qn_upmu_tof.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Event</th>\n",
       "      <th>NmHitDAQ1</th>\n",
       "      <th>MinFracDay</th>\n",
       "      <th>JulDay</th>\n",
       "      <th>SSDB</th>\n",
       "      <th>eventDateTime</th>\n",
       "      <th>Hit1.ch1</th>\n",
       "      <th>PW1</th>\n",
       "      <th>Hit1.ch2</th>\n",
       "      <th>PW2</th>\n",
       "      <th>...</th>\n",
       "      <th>Hit1.ch4</th>\n",
       "      <th>PW4</th>\n",
       "      <th>Hit2.ch1</th>\n",
       "      <th>PW1.1</th>\n",
       "      <th>Hit2.ch2</th>\n",
       "      <th>PW2.1</th>\n",
       "      <th>Hit2.ch3</th>\n",
       "      <th>PW3.1</th>\n",
       "      <th>Hit2.ch4</th>\n",
       "      <th>PW4.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500012</td>\n",
       "      <td>2459747</td>\n",
       "      <td>43201.03</td>\n",
       "      <td>6/17/2022 0:0:1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>25.00</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.01</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500012</td>\n",
       "      <td>2459747</td>\n",
       "      <td>43201.07</td>\n",
       "      <td>6/17/2022 0:0:1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>22.49</td>\n",
       "      <td>0.75</td>\n",
       "      <td>23.75</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.75</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500013</td>\n",
       "      <td>2459747</td>\n",
       "      <td>43201.16</td>\n",
       "      <td>6/17/2022 0:0:1</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.50</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Event  NmHitDAQ1  MinFracDay   JulDay      SSDB    eventDateTime  Hit1.ch1  \\\n",
       "0      1          4    0.500012  2459747  43201.03  6/17/2022 0:0:1      0.00   \n",
       "1      2          4    0.500012  2459747  43201.07  6/17/2022 0:0:1      0.75   \n",
       "2      3          2    0.500013  2459747  43201.16  6/17/2022 0:0:1     -1.00   \n",
       "\n",
       "     PW1  Hit1.ch2    PW2  ...  Hit1.ch4    PW4  Hit2.ch1  PW1.1  Hit2.ch2  \\\n",
       "0  17.50      2.50  25.00  ...       3.0  25.01      -1.0   -1.0      -1.0   \n",
       "1  22.49      0.75  23.75  ...       0.0  23.75      -1.0   -1.0      -1.0   \n",
       "2  -1.00     -1.00  -1.00  ...       0.0  32.50      -1.0   -1.0      -1.0   \n",
       "\n",
       "   PW2.1  Hit2.ch3  PW3.1  Hit2.ch4  PW4.1  \n",
       "0   -1.0      -1.0   -1.0      -1.0   -1.0  \n",
       "1   -1.0      -1.0   -1.0      -1.0   -1.0  \n",
       "2   -1.0      -1.0   -1.0      -1.0   -1.0  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "#read the file\n",
    "    \n",
    "data = pd.read_table('6119-hybridOut-June17-27.txt', skiprows=1)\n",
    "\n",
    "#drop columns with NaN\n",
    "\n",
    "data = data.dropna(axis='columns', how ='all')\n",
    "\n",
    "#fix the headers\n",
    "\n",
    "data = data.rename({'Event': 'Event', 'MinFracDay': 'NmHitDAQ1', \\\n",
    "\n",
    "                   'JulDay':'MinFracDay','Unnamed: 6':'JulDay', \\\n",
    "\n",
    "                   'Unnamed: 8':'SSDB', 'Unnamed: 10':'Hit1.ch1', \\\n",
    "\n",
    "                   'Unnamed: 13':'Hit1.ch2','Unnamed: 16':'Hit1.ch3', \\\n",
    "\n",
    "                   'Unnamed: 19':'Hit1.ch4','Unnamed: 22':'Hit2.ch1', \\\n",
    "\n",
    "                   'Unnamed: 25':'Hit2.ch2','Unnamed: 28':'Hit2.ch3', \\\n",
    "\n",
    "                   'Unnamed: 31':'Hit2.ch4'\n",
    "\n",
    "                   },\n",
    "\n",
    "                  axis=1)\n",
    "cols = data.columns\n",
    "\n",
    "cols = cols.map(lambda x: x.replace('#', '_') if isinstance(x, (str or unicode)) else x)\n",
    "\n",
    "data.columns = cols\n",
    "\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New Mean is 4.238023141245303\n",
      "New Standard Deviation is 2.5648528377844584\n",
      "New Outlier length is 114687\n",
      "Number of Signal Events 117\n"
     ]
    },
    {
     "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": [
    "#Creating New Filter Columns\n",
    "data['Filter_1'] = data.iloc[:, 6] != -1  \n",
    "data['Filter_2'] = data.iloc[:, 8] != -1\n",
    "data['Filter_3'] = data.iloc[:, 10] != -1 \n",
    "data['Filter_4'] = data.iloc[:, 12] != -1 \n",
    "\n",
    "#data.head(5)\n",
    "\n",
    "filter_to_data = data.query('Filter_1 == True and Filter_3 == True and Filter_2 == True and Filter_4 == True')\n",
    "\n",
    "#Creating a new Delta_T Column (Calculates difference in two different channel hit columns)\n",
    "filter_to_data['Delta_T'] = (filter_to_data['Hit1.ch3'] - filter_to_data['Hit1.ch1'])\n",
    "\n",
    "#data.head(3)\n",
    "\n",
    "\n",
    "no_outlier_data = list(filter(lambda x: x < 30 and x > -30, filter_to_data['Delta_T'])) #Filters out outliers in calculations\n",
    "#2 Sigma Cut \n",
    "signal_region = list(filter(lambda x: x < -5.2 and x > -15.2, filter_to_data['Delta_T'])) #Focuses on Signal Region  \n",
    "#1 sigma Cut\n",
    "#signal_region = list(filter(lambda x: x < -7.7 and x > -12.7, filter_to_data['Delta_T'])) \n",
    "\n",
    "\n",
    "print(\"New Mean is \" + str(np.mean(no_outlier_data)))\n",
    "print(\"New Standard Deviation is \" + str(np.std(no_outlier_data)))\n",
    "print(\"New Outlier length is \" + str(np.size(no_outlier_data)))\n",
    "print(\"Number of Signal Events \" + str(np.size(signal_region)))\n",
    "\n",
    "#Finding the min and max value in Delta_T column\n",
    "x_max = max(filter_to_data['Delta_T'])\n",
    "x_min = min(filter_to_data['Delta_T'])\n",
    "\n",
    "#Setting the x limits as -30 to 30 on Graph\n",
    "if(x_max > 30):\n",
    "    x_max = 30   \n",
    "    \n",
    "if(x_min < -30):\n",
    "    x_min = -30\n",
    "\n",
    "#Plotting Histogram\n",
    "plt.hist(filter_to_data['Delta_T'], bins=25, range=[x_min,x_max], log=False, color = \"blue\")  # makes the histogram\n",
    "plt.title(\"Time Difference ch3-ch1\")\n",
    "\n",
    "#Rounding the mean on graph\n",
    "new_mean = round(np.mean(no_outlier_data), 2)\n",
    "\n",
    "plt.xlabel(\"Time Between Channels (ns) (Mean: \" + str(new_mean) + \")\")\n",
    "plt.ylabel(\"Number of Entries\")\n",
    "plt.grid(True);\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.0000e+00, 0.0000e+00, 2.0000e+00, 6.0000e+00, 3.0000e+00,\n",
       "        4.0000e+00, 7.0000e+00, 7.0000e+00, 2.4000e+01, 2.6000e+01,\n",
       "        1.4400e+02, 1.1130e+03, 9.7280e+03, 3.5667e+04, 4.3615e+04,\n",
       "        1.9412e+04, 4.2160e+03, 5.5800e+02, 1.1100e+02, 1.1000e+01,\n",
       "        1.9000e+01, 7.0000e+00, 2.0000e+00, 2.0000e+00, 1.0000e+00]),\n",
       " array([-30. , -27.6, -25.2, -22.8, -20.4, -18. , -15.6, -13.2, -10.8,\n",
       "         -8.4,  -6. ,  -3.6,  -1.2,   1.2,   3.6,   6. ,   8.4,  10.8,\n",
       "         13.2,  15.6,  18. ,  20.4,  22.8,  25.2,  27.6,  30. ]),\n",
       " <BarContainer object of 25 artists>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Time Difference ch3-ch1')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time Between Channels (ns) (Mean: 4.24)')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Entries')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the Graph in Log Scale\n",
    "#Log can be useful to see the full distribution\n",
    "\n",
    "plt.hist(filter_to_data['Delta_T'], bins=25, range=[x_min,x_max], log=False, color = \"blue\")  # makes the histogram\n",
    "plt.title(\"Time Difference ch3-ch1\")\n",
    "\n",
    "new_mean = round(np.mean(no_outlier_data), 2)\n",
    "\n",
    "plt.xlabel(\"Time Between Channels (ns) (Mean: \" + str(new_mean) + \")\")\n",
    "plt.ylabel(\"Number of Entries\")\n",
    "plt.grid(True);\n",
    "plt.yscale('log') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New mean is 1.0487575739082675\n",
      "New standard deviation is 1.8456959071125276\n",
      "New Outlier length is 114703\n",
      "Number of Signal Events 792\n"
     ]
    },
    {
     "data": {
      "image/png": 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Zjb2izynsmht216RmZuNUkSuF04AbJV1Ldlvqm2jzVYJvSTUzK0fDK4WIuBB4LfC99HNIRPSXHViDmPw+BTOzEhS5UiAiNgCXlxyLmZm1mfs+MjOzCicFMzOrqJsUJG0nae1YBWNmZu1VNymkZxNulvSSMYrHzMzaqEhD8zRgnaSfAY8NTYyII0qLyszM2qJIUlhaehRmZtYRinSI9yNJewH7RsTVkl4ATCo/NDMzG2tFOsQ7DrgE+GqaNB24tIxgJB0p6WuSLpP01jL2YWZmtRW5JfXPgNcDmwEi4i7g94ruQNJ5kjYOv4tJ0nxJd0haL+mktO1LI+I44IPAe4vuw8zMRkeRNoUnIuJ3UvY2Tknbk715rajzgbOAfxmaIGkScDbwh8AgsFrS5RFxa1rkU2m+2bNoqRovZGYtU0T973dJZwCPAn8CfAz4U+DWiPhk4Z1IM4ErIuKANH4IsCQiDkvjQx3sfT79XBURV9fY1iJgEcDUqVPn9ve31g3T1q1b6enpaWndTjORyrJmw5oxjKZ1M3aaweATg+0Oo5C50+bWnT+RPl/dZCRl6evrWxMRvdXmFblSOAk4FvgF8FFgJfD1liLZZjpwX258EDiYLOkcCkyRNCsizhm+YkQsB5YD9Pb2xrx581oKYGBggFbX7TQTqSx9S/vGLpgRWDZ7GYvvXNzuMAqJhfVPDCfS56ublFWWIncfPZNerPNTsmqjO6LR5UVj1eoAIiK+BHyp4cruOtvMrBQNk4KktwPnAHeTfZnvLemjEfHDEex3ENgzNz4DeKDoyhGxAljR29t73AhiMDMat9Msm73sWVdoccpIzwmtkxWpPjoT6IuI9QCS9gF+AIwkKawG9pW0N3A/cAzwx0VX9pWCmVk5itySunEoIST3ABuL7kDShcD1wH6SBiUdGxFPAScAq4DbgIsiYl3RbfolO2Zm5ah5pSDpqDS4TtJK4CKyNoX3kJ3pFxIRC2tMX0nWaG1mZh2iXvXRgtzwg8Cb0/CvgReWFlEBrj4yMytHzaQQER8ay0Ca4YZmM7NyFLn7aG+y5wdm5pdvZ9fZvlIwMytHkbuPLgXOBVYAz5QaTUG+UjAzK0eRpPA/6aEyMzMb54okhX+SdApwJfDE0MSI+HlpUZmZWVsUSQqvAP4v8Ba2VR9FGm8LtymYmZWjSFJ4F/DSiPhd2cEU5TYFM7NyFHmi+WZg15LjMDOzDlDkSmEqcLuk1Ty7TaFtt6SamVk5iiSFU0qPokluUzAzK0eR9yn8aCwCaYbbFMzMylHkieYtbHsn847ADsBjEbFLmYGZmdnYK3KlsHN+XNKRwGvKCsgmluEveBn+QhczG1tF7j56loi4lDY+o2BmZuUpUn10VG50O6CXbdVJbeGGZjOzchS5+yj/XoWngHuBd5YSTUFuaDYzK0eRNoWOfa+CmZmNrnqv4/x0nfUiIk4tIR4zM2ujelcKj1WZNhk4FtgNcFIwMxtn6r2O88yhYUk7AycCHwL6gTNrrWdm49vw24gbiVPael+KNalum4KkFwF/CbwPuAB4dUQ8MhaBmZnZ2Kv5nIKkfwBWA1uAV0TEkk5JCJIWSFq+adOmdodiZjau1Ht47a+AFwOfAh6QtDn9bJG0eWzCqy4iVkTEoilTprQzDDOzcadem0LTTzubmVl38xe/mZlVOCmYmVmFk4KZmVU4KZiZWYWTgpmZVTgpmJlZhZOCmZlVdExSkPRSSedKuqTdsZiZTVSlJgVJ50naKGntsOnzJd0hab2kkwAi4p6IOLbMeMzMrL6yrxTOB+bnJ0iaBJwNHA7MARZKmlNyHGZmVoAiyu3WVtJM4IqIOCCNHwIsiYjD0vjJABFxWhq/JCKOrrO9RcAigKlTp87t7+9vKa6tW7fS09PT0rqdpqyyrNmwpqnl506bO+J9zNhpBoNPDDa9nU4zXsoBIy9LK5+Lsvj/PtPX17cmInqrzSvyjubRNh24Lzc+CBwsaTfgs8CrJJ08lCSGi4jlwHKA3t7emDdvXktBDAwM0Oq6naassvQt7Wtq+VjY/AnG8H0sm72MxXcubno7nWa8lANGXpZWPhdl8f99Y+1ICtXe0BER8Rvg+EIbkBYAC2bNmjWqgY13zb4cxcwmnnbcfTQI7JkbnwE80MwG3HW2mVk52pEUVgP7Stpb0o7AMcDlzWzAL9kxMytH2bekXghcD+wnaVDSsRHxFHACsAq4DbgoItY1s11fKZiZlaPUNoWIWFhj+kpgZZn7NjOz5nXME83NcPWRmVk5ujIpuPrIzKwc7bgldcR8S6pZ92jlVug4pXOebZhofKVgZmYVXZkUzMysHE4KZmZW4TYFGzXuRsOs+3XllYLbFMzMytGVScHMzMrhpGBmZhVuUzCzjtNs+5Sfaxg9XXml4DYFM7NydGVSMDOzcjgpmJlZhZOCmZlVuKHZzCYcN2TX1pVXCm5oNjMrR1cmBTMzK4eTgpmZVTgpmJlZhZOCmZlVOCmYmVmFb0ntYkO31S2bvYy+pX1tjsasfYreYjpW/ytj8W6Ra998bSnb7corBd+SamZWjq5MCmZmVg4nBTMzq3BSMDOzCicFMzOrcFIwM7MKJwUzM6twUjAzswonBTMzq+iYJ5olTQb+GfgdMBAR32pzSGZmE06pVwqSzpO0UdLaYdPnS7pD0npJJ6XJRwGXRMRxwBFlxmVmZtWVXX10PjA/P0HSJOBs4HBgDrBQ0hxgBnBfWuzpkuMyM7MqFFHuu0clzQSuiIgD0vghwJKIOCyNn5wWHQQeiYgrJPVHxDE1trcIWAQwderUuf39/S3F9eDDDzL4xGBL63aaGTvNcFk6zHgpB7gsnWq/nfejp6enpXX7+vrWRERvtXntaFOYzrYrAsiSwcHAl4CzJL0dWFFr5YhYDiwH6O3tjXnz5rUUxJkXnsniOxe3tG6nWTZ7mcvSYcZLOcBl6VTXvvlaWv3+q6cdSaFan7IREY8BHyq0AXedbWZWinbckjoI7JkbnwE80MwG3HW2mVk52pEUVgP7Stpb0o7AMcDlzWxA0gJJyzdt2lRKgGZmE1XZt6ReCFwP7CdpUNKxEfEUcAKwCrgNuCgi1jWzXV8pmJmVo9Q2hYhYWGP6SmBlq9t1m4KZWTm6spsLXymYmZWjK5OCmZmVoyuTghuazczKUfoTzWWS9GvgVy2uvjvw0CiG004uS+cZL+UAl6VTjaQse0XEHtVmdHVSGAlJN9R6zLvbuCydZ7yUA1yWTlVWWbqy+sjMzMrhpGBmZhUTOSksb3cAo8hl6TzjpRzgsnSqUsoyYdsUzMzsuSbylYKZmQ3jpGBmZhUTLilIOlXSLZJuknSlpBfn5p2c3ht9h6TD2hlnEZL+QdLtqTzfl7Rrbl7XlEXSeyStk/SMpN5h87qmHENqvIO8K1R7r7qkF0m6StJd6fcL2xljEZL2lHStpNvSZ+vENL0by/I8ST+TdHMqy9I0vZyyRMSE+gF2yQ1/HDgnDc8BbgZ2AvYG7gYmtTveBmV5K7B9Gj4dOL0bywK8HNgPGAB6c9O7qhwp5kkpzpcCO6b457Q7ribifxPwamBtbtoZwElp+KShz1kn/wDTgFen4Z2BO9PnqRvLIqAnDe8A/BR4bVllmXBXChGxOTc6GRhqaX8n0B8RT0TEL4H1wGvGOr5mRMSVkXVFDvCfZC8sgi4rS0TcFhF3VJnVVeVIXgOsj4h7IuJ3QD9ZObpCRPwYeHjY5HcCF6ThC4AjxzKmVkTEhoj4eRreQtZN/3S6sywREVvT6A7pJyipLBMuKQBI+qyk+4D3AZ9Ok6u9O3r6WMc2Ah8GfpiGu70sQ7qxHN0YcyNTI2IDZF+2wO+1OZ6mSJoJvIrsDLsryyJpkqSbgI3AVRFRWlnGZVKQdLWktVV+3gkQEZ+MiD2Bb5G98AdqvDt6rGKupVFZ0jKfBJ4iKw90YFmKlKPaalWmtf1v0kA3xjxuSeoBvgv8+bBagq4SEU9HxEFktQGvkXRAWfsq9SU77RIRhxZc9NvAD4BTGIV3R5ehUVkkfQB4B/AHkSoX6cCyNPE3yeu4chTQjTE38qCkaRGxQdI0srPVjidpB7KE8K2I+F6a3JVlGRIRj0oaAOZTUlnG5ZVCPZL2zY0eAdyehi8HjpG0k6S9gX2Bn411fM2QNB/4W+CIiPhtblbXlaWGbizHiN9B3oEuBz6Qhj8AXNbGWAqRJOBc4LaI+EJuVjeWZY+hOwslPR84lOx7q5yytLtlvQ0t+d8F1gK3ACuA6bl5nyS7c+QO4PB2x1qgLOvJ6q9vSj/ndGNZgHeRnWE/ATwIrOrGcuRifhvZ3S53A59sdzxNxn4hsAF4Mv1NjgV2A64B7kq/X9TuOAuU4w1k1Xa35P4/3talZTkQuDGVZS3w6TS9lLK4mwszM6uYcNVHZmZWm5OCmZlVOCmYmVmFk4KZmVU4KZiZWYWTQheStFvq5fUmSf8t6f40vFXSP5ewvyW5fdwu6SuS6n52JB0pac5ox9IsSTtI+nzqSXJt6m3y8DRva6P1S4xriaTFTSz/Kklfb3Ff/cOezxk+/xJJL03D90q6btj8m/K9po4VSS9Jn+mqx0l1egmutX56sr7je0ZtJyeFLhQRv4mIgyJ77P0c4ItpvCci/rSk3X4x7W8O8ArgzQ2WPzIt226nkvWYeUBEHAAsIOs1s9t8Avhyi+t+BfibajMk7U/W8+w9uck7S9ozzX95i/scDV9kW39e1VxF9nc9kOy5kJMLrP+vQFn/I+OCk8I4ImmepCvS8BJJFyh7Z8S9ko6SdIakX0j6t9QFAJLmSvqRpDWSVqXH5evZEXge8Ehaf5+0vTWSrpP0MkmvI3ta/B/SWebBktak5V8pKSS9JI3fLekF6anN70panX5en+ZPVtbH/2pJNw71lSTpg5K+l/Z9l6QzqhyPFwDHAR+LiCcAIuLBiLgot8xnlfVT/5+SpqZpCyT9NO3v6tz0JSmWAUn3SPp4mj5TWb/9X1PW3/2V6cnTqsenSpwfl3RrOuPtrzJ/Z+DAiLi5QRyTJf0glWetpPemTVwHHCqpWrc27+O5T8JeBAytu5DsgbahWCalM/TVKd6Ppuk9kq6R9PP0GRv6O9U8NvVIOhK4B1hXa5mo3UtwvfUvT2WyWtr9tJ5/Rvy04xJgcRqeB1yRm/4fZN3svhL4LemJYOD7ZGfyOwA/AfZI098LnFdjH/eTPRX6CPDt3LxrgH3T8MHAv6fh84Gjc8utA3Yh64BwNdmX0V7A9Wn+t4E3pOGXkHVPAPA54P1peFeyM8LJwAfJ/umnkCWpXwF7Dov7QODGOscugAVp+AzgU2n4hWx7f/lHgDNzx+EnZO932B34TTqGM8k6JDwoLXdRLuZaxyf/d3sA2GmojFXi7AO+O+zvUS2OdwNfyy03JTd8FTC3yrZ/BLwiN34vMBv4SRq/keyKb20aX5Q7TjsBN5C962J70rtKUkzryToHrHdsjgeOrxLTZOB6oCd/nBr8H6zIbbfu+mRPAO/W7v/dTv0Zlx3iWcUPI+JJSb8ge/nLv6XpvyD7Z90POAC4ShJpmQ01tvXFiFiWrjAukXQMcAXwOuDitD5kXxTV/AR4PdlLXD5H1qGXyM5iIevPZU5uO7ukM+S3Akfk6oWfR5Y0AK6JiE0Akm4lSzL5bqsb+V0qA8Aa4A/T8AzgO+mqaUfgl7l1fhDZVccTkjYCU9P0X0bETbltzVTWQ2eR43ML8C1JlwKXVpk/Dfj1sGnV4vgFsEzS6WQnB/m2gY3Ai1Nsjbb9MPBI+hvfRnZCMeStwIGSjk7jU8j6pBoEPifpTcAzZN2F1zw2ABFxTpWyAiwl+7xtzR23mvTcXoIbrT90LH7TcOMTkJPC+DZUZfKMpCcjnSaR/dNuT/alvC4iDim6wZRk/o3sy30l8GhkbQ2NXAe8keyL+zKyjvyCbV/K2wGHRMTj+ZWU/Ve/O4a9hEfSwUPlS57muZ/n9cBLJO0c2YtWhssfk/z6Xwa+EBGXS5pHdrY5pNY+h09/fipTkePzdrLjeQTwd5L2j23VIgCPkyXDvOfEERF3SppL1sfPaZKujIjPpGWel7YzXLVtA3wHOJvsiixPZNVxq541UfogsAfZ1ciTku7NbbfasannYODoVCW4K/CMpP+JiLOGL6jqvQQ3Wr/WsTDcpjDR3QHsIekQqNyps3+9FdKX9OuAuyPrn/6Xkt4zNE/SK9OiW3h2g+6PgfcDd0XEM2Rno28D/n+afyXb3m2BpIPS4CrgY2m/SHpV0cJF1nPsucCXlPVYiqRpkt7fYNUpZNVlsK0XyqY1OD6kaduRVXtdS9YYvCtZtUfebcCsRvtT9r7x30bEN4FlZK/VHDKb6vXztbb9fbIqtVXDpq8C/p+2tUnNljSZ7JhtTAmhjyz5tyQi3hgRMyNiJvCPwOdqJISqvQTXWz99jn6frJrMqnBSmMAie13k0cDpkm4mazN4XY3F/0LZm5/Wkp0dD936+j7g2LT+Ora9erIf+GtljbX7RMS9afqP0+//IDuLfiSNfxzoTY2Xt5LVN0N299AOwC3Kbos8tclifoqseuTWtP6lPLe6ZLglZFU+1wEPNbm/4WodnyGTgG+mKr4byao9Hs0vEBG3A1NSdVo9rwB+lv5OnwT+HkBZQ/njkd7SNcwPyNqiniUitkTE6ekzkvd14Fbg5+l4fpXs8/Atsr/fDanMt9OApOMlHd9ouWHrfF1Sbxo9i+zE4yplNzTUqo7Kmwv857ArMctxL6lmXUDSXwBbIqLpZxXSupsj4twq854PXAu8PiKeHnmknU3SPwGXR8Q17Y6lU/lKwaw7fIVn180341G2veD9WVIbzil0/3uki1rrhFCfrxTMzKzCVwpmZlbhpGBmZhVOCmZmVuGkYGZmFU4KZmZW8b9ro+JIIkrodAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graphing a Different Set of Counters\n",
    "\n",
    "\n",
    "filter_to_data['Delta_T'] = (filter_to_data['Hit1.ch4'] - filter_to_data['Hit1.ch2'])\n",
    "\n",
    "\n",
    "no_outlier_data = list(filter(lambda x: x < 21 and x > -19, filter_to_data['Delta_T'])) #Filters out outliers in calculations\n",
    "signal_region = list(filter(lambda x: x < -3.3  and x > -7.3, filter_to_data['Delta_T'])) #Focuses on Signal Region\n",
    "\n",
    "print(\"New mean is \" + str(np.mean(no_outlier_data)))\n",
    "print(\"New standard deviation is \" + str(np.std(no_outlier_data)))\n",
    "print(\"New Outlier length is \" + str(np.size(no_outlier_data)))\n",
    "print(\"Number of Signal Events \" + str(np.size(signal_region)))\n",
    "\n",
    "x_max = max(filter_to_data['Delta_T'])\n",
    "x_min = min(filter_to_data['Delta_T'])\n",
    "\n",
    "if(x_max > 30):\n",
    "    x_max = 30   \n",
    "    \n",
    "if(x_min < -30):\n",
    "    x_min = -30\n",
    "\n",
    "plt.hist(filter_to_data['Delta_T'], bins=25, range=[x_min,x_max], log=False, color = \"green\")  # makes the histogram\n",
    "plt.title(\"Time Difference ch4-ch2\")\n",
    "plt.xlabel(\"Time Between Channels (ns) (Mean: \" + str(new_mean) + \")\")\n",
    "plt.yscale('log') #Remember you can always change from log to linear\n",
    "plt.ylabel(\"Number of Entries\")\n",
    "plt.grid(True);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "vscode": {
   "interpreter": {
    "hash": "d254c79ca0780151aaa7e4cd16056ebdec783c71a8789cb97d8cd0ee929d8311"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}