def test_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()
t_max, f_max, R_max, shape_max = mf.match_test(ins)
print(shape_max)
assert t_max == 5, "Wrong time"
assert f_max == slice(0, 20), "Wrong freq"
assert shape_max == 'streak', "Wrong shape"
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_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_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)
