import platform

from unittest import mock

import numpy as np

import pytest

from matplotlib import cm

import matplotlib.colors as mcolors

import matplotlib as mpl

from matplotlib import rc_context

from matplotlib.testing.decorators import image_comparison

import matplotlib.pyplot as plt

from matplotlib.colors import (

BoundaryNorm, LogNorm, PowerNorm, Normalize, NoNorm

)

from matplotlib.colorbar import Colorbar

from matplotlib.ticker import FixedLocator, LogFormatter, StrMethodFormatter

from matplotlib.testing.decorators import check_figures_equal

def _get_cmap_norms():

"""

Define a colormap and appropriate norms for each of the four

possible settings of the extend keyword.

Helper function for _colorbar_extension_shape and

colorbar_extension_length.

"""

# 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)

return cmap, norms

def _colorbar_extension_shape(spacing):

"""

Produce 4 colorbars with rectangular extensions for either uniform

or proportional spacing.

Helper function for test_colorbar_extension_shape.

"""

# 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.

return fig

def _colorbar_extension_length(spacing):

"""

Produce 12 colorbars with variable length extensions for either

uniform or proportional spacing.

Helper function for test_colorbar_extension_length.

"""

# 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.

return fig

@image_comparison(['colorbar_extensions_shape_uniform.png',

'colorbar_extensions_shape_proportional.png'],

style='_classic_test')

def test_colorbar_extension_shape():

"""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')

_colorbar_extension_shape('proportional')

@image_comparison(['colorbar_extensions_uniform.png',

'colorbar_extensions_proportional.png'],

style='_classic_test', tol=1.0)

def test_colorbar_extension_length():

"""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')

_colorbar_extension_length('proportional')

@pytest.mark.parametrize("orientation", ["horizontal", "vertical"])

@pytest.mark.parametrize("extend,expected", [("min", (0, 0, 0, 1)),

("max", (1, 1, 1, 1)),

("both", (1, 1, 1, 1))])

def test_colorbar_extension_inverted_axis(orientation, extend, expected):

"""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:

assert len(cbar._extend_patches) == 1

@pytest.mark.parametrize('use_gridspec', [True, False])

@image_comparison(['cbar_with_orientation.png',

'cbar_locationing.png',

'double_cbar.png',

'cbar_sharing.png',

],

remove_text=True, savefig_kwarg={'dpi': 40}, style='mpl20')

def test_colorbar_positioning(use_gridspec):

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,

anchor=(0.8, 0.5), shrink=0.6, use_gridspec=use_gridspec)

def test_colorbar_single_ax_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)

assert ax.get_anchor() == 'N'

@pytest.mark.parametrize('constrained', [False, True],

ids=['standard', 'constrained'])

def test_colorbar_single_ax_panchor_east(constrained):

fig = plt.figure(constrained_layout=constrained)

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

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

plt.colorbar(panchor='E')

assert ax.get_anchor() == 'E'

@image_comparison(['contour_colorbar.png'], remove_text=True, style='_classic_test',

tol=0 if platform.machine() == 'x86_64' else 0.054)

def test_contour_colorbar():

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')

fig.colorbar(CS, orientation='vertical')

@image_comparison(['cbar_with_subplots_adjust.png'], remove_text=True,

savefig_kwarg={'dpi': 40}, style='_classic_test')

def test_gridspec_make_colorbar():

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')

plt.subplots_adjust(top=0.95, right=0.95, bottom=0.2, hspace=0.25)

@image_comparison(['colorbar_single_scatter.png'], remove_text=True,

savefig_kwarg={'dpi': 40}, style='_classic_test')

def test_colorbar_single_scatter():

# 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)

plt.colorbar(cs)

@pytest.mark.parametrize('use_gridspec', [True, False])

@pytest.mark.parametrize('nested_gridspecs', [True, False])

def test_remove_from_figure(nested_gridspecs, use_gridspec):

"""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()

assert (pre_position.get_points() == post_position.get_points()).all()

def test_remove_from_figure_cl():

"""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(),

post_position.get_points())

def test_axes_remove():

"""Removing the colorbar's axes should also remove the colorbar"""

fig, ax = plt.subplots()

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

cb = fig.colorbar(sc)

with mock.patch.object(cb, 'remove') as mock_cb_remove:

cb.ax.remove()

mock_cb_remove.assert_called()

def test_colorbarbase():

# smoke test from #3805

ax = plt.gca()

Colorbar(ax, cmap=plt.colormaps["bone"])

def test_parentless_mappable():

pc = mpl.collections.PatchCollection([], cmap=plt.get_cmap('viridis'), array=[])

with pytest.raises(ValueError, match='Unable to determine Axes to steal'):

plt.colorbar(pc)

@image_comparison(['colorbar_closed_patch.png'], remove_text=True,

style='_classic_test')

def test_colorbar_closed_patch():

# 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)

plt.colorbar(im, cax=ax5, extend='neither', **cbar_kw)

def test_colorbar_ticks():

# 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)

assert len(cbar.ax.xaxis.get_ticklocs()) == len(clevs)

def test_colorbar_minorticks_on_off():

# 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])

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

def test_cbar_minorticks_for_rc_xyminortickvisible():

"""

issue gh-16468.

Making sure that minor ticks on the colorbar are turned on

(internally) using the cbar.minorticks_on() method when

rcParams['xtick.minor.visible'] = True (for horizontal cbar)

rcParams['ytick.minor.visible'] = True (for vertical cbar).

Using cbar.minorticks_on() ensures that the minor ticks

don't overflow into the extend regions of the colorbar.

"""

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

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

def test_colorbar_autoticks():

# 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(),

np.arange(-20, 21, 10))

def test_colorbar_autotickslog():

# 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()),

[-36, -12, 12, +36])

def test_colorbar_get_ticks():

# 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))

assert len(cbar.get_ticks(minor=True)) == 0

@pytest.mark.parametrize("extend", ['both', 'min', 'max'])

def test_colorbar_lognorm_extension(extend):

# 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)

assert cb._values[0] >= 0.0

def test_colorbar_powernorm_extension():

# 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),

orientation='vertical', extend='both')

def test_colorbar_axes_kw():

# 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,

aspect=10, anchor=(0., 0.), panchor=(0., 1.))

def test_colorbar_log_minortick_labels():

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:

assert exp in lb

def test_colorbar_renorm():

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)

assert np.isclose(cbar.vmax, z.max() * 1000)

@pytest.mark.parametrize('fmt', ['%4.2e', '{x:.2e}'])

def test_colorbar_format(fmt):

# 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() ==

'$\\mathdefault{10^{-2}}$')

def test_colorbar_scale_reset():

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()

assert pcm.norm.vmax == z.max()

def test_colorbar_get_ticks_2():

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

fig, ax = plt.subplots()

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

cb = fig.colorbar(pc)

np.testing.assert_allclose(cb.get_ticks(), [0., 0.2, 0.4, 0.6, 0.8, 1.0])

def test_colorbar_inverted_ticks():

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())

np.testing.assert_allclose(minorticks, cbar.get_ticks(minor=True))

def test_mappable_no_alpha():

fig, ax = plt.subplots()

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

fig.colorbar(sm, ax=ax)

sm.set_cmap('plasma')

plt.draw()

def test_mappable_2d_alpha():

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 original alpha array

assert cb.alpha is None

assert pc.get_alpha() is x

fig.draw_without_rendering()

def test_colorbar_label():

"""

Test the label parameter. It should just be mapped to the xlabel/ylabel of

the axes, depending on the orientation.

"""

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')

assert cbar3.ax.get_xlabel() == 'horizontal cbar'

@image_comparison(['colorbar_keeping_xlabel.png'], style='mpl20')

def test_keeping_xlabel():

# 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')

cbar.set_label('YLabel')

@pytest.mark.parametrize("clim", [(-20000, 20000), (-32768, 0)])

def test_colorbar_int(clim):

# 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)

assert (im.norm.vmin, im.norm.vmax) == clim

def test_anchored_cbar_position_using_specgrid():

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],

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

@image_comparison(['colorbar_change_lim_scale.png'], remove_text=True,

style='mpl20')

def test_colorbar_change_lim_scale():

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')

cb.ax.set_ylim(20, 90)

@check_figures_equal()

def test_axes_handles_same_functions(fig_ref, fig_test):

# 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')

caxx.set_position([0.92, 0.1, 0.02, 0.7])

def test_inset_colorbar_layout():

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)

assert cb.ax in ax.child_axes

@image_comparison(['colorbar_twoslope.png'], remove_text=True,

style='mpl20')

def test_twoslope_colorbar():

# 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()

norm = mcolors.TwoSlopeNorm(20, 5, 95)

pc = ax.pcolormesh(np.arange(1, 11), np.arange(1, 11),

np.arange(100).reshape(10, 10),

norm=norm, cmap='RdBu_r')

fig.colorbar(pc)

def test_remove_cb_whose_mappable_has_no_figure():

fig, ax = plt.subplots()

assert fig.get_axes() != []

cb = fig.colorbar(cm.ScalarMappable(), cax=ax)

cb.remove()

assert fig.get_axes() == []

def test_aspects():

fig, ax = plt.subplots(3, 2, figsize=(8, 8))

aspects = [20, 20, 10]

extends = ['neither', 'both', 'both']

cb = [[None, None, None], [None, None, None]]

for nn, orient in enumerate(['vertical', 'horizontal']):

for mm, (aspect, extend) in enumerate(zip(aspects, extends)):

pc = ax[mm, nn].pcolormesh(np.arange(100).reshape(10, 10))

cb[nn][mm] = fig.colorbar(pc, ax=ax[mm, nn], orientation=orient,

aspect=aspect, extend=extend)

fig.draw_without_rendering()

# check the extends are right ratio:

np.testing.assert_almost_equal(cb[0][1].ax.get_position().height,

cb[0][0].ax.get_position().height * 0.9,

decimal=2)

# horizontal

np.testing.assert_almost_equal(cb[1][1].ax.get_position().width,

cb[1][0].ax.get_position().width * 0.9,

decimal=2)

# check correct aspect:

pos = cb[0][0].ax.get_position(original=False)

np.testing.assert_almost_equal(pos.height, pos.width * 20, decimal=2)

pos = cb[1][0].ax.get_position(original=False)

np.testing.assert_almost_equal(pos.height * 20, pos.width, decimal=2)

# check twice as wide if aspect is 10 instead of 20

np.testing.assert_almost_equal(

cb[0][0].ax.get_position(original=False).width * 2,

cb[0][2].ax.get_position(original=False).width, decimal=2)

np.testing.assert_almost_equal(

cb[1][0].ax.get_position(original=False).height * 2,

cb[1][2].ax.get_position(original=False).height, decimal=2)

@image_comparison(['proportional_colorbars.png'], remove_text=True,

style='mpl20')

def test_proportional_colorbars():

x = y = np.arange(-3.0, 3.01, 0.025)

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

Z1 = np.exp(-X**2 - Y**2)

Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)

Z = (Z1 - Z2) * 2

levels = [-1.25, -0.5, -0.125, 0.125, 0.5, 1.25]

cmap = mcolors.ListedColormap(

['0.3', '0.5', 'white', 'lightblue', 'steelblue'],

under='darkred',

over='crimson',

)

norm = mcolors.BoundaryNorm(levels, cmap.N)

extends = ['neither', 'both']

spacings = ['uniform', 'proportional']

fig, axs = plt.subplots(2, 2)

for i in range(2):

for j in range(2):

CS3 = axs[i, j].contourf(X, Y, Z, levels, cmap=cmap, norm=norm,

extend=extends[i])

fig.colorbar(CS3, spacing=spacings[j], ax=axs[i, j])

@image_comparison(['extend_drawedges.png'], remove_text=True, style='mpl20')

def test_colorbar_extend_drawedges():

params = [

('both', 1, [[[1.1, 0], [1.1, 1]],

[[2, 0], [2, 1]],

[[2.9, 0], [2.9, 1]]]),

('min', 0, [[[1.1, 0], [1.1, 1]],

[[2, 0], [2, 1]]]),

('max', 0, [[[2, 0], [2, 1]],

[[2.9, 0], [2.9, 1]]]),

('neither', -1, [[[2, 0], [2, 1]]]),

]

plt.rcParams['axes.linewidth'] = 2

fig = plt.figure(figsize=(10, 4))

subfigs = fig.subfigures(1, 2)

for orientation, subfig in zip(['horizontal', 'vertical'], subfigs):

if orientation == 'horizontal':

axs = subfig.subplots(4, 1)

else: