deepCR: Deep Learning Based Cosmic Ray Removal for Astronomical Images¶
| target: | https://deepcr.readthedocs.io/en/latest/?badge=latest |
|---|---|
| alt: | Documentation Status |
Welcome to the documentation for deepCR. You will use deepCR to apply a learned convolutional neural net (CNN) model to a 2d numpy array to identify and remove cosmic rays, on multi-core CPUs or GPUs.
Installation¶
pip install deepCR
Or you can install from source:
git clone https://github.com/profjsb/deepCR.git
cd deepCR/
pip install
Currently available models¶
mask:
ACS-WFC-F606W-2-4
ACS-WFC-F606W-2-32(*)
inpaint:
ACS-WFC-F606W-2-32(*)
ACS-WFC-F606W-3-32
Recommended models are marked in (*). Larger number indicate larger capacity.
Note that trained models may have input unit or preprocessing requirements. For the ACS-WFC-F606W models, input images must come from _flc.fits files which are in units of electrons.
Limitations and Caveats¶
In the current release, the included models have been built and tested only on Hubble Space Telescope (HST) ACS/WFC images in the F606W filter. Application to native-spatial resolution (ie. not drizzled), calibrated images from ACS/F606W (*_flc.fits) is expected to work well. Use of these prepackaged models in other observing modes with HST or spectroscopy is not encouraged. We are planning hosting a “model zoo” that would allow deepCR to be adapted to a wide range of instrument configurations.
Contributing¶
We are very interested in getting bug fixes, new functionality, and new trained models from the community (especially for ground-based imaging and spectroscopy). Please fork this repo and issue a PR with your changes. It will be especially helpful if you add some tests for your changes.
If you run into any issues, please don’t hesitate to open an issue on GitHub.