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Copy pathrat.cpp
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1462 lines (1270 loc) · 58.5 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;
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;
}
// 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 libray failed to load" << std::endl;
return;
}
};
~DylibEngine(){};
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 libray 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;
};
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 libray failed to load" << std::endl;
return;
}
this->callback = callback;
};
py::list unpackDataToCell(int rows, int cols, double* data, double* nData,
double* data2, double* nData2, int dataCol)
{
py::list allResults;
int dims[2] = {0, dataCol};
int offset = 0;
for (int i = 0; i < rows; i++){
py::list rowList;
dims[0] = (int)nData[i] / dataCol;
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();
rowList.append(result);
allResults.append(rowList);
}
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[i] / dataCol;
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, 2);
eventData.shiftedData = unpackDataToCell(pEvent->data->nContrast, 1,
pEvent->data->shiftedData, pEvent->data->nShiftedData, NULL, NULL, 3);
eventData.sldProfiles = unpackDataToCell(pEvent->data->nContrast, (pEvent->data->nSldProfiles2 == NULL) ? 1 : 2,
pEvent->data->sldProfiles, pEvent->data->nSldProfiles,
pEvent->data->sldProfiles2, pEvent->data->nSldProfiles2, 2);
eventData.resampledLayers = unpackDataToCell(pEvent->data->nContrast, (pEvent->data->nLayers2 == NULL) ? 1 : 2,
pEvent->data->layers, pEvent->data->nLayers,
pEvent->data->layers2, pEvent->data->nLayers, 2);
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::list reflectivityXData;
py::list sldXData;
py::array_t<real_T> sampleChi;
};
struct BestFitMean
{
py::list reflectivity;
py::list sld;
real_T chi;
py::list data;
};
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;
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
{
BestFitMean bestFitMean;
PredictionIntervals predictionIntervals;
ConfidenceIntervals confidenceIntervals;
DreamParams dreamParams;
DreamOutput dreamOutput;
NestedSamplerOutput nestedSamplerOutput;
py::array_t<real_T> chain;
};
struct Priors
{
py::list param;
py::list backgroundParam;
py::list resolutionParam;
py::list bulkIn;
py::list bulkOut;
py::list qzshift;
py::list scalefactor;
py::list domainRatio;
py::list priorNames;
py::array_t<real_T> priorValues;
};
struct Checks {
py::array_t<real_T> fitParam;
py::array_t<real_T> fitBackgroundParam;
py::array_t<real_T> fitQzshift;
py::array_t<real_T> fitScalefactor;
py::array_t<real_T> fitBulkIn;
py::array_t<real_T> fitBulkOut;
py::array_t<real_T> fitResolutionParam;
py::array_t<real_T> fitDomainRatio;
};
struct Calculation
{
py::array_t<real_T> chiValues;
real_T sumChi;
};
struct ContrastParams
{
py::array_t<real_T> backgroundParams;
py::array_t<real_T> qzshifts;
py::array_t<real_T> scalefactors;
py::array_t<real_T> bulkIn;
py::array_t<real_T> bulkOut;
py::array_t<real_T> resolutionParams;
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 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> param;
py::array_t<real_T> backgroundParam;
py::array_t<real_T> scalefactor;
py::array_t<real_T> qzshift;
py::array_t<real_T> bulkIn;
py::array_t<real_T> bulkOut;
py::array_t<real_T> resolutionParam;
py::array_t<real_T> domainRatio;
};
struct Cells {
py::list f1;
py::list f2;
py::list f3;
py::list f4;
py::list f5;
py::list f6;
py::list f7;
py::list f8;
py::list f9;
py::list f10;
py::list f11;
py::list f12;
py::list f13;
py::list f14;
py::list f15;
py::list f16;
py::list f17;
py::list f18;
py::list f19;
py::list f20;
};
struct ProblemDefinition {
py::array_t<real_T> contrastBackgrounds;
py::array_t<real_T> contrastBackgroundActions;
std::string TF {};
py::array_t<real_T> resample;
py::array_t<real_T> dataPresent;
py::array_t<real_T> oilChiDataPresent;
real_T numberOfContrasts;
std::string geometry {};
bool useImaginary {};
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::array_t<real_T> contrastResolutions;
py::array_t<real_T> backgroundParams;
py::array_t<real_T> qzshifts;
py::array_t<real_T> scalefactors;
py::array_t<real_T> bulkIn;
py::array_t<real_T> bulkOut;
py::array_t<real_T> resolutionParams;
py::array_t<real_T> params;
real_T numberOfLayers {};
std::string modelType {};
py::array_t<real_T> contrastCustomFiles;
py::array_t<real_T> contrastDomainRatios;
py::array_t<real_T> domainRatio;
real_T numberOfDomainContrasts {};
py::array_t<real_T> fitParams;
py::array_t<real_T> otherParams;
py::array_t<real_T> fitLimits;
py::array_t<real_T> otherLimits;
};
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 {};
py::array_t<real_T> resampleParams;
real_T updateFreq {};
real_T updatePlotFreq {};
real_T nSamples {};
real_T nChains {};
real_T jumpProbability {};
real_T pUnitGamma {};
std::string boundHandling {};
boolean_T adaptPCR;
Checks checks {};
};
void stringToRatArray(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];
}
}
coder::array<real_T, 2U> pyArrayToRatArray1d(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("Number of dimensions must be 1");
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("Number of dimensions must be 1");
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("Number of dimensions must be 2");
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_0, 1U> pyListToUnboundedCell0(py::list values)
{
coder::array<RAT::cell_0, 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)
throw std::runtime_error("Number of dimensions for each row must be 4");
stringToRatCharArray(value[0].cast<std::string>(), result[idx].f1);
stringToRatCharArray(value[1].cast<std::string>(), result[idx].f2);
result[idx].f3 = value[2].cast<real_T>();
result[idx].f4 = value[3].cast<real_T>();
idx++;
}
return result;
}
coder::array<RAT::cell_wrap_1, 1U> pyListToUnboundedCell1(py::list values)
{
coder::array<RAT::cell_wrap_1, 1U> result;
result.set_size(values.size());
int32_T idx {0};
for (py::handle list: values)
{
std::string value = py::cast<std::string>(list);
//TODO: validate dimension
stringToRatCharArray(value, result[idx].f1);
idx++;
}
return result;
}
RAT::struct0_T createStruct0(const ProblemDefinition& problem)
{
RAT::struct0_T problem_struct;
problem_struct.useImaginary = problem.useImaginary;
problem_struct.numberOfLayers = problem.numberOfLayers;
problem_struct.numberOfDomainContrasts = problem.numberOfDomainContrasts;
problem_struct.numberOfContrasts = problem.numberOfContrasts;
stringToRatArray(problem.modelType, problem_struct.modelType.data, problem_struct.modelType.size);
stringToRatArray(problem.geometry, problem_struct.geometry.data, problem_struct.geometry.size);
stringToRatArray(problem.TF, problem_struct.TF.data, problem_struct.TF.size);
problem_struct.contrastBackgrounds = pyArrayToRatArray1d(problem.contrastBackgrounds);
problem_struct.contrastBackgroundActions = pyArrayToRatArray1d(problem.contrastBackgroundActions);
problem_struct.resample = pyArrayToRatArray1d(problem.resample);
problem_struct.dataPresent = pyArrayToRatArray1d(problem.dataPresent);
problem_struct.oilChiDataPresent = pyArrayToRatArray1d(problem.oilChiDataPresent);
problem_struct.contrastQzshifts = pyArrayToRatArray1d(problem.contrastQzshifts);
problem_struct.contrastScalefactors = pyArrayToRatArray1d(problem.contrastScalefactors);
problem_struct.contrastBulkIns = pyArrayToRatArray1d(problem.contrastBulkIns);
problem_struct.contrastBulkOuts = pyArrayToRatArray1d(problem.contrastBulkOuts);
problem_struct.contrastResolutions = pyArrayToRatArray1d(problem.contrastResolutions);
problem_struct.backgroundParams = pyArrayToRatArray1d(problem.backgroundParams);
problem_struct.qzshifts = pyArrayToRatArray1d(problem.qzshifts);
problem_struct.scalefactors = pyArrayToRatArray1d(problem.scalefactors);
problem_struct.bulkIn = pyArrayToRatArray1d(problem.bulkIn);
problem_struct.bulkOut = pyArrayToRatArray1d(problem.bulkOut);
problem_struct.resolutionParams = pyArrayToRatArray1d(problem.resolutionParams);
problem_struct.params = pyArrayToRatArray1d(problem.params);
problem_struct.contrastCustomFiles = pyArrayToRatArray1d(problem.contrastCustomFiles);
problem_struct.contrastDomainRatios = pyArrayToRatArray1d(problem.contrastDomainRatios);
problem_struct.domainRatio = pyArrayToRatArray1d(problem.domainRatio);
problem_struct.fitParams = pyArrayToRatArray1d(problem.fitParams);
problem_struct.otherParams = pyArrayToRatArray1d(problem.otherParams);
problem_struct.fitLimits = pyArrayToRatArray2d(problem.fitLimits);
problem_struct.otherLimits = pyArrayToRatArray2d(problem.otherLimits);
return problem_struct;
}
RAT::struct1_T createStruct1(const Limits& limits)
{
RAT::struct1_T limits_struct;
limits_struct.param = pyArrayToRatArray2d(limits.param);
limits_struct.backgroundParam = pyArrayToRatArray2d(limits.backgroundParam);
limits_struct.qzshift = pyArrayToRatArray2d(limits.qzshift);
limits_struct.scalefactor = pyArrayToRatArray2d(limits.scalefactor);
limits_struct.bulkIn = pyArrayToRatArray2d(limits.bulkIn);
limits_struct.bulkOut = pyArrayToRatArray2d(limits.bulkOut);
limits_struct.resolutionParam = pyArrayToRatArray2d(limits.resolutionParam);
limits_struct.domainRatio = pyArrayToRatArray2d(limits.domainRatio);
return limits_struct;
}
RAT::struct3_T createStruct3(const Checks& checks)
{
RAT::struct3_T checks_struct;
checks_struct.fitParam = pyArrayToRatArray1d(checks.fitParam);
checks_struct.fitBackgroundParam = pyArrayToRatArray1d(checks.fitBackgroundParam);
checks_struct.fitQzshift = pyArrayToRatArray1d(checks.fitQzshift);
checks_struct.fitScalefactor = pyArrayToRatArray1d(checks.fitScalefactor);
checks_struct.fitBulkIn = pyArrayToRatArray1d(checks.fitBulkIn);
checks_struct.fitBulkOut = pyArrayToRatArray1d(checks.fitBulkOut);
checks_struct.fitResolutionParam = pyArrayToRatArray1d(checks.fitResolutionParam);
checks_struct.fitDomainRatio = pyArrayToRatArray1d(checks.fitDomainRatio);
return checks_struct;
}
RAT::struct4_T createStruct4(const Priors& priors)
{
RAT::struct4_T priors_struct;
priors_struct.param = pyListToUnboundedCell0(priors.param);
priors_struct.backgroundParam = pyListToUnboundedCell0(priors.backgroundParam);
priors_struct.resolutionParam = pyListToUnboundedCell0(priors.resolutionParam);
priors_struct.qzshift = pyListToUnboundedCell0(priors.qzshift);
priors_struct.scalefactor = pyListToUnboundedCell0(priors.scalefactor);
priors_struct.bulkIn = pyListToUnboundedCell0(priors.bulkIn);
priors_struct.bulkOut = pyListToUnboundedCell0(priors.bulkOut);
priors_struct.domainRatio = pyListToUnboundedCell0(priors.domainRatio);
priors_struct.priorNames = pyListToUnboundedCell1(priors.priorNames);
priors_struct.priorValues = pyArrayToRatArray2d(priors.priorValues);
return priors_struct;
}
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);
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 = pyArrayToRatArray2d(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 = pyArrayToRatArray1d(casted_array);
idx++;
}
return result;
}
coder::array<RAT::cell_wrap_5, 1U> pyListToRatCellWrap5(py::list values)
{
coder::array<RAT::cell_wrap_5, 1U> result;
result.set_size(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 = pyArrayToRatBoundedArray(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)
{
std::string name = py::cast<std::string>(array);
stringToRatArray(name, result[idx].f1.data, result[idx].f1.size);
idx++;
}
return result;
}
coder::array<RAT::cell_wrap_6, 2U> py_function_array_to_rat_cell_wrap_6(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)
{
auto func = py::cast<py::function>(array);
std::string func_ptr = convertPtr2String<CallbackInterface>(new Library(func));
stringToRatArray(func_ptr, result[idx].f1.data, result[idx].f1.size);
idx++;
}
return result;
}
RAT::cell_7 createCell7(const Cells& cells)
{
RAT::cell_7 cells_struct;
cells_struct.f1 = pyListToRatCellWrap2(cells.f1);
cells_struct.f2 = pyListToRatCellWrap3(cells.f2);
cells_struct.f3 = pyListToRatCellWrap2(cells.f3);
cells_struct.f4 = pyListToRatCellWrap2(cells.f4);
cells_struct.f5 = pyListToRatCellWrap4(cells.f5);
cells_struct.f6 = pyListToRatCellWrap5(cells.f6);
cells_struct.f7 = pyListToRatCellWrap6(cells.f7);
cells_struct.f8 = pyListToRatCellWrap6(cells.f8);
cells_struct.f9 = pyListToRatCellWrap6(cells.f9);
cells_struct.f10 = pyListToRatCellWrap6(cells.f10);
cells_struct.f11 = pyListToRatCellWrap6(cells.f11);
cells_struct.f12 = pyListToRatCellWrap6(cells.f12);
cells_struct.f13 = pyListToRatCellWrap6(cells.f13);
cells_struct.f14 = py_function_array_to_rat_cell_wrap_6(cells.f14);
cells_struct.f15 = pyListToRatCellWrap6(cells.f15);
cells_struct.f16 = pyListToRatCellWrap6(cells.f16);
cells_struct.f17 = pyListToRatCellWrap3(cells.f17);
cells_struct.f18 = pyListToRatCellWrap2(cells.f18);
cells_struct.f19 = pyListToRatCellWrap4(cells.f19);
cells_struct.f20 = pyListToRatCellWrap6(cells.f20);
return cells_struct;
}
RAT::struct2_T createStruct2T(const Control& control)
{
RAT::struct2_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;
stringToRatArray(control.parallel, control_struct.parallel.data, control_struct.parallel.size);
stringToRatArray(control.procedure, control_struct.procedure.data, control_struct.procedure.size);
stringToRatArray(control.display, control_struct.display.data, control_struct.display.size);
control_struct.xTolerance = control.xTolerance;
control_struct.resampleParams[0] = control.resampleParams.at(0);
control_struct.resampleParams[1] = control.resampleParams.at(1);
stringToRatArray(control.boundHandling, control_struct.boundHandling.data, control_struct.boundHandling.size);
control_struct.adaptPCR = control.adaptPCR;
control_struct.checks = createStruct3(control.checks);
return control_struct;
}
py::array_t<real_T> pyArrayFromRatArray1d(coder::array<real_T, 2U> array)
{
auto size = (array.size(0) > 1) ? array.size(0) : array.size(1);
auto result_array = py::array_t<real_T>(size);
std::memcpy(result_array.request().ptr, array.data(), result_array.nbytes());
return result_array;
}
py::array_t<real_T> pyArrayFromRatArray2d(coder::array<real_T, 2U> array)
{
auto result_array = py::array_t<real_T, py::array::f_style>({array.size(0), array.size(1)});
std::memcpy(result_array.request().ptr, array.data(), result_array.nbytes());
return result_array;
}
OutputResult OutputResultFromStruct5T(const RAT::struct5_T result)
{
// Copy problem to output
OutputResult output_result;
for (int32_T idx0{0}; idx0 < result.reflectivity.size(0); idx0++) {
py::list inner_list;
for (int32_T idx1{0}; idx1 < result.reflectivity.size(1); idx1++) {
auto tmp = result.reflectivity[idx0 + result.reflectivity.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.reflectivity.append(inner_list);
}
for (int32_T idx0{0}; idx0 < result.simulation.size(0); idx0++) {
py::list inner_list;
for (int32_T idx1{0}; idx1 < result.simulation.size(1); idx1++) {
auto tmp = result.simulation[idx0 + result.simulation.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.simulation.append(inner_list);
}
for (int32_T idx0{0}; idx0 < result.shiftedData.size(0); idx0++) {
py::list inner_list;
for (int32_T idx1{0}; idx1 < result.shiftedData.size(1); idx1++) {
auto tmp = result.shiftedData[idx0 + result.shiftedData.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.shiftedData.append(inner_list);
}
for (int32_T idx0{0}; idx0 < result.layerSlds.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];
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);
}
for (int32_T idx0{0}; idx0 < result.sldProfiles.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];
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);
}
for (int32_T idx0{0}; idx0 < result.resampledLayers.size(0); idx0++) {
py::list inner_list;
for (int32_T idx1{0}; idx1 < result.resampledLayers.size(1); idx1++) {
auto tmp = result.resampledLayers[idx0 + result.resampledLayers.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.resampledLayers.append(inner_list);
}
output_result.contrastParams.backgroundParams = py::array_t<real_T>(result.contrastParams.backgroundParams.size(0));
auto buffer = output_result.contrastParams.backgroundParams.request();
std::memcpy(buffer.ptr, result.contrastParams.backgroundParams.data(), output_result.contrastParams.backgroundParams.size()*sizeof(real_T));
output_result.contrastParams.qzshifts = py::array_t<real_T>(result.contrastParams.qzshifts.size(0));
buffer = output_result.contrastParams.qzshifts.request();
std::memcpy(buffer.ptr, result.contrastParams.qzshifts.data(), output_result.contrastParams.qzshifts.size()*sizeof(real_T));
output_result.contrastParams.scalefactors = py::array_t<real_T>(result.contrastParams.scalefactors.size(0));
buffer = output_result.contrastParams.scalefactors.request();
std::memcpy(buffer.ptr, result.contrastParams.scalefactors.data(), output_result.contrastParams.scalefactors.size()*sizeof(real_T));
output_result.contrastParams.bulkIn = py::array_t<real_T>(result.contrastParams.bulkIn.size(0));
buffer = output_result.contrastParams.bulkIn.request();
std::memcpy(buffer.ptr, result.contrastParams.bulkIn.data(), output_result.contrastParams.bulkIn.size()*sizeof(real_T));
output_result.contrastParams.bulkOut = py::array_t<real_T>(result.contrastParams.bulkOut.size(0));
buffer = output_result.contrastParams.bulkOut.request();
std::memcpy(buffer.ptr, result.contrastParams.bulkOut.data(), output_result.contrastParams.bulkOut.size()*sizeof(real_T));
output_result.contrastParams.resolutionParams = py::array_t<real_T>(result.contrastParams.resolutionParams.size(0));
buffer = output_result.contrastParams.resolutionParams.request();
std::memcpy(buffer.ptr, result.contrastParams.resolutionParams.data(), output_result.contrastParams.resolutionParams.size()*sizeof(real_T));
output_result.calculationResults.sumChi = result.calculationResults.sumChi;
output_result.calculationResults.chiValues = py::array_t<real_T>(result.calculationResults.chiValues.size(0));
buffer = output_result.calculationResults.chiValues.request();
std::memcpy(buffer.ptr, result.calculationResults.chiValues.data(), output_result.calculationResults.chiValues.size()*sizeof(real_T));
output_result.contrastParams.subRoughs = py::array_t<real_T>(result.contrastParams.subRoughs.size(0));
buffer = output_result.contrastParams.subRoughs.request();
std::memcpy(buffer.ptr, result.contrastParams.subRoughs.data(), output_result.contrastParams.subRoughs.size()*sizeof(real_T));
output_result.contrastParams.resample = py::array_t<real_T>(result.contrastParams.resample.size(1));
buffer = output_result.contrastParams.resample.request();
std::memcpy(buffer.ptr, result.contrastParams.resample.data(), output_result.contrastParams.resample.size()*sizeof(real_T));
return output_result;
}
ProblemDefinition problemDefinitionFromStruct0T(const RAT::struct0_T problem)
{
ProblemDefinition problem_def;
problem_def.useImaginary = problem.useImaginary;
problem_def.numberOfLayers = problem.numberOfLayers;
problem_def.numberOfDomainContrasts = problem.numberOfDomainContrasts;
problem_def.numberOfContrasts = problem.numberOfContrasts;
problem_def.modelType.resize(problem.modelType.size[1]);
memcpy(&problem_def.modelType[0], problem.modelType.data, problem.modelType.size[1]);