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#ifndef RAT_DEFINES_H
#define RAT_DEFINES_H
#include <cstdlib>
#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
namespace py = pybind11;
const std::string docsProgressEventData = R"(The Python binding for the C++ progressEventData struct.
The progress event shows the percentage completion for the calculation. This can be emitted by
the DREAM algorithm only.
Parameters
----------
message : str
The title text for the event.
percent : float
The percentage of the calculation completed (as a number between 0 and 1)
)";
struct ProgressEventData
{
std::string message;
double percent;
};
const std::string docsPlotEventData = R"(The Python binding for the C++ plotEventData struct.
The plot event data contains intermediate results from the calculation. This can be emitted
by the Simplex and DE algorithms only.
Parameters
----------
reflectivity : list
The reflectivity curves for each contrast, with the same range as the data
(``data_range`` in the contrast's ``Data`` object)
shiftedData : list
The data with scalefactors and background corrections applied.
sldProfiles : list
The SLD profiles for each contrast.
resampledLayers : list
If resampling is used, the SLD for each contrast after resampling has been performed.
subRoughs : np.ndarray[np.float]
The substrate roughness values for each contrast.
resample : np.ndarray[np.float]
An array containing whether each contrast was resampled.
dataPresent : np.ndarray[np.float]
Non-zero values indicates if data is present for the contrast.
modelType : str
The model type for the project.
contrastNames : list
The names of all contrasts in the project.
)";
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;
};
const std::string docsPredictionIntervals = R"(The Python binding for the C++ predictionIntervals struct.
The Bayesian prediction intervals for 95% and 65% confidence.
For ``reflectivity`` and ``sld``, each list item is an array
with five rows. The rows represent:
- 0: the 5th percentile;
- 1: the 35th percentile;
- 2: the mean value of the interval;
- 3: the 65th percentile;
- 4: the 95th percentile.
Parameters
----------
reflectivity : list
The prediction interval data for reflectivity of each contrast.
sld : list
The prediction interval data for SLD of each contrast.
sampleChi : np.ndarray[np.float]
The value of sumChi at each point of the Markov chain.
)";
struct PredictionIntervals
{
py::list reflectivity;
py::list sld;
py::array_t<real_T> sampleChi;
};
const std::string docsConfidenceIntervals = R"(The Python binding for the C++ confidenceIntervals struct.
The 65% and 95% confidence intervals for the best fit results.
Parameters
----------
percentile95 : np.ndarray[np.float]
The 95% confidence intervals for each fit parameter.
percentile65 : np.ndarray[np.float]
The 65% confidence intervals for each fit parameter.
mean : np.ndarray[np.float]
The mean values for each fit parameter.
)";
struct ConfidenceIntervals
{
py::array_t<real_T> percentile95;
py::array_t<real_T> percentile65;
py::array_t<real_T> mean;
};
const std::string docsNestedSamplerOutput = R"(The Python binding for the C++ nestedSamplerOutput struct.
The output information from the Nested Sampler (ns).
Parameters
----------
logZ : float
The natural logarithm of the evidence Z for the parameter values.
logZErr : float
The estimated uncertainty in the final value of logZ.
nestSamples : np.ndarray[np.float]
``NestedSamplerOutput.nestSamples[i, j]`` represents the values
sampled at iteration ``i``, where this value is:
- the value sampled for parameter ``j``, for ``j`` in ``0:nParams``,
- the minimum log-likelihood for ``j = nParams + 1``.
postSamples : np.ndarray[np.float]
The posterior values at the points sampled in ``NestedSamplerOutput.nestSamples``.
)";
struct NestedSamplerOutput
{
real_T logZ;
real_T logZErr;
py::array_t<real_T> nestSamples;
py::array_t<real_T> postSamples;
};
const std::string docsDreamParams = R"(The Python binding for the C++ dreamParams struct.
The parameters used by the inner DREAM algorithm.
Parameters
----------
nParams : float
The number of parameters used by the algorithm.
nChains : float
The number of MCMC chains used by the algorithm.
nGenerations : float
The number of DE generations calculated per iteration.
parallel : bool
Whether the algorithm should run chains in parallel.
CPU : float
The number of processor cores used for parallel chains.
jumpProbability : float
A probability range for the size of jumps when performing subspace sampling.
pUnitGamma : float
The probability that the scaling-down factor of jumps will be ignored
and a larger jump will be taken for one iteration.
nCR : float
The number of crossovers performed each iteration.
delta : float
The number of chain mutation pairs proposed each iteration.
steps : float
The number of MCMC steps to perform between conversion checks.
zeta : float
The ergodicity of the algorithm.
outlier : str
What test should be used to detect outliers.
adaptPCR : bool
Whether the crossover probability for differential evolution should be
adapted by the algorithm as it runs.
thinning : float
The thinning rate of each Markov chain (to reduce memory intensity)
epsilon : float
The cutoff threshold for Approximate Bayesian Computation (if used)
ABC : bool
Whether Approximate Bayesian Computation is used.
IO : bool
Whether the algorithm should perform IO writes of the model in parallel.
storeOutput : bool
Whether output model simulations are performed.
R : np.np.ndarray[np.float]
An array where row ``i`` is the list of chains
with which chain ``i`` can mutate.
)";
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;
};
const std::string docsDreamOutput = R"(The Python binding for the C++ DreamOutput struct.
The diagnostic output information from DREAM.
Parameters
----------
allChains : np.ndarray[np.float]
An ``nGenerations`` x ``nParams + 2`` x ``nChains`` size array,
where ``chain_k = DreamOutput.allChains[:, :, k]``
is the data of chain ``k`` in the final iteration;
for generation i of the final iteration, ``chain_k[i, j]`` represents:
- the sampled value of parameter ``j`` for ``j in 0:nParams``;
- the associated log-prior for those sampled values for ``j = nParams + 1``;
- the associated log-likelihood for those sampled values for ``j = nParams + 2``.
outlierChains : np.ndarray[np.float]
A two-column array where ``DreamOutput.AR[i, 1]`` is the index of a chain
and ``DreamOutput.AR[i, 0]`` is the length of that chain when it was removed
for being an outlier.
runtime : float
The runtime of the DREAM algorithm in seconds.
iteration : float
The number of iterations performed.
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
proposals for that iteration.
R_stat : np.ndarray[np.float]
An array where ``DreamOutput.R_stat[i, 0]`` is an iteration number and
``DreamOutput.R_stat[i, j]`` is the convergence statistic for parameter ``j``
at that iteration (where chains are indexed 1 to ``nParams`` inclusive).
CR : np.ndarray[np.float]
A four-column array where ``DreamOutput.CR[i, 0]`` is an iteration number,
``and DreamOutput.CR[i, j]`` is the selection probability of the ``j``'th crossover
value for that iteration.
)";
struct DreamOutput
{
py::array_t<real_T> allChains;
py::array_t<real_T> outlierChains;
real_T runtime;
real_T iteration;
py::array_t<real_T> AR;
py::array_t<real_T> R_stat;
py::array_t<real_T> CR;
};
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.PredictionIntervals
The prediction intervals.
confidenceIntervals : RATapi.rat_core.ConfidenceIntervals
The 65% and 95% confidence intervals for the best fit results.
dreamParams : RATapi.rat_core.DreamParams
The parameters used by DREAM, if relevant.
dreamOutput : RATapi.rat_core.DreamOutput
The output from DREAM if DREAM was used.
nestedSamplerOutput : RATapi.rat_core.NestedSamplerOutput
The output from nested sampling if ns was used.
chain : np.ndarray
The MCMC chains for each parameter.
The ``i``'th column of the array contains the chain for parameter ``fitNames[i]``.
)";
struct OutputBayesResult
{
PredictionIntervals predictionIntervals;
ConfidenceIntervals confidenceIntervals;
DreamParams dreamParams;
DreamOutput dreamOutput;
NestedSamplerOutput nestedSamplerOutput;
py::array_t<real_T> chain;
};
const std::string docsCalculation = R"(The Python binding for the C++ calculationResult struct.
The goodness of fit from the Abeles calculation.
Parameters
----------
chiValues : np.ndarray[np.float]
The chi-squared value for each contrast.
sumChi : float
The sum of the chiValues array.
)";
struct Calculation
{
py::array_t<real_T> chiValues;
real_T sumChi;
};
const std::string docsContrastParams = R"(The Python binding for the C++ contrastParams struct.
The experimental parameters for each contrast.
Parameters
----------
scalefactors : np.ndarray[np.float]
The scalefactor values for each contrast.
bulkIn : np.ndarray[np.float]
The bulk in values for each contrast.
bulkOut : np.ndarray[np.float]
The bulk out values for each contrast.
subRoughs : np.ndarray[np.float]
The substrate roughness values for each contrast.
resample : np.ndarray[np.float]
An array containing whether each contrast was resampled.
)";
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;
};
const std::string docsOutputResult = R"(The C++ result struct of a RAT calculation.
Parameters
----------
reflectivity : list
The reflectivity curves for each contrast, with the same range as the data
(``data_range`` in the contrast's ``Data`` object)
simulation : list
The reflectivity curves for each contrast, which can be a wider range to allow extrapolation
(``simulation_range`` in the contrast's ``Data`` object).
shiftedData : list
The data with scalefactors and background corrections applied.
backgrounds : list
The background for each contrast defined over the simulation range.
resolutions : list
The resolution for each contrast defined over the simulation range.
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 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
The experimental parameters for the contrasts.
fitParams : np.ndarray[np.float]
The best fit value of the parameter with name ``fitNames[i]``.
fitNames : list[str]
The names of the fit parameters, where ``fitNames[i]`` is the name
of the parameter with value given in ``fitParams[i]``.
)";
struct OutputResult {
py::list reflectivity;
py::list simulation;
py::list shiftedData;
py::list backgrounds;
py::list resolutions;
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 docsNameStore = R"(The Python binding for the C++ names struct which
contains names of all parameters in the project.
Parameters
----------
params : list
Names of params in the problem definition.
backgroundParams : list
Names of backgroundParams in the problem definition.
scalefactors : list
Names of scalefactors in the problem definition.
bulkIns : list
Names of bulkIns in the problem definition.
bulkName: list
Names of bulkOuts in the problem definition.
resolutionParams : list
Names of resolutionParams in the problem definition.
domainRatios : list
Names of domainRatios in the problem definition.
)";
struct NameStore {
py::list params;
py::list backgroundParams;
py::list scalefactors;
py::list bulkIns;
py::list bulkOuts;
py::list resolutionParams;
py::list domainRatios;
py::list contrasts;
};
const std::string docsChecks = R"(The Python binding for the C++ checks struct which contains
flags indicating which parameters should be fitted in the project.
For each attribute, if index ``i`` is non-zero, then parameter ``i`` in that attribute is fitted, e.g. if ``Checks.scalefactors = [0.0, 1.0, 1.0]``, then the second and third scalefactors are fitted and the first is not.
Parameters
----------
params : np.ndarray[np.float]
Non-zero values indicates which params is fitted.
backgroundParams : np.ndarray[np.float]
Non-zero values indicates which backgroundParams is fitted.
scalefactors : np.ndarray[np.float]
Non-zero values indicates which scalefactors is fitted.
bulkIns : np.ndarray[np.float]
Non-zero values indicates which bulkIns is fitted.
bulkOuts : np.ndarray[np.float]
Non-zero values indicates which bulkOuts is fitted.
resolutionParams : np.ndarray[np.float]
Non-zero values indicates which resolutionParams is fitted.
domainRatios : np.ndarray[np.float]
Non-zero values indicates which domainRatios is fitted.
)";
struct Checks {
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 docsProblemDefinition = R"(The Python binding for the C++ problem struct.
Parameters
----------
TF : str
The target function for the calculation which can be 'normal' or 'domains'.
resample : np.ndarray[np.float]
If ``resample[i]`` is non-zero, then contrast ``i`` will be resampled.
data : list
Data for each contrast.
dataPresent : np.ndarray[np.float]
If ``dataPresent[i]`` is non-zero, then contrast ``i`` has experimental data.
dataLimits : list
Data limits for each contrast.
simulationLimits : list;
Simulation for each contrast.
numberOfContrasts : int
Number of contrasts.
geometry : str
The geometry to use which can be 'air/substrate' or 'substrate/liquid'
useImaginary : bool
Indicates whether imaginary component is used for the SLD value in layers, i.e.
absorption is set to True for the project.
repeatLayers : list
Information about repeating layers for each contrast. This is currently not being used.
contrastBackgroundParams : list
Indices of backgroundParams used for each contrast
contrastBackgroundTypes : list
Background type for each contrast.
contrastBackgroundActions : list
Background action for each contrast.
contrastScalefactors : np.ndarray[np.float]
Indices of scalefactors used for each contrast.
contrastBulkIns : np.ndarray[np.float]
Indices of BulkIns used for each contrast.
contrastBulkOuts : np.ndarray[np.float]
Indices of BulkIns used for each contrast.
contrastResolutionParams : list
Indices of resolutionParams used for each contrast
contrastResolutionTypes : list
Resolution type for each contrast.
backgroundParams : np.ndarray[np.float]
Background parameter values.
scalefactors : np.ndarray[np.float]
Scalefactors values.
bulkIns : np.ndarray[np.float]
BulkIn values.
bulkOuts : np.ndarray[np.float]
BulkOut values.
resolutionParams : np.ndarray[np.float]
Resolution parameter values.
params : np.ndarray[np.float]
Parameter values.
numberOfLayers : int
Number of layers.
contrastLayers : list
Indices of layers added to the model of each contrast.
layersDetails : list
Indices of parameters in each layer.
customFiles : object
Iterable with custom file functions
modelType : str
The layer model type which can be 'standard layers', 'custom layers', or 'custom xy'.
contrastCustomFiles : np.ndarray[np.float]
Indices of CustomFiles used for each domain contrast
contrastDomainRatios : np.ndarray[np.float]
Indices of DomainRatios used for each domain contrast
domainRatios : np.ndarray[np.float]
Domain ratio values
numberOfDomainContrasts : int
Number of domain contrasts.
domainContrastLayers : list
Indices of layers added to the model of each domain contrast.
fitParams : np.ndarray[np.float]
Values of fitted parameters.
fitLimits : np.ndarray[np.float]
Limits of fitted parameters.
priorNames : list
Parameter names for for all parameters in the problem definition.
priorValues : np.ndarray[np.float]
Prior type, mu, and sigma for all parameters in the problem definition.
names : RATapi.rat_core.NameStore
Names of all parameters.
checks : RATapi.rat_core.Checks
Flags indicating which parameters should be fitted.
)";
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;
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> 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> 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> fitLimits;
py::list priorNames;
py::array_t<real_T> priorValues;
NameStore names;
Checks checks {};
};
const std::string docsControl = R"(The Python binding for the C++ controls struct.
Parameters
----------
parallel : str
How the calculation should be parallelised (This uses the Parallel Computing Toolbox). Can be 'single', 'contrasts' or 'points'.
procedure : str
Which procedure RAT should execute. Can be 'calculate', 'simplex', 'de', 'ns', or 'dream'.
calcSldDuringFit : bool
Whether SLD will be calculated during fit (for live plotting etc.)
resampleMinAngle : float
The upper threshold on the angle between three sampled points for resampling, in units of radians over pi.
resampleNPoints : int
The number of initial points to use for resampling.
display : str
How much RAT should print to the terminal. Can be 'off', 'iter', 'notify', or 'final'.
IPCFilePath : str
The path of the inter process communication file.
updateFreq : int
[SIMPLEX, DE] Number of iterations between printing progress updates to the terminal.
updatePlotFreq : int
[SIMPLEX, DE] Number of iterations between updates to live plots.
xTolerance : float
[SIMPLEX] The termination tolerance for step size.
funcTolerance : float
[SIMPLEX] The termination tolerance for change in chi-squared.
maxFuncEvals : int
[SIMPLEX] The maximum number of function evaluations before the algorithm terminates.
maxIterations : int
[SIMPLEX] The maximum number of iterations before the algorithm terminates.
populationSize : int
[DE] The number of candidate solutions that exist at any time.
fWeight : float
[DE] The step size for how different mutations are to their parents.
crossoverProbability : float
[DE] The probability of exchange of parameters between individuals at any iteration.
strategy : int
[DE] The algorithm used to generate new candidates.
targetValue : float
[DE] The value of chi-squared at which the algorithm will terminate.
numGenerations : int
[DE] The maximum number of iterations before the algorithm terminates.
nLive : int
[NS] The number of points to sample.
nMCMC : int
[NS] If non-zero, an MCMC process with ``nMCMC`` chains will be used instead of MultiNest.
propScale : float
[NS] A scaling factor for the ellipsoid generated by MultiNest.
nsTolerance : float
[NS] The tolerance threshold for when the algorithm should terminate.
nSamples : int
[DREAM] The number of samples in the initial population for each chain.
nChains : int
[DREAM] The number of Markov chains to use in the algorithm.
jumpProbability : float
[DREAM] The probability range for the size of jumps in sampling. Larger values mean more variable jumps.
pUnitGamma : float
[DREAM] The probability that the scaling-down factor of jumps will be ignored and a larger jump will be taken.
boundHandling : str
[DREAM] How steps past the space boundaries should be handled. Can be 'off', 'reflect', 'bound', or 'fold'.
adaptPCR : bool
[DREAM] Whether the crossover probability for differential evolution should be adapted during the run.
)";
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 {};
};
#endif // RAT_DEFINES_H
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