pz_stats_fakedatatest_properuniform.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as ss\n",
    "import qp\n",
    "import individual_metrics as inmet\n",
    "import skgof\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "func1 = ss.norm(loc=3.0,scale=0.5)\n",
    "x = np.arange(0.5,10.0,0.02)\n",
    "y1 = func1.pdf(x)*20000./0.25*0.1\n",
    "func2= ss.norm(loc=5.0,scale=0.35)\n",
    "y2 = func2.pdf(x)*40000./0.35*0.1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10eb48ad0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CwuFQ5rOWg3UrGT9IxAV8/wBcLiLbgMvc16jqZuApnCubVgC3q2q4y+kO4CFg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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103e43950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples1 = np.array(func1.rvs(size=20000))\n",
    "samples2 = np.array(func2.rvs(size=40000))\n",
    "plt.hist(samples1, normed=False, histtype='stepfilled', alpha=0.2)\n",
    "plt.hist(samples2, normed=False, histtype='stepfilled', alpha=0.2)\n",
    "plt.plot(x,y1,lw=2,c='r')\n",
    "plt.plot(x,y2,lw=2,c='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000\n"
     ]
    }
   ],
   "source": [
    "bigsample = np.append(samples1,samples2)\n",
    "print len(bigsample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "made the pool of 4 in 0.0623679161072\n",
      "made the catalog in 122.718238115\n"
     ]
    }
   ],
   "source": [
    "smallgrid=np.zeros([60000,len(y1)])\n",
    "for i in range(20000):\n",
    "    smallgrid[i,:] = y1\n",
    "for i in range(20000,60000):\n",
    "    smallgrid[i,:] = y2\n",
    "approx_pdf = qp.Ensemble(smallgrid.shape[0],gridded=(x,smallgrid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testme = inmet.EvaluateMetric(approx_pdf,bigsample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testPIT = testme.PIT()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ks_stat,ks_pval = testme.KS(using='gridded',dx=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00265728452701\n",
      "0.78956502173\n"
     ]
    }
   ],
   "source": [
    "print ks_stat\n",
    "print ks_pval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0513794073338\n",
      "0.867859453451\n"
     ]
    }
   ],
   "source": [
    "cvm_stat,cvm_pval = testme.CvM(using='gridded',dx=0.0001)\n",
    "print cvm_stat\n",
    "print cvm_pval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now with proper uniform range\n",
      "0.36740855471\n",
      "0.88034108617\n"
     ]
    }
   ],
   "source": [
    "ad_stat,ad_pval = testme.AD(using='gridded',dx=0.0001, vmin=0.01,vmax=0.99)\n",
    "print ad_stat\n",
    "print ad_pval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "change vmin,vmax, see effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now with proper uniform range\n",
      "0.353218731994\n",
      "0.893515473165\n"
     ]
    }
   ],
   "source": [
    "ad_stat,ad_pval = testme.AD(using='gridded',dx=0.0001, vmin=0.005,vmax=0.995)\n",
    "print ad_stat\n",
    "print ad_pval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now with proper uniform range\n",
      "0.331464031915\n",
      "0.912790359342\n"
     ]
    }
   ],
   "source": [
    "ad_stat,ad_pval = testme.AD(using='gridded',dx=0.0001, vmin=0.001,vmax=0.999)\n",
    "print ad_stat\n",
    "print ad_pval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now with proper uniform range\n",
      "0.323428609292\n",
      "0.919578277939\n"
     ]
    }
   ],
   "source": [
    "ad_stat,ad_pval = testme.AD(using='gridded',dx=0.0001, vmin=0.0001,vmax=0.9999)\n",
    "print ad_stat\n",
    "print ad_pval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now with proper uniform range\n",
      "0.322971105248\n",
      "0.919958810636\n"
     ]
    }
   ],
   "source": [
    "ad_stat,ad_pval = testme.AD(using='gridded',dx=0.0001, vmin=0.000,vmax=1.0)\n",
    "print ad_stat\n",
    "print ad_pval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vs7OzlZ2dnbQhAQBItNdekzp39lRQEITbnDmuTj6ZcEt1ubm5ys3Njcv3Cvuet5Mk9bPW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\nIw5vZmZmZjnyf3oky+EQ5PHaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1110eba50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qqplot = testme.QQplot(using='gridded',dx=0.0001,Nquants=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "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": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}