climakitae 1.4.6

Climate data analysis toolkit

Climakitae

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.

Key Features

• 🌡️ 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

About Cal-Adapt

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.

Getting Started

Installation via Conda

Prerequisites

• Python 3.12 | 3.13
• conda / miniconda

Install latest release with conda on Linux

For additional details on the latest version and step-by-step installation instructions please visit the wiki

# create a conda environment with 
conda create -n climakitae python=3.13 -y
conda activate climakitae
pip install climakitae

Installation via Pip

Prerequisites

• Python 3.12
• pip

Install latest release with pip on Linux

For additional details on the latest version and step-by-step installation instructions please visit the wiki

pip install --upgrade pip
pip install climakitae

Basic Usage

from 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)

Documentation

Resource	Description
AE Navigation Guide	Interactive notebook tutorial
API Reference	Complete API documentation
AE Notebooks	Sample notebooks and scripts
Contributing	Development guidelines

Development Setup

Prerequisites

• Python 3.12
• conda / miniconda

Dev Environment Setup (Linux)

git clone https://github.com/cal-adapt/climakitae.git
cd climakitae
conda create -n climakitae --file conda-linux-64.lock
conda activate climakitae

Running Tests

# Run basic tests
pytest -m "not advanced"

# Run all tests
pytest

# Run with coverage
pip install pytest-cov
pytest --cov=climakitae --cov-report=html

Contributing

We welcome contributions! Please see our contributing guidelines for details on:

• 🐛 Reporting bugs
• 💡 Requesting features
• 🔧 Submitting code changes
• 📖 Improving documentation

Quick Development Workflow

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 request

When submitting a pull request, please tag at least two project maintainers/developers for review.

License

This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.

Related Projects

• climakitaegui - Interactive GUI tools for climakitae
• cae-notebooks - Example notebooks and tutorials

Support

• 📧 Email: [e-mail]
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

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Contributors