A fast, flexible, differentiable, and automated astronomical image modelling tool for precise parallel multi-wavelength photometry
Project description
AutoProf is a fast, flexible, and automated astronomical image modelling tool for precise parallel multi-wavelength photometry. It is a python based package that uses PyTorch to quickly and efficiently perform analysis tasks. Written by Connor Stone for tasks such as LSB imaging, handling crowded fields, multi-band photometry, and analyzing massive data from future telescopes. AutoProf is flexible and fast for any astronomical image modelling task. While it uses PyTorch (originally developed for Machine Learning) it is NOT a machine learning based tool.
Installation
AutoProf can be installed with pip:
pip install autoprof
If PyTorch gives you any trouble on your system, just follow the instructions on the pytorch website to install a version for your system.
Also note that AutoProf is only available for python3.
See the documentation for more details.
Documentation
You can find the documentation at the GitHub Pages site connected with the AutoProf project which covers many of the main use cases for AutoProf. It is still in development, but lots of useful information is there. Feel free to contact the author, Connor Stone, for any questions not answered by the documentation or tutorials.
Credit / Citation
If you use AutoProf in your research, please follow the citation instructions here. A new paper for the updated AutoProf code is in the works.
Looking for the old AutoProf?
Don't worry, the old AutoProf is still available unchanged as AutoProf-Legacy simply follow this link to see the github page.
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 Distributions
Built Distribution
File details
Details for the file autoprof-0.7.1-py2.py3-none-any.whl.
File metadata
- Download URL: autoprof-0.7.1-py2.py3-none-any.whl
- Upload date:
- Size: 149.5 kB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.8.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | bcf0562ffabcf914d0ef6c8408bd912e2326453ebcb8028a531dde534d69f87a |
|
| MD5 | 443deed4827e0fe533a4f7ad78723072 |
|
| BLAKE2b-256 | 0a8e58bd5b53a04bcecb20e416dadda5fb1f70b6872423c262b949d8e8eeebca |
