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Readers for various magnetotelluric data file types from various data loggers

Project description

mt-io

Version: 0.0.1

This package is meant to be a repository for readers for various magnetotelluric data file types from various data loggers including:

Data Logger File Type Reader Maturity
LEMI 424 txt Y medium
LEMI 423 txt In progress weak
LEMI 417 ? In progress weak
Metronix atss Y medium
Metronix ats In progress weak
MiniSEED mseed Y strong
NIMS bin Y strong
Phoenix MTU-5C td_*, JSON, bin Y medium
Phoenix MTU-5A tbl* Y medium
USGS ASCII ascii Y medium
ZEN z3d Y strong

This package will read the data into either a mt_timeseries.ChannelTS or a mt_timeseries.RunTS object depending on the data included in the file.

Installation

From Source

git clone https://github.com/kujaku11/mt-io.git
cd mt-io
pip install .

For development installation with editable mode:

pip install -e .

Note: This package is currently in development. PyPI and conda-forge releases will be available in future versions.

Issues

If you encounter any problems or have suggestions for improvements, please report issues on the GitHub Issues page.

When reporting an issue, please include:

  • A clear description of the problem
  • Steps to reproduce the issue
  • Expected vs. actual behavior
  • Your Python version and operating system
  • Relevant code snippets or error messages

Contributing

Contributions are welcome! To contribute:

  1. Fork the repository on GitHub
  2. Create a new branch for your feature or bug fix (git checkout -b feature/your-feature-name)
  3. Make your changes and commit them with clear, descriptive messages
  4. Add tests for any new functionality
  5. Ensure all tests pass
  6. Push your branch to your fork (git push origin feature/your-feature-name)
  7. Submit a pull request to the main repository

Please ensure your code follows the project's coding standards and includes appropriate documentation.

For major changes, please open an issue first to discuss what you would like to change.

Test Data

If you have a small data set contribute to the repository here.

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