Radarx is a Python library built for radar data processing and visualization. The library integrates tightly with xradar and leverages xarray and DataTree structures to enable easy and efficient manipulation of radar sweeps and volume data.

• Xradar Integration: Uses xradar for reading radar data in different formats, providing a consistent interface for various radar types.
• IMD Radar Data Support: Special support for reading and processing IMD radar data in NetCDF format.
• Volume Scanning: Utilities to process radar sweeps and group them into complete volume scans.
• Data Gridding: Provides tools for converting radar data to regular Cartesian grids, supporting complex radar geometries.
• Xarray and DataTree Structured Data: Radar data is returned as xarray datasets, organized into DataTree structures for easy navigation and analysis.

You can install radarx using conda from the conda-forge channel (recommended):

conda install -c conda-forge radarx

You can also install radarx via pip from PyPI:

python -m pip install radarx

Alternatively, you can install it from source by cloning the repository and running:

git clone https://github.com/syedhamidali/radarx.git
cd radarx
python -m pip install .

Here's a simple example of how to use Radarx with [xradar]{.title-ref} to load and process a volume scan:

import radarx as rx

# List of radar files
files = [
    'radar_file1.nc',
    'radar_file2.nc',
    'radar_file3.nc'
]

# Read volume data using Radarx, with xradar integration
volume = rx.io.read_volume(files)

# Access a specific sweep or variable
dbz_data = volume['/volume_0']['DBZ']

Radarx leverages xradar to handle radar file formats and integrates smoothly with xarray and DataTree for organizing and analyzing radar data.

Radarx makes use of the powerful xradar library for radar data ingestion and format handling. This ensures that the package is flexible and can handle a variety of radar data formats, including ODIM_H5, Sigmet, and others. For more advanced users, xradar functionality can be directly accessed to extend Radarx's capabilities.

For full documentation, please visit the Radarx Documentation.

Contributions are welcome! If you'd like to contribute, please follow the steps below:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Write tests for your changes.
4. Submit a pull request.

Please ensure that your code passes the pre-commit hooks and test suite before submitting your PR.

Radarx is licensed under the MIT License. See the LICENSE file for more details.

• Syed Hamid Ali