def test_mask_to_flags():
obs = '1061313128_99bl_1pol_half_time'
testfile = os.path.join(DATA_PATH, '%s.uvfits' % obs)
file_type = 'uvfits'
ss = SS()
ss.read(testfile)
ins = INS(ss)
freq_inds_1 = np.arange(0, len(ins.freq_array), 2)
freq_inds_2 = np.arange(1, len(ins.freq_array), 2)
ins.metric_array[1, freq_inds_1] = np.ma.masked
ins.metric_array[3, freq_inds_1] = np.ma.masked
ins.metric_array[7, freq_inds_2] = np.ma.masked
ins.metric_array[-2, freq_inds_2] = np.ma.masked
flags = ins.mask_to_flags()
test_flags = np.zeros(flags.shape, dtype=bool)
test_flags[1:5, freq_inds_1] = True
test_flags[7, freq_inds_2] = True
test_flags[8, freq_inds_2] = True
test_flags[-3:-1, freq_inds_2] = True
assert np.all(
flags == test_flags), "Test flags were not equal to calculated flags."
def test_write_mwaf():
from astropy.io import fits
obs = '1061313128_99bl_1pol_half_time_SSINS'
testfile = os.path.join(DATA_PATH, '%s.h5' % obs)
prefix = os.path.join(DATA_PATH, '%s_test' % obs)
ins = INS(testfile)
mwaf_files = [os.path.join(DATA_PATH, '1061313128_12.mwaf')]
bad_mwaf_files = [os.path.join(DATA_PATH, 'bad_file_path')]
# Compatible shape with mwaf file
ins.metric_array = np.ma.ones([55, 384, 1])
ins.metric_array[50, 16 * 11: 16 * (11 + 1)] = np.ma.masked
# metadata from the input file, hardcoded for testing purposes
time_div = 4
freq_div = 2
NCHANS = 32
boxint = 11
Nbls = 8256
NSCANS = 224
flags = ins.mask_to_flags()
# Repeat in time
time_rep_flags = np.repeat(flags, time_div, axis=0)
# Repeat in freq
freq_time_rep_flags = np.repeat(time_rep_flags, freq_div, axis=1)
# Repeat in bls
freq_time_bls_rep_flags = np.repeat(freq_time_rep_flags[:, np.newaxis, NCHANS * boxint: NCHANS * (boxint + 1)], Nbls, axis=1)
# This shape is on MWA wiki. Reshape to this shape.
new_flags = freq_time_bls_rep_flags.reshape((NSCANS * Nbls, NCHANS))
# Test some defensive errors
with pytest.raises(IOError):
ins.write(prefix, output_type='mwaf', mwaf_files=bad_mwaf_files)
with pytest.raises(ValueError):
ins.write(prefix, output_type='mwaf', mwaf_files=mwaf_files,
mwaf_method='bad_method')
with pytest.raises(ValueError):
ins.write(prefix, output_type='mwaf', mwaf_files=None)
ins.write('%s_add' % prefix, output_type='mwaf', mwaf_files=mwaf_files)
ins.write('%s_replace' % prefix, output_type='mwaf', mwaf_files=mwaf_files,
mwaf_method='replace')
with fits.open(mwaf_files[0]) as old_mwaf_hdu:
with fits.open('%s_add_12.mwaf' % prefix) as add_mwaf_hdu:
assert np.all(add_mwaf_hdu[1].data['FLAGS'] == old_mwaf_hdu[1].data['FLAGS'] + new_flags)
with fits.open('%s_replace_12.mwaf' % prefix) as replace_mwaf_hdu:
assert np.all(replace_mwaf_hdu[1].data['FLAGS'] == new_flags)
for path in ['%s_add_12.mwaf' % prefix, '%s_replace_12.mwaf' % prefix]:
os.remove(path)
def test_write_mwaf():
from astropy.io import fits
obs = '1061313128_99bl_1pol_half_time_SSINS'
testfile = os.path.join(DATA_PATH, '%s.h5' % obs)
prefix = os.path.join(DATA_PATH, '%s_test' % obs)
ins = INS(testfile)
mwaf_files = [os.path.join(DATA_PATH, '1061313128_12.mwaf')]
bad_mwaf_files = [os.path.join(DATA_PATH, 'bad_file_path')]
# shape of that mwaf file
ins.metric_array = np.ma.ones([223, 768, 1])
ins.metric_array[100, 32 * 11:32 * (11 + 1)] = np.ma.masked
flags = ins.mask_to_flags()
new_flags = np.repeat(flags[:, np.newaxis, 32 * 11:32 * (11 + 1)],
8256,
axis=1).reshape((224 * 8256, 32))
# Test some defensive errors
with pytest.raises(IOError):
ins.write(prefix, output_type='mwaf', mwaf_files=bad_mwaf_files)
with pytest.raises(ValueError):
ins.write(prefix,
output_type='mwaf',
mwaf_files=mwaf_files,
mwaf_method='bad_method')
with pytest.raises(ValueError):
ins.write(prefix, output_type='mwaf', mwaf_files=None)
ins.write('%s_add' % prefix, output_type='mwaf', mwaf_files=mwaf_files)
ins.write('%s_replace' % prefix,
output_type='mwaf',
mwaf_files=mwaf_files,
mwaf_method='replace')
with fits.open(mwaf_files[0]) as old_mwaf_hdu:
with fits.open('%s_add_12.mwaf' % prefix) as add_mwaf_hdu:
assert np.all(add_mwaf_hdu[1].data['FLAGS'] ==
old_mwaf_hdu[1].data['FLAGS'] + new_flags)
with fits.open('%s_replace_12.mwaf' % prefix) as replace_mwaf_hdu:
assert np.all(replace_mwaf_hdu[1].data['FLAGS'] == new_flags)
for path in ['%s_add_12.mwaf' % prefix, '%s_replace_12.mwaf' % prefix]:
os.remove(path)
xlabel='Frequency (Mhz)', ylabel='Time (UTC)')
# Try to save memory - hope for garbage collector
del ss
# Set up MF flagging for routine shapes
shape_dict = {'TV6': [1.74e8, 1.81e8], 'TV7': [1.81e8, 1.88e8],
'TV8': [1.88e8, 1.95e8], 'TV9': [1.95e8, 2.02e8]}
sig_thresh = {shape: 5 for shape in shape_dict}
sig_thresh['narrow'] = 5
sig_thresh['streak'] = 8
mf = MF(ins.freq_array, sig_thresh, shape_dict=shape_dict,
N_samp_thresh=len(ins.time_array) // 2)
mf.apply_match_test(ins, apply_samp_thresh=False)
mf.apply_samp_thresh_test(ins, event_record=True)
Catalog_Plot.INS_plot(ins, '%s_flagged' % prefix,
xticks=xticks, yticks=yticks, xticklabels=xticklabels,
yticklabels=yticklabels, data_cmap=cm.plasma,
ms_vmin=-5, ms_vmax=5, title=args.obsid,
xlabel='Frequency (Mhz)', ylabel='Time (UTC)')
ins.write(prefix, output_type='mask')
uvd = UVData()
uvd.read(args.uvd, phase_to_pointing_center=True, correct_cable_len=True)
uvf = UVFlag(uvd, mode='flag', waterfall=True)
uvf.flag_array = ins.mask_to_flags()
utils.apply_uvflag(uvd, uvf, inplace=True)
if np.any(uvd.nsample_array == 0):
uvd.nsample_array[uvd.nsample_array == 0] = args.nsample_default
uvd.write_uvfits('%s/%s.uvfits' % (args.outdir, args.obsid), spoof_nonessential=True)
uv.select(ant_str='crosses')
shapes = ['TV4', 'TV5', 'TV6']
shape_dict = {
'TV4': [1.74e8, 1.82e8],
'TV5': [1.82e8, 1.9e8],
'TV6': [1.9e8, 1.98e8]
}
mf = MF(ins.freq_array, 5, shape_dict=shape_dict, streak=False, narrow=False)
for event in ins.match_events:
if event[-2] in shapes:
ins.metric_array[event[:2]] = np.ma.masked
ins_flags = ins.mask_to_flags()
for uvd in [auto_uv, uv]:
uvf = UVFlag(uvd, mode='flag', waterfall=True)
uvf.flag_array = ins_flags
uvf.to_baseline(uvd)
uvd.flag_array = uvf.flag_array
stat_dict = {
'TV4': {
'occ': 0,
'autopow': 0,
'crosspow': 0
},
'TV5': {
'occ': 0,
'autopow': 0,
