def test_fourier_synthesis_1D_input():
H = 0.7
c0 = 1.
n = 512
s = n * 4.
ls = 8
np.random.seed(0)
fourier_synthesis((n, ), (s, ),
H,
c0=c0,
long_cutoff=s / 2,
short_cutoff=ls,
amplitude_distribution=lambda n: np.ones(n))
def test_shortcut_vs_isotropic_filter():
print(SurfaceTopography.__file__)
n = 100
# t = SurfaceTopography(np.zeros(n,n), (2,3))
np.random.seed(0)
t = fourier_synthesis((n, n), (13, 13), 0.9, 1.)
cutoff_wavevector = 2 * np.pi / 13 * 0.4 * n
hc = t.shortcut(cutoff_wavevector=cutoff_wavevector)
fhc = t.filter(filter_function=lambda q: q <= cutoff_wavevector)
assert hc.is_filter_isotropic
assert fhc.is_filter_isotropic
if False:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.loglog(*hc.power_spectrum_2D(), "+", label="shortcut")
ax.loglog(*fhc.power_spectrum_2D(), "x", label="filter")
ax.loglog(*t.power_spectrum_2D(), label="original")
ax.legend()
fig.show()
np.testing.assert_allclose(fhc.heights(), hc.heights())
def test_uniform_scaled_topography():
surf = fourier_synthesis((5, 7), (1.2, 1.1), 0.8, rms_height=1)
sx, sy = surf.physical_sizes
for fac in [1.0, 2.0, np.pi]:
surf2 = surf.scale(fac)
np.testing.assert_almost_equal(fac * surf.rms_height_from_profile(),
surf2.rms_height_from_profile())
np.testing.assert_almost_equal(surf.positions(), surf2.positions())
surf2 = surf.scale(fac, 2 * fac)
np.testing.assert_almost_equal(fac * surf.rms_height_from_profile(),
surf2.rms_height_from_profile())
sx2, sy2 = surf2.physical_sizes
np.testing.assert_almost_equal(2 * fac * sx, sx2)
np.testing.assert_almost_equal(2 * fac * sy, sy2)
x, y = surf.positions()
x2, y2 = surf2.positions()
np.testing.assert_almost_equal(2 * fac * x, x2)
np.testing.assert_almost_equal(2 * fac * y, y2)
x2, y2, h2 = surf2.positions_and_heights()
np.testing.assert_almost_equal(2 * fac * x, x2)
np.testing.assert_almost_equal(2 * fac * y, y2)
def test_wrapped_bicubic_vs_fourier(sx, sy):
# test against fourier interpolation
# sx and sy are varied to ensure the unit conversions of the slopes are
# correct
nx, ny = [35, 42]
hc = 0.2 * sx
np.random.seed(0)
topography = fourier_synthesis(
(nx, ny),
(sx, sy),
0.8,
rms_height=1.,
short_cutoff=hc,
long_cutoff=hc + 1e-9,
)
topography = topography.scale(1 / topography.rms_height())
interp = topography.interpolate_bicubic()
fine_topography = topography.interpolate_fourier((4 * nx, 4 * ny))
interp_height, interp_slopex, interp_slopey = interp(
*fine_topography.positions(), derivative=1)
np.testing.assert_allclose(interp_height,
fine_topography.heights(),
atol=1e-2)
derx, dery = fine_topography.fourier_derivative()
rms_slope = topography.rms_slope()
np.testing.assert_allclose(interp_slopex, derx, atol=1e-1 * rms_slope)
np.testing.assert_allclose(interp_slopey, dery, atol=1e-1 * rms_slope)
def test_rms_slope_from_area():
r = 2048
res = [r, r]
for H in [0.3, 0.8]:
for s in [(1, 1), (1.4, 3.3)]:
t = fourier_synthesis(res,
s,
H,
short_cutoff=8 / r * np.mean(s),
rms_slope=0.1,
amplitude_distribution=lambda n: 1.0)
last_rms_slope = t.rms_gradient()
np.testing.assert_almost_equal(last_rms_slope, 0.1, decimal=2)
np.testing.assert_almost_equal(last_rms_slope,
t.scale(1.3).rms_gradient() / 1.3)
np.testing.assert_almost_equal(last_rms_slope,
t.scale(1.3, 1.3).rms_gradient())
# rms slope should not depend on filter for these cutoffs...
for cutoff in [4]:
rms_slope = t.rms_gradient(short_wavelength_cutoff=s[0] / r *
cutoff)
np.testing.assert_almost_equal(rms_slope, last_rms_slope)
# ...but starts being a monotonously decreasing function here
for cutoff in [16, 32]:
rms_slope = t.rms_gradient(short_wavelength_cutoff=s[0] / r *
cutoff)
assert rms_slope < last_rms_slope
last_rms_slope = rms_slope
def test_load_no_physical_sizes(comm_self):
nb_grid_pts = (128, 128)
size = (3, 3)
np.random.seed(1)
t = fourier_synthesis(nb_grid_pts, size, 0.8, rms_slope=0.1)
# Topographies always have physical size information, we need to create a
# NetCDF file without any manually
with Dataset('no_physical_sizes.nc', 'w', format='NETCDF3_CLASSIC') as nc:
nc.createDimension('x', nb_grid_pts[0])
nc.createDimension('y', nb_grid_pts[1])
nc.createVariable('heights', 'f8', ('x', 'y'))
nc.variables['heights'] = t.heights()
# Attempt to open full file on each process
# with pytest.raises(ValueError):
# # This raises an error because the physical sizes are not present
# t2 = read_topography('no_physical_sizes.nc')
t2 = read_topography('no_physical_sizes.nc', physical_sizes=size)
assert t.physical_sizes == t2.physical_sizes
assert 'unit' not in t2.info
np.testing.assert_array_almost_equal(t.heights(), t2.heights())
os.remove('no_physical_sizes.nc')
def test_fourier_synthesis_linescan_c0(n):
H = 0.7
c0 = 8.
s = n * 4.
ls = 32
qs = 2 * np.pi / ls
np.random.seed(0)
t = fourier_synthesis((n, ), (s, ),
c0=c0,
hurst=H,
long_cutoff=s / 2,
short_cutoff=ls,
amplitude_distribution=lambda n: np.ones(n))
if False:
import matplotlib.pyplot as plt
q, psd = t.power_spectrum_1D()
fig, ax = plt.subplots()
ax.plot(q, psd)
ax.plot(q, c0 * q**(-1 - 2 * H))
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_ylim(bottom=1)
fig.show()
ref_slope = np.sqrt(1 / (2 * np.pi) * c0 / (1 - H) * qs**(2 - 2 * H))
assert abs(t.rms_slope() - ref_slope) / ref_slope < 1e-1
def test_fourier_synthesis_rms_height_more_wavevectors(comm_self):
"""
Set amplitude to 0 (rolloff = 0) outside the self affine region.
Long cutoff wavelength is smaller then the box size so that we get closer
to a continuum of wavevectors
"""
n = 256
H = 0.74
rms_height = 7.
s = 1.
realised_rms_heights = []
for i in range(50):
topography = fourier_synthesis(
(n, n),
(s, s),
H,
rms_height=rms_height,
rolloff=0,
long_cutoff=s / 8,
short_cutoff=4 * s / n,
# amplitude_distribution=lambda n: np.ones(n)
)
realised_rms_heights.append(topography.rms_height())
# print(abs(np.mean(realised_rms_heights) - rms_height) / rms_height)
# TODO: this is not very accurate !
assert abs(np.mean(realised_rms_heights) - rms_height) / rms_height < 0.1
def test_fourier_synthesis_linescan_hrms_more_wavevectors():
"""
Set amplitude to 0 (rolloff = 0) outside the self affine region.
Long cutoff wavelength is smaller then the box size so that we get closer
to a continuum of wavevectors
"""
H = 0.7
hrms = 4.
n = 4096
s = n * 4.
ls = 8
np.random.seed(0)
realised_rms_heights = []
for i in range(50):
t = fourier_synthesis(
(n, ),
(s, ),
rms_height=hrms,
hurst=H,
rolloff=0,
long_cutoff=s / 8,
short_cutoff=ls,
)
realised_rms_heights.append(t.rms_height())
realised_rms_heights = np.array(realised_rms_heights)
ref_height = hrms
# print(np.sqrt(np.mean(
# (realised_rms_heights - np.mean(realised_rms_heights))**2)))
assert abs(np.mean(realised_rms_heights) -
ref_height) / ref_height < 0.1 #
def test_delegation():
t1 = fourier_synthesis((128,), (1,), 0.8, rms_slope=0.1)
t2 = t1.detrend()
t3 = t2.to_nonuniform()
t4 = t3.scale(2.0)
t4.scale_factor
with pytest.raises(AttributeError):
t2.scale_factor
t2.detrend_mode
# detrend_mode should not be delegated to the parent class
with pytest.raises(AttributeError):
t4.detrend_mode
# t2 should have 'to_nonuniform'
t2.to_nonuniform()
# but it should not have 'to_uniform'
with pytest.raises(AttributeError):
t2.to_uniform(100, 10)
# t4 should have 'to_uniform'
t5 = t4.to_uniform(100, 10)
# but it should not have 'to_nonuniform'
with pytest.raises(AttributeError):
t4.to_nonuniform()
# t5 should have 'to_nonuniform'
t5.to_nonuniform()
# but it should not have 'to_uniform'
with pytest.raises(AttributeError):
t5.to_uniform(100, 10)
def test_uniform_vs_nonuniform():
t1 = fourier_synthesis([12], [6], 0.8, rms_slope=0.1, periodic=False)
t2 = t1.to_nonuniform()
d1 = t1.derivative(1)
d2 = t2.derivative(1)
np.testing.assert_allclose(d1, d2)
def test_q0_1D():
surf = fourier_synthesis([1024, 512], [2.3, 1.5], 0.8, rms_height=0.87)
rms_height = surf.rms_height_from_profile(
) # Need to measure it since it can fluctuate wildly
q, C = surf.power_spectrum_from_profile()
ratio = rms_height**2 / (np.trapz(C, q) / np.pi)
assert ratio > 0.2
assert ratio < 5
def test_scale_dependent_rms_slope_from_profile():
t = fourier_synthesis((1024, 1024), (1, 1), 0.8, rms_slope=0.1)
x, A = t.autocorrelation_from_profile()
s = t.scale_dependent_statistical_property(lambda x, y: np.var(x), distance=x[1::20])
np.testing.assert_allclose(2*A[1::20]/x[1::20]**2, s)
def test_autocompletion():
t1 = fourier_synthesis((128,), (1,), 0.8, rms_slope=0.1)
t2 = t1.detrend()
t3 = t2.to_nonuniform()
assert 'detrend' in dir(t1)
assert 'to_nonuniform' in dir(t2)
assert 'to_uniform' in dir(t3)
def test_isotropic_1d():
n = 32
t = fourier_synthesis((n, ), (13, ), 0.9, 1.)
cutoff_wavevector = 2 * np.pi / 13 * n / 4
q, psd = t.filter(
filter_function=lambda q: q > cutoff_wavevector).power_spectrum_1D()
assert (psd[q < 0.9 * cutoff_wavevector] < 1e-10).all()
def test_q0_2D():
surf = fourier_synthesis([1024, 512], [2.3, 1.5], 0.8, rms_height=0.87)
rms_height = surf.rms_height_from_area(
) # Need to measure it since it can fluctuate wildly
q, C = surf.power_spectrum_from_area(nbins=200, bin_edges='quadratic')
# This is really not quantitative, it's just checking whether it's the right ballpark.
# Any bug in normalization would show up here as an order of magnitude
ratio = rms_height**2 / (np.trapz(q * C, q) / np.pi)
assert ratio > 0.2
assert ratio < 5
def test_noniform_mean_zero(self):
surface = fourier_synthesis((512,), (1.3,), 0.8,
rms_height=1).to_nonuniform()
self.assertTrue(not surface.is_uniform)
x, h = surface.positions_and_heights()
s, = surface.physical_sizes
self.assertAlmostEqual(surface.mean(), np.trapz(h, x) / s)
detrended_surface = surface.detrend(detrend_mode='height')
self.assertAlmostEqual(detrended_surface.mean(), 0)
x, h = detrended_surface.positions_and_heights()
self.assertAlmostEqual(np.trapz(h, x), 0)
def test_randomly_rough(self):
surface = fourier_synthesis((511, 511), (1., 1.), 0.8, rms_height=1)
self.assertTrue(surface.is_uniform)
cut = Topography(surface[:64, :64], physical_sizes=(64., 64.))
self.assertTrue(cut.is_uniform)
untilt1 = cut.detrend(detrend_mode='height')
untilt2 = cut.detrend(detrend_mode='slope')
self.assertTrue(untilt1.is_uniform)
self.assertTrue(untilt2.is_uniform)
self.assertTrue(untilt1.rms_height_from_area() < untilt2.rms_height_from_area())
self.assertTrue(untilt1.rms_gradient() > untilt2.rms_gradient())
def test_third_derivatives_fourier_vs_finite_differences(plot=False):
nx, ny = [512] * 2
sx, sy = [1.] * 2
lc = 0.5
topography = fourier_synthesis((nx, ny), (sx, sy),
0.8,
rms_height=1.,
short_cutoff=lc,
long_cutoff=lc + 1e-9)
topography = topography.scale(1 / topography.rms_height_from_area())
# Fourier derivative
dx3_topography = topography.filter(lambda qx, qy: (1j * qx)**3,
isotropic=False)
dx3 = dx3_topography.heights()
dy3 = topography.filter(lambda qx, qy: (1j * qy)**3,
isotropic=False).heights()
# Finite-differences. We use central differences because this produces the
# derivative at the same point as the Fourier derivative
dx3_num, dy3_num = topography.derivative(3, operator=third_2d)
np.testing.assert_allclose(dx3,
dx3_num,
atol=dx3_topography.rms_height_from_area() *
1e-1)
np.testing.assert_allclose(dy3,
dy3_num,
atol=dx3_topography.rms_height_from_area() *
1e-1)
if plot:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
x, y = topography.positions()
ax.plot(x[:, 0], topography.heights()[:, 0], label="height")
ax.plot(x[:, 0], dx3[:, 0], label="fourier der")
ax.plot(x[:, 0], dx3_num[:, 0], label="FD")
ax.set_title("x")
ax.legend()
fig.show()
fig, ax = plt.subplots()
ax.set_title("y")
x, y = topography.positions()
ax.plot(y[-1, :], topography.heights()[-1, :], label="height")
ax.plot(y[-1, :], dy3[-1, :], label="fourier der")
ax.plot(y[-1, :], dy3_num[-1, :], label="FD")
ax.legend()
fig.show()
def test_transposed_topography():
surf = fourier_synthesis([124, 368], [6, 3], 0.8, rms_slope=0.1)
nx, ny = surf.nb_grid_pts
sx, sy = surf.physical_sizes
surf2 = surf.transpose()
nx2, ny2 = surf2.nb_grid_pts
sx2, sy2 = surf2.physical_sizes
assert nx == ny2
assert ny == nx2
assert sx == sy2
assert sy == sx2
assert (surf.heights() == surf2.heights().T).all()
def test_self_affine_topography_2d(self):
r = 2048
res = [r, r]
for H in [0.3, 0.8]:
t = fourier_synthesis(res, (1, 1), H, rms_slope=0.1,
amplitude_distribution=lambda n: 1.0)
mag, bwidth, rms = t.variable_bandwidth(nb_grid_pts_cutoff=r // 32)
self.assertAlmostEqual(rms[0], t.detrend().rms_height())
# Since this is a self-affine surface, rms(mag) ~ mag^-H
b, a = np.polyfit(np.log(mag[1:]), np.log(rms[1:]), 1)
# The error is huge...
self.assertTrue(abs(H + b) < 0.1)
def test_self_affine_uniform_autocorrelation():
r = 2048
s = 1
H = 0.8
slope = 0.1
t = fourier_synthesis((r,), (s,), H, rms_slope=slope,
amplitude_distribution=lambda n: 1.0)
r, A = t.autocorrelation_from_profile()
m = np.logical_and(r > 1e-3, r < 10 ** (-1.5))
b, a = np.polyfit(np.log(r[m]), np.log(A[m]), 1)
assert abs(b / 2 - H) < 0.1
def test_nonuniform_rms_height():
r = 128
s = 1.3
H = 0.8
slope = 0.1
t = fourier_synthesis((r,), (s,), H, rms_slope=slope,
amplitude_distribution=lambda n: 1.0) \
.to_nonuniform().detrend(detrend_mode='center')
assert_almost_equal(t.mean(), 0)
r, A = height_height_autocorrelation(t, distances=[0])
s, = t.physical_sizes
assert_almost_equal(t.rms_height_from_profile() ** 2 * s, A[0])
def test_write():
nx, ny = 1782, 1302
t = fourier_synthesis((nx, ny), (1, 1), 0.8, rms_slope=0.1)
with tempfile.TemporaryDirectory() as d:
t.to_dzi('synthetic', d)
assert os.path.exists(f'{d}/synthetic_files')
for i in range(12):
assert os.path.exists(f'{d}/synthetic_files/{i}')
root = ET.parse(open(f'{d}/synthetic.xml')).getroot()
assert root.attrib['TileSize'] == '256'
assert root.attrib['Overlap'] == '1'
assert root.attrib['Format'] == 'jpg'
assert root[0].attrib['Width'] == f'{nx}'
assert root[0].attrib['Height'] == f'{ny}'
def test_self_affine_topography_1d(self):
r = 16384
for H in [0.3, 0.8]:
t0 = fourier_synthesis((r,), (1,), H, rms_slope=0.1,
amplitude_distribution=lambda n: 1.0)
for t in [t0, t0.to_nonuniform()]:
mag, bwidth, rms = t.variable_bandwidth(
nb_grid_pts_cutoff=r // 32)
self.assertAlmostEqual(rms[0], t.detrend().rms_height())
np.testing.assert_allclose(bwidth, t.physical_sizes[0] / mag)
# Since this is a self-affine surface, rms(mag) ~ mag^-H
b, a = np.polyfit(np.log(mag[1:]), np.log(rms[1:]), 1)
# The error is huge...
self.assertTrue(abs(H + b) < 0.1)
def test_save_and_load(comm):
nb_grid_pts = (128, 128)
size = (3, 3)
np.random.seed(1)
t = fourier_synthesis(nb_grid_pts, size, 0.8, rms_slope=0.1, unit='µm')
fft = FFT(nb_grid_pts, communicator=comm, fft="mpi")
fft.create_plan(1)
dt = t.domain_decompose(fft.subdomain_locations,
fft.nb_subdomain_grid_pts,
communicator=comm)
assert t.unit == 'µm'
assert dt.unit == 'µm'
assert t.info['unit'] == 'µm'
assert dt.info['unit'] == 'µm'
if comm.size > 1:
assert dt.is_domain_decomposed
# Save file
dt.to_netcdf('parallel_save_test.nc')
# Attempt to open full file on each MPI process
t2 = read_topography('parallel_save_test.nc')
assert t.physical_sizes == t2.physical_sizes
assert t.unit == t2.unit
assert t.info['unit'] == t2.info['unit']
np.testing.assert_array_almost_equal(t.heights(), t2.heights())
# Attempt to open file in parallel
r = NCReader('parallel_save_test.nc', communicator=comm)
assert r.channels[0].nb_grid_pts == nb_grid_pts
t3 = r.topography(subdomain_locations=fft.subdomain_locations,
nb_subdomain_grid_pts=fft.nb_subdomain_grid_pts)
assert t.physical_sizes == t3.physical_sizes
assert t.unit == t3.unit
assert t.info['unit'] == t3.info['unit']
np.testing.assert_array_almost_equal(dt.heights(), t3.heights())
assert t3.is_periodic
comm.barrier()
if comm.rank == 0:
os.remove('parallel_save_test.nc')
def test_c_vs_py_reference():
from _SurfaceTopography import nonuniform_autocorrelation
r = 16
s = 1
H = 0.8
slope = 0.1
t = fourier_synthesis((r,), (s,), H, rms_slope=slope,
amplitude_distribution=lambda n: 1.0).to_nonuniform()
r1, A1 = py_autocorrelation_from_profile(t)
s, = t.physical_sizes
r2, A2 = nonuniform_autocorrelation(*t.positions_and_heights(), s)
assert_array_almost_equal(r1, r2)
assert_array_almost_equal(A1, A2)
def test_fourier_synthesis(n):
H = 0.74
rms_slope = 1.2
s = 2e-6
topography = fourier_synthesis((n, n), (s, s),
H,
rms_slope=rms_slope,
long_cutoff=s / 4,
short_cutoff=4 * s / n)
qx, psdx = topography.power_spectrum_1D()
qy, psdy = topography.transpose().power_spectrum_1D()
assert psdy[-1] < 10 * psdx[-1] # assert psdy is not much bigger
assert abs(topography.rms_slope() - rms_slope) / rms_slope < 1e-1
def test_constrained_conjugate_gradients():
nb_grid_pts = (512, 512)
physical_sizes = (1., 1.)
hurst = 0.8
rms_slope = 0.1
modulus = 1
np.random.seed(999)
topography = fourier_synthesis(nb_grid_pts,
physical_sizes,
hurst,
rms_slope=rms_slope)
substrate = PeriodicFFTElasticHalfSpace(nb_grid_pts, modulus,
physical_sizes)
system = make_system(substrate, topography)
system.minimize_proxy(offset=0.1)
def test_save_and_load_no_unit(comm_self):
nb_grid_pts = (128, 128)
size = (3, 3)
np.random.seed(1)
t = fourier_synthesis(nb_grid_pts, size, 0.8, rms_slope=0.1)
# Save file
t.to_netcdf('no_unit.nc')
t2 = read_topography('no_unit.nc')
assert t.physical_sizes == t2.physical_sizes
assert 'unit' not in t2.info
np.testing.assert_array_almost_equal(t.heights(), t2.heights())
os.remove('no_unit.nc')