{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mc741/anaconda3/lib/python3.6/site-packages/mpl_toolkits/axes_grid/__init__.py:12: MatplotlibDeprecationWarning: \n",
      "The mpl_toolkits.axes_grid module was deprecated in Matplotlib 2.1 and will be removed two minor releases later. Use mpl_toolkits.axes_grid1 and mpl_toolkits.axisartist, which provide the same functionality instead.\n",
      "  obj_type='module')\n"
     ]
    }
   ],
   "source": [
    "import pylab as plt\n",
    "import pymoc\n",
    "import xidplus\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "from astropy.table import Table, join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook uses all the raw data from the CIGALE predictions and photoz catalogue, maps, PSF and relevant MOCs to create XID+ prior object and relevant tiling scheme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in MOCs\n",
    "The selection functions required are the main MOC associated with the masterlist. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Sel_func=pymoc.MOC()\n",
    "Sel_func.read('../../dmu4/dmu4_sm_NGP/data/holes_NGP_ukidss_k_O16_20190204_MOC.fits')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in CIGALE predictions catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cigale=Table.read('../../dmu28/dmu28_NGP/data/NGP_results_Ldust_prediction.fits')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cigale['id'].name = 'help_id'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "</table>"
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       "<Table length=1473955>\n",
       "          help_id           bayes.dust.luminosity  ... best.reduced_chi_square\n",
       "          bytes27                  float64         ...         float64        \n",
       "--------------------------- ---------------------- ... -----------------------\n",
       "HELP_J124043.436+340454.964 2.6255726460825376e+38 ...      1.9536878107221285\n",
       "HELP_J124044.261+340515.932  4.454774001790711e+36 ...        5.01640692971565\n",
       "HELP_J124044.423+340508.303  7.425850359374088e+38 ...      0.3329001148514677\n",
       "HELP_J124047.274+340554.983 1.4016155289703243e+38 ...       5.216188515720689\n",
       "HELP_J124047.353+340507.994 3.6873140591071903e+37 ...       4.502564721686689\n",
       "HELP_J124048.661+340635.189 2.3995085208439427e+35 ...      10.619529207514194\n",
       "HELP_J124049.481+340533.486   1.03643281738859e+37 ...       33.55232376011216\n",
       "HELP_J124049.705+340627.843  8.703772920561137e+36 ...      25.692735077431593\n",
       "HELP_J124050.462+340830.858  5.183459585477802e+38 ...      1.0393386071459774\n",
       "HELP_J124050.879+340445.811  5.653579254171697e+39 ...       22.33729860901667\n",
       "                        ...                    ... ...                     ...\n",
       "HELP_J135159.418+234354.890  7.461172966310608e+37 ...      1.2190372204843185\n",
       "HELP_J135159.427+233452.235  2.176014166381297e+38 ...      0.4908463762068152\n",
       "HELP_J135159.455+233704.417  7.191836237872661e+38 ...      1.8533983873199378\n",
       "HELP_J135159.631+233659.037 1.5629847355631444e+38 ...      1.9931499805995405\n",
       "HELP_J135159.634+234408.055 2.1001621135172227e+37 ...      1.8466457796569975\n",
       "HELP_J135159.652+233129.787  6.216848794517417e+37 ...      0.4722543994391028\n",
       "HELP_J135159.729+233139.022  7.190633460347819e+37 ...      0.7766191791429696\n",
       "HELP_J135159.854+233206.672  3.053461116040867e+38 ...     0.44493215863564783\n",
       "HELP_J135159.896+233644.734  5.153674999945474e+37 ...       1.585380645295891\n",
       "HELP_J135159.920+233945.349 1.4586030615052245e+38 ...      1.6107193694151216"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cigale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in photoz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "photoz=Table.read('../../dmu24/dmu24_NGP/data/master_catalogue_ngp_20180501_photoz_20180601_r_optimised.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<tr><td>HELP_J131410.500+302813.489</td><td>198.54375042943158</td><td>30.47041372674147</td><td>2020994</td><td>0.1631</td><td>0.1017</td><td>0.2345</td><td>0.784</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>0.15233281491278122</td><td>0.162043</td><td>0.4889565</td><td>0.74402625</td><td>0.10714735</td><td>rg5iii</td></tr>\n",
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       "<tr><td>HELP_J125306.685+333113.670</td><td>193.2778540874335</td><td>33.5204639597969</td><td>6081862</td><td>0.6199</td><td>0.2125</td><td>1.0481</td><td>0.798</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>0.517972022837197</td><td>0.000487424</td><td>0.019583675</td><td>0.071689425</td><td>1.0567145</td><td>m1v</td></tr>\n",
       "<tr><td>HELP_J124548.943+313103.543</td><td>191.45392821032934</td><td>31.5176509039592</td><td>6081863</td><td>0.5218</td><td>0.2569</td><td>0.7952</td><td>0.793</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>0.5362698216644108</td><td>0.48726766666666665</td><td>0.14310031666666667</td><td>0.13941498333333333</td><td>3.8422983333333334</td><td>m2i</td></tr>\n",
       "<tr><td>HELP_J125400.987+335609.752</td><td>193.50411202743348</td><td>33.9360422697969</td><td>6081868</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>0.0628029174171393</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td></td></tr>\n",
       "<tr><td>HELP_J132505.187+340052.360</td><td>201.271613098188</td><td>34.01454455049162</td><td>6081875</td><td>0.4392</td><td>0.1558</td><td>0.7422</td><td>0.66</td><td>0.8877</td><td>0.8333</td><td>0.964</td><td>0.131</td><td>0.4126721221584137</td><td>0.19716165</td><td>2.18888425</td><td>2.3407385</td><td>11.774875</td><td>k0iv</td></tr>\n",
       "<tr><td>HELP_J124622.195+335623.136</td><td>191.5924800774335</td><td>33.9397599497969</td><td>6081877</td><td>0.1264</td><td>0.0222</td><td>0.2017</td><td>0.797</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>0.12168143590239443</td><td>6.49939</td><td>16.357805</td><td>22.56374</td><td>27.0209</td><td>f0v</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3175339>\n",
       "          help_id                   RA         ... stellar_type\n",
       "          bytes27                float64       ...    bytes6   \n",
       "--------------------------- ------------------ ... ------------\n",
       "HELP_J131410.500+302813.489 198.54375042943158 ...       rg5iii\n",
       "HELP_J133855.810+281107.598 204.73254078095655 ...        m0iii\n",
       "HELP_J130324.552+285933.902 195.85230149733783 ...         g5ii\n",
       "HELP_J125051.674+284236.699 192.71530874422427 ...          m3v\n",
       "HELP_J133040.559+290320.044 202.66899411411032 ...         rf8v\n",
       "HELP_J125342.104+271050.555 193.42543477062267 ...        m2p5v\n",
       "HELP_J130655.638+304710.237  196.7318269709398 ...        a3iii\n",
       "HELP_J132321.501+264200.206 200.83958711978946 ...       rk4iii\n",
       "HELP_J130554.319+305415.277  196.4763310378609 ...       rk3iii\n",
       "HELP_J133509.654+295231.029 203.79022411876866 ...        m2p5v\n",
       "                        ...                ... ...          ...\n",
       "HELP_J134135.307+344421.576  205.3971133074335 ...             \n",
       "HELP_J124858.080+341216.830  192.2420010874335 ...          m5v\n",
       "HELP_J134424.709+335630.506 206.10295449743347 ...             \n",
       "HELP_J133709.162+345604.772 204.28817473743348 ...        f5iii\n",
       "HELP_J134920.644+332452.682  207.3360167874335 ...         m3ii\n",
       "HELP_J125306.685+333113.670  193.2778540874335 ...          m1v\n",
       "HELP_J124548.943+313103.543 191.45392821032934 ...          m2i\n",
       "HELP_J125400.987+335609.752 193.50411202743348 ...             \n",
       "HELP_J132505.187+340052.360   201.271613098188 ...         k0iv\n",
       "HELP_J124622.195+335623.136  191.5924800774335 ...          f0v"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "photoz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join CIGALE and photoz tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prior=join(cigale,photoz,keys='help_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.cosmology import Planck15 as cosmo\n",
    "from astropy import units as u\n",
    "f_pred=prior['bayes.dust.luminosity']/(4*np.pi*cosmo.luminosity_distance(prior['z1_median']).to(u.cm))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prior=prior[np.isfinite(f_pred.value)][np.log10(f_pred.value[np.isfinite(f_pred.value)])>8.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prior['DEC'].name='Dec'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in Maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "pswfits='../../dmu19/dmu19_HELP-SPIRE-maps/data/HATLAS-NGP_SPIRE250_v1.0.fits'#SPIRE 250 map\n",
    "pmwfits='../../dmu19/dmu19_HELP-SPIRE-maps/data/HATLAS-NGP_SPIRE350_v1.0.fits'#SPIRE 350 map\n",
    "plwfits='../../dmu19/dmu19_HELP-SPIRE-maps/data/HATLAS-NGP_SPIRE500_v1.0.fits'#SPIRE 500 map\n",
    "\n",
    "#output folder\n",
    "output_folder='./'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "from astropy import wcs\n",
    "\n",
    "#-----250-------------\n",
    "hdulist = fits.open(pswfits)\n",
    "im250phdu=hdulist[0].header\n",
    "im250hdu=hdulist[1].header\n",
    "\n",
    "im250=hdulist[1].data*1.0E3 #convert to mJy\n",
    "nim250=hdulist[3].data*1.0E3 #convert to mJy\n",
    "w_250 = wcs.WCS(hdulist[1].header)\n",
    "pixsize250=3600.0*w_250.wcs.cdelt #pixel size (in arcseconds)\n",
    "hdulist.close()\n",
    "#-----350-------------\n",
    "hdulist = fits.open(pmwfits)\n",
    "im350phdu=hdulist[0].header\n",
    "im350hdu=hdulist[1].header\n",
    "\n",
    "im350=hdulist[1].data*1.0E3 #convert to mJy\n",
    "nim350=hdulist[3].data*1.0E3 #convert to mJy\n",
    "w_350 = wcs.WCS(hdulist[1].header)\n",
    "pixsize350=3600.0*w_350.wcs.cdelt #pixel size (in arcseconds)\n",
    "hdulist.close()\n",
    "#-----500-------------\n",
    "hdulist = fits.open(plwfits)\n",
    "im500phdu=hdulist[0].header\n",
    "im500hdu=hdulist[1].header\n",
    "im500=hdulist[1].data*1.0E3 #convert to mJy\n",
    "nim500=hdulist[3].data*1.0E3 #convert to mJy\n",
    "w_500 = wcs.WCS(hdulist[1].header)\n",
    "pixsize500=3600.0*w_500.wcs.cdelt #pixel size (in arcseconds)\n",
    "hdulist.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Set XID+ prior class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: AstropyDeprecationWarning: \n",
      "Private attributes \"_naxis1\" and \"_naxis2\" have been deprecated since v3.1.\n",
      "Instead use the \"pixel_shape\" property which returns a list of NAXISj keyword values.\n",
      " [astropy.wcs.wcs]\n",
      "WARNING: AstropyDeprecationWarning: \n",
      "Private attributes \"_naxis1\" and \"_naxis2\" have been deprecated since v3.1.\n",
      "Instead use the \"pixel_shape\" property which returns a list of NAXISj keyword values.\n",
      " [astropy.wcs.wcs]\n"
     ]
    }
   ],
   "source": [
    "#---prior250--------\n",
    "prior250=xidplus.prior(im250,nim250,im250phdu,im250hdu, moc=Sel_func)#Initialise with map, uncertianty map, wcs info and primary header\n",
    "prior250.prior_cat(prior['RA'] ,prior['Dec'] ,'NGP_results_Ldust_prediction.fits',ID=prior['help_id'] )#Set input catalogue\n",
    "prior250.prior_bkg(-5.0,5)#Set prior on background (assumes Gaussian pdf with mu and sigma)\n",
    "#---prior350--------\n",
    "prior350=xidplus.prior(im350,nim350,im350phdu,im350hdu, moc=Sel_func)\n",
    "prior350.prior_cat(prior['RA'] ,prior['Dec'] ,'NGP_results_Ldust_prediction.fits',ID=prior['help_id'] )\n",
    "prior350.prior_bkg(-5.0,5)\n",
    "\n",
    "#---prior500--------\n",
    "prior500=xidplus.prior(im500,nim500,im500phdu,im500hdu, moc=Sel_func)\n",
    "prior500.prior_cat(prior['RA'] ,prior['Dec'] ,'NGP_results_Ldust_prediction.fits',ID=prior['help_id'] )\n",
    "prior500.prior_bkg(-5.0,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#pixsize array (size of pixels in arcseconds)\n",
    "pixsize=np.array([pixsize250,pixsize350,pixsize500])\n",
    "#point response function for the three bands\n",
    "prfsize=np.array([18.15,25.15,36.3])\n",
    "#use Gaussian2DKernel to create prf (requires stddev rather than fwhm hence pfwhm/2.355)\n",
    "from astropy.convolution import Gaussian2DKernel\n",
    "\n",
    "##---------fit using Gaussian beam-----------------------\n",
    "prf250=Gaussian2DKernel(prfsize[0]/2.355,x_size=101,y_size=101)\n",
    "prf250.normalize(mode='peak')\n",
    "prf350=Gaussian2DKernel(prfsize[1]/2.355,x_size=101,y_size=101)\n",
    "prf350.normalize(mode='peak')\n",
    "prf500=Gaussian2DKernel(prfsize[2]/2.355,x_size=101,y_size=101)\n",
    "prf500.normalize(mode='peak')\n",
    "\n",
    "pind250=np.arange(0,101,1)*1.0/pixsize[0,1] #get 250 scale in terms of pixel scale of map\n",
    "pind350=np.arange(0,101,1)*1.0/pixsize[1,1] #get 350 scale in terms of pixel scale of map\n",
    "pind500=np.arange(0,101,1)*1.0/pixsize[2,1] #get 500 scale in terms of pixel scale of map\n",
    "\n",
    "prior250.set_prf(prf250.array,pind250,pind250)#requires psf as 2d grid, and x and y bins for grid (in pixel scale)\n",
    "prior350.set_prf(prf350.array,pind350,pind350)\n",
    "prior500.set_prf(prf500.array,pind500,pind500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- There are 4959 tiles required for input catalogue and 102 large tiles\n",
      "writing total_bytes=1588798331...\n",
      "writing bytes [0, 1073741824)... done.\n",
      "writing bytes [1073741824, 1588798331)... done.\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "#from moc, get healpix pixels at a given order\n",
    "from xidplus import moc_routines\n",
    "order=9\n",
    "tiles=moc_routines.get_HEALPix_pixels(order,prior250.sra,prior250.sdec,unique=True)\n",
    "order_large=6\n",
    "tiles_large=moc_routines.get_HEALPix_pixels(order_large,prior250.sra,prior250.sdec,unique=True)\n",
    "print('----- There are '+str(len(tiles))+' tiles required for input catalogue and '+str(len(tiles_large))+' large tiles')\n",
    "output_folder='./data/'\n",
    "outfile=output_folder+'Master_prior.pkl'\n",
    "xidplus.io.pickle_dump({'priors':[prior250,prior350,prior500],'tiles':tiles,'order':order,'version':xidplus.io.git_version()},outfile)\n",
    "outfile=output_folder+'Tiles.pkl'\n",
    "with open(outfile, 'wb') as f:\n",
    "    pickle.dump({'tiles':tiles,'order':order,'tiles_large':tiles_large,'order_large':order_large,'version':xidplus.io.git_version()},f)\n",
    "raise SystemExit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prior250.nsrc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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