flip.covariance

flip.covariance#

Init file of the flip.covariance package.

Submodules#

Classes#

CovMatrix

Package Contents#

class flip.covariance.CovMatrix(model_name=None, model_kind=None, free_par=None, los_definition=None, covariance_dict=None, full_matrix=False, redshift_dict=None, variant=None)[source]#
model_name = None#
model_kind = None#
free_par = None#
los_definition = None#
covariance_dict = None#
full_matrix = False#
redshift_dict = None#
variant = None#
coefficients#
compute_covariance_sum = None#
compute_covariance_sum_jit = None#
classmethod init_from_flip(model_name, model_kind, power_spectrum_dict, coordinates_density=None, coordinates_velocity=None, additional_parameters_values=None, los_definition='bisector', variant=None, **kwargs)[source]#

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_kind: kind 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_kind – Determine the kind 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_kind, power_spectrum_dict, coordinates_velocity=None, coordinates_density=None, additional_parameters_values=None, variant=None, **kwargs)[source]#

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_kind: kind 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 kind of model used to generate the covariance matrix

  • model_kind – 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)[source]#
property kind#

The kind function is used to determine the kind 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 kind 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

init_compute_covariance_sum()[source]#
compute_covariance_sum_eigenvalues(parameter_values_dict, vector_variance)[source]#
compute_full_matrix()[source]#

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()[source]#
write(filename, file_format)[source]#

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)[source]#
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