Working with spectra

When working with spectroscopic data in chemotools and scikit-learn, you often need to reshape single spectra to fit the expected data shapes. This guide explains how to reshape single spectra for preprocessing in scikit-learn and chemotools.

Understanding Data Shapes

chemotools and scikit-learn preprocessing techniques expect 2D arrays (matrices) where:

• Each row represents a sample

• Each column represents a feature

However, spectroscopic data often comes as single spectrum in 1D arrays (vectors). Here’s an example of a single spectrum:

array([0.484434, 0.485629, 0.488754, 0.491942, 0.489923, 0.492869,
       0.497285, 0.501567, 0.500027, 0.50265])

To use chemotools and scikit-learn with single spectra, you need to reshape the 1D array into a 2D array with one row.

Reshaping for Preprocessing

Here’s how to reshape a 1D array into a 2D array with a single row:

import numpy as np

spectra_2d = spectra_1d.reshape(1, -1)

The reshape(1, -1) method converts the 1D array spectra_1d into a 2D array with a single row. The result (spectra_2d) looks like this:

array([[0.484434, 0.485629, 0.488754, 0.491942, 0.489923, 0.492869,
        0.497285, 0.501567, 0.500027, 0.50265]])

Note

The reshaped output is a 2D array with a single row - the format required by scikit-learn and chemotools preprocessing techniques.

Now, you can use the reshaped single spectrum with chemotools and scikit-learn preprocessing techniques:

import numpy as np
from chemotools.scatter import MultiplicativeScatterCorrection

msc = MultiplicativeScatterCorrection()
spectra_msc = msc.fit_transform(spectra_2d))