def test_variable_type(self):
# first, make a noise trace and shift it by tau * sampling rate\
file = source_file
duration = 400
shift = 30
noise_loc_1 = create_random_trace(sampling_rate=40, duration=duration)
noise_loc_1[0].data += create_random_trace(sampling_rate=40,
duration=duration)[0].data
noise_loc_1 = convert(noise_loc_1)
noise_loc_2 = xr.apply_ufunc(shift_trace,
noise_loc_1,
input_core_dims=[['time']],
output_core_dims=[['time']],
kwargs={
'time': shift,
'delta': noise_loc_1.attrs['delta']
},
keep_attrs=True)
# next, add an eq teleseism from file to both noise streams
noise_loc_1_eq = noise_loc_1.copy()
down = XArrayResample(10)
t_norm = XArrayTemporalNorm(time_window=2.0)
noise_loc_2_eq = down(noise_loc_1_eq)
noise_loc_2_eq_tnorm = t_norm(noise_loc_2_eq)
assert noise_loc_2_eq_tnorm.dtype == np.float64
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_correlation_length(self):
correlator = XArrayXCorrelate(max_tau_shift=5.0)
syth_trace1 = converter(create_random_trace(station='1', network='v'))
syth_trace2 = converter(create_random_trace(station='2', network='v'))
try:
correlation = correlator(syth_trace1, syth_trace2)
assert False, 'failed to catch exception'
except Exception:
assert True, 'failed to catch exception'
def test_filter_frequency_in_band(self):
process = XArrayBandpass(freqmin=0.5, freqmax=20.0)
trace = converter(create_random_trace(sampling_rate=100))
trace = process(trace, starttime=0, station=0)
source = self._get_frequency_of_trace(trace, sample_point=5)
target = 0.1
assert source > target, "bandpass filter removing desired frequency"
def test_filter_frequency_out_of_band(self):
process = XArrayBandpass(freqmin=0.5, freqmax=20.0)
trace = converter(create_random_trace(sampling_rate=100))
trace = process(trace, starttime=0, station=0)
source = self._get_frequency_of_trace(trace, sample_point=40.0)
target = 0
assert round(abs(source - target), 1) == 0, "frequency not removed"
def test_autocorrelation(self):
correlator = XArrayXCorrelate(max_tau_shift=5.0)
synth_trace1 = converter(create_random_trace(station='1',network='v',duration=20))
correlation = correlator(synth_trace1,synth_trace1)
zero_target_index = correlation.data.shape[4]//2
zero_source_index = np.argmax(correlation.data[0,0,0,:][0])
assert zero_source_index == zero_target_index,'autocorrelation failed'
def test_increase_earthquake_shift_time_xcorr_func(self):
# first, make a noise trace and shift it by tau * sampling rate\
file = source_file
duration = 400
shift = 3
noise_loc_1 = create_random_trace(sampling_rate=40, duration=duration)
noise_loc_1[0].data+= create_random_trace(sampling_rate=40, duration=duration)[0].data
noise_loc_1 = convert(noise_loc_1,starttime=0,station=0)
noise_loc_2 = xr.apply_ufunc(shift_trace, noise_loc_1, input_core_dims=[['time']],
output_core_dims=[['time']],
kwargs={'time': shift, 'delta': noise_loc_1.attrs['delta']}, keep_attrs=True)
# next, add an eq teleseism from file to both noise streams
noise_loc_1_eq = noise_loc_1.copy()
noise_loc_2_eq = noise_loc_2.copy()
attrs = noise_loc_1.attrs
source_eq = source_earthquake()
noise_loc_1_eq[:, :, :source_eq.data.shape[2]] += source_eq.data[:, :, :]
noise_loc_2_eq[:, :, :source_eq.data.shape[2]] += source_eq.data[:, :, :]
noise_loc_1.attrs = attrs
noise_loc_2.attrs = attrs
noise_loc_2_eq.attrs = attrs
noise_loc_1_eq.attrs = attrs
# downsample both to 10hz sampling rate
down = XArrayResample(10)
t_norm =XArrayTemporalNorm(time_window=2.0)
noise_loc_1_eq = down(noise_loc_1_eq)
noise_loc_2_eq = down(noise_loc_2_eq)
noise_loc_2_eq_tnorm = t_norm(noise_loc_2_eq.copy())
noise_loc_1_eq_tnorm = t_norm(noise_loc_1_eq.copy())
x_corr_eq = self.max_corr_norm(noise_loc_1_eq, noise_loc_2_eq)
x_corr_eq_tnorm= self.max_corr_norm(noise_loc_1_eq_tnorm, noise_loc_2_eq_tnorm)
zero_index = len(x_corr_eq)//2
assert x_corr_eq[zero_index+int(40*shift)] < x_corr_eq_tnorm[zero_index+int(40*shift)]
def test_array_is_real(self):
correlator = XArrayXCorrelate(max_tau_shift=5.0)
syth_trace1 = converter(create_random_trace(station='1',network='v',duration=20))
correlation = correlator(syth_trace1,syth_trace1)
assert correlation.data.dtype == np.float64,'improper data type'
def test_name(self):
correlator = XArrayXCorrelate(max_tau_shift=5.0)
syth_trace1 = converter(create_random_trace(station='1',network='v',duration=20))
correlation = correlator(syth_trace1,syth_trace1)
assert isinstance(correlation.name,str),'improper name type'
def test_autocorrelation_delta_attr(self):
correlator = XArrayXCorrelate(max_tau_shift=5.0)
syth_trace1 = converter(create_random_trace(station='1',network='v',duration=20))
correlation = correlator(syth_trace1,syth_trace1)
assert 'delta' in correlation.attrs['df'].columns,'did not propagate the delta value'
def test_one_stack(self):
correlator = XArrayXCorrelate(max_tau_shift=5.0)
syth_trace1 = converter(create_random_trace(station='1', network='v', duration=20))
attr = correlator(syth_trace1, syth_trace1).attrs
assert attr['df']['stacks'].values[0] == 1,'stacks assigned improper value'
def test_has_stacks(self):
correlator = XArrayXCorrelate(max_tau_shift=5.0)
syth_trace1 = converter(create_random_trace(station='1', network='v', duration=20))
keys = correlator(syth_trace1, syth_trace1).attrs['df'].columns
assert 'stacks' in keys, 'stacks not preserved through correlation'
def test_stacking_preserves_metadata(self):
correlator = XArrayXCorrelate(max_tau_shift=5.0)
combiner = XArrayCombine()
syth_trace1 = converter(
create_random_trace(station='1', network='v', duration=20))
result_1 = combiner(correlator(syth_trace1, syth_trace1), None)
result_2 = combiner(correlator(syth_trace1, syth_trace1), None)
stack = stacker(result_1, result_2)
df = stack.attrs['df']
assert df['stacks'].values[0] == 2, 'unexpected stack length'
assert 'operations' in df.columns, 'operations did not persist through stacking'
assert 'delta' in df.columns, 'delta did not persist through stacking'
