-5.0, 5) #Set prior on background (assumes Gaussian pdf with mu and sigma)
pind = np.arange(0, n_size) #*(1.0/radim_pixsize)
prior.set_prf(prf.array, pind, pind)
prior.get_pointing_matrix()
prior.upper_lim_map()
print('fitting ' + str(prior.nsrc) + ' sources \n')
print('using ' + str(prior.snpix) + ' pixels')
from xidplus.numpyro_fit import LOFAR150
fit = LOFAR150.LOFAR_150(prior, num_samples=1000, num_warmup=1000)
samples = fit.get_samples()['src_f']
posterior = xidplus.posterior_numpyro(fit, [prior])
if os.path.exists(
'data/xidplus_results/pybdsf/xidplus_run_{}'.format(n)) == True:
()
else:
os.mkdir('data/xidplus_results/pybdsf/xidplus_run_{}'.format(n))
xidplus.save(
[prior], posterior,
'data/xidplus_results/pybdsf/xidplus_run_{}/lofar_xidplus_fir_{}_rerun.pkl'
.format(n, n))
LOFAR_cat = cat.create_LOFAR_cat(posterior, prior, 0)
LOFAR_cat = Table.read(LOFAR_cat)
def test_all_bands(self):
fit=SPIRE.all_bands(self.priors)
fit.print_summary()
posterior_numpyro=xidplus.posterior_numpyro(fit,self.priors)
self.assertAlmostEqual(np.mean(posterior_numpyro.samples['bkg']),np.mean(self.posterior.samples['bkg']),places=0)
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.numpyro_fit import SPIRE
fit = SPIRE.all_bands(priors, num_chains=4)
posterior = xidplus.posterior_numpyro(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))
pind160=np.arange(0,radius160+1+radius160,1)*3600*np.abs(pacs160_psf[1].header['CDELT1'])/pixsize160 #get 160 scale in terms of pixel scale of map
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.numpyro_fit import PACS
fit=PACS.all_bands([prior100,prior160])
posterior=xidplus.posterior_numpyro(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/v2_numpyro/xidplus_run_{}'.format(taskid))==True:()
else:
os.mkdir('data/fir/PACS/v2_numpyro/xidplus_run_{}'.format(taskid))
xidplus.save([prior100,prior160],posterior,'data/fir/PACS/v2_numpyro/xidplus_run_{}/lofar_xidplus_fir_{}'.format(taskid,taskid))
#the next couple of lines are an alternative way to save astropy table since the Table.write method is currently broken