The ChemoPy Chemometric Toolbox is a Python package specifically tailored for chemometric analysis. It provides a versatile set of tools for seamlessly integrating various chemometric techniques into Scikit-learn pipelines. Whether you are working with spectral data, chemical analyses, or related fields, this package simplifies the integration of fundamental chemometric methods, streamlining data preprocessing, analysis, and modeling.
Please see the documentation for more information.
The ChemoPy Chemometric Toolbox offers a diverse range of modular chemometric methods, thoughtfully categorized into different classes. These methods can be effortlessly integrated into Scikit-learn pipelines to empower your data analysis. Here are some of the available techniques:
- MSC (Multiplicative Scatter Correction)
- SNV (Standard Normal Variate)
- Savitzky-Golay Smoothing
- Denoising Techniques
- ...
- PCA (Principal Component Analysis)
- Orthogonal Correction
- ...
These techniques are designed to be used individually or in combination to meet your specific data preprocessing and analysis requirements.
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Scikit-learn Compatibility: Seamlessly integrate all chemometric techniques into Scikit-learn pipelines, ensuring a smooth workflow for building machine learning models.
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User-Friendly: The ChemoPy Chemometric Toolbox is designed with user-friendliness in mind, making it accessible to both experts and newcomers to chemometrics.
To install the ChemoPy Chemometric Toolbox, clone the repository!