interface¶
import tensorwaves.interface
Defines top-level interface of tensorwaves.
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DataSample
¶ Mapping of variable names to a sequence of data points, used by
Function
.alias of Mapping[str, numpy.ndarray]
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class
DataTransformer
[source]¶ Bases:
abc.ABC
Interface of a data converter.
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abstract
transform
(dataset: Mapping[str, ndarray]) → Mapping[str, ndarray][source]¶ Transform a dataset into another dataset.
This changes the keys and values of the input
DataSample
to a specific outputDataSample
structure.
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abstract
-
class
Estimator
[source]¶ Bases:
abc.ABC
Estimator for discrepancy model and data.
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class
FitResult
(minimum_valid: bool, execution_time: float, function_calls: int, estimator_value: float, parameter_values: Optional[Dict[str, Union[complex, float]]] = None, parameter_errors: Optional[Dict[str, Union[complex, float]]] = None, iterations: Optional[int] = None, specifics: Optional[Any] = None)[source]¶ Bases:
object
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__eq__
(other)¶ Method generated by attrs for class FitResult.
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count_number_of_parameters
(complex_twice: bool = False) → int[source]¶ Compute the number of free parameters in a
FitResult
.- Parameters
fit_result (FitResult) – Fit result from which to count it’s
parameter_values
.complex_twice (bool) – Count complex-valued parameters twice.
-
specifics
: Optional[Any]¶ Any additional info provided by the specific optimizer.
-
-
FourMomentum
¶ Tuple of energy and three-momentum \((E, \vec{p})\).
alias of Tuple[float, float, float, float]
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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 (seeparameters()
). The reason for this separation is to facilitate the events when parameters have changed.
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class
Model
[source]¶ Bases:
abc.ABC
Interface of a model which can be lambdified into a callable.
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property
argument_order
¶ Order of arguments of lambdified function signature.
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abstract
lambdify
(backend: Union[str, tuple, dict]) → Callable[source]¶ Lambdify the model into a Callable.
- Parameters
backend – Choice of backend for fast evaluations.
The arguments of the Callable are union of the variables and parameters. The return value of the Callable is Any. In theory the return type should be a value type depending on the model. Currently, there no typing support is implemented for this.
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abstract property
parameters
¶ Get mapping of parameters to suggested initial values.
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abstract
performance_optimize
(fix_inputs: Mapping[str, ndarray]) → Model[source]¶ Create a performance optimized model, based on fixed inputs.
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abstract property
variables
¶ Expected input variable names.
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property
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MomentumSample
¶ Mapping of state ID to a event-wise sequence of their
FourMomentum
.alias of Mapping[int, Sequence[Tuple[float, float, float, float]]]
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ParameterValue
¶ Allowed types for parameter values.
alias of Union[complex, float]
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class
PhaseSpaceGenerator
[source]¶ Bases:
abc.ABC
Abstract class for generating phase space samples.
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abstract
generate
(size: int, rng: UniformRealNumberGenerator) → Tuple[Mapping[int, Sequence[Tuple[float, float, float, float]]], ndarray][source]¶ Generate phase space sample.
- Returns
A
tuple
of aMomentumSample
plus a event-wise sequence of weights.
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abstract
setup
(initial_state_mass: float, final_state_masses: Mapping[int, float]) → None[source]¶ Hook for initialization of the
PhaseSpaceGenerator
.Called before any
generate()
calls.- Parameters
initial_state_mass – Mass of the decaying state.
final_state_masses – A mapping of final state IDs to the corresponding masses.
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abstract