Preprocessing

Rampy offers handful functions to preprocess your spectra. Do not hesitate to propose/ask for new functionalities!

In the following, we assume you already performed library importation as

# importing rampy
import rampy as rp
# and for numpy we will respect the usual name:
import numpy as np
# for matplotlib
import matplotlib.pyplot as plt

Flip the X axis

Some spectra come with decreasing X values. Rampy offers a simple function to flip them. This can be necessary to resample them (as interpolation algorithms usually require the X values to increase). Here for our spectrum we can do:

spectrum_increasing = rp.flipsp(spectrum)

Remove spikes

Spikes can be removed via the rp.despiking() function. It takes as input X and Y values of a spectrum and a threshold. The threshold is the number of standard deviation above the mean noise value that a point must be to be considered as a spike. For instance, if the threshold is 3, then a point will be considered as a spike if it is 3 standard deviation above the mean of the noise. The function will then replace the spike by the mean of k points before and after the spike.

The function returns the new Y values.

Resampling a spectrum

We need sometime to resample a spectrum with a new X axis. rampy.resample() offers such ability. For instance, we have a spectrum that has a X axis from 400 to 1300 cm-1, with points each 0.9 cm-1. We want the same but with an X axis with a value each cm-1. We can do for our spectrum:

x_new = np.arange(400., 1300., 1.0) # we generate the new X values with numpy.arange()
y_new = rp.resample(spectrum[:,0], spectrum[:,1], x_new)

Sometime, this can fail because we have a new X point at higher or lower values than the original X axis. This is simply solved by asking rampy.resample() to extrapolate:

x_new = np.arange(400., 1300., 1.0) # we generate the new X values with numpy.arange()
y_new = rp.resample(spectrum[:,0], spectrum[:,1], x_new, fill_value="extrapolate")

rampy.resample uses scipy.interpolate.interp1d. It can takes all the arguments that the latter function takes, so see its doc for details!

We can eventually create a new array with the x_new and y_new like:

spectrum_resample = np.vstack((x_new,y_new)).T

Normalisation

Rampy provides the rampy.normalisation() function to normalise the Y values of a spectrum to

• the maximum intensity

• the trapezoidal area under the curve

• to min-max values of intensities

See the notebook on Github for examples

Smoothing

Rampy provides the rampy.smooth() function to smooth a spectrum. 10 different algorithms are available, see the notebook on Github for examples of use.

Baseline

Rampy allows you to fit polynomial, spline, generalized cross-validated spline, logarithms, exponential, ALS, arPLS, drPLS and rubberband baselines to your spectra, in order to remove the background.

See this notebook for an example of use, and the help of rampy.baseline() for details on each algorithm.

Temperature and excitation line effects

Raman spectra may need correction from temperature and excitation line effects. See the review of Brooker et al. 1988 for details. rampy offers several way to do so with the rampy.tlexcitation() function. See its documentation for details.