flip.data_vector

Init file of the flip.data_vector package.

Submodules

• flip.data_vector.basic
• flip.data_vector.cosmo_utils
• flip.data_vector.galaxypv_vectors
• flip.data_vector.snia_vectors
• flip.data_vector.vector_utils

Attributes

jax_installed
log

Classes

CovMatrix
DataVector	Helper class that provides a standard way to create an ABC using
Dens	Helper class that provides a standard way to create an ABC using
DirectVel	Helper class that provides a standard way to create an ABC using
DensVel	Helper class that provides a standard way to create an ABC using
VelFromHDres	Helper class that provides a standard way to create an ABC using
FisherVelFromHDres	Helper class that provides a standard way to create an ABC using
FisherDens	Helper class that provides a standard way to create an ABC using
FisherDensVel	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.

Package Contents

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

model_name = None

model_type = None

free_par = None

los_definition = None

covariance_dict = None

full_matrix = False

number_densities = None

number_velocities = None

redshift_dict = 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_variance, use_jit=False)

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_variance)

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

compute_flat_matrix()

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

mask(mask_vel=None, mask_dens=None)

flip.data_vector.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.data_vector.jax_installed = True

flip.data_vector.log

class flip.data_vector.DataVector(data, covariance_observation=None, **kwargs)

Bases: abc.ABC

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

_free_par = []

_kind = ''

property conditional_free_par

property free_par

property kind

property conditional_needed_keys

property needed_keys

property data

abstract _give_data_and_variance(**kwargs)

_check_keys(data)

_covariance_observation = None

_data

_kwargs

__call__(*args)

mask(bool_mask)

compute_covariance(model, power_spectrum_dict, **kwargs)

class flip.data_vector.Dens(data, covariance_observation=None, **kwargs)

Bases: DataVector

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

_kind = 'density'

_needed_keys = ['density', 'density_error']

_give_data_and_variance(*args)

class flip.data_vector.DirectVel(data, covariance_observation=None)

Bases: DataVector

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

_kind = 'velocity'

_needed_keys = ['velocity']

property conditional_needed_keys

_give_data_and_variance(*args)

class flip.data_vector.DensVel(density_vector, velocity_vector)

Bases: DataVector

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

_kind = 'cross'

property needed_keys

property free_par

_give_data_and_variance(*args)

densities

velocities

compute_covariance(model, power_spectrum_dict, **kwargs)

class flip.data_vector.VelFromHDres(data, covariance_observation=None, velocity_estimator='full', **kwargs)

Bases: DirectVel

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

_needed_keys = ['dmu', 'zobs']

_free_par = ['M_0']

property conditional_needed_keys

_give_data_and_variance(parameter_values_dict)

velocity_estimator = 'full'

class flip.data_vector.FisherVelFromHDres(data, velocity_estimator='full')

Bases: DataVector

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

_kind = 'velocity'

_needed_keys = ['zobs', 'ra', 'dec', 'rcom_zobs']

_free_par = ['sigma_M']

_give_data_and_variance(parameter_values_dict)

velocity_estimator = 'full'

class flip.data_vector.FisherDens(data, velocity_estimator='full', **kwargs)

Bases: DataVector

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

_kind = 'density'

_needed_keys = ['ra', 'dec', 'rcom_zobs']

_free_par = []

_give_data_and_variance(parameter_values_dict)

class flip.data_vector.FisherDensVel(fisher_density, fisher_velocity)

Bases: DataVector

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

_kind = 'cross'

_give_data_and_variance(*args)

densities

velocities

compute_covariance(model, power_spectrum_dict, **kwargs)