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Copy pathrat.cpp
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1934 lines (1690 loc) · 86.4 KB
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/*<%
import glob
cfg['sources'] = [*glob.glob('RAT/*.cpp'), *glob.glob('RAT/*.c')]
cfg['parallel'] = True
setup_pybind11(cfg)
%>*/
#include <cstdlib>
#include <iostream>
#include <functional>
#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
#include <pybind11/functional.h>
#include <pybind11/stl.h>
#include "RAT/RATMain.h"
#include "RAT/RATMain_initialize.h"
#include "RAT/RATMain_terminate.h"
#include "RAT/RATMain_types.h"
#include "RAT/makeSLDProfileXY.h"
#include "RAT/classHandle.hpp"
#include "RAT/dylib.hpp"
#include "RAT/events/eventManager.h"
namespace py = pybind11;
const int DEFAULT_DOMAIN = -1;
template <typename Function, typename ... Args>
auto customCaller(std::string identifier, Function f, Args&& ... args) -> decltype((*f)(std::forward<Args>(args)...))
{
try
{
return (*f)(std::forward<Args>(args)...);
}
catch(const std::runtime_error& re)
{
std::string errorMsg;
size_t start_pos = std::string(re.what()).find("$id");
if(start_pos == std::string::npos)
{
errorMsg = std::string("Error occurred when setting ") + identifier + ". " + re.what();
}
else
{
errorMsg = re.what();
errorMsg.replace(start_pos, 3, identifier);
}
throw std::runtime_error(errorMsg);
}
}
class Library: public CallbackInterface
{
public:
py::function function;
Library(const py::function function){
this->function = function;
};
void setOutput(py::tuple& result, std::vector<double>& output, double *outputSize)
{
int nRows = 0, idx = 0;
for (py::handle rowHandle : result[0])
{
py::list rows = py::cast<py::list>(rowHandle);
for (py::handle value : rows)
{
output.push_back(py::cast<double>(value));
idx++;
}
nRows++;
}
outputSize[0] = nRows;
outputSize[1] = (nRows == 0) ? 0 : idx / nRows;
}
// Backgrounds
void invoke(std::vector<double>& xdata, std::vector<double>& params, std::vector<double>& output)
{
auto f = py::cast<std::function<py::tuple(py::list, py::list)>>(this->function);
auto result = f(py::cast(xdata), py::cast(params));
for (py::handle rowHandle : result)
{
if (py::isinstance<py::list>(rowHandle))
output.push_back(py::cast<double>(py::cast<py::list>(rowHandle)[0]));
else
output.push_back(py::cast<double>(rowHandle));
}
};
// Domain overload
void invoke(std::vector<double>& params, std::vector<double>& bulkIn, std::vector<double>& bulkOut,
int contrast, int domainNumber, std::vector<double>& output, double *outputSize, double *roughness)
{
auto f = py::cast<std::function<py::tuple(py::list, py::list, py::list, int, int)>>(this->function);
auto result = f(py::cast(params), py::cast(bulkIn), py::cast(bulkOut), contrast, domainNumber);
*roughness = py::cast<double>(result[1]);
setOutput(result, output, outputSize);
};
// Non-Domain overload
void invoke(std::vector<double>& params, std::vector<double>& bulkIn, std::vector<double>& bulkOut,
int contrast, std::vector<double>& output, double *outputSize, double *roughness)
{
auto f = py::cast<std::function<py::tuple(py::list, py::list, py::list, int)>>(this->function);
auto result = f(py::cast(params), py::cast(bulkIn), py::cast(bulkOut), contrast);
*roughness = py::cast<double>(result[1]);
setOutput(result, output, outputSize);
};
};
class DylibEngine
{
public:
std::unique_ptr<dylib> library;
std::string functionName;
DylibEngine(std::string libName, std::string functionName)
{
this->functionName = functionName;
this->library = std::unique_ptr<dylib>(new dylib(libName));
if (!library)
{
std::cerr << "dynamic library failed to load" << std::endl;
return;
}
};
~DylibEngine(){};
py::list invoke(std::vector<double>& xdata, std::vector<double>& params)
{
try{
std::vector<double> output;
auto func = library->get_function<void(std::vector<double>&, std::vector<double>&, std::vector<double>&)>(functionName);
func(xdata, params, output);
return py::cast(output);
}catch (const dylib::symbol_error &) {
throw std::runtime_error("failed to get dynamic library symbol for " + functionName);
}
};
py::tuple invoke(std::vector<double>& params, std::vector<double>& bulkIn, std::vector<double>& bulkOut, int contrast, int domain=DEFAULT_DOMAIN)
{
try{
std::vector<double> tempOutput;
double *outputSize = new double[2];
double roughness = 0.0;
if (domain != -1) {
auto func = library->get_function<void(std::vector<double>&, std::vector<double>&, std::vector<double>&,
int, int, std::vector<double>&, double*, double*)>(functionName);
func(params, bulkIn, bulkOut, contrast, domain, tempOutput, outputSize, &roughness);
}
else {
auto func = library->get_function<void(std::vector<double>&, std::vector<double>&, std::vector<double>&,
int, std::vector<double>&, double*, double*)>(functionName);
func(params, bulkIn, bulkOut, contrast, tempOutput, outputSize, &roughness);
}
py::list output;
for (int32_T idx1{0}; idx1 < outputSize[0]; idx1++)
{
py::list rows;
for (int32_T idx2{0}; idx2 < outputSize[1]; idx2++)
{
rows.append(tempOutput[(int32_T)outputSize[1] * idx1 + idx2]);
}
output.append(rows);
}
return py::make_tuple(output, roughness);
}catch (const dylib::symbol_error &) {
throw std::runtime_error("failed to get dynamic library symbol for " + functionName);
}
};
};
struct ProgressEventData
{
std::string message;
double percent;
};
struct PlotEventData
{
py::list reflectivity;
py::list shiftedData;
py::list sldProfiles;
py::list resampledLayers;
py::array_t<double> subRoughs;
py::array_t<double> resample;
py::array_t<double> dataPresent;
std::string modelType;
py::list contrastNames;
};
class EventBridge
{
public:
std::unique_ptr<dylib> library;
py::function callback;
EventBridge(py::function callback)
{
std::string filename = "eventManager" + std::string(dylib::extension);
this->library = std::unique_ptr<dylib>(new dylib(std::getenv("RAT_PATH"), filename.c_str()));
if (!library)
{
std::cerr << "event manager dynamic library failed to load" << std::endl;
return;
}
this->callback = callback;
};
py::list unpackDataToCell(int rows, int cols, double* data, double* nData,
double* data2, double* nData2, bool isOutput2D=false)
{
py::list allResults;
int dims[2] = {0, 0};
int offset = 0;
for (int i = 0; i < rows; i++){
dims[0] = (int)nData[2*i];
dims[1] = (int)nData[2*i+1];
auto result = py::array_t<double, py::array::f_style>({dims[0], dims[1]});
std::memcpy(result.request().ptr, data + offset, result.nbytes());
offset += result.size();
if (isOutput2D){
py::list rowList;
rowList.append(result);
allResults.append(rowList);
}
else{
allResults.append(result);
}
}
if (data2 != NULL && nData2 != NULL)
{
// This is used to unpack the domains data into the second column
offset = 0;
for ( int i = 0; i < rows; i++){
dims[0] = (int)nData2[2*i];
dims[1] = (int)nData2[2*i+1];
auto result = py::array_t<double, py::array::f_style>({dims[0], dims[1]});
std::memcpy(result.request().ptr, data2 + offset, result.nbytes());
offset += result.size();
auto rowList = allResults[i].cast<py::list>();
rowList.append(result);
}
}
return allResults;
};
void eventCallback(const baseEvent& event)
{
if (event.type == EventTypes::Message) {
messageEvent* mEvent = (messageEvent*)&event;
this->callback(event.type, mEvent->msg);
} else if (event.type == EventTypes::Progress){
progressEvent* pEvent = (progressEvent*)&event;
ProgressEventData eventData;
eventData.message = pEvent->msg;
eventData.percent = pEvent->percent;
this->callback(event.type, eventData);
} else if (event.type == EventTypes::Plot){
plotEvent* pEvent = (plotEvent*)&event;
PlotEventData eventData;
eventData.modelType = std::string(pEvent->data->modelType);
eventData.subRoughs = py::array_t<double>(pEvent->data->nContrast);
std::memcpy(eventData.subRoughs.request().ptr, pEvent->data->subRoughs, eventData.subRoughs.nbytes());
eventData.resample = py::array_t<double>(pEvent->data->nContrast);
std::memcpy(eventData.resample.request().ptr, pEvent->data->resample, eventData.resample.nbytes());
eventData.dataPresent = py::array_t<double>(pEvent->data->nContrast);
std::memcpy(eventData.dataPresent.request().ptr, pEvent->data->dataPresent, eventData.dataPresent.nbytes());
eventData.reflectivity = unpackDataToCell(pEvent->data->nContrast, 1,
pEvent->data->reflect, pEvent->data->nReflect, NULL, NULL);
eventData.shiftedData = unpackDataToCell(pEvent->data->nContrast, 1,
pEvent->data->shiftedData, pEvent->data->nShiftedData, NULL, NULL);
eventData.sldProfiles = unpackDataToCell(pEvent->data->nContrast, (pEvent->data->nSldProfiles2 == NULL) ? 1 : 2,
pEvent->data->sldProfiles, pEvent->data->nSldProfiles,
pEvent->data->sldProfiles2, pEvent->data->nSldProfiles2, true);
eventData.resampledLayers = unpackDataToCell(pEvent->data->nContrast, (pEvent->data->nLayers2 == NULL) ? 1 : 2,
pEvent->data->layers, pEvent->data->nLayers,
pEvent->data->layers2, pEvent->data->nLayers2, true);
int offset = 0;
for (int i = 0; i < pEvent->data->nContrast; i++){
eventData.contrastNames.append(std::string(pEvent->data->contrastNames + offset,
pEvent->data->nContrastNames[i]));
offset += pEvent->data->nContrastNames[i];
}
this->callback(event.type, eventData);
}
};
void registerEvent(EventTypes eventType)
{
std::function<void(const baseEvent& event)> caller = std::bind(&EventBridge::eventCallback, this, std::placeholders::_1);
auto addListener = library->get_function<void(EventTypes, std::function<void(const baseEvent&)>)>("addListener");
addListener(eventType, caller);
};
void clear()
{
auto clearListeners = library->get_function<void(void)>("clearListeners");
clearListeners();
};
};
struct PredictionIntervals
{
py::list reflectivity;
py::list sld;
py::array_t<real_T> sampleChi;
};
struct ConfidenceIntervals
{
py::array_t<real_T> percentile95;
py::array_t<real_T> percentile65;
py::array_t<real_T> mean;
};
struct NestedSamplerOutput
{
real_T logZ;
real_T logZErr;
py::array_t<real_T> nestSamples;
py::array_t<real_T> postSamples;
};
struct DreamParams
{
real_T nParams;
real_T nChains;
real_T nGenerations;
boolean_T parallel;
real_T CPU;
real_T jumpProbability;
real_T pUnitGamma;
real_T nCR;
real_T delta;
real_T steps;
real_T zeta;
std::string outlier;
boolean_T adaptPCR;
real_T thinning;
real_T epsilon;
boolean_T ABC;
boolean_T IO;
boolean_T storeOutput;
py::array_t<real_T> R;
};
struct DreamOutput
{
py::array_t<real_T> allChains;
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;
};
struct BayesResults
{
PredictionIntervals predictionIntervals;
ConfidenceIntervals confidenceIntervals;
DreamParams dreamParams;
DreamOutput dreamOutput;
NestedSamplerOutput nestedSamplerOutput;
py::array_t<real_T> chain;
};
struct Priors
{
py::list params;
py::list backgroundParams;
py::list scalefactors;
py::list qzshifts;
py::list bulkIns;
py::list bulkOuts;
py::list resolutionParams;
py::list domainRatios;
py::list priorNames;
py::array_t<real_T> priorValues;
};
struct Checks {
py::array_t<real_T> params;
py::array_t<real_T> backgroundParams;
py::array_t<real_T> qzshifts;
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;
};
struct Calculation
{
py::array_t<real_T> chiValues;
real_T sumChi;
};
struct ContrastParams
{
py::array_t<real_T> scalefactors;
py::array_t<real_T> bulkIn;
py::array_t<real_T> bulkOut;
py::array_t<real_T> subRoughs;
py::array_t<real_T> resample;
};
struct OutputResult {
py::list reflectivity;
py::list simulation;
py::list shiftedData;
py::list backgrounds;
py::list resolutions;
py::list layerSlds;
py::list sldProfiles;
py::list resampledLayers;
Calculation calculationResults {};
ContrastParams contrastParams {};
py::array_t<real_T> fitParams;
py::list fitNames;
};
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> qzshifts;
py::array_t<real_T> bulkIns;
py::array_t<real_T> bulkOuts;
py::array_t<real_T> resolutionParams;
py::array_t<real_T> domainRatios;
};
struct NameStore {
py::list params;
py::list backgroundParams;
py::list scalefactors;
py::list qzshifts;
py::list bulkIns;
py::list bulkOuts;
py::list resolutionParams;
py::list domainRatios;
py::list contrasts;
};
struct ProblemDefinition {
std::string TF {};
py::array_t<real_T> resample;
py::list data;
py::array_t<real_T> dataPresent;
py::list dataLimits;
py::list simulationLimits;
py::array_t<real_T> oilChiDataPresent;
real_T numberOfContrasts;
std::string geometry {};
boolean_T useImaginary {};
py::list repeatLayers;
py::list contrastBackgroundParams;
py::list contrastBackgroundTypes;
py::list contrastBackgroundActions;
py::array_t<real_T> contrastQzshifts;
py::array_t<real_T> contrastScalefactors;
py::array_t<real_T> contrastBulkIns;
py::array_t<real_T> contrastBulkOuts;
py::list contrastResolutionParams;
py::list contrastResolutionTypes;
py::array_t<real_T> backgroundParams;
py::array_t<real_T> qzshifts;
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> params;
real_T numberOfLayers {};
py::list contrastLayers;
py::list layersDetails;
py::object customFiles;
std::string modelType {};
py::array_t<real_T> contrastCustomFiles;
py::array_t<real_T> contrastDomainRatios;
py::array_t<real_T> domainRatios;
real_T numberOfDomainContrasts {};
py::list domainContrastLayers;
py::array_t<real_T> fitParams;
py::array_t<real_T> otherParams;
py::array_t<real_T> fitLimits;
py::array_t<real_T> otherLimits;
NameStore names;
Checks checks {};
};
struct Control {
std::string parallel {};
std::string procedure {};
std::string display {};
real_T xTolerance {};
real_T funcTolerance {};
real_T maxFuncEvals {};
real_T maxIterations {};
real_T populationSize {};
real_T fWeight {};
real_T crossoverProbability {};
real_T targetValue {};
real_T numGenerations {};
real_T strategy {};
real_T nLive {};
real_T nMCMC {};
real_T propScale {};
real_T nsTolerance {};
boolean_T calcSldDuringFit {};
real_T resampleMinAngle {};
real_T resampleNPoints {};
real_T updateFreq {};
real_T updatePlotFreq {};
real_T nSamples {};
real_T nChains {};
real_T jumpProbability {};
real_T pUnitGamma {};
std::string boundHandling {};
boolean_T adaptPCR;
std::string IPCFilePath {};
};
void stringToRatBoundedArray(std::string value, char_T result_data[], int32_T result_size[2])
{
result_size[0] = 1;
result_size[1] = value.length();
for (int32_T idx1{0}; idx1 < value.length(); idx1++) {
result_data[idx1] = value[idx1];
}
}
void stringToRatCharArray(std::string value, coder::array<char_T, 2U>& result)
{
result.set_size(1, value.length());
for (int32_T idx{0}; idx < value.length(); idx++) {
result[idx] = value[idx];
}
}
void stringFromRatBoundedArray(const char_T array_data[], const int32_T array_size[2], std::string& result)
{
result.resize(array_size[1]);
memcpy(&result[0], array_data, array_size[1]);
}
coder::array<real_T, 2U> pyArrayToRatRowArray1d(py::array_t<real_T> value)
{
coder::array<real_T, 2U> result;
py::buffer_info buffer_info = value.request();
if (buffer_info.size == 0)
return result;
if (buffer_info.ndim != 1)
throw std::runtime_error("Expects a 1D numeric array");
result.set_size(1, buffer_info.shape[0]);
for (int32_T idx0{0}; idx0 < buffer_info.shape[0]; idx0++) {
result[idx0] = value.at(idx0);
}
return result;
}
coder::bounded_array<real_T, 10U, 2U> pyArrayToRatBoundedArray(py::array_t<real_T> value)
{
coder::bounded_array<real_T, 10U, 2U> result {};
py::buffer_info buffer_info = value.request();
if (buffer_info.size == 0)
return result;
if (buffer_info.ndim != 1)
throw std::runtime_error("Expects a 1D numeric array");
result.size[0] = 1;
result.size[1] = buffer_info.shape[0];
for (int32_T idx0{0}; idx0 < buffer_info.shape[0]; idx0++) {
result.data[idx0] = value.at(idx0);
}
return result;
}
coder::bounded_array<real_T, 5U, 2U> pyArrayToRatBoundedArray3(py::array_t<real_T> value)
{
coder::bounded_array<real_T, 5U, 2U> result {};
py::buffer_info buffer_info = value.request();
if (buffer_info.size == 0)
return result;
if (buffer_info.ndim != 1)
throw std::runtime_error("Expects a 1D numeric array");
result.size[0] = 1;
result.size[1] = buffer_info.shape[0];
for (int32_T idx0{0}; idx0 < buffer_info.shape[0]; idx0++) {
result.data[idx0] = value.at(idx0);
}
return result;
}
coder::array<real_T, 2U> pyArrayToRatArray2d(py::array_t<real_T> value)
{
coder::array<real_T, 2U> result;
py::buffer_info buffer_info = value.request();
if (buffer_info.size == 0)
return result;
if (buffer_info.ndim != 2)
throw std::runtime_error("Expects a 2D numeric array");
result.set_size(buffer_info.shape[0], buffer_info.shape[1]);
int32_T idx {0};
for (int32_T idx0{0}; idx0 < buffer_info.shape[0]; idx0++) {
for (int32_T idx1{0}; idx1 < buffer_info.shape[1]; idx1++) {
idx = idx0 + result.size(0) * idx1;
result[idx] = value.at(idx0, idx1);
}
}
return result;
}
coder::array<RAT::cell_7, 1U> pyListToUnboundedCell0(py::list values)
{
coder::array<RAT::cell_7, 1U> result;
result.set_size(values.size());
int32_T idx {0};
for (py::handle list: values)
{
py::list value = py::cast<py::list>(list);
if (py::len(list) != 4 || !py::isinstance<py::str>(value[0]) || !py::isinstance<py::str>(value[1]) ||
!py::isinstance<py::float_>(value[2]) || !py::isinstance<py::float_>(value[3]))
throw std::runtime_error("Expects a 2D list where each row must contain 4 elements. "
"Columns 1 and 2 must be strings and Columns 3 and 4 must be numeric arrays");
stringToRatBoundedArray(value[0].cast<std::string>(), result[idx].f1.data, result[idx].f1.size);
stringToRatBoundedArray(value[1].cast<std::string>(), result[idx].f2.data, result[idx].f2.size);
result[idx].f3 = value[2].cast<real_T>();
result[idx].f4 = value[3].cast<real_T>();
idx++;
}
return result;
}
coder::array<RAT::cell_wrap_8, 1U> pyListToUnboundedCell1(py::list values)
{
coder::array<RAT::cell_wrap_8, 1U> result;
result.set_size(values.size());
int32_T idx {0};
for (py::handle list: values)
{
if (py::isinstance<py::str>(list)) {
std::string value = py::cast<std::string>(list);
stringToRatCharArray(value, result[idx].f1);
idx++;
}
else
throw std::runtime_error("Expects a 1D list of strings");
}
return result;
}
coder::array<RAT::cell_wrap_1, 2U> pyListToRatCellWrap1(py::list values)
{
coder::array<RAT::cell_wrap_1, 2U> result;
result.set_size(1, values.size());
int32_T idx {0};
for (py::handle array: values)
{
py::array_t<real_T> casted_array = py::cast<py::array>(array);
result[idx].f1 = customCaller("$id[" + std::to_string(idx) +"]", pyArrayToRatArray2d, casted_array);
idx++;
}
return result;
}
coder::array<RAT::cell_wrap_2, 2U> pyListToRatCellWrap2(py::list values)
{
coder::array<RAT::cell_wrap_2, 2U> result;
result.set_size(1, values.size());
int32_T idx {0};
for (py::handle array: values)
{
py::array_t<real_T> casted_array = py::cast<py::array>(array);
if (casted_array.size() != 2)
throw std::runtime_error("Expects a 2D list where each row contains exactly 2 numbers");
result[idx].f1[0] = casted_array.at(0);
result[idx].f1[1] = casted_array.at(1);
idx++;
}
return result;
}
coder::array<RAT::cell_wrap_3, 2U> pyListToRatCellWrap3(py::list values)
{
coder::array<RAT::cell_wrap_3, 2U> result;
result.set_size(1, values.size());
int32_T idx {0};
for (py::handle array: values)
{
py::array_t<real_T> casted_array = py::cast<py::array>(array);
result[idx].f1 = customCaller("$id[" + std::to_string(idx) +"]", pyArrayToRatBoundedArray3, casted_array);
idx++;
}
return result;
}
coder::array<RAT::cell_wrap_4, 2U> pyListToRatCellWrap4(py::list values)
{
coder::array<RAT::cell_wrap_4, 2U> result;
result.set_size(1, values.size());
int32_T idx {0};
for (py::handle array: values)
{
py::array_t<real_T> casted_array = py::cast<py::array>(array);
result[idx].f1 = customCaller("$id[" + std::to_string(idx) +"]", pyArrayToRatBoundedArray3, casted_array);
idx++;
}
return result;
}
coder::array<RAT::cell_wrap_5, 2U> pyListToRatCellWrap5(py::list values)
{
coder::array<RAT::cell_wrap_5, 2U> result;
result.set_size(1, values.size());
int32_T idx {0};
for (py::handle array: values)
{
py::array_t<real_T> casted_array = py::cast<py::array>(array);
result[idx].f1 = customCaller("$id[" + std::to_string(idx) +"]", pyArrayToRatRowArray1d, casted_array);
idx++;
}
return result;
}
coder::array<RAT::cell_wrap_6, 2U> pyListToRatCellWrap6(py::list values)
{
coder::array<RAT::cell_wrap_6, 2U> result;
result.set_size(1, values.size());
int32_T idx {0};
for (py::handle array: values)
{
py::array_t<real_T> casted_array = py::cast<py::array>(array);
result[idx].f1 = customCaller("$id[" + std::to_string(idx) +"]", pyArrayToRatBoundedArray, casted_array);
idx++;
}
return result;
}
coder::array<RAT::cell_wrap_0, 2U> pyListToRatCellWrap0(py::list values)
{
coder::array<RAT::cell_wrap_0, 2U> result;
result.set_size(1, values.size());
int32_T idx {0};
for (py::handle array: values)
{
if (py::isinstance<py::str>(array)) {
std::string name = py::cast<std::string>(array);
stringToRatBoundedArray(name, result[idx].f1.data, result[idx].f1.size);
idx++;
}
else
throw std::runtime_error("Expects a 1D list of strings");
}
return result;
}
coder::array<RAT::cell_wrap_0, 2U> py_function_array_to_rat_cell_wrap_0(py::object values)
{
auto handles = py::cast<py::list>(values);
coder::array<RAT::cell_wrap_0, 2U> result;
result.set_size(1, handles.size());
int32_T idx {0};
for (py::handle array: handles)
{
auto func = py::cast<py::function>(array);
std::string func_ptr = convertPtr2String<CallbackInterface>(new Library(func));
stringToRatBoundedArray(func_ptr, result[idx].f1.data, result[idx].f1.size);
idx++;
}
return result;
}
RAT::struct1_T createStruct1(const NameStore& names)
{
RAT::struct1_T names_struct;
names_struct.params = customCaller("NameStore.params", pyListToRatCellWrap0, names.params);
names_struct.backgroundParams = customCaller("NameStore.backgroundParams", pyListToRatCellWrap0, names.backgroundParams);
names_struct.scalefactors = customCaller("NameStore.scalefactors", pyListToRatCellWrap0, names.scalefactors);
names_struct.qzshifts = customCaller("NameStore.qzshifts", pyListToRatCellWrap0, names.qzshifts);
names_struct.bulkIns = customCaller("NameStore.bulkIns", pyListToRatCellWrap0, names.bulkIns);
names_struct.bulkOuts = customCaller("NameStore.bulkOuts", pyListToRatCellWrap0, names.bulkOuts);
names_struct.resolutionParams = customCaller("NameStore.resolutionParams", pyListToRatCellWrap0, names.resolutionParams);
names_struct.domainRatios = customCaller("NameStore.domainRatios", pyListToRatCellWrap0, names.domainRatios);
names_struct.contrasts = customCaller("NameStore.contrasts", pyListToRatCellWrap0, names.contrasts);
return names_struct;
}
RAT::struct2_T createStruct2(const Checks& checks)
{
RAT::struct2_T checks_struct;
checks_struct.params = customCaller("Checks.params", pyArrayToRatRowArray1d, checks.params);
checks_struct.backgroundParams = customCaller("Checks.backgroundParams", pyArrayToRatRowArray1d, checks.backgroundParams);
checks_struct.scalefactors = customCaller("Checks.scalefactors", pyArrayToRatRowArray1d, checks.scalefactors);
checks_struct.qzshifts = customCaller("Checks.qzshifts", pyArrayToRatRowArray1d, checks.qzshifts);
checks_struct.bulkIns = customCaller("Checks.bulkIns", pyArrayToRatRowArray1d, checks.bulkIns);
checks_struct.bulkOuts = customCaller("Checks.bulkOuts", pyArrayToRatRowArray1d, checks.bulkOuts);
checks_struct.resolutionParams = customCaller("Checks.resolutionParams", pyArrayToRatRowArray1d, checks.resolutionParams);
checks_struct.domainRatios = customCaller("Checks.domainRatios", pyArrayToRatRowArray1d, checks.domainRatios);
return checks_struct;
}
RAT::struct0_T createStruct0(const ProblemDefinition& problem)
{
RAT::struct0_T problem_struct;
stringToRatBoundedArray(problem.TF, problem_struct.TF.data, problem_struct.TF.size);
problem_struct.resample = customCaller("Problem.resample", pyArrayToRatRowArray1d, problem.resample);
problem_struct.data = customCaller("Problem.data", pyListToRatCellWrap1, problem.data);
problem_struct.dataPresent = customCaller("Problem.dataPresent", pyArrayToRatRowArray1d, problem.dataPresent);
problem_struct.dataLimits = customCaller("Problem.dataLimits", pyListToRatCellWrap2, problem.dataLimits);
problem_struct.simulationLimits = customCaller("Problem.simulationLimits", pyListToRatCellWrap2, problem.simulationLimits);
problem_struct.oilChiDataPresent = customCaller("Problem.oilChiDataPresent", pyArrayToRatRowArray1d, problem.oilChiDataPresent);
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.contrastBackgroundParams = customCaller("Problem.contrastBackgroundParams", pyListToRatCellWrap3, problem.contrastBackgroundParams);
problem_struct.contrastBackgroundTypes = customCaller("Problem.contrastBackgroundTypes", pyListToRatCellWrap0, problem.contrastBackgroundTypes);
problem_struct.contrastBackgroundActions = customCaller("Problem.contrastBackgroundActions", pyListToRatCellWrap0, problem.contrastBackgroundActions);
problem_struct.contrastQzshifts = customCaller("Problem.contrastQzshifts", pyArrayToRatRowArray1d, problem.contrastQzshifts);
problem_struct.contrastScalefactors = customCaller("Problem.contrastScalefactors", pyArrayToRatRowArray1d, problem.contrastScalefactors);
problem_struct.contrastBulkIns = customCaller("Problem.contrastBulkIns", pyArrayToRatRowArray1d, problem.contrastBulkIns);
problem_struct.contrastBulkOuts = customCaller("Problem.contrastBulkOuts", pyArrayToRatRowArray1d, problem.contrastBulkOuts);
problem_struct.contrastResolutionParams = customCaller("Problem.contrastResolutionParams", pyListToRatCellWrap4, problem.contrastResolutionParams);
problem_struct.contrastResolutionTypes = customCaller("Problem.contrastResolutionTypes", pyListToRatCellWrap0, problem.contrastResolutionTypes);
problem_struct.backgroundParams = customCaller("Problem.backgroundParams", pyArrayToRatRowArray1d, problem.backgroundParams);
problem_struct.qzshifts = customCaller("Problem.qzshifts", pyArrayToRatRowArray1d, problem.qzshifts);
problem_struct.scalefactors = customCaller("Problem.scalefactors", pyArrayToRatRowArray1d, problem.scalefactors);
problem_struct.bulkIns = customCaller("Problem.bulkIns", pyArrayToRatRowArray1d, problem.bulkIns);
problem_struct.bulkOuts = customCaller("Problem.bulkOuts", pyArrayToRatRowArray1d, problem.bulkOuts);
problem_struct.resolutionParams = customCaller("Problem.resolutionParams", pyArrayToRatRowArray1d, problem.resolutionParams);
problem_struct.params = customCaller("Problem.params", pyArrayToRatRowArray1d, problem.params);
problem_struct.numberOfLayers = problem.numberOfLayers;
problem_struct.contrastLayers = customCaller("Problem.contrastLayers", pyListToRatCellWrap5, problem.contrastLayers);
problem_struct.layersDetails = customCaller("Problem.layersDetails", pyListToRatCellWrap6, problem.layersDetails);
problem_struct.customFiles = customCaller("Problem.customFiles", py_function_array_to_rat_cell_wrap_0, problem.customFiles);
stringToRatBoundedArray(problem.modelType, problem_struct.modelType.data, problem_struct.modelType.size);
problem_struct.contrastCustomFiles = customCaller("Problem.contrastCustomFiles", pyArrayToRatRowArray1d, problem.contrastCustomFiles);
problem_struct.contrastDomainRatios = customCaller("Problem.contrastDomainRatios", pyArrayToRatRowArray1d, problem.contrastDomainRatios);
problem_struct.domainRatios = customCaller("Problem.domainRatios", pyArrayToRatRowArray1d, problem.domainRatios);
problem_struct.numberOfDomainContrasts = problem.numberOfDomainContrasts;
problem_struct.domainContrastLayers = customCaller("Problem.domainContrastLayers", pyListToRatCellWrap5, problem.domainContrastLayers);
problem_struct.fitParams = customCaller("Problem.fitParams", pyArrayToRatRowArray1d, problem.fitParams);
problem_struct.otherParams = customCaller("Problem.otherParams", pyArrayToRatRowArray1d, problem.otherParams);
problem_struct.fitLimits = customCaller("Problem.fitLimits", pyArrayToRatArray2d, problem.fitLimits);
problem_struct.otherLimits = customCaller("Problem.otherLimits", pyArrayToRatArray2d, problem.otherLimits);
problem_struct.names = createStruct1(problem.names);
problem_struct.checks = createStruct2(problem.checks);
return problem_struct;
}
RAT::struct3_T createStruct3(const Limits& limits)
{
RAT::struct3_T 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.qzshifts = customCaller("Limits.qzshifts", pyArrayToRatArray2d, limits.qzshifts);
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::struct5_T createStruct5(const Priors& priors)
{
RAT::struct5_T priors_struct;
priors_struct.params = customCaller("Priors.params", pyListToUnboundedCell0, priors.params);
priors_struct.backgroundParams = customCaller("Priors.backgroundParams", pyListToUnboundedCell0, priors.backgroundParams);
priors_struct.scalefactors = customCaller("Priors.scalefactors", pyListToUnboundedCell0, priors.scalefactors);
priors_struct.qzshifts = customCaller("Priors.qzshifts", pyListToUnboundedCell0, priors.qzshifts);
priors_struct.bulkIns = customCaller("Priors.bulkIns", pyListToUnboundedCell0, priors.bulkIns);
priors_struct.bulkOuts = customCaller("Priors.bulkOuts", pyListToUnboundedCell0, priors.bulkOuts);
priors_struct.resolutionParams = customCaller("Priors.resolutionParams", pyListToUnboundedCell0, priors.resolutionParams);
priors_struct.domainRatios = customCaller("Priors.domainRatios", pyListToUnboundedCell0, priors.domainRatios);
priors_struct.priorNames = customCaller("Priors.priorNames", pyListToUnboundedCell1, priors.priorNames);
priors_struct.priorValues = customCaller("Priors.priorValues", pyArrayToRatArray2d, priors.priorValues);
return priors_struct;
}
RAT::struct4_T createStruct4(const Control& control)
{
RAT::struct4_T control_struct;
control_struct.funcTolerance = control.funcTolerance;
control_struct.maxFuncEvals = control.maxFuncEvals;
control_struct.maxIterations = control.maxIterations;
control_struct.populationSize = control.populationSize;
control_struct.fWeight = control.fWeight;
control_struct.crossoverProbability = control.crossoverProbability;
control_struct.targetValue = control.targetValue;
control_struct.numGenerations = control.numGenerations;
control_struct.strategy = control.strategy;
control_struct.nLive = control.nLive;
control_struct.nMCMC = control.nMCMC;
control_struct.propScale = control.propScale;
control_struct.nsTolerance = control.nsTolerance;
control_struct.calcSldDuringFit = control.calcSldDuringFit;
control_struct.updateFreq = control.updateFreq;
control_struct.updatePlotFreq = control.updatePlotFreq;
control_struct.nSamples = control.nSamples;
control_struct.nChains = control.nChains;
control_struct.jumpProbability = control.jumpProbability;
control_struct.pUnitGamma = control.pUnitGamma;
stringToRatBoundedArray(control.parallel, control_struct.parallel.data, control_struct.parallel.size);
stringToRatBoundedArray(control.procedure, control_struct.procedure.data, control_struct.procedure.size);
stringToRatBoundedArray(control.display, control_struct.display.data, control_struct.display.size);
control_struct.xTolerance = control.xTolerance;
control_struct.resampleMinAngle = control.resampleMinAngle;
control_struct.resampleNPoints = control.resampleNPoints;
stringToRatBoundedArray(control.boundHandling, control_struct.boundHandling.data, control_struct.boundHandling.size);
control_struct.adaptPCR = control.adaptPCR;
stringToRatBoundedArray(control.IPCFilePath, control_struct.IPCFilePath.data, control_struct.IPCFilePath.size);
return control_struct;
}
template <typename T>
py::array_t<real_T> pyArrayFromRatArray1d(T array, bool isCol=true)
{
auto size = isCol ? array.size(1) : array.size(0);
auto result_array = py::array_t<real_T>(size);
std::memcpy(result_array.request().ptr, array.data(), result_array.nbytes());
return result_array;