petitRADTRANS.ccf.ccf_core
==========================

.. py:module:: petitRADTRANS.ccf.ccf_core

.. autoapi-nested-parse::

   Core cross-correlation-related functions.



Functions
---------

.. autoapisummary::

   petitRADTRANS.ccf.ccf_core.co_add_cross_correlation
   petitRADTRANS.ccf.ccf_core.cross_correlate
   petitRADTRANS.ccf.ccf_core.cross_correlate_matrices


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

.. py:function:: co_add_cross_correlation(cross_correlation, velocities_ccf, co_added_velocities)

.. py:function:: cross_correlate(array_1, array_2)

   Cross-correlate two 1-D arrays.

   Args:
       array_1: first array
       array_2: second array

   Returns:
       The cross-correlation values.


.. py:function:: cross_correlate_matrices(matrix_1, matrix_2)

   Cross-correlate two N-D arrays.
   The cross-correlation is performed over the last dimension of the matrices. NaNs are ignored and replaced by zeros.
   For matrices of shape e.g. (2, 3, 10, 1000), the result is a matrix of shape (2, 3, 10). This is equivalent, but
   faster, to:
   >>> ccf = np.zeros((2, 3, 10))
   >>> for i in range(2):
   ...     for j in range(3):
   ...         for k in range(10):
   ...             ccf[i, j, k] = cross_correlate(matrix_1[i, j, k, :], matrix_2[i, j, k, :])

   For 1-D arrays, cross_correlate is faster, but NaNs are not treated.
   Arguments matrix_1 and matrix_2 can be 1-D arrays, in that case NaNs are treated then cross_correlate() is used. The
   NaN treatment is rather slow, so cross_correlate is still faster if no NaN are in the arrays.

   Args:
       matrix_1: first matrix
       matrix_2: second matrix

   Returns:
       The cross-correlation values.