Constructs python outputs and adds file "run.py" (#35)

* Adds file "run.py"

* Adds file "outputs.py", which constructs python results objects in "run.py"

* Adds test "test_outputs.py"

* Rewrites "plot_ref_sld" to use python objects

* Adds test cases to "test_outputs.py"

* Reorganises output tests and fixtures into "test_run.py" and "conftest.py"

* Fixes bug for "data" input to "project.py" and "test_inputs.py"

* Updates submodule

* Removes "bestFitsMean" from "bayesResults"

RAT/__init__.py

@@ -2,6 +2,7 @@
 from RAT.classlist import ClassList
 from RAT.project import Project
 from RAT.controls import set_controls
+from RAT.run import run
 import RAT.models
 
 dir_path = os.path.dirname(os.path.realpath(__file__))

RAT/inputs.py

@@ -133,8 +133,8 @@ def make_problem(project: RAT.Project) -> ProblemDefinition:
     problem.contrastBackgroundActions = [action_id[contrast.background_action] for contrast in project.contrasts]
     problem.contrastResolutions = [project.resolutions.index(contrast.resolution, True) for contrast in project.contrasts]
     problem.contrastCustomFiles = contrast_custom_files
-    problem.resample = [contrast.resample for contrast in project.contrasts]
-    problem.dataPresent = [1 if contrast.data else 0 for contrast in project.contrasts]
+    problem.resample = make_resample(project)
+    problem.dataPresent = make_data_present(project)
     problem.oilChiDataPresent = [0] * len(project.contrasts)
     problem.numberOfContrasts = len(project.contrasts)
     problem.numberOfLayers = len(project.layers)
@@ -151,6 +151,38 @@ def make_problem(project: RAT.Project) -> ProblemDefinition:
     return problem
 
 
+def make_resample(project: RAT.Project) -> list[int]:
+    """Constructs the "resample" field of the problem input required for the compiled RAT code.
+
+    Parameters
+    ----------
+    project : RAT.Project
+        The project model, which defines the physical system under study.
+
+    Returns
+    -------
+     : list[int]
+        The "resample" field of the problem input used in the compiled RAT code.
+    """
+    return [contrast.resample for contrast in project.contrasts]
+
+
+def make_data_present(project: RAT.Project) -> list[int]:
+    """Constructs the "dataPresent" field of the problem input required for the compiled RAT code.
+
+    Parameters
+    ----------
+    project : RAT.Project
+        The project model, which defines the physical system under study.
+
+    Returns
+    -------
+     : list[int]
+        The "dataPresent" field of the problem input used in the compiled RAT code.
+    """
+    return [1 if project.data[project.data.index(contrast.data)].data.size != 0 else 0 for contrast in project.contrasts]
+
+
 def make_cells(project: RAT.Project) -> Cells:
     """Constructs the cells input required for the compiled RAT code.
 
@@ -199,14 +231,10 @@ def make_cells(project: RAT.Project) -> Cells:
 
         data_index = project.data.index(contrast.data)
 
-        if 'data' in project.data[data_index].model_fields_set:
-            all_data.append(project.data[data_index].data)
-            data_limits.append(project.data[data_index].data_range)
-            simulation_limits.append(project.data[data_index].simulation_range)
-        else:
-            all_data.append([0.0, 0.0, 0.0])
-            data_limits.append([0.0, 0.0])
-            simulation_limits.append([0.0, 0.0])
+        all_data.append(project.data[data_index].data)
+        data_limits.append(project.data[data_index].data_range)
+        simulation_limits.append(project.data[data_index].simulation_range)
+
 
     # Populate the set of cells
     cells = Cells()

RAT/models.py

@@ -123,7 +123,7 @@ def model_post_init(self, __context: Any) -> None:
         """If the "data_range" and "simulation_range" fields are not set, but "data" is supplied, the ranges should be
         set to the min and max values of the first column (assumed to be q) of the supplied data.
         """
-        if len(self.data[:, 0]) > 0:
+        if self.data.shape[0] > 0:
             data_min = np.min(self.data[:, 0])
             data_max = np.max(self.data[:, 0])
             for field in ["data_range", "simulation_range"]:
@@ -135,7 +135,7 @@ def check_ranges(self) -> 'Data':
         """The limits of the "data_range" field must lie within the range of the supplied data, whilst the limits
         of the "simulation_range" field must lie outside the range of the supplied data.
         """
-        if len(self.data[:, 0]) > 0:
+        if self.data.shape[0] > 0:
             data_min = np.min(self.data[:, 0])
             data_max = np.max(self.data[:, 0])
             if "data_range" in self.model_fields_set and (self.data_range[0] < data_min or

RAT/outputs.py

@@ -0,0 +1,215 @@
+"""Converts outputs from the compiled RAT code to python dataclasses"""
+
+from dataclasses import dataclass
+import numpy as np
+from typing import Optional, Union
+from RAT.utils.enums import Procedures
+import RAT.rat_core
+
+
+@dataclass
+class CalculationResults:
+    chiValues: np.ndarray
+    sumChi: float
+
+
+@dataclass
+class ContrastParams:
+    backgroundParams: np.ndarray
+    scalefactors: np.ndarray
+    bulkIn: np.ndarray
+    bulkOut: np.ndarray
+    resolutionParams: np.ndarray
+    subRoughs: np.ndarray
+    resample: np.ndarray
+
+
+@dataclass
+class Results:
+    reflectivity: list
+    simulation: list
+    shiftedData: list
+    layerSlds: list
+    sldProfiles: list
+    resampledLayers: list
+    calculationResults: CalculationResults
+    contrastParams: ContrastParams
+    fitParams: np.ndarray
+    fitNames: list[str]
+
+
+@dataclass
+class PredictionIntervals:
+    reflectivity: list
+    sld: list
+    reflectivityXData: list
+    sldXData: list
+    sampleChi: np.ndarray
+
+
+@dataclass
+class ConfidenceIntervals:
+    percentile95: np.ndarray
+    percentile65: np.ndarray
+    mean: np.ndarray
+
+
+@dataclass
+class 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: np.ndarray
+
+
+@dataclass
+class DreamOutput:
+    allChains: np.ndarray
+    outlierChains: np.ndarray
+    runtime: float
+    iteration: float
+    modelOutput: float
+    AR: np.ndarray
+    R_stat: np.ndarray
+    CR: np.ndarray
+
+
+@dataclass
+class NestedSamplerOutput:
+    logZ: float
+    nestSamples: np.ndarray
+    postSamples: np.ndarray
+
+
+@dataclass
+class BayesResults(Results):
+    predictionIntervals: PredictionIntervals
+    confidenceIntervals: ConfidenceIntervals
+    dreamParams: DreamParams
+    dreamOutput: DreamOutput
+    nestedSamplerOutput: NestedSamplerOutput
+    chain: np.ndarray
+
+
+def make_results(procedure: Procedures, output_results: RAT.rat_core.OutputResult,
+                 bayes_results: Optional[RAT.rat_core.BayesResults] = None) -> Union[Results, BayesResults]:
+    """Initialise a python Results or BayesResults object using the outputs from a RAT calculation."""
+
+    calculation_results = CalculationResults(chiValues=output_results.calculationResults.chiValues,
+                                             sumChi=output_results.calculationResults.sumChi
+                                             )
+    contrast_params = ContrastParams(
+        backgroundParams=output_results.contrastParams.backgroundParams,
+        scalefactors=output_results.contrastParams.scalefactors,
+        bulkIn=output_results.contrastParams.bulkIn,
+        bulkOut=output_results.contrastParams.bulkOut,
+        resolutionParams=output_results.contrastParams.resolutionParams,
+        subRoughs=output_results.contrastParams.subRoughs,
+        resample=output_results.contrastParams.resample
+    )
+
+    if procedure in [Procedures.NS, Procedures.Dream]:
+
+        prediction_intervals = PredictionIntervals(
+            reflectivity=bayes_results.predictionIntervals.reflectivity,
+            sld=bayes_results.predictionIntervals.sld,
+            reflectivityXData=bayes_results.predictionIntervals.reflectivityXData,
+            sldXData=bayes_results.predictionIntervals.sldXData,
+            sampleChi=bayes_results.predictionIntervals.sampleChi
+        )
+
+        confidence_intervals = ConfidenceIntervals(
+            percentile95=bayes_results.confidenceIntervals.percentile95,
+            percentile65=bayes_results.confidenceIntervals.percentile65,
+            mean=bayes_results.confidenceIntervals.mean
+        )
+
+        dream_params = DreamParams(
+            nParams=bayes_results.dreamParams.nParams,
+            nChains=bayes_results.dreamParams.nChains,
+            nGenerations=bayes_results.dreamParams.nGenerations,
+            parallel=bool(bayes_results.dreamParams.parallel),
+            CPU=bayes_results.dreamParams.CPU,
+            jumpProbability=bayes_results.dreamParams.jumpProbability,
+            pUnitGamma=bayes_results.dreamParams.pUnitGamma,
+            nCR=bayes_results.dreamParams.nCR,
+            delta=bayes_results.dreamParams.delta,
+            steps=bayes_results.dreamParams.steps,
+            zeta=bayes_results.dreamParams.zeta,
+            outlier=bayes_results.dreamParams.outlier,
+            adaptPCR=bool(bayes_results.dreamParams.adaptPCR),
+            thinning=bayes_results.dreamParams.thinning,
+            epsilon=bayes_results.dreamParams.epsilon,
+            ABC=bool(bayes_results.dreamParams.ABC),
+            IO=bool(bayes_results.dreamParams.IO),
+            storeOutput=bool(bayes_results.dreamParams.storeOutput),
+            R=bayes_results.dreamParams.R
+        )
+
+        dream_output = DreamOutput(
+            allChains=bayes_results.dreamOutput.allChains,
+            outlierChains=bayes_results.dreamOutput.outlierChains,
+            runtime=bayes_results.dreamOutput.runtime,
+            iteration=bayes_results.dreamOutput.iteration,
+            modelOutput=bayes_results.dreamOutput.modelOutput,
+            AR=bayes_results.dreamOutput.AR,
+            R_stat=bayes_results.dreamOutput.R_stat,
+            CR=bayes_results.dreamOutput.CR
+        )
+
+        nested_sampler_output = NestedSamplerOutput(
+            logZ=bayes_results.nestedSamplerOutput.logZ,
+            nestSamples=bayes_results.nestedSamplerOutput.nestSamples,
+            postSamples=bayes_results.nestedSamplerOutput.postSamples
+        )
+
+        results = BayesResults(
+            reflectivity=output_results.reflectivity,
+            simulation=output_results.simulation,
+            shiftedData=output_results.shiftedData,
+            layerSlds=output_results.layerSlds,
+            sldProfiles=output_results.sldProfiles,
+            resampledLayers=output_results.resampledLayers,
+            calculationResults=calculation_results,
+            contrastParams=contrast_params,
+            fitParams=output_results.fitParams,
+            fitNames=output_results.fitNames,
+            predictionIntervals=prediction_intervals,
+            confidenceIntervals=confidence_intervals,
+            dreamParams=dream_params,
+            dreamOutput=dream_output,
+            nestedSamplerOutput=nested_sampler_output,
+            chain=bayes_results.chain
+        )
+
+    else:
+
+        results = Results(
+            reflectivity=output_results.reflectivity,
+            simulation=output_results.simulation,
+            shiftedData=output_results.shiftedData,
+            layerSlds=output_results.layerSlds,
+            sldProfiles=output_results.sldProfiles,
+            resampledLayers=output_results.resampledLayers,
+            calculationResults=calculation_results,
+            contrastParams=contrast_params,
+            fitParams=output_results.fitParams,
+            fitNames=output_results.fitNames
+        )
+
+    return results

RAT/project.py

@@ -124,7 +124,7 @@ class Project(BaseModel, validate_assignment=True, extra='forbid', arbitrary_typ
                                                              value_1='Resolution Param 1'))
 
     custom_files: ClassList = ClassList()
-    data: ClassList = ClassList(RAT.models.Data(name='Simulation'))
+    data: ClassList = ClassList()
     layers: ClassList = ClassList()
     domain_contrasts: ClassList = ClassList()
     contrasts: ClassList = ClassList()
@@ -187,6 +187,9 @@ def model_post_init(self, __context: Any) -> None:
             self.parameters.remove('Substrate Roughness')
             self.parameters.insert(0, RAT.models.ProtectedParameter(**substrate_roughness_values))
 
+        if 'Simulation' not in self.data.get_names():
+            self.data.insert(0, RAT.models.Data(name='Simulation'))
+
         self._all_names = self.get_all_names()
         self._contrast_model_field = self.get_contrast_model_field()
         self._protected_parameters = self.get_all_protected_parameters()

RAT/run.py

@@ -0,0 +1,30 @@
+from RAT.inputs import make_input
+from RAT.outputs import make_results
+import RAT.rat_core
+
+
+def run(project, controls):
+    """Run RAT for the given project and controls inputs."""
+
+    parameter_field = {'parameters': 'params',
+                       'bulk_in': 'bulkIn',
+                       'bulk_out': 'bulkOut',
+                       'scalefactors': 'scalefactors',
+                       'domain_ratios': 'domainRatio',
+                       'background_parameters': 'backgroundParams',
+                       'resolution_parameters': 'resolutionParams',
+                       }
+
+    problem_definition, cells, limits, priors, cpp_controls = make_input(project, controls)
+
+    problem_definition, output_results, bayes_results = RAT.rat_core.RATMain(problem_definition, cells, limits,
+                                                                             cpp_controls, priors)
+
+    results = RAT.outputs.make_results(controls.procedure, output_results, bayes_results)
+
+    # Update parameter values in project
+    for class_list in RAT.project.parameter_class_lists:
+        for (index, value) in enumerate(getattr(problem_definition, parameter_field[class_list])):
+            setattr(getattr(project, class_list)[index], 'value', value)
+
+    return project, results