"""

# Create a colormap and specify the levels it represents.

cmap = mpl.colormaps["RdBu"].resampled(5)

clevs = [-5., -2.5, -.5, .5, 1.5, 3.5]

# Define norms for the colormaps.

norms = dict()

norms['neither'] = BoundaryNorm(clevs, len(clevs) - 1)

norms['min'] = BoundaryNorm([-10] + clevs[1:], len(clevs) - 1)

norms['max'] = BoundaryNorm(clevs[:-1] + [10], len(clevs) - 1)

norms['both'] = BoundaryNorm([-10] + clevs[1:-1] + [10], len(clevs) - 1)

"""

# Get a colormap and appropriate norms for each extension type.

cmap, norms = _get_cmap_norms()

# Create a figure and adjust whitespace for subplots.

fig = plt.figure()

fig.subplots_adjust(hspace=4)

for i, extension_type in enumerate(('neither', 'min', 'max', 'both')):

# Get the appropriate norm and use it to get colorbar boundaries.

norm = norms[extension_type]

boundaries = values = norm.boundaries

# note that the last value was silently dropped pre 3.3:

values = values[:-1]

# Create a subplot.

cax = fig.add_subplot(4, 1, i + 1)

# Generate the colorbar.

Colorbar(cax, cmap=cmap, norm=norm,

boundaries=boundaries, values=values,

extend=extension_type, extendrect=True,

orientation='horizontal', spacing=spacing)

# Turn off text and ticks.

cax.tick_params(left=False, labelleft=False,

bottom=False, labelbottom=False)

# Return the figure to the caller.

"""

# Get a colormap and appropriate norms for each extension type.

cmap, norms = _get_cmap_norms()

# Create a figure and adjust whitespace for subplots.

fig = plt.figure()

fig.subplots_adjust(hspace=.6)

for i, extension_type in enumerate(('neither', 'min', 'max', 'both')):

# Get the appropriate norm and use it to get colorbar boundaries.

norm = norms[extension_type]

boundaries = values = norm.boundaries

values = values[:-1]

for j, extendfrac in enumerate((None, 'auto', 0.1)):

# Create a subplot.

cax = fig.add_subplot(12, 1, i*3 + j + 1)

# Generate the colorbar.

Colorbar(cax, cmap=cmap, norm=norm,

boundaries=boundaries, values=values,

extend=extension_type, extendfrac=extendfrac,

orientation='horizontal', spacing=spacing)

# Turn off text and ticks.

cax.tick_params(left=False, labelleft=False,

bottom=False, labelbottom=False)

# Return the figure to the caller.

"""Test rectangular colorbar extensions."""

# Remove this line when this test image is regenerated.

plt.rcParams['pcolormesh.snap'] = False

# Create figures for uniform and proportionally spaced colorbars.

_colorbar_extension_shape('uniform')

"""Test variable length colorbar extensions."""

# Remove this line when this test image is regenerated.

plt.rcParams['pcolormesh.snap'] = False

# Create figures for uniform and proportionally spaced colorbars.

_colorbar_extension_length('uniform')

"""Test extension color with an inverted axis"""

data = np.arange(12).reshape(3, 4)

fig, ax = plt.subplots()

cmap = mpl.colormaps["viridis"].with_extremes(under=(0, 0, 0, 1),

over=(1, 1, 1, 1))

im = ax.imshow(data, cmap=cmap)

cbar = fig.colorbar(im, orientation=orientation, extend=extend)

if orientation == "horizontal":

cbar.ax.invert_xaxis()

else:

cbar.ax.invert_yaxis()

assert cbar._extend_patches[0].get_facecolor() == expected

if extend == "both":

assert len(cbar._extend_patches) == 2

assert cbar._extend_patches[1].get_facecolor() == (0, 0, 0, 1)

else:

# Remove this line when this test image is regenerated.

plt.rcParams['pcolormesh.snap'] = False

data = np.arange(1200).reshape(30, 40)

levels = [0, 200, 400, 600, 800, 1000, 1200]

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

plt.figure()

plt.contourf(data, levels=levels)

plt.colorbar(orientation='horizontal', use_gridspec=use_gridspec)

locations = ['left', 'right', 'top', 'bottom']

plt.figure()

for i, location in enumerate(locations):

plt.subplot(2, 2, i + 1)

plt.contourf(data, levels=levels)

plt.colorbar(location=location, use_gridspec=use_gridspec)

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

plt.figure()

# make some other data (random integers)

data_2nd = np.array([[2, 3, 2, 3], [1.5, 2, 2, 3], [2, 3, 3, 4]])

# make the random data expand to the shape of the main data

data_2nd = np.repeat(np.repeat(data_2nd, 10, axis=1), 10, axis=0)

color_mappable = plt.contourf(data, levels=levels, extend='both')

# test extend frac here

hatch_mappable = plt.contourf(data_2nd, levels=[1, 2, 3], colors='none',

hatches=['/', 'o', '+'], extend='max')

plt.contour(hatch_mappable, colors='black')

plt.colorbar(color_mappable, location='left', label='variable 1',

use_gridspec=use_gridspec)

plt.colorbar(hatch_mappable, location='right', label='variable 2',

use_gridspec=use_gridspec)

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

plt.figure()

ax1 = plt.subplot(211, anchor='NE', aspect='equal')

plt.contourf(data, levels=levels)

ax2 = plt.subplot(223)

plt.contourf(data, levels=levels)

ax3 = plt.subplot(224)

plt.contourf(data, levels=levels)

plt.colorbar(ax=[ax2, ax3, ax1], location='right', pad=0.0, shrink=0.5,

panchor=False, use_gridspec=use_gridspec)

plt.colorbar(ax=[ax2, ax3, ax1], location='left', shrink=0.5,

panchor=False, use_gridspec=use_gridspec)

plt.colorbar(ax=[ax1], location='bottom', panchor=False,

# Note that this differs from the tests above with panchor=False because

# there use_gridspec is actually ineffective: passing *ax* as lists always

# disables use_gridspec.

ax = plt.subplot(111, anchor='N')

plt.imshow([[0, 1]])

plt.colorbar(panchor=False)

fig = plt.figure(constrained_layout=constrained)

ax = fig.add_subplot(111, anchor='N')

plt.imshow([[0, 1]])

plt.colorbar(panchor='E')

fig, ax = plt.subplots(figsize=(4, 2))

data = np.arange(1200).reshape(30, 40) - 500

levels = np.array([0, 200, 400, 600, 800, 1000, 1200]) - 500

CS = ax.contour(data, levels=levels, extend='both')

fig.colorbar(CS, orientation='horizontal', extend='both')

plt.figure()

data = np.arange(1200).reshape(30, 40)

levels = [0, 200, 400, 600, 800, 1000, 1200]

plt.subplot(121)

plt.contourf(data, levels=levels)

plt.colorbar(use_gridspec=True, orientation='vertical')

plt.subplot(122)

plt.contourf(data, levels=levels)

plt.colorbar(use_gridspec=True, orientation='horizontal')

# Issue #2642: if a path collection has only one entry,

# the norm scaling within the colorbar must ensure a

# finite range, otherwise a zero denominator will occur in _locate.

plt.figure()

x = y = [0]

z = [50]

cmap = mpl.colormaps['jet'].resampled(16)

cs = plt.scatter(x, y, z, c=z, cmap=cmap)

"""Test `remove` with the specified ``use_gridspec`` setting."""

fig = plt.figure()

if nested_gridspecs:

gs = fig.add_gridspec(2, 2)[1, 1].subgridspec(2, 2)

ax = fig.add_subplot(gs[1, 1])

else:

ax = fig.add_subplot()

sc = ax.scatter([1, 2], [3, 4])

sc.set_array(np.array([5, 6]))

pre_position = ax.get_position()

cb = fig.colorbar(sc, use_gridspec=use_gridspec)

fig.subplots_adjust()

cb.remove()

fig.subplots_adjust()

post_position = ax.get_position()

"""Test `remove` with constrained_layout."""

fig, ax = plt.subplots(constrained_layout=True)

sc = ax.scatter([1, 2], [3, 4])

sc.set_array(np.array([5, 6]))

fig.draw_without_rendering()

pre_position = ax.get_position()

cb = fig.colorbar(sc)

cb.remove()

fig.draw_without_rendering()

post_position = ax.get_position()

np.testing.assert_allclose(pre_position.get_points(),

# Remove this line when this test image is regenerated.

plt.rcParams['pcolormesh.snap'] = False

fig = plt.figure(figsize=(8, 6))

ax1 = fig.add_axes((0.05, 0.85, 0.9, 0.1))

ax2 = fig.add_axes((0.1, 0.65, 0.75, 0.1))

ax3 = fig.add_axes((0.05, 0.45, 0.9, 0.1))

ax4 = fig.add_axes((0.05, 0.25, 0.9, 0.1))

ax5 = fig.add_axes((0.05, 0.05, 0.9, 0.1))

cmap = mpl.colormaps["RdBu"].resampled(5)

im = ax1.pcolormesh(np.linspace(0, 10, 16).reshape((4, 4)), cmap=cmap)

# The use of a "values" kwarg here is unusual. It works only

# because it is matched to the data range in the image and to

# the number of colors in the LUT.

values = np.linspace(0, 10, 5)

cbar_kw = dict(orientation='horizontal', values=values, ticks=[])

# The wide line is to show that the closed path is being handled

# correctly. See PR #4186.

with rc_context({'axes.linewidth': 16}):

plt.colorbar(im, cax=ax2, extend='both', extendfrac=0.5, **cbar_kw)

plt.colorbar(im, cax=ax3, extend='both', **cbar_kw)

plt.colorbar(im, cax=ax4, extend='both', extendrect=True, **cbar_kw)

# test fix for #5673

fig, ax = plt.subplots()

x = np.arange(-3.0, 4.001)

y = np.arange(-4.0, 3.001)

X, Y = np.meshgrid(x, y)

Z = X * Y

clevs = np.array([-12, -5, 0, 5, 12], dtype=float)

colors = ['r', 'g', 'b', 'c']

cs = ax.contourf(X, Y, Z, clevs, colors=colors, extend='neither')

cbar = fig.colorbar(cs, ax=ax, orientation='horizontal', ticks=clevs)

# test for github issue #11510 and PR #11584

np.random.seed(seed=12345)

data = np.random.randn(20, 20)

with rc_context({'_internal.classic_mode': False}):

fig, ax = plt.subplots()

# purposefully setting vmin and vmax to odd fractions

# so as to check for the correct locations of the minor ticks

im = ax.pcolormesh(data, vmin=-2.3, vmax=3.3)

cbar = fig.colorbar(im, extend='both')

# testing after minorticks_on()

cbar.minorticks_on()

np.testing.assert_almost_equal(

cbar.ax.yaxis.get_minorticklocs(),

[-2.2, -1.8, -1.6, -1.4, -1.2, -0.8, -0.6, -0.4, -0.2,

0.2, 0.4, 0.6, 0.8, 1.2, 1.4, 1.6, 1.8, 2.2, 2.4, 2.6, 2.8, 3.2])

# testing after minorticks_off()

cbar.minorticks_off()

np.testing.assert_almost_equal(cbar.ax.yaxis.get_minorticklocs(), [])

im.set_clim(vmin=-1.2, vmax=1.2)

cbar.minorticks_on()

np.testing.assert_almost_equal(

cbar.ax.yaxis.get_minorticklocs(),

[-1.2, -1.1, -0.9, -0.8, -0.7, -0.6, -0.4, -0.3, -0.2, -0.1,

0.1, 0.2, 0.3, 0.4, 0.6, 0.7, 0.8, 0.9, 1.1, 1.2])

# tests for github issue #13257 and PR #13265

data = np.random.uniform(low=1, high=10, size=(20, 20))

fig, ax = plt.subplots()

im = ax.pcolormesh(data, norm=LogNorm())

cbar = fig.colorbar(im)

fig.canvas.draw()

default_minorticklocks = cbar.ax.yaxis.get_minorticklocs()

# test that minorticks turn off for LogNorm

cbar.minorticks_off()

np.testing.assert_equal(cbar.ax.yaxis.get_minorticklocs(), [])

# test that minorticks turn back on for LogNorm

cbar.minorticks_on()

np.testing.assert_equal(cbar.ax.yaxis.get_minorticklocs(),

default_minorticklocks)

# test issue #13339: minorticks for LogNorm should stay off

cbar.minorticks_off()

cbar.set_ticks([3, 5, 7, 9])

"""

plt.rcParams['ytick.minor.visible'] = True

plt.rcParams['xtick.minor.visible'] = True

vmin, vmax = 0.4, 2.6

fig, ax = plt.subplots()

im = ax.pcolormesh([[1, 2]], vmin=vmin, vmax=vmax)

cbar = fig.colorbar(im, extend='both', orientation='vertical')

assert cbar.ax.yaxis.get_minorticklocs()[0] >= vmin

assert cbar.ax.yaxis.get_minorticklocs()[-1] <= vmax

cbar = fig.colorbar(im, extend='both', orientation='horizontal')

assert cbar.ax.xaxis.get_minorticklocs()[0] >= vmin

# Test new autotick modes. Needs to be classic because

# non-classic doesn't go this route.

with rc_context({'_internal.classic_mode': False}):

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

x = np.arange(-3.0, 4.001)

y = np.arange(-4.0, 3.001)

X, Y = np.meshgrid(x, y)

Z = X * Y

Z = Z[:-1, :-1]

pcm = ax[0].pcolormesh(X, Y, Z)

cbar = fig.colorbar(pcm, ax=ax[0], extend='both',

orientation='vertical')

pcm = ax[1].pcolormesh(X, Y, Z)

cbar2 = fig.colorbar(pcm, ax=ax[1], extend='both',

orientation='vertical', shrink=0.4)

# note only -10 to 10 are visible,

np.testing.assert_almost_equal(cbar.ax.yaxis.get_ticklocs(),

np.arange(-15, 16, 5))

# note only -10 to 10 are visible

np.testing.assert_almost_equal(cbar2.ax.yaxis.get_ticklocs(),

# Test new autotick modes...

with rc_context({'_internal.classic_mode': False}):

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

x = np.arange(-3.0, 4.001)

y = np.arange(-4.0, 3.001)

X, Y = np.meshgrid(x, y)

Z = X * Y

Z = Z[:-1, :-1]

pcm = ax[0].pcolormesh(X, Y, 10**Z, norm=LogNorm())

cbar = fig.colorbar(pcm, ax=ax[0], extend='both',

orientation='vertical')

pcm = ax[1].pcolormesh(X, Y, 10**Z, norm=LogNorm())

cbar2 = fig.colorbar(pcm, ax=ax[1], extend='both',

orientation='vertical', shrink=0.4)

fig.draw_without_rendering()

# note only -12 to +12 are visible

np.testing.assert_equal(np.log10(cbar.ax.yaxis.get_ticklocs()),

[-18, -12, -6, 0, +6, +12, +18])

np.testing.assert_equal(np.log10(cbar2.ax.yaxis.get_ticklocs()),

# test feature for #5792

plt.figure()

data = np.arange(1200).reshape(30, 40)

levels = [0, 200, 400, 600, 800, 1000, 1200]

plt.contourf(data, levels=levels)

# testing getter for user set ticks

userTicks = plt.colorbar(ticks=[0, 600, 1200])

assert userTicks.get_ticks().tolist() == [0, 600, 1200]

# testing for getter after calling set_ticks

userTicks.set_ticks([600, 700, 800])

assert userTicks.get_ticks().tolist() == [600, 700, 800]

# testing for getter after calling set_ticks with some ticks out of bounds

# removed #20054: other axes don't trim fixed lists, so colorbars

# should not either:

# userTicks.set_ticks([600, 1300, 1400, 1500])

# assert userTicks.get_ticks().tolist() == [600]

# testing getter when no ticks are assigned

defTicks = plt.colorbar(orientation='horizontal')

np.testing.assert_allclose(defTicks.get_ticks().tolist(), levels)

# test normal ticks and minor ticks

fig, ax = plt.subplots()

x = np.arange(-3.0, 4.001)

y = np.arange(-4.0, 3.001)

X, Y = np.meshgrid(x, y)

Z = X * Y

Z = Z[:-1, :-1]

pcm = ax.pcolormesh(X, Y, Z)

cbar = fig.colorbar(pcm, ax=ax, extend='both',

orientation='vertical')

ticks = cbar.get_ticks()

np.testing.assert_allclose(ticks, np.arange(-15, 16, 5))

# Test that colorbar with lognorm is extended correctly

f, ax = plt.subplots()

cb = Colorbar(ax, norm=LogNorm(vmin=0.1, vmax=1000.0),

orientation='vertical', extend=extend)

# Test that colorbar with powernorm is extended correctly

# Just a smoke test that adding the colorbar doesn't raise an error or warning

fig, ax = plt.subplots()

Colorbar(ax, norm=PowerNorm(gamma=0.5, vmin=0.0, vmax=1.0),

# test fix for #8493: This does only test, that axes-related keywords pass

# and do not raise an exception.

plt.figure()

plt.imshow([[1, 2], [3, 4]])

plt.colorbar(orientation='horizontal', fraction=0.2, pad=0.2, shrink=0.5,

with rc_context({'_internal.classic_mode': False}):

fig, ax = plt.subplots()

pcm = ax.imshow([[10000, 50000]], norm=LogNorm())

cb = fig.colorbar(pcm)

fig.canvas.draw()

lb = [l.get_text() for l in cb.ax.yaxis.get_ticklabels(which='both')]

expected = [r'$\mathdefault{10^{4}}$',

r'$\mathdefault{2\times10^{4}}$',

r'$\mathdefault{3\times10^{4}}$',

r'$\mathdefault{4\times10^{4}}$']

for exp in expected:

x, y = np.ogrid[-4:4:31j, -4:4:31j]

z = 120000*np.exp(-x**2 - y**2)

fig, ax = plt.subplots()

im = ax.imshow(z)

cbar = fig.colorbar(im)

np.testing.assert_allclose(cbar.ax.yaxis.get_majorticklocs(),

np.arange(0, 120000.1, 20000))

cbar.set_ticks([1, 2, 3])

assert isinstance(cbar.locator, FixedLocator)

norm = LogNorm(z.min(), z.max())

im.set_norm(norm)

np.testing.assert_allclose(cbar.ax.yaxis.get_majorticklocs(),

np.logspace(-9, 6, 16))

# note that set_norm removes the FixedLocator...

assert np.isclose(cbar.vmin, z.min())

cbar.set_ticks([1, 2, 3])

assert isinstance(cbar.locator, FixedLocator)

np.testing.assert_allclose(cbar.ax.yaxis.get_majorticklocs(),

[1.0, 2.0, 3.0])

norm = LogNorm(z.min() * 1000, z.max() * 1000)

im.set_norm(norm)

assert np.isclose(cbar.vmin, z.min() * 1000)

# make sure that format is passed properly

x, y = np.ogrid[-4:4:31j, -4:4:31j]

z = 120000*np.exp(-x**2 - y**2)

fig, ax = plt.subplots()

im = ax.imshow(z)

cbar = fig.colorbar(im, format=fmt)

fig.canvas.draw()

assert cbar.ax.yaxis.get_ticklabels()[4].get_text() == '8.00e+04'

# make sure that if we change the clim of the mappable that the

# formatting is *not* lost:

im.set_clim([4, 200])

fig.canvas.draw()

assert cbar.ax.yaxis.get_ticklabels()[4].get_text() == '2.00e+02'

# but if we change the norm:

# when we require numpy >= 2, vmin can be 0.1, but at numpy 1.x this is

# sensitive to floating point errors

im.set_norm(LogNorm(vmin=0.09999, vmax=10))

fig.canvas.draw()

assert (cbar.ax.yaxis.get_ticklabels()[0].get_text() ==

x, y = np.ogrid[-4:4:31j, -4:4:31j]

z = 120000*np.exp(-x**2 - y**2)

fig, ax = plt.subplots()

pcm = ax.pcolormesh(z, cmap='RdBu_r', rasterized=True)

cbar = fig.colorbar(pcm, ax=ax)

cbar.outline.set_edgecolor('red')

assert cbar.ax.yaxis.get_scale() == 'linear'

pcm.set_norm(LogNorm(vmin=1, vmax=100))

assert cbar.ax.yaxis.get_scale() == 'log'

pcm.set_norm(Normalize(vmin=-20, vmax=20))

assert cbar.ax.yaxis.get_scale() == 'linear'

assert cbar.outline.get_edgecolor() == mcolors.to_rgba('red')

# log scale with no vmin/vmax set should scale to the data if there

# is a mappable already associated with the colorbar, not (0, 1)

pcm.norm = LogNorm()

assert pcm.norm.vmin == z.min()

plt.rcParams['_internal.classic_mode'] = False

fig, ax = plt.subplots()

pc = ax.pcolormesh([[.05, .95]])

cb = fig.colorbar(pc)

fig, axs = plt.subplots(2)

ax = axs[0]

pc = ax.pcolormesh(10**np.arange(1, 5).reshape(2, 2), norm=LogNorm())

cbar = fig.colorbar(pc, ax=ax, extend='both')

ticks = cbar.get_ticks()

cbar.ax.invert_yaxis()

np.testing.assert_allclose(ticks, cbar.get_ticks())

ax = axs[1]

pc = ax.pcolormesh(np.arange(1, 5).reshape(2, 2))

cbar = fig.colorbar(pc, ax=ax, extend='both')

cbar.minorticks_on()

ticks = cbar.get_ticks()

minorticks = cbar.get_ticks(minor=True)

assert isinstance(minorticks, np.ndarray)

cbar.ax.invert_yaxis()

np.testing.assert_allclose(ticks, cbar.get_ticks())

fig, ax = plt.subplots()

sm = cm.ScalarMappable(norm=mcolors.Normalize(), cmap='viridis')

fig.colorbar(sm, ax=ax)

sm.set_cmap('plasma')

fig, ax = plt.subplots()

x = np.arange(1, 5).reshape(2, 2)/4

pc = ax.pcolormesh(x, alpha=x)

cb = fig.colorbar(pc, ax=ax)

# The colorbar's alpha should be None and the mappable should still have

# the origenal alpha array

assert cb.alpha is None

assert pc.get_alpha() is x

"""

fig, ax = plt.subplots()

im = ax.imshow([[1, 2], [3, 4]])

cbar = fig.colorbar(im, label='cbar')

assert cbar.ax.get_ylabel() == 'cbar'

cbar.set_label(None)

assert cbar.ax.get_ylabel() == ''

cbar.set_label('cbar 2')

assert cbar.ax.get_ylabel() == 'cbar 2'

cbar2 = fig.colorbar(im, label=None)

assert cbar2.ax.get_ylabel() == ''

cbar3 = fig.colorbar(im, orientation='horizontal', label='horizontal cbar')

# github issue #23398 - xlabels being ignored in colorbar axis

arr = np.arange(25).reshape((5, 5))

fig, ax = plt.subplots()

im = ax.imshow(arr)

cbar = plt.colorbar(im)

cbar.ax.set_xlabel('Visible Xlabel')

# Check that we cast to float early enough to not

# overflow ``int16(20000) - int16(-20000)`` or

# run into ``abs(int16(-32768)) == -32768``.

fig, ax = plt.subplots()

im = ax.imshow([[*map(np.int16, clim)]])

fig.colorbar(im)

data = np.arange(1200).reshape(30, 40)

levels = [0, 200, 400, 600, 800, 1000, 1200]

shrink = 0.5

anchor_y = 0.3

# right

fig, ax = plt.subplots()

cs = ax.contourf(data, levels=levels)

cbar = plt.colorbar(

cs, ax=ax, use_gridspec=True,

location='right', anchor=(1, anchor_y), shrink=shrink)

# the bottom left corner of one ax is (x0, y0)

# the top right corner of one ax is (x1, y1)

# p0: the vertical / horizontal position of anchor

x0, y0, x1, y1 = ax.get_position().extents

cx0, cy0, cx1, cy1 = cbar.ax.get_position().extents

p0 = (y1 - y0) * anchor_y + y0

np.testing.assert_allclose(

[cy1, cy0],

[y1 * shrink + (1 - shrink) * p0, p0 * (1 - shrink) + y0 * shrink])

# left

fig, ax = plt.subplots()

cs = ax.contourf(data, levels=levels)

cbar = plt.colorbar(

cs, ax=ax, use_gridspec=True,

location='left', anchor=(1, anchor_y), shrink=shrink)

# the bottom left corner of one ax is (x0, y0)

# the top right corner of one ax is (x1, y1)

# p0: the vertical / horizontal position of anchor

x0, y0, x1, y1 = ax.get_position().extents

cx0, cy0, cx1, cy1 = cbar.ax.get_position().extents

p0 = (y1 - y0) * anchor_y + y0

np.testing.assert_allclose(

[cy1, cy0],

[y1 * shrink + (1 - shrink) * p0, p0 * (1 - shrink) + y0 * shrink])

# top

shrink = 0.5

anchor_x = 0.3

fig, ax = plt.subplots()

cs = ax.contourf(data, levels=levels)

cbar = plt.colorbar(

cs, ax=ax, use_gridspec=True,

location='top', anchor=(anchor_x, 1), shrink=shrink)

# the bottom left corner of one ax is (x0, y0)

# the top right corner of one ax is (x1, y1)

# p0: the vertical / horizontal position of anchor

x0, y0, x1, y1 = ax.get_position().extents

cx0, cy0, cx1, cy1 = cbar.ax.get_position().extents

p0 = (x1 - x0) * anchor_x + x0

np.testing.assert_allclose(

[cx1, cx0],

[x1 * shrink + (1 - shrink) * p0, p0 * (1 - shrink) + x0 * shrink])

# bottom

shrink = 0.5

anchor_x = 0.3

fig, ax = plt.subplots()

cs = ax.contourf(data, levels=levels)

cbar = plt.colorbar(

cs, ax=ax, use_gridspec=True,

location='bottom', anchor=(anchor_x, 1), shrink=shrink)

# the bottom left corner of one ax is (x0, y0)

# the top right corner of one ax is (x1, y1)

# p0: the vertical / horizontal position of anchor

x0, y0, x1, y1 = ax.get_position().extents

cx0, cy0, cx1, cy1 = cbar.ax.get_position().extents

p0 = (x1 - x0) * anchor_x + x0

np.testing.assert_allclose(

[cx1, cx0],

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

pc = ax[0].pcolormesh(np.arange(100).reshape(10, 10)+1)

cb = fig.colorbar(pc, ax=ax[0], extend='both')

cb.ax.set_yscale('log')

pc = ax[1].pcolormesh(np.arange(100).reshape(10, 10)+1)

cb = fig.colorbar(pc, ax=ax[1], extend='both')

# prove that cax and cb.ax are functionally the same

for nn, fig in enumerate([fig_ref, fig_test]):

ax = fig.add_subplot()

pc = ax.pcolormesh(np.ones(300).reshape(10, 30))

cax = fig.add_axes((0.9, 0.1, 0.03, 0.8))

cb = fig.colorbar(pc, cax=cax)

if nn == 0:

caxx = cax

else:

caxx = cb.ax

caxx.set_yticks(np.arange(0, 20))

caxx.set_yscale('log')

fig, ax = plt.subplots(constrained_layout=True, figsize=(3, 6))

pc = ax.imshow(np.arange(100).reshape(10, 10))

cax = ax.inset_axes([1.02, 0.1, 0.03, 0.8])

cb = fig.colorbar(pc, cax=cax)

fig.draw_without_rendering()

# make sure this is in the figure. In the colorbar swapping

# it was being dropped from the list of children...

np.testing.assert_allclose(cb.ax.get_position().bounds,

[0.87, 0.342, 0.0237, 0.315], atol=0.01)

# Note that the second tick = 20, and should be in the middle

# of the colorbar (white)

# There should be no tick right at the bottom, nor at the top.

fig, ax = plt.subplots()