mirror of
https://github.com/cdtools-developers/cdtools.git
synced 2026-09-09 21:12:42 +02:00
First work on the off-axis propagator
This commit is contained in:
Abe Levitan
@@ -23,6 +23,23 @@ from copy import copy
|
||||
# 4) Include a correction for the thickness of the sample
|
||||
#
|
||||
|
||||
#
|
||||
# How to do this properly?
|
||||
# First thing to note is that the two corrections (probe propagation before
|
||||
# interaction and high-NA correction for the final diffraction measurement)
|
||||
# should be able to be turned on separately, since they show up in different
|
||||
# situations. In fact, I would like to focus on the first aspect initially
|
||||
# since I think that's the dominant issue we will contend with at CSX.
|
||||
#
|
||||
|
||||
#
|
||||
# It also should be possible to choose an "auto" setting for the two
|
||||
# corrections, since the geometry information given should be enough to
|
||||
# decide if the correction is needed.
|
||||
# Probably the automatic check will have to be very conservative for the
|
||||
# probe propagation side since the model has no information about the
|
||||
# expected numerical aperture of the probe.
|
||||
#
|
||||
|
||||
class Bragg2DPtycho(CDIModel):
|
||||
|
||||
@@ -31,6 +48,367 @@ class Bragg2DPtycho(CDIModel):
|
||||
detector_slice=None,
|
||||
surface_normal=np.array([0.,0.,1.]),
|
||||
min_translation = t.Tensor([0,0]),
|
||||
median_propagation = t.Tensor(data=[0]),
|
||||
background = None, translation_offsets=None, mask=None,
|
||||
weights = None, translation_scale = 1, saturation=None,
|
||||
probe_support = None, obj_support=None, oversampling=1):
|
||||
probe_support = None, obj_support=None, oversampling=1,
|
||||
propagate_probe=None, correct_tilt=None):
|
||||
|
||||
|
||||
super(FancyPtycho,self).__init__()
|
||||
self.wavelength = t.Tensor([wavelength])
|
||||
self.detector_geometry = copy(detector_geometry)
|
||||
det_geo = self.detector_geometry
|
||||
if hasattr(det_geo, 'distance'):
|
||||
det_geo['distance'] = t.Tensor(det_geo['distance'])
|
||||
if hasattr(det_geo, 'basis'):
|
||||
det_geo['basis'] = t.Tensor(det_geo['basis'])
|
||||
if hasattr(det_geo, 'corner'):
|
||||
det_geo['corner'] = t.Tensor(det_geo['corner'])
|
||||
|
||||
self.min_translation = t.Tensor(min_translation)
|
||||
|
||||
self.probe_basis = t.Tensor(probe_basis)
|
||||
self.detector_slice = detector_slice
|
||||
self.surface_normal = t.Tensor(surface_normal)
|
||||
|
||||
self.saturation = saturation
|
||||
|
||||
if mask is None:
|
||||
self.mask = mask
|
||||
else:
|
||||
self.mask = t.BoolTensor(mask)
|
||||
|
||||
# We rescale the probe here so it learns at the same rate as the
|
||||
# object
|
||||
if probe_guess.dim() > 3:
|
||||
self.probe_norm = 1 * t.max(tools.cmath.cabs(probe_guess[0].to(t.float32)))
|
||||
else:
|
||||
self.probe_norm = 1 * t.max(tools.cmath.cabs(probe_guess.to(t.float32)))
|
||||
|
||||
self.probe = t.nn.Parameter(probe_guess.to(t.float32)
|
||||
/ self.probe_norm)
|
||||
|
||||
self.obj = t.nn.Parameter(obj_guess.to(t.float32))
|
||||
|
||||
if background is None:
|
||||
if detector_slice is not None:
|
||||
background = 1e-6 * t.ones(self.probe[0][self.detector_slice].shape[:-1])
|
||||
else:
|
||||
background = 1e-6 * t.ones(self.probe[0].shape[:-1])
|
||||
|
||||
|
||||
self.background = t.nn.Parameter(t.Tensor(background).to(t.float32))
|
||||
|
||||
if weights is None:
|
||||
self.weights = None
|
||||
else:
|
||||
self.weights = t.nn.Parameter(t.Tensor(weights).to(t.float32))
|
||||
|
||||
if translation_offsets is None:
|
||||
self.translation_offsets = None
|
||||
else:
|
||||
self.translation_offsets = t.nn.Parameter(t.Tensor(translation_offsets).to(t.float32)/ translation_scale)
|
||||
|
||||
self.translation_scale = translation_scale
|
||||
|
||||
if probe_support is not None:
|
||||
self.probe_support = probe_support
|
||||
else:
|
||||
self.probe_support = t.ones_like(self.probe[0])
|
||||
|
||||
if obj_support is not None:
|
||||
self.obj_support = obj_support
|
||||
self.obj.data = self.obj * obj_support
|
||||
else:
|
||||
self.obj_support = t.ones_like(self.obj)
|
||||
|
||||
self.oversampling = oversampling
|
||||
|
||||
# Here we need to implement a simple condition to choose whether
|
||||
# to propagate the probe or not
|
||||
if not( propagate_probe is True or propagate_probe is False):
|
||||
pass
|
||||
else:
|
||||
self.propagate_probe = propagate_probe
|
||||
|
||||
if not(correct_tilt is True or correct_tilt is False):
|
||||
pass
|
||||
else:
|
||||
self.correct_tilt = correct_tilt
|
||||
|
||||
|
||||
@classmethod
|
||||
def from_dataset(cls, dataset, probe_size=None, randomize_ang=0, padding=0, n_modes=1, translation_scale = 1, saturation=None, probe_support_radius=None, propagation_distance=None, restrict_obj=-1, scattering_mode=None, oversampling=1, auto_center=True):
|
||||
|
||||
wavelength = dataset.wavelength
|
||||
det_basis = dataset.detector_geometry['basis']
|
||||
det_shape = dataset[0][1].shape
|
||||
distance = dataset.detector_geometry['distance']
|
||||
|
||||
# always do this on the cpu
|
||||
get_as_args = dataset.get_as_args
|
||||
dataset.get_as(device='cpu')
|
||||
(indices, translations), patterns = dataset[:]
|
||||
dataset.get_as(*get_as_args[0],**get_as_args[1])
|
||||
|
||||
# Set to none to avoid issues with things outside the detector
|
||||
if auto_center:
|
||||
center = tools.image_processing.centroid(t.sum(patterns,dim=0))
|
||||
else:
|
||||
center = None
|
||||
|
||||
# Then, generate the probe geometry from the dataset
|
||||
ewg = tools.initializers.exit_wave_geometry
|
||||
probe_basis, probe_shape, det_slice = ewg(det_basis,
|
||||
det_shape,
|
||||
wavelength,
|
||||
distance,
|
||||
center=center,
|
||||
padding=padding,
|
||||
opt_for_fft=False,
|
||||
oversampling=oversampling)
|
||||
|
||||
|
||||
if hasattr(dataset, 'sample_info') and \
|
||||
dataset.sample_info is not None and \
|
||||
'orientation' in dataset.sample_info:
|
||||
surface_normal = dataset.sample_info['orientation'][2]
|
||||
else:
|
||||
surface_normal = np.array([0.,0.,1.])
|
||||
|
||||
|
||||
# If this information is supplied when the function is called,
|
||||
# then we override the information in the .cxi file
|
||||
if scattering_mode in {'t', 'transmission'}:
|
||||
surface_normal = np.array([0.,0.,1.])
|
||||
elif scattering_mode in {'r', 'reflection'}:
|
||||
outgoing_dir = np.cross(det_basis[:,0], det_basis[:,1])
|
||||
outgoing_dir /= np.linalg.norm(outgoing_dir)
|
||||
surface_normal = outgoing_dir + np.array([0.,0.,1.])
|
||||
surface_normal /= np.linalg.norm(outgoing_dir)
|
||||
|
||||
|
||||
# Next generate the object geometry from the probe geometry and
|
||||
# the translations
|
||||
pix_translations = tools.interactions.translations_to_pixel(probe_basis, translations, surface_normal=surface_normal)
|
||||
|
||||
obj_size, min_translation = tools.initializers.calc_object_setup(probe_shape, pix_translations, padding=200)
|
||||
|
||||
if hasattr(dataset, 'background') and dataset.background is not None:
|
||||
background = t.sqrt(dataset.background)
|
||||
else:
|
||||
background = None
|
||||
|
||||
# Finally, initialize the probe and object using this information
|
||||
if probe_size is None:
|
||||
probe = tools.initializers.SHARP_style_probe(dataset, probe_shape, det_slice, propagation_distance=propagation_distance, oversampling=oversampling)
|
||||
else:
|
||||
probe = tools.initializers.gaussian_probe(dataset, probe_basis, probe_shape, probe_size, propagation_distance=propagation_distance)
|
||||
|
||||
|
||||
# Now we initialize all the subdominant probe modes
|
||||
probe_max = t.max(cmath.cabs(probe))
|
||||
probe_stack = [0.01 * probe_max * t.rand(probe.shape,dtype=probe.dtype) for i in range(n_modes - 1)]
|
||||
probe = t.stack([probe,] + probe_stack)
|
||||
|
||||
obj = tools.cmath.expi(randomize_ang * (t.rand(obj_size)-0.5))
|
||||
|
||||
det_geo = dataset.detector_geometry
|
||||
|
||||
translation_offsets = 0 * (t.rand((len(dataset),2)) - 0.5)
|
||||
|
||||
weights = t.ones(len(dataset))
|
||||
|
||||
if hasattr(dataset, 'mask') and dataset.mask is not None:
|
||||
mask = dataset.mask.to(t.bool)
|
||||
else:
|
||||
mask = None
|
||||
|
||||
if probe_support_radius is not None:
|
||||
probe_support = t.zeros_like(probe[0].to(dtype=t.float32))
|
||||
p_cent = np.array(probe.shape[1:3]).astype(int) // 2
|
||||
psr = int(probe_support_radius)
|
||||
probe_support[p_cent[0]-psr:p_cent[0]+psr,
|
||||
p_cent[1]-psr:p_cent[1]+psr] = 1
|
||||
else:
|
||||
probe_support = None;
|
||||
|
||||
if restrict_obj != -1:
|
||||
ro = restrict_obj
|
||||
os = np.array(obj_size)
|
||||
ps = np.array(probe_shape)
|
||||
obj_support = t.zeros_like(obj.to(dtype=t.float32))
|
||||
obj_support[ps[0]//2-ro:os[0]+ro-ps[0]//2,
|
||||
ps[1]//2-ro:os[1]+ro-ps[1]//2] = 1
|
||||
else:
|
||||
obj_support = None
|
||||
|
||||
return cls(wavelength, det_geo, probe_basis, probe, obj,
|
||||
detector_slice=det_slice,
|
||||
surface_normal=surface_normal,
|
||||
min_translation=min_translation,
|
||||
translation_offsets = translation_offsets,
|
||||
weights=weights, mask=mask, background=background,
|
||||
translation_scale=translation_scale,
|
||||
saturation=saturation,
|
||||
probe_support=probe_support,
|
||||
obj_support=obj_support,
|
||||
oversampling=oversampling)
|
||||
|
||||
|
||||
def interaction(self, index, translations):
|
||||
pix_trans = tools.interactions.translations_to_pixel(self.probe_basis,
|
||||
translations,
|
||||
surface_normal=self.surface_normal)
|
||||
pix_trans -= self.min_translation
|
||||
|
||||
if self.translation_offsets is not None:
|
||||
pix_trans += self.translation_scale * self.translation_offsets[index]
|
||||
|
||||
all_exit_waves = []
|
||||
for i in range(self.probe.shape[0]):
|
||||
pr = self.probe[i] * self.probe_support
|
||||
exit_waves = self.probe_norm * tools.interactions.ptycho_2D_sinc(pr,
|
||||
self.obj_support * self.obj,
|
||||
pix_trans,
|
||||
shift_probe=True)
|
||||
exit_waves = exit_waves * self.probe_support[...,:,:]
|
||||
|
||||
|
||||
if exit_waves.dim() == 4:
|
||||
exit_waves = self.weights[index][:,None,None,None] * exit_waves
|
||||
else:
|
||||
exit_waves = self.weights[index] * exit_waves
|
||||
|
||||
all_exit_waves.append(exit_waves)
|
||||
|
||||
|
||||
return t.stack(all_exit_waves)
|
||||
|
||||
|
||||
def forward_propagator(self, wavefields):
|
||||
return tools.propagators.far_field(wavefields)
|
||||
|
||||
|
||||
def backward_propagator(self, wavefields):
|
||||
return tools.propagators.inverse_far_field(wavefields)
|
||||
|
||||
|
||||
def measurement(self, wavefields):
|
||||
return tools.measurements.quadratic_background(wavefields,
|
||||
self.background,
|
||||
detector_slice=self.detector_slice,
|
||||
measurement=tools.measurements.incoherent_sum,
|
||||
saturation=self.saturation,
|
||||
oversampling=self.oversampling)
|
||||
|
||||
|
||||
def loss(self, sim_data, real_data, mask=None):
|
||||
return tools.losses.amplitude_mse(real_data, sim_data, mask=mask)
|
||||
|
||||
|
||||
def to(self, *args, **kwargs):
|
||||
super(FancyPtycho, self).to(*args, **kwargs)
|
||||
self.wavelength = self.wavelength.to(*args,**kwargs)
|
||||
# move the detector geometry too
|
||||
det_geo = self.detector_geometry
|
||||
if hasattr(det_geo, 'distance'):
|
||||
det_geo['distance'] = det_geo['distance'].to(*args,**kwargs)
|
||||
if hasattr(det_geo, 'basis'):
|
||||
det_geo['basis'] = det_geo['basis'].to(*args,**kwargs)
|
||||
if hasattr(det_geo, 'corner'):
|
||||
det_geo['corner'] = det_geo['corner'].to(*args,**kwargs)
|
||||
|
||||
if self.mask is not None:
|
||||
self.mask = self.mask.to(*args, **kwargs)
|
||||
|
||||
|
||||
self.min_translation = self.min_translation.to(*args,**kwargs)
|
||||
self.probe_basis = self.probe_basis.to(*args,**kwargs)
|
||||
self.probe_norm = self.probe_norm.to(*args,**kwargs)
|
||||
self.probe_support = self.probe_support.to(*args,**kwargs)
|
||||
self.obj_support = self.obj_support.to(*args,**kwargs)
|
||||
self.surface_normal = self.surface_normal.to(*args, **kwargs)
|
||||
|
||||
|
||||
def sim_to_dataset(self, args_list):
|
||||
# In the future, potentially add more control
|
||||
# over what metadata is saved (names, etc.)
|
||||
|
||||
# First, I need to gather all the relevant data
|
||||
# that needs to be added to the dataset
|
||||
entry_info = {'program_name': 'CDTools',
|
||||
'instrument_n': 'Simulated Data',
|
||||
'start_time': datetime.now()}
|
||||
|
||||
surface_normal = self.surface_normal.detach().cpu().numpy()
|
||||
xsurfacevec = np.cross(np.array([0.,1.,0.]), surface_normal)
|
||||
xsurfacevec /= np.linalg.norm(xsurfacevec)
|
||||
ysurfacevec = np.cross(surface_normal, xsurfacevec)
|
||||
ysurfacevec /= np.linalg.norm(ysurfacevec)
|
||||
orientation = np.array([xsurfacevec, ysurfacevec, surface_normal])
|
||||
|
||||
sample_info = {'description': 'A simulated sample',
|
||||
'orientation': orientation}
|
||||
|
||||
|
||||
detector_geometry = self.detector_geometry
|
||||
mask = self.mask
|
||||
wavelength = self.wavelength
|
||||
indices, translations = args_list
|
||||
|
||||
# Then we simulate the results
|
||||
data = self.forward(indices, translations)
|
||||
|
||||
# And finally, we make the dataset
|
||||
return Ptycho2DDataset(translations, data,
|
||||
entry_info = entry_info,
|
||||
sample_info = sample_info,
|
||||
wavelength=wavelength,
|
||||
detector_geometry=detector_geometry,
|
||||
mask=mask)
|
||||
|
||||
|
||||
def corrected_translations(self,dataset):
|
||||
translations = dataset.translations.to(dtype=self.probe.dtype,device=self.probe.device)
|
||||
t_offset = tools.interactions.pixel_to_translations(self.probe_basis,self.translation_offsets*self.translation_scale,surface_normal=self.surface_normal)
|
||||
return translations + t_offset
|
||||
|
||||
|
||||
# Needs to be updated to allow for plotting to an existing figure
|
||||
plot_list = [
|
||||
('Dominant Probe Amplitude',
|
||||
lambda self, fig: p.plot_amplitude(self.probe[0], fig=fig, basis=self.probe_basis)),
|
||||
('Dominant Probe Phase',
|
||||
lambda self, fig: p.plot_phase(self.probe[0], fig=fig, basis=self.probe_basis)),
|
||||
('Subdominant Probe Amplitude',
|
||||
lambda self, fig: p.plot_amplitude(self.probe[1], fig=fig, basis=self.probe_basis),
|
||||
lambda self: len(self.probe) >=2),
|
||||
('Subdominant Probe Phase',
|
||||
lambda self, fig: p.plot_phase(self.probe[1], fig=fig, basis=self.probe_basis),
|
||||
lambda self: len(self.probe) >=2),
|
||||
('Object Amplitude',
|
||||
lambda self, fig: p.plot_amplitude(self.obj, fig=fig, basis=self.probe_basis)),
|
||||
('Object Phase',
|
||||
lambda self, fig: p.plot_phase(self.obj, fig=fig, basis=self.probe_basis)),
|
||||
('Corrected Translations',
|
||||
lambda self, fig, dataset: p.plot_translations(self.corrected_translations(dataset), fig=fig)),
|
||||
('Background',
|
||||
lambda self, fig: plt.figure(fig.number) and plt.imshow(self.background.detach().cpu().numpy()**2))
|
||||
]
|
||||
|
||||
|
||||
def save_results(self, dataset):
|
||||
basis = self.probe_basis.detach().cpu().numpy()
|
||||
translations = self.corrected_translations(dataset).detach().cpu().numpy()
|
||||
probe = cmath.torch_to_complex(self.probe.detach().cpu())
|
||||
probe = probe * self.probe_norm.detach().cpu().numpy()
|
||||
obj = cmath.torch_to_complex(self.obj.detach().cpu())
|
||||
background = self.background.detach().cpu().numpy()**2
|
||||
weights = self.weights.detach().cpu().numpy()
|
||||
|
||||
return {'basis':basis, 'translation':translations,
|
||||
'probe':probe,'obj':obj,
|
||||
'background':background,
|
||||
'weights':weights}
|
||||
|
||||
@@ -117,10 +117,68 @@ def generate_angular_spectrum_propagator(shape, spacing, wavelength, z, *args, *
|
||||
# the previous expression to ensure that complex frequencies
|
||||
# get mapped to values <1 instead of >1
|
||||
propagator = complex_to_torch(np.conj(propagator))
|
||||
|
||||
|
||||
return propagator.to(*args, **kwargs)
|
||||
|
||||
|
||||
def generate_generalized_angular_spectrum_propagator(shape, basis, wavelength, propagation_vector, *args, **kwargs):
|
||||
"""Generates an angular-spectrum based near-field propagator from experimental quantities
|
||||
|
||||
This function generates an angular-spectrum based near field
|
||||
propagator that will work on torch Tensors. The function is structured
|
||||
this way - to generate the propagator first - because the
|
||||
generation of the propagation mask is a bit expensive and if this
|
||||
propagator is used in a reconstruction program, then it will be best
|
||||
to calculate this mask once and close over it.
|
||||
|
||||
Formally, this propagator is the complex conjugate of the fourier
|
||||
transform of the convolution kernel for light propagation in free
|
||||
space
|
||||
|
||||
This function is written to work on any wavefield defined on any
|
||||
array of parallelograms. In addition, there is an assumed phase ramp
|
||||
applied to the wavefield before propagation, defined such that a feature
|
||||
with uniform phase will propagate along the direction of the
|
||||
defined propagation vector. This helps simplify
|
||||
|
||||
|
||||
Parameters
|
||||
----------
|
||||
shape : array
|
||||
The shape of the arrays to be propagated
|
||||
spacing : array
|
||||
The (2x3) set of basis vectors describing the array to be propagated
|
||||
wavelength : float
|
||||
The wavelength of light to simulate propagation of
|
||||
propagation_vector : array
|
||||
The displacement to propagate the wavefield along.
|
||||
tilt : float
|
||||
The tilt, in radians, of the plane that the wavefield is defined on
|
||||
|
||||
Returns
|
||||
-------
|
||||
propagator : torch.Tensor
|
||||
A phase mask which accounts for the phase change that each plane wave will undergo.
|
||||
"""
|
||||
|
||||
ki = 2 * np.pi * fftpack.fftfreq(shape[0],spacing[0])
|
||||
kj = 2 * np.pi * fftpack.fftfreq(shape[1],spacing[1])
|
||||
Kj, Ki = np.meshgrid(kj,ki)
|
||||
|
||||
# Define this as complex so the square root properly gives
|
||||
# k>k0 components imaginary frequencies
|
||||
k0 = np.complex128((2*np.pi/wavelength))
|
||||
|
||||
propagator = np.exp(1j*np.sqrt(k0**2 - Ki**2 - Kj**2) * z)
|
||||
|
||||
# Take the conjugate explicitly here instead of negating
|
||||
# the previous expression to ensure that complex frequencies
|
||||
# get mapped to values <1 instead of >1
|
||||
propagator = complex_to_torch(np.conj(propagator))
|
||||
|
||||
return propagator.to(**kwargs)
|
||||
|
||||
|
||||
def near_field(wavefront, angular_spectrum_propagator):
|
||||
""" Propagates a wavefront via the angular spectrum method
|
||||
|
||||
|
||||
Reference in New Issue
Block a user