prose 3.3.2

Modular image processing pipelines for Astronomy

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

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.