def test_fourier_interpolate_nyquist(plot=False):
# asserts that the interpolation follows the "minimal-osciallation"
# assumption for the nyquist frequency
topography = Topography(np.array([[1], [-1]]), physical_sizes=(1., 1.))
interpolated_topography = topography.interpolate_fourier((64, 1))
if plot:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
x, y = topography.positions()
ax.plot(x.flat, topography.heights().flat, "+")
x, y = interpolated_topography.positions()
ax.plot(x.flat, interpolated_topography.heights().flat, "-")
fig.show()
x, y = interpolated_topography.positions()
np.testing.assert_allclose(interpolated_topography.heights(),
np.cos(2 * np.pi * x), atol=1e-14)
def test_positions(comm):
nx, ny = (12 * comm.Get_size(), 10 * comm.Get_size() + 1)
sx = 33.
sy = 54.
fftengine = FFT((nx, ny), fft='mpi', communicator=comm)
surf = Topography(np.zeros(fftengine.nb_subdomain_grid_pts),
physical_sizes=(sx, sy),
decomposition='subdomain',
nb_grid_pts=(nx, ny),
subdomain_locations=fftengine.subdomain_locations,
communicator=comm)
x, y = surf.positions()
assert x.shape == fftengine.nb_subdomain_grid_pts
assert y.shape == fftengine.nb_subdomain_grid_pts
assert Reduction(comm).min(x) == 0
assert abs(Reduction(comm).max(x) - sx * (1 - 1. / nx)) \
< 1e-8 * sx / nx, "{}".format(x)
assert Reduction(comm).min(y) == 0
assert abs(Reduction(comm).max(y) - sy * (1 - 1. / ny)) < 1e-8
