plotSpectra-QA-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7ec11948",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Plot single module data taken by the DT5202"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94d13bb8",
   "metadata": {},
   "source": [
    "#### Created by Emily Pottebaum, 7/20/2023"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c8a32e",
   "metadata": {},
   "source": [
    "### Import needed libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f4699e1e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.22/00\n"
     ]
    }
   ],
   "source": [
    "import ROOT\n",
    "\n",
    "import sys\n",
    "import math\n",
    "\n",
    "import numpy as np\n",
    "from ROOT import TH1D,TF1, gRandom, gPad, gStyle, TCanvas\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from ROOT import TChain, TSelector, TTree\n",
    "import os\n",
    "import matplotlib.colors as mcolors\n",
    "import scipy\n",
    "from matplotlib.colors import LogNorm\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import matplotlib.colors as colors\n",
    "from scipy.optimize import curve_fit\n",
    "import scipy.signal\n",
    "from lmfit.models import GaussianModel, ConstantModel\n",
    "from datetime import datetime\n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98d11904",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_65889/833492359.py:5: MatplotlibDeprecationWarning: Support for setting an rcParam that expects a str value to a non-str value is deprecated since 3.5 and support will be removed two minor releases later.\n",
      "  mpl.rcParams['text.latex.preamble'] = [r'\\usepackage{amsmath}'] #for \\text command\n"
     ]
    }
   ],
   "source": [
    "mpl.rcParams['text.usetex'] = True\n",
    "mpl.rcParams['mathtext.rm'] = 'Times New Roman'\n",
    "mpl.rcParams['mathtext.it'] = 'Times New Roman:italic'\n",
    "mpl.rcParams['mathtext.bf'] = 'Times New Roman:bold'\n",
    "mpl.rcParams['text.latex.preamble'] = [r'\\usepackage{amsmath}'] #for \\text command\n",
    "\n",
    "mpl.rc('font', family='serif', size=14)\n",
    "mpl.rcParams['xtick.labelsize'] = 20\n",
    "mpl.rcParams['ytick.labelsize'] = 20\n",
    "mpl.rcParams['xtick.major.size'] = 8\n",
    "mpl.rcParams['ytick.major.size'] = 8\n",
    "\n",
    "mpl.rcParams['axes.titlesize'] = 18\n",
    "mpl.rcParams['axes.labelsize'] = 22\n",
    "mpl.rcParams['axes.linewidth'] = 1.2 # set the value globally\n",
    "\n",
    "# mpl.rc('font', size=16)\n",
    "mpl.rc('axes', titlesize=22)\n",
    "\n",
    "current_palette = sns.color_palette('colorblind', 10)\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0a059bf5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from dt5202 import dt5202_crunch\n",
    "\n",
    "from dt5202oldDAQ import dt5202_crunch as dt5202_crunch_oldDAQ\n",
    "ped = 50 #pedestal correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1325704b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# %cd /home/coure/Software/caenqdc_custom/scripts/PECollectionStudies\n",
    "from langaus import LanGausFit\n",
    "# %cd -\n",
    "from root_numpy import array2hist, hist2array, fill_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fcec1e18",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "sipms = {'PCB5': [18, 19, 20, 21], 'PCB6': [22, 23, 24, 25], 'PCB7': [26, 27, 28, 29],\n",
    "         'PCB8': [30, 31, 32, 33], 'PCB9': [34, 35, 36, 37], 'PCB10': [38, 39, 40, 41]}\n",
    "\n",
    "# sipms = {'PCB5': [18, 19, 20, 21]}\n",
    "# sipms = {'PCB5': [18, 19, 20, 21]}\n",
    "\n",
    "num_channels = 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "4207fae1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCB\n",
      "5\n",
      "18\n",
      "PCB\n",
      "6\n",
      "22\n"
     ]
    }
   ],
   "source": [
    "for key in sipms:\n",
    "    print(key[0:3])\n",
    "    print(key[3:])\n",
    "    print(sipms[key][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1c36fd2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "dataFolder = \"/data/SiPM_QA_data/\"\n",
    "data = []\n",
    "# print(sipms)\n",
    "for key in sipms:\n",
    "    pcbData = []\n",
    "    for sipm in sipms[key]:\n",
    "        sourceFolder = dataFolder + \"LED_\" + key + \"_SiPM\" + str(sipm) + \"/\"\n",
    "#         print(sourceFolder)\n",
    "        onlyfiles = [f for f in os.listdir(sourceFolder) if '.dat' in f]\n",
    "        data_list = []\n",
    "        for file in onlyfiles:\n",
    "            data_list.append(dt5202_crunch(str(sourceFolder)+str(file), num_ev=1000000000, num_ch=num_channels))\n",
    "        data_temp = data_list[0]\n",
    "        for i in range(1, len(data_list)):\n",
    "            data_temp = np.append(data_temp, data_list[i])\n",
    "        pcbData.append(data_temp)\n",
    "    data.append(pcbData)\n",
    "print(len(data))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a453371f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "keyIn = 0\n",
    "c = []\n",
    "hpx = []\n",
    "# funcs = []\n",
    "for key in sipms: # for each PCB, plot and fit low gain for all 4 SiPMs\n",
    "    cTitle = 'c' + str(keyIn)\n",
    "    c.append(ROOT.TCanvas(cTitle,\"\",1000,800))\n",
    "    c[keyIn].Divide(2,2)\n",
    "    title = ROOT.TPaveText(.4,0.48,.6,0.52)\n",
    "    title.AddText(key[0:3] + \" \" + key[3:])\n",
    "    title.SetMargin(0.1)\n",
    "#     hpx = []\n",
    "    func = []\n",
    "    for i in range(0,4): # for each SiPM on this PCB\n",
    "        c[keyIn].cd(i+1)\n",
    "#         title.Draw()\n",
    "        ROOT.gStyle.SetStatW(0.4)\n",
    "        hTitle = 'SiPM ' + str(sipms[key][i])\n",
    "        hpx.append(ROOT.TH1D(hTitle, '', 300, 0, 300))\n",
    "#         print(hTitle)\n",
    "        fill_hist(hpx[i+keyIn],np.array(data[keyIn][i]['data']['low_gain']))\n",
    "        hpx[i+keyIn].SetXTitle(\"ADC Counts\")\n",
    "        hpx[i+keyIn].Draw()\n",
    "        ROOT.gStyle.SetOptFit(2)\n",
    "        fit = LanGausFit()\n",
    "        func.append(fit.fit(hpx[i+keyIn], fitrange=(50,130)))\n",
    "        hpx[i+keyIn].Draw(\"same\")\n",
    "        func[i].Draw(\"same\")\n",
    "        c[keyIn].Draw(\"same\")\n",
    "#     hists.append(hpx)\n",
    "    c[keyIn].cd()\n",
    "    title.Draw()\n",
    "    keyIn += 1\n",
    "    c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "437bfdff",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "def drawHists(key, keyIn):\n",
    "# for key in sipms:\n",
    "#     keyIn = 0\n",
    "    c1 = ROOT.TCanvas('c1',\"\",1000,1000)\n",
    "    c1.Divide(2,2)\n",
    "    title = ROOT.TPaveText(.4,0.48,.6,0.52)\n",
    "    title.AddText(key[0:3] + \" \" + key[3:])\n",
    "    title.SetMargin(0.1)\n",
    "    hpx = []\n",
    "    func = []\n",
    "    for i in range(0,4): # for each SiPM on this PCB\n",
    "        c1.cd(i+1)\n",
    "#         title.Draw()\n",
    "        ROOT.gStyle.SetStatW(0.5)\n",
    "        hTitle = 'SiPM ' + str(sipms[key][i])\n",
    "        hpx.append(ROOT.TH1D(hTitle, '', 300, 0, 300))\n",
    "#         print(hTitle)\n",
    "        fill_hist(hpx[i],np.array(data[keyIn][i]['data']['low_gain']))\n",
    "        hpx[i].SetXTitle(\"ADC Counts\")\n",
    "        hpx[i].Draw()\n",
    "        ROOT.gStyle.SetOptFit(2)\n",
    "        fit = LanGausFit()\n",
    "        func.append(fit.fit(hpx[i], fitrange=(50,130)))\n",
    "        hpx[i].Draw(\"same\")\n",
    "        func[i].Draw(\"same\")\n",
    "        c1.Draw(\"same\")\n",
    "    c1.cd()\n",
    "    title.Draw()\n",
    "    c1.SaveAs(\"test/\" + key + \".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "829d6d83",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "keyIn = 0\n",
    "for key in sipms:\n",
    "    drawHists(key, keyIn)\n",
    "    keyIn += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "59276496",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "for key in sipms: # for each PCB, plot and fit low gain for all 4 SiPMs\n",
    "    c1 = ROOT.TCanvas('c1',\"\",1000,800)\n",
    "    c1.Divide(2,2)\n",
    "    title = ROOT.TPaveText(.4,0.48,.6,0.52)\n",
    "    title.AddText(key[0:3] + \" \" + key[3:])\n",
    "    title.SetMargin(0.1)\n",
    "    hpx = []\n",
    "    func = []\n",
    "    for i in range(0,4): # for each SiPM on this PCB\n",
    "        c1.cd(i+1)\n",
    "#         title.Draw()\n",
    "        ROOT.gStyle.SetStatW(0.4)\n",
    "        hTitle = 'SiPM ' + str(sipms[key][i])\n",
    "        hpx.append(ROOT.TH1D(hTitle, '', 300, 0, 300))\n",
    "#         print(hTitle)\n",
    "        fill_hist(hpx[i],np.array(data[0][i]['data']['low_gain']))\n",
    "        hpx[i].SetXTitle(\"ADC Counts\")\n",
    "        hpx[i].Draw()\n",
    "        ROOT.gStyle.SetOptFit(2)\n",
    "        fit = LanGausFit()\n",
    "        func.append(fit.fit(hpx[i], fitrange=(50,130)))\n",
    "        hpx[i].Draw(\"same\")\n",
    "        func[i].Draw(\"same\")\n",
    "        c1.Draw(\"same\")\n",
    "    c1.cd()\n",
    "    title.Draw()\n",
    "#     c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "a2573ee3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(259, [3, 0, 3], 1, 0, 288, 0.5, 1689877994325)]\n",
      "((33, 0, 1.68987799e+09, 0, 1), (0, 3, 108, 937))\n",
      "[(259, [3, 0, 3], 1, 0, 288, 0.5, 1689878085447)]\n",
      "((33, 0, 1.68987809e+09, 0, 1), (0, 3, 70, 457))\n",
      "[(259, [3, 0, 3], 1, 0, 288, 0.5, 1689878172553)]\n",
      "((33, 0, 1.68987817e+09, 0, 1), (0, 3, 78, 486))\n",
      "[(259, [3, 0, 3], 1, 0, 288, 0.5, 1689883642648)]\n",
      "((33, 0, 1.68988364e+09, 0, 1), (0, 3, 104, 912))\n"
     ]
    }
   ],
   "source": [
    "dataFolder = \"/data/SiPM_QA_data/\"\n",
    "data = []\n",
    "# print(sipms)\n",
    "for key in sipms:\n",
    "    for sipm in sipms[key]:\n",
    "        sourceFolder = dataFolder + \"LED_\" + key + \"_SiPM\" + str(sipm) + \"/\"\n",
    "#         print(sourceFolder)\n",
    "        onlyfiles = [f for f in os.listdir(sourceFolder) if '.dat' in f]\n",
    "        data_list = []\n",
    "        for file in onlyfiles:\n",
    "            data_list.append(dt5202_crunch(str(sourceFolder)+str(file), num_ev=1000000000, num_ch=num_channels))\n",
    "        data_temp = data_list[0]\n",
    "        for i in range(1, len(data_list)):\n",
    "            data_temp = np.append(data_temp, data_list[i])\n",
    "        data.append(data_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "4916bad4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/coure/Software/DataTakingVenv/lib/python3.8/site-packages/root_numpy/_hist.py:75: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  return _librootnumpy.fill_h1(\n",
      "/home/coure/Software/DataTakingVenv/lib/python3.8/site-packages/root_numpy/_hist.py:75: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  return _librootnumpy.fill_h1(\n",
      "/home/coure/Software/DataTakingVenv/lib/python3.8/site-packages/root_numpy/_hist.py:75: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  return _librootnumpy.fill_h1(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " FCN=2988.41 FROM MIGRAD    STATUS=CALL LIMIT   1483 CALLS        1484 TOTAL\n",
      "                     EDM=0.0500158    STRATEGY= 1  ERROR MATRIX UNCERTAINTY  34.4 per cent\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  LandauWidth   1.23730e+01   1.94161e-02   3.48469e-04   4.82399e+00\n",
      "   2  LandauMPV    7.36945e+01   3.07990e-02  -4.02083e-03  -3.18855e+00\n",
      "   3  Normalisation   2.02738e+06   1.07984e+05   1.63025e+04  -8.15919e-05\n",
      "   4  GaussianSigma   4.69164e-01   2.55806e-02  -3.81508e-03  -3.45582e+02\n",
      "                               ERR DEF= 0.5\n",
      " FCN=1087.28 FROM MIGRAD    STATUS=CALL LIMIT   1481 CALLS        1482 TOTAL\n",
      "                     EDM=0.0338517    STRATEGY= 1  ERROR MATRIX UNCERTAINTY  25.9 per cent\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  LandauWidth   1.08435e+01   2.09702e-02  -9.94627e-04   6.65353e+00\n",
      "   2  LandauMPV    7.04962e+01   2.74071e-02  -1.12711e-03   1.73542e-01\n",
      "   3  Normalisation   2.60119e+06   1.64817e+05   3.26253e+04  -5.68858e-05\n",
      "   4  GaussianSigma   3.68711e-01   2.38142e-02  -4.75352e-03  -3.91765e+02\n",
      "                               ERR DEF= 0.5\n",
      " FCN=1375.54 FROM MIGRAD    STATUS=CALL LIMIT   1488 CALLS        1489 TOTAL\n",
      "                     EDM=0.0429488    STRATEGY= 1  ERROR MATRIX UNCERTAINTY  22.1 per cent\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  LandauWidth   1.08092e+01   1.93807e-02   4.30372e-03   7.73526e+00\n",
      "   2  LandauMPV    7.09875e+01   2.49650e-02   5.71968e-03   4.38647e+00\n",
      "   3  Normalisation   3.21468e+06   2.22686e+05   4.82372e+04  -3.13560e-05\n",
      "   4  GaussianSigma   2.99512e-01   2.14483e-02  -4.49347e-03  -3.23407e+02\n",
      "                               ERR DEF= 0.5\n",
      " FCN=2002.58 FROM MIGRAD    STATUS=CALL LIMIT   1489 CALLS        1490 TOTAL\n",
      "                     EDM=0.125856    STRATEGY= 1  ERROR MATRIX UNCERTAINTY  26.6 per cent\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  LandauWidth   9.85688e+00   1.86663e-02   1.30508e-03   1.18009e+01\n",
      "   2  LandauMPV    6.84347e+01   2.43025e-02   4.61402e-04  -1.11599e+00\n",
      "   3  Normalisation  -4.50949e+06   2.27460e+05  -5.55067e+04   4.45060e-05\n",
      "   4  GaussianSigma  -2.19922e-01   1.12714e-02   2.74534e-03   8.92885e+02\n",
      "                               ERR DEF= 0.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/coure/Software/DataTakingVenv/lib/python3.8/site-packages/root_numpy/_hist.py:75: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  return _librootnumpy.fill_h1(\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c1\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: SiPM 38 (Potential memory leak).\n",
      "Warning in <Fit>: Abnormal termination of minimization.\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: SiPM 39 (Potential memory leak).\n",
      "Warning in <Fit>: Abnormal termination of minimization.\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: SiPM 40 (Potential memory leak).\n",
      "Warning in <Fit>: Abnormal termination of minimization.\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: SiPM 41 (Potential memory leak).\n",
      "Warning in <Fit>: Abnormal termination of minimization.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title.AddText(\"PCB 10 - no grease\")\n",
    "title.SetMargin(0.1)\n",
    "# ROOT.gStyle.SetStatFontSize(.08)\n",
    "ROOT.gStyle.SetStatW(0.4)\n",
    "c1.Divide(2,2)\n",
    "c1.cd(1)\n",
    "hpx = ROOT.TH1D('SiPM 38','',300,0,300)\n",
    "fill_hist(hpx,np.array(data[0]['data']['low_gain']))\n",
    "hpx.SetXTitle(\"ADC Counts\")\n",
    "hpx.Draw()\n",
    "# ROOT.gPad.SetLogy()\n",
    "ROOT.gStyle.SetOptFit(2)\n",
    "fit = LanGausFit()\n",
    "func = fit.fit(hpx,fitrange=(50,200)) # this is the fit range, may need to tweak it \n",
    "# hpx.SetLineWidth(2)\n",
    "hpx.Draw()\n",
    "func.Draw(\"same\")\n",
    "\n",
    "c1.cd(2)\n",
    "hpx2 = ROOT.TH1D('SiPM 39','',300,0,300)\n",
    "fill_hist(hpx2,np.array(data[1]['data']['low_gain']))\n",
    "\n",
    "hpx2.Draw()\n",
    "# ROOT.gPad.SetLogy()\n",
    "ROOT.gStyle.SetOptFit(2)\n",
    "fit2 = LanGausFit()\n",
    "func2 = fit2.fit(hpx2,fitrange=(50,130)) # this is the fit range, may need to tweak it \n",
    "hpx2.SetXTitle(\"ADC Counts\")\n",
    "hpx2.Draw()\n",
    "func2.Draw(\"same\")\n",
    "\n",
    "c1.cd(3)\n",
    "hpx3 = ROOT.TH1D('SiPM 40','',300,0,300)\n",
    "fill_hist(hpx3,np.array(data[2]['data']['low_gain']))\n",
    "hpx3.Draw()\n",
    "# ROOT.gPad.SetLogy()\n",
    "ROOT.gStyle.SetOptFit(2)\n",
    "fit3 = LanGausFit()\n",
    "func3 = fit3.fit(hpx3,fitrange=(50,130)) # this is the fit range, may need to tweak it \n",
    "hpx3.SetXTitle(\"ADC Counts\")\n",
    "hpx3.Draw()\n",
    "func3.Draw(\"same\")\n",
    "\n",
    "c1.cd(4)\n",
    "hpx4 = ROOT.TH1D('SiPM 41','',300,0,300)\n",
    "fill_hist(hpx4,np.array(data[3]['data']['low_gain']))\n",
    "hpx4.Draw()\n",
    "# ROOT.gPad.SetLogy()\n",
    "ROOT.gStyle.SetOptFit(2)\n",
    "fit4 = LanGausFit()\n",
    "func4 = fit.fit(hpx4,fitrange=(50,130)) # this is the fit range, may need to tweak it \n",
    "hpx4.SetXTitle(\"ADC Counts\")\n",
    "hpx4.Draw()\n",
    "func4.Draw(\"same\")\n",
    "\n",
    "c1.cd()\n",
    "title.Draw()\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "5fb53ded",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/data/SiPM_QA_data/LED_PCB5_SiPM']\n"
     ]
    }
   ],
   "source": [
    "dataFolder = \"/data/SiPM_QA_data/\"\n",
    "pcbList = []\n",
    "for key in sipms:\n",
    "    folderHead = dataFolder + \"LED_\" + key + \"_SiPM\"\n",
    "    pcbList.append(folderHead)\n",
    "print(pcbList)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd1f419f",
   "metadata": {},
   "source": [
    "### Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f65382ae",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Absolute path to folder containing data file(s):\n",
    "# sourceFolder1 = \"/data/SiPM_QA_data/LED_PCB5_SiPM18/\"\n",
    "# sourceFolder2 = \"/data/SiPM_QA_data/LED_PCB5_SiPM19/\"\n",
    "# sourceFolder3 = \"/data/SiPM_QA_data/LED_PCB5_SiPM20/\"\n",
    "# sourceFolder4 = \"/data/SiPM_QA_data/LED_PCB5_SiPM21/\"\n",
    "\n",
    "# sourceFolder1 = \"/data/SiPM_QA_data/LED_PCB6_SiPM22/\"\n",
    "# sourceFolder2 = \"/data/SiPM_QA_data/LED_PCB6_SiPM23/\"\n",
    "# sourceFolder3 = \"/data/SiPM_QA_data/LED_PCB6_SiPM24/\"\n",
    "# sourceFolder4 = \"/data/SiPM_QA_data/LED_PCB6_SiPM25/\"\n",
    "\n",
    "# sourceFolder1 = \"/data/SiPM_QA_data/LED_PCB7_SiPM26/\"\n",
    "# sourceFolder2 = \"/data/SiPM_QA_data/LED_PCB7_SiPM27/\"\n",
    "# sourceFolder3 = \"/data/SiPM_QA_data/LED_PCB7_SiPM28/\"\n",
    "# sourceFolder4 = \"/data/SiPM_QA_data/LED_PCB7_SiPM29/\"\n",
    "\n",
    "# sourceFolder1 = \"/data/SiPM_QA_data/LED_PCB8_SiPM30/\"\n",
    "# sourceFolder2 = \"/data/SiPM_QA_data/LED_PCB8_SiPM31/\"\n",
    "# sourceFolder3 = \"/data/SiPM_QA_data/LED_PCB8_SiPM32/\"\n",
    "# sourceFolder4 = \"/data/SiPM_QA_data/LED_PCB8_SiPM33/\"\n",
    "\n",
    "# sourceFolder1 = \"/data/SiPM_QA_data/LED_PCB9_SiPM34/\"\n",
    "# sourceFolder2 = \"/data/SiPM_QA_data/LED_PCB9_SiPM35/\"\n",
    "# sourceFolder3 = \"/data/SiPM_QA_data/LED_PCB9_SiPM36/\"\n",
    "# sourceFolder4 = \"/data/SiPM_QA_data/LED_PCB9_SiPM37/\"\n",
    "\n",
    "sourceFolder1 = \"/data/SiPM_QA_data/LED_PCB10_SiPM38/\"\n",
    "sourceFolder2 = \"/data/SiPM_QA_data/LED_PCB10_SiPM39_55/\"\n",
    "sourceFolder3 = \"/data/SiPM_QA_data/LED_PCB10_SiPM40/\"\n",
    "sourceFolder4 = \"/data/SiPM_QA_data/LED_PCB10_SiPM41/\"\n",
    "\n",
    "# Number of DT5202 channels used during this run:\n",
    "num_channels = 1\n",
    "\n",
    "# Run time in minutes\n",
    "run_time = 0.33 # 20 seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "17d38e10",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "onlyfiles = [f for f in os.listdir(sourceFolder1) if '.dat' in f]\n",
    "print(onlyfiles)\n",
    "data_list = []\n",
    "for file in onlyfiles:\n",
    "    data_list.append(dt5202_crunch(str(sourceFolder1)+str(file), num_ch=num_channels))\n",
    "data1 = data_list[0]\n",
    "for i in range(1, len(data_list)):\n",
    "    data1 = np.append(data2, data_list[i])\n",
    "    \n",
    "onlyfiles = [f for f in os.listdir(sourceFolder2) if '.dat' in f]\n",
    "print(onlyfiles)\n",
    "data_list = []\n",
    "for file in onlyfiles:\n",
    "    data_list.append(dt5202_crunch(str(sourceFolder2)+str(file), num_ch=num_channels))\n",
    "data2 = data_list[0]\n",
    "for i in range(1, len(data_list)):\n",
    "    data2 = np.append(data2, data_list[i])\n",
    "    \n",
    "onlyfiles = [f for f in os.listdir(sourceFolder3) if '.dat' in f]\n",
    "print(onlyfiles)\n",
    "data_list = []\n",
    "for file in onlyfiles:\n",
    "    data_list.append(dt5202_crunch(str(sourceFolder3)+str(file), num_ch=num_channels))\n",
    "data3 = data_list[0]\n",
    "for i in range(1, len(data_list)):\n",
    "    data3 = np.append(data3, data_list[i])\n",
    "    \n",
    "onlyfiles = [f for f in os.listdir(sourceFolder4) if '.dat' in f]\n",
    "print(onlyfiles)\n",
    "data_list = []\n",
    "for file in onlyfiles:\n",
    "    data_list.append(dt5202_crunch(str(sourceFolder4)+str(file), num_ch=num_channels))\n",
    "data4 = data_list[0]\n",
    "for i in range(1, len(data_list)):\n",
    "    data4 = np.append(data4, data_list[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "26619c45",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/coure/Software/DataTakingVenv/lib/python3.8/site-packages/root_numpy/_hist.py:75: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  return _librootnumpy.fill_h1(\n",
      "/home/coure/Software/DataTakingVenv/lib/python3.8/site-packages/root_numpy/_hist.py:75: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  return _librootnumpy.fill_h1(\n",
      "/home/coure/Software/DataTakingVenv/lib/python3.8/site-packages/root_numpy/_hist.py:75: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  return _librootnumpy.fill_h1(\n",
      "/home/coure/Software/DataTakingVenv/lib/python3.8/site-packages/root_numpy/_hist.py:75: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  return _librootnumpy.fill_h1(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " FCN=7124.53 FROM MIGRAD    STATUS=CALL LIMIT   1485 CALLS        1486 TOTAL\n",
      "                     EDM=0.399571    STRATEGY= 1  ERROR MATRIX UNCERTAINTY  26.5 per cent\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  LandauWidth   2.16985e+01   3.61990e-02  -1.38578e-02  -4.78577e+00\n",
      "   2  LandauMPV    1.03595e+02   5.57297e-02  -2.21632e-02  -3.41201e+00\n",
      "   3  Normalisation   1.59990e+06   5.04043e+04   2.23077e+04   3.77775e-05\n",
      "   4  GaussianSigma   6.71996e-01   2.18737e-02  -9.32107e-03   1.06288e+02\n",
      "                               ERR DEF= 0.5\n",
      " FCN=921.875 FROM MIGRAD    STATUS=CALL LIMIT   1489 CALLS        1490 TOTAL\n",
      "                     EDM=0.0200644    STRATEGY= 1  ERROR MATRIX UNCERTAINTY  19.2 per cent\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  LandauWidth   1.03019e+01   1.87077e-02  -7.05524e-05   6.13316e+00\n",
      "   2  LandauMPV    7.03092e+01   2.34974e-02  -3.63583e-03  -5.62117e+00\n",
      "   3  Normalisation  -3.50515e+06   2.75442e+05  -3.39248e+04   2.38258e-05\n",
      "   4  GaussianSigma  -2.69958e-01   2.17703e-02   2.62579e-03   2.99652e+02\n",
      "                               ERR DEF= 0.5\n",
      " FCN=1566.87 FROM MIGRAD    STATUS=CONVERGED     281 CALLS         282 TOTAL\n",
      "                     EDM=2.29043e-09    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  LandauWidth   1.02314e+01   2.99650e-02   3.83367e-04   6.21603e-04\n",
      "   2  LandauMPV    7.35551e+01   2.62744e-02   5.63857e-04   1.51437e-03\n",
      "   3  Normalisation   3.60275e+05   1.58667e+04   1.13243e+01  -2.60005e-08\n",
      "   4  GaussianSigma   2.66214e+00   1.15900e-01   8.43764e-05  -3.52593e-03\n",
      "                               ERR DEF= 0.5\n",
      " FCN=1748.13 FROM MIGRAD    STATUS=CALL LIMIT   1489 CALLS        1490 TOTAL\n",
      "                     EDM=0.0495924    STRATEGY= 1  ERROR MATRIX UNCERTAINTY  13.7 per cent\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  LandauWidth   1.33244e+01   2.81627e-02   1.04994e-03   6.67687e+00\n",
      "   2  LandauMPV    7.78600e+01   3.71700e-02  -6.46371e-04   4.44479e-01\n",
      "   3  Normalisation   2.79616e+06   1.36837e+05   2.77227e+04  -7.55429e-05\n",
      "   4  GaussianSigma   3.46536e-01   1.72287e-02  -3.48281e-03  -5.98830e+02\n",
      "                               ERR DEF= 0.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c1\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: SiPM 38 (Potential memory leak).\n",
      "Warning in <Fit>: Abnormal termination of minimization.\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: SiPM 39 (Potential memory leak).\n",
      "Warning in <Fit>: Abnormal termination of minimization.\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: SiPM 40 (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: SiPM 41 (Potential memory leak).\n",
      "Warning in <Fit>: Abnormal termination of minimization.\n",
      "Info in <TCanvas::Print>: png file plots/PCB10_38adj.png has been created\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit the muon peak and plot everything\n",
    "# leg = ROOT.TLegend()\n",
    "c1 = ROOT.TCanvas('c1',\"\",1000,800)\n",
    "title = ROOT.TPaveText(.4,0.48,.6,0.52)\n",
    "title.AddText(\"PCB 10 - no grease\")\n",
    "title.SetMargin(0.1)\n",
    "# ROOT.gStyle.SetStatFontSize(.08)\n",
    "ROOT.gStyle.SetStatW(0.4)\n",
    "c1.Divide(2,2)\n",
    "c1.cd(1)\n",
    "hpx = ROOT.TH1D('SiPM 38','',300,0,300)\n",
    "fill_hist(hpx,np.array(data1['data']['low_gain']))\n",
    "hpx.SetXTitle(\"ADC Counts\")\n",
    "hpx.Draw()\n",
    "# ROOT.gPad.SetLogy()\n",
    "ROOT.gStyle.SetOptFit(2)\n",
    "fit = LanGausFit()\n",
    "func = fit.fit(hpx,fitrange=(50,200)) # this is the fit range, may need to tweak it \n",
    "# hpx.SetLineWidth(2)\n",
    "hpx.Draw()\n",
    "func.Draw(\"same\")\n",
    "\n",
    "c1.cd(2)\n",
    "hpx2 = ROOT.TH1D('SiPM 39','',300,0,300)\n",
    "fill_hist(hpx2,np.array(data2['data']['low_gain']))\n",
    "\n",
    "hpx2.Draw()\n",
    "# ROOT.gPad.SetLogy()\n",
    "ROOT.gStyle.SetOptFit(2)\n",
    "fit2 = LanGausFit()\n",
    "func2 = fit2.fit(hpx2,fitrange=(50,130)) # this is the fit range, may need to tweak it \n",
    "hpx2.SetXTitle(\"ADC Counts\")\n",
    "hpx2.Draw()\n",
    "func2.Draw(\"same\")\n",
    "\n",
    "c1.cd(3)\n",
    "hpx3 = ROOT.TH1D('SiPM 40','',300,0,300)\n",
    "fill_hist(hpx3,np.array(data3['data']['low_gain']))\n",
    "hpx3.Draw()\n",
    "# ROOT.gPad.SetLogy()\n",
    "ROOT.gStyle.SetOptFit(2)\n",
    "fit3 = LanGausFit()\n",
    "func3 = fit3.fit(hpx3,fitrange=(50,130)) # this is the fit range, may need to tweak it \n",
    "hpx3.SetXTitle(\"ADC Counts\")\n",
    "hpx3.Draw()\n",
    "func3.Draw(\"same\")\n",
    "\n",
    "c1.cd(4)\n",
    "hpx4 = ROOT.TH1D('SiPM 41','',300,0,300)\n",
    "fill_hist(hpx4,np.array(data4['data']['low_gain']))\n",
    "hpx4.Draw()\n",
    "# ROOT.gPad.SetLogy()\n",
    "ROOT.gStyle.SetOptFit(2)\n",
    "fit4 = LanGausFit()\n",
    "func4 = fit.fit(hpx4,fitrange=(50,130)) # this is the fit range, may need to tweak it \n",
    "hpx4.SetXTitle(\"ADC Counts\")\n",
    "hpx4.Draw()\n",
    "func4.Draw(\"same\")\n",
    "\n",
    "c1.cd()\n",
    "title.Draw()\n",
    "c1.Draw()\n",
    "# c1.SaveAs(\"plots/PCB10_38adj.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d8ca271",
   "metadata": {},
   "source": [
    "### Plot high gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "09314d41-8983-4b4f-a437-dedffb48600b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# High gain for each channel\n",
    "plt.figure(figsize=(10,7))\n",
    "\n",
    "plt.hist(data1['data']['high_gain'], bins=500, range=(0,5000), histtype='step', label=\"SiPM 18\")\n",
    "plt.hist(data2['data']['high_gain'], bins=500, range=(0,5000), histtype='step', label=\"SiPM 19\")\n",
    "plt.hist(data3['data']['high_gain'], bins=500, range=(0,5000), histtype='step', label=\"SiPM 20\")\n",
    "plt.hist(data4['data']['high_gain'], bins=500, range=(0,5000), histtype='step', label=\"SiPM 21\")\n",
    "\n",
    "# plt.yscale('log')\n",
    "plt.xlabel('ADC Counts')\n",
    "plt.ylabel('Number of Events (Counts)')\n",
    "plt.title('Histogram of ADC Counts: HG')\n",
    "plt.legend()\n",
    "plt.grid(alpha = 0.5)\n",
    "plt.tight_layout()\n",
    "\n",
    "# uncommenting the line below will save the plot as a png with the specified name in the current directory.\n",
    "# plt.savefig('HG_allChannels.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6ec798f0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def gaus(x, A, mu, sigma):\n",
    "    return(A*np.exp(-(x-mu)**2 / (2*sigma**2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d830bc17",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pylandau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "18511df9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:  70.40978337493094  | sigma:  30.771261574046388\n",
      "[1.45318378e+04 7.23855446e+01 1.34222238e+01]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa8392cd820>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "\n",
    "bin_heights, bin_borders, _ = plt.hist(np.array(data1['data']['low_gain']), bins=200, range=(0,200))\n",
    "bin_centers = bin_borders[:-1] + np.diff(bin_borders) / 2\n",
    "\n",
    "x_data = bin_centers[60:80]\n",
    "y_data = bin_heights[60:80]\n",
    "mean = sum(x_data*y_data)/sum(y_data)\n",
    "sigma = sum(y_data*(x_data-mean)**2)/sum(y_data)\n",
    "print(\"mean: \", mean, \" | sigma: \", sigma)\n",
    "popt, pcov = curve_fit(gaus, x_data, y_data, p0=[max(y_data), mean, 5])\n",
    "fitspace = np.linspace(60,80,60)\n",
    "print(popt)\n",
    "plt.plot(fitspace, gaus(fitspace, *popt), lw=3, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5cd60abe",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'cython_function_or_method' object does not support vectorcall",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_58419/1833820474.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mbin_centers4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbin_borders4\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbin_borders4\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mpylandau\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlandau\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbin_centers1\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     16\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\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[1;32m     17\u001b[0m \u001b[0max2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\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;32mpyLandau\\cpp\\pylandau.pyx\u001b[0m in \u001b[0;36mpylandau.landau (pyLandau/cpp/pylandau.cpp:3904)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpyLandau\\cpp\\pylandau.pyx\u001b[0m in \u001b[0;36mpylandau._scale_to_mpv (pyLandau/cpp/pylandau.cpp:5421)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/Software/DataTakingVenv/lib/python3.8/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36mfmin\u001b[0;34m(func, x0, args, xtol, ftol, maxiter, maxfun, full_output, disp, retall, callback, initial_simplex)\u001b[0m\n\u001b[1;32m    578\u001b[0m             'initial_simplex': initial_simplex}\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_minimize_neldermead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mopts\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    581\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m         \u001b[0mretlist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fun'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'nit'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'nfev'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'status'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Software/DataTakingVenv/lib/python3.8/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36m_minimize_neldermead\u001b[0;34m(func, x0, args, callback, maxiter, maxfev, disp, return_all, initial_simplex, xatol, fatol, adaptive, bounds, **unknown_options)\u001b[0m\n\u001b[1;32m    748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    749\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\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[0;32m--> 750\u001b[0;31m         \u001b[0mfsim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\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    751\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    752\u001b[0m     \u001b[0mind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfsim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Software/DataTakingVenv/lib/python3.8/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36mfunction_wrapper\u001b[0;34m(x, *wrapper_args)\u001b[0m\n\u001b[1;32m    462\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mwrapper_args\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[1;32m    463\u001b[0m         \u001b[0mncalls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 464\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper_args\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\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    465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mncalls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'cython_function_or_method' object does not support vectorcall"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(15,7))\n",
    "\n",
    "bin_heights1, bin_borders1, _ = ax1.hist(data1['data']['low_gain'], bins=300, range=(0,300), histtype='step', label=\"SiPM 18\", lw=2)\n",
    "bin_centers1 = bin_borders1[:-1] + np.diff(bin_borders1) / 2\n",
    "\n",
    "bin_heights2, bin_borders2, _ = ax2.hist(data2['data']['low_gain'], bins=300, range=(0,300), histtype='step', label=\"SiPM 19\", lw=2)\n",
    "bin_centers2 = bin_borders2[:-1] + np.diff(bin_borders2) / 2\n",
    "\n",
    "bin_heights3, bin_borders3, _ = ax3.hist(data3['data']['low_gain'], bins=300, range=(0,300), histtype='step', label=\"SiPM 20\", lw=2)\n",
    "bin_centers3 = bin_borders3[:-1] + np.diff(bin_borders3) / 2\n",
    "\n",
    "bin_heights4, bin_borders4, _ = ax4.hist(data4['data']['low_gain'], bins=300, range=(0,300), histtype='step', label=\"SiPM 21\", lw=2)\n",
    "bin_centers4 = bin_borders4[:-1] + np.diff(bin_borders4) / 2\n",
    "\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "ax3.legend()\n",
    "ax4.legend()\n",
    "\n",
    "fig.suptitle('PCB 5, LG', fontsize=\"25\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6f81d77c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def make_model(num):\n",
    "    pref = \"f{0}_\".format(num)\n",
    "    model = GaussianModel(prefix = pref)\n",
    "    model.set_param_hint(pref+'amplitude', value=amplitude[num], min=1, max=200*amplitude[num])\n",
    "    model.set_param_hint(pref+'center', value=center[num], min=center[num]-10, max=center[num]+10)\n",
    "    model.set_param_hint(pref+'sigma', value=width[num], min=1, max=50)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1f3f235a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sigmas' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_58419/2124770884.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\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[1;32m     27\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Integrated ADC\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0msigmas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msigmas\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     29\u001b[0m \u001b[0msigm_avg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msigmas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\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[1;32m     30\u001b[0m \u001b[0msigm_std\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msigmas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msigmas\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;31mNameError\u001b[0m: name 'sigmas' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "\n",
    "bin_heights, bin_borders, _ = plt.hist(np.array(data4['data']['high_gain']-ped),histtype='step', bins=500, range=(0,3000))\n",
    "bin_centers = bin_borders[:-1] + np.diff(bin_borders) / 2\n",
    "\n",
    "peaks_in_interval = scipy.signal.find_peaks_cwt(bin_heights, widths=2)\n",
    "number_of_peaks = len(peaks_in_interval)\n",
    "amplitude = bin_heights[peaks_in_interval]\n",
    "width = np.zeros(number_of_peaks) + 10\n",
    "center = bin_centers[peaks_in_interval]\n",
    "\n",
    "mod = None\n",
    "for i in range(len(peaks_in_interval)):\n",
    "    this_mod = make_model(i)\n",
    "    if mod is None:\n",
    "        mod = this_mod\n",
    "    else:\n",
    "        mod = mod + this_mod\n",
    "\n",
    "offset = ConstantModel()\n",
    "offset.set_param_hint('c', value=np.average(bin_heights))\n",
    "\n",
    "out=mod.fit(bin_heights, x=bin_centers)\n",
    "\n",
    "plt.plot(bin_centers, out.best_fit, label='Best fit (Gaus sum)', lw=2)\n",
    "plt.legend()\n",
    "plt.xlabel(\"Integrated ADC\")\n",
    "sigmas = np.array(sigmas)\n",
    "sigm_avg.append(sigmas.mean())\n",
    "sigm_std.append(sigmas.std()/np.sqrt(len(sigmas)))\n",
    "redchi.append(out.redchi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "624b4b96",
   "metadata": {
    "scrolled": false
   },
   "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": [
    "bin_heights, bin_borders, _ = plt.hist(np.array(data4['data']['low_gain']-ped),histtype='step', bins=300, range=(0,300))\n",
    "bin_centers = bin_borders[:-1] + np.diff(bin_borders) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e004724f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'PCB 6, HG')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(15,7))\n",
    "\n",
    "ax1.hist(data1['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 22\", lw=2)\n",
    "ax2.hist(data2['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 23\", lw=2)\n",
    "ax3.hist(data3['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 24\", lw=2)\n",
    "ax4.hist(data4['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 25\", lw=2)\n",
    "\n",
    "# ax1.set_title('SiPM 18')\n",
    "# ax2.set_title('SiPM 19')\n",
    "# ax3.set_title('SiPM 20')\n",
    "# ax4.set_title('SiPM 21')\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "ax3.legend()\n",
    "ax4.legend()\n",
    "\n",
    "fig.suptitle('PCB 6, HG', fontsize=\"25\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b408db1a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'PCB 7, HG')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(15,7))\n",
    "\n",
    "ax1.hist(data1['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 26\", lw=2)\n",
    "ax2.hist(data2['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 27\", lw=2)\n",
    "ax3.hist(data3['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 28\", lw=2)\n",
    "ax4.hist(data4['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 29\", lw=2)\n",
    "\n",
    "# ax1.set_title('SiPM 18')\n",
    "# ax2.set_title('SiPM 19')\n",
    "# ax3.set_title('SiPM 20')\n",
    "# ax4.set_title('SiPM 21')\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "ax3.legend()\n",
    "ax4.legend()\n",
    "\n",
    "fig.suptitle('PCB 7, HG', fontsize=\"25\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0052fa6c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'PCB 8, HG')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(15,7))\n",
    "\n",
    "ax1.hist(data1['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 30\", lw=2)\n",
    "ax2.hist(data2['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 31\", lw=2)\n",
    "ax3.hist(data3['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 32\", lw=2)\n",
    "ax4.hist(data4['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 33\", lw=2)\n",
    "\n",
    "# ax1.set_title('SiPM 18')\n",
    "# ax2.set_title('SiPM 19')\n",
    "# ax3.set_title('SiPM 20')\n",
    "# ax4.set_title('SiPM 21')\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "ax3.legend()\n",
    "ax4.legend()\n",
    "\n",
    "fig.suptitle('PCB 8, HG', fontsize=\"25\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0ea03f0c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'PCB 9, HG')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(15,7))\n",
    "\n",
    "ax1.hist(data1['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 34\", lw=2)\n",
    "ax2.hist(data2['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 35\", lw=2)\n",
    "ax3.hist(data3['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 36\", lw=2)\n",
    "ax4.hist(data4['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 37\", lw=2)\n",
    "\n",
    "# ax1.set_title('SiPM 18')\n",
    "# ax2.set_title('SiPM 19')\n",
    "# ax3.set_title('SiPM 20')\n",
    "# ax4.set_title('SiPM 21')\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "ax3.legend()\n",
    "ax4.legend()\n",
    "\n",
    "fig.suptitle('PCB 9, HG', fontsize=\"25\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "83eace8d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'PCB 10, HG')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(15,7))\n",
    "\n",
    "ax1.hist(data1['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 38\", lw=2)\n",
    "ax2.hist(data2['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 39\", lw=2)\n",
    "ax3.hist(data3['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 40\", lw=2)\n",
    "ax4.hist(data4['data']['high_gain'], bins=500, range=(0,3000), histtype='step', label=\"SiPM 41\", lw=2)\n",
    "\n",
    "# ax1.set_title('SiPM 18')\n",
    "# ax2.set_title('SiPM 19')\n",
    "# ax3.set_title('SiPM 20')\n",
    "# ax4.set_title('SiPM 21')\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "ax3.legend()\n",
    "ax4.legend()\n",
    "\n",
    "fig.suptitle('PCB 10, HG', fontsize=\"25\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a07a4c02",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'PCB 5, LG')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(15,7))\n",
    "\n",
    "ax1.hist(data1['data']['low_gain'], bins=300, range=(0,300), histtype='step', label=\"SiPM 18\", lw=2)\n",
    "ax2.hist(data2['data']['low_gain'], bins=300, range=(0,300), histtype='step', label=\"SiPM 19\", lw=2)\n",
    "ax3.hist(data3['data']['low_gain'], bins=300, range=(0,300), histtype='step', label=\"SiPM 20\", lw=2)\n",
    "ax4.hist(data4['data']['low_gain'], bins=300, range=(0,300), histtype='step', label=\"SiPM 21\", lw=2)\n",
    "\n",
    "# ax1.set_title('SiPM 18')\n",
    "# ax2.set_title('SiPM 19')\n",
    "# ax3.set_title('SiPM 20')\n",
    "# ax4.set_title('SiPM 21')\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "ax3.legend()\n",
    "ax4.legend()\n",
    "\n",
    "fig.suptitle('PCB 5, LG', fontsize=\"25\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b5470c22",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "list indices must be integers or slices, not str",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_329900/3181973964.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Sum high gain data from each channel\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[0mnEvents_HG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'high_gain'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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      3\u001b[0m \u001b[0menergy_sum_HG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnEvents_HG\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_channels\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[1;32m      5\u001b[0m     \u001b[0menergy_sum_HG\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'high_gain'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mch\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not str"
     ]
    }
   ],
   "source": [
    "# Sum high gain data from each channel\n",
    "nEvents_HG = np.shape(data['data']['high_gain'])[0]\n",
    "energy_sum_HG = np.ones(nEvents_HG)\n",
    "for ch in range(0, num_channels):\n",
    "    energy_sum_HG += data['data']['high_gain'][:,ch]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2187b33",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Plot high gain sum (this will probably look like garbage but I'm including it just in case)\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.hist(energy_sum_HG, bins=400, range=(40,2000), histtype='step', label=\"Sum of energies from all channels\")\n",
    "plt.yscale('log')\n",
    "plt.xlabel('ADC counts (sum)')\n",
    "plt.ylabel('Number of Events (counts)')\n",
    "plt.title('Histogram of Summed ADC Counts: HG')\n",
    "plt.legend()\n",
    "plt.grid(alpha = 0.5)\n",
    "plt.tight_layout()\n",
    "\n",
    "# plt.savefig('HG_sum.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d75aec42",
   "metadata": {},
   "source": [
    "### Plot low gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9528e63",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Low gain for each channel\n",
    "plt.figure(figsize=(7,5))\n",
    "\n",
    "for ch in range(0, num_channels):\n",
    "    if ch==3:\n",
    "         plt.hist(data[ch]['data']['low_gain'][:,2]-ped, bins=200, range=(00,200), histtype='step', label=\"loc3 ch2\", lw=1)\n",
    "    else:\n",
    "        plt.hist(data[ch]['data']['low_gain'][:,ch]-ped, bins=200, range=(0,200),\n",
    "            histtype='step', label = \"Ch%i\"%ch, lw = 1);\n",
    "\n",
    "# plt.yscale('log')\n",
    "plt.xlabel('ADC Counts')\n",
    "plt.ylabel('Number of Events (Counts)')\n",
    "plt.title('Histogram of ADC Counts: LG')\n",
    "plt.legend()\n",
    "plt.grid(alpha = 0.5)\n",
    "plt.tight_layout()\n",
    "\n",
    "# plt.savefig('LG_allChannels_cs137.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e7531a1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "# print(type(data[0]['data']['low_gain'][:,0]))\n",
    "# sourceHist, sourceBins, _ = plt.hist(data[3]['data']['low_gain'][:,3], bins=500, range=(0,500), histtype='step', label=\"Ch0 source\", lw=1)\n",
    "# bkgHist, bkgBins, _ = plt.hist(data2['data']['low_gain'][:,0], bins=500, range=(0,500), \n",
    "#                                weights=2*np.ones_like(data2['data']['low_gain'][:,0]), histtype='step', label=\"Ch0 background\", lw=1)\n",
    "# resid = sourceHist - bkgHist\n",
    "# plt.plot(resid, lw=2)\n",
    "# plt.yscale('log')\n",
    "\n",
    "plt.hist(data3['data']['low_gain'][:,3], bins=200, range=(0,200), histtype='step')\n",
    "# plt.yscale('log')\n",
    "plt.xlabel('ADC Counts')\n",
    "plt.ylabel('Number of Events (Counts)')\n",
    "plt.title('Histogram of ADC Counts: LG, Cs-137')\n",
    "plt.legend()\n",
    "plt.grid(alpha=0.5)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71dbad75",
   "metadata": {},
   "source": [
    "### Perform fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85ac03ff",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Gaussian fit function\n",
    "def gaus(x, A, mu, sigma):\n",
    "    return(A*np.exp(-(x-mu)**2 / (2*sigma**2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1b46bd7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "csPeak_means = []\n",
    "csPeak_difs = []\n",
    "for ch in range(0, num_channels):\n",
    "    plt.figure(ch)\n",
    "    if ch==3:\n",
    "        bin_heights, bin_borders, _ = plt.hist(np.array(data[3]['data']['low_gain'][:,2]-ped), \n",
    "                                               bins=300, range=(0,300), lw=1, label=\"loc3 ch2\")\n",
    "    else:\n",
    "        bin_heights, bin_borders, _ = plt.hist(np.array(data[ch]['data']['low_gain'][:,ch]-ped),\n",
    "                                              bins=300, range=(0,300), lw=1)\n",
    "    bin_centers = bin_borders[:-1] + np.diff(bin_borders) / 2\n",
    "    x_data = bin_centers[40:140]\n",
    "    y_data = bin_heights[40:140]\n",
    "    mean = sum(x_data*y_data)/sum(y_data)\n",
    "    sigma = sum(y_data*(x_data-mean)**2)/sum(y_data)\n",
    "    popt, pcov = curve_fit(gaus, x_data, y_data, p0=[max(y_data), mean, sigma])\n",
    "    fitspace = np.linspace(200,40,140)\n",
    "    plt.plot(fitspace, gaus(fitspace, *popt), lw=3, color='red', label=\"Mean: %i, Sigma: %i\"%(popt[1], popt[2]))\n",
    "    plt.legend()\n",
    "    plt.xlabel(\"ADC Counts\")\n",
    "    plt.title(\"Cs137 Peak Fit: Ch%i\"%ch)\n",
    "    csPeak_means.append(popt[1])\n",
    "    if ch==0:\n",
    "        csPeak_difs.append(0.0)\n",
    "    else:\n",
    "        csPeak_difs.append((popt[1]) - (csPeak_means[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68980cb5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "print(csPeak_difs)\n",
    "# Low gain for each channel\n",
    "plt.figure(figsize=(7,5))\n",
    "\n",
    "for ch in range(0, num_channels):\n",
    "    if ch==3:\n",
    "         plt.hist(data[ch]['data']['low_gain'][:,2]-(ped+csPeak_difs[ch]), bins=200, range=(0,200), histtype='step', label=\"loc3 ch2\", lw=1)\n",
    "    else:\n",
    "        plt.hist(data[ch]['data']['low_gain'][:,ch]-(ped+csPeak_difs[ch]), bins=200, range=(0,200),\n",
    "            histtype='step', label = \"Ch%i\"%ch, lw = 1);\n",
    "\n",
    "# plt.yscale('log')\n",
    "plt.xlabel('ADC Counts')\n",
    "plt.ylabel('Number of Events (Counts)')\n",
    "plt.title('Histogram of ADC Counts, Compton maximums aligned')\n",
    "plt.legend()\n",
    "plt.grid(alpha = 0.5)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "920d0c32",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "print(csPeak_means)\n",
    "ADCToMev = [] # ADC/MeV\n",
    "for i in range(0, len(csPeak_means)):\n",
    "    ADCToMev.append(csPeak_means[i] / .662)\n",
    "# print(ADCToMev)\n",
    "# print(ADCToMev[0])\n",
    "# plt.hist(data[0]['data']['low_gain'][:,0], bins=500, range=(0,500), histtype='step', lw=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3825c605",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# _ = plt.hist((data[0]['data']['low_gain'][:,0])/ADCToMev[0], bins=500, range=(0,10), histtype='step', lw=1)\n",
    "# plt.xlabel(\"MeV\")\n",
    "\n",
    "for ch in range(0, num_channels):\n",
    "    _ = plt.hist((data[0]['data']['low_gain'][:,ch]-ped)/ADCToMev[ch], bins=300, range=(0,40),\n",
    "                histtype='step', label=\"Ch%i\"%ch, lw=1)\n",
    "plt.xlabel('MeV')\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.title('Low gain source data in MeV')\n",
    "plt.grid(alpha=0.5)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d911fc33",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# sum LG data\n",
    "nEvents_LG = np.shape(data[0]['data']['low_gain'])[0]\n",
    "energy_sum_LG = np.ones(nEvents_LG)\n",
    "energy_sum_cal = np.ones(nEvents_LG)\n",
    "for ch in range(0, num_channels):\n",
    "    energy_sum_LG += data[0]['data']['low_gain'][:,ch]-ped #/ADCToMev[ch]\n",
    "    energy_sum_cal += (data[0]['data']['low_gain'][:,ch]-ped)/ADCToMev[ch]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2393e88a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "_ = plt.hist(energy_sum_cal, bins=300, range=(0,50), histtype='step')\n",
    "plt.yscale('log')\n",
    "plt.xlabel('MeV')\n",
    "plt.title(\"Calibrated LG data, summed, single module loc0\")\n",
    "plt.grid(alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3990cd1b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "_ = plt.hist(energy_sum_LG, bins=300, range=(0,5000), histtype='step')\n",
    "plt.yscale('log')\n",
    "plt.xlabel('ADC Counts')\n",
    "plt.title(\"Uncalibrated LG data, summed, single module loc0\")\n",
    "plt.grid(alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "86341d0d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123.50683742505849 279.97252738638997\n",
      "[2.97223146e+04 1.21696193e+02 2.04532920e+01]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "121.69619331268984"
      ]
     },
     "execution_count": 149,
     "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": [
    "bin_heights, bin_borders, _ = plt.hist(np.array(data[2]['data']['low_gain'][:,2]), bins=300, range=(0,300), alpha=1)\n",
    "bin_centers = bin_borders[:-1] + np.diff(bin_borders) / 2\n",
    "# print(bin_centers[79:179])\n",
    "\n",
    "x_data = bin_centers[90:160]\n",
    "y_data = bin_heights[90:160]\n",
    "# plt.plot(y_data, color='red')\n",
    "mean = sum(x_data*y_data)/sum(y_data)\n",
    "sigma = sum(y_data*(x_data-mean)**2)/sum(y_data)\n",
    "# mean = sum(bin_centers*bin_heights)/sum(bin_heights)\n",
    "# sigma = sum(bin_heights*(bin_centers-mean)**2)/sum(bin_heights)\n",
    "print(mean, sigma)\n",
    "# print(max(bin_heights))\n",
    "popt, pcov = curve_fit(gaus, x_data, y_data, p0=[max(y_data), mean, 5])\n",
    "fitspace = np.linspace(90,160,90)\n",
    "print(popt)\n",
    "plt.plot(fitspace, gaus(fitspace, *popt), lw=3, color='red')\n",
    "popt[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "506be1ce",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff608f67d30>]"
      ]
     },
     "execution_count": 117,
     "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": [
    "plt.hist(np.array(data['data']['low_gain'][:,0]), bins=100, range=(0,300))\n",
    "plt.plot(fitspace, gaus(fitspace, *popt), lw=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e0f7a0aa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "[[Model]]\n",
      "    Model(gaussian)\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 465\n",
      "    # data points      = 300\n",
      "    # variables        = 3\n",
      "    chi-square         = 6.3176e+10\n",
      "    reduced chi-square = 2.1271e+08\n",
      "    Akaike info crit   = 5755.62066\n",
      "    Bayesian info crit = 5766.73201\n",
      "[[Variables]]\n",
      "    amplitude:  174075.000 +/- 77655.6029 (44.61%) (init = 34815)\n",
      "    center:     124.210517 +/- 4.21043815 (3.39%) (init = 130.5)\n",
      "    sigma:      7.08824000 +/- 4.21068379 (59.40%) (init = 1)\n",
      "    fwhm:       16.6915293 +/- 9.91540244 (59.40%) == '2.3548200*sigma'\n",
      "    height:     9797.33767 +/- 9074.58079 (92.62%) == '0.3989423*amplitude/max(1e-15, sigma)'\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(amplitude, sigma) = -0.577\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": [
    "bin_heights, bin_borders, _ = plt.hist(np.array(data['data']['low_gain'][:,0]), bins=300, range=(0,300))\n",
    "bin_centers = bin_borders[:-1] + np.diff(bin_borders) / 2\n",
    "\n",
    "peaks_in_interval = scipy.signal.find_peaks_cwt(bin_heights, widths=50)\n",
    "number_of_peaks = len(peaks_in_interval)\n",
    "print(number_of_peaks)\n",
    "amplitude = bin_heights[peaks_in_interval]\n",
    "# print(amplitude)\n",
    "width = np.zeros(number_of_peaks) + 1\n",
    "center = bin_centers[peaks_in_interval]\n",
    "# print(width)\n",
    "# print(center)\n",
    "def make_model(num):\n",
    "    model = GaussianModel()\n",
    "    model.set_param_hint('amplitude', value=amplitude[num], min=0.5*amplitude[num], max=5*amplitude[num])\n",
    "    model.set_param_hint('center', value=center[num], min=center[num]-20, max=center[num]+20)\n",
    "    model.set_param_hint('sigma', value=width[num])\n",
    "    return model\n",
    "mod = None\n",
    "for i in range(number_of_peaks):\n",
    "    this_mod = make_model(i)\n",
    "    if mod is None:\n",
    "        mod = this_mod\n",
    "    else:\n",
    "        mod = mod + this_mod\n",
    "        \n",
    "out=mod.fit(bin_heights, x=bin_centers)\n",
    "print(out.fit_report())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b34e75dc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'ADC Counts')"
      ]
     },
     "execution_count": 69,
     "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": [
    "plt.hist(np.array(data['data']['low_gain'][:,0]), bins=300, range=(0,300), alpha=0.6)\n",
    "plt.plot(bin_centers, out.best_fit, label='best fit')\n",
    "plt.legend()\n",
    "plt.xlabel(\"ADC Counts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "590caa4c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# sum low gain data\n",
    "nEvents_LG = np.shape(data['data']['low_gain'])[0] # should be the same as nEvents_HG I think\n",
    "energy_sum_LG = np.ones(nEvents_LG)\n",
    "for ch in range(0, num_channels):\n",
    "    energy_sum_LG += data['data']['low_gain'][:,ch]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "11cab2ef",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot low gain sum\n",
    "plt.figure(figsize=(7,5))\n",
    "entries, bin_edges, _ = plt.hist(energy_sum_LG, bins=400, range=(0,2000), histtype='step', label=\"Sum of energies from all channels\")\n",
    "plt.yscale('log')\n",
    "plt.xlabel('ADC counts (sum)')\n",
    "plt.ylabel('Number of Events (counts)')\n",
    "plt.title('Histogram of Summed ADC Counts: LG')\n",
    "plt.legend()\n",
    "plt.grid(alpha = 0.5)\n",
    "plt.tight_layout()\n",
    "\n",
    "# plt.savefig('LG_sum.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f6ee97f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7ae042cb",
   "metadata": {},
   "source": [
    "### Get muon flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9d474f23",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/coure/Software/caenqdc_custom/scripts/PECollectionStudies\n",
      "/home/coure/SiPMs_QA/dt5202\n"
     ]
    }
   ],
   "source": [
    "%cd /home/coure/Software/caenqdc_custom/scripts/PECollectionStudies\n",
    "from langaus import LanGausFit\n",
    "%cd -\n",
    "from root_numpy import array2hist, hist2array, fill_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e6c2d673",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " FCN=90863.8 FROM MIGRAD    STATUS=CONVERGED     127 CALLS         128 TOTAL\n",
      "                     EDM=8.12528e-11    STRATEGY= 1  ERROR MATRIX UNCERTAINTY   1.8 per cent\n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  Constant     8.10787e+04   1.34689e+02   6.62750e-01  -6.36597e-08\n",
      "   2  MPV          7.34140e+01   2.66711e-02  -1.93211e-04   6.00477e-04\n",
      "   3  Sigma        1.01207e+01   1.31031e-02  -2.92544e-06  -7.53095e-02\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/coure/Software/DataTakingVenv/lib/python3.8/site-packages/root_numpy/_hist.py:75: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  return _librootnumpy.fill_h1(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c1 = ROOT.TCanvas('c1','')\n",
    "hpx = ROOT.TH1D('Fit', 'LED pulse fit, LG', 300, 0, 300)\n",
    "fill_hist(hpx, np.array(data1['data']['low_gain']))\n",
    "hpx.Draw()\n",
    "fit = hpx.Fit(\"landau\")\n",
    "#fit.Draw(\"same\")\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "abdf11d0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73.98377573659774\n",
      "ADC per MeV:  14.796755147319548\n"
     ]
    }
   ],
   "source": [
    "MPV = func.GetParameter(1) # get the most probable value of the distribution\n",
    "print(MPV)\n",
    "ADCperMEV = MPV/5.0 # we expect the muon peak to be at 5 MeV\n",
    "print(\"ADC per MeV: \", ADCperMEV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ccac6470",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'MeV')"
      ]
     },
     "execution_count": 43,
     "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": [
    "_ = plt.hist(energy_sum_LG/ADCperMEV, bins=300, range=(0,15), \n",
    "            histtype='step', label=\"unnormalized sum\")\n",
    "_ = plt.hist(energy_sum_LG/ADCperMEV, bins=300, range=(0,15), weights=np.ones_like(energy_sum_LG/ADCperMEV)/8., \n",
    "             histtype='step', label=\"normalized sum\")\n",
    "plt.yscale('log')\n",
    "plt.title(\"Calibrated background spectrum, LG (normalized)\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"MeV\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e1cac6a2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cs137 run calibrated from background muon peak')"
      ]
     },
     "execution_count": 49,
     "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": [
    "nEvents_cs = np.shape(data2['data']['low_gain'])[0]\n",
    "energy_sum_cs = np.ones(nEvents_cs)\n",
    "for ch in range(0,8):\n",
    "    energy_sum_cs += data2['data']['low_gain'][:,ch]-ped\n",
    "\n",
    "_ = plt.hist(energy_sum_cs/ADCperMEV, bins=300, range=(0,15), histtype='step', label=\"LG summed ADC\")\n",
    "plt.yscale('log')\n",
    "plt.xlabel(\"MeV\")\n",
    "plt.title(\"Cs137 run calibrated from background muon peak\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e59a0566",
   "metadata": {
    "scrolled": false
   },
   "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": [
    "# eyeball where the muon peak begins (we want the x-axis value) \n",
    "plt.hist(energy_sum_LG, bins=700, range=(40,9000), histtype='step')\n",
    "plt.yscale(\"log\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "88476579-3473-45aa-8c84-bbac4f40ebed",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2600.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_edges[200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b1c7a1f4-45d0-4958-aaf0-e71bd115ba5b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.45696"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(entries[200:])/(10*25*25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "223f09a1-d5ba-4306-b7e1-e8f3799eae0c",
   "metadata": {
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "for ch in range(0,8):\n",
    "    plt.hist(data['data']['low_gain'][:,ch], bins=500, range=(0,500),\n",
    "             histtype='step', label = \"Ch%i\"%ch, lw = 2);\n",
    "#     plt.hist(data2['data']['low_gain'], bins=500, range=(0,9000),\n",
    "#              histtype='step', label = \"Swirly\");\n",
    "#plt.xlim(3200,4500)\n",
    "# plt.ylim(0,1000)\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.xlabel('ADC Counts')\n",
    "plt.ylabel('Number of Events (Counts)')\n",
    "plt.title('Histogram of ADC Counts: LG')\n",
    "plt.legend()\n",
    "plt.grid(alpha = 0.5)\n",
    "plt.tight_layout()\n",
    "# Can work on naming scheme\n",
    "# plt.savefig(figure_folder + '/' + 'ADCHist_HighGainBigPanel.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9a5b30fe",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_207205/1288339412.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\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[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\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[0;32m----> 3\u001b[0;31m     entries, bin_edges, _ = plt.hist(data2['data']['low_gain'], bins=500, range=(0,7500),\n\u001b[0m\u001b[1;32m      4\u001b[0m              histtype='step', label = \"Big panel ch0\");\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#     plt.hist(data['data']['high_gain'], bins=500, range=(0,4000),\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data2' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 504x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "for ch in range(0,1):\n",
    "    entries, bin_edges, _ = plt.hist(data2['data']['low_gain'], bins=500, range=(0,7500),\n",
    "             histtype='step', label = \"Big panel ch0\");\n",
    "#     plt.hist(data['data']['high_gain'], bins=500, range=(0,4000),\n",
    "#              histtype='step', label = \"Swirly panel\");\n",
    "#     plt.hist(data2['data']['high_gain'], bins=500, range=(0,4000),\n",
    "#              histtype='step', label = \"Swirly\");\n",
    "#plt.xlim(3200,4500)\n",
    "# plt.ylim(0,1000)\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.xlabel('ADC Counts')\n",
    "plt.ylabel('Counts')\n",
    "plt.title('Co60 on top of panel - HG, 2min')\n",
    "plt.legend()\n",
    "plt.grid(alpha = 0.5)\n",
    "plt.tight_layout()\n",
    "# Can work on naming scheme\n",
    "# plt.savefig('swirlyVsBiggerMod_Co60HG.pdf')\n",
    "# Can work on naming scheme\n",
    "# plt.savefig(figure_folder + '/' + 'ADCHist_HighGainBigPanel.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "acf76ad2-8548-44d5-b87c-7a9dce4bbe93",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_edges[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "983b5da6-777d-4f57-8e40-3f1f01d424eb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.26064"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(entries[120:])/(10*25*25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "0aadf9c7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,4.5))\n",
    "# for ch in range(0,1):\n",
    "#     plt.hist(data['data']['low_gain'][:,ch], bins=500, range=(0,500),\n",
    "#              histtype='step', label = \"Ch%i\"%ch);\n",
    "# plt.hist(data['data']['low_gain'], bins=500, range=(0,4000),\n",
    "#           histtype='step', label = \"Ch0\");\n",
    "plt.hist(data2['data']['low_gain'], bins=1000, range=(0,2000),\n",
    "          histtype='step', label = \"Big panel ch0\");\n",
    "plt.hist(data['data']['low_gain'], bins=1000, range=(0,2000),\n",
    "             histtype='step', label = \"Swirly panel\");\n",
    "# plt.hist(data['data']['high_gain'][:,1], bins=300, range=(0,3000),\n",
    "#           histtype='step', label = \"Swirly mini-module\");\n",
    "# # plt.ylim(0,1000)\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.xlabel('ADC Channels')\n",
    "plt.ylabel('Counts')\n",
    "plt.title('Co60 on top of panel - LG, 2min')\n",
    "plt.legend()\n",
    "plt.grid(alpha = 0.5)\n",
    "plt.tight_layout()\n",
    "# Can work on naming scheme\n",
    "plt.savefig('swirlyVsBiggerMod_Co60LG.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7dd6c9db-d846-4e2a-988c-e568622407fe",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_171314/2883849274.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\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 plt.hist(data['data']['low_gain'][:,0], bins=500, range=(0,8000),\n\u001b[0m\u001b[1;32m      3\u001b[0m          histtype='step', label = \"Big panel\");\n\u001b[1;32m      4\u001b[0m plt.hist(data['data']['low_gain'][:,1], bins=500, range=(10,8000),\n\u001b[1;32m      5\u001b[0m          histtype='step', label = \"Swirly\");\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 504x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.hist(data['data']['low_gain'][:,0], bins=500, range=(0,8000),\n",
    "         histtype='step', label = \"Big panel\");\n",
    "plt.hist(data['data']['low_gain'][:,1], bins=500, range=(10,8000),\n",
    "         histtype='step', label = \"Swirly\");\n",
    "# plt.ylim(0,1000)\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.xlabel('ADC Counts')\n",
    "plt.ylabel('Number of Events (Counts)')\n",
    "# plt.title('Histogram of ADC Counts: Prototype Panel')\n",
    "plt.legend()\n",
    "plt.grid(alpha = 0.5)\n",
    "plt.tight_layout()\n",
    "# Can work on naming scheme\n",
    "# plt.savefig(figure_folder + '/' + 'ADCHist_HighGainBigPanel.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf94b3c-f660-4ff0-9097-24fa668c999d",
   "metadata": {},
   "source": [
    "## Sum of energies for all channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "bb75df0e-d3e3-4e30-983e-fe061c0074de",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "342416"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nEvents = np.shape(data['data']['high_gain'])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "dacce756-1cbb-4737-bb2a-b1a69e02d2fe",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "energy_sum = np.ones(342416)\n",
    "for ch in range(0,7):\n",
    "    energy_sum += data['data']['low_gain'][:,ch]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "1f85c478-2eb3-47cb-9d43-2e6e2a61e2a8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.hist(energy_sum, bins=700, histtype='step', range=(40, 9000), label=\"Sum of energies from all channels\");    \n",
    "plt.yscale('log')\n",
    "plt.xlabel('ADC Counts (sum)')\n",
    "plt.ylabel('Counts')\n",
    "plt.title('Histogram of summed ADC counts: LG')\n",
    "plt.legend()\n",
    "plt.grid(alpha = 0.5)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"LG_spectrum_sumChans_singleModule.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "08cc088f-26d7-4b3f-a070-86b7715dde86",
   "metadata": {
    "scrolled": false
   },
   "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": [
    "entries, bin_edges, _ = plt.hist(energy_sum, bins=700, histtype='step', range=(0, 8000));\n",
    "plt.yscale(\"log\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "8eea8149-74d3-4dd6-a768-5bc3a0c20bc2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1314.2857142857142"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_edges[115]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "5a3d73e1-4fb1-499d-b9ae-93ab28e13a3d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90252"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(entries[115:])/(10*100*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00e78762-7fac-4c2e-8cd7-7f1638579490",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": []
  }
 ],
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