Outputs

This module contains the Results objects and the relevant objects to retrieve results data from the C++ module.

Converts outputs from the compiled RAT code to python dataclasses

class RATapi.outputs.BayesResults(reflectivity: list, simulation: list, shiftedData: list, backgrounds: list, resolutions: list, layerSlds: list, sldProfiles: list, resampledLayers: list, calculationResults: RATapi.outputs.CalculationResults, contrastParams: RATapi.outputs.ContrastParams, fitParams: numpy.ndarray, fitNames: list[str], predictionIntervals: RATapi.outputs.PredictionIntervals, confidenceIntervals: RATapi.outputs.ConfidenceIntervals, dreamParams: RATapi.outputs.DreamParams, dreamOutput: RATapi.outputs.DreamOutput, nestedSamplerOutput: RATapi.outputs.NestedSamplerOutput, chain: numpy.ndarray)

Parameters:

• reflectivity (list)

• simulation (list)

• shiftedData (list)

• backgrounds (list)

• resolutions (list)

• layerSlds (list)

• sldProfiles (list)

• resampledLayers (list)

• calculationResults (CalculationResults)

• contrastParams (ContrastParams)

• fitParams (ndarray)

• fitNames (list[str])

• predictionIntervals (PredictionIntervals)

• confidenceIntervals (ConfidenceIntervals)

• dreamParams (DreamParams)

• dreamOutput (DreamOutput)

• nestedSamplerOutput (NestedSamplerOutput)

• chain (ndarray)

class RATapi.outputs.CalculationResults(chiValues: numpy.ndarray, sumChi: float)

Parameters:

• chiValues (ndarray)

• sumChi (float)

class RATapi.outputs.ConfidenceIntervals(percentile95: numpy.ndarray, percentile65: numpy.ndarray, mean: numpy.ndarray)

Parameters:

• percentile95 (ndarray)

• percentile65 (ndarray)

• mean (ndarray)

class RATapi.outputs.ContrastParams(scalefactors: numpy.ndarray, bulkIn: numpy.ndarray, bulkOut: numpy.ndarray, subRoughs: numpy.ndarray, resample: numpy.ndarray)

Parameters:

• scalefactors (ndarray)

• bulkIn (ndarray)

• bulkOut (ndarray)

• subRoughs (ndarray)

• resample (ndarray)

class RATapi.outputs.DreamOutput(allChains: numpy.ndarray, outlierChains: numpy.ndarray, runtime: float, iteration: float, modelOutput: float, AR: numpy.ndarray, R_stat: numpy.ndarray, CR: numpy.ndarray)

Parameters:

• allChains (ndarray)

• outlierChains (ndarray)

• runtime (float)

• iteration (float)

• modelOutput (float)

• AR (ndarray)

• R_stat (ndarray)

• CR (ndarray)

class RATapi.outputs.DreamParams(nParams: float, nChains: float, nGenerations: float, parallel: bool, CPU: float, jumpProbability: float, pUnitGamma: float, nCR: float, delta: float, steps: float, zeta: float, outlier: str, adaptPCR: bool, thinning: float, epsilon: float, ABC: bool, IO: bool, storeOutput: bool, R: numpy.ndarray)

Parameters:

• nParams (float)

• nChains (float)

• nGenerations (float)

• parallel (bool)

• CPU (float)

• jumpProbability (float)

• pUnitGamma (float)

• nCR (float)

• delta (float)

• steps (float)

• zeta (float)

• outlier (str)

• adaptPCR (bool)

• thinning (float)

• epsilon (float)

• ABC (bool)

• IO (bool)

• storeOutput (bool)

• R (ndarray)

class RATapi.outputs.NestedSamplerOutput(logZ: float, logZErr: float, nestSamples: numpy.ndarray, postSamples: numpy.ndarray)

Parameters:

• logZ (float)

• logZErr (float)

• nestSamples (ndarray)

• postSamples (ndarray)

class RATapi.outputs.PredictionIntervals(reflectivity: list, sld: list, sampleChi: numpy.ndarray)

Parameters:

• reflectivity (list)

• sld (list)

• sampleChi (ndarray)

class RATapi.outputs.Results(reflectivity: list, simulation: list, shiftedData: list, backgrounds: list, resolutions: list, layerSlds: list, sldProfiles: list, resampledLayers: list, calculationResults: RATapi.outputs.CalculationResults, contrastParams: RATapi.outputs.ContrastParams, fitParams: numpy.ndarray, fitNames: list[str])

Parameters:

• reflectivity (list)

• simulation (list)

• shiftedData (list)

• backgrounds (list)

• resolutions (list)

• layerSlds (list)

• sldProfiles (list)

• resampledLayers (list)

• calculationResults (CalculationResults)

• contrastParams (ContrastParams)

• fitParams (ndarray)

• fitNames (list[str])

RATapi.outputs.get_field_string(field, value, array_limit)

Returns a string representation of class fields where large and multidimensional arrays are represented by their shape.

Parameters:

• field (str) – The name of the field in the RAT output class.

• value (Any) – The value of the given field in the RAT output class.

• array_limit (int) – The maximum length of 1D arrays which will be fully displayed.

Returns:

field_string – The string representation of the field in the RAT output class.

Return type:

str

RATapi.outputs.make_results(procedure, output_results, bayes_results=None)

Initialise a python Results or BayesResults object using the outputs from a RAT calculation.

Parameters:

• procedure (Procedures)

• output_results (OutputResult)

• bayes_results (BayesResults | None)

Return type:

Results | BayesResults