Updates submodule

RATapi/examples/convert_rascal_project/convert_rascal.py

@@ -26,7 +26,7 @@ def convert_rascal(mat_filename="lipid_bilayer.mat"):
 
     """
     project_path = pathlib.Path(__file__).parent / "R1monolayerVolumeModel.mat"
-    project = RAT.utils.convert.r1_to_project_class(project_path)
+    project = RAT.utils.convert.r1_to_project(project_path)
 
     # change values if you like, including ones not supported by R1
     project.parameters["Head Thickness"].prior_type = "gaussian"
@@ -35,10 +35,10 @@ def convert_rascal(mat_filename="lipid_bilayer.mat"):
 
     # convert DSPC standard layers example to a struct and save as file
     lipid_bilayer_project = RAT.examples.DSPC_standard_layers()[0]
-    RAT.utils.convert.project_class_to_r1(lipid_bilayer_project, filename=mat_filename)
+    RAT.utils.convert.project_to_r1(lipid_bilayer_project, filename=mat_filename)
 
     # convert and return as a Python dictionary
-    struct = RAT.utils.convert.project_class_to_r1(lipid_bilayer_project, return_struct=True)
+    struct = RAT.utils.convert.project_to_r1(lipid_bilayer_project, return_struct=True)
 
     return project, struct
 

RATapi/inputs.py

@@ -284,7 +284,7 @@ def make_problem(project: RATapi.Project) -> ProblemDefinition:
     problem.numberOfContrasts = len(project.contrasts)
     problem.geometry = project.geometry
     problem.useImaginary = project.absorption
-    problem.repeatLayers = [[0, 1]] * len(project.contrasts)  # This is marked as "to do" in RAT
+    problem.repeatLayers = [1] * len(project.contrasts)
     problem.contrastBackgroundParams = contrast_background_params
     problem.contrastBackgroundTypes = contrast_background_types
     problem.contrastBackgroundActions = [contrast.background_action for contrast in project.contrasts]

RATapi/outputs.py

@@ -409,7 +409,7 @@ class BayesResults(Results):
 def make_results(
     procedure: Procedures,
     output_results: RATapi.rat_core.OutputResult,
-    bayes_results: Optional[RATapi.rat_core.BayesResults] = None,
+    bayes_results: Optional[RATapi.rat_core.OutputBayesResult] = None,
 ) -> Union[Results, BayesResults]:
     """Initialise a python Results or BayesResults object using the outputs from a RAT calculation.
 
@@ -419,7 +419,7 @@ def make_results(
         The procedure used by the calculation.
     output_results : RATapi.rat_core.OutputResult
         The C++ output results from the calculation.
-    bayes_results : Optional[RATapi.rat_core.BayesResults]
+    bayes_results : Optional[RATapi.rat_core.OutputBayesResult]
         The optional extra C++ Bayesian output results from a Bayesian calculation.
 
     Returns

RATapi/utils/convert.py

@@ -15,7 +15,7 @@
 from RATapi.utils.enums import Geometries, Languages, LayerModels
 
 
-def r1_to_project_class(filename: Union[str, PathLike]) -> Project:
+def r1_to_project(filename: Union[str, PathLike]) -> Project:
     """Read a RasCAL1 project struct as a Python `Project`.
 
     Parameters
@@ -318,7 +318,7 @@ def fix_invalid_constraints(name: str, constrs: tuple[float, float], value: floa
     return project
 
 
-def project_class_to_r1(
+def project_to_r1(
     project: Project, filename: Union[str, PathLike] = "RAT_project", return_struct: bool = False
 ) -> Union[dict, None]:
     """Convert a RAT Project to a RasCAL1 project struct.
@@ -549,10 +549,10 @@ def convert_parameters(
     # scipy.io.savemat doesn't do cells properly:
     # https://github.com/scipy/scipy/issues/3756
     # rather than fiddling we just use matlab
-    eng = wrappers.start_matlab().result()
-    if eng is None:
+    loader = wrappers.MatlabWrapper.loader
+    if loader is None:
         raise ImportError("matlabengine is not installed.")
+    eng = loader.result()
     eng.workspace["problem"] = r1
     eng.save(str(filename), "problem", nargout=0)
-    eng.exit()
     return None

RATapi/utils/plotting.py

@@ -17,7 +17,7 @@
 import RATapi
 import RATapi.inputs
 import RATapi.outputs
-from RATapi.rat_core import PlotEventData, makeSLDProfileXY
+from RATapi.rat_core import PlotEventData, makeSLDProfile
 
 
 def plot_errorbars(ax: Axes, x: np.ndarray, y: np.ndarray, err: np.ndarray, one_sided: bool, color: str):
@@ -154,11 +154,11 @@ def plot_ref_sld_helper(
                 layer = data.resampledLayers[i][j]
                 if layers.shape[1] == 4:
                     layer = np.delete(layer, 2, 1)
-                new_profile = makeSLDProfileXY(
+                new_profile = makeSLDProfile(
                     layers[0, 1],  # Bulk In
                     layers[-1, 1],  # Bulk Out
-                    data.subRoughs[i],  # roughness
                     layer,
+                    data.subRoughs[i],  # roughness
                     1,
                 )
 

cpp/includes/defines.h

@@ -262,12 +262,12 @@ struct DreamOutput
     py::array_t<real_T> CR;
 };
 
-const std::string docsBayesResults = R"(The Python binding for the C++ bayesResults struct. 
+const std::string docsOutputBayesResult = R"(The Python binding for the C++ bayesResults struct. 
 The results of a Bayesian RAT calculation.
 
 Parameters
 ----------
-predictionIntervals : RATapi.rat_core.orePredictionIntervals
+predictionIntervals : RATapi.rat_core.PredictionIntervals
     The prediction intervals.
 confidenceIntervals : RATapi.rat_core.ConfidenceIntervals
     The 65% and 95% confidence intervals for the best fit results.
@@ -282,7 +282,7 @@ chain : np.ndarray
     The ``i``'th column of the array contains the chain for parameter ``fitNames[i]``.
 )";
 
-struct BayesResults
+struct OutputBayesResult
 {
     PredictionIntervals predictionIntervals;
     ConfidenceIntervals confidenceIntervals;

cpp/rat.cpp

@@ -16,7 +16,7 @@ setup_pybind11(cfg)
 #include "RAT/RATMain_initialize.h"
 #include "RAT/RATMain_terminate.h"
 #include "RAT/RATMain_types.h"
-#include "RAT/makeSLDProfileXY.h"
+#include "RAT/makeSLDProfile.h"
 #include "RAT/dylib.hpp"
 #include "RAT/events/eventManager.h"
 #include "includes/defines.h"
@@ -263,7 +263,7 @@ RAT::b_ProblemDefinition createProblemDefinitionStruct(const ProblemDefinition&
     problem_struct.numberOfContrasts = problem.numberOfContrasts;
     stringToRatBoundedArray(problem.geometry, problem_struct.geometry.data, problem_struct.geometry.size);
     problem_struct.useImaginary = problem.useImaginary;
-    problem_struct.repeatLayers = customCaller("Problem.repeatLayers", pyListToRatCellWrap2, problem.repeatLayers);
+    problem_struct.repeatLayers = customCaller("Problem.repeatLayers", pyArrayToRatRowArray1d, problem.repeatLayers);
     problem_struct.contrastBackgroundParams = customCaller("Problem.contrastBackgroundParams", pyListToRatCellWrap3, problem.contrastBackgroundParams); 
     problem_struct.contrastBackgroundTypes = customCaller("Problem.contrastBackgroundTypes", pyListToRatCellWrap02d, problem.contrastBackgroundTypes);
     problem_struct.contrastBackgroundActions = customCaller("Problem.contrastBackgroundActions", pyListToRatCellWrap02d, problem.contrastBackgroundActions);
@@ -460,7 +460,7 @@ ProblemDefinition problemDefinitionFromStruct(const RAT::b_ProblemDefinition pro
     problem_def.numberOfContrasts = problem.numberOfContrasts;
     stringFromRatBoundedArray(problem.geometry.data, problem.geometry.size, problem_def.geometry);
     problem_def.useImaginary = problem.useImaginary;       
-    problem_def.repeatLayers = pyListFromRatCellWrap2(problem.repeatLayers);
+    problem_def.repeatLayers = pyArrayFromRatArray1d<coder::array<real_T, 2U>>(problem.repeatLayers);
     problem_def.contrastBackgroundParams = pyListFromBoundedCellWrap<coder::array<RAT::cell_wrap_3, 2U>>(problem.contrastBackgroundParams);
     problem_def.contrastBackgroundTypes = pyListFromRatCellWrap02d(problem.contrastBackgroundTypes);
     problem_def.contrastBackgroundActions = pyListFromRatCellWrap02d(problem.contrastBackgroundActions);    
@@ -510,9 +510,9 @@ ProblemDefinition problemDefinitionFromStruct(const RAT::b_ProblemDefinition pro
     return problem_def;
 }
 
-BayesResults bayesResultsFromStruct(const RAT::BayesResults results)
+OutputBayesResult OutputBayesResultsFromStruct(const RAT::BayesResults results)
 {
-    BayesResults bayesResults;
+    OutputBayesResult bayesResults;
 
     bayesResults.chain = pyArrayFromRatArray2d(results.chain);
 
@@ -575,7 +575,7 @@ out_problem_def : Rat.rat_core.ProblemDefinition
     The project input with the updated fit values.
 results : Rat.rat_core.OutputResult
     The results from a RAT calculation.
-bayes_result : Rat.rat_core.BayesResults
+bayes_result : Rat.rat_core.OutputBayesResult
     The extra results if RAT calculation is Bayesian.
 )";
 
@@ -593,21 +593,21 @@ py::tuple RATMain(const ProblemDefinition& problem_def, const Control& control)
     out_problem_def.customFiles = problem_def.customFiles.attr("copy")(); 
     return py::make_tuple(out_problem_def, 
                           OutputResultFromStruct(results), 
-                          bayesResultsFromStruct(bayesResults));    
+                          OutputBayesResultsFromStruct(bayesResults));    
 }
 
-const std::string docsMakeSLDProfileXY = R"(Creates the profiles for the SLD plots
+const std::string docsMakeSLDProfile = R"(Creates the profiles for the SLD plots
 
 Parameters
 ----------
 bulk_in : float
     Bulk in value for contrast.
 bulk_out : float
     Bulk out value for contrast.
-ssub : float
-    Substrate roughness.
 layers : np.ndarray[np.float]
     Array of parameters for each layer in the contrast.
+ssub : float
+    Substrate roughness.
 number_of_repeats : int, default: 1
     Number of times the layers are repeated.
 
@@ -617,22 +617,20 @@ sld_profile : np.ndarray[np.float]
     Computed SLD profile
 )";
 
-py::array_t<real_T> makeSLDProfileXY(real_T bulk_in,
-                                     real_T bulk_out,
-                                     real_T ssub,
-                                     const py::array_t<real_T> &layers,
-                                     int number_of_repeats=DEFAULT_NREPEATS)
+py::array_t<real_T> makeSLDProfile(real_T bulk_in,
+                                   real_T bulk_out,
+                                   const py::array_t<real_T> &layers,
+                                   real_T ssub,
+                                   int number_of_repeats=DEFAULT_NREPEATS)
 {
     coder::array<real_T, 2U> out;
     coder::array<real_T, 2U> layers_array = pyArrayToRatArray2d(layers);
-    py::buffer_info buffer_info = layers.request();
-    RAT::makeSLDProfileXY(bulk_in,
-                          bulk_out,
-                          ssub,
-                          layers_array,
-                          buffer_info.shape[0],
-                          number_of_repeats,
-                          out);
+    RAT::makeSLDProfile(bulk_in,
+                        bulk_out,
+                        layers_array,
+                        ssub,
+                        number_of_repeats,
+                        out);
 
     return pyArrayFromRatArray2d(out);
 
@@ -787,14 +785,14 @@ PYBIND11_MODULE(rat_core, m) {
         .def_readwrite("R_stat", &DreamOutput::R_stat)
         .def_readwrite("CR", &DreamOutput::CR);
 
-    py::class_<BayesResults>(m, "BayesResults", docsBayesResults.c_str())
+    py::class_<OutputBayesResult>(m, "OutputBayesResult", docsOutputBayesResult.c_str())
         .def(py::init<>())
-        .def_readwrite("predictionIntervals", &BayesResults::predictionIntervals)
-        .def_readwrite("confidenceIntervals", &BayesResults::confidenceIntervals)
-        .def_readwrite("dreamParams", &BayesResults::dreamParams)
-        .def_readwrite("dreamOutput", &BayesResults::dreamOutput)
-        .def_readwrite("nestedSamplerOutput", &BayesResults::nestedSamplerOutput)
-        .def_readwrite("chain", &BayesResults::chain);
+        .def_readwrite("predictionIntervals", &OutputBayesResult::predictionIntervals)
+        .def_readwrite("confidenceIntervals", &OutputBayesResult::confidenceIntervals)
+        .def_readwrite("dreamParams", &OutputBayesResult::dreamParams)
+        .def_readwrite("dreamOutput", &OutputBayesResult::dreamOutput)
+        .def_readwrite("nestedSamplerOutput", &OutputBayesResult::nestedSamplerOutput)
+        .def_readwrite("chain", &OutputBayesResult::chain);
 
     py::class_<Calculation>(m, "Calculation", docsCalculation.c_str())
         .def(py::init<>())
@@ -1047,7 +1045,7 @@ PYBIND11_MODULE(rat_core, m) {
                 p.numberOfContrasts = t[6].cast<real_T>();
                 p.geometry = t[7].cast<std::string>(); 
                 p.useImaginary = t[8].cast<bool>();
-                p.repeatLayers = t[9].cast<py::list>();
+                p.repeatLayers = t[9].cast<py::array_t<real_T>>();
                 p.contrastBackgroundParams = t[10].cast<py::list>(); 
                 p.contrastBackgroundTypes = t[11].cast<py::list>(); 
                 p.contrastBackgroundActions = t[12].cast<py::list>(); 
@@ -1099,6 +1097,6 @@ PYBIND11_MODULE(rat_core, m) {
 
     m.def("RATMain", &RATMain, docsRATMain.c_str(), py::arg("problem_def"), py::arg("control"));
 
-    m.def("makeSLDProfileXY", &makeSLDProfileXY, docsMakeSLDProfileXY.c_str(), 
-          py::arg("bulk_in"), py::arg("bulk_out"), py::arg("ssub"), py::arg("layers"), py::arg("number_of_repeats") = DEFAULT_NREPEATS);
+    m.def("makeSLDProfile", &makeSLDProfile, docsMakeSLDProfile.c_str(), 
+          py::arg("bulk_in"), py::arg("bulk_out"), py::arg("layers"), py::arg("ssub"), py::arg("number_of_repeats") = DEFAULT_NREPEATS);
 }

tests/conftest.py

@@ -2253,7 +2253,7 @@ def dream_bayes():
 
     This optimisation used the parameters: nSamples=1, nChains=1.
     """
-    bayes = RATapi.rat_core.BayesResults()
+    bayes = RATapi.rat_core.OutputBayesResult()
     bayes.predictionIntervals = RATapi.rat_core.PredictionIntervals()
     bayes.predictionIntervals.reflectivity = [
         np.array(

tests/test_convert.py

@@ -8,7 +8,7 @@
 import pytest
 
 import RATapi
-from RATapi.utils.convert import project_class_to_r1, r1_to_project_class
+from RATapi.utils.convert import project_to_r1, r1_to_project
 
 TEST_DIR_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data")
 
@@ -58,9 +58,9 @@ def dspc_bilayer():
     ],
 )
 @pytest.mark.parametrize("path_type", [os.path.join, pathlib.Path])
-def test_r1_to_project_class(file, project, path_type, request):
+def test_r1_to_project(file, project, path_type, request):
     """Test that R1 to Project class conversion returns the expected Project."""
-    output_project = r1_to_project_class(path_type(TEST_DIR_PATH, file))
+    output_project = r1_to_project(path_type(TEST_DIR_PATH, file))
     expected_project = request.getfixturevalue(project)
 
     # assert statements have to be more careful due to R1 missing features
@@ -83,7 +83,7 @@ def test_r1_to_project_class(file, project, path_type, request):
 def test_r1_involution(project, request, monkeypatch):
     """Test that converting a Project to an R1 struct and back returns the same project."""
     original_project = request.getfixturevalue(project)
-    r1_struct = project_class_to_r1(original_project, return_struct=True)
+    r1_struct = project_to_r1(original_project, return_struct=True)
 
     # rather than writing the struct to a file and reading the file, just directly
     # hand the struct over
@@ -93,7 +93,7 @@ def mock_load(ignored_filename, **ignored_settings):
 
     monkeypatch.setattr("RATapi.utils.convert.loadmat", mock_load, raising=True)
 
-    converted_project = r1_to_project_class(project)
+    converted_project = r1_to_project(project)
 
     for class_list in RATapi.project.class_lists:
         assert getattr(converted_project, class_list) == getattr(original_project, class_list)
@@ -105,7 +105,7 @@ def test_invalid_constraints():
         match=r"The parameter (.+) has invalid constraints,"
         " these have been adjusted to satisfy the current value of the parameter."
     ):
-        output_project = r1_to_project_class(pathlib.Path(TEST_DIR_PATH, "R1DoubleBilayerVolumeModel.mat"))
+        output_project = r1_to_project(pathlib.Path(TEST_DIR_PATH, "R1DoubleBilayerVolumeModel.mat"))
 
     assert output_project.background_parameters[0].min == output_project.background_parameters[0].value
 
@@ -117,7 +117,7 @@ def test_matlab_save(path_type, request):
     project = request.getfixturevalue("r1_default_project")
     with tempfile.TemporaryDirectory() as temp:
         matfile = path_type(temp, "testfile.mat")
-        project_class_to_r1(project, filename=matfile)
-        converted_project = r1_to_project_class(matfile)
+        project_to_r1(project, filename=matfile)
+        converted_project = r1_to_project(matfile)
 
     assert project == converted_project

tests/test_inputs.py

@@ -174,7 +174,7 @@ def standard_layers_problem(test_names, test_checks):
     problem.simulationLimits = [[1.0, 1.0]]
     problem.numberOfContrasts = 1
     problem.numberOfLayers = 1
-    problem.repeatLayers = [[0, 1]]
+    problem.repeatLayers = [1]
     problem.layersDetails = [[2, 3, 4, float("NaN"), 2]]
     problem.contrastLayers = [[1]]
     problem.numberOfDomainContrasts = 0
@@ -242,7 +242,7 @@ def domains_problem(test_names, test_checks):
     problem.simulationLimits = [[1.0, 1.0]]
     problem.numberOfContrasts = 1
     problem.numberOfLayers = 1
-    problem.repeatLayers = [[0, 1]]
+    problem.repeatLayers = [1]
     problem.layersDetails = [[2, 3, 4, float("NaN"), 2]]
     problem.contrastLayers = [[2, 1]]
     problem.numberOfDomainContrasts = 2
@@ -311,7 +311,7 @@ def custom_xy_problem(test_names, test_checks):
     problem.data = [np.empty([0, 6])]
     problem.dataLimits = [[0.0, 0.0]]
     problem.simulationLimits = [[0.005, 0.7]]
-    problem.repeatLayers = [[0, 1]]
+    problem.repeatLayers = [1]
     problem.layersDetails = []
     problem.contrastLayers = [[]]
     problem.numberOfContrasts = 1