alexpearce/bcolz.ipynb

## bcolz.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import bcolz\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import root_pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 2.83 s per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "ctable = bcolz.ctable(rootdir='LcTopKpi_2015_MagUp.bcolz')\n",
    "lc_m = ctable.fetchwhere('(2220 < Lc_M) & (Lc_M < 2360) & (0.9 < Lc_p_ProbNNp)',\n",
    "                         outcols=['Lc_M'],\n",
    "                         out_flavor='numpy')\n",
    "df = pd.DataFrame(lc_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sJC9I8tmqOi/JARm++B+e5DnT11xkeFjc48btfqaqtiQ5YmyzLsnTWmvX7dNIAQDAKrIg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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12eb886438>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 390
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.Lc_M, bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 2.49 s per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "df = root_pandas.read_root('../output/data/LcTopKpi_2015_MagUp.root',\n",
    "                           columns=['Lc_M'],\n",
    "                           where=('(2220 < Lc_M) && (Lc_M < 2360) && (0.9 < Lc_p_ProbNNp)'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"import sys\n",
	"sys.path.insert(0, '..')"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 32,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"import bcolz\n",
	"import matplotlib.pyplot as plt\n",
	"import pandas as pd\n",
	"import root_pandas"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 35,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"1 loop, best of 3: 2.83 s per loop\n"
	]
	}
	],
	"source": [
	"%%timeit\n",
	"ctable = bcolz.ctable(rootdir='LcTopKpi_2015_MagUp.bcolz')\n",
	"lc_m = ctable.fetchwhere('(2220 < Lc_M) & (Lc_M < 2360) & (0.9 < Lc_p_ProbNNp)',\n",
	" outcols=['Lc_M'],\n",
	" out_flavor='numpy')\n",
	"df = pd.DataFrame(lc_m)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 29,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"image/png": 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sJC9I8tmqOi/JARm++B+e5DnT11xkeFjc48btfqaqtiQ5YmyzLsnTWmvX7dNIAQDAKrIg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\nAAAAAElFTkSuQmCC\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x7f12eb886438>"
	]
	},
	"metadata": {
	"image/png": {
	"height": 250,
	"width": 390
	}
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"plt.hist(df.Lc_M, bins=100);"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 36,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"1 loop, best of 3: 2.49 s per loop\n"
	]
	}
	],
	"source": [
	"%%timeit\n",
	"df = root_pandas.read_root('../output/data/LcTopKpi_2015_MagUp.root',\n",
	" columns=['Lc_M'],\n",
	" where=('(2220 < Lc_M) && (Lc_M < 2360) && (0.9 < Lc_p_ProbNNp)'))"
	]
	},
	{
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