track2jet_check.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandautils as pu\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -- open an ntuple produced using the 98% track2jet cut to test whether that was implemented correctly:\n",
    "df = pu.root2panda('../temp_samples/user.mpaganin.6591530.BTAGSTREAM._000198.root', 'bTag_AntiKt3PV0TrackJets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def deltaR(eta1, eta2, phi1, phi2):\n",
    "    '''\n",
    "    Definition:\n",
    "    -----------\n",
    "        Function that calculates DR between two objects given their etas and phis\n",
    "    Args:\n",
    "    -----\n",
    "        eta1 = float, eta of first object\n",
    "        eta2 = float, eta of second object\n",
    "        phi1 = float, phi of first object\n",
    "        phi2 = float, phi of second object\n",
    "    Output:\n",
    "    -------\n",
    "        deltaR = float, distance between the two objects \n",
    "    '''\n",
    "    DEta = abs(eta1-eta2)\n",
    "    DPhi = math.acos(math.cos( abs( phi1-phi2 ) ) ) # hack to avoid |phi1-phi2| larger than 180 degrees\n",
    "    return math.sqrt( pow(DEta,2) + pow(DPhi,2) ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Event 0 of 5000\n",
      "Event 100 of 5000\n",
      "Event 200 of 5000\n",
      "Event 300 of 5000\n",
      "Event 400 of 5000\n",
      "Event 500 of 5000\n",
      "Event 600 of 5000\n",
      "Event 700 of 5000\n",
      "Event 800 of 5000\n",
      "Event 900 of 5000\n",
      "Event 1000 of 5000\n",
      "Event 1100 of 5000\n",
      "Event 1200 of 5000\n",
      "Event 1300 of 5000\n",
      "Event 1400 of 5000\n",
      "Event 1500 of 5000\n",
      "Event 1600 of 5000\n",
      "Event 1700 of 5000\n",
      "Event 1800 of 5000\n",
      "Event 1900 of 5000\n",
      "Event 2000 of 5000\n",
      "Event 2100 of 5000\n",
      "Event 2200 of 5000\n",
      "Event 2300 of 5000\n",
      "Event 2400 of 5000\n",
      "Event 2500 of 5000\n",
      "Event 2600 of 5000\n",
      "Event 2700 of 5000\n",
      "Event 2800 of 5000\n",
      "Event 2900 of 5000\n",
      "Event 3000 of 5000\n",
      "Event 3100 of 5000\n",
      "Event 3200 of 5000\n",
      "Event 3300 of 5000\n",
      "Event 3400 of 5000\n",
      "Event 3500 of 5000\n",
      "Event 3600 of 5000\n",
      "Event 3700 of 5000\n",
      "Event 3800 of 5000\n",
      "Event 3900 of 5000\n",
      "Event 4000 of 5000\n",
      "Event 4100 of 5000\n",
      "Event 4200 of 5000\n",
      "Event 4300 of 5000\n",
      "Event 4400 of 5000\n",
      "Event 4500 of 5000\n",
      "Event 4600 of 5000\n",
      "Event 4700 of 5000\n",
      "Event 4800 of 5000\n",
      "Event 4900 of 5000\n"
     ]
    }
   ],
   "source": [
    "# -- C++ like loop to store DR of each track to its jet:\n",
    "DR = []\n",
    "for event in range(df.shape[0]): # loop thru events\n",
    "    if (event%100 == 0):\n",
    "        print 'Event {} of {}'.format(event, df.shape[0])\n",
    "    DR.append([])\n",
    "    for jet in range(df['jet_pt'][event].shape[0]): # loop thru jets\n",
    "        DR[event].append([])\n",
    "        for trk in range(df['jet_trk_pt'][event][jet].shape[0]): # loop thru tracks\n",
    "            DR[event][jet].append( deltaR(df['jet_eta'][event][jet], df['jet_trk_eta'][event][jet][trk],\n",
    "                                         df['jet_phi'][event][jet], df['jet_trk_phi'][event][jet][trk]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -- add DR column to the df:\n",
    "df['DR'] = DR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# -- save df to pickle for future use:\n",
    "# df.to_pickle('../temp_samples/dr.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Event 0 of 5000\n",
      "Event 100 of 5000\n",
      "Event 200 of 5000\n",
      "Event 300 of 5000\n",
      "Event 400 of 5000\n",
      "Event 500 of 5000\n",
      "Event 600 of 5000\n",
      "Event 700 of 5000\n",
      "Event 800 of 5000\n",
      "Event 900 of 5000\n",
      "Event 1000 of 5000\n",
      "Event 1100 of 5000\n",
      "Event 1200 of 5000\n",
      "Event 1300 of 5000\n",
      "Event 1400 of 5000\n",
      "Event 1500 of 5000\n",
      "Event 1600 of 5000\n",
      "Event 1700 of 5000\n",
      "Event 1800 of 5000\n",
      "Event 1900 of 5000\n",
      "Event 2000 of 5000\n",
      "Event 2100 of 5000\n",
      "Event 2200 of 5000\n",
      "Event 2300 of 5000\n",
      "Event 2400 of 5000\n",
      "Event 2500 of 5000\n",
      "Event 2600 of 5000\n",
      "Event 2700 of 5000\n",
      "Event 2800 of 5000\n",
      "Event 2900 of 5000\n",
      "Event 3000 of 5000\n",
      "Event 3100 of 5000\n",
      "Event 3200 of 5000\n",
      "Event 3300 of 5000\n",
      "Event 3400 of 5000\n",
      "Event 3500 of 5000\n",
      "Event 3600 of 5000\n",
      "Event 3700 of 5000\n",
      "Event 3800 of 5000\n",
      "Event 3900 of 5000\n",
      "Event 4000 of 5000\n",
      "Event 4100 of 5000\n",
      "Event 4200 of 5000\n",
      "Event 4300 of 5000\n",
      "Event 4400 of 5000\n",
      "Event 4500 of 5000\n",
      "Event 4600 of 5000\n",
      "Event 4700 of 5000\n",
      "Event 4800 of 5000\n",
      "Event 4900 of 5000\n"
     ]
    }
   ],
   "source": [
    "# -- get pt and DR info for plotting:\n",
    "# (kinda redundant - could be done in loop above)\n",
    "pts = []\n",
    "drs = []\n",
    "for event in range(df.shape[0]): # loop thru events\n",
    "    if (event%100 == 0):\n",
    "        print 'Event {} of {}'.format(event, df.shape[0])\n",
    "    for jet in range(df['jet_pt'][event].shape[0]): # loop thru jets\n",
    "        jet_pt = df['jet_pt'][event][jet]\n",
    "        tracks = df['DR'][event][jet]\n",
    "        pts.extend([jet_pt] * len(tracks))\n",
    "        drs.extend(tracks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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q5m4MBoPBeKP1gu6UzYkoH+JnHzDS74ekQZvGx2AwGCIiYZSNiDylqpeJyLsc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\nRs4xYWMYhmHkHBM2hmEYRs75fzWHHG0WuX7cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12288af90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -- plotting:\n",
    "plt.scatter(pts, drs, 0.2, marker = '.')\n",
    "\n",
    "a = 0.1978; b = -0.920; c = -2.817e-5;\n",
    "X = np.linspace(min(pts), max(pts), 5000)\n",
    "y = [(a + math.exp(b+c*x)) for x in X] \n",
    "plt.plot(X, y , color = 'red', label = '$y = a+e^{b+cx}$ (New)', linewidth = 2.0)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('jet pT (MeV)')\n",
    "plt.ylabel('dR between charged particle and jet')\n",
    "plt.title('dR vs. jet pT scatter plot with 98th percentile fits')\n",
    "plt.xlim(0, 1000000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}