tjpcov.covariance_cluster_counts_gaussian
=========================================

.. py:module:: tjpcov.covariance_cluster_counts_gaussian


Classes
-------

.. autoapisummary::

   tjpcov.covariance_cluster_counts_gaussian.ClusterCountsGaussian


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

.. py:class:: ClusterCountsGaussian(config)

   Bases: :py:obj:`tjpcov.covariance_cluster_counts.CovarianceClusterCounts`


   Implementation of gaussian covariance term.

   This class calculates the gaussian (shot-noise) contribution to the
   autocorrelation of cluster counts (NxN).

   Class to calculate the gaussian covariance of cluster counts

   :param config: If dict, it returns the configuration
                  dictionary directly. If string, it asumes a YAML file and
                  parses it.
   :type config: dict or str


   .. py:attribute:: cov_type
      :value: 'gauss'



   .. py:method:: _get_covariance_block_for_sacc(tracer_comb1, tracer_comb2, **kwargs)

      Compute a single covariance entry 'clusters_redshift_richness'

      :param tracer_comb1: e.g.
                           ('survey', 'bin_richness_1', 'bin_z_0')
      :type tracer_comb1: `tuple` of str
      :param tracer_comb2: e.g.
                           ('survey', 'bin_richness_0', 'bin_z_0')
      :type tracer_comb2: `tuple` of str

      :returns: Covariance for a single block
      :rtype: array_like



   .. py:method:: _get_covariance_gaussian(tracer_comb1, tracer_comb2)

      Compute a single covariance entry 'clusters_redshift_richness'

      :param tracer_comb1: e.g.
                           ('survey', 'bin_richness_1', 'bin_z_0')
      :type tracer_comb1: `tuple` of str
      :param tracer_comb2: e.g.
                           ('survey', 'bin_richness_0', 'bin_z_0')
      :type tracer_comb2: `tuple` of str

      :returns: Covariance for a single block
      :rtype: float



   .. py:method:: get_covariance_block(tracer_comb1, tracer_comb2, **kwargs)

      Compute a single covariance entry 'clusters_redshift_richness'

      :param tracer_comb1: e.g. ('clusters_0_0',)
      :type tracer_comb1: `tuple` of str
      :param tracer_comb2: e.g. ('clusters_0_1',)
      :type tracer_comb2: `tuple` of str

      :returns: Covariance for a single block
      :rtype: array_like



   .. py:method:: shot_noise(z_i, lbd_i)

      Returns the cluster shot noise contribution to the covariance.

      The covariance of number counts is a sum of a super sample
      covariance (SSC) term plus a gaussian diagonal term.  The diagonal
      term is also referred to as "shot noise" which we compute here.

      :param z_i: redshift bin i
      :type z_i: int
      :param lbd_i: richness bin i
      :type lbd_i: int

      :returns: Gaussian covariance contribution
      :rtype: float