Modular image processing pipelines for Astronomy
Project description
prose
Modular image processing pipelines for Astronomy
prose is a Python package to build image processing pipelines for Astronomy. Beyond featuring the blocks to build pipelines from scratch, it provides pre-implemented ones to perform common tasks such as automated calibration, reduction and photometry.
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
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file prose-3.1.0.tar.gz
.
File metadata
- Download URL: prose-3.1.0.tar.gz
- Upload date:
- Size: 79.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.4.2 CPython/3.10.11 Linux/5.15.0-1037-azure
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 44dcb83eeae4882e71ba3c2d3a6894c2e21c18f56602ae0b1ba586dac1cd04c0 |
|
MD5 | 37223f879ba6b22ca7743ddbefc33951 |
|
BLAKE2b-256 | c487d73c481371bbb2ca6240733a1848a977545fa2241e2d634acac65dd779cc |
Provenance
File details
Details for the file prose-3.1.0-py3-none-any.whl
.
File metadata
- Download URL: prose-3.1.0-py3-none-any.whl
- Upload date:
- Size: 91.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.4.2 CPython/3.10.11 Linux/5.15.0-1037-azure
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6df44e3d461ef0e2fea839fae51157264466839809aa00759843a2855f91d897 |
|
MD5 | 5a50ee6efed591bfea12fbe948025ed7 |
|
BLAKE2b-256 | 230632d1be3afa4b9474b80db87c3d3dbace8a1c0931c8d8d087e166a5b7f2ab |