{
 "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": 66,
   "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 matplotlib.colors import ListedColormap\n",
    "jtplot.style(context='paper', fscale=1.4, spines=False, gridlines='--',figsize=(6, 4.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "jtplot.style(theme='gruvboxd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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": 19,
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthquakes"
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  },
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    "outputId": "0f62af0c-15a7-473c-f785-09d507615795",
    "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": 21,
   "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": 22,
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-23-8cb0d3ec2c42>: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": 24,
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intervalos[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-26-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": 27,
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthquakes[[\"time\",\"year\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthquakes[\"year\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "earthquakes[\"year2\"]= earthquakes[\"year\"]-1970"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 31,
   "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": 32,
   "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": 33,
   "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": 36,
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.0,\n",
       " 1.6,\n",
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       " 2.6,\n",
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       " 0.0]"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frecuencia_intervalos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-38-1de5cb23f1b8>: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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       " 2.5,\n",
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       " inf]"
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     },
     "execution_count": 38,
     "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8646647167633873,\n",
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       " 0.99177025295098,\n",
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       " 0.7981034820053446,\n",
       " 0.3296799539643607,\n",
       " 0.18126924692201818,\n",
       " 0.0]"
      ]
     },
     "execution_count": 39,
     "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": 40,
   "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",
    "    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",
    "#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": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-40-eb5c2057330e>: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",
      "<ipython-input-40-eb5c2057330e>:37: 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": 42,
   "metadata": {},
   "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",
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       " 0.4511883639059735,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": 55,
   "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": 96,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-55-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": 97,
   "metadata": {},
   "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": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "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": {
      "image/png": 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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": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx, yy = np.meshgrid(np.linspace(0, 1, 101),\n",
    "                     np.linspace(0, 1,26))\n",
    "grid = np.c_[xx.ravel(), yy.ravel()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_classes =np.array(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_classes=np.tile(grid_classes,101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.18126925 0.32967995 ... 0.         0.         0.        ]\n"
     ]
    }
   ],
   "source": [
    "print(grid_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_classes = grid_classes.reshape(xx.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.18126925, 0.32967995, ..., 0.18126925, 0.        ,\n",
       "        0.45118836],\n",
       "       [0.        , 0.        , 0.        , ..., 0.55067104, 0.32967995,\n",
       "        0.18126925],\n",
       "       [0.18126925, 0.        , 0.45118836, ..., 0.        , 0.18126925,\n",
       "        0.32967995],\n",
       "       ...,\n",
       "       [0.55067104, 0.        , 0.18126925, ..., 0.        , 0.45118836,\n",
       "        0.18126925],\n",
       "       [0.        , 0.18126925, 0.32967995, ..., 0.        , 0.18126925,\n",
       "        0.32967995],\n",
       "       [0.        , 0.45118836, 0.18126925, ..., 0.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_classes2=np.zeros(len(grid_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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