././@PaxHeader0000000000000000000000000000003400000000000011452 xustar000000000000000028 mtime=1637317818.3478622 astroscrappy-1.1.0/0000755000175100001710000000000000000000000015043 5ustar00runnerdocker00000000000000././@PaxHeader0000000000000000000000000000003400000000000011452 xustar000000000000000028 mtime=1637317818.3398623 astroscrappy-1.1.0/.github/0000755000175100001710000000000000000000000016403 5ustar00runnerdocker00000000000000././@PaxHeader0000000000000000000000000000003400000000000011452 xustar000000000000000028 mtime=1637317818.3438623 astroscrappy-1.1.0/.github/workflows/0000755000175100001710000000000000000000000020440 5ustar00runnerdocker00000000000000././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/.github/workflows/python-tests.yml0000644000175100001710000000504500000000000023650 0ustar00runnerdocker00000000000000name: Run unit tests on: pull_request: push: branches: [ master ] tags: workflow_dispatch: jobs: tests: name: ${{ matrix.name }} (${{ matrix.os }}, ${{ matrix.toxenv }}) runs-on: ${{ matrix.os }} if: "!(contains(github.event.head_commit.message, '[skip ci]') || contains(github.event.head_commit.message, '[ci skip]'))" strategy: fail-fast: false matrix: include: - name: Python 3.7 with required dependencies os: macos-latest python-version: 3.7 toxenv: py37-test - name: Python 3.8 with required dependencies and measure coverage os: ubuntu-latest python-version: 3.8 toxenv: py38-test coverage: true - name: Documentation build os: ubuntu-latest python-version: 3.8 toxenv: build_docs - name: Python 3.8 with developer version of astropy and numpy os: ubuntu-latest python-version: 3.8 toxenv: py38-test-devdeps - name: Python 3.6 astropy LTS and Numpy 1.16 os: ubuntu-latest python-version: 3.6 toxenv: py36-test-astropylts-numpy116 - name: Python 3.8 with required dependencies os: windows-latest python-version: 3.8 toxenv: py38-test - name: Python 3.7 with older dependencies, astropy 3.0 and Numpy 1.17 os: ubuntu-latest python-version: 3.7 toxenv: py37-test-astropy30-numpy117 - name: Code style checks os: ubuntu-latest python-version: 3.8 toxenv: codestyle steps: - uses: actions/checkout@v2 with: fetch-depth: 0 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: python -m pip install tox - name: Install graphviz dependency if: "endsWith(matrix.toxenv, 'build_docs')" run: sudo apt-get -y install graphviz - name: Run tests if: "! matrix.coverage" run: tox -v -e ${{ matrix.toxenv }} - name: Run tests with coverage if: "matrix.coverage" run: | pip install Cython extension-helpers numpy COVERAGE=1 pip install -e .[test] pytest --pyargs astroscrappy docs --cov astroscrappy - name: Upload coverage to codecov if: "matrix.coverage" run: | pip install codecov codecov ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/.github/workflows/release.yml0000644000175100001710000000302500000000000022603 0ustar00runnerdocker00000000000000name: Build and upload to PyPI # Build on every branch push, tag push, and pull request change: on: [push, pull_request] jobs: build_wheels: name: Build wheels on ${{ matrix.os }} runs-on: ${{ matrix.os }} strategy: fail-fast: false matrix: os: [ubuntu-20.04, windows-2019, macos-10.15] steps: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 name: Install Python with: python-version: '3.8' - name: Build wheels uses: pypa/cibuildwheel@v2.2.2 - uses: actions/upload-artifact@v2 with: path: ./wheelhouse/*.whl build_sdist: name: Build source distribution runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 name: Install Python with: python-version: '3.8' - name: Build sdist run: | python -m pip install build python -m build --sdist - uses: actions/upload-artifact@v2 with: path: dist/*.tar.gz upload_pypi: needs: [build_wheels, build_sdist] runs-on: ubuntu-latest # upload to PyPI on every tag if: startsWith(github.ref, 'refs/tags') steps: - uses: actions/download-artifact@v2 with: name: artifact path: dist - uses: pypa/gh-action-pypi-publish@v1.4.2 with: user: __token__ password: ${{ secrets.pypi_token }} # To test: repository_url: https://test.pypi.org/legacy/ ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/.gitignore0000644000175100001710000000134100000000000017032 0ustar00runnerdocker00000000000000# Compiled files *.py[cod] *.a *.o *.so a.out __pycache__ .idea # Ignore .c files by default to avoid including generated code. If you want to # add a non-generated .c extension, use `git add -f filename.c`. *.c # Other generated files */version.py */cython_version.py htmlcov .coverage MANIFEST .ipynb_checkpoints # Sphinx docs/api docs/_build # Eclipse editor project files .project .pydevproject .settings # Pycharm editor project files .idea # Floobits project files .floo .flooignore # Packages/installer info *.egg *.egg-info dist build eggs .eggs parts bin var sdist develop-eggs .installed.cfg distribute-*.tar.gz # Other .cache .tox .*.sw[op] *~ .project .pydevproject .settings pip-wheel-metadata/ # Mac OSX .DS_Store ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/.readthedocs.yml0000644000175100001710000000025200000000000020130 0ustar00runnerdocker00000000000000version: 2 build: image: latest python: version: 3.7 install: - method: pip path: . extra_requirements: - docs - all formats: [] ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/CHANGES.rst0000644000175100001710000000175300000000000016653 0ustar00runnerdocker000000000000001.1.0 (2020-11-01) ------------------ - Added the option to add a variance array - Added the ability to subtract a background array rather than a single value. 1.0.5 (2016-08-16) ------------------ - Updated to newest version of astropy package template. - Fixed median cleaning. There was a subtle bug that the crmask was defined as a unit8 array. This was then used to clean the image, but this acted as indexes 0 and 1 rather than a boolean array that was intended 1.0.4 (2016-02-29) ------------------ - Fixed setup_requires so that it doesn't install astropy when using egg_info. - Pinned coverage version to 3.7.1. - Removed dependence on endianness in tests - Fixed build issues on windows 1.0.3 (2015-09-29) ------------------ - Updated URL in setup.cfg. 1.0.2 (2015-09-29) ------------------ - Added .h files to MANIFEST.in 1.0.1 (2015-09-29) ------------------ - Fixed bug in MANIFEST.in that was excluding *.pyx files. 1.0 (2015-09-29) ---------------- - Initial release. ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/MANIFEST.in0000644000175100001710000000066500000000000016610 0ustar00runnerdocker00000000000000include README.rst include CHANGES.rst include setup.cfg include LICENSE.rst include pyproject.toml include astroscrappy/tests/coveragerc recursive-exclude . astroscrappy.c recursive-exclude astroscrappy/utils image_utils.c median_utils.c recursive-include astroscrappy *.pyx *.pxd recursive-include docs * recursive-include licenses * recursive-include scripts * prune build prune docs/_build prune docs/api global-exclude *.pyc *.o ././@PaxHeader0000000000000000000000000000003400000000000011452 xustar000000000000000028 mtime=1637317818.3478622 astroscrappy-1.1.0/PKG-INFO0000644000175100001710000000650000000000000016141 0ustar00runnerdocker00000000000000Metadata-Version: 2.1 Name: astroscrappy Version: 1.1.0 Summary: Speedy Cosmic Ray Annihilation Package in Python Home-page: https://github.com/astropy/astroscrappy Author: Curtis McCully Author-email: cmccully@lco.global License: BSD 3-Clause Platform: UNKNOWN Requires-Python: >=3.6 Description-Content-Type: text/x-rst Provides-Extra: test Provides-Extra: docs License-File: licenses/LICENSE.rst Astro-SCRAPPY: The Speedy Cosmic Ray Annihilation Package in Python =================================================================== .. image:: https://readthedocs.org/projects/astroscrappy/badge/?version=latest :alt: Documentation Status :scale: 100% :target: https://astroscrappy.readthedocs.io/en/latest/?badge=latest .. image:: https://github.com/astropy/astroscrappy/workflows/Run%20unit%20tests/badge.svg :target: https://github.com/astropy/astroscrappy/actions :alt: CI Status .. image:: https://codecov.io/gh/astropy/astroscrappy/branch/master/graph/badge.svg :target: https://codecov.io/gh/astropy/astroscrappy :alt: AstroScrappy's Coverage Status .. image:: https://zenodo.org/badge/36837126.svg :target: https://zenodo.org/badge/latestdoi/36837126 An optimized cosmic ray detector. :Author: Curtis McCully Astro-SCRAPPY 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. If you use this code, please cite the Zendo DOI: https://zenodo.org/record/1482019 Please cite the original paper which can be found at: http://www.astro.yale.edu/dokkum/lacosmic/ van Dokkum 2001, PASP, 113, 789, 1420 (article : 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 ----- There are some differences from original LA Cosmic: - Automatic recognition of saturated stars. 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, improvment by a factor of ~90. The arrays always must be C-contiguous, thus all loops are y outer, x inner. This follows the astropy.io.fits (pyfits) convention. scipy is required for certain tests to pass, but the code itself does not depend on scipy. License ------- This project is Copyright (c) Astropy Developers and licensed under the terms of the BSD 3-Clause license. See the licenses folder for more information. ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/README.rst0000644000175100001710000000565600000000000016546 0ustar00runnerdocker00000000000000Astro-SCRAPPY: The Speedy Cosmic Ray Annihilation Package in Python =================================================================== .. image:: https://readthedocs.org/projects/astroscrappy/badge/?version=latest :alt: Documentation Status :scale: 100% :target: https://astroscrappy.readthedocs.io/en/latest/?badge=latest .. image:: https://github.com/astropy/astroscrappy/workflows/Run%20unit%20tests/badge.svg :target: https://github.com/astropy/astroscrappy/actions :alt: CI Status .. image:: https://codecov.io/gh/astropy/astroscrappy/branch/master/graph/badge.svg :target: https://codecov.io/gh/astropy/astroscrappy :alt: AstroScrappy's Coverage Status .. image:: https://zenodo.org/badge/36837126.svg :target: https://zenodo.org/badge/latestdoi/36837126 An optimized cosmic ray detector. :Author: Curtis McCully Astro-SCRAPPY 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. If you use this code, please cite the Zendo DOI: https://zenodo.org/record/1482019 Please cite the original paper which can be found at: http://www.astro.yale.edu/dokkum/lacosmic/ van Dokkum 2001, PASP, 113, 789, 1420 (article : 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 ----- There are some differences from original LA Cosmic: - Automatic recognition of saturated stars. 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, improvment by a factor of ~90. The arrays always must be C-contiguous, thus all loops are y outer, x inner. This follows the astropy.io.fits (pyfits) convention. scipy is required for certain tests to pass, but the code itself does not depend on scipy. License ------- This project is Copyright (c) Astropy Developers and licensed under the terms of the BSD 3-Clause license. See the licenses folder for more information. ././@PaxHeader0000000000000000000000000000003400000000000011452 xustar000000000000000028 mtime=1637317818.3438623 astroscrappy-1.1.0/astroscrappy/0000755000175100001710000000000000000000000017575 5ustar00runnerdocker00000000000000././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/__init__.py0000644000175100001710000000504100000000000021706 0ustar00runnerdocker00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst """ Astro-SCRAPPY: The Speedy Cosmic Ray Annihilation Package in Python =================================================================== An optimized cosmic ray detector :Author: Curtis McCully Astro-SCRAPPY is designed to detect cosmic rays in images (numpy arrays), originally 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 cite the Zendo DOI: https://zenodo.org/record/1482019 Please cite the original paper which can be found at: http://www.astro.yale.edu/dokkum/lacosmic/ van Dokkum 2001, PASP, 113, 789, 1420 (article : 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 ----- There are some differences from original LACosmic: - Automatic recognition of saturated stars. 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 28 depending on the given parameters. This implementation is much faster than the original IRAF version, by a factor of ~90. Note that arrays always must be C-contiguous, thus all loops are y outer, x inner. This follows the Pyfits convention. scipy is required for certain tests to pass, but the code itself does not depend on scipy. """ # Affiliated packages may add whatever they like to this file, but # should keep this content at the top. # ---------------------------------------------------------------------------- from ._astropy_init import * # noqa # ---------------------------------------------------------------------------- from .astroscrappy import * # noqa from .utils import * # noqa __all__ = ['detect_cosmics'] # noqa ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/_astropy_init.py0000644000175100001710000000114500000000000023033 0ustar00runnerdocker00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst __all__ = ['__version__'] # this indicates whether or not we are in the package's setup.py try: _ASTROPY_SETUP_ except NameError: import builtins builtins._ASTROPY_SETUP_ = False try: from .version import version as __version__ except ImportError: __version__ = '' if not _ASTROPY_SETUP_: # noqa import os # Create the test function for self test from astropy.tests.runner import TestRunner test = TestRunner.make_test_runner_in(os.path.dirname(__file__)) test.__test__ = False __all__ += ['test'] ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317818.0 astroscrappy-1.1.0/astroscrappy/_compiler.c0000644000175100001710000000524000000000000021713 0ustar00runnerdocker00000000000000#include /*************************************************************************** * Macros for determining the compiler version. * * These are borrowed from boost, and majorly abridged to include only * the compilers we care about. ***************************************************************************/ #define STRINGIZE(X) DO_STRINGIZE(X) #define DO_STRINGIZE(X) #X #if defined __clang__ /* Clang C++ emulates GCC, so it has to appear early. */ # define COMPILER "Clang version " __clang_version__ #elif defined(__INTEL_COMPILER) || defined(__ICL) || defined(__ICC) || defined(__ECC) /* Intel */ # if defined(__INTEL_COMPILER) # define INTEL_VERSION __INTEL_COMPILER # elif defined(__ICL) # define INTEL_VERSION __ICL # elif defined(__ICC) # define INTEL_VERSION __ICC # elif defined(__ECC) # define INTEL_VERSION __ECC # endif # define COMPILER "Intel C compiler version " STRINGIZE(INTEL_VERSION) #elif defined(__GNUC__) /* gcc */ # define COMPILER "GCC version " __VERSION__ #elif defined(__SUNPRO_CC) /* Sun Workshop Compiler */ # define COMPILER "Sun compiler version " STRINGIZE(__SUNPRO_CC) #elif defined(_MSC_VER) /* Microsoft Visual C/C++ Must be last since other compilers define _MSC_VER for compatibility as well */ # if _MSC_VER < 1200 # define COMPILER_VERSION 5.0 # elif _MSC_VER < 1300 # define COMPILER_VERSION 6.0 # elif _MSC_VER == 1300 # define COMPILER_VERSION 7.0 # elif _MSC_VER == 1310 # define COMPILER_VERSION 7.1 # elif _MSC_VER == 1400 # define COMPILER_VERSION 8.0 # elif _MSC_VER == 1500 # define COMPILER_VERSION 9.0 # elif _MSC_VER == 1600 # define COMPILER_VERSION 10.0 # else # define COMPILER_VERSION _MSC_VER # endif # define COMPILER "Microsoft Visual C++ version " STRINGIZE(COMPILER_VERSION) #else /* Fallback */ # define COMPILER "Unknown compiler" #endif /*************************************************************************** * Module-level ***************************************************************************/ struct module_state { /* The Sun compiler can't handle empty structs */ #if defined(__SUNPRO_C) || defined(_MSC_VER) int _dummy; #endif }; static struct PyModuleDef moduledef = { PyModuleDef_HEAD_INIT, "compiler_version", NULL, sizeof(struct module_state), NULL, NULL, NULL, NULL, NULL }; #define INITERROR return NULL PyMODINIT_FUNC PyInit_compiler_version(void) { PyObject* m; m = PyModule_Create(&moduledef); if (m == NULL) INITERROR; PyModule_AddStringConstant(m, "compiler", COMPILER); return m; } ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/astroscrappy.pyx0000644000175100001710000007566400000000000023113 0ustar00runnerdocker00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # cython: boundscheck=False, nonecheck=False, wraparound=False, language_level=3, cdivision=True """ Name : astroscrappy: The Speedy Cosmic Ray Annihilation Package in Python Author : Curtis McCully Date : October 2014 """ import numpy as np cimport numpy as np np.import_array() cimport cython from cython.parallel cimport parallel, prange from .utils import * from .utils.median_utils cimport cymedian from libc.stdint cimport uint8_t ctypedef uint8_t bool from libc.stdlib cimport malloc, free def detect_cosmics(indat, inmask=None, inbkg=None, invar=None, float sigclip=4.5, float sigfrac=0.3, float objlim=5.0, float gain=1.0, float readnoise=6.5, float satlevel=65536.0, int niter=4, sepmed=True, cleantype='meanmask', fsmode='median', psfmodel='gauss', float psffwhm=2.5, int psfsize=7, psfk=None, float psfbeta=4.765, verbose=False): """detect_cosmics(indat, inmask=None, bkg=None, var=None, sigclip=4.5, sigfrac=0.3, objlim=5.0, gain=1.0, readnoise=6.5, satlevel=65536.0, niter=4, sepmed=True, cleantype='meanmask', fsmode='median', psfmodel='gauss', psffwhm=2.5, psfsize=7, psfk=None, psfbeta=4.765, verbose=False)\n Detect cosmic rays in a numpy array. If you use this code, please cite the Zendo DOI: https://zenodo.org/record/1482019 Please cite the original paper which can be found at: http://www.astro.yale.edu/dokkum/lacosmic/ van Dokkum 2001, PASP, 113, 789, 1420 (article : http://adsabs.harvard.edu/abs/2001PASP..113.1420V) Parameters ---------- indat : float numpy array Input data array that will be used for cosmic ray detection. This should include the sky background (or a mean background level, added back in after sky subtraction), so that noise can be estimated correctly from the data values. This should be in units of "counts". inmask : boolean numpy array, optional Input bad pixel mask. Values of True will be ignored in the cosmic ray detection/cleaning process. Default: None. inbkg : float numpy array, optional A pre-determined background image, to be subtracted from ``indat`` before running the main detection algorithm. This is used primarily with spectroscopic data, to remove sky lines and the cross-section of an object continuum during iteration, "protecting" them from spurious rejection (see the above paper). This background is not removed from the final, cleaned output (`cleanarr`). This should be in units of "counts", the same units of indat. This inbkg should be free from cosmic rays. When estimating the cosmic-ray free noise of the image, we will treat ``inbkg`` as a constant Poisson contribution to the variance. invar : float numpy array, optional A pre-determined estimate of the data variance (ie. noise squared) in each pixel, generated by previous processing of ``indat``. If provided, this is used in place of an internal noise model based on ``indat``, ``gain`` and ``readnoise``. This still gets median filtered and cleaned internally, to estimate what the noise in each pixel *would* be in the absence of cosmic rays. This should be in units of "counts" squared. sigclip : float, optional Laplacian-to-noise limit for cosmic ray detection. Lower values will flag more pixels as cosmic rays. Default: 4.5. sigfrac : float, optional Fractional detection limit for neighboring pixels. For cosmic ray neighbor pixels, a lapacian-to-noise detection limit of sigfrac * sigclip will be used. Default: 0.3. objlim : float, optional Minimum contrast between Laplacian image and the fine structure image. Increase this value if cores of bright stars are flagged as cosmic rays. Default: 5.0. gain : float, optional Gain of the image (electrons / ADU). We always need to work in electrons for cosmic ray detection. Default: 1.0 readnoise : float, optional Read noise of the image (electrons). Used to generate the noise model of the image. Default: 6.5. satlevel : float, optional Saturation of level of the image (electrons). This value is used to detect saturated stars and pixels at or above this level are added to the mask. Default: 65536.0. niter : int, optional Number of iterations of the LA Cosmic algorithm to perform. Default: 4. sepmed : boolean, optional Use the separable median filter instead of the full median filter. The separable median is not identical to the full median filter, but they are approximately the same and the separable median filter is significantly faster and still detects cosmic rays well. Default: True cleantype : {'median', 'medmask', 'meanmask', 'idw'}, optional Set which clean algorithm is used:\n 'median': An umasked 5x5 median filter\n 'medmask': A masked 5x5 median filter\n 'meanmask': A masked 5x5 mean filter\n 'idw': A masked 5x5 inverse distance weighted interpolation\n Default: "meanmask". fsmode : {'median', 'convolve'}, optional Method to build the fine structure image:\n 'median': Use the median filter in the standard LA Cosmic algorithm 'convolve': Convolve the image with the psf kernel to calculate the fine structure image. Default: 'median'. psfmodel : {'gauss', 'gaussx', 'gaussy', 'moffat'}, optional Model to use to generate the psf kernel if fsmode == 'convolve' and psfk is None. The current choices are Gaussian and Moffat profiles. 'gauss' and 'moffat' produce circular PSF kernels. The 'gaussx' and 'gaussy' produce Gaussian kernels in the x and y directions respectively. Default: "gauss". psffwhm : float, optional Full Width Half Maximum of the PSF to use to generate the kernel. Default: 2.5. psfsize : int, optional Size of the kernel to calculate. Returned kernel will have size psfsize x psfsize. psfsize should be odd. Default: 7. psfk : float numpy array, optional PSF kernel array to use for the fine structure image if fsmode == 'convolve'. If None and fsmode == 'convolve', we calculate the psf kernel using 'psfmodel'. Default: None. psfbeta : float, optional Moffat beta parameter. Only used if fsmode=='convolve' and psfmodel=='moffat'. Default: 4.765. verbose : boolean, optional Print to the screen or not. Default: False. Returns ------- crmask : boolean numpy array The cosmic ray mask (boolean) array with values of True where there are cosmic ray detections. cleanarr : float numpy array The cleaned data array. Notes ----- To reproduce the most similar behavior to the original LA Cosmic (written in IRAF), set inmask = None, satlevel = np.inf, sepmed=False, cleantype='medmask', and fsmode='median'. The original IRAF version distinguishes between spectroscopic and imaging data. This version does not. For best results on spectra, we recommend that you include an estimate of the background. One can generally obtain this by fitting columns with a smooth function. To efficiently identify cosmic rays, LA Cosmic and therefore astroscrappy estimates the cosmic ray free noise by smoothing the variance using a median filter. To minimize false positives on bright sky lines, if ``inbkg`` is provided, we do not smooth the variance contribution from the provided background. We only smooth the variance that is in addition to the Poisson contribution from the background so that we do not underestimate the noise (and therefore run the risk of flagging false positives) near narrow, bright sky lines. """ # Grab the sizes of the input array cdef int nx = indat.shape[1] cdef int ny = indat.shape[0] # Tell the compiler about the loop indices so it can optimize them. cdef int i, j = 0 # Make a copy of the data as the cleanarr that we work on # This guarantees that that the data will be contiguous and makes sure we # don't edit the input data. cleanarr = np.empty((ny, nx), dtype=np.float32, order='C') # Set the initial values to those of the data array # Multiply by the gain; the statistics only work properly with electrons. cleanarr[:, :] = indat[:, :] * gain if inbkg is not None: bkg = np.empty((ny, nx), dtype=np.float32, order='C') bkg[:, :] = inbkg[:, :] * gain # Setup the mask if inmask is None: # By default don't mask anything mask = np.zeros((ny, nx), dtype=np.uint8, order='C') else: # Make a copy of the input mask mask = np.empty((ny, nx), dtype=np.uint8, order='C') mask[:, :] = inmask[:, :] # Copy any noise estimates so we can clean them like the data, otherwise # use the main data array instead. if invar is not None and inbkg is not None: clean_var = invar * (gain * gain) - bkg elif invar is not None and inbkg is None: clean_var = invar * (gain * gain) elif invar is None and inbkg is not None: clean_var = cleanarr - bkg + readnoise * readnoise else: clean_var = cleanarr + readnoise * readnoise # Find the saturated stars and add them to the mask update_mask(np.asarray(cleanarr), np.asarray(mask), satlevel, sepmed) # Subtract the input sky model, if applicable. if inbkg is not None: cleanarr -= bkg # Find the unmasked pixels to calculate the sky. gooddata = np.zeros(int(nx * ny - np.asarray(mask).sum()), dtype=np.float32, order='c') igoodpix = 0 gooddata[:] = cleanarr[np.logical_not(mask)] # Get the default background level for large cosmic rays. background_level = median(gooddata, len(gooddata)) goodvar = np.empty_like(gooddata, order='c') goodvar[:] = clean_var[np.logical_not(mask)] background_var_level = median(goodvar, len(goodvar)) del goodvar del gooddata # Set up the psf kernel if necessary. if psfk is None and fsmode == 'convolve': # calculate the psf kernel psfk if psfmodel == 'gauss': psfk = gausskernel(psffwhm, psfsize) elif psfmodel == 'gaussx': psfk = gaussxkernel(psffwhm, psfsize) elif psfmodel == 'gaussy': psfk = gaussykernel(psffwhm, psfsize) elif psfmodel == 'moffat': psfk = moffatkernel(psffwhm, psfbeta, psfsize) else: raise ValueError('Please choose a supported PSF model.') # Define a cosmic ray mask # This is what will be returned at the end crmask = np.zeros((ny, nx), dtype=np.uint8, order='C') # Calculate the detection limit for neighbor pixels cdef float sigcliplow = sigfrac * sigclip # Run lacosmic for up to maxiter iterations # We stop if no more cosmic ray pixels are found (quite rare) if verbose: print("Starting {} L.A.Cosmic iterations".format(niter)) for i in range(niter): if verbose: print("Iteration {}:".format(i + 1)) # Detect the cosmic rays # We subsample, convolve, clip negative values, # and rebin to original size subsam = subsample(cleanarr) conved = laplaceconvolve(subsam) del subsam conved[conved < 0] = 0.0 # This is called L+ in the original LA Cosmic/cosmics.py s = rebin(conved) del conved # Build a noise map, to compare the laplacian to, based on the input # data or (if supplied) input variance. If variance is used, it still # gets filtered, since we can best identify cosmic rays using what the # noise in a pixel *should* be, rather than what it actually is after # a cosmic ray hit. if sepmed: m5 = sepmedfilt7(clean_var) else: m5 = medfilt5(clean_var) # Here we add back in the background to get the full variance. m5 here is effectively the CR free variance if inbkg is not None: m5 += bkg # Clip noise so that we can take a square root m5[m5 < 0.00001] = 0.00001 noise = np.sqrt(m5) del m5 # Laplacian signal to noise ratio : s /= 2.0 * noise # the 2.0 is from the 2x2 subsampling and is denoted f_s in the original paper # This s is called sigmap in the original lacosmic.cl if sepmed: sp = sepmedfilt7(s) else: sp = medfilt5(s) # Remove the large structures (s prime) : sp = s - sp del s # Build the fine structure image : if fsmode == 'convolve': f = convolve(cleanarr, psfk) elif fsmode == 'median': if sepmed: f = sepmedfilt5(cleanarr) else: f = medfilt3(cleanarr) else: raise ValueError('Please choose a valid fine structure mode.') if sepmed: m7 = sepmedfilt9(f) else: m7 = medfilt7(f) # Note that original paper compares L+ / Fine Structure Image < threshold. # The original code and therefore we compare S' / Fine Structure Image < threshold. # As such, we scale the fine structure image by the noise here so it is in the same units as S'. f = (f - m7) / noise # Clip f as we will divide by f. Similar to the IRAF version. f[f < 0.01] = 0.01 del m7 del noise # Find the candidate cosmic rays goodpix = np.logical_not(mask) cosmics = np.logical_and(sp > sigclip, goodpix) # S' / F is still not exactly what is in the paper as we have subtracted the "sampling flux" from S # via the median filter. This should be an optimization because we have removed the smooth component # and are therefore comparing the candidate "CR flux" only. cosmics = np.logical_and(cosmics, (sp / f) > objlim) del f # What follows is a special treatment for neighbors, with more relaxed # constraints. # We grow these cosmics a first time to determine the immediate # neighborhood. cosmics = dilate3(cosmics) cosmics = np.logical_and(cosmics, goodpix) # From this grown set, we keep those that have sp > sigmaclip # Note that the only new pixels that should be added here are ones that were rejected by the # objlim test. As they are neighbors to true CRs, we take that as a prior that CR probability # of the neighboring pixel is enhanced. cosmics = np.logical_and(sp > sigclip, cosmics) # Now we repeat this procedure, but lower the detection limit to siglow cosmics = dilate3(cosmics) cosmics = np.logical_and(cosmics, goodpix) del goodpix cosmics = np.logical_and(sp > sigcliplow, cosmics) del sp # Our CR counter numcr = cosmics.sum() # Update the crmask with the cosmics we have found crmask[:, :] = np.logical_or(crmask, cosmics)[:, :] del cosmics if verbose: print("{} cosmic pixels this iteration".format(numcr)) # If we didn't find anything, we're done. if numcr == 0: break # otherwise clean the image and iterate if cleantype == 'median': # Unmasked median filter clean_median(cleanarr, crmask, nx, ny) clean_median(clean_var, crmask, nx, ny) # Masked mean filter elif cleantype == 'meanmask': clean_meanmask(cleanarr, crmask, mask, nx, ny, background_level) clean_meanmask(clean_var, crmask, mask, nx, ny, background_var_level) # Masked median filter elif cleantype == 'medmask': clean_medmask(cleanarr, crmask, mask, nx, ny, background_level) clean_medmask(clean_var, crmask, mask, nx, ny, background_var_level) # Inverse distance weighted interpolation elif cleantype == 'idw': clean_idwinterp(cleanarr, crmask, mask, nx, ny, background_level) clean_idwinterp(clean_var, crmask, mask, nx, ny, background_var_level) else: raise ValueError("""cleantype must be one of the following values: [median, meanmask, medmask, idw]""") # Restore the sky background if needed. if inbkg is not None: cleanarr += bkg cleanarr /= gain return crmask.astype(np.bool_), cleanarr def update_mask(np.ndarray[np.float32_t, ndim=2, mode='c', cast=True] data, np.ndarray[np.uint8_t, ndim=2, mode='c', cast=True] mask, float satlevel, bool sepmed): """update_mask(data, mask, satlevel, sepmed)\n Find staturated stars and puts them in the mask. This can then be used to avoid these regions in cosmic detection and cleaning procedures. The median filter is used to find large symmetric regions of saturated pixels (i.e. saturated stars). Parameters ---------- data : float numpy array The data array in which we look for saturated stars. mask : boolean numpy array Bad pixel mask. This mask will be dilated using dilate3 and then combined with the saturated star mask. satlevel : float Saturation level of the image. This value can be lowered if the cores of bright (saturated) stars are not being masked. sepmed : boolean Use the separable median or not. The separable median is not identical to the full median filter, but they are approximately the same and the separable median filter is significantly faster. """ # Find all of the saturated pixels satpixels = data >= satlevel # Use the median filter to estimate the large scale structure if sepmed: m5 = sepmedfilt7(data) else: m5 = medfilt5(data) # Use the median filtered image to find the cores of saturated stars # The 10 here is arbitray. Malte Tewes uses 2.0 in cosmics.py, but I # wanted to get more of the cores of saturated stars. satpixels = np.logical_and(satpixels, m5 > (satlevel / 10.0)) # Grow the input mask by one pixel to make sure we cover bad pixels grow_mask = dilate3(mask) # Dilate the saturated star mask to remove edge effects in the mask dilsatpixels = dilate5(satpixels, 2) del satpixels # Combine the saturated pixels with the given input mask # Note, we work on the mask pixels in place mask[:, :] = np.logical_or(dilsatpixels, grow_mask)[:, :] del grow_mask cdef void clean_meanmask(float[:, ::1] cleanarr, bool[:, ::1] crmask, bool[:, ::1] mask, int nx, int ny, float background_level): """clean_meanmask(cleanarr, crmask, mask, nx, ny, background_level)\n Clean the bad pixels in cleanarr using a 5x5 masked mean filter. Parameters ---------- cleanarr : float numpy array The array to be cleaned. crmask : boolean numpy array Cosmic ray mask. Pixels with a value of True in this mask will be cleaned. mask : boolean numpy array Bad pixel mask. Values of True indicate bad pixels. nx : int Size of cleanarr in the x-direction. Note cleanarr has dimensions ny x nx. ny : int Size of cleanarr in the y-direction. Note cleanarr has dimensions ny x nx. background_level : float Average value of the background. This value will be used if there are no good pixels in a 5x5 region. """ # Go through all of the pixels, ignore the borders cdef int i, j, k, l, numpix cdef float s cdef bool badpix with nogil, parallel(): # For each pixel for j in prange(2, ny - 2): for i in range(2, nx - 2): # if the pixel is in the crmask if crmask[j, i]: numpix = 0 s = 0.0 # sum the 25 pixels around the pixel # ignoring any pixels that are masked for l in range(-2, 3): for k in range(-2, 3): badpix = crmask[j + l, i + k] badpix = badpix or mask[j + l, i + k] if not badpix: s = s + cleanarr[j + l, i + k] numpix = numpix + 1 # if the pixels count is 0 # then put in the background of the image if numpix == 0: s = background_level else: # else take the mean s = s / float(numpix) cleanarr[j, i] = s cdef void clean_median(float[:, ::1] cleanarr, bool[:, ::1] crmask, int nx, int ny): """clean_medmask(cleanarr, crmask, mask, nx, ny, background_level)\n Clean the bad pixels in cleanarr using a 5x5 masked median filter. Parameters ---------- cleanarr : float numpy array The array to be cleaned. crmask : boolean numpy array Cosmic ray mask. Pixels with a value of True in this mask will be cleaned. nx : int size of cleanarr in the x-direction. Note cleanarr has dimensions ny x nx. ny : int size of cleanarr in the y-direction. Note cleanarr has dimensions ny x nx. """ # Go through all of the pixels, ignore the borders cdef int k, l, i, j, counter cdef float * medarr # For each pixel with nogil, parallel(): medarr = < float * > malloc(25 * sizeof(float)) for j in prange(2, ny - 2): for i in range(2, nx - 2): # if the pixel is in the crmask if crmask[j, i]: # median the 25 pixels around the pixel counter = 0 for l in range(-2, 3): for k in range(-2, 3): medarr[counter] = cleanarr[j + l, i + k] counter =+ 1 cleanarr[j, i] = cymedian(medarr, 25) free(medarr) cdef void clean_medmask(float[:, ::1] cleanarr, bool[:, ::1] crmask, bool[:, ::1] mask, int nx, int ny, float background_level): """clean_medmask(cleanarr, crmask, mask, nx, ny, background_level)\n Clean the bad pixels in cleanarr using a 5x5 masked median filter. Parameters ---------- cleanarr : float numpy array The array to be cleaned. crmask : boolean numpy array Cosmic ray mask. Pixels with a value of True in this mask will be cleaned. mask : boolean numpy array Bad pixel mask. Values of True indicate bad pixels. nx : int size of cleanarr in the x-direction. Note cleanarr has dimensions ny x nx. ny : int size of cleanarr in the y-direction. Note cleanarr has dimensions ny x nx. background_level : float Average value of the background. This value will be used if there are no good pixels in a 5x5 region. """ # Go through all of the pixels, ignore the borders cdef int k, l, i, j, numpix cdef float * medarr cdef bool badpixel # For each pixel with nogil, parallel(): medarr = < float * > malloc(25 * sizeof(float)) for j in prange(2, ny - 2): for i in range(2, nx - 2): # if the pixel is in the crmask if crmask[j, i]: numpix = 0 # median the 25 pixels around the pixel ignoring # any pixels that are masked for l in range(-2, 3): for k in range(-2, 3): badpixel = crmask[j + l, i + k] badpixel = badpixel or mask[j + l, i + k] if not badpixel: medarr[numpix] = cleanarr[j + l, i + k] numpix = numpix + 1 # if the pixels count is 0 then put in the background # of the image if numpix == 0: cleanarr[j, i] = background_level else: # else take the median cleanarr[j, i] = cymedian(medarr, numpix) free(medarr) cdef void clean_idwinterp(float[:, ::1] cleanarr, bool[:, ::1] crmask, bool[:, ::1] mask, int nx, int ny, float background_level): """clean_idwinterp(cleanarr, crmask, mask, nx, ny, background_level)\n Clean the bad pixels in cleanarr using a 5x5 using inverse distance weighted interpolation. Parameters ---------- cleanarr : float numpy array The array to be cleaned. crmask : boolean numpy array Cosmic ray mask. Pixels with a value of True in this mask will be cleaned. mask : boolean numpy array Bad pixel mask. Values of True indicate bad pixels. nx : int Size of cleanarr in the x-direction (int). Note cleanarr has dimensions ny x nx. ny : int Size of cleanarr in the y-direction (int). Note cleanarr has dimensions ny x nx. background_level : float Average value of the background. This value will be used if there are no good pixels in a 5x5 region. """ # Go through all of the pixels, ignore the borders cdef int i, j, k, l cdef float f11, f12, f21, f22 = background_level cdef int x1, x2, y1, y2 weightsarr = np.array([[0.35355339, 0.4472136, 0.5, 0.4472136, 0.35355339], [0.4472136, 0.70710678, 1., 0.70710678, 0.4472136], [0.5, 1., 0., 1., 0.5], [0.4472136, 0.70710678, 1., 0.70710678, 0.4472136], [0.35355339, 0.4472136, 0.5, 0.4472136, 0.35355339]], dtype=np.float32) cdef float[:, ::1] weights = weightsarr cdef float wsum cdef float val cdef int x, y # For each pixel with nogil, parallel(): for j in prange(2, ny - 2): for i in range(2, nx - 2): # if the pixel is in the crmask if crmask[j, i]: wsum = 0.0 val = 0.0 for l in range(-2, 3): y = j + l for k in range(-2, 3): x = i + k if not (crmask[y, x] or mask[y, x]): val = val + weights[l+2, k+2] * cleanarr[y, x] wsum = wsum + weights[l+2, k+2] if wsum < 1e-6: cleanarr[j, i] = background_level else: cleanarr[j, i] = val / wsum def gausskernel(float psffwhm, int kernsize): """gausskernel(psffwhm, kernsize)\n Calculate a circular Gaussian psf kernel. Parameters ---------- psffwhm : float Full Width Half Maximum of the PSF to use to generate the kernel. kernsize : int Size of the kernel to calculate. kernsize should be odd. Returned kernel will have size kernsize x kernsize. Returns ------- kernel : float numpy array Gaussian PSF kernel with size kernsize x kernsize. """ kernel = np.zeros((kernsize, kernsize), dtype=np.float32) # Make a grid of x and y values x = np.tile(np.arange(kernsize) - kernsize / 2, (kernsize, 1)) y = x.transpose().copy() # Calculate the offset, r r2 = x * x + y * y # Calculate the kernel sigma2 = psffwhm * psffwhm / 2.35482 / 2.35482 kernel[:, :] = np.exp(-0.5 * r2 / sigma2)[:, :] # Normalize the kernel kernel /= kernel.sum() return kernel def gaussxkernel(float psffwhm, int kernsize): """gaussxkernel(psffwhm, kernsize)\n Calculate a Gaussian kernel in the x-direction. This can be used for spectroscopic data. Parameters ---------- psffwhm : float Full Width Half Maximum of the PSF to use to generate the kernel. kernsize : int Size of the kernel to calculate. kernsize should be odd. Returned kernel will have size kernsize x kernsize. Returns ------- kernel : float numpy array Gaussian(x) kernel with size kernsize x kernsize. """ kernel = np.zeros((kernsize, kernsize), dtype=np.float32) # Make a grid of x and y values x = np.tile(np.arange(kernsize) - kernsize / 2, (kernsize, 1)) # Calculate the kernel sigma2 = psffwhm * psffwhm / 2.35482 / 2.35482 kernel[:, :] = np.exp(-0.5 * x * x / sigma2)[:, :] # Normalize the kernel kernel /= kernel.sum() return kernel def gaussykernel(float psffwhm, int kernsize): """gaussykernel(psffwhm, kernsize)\n Calculate a Gaussian kernel in the y-direction. This can be used for spectroscopic data. Parameters ---------- psffwhm : float Full Width Half Maximum of the PSF to use to generate the kernel. kernsize : int Size of the kernel to calculate. kernsize should be odd. Returned kernel will have size kernsize x kernsize. Returns ------- kernel : float numpy array Gaussian(y) kernel with size kernsize x kernsize. """ kernel = np.zeros((kernsize, kernsize), dtype=np.float32) # Make a grid of x and y values x = np.tile(np.arange(kernsize) - kernsize / 2, (kernsize, 1)) y = x.transpose().copy() # Calculate the kernel sigma2 = psffwhm * psffwhm / 2.35482 / 2.35482 kernel[:, :] = np.exp(-0.5 * y * y / sigma2)[:, :] # Normalize the kernel kernel /= kernel.sum() return kernel cdef moffatkernel(float psffwhm, float beta, int kernsize): """moffatkernel(psffwhm, beta, kernsize)\n Calculate a Moffat psf kernel. Parameters ---------- psffwhm : float Full Width Half Maximum of the PSF to use to generate the kernel. beta : float Moffat beta parameter kernsize : int Size of the kernel to calculate. Returned kernel will have size kernsize x kernsize. kernsize should be odd. Returns ------- kernel : float numpy array Moffat kernel with size kernsize x kernsize. """ kernel = np.zeros((kernsize, kernsize), dtype=np.float32) # Make a grid of x and y values x = np.tile(np.arange(kernsize) - kernsize / 2, (kernsize, 1)) y = x.transpose().copy() # Calculate the offset r r = np.sqrt(x * x + y * y) # Calculate the kernel hwhm = psffwhm / 2.0 alpha = hwhm / np.sqrt(np.power(2.0, (1.0 / beta)) - 1.0) kernel[:, :] = (np.power(1.0 + (r * r / alpha / alpha), -1.0 * beta))[:, :] # Normalize the kernel. kernel /= kernel.sum() return kernel ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/conftest.py0000644000175100001710000000364500000000000022004 0ustar00runnerdocker00000000000000# This file is used to configure the behavior of pytest when using the Astropy # test infrastructure. It needs to live inside the package in order for it to # get picked up when running the tests inside an interpreter using # packagename.test import os from astropy.version import version as astropy_version # For Astropy 3.0 and later, we can use the standalone pytest plugin if astropy_version < '3.0': from astropy.tests.pytest_plugins import * # noqa del pytest_report_header ASTROPY_HEADER = True else: try: from pytest_astropy_header.display import PYTEST_HEADER_MODULES, TESTED_VERSIONS ASTROPY_HEADER = True except ImportError: ASTROPY_HEADER = False def pytest_configure(config): if ASTROPY_HEADER: config.option.astropy_header = True # Customize the following lines to add/remove entries from the list of # packages for which version numbers are displayed when running the tests. PYTEST_HEADER_MODULES.pop('Pandas', None) PYTEST_HEADER_MODULES['scikit-image'] = 'skimage' from . import __version__ packagename = os.path.basename(os.path.dirname(__file__)) TESTED_VERSIONS[packagename] = __version__ # Uncomment the last two lines in this block to treat all DeprecationWarnings as # exceptions. For Astropy v2.0 or later, there are 2 additional keywords, # as follow (although default should work for most cases). # To ignore some packages that produce deprecation warnings on import # (in addition to 'compiler', 'scipy', 'pygments', 'ipykernel', and # 'setuptools'), add: # modules_to_ignore_on_import=['module_1', 'module_2'] # To ignore some specific deprecation warning messages for Python version # MAJOR.MINOR or later, add: # warnings_to_ignore_by_pyver={(MAJOR, MINOR): ['Message to ignore']} # from astropy.tests.helper import enable_deprecations_as_exceptions # noqa # enable_deprecations_as_exceptions() ././@PaxHeader0000000000000000000000000000003400000000000011452 xustar000000000000000028 mtime=1637317818.3438623 astroscrappy-1.1.0/astroscrappy/tests/0000755000175100001710000000000000000000000020737 5ustar00runnerdocker00000000000000././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/tests/__init__.py0000644000175100001710000000015400000000000023050 0ustar00runnerdocker00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst """ This module contains package tests. """ ././@PaxHeader0000000000000000000000000000003400000000000011452 xustar000000000000000028 mtime=1637317818.3438623 astroscrappy-1.1.0/astroscrappy/tests/data/0000755000175100001710000000000000000000000021650 5ustar00runnerdocker00000000000000././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/tests/data/gmos.fits0000644000175100001710000136730000000000000023516 0ustar00runnerdocker00000000000000SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T COMMENT FITS (Flexible Image Transport System) format is defined in 'AstronomyCOMMENT and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H INSTRUME= 'GMOS-S ' / Instrument used to acquire data OBJECT = 'LTT7379 ' / Object Name OBSTYPE = 'OBJECT ' / Observation type OBSCLASS= 'partnerCal' / Observe class GEMPRGID= 'GS-2019B-Q-305' / Gemini programme ID OBSID = 'GS-2019B-Q-305-7' / Observation ID / Data label DATALAB = 'GS-2019B-Q-305-7-004' / DHS data label OBSERVER= 'J. Chavez' / Observer OBSERVAT= 'Gemini-South' / Name of telescope (Gemini-North|Gemini-South) TELESCOP= 'Gemini-South' / Gemini-North PARALLAX= 0. / Parallax of Target RADVEL = 0. / Heliocentric Radial Velocity EPOCH = 2000. / Epoch for Target coordinates EQUINOX = 2000. / Equinox of coordinate system TRKEQUIN= 2000. / Tracking equinox SSA = 'P. Candia' / SSA RA = 279.108125 / Right Ascension DEC = -44.31025 / Declination of Target ELEVATIO= 66.91005 / Current Elevation AZIMUTH = 133.776055555556 / Current Azimuth CRPA = 0.49316948972528 / Current Cass Rotator Position Angle HA = '-01:33:23.08' / Telescope hour angle LT = '21:42:26.9' / Local time at start of observation TRKFRAME= 'FK5 ' / Tracking co-ordinate DECTRACK= 0. / Differential tracking rate Dec TRKEPOCH= 58703.021628 / Differential tracking reference epoch RATRACK = 0. / Differential tracking rate RA FRAME = 'FK5 ' / Target coordinate system PMDEC = -0.1615 / Proper Motion in Declination PMRA = -0.01603 / Proper Motion in RA WAVELENG= 7400. / Effective Target Wavelength RAWIQ = 'Any ' / Raw Image Quality RAWCC = '70-percentile' / Raw Cloud Cover RAWWV = 'UNKNOWN ' / Raw Water Vapour/Transparency RAWBG = 'UNKNOWN ' / Raw Background RAWPIREQ= 'YES ' / PI Requirements Met RAWGEMQA= 'USABLE ' / Gemini Quality Assessment CGUIDMOD= 'Basic ' / Driving mode for carousel UT = '00:42:27.4' / UT at observation start DATE = '2019-08-08' / UT Date of observation (YYYY-MM-DD) M2BAFFLE= 'VISIBLE ' / Position of M2 baffle M2CENBAF= 'CLOSED ' / Position of M2 central hole baffle ST = '17:04:28.7' / Sidereal time at the start of the exposure XOFFSET = 0.15432377504409 / Telescope offset in x in arcsec YOFFSET = 0.9061870598354 / Telescope offset in y in arcsec POFFSET = -0.156000000459329 / Telescope offset in p in arcsec QOFFSET = -0.905900002656847 / Telescope offset in q in arcsec RAOFFSET= -0.706532415359935 / Telescope offset in RA in arcsec DECOFFSE= 0.588049964716129 / Telescope offset in DEC in arcsec RATRGOFF= 0. / Target offset in RA in arcsec DECTRGOF= 0. / Target offset in DEC in arcsec PA = 120. / Sky Position Angle at start of exposure IAA = 359.894 / Instrument Alignment Angle SFRT2 = -0.417 / Science fold rotation angle (degrees) SFTILT = 44.94 / Science fold tilt angle (degrees) SFLINEAR= -3. / Science fold linear position (mm) AOFOLD = 'park-pos.' / AO Pick-Off Mirror Position PWFS1_ST= 'parked ' / PWFS1 probe state (frozen,guiding,parked) PWFS2_ST= 'parked ' / PWFS2 probe state (frozen,guiding,parked) OIWFS_ST= 'guiding ' / OIWFS probe state (frozen,guiding,parked) AOWFS_ST= 'parked ' / AOWFS probe state (frozen,guiding,parked) SCIBAND = 3 / Science Ranking Band REQIQ = 'Any ' / Requested Image Quality REQCC = 'Any ' / Requested Cloud Cover REQBG = 'Any ' / Requested Background REQWV = 'Any ' / Requested Water Vapour NUMREQTW= 0 / Number of Requested Timing Window REQTW entriesOIARA = 279.16629167 / RA of OIWFS guide star OIARV = 0. / OIWFS Heliocentric Radial Velocity OIAWAVEL= 6500. / OIWFS Effective Target Wavelength OIADEC = -44.3175 / Declination of OIWFS guide star OIAEPOCH= 2000. / Epoch for OIWFS guide star coordinates OIAEQUIN= 2000. / Equinox for OIWFS guide star coordinates OIAFRAME= 'FK5 ' / OIWFS Target co-ordinate system OIAOBJEC= 'GSC0791402580' / Object Name for OIWFS, Chop A OIAPMDEC= 0. / OIWFS Proper Motion in Declination OIAPMRA = 0. / OIWFS Proper Motion in RA OIAPARAL= 0. / OIWFS Parallax of Target OIFREQ = 200. / OIWFS sampling frequency (Hz) HUMIDITY= 23. / The Relative Humidity (fraction, 0..101). TAMBIENT= 4.3 / The ambient temp (C). TAMBIEN2= 39.74 / The ambient temp (F). PRESSURE= 544.70328 / The atmospheric pressure (mm Hg). PRESSUR2= 72600. / The atmospheric pressure (Pa). DEWPOINT= -15.3 / The dew point (C). DEWPOIN2= 4.46 / The dew point (F). WINDSPEE= 14.1 / The wind speed (m/s). WINDSPE2= 31.5417 / The wind speed (mph). WINDDIRE= 301. / The wind direction (degrees). INPORT = 3 / Number of ISS port where GMOS was located GMOSCC = 'GMOS-CP (V6-9)' / GMOS components controller s/w TIME-OBS= '00:42:27.4' / Time of observation PREIMAGE= F / MOS pre-imaging CONID = 'ARC-III ' / Detector controller ID DETECTOR= 'GMOS + Hamamatsu_new' / Detector name DEWAR = 'Lab Cryostat' / Dewar name ARRYTMPD= -100.998 / Array temperature D (Celsius) ARRYTMPA= -94.113 / Array temperature A (Celsius) ARRYTMPB= -94.379 / Array temperature B (Celsius) ARRYTMPC= -94.095 / Array temperature C (Celsius) ARRYTSET= -101. / Array temperature Setpoint (Celsius) DETSIZE = '[1:6144,1:4224]' / Detector size NCCDS = 3 / Number of CCD chips NAMPS = 4 / Number of amplifiers SHUTTER = 'OPEN ' / Shutter state during observation AMPINTEG= 11880 / Amplifier integration time OBSEPOCH= 2019.59898560676 / Epoch at start of exposure TIMESYS = 'UTC ' / Time system used DATE-OBS= '2019-08-08' / UT Date of observation (YYYY-MM-DD) GMOSTHT = 'WARNING ' / Shutter health NSUBEXP = 1 / Number of sub exposures UTSTART = '00:42:29.7167' / UT at observation start UTEND = '00:43:59.7167' / UT at observation end EXPTIME = 90. / Exposure time in seconds ELAPSED = 90. / Elapsed observation time in seconds DARKTIME= 98.5500249862671 / Dark current integration in seconds MASKID = 10005374 / Mask/IFU barcode MASKNAME= '1.0arcsec' / Mask name MASKTYP = 1 / Mask/IFU type (0=none/-1=IFU/1=mask) MASKLOC = 0 / Mask/IFU location (-1=unknown/0=FP/1=cassette) FILTER1 = 'GG455_G0329' / Filter 1 name FILTID1 = 20000044 / Filter 1 barcode FILTER2 = 'open2-8 ' / Filter 2 name FILTID2 = 20000041 / Filter 2 barcode GRATING = 'R400+_G5325' / Grating name GRATID = 30000019 / Grating barcode GRWLEN = 740. / Grating wavelength at slit (nm) CENTWAVE= 740. / Central wavelength (nm) GRORDER = 1 / Grating order GRTILT = 55.6015 / Grating tilt angle (degrees) GRSTEP = 5891.3684 / Requested grating motor step position DTAX = 42. / Detector translation X position (microns) DTAY = 127.68 / Detector translation Y position (microns) DTAZ = 1068. / Detector translation Z position (microns) DTAZST = 1068. / Focus at observation start (microns) DTAZEN = 1068. / Focus at observation end (microns) DTAZME = 1650. / Mean focus during observation (microns) DTMODE = 'FOLLOW ' / Detector translation stage mode ADCMODE = ' ' / ADC mode GMOSDC = 'gmosdc-6.4AT1.6' / GMOS detector controller s/w DETTYPE = 'S10892 ' / Detector array type DETID = 'BI5-36-4k-2,BI11-33-4k-1,BI12-34-4k-1' / Chip IDs EXPOSURE= 90. / Requested exposure time in seconds ADCUSED = 1 / ADC used? (0=yes/1=no) DETNROI = 1 / No. regions of interest ADCENPST= 0. / Start entrance prism angle of ADC ADCENPEN= 0. / End entrance prism angle of ADC ADCENPME= 0. / Mean entrance prism angle of ADC ADCEXPST= 0. / Start exit prism angle of ADC ADCEXPEN= 0. / End exit prism angle of ADC ADCEXPME= 0. / Mean exit prism angle of ADC ADCWLEN1= 0. / Lower wavelength for ADC calculation ADCWLEN2= 0. / Upper wavelength for ADC calculation DETRO1X = 1 / ROI 1 X start DETRO1XS= 3072 / ROI 1 X size DETRO1Y = 1625 / ROI 1 Y start DETRO1YS= 512 / ROI 1 Y size AIRMASS = 1.086 / Mean airmass for the observation AMSTART = 1.087 / Airmass at start of exposure AMEND = 1.085 / Airmass at end of exposure PROP_MD = F / Proprietary Metadata RELEASE = '2019-08-08' / End of proprietary period YYY-MM-DD ORIGNAME= 'S20190808S0048.fits' / Original filename prior to processing GEM-TLM = '2020-10-28T11:30:19' / UT last modification with GEMINI VALDATA = '2020-10-28T11:30:18' / UT time stamp for validateData ADDMDF = '2020-10-28T11:30:18' / UT time stamp for addMDF SDZSTRUC= '2020-10-28T11:30:18' / UT time stamp for standardizeStructure NSCIEXT = 12 / Number of science extensions SDZHDRSG= '2020-10-28T11:30:18' / UT time stamp for standardizeObservatoryHeaderSDZHDRSI= '2020-10-28T11:30:18' / UT time stamp for standardizeInstrumentHeadersSDZWCS = '2020-10-28T11:30:18' / UT time stamp for standardizeWCS PREPARE = '2020-10-28T11:30:18' / UT time stamp for PREPARE ADDDQ = '2020-10-28T11:30:18' / UT time stamp for addDQ ADDVAR = '2020-10-28T11:30:19' / UT time stamp for addVAR SUBOVER = '2020-10-28T11:30:19' / UT time stamp for subtractOverscan TRIMMED = 'yes ' / Overscan section trimmed TRIMOVER= '2020-10-28T11:30:19' / UT time stamp for trimOverscan ADUTOELE= '2020-10-28T11:30:19' / UT time stamp for ADUToElectrons MOSAIC = '2020-10-28T11:30:19' / UT time stamp for mosaicDetectors END XTENSION= 'IMAGE ' / Image extension BITPIX = -32 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 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fwhm): x = np.arange(image_shape[1]) y = np.arange(image_shape[0]) x2d, y2d = np.meshgrid(x, y) sig = fwhm / 2.35482 normfactor = brightness / 2.0 / np.pi * sig ** -2.0 exponent = -0.5 * sig ** -2.0 exponent *= (x2d - x0) ** 2.0 + (y2d - y0) ** 2.0 return normfactor * np.exp(exponent) def make_fake_data(): """ Generate fake data that can be used to test the detection and cleaning algorithms Returns ------- imdata : numpy float array Fake Image data crmask : numpy boolean array Boolean mask of locations of injected cosmic rays """ # Set a seed so that the tests are repeatable np.random.seed(200) # Create a simulated image to use in our tests imdata = np.zeros((1001, 1001), dtype=np.float32) # Add sky and sky noise imdata += 200 # Add some fake sources for i in range(100): x = np.random.uniform(low=0.0, high=1001) y = np.random.uniform(low=0.0, high=1001) brightness = np.random.uniform(low=1000., high=30000.) imdata += gaussian(imdata.shape, x, y, brightness, 3.5) # Add the poisson noise imdata = np.float32(np.random.poisson(imdata)) # Add readnoise imdata += np.random.normal(0.0, 10.0, size=(1001, 1001)) # Add 100 fake cosmic rays cr_x = np.random.randint(low=5, high=995, size=100) cr_y = np.random.randint(low=5, high=995, size=100) cr_brightnesses = np.random.uniform(low=1000.0, high=30000.0, size=100) imdata[cr_y, cr_x] += cr_brightnesses imdata = imdata.astype('f4') # Make a mask where the detected cosmic rays should be crmask = np.zeros((1001, 1001), dtype=bool) crmask[cr_y, cr_x] = True return imdata, crmask ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/tests/test_astroscrappy.py0000644000175100001710000000110300000000000025075 0ustar00runnerdocker00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst from ..astroscrappy import detect_cosmics from . import fake_data def test_main(): imdata, expected_crmask = fake_data.make_fake_data() # Because our image only contains single cosmics, turn off # neighbor detection. Also, our cosmic rays are high enough # contrast that we can turn our detection threshold up. mask, _clean = detect_cosmics(imdata, readnoise=10., gain=1.0, sigclip=6, sigfrac=1.0) assert (mask == expected_crmask).sum() == (1001 * 1001) ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/tests/test_cleaning.py0000644000175100001710000000556700000000000024145 0ustar00runnerdocker00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst from ..astroscrappy import detect_cosmics from . import fake_data # Get fake data to work on imdata, crmask = fake_data.make_fake_data() def test_median_clean(): # Because our image only contains single cosmics, turn off # neighbor detection. Also, our cosmic rays are high enough # contrast that we can turn our detection threshold up. _mask, clean = detect_cosmics(imdata, readnoise=10., gain=1.0, sigclip=6, sigfrac=1.0, cleantype='median') assert (clean[crmask] != imdata[crmask]).sum() == crmask.sum() # Run it again on the clean data. We shouldn't find any new cosmic rays _mask2, _clean2 = detect_cosmics(clean, readnoise=10., gain=1.0, sigclip=6, sigfrac=1.0, cleantype='median') assert _mask2.sum() == 0 def test_medmask_clean(): # Because our image only contains single cosmics, turn off # neighbor detection. Also, our cosmic rays are high enough # contrast that we can turn our detection threshold up. _mask, clean = detect_cosmics(imdata, readnoise=10., gain=1.0, sigclip=6, sigfrac=1.0, cleantype='medmask') assert (clean[crmask] != imdata[crmask]).sum() == crmask.sum() # Run it again on the clean data. We shouldn't find any new cosmic rays _mask2, _clean2 = detect_cosmics(clean, readnoise=10., gain=1.0, sigclip=6, sigfrac=1.0, cleantype='medmask') assert _mask2.sum() == 0 def test_meanmask_clean(): # Because our image only contains single cosmics, turn off # neighbor detection. Also, our cosmic rays are high enough # contrast that we can turn our detection threshold up. _mask, clean = detect_cosmics(imdata, readnoise=10., gain=1.0, sigclip=6, sigfrac=1.0, cleantype='meanmask') assert (clean[crmask] != imdata[crmask]).sum() == crmask.sum() # Run it again on the clean data. We shouldn't find any new cosmic rays _mask2, _clean2 = detect_cosmics(clean, readnoise=10., gain=1.0, sigclip=6, sigfrac=1.0, cleantype='meanmask') assert _mask2.sum() == 0 def test_idw_clean(): # Because our image only contains single cosmics, turn off # neighbor detection. Also, our cosmic rays are high enough # contrast that we can turn our detection threshold up. _mask, clean = detect_cosmics(imdata, readnoise=10., gain=1.0, sigclip=6, sigfrac=1.0, cleantype='idw') assert (clean[crmask] != imdata[crmask]).sum() == crmask.sum() # Run it again on the clean data. We shouldn't find any new cosmic rays _mask2, _clean2 = detect_cosmics(clean, readnoise=10., gain=1.0, sigclip=6, sigfrac=1.0, cleantype='idw') assert _mask2.sum() == 0 ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/tests/test_gmos.py0000644000175100001710000000302600000000000023316 0ustar00runnerdocker00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst import os import numpy as np import scipy.ndimage as ndi from astropy.io import fits from ..astroscrappy import detect_cosmics TESTFILE = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'data', 'gmos.fits') def test_gmos(): """This test uses a small cutout from a standard observation with GMOS (S20190808S0048). The extracted region is [362:, 480:680], and the file has been reduced with DRAGONS. """ with fits.open(TESTFILE) as hdul: data = hdul['SCI'].data var = hdul['VAR'].data sky = hdul['SKYFIT'].data m1, _ = detect_cosmics(data, readnoise=4.24, gain=1.933) m2, _ = detect_cosmics(data, inbkg=sky, readnoise=4.24, gain=1.933) m3, _ = detect_cosmics(data, inbkg=sky, invar=var, readnoise=4.24, gain=1.933) cosmic1 = (slice(41, 72), slice(142, 161)) cosmic2 = (slice(117, 147), slice(35, 43)) # We must find 2 cosmic rays, but m1 (without bkg and var) also flags # 2 additional pixels that are identified as independent regions label, nb = ndi.label(m1) assert nb == 4 objects = ndi.find_objects(label) assert cosmic1 in objects assert cosmic2 in objects areas = sorted([np.sum(label == (i+1)) for i in range(nb)]) assert areas == [1, 1, 74, 93] for mask in m2, m3: label, nb = ndi.label(mask) assert nb == 2 objects = ndi.find_objects(label) assert objects[0] == cosmic1 assert objects[1] == cosmic2 ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/tests/test_utils.py0000644000175100001710000001605400000000000023516 0ustar00runnerdocker00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst import numpy as np from numpy.testing import assert_allclose from ..utils import (median, optmed3, optmed5, optmed7, optmed9, optmed25, medfilt3, medfilt5, medfilt7, sepmedfilt3, sepmedfilt5, sepmedfilt7, sepmedfilt9, dilate3, dilate5, subsample, rebin, laplaceconvolve, convolve) from scipy.ndimage.morphology import binary_dilation from scipy import ndimage def test_median(): a = np.ascontiguousarray(np.random.random(1001)).astype('f4') assert np.float32(np.median(a)) == np.float32(median(a, 1001)) def test_optmed3(): a = np.ascontiguousarray(np.random.random(3)).astype('f4') assert np.float32(np.median(a)) == np.float32(optmed3(a)) def test_optmed5(): a = np.ascontiguousarray(np.random.random(5)).astype('f4') assert np.float32(np.median(a)) == np.float32(optmed5(a)) def test_optmed7(): a = np.ascontiguousarray(np.random.random(7)).astype('f4') assert np.float32(np.median(a)) == np.float32(optmed7(a)) def test_optmed9(): a = np.ascontiguousarray(np.random.random(9)).astype('f4') assert np.float32(np.median(a)) == np.float32(optmed9(a)) def test_optmed25(): a = np.ascontiguousarray(np.random.random(25)).astype('f4') assert np.float32(np.median(a)) == np.float32(optmed25(a)) def test_medfilt3(): a = np.ascontiguousarray(np.random.random((1001, 1001))).astype('f4') npmed3 = ndimage.filters.median_filter(a, size=(3, 3), mode='nearest') npmed3[:1, :] = a[:1, :] npmed3[-1:, :] = a[-1:, :] npmed3[:, :1] = a[:, :1] npmed3[:, -1:] = a[:, -1:] med3 = medfilt3(a) assert np.all(med3 == npmed3) def test_medfilt5(): a = np.ascontiguousarray(np.random.random((1001, 1001))).astype('f4') npmed5 = ndimage.filters.median_filter(a, size=(5, 5), mode='nearest') npmed5[:2, :] = a[:2, :] npmed5[-2:, :] = a[-2:, :] npmed5[:, :2] = a[:, :2] npmed5[:, -2:] = a[:, -2:] med5 = medfilt5(a) assert np.all(med5 == npmed5) def test_medfilt7(): a = np.ascontiguousarray(np.random.random((1001, 1001))).astype('f4') npmed7 = ndimage.filters.median_filter(a, size=(7, 7), mode='nearest') npmed7[:3, :] = a[:3, :] npmed7[-3:, :] = a[-3:, :] npmed7[:, :3] = a[:, :3] npmed7[:, -3:] = a[:, -3:] med7 = medfilt7(a) assert np.all(med7 == npmed7) def test_sepmedfilt3(): a = np.ascontiguousarray(np.random.random((1001, 1001))).astype('f4') npmed3 = ndimage.filters.median_filter(a, size=(1, 3), mode='nearest') npmed3[:, :1] = a[:, :1] npmed3[:, -1:] = a[:, -1:] npmed3 = ndimage.filters.median_filter(npmed3, size=(3, 1), mode='nearest') npmed3[:1, :] = a[:1, :] npmed3[-1:, :] = a[-1:, :] npmed3[:, :1] = a[:, :1] npmed3[:, -1:] = a[:, -1:] med3 = sepmedfilt3(a) assert np.all(med3 == npmed3) def test_sepmedfilt5(): a = np.ascontiguousarray(np.random.random((1001, 1001))).astype('f4') npmed5 = ndimage.filters.median_filter(a, size=(1, 5), mode='nearest') npmed5[:, :2] = a[:, :2] npmed5[:, -2:] = a[:, -2:] npmed5 = ndimage.filters.median_filter(npmed5, size=(5, 1), mode='nearest') npmed5[:2, :] = a[:2, :] npmed5[-2:, :] = a[-2:, :] npmed5[:, :2] = a[:, :2] npmed5[:, -2:] = a[:, -2:] med5 = sepmedfilt5(a) assert np.all(med5 == npmed5) def test_sepmedfilt7(): a = np.ascontiguousarray(np.random.random((1001, 1001))).astype('f4') npmed7 = ndimage.filters.median_filter(a, size=(1, 7), mode='nearest') npmed7[:, :3] = a[:, :3] npmed7[:, -3:] = a[:, -3:] npmed7 = ndimage.filters.median_filter(npmed7, size=(7, 1), mode='nearest') npmed7[:3, :] = a[:3, :] npmed7[-3:, :] = a[-3:, :] npmed7[:, :3] = a[:, :3] npmed7[:, -3:] = a[:, -3:] med7 = sepmedfilt7(a) assert np.all(med7 == npmed7) def test_sepmedfilt9(): a = np.ascontiguousarray(np.random.random((1001, 1001))).astype('f4') npmed9 = ndimage.filters.median_filter(a, size=(1, 9), mode='nearest') npmed9[:, :4] = a[:, :4] npmed9[:, -4:] = a[:, -4:] npmed9 = ndimage.filters.median_filter(npmed9, size=(9, 1), mode='nearest') npmed9[:4, :] = a[:4, :] npmed9[-4:, :] = a[-4:, :] npmed9[:, :4] = a[:, :4] npmed9[:, -4:] = a[:, -4:] med9 = sepmedfilt9(a) assert np.all(med9 == npmed9) def test_dilate5(): # Put 5% of the pixels into a mask a = np.zeros((1001, 1001), dtype=bool) a[np.random.random((1001, 1001)) < 0.05] = True kernel = np.ones((5, 5)) kernel[0, 0] = 0 kernel[0, 4] = 0 kernel[4, 0] = 0 kernel[4, 4] = 0 # Make a zero padded array for the numpy version to operate paddeda = np.zeros((1005, 1005), dtype=bool) paddeda[2:-2, 2:-2] = a[:, :] npdilate = binary_dilation(np.ascontiguousarray(paddeda), structure=kernel, iterations=2) cdilate = dilate5(a, 2) assert np.all(npdilate[2:-2, 2:-2] == cdilate) def test_dilate3(): # Put 5% of the pixels into a mask a = np.zeros((1001, 1001), dtype=bool) a[np.random.random((1001, 1001)) < 0.05] = True kernel = np.ones((3, 3)) npgrow = binary_dilation(np.ascontiguousarray(a), structure=kernel, iterations=1) cgrow = dilate3(a) npgrow[:, 0] = a[:, 0] npgrow[:, -1] = a[:, -1] npgrow[0, :] = a[0, :] npgrow[-1, :] = a[-1, :] assert np.all(npgrow == cgrow) def test_subsample(): a = np.ascontiguousarray(np.random.random((1001, 1001))).astype('f4') npsubsamp = np.zeros((a.shape[0] * 2, a.shape[1] * 2), dtype=np.float32) for i in range(a.shape[0]): for j in range(a.shape[1]): npsubsamp[2 * i, 2 * j] = a[i, j] npsubsamp[2 * i + 1, 2 * j] = a[i, j] npsubsamp[2 * i, 2 * j + 1] = a[i, j] npsubsamp[2 * i + 1, 2 * j + 1] = a[i, j] csubsamp = subsample(a) assert np.all(npsubsamp == csubsamp) def test_rebin(): a = np.ascontiguousarray(np.random.random((2002, 2002)), dtype=np.float32) a = a.astype('f4') nprebin = np.zeros((1001, 1001), dtype=np.float32).astype('f4') for i in range(1001): for j in range(1001): nprebin[i, j] = (a[2 * i, 2 * j] + a[2 * i + 1, 2 * j] + a[2 * i, 2 * j + 1] + a[2 * i + 1, 2 * j + 1]) nprebin[i, j] /= np.float32(4.0) crebin = rebin(a) assert_allclose(crebin, nprebin, rtol=0, atol=1.e-6) def test_laplaceconvolve(): a = np.ascontiguousarray(np.random.random((1001, 1001))).astype('f4') k = np.array([[0.0, -1.0, 0.0], [-1.0, 4.0, -1.0], [0.0, -1.0, 0.0]]) k = k.astype(' np.PyArray_DATA(dsub) cdef float * outdsubptr = < float * > np.PyArray_DATA(output) with nogil: PySubsample(dsubptr, outdsubptr, nx, ny) return output def rebin(np.ndarray[np.float32_t, ndim=2, mode='c', cast=True] drebin): """rebin(drebin)\n Rebin an array 2x2. Rebin the array by block averaging 4 pixels back into 1. Parameters ---------- drebin : float numpy array Array to be rebinned 2x2. Returns ------- output : float numpy array Rebinned array. The size of the output array will be 2 times smaller than drebin. Notes ----- This is effectively the opposite of subsample (although subsample does not do an average). The array needs to be C-contiguous order. Wrapper for PyRebin in imutils. """ cdef int nx = drebin.shape[1] / 2 cdef int ny = drebin.shape[0] / 2 # Allocate the output array here so that Python tracks the memory and will # free the memory when we are finished with the output array. output = np.zeros((ny, nx), dtype=np.float32) cdef float * drebinptr = < float * > np.PyArray_DATA(drebin) cdef float * outdrebinptr = < float * > np.PyArray_DATA(output) with nogil: PyRebin(drebinptr, outdrebinptr, nx, ny) return output def convolve(np.ndarray[np.float32_t, ndim=2, mode='c', cast=True] dconv, np.ndarray[np.float32_t, ndim=2, mode='c', cast=True] kernel): """convolve(dconv, kernel)\n Convolve an array with a kernel. Parameters ---------- dconv : float numpy array Array to be convolved. kernel : float numpy array Kernel to use in the convolution. Returns ------- output : float numpy array Convolved array. Notes ----- Both the data and kernel arrays need to be C-contiguous order. Wrapper for PyConvolve in imutils. """ cdef int nx = dconv.shape[1] cdef int ny = dconv.shape[0] # Allocate the output array here so that Python tracks the memory and will # free the memory when we are finished with the output array. output = np.zeros((ny, nx), dtype=np.float32) cdef float * dconvptr = < float * > np.PyArray_DATA(dconv) cdef float * outdconvptr = < float * > np.PyArray_DATA(output) cdef int knx = kernel.shape[1] cdef int kny = kernel.shape[0] cdef float * kernptr = < float * > np.PyArray_DATA(kernel) with nogil: PyConvolve(dconvptr, kernptr, outdconvptr, nx, ny, knx, kny) return output def laplaceconvolve(np.ndarray[np.float32_t, ndim=2, mode='c', cast=True] dl): """laplaceconvolve(dl)\n Convolve an array with the Laplacian kernel. Convolve with the discrete version of the Laplacian operator with kernel:\n 0 -1 0\n -1 4 -1\n 0 -1 0\n Parameters ---------- dl : float numpy array Array to be convolved. Returns ------- output: float numpy array Convolved array. Notes ----- The array needs to be C-contiguous order. Wrapper for PyLaplaceConvolve in imutils. """ cdef int nx = dl.shape[1] cdef int ny = dl.shape[0] # Allocate the output array here so that Python tracks the memory and will # free the memory when we are finished with the output array. output = np.zeros((ny, nx), dtype=np.float32) cdef float * dlapptr = < float * > np.PyArray_DATA(dl) cdef float * outdlapptr = < float * > np.PyArray_DATA(output) with nogil: PyLaplaceConvolve(dlapptr, outdlapptr, nx, ny) return output def dilate3(np.ndarray[np.uint8_t, ndim=2, mode='c', cast=True] dgrow): """dilate3(dgrow)\n Perform a boolean dilation on an array. Parameters ---------- dgrow : boolean numpy array Array to dilate. Returns ------- output : boolean numpy array Dilated array. Notes ----- Dilation is the boolean equivalent of a convolution but using logical ors instead of a sum. We apply the following kernel:\n 1 1 1\n 1 1 1\n 1 1 1\n The binary dilation is not computed for a 1 pixel border around the image. These pixels are copied from the input data. The array needs to be C-contiguous order. Wrapper for PyDilate3 in imutils. """ cdef int nx = dgrow.shape[1] cdef int ny = dgrow.shape[0] # Allocate the output array here so that Python tracks the memory and will # free the memory when we are finished with the output array. output = np.zeros((ny, nx), dtype=np.bool_) cdef uint8_t * dgrowptr = < uint8_t * > np.PyArray_DATA(dgrow) cdef uint8_t * outdgrowptr = < uint8_t * > np.PyArray_DATA(output) with nogil: PyDilate3(dgrowptr, outdgrowptr, nx, ny) return output def dilate5(np.ndarray[np.uint8_t, ndim=2, mode='c', cast=True] ddilate, int niter): """dilate5(data, niter)\n Do niter iterations of boolean dilation on an array. Parameters ---------- ddilate : boolean numpy array Array to dilate. niter : int Number of iterations. Returns ------- output : boolean numpy array Dilated array. Notes ----- Dilation is the boolean equivalent of a convolution but using logical ors instead of a sum. We apply the following kernel:\n 0 1 1 1 0\n 1 1 1 1 1\n 1 1 1 1 1\n 1 1 1 1 1\n 0 1 1 1 0\n The edges are padded with zeros so that the dilation operator is defined for all pixels. The array needs to be C-contiguous order. Wrapper for PyDilate5 in imutils. """ cdef int nx = ddilate.shape[1] cdef int ny = ddilate.shape[0] # Allocate the output array here so that Python tracks the memory and will # free the memory when we are finished with the output array. output = np.zeros((ny, nx), dtype=bool) cdef uint8_t * ddilateptr = < uint8_t * > np.PyArray_DATA(ddilate) cdef uint8_t * outddilateptr = < uint8_t * > np.PyArray_DATA(output) with nogil: PyDilate5(ddilateptr, outddilateptr, niter, nx, ny) return output ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/utils/imutils.c0000644000175100001710000004762400000000000022604 0ustar00runnerdocker00000000000000/* * Author: Curtis McCully * October 2014 * Licensed under a 3-clause BSD style license - see LICENSE.rst * * Originally written in C++ in 2011 * See also https://github.com/cmccully/lacosmicx * * This file contains image utility functions for SCRAPPY. These are the most * computationally expensive pieces of the calculation so they have been ported * to C. * * Many thanks to Nicolas Devillard who wrote the optimized methods for finding * the median and placed them in the public domain. I have noted in the * comments places that use Nicolas Devillard's code. * * Parallelization has been achieved using OpenMP. Using a compiler that does * not support OpenMP, e.g. clang currently, the code should still compile and * run serially without issue. I have tried to be explicit as possible about * specifying which variables are private and which should be shared, although * we never actually have any shared variables. We use firstprivate instead. * This does mean that it is important that we never have two threads write to * the same memory position at the same time. * * All calculations are done with 32 bit floats to keep the memory footprint * small. */ #include #include "imutils.h" /* Subsample an array 2x2 given an input array data with size nx x ny. Each * pixel is replicated into 4 pixels; no averaging is performed. The results * are saved in the output array. The output array should already be allocated * as we work on it in place. Data should be striped in the x direction such * that the memory location of pixel i,j is data[nx *j + i]. */ void PySubsample(float* data, float* output, int nx, int ny) { PyDoc_STRVAR(PySubsample__doc__, "PySubample(data, output, nx, ny) -> void\n\n" "Subsample an array 2x2 given an input array data with size " "nx x ny.The results are saved in the output array. The output " "array should already be allocated as we work on it in place. Each" " pixel is replicated into 4 pixels; no averaging is performed. " "Data should be striped in the x direction such that the memory " "location of pixel i,j is data[nx *j + i]."); /* Precalculate the new length; minor optimization */ int padnx = 2 * nx; /* Loop indices */ int i, j, nxj, padnxj; /* Loop over all pixels */ #pragma omp parallel for firstprivate(data, output, nx, ny, padnx) \ private(i, j, nxj, padnxj) for (j = 0; j < ny; j++) { nxj = nx * j; padnxj = 2 * padnx * j; for (i = 0; i < nx; i++) { /* Copy the pixel value into a 2x2 grid on the output image */ output[2 * i + padnxj] = data[i + nxj]; output[2 * i + padnxj + padnx] = data[i + nxj]; output[2 * i + 1 + padnxj + padnx] = data[i + nxj]; output[2 * i + 1 + padnxj] = data[i + nxj]; } } return; } /* Rebin an array 2x2, with size (2 * nx) x (2 * ny). Rebin the array by block * averaging 4 pixels back into 1. This is effectively the opposite of * subsample (although subsample does not do an average). The results are saved * in the output array. The output array should already be allocated as we work * on it in place. Data should be striped in the x direction such that the * memory location of pixel i,j is data[nx *j + i]. */ void PyRebin(float* data, float* output, int nx, int ny) { PyDoc_STRVAR(PyRebin__doc__, "PyRebin(data, output, nx, ny) -> void\n \n" "Rebin an array 2x2, with size (2 * nx) x (2 * ny). Rebin the " "array by block averaging 4 pixels back into 1. This is " "effectively the opposite of subsample (although subsample does " "not do an average). The results are saved in the output array. " "The output array should already be allocated as we work on it in " "place. Data should be striped in the x direction such that the " "memory location of pixel i,j is data[nx *j + i]."); /* Size of original array */ int padnx = nx * 2; /* Loop variables */ int i, j, nxj, padnxj; /* Pixel value p. Each thread needs its own copy of this variable so we * wait to initialize it until the pragma below */ float p; #pragma omp parallel for firstprivate(output, data, nx, ny, padnx) \ private(i, j, nxj, padnxj, p) /*Loop over all of the pixels */ for (j = 0; j < ny; j++) { nxj = nx * j; padnxj = 2 * padnx * j; for (i = 0; i < nx; i++) { p = data[2 * i + padnxj]; p += data[2 * i + padnxj + padnx]; p += data[2 * i + 1 + padnxj + padnx]; p += data[2 * i + 1 + padnxj]; p /= 4.0; output[i + nxj] = p; } } return; } /* Convolve an image of size nx x ny with a kernel of size kernx x kerny. The * results are saved in the output array. The output array should already be * allocated as we work on it in place. Data and kernel should both be striped * in the x direction such that the memory location of pixel i,j is * data[nx *j + i]. */ void PyConvolve(float* data, float* kernel, float* output, int nx, int ny, int kernx, int kerny) { PyDoc_STRVAR(PyConvolve__doc__, "PyConvolve(data, kernel, output, nx, ny, kernx, kerny) -> void\n\n" "Convolve an image of size nx x ny with a a kernel of size " "kernx x kerny. The results are saved in the output array. The " "output array should already be allocated as we work on it in " "place. Data and kernel should both be striped along the x " "direction such that the memory location of pixel i,j is " "data[nx *j + i]."); /* Get the width of the borders that we will pad with zeros */ int bnx = (kernx - 1) / 2; int bny = (kerny - 1) / 2; /* Calculate the dimensions of the array including padded border */ int padnx = nx + kernx - 1; int padny = ny + kerny - 1; /* Get the total number of pixels in the padded array */ int padnxny = padnx * padny; /*Get the total number of pixels in the output image */ int nxny = nx * ny; /*Allocate the padded array */ float* padarr = (float *) malloc(padnxny * sizeof(float)); /* Loop variables. These should all be thread private. */ int i, j; int nxj; int padnxj; /* Inner loop variables. Again thread private. */ int k, l; int kernxl, padnxl; /* Define a sum variable to use in the convolution calculation. Each * thread needs its own copy of this so it should be thread private. */ float sum; /* Precompute maximum good index in each dimension */ int xmaxgood = nx + bnx; int ymaxgood = ny + bny; /* Set the borders of padarr = 0.0 * Fill the rest of the padded array with the input data. */ #pragma omp parallel for \ firstprivate(padarr, data, nx, padnx, padny, bnx, bny, xmaxgood, ymaxgood)\ private(nxj, padnxj, i, j) for (j = 0; j < padny; j++) { padnxj = padnx * j; nxj = nx * (j - bny); for (i = 0; i < padnx; i++) { if (i < bnx || j < bny || j >= ymaxgood || i >= xmaxgood) { padarr[padnxj + i] = 0.0; } else { padarr[padnxj + i] = data[nxj + i - bnx]; } } } /* Calculate the convolution */ /* Loop over all pixels */ #pragma omp parallel for \ firstprivate(padarr, output, nx, ny, padnx, bnx, bny, kernx) \ private(nxj, padnxj, kernxl, padnxl, i, j, k, l, sum) for (j = 0; j < ny; j++) { nxj = nx * j; /* Note the + bvy in padnxj */ padnxj = padnx * (j + bny); for (i = 0; i < nx; i++) { sum = 0.0; /* Note that the sums in the definition of the convolution go from * -border width to + border width */ for (l = -bny; l <= bny; l++) { padnxl = padnx * (l + j + bny); kernxl = kernx * (-l + bny); for (k = -bnx; k <= bnx; k++) { sum += kernel[bnx - k + kernxl] * padarr[padnxl + k + i + bnx]; } } output[nxj + i] = sum; } } free(padarr); return; } /* Convolve an image of size nx x ny the following kernel: * 0 -1 0 * -1 4 -1 * 0 -1 0 * The results are saved in the output array. The output array should * already be allocated as we work on it in place. * This is a discrete version of the Laplacian operator. * Data should be striped in the x direction such that the memory location of * pixel i,j is data[nx *j + i]. */ void PyLaplaceConvolve(float* data, float* output, int nx, int ny) { PyDoc_STRVAR(PyLaplaceConvolve__doc__, "PyLaplaceConvolve(data, output, nx, ny) -> void\n\n" "Convolve an image of size nx x ny the following kernel:\n" " 0 -1 0\n" "-1 4 -1\n" " 0 -1 0\n" "This is a discrete version of the Laplacian operator. The results" " are saved in the output array. The output array should already " "be allocated as we work on it in place.Data should be striped in " "the x direction such that the memory location of pixel i,j is " "data[nx *j + i]."); /* Precompute the total number of pixels in the image */ int nxny = nx * ny; /* Loop variables */ int i, j, nxj; /* Pixel value p. Each thread will need its own copy of this so we need to * make it private*/ float p; /* Because we know the form of the kernel, we can short circuit the * convolution and calculate the results with inner nest for loops. */ /*Loop over all of the pixels except the edges which we will do explicitly * below */ #pragma omp parallel for firstprivate(nx, ny, output, data) \ private(i, j, nxj, p) for (j = 1; j < ny - 1; j++) { nxj = nx * j; for (i = 1; i < nx - 1; i++) { p = 4.0 * data[nxj + i]; p -= data[i + 1 + nxj]; p -= data[i - 1 + nxj]; p -= data[i + nxj + nx]; p -= data[i + nxj - nx]; output[nxj + i] = p; } } /* Leave the corners until the very end */ #pragma omp parallel firstprivate(output, data, nx, nxny) private(i) /* Top and Bottom Rows */ for (i = 1; i < nx - 1; i++) { output[i] = 4.0 * data[i] - data[i + 1] - data[i - 1] - data[i + nx]; p = 4.0 * data[i + nxny - nx]; p -= data[i + 1 + nxny - nx]; p -= data[i + nxny - nx - 1]; p -= data[i - nx + nxny - nx]; output[i + nxny - nx] = p; } #pragma omp parallel firstprivate(output, data, nx, ny) private(j, nxj) /* First and Last Column */ for (j = 1; j < ny - 1; j++) { nxj = nx * j; p = 4.0 * data[nxj]; p -= data[nxj + 1]; p -= data[nxj + nx]; p -= data[nxj - nx]; output[nxj] = p; p = 4.0 * data[nxj + nx - 1]; p -= data[nxj + nx - 2]; p -= data[nxj + nx + nx - 1]; p -= data[nxj - 1]; output[nxj + nx - 1] = p; } /* Bottom Left Corner */ output[0] = 4.0 * data[0] - data[1] - data[nx]; /* Bottom Right Corner */ output[nx - 1] = 4.0 * data[nx - 1] - data[nx - 2] - data[nx + nx - 1]; /* Top Left Corner */ p = 4.0 * data[nxny - nx]; p -= data[nxny - nx + 1]; p -= data[nxny - nx - nx]; output[nxny - nx] = p; /* Top Right Corner */ p = 4.0 * data[nxny - 1]; p -= data[nxny - 2]; p -= data[nxny - 1 - nx]; output[nxny - 1] = p; return; } /* Perform a boolean dilation on an array of size nx x ny. The results are * saved in the output array. The output array should already be allocated as * we work on it in place. * Dilation is the boolean equivalent of a convolution but using logical ors * instead of a sum. * We apply the following kernel: * 1 1 1 * 1 1 1 * 1 1 1 * The binary dilation is not computed for a 1 pixel border around the image. * These pixels are copied from the input data. Data should be striped along * the x direction such that the memory location of pixel i,j is * data[i + nx * j]. */ void PyDilate3(bool* data, bool* output, int nx, int ny) { PyDoc_STRVAR(PyDilate3__doc__, "PyDilate3(data, output, nx, ny) -> void\n\n" "Perform a boolean dilation on an array of size nx x ny. The " "results are saved in the output array which should already be " "allocated as we work on it in place. " "Dilation is the boolean equivalent of a convolution but using " "logical or instead of a sum. We apply a 3x3 kernel of all ones. " "Dilation is not computed for a 1 pixel border which is copied " "from the input data. Data should be striped along the x-axis " "such that the location of pixel i,j is data[i + nx * j]."); /* Precompute the total number of pixels; minor optimization */ int nxny = nx * ny; /* Loop variables */ int i, j, nxj; /* Pixel value p. Each thread needs its own unique copy of this so we don't initialize this until the pragma below. */ bool p; #pragma omp parallel for firstprivate(output, data, nxny, nx, ny) \ private(i, j, nxj, p) /* Loop through all of the pixels excluding the border */ for (j = 1; j < ny - 1; j++) { nxj = nx * j; for (i = 1; i < nx - 1; i++) { /*Start in the middle and work out */ p = data[i + nxj]; /* Right 1 */ p = p || data[i + 1 + nxj]; /* Left 1 */ p = p || data[i - 1 + nxj]; /* Up 1 */ p = p || data[i + nx + nxj]; /* Down 1 */ p = p || data[i - nx + nxj]; /* Up 1 Right 1 */ p = p || data[i + 1 + nx + nxj]; /* Up 1 Left 1 */ p = p || data[i - 1 + nx + nxj]; /* Down 1 Right 1 */ p = p || data[i + 1 - nx + nxj]; /* Down 1 Left 1 */ p = p || data[i - 1 - nx + nxj]; output[i + nxj] = p; } } #pragma omp parallel firstprivate(output, data, nx, nxny) private(i) /* For the borders, copy the data from the input array */ for (i = 0; i < nx; i++) { output[i] = data[i]; output[nxny - nx + i] = data[nxny - nx + i]; } #pragma omp parallel firstprivate(output, data, nx, ny) private(j, nxj) for (j = 0; j < ny; j++) { nxj = nx * j; output[nxj] = data[nxj]; output[nxj - 1 + nx] = data[nxj - 1 + nx]; } return; } /* Do niter iterations of boolean dilation on an array of size nx x ny. The * results are saved in the output array. The output array should already be * allocated as we work on it in place. * Dilation is the boolean equivalent of a convolution but using logical ors * instead of a sum. * We apply the following kernel: * 0 1 1 1 0 * 1 1 1 1 1 * 1 1 1 1 1 * 1 1 1 1 1 * 0 1 1 1 0 * The edges are padded with zeros so that the dilation operator is defined for * all pixels. Data should be striped along the x direction such that the * memory location of pixel i,j is data[i + nx * j]. */ void PyDilate5(bool* data, bool* output, int niter, int nx, int ny) { PyDoc_STRVAR(PyDilate5__doc__, "PyDilate5(data, output, nx, ny) -> void\n\n" "Do niter iterations of boolean dilation on an array of size " "nx x ny. The results are saved in the output array. The output " "array should already be allocated as we work on it in place. " "Dilation is the boolean equivalent of a convolution but using " "logical ors instead of a sum. We apply the following kernel:\n" "0 1 1 1 0\n" "1 1 1 1 1\n" "1 1 1 1 1\n" "1 1 1 1 1\n" "0 1 1 1 0\n" "Data should be striped along the x direction such that the " "location of pixel i,j is data[i + nx * j]."); /* Pad the array with a border of zeros */ int padnx = nx + 4; int padny = ny + 4; /* Precompute the total number of pixels; minor optimization */ int padnxny = padnx * padny; int nxny = nx * ny; /* The padded array to work on */ bool* padarr = (bool *) malloc(padnxny * sizeof(bool)); /*Loop indices */ int i, j, nxj, padnxj; int iter; /* Pixel value p. This needs to be unique for each thread so we initialize * it below inside the pragma. */ bool p; #pragma omp parallel firstprivate(padarr, padnx, padnxny) private(i) /* Initialize the borders of the padded array to zero */ for (i = 0; i < padnx; i++) { padarr[i] = false; padarr[i + padnx] = false; padarr[padnxny - padnx + i] = false; padarr[padnxny - padnx - padnx + i] = false; } #pragma omp parallel firstprivate(padarr, padnx, padny) private(j, padnxj) for (j = 0; j < padny; j++) { padnxj = padnx * j; padarr[padnxj] = false; padarr[padnxj + 1] = false; padarr[padnxj + padnx - 1] = false; padarr[padnxj + padnx - 2] = false; } #pragma omp parallel firstprivate(output, data, nxny) private(i) /* Initialize the output array to the input data */ for (i = 0; i < nxny; i++) { output[i] = data[i]; } /* Outer iteration loop */ for (iter = 0; iter < niter; iter++) { #pragma omp parallel for firstprivate(padarr, output, nx, ny, padnx, iter) \ private(nxj, padnxj, i, j) /* Initialize the padded array to the output from the latest * iteration*/ for (j = 0; j < ny; j++) { padnxj = padnx * j; nxj = nx * j; for (i = 0; i < nx; i++) { padarr[i + 2 + padnx + padnx + padnxj] = output[i + nxj]; } } /* Loop over all pixels */ #pragma omp parallel for firstprivate(padarr, output, nx, ny, padnx, iter) \ private(nxj, padnxj, i, j, p) for (j = 0; j < ny; j++) { nxj = nx * j; /* Note the + 2 padding in padnxj */ padnxj = padnx * (j + 2); for (i = 0; i < nx; i++) { /* Start with the middle pixel and work out */ p = padarr[i + 2 + padnxj]; /* Right 1 */ p = p || padarr[i + 3 + padnxj]; /* Left 1 */ p = p || padarr[i + 1 + padnxj]; /* Up 1 */ p = p || padarr[i + 2 + padnx + padnxj]; /* Down 1 */ p = p || padarr[i + 2 - padnx + padnxj]; /* Up 1 Right 1 */ p = p || padarr[i + 3 + padnx + padnxj]; /* Up 1 Left 1 */ p = p || padarr[i + 1 + padnx + padnxj]; /* Down 1 Right 1 */ p = p || padarr[i + 3 - padnx + padnxj]; /* Down 1 Left 1 */ p = p || padarr[i + 1 - padnx + padnxj]; /* Right 2 */ p = p || padarr[i + 4 + padnxj]; /* Left 2 */ p = p || padarr[i + padnxj]; /* Up 2 */ p = p || padarr[i + 2 + padnx + padnx + padnxj]; /* Down 2 */ p = p || padarr[i + 2 - padnx - padnx + padnxj]; /* Right 2 Up 1 */ p = p || padarr[i + 4 + padnx + padnxj]; /* Right 2 Down 1 */ p = p || padarr[i + 4 - padnx + padnxj]; /* Left 2 Up 1 */ p = p || padarr[i + padnx + padnxj]; /* Left 2 Down 1 */ p = p || padarr[i - padnx + padnxj]; /* Up 2 Right 1 */ p = p || padarr[i + 3 + padnx + padnx + padnxj]; /* Up 2 Left 1 */ p = p || padarr[i + 1 + padnx + padnx + padnxj]; /* Down 2 Right 1 */ p = p || padarr[i + 3 - padnx - padnx + padnxj]; /* Down 2 Left 1 */ p = p || padarr[i + 1 - padnx - padnx + padnxj]; output[i + nxj] = p; } } } free(padarr); return; } ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/utils/imutils.h0000644000175100001710000000702500000000000022600 0ustar00runnerdocker00000000000000/* * imutils.h * * Author: Curtis McCully * October 2014 * * Licensed under a 3-clause BSD style license - see LICENSE.rst */ #ifndef IMUTILS_H_ #define IMUTILS_H_ /* Including definitions of the standard int types is necesssary for Windows, * and does no harm on other platforms. */ #include /* Define a bool type because there isn't one built in ANSI C */ typedef uint8_t bool; #define true 1 #define false 0 /* Subsample an array 2x2 given an input array data with size nx x ny. Each * pixel is replicated into 4 pixels; no averaging is performed. The results * are saved in the output array. The output array should already be allocated * as we work on it in place. Data should be striped in the x direction such * that the memory location of pixel i,j is data[nx *j + i]. */ void PySubsample(float* data, float* output, int nx, int ny); /* Rebin an array 2x2, with size (2 * nx) x (2 * ny). Rebin the array by block * averaging 4 pixels back into 1. This is effectively the opposite of * subsample (although subsample does not do an average). The results are saved * in the output array. The output array should already be allocated as we work * on it in place. Data should be striped in the x direction such that the * memory location of pixel i,j is data[nx *j + i]. */ void PyRebin(float* data, float* output, int nx, int ny); /* Convolve an image of size nx x ny with a kernel of size kernx x kerny. The * results are saved in the output array. The output array should already be * allocated as we work on it in place. Data and kernel should both be striped * in the x direction such that the memory location of pixel i,j is * data[nx *j + i]. */ void PyConvolve(float* data, float* kernel, float* output, int nx, int ny, int kernx, int kerny); /* Convolve an image of size nx x ny the following kernel: * 0 -1 0 * -1 4 -1 * 0 -1 0 * The results are saved in the output array. The output array should * already be allocated as we work on it in place. * This is a discrete version of the Laplacian operator. * Data should be striped in the x direction such that the memory location of * pixel i,j is data[nx *j + i]. */ void PyLaplaceConvolve(float* data, float* output, int nx, int ny); /* Perform a boolean dilation on an array of size nx x ny. The results are * saved in the output array. The output array should already be allocated as * we work on it in place. * Dilation is the boolean equivalent of a convolution but using logical ors * instead of a sum. * We apply the following kernel: * 1 1 1 * 1 1 1 * 1 1 1 * The binary dilation is not computed for a 1 pixel border around the image. * These pixels are copied from the input data. Data should be striped along * the x direction such that the memory location of pixel i,j is * data[i + nx * j]. */ void PyDilate3(bool* data, bool* output, int nx, int ny); /* Do niter iterations of boolean dilation on an array of size nx x ny. The * results are saved in the output array. The output array should already be * allocated as we work on it in place. * Dilation is the boolean equivalent of a convolution but using logical ors * instead of a sum. * We apply the following kernel: * 0 1 1 1 0 * 1 1 1 1 1 * 1 1 1 1 1 * 1 1 1 1 1 * 0 1 1 1 0 * The edges are padded with zeros so that the dilation operator is defined for * all pixels. Data should be striped along the x direction such that the * memory location of pixel i,j is data[i + nx * j]. */ void PyDilate5(bool* data, bool* output, int iter, int nx, int ny); #endif /* IMUTILS_H_ */ ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/utils/median_utils.pxd0000644000175100001710000000050200000000000024124 0ustar00runnerdocker00000000000000# cython: language_level=3 """ Header file for Cython functions in the utils package. This allows the Cython code to call these routines directly without requiring the GIL. """ """ Calculate the median on the first n elements of C float array without requiring the GIL. """ cdef float cymedian(float* aptr, int n) nogil ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/utils/median_utils.pyx0000644000175100001710000003155200000000000024162 0ustar00runnerdocker00000000000000# Licensed under a 3-clause BSD style license - see LICENSE.rst # cython: boundscheck=False, nonecheck=False, wraparound=False, language_level=3, cdivision=True """ Name : median_utils Author : Curtis McCully Date : October 2014 """ import numpy as np cimport numpy as np np.import_array() cdef extern from "medutils.h": float PyMedian(float * a, int n) nogil float PyOptMed3(float * a) nogil float PyOptMed5(float * a) nogil float PyOptMed7(float * a) nogil float PyOptMed9(float * a) nogil float PyOptMed25(float * a) nogil void PyMedFilt3(float * data, float * output, int nx, int ny) nogil void PyMedFilt5(float * data, float * output, int nx, int ny) nogil void PyMedFilt7(float * data, float * output, int nx, int ny) nogil void PySepMedFilt3(float * data, float * output, int nx, int ny) nogil void PySepMedFilt5(float * data, float * output, int nx, int ny) nogil void PySepMedFilt7(float * data, float * output, int nx, int ny) nogil void PySepMedFilt9(float * data, float * output, int nx, int ny) nogil """ Wrappers for the C functions in medutils.c """ def median(np.ndarray[np.float32_t, mode='c', cast=True] a, int n): """median(a, n)\n Find the median of the first n elements of an array. Parameters ---------- a : float numpy array Input array to find the median. n : int Number of elements of the array to median. Returns ------- med : float The median value. Notes ----- Wrapper for PyMedian in medutils. """ cdef float * aptr = < float * > np.PyArray_DATA(a) cdef float med = 0.0 with nogil: med = PyMedian(aptr, n) return med cdef float cymedian(float* a, int n) nogil: """cymedian(a, n)\n Cython function to calculate the median without requiring the GIL. :param a: :param n: :return: """ cdef float med = 0.0 med = PyMedian(a, n) return med def optmed3(np.ndarray[np.float32_t, ndim=1, mode='c', cast=True] a): """optmed3(a)\n Optimized method to find the median value of an array of length 3. Parameters ---------- a : float numpy array Input array to find the median. Must be length 3. Returns ------- med3 : float The median of the 3-element array. Notes ----- Wrapper for PyOptMed3 in medutils. """ cdef float * aptr3 = < float * > np.PyArray_DATA(a) cdef float med3 = 0.0 with nogil: med3 = PyOptMed3(aptr3) return med3 def optmed5(np.ndarray[np.float32_t, ndim=1, mode='c', cast=True] a): """optmed5(a)\n Optimized method to find the median value of an array of length 5. Parameters ---------- a : float numpy array Input array to find the median. Must be length 5. Returns ------- med5 : float The median of the 5-element array. Notes ----- Wrapper for PyOptMed5 in medutils. """ cdef float * aptr5 = < float * > np.PyArray_DATA(a) cdef float med5 = 0.0 with nogil: med5 = PyOptMed5(aptr5) return med5 def optmed7(np.ndarray[np.float32_t, ndim=1, mode='c', cast=True] a): """optmed7(a)\n Optimized method to find the median value of an array of length 7. Parameters ---------- a : float numpy array Input array to find the median. Must be length 7. Returns ------- med7 : float The median of the 7-element array. Notes ----- Wrapper for PyOptMed7 in medutils. """ cdef float * aptr7 = < float * > np.PyArray_DATA(a) cdef float med7 = 0.0 with nogil: med7 = PyOptMed7(aptr7) return med7 def optmed9(np.ndarray[np.float32_t, ndim=1, mode='c', cast=True] a): """optmed9(a)\n Optimized method to find the median value of an array of length 9. Parameters ---------- a : float numpy array Input array to find the median. Must be length 9. Returns ------- med9 : float The median of the 9-element array. Notes ----- Wrapper for PyOptMed9 in medutils. """ cdef float * aptr9 = < float * > np.PyArray_DATA(a) cdef float med9 = 0.0 with nogil: med9 = PyOptMed9(aptr9) return med9 def optmed25(np.ndarray[np.float32_t, ndim=1, mode='c', cast=True] a): """optmed25(a)\n Optimized method to find the median value of an array of length 25. Parameters ---------- a : float numpy array Input array to find the median. Must be length 25. Returns ------- med25 : float The median of the 25-element array. Notes ----- Wrapper for PyOptMed25 in medutils. """ cdef float * aptr25 = < float * > np.PyArray_DATA(a) cdef float med25 = 0.0 with nogil: med25 = PyOptMed25(aptr25) return med25 def medfilt3(np.ndarray[np.float32_t, ndim=2, mode='c', cast=True] d3): """medfilt3(d3)\n Calculate the 3x3 median filter of an array. Parameters ---------- d3 : float numpy array Array to median filter. Returns ------- output : float numpy array Median filtered array. Notes ----- The median filter is not calculated for a 1 pixel border around the image. These pixel values are copied from the input data. The array needs to be C-contiguous order. Wrapper for PyMedFilt3 in medutils. """ cdef int nx = d3.shape[1] cdef int ny = d3.shape[0] # Allocate the output array here so that Python tracks the memory and will # free the memory when we are finished with the output array. output = np.zeros((ny, nx), dtype=np.float32) cdef float * d3ptr = < float * > np.PyArray_DATA(d3) cdef float * outd3ptr = < float * > np.PyArray_DATA(output) with nogil: PyMedFilt3(d3ptr, outd3ptr, nx, ny) return output def medfilt5(np.ndarray[np.float32_t, ndim=2, mode='c', cast=True] d5): """medfilt5(d5)\n Calculate the 5x5 median filter of an array. Parameters ---------- d5 : float numpy array Array to median filter. Returns ------- output : float numpy array Median filtered array. Notes ----- The median filter is not calculated for a 2 pixel border around the image. These pixel values are copied from the input data. The array needs to be C-contiguous order. Wrapper for PyMedFilt5 in medutils. """ cdef int nx = d5.shape[1] cdef int ny = d5.shape[0] # Allocate the output array here so that Python tracks the memory and will # free the memory when we are finished with the output array. output = np.zeros((ny, nx), dtype=np.float32) cdef float * d5ptr = < float * > np.PyArray_DATA(d5) cdef float * outd5ptr = < float * > np.PyArray_DATA(output) with nogil: PyMedFilt5(d5ptr, outd5ptr, nx, ny) return output def medfilt7(np.ndarray[np.float32_t, ndim=2, mode='c', cast=True] d7): """medfilt7(d7)\n Calculate the 7x7 median filter of an array. Parameters ---------- d7 : float numpy array Array to median filter. Returns ------- output : float numpy array Median filtered array. Notes ----- The median filter is not calculated for a 3 pixel border around the image. These pixel values are copied from the input data. The array needs to be C-contiguous order. Wrapper for PyMedFilt7 in medutils. """ cdef int nx = d7.shape[1] cdef int ny = d7.shape[0] # Allocate the output array here so that Python tracks the memory and will # free the memory when we are finished with the output array. output = np.zeros((ny, nx), dtype=np.float32) cdef float * d7ptr = < float * > np.PyArray_DATA(d7) cdef float * outd7ptr = < float * > np.PyArray_DATA(output) with nogil: PyMedFilt7(d7ptr, outd7ptr, nx, ny) return output def sepmedfilt3(np.ndarray[np.float32_t, ndim=2, mode='c', cast=True] dsep3): """sepmedfilt3(dsep3)\n Calculate the 3x3 separable median filter of an array. Parameters ---------- dsep3 : float numpy array Array to median filter. Returns ------- output : float numpy array Median filtered array. Notes ----- The separable median medians the rows followed by the columns instead of using a square window. Therefore it is not identical to the full median filter but it is approximately the same, but it is significantly faster. The median filter is not calculated for a 1 pixel border around the image. These pixel values are copied from the input data. The array needs to be C-contiguous order. Wrapper for PySepMedFilt3 in medutils. """ cdef int nx = dsep3.shape[1] cdef int ny = dsep3.shape[0] # Allocate the output array here so that Python tracks the memory and will # free the memory when we are finished with the output array. output = np.zeros((ny, nx), dtype=np.float32) cdef float * dsep3ptr = < float * > np.PyArray_DATA(dsep3) cdef float * outdsep3ptr = < float * > np.PyArray_DATA(output) with nogil: PySepMedFilt3(dsep3ptr, outdsep3ptr, nx, ny) return np.asarray(output) def sepmedfilt5(np.ndarray[np.float32_t, ndim=2, mode='c', cast=True] dsep5): """sepmedfilt5(dsep5)\n Calculate the 5x5 separable median filter of an array. Parameters ---------- dsep5 : float numpy array Array to median filter. Returns ------- output : float numpy array Median filtered array. Notes ----- The separable median medians the rows followed by the columns instead of using a square window. Therefore it is not identical to the full median filter but it is approximately the same, but it is significantly faster. The median filter is not calculated for a 2 pixel border around the image. These pixel values are copied from the input data. The array needs to be C-contiguous order. Wrapper for PySepMedFilt5 in medutils. """ cdef int nx = dsep5.shape[1] cdef int ny = dsep5.shape[0] # Allocate the output array here so that Python tracks the memory and will # free the memory when we are finished with the output array. output = np.zeros((ny, nx), dtype=np.float32) cdef float * dsep5ptr = < float * > np.PyArray_DATA(dsep5) cdef float * outdsep5ptr = < float * > np.PyArray_DATA(output) with nogil: PySepMedFilt5(dsep5ptr, outdsep5ptr, nx, ny) return output def sepmedfilt7(np.ndarray[np.float32_t, ndim=2, mode='c', cast=True] dsep7): """sepmedfilt7(dsep7)\n Calculate the 7x7 separable median filter of an array. Parameters ---------- dsep7 : float numpy array Array to median filter. Returns ------- output : float numpy array Median filtered array. Notes ----- The separable median medians the rows followed by the columns instead of using a square window. Therefore it is not identical to the full median filter but it is approximately the same, but it is significantly faster. The median filter is not calculated for a 3 pixel border around the image. These pixel values are copied from the input data. The array needs to be C-contiguous order. Wrapper for PySepMedFilt7 in medutils. """ cdef int nx = dsep7.shape[1] cdef int ny = dsep7.shape[0] # Allocate the output array here so that Python tracks the memory and will # free the memory when we are finished with the output array. output = np.zeros((ny, nx), dtype=np.float32) cdef float * dsep7ptr = < float * > np.PyArray_DATA(dsep7) cdef float * outdsep7ptr = < float * > np.PyArray_DATA(output) with nogil: PySepMedFilt7(dsep7ptr, outdsep7ptr, nx, ny) return output def sepmedfilt9(np.ndarray[np.float32_t, ndim=2, mode='c', cast=True] dsep9): """sepmedfilt9(dsep9)\n Calculate the 9x9 separable median filter of an array. Parameters ---------- dsep9 : float numpy array Array to median filter. Returns ------- output : float numpy array Median filtered array. Notes ----- The separable median medians the rows followed by the columns instead of using a square window. Therefore it is not identical to the full median filter but it is approximately the same, but it is significantly faster. The median filter is not calculated for a 4 pixel border around the image. These pixel values are copied from the input data. The array needs to be C-contiguous order. Wrapper for PySepMedFilt9 in medutils. """ cdef int nx = dsep9.shape[1] cdef int ny = dsep9.shape[0] # Allocate the output array here so that Python tracks the memory and will # free the memory when we are finished with the output array. output = np.zeros((ny, nx), dtype=np.float32) cdef float * dsep9ptr = < float * > np.PyArray_DATA(dsep9) cdef float * outdsep9ptr = < float * > np.PyArray_DATA(output) with nogil: PySepMedFilt9(dsep9ptr, outdsep9ptr, nx, ny) return output ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/utils/medutils.c0000644000175100001710000012514200000000000022734 0ustar00runnerdocker00000000000000/* * Author: Curtis McCully * October 2014 * Licensed under a 3-clause BSD style license - see LICENSE.rst * * Originally written in C++ in 2011 * See also https://github.com/cmccully/lacosmicx * * This file contains median utility functions for SCRAPPY. These are the most * computationally expensive pieces of the calculation so they have been ported * to C. * * Many thanks to Nicolas Devillard who wrote the optimized methods for finding * the median and placed them in the public domain. I have noted in the * comments places that use Nicolas Devillard's code. * * Parallelization has been achieved using OpenMP. Using a compiler that does * not support OpenMP, e.g. clang currently, the code should still compile and * run serially without issue. I have tried to be explicit as possible about * specifying which variables are private and which should be shared, although * we never actually have any shared variables. We use firstprivate instead. * This does mean that it is important that we never have two threads write to * the same memory position at the same time. * * All calculations are done with 32 bit floats to keep the memory footprint * small. */ #include #include "medutils.h" #define ELEM_SWAP(a,b) { float t=(a);(a)=(b);(b)=t; } float PyMedian(float* a, int n) { /* Get the median of an array "a" with length "n" * using the Quickselect algorithm. Returns a float. * This Quickselect routine is based on the algorithm described in * "Numerical recipes in C", Second Edition, Cambridge University Press, * 1992, Section 8.5, ISBN 0-521-43108-5 * This code by Nicolas Devillard - 1998. Public domain. */ PyDoc_STRVAR(PyMedian__doc__, "PyMedian(a, n) -> float\n\n" "Get the median of array a of length n using the Quickselect " "algorithm."); /* Make a copy of the array so that we don't alter the input array */ float* arr = (float *) malloc(n * sizeof(float)); /* Indices of median, low, and high values we are considering */ int low = 0; int high = n - 1; int median = (low + high) / 2; /* Running indices for the quick select algorithm */ int middle, ll, hh; /* The median to return */ float med; /* running index i */ int i; /* Copy the input data into the array we work with */ for (i = 0; i < n; i++) { arr[i] = a[i]; } /* Start an infinite loop */ while (true) { /* Only One or two elements left */ if (high <= low + 1) { /* Check if we need to swap the two elements */ if ((high == low + 1) && (arr[low] > arr[high])) ELEM_SWAP(arr[low], arr[high]); med = arr[median]; free(arr); return med; } /* Find median of low, middle and high items; * swap into position low */ middle = (low + high) / 2; if (arr[middle] > arr[high]) ELEM_SWAP(arr[middle], arr[high]); if (arr[low] > arr[high]) ELEM_SWAP(arr[low], arr[high]); if (arr[middle] > arr[low]) ELEM_SWAP(arr[middle], arr[low]); /* Swap low item (now in position middle) into position (low+1) */ ELEM_SWAP(arr[middle], arr[low + 1]); /* Nibble from each end towards middle, * swap items when stuck */ ll = low + 1; hh = high; while (true) { do ll++; while (arr[low] > arr[ll]); do hh--; while (arr[hh] > arr[low]); if (hh < ll) break; ELEM_SWAP(arr[ll], arr[hh]); } /* Swap middle item (in position low) back into * the correct position */ ELEM_SWAP(arr[low], arr[hh]); /* Re-set active partition */ if (hh <= median) low = ll; if (hh >= median) high = hh - 1; } } #undef ELEM_SWAP /* All of the optimized median methods below were written by * Nicolas Devillard and are in the public domain. */ #define PIX_SORT(a,b) { if (a>b) PIX_SWAP(a,b); } #define PIX_SWAP(a,b) { float temp=a; a=b; b=temp; } /* ---------------------------------------------------------------------------- Function : PyOptMed3() In : pointer to array of 3 pixel values Out : a pixel value Job : optimized search of the median of 3 pixel values Notice : found on sci.image.processing cannot go faster unless assumptions are made on the nature of the input signal. Code adapted from Nicolas Devillard. --------------------------------------------------------------------------- */ float PyOptMed3(float* p) { PyDoc_STRVAR(PyOptMed3__doc__, "PyOptMed3(a) -> float\n\n" "Get the median of array a of length 3 using a search tree."); PIX_SORT(p[0], p[1]); PIX_SORT(p[1], p[2]); PIX_SORT(p[0], p[1]); return p[1]; } /* ---------------------------------------------------------------------------- Function : PyOptMed5() In : pointer to array of 5 pixel values Out : a pixel value Job : optimized search of the median of 5 pixel values Notice : found on sci.image.processing cannot go faster unless assumptions are made on the nature of the input signal. Code adapted from Nicolas Devillard. --------------------------------------------------------------------------- */ float PyOptMed5(float* p) { PyDoc_STRVAR(PyOptMed5__doc__, "PyOptMed5(a) -> float\n\n" "Get the median of array a of length 5 using a search tree."); PIX_SORT(p[0], p[1]); PIX_SORT(p[3], p[4]); PIX_SORT(p[0], p[3]); PIX_SORT(p[1], p[4]); PIX_SORT(p[1], p[2]); PIX_SORT(p[2], p[3]); PIX_SORT(p[1], p[2]); return p[2]; } /* ---------------------------------------------------------------------------- Function : PyOptMed7() In : pointer to array of 7 pixel values Out : a pixel value Job : optimized search of the median of 7 pixel values Notice : found on sci.image.processing cannot go faster unless assumptions are made on the nature of the input signal. Code adapted from Nicolas Devillard. --------------------------------------------------------------------------- */ float PyOptMed7(float* p) { PyDoc_STRVAR(PyOptMed7__doc__, "PyOptMed7(a) -> float\n\n" "Get the median of array a of length 7 using a search tree."); PIX_SORT(p[0], p[5]); PIX_SORT(p[0], p[3]); PIX_SORT(p[1], p[6]); PIX_SORT(p[2], p[4]); PIX_SORT(p[0], p[1]); PIX_SORT(p[3], p[5]); PIX_SORT(p[2], p[6]); PIX_SORT(p[2], p[3]); PIX_SORT(p[3], p[6]); PIX_SORT(p[4], p[5]); PIX_SORT(p[1], p[4]); PIX_SORT(p[1], p[3]); PIX_SORT(p[3], p[4]); return p[3]; } /* ---------------------------------------------------------------------------- Function : PyOptMed9() In : pointer to an array of 9 pixel values Out : a pixel value Job : optimized search of the median of 9 pixel values Notice : in theory, cannot go faster without assumptions on the signal. Formula from: XILINX XCELL magazine, vol. 23 by John L. Smith The input array is modified in the process The result array is guaranteed to contain the median value in middle position, but other elements are NOT sorted. Code adapted from Nicolas Devillard. --------------------------------------------------------------------------- */ float PyOptMed9(float* p) { PyDoc_STRVAR(PyOptMed9__doc__, "PyOptMed9(a) -> float\n\n" "Get the median of array a of length 9 using a search tree."); PIX_SORT(p[1], p[2]); PIX_SORT(p[4], p[5]); PIX_SORT(p[7], p[8]); PIX_SORT(p[0], p[1]); PIX_SORT(p[3], p[4]); PIX_SORT(p[6], p[7]); PIX_SORT(p[1], p[2]); PIX_SORT(p[4], p[5]); PIX_SORT(p[7], p[8]); PIX_SORT(p[0], p[3]); PIX_SORT(p[5], p[8]); PIX_SORT(p[4], p[7]); PIX_SORT(p[3], p[6]); PIX_SORT(p[1], p[4]); PIX_SORT(p[2], p[5]); PIX_SORT(p[4], p[7]); PIX_SORT(p[4], p[2]); PIX_SORT(p[6], p[4]); PIX_SORT(p[4], p[2]); return p[4]; } /* ---------------------------------------------------------------------------- Function : PyOptMed25() In : pointer to an array of 25 pixel values Out : a pixel value Job : optimized search of the median of 25 pixel values Notice : in theory, cannot go faster without assumptions on the signal. Code taken from Graphic Gems. Code adapted from Nicolas Devillard. --------------------------------------------------------------------------- */ float PyOptMed25(float* p) { PyDoc_STRVAR(PyOptMed25__doc__, "PyOptMed25(a) -> float\n\n" "Get the median of array a of length 25 using a search tree."); PIX_SORT(p[0], p[1]); PIX_SORT(p[3], p[4]); PIX_SORT(p[2], p[4]); PIX_SORT(p[2], p[3]); PIX_SORT(p[6], p[7]); PIX_SORT(p[5], p[7]); PIX_SORT(p[5], p[6]); PIX_SORT(p[9], p[10]); PIX_SORT(p[8], p[10]); PIX_SORT(p[8], p[9]); PIX_SORT(p[12], p[13]); PIX_SORT(p[11], p[13]); PIX_SORT(p[11], p[12]); PIX_SORT(p[15], p[16]); PIX_SORT(p[14], p[16]); PIX_SORT(p[14], p[15]); PIX_SORT(p[18], p[19]); PIX_SORT(p[17], p[19]); PIX_SORT(p[17], p[18]); PIX_SORT(p[21], p[22]); PIX_SORT(p[20], p[22]); PIX_SORT(p[20], p[21]); PIX_SORT(p[23], p[24]); PIX_SORT(p[2], p[5]); PIX_SORT(p[3], p[6]); PIX_SORT(p[0], p[6]); PIX_SORT(p[0], p[3]); PIX_SORT(p[4], p[7]); PIX_SORT(p[1], p[7]); PIX_SORT(p[1], p[4]); PIX_SORT(p[11], p[14]); PIX_SORT(p[8], p[14]); PIX_SORT(p[8], p[11]); PIX_SORT(p[12], p[15]); PIX_SORT(p[9], p[15]); PIX_SORT(p[9], p[12]); PIX_SORT(p[13], p[16]); PIX_SORT(p[10], p[16]); PIX_SORT(p[10], p[13]); PIX_SORT(p[20], p[23]); PIX_SORT(p[17], p[23]); PIX_SORT(p[17], p[20]); PIX_SORT(p[21], p[24]); PIX_SORT(p[18], p[24]); PIX_SORT(p[18], p[21]); PIX_SORT(p[19], p[22]); PIX_SORT(p[8], p[17]); PIX_SORT(p[9], p[18]); PIX_SORT(p[0], p[18]); PIX_SORT(p[0], p[9]); PIX_SORT(p[10], p[19]); PIX_SORT(p[1], p[19]); PIX_SORT(p[1], p[10]); PIX_SORT(p[11], p[20]); PIX_SORT(p[2], p[20]); PIX_SORT(p[2], p[11]); PIX_SORT(p[12], p[21]); PIX_SORT(p[3], p[21]); PIX_SORT(p[3], p[12]); PIX_SORT(p[13], p[22]); PIX_SORT(p[4], p[22]); PIX_SORT(p[4], p[13]); PIX_SORT(p[14], p[23]); PIX_SORT(p[5], p[23]); PIX_SORT(p[5], p[14]); PIX_SORT(p[15], p[24]); PIX_SORT(p[6], p[24]); PIX_SORT(p[6], p[15]); PIX_SORT(p[7], p[16]); PIX_SORT(p[7], p[19]); PIX_SORT(p[13], p[21]); PIX_SORT(p[15], p[23]); PIX_SORT(p[7], p[13]); PIX_SORT(p[7], p[15]); PIX_SORT(p[1], p[9]); PIX_SORT(p[3], p[11]); PIX_SORT(p[5], p[17]); PIX_SORT(p[11], p[17]); PIX_SORT(p[9], p[17]); PIX_SORT(p[4], p[10]); PIX_SORT(p[6], p[12]); PIX_SORT(p[7], p[14]); PIX_SORT(p[4], p[6]); PIX_SORT(p[4], p[7]); PIX_SORT(p[12], p[14]); PIX_SORT(p[10], p[14]); PIX_SORT(p[6], p[7]); PIX_SORT(p[10], p[12]); PIX_SORT(p[6], p[10]); PIX_SORT(p[6], p[17]); PIX_SORT(p[12], p[17]); PIX_SORT(p[7], p[17]); PIX_SORT(p[7], p[10]); PIX_SORT(p[12], p[18]); PIX_SORT(p[7], p[12]); PIX_SORT(p[10], p[18]); PIX_SORT(p[12], p[20]); PIX_SORT(p[10], p[20]); PIX_SORT(p[10], p[12]); return p[12]; } #undef PIX_SORT #undef PIX_SWAP /* We have slightly unusual boundary conditions for all of the median filters * below. Rather than padding the data, we just don't calculate the median * filter for pixels around the border of the output image (n - 1) / 2 from * the edge, where we are using an n x n median filter. Edge effects often * look like cosmic rays and the edges are often blank so this shouldn't * matter. We fill the border with the original data values. */ /* Calculate the 3x3 median filter of an array data that has dimensions * nx x ny. The results are saved in the output array. The output array should * already be allocated as we work on it in place. The median filter is not * calculated for a 1 pixel border around the image. These pixel values are * copied from the input data. The data should be striped along the x * direction, such that pixel i,j in the 2D image should have memory location * data[i + nx *j]. */ void PyMedFilt3(float* data, float* output, int nx, int ny) { PyDoc_STRVAR(PyMedFilt3__doc__, "PyMedFilt3(data, output, nx, ny) -> void\n\n" "Calculate the 3x3 median filter on an array data with dimensions " "nx x ny. The results are saved in the output array. The output " "array should already be allocated as we work on it in place. The " "median filter is not calculated for a 1 pixel border around the " "image. These pixel values are copied from the input data. Note " "that the data array needs to be striped in the x direction such " "that pixel i,j has memory location data[i + nx * j]"); /*Total size of the array */ int nxny = nx * ny; /* Loop indices */ int i, j, nxj; int k, l, nxk; /* 9 element array to calculate the median and a counter index. Note that * these both need to be unique for each thread so they both need to be * private and we wait to allocate memory until the pragma below.*/ float* medarr; int medcounter; /* Each thread needs to access the data and the output so we make them * firstprivate. We make sure that our algorithm doesn't have multiple * threads read or write the same piece of memory. */ #pragma omp parallel firstprivate(output, data, nx, ny) \ private(i, j, k, l, medarr, nxj, nxk, medcounter) { /*Each thread allocates its own array. */ medarr = (float *) malloc(9 * sizeof(float)); /* Go through each pixel excluding the border.*/ #pragma omp for nowait for (j = 1; j < ny - 1; j++) { /* Precalculate the multiplication nx * j, minor optimization */ nxj = nx * j; for (i = 1; i < nx - 1; i++) { medcounter = 0; /* The compiler should optimize away these loops */ for (k = -1; k < 2; k++) { nxk = nx * k; for (l = -1; l < 2; l++) { medarr[medcounter] = data[nxj + i + nxk + l]; medcounter++; } } /* Calculate the median in the fastest way possible */ output[nxj + i] = PyOptMed9(medarr); } } /* Each thread needs to free its own copy of medarr */ free(medarr); } #pragma omp parallel firstprivate(output, data, nx, nxny) private(i) /* Copy the border pixels from the original data into the output array */ for (i = 0; i < nx; i++) { output[i] = data[i]; output[nxny - nx + i] = data[nxny - nx + i]; } #pragma omp parallel firstprivate(output, data, nx, ny) private(j, nxj) for (j = 0; j < ny; j++) { nxj = nx * j; output[nxj] = data[nxj]; output[nxj + nx - 1] = data[nxj + nx - 1]; } return; } /* Calculate the 5x5 median filter of an array data that has dimensions * nx x ny. The results are saved in the output array. The output array should * already be allocated as we work on it in place. The median filter is not * calculated for a 2 pixel border around the image. These pixel values are * copied from the input data. The data should be striped along the * x direction, such that pixel i,j in the 2D image should have memory * location data[i + nx *j]. */ void PyMedFilt5(float* data, float* output, int nx, int ny) { PyDoc_STRVAR(PyMedFilt5__doc__, "PyMedFilt5(data, output, nx, ny) -> void\n\n" "Calculate the 5x5 median filter on an array data with dimensions " "nx x ny. The results are saved in the output array. The output " "array should already be allocated as we work on it in place. The " "median filter is not calculated for a 2 pixel border around the " "image. These pixel values are copied from the input data. Note " "that the data array needs to be striped in the x direction such " "that pixel i,j has memory location data[i + nx * j]"); /*Total size of the array */ int nxny = nx * ny; /* Loop indices */ int i, j, nxj; int k, l, nxk; /* 25 element array to calculate the median and a counter index. Note that * these both need to be unique for each thread so they both need to be * private and we wait to allocate memory until the pragma below. */ float* medarr; int medcounter; /* Each thread needs to access the data and the output so we make them * firstprivate. We make sure that our algorithm doesn't have multiple * threads read or write the same piece of memory. */ #pragma omp parallel firstprivate(output, data, nx, ny) \ private(i, j, k, l, medarr, nxj, nxk, medcounter) { /*Each thread allocates its own array. */ medarr = (float *) malloc(25 * sizeof(float)); /* Go through each pixel excluding the border.*/ #pragma omp for nowait for (j = 2; j < ny - 2; j++) { /* Precalculate the multiplication nx * j, minor optimization */ nxj = nx * j; for (i = 2; i < nx - 2; i++) { medcounter = 0; /* The compiler should optimize away these loops */ for (k = -2; k < 3; k++) { nxk = nx * k; for (l = -2; l < 3; l++) { medarr[medcounter] = data[nxj + i + nxk + l]; medcounter++; } } /* Calculate the median in the fastest way possible */ output[nxj + i] = PyOptMed25(medarr); } } /* Each thread needs to free its own copy of medarr */ free(medarr); } #pragma omp parallel firstprivate(output, data, nx, nxny) private(i) /* Copy the border pixels from the original data into the output array */ for (i = 0; i < nx; i++) { output[i] = data[i]; output[i + nx] = data[i + nx]; output[nxny - nx + i] = data[nxny - nx + i]; output[nxny - nx - nx + i] = data[nxny - nx - nx + i]; } #pragma omp parallel firstprivate(output, data, nx, ny) private(j, nxj) for (j = 0; j < ny; j++) { nxj = nx * j; output[nxj] = data[nxj]; output[nxj + 1] = data[nxj + 1]; output[nxj + nx - 1] = data[nxj + nx - 1]; output[nxj + nx - 2] = data[nxj + nx - 2]; } return; } /* Calculate the 7x7 median filter of an array data that has dimensions * nx x ny. The results are saved in the output array. The output array should * already be allocated as we work on it in place. The median filter is not * calculated for a 3 pixel border around the image. These pixel values are * copied from the input data. The data should be striped along the * x direction, such that pixel i,j in the 2D image should have memory * location data[i + nx *j]. */ void PyMedFilt7(float* data, float* output, int nx, int ny) { PyDoc_STRVAR(PyMedFilt7__doc__, "PyMedFilt7(data, output, nx, ny) -> void\n\n" "Calculate the 7x7 median filter on an array data with dimensions " "nx x ny. The results are saved in the output array. The output " "array should already be allocated as we work on it in place. The " "median filter is not calculated for a 3 pixel border around the " "image. These pixel values are copied from the input data. Note " "that the data array needs to be striped in the x direction such " "that pixel i,j has memory location data[i + nx * j]"); /*Total size of the array */ int nxny = nx * ny; /* Loop indices */ int i, j, nxj; int k, l, nxk; /* 49 element array to calculate the median and a counter index. Note that * these both need to be unique for each thread so they both need to be * private and we wait to allocate memory until the pragma below. */ float* medarr; int medcounter; /* Each thread needs to access the data and the output so we make them * firstprivate. We make sure that our algorithm doesn't have multiple * threads read or write the same piece of memory. */ #pragma omp parallel firstprivate(output, data, nx, ny) \ private(i, j, k, l, medarr, nxj, nxk, medcounter) { /*Each thread allocates its own array. */ medarr = (float *) malloc(49 * sizeof(float)); /* Go through each pixel excluding the border.*/ #pragma omp for nowait for (j = 3; j < ny - 3; j++) { /* Precalculate the multiplication nx * j, minor optimization */ nxj = nx * j; for (i = 3; i < nx - 3; i++) { medcounter = 0; /* The compiler should optimize away these loops */ for (k = -3; k < 4; k++) { nxk = nx * k; for (l = -3; l < 4; l++) { medarr[medcounter] = data[nxj + i + nxk + l]; medcounter++; } } /* Calculate the median in the fastest way possible */ output[nxj + i] = PyMedian(medarr, 49); } } /* Each thread needs to free its own copy of medarr */ free(medarr); } #pragma omp parallel firstprivate(output, data, nx, nxny) private(i) /* Copy the border pixels from the original data into the output array */ for (i = 0; i < nx; i++) { output[i] = data[i]; output[i + nx] = data[i + nx]; output[i + nx + nx] = data[i + nx + nx]; output[nxny - nx + i] = data[nxny - nx + i]; output[nxny - nx - nx + i] = data[nxny - nx - nx + i]; output[nxny - nx - nx - nx + i] = data[nxny - nx - nx - nx + i]; } #pragma omp parallel firstprivate(output, data, nx, ny) private(j, nxj) for (j = 0; j < ny; j++) { nxj = nx * j; output[nxj] = data[nxj]; output[nxj + 1] = data[nxj + 1]; output[nxj + 2] = data[nxj + 2]; output[nxj + nx - 1] = data[nxj + nx - 1]; output[nxj + nx - 2] = data[nxj + nx - 2]; output[nxj + nx - 3] = data[nxj + nx - 3]; } return; } /* Calculate the 3x3 separable median filter of an array data that has * dimensions nx x ny. The results are saved in the output array. The output * array should already be allocated as we work on it in place. The median * filter is not calculated for a 1 pixel border around the image. These pixel * values are copied from the input data. The data should be striped along * the x direction, such that pixel i,j in the 2D image should have memory * location data[i + nx *j]. Note that the rows are median filtered first, * followed by the columns. */ void PySepMedFilt3(float* data, float* output, int nx, int ny) { PyDoc_STRVAR(PySepMedFilt3__doc__, "PySepMedFilt3(data, output, nx, ny) -> void\n\n" "Calculate the 3x3 separable median filter on an array data with" "dimensions nx x ny. The results are saved in the output array " "which should already be allocated as we work on it in place. The " "median filter is not calculated for a 1 pixel border which is " "copied from the input data. The data array should be striped in " "the x direction such that pixel i,j has memory location " "data[i + nx * j]. Note that the rows are median filtered first, " "followed by the columns."); /* Total number of pixels */ int nxny = nx * ny; /* Output array for the median filter of the rows. We later median filter * the columns of this array. */ float* rowmed = (float *) malloc(nxny * sizeof(float)); /* Loop indices */ int i, j, nxj; /* 3 element array to calculate the median and a counter index. Note that * this array needs to be unique for each thread so it needs to be * private and we wait to allocate memory until the pragma below. */ float* medarr; /* Median filter the rows first */ /* Each thread needs to access the data and rowmed so we make them * firstprivate. We make sure that our algorithm doesn't have multiple * threads read or write the same piece of memory. */ #pragma omp parallel firstprivate(data, rowmed, nx, ny) \ private(i, j, nxj, medarr) { /*Each thread allocates its own array. */ medarr = (float *) malloc(3 * sizeof(float)); /* For each pixel excluding the border */ #pragma omp for nowait for (j = 0; j < ny; j++) { nxj = nx * j; for (i = 1; i < nx - 1; i++) { medarr[0] = data[nxj + i]; medarr[1] = data[nxj + i - 1]; medarr[2] = data[nxj + i + 1]; /* Calculate the median in the fastest way possible */ rowmed[nxj + i] = PyOptMed3(medarr); } } /* Each thread needs to free its own medarr */ free(medarr); } /* Fill in the borders of rowmed with the original data values */ #pragma omp parallel for firstprivate(data, rowmed, nx, ny) private(j, nxj) for (j = 0; j < ny; j++) { nxj = nx * j; rowmed[nxj] = data[nxj]; rowmed[nxj + nx - 1] = data[nxj + nx - 1]; } /* Median filter the columns */ #pragma omp parallel firstprivate(rowmed, output, nx, ny) \ private(i, j, nxj, medarr) { /* Each thread needs to reallocate a new medarr */ medarr = (float *) malloc(3 * sizeof(float)); /* For each pixel excluding the border */ #pragma omp for nowait for (j = 1; j < ny - 1; j++) { nxj = nx * j; for (i = 1; i < nx - 1; i++) { medarr[0] = rowmed[i + nxj]; medarr[1] = rowmed[i + nxj - nx]; medarr[2] = rowmed[i + nxj + nx]; /* Calculate the median in the fastest way possible */ output[nxj + i] = PyOptMed3(medarr); } } /* Each thread needs to free its own medarr */ free(medarr); } /* Clean up rowmed */ free(rowmed); /* Copy the border pixels from the original data into the output array */ #pragma omp parallel for firstprivate(output, data, nx, nxny) private(i) for (i = 0; i < nx; i++) { output[i] = data[i]; output[nxny - nx + i] = data[nxny - nx + i]; } #pragma omp parallel for firstprivate(output, data, nx, ny) private(j, nxj) for (j = 0; j < ny; j++) { nxj = nx * j; output[nxj] = data[nxj]; output[nxj + nx - 1] = data[nxj + nx - 1]; } return; } /* Calculate the 5x5 separable median filter of an array data that has * dimensions nx x ny. The results are saved in the output array. The output * array should already be allocated as we work on it in place.The median * filter is not calculated for a 2 pixel border around the image. These pixel * values are copied from the input data. The data should be striped along the * x direction, such that pixel i,j in the 2D image should have memory location * data[i + nx *j]. Note that the rows are median filtered first, followed by * the columns. */ void PySepMedFilt5(float* data, float* output, int nx, int ny) { PyDoc_STRVAR(PySepMedFilt5__doc__, "PySepMedFilt5(data, output, nx, ny) -> void\n\n" "Calculate the 5x5 separable median filter on an array data with " "dimensions nx x ny. The results are saved in the output array " "which should already be allocated as we work on it in place. The " "median filter is not calculated for a 2 pixel border which is " "copied from the input data. The data array should be striped in " "the x direction such that pixel i,j has memory location " "data[i + nx * j]. Note that the rows are median filtered first, " "followed by the columns."); /* Total number of pixels */ int nxny = nx * ny; /* Output array for the median filter of the rows. We later median filter * the columns of this array. */ float* rowmed = (float *) malloc(nxny * sizeof(float)); /* Loop indices */ int i, j, nxj; /* 5 element array to calculate the median and a counter index. Note that * this array needs to be unique for each thread so it needs to be * private and we wait to allocate memory until the pragma below. */ float* medarr; /* Median filter the rows first */ /* Each thread needs to access the data and rowmed so we make them * firstprivate. We make sure that our algorithm doesn't have multiple * threads read or write the same piece of memory. */ #pragma omp parallel firstprivate(data, rowmed, nx, ny) \ private(i, j, nxj, medarr) { /*Each thread allocates its own array. */ medarr = (float *) malloc(5 * sizeof(float)); /* For each pixel excluding the border */ #pragma omp for nowait for (j = 0; j < ny; j++) { nxj = nx * j; for (i = 2; i < nx - 2; i++) { medarr[0] = data[nxj + i]; medarr[1] = data[nxj + i - 1]; medarr[2] = data[nxj + i + 1]; medarr[3] = data[nxj + i - 2]; medarr[4] = data[nxj + i + 2]; /* Calculate the median in the fastest way possible */ rowmed[nxj + i] = PyOptMed5(medarr); } } /* Each thread needs to free its own medarr */ free(medarr); } /* Fill in the borders of rowmed with the original data values */ #pragma omp parallel for firstprivate(rowmed, data, nx, ny) private(j, nxj) for (j = 0; j < ny; j++) { nxj = nx * j; rowmed[nxj] = data[nxj]; rowmed[nxj + 1] = data[nxj + 1]; rowmed[nxj + nx - 1] = data[nxj + nx - 1]; rowmed[nxj + nx - 2] = data[nxj + nx - 2]; } /* Median filter the columns */ #pragma omp parallel firstprivate(rowmed, output, nx, ny) \ private(i, j, nxj, medarr) { /* Each thread needs to reallocate a new medarr */ medarr = (float *) malloc(5 * sizeof(float)); /* For each pixel excluding the border */ #pragma omp for nowait for (j = 2; j < ny - 2; j++) { nxj = nx * j; for (i = 2; i < nx - 2; i++) { medarr[0] = rowmed[i + nxj]; medarr[1] = rowmed[i + nxj - nx]; medarr[2] = rowmed[i + nxj + nx]; medarr[3] = rowmed[i + nxj + nx + nx]; medarr[4] = rowmed[i + nxj - nx - nx]; /* Calculate the median in the fastest way possible */ output[nxj + i] = PyOptMed5(medarr); } } /* Each thread needs to free its own medarr */ free(medarr); } /* Clean up rowmed */ free(rowmed); /* Copy the border pixels from the original data into the output array */ #pragma omp parallel for firstprivate(output, data, nx, nxny) private(i) for (i = 0; i < nx; i++) { output[i] = data[i]; output[i + nx] = data[i + nx]; output[nxny - nx + i] = data[nxny - nx + i]; output[nxny - nx - nx + i] = data[nxny - nx - nx + i]; } #pragma omp parallel for firstprivate(output, data, nx, ny) private(j, nxj) for (j = 0; j < ny; j++) { nxj = nx * j; output[nxj] = data[nxj]; output[nxj + 1] = data[nxj + 1]; output[nxj + nx - 1] = data[nxj + nx - 1]; output[nxj + nx - 2] = data[nxj + nx - 2]; } return; } /* Calculate the 7x7 separable median filter of an array data that has * dimensions nx x ny. The results are saved in the output array. The output * array should already be allocated as we work on it in place. The median * filter is not calculated for a 3 pixel border around the image. These pixel * values are copied from the input data. The data should be striped along the * x direction, such that pixel i,j in the 2D image should have memory location * data[i + nx *j]. Note that the rows are median filtered first, followed by * the columns. */ void PySepMedFilt7(float* data, float* output, int nx, int ny) { PyDoc_STRVAR(PySepMedFilt7__doc__, "PySepMedFilt7(data, output, nx, ny) -> void\n\n" "Calculate the 7x7 separable median filter on an array data with " "dimensions nx x ny. The results are saved in the output array " "which should already be allocated as we work on it in place. The " "median filter is not calculated for a 3 pixel border which is " "copied from the input data. The data array should be striped in " "the x direction such that pixel i,j has memory location " "data[i + nx * j]. Note that the rows are median filtered first, " "followed by the columns."); /* Total number of pixels */ int nxny = nx * ny; /* Output array for the median filter of the rows. We later median filter * the columns of this array. */ float* rowmed = (float *) malloc(nxny * sizeof(float)); /* Loop indices */ int i, j, nxj; /* 7 element array to calculate the median and a counter index. Note that * this array needs to be unique for each thread so it needs to be * private and we wait to allocate memory until the pragma below. */ float* medarr; /* Median filter the rows first */ /* Each thread needs to access the data and rowmed so we make them * firstprivate. We make sure that our algorithm doesn't have multiple * threads read or write the same piece of memory. */ #pragma omp parallel firstprivate(data, rowmed, nx, ny) \ private(i, j, nxj, medarr) { /*Each thread allocates its own array. */ medarr = (float *) malloc(7 * sizeof(float)); /* For each pixel excluding the border */ #pragma omp for nowait for (j = 0; j < ny; j++) { nxj = nx * j; for (i = 3; i < nx - 3; i++) { medarr[0] = data[nxj + i]; medarr[1] = data[nxj + i - 1]; medarr[2] = data[nxj + i + 1]; medarr[3] = data[nxj + i - 2]; medarr[4] = data[nxj + i + 2]; medarr[5] = data[nxj + i - 3]; medarr[6] = data[nxj + i + 3]; /* Calculate the median in the fastest way possible */ rowmed[nxj + i] = PyOptMed7(medarr); } } /* Each thread needs to free its own medarr */ free(medarr); } /* Fill in the borders of rowmed with the original data values */ #pragma omp parallel for firstprivate(rowmed, data, nx, ny) private(j, nxj) for (j = 0; j < ny; j++) { nxj = nx * j; rowmed[nxj] = data[nxj]; rowmed[nxj + 1] = data[nxj + 1]; rowmed[nxj + 2] = data[nxj + 2]; rowmed[nxj + nx - 1] = data[nxj + nx - 1]; rowmed[nxj + nx - 2] = data[nxj + nx - 2]; rowmed[nxj + nx - 3] = data[nxj + nx - 3]; } /* Median filter the columns */ #pragma omp parallel firstprivate(rowmed, output, nx, ny) \ private(i, j, nxj, medarr) { /* Each thread needs to reallocate a new medarr */ medarr = (float *) malloc(7 * sizeof(float)); /* For each pixel excluding the border */ #pragma omp for nowait for (j = 3; j < ny - 3; j++) { nxj = nx * j; for (i = 3; i < nx - 3; i++) { medarr[0] = rowmed[i + nxj - nx]; medarr[1] = rowmed[i + nxj + nx]; medarr[2] = rowmed[i + nxj + nx + nx]; medarr[3] = rowmed[i + nxj - nx - nx]; medarr[4] = rowmed[i + nxj]; medarr[5] = rowmed[i + nxj + nx + nx + nx]; medarr[6] = rowmed[i + nxj - nx - nx - nx]; /* Calculate the median in the fastest way possible */ output[nxj + i] = PyOptMed7(medarr); } } /* Each thread needs to free its own medarr */ free(medarr); } /* Clean up rowmed */ free(rowmed); /* Copy the border pixels from the original data into the output array */ #pragma omp parallel for firstprivate(output, data, nx, nxny) private(i) for (i = 0; i < nx; i++) { output[i] = data[i]; output[i + nx] = data[i + nx]; output[i + nx + nx] = data[i + nx + nx]; output[nxny - nx + i] = data[nxny - nx + i]; output[nxny - nx - nx + i] = data[nxny - nx - nx + i]; output[nxny - nx - nx - nx + i] = data[nxny - nx - nx - nx + i]; } #pragma omp parallel for firstprivate(output, data, nx, ny) private(j, nxj) for (j = 0; j < ny; j++) { nxj = nx * j; output[nxj] = data[nxj]; output[nxj + 1] = data[nxj + 1]; output[nxj + 2] = data[nxj + 2]; output[nxj + nx - 1] = data[nxj + nx - 1]; output[nxj + nx - 2] = data[nxj + nx - 2]; output[nxj + nx - 3] = data[nxj + nx - 3]; } return; } /* Calculate the 9x9 separable median filter of an array data that has * dimensions nx x ny. The results are saved in the output array. The output * array should already be allocated as we work on it in place. The median * filter is not calculated for a 4 pixel border around the image. These pixel * values are copied from the input data. The data should be striped along the * x direction, such that pixel i,j in the 2D image should have memory location * data[i + nx *j]. Note that the rows are median filtered first, followed by * the columns. */ void PySepMedFilt9(float* data, float* output, int nx, int ny) { PyDoc_STRVAR(PySepMedFilt9__doc__, "PySepMedFilt9(data, output, nx, ny) -> void\n\n" "Calculate the 9x9 separable median filter on an array data with " "dimensions nx x ny. The results are saved in the output array " "which should already be allocated as we work on it in place. The " "median filter is not calculated for a 4 pixel border which is " "copied from the input data. The data array should be striped in " "the x direction such that pixel i,j has memory location " "data[i + nx * j]. Note that the rows are median filtered first, " "followed by the columns."); /* Total number of pixels */ int nxny = nx * ny; /* Output array for the median filter of the rows. We later median filter * the columns of this array. */ float* rowmed = (float *) malloc(nxny * sizeof(float)); /* Loop indices */ int i, j, nxj; /* 9 element array to calculate the median and a counter index. Note that * this array needs to be unique for each thread so it needs to be * private and we wait to allocate memory until the pragma below. */ float* medarr; /* Median filter the rows first */ /* Each thread needs to access the data and rowmed so we make them * firstprivate. We make sure that our algorithm doesn't have multiple * threads read or write the same piece of memory. */ #pragma omp parallel firstprivate(data, rowmed, nx, ny) \ private(i, j, nxj, medarr) { /*Each thread allocates its own array. */ medarr = (float *) malloc(9 * sizeof(float)); /* For each pixel excluding the border */ #pragma omp for nowait for (j = 0; j < ny; j++) { nxj = nx * j; for (i = 4; i < nx - 4; i++) { medarr[0] = data[nxj + i]; medarr[1] = data[nxj + i - 1]; medarr[2] = data[nxj + i + 1]; medarr[3] = data[nxj + i - 2]; medarr[4] = data[nxj + i + 2]; medarr[5] = data[nxj + i - 3]; medarr[6] = data[nxj + i + 3]; medarr[7] = data[nxj + i - 4]; medarr[8] = data[nxj + i + 4]; /* Calculate the median in the fastest way possible */ rowmed[nxj + i] = PyOptMed9(medarr); } } /* Each thread needs to free its own medarr */ free(medarr); } /* Fill in the borders of rowmed with the original data values */ #pragma omp parallel for firstprivate(rowmed, data, nx, ny) private(j, nxj) for (j = 0; j < ny; j++) { nxj = nx * j; rowmed[nxj] = data[nxj]; rowmed[nxj + 1] = data[nxj + 1]; rowmed[nxj + 2] = data[nxj + 2]; rowmed[nxj + 3] = data[nxj + 3]; rowmed[nxj + nx - 1] = data[nxj + nx - 1]; rowmed[nxj + nx - 2] = data[nxj + nx - 2]; rowmed[nxj + nx - 3] = data[nxj + nx - 3]; rowmed[nxj + nx - 4] = data[nxj + nx - 4]; } /* Median filter the columns */ #pragma omp parallel firstprivate(rowmed, output, nx, ny) \ private(i, j, nxj, medarr) { /* Each thread needs to reallocate a new medarr */ medarr = (float *) malloc(9 * sizeof(float)); /* For each pixel excluding the border */ #pragma omp for nowait for (j = 4; j < ny - 4; j++) { nxj = nx * j; for (i = 4; i < nx - 4; i++) { medarr[0] = rowmed[i + nxj]; medarr[1] = rowmed[i + nxj - nx]; medarr[2] = rowmed[i + nxj + nx]; medarr[3] = rowmed[i + nxj + nx + nx]; medarr[4] = rowmed[i + nxj - nx - nx]; medarr[5] = rowmed[i + nxj + nx + nx + nx]; medarr[6] = rowmed[i + nxj - nx - nx - nx]; medarr[7] = rowmed[i + nxj + nx + nx + nx + nx]; medarr[8] = rowmed[i + nxj - nx - nx - nx - nx]; /* Calculate the median in the fastest way possible */ output[nxj + i] = PyOptMed9(medarr); } } /* Each thread needs to free its own medarr */ free(medarr); } /* Clean up rowmed */ free(rowmed); /* Copy the border pixels from the original data into the output array */ #pragma omp parallel for firstprivate(output, data, nx, nxny) private(i) for (i = 0; i < nx; i++) { output[i] = data[i]; output[i + nx] = data[i + nx]; output[i + nx + nx] = data[i + nx + nx]; output[i + nx + nx + nx] = data[i + nx + nx + nx]; output[nxny - nx + i] = data[nxny - nx + i]; output[nxny - nx - nx + i] = data[nxny - nx - nx + i]; output[nxny - nx - nx - nx + i] = data[nxny - nx - nx - nx + i]; output[nxny - nx - nx - nx - nx + i] = data[nxny - nx - nx - nx - nx + i]; } #pragma omp parallel for firstprivate(output, data, nx, ny) private(j, nxj) for (j = 0; j < ny; j++) { nxj = nx * j; output[nxj] = data[nxj]; output[nxj + 1] = data[nxj + 1]; output[nxj + 2] = data[nxj + 2]; output[nxj + 3] = data[nxj + 3]; output[nxj + nx - 1] = data[nxj + nx - 1]; output[nxj + nx - 2] = data[nxj + nx - 2]; output[nxj + nx - 3] = data[nxj + nx - 3]; output[nxj + nx - 4] = data[nxj + nx - 4]; } return; } ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/utils/medutils.h0000644000175100001710000001207700000000000022743 0ustar00runnerdocker00000000000000/* * medutils.h * * Author: Curtis McCully * October 2014 * * Licensed under a 3-clause BSD style license - see LICENSE.rst */ #ifndef MEDUTILS_H_ #define MEDUTILS_H_ /* Including definitions of the standard int types is necesssary for Windows, * and does no harm on other platforms. */ #include /* Define a bool type because there isn't one built in ANSI C */ typedef uint8_t bool; #define true 1 #define false 0 /*Find the median value of an array "a" of length n. */ float PyMedian(float* a, int n); /*Optimized method to find the median value of an array "a" of length 3. */ float PyOptMed3(float* a); /*Optimized method to find the median value of an array "a" of length 5. */ float PyOptMed5(float* a); /*Optimized method to find the median value of an array "a" of length 7. */ float PyOptMed7(float* a); /*Optimized method to find the median value of an array "a" of length 9. */ float PyOptMed9(float* a); /*Optimized method to find the median value of an array "a" of length 25. */ float PyOptMed25(float* a); /* Calculate the 3x3 median filter of an array data that has dimensions * nx x ny. The results are saved in the output array. The output array should * already be allocated as we work on it in place. The median filter is not * calculated for a 1 pixel border around the image. These pixel values are * copied from the input data. The data should be striped along the x * direction, such that pixel i,j in the 2D image should have memory location * data[i + nx *j]. */ void PyMedFilt3(float* data, float* output, int nx, int ny); /* Calculate the 5x5 median filter of an array data that has dimensions * nx x ny. The results are saved in the output array. The output array should * already be allocated as we work on it in place. The median filter is not * calculated for a 2 pixel border around the image. These pixel values are * copied from the input data. The data should be striped along the * x direction, such that pixel i,j in the 2D image should have memory * location data[i + nx *j]. */ void PyMedFilt5(float* data, float* output, int nx, int ny); /* Calculate the 7x7 median filter of an array data that has dimensions * nx x ny. The results are saved in the output array. The output array should * already be allocated as we work on it in place. The median filter is not * calculated for a 3 pixel border around the image. These pixel values are * copied from the input data. The data should be striped along the * x direction, such that pixel i,j in the 2D image should have memory * location data[i + nx *j]. */ void PyMedFilt7(float* data, float* output, int nx, int ny); /* Calculate the 3x3 separable median filter of an array data that has * dimensions nx x ny. The results are saved in the output array. The output * array should already be allocated as we work on it in place. The median * filter is not calculated for a 1 pixel border around the image. These pixel * values are copied from the input data. The data should be striped along * the x direction, such that pixel i,j in the 2D image should have memory * location data[i + nx *j]. Note that the rows are median filtered first, * followed by the columns. */ void PySepMedFilt3(float* data, float* output, int nx, int ny); /* Calculate the 5x5 separable median filter of an array data that has * dimensions nx x ny. The results are saved in the output array. The output * array should already be allocated as we work on it in place.The median * filter is not calculated for a 2 pixel border around the image. These pixel * values are copied from the input data. The data should be striped along the * x direction, such that pixel i,j in the 2D image should have memory location * data[i + nx *j]. Note that the rows are median filtered first, followed by * the columns. */ void PySepMedFilt5(float* data, float* output, int nx, int ny); /* Calculate the 7x7 separable median filter of an array data that has * dimensions nx x ny. The results are saved in the output array. The output * array should already be allocated as we work on it in place. The median * filter is not calculated for a 3 pixel border around the image. These pixel * values are copied from the input data. The data should be striped along the * x direction, such that pixel i,j in the 2D image should have memory location * data[i + nx *j]. Note that the rows are median filtered first, followed by * the columns. */ void PySepMedFilt7(float* data, float* output, int nx, int ny); /* Calculate the 9x9 separable median filter of an array data that has * dimensions nx x ny. The results are saved in the output array. The output * array should already be allocated as we work on it in place. The median * filter is not calculated for a 4 pixel border around the image. These pixel * values are copied from the input data. The data should be striped along the * x direction, such that pixel i,j in the 2D image should have memory location * data[i + nx *j]. Note that the rows are median filtered first, followed by * the columns. */ void PySepMedFilt9(float* data, float* output, int nx, int ny); #endif /* MEDUTILS_H_ */ ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/astroscrappy/utils/setup_package.py0000644000175100001710000000263700000000000024132 0ustar00runnerdocker00000000000000import os import numpy as np from distutils.core import Extension from extension_helpers import add_openmp_flags_if_available UTIL_DIR = os.path.relpath(os.path.dirname(__file__)) def get_extensions(): med_sources = [str(os.path.join(UTIL_DIR, "median_utils.pyx")), str(os.path.join(UTIL_DIR, "medutils.c"))] im_sources = [str(os.path.join(UTIL_DIR, "image_utils.pyx")), str(os.path.join(UTIL_DIR, "imutils.c"))] include_dirs = [np.get_include(), UTIL_DIR] libraries = [] if 'CFLAGS' in os.environ: extra_compile_args = os.environ['CFLAGS'].split() else: extra_compile_args = ['-g', '-O3', '-funroll-loops', '-ffast-math'] ext_med = Extension(name=str('astroscrappy.utils.median_utils'), sources=med_sources, include_dirs=include_dirs, libraries=libraries, language="c", extra_compile_args=extra_compile_args) ext_im = Extension(name=str("astroscrappy.utils.image_utils"), sources=im_sources, include_dirs=include_dirs, libraries=libraries, language="c", extra_compile_args=extra_compile_args) add_openmp_flags_if_available(ext_med) add_openmp_flags_if_available(ext_im) return [ext_med, ext_im] ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317818.0 astroscrappy-1.1.0/astroscrappy/version.py0000644000175100001710000000052100000000000021632 0ustar00runnerdocker00000000000000# Note that we need to fall back to the hard-coded version if either # setuptools_scm can't be imported or setuptools_scm can't determine the # version, so we catch the generic 'Exception'. try: from setuptools_scm import get_version version = get_version(root='..', relative_to=__file__) except Exception: version = '1.1.0' ././@PaxHeader0000000000000000000000000000003400000000000011452 xustar000000000000000028 mtime=1637317818.3438623 astroscrappy-1.1.0/astroscrappy.egg-info/0000755000175100001710000000000000000000000021267 5ustar00runnerdocker00000000000000././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317818.0 astroscrappy-1.1.0/astroscrappy.egg-info/PKG-INFO0000644000175100001710000000650000000000000022365 0ustar00runnerdocker00000000000000Metadata-Version: 2.1 Name: astroscrappy Version: 1.1.0 Summary: Speedy Cosmic Ray Annihilation Package in Python Home-page: https://github.com/astropy/astroscrappy Author: Curtis McCully Author-email: cmccully@lco.global License: BSD 3-Clause Platform: UNKNOWN Requires-Python: >=3.6 Description-Content-Type: text/x-rst Provides-Extra: test Provides-Extra: docs License-File: licenses/LICENSE.rst Astro-SCRAPPY: The Speedy Cosmic Ray Annihilation Package in Python =================================================================== .. image:: https://readthedocs.org/projects/astroscrappy/badge/?version=latest :alt: Documentation Status :scale: 100% :target: https://astroscrappy.readthedocs.io/en/latest/?badge=latest .. image:: https://github.com/astropy/astroscrappy/workflows/Run%20unit%20tests/badge.svg :target: https://github.com/astropy/astroscrappy/actions :alt: CI Status .. image:: https://codecov.io/gh/astropy/astroscrappy/branch/master/graph/badge.svg :target: https://codecov.io/gh/astropy/astroscrappy :alt: AstroScrappy's Coverage Status .. image:: https://zenodo.org/badge/36837126.svg :target: https://zenodo.org/badge/latestdoi/36837126 An optimized cosmic ray detector. :Author: Curtis McCully Astro-SCRAPPY 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. If you use this code, please cite the Zendo DOI: https://zenodo.org/record/1482019 Please cite the original paper which can be found at: http://www.astro.yale.edu/dokkum/lacosmic/ van Dokkum 2001, PASP, 113, 789, 1420 (article : 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 ----- There are some differences from original LA Cosmic: - Automatic recognition of saturated stars. 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, improvment by a factor of ~90. The arrays always must be C-contiguous, thus all loops are y outer, x inner. This follows the astropy.io.fits (pyfits) convention. scipy is required for certain tests to pass, but the code itself does not depend on scipy. License ------- This project is Copyright (c) Astropy Developers and licensed under the terms of the BSD 3-Clause license. See the licenses folder for more information. ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317818.0 astroscrappy-1.1.0/astroscrappy.egg-info/SOURCES.txt0000644000175100001710000000240300000000000023152 0ustar00runnerdocker00000000000000.gitignore .readthedocs.yml CHANGES.rst MANIFEST.in README.rst pyproject.toml setup.cfg setup.py tox.ini .github/workflows/python-tests.yml .github/workflows/release.yml astroscrappy/__init__.py astroscrappy/_astropy_init.py astroscrappy/_compiler.c astroscrappy/astroscrappy.pyx astroscrappy/conftest.py astroscrappy/version.py astroscrappy.egg-info/PKG-INFO astroscrappy.egg-info/SOURCES.txt astroscrappy.egg-info/dependency_links.txt astroscrappy.egg-info/not-zip-safe astroscrappy.egg-info/requires.txt astroscrappy.egg-info/top_level.txt astroscrappy/tests/__init__.py astroscrappy/tests/fake_data.py astroscrappy/tests/test_astroscrappy.py astroscrappy/tests/test_cleaning.py astroscrappy/tests/test_gmos.py astroscrappy/tests/test_utils.py astroscrappy/tests/data/gmos.fits astroscrappy/utils/__init__.py astroscrappy/utils/image_utils.pyx astroscrappy/utils/imutils.c astroscrappy/utils/imutils.h astroscrappy/utils/median_utils.pxd astroscrappy/utils/median_utils.pyx astroscrappy/utils/medutils.c astroscrappy/utils/medutils.h astroscrappy/utils/setup_package.py docs/Makefile docs/conf.py docs/index.rst docs/make.bat docs/_templates/autosummary/base.rst docs/_templates/autosummary/class.rst docs/_templates/autosummary/module.rst licenses/LICENSE.rst licenses/README.rst././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317818.0 astroscrappy-1.1.0/astroscrappy.egg-info/dependency_links.txt0000644000175100001710000000000100000000000025335 0ustar00runnerdocker00000000000000 ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317818.0 astroscrappy-1.1.0/astroscrappy.egg-info/not-zip-safe0000644000175100001710000000000100000000000023515 0ustar00runnerdocker00000000000000 ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317818.0 astroscrappy-1.1.0/astroscrappy.egg-info/requires.txt0000644000175100001710000000011100000000000023660 0ustar00runnerdocker00000000000000astropy numpy [docs] sphinx-astropy [test] Cython pytest-astropy scipy ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317818.0 astroscrappy-1.1.0/astroscrappy.egg-info/top_level.txt0000644000175100001710000000001500000000000024015 0ustar00runnerdocker00000000000000astroscrappy ././@PaxHeader0000000000000000000000000000003400000000000011452 xustar000000000000000028 mtime=1637317818.3478622 astroscrappy-1.1.0/docs/0000755000175100001710000000000000000000000015773 5ustar00runnerdocker00000000000000././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/docs/Makefile0000644000175100001710000001074500000000000017442 0ustar00runnerdocker00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = BUILDDIR = _build # Internal variables. PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . .PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest #This is needed with git because git doesn't create a dir if it's empty $(shell [ -d "_static" ] || mkdir -p _static) help: @echo "Please use \`make ' where is one of" @echo " html to make standalone HTML files" @echo " dirhtml to make HTML files named index.html in directories" @echo " singlehtml to make a single large HTML file" @echo " pickle to make pickle files" @echo " json to make JSON files" @echo " htmlhelp to make HTML files and a HTML help project" @echo " qthelp to make HTML files and a qthelp project" @echo " devhelp to make HTML files and a Devhelp project" @echo " epub to make an epub" @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" @echo " latexpdf to make LaTeX files and run them through pdflatex" @echo " text to make text files" @echo " man to make manual pages" @echo " changes to make an overview of all changed/added/deprecated items" @echo " linkcheck to check all external links for integrity" clean: -rm -rf $(BUILDDIR) -rm -rf api -rm -rf generated html: $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." dirhtml: $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." singlehtml: $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml @echo @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." pickle: $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle @echo @echo "Build finished; now you can process the pickle files." json: $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json @echo @echo "Build finished; now you can process the JSON files." htmlhelp: $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp @echo @echo "Build finished; now you can run HTML Help Workshop with the" \ ".hhp project file in $(BUILDDIR)/htmlhelp." qthelp: $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ ".qhcp project file in $(BUILDDIR)/qthelp, like this:" @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/Astropy.qhcp" @echo "To view the help file:" @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/Astropy.qhc" devhelp: $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp @echo @echo "Build finished." @echo "To view the help file:" @echo "# mkdir -p $$HOME/.local/share/devhelp/Astropy" @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/Astropy" @echo "# devhelp" epub: $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub @echo @echo "Build finished. The epub file is in $(BUILDDIR)/epub." latex: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." @echo "Run \`make' in that directory to run these through (pdf)latex" \ "(use \`make latexpdf' here to do that automatically)." latexpdf: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo "Running LaTeX files through pdflatex..." make -C $(BUILDDIR)/latex all-pdf @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." text: $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text @echo @echo "Build finished. The text files are in $(BUILDDIR)/text." man: $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man @echo @echo "Build finished. The manual pages are in $(BUILDDIR)/man." changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." doctest: @echo "Run 'python setup.py test' in the root directory to run doctests " \ @echo "in the documentation." ././@PaxHeader0000000000000000000000000000003400000000000011452 xustar000000000000000028 mtime=1637317818.3398623 astroscrappy-1.1.0/docs/_templates/0000755000175100001710000000000000000000000020130 5ustar00runnerdocker00000000000000././@PaxHeader0000000000000000000000000000003400000000000011452 xustar000000000000000028 mtime=1637317818.3478622 astroscrappy-1.1.0/docs/_templates/autosummary/0000755000175100001710000000000000000000000022516 5ustar00runnerdocker00000000000000././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/docs/_templates/autosummary/base.rst0000644000175100001710000000037200000000000024164 0ustar00runnerdocker00000000000000{% extends "autosummary_core/base.rst" %} {# The template this is inherited from is in astropy/sphinx/ext/templates/autosummary_core. If you want to modify this template, it is strongly recommended that you still inherit from the astropy template. #}././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/docs/_templates/autosummary/class.rst0000644000175100001710000000037300000000000024360 0ustar00runnerdocker00000000000000{% extends "autosummary_core/class.rst" %} {# The template this is inherited from is in astropy/sphinx/ext/templates/autosummary_core. If you want to modify this template, it is strongly recommended that you still inherit from the astropy template. #}././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/docs/_templates/autosummary/module.rst0000644000175100001710000000037400000000000024541 0ustar00runnerdocker00000000000000{% extends "autosummary_core/module.rst" %} {# The template this is inherited from is in astropy/sphinx/ext/templates/autosummary_core. If you want to modify this template, it is strongly recommended that you still inherit from the astropy template. #}././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/docs/conf.py0000644000175100001710000001607700000000000017305 0ustar00runnerdocker00000000000000# -*- coding: utf-8 -*- # Licensed under a 3-clause BSD style license - see LICENSE.rst # # Astropy documentation build configuration file. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this file. # # All configuration values have a default. Some values are defined in # the global Astropy configuration which is loaded here before anything else. # See astropy.sphinx.conf for which values are set there. # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # sys.path.insert(0, os.path.abspath('..')) # IMPORTANT: the above commented section was generated by sphinx-quickstart, but # is *NOT* appropriate for astropy or Astropy affiliated packages. It is left # commented out with this explanation to make it clear why this should not be # done. If the sys.path entry above is added, when the astropy.sphinx.conf # import occurs, it will import the *source* version of astropy instead of the # version installed (if invoked as "make html" or directly with sphinx), or the # version in the build directory (if "python setup.py build_sphinx" is used). # Thus, any C-extensions that are needed to build the documentation will *not* # be accessible, and the documentation will not build correctly. import os import sys import datetime from importlib import import_module try: from sphinx_astropy.conf.v1 import * # noqa except ImportError: print('ERROR: the documentation requires the sphinx-astropy package to be installed') sys.exit(1) # Get configuration information from setup.cfg from configparser import ConfigParser conf = ConfigParser() conf.read([os.path.join(os.path.dirname(__file__), '..', 'setup.cfg')]) setup_cfg = dict(conf.items('metadata')) # -- General configuration ---------------------------------------------------- # By default, highlight as Python 3. highlight_language = 'python3' # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.2' # To perform a Sphinx version check that needs to be more specific than # major.minor, call `check_sphinx_version("x.y.z")` here. # check_sphinx_version("1.2.1") # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns.append('_templates') # This is added to the end of RST files - a good place to put substitutions to # be used globally. rst_epilog += """ """ # -- Project information ------------------------------------------------------ # This does not *have* to match the package name, but typically does project = setup_cfg['name'] author = setup_cfg['author'] copyright = '{0}, {1}'.format( datetime.datetime.now().year, setup_cfg['author']) # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. import_module(setup_cfg['name']) package = sys.modules[setup_cfg['name']] # The short X.Y version. version = package.__version__.split('-', 1)[0] # The full version, including alpha/beta/rc tags. release = package.__version__ # -- Options for HTML output -------------------------------------------------- # A NOTE ON HTML THEMES # The global astropy configuration uses a custom theme, 'bootstrap-astropy', # which is installed along with astropy. A different theme can be used or # the options for this theme can be modified by overriding some of the # variables set in the global configuration. The variables set in the # global configuration are listed below, commented out. # Add any paths that contain custom themes here, relative to this directory. # To use a different custom theme, add the directory containing the theme. #html_theme_path = [] # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. To override the custom theme, set this to the # name of a builtin theme or the name of a custom theme in html_theme_path. #html_theme = None # Please update these texts to match the name of your package. html_theme_options = { 'logotext1': 'astro', # white, semi-bold 'logotext2': 'scrappy', # orange, light 'logotext3': ':docs' # white, light } # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = '' # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = '' # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '' # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". html_title = '{0} v{1}'.format(project, release) # Output file base name for HTML help builder. htmlhelp_basename = project + 'doc' # -- Options for LaTeX output ------------------------------------------------- # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [('index', project + '.tex', project + u' Documentation', author, 'manual')] # -- Options for manual page output ------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [('index', project.lower(), project + u' Documentation', [author], 1)] # -- Options for the edit_on_github extension --------------------------------- if setup_cfg.get('edit_on_github').lower() == 'true': extensions += ['sphinx_astropy.ext.edit_on_github'] edit_on_github_project = setup_cfg['github_project'] edit_on_github_branch = "master" edit_on_github_source_root = "" edit_on_github_doc_root = "docs" # -- Resolving issue number to links in changelog ----------------------------- github_issues_url = 'https://github.com/{0}/issues/'.format(setup_cfg['github_project']) # -- Turn on nitpicky mode for sphinx (to warn about references not found) ---- # # nitpicky = True # nitpick_ignore = [] # # Some warnings are impossible to suppress, and you can list specific references # that should be ignored in a nitpick-exceptions file which should be inside # the docs/ directory. The format of the file should be: # # # # for example: # # py:class astropy.io.votable.tree.Element # py:class astropy.io.votable.tree.SimpleElement # py:class astropy.io.votable.tree.SimpleElementWithContent # # Uncomment the following lines to enable the exceptions: # # for line in open('nitpick-exceptions'): # if line.strip() == "" or line.startswith("#"): # continue # dtype, target = line.split(None, 1) # target = target.strip() # nitpick_ignore.append((dtype, six.u(target))) ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/docs/index.rst0000644000175100001710000000011500000000000017631 0ustar00runnerdocker00000000000000**************** ASTROSCRAPPY **************** .. automodapi:: astroscrappy ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/docs/make.bat0000644000175100001710000001064100000000000017402 0ustar00runnerdocker00000000000000@ECHO OFF REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set BUILDDIR=_build set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% . if NOT "%PAPER%" == "" ( set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% ) if "%1" == "" goto help if "%1" == "help" ( :help echo.Please use `make ^` where ^ is one of echo. html to make standalone HTML files echo. dirhtml to make HTML files named index.html in directories echo. singlehtml to make a single large HTML file echo. pickle to make pickle files echo. json to make JSON files echo. htmlhelp to make HTML files and a HTML help project echo. qthelp to make HTML files and a qthelp project echo. devhelp to make HTML files and a Devhelp project echo. epub to make an epub echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter echo. text to make text files echo. man to make manual pages echo. changes to make an overview over all changed/added/deprecated items echo. linkcheck to check all external links for integrity echo. doctest to run all doctests embedded in the documentation if enabled goto end ) if "%1" == "clean" ( for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i del /q /s %BUILDDIR%\* goto end ) if "%1" == "html" ( %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/html. goto end ) if "%1" == "dirhtml" ( %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. goto end ) if "%1" == "singlehtml" ( %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. goto end ) if "%1" == "pickle" ( %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the pickle files. goto end ) if "%1" == "json" ( %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the JSON files. goto end ) if "%1" == "htmlhelp" ( %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run HTML Help Workshop with the ^ .hhp project file in %BUILDDIR%/htmlhelp. goto end ) if "%1" == "qthelp" ( %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run "qcollectiongenerator" with the ^ .qhcp project file in %BUILDDIR%/qthelp, like this: echo.^> qcollectiongenerator %BUILDDIR%\qthelp\Astropy.qhcp echo.To view the help file: echo.^> assistant -collectionFile %BUILDDIR%\qthelp\Astropy.ghc goto end ) if "%1" == "devhelp" ( %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp if errorlevel 1 exit /b 1 echo. echo.Build finished. goto end ) if "%1" == "epub" ( %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub if errorlevel 1 exit /b 1 echo. echo.Build finished. The epub file is in %BUILDDIR%/epub. goto end ) if "%1" == "latex" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex if errorlevel 1 exit /b 1 echo. echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. goto end ) if "%1" == "text" ( %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text if errorlevel 1 exit /b 1 echo. echo.Build finished. The text files are in %BUILDDIR%/text. goto end ) if "%1" == "man" ( %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man if errorlevel 1 exit /b 1 echo. echo.Build finished. The manual pages are in %BUILDDIR%/man. goto end ) if "%1" == "changes" ( %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes if errorlevel 1 exit /b 1 echo. echo.The overview file is in %BUILDDIR%/changes. goto end ) if "%1" == "linkcheck" ( %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck if errorlevel 1 exit /b 1 echo. echo.Link check complete; look for any errors in the above output ^ or in %BUILDDIR%/linkcheck/output.txt. goto end ) if "%1" == "doctest" ( %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest if errorlevel 1 exit /b 1 echo. echo.Testing of doctests in the sources finished, look at the ^ results in %BUILDDIR%/doctest/output.txt. goto end ) :end ././@PaxHeader0000000000000000000000000000003400000000000011452 xustar000000000000000028 mtime=1637317818.3478622 astroscrappy-1.1.0/licenses/0000755000175100001710000000000000000000000016650 5ustar00runnerdocker00000000000000././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/licenses/LICENSE.rst0000644000175100001710000000272300000000000020470 0ustar00runnerdocker00000000000000Copyright (c) 2015-2018, Curtis McCully All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the Astropy Team nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/licenses/README.rst0000644000175100001710000000021400000000000020334 0ustar00runnerdocker00000000000000Licenses ======== This directory holds license and credit information for the package, works the package is derived from, and/or datasets. ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/pyproject.toml0000644000175100001710000000073300000000000017762 0ustar00runnerdocker00000000000000[build-system] requires = ["setuptools", "setuptools_scm", "wheel", "extension-helpers", "oldest-supported-numpy", "Cython>=0.29.21,<3.0"] build-backend = 'setuptools.build_meta' [tool.cibuildwheel] # Skip pypy on mac due to numpy/accelerate issues skip = "pp37* cp36* cp310-win* cp310-manylinux_x86_64 cp*-manylinux_i686 *musllinux*" test-requires = "pytest scipy" test-command = "pytest --pyargs astroscrappy" ././@PaxHeader0000000000000000000000000000003400000000000011452 xustar000000000000000028 mtime=1637317818.3478622 astroscrappy-1.1.0/setup.cfg0000644000175100001710000000276300000000000016674 0ustar00runnerdocker00000000000000[metadata] name = astroscrappy author = Curtis McCully author_email = cmccully@lco.global license = BSD 3-Clause license_file = licenses/LICENSE.rst url = https://github.com/astropy/astroscrappy description = Speedy Cosmic Ray Annihilation Package in Python long_description = file: README.rst long_description_content_type = text/x-rst edit_on_github = False github_project = astropy/astroscrappy [options] zip_safe = False packages = find: python_requires = >=3.6 setup_requires = setuptools_scm install_requires = astropy numpy [options.extras_require] test = Cython pytest-astropy scipy docs = sphinx-astropy [options.package_data] * = data/* [tool:pytest] testpaths = "astroscrappy" "docs" astropy_header = true doctest_plus = enabled text_file_format = rst addopts = -p no:warnings --doctest-rst doctest_norecursedirs = */setup_package.py [coverage:run] plugins = Cython.Coverage omit = astroscrappy/_astropy_init* astroscrappy/conftest.py astroscrappy/*setup_package* astroscrappy/tests/* astroscrappy/*/tests/* astroscrappy/extern/* astroscrappy/version* */astroscrappy/_astropy_init* */astroscrappy/conftest.py */astroscrappy/*setup_package* */astroscrappy/tests/* */astroscrappy/*/tests/* */astroscrappy/extern/* */astroscrappy/version* [coverage:report] exclude_lines = pragma: no cover except ImportError raise AssertionError raise NotImplementedError def main\(.*\): pragma: py{ignore_python_version} def _ipython_key_completions_ [egg_info] tag_build = tag_date = 0 ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/setup.py0000644000175100001710000000474200000000000016564 0ustar00runnerdocker00000000000000#!/usr/bin/env python # Licensed under a 3-clause BSD style license - see LICENSE.rst # NOTE: The configuration for the package, including the name, version, and # other information are set in the setup.cfg file. import os import sys from setuptools import setup # First provide helpful messages if contributors try and run legacy commands # for tests or docs. TEST_HELP = """ Note: running tests is no longer done using 'python setup.py test'. Instead you will need to run: tox -e test If you don't already have tox installed, you can install it with: pip install tox If you only want to run part of the test suite, you can also use pytest directly with:: pip install -e .[test] pytest For more information, see: http://docs.astropy.org/en/latest/development/testguide.html#running-tests """ if 'test' in sys.argv: print(TEST_HELP) sys.exit(1) DOCS_HELP = """ Note: building the documentation is no longer done using 'python setup.py build_docs'. Instead you will need to run: tox -e build_docs If you don't already have tox installed, you can install it with: pip install tox You can also build the documentation with Sphinx directly using:: pip install -e .[docs] cd docs make html For more information, see: http://docs.astropy.org/en/latest/install.html#builddocs """ if 'build_docs' in sys.argv or 'build_sphinx' in sys.argv: print(DOCS_HELP) sys.exit(1) VERSION_TEMPLATE = """ # Note that we need to fall back to the hard-coded version if either # setuptools_scm can't be imported or setuptools_scm can't determine the # version, so we catch the generic 'Exception'. try: from setuptools_scm import get_version version = get_version(root='..', relative_to=__file__) except Exception: version = '{version}' """.lstrip() # Import this later to allow checking deprecated options before from extension_helpers import get_extensions # noqa from Cython.Build import cythonize # noqa ext_modules = get_extensions() compiler_directives = {} if os.getenv('COVERAGE'): print('Adding linetrace directive') compiler_directives['profile'] = True compiler_directives['linetrace'] = True os.environ['CFLAGS'] = '-DCYTHON_TRACE_NOGIL=1 --coverage -fno-inline-functions -O0' ext_modules = cythonize(ext_modules, compiler_directives=compiler_directives) setup(use_scm_version={'write_to': os.path.join('astroscrappy', 'version.py'), 'write_to_template': VERSION_TEMPLATE}, ext_modules=ext_modules) ././@PaxHeader0000000000000000000000000000002600000000000011453 xustar000000000000000022 mtime=1637317803.0 astroscrappy-1.1.0/tox.ini0000644000175100001710000000533000000000000016357 0ustar00runnerdocker00000000000000[tox] envlist = py{36,37,38,39}-test{,-alldeps,-devdeps}{,-cov} py{36,37,38,39}-test-numpy{116,117,118,119} py{36,37,38,39}-test-astropy{30,40,41,lts} build_docs linkcheck codestyle requires = setuptools >= 30.3.0 pip >= 19.3.1 isolated_build = true indexserver = NIGHTLY = https://pypi.anaconda.org/scipy-wheels-nightly/simple [testenv] # Pass through the following environment variables which may be needed for the CI passenv = HOME WINDIR LC_ALL LC_CTYPE CC CI TRAVIS TRAVIS_* COVERAGE # Run the tests in a temporary directory to make sure that we don't import # this package from the source tree changedir = .tmp/{envname} # tox environments are constructed with so-called 'factors' (or terms) # separated by hyphens, e.g. test-devdeps-cov. Lines below starting with factor: # will only take effect if that factor is included in the environment name. To # see a list of example environments that can be run, along with a description, # run: # # tox -l -v # description = run tests alldeps: with all optional dependencies devdeps: with the latest developer version of key dependencies oldestdeps: with the oldest supported version of key dependencies cov: and test coverage numpy116: with numpy 1.16.* numpy117: with numpy 1.17.* numpy118: with numpy 1.18.* numpy119: with numpy 1.19.* astropy30: with astropy 3.0.* astropy40: with astropy 4.0.* astropy41: with astropy 4.1.* astropylts: with the latest astropy LTS # The following provides some specific pinnings for key packages deps = numpy116: numpy==1.16.* numpy117: numpy==1.17.* numpy118: numpy==1.18.* numpy119: numpy==1.19.* astropy30: astropy==3.0.* astropy40: astropy==4.0.* astropy41: astropy==4.1.* astropylts: astropy==4.0.* devdeps: :NIGHTLY:numpy devdeps: git+https://github.com/astropy/astropy.git#egg=astropy # The following indicates which extras_require from setup.cfg will be installed extras = test alldeps: all commands = pip freeze !cov: pytest --pyargs astroscrappy {toxinidir}/docs {posargs} cov: pytest --pyargs astroscrappy {toxinidir}/docs --cov astroscrappy --cov-config={toxinidir}/setup.cfg {posargs} [testenv:build_docs] changedir = docs description = invoke sphinx-build to build the HTML docs extras = docs commands = pip freeze sphinx-build -W -b html . _build/html [testenv:linkcheck] changedir = docs description = check the links in the HTML docs extras = docs commands = pip freeze sphinx-build -W -b linkcheck . _build/html [testenv:codestyle] skip_install = true changedir = . description = check code style, e.g. with flake8 deps = flake8 commands = flake8 astroscrappy --count --max-line-length=100