priors[0].cut_down_prior()
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 MIPS
priors[0].prior_bkg(0.0,1)
priors[0].get_pointing_matrix()
#priors[0].upper_lim_map()
#priors[0].prior_flux_upper=(priors[0].prior_flux_upper-10.0+0.02)/np.max(priors[0].prf)
fit=MIPS.MIPS_24(priors[0],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)
Bayes_P24=postmaps.Bayes_Pval_res(priors[0],post_rep_map[0])
cat=catalogue.create_MIPS_cat(posterior, priors[0], Bayes_P24)
kept_sources=moc_routines.sources_in_tile([tiles[taskid-1]],order,priors[0].sra,priors[0].sdec)
kept_sources=np.array(kept_sources)
cat[1].data=cat[1].data[kept_sources]
outfile=output_folder+'Tile_'+str(tiles[taskid-1])+'_'+str(order)
cat.writeto(outfile+'_MIPS24_cat.fits',overwrite=True)
) #requires PRF 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)
prior250.get_pointing_matrix()
prior350.get_pointing_matrix()
prior500.get_pointing_matrix()
prior250.upper_lim_map()
prior350.upper_lim_map()
prior500.upper_lim_map()
from xidplus.stan_fit import SPIRE
fit = SPIRE.all_bands(prior250, prior350, prior500, iter=1000)
posterior = xidplus.posterior_stan(fit, [prior250, prior350, prior500])
priors = [prior250, prior350, prior500]
import xidplus.catalogue as cat
SPIRE_cat = cat.create_SPIRE_cat(posterior, priors[0], priors[1], priors[2])
SPIRE_cat = Table.read(SPIRE_cat)
mask = [SPIRE_cat['HELP_ID'][i] in ids for i in range(len(SPIRE_cat))]
SPIRE_cat = SPIRE_cat[mask]
if os.path.exists('data/fir/v0_7/SPIRE/xidplus_run_{}'.format(taskid)) == True:
()
else:
os.mkdir('data/fir/v0_7/SPIRE/xidplus_run_{}'.format(taskid))
xidplus.save([prior250, prior350, prior500], posterior,
'data/fir/v0_7/SPIRE/xidplus_run_{}/lofar_xidplus_fir_{}'.format(
-5.0, 5) #Set prior on background (assumes Gaussian pdf with mu and sigma)
centre = np.long((MIPS_psf[1].header['NAXIS1'] - 1) / 2)
radius = 20
prior250.set_prf(
MIPS_psf[1].data[centre - radius:centre + radius + 1,
centre - radius:centre + radius + 1] / 1.0E6,
np.arange(0, 41 / 2.0, 0.5), np.arange(0, 41 / 2.0, 0.5)
) #requires PRF as 2d grid, and x and y bins for grid (in pixel scale)
prior250.get_pointing_matrix()
from xidplus.stan_fit import MIPS, SPIRE
fit = MIPS.MIPS_24(prior250, iter=1000)
posterior = xidplus.posterior_stan(fit, [prior250])
priors = [prior250]
import xidplus.catalogue as cat
MIPS_cat = cat.create_MIPS_cat(posterior, priors[0], 0)
MIPS_cat = Table.read(MIPS_cat)
mask = [MIPS_cat['help_id'][i] in ids for i in range(len(MIPS_cat))]
MIPS_cat = MIPS_cat[mask]
if os.path.exists(
'data/fir/MIPS_no_lofar/xidplus_run_{}'.format(taskid)) == True:
()
else:
os.mkdir('data/fir/MIPS_no_lofar/xidplus_run_{}'.format(taskid))
prior100.set_prf(
pacs100_psf[1].data[centre100 - radius100:centre100 + radius100 + 1,
centre100 - radius100:centre100 + radius100 + 1] /
1000.0, pind100, pind100)
prior160.set_prf(
pacs160_psf[1].data[centre160 - radius160:centre160 + radius160 + 1,
centre160 - radius160:centre160 + radius160 + 1] /
1000.0, pind160, pind160)
prior100.get_pointing_matrix()
prior160.get_pointing_matrix()
from xidplus.stan_fit import PACS
fit = PACS.all_bands(prior100, prior160, iter=1000)
posterior = xidplus.posterior_stan(fit, [prior100, prior160])
priors = [prior100, prior160]
import xidplus.catalogue as cat
PACS_cat = cat.create_PACS_cat(posterior, priors[0], priors[1])
PACS_cat = Table.read(PACS_cat)
mask = [PACS_cat['help_id'][i] in ids for i in range(len(PACS_cat))]
PACS_cat = PACS_cat[mask]
if os.path.exists(
'data/fir/PACS_no_lofar/xidplus_run_{}'.format(taskid)) == True:
()
else:
os.mkdir('data/fir/PACS_no_lofar/xidplus_run_{}'.format(taskid))
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
