firecrown.likelihood.gauss_family.statistic.statistic.Statistic

class firecrown.likelihood.gauss_family.statistic.statistic.Statistic(parameter_prefix=None)

Bases: Updatable

The abstract base class for all physics-related statistics.

Statistics read data from a SACC object as part of a multi-phase initialization. They manage a DataVector and, given a ModelingTools object, can compute a TheoryVector.

Statistics represent things like two-point functions and mass functions.

Parameters:

parameter_prefix (None | str) –

Public Methods:

__init__([parameter_prefix])	Updatable initialization.
read(_)	Read the data for this statistic and mark it as ready for use.
get_data_vector()	Gets the statistic data vector.
compute_theory_vector(tools)	Compute a statistic from sources, applying any systematics.
get_theory_vector()	Returns the last computed theory vector.

Inherited from Updatable

__init__([parameter_prefix])	Updatable initialization.
__setattr__(key, value)	Set the attribute named key to the supplied value.
set_parameter(key, value)	Sets the parameter to the given value.
set_internal_parameter(key, value)	Assure this InternalParameter has not already been set, and then set it.
set_sampler_parameter(value)	Assure this SamplerParameter has not already been set, and then set it.
update(params)	Update self by calling to prepare for the next MCMC sample.
is_updated()	Determine if the object has been updated.
reset()	Reset the updatable.
required_parameters()	Returns a RequiredParameters object.
get_derived_parameters()	Returns a collection of derived parameters.

Private Methods:

_reset()	Reset this statistic.
_compute_theory_vector(tools)	Compute a statistic from sources, concrete implementation.

Inherited from Updatable

_update(params)	Method for auxiliary updates to be made to an updatable.
_reset()	Abstract method implemented by all concrete classes to update self.
_required_parameters()	Return a RequiredParameters object containing the information for this class.
_get_derived_parameters()	Returns the derived parameters of an implementation.

---

abstract _compute_theory_vector(tools)

Compute a statistic from sources, concrete implementation.

Parameters:

tools (ModelingTools) –

Return type:

TheoryVector

_reset()

Reset this statistic.

All subclasses implementations must call super()._reset()

final compute_theory_vector(tools)

Compute a statistic from sources, applying any systematics.

Parameters:

tools (ModelingTools) –

Return type:

TheoryVector

abstract get_data_vector()

Gets the statistic data vector.

Return type:

DataVector

get_theory_vector()

Returns the last computed theory vector.

Raises a RuntimeError if the vector has not been computed.

Return type:

TheoryVector

read(_)

Read the data for this statistic and mark it as ready for use.

Derived classes that override this function should make sure to call the base class method using:

super().read(sacc_data)

as the last thing they do in __init__.

Note that currently the argument is not used; it is present so that this method will have the correct argument type for the override.

Parameters:

_ (Sacc) –

Return type:

None