Keysight.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Author:  Nolan Kowitt\n",
    "\n",
    "The code below connects to a Keysight DSOX3014G\n",
    "\n",
    "In all examples below we use Channel1 of the scope -- C1 for short. You can always change it to C2.\n",
    "\n",
    "* Documentation: https://www.batronix.com/files/Keysight/Oszilloskope/3000XT/3000XT-Programming.pdf\n",
    "\n",
    "Requirements:\n",
    "\n",
    "* pyvisa\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pyvisa\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm.notebook import tqdm #This is optional just if you want a progress bar\n",
    "plt.rcParams['figure.figsize'] = [10, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KEYSIGHT TECHNOLOGIES,DSO-X 3014G,MY62510218,07.57.2023033130\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rm = pyvisa.ResourceManager()\n",
    "rm.list_resources()\n",
    "scope = rm.open_resource('USB0::0x2A8D::0x1768::MY62510218::INSTR')\n",
    "print(scope.query(\"*IDN?\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scope.write('*RST')# Puts the scope into a KNOWN state, but will kill any settings you have, so run with caution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CH1Range = 0.2 #FULL SCALE RANGE [V]\n",
    "TIMEBASE = 100e-6 #FULL TIME RANGE [s]\n",
    "\n",
    "scope.write(':CHAN1:DISP 1 ')\n",
    "scope.write(':CHAN2:DISP 1 ')\n",
    "\n",
    "scope.write(f':CHAN1:RANG {CH1Range}')\n",
    "scope.write(f':CHAN2:RANG {CH2Range}')\n",
    "\n",
    "scope.write(f':CHAN1:OFFS {CH1Range/2-CH1Range*0.05}')\n",
    "scope.write(f':CHAN2:OFFS {CH2Range/2-CH2Range*0.05}')\n",
    "\n",
    "scope.write(f':TIM:RANG {TIMEBASE}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [],
   "source": [
    "def aquire(npoints):\n",
    "    ampoutmax = np.zeros(npoints)\n",
    "    ampoutarea= np.zeros(npoints)\n",
    "    scope.write(':MEAS:STAT:DISP 1')\n",
    "    scope.write(':MEAS:STAT CURR')\n",
    "    scope.write(':MEAS:STAT:RES')\n",
    "    scope.write(':MEAS:VMIN')\n",
    "    scope.write(':MEAS:AREA')\n",
    "    for i in tqdm(range(npoints)):\n",
    "        trigflag = 0\n",
    "        while trigflag==0:\n",
    "            trigflag = scope.query(':TER?')[1]\n",
    "        val = scope.query(':MEAS:RES? ').split(',')\n",
    "        ampoutmax[i] = val[0]\n",
    "        ampoutarea[i]= val[1]\n",
    "\n",
    "    return ampoutmax,ampoutarea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2cea1c894dd04586b1094b997ea641b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "CSheight,CSarea = aquire(100000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlwAAAE8CAYAAAAVAG93AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbOklEQVR4nO3df7CmZX3f8fdHNmg0qSyyoWQXuqSuWmKqklMgPyZRifzsuLRFBmvC6ux0Owm1aZq2WdtOaFFncNJKdCaSboVmcaJIiIadQKQ7iLFtBDmIIQIxnCjIblE27kKaMBpXv/3judYcyTmc5yznep7nPOf9mjlz7vu6r/t+ru+e5fDZ6/6VqkKSJEn9PGfcA5AkSZp2Bi5JkqTODFySJEmdGbgkSZI6M3BJkiR1ZuCSJEnqbN24B/BMTjjhhNq8efO4hyFJkrSke+6558+qasNC2yY6cG3evJnZ2dlxD0OSJGlJSR5ZbJunFCVJkjozcEmSJHU2VOBK8gtJ7k/yuSQfSvK8JKcmuSvJXJIPJzm29X1uW59r2zfPO87bWvvnk5zbqSZJkqSJsmTgSrIR+JfATFW9HDgGuBR4F3B1Vb0YOARsb7tsBw619qtbP5Kc1vb7QeA84H1JjlnZciRJkibPsKcU1wHfnWQd8HzgMeC1wE1t+27gora8ta3Ttp+dJK39hqr6elV9EZgDznjWFUiSJE24JQNXVe0H/gvwJQZB60ngHuCJqjrcuu0DNrbljcCjbd/Drf+L5rcvsI8kSdLUGuaU4noGs1OnAt8PvIDBKcEukuxIMptk9sCBA70+RpIkaWSGOaX4U8AXq+pAVX0D+AjwY8Bx7RQjwCZgf1veD5wM0La/EPjq/PYF9vm2qtpVVTNVNbNhw4LPDpMkSVpVhglcXwLOSvL8di3W2cADwB3Axa3PNuDmtrynrdO2f7yqqrVf2u5iPBXYAnx6ZcqQJEmaXEs+ab6q7kpyE/AZ4DBwL7ALuAW4Ick7Wtu1bZdrgQ8kmQMOMrgzkaq6P8mNDMLaYeDyqvrmCtcjSZI0cTKYfJpMMzMz5at9JEnSapDknqqaWWjbRL9LUQvbvPOWBdsfvurCEY9EkiQNw1f7SJIkdWbgkiRJ6szAJUmS1JmBS5IkqTMDlyRJUmcGLkmSpM4MXJIkSZ0ZuCRJkjozcEmSJHVm4JIkSerMwCVJktSZgUuSJKkzA5ckSVJnBi5JkqTODFySJEmdGbgkSZI6M3BJkiR1ZuCSJEnqzMAlSZLUmYFLkiSpMwOXJElSZwYuSZKkzgxckiRJna1bqkOSlwIfntf0A8AvA9e39s3Aw8AlVXUoSYD3ABcATwFvrqrPtGNtA/5jO847qmr3ypQxnTbvvGXcQ5AkSStgyRmuqvp8Vb2yql4J/DCDEPVRYCdwe1VtAW5v6wDnA1va1w7gGoAkxwNXAGcCZwBXJFm/otVIkiRNoOWeUjwb+NOqegTYChyZodoNXNSWtwLX18CdwHFJTgLOBfZW1cGqOgTsBc57tgVIkiRNuuUGrkuBD7XlE6vqsbb8ZeDEtrwReHTePvta22LtkiRJU23owJXkWOD1wG89fVtVFVArMaAkO5LMJpk9cODAShxSkiRprJYzw3U+8Jmq+kpb/0o7VUj7/nhr3w+cPG+/Ta1tsfbvUFW7qmqmqmY2bNiwjOFJkiRNpuUErjfy16cTAfYA29ryNuDmee2XZeAs4Ml26vE24Jwk69vF8ue0NkmSpKm25GMhAJK8AHgd8M/nNV8F3JhkO/AIcElrv5XBIyHmGNzR+BaAqjqY5O3A3a3flVV18FlXIEmSNOEyuPxqMs3MzNTs7Oy4hzE2K/kcroevunDFjiVJkv6mJPdU1cxC23zSvCRJUmcGLkmSpM4MXJIkSZ0ZuCRJkjozcEmSJHU21GMhtPotdsejdy9KktSfM1ySJEmdGbgkSZI6M3BJkiR1ZuCSJEnqzMAlSZLUmYFLkiSpMwOXJElSZwYuSZKkzgxckiRJnRm4JEmSOjNwSZIkdWbgkiRJ6szAJUmS1JmBS5IkqTMDlyRJUmcGLkmSpM4MXJIkSZ0ZuCRJkjobKnAlOS7JTUn+OMmDSX4kyfFJ9iZ5qH1f3/omyXuTzCW5L8np846zrfV/KMm2XkVJkiRNkmFnuN4DfKyqXga8AngQ2AncXlVbgNvbOsD5wJb2tQO4BiDJ8cAVwJnAGcAVR0KaJEnSNFsycCV5IfATwLUAVfVXVfUEsBXY3brtBi5qy1uB62vgTuC4JCcB5wJ7q+pgVR0C9gLnrWAtkiRJE2mYGa5TgQPA/0hyb5L3J3kBcGJVPdb6fBk4sS1vBB6dt/++1rZYuyRJ0lQbJnCtA04HrqmqVwF/yV+fPgSgqgqolRhQkh1JZpPMHjhwYCUOKUmSNFbDBK59wL6ququt38QggH2lnSqkfX+8bd8PnDxv/02tbbH271BVu6pqpqpmNmzYsJxaJEmSJtKSgauqvgw8muSlrels4AFgD3DkTsNtwM1teQ9wWbtb8SzgyXbq8TbgnCTr28Xy57Q2SZKkqbZuyH5vBX4zybHAF4C3MAhrNybZDjwCXNL63gpcAMwBT7W+VNXBJG8H7m79rqyqgytShSRJ0gQbKnBV1WeBmQU2nb1A3wIuX+Q41wHXLWN8kiRJq55PmpckSerMwCVJktSZgUuSJKkzA5ckSVJnBi5JkqTODFySJEmdGbgkSZI6M3BJkiR1ZuCSJEnqbNhX+2hKbd55y4LtD1914YhHIknS9HKGS5IkqTNnuMZssRkmSZI0PZzhkiRJ6szAJUmS1JmBS5IkqTMDlyRJUmcGLkmSpM4MXJIkSZ0ZuCRJkjozcEmSJHVm4JIkSerMwCVJktSZgUuSJKkzA5ckSVJnQwWuJA8n+aMkn00y29qOT7I3yUPt+/rWniTvTTKX5L4kp887zrbW/6Ek2/qUJEmSNFmWM8P1mqp6ZVXNtPWdwO1VtQW4va0DnA9saV87gGtgENCAK4AzgTOAK46ENEmSpGn2bE4pbgV2t+XdwEXz2q+vgTuB45KcBJwL7K2qg1V1CNgLnPcsPl+SJGlVGDZwFfA/k9yTZEdrO7GqHmvLXwZObMsbgUfn7buvtS3WLkmSNNXWDdnvx6tqf5LvA/Ym+eP5G6uqktRKDKgFuh0Ap5xyykocUpIkaayGmuGqqv3t++PARxlcg/WVdqqQ9v3x1n0/cPK83Te1tsXan/5Zu6pqpqpmNmzYsLxqJEmSJtCSgSvJC5J875Fl4Bzgc8Ae4MidhtuAm9vyHuCydrfiWcCT7dTjbcA5Sda3i+XPaW2SJElTbZhTiicCH01ypP8Hq+pjSe4GbkyyHXgEuKT1vxW4AJgDngLeAlBVB5O8Hbi79buyqg6uWCWSJEkTasnAVVVfAF6xQPtXgbMXaC/g8kWOdR1w3fKHKUmStHr5pHlJkqTODFySJEmdDftYCK0xm3fesui2h6+6cIQjkSRp9XOGS5IkqTMDlyRJUmcGLkmSpM4MXJIkSZ0ZuCRJkjozcEmSJHVm4JIkSerMwCVJktSZgUuSJKkzA5ckSVJnBi5JkqTODFySJEmdGbgkSZI6M3BJkiR1ZuCSJEnqbN24B6DVZ/POWxZsf/iqC0c8EkmSVgdnuCRJkjozcEmSJHVm4JIkSerMwCVJktSZgUuSJKmzoQNXkmOS3Jvkd9v6qUnuSjKX5MNJjm3tz23rc2375nnHeFtr/3ySc1e8GkmSpAm0nBmunwcenLf+LuDqqnoxcAjY3tq3A4da+9WtH0lOAy4FfhA4D3hfkmOe3fAlSZIm31CBK8km4ELg/W09wGuBm1qX3cBFbXlrW6dtP7v13wrcUFVfr6ovAnPAGStQgyRJ0kQbdobrV4F/B3yrrb8IeKKqDrf1fcDGtrwReBSgbX+y9f92+wL7SJIkTa0lA1eSfwg8XlX3jGA8JNmRZDbJ7IEDB0bxkZIkSV0NM8P1Y8DrkzwM3MDgVOJ7gOOSHHk10CZgf1veD5wM0La/EPjq/PYF9vm2qtpVVTNVNbNhw4ZlFyRJkjRplgxcVfW2qtpUVZsZXPT+8ap6E3AHcHHrtg24uS3vaeu07R+vqmrtl7a7GE8FtgCfXrFKJEmSJtSzeXn1LwE3JHkHcC9wbWu/FvhAkjngIIOQRlXdn+RG4AHgMHB5VX3zWXy+JEnSqrCswFVVnwA+0Za/wAJ3GVbV14A3LLL/O4F3LneQkiRJq5lPmpckSerMwCVJktTZs7mGS/oOm3fesmD7w1ddOOKRSJI0WZzhkiRJ6szAJUmS1JmBS5IkqTMDlyRJUmcGLkmSpM4MXJIkSZ0ZuCRJkjozcEmSJHVm4JIkSerMwCVJktSZgUuSJKkzA5ckSVJnBi5JkqTO1o17AJp+m3fesui2h6+6cIQjkSRpPJzhkiRJ6szAJUmS1JmBS5IkqTMDlyRJUmcGLkmSpM4MXJIkSZ35WIgReaZHI0iSpOm25AxXkucl+XSSP0xyf5L/3NpPTXJXkrkkH05ybGt/blufa9s3zzvW21r755Oc260qSZKkCTLMKcWvA6+tqlcArwTOS3IW8C7g6qp6MXAI2N76bwcOtfarWz+SnAZcCvwgcB7wviTHrGAtkiRJE2nJwFUDf9FWv6t9FfBa4KbWvhu4qC1vbeu07WcnSWu/oaq+XlVfBOaAM1aiCEmSpEk21EXzSY5J8lngcWAv8KfAE1V1uHXZB2xsyxuBRwHa9ieBF81vX2AfSZKkqTVU4Kqqb1bVK4FNDGalXtZrQEl2JJlNMnvgwIFeHyNJkjQyy3osRFU9AdwB/AhwXJIjdzluAva35f3AyQBt+wuBr85vX2Cf+Z+xq6pmqmpmw4YNyxmeJEnSRFrysRBJNgDfqKonknw38DoGF8LfAVwM3ABsA25uu+xp659q2z9eVZVkD/DBJO8Gvh/YAnx6hevRKrPY4zIevurCEY9EkqR+hnkO10nA7nZH4XOAG6vqd5M8ANyQ5B3AvcC1rf+1wAeSzAEHGdyZSFXdn+RG4AHgMHB5VX1zZcuRJEmaPEsGrqq6D3jVAu1fYIG7DKvqa8AbFjnWO4F3Ln+YkiRJq5ev9pEkSerMwCVJktSZgUuSJKkzA5ckSVJnBi5JkqTODFySJEmdGbgkSZI6M3BJkiR1ZuCSJEnqzMAlSZLU2TDvUpRGzpdaS5KmiTNckiRJnRm4JEmSOjNwSZIkdWbgkiRJ6szAJUmS1JmBS5IkqTMDlyRJUmcGLkmSpM4MXJIkSZ35pHmtKos9gR58Cr0kaXI5wyVJktSZgUuSJKkzA5ckSVJnSwauJCcnuSPJA0nuT/Lzrf34JHuTPNS+r2/tSfLeJHNJ7kty+rxjbWv9H0qyrV9ZkiRJk2OYGa7DwC9W1WnAWcDlSU4DdgK3V9UW4Pa2DnA+sKV97QCugUFAA64AzgTOAK44EtIkSZKm2ZKBq6oeq6rPtOX/BzwIbAS2Artbt93ARW15K3B9DdwJHJfkJOBcYG9VHayqQ8Be4LyVLEaSJGkSLesariSbgVcBdwEnVtVjbdOXgRPb8kbg0Xm77Wtti7VLkiRNtaEDV5LvAX4b+FdV9efzt1VVAbUSA0qyI8lsktkDBw6sxCElSZLGaqjAleS7GISt36yqj7Tmr7RThbTvj7f2/cDJ83bf1NoWa/8OVbWrqmaqambDhg3LqUWSJGkiDXOXYoBrgQer6t3zNu0BjtxpuA24eV77Ze1uxbOAJ9upx9uAc5KsbxfLn9PaJEmSptowr/b5MeBngD9K8tnW9u+Bq4Abk2wHHgEuadtuBS4A5oCngLcAVNXBJG8H7m79rqyqgytRxCR5plfPqK/F/ux95Y8kadyWDFxV9b+BLLL57AX6F3D5Ise6DrhuOQOUJEla7XzSvCRJUmcGLkmSpM4MXJIkSZ0ZuCRJkjozcEmSJHVm4JIkSerMwCVJktSZgUuSJKkzA5ckSVJnBi5JkqTOhnmXorSqPdP7LX3PoiRpFJzhkiRJ6szAJUmS1JmBS5IkqTMDlyRJUmcGLkmSpM4MXJIkSZ35WAitaYs9MsLHRUiSVpKBS1qAQUyStJI8pShJktSZgUuSJKkzA5ckSVJnXsMlLYPvZZQkHQ1nuCRJkjpbMnAluS7J40k+N6/t+CR7kzzUvq9v7Uny3iRzSe5Lcvq8fba1/g8l2danHEmSpMkzzAzXbwDnPa1tJ3B7VW0Bbm/rAOcDW9rXDuAaGAQ04ArgTOAM4IojIU2SJGnaLRm4quqTwMGnNW8Fdrfl3cBF89qvr4E7geOSnAScC+ytqoNVdQjYy98McZIkSVPpaK/hOrGqHmvLXwZObMsbgUfn9dvX2hZrlyRJmnrP+i7FqqoktRKDAUiyg8HpSE455ZSVOqw0Nj61XpJ0tIHrK0lOqqrH2inDx1v7fuDkef02tbb9wKuf1v6JhQ5cVbuAXQAzMzMrFuSk3p7pkRGSpLXtaE8p7gGO3Gm4Dbh5Xvtl7W7Fs4An26nH24BzkqxvF8uf09okSZKm3pIzXEk+xGB26oQk+xjcbXgVcGOS7cAjwCWt+63ABcAc8BTwFoCqOpjk7cDdrd+VVfX0C/ElSZKm0pKBq6reuMimsxfoW8DlixznOuC6ZY1OkiRpCvhqH2lMVvI1Qb5ySJImm6/2kSRJ6szAJUmS1JmnFKVVxEdPSNLq5AyXJElSZ85wSRNoJWeyfNK9JI2fgUtaowxikjQ6Bi5J38FHTEjSyjNwHQUvXJaGY3iTpAEDl6Sx8JSmpLXEwCVpaIYkSTo6PhZCkiSpM2e4JE0UZ9EkTSMDl6RVwQvwJa1mBi5Jz9q479x1VkzSpDNwPYNx/09EkiRNBy+alyRJ6swZLklT62hONS53n5WcCfcUqDS9DFyS1py1frnASoZKQ6I0HAOXJOmoecOCNBwDlyQNYRSzYis5k3Q0452mGic18BlQ1y4DlyStAqvtf9STGviOxtGMa1J/LhofA5ckrWKTGlLGbZr+XFZb2NbCUlXjHsOiZmZmanZ2dmyfP03/wUqS1gaD2PgkuaeqZhbaNvLncCU5L8nnk8wl2Tnqz5ckSRq1kZ5STHIM8GvA64B9wN1J9lTVA6MchyRJ0+pobibwtGV/o76G6wxgrqq+AJDkBmArYOCSJKmz5V4qsxrvBJ1Uow5cG4FH563vA84c8RgkSdKz5FsWlmfi7lJMsgPY0Vb/IsnnR/CxJwB/NoLPmURruXZY2/Vb+9q1lutfy7XDhNafd43kY0ZR+99ZbMOoA9d+4OR565ta27dV1S5g1ygHlWR2sbsKpt1arh3Wdv3WvjZrh7Vd/1quHdZ2/eOufdR3Kd4NbElyapJjgUuBPSMegyRJ0kiNdIarqg4n+RfAbcAxwHVVdf8oxyBJkjRqI7+Gq6puBW4d9ecuYaSnMCfMWq4d1nb91r52reX613LtsLbrH2vtE/2keUmSpGkw8ifNS5IkrTVrJnAt9UqhJM9N8uG2/a4km8cwzG6GqP9fJ3kgyX1Jbk+y6K2tq82wr5NK8k+SVJKpuoNnmPqTXNJ+/vcn+eCox9jLEH/vT0lyR5J729/9C8Yxzh6SXJfk8SSfW2R7kry3/dncl+T0UY+xlyFqf1Or+Y+S/EGSV4x6jD0tVf+8fv8gyeEkF49qbL0NU3uSVyf5bPt99/sjG1xVTf0Xgwv0/xT4AeBY4A+B057W5+eAX2/LlwIfHve4R1z/a4Dnt+WfnZb6h6m99fte4JPAncDMuMc94p/9FuBeYH1b/75xj3uEte8CfrYtnwY8PO5xr2D9PwGcDnxuke0XAL8HBDgLuGvcYx5h7T867+/7+dNU+zD1tz7HAB9ncE31xeMe8wh/9scxeLvNKW19ZL/v1soM17dfKVRVfwUceaXQfFuB3W35JuDsJBnhGHtasv6quqOqnmqrdzJ4Rto0GOZnD/B24F3A10Y5uBEYpv5/BvxaVR0CqKrHRzzGXoapvYC/1ZZfCPzfEY6vq6r6JHDwGbpsBa6vgTuB45KcNJrR9bVU7VX1B0f+vjNdv++AoX72AG8FfhuYlv/egaFq/6fAR6rqS63/yOpfK4FroVcKbVysT1UdBp4EXjSS0fU3TP3zbWfwL99psGTt7VTKyVW1cu+pmBzD/OxfArwkyf9JcmeS80Y2ur6Gqf0/AT+dZB+Df+m/dTRDmwjL/b0wrabp991QkmwE/hFwzbjHMgYvAdYn+USSe5JcNqoPnrhX+2i8kvw0MAP85LjHMgpJngO8G3jzmIcyTusYnFZ8NYN/6X8yyQ9V1RPjHNSIvBH4jar6r0l+BPhAkpdX1bfGPTD1l+Q1DALXj497LCP2q8AvVdW3pudEztDWAT8MnA18N/CpJHdW1Z+M4oPXgiVfKTSvz74k6xicXvjqaIbX3TD1k+SngP8A/GRVfX1EY+ttqdq/F3g58In2i+dvA3uSvL6qZkc2yn6G+dnvY3ANyzeALyb5EwYB7O7RDLGbYWrfDpwHUFWfSvI8Bu9bm6rTLIsY6vfCtEry94H3A+dX1bT8rh/WDHBD+513AnBBksNV9TtjHdVo7AO+WlV/Cfxlkk8CrwC6B661ckpxmFcK7QG2teWLgY9Xu6JuCixZf5JXAf8NeP0UXcMDS9ReVU9W1QlVtbmqNjO4nmNawhYM93f/dxjMbpHkBAZT7l8Y4Rh7Gab2LzH4ly5J/h7wPODASEc5PnuAy9rdimcBT1bVY+Me1CgkOQX4CPAzo5jZmDRVdeq833k3AT+3RsIWwM3AjydZl+T5wJnAg6P44DUxw1WLvFIoyZXAbFXtAa5lcDphjsEFd5eOb8Qra8j6fwX4HuC32r96vlRVrx/boFfIkLVPrSHrvw04J8kDwDeBfzsN/+IfsvZfBP57kl9gcAH9m6flH1pJPsQgSJ/QrlG7AvgugKr6dQbXrF0AzAFPAW8Zz0hX3hC1/zKDa3Tf137fHa4peqHzEPVPraVqr6oHk3wMuA/4FvD+qnrGx2es2Nim5HeLJEnSxForpxQlSZLGxsAlSZLUmYFLkiSpMwOXJElSZwYuSZI01YZ9ofeQx3pNe/n1ka+vJbloyf28S1GSJE2zJD8B/AWD94e+fAWPezyDR6tsmvc+4gU5wyVJkqbaQi+1TvJ3k3ysvVPxfyV52VEc+mLg95YKW2DgkiRJa9Mu4K1V9cPAvwHedxTHuBT40DAd18ST5iVJko5I8j3Aj/LXb1cBeG7b9o+BKxfYbX9VnTvvGCcBP8TgbRZLMnBJkqS15jnAE1X1yqdvqKqPMHjX5lIuAT5aVd8Y9gMlSZLWjKr6c+CLSd4A0F7i/oplHuaNDHk6EQxckiRpyrWXWn8KeGmSfUm2A28Ctif5Q+B+YOsyjrcZOBn4/aH38bEQkiRJfTnDJUmS1JmBS5IkqTMDlyRJUmcGLkmSpM4MXJIkSZ0ZuCRJkjozcEmSJHVm4JIkSers/wNG6RsaweIW4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "measured_counts_br, bin_edges_br,patches = plt.hist(np.abs(BRarea),80,[0,1.6e-7]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "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": [
    "plt.figure()\n",
    "measured_counts_CS, bin_edges_CS,patches = plt.hist(np.abs(CSarea),80,[0,1.6e-7]);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnUAAAFNCAYAAABmNpkJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAABQsUlEQVR4nO3dd5zdVZ3/8ddnJh1CAiS0BAgIooKKGhUFFUURsWBBREVQcVl3xb7+dl3dtbu2FduKIiDiKl2XItJJaElIoaZOeiaZlmmZXu79/P445yaXYcqdmdvn/Xw85jH3ftv93G/93PM953zN3RERERGR0lZR6ABEREREZPyU1ImIiIiUASV1IiIiImVASZ2IiIhIGVBSJyIiIlIGlNSJiIiIlAEldYCZXWNm3y10HKNlZt80s//N8jJPN7PqbC4zn3KxTkSywcy2mtlbx7mMv5vZRcOML8lz2URjZo+a2Svi65ycs7KxvxWCmS0ys0/l4XMWmJmb2aRRzvfvZnZlruJK+5yPm9kj8fVUM1tnZnNHmq+kkrq4k3aZWbuZNZvZ38zsyELHNZHk+qKR6xORmR1gZj8zs+1xP9oU388ZYb6TzOxuM9ttZs/r3NHM/tfMasxsj5ltSD8pmdlH42el/jrjyeRVufiOo1WqJ/+JyN3f4e5/gOee9MfKzK4ws/VmljSzjw8y/otmVhv366vNbGrauAVm9mDcn9cN3IdyNW+hjTUZSJv/3UCbuz+R5dAkD9z9++6e86RzwGf2AFcD/zbStCWV1EXvdvf9gcOBOuCXBY5nr7Ee5OWkmNeBmU0B7gdOBM4CDgBeBzQCrxlh9j7gRuDiIcb/F7DA3Q8A3gN8N5W0ufuf3H3/1B/wz8BmYNU4v1JeFPM2LZQyWidPEfbH5+2LZvZ2wkXkDOBo4FjgW2mTXAc8ARwMfA24OVWSkON5S92ngT8WOohMldG+Xur+DFw04g8cdy+ZP2Ar8Na092cDG9Lev5NwotgD7AC+OWD+04DHgJY4/uNx+DXAd+PrmcCDwC8AI5x0bo/LXA58F3gkbZkOfAaoArbEYf8AbASagNuAI+LwBXH6SWnzLwI+FV9/HHgE+AnQDGwB3pE27THAYqANuBf4FfC/Q6yrOcAd8bs2AQ8DFWkxH5c2bfr3Px2oBv4d2B3X+UfjuEsIyU0v0A7cnrZd/hV4GugBJhFOyptirGuA9w2I7x+AtWnjX0k40SWBrrj8/xenPSVtuz0FnD7GdfIpwg+B/YfZx/4V2BmXtx44Y8D44wAfYT89AagBzhti/IPAN4aZ/+OEpK8t7gMfTRv+aPyOrcC69PiAWcBV8bN3EvbVytGuc/btpxcD24GHUvvFUMcj8E3gJuB/4/KfAV4IfBWoJxxvZ45wbH8l7kMd8XscCvw9Lu8+4MC06YfbJz6R9j03A/+YxePiX4HauN4q2LefNxKS/oPS5v8YsC2O+xoDzl8D9uGWtDh+B9Snjf8j8IX08wXwYqAbSMTt1pIW8/8Af4vffxnwggzOrY8Qz4dpw/4MfD/t/RlAbXz9QsKxPjNt/MPAp3M57xCxD3VenwVcCzTE7fD1tHX8TdLOEww4N8f1/B3C8dYG3APMieO2x2nb49/rCOeFxYTjcjdwwxCxTiEca/PThn0TuBm4IX7WKuDlaeNHfS4d5Ph8MeFc8uH4/pWEa2Ub4bi9geH39anAz4Bd8e9nwNS089IjA2Laeywxwj4JvI1wLmslnNsWE6+Jg6y/1wBL4rauidNPGfC5nyZcj1vi51ocV0m4tu4mnBc+w4Dr8YDPGvRawPP3nQvZd5z/B88/L95I2A/bgNXAwky27RDrtQp407DH8kgHezH9DVhZM4A/ANemjT8deCnhZPsywgX8vXHc0XHFfRiYTEjWTk7b6b4bhz1O3LnjuOvj3wzgJYSTxsCk7l7gIGA68Ja407yScCD8EnhosBNH2skjPanrIxyklcA/EQ6g1E65BPhpXO4b4/cZKoH5L+A38btOBt6QtpyRLl79aZ/zJsJF9oSB0w7YLk8CRwLT47APAkfEbfGhuIzD08btBF5NSJyPA44euI3j+3mEg+XsuKy3xfdzx7BOrgf+MMz+dULcvulJ+AsGTDNkUgf8GuiM63cVgySPhP0wARwzxDL2I/yASK3vw4ET0/aPfuCLcZt+iHAiPCiO/yvw27iMQwj78j+OYZ0viN/h2ris6WSW1HUDbyck9dcSLiJfi7H+A/FHzzDH9lJCIjePkAiuAl4BTAMeICbCGewT7wReEL/nm+I2SV3oxntc/JCwr00HPh9jnh+H/Ra4Lk7/EsIF/41x3E/j/M9L6uL024FXxdfrCRedF6eNe8UQ54uBJ/1r2FfyPAn4E3B9BufWwZK6p4APpb2fE9fRwcD7gLUDpv8V8MtczjvE8TTUef1a4FbCD/UFwAbg4rT9daSkbhMhAZ0e3/9gsGnjsOsI+3oFYX89bYj1fCLQMWDYNwnn/XPjd/gXwrEzORvnUsK1aDvwrjh8CiEJ+Xz8vPcTfqgPt69/m7CvHwLMJSTR3xlmPxyY1A26T8Zt25b23b8YP3uopO5VhB90k+J2WEv8wZP2uXcAs4GjCAn9WXHcpwnJ45GE6/WDA7djJtcC0vYd9h3np8X1+pO4LQeeF88mXNP/C1ia9jnDbdvB1uttwOeGO5ZL8fbr/5lZC+Fi9jbgx6kR7r7I3Z9x96S7P0040N4UR38EuM/dr3P3PndvdPcn05Z7BOEXwk3u/nUAM6sEPkC4mHS6+xpCIjnQf7l7k7t3AR8Frnb3VR7ug38VeJ2ZLcjw+21z99+5eyJ+1uHAoWZ2FOHA/Q9373H3hwgliEPpi/MeHb/vwx73igylPmcx4RfWeSNM/wt33xHXAe5+k7vvitviBsIvjNQtzk8BP3L35R5sdPdtQyz3AuBOd78zLuteYAVw9hjWycGEX3dDSRBOYi8xs8nuvtXdN43wvfdy938mXEDeAPyFUBox0IXAw+6+ZZhFJYGTzGy6u9e4++q0cfXAz+I2vYGQALzTzA4lnDi+4O4d7l4PXAacH+cbzTpP+WZcVtcI06U87O53u3s/4df/XMKFsI+QUC8ws9nDzP9Ld69z952Ekptl7v6Eu3cTEtZXxOmG3CcA3P1v7r4pfs/FhFKWN8R5x3NcJAnngp64Tj4NfM3dq+Ox/k3g3Hi76lzgDnd/KI77jzj/UBYDbzKzw+L7m+P7YwjVBJ7KMEaAv7r743E7/Ak4eRTzptufcJ5NSb2eOci41PiZOZ53oEHP6/HcfT7wVXdvc/etwH8TSk8z9Xt33xC39Y0Mvx77CAnmEe7e7e5D1XWcTUhiBlrp7jfHY+WnhMTwFBj3ufQNhETgQne/Iw5LJUW/iOvsL4QfgOkG7usfBb7t7vXu3kC4HT6adTnUPnk2sDrtu/+MUDo4KHdf6e5L3b0/btPfsu8an/IDd29x9+2ExC31WecRzp073L2JkGANJdNrwbmEO1aPuHsv8J+ERDHdI/FclSCUer487fsMt20H00bYh4ZUiknde919NmGnvxRYnDoRmtlrY+XbBjNrJZx0UxXgjyT88hrKOwm/SH6TNmwuYeffkTYs/fVgw44g/AoCwN3bCb9S5o381YC0HdrdO+PL/eNym929I23a4S7KPybcAr7HzDab2YgVLNMM9jlHjDDPc9aLmV1oZk+aWUtMwk8i822R7mjgg6nlxGWdRrgwj3adNMb5BuXuG4EvEC7O9WZ2vZmN9L0HLiMRT+jzCSWtA13I4D8MUvN3EH6xfRqoiY2BXpQ2yc4BSUhq2xxN+KVbk7aefkv4ZQ2jW+cpg+3rw6lLe90F7I4nstR7CPtypvMPfJ+ad7h9AjN7h5ktNbOmOO5s9u174zkuGmKCmXI08Ne0GNYSLgaHErbJ3vUXt2vjMMteTCgheSPhdvciwsXqTYRkebiEcKD0i2Inw6/z4bQTEsqU1Ou2QcalxqcSllzNO9BQ+/UcwvGQfj7YRubnYRjdevx/hJKyx81stZl9cojpmhk8OU3fV5KE259HwLjPpZ8GHnP3RWnDjuD555GBx/rAff051zUyuyakG2pdDjxOfJBY9jKzF5rZHalGNMD32bcuRvVZDHOtGMW1YGD8nTz/OB8Yz7RUPcURtu1gZhJuKw+pFJM6YO/F8y+Ek+hpcfCfCb9KjnT3WYQEzeK4HYRbMkP5HXAXcKeZ7ReHNRCKguenTTdYa9v0g2MX4WQPQFzWwYQi8lTyMSNt+sPITA1wYFpsEIqXBxV/nX7Z3Y8lVNz/kpmdEUd3jhDDYJ+zK7XooT4y9cLMjiasz0sJt0xmA8+S2bYYuPwdwB/dfXba337u/gNGuU4I9bLePmD65364+5/d/TTCNnTCLYixmMSA72hmpxJOAjcPN6OH0q63EZKUdYR1mTLPzCztfWrb7CCUDM5JW08HuPuJcbrRrPPBhneQts/EkpARm9fnyJD7RKxEfAvhNsihcd+7k7jvjfO4GGzffMeAOKZ5KGmsIe1cYWYzCOeBoSwmlKqcHl8/ApxKSOoWDzHPaErex2I1aaUK8XWduzfGccea2cwB41fneN6Bhtqvd7Ov9CzlKMJ5GAbsz2R+HoZB1ru717r7P7j7EcA/Ar82s+MGmXcjYGY2MLlM31cqCNecXeM8l0JI6o4ys8vShtXw/PPIwOvawO/4nOsaz70mDDw3jGZdDjxObJBY0l1OOCce76FR2r+zb12M6rMY/lqR6bWghrT8wMymM/xxvlcG23YwL2aEUvuSTeosOAc4kPALGUIW2+Tu3Wb2GkLRfMqfgLea2XlmNsnMDjazkwcs9lLC7azb462vBOE22jfNbEYsMblwhNCuAz5hZifHC8z3CbeRtsZi653ABWZWGX/NDXdA7hWL1FcA3zKzKWZ2GvDuoaY3s3eZ2XHxIGklJL+pX/tPAh+JMZzF84uvSfucNwDvItxOg1B6cuwI4e5HOAgaYiyfIPwCSbkS+Bcze1XcjsfFHXyw5f8v8G4ze3uMd5qFvvTmj3adEIq+dwC3mNmLzKwi7gf/bmZnm9kJZvaWuN26CaVDyfgdzMymEepNEOOYGl8fYmbnm9n+Mca3E+r43D/g8y8CbnH3wUociMs61MzOiYlnD6HUIr2U5hDgc2Y22cw+SDjI73T3GsJtxv+20G1LhZm9wMxS23Y063wwGwi/MN9pZpMJlc4L1c3EkPsEYftMJf4gM7N3AGemZszCcZHuN8D3UuvRzObGcxKExP1dZnaahVbX32aY8627VxH2twuAxe6+h7BdPsDQSV0dMD8uf0zicTONcCGZHNdlKs5rgYvN7CUWbpt/nVA/CnffQFhf34jzvI9Qj/mWXM47iEHP6/HcfSNh+8yM2+hLhH2H+PlvNLOjzGwWoZpMphoI+8zeY8bMPhj3Pwilcc4gt9vjLbr7eP6+9Soze7+FEpwvEI79pYzvXAqhdPOs+F1/EIctIez3l8Z1dg4jt/6/Dvh63MfnEG4zptblU8CJ8Zo3jVC6lam/xXlT3/1zDJ9gzyTUOW63cD0e7G7IUG4knDvnm9mBDNM9yHDXggFuJpyLXh+Pw2+SeZI50rYdGNM8Ql3ApcMttBSTutvNrJ2wYb8HXOT76hz9M/BtM2sj7HQ3pmaK99fPBr5MaPX2JM/9NZgq+r2EUPR9a9xBLyW0okq1ArqOwetKpZZxH6H+zC2ELP4F7KvXBKHC+FcIRbQnEiqcZuojwGtj/N8gnPyGcjzh5NFOOIh/7e4PxnGfJyQ/LYS6Ev83YN5awolpF+Gk+Wl3XxfHXUWoZ9BiZgPnA8BD3cP/jp9bR2i88mja+JsI2+7PhJPO/xF2Vgj1HL4el/8v7r4DOIfwi6yBkJR9hX37bsbrxEPdprcSfundS9iHHicUdy8jJAM/IPzKryUkUKmT/dGEAzu1r3URfgBAODD/ibDfNBNKib7g7relPjvuS+cxzK3XqIJw8dkVv9ObeO6Jaxlh2+4mrMNz00owLiQkNWtiHDcTb0mOZp0PFpS7txKOryvZV+pckE6qh9snYsL8OcKx30zYP25Lm308x8VAP4/Lvieec5YS9kXiOekzhPVdE2MZaX0tBhrj90u9N4bu+uYBwv5Ya2a7R1j2UO4h7MuvB66Ir98Yv8NdwI8I9ZK2E25XfSNt3vOBhYTv9gPCvtiQh3n3GuG8/lnCfrqZUPL5Z0JfX3ioh3kDobX1SkLl+ozEW2zfAx6Nx8wphLq9y+K16Tbg8+6+eYhF/Jbn10e7lVDtojmOe7+H+m7jOZempmkh1D9/h5l9JyaW7ye0bm8h/JC4g2Gua4SGhCsI6+sZwj753bj8DYQfLfcR6oRl3Heiu+8mNBb4AeGaeHz69xvEvxCO6TZCKdcNmX5WnP5uQhK6ilBgM5ThrgXp8a8m7GfXE47zdkK95+HWZWreYbftID5CaOg37LJTrb4kQ2b2Q+Awd7+o0LHIxGKhc9hPxVsCIlKizOxR4FIvkg6IzWwZ8Bt3/32hYyllZrY/IVE+3odvDDfa5U4lJKNv9NAIbkjqVHAEsYh3CuHXyasJv27y2pu0iIiUD3c/tZCfH6tlrCeURH2UcPv7rkLGVKosPCHkfkKp+k8IucLWbH5GLJ170YgToqQuEzMJt1yPIBSR/jehqFxERKQUnUCoorAf4fb0ubFerozeOYSqWUa4RX2+F/AWqG6/ioiIiJSBUmwoISIiIiIDKKkTERERKQNlWaduzpw5vmDBgkKHISIiIjKilStX7nb3cXfoXpZJ3YIFC1ixYkWhwxAREREZkZmN9CzujOj2q4iIiEgZUFInIiIiUgaU1ImIiIiUASV1IiIiImVASZ2IiIhIGVBSJyIiIlIGlNSJiIiIlAEldSIiIiJlQEmdiIiISBlQUiciIlIialu7eWpHS6HDkCKlpE5ERKRE/Oetz/LB3yyhtrW70KFIEVJSJyIiUgK6ehMs2tBAbyLJrx6sKnQ4UoSU1ImIiJSAh6sa6O1PAnDd4zvY0dRZ4Iik2CipExERKQH3rKmjwsLrZNL5+f0qrZPnUlInIiJS5PoTSe5ZXUvSw3sHbllVzaaG9oLGJcVFSZ2IiEiRW7GtmT3d/c8ZZsBl924oTEBSlJTUiYiIFLl7VtdhA4YlHe54uoY1u/YUJCYpPkrqREREipi7c9fqWnyQcRUGP713fd5jkuKkpE5ERKSIrattY1dL16Djkg73ra3nie3NeY5KipGSOhERkSJ2z+q6YcdXGPzkHpXWiZI6ERGRonbX6prn1adLl3R4dGMjSzc35i0mKU5K6kRERIrUzpYu1ta0DVqfLl2FwY/uWof7SFNKOVNSJyIiUqTuXV2b0XRJh1XbW1i8oSHHEUkxU1InIiJSpO5eve8pEiOpMPjx3etVWjeBKakTEREpQi2dvSzb0rj3KRIjSTqs3rWHu0doWCHlS0mdiIhIEXpgXX3GCV1KKK1bR2K0M0pZUFInIiJShO5dk/mt15Skw6aGDm5/aldugpKipqRORESkyHT3JXhw/ehL6iCU1v33vevpSySzH5gUNSV1IiIiRebRjbvp7htbUpZ02NHUxS0rq7MclRQ7JXUiIiJF5p5RtHodjBlcdt8GevoT2QtKip6SOhERkSKSSDr3rKkd063XFHeo29PDdcu2Zy8wKXo5TerM7ItmttrMnjWz68xsmpkdY2bLzGyjmd1gZlPitFPj+41x/IK05Xw1Dl9vZm/PZcwiIiKF9MT2Zpo7+8a9HAN+8cBG1a2bQHKW1JnZPOBzwEJ3PwmoBM4Hfghc5u7HAc3AxXGWi4HmOPyyOB1m9pI434nAWcCvzawyV3GLiIgU0j1r6oZ91mumHGjq6KWmpTsLS5NSkOvbr5OA6WY2CZgB1ABvAW6O4/8AvDe+Pie+J44/w8wsDr/e3XvcfQuwEXhNjuMWERHJO3fnzmdqRnzW62hUN3dmcWlSzHKW1Ln7TuAnwHZCMtcKrARa3L0/TlYNzIuv5wE74rz9cfqD04cPMo+IiEjZqKpvp7q5K6vLrG7J7vKkeOXy9uuBhFK2Y4AjgP0It09z9XmXmNkKM1vR0KAHGouISOm5d032H/G1M8tJohSvXN5+fSuwxd0b3L0P+AtwKjA73o4FmA/sjK93AkcCxPGzgMb04YPMs5e7X+HuC9194dy5c3PxfURERHLqrmdrsWxUqIsqzdipkroJI5dJ3XbgFDObEevGnQGsAR4Ezo3TXATcGl/fFt8Txz/g7h6Hnx9bxx4DHA88nsO4RURE8q6mtYtndrbiWaxQl3RXSd0EMmnkScbG3ZeZ2c3AKqAfeAK4AvgbcL2ZfTcOuyrOchXwRzPbCDQRWrzi7qvN7EZCQtgPfMbd1ZuiiIiUlftycOvVge1NaigxUeQsqQNw928A3xgweDODtF51927gg0Ms53vA97IeoIiISJG4Z014isR4Oh0eTN2ebpJJp2I8j6iQkqAnSoiIiBRYa1cfj21szHpCB9CfdOrberK/YCk6SupEREQKbNH6ehLZrEw3wM4W3YKdCJTUiYiIFFjq1muuZLvvOylOSupEREQKqKc/wQNr63Ny6zVFSd3EoKRORESkgJZsaqSrL3edOlQY6qtuglBSJyIiUkC5vvXq6KkSE4WSOhERkQJaua05p7de3dVX3UShpE5ERKSAduXh1uiuli48h61rpTgoqRMRESmQrt4Ebd39Of+cnv4kzZ19Of8cKSwldSIiIgWyqzV/dd1Ur678KakTEREpkJqW7rx9VnWz6tWVOyV1IiIiBZLXkjp1a1L2lNSJiIgUSL5K6ipMHRBPBErqRERECqSmtYtKy2EndWlUUlf+lNSJiIgUyM6WLpLkvquRpMMO9VVX9pTUiYiIFEh1cxf56j5Ot1/Ln5I6ERGRAnB3alvz1/q1vaef9p7c94knhaOkTkREpAD2dPfT1ZfI62eqr7rypqRORESkAGry2J1Jys4W1asrZ0rqRERECiCfHQ+nqF5deVNSJyIiUgD57HgYwNDt13KnpE5ERKQA8l1SV2FGtfqqK2tK6kRERApgVx47HgZIuFOtvurKmpI6ERGRAqhp6SaZr07qoh26/VrWlNSJiIgUQHVzZx6eJfFcTR29dOe5GxXJHyV1IiIieebu1O3pKchn1+Sxw2PJLyV1IiIiedbU0UtvIlmQz1YL2PKlpE5ERCTPCllapg6Iy5eSOhERkTzbVcCuRdQBcflSUiciIpJnhSqpqzTT7dcypqROREQkz3a1dpG/Hur2SbirpK6MKakTERHJs5qWbiry2PFwuu3qgLhsKakTERHJs50tXSTy3PFwSn1bN/0FankruaWkTkREJM+qmwtXWpZ0qGsrTB95kltK6kRERPIokXQaCpxUqbFEeVJSJyIikkcNbT0kC3PndS/1VVeelNSJiIjk0a7WwpeSVTcVPgbJPiV1IiIieVTTUthnr1ZaaKgh5UdJnYiISB7VFLikLomSunKlpE5ERCSPdrV0U6Au6gBwh22NqlNXjpTUiYiI5FFNgZ4mka62tRsvUD95kjtK6kRERPJoZ0tXwVu/9iaS7G7vLWwQknVK6kRERPKoWPqIU7268qOkTkREJE96+5M0dRRHCVmxJJeSPUrqRERE8qRuTzfFUpNNHRCXHyV1IiIieVLTWtg+6lIqDKpVUld2lNSJiIjkSaH7qEun26/lJ6dJnZnNNrObzWydma01s9eZ2UFmdq+ZVcX/B8Zpzcx+YWYbzexpM3tl2nIuitNXmdlFuYxZREQkV3YV+GkSKUmH7U26/Vpucl1S93PgLnd/EfByYC3wb8D97n48cH98D/AO4Pj4dwlwOYCZHQR8A3gt8BrgG6lEUEREpJTUtHZRUehO6iK1fi0/OUvqzGwW8EbgKgB373X3FuAc4A9xsj8A742vzwGu9WApMNvMDgfeDtzr7k3u3gzcC5yVq7hFRERypVhK6gA6exO0dvUVOgzJolyW1B0DNAC/N7MnzOxKM9sPONTda+I0tcCh8fU8YEfa/NVx2FDDRURESsrOls6CdzycTvXqysuokjozO9DMXpbh5JOAVwKXu/srgA723WoFwMMzSrKye5vZJWa2wsxWNDQ0ZGORIiIiWVVsSZRuwZaXEZM6M1tkZgfEum2rgN+Z2U8zWHY1UO3uy+L7mwlJXl28rUr8Xx/H7wSOTJt/fhw21PDncPcr3H2huy+cO3duBuGJiIjkT1dvgj3d/YUO4zl2NquxRDnJpKRulrvvAd5PqPP2WuCtI83k7rXADjM7IQ46A1gD3AakWrBeBNwaX98GXBhbwZ4CtMbbtHcDZ8ZSwgOBM+MwERGRklFM3ZkAmKmkrtxMymSaWKJ2HvC1US7/s8CfzGwKsBn4BCGRvNHMLga2xeUC3AmcDWwEOuO0uHuTmX0HWB6n+7a7N40yDhERkYIqlo6HUypQB8TlJpOk7luEkrFH3H25mR0LVGWycHd/Elg4yKgzBpnWgc8MsZyrgasz+UwREZFitKvISsUSDjt0+7WsZJLU1bj73sYR7r45wzp1IiIiEhVbSR1AdVNxJZoyPpnUqftlhsNERERkCMXU8XBKS1cfXb2JQochWTJkSZ2ZvQ54PTDXzL6UNuoAoDLXgYmIiJSTXS3d2enDK8t2tnRx3CH7FzoMyYLhSuqmAPsTEr+ZaX97gHNzH5qIiEj5qG7uxIswq1ML2PIxZEmduy8GFpvZNe6+LY8xiYiIlJ1irFMHxdchsoxdJg0lpprZFcCC9Ond/S25CkpERKSctHX30VmEddeM8OgyKQ+ZJHU3Ab8BrgSKb48UEREpcsVaSldhppK6MpJJUtfv7pfnPBIREZEyVWx91KUk3NmhpK5sZNKlye1m9s9mdriZHZT6y3lkIiIiZaJYS+oAdjTp9mu5yKSkLvWc1q+kDXPg2OyHIyIiUn5qirSkDqChvYe+RJLJlZmU80gxGzGpc/dj8hGIiIhIudrV2k2lGYki7NPEHWpbuznyoBmFDkXGacSkzswuHGy4u1+b/XBERETKT01LF8kiTOhSqpu7lNSVgUxuv7467fU04AxgFaCkTkREJAM7mruK8mkSKeqAuDxkcvv1s+nvzWw2cH2uAhIRESkn7k7tnuJtKAHqgLhcjKVWZAegenYiIiIZaO7so7c/WegwhlRppg6Iy0Qmdepuh72lxpXAi4EbcxmUiIhIuSjWPupSku5Uq6SuLGRSp+4naa/7gW3uXp2jeERERMpKMfdRB6HUZrv6qisLI95+dffFwDpgJnAg0JvroERERMpFTWvxl4LVtnaTTBZzUw7JxIhJnZmdBzwOfBA4D1hmZufmOjAREZFysKulGyt0ECPoTzoN7T2FDkPGKZPbr18DXu3u9QBmNhe4D7g5l4GJiIiUg5rWLiqKtOPhdNXNXRx6wLRChyHjkEnr14pUQhc1ZjifiIjIhLerpavoEzpQX3XlIJOSurvM7G7guvj+Q8DfcxeSiIhI+SiVlqXqq670ZdL58FfM7P3AaXHQFe7+19yGJSIiUvqSSaehrfjrqlUY6quuDAyZ1JnZccCh7v6ou/8F+EscfpqZvcDdN+UrSBERkVK0u72H/hJpVaqSutI3XN24nwF7BhneGseJiIjIMHYVeR91KUmHbeqrruQNl9Qd6u7PDBwYhy3IWUQiIiJloqaEGh/saunCS6BBhwxtuKRu9jDjpmc5DhERkbJTKiV1AN19SVo6+wodhozDcEndCjP7h4EDzexTwMrchSQiIlIealq6sGLveThNqbTUlcEN1/r1C8Bfzeyj7EviFgJTgPflOC4REZGSV9PaTQVGgtK4rbmlsYOXzp9V6DBkjIZM6ty9Dni9mb0ZOCkO/pu7P5CXyERERErczhLpeDhlc0N7oUOQccikn7oHgQfzEIuIiEhZKaVuQirN2NzQUegwZBz0uC8REZEc6Esk2d1R/B0PpyTc2VivkrpSpqROREQkB+r2dFNCd14B2LK7Q92alDAldSIiIjlQU0LdmaR09SWo21M6pYvyXMM9JqwNhm6u4+4H5CQiERGRMrCrhDoeTre5oZ3DZk0rdBgyBsO1fp0JYGbfAWqAPwIGfBQ4PC/RiYiIlKhSLKkD2LS7g9cfN6fQYcgYZHL79T3u/mt3b3P3Pe5+OXBOrgMTEREpZTUtXVSUUMfDABWmbk1KWSZJXYeZfdTMKs2sInZGrDbPIiIiwyilR4SlU7cmpSuTpO4jwHlAXfz7YBwmIiIiQ9jZ3EWyxBqSJh2q6tsKHYaMUSadD29Ft1tFRERGpVQbStS0dNPdl2Da5MpChyKjNGJJnZm90MzuN7Nn4/uXmdnXcx+aiIhIaeruS9DS1VfoMMbEgW2NnYUOQ8Ygk9uvvwO+CvQBuPvTwPm5DKpcra3Zw/lXLKGjp7/QoYiISA7Vlmh9uhQ1lihNmSR1M9z98QHDlJWMwQPr6lm6uYk1NXsKHYqIiOTQrtbSvPWasnm3GkuUokySut1m9gJiR8Rmdi6h3zoZpVSLoi06WEREylpNS+mW1FUabFJJXUkasaEE8BngCuBFZrYT2ELogFhGaVNDaFG0VUmdiEhZqynhkrqEw6Z6JXWlaNikzswqgX9297ea2X5AhburrfMYuDub6kMyt7VRSZ2ISDnb1dpNhVFyXZqkbGrowN0xK7Hekye4YW+/unsCOC2+7hhLQhc7LX7CzO6I748xs2VmttHMbjCzKXH41Ph+Yxy/IG0ZX43D15vZ20cbQzFo6uilLTaQUMeOIiLlraZEuzNJae/pp7Gjt9BhyChlUqfuCTO7zcw+ZmbvT/2N4jM+D6xNe/9D4DJ3Pw5oBi6Owy8GmuPwy+J0mNlLCK1tTwTOAn4dSxBLSnql062N4ReQiIiUp+oS7Hh4IBVAlJ5MkrppQCPwFuDd8e9dmSzczOYD7wSujO8tLufmOMkfgPfG1+fE98TxZ8TpzwGud/ced98CbARek8nnF5P05uHdfUka2noKGI2IiOSKu7OzxEvqQN2alKJMnijxiXEs/2fA/wNmxvcHAy3unuoSpRqYF1/PA3bEz+w3s9Y4/Txgadoy0+cpGZsbOjBiE2JCC9hDDphWyJBERCQH6tt66OxNFDqMcTHUrUkpGjGpM7Pfsy8X2cvdPznCfO8C6t19pZmdPtYAM2VmlwCXABx11FG5/rhR29TQgRmk7rpubezgtcceXNigREQk66rqSr+Ey0wldaUoky5N7kh7PQ14H7Arg/lOBd5jZmfH+Q4Afg7MNrNJsbRuPrAzTr8TOBKoNrNJwCzCbd/U8JT0efZy9ysIXa+wcOHCoqvJUFXXtrd+hQFbdusRLCIi5aiqvvQ7iUh6eSSnE82Iderc/Za0vz8B5wELM5jvq+4+390XEBo6PODuHwUeBM6Nk10E3Bpf3xbfE8c/4KE1wW3A+bF17DHA8cDAJ1wUtb5Ekuq0+hVm6qtORKRcVdW3U1EGPYFUt3TRl0gWOgwZhUwaSgx0PHDIOD7zX4EvmdlGQp25q+Lwq4CD4/AvAf8G4O6rgRuBNcBdwGdiVyslY3tTJ4m0ZlBJV2/dIiLlqqqujXLo4CCRdHY06a5SKcmkTl0bz61TV0tIzDLm7ouARfH1ZgZpveru3cAHh5j/e8D3RvOZxWSwZuHbmjrVsaOISBnaUNf+/IroJWpzQwfHzt2/0GFIhjJp/TpzpGlkeINVNu3tT1K3p4fDZqkFrIhIuWhs76G1q6/QYWTN5t3twKGFDkMyNOLtVzM7NT4iDDO7wMx+amZH5z608rG5oYPKQQrktqhenYhIWakqo2emVpg6IC41mdSpuxzoNLOXA18GNgHX5jSqMrOpoZ3EIGXxegasiEh5KaekzlX/u+RkktT1x1ao5wC/cvf/YV9nwpKBjYMc5IZawIqIlJuNdW1l0fIVQmX6wa5fUrwySerazOyrwAXA38ysApic27DKR2tnHy2D1K8w0+1XEZFyU1XfXhYtX1OaO/vKqo5gucskqfsQ0ANc7O61hM5/f5zTqMrIpt2D/8pRtyYiIuVnfW1b2bR8TdGTJUpHJp0P17r7T9394fh+u7urTl2GhqtkuqOpi2Sy3A5/EZGJqbWzj8aO3kKHkXVqLFE6Mmn9eoqZLTezdjPrNbOEmbXmI7hysLmhnaGqV/QmktTu6c5rPCIikhsbG0r/8WADGaluTaQUZHL79VfAh4EqYDrwKeDXuQyqnGxu6GC4/oXVWEJEpDyU47NS1a1JacnoMWHuvhGodPeEu/8eOCu3YZWPqvo2hrvDukXdmoiIlIWq+vZhf8SXooSXVzct5W7EJ0oQ+qibAjxpZj8CahjbM2MnnETS2dY49HPz1K2JiEj52FgfqtuUW03pbY0dJJJOZbn01VLGMknOPhanuxToAI4EPpDLoMpFdXMn/cMU04VuTfSwZBGRcrCuds+wd2ZKVV/C2dXSVegwJAOZPPt1m5lNBw5392/lIaayMVI9BHVrIiJSHtp7+qnb01PoMHJmU0M7Rx40o9BhyAgyaf36buBJ4K74/mQzuy3HcZWFTBK2HU2dJMrxp52IyASyqczrnamxRGnI5PbrN4HXAC0A7v4kcEzOIiojm3d3jPi4mP6kU9OqYm0RkVJWzo0JKkzdmpSKTJK6Pncf2C+dipYysDnDx8VsVb06EZGSlmokUY7cYVO9SupKQSZJ3Woz+whQaWbHm9kvgcdyHFdZqKpvzyj7VbcmIiKlbWN9W9l1Z5LihKRVil8mSd1ngRMJz3+9DtgDfCGHMZWFtu7MHhdjpm5NRERK3bra4fskLXUN7T109PQXOgwZQSatXzuBr8U/ydCWDBM19VUnIlLauvsS7Gwu/7rRW3Z3cNK8WYUOQ4YxZFI3UgtXd39P9sMpH5m2FEo6bFS3JiIiJWtTQ2ZVbUrdpoZ2JXVFbriSutcBOwi3XJdB2dYBzYnNo0jUdjZ30Z9IMqlSD+oQESk1E6W+mbo1KX7DZRGHAf8OnAT8HHgbsNvdF7v74nwEV8o27e6gMsM0uD/p7Grpzm1AIiKSExMhqas0Y7OqChW9IZM6d0+4+13ufhFwCrARWGRml+YtuhK2sb6dxCjK47eqBayISEmqqmsfsU/SUpdwZ2N9W6HDkBEM21DCzKYC7wQ+DCwAfgH8NfdhlbZk0jNuKJGytbGDNzI3RxGJiEiulOszXwfasrsDd8fKte+WMjBcQ4lrCbde7wS+5e7P5i2qElezp5ve/mTG01dY5q1lRUSkePT2J9neNDE6kO/uS1K7p5vDZ00vdCgyhOHq1F0AHA98HnjMzPbEvzYz25Of8ErTaBpJpKhbExGR0rO1sWNClNKlqLFEcRuypM7d1RRzjEb7YOekwyYdKCIiJWciNJJIt7mhnVOPm1PoMGQIStxyYPPujlFXmt3ZEro1ERGR0lFVN3GSugpTAUSxU1KXA2PpiDKRdKonQI/kIiLlpKq+rexbvqZTtybFTUldDmysb8fHUMdii7o1EREpKevL/Jmv6ZIOVXXq1qSYKanLss7efur29Ixp3m36BSQiUjL6E8kJ13NBbWs33X2JQochQ1BSl2VjPcArDLY2Toxm8SIi5WBHcxf9E6WYLnLUWX4xU1KXZeNp7j3RfvGJiJSyiXorcosaSxStYZ8oIaM31qQudGsycVpRlYqWzl6WbWli6eZGlmxq5LXHHMS3zjmp0GGJSBGommDdmaSosUTxUlKXZZt3t1NpjOq5rym7WrroSySZXKkC1EJp7ezj8a0hiXt0427W17bhgBm4w7raNt7x0sM55diDCx2qiBTYxvp2Ks1IjKVlXImqNBVAFDMldVm2sb59TAkdhNK6HU2dHDt3/+wGJUNq7+lnWSyFe2xTI2tr9oQkLo5PbcrUObvC4D/+71n+/vk3MEnJt8iEtr62bUIldBAKLEbbwb7kj5K6LHL3cT9CZWtjh5K6PNmyu4MPXP4YTR29z0/ihpgn6eGWy5+Wbeei1y/IfZAiUpSSSZ+wJVabGjpwd8wmUAd9JUJFDVlU39ZD1zibem/drRaw+VDf1s0FVy6jpbMXCElcpr+3Dfjx3etpbB9b1zUiUvp2tnTR0z8xnwLU3tNPY0dvocOQQSipy6Lx/moL3ZqoAmqutXX3ceFVj7OrtWtMnYY60NHTz4/vXp/12ESkNEy0Z74ONN67UpIbSuqyaLw7uaNuTXKtpz/BJdeuDA0gxlEVxoEblu/g6eqWbIUmIiWkqn5idmeSsnmC3noudkrqsmhzQwfjqWLg6tYkp5JJ50s3PMWSzY2jfjbvYCw2mkhOsM5HRQSq6kJPBxORoW5NipWSuiza1NDOeI/xmtZueidoPY1ccne+dftq/vZMTdaWmXR4qrqVW1ZVZ22ZIlIaNtS1jbmng1JnppK6YqWkLouq6sf/YGd32N6kxhLZ9utFm/jDkm1ZX64B379zLXu6+7K+bBEpTu4+YTsehvCDdkPdxP3+xUxJXZZ09yWoaenOyrK2qlg7q25cviNnjRocaO7s42f3VuVk+SJSfOr29NDZO7Efar8zdpYvxUVJXZZsa+zMSj0tUAvYbLpvTR3/9penx31bfCTXPLaFDRP0OZAiE81EbyQBkEi67ioVISV1WZKt+gXq1iR7Vm5r4p//tGpUfdCNxzduXY1PsN7lRSaiKt16BPRkiWKkpC5LstUSyF3dmmRDVV0bH796Of3J5Li6LslU0mHJ5kb+/mxt7j9MRAqqqr6digna8jXFCOc8KS45S+rM7Egze9DM1pjZajP7fBx+kJnda2ZV8f+BcbiZ2S/MbKOZPW1mr0xb1kVx+iozuyhXMY/HpobwYOfxcmBTvZK68djV0sVHr1xGR2//uBuujEaFwbduX03XBK9rI1LuqurH189lOXDgzmdqdHeiyOSypK4f+LK7vwQ4BfiMmb0E+Dfgfnc/Hrg/vgd4B3B8/LsEuBxCEgh8A3gt8BrgG6lEsJhsqm/P2oOd6/Z00z3Ox41NVA1tPVxw1TJ2t/fkNaGDUFpXt6eHXy/amN8PFpG8cffQeXmhAykCdXt6eGZna6HDkDQ5S+rcvcbdV8XXbcBaYB5wDvCHONkfgPfG1+cA13qwFJhtZocDbwfudfcmd28G7gXOylXcY+HubMriI1Mc2KEKqKO2o6mT91/+KFt2d+Q9oUv3m8Wb2N6o7SdSjho7emnr7i90GEXjLlU5KSp5qVNnZguAVwDLgEPdPdUDbC1waHw9D9iRNlt1HDbU8IGfcYmZrTCzFQ0NDdn9AiNo7OilvSe7B7nq1Y3Oxvo23n/5Y+xs7ir4bZFE0vn2HasLG4SI5IQaSexjwN90C7ao5DypM7P9gVuAL7j7nvRxHvaErOwN7n6Fuy9094Vz587NxiIzlosHG6sFbOaerm7h/Zc/RmMBbrkOJulw39p6Fq2vL3QoIpJlG9WdyV5O6M5ro1rBFo1JuVy4mU0mJHR/cve/xMF1Zna4u9fE26upK99O4Mi02efHYTuB0wcMX5TLuEcr249LCd2a6PZdJpZsauST1yynpz9RFAldSoXBJ69ZzuwZUzhovynM3X8qB+0XXh+43xQOTvt/0H5TOHzWNGbPmFLosEVkBKmWr8V0vim0u56t5fhDZxY6DCGHSZ2ZGXAVsNbdf5o26jbgIuAH8f+tacMvNbPrCY0iWmPidzfw/bTGEWcCX81V3GOxeXcHRvb6QnOHLTko/Ss3962p45/+tJJE0ovuBJuKp6mjl6aOXjbWh9bRzuCxVppx7qvmc+lbjuPIg2bkN1gRyVhVXXvBq3gUEwPufLaGz55xfKFDEXJbUncq8DHgGTN7Mg77d0Iyd6OZXQxsA86L4+4EzgY2Ap3AJwDcvcnMvgMsj9N9292bchj3qG1uaMeMrB3oTugiRYb21yeq+fKNT4WOhUvkBDtc6+iEOzeu2MHNK6v5wKvmcembj+eog5XciRSb9XVq+ZrOgbU1bexo6tQP0iKQs6TO3R+BIZ/OdMYg0zvwmSGWdTVwdfaiy64Nde1ZLymqb+uhuy/BtMmV2V1wGbh2yVb+89bVWS0dLQZOSO5uWlHNzSurQ8mdkjuRotHSGUre5fnuXl3Lp95wbKHDmPD0RIlx6ksk2dnSlZNlb1O9uudwd355fxX/eWtoWVpOCV06J9y+vWllNaf/5EG+ctNTbFPDGZGCU4OAwVUYeppOkchpQ4mJYFtjJ4kcVejasruDEw5T5VMICd33/raWKx/ZUuhQ8sY9JHg3r6rmllXVvP+V87n0zcexYM5+hQ6t5Gysb+PBdQ30JpJUmFFhUFlhVJiF/xVGpRmVFWBmTK40TjpiFscdsj+WhSfFSHmoUlI3qKTDqm3N1Ld1c8jMaYUOZ0JTUjdO2W75mk7dmkBPf4JN9R1c/egWbl5ZXehwCiKV3N2yqpq/rKrmnJPncfoJczn5yNkcddAMJR1D2NHUye1P7+LWJ3exvnZs3VAcddAMzjrpMM58yaG84qgDqZzoD/yc4DbWZ7f+dDlx4N41dXz0tUcXOpQJTUndOG3OUSfBFQZbJ1AHxImks72pk/W1bayvbWNDXRtravawrbGwT4coJqnk7tYnd/LXJ3YCMGv6ZF511GxOPupATj5yNi8/cjazpk8ubKAFVL+nmzueruHWp3by1I7w+KLx5Lzbmzr53UObueKhzRw4YzJvP/EwzjzxUF7/gjmq7zoBVdWpj7qhVFjo2kRJXWEpqRunJZsac9JnkXtpPVWiuy/BZfdt4J7VdUybXMF+UyYxfUol0ydX7v0/Le319MmVJNypqmtnXe0equrb6e1P7l2e+oEaWvp6ae3qY9GGBh5Yv+8pKgsOnsErjz6QVxw5m5OPPJBj5u7H/lPL91Bv7ujlrtW13PrkTpZtbsIJ+0/KeEtVUrM3d/Zx44odXL98B9MmV/DmEw7hzBMP5S0nHMqsGRM3kZ5I1tW2qZRuCEmHxzY20trZp+OhgMr3TJ8HHT39PLpxd06SDyc3T6rIhaerW/jC9U8+r9TSCKUkqduD7r63tCml0mzQrj6U0GVu4Lra2tjJ9qZO/rJq595hM6dNYv6B0znqoBnMP3AG82ZPZ/6B08PrA6eXXOnezpYuFq2v5941dTy8YTcJdyps376Vq/0ntdzuviR3r67l78/WUmFw0rxZvHTeLE48YhYnzTuAFx46UyV5Zaa5o5f6tp5Ch1HUEu7cv66O979yfqFDmbCU1I3Dw1UN9Ocw+2ho76GrN8H0KcV5cehLJPnVAxv55QNVg47f24fcCP2zSfYN3C3buvtZWxNubQ+2SfabUsn8g2Zw2nFzOPdV83nx4QfkLdZM9PQnWLG1mUXr67l/Xf3eHzzpJbr5/iGQ/rnPVLfy7M7WvcMqzTjukP156fxZnHTEAZw0bxYvPvwA9ivjEtNyd/3yHSNPNMFVWOjaREld4egMMw73ra3P+W3CXzxQxbRJlXT09tPe009H/Gvv6aetO/x19PTT1Zfg0JnTOOGwmRx/6P4cd0j4e8Hc/XNSYlBV18YXbniS1bv2jDyxFI2h9tWO3kSoy1jbxlWPbOElhx/AeQvn856T53HQfmN/fFlvf5Knq1toaOvhwPhItIP2m8KBM6aM2OigurmTResbWLS+nkc27qa7L/m8vgmLpUR3YKKccGd9XRtV9W3cvDIMM+Dog2dw6nFz+NjrjuZFhxVX4ixD6+1PctUjm8uub8xsSzo8uL6Bzt5+ZkxRelEI5mVYUrJw4UJfsWJFTj8jkXRe+Z17ae3qy+nnpJilOhUc+lFT8Py6aAYcMXt6SPYO2ZfsvfjwA8aU7CWSztWPbOFHd68rysdzSXak9qPKCuNtLz6Ec191JG86YS6TK4fv2rKnP8HT1a0s3dTI0i2NLN/a/Jy6kilGuCV8YHwu7sH770v4unqTPLi+fm+d0nKrX5lqPfmaBQfxydMW8NYXH8qkEdarFNb/PbGTL9zwZKHDKBm/ueCVnHXS4YUOo6SY2Up3Xzje5SiVHqMntjfnLaGDcBFIhFfDTjfw4ueE+kc1rV08uK5+79yTK41XHX0gpx03h9cfN4eXzZs14oVle2MnX7rxSVZsax7jt5BSkdqPEknnnjV13LW6jgNnTOYDr5zPuQvn7y1l6ulP8NSOVpZubmTJpkZWbt+XxA2XjDmwp7ufPd39bGvspMJCopdwirY0LltSv6NXbGvi8a1NHHrAVC56/QLOf/VR4yoVldxwd654aHPZ/bjIlVQrWCV1haGSujH6r7+v5YrFm0u6KD79JDV9SiWvO/ZgTj1uDqcedzAnHDrzOQ0crnt8B9++YzW9/Umd2CawVML1ksMP4IDpk1i1rYXexMhJnAwttU6nVFbw3lccwUWvX8CJR8wqdFgSLdvcyIeuWFroMErKjCmVPPmfZzJlkkqgM6WSugK7Z3VdoUMYt/QLcFdvgkXr63lgXT0AB86YzGnHz+V1xx7M3atrWbyhQfVJZO/2X1cb6lKm70NK6MYmtdp6E0luWlnNjSuqWXj0gXzi1GM488RDR7zlLbl15cNb9INllDp7Ezy2aTenn3BIoUOZcJTUjcGW3R0l1YdcptJPWs2dffzt6V3c/tSuvZ236pwmKbrA5Ubqxsmq7c2s2NbM0QfN4M+XnMK82dMLG9gEtXV3B/etrdO5b5RSrWCV1OWffgKOwf1rS7+ULhOpC3cZ3qEXKWqpY29Hcycf+u0S6vZ0FzagCer3j06cZ01nU9Lh78/W5uy56DI0JXVjcM+aOvQISBHJtaTDrpYuPvTbJTSo49u8au3s4/rlO1RKN0YtnX2s2NpU6DAmHCV1o9TS2cuKrU26/SQieZF02NbUyflXLKGxXYldvvz58e30DNIdj2TGgLvLoO55qVFSN0qL1jcooRORvHKHzbs7+MiVy2jp7C10OGWvL5Hk6ke3oBsyY+fAnc/UUI49bBQzJXWjdO9a3XoVkfxzhw21bXz0ymV57SNzIrrzmRoa2np063Wcavd08+xOPXUon5TUjUJvf5IH1tWrpE5ECsKBNbv2cOFVy2jrVmKXC+7Obxdv1o/3LLlrdU2hQ5hQlNSNwrItjXT1JgodhohMYA48Xd3Kx3+/nI6e/kKHU3Ye39LEmpo9+vGeBQbc+UxtocOYUJTUjcJ9a+r29tkmIlIoDqzc1swnr1muH5pZdtUjW1RKlyVO6Nd1Y31boUOZMJTUZcg9PANTdT5FpFgs29LEp65dTnefErts2Lq7g3vX1KmULsvuelaldfmipC5D62rbqGlVB6AiUlwe3djIp/93JT39SuzGS50NZ59uweaXkroM3bdG/e2ISHFatL6Bf/rfVexo6ix0KCVLnQ3nhgNravZQ3ax9Mx+U1GXoHtWnE5Ei9sC6et74owf5xO8f54F1dXpE0yhdt1ydDefSjSuqCx3ChDCp0AGUgro93Tyzs7XQYYiIDMuBxRsaeHB9A4fPmsYFpxzNeQuPZO7MqYUOraj1JZJc9UjobFipcG788v4qXnzYTN7x0sMLHUpZU0ldBh5YV1/oEEREMpIqoKtt7ebHd6/nlP+6n0v/vIqlmxvVu/8Q1Nlwfnzu+id4fIueB5tLSuoycO8aPUVCREpLKkFJJJ07n6nh/CuWcsZ/L+b3j27REynSuDtXPKTOhnPNCfvixdcsp6pOXZzkipK6EXT29vNI1W41cReRkpU6f21p7OBbt6/hNd+7jysf3kxSJzaWb21m9S51NpwPSYeO3n4uuGoZtepNIieU1I3gkard9CZUeVZESl/q7mtPf5Lv/m0tH/7d0gnfKvHKh1VKl09Jh4a2Hi68ehl79Ki7rFNSN4L71urWq4iUp+Vbm3jbZQ9x04odE7K+3UMbGtTZcAEkHarq2vnHa1fSqxbHWaWkbhjJpOuAF5GylXTo7k3wlZuf5pJrV7K7vafQIeVFMun84v4qLrr6cXVVVSAOLNncyFduekrVALJISd0wnqxuoblTxcMiUr5Sl9P71tbx1p8u5u7V5d37f3NHL5+4Zjk/vXcDDvrRXmC3PrWLH969rtBhlA0ldcO4b00d+hEnIhOBA3u6+vjHP67kyzc+VZb1nZ7c0cI7fv4wD21oKHQokua3izfrEW1ZoqRuGOX+i1VEJF2q1OqWVdWc+dOHeGzT7sIGlCXuzh+XbOUDlz9GfVu3+qMrQt++fQ1/f6am0GGUPCV1Q9je2Mmmhg4d/CIyIdW3dfOR3y3j27evoas3Uehwxqyjp5/PX/8E/3HrahJJ1+3WIqbOicdPSd0Q7ltbV+gQREQKJpX8XP3oFt744we5acWOknue7Mb6Nt79y0e47SmVABW7VOfEn1TnxOOipG4IeoqEiEiwu72Hr9z8NO/4+UMlUx/t1id38q5fPsLWxo5ChyIZSnro8P+Dv13CkztaCh1OSVJSN4jWzj6WbWlUMb2ICPs6Ld5Y386FVz/OBVcuY23NnsIGNYQ93X38563P8vnrn6SnL6nzeIlJemiwc95vl3DvGt0xG61JhQ6gGC3aUK8TgYjIAKnz4mObdnP2zx/m3FfN58tnnsBhs6YVNK4dTZ3cv7aOe9bUsWxzE4mYheo0XpqSDn2JJJf8cQXffs+JfOx1CwodUslQUjfAym3N/Py+KipM/ReJiAwmdW68eWU1tz61i0vecCz/+KZjmTltcl4+3915Zmcr962p467VtWyoawfQebuMpEqH/+PW1VS3dPGvb38RFaoTNSIrx0fDLFy40FesWDGqeRraevjB39dxy6pqnRhEREZp9ozJfPltL+R1L5hDa1cvzR19NHf20tLZR0tXL82dfbR0huFNHb00d/ZiBocdMI1DDpjGITOncsjMacydOZVDZk4N/w+Yypz9pzK5soLuvgRLNjdy35o67l5dy+72XgAMlchNBO9+2eH85LyXM3VSZaFDyQkzW+nuC8e9nIme1PUnkly7ZBs/uWc9Xb0JnRxERMZgpOSq0sIUiUEmShXADPVjetb0yfT0J+juS+pH9wT2mgUH8bsLFzJrRn5KhPMpW0ndhG4osXRzI2f9/GG+fccaJXQiIuMw0vkz4YMndBCStOEStdauPrr7knunlYlp+dYm3nf5o1Q3dxY6lKI1IZO62tZuPnfdKs6/YimbG0JdDJ0nREREipcDW3d3cM7/PMrqXa2FDqcoTaikrrc/yW8Wb+L0nzzI7bEzSv3qExERKQ1Jh+aOXs69fAmLS6TPxHwqmaTOzM4ys/VmttHM/m208z9c1cCZly3mB39fR3dfUiVzIiIiJSjp0NOf4BO/f5xf3l/Fqu3NdPeV7qPssqkkGkqYWSWwAXgbUA0sBz7s7msGm35gQ4m+RJI3/2QRu1q6VDInIiJSBtIb51QYvGDu/rz8yNmcdMQBvHT+LF58+AHMmFIaPbdlq6FEaXxbeA2w0d03A5jZ9cA5wKBJ3UCTKyu47EMv54O/WZrDEEVERCRf0stokg5V9e1samjn5pVhmBkcM2c/Tp4/mxPnzeK4Q/ZnSmUFkyuNygpjUkUFkyqNSRXh/eTKijjcMLO9La67+xL09Cfo6UvSnTYs9b83kWRKZQXTp1QyfXIl0yZX7n09PfV677iKUNLYl6CnPxn/slfKWCpJ3TxgR9r7auC1o1nAMXP2Z/+ppfJ1RUREZLTcHQeSSSfhzrbGTrY3dvKXJ3YWOrS8KJssx8wuAS4BOOqoo543fs7+U3n2W2/Pd1giIiJSYPV7utnW1El/wulPJulPOon010mnL+Ekkkn6Eo67M3VSJVMnVzB1UihhmxZL4aZNrmDapPB66qQKpkyqoC+RpKsvQVdvgq6+BN19Cbp647C+BN1xeFdfgkqzuNyw7CmTKnjnD7PzPUslqdsJHJn2fn4ctpe7XwFcAaFOXf5CExERkWJ2SHxySS7NzunSM1MqrV+XA8eb2TFmNgU4H7itwDGJiIiIFI2SKKlz934zuxS4G6gErnb31QUOS0RERKRolERSB+DudwJ3FjoOERERkWJUKrdfRURERGQYSupEREREyoCSOhEREZEyoKROREREpAwoqRMREREpA0rqRERERMqAkjoRERGRMqCkTkRERKQMmHv5PSbVzNqA9YWOQ8ZsDrC70EHImGn7lS5tu9Km7Ve6TnD3meNdSMk8UWKU1rv7wkIHIWNjZiu0/UqXtl/p0rYrbdp+pcvMVmRjObr9KiIiIlIGlNSJiIiIlIFyTequKHQAMi7afqVN2690aduVNm2/0pWVbVeWDSVEREREJppyLakTERERmVBKOqkzs7PMbL2ZbTSzfxtk/FQzuyGOX2ZmCwoQpgwhg+33JTNbY2ZPm9n9ZnZ0IeKU5xtp26VN9wEzczNTi7wiksn2M7Pz4vG32sz+nO8YZXAZnDePMrMHzeyJeO48uxBxyuDM7GozqzezZ4cYb2b2i7h9nzazV45m+SWb1JlZJfA/wDuAlwAfNrOXDJjsYqDZ3Y8DLgN+mN8oZSgZbr8ngIXu/jLgZuBH+Y1SBpPhtsPMZgKfB5blN0IZTibbz8yOB74KnOruJwJfyHec8nwZHntfB25091cA5wO/zm+UMoJrgLOGGf8O4Pj4dwlw+WgWXrJJHfAaYKO7b3b3XuB64JwB05wD/CG+vhk4w8wsjzHK0Ebcfu7+oLt3xrdLgfl5jlEGl8mxB/Adwg+p7nwGJyPKZPv9A/A/7t4M4O71eY5RBpfJtnPggPh6FrArj/HJCNz9IaBpmEnOAa71YCkw28wOz3T5pZzUzQN2pL2vjsMGncbd+4FW4OC8RCcjyWT7pbsY+HtOI5JMjbjt4i2DI939b/kMTDKSybH3QuCFZvaomS01s+FKFiR/Mtl23wQuMLNq4E7gs/kJTbJktNfG5yjXJ0pIGTGzC4CFwJsKHYuMzMwqgJ8CHy9wKDJ2kwi3f04nlJA/ZGYvdfeWQgYlGfkwcI27/7eZvQ74o5md5O7JQgcmuVfKJXU7gSPT3s+PwwadxswmEYqiG/MSnYwkk+2Hmb0V+BrwHnfvyVNsMryRtt1M4CRgkZltBU4BblNjiaKRybFXDdzm7n3uvgXYQEjypLAy2XYXAzcCuPsSYBrhmbBSGjK6Ng6llJO65cDxZnaMmU0hVAi9bcA0twEXxdfnAg+4OuYrFiNuPzN7BfBbQkKnOj3FY9ht5+6t7j7H3Re4+wJCfcj3uHtWnm0o45bJufP/CKV0mNkcwu3YzXmMUQaXybbbDpwBYGYvJiR1DXmNUsbjNuDC2Ar2FKDV3Wsynblkb7+6e7+ZXQrcDVQCV7v7ajP7NrDC3W8DriIUPW8kVEw8v3ARS7oMt9+Pgf2Bm2L7lu3u/p6CBS1AxttOilSG2+9u4EwzWwMkgK+4u+5yFFiG2+7LwO/M7IuERhMfV2FG8TCz6wg/mObEeo/fACYDuPtvCPUgzwY2Ap3AJ0a1fG1rERERkdJXyrdfRURERCRSUiciIiJSBpTUiYiIiJQBJXUiIiIiZUBJnYiIiMggzOxqM6s3s2ezsKw3m9mTaX/dZvbeLIS5l5I6EcmImbVnMM0XzGxGHmJZYGYfGcN815jZuQOGvdzMnkx7/2Ez6zKzyfH9S83s6XEH/dzPvM7Mno7dTuSUmZ1sZq5HfYmMyTVAVo6d+Dzzk939ZOAthC5L7snGslOU1IlINn0ByEpSF58CM5QFwKiTuiE8AxxlZjPj+9cDa4FXpL1/LEufhZkdBrza3V/m7pdlOM94+hT9MPBI/D/Ysi0+2k1EBnD3hwj93O5lZi8ws7vMbKWZPWxmLxrDos8F/u7unVkJNNKBLCKjYmanm9kiM7vZzNaZ2Z9iYvA54AjgQTN7ME57ppktMbNVZnaTme0fh58d511pZr8wszvi8G+a2R/N7FFCx+EL4klzVfx7fQzjB8Ab4i2ML5pZpZn92MyWxxKwf4zLMzP7lZmtN7P7gEMGfp/4TMwVwGvjoFcB/0NI5oj/HzWz18Tv8oSZPWZmJ8TPWGpmJ6atn0VmttDM9ou3bh6P85wTJ7kHmBdjf0MsSVsa4/6rmR2YtpyfmdkK4PPx/WVmtsLM1prZq83sL2ZWZWbfHWJbGfBBwnN432Zm0+LwBXGdXAs8CxxpZl9JW3/fSlvG/8XttNrMLhl5DxEpe1cAn3X3VwH/Avx6DMs4H7guq1EBuLv+9Kc//Y34B7TH/6cDrYRnElYAS4DT4ritwJz4eg7wELBffP+vwH8SHlu0AzgmDr8OuCO+/iawEpge388ApsXXxxN6zU/FcEdabJcAX4+vpxKStGOA9wP3EnrfPwJoAc4d5Lt9I8a2H6FU6wXAjXFcVXx/ADApDnsrcEt8/UXgW/H14cD6+Pr7wAXx9WzC81P3I5QyPpv22U8Db4qvvw38LL5eBPw6bbpFwA/j688Du+LnTSU8q/XgQb7XqcD98fWfgQ/E1wuAJHBKfH8m4UJlcZveAbwxjjso/p9OSACf9zn60185/6Ufs4SnHHUBT6b9rY3j3h+PkYF/dw9Y3uGER7dNznasJfuYMBEpqMfdvRog1kdbQEiG0p0CvIRQygUwhZAAvgjY7OFB8RCSuvQSoNvcvSu+ngz8ysxOJjyu6oVDxHMm8LK0+nKzCEngG4Hr3D0B7DKzB4aY/zHC45UeBpa7+yYzO87M5gL7x/dHAn8ws+MJj1+aHOe9kVD69g3gPODmtJjeY2b/Et9PA44iXBAAMLNZwGx3XxwH/QG4KS2uGwbEmXoE2zPAao/PhDSzzYSHgA98lNeHgevj6+uBC4Fb4vtt7r40LdYzgSfi+/0J6+8h4HNm9r44/Mg4XI8Mk4mqAmjxUC/uOdz9L8BfMljGecBf3b0vy7EpqRORMelJe51g8HOJAfe6+3PqcsUEbTgdaa+/CNQBLyecTLuHmMcIt0PuHvBZZ4/wWSlLgVcTSraWxGHVhFskqfffAR509/eZ2QJCyRnuvtPMGs3sZcCHgE+nxfQBd18/IKYFGcYEz10XsG+9J3nuNkgyYBuYWSXwAeAcM/tajOfgtLqD6cs24L/c/bcDlnE6oVTyde7eaWaLCMmpyITk7nvMbIuZfdDdb4pVHF7m7k+NYjEfBr6ai/hUp05EsqkNSCUNS4FTzew4gFjH7IXAeuDYtOTmQ8MsbxZQ46He28cIt1EHfg6EB5z/k+1rsfpCM9uPUNL0oVjn7nDgzYN9iLu3EW4Jf4J9SdwSQsOPR9Ni2Rlff3zAIm4A/h8wy91TLWXvBj4bT/qY2SsGzIO7twLNZvaGOOhjwOKB043RGcDT7n6kuy9w96MJpXTvG2Tau4FPptV5nGdmhxC+c3NM6F5EKH0VmTDM7DrCueAEM6s2s4uBjwIXm9lTwGrgnOGWMWB5Cwgl3tk6zp9DJXUikk1XAHeZ2S53f7OZfRy4zsymxvFfd/cNZvbPcboOYPkwy/s1cIuZXQjcxb7SpaeBRDypXgP8nHALeFVMohqA9wJ/JXQdsAbYzr6EbTCPAue4+474fgmhXlyq5euPCLdfvw78bcC8N8cYvpM27DvAz4CnLbQu3QK8a5DPvQj4jYWuYDYTEsts+DDh+6e7BfgnQrK7l7vfY2YvBpbEHLQduICwzj9tZmsJyfhSRCaQgXca0oypmxN33wrMG3NAI7BYaU9EJG/MbH93b48J2P8AVZ5h9x4iIjI43X4VkUL4h9jAYjXhFt9vh59cRERGopI6ERERkTKgkjoRERGRMqCkTkRERKQMKKkTERERKQNK6kRERETKgJI6ERERkTKgpE5ERESkDPx/5z4FG7G0UM0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_center = (bin_edges_CS[1:]+bin_edges_CS[:-1])/2\n",
    "mask = bin_center>1e-7\n",
    "\n",
    "normalization = np.sum(measured_counts_CS[mask])/np.sum(measured_counts_br[mask])\n",
    "brsub = measured_counts_CS-measured_counts_br*normalization\n",
    "plt.figure()\n",
    "plt.plot(bin_center,brsub)\n",
    "plt.fill_between(bin_center,0,brsub)\n",
    "plt.xlim([0,1e-7])\n",
    "plt.xlabel('Integrated Waveform Area')\n",
    "plt.ylabel('Measured Counts')\n",
    "plt.title('Background subtracted Cs137 spectrum measured with 100000 counts (background and signal)');\n"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}