class MontePythonLike::Likelihood_sn

[No description available]

Inherits from MontePythonLike.Likelihood, MontePythonLike.Likelihood, object

Public Functions

	Name
def	init(self self, path path, data data, command_line command_line)
def	read_configuration_file(self self)
def	read_matrix(self self, path path)
def	read_light_curve_parameters(self self)
def	init(self self, path path, data data, command_line command_line)
def	read_configuration_file(self self)
def	read_matrix(self self, path path)
def	read_light_curve_parameters(self self)

Additional inherited members

Public Functions inherited from MontePythonLike.Likelihood

	Name
def	loglkl(self self, cosmo cosmo, data data)
def	raise_fiducial_model_err(self self)
def	read_from_file(self self, path path, data data, command_line command_line)
def	get_cl(self self, cosmo cosmo, l_max l_max =-1)
def	get_unlensed_cl(self self, cosmo cosmo, l_max l_max =-1)
def	need_cosmo_arguments(self self, data data, dictionary dictionary)
def	read_contamination_spectra(self self, data data)
def	add_contamination_spectra(self self, cl cl, data data)
def	add_nuisance_prior(self self, lkl lkl, data data)
def	computeLikelihood(self self, ctx ctx)
def	loglkl(self self, cosmo cosmo, data data)
def	raise_fiducial_model_err(self self)
def	read_from_file(self self, path path, data data, command_line command_line)
def	get_cl(self self, cosmo cosmo, l_max l_max =-1)
def	get_unlensed_cl(self self, cosmo cosmo, l_max l_max =-1)
def	need_cosmo_arguments(self self, data data, dictionary dictionary)
def	read_contamination_spectra(self self, data data)
def	add_contamination_spectra(self self, cl cl, data data)
def	add_nuisance_prior(self self, lkl lkl, data data)
def	computeLikelihood(self self, ctx ctx)

Public Attributes inherited from MontePythonLike.Likelihood

	Name
	name
	folder
	data_directory
	default_values
	need_update
	use_nuisance
	nuisance
	path
	dictionary

Public Functions inherited from MontePythonLike.Likelihood

	Name
def	loglkl(self self, cosmo cosmo, data data)
def	raise_fiducial_model_err(self self)
def	read_from_file(self self, path path, data data, command_line command_line)
def	get_cl(self self, cosmo cosmo, l_max l_max =-1)
def	get_unlensed_cl(self self, cosmo cosmo, l_max l_max =-1)
def	need_cosmo_arguments(self self, data data, dictionary dictionary)
def	read_contamination_spectra(self self, data data)
def	add_contamination_spectra(self self, cl cl, data data)
def	add_nuisance_prior(self self, lkl lkl, data data)
def	computeLikelihood(self self, ctx ctx)
def	loglkl(self self, cosmo cosmo, data data)
def	raise_fiducial_model_err(self self)
def	read_from_file(self self, path path, data data, command_line command_line)
def	get_cl(self self, cosmo cosmo, l_max l_max =-1)
def	get_unlensed_cl(self self, cosmo cosmo, l_max l_max =-1)
def	need_cosmo_arguments(self self, data data, dictionary dictionary)
def	read_contamination_spectra(self self, data data)
def	add_contamination_spectra(self self, cl cl, data data)
def	add_nuisance_prior(self self, lkl lkl, data data)
def	computeLikelihood(self self, ctx ctx)

Public Attributes inherited from MontePythonLike.Likelihood

	Name
	name
	folder
	data_directory
	default_values
	need_update
	use_nuisance
	nuisance
	path
	dictionary

Public Functions Documentation

function init

def __init__(
    self self,
    path path,
    data data,
    command_line command_line
)

Reimplements: MontePythonLike::Likelihood::init

It copies the content of self.path from the initialization routine of
the :class:`Data <data.Data>` class, and defines a handful of useful
methods, that every likelihood might need.

If the nuisance parameters required to compute this likelihood are not
defined (either fixed or varying), the code will stop.

Parameters
----------
data : class
    Initialized instance of :class:`Data <data.Data>`
command_line : NameSpace
    NameSpace containing the command line arguments```


### function read_configuration_file

def read_configuration_file( self self )

Extract Python variables from the configuration file

This routine performs the equivalent to the program “inih” used in the original c++ library.



### function read_matrix

def read_matrix( self self, path path )

extract the matrix from the path

This routine uses the blazing fast pandas library (0.10 seconds to load a 740x740 matrix). If not installed, it uses a custom routine that is twice as slow (but still 4 times faster than the straightforward numpy.loadtxt method)

.. note::

the length of the matrix is stored on the first line... then it has
to be unwrapped. The pandas routine read_csv understands this
immediatly, though.```

function read_light_curve_parameters

def read_light_curve_parameters(
    self self
)

Read the file jla_lcparams.txt containing the SN data

.. note::

    the length of the resulting array should be equal to the length of
    the covariance matrices stored in C00, etc...```


### function __init__

def init( self self, path path, data data, command_line command_line )



**Reimplements**: [MontePythonLike::Likelihood::__init__](/documentation/code/darkbit/classes/classmontepythonlike_1_1likelihood/#function---init--)

It copies the content of self.path from the initialization routine of the :class:Data <data.Data> class, and defines a handful of useful methods, that every likelihood might need.

If the nuisance parameters required to compute this likelihood are not defined (either fixed or varying), the code will stop.

Parameters

data : class Initialized instance of :class:Data <data.Data> command_line : NameSpace NameSpace containing the command line arguments```

function read_configuration_file

def read_configuration_file(
    self self
)

Extract Python variables from the configuration file

This routine performs the equivalent to the program "inih" used in the
original c++ library.

function read_matrix

def read_matrix(
    self self,
    path path
)

extract the matrix from the path

This routine uses the blazing fast pandas library (0.10 seconds to load
a 740x740 matrix). If not installed, it uses a custom routine that is
twice as slow (but still 4 times faster than the straightforward
numpy.loadtxt method)

.. note::

    the length of the matrix is stored on the first line... then it has
    to be unwrapped. The pandas routine read_csv understands this
    immediatly, though.```


### function read_light_curve_parameters

def read_light_curve_parameters( self self )

Read the file jla_lcparams.txt containing the SN data

.. note::

the length of the resulting array should be equal to the length of
the covariance matrices stored in C00, etc...```

Updated on 2022-08-03 at 12:58:02 +0000

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class MontePythonLike::Likelihood_sn

Public Functions #

Additional inherited members #

Public Functions Documentation #

function init #

function read_light_curve_parameters #

Parameters #

function read_configuration_file #

function read_matrix #