def test_auto_pick_first_arrivals(self):
s1 = swprocess.Sensor1C(np.concatenate(
(np.zeros(100), np.array([0.1, 0, 0]), np.zeros(100))),
dt=1,
x=1,
y=0,
z=0)
s2 = swprocess.Sensor1C(np.concatenate(
(np.zeros(100), np.array([0, 0.2, 0]), np.zeros(100))),
dt=1,
x=2,
y=0,
z=0)
source = swprocess.Source(0, 0, 0)
array = swprocess.Array1D([s1, s2], source)
# algorithm = "threshold"
position, times = array.auto_pick_first_arrivals(algorithm="threshold")
self.assertListEqual(array.position(), position)
self.assertListEqual([100, 101], times)
# algorithm = "bad"
self.assertRaises(NotImplementedError,
array.auto_pick_first_arrivals,
algorithm="bad")
def test_from_array1ds(self):
# Define source.
source = swprocess.Source(x=-5, y=0, z=0)
# Create signal array.
s_sensors = []
for n in range(5):
amp = 5 * np.random.random(100)
s_sensors.append(
swprocess.Sensor1C(amp, dt=0.01, x=2 * n, y=0, z=0))
signal = swprocess.Array1D(s_sensors, source)
# Create noise array.
n_sensors = []
for n in range(5):
amp = np.random.random(100)
n_sensors.append(
swprocess.Sensor1C(amp, dt=0.01, x=2 * n, y=0, z=0))
noise = swprocess.Array1D(n_sensors, source)
# Calculate SNR
fmin, fmax = 5, 50
snr = swprocess.snr.SignaltoNoiseRatio.from_array1ds(signal,
noise,
fmin=fmin,
fmax=fmax)
expected = np.arange(fmin, fmax + signal[0].df, signal[0].df)
returned = snr.frequencies
self.assertArrayAlmostEqual(expected, returned)
# Fail due to unequal samples
noise.trim(0, 0.25)
self.assertRaises(IndexError,
swprocess.snr.SignaltoNoiseRatio.from_array1ds,
signal,
noise,
fmin=fmin,
fmax=fmax)
# Pass after padding
snr = swprocess.snr.SignaltoNoiseRatio.from_array1ds(
signal,
noise,
fmin=fmin,
fmax=fmax,
pad_snr=True,
df_snr=signal[0].df)
expected = np.arange(fmin, fmax + signal[0].df, signal[0].df)
returned = snr.frequencies
self.assertArrayAlmostEqual(expected, returned)
def test_init(self):
# Basic
source = swprocess.Source(x=-5, y=0, z=0)
sensors = [self.sensor_0, self.sensor_1]
array = swprocess.Array1D(sensors=sensors, source=source)
self.assertEqual(array.source, source)
self.assertListEqual(array.sensors, sensors)
# Bad: Invalid sensors
self.assertRaises(ValueError,
swprocess.Array1D,
sensors=[self.sensor_5, self.sensor_5],
source=source)
# Bad: Incompatable sensors
sensor_bad = swprocess.Sensor1C(amplitude=[1, 2, 3, 4],
dt=1,
x=7,
y=0,
z=0,
nstacks=1,
delay=0)
self.assertRaises(ValueError,
swprocess.Array1D,
sensors=[self.sensor_5, sensor_bad],
source=source)
def test_to_and_from_su(self):
spacing = 2
sensors = []
nsamples = 1000
time = np.arange(0, 1, 1 / nsamples)
for n in range(24):
sensor = swprocess.Sensor1C(amplitude=np.sin(2 * np.pi * n * time),
dt=1 / nsamples,
x=spacing * n,
y=0,
z=0,
nstacks=1,
delay=0)
sensors.append(sensor)
source = swprocess.Source(x=-10, y=0, z=0)
expected = swprocess.Array1D(sensors, source)
# To and from : SU
fname = "to_file.su"
expected.to_file(fname)
returned = swprocess.Array1D.from_files(fname)
self.assertEqual(expected, returned)
os.remove(fname)
# Bad : format
self.assertRaises(ValueError, expected.to_file, fname, ftype="seg2")
def test_plot(self):
# Basic case (near-side, 2m spacing)
fname = self.wghs_path + "1.dat"
swprocess.Array1D.from_files(fname).plot()
# Non-linear spacing
sensors = [
swprocess.Sensor1C(
[1, 2, 3],
dt=1,
x=x,
y=0,
z=0,
) for x in [0, 1, 3]
]
source = swprocess.Source(x=-5, y=0, z=0)
array = swprocess.Array1D(sensors=sensors, source=source)
array.plot()
# Basic case (far-side, 2m spacing)
fname = self.wghs_path + "20.dat"
swprocess.Array1D.from_files(fname).plot()
plt.show(block=False)
plt.close("all")
def dummy_array(amp, dt, nstacks, delay, nsensors, spacing, source_x):
"""Make simple dummy array from timeseries for testing."""
sensors = []
for i in range(nsensors):
sensor = swprocess.Sensor1C(amp,
dt,
x=i * spacing,
y=0,
z=0,
nstacks=nstacks,
delay=delay)
sensors.append(sensor)
source = swprocess.Source(x=source_x, y=0, z=0)
return swprocess.Array1D(sensors=sensors, source=source)
def setUpClass(cls):
cls.full_path = get_full_path(__file__)
cls.wghs_path = cls.full_path + "../examples/masw/data/wghs/"
cls.sensor_0 = swprocess.Sensor1C(amplitude=[-.1] * 9 + [-0.11] +
[-.1] * 9,
dt=1,
x=0,
y=0,
z=0,
nstacks=1,
delay=0)
cls.sensor_1 = swprocess.Sensor1C(amplitude=[0.2] * 9 + [+0.25] +
[0.2] * 9,
dt=1,
x=1,
y=0,
z=0,
nstacks=1,
delay=0)
cls.sensor_5 = swprocess.Sensor1C(amplitude=[1.0] * 9 + [+1.05] +
[1.0] * 9,
dt=1,
x=5,
y=0,
z=0,
nstacks=1,
delay=0)
cls.sensor_6 = swprocess.Sensor1C(amplitude=[2.0] * 9 + [+2.02] +
[2.0] * 9,
dt=1,
x=6,
y=0,
z=0,
nstacks=1,
delay=0)
def test_from_array(self):
class SubEmpty(swprocess.wavefieldtransforms.EmptyWavefieldTransform):
@classmethod
def _create_vector(cls, *args, **kwargs):
cls._create_vector_args = args
cls._create_vector_kwargs = kwargs
sensors = [swprocess.Sensor1C([0]*100, 0.01, 2*n, 0, 0)
for n in range(5)]
source = swprocess.Source(-5, 0, 0)
array = swprocess.Array1D(sensors, source)
settings = dict(fmin=5, fmax=50, vmin=75,
vmax=300, nvel=30, vspace="lin")
empty = SubEmpty.from_array(array, settings)
self.assertArrayEqual(np.arange(5., 50+1, 1), empty.frequencies)
expected = (settings["vmin"], settings["vmax"], settings["nvel"], settings["vspace"])
returned = empty._create_vector_args
self.assertTupleEqual(expected, returned)
self.assertDictEqual({}, empty._create_vector_kwargs)