{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using HDF5 Posterior Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "\n",
    "from astropy.table import Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To open an HDF file using the h5py module we do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cosmos_hdf = h5py.File('../../dmu24/dmu24_COSMOS/data/pz_hb_cosmos.hdf', 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each HDF5 posterior file contains three different datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'help_id', u'pz', u'zgrid']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosmos_hdf.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These datasets contain the list of HELP IDs for which photometric redshifts are available (`help_id`), the grid of redshift steps used during template fitting (`zgrid`) and the 2D array of redshift posteriors evaluated on the redshift grid (`pz`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(694478, 694)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosmos_hdf['pz'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cosmos_hdf['help_id']) == cosmos_hdf['pz'].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cosmos_hdf['zgrid']) == cosmos_hdf['pz'].shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For large fields, the HDF5 file can be sliced directly without needing to load the entire array into memory. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_pz = cosmos_hdf['pz'][1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But the datasets can also be loaded into memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pz = cosmos_hdf['pz'].value\n",
    "zgrid = cosmos_hdf['zgrid'].value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we plot two redshift posteriors for sources in the COSMOS field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x17a2f1e10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KeARU3bXr9wH3i0gZTtGPwNEzigVaX+UIWm150VBOaCmeFaHpTGGCaEGOV/HJCBTffWwL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\nhmEkiQmoYRhGkvx/csI7lbTJuLkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107423b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fig, Ax = plt.subplots(1,1, figsize=(5,4))\n",
    "\n",
    "Ax.set_xlim([0,7])\n",
    "Ax.plot(zgrid, pz[-199], lw=3)\n",
    "Ax.plot(zgrid, pz[23492], lw=3)\n",
    "Ax.set_xlabel(r'Redshift, $z$')\n",
    "Ax.set_ylabel(r'$P(z)$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (herschelhelp_internal)",
   "language": "python",
   "name": "helpint"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}