quick and dirty unblind

quick_and_dirty

{
 "metadata": {
  "name": "quick_and_dirty"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from root_numpy import *\nfrom glob import glob\ndatadir = '../dumpEvtShape/output/bestcandcut/'",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "onpeak_pat = datadir+'OnPeak_KStar_Run*.root'\ndata = root2rec(onpeak_pat)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "hist(data.BsublistMes[data.CPFlavor<0],bins=100,histtype='step');\nhist(data.BsublistMes[data.CPFlavor>0],bins=100,histtype='step');\nxlabel('mES(GeV)')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": "<matplotlib.text.Text at 0x2aaaac053210>"
      },
      {
       "output_type": "display_data",
       "png": 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80PkBC0cE7d3aY6O1obF9Y0uHcpvvx31vvD9wIITJJFyrUy8WUSkqKumOyLFg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\ngg==\n",
       "text": "<matplotlib.figure.Figure at 0x2aaaac066dd0>"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "b_peak    = len(data.BsublistMes[(data.CPFlavor<0) & (data.BsublistMes>5.27)])*1.0 #b\nbbar_peak = len(data.BsublistMes[(data.CPFlavor>0) & (data.BsublistMes>5.27)])*1.0 #bbar\n\nb_sb    = len(data.BsublistMes[(data.CPFlavor<0) & (data.BsublistMes<5.27)])*1.0 #b\nbbar_sb = len(data.BsublistMes[(data.CPFlavor>0) & (data.BsublistMes<5.27)])*1.0 #bbar",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print b_peak, bbar_peak, b_sb, bbar_sb",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " 4883.0 5426.0 4959.0 5765.0\n"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "(b_peak-bbar_peak)/(b_peak+bbar_peak)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": "-0.0526724221553982"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "(b_sb-bbar_sb)/(b_sb+bbar_sb)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": "-0.07515852293920179"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#so... 2 sigma at best\nsqrt(10000.)/10000.",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": "0.01"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "1898+1260+142+508+5184",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": "8992"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "b_peak + bbar_peak",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": "10309.0"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}