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https://github.com/kazewong/jim
Gravitational-wave data analysis tools in Jax
https://github.com/kazewong/jim
Last synced: 14 days ago
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Gravitational-wave data analysis tools in Jax
- Host: GitHub
- URL: https://github.com/kazewong/jim
- Owner: kazewong
- License: mit
- Created: 2021-09-03T17:04:19.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-10-18T20:14:47.000Z (25 days ago)
- Last Synced: 2024-10-26T15:47:46.525Z (18 days ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 8.66 MB
- Stars: 57
- Watchers: 7
- Forks: 19
- Open Issues: 22
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Jim 
- A JAX-based gravitational-wave inference toolkit
Jim comprises a set of tools for estimating parameters of gravitational-wave sources thorugh Bayesian inference.
At its core, Jim relies on the JAX-based sampler [flowMC](https://github.com/kazewong/flowMC),
which leverages normalizing flows to enhance the convergence of a gradient-based MCMC sampler.
Since its based on JAX, Jim can also leverage hardware acceleration to achieve significant speedups on GPUs. Jim also takes advantage of likelihood-heterodyining, ([Cornish 2010](https://arxiv.org/abs/1007.4820), [Cornish 2021](https://arxiv.org/abs/2109.02728)) to compute the gravitational-wave likelihood more efficiently.
See the accompanying paper, [Wong, Isi, Edwards (2023)](https://github.com/kazewong/TurboPE/) for details.
> [!WARNING]
> Jim is under heavy development, so API is constantly changing. Use at your own risk!
> One way to mitigate this inconvience is to make your own fork over a version for now.
> We expect to hit a stable version this year. Stay tuned.
_[Documentatation and examples are a work in progress]_
## Installation
You may install the latest released version of Jim through pip by doing
```
pip install jimGW
```
You may install the bleeding edge version by cloning this repo, or doing
```
pip install git+https://github.com/kazewong/jim
```
If you would like to take advantage of CUDA, you will additionally need to install a specific version of JAX by doing
```
pip install --upgrade "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
```
_NOTE:_ Jim is only currently compatible with Python 3.10.
## Performance
The performance of Jim will vary depending on the hardware available. Under optimal conditions, the CUDA installation can achieve parameter estimation in ~1 min on an Nvidia A100 GPU for a binary neutron star (see [paper](https://github.com/kazewong/TurboPE/) for details). If a GPU is not available, JAX will fall back on CPUs, and you will see a message like this on execution:
```
No GPU/TPU found, falling back to CPU.
```
## Directory
Parameter estimation examples are in `example/ParameterEstimation`.
## Attribution
Please cite the accompanying paper, [Wong, Isi, Edwards (2023)](https://github.com/kazewong/TurboPE/).