for p in priors:
p.moc=moc
p.cut_down_prior()
p.prior_bkg(0.0,5)
p.get_pointing_matrix()
p.upper_lim_map()
print('fitting '+ str(priors[0].nsrc)+' sources \n')
print('there are '+ str(priors[0].snpix)+' pixels')
print('fitting tile:'+str(tiles[taskid-1])+' order: '+str(order)+' of '+str(len(tiles))+' tiles')
from xidplus.stan_fit import SPIRE
fit=SPIRE.all_bands(priors[0],priors[1],priors[2],iter=1000)
posterior=xidplus.posterior_stan(fit,priors)
outfile=output_folder+'Tile_'+str(tiles[taskid-1])+'_'+str(order)
posterior=xidplus.posterior_stan(fit,priors)
xidplus.save(priors,posterior,outfile)
post_rep_map=postmaps.replicated_maps(priors,posterior,nrep=2000)
band=['psw','pmw','plw']
for i,p in enumerate(priors):
Bayesian_Pval=postmaps.make_Bayesian_pval_maps(priors[i],post_rep_map[i])
wcs_temp=wcs.WCS(priors[i].imhdu)
ra,dec=wcs_temp.wcs_pix2world(priors[i].sx_pix,priors[i].sy_pix,0)
kept_pixels=np.array(moc_routines.sources_in_tile([tiles[taskid-1]],order,ra,dec))
def clus_get_xid(maps,
cats,
savemap=0,
simmap=0,
verbose=1,
confusionerrors=1):
err = False
thresh = 3.0
mf = 1.0
mJy2Jy = 1000.0 / mf
catfile = config.CLUSDATA + 'placeholder' #this doesn't actually seem to do anything in the xid code,
# but you need something for this for some reason.
#Old code not used anymore
# print('Retrieving data from cats')
# inra = []
# indec = []
# for i in range(len(cats)):
# for j in range(len(cats[i]['ra'])):
# if cats[i]['ra'][j] not in inra and cats[i]['dec'][j] not in indec:
# inra.append(cats[i]['ra'][j])
# indec.append(cats[i]['dec'][j])
# print('Done retrieving data from cats')
inra = np.array(cats['ra'])
indec = np.array(cats['dec'])
ra = inra * u.deg
dec = indec * u.deg
c = SkyCoord(ra, dec, unit='deg')
plt.scatter(ra, dec, c=cats['flux'], alpha=0.5)
plt.show()
print(inra)
#initializing data containers.
pinds = []
files = []
primary_hdus = []
noise_maps = []
data_maps = []
headers = []
pixsizes = []
prf_sizes = []
priors = []
prfs = []
for i in range(len(maps)):
bands = [18, 25, 36] #units of arcseconds
fwhm = bands[i] / maps[i]['pixsize'] #converts to arcseconds/pixel
pixs = maps[i]['pixsize']
size = pixs * 5
moc = pymoc.util.catalog.catalog_to_moc(c, size, 15)
#getting data from the fits files
files.append(maps[i]['file'])
hdul = fits.open(files[i])
headers.append(hdul[1].header)
primary_hdus.append(hdul[0].header)
img = hdul[1].data
data_maps.append(img)
noise_maps.append(hdul[2].data)
pixsizes.append(maps[i]['pixsize'])
prf_sizes.append(get_spire_beam_fwhm(maps[i]['band']))
pinds.append(np.arange(0, 101, 1) * 1.0 / pixsizes[i])
# print(maps[i]['file'])
# print(pixsizes[i])
#setting up priors
prior = xidplus.prior(data_maps[i],
noise_maps[i],
primary_hdus[i],
headers[i],
moc=moc)
prior.prior_cat(inra, indec, catfile, moc=moc)
prior.prior_bkg(-5.0, 5)
#setting up prfs.
# This prf doesnt quite look correct
# In previous set up we needed to rebin to accuratly describe our beam sizes
prf = Gaussian2DKernel(
bands[i] / 2.355, x_size=101,
y_size=101) #maybe x_size and y_size need to change.
prf.normalize(mode='peak')
prfs.append(prf.array)
# print(prfs)
exit()
#appending prf to prior and setting point matrix
prior.set_prf(prfs[i], pinds[i], pinds[i]) #prfs, xpinds, ypinds
prior.get_pointing_matrix()
prior.upper_lim_map()
#appending prior to priors list.
priors.append(prior)
print('fitting %s sources' % (priors[0].nsrc))
print('using %s %s %s pixels' %
(priors[0].snpix, priors[1].snpix, priors[2].snpix))
fit = SPIRE.all_bands(
priors[0], priors[1], priors[2], iter=1000
) #number of iterations should be at least 100 just set lower for testing.
posterior = xidplus.posterior_stan(fit, [priors[0], priors[1], priors[2]])
# figs, fig = xidplus.plots.plot_Bayes_pval_map(priors, posterior)
# print(type(figs)) #figs is list.
# print(figs) #fig is matplotlib.figure.figure object.
# print(type(fig))
# cols = ['PSW', 'PMW', 'PLW']
# counter = 0
# for figure in figs:
# figure.save('xid_%s.png' %(cols[counter]))
# counter += 1
# plt.imshow(figs)
spire_cat = cat.create_SPIRE_cat(posterior, priors[0], priors[1],
priors[2])
# spire_cat.writeto('xid_model_2_%s.fits' % (maps[0]['name']))
xid_data = spire_cat[1].data
xid = []
#in units of mJy for fluxes and degrees for RA/DEC
xid1 = {
'band': 'PSW',
'sra': xid_data.field('RA'),
'sdec': xid_data.field('DEC'),
'sflux': xid_data.field('F_SPIRE_250'),
'serr': xid_data.field(
'FErr_SPIRE_250_u'
), #there was also FErr_SPIRE_250_l don't know which to use.
'pflux': xid_data.field('F_SPIRE_250'),
'perr': xid_data.field('FErr_SPIRE_250_u'),
'model': None,
'mf': mf
} #idk if perr and pflux is right there may be a conversion needed for pflux.
#in mikes code it has pflux = output from xid / mJy2Jy.
xid2 = {
'band': 'PMW',
'sra': xid_data.field('RA'),
'sdec': xid_data.field('DEC'),
'sflux': xid_data.field('F_SPIRE_350'),
'serr': xid_data.field('FErr_SPIRE_350_u'),
'pflux': xid_data.field('F_SPIRE_350'),
'perr': xid_data.field('FErr_SPIRE_350_u'),
'model': None,
'mf': mf
}
xid3 = {
'band': 'PLW',
'sra': xid_data.field('RA'),
'sdec': xid_data.field('DEC'),
'sflux': xid_data.field('F_SPIRE_500'),
'serr': xid_data.field('FErr_SPIRE_500_u'),
'pflux': xid_data.field('F_SPIRE_500'),
'perr': xid_data.field('FErr_SPIRE_500_u'),
'model': None,
'mf': mf
}
#there was another term in the dictionary sflux, pflux and sflux looked like maybe the same thing, but I'm not sure.
#I left it out so if there are issues with that then it is because that is gone.
xid.append(xid1)
xid.append(xid2)
xid.append(xid3)
# models = create_model(maps, xid)
# for i in range(len(xid)):
# xid[i]['model'] = models[i]
# # only look at data with a flux lower than 0.0
# for i in range(len(xid)):
# whpl = []
# for j in range(xid[i]['model'].shape[0]):
# for k in range(xid[i]['model'].shape[1]):
# if xid[i]['pflux'][j] >= 0.0:
# whpl.append(j)
# whpl = np.array(whpl)
#
# xid[i]['sra'] = xid[i]['sra'][whpl]
# xid[i]['sdec'] = xid[i]['sdec'][whpl]
# xid[i]['x'] = xid[i]['x'][whpl]
# xid[i]['y'] = xid[i]['y'][whpl]
# xid[i]['sflux'] = xid[i]['sflux'][whpl]
# xid[i]['serr'] = xid[i]['serr'][whpl]
for i in range(len(xid)):
ra = xid[i]['sra'] * u.deg
dec = xid[i]['sdec'] * u.deg
c = SkyCoord(ra, dec)
#initializing w class.
hdul = fits.open(maps[i]['file'])
w = wcs(hdul[1].header)
#converting ra/dec to pixel coords.
px, py = skycoord_to_pixel(c, w)
xid[i]['x'] = px
xid[i]['y'] = py
xid[i]['sra'] = xid[i]['sra'].tolist()
xid[i]['sdec'] = xid[i]['sdec'].tolist()
xid[i]['sflux'] = xid[i]['sflux'].tolist()
xid[i]['serr'] = xid[i]['serr'].tolist()
xid[i]['pflux'] = xid[i]['pflux'].tolist()
xid[i]['perr'] = xid[i]['perr'].tolist()
xid[i]['x'] = xid[i]['x'].tolist()
xid[i]['y'] = xid[i]['y'].tolist()
#saving to json file for further analysis.
with open('xid_a0370_take_9_%s.json' % (xid[i]['band']),
'w') as f: #code for saving output to a file.
json.dump(xid[i], f)
#model = image_model(x,y, sflux, maps[i]['astr']['NAXIS'][0], maps[i]['astr']['NAXIS'][1],
#maps[i]['psf'])
#need to finish converting model over to python.
#
# if savemap:
# outfile = config.CLUSSBOX + 'clus_get_xid_model_' + maps[i]['band'] + '.fit'
# writefits(outfile, data=model, header_dict=maps[i]['shead'])
return xid, err