"""

=============================

Customized Colorbars Tutorial

=============================

This tutorial shows how to build colorbars without an attached plot.

Customized Colorbars

====================

:class:`~matplotlib.colorbar.ColorbarBase` derives from

:mod:`~matplotlib.cm.ScalarMappable` and puts a colorbar in a specified axes,

so it has everything needed for a standalone colorbar. It can be used as-is to

make a colorbar for a given colormap; it does not need a mappable object like

an image. In this tutorial we will explore what can be done with standalone

colorbar.

Basic continuous colorbar

-------------------------

Set the colormap and norm to correspond to the data for which the colorbar

will be used. Then create the colorbar by calling

:class:`~matplotlib.colorbar.ColorbarBase` and specify axis, colormap, norm

and orientation as parameters. Here we create a basic continuous colorbar

with ticks and labels. For more information see the

:mod:`~matplotlib.colorbar` API.

"""

import matplotlib.pyplot as plt

import matplotlib as mpl

fig, ax = plt.subplots(figsize=(6, 1))

fig.subplots_adjust(bottom=0.5)

cmap = mpl.cm.cool

norm = mpl.colors.Normalize(vmin=5, vmax=10)

cb1 = mpl.colorbar.ColorbarBase(ax, cmap=cmap,

norm=norm,

orientation='horizontal')

cb1.set_label('Some Units')

fig.show()

###############################################################################

# Discrete intervals colorbar

# ---------------------------

#

# The second example illustrates the use of a

# :class:`~matplotlib.colors.ListedColormap` which generates a colormap from a

# set of listed colors, :func:`colors.BoundaryNorm` which generates a colormap

# index based on discrete intervals and extended ends to show the "over" and

# "under" value colors. Over and under are used to display data outside of the

# normalized [0,1] range. Here we pass colors as gray shades as a string

# encoding a float in the 0-1 range.

#

# If a :class:`~matplotlib.colors.ListedColormap` is used, the length of the

# bounds array must be one greater than the length of the color list. The

# bounds must be monotonically increasing.

#

# This time we pass some more arguments in addition to previous arguments to

# :class:`~matplotlib.colorbar.ColorbarBase`. For the out-of-range values to

# display on the colorbar, we have to use the *extend* keyword argument. To use

# *extend*, you must specify two extra boundaries. Finally spacing argument

# ensures that intervals are shown on colorbar proportionally.

fig, ax = plt.subplots(figsize=(6, 1))

fig.subplots_adjust(bottom=0.5)

cmap = mpl.colors.ListedColormap(['red', 'green', 'blue', 'cyan'])

cmap.set_over('0.25')

cmap.set_under('0.75')

bounds = [1, 2, 4, 7, 8]

norm = mpl.colors.BoundaryNorm(bounds, cmap.N)

cb2 = mpl.colorbar.ColorbarBase(ax, cmap=cmap,

norm=norm,

boundaries=[0] + bounds + [13],

extend='both',

ticks=bounds,

spacing='proportional',

orientation='horizontal')

cb2.set_label('Discrete intervals, some other units')

fig.show()

###############################################################################

# Colorbar with custom extension lengths

# --------------------------------------

#

# Here we illustrate the use of custom length colorbar extensions, used on a

# colorbar with discrete intervals. To make the length of each extension the

# same as the length of the interior colors, use ``extendfrac='auto'``.

fig, ax = plt.subplots(figsize=(6, 1))

fig.subplots_adjust(bottom=0.5)

cmap = mpl.colors.ListedColormap(['royalblue', 'cyan',

'yellow', 'orange'])

cmap.set_over('red')

cmap.set_under('blue')

bounds = [-1.0, -0.5, 0.0, 0.5, 1.0]

norm = mpl.colors.BoundaryNorm(bounds, cmap.N)

cb3 = mpl.colorbar.ColorbarBase(ax, cmap=cmap,

norm=norm,

boundaries=[-10] + bounds + [10],

extend='both',

extendfrac='auto',

ticks=bounds,

spacing='uniform',

orientation='horizontal')

cb3.set_label('Custom extension lengths, some other units')

fig.show()