root->numpy

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Umsetzung Root->Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from tree2array import ReadTTree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pfade zu Root Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "signal_dir1 = ['/storage/jbod/welsch/ntuples/ttHbb/Even/' + x for x in \n",
    "               os.listdir('/storage/jbod/welsch/ntuples/ttHbb/Even/')]\n",
    "signal_dir2 = [ '/storage/jbod/welsch/ntuples/ttHbb/Odd/' + x for x in \n",
    "               os.listdir('/storage/jbod/welsch/ntuples/ttHbb/Odd/')]\n",
    "signal_dir = signal_dir1 + signal_dir2\n",
    "\n",
    "background_dir1 = ['/storage/jbod/welsch/ntuples/ttbarSL/Even/' + x for x in \n",
    "                   os.listdir('/storage/jbod/welsch/ntuples/ttbarSL/Even/')]\n",
    "background_dir2 = ['/storage/jbod/welsch/ntuples/ttbarSL/Odd/' + x for x in \n",
    "                   os.listdir('/storage/jbod/welsch/ntuples/ttbarSL/Odd/')]\n",
    "background_dir = background_dir1 + background_dir2\n",
    "\n",
    "treename = 'MVATree'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wandle Dateien um"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "convert= ReadTTree(signal_dir, background_dir,treename, read_new = False)\n",
    "array = convert.get_array_include('testbranches2.txt', 'UCIlikedata_2', \n",
    "                                 category='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.         -0.77377683 -0.93944979 -0.90936399 -1.39126837  0.58802342\n",
      " -0.77298152  1.18897867 -0.61258119  0.9871887   1.08780718  0.24216792\n",
      "  0.84163421 -0.13825552 -0.79785258 -2.04607296  0.39207008  0.27452457\n",
      "  1.06821191 -0.61810285 -0.17287344  0.          0.          0.93986824\n",
      " -0.30467564 -2.0600799  -0.91354035 -0.21903954 -0.58481437 -0.70923758\n",
      "  1.47277915 -0.77878302 -0.82648003 -0.2749474  -0.24532947 -1.56962574\n",
      " -1.04367363 -0.43350819 -2.11030293 -0.42432666 -1.4390775   1.62937534\n",
      " -1.10555732 -0.69825202 -0.83134842 -0.53475606 -0.77014762]\n"
     ]
    }
   ],
   "source": [
    "print(array[0])"
   ]
  },
  {
   "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}