firecrown.models.cluster.recipes.murata_binned_spec_z
=====================================================

.. py:module:: firecrown.models.cluster.recipes.murata_binned_spec_z

.. autoapi-nested-parse::

   Module for defining the classes used in the MurataBinnedSpecZ cluster recipe.



Classes
-------

.. autoapisummary::

   firecrown.models.cluster.recipes.murata_binned_spec_z.MurataBinnedSpecZRecipe


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

.. py:class:: MurataBinnedSpecZRecipe

   Bases: :py:obj:`firecrown.models.cluster.recipes.cluster_recipe.ClusterRecipe`

   .. autoapi-inheritance-diagram:: firecrown.models.cluster.recipes.murata_binned_spec_z.MurataBinnedSpecZRecipe
      :parts: 1


   Cluster recipe with Murata19 mass-richness and spec-zs.

   This recipe uses the Murata 2019 binned mass-richness relation and assumes
   perfectly measured spec-zs.


   .. py:attribute:: integrator


   .. py:attribute:: redshift_distribution


   .. py:attribute:: mass_distribution


   .. py:method:: get_theory_prediction(cluster_theory, average_on = None)

      Get a callable that evaluates a cluster theory prediction.

      Returns a callable function that accepts mass, redshift, mass proxy limits,
      and the sky area of your survey and returns the theoretical prediction for the
      expected number of clusters.



   .. py:method:: get_function_to_integrate(prediction)

      Returns a callable function that can be evaluated by an integrator.

      This function is responsible for mapping arguments from the numerical integrator
      to the arguments of the theoretical prediction function.



   .. py:method:: evaluate_theory_prediction(cluster_theory, this_bin, sky_area, average_on = None)

      Evaluate the theory prediction for this cluster recipe.

      Evaluate the theoretical prediction for the observable in the provided bin
      using the Murata 2019 binned mass-richness relation and assuming perfectly
      measured redshifts.