Extended sources in SPIRE maps

This notebook was originally written by Peter Hurley to investigate the possibility of using XID+ to get flux posterior distributions for resolved objects in the far infrared. This is to be done by using fitted profiles from optical images.



In [1]:

    
import warnings
from matplotlib.cbook import MatplotlibDeprecationWarning
warnings.simplefilter('ignore', MatplotlibDeprecationWarning)
warnings.simplefilter('ignore', UserWarning)
warnings.simplefilter('ignore', RuntimeWarning)
warnings.simplefilter('ignore',UnicodeWarning)
#warnings.simplefilter('ignore',VisibleDeprecationWarning)

import numpy as np
import scipy.stats as st
import pylab as plt
from pymoc import MOC

import xidplus
from xidplus.stan_fit import SPIRE
from xidplus import posterior_maps as postmaps

from astropy.io import fits
from astropy import wcs
from astropy.table import Table
from astropy.convolution import Gaussian2DKernel
from astropy.convolution import convolve
from astropy.coordinates import SkyCoord
from astropy import units as u

import pickle
import seaborn as sns
import pandas as pd
sns.set(color_codes=True)

%matplotlib inline
import aplpy



In [2]:

    
hdulist_250=fits.open('./data/XMM-LSS-NEST_image_250_SMAP_v6.0.fits')
im250phdu=hdulist_250[0].header
im250hdu=hdulist_250[1].header
im250=hdulist_250[1].data*1.0E3
nim250=hdulist_250[2].data*1.0E3
w_250 = wcs.WCS(hdulist_250[1].header)
pixsize250=3600.0*w_250.wcs.cd[1,1] #pixel size (in arcseconds)
#hdulist.close()

hdulist_350=fits.open('./data/XMM-LSS-NEST_image_350_SMAP_v6.0.fits')
im350phdu=hdulist_350[0].header
im350hdu=hdulist_350[1].header
im350=hdulist_350[1].data*1.0E3
nim350=hdulist_350[2].data*1.0E3
w_350 = wcs.WCS(hdulist_350[1].header)
pixsize350=3600.0*w_350.wcs.cd[1,1] #pixel size (in arcseconds)
#hdulist.close()

hdulist_500=fits.open('./data/XMM-LSS-NEST_image_500_SMAP_v6.0.fits')
im500phdu=hdulist_500[0].header
im500hdu=hdulist_500[1].header
im500=hdulist_500[1].data*1.0E3
nim500=hdulist_500[2].data*1.0E3
w_500 = wcs.WCS(hdulist_500[1].header)
pixsize500=3600.0*w_500.wcs.cd[1,1] #pixel size (in arcseconds)
#hdulist.close()



In [3]:

    
source=Table.read('./data/extended_source_test.xml')



In [4]:

    
IRAC_sources=Table.read('./data/UDS_PACSxID24_v1.fits')



In [5]:

    
IRAC_sources









    Out[5]:




<Table length=69614>

XID RA Dec F_PACS_100__A4 F_PACS_100__A5 F_PACS_100__A6 F_PACS_100__A7 F_PACS_100__A8 F_PACS_100__A10 F_PACS_100 Ferr_PACS_100__A4 Ferr_PACS_100__A5 Ferr_PACS_100__A6 Ferr_PACS_100__A7 Ferr_PACS_100__A8 Ferr_PACS_100__A10 Ferr_PACS_100 F_PACS_100__SKY F_PACS_160__A4 F_PACS_160__A5 F_PACS_160__A6 F_PACS_160__A7 F_PACS_160__A8 F_PACS_160__A10 F_PACS_160 Ferr_PACS_160__A4 Ferr_PACS_160__A5 Ferr_PACS_160__A6 Ferr_PACS_160__A7 Ferr_PACS_160__A8 Ferr_PACS_160__A10 Ferr_PACS_160 F_PACS_160__SKY HELP_ID
deg deg mJy mJy mJy mJy mJy mJy mJy mJy mJy mJy mJy mJy mJy mJy mJy / pix mJy mJy mJy mJy mJy mJy mJy mJy mJy mJy mJy mJy mJy mJy mJy / pix 
int32 float64 float64 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 str25
35164 33.400427 -3.532034 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-00001
69559 33.402787 -3.552514 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-00002
69612 33.402787 -3.529534 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-00003
35151 33.407827 -3.534404 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-00004
35141 33.409607 -3.534104 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-00005
35126 33.412387 -3.544484 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-00006
69545 33.413037 -3.555364 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-00007
69613 33.421437 -3.521034 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-00008
69556 33.422047 -3.545824 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-00009
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
35346 37.189607 -5.359364 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-69605
139 37.191917 -5.397384 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-69606
118 37.192737 -5.400864 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-69607
35313 37.192897 -5.372624 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-69608
222 37.192937 -5.360784 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-69609
35325 37.194807 -5.378524 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-69610
35322 37.196767 -5.379374 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-69611
35198 37.199537 -5.443994 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-69612
35185 37.210767 -5.442134 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-69613
44 37.212207 -5.434754 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 nan nan nan nan nan nan nan nan nan nan nan nan nan nan 0.0 XMM-PACSxID24-UDS-1-69614



In [6]:

    
WISE_sources=Table.read('./data/WXSC.phot.clean_all.fits')









    



WARNING: UnitsWarning: 'mag.asec' did not parse as fits unit: At col 4, Unit 'asec' not supported by the FITS standard. Did you mean arcsec? [astropy.units.core]
WARNING:astropy:UnitsWarning: 'mag.asec' did not parse as fits unit: At col 4, Unit 'asec' not supported by the FITS standard. Did you mean arcsec?
WARNING: UnitsWarning: 'dn' did not parse as fits unit: At col 0, Unit 'dn' not supported by the FITS standard. Did you mean dN or daN? [astropy.units.core]
WARNING:astropy:UnitsWarning: 'dn' did not parse as fits unit: At col 0, Unit 'dn' not supported by the FITS standard. Did you mean dN or daN?



In [7]:

    
#Set some color info

cmap=sns.cubehelix_palette(8, start=.5, rot=-.75,as_cmap=True)

vmin=-1.7E1/1.0E3
vmax=4.446e+01/1.0E3
ra_zoom=source['RAJ2000']
dec_zoom=source['DEJ2000']
radius=0.05



In [8]:

    
c = SkyCoord(ra=ra_zoom, dec=dec_zoom)  
catalog = SkyCoord(ra=WISE_sources['ra'], dec=WISE_sources['dec'])  
idx, d2d, d3d = c.match_to_catalog_sky(catalog)



In [9]:

    
WISE_sources[idx]









    Out[9]:




<Table length=1>

desig ra dec w1best w1berr w1f w2best w2berr w2f w3best w3berr w3f w4best w4berr w4f W1snr W2snr W3snr W4snr tile Riso R2iso R3iso R4iso ba pa flux_1 err_1 mag_1 merr_1 flg1 flux_2 err_2 mag_2 merr_2 flg2 flux_3 err_3 mag_3 merr_3 flg3 flux_4 err_4 mag_4 merr_4 flg4 W1W2 W1W2er W2W3 W2W3er W1W3 W1W3er W3W4 W3W4er meanSB_1 meanSB_2 meanSB_3 meanSB_4 sky_1 sig_1 sky_2 sig_2 sky_3 sig_3 sky_4 sig_4 SB_1 SB_2 SB_3 SB_4 scale_1a scale_1b beta_1a beta_1b scale_2a scale_2b beta_2a beta_2b scale_3a scale_3b beta_3a beta+3b scale_4a scale_4b beta_4a beta_4b Rtot_1 ftot_1 ftoterr_1 mtot_1 mterr1 Rtot_2 ftot_2 ftoterr_2 mtot_2 mterr2 Rtot_3 ftot_3 ftoterr_3 mtot_3 mterr3 Rtot_4 ftot_4 ftoterr_4 mtot_4 mterr4 Reff_1 SBeff_1 con_1 Reff_2 SBeff_2 con_2 Reff_3 SBeff_3 con_3 Reff_4 SBeff_4 con_4 R1conv W1conv uW1conv R2conv W2conv uW2conv R3conv W3conv uW3conv R4conv W4conv uW4conv Rinner Router w1zero w2zero w3zero w4zero w1mpro dw1mpro w1rchi2 w2mpro dw2mpro w2rchi2 w3mpro dw3mpro w3rchi2 w4mpro dw4mpro w4rchi2 Rw123 w123 dw123 Rw222 w222 dw222 Rw318 w318 dw318 Rw4155 w4155 dw4155 xscprox Rmoment Rminor Rfuzzy Rfuzz2 Rfuzz3 Rfuzz4 vc sepmin
deg deg mag mag mag mag mag mag mag mag arcsec arcsec arcsec arcsec deg mJy mJy mag mag mJy mJy mag mag mJy mJy mag mag mJy mJy mag mag mag mag mag mag mag mag mag mag mag.asec mag.asec mag.asec mag.asec dn dn dn dn dn dn dn dn mag.asec mag.asec mag.asec mag.asec arcsec arcsec arcsec arcsec arcsec arcsec arcsec arcsec arcsec mJy mJy mag mag arcsec mJy mJy mag mag arcsec mJy mJy mag mag arcsec mJy mJy mag mag arcsec mag.asec arcsec mag.asec arcsec mag.asec arcsec mag.asec arcsec mag mag arcsec mag mag arcsec mag mag arcsec mag mag arcsec arcsec mag mag mag mag mag mag mag mag mag mag mag mag arcsec mag mag arcsec mag mag arcsec mag mag arcsec mag mag arcsec arcsec arcsec arcsec arcsec arcsec arcsec arcsec
str23 float64 float64 float32 float32 int32 float32 float32 int32 float32 float32 int32 float32 float32 int32 float32 float32 float32 float32 str12 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 int32 float64 float64 float64 float64 int32 float64 float64 float64 float64 int32 float64 float64 float64 float64 int32 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float64 float64 float64 float64 float64 float64 float64 int32 float32
J022136.46-053116.6 35.40201 -5.52123 8.979 0.011 0 8.937 0.02 0 5.594 0.022 10 3.942 0.048 10 98.7 90.5 50.4 22.7 S_tile_133 122.19 108.57 93.53 82.59 0.69 116.9 79.337 0.84 8.979 0.011 1 44.7 0.49 8.955 0.012 1 152.26 1.76 5.701 0.013 1 183.888 3.69 4.079 0.022 1 0.042 0.017 3.253 0.017 3.296 0.017 1.688 0.027 17.298 17.263 14.384 13.213 2.169 0.056 5.949 0.061 345.36 0.71 27.182 0.07 23.624 22.536 18.371 15.892 8.05 46.92 0.73 0.61 7.93 46.91 0.89 0.61 10.03 58.85 0.77 0.5 8.6 52.22 0.82 0.52 183.28 80.71 0.831 8.96 0.011 183.28 45.477 0.562 8.936 0.013 218.77 168.042 3.332 5.594 0.022 194.61 208.533 9.172 3.942 0.048 40.6 18.596 3.14 39.5 18.513 3.08 45.4 15.475 2.72 48.2 13.951 2.39 122.19 8.979 0.011 108.57 8.955 0.012 93.53 5.701 0.013 99.0 3.985 0.025 153.2 199.2 20.5 19.5 18.0 13.0 11.54 0.023 65.15 11.553 0.023 21.75 8.497 0.025 9.985 6.425 0.056 0.2172 113.85 8.988 0.011 101.12 8.975 0.012 85.44 5.739 0.012 74.29 4.151 0.0211 0.4 30.9 25.9 30.4 30.8 34.5 70.5 -1 138.8



In [10]:

    
def extended_source(beta,r_e,x_0,y_0,x,y):
    source_grid=np.exp(-beta*((x-x_0)**2+(y-y_0)**2)/r_e)
    return source_grid



In [11]:

    
xx, yy = np.meshgrid(np.arange(0,101), np.arange(0,101), sparse=True)

The extended source as seen in optical, MIPS, WISE band 4, and the SPIRE bands



In [12]:

    
source









    Out[12]:




<Table masked=True length=1>

Field ID2MASX RAJ2000 DEJ2000 supRAdeg supDEdeg density r_K20e J_K20e e_J_K20e f_J_K20e H_K20e e_H_K20e f_H_K20e K_K20e e_K_K20e f_K_K20e Kb_a Kpa Sb_a Spa r_ext J_ext e_J_ext H_ext e_H_ext K_ext e_K_ext cc
deg deg deg deg arcs mag mag mag mag mag mag deg deg arcs mag mag mag mag mag mag 
object object float64 float64 float64 float64 float64 float64 float64 float64 int16 float64 float64 int16 float64 float64 int16 float64 int16 float64 int16 float64 float64 float64 float64 float64 float64 float64 object
XMM-LSS 02213646-0531170 35.401958 -5.5213939999999999 35.401961999999997 -5.5213799999999997 2.5499999999999998 51.0 10.523999999999999 0.024 0 9.9870000000000001 0.040000000000000001 0 9.6880000000000006 0.050999999999999997 0 0.66000000000000003 -60 0.47999999999999998 -70 86.959999999999994 10.145 0.024 9.7349999999999994 0.040000000000000001 9.4049999999999994 0.050999999999999997 0



In [13]:

    
def sersic(alpha,beta,x_0,y_0,x,y):
    source_grid=np.exp(-1*((((x-x_0)**2+(y-y_0)**2)**0.5)/alpha)**(1/beta))
    return source_grid
    

def extended_source(beta,r_e,x_0,y_0,x,y):
    source_grid=np.exp(-beta*((x-x_0)**2+(y-y_0)**2)/r_e)
    return source_grid



In [14]:

    
bulge=sersic(WISE_sources[idx]['scale_1a'],WISE_sources[idx]['beta_1a'],50,50,xx,yy) #Raphael added first 50 to make it run
disc=sersic(WISE_sources[idx]['scale_1b'],WISE_sources[idx]['beta_1b'],50,50,xx,yy)



In [15]:

    
plt.imshow(disc)
plt.colorbar()









    Out[15]:





<matplotlib.colorbar.Colorbar at 0x150d09518>



In [16]:

    
#pixsize array (size of pixels in arcseconds)
pixsize=np.array([pixsize250,pixsize350,pixsize500])
#point spread function for the three bands
prfsize=np.array([18.15,25.15,36.3])


##---------fit using Gaussian beam-----------------------
prf250=Gaussian2DKernel(prfsize[0]/2.355,x_size=101,y_size=101)
prf250.normalize(mode='peak')
prf350=Gaussian2DKernel(prfsize[1]/2.355,x_size=101,y_size=101)
prf350.normalize(mode='peak')
prf500=Gaussian2DKernel(prfsize[2]/2.355,x_size=101,y_size=101)
prf500.normalize(mode='peak')

pind250=np.arange(0,101,1)*1.0/pixsize[0] #get 250 scale in terms of pixel scale of map
pind350=np.arange(0,101,1)*1.0/pixsize[1] #get 350 scale in terms of pixel scale of map
pind500=np.arange(0,101,1)*1.0/pixsize[2] #get 500 scale in terms of pixel scale of map

#convolved250 = convolve(prf250)[::6,::6]



In [17]:

    
fig=plt.figure(figsize=(30,10))
plt.subplot(2,1,1)
plt.imshow(convolve(bulge,prf250.array)[::6,::6],interpolation='nearest')
plt.colorbar()
plt.subplot(2,1,2)
plt.imshow(convolve(disc,prf250.array)[::6,::6],interpolation='nearest')
plt.colorbar()









    Out[17]:





<matplotlib.colorbar.Colorbar at 0x14faab630>



In [18]:

    
w_250.wcs_world2pix(ra_zoom,dec_zoom,0)









    Out[18]:





[array([ 1605.60172062]), array([ 1167.31004696])]



In [19]:

    
def radial_profile(data, center):
    x, y = np.indices((data.shape))
    r = np.sqrt((x - center[0])**2 + (y - center[1])**2)
    r = r.astype(np.int)

    tbin = np.bincount(r.ravel(), data.ravel())
    nr = np.bincount(r.ravel())
    radialprofile = tbin / nr
    return radialprofile



In [20]:

    
rad_profile = radial_profile(hdulist_250[1].data,w_250.wcs_world2pix(ra_zoom,dec_zoom,0))

fig, ax = plt.subplots()
plt.plot(rad_profile[0:22], 'x-')









    Out[20]:





[<matplotlib.lines.Line2D at 0x1509053c8>]

Fit extended source with XID+



In [21]:

    
RA=np.concatenate((IRAC_sources['RA'].__array__(),source['RAJ2000'].__array__()))
Dec=np.concatenate((IRAC_sources['Dec'].__array__(),source['DEJ2000'].__array__()))



In [22]:

    
source









    Out[22]:




<Table masked=True length=1>

Field ID2MASX RAJ2000 DEJ2000 supRAdeg supDEdeg density r_K20e J_K20e e_J_K20e f_J_K20e H_K20e e_H_K20e f_H_K20e K_K20e e_K_K20e f_K_K20e Kb_a Kpa Sb_a Spa r_ext J_ext e_J_ext H_ext e_H_ext K_ext e_K_ext cc
deg deg deg deg arcs mag mag mag mag mag mag deg deg arcs mag mag mag mag mag mag 
object object float64 float64 float64 float64 float64 float64 float64 float64 int16 float64 float64 int16 float64 float64 int16 float64 int16 float64 int16 float64 float64 float64 float64 float64 float64 float64 object
XMM-LSS 02213646-0531170 35.401958 -5.5213939999999999 35.401961999999997 -5.5213799999999997 2.5499999999999998 51.0 10.523999999999999 0.024 0 9.9870000000000001 0.040000000000000001 0 9.6880000000000006 0.050999999999999997 0 0.66000000000000003 -60 0.47999999999999998 -70 86.959999999999994 10.145 0.024 9.7349999999999994 0.040000000000000001 9.4049999999999994 0.050999999999999997 0



In [23]:

    
prior_cat='IRAC'
#---prior250--------
prior250=xidplus.prior(im250,nim250,im250phdu,im250hdu)#Initialise with map, uncertianty map, wcs info and primary header
prior250.prior_cat(RA,Dec,prior_cat)#Set input catalogue
prior250.prior_bkg(-5.0,5)#Set prior on background (assumes Guassian pdf with mu and sigma)
#---prior350--------
prior350=xidplus.prior(im350,nim350,im350phdu,im350hdu)
prior350.prior_cat(RA,Dec,prior_cat)
prior350.prior_bkg(-5.0,5)

#---prior500--------
prior500=xidplus.prior(im500,nim500,im500phdu,im500hdu)
prior500.prior_cat(RA,Dec,prior_cat)
prior500.prior_bkg(-5.0,5)



In [24]:

    
#pixsize array (size of pixels in arcseconds)
pixsize=np.array([pixsize250,pixsize350,pixsize500])
#point response function for the three bands
prfsize=np.array([18.15,25.15,36.3])
#use Gaussian2DKernel to create prf (requires stddev rather than fwhm hence pfwhm/2.355)
from astropy.convolution import Gaussian2DKernel

##---------fit using Gaussian beam-----------------------
prf250=Gaussian2DKernel(prfsize[0]/2.355,x_size=101,y_size=101)
prf250.normalize(mode='peak')
prf350=Gaussian2DKernel(prfsize[1]/2.355,x_size=101,y_size=101)
prf350.normalize(mode='peak')
prf500=Gaussian2DKernel(prfsize[2]/2.355,x_size=101,y_size=101)
prf500.normalize(mode='peak')

pind250=np.arange(0,101,1)*1.0/pixsize[0] #get 250 scale in terms of pixel scale of map
pind350=np.arange(0,101,1)*1.0/pixsize[1] #get 350 scale in terms of pixel scale of map
pind500=np.arange(0,101,1)*1.0/pixsize[2] #get 500 scale in terms of pixel scale of map

prior250.set_prf(prf250.array,pind250,pind250)#requires psf as 2d grid, and x and y bins for grid (in pixel scale)
prior350.set_prf(prf350.array,pind350,pind350)
prior500.set_prf(prf500.array,pind500,pind500)



In [25]:

    
print( 'fitting ' + str(prior250.nsrc)+' sources \n')
print( 'using ' +  str(prior250.snpix)+', '+ str(prior250.snpix)+' and '+ str(prior500.snpix)+' pixels')









    



fitting 69615 sources 

using 3380938, 3380938 and 845235 pixels



In [26]:

    
moc=MOC()
moc.read('./data/extended_source_test_MOC_rad4.fits')
prior250.set_tile(moc)
prior350.set_tile(moc)
prior500.set_tile(moc)



In [27]:

    
print( 'fitting '+ str(prior250.nsrc)+' sources \n')
print( 'using ' +  str(prior250.snpix)+', '+ str(prior350.snpix)+' and '+ str(prior500.snpix)+' pixels')









    



fitting 127 sources 

using 5488, 2843 and 1372 pixels



In [28]:

    
prior250.get_pointing_matrix()
prior350.get_pointing_matrix()
prior500.get_pointing_matrix()



In [29]:

    
prior250.upper_lim_map()
prior350.upper_lim_map()
prior500.upper_lim_map()



In [30]:

    
fit=SPIRE.all_bands(prior250,prior350,prior500,iter=1500)









    



/XID+SPIRE found. Reusing



In [31]:

    
posterior=xidplus.posterior_stan(fit,[prior250,prior350,prior500])



In [32]:

    
hdurep_250=postmaps.make_fits_image(prior250,prior250.sim)
hdurep_350=postmaps.make_fits_image(prior350,prior350.sim)
hdurep_500=postmaps.make_fits_image(prior500,prior500.sim)



In [33]:

    
rep_maps=postmaps.replicated_maps([prior250, prior350, prior500],posterior,nrep=1000)



In [34]:

    
rep_maps









    Out[34]:





[array([[ -95.15871186,    9.6646541 ,   34.23258716, ...,   29.78146418,
           40.43826205,    9.09151328],
        [  -4.14585848,   53.19359969,   45.58618774, ...,   -9.70311013,
           11.07712368,   12.44218295],
        [  35.50114991,   -3.95510777,   28.21229014, ...,   21.85292699,
           -9.18307466,  -51.6020461 ],
        ..., 
        [ -31.0917198 ,   -5.93654978,  -13.14994426, ...,   24.74482587,
          -30.42967735,   51.2439061 ],
        [  50.61248221,   43.46473732,  -39.25446508, ...,  -25.02145441,
          -83.14067442,   19.43890849],
        [  17.27912463,   81.72239003,   25.24546498, ...,  110.85676235,
           13.1622351 ,  -18.95470862]]),
 array([[ 16.42307081, -25.45516102,  24.95580806, ...,  33.55797915,
          17.08750418, -54.3404259 ],
        [ 15.94924226, -45.87654087, -20.24761076, ...,  32.45133213,
          61.57367422, -13.68679266],
        [ -7.16009502,  31.23172322, -76.75048209, ...,  48.1314199 ,
          38.24646324,  88.61126662],
        ..., 
        [-47.23930747,  22.22454012,   7.31458678, ..., -40.2586409 ,
          37.22097222, -16.20577322],
        [ 72.03396453, -42.73479083,   8.72486486, ...,  -1.85594061,
           4.11008603,  36.80021382],
        [ 20.45555516, -10.23825638,  47.70064361, ..., -14.50447209,
          45.90664751,  -2.77036094]]),
 array([[ 26.69663456, -16.2484859 , -24.24689994, ...,   4.9639774 ,
         -11.57080342,  50.47903241],
        [ 14.58037212,  10.06637155,  16.31457931, ...,  27.34074897,
           3.88030931,   3.68617382],
        [  7.77969679,  28.49685942,   1.75688749, ...,  -0.18588177,
          22.59218494,  18.54975983],
        ..., 
        [-11.05512648,   1.87946992,  20.98454472, ..., -23.86384642,
         -17.02219915,  -4.23556235],
        [-14.03490837, -11.83580302,  -9.94061683, ...,  10.27165706,
          -7.03002479,   5.46537351],
        [ 11.60228358,   6.09969554,  15.68069085, ...,   6.01661344,
          -5.97750738,   4.08881869]])]



In [35]:

    
mod_map_250 = rep_maps[0]
mod_map_350 = rep_maps[1]
mod_map_500 = rep_maps[2]
mod_map_array_250 = rep_maps[0]
mod_map_array_350 = rep_maps[1]
mod_map_array_500 = rep_maps[2]



In [ ]:

    
pval_250=np.empty_like(prior250.sim)
for i in range(0,prior250.snpix):
    ind=mod_map_array_250[i,:]<prior250.sim[i]
    pval_250[i]=st.norm.ppf(sum(ind)/np.float(mod_map_array_250.shape[1]))
pval_250[np.isposinf(pval_250)]=6
    
pval_350=np.empty_like(prior350.sim)
for i in range(0,prior350.snpix):
    ind=mod_map_array_350[i,:]<prior350.sim[i]
    pval_350[i]=st.norm.ppf(sum(ind)/np.float(mod_map_array_350.shape[1]))
pval_350[np.isposinf(pval_350)]=6
    
pval_500=np.empty_like(prior500.sim)
for i in range(0,prior500.snpix):
    ind=mod_map_array_500[i,:]<prior500.sim[i]
    pval_500[i]=st.norm.ppf(sum(ind)/np.float(mod_map_array_500.shape[1]))
pval_500[np.isposinf(pval_500)]=6



In [ ]:

    
cmap=sns.cubehelix_palette(8, start=.5, rot=-.75,as_cmap=True)

vmin=-1.7E1/1.0E3
vmax=4.446e+01/1.0E3
ra_zoom=source['RAJ2000']
dec_zoom=source['DEJ2000']
radius=0.05
fig = plt.figure(figsize=(30,30))
cfhtls=aplpy.FITSFigure('./data/W1+1+2.U.11023_11534_3064_3575.fits',figure=fig,subplot=(3,3,1))
cfhtls.show_colorscale(vmin=-10,vmax=200,cmap=cmap)
cfhtls.recenter(ra_zoom, dec_zoom, radius=radius)


mips=aplpy.FITSFigure('./data/wp4_xmm-lss_mips24_map_v1.0.fits.gz',figure=fig,subplot=(3,3,2))
mips.show_colorscale(vmin=-0.001,vmax=5,cmap=cmap)
mips.recenter(ra_zoom, dec_zoom, radius=radius)

wise_band4=aplpy.FITSFigure('./data/L3a-0349m061_ac51-0349m061_ac51-w4-int-3_ra35.401958_dec-5.5213939_asec600.000.fits',figure=fig,subplot=(3,3,3))
wise_band4.show_colorscale(vmin=202,vmax=204,cmap=cmap)
wise_band4.recenter(ra_zoom, dec_zoom, radius=radius)
wise_band4.show_ellipses(ra_zoom, dec_zoom,2*WISE_sources[idx]['Riso']/3600.0,2*WISE_sources[idx]['Riso']*WISE_sources[idx]['ba']/3600.0, angle=360.0-WISE_sources[idx]['pa'],edgecolor='white',linewidth=2.0)


real_250 = aplpy.FITSFigure(hdulist_250[1],figure=fig,subplot=(3,3,4))
real_250.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap)
#real_250.show_markers(ra_list,dec_list, edgecolor='white', facecolor='white',
                #marker='o', s=40, alpha=0.5)
real_250.show_markers(IRAC_sources['RA'],IRAC_sources['DEC'], edgecolor='yellow', facecolor='yellow',
                marker='o', s=40, alpha=0.5)
real_250.recenter(ra_zoom, dec_zoom, radius=radius)
real_250.show_ellipses(ra_zoom, dec_zoom,2*source['r_K20e']/3600.0,2*source['r_K20e']*source['Kb_a']/3600.0, angle=360.0-source['Spa'],edgecolor='white',linewidth=2.0)
real_250.show_ellipses(ra_zoom, dec_zoom,2*WISE_sources[idx]['Riso']/3600.0,2*WISE_sources[idx]['Riso']*WISE_sources[idx]['ba']/3600.0, angle=360.0-WISE_sources[idx]['pa'],edgecolor='white',linewidth=2.0)

real_250.add_colorbar()
#real_250.show_markers(WISE_sources['ra'],WISE_sources['dec'], edgecolor='red', facecolor='red',
#                marker='o', s=40, alpha=0.5)

real_350 = aplpy.FITSFigure(hdulist_350[1],figure=fig,subplot=(3,3,5))
real_350.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap)
real_350.show_ellipses(ra_zoom, dec_zoom,2*source['r_K20e']/3600.0,2*source['r_K20e']*source['Kb_a']/3600.0, angle=360.0-source['Spa'],edgecolor='white',linewidth=2.0)

#real_350.show_markers(prior250.sra, prior250.sdec, edgecolor='black', facecolor='black',
                #marker='o', s=40, alpha=0.5)
real_350.recenter(ra_zoom, dec_zoom, radius=radius)

real_500 = aplpy.FITSFigure(hdulist_500[1],figure=fig,subplot=(3,3,6))
real_500.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap)
#real_500.show_markers(prior250.sra, prior250.sdec, edgecolor='black', facecolor='black',
                #marker='o', s=40, alpha=0.5)
real_500.recenter(ra_zoom, dec_zoom, radius=radius)
real_500.show_ellipses(ra_zoom, dec_zoom,2*source['r_K20e']/3600.0,2*source['r_K20e']*source['Kb_a']/3600.0, angle=360.0-source['Spa'],edgecolor='white',linewidth=2.0)

vmin=-6
vmax=6
cmap=sns.diverging_palette(220, 20,as_cmap=True)

res250=aplpy.FITSFigure(hdurep_250[1],figure=fig,subplot=(3,3,7))
res250.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res250.show_markers(prior250.sra, prior250.sdec, edgecolor='black', facecolor='black',
                marker='o', s=80, alpha=0.5)
res250.recenter(ra_zoom, dec_zoom, radius=radius)

res350=aplpy.FITSFigure(hdurep_350[1],figure=fig,subplot=(3,3,8))
res350.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res350.show_markers(prior350.sra, prior350.sdec, edgecolor='black', facecolor='black',
                marker='o', s=80, alpha=0.5)
res350.recenter(ra_zoom, dec_zoom, radius=radius)


res500=aplpy.FITSFigure(hdurep_500[1],figure=fig,subplot=(3,3,9))
res500.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res500.show_markers(prior500.sra, prior500.sdec, edgecolor='black', facecolor='black',
                marker='o', s=80, alpha=0.5)


res250._data[prior250.sy_pix-np.min(prior250.sy_pix)-1,prior250.sx_pix-np.min(prior250.sx_pix)-1]=pval_250
res350._data[prior350.sy_pix-np.min(prior350.sy_pix)-1,prior350.sx_pix-np.min(prior350.sx_pix)-1]=pval_350
res500._data[prior500.sy_pix-np.min(prior500.sy_pix)-1,prior500.sx_pix-np.min(prior500.sx_pix)-1]=pval_500
res500.recenter(ra_zoom, dec_zoom, radius=radius)
#res500.tick_labels.set_xformat('dd.dd')
#res500.tick_labels.set_yformat('dd.dd')


res250.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res350.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res500.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res250.add_colorbar()
res250.colorbar.set_location('top')
res350.add_colorbar()
res350.colorbar.set_location('top')
res500.add_colorbar()
res500.colorbar.set_location('top')



In [ ]:

    
def ellipse(x,y,x0,y0,angle,a,b):
    dx=x-x0
    dy=y-y0
    rad=((dx*np.cos(angle)-dy*np.sin(angle))/a)**2 + ((dx*np.sin(angle)+dy*np.cos(angle))/b)**2
    rad[rad<1.0]=1.0
    rad[rad>1.0]=0.0
    return rad
                                                                 
from astropy.modeling.models import Sersic2D

mod = Sersic2D(amplitude = 1, r_eff = 25, n=4, x_0=50, y_0=50,
               ellip=.5, theta=-1)



In [ ]:



In [ ]:

    
wcs_temp = wcs.WCS(prior250.imhdu)
source_pix_x,source_pix_y=wcs_temp.all_world2pix(ra_zoom,dec_zoom,0)



In [ ]:

    
a=WISE_sources[idx]['Riso']/pixsize[0]
b=WISE_sources[idx]['ba']*a
xx, yy = np.meshgrid(np.arange(0,WISE_sources[idx]['Riso']*3), np.arange(0,WISE_sources[idx]['Riso']*3))



In [ ]:

    
import scipy.signal as signal
extended_source_SMAP=signal.convolve(ellipse(xx,yy,xx.shape[1]/2.0,yy.shape[0]/2.0,WISE_sources[idx]['pa']*np.pi/180.0,a*pixsize[0],b*pixsize[0]),prf250.array,mode='same')



In [ ]:

    
plt.imshow(extended_source_SMAP)



In [ ]:

    
from astropy.modeling.models import Ellipse2D
from astropy.coordinates import Angle

theta = Angle(-1*WISE_sources[idx]['pa'], 'deg')
e = Ellipse2D(amplitude=100., x_0=xx.shape[1]/2.0,y_0=yy.shape[0]/2.0, a=a*pixsize[0], b=b*pixsize[0],
              theta=theta.radian)
plt.imshow(signal.convolve(e(xx,yy),prf250.array,mode='same'))



In [ ]:

    
from astropy.modeling.models import Sersic2D

mod = Sersic2D(amplitude = 1.0, r_eff = WISE_sources[idx]['scale_1b'], n=WISE_sources[idx]['beta_1b']	,x_0=xx.shape[1]/2.0,y_0=yy.shape[0]/2.0,
               ellip=1.0-WISE_sources[idx]['ba'], theta=theta.radian)
plt.imshow(signal.convolve(mod(xx,yy),prf250.array,mode='same'))



In [ ]:

    
pind250_source=np.arange(0,WISE_sources[idx]['Riso']*3)*1.0/pixsize[0]



In [ ]:

    
ipx=source_pix_x+xx*1.0/pixsize[0]-pind250_source[-1]/2
ipy=source_pix_y+yy*1.0/pixsize[0]-pind250_source[-1]/2
from scipy import interpolate
atemp = interpolate.griddata((ipx.ravel(), ipy.ravel()),extended_source_SMAP.ravel(), (prior250.sx_pix,prior250.sy_pix),
                                             method='nearest')



In [ ]:

    
def add_sersic_source(prior,source_no,angle,scale,beta,ba):
    from astropy.modeling.models import Sersic2D
    from astropy.coordinates import Angle
    import scipy.signal as signal


    import scipy.signal as signal
    wcs_temp = wcs.WCS(prior.imhdu)
    source_pix_x=prior.sx[source_no]
    source_pix_y=prior.sy[source_no]
    pixsize=np.absolute(prior.imhdu['CD2_2']*3600.0)

    mesh_length=np.max([prior.pindx.size,scale*5])
    xx, yy = np.meshgrid(np.arange(0,mesh_length), np.arange(0,mesh_length))
    theta = Angle(-1*angle, 'deg')

    mod = Sersic2D(amplitude = 1.0, r_eff =scale, n=beta,x_0=xx.shape[1]/2.0,y_0=yy.shape[0]/2.0,
               ellip=1.0-ba, theta=theta.radian)
    extended_source_SMAP=signal.convolve(mod(xx,yy),prior.prf,mode='same')
    pind_source=np.arange(0,mesh_length)*1.0/pixsize
    ipx=source_pix_x+xx*1.0/pixsize-pind_source[-1]/2
    ipy=source_pix_y+yy*1.0/pixsize-pind_source[-1]/2
    from scipy import interpolate
    atemp = interpolate.griddata((ipx.ravel(), ipy.ravel()),extended_source_SMAP.ravel(), (prior.sx_pix,prior.sy_pix),
                                             method='nearest')
    ind=atemp>0.001
    ind_not_prev=prior.amat_col != source_no
    prior.amat_data=np.append(prior.amat_data[ind_not_prev],atemp[ind])
    prior.amat_col=np.append(prior.amat_col[ind_not_prev],np.full(ind.sum(),source_no))
    prior.amat_row=np.append(prior.amat_row[ind_not_prev],np.arange(0,prior.snpix,dtype=int)[ind])
    return prior

    
def add_extended_source(prior,source_no,angle,semi_major,semi_minor):
    import scipy.signal as signal
    wcs_temp = wcs.WCS(prior.imhdu)
    source_pix_x=prior.sx[source_no]
    source_pix_y=prior.sy[source_no]
    pixsize=np.absolute(prior.imhdu['CD2_2']*3600.0)
    a=semi_major/pixsize
    b=semi_minor/pixsize
    xx, yy = np.meshgrid(np.arange(0,semi_major*2.5), np.arange(0,semi_major*2.5))
    extended_source_SMAP=signal.convolve(ellipse(xx,yy,xx.shape[1]/2.0,yy.shape[0]/2.0,angle*np.pi/180.0,a*pixsize,b*pixsize),prior.prf,mode='same')
    pind_source=np.arange(0,semi_major*2.5)*1.0/pixsize
    ipx=source_pix_x+xx*1.0/pixsize-pind_source[-1]/2
    ipy=source_pix_y+yy*1.0/pixsize-pind_source[-1]/2
    from scipy import interpolate
    atemp = interpolate.griddata((ipx.ravel(), ipy.ravel()),extended_source_SMAP.ravel(), (prior.sx_pix,prior.sy_pix),
                                             method='nearest')
    ind=atemp>0.001
    ind_not_prev=prior.amat_col != source_no
    prior.amat_data=np.append(prior.amat_data[ind_not_prev],atemp[ind])
    prior.amat_col=np.append(prior.amat_col[ind_not_prev],np.full(ind.sum(),source_no))
    prior.amat_row=np.append(prior.amat_row[ind_not_prev],np.arange(0,prior.snpix,dtype=int)[ind])
    return prior



In [ ]:

    
prior250.pindx.size



In [ ]:

    
prior250.imhdu['CD2_2']*3600.0



In [ ]:

    
extended_source_conv=postmaps.make_fits_image(prior250,atemp)



In [ ]:

    
vmin=-1.7E1/1.0E3
vmax=4.446e+01/1.0E3
cmap=sns.cubehelix_palette(8, start=.5, rot=-.75,as_cmap=True)
real_250 = aplpy.FITSFigure(extended_source_conv[1])
real_250.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap)
#real_250.show_markers(ra_list,dec_list, edgecolor='white', facecolor='white',
                #marker='o', s=40, alpha=0.5)
real_250.show_ellipses(ra_zoom, dec_zoom,2*WISE_sources[idx]['Riso']/3600.0,2*WISE_sources[idx]['Riso']*WISE_sources[idx]['ba']/3600.0, angle=360.0-WISE_sources[idx]['pa'],edgecolor='white',linewidth=2.0)

real_250.show_markers(IRAC_sources['RA'],IRAC_sources['DEC'], edgecolor='yellow', facecolor='yellow',
                marker='o', s=40, alpha=0.5)
#real_250.recenter(ra_zoom, dec_zoom, radius=radius)
real_250.add_colorbar()



In [ ]:

    
prior250.amat_data

Fit with disc and bulge



In [ ]:

    
#Concatenate the extended source twice, once for bulge once for disc
RA=np.concatenate((np.concatenate((IRAC_sources['RA'].__array__(),source['RAJ2000'].__array__())),source['RAJ2000'].__array__()))
Dec=np.concatenate((np.concatenate((IRAC_sources['DEC'].__array__(),source['DEJ2000'].__array__())),source['DEJ2000'].__array__()))



In [ ]:

    
prior_cat='IRAC'
#---prior250--------
prior250=xidplus.prior(im250,nim250,im250phdu,im250hdu)#Initialise with map, uncertianty map, wcs info and primary header
prior250.prior_cat(RA,Dec,prior_cat)#Set input catalogue
prior250.prior_bkg(-5.0,5)#Set prior on background (assumes Guassian pdf with mu and sigma)
#---prior350--------
prior350=xidplus.prior(im350,nim350,im350phdu,im350hdu)
prior350.prior_cat(RA,Dec,prior_cat)
prior350.prior_bkg(-5.0,5)

#---prior500--------
prior500=xidplus.prior(im500,nim500,im500phdu,im500hdu)
prior500.prior_cat(RA,Dec,prior_cat)
prior500.prior_bkg(-5.0,5)



In [ ]:

    
#pixsize array (size of pixels in arcseconds)
pixsize=np.array([pixsize250,pixsize350,pixsize500])
#point response function for the three bands
prfsize=np.array([18.15,25.15,36.3])
#use Gaussian2DKernel to create prf (requires stddev rather than fwhm hence pfwhm/2.355)
from astropy.convolution import Gaussian2DKernel

##---------fit using Gaussian beam-----------------------
prf250=Gaussian2DKernel(prfsize[0]/2.355,x_size=101,y_size=101)
prf250.normalize(mode='peak')
prf350=Gaussian2DKernel(prfsize[1]/2.355,x_size=101,y_size=101)
prf350.normalize(mode='peak')
prf500=Gaussian2DKernel(prfsize[2]/2.355,x_size=101,y_size=101)
prf500.normalize(mode='peak')

pind250=np.arange(0,101,1)*1.0/pixsize[0] #get 250 scale in terms of pixel scale of map
pind350=np.arange(0,101,1)*1.0/pixsize[1] #get 350 scale in terms of pixel scale of map
pind500=np.arange(0,101,1)*1.0/pixsize[2] #get 500 scale in terms of pixel scale of map

prior250.set_prf(prf250.array,pind250,pind250)#requires psf as 2d grid, and x and y bins for grid (in pixel scale)
prior350.set_prf(prf350.array,pind350,pind350)
prior500.set_prf(prf500.array,pind500,pind500)



In [ ]:

    
print 'fitting '+ str(prior250.nsrc)+' sources \n'
print 'using ' +  str(prior250.snpix)+', '+ str(prior250.snpix)+' and '+ str(prior500.snpix)+' pixels'



In [ ]:

    
from pymoc import MOC
moc=MOC()
moc.read('./data/extended_source_test_MOC_rad4.fits')
prior250.set_tile(moc)
prior350.set_tile(moc)
prior500.set_tile(moc)



In [ ]:

    
prior250.get_pointing_matrix()
prior350.get_pointing_matrix()
prior500.get_pointing_matrix()



In [ ]:

    
print 'fitting '+ str(prior250.nsrc)+' sources \n'
print 'using ' +  str(prior250.snpix)+', '+ str(prior250.snpix)+' and '+ str(prior500.snpix)+' pixels'



In [ ]:

    
prior250.upper_lim_map()
prior350.upper_lim_map()
prior500.upper_lim_map()



In [ ]:

    
prior250=add_extended_source(prior250,419,source['Spa'],source['r_K20e'],source['r_K20e']*source['Kb_a'])
prior250=add_extended_source(prior250,420,WISE_sources[idx]['pa'],WISE_sources[idx]['Riso'],WISE_sources[idx]['Riso']*WISE_sources[idx]['ba'])
prior350=add_extended_source(prior350,419,source['Spa'],source['r_K20e'],source['r_K20e']*source['Kb_a'])
prior350=add_extended_source(prior350,420,WISE_sources[idx]['pa'],WISE_sources[idx]['Riso'],WISE_sources[idx]['Riso']*WISE_sources[idx]['ba'])
prior500=add_extended_source(prior500,419,source['Spa'],source['r_K20e'],source['r_K20e']*source['Kb_a'])
prior500=add_extended_source(prior500,420,WISE_sources[idx]['pa'],WISE_sources[idx]['Riso'],WISE_sources[idx]['Riso']*WISE_sources[idx]['ba'])



In [ ]:

    
prior250=add_sersic_source(prior250,419,WISE_sources[idx]['pa'],WISE_sources[idx]['scale_1a'],WISE_sources[idx]['beta_1a'],WISE_sources[idx]['ba'])
prior250=add_sersic_source(prior250,420,WISE_sources[idx]['pa'],WISE_sources[idx]['scale_1b'],WISE_sources[idx]['beta_1b'],WISE_sources[idx]['ba'])

prior350=add_sersic_source(prior350,419,WISE_sources[idx]['pa'],WISE_sources[idx]['scale_1a'],WISE_sources[idx]['beta_1a'],WISE_sources[idx]['ba'])
prior350=add_sersic_source(prior350,420,WISE_sources[idx]['pa'],WISE_sources[idx]['scale_1b'],WISE_sources[idx]['beta_1b'],WISE_sources[idx]['ba'])

prior500=add_sersic_source(prior500,419,WISE_sources[idx]['pa'],WISE_sources[idx]['scale_1a'],WISE_sources[idx]['beta_1a'],WISE_sources[idx]['ba'])
prior500=add_sersic_source(prior500,420,WISE_sources[idx]['pa'],WISE_sources[idx]['scale_1b'],WISE_sources[idx]['beta_1b'],WISE_sources[idx]['ba'])



In [ ]:

    
SDSS=Table.read('./data/SDSS_extended_example.csv',format='ascii.csv')
SDSS



In [ ]:

    
prior250=add_sersic_source(prior250,419,SDSS['expPhi_u'],SDSS['expRad_u'],1.0,SDSS['expAB_u'])
prior350=add_sersic_source(prior350,419,SDSS['expPhi_u'],SDSS['expRad_u'],1.0,SDSS['expAB_u'])

prior500=add_sersic_source(prior500,419,SDSS['expPhi_u'],SDSS['expRad_u'],1.0,SDSS['expAB_u'])



In [ ]:

    
from xidplus.stan_fit import SPIRE
fit=SPIRE.all_bands(prior250,prior350,prior500,iter=1000)



In [ ]:

    
posterior=xidplus.posterior_stan(fit,[prior250,prior350,prior500])



In [ ]:

    
from xidplus import posterior_maps as postmaps

hdurep_250=postmaps.make_fits_image(prior250,prior250.sim)
hdurep_350=postmaps.make_fits_image(prior350,prior350.sim)
hdurep_500=postmaps.make_fits_image(prior500,prior500.sim)



In [ ]:

    
mod_map=np.full((hdurep_250[1].data.shape[1],hdurep_250[1].data.shape[0],500),np.nan)
mod_map_array=np.empty((prior250.snpix,500))

for i in range(0,500):
    mod_map_array[:,i]= postmaps.ymod_map(prior250,posterior.stan_fit[i,0,0:prior250.nsrc]).reshape(-1)+posterior.stan_fit[i,0,prior250.nsrc]+np.random.normal(scale=np.sqrt(prior250.snim**2+posterior.stan_fit[i,0,(prior250.nsrc+1)*3]**2))         
    mod_map[prior250.sx_pix-np.min(prior250.sx_pix)-1,prior250.sy_pix-np.min(prior250.sy_pix)-1,i]=mod_map_array[:,i]



In [ ]:

    
mod_map_350=np.full((hdurep_350[1].data.shape[1],hdurep_350[1].data.shape[0],500),np.nan)
mod_map_array_350=np.empty((prior350.snpix,500))

for i in range(0,500):
    mod_map_array_350[:,i]= postmaps.ymod_map(prior350,posterior.stan_fit[i,0,prior350.nsrc+1:2*prior350.nsrc+1]).reshape(-1)+posterior.stan_fit[i,0,2*prior350.nsrc+1]+np.random.normal(scale=np.sqrt(prior350.snim**2+posterior.stan_fit[i,0,1+(prior350.nsrc+1)*3]**2))         
    mod_map_350[prior350.sx_pix-np.min(prior350.sx_pix)-1,prior350.sy_pix-np.min(prior350.sy_pix)-1,i]=mod_map_array_350[:,i]



In [ ]:

    
mod_map_500=np.full((hdurep_500[1].data.shape[1],hdurep_500[1].data.shape[0],500),np.nan)
mod_map_array_500=np.empty((prior500.snpix,500))

for i in range(0,500):
    mod_map_array_500[:,i]= postmaps.ymod_map(prior500,posterior.stan_fit[i,0,2*prior500.nsrc+2:3*prior350.nsrc+2]).reshape(-1)+posterior.stan_fit[i,0,3*prior500.nsrc+2]+np.random.normal(scale=np.sqrt(prior500.snim**2+posterior.stan_fit[i,0,2+(prior500.nsrc+1)*3]**2))         
    mod_map_500[prior500.sx_pix-np.min(prior500.sx_pix)-1,prior500.sy_pix-np.min(prior500.sy_pix)-1,i]=mod_map_array_500[:,i]



In [ ]:

    
import scipy.stats as st

pval_250=np.empty_like(prior250.sim)
for i in range(0,prior250.snpix):
    ind=mod_map_array[i,:]<prior250.sim[i]
    pval_250[i]=st.norm.ppf(sum(ind)/np.float(mod_map_array.shape[1]))
pval_250[np.isposinf(pval_250)]=6
    
pval_350=np.empty_like(prior350.sim)
for i in range(0,prior350.snpix):
    ind=mod_map_array_350[i,:]<prior350.sim[i]
    pval_350[i]=st.norm.ppf(sum(ind)/np.float(mod_map_array_350.shape[1]))
pval_350[np.isposinf(pval_350)]=6
    
pval_500=np.empty_like(prior500.sim)
for i in range(0,prior500.snpix):
    ind=mod_map_array_500[i,:]<prior500.sim[i]
    pval_500[i]=st.norm.ppf(sum(ind)/np.float(mod_map_array_500.shape[1]))
pval_500[np.isposinf(pval_500)]=6



In [ ]:



In [ ]:

    
cmap=sns.cubehelix_palette(8, start=.5, rot=-.75,as_cmap=True)

vmin=-1.7E1/1.0E3
vmax=4.446e+01/1.0E3
ra_zoom=source['RAJ2000']
dec_zoom=source['DEJ2000']
radius=0.05
fig = plt.figure(figsize=(30,30))
cfhtls=aplpy.FITSFigure('./data/W1+1+2.U.11023_11534_3064_3575.fits',figure=fig,subplot=(3,3,1))
cfhtls.show_colorscale(vmin=-10,vmax=200,cmap=cmap)
cfhtls.recenter(ra_zoom, dec_zoom, radius=radius)


mips=aplpy.FITSFigure('./data/wp4_xmm-lss_mips24_map_v1.0.fits',figure=fig,subplot=(3,3,2))
mips.show_colorscale(vmin=-0.001,vmax=5,cmap=cmap)
mips.recenter(ra_zoom, dec_zoom, radius=radius)

wise_band4=aplpy.FITSFigure('./data/L3a-0349m061_ac51-0349m061_ac51-w4-int-3_ra35.401958_dec-5.5213939_asec600.000.fits',figure=fig,subplot=(3,3,3))
wise_band4.show_colorscale(vmin=202,vmax=204,cmap=cmap)
wise_band4.recenter(ra_zoom, dec_zoom, radius=radius)
wise_band4.show_ellipses(ra_zoom, dec_zoom,2*WISE_sources[idx]['Riso']/3600.0,2*WISE_sources[idx]['Riso']*WISE_sources[idx]['ba']/3600.0, angle=360.0-WISE_sources[idx]['pa'],edgecolor='white',linewidth=2.0)


real_250 = aplpy.FITSFigure(hdulist_250[1],figure=fig,subplot=(3,3,4))
real_250.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap)
#real_250.show_markers(ra_list,dec_list, edgecolor='white', facecolor='white',
                #marker='o', s=40, alpha=0.5)
real_250.show_markers(IRAC_sources['RA'],IRAC_sources['DEC'], edgecolor='yellow', facecolor='yellow',
                marker='o', s=40, alpha=0.5)
real_250.recenter(ra_zoom, dec_zoom, radius=radius)
real_250.show_ellipses(ra_zoom, dec_zoom,2*source['r_K20e']/3600.0,2*source['r_K20e']*source['Kb_a']/3600.0, angle=360.0-source['Spa'],edgecolor='white',linewidth=2.0)
real_250.show_ellipses(ra_zoom, dec_zoom,2*WISE_sources[idx]['Riso']/3600.0,2*WISE_sources[idx]['Riso']*WISE_sources[idx]['ba']/3600.0, angle=360.0-WISE_sources[idx]['pa'],edgecolor='white',linewidth=2.0)

real_250.add_colorbar()
#real_250.show_markers(WISE_sources['ra'],WISE_sources['dec'], edgecolor='red', facecolor='red',
#                marker='o', s=40, alpha=0.5)

real_350 = aplpy.FITSFigure(hdulist_350[1],figure=fig,subplot=(3,3,5))
real_350.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap)
real_350.show_ellipses(ra_zoom, dec_zoom,2*source['r_K20e']/3600.0,2*source['r_K20e']*source['Kb_a']/3600.0, angle=360.0-source['Spa'],edgecolor='white',linewidth=2.0)

#real_350.show_markers(prior250.sra, prior250.sdec, edgecolor='black', facecolor='black',
                #marker='o', s=40, alpha=0.5)
real_350.recenter(ra_zoom, dec_zoom, radius=radius)

real_500 = aplpy.FITSFigure(hdulist_500[1],figure=fig,subplot=(3,3,6))
real_500.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap)
#real_500.show_markers(prior250.sra, prior250.sdec, edgecolor='black', facecolor='black',
                #marker='o', s=40, alpha=0.5)
real_500.recenter(ra_zoom, dec_zoom, radius=radius)
real_500.show_ellipses(ra_zoom, dec_zoom,2*source['r_K20e']/3600.0,2*source['r_K20e']*source['Kb_a']/3600.0, angle=360.0-source['Spa'],edgecolor='white',linewidth=2.0)

vmin=-6
vmax=6
cmap=sns.diverging_palette(220, 20,as_cmap=True)

res250=aplpy.FITSFigure(hdurep_250[1],figure=fig,subplot=(3,3,7))
res250.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res250.show_markers(prior250.sra, prior250.sdec, edgecolor='black', facecolor='black',
                marker='o', s=80, alpha=0.5)
res250.recenter(ra_zoom, dec_zoom, radius=radius)

res350=aplpy.FITSFigure(hdurep_350[1],figure=fig,subplot=(3,3,8))
res350.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res350.show_markers(prior350.sra, prior350.sdec, edgecolor='black', facecolor='black',
                marker='o', s=80, alpha=0.5)
res350.recenter(ra_zoom, dec_zoom, radius=radius)


res500=aplpy.FITSFigure(hdurep_500[1],figure=fig,subplot=(3,3,9))
res500.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res500.show_markers(prior500.sra, prior500.sdec, edgecolor='black', facecolor='black',
                marker='o', s=80, alpha=0.5)


res250._data[prior250.sy_pix-np.min(prior250.sy_pix)-1,prior250.sx_pix-np.min(prior250.sx_pix)-1]=pval_250
res350._data[prior350.sy_pix-np.min(prior350.sy_pix)-1,prior350.sx_pix-np.min(prior350.sx_pix)-1]=pval_350
res500._data[prior500.sy_pix-np.min(prior500.sy_pix)-1,prior500.sx_pix-np.min(prior500.sx_pix)-1]=pval_500
res500.recenter(ra_zoom, dec_zoom, radius=radius)
#res500.tick_labels.set_xformat('dd.dd')
#res500.tick_labels.set_yformat('dd.dd')


res250.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res350.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res500.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res250.add_colorbar()
res250.colorbar.set_location('top')
res350.add_colorbar()
res350.colorbar.set_location('top')
res500.add_colorbar()
res500.colorbar.set_location('top')



In [ ]:

    
plt.imshow(mod_map[:,:,1],interpolation='nearest')
plt.colorbar()



In [ ]:

    
plt.hist(posterior.stan_fit[:,:,420])



In [ ]:

    
prior250.nsrc



In [ ]:



In [ ]:

    
cmap=sns.cubehelix_palette(8, start=.5, rot=-.75,as_cmap=True)

vmin=-1.7E1/1.0E3
vmax=4.446e+01/1.0E3
ra_zoom=source['RAJ2000']
dec_zoom=source['DEJ2000']
radius=0.05
fig = plt.figure(figsize=(30,30))
cfhtls=aplpy.FITSFigure('./data/W1+1+2.U.11023_11534_3064_3575.fits',figure=fig,subplot=(3,3,1))
cfhtls.show_colorscale(vmin=-10,vmax=200,cmap=cmap)
cfhtls.recenter(ra_zoom, dec_zoom, radius=radius)


mips=aplpy.FITSFigure('./data/wp4_xmm-lss_mips24_map_v1.0.fits',figure=fig,subplot=(3,3,2))
mips.show_colorscale(vmin=-0.001,vmax=5,cmap=cmap)
mips.recenter(ra_zoom, dec_zoom, radius=radius)

wise_band4=aplpy.FITSFigure('./data/L3a-0349m061_ac51-0349m061_ac51-w4-int-3_ra35.401958_dec-5.5213939_asec600.000.fits',figure=fig,subplot=(3,3,3))
wise_band4.show_colorscale(vmin=202,vmax=204,cmap=cmap)
wise_band4.recenter(ra_zoom, dec_zoom, radius=radius)
wise_band4.show_ellipses(ra_zoom, dec_zoom,2*WISE_sources[idx]['Riso']/3600.0,2*WISE_sources[idx]['Riso']*WISE_sources[idx]['ba']/3600.0, angle=360.0-WISE_sources[idx]['pa'],edgecolor='white',linewidth=2.0)


real_250 = aplpy.FITSFigure(hdulist_250[1],figure=fig,subplot=(3,3,4))
real_250.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap,stretch='arcsinh')
#real_250.show_markers(ra_list,dec_list, edgecolor='white', facecolor='white',
                #marker='o', s=40, alpha=0.5)
real_250.show_markers(IRAC_sources['RA'],IRAC_sources['DEC'], edgecolor='yellow', facecolor='yellow',
                marker='o', s=40, alpha=0.5)
real_250.recenter(ra_zoom, dec_zoom, radius=radius)
real_250.show_ellipses(ra_zoom, dec_zoom,2*source['r_K20e']/3600.0,2*source['r_K20e']*source['Kb_a']/3600.0, angle=360.0-source['Spa'],edgecolor='white',linewidth=2.0)
real_250.show_ellipses(ra_zoom, dec_zoom,2*WISE_sources[idx]['Riso']/3600.0,2*WISE_sources[idx]['Riso']*WISE_sources[idx]['ba']/3600.0, angle=360.0-WISE_sources[idx]['pa'],edgecolor='white',linewidth=2.0)

real_250.add_colorbar()
#real_250.show_markers(WISE_sources['ra'],WISE_sources['dec'], edgecolor='red', facecolor='red',
#                marker='o', s=40, alpha=0.5)

real_350 = aplpy.FITSFigure(hdulist_350[1],figure=fig,subplot=(3,3,5))
real_350.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap,stretch='arcsinh')
real_350.show_ellipses(ra_zoom, dec_zoom,2*source['r_K20e']/3600.0,2*source['r_K20e']*source['Kb_a']/3600.0, angle=360.0-source['Spa'],edgecolor='white',linewidth=2.0)

#real_350.show_markers(prior250.sra, prior250.sdec, edgecolor='black', facecolor='black',
                #marker='o', s=40, alpha=0.5)
real_350.recenter(ra_zoom, dec_zoom, radius=radius)

real_500 = aplpy.FITSFigure(hdulist_500[1],figure=fig,subplot=(3,3,6))
real_500.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap,stretch='arcsinh')
#real_500.show_markers(prior250.sra, prior250.sdec, edgecolor='black', facecolor='black',
                #marker='o', s=40, alpha=0.5)
real_500.recenter(ra_zoom, dec_zoom, radius=radius)
real_500.show_ellipses(ra_zoom, dec_zoom,2*source['r_K20e']/3600.0,2*source['r_K20e']*source['Kb_a']/3600.0, angle=360.0-source['Spa'],edgecolor='white',linewidth=2.0)

vmin=-1.7E1
vmax=800.0
#cmap=sns.diverging_palette(220, 20,as_cmap=True)

res250=aplpy.FITSFigure(hdurep_250[1],figure=fig,subplot=(3,3,7))
res250.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap,stretch='arcsinh')
res250.show_markers(prior250.sra, prior250.sdec, edgecolor='black', facecolor='black',
                marker='o', s=80, alpha=0.5)
res250.recenter(ra_zoom, dec_zoom, radius=radius)

res350=aplpy.FITSFigure(hdurep_350[1],figure=fig,subplot=(3,3,8))
res350.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap,stretch='arcsinh')
res350.show_markers(prior350.sra, prior350.sdec, edgecolor='black', facecolor='black',
                marker='o', s=80, alpha=0.5)
res350.recenter(ra_zoom, dec_zoom, radius=radius)


res500=aplpy.FITSFigure(hdurep_500[1],figure=fig,subplot=(3,3,9))
res500.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap,stretch='arcsinh')
res500.show_markers(prior500.sra, prior500.sdec, edgecolor='black', facecolor='black',
                marker='o', s=80, alpha=0.5)


res250._data[prior250.sy_pix-np.min(prior250.sy_pix)-1,prior250.sx_pix-np.min(prior250.sx_pix)-1]=mod_map_array[:,50]
res350._data[prior350.sy_pix-np.min(prior350.sy_pix)-1,prior350.sx_pix-np.min(prior350.sx_pix)-1]=mod_map_array_350[:,50]
res500._data[prior500.sy_pix-np.min(prior500.sy_pix)-1,prior500.sx_pix-np.min(prior500.sx_pix)-1]=mod_map_array_500[:,50]
res500.recenter(ra_zoom, dec_zoom, radius=radius)
#res500.tick_labels.set_xformat('dd.dd')
#res500.tick_labels.set_yformat('dd.dd')


res250.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap,stretch='arcsinh')
res350.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap,stretch='arcsinh')
res500.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap,stretch='arcsinh')
res250.add_colorbar()
res250.colorbar.set_location('top')
res350.add_colorbar()
res350.colorbar.set_location('top')
res500.add_colorbar()
res500.colorbar.set_location('top')



In [ ]:

    
plt.scatter(posterior.stan_fit[:,:,419].flatten(),posterior.stan_fit[:,:,420].flatten())



In [ ]:

    
plt.hist(posterior.stan_fit[:,:,-4].flatten())



In [ ]:

    
prior250
extended_source_conv=postmaps.make_fits_image(prior250,atemp)



In [ ]:

    
prior250.amat_col == 419



In [ ]:

    
prior250.nsrc



In [ ]:

    
atemp=np.empty_like(prior250.sim)
atemp[:]=0.0
ind=prior250.amat_col == 420
atemp[prior250.amat_row[ind]]=prior250.amat_data[ind]
extended_source_conv=postmaps.make_fits_image(prior250,atemp)
cmap=sns.cubehelix_palette(8, start=.5, rot=-.75,as_cmap=True)
real_250 = aplpy.FITSFigure(extended_source_conv[1])
real_250.show_colorscale(cmap=cmap)
#real_250.show_markers(ra_list,dec_list, edgecolor='white', facecolor='white',
                #marker='o', s=40, alpha=0.5)
real_250.show_ellipses(ra_zoom, dec_zoom,2*WISE_sources[idx]['Riso']/3600.0,2*WISE_sources[idx]['Riso']*WISE_sources[idx]['ba']/3600.0, angle=360.0-WISE_sources[idx]['pa'],edgecolor='white',linewidth=2.0)

real_250.show_markers(IRAC_sources['RA'],IRAC_sources['DEC'], edgecolor='yellow', facecolor='yellow',
                marker='o', s=40, alpha=0.5)
#real_250.recenter(ra_zoom, dec_zoom, radius=radius)



In [ ]:

    
fit=SPIRE.all_bands(prior250,prior350,prior500,iter=1500)



In [ ]:

    
posterior=xidplus.posterior_stan(fit,[prior250,prior350,prior500])



In [ ]:

    
from xidplus import posterior_maps as postmaps

hdurep_250=postmaps.make_fits_image(prior250,prior250.sim)
hdurep_350=postmaps.make_fits_image(prior350,prior350.sim)
hdurep_500=postmaps.make_fits_image(prior500,prior500.sim)



In [ ]:

    
mod_map=np.full((hdurep_250[1].data.shape[1],hdurep_250[1].data.shape[0],500),np.nan)
mod_map_array=np.empty((prior250.snpix,500))

for i in range(0,500):
    mod_map_array[:,i]= postmaps.ymod_map(prior250,posterior.stan_fit[i,0,0:prior250.nsrc]).reshape(-1)+posterior.stan_fit[i,0,prior250.nsrc]+np.random.normal(scale=np.sqrt(prior250.snim**2+posterior.stan_fit[i,0,(prior250.nsrc+1)*3]**2))         
    mod_map[prior250.sx_pix-np.min(prior250.sx_pix)-1,prior250.sy_pix-np.min(prior250.sy_pix)-1,i]=mod_map_array[:,i]



In [ ]:

    
mod_map_350=np.full((hdurep_350[1].data.shape[1],hdurep_350[1].data.shape[0],500),np.nan)
mod_map_array_350=np.empty((prior350.snpix,500))

for i in range(0,500):
    mod_map_array_350[:,i]= postmaps.ymod_map(prior350,posterior.stan_fit[i,0,prior350.nsrc+1:2*prior350.nsrc+1]).reshape(-1)+posterior.stan_fit[i,0,2*prior350.nsrc+1]+np.random.normal(scale=np.sqrt(prior350.snim**2+posterior.stan_fit[i,0,1+(prior350.nsrc+1)*3]**2))         
    mod_map_350[prior350.sx_pix-np.min(prior350.sx_pix)-1,prior350.sy_pix-np.min(prior350.sy_pix)-1,i]=mod_map_array_350[:,i]



In [ ]:

    
mod_map_500=np.full((hdurep_500[1].data.shape[1],hdurep_500[1].data.shape[0],500),np.nan)
mod_map_array_500=np.empty((prior500.snpix,500))

for i in range(0,500):
    mod_map_array_500[:,i]= postmaps.ymod_map(prior500,posterior.stan_fit[i,0,2*prior500.nsrc+2:3*prior350.nsrc+2]).reshape(-1)+posterior.stan_fit[i,0,3*prior500.nsrc+2]+np.random.normal(scale=np.sqrt(prior500.snim**2+posterior.stan_fit[i,0,2+(prior500.nsrc+1)*3]**2))         
    mod_map_500[prior500.sx_pix-np.min(prior500.sx_pix)-1,prior500.sy_pix-np.min(prior500.sy_pix)-1,i]=mod_map_array_500[:,i]



In [ ]:

    
import scipy.stats as st

pval_250=np.empty_like(prior250.sim)
for i in range(0,prior250.snpix):
    ind=mod_map_array[i,:]<prior250.sim[i]
    pval_250[i]=st.norm.ppf(sum(ind)/np.float(mod_map_array.shape[1]))
pval_250[np.isposinf(pval_250)]=6
    
pval_350=np.empty_like(prior350.sim)
for i in range(0,prior350.snpix):
    ind=mod_map_array_350[i,:]<prior350.sim[i]
    pval_350[i]=st.norm.ppf(sum(ind)/np.float(mod_map_array_350.shape[1]))
pval_350[np.isposinf(pval_350)]=6
    
pval_500=np.empty_like(prior500.sim)
for i in range(0,prior500.snpix):
    ind=mod_map_array_500[i,:]<prior500.sim[i]
    pval_500[i]=st.norm.ppf(sum(ind)/np.float(mod_map_array_500.shape[1]))
pval_500[np.isposinf(pval_500)]=6



In [ ]:

    
cmap=sns.cubehelix_palette(8, start=.5, rot=-.75,as_cmap=True)

vmin=-1.7E1/1.0E3
vmax=4.446e+01/1.0E3
ra_zoom=source['RAJ2000']
dec_zoom=source['DEJ2000']
radius=0.05
fig = plt.figure(figsize=(30,30))
cfhtls=aplpy.FITSFigure('./data/W1+1+2.U.11023_11534_3064_3575.fits',figure=fig,subplot=(3,3,1))
cfhtls.show_colorscale(vmin=-10,vmax=200,cmap=cmap)
cfhtls.recenter(ra_zoom, dec_zoom, radius=radius)


mips=aplpy.FITSFigure('./data/wp4_xmm-lss_mips24_map_v1.0.fits',figure=fig,subplot=(3,3,2))
mips.show_colorscale(vmin=-0.001,vmax=5,cmap=cmap)
mips.recenter(ra_zoom, dec_zoom, radius=radius)

wise_band4=aplpy.FITSFigure('./data/L3a-0349m061_ac51-0349m061_ac51-w4-int-3_ra35.401958_dec-5.5213939_asec600.000.fits',figure=fig,subplot=(3,3,3))
wise_band4.show_colorscale(vmin=202,vmax=204,cmap=cmap)
wise_band4.recenter(ra_zoom, dec_zoom, radius=radius)
wise_band4.show_ellipses(ra_zoom, dec_zoom,2*WISE_sources[idx]['Riso']/3600.0,2*WISE_sources[idx]['Riso']*WISE_sources[idx]['ba']/3600.0, angle=360.0-WISE_sources[idx]['pa'],edgecolor='white',linewidth=2.0)


real_250 = aplpy.FITSFigure(hdulist_250[1],figure=fig,subplot=(3,3,4))
real_250.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap)
#real_250.show_markers(ra_list,dec_list, edgecolor='white', facecolor='white',
                #marker='o', s=40, alpha=0.5)
real_250.show_markers(IRAC_sources['RA'],IRAC_sources['DEC'], edgecolor='yellow', facecolor='yellow',
                marker='o', s=40, alpha=0.5)
real_250.recenter(ra_zoom, dec_zoom, radius=radius)
real_250.show_ellipses(ra_zoom, dec_zoom,2*source['r_K20e']/3600.0,2*source['r_K20e']*source['Kb_a']/3600.0, angle=360.0-source['Spa'],edgecolor='white',linewidth=2.0)
real_250.show_ellipses(ra_zoom, dec_zoom,2*WISE_sources[idx]['Riso']/3600.0,2*WISE_sources[idx]['Riso']*WISE_sources[idx]['ba']/3600.0, angle=360.0-WISE_sources[idx]['pa'],edgecolor='white',linewidth=2.0)

real_250.add_colorbar()
#real_250.show_markers(WISE_sources['ra'],WISE_sources['dec'], edgecolor='red', facecolor='red',
#                marker='o', s=40, alpha=0.5)

real_350 = aplpy.FITSFigure(hdulist_350[1],figure=fig,subplot=(3,3,5))
real_350.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap)
real_350.show_ellipses(ra_zoom, dec_zoom,2*source['r_K20e']/3600.0,2*source['r_K20e']*source['Kb_a']/3600.0, angle=360.0-source['Spa'],edgecolor='white',linewidth=2.0)

#real_350.show_markers(prior250.sra, prior250.sdec, edgecolor='black', facecolor='black',
                #marker='o', s=40, alpha=0.5)
real_350.recenter(ra_zoom, dec_zoom, radius=radius)

real_500 = aplpy.FITSFigure(hdulist_500[1],figure=fig,subplot=(3,3,6))
real_500.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap)
#real_500.show_markers(prior250.sra, prior250.sdec, edgecolor='black', facecolor='black',
                #marker='o', s=40, alpha=0.5)
real_500.recenter(ra_zoom, dec_zoom, radius=radius)
real_500.show_ellipses(ra_zoom, dec_zoom,2*source['r_K20e']/3600.0,2*source['r_K20e']*source['Kb_a']/3600.0, angle=360.0-source['Spa'],edgecolor='white',linewidth=2.0)

vmin=-6
vmax=6
cmap=sns.diverging_palette(220, 20,as_cmap=True)

res250=aplpy.FITSFigure(hdurep_250[1],figure=fig,subplot=(3,3,7))
res250.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res250.show_markers(prior250.sra, prior250.sdec, edgecolor='black', facecolor='black',
                marker='o', s=80, alpha=0.5)
res250.recenter(ra_zoom, dec_zoom, radius=radius)

res350=aplpy.FITSFigure(hdurep_350[1],figure=fig,subplot=(3,3,8))
res350.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res350.show_markers(prior350.sra, prior350.sdec, edgecolor='black', facecolor='black',
                marker='o', s=80, alpha=0.5)
res350.recenter(ra_zoom, dec_zoom, radius=radius)


res500=aplpy.FITSFigure(hdurep_500[1],figure=fig,subplot=(3,3,9))
res500.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res500.show_markers(prior500.sra, prior500.sdec, edgecolor='black', facecolor='black',
                marker='o', s=80, alpha=0.5)


res250._data[prior250.sy_pix-np.min(prior250.sy_pix)-1,prior250.sx_pix-np.min(prior250.sx_pix)-1]=pval_250
res350._data[prior350.sy_pix-np.min(prior350.sy_pix)-1,prior350.sx_pix-np.min(prior350.sx_pix)-1]=pval_350
res500._data[prior500.sy_pix-np.min(prior500.sy_pix)-1,prior500.sx_pix-np.min(prior500.sx_pix)-1]=pval_500
res500.recenter(ra_zoom, dec_zoom, radius=radius)
#res500.tick_labels.set_xformat('dd.dd')
#res500.tick_labels.set_yformat('dd.dd')


res250.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res350.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res500.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
res250.add_colorbar()
res250.colorbar.set_location('top')
res350.add_colorbar()
res350.colorbar.set_location('top')
res500.add_colorbar()
res500.colorbar.set_location('top')



In [ ]:

    
cmap=sns.cubehelix_palette(8, start=.5, rot=-.75,as_cmap=True)

vmin=-1.7E1/1.0E3
vmax=4.446e+01/1.0E3
ra_zoom=source['RAJ2000']
dec_zoom=source['DEJ2000']
radius=0.05
fig = plt.figure(figsize=(30,30))
cfhtls=aplpy.FITSFigure('./data/W1+1+2.U.11023_11534_3064_3575.fits',figure=fig,subplot=(3,3,1))
cfhtls.show_colorscale(vmin=-10,vmax=200,cmap=cmap)
cfhtls.recenter(ra_zoom, dec_zoom, radius=radius)


mips=aplpy.FITSFigure('./data/wp4_xmm-lss_mips24_map_v1.0.fits',figure=fig,subplot=(3,3,2))
mips.show_colorscale(vmin=-0.001,vmax=5,cmap=cmap)
mips.recenter(ra_zoom, dec_zoom, radius=radius)

wise_band4=aplpy.FITSFigure('./data/L3a-0349m061_ac51-0349m061_ac51-w4-int-3_ra35.401958_dec-5.5213939_asec600.000.fits',figure=fig,subplot=(3,3,3))
wise_band4.show_colorscale(vmin=202,vmax=204,cmap=cmap)
wise_band4.recenter(ra_zoom, dec_zoom, radius=radius)
wise_band4.show_ellipses(ra_zoom, dec_zoom,2*WISE_sources[idx]['Riso']/3600.0,2*WISE_sources[idx]['Riso']*WISE_sources[idx]['ba']/3600.0, angle=360.0-WISE_sources[idx]['pa'],edgecolor='white',linewidth=2.0)


real_250 = aplpy.FITSFigure(hdulist_250[1],figure=fig,subplot=(3,3,4))
real_250.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap,stretch='arcsinh')
#real_250.show_markers(ra_list,dec_list, edgecolor='white', facecolor='white',
                #marker='o', s=40, alpha=0.5)
real_250.show_markers(IRAC_sources['RA'],IRAC_sources['DEC'], edgecolor='yellow', facecolor='yellow',
                marker='o', s=40, alpha=0.5)
real_250.recenter(ra_zoom, dec_zoom, radius=radius)
real_250.show_ellipses(ra_zoom, dec_zoom,2*source['r_K20e']/3600.0,2*source['r_K20e']*source['Kb_a']/3600.0, angle=360.0-source['Spa'],edgecolor='white',linewidth=2.0)
real_250.show_ellipses(ra_zoom, dec_zoom,2*WISE_sources[idx]['Riso']/3600.0,2*WISE_sources[idx]['Riso']*WISE_sources[idx]['ba']/3600.0, angle=360.0-WISE_sources[idx]['pa'],edgecolor='white',linewidth=2.0)

real_250.add_colorbar()
#real_250.show_markers(WISE_sources['ra'],WISE_sources['dec'], edgecolor='red', facecolor='red',
#                marker='o', s=40, alpha=0.5)

real_350 = aplpy.FITSFigure(hdulist_350[1],figure=fig,subplot=(3,3,5))
real_350.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap,stretch='arcsinh')
real_350.show_ellipses(ra_zoom, dec_zoom,2*source['r_K20e']/3600.0,2*source['r_K20e']*source['Kb_a']/3600.0, angle=360.0-source['Spa'],edgecolor='white',linewidth=2.0)

#real_350.show_markers(prior250.sra, prior250.sdec, edgecolor='black', facecolor='black',
                #marker='o', s=40, alpha=0.5)
real_350.recenter(ra_zoom, dec_zoom, radius=radius)

real_500 = aplpy.FITSFigure(hdulist_500[1],figure=fig,subplot=(3,3,6))
real_500.show_colorscale(vmin=vmin,vmax=0.8,cmap=cmap,stretch='arcsinh')
#real_500.show_markers(prior250.sra, prior250.sdec, edgecolor='black', facecolor='black',
                #marker='o', s=40, alpha=0.5)
real_500.recenter(ra_zoom, dec_zoom, radius=radius)
real_500.show_ellipses(ra_zoom, dec_zoom,2*source['r_K20e']/3600.0,2*source['r_K20e']*source['Kb_a']/3600.0, angle=360.0-source['Spa'],edgecolor='white',linewidth=2.0)

vmin=-1.7E1
vmax=800.0
#cmap=sns.diverging_palette(220, 20,as_cmap=True)

res250=aplpy.FITSFigure(hdurep_250[1],figure=fig,subplot=(3,3,7))
res250.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap,stretch='arcsinh')
res250.show_markers(prior250.sra, prior250.sdec, edgecolor='black', facecolor='black',
                marker='o', s=80, alpha=0.5)
res250.recenter(ra_zoom, dec_zoom, radius=radius)

res350=aplpy.FITSFigure(hdurep_350[1],figure=fig,subplot=(3,3,8))
res350.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap,stretch='arcsinh')
res350.show_markers(prior350.sra, prior350.sdec, edgecolor='black', facecolor='black',
                marker='o', s=80, alpha=0.5)
res350.recenter(ra_zoom, dec_zoom, radius=radius)


res500=aplpy.FITSFigure(hdurep_500[1],figure=fig,subplot=(3,3,9))
res500.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap,stretch='arcsinh')
res500.show_markers(prior500.sra, prior500.sdec, edgecolor='black', facecolor='black',
                marker='o', s=80, alpha=0.5)


res250._data[prior250.sy_pix-np.min(prior250.sy_pix)-1,prior250.sx_pix-np.min(prior250.sx_pix)-1]=mod_map_array[:,50]
res350._data[prior350.sy_pix-np.min(prior350.sy_pix)-1,prior350.sx_pix-np.min(prior350.sx_pix)-1]=mod_map_array_350[:,50]
res500._data[prior500.sy_pix-np.min(prior500.sy_pix)-1,prior500.sx_pix-np.min(prior500.sx_pix)-1]=mod_map_array_500[:,50]
res500.recenter(ra_zoom, dec_zoom, radius=radius)
#res500.tick_labels.set_xformat('dd.dd')
#res500.tick_labels.set_yformat('dd.dd')


res250.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap,stretch='arcsinh')
res350.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap,stretch='arcsinh')
res500.show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap,stretch='arcsinh')
res250.add_colorbar()
res250.colorbar.set_location('top')
res350.add_colorbar()
res350.colorbar.set_location('top')
res500.add_colorbar()
res500.colorbar.set_location('top')



In [ ]:

    
plt.scatter(posterior.stan_fit[:,:,421*2-1].flatten(),posterior.stan_fit[:,:,421*2].flatten())



In [ ]:

    
vmin=-1.7E1/1.0E3
vmax=4.446e+01/1.0E3
cmap=sns.cubehelix_palette(8, start=.5, rot=-.75,as_cmap=True)
real_250 = aplpy.FITSFigure(extended_source_conv[1])
real_250.show_colorscale(cmap=cmap)
#real_250.show_markers(ra_list,dec_list, edgecolor='white', facecolor='white',
                #marker='o', s=40, alpha=0.5)
real_250.show_ellipses(ra_zoom, dec_zoom,2*WISE_sources[idx]['Riso']/3600.0,2*WISE_sources[idx]['Riso']*WISE_sources[idx]['ba']/3600.0, angle=360.0-WISE_sources[idx]['pa'],edgecolor='white',linewidth=2.0)

real_250.show_markers(IRAC_sources['RA'],IRAC_sources['DEC'], edgecolor='yellow', facecolor='yellow',
                marker='o', s=40, alpha=0.5)
#real_250.recenter(ra_zoom, dec_zoom, radius=radius)
real_250.add_colorbar()



In [ ]:

XID	RA	Dec	F_PACS_100__A4	F_PACS_100__A5	F_PACS_100__A6	F_PACS_100__A7	F_PACS_100__A8	F_PACS_100__A10	F_PACS_100	Ferr_PACS_100__A4	Ferr_PACS_100__A5	Ferr_PACS_100__A6	Ferr_PACS_100__A7	Ferr_PACS_100__A8	Ferr_PACS_100__A10	Ferr_PACS_100	F_PACS_100__SKY	F_PACS_160__A4	F_PACS_160__A5	F_PACS_160__A6	F_PACS_160__A7	F_PACS_160__A8	F_PACS_160__A10	F_PACS_160	Ferr_PACS_160__A4	Ferr_PACS_160__A5	Ferr_PACS_160__A6	Ferr_PACS_160__A7	Ferr_PACS_160__A8	Ferr_PACS_160__A10	Ferr_PACS_160	F_PACS_160__SKY	HELP_ID
	deg	deg	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy / pix	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy	mJy / pix
int32	float64	float64	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	str25
35164	33.400427	-3.532034	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-00001
69559	33.402787	-3.552514	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-00002
69612	33.402787	-3.529534	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-00003
35151	33.407827	-3.534404	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-00004
35141	33.409607	-3.534104	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-00005
35126	33.412387	-3.544484	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-00006
69545	33.413037	-3.555364	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-00007
69613	33.421437	-3.521034	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-00008
69556	33.422047	-3.545824	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-00009
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
35346	37.189607	-5.359364	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-69605
139	37.191917	-5.397384	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-69606
118	37.192737	-5.400864	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-69607
35313	37.192897	-5.372624	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-69608
222	37.192937	-5.360784	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-69609
35325	37.194807	-5.378524	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-69610
35322	37.196767	-5.379374	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-69611
35198	37.199537	-5.443994	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-69612
35185	37.210767	-5.442134	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-69613
44	37.212207	-5.434754	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0	XMM-PACSxID24-UDS-1-69614

desig	ra	dec	w1best	w1berr	w1f	w2best	w2berr	w2f	w3best	w3berr	w3f	w4best	w4berr	w4f	W1snr	W2snr	W3snr	W4snr	tile	Riso	R2iso	R3iso	R4iso	ba	pa	flux_1	err_1	mag_1	merr_1	flg1	flux_2	err_2	mag_2	merr_2	flg2	flux_3	err_3	mag_3	merr_3	flg3	flux_4	err_4	mag_4	merr_4	flg4	W1W2	W1W2er	W2W3	W2W3er	W1W3	W1W3er	W3W4	W3W4er	meanSB_1	meanSB_2	meanSB_3	meanSB_4	sky_1	sig_1	sky_2	sig_2	sky_3	sig_3	sky_4	sig_4	SB_1	SB_2	SB_3	SB_4	scale_1a	scale_1b	beta_1a	beta_1b	scale_2a	scale_2b	beta_2a	beta_2b	scale_3a	scale_3b	beta_3a	beta+3b	scale_4a	scale_4b	beta_4a	beta_4b	Rtot_1	ftot_1	ftoterr_1	mtot_1	mterr1	Rtot_2	ftot_2	ftoterr_2	mtot_2	mterr2	Rtot_3	ftot_3	ftoterr_3	mtot_3	mterr3	Rtot_4	ftot_4	ftoterr_4	mtot_4	mterr4	Reff_1	SBeff_1	con_1	Reff_2	SBeff_2	con_2	Reff_3	SBeff_3	con_3	Reff_4	SBeff_4	con_4	R1conv	W1conv	uW1conv	R2conv	W2conv	uW2conv	R3conv	W3conv	uW3conv	R4conv	W4conv	uW4conv	Rinner	Router	w1zero	w2zero	w3zero	w4zero	w1mpro	dw1mpro	w1rchi2	w2mpro	dw2mpro	w2rchi2	w3mpro	dw3mpro	w3rchi2	w4mpro	dw4mpro	w4rchi2	Rw123	w123	dw123	Rw222	w222	dw222	Rw318	w318	dw318	Rw4155	w4155	dw4155	xscprox	Rmoment	Rminor	Rfuzzy	Rfuzz2	Rfuzz3	Rfuzz4	vc	sepmin
	deg	deg	mag	mag		mag	mag		mag	mag		mag	mag							arcsec	arcsec	arcsec	arcsec		deg	mJy	mJy	mag	mag		mJy	mJy	mag	mag		mJy	mJy	mag	mag		mJy	mJy	mag	mag		mag	mag	mag	mag	mag	mag	mag	mag	mag.asec	mag.asec	mag.asec	mag.asec	dn	dn	dn	dn	dn	dn	dn	dn	mag.asec	mag.asec	mag.asec	mag.asec	arcsec	arcsec			arcsec	arcsec			arcsec	arcsec			arcsec	arcsec			arcsec	mJy	mJy	mag	mag	arcsec	mJy	mJy	mag	mag	arcsec	mJy	mJy	mag	mag	arcsec	mJy	mJy	mag	mag	arcsec	mag.asec		arcsec	mag.asec		arcsec	mag.asec		arcsec	mag.asec		arcsec	mag	mag	arcsec	mag	mag	arcsec	mag	mag	arcsec	mag	mag	arcsec	arcsec	mag	mag	mag	mag	mag	mag		mag	mag		mag	mag		mag	mag		arcsec	mag	mag	arcsec	mag	mag	arcsec	mag	mag	arcsec	mag	mag	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec	arcsec		arcsec
str23	float64	float64	float32	float32	int32	float32	float32	int32	float32	float32	int32	float32	float32	int32	float32	float32	float32	float32	str12	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	int32	float64	float64	float64	float64	int32	float64	float64	float64	float64	int32	float64	float64	float64	float64	int32	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float64	float64	float64	float64	float64	float64	float64	int32	float32
J022136.46-053116.6	35.40201	-5.52123	8.979	0.011	0	8.937	0.02	0	5.594	0.022	10	3.942	0.048	10	98.7	90.5	50.4	22.7	S_tile_133	122.19	108.57	93.53	82.59	0.69	116.9	79.337	0.84	8.979	0.011	1	44.7	0.49	8.955	0.012	1	152.26	1.76	5.701	0.013	1	183.888	3.69	4.079	0.022	1	0.042	0.017	3.253	0.017	3.296	0.017	1.688	0.027	17.298	17.263	14.384	13.213	2.169	0.056	5.949	0.061	345.36	0.71	27.182	0.07	23.624	22.536	18.371	15.892	8.05	46.92	0.73	0.61	7.93	46.91	0.89	0.61	10.03	58.85	0.77	0.5	8.6	52.22	0.82	0.52	183.28	80.71	0.831	8.96	0.011	183.28	45.477	0.562	8.936	0.013	218.77	168.042	3.332	5.594	0.022	194.61	208.533	9.172	3.942	0.048	40.6	18.596	3.14	39.5	18.513	3.08	45.4	15.475	2.72	48.2	13.951	2.39	122.19	8.979	0.011	108.57	8.955	0.012	93.53	5.701	0.013	99.0	3.985	0.025	153.2	199.2	20.5	19.5	18.0	13.0	11.54	0.023	65.15	11.553	0.023	21.75	8.497	0.025	9.985	6.425	0.056	0.2172	113.85	8.988	0.011	101.12	8.975	0.012	85.44	5.739	0.012	74.29	4.151	0.0211	0.4	30.9	25.9	30.4	30.8	34.5	70.5	-1	138.8

Field	ID2MASX	RAJ2000	DEJ2000	supRAdeg	supDEdeg	density	r_K20e	J_K20e	e_J_K20e	f_J_K20e	H_K20e	e_H_K20e	f_H_K20e	K_K20e	e_K_K20e	f_K_K20e	Kb_a	Kpa	Sb_a	Spa	r_ext	J_ext	e_J_ext	H_ext	e_H_ext	K_ext	e_K_ext	cc
		deg	deg	deg	deg		arcs	mag	mag		mag	mag		mag	mag			deg		deg	arcs	mag	mag	mag	mag	mag	mag
object	object	float64	float64	float64	float64	float64	float64	float64	float64	int16	float64	float64	int16	float64	float64	int16	float64	int16	float64	int16	float64	float64	float64	float64	float64	float64	float64	object
XMM-LSS	02213646-0531170	35.401958	-5.5213939999999999	35.401961999999997	-5.5213799999999997	2.5499999999999998	51.0	10.523999999999999	0.024	0	9.9870000000000001	0.040000000000000001	0	9.6880000000000006	0.050999999999999997	0	0.66000000000000003	-60	0.47999999999999998	-70	86.959999999999994	10.145	0.024	9.7349999999999994	0.040000000000000001	9.4049999999999994	0.050999999999999997	0