struct Gambit::ColliderBit::AnalysisData #

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#include <AnalysisData.hpp>

Public Functions #

Public Attributes #

Detailed Description #

struct Gambit::ColliderBit::AnalysisData;

Todo: Access by name?

Guarantee ordering?

How to combine covariance matrices – require?

A container for the result of an analysis, potentially with many signal regions and correlations

Public Functions Documentation #

function AnalysisData #

inline AnalysisData()

Default constructor.

function AnalysisData #

inline AnalysisData(
    const std::string & name
)

Constructor with analysis name.

function AnalysisData #

inline AnalysisData(
    const std::vector< SignalRegionData > & srds,
    const Eigen::MatrixXd & cov =Eigen::MatrixXd(),
    const std::string path =""
)

Constructor from a list of SignalRegionData and an optional correlation (or covariance?) matrix.

If corrs is a null matrix (the default), this AnalysisData is to be interpreted as having no correlation information, and hence the likelihood calculation should use the single best-expected-limit SR.

function clear #

inline void clear()

Todo: It’d be good to not have to re-enter most of the SRData and the covariance on every point: they don’t change

Clear the list of SignalRegionData, and nullify the covariance matrix

function size #

inline size_t size() const

Number of analyses.

function empty #

inline bool empty() const

Is this container empty of signal regions?

function hasCorrs #

inline bool hasCorrs() const

Is there non-null correlation data?

function hasFullLikes #

inline bool hasFullLikes() const

Is there non-null correlation data?

function add #

inline void add(
    const SignalRegionData & srd
)

Add a SignalRegionData.

Todo: Allow naming the SRs?

function check #

inline bool check() const

Check that the SRData list and the covariance matrix are consistent.

function pythonize_me #

void pythonize_me() const

bjf> Experimental! But already useful for helping me convert the key numbers from these analyses to Python for the p-value calculuations.

bjf> Experimental! But already useful for helping me convert the key numbers from these analyses to Python for the p-value calculuations. This is a dumb place to define this, but there is no cpp file for AnalysisData and I can’t be bothered making one.

Could record or something, but for now just dump to stdout

function operator[] #

inline SignalRegionData & operator[](
    size_t i
)

Access the i’th signal region’s data.

function operator[] #

inline const SignalRegionData & operator[](
    size_t i
) const

Access the i’th signal region’s data (const)

function begin #

inline std::vector< SignalRegionData >::iterator begin()

Iterators (sugar for direct access to this->srdata)

function begin #

inline std::vector< SignalRegionData >::const_iterator begin() const

function end #

inline std::vector< SignalRegionData >::iterator end()

function end #

inline std::vector< SignalRegionData >::const_iterator end() const

Public Attributes Documentation #

variable event_gen_BYPASS #

bool event_gen_BYPASS = false;

variable analysis_name #

std::string analysis_name;

Analysis name.

variable srdata #

std::vector< SignalRegionData > srdata;

List of signal regions’ data summaries.

variable srdata_identifiers #

std::map< std::string, int > srdata_identifiers;

Map of names and indices of all entries in srdata, for easy lookup.

variable srcov #

Eigen::MatrixXd srcov;

Optional covariance matrix between SRs (0x0 null matrix = no correlation info)

variable bkgjson_path #

std::string bkgjson_path;

FullLikes bkg json file path realtive to the GAMBIT directory.

Updated on 2024-07-18 at 13:53:31 +0000