aar.grid_convolve(pol='P1',
method='NN',
distNN=0.5 * FCNST.c / f0,
tol=1.0e-6,
maxmatch=1,
identical_antennas=True,
cal_loop=False,
gridfunc_freq='scale',
mapping='weighted',
wts_change=False,
parallel=True,
pp_method='pool')
# fp1 = [ad['flags']['P1'] for ad in update_info['antennas']]
# p1f = [a.antpol.flag['P1'] for a in aar.antennas.itervalues()]
imgobj = AA.NewImage(antenna_array=aar, pol='P1')
imgobj.imagr(weighting='natural', pol='P1')
img = imgobj.img['P1']
# for chan in xrange(imgobj.holograph_P1.shape[2]):
# imval = NP.abs(imgobj.holograph_P1[imgobj.mf_P1.shape[0]/2,:,chan])**2 # a horizontal slice
# imval = imval[NP.logical_not(NP.isnan(imval))]
# immax2[it,chan,:] = NP.sort(imval)[-2:]
if it == 0:
avg_img = NP.copy(img)
else:
avg_img += NP.copy(img)
if NP.any(NP.isnan(avg_img)):
PDB.set_trace()
tempdata = calarr_full['P1'].apply_cal(tempdata,meas=True) # doesn't matter which calarr we use cause it's a measurement
tempdata += NP.sqrt(rxr_noise) / NP.sqrt(2) * (NP.random.normal(loc=0.0, scale=1, size=tempdata.shape) + 1j * NP.random.normal(loc=0.0, scale=1, size=tempdata.shape))
aar.caldata['P1']['E-fields'][0,:,:]=calarr_full['P1'].apply_cal(tempdata)
if i < Nmeas_eff:
for anti in xrange(n_antennas):
visdata[anti,:,:] += tempdata[anti,:].reshape(1,nchan) * NP.conj(tempdata)/Nmeas_eff
if grid_map_method == 'regular':
aar.grid_convolve_new(pol='P1', method='NN',distNN=0.5*FCNST.c/f0, tol=1.0e-6,maxmatch=1,identical_antennas=True,gridfunc_freq='scale',mapping='weighted',wts_change=False,parallel=False, pp_method='queue',nproc=16, cal_loop=True,verbose=False)
else:
if i == 0:
aar.genMappingMatrix(pol='P1',method='NN',distNN=0.5*NP.sqrt(ant_sizex**2+ant_sizey**2)/NP.sqrt(2),identical_antennas=True,gridfunc_freq='scale',wts_change=False,parallel=False)
if i == 0:
imgobj = AA.NewImage(antenna_array=aar,pol='P1',verbose=False)
imgobj.imagr(weighting='natural',pol='P1',pad=0,verbose=False,grid_map_method=grid_map_method,cal_loop=True,stack=False)
else:
#imgobj.update(antenna_array=aar,reset=True,verbose=False)
#imgobj.holimg['P1'][64,64,:] = NP.mean(tempdata,axis=0)
imgobj.holimg['P1'][64,64,:] = NP.mean(calarr_full['P1'].apply_cal(tempdata),axis=0)
#imgobj.imagr(weighting='natural',pol='P1',pad=0,verbose=False,grid_map_method=grid_map_method,cal_loop=True,stack=False)
# update calibration
calarr_full['P1'].update_cal(tempdata,imgobj)
imgobj.holimg['P1'][64,64,:] = NP.mean(calarr_one['P1'].apply_cal(tempdata),axis=0)
calarr_one['P1'].update_cal(tempdata,imgobj)
if True in NP.isnan(calarr_full['P1'].cal_corr):
print 'NAN in calibration gains! exiting!'
do_correlate='FX',
parallel=True,
verbose=True)
iar.grid_convolve(pol='P11',
method='NN',
distNN=0.5 * FCNST.c / f0,
tol=1.0e-6,
maxmatch=1,
identical_interferometers=True,
gridfunc_freq='scale',
mapping='weighted',
wts_change=False,
parallel=True,
pp_method='queue')
imgobj = AA.NewImage(interferometer_array=iar, pol='P11')
imgobj.imagr(weighting='natural', pol='P11')
if i == 0:
avg_img = imgobj.img['P11']
else:
avg_img += imgobj.img['P11']
avg_img /= max_n_timestamps
fig = PLT.figure()
ax = fig.add_subplot(111)
imgplot = ax.imshow(NP.mean(avg_img, axis=2),
aspect='equal',
origin='lower',
extent=(imgobj.gridl.min(), imgobj.gridl.max(),
adict['flags'][pol] = False
adict['Et'][pol] = E_timeseries_dict['Et'][:,ind]
# adict['wtsinfo'][pol] = [{'orientation':0.0, 'lookup':'/data3/t_nithyanandan/project_MOFF/simulated/MWA/data/lookup/E_illumination_lookup_zenith.txt'}]
adict['wtsinfo'][pol] = [{'orientation':0.0, 'lookup':'/data3/t_nithyanandan/project_MOFF/simulated/LWA/data/lookup/E_illumination_isotropic_radiators_lookup_zenith.txt'}]
update_info['antennas'] += [adict]
progress.update(antnum+1)
antnum += 1
progress.finish()
aar.update(update_info, parallel=True, verbose=True)
aar.grid_convolve(pol=None, method='NN', distNN=0.5*FCNST.c/f0, tol=1.0e-6, maxmatch=1, identical_antennas=True, cal_loop=False, gridfunc_freq='scale', mapping='weighted', wts_change=False, parallel=True, pp_method='pool')
aar.make_grid_cube()
aar_psf_info = aar.quick_beam_synthesis(pol='P1', keep_zero_spacing=False)
efimgobj = AA.NewImage(antenna_array=aar, pol='P1')
efimgobj.imagr(pol='P1', weighting='uniform', pad='on')
efimg = efimgobj.img['P1']
efimgmax += [efimg[tuple(NP.array(efimg.shape)/2)]]
if i == 0:
avg_efimg = NP.copy(efimg)
else:
avg_efimg += NP.copy(efimg)
if NP.any(NP.isnan(avg_efimg)):
PDB.set_trace()
# Begin interferometry FX processing
iar = AA.InterferometerArray(antenna_array=aar)
iar.refresh_antenna_pairs()
iar.stack(on_flags=True, on_data=True, parallel=False, nproc=None)
