deepCR 0.1.3

deepCR: Deep Learning Based Cosmic Ray Removal for Astronomical Images

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.

This is the installable package which implements the methods described in the paper: Zhang & Bloom (2019), submitted. Code to benchmark the model and to generate figures and tables in the paper can be found in the deepCR-paper Github repo: https://github.com/kmzzhang/deepCR-paper

Installation

pip install deepCR

Or you can install from source:

git clone https://github.com/profjsb/deepCR.git
cd deepCR/
pip install

Quick Start

With Python >=3.5:

from deepCR import deepCR
from astropy.io import fits

image = fits.getdata("*********_flc.fits")
mdl = deepCR(mask="ACS-WFC-F606W-2-32",
	     inpaint="ACS-WFC-F606W-2-32",
             device="GPU")
mask, cleaned_image = mdl.clean(image, threshold = 0.5)

Note: Input image must be in units of electrons

To reduce memory consumption (recommended for image larger than 1k x 1k):

mask, cleaned_image = mdl.clean(image, threshold = 0.5, seg = 256)

which segments the input image into patches of 256*256, seperately perform CR rejection on the patches, before stitching back to original image size.

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(*)

The two numbers following instrument configuration specifies model size, with larger number indicating better performing model at the expense of runtime. Recommanded models are marked in (*). For benchmarking of these models, please refer to the original paper.

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.