prose

Modular image processing pipelines for Astronomy

prose is a Python package to build modular image processing pipelines for Astronomy.

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.

Metadata

Release files for prose 3.3.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for prose 3.3.4
File	Size	Uploaded
prose-3.3.4.tar.gz	83.5 kB	Jul 26, 2024	Details

Built distribution (wheel)

Table of built distributions (wheels) for prose 3.3.4
File	Interpreter	ABI	Platform	Reset
prose-3.3.4-py3-none-any.whl 96.1 kB Jul 26, 2024	Python 3	none	any	Details

Total release size: 179.6 kB

Release files / prose-3.3.4.tar.gz

Download URL	prose-3.3.4.tar.gz
Size	83.5 kB
Tags	Source
SHA-256 checksum How to use checksums	`5a5340bd65c572f0e36b1cdb87cae549a15331201ed3c608141f3ad179cbba95`
BLAKE2b-256 checksum How to use checksums	`f70df3d10f884b6eb272a98655aa3711fdfa6e7ffea2e82db80c90ab38af8fd4`
Upload date	Jul 26, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.8.3 CPython/3.10.14 Linux/6.5.0-1024-azure`

Release files / prose-3.3.4-py3-none-any.whl

Download URL	prose-3.3.4-py3-none-any.whl
Size	96.1 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`74d83e153141adec1bd679f9a5c476b7032f7131cbc172b216a126ec68d2be99`
BLAKE2b-256 checksum How to use checksums	`41ee2d72b259b8e2676c4134078593253100c9140c12210d0651db2f19192eb3`
Upload date	Jul 26, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.8.3 CPython/3.10.14 Linux/6.5.0-1024-azure`