| Name | astroalign JSON |
| Version |
2.5.1
JSON |
| download |
| home_page | |
| Summary | Astrometric Alignment of Images |
| upload_time | 2023-10-14 01:12:58 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.7 |
| license | MIT License Copyright (c) 2016-2019 Martin Beroiz Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
astronomy
registration
alignment
asterism-matching
stellar-astronomical-images
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
***
[](https://quatrope.github.io/)
[](https://github.com/quatrope/astroalign/actions/workflows/aa-ci.yml)
[](https://codecov.io/github/quatrope/astroalign)
[](http://astroalign.readthedocs.org/en/latest/?badge=latest)
[](https://pypi.org/project/astroalign/)
[](http://ascl.net/1906.001)
**ASTROALIGN** is a python module that will try to align two stellar astronomical images, especially when there is no WCS information available.
It does so by finding similar 3-point asterisms (triangles) in both images and deducing the affine transformation between them.
Generic registration routines try to match feature points, using corner
detection routines to make the point correspondence.
These generally fail for stellar astronomical images, since stars have very
little stable structure and so, in general, indistinguishable from each other.
Asterism matching is more robust, and closer to the human way of matching stellar images.
Astroalign can match images of very different field of view, point-spread function, seeing and atmospheric conditions.
It may not work, or work with special care, on images of extended objects with few point-like sources or in very crowded fields.
You can find a Jupyter notebook example with the main features at [http://quatrope.github.io/astroalign/](http://quatrope.github.io/astroalign/).
**Full documentation:** https://astroalign.readthedocs.io/
# Installation
Using setuptools:
```bash
$ pip install astroalign
```
or from this distribution with
```bash
$ python setup.py install
```
## Performance: Optional
This library is optionally compatible with [bottleneck](https://github.com/pydata/bottleneck) and may offer performance improvements in some cases.
Install bottleneck in your project as a peer to astroalign using:
```bash
pip install bottleneck
```
`Astroalign` will pick this optional dependency up and use it's performance improved functions for computing transforms.
## Running Tests
```bash
python tests/test_align.py
```
# Usage example
```
>>> import astroalign as aa
>>> aligned_image, footprint = aa.register(source_image, target_image)
```
In this example `source_image` will be interpolated by a transformation to coincide pixel to pixel with `target_image` and stored in `aligned_image`.
If we are only interested in knowing the transformation and the correspondence of control points in both images, use `find_transform` will return the transformation in a [Scikit-Image](https://scikit-image.org/) `SimilarityTransform` object and a list of stars in source with the corresponding stars in target.
```
>>> transf, (s_list, t_list) = aa.find_transform(source, target)
```
`source` and `target` can each either be the numpy array of the image (grayscale or color),
or an iterable of (x, y) pairs of star positions on the image.
The returned `transf` object is a scikit-image [`SimilarityTranform`](http://scikit-image.org/docs/dev/api/skimage.transform.html#skimage.transform.SimilarityTransform) object that contains the transformation matrix along with the scale, rotation and translation parameters.
`s_list` and `t_list` are numpy arrays of (x, y) point correspondence between `source` and `target`. `transf` applied to `s_list` will approximately render `t_list`.
# Related Software
There are other related software that may offer similar functionality as astroalign.
This list is not exhaustive and may be others.
* [astrometry.net](https://github.com/dstndstn/astrometry.net)
* [reproject](https://github.com/astropy/reproject)
* [Watney Astrometry Engine](https://github.com/Jusas/WatneyAstrometry)
* [Stellar Solver](https://github.com/rlancaste/stellarsolver)
* [Montage](https://github.com/Caltech-IPAC/Montage)
# Citation
If you use astroalign in a scientific publication, we would appreciate citations to the following [paper](https://www.sciencedirect.com/science/article/pii/S221313372030038X):
Astroalign: A Python module for astronomical image registration.
Beroiz, M., Cabral, J. B., & Sanchez, B.
Astronomy & Computing, Volume 32, July 2020, 100384.
***
TOROS Dev Team
<martinberoiz@gmail.com>
Raw data
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"description": "\n\n***\n\n[](https://quatrope.github.io/)\n[](https://github.com/quatrope/astroalign/actions/workflows/aa-ci.yml)\n[](https://codecov.io/github/quatrope/astroalign)\n[](http://astroalign.readthedocs.org/en/latest/?badge=latest)\n[](https://pypi.org/project/astroalign/)\n\n[](http://ascl.net/1906.001)\n\n\n**ASTROALIGN** is a python module that will try to align two stellar astronomical images, especially when there is no WCS information available.\n\nIt does so by finding similar 3-point asterisms (triangles) in both images and deducing the affine transformation between them.\n\nGeneric registration routines try to match feature points, using corner\ndetection routines to make the point correspondence.\nThese generally fail for stellar astronomical images, since stars have very\nlittle stable structure and so, in general, indistinguishable from each other.\nAsterism matching is more robust, and closer to the human way of matching stellar images.\n\nAstroalign can match images of very different field of view, point-spread function, seeing and atmospheric conditions.\n\nIt may not work, or work with special care, on images of extended objects with few point-like sources or in very crowded fields.\n\nYou can find a Jupyter notebook example with the main features at [http://quatrope.github.io/astroalign/](http://quatrope.github.io/astroalign/).\n\n**Full documentation:** https://astroalign.readthedocs.io/\n\n# Installation\n\nUsing setuptools:\n\n```bash\n$ pip install astroalign\n```\n\nor from this distribution with\n\n```bash\n$ python setup.py install\n```\n\n## Performance: Optional\n\nThis library is optionally compatible with [bottleneck](https://github.com/pydata/bottleneck) and may offer performance improvements in some cases.\nInstall bottleneck in your project as a peer to astroalign using:\n\n```bash\npip install bottleneck\n```\n\n`Astroalign` will pick this optional dependency up and use it's performance improved functions for computing transforms.\n\n## Running Tests\n\n```bash\npython tests/test_align.py\n```\n\n# Usage example\n\n```\n>>> import astroalign as aa\n>>> aligned_image, footprint = aa.register(source_image, target_image)\n```\n\nIn this example `source_image` will be interpolated by a transformation to coincide pixel to pixel with `target_image` and stored in `aligned_image`.\n\nIf we are only interested in knowing the transformation and the correspondence of control points in both images, use `find_transform` will return the transformation in a [Scikit-Image](https://scikit-image.org/) `SimilarityTransform` object and a list of stars in source with the corresponding stars in target.\n\n```\n>>> transf, (s_list, t_list) = aa.find_transform(source, target)\n```\n\n`source` and `target` can each either be the numpy array of the image (grayscale or color),\nor an iterable of (x, y) pairs of star positions on the image.\n\nThe returned `transf` object is a scikit-image [`SimilarityTranform`](http://scikit-image.org/docs/dev/api/skimage.transform.html#skimage.transform.SimilarityTransform) object that contains the transformation matrix along with the scale, rotation and translation parameters.\n\n`s_list` and `t_list` are numpy arrays of (x, y) point correspondence between `source` and `target`. `transf` applied to `s_list` will approximately render `t_list`.\n\n# Related Software\n\nThere are other related software that may offer similar functionality as astroalign.\nThis list is not exhaustive and may be others.\n\n* [astrometry.net](https://github.com/dstndstn/astrometry.net)\n* [reproject](https://github.com/astropy/reproject)\n* [Watney Astrometry Engine](https://github.com/Jusas/WatneyAstrometry)\n* [Stellar Solver](https://github.com/rlancaste/stellarsolver)\n* [Montage](https://github.com/Caltech-IPAC/Montage)\n\n# Citation\n\nIf you use astroalign in a scientific publication, we would appreciate citations to the following [paper](https://www.sciencedirect.com/science/article/pii/S221313372030038X):\n\n Astroalign: A Python module for astronomical image registration.\n Beroiz, M., Cabral, J. B., & Sanchez, B.\n Astronomy & Computing, Volume 32, July 2020, 100384.\n\n***\n\nTOROS Dev Team\n\n<martinberoiz@gmail.com>\n",
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