Metadata-Version: 2.4
Name: climemu
Version: 0.1.8
Summary: Score-based generative emulation of impact-relevant earth system model outputs
Author-email: Shahine Bouabid <shahineb@mit.edu>
License: MIT
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: diffrax>=0.7.0
Requires-Dist: einops>=0.8.1
Requires-Dist: equinox>=0.13.0
Requires-Dist: huggingface-hub>=0.35.0
Requires-Dist: jax>=0.7.1
Requires-Dist: netcdf4>=1.7.2
Requires-Dist: numpy>=2.3.2
Requires-Dist: xarray>=2025.9.0
Provides-Extra: gpu
Requires-Dist: jax[cuda12]>=0.7.1; extra == "gpu"
Provides-Extra: train
Requires-Dist: dask>=2025.7.0; extra == "train"
Requires-Dist: torch>=2.8.0; extra == "train"
Requires-Dist: healpy>=1.18.1; extra == "train"
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Requires-Dist: scikit-learn>=1.7.1; extra == "train"
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Provides-Extra: plots
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Requires-Dist: seaborn>=0.13.2; extra == "plots"
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Provides-Extra: test
Requires-Dist: pytest>=8.0.0; extra == "test"
Requires-Dist: pytest-cov>=4.0.0; extra == "test"
Requires-Dist: pytest-mock>=3.10.0; extra == "test"
Requires-Dist: jaxlib>=0.4.0; extra == "test"
Dynamic: license-file

[![image](https://github.com/shahineb/climemu/actions/workflows/ci.yml/badge.svg)](https://github.com/shahineb/climemu/actions/workflows/ci.yml)
[![image](https://img.shields.io/pypi/v/climemu)](https://pypi.org/project/climemu/) 

# ESM Emulation with diffusion models in JAX

This repository contains the implementation for the paper **"Score-based generative emulation of impact-relevant earth system model outputs"**. Codebase allows to run emulators of monthly averaged near-surface temperature, precipitation, relative humidity, windspeed for MPI-ESM1-2-LR, MIROC6, ACCESS-ESM1-5.


## Installation

Code tested on Python 3.10, 3.11, 3.12. GPU support is required for efficient usage. Install from PyPI:
```bash
pip install climemu
```

## Usage

```python
import climemu

# Instantiate emulator
emulator = climemu.build_emulator("MPI-ESM1-2-LR")

# Download pretrained weights and compile (~1min)
emulator.load()
emulator.compile(n_samples=5)   # Nb of samples generated at each function call

# Generate 5 samples for a given gmst and month
samples = emulator(gmst=2,       # GMST anomaly wrt piControl (°C)
                   month=3,      # Month index (1-12)
                   seed=0,       # Random seed
                   xarray=True)  # Return xr.Dataset
```

:warning: _Default model files for usage are trained on the full set of Tier I SSP simulations. To reproduce the paper results follow [instructions](docs/reproducepaper.md)_.


## Citation
_add_
