Custom methods

Users can use RamanSPy to also define their own preprocessing methods, which can then be directly integrated into the preprocessing core of RamanSPy. This can be done by wrapping custom preprocessing methods into ramanspy.preprocessing.PreprocessingStep instances.

Below, we will use RamanSPy to define and apply a custom preprocessing method to the volumetric Volumetric cell data provided in RamanSPy.

import ramanspy

dir_ = r'../../../../data/kallepitis_data'

volumes = ramanspy.datasets.volumetric_cells(cell_type='THP-1', folder=dir_)

We will use the first volume

cell_volume = volumes[0]

# selecting a random spectrum for visualisation purposes
random_spectrum = cell_volume[25, 25, 5]
random_spectrum.plot(title='Original Raman spectra')

<Axes: title={'center': 'Original Raman spectra'}, xlabel='Raman shift (cm$^{{{-1}}}$)', ylabel='Intensity (a.u.)'>

To do so, users can define their own preprocessing methods, which must be of the form given below and return the updated intensity_data and spectral_axis.

def func(intensity_data, spectral_axis, **kwargs):
    # Preprocess intensity_data and spectral axis
    updated_intensity_data = ...
    updated_spectral_axis = ...

    return updated_intensity_data, updated_spectral_axis

For instance, we can define a simple example function, which simply subtracts a given offset from each value in the intensity_data array as follows:

def offset_func(intensity_data, spectral_axis, *, offset):
    return intensity_data - offset, spectral_axis

Then, one must simply wrap the function of interest using the ramanspy.preprocessing.PreprocessingStep class. That is done by creating a ramanspy.preprocessing.PreprocessingStep object by invoking its ramanspy.preprocessing.PreprocessingStep.__init__() method on the function one wants to wrap and the *args, **kwargs needed.

offsetter = ramanspy.preprocessing.PreprocessingStep(offset_func, offset=500)

Having done that, the ramanspy.preprocessing.PreprocessingStep object can now be used as any of RamanSPy’s preprocessing methods through its ramanspy.preprocessing.PreprocessingStep.apply() method.

preprocessed_random_spectrum = offsetter.apply(random_spectrum)
preprocessed_random_spectrum.plot()

<Axes: title={'center': 'Raman spectra'}, xlabel='Raman shift (cm$^{{{-1}}}$)', ylabel='Intensity (a.u.)'>

The custom method can also be directly integrated into pipelines as follows:

preprocessing_pipeline = ramanspy.preprocessing.Pipeline([
    ramanspy.preprocessing.despike.WhitakerHayes(),
    ramanspy.preprocessing.denoise.SavGol(window_length=7, polyorder=3),
    offsetter
])
preprocessed_random_spectrum = preprocessing_pipeline.apply(random_spectrum)

Visualising the preprocessed spectrum.

preprocessed_random_spectrum.plot(title='Preprocessed Raman spectra')

<Axes: title={'center': 'Preprocessed Raman spectra'}, xlabel='Raman shift (cm$^{{{-1}}}$)', ylabel='Intensity (a.u.)'>

Note

Custom preprocessing methods defined with RamanSPy work equally well on the other spectral data containers, as well as on collection of those.