Skip to main content

Modular image processing pipelines for Astronomy

Project description

prose

Modular image processing pipelines for Astronomy

github license paper documentation

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

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:

pip install prose

For the latest version

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

prose-3.3.3.tar.gz (83.5 kB view hashes)

Uploaded Source

Built Distribution

prose-3.3.3-py3-none-any.whl (96.1 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page