"""
Combine multiple classification plots
=====================================
This is an example showing how to use multiple classification
plotting functions in a single figure.
"""

#%%
# First we shall create a random 3D grid of classifications that can be plotted.
# Usually you would use a method such as
# :meth:`mcalf.models.ModelBase.classify_spectra`
# to classify an array of spectra.

from mcalf.tests.helpers import class_map as c

t = 1  # One image
x = 20  # 20 coordinates along x-axis
y = 20  # 20 coordinates along y-axis
n = 3  # Possible classifications [0, 1, 2]

class_map = c(t, x, y, n)  # 3D array of classifications (t, y, x)

#%%
# Next 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.
# Or you could provide a set of spectra labelled by the classifier.

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

n = 400  # 200 spectra
w = 20  # 20 wavelength points for each spectrum
low, high = 0, 3  # Possible classifications [0, 1, 2]

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

#%%
# If a GridSpec returned by the plot_classification function has
# free space, a new axes can be added to the returned GridSpec.
# We can then request plot_class_map to plot onto this
# new axes.
# The colorbar axes can be set to ``fig.axes`` such that
# the colorbar takes the full height of the figure, as
# in this case, its colours are the same as the line plots.

import matplotlib.pyplot as plt
from mcalf.visualisation import plot_classifications, plot_class_map

fig = plt.figure(constrained_layout=True)

gs = plot_classifications(spectra, labels, nrows=2, show_labels=False)

ax = fig.add_subplot(gs[-1])
plot_class_map(class_map, ax=ax, colorbar_settings={
    'ax': fig.axes,
    'label': 'classification',
})

#%%
# The function :func:`mcalf.visualisation.init_class_data`` is
# intended to be an internal function for generating data that
# is common to multiple plotting functions. However, it may be
# used externally if necessary.

from mcalf.visualisation import init_class_data, bar

fig, ax = plt.subplots(1, 2, constrained_layout=True)

data = init_class_data(class_map, resolution=(0.25, 0.25), ax=ax[1])

bar(data=data, ax=ax[0])
plot_class_map(data=data, ax=ax[1], show_colorbar=False)

#%%
# The following example should be equivalent to the example above,

fig, ax = plt.subplots(1, 2, constrained_layout=True)

bar(class_map, ax=ax[0])
plot_class_map(class_map, ax=ax[1], show_colorbar=False,
               resolution=(0.25, 0.25))