wdfkit 0.1.0

Python package for WDF data treatment

About wdfkit

wdfkit is a Python toolkit for Renishaw WiRE .wdf spectroscopy data—especially Raman and photoluminescence work. It helps you bring measurements out of the instrument format so you can explore and analyse them in Python: load single spectra, line scans, and maps; interpret wavelength or Raman-shift axes consistently; and prepare data with everyday steps such as normalization, cosmic-ray spike removal, reduction of laser-related spectral artefacts, and noise suppression for stacks of spectra.

The project is inspired by spectrapy by Dejan Skrelic—an earlier tool that shaped how spectroscopy users treat this kind of data.

For more information about the wdfkit library, please consult our online documentation.

Citation

If you use wdfkit in a scientific publication, we would like you to cite this package as

wdfkit Package, https://github.com/dshirya/wdfkit

Installation

The preferred method is to use Miniconda Python and install from the “conda-forge” channel of Conda packages.

To add “conda-forge” to the conda channels, run the following in a terminal.

conda config --add channels conda-forge

We want to install our packages in a suitable conda environment. The following creates and activates a new environment named wdfkit_env

conda create -n wdfkit_env wdfkit
conda activate wdfkit_env

The output should print the latest version displayed on the badges above.

If the above does not work, you can use pip to download and install the latest release from Python Package Index. To install using pip into your wdfkit_env environment, type

pip install wdfkit

If you prefer to install from sources, after installing the dependencies, obtain the source archive from GitHub. Once installed, cd into your wdfkit directory and run the following

pip install .

This package also provides command-line utilities. To check the software has been installed correctly, type

wdfkit --version

You can also type the following command to verify the installation.

python -c "import wdfkit; print(wdfkit.__version__)"

To view the basic usage and available commands, type

wdfkit -h

Getting Started

You may consult our online documentation for tutorials and API references.

Support and Contribute

If you see a bug or want to request a feature, please report it as an issue and/or submit a fix as a PR.

Feel free to fork the project and contribute. To install wdfkit in a development mode, with its sources being directly used by Python rather than copied to a package directory, use the following in the root directory

pip install -e .

To ensure code quality and to prevent accidental commits into the default branch, please set up the use of our pre-commit hooks.

1. Install pre-commit in your working environment by running conda install pre-commit.

2. Initialize pre-commit (one time only) pre-commit install.

Thereafter your code will be linted by black and isort and checked against flake8 before you can commit. If it fails by black or isort, just rerun and it should pass (black and isort will modify the files so should pass after they are modified). If the flake8 test fails please see the error messages and fix them manually before trying to commit again.

Improvements and fixes are always appreciated.

Before contributing, please read our Code of Conduct.

Contact

For more information on wdfkit please visit the project web-page or email the maintainers Danila Shiryaev([e-mail]).

Acknowledgements

wdfkit draws conceptual inspiration from spectrapy by Dejan Skrelic.

wdfkit is built and maintained with scikit-package.