def test_GaN_synthetic_empty(self):
# Reconstruct empty for displacements
empty_param = self.default_GaN_param()
del empty_param["object_names"]
carrier = (np.array(empty_param["sideband_pos"], dtype=float) - 1024) / 2048
carrier /= np.dot(carrier, carrier)
holoaverage(empty_param, verbose=self.VERBOSE)
with h5py.File(empty_param["output_name"], "r") as output:
empty = output["empty"][...]
# Create displacements from empty hologram
shift = np.angle(rescale_fourier(empty, (2048, 2048))) / 2.0 / np.pi
displacement_file = self.touch_temp_output()
with h5py.File(displacement_file, "w") as output:
output.create_dataset("dx", data=shift * carrier[0])
output.create_dataset("dy", data=shift * carrier[1])
# Reconstruct data
object_param = self.default_GaN_param()
object_param["empty_names"] = "/dev/null/some_invalid_filename"
object_param["empty_override"] = "/dev/null/some_invalid_filename"
object_param["synthesize_empty"] = True
object_param["camera_distortions"] = [displacement_file + "?dataset=dx", displacement_file + "?dataset=dy"]
holoaverage(object_param, verbose=self.VERBOSE)
with h5py.File(object_param["output_name"], "r") as output:
new_empty = output["empty"][...]
data_convergence = output["data"].attrs["convergence"]
# Test for empty equivalence
np.testing.assert_allclose(np.angle(empty / new_empty)[10:-10, 10:-10], 0.0, atol=0.1)
# Test for data convergence
self.assertTrue(np.allclose(data_convergence[-1], 2.25341306e+11, rtol=1e-4))
delta = (data_convergence[1:] - data_convergence[:-1]) / data_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
def test_GaN_separate_empty_and_object(self):
# Reconstruct empty
empty_param = self.default_GaN_param()
del empty_param["object_names"]
holoaverage(empty_param, verbose=self.VERBOSE)
with h5py.File(empty_param["output_name"], "r") as output:
empty_convergence = output["empty"].attrs["convergence"]
# Test for empty convergence
self.assertTrue(np.allclose(empty_convergence[-1], self.GAN_EMPTY_CONVERGENCE_384, rtol=1e-4))
delta = (empty_convergence[1:] - empty_convergence[:-1]) / empty_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
# Reconstruct data
object_param = self.default_GaN_param()
object_param["empty_names"] = "/dev/null/some_invalid_filename"
object_param["empty_override"] = empty_param["output_name"] + "?dataset=empty"
holoaverage(object_param, verbose=self.VERBOSE)
with h5py.File(object_param["output_name"], "r") as output:
data_convergence = output["data"].attrs["convergence"]
# Test for data convergence
self.assertTrue(np.allclose(data_convergence[-1], self.GAN_DATA_CONVERGENCE_256, rtol=1e-4))
delta = (data_convergence[1:] - data_convergence[:-1]) / data_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
def test_GaN_single_empty_and_object(self):
# Reconstruct empty
param = self.default_GaN_param()
param["object_last"] = 1
param["empty_last"] = 1
holoaverage(param, verbose=self.VERBOSE)
with h5py.File(param["output_name"], "r") as output:
data = output["data"][...]
empty = output["empty"][...]
# Test for shape
self.assertEqual(data.shape, (param["object_size"],) * 2)
self.assertEqual(empty.shape, (param["empty_size"],) * 2)
def test_GaN_no_empty(self):
param = self.default_GaN_param()
del param["empty_names"]
del param["empty_size"]
holoaverage(param, verbose=self.VERBOSE)
with h5py.File(param["output_name"], "r") as output:
self.assertFalse("empty" in output)
data_convergence = output["data"].attrs["convergence"]
# Test for data convergence
self.assertTrue(np.allclose(data_convergence[-1], 4.88520723e+13, rtol=1e-4))
delta = (data_convergence[1:] - data_convergence[:-1]) / data_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
def test_GaN_no_objects(self):
param = self.default_GaN_param()
del param["object_names"]
holoaverage(param, verbose=self.VERBOSE)
with h5py.File(param["output_name"], "r") as output:
self.assertFalse("data" in output)
self.assertFalse("variance" in output)
empty_convergence = output["empty"].attrs["convergence"]
# Test for empty convergence
self.assertTrue(np.allclose(empty_convergence[-1], self.GAN_EMPTY_CONVERGENCE_384, rtol=1e-4))
delta = (empty_convergence[1:] - empty_convergence[:-1]) / empty_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
def test_GaN_example(self):
param = self.default_GaN_param()
holoaverage(param, verbose=self.VERBOSE)
with h5py.File(param["output_name"], "r") as output:
data_convergence = output["data"].attrs["convergence"]
empty_convergence = output["empty"].attrs["convergence"]
# Test for empty convergence
self.assertTrue(np.allclose(empty_convergence[-1], self.GAN_EMPTY_CONVERGENCE_384, rtol=1e-4))
delta = (empty_convergence[1:] - empty_convergence[:-1]) / empty_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
# Test for data convergence
self.assertTrue(np.allclose(data_convergence[-1], self.GAN_DATA_CONVERGENCE_256, rtol=1e-4))
delta = (data_convergence[1:] - data_convergence[:-1]) / data_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
def test_GaN_non_square_roi(self):
param = self.default_GaN_param()
param["object_last"] = 5 # Smaller series for speed
param["empty_last"] = 5
param["roi"] = [256, 640, 1792, 1408]
holoaverage(param, verbose=self.VERBOSE)
with h5py.File(param["output_name"], "r") as output:
data_convergence = output["data"].attrs["convergence"]
empty_convergence = output["empty"].attrs["convergence"]
# Test for empty convergence
delta = (empty_convergence[1:] - empty_convergence[:-1]) / empty_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
# Test for data convergence
delta = (data_convergence[1:] - data_convergence[:-1]) / data_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
def test_GaN_empty_from_raw(self):
param = self.default_GaN_param()
empty_raw = self.touch_temp_output(suffix=".dat")
with open(empty_raw, "wb") as file:
np.zeros(100000, dtype=np.uint8).tofile(file)
np.ones(256 * 256, dtype=np.complex64).tofile(file)
del param["empty_names"]
del param["empty_size"]
param["empty_override"] = "%s?type=raw&xsize=256&ysize=256&dtype=complex64&offset=100000" % empty_raw
holoaverage(param, verbose=self.VERBOSE)
with h5py.File(param["output_name"], "r") as output:
self.assertFalse("empty" in output)
data_convergence = output["data"].attrs["convergence"]
# Test for data convergence
self.assertTrue(np.allclose(data_convergence[-1], 2.47313565e+14, rtol=1e-4))
delta = (data_convergence[1:] - data_convergence[:-1]) / data_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
def test_GaN_focus_and_tilt_alignment(self):
param = self.default_GaN_param()
param["adjust_defocus"] = True
param["adjust_tilt"] = True
holoaverage(param, verbose=self.VERBOSE)
with h5py.File(param["output_name"], "r") as output:
data_convergence = output["data"].attrs["convergence"]
empty_convergence = output["empty"].attrs["convergence"]
# Test for empty convergence
self.assertTrue(np.allclose(empty_convergence[-1], self.GAN_EMPTY_CONVERGENCE_384, rtol=1e-1))
delta = (empty_convergence[1:] - empty_convergence[:-1]) / empty_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
# Test for data convergence
self.assertTrue(np.allclose(data_convergence[-1], self.GAN_DATA_CONVERGENCE_256_FOCUS, rtol=1e-4))
delta = (data_convergence[1:] - data_convergence[:-1]) / data_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
def test_GaN_example_disabled_pyfftw(self):
from holoaverage.fft import pyfftw_present, disable_pyfftw
if not pyfftw_present():
return # Disable test: already tested by other test
disable_pyfftw()
param = self.default_GaN_param()
holoaverage(param, verbose=self.VERBOSE)
with h5py.File(param["output_name"], "r") as output:
data_convergence = output["data"].attrs["convergence"]
empty_convergence = output["empty"].attrs["convergence"]
# Test for empty convergence
self.assertTrue(np.allclose(empty_convergence[-1], self.GAN_EMPTY_CONVERGENCE_384, rtol=1e-4))
delta = (empty_convergence[1:] - empty_convergence[:-1]) / empty_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
# Test for data convergence
self.assertTrue(np.allclose(data_convergence[-1], self.GAN_DATA_CONVERGENCE_256, rtol=1e-4))
delta = (data_convergence[1:] - data_convergence[:-1]) / data_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
def test_GaN_filename_list(self):
param = self.default_GaN_param()
# Use smaller series for speed
object_names = param["object_names"]
empty_names = param["empty_names"]
param["object_names"] = [object_names % n for n in range(1, 12, 2)]
param["empty_names"] = [empty_names % n for n in range(1, 12, 2)]
del param["object_first"]
del param["object_last"]
del param["empty_first"]
del param["empty_last"]
holoaverage(param, verbose=self.VERBOSE)
with h5py.File(param["output_name"], "r") as output:
data_convergence = output["data"].attrs["convergence"]
empty_convergence = output["empty"].attrs["convergence"]
# Test for empty convergence
delta = (empty_convergence[1:] - empty_convergence[:-1]) / empty_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
# Test for data convergence
delta = (data_convergence[1:] - data_convergence[:-1]) / data_convergence[:-1]
self.assertTrue(np.all(delta < +1e-4))
def test_odd_sizes(self):
param = self.default_n1_param()
param["object_size"] = 3**5
holoaverage(param, verbose=self.VERBOSE)
phase_mean, phase_std = self.inner_quarter_stats(param)
self.assertLess(abs(phase_std), 0.15)
param["object_size"] = 384
param["empty_size"] = 3**5
holoaverage(param, verbose=self.VERBOSE)
phase_mean, phase_std = self.inner_quarter_stats(param)
self.assertLess(abs(phase_std), 0.15)
param["empty_size"] = 512
param["roi"] = [
param["roi"][0], param["roi"][1], param["roi"][0] + 1215,
param["roi"][1] + 1215
]
holoaverage(param, verbose=self.VERBOSE)
phase_mean, phase_std = self.inner_quarter_stats(param)
self.assertLess(abs(phase_std), 0.15)
def test_same_empty_as_object(self):
param = self.default_n1_param()
holoaverage(param, verbose=self.VERBOSE)
phase_mean, phase_std = self.inner_quarter_stats(param)
self.assertLess(abs(phase_std), 0.15)
