prose
Modular image processing pipelines with Python. Built for Astronomy.
Science Score: 49.0%
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org -
○Committers with academic emails
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (15.9%) to scientific vocabulary
Keywords
Repository
Modular image processing pipelines with Python. Built for Astronomy.
Basic Info
- Host: GitHub
- Owner: lgrcia
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://prose.readthedocs.io
- Size: 86.1 MB
Statistics
- Stars: 58
- Watchers: 4
- Forks: 16
- Open Issues: 43
- Releases: 26
Topics
Metadata Files
README.md
[!CAUTION] prose maintenance has stopped. More information here.
prose
Modular image processing pipelines for Astronomy
prose is a Python package to build modular image processing pipelines for Astronomy.
powered by astropy and photutils!
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 and What is a pipeline?
Installation
latest
prose is written for python 3 and can be installed from pypi with:
shell
pip install prose
For the latest version
shell
pip install 'prose @ git+https://github.com/lgrcia/prose'
Contributions
See our contributions guidelines
Attribution
If you find prose useful for your research, cite Garcia et. al 2022. 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.
Owner
- Name: Lionel J. Garcia
- Login: lgrcia
- Kind: user
- Website: lgrcia.github.io
- Repositories: 4
- Profile: https://github.com/lgrcia
GitHub Events
Total
- Issues event: 3
- Watch event: 5
- Delete event: 1
- Issue comment event: 14
- Push event: 9
- Pull request event: 8
- Fork event: 6
- Create event: 1
Last Year
- Issues event: 3
- Watch event: 5
- Delete event: 1
- Issue comment event: 14
- Push event: 9
- Pull request event: 8
- Fork event: 6
- Create event: 1
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 989
- Total Committers: 7
- Avg Commits per committer: 141.286
- Development Distribution Score (DDS): 0.546
Top Committers
| Name | Commits | |
|---|---|---|
| lgrcia | l****a@l****r | 449 |
| Lionel J. Garcia | l****a@u****e | 176 |
| Lionel Garcia | l****a@n****e | 134 |
| Lionel Garcia | l****a@M****l | 127 |
| mathtimm | m****s@h****m | 90 |
| Lionel Garcia | l****a@p****e | 12 |
| exotic | e****c@e****g | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 9 months ago
All Time
- Total issues: 102
- Total pull requests: 85
- Average time to close issues: 7 months
- Average time to close pull requests: 16 days
- Total issue authors: 15
- Total pull request authors: 8
- Average comments per issue: 1.2
- Average comments per pull request: 0.26
- Merged pull requests: 63
- Bot issues: 0
- Bot pull requests: 29
Past Year
- Issues: 6
- Pull requests: 9
- Average time to close issues: N/A
- Average time to close pull requests: 3 months
- Issue authors: 4
- Pull request authors: 4
- Average comments per issue: 1.17
- Average comments per pull request: 0.33
- Merged pull requests: 3
- Bot issues: 0
- Bot pull requests: 4
Top Authors
Issue Authors
- lgrcia (64)
- mathtimm (8)
- educrot (7)
- schackey (3)
- mathvdd (2)
- EAlexJ (2)
- alexdumitriu (2)
- vdeloupy (2)
- smoh (2)
- manishprasad0 (1)
- dan-adi (1)
- Tim171717 (1)
- hauretc (1)
- MouradGHACHOUI (1)
Pull Request Authors
- dependabot[bot] (46)
- lgrcia (40)
- michealroberts (6)
- mathtimm (5)
- schackey (4)
- shfxia (2)
- AlexKurek (2)
- hauretc (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- astroalign *
- astropy ==4.0
- astroquery *
- celerite2 *
- colorama *
- docutils *
- imageio *
- ipython *
- jinja2 *
- matplotlib *
- nbsphinx *
- netcdf4 *
- numba *
- numpy *
- pandas >=1.1
- photutils *
- pyyaml *
- requests *
- scikit-image *
- scipy *
- sep *
- sphinx *
- sphinx-copybutton *
- sphinx_rtd_theme *
- tabulate *
- tensorflow *
- tqdm *
- xarray *
- astropy *
- astroquery *
- celerite2 *
- imageio *
- ipython *
- jinja2 *
- matplotlib *
- netcdf4 *
- numba *
- numpy *
- pandas >=1.1
- photutils *
- pyyaml *
- requests *
- scikit-image *
- scipy *
- sep *
- tabulate *
- tensorflow *
- tqdm *
- twirl *
- xarray *