diff --git a/.gitignore b/.gitignore
index 4486d3ab0ccd86eefd99deb2adaa117f66b72cad..b09926f4d223508a8743fe195bfed3fea15f60cb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,5 @@
 data/
 .ipynb_checkpoints/
 src/
+*.zip
+*.dat
diff --git a/calibracion.ipynb b/calibracion.ipynb
index 2f95eec03e1a65a77bb267e723ac2aab1e93f628..ad524b622b340c74ad4f93d5837606fac56a1586 100644
--- a/calibracion.ipynb
+++ b/calibracion.ipynb
@@ -2,7 +2,6 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "a4d202eb-9d92-4857-b2af-aba670f9090b",
    "metadata": {},
    "source": [
     "# Análisis de Datos Racimo Tormenta"
@@ -10,7 +9,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "52bc4c5f-1baf-4e8f-8a1d-0b0e51fa8695",
    "metadata": {},
    "source": [
     "Notebook para el análisis de datos del proyecto racimo tormenta. \n",
@@ -24,7 +22,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d277a53c-cccc-4996-8944-620130575372",
    "metadata": {},
    "source": [
     "## Cargar Librerias "
@@ -32,7 +29,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e96a3270-365f-4f90-9d5a-d2673f176f11",
    "metadata": {},
    "source": [
     "Importar las librerias necesarias para el análisis e interacciones de los datos"
@@ -40,10 +36,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "5d4d6478-6b92-46a4-a849-7fc4d5a3b119",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'utf-8'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pylab as plt\n",
@@ -67,7 +73,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e994b24-64db-40ca-8d89-698456ba2f5b",
    "metadata": {},
    "source": [
     "---"
@@ -75,7 +80,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "97659221-fc8b-4e1a-b6d9-2ef11fd5410c",
    "metadata": {},
    "source": [
     "## Descargar datos "
@@ -83,23 +87,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14c40ec4-7904-4690-8619-f9cf1cbdafec",
    "metadata": {},
    "source": [
-    "En esta sección descargaremos el archivo de datos desde el repositorio **Dataverse**."
+    "En esta sección descargaremos datos desde el repositorio **dataLab**."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "14901417-d762-42f9-83e5-2bed2e3c3138",
    "metadata": {},
    "source": [
-    "<img src=\"imagenes/dataverse-jupyter_datos.jpg\" alt=\"drawing\" width=\"600\"/>"
+    "<img src=\"imagenes/dataverse-jupyter_datos.jpg\" alt=\"drawing\" width=\"400\"/>"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b0683242-aecf-4fee-91f6-1fbd91aff17e",
    "metadata": {},
    "source": [
     "Ir a https://dataverse.redclara.net/dataverseuser.xhtml?selectTab=apiTokenTab y copiar el **Token** de usuario"
@@ -107,38 +108,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "89723f93-88a1-4bd4-ac98-76a5d4e75c48",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: API_TOKEN=TOKENdeUSUARIO\n"
+     ]
+    }
+   ],
    "source": [
     "%env API_TOKEN=TOKENdeUSUARIO"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "eac7066f-c26e-4297-85ad-4f1c632ade0b",
    "metadata": {},
    "source": [
-    "Descargar dataset"
+    "Ir a dataverse y copiar el indentificador *DOI* del enlace, por ejemplo para https://dataverse.redclara.net/dataset.xhtml?persistentId=doi:10.21348/FK2/RBNNPC se debe copiar **doi:10.21348/FK2/RBNNPC** y definir en la siguiente variable junto a la versión del dataset (Por defecto se toma la versión borrador)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "bf02c4eb-bfed-478e-8315-2ca2bfbe6470",
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: PERSISTENT_ID=DOI\n",
+      "env: VERSION=DRAFT\n"
+     ]
+    }
+   ],
+   "source": [
+    "%env PERSISTENT_ID=DOI\n",
+    "%env VERSION=DRAFT "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: SERVER_URL=https://dataverse.redclara.net\n",
+      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+      "100    43  100    43    0     0    139      0 --:--:-- --:--:-- --:--:--   139\n"
+     ]
+    }
+   ],
    "source": [
     "%env SERVER_URL=https://dataverse.redclara.net\n",
-    "%env PERSISTENT_ID=doi:10.21348/FK2/RBNNPC\n",
-    "%env VERSION=DRAFT \n",
     "!curl -L -O -J -H \"X-Dataverse-key:$API_TOKEN\" $SERVER_URL/api/access/dataset/:persistentId/?persistentId=$PERSISTENT_ID"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "52c1b78f-5325-4a21-9933-d25cf8e927ab",
    "metadata": {},
    "source": [
     "Descomprimir archivo de datos"
@@ -147,7 +180,6 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0c7d3e5e-31fe-42b7-be08-e9c9f793a7da",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -158,7 +190,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "927a9fb3-a065-45f2-b075-cf650d73d8b5",
    "metadata": {},
    "source": [
     "---"
@@ -166,7 +197,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "960fdf13-2b85-4fca-a1fb-ed0aa016e468",
    "metadata": {},
    "source": [
     "## Calibración del detector"
@@ -174,7 +204,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b8b509b-c69e-4c8d-98e2-6f9d7cc825c3",
    "metadata": {
     "tags": []
    },
@@ -184,7 +213,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "440109c1-3272-4702-9a0c-fd5e2b8e6b1d",
    "metadata": {},
    "source": [
     "### Vista preliminar de los datos de calibración"
@@ -192,7 +220,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4ed58223-c091-4de4-b9c7-2cf4b3dd674f",
    "metadata": {},
    "source": [
     "Cargar datos en formato *array numpy*"
@@ -200,17 +227,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "78a31578-66f3-4363-80b4-856aabede298",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "ename": "OSError",
+     "evalue": "Descargas/Lighting_2021_11_10_01_56.dat not found.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-55c45f828f11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Descargas/Lighting_2021_11_10_01_56.dat'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcomments\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'#'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mloadtxt\u001b[0;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding, max_rows, like)\u001b[0m\n\u001b[1;32m   1063\u001b[0m             \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1064\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_is_string_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1065\u001b[0;31m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_datasource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1066\u001b[0m             \u001b[0mfencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'encoding'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'latin1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1067\u001b[0m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/numpy/lib/_datasource.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(path, mode, destpath, encoding, newline)\u001b[0m\n\u001b[1;32m    192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m     \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataSource\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdestpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 194\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnewline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/numpy/lib/_datasource.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, path, mode, encoding, newline)\u001b[0m\n\u001b[1;32m    529\u001b[0m                                       encoding=encoding, newline=newline)\n\u001b[1;32m    530\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s not found.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    532\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mOSError\u001b[0m: Descargas/Lighting_2021_11_10_01_56.dat not found."
+     ]
+    }
+   ],
    "source": [
     "data = np.loadtxt('Descargas/Lighting_2021_11_10_01_56.dat', comments='#')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7c03fdf2-6a4a-4d30-87e3-3b025a43f6c1",
    "metadata": {},
    "source": [
     "Describir datos "
@@ -218,10 +263,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "6733522b-ab2c-4b49-841a-a45bc4dfd377",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DescribeResult(nobs=119998, minmax=(array([0., 0.]), array([  1.19997, 509.     ])), mean=array([ 0.599985, 48.600035]), variance=array([1.19997000e-01, 2.69726649e+02]), skewness=array([9.41028435e-13, 1.90329167e-01]), kurtosis=array([-1.2       ,  5.07317904]))"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from scipy import stats\n",
     "stats.describe(data)"
@@ -229,7 +284,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c754f55c-22ff-46b9-a414-7fa07a4497d6",
    "metadata": {},
    "source": [
     "### Amplitud y frecuencia de la señal"
@@ -237,7 +291,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "badb8819-ef05-4e8c-867c-8b4a03f554d2",
    "metadata": {},
    "source": [
     "Función para graficar amplitud y frecuencia."
@@ -245,8 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "05f9f0b6-dd57-4e7e-92ca-4718cf85808e",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -319,7 +371,7 @@
     "        plt.axhline(threshold, color='red')\n",
     "        plt.xlabel('Time [s]', fontsize = 20)\n",
     "        plt.ylabel('Amplitude [ADC]', fontsize = 20)\n",
-    "        plt.savefig(\"amplitude.png\", dpi=150)\n",
+    "        # plt.savefig(\"amplitude.png\", dpi=150)\n",
     "\n",
     "        # Spectrum plotting\n",
     "\n",
@@ -336,7 +388,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4da5b0b2-97c4-455b-b9f9-028e817f3fa9",
    "metadata": {},
    "source": [
     "Graficar "
@@ -344,10 +395,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "66982705-0db4-43d7-8bd0-22ec2ae7b907",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Terminations above 5σ = 4\n",
+      "\n",
+      "Number of strokes = 4\n",
+      "\n",
+      "Sample Time = 0.00001 s\n",
+      "Frequency = 100000.00 Hz\n",
+      "Maximum frequency = 868.35 Hz\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "dt = 10e-6 # sampling period\n",
     "Np = 0. # filter signals per number of peaks above 5 sigma\n",
@@ -357,7 +433,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eccbba23-922e-430b-816b-41ca7212cae5",
    "metadata": {},
    "source": [
     "### Escalar señal y enfocar"
@@ -365,7 +440,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c15cc352-a82b-404d-9a47-09860ae25950",
    "metadata": {},
    "source": [
     "Definir parámetros de escalado y foco"
@@ -373,8 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "aaea4b8a-0639-479e-8f66-ad59fb179ac8",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -385,7 +458,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a59c5cb-19db-4872-9046-6f0471498da0",
    "metadata": {},
    "source": [
     "Crear nuevo *array* de datos."
@@ -393,8 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "0d8a87c1-5ab1-4e9f-b2d8-8d10572ebad8",
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -403,7 +474,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6abec110-ecf4-44e0-b88c-3a3a94b8b4aa",
    "metadata": {},
    "source": [
     "Graficar señal escalada"
@@ -411,16 +481,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "53d02f88-4245-4b93-a2c4-7da1cb9c4448",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Terminations above 5σ = 1\n",
+      "\n",
+      "Number of strokes = 1\n",
+      "\n",
+      "Sample Time = 0.00001 s\n",
+      "Frequency = 100000.00 Hz\n",
+      "Maximum frequency = 868.35 Hz\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "dt = 10e-6 # sampling period\n",
     "Np = 0. # filter signals per number of peaks above 5 sigma\n",
     "\n",
     "fp1, peaks1, MTFt, MTSt, pN1, MTFc, MTSc = Lightning_Analysis(newdata, dt, Np) # Returns maximum peak frequency and peak positions"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -439,7 +541,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,