Discrete Differentiation Testing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing the Discrete Differentiation Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/sw/lib/python2.7/site-packages/matplotlib/__init__.py:1350: UserWarning:  This call to matplotlib.use() has no effect\n",
      "because the backend has already been chosen;\n",
      "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
      "or matplotlib.backends is imported for the first time.\n",
      "\n",
      "  warnings.warn(_use_error_msg)\n"
     ]
    }
   ],
   "source": [
    "import crosspower as cp\n",
    "import FisherCl2 as fcl\n",
    "from numpy.linalg import eigh\n",
    "\n",
    "# set plotting to be in the notebook instead of its own qt window\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'FisherCl2' from 'FisherCl2.pyc'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reload any changes that have been made to crosspower.py\n",
    "reload(cp)\n",
    "reload(fcl.cp)\n",
    "reload(fcl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Do some testing with discrete differentiation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating matter power spectrum object...\n",
      "zs.size:  9998\n",
      "creating more matter power objects...\n",
      "creating matter power spectra for  ombh2  derivative...\n",
      "zs.size:  9998\n",
      "zs.size:  9998\n",
      "creating matter power spectra for  omch2  derivative...\n",
      "zs.size:  9998\n",
      "zs.size:  9998\n",
      "creating matter power spectra for  cosmomc_theta  derivative...\n",
      "zs.size:  9998\n",
      "zs.size:  9998\n",
      "creating matter power spectra for  As  derivative...\n",
      "zs.size:  9998\n",
      "zs.size:  9998\n",
      "creating matter power spectra for  ns  derivative...\n",
      "zs.size:  9998\n",
      "zs.size:  9998\n",
      "creating matter power spectra for  tau  derivative...\n",
      "zs.size:  9998\n",
      "zs.size:  9998\n",
      "creating matter power spectra for  mnu  derivative...\n",
      "zs.size:  9998\n",
      "zs.size:  9998\n",
      "fiducial bs:  [ 1.31336283  1.66697151  2.06888487  2.48113088  2.89695688  3.31442616\n",
      "  3.73278304  4.15167301]\n",
      "fiducial As:  [ 1.00205877  1.00134013  1.00118993  1.00112731  1.00109399  1.00107367\n",
      "  1.00106014  1.00105055]\n",
      "starting cross power with entire kappa... \n",
      "starting angular cross power spectrum  0 ,  0 ... \n",
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      "starting angular cross power spectrum  0 ,  1 ... \n",
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      "starting angular cross power spectrum  0 ,  3 ... \n",
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      "starting angular cross power spectrum  0 ,  4 ... \n",
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      "starting angular cross power spectrum  0 ,  5 ... \n",
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      "window  1 : galaxies  6\n",
      "window  2 : galaxies  6\n",
      "window  1 : galaxies  6\n",
      "window  2 : galaxies  6\n",
      "window  1 : galaxies  6\n",
      "window  2 : galaxies  6\n",
      "window  1 : galaxies  6\n",
      "window  2 : galaxies  6\n",
      "window  1 : galaxies  6\n",
      "window  2 : galaxies  6\n",
      "window  1 : galaxies  6\n",
      "window  2 : galaxies  6\n",
      "window  1 : galaxies  6\n",
      "window  2 : galaxies  6\n",
      "window  1 : galaxies  6\n",
      "window  2 : galaxies  6\n",
      "window  1 : galaxies  6\n",
      "window  2 : galaxies  6\n",
      "window  1 : galaxies  6\n",
      "window  2 : galaxies  6\n",
      "window  1 : galaxies  6\n",
      "window  2 : galaxies  6\n",
      "starting angular cross power spectrum  6 ,  7 ... \n",
      "starting angular cross power spectrum  6 ,  8 ... \n",
      "starting angular cross power spectrum  7 ,  7 ... \n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "starting angular cross power spectrum  7 ,  8 ... \n",
      "starting angular cross power spectrum  8 ,  8 ... \n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "starting cross power with binned kappa... \n",
      "starting angular cross power spectrum  0 ,  0 ... \n",
      "window  1 : kappa  1\n",
      "window  2 : kappa  1\n",
      "window  1 : kappa  1\n",
      "window  2 : galaxies  1\n",
      "window  1 : galaxies  1\n",
      "window  2 : galaxies  1\n",
      "starting angular cross power spectrum  1 ,  1 ... \n",
      "window  1 : kappa  2\n",
      "window  2 : kappa  2\n",
      "window  1 : kappa  2\n",
      "window  2 : galaxies  2\n",
      "window  1 : galaxies  2\n",
      "window  2 : galaxies  2\n",
      "starting angular cross power spectrum  2 ,  2 ... \n",
      "window  1 : kappa  3\n",
      "window  2 : kappa  3\n",
      "window  1 : kappa  3\n",
      "window  2 : galaxies  3\n",
      "window  1 : galaxies  3\n",
      "window  2 : galaxies  3\n",
      "starting angular cross power spectrum  3 ,  3 ... \n",
      "window  1 : kappa  4\n",
      "window  2 : kappa  4\n",
      "window  1 : kappa  4\n",
      "window  2 : galaxies  4\n",
      "window  1 : galaxies  4\n",
      "window  2 : galaxies  4\n",
      "starting angular cross power spectrum  4 ,  4 ... \n",
      "window  1 : kappa  5\n",
      "window  2 : kappa  5\n",
      "window  1 : kappa  5\n",
      "window  2 : galaxies  5\n",
      "window  1 : galaxies  5\n",
      "window  2 : galaxies  5\n",
      "starting angular cross power spectrum  5 ,  5 ... \n",
      "window  1 : kappa  6\n",
      "window  2 : kappa  6\n",
      "window  1 : kappa  6\n",
      "window  2 : galaxies  6\n",
      "window  1 : galaxies  6\n",
      "window  2 : galaxies  6\n",
      "starting angular cross power spectrum  6 ,  6 ... \n",
      "window  1 : kappa  7\n",
      "window  2 : kappa  7\n",
      "window  1 : kappa  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "starting angular cross power spectrum  7 ,  7 ... \n",
      "window  1 : kappa  8\n",
      "window  2 : kappa  8\n",
      "window  1 : kappa  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "building covariance matrix... \n",
      "starting covariance set  1  of  9 ... \n",
      "starting covariance set  2  of  9 ... \n",
      "starting covariance set  3  of  9 ... \n",
      "starting covariance set  4  of  9 ... \n",
      "starting covariance set  5  of  9 ... \n",
      "starting covariance set  6  of  9 ... \n",
      "starting covariance set  7  of  9 ... \n",
      "starting covariance set  8  of  9 ... \n",
      "starting covariance set  9  of  9 ... \n",
      "inverting covariance matrix... \n",
      "starting creation of C_l derivatives... \n",
      "starting derivative set  1  of  9 ... \n",
      "starting derivative set  2  of  9 ... \n",
      "starting derivative set  3  of  9 ... \n",
      "starting derivative set  4  of  9 ... \n",
      "starting derivative set  5  of  9 ... \n",
      "starting derivative set  6  of  9 ... \n",
      "starting derivative set  7  of  9 ... \n",
      "starting derivative set  8  of  9 ... \n",
      "starting derivative set  9  of  9 ... \n",
      "building Fisher matrix from components...\n",
      "invCov.shape:  (45, 45, 1999) , dClVecs.shape:  (45, 15, 1999)\n",
      "starting bin set  1  of  15\n",
      "starting bin set  2  of  15\n",
      "starting bin set  3  of  15\n",
      "starting bin set  4  of  15\n",
      "starting bin set  5  of  15\n",
      "starting bin set  6  of  15\n",
      "starting bin set  7  of  15\n",
      "starting bin set  8  of  15\n",
      "starting bin set  9  of  15\n",
      "starting bin set  10  of  15\n",
      "starting bin set  11  of  15\n",
      "starting bin set  12  of  15\n",
      "starting bin set  13  of  15\n",
      "starting bin set  14  of  15\n",
      "starting bin set  15  of  15\n",
      "creation of Fisher Matrix complete!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# get a Fisher Matrix object using new version FisherCl2\n",
    "reload(fcl)\n",
    "zmax=4\n",
    "nBins=8\n",
    "nz=10000\n",
    "Fobj = fcl.FisherMatrix(zmax=zmax,nBins=nBins,nz=nz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ombh2 : 0.000604624126789\n",
      "omch2 : 0.00155314762976\n",
      "cosmomc_theta : 1.78161804649e-05\n",
      "As : 2.16537404113e-11\n",
      "ns : 0.00299054454839\n",
      "tau : 1.22355881919\n",
      "mnu : 0.0159371478595\n",
      "bin1 : 0.0199824209983\n",
      "bin2 : 0.0245806962699\n",
      "bin3 : 0.031043262345\n",
      "bin4 : 0.0375744646412\n",
      "bin5 : 0.0429502085328\n",
      "bin6 : 0.0498493534157\n",
      "bin7 : 0.0563543408301\n",
      "bin8 : 0.0622921567252\n"
     ]
    }
   ],
   "source": [
    "sigmas = np.sqrt(np.diag(np.linalg.inv(Fobj.Fij)))\n",
    "for paramNum in range(Fobj.nParams):\n",
    "    print Fobj.paramList[paramNum],':',sigmas[paramNum]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AXdz9J4Q9pJ6Nsxgz+wFh5nRZNLv+3tNZ/krBj2kDNRbF8XT3+qiGTsABWd6E\niuG1nkudsb/Wzex44PPotz4j+/+HjT6ehQz/eUD6hxCdgPkZY+YCOwFEJ4tt7u4NtQzyrcE63X1p\n1BICeATYr0C1NcY8omMZyXa8Y+fuK7751dvdxwEbxbU1iJm1I4TqGHd/LsuQ2I9pQzUW0/GMalgO\npIB+GU8Vw2v9W+urs0he64cAJ5rZx8ATwBFm9ljGmEYfz0KG/7cni5lZe8LJYpmfWn9zshis/2Sx\nltZgnWa2fdrdcmBmAev7Ximsvx85Fjgbvj1Le5m7f16owjKst870nrmZ9SYsP15SqMIyjAJmuvu9\n63m+GI7pBmsshuNpZtuY2RbR7U2Ao4DMc3tif63nUmcxvNbd/Xp37+zuuxLyaIK7n50xrNHHM5cz\nfPPC3evMbCgwnu9OFqu2tJPFgJHAGDOrJTpZrFD1NbLOS83sRGAtsAQ4p9B1mtn/AmXA1mb2KXAz\n4UQ6d/eH3f0lMzvOzD4CVgLnFrrGXOoETjWzIYRjuYqwUiGOOg8BzgDej3rADlxPWPVVFMc0lxop\njuO5AzDawnbwbYA/RceuqF7rOdYZ+2t9fZp7PHWSl4hIAukyjiIiCaTwFxFJIIW/iEgCKfxFRBJI\n4S8ikkAKfxGRBFL4i4gkkMJfRCSB/h/dJ69kJPfuTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a3d050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.25  0.75  1.25  1.75  2.25  2.75  3.25  3.75]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "zs = Fobj.getBinCenters()\n",
    "sigmaBs = sigmas[7:]\n",
    "plt.plot(zs,sigmaBs)\n",
    "plt.show();\n",
    "print zs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CNEbE0ohoAuqBEUVtRgBTsuVbgCOz5eFAfURsiIjngcasPyJiFrC2xOcV9jUF+Fxlu2LW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115995550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# why the bump?  Check just the b_i portion\n",
    "sigmasBonly = np.sqrt(np.diag(np.linalg.inv(Fobj.Fij[7:,7:])))\n",
    "plt.plot(zs,sigmasBonly)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating matter power spectrum object...\n",
      "zs.size:  998\n",
      "fiducial bs:  [ 1.31336283  1.66697151  2.06888487  2.48113088  2.89695688  3.31442616\n",
      "  3.73278304  4.15167301]\n",
      "fiducial As:  [ 1.00205884  1.00134017  1.00118995  1.00112732  1.00109399  1.00107368\n",
      "  1.00106014  1.00105056]\n",
      "starting cross power with entire kappa... \n",
      "starting angular cross power spectrum  0 ,  0 ... \n",
      "window  1 : kappa  0\n",
      "window  2 : kappa  0\n",
      "starting angular cross power spectrum  0 ,  1 ... \n",
      "window  1 : kappa  0\n",
      "window  2 : galaxies  1\n",
      "starting angular cross power spectrum  0 ,  2 ... \n",
      "window  1 : kappa  0\n",
      "window  2 : galaxies  2\n",
      "starting angular cross power spectrum  0 ,  3 ... \n",
      "window  1 : kappa  0\n",
      "window  2 : galaxies  3\n",
      "starting angular cross power spectrum  0 ,  4 ... \n",
      "window  1 : kappa  0\n",
      "window  2 : galaxies  4\n",
      "starting angular cross power spectrum  0 ,  5 ... \n",
      "window  1 : kappa  0\n",
      "window  2 : galaxies  5\n",
      "starting angular cross power spectrum  0 ,  6 ... \n",
      "window  1 : kappa  0\n",
      "window  2 : galaxies  6\n",
      "starting angular cross power spectrum  0 ,  7 ... \n",
      "window  1 : kappa  0\n",
      "window  2 : galaxies  7\n",
      "starting angular cross power spectrum  0 ,  8 ... \n",
      "window  1 : kappa  0\n",
      "window  2 : galaxies  8\n",
      "starting angular cross power spectrum  1 ,  1 ... \n",
      "window  1 : galaxies  1\n",
      "window  2 : galaxies  1\n",
      "starting angular cross power spectrum  1 ,  2 ... \n",
      "starting angular cross power spectrum  1 ,  3 ... \n",
      "starting angular cross power spectrum  1 ,  4 ... \n",
      "starting angular cross power spectrum  1 ,  5 ... \n",
      "starting angular cross power spectrum  1 ,  6 ... \n",
      "starting angular cross power spectrum  1 ,  7 ... \n",
      "starting angular cross power spectrum  1 ,  8 ... \n",
      "starting angular cross power spectrum  2 ,  2 ... \n",
      "window  1 : galaxies  2\n",
      "window  2 : galaxies  2\n",
      "starting angular cross power spectrum  2 ,  3 ... \n",
      "starting angular cross power spectrum  2 ,  4 ... \n",
      "starting angular cross power spectrum  2 ,  5 ... \n",
      "starting angular cross power spectrum  2 ,  6 ... \n",
      "starting angular cross power spectrum  2 ,  7 ... \n",
      "starting angular cross power spectrum  2 ,  8 ... \n",
      "starting angular cross power spectrum  3 ,  3 ... \n",
      "window  1 : galaxies  3\n",
      "window  2 : galaxies  3\n",
      "starting angular cross power spectrum  3 ,  4 ... \n",
      "starting angular cross power spectrum  3 ,  5 ... \n",
      "starting angular cross power spectrum  3 ,  6 ... \n",
      "starting angular cross power spectrum  3 ,  7 ... \n",
      "starting angular cross power spectrum  3 ,  8 ... \n",
      "starting angular cross power spectrum  4 ,  4 ... \n",
      "window  1 : galaxies  4\n",
      "window  2 : galaxies  4\n",
      "starting angular cross power spectrum  4 ,  5 ... \n",
      "starting angular cross power spectrum  4 ,  6 ... \n",
      "starting angular cross power spectrum  4 ,  7 ... \n",
      "starting angular cross power spectrum  4 ,  8 ... \n",
      "starting angular cross power spectrum  5 ,  5 ... \n",
      "window  1 : galaxies  5\n",
      "window  2 : galaxies  5\n",
      "starting angular cross power spectrum  5 ,  6 ... \n",
      "starting angular cross power spectrum  5 ,  7 ... \n",
      "starting angular cross power spectrum  5 ,  8 ... \n",
      "starting angular cross power spectrum  6 ,  6 ... \n",
      "window  1 : galaxies  6\n",
      "window  2 : galaxies  6\n",
      "starting angular cross power spectrum  6 ,  7 ... \n",
      "starting angular cross power spectrum  6 ,  8 ... \n",
      "starting angular cross power spectrum  7 ,  7 ... \n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "starting angular cross power spectrum  7 ,  8 ... \n",
      "starting angular cross power spectrum  8 ,  8 ... \n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "starting cross power with binned kappa... \n",
      "starting angular cross power spectrum  0 ,  0 ... \n",
      "window  1 : kappa  1\n",
      "window  2 : kappa  1\n",
      "window  1 : kappa  1\n",
      "window  2 : galaxies  1\n",
      "window  1 : galaxies  1\n",
      "window  2 : galaxies  1\n",
      "starting angular cross power spectrum  1 ,  1 ... \n",
      "window  1 : kappa  2\n",
      "window  2 : kappa  2\n",
      "window  1 : kappa  2\n",
      "window  2 : galaxies  2\n",
      "window  1 : galaxies  2\n",
      "window  2 : galaxies  2\n",
      "starting angular cross power spectrum  2 ,  2 ... \n",
      "window  1 : kappa  3\n",
      "window  2 : kappa  3\n",
      "window  1 : kappa  3\n",
      "window  2 : galaxies  3\n",
      "window  1 : galaxies  3\n",
      "window  2 : galaxies  3\n",
      "starting angular cross power spectrum  3 ,  3 ... \n",
      "window  1 : kappa  4\n",
      "window  2 : kappa  4\n",
      "window  1 : kappa  4\n",
      "window  2 : galaxies  4\n",
      "window  1 : galaxies  4\n",
      "window  2 : galaxies  4\n",
      "starting angular cross power spectrum  4 ,  4 ... \n",
      "window  1 : kappa  5\n",
      "window  2 : kappa  5\n",
      "window  1 : kappa  5\n",
      "window  2 : galaxies  5\n",
      "window  1 : galaxies  5\n",
      "window  2 : galaxies  5\n",
      "starting angular cross power spectrum  5 ,  5 ... \n",
      "window  1 : kappa  6\n",
      "window  2 : kappa  6\n",
      "window  1 : kappa  6\n",
      "window  2 : galaxies  6\n",
      "window  1 : galaxies  6\n",
      "window  2 : galaxies  6\n",
      "starting angular cross power spectrum  6 ,  6 ... \n",
      "window  1 : kappa  7\n",
      "window  2 : kappa  7\n",
      "window  1 : kappa  7\n",
      "window  2 : galaxies  7\n",
      "window  1 : galaxies  7\n",
      "window  2 : galaxies  7\n",
      "starting angular cross power spectrum  7 ,  7 ... \n",
      "window  1 : kappa  8\n",
      "window  2 : kappa  8\n",
      "window  1 : kappa  8\n",
      "window  2 : galaxies  8\n",
      "window  1 : galaxies  8\n",
      "window  2 : galaxies  8\n",
      "building covariance matrix... \n",
      "starting covariance set  1  of  9 ... \n",
      "starting covariance set  2  of  9 ... \n",
      "starting covariance set  3  of  9 ... \n",
      "starting covariance set  4  of  9 ... \n",
      "starting covariance set  5  of  9 ... \n",
      "starting covariance set  6  of  9 ... \n",
      "starting covariance set  7  of  9 ... \n",
      "starting covariance set  8  of  9 ... \n",
      "starting covariance set  9  of  9 ... \n",
      "inverting covariance matrix... \n",
      "starting creation of C_l derivatives... \n",
      "starting derivative set  1  of  9 ... \n",
      "starting derivative set  2  of  9 ... \n",
      "starting derivative set  3  of  9 ... \n",
      "starting derivative set  4  of  9 ... \n",
      "starting derivative set  5  of  9 ... \n",
      "starting derivative set  6  of  9 ... \n",
      "starting derivative set  7  of  9 ... \n",
      "starting derivative set  8  of  9 ... \n",
      "starting derivative set  9  of  9 ... \n",
      "building Fisher matrix from components...\n",
      "invCov.shape:  (45, 45, 1999) , dClVecs.shape:  (45, 16, 1999)\n",
      "starting bin set  1  of  16\n",
      "starting bin set  2  of  16\n",
      "starting bin set  3  of  16\n",
      "starting bin set  4  of  16\n",
      "starting bin set  5  of  16\n",
      "starting bin set  6  of  16\n",
      "starting bin set  7  of  16\n",
      "starting bin set  8  of  16\n",
      "starting bin set  9  of  16\n",
      "starting bin set  10  of  16\n",
      "starting bin set  11  of  16\n",
      "starting bin set  12  of  16\n",
      "starting bin set  13  of  16\n",
      "starting bin set  14  of  16\n",
      "starting bin set  15  of  16\n",
      "starting bin set  16  of  16\n",
      "creation of Fisher Matrix complete!\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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1wHJgITB9y80RJP0U+C2wt6QnJX2mZ9+amZltq7q/yY6ZmfWduhjR3J3BcXVW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\nJ3FX29kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c495d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# why the bump?  Try with other Fcl version\n",
    "import FisherCl as fcl0\n",
    "Fobj0 = fcl0.FisherMatrix(zmax=4,nBins=8)\n",
    "zs0 = Fobj0.getBinCenters()\n",
    "sigmasA0,sigmasB0 = Fobj0.getSigmas()\n",
    "plt.plot(zs0,sigmasB0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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T8szMrMNUlCwkDZO0UNIzks4t8XoPSVMkNUqaJalPwWvjs/UNkoa2oM/LJb3e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\nzMzK+v8r7hobmQvLpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c365210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check just the b_i version\n",
    "sigmasB0only = np.sqrt(np.diag(np.linalg.inv(Fobj0.Fij[8:,8:])))\n",
    "plt.plot(zs,sigmasB0only)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save and load some Fisher Matrix data\n",
    "reload(fcl)\n",
    "filename='myFish.npz'\n",
    "fcl.saveFish(Fobj,filename=filename)\n",
    "x,y,z = fcl.loadFish(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx, dy, p:  53126.9962548 2072.10129598 -0.672325680068\n",
      "fid0, fid1:  0.1193 0.06\n",
      "var0, var1:  omch2 mnu\n"
     ]
    },
    {
     "data": {
      "image/png": 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MLAxjZ0pL4Y03dEnXFStUNHJztT6jWbOgrUsuzmlMZ9Uq3ZYu1Wms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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157a9850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot error ellipses!\n",
    "reload(fcl)\n",
    "myFish = fcl.Fisher(filename)\n",
    "#select parameters\n",
    "p0=1\n",
    "p1=6\n",
    "dx,dy,p = myFish.dxdyp(p0,p1)\n",
    "print 'dx, dy, p: ',dx,dy,p\n",
    "print 'fid0, fid1: ',myFish.paramVals[p0],myFish.paramVals[p1]\n",
    "print 'var0, var1: ',myFish.paramList[p0],myFish.paramList[p1]\n",
    "myFish.twoParamConf(p0,p1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try multiple plots as subplots\n",
    "#f, axarr = plt.subplots(2, 2)\n",
    "p0=1\n",
    "p1=3\n",
    "p2=6\n",
    "\n",
    "# apparently this works better for corner plots than %matplotlib inline\n",
    "%matplotlib notebook\n",
    "#plt.figure(0)\n",
    "\n",
    "nParams = 7 # arbitrary low number\n",
    "f, axarr = plt.subplots(nParams,nParams,figsize=(13,13))\n",
    "for i in range(nParams):\n",
    "    for j in range(i+1):\n",
    "        #counter = i*nParams+j+1\n",
    "        #print i,j,counter\n",
    "        plt.sca(axarr[i,j])\n",
    "        if i != nParams-1:\n",
    "            axarr[i,j].tick_params(labelbottom='off') \n",
    "        else:\n",
    "            axarr[i,j].set_xlabel(myFish.paramList[j])\n",
    "        if j != 0:\n",
    "            axarr[i,j].tick_params(labelleft='off')\n",
    "        else:\n",
    "            axarr[i,j].set_ylabel(myFish.paramList[i])\n",
    "        myFish.twoParamConf(j,i)\n",
    "    # get rid of other boxes!\n",
    "    for j in range(i+1,nParams):\n",
    "        axarr[i,j].axis('off')\n",
    "        \n",
    "f.subplots_adjust(hspace=0)\n",
    "f.subplots_adjust(wspace=0)\n",
    "#plt.tight_layout()\n",
    "plt.gcf().subplots_adjust(bottom=0.15)\n",
    "plt.gcf().subplots_adjust(left=0.15)\n",
    "plt.show()"
   ]
  },
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   "cell_type": "code",
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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