"""
Plot a grid of spectra grouped by classification
================================================
This is an example showing how to produce a grid of line plots of
an array of spectra labelled with a classification.
"""

#%%
# First we shall create a random array of spectra each labelled
# with a random classifications.
# Usually you would provide your own set of hand labelled spectra
# taken from spectral imaging observations of the Sun.

from mcalf.tests.visualisation.test_classifications import spectra as s

n = 200  # 200 spectra
w = 20  # 20 wavelength points for each spectrum
low, high = 1, 5  # Possible classifications [1, 2, 3, 4]

# 2D array of spectra (n, w), 1D array of labels (n,)
spectra, labels = s(n, w, low, high)

#%%
# Next, we shall import :func:`mcalf.visualisation.plot_classifications`.

from mcalf.visualisation import plot_classifications

#%%
# We can now plot a simple grid of the spectra grouped by their
# classification. By default, a maximum of 20 spectra are plotted
# for each classification. The first 20 spectra are selected.

plot_classifications(spectra, labels)

#%%
# A specific number of rows or columns can be requested,

plot_classifications(spectra, labels, ncols=1)

#%%
# The plot settings can be adjusted,

plot_classifications(spectra, labels, show_labels=False, nlines=5,
                     style='viridis', plot_settings={'ls': '--', 'marker': 'x'})

#%%
# By default, the y-axis goes from 0 to 1. This is because
# labelled training data will typically be rescaled
# between 0 and 1. However, if a particular classification
# has spectra that are not within 0 and 1, the y-axis limits
# are determined by matplotlib.

spectra[labels == 2] += 0.5
plot_classifications(spectra, labels)