",
    "    earthquakes = pd.read_csv(obtenerUrlSismo([-14.796358, -73.850519],[-41.897113, -68.597967]))  \n",
    "#se definen los intervalos    \n",
    "    limites = np.linspace(-14.796358,-41.897113,n_intervalos)\n",
    "    intervalos = []  #lista de los datasets de los sismos en cada intervalo\n",
    "    i = 0\n",
    "    while i <(len(limites)-1):  #añade recursivamente el dataset de cada intervalo\n",
    "        intervalo_iesimo =pd.read_csv(obtenerUrlSismo([limites[i], -73.850519],[limites[i+1], -68.597967]))\n",
    "        intervalo_iesimo[\"year\"]=intervalo_iesimo[\"time\"]\n",
    "        k=0\n",
    "        while k <len(intervalo_iesimo.index):\n",
    "            intervalo_iesimo[\"year\"][k] = float(intervalo_iesimo[\"time\"][k][:4])  #crea una columna con el valor float del año de cada terremoto\n",
    "            k+=1\n",
    "        intervalos.append(intervalo_iesimo)\n",
    "        i+=1\n",
    "    \n",
    "    lagunas=[]\n",
    "    i=0\n",
    "    while i <(len(limites)-1):\n",
    "        lagunas.append(es_laguna(separaDecadas(intervalos[i])))\n",
    "        i+=1\n",
    "    return lagunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-28-e5540f490a9f>:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  intervalo_iesimo[\"year\"][k] = float(intervalo_iesimo[\"time\"][k][:4])  #crea una columna con el valor float del año de cada terremoto\n"
     ]
    }
   ],
   "source": [
    "f = lagunas_en_chile(mag=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " True,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " True,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(30,30))\n",
    "ax = plt.axes(projection=ccrs.PlateCarree(-46.0))\n",
    "lat = earthquakes[\"latitude\"]\n",
    "lon= earthquakes[\"longitude\"]\n",
    "ax.set_extent([lon.min(),lon.max(),lat.min(),lat.max()],crs=ccrs.PlateCarree())\n",
    "ax.coastlines()\n",
    "ax.stock_img()\n",
    "ax.gridlines()\n",
    "plt.scatter(earthquakes['longitude'],earthquakes['latitude'],marker='o',transform=ccrs.PlateCarree(),color=\"red\")\n",
    "grid_lines = ax.gridlines(draw_labels = True)\n",
    "grid_lines.xformatter = LONGITUDE_FORMATTER\n",
    "grid_lines.yformatter = LATITUDE_FORMATTER\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx, yy = np.meshgrid( np.linspace(-74,-67,101),\n",
    "                      np.linspace(-14.796358,-41.897113,27))\n",
    "grid = np.c_[xx.ravel(), yy.ravel()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 345.6x259.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(xx, yy, s=1)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmap = ListedColormap(['red', 'orange', 'yellow'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_classes =np.zeros(+1+len(np.array(g)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_classes=np.tile(grid_classes,101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. ... 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "print(grid_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_classes = grid_classes.reshape(xx.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "descriptor '_as_mpl_transform' for 'cartopy._crs.CRS' objects doesn't apply to a 'GeoAxesSubplot' object",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-137-6b883d1205dc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mlon\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mearthquakes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"longitude\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_extent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlon\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlon\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mccrs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlateCarree\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontourf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrid_classes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mccrs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlateCarree\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mcontourf\u001b[1;34m(data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2559\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0m_copy_docstring_and_deprecators\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontourf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2560\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcontourf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2561\u001b[1;33m     __ret = gca().contourf(\n\u001b[0m\u001b[0;32m   2562\u001b[0m         \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m\"data\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m}\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2563\u001b[0m         **kwargs)\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\cartopy\\mpl\\geoaxes.py\u001b[0m in \u001b[0;36mcontourf\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1398\u001b[0m                         \u001b[0msub_trans\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforce_path_ccw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1399\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1400\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontourf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1401\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1402\u001b[0m         \u001b[1;31m# We need to compute the dataLim correctly for contours.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1436\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1437\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1438\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1439\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1440\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mcontourf\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   6330\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcontourf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6331\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'filled'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6332\u001b[1;33m         \u001b[0mcontours\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmcontour\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQuadContourSet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6333\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_request_autoscale_view\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6334\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcontours\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\contour.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, ax, levels, filled, linewidths, linestyles, hatches, alpha, origin, extent, cmap, colors, norm, vmin, vmax, extend, antialiased, nchunk, locator, transform, *args, **kwargs)\u001b[0m\n\u001b[0;32m    814\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_transform\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    815\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 816\u001b[1;33m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    817\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process_levels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\contour.py\u001b[0m in \u001b[0;36m_process_args\u001b[1;34m(self, corner_mask, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1437\u001b[0m                 x, y, z.filled(), _mask, self._corner_mask, self.nchunk)\n\u001b[0;32m   1438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1439\u001b[1;33m             \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1440\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1441\u001b[0m             \u001b[1;31m# if the transform is not trans data, and some part of it\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\contour.py\u001b[0m in \u001b[0;36mget_transform\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    943\u001b[0m         elif (not isinstance(self._transform, mtransforms.Transform)\n\u001b[0;32m    944\u001b[0m               and hasattr(self._transform, '_as_mpl_transform')):\n\u001b[1;32m--> 945\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_transform\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_transform\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_as_mpl_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    946\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_transform\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    947\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: descriptor '_as_mpl_transform' for 'cartopy._crs.CRS' objects doesn't apply to a 'GeoAxesSubplot' object"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 345.6x259.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = ListedColormap([\"red\"])\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "lat = earthquakes[\"latitude\"]\n",
    "lon= earthquakes[\"longitude\"]\n",
    "ax.set_extent([lon.min(),lon.max(),lat.min(),lat.max()],crs=ccrs.PlateCarree())\n",
    "plt.contourf(xx, yy, grid_classes, cmap=cmap,transform=ccrs.PlateCarree)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'lower'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-132-01d2c52e86e3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mgrid_lines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgridlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdraw_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpcolormesh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrid_classes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mpcolormesh\u001b[1;34m(alpha, norm, cmap, vmin, vmax, shading, antialiased, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2783\u001b[0m         \u001b[0mvmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshading\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mantialiased\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2784\u001b[0m         **kwargs):\n\u001b[1;32m-> 2785\u001b[1;33m     __ret = gca().pcolormesh(\n\u001b[0m\u001b[0;32m   2786\u001b[0m         \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2787\u001b[0m         \u001b[0mvmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshading\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshading\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mantialiased\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mantialiased\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\cartopy\\mpl\\geoaxes.py\u001b[0m in \u001b[0;36mpcolormesh\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1457\u001b[0m                              ' consider using PlateCarree/RotatedPole.')\n\u001b[0;32m   1458\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'transform'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1459\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pcolormesh_patched\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1460\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautoscale_view\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1461\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\cartopy\\mpl\\geoaxes.py\u001b[0m in \u001b[0;36m_pcolormesh_patched\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1483\u001b[0m         \u001b[0mvmin\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'vmin'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1484\u001b[0m         \u001b[0mvmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'vmax'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1485\u001b[1;33m         \u001b[0mshading\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'shading'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'flat'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1486\u001b[0m         \u001b[0mantialiased\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'antialiased'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1487\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'edgecolors'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'None'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'lower'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(30,30))\n",
    "ax = plt.axes(projection=ccrs.PlateCarree(-46.0))\n",
    "lat = earthquakes[\"latitude\"]\n",
    "lon= earthquakes[\"longitude\"]\n",
    "ax.set_extent([lon.min(),lon.max(),lat.min(),lat.max()],crs=ccrs.PlateCarree())\n",
    "\n",
    "plt.scatter(earthquakes['longitude'],earthquakes['latitude'],marker='o',transform=ccrs.PlateCarree(),color=\"red\")\n",
    "grid_lines = ax.gridlines(draw_labels = True)\n",
    "\n",
    "plt.pcolormesh(xx, yy, grid_classes, cmap=cmap)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "frecuencias_ultima_decada=[]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "frecuencias_penultima_decada=[]\n",
    "\n",
    "i=0\n",
    "while i <(len(limites)-1):\n",
    "    decadas_intervalos= separaDecadas(intervalos[i])\n",
    "    frecuencias_penultima_decada.append(decadas_intervalos[3][\"mag\"].count())\n",
    "    i+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#se crea un array con las frecuencias de la úlima decada (2010-2020)\n",
    "frecuencias_ultima_decada=[]\n",
    "\n",
    "i=0\n",
    "while i <(len(limites)-1):\n",
    "    decadas_intervalos= separaDecadas(intervalos[i])\n",
    "    frecuencias_ultima_decada.append(decadas_intervalos[4][\"mag\"].count())\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 2, 4, 0, 4, 2, 0, 5, 3, 1, 0, 2, 1, 2, 2, 4, 4, 1, 1, 1, 0, 1, 0, 1, 0, 0]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frecuencias_penultima_decada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3,\n",
       " 2,\n",
       " 1,\n",
       " 6,\n",
       " 6,\n",
       " 6,\n",
       " 1,\n",
       " 0,\n",
       " 0,\n",
       " 1,\n",
       " 1,\n",
       " 0,\n",
       " 6,\n",
       " 1,\n",
       " 9,\n",
       " 7,\n",
       " 13,\n",
       " 7,\n",
       " 7,\n",
       " 10,\n",
       " 15,\n",
       " 5,\n",
       " 4,\n",
       " 0,\n",
       " 1,\n",
       " 0]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frecuencias_ultima_decada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2, 3],\n",
       " [2, 2],\n",
       " [4, 1],\n",
       " [0, 6],\n",
       " [4, 6],\n",
       " [2, 6],\n",
       " [0, 1],\n",
       " [5, 0],\n",
       " [3, 0],\n",
       " [1, 1],\n",
       " [0, 1],\n",
       " [2, 0],\n",
       " [1, 6],\n",
       " [2, 1],\n",
       " [2, 9],\n",
       " [4, 7],\n",
       " [4, 13],\n",
       " [1, 7],\n",
       " [1, 7],\n",
       " [1, 10],\n",
       " [0, 15],\n",
       " [1, 5],\n",
       " [0, 4],\n",
       " [1, 0],\n",
       " [0, 1],\n",
       " [0, 0]]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frecuencias_entrenamiento=[]\n",
    "i=0\n",
    "while i<(len(limites)-1):\n",
    "    intervalo_i = [frecuencias_penultima_decada[i],frecuencias_ultima_decada[i]]\n",
    "    frecuencias_entrenamiento.append(intervalo_i)\n",
    "    i+=1\n",
    "frecuencias_entrenamiento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-24-9c6b161cfdcd>:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  intervalo_iesimo[\"year\"][k] = float(intervalo_iesimo[\"time\"][k][:4])  #crea una columna con el valor float del año de cada terremoto\n",
      "<ipython-input-24-9c6b161cfdcd>:36: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  Periodos_de_retorno.append(i**(-1))\n"
     ]
    }
   ],
   "source": [
    "h = Prob_sismos(mag=7,n_intervalos=27,periodo=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0., 18., 33.,  0., 45., 18.,  0., 18., 33., 55.,  0., 18.,  0.,\n",
       "        0.,  0., 18., 33., 55., 33., 18., 18.,  0., 45.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g=np.copy(h)\n",
    "\n",
    "i=0\n",
    "while i<(len(limites)-1):\n",
    "    g[i]=g[i]*100\n",
    "    g[i]=int(g[i].round(0))\n",
    "    i+=1\n",
    "g.tolist()\n",
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0., 18., 33.,  0., 45., 18.,  0., 18., 33., 55.,  0., 18.,  0.,\n",
       "        0.,  0., 18., 33., 55., 33., 18., 18.,  0., 45.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "classifier_svr = SVR(kernel=\"linear\",degree=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.243976777939045"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "# split the data with 50% in each set\n",
    "X1, X2, y1, y2 = train_test_split(frecuencias_entrenamiento,g,train_size=0.5)\n",
    "\n",
    "# fit the model on one set of data\n",
    "classifier_svr.fit(X1, y1)\n",
    "\n",
    "# evaluate the model on the second set of data\n",
    "y2_model = classifier_svr.predict(X2)\n",
    "mean_absolute_error(y2,y2_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0., 18., 33.,  0., 18., 55.,  0., 18., 55.,  0., 33.,  0.,  0.])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_squared_error?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14.88113208])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_svr.predict([[1,5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Proyecto Riesgo Sismico.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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