a-dataset-and-plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasets\n",
    "Author: Javier Duarte\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load datasets from `ROOT` files using `uproot`\n",
    "Here we load the `ROOT` datasets in python using `uproot`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load `ROOT` files\n",
    "Here we load the `ROOT` datasets with `uproot` (see: https://github.com/scikit-hep/uproot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "treename = 'HZZ4LeptonsAnalysisReduced'\n",
    "filename = {}\n",
    "upfile = {}\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",
    "upfile['VV'] = uproot.open(filename['VV'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert tree to `pandas` DataFrames\n",
    "In my opinion, `pandas` DataFrames are a more convenient/flexible data container in python: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(58107, 2)\n",
      "(25817, 2)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "VARS = ['f_mass4l','f_massjj'] # choose which vars to use (2d)\n",
    "\n",
    "df = {}\n",
    "\n",
    "df['bkg'] = upfile['bkg'][treename].pandas.df(branches=VARS)\n",
    "df['VV'] = upfile['VV'][treename].pandas.df(branches=VARS)\n",
    "\n",
    "# print shape of DataFrame\n",
    "print(df['bkg'].shape)\n",
    "print(df['VV'].shape)\n",
    "\n",
    "# print first entry\n",
    "# print(df['bkg'].iloc[:1])\n",
    "\n",
    "# print first entry for f_mass4l and f_massjj\n",
    "# print(df['bkg'][['f_mass4l','f_massjj']].iloc[:1])\n",
    "\n",
    "# convert back into unstructured NumPY array\n",
    "# print(df['bkg'].values)\n",
    "# print(df['bkg'].values.shape)\n",
    "\n",
    "# get boolean mask array\n",
    "# mask = (df['bkg']['f_mass4l'] > 125)\n",
    "# print(mask)\n",
    "\n",
    "# cut using this boolean mask array\n",
    "# print(df['bkg']['f_mass4l'][mask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2000)"
      ]
     },
     "execution_count": 5,
     "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": {
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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",
    "\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=bins, alpha=1, label='bkg',histtype='step')\n",
    "df['VV'][VARS[0]].plot.hist(bins=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=bins, alpha=1, label='bkg',histtype='step')\n",
    "df['VV'][VARS[1]].plot.hist(bins=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 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}