astropipe is a pipeline aim to produce reliable surface brightness profiles of galaxies. It has built-in functions to reduce, analyse and visualize astronomical images in general. It is meant to help me analyse all the data for my PhD and being able to share it with other colleagues.
This is a work in progress, use at your own risk!
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All this code is the result of my Ph.D. The aim of this "library" is to help analyse astronomical images. More specifically, to obtain reliable surface brightness profiles. It can also offer more features to reduce, smooth, fit, and visualize images. It is written in Python and mostly uses common libraries (see the prerequisites).
Since this is a multiple-purpose library it depends on different Python libraries and astronomical software. It also depends on the modules you would like to use. Here I explained briefly the biggest dependencies of the different modules.
To use the masking module you need to have different external software for Astronomical Image Segmentation. Specifically the following software:
and the aliases need to be stored in your environment variable so you can call the different software. In particular, for Gnuastro, it uses NoiseChisel. This needs to be installed if you want to create masks. However there are different methods that use different software, so it's not mandatory to have all installed but at least, one of them. To be able to use the MTObjects in this library, a github patch needs to be apply to create some changes. The patch can be found in mtobjects.patch.
The library is written in Python and uses different libraries. Most of them can be installed using pip. You can find the main requisites in the pyproject.toml. Furthermore, you must install the sewpy library apart from the ones installed automatically with the requirements.txt file.
You can install the library using pip as follows:
git clone https://github.com/PabloMSanAla/astropipe.git
cd astropipe
pip install -e .I strongly recommend to install it in a separate virtual environment. You could also add this to your Python path in this repository and you would not depend on pip.
Work in progress...
I created different Jupyter notebooks to help you use the pipeline and get a sense of the methods built-in.
Contributions are what makes the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the GNU General Public License. See LICENSE.txt for more information.
Pablo M Sánchez-Alarcón - pmsa.astro@gmail.com
Project Link: https://github.com/PabloMSanAla/astropipe
Thank you for using astropipe.
Readme file taken from Best README Template.