{
 "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": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XLwOfiGjVUIS",
    "outputId": "f74d060c-ff8f-41d5-cc99-f5ca0fe40731"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "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": 4,
   "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]"
      ]
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     "metadata": {},
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   "source": [
    "earthquakes"
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    {
     "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": {
      "needs_background": "light"
     },
     "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())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "single positional indexer is out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-8e17584f7e6a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mearthquakes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m824\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    877\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    878\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 879\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\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    880\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    881\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\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\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1494\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1495\u001b[0m             \u001b[1;31m# validate the location\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1496\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\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   1497\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1498\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ixs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\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\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_integer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1435\u001b[0m         \u001b[0mlen_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\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   1436\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mlen_axis\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0mlen_axis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1437\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"single positional indexer is out-of-bounds\"\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   1438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1439\u001b[0m     \u001b[1;31m# -------------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
     ]
    }
   ],
   "source": [
    "earthquakes.iloc[824]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 337
    },
    "id": "C37166ViX0ZG",
    "outputId": "fdd64528-d789-44ed-f0e6-6fac339a9e3f",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.scatter(earthquakes[\"longitude\"],-earthquakes[\"depth\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 75,
   "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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limites\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-49-2d0fd2f8255f>: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])\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": 63,
   "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>2020-09-11T07:35:57.187Z</td>\n",
       "      <td>-21.3968</td>\n",
       "      <td>-69.9096</td>\n",
       "      <td>51</td>\n",
       "      <td>6.2</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.88</td>\n",
       "      <td>...</td>\n",
       "      <td>82 km NNE of Tocopilla, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.071</td>\n",
       "      <td>19</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>us</td>\n",
       "      <td>us</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       time  latitude  longitude  depth  mag magType  nst  \\\n",
       "0  2020-09-11T07:35:57.187Z  -21.3968   -69.9096     51  6.2     mww  NaN   \n",
       "\n",
       "   gap   dmin   rms  ...                          place        type  \\\n",
       "0   72  0.077  0.88  ...  82 km NNE of Tocopilla, Chile  earthquake   \n",
       "\n",
       "  horizontalError depthError magError  magNst    status  locationSource  \\\n",
       "0             6.3        1.9    0.071      19  reviewed              us   \n",
       "\n",
       "   magSource  year  \n",
       "0         us  2020  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intervalos[6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-dfab5c183b74>: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])\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "earthquakes[\"year\"]=earthquakes[\"time\"]\n",
    "while i <280:\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": 19,
   "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",
       "      <th>year2</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",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-06-23T20:33:14.130Z</td>\n",
       "      <td>2001</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-16T22:54:32.860Z</td>\n",
       "      <td>2015</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-04-01T23:46:47.260Z</td>\n",
       "      <td>2014</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995-07-30T05:11:23.630Z</td>\n",
       "      <td>1995</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>2016</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>2016-10-27T20:32:55.790Z</td>\n",
       "      <td>2016</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>2016-11-08T04:55:45.900Z</td>\n",
       "      <td>2016</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>2017-04-23T02:36:07.830Z</td>\n",
       "      <td>2017</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>2019-12-03T08:46:35.780Z</td>\n",
       "      <td>2019</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>280 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         time  year year2\n",
       "0    2010-02-27T06:34:11.530Z  2010    40\n",
       "1    2001-06-23T20:33:14.130Z  2001    31\n",
       "2    2015-09-16T22:54:32.860Z  2015    45\n",
       "3    2014-04-01T23:46:47.260Z  2014    44\n",
       "4    1995-07-30T05:11:23.630Z  1995    25\n",
       "..                        ...   ...   ...\n",
       "275  2016-02-22T06:36:59.400Z  2016    46\n",
       "276  2016-10-27T20:32:55.790Z  2016    46\n",
       "277  2016-11-08T04:55:45.900Z  2016    46\n",
       "278  2017-04-23T02:36:07.830Z  2017    47\n",
       "279  2019-12-03T08:46:35.780Z  2019    49\n",
       "\n",
       "[280 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthquakes[[\"time\",\"year\",\"year2\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     280.0\n",
       "unique     49.0\n",
       "top        40.0\n",
       "freq       29.0\n",
       "Name: year2, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earthquakes[\"year\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "earthquakes[\"year2\"]= earthquakes[\"year\"]-1970"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 26,
   "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": 64,
   "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": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FrecuenciaProm(decadas):  # Calcula el promedio de las frecuencias de terremotos por década\n",
    "    i=0\n",
    "    suma=0\n",
    "    while i <5:\n",
    "        x = decadas[i][\"mag\"].count()\n",
    "        i+=1\n",
    "        suma+=x\n",
    "    return suma/5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.8"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FrecuenciaProm(decadas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.0,\n",
       " 1.6,\n",
       " 2.2,\n",
       " 2.0,\n",
       " 2.6,\n",
       " 2.2,\n",
       " 0.2,\n",
       " 1.8,\n",
       " 2.8,\n",
       " 1.8,\n",
       " 0.8,\n",
       " 2.2,\n",
       " 2.0,\n",
       " 1.4,\n",
       " 3.6,\n",
       " 4.6,\n",
       " 4.8,\n",
       " 4.8,\n",
       " 3.4,\n",
       " 2.4,\n",
       " 3.2,\n",
       " 1.4,\n",
       " 1.6,\n",
       " 0.4,\n",
       " 0.2,\n",
       " 0.0]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frecuencia_intervalos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-69-96fbaa7680e0>: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",
       " 0.45454545454545453,\n",
       " 0.5,\n",
       " 0.3846153846153846,\n",
       " 0.45454545454545453,\n",
       " 5.0,\n",
       " 0.5555555555555556,\n",
       " 0.35714285714285715,\n",
       " 0.5555555555555556,\n",
       " 1.25,\n",
       " 0.45454545454545453,\n",
       " 0.5,\n",
       " 0.7142857142857143,\n",
       " 0.2777777777777778,\n",
       " 0.2173913043478261,\n",
       " 0.20833333333333334,\n",
       " 0.20833333333333334,\n",
       " 0.29411764705882354,\n",
       " 0.4166666666666667,\n",
       " 0.3125,\n",
       " 0.7142857142857143,\n",
       " 0.625,\n",
       " 2.5,\n",
       " 5.0,\n",
       " inf]"
      ]
     },
     "execution_count": 69,
     "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": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8646647167633873,\n",
       " 0.7981034820053446,\n",
       " 0.8891968416376661,\n",
       " 0.8646647167633873,\n",
       " 0.9257264217856661,\n",
       " 0.8891968416376661,\n",
       " 0.18126924692201818,\n",
       " 0.8347011117784134,\n",
       " 0.9391899373747821,\n",
       " 0.8347011117784134,\n",
       " 0.5506710358827784,\n",
       " 0.8891968416376661,\n",
       " 0.8646647167633873,\n",
       " 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": 76,
     "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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