# ======================================================================================
# Copyright (©) 2014-2026 Laboratoire Catalyse et Spectrochimie (LCS), Caen, France.
# CeCILL-B FREE SOFTWARE LICENSE AGREEMENT
# See full LICENSE agreement in the root directory.
# ======================================================================================
# ruff: noqa
"""
PCA analysis example
--------------------
In this example, we perform the PCA dimensionality reduction of a spectra
dataset

"""

# %%
# Import the spectrochempy API package
import spectrochempy as scp

# %%
# Load a dataset
dataset = scp.read_omnic("irdata/nh4y-activation.spg")[::5]
print(dataset)
dataset.plot()
# %%
# Create a PCA object and fit the dataset so that the explained variance is greater or
# equal to 99.9%
pca = scp.PCA(n_components=0.999)
pca.fit(dataset)

# %%
# The number of fitted components is given by the n_components attribute
# (We obtain 23 components)
pca.n_components

# %%
# Transform the dataset to a lower dimensionality using all the fitted components
scores = pca.transform()
scores

# %%
# Finally, display the results graphically

# first we can set some preferences for the plot
prefs = scp.preferences
prefs.lines.markersize = 7

# ScreePlot
_ = pca.plot_scree()
# %%
# Score Plot
# first we can set some preferences for the plot
prefs.lines.markersize = 10

_ = pca.plot_score()
# %%
# Score Plot for 3 PC's in 3D
pca.plot_score(components=(1, 2, 3))
# %%
# Displays 4 loadings
pca.loadings[:4].plot(legend=True)
# %%
# Here we do a masking of the saturated region between 882 and 1280 cm^-1
dataset[
    :, 882.0:1280.0
] = scp.MASKED  # remember: use float numbers for slicing (not integer)
_ = dataset.plot()
# %%
# Apply the PCA model
pca = scp.PCA(n_components=0.999)
pca.fit(dataset)
pca.n_components

# %%
# As seen above, now only 4 components instead of 23 are necessary to 99.9% of
# explained variance.
_ = pca.plot_scree()
# %%
# Displays the loadings
_ = pca.loadings.plot(legend=True)
# %%
# Let's plot the scores
scores = pca.transform()
_ = pca.plot_score((1, 2))
# %%
# Labeling scoreplot with spectra labels
# Our dataset has already two columns of labels for the spectra but there are little
# too long for display on plots.
scores.y.labels

# %%
# So we define some short labels for each component, and add them as a third column:
labels = [lab[:6] for lab in dataset.y.labels[:, 1]]
scores.y.labels = labels  # Note this does not replace previous labels,
# but adds a column.

# %%
# now display thse
_ = pca.plot_score(scores=scores, show_labels=True, labels_column=2)

# %%
# This ends the example ! The following line can be uncommented if no plot shows when
# running the .py script with python

# scp.show()