flip.covariance =============== .. py:module:: flip.covariance .. autoapi-nested-parse:: Init file of the flip.covariance package. Submodules ---------- .. toctree:: :maxdepth: 1 /autoapi/flip/covariance/adamsblake17/index /autoapi/flip/covariance/adamsblake17plane/index /autoapi/flip/covariance/adamsblake20/index /autoapi/flip/covariance/carreres23/index /autoapi/flip/covariance/contraction/index /autoapi/flip/covariance/cov_utils/index /autoapi/flip/covariance/covariance/index /autoapi/flip/covariance/generator/index /autoapi/flip/covariance/lai22/index /autoapi/flip/covariance/ravouxcarreres/index /autoapi/flip/covariance/ravouxnoanchor25/index /autoapi/flip/covariance/rcrk24/index /autoapi/flip/covariance/symbolic/index Classes ------- .. autoapisummary:: flip.covariance.CovMatrix Package Contents ---------------- .. py:class:: CovMatrix(model_name=None, model_kind=None, free_par=None, los_definition=None, covariance_dict=None, full_matrix=False, redshift_dict=None, variant=None) .. py:attribute:: model_name :value: None .. py:attribute:: model_kind :value: None .. py:attribute:: free_par :value: None .. py:attribute:: los_definition :value: None .. py:attribute:: covariance_dict :value: None .. py:attribute:: full_matrix :value: False .. py:attribute:: redshift_dict :value: None .. py:attribute:: variant :value: None .. py:attribute:: coefficients .. py:attribute:: compute_covariance_sum :value: None .. py:attribute:: compute_covariance_sum_jit :value: None .. py:method:: 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) :classmethod: 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 :param cls: Indicate that the function is a class method :param model_name: Determine which model to use for the covariance matrix :param model_kind: Determine the kind of model to be used :param power_spectrum_dict: Pass the power spectrum of the model :param coordinates_density: Specify the coordinates of the density field :param coordinates_velocity: Define the velocity coordinates of the covariance matrix :param additional_parameters_values: Pass the values of additional parameters to the flip code :param \*\*kwargs: Pass a variable number of keyword arguments to the function :returns: A covariancematrix object .. py:method:: init_from_generator(model_name, model_kind, power_spectrum_dict, coordinates_velocity=None, coordinates_density=None, additional_parameters_values=None, variant=None, **kwargs) :classmethod: 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 :param cls: Refer to the class itself :param model_name: Specify the kind of model used to generate the covariance matrix :param model_kind: Determine which model to use :param power_spectrum_dict: Pass the power spectrum to the generate_* functions :param coordinates_velocity: Generate the velocity covariance matrix :param coordinates_density: Generate the density field :param additional_parameters_values: Pass additional parameters to the generator function :param \*\*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 .. py:method:: init_from_file(filename, file_format) :classmethod: .. py: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 :param self: Represent the instance of the class :returns: The kind of the model .. py:property:: loaded The loaded function checks if the covariance matrix is loaded. :param self: Refer to the object itself :returns: A boolean .. py:method:: init_compute_covariance_sum() .. py:method:: compute_covariance_sum_eigenvalues(parameter_values_dict, vector_variance) .. py:method:: compute_full_matrix() The compute_full_matrix function takes the covariance matrix and fills in all of the missing values. :param self: Bind the method to the object :returns: A dictionary with the full covariance matrices for each redshift bin .. py:method:: compute_flat_matrix() .. py:method:: write(filename, file_format) The write function writes the covariance matrix to a file. :param self: Represent the instance of the class :param 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 .. py:method:: mask(mask_vel=None, mask_dens=None)