fminax = fmajax
fpa = NP.zeros(n_src)
ctlgobj = CTLG.Catalog('PS',
freq_catalog * 1e9,
NP.hstack(
(ra_deg.reshape(-1, 1), dec_deg.reshape(-1,
1))),
fpeak,
spectral_index=spindex,
src_shape=NP.hstack(
(fmajax.reshape(-1, 1), fminax.reshape(-1, 1),
fpa.reshape(-1, 1))),
src_shape_units=['arcmin', 'arcmin', 'degree'])
fg_str = 'point'
skymod = CTLG.SkyModel(ctlgobj)
## Start the observation
# PDB.set_trace()
# progress = PGB.ProgressBar(widgets=[PGB.Percentage(), PGB.Bar(), PGB.ETA()], maxval=len(bl_chunk)).start()
# for i in range(len(bl_chunk)):
# outfile = '/data3/t_nithyanandan/project_MWA/multi_baseline_visibilities_'+obs_mode+'_baseline_range_{0:.1f}-{1:.1f}_'.format(bl_length[baseline_bin_indices[bl_chunk[i]]],bl_length[min(baseline_bin_indices[bl_chunk[i]]+baseline_chunk_size-1,total_baselines-1)])+'gaussian_FG_model_'+fg_str+'_{0:0d}_'.format(nside)+bpass_shape+'{0:.1f}'.format(oversampling_factor)+'_part_{0:0d}'.format(i)
# ia = RI.InterferometerArray(labels[baseline_bin_indices[bl_chunk[i]]:min(baseline_bin_indices[bl_chunk[i]]+baseline_chunk_size,total_baselines)], bl[baseline_bin_indices[bl_chunk[i]]:min(baseline_bin_indices[bl_chunk[i]]+baseline_chunk_size,total_baselines),:], chans, telescope=telescope, latitude=latitude, A_eff=A_eff, freq_scale='GHz')
# ts = time.time()
# ia.observing_run(pointing_init, skymod, t_snap, t_obs, chans, bpass, Tsys, lst_init, mode=obs_mode, freq_scale='GHz', brightness_units=flux_unit, memsave=True)
# print 'The last chunk of {0:0d} baselines required {1:.1f} minutes'.format(baseline_chunk_size, (time.time()-ts)/60.0)
# ia.delay_transform(oversampling_factor-1.0, freq_wts=window)
# ia.save(outfile, verbose=True, tabtype='BinTableHDU', overwrite=True)
# progress.update(i+1)
# progress.finish()
ec = gc.transform_to('icrs')
else:
ec = SkyCoord(ra=NP.degrees(phi),
dec=90.0 - NP.degrees(theta),
unit='deg',
frame='icrs')
spec_parms = None
spec_type = 'spectrum'
dsm_inspec = NP.asarray(dsm_inspec)
catlabel = NP.repeat('DSM', dsm_inspec.shape[0])
dsm_in = SM.SkyModel(catlabel,
freqs[chan],
NP.hstack((ec.ra.degree.reshape(-1, 1),
ec.dec.degree.reshape(-1, 1))),
spec_type,
spectrum=indata,
spec_parms=None)
dsm_out_dict = dsm_in.to_healpix(freqs[chan],
out_nside,
in_units=dsm_units,
dsm_out_coords=out_coord,
out_units=out_units)
dsm_out[:, chan] = dsm_out_dict['spectrum']
if ('csm' in inp_skymodels) or ('CSM' in inp_skymodels):
freq_SUMSS = 0.843 # in GHz
catalog = NP.loadtxt(SUMSS_file,
usecols=(0, 1, 2, 3, 4, 5, 10, 12, 13, 14, 15, 16))