python-cdo-wrapper 0.1.0

A simple, universal Python wrapper for CDO (Climate Data Operators) with seamless xarray integration

Python CDO Wrapper

A simple, universal Python wrapper for CDO (Climate Data Operators) with seamless xarray integration. Perfect for Jupyter notebooks and climate data analysis workflows.

Features

• 🚀 Simple API: Single function to handle all CDO operations
• 📊 Auto-detection: Automatically detects text vs. data commands
• 🔄 xarray Integration: Returns xarray.Dataset for data operations
• 🧹 Clean Output: Automatic temp file management
• 🐛 Debug Mode: Easy troubleshooting with detailed output
• 📝 Type Hints: Full typing support for IDE autocompletion
• ⚡ Zero Config: Works out of the box

Installation

pip install python-cdo-wrapper

Prerequisites

CDO must be installed on your system:

# macOS (Homebrew)
brew install cdo

# Ubuntu/Debian
sudo apt install cdo

# Conda (recommended for HPC)
conda install -c conda-forge cdo

Quick Start

from python_cdo_wrapper import cdo

# Text commands return strings
info = cdo("sinfo data.nc")
print(info)

# Data commands return xarray.Dataset
ds, log = cdo("yearmean data.nc")
print(ds)

# Chain operators
ds, log = cdo("-yearmean -selname,temperature input.nc")

Usage Examples

Getting File Information

from python_cdo_wrapper import cdo

# File structure info
info = cdo("sinfo data.nc")
print(info)

# Grid description
grid = cdo("griddes data.nc")
print(grid)

# Time information
times = cdo("showtimestamp data.nc")
print(times)

# Variable names
vars = cdo("showname data.nc")
print(vars)

Data Processing

from python_cdo_wrapper import cdo

# Calculate yearly mean
ds, log = cdo("yearmean input.nc")

# Select specific variable
ds, log = cdo("-selname,temperature input.nc")

# Select time range
ds, log = cdo("-seldate,2020-01-01,2020-12-31 input.nc")

# Regrid to different resolution
ds, log = cdo("-remapbil,r360x180 input.nc")

# Calculate field mean
ds, log = cdo("fldmean input.nc")

# Chain multiple operators
ds, log = cdo("-yearmean -selname,temp -sellonlatbox,-10,30,35,70 input.nc")

Saving Output

from python_cdo_wrapper import cdo

# Save to specific file (file persists)
ds, log = cdo("yearmean input.nc", output_file="output.nc")

# Without loading into xarray (useful for large files)
_, log = cdo("yearmean input.nc", output_file="output.nc", return_xr=False)

Debugging

from python_cdo_wrapper import cdo

# Enable debug mode for detailed output
ds, log = cdo("yearmean input.nc", debug=True)
# Output:
# CDO Command: cdo yearmean input.nc /tmp/xxx.nc
# Return code: 0
# Stdout: ...
# Stderr: ...

Error Handling

from python_cdo_wrapper import cdo, CDOError

try:
    ds, log = cdo("invalid_command data.nc")
except CDOError as e:
    print(f"CDO failed with code {e.returncode}")
    print(f"Command: {e.command}")
    print(f"Error: {e.stderr}")
except FileNotFoundError as e:
    print(f"File or CDO not found: {e}")

Utility Functions

from python_cdo_wrapper.core import get_cdo_version, list_operators

# Get CDO version
version = get_cdo_version()
print(version)

# List available operators
ops = list_operators()
print(ops)

Text vs Data Commands

The wrapper automatically detects command types:

Text Commands (return str)

These operators print information and don't produce NetCDF output:

Category	Operators
File Info	sinfo, info, ninfo, tinfo, vlist
Grid	griddes, showgrid, showprojection
Variables	showname, showvar, showcode, showunit
Time	showdate, showtimestamp, showyear, showmonth
Counts	ntsteps, nvars, nlevels, ngrids

Data Commands (return tuple[xr.Dataset, str])

All other operators that produce NetCDF output:

Category	Example Operators
Statistics	yearmean, monmean, daymean, fldmean
Selection	selname, seldate, sellevel, sellonlatbox
Remapping	remapbil, remapcon, remapnn
Arithmetic	add, sub, mul, div, expr
Comparison	eq, ne, gt, lt

API Reference

cdo(cmd, *, output_file=None, return_xr=True, debug=False, check_files=True)

Execute a CDO command and return results as Python objects.

Parameters:

Parameter	Type	Default	Description
cmd	str	required	CDO command (without leading "cdo")
output_file	str | Path | None	None	Output file path (temp file if None)
return_xr	bool	True	Return xarray.Dataset for data commands
debug	bool	False	Print detailed execution info
check_files	bool	True	Validate input files exist

Returns:

• Text commands: str
• Data commands: tuple[xr.Dataset, str] or tuple[None, str]

Raises:

• CDOError: CDO command failed
• FileNotFoundError: CDO not installed or input file missing

Configuration

Environment Variables

The wrapper uses the system CDO installation. You can configure CDO behavior with standard environment variables:

# Set CDO temp directory
export CDO_TMPDIR=/path/to/tmp

# Set number of OpenMP threads
export OMP_NUM_THREADS=4

Comparison with Other Libraries

Feature	python-cdo-wrapper	python-cdo	cdo-bindings
Simple API	✅ Single function	❌ Object-oriented	❌ Complex
Auto text detection	✅	❌	❌
xarray integration	✅ Native	⚠️ Manual	⚠️ Manual
Temp file cleanup	✅ Automatic	⚠️ Manual	⚠️ Manual
Type hints	✅ Full	❌	❌
Dependencies	Minimal	Heavy	Heavy

Development

Setup

# Clone the repository
git clone https://github.com/NarenKarthikBM/python-cdo-wrapper.git
cd python-cdo-wrapper

# Install with dev dependencies
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=python_cdo_wrapper

# Run only unit tests (no CDO required)
pytest -m "not integration"

# Run integration tests (requires CDO)
pytest -m integration

Code Quality

# Format code
ruff format .

# Lint code
ruff check .

# Type check
mypy python_cdo_wrapper

Building

# Build package
hatch build

# Check package
twine check dist/*

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• CDO (Climate Data Operators) by MPI-M
• xarray for N-dimensional labeled arrays
• Climate research community for feedback and testing

Citation

If you use this package in your research, please consider citing:

@software{python_cdo_wrapper,
  title = {Python CDO Wrapper},
  author = {Your Name},
  year = {2024},
  url = {https://github.com/NarenKarthikBM/python-cdo-wrapper},
}

Changelog

See CHANGELOG.md for a list of changes.

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Made with ❤️ for the climate science community