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version](https://badge.fury.io/py/ringdown.svg)](https://badge.fury.io/py/ringdown)\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/maxisi/ringdown/HEAD)\n[![DOI](https://zenodo.org/badge/368680640.svg)](https://zenodo.org/badge/latestdoi/368680640)\n![pytest](https://github.com/maxisi/ringdown/actions/workflows/python-app.yml/badge.svg?event=push)\n[![Documentation Status](https://readthedocs.org/projects/ringdown/badge/?version=latest)](https://ringdown.readthedocs.io/en/latest/?badge=latest)\n\nBayesian analysis of black hole ringdowns.  The original paper that inspired this code package is [Isi, et al. (2019)](https://arxiv.org/abs/1905.00869); a full description of the code and method can be found in [Isi \u0026 Farr (2021)](https://arxiv.org/abs/2107.05609).\n\n## Installation\n\nThis package is pip installable:\n\n```shell\npip install ringdown\n```\n\nFor the latest and greatest version, you can install directly from the git repo:\n\n```shell\npip install git+https://github.com/maxisi/ringdown.git\n```\n\nAdditionally, you may follow the [JAX documentation](https://jax.readthedocs.io/en/latest/installation.html) to install JAX with GPU support.\n\n### Complete Environments\n\nA complete [conda](https://docs.conda.io/en/latest/) environment that includes all the prerequisites (and more!) to install `ringdown` can be found in  `environment.yml` in the current directory:\n\n```shell\nconda env create -f environment.yml\nconda activate ringdown\npip install ringdown\n```\n\nwill leave the shell in an environment that includes `jupyterlab` ready to explore the `ringdown` package.  \n\nThe `environment.yml` file enables running `ringdown` in JupyterHub services like [MyBinder](https://mybinder.org/) by pointing MyBinder at this repository or clicking the button at the top of this README.\n\n## Examples and tips\n\nSee the [example gallery](https://ringdown.readthedocs.io/en/latest/gallery.html) in the docs for several examples. You can download the Jupyter notebooks featured in the docs from the `docs/examples`.\n\n### Performance notes\n\nIn order to run Jax on a CPU with four cores and use double precision, you can do the following at the top of your script:\n```python\n# disable numpy multithreading to avoid conflicts\n# with jax multiprocessing in numpyro\nimport os\nos.environ[\"OMP_NUM_THREADS\"] = \"1\"\n\n# set jax up to use double precision\nfrom jax import config\nconfig.update(\"jax_enable_x64\", True)\n\n# import numpyro and set it up to use 4 CPU devices\nimport numpyro\nnumpyro.set_host_device_count(4)\nnumpyro.set_platform('cpu')\n```\n\nTo run on a GPU with single precision you can instead do:\n```python\n# set jax to use single precision (this is the default so no need to run the lines below)\n# from jax import config\n# config.update(\"jax_enable_x64\", False)\n\n# import numpyro and set it up to use 4 CPU devices\nimport numpyro\nnumpyro.set_platform('gpu')\n```\n\nYou will see significant performance enhancements when running ona GPU with 32-bit precision. If you have multiple GPUs, `numpyro` can use them in parallel to run different chains, just as with CPUs. Sampling one chain for a GW150914-like system takes O(s) on an Nvidia A100 GPU.\n\n⚠️ _Caveat emptor:_ depending on the autocovariance function (ACF), using `float32` can cause numerical problems when computing the likelihood; _ringdown_ will automatically rescale the strain in an attempt to prevent this, but you should use this feature at your own risk.\n\n## Citations\n\nWe ask that scientific users of this code cite the corresponding Zenodo entry (see blue DOI badge above), as well as [Isi \u0026 Farr (2021)](https://arxiv.org/abs/2107.05609):\n\n```bibtex\n@article{Isi:2021iql,\n    author = \"Isi, Maximiliano and Farr, Will M.\",\n    title = \"{Analyzing black-hole ringdowns}\",\n    eprint = \"2107.05609\",\n    archivePrefix = \"arXiv\",\n    primaryClass = \"gr-qc\",\n    reportNumber = \"LIGO-P2100227\",\n    month = \"7\",\n    year = \"2021\"\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaxisi%2Fringdown","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmaxisi%2Fringdown","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaxisi%2Fringdown/lists"}