LifetimePlots-xi0pipipi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.24/00\n"
     ]
    }
   ],
   "source": [
    "#Xic+ -> Xi0 pi pi pi\n",
    "#Xi0 -> Lambda0 pi0\n",
    "#Lambda0 -> p pi-\n",
    "\n",
    "from root_pandas import read_root\n",
    "import ROOT\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "signalfile = '/belle2work/harsikad/ntuples_Signal_630K.root' \n",
    "ccbar = '/belle2work/harsikad/harshika_CCbarBkg1.root'\n",
    "uubar = '/belle2work/harsikad/harshika_UUbarBkg1.root'\n",
    "ddbar = '/belle2work/harsikad/harshika_DDbarBkg1.root'\n",
    "ssbar = '/belle2work/harsikad/harshika_SSbarBkg1.root'\n",
    "mixed = '/belle2work/harsikad/harshika_MixedBkg1.root'\n",
    "charged = '/belle2work/harsikad/harshika_ChargedBkg1.root'\n",
    "\n",
    "mycols = ['xic_isSignal','xic_M','xi_mcDecayVertexFromIPDistance','xi_significanceOfDistance','xi_ErrM','xic_flightTime','xic_flightTimeErr','xic_mcFlightTime']\n",
    "\n",
    "df = read_root(signalfile, key=\"xic_tree\", columns=mycols)\n",
    "df_ccbar = read_root(ccbar, key=\"xic_tree\", columns=mycols)\n",
    "df_uubar = read_root(uubar, key=\"xic_tree\", columns=mycols)\n",
    "df_ddbar = read_root(ddbar, key=\"xic_tree\", columns=mycols)\n",
    "df_ssbar = read_root(ssbar, key=\"xic_tree\", columns=mycols)\n",
    "df_mixed = read_root(mixed, key=\"xic_tree\", columns=mycols)\n",
    "df_charged = read_root(charged, key=\"xic_tree\", columns=mycols)      \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate optimal value to apply cut (>)\n",
    "\n",
    "def optimalCut(var):\n",
    "    \n",
    "\n",
    "    signal = '(xic_isSignal==1)'\n",
    "    cuts = 'xic_M >2.3 & xic_M<2.7'\n",
    "\n",
    "    # define a numpy array from the \"M\" column in the dataframe\n",
    "    #run over new MC\n",
    "    nptrue = df.query(cuts+'and xic_isSignal==1')[var].to_numpy()\n",
    "    np_dd= df_ddbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_uu= df_uubar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_ss= df_ssbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_cc= df_ccbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_charged = df_charged.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_mixed = df_mixed.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "\n",
    "    myrange=(min(nptrue),max(nptrue))\n",
    "\n",
    "    signal_without_cuts = len(nptrue)\n",
    "    bkg_without_cuts = len(np_dd)+len(np_uu)+len(np_ss)+len(np_cc)+len(np_charged)+len(np_mixed)\n",
    "    \n",
    "    mycolor = 0\n",
    "    testvalue = min(nptrue)\n",
    "    mytestvalue=[]\n",
    "    mytotal=[]\n",
    "\n",
    "    for i in range(125):\n",
    "        cuts = f\"xic_M >2.3 & xic_M<2.7 & {var} > {testvalue}\"\n",
    "\n",
    "        nptrue = df.query(cuts+'and xic_isSignal==1')[var].to_numpy()\n",
    "        np_dd= df_ddbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_uu= df_uubar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_ss= df_ssbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_cc= df_ccbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_charged = df_charged.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_mixed = df_mixed.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "\n",
    "        signal_efficiency = (len(nptrue))/signal_without_cuts\n",
    "        background_rejection = 1-((len(np_dd)+len(np_uu)+len(np_ss)+len(np_cc)+len(np_charged)+len(np_mixed))/bkg_without_cuts)\n",
    "        total=signal_efficiency+background_rejection\n",
    "\n",
    "        mytestvalue.append(testvalue)\n",
    "        mytotal.append(total)\n",
    "\n",
    "        testvalue+=((max(nptrue)-min(nptrue))/125)\n",
    "        mycolor+=0.0072\n",
    "\n",
    "\n",
    "    print(var+\": \"+str(max(mytotal)))\n",
    "    print(mytestvalue[mytotal.index(max(mytotal))])\n",
    "#CALCULATE OPTIMAL CUT VALUE AND PRINT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate optimal value to apply cut (<)\n",
    "\n",
    "def optimalCutLess(var):\n",
    "    \n",
    "\n",
    "    signal = '(xic_isSignal==1)'\n",
    "    cuts = 'xic_M >2.3 & xic_M<2.7'\n",
    "\n",
    "    # define a numpy array from the \"M\" column in the dataframe\n",
    "    #run over new MC\n",
    "    nptrue = df.query(cuts+'and xic_isSignal==1')[var].to_numpy()\n",
    "    np_dd= df_ddbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_uu= df_uubar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_ss= df_ssbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_cc= df_ccbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_charged = df_charged.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_mixed = df_mixed.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "\n",
    "    myrange=(min(nptrue),max(nptrue))\n",
    "\n",
    "    signal_without_cuts = len(nptrue)\n",
    "    bkg_without_cuts = len(np_dd)+len(np_uu)+len(np_ss)+len(np_cc)+len(np_charged)+len(np_mixed)\n",
    "\n",
    "    mycolor = 0\n",
    "    testvalue = max(nptrue)\n",
    "    mytestvalue=[]\n",
    "    mytotal=[]\n",
    "\n",
    "    for i in range(125):\n",
    "        cuts = f\"xic_M >2.3 & xic_M<2.7 & {var} < {testvalue}\"\n",
    "\n",
    "        nptrue = df.query(cuts+'and xic_isSignal==1')[var].to_numpy()\n",
    "        np_dd= df_ddbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_uu= df_uubar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_ss= df_ssbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_cc= df_ccbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_charged = df_charged.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_mixed = df_mixed.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "\n",
    "        signal_efficiency = (len(nptrue))/signal_without_cuts\n",
    "        background_rejection = 1-((len(np_dd)+len(np_uu)+len(np_ss)+len(np_cc)+len(np_charged)+len(np_mixed))/bkg_without_cuts)\n",
    "        total=signal_efficiency+background_rejection\n",
    "\n",
    "        mytestvalue.append(testvalue)\n",
    "        mytotal.append(total)\n",
    "\n",
    "        testvalue-=((max(nptrue)-min(nptrue))/125)\n",
    "        mycolor+=0.0072\n",
    "\n",
    "\n",
    "    print(var+\": \"+str(max(mytotal)))\n",
    "    print(mytestvalue[mytotal.index(max(mytotal))])\n",
    "#CALCULATE OPTIMAL CUT VALUE AND PRINT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotVar(var, var_range, mycuts):\n",
    "    \n",
    "    # Make nice looking plots\n",
    "    plt.rcParams.update({\n",
    "              'font.size': 20,\n",
    "              'figure.figsize': (10, 8),\n",
    "    })\n",
    "\n",
    "\n",
    "    signal = '(xic_isSignal==1)'\n",
    "    cuts = mycuts\n",
    "    nbins=100\n",
    "    myrange=var_range\n",
    "\n",
    "    ax = plt.subplot()\n",
    "\n",
    "    # define a numpy array from the \"M\" column in the dataframe\n",
    "    #run over new MC\n",
    "    nptrue = df.query(cuts+'and xic_isSignal==1')[var].to_numpy()\n",
    "    np_dd= df_ddbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_uu= df_uubar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_ss= df_ssbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_cc= df_ccbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_charged = df_charged.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_mixed = df_mixed.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "\n",
    "    scale = 1 # scale factor by eye\n",
    "    wnp_dd = [scale] * len(np_dd)\n",
    "    wnp_uu = [scale] * len(np_uu)\n",
    "    wnp_ss = [scale] * len(np_ss)\n",
    "    wnp_cc = [scale] * len(np_cc)\n",
    "    wnp_charged = [scale]*len(np_charged)\n",
    "    wnp_mixed = [scale]*len(np_mixed)\n",
    "    wnptrue = [scale] * len(nptrue)\n",
    "\n",
    "\n",
    "    # use the numpy data to create a matplotlib histogram\n",
    "    ax.hist([np_dd, np_uu, np_ss, np_cc, np_charged, np_mixed, nptrue], bins=nbins, range=myrange,\n",
    "            label=[\"ddbar\", \"uubar\", \"ssbar\", \"ccbar\",\"charged\",\"mixed\", \"signal\"], weights= [wnp_dd, wnp_uu, wnp_ss, wnp_cc, wnp_charged, wnp_mixed, wnptrue], stacked=True)\n",
    "\n",
    "\n",
    "    plt.xlim(myrange)\n",
    "\n",
    "    plt.ylabel('Number of Events')\n",
    "    plt.xlabel(var)\n",
    "    plt.legend()\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "#PLOT A VARIABLE - STANDARD GRAPH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Before selection criteria\n",
    "cuts = 'xic_M > 2.3 & xic_M < 2.7'\n",
    "plotVar('xic_M',(2.3, 2.7), cuts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#After selection criteria\n",
    "cuts = 'xic_M > 2.3 & xic_M < 2.7 & xi_mcDecayVertexFromIPDistance > 0.3617 & xi_significanceOfDistance > 3.46215456 & xi_ErrM < 0.00695869'\n",
    "plotVar('xic_M',(2.3, 2.7), cuts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotLifetime(var, cuts, myrange, mylabel):\n",
    "    \n",
    "    # Make nice looking plots\n",
    "    plt.rcParams.update({\n",
    "              'font.size': 20,\n",
    "              'figure.figsize': (10, 8),\n",
    "    })\n",
    "\n",
    "    ax = plt.subplot()\n",
    "    nbins = 100\n",
    "\n",
    "    nptrue = df.query(cuts+'and xic_isSignal==1')[var].to_numpy()*1000\n",
    "    np_dd= df_ddbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000\n",
    "    np_uu= df_uubar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000\n",
    "    np_ss= df_ssbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000\n",
    "    np_cc= df_ccbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000\n",
    "    np_charged = df_charged.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000\n",
    "    np_mixed = df_mixed.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000\n",
    "\n",
    "    scale = 1 # scale factor by eye\n",
    "    wnp_dd = [scale] * len(np_dd)\n",
    "    wnp_uu = [scale] * len(np_uu)\n",
    "    wnp_ss = [scale] * len(np_ss)\n",
    "    wnp_cc = [scale] * len(np_cc)\n",
    "    wnp_charged = [scale]*len(np_charged)\n",
    "    wnp_mixed = [scale]*len(np_mixed)\n",
    "    wnptrue = [scale] * len(nptrue)\n",
    "\n",
    "    # create histograms\n",
    "    ydata, bin_edges = np.histogram(nptrue, bins=nbins, range=myrange)\n",
    "    ax.hist([np_mixed,np_charged,np_uu,np_dd,np_ss,np_cc,nptrue], bins=nbins, range=myrange,\n",
    "            label=[\"mixed\",\"charged\",\"uubar\",\"ddbar\",\"ssbar\",\"ccbar\",\"signal\"], \n",
    "            weights=[wnp_mixed,wnp_charged,wnp_uu,wnp_dd,wnp_ss,wnp_cc,wnptrue], stacked=True)\n",
    "    bin_centers = 0.5*(bin_edges[1:] + bin_edges[:-1])\n",
    "    ax.errorbar(bin_centers, ydata, yerr = ydata**0.5, fmt='ko', label=\"Data\")\n",
    "\n",
    "    # set plot features\n",
    "    #plt.ylim(0.1,2000)\n",
    "    #plt.yscale(\"log\")\n",
    "    plt.xlim(myrange)\n",
    "    plt.xlabel(mylabel)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cuts = 'xic_M > 2.3 & xic_M < 2.7 & xi_mcDecayVertexFromIPDistance > 0.3617 & xi_significanceOfDistance > 3.46215456 & xi_ErrM < 0.00695869'\n",
    "plotLifetime(\"xic_flightTime\",cuts,(-2,4), r'$\\Xi_{c}^{+}$ flight time [ps]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotLogLifetime(var, cuts, myrange, mylabel):\n",
    "    \n",
    "    # Make nice looking plots\n",
    "    plt.rcParams.update({\n",
    "              'font.size': 20,\n",
    "              'figure.figsize': (10, 8),\n",
    "    })\n",
    "\n",
    "    ax = plt.subplot()\n",
    "    nbins = 100\n",
    "\n",
    "    nptrue = df.query(cuts+'and xic_isSignal==1')[var].to_numpy()*1000\n",
    "    np_dd= df_ddbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000\n",
    "    np_uu= df_uubar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000\n",
    "    np_ss= df_ssbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000\n",
    "    np_cc= df_ccbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000\n",
    "    np_charged = df_charged.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000\n",
    "    np_mixed = df_mixed.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000\n",
    "\n",
    "    scale = 1 # scale factor by eye\n",
    "    wnp_dd = [scale] * len(np_dd)\n",
    "    wnp_uu = [scale] * len(np_uu)\n",
    "    wnp_ss = [scale] * len(np_ss)\n",
    "    wnp_cc = [scale] * len(np_cc)\n",
    "    wnp_charged = [scale]*len(np_charged)\n",
    "    wnp_mixed = [scale]*len(np_mixed)\n",
    "    wnptrue = [scale] * len(nptrue)\n",
    "\n",
    "    # create histograms\n",
    "    ydata, bin_edges = np.histogram(nptrue, bins=nbins, range=myrange)\n",
    "    ax.hist([np_mixed,np_charged,np_uu,np_dd,np_ss,np_cc,nptrue], bins=nbins, range=myrange,\n",
    "            label=[\"mixed\",\"charged\",\"uubar\",\"ddbar\",\"ssbar\",\"ccbar\",\"signal\"], \n",
    "            weights=[wnp_mixed,wnp_charged,wnp_uu,wnp_dd,wnp_ss,wnp_cc,wnptrue], stacked=True)\n",
    "    bin_centers = 0.5*(bin_edges[1:] + bin_edges[:-1])\n",
    "    ax.errorbar(bin_centers, ydata, yerr = ydata**0.5, fmt='ko', label=\"Data\")\n",
    "\n",
    "    # set plot features\n",
    "    #plt.ylim(0.1,2000)\n",
    "    plt.yscale(\"log\")\n",
    "    plt.xlim(myrange)\n",
    "    plt.xlabel(mylabel)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotLogLifetime(\"xic_flightTime\",cuts,(-2,4), r'$\\Xi_{c}^{+}$ flight time [ps]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotLogLifetime(\"xic_flightTimeErr\",cuts,(0,1), 'Decay-time residual [ps]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotLogDiff(var, var2, cuts, myrange, mylabel):\n",
    "    \n",
    "    # Make nice looking plots\n",
    "    plt.rcParams.update({\n",
    "              'font.size': 20,\n",
    "              'figure.figsize': (10, 8),\n",
    "    })\n",
    "    \n",
    "    nbins = 100\n",
    "\n",
    "    ax = plt.subplot()\n",
    "\n",
    "    # define a numpy array from the \"M\" column in the dataframe\n",
    "    nptrue = df.query(cuts+'and xic_isSignal==1')[var].to_numpy()*1000 - df.query(cuts+'and xic_isSignal==1')[var2].to_numpy()*1000\n",
    "    np_dd= df_ddbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000 - df_ddbar.query(cuts+' and not xic_isSignal==1')[var2].to_numpy()*1000\n",
    "    np_uu= df_uubar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000 - df_uubar.query(cuts+' and not xic_isSignal==1')[var2].to_numpy()*1000\n",
    "    np_ss= df_ssbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000 - df_ssbar.query(cuts+' and not xic_isSignal==1')[var2].to_numpy()*1000\n",
    "    np_cc= df_ccbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000 - df_ccbar.query(cuts+' and not xic_isSignal==1')[var2].to_numpy()*1000\n",
    "    np_charged = df_charged.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000 - df_charged.query(cuts+' and not xic_isSignal==1')[var2].to_numpy()*1000\n",
    "    np_mixed = df_mixed.query(cuts+' and not xic_isSignal==1')[var].to_numpy()*1000 - df_mixed.query(cuts+' and not xic_isSignal==1')[var2].to_numpy()*1000\n",
    "\n",
    "    scale = 1 # scale factor by eye\n",
    "    wnp_dd = [scale] * len(np_dd)\n",
    "    wnp_uu = [scale] * len(np_uu)\n",
    "    wnp_ss = [scale] * len(np_ss)\n",
    "    wnp_cc = [scale] * len(np_cc)\n",
    "    wnp_charged = [scale]*len(np_charged)\n",
    "    wnp_mixed = [scale]*len(np_mixed)\n",
    "    wnptrue = [scale] * len(nptrue)\n",
    "\n",
    "    # use the numpy histogram to create a matplotlib histogram\n",
    "    ax.hist([np_mixed,np_charged,np_uu,np_dd,np_ss,np_cc,nptrue], bins=nbins, range=myrange,\n",
    "            label=[\"mixed\",\"charged\",\"uubar\",\"ddbar\",\"ssbar\",\"ccbar\",\"signal\"], \n",
    "            weights=[wnp_mixed,wnp_charged,wnp_uu,wnp_dd,wnp_ss,wnp_cc,wnptrue], stacked=True)\n",
    "\n",
    "    # set plot features\n",
    "    #plt.ylim(0.1,2000)\n",
    "    plt.yscale(\"log\")\n",
    "    plt.xlim(myrange)\n",
    "    plt.xlabel(mylabel)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotLogDiff(\"xic_flightTime\",\"xic_mcFlightTime\",cuts,(-1.2,1.2),'Measured-true flight time [ps]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "xicsdbnds = \"((xic_M > 2.35 & xic_M < 2.39) or (xic_M > 2.52 & xic_M < 2.56))\"\n",
    "xicsdbndlow = \"(xic_M > 2.35 & xic_M < 2.39)\"\n",
    "xicsdbndhigh = \"(xic_M > 2.52 & xic_M < 2.56)\"\n",
    "xicmassrange = \"xic_M > 2.435 & xic_M < 2.495\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotLifetimeCompSignal(var, cuts, myrange, mylabel):\n",
    "    fig = plt.figure()\n",
    "    fig.subplots_adjust(hspace=0.4)\n",
    "    \n",
    "    nbins = 100\n",
    "    \n",
    "    gs = gridspec.GridSpec(2, 1, height_ratios=[5, 1]) \n",
    "    ax0 = plt.subplot(gs[0])\n",
    "    ax1 = plt.subplot(gs[1])\n",
    "\n",
    "    npcc = np.array(df_ccbar.query(cuts+' and not xic_isSignal==1 and '+xicmassrange).xic_flightTime.to_numpy()*1000)\n",
    "    npccsblow = np.array(df_ccbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndlow).xic_flightTime.to_numpy()*1000)\n",
    "    npccsbhigh = np.array(df_ccbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndhigh).xic_flightTime.to_numpy()*1000)\n",
    "    npccsb = np.array(df_ccbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbnds).xic_flightTime.to_numpy()*1000)\n",
    "\n",
    "    scale = 1 # optional scale factor\n",
    "    wcc = [scale] * len(npcc)\n",
    "    wccsblow = [scale] * len(npccsblow)\n",
    "    wccsbhigh = [scale] * len(npccsbhigh)\n",
    "    wccsb = [scale/2] * len(npccsb)\n",
    "\n",
    "    ycc, bin_edges = np.histogram(npcc, bins=nbins, range=myrange, weights=wcc)\n",
    "    yccsblow, bin_edges = np.histogram(npccsblow, bins=nbins, range=myrange,  weights=wccsblow)\n",
    "    yccsbhigh, bin_edges = np.histogram(npccsbhigh, bins=nbins, range=myrange,  weights=wccsbhigh)\n",
    "    yccsb, bin_edges = np.histogram(npccsb, bins=nbins, range=myrange,  weights=wccsb)\n",
    "\n",
    "    bin_centers = 0.5*(bin_edges[1:] + bin_edges[:-1])\n",
    "    ax0.errorbar(bin_centers, ycc, yerr = ycc**0.5, fmt='kp', label=\"Signal region\")\n",
    "    ax0.errorbar(bin_centers, yccsblow, yerr = yccsblow**0.5, fmt='gp', label=\"Low sideband\")\n",
    "    ax0.errorbar(bin_centers, yccsbhigh, yerr = yccsbhigh**0.5, fmt='bp', label=\"High sideband\")\n",
    "    ax0.errorbar(bin_centers, yccsb, yerr = yccsb**0.5, fmt='rp', label=\"Sidebands\")\n",
    "\n",
    "    # set plot features\n",
    "    #ax0.set_ylim(0.1,2e3)\n",
    "    #ax0.set_yscale(\"log\")\n",
    "    ax0.set_xlim(myrange)\n",
    "    ax0.set_xlabel(mylabel)\n",
    "    ax0.legend()\n",
    "\n",
    "    yccerr = ycc**0.5\n",
    "    X = np.ma.masked_equal(yccerr,0)\n",
    "    ysbpull = (ycc-yccsb)/X\n",
    "    ax1.hist(bin_edges[:-1], bin_edges, weights=ysbpull, histtype='step', color='Black')\n",
    "    ysblhpull = (yccsbhigh-yccsblow)/X\n",
    "    ax1.hist(bin_edges[:-1], bin_edges, weights=ysblhpull, histtype='step', color='Red')\n",
    "\n",
    "    # set plot features\n",
    "    ax1.set_xlim(myrange)\n",
    "    plt.axhline(y=0.0, color='k', linestyle='-', linewidth=0.5)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotLifetimeCompSignal(\"xic_flightTime\",cuts,(-2,4),r'$\\Xi_{c}^{+}$ flight time [ps]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotLogLifetimeCompSignal(var, cuts, myrange, mylabel):\n",
    "    fig = plt.figure()\n",
    "    fig.subplots_adjust(hspace=0.4)\n",
    "    \n",
    "    nbins = 100\n",
    "    \n",
    "    gs = gridspec.GridSpec(2, 1, height_ratios=[5, 1]) \n",
    "    ax0 = plt.subplot(gs[0])\n",
    "    ax1 = plt.subplot(gs[1])\n",
    "\n",
    "    npcc = np.array(df_ccbar.query(cuts+' and not xic_isSignal==1 and '+xicmassrange).xic_flightTime.to_numpy()*1000)\n",
    "    npccsblow = np.array(df_ccbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndlow).xic_flightTime.to_numpy()*1000)\n",
    "    npccsbhigh = np.array(df_ccbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndhigh).xic_flightTime.to_numpy()*1000)\n",
    "    npccsb = np.array(df_ccbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbnds).xic_flightTime.to_numpy()*1000)\n",
    "\n",
    "    scale = 1 # optional scale factor\n",
    "    wcc = [scale] * len(npcc)\n",
    "    wccsblow = [scale] * len(npccsblow)\n",
    "    wccsbhigh = [scale] * len(npccsbhigh)\n",
    "    wccsb = [scale/2] * len(npccsb)\n",
    "\n",
    "    ycc, bin_edges = np.histogram(npcc, bins=nbins, range=myrange, weights=wcc)\n",
    "    yccsblow, bin_edges = np.histogram(npccsblow, bins=nbins, range=myrange,  weights=wccsblow)\n",
    "    yccsbhigh, bin_edges = np.histogram(npccsbhigh, bins=nbins, range=myrange,  weights=wccsbhigh)\n",
    "    yccsb, bin_edges = np.histogram(npccsb, bins=nbins, range=myrange,  weights=wccsb)\n",
    "\n",
    "    bin_centers = 0.5*(bin_edges[1:] + bin_edges[:-1])\n",
    "    ax0.errorbar(bin_centers, ycc, yerr = ycc**0.5, fmt='kp', label=\"Signal region\")\n",
    "    ax0.errorbar(bin_centers, yccsblow, yerr = yccsblow**0.5, fmt='gp', label=\"Low sideband\")\n",
    "    ax0.errorbar(bin_centers, yccsbhigh, yerr = yccsbhigh**0.5, fmt='bp', label=\"High sideband\")\n",
    "    ax0.errorbar(bin_centers, yccsb, yerr = yccsb**0.5, fmt='rp', label=\"Sidebands\")\n",
    "\n",
    "    # set plot features\n",
    "    #ax0.set_ylim(0.1,2e3)\n",
    "    ax0.set_yscale(\"log\")\n",
    "    ax0.set_xlim(myrange)\n",
    "    ax0.set_xlabel(mylabel)\n",
    "    ax0.legend()\n",
    "\n",
    "    yccerr = ycc**0.5\n",
    "    X = np.ma.masked_equal(yccerr,0)\n",
    "    ysbpull = (ycc-yccsb)/X\n",
    "    ax1.hist(bin_edges[:-1], bin_edges, weights=ysbpull, histtype='step', color='Black')\n",
    "    ysblhpull = (yccsbhigh-yccsblow)/X\n",
    "    ax1.hist(bin_edges[:-1], bin_edges, weights=ysblhpull, histtype='step', color='Red')\n",
    "\n",
    "    # set plot features\n",
    "    ax1.set_xlim(myrange)\n",
    "    plt.axhline(y=0.0, color='k', linestyle='-', linewidth=0.5)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotLogLifetimeCompSignal(\"xic_flightTime\",cuts,(-2,4),r'$\\Xi_{c}^{+}$ flight time [ps]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotLifetimeCompUDS(var, cuts, myrange, mylabel):\n",
    "    nbins = 100\n",
    "    fig = plt.figure()\n",
    "    fig.subplots_adjust(hspace=0.4)\n",
    "\n",
    "    gs = gridspec.GridSpec(2, 1, height_ratios=[5, 1]) \n",
    "    ax0 = plt.subplot(gs[0])\n",
    "    ax1 = plt.subplot(gs[1])\n",
    "\n",
    "    npmi = np.array(df_mixed.query(cuts+' and not xic_isSignal==1 and '+xicmassrange).xic_flightTime.to_numpy()*1000)\n",
    "    npmisblow = np.array(df_mixed.query(cuts+' and not xic_isSignal==1 and '+xicsdbndlow).xic_flightTime.to_numpy()*1000)\n",
    "    npmisbhigh = np.array(df_mixed.query(cuts+' and not xic_isSignal==1 and '+xicsdbndhigh).xic_flightTime.to_numpy()*1000)\n",
    "    npmisb = np.array(df_mixed.query(cuts+' and not xic_isSignal==1 and '+xicsdbnds).xic_flightTime.to_numpy()*1000)\n",
    "\n",
    "    npch = np.array(df_charged.query(cuts+' and not xic_isSignal==1 and '+xicmassrange).xic_flightTime.to_numpy()*1000)\n",
    "    npchsblow = np.array(df_charged.query(cuts+' and not xic_isSignal==1 and '+xicsdbndlow).xic_flightTime.to_numpy()*1000)\n",
    "    npchsbhigh = np.array(df_charged.query(cuts+' and not xic_isSignal==1 and '+xicsdbndhigh).xic_flightTime.to_numpy()*1000)\n",
    "    npchsb = np.array(df_charged.query(cuts+' and not xic_isSignal==1 and '+xicsdbnds).xic_flightTime.to_numpy()*1000)\n",
    "\n",
    "    npuu = np.array(df_uubar.query(cuts+' and not xic_isSignal==1 and '+xicmassrange).xic_flightTime.to_numpy()*1000)\n",
    "    npuusblow = np.array(df_uubar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndlow).xic_flightTime.to_numpy()*1000)\n",
    "    npuusbhigh = np.array(df_uubar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndhigh).xic_flightTime.to_numpy()*1000)\n",
    "    npuusb = np.array(df_uubar.query(cuts+' and not xic_isSignal==1 and '+xicsdbnds).xic_flightTime.to_numpy()*1000)\n",
    "\n",
    "    npdd = np.array(df_ddbar.query(cuts+' and not xic_isSignal==1 and '+xicmassrange).xic_flightTime.to_numpy()*1000)\n",
    "    npddsblow = np.array(df_ddbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndlow).xic_flightTime.to_numpy()*1000)\n",
    "    npddsbhigh = np.array(df_ddbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndhigh).xic_flightTime.to_numpy()*1000)\n",
    "    npddsb = np.array(df_ddbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbnds).xic_flightTime.to_numpy()*1000)\n",
    "\n",
    "    npss = np.array(df_ssbar.query(cuts+' and not xic_isSignal==1 and '+xicmassrange).xic_flightTime.to_numpy()*1000)\n",
    "    npsssblow = np.array(df_ssbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndlow).xic_flightTime.to_numpy()*1000)\n",
    "    npsssbhigh = np.array(df_ssbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndhigh).xic_flightTime.to_numpy()*1000)\n",
    "    npsssb = np.array(df_ssbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbnds).xic_flightTime.to_numpy()*1000)\n",
    "\n",
    "    npcc = np.concatenate((npmi,npch,npuu,npdd,npss))\n",
    "    npccsblow = np.concatenate((npmisblow,npchsblow,npuusblow,npddsblow,npsssblow))\n",
    "    npccsbhigh = np.concatenate((npmisbhigh,npchsbhigh,npuusbhigh,npddsbhigh,npsssbhigh))\n",
    "    npccsb = np.concatenate((npmisb,npchsb,npuusb,npddsb,npsssb))\n",
    "\n",
    "    scale = 1 # optional scale factor\n",
    "    wcc = [scale] * len(npcc)\n",
    "    wccsblow = [scale] * len(npccsblow)\n",
    "    wccsbhigh = [scale] * len(npccsbhigh)\n",
    "    wccsb = [scale/2] * len(npccsb)\n",
    "\n",
    "    ycc, bin_edges = np.histogram(npcc, bins=nbins, range=myrange, weights=wcc)\n",
    "    yccsblow, bin_edges = np.histogram(npccsblow, bins=nbins, range=myrange,  weights=wccsblow)\n",
    "    yccsbhigh, bin_edges = np.histogram(npccsbhigh, bins=nbins, range=myrange,  weights=wccsbhigh)\n",
    "    yccsb, bin_edges = np.histogram(npccsb, bins=nbins, range=myrange,  weights=wccsb)\n",
    "\n",
    "    bin_centers = 0.5*(bin_edges[1:] + bin_edges[:-1])\n",
    "    ax0.errorbar(bin_centers, ycc, yerr = ycc**0.5, fmt='kp', label=\"Signal region\")\n",
    "    ax0.errorbar(bin_centers, yccsblow, yerr = yccsblow**0.5, fmt='gp', label=\"Low sideband\")\n",
    "    ax0.errorbar(bin_centers, yccsbhigh, yerr = yccsbhigh**0.5, fmt='bp', label=\"High sideband\")\n",
    "    ax0.errorbar(bin_centers, yccsb, yerr = yccsb**0.5, fmt='rp', label=\"Sidebands\")\n",
    "\n",
    "    # set plot features\n",
    "    #ax0.set_ylim(0.1,2e3)\n",
    "    #ax0.set_yscale(\"log\")\n",
    "    ax0.set_xlim(myrange)\n",
    "    ax0.set_xlabel(mylabel)\n",
    "    ax0.legend()\n",
    "\n",
    "    yccerr = ycc**0.5\n",
    "    X = np.ma.masked_equal(yccerr,0)\n",
    "    ysbpull = (ycc-yccsb)/X\n",
    "    ax1.hist(bin_edges[:-1], bin_edges, weights=ysbpull, histtype='step', color='Black')\n",
    "    ysblhpull = (yccsbhigh-yccsblow)/X\n",
    "    ax1.hist(bin_edges[:-1], bin_edges, weights=ysblhpull, histtype='step', color='Red')\n",
    "\n",
    "    # set plot features\n",
    "    ax1.set_xlim(myrange)\n",
    "    plt.axhline(y=0.0, color='k', linestyle='-', linewidth=0.5)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotLifetimeCompUDS(\"xic_flightTime\",cuts,(-2,4),r'$\\Xi_{c}^{+}$ flight time [ps]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotLogLifetimeCompUDS(var, cuts, myrange, mylabel):\n",
    "    nbins = 100\n",
    "    fig = plt.figure()\n",
    "    fig.subplots_adjust(hspace=0.4)\n",
    "\n",
    "    gs = gridspec.GridSpec(2, 1, height_ratios=[5, 1]) \n",
    "    ax0 = plt.subplot(gs[0])\n",
    "    ax1 = plt.subplot(gs[1])\n",
    "\n",
    "    npmi = np.array(df_mixed.query(cuts+' and not xic_isSignal==1 and '+xicmassrange).xic_flightTime.to_numpy()*1000)\n",
    "    npmisblow = np.array(df_mixed.query(cuts+' and not xic_isSignal==1 and '+xicsdbndlow).xic_flightTime.to_numpy()*1000)\n",
    "    npmisbhigh = np.array(df_mixed.query(cuts+' and not xic_isSignal==1 and '+xicsdbndhigh).xic_flightTime.to_numpy()*1000)\n",
    "    npmisb = np.array(df_mixed.query(cuts+' and not xic_isSignal==1 and '+xicsdbnds).xic_flightTime.to_numpy()*1000)\n",
    "\n",
    "    npch = np.array(df_charged.query(cuts+' and not xic_isSignal==1 and '+xicmassrange).xic_flightTime.to_numpy()*1000)\n",
    "    npchsblow = np.array(df_charged.query(cuts+' and not xic_isSignal==1 and '+xicsdbndlow).xic_flightTime.to_numpy()*1000)\n",
    "    npchsbhigh = np.array(df_charged.query(cuts+' and not xic_isSignal==1 and '+xicsdbndhigh).xic_flightTime.to_numpy()*1000)\n",
    "    npchsb = np.array(df_charged.query(cuts+' and not xic_isSignal==1 and '+xicsdbnds).xic_flightTime.to_numpy()*1000)\n",
    "\n",
    "    npuu = np.array(df_uubar.query(cuts+' and not xic_isSignal==1 and '+xicmassrange).xic_flightTime.to_numpy()*1000)\n",
    "    npuusblow = np.array(df_uubar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndlow).xic_flightTime.to_numpy()*1000)\n",
    "    npuusbhigh = np.array(df_uubar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndhigh).xic_flightTime.to_numpy()*1000)\n",
    "    npuusb = np.array(df_uubar.query(cuts+' and not xic_isSignal==1 and '+xicsdbnds).xic_flightTime.to_numpy()*1000)\n",
    "\n",
    "    npdd = np.array(df_ddbar.query(cuts+' and not xic_isSignal==1 and '+xicmassrange).xic_flightTime.to_numpy()*1000)\n",
    "    npddsblow = np.array(df_ddbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndlow).xic_flightTime.to_numpy()*1000)\n",
    "    npddsbhigh = np.array(df_ddbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndhigh).xic_flightTime.to_numpy()*1000)\n",
    "    npddsb = np.array(df_ddbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbnds).xic_flightTime.to_numpy()*1000)\n",
    "\n",
    "    npss = np.array(df_ssbar.query(cuts+' and not xic_isSignal==1 and '+xicmassrange).xic_flightTime.to_numpy()*1000)\n",
    "    npsssblow = np.array(df_ssbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndlow).xic_flightTime.to_numpy()*1000)\n",
    "    npsssbhigh = np.array(df_ssbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbndhigh).xic_flightTime.to_numpy()*1000)\n",
    "    npsssb = np.array(df_ssbar.query(cuts+' and not xic_isSignal==1 and '+xicsdbnds).xic_flightTime.to_numpy()*1000)\n",
    "\n",
    "    npcc = np.concatenate((npmi,npch,npuu,npdd,npss))\n",
    "    npccsblow = np.concatenate((npmisblow,npchsblow,npuusblow,npddsblow,npsssblow))\n",
    "    npccsbhigh = np.concatenate((npmisbhigh,npchsbhigh,npuusbhigh,npddsbhigh,npsssbhigh))\n",
    "    npccsb = np.concatenate((npmisb,npchsb,npuusb,npddsb,npsssb))\n",
    "\n",
    "    scale = 1 # optional scale factor\n",
    "    wcc = [scale] * len(npcc)\n",
    "    wccsblow = [scale] * len(npccsblow)\n",
    "    wccsbhigh = [scale] * len(npccsbhigh)\n",
    "    wccsb = [scale/2] * len(npccsb)\n",
    "\n",
    "    ycc, bin_edges = np.histogram(npcc, bins=nbins, range=myrange, weights=wcc)\n",
    "    yccsblow, bin_edges = np.histogram(npccsblow, bins=nbins, range=myrange,  weights=wccsblow)\n",
    "    yccsbhigh, bin_edges = np.histogram(npccsbhigh, bins=nbins, range=myrange,  weights=wccsbhigh)\n",
    "    yccsb, bin_edges = np.histogram(npccsb, bins=nbins, range=myrange,  weights=wccsb)\n",
    "\n",
    "    bin_centers = 0.5*(bin_edges[1:] + bin_edges[:-1])\n",
    "    ax0.errorbar(bin_centers, ycc, yerr = ycc**0.5, fmt='kp', label=\"Signal region\")\n",
    "    ax0.errorbar(bin_centers, yccsblow, yerr = yccsblow**0.5, fmt='gp', label=\"Low sideband\")\n",
    "    ax0.errorbar(bin_centers, yccsbhigh, yerr = yccsbhigh**0.5, fmt='bp', label=\"High sideband\")\n",
    "    ax0.errorbar(bin_centers, yccsb, yerr = yccsb**0.5, fmt='rp', label=\"Sidebands\")\n",
    "\n",
    "    # set plot features\n",
    "    #ax0.set_ylim(0.1,2e3)\n",
    "    ax0.set_yscale(\"log\")\n",
    "    ax0.set_xlim(myrange)\n",
    "    ax0.set_xlabel(mylabel)\n",
    "    ax0.legend()\n",
    "\n",
    "    yccerr = ycc**0.5\n",
    "    X = np.ma.masked_equal(yccerr,0)\n",
    "    ysbpull = (ycc-yccsb)/X\n",
    "    ax1.hist(bin_edges[:-1], bin_edges, weights=ysbpull, histtype='step', color='Black')\n",
    "    ysblhpull = (yccsbhigh-yccsblow)/X\n",
    "    ax1.hist(bin_edges[:-1], bin_edges, weights=ysblhpull, histtype='step', color='Red')\n",
    "\n",
    "    # set plot features\n",
    "    ax1.set_xlim(myrange)\n",
    "    plt.axhline(y=0.0, color='k', linestyle='-', linewidth=0.5)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotLogLifetimeCompUDS(\"xic_flightTime\",cuts,(-2,4),r'$\\Xi_{c}^{+}$ flight time [ps]')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (Belle2)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}