# <img alt="Biolith logo" src="assets/biolith.svg" style="height: 2em;"> Biolith: <ins>B</ins>ayes<ins>i</ins>an Ec<ins>ol</ins>ogical Modeling <ins>i</ins>n Py<ins>th</ins>on
[](https://github.com/timmh/biolith/actions/workflows/test.yml) [](https://pypi.org/project/biolith/) [](https://doi.org/10.5281/zenodo.15706627)
Biolith is a Python package designed for Bayesian ecological modeling and analysis with a focus on occupancy modeling. It has similar goals to [Unmarked](https://github.com/biodiverse/unmarked) and [spOccupancy](https://github.com/biodiverse/spOccupancy/), but is written in Python and uses [NumPyro](https://num.pyro.ai) and [JAX](https://jax.readthedocs.io) to enable rapid model fitting and iteration.
## Features
- **Hackable**: Models are easy to understand and implement, no likelihood derivations needed.
- **Fast**: Models can be fit on GPUs, which is _fast_.
- **Familiar**: Everything is written in Python, making it easy to integrate into existing pipelines.
## Installation
You can install Biolith using pip:
```bash
pip install biolith
```
## Usage
Here is a simple example using simulated data to get you started:
```python
from biolith.models import occu, simulate
from biolith.utils import fit
# Simulate dataset
data, true_params = simulate()
# Fit model to simulated data
results = fit(occu, **data)
# Compare estimated occupancy probability to the true mean occupancy
print(f"Mean estimated psi: {results.samples['psi'].mean():.2f}")
print(f"Mean true occupancy: {true_params['z'].mean():.2f}")
```
## Real-world Example
To see a real-world example on camera trap data, see [this Jupyter Notebook](./assets/CameraTrapExample.ipynb) from the [EFI Statistical Methods Seminar Series](https://github.com/eco4cast/Statistical-Methods-Seminar-Series/tree/main/beery-haucke_biolith) or [](https://colab.research.google.com/github/timmh/biolith/blob/main/assets/CameraTrapExample.ipynb)
## Documentation
API Documentation and examples are available [here](https://timm.haucke.xyz/biolith/).
## Development
Run `python -m unittest` to run unit tests.
Run `scripts/format.sh` to format the codebase. Execute `scripts/check.sh` to run
isort, docformatter and black in check mode along with pylint and pyright.
To install the pre-commit hook for formatting and code linting, run:
```bash
./scripts/install_precommit.sh
```
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Contact
For questions or feedback, please open an issue or email [haucke@mit.edu](mailto:haucke@mit.edu).
## Acknowledgements
This work was supported by the AI and Biodiversity Change (ABC) Global Center, which is funded by the [US National Science Foundation under Award No. 2330423](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330423&HistoricalAwards=false) and [Natural Sciences and Engineering Research Council of Canada under Award No. 585136](https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=782440). This work draws on research supported in part by the Social Sciences and Humanities Research Council.
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"description": "# <img alt=\"Biolith logo\" src=\"assets/biolith.svg\" style=\"height: 2em;\"> Biolith: <ins>B</ins>ayes<ins>i</ins>an Ec<ins>ol</ins>ogical Modeling <ins>i</ins>n Py<ins>th</ins>on\n\n[](https://github.com/timmh/biolith/actions/workflows/test.yml) [](https://pypi.org/project/biolith/) [](https://doi.org/10.5281/zenodo.15706627)\n\nBiolith is a Python package designed for Bayesian ecological modeling and analysis with a focus on occupancy modeling. It has similar goals to [Unmarked](https://github.com/biodiverse/unmarked) and [spOccupancy](https://github.com/biodiverse/spOccupancy/), but is written in Python and uses [NumPyro](https://num.pyro.ai) and [JAX](https://jax.readthedocs.io) to enable rapid model fitting and iteration.\n\n## Features\n\n- **Hackable**: Models are easy to understand and implement, no likelihood derivations needed.\n- **Fast**: Models can be fit on GPUs, which is _fast_.\n- **Familiar**: Everything is written in Python, making it easy to integrate into existing pipelines.\n\n## Installation\n\nYou can install Biolith using pip:\n\n```bash\npip install biolith\n```\n\n## Usage\n\nHere is a simple example using simulated data to get you started:\n\n```python\nfrom biolith.models import occu, simulate\nfrom biolith.utils import fit\n\n# Simulate dataset\ndata, true_params = simulate()\n\n# Fit model to simulated data\nresults = fit(occu, **data)\n\n# Compare estimated occupancy probability to the true mean occupancy\nprint(f\"Mean estimated psi: {results.samples['psi'].mean():.2f}\")\nprint(f\"Mean true occupancy: {true_params['z'].mean():.2f}\")\n```\n\n## Real-world Example\nTo see a real-world example on camera trap data, see [this Jupyter Notebook](./assets/CameraTrapExample.ipynb) from the [EFI Statistical Methods Seminar Series](https://github.com/eco4cast/Statistical-Methods-Seminar-Series/tree/main/beery-haucke_biolith) or [](https://colab.research.google.com/github/timmh/biolith/blob/main/assets/CameraTrapExample.ipynb)\n\n## Documentation\nAPI Documentation and examples are available [here](https://timm.haucke.xyz/biolith/).\n\n## Development\n\nRun `python -m unittest` to run unit tests.\n\nRun `scripts/format.sh` to format the codebase. Execute `scripts/check.sh` to run\nisort, docformatter and black in check mode along with pylint and pyright.\n\nTo install the pre-commit hook for formatting and code linting, run:\n\n```bash\n./scripts/install_precommit.sh\n```\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n## Contact\n\nFor questions or feedback, please open an issue or email [haucke@mit.edu](mailto:haucke@mit.edu).\n\n## Acknowledgements\nThis work was supported by the AI and Biodiversity Change (ABC) Global Center, which is funded by the [US National Science Foundation under Award No. 2330423](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330423&HistoricalAwards=false) and [Natural Sciences and Engineering Research Council of Canada under Award No. 585136](https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=782440). This work draws on research supported in part by the Social Sciences and Humanities Research Council.\n",
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