"""
=================
Placing Colorbars
=================

Colorbars indicate the quantitative extent of image data.  Placing in
a figure is non-trivial because room needs to be made for them.

The simplest case is just attaching a colorbar to each axes:
"""
import matplotlib.pyplot as plt
import numpy as np


# Fixing random state for reproducibility
np.random.seed(19680801)

fig, axs = plt.subplots(2, 2)
cmaps = ['RdBu_r', 'viridis']
for col in range(2):
    for row in range(2):
        ax = axs[row, col]
        pcm = ax.pcolormesh(np.random.random((20, 20)) * (col + 1),
                            cmap=cmaps[col])
        fig.colorbar(pcm, ax=ax)
plt.show()

######################################################################
# The first column has the same type of data in both rows, so it may
# be desirable to combine the colorbar which we do by calling
# `.Figure.colorbar` with a list of axes instead of a single axes.

fig, axs = plt.subplots(2, 2)
cmaps = ['RdBu_r', 'viridis']
for col in range(2):
    for row in range(2):
        ax = axs[row, col]
        pcm = ax.pcolormesh(np.random.random((20, 20)) * (col + 1),
                            cmap=cmaps[col])
    fig.colorbar(pcm, ax=axs[:, col], shrink=0.6)
plt.show()

######################################################################
# Relatively complicated colorbar layouts are possible using this
# paradigm.  Note that this example works far better with
# ``constrained_layout=True``

fig, axs = plt.subplots(3, 3, constrained_layout=True)
for ax in axs.flat:
    pcm = ax.pcolormesh(np.random.random((20, 20)))

fig.colorbar(pcm, ax=axs[0, :2], shrink=0.6, location='bottom')
fig.colorbar(pcm, ax=[axs[0, 2]], location='bottom')
fig.colorbar(pcm, ax=axs[1:, :], location='right', shrink=0.6)
fig.colorbar(pcm, ax=[axs[2, 1]], location='left')
plt.show()

######################################################################
# Colorbars with fixed-aspect-ratio axes
# ======================================
#
# Placing colorbars for axes with a fixed aspect ratio pose a particular
# challenge as the parent axes changes size depending on the data view.

fig, axs = plt.subplots(2, 2,  constrained_layout=True)
cmaps = ['RdBu_r', 'viridis']
for col in range(2):
    for row in range(2):
        ax = axs[row, col]
        pcm = ax.pcolormesh(np.random.random((20, 20)) * (col + 1),
                            cmap=cmaps[col])
        if col == 0:
            ax.set_aspect(2)
        else:
            ax.set_aspect(1/2)
        if row == 1:
            fig.colorbar(pcm, ax=ax, shrink=0.6)
plt.show()

######################################################################
# One way around this issue is to use an `.Axes.inset_axes` to locate the
# axes in axes coordinates.  Note that if you zoom in on the axes, and
# change the shape of the axes, the colorbar will also change position.

fig, axs = plt.subplots(2, 2, constrained_layout=True)
cmaps = ['RdBu_r', 'viridis']
for col in range(2):
    for row in range(2):
        ax = axs[row, col]
        pcm = ax.pcolormesh(np.random.random((20, 20)) * (col + 1),
                            cmap=cmaps[col])
        if col == 0:
            ax.set_aspect(2)
        else:
            ax.set_aspect(1/2)
        if row == 1:
            cax = ax.inset_axes([1.04, 0.2, 0.05, 0.6], transform=ax.transAxes)
            fig.colorbar(pcm, ax=ax, cax=cax)
plt.show()
