Imaging Models

Imaging(configs)	Base class for imaging models in real-space.
Tomography([configs])	Tomography forward model.
CDI([configs])	Base class for coherent-diffraction imaging (CDI) models.
FourierPtychography([configs])	Fourier-based Ptychography imaging model
BraggPtychography([configs])	Bragg Ptychography imaging model

create_Tomography_model([n_voxels, ...])	Simple interface to create an instace of Tomography
create_CDI_model(*args, **kwargs)	Simple interface to create an instace of CDI
create_FourierPtychography_model(*args, **kwargs)	Simple interface to create an instace of FourierPtychography
create_BraggPtychography_model(*args, **kwargs)	Simple interface to create an instace of BragPtychography

A module that provides implementation(s) of imaging models (tomography, coherent diffraction imaging (CDI), etc.)

This module is incomplete, and will be updated gradually.

class Imaging(configs)

Bases: TimeIndependentModel

Base class for imaging models in real-space.

class TomographyConfigs(*, debug=False, verbose=False, output_dir='./_PYOED_RESULTS_', model_name='Radon-Tomography', screen_output_iter=1, file_output_iter=1, n_voxels=50, n_angles=10, theta=None, scale_matrix=True)

Bases: TimeIndependentModelConfigs

Configurations for the Tomography model.

Parameters:

• verbose (bool) – a boolean flag to control verbosity of the object.

• debug (bool) – a boolean flag that enables adding extra functionlity in a debug mode

• output_dir (str | Path) – the base directory where the output files will be saved.

• screen_output_iter (int) – iteration interval for screen output. Default is 1. Note that this should be a positive integer to enforce proper effect.

• file_out_iter – iteration interval for file output. Default is 1. Note that this should be a positive integer to enforce proper effect.

• model_name (str) – ‘Radon-Tomography’ this is just for verbosity and output.

• n_voxels (int) – Number of pixels along each edge of the image. Default is 50.

• n_angles (int) – Number of angles at which to probe the object used to create theta (if None found); as linearly spaced <n_angles> angles between 1, 360. This is overridden if theta is not None. Default is 10.

• theta (ndarray | None) – If passed, array-like objects that contains angles (all must be between 0, 360). Overridess n_angles if passed. Default is None.

• scale_matrix (bool) – default True; scale A, by maximum sum of rows!

n_voxels: int

n_angles: int

theta: ndarray | None

scale_matrix: bool

__init__(*, debug=False, verbose=False, output_dir='./_PYOED_RESULTS_', model_name='Radon-Tomography', screen_output_iter=1, file_output_iter=1, n_voxels=50, n_angles=10, theta=None, scale_matrix=True)

class Tomography(configs=None)

Bases: Imaging

Tomography forward model.

Parameters:

configs (TomographyConfigs | dict | None) – Object containing the configurations of the Tomography model.

Warning

This implementation is by no means efficient. In fact, it relies on a very inefficient implementation of radon transform. We will consider more efficient approaches if we resort to this much!

__init__(configs=None)

validate_configurations(configs, raise_for_invalid=True)

Check the passed configuratios and make sure they are conformable with each other, and with current configurations once combined. This guarantees that any key-value pair passed in configs can be properly used

Note

Here only the locally-defined configurations in TomographyConfigs are validated. Finally, super classes validators are called.

Parameters:

• configs (dict | TomographyConfigs) – full or partial (subset) configurations to be validated

• raise_for_invalid (bool) – if True raise TypeError for invalid configrations type/key. Default True

Returns:

flag indicating whether passed configurations dictionary is valid or not

Raises:

• AttributeError – if any (or a group) of the configurations does not exist in the model configurations TomographyConfigs.

• PyOEDConfigsValidationError – if the configurations are invalid and raise_for_invalid is set to True.

Return type:

bool

create_radon_forward_operator(n_voxels, n_angles, theta, scale_matrix, verbose=False)

Construct a radon-transoform matrix A for multiple projection angles. Each row of A, takes as an input an image (flattened out as a vector), and create a radon transform.

See TomographyConfigs for more details on the parameters.

Returns:

2D Numpy array representing the radon transform (Linear model for an image)

state_vector(init_val=0)

Create an empty model state vector

is_state_vector(state)

Test if the passed state is of appropriate shape/type/size

parameter_vector(init_val=0)

Create an instance of model parameter vector. Here, the parameter is the model initial state, and thus parameter_vector() actually calls state_vector()

Parameters:

init_val (float) – (optional) value assigned to entries of the parameter vector upon initialization

is_parameter_vector(parameter)

Test whether the passed parameter vector is valid or not Here, the parameter is the model initial state, and thus is_parameter_vector() actually calls is_state_vector()

solve_forward(state, design=None, verbose=False)

Apply/Solve the model (apply the radon transform to an image or a vectorized

version thereof)

Parameters:

• state – either 1d flattened image, or 2d array describing an image

• design – observational design; typically a binary array of the same size as design, or an array of indexes to extract active observations from the result of applying the forward operator.

Returns:

result of applying radon transorm to the passed state/image

Raises:

AssertionError is raised if state or design don’t have the right shape/size

get_model_array()

return a copy of the model operator (matrix)

get_model_grid()

return a copy of the model grid

class CDI(configs=None)

Bases: TimeIndependentModel

Base class for coherent-diffraction imaging (CDI) models. The infrastructure, however, might be different because the domain is in complex space, rather than real space This might end up being similar to Imaging; we will see!

__init__(configs=None)

class FourierPtychography(configs=None)

Bases: CDI

Fourier-based Ptychography imaging model

__init__(configs=None)

class BraggPtychography(configs=None)

Bases: CDI

Bragg Ptychography imaging model

__init__(configs=None)

create_Tomography_model(n_voxels=50, n_angles=10, theta=None, scale_matrix=True)

Simple interface to create an instace of Tomography

create_CDI_model(*args, **kwargs)

Simple interface to create an instace of CDI

create_FourierPtychography_model(*args, **kwargs)

Simple interface to create an instace of FourierPtychography

create_BraggPtychography_model(*args, **kwargs)

Simple interface to create an instace of BragPtychography