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"""
========
Colorbar
========
Use `~.Figure.colorbar` by specifying the mappable object (here
the `.AxesImage` returned by `~.axes.Axes.imshow`)
and the Axes to attach the colorbar to.
"""
import matplotlib.pyplot as plt
import numpy as np
# setup some generic data
N = 37
x, y = np.mgrid[:N, :N]
Z = (np.cos(x*0.2) + np.sin(y*0.3))
# mask out the negative and positive values, respectively
Zpos = np.ma.masked_less(Z, 0)
Zneg = np.ma.masked_greater(Z, 0)
fig, (ax1, ax2, ax3) = plt.subplots(figsize=(13, 3), ncols=3)
# plot just the positive data and save the
# color "mappable" object returned by ax1.imshow
pos = ax1.imshow(Zpos, cmap='Blues', interpolation='none')
# add the colorbar using the figure's method,
# telling which mappable we're talking about and
# which Axes object it should be near
fig.colorbar(pos, ax=ax1)
# repeat everything above for the negative data
# you can specify location, anchor and shrink the colorbar
neg = ax2.imshow(Zneg, cmap='Reds_r', interpolation='none')
fig.colorbar(neg, ax=ax2, location='right', anchor=(0, 0.3), shrink=0.7)
# Plot both positive and negative values between +/- 1.2
pos_neg_clipped = ax3.imshow(Z, cmap='RdBu', vmin=-1.2, vmax=1.2,
interpolation='none')
# Add minorticks on the colorbar to make it easy to read the
# values off the colorbar.
cbar = fig.colorbar(pos_neg_clipped, ax=ax3, extend='both')
cbar.minorticks_on()
plt.show()
# %%
#
# .. admonition:: References
#
# The use of the following functions, methods, classes and modules is shown
# in this example:
#
# - `matplotlib.axes.Axes.imshow` / `matplotlib.pyplot.imshow`
# - `matplotlib.figure.Figure.colorbar` / `matplotlib.pyplot.colorbar`
# - `matplotlib.colorbar.Colorbar.minorticks_on`
# - `matplotlib.colorbar.Colorbar.minorticks_off`
#
# .. tags::
#
# component: colorbar
# styling: color
# plot-type: imshow
# level: beginner
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