THESIS_Gutenberg_Richter.ipynb

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       "<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",
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       "        text-align: right;\n",
       "    }\n",
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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>1985-01-02 23:26:09.581820</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>1985-01-03 01:14:43.481820</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>1985-01-04 17:26:06.731820</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>1985-01-05 00:28:21.311820</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>1985-01-05 04:15:30.131820</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181772</th>\n",
       "      <td>24334191</td>\n",
       "      <td>2020-04-29 11:45:50.661820</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>2020-04-29 14:43:14.461820</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>2020-04-29 16:52:06.501820</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>2020-04-29 16:58:41.341820</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>2020-04-29 19:49:26.781820</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  1985-01-02 23:26:09.581820   41.7150    13.9410       5.8   \n",
       "1             89  1985-01-03 01:14:43.481820   41.6770    13.9310       6.1   \n",
       "2            199  1985-01-04 17:26:06.731820   46.1580     7.1740      10.0   \n",
       "3            229  1985-01-05 00:28:21.311820   45.3380     6.6410       5.0   \n",
       "4            239  1985-01-05 04:15:30.131820   45.4090     6.6270      10.0   \n",
       "...          ...                         ...       ...        ...       ...   \n",
       "181772  24334191  2020-04-29 11:45:50.661820   45.9167    12.1917      10.1   \n",
       "181773  24335541  2020-04-29 14:43:14.461820   38.4090    13.5617      28.2   \n",
       "181774  24336421  2020-04-29 16:52:06.501820   42.8882    13.0363      10.0   \n",
       "181775  24336481  2020-04-29 16:58:41.341820   37.7683    14.6293      38.0   \n",
       "181776  24337031  2020-04-29 19:49:26.781820   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": {},
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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",
    "import pandas as pd\n",
    "DATA=pd.read_csv(r\"C:\\\\Users\\Ogeid99\\Desktop\\cataloguess\\DATA.csv\")\n",
    "DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
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     "data": {
      "text/plain": [
       "0         1985-01-02 22:58:04.594130\n",
       "1         1985-01-03 00:46:38.494130\n",
       "2         1985-01-04 16:58:01.744130\n",
       "3         1985-01-05 00:00:16.324130\n",
       "4         1985-01-05 03:47:25.144130\n",
       "                     ...            \n",
       "181772    2020-04-29 11:17:45.674130\n",
       "181773    2020-04-29 14:15:09.474130\n",
       "181774    2020-04-29 16:24:01.514130\n",
       "181775    2020-04-29 16:30:36.354130\n",
       "181776    2020-04-29 19:21:21.794130\n",
       "Name: Time, Length: 181777, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CALCULATING B-VALUES AND THE ASSOCIATED STANDARD ERRORS WITH AKI AND SHI & BOLT METHODS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "from math import sqrt\n",
    "binning=0.1\n",
    "Mmin=3.0\n",
    "Mc=1.6\n",
    "list_bin=[]\n",
    "list_b=[]\n",
    "list_sigma1=[]\n",
    "list_sigma2=[]\n",
    "for k in range(15,62,1):\n",
    "    M_cutoff=k/10 #use this\n",
    "    #evaluation of maximum likelihood B^\n",
    "    magnitude=DATA[DATA['Magnitude']>=M_cutoff].loc[:,'Magnitude']\n",
    "    mean_magn=np.mean(magnitude)\n",
    "    b1=math.log10(math.exp(1))/(mean_magn-(M_cutoff-binning/2))\n",
    "    sigma1=b1/sqrt(len(magnitude))\n",
    "    ## standard error (Aki and Shi & Bolt)\n",
    "    scarto_list=[]\n",
    "    for j in magnitude:\n",
    "        scarto=(j-mean_magn)**2\n",
    "        scarto_list.append(scarto)\n",
    "    sum_scarto_list=sum(scarto_list)\n",
    "    sigma2=2.30*b1**2*sqrt(sum_scarto_list/(len(magnitude)*(len(magnitude)-1)))\n",
    "    list_bin.append(M_cutoff)\n",
    "    list_b.append(b1)\n",
    "    list_sigma1.append(sigma1)\n",
    "    list_sigma2.append(sigma2)\n",
    "#plot Gutemberg-Richter (0.2 binning)\n",
    "b=1.21\n",
    "magnitude=DATA['Magnitude']\n",
    "list_1=pd.Series([1 for i in range(181777)],name='1' )\n",
    "#bin_magnitude=pd.Series(np.array(np.arange(np.min(magnitude),np.max(magnitude)+0.1,0.2)))\n",
    "bin_magnitude=pd.Series(np.array(np.arange(np.min(magnitude),np.max(magnitude)+0.2,0.2)))\n",
    "number_of_earthquakes= pd.Series(np.array([np.count_nonzero((magnitude >= i ) & (magnitude <= i+0.2))\n",
    "                                           for i in bin_magnitude]))\n",
    "#bin_magnitude=np.add(bin_magnitude,0.2)\n",
    "df1=pd.concat([bin_magnitude,number_of_earthquakes],axis=1)\n",
    "df1.columns=['bin_magnitude','number_of_earthquakes']\n",
    "\n",
    "plot=sns.scatterplot(x='bin_magnitude',y='number_of_earthquakes',data=df1)\n",
    "plt.xlabel(\"Years\",fontsize=18,labelpad=5) #set font and title of x label\n",
    "plt.ylabel(\"Magnitude Types\",fontsize=18,labelpad=5)\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.title('Magnitude types throughout the years',fontsize=18,pad=15.5)\n",
    "\n",
    "plt.yscale('log')# an alternative:  plot.set(yscale=\"log\")\n",
    "plt.ylim((0.7, 200000)) # DO NOT put 0 or negative values 'cause there is a log scale\n",
    "plt.xlim((1.3, 6.7))\n",
    "#explain influence of old catalogues\n",
    "#fig.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\N.earthquakes_bin_magnitude.png\",dpi=600)\n",
    "list_bin,list_b,list_sigma1,list_sigma2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>Magnitude</th>\n",
       "      <th>B_value</th>\n",
       "      <th>$\\sigma_{st-error}$ Aki</th>\n",
       "      <th>$\\sigma_{st-error}$ Shi &amp; Bolt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.686260</td>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.001281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.6</td>\n",
       "      <td>0.707021</td>\n",
       "      <td>0.001770</td>\n",
       "      <td>0.001398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.7</td>\n",
       "      <td>0.729553</td>\n",
       "      <td>0.001954</td>\n",
       "      <td>0.001528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.8</td>\n",
       "      <td>0.758944</td>\n",
       "      <td>0.002171</td>\n",
       "      <td>0.001696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.9</td>\n",
       "      <td>0.797301</td>\n",
       "      <td>0.002434</td>\n",
       "      <td>0.001919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.845379</td>\n",
       "      <td>0.002755</td>\n",
       "      <td>0.002220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.1</td>\n",
       "      <td>0.904676</td>\n",
       "      <td>0.003152</td>\n",
       "      <td>0.002633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.2</td>\n",
       "      <td>0.963178</td>\n",
       "      <td>0.003621</td>\n",
       "      <td>0.003130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.3</td>\n",
       "      <td>1.019706</td>\n",
       "      <td>0.004177</td>\n",
       "      <td>0.003731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.070730</td>\n",
       "      <td>0.004829</td>\n",
       "      <td>0.004441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.5</td>\n",
       "      <td>1.110043</td>\n",
       "      <td>0.005576</td>\n",
       "      <td>0.005236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.6</td>\n",
       "      <td>1.138723</td>\n",
       "      <td>0.006432</td>\n",
       "      <td>0.006122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.7</td>\n",
       "      <td>1.169282</td>\n",
       "      <td>0.007448</td>\n",
       "      <td>0.007223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.8</td>\n",
       "      <td>1.177423</td>\n",
       "      <td>0.008561</td>\n",
       "      <td>0.008316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.9</td>\n",
       "      <td>1.190919</td>\n",
       "      <td>0.009875</td>\n",
       "      <td>0.009662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.210169</td>\n",
       "      <td>0.011438</td>\n",
       "      <td>0.011358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.1</td>\n",
       "      <td>1.221397</td>\n",
       "      <td>0.013228</td>\n",
       "      <td>0.013290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.2</td>\n",
       "      <td>1.233552</td>\n",
       "      <td>0.015325</td>\n",
       "      <td>0.015647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.3</td>\n",
       "      <td>1.241494</td>\n",
       "      <td>0.017745</td>\n",
       "      <td>0.018431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.4</td>\n",
       "      <td>1.215782</td>\n",
       "      <td>0.020252</td>\n",
       "      <td>0.020883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.5</td>\n",
       "      <td>1.200024</td>\n",
       "      <td>0.023146</td>\n",
       "      <td>0.023836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.6</td>\n",
       "      <td>1.187678</td>\n",
       "      <td>0.026445</td>\n",
       "      <td>0.027259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.7</td>\n",
       "      <td>1.182608</td>\n",
       "      <td>0.030274</td>\n",
       "      <td>0.031446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.8</td>\n",
       "      <td>1.162662</td>\n",
       "      <td>0.034390</td>\n",
       "      <td>0.035579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.9</td>\n",
       "      <td>1.148899</td>\n",
       "      <td>0.039086</td>\n",
       "      <td>0.040440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.136860</td>\n",
       "      <td>0.044387</td>\n",
       "      <td>0.045986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.1</td>\n",
       "      <td>1.088261</td>\n",
       "      <td>0.049415</td>\n",
       "      <td>0.049440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4.2</td>\n",
       "      <td>1.062344</td>\n",
       "      <td>0.055303</td>\n",
       "      <td>0.053993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4.3</td>\n",
       "      <td>1.055845</td>\n",
       "      <td>0.062325</td>\n",
       "      <td>0.060156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.4</td>\n",
       "      <td>1.009770</td>\n",
       "      <td>0.068706</td>\n",
       "      <td>0.062386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.5</td>\n",
       "      <td>1.029526</td>\n",
       "      <td>0.078048</td>\n",
       "      <td>0.070720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.020104</td>\n",
       "      <td>0.087473</td>\n",
       "      <td>0.076200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4.7</td>\n",
       "      <td>1.069033</td>\n",
       "      <td>0.101014</td>\n",
       "      <td>0.089755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4.8</td>\n",
       "      <td>1.117986</td>\n",
       "      <td>0.117197</td>\n",
       "      <td>0.106381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.9</td>\n",
       "      <td>1.131556</td>\n",
       "      <td>0.134291</td>\n",
       "      <td>0.120195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.163660</td>\n",
       "      <td>0.155501</td>\n",
       "      <td>0.139280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.109413</td>\n",
       "      <td>0.173261</td>\n",
       "      <td>0.140145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>5.2</td>\n",
       "      <td>1.160463</td>\n",
       "      <td>0.202011</td>\n",
       "      <td>0.161208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>5.3</td>\n",
       "      <td>1.361572</td>\n",
       "      <td>0.252838</td>\n",
       "      <td>0.230945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.4</td>\n",
       "      <td>1.426042</td>\n",
       "      <td>0.304033</td>\n",
       "      <td>0.284935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.5</td>\n",
       "      <td>1.343179</td>\n",
       "      <td>0.346807</td>\n",
       "      <td>0.293321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>5.6</td>\n",
       "      <td>1.489010</td>\n",
       "      <td>0.429840</td>\n",
       "      <td>0.389266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>5.7</td>\n",
       "      <td>1.389742</td>\n",
       "      <td>0.491348</td>\n",
       "      <td>0.384131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.043739</td>\n",
       "      <td>0.722571</td>\n",
       "      <td>0.830734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.9</td>\n",
       "      <td>1.579253</td>\n",
       "      <td>0.789626</td>\n",
       "      <td>0.754309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.737178</td>\n",
       "      <td>1.002960</td>\n",
       "      <td>1.060242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>6.1</td>\n",
       "      <td>1.737178</td>\n",
       "      <td>1.228370</td>\n",
       "      <td>1.388182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Magnitude   B_value  $\\sigma_{st-error}$ Aki  \\\n",
       "0         1.5  0.686260                 0.001610   \n",
       "1         1.6  0.707021                 0.001770   \n",
       "2         1.7  0.729553                 0.001954   \n",
       "3         1.8  0.758944                 0.002171   \n",
       "4         1.9  0.797301                 0.002434   \n",
       "5         2.0  0.845379                 0.002755   \n",
       "6         2.1  0.904676                 0.003152   \n",
       "7         2.2  0.963178                 0.003621   \n",
       "8         2.3  1.019706                 0.004177   \n",
       "9         2.4  1.070730                 0.004829   \n",
       "10        2.5  1.110043                 0.005576   \n",
       "11        2.6  1.138723                 0.006432   \n",
       "12        2.7  1.169282                 0.007448   \n",
       "13        2.8  1.177423                 0.008561   \n",
       "14        2.9  1.190919                 0.009875   \n",
       "15        3.0  1.210169                 0.011438   \n",
       "16        3.1  1.221397                 0.013228   \n",
       "17        3.2  1.233552                 0.015325   \n",
       "18        3.3  1.241494                 0.017745   \n",
       "19        3.4  1.215782                 0.020252   \n",
       "20        3.5  1.200024                 0.023146   \n",
       "21        3.6  1.187678                 0.026445   \n",
       "22        3.7  1.182608                 0.030274   \n",
       "23        3.8  1.162662                 0.034390   \n",
       "24        3.9  1.148899                 0.039086   \n",
       "25        4.0  1.136860                 0.044387   \n",
       "26        4.1  1.088261                 0.049415   \n",
       "27        4.2  1.062344                 0.055303   \n",
       "28        4.3  1.055845                 0.062325   \n",
       "29        4.4  1.009770                 0.068706   \n",
       "30        4.5  1.029526                 0.078048   \n",
       "31        4.6  1.020104                 0.087473   \n",
       "32        4.7  1.069033                 0.101014   \n",
       "33        4.8  1.117986                 0.117197   \n",
       "34        4.9  1.131556                 0.134291   \n",
       "35        5.0  1.163660                 0.155501   \n",
       "36        5.1  1.109413                 0.173261   \n",
       "37        5.2  1.160463                 0.202011   \n",
       "38        5.3  1.361572                 0.252838   \n",
       "39        5.4  1.426042                 0.304033   \n",
       "40        5.5  1.343179                 0.346807   \n",
       "41        5.6  1.489010                 0.429840   \n",
       "42        5.7  1.389742                 0.491348   \n",
       "43        5.8  2.043739                 0.722571   \n",
       "44        5.9  1.579253                 0.789626   \n",
       "45        6.0  1.737178                 1.002960   \n",
       "46        6.1  1.737178                 1.228370   \n",
       "\n",
       "    $\\sigma_{st-error}$ Shi & Bolt   \n",
       "0                          0.001281  \n",
       "1                          0.001398  \n",
       "2                          0.001528  \n",
       "3                          0.001696  \n",
       "4                          0.001919  \n",
       "5                          0.002220  \n",
       "6                          0.002633  \n",
       "7                          0.003130  \n",
       "8                          0.003731  \n",
       "9                          0.004441  \n",
       "10                         0.005236  \n",
       "11                         0.006122  \n",
       "12                         0.007223  \n",
       "13                         0.008316  \n",
       "14                         0.009662  \n",
       "15                         0.011358  \n",
       "16                         0.013290  \n",
       "17                         0.015647  \n",
       "18                         0.018431  \n",
       "19                         0.020883  \n",
       "20                         0.023836  \n",
       "21                         0.027259  \n",
       "22                         0.031446  \n",
       "23                         0.035579  \n",
       "24                         0.040440  \n",
       "25                         0.045986  \n",
       "26                         0.049440  \n",
       "27                         0.053993  \n",
       "28                         0.060156  \n",
       "29                         0.062386  \n",
       "30                         0.070720  \n",
       "31                         0.076200  \n",
       "32                         0.089755  \n",
       "33                         0.106381  \n",
       "34                         0.120195  \n",
       "35                         0.139280  \n",
       "36                         0.140145  \n",
       "37                         0.161208  \n",
       "38                         0.230945  \n",
       "39                         0.284935  \n",
       "40                         0.293321  \n",
       "41                         0.389266  \n",
       "42                         0.384131  \n",
       "43                         0.830734  \n",
       "44                         0.754309  \n",
       "45                         1.060242  \n",
       "46                         1.388182  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old=[0,1,2,3]\n",
    "new=['Magnitude','B_value',r'$\\sigma_{st-error}$ Aki', r'$\\sigma_{st-error}$ Shi & Bolt ']\n",
    "table_errors_b_value_magnitude=pd.DataFrame([list_bin,list_b,list_sigma1,list_sigma2]).T\n",
    "table_errors_b_value_magnitude.rename(columns=dict(zip(old,new)),inplace=True)\n",
    "table_errors_b_value_magnitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1.5\n",
       "1     1.7\n",
       "2     1.9\n",
       "3     2.1\n",
       "4     2.3\n",
       "5     2.5\n",
       "6     2.7\n",
       "7     2.9\n",
       "8     3.1\n",
       "9     3.3\n",
       "10    3.5\n",
       "11    3.7\n",
       "12    3.9\n",
       "13    4.1\n",
       "14    4.3\n",
       "15    4.5\n",
       "16    4.7\n",
       "17    4.9\n",
       "18    5.1\n",
       "19    5.3\n",
       "20    5.5\n",
       "21    5.7\n",
       "22    5.9\n",
       "23    6.1\n",
       "24    6.3\n",
       "25    6.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#a=DATA[(DATA['Magnitude']>=3.0)  & (DATA['Magnitude']<3.1)].loc[:,'Magnitude']\n",
    "#print(a)\n",
    "#DATA['Magnitude']\n",
    "bin_magnitude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PLOT B-VALUE BY MAGNITUDE CUT-OFF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10,7))\n",
    "\n",
    "plt.scatter(list_bin,list_b,c='g',)\n",
    "plt.errorbar(list_bin,list_b, yerr=list_sigma1,fmt='o',ecolor='r')\n",
    "plt.xlabel('Magnitude',fontsize=17)\n",
    "plt.ylabel('b-value',fontsize=17)\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.plot()\n",
    "#plt.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\b_value_error_bar.png\",dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PLOT GUTENBERG-RICHTER (ITERATIVE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a_value=7.35\n",
    "b=1.21\n",
    "#b=math.log(1/((np.mean(magnitude)-(Mmin-binning/2))*(math.log(10))))# according to 'a non robust method to estimate the b value'\n",
    "#b=math.log10(math.exp(1)/(np.mean(magnitude)-(Mmin-binning/2)))\n",
    "Gutemberg_Richter=np.power(10,a_value-b*bin_magnitude) #here you can consider b or not\n",
    "plt.figure(figsize=(10,7))\n",
    "plot=sns.scatterplot(x='bin_magnitude',y='number_of_earthquakes',data=df1,s=66)\n",
    "plt.title('Gutenberg-Richter (iterative events)',fontsize=18,pad=15.5)\n",
    "plt.yscale('log')# an alternative:  plot.set(yscale=\"log\")\n",
    "plt.ylim((0.7, 200000)) # DO NOT put 0 or negative values 'cause there is a log scale\n",
    "plt.plot(bin_magnitude,Gutemberg_Richter)\n",
    "plt.xlabel('Magnitude',fontsize=17,labelpad=5) #set font and title of x label\n",
    "plt.ylabel('N. of earthquakes',fontsize=17,labelpad=5) #set font and title of y label\n",
    "plt.tick_params(labelsize=12)\n",
    "#plt.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\gut_Richter.png\",dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PLOT GUTENBERG-RICHTER (ITERATIVE AND CUMULATIVE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "list_1=pd.Series([1 for i in range(181777)],name='1' )\n",
    "a_value_cum=7.679\n",
    "#another method\n",
    "DATA_with_one=pd.concat(  [DATA,list_1],axis=1 )  \n",
    "\n",
    "grouped=DATA_with_one.iloc[:,13].groupby(DATA['Magnitude'])\n",
    "DATA['Magnitude'].sort_values(ascending=False) \n",
    "grouped_DATA_Magnitude=grouped.sum()\n",
    "index_grouped_DATA_Magnitude=grouped_DATA_Magnitude.index\n",
    "Gutemberg_Richter_cum=np.power(10,a_value_cum-b*bin_magnitude)\n",
    "cum_series=pd.Series(np.add.accumulate(grouped_DATA_Magnitude.sort_index(ascending=False)))\n",
    "#cum_ser_bin=[]\n",
    "#for i in cum_series:\n",
    "#    if\n",
    "#    cum_ser_bin.append(i)\n",
    "cum_series_1=[]\n",
    "cum_series_1_index=[]\n",
    "k=1\n",
    "for i,z in zip(cum_series.index,cum_series):\n",
    "    if k%2!=0 and k>2:\n",
    "        pass\n",
    "    else:\n",
    "        cum_series_1_index.append([i])\n",
    "        cum_series_1.append([z])\n",
    "    k+=1\n",
    "plt.figure(figsize=(10,7))    \n",
    "plot=plt.scatter(cum_series_1_index,cum_series_1,s=46,color='red')\n",
    "plt.yscale('log')# an alternative:  plot.set(yscale=\"log\")\n",
    "plt.title('Gutenberg-Richter (cumulative and iterative events)',fontsize=18,pad=15.5)\n",
    "plt.ylim((0.7, 600000)) # DO NOT put 0 or negative values 'cause there is a log scale\n",
    "plt.plot(bin_magnitude,Gutemberg_Richter_cum,color='red')\n",
    "plt.xlabel('Number of earthquakes',fontsize=17) #set font and title of x label\n",
    "plt.ylabel('Magnitude',fontsize=17) #set font and title of y label\n",
    "plt.tick_params(labelsize=12)\n",
    "plot=sns.scatterplot(x='bin_magnitude',y='number_of_earthquakes',data=df1,s=66)\n",
    "plt.plot(bin_magnitude,Gutemberg_Richter)\n",
    "plt.xlabel('Magnitude',fontsize=17) #set font and title of x label\n",
    "plt.ylabel('Number of earthquakes',fontsize=17)\n",
    "plt.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\gut_Richter_iterativeandcumulative.png\",dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2.431673e-01\n",
       "1     1.840370e-01\n",
       "2     1.392852e-01\n",
       "3     1.054157e-01\n",
       "4     7.978204e-02\n",
       "5     6.038167e-02\n",
       "6     4.569883e-02\n",
       "7     3.458638e-02\n",
       "8     2.617611e-02\n",
       "9     1.981094e-02\n",
       "10    1.499357e-02\n",
       "11    1.134763e-02\n",
       "12    8.588259e-03\n",
       "13    6.499877e-03\n",
       "14    4.919320e-03\n",
       "15    3.723104e-03\n",
       "16    2.817767e-03\n",
       "17    2.132579e-03\n",
       "18    1.614006e-03\n",
       "19    1.221533e-03\n",
       "20    9.244962e-04\n",
       "21    6.996891e-04\n",
       "22    5.295477e-04\n",
       "23    4.007791e-04\n",
       "24    3.033228e-04\n",
       "25    2.295647e-04\n",
       "26    1.737421e-04\n",
       "27    1.314938e-04\n",
       "28    9.951880e-05\n",
       "29    7.531910e-05\n",
       "30    5.700397e-05\n",
       "31    4.314248e-05\n",
       "32    3.265165e-05\n",
       "33    2.471184e-05\n",
       "34    1.870273e-05\n",
       "35    1.415484e-05\n",
       "36    1.071285e-05\n",
       "37    8.107839e-06\n",
       "38    6.136279e-06\n",
       "39    4.644138e-06\n",
       "40    3.514836e-06\n",
       "41    2.660144e-06\n",
       "42    2.013284e-06\n",
       "43    1.523720e-06\n",
       "44    1.153201e-06\n",
       "45    8.727807e-07\n",
       "46    6.605491e-07\n",
       "47    4.999253e-07\n",
       "48    3.783599e-07\n",
       "49    2.863552e-07\n",
       "50    2.167231e-07\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "magnitude=DATA['Magnitude']\n",
    "new_bin_magnitude=pd.Series(np.array(np.arange(np.min(magnitude),np.max(magnitude)+0.1,0.1)))\n",
    "new_Gutemberg_Richter=np.power(10,7.46-(b*new_bin_magnitude)) #here you can consider b or not\n",
    "sumGR=new_Gutemberg_Richter.sum()\n",
    "probabilities=np.divide(new_Gutemberg_Richter,sumGR)\n",
    "probabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GENERATE 1000 SYNTHETIC CATALOGUES FOR ITERATIVE EVENTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#draw from an exponential distribution\n",
    "#for i in new_bin_magnitude:\n",
    "new_bin_df=pd.Series(new_bin_magnitude)\n",
    "ranger=range(1000)\n",
    "for i in ranger:\n",
    "    random_sample=np.random.choice(new_bin_magnitude,100000,p=probabilities)\n",
    "    counts=np.array([])\n",
    "    for j in new_bin_magnitude:\n",
    "        counts=pd.Series(np.append(counts,np.count_nonzero(random_sample==j)))\n",
    "    new_bin_df=pd.concat([new_bin_df,counts],axis=1)\n",
    "new_bin_df.columns=[s for s in range(len(ranger)+1)]\n",
    "\n",
    "\n",
    "#sns.distplot(random_sample,kde=False)\n",
    "\n",
    "#plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1.5</th>\n",
       "      <th>1.6</th>\n",
       "      <th>1.7</th>\n",
       "      <th>1.8</th>\n",
       "      <th>1.9</th>\n",
       "      <th>2.0</th>\n",
       "      <th>2.1</th>\n",
       "      <th>2.2</th>\n",
       "      <th>2.3</th>\n",
       "      <th>2.4</th>\n",
       "      <th>...</th>\n",
       "      <th>5.6</th>\n",
       "      <th>5.7</th>\n",
       "      <th>5.8</th>\n",
       "      <th>5.9</th>\n",
       "      <th>6.0</th>\n",
       "      <th>6.1</th>\n",
       "      <th>6.2</th>\n",
       "      <th>6.3</th>\n",
       "      <th>6.4</th>\n",
       "      <th>6.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24374.0</td>\n",
       "      <td>18377.0</td>\n",
       "      <td>13864.0</td>\n",
       "      <td>10465.0</td>\n",
       "      <td>7989.0</td>\n",
       "      <td>6114.0</td>\n",
       "      <td>4542.0</td>\n",
       "      <td>3472.0</td>\n",
       "      <td>2630.0</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24346.0</td>\n",
       "      <td>18449.0</td>\n",
       "      <td>14065.0</td>\n",
       "      <td>10509.0</td>\n",
       "      <td>8121.0</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>4507.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>2545.0</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24305.0</td>\n",
       "      <td>18390.0</td>\n",
       "      <td>14025.0</td>\n",
       "      <td>10650.0</td>\n",
       "      <td>8033.0</td>\n",
       "      <td>6030.0</td>\n",
       "      <td>4486.0</td>\n",
       "      <td>3519.0</td>\n",
       "      <td>2648.0</td>\n",
       "      <td>1927.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24272.0</td>\n",
       "      <td>18509.0</td>\n",
       "      <td>13846.0</td>\n",
       "      <td>10532.0</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>6018.0</td>\n",
       "      <td>4480.0</td>\n",
       "      <td>3485.0</td>\n",
       "      <td>2683.0</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24433.0</td>\n",
       "      <td>18478.0</td>\n",
       "      <td>13892.0</td>\n",
       "      <td>10382.0</td>\n",
       "      <td>7864.0</td>\n",
       "      <td>6120.0</td>\n",
       "      <td>4574.0</td>\n",
       "      <td>3470.0</td>\n",
       "      <td>2591.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <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>996</th>\n",
       "      <td>24455.0</td>\n",
       "      <td>18188.0</td>\n",
       "      <td>13934.0</td>\n",
       "      <td>10594.0</td>\n",
       "      <td>8034.0</td>\n",
       "      <td>6090.0</td>\n",
       "      <td>4580.0</td>\n",
       "      <td>3448.0</td>\n",
       "      <td>2552.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>24299.0</td>\n",
       "      <td>18456.0</td>\n",
       "      <td>14226.0</td>\n",
       "      <td>10435.0</td>\n",
       "      <td>8156.0</td>\n",
       "      <td>5888.0</td>\n",
       "      <td>4527.0</td>\n",
       "      <td>3477.0</td>\n",
       "      <td>2630.0</td>\n",
       "      <td>1946.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>24249.0</td>\n",
       "      <td>18499.0</td>\n",
       "      <td>13942.0</td>\n",
       "      <td>10547.0</td>\n",
       "      <td>7784.0</td>\n",
       "      <td>6021.0</td>\n",
       "      <td>4729.0</td>\n",
       "      <td>3442.0</td>\n",
       "      <td>2598.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>24471.0</td>\n",
       "      <td>18279.0</td>\n",
       "      <td>13969.0</td>\n",
       "      <td>10565.0</td>\n",
       "      <td>7908.0</td>\n",
       "      <td>6032.0</td>\n",
       "      <td>4647.0</td>\n",
       "      <td>3506.0</td>\n",
       "      <td>2612.0</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>24472.0</td>\n",
       "      <td>18479.0</td>\n",
       "      <td>13807.0</td>\n",
       "      <td>10462.0</td>\n",
       "      <td>7966.0</td>\n",
       "      <td>6036.0</td>\n",
       "      <td>4587.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>2508.0</td>\n",
       "      <td>2036.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0         1.5      1.6      1.7      1.8     1.9     2.0     2.1     2.2  \\\n",
       "1     24374.0  18377.0  13864.0  10465.0  7989.0  6114.0  4542.0  3472.0   \n",
       "2     24346.0  18449.0  14065.0  10509.0  8121.0  6012.0  4507.0  3364.0   \n",
       "3     24305.0  18390.0  14025.0  10650.0  8033.0  6030.0  4486.0  3519.0   \n",
       "4     24272.0  18509.0  13846.0  10532.0  7969.0  6018.0  4480.0  3485.0   \n",
       "5     24433.0  18478.0  13892.0  10382.0  7864.0  6120.0  4574.0  3470.0   \n",
       "...       ...      ...      ...      ...     ...     ...     ...     ...   \n",
       "996   24455.0  18188.0  13934.0  10594.0  8034.0  6090.0  4580.0  3448.0   \n",
       "997   24299.0  18456.0  14226.0  10435.0  8156.0  5888.0  4527.0  3477.0   \n",
       "998   24249.0  18499.0  13942.0  10547.0  7784.0  6021.0  4729.0  3442.0   \n",
       "999   24471.0  18279.0  13969.0  10565.0  7908.0  6032.0  4647.0  3506.0   \n",
       "1000  24472.0  18479.0  13807.0  10462.0  7966.0  6036.0  4587.0  3400.0   \n",
       "\n",
       "0        2.3     2.4  ...  5.6  5.7  5.8  5.9  6.0  6.1  6.2  6.3  6.4  6.5  \n",
       "1     2630.0  1962.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2     2545.0  1978.0  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  \n",
       "3     2648.0  1927.0  ...  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "4     2683.0  1954.0  ...  0.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "5     2591.0  1987.0  ...  0.0  1.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  \n",
       "...      ...     ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "996   2552.0  2012.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "997   2630.0  1946.0  ...  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "998   2598.0  1975.0  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  \n",
       "999   2612.0  1931.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "1000  2508.0  2036.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[1000 rows x 51 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_bin_df2=new_bin_df.T\n",
    "new_bin_df2\n",
    "binned_array=new_bin_df2.loc[0,:]\n",
    "dropped_new_bin_df2=new_bin_df2.drop([0])  #drop row if axis=0 and columns if 1\n",
    "dropped_new_bin_df2.columns=binned_array\n",
    "dropped_new_bin_df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PLOT HISTOGRAMS OF ITERATIVE PLOTS FOR A GIVEN COLUMN IN THE GENERATED CATALOGUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 19.,  87., 228., 280., 170., 153.,  45.,  15.,   1.,   2.]),\n",
       " array([ 7. ,  9.8, 12.6, 15.4, 18.2, 21. , 23.8, 26.6, 29.4, 32.2, 35. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MJDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkN+n/mC/kpw+5A1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plt.hist(dropped_new_bin_df2.iloc[:,26],color='lime') #bins=bins\n",
    "#bins_labels(bins)      \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PLOT B-VALUE BY MAGNITUDE CUT-OFF FOR TWO SYNTHETIC CATALOGUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "list_bin=[]\n",
    "list_b=[]\n",
    "list_sigma1=[]\n",
    "def funct_synthetic(n,dropped_new_bin_df2):\n",
    "    for k in range(15,58,1):\n",
    "        M_cutoff_sample=k/10 #use this\n",
    "        #evaluation of maximum likelihood B^\n",
    "        magnitude_sample=dropped_new_bin_df2.columns.values\n",
    "        count_sample=dropped_new_bin_df2.iloc[n][magnitude_sample>=M_cutoff_sample]\n",
    "        mean_magnitude= np.average(count_sample.index.values, weights=count_sample)\n",
    "        b1=math.log10(math.exp(1))/(mean_magnitude-(M_cutoff_sample-binning/2))\n",
    "        sigma1=b1/sqrt(sum(count_sample))\n",
    "        list_bin.append(M_cutoff_sample)\n",
    "        list_b.append(b1)\n",
    "        list_sigma1.append(sigma1)\n",
    "funct_synthetic(3,dropped_new_bin_df2)\n",
    "list_bin,list_b,list_sigma1\n",
    "fig=plt.figure(figsize=(10,7))\n",
    "#fig, ax1 = plt.subplots(1)\n",
    "plt.scatter(list_bin,list_b,c='g',)\n",
    "plt.errorbar(list_bin,list_b, yerr=list_sigma1,fmt='o',ecolor='r')\n",
    "plt.xlabel('Magnitude',fontsize=17)\n",
    "plt.ylabel('b-value',fontsize=17)\n",
    "plt.tick_params(labelsize=12)\n",
    "\n",
    "plt.plot()\n",
    "plt.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\b_value_error_bar_sinthetic1.png\",bbox_inches='tight',dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "list_bin=[]\n",
    "list_b=[]\n",
    "list_sigma1=[]\n",
    "funct_synthetic(2,dropped_new_bin_df2)\n",
    "#fig=plt.figure(figsize=(16,10))\n",
    "fig, ax1 = plt.subplots(1,figsize=(10,7))\n",
    "ax1.scatter(list_bin,list_b,c='g',)\n",
    "ax1.errorbar(list_bin,list_b, yerr=list_sigma1,fmt='o',ecolor='r')\n",
    "ax1.set_xlabel('Magnitude',fontsize=17)\n",
    "ax1.set_ylabel('b-value',fontsize=17)\n",
    "ax1.tick_params(labelsize=12)\n",
    "#funct_synthetic(996)\n",
    "ax1.plot()\n",
    "fig.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\b_value_error_bar_sinthetic2.png\",bbox_inches='tight',dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GENERATE 0.95/ 0.05 CONFIDENCE INTERVAL FOR ITERATIVE SYNTHETIC CATALOGUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1.5, 1.0, 0.0],\n",
       " [1.6, 1.0, 0.0],\n",
       " [1.7, 1.0, 0.0],\n",
       " [1.8, 1.0, 0.0],\n",
       " [1.9, 1.0, 0.0],\n",
       " [2.0, 1.0, 0.0],\n",
       " [2.1, 1.0, 0.0],\n",
       " [2.2, 1.0, 0.0],\n",
       " [2.3, 1.0, 0.0],\n",
       " [2.4, 1.0, 0.0],\n",
       " [2.5, 1.0, 0.0],\n",
       " [2.6, 1.0, 0.0],\n",
       " [2.7, 1.0, 0.0],\n",
       " [2.8, 1.0, 0.0],\n",
       " [2.9, 1.0, 0.0],\n",
       " [3.0, 1.0, 0.0],\n",
       " [3.1, 1.0, 0.0],\n",
       " [3.2, 1.0, 0.0],\n",
       " [3.3, 1.0, 0.0],\n",
       " [3.4, 1.0, 0.0],\n",
       " [3.5, 1.0, 0.0],\n",
       " [3.6, 1.0, 0.0],\n",
       " [3.7, 1.0, 0.0],\n",
       " [3.8, 1.0, 0.0],\n",
       " [3.9, 1.0, 0.0],\n",
       " [4.0, 1.0, 0.0],\n",
       " [4.1, 0.9999999999987201, 0.0],\n",
       " [4.2, 0.9999683287581669, 0.0],\n",
       " [4.3, 0.9946501019683699, 0.0],\n",
       " [4.4, 0.9746501019683699, 0.0],\n",
       " [4.5, 0.9572498680518208, 0.0],\n",
       " [4.6, 0.9052498680518208, 0.0],\n",
       " [4.7, 0.8132498680518208, 0.0],\n",
       " [4.8, 0.741344746068543, 0.0],\n",
       " [4.9, 0.6803447460685429, 0.0],\n",
       " [5.0, 0.5873447460685429, 0.0],\n",
       " [5.1, 0.519344746068543, 2.356178635731473e-251],\n",
       " [5.2, 0.5, 1.064517291557782e-231],\n",
       " [5.3, 0.5, 1.064517291557782e-231],\n",
       " [5.4, 0.5, 1.064517291557782e-231],\n",
       " [5.5, 0.5, 1.064517291557782e-231],\n",
       " [5.6, 0.5, 1.064517291557782e-231],\n",
       " [5.7, 0.5, 1.064517291557782e-231],\n",
       " [5.8, 0.5, 1.064517291557782e-231],\n",
       " [5.9, 0.5, 1.064517291557782e-231],\n",
       " [6.0, 0.5, 1.064517291557782e-231],\n",
       " [6.1, 0.5, 1.064517291557782e-231],\n",
       " [6.2, 0.5, 1.064517291557782e-231],\n",
       " [6.3, 0.5, 1.064517291557782e-231],\n",
       " [6.4, 0.5, 1.064517291557782e-231],\n",
       " [6.5, 0.5, 1.064517291557782e-231]]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import poisson, kstest\n",
    "low=np.array([])\n",
    "up=np.array([])\n",
    "medium=np.array([])\n",
    "mean_arr=np.array([])\n",
    "for i in dropped_new_bin_df2.columns: #iterate over columns\n",
    "    mean_c=dropped_new_bin_df2[i].mean()\n",
    "    mean_arr=np.append(mean_arr,mean_c)\n",
    "    low=np.append(low,poisson.ppf(0.05, mean_c))\n",
    "    up=np.append(up,poisson.ppf(0.95, mean_c))\n",
    "    #cumulate curve\n",
    "    #np.add.accumulate(dropped_new_bin_df2[i]) #cut this\n",
    "    \n",
    "#dropped_new_bin_df2 \n",
    "#kolm_smirn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KOLMOGOROV-SMIRNOV TEST FOR NORMALITY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "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>Magnitude</th>\n",
       "      <th>KS_test</th>\n",
       "      <th>p-value</th>\n",
       "      <th>Skewness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.013494</td>\n",
       "      <td>9.932914e-01</td>\n",
       "      <td>-0.027819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.6</td>\n",
       "      <td>0.014802</td>\n",
       "      <td>9.808003e-01</td>\n",
       "      <td>0.007952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.7</td>\n",
       "      <td>0.022897</td>\n",
       "      <td>6.708743e-01</td>\n",
       "      <td>0.150867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.8</td>\n",
       "      <td>0.014290</td>\n",
       "      <td>9.868076e-01</td>\n",
       "      <td>-0.051815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.9</td>\n",
       "      <td>0.014781</td>\n",
       "      <td>9.810754e-01</td>\n",
       "      <td>-0.010402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.023507</td>\n",
       "      <td>6.383821e-01</td>\n",
       "      <td>0.024368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.1</td>\n",
       "      <td>0.022016</td>\n",
       "      <td>7.175397e-01</td>\n",
       "      <td>0.079084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.2</td>\n",
       "      <td>0.036220</td>\n",
       "      <td>1.415670e-01</td>\n",
       "      <td>-0.048392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.3</td>\n",
       "      <td>0.019348</td>\n",
       "      <td>8.482159e-01</td>\n",
       "      <td>0.022039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.4</td>\n",
       "      <td>0.018606</td>\n",
       "      <td>8.793121e-01</td>\n",
       "      <td>-0.035535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.5</td>\n",
       "      <td>0.023153</td>\n",
       "      <td>6.572262e-01</td>\n",
       "      <td>0.023920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.6</td>\n",
       "      <td>0.015823</td>\n",
       "      <td>9.637115e-01</td>\n",
       "      <td>-0.011666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.7</td>\n",
       "      <td>0.034313</td>\n",
       "      <td>1.855322e-01</td>\n",
       "      <td>0.114523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.8</td>\n",
       "      <td>0.033419</td>\n",
       "      <td>2.095336e-01</td>\n",
       "      <td>-0.105112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.9</td>\n",
       "      <td>0.026643</td>\n",
       "      <td>4.750926e-01</td>\n",
       "      <td>0.014474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.020614</td>\n",
       "      <td>7.891107e-01</td>\n",
       "      <td>-0.022477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.1</td>\n",
       "      <td>0.027412</td>\n",
       "      <td>4.369539e-01</td>\n",
       "      <td>0.077585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.2</td>\n",
       "      <td>0.028942</td>\n",
       "      <td>3.673662e-01</td>\n",
       "      <td>0.051875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.3</td>\n",
       "      <td>0.031497</td>\n",
       "      <td>2.692819e-01</td>\n",
       "      <td>0.007265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.4</td>\n",
       "      <td>0.029156</td>\n",
       "      <td>3.582936e-01</td>\n",
       "      <td>0.114589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.5</td>\n",
       "      <td>0.030229</td>\n",
       "      <td>3.151886e-01</td>\n",
       "      <td>0.099948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.6</td>\n",
       "      <td>0.050600</td>\n",
       "      <td>1.152659e-02</td>\n",
       "      <td>0.268643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.7</td>\n",
       "      <td>0.057563</td>\n",
       "      <td>2.539895e-03</td>\n",
       "      <td>0.157334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.8</td>\n",
       "      <td>0.047710</td>\n",
       "      <td>2.039436e-02</td>\n",
       "      <td>0.150258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.9</td>\n",
       "      <td>0.048473</td>\n",
       "      <td>1.760055e-02</td>\n",
       "      <td>0.036292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.054860</td>\n",
       "      <td>4.675797e-03</td>\n",
       "      <td>0.178349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.1</td>\n",
       "      <td>0.067599</td>\n",
       "      <td>2.037780e-04</td>\n",
       "      <td>0.275436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4.2</td>\n",
       "      <td>0.074343</td>\n",
       "      <td>2.978903e-05</td>\n",
       "      <td>0.387578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4.3</td>\n",
       "      <td>0.084084</td>\n",
       "      <td>1.340627e-06</td>\n",
       "      <td>0.187324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.4</td>\n",
       "      <td>0.098347</td>\n",
       "      <td>7.163147e-09</td>\n",
       "      <td>0.340189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.5</td>\n",
       "      <td>0.115079</td>\n",
       "      <td>5.409877e-12</td>\n",
       "      <td>0.500547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4.6</td>\n",
       "      <td>0.141100</td>\n",
       "      <td>7.824873e-18</td>\n",
       "      <td>0.540921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4.7</td>\n",
       "      <td>0.131749</td>\n",
       "      <td>1.350612e-15</td>\n",
       "      <td>0.496530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4.8</td>\n",
       "      <td>0.146370</td>\n",
       "      <td>3.664455e-19</td>\n",
       "      <td>0.658550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.9</td>\n",
       "      <td>0.181332</td>\n",
       "      <td>3.032335e-29</td>\n",
       "      <td>0.646976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.215178</td>\n",
       "      <td>4.031371e-41</td>\n",
       "      <td>0.797469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.1</td>\n",
       "      <td>0.238038</td>\n",
       "      <td>2.437411e-50</td>\n",
       "      <td>1.067624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>5.2</td>\n",
       "      <td>0.241464</td>\n",
       "      <td>8.337047e-52</td>\n",
       "      <td>1.011539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>5.3</td>\n",
       "      <td>0.326586</td>\n",
       "      <td>1.787124e-95</td>\n",
       "      <td>1.214788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.4</td>\n",
       "      <td>0.373215</td>\n",
       "      <td>1.509310e-125</td>\n",
       "      <td>1.431748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.5</td>\n",
       "      <td>0.439307</td>\n",
       "      <td>3.392530e-176</td>\n",
       "      <td>1.734529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>5.6</td>\n",
       "      <td>0.455374</td>\n",
       "      <td>5.153172e-190</td>\n",
       "      <td>2.040446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>5.7</td>\n",
       "      <td>0.492335</td>\n",
       "      <td>3.496834e-224</td>\n",
       "      <td>2.181094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.8</td>\n",
       "      <td>0.501455</td>\n",
       "      <td>3.827521e-233</td>\n",
       "      <td>2.317372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.525332</td>\n",
       "      <td>1.218014e-257</td>\n",
       "      <td>3.130761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.536584</td>\n",
       "      <td>1.008874e-269</td>\n",
       "      <td>3.625914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>6.1</td>\n",
       "      <td>0.535224</td>\n",
       "      <td>3.040419e-268</td>\n",
       "      <td>3.771272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.539937</td>\n",
       "      <td>2.162675e-273</td>\n",
       "      <td>3.781924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.537791</td>\n",
       "      <td>4.860457e-271</td>\n",
       "      <td>5.649292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>6.4</td>\n",
       "      <td>0.538278</td>\n",
       "      <td>1.426440e-271</td>\n",
       "      <td>6.176931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>6.5</td>\n",
       "      <td>0.536314</td>\n",
       "      <td>1.984660e-269</td>\n",
       "      <td>7.046343</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Magnitude   KS_test        p-value  Skewness\n",
       "0         1.5  0.013494   9.932914e-01 -0.027819\n",
       "1         1.6  0.014802   9.808003e-01  0.007952\n",
       "2         1.7  0.022897   6.708743e-01  0.150867\n",
       "3         1.8  0.014290   9.868076e-01 -0.051815\n",
       "4         1.9  0.014781   9.810754e-01 -0.010402\n",
       "5         2.0  0.023507   6.383821e-01  0.024368\n",
       "6         2.1  0.022016   7.175397e-01  0.079084\n",
       "7         2.2  0.036220   1.415670e-01 -0.048392\n",
       "8         2.3  0.019348   8.482159e-01  0.022039\n",
       "9         2.4  0.018606   8.793121e-01 -0.035535\n",
       "10        2.5  0.023153   6.572262e-01  0.023920\n",
       "11        2.6  0.015823   9.637115e-01 -0.011666\n",
       "12        2.7  0.034313   1.855322e-01  0.114523\n",
       "13        2.8  0.033419   2.095336e-01 -0.105112\n",
       "14        2.9  0.026643   4.750926e-01  0.014474\n",
       "15        3.0  0.020614   7.891107e-01 -0.022477\n",
       "16        3.1  0.027412   4.369539e-01  0.077585\n",
       "17        3.2  0.028942   3.673662e-01  0.051875\n",
       "18        3.3  0.031497   2.692819e-01  0.007265\n",
       "19        3.4  0.029156   3.582936e-01  0.114589\n",
       "20        3.5  0.030229   3.151886e-01  0.099948\n",
       "21        3.6  0.050600   1.152659e-02  0.268643\n",
       "22        3.7  0.057563   2.539895e-03  0.157334\n",
       "23        3.8  0.047710   2.039436e-02  0.150258\n",
       "24        3.9  0.048473   1.760055e-02  0.036292\n",
       "25        4.0  0.054860   4.675797e-03  0.178349\n",
       "26        4.1  0.067599   2.037780e-04  0.275436\n",
       "27        4.2  0.074343   2.978903e-05  0.387578\n",
       "28        4.3  0.084084   1.340627e-06  0.187324\n",
       "29        4.4  0.098347   7.163147e-09  0.340189\n",
       "30        4.5  0.115079   5.409877e-12  0.500547\n",
       "31        4.6  0.141100   7.824873e-18  0.540921\n",
       "32        4.7  0.131749   1.350612e-15  0.496530\n",
       "33        4.8  0.146370   3.664455e-19  0.658550\n",
       "34        4.9  0.181332   3.032335e-29  0.646976\n",
       "35        5.0  0.215178   4.031371e-41  0.797469\n",
       "36        5.1  0.238038   2.437411e-50  1.067624\n",
       "37        5.2  0.241464   8.337047e-52  1.011539\n",
       "38        5.3  0.326586   1.787124e-95  1.214788\n",
       "39        5.4  0.373215  1.509310e-125  1.431748\n",
       "40        5.5  0.439307  3.392530e-176  1.734529\n",
       "41        5.6  0.455374  5.153172e-190  2.040446\n",
       "42        5.7  0.492335  3.496834e-224  2.181094\n",
       "43        5.8  0.501455  3.827521e-233  2.317372\n",
       "44        5.9  0.525332  1.218014e-257  3.130761\n",
       "45        6.0  0.536584  1.008874e-269  3.625914\n",
       "46        6.1  0.535224  3.040419e-268  3.771272\n",
       "47        6.2  0.539937  2.162675e-273  3.781924\n",
       "48        6.3  0.537791  4.860457e-271  5.649292\n",
       "49        6.4  0.538278  1.426440e-271  6.176931\n",
       "50        6.5  0.536314  1.984660e-269  7.046343"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import skew\n",
    "KS=[]\n",
    "it=0\n",
    "old=[0,1,2,3]\n",
    "for i in dropped_new_bin_df2.columns: #iterate over columns\n",
    "    mean_c=dropped_new_bin_df2[i].mean()\n",
    "    st_dev=statistics.stdev(dropped_new_bin_df2[i])\n",
    "    standardised_sample=[]\n",
    "    skew_item=skew(dropped_new_bin_df2[i])\n",
    "    for item in dropped_new_bin_df2[i]:\n",
    "        standardised_item=(item-mean_c)/st_dev \n",
    "        standardised_sample.append(standardised_item)\n",
    "    kolm_smirn,p=kstest( standardised_sample ,'norm')\n",
    "    KS.append([round(i,1),kolm_smirn,p,skew_item]) #KS test\n",
    "    it+=1\n",
    "KS_df=pd.DataFrame(KS)\n",
    "new=['Magnitude','KS_test', 'p-value','Skewness']\n",
    "KS_df.rename(columns=dict(zip(old,new)),inplace=True)\n",
    "KS_df   \n",
    "#KS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PLOT 0.95/ 0.05 CONFIDENCE INTERVAL FOR ITERATIVE SYNTHETIC CATALOGUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.plot(binned_array,low)\n",
    "plt.plot(binned_array,mean_arr)\n",
    "plt.plot(binned_array,up)\n",
    "plt.yscale('log') # an alternative:  plot.set(yscale=\"log\")\n",
    "plt.ylim((0.7, 200000))\n",
    "plt.fill_between(binned_array, low, up, where=up >= low, facecolor='aquamarine', interpolate=True)\n",
    "plt.xlabel('Magnitude',fontsize=17)\n",
    "plt.ylabel('Number of events',fontsize=17)\n",
    "plt.tick_params(labelsize=12)\n",
    "#plt.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\gut_Richter_simulated_iterative.png\",dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GENERATE 1000 SYNTHETIC CATALOGUES FOR CUMULATIVE EVENTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1.5</th>\n",
       "      <th>1.6</th>\n",
       "      <th>1.7</th>\n",
       "      <th>1.8</th>\n",
       "      <th>1.9</th>\n",
       "      <th>2.0</th>\n",
       "      <th>2.1</th>\n",
       "      <th>2.2</th>\n",
       "      <th>2.3</th>\n",
       "      <th>2.4</th>\n",
       "      <th>...</th>\n",
       "      <th>5.6</th>\n",
       "      <th>5.7</th>\n",
       "      <th>5.8</th>\n",
       "      <th>5.9</th>\n",
       "      <th>6.0</th>\n",
       "      <th>6.1</th>\n",
       "      <th>6.2</th>\n",
       "      <th>6.3</th>\n",
       "      <th>6.4</th>\n",
       "      <th>6.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>75626.0</td>\n",
       "      <td>57249.0</td>\n",
       "      <td>43385.0</td>\n",
       "      <td>32920.0</td>\n",
       "      <td>24931.0</td>\n",
       "      <td>18817.0</td>\n",
       "      <td>14275.0</td>\n",
       "      <td>10803.0</td>\n",
       "      <td>8173.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>75654.0</td>\n",
       "      <td>57205.0</td>\n",
       "      <td>43140.0</td>\n",
       "      <td>32631.0</td>\n",
       "      <td>24510.0</td>\n",
       "      <td>18498.0</td>\n",
       "      <td>13991.0</td>\n",
       "      <td>10627.0</td>\n",
       "      <td>8082.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>75695.0</td>\n",
       "      <td>57305.0</td>\n",
       "      <td>43280.0</td>\n",
       "      <td>32630.0</td>\n",
       "      <td>24597.0</td>\n",
       "      <td>18567.0</td>\n",
       "      <td>14081.0</td>\n",
       "      <td>10562.0</td>\n",
       "      <td>7914.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>75728.0</td>\n",
       "      <td>57219.0</td>\n",
       "      <td>43373.0</td>\n",
       "      <td>32841.0</td>\n",
       "      <td>24872.0</td>\n",
       "      <td>18854.0</td>\n",
       "      <td>14374.0</td>\n",
       "      <td>10889.0</td>\n",
       "      <td>8206.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>75567.0</td>\n",
       "      <td>57089.0</td>\n",
       "      <td>43197.0</td>\n",
       "      <td>32815.0</td>\n",
       "      <td>24951.0</td>\n",
       "      <td>18831.0</td>\n",
       "      <td>14257.0</td>\n",
       "      <td>10787.0</td>\n",
       "      <td>8196.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <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>996</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>75545.0</td>\n",
       "      <td>57357.0</td>\n",
       "      <td>43423.0</td>\n",
       "      <td>32829.0</td>\n",
       "      <td>24795.0</td>\n",
       "      <td>18705.0</td>\n",
       "      <td>14125.0</td>\n",
       "      <td>10677.0</td>\n",
       "      <td>8125.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>75701.0</td>\n",
       "      <td>57245.0</td>\n",
       "      <td>43019.0</td>\n",
       "      <td>32584.0</td>\n",
       "      <td>24428.0</td>\n",
       "      <td>18540.0</td>\n",
       "      <td>14013.0</td>\n",
       "      <td>10536.0</td>\n",
       "      <td>7906.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>75751.0</td>\n",
       "      <td>57252.0</td>\n",
       "      <td>43310.0</td>\n",
       "      <td>32763.0</td>\n",
       "      <td>24979.0</td>\n",
       "      <td>18958.0</td>\n",
       "      <td>14229.0</td>\n",
       "      <td>10787.0</td>\n",
       "      <td>8189.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>75529.0</td>\n",
       "      <td>57250.0</td>\n",
       "      <td>43281.0</td>\n",
       "      <td>32716.0</td>\n",
       "      <td>24808.0</td>\n",
       "      <td>18776.0</td>\n",
       "      <td>14129.0</td>\n",
       "      <td>10623.0</td>\n",
       "      <td>8011.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>75528.0</td>\n",
       "      <td>57049.0</td>\n",
       "      <td>43242.0</td>\n",
       "      <td>32780.0</td>\n",
       "      <td>24814.0</td>\n",
       "      <td>18778.0</td>\n",
       "      <td>14191.0</td>\n",
       "      <td>10791.0</td>\n",
       "      <td>8283.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0          1.5      1.6      1.7      1.8      1.9      2.0      2.1      2.2  \\\n",
       "1     100000.0  75626.0  57249.0  43385.0  32920.0  24931.0  18817.0  14275.0   \n",
       "2     100000.0  75654.0  57205.0  43140.0  32631.0  24510.0  18498.0  13991.0   \n",
       "3     100000.0  75695.0  57305.0  43280.0  32630.0  24597.0  18567.0  14081.0   \n",
       "4     100000.0  75728.0  57219.0  43373.0  32841.0  24872.0  18854.0  14374.0   \n",
       "5     100000.0  75567.0  57089.0  43197.0  32815.0  24951.0  18831.0  14257.0   \n",
       "...        ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "996   100000.0  75545.0  57357.0  43423.0  32829.0  24795.0  18705.0  14125.0   \n",
       "997   100000.0  75701.0  57245.0  43019.0  32584.0  24428.0  18540.0  14013.0   \n",
       "998   100000.0  75751.0  57252.0  43310.0  32763.0  24979.0  18958.0  14229.0   \n",
       "999   100000.0  75529.0  57250.0  43281.0  32716.0  24808.0  18776.0  14129.0   \n",
       "1000  100000.0  75528.0  57049.0  43242.0  32780.0  24814.0  18778.0  14191.0   \n",
       "\n",
       "0         2.3     2.4  ...  5.6  5.7  5.8  5.9  6.0  6.1  6.2  6.3  6.4  6.5  \n",
       "1     10803.0  8173.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2     10627.0  8082.0  ...  2.0  2.0  2.0  2.0  2.0  1.0  1.0  1.0  1.0  0.0  \n",
       "3     10562.0  7914.0  ...  2.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "4     10889.0  8206.0  ...  2.0  2.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "5     10787.0  8196.0  ...  3.0  3.0  2.0  2.0  1.0  1.0  1.0  0.0  0.0  0.0  \n",
       "...       ...     ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "996   10677.0  8125.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "997   10536.0  7906.0  ...  1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "998   10787.0  8189.0  ...  1.0  1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  \n",
       "999   10623.0  8011.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "1000  10791.0  8283.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[1000 rows x 51 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cumulate data set\n",
    "dropped_new_bin_df2_cum=dropped_new_bin_df2.T\n",
    "columns_to_sort=[i for i in dropped_new_bin_df2_cum]\n",
    "low_accumulate=np.array([])\n",
    "up_accumulate=np.array([])\n",
    "medium_accumulate=np.array([])\n",
    "binned_array.index=binned_array\n",
    "dropped_new_bin_df2_cumulate=binned_array\n",
    "for i in dropped_new_bin_df2_cum: #iterate over columns\n",
    "    cum_series=pd.Series(dropped_new_bin_df2_cum[i])\n",
    "    cum_series=pd.Series(np.add.accumulate(cum_series[::-1])[::-1])\n",
    "    #cum_series=cum_series.reset_index(drop=True,inplace=True)\n",
    "    dropped_new_bin_df2_cumulate=pd.concat((dropped_new_bin_df2_cumulate,cum_series),axis=1)\n",
    "    #dropped_new_bin_df2_cumulate=dropped_new_bin_df2_cumulate.reshape((2,98))\n",
    "\n",
    "dropped_new_bin_df2_cumulate=dropped_new_bin_df2_cumulate.T.drop([0])\n",
    "dropped_new_bin_df2_cumulate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GENERATE AND PLOT 0.95/ 0.05 CONFIDENCE INTERVAL FOR CUMULATIVE SYNTHETIC CATALOGUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cumulative events')"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statistics\n",
    "#cumulate function\n",
    "low_cum=np.array([])\n",
    "up_cum=np.array([])\n",
    "medium_cum=np.array([])\n",
    "mean_arr_cum=np.array([])\n",
    "for i in dropped_new_bin_df2_cumulate:\n",
    "    mean_cc=dropped_new_bin_df2_cumulate[i].mean()\n",
    "    mean_arr_cum=np.append(mean_arr_cum,mean_cc)\n",
    "    low_cum=np.append(low_cum,poisson.ppf(0.05, mean_cc))\n",
    "    up_cum=np.append(up_cum,poisson.ppf(0.95, mean_cc))  \n",
    "#cumulate curve\n",
    "fig=plt.figure()\n",
    "ax=fig.add_subplot(1,1,1)\n",
    "ax1_cum=ax.plot(binned_array,low_cum)\n",
    "ax2_cum=ax.plot(binned_array,mean_arr_cum)\n",
    "ax3_cum=ax.plot(binned_array,up_cum)\n",
    "ax.set_yscale('log') # an alternative:  plot.set(yscale=\"log\")\n",
    "ax.set_ylim((0.7, 60000))\n",
    "ax.fill_between(binned_array, low_cum, up_cum, where=up_cum >= low_cum, facecolor='aquamarine', interpolate=True)\n",
    "ax.set_xlabel('Magnitude')\n",
    "ax.set_ylabel('Number of events')\n",
    "ax.set_title('Cumulative events')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PLOT 0.95/ 0.05 CONFIDENCE INTERVAL FOR ITERATIVE AND CUMULATIVE SYNTHETIC CATALOGUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10,7))\n",
    "ax=fig.add_subplot(1,1,1)\n",
    "ax1=ax.plot(binned_array,low)\n",
    "ax2=ax.plot(binned_array,mean_arr)\n",
    "ax3=ax.plot(binned_array,up)\n",
    "ax.set_yscale('log') # an alternative:  plot.set(yscale=\"log\")\n",
    "ax.fill_between(binned_array, low, up, where=up >= low, facecolor='aquamarine', interpolate=True)\n",
    "ax1_cum=ax.plot(binned_array,low_cum)\n",
    "ax2_cum=ax.plot(binned_array,mean_arr_cum)\n",
    "ax3_cum=ax.plot(binned_array,up_cum)\n",
    "ax.set_yscale('log') # an alternative:  plot.set(yscale=\"log\")\n",
    "ax.fill_between(binned_array, low_cum, up_cum, where=up_cum >= low_cum, facecolor='thistle', interpolate=True)\n",
    "ax.set_ylim((0.7, 120000))\n",
    "ax.set_xlabel('Magnitude',fontsize=17)\n",
    "ax.set_ylabel('Number of events',fontsize=17)\n",
    "plt.tick_params(labelsize=12)\n",
    "fig.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\gut_Richter_simulated_iterative_cumulative.png\",dpi=600)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PLOT 0.95/ 0.05 CONFIDENCE INTERVAL FOR ITERATIVE AND CUMULATIVE SYNTHETIC CATALOGUES WITH A SINTHETIC CATALOGUE WITHIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig=plt.figure(figsize=(10,7))\n",
    "ax=fig.add_subplot(1,1,1)\n",
    "\n",
    "ax1=ax.plot(binned_array,low)\n",
    "ax2=ax.plot(binned_array,mean_arr)\n",
    "ax3=ax.plot(binned_array,up)\n",
    "ax.set_yscale('log') # an alternative:  plot.set(yscale=\"log\")\n",
    "ax.fill_between(binned_array, low, up, where=up >= low, facecolor='aquamarine', interpolate=True)\n",
    "ax1_cum=ax.plot(binned_array,low_cum)\n",
    "ax2_cum=ax.plot(binned_array,mean_arr_cum)\n",
    "ax3_cum=ax.plot(binned_array,up_cum)\n",
    "ax.set_yscale('log') # an alternative:  plot.set(yscale=\"log\")\n",
    "ax.fill_between(binned_array, low_cum, up_cum, where=up_cum >= low_cum, facecolor='thistle', interpolate=True)\n",
    "ax.set_ylim((0.7, 1200000))\n",
    "ax.set_xlabel('Magnitude',fontsize=17)\n",
    "ax.set_ylabel('Number of events',fontsize=17)\n",
    "plt.tick_params(labelsize=12)\n",
    "plot=plt.scatter(dropped_new_bin_df2_cumulate.columns.values, dropped_new_bin_df2_cumulate.iloc[2],s=46,color='red')\n",
    "#replace 2 with 3 if you want to plot the first synth. catalogue\n",
    "plot2=plt.scatter(dropped_new_bin_df2.columns.values, dropped_new_bin_df2.iloc[2],s=46,color='blue')\n",
    "#replace 2 with 3 if you want to plot the first synth. catalogue\n",
    "#plot=sns.scatterplot(x='bin_magnitude',y='number_of_earthquakes',data=df1,s=66)\n",
    "#fig.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\gut_Richter_simulated_iterative_cumulative_synthetic2.png\",dpi=600)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PLOT A SINTHETIC CATALOGUE WITHIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10,7))\n",
    "plot=plt.scatter(dropped_new_bin_df2_cumulate.columns.values, dropped_new_bin_df2_cumulate.iloc[3],s=46,color='red')\n",
    "plot2=plt.scatter(dropped_new_bin_df2.columns.values, dropped_new_bin_df2.iloc[3],s=46,color='blue')\n",
    "plt.xlabel(\"Magnitude\",fontsize=18,labelpad=5) #set font and title of x label\n",
    "plt.ylabel(\"Number of events\",fontsize=18,labelpad=5)\n",
    "plt.tick_params(labelsize=12)\n",
    "#plt.title('Magnitude types throughout the years',fontsize=18,pad=15.5)\n",
    "plt.yscale('log')# an alternative:  plot.set(yscale=\"log\")\n",
    "plt.ylim((0.7, 200000)) # DO NOT put 0 or negative values 'cause there is a log scale\n",
    "plt.xlim((1.3, 6.7))\n",
    "#fig.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\gut_Richter_simulated_iterative_cumulative_synthetic1_simple.png\",dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PLOT A SECOND SINTHETIC CATALOGUE WITHIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10,7))\n",
    "plot=plt.scatter(dropped_new_bin_df2_cumulate.columns.values, dropped_new_bin_df2_cumulate.iloc[2],s=46,color='red')\n",
    "plot2=plt.scatter(dropped_new_bin_df2.columns.values, dropped_new_bin_df2.iloc[2],s=46,color='blue')\n",
    "plt.xlabel(\"Magnitude\",fontsize=18,labelpad=5) #set font and title of x label\n",
    "plt.ylabel(\"Number of events\",fontsize=18,labelpad=5)\n",
    "plt.tick_params(labelsize=12)\n",
    "#plt.title('Magnitude types throughout the years',fontsize=18,pad=15.5)\n",
    "plt.yscale('log')# an alternative:  plot.set(yscale=\"log\")\n",
    "plt.ylim((0.7, 200000)) # DO NOT put 0 or negative values 'cause there is a log scale\n",
    "plt.xlim((1.3, 6.7))\n",
    "#fig.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\gut_Richter_simulated_iterative_cumulative_synthetic2_simple.png\",dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STOP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))    \n",
    "plot=plt.scatter(cum_series_1_index,cum_series_1,s=46,color='red')\n",
    "plt.yscale('log')# an alternative:  plot.set(yscale=\"log\")\n",
    "#plt.title('Gutemberg Richter (cumulative and iterative events)',fontsize=18,pad=15.5)\n",
    "plt.ylim((0.7, 600000)) # DO NOT put 0 or negative values 'cause there is a log scale\n",
    "plt.ylabel('Number of earthquakes',fontsize=17) #set font and title of x label\n",
    "plt.xlabel('Magnitude',fontsize=17) #set font and title of y label\n",
    "plt.tick_params(labelsize=12)\n",
    "plot2=plt.scatter(x='bin_magnitude',y='number_of_earthquakes',data=df1,s=46,color='blue')\n",
    "plt.ylabel('Number of events',fontsize=17) #set font and title of x label\n",
    "plt.xlabel('Magnitude',fontsize=17)\n",
    "#plt.savefig(r\"C:\\\\Users\\Ogeid99\\Desktop\\tesi\\photos_thesis\\grafici_finali_jupyter\\gut_Richter_iterativeandcumulative_catalogue.png\",dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_grouped_magnitude=DATA_with_one.groupby(\"Magnitude\")['Magnitude'].count()\n",
    "data_thresh=data_grouped_magnitude.iloc[25:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dropped_new_bin_df2.loc[]"
   ]
  }
 ],
 "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}