:py:mod:`flip.covariance`
=========================

.. py:module:: flip.covariance

.. autoapi-nested-parse::

   Init file of the flip.covariance package.



Subpackages
-----------
.. toctree::
   :titlesonly:
   :maxdepth: 3

   adamsblake17plane/index.rst
   adamsblake20/index.rst
   carreres23/index.rst
   lai22/index.rst
   ravouxcarreres/index.rst


Submodules
----------
.. toctree::
   :titlesonly:
   :maxdepth: 1

   contraction/index.rst
   cov_utils/index.rst
   covariance/index.rst
   generator/index.rst
   symbolic/index.rst


Package Contents
----------------

Classes
~~~~~~~

.. autoapisummary::

   flip.covariance.CovMatrix




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


   .. py: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

      :param self: Represent the instance of the class

      :returns: The type 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_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)
      :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_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

      :param cls: Indicate that the function is a class method
      :param model_name: Determine which model to use for the covariance matrix
      :param model_type: Determine the type 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_type, 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_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

      :param cls: Refer to the class itself
      :param model_name: Specify the type of model used to generate the covariance matrix
      :param model_type: 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:method:: 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.

      :param self: Access the attributes of the class
      :param 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


   .. py:method:: compute_covariance_sum_eigenvalues(parameter_values_dict, vector_err)


   .. 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:: 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