firecrown.models.two_point
==========================

.. py:module:: firecrown.models.two_point

.. autoapi-nested-parse::

   TwoPoint theory support.



Classes
-------

.. autoapisummary::

   firecrown.models.two_point.ApplyInterpolationWhen
   firecrown.models.two_point.TwoPointTheory


Functions
---------

.. autoapisummary::

   firecrown.models.two_point.at_least_one_tracer_has_hm
   firecrown.models.two_point.at_least_one_tracer_has_pt
   firecrown.models.two_point.calculate_pk
   firecrown.models.two_point.determine_ccl_kind


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

.. py:class:: ApplyInterpolationWhen

   Bases: :py:obj:`enum.Flag`

   .. autoapi-inheritance-diagram:: firecrown.models.two_point.ApplyInterpolationWhen
      :parts: 1


   Flags controlling when to apply interpolation of multipole moments.

   These flags specify the contexts in which interpolation should be used instead of
   computing all multipoles exactly. When `NONE` is set, all multipoles are computed
   directly. When any flag is set, only the multipoles defined in `LogLinearElls()` are
   computed exactly; the others will be interpolated.

   Note: For real-space correlation functions xi(theta), interpolation is handled
   internally by CCL and does not depend on these flags.


   .. py:attribute:: NONE
      :value: 0



   .. py:attribute:: REAL


   .. py:attribute:: HARMONIC


   .. py:attribute:: HARMONIC_WINDOW


   .. py:attribute:: DEFAULT


   .. py:attribute:: ALL


   .. py:method:: __get_pydantic_core_schema__(_source_type, _handler)
      :classmethod:


      Custom schema for Pydantic to support Flag (de)serialization from string.



.. py:function:: at_least_one_tracer_has_hm(tools, tracer0, tracer1)

   Compute a power spectrum with the halo model.

   :param tools: The modeling tools to use.
   :param tracer0: The first tracer to use.
   :param tracer1: The second tracer to use.
   :return: The power spectrum.


.. py:function:: at_least_one_tracer_has_pt(tools, tracer0, tracer1)

   Compute the power spectrum with the perturbation theory.

   If one of the tracers does not have a perturbation theory (PT) tracer, a dummy
   matter PT tracer is created for it. This is useful for doing cross-correlations
   between a PT tracer and a non-PT tracer.

   :param tools: The modeling tools to use.
   :param tracer0: The first tracer to use.
   :param tracer1: The second tracer to use.
   :return: The power spectrum.


.. py:function:: calculate_pk(pk_name, tools, tracer0, tracer1)

   Return the power spectrum named by pk_name.

   If the modeling tools already has the power spectrum, it is returned.
   If not, is is computed with the help of the modeling tools.

   :param pk_name: The name of the power spectrum to return.
   :param tools: The modeling tools to use.
   :param tracer0: The first tracer to use.
   :param tracer1: The second tracer to use.
   :return: The power spectrum.


.. py:function:: determine_ccl_kind(sacc_data_type)

   Determine the CCL kind for this SACC data type.

   :param sacc_data_type: the name of the SACC data type
   :return: the CCL kind


.. py:class:: TwoPointTheory(*, sacc_data_type, sources, interp_ells_gen = LogLinearElls(), ell_or_theta = None, tracers = None, int_options = None, apply_interp = ApplyInterpolationWhen.DEFAULT)

   Bases: :py:obj:`firecrown.updatable.Updatable`

   .. autoapi-inheritance-diagram:: firecrown.models.two_point.TwoPointTheory
      :parts: 1


   Making predictions for TwoPoint statistics.


   .. py:attribute:: sacc_data_type


   .. py:attribute:: ccl_kind
      :value: 'cl'



   .. py:attribute:: sources


   .. py:attribute:: interp_ells_gen


   .. py:attribute:: ell_or_theta_config
      :type:  None | firecrown.generators.two_point.EllOrThetaConfig
      :value: None



   .. py:attribute:: window
      :type:  None | numpy.typing.NDArray[numpy.float64]
      :value: None



   .. py:attribute:: sacc_tracers
      :value: None



   .. py:attribute:: ells
      :type:  None | numpy.typing.NDArray[numpy.int64]
      :value: None



   .. py:attribute:: thetas
      :type:  None | numpy.typing.NDArray[numpy.float64]
      :value: None



   .. py:attribute:: mean_ells
      :type:  None | numpy.typing.NDArray[numpy.float64]
      :value: None



   .. py:attribute:: cells
      :type:  dict[firecrown.metadata_types.TracerNames, numpy.typing.NDArray[numpy.float64]]


   .. py:attribute:: ells_for_xi
      :type:  numpy.typing.NDArray[numpy.int64]


   .. py:attribute:: int_options
      :value: None



   .. py:attribute:: apply_interp


   .. py:property:: source0
      :type: firecrown.likelihood.source.Source


      Return the first source.



   .. py:property:: source1
      :type: firecrown.likelihood.source.Source


      Return the second source.



   .. py:method:: initialize_sources(sacc_data)

      Initialize this TwoPointTheory's sources  and tracer names.

      :param sacc_data: The data in the from which we read the data.
      :return: The tracer names.



   .. py:method:: get_tracers_and_scales(tools)

      Get tracers and scales for both sources.

      :param tools: The modeling tools to use.
      :result: The tracers and scales for both sources.



   .. py:method:: generate_ells_for_interpolation()

      Generate ells for interpolation.