def test_radial_average_spectrum_distances():
shape = (201, 201)
area = (-100, 100, -100, 100)
x, y = gridder.regular(area, shape)
x, y = x.reshape(shape), y.reshape(shape)
x, y = np.fft.ifftshift(x), np.fft.ifftshift(y)
distances = np.sqrt(x**2 + y**2)
k, radial_distances = transform.radial_average_spectrum(x, y, distances)
npt.assert_allclose(k[2:], radial_distances[2:], rtol=0.1) # doesn't fail
def test_radial_average_spectrum_distances():
shape = (201, 201)
area = (-100, 100, -100, 100)
x, y = gridder.regular(area, shape)
x, y = x.reshape(shape), y.reshape(shape)
x, y = np.fft.ifftshift(x), np.fft.ifftshift(y)
distances = np.sqrt(x**2 + y**2)
k, radial_distances = transform.radial_average_spectrum(x, y, distances)
npt.assert_allclose(k[2:], radial_distances[2:], rtol=0.1) # doesn't fail
def test_radial_average_spectrum_integers():
x = np.arange(-10, 11, 1)
y = np.arange(-10, 11, 1)
y, x = np.meshgrid(y, x)
x, y = np.fft.ifftshift(x), np.fft.ifftshift(y)
r = np.sqrt(x**2 + y**2)
z = np.zeros(x.shape)
integers = np.arange(0, x.max() + 1, 1)
for i in integers:
if i == 0:
inside = r <= 0.5
else:
inside = np.logical_and(r > i - 0.5, r <= i + 0.5)
z[inside] = i
k, radial_z = transform.radial_average_spectrum(x, y, z)
npt.assert_allclose(integers, radial_z, rtol=0.1)
def test_radial_average_spectrum_integers():
x = np.arange(-10, 11, 1)
y = np.arange(-10, 11, 1)
y, x = np.meshgrid(y, x)
x, y = np.fft.ifftshift(x), np.fft.ifftshift(y)
r = np.sqrt(x**2 + y**2)
z = np.zeros(x.shape)
integers = np.arange(0, x.max() + 1, 1)
for i in integers:
if i == 0:
inside = r <= 0.5
else:
inside = np.logical_and(r > i - 0.5, r <= i + 0.5)
z[inside] = i
k, radial_z = transform.radial_average_spectrum(x, y, z)
npt.assert_allclose(integers, radial_z, rtol=0.1)
# Fetch Hawaii data
data = fetch_hawaii_gravity()
# When the data is projected using UTM zone 4, it is no longer regular gridded.
# We must regrid it.
area = (1.483e6, 3.079e6, -60e3, 1.326e6)
shape = (77, 67)
x, y, gravity = gridder.interp(data['x'], data['y'],
data['topo-free'],
shape, area=area)
# Lets compute the Power Density Spectra (2d arrays)
kx, ky, pds = transform.power_density_spectra(x, y, gravity, shape)
# And then compute the radial average of the PDS
k_radial, pds_radial = transform.radial_average_spectrum(kx, ky, pds)
# Plot Hawaii gravity and radially averaged power spectrum
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 4))
cm = ax1.contourf(y.reshape(shape),
x.reshape(shape),
gravity.reshape(shape), 100)
ax1.contour(y.reshape(shape),
x.reshape(shape),
gravity.reshape(shape),
8, colors='k', linewidths=0.4)
ax1.set_aspect('equal')
ax1.set_xlabel('m')
ax1.set_ylabel('m')
ax1.ticklabel_format(axis='x', style='sci', scilimits=(1, 1))
ax1.ticklabel_format(axis='y', style='sci', scilimits=(1, 1))
# When the data is projected using UTM zone 4, it is no longer regular gridded.
# We must regrid it.
area = (1.483e6, 3.079e6, -60e3, 1.326e6)
shape = (77, 67)
x, y, gravity = gridder.interp(data['x'],
data['y'],
data['topo-free'],
shape,
area=area)
# Lets compute the Power Density Spectra (2d arrays)
kx, ky, pds = transform.power_density_spectra(x, y, gravity, shape)
# And then compute the radial average of the PDS
k_radial, pds_radial = transform.radial_average_spectrum(kx, ky, pds)
# Plot Hawaii gravity and radially averaged power spectrum
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 4))
cm = ax1.contourf(y.reshape(shape), x.reshape(shape), gravity.reshape(shape),
100)
ax1.contour(y.reshape(shape),
x.reshape(shape),
gravity.reshape(shape),
8,
colors='k',
linewidths=0.4)
ax1.set_aspect('equal')
ax1.set_xlabel('m')
ax1.set_ylabel('m')
ax1.ticklabel_format(axis='x', style='sci', scilimits=(1, 1))
