| Name | prose JSON |
| Version |
3.3.3
JSON |
| download |
| home_page | None |
| Summary | Modular image processing pipelines for Astronomy |
| upload_time | 2024-05-17 18:43:35 |
| maintainer | None |
| docs_url | None |
| author | Lionel Garcia |
| requires_python | <3.12,>=3.9 |
| license | MIT |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# prose
<p align="center" style="margin-bottom:-50px">
<img src="docs/_static/prose3.png" width="450">
</p>
<p align="center">
Modular image processing pipelines for Astronomy
<br>
<p align="center">
<a href="https://github.com/lgrcia/prose"><img src="https://img.shields.io/badge/github-lgrcia/prose-03A487.svg?style=flat" alt="github"/></a>
<a href="https://github.com/lgrcia/prose/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-MIT-lightgray.svg?style=flat" alt="license"/></a>
<a href="https://arxiv.org/abs/2111.02814"><img src="https://img.shields.io/badge/paper-B166A9.svg?style=flat" alt="paper"/></a>
<a href="https://prose.readthedocs.io/en/latest"><img src="https://img.shields.io/badge/documentation-black.svg?style=flat" alt="documentation"/></a>
</p>
</p>
*prose* is a Python package to build modular image processing pipelines for Astronomy.
*powered by [astropy](https://www.astropy.org/) and [photutils](https://photutils.readthedocs.io)*!
## Example
Here is a quick example pipeline to characterize the point-spread-function (PSF) of an example image
```python
import matplotlib.pyplot as plt
from prose import Sequence, blocks
from prose.simulations import example_image
# getting the example image
image = example_image()
sequence = Sequence(
[
blocks.PointSourceDetection(), # stars detection
blocks.Cutouts(shape=21), # cutouts extraction
blocks.MedianEPSF(), # PSF building
blocks.Moffat2D(), # PSF modeling
]
)
sequence.run(image)
# plotting
image.show() # detected stars
# effective PSF parameters
image.epsf.params
```
While being run on a single image, a Sequence is designed to be run on list of images (paths) and provides the architecture to build powerful pipelines. For more details check [Quickstart](https://prose.readthedocs.io/en/latest/ipynb/quickstart.html) and [What is a pipeline?](https://prose.readthedocs.io/en/latest/ipynb/core.html)
## Installation
### latest
*prose* is written for python 3 and can be installed from [pypi](https://pypi.org/project/prose/) with:
```shell
pip install prose
```
For the latest version
```shell
pip install 'prose @ git+https://github.com/lgrcia/prose'
```
## Contributions
See our [contributions guidelines](docs/CONTRIBUTING.md)
## Attribution
If you find `prose` useful for your research, cite [Garcia et. al 2022](https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.4817G). The BibTeX entry for the paper is:
```
@ARTICLE{prose,
author = {{Garcia}, Lionel J. and {Timmermans}, Mathilde and {Pozuelos}, Francisco J. and {Ducrot}, Elsa and {Gillon}, Micha{\"e}l and {Delrez}, Laetitia and {Wells}, Robert D. and {Jehin}, Emmanu{\"e}l},
title = "{PROSE: a PYTHON framework for modular astronomical images processing}",
journal = {\mnras},
keywords = {instrumentation: detectors, methods: data analysis, planetary systems, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Earth and Planetary Astrophysics},
year = 2022,
month = feb,
volume = {509},
number = {4},
pages = {4817-4828},
doi = {10.1093/mnras/stab3113},
archivePrefix = {arXiv},
eprint = {2111.02814},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.4817G},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```
and read about how to cite the dependencies of your sequences [here](https://prose.readthedocs.io/en/latest/ipynb/acknowledgement.html).
Raw data
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"description": "# prose\n\n<p align=\"center\" style=\"margin-bottom:-50px\">\n <img src=\"docs/_static/prose3.png\" width=\"450\">\n</p>\n\n<p align=\"center\">\n Modular image processing pipelines for Astronomy\n <br>\n <p align=\"center\">\n <a href=\"https://github.com/lgrcia/prose\"><img src=\"https://img.shields.io/badge/github-lgrcia/prose-03A487.svg?style=flat\" alt=\"github\"/></a>\n <a href=\"https://github.com/lgrcia/prose/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/license-MIT-lightgray.svg?style=flat\" alt=\"license\"/></a>\n <a href=\"https://arxiv.org/abs/2111.02814\"><img src=\"https://img.shields.io/badge/paper-B166A9.svg?style=flat\" alt=\"paper\"/></a>\n <a href=\"https://prose.readthedocs.io/en/latest\"><img src=\"https://img.shields.io/badge/documentation-black.svg?style=flat\" alt=\"documentation\"/></a>\n </p>\n</p>\n\n *prose* is a Python package to build modular image processing pipelines for Astronomy.\n\n*powered by [astropy](https://www.astropy.org/) and [photutils](https://photutils.readthedocs.io)*!\n\n## Example\n\nHere is a quick example pipeline to characterize the point-spread-function (PSF) of an example image\n\n\n```python\nimport matplotlib.pyplot as plt\nfrom prose import Sequence, blocks\nfrom prose.simulations import example_image\n\n# getting the example image\nimage = example_image()\n\nsequence = Sequence(\n [\n blocks.PointSourceDetection(), # stars detection\n blocks.Cutouts(shape=21), # cutouts extraction\n blocks.MedianEPSF(), # PSF building\n blocks.Moffat2D(), # PSF modeling\n ]\n)\n\nsequence.run(image)\n\n# plotting\nimage.show() # detected stars\n\n# effective PSF parameters\nimage.epsf.params\n```\n\nWhile being run on a single image, a Sequence is designed to be run on list of images (paths) and provides the architecture to build powerful pipelines. For more details check [Quickstart](https://prose.readthedocs.io/en/latest/ipynb/quickstart.html) and [What is a pipeline?](https://prose.readthedocs.io/en/latest/ipynb/core.html)\n\n## Installation\n\n### latest\n\n*prose* is written for python 3 and can be installed from [pypi](https://pypi.org/project/prose/) with:\n\n```shell\npip install prose\n```\n\nFor the latest version \n\n```shell\npip install 'prose @ git+https://github.com/lgrcia/prose'\n```\n\n## Contributions\nSee our [contributions guidelines](docs/CONTRIBUTING.md)\n\n## Attribution\n\nIf you find `prose` useful for your research, cite [Garcia et. al 2022](https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.4817G). The BibTeX entry for the paper is:\n```\n@ARTICLE{prose,\n author = {{Garcia}, Lionel J. and {Timmermans}, Mathilde and {Pozuelos}, Francisco J. and {Ducrot}, Elsa and {Gillon}, Micha{\\\"e}l and {Delrez}, Laetitia and {Wells}, Robert D. and {Jehin}, Emmanu{\\\"e}l},\n title = \"{PROSE: a PYTHON framework for modular astronomical images processing}\",\n journal = {\\mnras},\n keywords = {instrumentation: detectors, methods: data analysis, planetary systems, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Earth and Planetary Astrophysics},\n year = 2022,\n month = feb,\n volume = {509},\n number = {4},\n pages = {4817-4828},\n doi = {10.1093/mnras/stab3113},\narchivePrefix = {arXiv},\n eprint = {2111.02814},\n primaryClass = {astro-ph.IM},\n adsurl = {https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.4817G},\n adsnote = {Provided by the SAO/NASA Astrophysics Data System}\n}\n```\n\nand read about how to cite the dependencies of your sequences [here](https://prose.readthedocs.io/en/latest/ipynb/acknowledgement.html).\n\n",
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