climate-diagnostics 1.1

Climate diagnostics tools 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 robust support for parallel processing and publication-quality figures.

Key Features

• Seamless xarray Integration: Access all features via .climate_plots, .climate_timeseries, and .climate_trends on xarray Datasets.
• Temporal Analysis: Trend detection, STL decomposition, and variability analysis.
• 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.
• Performance: Dask-powered parallel processing 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 dataset
ds = xr.open_dataset("/path/to/air.mon.mean.nc")

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

# Analyze trends
ds.climate_trends.calculate_spatial_trends(
    variable="air",
    num_years=10,
    latitude=slice(40, 6),
    longitude=slice(60, 110)
)

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, 6),
    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, 6),
    longitude=slice(60, 110)
)

Documentation

Full API documentation and usage examples are available in the docs/ folder. To build and view locally:

cd docs
make html
# Then open _build/html/index.html in your browser

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]}
}