flip.fitter

Attributes

log

Classes

CovMatrix
BaseFitter	Helper class that provides a standard way to create an ABC using
FitMinuit	Class to maximize likelihood with Minuit.
FitMCMC	Class to create and run a MCMC sampler with emcee package.
Sampler	Helper class that provides a standard way to create an ABC using
EMCEESampler	Helper class that provides a standard way to create an ABC using

Functions

create_log([log_level])

The create_log function creates a logger object that can be used to log messages.

Module Contents

class flip.fitter.CovMatrix(model_name=None, model_type=None, los_definition=None, covariance_dict=None, full_matrix=False, number_densities=None, number_velocities=None, variant=None)

classmethod init_from_flip(model_name, model_type, power_spectrum_dict, coordinates_density=None, coordinates_velocity=None, additional_parameters_values=None, los_definition='bisector', variant=None, **kwargs)

The init_from_flip function is a function that initializes the covariance matrix from the flip code. It takes as input:

• model_name: name of the model used to generate the covariance matrix (e.g., ‘lai22’)

• model_type: type of data used to generate the covariance matrix (e.g., ‘density’ or ‘velocity’)

• power_spectrum_dict: dictionary containing all information about power spectrum, including k and P(k) values, redshift, etc…

  It is generated by calling getPowerSpectrumDict() in

Parameters:

• cls – Indicate that the function is a class method

• model_name – Determine which model to use for the covariance matrix

• model_type – Determine the type of model to be used

• power_spectrum_dict – Pass the power spectrum of the model

• coordinates_density – Specify the coordinates of the density field

• coordinates_velocity – Define the velocity coordinates of the covariance matrix

• additional_parameters_values – Pass the values of additional parameters to the flip code

• **kwargs – Pass a variable number of keyword arguments to the function

Returns:

A covariancematrix object

classmethod init_from_generator(model_name, model_type, power_spectrum_dict, coordinates_velocity=None, coordinates_density=None, additional_parameters_values=None, variant=None, **kwargs)

The init_from_generator function is a helper function that allows the user to initialize a Covariance object from a generator. The init_from_generator function takes in as arguments:

• cls: the class of the object being initialized (Covariance)

• model_name: name of covariance model used to generate covariance matrix (e.g., ‘lai22’)

• model_type: type of covariance matrix generated (‘density’ or ‘velocity’)

• power spectrum dictionary containing keys for each redshift bin and values corresponding to

  power spectra at those red

Parameters:

• cls – Refer to the class itself

• model_name – Specify the type of model used to generate the covariance matrix

• model_type – Determine which model to use

• power_spectrum_dict – Pass the power spectrum to the generate_* functions

• coordinates_velocity – Generate the velocity covariance matrix

• coordinates_density – Generate the density field

• additional_parameters_values – Pass additional parameters to the generator function

• **kwargs – Pass a variable number of keyword arguments to the function

:param : Generate the covariance matrix from a given model

Returns:

An object of the class covariancematrix

classmethod init_from_file(filename, file_format)

property type

The type function is used to determine the type of covariance model that will be computed.

The options are:

- velocity: The covariance model is computed for velocity only.

- density: The covariance model is computed for density only.

- density_velocity: The covariance model is computed for both velocity and density, without cross-term (i.e., the covariances between velocities and densities are zero). This option should be used when computing a full 3D tomography in which we want to compute a separate 1D tomography along each axis (x, y, z

Parameters:

self – Represent the instance of the class

Returns:

The type of the model

property loaded

The loaded function checks if the covariance matrix is loaded.

Parameters:

self – Refer to the object itself

Returns:

A boolean

compute_covariance_sum(parameter_values_dict, vector_err)

The compute_covariance_sum function computes the sum of all covariance matrices

and adds the diagonal terms.

Parameters:

• self – Access the attributes of the class

• parameter_values_dict – Pass the values of the parameters

:param : Compute the covariance matrix

Returns:

The sum of the covariance matrices with their respective coefficients

compute_covariance_sum_eigenvalues(parameter_values_dict, vector_err)

compute_full_matrix()

The compute_full_matrix function takes the covariance matrix and fills in all of the missing values.

Parameters:

self – Bind the method to the object

Returns:

A dictionary with the full covariance matrices for each redshift bin

write(filename, file_format)

The write function writes the covariance matrix to a file.

Parameters:

• self – Represent the instance of the class

• filename – Specify the name of the file to be written

:param : Specify the name of the file in which we want to save our covariance matrix

Returns:

Nothing

flip.fitter.create_log(log_level='info')

The create_log function creates a logger object that can be used to log messages. The function takes one argument, the log_level, which is set to "info" by default. The function returns a Logger object with the specified logging level.

Parameters:

log_level – Set the logging level

Returns:

A logger object

flip.fitter.log

class flip.fitter.BaseFitter(covariance=None, data=None)

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

abstract init_from_covariance()

The init_from_covariance function is a class method that initializes the

fitter from the covariance matrix. It is here an abstract method that needs to be override

Parameters:

cls – Pass a class object into a method

abstract init_from_file()

The init_from_covariance function is a class method that initializes the

fitter from the a file containing covariance matrix. It is here an abstract method that needs to be override

Parameters:

cls – Pass a class object into a method

get_likelihood(parameter_dict, likelihood_type='multivariate_gaussian', likelihood_properties=None, **kwargs)

The get_likelihood function is used to create a likelihood object from the covariance matrix. The function takes in a dictionary of parameters, and returns an instance of the likelihood class.

Parameters:

• self – Bind the method to a class

• parameter_dict – Pass the parameters to be used in the likelihood function

• likelihood_type – Select the likelihood class

:param : Select the likelihood function

Returns:

A likelihood object

static select_likelihood(likelihood_type)

The select_likelihood function takes in a string, likelihood_type, and returns the corresponding class.

Parameters:

likelihood_type – Determine which likelihood function to use

Returns:

The likelihood class

class flip.fitter.FitMinuit(covariance=None, data=None, likelihood=None, minuit=None)

Bases: BaseFitter

Class to maximize likelihood with Minuit.

classmethod init_from_covariance(covariance, data, parameter_dict, likelihood_type='multivariate_gaussian', likelihood_properties={}, **kwargs)

The init_from_covariance function is a class method that initializes the MinuitFitter object. It takes in the covariance matrix, data, parameter dictionary and likelihood type as arguments. The minuit_fitter object is initialized with the covariance matrix and data. The likelihood function is then calculated using get_likelihood() which returns an instance of LikelihoodFunction(). This instance is assigned to minuit_fitter’s attribute ‘likelihood’. The parameter values are extracted from the parameter dictionary and stored in a list called ‘parameter_values’. A Minuit object called ‘minuit’ is

Parameters:

• cls – Create a new instance of the class

• covariance – Initialize the covariance matrix of the likelihood

• data – Pass the data to the likelihood function

• parameter_dict – Pass the parameters to be fitted

• likelihood_type – Specify the type of likelihood function to be used

:param : Set the covariance matrix of the data

Returns:

A minuit_fitter object

classmethod init_from_file(model_name, model_type, filename, data, parameter_dict, likelihood_type='multivariate_gaussian', likelihood_properties=None)

The init_from_file function is a class method that initializes the fitter object from a covariance matrix.

Parameters:

• cls – Pass the class object to the function

• model_name – Specify the name of the model

• model_type – Specify the type of model

• filename – Load the covariance matrix from a file

• data – Initialize the fitter’s data attribute

• parameter_dict – Pass in the parameters that are used to

• likelihood_type – Specify the type of likelihood function to use

:param : Specify the type of likelihood

Returns:

A fitter object

setup_minuit(parameter_dict)

The setup_minuit function is used to set up the minuit object. It takes a dictionary of parameters as input and sets the errors, fixed values, and limits for each parameter. The error is set to be equal to the value if no error is specified in the dictionary. If a parameter has been fixed then its error will be zero.

Parameters:

• self – Refer to the object itself

• parameter_dict – Set the initial values of the parameters

Returns:

A minuit object

run(migrad=True, hesse=False, minos=False, n_iter=1)

The run function is the main function of the class. It takes in a number of arguments, and then runs them through Minuit. The arguments are:

Parameters:

• self – Bind the method to the object

• migrad – Run the migrad algorithm

• hesse – Run the hesse function

• minos – Run the minos function, which is a

:param : Set the number of iterations for migrad

Returns:

A dictionary with the results of the minimization

class flip.fitter.FitMCMC(covariance=None, data=None, sampler_name='emcee')

Bases: BaseFitter

Class to create and run a MCMC sampler with emcee package.

classmethod init_from_covariance(covariance, data, parameter_dict, likelihood_type='multivariate_gaussian', likelihood_properties=None, sampler_name='emcee', nwalkers=1, backend_file=None)

The init_from_covariance function is a class method that initializes the MCMC fitter from a covariance matrix.

Parameters:

• cls – Create a new instance of the class

• covariance – Set the covariance matrix of the multivariate gaussian

• data – Calculate the likelihood

• parameter_dict – Pass in the parameters of the model

• likelihood_type – Specify the type of likelihood function to use

:param : Set the covariance matrix

Returns:

A mcmc_fitter object

classmethod init_from_file()

Abstractmethod:

The init_from_covariance function is a class method that initializes the

fitter from the a file containing covariance matrix. It is here an abstract method that needs to be override

Parameters:

cls – Pass a class object into a method

set_sampler(likelihood, p0=None, **kwargs)

class flip.fitter.Sampler(likelihood, p0=None)

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

abstract run_chains(nsteps)

property ndim

property p0

class flip.fitter.EMCEESampler(likelihood, p0=None, backend_file=None)

Bases: Sampler

Helper class that provides a standard way to create an ABC using inheritance.

run_chains(nsteps, number_worker=1, progress=False)

run_chains_untilconv(number_worker=1, maxstep=100, tau_conv=0.01, progress=False)

Run chains until reaching auto correlation convergence criteria.