def _testAdjointMany(self, backend, dtype, ntransforms=2):
if not backend_available(backend):
raise SkipTest
complex_dtype = complex_type(dtype)
plan = Plan(
self.plane.shape,
self.fourier_pts[:2].astype(dtype),
backend=backend,
ntransforms=ntransforms,
)
batch = np.empty(
(ntransforms, *self.recip_space_plane.shape), dtype=complex_dtype
)
for i in range(ntransforms):
batch[i] = self.recip_space_plane
# Test Adjoint
result = plan.adjoint(batch)
for r in range(ntransforms):
self.assertTrue(
np.allclose(result[r], self.adjoint_plane, atol=utest_tolerance(dtype))
)
def testSimulationImagesNoisy(self):
images = self.sim.images(0, 512).asnumpy()
self.assertTrue(
np.allclose(
images,
np.load(os.path.join(DATA_DIR, "sim_images_with_noise.npy")),
rtol=1e-2,
atol=utest_tolerance(self.sim.dtype),
))
def testAnisotropicNoiseVariance(self):
noise_estimator = AnisotropicNoiseEstimator(self.sim, batchSize=512)
noise_variance = noise_estimator.estimate()
self.assertTrue(
np.allclose(
0.04534846544265747,
noise_variance,
atol=utest_tolerance(self.sim.dtype),
))
def testSimulationImagesDownsample(self):
# The simulation already generates images of size 8 x 8; Downsampling to resolution 8 should thus have no effect
self.sim.downsample(8)
images = self.sim.clean_images(0, 512).asnumpy()
self.assertTrue(
np.allclose(
images,
np.load(os.path.join(DATA_DIR, "sim_clean_images.npy")),
rtol=1e-2,
atol=utest_tolerance(self.sim.dtype),
))
def _testAdjoint(self, backend, dtype):
if not backend_available(backend):
raise SkipTest
complex_dtype = complex_type(dtype)
plan = Plan(self.vol.shape, self.fourier_pts.astype(dtype), backend=backend)
# Test Adjoint
result = plan.adjoint(self.recip_space.astype(complex_dtype))
self.assertTrue(
np.allclose(result, self.adjoint_vol, atol=utest_tolerance(dtype))
)