WidthStudy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ad1b8d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imports\n",
    "import time                     #For progress indicator\n",
    "import sys\n",
    "import numpy as np              #Will be used for binning\n",
    "import pandas as pd             #Will be used for data structuring\n",
    "import lmfit as lf              #Fitting\n",
    "import matplotlib.pyplot as plt #Basic plotting library\n",
    "sys.path.append('./')\n",
    "from R76Tools import *          #Tools written by me for, e.x., importing our files\n",
    "\n",
    "#Constants\n",
    "frittspath = \"/data/chocula/fritts/data/k100proc/midasrq/byseries/\"\n",
    "neogpath = \"/data/chocula/neog/rq/\"\n",
    "baselinecorrections = pd.read_csv(\"./baselinecorrectionvalues.csv\")\n",
    "gaus_mod = lf.Model(gaus)\n",
    "gaus_params = gaus_mod.make_params()\n",
    "gaus_params.add('A',min=0,value=300)\n",
    "gaus_params.add('sigma',min=0,value=3,max=200)\n",
    "gaus_params.add('mu',min=100,max=300,value=150)\n",
    "cal_mod = lf.Model(E_cal)\n",
    "cal_params = cal_mod.make_params(a=300,c=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3d09b2ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "1691522854\n",
      "Progress: 0/6. This action took 0.0 minutes.\n",
      "Progress: 1/6. This action took 6.8 minutes.\n",
      "Progress: 2/6. This action took 1.7 minutes.\n",
      "Progress: 3/6. This action took 2.1 minutes.\n",
      "Progress: 4/6. This action took 3.1 minutes.\n",
      "Progress: 5/6. This action took 2.5 minutes.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_2470098/599924820.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Progress: \"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"/\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mser\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\". This action took \"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\" minutes.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m     \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz_chain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmakechain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpaths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"/umn_*.root\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfilters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfittingfilters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"PTOFamps0\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maliases\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcalibrationaliases\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m     \u001b[0mz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz_chain\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Do it this roundabout way to accommodate having Matt's and Himangshu's data in ser\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Progress: \"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"/\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mser\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\". This action took \"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\" minutes.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/chocula/robbtanner/usr/local/bin/R76Analysis/R76Tools.py\u001b[0m in \u001b[0;36mmakechain\u001b[0;34m(filelist, filters, friends, aliases, trees)\u001b[0m\n\u001b[1;32m    146\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzthing\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzstuff\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m             \u001b[0mlistframes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzthing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 148\u001b[0;31m             \u001b[0mz_chain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlistframes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m\"z4\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrees\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mz4thing\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mz4stuff\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/chocula/test_cdmsbats/usr/local/lib/python3.7/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/chocula/test_cdmsbats/usr/local/lib/python3.7/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    305\u001b[0m     )\n\u001b[1;32m    306\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/chocula/test_cdmsbats/usr/local/lib/python3.7/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    532\u001b[0m             new_data = concatenate_managers(\n\u001b[0;32m--> 533\u001b[0;31m                 \u001b[0mmgrs_indexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_axes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconcat_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbm_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    534\u001b[0m             )\n\u001b[1;32m    535\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/chocula/test_cdmsbats/usr/local/lib/python3.7/site-packages/pandas/core/internals/concat.py\u001b[0m in \u001b[0;36mconcatenate_managers\u001b[0;34m(mgrs_indexers, axes, concat_axis, copy)\u001b[0m\n\u001b[1;32m    214\u001b[0m                 \u001b[0;31m#  we can use np.concatenate, which is more performant\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m                 \u001b[0;31m#  than concat_compat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m                 \u001b[0;31m# TODO(EA2D): special-casing not needed with 2D EAs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#User-set Variables\n",
    "ser = [\"07221119_1016\",\"07221124_1408\",\"07221124_1603\",\"07221124_1748\",\"07221124_2022\"\\\n",
    "      ,\"07221124_2331\"]\n",
    "paths=[frittspath,frittspath,frittspath,frittspath,frittspath,frittspath]\n",
    "lab = [-81.5,-8.3,-16.3,-24,-34,-34] # Actually relevant here\n",
    "for i in range(len(ser)):\n",
    "    m,b=0,0\n",
    "    for j in range(len(baselinecorrections)): # Pull m and b values from the csv\n",
    "        if baselinecorrections.series[j]==ser[i]:\n",
    "            m,b=1,1\n",
    "    if ((m==0) or (b==0)):\n",
    "        print(\"Oops! Series\",ser[i],\"doesn't have its correction values in the proper csv.\")\n",
    "print((len(ser)==len(paths))and(len(paths)==len(lab)))\n",
    "z=[]\n",
    "t=int(time.time())\n",
    "print(t)\n",
    "#Derived variables\n",
    "for i,s in enumerate(ser):\n",
    "    print(\"Progress: \"+str(i)+\"/\"+str(len(ser))+\". This action took \"+str(round((time.time()-t)/60,1))+\" minutes.\")\n",
    "    t=int(time.time())\n",
    "    _,z_chain = makechain(paths[i]+s+\"/umn_*.root\",filters=[fittingfilters,\"PTOFamps0\"],aliases=calibrationaliases)\n",
    "    z.append(z_chain) # Do it this roundabout way to accommodate having Matt's and Himangshu's data in ser\n",
    "print(\"Progress: \"+str(i+1)+\"/\"+str(len(ser))+\". This action took \"+str(round((time.time()-t)/60,1))+\" minutes.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "78f40bf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fix up the names\n",
    "cam=[]\n",
    "for i,x in enumerate(z):\n",
    "    cam.append((x['phidel']>-70)&(x['phidel']<50))\n",
    "    m,b=0,0\n",
    "    for j in range(len(baselinecorrections)): # Pull m and b values from the csv\n",
    "        if baselinecorrections.series[j]==ser[i]:\n",
    "            m=baselinecorrections.m[j]\n",
    "            b=baselinecorrections.b[j]\n",
    "    if ((m==0) or (b==0)):\n",
    "        print(\"Oops! Series\",ser[i],\"doesn't have its correction values in the proper csv.\")\n",
    "    x[\"pt_keV_bscorr\"] = x[\"pt_keV\"]/(1+m*x[\"BSel\"]/b)\n",
    "    x[\"pt0_keV_bscorr\"] = x[\"pt0_keV\"]/(1+m*x[\"BSel\"]/b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "226fca09",
   "metadata": {},
   "source": [
    "# Finding sigma_0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "192a15b6",
   "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"
    },
    {
     "data": {
      "text/plain": [
       "[0.00925046108382066]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hn=[]; sigma0_values=[]\n",
    "for i,x in enumerate(z):\n",
    "    x[\"pt_keVee_root\"] = 13.1849*x[\"pt_keV_bscorr\"]/(297.667-x[\"pt_keV_bscorr\"])\n",
    "    x[\"pt0_keVee_root\"] = 13.1849*x[\"pt0_keV_bscorr\"]/(297.667-x[\"pt0_keV_bscorr\"])\n",
    "    hn.append(plt.hist(x[\"pt0_keVee_root\"][x[\"crand\"] & x[\"cbs\"]],bins=np.linspace(-0.1,0.1,200)))\n",
    "    gaus_params.add('mu',value=0,min=-0.1,max=0.1)\n",
    "    bins = (hn[i][1][:-1]+hn[i][1][1:])/2\n",
    "    fit_NTL = gaus_mod.fit(hn[i][0],gaus_params,x=bins)#,weights=1/np.sqrt(h4n[0]))\n",
    "    sigma0_values.append(fit_NTL.params['sigma'].value)\n",
    "    plt.plot(bins,fit_NTL.best_fit)\n",
    "plt.show() # Ignore this plot\n",
    "sigma0_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6a55501e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "07221118_1404,0.0093,-81.6\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_2470098/224472893.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mser\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mser\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\",\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msigma0_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\",\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlab\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "for i in range(len(ser)):\n",
    "    print(ser[i]+\",\"+str(round(sigma0_values[i],4))+\",\"+str(lab[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f95e5a4",
   "metadata": {},
   "source": [
    "# Is there a correlation between lab voltage and sigma values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ef58dfba",
   "metadata": {},
   "outputs": [],
   "source": [
    "widthvalues = pd.read_csv(\"./widthstudyvalues.csv\")\n",
    "voltagevalues, sigma0values, sigma80, sigma200=[],[],[],[]\n",
    "for i in range(len(widthvalues.series)):\n",
    "    voltagevalues.append(widthvalues.lab[i])\n",
    "    sigma0values.append(widthvalues.sigma0[i])\n",
    "    if ((voltagevalues[i]<-75)and(voltagevalues[i]>-85)):\n",
    "        sigma80.append(widthvalues.sigma0[i])\n",
    "    if ((voltagevalues[i]<-195)and(voltagevalues[i]>-205)):\n",
    "        sigma200.append(widthvalues.sigma0[i])\n",
    "voltagevalues=np.asarray(voltagevalues)\n",
    "sigma0values=np.asarray(sigma0values)\n",
    "sigma200=np.asarray(sigma200)\n",
    "sigma80=np.asarray(sigma80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3ccf27e3",
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sigma80 average: 9.776666666666664 eVee. sigma80 std. deviation: 3.9618752574450844 eVee\n",
      "sigma200 average: 12.542857142857144 eVee. sigma200 std. deviation: 5.631127048534307 eVee\n"
     ]
    }
   ],
   "source": [
    "plt.hist(sigma0values)\n",
    "plt.show()\n",
    "plt.scatter(voltagevalues,sigma0values)\n",
    "plt.show()\n",
    "plt.hist(sigma80,histtype=\"step\",bins=15)\n",
    "plt.hist(sigma200,histtype=\"step\",bins=15)\n",
    "plt.legend([\"sigma80\",\"sigma200\"])\n",
    "plt.show()\n",
    "print(\"sigma80 average:\",np.mean(sigma80)*1000,\"eVee.\",\"sigma80 std. deviation:\",np.std(sigma80)*1000,\"eVee\")\n",
    "print(\"sigma200 average:\",np.mean(sigma200)*1000,\"eVee.\",\"sigma200 std. deviation:\",np.std(sigma200)*1000,\"eVee\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "718f03a8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}