"""
Built-in clustering methods
================================

Below, we will use `RamanSPy's` built-in clustering methods to perform KMeans clustering and cluster a Raman spectroscopic image.

In particular, we will cluster the fourth layer of the volumetric :ref:`Volumetric cell data` provided in `RamanSPy`.
"""

# sphinx_gallery_start_ignore
# sphinx_gallery_thumbnail_number = 6
# sphinx_gallery_end_ignore

import ramanspy

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

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

cell_layer = volumes[0].layer(5)  # only selecting the fourth layer of the volume


# %%
# We will first preprocess the spectral image to improve the results of our consecutive analysis.
preprocessing_pipeline = ramanspy.preprocessing.Pipeline([
    ramanspy.preprocessing.misc.Cropper(region=(500, 1800)),
    ramanspy.preprocessing.despike.WhitakerHayes(),
    ramanspy.preprocessing.denoise.SavGol(window_length=7, polyorder=3),
    ramanspy.preprocessing.baseline.ASLS(),
    ramanspy.preprocessing.normalise.MinMax(pixelwise=False),
])
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=[1008])


# %%
# We can then access and use `RamanSPy's` implementation of KMeans clustering with 4 clusters.
kmeans = ramanspy.analysis.cluster.KMeans(n_clusters=4)
# %%
#
clusters, cluster_centres = kmeans.apply(preprocessed_cell_layer)


# %%
# Finally, we can use `RamanSPy's` :meth:`ramanspy.plot.spectra` and :meth:`ramanspy.plot.image` methods to visualise the derived
# clusters.
ramanspy.plot.spectra(cluster_centres, preprocessed_cell_layer.spectral_axis, plot_type="single stacked", label=[f"Cluster centre {i + 1}" for i in range(len(cluster_centres))])

# %%
#
ramanspy.plot.image(clusters, title=[f"Clusters {i + 1}" for i in range(len(clusters))], cbar=False)