# STARRED: STARlet REgularized Deconvolution
[](https://gitlab.com/cosmograil/starred/commits/main)
[](https://cosmograil.gitlab.io/starred/coverage/)
[](https://www.python.org/downloads/release/python-390/)
[](https://www.gnu.org/licenses/gpl-3.0)
[](https://doi.org/10.21105/joss.05340)
[](https://pypi.org/project/starred-astro/)
STARlet REgularized Deconvolution (STARRED) is a Python deconvolution method powered by Starlet regularization and JAX automatic differentiation. It uses a Point Spread Function (PSF) narrower than the original one as kernel.
## Installation
### Through PyPI
STARRED releases are distributed through the Python Package Index (PyPI). To install the latest version use `pip`:
```bash
$ pip install starred-astro
```
### Through Anaconda
We provide an Anaconda environment that satisfies all the dependencies in `starred-env.yml`.
```bash
$ git clone https://gitlab.com/cosmograil/starred.git
$ cd starred
$ conda env create -f starred-env.yml
$ conda activate starred-env
$ pip install .
```
In case you have an NVIDIA GPU, this should automatically download the right version of JAX as well as cuDNN.
Next, you can run the tests to make sure your installation is working correctly.
```bash
# While still in the STARRED directory:
$ pytest .
```
### Manually handling the dependencies
If you want to use an existing environment, just omit the Anaconda commands above:
```bash
$ git clone https://gitlab.com/cosmograil/starred
$ cd starred
$ pip install .
```
or if you need to install it for your user only:
```bash
$ python setup.py install --user
```
STARRED runs much faster on GPUs, so make sure you install a version of JAX that is compatible
with your version of CUDA and cuDNN:
``` bash
$ pip install "jax[cuda11_cudnn86]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
```
## Requirements
STARRED requires the following Python packages:
* `astropy`
* `dill`
* `jax`
* `jaxlib`
* `jaxopt`
* `matplotlib`
* `numpy`
* `scipy`
* `optax`
* `pyregion`
* `tqdm`
* `h5py`
Additionnaly, the following package needs to be installed if you want to sample posterior distribution:
* `emcee`
* `mclmc`
## Example Notebooks and Documentation
We provide several notebooks to help you get started.
> [Start here to grasp the basic STARRED workflow](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/start_here.ipynb).
More example notebooks going in more detail of how the internals work can be found in the [notebooks](https://gitlab.com/cosmograil/starred/-/tree/main/notebooks/more_examples) directory:
* [Ground-based narrow PSF generation](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/more_examples/1_WFI%20narrow%20PSF%20generation.ipynb)
* [Ground-based joint deconvolution](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/more_examples/2_DESJ0602-4335%20joint%20deconvolution.ipynb)
* [Another ground-based joint deconvolution](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/more_examples/3_Another%20lensed%20quasar%20-%20joint%20deconvolution.ipynb)
* [JWST PSF generation and deconvolution](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/more_examples/4_JWST%20deconvolution.ipynb)
* [DES2038 joint deconvolution](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/more_examples/5_DES2038_from_WFI_joint_deconvolution.ipynb)
* [HST PSF reconstruction](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/more_examples/6_HST-PSF%20reconstruction.ipynb)
* [JWST PSF reconstruction](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/more_examples/7_JWST-PSF_reconstruction.ipynb)
The mathematical formalism along with further examples are also presented
in [Millon et al. (2024)](https://arxiv.org/abs/2402.08725). All the examples and tests presented in this paper can be
reproduced from this repository:
* [STARRED Examples](https://gitlab.com/cosmograil/starred-examples)
You can also run STARRED from the command line by following
these [instructions](https://gitlab.com/cosmograil/starred/-/tree/main/scripts?ref_type=heads).
Finally, the full documentation can be found [here](https://cosmograil.gitlab.io/starred/) and a video presentation of
STARRED is accessible on [Youtube](https://www.youtube.com/watch?v=04FKFMBpSlo).
## Attribution
If you use this code, please cite [Michalewicz et al. 2023](https://joss.theoj.org/papers/10.21105/joss.05340)
and [Millon et al. 2024](https://arxiv.org/abs/2402.08725)
as indicated in the [documentation](https://cosmograil.gitlab.io/starred/citing.html).
## License
STARRED is a free software. You can redistribute it and/or modify it under the terms of the
GNU General Public License as published by the Free Software Foundation.
STARRED is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY, without
even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details ([LICENSE.txt](LICENSE)).
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"author": "Martin Millon, Fr\u00e9d\u00e9ric Dux",
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"description": "# STARRED: STARlet REgularized Deconvolution \n\n[](https://gitlab.com/cosmograil/starred/commits/main)\n[](https://cosmograil.gitlab.io/starred/coverage/)\n[](https://www.python.org/downloads/release/python-390/)\n[](https://www.gnu.org/licenses/gpl-3.0)\n[](https://doi.org/10.21105/joss.05340)\n[](https://pypi.org/project/starred-astro/)\n\nSTARlet REgularized Deconvolution (STARRED) is a Python deconvolution method powered by Starlet regularization and JAX automatic differentiation. It uses a Point Spread Function (PSF) narrower than the original one as kernel. \n\n## Installation \n\n### Through PyPI\n\nSTARRED releases are distributed through the Python Package Index (PyPI). To install the latest version use `pip`:\n\n```bash\n$ pip install starred-astro\n```\n\n### Through Anaconda\nWe provide an Anaconda environment that satisfies all the dependencies in `starred-env.yml`. \n```bash\n$ git clone https://gitlab.com/cosmograil/starred.git\n$ cd starred\n$ conda env create -f starred-env.yml\n$ conda activate starred-env\n$ pip install .\n```\nIn case you have an NVIDIA GPU, this should automatically download the right version of JAX as well as cuDNN.\nNext, you can run the tests to make sure your installation is working correctly.\n\n```bash\n# While still in the STARRED directory:\n$ pytest . \n```\n\n### Manually handling the dependencies\nIf you want to use an existing environment, just omit the Anaconda commands above:\n```bash\n$ git clone https://gitlab.com/cosmograil/starred\n$ cd starred \n$ pip install .\n```\n\nor if you need to install it for your user only: \n```bash\n$ python setup.py install --user \n```\n\nSTARRED runs much faster on GPUs, so make sure you install a version of JAX that is compatible \nwith your version of CUDA and cuDNN: \n``` bash \n$ pip install \"jax[cuda11_cudnn86]\" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html\n```\n\n## Requirements \n\nSTARRED requires the following Python packages: \n* `astropy`\n* `dill`\n* `jax`\n* `jaxlib`\n* `jaxopt`\n* `matplotlib`\n* `numpy`\n* `scipy`\n* `optax`\n* `pyregion`\n* `tqdm`\n* `h5py`\n\nAdditionnaly, the following package needs to be installed if you want to sample posterior distribution: \n* `emcee`\n* `mclmc`\n\n## Example Notebooks and Documentation\n\nWe provide several notebooks to help you get started.\n\n> [Start here to grasp the basic STARRED workflow](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/start_here.ipynb).\n\nMore example notebooks going in more detail of how the internals work can be found in the [notebooks](https://gitlab.com/cosmograil/starred/-/tree/main/notebooks/more_examples) directory: \n* [Ground-based narrow PSF generation](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/more_examples/1_WFI%20narrow%20PSF%20generation.ipynb)\n* [Ground-based joint deconvolution](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/more_examples/2_DESJ0602-4335%20joint%20deconvolution.ipynb)\n* [Another ground-based joint deconvolution](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/more_examples/3_Another%20lensed%20quasar%20-%20joint%20deconvolution.ipynb)\n* [JWST PSF generation and deconvolution](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/more_examples/4_JWST%20deconvolution.ipynb)\n* [DES2038 joint deconvolution](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/more_examples/5_DES2038_from_WFI_joint_deconvolution.ipynb)\n* [HST PSF reconstruction](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/more_examples/6_HST-PSF%20reconstruction.ipynb)\n* [JWST PSF reconstruction](https://gitlab.com/cosmograil/starred/-/blob/main/notebooks/more_examples/7_JWST-PSF_reconstruction.ipynb)\n\nThe mathematical formalism along with further examples are also presented\nin [Millon et al. (2024)](https://arxiv.org/abs/2402.08725). All the examples and tests presented in this paper can be\nreproduced from this repository:\n\n* [STARRED Examples](https://gitlab.com/cosmograil/starred-examples)\n\nYou can also run STARRED from the command line by following\nthese [instructions](https://gitlab.com/cosmograil/starred/-/tree/main/scripts?ref_type=heads).\n\nFinally, the full documentation can be found [here](https://cosmograil.gitlab.io/starred/) and a video presentation of\nSTARRED is accessible on [Youtube](https://www.youtube.com/watch?v=04FKFMBpSlo).\n\n## Attribution\n\nIf you use this code, please cite [Michalewicz et al. 2023](https://joss.theoj.org/papers/10.21105/joss.05340)\nand [Millon et al. 2024](https://arxiv.org/abs/2402.08725)\nas indicated in the [documentation](https://cosmograil.gitlab.io/starred/citing.html).\n\n## License\nSTARRED is a free software. 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