flip.covariance

Init file of the flip.covariance package.

Subpackages

• flip.covariance.adamsblake17plane
  • flip.covariance.adamsblake17plane.coefficients
  • flip.covariance.adamsblake17plane.flip_terms
  • flip.covariance.adamsblake17plane.generator
• flip.covariance.adamsblake20
  • flip.covariance.adamsblake20.coefficients
  • flip.covariance.adamsblake20.flip_terms
• flip.covariance.carreres23
  • flip.covariance.carreres23.coefficients
  • flip.covariance.carreres23.flip_terms
  • flip.covariance.carreres23.generator
• flip.covariance.lai22
  • flip.covariance.lai22.coefficients
  • flip.covariance.lai22.flip_terms
  • flip.covariance.lai22.generator
  • flip.covariance.lai22.h_terms
  • flip.covariance.lai22.symbolic
• flip.covariance.ravouxcarreres
  • flip.covariance.ravouxcarreres.coefficients
  • flip.covariance.ravouxcarreres.flip_terms
  • flip.covariance.ravouxcarreres.flip_terms_lmax

Submodules

• flip.covariance.contraction
• flip.covariance.cov_utils
• flip.covariance.covariance
• flip.covariance.generator
• flip.covariance.symbolic

Package Contents

Classes

CovMatrix

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

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

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)

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