qp_test_ensemble_QQ.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as ss\n",
    "import qp\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111171\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": "markdown",
   "metadata": {},
   "source": [
    "Ensemble has an integrate function that takes a ndarray of limits for each, or a tuple if the same for all\n",
    "for PIT, need the ndarray to be 0.0 to zspec for each galaxy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.     0.038]\n",
      " [ 0.     0.058]\n",
      " [ 0.     0.064]\n",
      " ..., \n",
      " [ 0.     1.895]\n",
      " [ 0.     1.891]\n",
      " [ 0.     1.97 ]]\n"
     ]
    }
   ],
   "source": [
    "alllimits = np.zeros([ngals,2])\n",
    "for i in range(ngals):\n",
    "    alllimits[i,1] = z_trues[i]\n",
    "print alllimits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "PIT = approx_pdf.integrate(limits=alllimits,using='gridded',dx=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111171\n"
     ]
    }
   ],
   "source": [
    "print len(PIT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "npct = 100\n",
    "dpct = 100./(npct)\n",
    "curpct = 0.0\n",
    "finpct = []\n",
    "percentiles = np.arange(0,100,dpct)\n",
    "for i in range(npct):\n",
    "    tmpx = np.percentile(PIT,curpct)\n",
    "    finpct.append(tmpx)\n",
    "    curpct += dpct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "QT = percentiles/100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11192b210>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm8AAAJmCAYAAAAKOWdAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f2aca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(QT,finpct)\n",
    "plt.plot([0,1], [0, 1], color='k', linestyle='-', linewidth=2)\n",
    "plt.xlabel(\"QTheory\",fontsize=18)\n",
    "plt.ylabel(\"QData\",fontsize=18)"
   ]
  },
  {
   "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
}