def test_combine_ins_use_nsample():
obs = "1061313128_99bl_1pol_half_time"
testfile = os.path.join(DATA_PATH, f"{obs}.uvfits")
ss = SS()
ss.read(testfile, diff=True)
whole_ins = INS(ss, use_integration_weights=True)
all_bls = ss.get_antpairs()
first_50 = all_bls[:50]
remaining = all_bls[50:]
ss_first_50 = ss.select(bls=first_50, inplace=False)
ss_remaining = ss.select(bls=remaining, inplace=False)
ins_first_50 = INS(ss_first_50, use_integration_weights=True)
ins_remaining = INS(ss_remaining, use_integration_weights=True)
test_ins = util.combine_ins(ins_first_50, ins_remaining)
# Metric arrays are off by 10^-14 (1 part in 10^16)
for attr in whole_ins._data_params:
assert np.all(
np.isclose(getattr(whole_ins, attr), getattr(
test_ins, attr))), f"{attr} is not equal between the two INS"
def test_combine_ins_errors():
obs = "1061313128_99bl_1pol_half_time"
testfile = os.path.join(DATA_PATH, f"{obs}.uvfits")
autofile = os.path.join(DATA_PATH, "1061312640_autos.uvfits")
mixfile = os.path.join(DATA_PATH, "1061312640_mix.uvfits")
ss = SS()
ss.read(testfile, diff=True)
all_bls = ss.get_antpairs()
first_50 = all_bls[:50]
remaining = all_bls[50:]
ss_first_50 = ss.select(bls=first_50, inplace=False)
ss_remaining = ss.select(bls=remaining, inplace=False)
ins_first_50 = INS(ss_first_50, use_integration_weights=True)
ins_remaining = INS(ss_remaining, use_integration_weights=True)
ins_sig_arr = np.ma.copy(ins_first_50.sig_array)
ins_first_50.sig_array = ins_first_50.sig_array + 1
with pytest.warns(UserWarning, match="sig_array attribute"):
new_ins = util.combine_ins(ins_first_50, ins_remaining)
ss_autos = SS()
ss_autos.read(autofile, diff=True)
ss_cross = SS()
ss_cross.read(mixfile, diff=True)
auto_ins = INS(ss_autos, spectrum_type="auto")
cross_ins = INS(ss_cross, spectrum_type="cross")
with pytest.raises(ValueError, match="ins1 is of type"):
new_ins = util.combine_ins(auto_ins, cross_ins)
ins_remaining.polarization_array = np.array([-6])
with pytest.raises(ValueError,
match="The spectra do not have the same pols"):
new_ins = util.combine_ins(ins_first_50, ins_remaining)
ins_remaining.freq_array = ins_remaining.freq_array + 1
with pytest.raises(ValueError,
match="The spectra do not have the same frequencies"):
new_ins = util.combine_ins(ins_first_50, ins_remaining)
ins_remaining.time_array = ins_remaining.time_array + 1
with pytest.raises(ValueError,
match="The spectra do not have matching time"):
new_ins = util.combine_ins(ins_first_50, ins_remaining)
# Make the SS object
ss = SS()
if args.num_baselines > 0:
ss.read(args.filename, bls=bls[:args.num_baselines], diff=args.no_diff)
ins = INS(ss)
Nbls = len(bls)
for slice_ind in range(args.num_baselines, Nbls, args.num_baselines):
ss = SS()
ss.read(args.filename,
bls=bls[slice_ind:slice_ind + args.num_baselines],
diff=args.no_diff)
new_ins = INS(ss)
ins = util.combine_ins(ins, new_ins)
else:
ss.read(args.filename, antenna_nums=use_ants, diff=args.no_diff)
ss.select(ant_str='cross')
ins = INS(ss)
# Clear some memory??
del ss
# Write the raw data and z-scores to h5 format
ins.write(args.prefix, sep='.', clobber=args.clobber)
ins.write(args.prefix, output_type='z_score', sep='.', clobber=args.clobber)
# Flag FM radio
where_FM = np.where(
np.logical_and(ins.freq_array > 87.5e6, ins.freq_array < 108e6))
ins.metric_array[:, where_FM] = np.ma.masked
ins.metric_ms = ins.mean_subtract()
ins.history += "Manually flagged the FM band. "
diff=args.no_diff)
ins = INS(ss)
Nbls = len(bls)
for slice_ind in range(args.num_baselines, Nbls, args.num_baselines):
ss = SS()
ss.read(file_names[i * 10:i * 10 + 10],
bls=bls[slice_ind:slice_ind + args.num_baselines],
diff=args.no_diff)
new_ins = INS(ss)
ins = util.combine_ins(ins, new_ins)
else:
ss.read(file_names[i * 10:i * 10 + 10],
diff=args.no_diff,
antenna_nums=use_ants)
if args.internode_only == 1:
ss.select(bls=int_bls)
elif args.intersnap_only == 1:
ss.select(bls=snap_bls)
ss.select(ant_str='cross')
ins = INS(ss)
# Clear some memory
del ss
# Write the raw data and z-scores to h5 format
ins.write(prefix, sep='.', clobber=args.clobber)
ins.write(prefix, output_type='z_score', sep='.', clobber=args.clobber)
# Write out plots
cp.INS_plot(ins,
