https://github.com/cy-xu/cosmic-conn
Tip revision: 234c5f3ef14615880ab70c45b99abef0f250ae10 authored by CY Xu on 06 December 2021, 20:32:25 UTC
Merge pull request #5 from cy-xu/feature/add_models_to_repo
Merge pull request #5 from cy-xu/feature/add_models_to_repo
Tip revision: 234c5f3
README.md
# Cosmic-CoNN
## A Cosmic Ray Detection Deep Learning Framework, Dataset, and Toolkit
## [Github](https://github.com/cy-xu/cosmic-conn) • [Documentation](https://cosmic-conn.readthedocs.io/) • [PyPI Release](https://pypi.org/project/cosmic-conn/) • [LCO CR Dataset](https://zenodo.org/record/5034763) • [Publication](https://github.com/cy-xu/cosmic-conn#publication)
[](https://arxiv.org/abs/2106.14922) [](https://doi.org/10.5281/zenodo.5034763) [](https://badge.fury.io/py/cosmic-conn) [](https://cosmic-conn.readthedocs.io) [](http://www.astropy.org/) [](https://tldrlegal.com/license/gnu-lesser-general-public-license-v3-(lgpl-3))
## About
Cosmic-CoNN is an end-to-end solution to help tackle the cosmic ray (CR) detection problem in CCD-captured astronomical images. It includes a deep-learning framework, high-performance CR detection models, a new dataset, and a suite of tools to use to the models, shown in the figure above:
1. [LCO CR dataset](https://zenodo.org/record/5034763), a large, diverse cosmic ray dataset that consists of over 4,500 scientific images from [Las Cumbres Observatory](https://lco.global/) (LCO) global telescope network's 23 instruments. CRs are labeled accurately and consistently across many diverse observations from various instruments. To the best of our knowledge, this is the largest dataset of its kind.
2. A PyTorch deep-learning framework that trains generic, robust CR detection models for ground- and space-based imaging data, as well as spectroscopic observations.
3. A suite of tools including console commands, a web app, and Python APIs to make deep-learning models easily accessible to astronomers.
Visual inspection of Cosmic-CoNNCR detection results. Detecting CRs in a Gemini GMOS-N 1×1 binning image with our generic ``ground-imaging`` model. The model was trained entirely on LCO data yet all visible CRs in the image stamp are correctly detected regardless of their shapes or sizes.
The Cosmic-CoNN ``NRES`` model detects CRs over the spectrum robustly on a LCO NRES spectroscopic image. The horizontal bands in the left image are the spectroscopic orders, which are left out of the CR mask.
## Publication
<p>
<a href="https://arxiv.org/abs/2106.14922"><img style="float: left; padding-right:30px;" src="https://cosmic-conn.readthedocs.io/en/latest/_images/paper_with_shadow.png" width="220"/></a>
This repository is part of our Cosmic-CoNN research paper. Our methods and a thorough evaluation of models' performance are available in the paper. If you used the Cosmic-CoNN or the LCO CR dataset for your research, please cite our paper:
[arXiv:2106.14922](https://arxiv.org/abs/2106.14922), [NASA ADS](https://ui.adsabs.harvard.edu/abs/2021arXiv210614922X/abstract)
Please also cite the LCO CR dataset if you used the Cosmic-CoNN `ground_imaging` model or the data in your research:
Xu, Chengyuan, McCully, Curtis, Dong, Boning, Howell, D. Andrew, & Sen, Pradeep. (2021). Cosmic-CoNN LCO CR Dataset (Version 0.1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.5034763
</p>
<br/><br/>
## Installation
*We recently added optional dependencies install for pip.*
We recommend installing Cosmic-CoNN in a new virtual environment, see the step-by-step [installation guide](https://cosmic-conn.readthedocs.io/en/latest/source/installation.html). To get a ~10x speed-up with GPU acceleration, see [Install for a CUDA-enabled GPU](https://cosmic-conn.readthedocs.io/en/latest/source/installation.html).
```console
# basic install for CR detection or library integration
$ pip install cosmic-conn
# include Flask for the web app interface
$ pip install cosmic-conn[webapp]
# install all dependencies for development
$ pip install cosmic-conn[develop]
```
## Command line interface
After installation, you can start CR detection in FITS files right from the terminal:
```console
$ cosmic-conn -m ground_imaging -e SCI -i input_dir
```
This command launches a generic `gorund_imaging` model to detect cosmic rays. It reads data from the SCI extension in a FITS file and processes all files in the input_dir. We also provide the `NRES` model for CR detection in spectroscopic data and the `HST_ACS_WFC` model for Hubble ACS/WFC imaging data. You could also find more Hubble Space Telescope CR detection and inpainting models trained by [deepCR](https://github.com/profjsb/deepCR).
See [documentation](https://cosmic-conn.readthedocs.io/en/latest/source/user_guide.html) for the complete user guide.
## Python APIs
It is also easy to integrate Cosmic-CoNN CR detection into your data workflow. Let `image` be a two-dimensional `float32 numpy` array of any size:
```Python
from cosmic_conn import init_model
# initialize a Cosmic-CoNN model
cr_model = init_model("ground_imaging")
# the model outputs a CR probability map in np.float32
cr_prob = cr_model.detect_cr(image)
# convert the probability map to a boolean mask with a 0.5 threshold
cr_mask = cr_prob > 0.5
```
## Web app
```console
$ cosmic-conn -a ground_imaging -e SCI
```
The Cosmic-CoNN web app automatically finds large CRs for close inspection. It supports live CR mask editing and is especially useful to find the suitable thresholds for different types of observations:
<img src="https://cosmic-conn.readthedocs.io/en/latest/_images/cosmic_conn_web_app_interface.png" alt="web-based CR detector interface" width="600"/>
The Cosmic-CoNN web app interface.
## Train new models with Cosmic-CoNN
See [documentation](https://cosmic-conn.readthedocs.io/en/latest/source/lco_cr_dataset.html) for the developer guide on using LCO CR dataset, data reduction, and model training.
## Credits
This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template.
