{ "cells": [ { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pylab\n", "import pymoc\n", "import xidplus\n", "import numpy as np\n", "%matplotlib inline\n", "from astropy.io import fits\n", "from astropy import wcs\n", "from astropy.table import Table\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook uses all the raw data from the XID+MIPS 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 coming from two different surveys at different depths (SWIRE and SPUDS) I will split the XID+ run into two different runs." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Sel_func=pymoc.MOC()\n", "Sel_func.read('../../dmu4/dmu4_sm_XMM-LSS/data/holes_XMM-LSS_irac1_O16_20180420_MOC.fits')\n", "SWIRE_MOC=pymoc.MOC()\n", "SWIRE_MOC.read('../../dmu0/dmu0_DataFusion-Spitzer/data/Sub_wp4_xmm-lss_mips24_map_v1-1-_MOCmips_mosaic_MOC.fits')\n", "Final=Sel_func.intersection(SWIRE_MOC)\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "#pymoc.io.fits.write_moc_fits(Final, 'SWIREnSPUDS.fits')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read in XID+MIPS catalogue" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "XID_MIPS=Table.read('../dmu26_XID+MIPS_XMM-LSS/data/dmu26_XID+MIPS_XMM-LSS_SWIREnSPUDS_concat_20190106.fits')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=10\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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", "use = skew < 5 \n", "n_use=skew>5\n", "g=sns.jointplot(x=np.log10(XID_MIPS['F_MIPS_24'][use]),y=skew[use] ,kind='hex')\n", "print(np.max(skew[use]))\n", "print(len(skew[n_use]))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The uncertianties become Gaussian by $\\sim 20 \\mathrm{\\mu Jy}$" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "461805\n" ] } ], "source": [ "good=XID_MIPS['F_MIPS_24']>20\n", "print(len(good))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "287595" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "good.sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read in Maps" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pswfits='../../dmu19/dmu19_HELP-SPIRE-maps/data/XMM-LSS_SPIRE250_v1.0.fits'#SPIRE 250 map\n", "pmwfits='../../dmu19/dmu19_HELP-SPIRE-maps/data/XMM-LSS_SPIRE350_v1.0.fits'#SPIRE 350 map\n", "plwfits='../../dmu19/dmu19_HELP-SPIRE-maps/data/XMM-LSS_SPIRE500_v1.0.fits'#SPIRE 500 map\n", "\n", "#output folder\n", "output_folder='./data/'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "\n", "#-----250-------------\n", "hdulist = fits.open(pswfits)\n", "im250phdu=hdulist[0].header\n", "im250hdu=hdulist['IMAGE'].header\n", "\n", "im250=hdulist['IMAGE'].data*1.0E3 #convert to mJy\n", "nim250=hdulist['ERROR'].data*1.0E3 #convert to mJy\n", "w_250 = wcs.WCS(hdulist['IMAGE'].header)\n", "pixsize250=3600.0*w_250.wcs.cd[1,1] #pixel size (in arcseconds)\n", "hdulist.close()\n", "#-----350-------------\n", "hdulist = fits.open(pmwfits)\n", "im350phdu=hdulist[0].header\n", "im350hdu=hdulist['IMAGE'].header\n", "\n", "im350=hdulist['IMAGE'].data*1.0E3 #convert to mJy\n", "nim350=hdulist['ERROR'].data*1.0E3 #convert to mJy\n", "w_350 = wcs.WCS(hdulist['IMAGE'].header)\n", "pixsize350=3600.0*w_350.wcs.cd[1,1] #pixel size (in arcseconds)\n", "hdulist.close()\n", "#-----500-------------\n", "hdulist = fits.open(plwfits)\n", "im500phdu=hdulist[0].header\n", "im500hdu=hdulist['IMAGE'].header\n", "\n", "im500=hdulist['IMAGE'].data*1.0E3 #convert to mJy\n", "nim500=hdulist['ERROR'].data*1.0E3 #convert to mJy\n", "w_500 = wcs.WCS(hdulist['IMAGE'].header)\n", "pixsize500=3600.0*w_500.wcs.cd[1,1] #pixel size (in arcseconds)\n", "hdulist.close()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Set XID+ prior class" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "#---prior250--------\n", "prior250=xidplus.prior(im250,nim250,im250phdu,im250hdu, moc=Final)#Initialise with map, uncertianty map, wcs info and primary header\n", "prior250.prior_cat(XID_MIPS['RA'][good],XID_MIPS['Dec'][good],'dmu26_XID+MIPS_XMM-LSS_SWIREnSPUDS_cat_20190106.fits',ID=XID_MIPS['help_id'][good])#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=Final)\n", "prior350.prior_cat(XID_MIPS['RA'][good],XID_MIPS['Dec'][good],'dmu26_XID+MIPS_XMM-LSS_SWIREnSPUDS_cat_20190106.fits',ID=XID_MIPS['help_id'][good])\n", "prior350.prior_bkg(-5.0,5)\n", "\n", "#---prior500--------\n", "prior500=xidplus.prior(im500,nim500,im500phdu,im500hdu, moc=Final)\n", "prior500.prior_cat(XID_MIPS['RA'][good],XID_MIPS['Dec'][good],'dmu26_XID+MIPS_XMM-LSS_SWIREnSPUDS_cat_20190106.fits',ID=XID_MIPS['help_id'][good])\n", "prior500.prior_bkg(-5.0,5)" ] }, { "cell_type": "code", "execution_count": 26, "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] #get 250 scale in terms of pixel scale of map\n", "pind350=np.arange(0,101,1)*1.0/pixsize[1] #get 350 scale in terms of pixel scale of map\n", "pind500=np.arange(0,101,1)*1.0/pixsize[2] #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": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----- There are 697 tiles required for input catalogue and 21 large tiles\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_SWIRE.pkl'\n", "with open(outfile, 'wb') as f:\n", " pickle.dump({'priors':[prior250,prior350,prior500],'tiles':tiles,'order':order,'version':xidplus.io.git_version()},f)\n", "outfile=output_folder+'Tiles_SWIRE.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": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python (herschelhelp_internal)", "language": "python", "name": "helpint" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }