Version 2.5.0

In this release:

• Mask in source or target image passes over the mask to source detection. Now is possible to exclude regions outside the region of interest ROI to find the match between images.
• Use of numpy's new random number generator API. This requires numpy version to be at least 1.17.
• Updates to README.md.

Version 2.4.2

This release:

• Gets rid of ez_setup.py and uses pyproject.toml instead.
• Lazy loads estimate_transform and matrix_transform from scikit-image for faster import time.
• Drops support for python 3.6 and 3.7 from unit testing.

Version 2.4

In this release:

Improved RANSAC algorithm

• The new ransac will exhaust all possibilities in a pseudo-random way. The exploration is done on a single (fixed) realization of all possible matches and it will exhaust the list of candidate matches before failing.
• ransac now iterates 3 times searching for the maximum number of matches before returning. This ensures (almost certainly in a statistical sense) that the results will be the same between calls.
• The new ransac is virtually deterministic, despite its stochastic search nature.

Version 2.3.2

In this release:

• Fix bug (#65) that would cause register to fail when it's passed a Image object (from pillow)
• Migrated unit tests to Github Actions

Version 2.3.1

In this release

• Added support to work with color images.

Check the details in the documentation.

(Note: 2.3.1 fixes an PyPI upload issue for 2.3)

Version 2.2

In this release:

• The functions register and find_transform now work with CCDData and NDData input.
• Docstrings and documentation changed to reflect the change.
• Added more exhaustive tests for new kind of input.

Version 2.1

In this Release

• Keyword argument detection_sigma to control the std-dev above noise used in source detection. Default now is 5 instead of 3.
• Keyword argument max_control_points to control how many bright sources should be detected on an image.
• Keyword argument min_area to control minimum number of connected pixels of sources detected. Defaults to 5.
• Docs examples for few sources on the field.
• Docs examples for faint sources.
• Other code improvements.

Version 2.0.2

Bug fix:

Very similar triangles made from very near stars may cause multiple correspondences between stars.

• Added a purge where only the best correspondence (min trasnsf error) is kept.
• Added test for this specific case.

Version 2.0.1

Bug fix:

• Images with small number of sources < 5 would not work with find_transform and register. (see #36)

This only tests the case when source and target are input as a list of (x, y) positions (as opposed to the image array).

Separate cases for 3, 4, 5, and 6 sources.

Note: Even though this version may still work with Python 2.7, this version drops support for testing Python < 3.

Version 2.0

Version 2.0

What's new

• register now accepts as input images Astropy's NDData objects (this includes the CCDProc's CCDData subclass) as well as Numpy's ndarray.

• register returns a footprint boolean image, True for masked pixels with no information.

registered_image, footprint = aa.register(source, target)

• New fill_value parameter: convenience argument to fill out untouched areas on the registered image.

registered_image, footprint = aa.register(source, target, fill_value=-99999.99)

• For data objects like NDData or Numpy masked arrays, register now propagates the mask if any.

Note:
This version drops support for the deprecated functions align_image and find_affine_transform.

More information and examples in the documentation.