sacc_like
================================================

Generic 2-point measurement Gaussian likelihood using sacc format

+-------------+------------------------------+
| File        | likelihood/sacc/sacc_like.py |
+-------------+------------------------------+
| Attribution | CosmoSIS Team                |
+-------------+------------------------------+
| URL         |                              |
+-------------+------------------------------+

This module implements a general likelihood of tomographic 2-point measuremnts
of various quantities, including galaxy density, cosmic shear, intrinsic alignments,
CMB lensing, and the various cross powers between these.

This is a first version of a general code using the SACC format developed for DESC.
See github.com/LSSTDESC/sacc for more details

Note a recent change: by default we now do not renormalize any bandpower weights,
whereas before we did.  This is to allow for non-unit-sum weights such as those
produced by NaMaster.



Assumptions
-----------

 - A Gaussian likelihood approximation for two-point measurements
 - Data supplied in a specific file format



Setup Parameters
----------------

.. list-table::
   :header-rows: 1

   * - Name
     - Type
     - Default
     - Description

   * - data_file
     - str
     - 
     - Filename of the sacc file to use.
   * - data_sets
     - str
     - all
     - Space-separated list of which data sets from within the file to use for likelihoods.
   * - keep_tracers
     - str
     - 
     - Regular expression to select tracers to use.
   * - angle_range_{dataset}_{i}_{j}
     - str
     - 
     - Pair of real numbers. If set, for the given data set and pair of bins, cut down the data used to this angular range  (min and max)
   * - cut_{dataset}
     - str
     - 
     - Space-separated list of i,j pairs. (no spaces within the pair, just betwen them, e.g. cut_lss = 1,2  1,1  3,4.  Remove this bin from the likelihood.
   * - {name}_section
     - str
     - Various depending on name.
     - For each {name} in the data types used from the file, a cosmosis block section to look for the theory predictions in.
   * - save_theory
     - str
     - 
     - If set, save the theory predictions used in the likelihood to this sacc file.
   * - save_realization
     - str
     - 
     - If set, save a simulated data set to this sacc file.
   * - covariance_realizations
     - int
     - -1
     - If >0, assume that the Covariance matrix was estimated from a set of MC simulations and should thus have the Anderson-Hartlap factor applied to increase its size. If zero, assume infinite number of realizations.
   * - sellentin
     - bool
     - False
     - If set, use the Sellentin-Heavens 2016 change to the likelihood to account for this distribution of the covariance estimates. This changes the likelihood to a student's-t form. Note that this invalidates the simulated data sets used for the ABC sampler.
   * - like_name
     - str
     - 2pt
     - The name of the likelihood to save.
   * - likelihood_only
     - bool
     - False
     - Skip saving the covariance, inverse, simulation, etc. Saves some time.
   * - kind
     - str
     - cubic
     - The interpolation to do into the theory splines. See scipy.interpolate.interp1d.


Input values
----------------

.. list-table::
   :header-rows: 1

   * - Section
     - Name
     - Type
     - Default
     - Description

   * - shear_cl
     - ell
     - real 1d
     - 
     - If a Fourier-space measurement is used, the angular wave-number of the predicted theory curves.  The name of the section here depends on the data type used from the file. It might be galaxy_cl or shear_cl, for example.
   * - 
     - bin_{i}_{j}
     - real 1d
     - 
     - For various i,j depending what is found in the file, the theory predictions for this value. For example, C_ell.


Output values
----------------


.. list-table:: Output values
   :header-rows: 1

   * - Section
     - Name
     - Type
     - Description

   * - likelihoods
     - 2pt_like
     - real
     - Gaussian likelihood value. Name can be changed in parameter file (see above) for this and the other outputs below.
   * - data_vector
     - 2pt_data
     - real 1d
     - The full vector of data points used in the likelihood
   * - 
     - 2pt_theory
     - real 1d
     - The full vector of theory points used in the likelihood
   * - 
     - 2pt_covariance
     - real 2d
     - The covariance matrix used
   * - 
     - 2pt_inverse_covariance
     - real 2d
     - The inverse covariance matrix (precision matrix) used.
   * - 
     - 2pt_simulation
     - real 1d
     - A simulated data set from the given theory and covariance matrix.
   * - 
     - 2pt_angle
     - real 1d
     - The angular scale used for each data point.
   * - 
     - 2pt_bin1
     - int 1d
     - The first bin index used for each data point
   * - 
     - 2pt_bin2
     - int 1d
     - The second bin index used for each data point