# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Optimized implementation of the LA Cosmic algorithm

Name : Lacosmicx
Author : Curtis McCully
Date : October 2014

Lacosmicx is designed to detect cosmic rays in images (numpy arrays),
based on Pieter van Dokkum's L.A.Cosmic algorithm.

Much of this was originally adapted from cosmics.py written by Malte Tewes.
I have ported all of the slow functions to Cython/C, and optimized
where I can. This is designed to be as fast as possible so some of the
readability has been sacrificed, specifically in the C code.

L.A.Cosmic = LAplacian Cosmic ray detection

If you use this code, please consider adding this repository address in a
footnote: https://github.com/cmccully/lacosmicx

Also please cite the original paper which can be found at:
U{http://www.astro.yale.edu/dokkum/lacosmic/}

van Dokkum 2001, PASP, 113, 789, 1420
(article : U{http://adsabs.harvard.edu/abs/2001PASP..113.1420V})

This code requires Cython, preferably version >= 0.21.

Parallelization is achieved using OpenMP. This code should compile (although
the Cython files may have issues) using a compiler that does not support OMP,
e.g. clang.

Notes
-----
Differences from original LACosmic:

- Automatic recognition of saturated stars, including their trails.
This avoids treating such stars as large cosmic rays.

-I have tried to optimize all of the code as much as possible while
maintaining the integrity of the algorithm. One of the key speedups is to
use a separable median filter instead of the true median filter. While these
are not identical, they produce comparable results and the separable version
is much faster.

-This implementation is much faster than the Python by as much as a factor of
17 depending on the given parameters, even without running multiple threads.
With multiple threads, this can be increased easily by another factor of 2.
This implementation is much faster than the original IRAF version
(orders of magnitude).

-The arrays always must be C-contiguous, thus all loops are y outer, x inner.
Note that this follows the Pyfits convention.
"""
# For egg_info test builds to pass, put package imports here.

#    from ..laxwrappers import *
from _lacosmicx import lacosmicx
__all__ = ['lacosmicx']
