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554 lines
24 KiB
Python
554 lines
24 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 CDTools.tools import analysis
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from matplotlib import pyplot as plt
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from datetime import datetime
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import numpy as np
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from scipy import linalg as sla
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from copy import copy
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__all__ = ['FancyPtycho']
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class FancyPtycho(CDIModel):
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def __init__(self, wavelength, detector_geometry,
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probe_basis,
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probe_guess, obj_guess,
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detector_slice=None,
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surface_normal=t.tensor([0., 0., 1.], dtype=t.float32),
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min_translation=t.tensor([0, 0], dtype=t.float32),
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background=None, translation_offsets=None, mask=None,
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weights=None, translation_scale=1, saturation=None,
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probe_support=None, oversampling=1,
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loss='amplitude mse', units='um'):
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super(FancyPtycho, 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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self.saturation = saturation
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self.units = units
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if mask is None:
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self.mask = mask
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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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if probe_guess.dim() > 2:
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self.probe_norm = 1 * t.max(t.abs(probe_guess[0]))
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else:
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self.probe_norm = 1 * 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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if background is None:
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if detector_slice is not None:
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background = 1e-6 * t.ones(
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self.probe[0][self.detector_slice].shape,
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dtype=t.float32)
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else:
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background = 1e-6 * t.ones(self.probe[0].shape,
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dtype=t.float32)
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self.background = t.nn.Parameter(background)
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if weights is None:
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self.weights = None
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else:
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# We now need to distinguish between real-valued per-image
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# weights and complex-valued per-mode weight matrices
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if len(weights.shape) == 1:
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# This is if it's just a list of numbers
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self.weights = t.nn.Parameter(t.tensor(weights,
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dtype=t.float32))
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else:
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# Now this is a matrix of weights, so it needs to be complex
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self.weights = t.nn.Parameter(t.tensor(weights,
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dtype=t.complex64))
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if translation_offsets is None:
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self.translation_offsets = None
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else:
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t_o = t.tensor(translation_offsets, dtype=t.float32)
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t_o = t_o / translation_scale
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self.translation_offsets = t.nn.Parameter(t_o)
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self.translation_scale = translation_scale
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if probe_support is not None:
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self.probe_support = probe_support
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else:
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self.probe_support = t.ones_like(self.probe[0], dtype=t.bool)
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self.oversampling = oversampling
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# Here we set the appropriate loss function
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if (loss.lower().strip() == 'amplitude mse'
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or loss.lower().strip() == 'amplitude_mse'):
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self.loss = tools.losses.amplitude_mse
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elif (loss.lower().strip() == 'poisson nll'
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or loss.lower().strip() == 'poisson_nll'):
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self.loss = tools.losses.poisson_nll
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else:
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raise KeyError('Specified loss function not supported')
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@classmethod
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def from_dataset(cls, dataset, probe_size=None, randomize_ang=0, padding=0, n_modes=1, dm_rank=None, translation_scale=1, saturation=None, probe_support_radius=None, propagation_distance=None, scattering_mode=None, oversampling=1, auto_center=False, opt_for_fft=False, loss='amplitude mse', units='um'):
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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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# We include the *extras to make this work even with datasets, like
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# polarization dependent datasets, that might toss out extra inputs
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(indices, translations, *extras), patterns = dataset[:]
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dataset.get_as(*get_as_args[0], **get_as_args[1])
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# Set to none to avoid issues with things outside the detector
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if auto_center:
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center = tools.image_processing.centroid(t.sum(patterns, dim=0))
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else:
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center = None
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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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padding=padding,
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opt_for_fft=opt_for_fft,
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oversampling=oversampling)
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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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# If this information is supplied when the function is called,
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# then we override the information in the .cxi file
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if scattering_mode in {'t', 'transmission'}:
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surface_normal = np.array([0., 0., 1.])
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elif scattering_mode in {'r', 'reflection'}:
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outgoing_dir = np.cross(det_basis[:, 0], det_basis[:, 1])
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outgoing_dir /= np.linalg.norm(outgoing_dir)
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surface_normal = outgoing_dir + np.array([0., 0., 1.])
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surface_normal /= -np.linalg.norm(surface_normal)
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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, padding=200)
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if hasattr(dataset, 'background') and dataset.background is not None:
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background = t.sqrt(dataset.background)
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else:
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background = None
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# Finally, initialize the probe and object using this information
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if probe_size is None:
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probe = tools.initializers.SHARP_style_probe(dataset, probe_shape, det_slice, propagation_distance=propagation_distance, oversampling=oversampling)
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else:
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probe = tools.initializers.gaussian_probe(dataset, probe_basis, probe_shape, probe_size, propagation_distance=propagation_distance)
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# Now we initialize all the subdominant probe modes
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probe_max = t.max(t.abs(probe))
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probe_stack = [0.01 * probe_max * t.rand(probe.shape, dtype=probe.dtype) for i in range(n_modes - 1)]
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probe = t.stack([probe, ] + probe_stack)
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# probe = t.stack([tools.propagators.far_field(probe),] + probe_stack)
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obj = t.exp(1j * randomize_ang * (t.rand(obj_size)-0.5))
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det_geo = dataset.detector_geometry
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translation_offsets = 0 * (t.rand((len(dataset), 2)) - 0.5)
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if dm_rank is not None and dm_rank != 0:
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if dm_rank > n_modes:
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raise KeyError('Density matrix rank cannot be greater than the number of modes. Use dm_rank = -1 to use a full rank matrix.')
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elif dm_rank == -1:
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# dm_rank == -1 is defined to mean full-rank
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dm_rank = n_modes
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Ws = t.zeros(len(dataset), dm_rank, n_modes, dtype=t.complex64)
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# Start with as close to the identity matrix as possible,
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# cutting of when we hit the specified maximum rank
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for i in range(0, dm_rank):
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Ws[:, i, i] = 1
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else:
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# dm_rank == None or dm_rank = 0 triggers a special case where
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# a standard incoherent multi-mode model is used. This is the
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# default, because it is so common.
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# In this case, we define a set of weights which only has one index
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Ws = t.ones(len(dataset))
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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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if probe_support_radius is not None:
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probe_support = t.zeros(probe[0].shape, dtype=t.bool)
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xs, ys = np.mgrid[:probe.shape[-2], :probe.shape[-1]]
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xs = xs - np.mean(xs)
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ys = ys - np.mean(ys)
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Rs = np.sqrt(xs**2 + ys**2)
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probe_support[Rs < probe_support_radius] = 1
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probe = probe * probe_support[None, :, :]
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else:
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probe_support = None
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return cls(wavelength, det_geo, probe_basis, probe, obj,
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detector_slice=det_slice,
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surface_normal=surface_normal,
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min_translation=min_translation,
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translation_offsets=translation_offsets,
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weights=Ws, mask=mask, background=background,
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translation_scale=translation_scale,
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saturation=saturation,
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probe_support=probe_support,
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oversampling=oversampling,
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loss=loss, units=units)
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def interaction(self, index, translations, *args):
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# The *args is included so that this can work even when given, say,
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# a polarized ptycho dataset that might spit out more inputs.
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# Step 1 is to convert the translations for each position into a
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# value in pixels
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pix_trans = tools.interactions.translations_to_pixel(
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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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# We then add on any recovered translation offset, if they exist
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if self.translation_offsets is not None:
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pix_trans += (self.translation_scale *
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self.translation_offsets[index])
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# This restricts the basis probes to stay within the probe support
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basis_prs = self.probe * self.probe_support[..., :, :]
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# Now we construct the probes for each shot from the basis probes
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Ws = self.weights[index]
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if len(self.weights[0].shape) == 0:
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# If a purely stable coherent illumination is defined
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prs = Ws[..., None, None, None] * basis_prs
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else:
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# If a frame-by-frame weight matrix is defined
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# This takes the dot product of all the weight matrices with
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# the probes. The output has dimensions of translation, then
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# coherent mode index, then x,y, and then complex index
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# Maybe this can be done with a matmul now?
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prs = t.sum(Ws[..., None, None] * basis_prs, axis=-3)
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# Now we actually do the interaction, using the sinc subpixel
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# translation model as per usual
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exit_waves = self.probe_norm * tools.interactions.ptycho_2D_sinc(
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prs, self.obj, pix_trans,
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shift_probe=True, multiple_modes=True)
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return exit_waves
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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.quadratic_background(
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wavefields,
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self.background,
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detector_slice=self.detector_slice,
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measurement=tools.measurements.incoherent_sum,
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saturation=self.saturation,
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oversampling=self.oversampling)
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# Note: No "loss" function is defined here, because it is added
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# dynamically during object creation in __init__
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def to(self, *args, **kwargs):
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super(FancyPtycho, 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.probe_support = self.probe_support.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(
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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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def corrected_translations(self, dataset):
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translations = dataset.translations.to(
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dtype=t.float32, device=self.probe.device)
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if (hasattr(self, 'translation_offsets') and
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self.translation_offsets is not None):
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t_offset = tools.interactions.pixel_to_translations(
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self.probe_basis,
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self.translation_offsets * self.translation_scale,
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surface_normal=self.surface_normal)
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return translations + t_offset
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else:
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return translations
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def get_rhos(self):
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# If this is the general unified mode model
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if self.weights.dim() >= 2:
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Ws = self.weights.detach().cpu().numpy()
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rhos_out = np.matmul(np.swapaxes(Ws, 1, 2), Ws.conj())
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return rhos_out
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# This is the purely incoherent case
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else:
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return np.array([np.eye(self.probe.shape[0])]*self.weights.shape[0],
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dtype=np.complex64)
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def tidy_probes(self, normalization=1, normalize=False):
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"""Tidies up the probes
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What we want to do here is use all the information on all the probes
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to calculate a natural basis for the experiment, and update all the
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density matrices to operate in that updated basis
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"""
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# First we treat the purely incoherent case
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# I don't love this pattern of using an if statement with a return
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# to catch this case, but because it's so much simpler than the
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# unified mode case I think it's appropriate
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if self.weights.dim() == 1:
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probe = self.probe.detach().cpu().numpy()
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ortho_probes = analysis.orthogonalize_probes(probe)
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self.probe.data = t.as_tensor(
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ortho_probes,
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device=self.probe.device,
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dtype=self.probe.dtype)
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return
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# This is for the unified mode case
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# Note to future: We could probably do this more cleanly with an
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# SVD directly on the Ws matrix, instead of an eigendecomposition
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# of the rho matrix.
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rhos = self.get_rhos()
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overall_rho = np.mean(rhos, axis=0)
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probe = self.probe.detach().cpu().numpy()
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ortho_probes, A = analysis.orthogonalize_probes(
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probe, density_matrix=overall_rho,
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keep_transform=True, normalize=normalize)
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Aconj = A.conj()
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Atrans = np.transpose(A)
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new_rhos = np.matmul(Atrans, np.matmul(rhos, Aconj))
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new_rhos /= normalization
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ortho_probes *= np.sqrt(normalization)
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dm_rank = self.weights.shape[1]
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new_Ws = []
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for rho in new_rhos:
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# These are returned from smallest to largest - we want to keep
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# the largest ones
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w, v = sla.eigh(rho)
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w = w[::-1][:dm_rank]
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v = v[:, ::-1][:, :dm_rank]
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# For situations where the rank of the density matrix is not
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# full in reality, but we keep more modes around than needed,
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# some ws can go negative due to numerical error! This is
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# extremely rare, but comon enough to cause crashes occasionally
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# when there are thousands of individual matrices to transform
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# every time this is called.
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w = np.maximum(w, 0)
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new_Ws.append(np.dot(np.diag(np.sqrt(w)), v.transpose()))
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new_Ws = np.array(new_Ws)
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self.weights.data = t.as_tensor(
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new_Ws, dtype=self.weights.dtype, device=self.weights.device)
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self.probe.data = t.as_tensor(
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ortho_probes, device=self.probe.device, dtype=self.probe.dtype)
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def plot_wavefront_variation(self, dataset, fig=None, mode='amplitude', **kwargs):
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def get_probes(idx):
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basis_prs = self.probe * self.probe_support[..., :, :]
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prs = t.sum(self.weights[idx, :, :, None, None] * basis_prs,
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axis=-4)
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ortho_probes = analysis.orthogonalize_probes(prs)
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|
|
|
if mode.lower() == 'amplitude':
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return np.abs(ortho_probes.detach().cpu().numpy())
|
|
if mode.lower() == 'root_sum_intensity':
|
|
return np.sum(np.abs(ortho_probes.detach().cpu().numpy())**2,
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|
axis=0)
|
|
if mode.lower() == 'phase':
|
|
return np.angle(ortho_probes.detach().cpu().numpy())
|
|
|
|
probe_matrix = np.zeros([self.probe.shape[0]]*2,
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|
dtype=np.complex64)
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|
np_probes = self.probe.detach().cpu().numpy()
|
|
for i in range(probe_matrix.shape[0]):
|
|
for j in range(probe_matrix.shape[0]):
|
|
probe_matrix[i,j] = np.sum(np_probes[i]*np_probes[j].conj())
|
|
|
|
weights = self.weights.detach().cpu().numpy()
|
|
|
|
probe_intensities = np.sum(np.tensordot(weights, probe_matrix, axes=1)
|
|
* weights.conj(), axis=2)
|
|
|
|
# Imaginary part is already essentially zero up to rounding error
|
|
probe_intensities = np.real(probe_intensities)
|
|
|
|
values = np.sum(probe_intensities, axis=1)
|
|
if mode.lower() == 'amplitude' or mode.lower() == 'root_sum_intensity':
|
|
cmap = 'viridis'
|
|
else:
|
|
cmap = 'twilight'
|
|
|
|
p.plot_nanomap_with_images(self.corrected_translations(dataset), get_probes, values=values, fig=fig, units=self.units, basis=self.probe_basis, nanomap_colorbar_title='Total Probe Intensity', cmap=cmap, **kwargs),
|
|
|
|
|
|
plot_list = [
|
|
('',
|
|
lambda self, fig, dataset: self.plot_wavefront_variation(dataset, fig=fig, mode='root_sum_intensity', image_title='Root Summed Probe Intensities', image_colorbar_title='Square Root of Intensity'),
|
|
lambda self: len(self.weights.shape) >= 2),
|
|
('',
|
|
lambda self, fig, dataset: self.plot_wavefront_variation(dataset, fig=fig, mode='amplitude', image_title='Probe Amplitudes (scroll to view modes)', image_colorbar_title='Probe Amplitude'),
|
|
lambda self: len(self.weights.shape) >= 2),
|
|
('',
|
|
lambda self, fig, dataset: self.plot_wavefront_variation(dataset, fig=fig, mode='phase', image_title='Probe Phases (scroll to view modes)', image_colorbar_title='Probe Phase'),
|
|
lambda self: len(self.weights.shape) >= 2),
|
|
('Basis Probe Amplitudes (scroll to view modes)',
|
|
lambda self, fig: p.plot_amplitude(self.probe, fig=fig, basis=self.probe_basis, units=self.units)),
|
|
('Basis Probe Phases (scroll to view modes)',
|
|
lambda self, fig: p.plot_phase(self.probe, fig=fig, basis=self.probe_basis, units=self.units)),
|
|
('Average Density Matrix Amplitudes',
|
|
lambda self, fig: p.plot_amplitude(np.nanmean(np.abs(self.get_rhos()), axis=0), fig=fig),
|
|
lambda self: len(self.weights.shape) >= 2),
|
|
('% Power in Top Mode (only accurate after tidy_probes)',
|
|
lambda self, fig, dataset: p.plot_nanomap(self.corrected_translations(dataset), analysis.calc_top_mode_fraction(self.get_rhos()), fig=fig, units=self.units),
|
|
lambda self: len(self.weights.shape) >= 2),
|
|
('Object Amplitude',
|
|
lambda self, fig: p.plot_amplitude(self.obj, fig=fig, basis=self.probe_basis, units=self.units)),
|
|
('Object Phase',
|
|
lambda self, fig: p.plot_phase(self.obj, fig=fig, basis=self.probe_basis, units=self.units)),
|
|
('Corrected Translations',
|
|
lambda self, fig, dataset: p.plot_translations(self.corrected_translations(dataset), fig=fig, units=self.units)),
|
|
('Background',
|
|
lambda self, fig: plt.figure(fig.number) and plt.imshow(self.background.detach().cpu().numpy()**2))
|
|
]
|
|
|
|
# def plot_errors(self, dataset):
|
|
|
|
|
|
|
|
def save_results(self, dataset):
|
|
basis = self.probe_basis.detach().cpu().numpy()
|
|
translations = self.corrected_translations(dataset).detach().cpu().numpy()
|
|
probe = self.probe.detach().cpu().numpy()
|
|
probe = probe * self.probe_norm.detach().cpu().numpy()
|
|
obj = self.obj.detach().cpu().numpy()
|
|
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}
|