THESIS_correlation_metrics_secondpart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the result of the first part\n",
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11191"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[28854386.69198039, 1907, 0],\n",
       " [3537832.369221905, 2, 1],\n",
       " [8460595.1654502, 9, 2],\n",
       " [35316376.37629168, 6208, 3],\n",
       " [2325440.926930323, 36, 4],\n",
       " [21572849.029225595, 23, 5],\n",
       " [358940.0695735319, 3045, 6],\n",
       " [18161449.493397087, 1973, 7],\n",
       " [885117.7500843582, 13, 8],\n",
       " [6608620.671072653, 394, 9],\n",
       " [2647141.7704029223, 389, 10],\n",
       " [387519.15322987194, 2065, 11],\n",
       " [106162.65938668484, 14, 12],\n",
       " [5076695.296603572, 14, 13],\n",
       " [3281327.999418661, 3238, 14],\n",
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     "metadata": {},
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "import warnings\n",
    "a_df=pd.DataFrame(a)\n",
    "a_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "      <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>11189</th>\n",
       "      <td>529</td>\n",
       "      <td>1985-01-07T00:34:49.290000</td>\n",
       "      <td>43.2250</td>\n",
       "      <td>12.73000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.0</td>\n",
       "      <td>--</td>\n",
       "      <td>5 km W Gualdo Tadino (PG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11190</th>\n",
       "      <td>489</td>\n",
       "      <td>1985-01-06T17:22:58.540000</td>\n",
       "      <td>39.2910</td>\n",
       "      <td>16.76500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.0</td>\n",
       "      <td>--</td>\n",
       "      <td>2 km N Castelsilano (KR)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11191</th>\n",
       "      <td>239</td>\n",
       "      <td>1985-01-05T03:47:03.640000</td>\n",
       "      <td>45.4090</td>\n",
       "      <td>6.62700</td>\n",
       "      <td>10.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>3.2</td>\n",
       "      <td>--</td>\n",
       "      <td>Confine Italia-Francia (FRANCIA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11192</th>\n",
       "      <td>229</td>\n",
       "      <td>1985-01-04T23:59:54.820000</td>\n",
       "      <td>45.3380</td>\n",
       "      <td>6.64100</td>\n",
       "      <td>5.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>3.7</td>\n",
       "      <td>--</td>\n",
       "      <td>Confine Italia-Francia (FRANCIA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11193</th>\n",
       "      <td>199</td>\n",
       "      <td>1985-01-04T16:57:40.240000</td>\n",
       "      <td>46.1580</td>\n",
       "      <td>7.17400</td>\n",
       "      <td>10.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>3.8</td>\n",
       "      <td>--</td>\n",
       "      <td>Confine Italia-Svizzera (SVIZZERA)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11194 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       #EventID                        Time  Latitude  Longitude  Depth/Km  \\\n",
       "0                                                                            \n",
       "0      24328471  2020-04-28T13:00:30.050000   38.4607   13.64330      17.7   \n",
       "1      24326871  2020-04-28T07:14:45.750000   38.5943   14.17220      27.5   \n",
       "2      24326451  2020-04-28T05:57:28.820000   38.4052   13.60600      25.4   \n",
       "3      24317311  2020-04-26T02:59:02.450000   40.8285   14.15167       2.5   \n",
       "4      24313381  2020-04-25T07:21:54.480000   37.7523   14.61750      23.5   \n",
       "...         ...                         ...       ...        ...       ...   \n",
       "11189       529  1985-01-07T00:34:49.290000   43.2250   12.73000      10.0   \n",
       "11190       489  1985-01-06T17:22:58.540000   39.2910   16.76500       5.0   \n",
       "11191       239  1985-01-05T03:47:03.640000   45.4090    6.62700      10.0   \n",
       "11192       229  1985-01-04T23:59:54.820000   45.3380    6.64100       5.0   \n",
       "11193       199  1985-01-04T16:57:40.240000   46.1580    7.17400      10.0   \n",
       "\n",
       "               Author  Catalog  Contributor  ContributorID MagType  Magnitude  \\\n",
       "0                                                                               \n",
       "0         SURVEY-INGV      NaN          NaN            NaN      ML        3.5   \n",
       "1         SURVEY-INGV      NaN          NaN            NaN      ML        3.0   \n",
       "2         SURVEY-INGV      NaN          NaN            NaN      ML        3.1   \n",
       "3      SURVEY-INGV-OV      NaN          NaN            NaN      Md        3.1   \n",
       "4       BULLETIN-INGV      NaN          NaN            NaN      ML        3.4   \n",
       "...               ...      ...          ...            ...     ...        ...   \n",
       "11189    BULLETIN-VAX      NaN          NaN            NaN      Md        3.0   \n",
       "11190    BULLETIN-VAX      NaN          NaN            NaN      Md        3.0   \n",
       "11191    BULLETIN-VAX      NaN          NaN            NaN       M        3.2   \n",
       "11192    BULLETIN-VAX      NaN          NaN            NaN       M        3.7   \n",
       "11193    BULLETIN-VAX      NaN          NaN            NaN       M        3.8   \n",
       "\n",
       "      MagAuthor                                EventLocationName  \n",
       "0                                                                 \n",
       "0            --                       Tirreno Meridionale (MARE)  \n",
       "1            --                       Tirreno Meridionale (MARE)  \n",
       "2            --  Costa Siciliana centro-settentrionale (Palermo)  \n",
       "3            --                             5 km E Pozzuoli (NA)  \n",
       "4            --                              4 km SE Troina (EN)  \n",
       "...         ...                                              ...  \n",
       "11189        --                        5 km W Gualdo Tadino (PG)  \n",
       "11190        --                         2 km N Castelsilano (KR)  \n",
       "11191        --                 Confine Italia-Francia (FRANCIA)  \n",
       "11192        --                 Confine Italia-Francia (FRANCIA)  \n",
       "11193        --               Confine Italia-Svizzera (SVIZZERA)  \n",
       "\n",
       "[11194 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "DATA=pd.read_csv(r\"C:\\\\Users\\Ogeid99\\Desktop\\cataloguess\\DATA3_magn.csv\")\n",
    "#put the index in ascending way\n",
    "DATA.sort_values(by='Time',ascending=False,inplace=True) #sort values according to time\n",
    "num=pd.Series([i for i in reversed(range(0,int(DATA.size/len(DATA.columns))))])  #range(0,2,-1) gives [2,1], riversed gives [1,0]\n",
    "DATA=pd.concat([DATA,num],axis=1)\n",
    "DATA.set_index(0,inplace=True) #remember inplace\n",
    "DATA=DATA.iloc[::-1] #invert the order\n",
    "DATA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (Recall twice the following script)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1559758.5749845775\n"
     ]
    }
   ],
   "source": [
    "#verify the algorithm\n",
    "from datetime import datetime\n",
    "import time\n",
    "from math import sin, cos, sqrt, atan2, radians, pow\n",
    "d=1.6   #it could change... try different values\n",
    "b=1.2\n",
    "R = 6373.0  \n",
    "a_time=pd.Series(DATA['Time'])\n",
    "DATA['Time']=a_time.astype(np.datetime64)\n",
    "space_time_total=[]\n",
    "start_time=time.time()\n",
    "def funct_lat_long(i,j):\n",
    "    #timei=a_time.iloc[i+j]\n",
    "    timej=a_time.iloc[j] \n",
    "    #magnitude=DATA.iloc[i+j]['Magnitude']\n",
    "    latj = radians(DATA.iloc[j]['Latitude'])\n",
    "    lonj = radians(DATA.iloc[j]['Longitude'])\n",
    "    dtime=((timej-a_time.iloc[i+j])).total_seconds()\n",
    "    lati = radians(DATA.iloc[i+j]['Latitude'])\n",
    "    loni = radians(DATA.iloc[i+j]['Longitude'])\n",
    "    a2 = sin((lati-latj) / 2)**2 + cos(latj) * cos(lati) * sin((loni-lonj) / 2)**2\n",
    "    distance = R * (sqrt((2 * atan2(sqrt(a2), sqrt(1 - a2)))**2+(DATA.iloc[i+j]['Depth/Km']-DATA.iloc[j]['Depth/Km'])**2))\n",
    "    space_time=round(dtime*distance**d*pow(10,-b*float(DATA.iloc[i+j]['Magnitude'])),12)\n",
    "    #j_real=elem[2]\n",
    "    #i_real=elem[1]-elem[2]\n",
    "    print(space_time)\n",
    "funct_lat_long(126,5108) # <---- pass i-j,j\n",
    "#2925351.2523385026, 8337, 7918"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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>Spacetime_distance</th>\n",
       "      <th>Mainshock</th>\n",
       "      <th>Aftershock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.885439e+07</td>\n",
       "      <td>1907</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.537832e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.460595e+06</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.531638e+07</td>\n",
       "      <td>6208</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.325441e+06</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11186</th>\n",
       "      <td>1.353561e+07</td>\n",
       "      <td>11187</td>\n",
       "      <td>11186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11187</th>\n",
       "      <td>3.940260e+03</td>\n",
       "      <td>11189</td>\n",
       "      <td>11187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11188</th>\n",
       "      <td>7.166286e+04</td>\n",
       "      <td>11191</td>\n",
       "      <td>11188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11189</th>\n",
       "      <td>5.534019e+05</td>\n",
       "      <td>11191</td>\n",
       "      <td>11189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11190</th>\n",
       "      <td>3.797452e+05</td>\n",
       "      <td>11192</td>\n",
       "      <td>11190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11191 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Spacetime_distance  Mainshock  Aftershock\n",
       "0            2.885439e+07       1907           0\n",
       "1            3.537832e+06          2           1\n",
       "2            8.460595e+06          9           2\n",
       "3            3.531638e+07       6208           3\n",
       "4            2.325441e+06         36           4\n",
       "...                   ...        ...         ...\n",
       "11186        1.353561e+07      11187       11186\n",
       "11187        3.940260e+03      11189       11187\n",
       "11188        7.166286e+04      11191       11188\n",
       "11189        5.534019e+05      11191       11189\n",
       "11190        3.797452e+05      11192       11190\n",
       "\n",
       "[11191 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_df.rename({0:'Spacetime_distance',1:'Mainshock',2:'Aftershock'},axis=1,inplace=True)\n",
    "a_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"list_dtm=[]\\nfor elem in a:\\n    def funct_lat_long2(i,j):\\n        #timei=a_time.iloc[i+j]\\n        timej=a_time.iloc[j] \\n        magnitude=DATA.iloc[i+j]['Magnitude']\\n        latj = radians(DATA.iloc[j]['Latitude'])\\n        lonj = radians(DATA.iloc[j]['Longitude'])\\n        dtime=((timej-a_time.iloc[i+j])).total_seconds()\\n        lati = radians(DATA.iloc[i+j]['Latitude'])\\n        loni = radians(DATA.iloc[i+j]['Longitude'])\\n        a2 = sin((lati-latj) / 2)**2 + cos(latj) * cos(lati) * sin((loni-lonj) / 2)**2\\n        distance = R * (sqrt((2 * atan2(sqrt(a2), sqrt(1 - a2)))**2+(DATA.iloc[i+j]['Depth/Km']-DATA.iloc[j]['Depth/Km'])**2))\\n        space_time=round(dtime*distance**d*pow(10,-b*float(DATA.iloc[i+j]['Magnitude'])),12)\\n        list_dtm.append( [space_time,i,j])\\n    j=elem[2]\\n    i=elem[1]\\n    funct_lat_long2(i,j)\\nlist_dtm\""
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''list_dtm=[]\n",
    "for elem in a:\n",
    "    def funct_lat_long2(i,j):\n",
    "        #timei=a_time.iloc[i+j]\n",
    "        timej=a_time.iloc[j] \n",
    "        magnitude=DATA.iloc[i+j]['Magnitude']\n",
    "        latj = radians(DATA.iloc[j]['Latitude'])\n",
    "        lonj = radians(DATA.iloc[j]['Longitude'])\n",
    "        dtime=((timej-a_time.iloc[i+j])).total_seconds()\n",
    "        lati = radians(DATA.iloc[i+j]['Latitude'])\n",
    "        loni = radians(DATA.iloc[i+j]['Longitude'])\n",
    "        a2 = sin((lati-latj) / 2)**2 + cos(latj) * cos(lati) * sin((loni-lonj) / 2)**2\n",
    "        distance = R * (sqrt((2 * atan2(sqrt(a2), sqrt(1 - a2)))**2+(DATA.iloc[i+j]['Depth/Km']-DATA.iloc[j]['Depth/Km'])**2))\n",
    "        space_time=round(dtime*distance**d*pow(10,-b*float(DATA.iloc[i+j]['Magnitude'])),12)\n",
    "        list_dtm.append( [space_time,i,j])\n",
    "    j=elem[2]\n",
    "    i=elem[1]\n",
    "    funct_lat_long2(i,j)\n",
    "list_dtm'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(1,1,figsize=(10,6))\n",
    "sns.distplot(a_df['Spacetime_distance'],bins=30,kde=False,color='dodgerblue')\n",
    "plt.xlabel(\"$n_{j*}$\",fontsize=17,labelpad=5) #set font and title of x label\n",
    "plt.ylabel(\"Frequency (log scale)\",fontsize=17,labelpad=5) #set font and title of y label\n",
    "#plt.yscale('log')\n",
    "plt.tick_params(labelsize=12) #change labelsize\n",
    "plt.title('$n_{j*}$ distribution',fontsize=22,pad=19)\n",
    "plt.yscale('log')\n",
    "#fig.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\spacetime_distance_distribution.png\",dpi=600)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 500000000)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(1,1,figsize=(10,6))\n",
    "sns.distplot(a_df['Spacetime_distance'],bins=5000,kde=False,color='dodgerblue')\n",
    "plt.yscale('log')\n",
    "plt.xlabel(\"$n_{j*}$\",fontsize=17,labelpad=5) #set font and title of x label\n",
    "plt.ylabel(\"Frequency (log scale)\",fontsize=17,labelpad=5) #set font and title of y label\n",
    "plt.tick_params(labelsize=12) #change labelsize\n",
    "plt.title('$n_{j*}$ distribution (for small $n_{j*}$ values)',fontsize=22,pad=19)\n",
    "plt.yscale('log')\n",
    "plt.xlim(0,500000000)\n",
    "#fig.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\spacetime_distance_distribution_zoom.png\",dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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>Spacetime_distance</th>\n",
       "      <th>Mainshock</th>\n",
       "      <th>Aftershock</th>\n",
       "      <th>#EventID</th>\n",
       "      <th>Time</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Depth/Km</th>\n",
       "      <th>Author</th>\n",
       "      <th>Catalog</th>\n",
       "      <th>Contributor</th>\n",
       "      <th>ContributorID</th>\n",
       "      <th>MagType</th>\n",
       "      <th>Magnitude</th>\n",
       "      <th>MagAuthor</th>\n",
       "      <th>EventLocationName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.885439e+07</td>\n",
       "      <td>1907.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24328471</td>\n",
       "      <td>2020-04-28 13:00:30.050</td>\n",
       "      <td>38.4607</td>\n",
       "      <td>13.64330</td>\n",
       "      <td>17.7</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>3.5</td>\n",
       "      <td>--</td>\n",
       "      <td>Tirreno Meridionale (MARE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.537832e+06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24326871</td>\n",
       "      <td>2020-04-28 07:14:45.750</td>\n",
       "      <td>38.5943</td>\n",
       "      <td>14.17220</td>\n",
       "      <td>27.5</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>3.0</td>\n",
       "      <td>--</td>\n",
       "      <td>Tirreno Meridionale (MARE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.460595e+06</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24326451</td>\n",
       "      <td>2020-04-28 05:57:28.820</td>\n",
       "      <td>38.4052</td>\n",
       "      <td>13.60600</td>\n",
       "      <td>25.4</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>3.1</td>\n",
       "      <td>--</td>\n",
       "      <td>Costa Siciliana centro-settentrionale (Palermo)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.531638e+07</td>\n",
       "      <td>6208.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24317311</td>\n",
       "      <td>2020-04-26 02:59:02.450</td>\n",
       "      <td>40.8285</td>\n",
       "      <td>14.15167</td>\n",
       "      <td>2.5</td>\n",
       "      <td>SURVEY-INGV-OV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.1</td>\n",
       "      <td>--</td>\n",
       "      <td>5 km E Pozzuoli (NA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.325441e+06</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24313381</td>\n",
       "      <td>2020-04-25 07:21:54.480</td>\n",
       "      <td>37.7523</td>\n",
       "      <td>14.61750</td>\n",
       "      <td>23.5</td>\n",
       "      <td>BULLETIN-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>3.4</td>\n",
       "      <td>--</td>\n",
       "      <td>4 km SE Troina (EN)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11186</th>\n",
       "      <td>1.353561e+07</td>\n",
       "      <td>11187.0</td>\n",
       "      <td>11186.0</td>\n",
       "      <td>809</td>\n",
       "      <td>1985-01-09 03:13:01.090</td>\n",
       "      <td>43.3020</td>\n",
       "      <td>12.63800</td>\n",
       "      <td>4.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.3</td>\n",
       "      <td>--</td>\n",
       "      <td>8 km SE Gubbio (PG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11187</th>\n",
       "      <td>3.940260e+03</td>\n",
       "      <td>11189.0</td>\n",
       "      <td>11187.0</td>\n",
       "      <td>769</td>\n",
       "      <td>1985-01-08 23:33:34.130</td>\n",
       "      <td>43.3030</td>\n",
       "      <td>12.55000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.6</td>\n",
       "      <td>--</td>\n",
       "      <td>6 km SW Gubbio (PG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11188</th>\n",
       "      <td>7.166286e+04</td>\n",
       "      <td>11191.0</td>\n",
       "      <td>11188.0</td>\n",
       "      <td>599</td>\n",
       "      <td>1985-01-07 11:07:55.060</td>\n",
       "      <td>44.2220</td>\n",
       "      <td>6.77800</td>\n",
       "      <td>10.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>3.0</td>\n",
       "      <td>--</td>\n",
       "      <td>Confine Italia-Francia (FRANCIA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11189</th>\n",
       "      <td>5.534019e+05</td>\n",
       "      <td>11191.0</td>\n",
       "      <td>11189.0</td>\n",
       "      <td>529</td>\n",
       "      <td>1985-01-07 00:34:49.290</td>\n",
       "      <td>43.2250</td>\n",
       "      <td>12.73000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.0</td>\n",
       "      <td>--</td>\n",
       "      <td>5 km W Gualdo Tadino (PG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11190</th>\n",
       "      <td>3.797452e+05</td>\n",
       "      <td>11192.0</td>\n",
       "      <td>11190.0</td>\n",
       "      <td>489</td>\n",
       "      <td>1985-01-06 17:22:58.540</td>\n",
       "      <td>39.2910</td>\n",
       "      <td>16.76500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.0</td>\n",
       "      <td>--</td>\n",
       "      <td>2 km N Castelsilano (KR)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11191 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Spacetime_distance  Mainshock  Aftershock  #EventID  \\\n",
       "0            2.885439e+07     1907.0         0.0  24328471   \n",
       "1            3.537832e+06        2.0         1.0  24326871   \n",
       "2            8.460595e+06        9.0         2.0  24326451   \n",
       "3            3.531638e+07     6208.0         3.0  24317311   \n",
       "4            2.325441e+06       36.0         4.0  24313381   \n",
       "...                   ...        ...         ...       ...   \n",
       "11186        1.353561e+07    11187.0     11186.0       809   \n",
       "11187        3.940260e+03    11189.0     11187.0       769   \n",
       "11188        7.166286e+04    11191.0     11188.0       599   \n",
       "11189        5.534019e+05    11191.0     11189.0       529   \n",
       "11190        3.797452e+05    11192.0     11190.0       489   \n",
       "\n",
       "                         Time  Latitude  Longitude  Depth/Km          Author  \\\n",
       "0     2020-04-28 13:00:30.050   38.4607   13.64330      17.7     SURVEY-INGV   \n",
       "1     2020-04-28 07:14:45.750   38.5943   14.17220      27.5     SURVEY-INGV   \n",
       "2     2020-04-28 05:57:28.820   38.4052   13.60600      25.4     SURVEY-INGV   \n",
       "3     2020-04-26 02:59:02.450   40.8285   14.15167       2.5  SURVEY-INGV-OV   \n",
       "4     2020-04-25 07:21:54.480   37.7523   14.61750      23.5   BULLETIN-INGV   \n",
       "...                       ...       ...        ...       ...             ...   \n",
       "11186 1985-01-09 03:13:01.090   43.3020   12.63800       4.0    BULLETIN-VAX   \n",
       "11187 1985-01-08 23:33:34.130   43.3030   12.55000      10.0    BULLETIN-VAX   \n",
       "11188 1985-01-07 11:07:55.060   44.2220    6.77800      10.0    BULLETIN-VAX   \n",
       "11189 1985-01-07 00:34:49.290   43.2250   12.73000      10.0    BULLETIN-VAX   \n",
       "11190 1985-01-06 17:22:58.540   39.2910   16.76500       5.0    BULLETIN-VAX   \n",
       "\n",
       "       Catalog  Contributor  ContributorID MagType  Magnitude MagAuthor  \\\n",
       "0          NaN          NaN            NaN      ML        3.5        --   \n",
       "1          NaN          NaN            NaN      ML        3.0        --   \n",
       "2          NaN          NaN            NaN      ML        3.1        --   \n",
       "3          NaN          NaN            NaN      Md        3.1        --   \n",
       "4          NaN          NaN            NaN      ML        3.4        --   \n",
       "...        ...          ...            ...     ...        ...       ...   \n",
       "11186      NaN          NaN            NaN      Md        3.3        --   \n",
       "11187      NaN          NaN            NaN      Md        3.6        --   \n",
       "11188      NaN          NaN            NaN       M        3.0        --   \n",
       "11189      NaN          NaN            NaN      Md        3.0        --   \n",
       "11190      NaN          NaN            NaN      Md        3.0        --   \n",
       "\n",
       "                                     EventLocationName  \n",
       "0                           Tirreno Meridionale (MARE)  \n",
       "1                           Tirreno Meridionale (MARE)  \n",
       "2      Costa Siciliana centro-settentrionale (Palermo)  \n",
       "3                                 5 km E Pozzuoli (NA)  \n",
       "4                                  4 km SE Troina (EN)  \n",
       "...                                                ...  \n",
       "11186                              8 km SE Gubbio (PG)  \n",
       "11187                              6 km SW Gubbio (PG)  \n",
       "11188                 Confine Italia-Francia (FRANCIA)  \n",
       "11189                        5 km W Gualdo Tadino (PG)  \n",
       "11190                         2 km N Castelsilano (KR)  \n",
       "\n",
       "[11191 rows x 16 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold=100000\n",
    "DATA_new=pd.concat([a_df,DATA],axis=1)\n",
    "DATA_new.dropna(subset=['Spacetime_distance','Mainshock','Aftershock'],inplace=True)\n",
    "DATA_new_condition=DATA_new[DATA_new['Spacetime_distance']<threshold]\n",
    "DATA_new\n",
    "DATA_new.to_csv(r\"C:\\\\Users\\Ogeid99\\Desktop\\cataloguess\\space_time_total_catalogue.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "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>Spacetime_distance</th>\n",
       "      <th>Mainshock</th>\n",
       "      <th>Aftershock</th>\n",
       "      <th>#EventID</th>\n",
       "      <th>Time</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Depth/Km</th>\n",
       "      <th>Author</th>\n",
       "      <th>Catalog</th>\n",
       "      <th>Contributor</th>\n",
       "      <th>ContributorID</th>\n",
       "      <th>MagType</th>\n",
       "      <th>Magnitude</th>\n",
       "      <th>MagAuthor</th>\n",
       "      <th>EventLocationName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3942.100864</td>\n",
       "      <td>31.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24174311</td>\n",
       "      <td>2020-03-28 12:58:29.130</td>\n",
       "      <td>43.7922</td>\n",
       "      <td>13.3600</td>\n",
       "      <td>10.1</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>3.2</td>\n",
       "      <td>--</td>\n",
       "      <td>Costa Marchigiana Anconetana (Ancona)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>301.989933</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>24136051</td>\n",
       "      <td>2020-03-22 06:01:20.808</td>\n",
       "      <td>45.8754</td>\n",
       "      <td>15.9717</td>\n",
       "      <td>9.8</td>\n",
       "      <td>SURVEY-INGV-A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mb</td>\n",
       "      <td>4.9</td>\n",
       "      <td>--</td>\n",
       "      <td>Confine Slovenia-Croazia (SLOVENIA, CROAZIA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>52856.645297</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>24134961</td>\n",
       "      <td>2020-03-22 05:24:03.905</td>\n",
       "      <td>45.8517</td>\n",
       "      <td>15.9460</td>\n",
       "      <td>10.1</td>\n",
       "      <td>SURVEY-INGV-A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mw</td>\n",
       "      <td>5.1</td>\n",
       "      <td>--</td>\n",
       "      <td>Confine Slovenia-Croazia (SLOVENIA, CROAZIA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>15384.816094</td>\n",
       "      <td>684.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>24128501</td>\n",
       "      <td>2020-03-20 21:58:06.140</td>\n",
       "      <td>42.4868</td>\n",
       "      <td>13.2963</td>\n",
       "      <td>10.8</td>\n",
       "      <td>BULLETIN-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>3.5</td>\n",
       "      <td>--</td>\n",
       "      <td>4 km S Capitignano (AQ)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>82897.808607</td>\n",
       "      <td>1486.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>24124461</td>\n",
       "      <td>2020-03-20 04:49:43.520</td>\n",
       "      <td>42.9817</td>\n",
       "      <td>13.0625</td>\n",
       "      <td>9.1</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>3.3</td>\n",
       "      <td>--</td>\n",
       "      <td>5 km E Monte Cavallo (MC)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11148</th>\n",
       "      <td>54245.025534</td>\n",
       "      <td>11164.0</td>\n",
       "      <td>11148.0</td>\n",
       "      <td>7519</td>\n",
       "      <td>1985-03-06 06:31:42.030</td>\n",
       "      <td>43.8740</td>\n",
       "      <td>17.2460</td>\n",
       "      <td>10.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>3.1</td>\n",
       "      <td>--</td>\n",
       "      <td>Bosnia and Herz. [Land]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11149</th>\n",
       "      <td>7989.426600</td>\n",
       "      <td>11150.0</td>\n",
       "      <td>11149.0</td>\n",
       "      <td>7079</td>\n",
       "      <td>1985-03-04 02:32:17.340</td>\n",
       "      <td>46.4400</td>\n",
       "      <td>13.6080</td>\n",
       "      <td>10.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.2</td>\n",
       "      <td>--</td>\n",
       "      <td>8 km SE Tarvisio (UD)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11182</th>\n",
       "      <td>71.701027</td>\n",
       "      <td>11187.0</td>\n",
       "      <td>11182.0</td>\n",
       "      <td>1209</td>\n",
       "      <td>1985-01-13 10:13:56.090</td>\n",
       "      <td>43.3240</td>\n",
       "      <td>12.5520</td>\n",
       "      <td>10.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.4</td>\n",
       "      <td>--</td>\n",
       "      <td>4 km SW Gubbio (PG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11187</th>\n",
       "      <td>3940.260318</td>\n",
       "      <td>11189.0</td>\n",
       "      <td>11187.0</td>\n",
       "      <td>769</td>\n",
       "      <td>1985-01-08 23:33:34.130</td>\n",
       "      <td>43.3030</td>\n",
       "      <td>12.5500</td>\n",
       "      <td>10.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.6</td>\n",
       "      <td>--</td>\n",
       "      <td>6 km SW Gubbio (PG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11188</th>\n",
       "      <td>71662.856286</td>\n",
       "      <td>11191.0</td>\n",
       "      <td>11188.0</td>\n",
       "      <td>599</td>\n",
       "      <td>1985-01-07 11:07:55.060</td>\n",
       "      <td>44.2220</td>\n",
       "      <td>6.7780</td>\n",
       "      <td>10.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>3.0</td>\n",
       "      <td>--</td>\n",
       "      <td>Confine Italia-Francia (FRANCIA)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6599 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Spacetime_distance  Mainshock  Aftershock  #EventID  \\\n",
       "27            3942.100864       31.0        27.0  24174311   \n",
       "30             301.989933       31.0        30.0  24136051   \n",
       "31           52856.645297     1984.0        31.0  24134961   \n",
       "32           15384.816094      684.0        32.0  24128501   \n",
       "33           82897.808607     1486.0        33.0  24124461   \n",
       "...                   ...        ...         ...       ...   \n",
       "11148        54245.025534    11164.0     11148.0      7519   \n",
       "11149         7989.426600    11150.0     11149.0      7079   \n",
       "11182           71.701027    11187.0     11182.0      1209   \n",
       "11187         3940.260318    11189.0     11187.0       769   \n",
       "11188        71662.856286    11191.0     11188.0       599   \n",
       "\n",
       "                         Time  Latitude  Longitude  Depth/Km         Author  \\\n",
       "27    2020-03-28 12:58:29.130   43.7922    13.3600      10.1    SURVEY-INGV   \n",
       "30    2020-03-22 06:01:20.808   45.8754    15.9717       9.8  SURVEY-INGV-A   \n",
       "31    2020-03-22 05:24:03.905   45.8517    15.9460      10.1  SURVEY-INGV-A   \n",
       "32    2020-03-20 21:58:06.140   42.4868    13.2963      10.8  BULLETIN-INGV   \n",
       "33    2020-03-20 04:49:43.520   42.9817    13.0625       9.1    SURVEY-INGV   \n",
       "...                       ...       ...        ...       ...            ...   \n",
       "11148 1985-03-06 06:31:42.030   43.8740    17.2460      10.0   BULLETIN-VAX   \n",
       "11149 1985-03-04 02:32:17.340   46.4400    13.6080      10.0   BULLETIN-VAX   \n",
       "11182 1985-01-13 10:13:56.090   43.3240    12.5520      10.0   BULLETIN-VAX   \n",
       "11187 1985-01-08 23:33:34.130   43.3030    12.5500      10.0   BULLETIN-VAX   \n",
       "11188 1985-01-07 11:07:55.060   44.2220     6.7780      10.0   BULLETIN-VAX   \n",
       "\n",
       "       Catalog  Contributor  ContributorID MagType  Magnitude MagAuthor  \\\n",
       "27         NaN          NaN            NaN      ML        3.2        --   \n",
       "30         NaN          NaN            NaN      mb        4.9        --   \n",
       "31         NaN          NaN            NaN      Mw        5.1        --   \n",
       "32         NaN          NaN            NaN      ML        3.5        --   \n",
       "33         NaN          NaN            NaN      ML        3.3        --   \n",
       "...        ...          ...            ...     ...        ...       ...   \n",
       "11148      NaN          NaN            NaN       M        3.1        --   \n",
       "11149      NaN          NaN            NaN      Md        3.2        --   \n",
       "11182      NaN          NaN            NaN      Md        3.4        --   \n",
       "11187      NaN          NaN            NaN      Md        3.6        --   \n",
       "11188      NaN          NaN            NaN       M        3.0        --   \n",
       "\n",
       "                                  EventLocationName  \n",
       "27            Costa Marchigiana Anconetana (Ancona)  \n",
       "30     Confine Slovenia-Croazia (SLOVENIA, CROAZIA)  \n",
       "31     Confine Slovenia-Croazia (SLOVENIA, CROAZIA)  \n",
       "32                          4 km S Capitignano (AQ)  \n",
       "33                        5 km E Monte Cavallo (MC)  \n",
       "...                                             ...  \n",
       "11148                       Bosnia and Herz. [Land]  \n",
       "11149                         8 km SE Tarvisio (UD)  \n",
       "11182                           4 km SW Gubbio (PG)  \n",
       "11187                           6 km SW Gubbio (PG)  \n",
       "11188              Confine Italia-Francia (FRANCIA)  \n",
       "\n",
       "[6599 rows x 16 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA_new_condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " [7.9093207526783, 0.0004882837439222868, 3.8, 999],\n",
       " ...]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "list_TR=[]\n",
    "b=1.2\n",
    "DATA_new['Time']=DATA_new['Time'].astype(np.datetime64)\n",
    "for e in DATA_new.index:\n",
    "    aft=int(DATA_new['Aftershock'].iloc[e])\n",
    "    main=int(DATA_new['Mainshock'].iloc[e])\n",
    "    if main <11191:\n",
    "        dtime=((DATA_new['Time'].iloc[e]-DATA_new['Time'].iloc[main])).total_seconds()\n",
    "        mag=DATA_new['Magnitude'].iloc[main]\n",
    "        T=dtime*pow(10,-b*((mag)/2))\n",
    "        R=(DATA_new['Spacetime_distance'].iloc[e]*pow(10,b*(mag)/2))/dtime\n",
    "        list_TR.append([T,R,mag,e])\n",
    "list_TR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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>T</th>\n",
       "      <th>R</th>\n",
       "      <th>Magnitude</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.811952e+06</td>\n",
       "      <td>15.924472</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.400745e+01</td>\n",
       "      <td>55272.194943</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.600109e+03</td>\n",
       "      <td>1839.216398</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.791618e+06</td>\n",
       "      <td>9.314328</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.550873e+04</td>\n",
       "      <td>149.944043</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11181</th>\n",
       "      <td>1.320872e+03</td>\n",
       "      <td>65550.018920</td>\n",
       "      <td>3.5</td>\n",
       "      <td>11183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11182</th>\n",
       "      <td>7.025166e+03</td>\n",
       "      <td>139.258515</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11183</th>\n",
       "      <td>1.094273e+03</td>\n",
       "      <td>153.209704</td>\n",
       "      <td>3.6</td>\n",
       "      <td>11185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11184</th>\n",
       "      <td>9.109311e+01</td>\n",
       "      <td>148590.923626</td>\n",
       "      <td>3.6</td>\n",
       "      <td>11186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11185</th>\n",
       "      <td>2.680448e+03</td>\n",
       "      <td>1.470001</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11186 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  T              R  Magnitude  Index\n",
       "0      1.811952e+06      15.924472        3.1      0\n",
       "1      6.400745e+01   55272.194943        3.1      1\n",
       "2      4.600109e+03    1839.216398        3.7      2\n",
       "3      3.791618e+06       9.314328        3.7      3\n",
       "4      1.550873e+04     149.944043        3.9      4\n",
       "...             ...            ...        ...    ...\n",
       "11181  1.320872e+03   65550.018920        3.5  11183\n",
       "11182  7.025166e+03     139.258515        3.0  11184\n",
       "11183  1.094273e+03     153.209704        3.6  11185\n",
       "11184  9.109311e+01  148590.923626        3.6  11186\n",
       "11185  2.680448e+03       1.470001        3.0  11187\n",
       "\n",
       "[11186 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TR=pd.DataFrame(list_TR)\n",
    "TR.rename({0:'T',1:'R',2:'Magnitude',3:'Index'},axis=1,inplace=True)\n",
    "TR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import gaussian_kde\n",
    "fig,axes=plt.subplots(1,1,figsize=(10,12))\n",
    "s = plt.scatter(TR['T'],TR['R'],s=9) #,c=z, s=10, cmap='jet', edgecolor='', label=None, picker=True, zorder=2 )   \n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.ylim((0.00003, 500000000))\n",
    "plt.xlim((0.009, 13000000))                                                                                                                                    \n",
    "plt.xlabel(\"T\",fontsize=17,labelpad=5) #set font and title of x label\n",
    "plt.ylabel(\"R\",fontsize=17,labelpad=5) #set font and title of y label\n",
    "plt.tick_params(labelsize=12) #change labelsize\n",
    "plt.title('T vs R plot',fontsize=22,pad=19)\n",
    "#fig.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\TvsR_scatter.png\",dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "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>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6491</th>\n",
       "      <td>11181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6492</th>\n",
       "      <td>11183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6493</th>\n",
       "      <td>11184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6494</th>\n",
       "      <td>11185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6495</th>\n",
       "      <td>11186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6496 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index\n",
       "0         0\n",
       "1         1\n",
       "2         2\n",
       "3         3\n",
       "4         4\n",
       "...     ...\n",
       "6491  11181\n",
       "6492  11183\n",
       "6493  11184\n",
       "6494  11185\n",
       "6495  11186\n",
       "\n",
       "[6496 rows x 1 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indici=[]\n",
    "for tr in TR.index:\n",
    "    if TR['T'].iloc[tr]*TR['R'].iloc[tr]>10000:\n",
    "        indici.append(TR['Index'].iloc[tr])\n",
    "indici=pd.Series(indici)\n",
    "indici.rename='index'\n",
    "indici = indici.to_frame('index')\n",
    "indici"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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>Spacetime_distance</th>\n",
       "      <th>Mainshock</th>\n",
       "      <th>Aftershock</th>\n",
       "      <th>#EventID</th>\n",
       "      <th>Time</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Depth/Km</th>\n",
       "      <th>Author</th>\n",
       "      <th>Catalog</th>\n",
       "      <th>Contributor</th>\n",
       "      <th>ContributorID</th>\n",
       "      <th>MagType</th>\n",
       "      <th>Magnitude</th>\n",
       "      <th>MagAuthor</th>\n",
       "      <th>EventLocationName</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.885439e+07</td>\n",
       "      <td>1907.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24328471</td>\n",
       "      <td>2020-04-28 13:00:30.050</td>\n",
       "      <td>38.4607</td>\n",
       "      <td>13.64330</td>\n",
       "      <td>17.7</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>3.5</td>\n",
       "      <td>--</td>\n",
       "      <td>Tirreno Meridionale (MARE)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.537832e+06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24326871</td>\n",
       "      <td>2020-04-28 07:14:45.750</td>\n",
       "      <td>38.5943</td>\n",
       "      <td>14.17220</td>\n",
       "      <td>27.5</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>3.0</td>\n",
       "      <td>--</td>\n",
       "      <td>Tirreno Meridionale (MARE)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.460595e+06</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24326451</td>\n",
       "      <td>2020-04-28 05:57:28.820</td>\n",
       "      <td>38.4052</td>\n",
       "      <td>13.60600</td>\n",
       "      <td>25.4</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>3.1</td>\n",
       "      <td>--</td>\n",
       "      <td>Costa Siciliana centro-settentrionale (Palermo)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.531638e+07</td>\n",
       "      <td>6208.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24317311</td>\n",
       "      <td>2020-04-26 02:59:02.450</td>\n",
       "      <td>40.8285</td>\n",
       "      <td>14.15167</td>\n",
       "      <td>2.5</td>\n",
       "      <td>SURVEY-INGV-OV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.1</td>\n",
       "      <td>--</td>\n",
       "      <td>5 km E Pozzuoli (NA)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.325441e+06</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24313381</td>\n",
       "      <td>2020-04-25 07:21:54.480</td>\n",
       "      <td>37.7523</td>\n",
       "      <td>14.61750</td>\n",
       "      <td>23.5</td>\n",
       "      <td>BULLETIN-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>3.4</td>\n",
       "      <td>--</td>\n",
       "      <td>4 km SE Troina (EN)</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6491</th>\n",
       "      <td>1.261267e+08</td>\n",
       "      <td>11187.0</td>\n",
       "      <td>11181.0</td>\n",
       "      <td>1669</td>\n",
       "      <td>1985-01-19 15:34:58.090</td>\n",
       "      <td>40.4540</td>\n",
       "      <td>16.12700</td>\n",
       "      <td>8.3</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.0</td>\n",
       "      <td>--</td>\n",
       "      <td>5 km SW Accettura (MT)</td>\n",
       "      <td>11181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6492</th>\n",
       "      <td>8.658320e+07</td>\n",
       "      <td>11185.0</td>\n",
       "      <td>11183.0</td>\n",
       "      <td>1159</td>\n",
       "      <td>1985-01-12 17:41:12.700</td>\n",
       "      <td>39.4500</td>\n",
       "      <td>16.39100</td>\n",
       "      <td>13.3</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.0</td>\n",
       "      <td>--</td>\n",
       "      <td>4 km S Acri (CS)</td>\n",
       "      <td>11183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6493</th>\n",
       "      <td>9.783141e+05</td>\n",
       "      <td>11190.0</td>\n",
       "      <td>11184.0</td>\n",
       "      <td>1099</td>\n",
       "      <td>1985-01-11 20:30:36.530</td>\n",
       "      <td>37.4040</td>\n",
       "      <td>14.44300</td>\n",
       "      <td>5.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.3</td>\n",
       "      <td>--</td>\n",
       "      <td>2 km SW Aidone (EN)</td>\n",
       "      <td>11184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6494</th>\n",
       "      <td>1.676533e+05</td>\n",
       "      <td>11187.0</td>\n",
       "      <td>11185.0</td>\n",
       "      <td>1019</td>\n",
       "      <td>1985-01-10 19:29:44.730</td>\n",
       "      <td>39.6090</td>\n",
       "      <td>16.37100</td>\n",
       "      <td>10.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.5</td>\n",
       "      <td>--</td>\n",
       "      <td>5 km N San Demetrio Corone (CS)</td>\n",
       "      <td>11185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6495</th>\n",
       "      <td>1.353561e+07</td>\n",
       "      <td>11187.0</td>\n",
       "      <td>11186.0</td>\n",
       "      <td>809</td>\n",
       "      <td>1985-01-09 03:13:01.090</td>\n",
       "      <td>43.3020</td>\n",
       "      <td>12.63800</td>\n",
       "      <td>4.0</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>3.3</td>\n",
       "      <td>--</td>\n",
       "      <td>8 km SE Gubbio (PG)</td>\n",
       "      <td>11186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6496 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Spacetime_distance  Mainshock  Aftershock  #EventID  \\\n",
       "0           2.885439e+07     1907.0         0.0  24328471   \n",
       "1           3.537832e+06        2.0         1.0  24326871   \n",
       "2           8.460595e+06        9.0         2.0  24326451   \n",
       "3           3.531638e+07     6208.0         3.0  24317311   \n",
       "4           2.325441e+06       36.0         4.0  24313381   \n",
       "...                  ...        ...         ...       ...   \n",
       "6491        1.261267e+08    11187.0     11181.0      1669   \n",
       "6492        8.658320e+07    11185.0     11183.0      1159   \n",
       "6493        9.783141e+05    11190.0     11184.0      1099   \n",
       "6494        1.676533e+05    11187.0     11185.0      1019   \n",
       "6495        1.353561e+07    11187.0     11186.0       809   \n",
       "\n",
       "                        Time  Latitude  Longitude  Depth/Km          Author  \\\n",
       "0    2020-04-28 13:00:30.050   38.4607   13.64330      17.7     SURVEY-INGV   \n",
       "1    2020-04-28 07:14:45.750   38.5943   14.17220      27.5     SURVEY-INGV   \n",
       "2    2020-04-28 05:57:28.820   38.4052   13.60600      25.4     SURVEY-INGV   \n",
       "3    2020-04-26 02:59:02.450   40.8285   14.15167       2.5  SURVEY-INGV-OV   \n",
       "4    2020-04-25 07:21:54.480   37.7523   14.61750      23.5   BULLETIN-INGV   \n",
       "...                      ...       ...        ...       ...             ...   \n",
       "6491 1985-01-19 15:34:58.090   40.4540   16.12700       8.3    BULLETIN-VAX   \n",
       "6492 1985-01-12 17:41:12.700   39.4500   16.39100      13.3    BULLETIN-VAX   \n",
       "6493 1985-01-11 20:30:36.530   37.4040   14.44300       5.0    BULLETIN-VAX   \n",
       "6494 1985-01-10 19:29:44.730   39.6090   16.37100      10.0    BULLETIN-VAX   \n",
       "6495 1985-01-09 03:13:01.090   43.3020   12.63800       4.0    BULLETIN-VAX   \n",
       "\n",
       "      Catalog  Contributor  ContributorID MagType  Magnitude MagAuthor  \\\n",
       "0         NaN          NaN            NaN      ML        3.5        --   \n",
       "1         NaN          NaN            NaN      ML        3.0        --   \n",
       "2         NaN          NaN            NaN      ML        3.1        --   \n",
       "3         NaN          NaN            NaN      Md        3.1        --   \n",
       "4         NaN          NaN            NaN      ML        3.4        --   \n",
       "...       ...          ...            ...     ...        ...       ...   \n",
       "6491      NaN          NaN            NaN      Md        3.0        --   \n",
       "6492      NaN          NaN            NaN      Md        3.0        --   \n",
       "6493      NaN          NaN            NaN      Md        3.3        --   \n",
       "6494      NaN          NaN            NaN      Md        3.5        --   \n",
       "6495      NaN          NaN            NaN      Md        3.3        --   \n",
       "\n",
       "                                    EventLocationName  index  \n",
       "0                          Tirreno Meridionale (MARE)      0  \n",
       "1                          Tirreno Meridionale (MARE)      1  \n",
       "2     Costa Siciliana centro-settentrionale (Palermo)      2  \n",
       "3                                5 km E Pozzuoli (NA)      3  \n",
       "4                                 4 km SE Troina (EN)      4  \n",
       "...                                               ...    ...  \n",
       "6491                           5 km SW Accettura (MT)  11181  \n",
       "6492                                 4 km S Acri (CS)  11183  \n",
       "6493                              2 km SW Aidone (EN)  11184  \n",
       "6494                  5 km N San Demetrio Corone (CS)  11185  \n",
       "6495                              8 km SE Gubbio (PG)  11186  \n",
       "\n",
       "[6496 rows x 17 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DECLUSTERED=pd.merge(DATA_new,indici,left_index=True,right_on='index')\n",
    "DECLUSTERED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=DECLUSTERED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import gaussian_kde\n",
    "from scipy.stats import kde\n",
    "\n",
    "fig,axes=plt.subplots(1,1,figsize=(10,12))\n",
    "bins = np.logspace(-5,10,num=76) \n",
    "cmap = plt.get_cmap('RdBu_r')\n",
    "norm = plt.Normalize(0, 400)\n",
    "color = cmap(norm(100.))\n",
    "\n",
    "s = plt.hist2d(TR['T'],TR['R'],bins,cmap=cmap,vmax=27,norm=norm)\n",
    "#plt.cm.RdBu_r) #,c=z, s=10, cmap='jet', edgecolor='', label=None, picker=True, zorder=2 )   \n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "\n",
    "\n",
    "# and apply the new colormap\n",
    "\n",
    "plt.ylim((0.00003, 500000000))\n",
    "plt.xlim((0.009, 13000000))                                                                                                                                    \n",
    "plt.xlabel(\"T\",fontsize=17,labelpad=5) #set font and title of x label\n",
    "plt.ylabel(\"R\",fontsize=17,labelpad=5) #set font and title of y label\n",
    "plt.tick_params(labelsize=12) #change labelsize\n",
    "plt.title('T vs R plot',fontsize=22,pad=19)\n",
    "plt.colorbar()\n",
    "#fig.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\TvsR_scatter_hist2d.png\",dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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