NEW_PIT_QQ_test.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",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1999\n"
     ]
    }
   ],
   "source": [
    "#infile = \"/Users/samschmidt/PZPAPER/mystuff/test_magscat_trainingfile_probs.out\"\n",
    "#szfile = \"/Users/samschmidt/PZPAPER/mystuff/train_justsz.out\"\n",
    "infile = \"/Users/samschmidt/PZPAPER/mystuff/test_magscat_trainingfile_probs_top2000.out\"\n",
    "szfile = \"/Users/samschmidt/PZPAPER/mystuff/train_justsz_top2000.out\"\n",
    "z_array = np.arange(0.0050,2.1100,0.0100) #this is the grid output by BPZ for my runs\n",
    "z_trues = np.loadtxt(szfile,skiprows=1)\n",
    "alldata = np.loadtxt(infile,skiprows=1)\n",
    "ID = alldata[:,0]\n",
    "pzs = alldata[:,1:]\n",
    "ngals = len(ID)\n",
    "print ngals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "approx_pdf = qp.Ensemble(pzs.shape[0],gridded=(z_array,pzs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "testme = inmet.EvaluateMetric(approx_pdf,z_trues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2aPeTtxDCCsDKLCIAxhhf7EhRkqTK0djYyLBhwxgzZgwAtbW1jBw5ki5VdNfT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\n0Ru9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10361f850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qqplot = testme.QQplot(using='gridded',dx=0.0001,Nquants=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "testPIT = testme.PIT()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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0.29538529081359943, 0.013476482033152878, 7.7160088456062056e-15, 0.62854003743969056, 0.32072319450686632, 0.49309755226810914, 0.9024146760517161, 7.7171353885303827e-15, 0.88990422652406742, 0.82367331220183049, 0.93318352531910242, 0.13280041295384259, 0.211531252067961, 0.27242631613700685, 0.41809129670826933, 0.28210712164355095, 0.17209350177673433, 0.70151882478650085, 0.76174165866719679, 0.12423917479296033, 0.47387673254056389, 0.26139591097257825, 0.39228708123419542, 7.7622139765852535e-15]\n"
     ]
    }
   ],
   "source": [
    "print testPIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "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": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}