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ENH: Add Axes.colorbar() method by timhoffm · Pull Request #31029 · matplotlib/matplotlib · GitHub

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ENH: Add Axes.colorbar() method #31029

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ENH: Add Axes.colorbar() method #31029

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6d57fc1
ENH: Add Axes.colorbar() method
timhoffm Jan 8, 2026
3beaebf
Apply ax.colorbar() to examples
timhoffm Jan 24, 2026
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21 changes: 16 additions & 5 deletions doc/api/axes_api.rst
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Expand Up @@ -314,8 +314,8 @@ Axis limits and direction
Axes.set_ybound
Axes.get_ybound

Axis labels, title, and legend
------------------------------
Axis labels and title
---------------------

.. autosummary::
:toctree: _as_gen
Expand All @@ -330,9 +330,6 @@ Axis labels, title, and legend

Axes.set_title
Axes.get_title
Axes.legend
Axes.get_legend
Axes.get_legend_handles_labels

Axis scales
-----------
Expand Down Expand Up @@ -438,6 +435,20 @@ Ticks and tick labels
Axes.locator_params


Legend and colorbar
===================

.. autosummary::
:toctree: _as_gen
:template: autosummary.rst
:nosignatures:

Axes.legend
Axes.get_legend
Axes.get_legend_handles_labels
Axes.colorbar


Units
=====

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20 changes: 7 additions & 13 deletions galleries/examples/color/colorbar_basics.py
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Expand Up @@ -22,26 +22,20 @@

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)
# plot just the positive data and add a colorbar
ax1.imshow(Zpos, cmap='Blues', interpolation='none')
ax1.colorbar()

# 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)
ax2.imshow(Zneg, cmap='Reds_r', interpolation='none')
ax2.colorbar(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')
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 = ax3.colorbar(extend='both')
cbar.minorticks_on()
plt.show()

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22 changes: 11 additions & 11 deletions galleries/examples/color/custom_cmap.py
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Expand Up @@ -122,9 +122,9 @@
# Create the colormap
cmap = LinearSegmentedColormap.from_list(cmap_name, colors, N=n_bin)
# Fewer bins will result in "coarser" colomap interpolation
im = ax.imshow(Z, origin='lower', cmap=cmap)
ax.imshow(Z, origin='lower', cmap=cmap)
ax.colorbar()
ax.set_title("N bins: %s" % n_bin)
fig.colorbar(im, ax=ax)


# %%
Expand Down Expand Up @@ -226,20 +226,20 @@
fig, axs = plt.subplots(2, 2, figsize=(6, 9))
fig.subplots_adjust(left=0.02, bottom=0.06, right=0.95, top=0.94, wspace=0.05)

im1 = axs[0, 0].imshow(Z, cmap=blue_red1)
fig.colorbar(im1, ax=axs[0, 0])
axs[0, 0].imshow(Z, cmap=blue_red1)
axs[0, 0].colorbar()

im2 = axs[1, 0].imshow(Z, cmap='BlueRed2')
fig.colorbar(im2, ax=axs[1, 0])
axs[1, 0].imshow(Z, cmap='BlueRed2')
axs[1, 0].colorbar()

# Now we will set the third cmap as the default. One would
# not normally do this in the middle of a script like this;
# it is done here just to illustrate the method.

plt.rcParams['image.cmap'] = 'BlueRed3'

im3 = axs[0, 1].imshow(Z)
fig.colorbar(im3, ax=axs[0, 1])
axs[0, 1].imshow(Z)
axs[0, 1].colorbar()
axs[0, 1].set_title("Alpha = 1")

# Or as yet another variation, we can replace the rcParams
Expand All @@ -252,12 +252,12 @@
# Draw a line with low zorder so it will be behind the image.
axs[1, 1].plot([0, 10 * np.pi], [0, 20 * np.pi], color='c', lw=20, zorder=-1)

im4 = axs[1, 1].imshow(Z)
fig.colorbar(im4, ax=axs[1, 1])
im = axs[1, 1].imshow(Z)
axs[1, 1].colorbar()

# Here it is: changing the colormap for the current image and its
# colorbar after they have been plotted.
im4.set_cmap('BlueRedAlpha')
im.set_cmap('BlueRedAlpha')
axs[1, 1].set_title("Varying alpha")

fig.suptitle('Custom Blue-Red colormaps', fontsize=16)
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5 changes: 2 additions & 3 deletions galleries/examples/images_contours_and_fields/colormap_interactive_adjustment.py
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Expand Up @@ -24,10 +24,9 @@
data2d = np.sin(t)[:, np.newaxis] * np.cos(t)[np.newaxis, :]

fig, ax = plt.subplots()
im = ax.imshow(data2d)
ax.imshow(data2d)
ax.colorbar(label='Interactive colorbar')
ax.set_title('Pan on the colorbar to shift the color mapping\n'
'Zoom on the colorbar to scale the color mapping')

fig.colorbar(im, ax=ax, label='Interactive colorbar')

plt.show()
63 changes: 29 additions & 34 deletions galleries/examples/images_contours_and_fields/colormap_normalizations.py
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Expand Up @@ -32,12 +32,12 @@

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

pcm = ax[0].pcolor(X, Y, Z, cmap='PuBu_r', shading='nearest')
fig.colorbar(pcm, ax=ax[0], extend='max', label='linear scaling')
ax[0].pcolor(X, Y, Z, cmap='PuBu_r', shading='nearest')
ax[0].colorbar(extend='max', label='linear scaling')

pcm = ax[1].pcolor(X, Y, Z, cmap='PuBu_r', shading='nearest',
norm=colors.LogNorm(vmin=Z.min(), vmax=Z.max()))
fig.colorbar(pcm, ax=ax[1], extend='max', label='LogNorm')
ax[1].pcolor(X, Y, Z, cmap='PuBu_r', shading='nearest',
norm=colors.LogNorm(vmin=Z.min(), vmax=Z.max()))
ax[1].colorbar(extend='max', label='LogNorm')

# %%
# PowerNorm
Expand All @@ -53,12 +53,12 @@

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

pcm = ax[0].pcolormesh(X, Y, Z, cmap='PuBu_r', shading='nearest')
fig.colorbar(pcm, ax=ax[0], extend='max', label='linear scaling')
ax[0].pcolormesh(X, Y, Z, cmap='PuBu_r', shading='nearest')
ax[0].colorbar(extend='max', label='linear scaling')

pcm = ax[1].pcolormesh(X, Y, Z, cmap='PuBu_r', shading='nearest',
norm=colors.PowerNorm(gamma=0.5))
fig.colorbar(pcm, ax=ax[1], extend='max', label='PowerNorm')
ax[1].pcolormesh(X, Y, Z, cmap='PuBu_r', shading='nearest',
norm=colors.PowerNorm(gamma=0.5))
ax[1].colorbar(extend='max', label='PowerNorm')

# %%
# SymLogNorm
Expand All @@ -79,14 +79,13 @@

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

pcm = ax[0].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
vmin=-np.max(Z))
fig.colorbar(pcm, ax=ax[0], extend='both', label='linear scaling')
ax[0].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest', vmin=-np.max(Z))
ax[0].colorbar(extend='both', label='linear scaling')

pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
norm=colors.SymLogNorm(linthresh=0.015,
vmin=-10.0, vmax=10.0, base=10))
fig.colorbar(pcm, ax=ax[1], extend='both', label='SymLogNorm')
ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
norm=colors.SymLogNorm(linthresh=0.015,
vmin=-10.0, vmax=10.0, base=10))
ax[1].colorbar(extend='both', label='SymLogNorm')

# %%
# Custom Norm
Expand All @@ -112,13 +111,12 @@ def __call__(self, value, clip=None):
# %%
fig, ax = plt.subplots(2, 1)

pcm = ax[0].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
vmin=-np.max(Z))
fig.colorbar(pcm, ax=ax[0], extend='both', label='linear scaling')
ax[0].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest', vmin=-np.max(Z))
ax[0].colorbar(extend='both', label='linear scaling')

pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
norm=MidpointNormalize(midpoint=0))
fig.colorbar(pcm, ax=ax[1], extend='both', label='Custom norm')
ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
norm=MidpointNormalize(midpoint=0))
ax[1].colorbar(extend='both', label='Custom norm')

# %%
# BoundaryNorm
Expand All @@ -129,25 +127,22 @@ def __call__(self, value, clip=None):

fig, ax = plt.subplots(3, 1, layout='constrained')

pcm = ax[0].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
vmin=-np.max(Z))
fig.colorbar(pcm, ax=ax[0], extend='both', orientation='vertical',
label='linear scaling')
ax[0].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest', vmin=-np.max(Z))
ax[0].colorbar(extend='both', orientation='vertical', label='linear scaling')

# Evenly-spaced bounds gives a contour-like effect.
bounds = np.linspace(-2, 2, 11)
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
norm=norm)
fig.colorbar(pcm, ax=ax[1], extend='both', orientation='vertical',
label='BoundaryNorm\nlinspace(-2, 2, 11)')
ax[1].colorbar(extend='both', orientation='vertical',
label='BoundaryNorm\nlinspace(-2, 2, 11)')

# Unevenly-spaced bounds changes the colormapping.
bounds = np.array([-1, -0.5, 0, 2.5, 5])
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
pcm = ax[2].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
norm=norm)
fig.colorbar(pcm, ax=ax[2], extend='both', orientation='vertical',
label='BoundaryNorm\n[-1, -0.5, 0, 2.5, 5]')
ax[2].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest', norm=norm)
ax[2].colorbar(extend='both', orientation='vertical',
label='BoundaryNorm\n[-1, -0.5, 0, 2.5, 5]')

plt.show()
33 changes: 15 additions & 18 deletions galleries/examples/images_contours_and_fields/colormap_normalizations_symlognorm.py
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Expand Up @@ -40,16 +40,14 @@ def rbf(x, y):

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

pcm = ax[0].pcolormesh(X, Y, Z,
norm=colors.SymLogNorm(linthresh=lnrwidth, linscale=1,
vmin=-gain, vmax=gain, base=10),
**shadeopts)
fig.colorbar(pcm, ax=ax[0], extend='both')
ax[0].pcolormesh(X, Y, Z, norm=colors.SymLogNorm(linthresh=lnrwidth, linscale=1,
vmin=-gain, vmax=gain, base=10),
**shadeopts)
ax[0].colorbar(extend='both')
ax[0].text(-2.5, 1.5, 'symlog')

pcm = ax[1].pcolormesh(X, Y, Z, vmin=-gain, vmax=gain,
**shadeopts)
fig.colorbar(pcm, ax=ax[1], extend='both')
ax[1].pcolormesh(X, Y, Z, vmin=-gain, vmax=gain, **shadeopts)
ax[1].colorbar(extend='both')
ax[1].text(-2.5, 1.5, 'linear')


Expand All @@ -67,18 +65,17 @@ def rbf(x, y):

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

pcm = ax[0].pcolormesh(X, Y, Z,
norm=colors.SymLogNorm(linthresh=lnrwidth, linscale=1,
vmin=-gain, vmax=gain, base=10),
**shadeopts)
fig.colorbar(pcm, ax=ax[0], extend='both')
ax[0].pcolormesh(X, Y, Z,
norm=colors.SymLogNorm(linthresh=lnrwidth, linscale=1,
vmin=-gain, vmax=gain, base=10),
**shadeopts)
ax[0].colorbar(extend='both')
ax[0].text(-2.5, 1.5, 'symlog')

pcm = ax[1].pcolormesh(X, Y, Z,
norm=colors.AsinhNorm(linear_width=lnrwidth,
vmin=-gain, vmax=gain),
**shadeopts)
fig.colorbar(pcm, ax=ax[1], extend='both')
ax[1].pcolormesh(X, Y, Z,
norm=colors.AsinhNorm(linear_width=lnrwidth, vmin=-gain, vmax=gain),
**shadeopts)
ax[1].colorbar(extend='both')
ax[1].text(-2.5, 1.5, 'asinh')


Expand Down
8 changes: 3 additions & 5 deletions galleries/examples/images_contours_and_fields/contour_image.py
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Expand Up @@ -65,8 +65,7 @@

cset3 = axs[0].contour(X, Y, Z, (0,), colors='g', linewidths=2)
axs[0].set_title('Filled contours')
fig.colorbar(cset1, ax=axs[0])

axs[0].colorbar(cset1)

axs[1].imshow(Z, extent=extent, cmap=cmap, norm=norm)
axs[1].contour(Z, levels, colors='k', origin='upper', extent=extent)
Expand All @@ -82,13 +81,12 @@
# This is intentional. The Z values are defined at the center of each
# image pixel (each color block on the following subplot), so the
# domain that is contoured does not extend beyond these pixel centers.
im = axs[3].imshow(Z, interpolation='nearest', extent=extent,
cmap=cmap, norm=norm)
im = axs[3].imshow(Z, interpolation='nearest', extent=extent, cmap=cmap, norm=norm)
axs[3].contour(Z, levels, colors='k', origin='image', extent=extent)
ylim = axs[3].get_ylim()
axs[3].set_ylim(ylim[::-1])
axs[3].set_title("Origin from rc, reversed y-axis")
fig.colorbar(im, ax=axs[3])
axs[3].colorbar(im)

fig.tight_layout()
plt.show()
Expand Down
4 changes: 2 additions & 2 deletions galleries/examples/images_contours_and_fields/contourf_demo.py
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Expand Up @@ -96,8 +96,8 @@
fig, axs = plt.subplots(2, 2, layout="constrained")

for ax, extend in zip(axs.flat, extends):
cs = ax.contourf(X, Y, Z, levels, cmap=cmap, extend=extend)
fig.colorbar(cs, ax=ax, shrink=0.9)
ax.contourf(X, Y, Z, levels, cmap=cmap, extend=extend)
ax.colorbar(shrink=0.9)
ax.set_title("extend = %s" % extend)
ax.locator_params(nbins=4)

Expand Down
6 changes: 3 additions & 3 deletions galleries/examples/images_contours_and_fields/contourf_log.py
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Expand Up @@ -36,16 +36,16 @@
# Automatic selection of levels works; setting the
# log locator tells contourf to use a log scale:
fig, ax = plt.subplots()
cs = ax.contourf(X, Y, z, locator=ticker.LogLocator(), cmap="PuBu_r")
ax.contourf(X, Y, z, locator=ticker.LogLocator(), cmap="PuBu_r")

# Alternatively, you can manually set the levels
# and the norm:
# lev_exp = np.arange(np.floor(np.log10(z.min())-1),
# np.ceil(np.log10(z.max())+1))
# levs = np.power(10, lev_exp)
# cs = ax.contourf(X, Y, z, levs, norm=colors.LogNorm())
# ax.contourf(X, Y, z, levs, norm=colors.LogNorm())

cbar = fig.colorbar(cs)
ax.colorbar()

plt.show()

Expand Down
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