# Customized Colorbars Tutorial¶

This tutorial shows how to build and customize standalone colorbars, i.e. without an attached plot.

## Customized Colorbars¶

A colorbar needs a "mappable" (matplotlib.cm.ScalarMappable) object (typically, an image) which indicates the colormap and the norm to be used. In order to create a colorbar without an attached image, one can instead use a ScalarMappable with no associated data.

### Basic continuous colorbar¶

Here we create a basic continuous colorbar with ticks and labels.

The arguments to the colorbar call are the ScalarMappable (constructed using the norm and cmap arguments), the axes where the colorbar should be drawn, and the colorbar's orientation.

For more information see the colorbar API.

import matplotlib.pyplot as plt
import matplotlib as mpl

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

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

fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),
cax=ax, orientation='horizontal', label='Some Units')


Out:

<matplotlib.colorbar.Colorbar object at 0x7fe6483bf670>


### Extended colorbar with continuous colorscale¶

The second example shows how to make a discrete colorbar based on a continuous cmap. With the "extend" keyword argument the appropriate colors are chosen to fill the colorspace, including the extensions:

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

cmap = mpl.cm.viridis
bounds = [-1, 2, 5, 7, 12, 15]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N, extend='both')

fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),
cax=ax, orientation='horizontal',
label="Discrete intervals with extend='both' keyword")


Out:

<matplotlib.colorbar.Colorbar object at 0x7fe64b64eeb0>


### Discrete intervals colorbar¶

The third example illustrates the use of a ListedColormap which generates a colormap from a set of listed colors, 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 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 additional arguments to colorbar. For the out-of-range values to display on the colorbar without using the extend keyword with colors.BoundaryNorm, we have to use the extend keyword argument directly in the colorbar call, and supply an additional boundary on each end of the range. Here we also use the spacing argument to make the length of each colorbar segment proportional to its corresponding interval.

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

cmap = (mpl.colors.ListedColormap(['red', 'green', 'blue', 'cyan'])
.with_extremes(over='0.25', under='0.75'))

bounds = [1, 2, 4, 7, 8]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
fig.colorbar(
mpl.cm.ScalarMappable(cmap=cmap, norm=norm),
cax=ax,
boundaries=[0] + bounds + [13],  # Adding values for extensions.
extend='both',
ticks=bounds,
spacing='proportional',
orientation='horizontal',
label='Discrete intervals, some other units',
)


Out:

<matplotlib.colorbar.Colorbar object at 0x7fe64b6ac790>


### Colorbar with custom extension lengths¶

Here we illustrate the use of custom length colorbar extensions, 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))

cmap = (mpl.colors.ListedColormap(['royalblue', 'cyan', 'yellow', 'orange'])
.with_extremes(over='red', under='blue'))

bounds = [-1.0, -0.5, 0.0, 0.5, 1.0]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
fig.colorbar(
mpl.cm.ScalarMappable(cmap=cmap, norm=norm),
cax=ax,
boundaries=[-10] + bounds + [10],
extend='both',
extendfrac='auto',
ticks=bounds,
spacing='uniform',
orientation='horizontal',
label='Custom extension lengths, some other units',
)

plt.show()


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