swiftemulator.mocking package

Sub-module that uses the created emulators to re-sample the parameter space completely, effectively creating a higher ‘resolution’ (in sub-grid parameters) hypercube.

swiftemulator.mocking.mock_hypercube(emulator, model_specification: ModelSpecification, samples: int, predict_values_kwargs: Optional[Dict[str, Any]] = None) → Tuple[ModelValues, ModelParameters]

Create a mocked version of the cube, interpolated at random points using the emulator.

Parameters:

• emulator – An emulator object that provides a predict_values function.

• model_spec (ModelSpecification) – A model specification for your chosen model. The cube will be generated for points within the ranges specified here.

• samples (int) – Number of samples to create within your model specification.

• predict_value_kwargs (dict, optional) – Keyword arguments to pass to predict_values on the emulator object.

Returns:

• values (ModelValues) – Model values container with the predictions from the provided emulator within a new cube.

• parameters (ModelParameters) – New model parameters generated in a random cube, corresponding to the unique identifiers in values.

Notes

The unique identifiers for the new simulations are prefixed with emulated_ to prevent confusion when comparing with ‘real’ data. Samples are generated at all of the independent variable points present within the provided emulator’s data.

swiftemulator.mocking.mock_sweep(emulator, model_specification: ModelSpecification, samples: int, sweep_parameter: str, center_point: Dict[str, float], predict_values_kwargs: Optional[Dict[str, Any]] = None) → Tuple[ModelValues, ModelParameters]

Create a mocked version of the cube, interpolated evenly spaced along one dimension.

Parameters:

• emulator – An emulator object that provides a predict_values function.

• model_spec (ModelSpecification) – A model specification for your chosen model. The cube will be generated for points within the ranges specified here.

• samples (int) – Number of samples to create within your model specification.

• sweep_parameter (str) – Parameter to sweep along, from minimum to maximum in the model_spec.

• center_point (str) – Model parameters for the center point of the sweep. The other model parameters will remain as these values throughout the sweep.

• predict_value_kwargs (dict, optional) – Keyword arguments to pass to predict_values on the emulator object.

Returns:

• values (ModelValues) – Model values container with the predictions from the provided emulator within a new cube.

• parameters (ModelParameters) – New model parameters generated in a sweep, corresponding to the unique identifiers in values.

Notes

The unique identifiers for the new simulations are prefixed with emulated_ to prevent confusion when comparing with ‘real’ data. Samples are generated at all of the independent variable points present within the provided emulator’s data.