"""
Plot a bar chart of classifications
===================================
This is an example showing how to produce a bar chart showing the percentage
abundance of each classification in a 2D or 3D array of classifications.
"""

#%%
# 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 = 3  # Three images
x = 50  # 50 coordinates along x-axis
y = 20  # 20 coordinates along y-axis
n = 5  # Possible classifications [0, 1, 2, 3, 4]

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

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

from mcalf.visualisation import bar

#%%
# We can now simply plot the 3D array.
# By default, the first dimension of a 3D array will be averaged to
# produce a time average, selecting the most common classification
# at each (x, y) coordinate.
# This means the percentage abundances will correspond to the
# most common classification at each coordinate.

bar(class_map)

#%%
# Instead, the percentage abundances can be determined for the whole
# 3D array of classifications by setting ``reduce=True``.
# This skips the averaging process.

bar(class_map, reduce=False)

#%%
# Alternatively, a 2D array can be passed to the function.
# If a 2D array is passed, no averaging is needed, and
# the ``reduce`` parameter is ignored.

#%%
# A narrower range of classifications to be plotted can be
# requested with the ``vmin`` and ``vmax`` parameters.
# To show bars for only classifcations 1, 2, and 3,

bar(class_map, vmin=1, vmax=3)

#%%
# An alternative set of colours can be requested.
# Passing a name of a matplotlib colormap to the
# ``style`` parameter will produce a corresponding
# list of colours for each of the bars.
# For advanced use, explore the ``cmap`` parameter.

bar(class_map, style='viridis')

#%%
# The bar function integrates well with matplotlib, allowing
# extensive flexibility.

import matplotlib.pyplot as plt

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

bar(class_map, vmax=2, style='viridis', ax=ax[0])
bar(class_map, vmin=3, style='cividis', ax=ax[1])

ax[0].set_title('first 3')
ax[1].set_title('last 2')

ax[1].set_ylabel('')

plt.show()

#%%
# Note that ``vmin`` and ``vmax`` are applied before the
# ``reduce`` value is applied. So setting these ranges
# can change the calculated abundances for other
# classifications if ``class_map`` is 3D and
# ``reduce=True``.
#
# The bars do not add up to 100% as a bar for
# negative, invalid classifications (and therefore
# classifications out of the ``vmin`` and ``vmax``
# range) is not shown.