"""
Custom pipelines
-------------------------------------------------

`RamanSPy` makes the construction and execution of diverse preprocessing pipelines significantly easier and less
software intensive. This is possible through the :class:`preprocessing.Pipeline` class.

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

# sphinx_gallery_start_ignore
# sphinx_gallery_thumbnail_number = -2
# 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 the fourth layer of the volume
cell_layer = cell_volume.layer(4)


# %%
# Let's first plot a spectral slice across the 2930 cm :sup:`-1` band of the image to visualise what has been captured in the image.
cell_layer.plot(bands=[2930, 1600], title='Original Raman image')


# %%
# We can also visualise a specific spectrum within the image.
cell_layer[30, 30].plot(title='Original Raman spectra')


# %%
# To create a complete preprocessing pipeline, users need to simply stack the relevant preprocessing algorithms they wish
# to perform (in the execution order they wish to adopt) into a :class:`Pipeline` object. This can include both methods
# provided by `RamanSPy` and custom methods wrapped within :class:`PreprocessingStep` objects.
preprocessing_pipeline = ramanspy.preprocessing.Pipeline([
    ramanspy.preprocessing.misc.Cropper(region=(300, None)),
    ramanspy.preprocessing.despike.WhitakerHayes(),
    ramanspy.preprocessing.denoise.SavGol(window_length=7, polyorder=3),
    ramanspy.preprocessing.baseline.IARPLS(),
    ramanspy.preprocessing.normalise.MinMax()
])


# %%
# Then, one can straightforwardly use the pipeline to preprocess different data through its
# :meth:`ramanspy.preprocessing.Pipeline.apply` method.
preprocessed_cell_layer = preprocessing_pipeline.apply(cell_layer)


# %%
# To check the effect of our preprocessing protocol, we can re-plot the same spectral slice as before
preprocessed_cell_layer.plot(bands=[2930, 1600], title='Preprocessed Raman spectra')


# %%
# as well as the same spectra we visualised before.
preprocessed_cell_layer[30, 30].plot(title='Preprocessed Raman spectra')

# %%
# .. note:: Custom preprocessing protocols defined with `RamanSPy` work equally well on the other spectral data containers, as well as on collection of those.