Updates inputs, outputs, and build process

RATapi/inputs.py

@@ -10,7 +10,7 @@
 import RATapi
 import RATapi.controls
 import RATapi.wrappers
-from RATapi.rat_core import Checks, Control, Limits, NameStore, ProblemDefinition
+from RATapi.rat_core import Checks, Control, NameStore, ProblemDefinition
 from RATapi.utils.enums import Calculations, Languages, LayerModels, TypeOptions
 
 parameter_field = {
@@ -113,7 +113,7 @@ def __len__(self):
         return len(self.files)
 
 
-def make_input(project: RATapi.Project, controls: RATapi.Controls) -> tuple[ProblemDefinition, Limits, Control]:
+def make_input(project: RATapi.Project, controls: RATapi.Controls) -> tuple[ProblemDefinition, Control]:
     """Construct the inputs required for the compiled RAT code using the data defined in the input project and controls.
 
     Parameters
@@ -127,28 +127,14 @@ def make_input(project: RATapi.Project, controls: RATapi.Controls) -> tuple[Prob
     -------
     problem : RAT.rat_core.ProblemDefinition
         The problem input used in the compiled RAT code.
-    limits : RAT.rat_core.Limits
-        A list of min/max values for each parameter defined in the project.
     cpp_controls : RAT.rat_core.Control
         The controls object used in the compiled RAT code.
 
     """
-    limits = Limits()
-
-    for class_list in RATapi.project.parameter_class_lists:
-        setattr(
-            limits,
-            parameter_field[class_list],
-            [[element.min, element.max] for element in getattr(project, class_list)],
-        )
-
-    if project.model == LayerModels.CustomXY:
-        controls.calcSldDuringFit = True
-
     problem = make_problem(project)
     cpp_controls = make_controls(controls)
 
-    return problem, limits, cpp_controls
+    return problem, cpp_controls
 
 
 def make_problem(project: RATapi.Project) -> ProblemDefinition:

RATapi/outputs.py

@@ -141,12 +141,13 @@ class Results:
         The background for each contrast defined over the simulation range.
     resolutions : list
         The resolution for each contrast defined over the simulation range.
-    layerSlds : list
-        The array of layer parameter values for each contrast.
     sldProfiles : list
         The SLD profiles for each contrast.
+    layers : list
+        The array of layer parameter values for each contrast.
     resampledLayers : list
-        If resampling is used, the SLD for each contrast after resampling has been performed.
+        If resampling is used, the array of layer parameter values for each contrast after resampling has been
+        performed.
     calculationResults : CalculationResults
         The chi-squared fit results from the final calculation and fit.
     contrastParams : ContrastParams
@@ -164,8 +165,8 @@ class Results:
     shiftedData: list
     backgrounds: list
     resolutions: list
-    layerSlds: list
     sldProfiles: list
+    layers: list
     resampledLayers: list
     calculationResults: CalculationResults
     contrastParams: ContrastParams
@@ -323,8 +324,6 @@ class DreamOutput(RATResult):
         The runtime of the DREAM algorithm in seconds.
     iteration : float
         The number of iterations performed.
-    modelOutput : float
-        Unused. Will always be 0.
     AR : np.ndarray
         A two-column array where ``DreamOutput.AR[i, 0]`` is an iteration number
         and ``DreamOutput.AR[i, 1]`` is the average acceptance rate of chain step
@@ -344,7 +343,6 @@ class DreamOutput(RATResult):
     outlierChains: np.ndarray
     runtime: float
     iteration: float
-    modelOutput: float
     AR: np.ndarray
     R_stat: np.ndarray
     CR: np.ndarray
@@ -483,7 +481,6 @@ def make_results(
             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,
@@ -502,8 +499,8 @@ def make_results(
             shiftedData=output_results.shiftedData,
             backgrounds=output_results.backgrounds,
             resolutions=output_results.resolutions,
-            layerSlds=output_results.layerSlds,
             sldProfiles=output_results.sldProfiles,
+            layers=output_results.layers,
             resampledLayers=output_results.resampledLayers,
             calculationResults=calculation_results,
             contrastParams=contrast_params,
@@ -524,8 +521,8 @@ def make_results(
             shiftedData=output_results.shiftedData,
             backgrounds=output_results.backgrounds,
             resolutions=output_results.resolutions,
-            layerSlds=output_results.layerSlds,
             sldProfiles=output_results.sldProfiles,
+            layers=output_results.layers,
             resampledLayers=output_results.resampledLayers,
             calculationResults=calculation_results,
             contrastParams=contrast_params,

RATapi/run.py

@@ -109,7 +109,7 @@ def run(project, controls):
 
     horizontal_line = "\u2500" * 107 + "\n"
     display_on = controls.display != Display.Off
-    problem_definition, limits, cpp_controls = make_input(project, controls)
+    problem_definition, cpp_controls = make_input(project, controls)
 
     if display_on:
         print("Starting RAT " + horizontal_line)
@@ -118,7 +118,6 @@ def run(project, controls):
     with ProgressBar(display=display_on), TextOutput(display=display_on):
         problem_definition, output_results, bayes_results = RATapi.rat_core.RATMain(
             problem_definition,
-            limits,
             cpp_controls,
         )
     end = time.time()

cpp/includes/defines.h

@@ -237,8 +237,6 @@ runtime : float
     The runtime of the DREAM algorithm in seconds.
 iteration : float
     The number of iterations performed.
-modelOutput : float
-    Unused. Will always be 0.
 AR : np.ndarray[np.float]
     A two-column array where ``DreamOutput.AR[i, 0]`` is an iteration number
     and ``DreamOutput.AR[i, 1]`` is the average acceptance rate of chain step
@@ -259,7 +257,6 @@ struct DreamOutput
     py::array_t<real_T> outlierChains;
     real_T runtime;
     real_T iteration;
-    real_T modelOutput;
     py::array_t<real_T> AR;
     py::array_t<real_T> R_stat;
     py::array_t<real_T> CR;
@@ -354,12 +351,12 @@ backgrounds : list
     The background for each contrast defined over the simulation range.
 resolutions : list
     The resolution for each contrast defined over the simulation range.
-layerSlds : list
-    The array of layer parameter values for each contrast.
 sldProfiles : list
     The SLD profiles for each contrast.
+layers : list
+    The array of layer parameter values for each contrast.
 resampledLayers : list
-    If resampling is used, the SLD for each contrast after resampling has been performed.
+    If resampling is used, the array of layer parameter values for each contrast after resampling has been performed.
 calculationResults : RATapi.rat_core.Calculation
     The chi-squared fit results from the final calculation and fit.
 contrastParams : RATapi.rat_core.ContrastParams
@@ -377,46 +374,15 @@ struct OutputResult {
     py::list shiftedData;
     py::list backgrounds;
     py::list resolutions;
-    py::list layerSlds;
     py::list sldProfiles;
+    py::list layers;
     py::list resampledLayers;
     Calculation calculationResults {};
     ContrastParams contrastParams {};
     py::array_t<real_T> fitParams;
     py::list fitNames;
 };
 
-const std::string docsLimits = R"(The Python binding for the C++ limit struct which contains
-Min and max values for each parameter defined in the project.
-
-Parameters
-----------
-params : np.ndarray[np.float] 
-    Limits for params in the problem definition.
-backgroundParams : np.ndarray[np.float] 
-    Limits for backgroundParams in the problem definition.
-scalefactors : np.ndarray[np.float] 
-    Limits for scalefactors in the problem definition.
-bulkIns : np.ndarray[np.float] 
-    Limits for bulkIns in the problem definition.
-bulkOuts : np.ndarray[np.float] 
-    Limits for bulkOuts in the problem definition.
-resolutionParams : np.ndarray[np.float] 
-    Limits for resolutionParams in the problem definition.
-domainRatios : np.ndarray[np.float] 
-    Limits for domainRatios in the problem definition.
-)";
-
-struct Limits {
-    py::array_t<real_T> params;
-    py::array_t<real_T> backgroundParams;
-    py::array_t<real_T> scalefactors;
-    py::array_t<real_T> bulkIns;
-    py::array_t<real_T> bulkOuts;
-    py::array_t<real_T> resolutionParams;
-    py::array_t<real_T> domainRatios;
-};
-
 const std::string docsNameStore = R"(The Python binding for the C++ names struct which 
 contains names of all parameters in the project.
 

cpp/rat.cpp

@@ -301,21 +301,6 @@ RAT::b_ProblemDefinition createProblemDefinitionStruct(const ProblemDefinition&
 }
 
 
-RAT::ProblemLimits createProblemLimitsStruct(const Limits& limits)
-{
-    RAT::ProblemLimits limits_struct;
-    limits_struct.params = customCaller("Limits.params", pyArrayToRatArray2d, limits.params);
-    limits_struct.backgroundParams = customCaller("Limits.backgroundParams", pyArrayToRatArray2d, limits.backgroundParams);
-    limits_struct.scalefactors = customCaller("Limits.scalefactors", pyArrayToRatArray2d, limits.scalefactors);
-    limits_struct.bulkIns = customCaller("Limits.bulkIns", pyArrayToRatArray2d, limits.bulkIns);
-    limits_struct.bulkOuts = customCaller("Limits.bulkOuts", pyArrayToRatArray2d, limits.bulkOuts);
-    limits_struct.resolutionParams = customCaller("Limits.resolutionParams", pyArrayToRatArray2d, limits.resolutionParams);
-    limits_struct.domainRatios = customCaller("Limits.domainRatios", pyArrayToRatArray2d, limits.domainRatios);
-
-    return limits_struct;
-}
-
-
 RAT::Controls createControlsStruct(const Control& control)
 {
     RAT::Controls control_struct;
@@ -391,26 +376,26 @@ OutputResult OutputResultFromStruct(const RAT::Results result)
         output_result.resolutions.append(array);
     }
 
-    for (int32_T idx0{0}; idx0 < result.layerSlds.size(0); idx0++) {
+    for (int32_T idx0{0}; idx0 < result.sldProfiles.size(0); idx0++) {
         py::list inner_list;
-        for (int32_T idx1{0}; idx1 < result.layerSlds.size(1); idx1++) {
-            auto tmp = result.layerSlds[idx0 +  result.layerSlds.size(0) * idx1];
+        for (int32_T idx1{0}; idx1 < result.sldProfiles.size(1); idx1++) {
+            auto tmp = result.sldProfiles[idx0 +  result.sldProfiles.size(0) * idx1];
             auto array = py::array_t<real_T, py::array::f_style>({tmp.f1.size(0), tmp.f1.size(1)});
             std::memcpy(array.request().ptr, tmp.f1.data(), array.nbytes());
             inner_list.append(array);
         }
-        output_result.layerSlds.append(inner_list);
+        output_result.sldProfiles.append(inner_list);
     }
 
-    for (int32_T idx0{0}; idx0 < result.sldProfiles.size(0); idx0++) {
+    for (int32_T idx0{0}; idx0 < result.layers.size(0); idx0++) {
         py::list inner_list;
-        for (int32_T idx1{0}; idx1 < result.sldProfiles.size(1); idx1++) {
-            auto tmp = result.sldProfiles[idx0 +  result.sldProfiles.size(0) * idx1];
+        for (int32_T idx1{0}; idx1 < result.layers.size(1); idx1++) {
+            auto tmp = result.layers[idx0 +  result.layers.size(0) * idx1];
             auto array = py::array_t<real_T, py::array::f_style>({tmp.f1.size(0), tmp.f1.size(1)});
             std::memcpy(array.request().ptr, tmp.f1.data(), array.nbytes());
             inner_list.append(array);
         }
-        output_result.sldProfiles.append(inner_list);
+        output_result.layers.append(inner_list);
     }
 
     for (int32_T idx0{0}; idx0 < result.resampledLayers.size(0); idx0++) {
@@ -548,7 +533,6 @@ BayesResults bayesResultsFromStruct(const RAT::BayesResults results)
     bayesResults.dreamOutput.outlierChains = pyArray2dFromBoundedArray<coder::bounded_array<real_T, 2000U, 2U>>(results.dreamOutput.outlierChains);
     bayesResults.dreamOutput.runtime = results.dreamOutput.runtime;
     bayesResults.dreamOutput.iteration = results.dreamOutput.iteration;
-    bayesResults.dreamOutput.modelOutput = results.dreamOutput.modelOutput;
     bayesResults.dreamOutput.R_stat = pyArrayFromRatArray2d(results.dreamOutput.R_stat);
     bayesResults.dreamOutput.CR = pyArrayFromRatArray2d(results.dreamOutput.CR);
     bayesResults.dreamOutput.AR = pyArray2dFromBoundedArray<coder::bounded_array<real_T, 2000U, 2U>>(results.dreamOutput.AR);
@@ -582,8 +566,6 @@ Parameters
 ----------
 problem_def : Rat.rat_core.ProblemDefinition
     The project input for the RAT calculation.
-limits : RATapi.rat_core.Limits
-    Min and max values for each parameter defined in the problem definition.
 control : RATapi.rat_core.Control
     The controls object for the RAT calculation.
 
@@ -597,16 +579,15 @@ bayes_result : Rat.rat_core.BayesResults
     The extra results if RAT calculation is Bayesian.
 )";
 
-py::tuple RATMain(const ProblemDefinition& problem_def, const Limits& limits, const Control& control)
+py::tuple RATMain(const ProblemDefinition& problem_def, const Control& control)
 {
     RAT::b_ProblemDefinition problem_def_struct = createProblemDefinitionStruct(problem_def);
-    RAT::ProblemLimits limits_struct = createProblemLimitsStruct(limits);
     RAT::Controls control_struct = createControlsStruct(control);
     // Output
     RAT::Results results;
     RAT::BayesResults bayesResults;
     // Call the entry-point
-    RAT::RATMain(&problem_def_struct, &limits_struct, &control_struct, &results, &bayesResults);
+    RAT::RATMain(&problem_def_struct, &control_struct, &results, &bayesResults);
     // Copy result to output
     auto out_problem_def = problemDefinitionFromStruct(problem_def_struct);
     out_problem_def.customFiles = problem_def.customFiles.attr("copy")(); 
@@ -802,7 +783,6 @@ PYBIND11_MODULE(rat_core, m) {
         .def_readwrite("outlierChains", &DreamOutput::outlierChains)
         .def_readwrite("runtime", &DreamOutput::runtime)
         .def_readwrite("iteration", &DreamOutput::iteration)
-        .def_readwrite("modelOutput", &DreamOutput::modelOutput)
         .def_readwrite("AR", &DreamOutput::AR)
         .def_readwrite("R_stat", &DreamOutput::R_stat)
         .def_readwrite("CR", &DreamOutput::CR);
@@ -836,8 +816,8 @@ PYBIND11_MODULE(rat_core, m) {
         .def_readwrite("shiftedData", &OutputResult::shiftedData)
         .def_readwrite("backgrounds", &OutputResult::backgrounds)
         .def_readwrite("resolutions", &OutputResult::resolutions)
-        .def_readwrite("layerSlds", &OutputResult::layerSlds)
         .def_readwrite("sldProfiles", &OutputResult::sldProfiles)
+        .def_readwrite("layers", &OutputResult::layers)
         .def_readwrite("resampledLayers", &OutputResult::resampledLayers)
         .def_readwrite("calculationResults", &OutputResult::calculationResults)
         .def_readwrite("contrastParams", &OutputResult::contrastParams)        
@@ -912,39 +892,6 @@ PYBIND11_MODULE(rat_core, m) {
                 return chk;
             }));
 
-    py::class_<Limits>(m, "Limits", docsLimits.c_str())
-        .def(py::init<>())
-        .def_readwrite("params", &Limits::params)
-        .def_readwrite("backgroundParams", &Limits::backgroundParams)
-        .def_readwrite("scalefactors", &Limits::scalefactors)
-        .def_readwrite("bulkIns", &Limits::bulkIns)
-        .def_readwrite("bulkOuts", &Limits::bulkOuts)
-        .def_readwrite("resolutionParams", &Limits::resolutionParams)
-        .def_readwrite("domainRatios", &Limits::domainRatios)
-        .def(py::pickle(
-            [](const Limits &lim) { // __getstate__
-                /* Return a tuple that fully encodes the state of the object */
-                return py::make_tuple(lim.params, lim.backgroundParams, lim.scalefactors, lim.bulkIns, lim.bulkOuts, 
-                                      lim.resolutionParams, lim.domainRatios);
-            },
-            [](py::tuple t) { // __setstate__
-                if (t.size() != 7)
-                    throw std::runtime_error("Encountered invalid state unpickling Limits object!");
-
-                /* Create a new C++ instance */
-                Limits lim;
-
-                lim.params = t[0].cast<py::array_t<real_T>>(); 
-                lim.backgroundParams = t[1].cast<py::array_t<real_T>>(); 
-                lim.scalefactors = t[2].cast<py::array_t<real_T>>(); 
-                lim.bulkIns = t[3].cast<py::array_t<real_T>>(); 
-                lim.bulkOuts = t[4].cast<py::array_t<real_T>>(); 
-                lim.resolutionParams = t[5].cast<py::array_t<real_T>>(); 
-                lim.domainRatios = t[6].cast<py::array_t<real_T>>();
-
-                return lim;
-            }));
-
     py::class_<Control>(m, "Control", docsControl.c_str())
         .def(py::init<>())
         .def_readwrite("parallel", &Control::parallel)
@@ -1150,7 +1097,7 @@ PYBIND11_MODULE(rat_core, m) {
                 return p;
             }));
 
-    m.def("RATMain", &RATMain, docsRATMain.c_str(), py::arg("problem_def"), py::arg("limits"), py::arg("control"));
+    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);