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_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 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_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_correct_pair_ez24(self):
correlator = XArrayXCorrelate(max_tau_shift=None)
synth_trace_1, synth_trace_2 = create_example_xarrays_missing_channel()
src_chan = 'z'
rec_chan = 'e'
correlation_source = correlator(synth_trace_1.copy(), synth_trace_2.copy())
test_correlation = correlator(synth_trace_1.sel(dict(channel=src_chan)).expand_dims('channel'),
synth_trace_2.sel(dict(channel=rec_chan)).expand_dims('channel'))
result_1 = correlation_source.loc[dict(src='v.h',rec='v.k',src_chan=src_chan, rec_chan=rec_chan)] - test_correlation
assert 0 == np.sum(result_1.data)
def test_scipy_equivalent(self):
max_tau_shift = 19.99
correlator = XArrayXCorrelate(max_tau_shift=max_tau_shift)
synth_trace_1, synth_trace_2 = create_example_xarrays()
src_chan = 'z'
rec_chan = 'z'
correlation_source = correlator(synth_trace_1.copy(), synth_trace_2.copy())
test_correlation = sig.correlate(np.asarray(synth_trace_1.sel(dict(channel=src_chan)).expand_dims('channel').data).squeeze(),
np.asarray(synth_trace_2.sel(dict(channel=rec_chan)).expand_dims('channel').data).squeeze(),mode='full')
corr_source = correlation_source.loc[dict(src='v.h',rec='v.k',src_chan=src_chan, rec_chan=rec_chan)].data
np.testing.assert_allclose(test_correlation,corr_source,atol=1e-14)
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'
def __init__(self, window_length=None, **kwargs):
self._single_station_include = []
self._station_pair_include = []
self._station_pair_exclude = []
self._single_station_exclude = []
self._window_length = window_length
self._tasks = {
'data': DataLoader(**kwargs),
'xconvert': XArrayConverter(),
'process': {},
'crosscorrelate': XArrayXCorrelate(),
'post-correlate': NullTask('post-correlate'),
'combine': XArrayCombine(),
'post-combine': NullTask('post-combine'),
'pre-stack': NullDualTask('pre-stack'),
'stack': XArrayStack(),
'post-stack': NullTask('post-stack')
}
self._process_order = []
def test_nonetype_in_out(self):
correlator = XArrayXCorrelate()
result = correlator(None, None, starttime=0, station=0)
assert result == None
# relative import
#from synthetic_trace_factory import create_random_trace, create_sinsoidal_trace
from obspy.core import read
from scipy.signal import correlate
from anxcor.xarray_routines import XArrayRemoveMeanTrend
import pytest
from obspy.clients.fdsn import Client
from obspy.core import UTCDateTime
import numpy as np
import xarray as xr
whiten = XArrayWhiten(smoothing_window_ratio=0.025,
freqmax=25.0,
freqmin=0.001,
order=2)
convert = XArrayConverter()
xarraycorrelate = XArrayXCorrelate(max_tau_shift=40)
source_file = 'tests/test_data/test_teleseism/test_teleseism.BHE.SAC'
def source_earthquake():
earthquake_trace = read(source_file, format='sac')[0]
earthquake_trace.stats.data_type = 'eq'
earthquake_trace.data /= max(earthquake_trace.data)
earthquake_trace.stats.starttime = 0
earthquake_trace.detrend(type='linear')
earthquake_trace.detrend(type='constant')
earthquake_trace.taper(max_percentage=0.05)
earthquake_trace.stats.channel = 'Z'
earthquake_trace.stats.station = 'test'
earthquake_trace.stats.network = ''
