From f48f219c675b08026ba18ca20437ef0ce0a9ff43 Mon Sep 17 00:00:00 2001 From: mj-will Date: Fri, 31 May 2024 10:27:16 +0100 Subject: [PATCH] DOC: update readme --- README.md | 76 ++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 75 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 8f3b7ac..f7f8ee0 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,76 @@ # nessai-bilby -Interface and plugin for using nessai in bilby + +Interface and plugin for using `nessai` in `bilby`. + +This plugin provides two samplers that can be used in `bilby`: + +- `nessai`: the standard nested sampler from `nessai` +- `inessai`: the importance nested sampler from nessai + + +It also provides a means to use `bilby` likelihoods and priors directly in +`nessai`, see [using bilby likelihoods in nessai](#using-bilby-likelihoods-in-nessai) + +## Installation + +The package can be installed using pip + +``` +pip install nessai-bilby +``` +or conda + +``` +conda install conda-forge::nessai-bilby +``` + +However, we recommend following installing PyTorch manually to ensure the +correct device support. + +**Note:** this plugin requires "bilby>=2.3.0". + + +## Usage + +### In bilby + +One `nessai-bilby` is installed, both samplers can be used directly in `bilby` +via the `run_sampler` function. See the bilby documentation for more details +on how to run different samplers. + + +### Using bilby likelihoods in nessai + +`nessai-bilby` also provides two model classes that allow bilby likelihood and +priors to be used directly with nessai: + +- `nessai_bilby.model.BilbyModel`: +- `nessai_bilby.model.BilbyModelLikelihoodConstraint`: + + +Either model can be used by creating an instance of the model and running `nessai` as usual: + +```python +from nessai.flowsampler import FlowSampler +from nessai_bilby.model import BilbyModel + +likelihood = ... # bilby likelihood object +priors = ... # bilby PriorDict + +model = BilbyModel( + priors=priors, + likelihood=likelihood, + use_ratio=True # Whether to use the log-likelihood ratio +) + +fs = FlowSampler( + model, + ..., +) + +fs.run() +``` + +## Citing + +If you use `nessai-bilby`, please cite the `nessai` and `bilby` code bases and the corresponding papers.