{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistics in physt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Necessary import evil\n",
    "%matplotlib inline\n",
    "import physt\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.random.seed(42)   # Repeatable results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.random.normal(150, 25, 1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w6jV16tTscklJCSUlJY3UHDOztqGsrIyysrJGrTNVEomI13K+vQ+YnyxXAt1z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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe1704e57f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = physt.h1(data)\n",
    "h.plot(stats_box=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cli7vrwi67F5EpIZTiIuIBJhCXEQkwBTiIiIBphAXEQkwhbiISIApxEVEAkwh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\nSIApxEVEAkwhLiISYApxEZEA+z9hYorL3VSVAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe14cb25c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = physt.h1(data, weights=data)\n",
    "h.plot(stats_box=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7o3L382aIldswJ06c8LS0NE9JSfGpU6f6G2+8EX+vR48eXlRU5O7uhw4d8m7duvlnn33m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\nKEREJJSCQkREQv1/Olwzk0/7uSoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe14ca35fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = physt.h1(data, range=(120, 140))\n",
    "h.plot(stats_box=True);"
   ]
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}