samples.h File Reference

#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <pybind11/numpy.h>
#include "Samples/SampleHandlerBase.h"
#include "TH1.h"
#include "TH2.h"
#include "histutils.h"

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Classes
class	PySampleHandlerInterface
	EW: As SampleHandlerBase is an abstract base class we have to do some gymnastics to get it to get it into python. More...
class	PySampleHandlerBase
	As SampleHandlerBase is an abstract base class we have to do some gymnastics to get it to get it into python. More...

Functions
void	initSamplesModule (py::module &m_samples)

Detailed Description

Author

Ewan Miller

Definition in file samples.h.

Function Documentation

initSamplesModule()

void initSamplesModule

(

py::module &

m_samples

)

Definition at line 343 of file samples.h.

                                           {
    // Bind the systematic type enum that lets us set different types of systematics
    py::enum_<TestStatistic>(m_samples, "TestStatistic")
        .value("Poisson", TestStatistic::kPoisson)
        .value("Barlow_Beeston", TestStatistic::kBarlowBeeston)
        .value("Ice_Cube", TestStatistic::kIceCube)
        .value("Pearson", TestStatistic::kPearson)
        .value("Dembinski_Abdelmottele", TestStatistic::kDembinskiAbdelmotteleb)
        .value("N_Test_Statistics", TestStatistic::kNTestStatistics);
 
    py::class_<SampleHandlerInterface, PySampleHandlerInterface /* <--- trampoline*/>(m_samples, "SampleHandlerInterface")
        .def(py::init())
 
        .def(
            "reweight",
            &SampleHandlerInterface::Reweight,
            "reweight the MC events in this sample. You will need to override this.")
 
        .def(
            "get_n_samples",
            &SampleHandlerInterface::GetNSamples,
            "Get the total number of samples"
        )
 
        .def(
            "get_n_dim",
            &SampleHandlerInterface::GetNDim,
            py::arg("sample"),
            "Get the dimension of a given sample"
        )
 
        .def(
            "get_mc_hist",
            [](SampleHandlerBase &self, const int sample)
            {
                auto hist_original = M3::Clone(self.GetMCHist(sample));
 
                auto edges = HistToNumpy(hist_original);
                return edges;
            },
 
            py::return_value_policy::reference_internal,
            py::arg("sample"),
            "Get MC histogram as numpy arrays.\n"
            "For 1D: Returns (contents, edges)\n"
            "For 2D: Returns (contents, edgesX, edgesY)\n"
            "where contents is shape (nbinsY, nbinsX) for 2D")
 
        .def(
            "get_likelihood",
            &SampleHandlerInterface::GetLikelihood,
            "Get the sample likelihood at the current point in your model space. You will need to override this.")
 
        .def(
            "set_test_stat",
            &SampleHandlerInterface::SetTestStatistic,
            "Set the test statistic that should be used when calculating likelihoods. \n\
            :param test_stat: The new test statistic to use",
            py::arg("test_stat"))
 
        .def(
            "get_bin_LLH",
            py::overload_cast<double, double, double>(&SampleHandlerInterface::GetTestStatLLH, py::const_),
            "Get the LLH for a bin by comparing the data and MC. The result depends on having previously set the test statistic using :py:meth:`pyMaCh3.samples.SampleHandlerInterface.set_test_stat` \n\
            :param data: The data content of the bin. \n\
            :param mc: The mc content of the bin \n\
            :param w2: The Sum(w_{i}^2) (sum of weights squared) in the bin, which is sigma^2_{MC stats}",
            py::arg("data"),
            py::arg("mc"),
            py::arg("w2"))
 
        ; // End of SampleHandlerInterface binding
 
    py::class_<SampleHandlerBase, PySampleHandlerBase /* <--- trampoline*/,
               SampleHandlerInterface>(m_samples, "SampleHandlerBase")
        .def(
            py::init<std::string, ParameterHandlerGeneric *>(),
            "This should never be called directly as SampleHandlerBase is an abstract base class. \n\
            However when creating a derived class, in the __init__() method, you should call the parent constructor i.e. this one by doing:: \n\
            \n\
            \tsuper(<your derived SampleHandler class>, self).__init__(*args) \n\
            \n ",
            py::arg("mc_version"), py::arg("xsec_cov"))
 
        .def(
            "add_data",
            py::overload_cast<const int, const std::vector<double>&>(&SampleHandlerBase::AddData),
            py::arg("sample"),
            py::arg("data_array"),
            "Set the data for your sample handler (assumes the binning is the same as your MC!)"
        )
 
        // ================
        // Useful getters
        // ===============
        .def(
            "get_sample_title",
            &SampleHandlerBase::GetSampleTitle,
            py::arg("sample"),
            "Get the title for a given sample"
        )
        .def(
            "get_data_array", 
            py::overload_cast<const int>(&SampleHandlerBase::GetDataArray, py::const_),
            py::arg("sample"),
            "Returns the contents of the MC histogram as a flat list"
        )
 
        .def(
            "get_mc_array", 
            py::overload_cast<const int>(&SampleHandlerBase::GetMCArray, py::const_),
            py::arg("sample"),
            "Returns the contents of the MC histogram as a flat list"
        )
 
        .def(
            "get_w2_array", 
            py::overload_cast<const int>(&SampleHandlerBase::GetW2Array, py::const_),
            py::arg("sample"),
            "Returns the contents of the W2 histogram as a flat list"
        )
 
        .def(
            "get_data_hist",
            [](SampleHandlerBase &self, const int sample)
            {
                auto hist_original = M3::Clone(self.GetDataHist(sample));
 
                auto edges = HistToNumpy(hist_original);
                return edges;
            },
            py::arg("Dimension"),
            "Get Data histogram as numpy arrays.\n"
            "For 1D: Returns (contents, edges)\n"
            "For 2D: Returns (contents, edgesX, edgesY)\n"
            "where contents is shape (nbinsY, nbinsX) for 2D")
 
        .def(
            "get_w2_hist",
            [](SampleHandlerBase &self, const int sample)
            {
                auto hist_original = M3::Clone(self.GetW2Hist(sample));
 
                auto edges = HistToNumpy(hist_original);
                return edges;
            },
 
 
            py::arg("sample"),
            "Get W2 histogram as numpy arrays.\n"
            "For 1D: Returns (contents, edges)\n"
            "For 2D: Returns (contents, edgesX, edgesY)\n"
            "where contents is shape (nbinsY, nbinsX) for 2D")
        
        .def("get_var_hist", 
            [](SampleHandlerBase &self, const int iSample,
                                    const std::string& ProjectionVarX,
                                    const std::string& ProjectionVarY="",
                                    const std::vector<KinematicCut> &EventSelectionVec = {},
                                    int WeightStyle = 0,
                                    const std::vector< KinematicCut >& SubEventSelectionVec = {})
            {
                py::array_t<M3::float_t> edgesY, edgesX, contents;
                std::unique_ptr<TH1> hist;
                int dim;
                if(ProjectionVarY==""){
                    hist = self.Get1DVarHist(iSample,
                                             ProjectionVarX,
                                             EventSelectionVec,
                                             WeightStyle,
                                             SubEventSelectionVec);
                } else{
                    hist = self.Get2DVarHist(iSample, 
                                     ProjectionVarX,
                                     ProjectionVarY,
                                     EventSelectionVec,
                                     WeightStyle,
                                     SubEventSelectionVec);
                }                    
                return HistToNumpy(hist);
            }
            )
        ; // End of SampleHandler Base
 
 
    py::class_<KinematicCut>(m_samples, "KinematicCut")
        .def(py::init<>(), "Simple wrapper around Kinematic cuts")
        .def_readwrite("param_name", &KinematicCut::ParamToCutOnIt, "Parameter to cut on")
        .def_readwrite("lower_bound", &KinematicCut::LowerBound, "Lower bound")
        .def_readwrite("upper_bound", &KinematicCut::UpperBound, "Upper Bound");
 
        
    /* Not sure if this will be needed in future versions of MaCh3 so leaving commented for now
    py::class_<fdmc_base>(m_samples, "MCstruct")
        .def(py::init())
        
        // Because a lot of the variables in fdmc_base use c style arrays,
        // we need to provide some setter functions to be able to set them using more
        // "pythony" objects, e.g. lists and numpy arrays
        .def(
            "set_event_variable_values", 
            [](fdmc_base &self, int dim, py::array_t<M3::float_t, py::array::c_style> &array)
            {
                py::buffer_info bufInfo = array.request();
 
                if ( dim > 2 )
                    throw MaCh3Exception(__FILE__, __LINE__, "Currently only dimensions of 1 or 2 are supported sorry :(");
 
                if ( bufInfo.ndim != 1 )
                    throw MaCh3Exception(__FILE__, __LINE__, "Number of dimensions in parameter array must be one if setting only one of the event variable arrays!");
                
                if( dim ==1 )
                    self.x_var = array.data();
                    
                else if ( dim == 2)
                    self.y_var = array.data();
            }
        )
    ;
    */
}