LYSO_OPT2_bar_properties_spread.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e6c1c1be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.24/02\n"
     ]
    }
   ],
   "source": [
    "import ROOT as R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dbfcc55e",
   "metadata": {},
   "outputs": [],
   "source": [
    "files={}\n",
    "files['OPT2_DRY']=R.TFile(\"data/OPT2/prodAnalysis_c40_OptStep2_Sep2022.root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4b9da33",
   "metadata": {},
   "outputs": [],
   "source": [
    "map_prod ={1:0, 5:1, 10:2}\n",
    "g={}\n",
    "for tag in ['OPT2_DRY']:\n",
    "    for t in ['LY_arrayRms','sigmaT_arrayRms','XT_arrayRms','LY_globRms','sigmaT_globRms','XT_globRms']:\n",
    "        g['%s_%s'%(t,tag)]=R.TGraphErrors(len(map_prod))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1b7e978f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=4.62657, ex[0]=0, ey[0]=0.407952\n",
      "x[1]=1, y[1]=3.78524, ex[1]=0, ey[1]=0.38438\n",
      "x[2]=2, y[2]=5.89579, ex[2]=0, ey[2]=0.613654\n",
      "x[0]=0.1, y[0]=5.32628, ex[0]=0, ey[0]=0.410931\n",
      "x[1]=1.1, y[1]=3.83768, ex[1]=0, ey[1]=0.296084\n",
      "x[2]=2.1, y[2]=5.3326, ex[2]=0, ey[2]=0.421579\n"
     ]
    }
   ],
   "source": [
    "for tag in ['OPT2_DRY']:\n",
    "    opt2_ly_histos = files['OPT2_DRY']\n",
    "\n",
    "    ly_arrayRms_gr = opt2_ly_histos.Get('ly_arrayRms_byProd_geo2_PREIRR')\n",
    "    xt_arrayRms_gr = opt2_ly_histos.Get('xt_arrayRms_byProd_geo2_PREIRR')\n",
    "    sigmaT_arrayRms_gr = opt2_ly_histos.Get('sigmaT_arrayRms_byProd_geo2_PREIRR')\n",
    "\n",
    "    ly_globRms_gr = opt2_ly_histos.Get('ly_rms_byProd_geo2_PREIRR')\n",
    "    xt_globRms_gr = opt2_ly_histos.Get('xt_rms_byProd_geo2_PREIRR')\n",
    "    sigmaT_globRms_gr = opt2_ly_histos.Get('sigmaT_rms_byProd_geo2_PREIRR')\n",
    "\n",
    "    gr_map_prod ={1:0, 5:4, 10:8}\n",
    "    for prod in [1,5,10]:\n",
    "        iv=map_prod[prod]\n",
    "        pos=iv\n",
    "        \n",
    "        ly_arrayRms = ly_arrayRms_gr.GetPointY(gr_map_prod[prod])\n",
    "        xt_arrayRms = xt_arrayRms_gr.GetPointY(gr_map_prod[prod]) \n",
    "        sigmaT_arrayRms = sigmaT_arrayRms_gr.GetPointY(gr_map_prod[prod]) \n",
    "        \n",
    "        ly_arrayRms_err = ly_arrayRms_gr.GetErrorY(gr_map_prod[prod])\n",
    "        xt_arrayRms_err = xt_arrayRms_gr.GetErrorY(gr_map_prod[prod]) \n",
    "        sigmaT_arrayRms_err = sigmaT_arrayRms_gr.GetErrorY(gr_map_prod[prod]) \n",
    "\n",
    "        ly_globRms = ly_globRms_gr.GetPointY(gr_map_prod[prod])\n",
    "        xt_globRms = xt_globRms_gr.GetPointY(gr_map_prod[prod]) \n",
    "        sigmaT_globRms = sigmaT_globRms_gr.GetPointY(gr_map_prod[prod]) \n",
    "        \n",
    "        ly_globRms_err = ly_globRms_gr.GetErrorY(gr_map_prod[prod])\n",
    "        xt_globRms_err = xt_globRms_gr.GetErrorY(gr_map_prod[prod]) \n",
    "        sigmaT_globRms_err = sigmaT_globRms_gr.GetErrorY(gr_map_prod[prod]) \n",
    "        \n",
    "        g['LY_arrayRms_%s'%tag].SetPoint(iv,pos,ly_arrayRms)\n",
    "        g['LY_arrayRms_%s'%tag].SetPointError(iv,0,ly_arrayRms_err)\n",
    "        g['sigmaT_arrayRms_%s'%tag].SetPoint(iv,pos,sigmaT_arrayRms)\n",
    "        g['sigmaT_arrayRms_%s'%tag].SetPointError(iv,0,sigmaT_arrayRms_err)\n",
    "        g['XT_arrayRms_%s'%tag].SetPoint(iv,pos,xt_arrayRms)\n",
    "        g['XT_arrayRms_%s'%tag].SetPointError(iv,0,xt_arrayRms_err)\n",
    "        \n",
    "        g['LY_globRms_%s'%tag].SetPoint(iv,pos+0.1,ly_globRms)\n",
    "        g['LY_globRms_%s'%tag].SetPointError(iv,0,ly_globRms_err)\n",
    "        g['sigmaT_globRms_%s'%tag].SetPoint(iv,pos+0.1,sigmaT_globRms)\n",
    "        g['sigmaT_globRms_%s'%tag].SetPointError(iv,0,sigmaT_globRms_err)\n",
    "        g['XT_globRms_%s'%tag].SetPoint(iv,pos+0.1,xt_globRms)\n",
    "        g['XT_globRms_%s'%tag].SetPointError(iv,0,xt_globRms_err)\n",
    "g['LY_arrayRms_OPT2_DRY'].Print()\n",
    "g['LY_globRms_OPT2_DRY'].Print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "85013162",
   "metadata": {},
   "outputs": [],
   "source": [
    "c1=R.TCanvas('c1','c1',800,600)\n",
    "l=R.TLegend(0.15,0.65,0.9,0.88)\n",
    "l.SetFillColorAlpha(0,0)\n",
    "l.SetBorderSize(0)\n",
    "l.SetTextSize(0.04)\n",
    "\n",
    "tL=R.TLatex()#pm                                                                                                                               \n",
    "tL.SetTextSize(0.04)\n",
    "R.gStyle.SetOptTitle(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "220b8015",
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in ['OPT2_DRY']:\n",
    "    for h in ['LY_arrayRms','sigmaT_arrayRms','XT_arrayRms','LY_globRms','sigmaT_globRms','XT_globRms']:\n",
    "        g['%s_%s'%(h,k)].GetHistogram().SetBins(3,-0.5,3-0.5)\n",
    "        for kk,vendor in enumerate([1,5,10]):\n",
    "            g['%s_%s'%(h,k)].GetHistogram().GetXaxis().SetBinLabel(kk+1,'prod%d'%vendor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "252a939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=4.62657, ex[0]=0, ey[0]=0.407952\n",
      "x[1]=1, y[1]=3.78524, ex[1]=0, ey[1]=0.38438\n",
      "x[2]=2, y[2]=5.89579, ex[2]=0, ey[2]=0.613654\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file plots/LYSO_OPT2_LY_ARRAYRMS_DRY_byProd.png has been created\n",
      "Info in <TCanvas::Print>: pdf file plots/LYSO_OPT2_LY_ARRAYRMS_DRY_byProd.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l.Clear()\n",
    "g['LY_arrayRms_OPT2_DRY'].Print()\n",
    "g['LY_arrayRms_OPT2_DRY'].Draw(\"AP*\")\n",
    "g['LY_arrayRms_OPT2_DRY'].GetHistogram().GetYaxis().SetLimits(0,17)\n",
    "g['LY_arrayRms_OPT2_DRY'].GetHistogram().GetYaxis().SetRangeUser(0,17)\n",
    "g['LY_arrayRms_OPT2_DRY'].GetHistogram().GetYaxis().SetTitle(\"LO Dev.St / Mean [%]\")\n",
    "g['LY_arrayRms_OPT2_DRY'].SetMarkerStyle(22)\n",
    "\n",
    "g['LY_globRms_OPT2_DRY'].SetMarkerStyle(26)\n",
    "g['LY_globRms_OPT2_DRY'].Draw(\"PSAME\")\n",
    "\n",
    "hl5 = R.TLine(-0.5,5,2.5,5)\n",
    "hl5.SetLineStyle(R.kDashed)\n",
    "hl5.SetLineColor(R.kRed)\n",
    "hl5.Draw(\"SAME\")\n",
    "\n",
    "hl10 = R.TLine(-0.5,10,2.5,10)\n",
    "hl10.SetLineStyle(R.kDotted)\n",
    "hl10.SetLineColor(R.kBlack)\n",
    "hl10.Draw(\"SAME\")\n",
    "\n",
    "l.AddEntry(g['LY_globRms_OPT2_DRY'],\"Dev.St/Mean\",\"PL\")\n",
    "l.AddEntry(hl10,\"Tolerance\",\"l\")\n",
    "l.AddEntry(g['LY_arrayRms_OPT2_DRY'],\"Dev.St/Mean in array (average over 6 arrays)\",\"PL\")\n",
    "l.AddEntry(hl5,\"Tolerance\",\"l\")\n",
    "\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - Optimization Step 2 - Array dry coupling')\n",
    "c1.Draw()\n",
    "for ext in ['png','pdf']:\n",
    "    c1.SaveAs('plots/LYSO_OPT2_LY_ARRAYRMS_DRY_byProd.%s'%ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3e9a77e8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=3.11974, ex[0]=0, ey[0]=0.463607\n",
      "x[1]=1, y[1]=3.95965, ex[1]=0, ey[1]=0.912748\n",
      "x[2]=2, y[2]=4.52227, ex[2]=0, ey[2]=0.715579\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file plots/LYSO_OPT2_sigmaT_ARRAYRMS_DRY_byProd.png has been created\n",
      "Info in <TCanvas::Print>: pdf file plots/LYSO_OPT2_sigmaT_ARRAYRMS_DRY_byProd.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['sigmaT_arrayRms_OPT2_DRY'].Print()\n",
    "g['sigmaT_arrayRms_OPT2_DRY'].Draw(\"AP*\")\n",
    "g['sigmaT_arrayRms_OPT2_DRY'].GetHistogram().GetYaxis().SetLimits(0,17)\n",
    "g['sigmaT_arrayRms_OPT2_DRY'].GetHistogram().GetYaxis().SetRangeUser(0,17)\n",
    "g['sigmaT_arrayRms_OPT2_DRY'].GetHistogram().GetYaxis().SetTitle(\"#sigma_{T} Dev.St / Mean [%]\")\n",
    "g['sigmaT_arrayRms_OPT2_DRY'].SetMarkerStyle(22)\n",
    "\n",
    "g['sigmaT_globRms_OPT2_DRY'].SetMarkerStyle(26)\n",
    "g['sigmaT_globRms_OPT2_DRY'].Draw(\"PSAME\")\n",
    "\n",
    "hl5.Draw(\"SAME\")\n",
    "hl10.Draw(\"SAME\")\n",
    "\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - Optimization Step 2 - Array dry coupling')\n",
    "c1.Draw()\n",
    "\n",
    "for ext in ['png','pdf']:\n",
    "    c1.SaveAs('plots/LYSO_OPT2_sigmaT_ARRAYRMS_DRY_byProd.%s'%ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f436388b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=2.70593, ex[0]=0, ey[0]=0.132026\n",
      "x[1]=1, y[1]=2.49971, ex[1]=0, ey[1]=0.11263\n",
      "x[2]=2, y[2]=2.42384, ex[2]=0, ey[2]=0.159129\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file plots/LYSO_OPT2_XT_ARRAYRMS_DRY_byProd.png has been created\n",
      "Info in <TCanvas::Print>: pdf file plots/LYSO_OPT2_XT_ARRAYRMS_DRY_byProd.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['XT_arrayRms_OPT2_DRY'].Print()\n",
    "g['XT_arrayRms_OPT2_DRY'].Draw(\"AP*\")\n",
    "g['XT_arrayRms_OPT2_DRY'].GetHistogram().GetYaxis().SetLimits(0,17)\n",
    "g['XT_arrayRms_OPT2_DRY'].GetHistogram().GetYaxis().SetRangeUser(0,17)\n",
    "g['XT_arrayRms_OPT2_DRY'].GetHistogram().GetYaxis().SetTitle(\"XT Dev.St / Mean [%]\")\n",
    "g['XT_arrayRms_OPT2_DRY'].SetMarkerStyle(22)\n",
    "\n",
    "g['XT_globRms_OPT2_DRY'].SetMarkerStyle(26)\n",
    "g['XT_globRms_OPT2_DRY'].Draw(\"PSAME\")\n",
    "\n",
    "hl5.Draw(\"SAME\")\n",
    "hl10.Draw(\"SAME\")\n",
    "\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - Optimization Step 2 - Array dry coupling')\n",
    "c1.Draw()\n",
    "\n",
    "for ext in ['png','pdf']:\n",
    "    c1.SaveAs('plots/LYSO_OPT2_XT_ARRAYRMS_DRY_byProd.%s'%ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "353c035c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}