def test_init(self):
# Successful init
ns = sigpropy.TimeSeries([1, 1, 1], dt=1)
ew = sigpropy.TimeSeries([1, 1, 1], dt=1)
vt = sigpropy.TimeSeries([1, 1, 1], dt=1)
sensor = hvsrpy.Sensor3c(ns, ew, vt)
# Check timeseries
for attr, expected in zip(["ns", "ew", "vt"], [ns, ew, vt]):
returned = getattr(sensor, attr)
self.assertEqual(expected, returned)
# Bad ns, should be TimeSeries
_ns = [1, 1, 1]
self.assertRaises(TypeError, hvsrpy.Sensor3c, _ns, ew, vt)
# Bad ew, should be TimeSeries
_ew = [1, 1, 1]
self.assertRaises(TypeError, hvsrpy.Sensor3c, ns, _ew, vt)
# Bad dt, should be 1
ns = sigpropy.TimeSeries([1, 1, 1], dt=2)
self.assertRaises(ValueError, hvsrpy.Sensor3c, ns, ew, vt)
# Bad length, will trim
amp = np.array([1, 1])
ns = sigpropy.TimeSeries(amp, dt=1)
hvsrpy.Sensor3c(ns, ew, vt)
self.assertArrayEqual(amp, ew.amp)
self.assertArrayEqual(amp, vt.amp)
def test_transform(self):
# Setup.
amplitude = [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(amplitude)
# nseries = 1
tseries = sigpropy.TimeSeries(amplitude, dt)
sensor = hvsrpy.Sensor3c(tseries, tseries, tseries)
fft = sensor.transform()
for val in fft.values():
for expected, returned in [(expected_frq, val.frequency),
(expected_amp, val.amplitude)]:
self.assertArrayAlmostEqual(expected, returned)
# nseries = 2
amplitude = np.array([amplitude, amplitude])
tseries = sigpropy.TimeSeries(amplitude, dt)
sensor = hvsrpy.Sensor3c(tseries, tseries, tseries)
fft = sensor.transform()
for val in fft.values():
for expected, returned in [(expected_frq, val.frequency),
(expected_amp, val.amplitude[0])]:
self.assertArrayAlmostEqual(expected, returned)
# Bad TimeSeries
sensor.ew = "bad TimeSeries"
self.assertRaises(TypeError, sensor.transform)
def test_detrend(self):
# Simple case
signal = np.array([0, -0.2, -0.5, -0.2, 0, 0.2, 0.5, 0.2]*5)
noise = np.linspace(0, 5, 40)
component = sigpropy.TimeSeries(signal + noise, dt=1)
sensor = hvsrpy.Sensor3c(component, component, component)
sensor.detrend()
expected = signal
for returned in sensor:
self.assertArrayAlmostEqual(expected, returned.amp, delta=0.1)
def test_split(self):
# Simple Case
component = sigpropy.TimeSeries([0, 1, 2, 3, 4, 5, 6], dt=1)
sensor = hvsrpy.Sensor3c(component, component, component)
wlen = 2
sensor.split(windowlength=wlen)
expected = sigpropy.WindowedTimeSeries.from_timeseries(component,
windowlength=wlen)
for returned in sensor:
self.assertEqual(expected, returned)
def test_normalization_factor(self):
ns = sigpropy.TimeSeries([-1, 1, 1], dt=1)
ew = sigpropy.TimeSeries([1, 2, 1], dt=1)
vt = sigpropy.TimeSeries([1, 1, -5], dt=1)
sensor = hvsrpy.Sensor3c(ns, ew, vt)
expected = 5
self.assertEqual(expected, sensor.normalization_factor)
# Find second maximum
sensor.vt.amp[2] = 0
expected = 2
self.assertEqual(expected, sensor.normalization_factor)
def test_to_and_from_json(self):
# Simple Case
ns = sigpropy.TimeSeries([1, 2, 3], dt=1)
ew = sigpropy.TimeSeries([1, 4, 5], dt=1)
vt = sigpropy.TimeSeries([1, -1, 1], dt=1)
expected = hvsrpy.Sensor3c(ns, ew, vt)
json_repr = expected.to_json()
returned = hvsrpy.Sensor3c.from_json(json_repr)
for comp in ["ns", "ew", "vt"]:
exp = getattr(expected, comp).amp
ret = getattr(returned, comp).amp
self.assertArrayEqual(exp, ret)
def test_bandpassfilter(self):
# Simple case
tseries = np.random.random(50)
component = sigpropy.TimeSeries(tseries, dt=1 / 10)
sensor = hvsrpy.Sensor3c(component, component, component)
settings = dict(flow=0.3, fhigh=3, order=5)
sensor.bandpassfilter(**settings)
expected = sigpropy.TimeSeries(tseries, dt=1 / 10)
expected.bandpassfilter(**settings)
for returned in sensor:
self.assertArrayEqual(expected.amplitude, returned.amplitude)
def test_to_and_from_json(self):
# Simple Case
ns = sigpropy.TimeSeries([1, 2, 3], dt=1)
ew = sigpropy.TimeSeries([1, 4, 5], dt=1)
vt = sigpropy.TimeSeries([1, -1, 1], dt=1)
original = hvsrpy.Sensor3c(ns, ew, vt)
original_as_json_string = original.to_json()
recovered = hvsrpy.Sensor3c.from_json(original_as_json_string)
for key in ["ns", "ew", "vt", "meta"]:
expected = getattr(original, key)
returned = getattr(recovered, key)
self.assertEqual(expected, returned)
def test_to_and_from_dict(self):
# Simple Case
ns = sigpropy.TimeSeries([1., 2, 3], dt=1)
ew = sigpropy.TimeSeries([1., 4, 5], dt=1)
vt = sigpropy.TimeSeries([1., 1, 1], dt=1)
original = hvsrpy.Sensor3c(ns, ew, vt, meta={"windowlength": 1})
original_as_dict = original.to_dict()
recovered = hvsrpy.Sensor3c.from_dict(original_as_dict)
for key in ["ns", "ew", "vt", "meta"]:
expected = getattr(original, key)
returned = getattr(recovered, key)
self.assertEqual(expected, returned)
def test_to_and_from_dict(self):
# Simple Case
ns = sigpropy.TimeSeries([1., 2, 3], dt=1)
ew = sigpropy.TimeSeries([1., 4, 5], dt=1)
vt = sigpropy.TimeSeries([1., 1, 1], dt=1)
expected = hvsrpy.Sensor3c(ns, ew, vt, meta={"windowlength": 1})
dict_repr = expected.to_dict()
returned = hvsrpy.Sensor3c.from_dict(dict_repr)
for comp in ["ns", "ew", "vt", "meta"]:
exp = getattr(expected, comp)
ret = getattr(returned, comp)
self.assertEqual(exp, ret)
def test_combine_horizontals(self):
dt = 0.01
amplitude = np.sin(2 * np.pi * 1 * np.arange(0, 4, dt))
tseries = sigpropy.TimeSeries(amplitude, dt)
fseries = sigpropy.FourierTransform.from_timeseries(tseries)
sensor = hvsrpy.Sensor3c(tseries, tseries, tseries)
for invalid_method in ["average"]:
self.assertRaises(NotImplementedError,
sensor._combine_horizontal_td,
method=invalid_method,
azimuth=2.)
self.assertRaises(NotImplementedError,
sensor._combine_horizontal_fd,
method=invalid_method,
ns=fseries,
ew=fseries)
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)
