def test_convert_is_accurate(self):
max_tau_shift = None
correlator = XArrayXCorrelate(max_tau_shift=max_tau_shift)
e2 = create_sinsoidal_trace_w_decay(decay=0.8,
station='k',
network='v',
channel='e',
duration=20)
n2 = create_random_trace(station='k',
network='v',
channel='n',
duration=20)
z2 = create_sinsoidal_trace_w_decay(decay=0.3,
station='k',
network='v',
channel='z',
duration=20)
b2 = create_random_trace(station='k',
network='v',
channel='b',
duration=20)
new_traces = e2.traces + n2.traces + z2.traces + b2.traces
syth_trace2 = converter(new_traces)
result_1 = syth_trace2.loc['e', :, :].data.ravel() - e2[0].data
assert 0 == np.sum(result_1)
result_1 = syth_trace2.loc['n', :, :].data.ravel() - n2[0].data
assert 0 == np.sum(result_1)
result_1 = syth_trace2.loc['z', :, :].data.ravel() - z2[0].data
assert 0 == np.sum(result_1)
result_1 = syth_trace2.loc['b', :, :].data.ravel() - b2[0].data
assert 0 == np.sum(result_1)
def test_shift_trace_time_slice(self):
max_tau_shift = 10.0
correlator = XArrayXCorrelate(max_tau_shift=max_tau_shift)
time_shift = 4
synth_trace1 = converter(
create_sinsoidal_trace_w_decay(decay=0.6,
station='h',
network='v',
channel='z',
duration=20))
synth_trace2 = converter(
create_sinsoidal_trace_w_decay(decay=0.4,
station='h',
network='w',
channel='z',
duration=20))
synth_trace2 = xr.apply_ufunc(shift_trace,
synth_trace2,
input_core_dims=[['time']],
output_core_dims=[['time']],
kwargs={
'time': time_shift,
'delta': synth_trace2.attrs['delta']
},
keep_attrs=True)
correlation = correlator(synth_trace1, synth_trace2)
correlation /= correlation.max()
time_array = correlation.coords['time'].values
max_index = np.argmax(correlation.data)
tau_shift = (time_array[max_index] - np.datetime64(
UTCDateTime(0.0).datetime)) / np.timedelta64(1, 's')
assert tau_shift == -time_shift, 'failed to correctly identify tau shift'
def create_example_xarrays_missing_channel(duration=20):
e1 = create_sinsoidal_trace_w_decay(decay=0.9,
station='h',
network='v',
channel='e',
duration=duration)
z1 = create_sinsoidal_trace_w_decay(decay=0.4,
station='h',
network='v',
channel='z',
duration=duration)
e2 = create_sinsoidal_trace_w_decay(decay=0.8,
station='k',
network='v',
channel='e',
duration=duration)
n2 = create_sinsoidal_trace_w_decay(decay=0.7,
station='k',
network='v',
channel='n',
duration=duration)
z2 = create_sinsoidal_trace_w_decay(decay=0.6,
station='k',
network='v',
channel='z',
duration=duration)
syth_trace1 = converter(e1.copy() + z1.copy())
syth_trace2 = converter(e2.copy() + z2.copy() + n2.copy())
return syth_trace1, syth_trace2
def test_into_freq_and_back_phase_shift(self):
stream = synthfactory.create_sinsoidal_trace_w_decay(
sampling_rate=40.0, duration=1000.0, period=0.5)
xarray = convert(stream)
xarray_freq = filt_ops.xarray_time_2_freq(xarray)
xarray_time = filt_ops.xarray_freq_2_time(xarray_freq, xarray)
a = xarray.data[0, 0, :]
b = xarray_time.data[0, 0, :]
xcorr = correlate(a, b)
# delta time array to match xcorr
dt = np.arange(1 - a.shape[-1], a.shape[-1])
recovered_time_shift = dt[xcorr.argmax()]
assert recovered_time_shift == 0
def test_taper_phase_shift(self):
stream = synthfactory.create_sinsoidal_trace_w_decay(
sampling_rate=40.0, duration=1000.0, period=0.5)
xarray = convert(stream)
filtered_array = xr.apply_ufunc(filt_ops.taper_func,
xarray,
input_core_dims=[['time']],
output_core_dims=[['time']],
kwargs={'taper': 0.1},
keep_attrs=True)
a = xarray.data[0, 0, :]
b = filtered_array.data[0, 0, :]
xcorr = correlate(a, b)
# delta time array to match xcorr
dt = np.arange(1 - a.shape[-1], a.shape[-1])
recovered_time_shift = dt[xcorr.argmax()]
assert recovered_time_shift == 0