2.1.1.1.1.11. emicroml.modelling.cbed.distortion.estimation.DefaultDistortionModelGenerator

class DefaultDistortionModelGenerator(reference_pt=(0.5, 0.5), rng_seed=None, sampling_grid_dims_in_pixels=(512, 512), least_squares_alg_params=None, device_name=None, skip_validation_and_conversion=False)[source]

Bases: _DefaultDistortionModelGenerator

The default class of random distortion model generators.

The current class is a subclass of fancytypes.PreSerializableAndUpdatable.

The current class represents the default random distortion model generators used to generate random “fake” CBED patterns. The current class is used in the class emicroml.modelling.cbed.distortion.estimation.DefaultCBEDPatternGenerator, with the latter class representing the default random fake CBED pattern generators. See the documentation for the latter class for further discussion on the default random fake CBED pattern generators.

A random number generator is used to generate random distortion models. Upon construction of an instance of the current class, or core attribute update via the method update(), a random numpy generator rng is constructed via import numpy; rng=numpy.random.default_rng(rng_seed), where rng_seed is the construction parameter or core attribute that specifies the seed used in the random number generator. See the documentation for the class core_attrs for a discussion on core attributes.

The distortion models generated by instances of the current class are “standard”, meaning that the corresponding coordinate transformation \(T_{⌑;x}\left(u_{x},u_{y}\right)\) that describes the optical distortions can be specified equivalently by an instance standard_coord_transform_params of distoptica.StandardCoordTransformParams. The distortion models are instances of the class distoptica.DistortionModel. See the documentation for the class distoptica.DistortionModel for further discussion on how the optical distortions are modelled.

The construction parameters of distoptica.StandardCoordTransformParams that specify \(T_{⌑;x}\left(u_{x},u_{y}\right)\) are center, quadratic_radial_distortion_amplitude, elliptical_distortion_vector, spiral_distortion_amplitude, and parabolic_distortion_vector. For each candidate distortion model, these parameters are calculated by

import numpy as np

r_c_D = rng.normal(loc=0, scale=1/20)
phi_c_D = rng.uniform(low=0, high=2*np.pi)
x_c_D = reference_pt[0] + r_c_D*np.cos(phi_c_D)
y_c_D = reference_pt[1] + r_c_D*np.sin(phi_c_D)
center = (x_c_D, y_c_D)

quadratic_radial_distortion_amplitude = rng.uniform(low=-0.5, high=2)
spiral_distortion_amplitude = rng.uniform(low=-1.5, high=1.5)

amplitude = rng.uniform(low=0, high=0.2)
phase = rng.uniform(low=0, high=np.pi)
elliptical_distortion_vector = (amplitude*np.cos(2*phase),
                                amplitude*np.sin(2*phase))

amplitude = rng.uniform(low=0, high=0.4)
phase = rng.uniform(low=0, high=2*np.pi)
parabolic_distortion_vector = (amplitude*np.cos(phase),
                               amplitude*np.sin(phase))

where reference_pt is a pair of floating-point numbers that users specify as a construction parameter of the current class.

If the floating-point numbers stored in the instance attribute distoptica.DistortionModel.mask_frame_of_distorted_then_resampled_images of the resulting candidate distortion model are less than or equal to 1/8, then the candidate distortion model is accepted as valid and returned as output after calling the method emicroml.modelling.cbed.distortion.estimation.DefaultDistortionModelGenerator.generate(). Otherwise, the candidate distortion model is rejected, and new candidate distortion models are generated until either: one of them is accepted as valid; or 10 candidate distortion models have been rejected in total. In the latter case, an exception is raised. See the documentation for the attribute distoptica.DistortionModel.mask_frame_of_distorted_then_resampled_images for a description of said attribute.

Parameters:

reference_ptarray_like (float, shape=(2,)), optional

A reference point from which to randomly generate distortion centers. See the summary documentation above for context.

rng_seedint | None, optional

rng_seed specifies the seed used in the random number generator.

sampling_grid_dims_in_pixelsarray_like (int, shape=(2,)), optional

The dimensions of the sampling grid, in units of pixels, used for all distortion models.

least_squares_alg_paramsdistoptica.LeastSquaresAlgParams | None, optional

least_squares_alg_params specifies the parameters of the least-squares algorithm to be used to calculate the mappings of fractional Cartesian coordinates of distorted images to those of the corresponding undistorted images. If least_squares_alg_params is set to None, then the parameter will be reassigned to the value distoptica.LeastSquaresAlgParams(). See the documentation for the class distoptica.LeastSquaresAlgParams for details on the parameters of the least-squares algorithm.

device_namestr | None, optional

This parameter specifies the device to be used to perform computationally intensive calls to PyTorch functions and where to store attributes of the type torch.Tensor for each distortion model represented by the class distoptica.DistortionModel. If device_name is a string, then it is the name of the device to be used, e.g. ”cuda” or ”cpu”. If device_name is set to None and a GPU device is available, then a GPU device is to be used. Otherwise, the CPU is used.

skip_validation_and_conversionbool, optional

Let validation_and_conversion_funcs and core_attrs denote the attributes validation_and_conversion_funcs and core_attrs respectively, both of which being dict objects.

Let params_to_be_mapped_to_core_attrs denote the dict representation of the constructor parameters excluding the parameter skip_validation_and_conversion, where each dict key key is a different constructor parameter name, excluding the name "skip_validation_and_conversion", and params_to_be_mapped_to_core_attrs[key] would yield the value of the constructor parameter with the name given by key.

If skip_validation_and_conversion is set to False, then for each key key in params_to_be_mapped_to_core_attrs, core_attrs[key] is set to validation_and_conversion_funcs[key] (params_to_be_mapped_to_core_attrs).

Otherwise, if skip_validation_and_conversion is set to True, then core_attrs is set to params_to_be_mapped_to_core_attrs.copy(). This option is desired primarily when the user wants to avoid potentially expensive deep copies and/or conversions of the dict values of params_to_be_mapped_to_core_attrs, as it is guaranteed that no copies or conversions are made in this case.

Attributes:

core_attrs: dict: The “core attributes”.
de_pre_serialization_funcs: dict: The de-pre-serialization functions.
pre_serialization_funcs: dict: The pre-serialization functions.
validation_and_conversion_funcs: dict: The validation and conversion functions.

Methods

`de_pre_serialize`([serializable_rep, ...])	Construct an instance from a serializable representation.
`dump`([filename, overwrite])	Serialize instance and save the result in a JSON file.
`dumps`()	Serialize instance.
`generate`()	Generate a random distortion model.
`get_core_attrs`([deep_copy])	Return the core attributes.
`get_de_pre_serialization_funcs`()	Return the de-pre-serialization functions.
`get_pre_serialization_funcs`()	Return the pre-serialization functions.
`get_validation_and_conversion_funcs`()	Return the validation and conversion functions.
`load`([filename, skip_validation_and_conversion])	Construct an instance from a serialized representation that is stored in a JSON file.
`loads`([serialized_rep, ...])	Construct an instance from a serialized representation.
`pre_serialize`()	Pre-serialize instance.
`update`(new_core_attr_subset_candidate[, ...])	Update a subset of the core attributes.

execute_post_core_attrs_update_actions

Methods

`de_pre_serialize`	Construct an instance from a serializable representation.
`dump`	Serialize instance and save the result in a JSON file.
`dumps`	Serialize instance.
`execute_post_core_attrs_update_actions`
`generate`	Generate a random distortion model.
`get_core_attrs`	Return the core attributes.
`get_de_pre_serialization_funcs`	Return the de-pre-serialization functions.
`get_pre_serialization_funcs`	Return the pre-serialization functions.
`get_validation_and_conversion_funcs`	Return the validation and conversion functions.
`load`	Construct an instance from a serialized representation that is stored in a JSON file.
`loads`	Construct an instance from a serialized representation.
`pre_serialize`	Pre-serialize instance.
`update`	Update a subset of the core attributes.

Attributes

`core_attrs`	dict: The "core attributes".
`de_pre_serialization_funcs`	dict: The de-pre-serialization functions.
`pre_serialization_funcs`	dict: The pre-serialization functions.
`validation_and_conversion_funcs`	dict: The validation and conversion functions.

property core_attrs

dict: The “core attributes”.

The keys of core_attrs are the same as the attribute validation_and_conversion_funcs, which is also a dict object.

Note that core_attrs should be considered read-only.

property de_pre_serialization_funcs

dict: The de-pre-serialization functions.

de_pre_serialization_funcs has the same keys as the attribute validation_and_conversion_funcs, which is also a dict object.

Let validation_and_conversion_funcs and pre_serialization_funcs denote the attributes validation_and_conversion_funcs pre_serialization_funcs respectively, the last of which being a dict object as well.

Let core_attrs_candidate_1 be any dict object that has the same keys as validation_and_conversion_funcs, where for each dict key key in core_attrs_candidate_1, validation_and_conversion_funcs[key](core_attrs_candidate_1) does not raise an exception.

Let serializable_rep be a dict object that has the same keys as core_attrs_candidate_1, where for each dict key key in core_attrs_candidate_1, serializable_rep[key] is set to pre_serialization_funcs[key](core_attrs_candidate_1[key]).

The items of de_pre_serialization_funcs are expected to be set to callable objects that would lead to de_pre_serialization_funcs[key](serializable_rep[key]) not raising an exception for each dict key key in serializable_rep.

Let core_attrs_candidate_2 be a dict object that has the same keys as serializable_rep, where for each dict key key in validation_and_conversion_funcs, core_attrs_candidate_2[key] is set to de_pre_serialization_funcs[key](serializable_rep[key]).

The items of de_pre_serialization_funcs are also expected to be set to callable objects that would lead to validation_and_conversion_funcs[key](core_attrs_candidate_2) not raising an exception for each dict key key in core_attrs_candidate_2.

Note that de_pre_serialization_funcs should be considered read-only.

classmethod de_pre_serialize(serializable_rep={}, skip_validation_and_conversion=False)

Construct an instance from a serializable representation.

Parameters:

serializable_repdict, optional

A dict object that has the same keys as the attribute validation_and_conversion_funcs, which is also a dict object.

Let validation_and_conversion_funcs and de_pre_serialization_funcs denote the attributes validation_and_conversion_funcs de_pre_serialization_funcs respectively, the last of which being a dict object as well.

The items of serializable_rep are expected to be objects that would lead to de_pre_serialization_funcs[key](serializable_rep[key]) not raising an exception for each dict key key in serializable_rep.

Let core_attrs_candidate be a dict object that has the same keys as serializable_rep, where for each dict key key in serializable_rep, core_attrs_candidate[key] is set to de_pre_serialization_funcs[key](serializable_rep[key])``.

The items of serializable_rep are also expected to be set to objects that would lead to validation_and_conversion_funcs[key](core_attrs_candidate) not raising an exception for each dict key key in serializable_rep.

skip_validation_and_conversionbool, optional

Let core_attrs denote the attribute core_attrs, which is a dict object.

If skip_validation_and_conversion is set to False, then for each key key in serializable_rep, core_attrs[key] is set to validation_and_conversion_funcs[key] (core_attrs_candidate), with validation_and_conversion_funcs and core_attrs_candidate_1 being introduced in the above description of serializable_rep.

Otherwise, if skip_validation_and_conversion is set to True, then core_attrs is set to core_attrs_candidate.copy(). This option is desired primarily when the user wants to avoid potentially expensive deep copies and/or conversions of the dict values of core_attrs_candidate, as it is guaranteed that no copies or conversions are made in this case.

Returns:

instance_of_current_clsCurrent class: An instance constructed from the serializable representation serializable_rep.

dump(filename='serialized_rep_of_fancytype.json', overwrite=False)

Serialize instance and save the result in a JSON file.

Parameters:

filenamestr, optional: The relative or absolute path to the JSON file in which to store the serialized representation of an instance.
overwritebool, optional: If overwrite is set to False and a file exists at the path filename, then the serialized instance is not written to that file and an exception is raised. Otherwise, the serialized instance will be written to that file barring no other issues occur.

Returns:

dumps()

Serialize instance.

Returns:

serialized_repdict: A serialized representation of an instance.

generate()

Generate a random distortion model.

See the summary documentation for the current class for details on how distortion models are randomly generated.

Returns:

distortion_modeldistoptica.DistortionModel: The distortion model.

get_core_attrs(deep_copy=True)

Return the core attributes.

Parameters:

deep_copybool, optional

Let core_attrs denote the attribute core_attrs, which is a dict object.

If deep_copy is set to True, then a deep copy of core_attrs is returned. Otherwise, a shallow copy of core_attrs is returned.

Returns:

core_attrsdict: The attribute core_attrs.

classmethod get_de_pre_serialization_funcs()

Return the de-pre-serialization functions.

Returns:

de_pre_serialization_funcsdict: The attribute de_pre_serialization_funcs.

classmethod get_pre_serialization_funcs()

Return the pre-serialization functions.

Returns:

pre_serialization_funcsdict: The attribute pre_serialization_funcs.

classmethod get_validation_and_conversion_funcs()

Return the validation and conversion functions.

Returns:

validation_and_conversion_funcsdict: The attribute validation_and_conversion_funcs.

classmethod load(filename='serialized_rep_of_fancytype.json', skip_validation_and_conversion=False)

Construct an instance from a serialized representation that is stored in a JSON file.

Users can save serialized representations to JSON files using the method fancytypes.PreSerializable.dump().

Parameters:

filenamestr, optional

The relative or absolute path to the JSON file that is storing the serialized representation of an instance.

filename is expected to be such that json.load(open(filename, "r")) does not raise an exception.

Let serializable_rep=json.load(open(filename, "r")).

Let validation_and_conversion_funcs and de_pre_serialization_funcs denote the attributes validation_and_conversion_funcs de_pre_serialization_funcs respectively, both of which being dict objects as well.

filename is also expected to be such that de_pre_serialization_funcs[key](serializable_rep[key]) does not raise an exception for each dict key key in de_pre_serialization_funcs.

Let core_attrs_candidate be a dict object that has the same keys as de_pre_serialization_funcs, where for each dict key key in serializable_rep, core_attrs_candidate[key] is set to de_pre_serialization_funcs[key](serializable_rep[key])``.

filename is also expected to be such that validation_and_conversion_funcs[key](core_attrs_candidate) does not raise an exception for each dict key key in serializable_rep.

skip_validation_and_conversionbool, optional

Let core_attrs denote the attribute core_attrs, which is a dict object.

Let core_attrs_candidate be as defined in the above description of filename.

If skip_validation_and_conversion is set to False, then for each key key in core_attrs_candidate, core_attrs[key] is set to validation_and_conversion_funcs[key] (core_attrs_candidate), , with validation_and_conversion_funcs and core_attrs_candidate being introduced in the above description of filename.

Otherwise, if skip_validation_and_conversion is set to True, then core_attrs is set to core_attrs_candidate.copy(). This option is desired primarily when the user wants to avoid potentially expensive deep copies and/or conversions of the dict values of core_attrs_candidate, as it is guaranteed that no copies or conversions are made in this case.

Returns:

instance_of_current_clsCurrent class: An instance constructed from the serialized representation stored in the JSON file.

classmethod loads(serialized_rep='{}', skip_validation_and_conversion=False)

Construct an instance from a serialized representation.

Users can generate serialized representations using the method dumps().

Parameters:

serialized_repstr | bytes | bytearray, optional

The serialized representation.

serialized_rep is expected to be such that json.loads(serialized_rep) does not raise an exception.

Let serializable_rep=json.loads(serialized_rep).

Let validation_and_conversion_funcs and de_pre_serialization_funcs denote the attributes validation_and_conversion_funcs de_pre_serialization_funcs respectively, both of which being dict objects as well.

serialized_rep is also expected to be such that de_pre_serialization_funcs[key](serializable_rep[key]) does not raise an exception for each dict key key in de_pre_serialization_funcs.

Let core_attrs_candidate be a dict object that has the same keys as serializable_rep, where for each dict key key in de_pre_serialization_funcs, core_attrs_candidate[key] is set to de_pre_serialization_funcs[key](serializable_rep[key])``.

serialized_rep is also expected to be such that validation_and_conversion_funcs[key](core_attrs_candidate) does not raise an exception for each dict key key in serializable_rep.

skip_validation_and_conversionbool, optional

Let core_attrs denote the attribute core_attrs, which is a dict object.

If skip_validation_and_conversion is set to False, then for each key key in core_attrs_candidate, core_attrs[key] is set to validation_and_conversion_funcs[key] (core_attrs_candidate), with validation_and_conversion_funcs and core_attrs_candidate_1 being introduced in the above description of serialized_rep.

Otherwise, if skip_validation_and_conversion is set to True, then core_attrs is set to core_attrs_candidate.copy(). This option is desired primarily when the user wants to avoid potentially expensive deep copies and/or conversions of the dict values of core_attrs_candidate, as it is guaranteed that no copies or conversions are made in this case.

Returns:

instance_of_current_clsCurrent class: An instance constructed from the serialized representation.

property pre_serialization_funcs

dict: The pre-serialization functions.

pre_serialization_funcs has the same keys as the attribute validation_and_conversion_funcs, which is also a dict object.

Let validation_and_conversion_funcs and core_attrs denote the attributes validation_and_conversion_funcs and core_attrs respectively, the last of which being a dict object as well.

For each dict key key in core_attrs, pre_serialization_funcs[key](core_attrs[key]) is expected to yield a serializable object, i.e. it should yield an object that can be passed into the function json.dumps without raising an exception.

Note that pre_serialization_funcs should be considered read-only.

pre_serialize()

Pre-serialize instance.

Returns:

serializable_repdict: A serializable representation of an instance.

update(new_core_attr_subset_candidate, skip_validation_and_conversion=False)

Update a subset of the core attributes.

Parameters:

new_core_attr_subset_candidatedict, optional

A dict object.

skip_validation_and_conversionbool, optional

Let validation_and_conversion_funcs and core_attrs denote the attributes validation_and_conversion_funcs and core_attrs respectively, both of which being dict objects.

If skip_validation_and_conversion is set to False, then for each key key in core_attrs that is also in new_core_attr_subset_candidate, core_attrs[key] is set to validation_and_conversion_funcs[key] (new_core_attr_subset_candidate).

Otherwise, if skip_validation_and_conversion is set to True, then for each key key in core_attrs that is also in new_core_attr_subset_candidate, core_attrs[key] is set to new_core_attr_subset_candidate[key]. This option is desired primarily when the user wants to avoid potentially expensive deep copies and/or conversions of the dict values of new_core_attr_subset_candidate, as it is guaranteed that no copies or conversions are made in this case.

property validation_and_conversion_funcs

dict: The validation and conversion functions.

The keys of validation_and_conversion_funcs are the names of the constructor parameters, excluding skip_validation_and_conversion if it exists as a construction parameter.

Let core_attrs denote the attribute core_attrs, which is also a dict object.

For each dict key key in core_attrs, validation_and_conversion_funcs[key](core_attrs) is expected to not raise an exception.

Note that validation_and_conversion_funcs should be considered read-only.