def test_transform(self):
amp = [0, 1, 2, 3, 4, 5, 4, 3, 2, 1, 0]
dt = 1
expected_frq = np.array([0.00000000000000000, 0.09090909090909091,
0.18181818181818182, 0.2727272727272727,
0.36363636363636365, 0.4545454545454546])
expected_amp = np.array([25.0+0.0*1j,
-11.843537519677056+-3.477576385886737*1j,
0.22844587066117938+0.14681324646918337*1j,
-0.9486905697966428+-1.0948472814948405*1j,
0.1467467171062613+0.3213304885841657*1j,
-0.08296449829374097+-0.5770307602665046*1j])
expected_amp *= 2/len(amp)
# TimeSeries
tseries = sigpropy.TimeSeries(amp, dt)
sensor = hvsrpy.Sensor3c(tseries, tseries, tseries)
fft = sensor.transform()
for val in fft.values():
for expected, returned in [(expected_frq, val.frq), (expected_amp, val.amp)]:
self.assertArrayAlmostEqual(expected, returned)
# WindowedTimeSeries
amps = np.array([amp, amp])
tseries = sigpropy.WindowedTimeSeries(amps, dt)
sensor = hvsrpy.Sensor3c(tseries, tseries, tseries)
fft = sensor.transform()
for val in fft.values():
for expected, returned in [(expected_frq, val.frq), (expected_amp, val.amp[0])]:
self.assertArrayAlmostEqual(expected, returned)
# Bad TimeSeries
sensor.ew = "bad TimeSeries"
self.assertRaises(NotImplementedError, sensor.transform)
def test_from_timeseries(self):
# TODO (jpv): Replace these tests with something more meaningful.
# Early example.
amplitude = [0, 1, 2, 3, 4, 5, 4, 3, 2, 1, 0]
dt = 1
tseries = sigpropy.TimeSeries(amplitude, dt)
fft = sigpropy.FourierTransform.from_timeseries(tseries)
true_frq = np.array([0.00000000000000000, 0.09090909090909091,
0.18181818181818182, 0.2727272727272727,
0.36363636363636365, 0.4545454545454546])
true_amp = np.array([25.0+0.0*1j,
-11.843537519677056+-3.477576385886737*1j,
0.22844587066117938+0.14681324646918337*1j,
-0.9486905697966428+-1.0948472814948405*1j,
0.1467467171062613+0.3213304885841657*1j,
-0.08296449829374097+-0.5770307602665046*1j])
true_amp *= 2/len(amplitude)
for expected, returned in [(true_frq, fft.frequency), (true_amp, fft.amplitude)]:
self.assertArrayAlmostEqual(expected, returned)
# Load and transform ambient noise record.
fname = "data/a2/UT.STN11.A2_C50.miniseed"
trace = obspy.read(self.full_path+fname)[0]
tseries = sigpropy.TimeSeries.from_trace(trace)
tseries.split(windowlength=120)
fsuite = sigpropy.FourierTransform.from_timeseries(tseries)
returned = fsuite.amplitude.shape
expected = (tseries.nseries, fsuite.frequency.size)
self.assertTupleEqual(expected, returned)
# Bad timeseries.
self.assertRaises(TypeError,
sigpropy.FourierTransform.from_timeseries,
"bad timeseries")
def test_cosine_taper(self):
## nseries = 1
# 0% Window - (i.e., no taper)
amp = np.ones(10)
dt = 1
tseries = sigpropy.TimeSeries(amp, dt)
tseries.cosine_taper(0)
returned = tseries.amplitude
expected = amp
self.assertArrayEqual(expected, returned)
# 50% window
amp = np.ones(10)
dt = 1
tseries = sigpropy.TimeSeries(amp, dt)
tseries.cosine_taper(0.5)
returned = tseries.amplitude
expected = np.array([
0.000000000000000e+00, 4.131759111665348e-01,
9.698463103929542e-01, 1.000000000000000e+00,
1.000000000000000e+00, 1.000000000000000e+00,
1.000000000000000e+00, 9.698463103929542e-01,
4.131759111665348e-01, 0.000000000000000e+00
])
self.assertArrayAlmostEqual(expected, returned, places=6)
# 100% Window
amp = np.ones(10)
dt = 1
tseries = sigpropy.TimeSeries(amp, dt)
tseries.cosine_taper(1)
returned = tseries.amplitude
expected = np.array([
0.000000000000000e+00, 1.169777784405110e-01,
4.131759111665348e-01, 7.499999999999999e-01,
9.698463103929542e-01, 9.698463103929542e-01,
7.500000000000002e-01, 4.131759111665350e-01,
1.169777784405111e-01, 0.000000000000000e+00
])
self.assertArrayAlmostEqual(expected, returned, places=6)
## nseries = 3
# 0% Window - (i.e., no taper)
amplitude = np.ones(30)
dt = 1
tseries = sigpropy.TimeSeries(amplitude, dt)
tseries.split(windowlength=9)
tseries.cosine_taper(0)
expected = np.ones(10)
for returned in tseries.amplitude:
self.assertArrayAlmostEqual(expected, returned, places=6)
# 50% window
amplitude = np.ones(30)
dt = 1
tseries = sigpropy.TimeSeries(amplitude, dt)
tseries.split(windowlength=9)
tseries.cosine_taper(0.5)
expected = np.array([
0.000000000000000e+00, 4.131759111665348e-01,
9.698463103929542e-01, 1.000000000000000e+00,
1.000000000000000e+00, 1.000000000000000e+00,
1.000000000000000e+00, 9.698463103929542e-01,
4.131759111665348e-01, 0.000000000000000e+00
])
for returned in tseries.amplitude:
self.assertArrayAlmostEqual(expected, returned, places=6)
# 100% Window
amplitude = np.ones(30)
dt = 1
tseries = sigpropy.TimeSeries(amplitude, dt)
tseries.split(windowlength=9)
tseries.cosine_taper(1)
expected = np.array([
0.000000000000000e+00, 1.169777784405110e-01,
4.131759111665348e-01, 7.499999999999999e-01,
9.698463103929542e-01, 9.698463103929542e-01,
7.500000000000002e-01, 4.131759111665350e-01,
1.169777784405111e-01, 0.000000000000000e+00
])
for returned in tseries.amplitude:
self.assertArrayAlmostEqual(expected, returned, places=6)
def test_trim(self):
# nseries = 1
tseries = sigpropy.TimeSeries(amplitude=[0, 1, 2, 3, 4], dt=0.5)
tseries.trim(0, 1)
self.assertListEqual([0, 1, 2], tseries.amplitude.tolist())
self.assertEqual(3, tseries.nsamples)
self.assertEqual(0, min(tseries.time))
self.assertEqual(1, max(tseries.time))
# nseries = 3, no trim
amplitude = np.array([[0., 1, 2, 3], [3., 4, 5, 6], [6., 7, 8, 9]])
tseries = sigpropy.TimeSeries(amplitude=amplitude, dt=1)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
tseries.trim(0, 9)
expected = amplitude
returned = tseries.amplitude
self.assertArrayEqual(expected, returned)
# nseries = 3, remove last window
for end_time in [6, 7, 8]:
amplitude = np.array([[0., 1, 2, 3], [3., 4, 5, 6], [6., 7, 8, 9]])
tseries = sigpropy.TimeSeries(amplitude=amplitude, dt=1)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
tseries.trim(0, end_time)
returned = tseries.amplitude
expected = amplitude[:-1]
self.assertArrayEqual(expected, returned)
# nseries = 3, leave only the middle window.
for end_time in [6, 7, 8]:
amplitude = np.array([[0., 1, 2, 3], [3., 4, 5, 6], [6., 7, 8, 9]])
tseries = sigpropy.TimeSeries(amplitude=amplitude, dt=1)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
tseries.trim(3, end_time)
returned = tseries.amplitude
expected = amplitude[1:2]
self.assertArrayEqual(expected, returned)
# nseries = 3, leave only the last window.
amplitude = np.array([[0., 1, 2, 3], [3., 4, 5, 6], [6., 7, 8, 9]])
tseries = sigpropy.TimeSeries(amplitude=amplitude, dt=1)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
tseries.trim(6, 9)
returned = tseries.amplitude
expected = amplitude[2:3]
self.assertArrayEqual(expected, returned)
# nseries = 3, leave only the first window.
for end_time in [3, 4, 5]:
amplitude = np.array([[0., 1, 2, 3], [3., 4, 5, 6], [6., 7, 8, 9]])
tseries = sigpropy.TimeSeries(amplitude=amplitude, dt=1)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
tseries.trim(0, end_time)
returned = tseries.amplitude
expected = amplitude[0:1]
self.assertArrayEqual(expected, returned)
# Illogical start_time (start_time < 0).
amplitude = list(range(5))
dt = 1
tseries = sigpropy.TimeSeries(amplitude, dt)
self.assertRaises(IndexError, tseries.trim, start_time=-1, end_time=2)
# Illogical start_time (start_time > end_time).
amplitude = list(range(5))
dt = 1
tseries = sigpropy.TimeSeries(amplitude, dt)
self.assertRaises(IndexError, tseries.trim, start_time=2, end_time=2)
# Illogical end_time (end_time > max_time).
amplitude = list(range(5))
dt = 1
tseries = sigpropy.TimeSeries(amplitude, dt)
self.assertRaises(IndexError, tseries.trim, start_time=1, end_time=5)
def new_sensor():
ns = sigpropy.TimeSeries(np.ones(10), dt=1)
ew = sigpropy.TimeSeries(np.ones(10), dt=1)
vt = sigpropy.TimeSeries(np.ones(10), dt=1)
return hvsrpy.Sensor3c(ns, ew, vt)
def new_sensor():
component = sigpropy.TimeSeries(np.ones(10), dt=1)
return hvsrpy.Sensor3c(component, component, component)