"""
Built-in protocols
-----------------------------------------------------

To further ease the preprocessing workflow, `RamanSPy` provides a selection of established preprocessing pipelines, which
have proved useful in the literature. Once again, users can directly access and use these out of the box.

Below, we will use `RamanSPy` to apply one of the available preprocessing protocols to the volumetric :ref:`Volumetric cell data` provided in `RamanSPy`.

.. seealso:: For more information on the available protocols, check :ref:`Established protocols`.
"""

# 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 and visualising it before it gets preprocessed
cell_layer = cell_volume.layer(4)
cell_layer.plot(1008, title='Original Raman image')

# %%
# Instead of manually creating a custom preprocessing pipeline, users can simply access and use some of the established
# preprocessing protocols offered within `RamanSPy`. These can be accessed within the `preprocessing.protocols` submodule.
preprocessing_pipeline = ramanspy.preprocessing.protocols.default_fingerprint()

# %%
# And, again, these protocols can be used directly as any :class:`Pipeline` object through their
# :meth:`ramanspy.preprocessing.Pipeline.apply` method.
preprocessed_cell_layer = preprocessing_pipeline.apply(cell_layer)

# %%
# Visualising the preprocessed layer.
preprocessed_cell_layer.plot(1008, title='Preprocessed Raman image')


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