First Steps

You can consult the help of each function/class/method on this website, please click on the rampy package item on the navigation bar on the left side.

Below, some quick examples of the use of those functions/classes are shown to highlight some of the possibilities offered by Rampy.

For complete examples of use, please directly see the example Jupyter notebooks here

Library importation

Python does not come with a lot of possibilities. This is why you usually need to import a few libraries. You can assign a shortname to a library, as usually people import/use functions with libraries’ prefix, to avoid confusion (two libraries can have a function with the same name…). A useful shortname for rampy is rp:

# 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

Data importation

This is done using directly Pandas or numpy, except for: - importing multiple spectra can be done with the rampy.spectrarray() function; - maps for which rampy provides a function to import the CSV files generated by the Renishaw or Horiba Raman spectrometers.

To import a spectrum saved in a 2 column text file “data.txt” with space separator, we can use the numpy genfromtxt function:

import numpy as np
spectrum = np.genfromtxt("data.txt")

If our file has a 10 line header, we can skip it like

spectrum = np.genfromtxt("data.txt", skip_header=10)

See the documentation of numpy for further details, as well as that of Pandas.

Plot a spectrum

This can be done with Matplotlib directly. For instance, to plot our spectrum we can do:

plt.figure(figsize=(5,5))
plt.plot(spectrum[:,0],spectrum[:,1],'k.',markersize=1)
plt.xlabel("Raman shift, cm$^{-1}$", fontsize = 12)
plt.ylabel("Normalized intensity, a. u.", fontsize = 12)
plt.title("Fig. 1: the raw data",fontsize = 12,fontweight="bold")

This will give the image: