"""
Built-in methods
-------------------------------------------

`RamanSPy` provides a collection of various preprocessing methods, which users can directly access and use out of the box.

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

.. seealso:: The list of available preprocessing procedures offered by `RamanSPy` can be checked at :ref:`Predefined algorithms`.
"""

# sphinx_gallery_start_ignore
# sphinx_gallery_thumbnail_number = -1
# sphinx_gallery_end_ignore

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')

# %%
# For instance, we can pergorm spectral cropping. This can be done by accessing the corresponding
# method in `RamanSPy` and providing the desired parameters as arguments in the initialisation of the preprocessing step as given below.
# To check what parameters may be needed, check the documentation of the selected method.
cropper = ramanspy.preprocessing.misc.Cropper(region=(300, None))

# %%
# Then, users can straightforwardly use the selected algorithm to preprocess different data through its
# :meth:`ramanspy.preprocessing.PreprocessingStep.apply` method.
cropped_random_spectrum = cropper.apply(random_spectrum)
cropped_random_spectrum.plot(title='Cropped Raman spectra')

# %%
# We can further apply other preprocessing procedure, such as denoising:
savgol = ramanspy.preprocessing.denoise.SavGol(window_length=7, polyorder=3)

denoised_random_spectrum = savgol.apply(cropped_random_spectrum)
denoised_random_spectrum.plot(title='Smoothened Raman spectra')

# %%
# baseline correction:
baseline_corrector = ramanspy.preprocessing.baseline.IARPLS()

baselined_random_spectrum = baseline_corrector.apply(denoised_random_spectrum)
baselined_random_spectrum.plot(title='Baseline corrected Raman spectra')


# %%
# normalisation:
vector_normaliser = ramanspy.preprocessing.normalise.Vector()

normalised_random_spectrum = vector_normaliser.apply(baselined_random_spectrum)
normalised_random_spectrum.plot(title='Normalised Raman spectra', ylabel="Normalised intensity (a.u.)")


# %%
# and others.

ramanspy.plot.spectra(
    [random_spectrum, cropped_random_spectrum, denoised_random_spectrum, baselined_random_spectrum, normalised_random_spectrum],
    plot_type='stacked',
    label=['Original', 'Cropped', 'Smoothened', 'Baseline corrected', 'Normalised'],
    title='Preprocessing showcase')

# %%
# .. note:: The methods provided in `RamanSPy` work equally well on the other spectral data containers, as well as on collection of those.