diff --git a/WCD_avances.ipynb b/WCD_avances.ipynb
index 0425ace50f6801bf07bc8315eab5ecb605f0c542..eccc276bf2dce389a3f3ed42ac5d69010d79e692 100644
--- a/WCD_avances.ipynb
+++ b/WCD_avances.ipynb
@@ -18,24 +18,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "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",
+    "from scipy.optimize import curve_fit\n",
+    "import io\n",
+    "import os\n",
+    "import random\n",
     "\n",
-    "%matplotlib inline  "
+    "%matplotlib inline "
    ]
   },
   {
@@ -111,24 +105,771 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "36029\n"
+     ]
+    }
+   ],
+   "source": [
+    "i=0\n",
+    "channel1=[]\n",
+    "channel2=[]\n",
+    "channel3=[]\n",
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos500=0\n",
+    "\n",
+    "archivo = open('../Data/WCD_calibration_500V_nogps_2021_04_14_20h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    if linea[0] == '#' :\n",
+    "        i+=1\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos500+=1\n",
+    "        contador_evento = 1 \n",
+    "print(eventos500)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "76512\n"
+     ]
+    }
+   ],
+   "source": [
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos550=0\n",
+    "\n",
+    "archivo = open('../Data/WCD_calibration_550V_nogps_2021_04_14_20h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    if linea[0] == '#' :\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos550+=1\n",
+    "        contador_evento = 1 \n",
+    "print(eventos550)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "84466\n",
+      "92586\n",
+      "65559\n",
+      "92545\n",
+      "99444\n",
+      "91094\n",
+      "96050\n",
+      "108326\n"
+     ]
+    }
+   ],
+   "source": [
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos560=0\n",
+    "eventos570=0\n",
+    "eventos580=0\n",
+    "eventos590=0\n",
+    "eventos610=0\n",
+    "eventos620=0\n",
+    "eventos630=0\n",
+    "eventos640=0\n",
+    "\n",
+    "archivo = open('../Data/WCD_calibration_560V_nogps_2021_04_14_23h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    if linea[0] == '#' :\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos560+=1\n",
+    "        contador_evento = 1 \n",
+    "#print(eventos560)\n",
+    "archivo1 = open('../Data/WCD_calibration_560V_nogps_2021_04_15_00h00.dat', 'r')\n",
+    "for linea in archivo1:\n",
+    "    if linea[0] == '#' :\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos560+=1\n",
+    "        contador_evento = 1 \n",
+    "print(eventos560)\n",
+    "\n",
+    "\n",
+    "archivo2 = open('../Data/WCD_calibration_570V_nogps_2021_04_15_00h00.dat', 'r')\n",
+    "for linea in archivo2:\n",
+    "    if linea[0] == '#' :\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos570+=1\n",
+    "        contador_evento = 1 \n",
+    "print(eventos570)\n",
+    "\n",
+    "archivo3 = open('../Data/WCD_calibration_580V_nogps_2021_04_15_00h00.dat', 'r')\n",
+    "for linea in archivo3:\n",
+    "    if linea[0] == '#' :\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos580+=1\n",
+    "        contador_evento = 1 \n",
+    "print(eventos580)\n",
+    "\n",
+    "archivo4 = open('../Data/WCD_calibration_590V_nogps_2021_04_15_00h00.dat', 'r')\n",
+    "for linea in archivo4:\n",
+    "    if linea[0] == '#' :\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos590+=1\n",
+    "        contador_evento = 1 \n",
+    "print(eventos590)\n",
+    "\n",
+    "archivo5 = open('../Data/WCD_calibration_610V_nogps_2021_04_15_00h00.dat', 'r')\n",
+    "for linea in archivo5:\n",
+    "    if linea[0] == '#' :\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos610+=1\n",
+    "        contador_evento = 1 \n",
+    "print(eventos610)\n",
+    "\n",
+    "archivo6 = open('../Data/WCD_calibration_620V_nogps_2021_04_15_00h00.dat', 'r')\n",
+    "for linea in archivo6:\n",
+    "    if linea[0] == '#' :\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos620+=1\n",
+    "        contador_evento = 1 \n",
+    "print(eventos620)\n",
+    "\n",
+    "archivo7 = open('../Data/WCD_calibration_630V_nogps_2021_04_15_00h00.dat', 'r')\n",
+    "for linea in archivo7:\n",
+    "    if linea[0] == '#' :\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos630+=1\n",
+    "        contador_evento = 1 \n",
+    "print(eventos630)\n",
+    "\n",
+    "archivo8 = open('../Data/WCD_calibration_640V_nogps_2021_04_15_00h00.dat', 'r')\n",
+    "for linea in archivo8:\n",
+    "    if linea[0] == '#' :\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos640+=1\n",
+    "        contador_evento = 1 \n",
+    "print(eventos640)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "121199\n"
+     ]
+    }
+   ],
+   "source": [
+    "i=0\n",
+    "channel1=[]\n",
+    "channel2=[]\n",
+    "channel3=[]\n",
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos600=0\n",
+    "\n",
+    "archivo = open('../Data/WCD_calibration_600V_nogps_2021_04_14_20h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    if linea[0] == '#' :\n",
+    "        i+=1\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos600+=1\n",
+    "        contador_evento = 1 \n",
+    "        if j==\" \":\n",
+    "            channel1.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "            continue\n",
+    "        if j==\"\\n\":\n",
+    "            channel3.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "        a.append(j)\n",
+    "print(eventos600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "121407\n"
+     ]
+    }
+   ],
+   "source": [
+    "i=0\n",
+    "channel1=[]\n",
+    "channel2=[]\n",
+    "channel3=[]\n",
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos650=0\n",
+    "\n",
+    "archivo = open('../Data/WCD_calibration_650V_nogps_2021_04_14_19h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    if linea[0] == '#' :\n",
+    "        i+=1\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos650+=1\n",
+    "        contador_evento = 1 \n",
+    "        if j==\" \":\n",
+    "            channel1.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "            continue\n",
+    "        if j==\"\\n\":\n",
+    "            channel3.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "        a.append(j)\n",
+    "print(eventos650)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "185018\n"
+     ]
+    }
+   ],
+   "source": [
+    "i=0\n",
+    "channel1=[]\n",
+    "channel2=[]\n",
+    "channel3=[]\n",
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos700=0\n",
+    "\n",
+    "archivo = open('../Data/WCD_calibration_700V_nogps_2021_04_14_19h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    if linea[0] == '#' :\n",
+    "        i+=1\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos700+=1\n",
+    "        contador_evento = 1 \n",
+    "        if j==\" \":\n",
+    "            channel1.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "            #a=[]\n",
+    "            continue\n",
+    "        if j==\"\\n\":\n",
+    "            channel3.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "        a.append(j)\n",
+    "print(eventos700)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "326930\n"
+     ]
+    }
+   ],
+   "source": [
+    "i=0\n",
+    "channel1=[]\n",
+    "channel2=[]\n",
+    "channel3=[]\n",
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos750=0\n",
+    "\n",
+    "archivo = open('../Data/WCD_calibration_750V_nogps_2021_04_14_19h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    if linea[0] == '#' :\n",
+    "        i+=1\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos750+=1\n",
+    "        contador_evento = 1 \n",
+    "        if j==\" \":\n",
+    "            channel1.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "            continue\n",
+    "        if j==\"\\n\":\n",
+    "            channel3.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "        a.append(j)\n",
+    "print(eventos750)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "181228\n",
+      "800467\n"
+     ]
+    }
+   ],
+   "source": [
+    "i=0\n",
+    "channel1=[]\n",
+    "channel2=[]\n",
+    "channel3=[]\n",
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos800=0\n",
+    "\n",
+    "archivo = open('../Data/WCD_calibration_800V_nogps_2021_04_14_19h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    if linea[0] == '#' :\n",
+    "        i+=1\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos800+=1\n",
+    "        contador_evento = 1 \n",
+    "        if j==\" \":\n",
+    "            channel1.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "            continue\n",
+    "        if j==\"\\n\":\n",
+    "            channel3.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "        a.append(j)\n",
+    "print(eventos800)\n",
+    "\n",
+    "archivo1 = open('../Data/WCD_calibration_800V_nogps_2021_04_14_20h00.dat', 'r')\n",
+    "for linea in archivo1:\n",
+    "    if linea[0] == '#' :\n",
+    "        i+=1\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos800+=1\n",
+    "        contador_evento = 1 \n",
+    "        if j==\" \":\n",
+    "            channel1.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "            continue\n",
+    "        if j==\"\\n\":\n",
+    "            channel3.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "        a.append(j)\n",
+    "print(eventos800)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3130986\n"
+     ]
+    }
+   ],
+   "source": [
+    "i=0\n",
+    "channel1=[]\n",
+    "channel2=[]\n",
+    "channel3=[]\n",
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos850=0\n",
+    "\n",
+    "archivo = open('../Data/WCD_calibration_850V_nogps_2021_04_14_20h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    if linea[0] == '#' :\n",
+    "        i+=1\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos850+=1\n",
+    "        contador_evento = 1 \n",
+    "        if j==\" \":\n",
+    "            channel1.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "            #a=[]\n",
+    "            continue\n",
+    "        if j==\"\\n\":\n",
+    "            channel3.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "        a.append(j)\n",
+    "print(eventos850)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10565385\n"
+     ]
+    }
+   ],
+   "source": [
+    "i=0\n",
+    "channel1=[]\n",
+    "channel2=[]\n",
+    "channel3=[]\n",
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos900=0\n",
+    "\n",
+    "archivo = open('../Data/WCD_calibration_900V_nogps_2021_04_14_20h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    if linea[0] == '#' :\n",
+    "        contador_evento = 0 \n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        if contador_evento==0:\n",
+    "            eventos900+=1\n",
+    "        contador_evento = 1 \n",
+    "print(eventos900)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eventosy=[eventos500*(5./4.97),eventos550*(5./5.13),eventos600*(5./6.86),eventos650*(5./4.97),eventos700,eventos750,eventos800,eventos850,eventos900]\n",
+    "Voltaje=[500,550,600,650,700,750,800,850,900]\n",
+    "plt.figure(figsize =(20,6))\n",
+    "#plt.plot(CPM_Trig , Trig, 'ob', fillstyle = 'none')\n",
+    "plt.xlabel(\"Tensión terminal del PMT\", fontsize=15)\n",
+    "plt.ylabel(\"Eventos\", fontsize=15)\n",
+    "plt.title(\" Zona de plateau  \",fontdict={'family': 'serif', 'color' : 'darkblue','size': 25})\n",
+    "plt.plot( Voltaje,eventosy , 'ro-')\n",
+    "plt.yscale(u'log')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eventosy=[eventos500*(5./4.97),eventos550*(5./5.13),eventos600*(5./6.86),eventos650*(5./4.97),eventos700,eventos750]\n",
+    "Voltaje=[500,550,600,650,700,750]\n",
+    "plt.figure(figsize =(20,6))\n",
+    "plt.xlabel(\" Tensión terminal del PMT\", fontsize=15)\n",
+    "plt.ylabel(\"Eventos\", fontsize=15)\n",
+    "plt.title(\" Zona de plateau  \",fontdict={'family': 'serif', 'color' : 'darkblue','size': 25})\n",
+    "plt.plot( Voltaje,eventosy , 'ro-')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "log_eventosy = np.log10(eventosy)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "dev_log_eventosy = []\n",
+    "for i in range(1,len(log_eventosy),1):\n",
+    "    dev_log_eventosy.append( (log_eventosy[i]-log_eventosy[i-1])/50 )\n",
+    "    #print(log_eventosy[i]-log_eventosy[i-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Voltaje1 =[525,575,625,675,725]\n",
+    "plt.figure(figsize =(20,7))\n",
+    "plt.xlabel(\"Tensión terminal del PMT\", fontsize=15)\n",
+    "plt.ylabel(r' $\\dfrac{d }{d V} eventos$', fontsize=15)\n",
+    "plt.title(\" Zona de plateau  \",fontdict={'family': 'serif', 'color' : 'darkblue','size': 25})\n",
+    "plt.plot( Voltaje1,dev_log_eventosy , 'ro-')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NUEVOS DATOS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eventos500fix = eventos500*(1/4.97)\n",
+    "eventos550fix = eventos550*(1/5.13)\n",
+    "eventos560fix = eventos560*(1/6.23)\n",
+    "eventos570fix = eventos570*(1/6.93)\n",
+    "eventos580fix = eventos580*(1/5.4)\n",
+    "eventos590fix = eventos590*(1/6.63)\n",
+    "eventos600fix = eventos600*(1/6.86)\n",
+    "eventos610fix = eventos610*(1/6.33)\n",
+    "eventos620fix = eventos620*(1/5.2)\n",
+    "eventos630fix = eventos630*(1/5.15)\n",
+    "eventos640fix = eventos640*(1/5.75)\n",
+    "eventos650fix = eventos650*(1/4.97)\n",
+    "eventos700fix = eventos700*(1/5)\n",
+    "eventos750fix = eventos750*(1/5)\n",
+    "\n",
+    "eventosy=[eventos500fix,eventos550fix,eventos560fix,eventos570fix,eventos580fix,eventos590fix,eventos610fix,eventos620fix,eventos630fix,eventos640fix,eventos650fix,eventos700fix,eventos750fix,eventos800]# eventos600fix\n",
+    "Voltaje=[500,550,560,570,580,590,610,620,630,640,650,700,750,800]# fue extraido el punto de 600 y eventos600fix\n",
+    "plt.figure(figsize =(20,6))\n",
+    "#plt.plot(CPM_Trig , Trig, 'ob', fillstyle = 'none')\n",
+    "plt.xlabel(\"Tensión terminal del PMT\", fontsize=15)\n",
+    "plt.ylabel(\"Eventos\", fontsize=15)\n",
+    "plt.title(\" Zona de plateau  \",fontdict={'family': 'serif', 'color' : 'darkblue','size': 25})\n",
+    "plt.plot( Voltaje,eventosy , 'ro-')\n",
+    "plt.yscale(u'log')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 10, 10, 10, 10, 20, 10, 10, 10, 10, 50, 50, 50]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_eventosy = np.log10(eventosy)\n",
+    "diff_Voltaje = []\n",
+    "for i in range (1,len(Voltaje),1):\n",
+    "    diff_Voltaje.append( Voltaje[i]-Voltaje[i-1] )\n",
+    "diff_Voltaje"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.3133163697927701\n",
+      "-0.041418307907410856\n",
+      "-0.006381794383188755\n",
+      "-0.04157352639026346\n",
+      "0.060600870712478994\n",
+      "0.0513354435184894\n",
+      "0.047311554153523616\n",
+      "0.027203711129027752\n",
+      "0.004374722022294719\n",
+      "0.11282247679686641\n",
+      "0.180356638715085\n",
+      "0.24724079244363484\n"
+     ]
+    }
+   ],
+   "source": [
+    "dev_log_eventosy = []\n",
+    "for i in range(1,len(log_eventosy)-1,1):\n",
+    "    dev_log_eventosy.append( (log_eventosy[i]-log_eventosy[i-1])/diff_Voltaje[i] )\n",
+    "    print(log_eventosy[i]-log_eventosy[i-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Voltaje_prom = []\n",
+    "for i in range(1,len(log_eventosy)-1,1):\n",
+    "    Voltaje_prom.append( Voltaje[i] + diff_Voltaje[i]/2 )\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize =(20,6))\n",
+    "plt.xlabel(\"Tensión terminal del PMT\", fontsize=15)\n",
+    "plt.ylabel(r' $\\dfrac{d }{d V} eventos$', fontsize=15)\n",
+    "plt.title(\" Zona de plateau  \",fontdict={'family': 'serif', 'color' : 'darkblue','size': 25})\n",
+    "plt.plot( Voltaje_prom,dev_log_eventosy , 'ro-')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "code",
@@ -161,7 +902,1285 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Para realizar la calibración en energía se establece el umbral del trigger en 100mV y el voltaje de polarización el valor establecido en la calibración anterior (PONER AQUÍ VALOR). A partir de esta configuración, se toma 1 hora de datos para realizar offline el histograma de carga."
+    "Para realizar la calibración en energía se establece el umbral del trigger en 100mV y el voltaje de polarización el valor establecido en la calibración anterior entre 870 y 948 Voltios. A partir de esta configuración, se toma 1 hora de datos para realizar offline el histograma de carga."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import curve_fit\n",
+    "import io\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def lineal_response(adc):\n",
+    "    y = (adc - 14.6026)/0.6439\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def suma_bines(array):\n",
+    "    cantidad_datos = 12\n",
+    "    suma_parcial=0\n",
+    "    array_out=[]\n",
+    "    for i in range(0,len(array),1):\n",
+    "        suma_parcial+=array[i]\n",
+    "        if (i+1)%cantidad_datos==0 :\n",
+    "            array_out.append(suma_parcial- 50*cantidad_datos)\n",
+    "            suma_parcial=0\n",
+    "            #print(\"el ultimo que suma es:\", array[i]  )\n",
+    "    return array_out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tiene problemas de precision en algunos intervalos, mejor no usarla y usar plt.hist\n",
+    "def make_histogram(array,bins,minn,maxx):\n",
+    "    intervalo = maxx - minn\n",
+    "    array_out = np.zeros(bins)\n",
+    "    for i in array:\n",
+    "        a = ((i-minn)/intervalo)*bins \n",
+    "        if a>=0 and a< bins:\n",
+    "            #array_out_append(a)\n",
+    "            array_out[int(a)] += 1 \n",
+    "    return array_out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 575"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i=0\n",
+    "channel1=[]\n",
+    "channel2=[]\n",
+    "channel3=[]\n",
+    "channel2_aux=[]\n",
+    "channel3_aux=[]\n",
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos560=0\n",
+    "number_bines=0\n",
+    "\n",
+    "archivo = open('../energy_cal/energy_cal_575V_nogps_2021_04_15_13h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    number_bines += 1\n",
+    "    if linea[0] == '#' :\n",
+    "        channel3_aux=[]\n",
+    "        channel2_aux=[]\n",
+    "        number_bines=0\n",
+    "        contador_evento=0\n",
+    "        i+=1\n",
+    "        continue\n",
+    "    espacios=0\n",
+    "    for j in linea:\n",
+    "        a.append(j)\n",
+    "        if contador_evento==0:\n",
+    "            eventos560+=1 \n",
+    "        contador_evento=1\n",
+    "        if j==\" \":\n",
+    "            if espacios == 1: # segundo dato asociado a 1\n",
+    "                channel2_aux.append(int(\"\".join(a)))\n",
+    "            if number_bines==12 and espacios == 1: \n",
+    "                for h in channel2_aux:\n",
+    "                    channel2.append(h)\n",
+    "            espacios += 1\n",
+    "            a.clear()\n",
+    "            a=[]\n",
+    "            continue\n",
+    "        if j==\"\\n\":\n",
+    "            channel3_aux.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "            if number_bines==12:\n",
+    "                for h in channel3_aux:\n",
+    "                    channel3.append(h)\n",
+    "\n",
+    "archivo = open('../energy_cal/energy_cal_575V_nogps_2021_04_15_12h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    number_bines += 1\n",
+    "    if linea[0] == '#' :\n",
+    "        channel3_aux=[]\n",
+    "        channel2_aux=[]\n",
+    "        number_bines=0\n",
+    "        contador_evento=0\n",
+    "        i+=1\n",
+    "        continue\n",
+    "    espacios=0\n",
+    "    for j in linea:\n",
+    "        a.append(j)\n",
+    "        if contador_evento==0:\n",
+    "            eventos560+=1 \n",
+    "        contador_evento=1\n",
+    "        if j==\" \":\n",
+    "            if espacios == 1: # segundo dato asociado a 1\n",
+    "                channel2_aux.append(int(\"\".join(a)))\n",
+    "            if number_bines==12 and espacios == 1: \n",
+    "                for h in channel2_aux:\n",
+    "                    channel2.append(h)\n",
+    "            espacios += 1\n",
+    "            a.clear()\n",
+    "            a=[]\n",
+    "            continue\n",
+    "        if j==\"\\n\":\n",
+    "            channel3_aux.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "            if number_bines==12:\n",
+    "                for h in channel3_aux:\n",
+    "                    channel3.append(h)\n",
+    "\n",
+    "hh575=suma_bines(channel3)\n",
+    "min_hist = 500.\n",
+    "max_hist = 1100.\n",
+    "bin_hist = 100\n",
+    "hist_Xlabel=[]\n",
+    "hist=make_histogram(hh575,bin_hist,min_hist,max_hist)\n",
+    "for i in range (0,bin_hist,1):\n",
+    "    f = min_hist + ((max_hist - min_hist)*i)/bin_hist\n",
+    "    hist_Xlabel.append(f)\n",
+    "#plt.figure(figsize =(10,10))\n",
+    "#plt.xlabel(\"V$_{Trigger}$ [V]\", fontsize=15)\n",
+    "#plt.ylabel(\"Numero Eventos\", fontsize=15)\n",
+    "#plt.title(\" Histograma 575 V \",fontdict={'family': 'serif', 'color' : 'darkblue','size': 25})\n",
+    "#plt.plot( hist_Xlabel,hist , 'ro-')\n",
+    "#plt.grid()\n",
+    "#plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize =(20,10))\n",
+    "plt.hist(hh575,bins=400 , range=[0, 400] , histtype='step' ) \n",
+    "plt.xlabel(\"Q\", fontsize=21)\n",
+    "plt.ylabel(\"Eventos\", fontsize=18)\n",
+    "plt.title(\"Histograma 1 hora a 575 (870 V) \", fontdict={'family': 'serif', 'color' : 'darkred','weight': 'bold','size': 22})\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 600"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i=0\n",
+    "channel1=[]\n",
+    "channel2=[]\n",
+    "channel3=[]\n",
+    "channel3_aux=[]\n",
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos560=0\n",
+    "number_bines=0\n",
+    "\n",
+    "archivo = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_05h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    number_bines += 1\n",
+    "    if linea[0] == '#' :\n",
+    "        channel3_aux=[]\n",
+    "        channel2_aux=[]\n",
+    "        number_bines=0\n",
+    "        contador_evento=0\n",
+    "        i+=1\n",
+    "        continue\n",
+    "    espacios=0\n",
+    "    for j in linea:\n",
+    "        a.append(j)\n",
+    "        if contador_evento==0:\n",
+    "            eventos560+=1 \n",
+    "        contador_evento=1\n",
+    "        if j==\" \":\n",
+    "            if espacios == 1: # segundo dato asociado a 1\n",
+    "                channel2_aux.append(int(\"\".join(a)))\n",
+    "            if number_bines==12 and espacios == 1: \n",
+    "                for h in channel2_aux:\n",
+    "                    channel2.append(h)\n",
+    "            espacios += 1\n",
+    "            a.clear()\n",
+    "            a=[]\n",
+    "            continue\n",
+    "        if j==\"\\n\":\n",
+    "            channel3_aux.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "            if number_bines==12:\n",
+    "                #print (channel3_aux)\n",
+    "                for h in channel3_aux:\n",
+    "                    channel3.append(h)\n",
+    "                    \n",
+    "archivo = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_06h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    number_bines += 1\n",
+    "    if linea[0] == '#' :\n",
+    "        channel3_aux=[]\n",
+    "        channel2_aux=[]\n",
+    "        number_bines=0\n",
+    "        contador_evento=0\n",
+    "        i+=1\n",
+    "        continue\n",
+    "    espacios=0\n",
+    "    for j in linea:\n",
+    "        a.append(j)\n",
+    "        if contador_evento==0:\n",
+    "            eventos560+=1 \n",
+    "        contador_evento=1\n",
+    "        if j==\" \":\n",
+    "            if espacios == 1: # segundo dato asociado a 1\n",
+    "                channel2_aux.append(int(\"\".join(a)))\n",
+    "            if number_bines==12 and espacios == 1: \n",
+    "                for h in channel2_aux:\n",
+    "                    channel2.append(h)\n",
+    "            espacios += 1\n",
+    "            a.clear()\n",
+    "            a=[]\n",
+    "            continue\n",
+    "        if j==\"\\n\":\n",
+    "            channel3_aux.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "            if number_bines==12:\n",
+    "                for h in channel3_aux:\n",
+    "                    channel3.append(h)\n",
+    "\n",
+    "archivo = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_07h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    number_bines += 1\n",
+    "    if linea[0] == '#' :\n",
+    "        channel3_aux=[]\n",
+    "        channel2_aux=[]\n",
+    "        number_bines=0\n",
+    "        contador_evento=0\n",
+    "        i+=1\n",
+    "        continue\n",
+    "    espacios=0\n",
+    "    for j in linea:\n",
+    "        a.append(j)\n",
+    "        if contador_evento==0:\n",
+    "            eventos560+=1 \n",
+    "        contador_evento=1\n",
+    "        if j==\" \":\n",
+    "            if espacios == 1: # segundo dato asociado a 1\n",
+    "                channel2_aux.append(int(\"\".join(a)))\n",
+    "            if number_bines==12 and espacios == 1: \n",
+    "                for h in channel2_aux:\n",
+    "                    channel2.append(h)\n",
+    "            espacios += 1\n",
+    "            a.clear()\n",
+    "            a=[]\n",
+    "            continue\n",
+    "        if j==\"\\n\":\n",
+    "            channel3_aux.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "            if number_bines==12:\n",
+    "                for h in channel3_aux:\n",
+    "                    channel3.append(h)\n",
+    "\n",
+    "hh600=suma_bines(channel3)\n",
+    "min_hist = 550.\n",
+    "max_hist = 770.\n",
+    "bin_hist = 110\n",
+    "hist_Xlabel=[]\n",
+    "hist=make_histogram(hh600,bin_hist,min_hist,max_hist)\n",
+    "for i in range (0,bin_hist,1):\n",
+    "    f = min_hist + ((max_hist - min_hist)*i)/bin_hist\n",
+    "    hist_Xlabel.append(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize =(20,10))\n",
+    "plt.hist(hh600,bins=400 , range=[0, 400]  , histtype='step' ) \n",
+    "plt.xlabel(\"Q\", fontsize=21)\n",
+    "plt.ylabel(\"Eventos\", fontsize=18)\n",
+    "plt.title(\"Histograma 3 horas a 600 (909 V) \", fontdict={'family': 'serif', 'color' : 'darkred','weight': 'bold','size': 22})\n",
+    "plt.axvline(x=127, label='line at x = {}'.format(128) , c='r')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 625"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i=0\n",
+    "channel1=[]\n",
+    "channel2=[]\n",
+    "channel3=[]\n",
+    "channel3_aux=[]\n",
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos560=0\n",
+    "number_bines=0\n",
+    "\n",
+    "archivo = open('../energy_cal/energy_cal_625V_nogps_2021_04_15_13h00.dat', 'r') \n",
+    "for linea in archivo:\n",
+    "    number_bines += 1\n",
+    "    if linea[0] == '#' :\n",
+    "        channel3_aux=[]\n",
+    "        number_bines=0\n",
+    "        contador_evento=0\n",
+    "        i+=1\n",
+    "        continue\n",
+    "    for j in linea:\n",
+    "        a.append(j)\n",
+    "        if contador_evento==0:\n",
+    "            eventos560+=1 \n",
+    "        contador_evento=1\n",
+    "        if j==\" \":\n",
+    "            a.clear()\n",
+    "            a=[]\n",
+    "            continue\n",
+    "        if j==\"\\n\":\n",
+    "            channel3_aux.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "            if number_bines==12:\n",
+    "                for h in channel3_aux:\n",
+    "                    channel3.append(h)\n",
+    "\n",
+    "hh625=suma_bines(channel3)\n",
+    "min_hist = 550.\n",
+    "max_hist = 1100.\n",
+    "bin_hist = 100\n",
+    "hist_Xlabel=[]\n",
+    "hist=make_histogram(hh625,bin_hist,min_hist,max_hist)\n",
+    "for i in range (0,bin_hist,1):\n",
+    "    f = min_hist + ((max_hist - min_hist)*i)/bin_hist\n",
+    "    hist_Xlabel.append(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize =(20,10))\n",
+    "plt.hist(hh625,bins= 200, range=[0, 400]  , histtype='step' ) \n",
+    "plt.xlabel(\"Q\", fontsize=21)\n",
+    "plt.ylabel(\"Eventos\", fontsize=18)\n",
+    "plt.title(\"Histograma 1 hora a 625 (948 V) \", fontdict={'family': 'serif', 'color' : 'darkred','weight': 'bold','size': 22})\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Aproximación parabolica"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import curve_fit\n",
+    "import io\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "charges= []\n",
+    "hist_= []\n",
+    "add= 0\n",
+    "count = 0\n",
+    "baseline = 50\n",
+    "\n",
+    "archivo = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_05h00.dat', 'r')\n",
+    "\n",
+    "for line in archivo:\n",
+    "    if line[0] != '#' :\n",
+    "        channels =  line.split(' ')  \n",
+    "        add = add + int(channels[2]) - baseline \n",
+    "        count += 1\n",
+    "        \n",
+    "    if line [0] == '#':\n",
+    "        if count == 12:\n",
+    "            charges.append(add)\n",
+    "        add=0\n",
+    "        count=0\n",
+    "archivo1 = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_06h00.dat', 'r')\n",
+    "\n",
+    "for line in archivo1:\n",
+    "    if line[0] != '#' :\n",
+    "        channels =  line.split(' ')  \n",
+    "        add = add + int(channels[2]) - baseline \n",
+    "        count += 1\n",
+    "        \n",
+    "    if line [0] == '#':\n",
+    "        if count == 12:\n",
+    "            a=0#charges.append(add)\n",
+    "        add=0\n",
+    "        count=0\n",
+    "archivo2 = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_07h00.dat', 'r')\n",
+    "\n",
+    "for line in archivo2:\n",
+    "    if line[0] != '#' :\n",
+    "        channels =  line.split(' ')  \n",
+    "        add = add + int(channels[2]) - baseline \n",
+    "        count += 1\n",
+    "        \n",
+    "    if line [0] == '#':\n",
+    "        if count == 12:\n",
+    "            a=0#charges.append(add)\n",
+    "        add=0\n",
+    "        count=0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = len(charges)\n",
+    "Hist = np.zeros(np.max(charges) + 1)\n",
+    "\n",
+    "for i in charges:\n",
+    "    Hist[i] = Hist[i] + 1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "charges= []\n",
+    "hist_= []\n",
+    "add= 0\n",
+    "count = 0\n",
+    "baseline = 50\n",
+    "\n",
+    "archivo = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_06h00.dat', 'r')\n",
+    "\n",
+    "for line in archivo:\n",
+    "    if line[0] != '#' :\n",
+    "        channels =  line.split(' ')  \n",
+    "        add = add + int(channels[2]) - baseline \n",
+    "        count += 1\n",
+    "        \n",
+    "    if line [0] == '#':\n",
+    "        if count == 12:\n",
+    "            charges.append(add)\n",
+    "        add=0\n",
+    "        count=0\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.optimize import curve_fit\n",
+    "def cuadratic(x, a, b,c):\n",
+    "    return a*(x-b)*(x-b) + c\n",
+    "\n",
+    "histograma , otro, otro2 =plt.hist(charges,bins=400 , range=[0, 400] , histtype='step' ) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cuadratic600 =[]\n",
+    "cuadratic600X=[]\n",
+    "init =107\n",
+    "maxx = 135\n",
+    "\n",
+    "for i in range (init,maxx,1):\n",
+    "    cuadratic600.append(histograma[i])\n",
+    "    cuadratic600X.append(i-init)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-1.14089961e-01  1.83850312e+00  4.97495236e+02]\n"
+     ]
+    }
+   ],
+   "source": [
+    "init_vals = [2,2,2]  # for [amp, cen, wid]\n",
+    "poptNa1,pcovNa1 = curve_fit(cuadratic,cuadratic600X,cuadratic600,p0=init_vals)\n",
+    "print(poptNa1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cuadratic600Y=[]\n",
+    "\n",
+    "cuadratic600Yfinal=[]\n",
+    "cuadratic600Xfinal=[]\n",
+    "\n",
+    "\n",
+    "rang=25\n",
+    "for a in range (init-rang,maxx+rang,1):\n",
+    "    cuadratic600Xfinal.append(a)\n",
+    "\n",
+    "for i in cuadratic600X: cuadratic600Y.append(  cuadratic(i,*poptNa1)  )\n",
+    "    \n",
+    "for i in range (-rang,maxx-init+rang,1): cuadratic600Yfinal.append(  cuadratic(i,*poptNa1)  )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize =(20,10))\n",
+    "plt.plot(histograma,'g+:',label='data')\n",
+    "plt.plot(cuadratic600Xfinal,cuadratic600Yfinal,'r',label = r'$y= -0.161*(x - 107.5)^2 + 451 $')\n",
+    "plt.xlabel(\"Q\", fontsize=22)\n",
+    "plt.ylabel(\"Eventos\", fontsize=20)\n",
+    "plt.title(\"Histograma 1 horas a 600 (909 V) \", fontdict={'family': 'serif', 'color' : 'darkred','weight': 'bold','size': 23})\n",
+    "plt.legend(loc=0, fontsize = 'xx-large' )\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PRUEBAS DEL FUNCIONAMIENTO DEL CODIGO\n",
+    "\n",
+    "Se haran varias pruebas para verificar el correcto funcionamiento del codigo y ademas se comprobara con otros programas hechos y dados para comprobar que realmente esta bien hecho el codigo usado."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def lineal_response(adc):\n",
+    "    y = (adc - 14.6026)/0.6439\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.system('rm test.txt')\n",
+    "f= open(\"test.txt\",\"w\")\n",
+    "a11=0\n",
+    "a12=0\n",
+    "for j in range (0,1000,1): #\n",
+    "    rand = random.randrange(20)\n",
+    "    if rand <8:\n",
+    "        f.write( \"##################\" +  \"linea de 12 datos\" +\"\\n\" )\n",
+    "        for i in range (0,12,1):\n",
+    "            a = i%12 + 1 + a12\n",
+    "            b = i%12 + 101 + a12\n",
+    "            c = i%12 + 51 + a12\n",
+    "            string = str(a) + \" \" + str(b) + \" \" + str(c) + \"\\n\"\n",
+    "            f.write( string )\n",
+    "            #f.writelines(\"string\")\n",
+    "        a12 +=1\n",
+    "    if rand <12 and rand >=8 :\n",
+    "        f.write( \"##################\" +  \"linea de 11 datos\" +\"\\n\" )\n",
+    "        for i in range (0,11,1):\n",
+    "            a = i%12 + 1 +a11\n",
+    "            b = i%12 + 101 +a11\n",
+    "            c = i%12 + 51 +a11\n",
+    "            string = str(a) + \" \" + str(b) + \" \" + str(c) + \"\\n\"\n",
+    "            f.write( string )\n",
+    "            #f.writelines(\"string\")\n",
+    "        a11 +=1\n",
+    "    if rand >=12 :\n",
+    "        f.write( \"##################\" +  \"linea de 5 datos\" +\"\\n\" )\n",
+    "        for i in range (0,5,1):\n",
+    "            a = 0 #i%12 + 1 \n",
+    "            b = 1 #i%12 + 101 \n",
+    "            c = 2 #i%12 + 51 \n",
+    "            string = str(a) + \" \" + str(b) + \" \" + str(c) + \"\\n\"\n",
+    "            f.write( string )\n",
+    "            #f.writelines(\"string\")\n",
+    "    for i in range (0,1,1):\n",
+    "        f.write( \"#############################\\n\" )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def suma_bines(array):\n",
+    "    cantidad_datos = 12\n",
+    "    suma_parcial=0\n",
+    "    array_out=[]\n",
+    "    for i in range(0,len(array),1):\n",
+    "        suma_parcial+=array[i]\n",
+    "        if (i+1)%cantidad_datos==0 :\n",
+    "            array_out.append(suma_parcial- 50*cantidad_datos)\n",
+    "            suma_parcial=0\n",
+    "            #print(\"el ultimo que suma es:\", array[i]  )\n",
+    "    return array_out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tiene problemas de precision en algunos intervalos, mejor no usarla y usar plt.hist\n",
+    "def make_histogram(array,bins,minn,maxx):\n",
+    "    intervalo = maxx - minn\n",
+    "    array_out = np.zeros(bins)\n",
+    "    for i in array:\n",
+    "        a = ((i-minn)/intervalo)*bins \n",
+    "        if a>=0 and a< bins:\n",
+    "            #array_out_append(a)\n",
+    "            array_out[int(a)] += 1 \n",
+    "    return array_out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# test programa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i=0\n",
+    "channel1=[]\n",
+    "channel2=[]\n",
+    "channel3=[]\n",
+    "channel3_aux=[]\n",
+    "channel2_aux=[]\n",
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos560=0\n",
+    "number_bines=0\n",
+    "\n",
+    "archivo = open('test.txt', 'r')\n",
+    "\n",
+    "for linea in archivo:\n",
+    "    number_bines += 1\n",
+    "    if linea[0] == '#' :\n",
+    "        channel3_aux=[]\n",
+    "        channel2_aux=[]\n",
+    "        number_bines=0\n",
+    "        contador_evento=0\n",
+    "        i+=1\n",
+    "        continue\n",
+    "    espacios=0\n",
+    "    for j in linea:\n",
+    "        a.append(j)\n",
+    "        if contador_evento==0:\n",
+    "            eventos560+=1 \n",
+    "        contador_evento=1\n",
+    "        if j==\" \":\n",
+    "            espacios += 1\n",
+    "            a.clear()\n",
+    "            a=[]\n",
+    "            continue\n",
+    "        if j==\"\\n\":\n",
+    "            channel3_aux.append(int(\"\".join(a)))\n",
+    "            a.clear()\n",
+    "            if number_bines==12:\n",
+    "                #print (channel3_aux)\n",
+    "                for h in channel3_aux:\n",
+    "                    channel3.append(h)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "hhtest=suma_bines(channel3)\n",
+    "min_hist = 500.\n",
+    "max_hist = 1100.\n",
+    "bin_hist = 100\n",
+    "hist_Xlabel=[]\n",
+    "hist=make_histogram(hh575,bin_hist,min_hist,max_hist)\n",
+    "for i in range (0,bin_hist,1):\n",
+    "    f = min_hist + ((max_hist - min_hist)*i)/bin_hist\n",
+    "    hist_Xlabel.append(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize =(20,10))\n",
+    "plt.hist(hhtest,bins=80 , range=[75, 115] , histtype='step' ) \n",
+    "plt.xlabel(\" Suma de 12 eventos\", fontsize=18)\n",
+    "plt.ylabel(\"Eventos\", fontsize=18)\n",
+    "plt.title(\"Histograma Test \", fontdict={'family': 'serif', 'color' : 'darkred','weight': 'bold','size': 21})\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# prueba con codigo dado"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import curve_fit\n",
+    "import io\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "charges= []\n",
+    "hist_= []\n",
+    "add= 0\n",
+    "count = 0\n",
+    "baseline = 50\n",
+    "\n",
+    "archivo = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_05h00.dat', 'r')\n",
+    "\n",
+    "for line in archivo:\n",
+    "    if line[0] != '#' :\n",
+    "        channels =  line.split(' ')  \n",
+    "        add = add + int(channels[2]) - baseline \n",
+    "        count += 1\n",
+    "        \n",
+    "    if line [0] == '#':\n",
+    "        if count == 12:\n",
+    "            charges.append(add)\n",
+    "        add=0\n",
+    "        count=0\n",
+    "archivo1 = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_06h00.dat', 'r')\n",
+    "\n",
+    "for line in archivo1:\n",
+    "    if line[0] != '#' :\n",
+    "        channels =  line.split(' ')  \n",
+    "        add = add + int(channels[2]) - baseline \n",
+    "        count += 1\n",
+    "        \n",
+    "    if line [0] == '#':\n",
+    "        if count == 12:\n",
+    "            a=0#charges.append(add)\n",
+    "        add=0\n",
+    "        count=0\n",
+    "archivo2 = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_07h00.dat', 'r')\n",
+    "\n",
+    "for line in archivo2:\n",
+    "    if line[0] != '#' :\n",
+    "        channels =  line.split(' ')  \n",
+    "        add = add + int(channels[2]) - baseline \n",
+    "        count += 1\n",
+    "        \n",
+    "    if line [0] == '#':\n",
+    "        if count == 12:\n",
+    "            a=0#charges.append(add)\n",
+    "        add=0\n",
+    "        count=0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = len(charges)\n",
+    "Hist = np.zeros(np.max(charges) + 1)\n",
+    "\n",
+    "for i in charges:\n",
+    "    Hist[i] = Hist[i] + 1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize =(20,10))\n",
+    "plt.plot(Hist ) \n",
+    "plt.xlabel(\" Suma de 12 eventos\", fontsize=18)\n",
+    "plt.ylabel(\"Eventos\", fontsize=18)\n",
+    "plt.title(\"Histograma Test \", fontdict={'family': 'serif', 'color' : 'darkred','weight': 'bold','size': 21})\n",
+    "plt.xlim([0, 400])\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "charges= []\n",
+    "add= 0\n",
+    "count = 0\n",
+    "baseline = 50\n",
+    "\n",
+    "archivo = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_05h00.dat', 'r')\n",
+    "\n",
+    "for line in archivo:\n",
+    "    if line[0] != '#' :\n",
+    "        channels =  line.split(' ')  \n",
+    "        add = add + int(channels[2]) - baseline \n",
+    "        count += 1\n",
+    "        \n",
+    "    if line [0] == '#':\n",
+    "        if count == 12:\n",
+    "            charges.append(add)\n",
+    "        add=0\n",
+    "        count=0\n",
+    "archivo1 = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_06h00.dat', 'r')\n",
+    "\n",
+    "for line in archivo1:\n",
+    "    if line[0] != '#' :\n",
+    "        channels =  line.split(' ')  \n",
+    "        add = add + int(channels[2]) - baseline \n",
+    "        count += 1\n",
+    "        \n",
+    "    if line [0] == '#':\n",
+    "        if count == 12:\n",
+    "            charges.append(add)\n",
+    "        add=0\n",
+    "        count=0\n",
+    "archivo2 = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_07h00.dat', 'r')\n",
+    "\n",
+    "for line in archivo2:\n",
+    "    if line[0] != '#' :\n",
+    "        channels =  line.split(' ')  \n",
+    "        add = add + int(channels[2]) - baseline \n",
+    "        count += 1\n",
+    "        \n",
+    "    if line [0] == '#':\n",
+    "        if count == 12:\n",
+    "            charges.append(add)\n",
+    "        add=0\n",
+    "        count=0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Solo una prueba de si mi histograma es correcto\n",
+    "plt.figure(figsize =(20,10))\n",
+    "plt.hist(charges,bins=300 , range=[0, 300] , histtype='step' ) \n",
+    "plt.xlabel(\" Suma de 12 eventos\", fontsize=18)\n",
+    "plt.ylabel(\"Eventos\", fontsize=18)\n",
+    "plt.title(\"Histograma Test \", fontdict={'family': 'serif', 'color' : 'darkred','weight': 'bold','size': 21})\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## crear archivo output para verificar que todo se lea bien"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.system('rm Data_test.txt')\n",
+    "Data_test= open(\"Data_test.txt\",\"w\")\n",
+    "i=0\n",
+    "channel3=[]\n",
+    "channel2_aux=[]\n",
+    "channel3_aux=[]\n",
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos560=0\n",
+    "number_bines=0\n",
+    "test = 0\n",
+    "\n",
+    "archivo = open('../energy_cal/WCD_calibration_610V_nogps_2021_04_15_00h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    if linea[0] == '#' and linea[2] == 't' :\n",
+    "        channel3_aux=[]\n",
+    "        channel2_aux=[]\n",
+    "        if number_bines==12:\n",
+    "            channel3.append(test) # sin ponewr los espacios tab extras de este if funciona bien\n",
+    "        number_bines=0\n",
+    "        contador_evento=0\n",
+    "        i+=1\n",
+    "        test = 0\n",
+    "        linea_out = linea[:-1]\n",
+    "        Data_test.write( linea_out  + \"---> NO SE TOMA ademas linea con t\\n\" )\n",
+    "        continue\n",
+    "    espacios=0\n",
+    "    if linea[0] == '#':\n",
+    "        number_bines=0\n",
+    "        test = 0\n",
+    "        linea_out = linea[:-1]\n",
+    "        Data_test.write( linea_out  + \"---> NO SE TOMA\\n\" )\n",
+    "    if linea[0] != '#' :\n",
+    "        number_bines += 1\n",
+    "        \n",
+    "        for j in linea:\n",
+    "            a.append(j)\n",
+    "            if contador_evento==0:\n",
+    "                eventos560+=1 \n",
+    "            contador_evento=1\n",
+    "            if j==\" \":\n",
+    "                espacios += 1\n",
+    "                a.clear()\n",
+    "                a=[]\n",
+    "                continue\n",
+    "            if j==\"\\n\":\n",
+    "                \n",
+    "                #channel3_aux.append(int(\"\".join(a)))\n",
+    "                test += int(\"\".join(a)) - 50\n",
+    "                linea_out = linea[:-1]\n",
+    "                if number_bines != 12:\n",
+    "                    Data_test.write( linea_out  + \"---> termino de suma #:\" + str(number_bines) + '  Suma_parcial:'+str(test) +\"\\n\" )\n",
+    "                if number_bines == 12:\n",
+    "                    Data_test.write( linea_out  + \"---> SIIIUUUUUUUUU termino de suma:\" + str(number_bines) + '  Suma_parcial:'+str(test)+\"\\n\" )\n",
+    "                a.clear()\n",
+    "                #print (test, number_bines)\n",
+    "                #if number_bines==12:\n",
+    "                    #print (channel3_aux)0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## crear histograma con otro programa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i=0\n",
+    "channel3=[]\n",
+    "a=[]\n",
+    "contador_evento=0\n",
+    "eventos600=0\n",
+    "number_bines=0\n",
+    "test = 0\n",
+    "\n",
+    "archivo = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_05h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    if linea[0] == '#' and linea[2] == 't' :\n",
+    "        if number_bines==12:\n",
+    "            channel3.append(test) # sin ponewr los espacios tab extras de este if funciona bien\n",
+    "        number_bines=0\n",
+    "        contador_evento=0\n",
+    "        i+=1\n",
+    "        test = 0\n",
+    "        continue\n",
+    "    espacios=0\n",
+    "    if linea[0] == '#':\n",
+    "        number_bines=0\n",
+    "        test = 0\n",
+    "    if linea[0] != '#' :\n",
+    "        number_bines += 1\n",
+    "        for j in linea:\n",
+    "            a.append(j)\n",
+    "            if contador_evento==0: # solo contar eventos\n",
+    "                eventos600+=1 \n",
+    "            contador_evento=1\n",
+    "            if j==\" \":\n",
+    "                a.clear()\n",
+    "                a=[]\n",
+    "                continue\n",
+    "            if j==\"\\n\":\n",
+    "                test += int(\"\".join(a)) - 50\n",
+    "                a.clear()\n",
+    "\n",
+    "                    \n",
+    "archivo = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_06h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    if linea[0] == '#' and linea[2] == 't' :\n",
+    "        if number_bines==12:\n",
+    "            channel3.append(test) # sin ponewr los espacios tab extras de este if funciona bien\n",
+    "        number_bines=0\n",
+    "        contador_evento=0\n",
+    "        i+=1\n",
+    "        test = 0\n",
+    "        continue\n",
+    "    espacios=0\n",
+    "    if linea[0] == '#':\n",
+    "        number_bines=0\n",
+    "        test = 0\n",
+    "    if linea[0] != '#' :\n",
+    "        number_bines += 1\n",
+    "        for j in linea:\n",
+    "            a.append(j)\n",
+    "            if contador_evento==0: # solo contar eventos\n",
+    "                eventos560+=1 \n",
+    "            contador_evento=1\n",
+    "            if j==\" \":\n",
+    "                a.clear()\n",
+    "                a=[]\n",
+    "                continue\n",
+    "            if j==\"\\n\":\n",
+    "                test += int(\"\".join(a)) - 50\n",
+    "                a.clear()\n",
+    "\n",
+    "                    \n",
+    "archivo = open('../energy_cal/energy_cal_600V_nogps_2021_04_15_07h00.dat', 'r')\n",
+    "for linea in archivo:\n",
+    "    if linea[0] == '#' and linea[2] == 't' :\n",
+    "        if number_bines==12:\n",
+    "            channel3.append(test) # sin ponewr los espacios tab extras de este if funciona bien\n",
+    "        number_bines=0\n",
+    "        contador_evento=0\n",
+    "        i+=1\n",
+    "        test = 0\n",
+    "        continue\n",
+    "    espacios=0\n",
+    "    if linea[0] == '#':\n",
+    "        number_bines=0\n",
+    "        test = 0\n",
+    "    if linea[0] != '#' :\n",
+    "        number_bines += 1\n",
+    "        for j in linea:\n",
+    "            a.append(j)\n",
+    "            if contador_evento==0: # solo contar eventos\n",
+    "                eventos560+=1 \n",
+    "            contador_evento=1\n",
+    "            if j==\" \":\n",
+    "                a.clear()\n",
+    "                a=[]\n",
+    "                continue\n",
+    "            if j==\"\\n\":\n",
+    "                test += int(\"\".join(a)) - 50\n",
+    "                a.clear()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Solo una prueba de si mi histograma es correcto\n",
+    "plt.figure(figsize =(20,10))\n",
+    "plt.hist(channel3,bins=200 , range=[0, 400] , histtype='step' ) \n",
+    "#plt.xlabel(\" ... label en x\", fontsize=15)\n",
+    "plt.ylabel(\"Eventos\", fontsize=15)\n",
+    "plt.title(\"Histograma V=600 TEST \", fontdict={'family': 'serif', 'color' : 'darkred','weight': 'bold','size': 18})\n",
+    "#plt.yscale(\"log\")\n",
+    "plt.grid()\n",
+    "plt.show()"
    ]
   },
   {
@@ -209,7 +2228,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,