interfaces¶

Defines top-level interfaces of tensorwaves.

class Estimator[source]¶

Bases: abc.ABC

Estimator for discrepancy model and data.

abstract __call__()[source]¶

Evaluate discrepancy.

Return type: float

abstract gradient()[source]¶

Compute the gradient of the data set.

Return type: list

abstract property parameters¶

Get dict of parameters.

Return type: dict

abstract update_parameters(new_parameters)[source]¶

Update the collection of parameters.

Parameters: new_parameters (dict) –
Return type: None

class Function[source]¶

Bases: abc.ABC

Interface of a callable function.

The parameters of the model are separated from the domain variables. This follows the mathematical definition, in which a function defines its domain and parameters. However specific points in the domain are not relevant. Hence while the domain variables are the argument of the evaluation (see __call__()), the parameters are controlled via a getter and setter (see parameters()). The reason for this separation is to facilitate the events when parameters have changed.

abstract __call__(dataset)[source]¶

Evaluate the function.

Parameters: dataset (dict) – a dict with domain variable names as keys.
Return type: list
Returns: list or numpy.array of values.

abstract property parameters¶

Get dict of parameters.

Return type: dict

abstract update_parameters(new_parameters)[source]¶

Update the collection of parameters.

Parameters: new_parameters (dict) –
Return type: None

class Kinematics[source]¶

Bases: abc.ABC

Abstract interface for computation of kinematic variables.

abstract convert(events)[source]¶

Convert a set of momentum tuples (events) to kinematic variables.

Parameters: events (dict) –
Return type: dict

abstract is_within_phase_space(events)[source]¶

Check which events lie within phase space.

Parameters: events (dict) –
Return type: Tuple[bool]

abstract property phase_space_volume¶

Compute volume of the phase space.

Return type: float

class Optimizer[source]¶

Bases: abc.ABC

Optimize a fit model to a data set.

abstract optimize(estimator, initial_parameters)[source]¶

Execute optimization.

Parameters

estimator (Estimator) –
initial_parameters (dict) –

Return type

dict

class PhaseSpaceGenerator[source]¶

Bases: abc.ABC

Abstract class for generating phase space samples.

abstract generate(size, random_generator)[source]¶

Generate phase space sample.

Parameters

size (int) –
random_generator (UniformRealNumberGenerator) –

Return type

dict

class UniformRealNumberGenerator[source]¶

Bases: abc.ABC

Abstract class for generating uniform real numbers.

abstract __call__(size, min_value=0.0, max_value=1.0)[source]¶

Generate random floats in the range from [min_value,max_value).

Parameters

size (int) –
min_value (float) –
max_value (float) –

Return type

Union[float, list]

abstract property seed¶

Get random seed.

Return type: float