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275 lines
11 KiB
Python
275 lines
11 KiB
Python
import torch as t
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from CDTools.models import CDIModel
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from CDTools.datasets import Ptycho2DDataset
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from CDTools import tools
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from CDTools.tools import plotting as p
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from copy import copy
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from torch.utils import data as torchdata
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from datetime import datetime
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import numpy as np
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__all__ = ['SimplePtycho']
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class SimplePtycho(CDIModel):
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"""A simple ptychography model for exploring ideas and extensions
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"""
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def __init__(self, wavelength, detector_geometry,
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probe_basis, detector_slice,
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probe_guess, obj_guess, min_translation = [0,0],
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surface_normal=np.array([0.,0.,1.]), mask=None):
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super(SimplePtycho,self).__init__()
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self.wavelength = t.tensor(wavelength)
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self.detector_geometry = copy(detector_geometry)
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det_geo = self.detector_geometry
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if hasattr(det_geo, 'distance'):
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det_geo['distance'] = t.tensor(det_geo['distance'])
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if hasattr(det_geo, 'basis'):
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det_geo['basis'] = t.tensor(det_geo['basis'])
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if hasattr(det_geo, 'corner'):
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det_geo['corner'] = t.tensor(det_geo['corner'])
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self.min_translation = t.tensor(min_translation)
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self.probe_basis = t.tensor(probe_basis)
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self.detector_slice = copy(detector_slice)
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self.surface_normal = t.tensor(surface_normal)
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if mask is None:
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self.mask = None
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else:
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self.mask = t.tensor(mask, dtype=t.bool)
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probe_guess = t.tensor(probe_guess, dtype=t.complex64)
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obj_guess = t.tensor(obj_guess, dtype=t.complex64)
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# We rescale the probe here so it learns at the same rate as the
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# object
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self.probe_norm = t.max(t.abs(probe_guess))
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self.probe = t.nn.Parameter(probe_guess / self.probe_norm)
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self.obj = t.nn.Parameter(obj_guess)
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@classmethod
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def from_dataset(cls, dataset):
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wavelength = dataset.wavelength
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det_basis = dataset.detector_geometry['basis']
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det_shape = dataset[0][1].shape
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distance = dataset.detector_geometry['distance']
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# always do this on the cpu
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get_as_args = dataset.get_as_args
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dataset.get_as(device='cpu')
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(indices, translations), patterns = dataset[:]
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dataset.get_as(*get_as_args[0],**get_as_args[1])
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center = tools.image_processing.centroid(t.sum(patterns,dim=0))
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# Then, generate the probe geometry from the dataset
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ewg = tools.initializers.exit_wave_geometry
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probe_basis, probe_shape, det_slice = ewg(det_basis,
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det_shape,
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wavelength,
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distance,
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center=center)
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if hasattr(dataset, 'sample_info') and \
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dataset.sample_info is not None and \
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'orientation' in dataset.sample_info:
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surface_normal = dataset.sample_info['orientation'][2]
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else:
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surface_normal = np.array([0.,0.,1.])
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# Next generate the object geometry from the probe geometry and
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# the translations
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pix_translations = tools.interactions.translations_to_pixel(probe_basis, translations, surface_normal=surface_normal)
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obj_size, min_translation = tools.initializers.calc_object_setup(probe_shape, pix_translations)
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# Finally, initialize the probe and object using this information
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probe = tools.initializers.SHARP_style_probe(dataset, probe_shape, det_slice)
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obj = t.ones(obj_size).to(dtype=t.complex64)
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det_geo = dataset.detector_geometry
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if hasattr(dataset, 'mask') and dataset.mask is not None:
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mask = dataset.mask.to(t.bool)
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else:
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mask = None
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return cls(wavelength, det_geo, probe_basis, det_slice, probe, obj, min_translation=min_translation, mask=mask, surface_normal=surface_normal)
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def interaction(self, index, translations):
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pix_trans = tools.interactions.translations_to_pixel(self.probe_basis,
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translations,
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surface_normal=self.surface_normal)
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pix_trans -= self.min_translation
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return tools.interactions.ptycho_2D_round(self.probe_norm * self.probe,
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self.obj,
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pix_trans)
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def forward_propagator(self, wavefields):
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return tools.propagators.far_field(wavefields)
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def backward_propagator(self, wavefields):
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return tools.propagators.inverse_far_field(wavefields)
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def measurement(self, wavefields):
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return tools.measurements.intensity(wavefields,
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detector_slice=self.detector_slice)
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def loss(self, real_data, sim_data, mask=None):
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return tools.losses.amplitude_mse(real_data, sim_data, mask=mask)
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def to(self, *args, **kwargs):
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super(SimplePtycho, self).to(*args, **kwargs)
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self.wavelength = self.wavelength.to(*args,**kwargs)
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# move the detector geometry too
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det_geo = self.detector_geometry
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if hasattr(det_geo, 'distance'):
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det_geo['distance'] = det_geo['distance'].to(*args,**kwargs)
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if hasattr(det_geo, 'basis'):
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det_geo['basis'] = det_geo['basis'].to(*args,**kwargs)
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if hasattr(det_geo, 'corner'):
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det_geo['corner'] = det_geo['corner'].to(*args,**kwargs)
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if self.mask is not None:
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self.mask = self.mask.to(*args, **kwargs)
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self.min_translation = self.min_translation.to(*args,**kwargs)
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self.probe_basis = self.probe_basis.to(*args,**kwargs)
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self.probe_norm = self.probe_norm.to(*args,**kwargs)
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self.surface_normal = self.surface_normal.to(*args, **kwargs)
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def sim_to_dataset(self, args_list):
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# In the future, potentially add more control
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# over what metadata is saved (names, etc.)
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# First, I need to gather all the relevant data
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# that needs to be added to the dataset
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entry_info = {'program_name': 'CDTools',
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'instrument_n': 'Simulated Data',
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'start_time': datetime.now()}
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surface_normal = self.surface_normal.detach().cpu().numpy()
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xsurfacevec = np.cross(np.array([0.,1.,0.]), surface_normal)
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xsurfacevec /= np.linalg.norm(xsurfacevec)
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ysurfacevec = np.cross(surface_normal, xsurfacevec)
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ysurfacevec /= np.linalg.norm(ysurfacevec)
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orientation = np.array([xsurfacevec, ysurfacevec, surface_normal])
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sample_info = {'description': 'A simulated sample',
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'orientation': orientation}
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detector_geometry = self.detector_geometry
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mask = self.mask
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wavelength = self.wavelength
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indices, translations = args_list
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# Then we simulate the results
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data = self.forward(indices, translations)
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# And finally, we make the dataset
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return Ptycho2DDataset(translations, data,
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entry_info = entry_info,
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sample_info = sample_info,
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wavelength=wavelength,
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detector_geometry=detector_geometry,
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mask=mask)
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plot_list = [
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('Probe Amplitude',
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lambda self, fig: p.plot_amplitude(self.probe, fig=fig, basis=self.probe_basis)),
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('Probe Phase',
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lambda self, fig: p.plot_phase(self.probe, fig=fig, basis=self.probe_basis)),
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('Object Amplitude',
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lambda self, fig: p.plot_amplitude(self.obj, fig=fig, basis=self.probe_basis)),
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('Object Phase',
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lambda self, fig: p.plot_phase(self.obj, fig=fig, basis=self.probe_basis))
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]
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def save_results(self):
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probe = self.probe.detach().cpu().numpy()
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probe = probe * self.probe_norm.detach().cpu().numpy()
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obj = self.obj.detach().cpu().numpy()
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return {'probe':probe,'obj':obj}
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def ePIE(self, iterations, dataset, beta = 1.0):
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"""Runs an ePIE reconstruction as described in `Maiden et al. (2017) <https://www.osapublishing.org/optica/abstract.cfm?uri=optica-4-7-736>`_.
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Optional parameters are:
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:arg ``iterations``: Controls the number of iterations run, defaults to 1.
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:arg ``beta``: Algorithmic parameter described in Maiden's implementation of rPIE. Defaults to 0.15.
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:arg ``probe``: Initial probe wavefunction.
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:arg ``object``: Initial object wavefunction.
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"""
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probe_shape = self.probe.shape
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if self.mask is not None:
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mask = self.mask[...,None]
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else:
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mask=None
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def probe_update(exit_wave, exit_wave_corrected, probe, object, translation):
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new_probe = probe + tools.cmath.cmult(beta * tools.cmath.cconj(object[translation])/(self.probe_norm*t.max(tools.cmath.cabssq(object))), exit_wave_corrected-exit_wave)
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return new_probe
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def object_update(exit_wave, exit_wave_corrected, probe, object, translation):
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new_object = object.clone()
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new_object[translation] = object[translation] + tools.cmath.cmult(beta * tools.cmath.cconj(probe)/(self.probe_norm*t.max(tools.cmath.cabssq(probe))), exit_wave_corrected-exit_wave)
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return new_object
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with t.no_grad():
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data_loader = torchdata.DataLoader(dataset, shuffle=True)
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for it in range(iterations):
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loss = []
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for (i, [translations]), [patterns] in data_loader:
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probe = self.probe.data.clone()
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object = self.obj.data.clone()
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exit_wave = self.interaction(i, translations).clone()
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# Apply modulus constraint
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exit_wave_corrected = exit_wave.clone()
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exit_wave_corrected = self.forward_propagator(exit_wave_corrected.clone())
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exit_wave_corrected[self.detector_slice] = tools.projectors.modulus(exit_wave_corrected.clone()[self.detector_slice], patterns, mask = mask)
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exit_wave_corrected = self.backward_propagator(exit_wave_corrected.clone())
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# Calculate the section of the object wavefunction to be modified
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pix_trans = tools.interactions.translations_to_pixel(self.probe_basis,
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translations)
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pix_trans -= self.min_translation
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pix_trans = t.round(pix_trans).to(dtype=t.int32).detach().cpu().numpy()
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object_slice = np.s_[pix_trans[0]:
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pix_trans[0]+probe_shape[0],
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pix_trans[1]:
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pix_trans[1]+probe_shape[1]]
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# Apply probe and object updates
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self.probe.data = probe_update(exit_wave, exit_wave_corrected, probe, object, object_slice)
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self.obj.data = object_update(exit_wave, exit_wave_corrected, probe, object, object_slice)
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# Calculate loss
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loss.append(self.loss(self.measurement(self.interaction(i, translations)), patterns))
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yield t.mean(t.tensor(loss)).cpu().numpy()
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