# fiesta 🎉
`fiesta`: **F**ast **I**nference of **E**lectromagnetic **S**ignals and **T**ransients with j**A**x
**NOTE:** `fiesta` is currently under development -- stay tuned!
## Installation
pip installation is currently work in progress. Install from source by cloning this Github repository and running
```
pip install -e .
```
NOTE: This is using an older and custom version of `flowMC`. Install by cloning the `flowMC` version at [this fork](https://github.com/ThibeauWouters/flowMC/tree/fiesta) (branch `fiesta`).
## Training surrogate models
To train your own surrogate models, have a look at some of the example scripts in the repository for inspiration, under `trained_models`
- `train_Bu2019lm.py`: Example script showing how to train a surrogate model for the POSSIS `Bu2019lm` kilonova model.
- `train_afterglowpy_tophat.py`: Example script showing how to train a surrogate model for `afterglowpy`, using a tophat jet structure.
## Examples
- `run_AT2017gfo_Bu2019lm.py`: Example where we infer the parameters of the AT2017gfo kilonova with the `Bu2019lm` model.
- `run_GRB170817_tophat.py`: Example where we infer the parameters of the GRB170817 GRB with a surrogate model for `afterglowpy`'s tophat jet. **NOTE** This currently only uses one specific filter. The complete inference will be updated soon.
## Acknowledgements
The logo was created by [ideogram AI](https://ideogram.ai/).
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"description": "# fiesta \ud83c\udf89\n\n`fiesta`: **F**ast **I**nference of **E**lectromagnetic **S**ignals and **T**ransients with j**A**x\n\n\n\n**NOTE:** `fiesta` is currently under development -- stay tuned!\n\n## Installation\n\npip installation is currently work in progress. Install from source by cloning this Github repository and running\n```\npip install -e .\n```\n\nNOTE: This is using an older and custom version of `flowMC`. Install by cloning the `flowMC` version at [this fork](https://github.com/ThibeauWouters/flowMC/tree/fiesta) (branch `fiesta`).\n\n## Training surrogate models\n\nTo train your own surrogate models, have a look at some of the example scripts in the repository for inspiration, under `trained_models`\n\n- `train_Bu2019lm.py`: Example script showing how to train a surrogate model for the POSSIS `Bu2019lm` kilonova model. \n- `train_afterglowpy_tophat.py`: Example script showing how to train a surrogate model for `afterglowpy`, using a tophat jet structure. \n\n## Examples\n\n- `run_AT2017gfo_Bu2019lm.py`: Example where we infer the parameters of the AT2017gfo kilonova with the `Bu2019lm` model.\n- `run_GRB170817_tophat.py`: Example where we infer the parameters of the GRB170817 GRB with a surrogate model for `afterglowpy`'s tophat jet. **NOTE** This currently only uses one specific filter. The complete inference will be updated soon.\n\n## Acknowledgements\n\nThe logo was created by [ideogram AI](https://ideogram.ai/). \n",
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