https://github.com/maruedt/chemometrics

Python library for chemometric data analysis
https://github.com/maruedt/chemometrics

chemometrics data-analysis ihm mcr mvda pca pls preprocessing python scikit-learn spectroscopy statistics

Last synced: 28 days ago
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Python library for chemometric data analysis

• Host: GitHub
• URL: https://github.com/maruedt/chemometrics
• Owner: maruedt
• License: gpl-3.0
• Created: 2020-05-03T14:52:16.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2022-10-16T12:58:07.000Z (about 2 years ago)
• Last Synced: 2024-09-29T16:20:57.449Z (about 1 month ago)
• Topics: chemometrics, data-analysis, ihm, mcr, mvda, pca, pls, preprocessing, python, scikit-learn, spectroscopy, statistics
• Language: Python
• Homepage:
• Size: 37.9 MB
• Stars: 29
• Watchers: 3
• Forks: 5
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: COPYING

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README

        
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# chemometrics

**chemometrics** is a free and open source library for visualization, modeling, and prediction of multivariate data.

The package streamlines chemometric workflows by providing powerful preprocessing algorithms combined with simple

functional calls for generating fully calibrated chemometric models. A variety of analytical visualizations help to

understand the data and build trust into the generated models. Looking for further extending the models? chemometrics is

fully compatible with scikit-learn. Use advanced machine learning algorithms to get most out of your data.

The documentation is hosted at https://chemometrics.readthedocs.io

## Installation

chemometrics is distributed over PyPI. The simplest way to install chemometrics is by running

```

python -m pip install chemometrics

```

## Example applications

Plotting with reference coloring:

```python

import numpy as np

import matplotlib.pyplot as plt

import chemometrics as cm

plt.figure(figsize=[12, 5])

lines = cm.plot_colored_series(D.T, x=wavenumbers, reference=C[:, 1])

plt.xlabel(r'Wavenumber / $\mathrm{cm^{-1}}$')

plt.ylabel('Intensity / AU')

```

![spectra](docs/source/examples/colored_spectra_by_reference.png "colored spectra")

Preprocessing by smoothing and performing a second derivative:

```python

X_deriv = cm.Whittaker(constraint_order=3, deriv=2).fit_transform(X)

cm.plot_colored_series(X_deriv.T, reference=Y)

plt.xlabel('Wavenumber / nm')

plt.ylabel('$d^2A/dl^2$ / $mAU/nm^2$')

```

![derived spectra](docs/source/examples/peaches_deriv.png "Second derivative NIR spectra")

A working PLS model is just one function call away:

```python

cm.fit_pls(X_deriv, Y)

```

![CV scores](docs/source/examples/pls_cv.png "Cross-validation scores")

![PLS analytics](docs/source/examples/pls_analysis.png "PLS analytical plots")

Interested in more? Check out following pages:

- [Full workflow](https://chemometrics.readthedocs.io/en/stable/examples/basic_pls_example.html)  from loading the data to interpreting the calibrated PLS model

- A variety of [examples](https://chemometrics.readthedocs.io/en/stable/examples.html)

- [Overview of the API](https://chemometrics.readthedocs.io/en/stable/api.html)

## Requirements

- Python >= 3.8

- NumPy >= 1.19.2

- SciPy >= 1.5.2

- scikit-learn >= 0.23.2

- matplotlib >= 3.3.2

Earlier versions of the required libraries may work but have not been tested.

## Copyright and license

chemometrics is released under GPLv3.

Copyright 2021, 2022 Matthias Rüdt