def test_freq_broadcast_whole_band():
obs = '1061313128_99bl_1pol_half_time'
insfile = os.path.join(DATA_PATH, f'{obs}_SSINS.h5')
out_prefix = os.path.join(DATA_PATH, f'{obs}_test')
match_outfile = f'{out_prefix}_SSINS_match_events.yml'
ins = INS(insfile)
# spoof the metric array
ins.metric_array[:] = 1
ins.metric_array[2, 10:20] = 10
ins.metric_array[4, 40:50] = 10
ins.metric_ms = ins.mean_subtract()
shape_dict = {
'shape1': [ins.freq_array[10], ins.freq_array[20]],
'shape2': [ins.freq_array[40], ins.freq_array[50]]
}
mf = MF(ins.freq_array, 5, shape_dict=shape_dict, broadcast_streak=True)
mf.apply_match_test(ins, event_record=True, freq_broadcast=True)
assert np.all(ins.metric_array.mask[2])
assert np.all(ins.metric_array.mask[4])
def test_sig_plot():
matplotlib = pytest.importorskip("matplotlib")
obs = '1061313128_99bl_1pol_half_time'
insfile = os.path.join(DATA_PATH, '%s_SSINS.h5' % obs)
outdir = os.path.join(DATA_PATH, 'test_plots')
prefix = '%s/%s_flagged' % (outdir, obs)
dataplotfile = '%s_SSINS.pdf' % prefix
outfile = '%s_SSINS_sig.pdf' % prefix
ins = INS(insfile)
shape_dict = {
'TV6': [1.74e8, 1.81e8],
'TV7': [1.81e8, 1.88e8],
'TV8': [1.88e8, 1.95e8]
}
sig_thresh = {'TV6': 5, 'TV7': 5, 'TV8': 5, 'narrow': 5, 'streak': 5}
mf = MF(ins.freq_array, sig_thresh, shape_dict=shape_dict)
mf.apply_match_test(ins)
xticks = np.arange(0, 384, 96)
xticklabels = ['%.1f' % (10**-6 * ins.freq_array[tick]) for tick in xticks]
yticks = np.arange(0, 50, 10)
yticklabels = ['%i' % (2 * tick) for tick in yticks]
cp.INS_plot(ins, prefix)
assert os.path.exists(outfile), "The first plot was not made"
assert os.path.exists(dataplotfile), "The second plot was not made"
os.remove(outfile)
os.remove(dataplotfile)
os.rmdir(outdir)
def test_time_broadcast_no_new_event():
"""
Tests that a new event is not added because the agression threshold is not
exceeded.
"""
obs = '1061313128_99bl_1pol_half_time'
insfile = os.path.join(DATA_PATH, f'{obs}_SSINS.h5')
out_prefix = os.path.join(DATA_PATH, f'{obs}_test')
match_outfile = f'{out_prefix}_SSINS_match_events.yml'
ins = INS(insfile)
# Mock a simple metric_array and freq_array (and weights...)
ins.metric_array[:] = 1
ins.weights_array = np.copy(ins.metric_array)
ins.weights_square_array = np.copy(ins.weights_array)
ins.metric_ms = ins.mean_subtract()
ins.sig_array = np.ma.copy(ins.metric_ms)
sig_thresh = {'narrow': 5}
mf = MF(ins.freq_array, sig_thresh, streak=False, tb_aggro=0.5)
# Put in an outlier so it gets to samp_thresh_test
ins.metric_array[1, 10] = 100
ins.metric_ms = ins.mean_subtract()
mf.apply_match_test(ins, event_record=True, time_broadcast=True)
event = mf.time_broadcast(ins, ins.match_events[0], event_record=True)
assert event == ins.match_events[0]
def test_freq_broadcast_no_new_event():
obs = '1061313128_99bl_1pol_half_time'
insfile = os.path.join(DATA_PATH, f'{obs}_SSINS.h5')
out_prefix = os.path.join(DATA_PATH, f'{obs}_test')
match_outfile = f'{out_prefix}_SSINS_match_events.yml'
ins = INS(insfile)
# spoof the metric array
ins.metric_array[:] = 1
ins.metric_array[2, 10:20] = 10
ins.metric_ms = ins.mean_subtract()
# Slice will go up to 21 since shapes are inclusive at the boundaries
# when boundary is on channel center
shape_dict = {'shape1': [ins.freq_array[10], ins.freq_array[20]]}
broadcast_dict = {'sb2': [ins.freq_array[30], ins.freq_array[59]]}
test_event = (slice(2, 3), slice(10, 21), 'shape1', 10)
mf = MF(ins.freq_array,
5,
shape_dict=shape_dict,
broadcast_streak=False,
broadcast_dict=broadcast_dict)
mf.apply_match_test(ins, event_record=True, freq_broadcast=True)
event = mf.freq_broadcast(ins, event=test_event, event_record=True)
assert event == test_event
assert ins.match_events[0][:-1] == test_event[:-1]
def test_time_broadcast():
obs = '1061313128_99bl_1pol_half_time'
insfile = os.path.join(DATA_PATH, f'{obs}_SSINS.h5')
out_prefix = os.path.join(DATA_PATH, f'{obs}_test')
match_outfile = f'{out_prefix}_SSINS_match_events.yml'
ins = INS(insfile)
# Mock a simple metric_array and freq_array
ins.metric_array[:] = 1
ins.weights_array = np.copy(ins.metric_array)
ins.weights_square_array = np.copy(ins.weights_array)
ins.metric_ms = ins.mean_subtract()
ins.sig_array = np.ma.copy(ins.metric_ms)
# Arbitrarily flag enough data in channel 10
sig_thresh = {'narrow': 5}
mf = MF(ins.freq_array, sig_thresh, streak=False, tb_aggro=0.5)
ins.metric_array[4:, 9] = np.ma.masked
ins.metric_array[4:, 10] = np.ma.masked
# Put in an outlier so it gets to samp_thresh_test
ins.metric_array[2, 9] = 100
ins.metric_array[1, 10] = 100
ins.metric_ms = ins.mean_subtract()
bool_ind = np.zeros(ins.metric_array.shape, dtype=bool)
bool_ind[:, 10] = 1
bool_ind[:, 9] = 1
mf.apply_match_test(ins, event_record=True, time_broadcast=True)
print(ins.match_events)
test_match_events = [
(slice(1, 2), slice(10, 11),
'narrow_%.3fMHz' % (ins.freq_array[10] * 10**(-6))),
(slice(0, ins.Ntimes), slice(10, 11),
'time_broadcast_narrow_%.3fMHz' % (ins.freq_array[10] * 10**(-6))),
(slice(2, 3), slice(9, 10),
'narrow_%.3fMHz' % (ins.freq_array[9] * 10**(-6))),
(slice(0, ins.Ntimes), slice(9, 10),
'time_broadcast_narrow_%.3fMHz' % (ins.freq_array[9] * 10**(-6)))
]
# Test stuff
assert np.all(
ins.metric_array.mask == bool_ind), "The right flags were not applied"
for i, event in enumerate(test_match_events):
assert ins.match_events[
i][:-1] == event, "The events weren't appended correctly"
# Test that writing with samp_thresh flags is OK
ins.write(out_prefix, output_type='match_events')
test_match_events_read = ins.match_events_read(match_outfile)
os.remove(match_outfile)
assert ins.match_events == test_match_events_read
# Test that exception is raised when tb_aggro is too high
with pytest.raises(ValueError):
mf = MF(ins.freq_array, {'narrow': 5, 'streak': 5}, tb_aggro=100)
mf.apply_samp_thresh_test(ins, (slice(1, 2), slice(10, 11), 'narrow'))
def test_calc_occ():
obs = "1061313128_99bl_1pol_half_time_SSINS"
testfile = os.path.join(DATA_PATH, f"{obs}.h5")
ins = INS(testfile)
# Mock some flaggable data
ins.select(freq_chans=np.arange(32), times=ins.time_array[:22])
ins.metric_array[:] = 1
ins.weights_array[:] = 10
ins.weights_square_array[:] = 10
# Make some outliers
# Narrowband in 1th, 26th, and 31th frequency
ins.metric_array[1, 1, :] = 100
ins.metric_array[1, 30, :] = 100
ins.metric_array[3:14, 26, :] = 100
# Arbitrary shape in 2, 3, 4
ins.metric_array[3:14, 2:25, :] = 100
ins.metric_array[[0, -1], :, :] = np.ma.masked
ins.metric_array[:, [0, -1], :] = np.ma.masked
ins.metric_ms = ins.mean_subtract()
num_int_flag = 2
num_chan_flag = 2
num_init_flag = np.sum(ins.metric_array.mask)
ch_wid = ins.freq_array[1] - ins.freq_array[0]
shape_dict = {"shape": [ins.freq_array[2] + 0.1 * ch_wid, ins.freq_array[24] - 0.1 * ch_wid]}
mf = MF(ins.freq_array, 5, tb_aggro=0.5, shape_dict=shape_dict)
mf.apply_match_test(ins, time_broadcast=True)
occ_dict = util.calc_occ(ins, mf, num_init_flag, num_int_flag=2,
lump_narrowband=False)
assert occ_dict["streak"] == 0
assert occ_dict["narrow_%.3fMHz" % (ins.freq_array[1] * 10**(-6))] == 0.05
assert occ_dict["narrow_%.3fMHz" % (ins.freq_array[26] * 10**(-6))] == 1
assert occ_dict["narrow_%.3fMHz" % (ins.freq_array[30] * 10**(-6))] == 0.05
assert occ_dict["shape"] == 1
occ_dict = util.calc_occ(ins, mf, num_init_flag, num_int_flag=2,
lump_narrowband=True)
# total narrow over total valid
assert occ_dict["narrow"] == 24 / 600
assert occ_dict["streak"] == 0
assert occ_dict["shape"] == 1
assert "narrow_%.3fMHz" % (ins.freq_array[1] * 10**(-6)) not in occ_dict.keys()
assert "narrow_%.3fMHz" % (ins.freq_array[30] * 10**(-6)) not in occ_dict.keys()
yml_outpath = os.path.join(DATA_PATH, "test_occ_.yml")
with open(yml_outpath, "w") as occ_file:
yaml.safe_dump(occ_dict, occ_file)
os.remove(yml_outpath)
def test_apply_match_test():
obs = '1061313128_99bl_1pol_half_time'
insfile = os.path.join(DATA_PATH, '%s_SSINS.h5' % obs)
ins = INS(insfile)
# Mock a simple metric_array and freq_array
ins.metric_array = np.ma.ones([10, 20, 1])
ins.weights_array = np.copy(ins.metric_array)
ins.freq_array = np.zeros([1, 20])
ins.freq_array = np.arange(20)
# Make a shape dictionary for a shape that will be injected later
shape = [7.9, 12.1]
shape_dict = {'shape': shape}
sig_thresh = {'shape': 5, 'narrow': 5, 'streak': 5}
mf = MF(ins.freq_array, sig_thresh, shape_dict=shape_dict)
# Inject a shape, narrow, and streak event
ins.metric_array[3, 5] = 10
ins.metric_array[5] = 10
ins.metric_array[7, 7:13] = 10
ins.metric_ms = ins.mean_subtract()
ins.sig_array = np.ma.copy(ins.metric_ms)
mf.apply_match_test(ins, event_record=True)
# Check that the right events are flagged
test_mask = np.zeros(ins.metric_array.shape, dtype=bool)
test_mask[3, 5] = 1
test_mask[5] = 1
test_mask[7, 7:13] = 1
assert np.all(test_mask == ins.metric_array.mask), "Flags are incorrect"
test_match_events_slc = [(5, slice(0, 20), 'streak'),
(7, slice(7, 13), 'shape'),
(3, slice(5, 6), 'narrow')]
for i, event in enumerate(test_match_events_slc):
assert ins.match_events[i][:-1] == test_match_events_slc[
i], "%ith event is wrong" % i
assert not np.any([ins.match_events[i][-1] < 5 for i in range(3)
]), "Some significances were less than 5"
# Test a funny if block that is required when the last time in a shape is flagged
ins.metric_array[1:, 7:13] = np.ma.masked
ins.metric_ms[0, 7:13] = 10
mf.apply_match_test(ins, event_record=True)
assert np.all(ins.metric_ms.mask[:, 7:13]
), "All the times were not flagged for the shape"
def test_apply_samp_thresh_dep_error_match_test():
obs = '1061313128_99bl_1pol_half_time'
insfile = os.path.join(DATA_PATH, f'{obs}_SSINS.h5')
out_prefix = os.path.join(DATA_PATH, f'{obs}_test')
match_outfile = f'{out_prefix}_SSINS_match_events.yml'
ins = INS(insfile)
mf = MF(ins.freq_array, 5, tb_aggro=0.2)
with pytest.raises(ValueError,
match="apply_samp_thresh has been deprecated"):
mf.apply_match_test(ins, apply_samp_thresh=True)
def test_samp_thresh():
obs = '1061313128_99bl_1pol_half_time'
insfile = os.path.join(DATA_PATH, '%s_SSINS.h5' % obs)
out_prefix = os.path.join(DATA_PATH, '%s_test' % obs)
match_outfile = '%s_SSINS_match_events.yml' % out_prefix
ins = INS(insfile)
# Mock a simple metric_array and freq_array
ins.metric_array = np.ma.ones([10, 20, 1])
ins.weights_array = np.copy(ins.metric_array)
ins.metric_ms = ins.mean_subtract()
ins.sig_array = np.ma.copy(ins.metric_ms)
ins.freq_array = np.zeros([1, 20])
ins.freq_array = np.arange(20)
# Arbitrarily flag enough data in channel 10
sig_thresh = {'narrow': 5}
mf = MF(ins.freq_array, sig_thresh, streak=False, N_samp_thresh=5)
ins.metric_array[3:, 10] = np.ma.masked
ins.metric_array[3:, 9] = np.ma.masked
# Put in an outlier so it gets to samp_thresh_test
ins.metric_array[0, 11] = 10
ins.metric_ms = ins.mean_subtract()
bool_ind = np.zeros(ins.metric_array.shape, dtype=bool)
bool_ind[:, 10] = 1
bool_ind[:, 9] = 1
bool_ind[0, 11] = 1
mf.apply_match_test(ins, event_record=True, apply_samp_thresh=True)
test_match_events = [(0, slice(11, 12), 'narrow')]
test_match_events += [(ind, slice(9, 10), 'samp_thresh')
for ind in range(3)]
test_match_events += [(ind, slice(10, 11), 'samp_thresh')
for ind in range(3)]
# Test stuff
assert np.all(
ins.metric_array.mask == bool_ind), "The right flags were not applied"
for i, event in enumerate(test_match_events):
assert ins.match_events[
i][:-1] == event, "The events weren't appended correctly"
# Test that writing with samp_thresh flags is OK
ins.write(out_prefix, output_type='match_events')
test_match_events_read = ins.match_events_read(match_outfile)
os.remove(match_outfile)
assert ins.match_events == test_match_events_read
# Test that exception is raised when N_samp_thresh is too high
with pytest.raises(ValueError):
mf = MF(ins.freq_array, {'narrow': 5, 'streak': 5}, N_samp_thresh=100)
mf.apply_samp_thresh_test(ins)
def test_freq_broadcast_no_dict():
obs = '1061313128_99bl_1pol_half_time'
insfile = os.path.join(DATA_PATH, f'{obs}_SSINS.h5')
out_prefix = os.path.join(DATA_PATH, f'{obs}_test')
match_outfile = f'{out_prefix}_SSINS_match_events.yml'
ins = INS(insfile)
mf = MF(ins.freq_array, 5)
with pytest.raises(ValueError,
match="MF object does not have a broadcast_dict"):
mf.apply_match_test(ins, freq_broadcast=True)
def test_freq_broadcast_subbands():
obs = '1061313128_99bl_1pol_half_time'
insfile = os.path.join(DATA_PATH, f'{obs}_SSINS.h5')
out_prefix = os.path.join(DATA_PATH, f'{obs}_test')
match_outfile = f'{out_prefix}_SSINS_match_events.yml'
ins = INS(insfile)
# spoof the metric array
ins.metric_array[:] = 1
ins.metric_array[2, 10:20] = 10
ins.metric_array[4, 40:50] = 10
ins.metric_ms = ins.mean_subtract()
# Slice will go up to 21 since shapes are inclusive at the boundaries
# when boundary is on channel center
shape_dict = {
'shape1': [ins.freq_array[10], ins.freq_array[20]],
'shape2': [ins.freq_array[40], ins.freq_array[50]]
}
# boundaries are INCLUSIVE
broadcast_dict = {
'sb1': [ins.freq_array[0], ins.freq_array[29]],
'sb2': [ins.freq_array[30], ins.freq_array[59]]
}
mf = MF(ins.freq_array,
5,
shape_dict=shape_dict,
broadcast_streak=False,
broadcast_dict=broadcast_dict)
mf.apply_match_test(ins, event_record=True, freq_broadcast=True)
assert np.all(ins.metric_array.mask[2, :30])
assert not np.any(ins.metric_array.mask[2, 30:])
assert np.all(ins.metric_array.mask[4, 30:60])
assert not np.any(ins.metric_array.mask[4, :30])
assert not np.any(ins.metric_array.mask[4, 60:])
print(ins.match_events)
test_match_events = [(slice(2, 3), slice(10, 21), 'shape1'),
(slice(2, 3), slice(0, 30), 'freq_broadcast_sb1'),
(slice(4, 5), slice(40, 51), 'shape2'),
(slice(4, 5), slice(30, 60), 'freq_broadcast_sb2')]
for event, test_event in zip(ins.match_events, test_match_events):
assert event[:3] == test_event
def test_write_meta():
obs = "1061313128_99bl_1pol_half_time"
testfile = os.path.join(DATA_PATH, f"{obs}.uvfits")
prefix = os.path.join(DATA_PATH, f"{obs}_test")
uvd = UVData()
uvd.read(testfile, freq_chans=np.arange(32))
ss = SS()
ss.read(testfile, freq_chans=np.arange(32), diff=True)
uvf = UVFlag(uvd, mode="flag", waterfall=True)
ins = INS(ss)
ins.metric_array[:] = 1
ins.weights_array[:] = 10
ins.weights_square_array[:] = 10
# Make some outliers
# Narrowband in 1th, 26th, and 31th frequency
ins.metric_array[1, 1, :] = 100
ins.metric_array[1, 30, :] = 100
ins.metric_array[3:14, 26, :] = 100
# Arbitrary shape in 2, 3, 4
ins.metric_array[3:14, 2:25, :] = 100
ins.metric_array[[0, -1], :, :] = np.ma.masked
ins.metric_array[:, [0, -1], :] = np.ma.masked
ins.metric_ms = ins.mean_subtract()
ch_wid = ins.freq_array[1] - ins.freq_array[0]
shape_dict = {
"shape":
[ins.freq_array[2] + 0.1 * ch_wid, ins.freq_array[24] - 0.1 * ch_wid]
}
mf = MF(ins.freq_array, 5, tb_aggro=0.5, shape_dict=shape_dict)
mf.apply_match_test(ins, time_broadcast=True)
util.write_meta(prefix, ins, uvf=uvf, mf=mf, clobber=True)
for data_type in ["data", "mask", "flags"]:
path = f"{prefix}_SSINS_{data_type}.h5"
assert os.path.exists(path)
os.remove(path)
for data_type in ["match_events", "matchfilter"]:
path = f"{prefix}_SSINS_{data_type}.yml"
assert os.path.exists(path)
os.remove(path)
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'] = 10
mf = MF(ins.freq_array, sig_thresh, shape_dict=shape_dict,
tb_aggro=0.5, broadcast_streak=True)
# Do the flagging
mf.apply_match_test(ins, event_record=True, time_broadcast=True,
freq_broadcast=True)
Catalog_Plot.INS_plot(ins, f'{prefix}_flagged', data_cmap=cm.plasma,
ms_vmin=-5, ms_vmax=5, title=args.obsid,
xlabel='Frequency (Mhz)', ylabel='Time (UTC)')
# Make the flag object
uvd = UVData()
uvd.read(args.uvd, read_data=False)
uvf = UVFlag(uvd, mode='flag', waterfall=True)
uvf.flag_array = ins.mask_to_flags()
# Write SSINS outputs
util.write_meta(prefix, ins, uvf=uvf, mf=mf)
if args.write_uvfits:
if (not args.rfi_flag) and (not args.correct):
occ_dict = {sig: {shape: {} for shape in shapes} for sig in sig_list}
for obs in obslist:
flist = glob.glob('%s/metadata/%s*' % (basedir, obs))
if len(flist):
read_paths = util.read_paths_construct(basedir, 'original', obs, 'INS')
for sig_thresh in sig_list:
ins = INS(read_paths=read_paths,
obs=obs,
outpath=outdir,
flag_choice='original')
mf = MF(ins,
sig_thresh=sig_thresh,
N_thresh=15,
shape_dict=shape_dict)
mf.apply_match_test(apply_N_thresh=True)
occ_dict[sig_thresh]['total'][obs] = np.mean(ins.data.mask[:, 0, :,
0],
axis=0)
if len(ins.match_events):
event_frac = util.event_fraction(ins.match_events,
ins.data.shape[0], shapes,
384)
for shape in shapes[:-1]:
occ_dict[sig_thresh][shape][obs] = event_frac[shape]
if obs == '1061312152' and sig_thresh is 5:
pickle.dump(mf.slice_dict,
open('%s/long_run_shape_dict.pik' % outdir, 'wb'))
del ins
del mf
ax,
ins.data[:, 0, :, 0],
cbar_label='Amplitude (UNCALIB)',
freq_array=ins.freq_array[0],
aspect=aspect)
fig.savefig('%s/%s_INS.png' % (outpath, obs))
fig_new, ax_new = plt.subplots(figsize=(16, 9), ncols=2)
plot_lib.image_plot(fig,
ax_new[0],
ins.data_ms[:, 0, :, 0],
cbar_label='Deviation ($\hat{\sigma}$)',
freq_array=ins.freq_array[0],
cmap=cm.coolwarm,
aspect=aspect)
mf = MF(ins, sig_thresh=5)
mf.apply_match_test()
plot_lib.image_plot(fig_new,
ax_new[1],
ins.data_ms[:, 0, :, 0],
cbar_label='Deviation ($\hat{\sigma}$)',
freq_array=ins.freq_array[0],
cmap=cm.coolwarm,
aspect=aspect,
mask_color='black')
fig_new.savefig('%s/%s_INS_MS.png' % (outpath, obs))
action='store',
help='The base directory for saving all outputs')
args = parser.parse_args()
ss = SS()
ss.read(args.inpath, ant_str='cross')
ins = INS(ss)
ins.metric_array[:, :82] = np.ma.masked
ins.metric_array[:, :-21] = np.ma.masked
ins.order = 1
ins.metric_ms = ins.mean_subtract()
shape_dict = {
'TV4': [1.74e8, 1.82e8],
'TV5': [1.82e8, 1.9e8],
'TV6': [1.9e8, 1.98e8],
'dig1': [1.125e8, 1.15625e8],
'dig2': [1.375e8, 1.40625e8],
'dig3': [1.625e8, 1.65625e8],
'dig4': [1.875e8, 1.90625e8]
}
mf = MF(ins.freq_array, 5, shape_dict=shape_dict, N_samp_thresh=15)
mf.apply_match_test(ins, apply_samp_thresh=True)
prefix = '%s/%s' % (args.outpath, args.obs)
ins.write(prefix)
ins.write(prefix, output_type='mask')
ins.write(prefix, output_type='match_events')
outpath = '/Users/mike_e_dubs/General/%s' % obs
read_paths = util.read_paths_construct(indir, None, obs, 'INS')
ins = INS(read_paths=read_paths, obs=obs, outpath=outpath)
mf = MF(ins, sig_thresh=5)
fig, ax = plt.subplots(figsize=(14, 8), nrows=2)
plot_lib.image_plot(fig,
ax[0],
ins.data_ms[:, 0, :, 0],
cmap=cm.coolwarm,
freq_array=ins.freq_array[0],
title='MWA Soft Breather',
cbar_label='Deviation ($\hat{\sigma}$)')
mf.apply_match_test()
plot_lib.image_plot(fig,
ax[1],
ins.data_ms[:, 0, :, 0],
cmap=cm.coolwarm,
vmin=-5,
vmax=5,
freq_array=ins.freq_array[0],
title='MWA Soft Breather',
mask_color='black',
cbar_label='Deviation ($\hat{\sigma}$)')
fig.savefig('%s/figs/%s_Order_0.png' % (outpath, obs))
fig2, ax2 = plt.subplots(figsize=(14, 8))
ins.data.mask = False
ins.data_ms = ins.mean_subtract(order=1)
mf.apply_match_test(order=1)
def execbody(ins_filepath):
slash_ind = ins_filepath.rfind('/')
obsid = ins_filepath[slash_ind + 1:slash_ind + 11]
ins = INS(ins_filepath)
ins.select(times=ins.time_array[3:-3])
ins.metric_ms = ins.mean_subtract()
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)
ins.metric_array[ins.metric_array == 0] = np.ma.masked
ins.metric_ms = ins.mean_subtract()
ins.sig_array = np.ma.copy(ins.metric_ms)
#write plots if command flagged to do so
if args.plots:
prefix = '%s/%s_trimmed_zeromask' % (args.outdir, obsid)
freqs = np.arange(1.7e8, 2e8, 5e6)
xticks, xticklabels = util.make_ticks_labels(freqs,
ins.freq_array,
sig_fig=0)
yticks = [0, 20, 40]
yticklabels = []
for tick in yticks:
yticklabels.append(
Time(ins.time_array[tick], format='jd').iso[:-4])
Catalog_Plot.INS_plot(ins,
prefix,
xticks=xticks,
yticks=yticks,
xticklabels=xticklabels,
yticklabels=yticklabels,
data_cmap=cm.plasma,
ms_vmin=-5,
ms_vmax=5,
title=obsid,
xlabel='Frequency (Mhz)',
ylabel='Time (UTC)')
if args.verbose:
print("wrote trimmed zeromask plot for " + obsid)
mf.apply_match_test(ins, apply_samp_thresh=False)
mf.apply_samp_thresh_test(ins, event_record=True)
flagged_prefix = '%s/%s_trimmed_zeromask_MF_s8' % (args.outdir, obsid)
#write data/mask/match/ if command flagged to do so
if args.write:
ins.write(flagged_prefix, output_type='data', clobber=True)
ins.write(flagged_prefix, output_type='mask', clobber=True)
ins.write(flagged_prefix, output_type='match_events')
if args.verbose:
print("wrote data/mask/match files for " + obsid)
#write plots if command flagged to do so
if args.plots:
Catalog_Plot.INS_plot(ins,
flagged_prefix,
xticks=xticks,
yticks=yticks,
xticklabels=xticklabels,
yticklabels=yticklabels,
data_cmap=cm.plasma,
ms_vmin=-5,
ms_vmax=5,
title=obsid,
xlabel='Frequency (Mhz)',
ylabel='Time (UTC)')
if args.verbose:
print("wrote trimmed zeromask (w/ match filter) for " + obsid)
#a hardcoded csv generator for occ_csv
if args.gencsv is not None:
csv = "" + obsid + "," + flagged_prefix + "_SSINS_data.h5," + flagged_prefix + "_SSINS_mask.h5," + flagged_prefix + "_SSINS_match_events.yml\n"
with open(args.gencsv, "a") as csvfile:
csvfile.write(csv)
print("wrote entry for " + obsid)
num_init_flag = np.sum(ins.metric_array.mask)
int_time = uvd.integration_time[0]
print(f"Using int_time {int_time}")
num_int_flag = (args.start_flag + args.end_flag) / int_time
with open(f"{DATA_PATH}/MWA_EoR_Highband_shape_dict.yml",
"r") as shape_file:
shape_dict = yaml.safe_load(shape_file)
sig_thresh = {shape: 5 for shape in shape_dict}
sig_thresh["narrow"] = 5
sig_thresh["streak"] = 10
print(f"Flagging these shapes: {shape_dict}")
mf = MF(ins.freq_array, sig_thresh, shape_dict=shape_dict, tb_aggro=0.4)
mf.apply_match_test(ins, time_broadcast=True)
occ_dict = util.calc_occ(ins,
mf,
num_init_flag,
num_int_flag=num_int_flag,
lump_narrowband=True)
with open(f"{prefix}_occ.yml", "w") as occ_file:
yaml.safe_dump(occ_dict, occ_file)
ins.write(prefix, output_type='mask', clobber=True)
print(ins.Nfreqs)
print(uvf.Nfreqs)
ins.write(prefix, output_type='flags', uvf=uvf, clobber=True)
ins.write(prefix, output_type='match_events', clobber=True)
if args.write_mwaf:
vmax=5,
ylabel='Time (10 s)')
fig_mf, ax_mf = plt.subplots(figsize=(16, 9), ncols=2)
ins.data.mask[:, 0, :82, 0] = True
ins.data.mask[:, 0, -21:, 0] = True
ins.data_ms = ins.mean_subtract(order=1)
mf = MF(ins,
sig_thresh=5,
N_thresh=0,
shape_dict={
'TV4': [1.74e8, 1.82e8],
'TV5': [1.82e8, 1.9e8],
'TV6': [1.9e8, 1.98e8]
})
mf.apply_match_test(apply_N_thresh=False, order=1)
plot_lib.image_plot(fig_mf,
ax_mf[0],
ins.data[:, 0, :, 0],
freq_array=ins.freq_array[0],
cbar_label='Amplitude (UNCALIB)',
aspect=aspect,
vmin=0,
vmax=0.03,
cmap=cm.viridis,
mask_color='white',
ylabel='Time (10 s)')
plot_lib.image_plot(fig_mf,
ax[1],
ins.data_ms[:, 0, :, 0],
'vmin': -5,
'vmax': 5,
'mask_color': 'black'
}]
for i in range(2):
for k, obj in zip([0, 2], [raw_ins, OR_ins]):
pl.image_plot(fig,
ax[k, i],
getattr(obj, attr[i])[:, 0, :, 0],
cbar_label=cbar_label[i],
freq_array=obj.freq_array[0],
**kwargs[i])
raw_ins.data[:, 0, :82] = np.ma.masked
raw_ins.data[:, 0, -21:] = np.ma.masked
mf = MF(raw_ins, sig_thresh=5, shape_dict=shape_dict, N_thresh=20)
raw_ins.data_ms = raw_ins.mean_subtract(order=1)
mf.apply_match_test(apply_N_thresh=True, order=1)
raw_ins.data_ms = raw_ins.mean_subtract()
for i in range(2):
pl.image_plot(fig,
ax[1, i],
getattr(raw_ins, attr[i])[:, 0, :, 0],
cbar_label=cbar_label[i],
freq_array=raw_ins.freq_array[0],
**kwargs[i])
fig.savefig('%s/%s_flag_comp.png' % (figpath, obs))
raw_ins.save()
plt.close(fig)
if args.labels is not None:
shape_dict = {label: [min, max] for (label, min, max) in zip(args.labels, args.mins, args.maxs)}
else:
shape_dict = {}
obslist = util.make_obslist(args.obsfile)
edges = [0 + 16 * i for i in range(24)] + [15 + 16 * i for i in range(24)]
freqs = np.load('/Users/mike_e_dubs/python_stuff/MJW-MWA/Useful_Information/MWA_Highband_Freq_Array.npy')
for i, obs in enumerate(obslist):
read_paths = util.read_paths_INS(args.basedir, args.flag_choice, obs)
ins = INS(read_paths=read_paths, flag_choice=args.flag_choice, obs=obs,
outpath=args.outdir, order=args.order)
cp.INS_plot(ins)
mf = MF(ins, sig_thresh=5, shape_dict=shape_dict)
mf.apply_match_test(order=args.order)
cp.MF_plot(mf)
if not i:
occ_num = np.ma.masked_array(mf.INS.data.mask)
occ_den = np.ma.masked_array(np.ones(occ_num.shape))
occ_num[:, 0, edges] = np.ma.masked
occ_den[:, 0, edges] = np.ma.masked
else:
occ_num = occ_num + mf.INS.data.mask
occ_den = occ_den + np.ones(occ_num.shape)
occ_freq = occ_num.sum(axis=(0, 1, 3)) / occ_den.sum(axis=(0, 1, 3)) * 100
occ_total = occ_num.sum() / occ_den.sum() * 100
fig, ax = plt.subplots(figsize=(14, 8))
plot_lib.error_plot(fig, ax, freqs * 10**(-6), occ_freq,
title='Golden Set RFI Frequency Post COTTER',
Catalog_Plot.INS_plot(ins, 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)')
# 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):
basedir = '/Users/mike_e_dubs/MWA/INS/Long_Run/All'
obs = '1066742016'
outpath = '/Users/mike_e_dubs/General/Movie'
if not os.path.exists(outpath):
os.makedirs(outpath)
flag_choice = 'None'
read_paths = util.read_paths_construct(basedir, flag_choice, obs, 'INS')
shape_dict = {
'TV6': [1.74e8, 1.81e8],
'TV7': [1.81e8, 1.88e8],
'TV8': [1.88e8, 1.95e8]
}
order = 0
ins = INS(obs=obs, read_paths=read_paths, outpath=outpath, order=order)
mf = MF(ins, shape_dict=shape_dict, sig_thresh=5)
mf.apply_match_test(order=order)
ins.data.mask = False
ins.data_ms = ins.mean_subtract(order=order)
ins.outpath = '%s_0' % outpath
labels = ['', '_ms']
titles = ['', ' (Mean-Subtracted)']
cbar_labels = [ins.vis_units, 'Deviation ($\hat{\sigma}$)']
cmaps = [cm.viridis, cm.coolwarm]
for i in range(2):
fig, ax = plt.subplots(figsize=(16, 9))
if i:
vmin = -4
vmax = 4
else:
vmin = None
vmax = None