3-plotting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting\n",
    "Author: Javier Duarte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting in `pyROOT`\n",
    "Here we do standard plotting in `pyROOT`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.14/04\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ROOT as rt\n",
    "rt.gStyle.SetOptTitle(0)\n",
    "rt.gStyle.SetOptStat(0)\n",
    "\n",
    "treename = 'HZZ4LeptonsAnalysisReduced'\n",
    "\n",
    "tfile = {}\n",
    "tree = {}\n",
    "hist = {}\n",
    "tfile['VV'] = rt.TFile.Open('data/ntuple_4mu_VV.root')\n",
    "tfile['bkg'] = rt.TFile.Open('data/ntuple_4mu_bkg.root')\n",
    "tree['VV'] = tfile['VV'].Get(treename)\n",
    "tree['bkg'] = tfile['bkg'].Get(treename)\n",
    "\n",
    "hist['VV_f_mass4l'] = rt.TH1D('VV_f_mass4l','VV_f_mass4l',100, 80, 140)\n",
    "hist['bkg_f_mass4l'] = rt.TH1D('bkg_f_mass4l','bkg_f_mass4l',100, 80, 140)\n",
    "\n",
    "tree['VV'].Project('VV_f_mass4l','f_mass4l') \n",
    "tree['bkg'].Project('bkg_f_mass4l','f_mass4l')\n",
    "\n",
    "hist['VV_f_massjj'] = rt.TH1D('VV_f_massjj','VV_f_massjj',100, 0, 2000)\n",
    "hist['bkg_f_massjj'] = rt.TH1D('bkg_f_massjj','bkg_f_massjj',100, 0, 2000)\n",
    "\n",
    "tree['VV'].Project('VV_f_massjj','f_massjj')\n",
    "tree['bkg'].Project('bkg_f_massjj','f_massjj')\n",
    "\n",
    "hist['VV_f_mass4l'].SetLineColor(rt.kRed)\n",
    "hist['VV_f_massjj'].SetLineColor(rt.kRed)\n",
    "\n",
    "%jsroot off\n",
    "c1 = rt.TCanvas('c1','c1',500,400)\n",
    "hist['VV_f_mass4l'].GetXaxis().SetTitle('f_mass4l')\n",
    "hist['VV_f_mass4l'].GetYaxis().SetTitle('Frequency')\n",
    "hist['VV_f_mass4l'].Draw('hist')\n",
    "hist['bkg_f_mass4l'].Draw('same')\n",
    "\n",
    "\n",
    "legend = rt.TLegend(0.75,0.75,0.9,0.9)\n",
    "legend.AddEntry(hist['bkg_f_mass4l'], ' bkg', 'f')\n",
    "legend.AddEntry(hist['VV_f_mass4l'], ' VV', 'f')\n",
    "legend.Draw()\n",
    "\n",
    "c1.Draw()\n",
    "\n",
    "c2 = rt.TCanvas('c2','c2',500,400)\n",
    "hist['bkg_f_massjj'].GetXaxis().SetTitle('f_massjj')\n",
    "hist['bkg_f_massjj'].GetYaxis().SetTitle('Frequency')\n",
    "hist['bkg_f_massjj'].Draw('hist')\n",
    "hist['VV_f_massjj'].Draw('same')\n",
    "\n",
    "legend.Draw()\n",
    "\n",
    "c2.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting in `matplotlib`\n",
    "Here we do the analagous plotting in `matplotlib` with `uproot`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import h5py\n",
    "\n",
    "treename = 'HZZ4LeptonsAnalysisReduced'\n",
    "filename = {}\n",
    "upfile = {}\n",
    "params = {}\n",
    "\n",
    "filename['bkg'] = 'data/ntuple_4mu_bkg.root'\n",
    "filename['VV'] = 'data/ntuple_4mu_VV.root'\n",
    "\n",
    "upfile['bkg'] = uproot.open(filename['bkg'])\n",
    "params['bkg'] = upfile['bkg'][treename].arrays() # dictionary of NumPy arrays\n",
    "upfile['VV'] = uproot.open(filename['VV'])\n",
    "params['VV'] = upfile['VV'][treename].arrays() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = {}\n",
    "VARS = ['f_mass4l', 'f_massjj']\n",
    "df['bkg'] = pd.DataFrame(params['bkg'],columns=VARS)\n",
    "df['VV'] = pd.DataFrame(params['VV'],columns=VARS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2000)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(5, 4),dpi=100)\n",
    "plt.xlabel(VARS[0])\n",
    "bins = np.linspace(80, 140, 100)\n",
    "df['bkg'][VARS[0]].plot.hist(bins, alpha=1, label='bkg',histtype='step')\n",
    "df['VV'][VARS[0]].plot.hist(bins, alpha=1, label='VV',histtype='step')\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlim(80,140)\n",
    "plt.figure(figsize=(5, 4),dpi=100)\n",
    "plt.xlabel(VARS[1])\n",
    "bins = np.linspace(0, 2000, 100)\n",
    "df['bkg'][VARS[1]].plot.hist(bins, alpha=1, label='bkg',histtype='step')\n",
    "df['VV'][VARS[1]].plot.hist(bins, alpha=1, label='VV',histtype='step')\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlim(0,2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}