:py:mod:`flip.covariance.lai22.generator`
=========================================

.. py:module:: flip.covariance.lai22.generator


Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   flip.covariance.lai22.generator.compute_correlation_coefficient_simple_integration
   flip.covariance.lai22.generator.compute_correlation_coefficient_hankel
   flip.covariance.lai22.generator.compute_cov_vv
   flip.covariance.lai22.generator.coefficient_vv
   flip.covariance.lai22.generator.compute_cov_gg
   flip.covariance.lai22.generator.compute_cov_gg_add
   flip.covariance.lai22.generator.coefficient_gg_b2_m
   flip.covariance.lai22.generator.coefficient_gg_f2_m
   flip.covariance.lai22.generator.coefficient_gg_bf_m
   flip.covariance.lai22.generator.compute_cov_gv
   flip.covariance.lai22.generator.coefficient_gv_f2_p
   flip.covariance.lai22.generator.coefficient_gv_bf_p
   flip.covariance.lai22.generator.return_full_cov
   flip.covariance.lai22.generator.return_correlation_matrix
   flip.covariance.lai22.generator.compute_all_matrices
   flip.covariance.lai22.generator.generate_covariance



.. py:function:: compute_correlation_coefficient_simple_integration(p, q, l, r, k, pk)

   " Here the sigma_u is added to pk later.
   The (2*np.pi**2) is added here in the Lai et al. formalism.


.. py:function:: compute_correlation_coefficient_hankel(p, q, l, r, k, pk, hankel_overhead_coefficient=2)

   Highly decrease time and memory consumption.
   Cosmoprimo prefactor is removed here
   When r is too small for the hankel range, standard integration is used.
   To avoid edge effects when using hankel, the mask have an overhead


.. py:function:: compute_cov_vv(ra, dec, comoving_distance, wavenumber_tt, power_spectrum_tt, grid_window_v_tt=None, size_batch=10000, number_worker=8, hankel=True, los_definition='bisector')


.. py:function:: coefficient_vv(wavenumber, power_spectrum_tt, coord, hankel=True)


.. py:function:: compute_cov_gg(pmax, qmax, ra, dec, comoving_distance, wavenumber_mm, wavenumber_mt, wavenumber_tt, power_spectrum_mm, power_spectrum_mt, power_spectrum_tt, grid_window_m_mm=None, grid_window_m_mt=None, grid_window_v_mt=None, grid_window_v_tt=None, size_batch=10000, number_worker=8, sig_damp_mm_gg_m=None, hankel=True, los_definition='bisector')


.. py:function:: compute_cov_gg_add(pmax, qmax, ra, dec, comoving_distance, wavenumber_mm, power_spectrum_mm, grid_window_m_mm=None, size_batch=10000, number_worker=8, sig_damp_mm_gg_m=None, hankel=True, los_definition='bisector')


.. py:function:: coefficient_gg_b2_m(wavenumber_mm, power_spectrum_mm, iter_pq, sum_iter_pq, m_value, sig_damp_mm_gg_m, coord, hankel=True)


.. py:function:: coefficient_gg_f2_m(wavenumber_tt, power_spectrum_tt, iter_pq, sum_iter_pq, m_value, coord, hankel=True)


.. py:function:: coefficient_gg_bf_m(wavenumber_mt, power_spectrum_mt, iter_pq, sum_iter_pq, m_value, coord, hankel=True)


.. py:function:: compute_cov_gv(pmax, ra_g, dec_g, comoving_distance_g, ra_v, dec_v, comoving_distance_v, wavenumber_mt, wavenumber_tt, power_spectrum_mt, power_spectrum_tt, grid_window_m_mt=None, grid_window_v_mt=None, grid_window_v_tt=None, size_batch=10000, number_worker=8, hankel=True, los_definition='bisector')


.. py:function:: coefficient_gv_f2_p(wavenumber_tt, power_spectrum_tt, p, coord, hankel=True)


.. py:function:: coefficient_gv_bf_p(wavenumber_mt, power_spectrum_mt, p, coord, hankel=True)


.. py:function:: return_full_cov(cov)


.. py:function:: return_correlation_matrix(cov)


.. py:function:: compute_all_matrices(ra_density, dec_density, rcom_density, ra_vel, dec_vel, rcom_vel, wavenumber_mm, wavenumber_mt, wavenumber_tt, power_spectrum_gg_mm, power_spectrum_gg_mt, power_spectrum_gg_tt, power_spectrum_gv_mt, power_spectrum_gv_tt, power_spectrum_vv_tt, grid_window_m_mm=None, grid_window_m_mt=None, grid_window_v_mt=None, grid_window_v_tt=None, pmax=3, qmax=3, size_batch=10000, number_worker=1, hankel=True)


.. py:function:: generate_covariance(model_type, power_spectrum_dict, coordinates_velocity=None, coordinates_density=None, pmax=3, qmax=3, **kwargs)

   The generate_covariance function generates the covariance matrix for a given model type.

   :param model_type: Determine which covariance matrices are computed
   :param power_spectrum_dict: Pass the power spectrum of the density and velocity fields
   :param coordinates_velocity: Pass the coordinates of the velocity field
   :param coordinates_density: Define the coordinates of the density field
   :param pmax: Set the maximum order of legendre polynomials used to compute the covariance matrix
   :param qmax: Set the maximum order of legendre polynomials used in the expansion
   :param Wide angle defined in Lai et al. 2022 by the bisector.:
   :param \*\*kwargs: Pass keyword arguments to the function
   :param : Define the model type

   :returns: A dictionary of covariance matrices, the number of density points and the number of velocity points