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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lluvias aéreas extensas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"Montaje_EAS-min.png\" style=\"width: 500px;\"/>\n",
+    "\n",
+    "Este notebook describe el procediminto para la toma de datos, control de parámetros y descarga de datos para su posterior análisis.\n",
+    "\n",
+    "El acceso al laboratorio se hace por SSH desde un terminal. Primero debemos ingresar al servidor central Obatala mediante el comando:\n",
+    "\n",
+    "**`ssh lacongalab@200.16.117.76`**\n",
+    "\n",
+    "pass: HMcvmA4ee3\n",
+    "\n",
+    "Luego para ingresar al sistema de adquisición del detector de centelleo usaremos:\n",
+    "\n",
+    "**`ssh root@10.1.28.86`**\n",
+    "\n",
+    "pass: laconga2021\n",
+    "\n",
+    "Allí accedemos a la carpeta de la práctica:\n",
+    "\n",
+    "**`cd /home/pi/LACoNGA`**\n",
+    "\n",
+    "## Configuración del detector\n",
+    "\n",
+    "La configuración de los parámetros de adquisición así como la toma de datos se hacen mediante la interfaz **minicom**. Para configurar los parámetros (umbral de discriminación, ventanas de coincidencias y número de coincidencias) se emplean los comandos descritos en el archivo (Escaramujo_User_Guide). \n",
+    "\n",
+    "Para configurar los parámetros de adquisición entramos a la intefaz ejecutando:\n",
+    "\n",
+    "**`minicom`**\n",
+    "\n",
+    "Un vez dentro configuramos por ejemplo el umbral de adquisición tecleando el comando:\n",
+    "\n",
+    "**`TL 4 30`**\n",
+    "\n",
+    "Esto estalece un umbral de discriminación de 30 mV en los 4 canales de adquisición. Para salir de la interfaz tecleamos.\n",
+    "\n",
+    "**`Ctrl+A x`** y enter\n",
+    "\n",
+    "Cuando configuremos los parámetros de adquisición procedemos a tomar los datos. Para ello ejecutamos:\n",
+    "\n",
+    "**`minicom -C File_name.dat`**\n",
+    "\n",
+    "y dentro de la interfaz ejecutamos\n",
+    "\n",
+    "**`CE`** -- habilita el contador de eventos\n",
+    "\n",
+    "Los datos mostrados en pantalla serán almacenados en el archivo **File_name.dat**. Para terminar la adquisición ejecutamos:\n",
+    "\n",
+    "**`CD`** -- deshabilita el contador de eventos\n",
+    "\n",
+    "Una vez más salimos del minicom con **`Ctrl+A x`** y enter. Este procedimiento se ejecuta siempre que se quiera adquirir cambiando los parámetros de adquisición.\n",
+    "\n",
+    "## Calibración del detector\n",
+    "\n",
+    "Primero el estudiante debe calibrar el umbral de detección. Para ello el estudiante debe tomar 5 minutos de datos cambiando el umbral de discriminación de 50 a 300 mV con paso de 25 mV. Luego se determina el umbral óptimo mediante la gráfica de flujo (conteos/s o conteos/min) vs umbral y estimando el flujo esperado en los 3 paneles centelladores de (25 cm x 25 cm) a 990 m s.n.m.\n",
+    "\n",
+    "**NOTA :** Tener cuidado con el nombre del archivo, use uno inconfundible ;).\n",
+    "\n",
+    "## Medición de la tasa de EAS\n",
+    "\n",
+    "Para la medición de lluvias aréreas extensas configurar el detector en coincidencia 2-Fold y establecer la ventana de coincidencia (ver Escaramujo User Guide).\n",
+    "\n",
+    "## Descarga de los archivos\n",
+    "\n",
+    "Ahora debemos copiar los datos a nuestro PC. Para ello primero lo copiamos al servidor **Obatala** y luego a nuestro PC. Ejecutamos:\n",
+    "\n",
+    "**`scp File_name.dat lacongalab@200.16.117.76:/home/lacongalab`**\n",
+    "\n",
+    "pass: HMcvmA4ee3\n",
+    "\n",
+    "Y ahora desde nuestra carpeta local (en nuestro PC) desde otro terminal ejecutamos:\n",
+    "\n",
+    "**`scp lacongalab@200.16.117.76:/home/lacongalab/File_name.dat .`**\n",
+    "\n",
+    "pass: HMcvmA4ee3\n",
+    "\n",
+    "En este puto ya tenemos los datos en nuestro PC y podemos procesarlos :) :) :)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Procesamiento"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import math\n",
+    "import csv, operator\n",
+    "import scipy.stats as st\n",
+    "from numpy import random\n",
+    "import pandas as pd\n",
+    "from datetime import datetime\n",
+    "from pandas import DataFrame as df\n",
+    "\n",
+    "matplotlib.pyplot.savefig\n",
+    "\n",
+    "%matplotlib inline  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def data_analysis(data):\n",
+    "    \n",
+    "    fn = np.unique(data['FECHA_HORA'])\n",
+    "    \n",
+    "    frec = []\n",
+    "\n",
+    "    for i in fn:\n",
+    "        frec.append(np.sum(data['FECHA_HORA']==i))\n",
+    "\n",
+    "    frec = np.array(frec)\n",
+    "    \n",
+    "    data2 = pd.DataFrame({'fecha': fn, 'frec': frec})\n",
+    "    \n",
+    "    data2.describe().transpose()\n",
+    "    \n",
+    "    mean = np.mean(frec)\n",
+    "    std = np.std(frec)\n",
+    "    \n",
+    "    print \"Mean rate : %.2f +/- %.2f\" %(mean, std)\n",
+    "    \n",
+    "#     plt.figure(figsize=(8,5))\n",
+    "#     plt.xlabel('\\n' + r'Time [HH:mm:ss]', linespacing=1, fontsize = 18)\n",
+    "#     plt.ylabel('\\n' + r'Flux [counts/s]', linespacing=1, fontsize = 18)\n",
+    "#     plt.plot(fn, frec)\n",
+    "#     plt.show()\n",
+    "    \n",
+    "    return mean, std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "500\n",
+      "Mean rate : 74.90 +/- 18.03\n",
+      "1000\n",
+      "Mean rate : 23.41 +/- 8.87\n",
+      "1500\n",
+      "Mean rate : 10.31 +/- 5.36\n",
+      "2000\n",
+      "Mean rate : 6.07 +/- 3.55\n",
+      "2500\n",
+      "Mean rate : 4.37 +/- 2.64\n",
+      "3000\n",
+      "Mean rate : 3.60 +/- 2.03\n"
+     ]
+    }
+   ],
+   "source": [
+    "rate = []\n",
+    "error = []\n",
+    "threshold = []\n",
+    "\n",
+    "for i in range(500,3500,500):\n",
+    "    \n",
+    "    print i\n",
+    "    \n",
+    "    data = pd.read_csv('Data/Flujo_Th_' + str(i) + '.dat', delimiter=' ', sep = 's*', comment='S',\n",
+    "                   names = ['T1', 'CH1a', 'CH1b', 'CH2a', 'CH2b', 'CH3a', 'CH3b', 'CH4a', 'CH4b', 'T2', 'hora', 'fecha', 'rec1', 'rec2', 'rec3', 'rec4'], \n",
+    "                   parse_dates={'FECHA_HORA': [11,10]}, date_parser= lambda x, y: pd.datetime.strptime(x + ' '+ y , '%d%m%y %H%M%S.%f'))\n",
+    "\n",
+    "    mean, std = data_analysis(data)\n",
+    "    \n",
+    "    rate.append(mean)\n",
+    "    error.append(std)\n",
+    "    threshold.append(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MrK+ZzTKzt83sLTP7Wri8m5nNNLPl4X3XqGuV9pWTkcbXzh7E7G9O5L9GHsNd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\nSQoLIiIi0qT/B9jMjl29voc+AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,5))\n",
+    "plt.plot(np.array(threshold)/10.0, rate)\n",
+    "plt.xlabel('\\n' + r'Threshold [mV]', linespacing=1, fontsize = 18)\n",
+    "plt.ylabel('\\n' + r'Flux [counts/s]', linespacing=1, fontsize = 18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Estudiantes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50\n",
+      "Data/calibration_50mV.dat\n",
+      "Mean rate : 70.96 +/- 17.01\n",
+      "75\n",
+      "Data/calibration_75mV.dat\n",
+      "Mean rate : 38.09 +/- 11.91\n",
+      "100\n",
+      "Data/calibration_100mV.dat\n",
+      "Mean rate : 21.70 +/- 8.84\n",
+      "125\n",
+      "Data/calibration_125mV.dat\n",
+      "Mean rate : 14.51 +/- 6.48\n",
+      "150\n",
+      "Data/calibration_150mV.dat\n",
+      "Mean rate : 14.33 +/- 6.73\n",
+      "175\n",
+      "Data/calibration_175mV.dat\n",
+      "Mean rate : 7.00 +/- 3.91\n",
+      "200\n",
+      "Data/calibration_200mV.dat\n",
+      "Mean rate : 5.46 +/- 3.08\n",
+      "225\n",
+      "Data/calibration_225mV.dat\n",
+      "Mean rate : 4.88 +/- 2.72\n",
+      "250\n",
+      "Data/calibration_250mV.dat\n",
+      "Mean rate : 4.38 +/- 2.66\n",
+      "275\n",
+      "Data/calibration_275mV.dat\n",
+      "Mean rate : 3.98 +/- 2.16\n",
+      "300\n",
+      "Data/calibration_300mV.dat\n",
+      "Mean rate : 3.28 +/- 1.76\n"
+     ]
+    }
+   ],
+   "source": [
+    "rate = []\n",
+    "error = []\n",
+    "threshold = []\n",
+    "\n",
+    "for i in range(50,325,25):\n",
+    "    \n",
+    "    print i\n",
+    "    file_name = 'Data/calibration_' + str(i) + 'mV.dat'\n",
+    "    print file_name\n",
+    "    \n",
+    "    data = pd.read_csv(file_name, delimiter=' ', sep = 's*', comment='S',\n",
+    "                   names = ['T1', 'CH1a', 'CH1b', 'CH2a', 'CH2b', 'CH3a', 'CH3b', 'CH4a', 'CH4b', 'T2', 'hora', 'fecha', 'rec1', 'rec2', 'rec3', 'rec4'], \n",
+    "                   parse_dates={'FECHA_HORA': [11,10]}, date_parser= lambda x, y: pd.datetime.strptime(x + ' '+ y , '%d%m%y %H%M%S.%f'))\n",
+    "\n",
+    "    mean, std = data_analysis(data)\n",
+    "    \n",
+    "    rate.append(mean)\n",
+    "    error.append(std)\n",
+    "    threshold.append(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JQFLCgpkdamb9k/FZklnKh/dn5leP5svHjeKBl1dx0nVPMbNyta6UEBFJomSd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\nFBZEREQkrv8Hj5YFNTY+no4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,5))\n",
+    "plt.plot(np.array(threshold), rate)\n",
+    "plt.xlabel('\\n' + r'Threshold [mV]', linespacing=1, fontsize = 18)\n",
+    "plt.ylabel('\\n' + r'Flux [counts/s]', linespacing=1, fontsize = 18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/proyecto-EAS-19-04-2021.ipynb b/proyecto-EAS-19-04-2021.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5d2af1d5c5ae00206ac928c9d3706b97354e6d11
--- /dev/null
+++ b/proyecto-EAS-19-04-2021.ipynb
@@ -0,0 +1,4868 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lluvias aéreas extensas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"Montaje_EAS-min.png\" style=\"width: 500px;\"/>\n",
+    "\n",
+    "Este notebook describe el procediminto para la toma de datos, control de parámetros y descarga de datos para su posterior análisis.\n",
+    "\n",
+    "El acceso al laboratorio se hace por SSH desde un terminal. Primero debemos ingresar al servidor central Obatala mediante el comando:\n",
+    "\n",
+    "**`ssh lacongalab@200.16.117.76`**\n",
+    "\n",
+    "pass: HMcvmA4ee3\n",
+    "\n",
+    "Luego para ingresar al sistema de adquisición del detector de centelleo usaremos:\n",
+    "\n",
+    "**`ssh pi@10.1.28.86`**\n",
+    "\n",
+    "pass: laconga2021\n",
+    "\n",
+    "Allí accedemos a la carpeta de la práctica:\n",
+    "\n",
+    "**`cd /home/pi/LACoNGA`**\n",
+    "\n",
+    "## Configuración del detector\n",
+    "\n",
+    "La configuración de los parámetros de adquisición así como la toma de datos se hacen mediante la interfaz **minicom**. Para configurar los parámetros (umbral de discriminación, ventanas de coincidencias y número de coincidencias) se emplean los comandos descritos en el archivo (Escaramujo_User_Guide). \n",
+    "\n",
+    "Para configurar los parámetros de adquisición entramos a la intefaz ejecutando:\n",
+    "\n",
+    "**`minicom`**\n",
+    "\n",
+    "Un vez dentro configuramos por ejemplo el umbral de adquisición tecleando el comando:\n",
+    "\n",
+    "**`TL 4 30`**\n",
+    "\n",
+    "Esto estalece un umbral de discriminación de 30 mV en los 4 canales de adquisición. Para salir de la interfaz tecleamos.\n",
+    "\n",
+    "**`Ctrl+A x`** y enter\n",
+    "\n",
+    "Cuando configuremos los parámetros de adquisición procedemos a tomar los datos. Para ello ejecutamos:\n",
+    "\n",
+    "**`minicom -C File_name.dat`**\n",
+    "\n",
+    "y dentro de la interfaz ejecutamos\n",
+    "\n",
+    "**`CE`** -- habilita el contador de eventos\n",
+    "\n",
+    "Los datos mostrados en pantalla serán almacenados en el archivo **File_name.dat**. Para terminar la adquisición ejecutamos:\n",
+    "\n",
+    "**`CD`** -- deshabilita el contador de eventos\n",
+    "\n",
+    "Una vez más salimos del minicom con **`Ctrl+A x`** y enter. Este procedimiento se ejecuta siempre que se quiera adquirir cambiando los parámetros de adquisición.\n",
+    "\n",
+    "## Calibración del detector\n",
+    "\n",
+    "Primero el estudiante debe calibrar el umbral de detección. Para ello el estudiante debe tomar 5 minutos de datos cambiando el umbral de discriminación de 50 a 300 mV con paso de 25 mV. Luego se determina el umbral óptimo mediante la gráfica de flujo (conteos/s o conteos/min) vs umbral y estimando el flujo esperado en los 3 paneles centelladores de (25 cm x 25 cm) a 990 m s.n.m.\n",
+    "\n",
+    "**NOTA :** Tener cuidado con el nombre del archivo, use uno inconfundible ;).\n",
+    "\n",
+    "## Medición de la tasa de EAS\n",
+    "\n",
+    "Para la medición de lluvias aréreas extensas configurar el detector en coincidencia 2-Fold y establecer la ventana de coincidencia (ver Escaramujo User Guide). Se recomienda adquirir 3 o 4 horas de datos para obtener una buena estadística.\n",
+    "\n",
+    "## Descarga de los archivos\n",
+    "\n",
+    "Ahora debemos copiar los datos a nuestro PC. Para ello primero lo copiamos al servidor **Obatala** y luego a nuestro PC. Ejecutamos:\n",
+    "\n",
+    "**`scp File_name.dat lacongalab@200.16.117.76:/home/lacongalab`**\n",
+    "\n",
+    "pass: HMcvmA4ee3\n",
+    "\n",
+    "Y ahora desde nuestra carpeta local (en nuestro PC) desde otro terminal ejecutamos:\n",
+    "\n",
+    "**`scp lacongalab@200.16.117.76:/home/lacongalab/File_name.dat .`**\n",
+    "\n",
+    "pass: HMcvmA4ee3\n",
+    "\n",
+    "En este puto ya tenemos los datos en nuestro PC y podemos procesarlos :) :) :)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Procesamiento"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Para el procesamiento de los datos se recomienda entender la estructura de los datos (ver Escaramujo User Guide)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import matplotlib\n",
+    "import csv, operator\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scipy.stats as st\n",
+    "import matplotlib.pyplot as plt\n",
+    "from numpy import random\n",
+    "from scipy import interpolate\n",
+    "from datetime import datetime\n",
+    "from pandas import DataFrame as df\n",
+    "\n",
+    "matplotlib.pyplot.savefig\n",
+    "\n",
+    "%matplotlib inline  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cargar datos ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def load_data(file):\n",
+    "    data = np.loadtxt(file,usecols=0,skiprows=1,dtype=str,delimiter=\"  \")\n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Funciones"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Extraer el tiempo del conjunto de datos\n",
+    "def time_list(data):\n",
+    "    times=[]\n",
+    "    for i in data:\n",
+    "        a=str(i)\n",
+    "        b=a.split(' ')\n",
+    "        if len(b)==16:\n",
+    "            t=b[10]\n",
+    "        times.append(t)\n",
+    "    time_list = pd.to_datetime(times, format=\"%H%M%S.%f\").time\n",
+    "    return time_list\n",
+    "\n",
+    "# Crear dataframe del conjunto de datos\n",
+    "def create_df(data):\n",
+    "    array = []\n",
+    "    for i in data:\n",
+    "        new_i = i.split(sep=' ')\n",
+    "        array_i = np.array(new_i)\n",
+    "        array.append(array_i)\n",
+    "    df = pd.DataFrame(array)\n",
+    "    df = df.mask(df.eq('None')).dropna()\n",
+    "    # Corregir fecha de la data\n",
+    "    df[11] = np.where((df[11] == '240801'),'090421', df[11])\n",
+    "    df[11] = np.where((df[11] == '250801'),'100421', df[11])\n",
+    "    return df\n",
+    "\n",
+    "# Ajustar el formato de str como datetime\n",
+    "def time_date(df):\n",
+    "    df[10] = df[11].str.cat(df[10], sep =\" \")\n",
+    "    df = df.drop(11, axis=1)\n",
+    "    df.columns = [x for x in range(0,15)]\n",
+    "    time = pd.to_datetime(df[10], format=\"%d%m%y %H%M%S.%f\")\n",
+    "    return df, time\n",
+    "\n",
+    "# Funciones para plots\n",
+    "\n",
+    "def plot_altitude(altitude, muons):\n",
+    "    plt.figure(figsize =(10,5))\n",
+    "    plt.plot(altitude,muons,'ob',markersize = 4, label= 'Data')\n",
+    "    plt.xlabel(\"Altitude $[m]$\", fontsize=15)\n",
+    "    plt.ylabel(\"Muons $cṃ^{-2}s^{-1}$\", fontsize=15)\n",
+    "    plt.legend()\n",
+    "    plt.grid()\n",
+    "    return \n",
+    "\n",
+    "def plot_voltage_flux(voltage, flux, fit_voltage, fit_flux):\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    plt.plot(fit_voltage, fit_flux, \"r-\",label='Fit')\n",
+    "    plt.plot(voltage, flux, \"ob\", markersize = 6, label= 'Data')\n",
+    "    plt.xlabel(\"Voltage $[mV]$\", fontsize=15)\n",
+    "    plt.ylabel(\"Flux [muons per second]\", fontsize=15)\n",
+    "    plt.title(\"Flux vs Voltage\", fontsize=20)\n",
+    "    plt.legend()\n",
+    "    plt.grid()\n",
+    "    return\n",
+    "\n",
+    "def plot_date(events,counts, mean, std, title):\n",
+    "    dates = matplotlib.dates.date2num(events)\n",
+    "    plt.figure(figsize=(20,10))\n",
+    "    plt.plot_date(dates, counts, 'ob', fillstyle=\"none\", label=\"Data\")\n",
+    "    plt.plot([], [], ' ', label=f\"Mean {round(mean,2)}\")\n",
+    "    plt.plot([], [], ' ', label=f\"\\u03C3 {round(std,2)}\")\n",
+    "    plt.xlabel(\"Tiempo [dd hh:mm]\", fontsize=15)\n",
+    "    plt.ylabel(\"#eventos\", fontsize=15)\n",
+    "    plt.title(title, fontsize=20)\n",
+    "    plt.legend(fontsize=15)\n",
+    "    plt.show\n",
+    "    return\n",
+    "\n",
+    "# Hallar los eventos\n",
+    "def events_counts(time):\n",
+    "    events, counts = np.unique(time, return_counts=True)\n",
+    "    mean = np.mean(counts)\n",
+    "    std = np.std(counts)\n",
+    "    return events, counts, mean, std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Estimación de muones"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Antes de hacer la calibración del detector, es necesario estimar el flujo esperado en los 3 paneles centelladores de (25 cm x 25 cm) a 990 m s.n.m. En principio, se espera un flujo de 1 muon por cm² por segundo a 0msnm. Para conocer el valor tabulado correspondiente a 990 msnm, se estudiará la dependecia del número de muones respecto a la altura, usando datos experimentales de *Report No. 094 - Exposure of the Population in the United States and Canada from Natural Background Radiation*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "muon_data = pd.read_csv(\"muon_data.csv\", sep=\" \")\n",
+    "muon_data[\"altitude\"] = muon_data[\"altitude\"]*1000\n",
+    "muon_data = muon_data.sort_index(ascending=False, ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>altitude</th>\n",
+       "      <th>muons</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>710.0</td>\n",
+       "      <td>0.0215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1150.0</td>\n",
+       "      <td>0.0234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1620.0</td>\n",
+       "      <td>0.0256</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2100.0</td>\n",
+       "      <td>0.0281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2620.0</td>\n",
+       "      <td>0.0310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3170.0</td>\n",
+       "      <td>0.0344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3750.0</td>\n",
+       "      <td>0.0384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>4350.0</td>\n",
+       "      <td>0.0431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>5020.0</td>\n",
+       "      <td>0.0492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>5730.0</td>\n",
+       "      <td>0.0556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>0.0631</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>7330.0</td>\n",
+       "      <td>0.0716</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>8260.0</td>\n",
+       "      <td>0.0833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>9310.0</td>\n",
+       "      <td>0.0934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10500.0</td>\n",
+       "      <td>0.1030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>11900.0</td>\n",
+       "      <td>0.1110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>13700.0</td>\n",
+       "      <td>0.1130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>16300.0</td>\n",
+       "      <td>0.1010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20800.0</td>\n",
+       "      <td>0.0664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>26700.0</td>\n",
+       "      <td>0.0211</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    altitude   muons\n",
+       "0        0.0  0.0190\n",
+       "1      710.0  0.0215\n",
+       "2     1150.0  0.0234\n",
+       "3     1620.0  0.0256\n",
+       "4     2100.0  0.0281\n",
+       "5     2620.0  0.0310\n",
+       "6     3170.0  0.0344\n",
+       "7     3750.0  0.0384\n",
+       "8     4350.0  0.0431\n",
+       "9     5020.0  0.0492\n",
+       "10    5730.0  0.0556\n",
+       "11    6500.0  0.0631\n",
+       "12    7330.0  0.0716\n",
+       "13    8260.0  0.0833\n",
+       "14    9310.0  0.0934\n",
+       "15   10500.0  0.1030\n",
+       "16   11900.0  0.1110\n",
+       "17   13700.0  0.1130\n",
+       "18   16300.0  0.1010\n",
+       "19   20800.0  0.0664\n",
+       "20   26700.0  0.0211"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "muon_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "altitude = muon_data[\"altitude\"] \n",
+    "muons = muon_data[\"muons\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_altitude(altitude, muons)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fit = interpolate.interp1d(altitude, muons)\n",
+    "fit_altitude = np.arange(0, 26800, 100)\n",
+    "fit_muons = fit(fit_altitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f87a982feb8>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_altitude(altitude, muons)\n",
+    "plt.plot(fit_altitude, fit_muons,'r-',label='Fit')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se puede confirmar el valor tabulado considerando que:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Altitude: 0.0 msnm (sea level)\n",
+      "Muon flux at sea level: 1 muon per cm² per min\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Altitude: {altitude[0]} msnm (sea level)\")\n",
+    "print(f\"Muon flux at sea level: {round(muons[0]*60)} muon per cm² per min\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Para 990msnm se espera el siguiente número de muones:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estimated muons per cm² per min at 990 msnm: 1.4 muons\n"
+     ]
+    }
+   ],
+   "source": [
+    "muon_990 = round(fit(990)*60, 1)\n",
+    "print(f\"Estimated muons per cm² per min at 990 msnm: {muon_990} muons\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Considerando la ecuación y el flujo de muones tabulado a nivel del mar:\n",
+    "$$\\Phi= \\frac{\\# events}{(area)(time)}$$\n",
+    "$$\\Phi_{\\mu}= \\frac{1 muon}{(1 cm^{2})(1 min)}$$\n",
+    "y el área total para los tres paneles:\n",
+    "$$ A = (25*25)cm² * 3 $$\n",
+    "el número de muones esperados:\n",
+    "$$ N\\mu= \\Phi_{\\mu}*A*t$$\n",
+    "Cálculo de área total:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total area: 1875 cm²\n"
+     ]
+    }
+   ],
+   "source": [
+    "area = 25*25*3\n",
+    "print(f\"Total area: {area} cm²\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cálculo de muones estimados para el área total, dada por los tres paneles del experimento, en minutos y segundos:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected muons per minute for the total area at 990 msnm: 2625.0 muons\n"
+     ]
+    }
+   ],
+   "source": [
+    "muons_min = area*muon_990\n",
+    "print(f\"Expected muons per minute for the total area at 990 msnm: {muons_min} muons\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected muons per second for the total area at 990 msnm: 43.75 muons\n"
+     ]
+    }
+   ],
+   "source": [
+    "muons_sec = muons_min/60\n",
+    "print(f\"Expected muons per second for the total area at 990 msnm: {muons_sec} muons\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Calibración"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flux=np.zeros(11)\n",
+    "flux"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Voltaje umbral de 50 mV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "V50=load_data(\"calibrationData/calibration_50mV.dat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "time_V50 = time_list(V50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([datetime.time(15, 21, 16, 6000), datetime.time(15, 21, 16, 6000),\n",
+       "       datetime.time(15, 21, 16, 6000), ..., datetime.time(15, 26, 49),\n",
+       "       datetime.time(15, 26, 49), datetime.time(15, 26, 49)], dtype=object)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_V50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flujo para 50mV: 70.97604790419162\n"
+     ]
+    }
+   ],
+   "source": [
+    "events_V50, counts_V50, flux_V50, std_V50 = events_counts(time_V50)\n",
+    "flux[0] = flux_V50\n",
+    "print(f\"Flujo para 50mV: {flux[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Voltaje umbral de 75 mV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "V75=load_data(\"calibrationData/calibration_75mV.dat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "time_V75 = time_list(V75)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([datetime.time(15, 34, 47, 3000), datetime.time(15, 34, 47, 3000),\n",
+       "       datetime.time(15, 34, 47, 3000), ...,\n",
+       "       datetime.time(15, 39, 48, 13000), datetime.time(15, 39, 48, 13000),\n",
+       "       datetime.time(15, 39, 48, 13000)], dtype=object)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_V75"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flujo para 75mV: 38.09933774834437\n"
+     ]
+    }
+   ],
+   "source": [
+    "events_V75, counts_V75, flux_V75, std_V75 = events_counts(time_V75)\n",
+    "flux[1] = flux_V75\n",
+    "print(f\"Flujo para 75mV: {flux[1]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Voltaje umbral de 100 mV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "V100=load_data(\"calibrationData/calibration_100mV.dat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "time_V100 = time_list(V100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([datetime.time(15, 41, 45, 6000), datetime.time(15, 41, 45, 6000),\n",
+       "       datetime.time(15, 41, 46, 14000), ..., datetime.time(15, 46, 56),\n",
+       "       datetime.time(15, 46, 56), datetime.time(15, 46, 56)], dtype=object)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_V100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flujo para 100mV: 21.708333333333332\n"
+     ]
+    }
+   ],
+   "source": [
+    "events_V100, counts_V100, flux_V100, std_V100 = events_counts(time_V100)\n",
+    "flux[2] = flux_V100\n",
+    "print(f\"Flujo para 100mV: {flux[2]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Voltaje umbral de 125 mV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "V125=load_data(\"calibrationData/calibration_125mV.dat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "time_V125 = time_list(V125)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([datetime.time(15, 49, 4, 1000), datetime.time(15, 49, 4, 1000),\n",
+       "       datetime.time(15, 49, 5, 9000), ...,\n",
+       "       datetime.time(15, 53, 28, 2000), datetime.time(15, 53, 28, 2000),\n",
+       "       datetime.time(15, 53, 28, 2000)], dtype=object)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_V125"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flujo para 125mV: 14.513307984790874\n"
+     ]
+    }
+   ],
+   "source": [
+    "events_V125, counts_V125, flux_V125, std_V125 = events_counts(time_V125)\n",
+    "flux[3] = flux_V125\n",
+    "print(f\"Flujo para 125mV: {flux[3]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Voltaje umbral de 150 mV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "V150=load_data(\"calibrationData/calibration_150mV.dat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "time_V150 = time_list(V150)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([datetime.time(15, 55, 58, 3000), datetime.time(15, 55, 58, 3000),\n",
+       "       datetime.time(15, 55, 58, 3000), ...,\n",
+       "       datetime.time(16, 0, 21, 13000), datetime.time(16, 0, 21, 13000),\n",
+       "       datetime.time(16, 0, 21, 13000)], dtype=object)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_V150"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flujo para 150mV: 14.338403041825096\n"
+     ]
+    }
+   ],
+   "source": [
+    "events_V150, counts_V150, flux_V150, std_V150 = events_counts(time_V150)\n",
+    "flux[4] = flux_V150\n",
+    "print(f\"Flujo para 150mV: {flux[4]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Voltaje umbral de 175 mV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "V175=load_data(\"calibrationData/calibration_175mV.dat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "time_V175 = time_list(V175)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([datetime.time(16, 1, 44, 6000), datetime.time(16, 1, 44, 6000),\n",
+       "       datetime.time(16, 1, 44, 6000), ...,\n",
+       "       datetime.time(16, 6, 34, 8000), datetime.time(16, 6, 34, 8000),\n",
+       "       datetime.time(16, 6, 34, 8000)], dtype=object)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_V175"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flujo para 175mV: 7.003703703703704\n"
+     ]
+    }
+   ],
+   "source": [
+    "events_V175, counts_V175, flux_V175, std_V175 = events_counts(time_V175)\n",
+    "flux[5] = flux_V175\n",
+    "print(f\"Flujo para 175mV: {flux[5]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Voltaje umbral de 200 mV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "V200=load_data(\"calibrationData/calibration_200mV.dat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "time_V200 = time_list(V200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([datetime.time(16, 7, 41), datetime.time(16, 7, 41),\n",
+       "       datetime.time(16, 7, 41), ..., datetime.time(16, 12, 44, 10000),\n",
+       "       datetime.time(16, 12, 45, 2000), datetime.time(16, 12, 45, 2000)],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_V200"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flujo para 200mV: 5.467153284671533\n"
+     ]
+    }
+   ],
+   "source": [
+    "events_V200, counts_V200, flux_V200, std_V200 = events_counts(time_V200)\n",
+    "flux[6] = flux_V200\n",
+    "print(f\"Flujo para 200mV: {flux[6]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Voltaje umbral de 225 mV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "V225=load_data(\"calibrationData/calibration_225mV.dat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "time_V225 = time_list(V225)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([datetime.time(16, 13, 58, 11000), datetime.time(16, 13, 58, 11000),\n",
+       "       datetime.time(16, 13, 58, 11000), ...,\n",
+       "       datetime.time(16, 19, 2, 13000), datetime.time(16, 19, 2, 13000),\n",
+       "       datetime.time(16, 19, 2, 13000)], dtype=object)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_V225"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flujo para 225mV: 4.888888888888889\n"
+     ]
+    }
+   ],
+   "source": [
+    "events_V225, counts_V225, flux_V225, std_V225 = events_counts(time_V225)\n",
+    "flux[7] = flux_V225\n",
+    "print(f\"Flujo para 225mV: {flux[7]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Voltaje umbral de 250 mV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "V250=load_data(\"calibrationData/calibration_250mV.dat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "time_V250 = time_list(V250)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([datetime.time(16, 20, 14, 13000), datetime.time(16, 20, 14, 13000),\n",
+       "       datetime.time(16, 20, 15, 5000), datetime.time(16, 20, 15, 5000),\n",
+       "       datetime.time(16, 20, 19, 5000), datetime.time(16, 20, 19, 5000),\n",
+       "       datetime.time(16, 20, 19, 5000), datetime.time(16, 20, 19, 5000),\n",
+       "       datetime.time(16, 20, 20, 13000), datetime.time(16, 20, 20, 13000),\n",
+       "       datetime.time(16, 20, 20, 13000), datetime.time(16, 20, 20, 13000),\n",
+       "       datetime.time(16, 20, 21, 5000), datetime.time(16, 20, 21, 5000),\n",
+       "       datetime.time(16, 20, 21, 5000), datetime.time(16, 20, 21, 5000),\n",
+       "       datetime.time(16, 20, 21, 5000), datetime.time(16, 20, 21, 5000),\n",
+       "       datetime.time(16, 20, 21, 5000), datetime.time(16, 20, 21, 5000),\n",
+       "       datetime.time(16, 20, 22, 13000), datetime.time(16, 20, 22, 13000),\n",
+       "       datetime.time(16, 20, 22, 13000), datetime.time(16, 20, 22, 13000),\n",
+       "       datetime.time(16, 20, 23, 5000), datetime.time(16, 20, 23, 5000),\n",
+       "       datetime.time(16, 20, 24, 13000), datetime.time(16, 20, 24, 13000),\n",
+       "       datetime.time(16, 20, 24, 13000), datetime.time(16, 20, 24, 13000),\n",
+       "       datetime.time(16, 20, 24, 13000), datetime.time(16, 20, 24, 13000),\n",
+       "       datetime.time(16, 20, 24, 13000), datetime.time(16, 20, 24, 13000),\n",
+       "       datetime.time(16, 20, 24, 13000), datetime.time(16, 20, 26, 13000),\n",
+       "       datetime.time(16, 20, 26, 13000), datetime.time(16, 20, 27, 5000),\n",
+       "       datetime.time(16, 20, 27, 5000), datetime.time(16, 20, 28, 13000),\n",
+       "       datetime.time(16, 20, 28, 13000), datetime.time(16, 20, 30, 13000),\n",
+       "       datetime.time(16, 20, 30, 13000), datetime.time(16, 20, 31, 5000),\n",
+       "       datetime.time(16, 20, 31, 5000), datetime.time(16, 20, 31, 5000),\n",
+       "       datetime.time(16, 20, 31, 5000), datetime.time(16, 20, 31, 5000),\n",
+       "       datetime.time(16, 20, 31, 5000), datetime.time(16, 20, 31, 5000),\n",
+       "       datetime.time(16, 20, 31, 5000), datetime.time(16, 20, 34, 13000),\n",
+       "       datetime.time(16, 20, 34, 13000), datetime.time(16, 20, 34, 13000),\n",
+       "       datetime.time(16, 20, 34, 13000), datetime.time(16, 20, 34, 13000),\n",
+       "       datetime.time(16, 20, 34, 13000), datetime.time(16, 20, 34, 13000),\n",
+       "       datetime.time(16, 20, 35, 5000), datetime.time(16, 20, 35, 5000),\n",
+       "       datetime.time(16, 20, 37, 5000), datetime.time(16, 20, 37, 5000),\n",
+       "       datetime.time(16, 20, 37, 5000), datetime.time(16, 20, 37, 5000),\n",
+       "       datetime.time(16, 20, 37, 5000), datetime.time(16, 20, 37, 5000),\n",
+       "       datetime.time(16, 20, 37, 5000), datetime.time(16, 20, 37, 5000),\n",
+       "       datetime.time(16, 20, 37, 5000), datetime.time(16, 20, 37, 5000),\n",
+       "       datetime.time(16, 20, 37, 5000), datetime.time(16, 20, 38, 13000),\n",
+       "       datetime.time(16, 20, 38, 13000), datetime.time(16, 20, 39, 5000),\n",
+       "       datetime.time(16, 20, 39, 5000), datetime.time(16, 20, 39, 5000),\n",
+       "       datetime.time(16, 20, 39, 5000), datetime.time(16, 20, 39, 5000),\n",
+       "       datetime.time(16, 20, 40, 13000), datetime.time(16, 20, 40, 13000),\n",
+       "       datetime.time(16, 20, 40, 13000), datetime.time(16, 20, 40, 13000),\n",
+       "       datetime.time(16, 20, 40, 13000), datetime.time(16, 20, 42, 13000),\n",
+       "       datetime.time(16, 20, 42, 13000), datetime.time(16, 20, 43, 5000),\n",
+       "       datetime.time(16, 20, 43, 5000), datetime.time(16, 20, 44, 13000),\n",
+       "       datetime.time(16, 20, 44, 13000), datetime.time(16, 20, 45, 5000),\n",
+       "       datetime.time(16, 20, 45, 5000), datetime.time(16, 20, 45, 5000),\n",
+       "       datetime.time(16, 20, 45, 5000), datetime.time(16, 20, 45, 5000),\n",
+       "       datetime.time(16, 20, 45, 5000), datetime.time(16, 20, 46, 13000),\n",
+       "       datetime.time(16, 20, 46, 13000), datetime.time(16, 20, 46, 13000),\n",
+       "       datetime.time(16, 20, 48, 13000), datetime.time(16, 20, 48, 13000),\n",
+       "       datetime.time(16, 20, 48, 13000), datetime.time(16, 20, 48, 13000),\n",
+       "       datetime.time(16, 20, 49, 5000), datetime.time(16, 20, 49, 5000),\n",
+       "       datetime.time(16, 20, 51, 5000), datetime.time(16, 20, 51, 5000),\n",
+       "       datetime.time(16, 20, 52, 13000), datetime.time(16, 20, 52, 13000),\n",
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+       "       datetime.time(16, 23, 5, 6000), datetime.time(16, 23, 5, 6000),\n",
+       "       datetime.time(16, 23, 5, 6000), datetime.time(16, 23, 6, 14000),\n",
+       "       datetime.time(16, 23, 6, 14000), datetime.time(16, 23, 8, 14000),\n",
+       "       datetime.time(16, 23, 8, 14000), datetime.time(16, 23, 8, 14000),\n",
+       "       datetime.time(16, 23, 9, 6000), datetime.time(16, 23, 9, 6000),\n",
+       "       datetime.time(16, 23, 9, 6000), datetime.time(16, 23, 11, 6000),\n",
+       "       datetime.time(16, 23, 11, 6000), datetime.time(16, 23, 11, 6000),\n",
+       "       datetime.time(16, 23, 13, 6000), datetime.time(16, 23, 13, 6000),\n",
+       "       datetime.time(16, 23, 13, 6000), datetime.time(16, 23, 13, 6000),\n",
+       "       datetime.time(16, 23, 14, 14000), datetime.time(16, 23, 14, 14000),\n",
+       "       datetime.time(16, 23, 15, 6000), datetime.time(16, 23, 15, 6000),\n",
+       "       datetime.time(16, 23, 16, 14000), datetime.time(16, 23, 16, 14000),\n",
+       "       datetime.time(16, 23, 17, 6000), datetime.time(16, 23, 17, 6000),\n",
+       "       datetime.time(16, 23, 17, 6000), datetime.time(16, 23, 17, 6000),\n",
+       "       datetime.time(16, 23, 17, 6000), datetime.time(16, 23, 18, 14000),\n",
+       "       datetime.time(16, 23, 18, 14000), datetime.time(16, 23, 19, 6000),\n",
+       "       datetime.time(16, 23, 19, 6000), datetime.time(16, 23, 19, 6000),\n",
+       "       datetime.time(16, 23, 19, 6000), datetime.time(16, 23, 19, 6000),\n",
+       "       datetime.time(16, 23, 19, 6000), datetime.time(16, 23, 19, 6000),\n",
+       "       datetime.time(16, 23, 19, 6000), datetime.time(16, 23, 19, 6000),\n",
+       "       datetime.time(16, 23, 19, 6000), datetime.time(16, 23, 19, 6000),\n",
+       "       datetime.time(16, 23, 19, 6000), datetime.time(16, 23, 19, 6000),\n",
+       "       datetime.time(16, 23, 19, 6000), datetime.time(16, 23, 19, 6000),\n",
+       "       datetime.time(16, 23, 19, 6000), datetime.time(16, 23, 19, 6000),\n",
+       "       datetime.time(16, 23, 19, 6000), datetime.time(16, 23, 19, 6000),\n",
+       "       datetime.time(16, 23, 19, 6000), datetime.time(16, 23, 21, 6000),\n",
+       "       datetime.time(16, 23, 21, 6000), datetime.time(16, 23, 21, 6000),\n",
+       "       datetime.time(16, 23, 21, 6000), datetime.time(16, 23, 21, 6000),\n",
+       "       datetime.time(16, 23, 24, 14000), datetime.time(16, 23, 24, 14000),\n",
+       "       datetime.time(16, 23, 25, 6000), datetime.time(16, 23, 25, 6000),\n",
+       "       datetime.time(16, 23, 25, 6000), datetime.time(16, 23, 25, 6000),\n",
+       "       datetime.time(16, 23, 26, 14000), datetime.time(16, 23, 26, 14000),\n",
+       "       datetime.time(16, 23, 26, 14000), datetime.time(16, 23, 26, 14000),\n",
+       "       datetime.time(16, 23, 27, 6000), datetime.time(16, 23, 27, 6000),\n",
+       "       datetime.time(16, 23, 28, 14000), datetime.time(16, 23, 28, 14000),\n",
+       "       datetime.time(16, 23, 28, 14000), datetime.time(16, 23, 28, 14000),\n",
+       "       datetime.time(16, 23, 28, 14000), datetime.time(16, 23, 28, 14000),\n",
+       "       datetime.time(16, 23, 28, 14000), datetime.time(16, 23, 29, 6000),\n",
+       "       datetime.time(16, 23, 29, 6000), datetime.time(16, 23, 29, 6000),\n",
+       "       datetime.time(16, 23, 29, 6000), datetime.time(16, 23, 29, 6000),\n",
+       "       datetime.time(16, 23, 29, 6000), datetime.time(16, 23, 29, 6000),\n",
+       "       datetime.time(16, 23, 29, 6000), datetime.time(16, 23, 29, 6000),\n",
+       "       datetime.time(16, 23, 29, 6000), datetime.time(16, 23, 30, 14000),\n",
+       "       datetime.time(16, 23, 30, 14000), datetime.time(16, 23, 30, 14000),\n",
+       "       datetime.time(16, 23, 30, 14000), datetime.time(16, 23, 30, 14000),\n",
+       "       datetime.time(16, 23, 30, 14000), datetime.time(16, 23, 31, 6000),\n",
+       "       datetime.time(16, 23, 31, 6000), datetime.time(16, 23, 31, 6000),\n",
+       "       datetime.time(16, 23, 31, 6000), datetime.time(16, 23, 32, 14000),\n",
+       "       datetime.time(16, 23, 32, 14000), datetime.time(16, 23, 32, 14000),\n",
+       "       datetime.time(16, 23, 32, 14000), datetime.time(16, 23, 32, 14000),\n",
+       "       datetime.time(16, 23, 32, 14000), datetime.time(16, 23, 33, 6000),\n",
+       "       datetime.time(16, 23, 33, 6000), datetime.time(16, 23, 33, 6000),\n",
+       "       datetime.time(16, 23, 33, 6000), datetime.time(16, 23, 33, 6000),\n",
+       "       datetime.time(16, 23, 33, 6000), datetime.time(16, 23, 33, 6000),\n",
+       "       datetime.time(16, 23, 33, 6000), datetime.time(16, 23, 33, 6000),\n",
+       "       datetime.time(16, 23, 35, 6000), datetime.time(16, 23, 35, 6000),\n",
+       "       datetime.time(16, 23, 37, 6000), datetime.time(16, 23, 37, 6000),\n",
+       "       datetime.time(16, 23, 37, 6000), datetime.time(16, 23, 37, 6000),\n",
+       "       datetime.time(16, 23, 38, 14000), datetime.time(16, 23, 38, 14000),\n",
+       "       datetime.time(16, 23, 38, 14000), datetime.time(16, 23, 38, 14000),\n",
+       "       datetime.time(16, 23, 38, 14000), datetime.time(16, 23, 38, 14000),\n",
+       "       datetime.time(16, 23, 38, 14000), datetime.time(16, 23, 39, 6000),\n",
+       "       datetime.time(16, 23, 39, 6000), datetime.time(16, 23, 40, 14000),\n",
+       "       datetime.time(16, 23, 40, 14000), datetime.time(16, 23, 41, 6000),\n",
+       "       datetime.time(16, 23, 41, 6000), datetime.time(16, 23, 42, 14000),\n",
+       "       datetime.time(16, 23, 42, 14000), datetime.time(16, 23, 43, 6000),\n",
+       "       datetime.time(16, 23, 43, 6000), datetime.time(16, 23, 43, 6000),\n",
+       "       datetime.time(16, 23, 43, 6000), datetime.time(16, 23, 44, 14000),\n",
+       "       datetime.time(16, 23, 44, 14000), datetime.time(16, 23, 45, 6000),\n",
+       "       datetime.time(16, 23, 45, 6000), datetime.time(16, 23, 45, 6000),\n",
+       "       datetime.time(16, 23, 45, 6000), datetime.time(16, 23, 45, 6000),\n",
+       "       datetime.time(16, 23, 45, 6000), datetime.time(16, 23, 45, 6000),\n",
+       "       datetime.time(16, 23, 45, 6000), datetime.time(16, 23, 45, 6000),\n",
+       "       datetime.time(16, 23, 46, 14000), datetime.time(16, 23, 46, 14000),\n",
+       "       datetime.time(16, 23, 46, 14000), datetime.time(16, 23, 46, 14000),\n",
+       "       datetime.time(16, 23, 46, 14000), datetime.time(16, 23, 46, 14000),\n",
+       "       datetime.time(16, 23, 47, 6000), datetime.time(16, 23, 47, 6000),\n",
+       "       datetime.time(16, 23, 47, 6000), datetime.time(16, 23, 47, 6000),\n",
+       "       datetime.time(16, 23, 47, 6000), datetime.time(16, 23, 47, 6000),\n",
+       "       datetime.time(16, 23, 50, 14000), datetime.time(16, 23, 50, 14000),\n",
+       "       datetime.time(16, 23, 51, 6000), datetime.time(16, 23, 51, 6000),\n",
+       "       datetime.time(16, 23, 51, 6000), datetime.time(16, 23, 51, 6000),\n",
+       "       datetime.time(16, 23, 51, 6000), datetime.time(16, 23, 51, 6000),\n",
+       "       datetime.time(16, 23, 51, 6000), datetime.time(16, 23, 52, 14000),\n",
+       "       datetime.time(16, 23, 52, 14000), datetime.time(16, 23, 53, 6000),\n",
+       "       datetime.time(16, 23, 53, 6000), datetime.time(16, 23, 53, 6000),\n",
+       "       datetime.time(16, 23, 53, 6000), datetime.time(16, 23, 55, 6000),\n",
+       "       datetime.time(16, 23, 55, 6000), datetime.time(16, 23, 55, 6000),\n",
+       "       datetime.time(16, 23, 55, 6000), datetime.time(16, 23, 56, 14000),\n",
+       "       datetime.time(16, 23, 56, 14000), datetime.time(16, 23, 57, 6000),\n",
+       "       datetime.time(16, 23, 57, 6000), datetime.time(16, 23, 57, 6000),\n",
+       "       datetime.time(16, 23, 57, 6000), datetime.time(16, 23, 58, 14000),\n",
+       "       datetime.time(16, 23, 58, 14000), datetime.time(16, 23, 58, 14000),\n",
+       "       datetime.time(16, 23, 59, 7000), datetime.time(16, 23, 59, 7000),\n",
+       "       datetime.time(16, 24, 0, 15000), datetime.time(16, 24, 0, 15000),\n",
+       "       datetime.time(16, 24, 0, 15000), datetime.time(16, 24, 0, 15000),\n",
+       "       datetime.time(16, 24, 0, 15000), datetime.time(16, 24, 0, 15000),\n",
+       "       datetime.time(16, 24, 1, 7000), datetime.time(16, 24, 1, 7000),\n",
+       "       datetime.time(16, 24, 1, 7000), datetime.time(16, 24, 1, 7000),\n",
+       "       datetime.time(16, 24, 1, 7000), datetime.time(16, 24, 1, 7000),\n",
+       "       datetime.time(16, 24, 3, 7000), datetime.time(16, 24, 3, 7000),\n",
+       "       datetime.time(16, 24, 3, 7000), datetime.time(16, 24, 3, 7000),\n",
+       "       datetime.time(16, 24, 4, 15000), datetime.time(16, 24, 4, 15000),\n",
+       "       datetime.time(16, 24, 4, 15000), datetime.time(16, 24, 4, 15000),\n",
+       "       datetime.time(16, 24, 4, 15000), datetime.time(16, 24, 5, 7000),\n",
+       "       datetime.time(16, 24, 5, 7000), datetime.time(16, 24, 6, 15000),\n",
+       "       datetime.time(16, 24, 6, 15000), datetime.time(16, 24, 6, 15000),\n",
+       "       datetime.time(16, 24, 6, 15000), datetime.time(16, 24, 6, 15000),\n",
+       "       datetime.time(16, 24, 6, 15000), datetime.time(16, 24, 6, 15000),\n",
+       "       datetime.time(16, 24, 6, 15000), datetime.time(16, 24, 7, 7000),\n",
+       "       datetime.time(16, 24, 7, 7000), datetime.time(16, 24, 7, 7000),\n",
+       "       datetime.time(16, 24, 11, 7000), datetime.time(16, 24, 11, 7000),\n",
+       "       datetime.time(16, 24, 13, 7000), datetime.time(16, 24, 13, 7000),\n",
+       "       datetime.time(16, 24, 15, 7000), datetime.time(16, 24, 15, 7000),\n",
+       "       datetime.time(16, 24, 15, 7000), datetime.time(16, 24, 15, 7000),\n",
+       "       datetime.time(16, 24, 15, 7000), datetime.time(16, 24, 15, 7000),\n",
+       "       datetime.time(16, 24, 16, 15000), datetime.time(16, 24, 16, 15000),\n",
+       "       datetime.time(16, 24, 16, 15000), datetime.time(16, 24, 16, 15000),\n",
+       "       datetime.time(16, 24, 18, 15000), datetime.time(16, 24, 18, 15000),\n",
+       "       datetime.time(16, 24, 18, 15000), datetime.time(16, 24, 18, 15000),\n",
+       "       datetime.time(16, 24, 18, 15000), datetime.time(16, 24, 18, 15000),\n",
+       "       datetime.time(16, 24, 19, 7000), datetime.time(16, 24, 19, 7000),\n",
+       "       datetime.time(16, 24, 19, 7000), datetime.time(16, 24, 19, 7000),\n",
+       "       datetime.time(16, 24, 19, 7000), datetime.time(16, 24, 19, 7000),\n",
+       "       datetime.time(16, 24, 19, 7000), datetime.time(16, 24, 19, 7000),\n",
+       "       datetime.time(16, 24, 19, 7000), datetime.time(16, 24, 19, 7000),\n",
+       "       datetime.time(16, 24, 20, 15000), datetime.time(16, 24, 20, 15000),\n",
+       "       datetime.time(16, 24, 20, 15000), datetime.time(16, 24, 20, 15000),\n",
+       "       datetime.time(16, 24, 20, 15000), datetime.time(16, 24, 22, 15000),\n",
+       "       datetime.time(16, 24, 22, 15000), datetime.time(16, 24, 22, 15000),\n",
+       "       datetime.time(16, 24, 22, 15000), datetime.time(16, 24, 22, 15000),\n",
+       "       datetime.time(16, 24, 22, 15000), datetime.time(16, 24, 22, 15000),\n",
+       "       datetime.time(16, 24, 22, 15000), datetime.time(16, 24, 22, 15000),\n",
+       "       datetime.time(16, 24, 22, 15000), datetime.time(16, 24, 23, 7000),\n",
+       "       datetime.time(16, 24, 23, 7000), datetime.time(16, 24, 25, 7000),\n",
+       "       datetime.time(16, 24, 25, 7000), datetime.time(16, 24, 26, 15000),\n",
+       "       datetime.time(16, 24, 26, 15000), datetime.time(16, 24, 26, 15000),\n",
+       "       datetime.time(16, 24, 27, 7000), datetime.time(16, 24, 27, 7000),\n",
+       "       datetime.time(16, 24, 27, 7000), datetime.time(16, 24, 27, 7000),\n",
+       "       datetime.time(16, 24, 28, 15000), datetime.time(16, 24, 28, 15000),\n",
+       "       datetime.time(16, 24, 29, 7000), datetime.time(16, 24, 29, 7000),\n",
+       "       datetime.time(16, 24, 29, 7000), datetime.time(16, 24, 29, 7000),\n",
+       "       datetime.time(16, 24, 29, 7000), datetime.time(16, 24, 29, 7000),\n",
+       "       datetime.time(16, 24, 31, 7000), datetime.time(16, 24, 31, 7000),\n",
+       "       datetime.time(16, 24, 31, 7000), datetime.time(16, 24, 31, 7000),\n",
+       "       datetime.time(16, 24, 31, 7000), datetime.time(16, 24, 31, 7000),\n",
+       "       datetime.time(16, 24, 32, 15000), datetime.time(16, 24, 32, 15000),\n",
+       "       datetime.time(16, 24, 33, 7000), datetime.time(16, 24, 33, 7000),\n",
+       "       datetime.time(16, 24, 33, 7000), datetime.time(16, 24, 33, 7000),\n",
+       "       datetime.time(16, 24, 33, 7000), datetime.time(16, 24, 33, 7000),\n",
+       "       datetime.time(16, 24, 34, 15000), datetime.time(16, 24, 34, 15000),\n",
+       "       datetime.time(16, 24, 34, 15000), datetime.time(16, 24, 34, 15000),\n",
+       "       datetime.time(16, 24, 34, 15000), datetime.time(16, 24, 34, 15000),\n",
+       "       datetime.time(16, 24, 34, 15000), datetime.time(16, 24, 34, 15000),\n",
+       "       datetime.time(16, 24, 35, 7000), datetime.time(16, 24, 35, 7000),\n",
+       "       datetime.time(16, 24, 35, 7000), datetime.time(16, 24, 36, 15000),\n",
+       "       datetime.time(16, 24, 36, 15000), datetime.time(16, 24, 36, 15000),\n",
+       "       datetime.time(16, 24, 36, 15000), datetime.time(16, 24, 36, 15000),\n",
+       "       datetime.time(16, 24, 36, 15000), datetime.time(16, 24, 36, 15000),\n",
+       "       datetime.time(16, 24, 37, 7000), datetime.time(16, 24, 37, 7000),\n",
+       "       datetime.time(16, 24, 41, 7000), datetime.time(16, 24, 41, 7000),\n",
+       "       datetime.time(16, 24, 41, 7000), datetime.time(16, 24, 41, 7000),\n",
+       "       datetime.time(16, 24, 41, 7000), datetime.time(16, 24, 42, 15000),\n",
+       "       datetime.time(16, 24, 42, 15000), datetime.time(16, 24, 42, 15000),\n",
+       "       datetime.time(16, 24, 42, 15000), datetime.time(16, 24, 42, 15000),\n",
+       "       datetime.time(16, 24, 43, 7000), datetime.time(16, 24, 43, 7000),\n",
+       "       datetime.time(16, 24, 44, 15000), datetime.time(16, 24, 44, 15000),\n",
+       "       datetime.time(16, 24, 44, 15000), datetime.time(16, 24, 44, 15000),\n",
+       "       datetime.time(16, 24, 45, 7000), datetime.time(16, 24, 45, 7000),\n",
+       "       datetime.time(16, 24, 47, 7000), datetime.time(16, 24, 47, 7000),\n",
+       "       datetime.time(16, 24, 48, 15000), datetime.time(16, 24, 48, 15000),\n",
+       "       datetime.time(16, 24, 48, 15000), datetime.time(16, 24, 48, 15000),\n",
+       "       datetime.time(16, 24, 49, 7000), datetime.time(16, 24, 49, 7000),\n",
+       "       datetime.time(16, 24, 49, 7000), datetime.time(16, 24, 49, 7000),\n",
+       "       datetime.time(16, 24, 51, 7000), datetime.time(16, 24, 51, 7000),\n",
+       "       datetime.time(16, 24, 51, 7000), datetime.time(16, 24, 51, 7000),\n",
+       "       datetime.time(16, 24, 51, 7000), datetime.time(16, 24, 51, 7000),\n",
+       "       datetime.time(16, 24, 51, 7000), datetime.time(16, 24, 52, 15000),\n",
+       "       datetime.time(16, 24, 52, 15000), datetime.time(16, 24, 52, 15000),\n",
+       "       datetime.time(16, 24, 52, 15000), datetime.time(16, 24, 54, 15000),\n",
+       "       datetime.time(16, 24, 54, 15000), datetime.time(16, 24, 54, 15000),\n",
+       "       datetime.time(16, 24, 54, 15000), datetime.time(16, 24, 54, 15000),\n",
+       "       datetime.time(16, 24, 54, 15000), datetime.time(16, 24, 55, 7000),\n",
+       "       datetime.time(16, 24, 55, 7000), datetime.time(16, 24, 55, 7000),\n",
+       "       datetime.time(16, 24, 55, 7000), datetime.time(16, 24, 55, 7000),\n",
+       "       datetime.time(16, 24, 56, 15000), datetime.time(16, 24, 56, 15000),\n",
+       "       datetime.time(16, 24, 56, 15000), datetime.time(16, 24, 56, 15000),\n",
+       "       datetime.time(16, 24, 58, 15000), datetime.time(16, 24, 58, 15000),\n",
+       "       datetime.time(16, 25, 1, 7000), datetime.time(16, 25, 1, 7000),\n",
+       "       datetime.time(16, 25, 1, 7000), datetime.time(16, 25, 1, 7000),\n",
+       "       datetime.time(16, 25, 1, 7000), datetime.time(16, 25, 1, 7000),\n",
+       "       datetime.time(16, 25, 3, 7000), datetime.time(16, 25, 3, 7000),\n",
+       "       datetime.time(16, 25, 3, 7000), datetime.time(16, 25, 4, 15000),\n",
+       "       datetime.time(16, 25, 4, 15000), datetime.time(16, 25, 4, 15000),\n",
+       "       datetime.time(16, 25, 4, 15000), datetime.time(16, 25, 4, 15000)],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_V250"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flujo para 250mV: 4.385650224215246\n"
+     ]
+    }
+   ],
+   "source": [
+    "events_V250, counts_V250, flux_V250, std_V250 = events_counts(time_V250)\n",
+    "flux[8] = flux_V250\n",
+    "print(f\"Flujo para 250mV: {flux[8]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Voltaje umbral de 275 mV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "V275=load_data(\"calibrationData/calibration_275mV.dat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "time_V275 = time_list(V275)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([datetime.time(16, 26, 54), datetime.time(16, 26, 54),\n",
+       "       datetime.time(16, 26, 55, 8000), datetime.time(16, 26, 55, 8000),\n",
+       "       datetime.time(16, 26, 55, 8000), datetime.time(16, 26, 55, 8000),\n",
+       "       datetime.time(16, 26, 55, 8000), datetime.time(16, 26, 56),\n",
+       "       datetime.time(16, 26, 56), datetime.time(16, 26, 58),\n",
+       "       datetime.time(16, 26, 58), datetime.time(16, 26, 58),\n",
+       "       datetime.time(16, 26, 58), datetime.time(16, 27),\n",
+       "       datetime.time(16, 27), datetime.time(16, 27, 1, 8000),\n",
+       "       datetime.time(16, 27, 1, 8000), datetime.time(16, 27, 1, 8000),\n",
+       "       datetime.time(16, 27, 1, 8000), datetime.time(16, 27, 3, 8000),\n",
+       "       datetime.time(16, 27, 3, 8000), datetime.time(16, 27, 3, 8000),\n",
+       "       datetime.time(16, 27, 3, 8000), datetime.time(16, 27, 5, 8000),\n",
+       "       datetime.time(16, 27, 5, 8000), datetime.time(16, 27, 5, 8000),\n",
+       "       datetime.time(16, 27, 5, 8000), datetime.time(16, 27, 6),\n",
+       "       datetime.time(16, 27, 6), datetime.time(16, 27, 6),\n",
+       "       datetime.time(16, 27, 7, 8000), datetime.time(16, 27, 7, 8000),\n",
+       "       datetime.time(16, 27, 9, 8000), datetime.time(16, 27, 9, 8000),\n",
+       "       datetime.time(16, 27, 11, 8000), datetime.time(16, 27, 11, 8000),\n",
+       "       datetime.time(16, 27, 12), datetime.time(16, 27, 12),\n",
+       "       datetime.time(16, 27, 12), datetime.time(16, 27, 14),\n",
+       "       datetime.time(16, 27, 14), datetime.time(16, 27, 15, 8000),\n",
+       "       datetime.time(16, 27, 15, 8000), datetime.time(16, 27, 16),\n",
+       "       datetime.time(16, 27, 16), datetime.time(16, 27, 17, 8000),\n",
+       "       datetime.time(16, 27, 17, 8000), datetime.time(16, 27, 17, 8000),\n",
+       "       datetime.time(16, 27, 17, 8000), datetime.time(16, 27, 17, 8000),\n",
+       "       datetime.time(16, 27, 17, 8000), datetime.time(16, 27, 18),\n",
+       "       datetime.time(16, 27, 18), datetime.time(16, 27, 19, 8000),\n",
+       "       datetime.time(16, 27, 19, 8000), datetime.time(16, 27, 19, 8000),\n",
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+       "       datetime.time(16, 30, 32, 1000), datetime.time(16, 30, 32, 1000),\n",
+       "       datetime.time(16, 30, 32, 1000), datetime.time(16, 30, 33, 9000),\n",
+       "       datetime.time(16, 30, 33, 9000), datetime.time(16, 30, 33, 9000),\n",
+       "       datetime.time(16, 30, 33, 9000), datetime.time(16, 30, 34, 1000),\n",
+       "       datetime.time(16, 30, 34, 1000), datetime.time(16, 30, 34, 1000),\n",
+       "       datetime.time(16, 30, 34, 1000), datetime.time(16, 30, 34, 1000),\n",
+       "       datetime.time(16, 30, 34, 1000), datetime.time(16, 30, 34, 1000),\n",
+       "       datetime.time(16, 30, 34, 1000), datetime.time(16, 30, 34, 1000),\n",
+       "       datetime.time(16, 30, 34, 1000), datetime.time(16, 30, 35, 9000),\n",
+       "       datetime.time(16, 30, 35, 9000), datetime.time(16, 30, 35, 9000),\n",
+       "       datetime.time(16, 30, 35, 9000), datetime.time(16, 30, 35, 9000),\n",
+       "       datetime.time(16, 30, 37, 9000), datetime.time(16, 30, 37, 9000),\n",
+       "       datetime.time(16, 30, 37, 9000), datetime.time(16, 30, 37, 9000),\n",
+       "       datetime.time(16, 30, 37, 9000), datetime.time(16, 30, 37, 9000),\n",
+       "       datetime.time(16, 30, 37, 9000), datetime.time(16, 30, 37, 9000),\n",
+       "       datetime.time(16, 30, 37, 9000), datetime.time(16, 30, 37, 9000),\n",
+       "       datetime.time(16, 30, 38, 1000), datetime.time(16, 30, 38, 1000),\n",
+       "       datetime.time(16, 30, 38, 1000), datetime.time(16, 30, 38, 1000),\n",
+       "       datetime.time(16, 30, 39, 9000), datetime.time(16, 30, 39, 9000),\n",
+       "       datetime.time(16, 30, 39, 9000), datetime.time(16, 30, 39, 9000),\n",
+       "       datetime.time(16, 30, 39, 9000), datetime.time(16, 30, 40, 1000),\n",
+       "       datetime.time(16, 30, 40, 1000), datetime.time(16, 30, 41, 9000),\n",
+       "       datetime.time(16, 30, 41, 9000), datetime.time(16, 30, 42, 1000),\n",
+       "       datetime.time(16, 30, 42, 1000), datetime.time(16, 30, 42, 1000),\n",
+       "       datetime.time(16, 30, 42, 1000), datetime.time(16, 30, 42, 1000),\n",
+       "       datetime.time(16, 30, 42, 1000), datetime.time(16, 30, 42, 1000),\n",
+       "       datetime.time(16, 30, 43, 9000), datetime.time(16, 30, 43, 9000),\n",
+       "       datetime.time(16, 30, 44, 1000), datetime.time(16, 30, 44, 1000),\n",
+       "       datetime.time(16, 30, 44, 1000), datetime.time(16, 30, 44, 1000),\n",
+       "       datetime.time(16, 30, 45, 9000), datetime.time(16, 30, 45, 9000),\n",
+       "       datetime.time(16, 30, 45, 9000), datetime.time(16, 30, 45, 9000),\n",
+       "       datetime.time(16, 30, 45, 9000), datetime.time(16, 30, 45, 9000),\n",
+       "       datetime.time(16, 30, 45, 9000), datetime.time(16, 30, 45, 9000),\n",
+       "       datetime.time(16, 30, 46, 1000), datetime.time(16, 30, 46, 1000),\n",
+       "       datetime.time(16, 30, 46, 1000), datetime.time(16, 30, 46, 1000),\n",
+       "       datetime.time(16, 30, 46, 1000), datetime.time(16, 30, 46, 1000),\n",
+       "       datetime.time(16, 30, 46, 1000), datetime.time(16, 30, 48, 1000),\n",
+       "       datetime.time(16, 30, 48, 1000), datetime.time(16, 30, 48, 1000),\n",
+       "       datetime.time(16, 30, 48, 1000), datetime.time(16, 30, 48, 1000),\n",
+       "       datetime.time(16, 30, 48, 1000), datetime.time(16, 30, 48, 1000),\n",
+       "       datetime.time(16, 30, 48, 1000), datetime.time(16, 30, 49, 9000),\n",
+       "       datetime.time(16, 30, 49, 9000), datetime.time(16, 30, 50, 1000),\n",
+       "       datetime.time(16, 30, 50, 1000), datetime.time(16, 30, 50, 1000),\n",
+       "       datetime.time(16, 30, 50, 1000), datetime.time(16, 30, 51, 9000),\n",
+       "       datetime.time(16, 30, 51, 9000), datetime.time(16, 30, 53, 9000),\n",
+       "       datetime.time(16, 30, 53, 9000), datetime.time(16, 30, 53, 9000),\n",
+       "       datetime.time(16, 30, 53, 9000), datetime.time(16, 30, 53, 9000),\n",
+       "       datetime.time(16, 30, 55, 9000), datetime.time(16, 30, 55, 9000),\n",
+       "       datetime.time(16, 30, 55, 9000), datetime.time(16, 30, 55, 9000),\n",
+       "       datetime.time(16, 30, 55, 9000), datetime.time(16, 30, 55, 9000),\n",
+       "       datetime.time(16, 30, 55, 9000), datetime.time(16, 30, 55, 9000),\n",
+       "       datetime.time(16, 30, 55, 9000), datetime.time(16, 30, 55, 9000),\n",
+       "       datetime.time(16, 30, 56, 1000), datetime.time(16, 30, 56, 1000),\n",
+       "       datetime.time(16, 30, 56, 1000), datetime.time(16, 30, 56, 1000),\n",
+       "       datetime.time(16, 30, 56, 1000), datetime.time(16, 30, 56, 1000),\n",
+       "       datetime.time(16, 30, 57, 9000), datetime.time(16, 30, 57, 9000),\n",
+       "       datetime.time(16, 30, 57, 9000), datetime.time(16, 30, 57, 9000),\n",
+       "       datetime.time(16, 30, 58, 1000), datetime.time(16, 30, 58, 1000),\n",
+       "       datetime.time(16, 30, 58, 1000), datetime.time(16, 30, 58, 1000),\n",
+       "       datetime.time(16, 30, 59, 9000), datetime.time(16, 30, 59, 9000),\n",
+       "       datetime.time(16, 30, 59, 9000), datetime.time(16, 30, 59, 9000),\n",
+       "       datetime.time(16, 30, 59, 9000), datetime.time(16, 30, 59, 9000),\n",
+       "       datetime.time(16, 30, 59, 9000), datetime.time(16, 30, 59, 9000),\n",
+       "       datetime.time(16, 31, 0, 1000), datetime.time(16, 31, 0, 1000),\n",
+       "       datetime.time(16, 31, 1, 9000), datetime.time(16, 31, 1, 9000),\n",
+       "       datetime.time(16, 31, 1, 9000), datetime.time(16, 31, 1, 9000),\n",
+       "       datetime.time(16, 31, 1, 9000), datetime.time(16, 31, 1, 9000),\n",
+       "       datetime.time(16, 31, 1, 9000), datetime.time(16, 31, 2, 1000),\n",
+       "       datetime.time(16, 31, 2, 1000), datetime.time(16, 31, 4, 1000),\n",
+       "       datetime.time(16, 31, 4, 1000), datetime.time(16, 31, 4, 1000),\n",
+       "       datetime.time(16, 31, 4, 1000), datetime.time(16, 31, 4, 1000),\n",
+       "       datetime.time(16, 31, 4, 1000), datetime.time(16, 31, 4, 1000),\n",
+       "       datetime.time(16, 31, 4, 1000), datetime.time(16, 31, 6, 1000),\n",
+       "       datetime.time(16, 31, 6, 1000), datetime.time(16, 31, 6, 1000),\n",
+       "       datetime.time(16, 31, 6, 1000), datetime.time(16, 31, 6, 1000),\n",
+       "       datetime.time(16, 31, 6, 1000), datetime.time(16, 31, 6, 1000),\n",
+       "       datetime.time(16, 31, 6, 1000), datetime.time(16, 31, 8, 1000),\n",
+       "       datetime.time(16, 31, 8, 1000), datetime.time(16, 31, 10, 1000),\n",
+       "       datetime.time(16, 31, 10, 1000), datetime.time(16, 31, 10, 1000),\n",
+       "       datetime.time(16, 31, 10, 1000), datetime.time(16, 31, 11, 9000),\n",
+       "       datetime.time(16, 31, 11, 9000), datetime.time(16, 31, 11, 9000),\n",
+       "       datetime.time(16, 31, 11, 9000), datetime.time(16, 31, 11, 9000),\n",
+       "       datetime.time(16, 31, 11, 9000), datetime.time(16, 31, 11, 9000),\n",
+       "       datetime.time(16, 31, 12, 1000), datetime.time(16, 31, 12, 1000),\n",
+       "       datetime.time(16, 31, 12, 1000), datetime.time(16, 31, 12, 1000),\n",
+       "       datetime.time(16, 31, 12, 1000), datetime.time(16, 31, 12, 1000),\n",
+       "       datetime.time(16, 31, 14, 1000), datetime.time(16, 31, 14, 1000),\n",
+       "       datetime.time(16, 31, 15, 9000), datetime.time(16, 31, 15, 9000),\n",
+       "       datetime.time(16, 31, 15, 9000), datetime.time(16, 31, 18, 1000),\n",
+       "       datetime.time(16, 31, 18, 1000), datetime.time(16, 31, 18, 1000),\n",
+       "       datetime.time(16, 31, 19, 9000), datetime.time(16, 31, 19, 9000),\n",
+       "       datetime.time(16, 31, 20, 1000), datetime.time(16, 31, 20, 1000),\n",
+       "       datetime.time(16, 31, 21, 9000), datetime.time(16, 31, 21, 9000),\n",
+       "       datetime.time(16, 31, 21, 9000), datetime.time(16, 31, 21, 9000),\n",
+       "       datetime.time(16, 31, 22, 1000), datetime.time(16, 31, 22, 1000),\n",
+       "       datetime.time(16, 31, 23, 9000), datetime.time(16, 31, 23, 9000),\n",
+       "       datetime.time(16, 31, 23, 9000), datetime.time(16, 31, 23, 9000),\n",
+       "       datetime.time(16, 31, 23, 9000), datetime.time(16, 31, 24, 1000),\n",
+       "       datetime.time(16, 31, 24, 1000), datetime.time(16, 31, 24, 1000),\n",
+       "       datetime.time(16, 31, 24, 1000), datetime.time(16, 31, 24, 1000),\n",
+       "       datetime.time(16, 31, 24, 1000), datetime.time(16, 31, 25, 9000),\n",
+       "       datetime.time(16, 31, 25, 9000), datetime.time(16, 31, 25, 9000),\n",
+       "       datetime.time(16, 31, 25, 9000), datetime.time(16, 31, 25, 9000),\n",
+       "       datetime.time(16, 31, 25, 9000), datetime.time(16, 31, 26, 1000),\n",
+       "       datetime.time(16, 31, 26, 1000), datetime.time(16, 31, 27, 9000),\n",
+       "       datetime.time(16, 31, 27, 9000), datetime.time(16, 31, 28, 1000),\n",
+       "       datetime.time(16, 31, 28, 1000), datetime.time(16, 31, 29, 9000),\n",
+       "       datetime.time(16, 31, 29, 9000), datetime.time(16, 31, 29, 9000),\n",
+       "       datetime.time(16, 31, 29, 9000), datetime.time(16, 31, 30, 1000),\n",
+       "       datetime.time(16, 31, 30, 1000), datetime.time(16, 31, 31, 9000),\n",
+       "       datetime.time(16, 31, 31, 9000), datetime.time(16, 31, 31, 9000),\n",
+       "       datetime.time(16, 31, 31, 9000), datetime.time(16, 31, 31, 9000),\n",
+       "       datetime.time(16, 31, 31, 9000), datetime.time(16, 31, 32, 1000),\n",
+       "       datetime.time(16, 31, 32, 1000), datetime.time(16, 31, 32, 1000),\n",
+       "       datetime.time(16, 31, 32, 1000), datetime.time(16, 31, 34, 1000),\n",
+       "       datetime.time(16, 31, 34, 1000), datetime.time(16, 31, 36, 2000),\n",
+       "       datetime.time(16, 31, 36, 2000), datetime.time(16, 31, 36, 2000),\n",
+       "       datetime.time(16, 31, 37, 10000), datetime.time(16, 31, 37, 10000),\n",
+       "       datetime.time(16, 31, 37, 10000), datetime.time(16, 31, 37, 10000),\n",
+       "       datetime.time(16, 31, 37, 10000), datetime.time(16, 31, 38, 2000),\n",
+       "       datetime.time(16, 31, 38, 2000), datetime.time(16, 31, 38, 2000),\n",
+       "       datetime.time(16, 31, 38, 2000), datetime.time(16, 31, 38, 2000),\n",
+       "       datetime.time(16, 31, 38, 2000), datetime.time(16, 31, 38, 2000),\n",
+       "       datetime.time(16, 31, 38, 2000), datetime.time(16, 31, 38, 2000),\n",
+       "       datetime.time(16, 31, 38, 2000), datetime.time(16, 31, 39, 10000),\n",
+       "       datetime.time(16, 31, 39, 10000), datetime.time(16, 31, 40, 2000),\n",
+       "       datetime.time(16, 31, 40, 2000), datetime.time(16, 31, 41, 10000),\n",
+       "       datetime.time(16, 31, 41, 10000), datetime.time(16, 31, 41, 10000),\n",
+       "       datetime.time(16, 31, 42, 2000), datetime.time(16, 31, 42, 2000),\n",
+       "       datetime.time(16, 31, 42, 2000), datetime.time(16, 31, 42, 2000),\n",
+       "       datetime.time(16, 31, 42, 2000), datetime.time(16, 31, 42, 2000),\n",
+       "       datetime.time(16, 31, 44, 2000), datetime.time(16, 31, 44, 2000),\n",
+       "       datetime.time(16, 31, 44, 2000), datetime.time(16, 31, 44, 2000),\n",
+       "       datetime.time(16, 31, 44, 2000), datetime.time(16, 31, 44, 2000),\n",
+       "       datetime.time(16, 31, 44, 2000), datetime.time(16, 31, 44, 2000),\n",
+       "       datetime.time(16, 31, 44, 2000), datetime.time(16, 31, 44, 2000),\n",
+       "       datetime.time(16, 31, 45, 10000), datetime.time(16, 31, 45, 10000),\n",
+       "       datetime.time(16, 31, 45, 10000), datetime.time(16, 31, 46, 2000),\n",
+       "       datetime.time(16, 31, 46, 2000), datetime.time(16, 31, 48, 2000),\n",
+       "       datetime.time(16, 31, 48, 2000), datetime.time(16, 31, 48, 2000),\n",
+       "       datetime.time(16, 31, 48, 2000), datetime.time(16, 31, 48, 2000),\n",
+       "       datetime.time(16, 31, 48, 2000), datetime.time(16, 31, 48, 2000),\n",
+       "       datetime.time(16, 31, 50, 2000), datetime.time(16, 31, 50, 2000),\n",
+       "       datetime.time(16, 31, 50, 2000), datetime.time(16, 31, 50, 2000),\n",
+       "       datetime.time(16, 31, 52, 2000), datetime.time(16, 31, 52, 2000),\n",
+       "       datetime.time(16, 31, 52, 2000), datetime.time(16, 31, 52, 2000),\n",
+       "       datetime.time(16, 31, 54, 2000), datetime.time(16, 31, 54, 2000),\n",
+       "       datetime.time(16, 31, 54, 2000), datetime.time(16, 31, 54, 2000),\n",
+       "       datetime.time(16, 31, 54, 2000), datetime.time(16, 31, 54, 2000),\n",
+       "       datetime.time(16, 31, 54, 2000), datetime.time(16, 31, 55, 10000),\n",
+       "       datetime.time(16, 31, 55, 10000), datetime.time(16, 31, 55, 10000),\n",
+       "       datetime.time(16, 31, 56, 2000), datetime.time(16, 31, 56, 2000),\n",
+       "       datetime.time(16, 31, 56, 2000)], dtype=object)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_V275"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flujo para 275mV: 3.9863636363636363\n"
+     ]
+    }
+   ],
+   "source": [
+    "events_V275, counts_V275, flux_V275, std_V275 = events_counts(time_V275)\n",
+    "flux[9] = flux_V275\n",
+    "print(f\"Flujo para 275mV: {flux[9]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Voltaje umbral de 300 mV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "V300=load_data(\"calibrationData/calibration_300mV.dat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "time_V300 = time_list(V300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([datetime.time(16, 32, 54, 2000), datetime.time(16, 32, 54, 2000),\n",
+       "       datetime.time(16, 32, 54, 2000), datetime.time(16, 32, 55, 10000),\n",
+       "       datetime.time(16, 32, 55, 10000), datetime.time(16, 32, 55, 10000),\n",
+       "       datetime.time(16, 32, 55, 10000), datetime.time(16, 32, 56, 2000),\n",
+       "       datetime.time(16, 32, 56, 2000), datetime.time(16, 32, 56, 2000),\n",
+       "       datetime.time(16, 32, 56, 2000), datetime.time(16, 32, 58, 2000),\n",
+       "       datetime.time(16, 32, 58, 2000), datetime.time(16, 33, 1, 10000),\n",
+       "       datetime.time(16, 33, 1, 10000), datetime.time(16, 33, 2, 2000),\n",
+       "       datetime.time(16, 33, 2, 2000), datetime.time(16, 33, 2, 2000),\n",
+       "       datetime.time(16, 33, 2, 2000), datetime.time(16, 33, 2, 2000),\n",
+       "       datetime.time(16, 33, 5, 10000), datetime.time(16, 33, 5, 10000),\n",
+       "       datetime.time(16, 33, 5, 10000), datetime.time(16, 33, 5, 10000),\n",
+       "       datetime.time(16, 33, 6, 2000), datetime.time(16, 33, 6, 2000),\n",
+       "       datetime.time(16, 33, 6, 2000), datetime.time(16, 33, 9, 10000),\n",
+       "       datetime.time(16, 33, 9, 10000), datetime.time(16, 33, 9, 10000),\n",
+       "       datetime.time(16, 33, 9, 10000), datetime.time(16, 33, 9, 10000),\n",
+       "       datetime.time(16, 33, 9, 10000), datetime.time(16, 33, 9, 10000),\n",
+       "       datetime.time(16, 33, 12, 2000), datetime.time(16, 33, 12, 2000),\n",
+       "       datetime.time(16, 33, 12, 2000), datetime.time(16, 33, 12, 2000),\n",
+       "       datetime.time(16, 33, 13, 10000), datetime.time(16, 33, 13, 10000),\n",
+       "       datetime.time(16, 33, 13, 10000), datetime.time(16, 33, 13, 10000),\n",
+       "       datetime.time(16, 33, 13, 10000), datetime.time(16, 33, 13, 10000),\n",
+       "       datetime.time(16, 33, 14, 2000), datetime.time(16, 33, 14, 2000),\n",
+       "       datetime.time(16, 33, 15, 10000), datetime.time(16, 33, 15, 10000),\n",
+       "       datetime.time(16, 33, 15, 10000), datetime.time(16, 33, 15, 10000),\n",
+       "       datetime.time(16, 33, 16, 2000), datetime.time(16, 33, 16, 2000),\n",
+       "       datetime.time(16, 33, 17, 10000), datetime.time(16, 33, 17, 10000),\n",
+       "       datetime.time(16, 33, 17, 10000), datetime.time(16, 33, 17, 10000),\n",
+       "       datetime.time(16, 33, 17, 10000), datetime.time(16, 33, 17, 10000),\n",
+       "       datetime.time(16, 33, 19, 10000), datetime.time(16, 33, 19, 10000),\n",
+       "       datetime.time(16, 33, 22, 2000), datetime.time(16, 33, 22, 2000),\n",
+       "       datetime.time(16, 33, 24, 2000), datetime.time(16, 33, 24, 2000),\n",
+       "       datetime.time(16, 33, 24, 2000), datetime.time(16, 33, 24, 2000),\n",
+       "       datetime.time(16, 33, 27, 10000), datetime.time(16, 33, 27, 10000),\n",
+       "       datetime.time(16, 33, 29, 10000), datetime.time(16, 33, 29, 10000),\n",
+       "       datetime.time(16, 33, 29, 10000), datetime.time(16, 33, 29, 10000),\n",
+       "       datetime.time(16, 33, 30, 2000), datetime.time(16, 33, 30, 2000),\n",
+       "       datetime.time(16, 33, 31, 10000), datetime.time(16, 33, 31, 10000),\n",
+       "       datetime.time(16, 33, 32, 2000), datetime.time(16, 33, 32, 2000),\n",
+       "       datetime.time(16, 33, 33, 10000), datetime.time(16, 33, 33, 10000),\n",
+       "       datetime.time(16, 33, 33, 10000), datetime.time(16, 33, 33, 10000),\n",
+       "       datetime.time(16, 33, 35, 10000), datetime.time(16, 33, 35, 10000),\n",
+       "       datetime.time(16, 33, 36, 2000), datetime.time(16, 33, 36, 2000),\n",
+       "       datetime.time(16, 33, 37, 10000), datetime.time(16, 33, 37, 10000),\n",
+       "       datetime.time(16, 33, 38, 2000), datetime.time(16, 33, 38, 2000),\n",
+       "       datetime.time(16, 33, 39, 10000), datetime.time(16, 33, 39, 10000),\n",
+       "       datetime.time(16, 33, 39, 10000), datetime.time(16, 33, 39, 10000),\n",
+       "       datetime.time(16, 33, 39, 10000), datetime.time(16, 33, 39, 10000),\n",
+       "       datetime.time(16, 33, 41, 10000), datetime.time(16, 33, 41, 10000),\n",
+       "       datetime.time(16, 33, 44, 2000), datetime.time(16, 33, 44, 2000),\n",
+       "       datetime.time(16, 33, 45, 10000), datetime.time(16, 33, 45, 10000),\n",
+       "       datetime.time(16, 33, 47, 10000), datetime.time(16, 33, 47, 10000),\n",
+       "       datetime.time(16, 33, 47, 10000), datetime.time(16, 33, 47, 10000),\n",
+       "       datetime.time(16, 33, 48, 2000), datetime.time(16, 33, 48, 2000),\n",
+       "       datetime.time(16, 33, 48, 2000), datetime.time(16, 33, 48, 2000),\n",
+       "       datetime.time(16, 33, 48, 2000), datetime.time(16, 33, 48, 2000),\n",
+       "       datetime.time(16, 33, 49, 10000), datetime.time(16, 33, 49, 10000),\n",
+       "       datetime.time(16, 33, 50, 2000), datetime.time(16, 33, 50, 2000),\n",
+       "       datetime.time(16, 33, 53, 10000), datetime.time(16, 33, 53, 10000),\n",
+       "       datetime.time(16, 33, 54, 2000), datetime.time(16, 33, 54, 2000),\n",
+       "       datetime.time(16, 33, 54, 2000), datetime.time(16, 33, 54, 2000),\n",
+       "       datetime.time(16, 33, 55, 10000), datetime.time(16, 33, 55, 10000),\n",
+       "       datetime.time(16, 33, 58, 2000), datetime.time(16, 33, 58, 2000),\n",
+       "       datetime.time(16, 34, 2, 2000), datetime.time(16, 34, 2, 2000),\n",
+       "       datetime.time(16, 34, 3, 10000), datetime.time(16, 34, 3, 10000),\n",
+       "       datetime.time(16, 34, 4, 2000), datetime.time(16, 34, 4, 2000),\n",
+       "       datetime.time(16, 34, 7, 11000), datetime.time(16, 34, 7, 11000),\n",
+       "       datetime.time(16, 34, 8, 3000), datetime.time(16, 34, 8, 3000),\n",
+       "       datetime.time(16, 34, 10, 3000), datetime.time(16, 34, 10, 3000),\n",
+       "       datetime.time(16, 34, 10, 3000), datetime.time(16, 34, 10, 3000),\n",
+       "       datetime.time(16, 34, 10, 3000), datetime.time(16, 34, 10, 3000),\n",
+       "       datetime.time(16, 34, 10, 3000), datetime.time(16, 34, 10, 3000),\n",
+       "       datetime.time(16, 34, 14, 3000), datetime.time(16, 34, 14, 3000),\n",
+       "       datetime.time(16, 34, 17, 11000), datetime.time(16, 34, 17, 11000),\n",
+       "       datetime.time(16, 34, 19, 11000), datetime.time(16, 34, 19, 11000),\n",
+       "       datetime.time(16, 34, 19, 11000), datetime.time(16, 34, 20, 3000),\n",
+       "       datetime.time(16, 34, 20, 3000), datetime.time(16, 34, 22, 3000),\n",
+       "       datetime.time(16, 34, 22, 3000), datetime.time(16, 34, 22, 3000),\n",
+       "       datetime.time(16, 34, 22, 3000), datetime.time(16, 34, 24, 3000),\n",
+       "       datetime.time(16, 34, 24, 3000), datetime.time(16, 34, 24, 3000),\n",
+       "       datetime.time(16, 34, 24, 3000), datetime.time(16, 34, 25, 11000),\n",
+       "       datetime.time(16, 34, 25, 11000), datetime.time(16, 34, 27, 11000),\n",
+       "       datetime.time(16, 34, 27, 11000), datetime.time(16, 34, 28, 3000),\n",
+       "       datetime.time(16, 34, 28, 3000), datetime.time(16, 34, 29, 11000),\n",
+       "       datetime.time(16, 34, 29, 11000), datetime.time(16, 34, 29, 11000),\n",
+       "       datetime.time(16, 34, 30, 3000), datetime.time(16, 34, 30, 3000),\n",
+       "       datetime.time(16, 34, 30, 3000), datetime.time(16, 34, 30, 3000),\n",
+       "       datetime.time(16, 34, 30, 3000), datetime.time(16, 34, 31, 11000),\n",
+       "       datetime.time(16, 34, 31, 11000), datetime.time(16, 34, 31, 11000),\n",
+       "       datetime.time(16, 34, 31, 11000), datetime.time(16, 34, 31, 11000),\n",
+       "       datetime.time(16, 34, 31, 11000), datetime.time(16, 34, 34, 3000),\n",
+       "       datetime.time(16, 34, 34, 3000), datetime.time(16, 34, 34, 3000),\n",
+       "       datetime.time(16, 34, 34, 3000), datetime.time(16, 34, 34, 3000),\n",
+       "       datetime.time(16, 34, 34, 3000), datetime.time(16, 34, 34, 3000),\n",
+       "       datetime.time(16, 34, 35, 11000), datetime.time(16, 34, 35, 11000),\n",
+       "       datetime.time(16, 34, 37, 11000), datetime.time(16, 34, 37, 11000),\n",
+       "       datetime.time(16, 34, 38, 3000), datetime.time(16, 34, 38, 3000),\n",
+       "       datetime.time(16, 34, 38, 3000), datetime.time(16, 34, 38, 3000),\n",
+       "       datetime.time(16, 34, 39, 11000), datetime.time(16, 34, 39, 11000),\n",
+       "       datetime.time(16, 34, 40, 3000), datetime.time(16, 34, 40, 3000),\n",
+       "       datetime.time(16, 34, 40, 3000), datetime.time(16, 34, 40, 3000),\n",
+       "       datetime.time(16, 34, 42, 3000), datetime.time(16, 34, 42, 3000),\n",
+       "       datetime.time(16, 34, 42, 3000), datetime.time(16, 34, 42, 3000),\n",
+       "       datetime.time(16, 34, 43, 11000), datetime.time(16, 34, 43, 11000),\n",
+       "       datetime.time(16, 34, 43, 11000), datetime.time(16, 34, 43, 11000),\n",
+       "       datetime.time(16, 34, 45, 11000), datetime.time(16, 34, 45, 11000),\n",
+       "       datetime.time(16, 34, 46, 3000), datetime.time(16, 34, 46, 3000),\n",
+       "       datetime.time(16, 34, 46, 3000), datetime.time(16, 34, 46, 3000),\n",
+       "       datetime.time(16, 34, 46, 3000), datetime.time(16, 34, 46, 3000),\n",
+       "       datetime.time(16, 34, 46, 3000), datetime.time(16, 34, 48, 3000),\n",
+       "       datetime.time(16, 34, 48, 3000), datetime.time(16, 34, 48, 3000),\n",
+       "       datetime.time(16, 34, 51, 11000), datetime.time(16, 34, 51, 11000),\n",
+       "       datetime.time(16, 34, 56, 3000), datetime.time(16, 34, 56, 3000),\n",
+       "       datetime.time(16, 34, 59, 11000), datetime.time(16, 34, 59, 11000),\n",
+       "       datetime.time(16, 35, 1, 11000), datetime.time(16, 35, 1, 11000),\n",
+       "       datetime.time(16, 35, 3, 11000), datetime.time(16, 35, 3, 11000),\n",
+       "       datetime.time(16, 35, 3, 11000), datetime.time(16, 35, 3, 11000),\n",
+       "       datetime.time(16, 35, 4, 3000), datetime.time(16, 35, 4, 3000),\n",
+       "       datetime.time(16, 35, 4, 3000), datetime.time(16, 35, 5, 11000),\n",
+       "       datetime.time(16, 35, 5, 11000), datetime.time(16, 35, 6, 3000),\n",
+       "       datetime.time(16, 35, 6, 3000), datetime.time(16, 35, 7, 11000),\n",
+       "       datetime.time(16, 35, 7, 11000), datetime.time(16, 35, 8, 3000),\n",
+       "       datetime.time(16, 35, 8, 3000), datetime.time(16, 35, 10, 3000),\n",
+       "       datetime.time(16, 35, 10, 3000), datetime.time(16, 35, 11, 11000),\n",
+       "       datetime.time(16, 35, 11, 11000), datetime.time(16, 35, 11, 11000),\n",
+       "       datetime.time(16, 35, 11, 11000), datetime.time(16, 35, 12, 3000),\n",
+       "       datetime.time(16, 35, 12, 3000), datetime.time(16, 35, 12, 3000),\n",
+       "       datetime.time(16, 35, 12, 3000), datetime.time(16, 35, 12, 3000),\n",
+       "       datetime.time(16, 35, 14, 3000), datetime.time(16, 35, 14, 3000),\n",
+       "       datetime.time(16, 35, 15, 11000), datetime.time(16, 35, 15, 11000),\n",
+       "       datetime.time(16, 35, 15, 11000), datetime.time(16, 35, 15, 11000),\n",
+       "       datetime.time(16, 35, 15, 11000), datetime.time(16, 35, 15, 11000),\n",
+       "       datetime.time(16, 35, 15, 11000), datetime.time(16, 35, 15, 11000),\n",
+       "       datetime.time(16, 35, 15, 11000), datetime.time(16, 35, 15, 11000),\n",
+       "       datetime.time(16, 35, 15, 11000), datetime.time(16, 35, 17, 11000),\n",
+       "       datetime.time(16, 35, 17, 11000), datetime.time(16, 35, 17, 11000),\n",
+       "       datetime.time(16, 35, 17, 11000), datetime.time(16, 35, 19, 11000),\n",
+       "       datetime.time(16, 35, 19, 11000), datetime.time(16, 35, 20, 3000),\n",
+       "       datetime.time(16, 35, 20, 3000), datetime.time(16, 35, 22, 3000),\n",
+       "       datetime.time(16, 35, 22, 3000), datetime.time(16, 35, 22, 3000),\n",
+       "       datetime.time(16, 35, 23, 11000), datetime.time(16, 35, 23, 11000),\n",
+       "       datetime.time(16, 35, 23, 11000), datetime.time(16, 35, 23, 11000),\n",
+       "       datetime.time(16, 35, 25, 11000), datetime.time(16, 35, 25, 11000),\n",
+       "       datetime.time(16, 35, 25, 11000), datetime.time(16, 35, 25, 11000),\n",
+       "       datetime.time(16, 35, 27, 11000), datetime.time(16, 35, 27, 11000),\n",
+       "       datetime.time(16, 35, 27, 11000), datetime.time(16, 35, 27, 11000),\n",
+       "       datetime.time(16, 35, 29, 11000), datetime.time(16, 35, 29, 11000),\n",
+       "       datetime.time(16, 35, 29, 11000), datetime.time(16, 35, 29, 11000),\n",
+       "       datetime.time(16, 35, 29, 11000), datetime.time(16, 35, 30, 3000),\n",
+       "       datetime.time(16, 35, 30, 3000), datetime.time(16, 35, 31, 11000),\n",
+       "       datetime.time(16, 35, 31, 11000), datetime.time(16, 35, 31, 11000),\n",
+       "       datetime.time(16, 35, 31, 11000), datetime.time(16, 35, 33, 11000),\n",
+       "       datetime.time(16, 35, 33, 11000), datetime.time(16, 35, 33, 11000),\n",
+       "       datetime.time(16, 35, 34, 3000), datetime.time(16, 35, 34, 3000),\n",
+       "       datetime.time(16, 35, 35, 11000), datetime.time(16, 35, 35, 11000),\n",
+       "       datetime.time(16, 35, 37, 11000), datetime.time(16, 35, 37, 11000),\n",
+       "       datetime.time(16, 35, 38, 3000), datetime.time(16, 35, 38, 3000),\n",
+       "       datetime.time(16, 35, 39, 11000), datetime.time(16, 35, 39, 11000),\n",
+       "       datetime.time(16, 35, 39, 11000), datetime.time(16, 35, 39, 11000),\n",
+       "       datetime.time(16, 35, 39, 11000), datetime.time(16, 35, 39, 11000),\n",
+       "       datetime.time(16, 35, 39, 11000), datetime.time(16, 35, 39, 11000),\n",
+       "       datetime.time(16, 35, 39, 11000), datetime.time(16, 35, 40, 3000),\n",
+       "       datetime.time(16, 35, 40, 3000), datetime.time(16, 35, 41, 11000),\n",
+       "       datetime.time(16, 35, 41, 11000), datetime.time(16, 35, 41, 11000),\n",
+       "       datetime.time(16, 35, 41, 11000), datetime.time(16, 35, 41, 11000),\n",
+       "       datetime.time(16, 35, 41, 11000), datetime.time(16, 35, 41, 11000),\n",
+       "       datetime.time(16, 35, 41, 11000), datetime.time(16, 35, 42, 3000),\n",
+       "       datetime.time(16, 35, 42, 3000), datetime.time(16, 35, 42, 3000),\n",
+       "       datetime.time(16, 35, 43, 11000), datetime.time(16, 35, 43, 11000),\n",
+       "       datetime.time(16, 35, 43, 11000), datetime.time(16, 35, 44, 3000),\n",
+       "       datetime.time(16, 35, 44, 3000), datetime.time(16, 35, 44, 3000),\n",
+       "       datetime.time(16, 35, 44, 3000), datetime.time(16, 35, 46, 3000),\n",
+       "       datetime.time(16, 35, 46, 3000), datetime.time(16, 35, 49, 11000),\n",
+       "       datetime.time(16, 35, 49, 11000), datetime.time(16, 35, 50, 3000),\n",
+       "       datetime.time(16, 35, 50, 3000), datetime.time(16, 35, 50, 3000),\n",
+       "       datetime.time(16, 35, 50, 3000), datetime.time(16, 35, 53, 11000),\n",
+       "       datetime.time(16, 35, 53, 11000), datetime.time(16, 35, 53, 11000),\n",
+       "       datetime.time(16, 35, 53, 11000), datetime.time(16, 35, 53, 11000),\n",
+       "       datetime.time(16, 35, 54, 3000), datetime.time(16, 35, 54, 3000),\n",
+       "       datetime.time(16, 35, 54, 3000), datetime.time(16, 35, 54, 3000),\n",
+       "       datetime.time(16, 35, 54, 3000), datetime.time(16, 35, 55, 11000),\n",
+       "       datetime.time(16, 35, 55, 11000), datetime.time(16, 35, 57, 11000),\n",
+       "       datetime.time(16, 35, 57, 11000), datetime.time(16, 36, 2, 3000),\n",
+       "       datetime.time(16, 36, 2, 3000), datetime.time(16, 36, 2, 3000),\n",
+       "       datetime.time(16, 36, 2, 3000), datetime.time(16, 36, 4, 3000),\n",
+       "       datetime.time(16, 36, 4, 3000), datetime.time(16, 36, 6, 3000),\n",
+       "       datetime.time(16, 36, 6, 3000), datetime.time(16, 36, 7, 11000),\n",
+       "       datetime.time(16, 36, 7, 11000), datetime.time(16, 36, 7, 11000),\n",
+       "       datetime.time(16, 36, 7, 11000), datetime.time(16, 36, 8, 3000),\n",
+       "       datetime.time(16, 36, 8, 3000), datetime.time(16, 36, 9, 11000),\n",
+       "       datetime.time(16, 36, 9, 11000), datetime.time(16, 36, 9, 11000),\n",
+       "       datetime.time(16, 36, 9, 11000), datetime.time(16, 36, 11, 11000),\n",
+       "       datetime.time(16, 36, 11, 11000), datetime.time(16, 36, 13, 11000),\n",
+       "       datetime.time(16, 36, 13, 11000), datetime.time(16, 36, 15, 11000),\n",
+       "       datetime.time(16, 36, 15, 11000), datetime.time(16, 36, 15, 11000),\n",
+       "       datetime.time(16, 36, 15, 11000), datetime.time(16, 36, 15, 11000),\n",
+       "       datetime.time(16, 36, 15, 11000), datetime.time(16, 36, 15, 11000),\n",
+       "       datetime.time(16, 36, 16, 3000), datetime.time(16, 36, 16, 3000),\n",
+       "       datetime.time(16, 36, 16, 3000), datetime.time(16, 36, 18, 3000),\n",
+       "       datetime.time(16, 36, 18, 3000), datetime.time(16, 36, 18, 3000),\n",
+       "       datetime.time(16, 36, 18, 3000), datetime.time(16, 36, 18, 3000),\n",
+       "       datetime.time(16, 36, 19, 11000), datetime.time(16, 36, 19, 11000),\n",
+       "       datetime.time(16, 36, 20, 3000), datetime.time(16, 36, 20, 3000),\n",
+       "       datetime.time(16, 36, 20, 3000), datetime.time(16, 36, 20, 3000),\n",
+       "       datetime.time(16, 36, 20, 3000), datetime.time(16, 36, 20, 3000),\n",
+       "       datetime.time(16, 36, 20, 3000), datetime.time(16, 36, 20, 3000),\n",
+       "       datetime.time(16, 36, 21, 11000), datetime.time(16, 36, 21, 11000),\n",
+       "       datetime.time(16, 36, 22, 3000), datetime.time(16, 36, 22, 3000),\n",
+       "       datetime.time(16, 36, 22, 3000), datetime.time(16, 36, 22, 3000),\n",
+       "       datetime.time(16, 36, 23, 11000), datetime.time(16, 36, 23, 11000),\n",
+       "       datetime.time(16, 36, 23, 11000), datetime.time(16, 36, 23, 11000),\n",
+       "       datetime.time(16, 36, 23, 11000), datetime.time(16, 36, 26, 3000),\n",
+       "       datetime.time(16, 36, 26, 3000), datetime.time(16, 36, 28, 3000),\n",
+       "       datetime.time(16, 36, 28, 3000), datetime.time(16, 36, 28, 3000),\n",
+       "       datetime.time(16, 36, 28, 3000), datetime.time(16, 36, 28, 3000),\n",
+       "       datetime.time(16, 36, 29, 11000), datetime.time(16, 36, 29, 11000),\n",
+       "       datetime.time(16, 36, 29, 11000), datetime.time(16, 36, 29, 11000),\n",
+       "       datetime.time(16, 36, 31, 11000), datetime.time(16, 36, 31, 11000),\n",
+       "       datetime.time(16, 36, 31, 11000), datetime.time(16, 36, 31, 11000),\n",
+       "       datetime.time(16, 36, 31, 11000), datetime.time(16, 36, 31, 11000),\n",
+       "       datetime.time(16, 36, 31, 11000), datetime.time(16, 36, 31, 11000),\n",
+       "       datetime.time(16, 36, 31, 11000), datetime.time(16, 36, 31, 11000),\n",
+       "       datetime.time(16, 36, 33, 11000), datetime.time(16, 36, 33, 11000),\n",
+       "       datetime.time(16, 36, 35, 11000), datetime.time(16, 36, 35, 11000),\n",
+       "       datetime.time(16, 36, 35, 11000), datetime.time(16, 36, 36, 3000),\n",
+       "       datetime.time(16, 36, 36, 3000), datetime.time(16, 36, 37, 11000),\n",
+       "       datetime.time(16, 36, 37, 11000), datetime.time(16, 36, 37, 11000),\n",
+       "       datetime.time(16, 36, 37, 11000), datetime.time(16, 36, 41, 12000),\n",
+       "       datetime.time(16, 36, 41, 12000), datetime.time(16, 36, 42, 4000),\n",
+       "       datetime.time(16, 36, 42, 4000), datetime.time(16, 36, 42, 4000),\n",
+       "       datetime.time(16, 36, 42, 4000), datetime.time(16, 36, 43, 12000),\n",
+       "       datetime.time(16, 36, 43, 12000), datetime.time(16, 36, 44, 4000),\n",
+       "       datetime.time(16, 36, 44, 4000), datetime.time(16, 36, 45, 12000),\n",
+       "       datetime.time(16, 36, 45, 12000), datetime.time(16, 36, 45, 12000),\n",
+       "       datetime.time(16, 36, 45, 12000), datetime.time(16, 36, 46, 4000),\n",
+       "       datetime.time(16, 36, 46, 4000), datetime.time(16, 36, 46, 4000),\n",
+       "       datetime.time(16, 36, 46, 4000), datetime.time(16, 36, 46, 4000),\n",
+       "       datetime.time(16, 36, 47, 12000), datetime.time(16, 36, 47, 12000),\n",
+       "       datetime.time(16, 36, 52, 4000), datetime.time(16, 36, 52, 4000),\n",
+       "       datetime.time(16, 36, 54, 4000), datetime.time(16, 36, 54, 4000),\n",
+       "       datetime.time(16, 36, 54, 4000), datetime.time(16, 36, 54, 4000),\n",
+       "       datetime.time(16, 36, 58, 4000), datetime.time(16, 36, 58, 4000),\n",
+       "       datetime.time(16, 36, 59, 12000), datetime.time(16, 36, 59, 12000),\n",
+       "       datetime.time(16, 37, 0, 4000), datetime.time(16, 37, 0, 4000),\n",
+       "       datetime.time(16, 37, 2, 4000), datetime.time(16, 37, 2, 4000),\n",
+       "       datetime.time(16, 37, 4, 4000), datetime.time(16, 37, 4, 4000),\n",
+       "       datetime.time(16, 37, 4, 4000), datetime.time(16, 37, 4, 4000),\n",
+       "       datetime.time(16, 37, 4, 4000), datetime.time(16, 37, 4, 4000),\n",
+       "       datetime.time(16, 37, 4, 4000), datetime.time(16, 37, 4, 4000),\n",
+       "       datetime.time(16, 37, 4, 4000), datetime.time(16, 37, 6, 4000),\n",
+       "       datetime.time(16, 37, 6, 4000), datetime.time(16, 37, 6, 4000),\n",
+       "       datetime.time(16, 37, 6, 4000), datetime.time(16, 37, 6, 4000),\n",
+       "       datetime.time(16, 37, 7, 12000), datetime.time(16, 37, 7, 12000),\n",
+       "       datetime.time(16, 37, 9, 12000), datetime.time(16, 37, 9, 12000),\n",
+       "       datetime.time(16, 37, 10, 4000), datetime.time(16, 37, 10, 4000),\n",
+       "       datetime.time(16, 37, 11, 12000), datetime.time(16, 37, 11, 12000),\n",
+       "       datetime.time(16, 37, 11, 12000), datetime.time(16, 37, 14, 4000),\n",
+       "       datetime.time(16, 37, 14, 4000), datetime.time(16, 37, 14, 4000),\n",
+       "       datetime.time(16, 37, 15, 12000), datetime.time(16, 37, 15, 12000),\n",
+       "       datetime.time(16, 37, 15, 12000), datetime.time(16, 37, 15, 12000),\n",
+       "       datetime.time(16, 37, 16, 4000), datetime.time(16, 37, 16, 4000),\n",
+       "       datetime.time(16, 37, 16, 4000), datetime.time(16, 37, 16, 4000),\n",
+       "       datetime.time(16, 37, 16, 4000), datetime.time(16, 37, 16, 4000),\n",
+       "       datetime.time(16, 37, 16, 4000), datetime.time(16, 37, 17, 12000),\n",
+       "       datetime.time(16, 37, 17, 12000), datetime.time(16, 37, 17, 12000),\n",
+       "       datetime.time(16, 37, 19, 12000), datetime.time(16, 37, 19, 12000),\n",
+       "       datetime.time(16, 37, 22, 4000), datetime.time(16, 37, 22, 4000),\n",
+       "       datetime.time(16, 37, 22, 4000), datetime.time(16, 37, 22, 4000),\n",
+       "       datetime.time(16, 37, 24, 4000), datetime.time(16, 37, 24, 4000),\n",
+       "       datetime.time(16, 37, 24, 4000), datetime.time(16, 37, 24, 4000),\n",
+       "       datetime.time(16, 37, 24, 4000), datetime.time(16, 37, 26, 4000),\n",
+       "       datetime.time(16, 37, 26, 4000), datetime.time(16, 37, 27, 12000),\n",
+       "       datetime.time(16, 37, 27, 12000), datetime.time(16, 37, 27, 12000),\n",
+       "       datetime.time(16, 37, 27, 12000), datetime.time(16, 37, 28, 4000),\n",
+       "       datetime.time(16, 37, 28, 4000), datetime.time(16, 37, 30, 4000),\n",
+       "       datetime.time(16, 37, 30, 4000), datetime.time(16, 37, 31, 12000),\n",
+       "       datetime.time(16, 37, 31, 12000), datetime.time(16, 37, 32, 4000),\n",
+       "       datetime.time(16, 37, 32, 4000), datetime.time(16, 37, 32, 4000),\n",
+       "       datetime.time(16, 37, 39, 12000), datetime.time(16, 37, 39, 12000),\n",
+       "       datetime.time(16, 37, 42, 4000), datetime.time(16, 37, 42, 4000),\n",
+       "       datetime.time(16, 37, 43, 12000), datetime.time(16, 37, 43, 12000),\n",
+       "       datetime.time(16, 37, 43, 12000), datetime.time(16, 37, 43, 12000),\n",
+       "       datetime.time(16, 37, 44, 4000), datetime.time(16, 37, 44, 4000),\n",
+       "       datetime.time(16, 37, 44, 4000), datetime.time(16, 37, 44, 4000),\n",
+       "       datetime.time(16, 37, 44, 4000), datetime.time(16, 37, 44, 4000),\n",
+       "       datetime.time(16, 37, 48, 4000), datetime.time(16, 37, 48, 4000),\n",
+       "       datetime.time(16, 37, 48, 4000), datetime.time(16, 37, 48, 4000),\n",
+       "       datetime.time(16, 37, 49, 12000), datetime.time(16, 37, 49, 12000),\n",
+       "       datetime.time(16, 37, 52, 4000), datetime.time(16, 37, 52, 4000),\n",
+       "       datetime.time(16, 37, 53, 12000), datetime.time(16, 37, 53, 12000),\n",
+       "       datetime.time(16, 37, 55, 12000), datetime.time(16, 37, 55, 12000),\n",
+       "       datetime.time(16, 37, 55, 12000), datetime.time(16, 37, 55, 12000),\n",
+       "       datetime.time(16, 37, 57, 12000), datetime.time(16, 37, 57, 12000),\n",
+       "       datetime.time(16, 37, 57, 12000), datetime.time(16, 37, 59, 12000),\n",
+       "       datetime.time(16, 37, 59, 12000)], dtype=object)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_V300"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flujo para 300mV: 3.2905027932960893\n"
+     ]
+    }
+   ],
+   "source": [
+    "events_V300, counts_V300, flux_V300, std_V300 = events_counts(time_V300)\n",
+    "flux[10] = flux_V300\n",
+    "print(f\"Flujo para 300mV: {flux[10]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([70.9760479 , 38.09933775, 21.70833333, 14.51330798, 14.33840304,\n",
+       "        7.0037037 ,  5.46715328,  4.88888889,  4.38565022,  3.98636364,\n",
+       "        3.29050279])"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flux"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "voltage = np.arange(50,325,25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fit = interpolate.interp1d(voltage, flux)\n",
+    "fit_voltage = np.arange(50, 305, 5)\n",
+    "fit_flux = fit(fit_voltage)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_voltage_flux(voltage, flux, fit_voltage, fit_flux)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(43.35961137)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fit(71)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f87a9556be0>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ShYVBeU0mY4nWrVvH8OHD+eSTT8jIyOCbb76p1nlSc5SMVZVZ0Dp21lnw9tuw//5xRyQiInXMvHlVK99U3333HRkZGbRv356RI0eSk5PDp59+SnFxMc2aNUt6zS233BLpPKk5GjO2KQYNCpZJ0jQXIiKyCTp1qlr5pliyZAl//OMfGT58OGbG8uXL6dChA40aNWLcuHEUFRUB0LJlS1auXFl6XUXnSeooGdsUWVlw9tnw3HMwf37c0YiISB0zahRkZ5cty84Oyqtj1apVpVNbDBw4kIMPPpirrroKgPPPP5+xY8fSq1cvvvrqK5o3bw5Az549ycjIoFevXtxyyy0Vniepo27KTXXeeXDTTXDfffCPf8QdjYiI1CEl48JGjAi6Jjt1ChKx6o4Xq6wVq2vXrkyfPr10/4YbbgAgMzOTN998s8y5yc6T1FHL2KbKzYUjj4TRo2HNmrijERGROiYvD+bMCZY8njMndQP3Jf0pGauO4cNhyRJ46qm4IxEREZE6SslYdRx4IHTrpoH8IiIissmUjFVHyTQX//sffPhh3NGIiIhIHaRkrLqGDIGWLdU6JiIiIptEyVh1tWwZJGTjxwfjx0RERESqQMlYTRg2DNauhQceiDsSERFpwH788UcGDRrEdtttR9++fTn88MM3upxRixYtAPj+++854YQTABgzZgzDhw+vViy33norhQlrPh1++OEsW7asWvd866232HPPPcuUrV+/npycHL7//vuk1yR+L88//zxffvlltWJIBSVjNWGHHWDgQLjnHli/Pu5oRESkAXJ3jj32WPr378+sWbOYNm0a1113HYsWLYp0/VZbbcXTTz9dpecVFxdXeLx8MvbKK6/QunXryPdPZt9992XBggXMnTu3tOy///0vPXr0YKutttro9UrG6rvhw4PZ+F98Me5IRESkDsj/LJ/cW3NpNLIRubfmkv9ZfrXuN2nSJDIzM/njH/9YWtarVy/23XdfCgoKOPDAA+nTpw8777wzL7zwwgbXz5kzh5122ql0f/78+fTv35+uXbsycuTI0nO6devG6aefzk477cT8+fM577zz6NevHz169Cid7f/222/n+++/Z8CAAQwYMACA3NxcfvrpJwD+/e9/s9NOO7HTTjtx6623lt67e/funHPOOfTo0YODDz6YVatWlYmxUaNGnHTSSYwfP760bPz48QwePJiff/6ZY445hp49e7LHHnuUmbgW4N133+XFF1/k4osvpnfv3syaNYv777+f/fffn169enH88ceXJo+zZs1ijz32YOedd+aKK64obT0E+Ne//sWuu+5Kz549S7/fanP3Orn17dvXU23SpEnRT16/3r1zZ/cBA1IVjoSqVC9SK1Qn6Un1Uru+/PLLjZ6zYsUKd3d/dPqjnj0q27ma0i17VLY/Ov3RTX7+bbfd5hdeeGHSY+vWrfPly5e7u/uSJUt8u+228+LiYnd3b968ubu7z54923v06OHu7g8//LBvueWW/tNPP3lhYaH36NHDP/zwQ589e7abmb/33nul9166dKm7u69fv973339///TTT93dvXPnzr5kyZLS80r2p06d6jvttJMXFBT4ypUrfccdd/SPPvrIZ8+e7RkZGf7xxx+7u/uJJ57o48aN2+B7+fDDD713797u7r569WrfYostfOnSpT58+HC/+uqr3d194sSJ3qtXr9LvZdiwYe7uPmTIEH/qqadK7/XTTz+V1smIESP89ttvd3f3I444wh977DF3d7/nnntKf0avv/66n3POOV5cXOxFRUV+xBFH+FtvvbVBjMn+LABTvYKcRi1jNSUjA84/HyZNgi++iDsaERFJYyMmjqBwXWGZssJ1hYyYOCIlz3N3Lr/8cnr27MnAgQNZuHDhRrsvDzroINq2bUtWVhbHHXcc77zzDgCdO3dmjz32KD3vySefpE+fPuyyyy588cUXG+0GfOeddzj22GNp3rw5LVq04LjjjmPKlCkAdOnShd69ewPQt29f5syZs8H1/fr1o6CggK+//ppXX32V3XffnTZt2vDOO+9w2mmnAXDAAQewdOlSVqxYUWksn3/+OYcccgg777wz+fn5fBH++/3ee+9x4oknAnDKKaeUnv/GG2/wxhtvsMsuu9CnTx+++uorvv3220qfEYXWpqxJf/gDXHUV3HUX3H133NGIiEiamrd8XpXKo+jRo0eFY77y8/NZsmQJ06ZNIzMzk9zcXFavXl3p/cws6X7iwuGzZ8/mpptu4sMPP2TzzTfnjDPO2Oh9K9O0adPSrzMyMjbopiwxePBgxo8fz4wZMxg8ePAmP++MM84gPz+fvfbaizFjxjB58uRKz3d3LrvsMs4999xNfmYyFbaMmdniTdgWmdnONRphXdK2LQweDI88AsuXxx2NiIikqU6tOlWpPIoDDjiANWvWMHr06NKy6dOnM2XKFJYvX0779u3JzMxk0qRJZQbAV2TChAn8/PPPrFq1iueff5699957g3NWrFhB8+bNadWqFYsWLeLVV18tPdayZUtWrly5wTX77rsvzz//PIWFhfz6668899xz7LvvvlX6XgcPHsyjjz7Km2++ydFHH1163/z8YNzd5MmTadeuHZtttlmZ68rHtHLlSrbcckvWrVtXei3AHnvswTPPPANQZnzaIYccwkMPPURBQQEACxcuZPHixVWKPZnKWsbaAQ8CCyLeKwMYAWRWN6g6bfhwePhhGDMG/vznuKMREZE0NOrAUQx9aWiZrsrszGxGHThqk+9pZjz33HNceOGF3HDDDTRr1ozc3FxuvfVW8vLyOPLII9l5553p168fO+yww0bvt9tuu3H88cezYMECTj31VPr167dBt2GvXr3YZZdd2GGHHdhmm23KJGxDhw7l0EMPZauttmLSpEml5X369OGMM85gt912A+Dss89ml112SdolWZHu3bvTvHlz+vbtW9pSd/XVV3PWWWfRs2dPsrOzGTt27AbXDRo0iHPOOYfbb7+dp59+mmuvvZYDDjiA9u3bs/vuu5cmarfeeiunnnoqo0aN4tBDD6VVq1YAHHzwwcyYMaN0eo0WLVrw6KOP0r59+8ixJ2PBmLIkB8yKgT3c/YNINzLLANYB/dz9o2pFFUG/fv186tSpKX3G5MmT6d+/f9Uv3Gsv+Okn+OoraKRheTVtk+tFUkZ1kp5UL7VrxowZdO/evdJzVq5cScuWLYHgbcoRE0cwb/k8OrXqxKgDR5G3c15thCoJEuukRGFhIVlZWZgZ48eP5/HHH0/6BmpFkv1ZMLNp7t4v2fmVtYwNACJPxuHuRWY2APg66jX11gUXwCmnwIQJcMghcUcjIiJpKG/nPCVfaWratGkMHz4cd6d169Y89NBDKX1ehcmYu79V1ZtFucbMWgMPADsBDpxFkMA9AeQCc4CT3P2Xqj4/bRx/POTkwB13KBkTERGpY/bdd18+/fTTWnteHH1otwGvufsOQC9gBnApMNHduwITw/26q0kTOPdceOUVmDUr7mhEREQkjVX2NuVsM/su6hblYWbWCtiP4MUA3H2tuy8DjgZKRtqNBY6pzjeVFs49N5h77J574o5ERERqSUXjsKXh2JQ/A5UN4L+JoBuxxCAgG5gALAbaAwcBvwLj3f1vG32YWW9gNMFYtF7ANODPwEJ3bx2eY8AvJfvlrh8KDAXIycnpm/i6aSoUFBSUWQKhqnYcOZLNp07lvaeeorhZsxqMrGGrbr1IzVOdpCfVS+1q0aIFOTk5tGrVaoM5ukoUFRWRkZFRy5FJZWqyTtyd5cuXs2jRotLpL0oMGDCg6gP43f2vJV+b2eXALOAId/81obwF8DJQ+RS3ZZ/XB7jA3f9nZrdRrkvS3d3MkmaI7j6aIJmjX79+nuq3hKr9JlLjxrDvvuy3YAGcfXaNxdXQ6Q2x9KM6SU+ql9q1bt06FixYwMKFCys8Z/Xq1TTTf87TSk3XSbNmzejVqxeZmdFn+oo6A/8wYGhiIgbg7gVhC9r9wD8i3GcBsMDd/xfuP02QjC0ysw7u/oOZdSBoeav79t4bevUKBvL/4Q9Qwf+URESk7svMzKRLly6VnjN58mR22WWXWopIokiHOok6gH8zIKeCY1sCkdrB3f1HYL6ZdQuLDiTosnwRGBKWDQGiT+aRzsyCSWCnT4dwTS8RERGRRFGTsZeAf5nZCWbWBMDMmpjZicAN4fGoLgDyzWw60Bv4J3A9cJCZfQsMDPfrh1NOgdat4c47445ERERE0lDUbsrzgDHAk4Cb2UqgJWAErVrnRX2gu38CJBvAdmDUe9Qp2dlBF+Vtt8HChbD11nFHJCIiImkkUsuYuy9392MJJmo9C7gu/NzJ3Y9xd62KXZnzz4eiIrjvvrgjERERkTQTtWUMAHf/kioskSShbbeFI44IkrERI6Bp07gjEhERkTRRpWTMzH4HdAQ2eAfU3V+pqaDqpeHD4dBD4emnIU9rkYmIiEggUjJmZjsC44EeBOPEynNAs9hV5qCDoGtXuOsuJWMiIiJSKurblPcBTYHjgG5Al3LbtimJrj5p1AiGDYP33oNp0+KORkRERNJE1GRsF+Av7v6Cu3/r7nPLb6kMst444wxo3lzTXIiIiEipqMnYLJKME5MqatUKTj8dHn8cfvop7mhEREQkDURNxv4CXG5m6o6srmHDYM0aePDBuCMRERGRNBA1GbsO2Br4ysy+MbMPym8pjLF+6dEDBgyAu+8O5h4TERGRBi3q1Bafh5vUhOHD4fjj4eWX4eij445GREREYhQpGXP3M1MdSINy1FGwzTZwxx1KxkRERBq4qN2UpcysrZl1NbO2qQioQWjcGM47DyZOhBkz4o5GREREYhQ5GTOzk81sBrAY+ApYbGYzzOzElEVXn519NjRpEkwCKyIiIg1WpGTMzAYDjwPfAWcCh4ef3wHjzWxQyiKsr7bYAgYNgrFjYcWKuKMRERGRmERtGRsBjHb3I9z9EXd/Pfw8ArgfuCJ1IdZjw4dDQQE88kjckYiIiEhMoiZj2wPPVHDsmfC4VNWuu8JuuwUz8hcXxx2NiIiIxCBqMrYI6FfBsX7hcdkUF1wAX38dDOYXERGRBidqMvYwcLWZXWFmO5jZ5mbWzcyuAK4CHkpdiPXciScG48e0XqWIiEiDFHXS12uATOBSYGRC+SrgpvC4bIqmTWHoUPjnP2HOHMjNjTsiERERqUWRWsbcvdjdRwDbAP2BweHnNu5+hbt7yiJsCM49Fxo1gnvuiTsSERERqWVVmvTV3X9x9ynu/mT4+UuqAmtQttkGjjkGHngAVq2KOxoRERGpRVHnGRtlZvdVcOxeM7u2ZsNqgC64AH7+GR5/PO5IREREpBZFbRkbDEyp4NgU4JSaCacB228/2GmnYL1K9fqKiIg0GFGTsa2AhRUc+z48LtVhFkwC+8kn8N57cUcjIiIitSRqMvYj0KeCY32AJTUTTgOXlwetWmmaCxERkQYkajL2JPB3MzsisdDMDgeuBMbXdGANUosWcOaZ8NRT8MMPcUcjIiIitSBqMvZ34H/AS2a2xMymm9kS4CXgPYKETGrC+efD+vUwenTckYiIiEgtiDrP2Gp3Pxg4DHiQIDF7EDjU3Q9z9zVRH2hmc8zsMzP7xMymhmVtzGyCmX0bfm6+Cd9L/dC1Kxx2GNx7L6xdG3c0IiIikmJVnWfsdXe/1N3PCT8nbOJzB7h7b3cvWe/yUmCiu3cFJob7Ddfw4fDjj/Dcc3FHIiIiIikWORkzs6Zmdp6ZPWhmr5tZ17D8ZDPrXs04jgbGhl+PBY6p5v3qtkMPhW231UB+ERGRBiDqpK+/A74BrgNygYFAy/DwvsBlVXimA2+Y2TQzGxqW5bh7yYj1H4GcKtyv/mnUCIYNg3feCaa6EBERkXrLoiwraWavAc2BI4ECYC3Qz90/MrMTgRvcfdtIDzTb2t0Xmll7YAJwAfCiu7dOOOcXd99g3FiYvA0FyMnJ6Tt+fGpf4iwoKKBFixYpfUZFGq9cyZ4nncTiAw7g64svjiWGdBVnvUhyqpP0pHpJP6qT9FNbdTJgwIBpCcOzymgc8R77Aie6+zIzyyh3bBHQIWow7r4w/FxsZs8BuwGLzKyDu/9gZh2AxRVcOxoYDdCvXz/v379/1MduksmTJ5PqZ1Tq9NPp8MgjdBg3Dtq0iS+ONBN7vcgGVCfpSfWSflQn6Scd6iTqmLHVQFYFx7YGlkW5iZk1N7OWJV8DBwOfAy8CQ8LThgAvRIyrfhs2DFavhoceijsSERERSZGoydgE4HIza5VQ5mbWlKCb8ZWI98kB3jGzT4EPgP+4+2vA9cBBZvYtwXi06yPer37r2TNYs/Luu6GoKO5oREREJAWidlNeDPwfMJMgMXOCiWB7AE2A46LcxN2/A3olKV8KHBgxloZl+HA46SR49VX4/e+rdasVK1awZMkSfv31V6KMFYyLmZGVlcUWW2xB69at4w5HREQkpSIlY+4+38x6ARcRJE2zCMaJPQX8O0ymJBWOOQa23hruuKNaydgvv/zC/Pnz2WqrrejYsSONGlVpirla5e4UFBQwf/581q9fT7t27eIOSUREJGWitozh7r8QLHukpY9qU2Ym/PGPcOWV8PXX0K3bJt3mhx9+IDc3l80226yGA0yNNm3a0KxZM2bOnEnbtm0xs7hDEhERSYmo84w1DseHJZYdbGYXmlmf1IQmpc45J0jK7r57ky5fv349a9asoWXLlhs/OY1kZ2djZqxZE3m1LRERkTonal/VE8A9JTtm9ifgNYJJYN83s+oNZpLK5eQE48bGjIGVK6t8eVFREY0bN66TrUsZGRkU6eUFERGpx6ImY3tQ9o3Ji4Gb3T0LeAAYUdOBSTnDh8OKFfDoozV2yxYtWpTZMjIyuOCCCwBYu3YtJ5xwArm5uZgZkydPrrHn7rDDDjyUZLqO2267jX79ys6HVxcTSBERkaqImoy1JVimCDPbGdgKuDc89hSwY82HJmXsvjv07RusV1lDb0IWFBSUbj/++CNZWVmceOKJpcf32WcfHn30UbbccssaeV6JIUOG8Mgjj2xQPm7cOIYMGZLkChERkforajK2iGBNSoBDgbnuPivczwKKazguKc8MLrgAvvwSJk2q8ds/88wztG/fnn333ReAJk2acOGFF7LPPvuQkVF+0YUN9e/fnyuuuIK99tqLFi1acOSRR7J06VLy8vLYbLPN2HXXXZkzZw4Ap512Gu+88w5z584tvf7LL79k+vTpDB48uMa/NxERkXQWNRl7CrjBzP4FXAIkNmvsAnxb04FJEiefDG3bBq1jNWzs2LGcfvrp1eoWHD9+POPGjWPhwoXMmjWLPffckzPPPJOff/6Z7t27M3LkSAA6duzIgAEDGDduXOm148aN4/DDD9c0FiIi0uBETcYuBe4DdiAYyP/PhGN9CQb4S6o1axa8WfnCCzBvXo3ddu7cubz11lvV7iI888wz2W677WjVqhWHHXYY2223HQMHDqRx48aceOKJfPzxx6XnDhkypDQZKy4uJj8/X12UIiLSIEVKxtx9vbtf4+5HuvuV7r424dhx7n5z6kKUMv74x+Dz3nsrP68Kxo0bxz777EOXLl2qdZ+cnJzSr7OysjbYLygoKN0/7rjj+OGHH3j//feZPHkyhYWFHHHEEdV6voiISF2UvtOwS3KdO8NRR8H99weLiNeARx55pNZbpbKzsznhhBN45JFHGDduHIMGDaJJkya1GoOIiEg6iDwDv6SRCy6A55+HJ56AaiZR7777LgsXLizzFmWJNWvWlK5huXbtWlavXk3Tpk1rbLqJIUOGcNxxx7Fu3TomTpxYI/cUERGpa9QyVhcNGADduwfrVVZzmouxY8dy3HHHJZ2dv1u3bmRlZbFw4UIOOeQQsrKyyrwBWV377bcfrVq1omPHjuy66641dl8REZG6RC1jdZFZMAnssGHwwQfBHGSb6L777qvwWMlUFFGUnxT2H//4R5n9gQMHMnPmzDJlZsZ3330X+RkiIiL10UZbxsysmZndb2Z71EZAEtFpp0HLlkHrmIiIiNRZG03G3H01MAholvpwJLKWLeGMM+Cpp2DRorijERERkU0UdczYm8CAVAYim2DYMPK7rSX33m40GtmI3Ftzyf8sf4PTzIzi4rq5SEJxcbHWpxQRkXot6pixu4AHzKw5wYLhi4AyI8fd/csajk02In/tVIYe04hClgMwd/lchr40FIC8nfNKz8vMzASCtyObNm1a+4FuovXr17N27do6FbOIiEhVRW0Zew3oCFwE/BeYDnwWbp+Hn1LLRkwcQWFG2RavwnWFjJg4okyZmdGmTRvmz59PUVFRbYa4yYqLi5k/fz6tW7eOtDamiIhIXRW1ZUxdlGlo3vLkSyIlK996662ZN28e06dPJzs7m0aN0ndWk+LiYlatWkXLli2rvSqAiIhIuouUjLn7W6kORKquU6tOzF2+4bxfnVp12qCsUaNG5Obm0rFjR1atWpXWY8jMjKysrNLuVRERkfqsSvOMmdlhQD9gG+Af7j7PzPYDZrr796kIUCo26sBRDH1pKIXrCkvLsjOzGXXgqAqvady4cdIJXkVERCQekfqqzCzHzP4HvAQMAf4AtAsPnwlcmZrwpDJ5O+cx+sjRdG7VGXPovNwYfcAtZQbvi4iISHqL2jJ2B9AC2AGYA6xNOPZf4KqaDUuiyts5L0i+Pv4Y+vSBbX4FTc8rIiJSZ0QdxX0ocIW7z6TclBbAAmDrGo1Kqm6XXWDvveGuuyCNx4OJiIhIWVV5pW59BeXtgFU1EItU1wUXwKxZ8NprcUciIiIiEUVNxqYAfzKzxAmfSlrIziKYoV/iduyx0KED3Hln3JGIiIhIRFGTsUuAXQkmeL2WIBE7x8zeAvYErkhNeFIlTZrAuefCq6/CzJlxRyMiIiIRRErG3P1zoC8wFTgDKAKOIxgvtru7f1OVh5pZhpl9bGYvh/tdzOx/ZjbTzJ4wsyZVuZ8kGDoUGjeGu++OOxIRERGJIPKYMXef5e6nuftW7t7E3bd09zx3/3YTnvtnYEbC/g3ALe6+PfALwdQZsik6dIATT4SHHoJff407GhEREdmIKq+JY2YdzWxXM9ukNyjNrCNwBPBAuG/AAcDT4SljgWM25d4SGj4cli+HRx+NOxIRERHZCHMvP1NFBSeanQdcDmwFGMG4sR+Af7p75D4xM3sauA5oCfyVoNvz/bBVDDPbBnjV3XdKcu1QYChATk5O3/Hjx0d97CYpKCigRYsWKX1GSrjT99xzsfXrmfrgg2AWd0Q1qs7WSz2mOklPqpf0ozpJP7VVJwMGDJjm7v2SHYs06auZ/Z1gYtcHgWeBxUB74HjgdjNr5+7XRLjP74HF7j7NzPpHC/837j4aGA3Qr18/79+/yreoksmTJ5PqZ6TMpZfCH/5A/0aNYP/9446mRtXpeqmnVCfpSfWSflQn6Scd6iRqN+Uwghawoe7+mrt/FH6eA1wfHo9ib+AoM5sDjCfonrwNaG1mJYlhR2Bh5O9Akhs8GNq00TQXIiIiaS5qMpYFvF3BsbeAZlFu4u6XuXtHd88FBgFvunseMAk4ITxtCPBCxLikIllZcPbZ8NxzsGBB3NGIiIhIBaImY88TTGWRzPHAy9WM4xLgIjObCbQl6A6V6jrvPPKLTia3R3MaNYLcXMjPjzsoERERSRR1ofBXgRvNLJcgMSsZM3Ys0AP4m5kdXnKyu7+ysRu6+2Rgcvj1d8Bu0cOWKPL/L5ehGQ9SuCJouJw7N5iGDCAvL8bAREREpFTUZKykPWVr4JBKjkPwlmVGknOklo0YAYVFZXuQCwuDciVjIiIi6SFqMtYlpVFISsybV7VyERERqX2RkjF3n5vqQKTmdeoUdE0mKxcREZH0UOUZ+KXuGDUKsrPLlmVnrGbUtcXxBCQiIiIbUDJWj+XlwejR0LlzMAl/581XMLroLPI+uTju0ERERCSkZKyey8uDOXOguBjm/LwZecPbwr//HWRpIiIiEruoA/ilvrjlFpg1C84/H7bdFgYOjDsiERGRBk0tYw1N48Ywfjx07w4nnAAzZsQdkYiISIMWKRkzs+PN7A8J+13M7F0zW2Zmz5hZ65RFKDVvs83g5ZehaVP4/e/hp5/ijkhERKTBitoydgWwWcL+HUA7gkXC+wCjajguSbXOneGFF2DhQjj2WFizJu6IREREGqSoydi2wGcAZtYKOBj4f+5+PTACODI14UlK7bEHjB0L77wTLCruHndEIiIiDU5VBvCX/Eu9P1AE/DfcXwBsUZNBSS06+WT49lu48kro1g2uuCLuiERERBqUqC1jnwJ5ZtYcOBuY5O4l/VqdCBYOl7pqxAg47bQgIXviibijERERaVCitoxdDrwEDAEKgIMSjh0D/K9mw5JaZQb33w+zZ8MZZ0BuLuy+e9xRiYiINAiRWsbc/R2CFrDdgM7u/kHC4YcIBvhLXda0KTz3HGy1FRx1VPJFLUVERKTGRZ5nzN1Xuvs0d19WrvwVd/+mxiOT2teuHfznP8Gblb//PaxYEXdEIiIi9V7kAfxmthXwe6Aj0KzcYXf3S2oyMInJDjvA00/DoYfCoEHw4ovBRLEiIiKSEpH+lTWzY4HHgQyCwfpry53igJKx+mLgQLj7bjj3XLjoIrj99rgjEhERqbeiNnn8E3gDOMPdf05hPJIuhg6Fr78OFhXv1g2GDYs7IhERkXopajK2DXCBErEG5sYbYeZM+NOfYLvtgq5LERERqVFRB/C/C3RLZSCShjIyID8fevaEk06Czz+POyIREZF6J2oydhEw1MyGmNlWZpZdfktlkBKjFi3gpZeCz9//HhYtijsiERGReiVqMjYd2Bl4GJgPrEyySX3VsWPwVuXixXDMMbBqVdwRiYiI1BtRx4ydxW9rU0pD1K8fPPooHH88nHUWPPZYMHO/iIiIVEukZMzdx6Q4DqkLjjsOrrsOLrsMfvc7GDky7ohERETqvCrN5hlO/Lon0Ab4GXjP3b9PRWCSpi65BL75Bq65JkjI8vLijkhERKROizrpawZwB3AOwcSvJYrMbDTBtBfFKYhP0o0Z3HsvfPdd0F2Zmwt77x13VCIiInVW1AH8IwnGjV0O5AJZ4eflYfnVUW5iZs3M7AMz+9TMvjCzkWF5FzP7n5nNNLMnzKxJ1b4NqVVNmsCzz0LnzsGA/u++izsiERGROitqMnY6cIW7/8vd57n7mvDzX8CVwBkR77MGOMDdewG9gUPNbA/gBuAWd98e+AX4Q1W+CYlBmzbBouJFRcGUF8uWxR2RiIhInRQ1GWtPML1FMtPD4xvlgYJwNzPcHDgAeDosHwscEzEuiVPXrkEL2cyZwaSw69bFHZGIiEidEzUZ+wYYVMGxQcDXUR9oZhlm9gnBguMTgFnAMndfH56yANg66v0kZv37w333wYQJwbJJrhlQREREqiLq25T/AMabWSeCFqxFBK1hJwIDqDhR24C7FwG9zaw18BywQ9RrzWwoMBQgJyeHyZMnR710kxQUFKT8GfVCly5sO3gwne69l5kZGSw44YSUPk71kn5UJ+lJ9ZJ+VCfpJx3qJOo8Y0+a2TKCgfy3EXQvrgOmAYe6+4SqPtjdl5nZJIKpMlqbWeOwdawjsLCCa0YDowH69evn/fv3r+pjq2Ty5Mmk+hn1xn77wZo1bH/33Wx/yCFw5JEpe5TqJf2oTtKT6iX9qE7STzrUSdRuStz9DXffk+BNyi2BLHffqyqJmJltEbaIYWZZwEHADGASUNKcMgR4Ieo9JU00agTjxkGfPjB4MHzySdwRiYiI1AmRk7ES7l7s7os3cV6xDsAkM5sOfAhMcPeXgUuAi8xsJtAWeHAT7i1xy84O1rDcfPOgZeyHH+KOSEREJO1FnfT1yY2d4+4nRThnOrBLkvLvgN2ixCJpbqut4KWXYJ994Kij4K23giRNREREkoraMrZFku13wFHA3kC7lEQndVPv3vD44zBtGpx2GhRrcQYREZGKRErG3H1Akq030BX4AbgllUFKHXTkkXDTTcE8ZCNGxB2NiIhI2qrymLFE7j4fuA64sWbCkXrl//0/GDoUrr8eHn447mhERETSUrWSsVARwXQUImWZwZ13wsCBcO65wfgxERERKSPqAP4dkxQ3AboD1xK8GSmyocxMeOop2HNPOO44eP/9YBklERERAaLPwP85wRqS5RkwFTi7xiKS+qd1a3j5Zdh9dzjiiCAha9Mm7qhERETSQtRkbECSstXAAndPOlu+SBnbbQfPPw8HHgjHHw+vvw5NmsQdlYiISOyiLoekwT5SffvsAw8+GEx3cd558MADwbgyERGRBixqyxgAZtYM2ApoVv6Yu39ZU0FJPXbqqfDNN3DttdCtG/ztb3FHJCIiEquoA/g7EizQfUiywwTjyTJqMC6pz0aODBKySy8NBvMfe2zcEYmIiMQmasvYOGBbYDgwE1ibsoik/jML5h2bOxfy8mDKFOjbN+6oREREYhE1GesH5Ln7i6kMRhqQrKxgQP/uuwez9X/wAXTUdHUiItLwRJ309UtAqz1LzcrJCaa8KCgIErKCgrgjEhERqXVRk7ELgEvMbO9UBiMN0E47wRNPwPTpQZdlUVHcEYmIiNSqqMnYJ8AHwNtmtsrMFpffUhei1HuHHQa33QYvvgiXXBJ3NCIiIrUq6pixB4ATgafRAH5JheHD4euv4eabgykvzjkn7ohERERqRdRk7Fjg/7n7vakMRhq4W26BWbPg/PNh222D2fpFRETquajdlEuAeakMRITGjWH8eNhhh2DJpK++ijsiERGRlIuajF0D/NXMWqQyGBE22yx4w7Jp02BR8Z9+ijsiERGRlIraTXkE0BWYZ2ZTgWXljru7n1yTgUkD1rkzvPAC9O8fzM7/3/8GyZmIiEg9FLVlrB3BwP1PgUxgi3Jb+5REJw3XHnvA2LHwzjvBYH73uCMSERFJiUgtY+4+INWBiGzg5JODNSz//vfgDcsRI+KOSEREpMZFbRkTiccVV8Cpp8IVV5B/wXvk5sIBB+xPbi7k58cdnIiISPVVmIyZ2Z/MrErdj+E17aoflkjIDB54gPyuVzH0zp7MnQvuxty5MHSoEjIREan7KmsZuwXoHPVGZpYRXtOpukGJlNG0KSNWXUkhzcsUFxaq51JEROq+ysaMGXCdmf0c8V5WA/GIJDVvYUbycs1+JyIidVxlydjbQAbB25JRvQ2srFZEIkl06gRz5yYpz1oCM5fD9tvXflAiIiI1oMJkzN3712IcIpUaNSoYI1ZY+FtZduO1jFp3MezwKJx5Jlx5ZZC1iYiI1CG1+jalmW1jZpPM7Esz+8LM/hyWtzGzCWb2bfi5eW3GJekvLw9Gjw7mgzVzOneG0WOakDfv+mAty0cega5d4c9/hh9/jDtcERGRyGp7aov1wF/cfUdgD2CYme0IXApMdPeuwMRwX6SMvDyYMwfefPMt5swJ9tlyS7j9dvj2Wzj9dLjrLthuO7j0Uli6NOaIRURENq5WkzF3/8HdPwq/XgnMALYGjgbGhqeNBY6pzbikHujUCe6/P1hc/Nhj4cYbYdttYeRIWLEi7uhEREQqZB7TMjNmlksw4H8nYJ67tw7LDfilZL/cNUOBoQA5OTl9x48fn9IYCwoKaNFCa6Onmyj1kj17Nl0efpgtpkxh3WabMW/QIBYecwzFWVm1FGXDot+V9KR6ST+qk/RTW3UyYMCAae7eL9mxWJIxM2sBvAWMcvdnzWxZYvJlZr+4e6Xjxvr16+dTp05NaZyTJ0+mf//+KX2GVF2V6mXatGBg/6uvQk5OMDHZ0KFaeLyG6XclPale0o/qJP3UVp2YWYXJWKRuSjMbuJHjw6oQTCbwDJDv7s+GxYvMrEN4vAOwOOr9RCrVty+88gpMmQI77AB/+lMw0P+BB2DdurijExERiTxm7A0zu9PMyvTxmFknM5sI3BzlJmEX5IPADHf/d8KhF4Eh4ddDgBcixiUSzT77wKRJMGECdOgA55wDO+4Ijz0GRUVxRyciIg1Y1GTsxHCbbmZ7AZjZ2cBnQDtg94j32Rs4DTjAzD4Jt8OB64GDzOxbYGC4L1KzzGDgQHj/fXjxRcjODl7J7NULnn0WYho/KSIiDVukZMzdnwF2Jki+3jKzj4G7gTuAfu7+acT7vOPu5u493b13uL3i7kvd/UB37+ruA9096hJMIlVnBkceCR9/DOPHw/r1cPzxsOuu8NprSspERKRWRZ7awt0XA88SzBXWC/gSuN/dNfBG6qZGjeDkk+Hzz2HMmGBessMOg/32g7feijs6ERFpIKIO4G9nZs8ADwO3A30JkrLpYXelSN3VuDEMGQJffw133w3ffQf9+8NBB8H//hd3dCIiUs9FbRn7kqCbcn93v8TdPyYYJ3YzcJeZvZKqAEVqTZMmcN55MHMm3HwzfPIJ7LEHHHUUfBqpJ15ERKTKoiZjTwK93f3dkgJ3L3L3awiWNdo6FcGJxCIrCy66KGgh+8c/4O23oXfvoEvzq6/ijk5EROqZqAP4h7t7YQXHPgaSTmImUqe1bBlMEjt7dvD5n/9Ajx5w5plBmYiISA2IOmYsu7INyExxnCLx2XzzoIVs9my48EJ4/HH43e+CLs2FC+OOTkRE6rio3ZQFwMqNbCL12xZbBGPJZs0KJo198EHYbrugS3OxFo0QEZFNEzUZOyvJ9hfgDWABcG5KohNJR1tvHbx1+fXXMHgw3HYbbLtt0JX5yy9xRyciInVM1DFjY9x9bLntVnc/DHgF6JHaMEXSUJcu8PDD8OWX8Pvfwz//GZT94x+wUo3FIiISTeRJXyvxDHB6DdxHpG7q1i2Yyf/TT2H//eHKK4OWsptvhlWr4o5ORETSXE0kY7sCa2rgPiJ1W8+e8MILwUSxu+wCf/1rMKbs7rth7dq4oxMRkTQV9W3KG5Nst5rZ68C1wGOpDVOkDtltN3jjDZg8OWghGzYsaD0bMwbWryc/H3Jzg9WYcnMhPz/ecEVEJF5RW8ZOTLIdEl7/J+DSlEQnUpftvz9MmQKvvgpt28KZZ5K/zSUMPWs9c+cG65HPnQtDh9bdhKwksTzggP2VWIqIbKLGUU5y9y6pDkSkXjKDQw+FQw6B559nxMm7U7iu7K9dYSGM+Mtq8rp8FKyTmZHx21Z+v6KyxPJGjYLnplh+fpBIFhYCWGliCZCXl/LHi4jUG5GSMRGpJjM49ljmrfekh+ctagJ7711zz2vUaOOJXVX3y5WPeP0+Cle1L/PYwsJghg8lYyIi0VWYjJnZ+VW4j7v7PTUQj0i91qlT0IK0QXn7NfDIa1BU9Nu2fn3Z/WRliftVPb8q91u7doOyeavaJf0e581zIPUtcyIi9UVlLWN3VuE+DigZE9mIUaMSu/YC2dkw6t9ZQVdmHdIpl+SJpS2Au1+Cs8+GJk1qPS4RkbqmwgH87t6oCltGbQYtUlfl5cHo0dC5c9Bz2blzsF8Xu/VGjQoSyUTZTYsYtf1DwRuk3bvDo48GLWkiIlKhmphnTESqIC8P5syB4uLgsy4mYlA+sfQgsXwwg7yv/g7/+Q+0bAmnnQa9e8NLLwWvj4qIyAYqTMbM7A0z61au7AAza576sESkLihJLN98863fEkszOPxw+OgjePxxWL0ajjoqeEHhrbdijlhEJP1U1jI2EGhVsmNmGcAEoFuFV4iIlGjUCAYNCtbuvO++YIBZ//7BVB8ffRR3dCIiaaOq3ZR6RUpEqiYzM3hrYeZM+Ne/4MMPoW9fOOkk+PrruKMTEYmdxoyJSO3IygrW6/zuu2Ax9VdegR49grcu58+POzoRkdhsLBlLNuJWo3BFZNO1agXXXBMkZcOGwbhx0LUr/OUv8NNPcUcnIlLrNpaMvW5mi81sMfBDWDaxpCxxS3GcIlLftG8Pt90G33wDgwfDrbcGC6uPHAkrV8YdnYhIrals0teRtRaFiDRcnTvDww/DxRcH3ZdXXw133gmXXw7nnQfNmsUdoYhISlWYjLm7kjERqT077gjPPBMM8L/8crjoIrjlFrjqKhgyJFgTU0SkHqrVAfxm9lDYrfl5QlkbM5tgZt+Gn5vXZkwikmZ23RUmTID//hc6dAgG+O+0Ezz9tCaOFZF6qbbfphwDHFqu7FJgort3BSaG+yLS0B14ILz/Pjz7bDBn2YknBonaG28oKROReqVWkzF3fxv4uVzx0cDY8OuxwDG1GZOIpDEzOPZY+OwzGDMGliwJFlQ/4IAgURMRqQfSYZ6xHHcveVPzRyAnzmBEJA1lZATjxr75JngD84svYM894Zhj4PPPN3q5iEg6M6/l5n4zywVedvedwv1l7t464fgv7p503JiZDQWGAuTk5PQdP358SmMtKCigRYsWKX2GVJ3qJf3Udp1krFpFx6efZpsnniCjsJBFAwcy58wzWd2hQ63FUBfodyX9qE7ST23VyYABA6a5e79kx9IhGfsa6O/uP5hZB2Cyu290/ct+/fr51KlTUxrr5MmT6d+/f0qfIVWnekk/sdXJ0qVwww1wxx1QVBQsu3TFFbDllrUfSxrS70r6UZ2kn9qqEzOrMBlLh27KF4Eh4ddDgBdijEVE6pK2beHGG4N1L886C+69F7bbLpgaY9myuKMTEYmktqe2eBx4D+hmZgvM7A/A9cBBZvYtMDDcFxGJbuutg0Rsxgw46ii47jro0gWuvx4KC+OOTkSkUrX9NuVgd+/g7pnu3tHdH3T3pe5+oLt3dfeB7l7+bUsRkWi6doXHH4ePP4a99oLLLgtayu6+G9aujTs6EZGk0qGbUkSkZvXuDf/5D7z9Nmy/fbAgeffukJ8PxcVxRyciUoaSMRGpv/bdN0jI/vMfaNkSTj01SNReekkTx4pI2lAyJiL1mxkcfjh89BE89lgwhuyoo2CffeCtt+KOTkREyZiINBCNGsHgwcEg/3vvhTlzoH9/OPRQ+Ogj8vMhNzc4LTc36NEUEakNSsZEpGHJzIRzzw2mw7jxRvjgA/L73sTQIauZOzfovZw7N5iyTAmZiNQGJWMi0jBlZcHFF8Ps2YxodReFRc3KHC4shBHDlwddm1OmwOzZsGZNTMGKSH3WOO4ARERi1aoV81YkPzRvWUvIyytb2L49bLMNdOwYbIlfl2xNm6Y+bhGpN5SMiUiD16lT0DW5Qfk2wOtfwvz5sGDBb9v8+TBrVvACQLKZ/rfYYsNErXzS1qzZhteJSIOkZExEGrxRo4IxYomT9Wdnw6jrGgXzk3XvXvHFBQUbJmolX8+eHUytkSxha9eu4mRtm22CVQWysjbp+8nPhxEjYN68/enUKfj+yjfwiUj6UDImIg1eSaISJDBULYFp0QJ22CHYKvLrr7BwYdlEreTrefPg3XeDRc/La9t2wxa18vvZ2WUuyc9PTCyt9GWExO9TRNKLkjEREYJEJWXJSvPm8LvfBVtFCguDhK1861rJ/vvvw08/bXhdmzZlErUR46+nsLD1BrcecWkReUcVBslbRkbNfn8iUi1KxkRE0kF2drC2ZteuFZ+zatVvCVv5pG3+fPjwQ+YtvzvppfMWGGy2WbDTrFnwvObNg60qX0c5XkPJ3m/drVVsrRSpY5SMiYjUFVlZwVqb229f4SmdOhUzd36S8s0L4PJ/BV2mJVthYdmvly4NMp/y5VXVtGn1Errmzcl/rwtDb+lO4eogsZs7F4ae47B+PXmnNw5WVqhDNI5PKqNkTESkHhl1XaPkLyPcsRnk/bXqN3QPWuQSk7fyyVrUr3/5JWjFK3+vJOuEjmA2hZRtYStcZYw4YyF5Z24bJKbZ2cFnyZa4n+zrqOeX369mS5/G8cnGKBkTEalHyr6M4HTqZNVrhTELEpNyLwrUGHdYvXqDxG3ebp0hyVru8+gcfHOrVgXnrlq14dfLlpXdLzleVLRpMWZmbnoil5XFiGvOorCwZZlbFhbCiL+uIa/nN0FLYtOmQfdxyddNmwZrc6UhdR/XPCVjIiL1TMnLCJMnv0X//v3jDqdyZr8lLu3alRZXOPdbZ4Nrr920Z61bt2HiVt39FSvgxx+THwvN44Kk4cz7MRN69qw43szMsklaYrJWWVlVjke5tkmT0m7hsq18qJWvhigZExGRtFPh3G+jqnHTzMxgK3mRIZXcg+WzCgvp1LOYuQs3bOXqtMVquOfpoGVwzZpgK/k6WVmy40uX/nYs2bnFxTXz/YSJ2oiCzyks3qbMocJCGDF0CXmTLoOWLYPpXlq2LLtVVBbjm73pNI5PyZiIiKSdas39lg7MgpalZs0YdUMFieUt2XD88amNY/36qiV3Gzlv3m0dkz5mXmFbePVVWLkymAg5yTjApLKyoiduGyurQnKXbuP4lIyJiEhaSuncb7WoxsfxVUXjxsHWvHmN3K7T8xV1HzeCOQuDneLiIMtZufK35Kzk6yhlixcHy40llkeVnZ08cSuXvI244y8UFrYqc2lhYVBHSsZERETqoTo1jq8SkbqPGzUKEp8WLaBDh+o/tHxyV1Eyl6y8oAAWLYKZM8uUzePqpI+aN6/64W4KJWMiIiISSSzdxylI7jrlknw+vk7Vv/2mSM/3ZkVERCQt5eXBnDlBg9WcOXWwK7lRI0Zd12iD2Vqq/YJIdUKK57EiIiIi8cjLg9GjoXNnMHM6dw7240oslYyJiIhIg1PSwvfmm2/F3sKnZExEREQkRkrGRERERGKkZExEREQkRkrGRERERGKUNsmYmR1qZl+b2UwzuzTueERERERqQ1okY2aWAdwFHAbsCAw2sx3jjUpEREQk9dIiGQN2A2a6+3fuvhYYDxwdc0wiIiIiKWcedWX1VAZhdgJwqLufHe6fBuzu7sPLnTcUGAqQk5PTd/z48SmNq6CggBYtWqT0GVJ1qpf0ozpJT6qX9KM6ST+1VScDBgyY5u79kh2rU2tTuvtoYDSAmS0ZMGBAkrXja1Q74KcUP0OqTvWSflQn6Un1kn5UJ+mntuqkc0UH0iUZWwhsk7DfMSyrkLtvkdKIADObWlEWK/FRvaQf1Ul6Ur2kH9VJ+kmHOkmXMWMfAl3NrIuZNQEGAS/GHJOIiIhIyqVFy5i7rzez4cDrQAbwkLt/EXNYIiIiIimXFskYgLu/ArwSdxzljI47AElK9ZJ+VCfpSfWSflQn6Sf2OkmLtylFREREGqp0GTMmIiIi0iApGUtgZnPM7DMz+8TMpoZlbcxsgpl9G35uHnec9ZmZPWRmi83s84SypHVggdvDJbSmm1mf+CKv3yqol6vNbGH4+/KJmR2ecOyysF6+NrND4om6fjOzbcxskpl9aWZfmNmfw3L9vsSkkjrR70qMzKyZmX1gZp+G9TIyLO9iZv8Lf/5PhC8QYmZNw/2Z4fHcVMeoZGxDA9y9d8JrrpcCE929KzAx3JfUGQMcWq6sojo4DOgabkOBe2opxoZoDBvWC8At4e9L73DcJ+FSZoOAHuE1d4dLnknNWg/8xd13BPYAhoU/e/2+xKeiOgH9rsRpDXCAu/cCegOHmtkewA0E9bI98Avwh/D8PwC/hOW3hOellJKxjTsaGBt+PRY4Jr5Q6j93fxv4uVxxRXVwNPCIB94HWptZh1oJtIGpoF4qcjQw3t3XuPtsYCbBkmdSg9z9B3f/KPx6JTAD2Br9vsSmkjqpiH5XakH4Z74g3M0MNwcOAJ4Oy8v/rpT8Dj0NHGhmlsoYlYyV5cAbZjYtXHoJIMfdfwi//hHIiSe0Bq2iOtgamJ9w3gIq/4tPat7wsMvroYQufNVLLQu7UXYB/od+X9JCuToB/a7EyswyzOwTYDEwAZgFLHP39eEpiT/70noJjy8H2qYyPiVjZe3j7n0ImvOHmdl+iQc9ePVUr5/GSHWQVu4BtiNo9v8BuDnWaBooM2sBPANc6O4rEo/p9yUeSepEvysxc/cid+9NsMLPbsAO8UZUlpKxBO6+MPxcDDxHUGGLSpryw8/F8UXYYFVUB1VeRktqjrsvCv+CKwbu57fuFdVLLTGzTIJ/9PPd/dmwWL8vMUpWJ/pdSR/uvgyYBOxJ0FVfMt9q4s++tF7C462ApamMS8lYyMyam1nLkq+Bg4HPCZZlGhKeNgR4IZ4IG7SK6uBF4PTwLbE9gOUJ3TOSYuXGGx1L8PsCQb0MCt9I6kIwYPyD2o6vvgvHsDwIzHD3fycc0u9LTCqqE/2uxMvMtjCz1uHXWcBBBOP5JgEnhKeV/10p+R06AXjTUzwpqyZ9DZnZtgStYRCsTPCYu48ys7bAk0AnYC5wkrtHHcgsVWRmjwP9gXbAIuAq4HmS1EH4F9+dBG8hFQJnuvvUGMKu9yqol/4E3S4OzAHOLfnH3cxGAGcRvF12obu/Wtsx13dmtg8wBfgMKA6LLycYo6TflxhUUieD0e9KbMysJ8GA/AyCRqgn3f2a8N/98UAb4GPgVHdfY2bNgHEEY/5+Bga5+3cpjVHJmIiIiEh81E0pIiIiEiMlYyIiIiIxUjImIiIiEiMlYyIiIiIxUjImIiIiEiMlYyIiIiIxUjImIiIiEiMlYyJSY8zsJTP7rJLjd5rZMjNrGvF+Y8xsasL+SWZ2Rg2EWuPMbLKZebhdWAP3m2RmL1Vy/E0z+9bM/pHw3Ker+1wRqX1KxkSkJj0O7GRmO5Y/YGYZBEuLPOvuazbx/icBZ2x6eClXsubd+Bq41wwqWMzYzAYAA4C/A/eGz/y4Bp4pIjFQMiYiNekFgqV2Bic5NgDIIUjY6quf3f19d/+xBu41A+hiZk2SHLsW+BQY7+4L3P19YEUNPFNEYqBkTERqjLv/CrwEnJzk8CBgMfBmSUHY7fiZma0xs/lmNsrMGie7t5mNAY4H9k/olrs6PLanmb1oZj+Y2a9m9omZ5SW5x/DwOb+a2fNmdmB4n/7lztvXzN4ys0IzW2pm95tZy035mZhZIzMrMLMLzezfZrbYzH4xs4vD46eZ2ZfhOc+GCxlDkIxlANuXu9+hwN7AiFQvXiwitUPJmIjUtMeBrmbWt6TAzDKB4wgW6C0Kyw4GngA+Ao4G7gD+SrCYdTLXEnQDfkzQLbcn8EB4rDPwf8AfgCOBZ4CHzay0hc7Mjg2f8SJwLDAdeLD8Q8xsb+C/wI8E3aoXAocDD1fpp/CbbYHm4X3WAKcA/wFuNLM7CZLUi4FLgWOAM8PrZoSf3crd71rg/9z9P5sYj4ikmaT/AxURqYZXgWUESca0sOwQYHPKdlFeA0x29yHh/mtmBnCdmf3D3Rck3tTdZ5nZz0CjsFsu8VjpGC0LbvI20BE4J+GZlwOvuPuwcP8NM2sHnFcu/uuBd929tHXPzBYCE81sJ3f/PNqPodTO4ee/3f328H7fAnlAd2BgSQuXmQ0lTL7cfaGZrSBh3JiZHQ30A/arYgwiksbUMiYiNcrd1wLPAieFiREE3ZZzgfegdDB/H+Cpcpc/QfD30p5VeaaZbW5mt5vZXGBduA0FfhcebwzsQtAqlujFcvfJDp/9pJk1LtmAd8J79qXqehIkp/cklDUPP68v19XYHPg5Yf8rwuQs/FleA7zq7lM2IQ4RSVNKxkQkFR4HOgF7mlkzgm7I8QmJRzsgE1hU7rqS/TZVfN4YgoTvX8DBwK7AQ0CzhOdlAEvKXVd+f/PwvLv5LalbR9C9mAlsU8W4IGgZe8fd1yWU9QTWE7TgAaWJYC6Q2PKW+EblieG9RmxCDCKSxtRNKSKpMIkgsRoEdABaUraL8ieCJKd9uetyws+fiShM9n4PDHP3exPKE/+z+RNQBGxR7vLy+8sAB64GXknyuO+jxpVgZ+DJcmW9gK/KTfGxM8F/kKcnlM0Ajg5bEq8mGHOnKSxE6hklYyJS49y9yMyeJGjN2RqY4e6fljs+LTye2H13ElBM2J2ZxFp+a+0q0ZQgiSlNbMI3H48iSKxw9/Vm9jFBC919CdceVS7uX83sfaCbu18T8dutUPhm5PYE01Ak6llB2a/ArISyGUBr4CKgaxi/iNQzSsZEJFUeBy4geHPxqiTHrwJeN7OHCSZJ3ZngTcH7yw/eT/AVQUvRMcAC4Ht3/97MPgT+Hg54LyZ4M3E5sFnCtdcBz4RvML5IMD3EEeGx4oTz/kYwWL8YeBpYSdDlegTBdBLfRP8R0IMgUUyWeN2epOwLd0+MpeSNymuAh9392yo8W0TqCI0ZE5GUcPf3gDmAkWSiV3d/g6Absx/B3GQXAjcDwyu57d3AGwTjwT4kGKQPwXQR3wGPALcRTG3xSLnnPQv8iWD6iOcJxpX9NTy8IuG8dwjeVtwCGBfG9jdgPhuOcduYnSnX2mVmmxO86Tm93Lk9k5R9R9AaCEFCJiL1kGnOQBFpqMzsCoIB8W3cfVU17zUZWErwIkFRbU3IGo6NawRMBJa4+wm18VwRqTlqGRORBsHMtghnwD8ynHn/aoJE7MHqJmIJjiN4MeHPNXS/KP4ePlNzj4nUUWoZE5EGwcxaEXSX7ga0An4AHgOuLDftxKbevxvBW6MA89x9cXXvGfG5WwFbhbs/u/t3tfFcEak5SsZEREREYqRuShEREZEYKRkTERERiZGSMREREZEYKRkTERERiZGSMREREZEYKRkTERERiZGSMREREZEYKRkTERERidH/B7Pd6U+oqhORAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_voltage_flux(voltage, flux, fit_voltage, fit_flux)\n",
+    "plt.plot(71, fit(71), \"o\", c=\"g\", markersize = 6, label= 'Calibration Voltage')\n",
+    "plt.annotate(\"71 mV\", (77, 45), fontsize=12, bbox=dict(boxstyle=\"round\", fc='w', ec='0.8'))\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tasa de lluvias aéreas extensas ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "#### Data 1 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data_1 = load_data(\"Data/EAS_data_1.dat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "EAS_1 = create_df(data_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "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>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>10</th>\n",
+       "      <th>11</th>\n",
+       "      <th>12</th>\n",
+       "      <th>13</th>\n",
+       "      <th>14</th>\n",
+       "      <th>15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2041B05A</td>\n",
+       "      <td>80</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>2A</td>\n",
+       "      <td>00</td>\n",
+       "      <td>2E</td>\n",
+       "      <td>00</td>\n",
+       "      <td>2022BD66</td>\n",
+       "      <td>231158.000</td>\n",
+       "      <td>090421</td>\n",
+       "      <td>A</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2041B061</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>26</td>\n",
+       "      <td>2022BD66</td>\n",
+       "      <td>231158.000</td>\n",
+       "      <td>090421</td>\n",
+       "      <td>A</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2041B065</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>2022BD66</td>\n",
+       "      <td>231158.000</td>\n",
+       "      <td>090421</td>\n",
+       "      <td>A</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2E51E182</td>\n",
+       "      <td>80</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>36</td>\n",
+       "      <td>00</td>\n",
+       "      <td>31</td>\n",
+       "      <td>00</td>\n",
+       "      <td>2D8BF7A6</td>\n",
+       "      <td>231207.008</td>\n",
+       "      <td>090421</td>\n",
+       "      <td>A</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0066</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2E51E185</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>34</td>\n",
+       "      <td>31</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>2D8BF7A6</td>\n",
+       "      <td>231207.008</td>\n",
+       "      <td>090421</td>\n",
+       "      <td>A</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0066</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2199</th>\n",
+       "      <td>F3559FF6</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>3D</td>\n",
+       "      <td>3A</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>F247F3A6</td>\n",
+       "      <td>001135.015</td>\n",
+       "      <td>100421</td>\n",
+       "      <td>A</td>\n",
+       "      <td>08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2200</th>\n",
+       "      <td>F3559FF7</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>20</td>\n",
+       "      <td>24</td>\n",
+       "      <td>21</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>F247F3A6</td>\n",
+       "      <td>001135.015</td>\n",
+       "      <td>100421</td>\n",
+       "      <td>A</td>\n",
+       "      <td>08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2201</th>\n",
+       "      <td>F3559FF7</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>2C</td>\n",
+       "      <td>29</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>F247F3A6</td>\n",
+       "      <td>001135.015</td>\n",
+       "      <td>100421</td>\n",
+       "      <td>A</td>\n",
+       "      <td>08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2202</th>\n",
+       "      <td>F3559FF7</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>34</td>\n",
+       "      <td>31</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>F247F3A6</td>\n",
+       "      <td>001135.015</td>\n",
+       "      <td>100421</td>\n",
+       "      <td>A</td>\n",
+       "      <td>08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2203</th>\n",
+       "      <td>F3559FF7</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>38</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>F247F3A6</td>\n",
+       "      <td>001135.015</td>\n",
+       "      <td>100421</td>\n",
+       "      <td>A</td>\n",
+       "      <td>08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0058</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2192 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            0   1   2   3   4   5   6   7   8         9           10      11  \\\n",
+       "0     2041B05A  80  00  00  00  2A  00  2E  00  2022BD66  231158.000  090421   \n",
+       "1     2041B061  00  00  00  00  00  00  00  26  2022BD66  231158.000  090421   \n",
+       "2     2041B065  00  00  00  00  00  30  00  00  2022BD66  231158.000  090421   \n",
+       "3     2E51E182  80  00  00  00  36  00  31  00  2D8BF7A6  231207.008  090421   \n",
+       "4     2E51E185  00  00  00  00  34  31  00  00  2D8BF7A6  231207.008  090421   \n",
+       "...        ...  ..  ..  ..  ..  ..  ..  ..  ..       ...         ...     ...   \n",
+       "2199  F3559FF6  00  00  3D  3A  00  00  00  00  F247F3A6  001135.015  100421   \n",
+       "2200  F3559FF7  00  00  00  20  24  21  00  00  F247F3A6  001135.015  100421   \n",
+       "2201  F3559FF7  00  00  00  00  2C  29  00  00  F247F3A6  001135.015  100421   \n",
+       "2202  F3559FF7  00  00  00  00  34  31  00  00  F247F3A6  001135.015  100421   \n",
+       "2203  F3559FF7  00  00  00  00  00  38  00  00  F247F3A6  001135.015  100421   \n",
+       "\n",
+       "     12  13 14     15  \n",
+       "0     A  11  0  +0074  \n",
+       "1     A  11  0  +0074  \n",
+       "2     A  11  0  +0074  \n",
+       "3     A  10  0  +0066  \n",
+       "4     A  10  0  +0066  \n",
+       "...  ..  .. ..    ...  \n",
+       "2199  A  08  0  +0058  \n",
+       "2200  A  08  0  +0058  \n",
+       "2201  A  08  0  +0058  \n",
+       "2202  A  08  0  +0058  \n",
+       "2203  A  08  0  +0058  \n",
+       "\n",
+       "[2192 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EAS_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "EAS_1, time_1 = time_date(EAS_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
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+       "      <th>9</th>\n",
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+       "      <th>11</th>\n",
+       "      <th>12</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
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+       "      <td>2A</td>\n",
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+       "      <td>2E</td>\n",
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+       "      <td>2022BD66</td>\n",
+       "      <td>090421 231158.000</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
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+       "      <td>00</td>\n",
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+       "      <td>00</td>\n",
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+       "      <td>26</td>\n",
+       "      <td>2022BD66</td>\n",
+       "      <td>090421 231158.000</td>\n",
+       "      <td>A</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2041B065</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
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+       "      <td>+0074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2E51E182</td>\n",
+       "      <td>80</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
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+       "      <td>36</td>\n",
+       "      <td>00</td>\n",
+       "      <td>31</td>\n",
+       "      <td>00</td>\n",
+       "      <td>2D8BF7A6</td>\n",
+       "      <td>090421 231207.008</td>\n",
+       "      <td>A</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0066</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2E51E185</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>34</td>\n",
+       "      <td>31</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>2D8BF7A6</td>\n",
+       "      <td>090421 231207.008</td>\n",
+       "      <td>A</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0066</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2199</th>\n",
+       "      <td>F3559FF6</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>3D</td>\n",
+       "      <td>3A</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>F247F3A6</td>\n",
+       "      <td>100421 001135.015</td>\n",
+       "      <td>A</td>\n",
+       "      <td>08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2200</th>\n",
+       "      <td>F3559FF7</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>20</td>\n",
+       "      <td>24</td>\n",
+       "      <td>21</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>F247F3A6</td>\n",
+       "      <td>100421 001135.015</td>\n",
+       "      <td>A</td>\n",
+       "      <td>08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2201</th>\n",
+       "      <td>F3559FF7</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>2C</td>\n",
+       "      <td>29</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>F247F3A6</td>\n",
+       "      <td>100421 001135.015</td>\n",
+       "      <td>A</td>\n",
+       "      <td>08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2202</th>\n",
+       "      <td>F3559FF7</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>34</td>\n",
+       "      <td>31</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>F247F3A6</td>\n",
+       "      <td>100421 001135.015</td>\n",
+       "      <td>A</td>\n",
+       "      <td>08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2203</th>\n",
+       "      <td>F3559FF7</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>38</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>F247F3A6</td>\n",
+       "      <td>100421 001135.015</td>\n",
+       "      <td>A</td>\n",
+       "      <td>08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0058</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2192 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            0   1   2   3   4   5   6   7   8         9                  10  \\\n",
+       "0     2041B05A  80  00  00  00  2A  00  2E  00  2022BD66  090421 231158.000   \n",
+       "1     2041B061  00  00  00  00  00  00  00  26  2022BD66  090421 231158.000   \n",
+       "2     2041B065  00  00  00  00  00  30  00  00  2022BD66  090421 231158.000   \n",
+       "3     2E51E182  80  00  00  00  36  00  31  00  2D8BF7A6  090421 231207.008   \n",
+       "4     2E51E185  00  00  00  00  34  31  00  00  2D8BF7A6  090421 231207.008   \n",
+       "...        ...  ..  ..  ..  ..  ..  ..  ..  ..       ...                ...   \n",
+       "2199  F3559FF6  00  00  3D  3A  00  00  00  00  F247F3A6  100421 001135.015   \n",
+       "2200  F3559FF7  00  00  00  20  24  21  00  00  F247F3A6  100421 001135.015   \n",
+       "2201  F3559FF7  00  00  00  00  2C  29  00  00  F247F3A6  100421 001135.015   \n",
+       "2202  F3559FF7  00  00  00  00  34  31  00  00  F247F3A6  100421 001135.015   \n",
+       "2203  F3559FF7  00  00  00  00  00  38  00  00  F247F3A6  100421 001135.015   \n",
+       "\n",
+       "     11  12 13     14  \n",
+       "0     A  11  0  +0074  \n",
+       "1     A  11  0  +0074  \n",
+       "2     A  11  0  +0074  \n",
+       "3     A  10  0  +0066  \n",
+       "4     A  10  0  +0066  \n",
+       "...  ..  .. ..    ...  \n",
+       "2199  A  08  0  +0058  \n",
+       "2200  A  08  0  +0058  \n",
+       "2201  A  08  0  +0058  \n",
+       "2202  A  08  0  +0058  \n",
+       "2203  A  08  0  +0058  \n",
+       "\n",
+       "[2192 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EAS_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "events_1, counts_1, mean_1, std_1 = events_counts(time_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_date(events_1, counts_1, mean_1, std_1, \"Eventos de 09/04/21 a 10/04/21\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean: 5.63\n",
+      "Standard deviation: 2.4\n",
+      "Rate: 2192 events per hour\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Mean: {round(mean_1,2)}\")\n",
+    "print(f\"Standard deviation: {round(std_1,2)}\")\n",
+    "print(f\"Rate: {sum(counts_1)} events per hour\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Data 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data_2 = load_data(\"Data/EAS_data_2.dat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "EAS_2 = create_df(data_2) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "[3211 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EAS_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "EAS_2, time_2 = time_date(EAS_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
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+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>3E</td>\n",
+       "      <td>00</td>\n",
+       "      <td>00</td>\n",
+       "      <td>F1C4D266</td>\n",
+       "      <td>100421 021442.008</td>\n",
+       "      <td>A</td>\n",
+       "      <td>06</td>\n",
+       "      <td>0</td>\n",
+       "      <td>+0058</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3211 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            0   1   2   3   4   5   6   7   8         9                  10  \\\n",
+       "0     E7552E76  80  00  2A  00  00  00  2C  00  E6FAC526  100421 004549.013   \n",
+       "1     E7552E76  00  00  00  00  30  00  00  00  E6FAC526  100421 004549.013   \n",
+       "2     E7552E7A  00  00  35  31  00  00  00  00  E6FAC526  100421 004549.013   \n",
+       "3     E7552E7A  00  00  00  38  00  00  00  00  E6FAC526  100421 004549.013   \n",
+       "4     E7552E7C  00  00  00  00  00  2D  00  00  E6FAC526  100421 004549.013   \n",
+       "...        ...  ..  ..  ..  ..  ..  ..  ..  ..       ...                ...   \n",
+       "3223  DF830814  00  00  00  00  00  00  00  28  DE65B726  100421 021429.000   \n",
+       "3224  F1C8B587  80  00  00  00  3B  00  00  00  F1C4D266  100421 021442.008   \n",
+       "3225  F1C8B588  00  00  00  00  00  00  21  00  F1C4D266  100421 021442.008   \n",
+       "3226  F1C8B589  00  00  00  00  00  00  00  2F  F1C4D266  100421 021442.008   \n",
+       "3227  F1C8B58A  00  00  00  00  00  3E  00  00  F1C4D266  100421 021442.008   \n",
+       "\n",
+       "     11  12 13     14  \n",
+       "0     A  08  0  +0055  \n",
+       "1     A  08  0  +0055  \n",
+       "2     A  08  0  +0055  \n",
+       "3     A  08  0  +0055  \n",
+       "4     A  08  0  +0055  \n",
+       "...  ..  .. ..    ...  \n",
+       "3223  A  07  0  +0034  \n",
+       "3224  A  06  0  +0058  \n",
+       "3225  A  06  0  +0058  \n",
+       "3226  A  06  0  +0058  \n",
+       "3227  A  06  0  +0058  \n",
+       "\n",
+       "[3211 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EAS_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "events_2, counts_2, mean_2, std_2 = events_counts(time_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_date(events_2, counts_2, mean_2, std_2, \"Eventos de 10/04/21\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean: 5.7\n",
+      "Standard deviation: 2.38\n",
+      "Rate: 2140.6666666666665 events per hour\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Mean: {round(mean_2,2)}\")\n",
+    "print(f\"Standard deviation: {round(std_2,2)}\")\n",
+    "print(f\"Rate: {sum(counts_2)/1.5} events per hour\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Data 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data_3 = load_data(\"Data/EAS_data_3.dat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "EAS_3 = create_df(data_3) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "    <tr>\n",
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+       "      <td>1EFFF90D</td>\n",
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+       "      <td>00</td>\n",
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+       "      <td>1EFFF912</td>\n",
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+       "      <td>1E96DAE6</td>\n",
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2333 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            0   1   2   3   4   5   6   7   8         9           10      11  \\\n",
+       "0     5C470C80  80  00  00  00  00  00  2C  00  5B585726  040733.013  100421   \n",
+       "1     5C470C80  00  00  00  00  30  00  00  00  5B585726  040733.013  100421   \n",
+       "2     5C470C81  00  00  00  00  00  39  00  00  5B585726  040733.013  100421   \n",
+       "3     5C470C83  00  00  00  00  00  00  00  2E  5B585726  040733.013  100421   \n",
+       "4     6E5FFE22  80  00  00  00  25  00  23  00  6D39FA26  040745.013  100421   \n",
+       "...        ...  ..  ..  ..  ..  ..  ..  ..  ..       ...         ...     ...   \n",
+       "2340  1E2CDB74  00  00  00  00  00  30  00  00  1D1962A6  050659.004  100421   \n",
+       "2341  1EFFF90D  80  00  3E  00  00  00  00  00  1E96DAE6  050700.012  100421   \n",
+       "2342  1EFFF90E  00  00  00  00  00  00  20  00  1E96DAE6  050700.012  100421   \n",
+       "2343  1EFFF912  00  00  00  00  00  00  00  3A  1E96DAE6  050700.012  100421   \n",
+       "2344  1EFFF914  00  00  00  31  00  00  00  00  1E96DAE6  050700.012  100421   \n",
+       "\n",
+       "     12  13 14     15  \n",
+       "0     A  10  0  -0009  \n",
+       "1     A  10  0  -0009  \n",
+       "2     A  10  0  -0009  \n",
+       "3     A  10  0  -0009  \n",
+       "4     A  09  0  +0055  \n",
+       "...  ..  .. ..    ...  \n",
+       "2340  A  09  0  +0062  \n",
+       "2341  A  09  0  +0054  \n",
+       "2342  A  09  0  +0054  \n",
+       "2343  A  09  0  +0054  \n",
+       "2344  A  09  0  +0054  \n",
+       "\n",
+       "[2333 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EAS_3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "EAS_3, time_3 = time_date(EAS_3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <td>A</td>\n",
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+       "      <td>3A</td>\n",
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+       "    <tr>\n",
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+       "      <td>A</td>\n",
+       "      <td>09</td>\n",
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+       "      <td>+0054</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2333 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            0   1   2   3   4   5   6   7   8         9                  10  \\\n",
+       "0     5C470C80  80  00  00  00  00  00  2C  00  5B585726  100421 040733.013   \n",
+       "1     5C470C80  00  00  00  00  30  00  00  00  5B585726  100421 040733.013   \n",
+       "2     5C470C81  00  00  00  00  00  39  00  00  5B585726  100421 040733.013   \n",
+       "3     5C470C83  00  00  00  00  00  00  00  2E  5B585726  100421 040733.013   \n",
+       "4     6E5FFE22  80  00  00  00  25  00  23  00  6D39FA26  100421 040745.013   \n",
+       "...        ...  ..  ..  ..  ..  ..  ..  ..  ..       ...                ...   \n",
+       "2340  1E2CDB74  00  00  00  00  00  30  00  00  1D1962A6  100421 050659.004   \n",
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+       "2344  1EFFF914  00  00  00  31  00  00  00  00  1E96DAE6  100421 050700.012   \n",
+       "\n",
+       "     11  12 13     14  \n",
+       "0     A  10  0  -0009  \n",
+       "1     A  10  0  -0009  \n",
+       "2     A  10  0  -0009  \n",
+       "3     A  10  0  -0009  \n",
+       "4     A  09  0  +0055  \n",
+       "...  ..  .. ..    ...  \n",
+       "2340  A  09  0  +0062  \n",
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+       "2342  A  09  0  +0054  \n",
+       "2343  A  09  0  +0054  \n",
+       "2344  A  09  0  +0054  \n",
+       "\n",
+       "[2333 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EAS_3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "events_3, counts_3, mean_3, std_3 = events_counts(time_3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_date(events_3, counts_3, mean_3, std_3, \"Eventos de 10/04/21\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean: 5.59\n",
+      "Standard deviation: 2.38\n",
+      "Rate: 2333 events per hour\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Mean: {round(mean_3,2)}\")\n",
+    "print(f\"Standard deviation: {round(std_3,2)}\")\n",
+    "print(f\"Rate: {sum(counts_3)} events per hour\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}