nmrpca

Principal Component Analysis for Nuclear Magnetic Resonance Spectrometry data

A collection of tools for Principal Component Analysis (PCA) on NMR data. The focus is on in situ or in operando NMR and potentially on T1 / T2 fitting.

Basic usage

import sklearn.decomposition
import nmrpca

# Get complex data ...

flat_data = nmrpca.nmr_flatten(complex_data)
pca = sklearn.decomposition.PCA()
coefficients = pca.fit_transform(flat_data)
components = nmrpca.nmr_rebuild(pca.components_)