THESIS_declustering_Knopoff_Gardner(only_distance_time_plot_windows).ipynb

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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>79</td>\n",
       "      <td>1985-01-02T22:57:43.090000</td>\n",
       "      <td>41.7150</td>\n",
       "      <td>13.9410</td>\n",
       "      <td>5.8</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>2.6</td>\n",
       "      <td>--</td>\n",
       "      <td>6 km S Civitella Alfedena (AQ)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89</td>\n",
       "      <td>1985-01-03T00:46:16.990000</td>\n",
       "      <td>41.6770</td>\n",
       "      <td>13.9310</td>\n",
       "      <td>6.1</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>2.5</td>\n",
       "      <td>--</td>\n",
       "      <td>6 km NE Picinisco (FR)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>199</td>\n",
       "      <td>1985-01-04T16:57:40.240000</td>\n",
       "      <td>46.1580</td>\n",
       "      <td>7.1740</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>229</td>\n",
       "      <td>1985-01-04T23:59:54.820000</td>\n",
       "      <td>45.3380</td>\n",
       "      <td>6.6410</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>4</th>\n",
       "      <td>239</td>\n",
       "      <td>1985-01-05T03:47:03.640000</td>\n",
       "      <td>45.4090</td>\n",
       "      <td>6.6270</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>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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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",
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       "    <tr>\n",
       "      <th>181772</th>\n",
       "      <td>24334191</td>\n",
       "      <td>2020-04-29T11:17:24.170000</td>\n",
       "      <td>45.9167</td>\n",
       "      <td>12.1917</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>1.7</td>\n",
       "      <td>--</td>\n",
       "      <td>2 km SW Refrontolo (TV)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181773</th>\n",
       "      <td>24335541</td>\n",
       "      <td>2020-04-29T14:14:47.970000</td>\n",
       "      <td>38.4090</td>\n",
       "      <td>13.5617</td>\n",
       "      <td>28.2</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>2.3</td>\n",
       "      <td>--</td>\n",
       "      <td>Costa Siciliana centro-settentrionale (Palermo)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181774</th>\n",
       "      <td>24336421</td>\n",
       "      <td>2020-04-29T16:23:40.010000</td>\n",
       "      <td>42.8882</td>\n",
       "      <td>13.0363</td>\n",
       "      <td>10.0</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>1.8</td>\n",
       "      <td>--</td>\n",
       "      <td>1 km N Preci (PG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181775</th>\n",
       "      <td>24336481</td>\n",
       "      <td>2020-04-29T16:30:14.850000</td>\n",
       "      <td>37.7683</td>\n",
       "      <td>14.6293</td>\n",
       "      <td>38.0</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>2.2</td>\n",
       "      <td>--</td>\n",
       "      <td>3 km SE Troina (EN)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181776</th>\n",
       "      <td>24337031</td>\n",
       "      <td>2020-04-29T19:21:00.290000</td>\n",
       "      <td>37.8797</td>\n",
       "      <td>12.9638</td>\n",
       "      <td>10.0</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>1.6</td>\n",
       "      <td>--</td>\n",
       "      <td>10 km E Calatafimi-Segesta (TP)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181777 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        #EventID                        Time  Latitude  Longitude  Depth/Km  \\\n",
       "0             79  1985-01-02T22:57:43.090000   41.7150    13.9410       5.8   \n",
       "1             89  1985-01-03T00:46:16.990000   41.6770    13.9310       6.1   \n",
       "2            199  1985-01-04T16:57:40.240000   46.1580     7.1740      10.0   \n",
       "3            229  1985-01-04T23:59:54.820000   45.3380     6.6410       5.0   \n",
       "4            239  1985-01-05T03:47:03.640000   45.4090     6.6270      10.0   \n",
       "...          ...                         ...       ...        ...       ...   \n",
       "181772  24334191  2020-04-29T11:17:24.170000   45.9167    12.1917      10.1   \n",
       "181773  24335541  2020-04-29T14:14:47.970000   38.4090    13.5617      28.2   \n",
       "181774  24336421  2020-04-29T16:23:40.010000   42.8882    13.0363      10.0   \n",
       "181775  24336481  2020-04-29T16:30:14.850000   37.7683    14.6293      38.0   \n",
       "181776  24337031  2020-04-29T19:21:00.290000   37.8797    12.9638      10.0   \n",
       "\n",
       "              Author  Catalog  Contributor  ContributorID MagType  Magnitude  \\\n",
       "0       BULLETIN-VAX      NaN          NaN            NaN      Md        2.6   \n",
       "1       BULLETIN-VAX      NaN          NaN            NaN      Md        2.5   \n",
       "2       BULLETIN-VAX      NaN          NaN            NaN       M        3.8   \n",
       "3       BULLETIN-VAX      NaN          NaN            NaN       M        3.7   \n",
       "4       BULLETIN-VAX      NaN          NaN            NaN       M        3.2   \n",
       "...              ...      ...          ...            ...     ...        ...   \n",
       "181772   SURVEY-INGV      NaN          NaN            NaN      ML        1.7   \n",
       "181773   SURVEY-INGV      NaN          NaN            NaN      ML        2.3   \n",
       "181774   SURVEY-INGV      NaN          NaN            NaN      ML        1.8   \n",
       "181775   SURVEY-INGV      NaN          NaN            NaN      ML        2.2   \n",
       "181776   SURVEY-INGV      NaN          NaN            NaN      ML        1.6   \n",
       "\n",
       "       MagAuthor                                EventLocationName  \n",
       "0             --                   6 km S Civitella Alfedena (AQ)  \n",
       "1             --                           6 km NE Picinisco (FR)  \n",
       "2             --               Confine Italia-Svizzera (SVIZZERA)  \n",
       "3             --                 Confine Italia-Francia (FRANCIA)  \n",
       "4             --                 Confine Italia-Francia (FRANCIA)  \n",
       "...          ...                                              ...  \n",
       "181772        --                          2 km SW Refrontolo (TV)  \n",
       "181773        --  Costa Siciliana centro-settentrionale (Palermo)  \n",
       "181774        --                                1 km N Preci (PG)  \n",
       "181775        --                              3 km SE Troina (EN)  \n",
       "181776        --                  10 km E Calatafimi-Segesta (TP)  \n",
       "\n",
       "[181777 rows x 13 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "sns.set(style=\"darkgrid\")\n",
    "a=pd.read_csv(r\"C:\\\\Users\\Ogeid99\\Desktop\\cataloguess\\DATA_no_modify.csv\",sep=',')  #\"C:\\\\Users\\Ogeid99\\Desktop\\DS\\excel\\catalogo_l'aquila.txt\"\n",
    "#b=(a['Time'] > '2020-03-10').astype(int)\n",
    "#c=[a,b]\n",
    "#a=pd.concat(c, axis=1)\n",
    "#column_names = a.columns.values\n",
    "#column_names[13] = 'Time_date'\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Magnitudo</th>\n",
       "      <th>tempo(giorni)</th>\n",
       "      <th>distanza(Km)</th>\n",
       "      <th>#EventID</th>\n",
       "      <th>Time</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
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       "      <th>Contributor</th>\n",
       "      <th>ContributorID</th>\n",
       "      <th>MagType</th>\n",
       "      <th>Magnitude</th>\n",
       "      <th>MagAuthor</th>\n",
       "      <th>EventLocationName</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.6</td>\n",
       "      <td>7.592766</td>\n",
       "      <td>20.178087</td>\n",
       "      <td>79</td>\n",
       "      <td>1985-01-02T22:57:43.090000</td>\n",
       "      <td>41.7150</td>\n",
       "      <td>13.9410</td>\n",
       "      <td>5.8</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>2.6</td>\n",
       "      <td>--</td>\n",
       "      <td>6 km S Civitella Alfedena (AQ)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.5</td>\n",
       "      <td>6.691138</td>\n",
       "      <td>19.611012</td>\n",
       "      <td>89</td>\n",
       "      <td>1985-01-03T00:46:16.990000</td>\n",
       "      <td>41.6770</td>\n",
       "      <td>13.9310</td>\n",
       "      <td>6.1</td>\n",
       "      <td>BULLETIN-VAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Md</td>\n",
       "      <td>2.5</td>\n",
       "      <td>--</td>\n",
       "      <td>6 km NE Picinisco (FR)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.8</td>\n",
       "      <td>34.609873</td>\n",
       "      <td>28.407957</td>\n",
       "      <td>199</td>\n",
       "      <td>1985-01-04T16:57:40.240000</td>\n",
       "      <td>46.1580</td>\n",
       "      <td>7.1740</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.7</td>\n",
       "      <td>30.500011</td>\n",
       "      <td>27.609593</td>\n",
       "      <td>229</td>\n",
       "      <td>1985-01-04T23:59:54.820000</td>\n",
       "      <td>45.3380</td>\n",
       "      <td>6.6410</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>4</th>\n",
       "      <td>3.2</td>\n",
       "      <td>16.210634</td>\n",
       "      <td>23.941976</td>\n",
       "      <td>239</td>\n",
       "      <td>1985-01-05T03:47:03.640000</td>\n",
       "      <td>45.4090</td>\n",
       "      <td>6.6270</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>...</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>181772</th>\n",
       "      <td>1.7</td>\n",
       "      <td>2.433885</td>\n",
       "      <td>15.612052</td>\n",
       "      <td>24334191</td>\n",
       "      <td>2020-04-29T11:17:24.170000</td>\n",
       "      <td>45.9167</td>\n",
       "      <td>12.1917</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>1.7</td>\n",
       "      <td>--</td>\n",
       "      <td>2 km SW Refrontolo (TV)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181773</th>\n",
       "      <td>2.3</td>\n",
       "      <td>5.196369</td>\n",
       "      <td>18.524223</td>\n",
       "      <td>24335541</td>\n",
       "      <td>2020-04-29T14:14:47.970000</td>\n",
       "      <td>38.4090</td>\n",
       "      <td>13.5617</td>\n",
       "      <td>28.2</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>2.3</td>\n",
       "      <td>--</td>\n",
       "      <td>Costa Siciliana centro-settentrionale (Palermo)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181774</th>\n",
       "      <td>1.8</td>\n",
       "      <td>2.761849</td>\n",
       "      <td>16.063493</td>\n",
       "      <td>24336421</td>\n",
       "      <td>2020-04-29T16:23:40.010000</td>\n",
       "      <td>42.8882</td>\n",
       "      <td>13.0363</td>\n",
       "      <td>10.0</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>1.8</td>\n",
       "      <td>--</td>\n",
       "      <td>1 km N Preci (PG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181775</th>\n",
       "      <td>2.2</td>\n",
       "      <td>4.579310</td>\n",
       "      <td>18.003627</td>\n",
       "      <td>24336481</td>\n",
       "      <td>2020-04-29T16:30:14.850000</td>\n",
       "      <td>37.7683</td>\n",
       "      <td>14.6293</td>\n",
       "      <td>38.0</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>2.2</td>\n",
       "      <td>--</td>\n",
       "      <td>3 km SE Troina (EN)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181776</th>\n",
       "      <td>1.6</td>\n",
       "      <td>2.144865</td>\n",
       "      <td>15.173298</td>\n",
       "      <td>24337031</td>\n",
       "      <td>2020-04-29T19:21:00.290000</td>\n",
       "      <td>37.8797</td>\n",
       "      <td>12.9638</td>\n",
       "      <td>10.0</td>\n",
       "      <td>SURVEY-INGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ML</td>\n",
       "      <td>1.6</td>\n",
       "      <td>--</td>\n",
       "      <td>10 km E Calatafimi-Segesta (TP)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181777 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Magnitudo  tempo(giorni)  distanza(Km)  #EventID  \\\n",
       "0             2.6       7.592766     20.178087        79   \n",
       "1             2.5       6.691138     19.611012        89   \n",
       "2             3.8      34.609873     28.407957       199   \n",
       "3             3.7      30.500011     27.609593       229   \n",
       "4             3.2      16.210634     23.941976       239   \n",
       "...           ...            ...           ...       ...   \n",
       "181772        1.7       2.433885     15.612052  24334191   \n",
       "181773        2.3       5.196369     18.524223  24335541   \n",
       "181774        1.8       2.761849     16.063493  24336421   \n",
       "181775        2.2       4.579310     18.003627  24336481   \n",
       "181776        1.6       2.144865     15.173298  24337031   \n",
       "\n",
       "                              Time  Latitude  Longitude  Depth/Km  \\\n",
       "0       1985-01-02T22:57:43.090000   41.7150    13.9410       5.8   \n",
       "1       1985-01-03T00:46:16.990000   41.6770    13.9310       6.1   \n",
       "2       1985-01-04T16:57:40.240000   46.1580     7.1740      10.0   \n",
       "3       1985-01-04T23:59:54.820000   45.3380     6.6410       5.0   \n",
       "4       1985-01-05T03:47:03.640000   45.4090     6.6270      10.0   \n",
       "...                            ...       ...        ...       ...   \n",
       "181772  2020-04-29T11:17:24.170000   45.9167    12.1917      10.1   \n",
       "181773  2020-04-29T14:14:47.970000   38.4090    13.5617      28.2   \n",
       "181774  2020-04-29T16:23:40.010000   42.8882    13.0363      10.0   \n",
       "181775  2020-04-29T16:30:14.850000   37.7683    14.6293      38.0   \n",
       "181776  2020-04-29T19:21:00.290000   37.8797    12.9638      10.0   \n",
       "\n",
       "              Author  Catalog  Contributor  ContributorID MagType  Magnitude  \\\n",
       "0       BULLETIN-VAX      NaN          NaN            NaN      Md        2.6   \n",
       "1       BULLETIN-VAX      NaN          NaN            NaN      Md        2.5   \n",
       "2       BULLETIN-VAX      NaN          NaN            NaN       M        3.8   \n",
       "3       BULLETIN-VAX      NaN          NaN            NaN       M        3.7   \n",
       "4       BULLETIN-VAX      NaN          NaN            NaN       M        3.2   \n",
       "...              ...      ...          ...            ...     ...        ...   \n",
       "181772   SURVEY-INGV      NaN          NaN            NaN      ML        1.7   \n",
       "181773   SURVEY-INGV      NaN          NaN            NaN      ML        2.3   \n",
       "181774   SURVEY-INGV      NaN          NaN            NaN      ML        1.8   \n",
       "181775   SURVEY-INGV      NaN          NaN            NaN      ML        2.2   \n",
       "181776   SURVEY-INGV      NaN          NaN            NaN      ML        1.6   \n",
       "\n",
       "       MagAuthor                                EventLocationName  \n",
       "0             --                   6 km S Civitella Alfedena (AQ)  \n",
       "1             --                           6 km NE Picinisco (FR)  \n",
       "2             --               Confine Italia-Svizzera (SVIZZERA)  \n",
       "3             --                 Confine Italia-Francia (FRANCIA)  \n",
       "4             --                 Confine Italia-Francia (FRANCIA)  \n",
       "...          ...                                              ...  \n",
       "181772        --                          2 km SW Refrontolo (TV)  \n",
       "181773        --  Costa Siciliana centro-settentrionale (Palermo)  \n",
       "181774        --                                1 km N Preci (PG)  \n",
       "181775        --                              3 km SE Troina (EN)  \n",
       "181776        --                  10 km E Calatafimi-Segesta (TP)  \n",
       "\n",
       "[181777 rows x 16 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "magnitude_threshold= 6.5\n",
    "a_new=a['Magnitude']\n",
    "conditional_Magnitude=(a_new > magnitude_threshold).astype(int)\n",
    "conditional_Magnitude=pd.Series(conditional_Magnitude)\n",
    "first_parameter=np.power(10,0.032*a_new+2.7389)\n",
    "second_parameter=np.power(10,0.549* a_new-0.547)\n",
    "array_algo=pd.Series(np.where(conditional_Magnitude, first_parameter, second_parameter)  ) \n",
    "array_algo_d=pd.Series(np.power(10,0.1238*a_new+0.983) )\n",
    "#the function pow converts into float \n",
    "#instead of ** that gives the integer\n",
    "magnitude=pd.Series(a_new)\n",
    "magnitude.name='Magnitudo'\n",
    "array_algo.name='tempo(giorni)' #GIVE THE NAME TO THE DATASETS IF YOU WANNA MERGE  THEM !!\n",
    "#magnitude,array_algo\n",
    "array_algo_d.name='distanza(Km)'\n",
    "#df_columns=np.array([['  ', 'Magnitude'], ['Declustering', 'distanza(Km)'], ['Declustering', 'tempo(giorni)'],])\n",
    "#columns_declustering=pd.MultiIndex.from_frame(df_columns)\n",
    "final_df=pd.concat([magnitude,array_algo,array_algo_d], axis=1)\n",
    "#final_df=pd.DataFrame(final_df,columns=columns_declustering) #merge two datasets\n",
    "final_df.style.set_caption(\"Declustering method by Knopoff and Gardner (1972) \") # set a title to the table\n",
    "FINAL=pd.concat([final_df,a],axis=1)\n",
    "FINAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Magnitudo</th>\n",
       "      <th>tempo(giorni)</th>\n",
       "      <th>distanza(Km)</th>\n",
       "      <th>Time</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Depth/Km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.6</td>\n",
       "      <td>7.592766</td>\n",
       "      <td>20.178087</td>\n",
       "      <td>1985-01-02T22:57:43.090000</td>\n",
       "      <td>41.7150</td>\n",
       "      <td>13.9410</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.5</td>\n",
       "      <td>6.691138</td>\n",
       "      <td>19.611012</td>\n",
       "      <td>1985-01-03T00:46:16.990000</td>\n",
       "      <td>41.6770</td>\n",
       "      <td>13.9310</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.8</td>\n",
       "      <td>34.609873</td>\n",
       "      <td>28.407957</td>\n",
       "      <td>1985-01-04T16:57:40.240000</td>\n",
       "      <td>46.1580</td>\n",
       "      <td>7.1740</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.7</td>\n",
       "      <td>30.500011</td>\n",
       "      <td>27.609593</td>\n",
       "      <td>1985-01-04T23:59:54.820000</td>\n",
       "      <td>45.3380</td>\n",
       "      <td>6.6410</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.2</td>\n",
       "      <td>16.210634</td>\n",
       "      <td>23.941976</td>\n",
       "      <td>1985-01-05T03:47:03.640000</td>\n",
       "      <td>45.4090</td>\n",
       "      <td>6.6270</td>\n",
       "      <td>10.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181772</th>\n",
       "      <td>1.7</td>\n",
       "      <td>2.433885</td>\n",
       "      <td>15.612052</td>\n",
       "      <td>2020-04-29T11:17:24.170000</td>\n",
       "      <td>45.9167</td>\n",
       "      <td>12.1917</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181773</th>\n",
       "      <td>2.3</td>\n",
       "      <td>5.196369</td>\n",
       "      <td>18.524223</td>\n",
       "      <td>2020-04-29T14:14:47.970000</td>\n",
       "      <td>38.4090</td>\n",
       "      <td>13.5617</td>\n",
       "      <td>28.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181774</th>\n",
       "      <td>1.8</td>\n",
       "      <td>2.761849</td>\n",
       "      <td>16.063493</td>\n",
       "      <td>2020-04-29T16:23:40.010000</td>\n",
       "      <td>42.8882</td>\n",
       "      <td>13.0363</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181775</th>\n",
       "      <td>2.2</td>\n",
       "      <td>4.579310</td>\n",
       "      <td>18.003627</td>\n",
       "      <td>2020-04-29T16:30:14.850000</td>\n",
       "      <td>37.7683</td>\n",
       "      <td>14.6293</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181776</th>\n",
       "      <td>1.6</td>\n",
       "      <td>2.144865</td>\n",
       "      <td>15.173298</td>\n",
       "      <td>2020-04-29T19:21:00.290000</td>\n",
       "      <td>37.8797</td>\n",
       "      <td>12.9638</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181777 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Magnitudo  tempo(giorni)  distanza(Km)                        Time  \\\n",
       "0             2.6       7.592766     20.178087  1985-01-02T22:57:43.090000   \n",
       "1             2.5       6.691138     19.611012  1985-01-03T00:46:16.990000   \n",
       "2             3.8      34.609873     28.407957  1985-01-04T16:57:40.240000   \n",
       "3             3.7      30.500011     27.609593  1985-01-04T23:59:54.820000   \n",
       "4             3.2      16.210634     23.941976  1985-01-05T03:47:03.640000   \n",
       "...           ...            ...           ...                         ...   \n",
       "181772        1.7       2.433885     15.612052  2020-04-29T11:17:24.170000   \n",
       "181773        2.3       5.196369     18.524223  2020-04-29T14:14:47.970000   \n",
       "181774        1.8       2.761849     16.063493  2020-04-29T16:23:40.010000   \n",
       "181775        2.2       4.579310     18.003627  2020-04-29T16:30:14.850000   \n",
       "181776        1.6       2.144865     15.173298  2020-04-29T19:21:00.290000   \n",
       "\n",
       "        Latitude  Longitude  Depth/Km  \n",
       "0        41.7150    13.9410       5.8  \n",
       "1        41.6770    13.9310       6.1  \n",
       "2        46.1580     7.1740      10.0  \n",
       "3        45.3380     6.6410       5.0  \n",
       "4        45.4090     6.6270      10.0  \n",
       "...          ...        ...       ...  \n",
       "181772   45.9167    12.1917      10.1  \n",
       "181773   38.4090    13.5617      28.2  \n",
       "181774   42.8882    13.0363      10.0  \n",
       "181775   37.7683    14.6293      38.0  \n",
       "181776   37.8797    12.9638      10.0  \n",
       "\n",
       "[181777 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_cleaned=FINAL.iloc[:,[0,1,2,4,5,6,7]]\n",
    "final_cleaned\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119044    1.5\n",
       "110085    1.5\n",
       "110087    1.5\n",
       "110090    1.5\n",
       "110091    1.5\n",
       "         ... \n",
       "24892     5.8\n",
       "128998    5.9\n",
       "119509    6.0\n",
       "62379     6.1\n",
       "131467    6.5\n",
       "Name: Magnitudo, Length: 181777, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_magnitudo=final_cleaned['Magnitudo'].sort_values()\n",
    "final_magnitudo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#change time1,time2,distance to do this with an other method\n",
    "line_marker2=np.arange(0,6.5,0.1)\n",
    "line_marker=np.arange(6.5,9.1,0.1)\n",
    "total_line=np.arange(0,9.1,0.1)\n",
    "total_line_series=pd.Series(total_line)\n",
    "time1=np.power(10,0.032*line_marker+2.7389)\n",
    "time2=np.power(10,0.5409* line_marker2-0.547)\n",
    "time1_df=pd.concat([pd.Series(time2),pd.Series(line_marker2)], axis=1)\n",
    "time2_df=pd.concat([pd.Series(time1),pd.Series(line_marker)],axis=1)\n",
    "time1_add=pd.Series(np.full(np.count_nonzero(time2),'time'))\n",
    "time2_add=pd.Series(np.full(np.count_nonzero(time1),'time'))\n",
    "time11=pd.concat([time1_df,time1_add], axis=1)\n",
    "time22=pd.concat([time2_df,time2_add], axis=1)\n",
    "time1_time2=pd.concat([time11,time22], axis=0)\n",
    "distance=pd.Series(np.power(10,0.1238*(total_line)+0.983))\n",
    "distance_add=pd.Series(np.full(np.count_nonzero(distance),'distance'))\n",
    "distance11=pd.concat([distance,total_line_series, distance_add], axis=1)\n",
    "val=time1_time2.columns.values\n",
    "val[2]='2'\n",
    "time1_time2\n",
    "df=pd.concat([time1_time2,distance11], axis=0)\n",
    "df.columns=['Declustering','Magnitude','Legend']\n",
    "#df_new=pd.concat([total_line_series,df], axis=1)\n",
    "#df_new\n",
    "#b=(a['Time'] > '2020-03-10').astype(int)\n",
    "#pd.DataFrame({})\n",
    "fig,axes=plt.subplots(1,1,figsize=(10,6))\n",
    "sns.lineplot(x=\"Magnitude\", y='Declustering', hue='Legend', data=df)\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel('Magnitude',fontsize=18,labelpad=5)\n",
    "plt.ylabel('Time [days]/ Distance [km] ',fontsize=18,labelpad=5)\n",
    "#plt.title('Number of earthquakes greater than 1.5 from 1985 to 2020',fontsize=16,pad=15.0)   \n",
    "plt.tick_params(labelsize=12)\n",
    "#fig.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\declustering_GardnerKnopoff_windows.png\",dpi=600)"
   ]
  },
  {
   "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",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}