chemtools is a Python library designed to simplify chemometric analyses.
This project draws heavy inspiration from the following sources:
- Statsmodels: ror statistical calculations and modeling.
- Scikit-learn: utilized for machine learning functionalities.
It provides a user-friendly interface for performing various operations, including:
1. Data Preprocessing:
- Autoscaling
- Correlation matrix calculation
- Diagonalization of matrices
- Calculation of matrix mean and standard deviation
- Variance calculation
2. Exploratory Data Analysis:
- Principal Component Analysis (PCA)
3. Regression Analysis:
- Linear Regression (including confidence and prediction bands)
4. Utility Functions:
- Heatmap generation and annotation
- Array manipulation (e.g., converting arrays to columns, reordering)
- Variable type checking
- Random color generation
- Saving and loading models
The library is organized into four main modules:
- preprocessing: Contains functions for data preprocessing.
- explotation: Houses classes and methods for exploratory data analysis (e.g., PCA).
- regression: Provides implementations of regression models (e.g., Linear Regression).
- utility: Includes various utility functions to assist with data handling, visualization, and more.
Refer to the documentation for detailed information on how to use the library.
Check out the Jupyter notebooks (.ipynb) in the repository for practical examples of how to use chemtools.
Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request.