{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XID+PACS Lockman-SWIRE Prior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pylab\n",
    "import pymoc\n",
    "import xidplus\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "from astropy.table import Table"
   ]
  },
  {
   "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 XID+PACS 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. As the prior for XID+ is based on IRAC detected sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Sel_func=pymoc.MOC()\n",
    "Sel_func.read('../data/Lockman-SWIRE/holes_Lockman-SWIRE_irac1_20171214_O16_MOC.fits')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in XID+MIPS catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "XID_MIPS=Table.read('../data/Lockman-SWIRE/MIPS/dmu26_XID+MIPS_Lockman-SWIRE_cat_20171214.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<Table length=10>\n",
       "<table id=\"table4507813704\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>help_id</th><th>RA</th><th>Dec</th><th>F_MIPS_24</th><th>FErr_MIPS_24_u</th><th>FErr_MIPS_24_l</th><th>Bkg_MIPS_24</th><th>Sig_conf_MIPS_24</th><th>Rhat_MIPS_24</th><th>n_eff_MIPS_24</th><th>Pval_res_24</th><th>flag_mips_24</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th><th>uJy</th><th>uJy</th><th>uJy</th><th>MJy / sr</th><th>MJy / sr</th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>str27</th><th>float64</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>bool</th></tr></thead>\n",
       "<tr><td>HELP_J104834.640+553618.070</td><td>162.1443344</td><td>55.6050193754</td><td>953.652</td><td>1391.02</td><td>558.782</td><td>-0.263134</td><td>4.94239e-06</td><td>1.0021</td><td>959.0</td><td>0.0</td><td>False</td></tr>\n",
       "<tr><td>HELP_J104844.111+553806.455</td><td>162.18379776</td><td>55.6351263157</td><td>5.835</td><td>15.6855</td><td>1.42353</td><td>-0.124418</td><td>5.0304e-06</td><td>nan</td><td>1428.0</td><td>0.0</td><td>True</td></tr>\n",
       "<tr><td>HELP_J104909.113+554129.002</td><td>162.287969389</td><td>55.6913894849</td><td>16.5938</td><td>33.347</td><td>5.67531</td><td>3.73435e-05</td><td>4.89605e-06</td><td>1.00059</td><td>1623.0</td><td>0.0</td><td>True</td></tr>\n",
       "<tr><td>HELP_J104910.454+554135.336</td><td>162.293559631</td><td>55.6931488629</td><td>47.5049</td><td>66.385</td><td>28.2103</td><td>3.73435e-05</td><td>4.89605e-06</td><td>nan</td><td>775.0</td><td>0.0</td><td>False</td></tr>\n",
       "<tr><td>HELP_J104911.099+554218.548</td><td>162.29624584</td><td>55.7051523559</td><td>187.706</td><td>204.257</td><td>169.476</td><td>-0.00208601</td><td>5.06499e-06</td><td>1.00105</td><td>1615.0</td><td>0.0</td><td>False</td></tr>\n",
       "<tr><td>HELP_J104919.290+554303.561</td><td>162.330374299</td><td>55.7176559469</td><td>39.9872</td><td>58.7282</td><td>22.266</td><td>-0.00208601</td><td>5.06499e-06</td><td>nan</td><td>2000.0</td><td>0.0</td><td>False</td></tr>\n",
       "<tr><td>HELP_J104916.682+554301.860</td><td>162.319507377</td><td>55.7171832419</td><td>18.1097</td><td>33.5374</td><td>6.31178</td><td>-0.00208601</td><td>5.06499e-06</td><td>1.00147</td><td>1201.0</td><td>0.0</td><td>True</td></tr>\n",
       "<tr><td>HELP_J104914.098+554211.057</td><td>162.308741513</td><td>55.7030714689</td><td>270.506</td><td>288.916</td><td>253.788</td><td>-0.00208601</td><td>5.06499e-06</td><td>nan</td><td>1763.0</td><td>0.0</td><td>False</td></tr>\n",
       "<tr><td>HELP_J104910.874+554227.417</td><td>162.295306988</td><td>55.7076158319</td><td>30.2717</td><td>47.4151</td><td>13.7691</td><td>-0.00208601</td><td>5.06499e-06</td><td>0.999344</td><td>1381.0</td><td>0.0</td><td>False</td></tr>\n",
       "<tr><td>HELP_J104914.130+554220.288</td><td>162.308873375</td><td>55.7056354849</td><td>251.413</td><td>270.354</td><td>232.821</td><td>-0.00208601</td><td>5.06499e-06</td><td>nan</td><td>1918.0</td><td>0.0</td><td>False</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=10>\n",
       "          help_id                 RA      ... Pval_res_24 flag_mips_24\n",
       "                                 deg      ...                         \n",
       "           str27               float64    ...   float32       bool    \n",
       "--------------------------- ------------- ... ----------- ------------\n",
       "HELP_J104834.640+553618.070   162.1443344 ...         0.0        False\n",
       "HELP_J104844.111+553806.455  162.18379776 ...         0.0         True\n",
       "HELP_J104909.113+554129.002 162.287969389 ...         0.0         True\n",
       "HELP_J104910.454+554135.336 162.293559631 ...         0.0        False\n",
       "HELP_J104911.099+554218.548  162.29624584 ...         0.0        False\n",
       "HELP_J104919.290+554303.561 162.330374299 ...         0.0        False\n",
       "HELP_J104916.682+554301.860 162.319507377 ...         0.0         True\n",
       "HELP_J104914.098+554211.057 162.308741513 ...         0.0        False\n",
       "HELP_J104910.874+554227.417 162.295306988 ...         0.0        False\n",
       "HELP_J104914.130+554220.288 162.308873375 ...         0.0        False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XID_MIPS[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S8a9rnGX68XVbJc62XAuWfJpMXH8I/FH0WwB+ZvsDgedIKS8E/kQp9WLg4cB7AZRSH5VS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\nCSWiIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112caa358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skew=(XID_MIPS['FErr_MIPS_24_u']-XID_MIPS['F_MIPS_24'])/(XID_MIPS['F_MIPS_24']-XID_MIPS['FErr_MIPS_24_l'])\n",
    "skew.name='(84th-50th)/(50th-16th) percentile'\n",
    "g=sns.jointplot(x=np.log10(XID_MIPS['F_MIPS_24']),y=skew, kind='hex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The uncertianties become Gaussian by $\\sim 20 \\mathrm{\\mu Jy}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "good=XID_MIPS['F_MIPS_24']>20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249732"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in Maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "im100fits='../data/Lockman-SWIRE/PACS/Lockman-SWIRE_PACS100_20160921_img_gls.fits'#PACS 100 map\n",
    "nim100fits='../data/Lockman-SWIRE/PACS/Lockman-SWIRE_PACS100_20160921_img_noise.fits'#PACS 100 noise map\n",
    "im160fits='../data/Lockman-SWIRE/PACS/Lockman-SWIRE_PACS160_20160921_img_gls.fits'#PACS 160 map\n",
    "nim160fits='../data/Lockman-SWIRE/PACS/Lockman-SWIRE_PACS160_20160921_img_noise.fits'#PACS 100 noise map\n",
    "#output folder\n",
    "output_folder='./'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "from astropy import wcs\n",
    "\n",
    "#-----100-------------\n",
    "hdulist = fits.open(im100fits)\n",
    "im100phdu=hdulist[0].header\n",
    "im100hdu=hdulist[0].header\n",
    "im100=hdulist[0].data\n",
    "w_100 = wcs.WCS(hdulist[0].header)\n",
    "pixsize100=3600.0*np.abs(hdulist[0].header['CDELT1']) #pixel size (in arcseconds)\n",
    "hdulist.close()\n",
    "\n",
    "hdulist = fits.open(nim100fits)\n",
    "nim100=hdulist[0].data\n",
    "hdulist.close()\n",
    "\n",
    "#-----160-------------\n",
    "hdulist = fits.open(im160fits)\n",
    "im160phdu=hdulist[0].header\n",
    "im160hdu=hdulist[0].header\n",
    "\n",
    "im160=hdulist[0].data #convert to mJy\n",
    "w_160 = wcs.WCS(hdulist[0].header)\n",
    "pixsize160=3600.0*np.abs(hdulist[0].header['CDELT1']) #pixel size (in arcseconds)\n",
    "hdulist.close()\n",
    "\n",
    "hdulist = fits.open(nim160fits)\n",
    "nim160=hdulist[0].data\n",
    "hdulist.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in PSF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pacs100_psf=fits.open('../../dmu18/dmu18_Lockman-SWIRE/dmu18_PACS_100_PSF_Lockman-SWIRE_20171214.fits')\n",
    "pacs160_psf=fits.open('../../dmu18/dmu18_Lockman-SWIRE/dmu18_PACS_160_PSF_Lockman-SWIRE_20171214.fits')\n",
    "\n",
    "centre100=np.long((pacs100_psf[1].header['NAXIS1']-1)/2)\n",
    "radius100=15\n",
    "centre160=np.long((pacs160_psf[1].header['NAXIS1']-1)/2)\n",
    "radius160=25\n",
    "\n",
    "pind100=np.arange(0,radius100+1+radius100,1)*3600*np.abs(pacs100_psf[1].header['CDELT1'])/pixsize100 #get 100 scale in terms of pixel scale of map\n",
    "pind160=np.arange(0,radius160+1+radius160,1)*3600*np.abs(pacs160_psf[1].header['CDELT1'])/pixsize160 #get 160 scale in terms of pixel scale of map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.           0.33333333   0.66666667   1.           1.33333333\n",
      "   1.66666667   2.           2.33333333   2.66666667   3.           3.33333333\n",
      "   3.66666667   4.           4.33333333   4.66666667   5.           5.33333333\n",
      "   5.66666667   6.           6.33333333   6.66666667   7.           7.33333333\n",
      "   7.66666667   8.           8.33333333   8.66666667   9.           9.33333333\n",
      "   9.66666667  10.        ]\n"
     ]
    }
   ],
   "source": [
    "print(pind100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1143f3438>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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442PDXvrSl+a3f/u3b9v+Z3/2Z1Mf57Aubf3ud787P/RDPzQybEhLW3/Xd31X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\nQEOSMQAAAAANScYAAAAANPS/R59JY3wzyugAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e3bfb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab as plt\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(pacs100_psf[1].data[centre100-radius100:centre100+radius100+1,centre100-radius100:centre100+radius100+1])\n",
    "plt.colorbar()\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(pacs160_psf[1].data[centre160-radius160:centre160+radius160+1,centre160-radius160:centre160+radius160+1])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Set XID+ prior class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#---prior100--------\n",
    "prior100=xidplus.prior(im100,nim100,im100phdu,im100hdu, moc=Sel_func)#Initialise with map, uncertianty map, wcs info and primary header\n",
    "prior100.prior_cat(XID_MIPS['RA'][good],XID_MIPS['Dec'][good],'dmu26_XID+MIPS_Lockman-SWIRE_cat_20171214.fits',ID=XID_MIPS['help_id'][good])#Set input catalogue\n",
    "prior100.prior_bkg(0.0,5)#Set prior on background (assumes Gaussian pdf with mu and sigma)\n",
    "\n",
    "#---prior160--------\n",
    "prior160=xidplus.prior(im160,nim160,im160phdu,im160hdu, moc=Sel_func)\n",
    "prior160.prior_cat(XID_MIPS['RA'][good],XID_MIPS['Dec'][good],'dmu26_XID+MIPS_Lockman-SWIRE_cat_20171214.fits',ID=XID_MIPS['help_id'][good])\n",
    "prior160.prior_bkg(0.0,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Divide by 1000 so that units are mJy\n",
    "prior100.set_prf(pacs100_psf[1].data[centre100-radius100:centre100+radius100+1,centre100-radius100:centre100+radius100+1]/1000.0,\n",
    "                pind100,pind100)\n",
    "prior160.set_prf(pacs160_psf[1].data[centre160-radius160:centre160+radius160+1,centre160-radius160:centre160+radius160+1]/1000.0,\n",
    "                pind160,pind160)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- There are 13996 tiles required for input catalogue and 24 large tiles\n",
      "writing total_bytes=1342530523...\n",
      "writing bytes [0, 1073741824)... done.\n",
      "writing bytes [1073741824, 1342530523)... done.\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\n"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "import pickle\n",
    "#from moc, get healpix pixels at a given order\n",
    "from xidplus import moc_routines\n",
    "order=11\n",
    "tiles=moc_routines.get_HEALPix_pixels(order,prior100.sra,prior100.sdec,unique=True)\n",
    "order_large=6\n",
    "tiles_large=moc_routines.get_HEALPix_pixels(order_large,prior100.sra,prior100.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='./'\n",
    "xidplus.io.pickle_dump({'priors':[prior100,prior160],'tiles':tiles,'order':order,'version':xidplus.io.git_version()},'Master_prior.pkl')\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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 2622552\r\n",
      "-rw-rw-r--  1 pdh21  pdh21   111K 15 Dec 13:11 Tiles.pkl\r\n",
      "-rw-rw-r--  1 pdh21  pdh21   1.3G 15 Dec 13:11 Master_prior.pkl\r\n",
      "-rw-rw-r--  1 pdh21  pdh21    95K 15 Dec 13:12 XID+PACS_prior.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "ls -ltrh"
   ]
  },
  {
   "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": [
    ""
   ]
  }
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