A powerful Python toolkit for climate data analysis and retrieval from the Cal-Adapt Analytics Engine (AE).
Climakitae provides intuitive tools for accessing, analyzing, and visualizing downscaled CMIP6 data, enabling researchers and practitioners to perform comprehensive climate impact assessments for California.
Warning
This package is under active development. APIs may change between versions.
- 🌡️ Comprehensive Climate Data Access: Retrieve climate variables from hosted climate models
- 📊 Downscaled Climate Models: Access dynamical (WRF) and statistical (LOCA2) downscaling methods
- 🗺️ Spatial Analysis Tools: Built-in support for geographic subsetting and spatial aggregation
- 📈 Climate Indices: Calculate heat indices, warming levels, and extreme event metrics
- 🔧 Flexible Data Export: Export to NetCDF, CSV, and Zarr
- 📱 GUI Integration: Works seamlessly with climakitaegui for interactive analysis
Climakitae is developed as part of the Cal-Adapt Analytics Engine, a platform for California climate data and tools. Cal-Adapt provides access to cutting-edge climate science to support adaptation planning and decision-making.
- Python 3.12
- conda / miniconda
For additional details on the latest version and step-by-step installation instructions please visit the wiki
# get the conda lock file from github
curl https://raw.githubusercontent.com/cal-adapt/cae-environments/refs/heads/main/conda-lock/climakitae/1.3.0/conda-linux-64.lock -o conda-linux-64.lock
# create and activate your environment
conda create -n climakitae --file conda-linux-64.lock
conda activate climakitae
# install climakitae
pip install https://github.com/cal-adapt/climakitae/archive/refs/tags/1.3.0.zip- Python 3.12
- pip
For additional details on the latest version and step-by-step installation instructions please visit the wiki
# get the requirements.txt file from github
curl https://raw.githubusercontent.com/cal-adapt/climakitae/refs/heads/release-1.3.0/requirements.txt -o requirements.txt
# load packages from requirements.txt
pip install -r requirements.txt
# install climakitae
pip install https://github.com/cal-adapt/climakitae/archive/refs/tags/1.3.0.zipfrom climakitae.core.data_interface import get_data
# Retrieve temperature data for California
data = get_data(
variable="Air Temperature at 2m",
downscaling_method="Dynamical",
resolution="9 km",
timescale="monthly",
scenario="SSP 3-7.0",
cached_area="CA"
)
# Data is returned as an xarray Dataset
print(data)| Resource | Description |
|---|---|
| AE Navigation Guide | Interactive notebook tutorial |
| API Reference | Complete API documentation |
| AE Notebooks | Sample notebooks and scripts |
| Contributing | Development guidelines |
- Python 3.12
- conda / miniconda
git clone https://github.com/cal-adapt/climakitae.git
cd climakitae
conda create -n climakitae --file conda-linux-64.lock
conda activate climakitae# Run basic tests
pytest -m "not advanced"
# Run all tests
pytest
# Run with coverage
pip install pytest-cov
pytest --cov=climakitae --cov-report=htmlWe welcome contributions! Please see our contributing guidelines for details on:
- 🐛 Reporting bugs
- 💡 Requesting features
- 🔧 Submitting code changes
- 📖 Improving documentation
Open a ⚙️ code improvement issue describing the feature you'd like to develop.
Then, checkout and setup your branch:
# Fork the repo and create a feature branch
git checkout -b feature/your-feature-name
# Make your changes and add tests
# ...
# Run tests and linting
pytest
black climakitae/
isort climakitae/
# Submit a pull requestWhen submitting a pull request, please tag at least two project maintainers/developers for review.
This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.
- climakitaegui - Interactive GUI tools for climakitae
- cae-notebooks - Example notebooks and tutorials
- 📧 Email: analytics@cal-adapt.org
- 🐛 Issues: GitHub Issues
- 💬 Discussions: GitHub Discussions