# Jim <img src="https://user-images.githubusercontent.com/4642979/218163532-1c8a58e5-6f36-42de-96d3-f245eee93cf8.png" alt="jim" width="35"/> - 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.
_[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[cuda]"==0.4.1 -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/).
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