data

data#

import tensorwaves.data

The data module takes care of data generation.

class NumpyDomainGenerator(boundaries: dict[str, tuple[float, float]])[source]#

Bases: DataGenerator

Generate a uniform DataSample as a domain for a Function.

Parameters:: boundaries – A mapping of the keys in the DataSample that is to be generated. The boundaries have to be a tuple of a minimum and a maximum value that define the range for each key in the DataSample.

generate(size: int, rng: RealNumberGenerator) → DataSample[source]#

Generate a DataSample with size events.

Returns:: A tuple of a DataSample with an array of weights.

class IntensityDistributionGenerator(domain_generator: DataGenerator, function: Function[TypeAliasForwardRef('tensorwaves.interface.DataSample'), ndarray], domain_transformer: DataTransformer | None = None, bunch_size: int = 50000)[source]#

Bases: DataGenerator

Generate an hit-and-miss DataSample distribution for a Function.

Parameters:

domain_generator – A DataGenerator that can be used to generate a domain DataSample over which to evaluate the function.
function – An intensity Function with which the output distribution DataSample is generated using a hit-and-miss strategy.
domain_transformer – Optional DataTransformer that can convert a generated domain DataSample to a DataSample that the function can take as input.
bunch_size – Size of a bunch that is generated during a hit-and-miss iteration.

generate(size: int, rng: RealNumberGenerator) → DataSample[source]#

Generate a DataSample with size events.

Returns:: A tuple of a DataSample with an array of weights.

Submodules and Subpackages

data

Contents

data#