def test_1dsprectraldecomp(self):
# test 1d
dt = 1.0 / self.fs
f = (self.f1, self.f2, self.f3)
test = spectraldecomp(self.data, window_length=1.0, f=f, dt=dt)
self.assertAlmostEquals(test[5, 0] / test[5, 1], self.a1 / self.a2)
self.assertAlmostEquals(test[5, 0] / test[5, 2], self.a1 / self.a3)
def test_2dspectraldecomp(self):
dt = 1.0 / self.fs
f = (self.f1, self.f2, self.f3)
data = np.zeros((int(self.data.size), 2))
data[:, 0] = self.data
data[:, 1] = self.data
test = spectraldecomp(data, window_length=1, f=f, dt=dt)
self.assertAlmostEquals(test[5, 0, 0] / test[5, 0, 1], self.a1/self.a2)
self.assertAlmostEquals(test[5, 0, 0] / test[5, 0, 2], self.a1/self.a3)
self.assertAlmostEquals(test[5, 1, 0] / test[5, 1, 1], self.a1/self.a2)
self.assertAlmostEquals(test[5, 1, 0] / test[5, 1, 2], self.a1/self.a3)
def test_1dsprectraldecomp( self ):
# test 1d
dt = 1.0 / self.fs
f = (self.f1, self.f2, self.f3)
test = spectraldecomp( self.data,window_length = 1.0,
f=f, dt=dt )
self.assertAlmostEquals( test[5,0] / test[5,1],
self.a1/self.a2)
self.assertAlmostEquals( test[5,0] / test[5,2],
self.a1/self.a3)
def test_2dspectraldecomp(self):
dt = 1.0 / self.fs
f = (self.f1, self.f2, self.f3)
data = np.zeros((int(self.data.size), 2))
data[:, 0] = self.data
data[:, 1] = self.data
test = spectraldecomp(data, window_length=1, f=f, dt=dt)
self.assertAlmostEquals(test[5, 0, 0] / test[5, 0, 1],
self.a1 / self.a2)
self.assertAlmostEquals(test[5, 0, 0] / test[5, 0, 2],
self.a1 / self.a3)
self.assertAlmostEquals(test[5, 1, 0] / test[5, 1, 1],
self.a1 / self.a2)
self.assertAlmostEquals(test[5, 1, 0] / test[5, 1, 2],
self.a1 / self.a3)
#hhpf = -hhpf
#hhpf[(N - 1) / 2] += 1
# Convolve both filters.
#h = np.convolve(hlpf, hhpf)
#Applying the filter h to the seismic cube c
#cfilt = np.convolve(c, h)
#plt.imshow(cfilt[100].T, cmap='Greys')
from bruges.attribute import spectraldecomp
sub_volume = c[100:200, 100:200, 950:]
out = []
print(c.shape)
freqs = np.arange(2, 10, 4)
for freq in freqs:
for i, section in enumerate(sub_volume):
d = spectraldecomp(section,
f=(freq, freq),
window_length=0.064,
dt=0.004)
print('done processing inline: ', i)
out.append(d)
tiles = np.array(out)
tiles = tiles[..., 0]
np.save(f'Piedras{freq}Hz.npy', tiles)
plt.imshow(d[..., 0].T, cmap='Reds', alpha=0.3)
#plt.imshow(d[...,1].T, cmap='Greens', alpha=0.3)
#plt.imshow(d[...,2].T, cmap='Blues', alpha=0.3)
