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https://github.com/nevernervous78/nirpyresearch

Snippets and data from the blog of Nirpy Research
https://github.com/nevernervous78/nirpyresearch

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Snippets and data from the blog of Nirpy Research

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# Nirpy Research

Snippets and data from the blog of [Nirpy Research](https://nirpyresearch.com/).



## Data

This folder contains the original datasets used in some of the posts. 



### milk.csv

This dataset is used in the following posts:

* [Classification of NIR spectra by Principal Components Analysis](https://nirpyresearch.com/classification-nir-spectra-principal-component-analysis-python/)



### milk-powder.csv 

Used in the following posts:

* [Classification of NIR Spectra by Linear Discriminant Analysis in Python](https://nirpyresearch.com/classification-nir-spectra-linear-discriminant-analysis-python/)

* [PLS Discriminant Analysis for binary classification in Python](https://nirpyresearch.com/pls-discriminant-analysis-binary-classification-python/)



### peach_spectra+brixvalues.csv

This dataset is used in the following posts:

* [Principal Component Regression in Python](https://nirpyresearch.com/principal-component-regression-python/)

* [Partial Least Square Regression in Python](https://nirpyresearch.com/partial-least-squares-regression-python/)

* [A Variable Selection Method for PLS in Python](https://nirpyresearch.com/variable-selection-method-pls-python/)

* [Two Scatter Correction Techniques for NIR Spectroscopy in Python](https://nirpyresearch.com/two-scatter-correction-techniques-nir-spectroscopy-python/)

* [Exporting NIR Regression Models Built in Python](https://nirpyresearch.com/exporting-nir-regression-models-built-in-python/)

* [Principal Component Regression in Python Revisited](https://nirpyresearch.com/principal-component-regression-python-revisited/)

* [Principal Component Selection with a Greedy Algorithm](https://nirpyresearch.com/principal-component-selection-greedy-algorithm/)

* [Principal Component Selection with Simulated Annealing](https://nirpyresearch.com/principal-component-selection-with-simulated-annealing/)

* [Minimal prediction models for linear regression](https://nirpyresearch.com/minimal-prediction-models-linear-regression/)

* [The Akaike Information Criterion for model selection](https://nirpyresearch.com/akaike-information-criterion-for-model-selection/)

* [Parallel computation of loops for cross-validation analysis](https://nirpyresearch.com/parallel-computation-cross-validation/)



### plums.csv

Used in the following posts:

* [Detecting outliers using the Mahalanobis distance with PCA in Python](https://nirpyresearch.com/detecting-outliers-using-mahalanobis-distance-pca-python/)

* [PCA and kernel PCA explained](https://nirpyresearch.com/pca-kernel-pca-explained/)



### Honey2021.csv

Used in the following posts:

* [Aquagrams with Python and Matplotlib](https://nirpyresearch.com/aquagrams-python-matplotlib/)



### Raman_spectra_mixtures.csv

Used in the following posts:

* [Multivariate curve resolution: an introduction](https://nirpyresearch.com/multivariate-curve-resolution-introduction/)



### ABS_plastic.csv

Used in the following posts:

* [Optimal spectra smoothing with Fourier ring correlation](https://nirpyresearch.com/optimal-spectra-smoothing-fourier-ring-correlation/)



### Incombustible Material NIR.csv

Kindly provided by S. D'Hyon. About 60 samples with varying "incombustible content". Mix is coal and calcium carbonate. The data is raw and unmodified using a ChemWiz-ADK NIR spectrometer. 



## Snippets

Whatever piece of code that can be of general use or will not make it in the last versions of the posts will be (in time) posted here.



* [bootstrap.py](https://github.com/nevernervous78/nirpyresearch/blob/master/snippets/bootstrap.py) - Data splitter implementing the Bootstrap cross-validation method. This is not currently available in scikit-learn. This class is consistent with scikit-learn usage in a limited case. For more info on how to use this class, read the tutorial [K-fold and Montecarlo cross-validation vs Bootstrap: a primer](https://nirpyresearch.com/kfold-montecarlo-cross-validation-bootstrap-primer/).

* [Scatter Correction](https://github.com/nevernervous78/nirpyresearch/blob/master/snippets/Scatter_corrections_techniques.ipynb) - Jupyter notebook associated with the post: [Two Scatter Correction Techniques for NIR Spectroscopy in Python](https://nirpyresearch.com/two-scatter-correction-techniques-nir-spectroscopy-python/).

* [Basic PLS Regression](https://github.com/nevernervous78/nirpyresearch/blob/master/snippets/Basic%20PLS%20regression%20in%20Python.ipynb) - Jupyter notebook associated with the [Partial Least Square Regression in Python](https://nirpyresearch.com/partial-least-squares-regression-python/) post.

* [PCA vs Kernel PCA](https://github.com/nevernervous78/nirpyresearch/blob/master/snippets/PCA_and_kernelPCA_explained.ipynb) - Jupyter notebook with the code described in the [PCA and kernel PCA explained](https://nirpyresearch.com/pca-kernel-pca-explained/) post.

* [Simulated annealing](https://github.com/nevernervous78/nirpyresearch/blob/master/snippets/Wavelength%20band%20selection%20with%20simulated%20annealing.ipynb) - Companion Jupyter notebook to the [simulated annealing](https://nirpyresearch.com/principal-component-selection-with-simulated-annealing/) post.