climate-diagnostics 1.1.1

Production-ready climate diagnostics tools with advanced chunking optimization for analyzing and visualizing climate data

Climate Diagnostics Toolkit

A comprehensive Python toolkit for analyzing, processing, and visualizing climate data from model output, reanalysis, and observations. Built on xarray, it provides specialized accessors for time series, trends, and spatial diagnostics, with sophisticated disk-aware chunking optimization and robust support for parallel processing and publication-quality figures.

🌍 Perfect for climate scientists, researchers, and data analysts working with atmospheric and oceanic datasets.

📋 Table of Contents

• ✨ Key Features
• 📦 Installation
• 🚀 Quick Start
• 🔧 API Overview
• 📚 Documentation
• 🤝 Contributing & Support
• 🚀 Development & Testing
• 📄 License
• 📖 Citation

✨ Key Features

• 🔌 Seamless xarray Integration: Access all features via .climate_plots, .climate_timeseries, and .climate_trends on xarray Datasets.
• 🚀 Sophisticated Chunking: Advanced disk-aware chunking strategies with automatic memory optimization and performance profiling.
• 📈 Temporal Analysis: Trend detection, STL decomposition, and variability analysis with optimized chunking.
• 🗺️ Spatial Visualization: Publication-quality maps with Cartopy, custom projections, and area-weighted statistics.
• 📊 Statistical Diagnostics: Advanced methods for climate science, including ETCCDI indices.
• 🔬 Multi-model Analysis: Compare and evaluate climate model outputs with intelligent chunking.
• ⚡ Performance: Dask-powered parallel processing with dynamic chunk optimization for large datasets.

📦 Installation

With pip

pip install climate_diagnostics

With conda (recommended for all dependencies)

conda env create -f environment.yml
conda activate climate-diagnostics
pip install -e .

🚀 Quick Start

import xarray as xr
from climate_diagnostics import accessors

# Open a large dataset
ds = xr.open_dataset("/path/to/air.mon.mean.nc")

# Optimize chunking for your analysis
ds = ds.climate_timeseries.optimize_chunks_advanced(
    operation_type='timeseries',
    performance_priority='balanced'
)

# Plot a mean map
ds.climate_plots.plot_mean(variable="air", season="djf")

# Analyze trends with optimized chunking
ds.climate_trends.calculate_spatial_trends(
    variable="air",
    num_years=10,
    latitude=slice(40, 60),
    longitude=slice(60, 110),
    optimize_chunks=True
)

# Get chunking recommendations
ds.climate_timeseries.analyze_chunking_strategy()

💡 Tip: Check out the Quick Start Guide for a complete walkthrough including advanced chunking strategies!

🔧 API Overview

Accessors

• climate_plots: Geographic and statistical visualizations
• climate_timeseries: Time series analysis and decomposition
• climate_trends: Trend calculation and significance testing

Example: Time Series

ds.climate_timeseries.plot_time_series(
    latitude=slice(40, 60),
    longitude=slice(60, 110),
    level=850,
    variable="air",
    season="jjas"
)

Example: Climate Indices

ds.climate_plots.plot_consecutive_wet_days(
    variable="prate",
    threshold=1.0,
    latitude=slice(40, 60),
    longitude=slice(60, 110)
)

📚 Documentation

📖 Complete Documentation

• Quick Start Guide - Get started in minutes
• API Reference - Complete function documentation
• User Guide - In-depth tutorials
• Examples - Real-world usage examples

Local Documentation Build

To build and view documentation locally:

cd docs
make html
# Open build/html/index.html in your browser

🤝 Contributing & Support

• 🐛 Report Issues - Bug reports and feature requests
• 💬 Discussions - Questions and community support
• 📖 Contributing Guide - How to contribute

🚀 Development & Testing

git clone https://github.com/pranay-chakraborty/climate_diagnostics.git
cd climate_diagnostics
conda env create -f environment.yml
conda activate climate-diagnostics
pip install -e ".[dev]"
pytest

📄 License

This project is licensed under the MIT LICENSE.

📖 Citation

If you use Climate Diagnostics Toolkit in your research, please cite:

Chakraborty, P. (2025) & Muhammed I. K., A. (2025). Climate Diagnostics Toolkit: Tools for analyzing and visualizing climate data using xarray accessors. Version 1.1. https://github.com/pranay-chakraborty/climate_diagnostics

For LaTeX users:

@software{chakraborty2025climate,
  author = {Chakraborty, Pranay and Muhammed I. K., Adil},
  title = {{Climate Diagnostics Toolkit: Tools for analyzing and visualizing climate data using xarray accessors}},
  year = {2025},
  version = {1.1},
  publisher = {GitHub},
  url = {https://github.com/pranay-chakraborty/climate_diagnostics},
  note = {[Computer software]}
}

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📚 Documentation | 🐛 Issues | 💬 Discussions

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