SEP-PJW

Python library for Source Extraction and Photometry

About

Source Extractor (Bertin & Arnouts 1996) is a widely used command-line program for segmentation and analysis of astronomical images. It reads in FITS format files, performs a configurable series of tasks, including background estimation, source detection, deblending and a wide array of source measurements, and finally outputs a FITS format catalog file.

While Source Extractor is highly useful, the fact that it can only be used as an executable can limit its applicability or lead to awkward workflows. There is often a desire to have programmatic access to perform one or more of the above tasks on in-memory images as part of a larger custom analysis.

SEP makes the core algorithms of Source Extractor available as a library of stand-alone functions and classes. These operate directly on in-memory arrays (no FITS files or configuration files). The code is derived from the Source Extractor code base (written in C) and aims to produce results compatible with Source Extractor whenever possible. SEP consists of a C library with no dependencies outside the standard library, and a Python module that wraps the C library in a Pythonic API. The Python wrapper operates on NumPy arrays with NumPy as its only dependency. See below for language-specfic build and usage instructions.

Some features:

• spatially variable background and noise estimation

• source extraction, with on-the-fly convolution and source deblending

• circular and elliptical aperture photometry

• fast: implemented in C with Python bindings via Cython

Additional features not in Source Extractor:

• Optimized matched filter for variable noise in source extraction.

• Circular annulus and elliptical annulus aperture photometry functions.

• Local background subtraction in shape consistent with aperture in aperture photometry functions.

• Exact pixel overlap mode in all aperture photometry functions.

• Masking of elliptical regions on images.

SEP-PJW vs. SEP

The original release of sep by Kyle Barbary, kbarbary/sep, no longer appears to be maintained. Whilst the package is incredibly useful, there are a few outstanding bugs, and support for the latest versions of Python will be limited. The aim of sep-pjw is to offer a version of sep that resolves these issues, whilst maintaining compatibility as much as is feasibly possible.

Installation

with conda

SEP-PJW can be installed with conda from the conda-forge channel:

conda install -c conda-forge sep-pjw

with pip

SEP-PJW can also be installed with pip. After ensuring that numpy is installed, run

python -m pip install sep-pjw

If you get an error about permissions, you are probably using your system Python. In this case, I recommend using pip’s “user install” option to install sep into your user directory

python -m pip install --user sep-pjw

Do not install sep-pjw or other third-party Python packages using sudo unless you are fully aware of the risks.

Usage Guide

• Tutorial
• Matched Filter (Convolution)
• Aperture photometry
• Changelog

For complete API documentation, see Reference/API.

Contributing

Report a bug or documentation issue: http://github.com/PJ-Watson/sep-pjw/issues

Development of sep-pjw takes place on GitHub at http://github.com/PJ-Watson/sep-pjw. Contributions of bug fixes, documentation improvements and minor feature additions are welcome via GitHub pull requests. For major features, it is best to open an issue discussing the change first.

License and Citation

The license for SEP is the Lesser GNU Public License (LGPL), granted with the permission from the original author of Source Extractor.

If you use SEP in a publication, please cite Barbary (2016) and the original Source Extractor paper: Bertin & Arnouts 1996.

The DOI for the sep v1.0.0 code release is 10.5281/zenodo.159035.