uproot_tutorial_extended.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Including selections in `uproot` and `JaggedArray` \n",
    "\n",
    "Here we now investigate more robust features of `JaggedArray`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import uproot_methods\n",
    "import awkward\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in\n",
    "\n",
    "Here, we read in the ROOT file with uproot. This will convert the `ROOT` data branches into `JaggedArrays`. We can read whichever branches we want. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = uproot.open(\"/home/physicist/results/zjets_pt170.root\")[\"T\"]\n",
    "ja = T.arrays(['gen_pt', 'gen_eta', 'gen_phi', 'gen_m', \n",
    "               'gen_id', 'gen_status', \n",
    "               'gen_mother1', 'gen_mother2', \n",
    "               'gen_daughter1', 'gen_daughter2', \n",
    "               'gen_col'])\n",
    "\n",
    "gen_pt = ja[b'gen_pt']\n",
    "gen_eta = ja[b'gen_eta']\n",
    "gen_phi = ja[b'gen_phi']\n",
    "gen_m = ja[b'gen_m']\n",
    "gen_id = ja[b'gen_id']\n",
    "gen_status = ja[b'gen_status']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get a list of particles as TLorentzVectorArrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "particles = uproot_methods.TLorentzVectorArray.from_ptetaphim(gen_pt, gen_eta, gen_phi, gen_m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select $Z$ bosons \n",
    "\n",
    "Here, we select the \"hard\" $Z$ bosons. They have a PDG ID of 23, and a \"status\" of 22. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "isz = abs(gen_id) == 23\n",
    "ishard = abs(gen_status) == 62\n",
    "selected = isz & ishard\n",
    "zbosons = particles[ selected ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.68086 , 27.715792, 20.510754, ..., 29.025118, 12.826066,\n",
       "       70.3555  ], dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zbosons.pt.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now plot the Z pt and mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111e3b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ptbins = [0, 100, 200, 300, 400, 500, 600, 700, 2000]\n",
    "plt.hist(x=zbosons.pt.flatten(), bins=ptbins, log=True, density=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1195d52d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mbins = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 2000]\n",
    "plt.hist(x=zbosons.mass.flatten(), bins=mbins, log=True, density=True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "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
}