import contextlib from collections import namedtuple, deque import datetime from decimal import Decimal from functools import partial import gc import inspect import io from itertools import product import platform import sys from types import SimpleNamespace import dateutil.tz import numpy as np from numpy import ma from cycler import cycler import pytest import matplotlib import matplotlib as mpl from matplotlib import rc_context, patheffects import matplotlib.colors as mcolors import matplotlib.dates as mdates from matplotlib.figure import Figure from matplotlib.axes import Axes from matplotlib.lines import Line2D from matplotlib.collections import PathCollection import matplotlib.font_manager as mfont_manager import matplotlib.markers as mmarkers import matplotlib.patches as mpatches import matplotlib.path as mpath from matplotlib.projections.geo import HammerAxes from matplotlib.projections.polar import PolarAxes import matplotlib.pyplot as plt import matplotlib.text as mtext import matplotlib.ticker as mticker import matplotlib.transforms as mtransforms import mpl_toolkits.axisartist as AA # type: ignore[import] from numpy.testing import ( assert_allclose, assert_array_equal, assert_array_almost_equal) from matplotlib.testing.decorators import ( image_comparison, check_figures_equal, remove_ticks_and_titles) from matplotlib.testing._markers import needs_usetex # Note: Some test cases are run twice: once normally and once with labeled data # These two must be defined in the same test function or need to have # different baseline images to prevent race conditions when pytest runs # the tests with multiple threads. @check_figures_equal(extensions=["png"]) def test_invisible_axes(fig_test, fig_ref): ax = fig_test.subplots() ax.set_visible(False) def test_get_labels(): fig, ax = plt.subplots() ax.set_xlabel('x label') ax.set_ylabel('y label') assert ax.get_xlabel() == 'x label' assert ax.get_ylabel() == 'y label' def test_repr(): fig, ax = plt.subplots() ax.set_label('label') ax.set_title('title') ax.set_xlabel('x') ax.set_ylabel('y') assert repr(ax) == ( "") @check_figures_equal() def test_label_loc_vertical(fig_test, fig_ref): ax = fig_test.subplots() sc = ax.scatter([1, 2], [1, 2], c=[1, 2], label='scatter') ax.legend() ax.set_ylabel('Y Label', loc='top') ax.set_xlabel('X Label', loc='right') cbar = fig_test.colorbar(sc) cbar.set_label("Z Label", loc='top') ax = fig_ref.subplots() sc = ax.scatter([1, 2], [1, 2], c=[1, 2], label='scatter') ax.legend() ax.set_ylabel('Y Label', y=1, ha='right') ax.set_xlabel('X Label', x=1, ha='right') cbar = fig_ref.colorbar(sc) cbar.set_label("Z Label", y=1, ha='right') @check_figures_equal() def test_label_loc_horizontal(fig_test, fig_ref): ax = fig_test.subplots() sc = ax.scatter([1, 2], [1, 2], c=[1, 2], label='scatter') ax.legend() ax.set_ylabel('Y Label', loc='bottom') ax.set_xlabel('X Label', loc='left') cbar = fig_test.colorbar(sc, orientation='horizontal') cbar.set_label("Z Label", loc='left') ax = fig_ref.subplots() sc = ax.scatter([1, 2], [1, 2], c=[1, 2], label='scatter') ax.legend() ax.set_ylabel('Y Label', y=0, ha='left') ax.set_xlabel('X Label', x=0, ha='left') cbar = fig_ref.colorbar(sc, orientation='horizontal') cbar.set_label("Z Label", x=0, ha='left') @check_figures_equal() def test_label_loc_rc(fig_test, fig_ref): with matplotlib.rc_context({"xaxis.labellocation": "right", "yaxis.labellocation": "top"}): ax = fig_test.subplots() sc = ax.scatter([1, 2], [1, 2], c=[1, 2], label='scatter') ax.legend() ax.set_ylabel('Y Label') ax.set_xlabel('X Label') cbar = fig_test.colorbar(sc, orientation='horizontal') cbar.set_label("Z Label") ax = fig_ref.subplots() sc = ax.scatter([1, 2], [1, 2], c=[1, 2], label='scatter') ax.legend() ax.set_ylabel('Y Label', y=1, ha='right') ax.set_xlabel('X Label', x=1, ha='right') cbar = fig_ref.colorbar(sc, orientation='horizontal') cbar.set_label("Z Label", x=1, ha='right') def test_label_shift(): fig, ax = plt.subplots() # Test label re-centering on x-axis ax.set_xlabel("Test label", loc="left") ax.set_xlabel("Test label", loc="center") assert ax.xaxis.label.get_horizontalalignment() == "center" ax.set_xlabel("Test label", loc="right") assert ax.xaxis.label.get_horizontalalignment() == "right" ax.set_xlabel("Test label", loc="center") assert ax.xaxis.label.get_horizontalalignment() == "center" # Test label re-centering on y-axis ax.set_ylabel("Test label", loc="top") ax.set_ylabel("Test label", loc="center") assert ax.yaxis.label.get_horizontalalignment() == "center" ax.set_ylabel("Test label", loc="bottom") assert ax.yaxis.label.get_horizontalalignment() == "left" ax.set_ylabel("Test label", loc="center") assert ax.yaxis.label.get_horizontalalignment() == "center" @check_figures_equal(extensions=["png"]) def test_acorr(fig_test, fig_ref): np.random.seed(19680801) Nx = 512 x = np.random.normal(0, 1, Nx).cumsum() maxlags = Nx-1 ax_test = fig_test.subplots() ax_test.acorr(x, maxlags=maxlags) ax_ref = fig_ref.subplots() # Normalized autocorrelation norm_auto_corr = np.correlate(x, x, mode="full")/np.dot(x, x) lags = np.arange(-maxlags, maxlags+1) norm_auto_corr = norm_auto_corr[Nx-1-maxlags:Nx+maxlags] ax_ref.vlines(lags, [0], norm_auto_corr) ax_ref.axhline(y=0, xmin=0, xmax=1) @check_figures_equal(extensions=["png"]) def test_acorr_integers(fig_test, fig_ref): np.random.seed(19680801) Nx = 51 x = (np.random.rand(Nx) * 10).cumsum() x = (np.ceil(x)).astype(np.int64) maxlags = Nx-1 ax_test = fig_test.subplots() ax_test.acorr(x, maxlags=maxlags) ax_ref = fig_ref.subplots() # Normalized autocorrelation norm_auto_corr = np.correlate(x, x, mode="full")/np.dot(x, x) lags = np.arange(-maxlags, maxlags+1) norm_auto_corr = norm_auto_corr[Nx-1-maxlags:Nx+maxlags] ax_ref.vlines(lags, [0], norm_auto_corr) ax_ref.axhline(y=0, xmin=0, xmax=1) @check_figures_equal(extensions=["png"]) def test_spy(fig_test, fig_ref): np.random.seed(19680801) a = np.ones(32 * 32) a[:16 * 32] = 0 np.random.shuffle(a) a = a.reshape((32, 32)) axs_test = fig_test.subplots(2) axs_test[0].spy(a) axs_test[1].spy(a, marker=".", origin="lower") axs_ref = fig_ref.subplots(2) axs_ref[0].imshow(a, cmap="gray_r", interpolation="nearest") axs_ref[0].xaxis.tick_top() axs_ref[1].plot(*np.nonzero(a)[::-1], ".", markersize=10) axs_ref[1].set( aspect=1, xlim=axs_ref[0].get_xlim(), ylim=axs_ref[0].get_ylim()[::-1]) for ax in axs_ref: ax.xaxis.set_ticks_position("both") def test_spy_invalid_kwargs(): fig, ax = plt.subplots() for unsupported_kw in [{'interpolation': 'nearest'}, {'marker': 'o', 'linestyle': 'solid'}]: with pytest.raises(TypeError): ax.spy(np.eye(3, 3), **unsupported_kw) @check_figures_equal(extensions=["png"]) def test_matshow(fig_test, fig_ref): mpl.style.use("mpl20") a = np.random.rand(32, 32) fig_test.add_subplot().matshow(a) ax_ref = fig_ref.add_subplot() ax_ref.imshow(a) ax_ref.xaxis.tick_top() ax_ref.xaxis.set_ticks_position('both') @image_comparison(['formatter_ticker_001', 'formatter_ticker_002', 'formatter_ticker_003', 'formatter_ticker_004', 'formatter_ticker_005', ], tol=0 if platform.machine() == 'x86_64' else 0.031) def test_formatter_ticker(): import matplotlib.testing.jpl_units as units units.register() # This should affect the tick size. (Tests issue #543) matplotlib.rcParams['lines.markeredgewidth'] = 30 # This essentially test to see if user specified labels get overwritten # by the auto labeler functionality of the axes. xdata = [x*units.sec for x in range(10)] ydata1 = [(1.5*y - 0.5)*units.km for y in range(10)] ydata2 = [(1.75*y - 1.0)*units.km for y in range(10)] ax = plt.figure().subplots() ax.set_xlabel("x-label 001") ax = plt.figure().subplots() ax.set_xlabel("x-label 001") ax.plot(xdata, ydata1, color='blue', xunits="sec") ax = plt.figure().subplots() ax.set_xlabel("x-label 001") ax.plot(xdata, ydata1, color='blue', xunits="sec") ax.set_xlabel("x-label 003") ax = plt.figure().subplots() ax.plot(xdata, ydata1, color='blue', xunits="sec") ax.plot(xdata, ydata2, color='green', xunits="hour") ax.set_xlabel("x-label 004") # See SF bug 2846058 # https://sourceforge.net/tracker/?func=detail&aid=2846058&group_id=80706&atid=560720 ax = plt.figure().subplots() ax.plot(xdata, ydata1, color='blue', xunits="sec") ax.plot(xdata, ydata2, color='green', xunits="hour") ax.set_xlabel("x-label 005") ax.autoscale_view() def test_funcformatter_auto_formatter(): def _formfunc(x, pos): return '' ax = plt.figure().subplots() assert ax.xaxis.isDefault_majfmt assert ax.xaxis.isDefault_minfmt assert ax.yaxis.isDefault_majfmt assert ax.yaxis.isDefault_minfmt ax.xaxis.set_major_formatter(_formfunc) assert not ax.xaxis.isDefault_majfmt assert ax.xaxis.isDefault_minfmt assert ax.yaxis.isDefault_majfmt assert ax.yaxis.isDefault_minfmt targ_funcformatter = mticker.FuncFormatter(_formfunc) assert isinstance(ax.xaxis.get_major_formatter(), mticker.FuncFormatter) assert ax.xaxis.get_major_formatter().func == targ_funcformatter.func def test_strmethodformatter_auto_formatter(): formstr = '{x}_{pos}' ax = plt.figure().subplots() assert ax.xaxis.isDefault_majfmt assert ax.xaxis.isDefault_minfmt assert ax.yaxis.isDefault_majfmt assert ax.yaxis.isDefault_minfmt ax.yaxis.set_minor_formatter(formstr) assert ax.xaxis.isDefault_majfmt assert ax.xaxis.isDefault_minfmt assert ax.yaxis.isDefault_majfmt assert not ax.yaxis.isDefault_minfmt targ_strformatter = mticker.StrMethodFormatter(formstr) assert isinstance(ax.yaxis.get_minor_formatter(), mticker.StrMethodFormatter) assert ax.yaxis.get_minor_formatter().fmt == targ_strformatter.fmt @image_comparison(["twin_axis_locators_formatters"]) def test_twin_axis_locators_formatters(): vals = np.linspace(0, 1, num=5, endpoint=True) locs = np.sin(np.pi * vals / 2.0) majl = plt.FixedLocator(locs) minl = plt.FixedLocator([0.1, 0.2, 0.3]) fig = plt.figure() ax1 = fig.add_subplot(1, 1, 1) ax1.plot([0.1, 100], [0, 1]) ax1.yaxis.set_major_locator(majl) ax1.yaxis.set_minor_locator(minl) ax1.yaxis.set_major_formatter(plt.FormatStrFormatter('%08.2lf')) ax1.yaxis.set_minor_formatter(plt.FixedFormatter(['tricks', 'mind', 'jedi'])) ax1.xaxis.set_major_locator(plt.LinearLocator()) ax1.xaxis.set_minor_locator(plt.FixedLocator([15, 35, 55, 75])) ax1.xaxis.set_major_formatter(plt.FormatStrFormatter('%05.2lf')) ax1.xaxis.set_minor_formatter(plt.FixedFormatter(['c', '3', 'p', 'o'])) ax1.twiny() ax1.twinx() def test_twinx_cla(): fig, ax = plt.subplots() ax2 = ax.twinx() ax3 = ax2.twiny() plt.draw() assert not ax2.xaxis.get_visible() assert not ax2.patch.get_visible() ax2.cla() ax3.cla() assert not ax2.xaxis.get_visible() assert not ax2.patch.get_visible() assert ax2.yaxis.get_visible() assert ax3.xaxis.get_visible() assert not ax3.patch.get_visible() assert not ax3.yaxis.get_visible() assert ax.xaxis.get_visible() assert ax.patch.get_visible() assert ax.yaxis.get_visible() @pytest.mark.parametrize('twin', ('x', 'y')) def test_twin_units(twin): axis_name = f'{twin}axis' twin_func = f'twin{twin}' a = ['0', '1'] b = ['a', 'b'] fig = Figure() ax1 = fig.subplots() ax1.plot(a, b) assert getattr(ax1, axis_name).units is not None ax2 = getattr(ax1, twin_func)() assert getattr(ax2, axis_name).units is not None assert getattr(ax2, axis_name).units is getattr(ax1, axis_name).units @pytest.mark.parametrize('twin', ('x', 'y')) @check_figures_equal(extensions=['png'], tol=0.19) def test_twin_logscale(fig_test, fig_ref, twin): twin_func = f'twin{twin}' # test twinx or twiny set_scale = f'set_{twin}scale' x = np.arange(1, 100) # Change scale after twinning. ax_test = fig_test.add_subplot(2, 1, 1) ax_twin = getattr(ax_test, twin_func)() getattr(ax_test, set_scale)('log') ax_twin.plot(x, x) # Twin after changing scale. ax_test = fig_test.add_subplot(2, 1, 2) getattr(ax_test, set_scale)('log') ax_twin = getattr(ax_test, twin_func)() ax_twin.plot(x, x) for i in [1, 2]: ax_ref = fig_ref.add_subplot(2, 1, i) getattr(ax_ref, set_scale)('log') ax_ref.plot(x, x) # This is a hack because twinned Axes double-draw the frame. # Remove this when that is fixed. Path = matplotlib.path.Path fig_ref.add_artist( matplotlib.patches.PathPatch( Path([[0, 0], [0, 1], [0, 1], [1, 1], [1, 1], [1, 0], [1, 0], [0, 0]], [Path.MOVETO, Path.LINETO] * 4), transform=ax_ref.transAxes, facecolor='none', edgecolor=mpl.rcParams['axes.edgecolor'], linewidth=mpl.rcParams['axes.linewidth'], capstyle='projecting')) remove_ticks_and_titles(fig_test) remove_ticks_and_titles(fig_ref) @image_comparison(['twin_autoscale.png'], tol=0 if platform.machine() == 'x86_64' else 0.009) def test_twinx_axis_scales(): x = np.array([0, 0.5, 1]) y = 0.5 * x x2 = np.array([0, 1, 2]) y2 = 2 * x2 fig = plt.figure() ax = fig.add_axes((0, 0, 1, 1), autoscalex_on=False, autoscaley_on=False) ax.plot(x, y, color='blue', lw=10) ax2 = plt.twinx(ax) ax2.plot(x2, y2, 'r--', lw=5) ax.margins(0, 0) ax2.margins(0, 0) def test_twin_inherit_autoscale_setting(): fig, ax = plt.subplots() ax_x_on = ax.twinx() ax.set_autoscalex_on(False) ax_x_off = ax.twinx() assert ax_x_on.get_autoscalex_on() assert not ax_x_off.get_autoscalex_on() ax_y_on = ax.twiny() ax.set_autoscaley_on(False) ax_y_off = ax.twiny() assert ax_y_on.get_autoscaley_on() assert not ax_y_off.get_autoscaley_on() def test_inverted_cla(): # GitHub PR #5450. Setting autoscale should reset # axes to be non-inverted. # plotting an image, then 1d graph, axis is now down fig = plt.figure(0) ax = fig.gca() # 1. test that a new axis is not inverted per default assert not ax.xaxis_inverted() assert not ax.yaxis_inverted() img = np.random.random((100, 100)) ax.imshow(img) # 2. test that a image axis is inverted assert not ax.xaxis_inverted() assert ax.yaxis_inverted() # 3. test that clearing and plotting a line, axes are # not inverted ax.cla() x = np.linspace(0, 2*np.pi, 100) ax.plot(x, np.cos(x)) assert not ax.xaxis_inverted() assert not ax.yaxis_inverted() # 4. autoscaling should not bring back axes to normal ax.cla() ax.imshow(img) plt.autoscale() assert not ax.xaxis_inverted() assert ax.yaxis_inverted() for ax in fig.axes: ax.remove() # 5. two shared axes. Inverting the leader axis should invert the shared # axes; clearing the leader axis should bring axes in shared # axes back to normal. ax0 = plt.subplot(211) ax1 = plt.subplot(212, sharey=ax0) ax0.yaxis.set_inverted(True) assert ax1.yaxis_inverted() ax1.plot(x, np.cos(x)) ax0.cla() assert not ax1.yaxis_inverted() ax1.cla() # 6. clearing the follower should not touch limits ax0.imshow(img) ax1.plot(x, np.cos(x)) ax1.cla() assert ax.yaxis_inverted() # clean up plt.close(fig) def test_subclass_clear_cla(): # Ensure that subclasses of Axes call cla/clear correctly. # Note, we cannot use mocking here as we want to be sure that the # superclass fallback does not recurse. with pytest.warns(PendingDeprecationWarning, match='Overriding `Axes.cla`'): class ClaAxes(Axes): def cla(self): nonlocal called called = True with pytest.warns(PendingDeprecationWarning, match='Overriding `Axes.cla`'): class ClaSuperAxes(Axes): def cla(self): nonlocal called called = True super().cla() class SubClaAxes(ClaAxes): pass class ClearAxes(Axes): def clear(self): nonlocal called called = True class ClearSuperAxes(Axes): def clear(self): nonlocal called called = True super().clear() class SubClearAxes(ClearAxes): pass fig = Figure() for axes_class in [ClaAxes, ClaSuperAxes, SubClaAxes, ClearAxes, ClearSuperAxes, SubClearAxes]: called = False ax = axes_class(fig, [0, 0, 1, 1]) # Axes.__init__ has already called clear (which aliases to cla or is in # the subclass). assert called called = False ax.cla() assert called def test_cla_not_redefined_internally(): for klass in Axes.__subclasses__(): # Check that cla does not get redefined in our Axes subclasses, except # for in the above test function. if 'test_subclass_clear_cla' not in klass.__qualname__: assert 'cla' not in klass.__dict__ @check_figures_equal(extensions=["png"]) def test_minorticks_on_rcParams_both(fig_test, fig_ref): with matplotlib.rc_context({"xtick.minor.visible": True, "ytick.minor.visible": True}): ax_test = fig_test.subplots() ax_test.plot([0, 1], [0, 1]) ax_ref = fig_ref.subplots() ax_ref.plot([0, 1], [0, 1]) ax_ref.minorticks_on() @image_comparison(["autoscale_tiny_range"], remove_text=True) def test_autoscale_tiny_range(): # github pull #904 fig, axs = plt.subplots(2, 2) for i, ax in enumerate(axs.flat): y1 = 10**(-11 - i) ax.plot([0, 1], [1, 1 + y1]) @mpl.style.context('default') def test_autoscale_tight(): fig, ax = plt.subplots(1, 1) ax.plot([1, 2, 3, 4]) ax.autoscale(enable=True, axis='x', tight=False) ax.autoscale(enable=True, axis='y', tight=True) assert_allclose(ax.get_xlim(), (-0.15, 3.15)) assert_allclose(ax.get_ylim(), (1.0, 4.0)) # Check that autoscale is on assert ax.get_autoscalex_on() assert ax.get_autoscaley_on() assert ax.get_autoscale_on() # Set enable to None ax.autoscale(enable=None) # Same limits assert_allclose(ax.get_xlim(), (-0.15, 3.15)) assert_allclose(ax.get_ylim(), (1.0, 4.0)) # autoscale still on assert ax.get_autoscalex_on() assert ax.get_autoscaley_on() assert ax.get_autoscale_on() @mpl.style.context('default') def test_autoscale_log_shared(): # related to github #7587 # array starts at zero to trigger _minpos handling x = np.arange(100, dtype=float) fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) ax1.loglog(x, x) ax2.semilogx(x, x) ax1.autoscale(tight=True) ax2.autoscale(tight=True) plt.draw() lims = (x[1], x[-1]) assert_allclose(ax1.get_xlim(), lims) assert_allclose(ax1.get_ylim(), lims) assert_allclose(ax2.get_xlim(), lims) assert_allclose(ax2.get_ylim(), (x[0], x[-1])) @mpl.style.context('default') def test_use_sticky_edges(): fig, ax = plt.subplots() ax.imshow([[0, 1], [2, 3]], origin='lower') assert_allclose(ax.get_xlim(), (-0.5, 1.5)) assert_allclose(ax.get_ylim(), (-0.5, 1.5)) ax.use_sticky_edges = False ax.autoscale() xlim = (-0.5 - 2 * ax._xmargin, 1.5 + 2 * ax._xmargin) ylim = (-0.5 - 2 * ax._ymargin, 1.5 + 2 * ax._ymargin) assert_allclose(ax.get_xlim(), xlim) assert_allclose(ax.get_ylim(), ylim) # Make sure it is reversible: ax.use_sticky_edges = True ax.autoscale() assert_allclose(ax.get_xlim(), (-0.5, 1.5)) assert_allclose(ax.get_ylim(), (-0.5, 1.5)) @check_figures_equal(extensions=["png"]) def test_sticky_shared_axes(fig_test, fig_ref): # Check that sticky edges work whether they are set in an Axes that is a # "leader" in a share, or an Axes that is a "follower". Z = np.arange(15).reshape(3, 5) ax0 = fig_test.add_subplot(211) ax1 = fig_test.add_subplot(212, sharex=ax0) ax1.pcolormesh(Z) ax0 = fig_ref.add_subplot(212) ax1 = fig_ref.add_subplot(211, sharex=ax0) ax0.pcolormesh(Z) @image_comparison(['sticky_tolerance.png'], remove_text=True, style="mpl20") def test_sticky_tolerance(): fig, axs = plt.subplots(2, 2) width = .1 axs.flat[0].bar(x=0, height=width, bottom=20000.6) axs.flat[0].bar(x=1, height=width, bottom=20000.1) axs.flat[1].bar(x=0, height=-width, bottom=20000.6) axs.flat[1].bar(x=1, height=-width, bottom=20000.1) axs.flat[2].barh(y=0, width=-width, left=-20000.6) axs.flat[2].barh(y=1, width=-width, left=-20000.1) axs.flat[3].barh(y=0, width=width, left=-20000.6) axs.flat[3].barh(y=1, width=width, left=-20000.1) @image_comparison(['sticky_tolerance_cf.png'], remove_text=True, style="mpl20") def test_sticky_tolerance_contourf(): fig, ax = plt.subplots() x = y = [14496.71, 14496.75] data = [[0, 1], [2, 3]] ax.contourf(x, y, data) def test_nargs_stem(): with pytest.raises(TypeError, match='0 were given'): # stem() takes 1-3 arguments. plt.stem() def test_nargs_legend(): with pytest.raises(TypeError, match='3 were given'): ax = plt.subplot() # legend() takes 0-2 arguments. ax.legend(['First'], ['Second'], 3) def test_nargs_pcolorfast(): with pytest.raises(TypeError, match='2 were given'): ax = plt.subplot() # pcolorfast() takes 1 or 3 arguments, # not passing any arguments fails at C = args[-1] # before nargs_err is raised. ax.pcolorfast([(0, 1), (0, 2)], [[1, 2, 3], [1, 2, 3]]) @image_comparison(['offset_points'], remove_text=True) def test_basic_annotate(): # Setup some data t = np.arange(0.0, 5.0, 0.01) s = np.cos(2.0*np.pi * t) # Offset Points fig = plt.figure() ax = fig.add_subplot(autoscale_on=False, xlim=(-1, 5), ylim=(-3, 5)) line, = ax.plot(t, s, lw=3, color='purple') ax.annotate('local max', xy=(3, 1), xycoords='data', xytext=(3, 3), textcoords='offset points') @image_comparison(['arrow_simple.png'], remove_text=True) def test_arrow_simple(): # Simple image test for ax.arrow # kwargs that take discrete values length_includes_head = (True, False) shape = ('full', 'left', 'right') head_starts_at_zero = (True, False) # Create outer product of values kwargs = product(length_includes_head, shape, head_starts_at_zero) fig, axs = plt.subplots(3, 4) for i, (ax, kwarg) in enumerate(zip(axs.flat, kwargs)): ax.set_xlim(-2, 2) ax.set_ylim(-2, 2) # Unpack kwargs (length_includes_head, shape, head_starts_at_zero) = kwarg theta = 2 * np.pi * i / 12 # Draw arrow ax.arrow(0, 0, np.sin(theta), np.cos(theta), width=theta/100, length_includes_head=length_includes_head, shape=shape, head_starts_at_zero=head_starts_at_zero, head_width=theta / 10, head_length=theta / 10) def test_arrow_empty(): _, ax = plt.subplots() # Create an empty FancyArrow ax.arrow(0, 0, 0, 0, head_length=0) def test_arrow_in_view(): _, ax = plt.subplots() ax.arrow(1, 1, 1, 1) assert ax.get_xlim() == (0.8, 2.2) assert ax.get_ylim() == (0.8, 2.2) def test_annotate_default_arrow(): # Check that we can make an annotation arrow with only default properties. fig, ax = plt.subplots() ann = ax.annotate("foo", (0, 1), xytext=(2, 3)) assert ann.arrow_patch is None ann = ax.annotate("foo", (0, 1), xytext=(2, 3), arrowprops={}) assert ann.arrow_patch is not None def test_annotate_signature(): """Check that the signature of Axes.annotate() matches Annotation.""" fig, ax = plt.subplots() annotate_params = inspect.signature(ax.annotate).parameters annotation_params = inspect.signature(mtext.Annotation).parameters assert list(annotate_params.keys()) == list(annotation_params.keys()) for p1, p2 in zip(annotate_params.values(), annotation_params.values()): assert p1 == p2 @image_comparison(['fill_units.png'], savefig_kwarg={'dpi': 60}) def test_fill_units(): import matplotlib.testing.jpl_units as units units.register() # generate some data t = units.Epoch("ET", dt=datetime.datetime(2009, 4, 27)) value = 10.0 * units.deg day = units.Duration("ET", 24.0 * 60.0 * 60.0) dt = np.arange('2009-04-27', '2009-04-29', dtype='datetime64[D]') dtn = mdates.date2num(dt) fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2) ax1.plot([t], [value], yunits='deg', color='red') ind = [0, 0, 1, 1] ax1.fill(dtn[ind], [0.0, 0.0, 90.0, 0.0], 'b') ax2.plot([t], [value], yunits='deg', color='red') ax2.fill([t, t, t + day, t + day], [0.0, 0.0, 90.0, 0.0], 'b') ax3.plot([t], [value], yunits='deg', color='red') ax3.fill(dtn[ind], [0 * units.deg, 0 * units.deg, 90 * units.deg, 0 * units.deg], 'b') ax4.plot([t], [value], yunits='deg', color='red') ax4.fill([t, t, t + day, t + day], [0 * units.deg, 0 * units.deg, 90 * units.deg, 0 * units.deg], facecolor="blue") fig.autofmt_xdate() def test_plot_format_kwarg_redundant(): with pytest.warns(UserWarning, match="marker .* redundantly defined"): plt.plot([0], [0], 'o', marker='x') with pytest.warns(UserWarning, match="linestyle .* redundantly defined"): plt.plot([0], [0], '-', linestyle='--') with pytest.warns(UserWarning, match="color .* redundantly defined"): plt.plot([0], [0], 'r', color='blue') # smoke-test: should not warn plt.errorbar([0], [0], fmt='none', color='blue') @check_figures_equal(extensions=["png"]) def test_errorbar_dashes(fig_test, fig_ref): x = [1, 2, 3, 4] y = np.sin(x) ax_ref = fig_ref.gca() ax_test = fig_test.gca() line, *_ = ax_ref.errorbar(x, y, xerr=np.abs(y), yerr=np.abs(y)) line.set_dashes([2, 2]) ax_test.errorbar(x, y, xerr=np.abs(y), yerr=np.abs(y), dashes=[2, 2]) def test_errorbar_mapview_kwarg(): D = {ii: ii for ii in range(10)} fig, ax = plt.subplots() ax.errorbar(x=D.keys(), y=D.values(), xerr=D.values()) @image_comparison(['single_point', 'single_point']) def test_single_point(): # Issue #1796: don't let lines.marker affect the grid matplotlib.rcParams['lines.marker'] = 'o' matplotlib.rcParams['axes.grid'] = True fig, (ax1, ax2) = plt.subplots(2) ax1.plot([0], [0], 'o') ax2.plot([1], [1], 'o') # Reuse testcase from above for a labeled data test data = {'a': [0], 'b': [1]} fig, (ax1, ax2) = plt.subplots(2) ax1.plot('a', 'a', 'o', data=data) ax2.plot('b', 'b', 'o', data=data) @image_comparison(['single_date.png'], style='mpl20') def test_single_date(): # use former defaults to match existing baseline image plt.rcParams['axes.formatter.limits'] = -7, 7 dt = mdates.date2num(np.datetime64('0000-12-31')) time1 = [721964.0] data1 = [-65.54] fig, ax = plt.subplots(2, 1) with pytest.warns(mpl.MatplotlibDeprecationWarning): ax[0].plot_date(time1 + dt, data1, 'o', color='r') ax[1].plot(time1, data1, 'o', color='r') @check_figures_equal(extensions=["png"]) def test_shaped_data(fig_test, fig_ref): row = np.arange(10).reshape((1, -1)) col = np.arange(0, 100, 10).reshape((-1, 1)) axs = fig_test.subplots(2) axs[0].plot(row) # Actually plots nothing (columns are single points). axs[1].plot(col) # Same as plotting 1d. axs = fig_ref.subplots(2) # xlim from the implicit "x=0", ylim from the row datalim. axs[0].set(xlim=(-.06, .06), ylim=(0, 9)) axs[1].plot(col.ravel()) def test_structured_data(): # support for structured data pts = np.array([(1, 1), (2, 2)], dtype=[("ones", float), ("twos", float)]) # this should not read second name as a format and raise ValueError axs = plt.figure().subplots(2) axs[0].plot("ones", "twos", data=pts) axs[1].plot("ones", "twos", "r", data=pts) @image_comparison(['aitoff_proj'], extensions=["png"], remove_text=True, style='mpl20') def test_aitoff_proj(): """ Test aitoff projection ref.: https://github.com/matplotlib/matplotlib/pull/14451 """ x = np.linspace(-np.pi, np.pi, 20) y = np.linspace(-np.pi / 2, np.pi / 2, 20) X, Y = np.meshgrid(x, y) fig, ax = plt.subplots(figsize=(8, 4.2), subplot_kw=dict(projection="aitoff")) ax.grid() ax.plot(X.flat, Y.flat, 'o', markersize=4) @image_comparison(['axvspan_epoch']) def test_axvspan_epoch(): import matplotlib.testing.jpl_units as units units.register() # generate some data t0 = units.Epoch("ET", dt=datetime.datetime(2009, 1, 21)) tf = units.Epoch("ET", dt=datetime.datetime(2009, 1, 22)) dt = units.Duration("ET", units.day.convert("sec")) ax = plt.gca() ax.axvspan(t0, tf, facecolor="blue", alpha=0.25) ax.set_xlim(t0 - 5.0*dt, tf + 5.0*dt) @image_comparison(['axhspan_epoch'], tol=0.02) def test_axhspan_epoch(): import matplotlib.testing.jpl_units as units units.register() # generate some data t0 = units.Epoch("ET", dt=datetime.datetime(2009, 1, 21)) tf = units.Epoch("ET", dt=datetime.datetime(2009, 1, 22)) dt = units.Duration("ET", units.day.convert("sec")) ax = plt.gca() ax.axhspan(t0, tf, facecolor="blue", alpha=0.25) ax.set_ylim(t0 - 5.0*dt, tf + 5.0*dt) @image_comparison(['hexbin_extent.png', 'hexbin_extent.png'], remove_text=True) def test_hexbin_extent(): # this test exposes sf bug 2856228 fig, ax = plt.subplots() data = (np.arange(2000) / 2000).reshape((2, 1000)) x, y = data ax.hexbin(x, y, extent=[.1, .3, .6, .7]) # Reuse testcase from above for a labeled data test data = {"x": x, "y": y} fig, ax = plt.subplots() ax.hexbin("x", "y", extent=[.1, .3, .6, .7], data=data) def test_hexbin_bad_extents(): fig, ax = plt.subplots() data = (np.arange(20) / 20).reshape((2, 10)) x, y = data with pytest.raises(ValueError, match="In extent, xmax must be greater than xmin"): ax.hexbin(x, y, extent=(1, 0, 0, 1)) with pytest.raises(ValueError, match="In extent, ymax must be greater than ymin"): ax.hexbin(x, y, extent=(0, 1, 1, 0)) def test_hexbin_string_norm(): fig, ax = plt.subplots() hex = ax.hexbin(np.random.rand(10), np.random.rand(10), norm="log", vmin=2, vmax=5) assert isinstance(hex, matplotlib.collections.PolyCollection) assert isinstance(hex.norm, matplotlib.colors.LogNorm) assert hex.norm.vmin == 2 assert hex.norm.vmax == 5 @image_comparison(['hexbin_empty.png'], remove_text=True) def test_hexbin_empty(): # From #3886: creating hexbin from empty dataset raises ValueError fig, ax = plt.subplots() ax.hexbin([], []) # From #23922: creating hexbin with log scaling from empty # dataset raises ValueError ax.hexbin([], [], bins='log') # From #27103: np.max errors when handed empty data ax.hexbin([], [], C=[], reduce_C_function=np.max) # No string-comparison warning from NumPy. ax.hexbin([], [], bins=np.arange(10)) def test_hexbin_pickable(): # From #1973: Test that picking a hexbin collection works fig, ax = plt.subplots() data = (np.arange(200) / 200).reshape((2, 100)) x, y = data hb = ax.hexbin(x, y, extent=[.1, .3, .6, .7], picker=-1) mouse_event = SimpleNamespace(x=400, y=300) assert hb.contains(mouse_event)[0] @image_comparison(['hexbin_log.png'], style='mpl20') def test_hexbin_log(): # Issue #1636 (and also test log scaled colorbar) # Remove this line when this test image is regenerated. plt.rcParams['pcolormesh.snap'] = False np.random.seed(19680801) n = 100000 x = np.random.standard_normal(n) y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n) y = np.power(2, y * 0.5) fig, ax = plt.subplots() h = ax.hexbin(x, y, yscale='log', bins='log', marginals=True, reduce_C_function=np.sum) plt.colorbar(h) # Make sure offsets are set assert h.get_offsets().shape == (11558, 2) def test_hexbin_log_offsets(): x = np.geomspace(1, 100, 500) fig, ax = plt.subplots() h = ax.hexbin(x, x, xscale='log', yscale='log', gridsize=2) np.testing.assert_almost_equal( h.get_offsets(), np.array( [[0, 0], [0, 2], [1, 0], [1, 2], [2, 0], [2, 2], [0.5, 1], [1.5, 1]])) @image_comparison(["hexbin_linear.png"], style="mpl20", remove_text=True) def test_hexbin_linear(): # Issue #21165 np.random.seed(19680801) n = 100000 x = np.random.standard_normal(n) y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n) fig, ax = plt.subplots() ax.hexbin(x, y, gridsize=(10, 5), marginals=True, reduce_C_function=np.sum) def test_hexbin_log_clim(): x, y = np.arange(200).reshape((2, 100)) fig, ax = plt.subplots() h = ax.hexbin(x, y, bins='log', vmin=2, vmax=100) assert h.get_clim() == (2, 100) @check_figures_equal(extensions=['png']) def test_hexbin_mincnt_behavior_upon_C_parameter(fig_test, fig_ref): # see: gh:12926 datapoints = [ # list of (x, y) (0, 0), (0, 0), (6, 0), (0, 6), ] X, Y = zip(*datapoints) C = [1] * len(X) extent = [-10., 10, -10., 10] gridsize = (7, 7) ax_test = fig_test.subplots() ax_ref = fig_ref.subplots() # without C parameter ax_ref.hexbin( X, Y, extent=extent, gridsize=gridsize, mincnt=1, ) ax_ref.set_facecolor("green") # for contrast of background # with C parameter ax_test.hexbin( X, Y, C=[1] * len(X), reduce_C_function=lambda v: sum(v), mincnt=1, extent=extent, gridsize=gridsize, ) ax_test.set_facecolor("green") def test_inverted_limits(): # Test gh:1553 # Calling invert_xaxis prior to plotting should not disable autoscaling # while still maintaining the inverted direction fig, ax = plt.subplots() ax.invert_xaxis() ax.plot([-5, -3, 2, 4], [1, 2, -3, 5]) assert ax.get_xlim() == (4, -5) assert ax.get_ylim() == (-3, 5) plt.close() fig, ax = plt.subplots() ax.invert_yaxis() ax.plot([-5, -3, 2, 4], [1, 2, -3, 5]) assert ax.get_xlim() == (-5, 4) assert ax.get_ylim() == (5, -3) # Test inverting nonlinear axes. fig, ax = plt.subplots() ax.set_yscale("log") ax.set_ylim(10, 1) assert ax.get_ylim() == (10, 1) @image_comparison(['nonfinite_limits']) def test_nonfinite_limits(): x = np.arange(0., np.e, 0.01) # silence divide by zero warning from log(0) with np.errstate(divide='ignore'): y = np.log(x) x[len(x)//2] = np.nan fig, ax = plt.subplots() ax.plot(x, y) @mpl.style.context('default') @pytest.mark.parametrize('plot_fun', ['scatter', 'plot', 'fill_between']) @check_figures_equal(extensions=["png"]) def test_limits_empty_data(plot_fun, fig_test, fig_ref): # Check that plotting empty data doesn't change autoscaling of dates x = np.arange("2010-01-01", "2011-01-01", dtype="datetime64[D]") ax_test = fig_test.subplots() ax_ref = fig_ref.subplots() getattr(ax_test, plot_fun)([], []) for ax in [ax_test, ax_ref]: getattr(ax, plot_fun)(x, range(len(x)), color='C0') @image_comparison(['imshow', 'imshow'], remove_text=True, style='mpl20') def test_imshow(): # use former defaults to match existing baseline image matplotlib.rcParams['image.interpolation'] = 'nearest' # Create a NxN image N = 100 (x, y) = np.indices((N, N)) x -= N//2 y -= N//2 r = np.sqrt(x**2+y**2-x*y) # Create a contour plot at N/4 and extract both the clip path and transform fig, ax = plt.subplots() ax.imshow(r) # Reuse testcase from above for a labeled data test data = {"r": r} fig, ax = plt.subplots() ax.imshow("r", data=data) @image_comparison(['imshow_clip'], style='mpl20', tol=0 if platform.machine() == 'x86_64' else 1.24) def test_imshow_clip(): # As originally reported by Gellule Xg # use former defaults to match existing baseline image matplotlib.rcParams['image.interpolation'] = 'nearest' # Create a NxN image N = 100 (x, y) = np.indices((N, N)) x -= N//2 y -= N//2 r = np.sqrt(x**2+y**2-x*y) # Create a contour plot at N/4 and extract both the clip path and transform fig, ax = plt.subplots() c = ax.contour(r, [N/4]) clip_path = mtransforms.TransformedPath(c.get_paths()[0], c.get_transform()) # Plot the image clipped by the contour ax.imshow(r, clip_path=clip_path) def test_imshow_norm_vminvmax(): """Parameters vmin, vmax should error if norm is given.""" a = [[1, 2], [3, 4]] ax = plt.axes() with pytest.raises(ValueError, match="Passing a Normalize instance simultaneously " "with vmin/vmax is not supported."): ax.imshow(a, norm=mcolors.Normalize(-10, 10), vmin=0, vmax=5) @image_comparison(['polycollection_joinstyle'], remove_text=True) def test_polycollection_joinstyle(): # Bug #2890979 reported by Matthew West fig, ax = plt.subplots() verts = np.array([[1, 1], [1, 2], [2, 2], [2, 1]]) c = mpl.collections.PolyCollection([verts], linewidths=40) ax.add_collection(c) ax.set_xbound(0, 3) ax.set_ybound(0, 3) @pytest.mark.parametrize( 'x, y1, y2', [ (np.zeros((2, 2)), 3, 3), (np.arange(0.0, 2, 0.02), np.zeros((2, 2)), 3), (np.arange(0.0, 2, 0.02), 3, np.zeros((2, 2))) ], ids=[ '2d_x_input', '2d_y1_input', '2d_y2_input' ] ) def test_fill_between_input(x, y1, y2): fig, ax = plt.subplots() with pytest.raises(ValueError): ax.fill_between(x, y1, y2) @pytest.mark.parametrize( 'y, x1, x2', [ (np.zeros((2, 2)), 3, 3), (np.arange(0.0, 2, 0.02), np.zeros((2, 2)), 3), (np.arange(0.0, 2, 0.02), 3, np.zeros((2, 2))) ], ids=[ '2d_y_input', '2d_x1_input', '2d_x2_input' ] ) def test_fill_betweenx_input(y, x1, x2): fig, ax = plt.subplots() with pytest.raises(ValueError): ax.fill_betweenx(y, x1, x2) @image_comparison(['fill_between_interpolate'], remove_text=True, tol=0 if platform.machine() == 'x86_64' else 0.012) def test_fill_between_interpolate(): x = np.arange(0.0, 2, 0.02) y1 = np.sin(2*np.pi*x) y2 = 1.2*np.sin(4*np.pi*x) fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) ax1.plot(x, y1, x, y2, color='black') ax1.fill_between(x, y1, y2, where=y2 >= y1, facecolor='white', hatch='/', interpolate=True) ax1.fill_between(x, y1, y2, where=y2 <= y1, facecolor='red', interpolate=True) # Test support for masked arrays. y2 = np.ma.masked_greater(y2, 1.0) # Test that plotting works for masked arrays with the first element masked y2[0] = np.ma.masked ax2.plot(x, y1, x, y2, color='black') ax2.fill_between(x, y1, y2, where=y2 >= y1, facecolor='green', interpolate=True) ax2.fill_between(x, y1, y2, where=y2 <= y1, facecolor='red', interpolate=True) @image_comparison(['fill_between_interpolate_decreasing'], style='mpl20', remove_text=True) def test_fill_between_interpolate_decreasing(): p = np.array([724.3, 700, 655]) t = np.array([9.4, 7, 2.2]) prof = np.array([7.9, 6.6, 3.8]) fig, ax = plt.subplots(figsize=(9, 9)) ax.plot(t, p, 'tab:red') ax.plot(prof, p, 'k') ax.fill_betweenx(p, t, prof, where=prof < t, facecolor='blue', interpolate=True, alpha=0.4) ax.fill_betweenx(p, t, prof, where=prof > t, facecolor='red', interpolate=True, alpha=0.4) ax.set_xlim(0, 30) ax.set_ylim(800, 600) @image_comparison(['fill_between_interpolate_nan'], remove_text=True) def test_fill_between_interpolate_nan(): # Tests fix for issue #18986. x = np.arange(10) y1 = np.asarray([8, 18, np.nan, 18, 8, 18, 24, 18, 8, 18]) y2 = np.asarray([18, 11, 8, 11, 18, 26, 32, 30, np.nan, np.nan]) fig, ax = plt.subplots() ax.plot(x, y1, c='k') ax.plot(x, y2, c='b') ax.fill_between(x, y1, y2, where=y2 >= y1, facecolor="green", interpolate=True, alpha=0.5) ax.fill_between(x, y1, y2, where=y1 >= y2, facecolor="red", interpolate=True, alpha=0.5) # test_symlog and test_symlog2 used to have baseline images in all three # formats, but the png and svg baselines got invalidated by the removal of # minor tick overstriking. @image_comparison(['symlog.pdf']) def test_symlog(): x = np.array([0, 1, 2, 4, 6, 9, 12, 24]) y = np.array([1000000, 500000, 100000, 100, 5, 0, 0, 0]) fig, ax = plt.subplots() ax.plot(x, y) ax.set_yscale('symlog') ax.set_xscale('linear') ax.set_ylim(-1, 10000000) @image_comparison(['symlog2.pdf'], remove_text=True) def test_symlog2(): # Numbers from -50 to 50, with 0.1 as step x = np.arange(-50, 50, 0.001) fig, axs = plt.subplots(5, 1) for ax, linthresh in zip(axs, [20., 2., 1., 0.1, 0.01]): ax.plot(x, x) ax.set_xscale('symlog', linthresh=linthresh) ax.grid(True) axs[-1].set_ylim(-0.1, 0.1) def test_pcolorargs_5205(): # Smoketest to catch issue found in gh:5205 x = [-1.5, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5] y = [-1.5, -1.25, -1.0, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5] X, Y = np.meshgrid(x, y) Z = np.hypot(X, Y) plt.pcolor(Z) plt.pcolor(list(Z)) plt.pcolor(x, y, Z[:-1, :-1]) plt.pcolor(X, Y, list(Z[:-1, :-1])) @image_comparison(['pcolormesh'], remove_text=True) def test_pcolormesh(): # Remove this line when this test image is regenerated. plt.rcParams['pcolormesh.snap'] = False n = 12 x = np.linspace(-1.5, 1.5, n) y = np.linspace(-1.5, 1.5, n*2) X, Y = np.meshgrid(x, y) Qx = np.cos(Y) - np.cos(X) Qz = np.sin(Y) + np.sin(X) Qx = (Qx + 1.1) Z = np.hypot(X, Y) / 5 Z = (Z - Z.min()) / np.ptp(Z) # The color array can include masked values: Zm = ma.masked_where(np.abs(Qz) < 0.5 * np.max(Qz), Z) _, (ax1, ax2, ax3) = plt.subplots(1, 3) ax1.pcolormesh(Qx, Qz, Zm[:-1, :-1], lw=0.5, edgecolors='k') ax2.pcolormesh(Qx, Qz, Zm[:-1, :-1], lw=2, edgecolors=['b', 'w']) ax3.pcolormesh(Qx, Qz, Zm, shading="gouraud") @image_comparison(['pcolormesh_small'], extensions=["eps"]) def test_pcolormesh_small(): n = 3 x = np.linspace(-1.5, 1.5, n) y = np.linspace(-1.5, 1.5, n*2) X, Y = np.meshgrid(x, y) Qx = np.cos(Y) - np.cos(X) Qz = np.sin(Y) + np.sin(X) Qx = (Qx + 1.1) Z = np.hypot(X, Y) / 5 Z = (Z - Z.min()) / np.ptp(Z) Zm = ma.masked_where(np.abs(Qz) < 0.5 * np.max(Qz), Z) Zm2 = ma.masked_where(Qz < -0.5 * np.max(Qz), Z) fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2) ax1.pcolormesh(Qx, Qz, Zm[:-1, :-1], lw=0.5, edgecolors='k') ax2.pcolormesh(Qx, Qz, Zm[:-1, :-1], lw=2, edgecolors=['b', 'w']) # gouraud with Zm yields a blank plot; there are no unmasked triangles. ax3.pcolormesh(Qx, Qz, Zm, shading="gouraud") # Reduce the masking to get a plot. ax4.pcolormesh(Qx, Qz, Zm2, shading="gouraud") for ax in fig.axes: ax.set_axis_off() @image_comparison(['pcolormesh_alpha'], extensions=["png", "pdf"], remove_text=True) def test_pcolormesh_alpha(): # Remove this line when this test image is regenerated. plt.rcParams['pcolormesh.snap'] = False n = 12 X, Y = np.meshgrid( np.linspace(-1.5, 1.5, n), np.linspace(-1.5, 1.5, n*2) ) Qx = X Qy = Y + np.sin(X) Z = np.hypot(X, Y) / 5 Z = (Z - Z.min()) / np.ptp(Z) vir = mpl.colormaps["viridis"].resampled(16) # make another colormap with varying alpha colors = vir(np.arange(16)) colors[:, 3] = 0.5 + 0.5*np.sin(np.arange(16)) cmap = mcolors.ListedColormap(colors) fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2) for ax in ax1, ax2, ax3, ax4: ax.add_patch(mpatches.Rectangle( (0, -1.5), 1.5, 3, facecolor=[.7, .1, .1, .5], zorder=0 )) # ax1, ax2: constant alpha ax1.pcolormesh(Qx, Qy, Z[:-1, :-1], cmap=vir, alpha=0.4, shading='flat', zorder=1) ax2.pcolormesh(Qx, Qy, Z, cmap=vir, alpha=0.4, shading='gouraud', zorder=1) # ax3, ax4: alpha from colormap ax3.pcolormesh(Qx, Qy, Z[:-1, :-1], cmap=cmap, shading='flat', zorder=1) ax4.pcolormesh(Qx, Qy, Z, cmap=cmap, shading='gouraud', zorder=1) @pytest.mark.parametrize("dims,alpha", [(3, 1), (4, 0.5)]) @check_figures_equal(extensions=["png"]) def test_pcolormesh_rgba(fig_test, fig_ref, dims, alpha): ax = fig_test.subplots() c = np.ones((5, 6, dims), dtype=float) / 2 ax.pcolormesh(c) ax = fig_ref.subplots() ax.pcolormesh(c[..., 0], cmap="gray", vmin=0, vmax=1, alpha=alpha) @check_figures_equal(extensions=["png"]) def test_pcolormesh_nearest_noargs(fig_test, fig_ref): x = np.arange(4) y = np.arange(7) X, Y = np.meshgrid(x, y) C = X + Y ax = fig_test.subplots() ax.pcolormesh(C, shading="nearest") ax = fig_ref.subplots() ax.pcolormesh(x, y, C, shading="nearest") @image_comparison(['pcolormesh_datetime_axis.png'], style='mpl20') def test_pcolormesh_datetime_axis(): # Remove this line when this test image is regenerated. plt.rcParams['pcolormesh.snap'] = False fig = plt.figure() fig.subplots_adjust(hspace=0.4, top=0.98, bottom=.15) base = datetime.datetime(2013, 1, 1) x = np.array([base + datetime.timedelta(days=d) for d in range(21)]) y = np.arange(21) z1, z2 = np.meshgrid(np.arange(20), np.arange(20)) z = z1 * z2 plt.subplot(221) plt.pcolormesh(x[:-1], y[:-1], z[:-1, :-1]) plt.subplot(222) plt.pcolormesh(x, y, z) x = np.repeat(x[np.newaxis], 21, axis=0) y = np.repeat(y[:, np.newaxis], 21, axis=1) plt.subplot(223) plt.pcolormesh(x[:-1, :-1], y[:-1, :-1], z[:-1, :-1]) plt.subplot(224) plt.pcolormesh(x, y, z) for ax in fig.get_axes(): for label in ax.get_xticklabels(): label.set_ha('right') label.set_rotation(30) @image_comparison(['pcolor_datetime_axis.png'], style='mpl20') def test_pcolor_datetime_axis(): fig = plt.figure() fig.subplots_adjust(hspace=0.4, top=0.98, bottom=.15) base = datetime.datetime(2013, 1, 1) x = np.array([base + datetime.timedelta(days=d) for d in range(21)]) y = np.arange(21) z1, z2 = np.meshgrid(np.arange(20), np.arange(20)) z = z1 * z2 plt.subplot(221) plt.pcolor(x[:-1], y[:-1], z[:-1, :-1]) plt.subplot(222) plt.pcolor(x, y, z) x = np.repeat(x[np.newaxis], 21, axis=0) y = np.repeat(y[:, np.newaxis], 21, axis=1) plt.subplot(223) plt.pcolor(x[:-1, :-1], y[:-1, :-1], z[:-1, :-1]) plt.subplot(224) plt.pcolor(x, y, z) for ax in fig.get_axes(): for label in ax.get_xticklabels(): label.set_ha('right') label.set_rotation(30) def test_pcolorargs(): n = 12 x = np.linspace(-1.5, 1.5, n) y = np.linspace(-1.5, 1.5, n*2) X, Y = np.meshgrid(x, y) Z = np.hypot(X, Y) / 5 _, ax = plt.subplots() with pytest.raises(TypeError): ax.pcolormesh(y, x, Z) with pytest.raises(TypeError): ax.pcolormesh(X, Y, Z.T) with pytest.raises(TypeError): ax.pcolormesh(x, y, Z[:-1, :-1], shading="gouraud") with pytest.raises(TypeError): ax.pcolormesh(X, Y, Z[:-1, :-1], shading="gouraud") x[0] = np.nan with pytest.raises(ValueError): ax.pcolormesh(x, y, Z[:-1, :-1]) with np.errstate(invalid='ignore'): x = np.ma.array(x, mask=(x < 0)) with pytest.raises(ValueError): ax.pcolormesh(x, y, Z[:-1, :-1]) # If the X or Y coords do not possess monotonicity in their respective # directions, a warning indicating a bad grid will be triggered. # The case of specifying coordinates by inputting 1D arrays. x = [359, 0, 1] y = [-10, 10] X, Y = np.meshgrid(x, y) Z = np.zeros(X.shape) with pytest.warns(UserWarning, match='are not monotonically increasing or decreasing'): ax.pcolormesh(X, Y, Z, shading='auto') # The case of specifying coordinates by inputting 2D arrays. x = np.linspace(-1, 1, 3) y = np.linspace(-1, 1, 3) X, Y = np.meshgrid(x, y) Z = np.zeros(X.shape) np.random.seed(19680801) noise_X = np.random.random(X.shape) noise_Y = np.random.random(Y.shape) with pytest.warns(UserWarning, match='are not monotonically increasing or ' 'decreasing') as record: # Small perturbations in coordinates will not disrupt the monotonicity # of the X-coords and Y-coords in their respective directions. # Therefore, no warnings will be triggered. ax.pcolormesh(X+noise_X, Y+noise_Y, Z, shading='auto') assert len(record) == 0 # Large perturbations have disrupted the monotonicity of the X-coords # and Y-coords in their respective directions, thus resulting in two # bad grid warnings. ax.pcolormesh(X+10*noise_X, Y+10*noise_Y, Z, shading='auto') assert len(record) == 2 def test_pcolormesh_underflow_error(): """ Test that underflow errors don't crop up in pcolormesh. Probably a numpy bug (https://github.com/numpy/numpy/issues/25810). """ with np.errstate(under="raise"): x = np.arange(0, 3, 0.1) y = np.arange(0, 6, 0.1) z = np.random.randn(len(y), len(x)) fig, ax = plt.subplots() ax.pcolormesh(x, y, z) def test_pcolorargs_with_read_only(): x = np.arange(6).reshape(2, 3) xmask = np.broadcast_to([False, True, False], x.shape) # read-only array assert xmask.flags.writeable is False masked_x = np.ma.array(x, mask=xmask) plt.pcolormesh(masked_x) x = np.linspace(0, 1, 10) y = np.linspace(0, 1, 10) X, Y = np.meshgrid(x, y) Z = np.sin(2 * np.pi * X) * np.cos(2 * np.pi * Y) mask = np.zeros(10, dtype=bool) mask[-1] = True mask = np.broadcast_to(mask, Z.shape) assert mask.flags.writeable is False masked_Z = np.ma.array(Z, mask=mask) plt.pcolormesh(X, Y, masked_Z) masked_X = np.ma.array(X, mask=mask) masked_Y = np.ma.array(Y, mask=mask) plt.pcolor(masked_X, masked_Y, masked_Z) @check_figures_equal(extensions=["png"]) def test_pcolornearest(fig_test, fig_ref): ax = fig_test.subplots() x = np.arange(0, 10) y = np.arange(0, 3) np.random.seed(19680801) Z = np.random.randn(2, 9) ax.pcolormesh(x, y, Z, shading='flat') ax = fig_ref.subplots() # specify the centers x2 = x[:-1] + np.diff(x) / 2 y2 = y[:-1] + np.diff(y) / 2 ax.pcolormesh(x2, y2, Z, shading='nearest') @check_figures_equal(extensions=["png"]) def test_pcolornearestunits(fig_test, fig_ref): ax = fig_test.subplots() x = [datetime.datetime.fromtimestamp(x * 3600) for x in range(10)] y = np.arange(0, 3) np.random.seed(19680801) Z = np.random.randn(2, 9) ax.pcolormesh(x, y, Z, shading='flat') ax = fig_ref.subplots() # specify the centers x2 = [datetime.datetime.fromtimestamp((x + 0.5) * 3600) for x in range(9)] y2 = y[:-1] + np.diff(y) / 2 ax.pcolormesh(x2, y2, Z, shading='nearest') def test_pcolorflaterror(): fig, ax = plt.subplots() x = np.arange(0, 9) y = np.arange(0, 3) np.random.seed(19680801) Z = np.random.randn(3, 9) with pytest.raises(TypeError, match='Dimensions of C'): ax.pcolormesh(x, y, Z, shading='flat') def test_samesizepcolorflaterror(): fig, ax = plt.subplots() x, y = np.meshgrid(np.arange(5), np.arange(3)) Z = x + y with pytest.raises(TypeError, match=r".*one smaller than X"): ax.pcolormesh(x, y, Z, shading='flat') @pytest.mark.parametrize('snap', [False, True]) @check_figures_equal(extensions=["png"]) def test_pcolorauto(fig_test, fig_ref, snap): ax = fig_test.subplots() x = np.arange(0, 10) y = np.arange(0, 4) np.random.seed(19680801) Z = np.random.randn(3, 9) # this is the same as flat; note that auto is default ax.pcolormesh(x, y, Z, snap=snap) ax = fig_ref.subplots() # specify the centers x2 = x[:-1] + np.diff(x) / 2 y2 = y[:-1] + np.diff(y) / 2 # this is same as nearest: ax.pcolormesh(x2, y2, Z, snap=snap) @image_comparison(['canonical'], tol=0 if platform.machine() == 'x86_64' else 0.02) def test_canonical(): fig, ax = plt.subplots() ax.plot([1, 2, 3]) @image_comparison(['arc_angles.png'], remove_text=True, style='default') def test_arc_angles(): # Ellipse parameters w = 2 h = 1 centre = (0.2, 0.5) scale = 2 fig, axs = plt.subplots(3, 3) for i, ax in enumerate(axs.flat): theta2 = i * 360 / 9 theta1 = theta2 - 45 ax.add_patch(mpatches.Ellipse(centre, w, h, alpha=0.3)) ax.add_patch(mpatches.Arc(centre, w, h, theta1=theta1, theta2=theta2)) # Straight lines intersecting start and end of arc ax.plot([scale * np.cos(np.deg2rad(theta1)) + centre[0], centre[0], scale * np.cos(np.deg2rad(theta2)) + centre[0]], [scale * np.sin(np.deg2rad(theta1)) + centre[1], centre[1], scale * np.sin(np.deg2rad(theta2)) + centre[1]]) ax.set_xlim(-scale, scale) ax.set_ylim(-scale, scale) # This looks the same, but it triggers a different code path when it # gets large enough. w *= 10 h *= 10 centre = (centre[0] * 10, centre[1] * 10) scale *= 10 @image_comparison(['arc_ellipse'], remove_text=True) def test_arc_ellipse(): xcenter, ycenter = 0.38, 0.52 width, height = 1e-1, 3e-1 angle = -30 theta = np.deg2rad(np.arange(360)) x = width / 2. * np.cos(theta) y = height / 2. * np.sin(theta) rtheta = np.deg2rad(angle) R = np.array([ [np.cos(rtheta), -np.sin(rtheta)], [np.sin(rtheta), np.cos(rtheta)]]) x, y = np.dot(R, [x, y]) x += xcenter y += ycenter fig = plt.figure() ax = fig.add_subplot(211, aspect='auto') ax.fill(x, y, alpha=0.2, facecolor='yellow', edgecolor='yellow', linewidth=1, zorder=1) e1 = mpatches.Arc((xcenter, ycenter), width, height, angle=angle, linewidth=2, fill=False, zorder=2) ax.add_patch(e1) ax = fig.add_subplot(212, aspect='equal') ax.fill(x, y, alpha=0.2, facecolor='green', edgecolor='green', zorder=1) e2 = mpatches.Arc((xcenter, ycenter), width, height, angle=angle, linewidth=2, fill=False, zorder=2) ax.add_patch(e2) def test_marker_as_markerstyle(): fix, ax = plt.subplots() m = mmarkers.MarkerStyle('o') ax.plot([1, 2, 3], [3, 2, 1], marker=m) ax.scatter([1, 2, 3], [4, 3, 2], marker=m) ax.errorbar([1, 2, 3], [5, 4, 3], marker=m) @image_comparison(['markevery'], remove_text=True) def test_markevery(): x = np.linspace(0, 10, 100) y = np.sin(x) * np.sqrt(x/10 + 0.5) # check marker only plot fig, ax = plt.subplots() ax.plot(x, y, 'o', label='default') ax.plot(x, y, 'd', markevery=None, label='mark all') ax.plot(x, y, 's', markevery=10, label='mark every 10') ax.plot(x, y, '+', markevery=(5, 20), label='mark every 5 starting at 10') ax.legend() @image_comparison(['markevery_line'], remove_text=True, tol=0.005) def test_markevery_line(): # TODO: a slight change in rendering between Inkscape versions may explain # why one had to introduce a small non-zero tolerance for the SVG test # to pass. One may try to remove this hack once Travis' Inkscape version # is modern enough. FWIW, no failure with 0.92.3 on my computer (#11358). x = np.linspace(0, 10, 100) y = np.sin(x) * np.sqrt(x/10 + 0.5) # check line/marker combos fig, ax = plt.subplots() ax.plot(x, y, '-o', label='default') ax.plot(x, y, '-d', markevery=None, label='mark all') ax.plot(x, y, '-s', markevery=10, label='mark every 10') ax.plot(x, y, '-+', markevery=(5, 20), label='mark every 5 starting at 10') ax.legend() @image_comparison(['markevery_linear_scales'], remove_text=True, tol=0.001) def test_markevery_linear_scales(): cases = [None, 8, (30, 8), [16, 24, 30], [0, -1], slice(100, 200, 3), 0.1, 0.3, 1.5, (0.0, 0.1), (0.45, 0.1)] cols = 3 gs = matplotlib.gridspec.GridSpec(len(cases) // cols + 1, cols) delta = 0.11 x = np.linspace(0, 10 - 2 * delta, 200) + delta y = np.sin(x) + 1.0 + delta for i, case in enumerate(cases): row = (i // cols) col = i % cols plt.subplot(gs[row, col]) plt.title('markevery=%s' % str(case)) plt.plot(x, y, 'o', ls='-', ms=4, markevery=case) @image_comparison(['markevery_linear_scales_zoomed'], remove_text=True) def test_markevery_linear_scales_zoomed(): cases = [None, 8, (30, 8), [16, 24, 30], [0, -1], slice(100, 200, 3), 0.1, 0.3, 1.5, (0.0, 0.1), (0.45, 0.1)] cols = 3 gs = matplotlib.gridspec.GridSpec(len(cases) // cols + 1, cols) delta = 0.11 x = np.linspace(0, 10 - 2 * delta, 200) + delta y = np.sin(x) + 1.0 + delta for i, case in enumerate(cases): row = (i // cols) col = i % cols plt.subplot(gs[row, col]) plt.title('markevery=%s' % str(case)) plt.plot(x, y, 'o', ls='-', ms=4, markevery=case) plt.xlim((6, 6.7)) plt.ylim((1.1, 1.7)) @image_comparison(['markevery_log_scales'], remove_text=True) def test_markevery_log_scales(): cases = [None, 8, (30, 8), [16, 24, 30], [0, -1], slice(100, 200, 3), 0.1, 0.3, 1.5, (0.0, 0.1), (0.45, 0.1)] cols = 3 gs = matplotlib.gridspec.GridSpec(len(cases) // cols + 1, cols) delta = 0.11 x = np.linspace(0, 10 - 2 * delta, 200) + delta y = np.sin(x) + 1.0 + delta for i, case in enumerate(cases): row = (i // cols) col = i % cols plt.subplot(gs[row, col]) plt.title('markevery=%s' % str(case)) plt.xscale('log') plt.yscale('log') plt.plot(x, y, 'o', ls='-', ms=4, markevery=case) @image_comparison(['markevery_polar'], style='default', remove_text=True) def test_markevery_polar(): cases = [None, 8, (30, 8), [16, 24, 30], [0, -1], slice(100, 200, 3), 0.1, 0.3, 1.5, (0.0, 0.1), (0.45, 0.1)] cols = 3 gs = matplotlib.gridspec.GridSpec(len(cases) // cols + 1, cols) r = np.linspace(0, 3.0, 200) theta = 2 * np.pi * r for i, case in enumerate(cases): row = (i // cols) col = i % cols plt.subplot(gs[row, col], polar=True) plt.title('markevery=%s' % str(case)) plt.plot(theta, r, 'o', ls='-', ms=4, markevery=case) @image_comparison(['markevery_linear_scales_nans'], remove_text=True) def test_markevery_linear_scales_nans(): cases = [None, 8, (30, 8), [16, 24, 30], [0, -1], slice(100, 200, 3), 0.1, 0.3, 1.5, (0.0, 0.1), (0.45, 0.1)] cols = 3 gs = matplotlib.gridspec.GridSpec(len(cases) // cols + 1, cols) delta = 0.11 x = np.linspace(0, 10 - 2 * delta, 200) + delta y = np.sin(x) + 1.0 + delta y[:10] = y[-20:] = y[50:70] = np.nan for i, case in enumerate(cases): row = (i // cols) col = i % cols plt.subplot(gs[row, col]) plt.title('markevery=%s' % str(case)) plt.plot(x, y, 'o', ls='-', ms=4, markevery=case) @image_comparison(['marker_edges'], remove_text=True) def test_marker_edges(): x = np.linspace(0, 1, 10) fig, ax = plt.subplots() ax.plot(x, np.sin(x), 'y.', ms=30.0, mew=0, mec='r') ax.plot(x+0.1, np.sin(x), 'y.', ms=30.0, mew=1, mec='r') ax.plot(x+0.2, np.sin(x), 'y.', ms=30.0, mew=2, mec='b') @image_comparison(['bar_tick_label_single.png', 'bar_tick_label_single.png']) def test_bar_tick_label_single(): # From 2516: plot bar with array of string labels for x axis ax = plt.gca() ax.bar(0, 1, align='edge', tick_label='0') # Reuse testcase from above for a labeled data test data = {"a": 0, "b": 1} fig, ax = plt.subplots() ax = plt.gca() ax.bar("a", "b", align='edge', tick_label='0', data=data) def test_nan_bar_values(): fig, ax = plt.subplots() ax.bar([0, 1], [np.nan, 4]) def test_bar_ticklabel_fail(): fig, ax = plt.subplots() ax.bar([], []) @image_comparison(['bar_tick_label_multiple.png']) def test_bar_tick_label_multiple(): # From 2516: plot bar with array of string labels for x axis ax = plt.gca() ax.bar([1, 2.5], [1, 2], width=[0.2, 0.5], tick_label=['a', 'b'], align='center') @image_comparison(['bar_tick_label_multiple_old_label_alignment.png']) def test_bar_tick_label_multiple_old_alignment(): # Test that the alignment for class is backward compatible matplotlib.rcParams["ytick.alignment"] = "center" ax = plt.gca() ax.bar([1, 2.5], [1, 2], width=[0.2, 0.5], tick_label=['a', 'b'], align='center') @check_figures_equal(extensions=["png"]) def test_bar_decimal_center(fig_test, fig_ref): ax = fig_test.subplots() x0 = [1.5, 8.4, 5.3, 4.2] y0 = [1.1, 2.2, 3.3, 4.4] x = [Decimal(x) for x in x0] y = [Decimal(y) for y in y0] # Test image - vertical, align-center bar chart with Decimal() input ax.bar(x, y, align='center') # Reference image ax = fig_ref.subplots() ax.bar(x0, y0, align='center') @check_figures_equal(extensions=["png"]) def test_barh_decimal_center(fig_test, fig_ref): ax = fig_test.subplots() x0 = [1.5, 8.4, 5.3, 4.2] y0 = [1.1, 2.2, 3.3, 4.4] x = [Decimal(x) for x in x0] y = [Decimal(y) for y in y0] # Test image - horizontal, align-center bar chart with Decimal() input ax.barh(x, y, height=[0.5, 0.5, 1, 1], align='center') # Reference image ax = fig_ref.subplots() ax.barh(x0, y0, height=[0.5, 0.5, 1, 1], align='center') @check_figures_equal(extensions=["png"]) def test_bar_decimal_width(fig_test, fig_ref): x = [1.5, 8.4, 5.3, 4.2] y = [1.1, 2.2, 3.3, 4.4] w0 = [0.7, 1.45, 1, 2] w = [Decimal(i) for i in w0] # Test image - vertical bar chart with Decimal() width ax = fig_test.subplots() ax.bar(x, y, width=w, align='center') # Reference image ax = fig_ref.subplots() ax.bar(x, y, width=w0, align='center') @check_figures_equal(extensions=["png"]) def test_barh_decimal_height(fig_test, fig_ref): x = [1.5, 8.4, 5.3, 4.2] y = [1.1, 2.2, 3.3, 4.4] h0 = [0.7, 1.45, 1, 2] h = [Decimal(i) for i in h0] # Test image - horizontal bar chart with Decimal() height ax = fig_test.subplots() ax.barh(x, y, height=h, align='center') # Reference image ax = fig_ref.subplots() ax.barh(x, y, height=h0, align='center') def test_bar_color_none_alpha(): ax = plt.gca() rects = ax.bar([1, 2], [2, 4], alpha=0.3, color='none', edgecolor='r') for rect in rects: assert rect.get_facecolor() == (0, 0, 0, 0) assert rect.get_edgecolor() == (1, 0, 0, 0.3) def test_bar_edgecolor_none_alpha(): ax = plt.gca() rects = ax.bar([1, 2], [2, 4], alpha=0.3, color='r', edgecolor='none') for rect in rects: assert rect.get_facecolor() == (1, 0, 0, 0.3) assert rect.get_edgecolor() == (0, 0, 0, 0) @image_comparison(['barh_tick_label.png']) def test_barh_tick_label(): # From 2516: plot barh with array of string labels for y axis ax = plt.gca() ax.barh([1, 2.5], [1, 2], height=[0.2, 0.5], tick_label=['a', 'b'], align='center') def test_bar_timedelta(): """Smoketest that bar can handle width and height in delta units.""" fig, ax = plt.subplots() ax.bar(datetime.datetime(2018, 1, 1), 1., width=datetime.timedelta(hours=3)) ax.bar(datetime.datetime(2018, 1, 1), 1., xerr=datetime.timedelta(hours=2), width=datetime.timedelta(hours=3)) fig, ax = plt.subplots() ax.barh(datetime.datetime(2018, 1, 1), 1, height=datetime.timedelta(hours=3)) ax.barh(datetime.datetime(2018, 1, 1), 1, height=datetime.timedelta(hours=3), yerr=datetime.timedelta(hours=2)) fig, ax = plt.subplots() ax.barh([datetime.datetime(2018, 1, 1), datetime.datetime(2018, 1, 1)], np.array([1, 1.5]), height=datetime.timedelta(hours=3)) ax.barh([datetime.datetime(2018, 1, 1), datetime.datetime(2018, 1, 1)], np.array([1, 1.5]), height=[datetime.timedelta(hours=t) for t in [1, 2]]) ax.broken_barh([(datetime.datetime(2018, 1, 1), datetime.timedelta(hours=1))], (10, 20)) def test_bar_datetime_start(): """test that tickers are correct for datetimes""" start = np.array([np.datetime64('2012-01-01'), np.datetime64('2012-02-01'), np.datetime64('2012-01-15')]) stop = np.array([np.datetime64('2012-02-07'), np.datetime64('2012-02-13'), np.datetime64('2012-02-12')]) fig, ax = plt.subplots() ax.bar([0, 1, 3], height=stop-start, bottom=start) assert isinstance(ax.yaxis.get_major_formatter(), mdates.AutoDateFormatter) fig, ax = plt.subplots() ax.barh([0, 1, 3], width=stop-start, left=start) assert isinstance(ax.xaxis.get_major_formatter(), mdates.AutoDateFormatter) def test_boxplot_dates_pandas(pd): # smoke test for boxplot and dates in pandas data = np.random.rand(5, 2) years = pd.date_range('1/1/2000', periods=2, freq=pd.DateOffset(years=1)).year plt.figure() plt.boxplot(data, positions=years) def test_boxplot_capwidths(): data = np.random.rand(5, 3) fig, axs = plt.subplots(9) axs[0].boxplot(data, capwidths=[0.3, 0.2, 0.1], widths=[0.1, 0.2, 0.3]) axs[1].boxplot(data, capwidths=[0.3, 0.2, 0.1], widths=0.2) axs[2].boxplot(data, capwidths=[0.3, 0.2, 0.1]) axs[3].boxplot(data, capwidths=0.5, widths=[0.1, 0.2, 0.3]) axs[4].boxplot(data, capwidths=0.5, widths=0.2) axs[5].boxplot(data, capwidths=0.5) axs[6].boxplot(data, widths=[0.1, 0.2, 0.3]) axs[7].boxplot(data, widths=0.2) axs[8].boxplot(data) def test_pcolor_regression(pd): from pandas.plotting import ( register_matplotlib_converters, deregister_matplotlib_converters, ) fig = plt.figure() ax = fig.add_subplot(111) times = [datetime.datetime(2021, 1, 1)] while len(times) < 7: times.append(times[-1] + datetime.timedelta(seconds=120)) y_vals = np.arange(5) time_axis, y_axis = np.meshgrid(times, y_vals) shape = (len(y_vals) - 1, len(times) - 1) z_data = np.arange(shape[0] * shape[1]) z_data.shape = shape try: register_matplotlib_converters() im = ax.pcolormesh(time_axis, y_axis, z_data) # make sure this does not raise! fig.canvas.draw() finally: deregister_matplotlib_converters() def test_bar_pandas(pd): # Smoke test for pandas df = pd.DataFrame( {'year': [2018, 2018, 2018], 'month': [1, 1, 1], 'day': [1, 2, 3], 'value': [1, 2, 3]}) df['date'] = pd.to_datetime(df[['year', 'month', 'day']]) monthly = df[['date', 'value']].groupby(['date']).sum() dates = monthly.index forecast = monthly['value'] baseline = monthly['value'] fig, ax = plt.subplots() ax.bar(dates, forecast, width=10, align='center') ax.plot(dates, baseline, color='orange', lw=4) def test_bar_pandas_indexed(pd): # Smoke test for indexed pandas df = pd.DataFrame({"x": [1., 2., 3.], "width": [.2, .4, .6]}, index=[1, 2, 3]) fig, ax = plt.subplots() ax.bar(df.x, 1., width=df.width) @mpl.style.context('default') @check_figures_equal() def test_bar_hatches(fig_test, fig_ref): ax_test = fig_test.subplots() ax_ref = fig_ref.subplots() x = [1, 2] y = [2, 3] hatches = ['x', 'o'] for i in range(2): ax_ref.bar(x[i], y[i], color='C0', hatch=hatches[i]) ax_test.bar(x, y, hatch=hatches) @pytest.mark.parametrize( ("x", "width", "label", "expected_labels", "container_label"), [ ("x", 1, "x", ["_nolegend_"], "x"), (["a", "b", "c"], [10, 20, 15], ["A", "B", "C"], ["A", "B", "C"], "_nolegend_"), (["a", "b", "c"], [10, 20, 15], ["R", "Y", "_nolegend_"], ["R", "Y", "_nolegend_"], "_nolegend_"), (["a", "b", "c"], [10, 20, 15], "bars", ["_nolegend_", "_nolegend_", "_nolegend_"], "bars"), ] ) def test_bar_labels(x, width, label, expected_labels, container_label): _, ax = plt.subplots() bar_container = ax.bar(x, width, label=label) bar_labels = [bar.get_label() for bar in bar_container] assert expected_labels == bar_labels assert bar_container.get_label() == container_label def test_bar_labels_length(): _, ax = plt.subplots() with pytest.raises(ValueError): ax.bar(["x", "y"], [1, 2], label=["X", "Y", "Z"]) _, ax = plt.subplots() with pytest.raises(ValueError): ax.bar(["x", "y"], [1, 2], label=["X"]) def test_pandas_minimal_plot(pd): # smoke test that series and index objects do not warn for x in [pd.Series([1, 2], dtype="float64"), pd.Series([1, 2], dtype="Float64")]: plt.plot(x, x) plt.plot(x.index, x) plt.plot(x) plt.plot(x.index) df = pd.DataFrame({'col': [1, 2, 3]}) plt.plot(df) plt.plot(df, df) @image_comparison(['hist_log'], remove_text=True) def test_hist_log(): data0 = np.linspace(0, 1, 200)**3 data = np.concatenate([1 - data0, 1 + data0]) fig, ax = plt.subplots() ax.hist(data, fill=False, log=True) @check_figures_equal(extensions=["png"]) def test_hist_log_2(fig_test, fig_ref): axs_test = fig_test.subplots(2, 3) axs_ref = fig_ref.subplots(2, 3) for i, histtype in enumerate(["bar", "step", "stepfilled"]): # Set log scale, then call hist(). axs_test[0, i].set_yscale("log") axs_test[0, i].hist(1, 1, histtype=histtype) # Call hist(), then set log scale. axs_test[1, i].hist(1, 1, histtype=histtype) axs_test[1, i].set_yscale("log") # Use hist(..., log=True). for ax in axs_ref[:, i]: ax.hist(1, 1, log=True, histtype=histtype) def test_hist_log_barstacked(): fig, axs = plt.subplots(2) axs[0].hist([[0], [0, 1]], 2, histtype="barstacked") axs[0].set_yscale("log") axs[1].hist([0, 0, 1], 2, histtype="barstacked") axs[1].set_yscale("log") fig.canvas.draw() assert axs[0].get_ylim() == axs[1].get_ylim() @image_comparison(['hist_bar_empty.png'], remove_text=True) def test_hist_bar_empty(): # From #3886: creating hist from empty dataset raises ValueError ax = plt.gca() ax.hist([], histtype='bar') def test_hist_float16(): np.random.seed(19680801) values = np.clip( np.random.normal(0.5, 0.3, size=1000), 0, 1).astype(np.float16) h = plt.hist(values, bins=3, alpha=0.5) bc = h[2] # Check that there are no overlapping rectangles for r in range(1, len(bc)): rleft = bc[r-1].get_corners() rright = bc[r].get_corners() # right hand position of left rectangle <= # left hand position of right rectangle assert rleft[1][0] <= rright[0][0] @image_comparison(['hist_step_empty.png'], remove_text=True) def test_hist_step_empty(): # From #3886: creating hist from empty dataset raises ValueError ax = plt.gca() ax.hist([], histtype='step') @image_comparison(['hist_step_filled.png'], remove_text=True) def test_hist_step_filled(): np.random.seed(0) x = np.random.randn(1000, 3) n_bins = 10 kwargs = [{'fill': True}, {'fill': False}, {'fill': None}, {}]*2 types = ['step']*4+['stepfilled']*4 fig, axs = plt.subplots(nrows=2, ncols=4) for kg, _type, ax in zip(kwargs, types, axs.flat): ax.hist(x, n_bins, histtype=_type, stacked=True, **kg) ax.set_title(f'{kg}/{_type}') ax.set_ylim(bottom=-50) patches = axs[0, 0].patches assert all(p.get_facecolor() == p.get_edgecolor() for p in patches) @image_comparison(['hist_density.png']) def test_hist_density(): np.random.seed(19680801) data = np.random.standard_normal(2000) fig, ax = plt.subplots() ax.hist(data, density=True) def test_hist_unequal_bins_density(): # Test correct behavior of normalized histogram with unequal bins # https://github.com/matplotlib/matplotlib/issues/9557 rng = np.random.RandomState(57483) t = rng.randn(100) bins = [-3, -1, -0.5, 0, 1, 5] mpl_heights, _, _ = plt.hist(t, bins=bins, density=True) np_heights, _ = np.histogram(t, bins=bins, density=True) assert_allclose(mpl_heights, np_heights) def test_hist_datetime_datasets(): data = [[datetime.datetime(2017, 1, 1), datetime.datetime(2017, 1, 1)], [datetime.datetime(2017, 1, 1), datetime.datetime(2017, 1, 2)]] fig, ax = plt.subplots() ax.hist(data, stacked=True) ax.hist(data, stacked=False) @pytest.mark.parametrize("bins_preprocess", [mpl.dates.date2num, lambda bins: bins, lambda bins: np.asarray(bins, 'datetime64')], ids=['date2num', 'datetime.datetime', 'np.datetime64']) def test_hist_datetime_datasets_bins(bins_preprocess): data = [[datetime.datetime(2019, 1, 5), datetime.datetime(2019, 1, 11), datetime.datetime(2019, 2, 1), datetime.datetime(2019, 3, 1)], [datetime.datetime(2019, 1, 11), datetime.datetime(2019, 2, 5), datetime.datetime(2019, 2, 18), datetime.datetime(2019, 3, 1)]] date_edges = [datetime.datetime(2019, 1, 1), datetime.datetime(2019, 2, 1), datetime.datetime(2019, 3, 1)] fig, ax = plt.subplots() _, bins, _ = ax.hist(data, bins=bins_preprocess(date_edges), stacked=True) np.testing.assert_allclose(bins, mpl.dates.date2num(date_edges)) _, bins, _ = ax.hist(data, bins=bins_preprocess(date_edges), stacked=False) np.testing.assert_allclose(bins, mpl.dates.date2num(date_edges)) @pytest.mark.parametrize('data, expected_number_of_hists', [([], 1), ([[]], 1), ([[], []], 2)]) def test_hist_with_empty_input(data, expected_number_of_hists): hists, _, _ = plt.hist(data) hists = np.asarray(hists) if hists.ndim == 1: assert 1 == expected_number_of_hists else: assert hists.shape[0] == expected_number_of_hists @pytest.mark.parametrize("histtype, zorder", [("bar", mpl.patches.Patch.zorder), ("step", mpl.lines.Line2D.zorder), ("stepfilled", mpl.patches.Patch.zorder)]) def test_hist_zorder(histtype, zorder): ax = plt.figure().add_subplot() ax.hist([1, 2], histtype=histtype) assert ax.patches for patch in ax.patches: assert patch.get_zorder() == zorder def test_stairs_no_baseline_fill_warns(): fig, ax = plt.subplots() with pytest.warns(UserWarning, match="baseline=None and fill=True"): ax.stairs( [4, 5, 1, 0, 2], [1, 2, 3, 4, 5, 6], facecolor="blue", baseline=None, fill=True ) @check_figures_equal(extensions=['png']) def test_stairs(fig_test, fig_ref): import matplotlib.lines as mlines y = np.array([6, 14, 32, 37, 48, 32, 21, 4]) # hist x = np.array([1., 2., 3., 4., 5., 6., 7., 8., 9.]) # bins test_axes = fig_test.subplots(3, 2).flatten() test_axes[0].stairs(y, x, baseline=None) test_axes[1].stairs(y, x, baseline=None, orientation='horizontal') test_axes[2].stairs(y, x) test_axes[3].stairs(y, x, orientation='horizontal') test_axes[4].stairs(y, x) test_axes[4].semilogy() test_axes[5].stairs(y, x, orientation='horizontal') test_axes[5].semilogx() # defaults of `PathPatch` to be used for all following Line2D style = {'solid_joinstyle': 'miter', 'solid_capstyle': 'butt'} ref_axes = fig_ref.subplots(3, 2).flatten() ref_axes[0].plot(x, np.append(y, y[-1]), drawstyle='steps-post', **style) ref_axes[1].plot(np.append(y[0], y), x, drawstyle='steps-post', **style) ref_axes[2].plot(x, np.append(y, y[-1]), drawstyle='steps-post', **style) ref_axes[2].add_line(mlines.Line2D([x[0], x[0]], [0, y[0]], **style)) ref_axes[2].add_line(mlines.Line2D([x[-1], x[-1]], [0, y[-1]], **style)) ref_axes[2].set_ylim(0, None) ref_axes[3].plot(np.append(y[0], y), x, drawstyle='steps-post', **style) ref_axes[3].add_line(mlines.Line2D([0, y[0]], [x[0], x[0]], **style)) ref_axes[3].add_line(mlines.Line2D([0, y[-1]], [x[-1], x[-1]], **style)) ref_axes[3].set_xlim(0, None) ref_axes[4].plot(x, np.append(y, y[-1]), drawstyle='steps-post', **style) ref_axes[4].add_line(mlines.Line2D([x[0], x[0]], [0, y[0]], **style)) ref_axes[4].add_line(mlines.Line2D([x[-1], x[-1]], [0, y[-1]], **style)) ref_axes[4].semilogy() ref_axes[5].plot(np.append(y[0], y), x, drawstyle='steps-post', **style) ref_axes[5].add_line(mlines.Line2D([0, y[0]], [x[0], x[0]], **style)) ref_axes[5].add_line(mlines.Line2D([0, y[-1]], [x[-1], x[-1]], **style)) ref_axes[5].semilogx() @check_figures_equal(extensions=['png']) def test_stairs_fill(fig_test, fig_ref): h, bins = [1, 2, 3, 4, 2], [0, 1, 2, 3, 4, 5] bs = -2 # Test test_axes = fig_test.subplots(2, 2).flatten() test_axes[0].stairs(h, bins, fill=True) test_axes[1].stairs(h, bins, orientation='horizontal', fill=True) test_axes[2].stairs(h, bins, baseline=bs, fill=True) test_axes[3].stairs(h, bins, baseline=bs, orientation='horizontal', fill=True) # # Ref ref_axes = fig_ref.subplots(2, 2).flatten() ref_axes[0].fill_between(bins, np.append(h, h[-1]), step='post', lw=0) ref_axes[0].set_ylim(0, None) ref_axes[1].fill_betweenx(bins, np.append(h, h[-1]), step='post', lw=0) ref_axes[1].set_xlim(0, None) ref_axes[2].fill_between(bins, np.append(h, h[-1]), np.ones(len(h)+1)*bs, step='post', lw=0) ref_axes[2].set_ylim(bs, None) ref_axes[3].fill_betweenx(bins, np.append(h, h[-1]), np.ones(len(h)+1)*bs, step='post', lw=0) ref_axes[3].set_xlim(bs, None) @check_figures_equal(extensions=['png']) def test_stairs_update(fig_test, fig_ref): # fixed ylim because stairs() does autoscale, but updating data does not ylim = -3, 4 # Test test_ax = fig_test.add_subplot() h = test_ax.stairs([1, 2, 3]) test_ax.set_ylim(ylim) h.set_data([3, 2, 1]) h.set_data(edges=np.arange(4)+2) h.set_data([1, 2, 1], np.arange(4)/2) h.set_data([1, 2, 3]) h.set_data(None, np.arange(4)) assert np.allclose(h.get_data()[0], np.arange(1, 4)) assert np.allclose(h.get_data()[1], np.arange(4)) h.set_data(baseline=-2) assert h.get_data().baseline == -2 # Ref ref_ax = fig_ref.add_subplot() h = ref_ax.stairs([1, 2, 3], baseline=-2) ref_ax.set_ylim(ylim) @check_figures_equal(extensions=['png']) def test_stairs_baseline_None(fig_test, fig_ref): x = np.array([0, 2, 3, 5, 10]) y = np.array([1.148, 1.231, 1.248, 1.25]) test_axes = fig_test.add_subplot() test_axes.stairs(y, x, baseline=None) style = {'solid_joinstyle': 'miter', 'solid_capstyle': 'butt'} ref_axes = fig_ref.add_subplot() ref_axes.plot(x, np.append(y, y[-1]), drawstyle='steps-post', **style) def test_stairs_empty(): ax = plt.figure().add_subplot() ax.stairs([], [42]) assert ax.get_xlim() == (39, 45) assert ax.get_ylim() == (-0.06, 0.06) def test_stairs_invalid_nan(): with pytest.raises(ValueError, match='Nan values in "edges"'): plt.stairs([1, 2], [0, np.nan, 1]) def test_stairs_invalid_mismatch(): with pytest.raises(ValueError, match='Size mismatch'): plt.stairs([1, 2], [0, 1]) def test_stairs_invalid_update(): h = plt.stairs([1, 2], [0, 1, 2]) with pytest.raises(ValueError, match='Nan values in "edges"'): h.set_data(edges=[1, np.nan, 2]) def test_stairs_invalid_update2(): h = plt.stairs([1, 2], [0, 1, 2]) with pytest.raises(ValueError, match='Size mismatch'): h.set_data(edges=np.arange(5)) @image_comparison(['test_stairs_options.png'], remove_text=True) def test_stairs_options(): x, y = np.array([1, 2, 3, 4, 5]), np.array([1, 2, 3, 4]).astype(float) yn = y.copy() yn[1] = np.nan fig, ax = plt.subplots() ax.stairs(y*3, x, color='green', fill=True, label="A") ax.stairs(y, x*3-3, color='red', fill=True, orientation='horizontal', label="B") ax.stairs(yn, x, color='orange', ls='--', lw=2, label="C") ax.stairs(yn/3, x*3-2, ls='--', lw=2, baseline=0.5, orientation='horizontal', label="D") ax.stairs(y[::-1]*3+13, x-1, color='red', ls='--', lw=2, baseline=None, label="E") ax.stairs(y[::-1]*3+14, x, baseline=26, color='purple', ls='--', lw=2, label="F") ax.stairs(yn[::-1]*3+15, x+1, baseline=np.linspace(27, 25, len(y)), color='blue', ls='--', label="G", fill=True) ax.stairs(y[:-1][::-1]*2+11, x[:-1]+0.5, color='black', ls='--', lw=2, baseline=12, hatch='//', label="H") ax.legend(loc=0) @image_comparison(['test_stairs_datetime.png']) def test_stairs_datetime(): f, ax = plt.subplots(constrained_layout=True) ax.stairs(np.arange(36), np.arange(np.datetime64('2001-12-27'), np.datetime64('2002-02-02'))) plt.xticks(rotation=30) @check_figures_equal(extensions=['png']) def test_stairs_edge_handling(fig_test, fig_ref): # Test test_ax = fig_test.add_subplot() test_ax.stairs([1, 2, 3], color='red', fill=True) # Ref ref_ax = fig_ref.add_subplot() st = ref_ax.stairs([1, 2, 3], fill=True) st.set_color('red') def contour_dat(): x = np.linspace(-3, 5, 150) y = np.linspace(-3, 5, 120) z = np.cos(x) + np.sin(y[:, np.newaxis]) return x, y, z @image_comparison(['contour_hatching'], remove_text=True, style='mpl20') def test_contour_hatching(): x, y, z = contour_dat() fig, ax = plt.subplots() ax.contourf(x, y, z, 7, hatches=['/', '\\', '//', '-'], cmap=mpl.colormaps['gray'], extend='both', alpha=0.5) @image_comparison(['contour_colorbar'], style='mpl20', tol=0 if platform.machine() == 'x86_64' else 0.54) def test_contour_colorbar(): x, y, z = contour_dat() fig, ax = plt.subplots() cs = ax.contourf(x, y, z, levels=np.arange(-1.8, 1.801, 0.2), cmap=mpl.colormaps['RdBu'], vmin=-0.6, vmax=0.6, extend='both') cs1 = ax.contour(x, y, z, levels=np.arange(-2.2, -0.599, 0.2), colors=['y'], linestyles='solid', linewidths=2) cs2 = ax.contour(x, y, z, levels=np.arange(0.6, 2.2, 0.2), colors=['c'], linewidths=2) cbar = fig.colorbar(cs, ax=ax) cbar.add_lines(cs1) cbar.add_lines(cs2, erase=False) @image_comparison(['hist2d', 'hist2d'], remove_text=True, style='mpl20') def test_hist2d(): # Remove this line when this test image is regenerated. plt.rcParams['pcolormesh.snap'] = False np.random.seed(0) # make it not symmetric in case we switch x and y axis x = np.random.randn(100)*2+5 y = np.random.randn(100)-2 fig, ax = plt.subplots() ax.hist2d(x, y, bins=10, rasterized=True) # Reuse testcase from above for a labeled data test data = {"x": x, "y": y} fig, ax = plt.subplots() ax.hist2d("x", "y", bins=10, data=data, rasterized=True) @image_comparison(['hist2d_transpose'], remove_text=True, style='mpl20') def test_hist2d_transpose(): # Remove this line when this test image is regenerated. plt.rcParams['pcolormesh.snap'] = False np.random.seed(0) # make sure the output from np.histogram is transposed before # passing to pcolorfast x = np.array([5]*100) y = np.random.randn(100)-2 fig, ax = plt.subplots() ax.hist2d(x, y, bins=10, rasterized=True) def test_hist2d_density(): x, y = np.random.random((2, 100)) ax = plt.figure().subplots() for obj in [ax, plt]: obj.hist2d(x, y, density=True) class TestScatter: @image_comparison(['scatter'], style='mpl20', remove_text=True) def test_scatter_plot(self): data = {"x": np.array([3, 4, 2, 6]), "y": np.array([2, 5, 2, 3]), "c": ['r', 'y', 'b', 'lime'], "s": [24, 15, 19, 29], "c2": ['0.5', '0.6', '0.7', '0.8']} fig, ax = plt.subplots() ax.scatter(data["x"] - 1., data["y"] - 1., c=data["c"], s=data["s"]) ax.scatter(data["x"] + 1., data["y"] + 1., c=data["c2"], s=data["s"]) ax.scatter("x", "y", c="c", s="s", data=data) @image_comparison(['scatter_marker.png'], remove_text=True) def test_scatter_marker(self): fig, (ax0, ax1, ax2) = plt.subplots(ncols=3) ax0.scatter([3, 4, 2, 6], [2, 5, 2, 3], c=[(1, 0, 0), 'y', 'b', 'lime'], s=[60, 50, 40, 30], edgecolors=['k', 'r', 'g', 'b'], marker='s') ax1.scatter([3, 4, 2, 6], [2, 5, 2, 3], c=[(1, 0, 0), 'y', 'b', 'lime'], s=[60, 50, 40, 30], edgecolors=['k', 'r', 'g', 'b'], marker=mmarkers.MarkerStyle('o', fillstyle='top')) # unit area ellipse rx, ry = 3, 1 area = rx * ry * np.pi theta = np.linspace(0, 2 * np.pi, 21) verts = np.column_stack([np.cos(theta) * rx / area, np.sin(theta) * ry / area]) ax2.scatter([3, 4, 2, 6], [2, 5, 2, 3], c=[(1, 0, 0), 'y', 'b', 'lime'], s=[60, 50, 40, 30], edgecolors=['k', 'r', 'g', 'b'], marker=verts) @image_comparison(['scatter_2D'], remove_text=True, extensions=['png']) def test_scatter_2D(self): x = np.arange(3) y = np.arange(2) x, y = np.meshgrid(x, y) z = x + y fig, ax = plt.subplots() ax.scatter(x, y, c=z, s=200, edgecolors='face') @check_figures_equal(extensions=["png"]) def test_scatter_decimal(self, fig_test, fig_ref): x0 = np.array([1.5, 8.4, 5.3, 4.2]) y0 = np.array([1.1, 2.2, 3.3, 4.4]) x = np.array([Decimal(i) for i in x0]) y = np.array([Decimal(i) for i in y0]) c = ['r', 'y', 'b', 'lime'] s = [24, 15, 19, 29] # Test image - scatter plot with Decimal() input ax = fig_test.subplots() ax.scatter(x, y, c=c, s=s) # Reference image ax = fig_ref.subplots() ax.scatter(x0, y0, c=c, s=s) def test_scatter_color(self): # Try to catch cases where 'c' kwarg should have been used. with pytest.raises(ValueError): plt.scatter([1, 2], [1, 2], color=[0.1, 0.2]) with pytest.raises(ValueError): plt.scatter([1, 2, 3], [1, 2, 3], color=[1, 2, 3]) @pytest.mark.parametrize('kwargs', [ {'cmap': 'gray'}, {'norm': mcolors.Normalize()}, {'vmin': 0}, {'vmax': 0} ]) def test_scatter_color_warning(self, kwargs): warn_match = "No data for colormapping provided " # Warn for cases where 'cmap', 'norm', 'vmin', 'vmax' # kwargs are being overridden with pytest.warns(Warning, match=warn_match): plt.scatter([], [], **kwargs) with pytest.warns(Warning, match=warn_match): plt.scatter([1, 2], [3, 4], c=[], **kwargs) # Do not warn for cases where 'c' matches 'x' and 'y' plt.scatter([], [], c=[], **kwargs) plt.scatter([1, 2], [3, 4], c=[4, 5], **kwargs) def test_scatter_unfilled(self): coll = plt.scatter([0, 1, 2], [1, 3, 2], c=['0.1', '0.3', '0.5'], marker=mmarkers.MarkerStyle('o', fillstyle='none'), linewidths=[1.1, 1.2, 1.3]) assert coll.get_facecolors().shape == (0, 4) # no facecolors assert_array_equal(coll.get_edgecolors(), [[0.1, 0.1, 0.1, 1], [0.3, 0.3, 0.3, 1], [0.5, 0.5, 0.5, 1]]) assert_array_equal(coll.get_linewidths(), [1.1, 1.2, 1.3]) @mpl.style.context('default') def test_scatter_unfillable(self): coll = plt.scatter([0, 1, 2], [1, 3, 2], c=['0.1', '0.3', '0.5'], marker='x', linewidths=[1.1, 1.2, 1.3]) assert_array_equal(coll.get_facecolors(), coll.get_edgecolors()) assert_array_equal(coll.get_edgecolors(), [[0.1, 0.1, 0.1, 1], [0.3, 0.3, 0.3, 1], [0.5, 0.5, 0.5, 1]]) assert_array_equal(coll.get_linewidths(), [1.1, 1.2, 1.3]) def test_scatter_size_arg_size(self): x = np.arange(4) with pytest.raises(ValueError, match='same size as x and y'): plt.scatter(x, x, x[1:]) with pytest.raises(ValueError, match='same size as x and y'): plt.scatter(x[1:], x[1:], x) with pytest.raises(ValueError, match='float array-like'): plt.scatter(x, x, 'foo') def test_scatter_edgecolor_RGB(self): # GitHub issue 19066 coll = plt.scatter([1, 2, 3], [1, np.nan, np.nan], edgecolor=(1, 0, 0)) assert mcolors.same_color(coll.get_edgecolor(), (1, 0, 0)) coll = plt.scatter([1, 2, 3, 4], [1, np.nan, np.nan, 1], edgecolor=(1, 0, 0, 1)) assert mcolors.same_color(coll.get_edgecolor(), (1, 0, 0, 1)) @check_figures_equal(extensions=["png"]) def test_scatter_invalid_color(self, fig_test, fig_ref): ax = fig_test.subplots() cmap = mpl.colormaps["viridis"].resampled(16) cmap.set_bad("k", 1) # Set a nonuniform size to prevent the last call to `scatter` (plotting # the invalid points separately in fig_ref) from using the marker # stamping fast path, which would result in slightly offset markers. ax.scatter(range(4), range(4), c=[1, np.nan, 2, np.nan], s=[1, 2, 3, 4], cmap=cmap, plotnonfinite=True) ax = fig_ref.subplots() cmap = mpl.colormaps["viridis"].resampled(16) ax.scatter([0, 2], [0, 2], c=[1, 2], s=[1, 3], cmap=cmap) ax.scatter([1, 3], [1, 3], s=[2, 4], color="k") @check_figures_equal(extensions=["png"]) def test_scatter_no_invalid_color(self, fig_test, fig_ref): # With plotnonfinite=False we plot only 2 points. ax = fig_test.subplots() cmap = mpl.colormaps["viridis"].resampled(16) cmap.set_bad("k", 1) ax.scatter(range(4), range(4), c=[1, np.nan, 2, np.nan], s=[1, 2, 3, 4], cmap=cmap, plotnonfinite=False) ax = fig_ref.subplots() ax.scatter([0, 2], [0, 2], c=[1, 2], s=[1, 3], cmap=cmap) def test_scatter_norm_vminvmax(self): """Parameters vmin, vmax should error if norm is given.""" x = [1, 2, 3] ax = plt.axes() with pytest.raises(ValueError, match="Passing a Normalize instance simultaneously " "with vmin/vmax is not supported."): ax.scatter(x, x, c=x, norm=mcolors.Normalize(-10, 10), vmin=0, vmax=5) @check_figures_equal(extensions=["png"]) def test_scatter_single_point(self, fig_test, fig_ref): ax = fig_test.subplots() ax.scatter(1, 1, c=1) ax = fig_ref.subplots() ax.scatter([1], [1], c=[1]) @check_figures_equal(extensions=["png"]) def test_scatter_different_shapes(self, fig_test, fig_ref): x = np.arange(10) ax = fig_test.subplots() ax.scatter(x, x.reshape(2, 5), c=x.reshape(5, 2)) ax = fig_ref.subplots() ax.scatter(x.reshape(5, 2), x, c=x.reshape(2, 5)) # Parameters for *test_scatter_c*. NB: assuming that the # scatter plot will have 4 elements. The tuple scheme is: # (*c* parameter case, exception regexp key or None if no exception) params_test_scatter_c = [ # single string: ('0.5', None), # Single letter-sequences (["rgby"], "conversion"), # Special cases ("red", None), ("none", None), (None, None), (["r", "g", "b", "none"], None), # Non-valid color spec (FWIW, 'jaune' means yellow in French) ("jaune", "conversion"), (["jaune"], "conversion"), # wrong type before wrong size (["jaune"]*4, "conversion"), # Value-mapping like ([0.5]*3, None), # should emit a warning for user's eyes though ([0.5]*4, None), # NB: no warning as matching size allows mapping ([0.5]*5, "shape"), # list of strings: (['0.5', '0.4', '0.6', '0.7'], None), (['0.5', 'red', '0.6', 'C5'], None), (['0.5', 0.5, '0.6', 'C5'], "conversion"), # RGB values ([[1, 0, 0]], None), ([[1, 0, 0]]*3, "shape"), ([[1, 0, 0]]*4, None), ([[1, 0, 0]]*5, "shape"), # RGBA values ([[1, 0, 0, 0.5]], None), ([[1, 0, 0, 0.5]]*3, "shape"), ([[1, 0, 0, 0.5]]*4, None), ([[1, 0, 0, 0.5]]*5, "shape"), # Mix of valid color specs ([[1, 0, 0, 0.5]]*3 + [[1, 0, 0]], None), ([[1, 0, 0, 0.5], "red", "0.0"], "shape"), ([[1, 0, 0, 0.5], "red", "0.0", "C5"], None), ([[1, 0, 0, 0.5], "red", "0.0", "C5", [0, 1, 0]], "shape"), # Mix of valid and non valid color specs ([[1, 0, 0, 0.5], "red", "jaune"], "conversion"), ([[1, 0, 0, 0.5], "red", "0.0", "jaune"], "conversion"), ([[1, 0, 0, 0.5], "red", "0.0", "C5", "jaune"], "conversion"), ] @pytest.mark.parametrize('c_case, re_key', params_test_scatter_c) def test_scatter_c(self, c_case, re_key): def get_next_color(): # pragma: no cover return 'blue' # currently unused xsize = 4 # Additional checking of *c* (introduced in #11383). REGEXP = { "shape": "^'c' argument has [0-9]+ elements", # shape mismatch "conversion": "^'c' argument must be a color", # bad vals } assert_context = ( pytest.raises(ValueError, match=REGEXP[re_key]) if re_key is not None else pytest.warns(match="argument looks like a single numeric RGB") if isinstance(c_case, list) and len(c_case) == 3 else contextlib.nullcontext() ) with assert_context: mpl.axes.Axes._parse_scatter_color_args( c=c_case, edgecolors="black", kwargs={}, xsize=xsize, get_next_color_func=get_next_color) @mpl.style.context('default') @check_figures_equal(extensions=["png"]) def test_scatter_single_color_c(self, fig_test, fig_ref): rgb = [[1, 0.5, 0.05]] rgba = [[1, 0.5, 0.05, .5]] # set via color kwarg ax_ref = fig_ref.subplots() ax_ref.scatter(np.ones(3), range(3), color=rgb) ax_ref.scatter(np.ones(4)*2, range(4), color=rgba) # set via broadcasting via c ax_test = fig_test.subplots() ax_test.scatter(np.ones(3), range(3), c=rgb) ax_test.scatter(np.ones(4)*2, range(4), c=rgba) def test_scatter_linewidths(self): x = np.arange(5) fig, ax = plt.subplots() for i in range(3): pc = ax.scatter(x, np.full(5, i), c=f'C{i}', marker='x', s=100, linewidths=i + 1) assert pc.get_linewidths() == i + 1 pc = ax.scatter(x, np.full(5, 3), c='C3', marker='x', s=100, linewidths=[*range(1, 5), None]) assert_array_equal(pc.get_linewidths(), [*range(1, 5), mpl.rcParams['lines.linewidth']]) def test_scatter_singular_plural_arguments(self): with pytest.raises(TypeError, match="Got both 'linewidth' and 'linewidths',\ which are aliases of one another"): plt.scatter([1, 2, 3], [1, 2, 3], linewidths=[0.5, 0.4, 0.3], linewidth=0.2) with pytest.raises(TypeError, match="Got both 'edgecolor' and 'edgecolors',\ which are aliases of one another"): plt.scatter([1, 2, 3], [1, 2, 3], edgecolors=["#ffffff", "#000000", "#f0f0f0"], edgecolor="#ffffff") with pytest.raises(TypeError, match="Got both 'facecolors' and 'facecolor',\ which are aliases of one another"): plt.scatter([1, 2, 3], [1, 2, 3], facecolors=["#ffffff", "#000000", "#f0f0f0"], facecolor="#ffffff") def _params(c=None, xsize=2, *, edgecolors=None, **kwargs): return (c, edgecolors, kwargs, xsize) _result = namedtuple('_result', 'c, colors') @pytest.mark.parametrize( 'params, expected_result', [(_params(), _result(c='b', colors=np.array([[0, 0, 1, 1]]))), (_params(c='r'), _result(c='r', colors=np.array([[1, 0, 0, 1]]))), (_params(c='r', colors='b'), _result(c='r', colors=np.array([[1, 0, 0, 1]]))), # color (_params(color='b'), _result(c='b', colors=np.array([[0, 0, 1, 1]]))), (_params(color=['b', 'g']), _result(c=['b', 'g'], colors=np.array([[0, 0, 1, 1], [0, .5, 0, 1]]))), ]) def test_parse_scatter_color_args(params, expected_result): def get_next_color(): # pragma: no cover return 'blue' # currently unused c, colors, _edgecolors = mpl.axes.Axes._parse_scatter_color_args( *params, get_next_color_func=get_next_color) assert c == expected_result.c assert_allclose(colors, expected_result.colors) del _params del _result @pytest.mark.parametrize( 'kwargs, expected_edgecolors', [(dict(), None), (dict(c='b'), None), (dict(edgecolors='r'), 'r'), (dict(edgecolors=['r', 'g']), ['r', 'g']), (dict(edgecolor='r'), 'r'), (dict(edgecolors='face'), 'face'), (dict(edgecolors='none'), 'none'), (dict(edgecolor='r', edgecolors='g'), 'r'), (dict(c='b', edgecolor='r', edgecolors='g'), 'r'), (dict(color='r'), 'r'), (dict(color='r', edgecolor='g'), 'g'), ]) def test_parse_scatter_color_args_edgecolors(kwargs, expected_edgecolors): def get_next_color(): # pragma: no cover return 'blue' # currently unused c = kwargs.pop('c', None) edgecolors = kwargs.pop('edgecolors', None) _, _, result_edgecolors = \ mpl.axes.Axes._parse_scatter_color_args( c, edgecolors, kwargs, xsize=2, get_next_color_func=get_next_color) assert result_edgecolors == expected_edgecolors def test_parse_scatter_color_args_error(): def get_next_color(): # pragma: no cover return 'blue' # currently unused with pytest.raises(ValueError, match="RGBA values should be within 0-1 range"): c = np.array([[0.1, 0.2, 0.7], [0.2, 0.4, 1.4]]) # value > 1 mpl.axes.Axes._parse_scatter_color_args( c, None, kwargs={}, xsize=2, get_next_color_func=get_next_color) # Warning message tested in the next two tests. WARN_MSG = ( "You passed both c and facecolor/facecolors for the markers. " "c has precedence over facecolor/facecolors. This behavior may " "change in the future." ) # Test cases shared between direct and integration tests COLOR_TEST_CASES = [ ('red', 'blue'), (['red', 'blue'], ['green', 'yellow']), ([[1, 0, 0], [0, 1, 0]], [[0, 0, 1], [1, 1, 0]]) ] @pytest.mark.parametrize('c, facecolor', COLOR_TEST_CASES) def test_parse_c_facecolor_warning_direct(c, facecolor): """Test the internal _parse_scatter_color_args method directly.""" def get_next_color(): # pragma: no cover return 'blue' # currently unused # Test with facecolors (plural) with pytest.warns(UserWarning, match=WARN_MSG): mpl.axes.Axes._parse_scatter_color_args( c=c, edgecolors=None, kwargs={'facecolors': facecolor}, xsize=2, get_next_color_func=get_next_color) # Test with facecolor (singular) with pytest.warns(UserWarning, match=WARN_MSG): mpl.axes.Axes._parse_scatter_color_args( c=c, edgecolors=None, kwargs={'facecolor': facecolor}, xsize=2, get_next_color_func=get_next_color) @pytest.mark.parametrize('c, facecolor', COLOR_TEST_CASES) def test_scatter_c_facecolor_warning_integration(c, facecolor): """Test the warning through the actual scatter plot creation.""" fig, ax = plt.subplots() x = [0, 1] if isinstance(c, (list, tuple)) else [0] y = x # Test with facecolors (plural) with pytest.warns(UserWarning, match=WARN_MSG): ax.scatter(x, y, c=c, facecolors=facecolor) # Test with facecolor (singular) with pytest.warns(UserWarning, match=WARN_MSG): ax.scatter(x, y, c=c, facecolor=facecolor) def test_as_mpl_axes_api(): # tests the _as_mpl_axes api class Polar: def __init__(self): self.theta_offset = 0 def _as_mpl_axes(self): # implement the matplotlib axes interface return PolarAxes, {'theta_offset': self.theta_offset} prj = Polar() prj2 = Polar() prj2.theta_offset = np.pi # testing axes creation with plt.axes ax = plt.axes((0, 0, 1, 1), projection=prj) assert type(ax) is PolarAxes plt.close() # testing axes creation with subplot ax = plt.subplot(121, projection=prj) assert type(ax) is PolarAxes plt.close() def test_pyplot_axes(): # test focusing of Axes in other Figure fig1, ax1 = plt.subplots() fig2, ax2 = plt.subplots() plt.sca(ax1) assert ax1 is plt.gca() assert fig1 is plt.gcf() plt.close(fig1) plt.close(fig2) def test_log_scales(): fig, ax = plt.subplots() ax.plot(np.log(np.linspace(0.1, 100))) ax.set_yscale('log', base=5.5) ax.invert_yaxis() ax.set_xscale('log', base=9.0) xticks, yticks = ( [(t.get_loc(), t.label1.get_text()) for t in axis._update_ticks()] for axis in [ax.xaxis, ax.yaxis] ) assert xticks == [ (1.0, '$\\mathdefault{9^{0}}$'), (9.0, '$\\mathdefault{9^{1}}$'), (81.0, '$\\mathdefault{9^{2}}$'), (2.0, ''), (3.0, ''), (4.0, ''), (5.0, ''), (6.0, ''), (7.0, ''), (8.0, ''), (18.0, ''), (27.0, ''), (36.0, ''), (45.0, ''), (54.0, ''), (63.0, ''), (72.0, ''), ] assert yticks == [ (0.18181818181818182, '$\\mathdefault{5.5^{-1}}$'), (1.0, '$\\mathdefault{5.5^{0}}$'), (5.5, '$\\mathdefault{5.5^{1}}$'), (0.36363636363636365, ''), (0.5454545454545454, ''), (0.7272727272727273, ''), (0.9090909090909092, ''), (2.0, ''), (3.0, ''), (4.0, ''), (5.0, ''), ] def test_log_scales_no_data(): _, ax = plt.subplots() ax.set(xscale="log", yscale="log") ax.xaxis.set_major_locator(mticker.MultipleLocator(1)) assert ax.get_xlim() == ax.get_ylim() == (1, 10) def test_log_scales_invalid(): fig, ax = plt.subplots() ax.set_xscale('log') with pytest.warns(UserWarning, match='Attempt to set non-positive'): ax.set_xlim(-1, 10) ax.set_yscale('log') with pytest.warns(UserWarning, match='Attempt to set non-positive'): ax.set_ylim(-1, 10) @image_comparison(['stackplot_test_image', 'stackplot_test_image'], tol=0 if platform.machine() == 'x86_64' else 0.031) def test_stackplot(): fig = plt.figure() x = np.linspace(0, 10, 10) y1 = 1.0 * x y2 = 2.0 * x + 1 y3 = 3.0 * x + 2 ax = fig.add_subplot(1, 1, 1) ax.stackplot(x, y1, y2, y3) ax.set_xlim((0, 10)) ax.set_ylim((0, 70)) # Reuse testcase from above for a test with labeled data and with colours # from the Axes property cycle. data = {"x": x, "y1": y1, "y2": y2, "y3": y3} fig, ax = plt.subplots() ax.stackplot("x", "y1", "y2", "y3", data=data, colors=["C0", "C1", "C2"]) ax.set_xlim((0, 10)) ax.set_ylim((0, 70)) @image_comparison(['stackplot_test_baseline'], remove_text=True) def test_stackplot_baseline(): np.random.seed(0) def layers(n, m): a = np.zeros((m, n)) for i in range(n): for j in range(5): x = 1 / (.1 + np.random.random()) y = 2 * np.random.random() - .5 z = 10 / (.1 + np.random.random()) a[:, i] += x * np.exp(-((np.arange(m) / m - y) * z) ** 2) return a d = layers(3, 100) d[50, :] = 0 # test for fixed weighted wiggle (issue #6313) fig, axs = plt.subplots(2, 2) axs[0, 0].stackplot(range(100), d.T, baseline='zero') axs[0, 1].stackplot(range(100), d.T, baseline='sym') axs[1, 0].stackplot(range(100), d.T, baseline='wiggle') axs[1, 1].stackplot(range(100), d.T, baseline='weighted_wiggle') @check_figures_equal() def test_stackplot_hatching(fig_ref, fig_test): x = np.linspace(0, 10, 10) y1 = 1.0 * x y2 = 2.0 * x + 1 y3 = 3.0 * x + 2 # stackplot with different hatching styles (issue #27146) ax_test = fig_test.subplots() ax_test.stackplot(x, y1, y2, y3, hatch=["x", "//", "\\\\"], colors=["white"]) ax_test.set_xlim((0, 10)) ax_test.set_ylim((0, 70)) # compare with result from hatching each layer individually stack_baseline = np.zeros(len(x)) ax_ref = fig_ref.subplots() ax_ref.fill_between(x, stack_baseline, y1, hatch="x", facecolor="white") ax_ref.fill_between(x, y1, y1+y2, hatch="//", facecolor="white") ax_ref.fill_between(x, y1+y2, y1+y2+y3, hatch="\\\\", facecolor="white") ax_ref.set_xlim((0, 10)) ax_ref.set_ylim((0, 70)) def _bxp_test_helper( stats_kwargs={}, transform_stats=lambda s: s, bxp_kwargs={}): np.random.seed(937) logstats = mpl.cbook.boxplot_stats( np.random.lognormal(mean=1.25, sigma=1., size=(37, 4)), **stats_kwargs) fig, ax = plt.subplots() if bxp_kwargs.get('orientation', 'vertical') == 'vertical': ax.set_yscale('log') else: ax.set_xscale('log') # Work around baseline images generate back when bxp did not respect the # boxplot.boxprops.linewidth rcParam when patch_artist is False. if not bxp_kwargs.get('patch_artist', False): mpl.rcParams['boxplot.boxprops.linewidth'] = \ mpl.rcParams['lines.linewidth'] ax.bxp(transform_stats(logstats), **bxp_kwargs) @image_comparison(['bxp_baseline.png'], savefig_kwarg={'dpi': 40}, style='default') def test_bxp_baseline(): _bxp_test_helper() @image_comparison(['bxp_rangewhis.png'], savefig_kwarg={'dpi': 40}, style='default') def test_bxp_rangewhis(): _bxp_test_helper(stats_kwargs=dict(whis=[0, 100])) @image_comparison(['bxp_percentilewhis.png'], savefig_kwarg={'dpi': 40}, style='default') def test_bxp_percentilewhis(): _bxp_test_helper(stats_kwargs=dict(whis=[5, 95])) @image_comparison(['bxp_with_xlabels.png'], savefig_kwarg={'dpi': 40}, style='default') def test_bxp_with_xlabels(): def transform(stats): for s, label in zip(stats, list('ABCD')): s['label'] = label return stats _bxp_test_helper(transform_stats=transform) @image_comparison(['bxp_horizontal.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default', tol=0.1) def test_bxp_horizontal(): _bxp_test_helper(bxp_kwargs=dict(orientation='horizontal')) @image_comparison(['bxp_with_ylabels.png'], savefig_kwarg={'dpi': 40}, style='default', tol=0.1) def test_bxp_with_ylabels(): def transform(stats): for s, label in zip(stats, list('ABCD')): s['label'] = label return stats _bxp_test_helper(transform_stats=transform, bxp_kwargs=dict(orientation='horizontal')) @image_comparison(['bxp_patchartist.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_patchartist(): _bxp_test_helper(bxp_kwargs=dict(patch_artist=True)) @image_comparison(['bxp_custompatchartist.png'], remove_text=True, savefig_kwarg={'dpi': 100}, style='default') def test_bxp_custompatchartist(): _bxp_test_helper(bxp_kwargs=dict( patch_artist=True, boxprops=dict(facecolor='yellow', edgecolor='green', ls=':'))) @image_comparison(['bxp_customoutlier.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_customoutlier(): _bxp_test_helper(bxp_kwargs=dict( flierprops=dict(linestyle='none', marker='d', mfc='g'))) @image_comparison(['bxp_withmean_custompoint.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_showcustommean(): _bxp_test_helper(bxp_kwargs=dict( showmeans=True, meanprops=dict(linestyle='none', marker='d', mfc='green'), )) @image_comparison(['bxp_custombox.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_custombox(): _bxp_test_helper(bxp_kwargs=dict( boxprops=dict(linestyle='--', color='b', lw=3))) @image_comparison(['bxp_custommedian.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_custommedian(): _bxp_test_helper(bxp_kwargs=dict( medianprops=dict(linestyle='--', color='b', lw=3))) @image_comparison(['bxp_customcap.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_customcap(): _bxp_test_helper(bxp_kwargs=dict( capprops=dict(linestyle='--', color='g', lw=3))) @image_comparison(['bxp_customwhisker.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_customwhisker(): _bxp_test_helper(bxp_kwargs=dict( whiskerprops=dict(linestyle='-', color='m', lw=3))) @check_figures_equal() def test_boxplot_median_bound_by_box(fig_test, fig_ref): data = np.arange(3) medianprops_test = {"linewidth": 12} medianprops_ref = {**medianprops_test, "solid_capstyle": "butt"} fig_test.subplots().boxplot(data, medianprops=medianprops_test) fig_ref.subplots().boxplot(data, medianprops=medianprops_ref) @image_comparison(['bxp_withnotch.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_shownotches(): _bxp_test_helper(bxp_kwargs=dict(shownotches=True)) @image_comparison(['bxp_nocaps.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_nocaps(): _bxp_test_helper(bxp_kwargs=dict(showcaps=False)) @image_comparison(['bxp_nobox.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_nobox(): _bxp_test_helper(bxp_kwargs=dict(showbox=False)) @image_comparison(['bxp_no_flier_stats.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_no_flier_stats(): def transform(stats): for s in stats: s.pop('fliers', None) return stats _bxp_test_helper(transform_stats=transform, bxp_kwargs=dict(showfliers=False)) @image_comparison(['bxp_withmean_point.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_showmean(): _bxp_test_helper(bxp_kwargs=dict(showmeans=True, meanline=False)) @image_comparison(['bxp_withmean_line.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_showmeanasline(): _bxp_test_helper(bxp_kwargs=dict(showmeans=True, meanline=True)) @image_comparison(['bxp_scalarwidth.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_scalarwidth(): _bxp_test_helper(bxp_kwargs=dict(widths=.25)) @image_comparison(['bxp_customwidths.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_customwidths(): _bxp_test_helper(bxp_kwargs=dict(widths=[0.10, 0.25, 0.65, 0.85])) @image_comparison(['bxp_custompositions.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_bxp_custompositions(): _bxp_test_helper(bxp_kwargs=dict(positions=[1, 5, 6, 7])) def test_bxp_bad_widths(): with pytest.raises(ValueError): _bxp_test_helper(bxp_kwargs=dict(widths=[1])) def test_bxp_bad_positions(): with pytest.raises(ValueError): _bxp_test_helper(bxp_kwargs=dict(positions=[2, 3])) @image_comparison(['bxp_custom_capwidths.png'], savefig_kwarg={'dpi': 40}, style='default') def test_bxp_custom_capwidths(): _bxp_test_helper(bxp_kwargs=dict(capwidths=[0.0, 0.1, 0.5, 1.0])) @image_comparison(['bxp_custom_capwidth.png'], savefig_kwarg={'dpi': 40}, style='default') def test_bxp_custom_capwidth(): _bxp_test_helper(bxp_kwargs=dict(capwidths=0.6)) def test_bxp_bad_capwidths(): with pytest.raises(ValueError): _bxp_test_helper(bxp_kwargs=dict(capwidths=[1])) @image_comparison(['boxplot', 'boxplot'], tol=1.28, style='default') def test_boxplot(): # Randomness used for bootstrapping. np.random.seed(937) x = np.linspace(-7, 7, 140) x = np.hstack([-25, x, 25]) fig, ax = plt.subplots() ax.boxplot([x, x], bootstrap=10000, notch=1) ax.set_ylim((-30, 30)) # Reuse testcase from above for a labeled data test data = {"x": [x, x]} fig, ax = plt.subplots() ax.boxplot("x", bootstrap=10000, notch=1, data=data) ax.set_ylim((-30, 30)) @check_figures_equal(extensions=["png"]) def test_boxplot_masked(fig_test, fig_ref): # Check that masked values are ignored when plotting a boxplot x_orig = np.linspace(-1, 1, 200) ax = fig_test.subplots() x = x_orig[x_orig >= 0] ax.boxplot(x) x = np.ma.masked_less(x_orig, 0) ax = fig_ref.subplots() ax.boxplot(x) @image_comparison(['boxplot_custom_capwidths.png'], savefig_kwarg={'dpi': 40}, style='default') def test_boxplot_custom_capwidths(): x = np.linspace(-7, 7, 140) x = np.hstack([-25, x, 25]) fig, ax = plt.subplots() ax.boxplot([x, x], notch=1, capwidths=[0.01, 0.2]) @image_comparison(['boxplot_sym2.png'], remove_text=True, style='default') def test_boxplot_sym2(): # Randomness used for bootstrapping. np.random.seed(937) x = np.linspace(-7, 7, 140) x = np.hstack([-25, x, 25]) fig, [ax1, ax2] = plt.subplots(1, 2) ax1.boxplot([x, x], bootstrap=10000, sym='^') ax1.set_ylim((-30, 30)) ax2.boxplot([x, x], bootstrap=10000, sym='g') ax2.set_ylim((-30, 30)) @image_comparison(['boxplot_sym.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_boxplot_sym(): x = np.linspace(-7, 7, 140) x = np.hstack([-25, x, 25]) fig, ax = plt.subplots() ax.boxplot([x, x], sym='gs') ax.set_ylim((-30, 30)) @image_comparison(['boxplot_autorange_false_whiskers.png', 'boxplot_autorange_true_whiskers.png'], style='default') def test_boxplot_autorange_whiskers(): # Randomness used for bootstrapping. np.random.seed(937) x = np.ones(140) x = np.hstack([0, x, 2]) fig1, ax1 = plt.subplots() ax1.boxplot([x, x], bootstrap=10000, notch=1) ax1.set_ylim((-5, 5)) fig2, ax2 = plt.subplots() ax2.boxplot([x, x], bootstrap=10000, notch=1, autorange=True) ax2.set_ylim((-5, 5)) def _rc_test_bxp_helper(ax, rc_dict): x = np.linspace(-7, 7, 140) x = np.hstack([-25, x, 25]) with matplotlib.rc_context(rc_dict): ax.boxplot([x, x]) return ax @image_comparison(['boxplot_rc_parameters'], savefig_kwarg={'dpi': 100}, remove_text=True, tol=1, style='default') def test_boxplot_rc_parameters(): # Randomness used for bootstrapping. np.random.seed(937) fig, ax = plt.subplots(3) rc_axis0 = { 'boxplot.notch': True, 'boxplot.whiskers': [5, 95], 'boxplot.bootstrap': 10000, 'boxplot.flierprops.color': 'b', 'boxplot.flierprops.marker': 'o', 'boxplot.flierprops.markerfacecolor': 'g', 'boxplot.flierprops.markeredgecolor': 'b', 'boxplot.flierprops.markersize': 5, 'boxplot.flierprops.linestyle': '--', 'boxplot.flierprops.linewidth': 2.0, 'boxplot.boxprops.color': 'r', 'boxplot.boxprops.linewidth': 2.0, 'boxplot.boxprops.linestyle': '--', 'boxplot.capprops.color': 'c', 'boxplot.capprops.linewidth': 2.0, 'boxplot.capprops.linestyle': '--', 'boxplot.medianprops.color': 'k', 'boxplot.medianprops.linewidth': 2.0, 'boxplot.medianprops.linestyle': '--', } rc_axis1 = { 'boxplot.whiskers': [0, 100], 'boxplot.patchartist': True, } rc_axis2 = { 'boxplot.whiskers': 2.0, 'boxplot.showcaps': False, 'boxplot.showbox': False, 'boxplot.showfliers': False, 'boxplot.showmeans': True, 'boxplot.meanline': True, 'boxplot.meanprops.color': 'c', 'boxplot.meanprops.linewidth': 2.0, 'boxplot.meanprops.linestyle': '--', 'boxplot.whiskerprops.color': 'r', 'boxplot.whiskerprops.linewidth': 2.0, 'boxplot.whiskerprops.linestyle': '-.', } dict_list = [rc_axis0, rc_axis1, rc_axis2] for axis, rc_axis in zip(ax, dict_list): _rc_test_bxp_helper(axis, rc_axis) assert (matplotlib.patches.PathPatch in [type(t) for t in ax[1].get_children()]) @image_comparison(['boxplot_with_CIarray.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_boxplot_with_CIarray(): # Randomness used for bootstrapping. np.random.seed(937) x = np.linspace(-7, 7, 140) x = np.hstack([-25, x, 25]) fig, ax = plt.subplots() CIs = np.array([[-1.5, 3.], [-1., 3.5]]) # show a boxplot with Matplotlib medians and confidence intervals, and # another with manual values ax.boxplot([x, x], bootstrap=10000, usermedians=[None, 1.0], conf_intervals=CIs, notch=1) ax.set_ylim((-30, 30)) @image_comparison(['boxplot_no_inverted_whisker.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_boxplot_no_weird_whisker(): x = np.array([3, 9000, 150, 88, 350, 200000, 1400, 960], dtype=np.float64) ax1 = plt.axes() ax1.boxplot(x) ax1.set_yscale('log') ax1.yaxis.grid(False, which='minor') ax1.xaxis.grid(False) def test_boxplot_bad_medians(): x = np.linspace(-7, 7, 140) x = np.hstack([-25, x, 25]) fig, ax = plt.subplots() with pytest.raises(ValueError): ax.boxplot(x, usermedians=[1, 2]) with pytest.raises(ValueError): ax.boxplot([x, x], usermedians=[[1, 2], [1, 2]]) def test_boxplot_bad_ci(): x = np.linspace(-7, 7, 140) x = np.hstack([-25, x, 25]) fig, ax = plt.subplots() with pytest.raises(ValueError): ax.boxplot([x, x], conf_intervals=[[1, 2]]) with pytest.raises(ValueError): ax.boxplot([x, x], conf_intervals=[[1, 2], [1]]) def test_boxplot_zorder(): x = np.arange(10) fix, ax = plt.subplots() assert ax.boxplot(x)['boxes'][0].get_zorder() == 2 assert ax.boxplot(x, zorder=10)['boxes'][0].get_zorder() == 10 def test_boxplot_marker_behavior(): plt.rcParams['lines.marker'] = 's' plt.rcParams['boxplot.flierprops.marker'] = 'o' plt.rcParams['boxplot.meanprops.marker'] = '^' fig, ax = plt.subplots() test_data = np.arange(100) test_data[-1] = 150 # a flier point bxp_handle = ax.boxplot(test_data, showmeans=True) for bxp_lines in ['whiskers', 'caps', 'boxes', 'medians']: for each_line in bxp_handle[bxp_lines]: # Ensure that the rcParams['lines.marker'] is overridden by '' assert each_line.get_marker() == '' # Ensure that markers for fliers and means aren't overridden with '' assert bxp_handle['fliers'][0].get_marker() == 'o' assert bxp_handle['means'][0].get_marker() == '^' @image_comparison(['boxplot_mod_artists_after_plotting.png'], remove_text=True, savefig_kwarg={'dpi': 40}, style='default') def test_boxplot_mod_artist_after_plotting(): x = [0.15, 0.11, 0.06, 0.06, 0.12, 0.56, -0.56] fig, ax = plt.subplots() bp = ax.boxplot(x, sym="o") for key in bp: for obj in bp[key]: obj.set_color('green') @image_comparison(['violinplot_vert_baseline.png', 'violinplot_vert_baseline.png']) def test_vert_violinplot_baseline(): # First 9 digits of frac(sqrt(2)) np.random.seed(414213562) data = [np.random.normal(size=100) for _ in range(4)] ax = plt.axes() ax.violinplot(data, positions=range(4), showmeans=False, showextrema=False, showmedians=False) # Reuse testcase from above for a labeled data test data = {"d": data} fig, ax = plt.subplots() ax.violinplot("d", positions=range(4), showmeans=False, showextrema=False, showmedians=False, data=data) @image_comparison(['violinplot_vert_showmeans.png']) def test_vert_violinplot_showmeans(): ax = plt.axes() # First 9 digits of frac(sqrt(3)) np.random.seed(732050807) data = [np.random.normal(size=100) for _ in range(4)] ax.violinplot(data, positions=range(4), showmeans=True, showextrema=False, showmedians=False) @image_comparison(['violinplot_vert_showextrema.png']) def test_vert_violinplot_showextrema(): ax = plt.axes() # First 9 digits of frac(sqrt(5)) np.random.seed(236067977) data = [np.random.normal(size=100) for _ in range(4)] ax.violinplot(data, positions=range(4), showmeans=False, showextrema=True, showmedians=False) @image_comparison(['violinplot_vert_showmedians.png']) def test_vert_violinplot_showmedians(): ax = plt.axes() # First 9 digits of frac(sqrt(7)) np.random.seed(645751311) data = [np.random.normal(size=100) for _ in range(4)] ax.violinplot(data, positions=range(4), showmeans=False, showextrema=False, showmedians=True) @image_comparison(['violinplot_vert_showall.png']) def test_vert_violinplot_showall(): ax = plt.axes() # First 9 digits of frac(sqrt(11)) np.random.seed(316624790) data = [np.random.normal(size=100) for _ in range(4)] ax.violinplot(data, positions=range(4), showmeans=True, showextrema=True, showmedians=True, quantiles=[[0.1, 0.9], [0.2, 0.8], [0.3, 0.7], [0.4, 0.6]]) @image_comparison(['violinplot_vert_custompoints_10.png']) def test_vert_violinplot_custompoints_10(): ax = plt.axes() # First 9 digits of frac(sqrt(13)) np.random.seed(605551275) data = [np.random.normal(size=100) for _ in range(4)] ax.violinplot(data, positions=range(4), showmeans=False, showextrema=False, showmedians=False, points=10) @image_comparison(['violinplot_vert_custompoints_200.png']) def test_vert_violinplot_custompoints_200(): ax = plt.axes() # First 9 digits of frac(sqrt(17)) np.random.seed(123105625) data = [np.random.normal(size=100) for _ in range(4)] ax.violinplot(data, positions=range(4), showmeans=False, showextrema=False, showmedians=False, points=200) @image_comparison(['violinplot_horiz_baseline.png']) def test_horiz_violinplot_baseline(): ax = plt.axes() # First 9 digits of frac(sqrt(19)) np.random.seed(358898943) data = [np.random.normal(size=100) for _ in range(4)] ax.violinplot(data, positions=range(4), orientation='horizontal', showmeans=False, showextrema=False, showmedians=False) @image_comparison(['violinplot_horiz_showmedians.png']) def test_horiz_violinplot_showmedians(): ax = plt.axes() # First 9 digits of frac(sqrt(23)) np.random.seed(795831523) data = [np.random.normal(size=100) for _ in range(4)] ax.violinplot(data, positions=range(4), orientation='horizontal', showmeans=False, showextrema=False, showmedians=True) @image_comparison(['violinplot_horiz_showmeans.png']) def test_horiz_violinplot_showmeans(): ax = plt.axes() # First 9 digits of frac(sqrt(29)) np.random.seed(385164807) data = [np.random.normal(size=100) for _ in range(4)] ax.violinplot(data, positions=range(4), orientation='horizontal', showmeans=True, showextrema=False, showmedians=False) @image_comparison(['violinplot_horiz_showextrema.png']) def test_horiz_violinplot_showextrema(): ax = plt.axes() # First 9 digits of frac(sqrt(31)) np.random.seed(567764362) data = [np.random.normal(size=100) for _ in range(4)] ax.violinplot(data, positions=range(4), orientation='horizontal', showmeans=False, showextrema=True, showmedians=False) @image_comparison(['violinplot_horiz_showall.png']) def test_horiz_violinplot_showall(): ax = plt.axes() # First 9 digits of frac(sqrt(37)) np.random.seed(82762530) data = [np.random.normal(size=100) for _ in range(4)] ax.violinplot(data, positions=range(4), orientation='horizontal', showmeans=True, showextrema=True, showmedians=True, quantiles=[[0.1, 0.9], [0.2, 0.8], [0.3, 0.7], [0.4, 0.6]]) @image_comparison(['violinplot_horiz_custompoints_10.png']) def test_horiz_violinplot_custompoints_10(): ax = plt.axes() # First 9 digits of frac(sqrt(41)) np.random.seed(403124237) data = [np.random.normal(size=100) for _ in range(4)] ax.violinplot(data, positions=range(4), orientation='horizontal', showmeans=False, showextrema=False, showmedians=False, points=10) @image_comparison(['violinplot_horiz_custompoints_200.png']) def test_horiz_violinplot_custompoints_200(): ax = plt.axes() # First 9 digits of frac(sqrt(43)) np.random.seed(557438524) data = [np.random.normal(size=100) for _ in range(4)] ax.violinplot(data, positions=range(4), orientation='horizontal', showmeans=False, showextrema=False, showmedians=False, points=200) @image_comparison(['violinplot_sides.png'], remove_text=True, style='mpl20') def test_violinplot_sides(): ax = plt.axes() np.random.seed(19680801) data = [np.random.normal(size=100)] # Check horizontal violinplot for pos, side in zip([0, -0.5, 0.5], ['both', 'low', 'high']): ax.violinplot(data, positions=[pos], orientation='horizontal', showmeans=False, showextrema=True, showmedians=True, side=side) # Check vertical violinplot for pos, side in zip([4, 3.5, 4.5], ['both', 'low', 'high']): ax.violinplot(data, positions=[pos], orientation='vertical', showmeans=False, showextrema=True, showmedians=True, side=side) def test_violinplot_bad_positions(): ax = plt.axes() # First 9 digits of frac(sqrt(47)) np.random.seed(855654600) data = [np.random.normal(size=100) for _ in range(4)] with pytest.raises(ValueError): ax.violinplot(data, positions=range(5)) def test_violinplot_bad_widths(): ax = plt.axes() # First 9 digits of frac(sqrt(53)) np.random.seed(280109889) data = [np.random.normal(size=100) for _ in range(4)] with pytest.raises(ValueError): ax.violinplot(data, positions=range(4), widths=[1, 2, 3]) def test_violinplot_bad_quantiles(): ax = plt.axes() # First 9 digits of frac(sqrt(73)) np.random.seed(544003745) data = [np.random.normal(size=100)] # Different size quantile list and plots with pytest.raises(ValueError): ax.violinplot(data, quantiles=[[0.1, 0.2], [0.5, 0.7]]) def test_violinplot_outofrange_quantiles(): ax = plt.axes() # First 9 digits of frac(sqrt(79)) np.random.seed(888194417) data = [np.random.normal(size=100)] # Quantile value above 100 with pytest.raises(ValueError): ax.violinplot(data, quantiles=[[0.1, 0.2, 0.3, 1.05]]) # Quantile value below 0 with pytest.raises(ValueError): ax.violinplot(data, quantiles=[[-0.05, 0.2, 0.3, 0.75]]) @check_figures_equal(extensions=["png"]) def test_violinplot_single_list_quantiles(fig_test, fig_ref): # Ensures quantile list for 1D can be passed in as single list # First 9 digits of frac(sqrt(83)) np.random.seed(110433579) data = [np.random.normal(size=100)] # Test image ax = fig_test.subplots() ax.violinplot(data, quantiles=[0.1, 0.3, 0.9]) # Reference image ax = fig_ref.subplots() ax.violinplot(data, quantiles=[[0.1, 0.3, 0.9]]) @check_figures_equal(extensions=["png"]) def test_violinplot_pandas_series(fig_test, fig_ref, pd): np.random.seed(110433579) s1 = pd.Series(np.random.normal(size=7), index=[9, 8, 7, 6, 5, 4, 3]) s2 = pd.Series(np.random.normal(size=9), index=list('ABCDEFGHI')) s3 = pd.Series(np.random.normal(size=11)) fig_test.subplots().violinplot([s1, s2, s3]) fig_ref.subplots().violinplot([s1.values, s2.values, s3.values]) def test_manage_xticks(): _, ax = plt.subplots() ax.set_xlim(0, 4) old_xlim = ax.get_xlim() np.random.seed(0) y1 = np.random.normal(10, 3, 20) y2 = np.random.normal(3, 1, 20) ax.boxplot([y1, y2], positions=[1, 2], manage_ticks=False) new_xlim = ax.get_xlim() assert_array_equal(old_xlim, new_xlim) def test_boxplot_not_single(): fig, ax = plt.subplots() ax.boxplot(np.random.rand(100), positions=[3]) ax.boxplot(np.random.rand(100), positions=[5]) fig.canvas.draw() assert ax.get_xlim() == (2.5, 5.5) assert list(ax.get_xticks()) == [3, 5] assert [t.get_text() for t in ax.get_xticklabels()] == ["3", "5"] def test_tick_space_size_0(): # allow font size to be zero, which affects ticks when there is # no other text in the figure. plt.plot([0, 1], [0, 1]) matplotlib.rcParams.update({'font.size': 0}) b = io.BytesIO() plt.savefig(b, dpi=80, format='raw') @image_comparison(['errorbar_basic', 'errorbar_mixed', 'errorbar_basic']) def test_errorbar(): # longdouble due to floating point rounding issues with certain # computer chipsets x = np.arange(0.1, 4, 0.5, dtype=np.longdouble) y = np.exp(-x) yerr = 0.1 + 0.2*np.sqrt(x) xerr = 0.1 + yerr # First illustrate basic pyplot interface, using defaults where possible. fig = plt.figure() ax = fig.gca() ax.errorbar(x, y, xerr=0.2, yerr=0.4) ax.set_title("Simplest errorbars, 0.2 in x, 0.4 in y") # Now switch to a more OO interface to exercise more features. fig, axs = plt.subplots(nrows=2, ncols=2, sharex=True) ax = axs[0, 0] ax.errorbar(x, y, yerr=yerr, fmt='o') ax.set_title('Vert. symmetric') # With 4 subplots, reduce the number of axis ticks to avoid crowding. ax.locator_params(nbins=4) ax = axs[0, 1] ax.errorbar(x, y, xerr=xerr, fmt='o', alpha=0.4) ax.set_title('Hor. symmetric w/ alpha') ax = axs[1, 0] ax.errorbar(x, y, yerr=[yerr, 2*yerr], xerr=[xerr, 2*xerr], fmt='--o') ax.set_title('H, V asymmetric') ax = axs[1, 1] ax.set_yscale('log') # Here we have to be careful to keep all y values positive: ylower = np.maximum(1e-2, y - yerr) yerr_lower = y - ylower ax.errorbar(x, y, yerr=[yerr_lower, 2*yerr], xerr=xerr, fmt='o', ecolor='g', capthick=2) ax.set_title('Mixed sym., log y') # Force limits due to floating point slop potentially expanding the range ax.set_ylim(1e-2, 1e1) fig.suptitle('Variable errorbars') # Reuse the first testcase from above for a labeled data test data = {"x": x, "y": y} fig = plt.figure() ax = fig.gca() ax.errorbar("x", "y", xerr=0.2, yerr=0.4, data=data) ax.set_title("Simplest errorbars, 0.2 in x, 0.4 in y") @image_comparison(['mixed_errorbar_polar_caps'], extensions=['png'], remove_text=True) def test_mixed_errorbar_polar_caps(): """ Mix several polar errorbar use cases in a single test figure. It is advisable to position individual points off the grid. If there are problems with reproducibility of this test, consider removing grid. """ fig = plt.figure() ax = plt.subplot(111, projection='polar') # symmetric errorbars th_sym = [1, 2, 3] r_sym = [0.9]*3 ax.errorbar(th_sym, r_sym, xerr=0.35, yerr=0.2, fmt="o") # long errorbars th_long = [np.pi/2 + .1, np.pi + .1] r_long = [1.8, 2.2] ax.errorbar(th_long, r_long, xerr=0.8 * np.pi, yerr=0.15, fmt="o") # asymmetric errorbars th_asym = [4*np.pi/3 + .1, 5*np.pi/3 + .1, 2*np.pi-0.1] r_asym = [1.1]*3 xerr = [[.3, .3, .2], [.2, .3, .3]] yerr = [[.35, .5, .5], [.5, .35, .5]] ax.errorbar(th_asym, r_asym, xerr=xerr, yerr=yerr, fmt="o") # overlapping errorbar th_over = [2.1] r_over = [3.1] ax.errorbar(th_over, r_over, xerr=10, yerr=.2, fmt="o") def test_errorbar_colorcycle(): f, ax = plt.subplots() x = np.arange(10) y = 2*x e1, _, _ = ax.errorbar(x, y, c=None) e2, _, _ = ax.errorbar(x, 2*y, c=None) ln1, = ax.plot(x, 4*y) assert mcolors.to_rgba(e1.get_color()) == mcolors.to_rgba('C0') assert mcolors.to_rgba(e2.get_color()) == mcolors.to_rgba('C1') assert mcolors.to_rgba(ln1.get_color()) == mcolors.to_rgba('C2') @check_figures_equal() def test_errorbar_cycle_ecolor(fig_test, fig_ref): x = np.arange(0.1, 4, 0.5) y = [np.exp(-x+n) for n in range(4)] axt = fig_test.subplots() axr = fig_ref.subplots() for yi, color in zip(y, ['C0', 'C1', 'C2', 'C3']): axt.errorbar(x, yi, yerr=(yi * 0.25), linestyle='-', marker='o', ecolor='black') axr.errorbar(x, yi, yerr=(yi * 0.25), linestyle='-', marker='o', color=color, ecolor='black') def test_errorbar_shape(): fig = plt.figure() ax = fig.gca() x = np.arange(0.1, 4, 0.5) y = np.exp(-x) yerr1 = 0.1 + 0.2*np.sqrt(x) yerr = np.vstack((yerr1, 2*yerr1)).T xerr = 0.1 + yerr with pytest.raises(ValueError): ax.errorbar(x, y, yerr=yerr, fmt='o') with pytest.raises(ValueError): ax.errorbar(x, y, xerr=xerr, fmt='o') with pytest.raises(ValueError): ax.errorbar(x, y, yerr=yerr, xerr=xerr, fmt='o') @image_comparison(['errorbar_limits']) def test_errorbar_limits(): x = np.arange(0.5, 5.5, 0.5) y = np.exp(-x) xerr = 0.1 yerr = 0.2 ls = 'dotted' fig, ax = plt.subplots() # standard error bars ax.errorbar(x, y, xerr=xerr, yerr=yerr, ls=ls, color='blue') # including upper limits uplims = np.zeros_like(x) uplims[[1, 5, 9]] = True ax.errorbar(x, y+0.5, xerr=xerr, yerr=yerr, uplims=uplims, ls=ls, color='green') # including lower limits lolims = np.zeros_like(x) lolims[[2, 4, 8]] = True ax.errorbar(x, y+1.0, xerr=xerr, yerr=yerr, lolims=lolims, ls=ls, color='red') # including upper and lower limits ax.errorbar(x, y+1.5, marker='o', ms=8, xerr=xerr, yerr=yerr, lolims=lolims, uplims=uplims, ls=ls, color='magenta') # including xlower and xupper limits xerr = 0.2 yerr = np.full_like(x, 0.2) yerr[[3, 6]] = 0.3 xlolims = lolims xuplims = uplims lolims = np.zeros_like(x) uplims = np.zeros_like(x) lolims[[6]] = True uplims[[3]] = True ax.errorbar(x, y+2.1, marker='o', ms=8, xerr=xerr, yerr=yerr, xlolims=xlolims, xuplims=xuplims, uplims=uplims, lolims=lolims, ls='none', mec='blue', capsize=0, color='cyan') ax.set_xlim((0, 5.5)) ax.set_title('Errorbar upper and lower limits') def test_errorbar_nonefmt(): # Check that passing 'none' as a format still plots errorbars x = np.arange(5) y = np.arange(5) plotline, _, barlines = plt.errorbar(x, y, xerr=1, yerr=1, fmt='none') assert plotline is None for errbar in barlines: assert np.all(errbar.get_color() == mcolors.to_rgba('C0')) def test_errorbar_remove(): x = np.arange(5) y = np.arange(5) fig, ax = plt.subplots() ec = ax.errorbar(x, y, xerr=1, yerr=1) assert len(ax.containers) == 1 assert len(ax.lines) == 5 assert len(ax.collections) == 2 ec.remove() assert not ax.containers assert not ax.lines assert not ax.collections def test_errorbar_line_specific_kwargs(): # Check that passing line-specific keyword arguments will not result in # errors. x = np.arange(5) y = np.arange(5) plotline, _, _ = plt.errorbar(x, y, xerr=1, yerr=1, ls='None', marker='s', fillstyle='full', drawstyle='steps-mid', dash_capstyle='round', dash_joinstyle='miter', solid_capstyle='butt', solid_joinstyle='bevel') assert plotline.get_fillstyle() == 'full' assert plotline.get_drawstyle() == 'steps-mid' @check_figures_equal(extensions=['png']) def test_errorbar_with_prop_cycle(fig_test, fig_ref): ax = fig_ref.subplots() ax.errorbar(x=[2, 4, 10], y=[0, 1, 2], yerr=0.5, ls='--', marker='s', mfc='k') ax.errorbar(x=[2, 4, 10], y=[2, 3, 4], yerr=0.5, color='tab:green', ls=':', marker='s', mfc='y') ax.errorbar(x=[2, 4, 10], y=[4, 5, 6], yerr=0.5, fmt='tab:blue', ls='-.', marker='o', mfc='c') ax.set_xlim(1, 11) _cycle = cycler(ls=['--', ':', '-.'], marker=['s', 's', 'o'], mfc=['k', 'y', 'c'], color=['b', 'g', 'r']) plt.rc("axes", prop_cycle=_cycle) ax = fig_test.subplots() ax.errorbar(x=[2, 4, 10], y=[0, 1, 2], yerr=0.5) ax.errorbar(x=[2, 4, 10], y=[2, 3, 4], yerr=0.5, color='tab:green') ax.errorbar(x=[2, 4, 10], y=[4, 5, 6], yerr=0.5, fmt='tab:blue') ax.set_xlim(1, 11) def test_errorbar_every_invalid(): x = np.linspace(0, 1, 15) y = x * (1-x) yerr = y/6 ax = plt.figure().subplots() with pytest.raises(ValueError, match='not a tuple of two integers'): ax.errorbar(x, y, yerr, errorevery=(1, 2, 3)) with pytest.raises(ValueError, match='not a tuple of two integers'): ax.errorbar(x, y, yerr, errorevery=(1.3, 3)) with pytest.raises(ValueError, match='not a valid NumPy fancy index'): ax.errorbar(x, y, yerr, errorevery=[False, True]) with pytest.raises(ValueError, match='not a recognized value'): ax.errorbar(x, y, yerr, errorevery='foobar') def test_xerr_yerr_not_negative(): ax = plt.figure().subplots() with pytest.raises(ValueError, match="'xerr' must not contain negative values"): ax.errorbar(x=[0], y=[0], xerr=[[-0.5], [1]], yerr=[[-0.5], [1]]) with pytest.raises(ValueError, match="'xerr' must not contain negative values"): ax.errorbar(x=[0], y=[0], xerr=[[-0.5], [1]]) with pytest.raises(ValueError, match="'yerr' must not contain negative values"): ax.errorbar(x=[0], y=[0], yerr=[[-0.5], [1]]) with pytest.raises(ValueError, match="'yerr' must not contain negative values"): x = np.arange(5) y = [datetime.datetime(2021, 9, i * 2 + 1) for i in x] ax.errorbar(x=x, y=y, yerr=datetime.timedelta(days=-10)) def test_xerr_yerr_not_none(): ax = plt.figure().subplots() with pytest.raises(ValueError, match="'xerr' must not contain None"): ax.errorbar(x=[0], y=[0], xerr=[[None], [1]], yerr=[[None], [1]]) with pytest.raises(ValueError, match="'xerr' must not contain None"): ax.errorbar(x=[0], y=[0], xerr=[[None], [1]]) with pytest.raises(ValueError, match="'yerr' must not contain None"): ax.errorbar(x=[0], y=[0], yerr=[[None], [1]]) @check_figures_equal() def test_errorbar_every(fig_test, fig_ref): x = np.linspace(0, 1, 15) y = x * (1-x) yerr = y/6 ax_ref = fig_ref.subplots() ax_test = fig_test.subplots() for color, shift in zip('rgbk', [0, 0, 2, 7]): y += .02 # Check errorevery using an explicit offset and step. ax_test.errorbar(x, y, yerr, errorevery=(shift, 4), capsize=4, c=color) # Using manual errorbars # n.b. errorbar draws the main plot at z=2.1 by default ax_ref.plot(x, y, c=color, zorder=2.1) ax_ref.errorbar(x[shift::4], y[shift::4], yerr[shift::4], capsize=4, c=color, fmt='none') # Check that markevery is propagated to line, without affecting errorbars. ax_test.errorbar(x, y + 0.1, yerr, markevery=(1, 4), capsize=4, fmt='o') ax_ref.plot(x[1::4], y[1::4] + 0.1, 'o', zorder=2.1) ax_ref.errorbar(x, y + 0.1, yerr, capsize=4, fmt='none') # Check that passing a slice to markevery/errorevery works. ax_test.errorbar(x, y + 0.2, yerr, errorevery=slice(2, None, 3), markevery=slice(2, None, 3), capsize=4, c='C0', fmt='o') ax_ref.plot(x[2::3], y[2::3] + 0.2, 'o', c='C0', zorder=2.1) ax_ref.errorbar(x[2::3], y[2::3] + 0.2, yerr[2::3], capsize=4, c='C0', fmt='none') # Check that passing an iterable to markevery/errorevery works. ax_test.errorbar(x, y + 0.2, yerr, errorevery=[False, True, False] * 5, markevery=[False, True, False] * 5, capsize=4, c='C1', fmt='o') ax_ref.plot(x[1::3], y[1::3] + 0.2, 'o', c='C1', zorder=2.1) ax_ref.errorbar(x[1::3], y[1::3] + 0.2, yerr[1::3], capsize=4, c='C1', fmt='none') @pytest.mark.parametrize('elinewidth', [[1, 2, 3], np.array([1, 2, 3]), 1]) def test_errorbar_linewidth_type(elinewidth): plt.errorbar([1, 2, 3], [1, 2, 3], yerr=[1, 2, 3], elinewidth=elinewidth) @check_figures_equal(extensions=["png"]) def test_errorbar_nan(fig_test, fig_ref): ax = fig_test.add_subplot() xs = range(5) ys = np.array([1, 2, np.nan, np.nan, 3]) es = np.array([4, 5, np.nan, np.nan, 6]) ax.errorbar(xs, ys, es) ax = fig_ref.add_subplot() ax.errorbar([0, 1], [1, 2], [4, 5]) ax.errorbar([4], [3], [6], fmt="C0") @image_comparison(['hist_stacked_stepfilled', 'hist_stacked_stepfilled']) def test_hist_stacked_stepfilled(): # make some data d1 = np.linspace(1, 3, 20) d2 = np.linspace(0, 10, 50) fig, ax = plt.subplots() ax.hist((d1, d2), histtype="stepfilled", stacked=True) # Reuse testcase from above for a labeled data test data = {"x": (d1, d2)} fig, ax = plt.subplots() ax.hist("x", histtype="stepfilled", stacked=True, data=data) @image_comparison(['hist_offset']) def test_hist_offset(): # make some data d1 = np.linspace(0, 10, 50) d2 = np.linspace(1, 3, 20) fig, ax = plt.subplots() ax.hist(d1, bottom=5) ax.hist(d2, bottom=15) @image_comparison(['hist_step.png'], remove_text=True) def test_hist_step(): # make some data d1 = np.linspace(1, 3, 20) fig, ax = plt.subplots() ax.hist(d1, histtype="step") ax.set_ylim(0, 10) ax.set_xlim(-1, 5) @image_comparison(['hist_step_horiz.png']) def test_hist_step_horiz(): # make some data d1 = np.linspace(0, 10, 50) d2 = np.linspace(1, 3, 20) fig, ax = plt.subplots() ax.hist((d1, d2), histtype="step", orientation="horizontal") @image_comparison(['hist_stacked_weights']) def test_hist_stacked_weighted(): # make some data d1 = np.linspace(0, 10, 50) d2 = np.linspace(1, 3, 20) w1 = np.linspace(0.01, 3.5, 50) w2 = np.linspace(0.05, 2., 20) fig, ax = plt.subplots() ax.hist((d1, d2), weights=(w1, w2), histtype="stepfilled", stacked=True) @image_comparison(['stem.png'], style='mpl20', remove_text=True) def test_stem(): x = np.linspace(0.1, 2 * np.pi, 100) fig, ax = plt.subplots() # Label is a single space to force a legend to be drawn, but to avoid any # text being drawn ax.stem(x, np.cos(x), linefmt='C2-.', markerfmt='k+', basefmt='C1-.', label=' ') ax.legend() def test_stem_args(): """Test that stem() correctly identifies x and y values.""" def _assert_equal(stem_container, expected): x, y = map(list, stem_container.markerline.get_data()) assert x == expected[0] assert y == expected[1] fig, ax = plt.subplots() x = [1, 3, 5] y = [9, 8, 7] # Test the call signatures _assert_equal(ax.stem(y), expected=([0, 1, 2], y)) _assert_equal(ax.stem(x, y), expected=(x, y)) _assert_equal(ax.stem(x, y, linefmt='r--'), expected=(x, y)) _assert_equal(ax.stem(x, y, 'r--'), expected=(x, y)) _assert_equal(ax.stem(x, y, linefmt='r--', basefmt='b--'), expected=(x, y)) _assert_equal(ax.stem(y, linefmt='r--'), expected=([0, 1, 2], y)) _assert_equal(ax.stem(y, 'r--'), expected=([0, 1, 2], y)) def test_stem_markerfmt(): """Test that stem(..., markerfmt=...) produces the intended markers.""" def _assert_equal(stem_container, linecolor=None, markercolor=None, marker=None): """ Check that the given StemContainer has the properties listed as keyword-arguments. """ if linecolor is not None: assert mcolors.same_color( stem_container.stemlines.get_color(), linecolor) if markercolor is not None: assert mcolors.same_color( stem_container.markerline.get_color(), markercolor) if marker is not None: assert stem_container.markerline.get_marker() == marker assert stem_container.markerline.get_linestyle() == 'None' fig, ax = plt.subplots() x = [1, 3, 5] y = [9, 8, 7] # no linefmt _assert_equal(ax.stem(x, y), markercolor='C0', marker='o') _assert_equal(ax.stem(x, y, markerfmt='x'), markercolor='C0', marker='x') _assert_equal(ax.stem(x, y, markerfmt='rx'), markercolor='r', marker='x') # positional linefmt _assert_equal( ax.stem(x, y, 'r'), # marker color follows linefmt if not given linecolor='r', markercolor='r', marker='o') _assert_equal( ax.stem(x, y, 'rx'), # the marker is currently not taken from linefmt linecolor='r', markercolor='r', marker='o') _assert_equal( ax.stem(x, y, 'r', markerfmt='x'), # only marker type specified linecolor='r', markercolor='r', marker='x') _assert_equal( ax.stem(x, y, 'r', markerfmt='g'), # only marker color specified linecolor='r', markercolor='g', marker='o') _assert_equal( ax.stem(x, y, 'r', markerfmt='gx'), # marker type and color specified linecolor='r', markercolor='g', marker='x') _assert_equal( ax.stem(x, y, 'r', markerfmt=' '), # markerfmt=' ' for no marker linecolor='r', markercolor='r', marker='None') _assert_equal( ax.stem(x, y, 'r', markerfmt=''), # markerfmt='' for no marker linecolor='r', markercolor='r', marker='None') # with linefmt kwarg _assert_equal( ax.stem(x, y, linefmt='r'), linecolor='r', markercolor='r', marker='o') _assert_equal( ax.stem(x, y, linefmt='r', markerfmt='x'), linecolor='r', markercolor='r', marker='x') _assert_equal( ax.stem(x, y, linefmt='r', markerfmt='gx'), linecolor='r', markercolor='g', marker='x') def test_stem_dates(): fig, ax = plt.subplots(1, 1) xs = [dateutil.parser.parse("2013-9-28 11:00:00"), dateutil.parser.parse("2013-9-28 12:00:00")] ys = [100, 200] ax.stem(xs, ys) @image_comparison(['stem_orientation.png'], style='mpl20', remove_text=True) def test_stem_orientation(): x = np.linspace(0.1, 2*np.pi, 50) fig, ax = plt.subplots() ax.stem(x, np.cos(x), linefmt='C2-.', markerfmt='kx', basefmt='C1-.', orientation='horizontal') @image_comparison(['hist_stacked_stepfilled_alpha']) def test_hist_stacked_stepfilled_alpha(): # make some data d1 = np.linspace(1, 3, 20) d2 = np.linspace(0, 10, 50) fig, ax = plt.subplots() ax.hist((d1, d2), histtype="stepfilled", stacked=True, alpha=0.5) @image_comparison(['hist_stacked_step']) def test_hist_stacked_step(): # make some data d1 = np.linspace(1, 3, 20) d2 = np.linspace(0, 10, 50) fig, ax = plt.subplots() ax.hist((d1, d2), histtype="step", stacked=True) @image_comparison(['hist_stacked_normed']) def test_hist_stacked_density(): # make some data d1 = np.linspace(1, 3, 20) d2 = np.linspace(0, 10, 50) fig, ax = plt.subplots() ax.hist((d1, d2), stacked=True, density=True) @image_comparison(['hist_step_bottom.png'], remove_text=True) def test_hist_step_bottom(): # make some data d1 = np.linspace(1, 3, 20) fig, ax = plt.subplots() ax.hist(d1, bottom=np.arange(10), histtype="stepfilled") def test_hist_step_geometry(): bins = [0, 1, 2, 3] data = [0, 0, 1, 1, 1, 2] top = [[0, 0], [0, 2], [1, 2], [1, 3], [2, 3], [2, 1], [3, 1], [3, 0]] bottom = [[2, 0], [2, 0], [1, 0], [1, 0], [0, 0]] for histtype, xy in [('step', top), ('stepfilled', top + bottom)]: _, _, (polygon, ) = plt.hist(data, bins=bins, histtype=histtype) assert_array_equal(polygon.get_xy(), xy) def test_hist_step_bottom_geometry(): bins = [0, 1, 2, 3] data = [0, 0, 1, 1, 1, 2] top = [[0, 1], [0, 3], [1, 3], [1, 5], [2, 5], [2, 2.5], [3, 2.5], [3, 1.5]] bottom = [[2, 1.5], [2, 2], [1, 2], [1, 1], [0, 1]] for histtype, xy in [('step', top), ('stepfilled', top + bottom)]: _, _, (polygon, ) = plt.hist(data, bins=bins, bottom=[1, 2, 1.5], histtype=histtype) assert_array_equal(polygon.get_xy(), xy) def test_hist_stacked_step_geometry(): bins = [0, 1, 2, 3] data_1 = [0, 0, 1, 1, 1, 2] data_2 = [0, 1, 2] tops = [ [[0, 0], [0, 2], [1, 2], [1, 3], [2, 3], [2, 1], [3, 1], [3, 0]], [[0, 2], [0, 3], [1, 3], [1, 4], [2, 4], [2, 2], [3, 2], [3, 1]], ] bottoms = [ [[2, 0], [2, 0], [1, 0], [1, 0], [0, 0]], [[2, 1], [2, 3], [1, 3], [1, 2], [0, 2]], ] combined = [t + b for t, b in zip(tops, bottoms)] for histtype, xy in [('step', tops), ('stepfilled', combined)]: _, _, patches = plt.hist([data_1, data_2], bins=bins, stacked=True, histtype=histtype) assert len(patches) == 2 polygon, = patches[0] assert_array_equal(polygon.get_xy(), xy[0]) polygon, = patches[1] assert_array_equal(polygon.get_xy(), xy[1]) def test_hist_stacked_step_bottom_geometry(): bins = [0, 1, 2, 3] data_1 = [0, 0, 1, 1, 1, 2] data_2 = [0, 1, 2] tops = [ [[0, 1], [0, 3], [1, 3], [1, 5], [2, 5], [2, 2.5], [3, 2.5], [3, 1.5]], [[0, 3], [0, 4], [1, 4], [1, 6], [2, 6], [2, 3.5], [3, 3.5], [3, 2.5]], ] bottoms = [ [[2, 1.5], [2, 2], [1, 2], [1, 1], [0, 1]], [[2, 2.5], [2, 5], [1, 5], [1, 3], [0, 3]], ] combined = [t + b for t, b in zip(tops, bottoms)] for histtype, xy in [('step', tops), ('stepfilled', combined)]: _, _, patches = plt.hist([data_1, data_2], bins=bins, stacked=True, bottom=[1, 2, 1.5], histtype=histtype) assert len(patches) == 2 polygon, = patches[0] assert_array_equal(polygon.get_xy(), xy[0]) polygon, = patches[1] assert_array_equal(polygon.get_xy(), xy[1]) @image_comparison(['hist_stacked_bar']) def test_hist_stacked_bar(): # make some data d = [[100, 100, 100, 100, 200, 320, 450, 80, 20, 600, 310, 800], [20, 23, 50, 11, 100, 420], [120, 120, 120, 140, 140, 150, 180], [60, 60, 60, 60, 300, 300, 5, 5, 5, 5, 10, 300], [555, 555, 555, 30, 30, 30, 30, 30, 100, 100, 100, 100, 30, 30], [30, 30, 30, 30, 400, 400, 400, 400, 400, 400, 400, 400]] colors = [(0.5759849696758961, 1.0, 0.0), (0.0, 1.0, 0.350624650815206), (0.0, 1.0, 0.6549834156005998), (0.0, 0.6569064625276622, 1.0), (0.28302699607823545, 0.0, 1.0), (0.6849123462299822, 0.0, 1.0)] labels = ['green', 'orange', ' yellow', 'magenta', 'black'] fig, ax = plt.subplots() ax.hist(d, bins=10, histtype='barstacked', align='mid', color=colors, label=labels) ax.legend(loc='upper right', bbox_to_anchor=(1.0, 1.0), ncols=1) @pytest.mark.parametrize('kwargs', ({'facecolor': ["b", "g", "r"]}, {'edgecolor': ["b", "g", "r"]}, {'hatch': ["/", "\\", "."]}, {'linestyle': ["-", "--", ":"]}, {'linewidth': [1, 1.5, 2]}, {'color': ["b", "g", "r"]})) @check_figures_equal(extensions=["png"]) def test_hist_vectorized_params(fig_test, fig_ref, kwargs): np.random.seed(19680801) xs = [np.random.randn(n) for n in [20, 50, 100]] (axt1, axt2) = fig_test.subplots(2) (axr1, axr2) = fig_ref.subplots(2) for histtype, axt, axr in [("stepfilled", axt1, axr1), ("step", axt2, axr2)]: _, bins, _ = axt.hist(xs, bins=10, histtype=histtype, **kwargs) kw, values = next(iter(kwargs.items())) for i, (x, value) in enumerate(zip(xs, values)): axr.hist(x, bins=bins, histtype=histtype, **{kw: value}, zorder=(len(xs)-i)/2) @pytest.mark.parametrize('kwargs, patch_face, patch_edge', # 'C0'(blue) stands for the first color of the # default color cycle as well as the patch.facecolor rcParam # When the expected edgecolor is 'k'(black), # it corresponds to the patch.edgecolor rcParam [({'histtype': 'stepfilled', 'color': 'r', 'facecolor': 'y', 'edgecolor': 'g'}, 'y', 'g'), ({'histtype': 'step', 'color': 'r', 'facecolor': 'y', 'edgecolor': 'g'}, ('y', 0), 'g'), ({'histtype': 'stepfilled', 'color': 'r', 'edgecolor': 'g'}, 'r', 'g'), ({'histtype': 'step', 'color': 'r', 'edgecolor': 'g'}, ('r', 0), 'g'), ({'histtype': 'stepfilled', 'color': 'r', 'facecolor': 'y'}, 'y', 'k'), ({'histtype': 'step', 'color': 'r', 'facecolor': 'y'}, ('y', 0), 'r'), ({'histtype': 'stepfilled', 'facecolor': 'y', 'edgecolor': 'g'}, 'y', 'g'), ({'histtype': 'step', 'facecolor': 'y', 'edgecolor': 'g'}, ('y', 0), 'g'), ({'histtype': 'stepfilled', 'color': 'r'}, 'r', 'k'), ({'histtype': 'step', 'color': 'r'}, ('r', 0), 'r'), ({'histtype': 'stepfilled', 'facecolor': 'y'}, 'y', 'k'), ({'histtype': 'step', 'facecolor': 'y'}, ('y', 0), 'C0'), ({'histtype': 'stepfilled', 'edgecolor': 'g'}, 'C0', 'g'), ({'histtype': 'step', 'edgecolor': 'g'}, ('C0', 0), 'g'), ({'histtype': 'stepfilled'}, 'C0', 'k'), ({'histtype': 'step'}, ('C0', 0), 'C0')]) def test_hist_color_semantics(kwargs, patch_face, patch_edge): _, _, patches = plt.figure().subplots().hist([1, 2, 3], **kwargs) assert all(mcolors.same_color([p.get_facecolor(), p.get_edgecolor()], [patch_face, patch_edge]) for p in patches) def test_hist_barstacked_bottom_unchanged(): b = np.array([10, 20]) plt.hist([[0, 1], [0, 1]], 2, histtype="barstacked", bottom=b) assert b.tolist() == [10, 20] def test_hist_emptydata(): fig, ax = plt.subplots() ax.hist([[], range(10), range(10)], histtype="step") def test_hist_unused_labels(): # When a list with one dataset and N elements is provided and N labels, ensure # that the first label is used for the dataset and all other labels are ignored fig, ax = plt.subplots() ax.hist([[1, 2, 3]], label=["values", "unused", "also unused"]) _, labels = ax.get_legend_handles_labels() assert labels == ["values"] def test_hist_labels(): # test singleton labels OK fig, ax = plt.subplots() _, _, bars = ax.hist([0, 1], label=0) assert bars[0].get_label() == '0' _, _, bars = ax.hist([0, 1], label=[0]) assert bars[0].get_label() == '0' _, _, bars = ax.hist([0, 1], label=None) assert bars[0].get_label() == '_nolegend_' _, _, bars = ax.hist([0, 1], label='0') assert bars[0].get_label() == '0' _, _, bars = ax.hist([0, 1], label='00') assert bars[0].get_label() == '00' @image_comparison(['transparent_markers'], remove_text=True) def test_transparent_markers(): np.random.seed(0) data = np.random.random(50) fig, ax = plt.subplots() ax.plot(data, 'D', mfc='none', markersize=100) @image_comparison(['rgba_markers'], remove_text=True) def test_rgba_markers(): fig, axs = plt.subplots(ncols=2) rcolors = [(1, 0, 0, 1), (1, 0, 0, 0.5)] bcolors = [(0, 0, 1, 1), (0, 0, 1, 0.5)] alphas = [None, 0.2] kw = dict(ms=100, mew=20) for i, alpha in enumerate(alphas): for j, rcolor in enumerate(rcolors): for k, bcolor in enumerate(bcolors): axs[i].plot(j+1, k+1, 'o', mfc=bcolor, mec=rcolor, alpha=alpha, **kw) axs[i].plot(j+1, k+3, 'x', mec=rcolor, alpha=alpha, **kw) for ax in axs: ax.axis([-1, 4, 0, 5]) @image_comparison(['mollweide_grid'], remove_text=True) def test_mollweide_grid(): # test that both horizontal and vertical gridlines appear on the Mollweide # projection fig = plt.figure() ax = fig.add_subplot(projection='mollweide') ax.grid() def test_mollweide_forward_inverse_closure(): # test that the round-trip Mollweide forward->inverse transformation is an # approximate identity fig = plt.figure() ax = fig.add_subplot(projection='mollweide') # set up 1-degree grid in longitude, latitude lon = np.linspace(-np.pi, np.pi, 360) # The poles are degenerate and thus sensitive to floating point precision errors lat = np.linspace(-np.pi / 2.0, np.pi / 2.0, 180)[1:-1] lon, lat = np.meshgrid(lon, lat) ll = np.vstack((lon.flatten(), lat.flatten())).T # perform forward transform xy = ax.transProjection.transform(ll) # perform inverse transform ll2 = ax.transProjection.inverted().transform(xy) # compare np.testing.assert_array_almost_equal(ll, ll2, 3) def test_mollweide_inverse_forward_closure(): # test that the round-trip Mollweide inverse->forward transformation is an # approximate identity fig = plt.figure() ax = fig.add_subplot(projection='mollweide') # set up grid in x, y x = np.linspace(0, 1, 500) x, y = np.meshgrid(x, x) xy = np.vstack((x.flatten(), y.flatten())).T # perform inverse transform ll = ax.transProjection.inverted().transform(xy) # perform forward transform xy2 = ax.transProjection.transform(ll) # compare np.testing.assert_array_almost_equal(xy, xy2, 3) @image_comparison(['test_alpha'], remove_text=True) def test_alpha(): np.random.seed(0) data = np.random.random(50) fig, ax = plt.subplots() # alpha=.5 markers, solid line ax.plot(data, '-D', color=[1, 0, 0], mfc=[1, 0, 0, .5], markersize=20, lw=10) # everything solid by kwarg ax.plot(data + 2, '-D', color=[1, 0, 0, .5], mfc=[1, 0, 0, .5], markersize=20, lw=10, alpha=1) # everything alpha=.5 by kwarg ax.plot(data + 4, '-D', color=[1, 0, 0], mfc=[1, 0, 0], markersize=20, lw=10, alpha=.5) # everything alpha=.5 by colors ax.plot(data + 6, '-D', color=[1, 0, 0, .5], mfc=[1, 0, 0, .5], markersize=20, lw=10) # alpha=.5 line, solid markers ax.plot(data + 8, '-D', color=[1, 0, 0, .5], mfc=[1, 0, 0], markersize=20, lw=10) @image_comparison(['eventplot', 'eventplot'], remove_text=True) def test_eventplot(): np.random.seed(0) data1 = np.random.random([32, 20]).tolist() data2 = np.random.random([6, 20]).tolist() data = data1 + data2 num_datasets = len(data) colors1 = [[0, 1, .7]] * len(data1) colors2 = [[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, .75, 0], [1, 0, 1], [0, 1, 1]] colors = colors1 + colors2 lineoffsets1 = 12 + np.arange(0, len(data1)) * .33 lineoffsets2 = [-15, -3, 1, 1.5, 6, 10] lineoffsets = lineoffsets1.tolist() + lineoffsets2 linelengths1 = [.33] * len(data1) linelengths2 = [5, 2, 1, 1, 3, 1.5] linelengths = linelengths1 + linelengths2 fig = plt.figure() axobj = fig.add_subplot() colls = axobj.eventplot(data, colors=colors, lineoffsets=lineoffsets, linelengths=linelengths) num_collections = len(colls) assert num_collections == num_datasets # Reuse testcase from above for a labeled data test data = {"pos": data, "c": colors, "lo": lineoffsets, "ll": linelengths} fig = plt.figure() axobj = fig.add_subplot() colls = axobj.eventplot("pos", colors="c", lineoffsets="lo", linelengths="ll", data=data) num_collections = len(colls) assert num_collections == num_datasets @image_comparison(['test_eventplot_defaults.png'], remove_text=True) def test_eventplot_defaults(): """ test that eventplot produces the correct output given the default params (see bug #3728) """ np.random.seed(0) data1 = np.random.random([32, 20]).tolist() data2 = np.random.random([6, 20]).tolist() data = data1 + data2 fig = plt.figure() axobj = fig.add_subplot() axobj.eventplot(data) @pytest.mark.parametrize(('colors'), [ ('0.5',), # string color with multiple characters: not OK before #8193 fix ('tab:orange', 'tab:pink', 'tab:cyan', 'bLacK'), # case-insensitive ('red', (0, 1, 0), None, (1, 0, 1, 0.5)), # a tricky case mixing types ]) def test_eventplot_colors(colors): """Test the *colors* parameter of eventplot. Inspired by issue #8193.""" data = [[0], [1], [2], [3]] # 4 successive events of different nature # Build the list of the expected colors expected = [c if c is not None else 'C0' for c in colors] # Convert the list into an array of RGBA values # NB: ['rgbk'] is not a valid argument for to_rgba_array, while 'rgbk' is. if len(expected) == 1: expected = expected[0] expected = np.broadcast_to(mcolors.to_rgba_array(expected), (len(data), 4)) fig, ax = plt.subplots() if len(colors) == 1: # tuple with a single string (like '0.5' or 'rgbk') colors = colors[0] collections = ax.eventplot(data, colors=colors) for coll, color in zip(collections, expected): assert_allclose(coll.get_color(), color) def test_eventplot_alpha(): fig, ax = plt.subplots() # one alpha for all collections = ax.eventplot([[0, 2, 4], [1, 3, 5, 7]], alpha=0.7) assert collections[0].get_alpha() == 0.7 assert collections[1].get_alpha() == 0.7 # one alpha per collection collections = ax.eventplot([[0, 2, 4], [1, 3, 5, 7]], alpha=[0.5, 0.7]) assert collections[0].get_alpha() == 0.5 assert collections[1].get_alpha() == 0.7 with pytest.raises(ValueError, match="alpha and positions are unequal"): ax.eventplot([[0, 2, 4], [1, 3, 5, 7]], alpha=[0.5, 0.7, 0.9]) with pytest.raises(ValueError, match="alpha and positions are unequal"): ax.eventplot([0, 2, 4], alpha=[0.5, 0.7]) @image_comparison(['test_eventplot_problem_kwargs.png'], remove_text=True) def test_eventplot_problem_kwargs(recwarn): """ test that 'singular' versions of LineCollection props raise an MatplotlibDeprecationWarning rather than overriding the 'plural' versions (e.g., to prevent 'color' from overriding 'colors', see issue #4297) """ np.random.seed(0) data1 = np.random.random([20]).tolist() data2 = np.random.random([10]).tolist() data = [data1, data2] fig = plt.figure() axobj = fig.add_subplot() axobj.eventplot(data, colors=['r', 'b'], color=['c', 'm'], linewidths=[2, 1], linewidth=[1, 2], linestyles=['solid', 'dashed'], linestyle=['dashdot', 'dotted']) assert len(recwarn) == 3 assert all(issubclass(wi.category, mpl.MatplotlibDeprecationWarning) for wi in recwarn) def test_empty_eventplot(): fig, ax = plt.subplots(1, 1) ax.eventplot([[]], colors=[(0.0, 0.0, 0.0, 0.0)]) plt.draw() @pytest.mark.parametrize('data', [[[]], [[], [0, 1]], [[0, 1], []]]) @pytest.mark.parametrize('orientation', [None, 'vertical', 'horizontal']) def test_eventplot_orientation(data, orientation): """Introduced when fixing issue #6412.""" opts = {} if orientation is None else {'orientation': orientation} fig, ax = plt.subplots(1, 1) ax.eventplot(data, **opts) plt.draw() @check_figures_equal(extensions=['png']) def test_eventplot_units_list(fig_test, fig_ref): # test that list of lists converted properly: ts_1 = [datetime.datetime(2021, 1, 1), datetime.datetime(2021, 1, 2), datetime.datetime(2021, 1, 3)] ts_2 = [datetime.datetime(2021, 1, 15), datetime.datetime(2021, 1, 16)] ax = fig_ref.subplots() ax.eventplot(ts_1, lineoffsets=0) ax.eventplot(ts_2, lineoffsets=1) ax = fig_test.subplots() ax.eventplot([ts_1, ts_2]) @image_comparison(['marker_styles.png'], remove_text=True) def test_marker_styles(): fig, ax = plt.subplots() # Since generation of the test image, None was removed but 'none' was # added. By moving 'none' to the front (=former sorted place of None) # we can avoid regenerating the test image. This can be removed if the # test image has to be regenerated for other reasons. markers = sorted(matplotlib.markers.MarkerStyle.markers, key=lambda x: str(type(x))+str(x)) markers.remove('none') markers = ['none', *markers] for y, marker in enumerate(markers): ax.plot((y % 2)*5 + np.arange(10)*10, np.ones(10)*10*y, linestyle='', marker=marker, markersize=10+y/5, label=marker) @image_comparison(['rc_markerfill.png'], tol=0 if platform.machine() == 'x86_64' else 0.037) def test_markers_fillstyle_rcparams(): fig, ax = plt.subplots() x = np.arange(7) for idx, (style, marker) in enumerate( [('top', 's'), ('bottom', 'o'), ('none', '^')]): matplotlib.rcParams['markers.fillstyle'] = style ax.plot(x+idx, marker=marker) @image_comparison(['vertex_markers.png'], remove_text=True) def test_vertex_markers(): data = list(range(10)) marker_as_tuple = ((-1, -1), (1, -1), (1, 1), (-1, 1)) marker_as_list = [(-1, -1), (1, -1), (1, 1), (-1, 1)] fig, ax = plt.subplots() ax.plot(data, linestyle='', marker=marker_as_tuple, mfc='k') ax.plot(data[::-1], linestyle='', marker=marker_as_list, mfc='b') ax.set_xlim([-1, 10]) ax.set_ylim([-1, 10]) @image_comparison(['vline_hline_zorder', 'errorbar_zorder'], tol=0 if platform.machine() == 'x86_64' else 0.026) def test_eb_line_zorder(): x = list(range(10)) # First illustrate basic pyplot interface, using defaults where possible. fig = plt.figure() ax = fig.gca() ax.plot(x, lw=10, zorder=5) ax.axhline(1, color='red', lw=10, zorder=1) ax.axhline(5, color='green', lw=10, zorder=10) ax.axvline(7, color='m', lw=10, zorder=7) ax.axvline(2, color='k', lw=10, zorder=3) ax.set_title("axvline and axhline zorder test") # Now switch to a more OO interface to exercise more features. fig = plt.figure() ax = fig.gca() x = list(range(10)) y = np.zeros(10) yerr = list(range(10)) ax.errorbar(x, y, yerr=yerr, zorder=5, lw=5, color='r') for j in range(10): ax.axhline(j, lw=5, color='k', zorder=j) ax.axhline(-j, lw=5, color='k', zorder=j) ax.set_title("errorbar zorder test") @check_figures_equal() def test_axline_loglog(fig_test, fig_ref): ax = fig_test.subplots() ax.set(xlim=(0.1, 10), ylim=(1e-3, 1)) ax.loglog([.3, .6], [.3, .6], ".-") ax.axline((1, 1e-3), (10, 1e-2), c="k") ax = fig_ref.subplots() ax.set(xlim=(0.1, 10), ylim=(1e-3, 1)) ax.loglog([.3, .6], [.3, .6], ".-") ax.loglog([1, 10], [1e-3, 1e-2], c="k") @check_figures_equal() def test_axline(fig_test, fig_ref): ax = fig_test.subplots() ax.set(xlim=(-1, 1), ylim=(-1, 1)) ax.axline((0, 0), (1, 1)) ax.axline((0, 0), (1, 0), color='C1') ax.axline((0, 0.5), (1, 0.5), color='C2') # slopes ax.axline((-0.7, -0.5), slope=0, color='C3') ax.axline((1, -0.5), slope=-0.5, color='C4') ax.axline((-0.5, 1), slope=float('inf'), color='C5') ax = fig_ref.subplots() ax.set(xlim=(-1, 1), ylim=(-1, 1)) ax.plot([-1, 1], [-1, 1]) ax.axhline(0, color='C1') ax.axhline(0.5, color='C2') # slopes ax.axhline(-0.5, color='C3') ax.plot([-1, 1], [0.5, -0.5], color='C4') ax.axvline(-0.5, color='C5') @check_figures_equal() def test_axline_transaxes(fig_test, fig_ref): ax = fig_test.subplots() ax.set(xlim=(-1, 1), ylim=(-1, 1)) ax.axline((0, 0), slope=1, transform=ax.transAxes) ax.axline((1, 0.5), slope=1, color='C1', transform=ax.transAxes) ax.axline((0.5, 0.5), slope=0, color='C2', transform=ax.transAxes) ax.axline((0.5, 0), (0.5, 1), color='C3', transform=ax.transAxes) ax = fig_ref.subplots() ax.set(xlim=(-1, 1), ylim=(-1, 1)) ax.plot([-1, 1], [-1, 1]) ax.plot([0, 1], [-1, 0], color='C1') ax.plot([-1, 1], [0, 0], color='C2') ax.plot([0, 0], [-1, 1], color='C3') @check_figures_equal() def test_axline_transaxes_panzoom(fig_test, fig_ref): # test that it is robust against pan/zoom and # figure resize after plotting ax = fig_test.subplots() ax.set(xlim=(-1, 1), ylim=(-1, 1)) ax.axline((0, 0), slope=1, transform=ax.transAxes) ax.axline((0.5, 0.5), slope=2, color='C1', transform=ax.transAxes) ax.axline((0.5, 0.5), slope=0, color='C2', transform=ax.transAxes) ax.set(xlim=(0, 5), ylim=(0, 10)) fig_test.set_size_inches(3, 3) ax = fig_ref.subplots() ax.set(xlim=(0, 5), ylim=(0, 10)) fig_ref.set_size_inches(3, 3) ax.plot([0, 5], [0, 5]) ax.plot([0, 5], [0, 10], color='C1') ax.plot([0, 5], [5, 5], color='C2') def test_axline_args(): """Exactly one of *xy2* and *slope* must be specified.""" fig, ax = plt.subplots() with pytest.raises(TypeError): ax.axline((0, 0)) # missing second parameter with pytest.raises(TypeError): ax.axline((0, 0), (1, 1), slope=1) # redundant parameters ax.set_xscale('log') with pytest.raises(TypeError): ax.axline((0, 0), slope=1) ax.set_xscale('linear') ax.set_yscale('log') with pytest.raises(TypeError): ax.axline((0, 0), slope=1) ax.set_yscale('linear') with pytest.raises(ValueError): ax.axline((0, 0), (0, 0)) # two identical points are not allowed plt.draw() @image_comparison(['vlines_basic', 'vlines_with_nan', 'vlines_masked'], extensions=['png']) def test_vlines(): # normal x1 = [2, 3, 4, 5, 7] y1 = [2, -6, 3, 8, 2] fig1, ax1 = plt.subplots() ax1.vlines(x1, 0, y1, colors='g', linewidth=5) # GH #7406 x2 = [2, 3, 4, 5, 6, 7] y2 = [2, -6, 3, 8, np.nan, 2] fig2, (ax2, ax3, ax4) = plt.subplots(nrows=3, figsize=(4, 8)) ax2.vlines(x2, 0, y2, colors='g', linewidth=5) x3 = [2, 3, 4, 5, 6, 7] y3 = [np.nan, 2, -6, 3, 8, 2] ax3.vlines(x3, 0, y3, colors='r', linewidth=3, linestyle='--') x4 = [2, 3, 4, 5, 6, 7] y4 = [np.nan, 2, -6, 3, 8, np.nan] ax4.vlines(x4, 0, y4, colors='k', linewidth=2) # tweak the x-axis so we can see the lines better for ax in [ax1, ax2, ax3, ax4]: ax.set_xlim(0, 10) # check that the y-lims are all automatically the same assert ax1.get_ylim() == ax2.get_ylim() assert ax1.get_ylim() == ax3.get_ylim() assert ax1.get_ylim() == ax4.get_ylim() fig3, ax5 = plt.subplots() x5 = np.ma.masked_equal([2, 4, 6, 8, 10, 12], 8) ymin5 = np.ma.masked_equal([0, 1, -1, 0, 2, 1], 2) ymax5 = np.ma.masked_equal([13, 14, 15, 16, 17, 18], 18) ax5.vlines(x5, ymin5, ymax5, colors='k', linewidth=2) ax5.set_xlim(0, 15) def test_vlines_default(): fig, ax = plt.subplots() with mpl.rc_context({'lines.color': 'red'}): lines = ax.vlines(0.5, 0, 1) assert mpl.colors.same_color(lines.get_color(), 'red') @image_comparison(['hlines_basic', 'hlines_with_nan', 'hlines_masked'], extensions=['png']) def test_hlines(): # normal y1 = [2, 3, 4, 5, 7] x1 = [2, -6, 3, 8, 2] fig1, ax1 = plt.subplots() ax1.hlines(y1, 0, x1, colors='g', linewidth=5) # GH #7406 y2 = [2, 3, 4, 5, 6, 7] x2 = [2, -6, 3, 8, np.nan, 2] fig2, (ax2, ax3, ax4) = plt.subplots(nrows=3, figsize=(4, 8)) ax2.hlines(y2, 0, x2, colors='g', linewidth=5) y3 = [2, 3, 4, 5, 6, 7] x3 = [np.nan, 2, -6, 3, 8, 2] ax3.hlines(y3, 0, x3, colors='r', linewidth=3, linestyle='--') y4 = [2, 3, 4, 5, 6, 7] x4 = [np.nan, 2, -6, 3, 8, np.nan] ax4.hlines(y4, 0, x4, colors='k', linewidth=2) # tweak the y-axis so we can see the lines better for ax in [ax1, ax2, ax3, ax4]: ax.set_ylim(0, 10) # check that the x-lims are all automatically the same assert ax1.get_xlim() == ax2.get_xlim() assert ax1.get_xlim() == ax3.get_xlim() assert ax1.get_xlim() == ax4.get_xlim() fig3, ax5 = plt.subplots() y5 = np.ma.masked_equal([2, 4, 6, 8, 10, 12], 8) xmin5 = np.ma.masked_equal([0, 1, -1, 0, 2, 1], 2) xmax5 = np.ma.masked_equal([13, 14, 15, 16, 17, 18], 18) ax5.hlines(y5, xmin5, xmax5, colors='k', linewidth=2) ax5.set_ylim(0, 15) def test_hlines_default(): fig, ax = plt.subplots() with mpl.rc_context({'lines.color': 'red'}): lines = ax.hlines(0.5, 0, 1) assert mpl.colors.same_color(lines.get_color(), 'red') @pytest.mark.parametrize('data', [[1, 2, 3, np.nan, 5], np.ma.masked_equal([1, 2, 3, 4, 5], 4)]) @check_figures_equal(extensions=["png"]) def test_lines_with_colors(fig_test, fig_ref, data): test_colors = ['red', 'green', 'blue', 'purple', 'orange'] fig_test.add_subplot(2, 1, 1).vlines(data, 0, 1, colors=test_colors, linewidth=5) fig_test.add_subplot(2, 1, 2).hlines(data, 0, 1, colors=test_colors, linewidth=5) expect_xy = [1, 2, 3, 5] expect_color = ['red', 'green', 'blue', 'orange'] fig_ref.add_subplot(2, 1, 1).vlines(expect_xy, 0, 1, colors=expect_color, linewidth=5) fig_ref.add_subplot(2, 1, 2).hlines(expect_xy, 0, 1, colors=expect_color, linewidth=5) @image_comparison(['vlines_hlines_blended_transform'], extensions=['png'], style='mpl20') def test_vlines_hlines_blended_transform(): t = np.arange(5.0, 10.0, 0.1) s = np.exp(-t) + np.sin(2 * np.pi * t) + 10 fig, (hax, vax) = plt.subplots(2, 1, figsize=(6, 6)) hax.plot(t, s, '^') hax.hlines([10, 9], xmin=0, xmax=0.5, transform=hax.get_yaxis_transform(), colors='r') vax.plot(t, s, '^') vax.vlines([6, 7], ymin=0, ymax=0.15, transform=vax.get_xaxis_transform(), colors='r') @image_comparison(['step_linestyle', 'step_linestyle'], remove_text=True, tol=0.2) def test_step_linestyle(): # Tolerance caused by reordering of floating-point operations # Remove when regenerating the images x = y = np.arange(10) # First illustrate basic pyplot interface, using defaults where possible. fig, ax_lst = plt.subplots(2, 2) ax_lst = ax_lst.flatten() ln_styles = ['-', '--', '-.', ':'] for ax, ls in zip(ax_lst, ln_styles): ax.step(x, y, lw=5, linestyle=ls, where='pre') ax.step(x, y + 1, lw=5, linestyle=ls, where='mid') ax.step(x, y + 2, lw=5, linestyle=ls, where='post') ax.set_xlim([-1, 5]) ax.set_ylim([-1, 7]) # Reuse testcase from above for a labeled data test data = {"X": x, "Y0": y, "Y1": y+1, "Y2": y+2} fig, ax_lst = plt.subplots(2, 2) ax_lst = ax_lst.flatten() ln_styles = ['-', '--', '-.', ':'] for ax, ls in zip(ax_lst, ln_styles): ax.step("X", "Y0", lw=5, linestyle=ls, where='pre', data=data) ax.step("X", "Y1", lw=5, linestyle=ls, where='mid', data=data) ax.step("X", "Y2", lw=5, linestyle=ls, where='post', data=data) ax.set_xlim([-1, 5]) ax.set_ylim([-1, 7]) @image_comparison(['mixed_collection'], remove_text=True) def test_mixed_collection(): # First illustrate basic pyplot interface, using defaults where possible. fig, ax = plt.subplots() c = mpatches.Circle((8, 8), radius=4, facecolor='none', edgecolor='green') # PDF can optimize this one p1 = mpl.collections.PatchCollection([c], match_original=True) p1.set_offsets([[0, 0], [24, 24]]) p1.set_linewidths([1, 5]) # PDF can't optimize this one, because the alpha of the edge changes p2 = mpl.collections.PatchCollection([c], match_original=True) p2.set_offsets([[48, 0], [-32, -16]]) p2.set_linewidths([1, 5]) p2.set_edgecolors([[0, 0, 0.1, 1.0], [0, 0, 0.1, 0.5]]) ax.patch.set_color('0.5') ax.add_collection(p1) ax.add_collection(p2) ax.set_xlim(0, 16) ax.set_ylim(0, 16) def test_subplot_key_hash(): ax = plt.subplot(np.int32(5), np.int64(1), 1) ax.twinx() assert ax.get_subplotspec().get_geometry() == (5, 1, 0, 0) @image_comparison( ["specgram_freqs.png", "specgram_freqs_linear.png", "specgram_noise.png", "specgram_noise_linear.png"], remove_text=True, tol=0.07, style="default") def test_specgram(): """Test axes.specgram in default (psd) mode.""" # use former defaults to match existing baseline image matplotlib.rcParams['image.interpolation'] = 'nearest' n = 1000 Fs = 10. fstims = [[Fs/4, Fs/5, Fs/11], [Fs/4.7, Fs/5.6, Fs/11.9]] NFFT_freqs = int(10 * Fs / np.min(fstims)) x = np.arange(0, n, 1/Fs) y_freqs = np.concatenate( np.sin(2 * np.pi * np.multiply.outer(fstims, x)).sum(axis=1)) NFFT_noise = int(10 * Fs / 11) np.random.seed(0) y_noise = np.concatenate([np.random.standard_normal(n), np.random.rand(n)]) all_sides = ["default", "onesided", "twosided"] for y, NFFT in [(y_freqs, NFFT_freqs), (y_noise, NFFT_noise)]: noverlap = NFFT // 2 pad_to = int(2 ** np.ceil(np.log2(NFFT))) for ax, sides in zip(plt.figure().subplots(3), all_sides): ax.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to, sides=sides) for ax, sides in zip(plt.figure().subplots(3), all_sides): ax.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to, sides=sides, scale="linear", norm=matplotlib.colors.LogNorm()) @image_comparison( ["specgram_magnitude_freqs.png", "specgram_magnitude_freqs_linear.png", "specgram_magnitude_noise.png", "specgram_magnitude_noise_linear.png"], remove_text=True, tol=0.07, style="default") def test_specgram_magnitude(): """Test axes.specgram in magnitude mode.""" # use former defaults to match existing baseline image matplotlib.rcParams['image.interpolation'] = 'nearest' n = 1000 Fs = 10. fstims = [[Fs/4, Fs/5, Fs/11], [Fs/4.7, Fs/5.6, Fs/11.9]] NFFT_freqs = int(100 * Fs / np.min(fstims)) x = np.arange(0, n, 1/Fs) y = np.sin(2 * np.pi * np.multiply.outer(fstims, x)).sum(axis=1) y[:, -1] = 1 y_freqs = np.hstack(y) NFFT_noise = int(10 * Fs / 11) np.random.seed(0) y_noise = np.concatenate([np.random.standard_normal(n), np.random.rand(n)]) all_sides = ["default", "onesided", "twosided"] for y, NFFT in [(y_freqs, NFFT_freqs), (y_noise, NFFT_noise)]: noverlap = NFFT // 2 pad_to = int(2 ** np.ceil(np.log2(NFFT))) for ax, sides in zip(plt.figure().subplots(3), all_sides): ax.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to, sides=sides, mode="magnitude") for ax, sides in zip(plt.figure().subplots(3), all_sides): ax.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to, sides=sides, mode="magnitude", scale="linear", norm=matplotlib.colors.LogNorm()) @image_comparison( ["specgram_angle_freqs.png", "specgram_phase_freqs.png", "specgram_angle_noise.png", "specgram_phase_noise.png"], remove_text=True, tol=0.07, style="default") def test_specgram_angle(): """Test axes.specgram in angle and phase modes.""" # use former defaults to match existing baseline image matplotlib.rcParams['image.interpolation'] = 'nearest' n = 1000 Fs = 10. fstims = [[Fs/4, Fs/5, Fs/11], [Fs/4.7, Fs/5.6, Fs/11.9]] NFFT_freqs = int(10 * Fs / np.min(fstims)) x = np.arange(0, n, 1/Fs) y = np.sin(2 * np.pi * np.multiply.outer(fstims, x)).sum(axis=1) y[:, -1] = 1 y_freqs = np.hstack(y) NFFT_noise = int(10 * Fs / 11) np.random.seed(0) y_noise = np.concatenate([np.random.standard_normal(n), np.random.rand(n)]) all_sides = ["default", "onesided", "twosided"] for y, NFFT in [(y_freqs, NFFT_freqs), (y_noise, NFFT_noise)]: noverlap = NFFT // 2 pad_to = int(2 ** np.ceil(np.log2(NFFT))) for mode in ["angle", "phase"]: for ax, sides in zip(plt.figure().subplots(3), all_sides): ax.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to, sides=sides, mode=mode) with pytest.raises(ValueError): ax.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to, sides=sides, mode=mode, scale="dB") def test_specgram_fs_none(): """Test axes.specgram when Fs is None, should not throw error.""" spec, freqs, t, im = plt.specgram(np.ones(300), Fs=None, scale='linear') xmin, xmax, freq0, freq1 = im.get_extent() assert xmin == 32 and xmax == 96 @check_figures_equal(extensions=["png"]) def test_specgram_origin_rcparam(fig_test, fig_ref): """Test specgram ignores image.origin rcParam and uses origin 'upper'.""" t = np.arange(500) signal = np.sin(t) plt.rcParams["image.origin"] = 'upper' # Reference: First graph using default origin in imshow (upper), fig_ref.subplots().specgram(signal) # Try to overwrite the setting trying to flip the specgram plt.rcParams["image.origin"] = 'lower' # Test: origin='lower' should be ignored fig_test.subplots().specgram(signal) def test_specgram_origin_kwarg(): """Ensure passing origin as a kwarg raises a TypeError.""" t = np.arange(500) signal = np.sin(t) with pytest.raises(TypeError): plt.specgram(signal, origin='lower') @image_comparison( ["psd_freqs.png", "csd_freqs.png", "psd_noise.png", "csd_noise.png"], remove_text=True, tol=0.002) def test_psd_csd(): n = 10000 Fs = 100. fstims = [[Fs/4, Fs/5, Fs/11], [Fs/4.7, Fs/5.6, Fs/11.9]] NFFT_freqs = int(1000 * Fs / np.min(fstims)) x = np.arange(0, n, 1/Fs) ys_freqs = np.sin(2 * np.pi * np.multiply.outer(fstims, x)).sum(axis=1) NFFT_noise = int(1000 * Fs / 11) np.random.seed(0) ys_noise = [np.random.standard_normal(n), np.random.rand(n)] all_kwargs = [{"sides": "default"}, {"sides": "onesided", "return_line": False}, {"sides": "twosided", "return_line": True}] for ys, NFFT in [(ys_freqs, NFFT_freqs), (ys_noise, NFFT_noise)]: noverlap = NFFT // 2 pad_to = int(2 ** np.ceil(np.log2(NFFT))) for ax, kwargs in zip(plt.figure().subplots(3), all_kwargs): ret = ax.psd(np.concatenate(ys), NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to, **kwargs) assert len(ret) == 2 + kwargs.get("return_line", False) ax.set(xlabel="", ylabel="") for ax, kwargs in zip(plt.figure().subplots(3), all_kwargs): ret = ax.csd(*ys, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to, **kwargs) assert len(ret) == 2 + kwargs.get("return_line", False) ax.set(xlabel="", ylabel="") @image_comparison( ["magnitude_spectrum_freqs_linear.png", "magnitude_spectrum_freqs_dB.png", "angle_spectrum_freqs.png", "phase_spectrum_freqs.png", "magnitude_spectrum_noise_linear.png", "magnitude_spectrum_noise_dB.png", "angle_spectrum_noise.png", "phase_spectrum_noise.png"], remove_text=True) def test_spectrum(): n = 10000 Fs = 100. fstims1 = [Fs/4, Fs/5, Fs/11] NFFT = int(1000 * Fs / min(fstims1)) pad_to = int(2 ** np.ceil(np.log2(NFFT))) x = np.arange(0, n, 1/Fs) y_freqs = ((np.sin(2 * np.pi * np.outer(x, fstims1)) * 10**np.arange(3)) .sum(axis=1)) np.random.seed(0) y_noise = np.hstack([np.random.standard_normal(n), np.random.rand(n)]) - .5 all_sides = ["default", "onesided", "twosided"] kwargs = {"Fs": Fs, "pad_to": pad_to} for y in [y_freqs, y_noise]: for ax, sides in zip(plt.figure().subplots(3), all_sides): spec, freqs, line = ax.magnitude_spectrum(y, sides=sides, **kwargs) ax.set(xlabel="", ylabel="") for ax, sides in zip(plt.figure().subplots(3), all_sides): spec, freqs, line = ax.magnitude_spectrum(y, sides=sides, **kwargs, scale="dB") ax.set(xlabel="", ylabel="") for ax, sides in zip(plt.figure().subplots(3), all_sides): spec, freqs, line = ax.angle_spectrum(y, sides=sides, **kwargs) ax.set(xlabel="", ylabel="") for ax, sides in zip(plt.figure().subplots(3), all_sides): spec, freqs, line = ax.phase_spectrum(y, sides=sides, **kwargs) ax.set(xlabel="", ylabel="") def test_psd_csd_edge_cases(): # Inverted yaxis or fully zero inputs used to throw exceptions. axs = plt.figure().subplots(2) for ax in axs: ax.yaxis.set(inverted=True) with np.errstate(divide="ignore"): axs[0].psd(np.zeros(5)) axs[1].csd(np.zeros(5), np.zeros(5)) @check_figures_equal(extensions=['png']) def test_twin_remove(fig_test, fig_ref): ax_test = fig_test.add_subplot() ax_twinx = ax_test.twinx() ax_twiny = ax_test.twiny() ax_twinx.remove() ax_twiny.remove() ax_ref = fig_ref.add_subplot() # Ideally we also undo tick changes when calling ``remove()``, but for now # manually set the ticks of the reference image to match the test image ax_ref.xaxis.tick_bottom() ax_ref.yaxis.tick_left() @image_comparison(['twin_spines.png'], remove_text=True, tol=0 if platform.machine() == 'x86_64' else 0.022) def test_twin_spines(): def make_patch_spines_invisible(ax): ax.set_frame_on(True) ax.patch.set_visible(False) ax.spines[:].set_visible(False) fig = plt.figure(figsize=(4, 3)) fig.subplots_adjust(right=0.75) host = fig.add_subplot() par1 = host.twinx() par2 = host.twinx() # Offset the right spine of par2. The ticks and label have already been # placed on the right by twinx above. par2.spines.right.set_position(("axes", 1.2)) # Having been created by twinx, par2 has its frame off, so the line of # its detached spine is invisible. First, activate the frame but make # the patch and spines invisible. make_patch_spines_invisible(par2) # Second, show the right spine. par2.spines.right.set_visible(True) p1, = host.plot([0, 1, 2], [0, 1, 2], "b-") p2, = par1.plot([0, 1, 2], [0, 3, 2], "r-") p3, = par2.plot([0, 1, 2], [50, 30, 15], "g-") host.set_xlim(0, 2) host.set_ylim(0, 2) par1.set_ylim(0, 4) par2.set_ylim(1, 65) host.yaxis.label.set_color(p1.get_color()) par1.yaxis.label.set_color(p2.get_color()) par2.yaxis.label.set_color(p3.get_color()) tkw = dict(size=4, width=1.5) host.tick_params(axis='y', colors=p1.get_color(), **tkw) par1.tick_params(axis='y', colors=p2.get_color(), **tkw) par2.tick_params(axis='y', colors=p3.get_color(), **tkw) host.tick_params(axis='x', **tkw) @image_comparison(['twin_spines_on_top.png', 'twin_spines_on_top.png'], remove_text=True) def test_twin_spines_on_top(): matplotlib.rcParams['axes.linewidth'] = 48.0 matplotlib.rcParams['lines.linewidth'] = 48.0 fig = plt.figure() ax1 = fig.add_subplot(1, 1, 1) data = np.array([[1000, 1100, 1200, 1250], [310, 301, 360, 400]]) ax2 = ax1.twinx() ax1.plot(data[0], data[1]/1E3, color='#BEAED4') ax1.fill_between(data[0], data[1]/1E3, color='#BEAED4', alpha=.8) ax2.plot(data[0], data[1]/1E3, color='#7FC97F') ax2.fill_between(data[0], data[1]/1E3, color='#7FC97F', alpha=.5) # Reuse testcase from above for a labeled data test data = {"i": data[0], "j": data[1]/1E3} fig = plt.figure() ax1 = fig.add_subplot(1, 1, 1) ax2 = ax1.twinx() ax1.plot("i", "j", color='#BEAED4', data=data) ax1.fill_between("i", "j", color='#BEAED4', alpha=.8, data=data) ax2.plot("i", "j", color='#7FC97F', data=data) ax2.fill_between("i", "j", color='#7FC97F', alpha=.5, data=data) @pytest.mark.parametrize("grid_which, major_visible, minor_visible", [ ("both", True, True), ("major", True, False), ("minor", False, True), ]) def test_rcparam_grid_minor(grid_which, major_visible, minor_visible): mpl.rcParams.update({"axes.grid": True, "axes.grid.which": grid_which}) fig, ax = plt.subplots() fig.canvas.draw() assert all(tick.gridline.get_visible() == major_visible for tick in ax.xaxis.majorTicks) assert all(tick.gridline.get_visible() == minor_visible for tick in ax.xaxis.minorTicks) def test_grid(): fig, ax = plt.subplots() ax.grid() fig.canvas.draw() assert ax.xaxis.majorTicks[0].gridline.get_visible() ax.grid(visible=False) fig.canvas.draw() assert not ax.xaxis.majorTicks[0].gridline.get_visible() ax.grid(visible=True) fig.canvas.draw() assert ax.xaxis.majorTicks[0].gridline.get_visible() ax.grid() fig.canvas.draw() assert not ax.xaxis.majorTicks[0].gridline.get_visible() def test_reset_grid(): fig, ax = plt.subplots() ax.tick_params(reset=True, which='major', labelsize=10) assert not ax.xaxis.majorTicks[0].gridline.get_visible() ax.grid(color='red') # enables grid assert ax.xaxis.majorTicks[0].gridline.get_visible() with plt.rc_context({'axes.grid': True}): ax.clear() ax.tick_params(reset=True, which='major', labelsize=10) assert ax.xaxis.majorTicks[0].gridline.get_visible() @check_figures_equal(extensions=['png']) def test_reset_ticks(fig_test, fig_ref): for fig in [fig_ref, fig_test]: ax = fig.add_subplot() ax.grid(True) ax.tick_params( direction='in', length=10, width=5, color='C0', pad=12, labelsize=14, labelcolor='C1', labelrotation=45, grid_color='C2', grid_alpha=0.8, grid_linewidth=3, grid_linestyle='--') fig.draw_without_rendering() # After we've changed any setting on ticks, reset_ticks will mean # re-creating them from scratch. This *should* appear the same as not # resetting them. for ax in fig_test.axes: ax.xaxis.reset_ticks() ax.yaxis.reset_ticks() @mpl.style.context('mpl20') def test_context_ticks(): with plt.rc_context({ 'xtick.direction': 'in', 'xtick.major.size': 30, 'xtick.major.width': 5, 'xtick.color': 'C0', 'xtick.major.pad': 12, 'xtick.bottom': True, 'xtick.top': True, 'xtick.labelsize': 14, 'xtick.labelcolor': 'C1'}): fig, ax = plt.subplots() # Draw outside the context so that all-but-first tick are generated with the normal # mpl20 style in place. fig.draw_without_rendering() first_tick = ax.xaxis.majorTicks[0] for tick in ax.xaxis.majorTicks[1:]: assert tick._size == first_tick._size assert tick._width == first_tick._width assert tick._base_pad == first_tick._base_pad assert tick._labelrotation == first_tick._labelrotation assert tick._zorder == first_tick._zorder assert tick._tickdir == first_tick._tickdir def test_vline_limit(): fig = plt.figure() ax = fig.gca() ax.axvline(0.5) ax.plot([-0.1, 0, 0.2, 0.1]) assert_allclose(ax.get_ylim(), (-.1, .2)) @pytest.mark.parametrize('fv, fh, args', [[plt.axvline, plt.axhline, (1,)], [plt.axvspan, plt.axhspan, (1, 1)]]) def test_axline_minmax(fv, fh, args): bad_lim = matplotlib.dates.num2date(1) # Check vertical functions with pytest.raises(ValueError, match='ymin must be a single scalar value'): fv(*args, ymin=bad_lim, ymax=1) with pytest.raises(ValueError, match='ymax must be a single scalar value'): fv(*args, ymin=1, ymax=bad_lim) # Check horizontal functions with pytest.raises(ValueError, match='xmin must be a single scalar value'): fh(*args, xmin=bad_lim, xmax=1) with pytest.raises(ValueError, match='xmax must be a single scalar value'): fh(*args, xmin=1, xmax=bad_lim) def test_empty_shared_subplots(): # empty plots with shared axes inherit limits from populated plots fig, axs = plt.subplots(nrows=1, ncols=2, sharex=True, sharey=True) axs[0].plot([1, 2, 3], [2, 4, 6]) x0, x1 = axs[1].get_xlim() y0, y1 = axs[1].get_ylim() assert x0 <= 1 assert x1 >= 3 assert y0 <= 2 assert y1 >= 6 def test_shared_with_aspect_1(): # allow sharing one axis for adjustable in ['box', 'datalim']: fig, axs = plt.subplots(nrows=2, sharex=True) axs[0].set_aspect(2, adjustable=adjustable, share=True) assert axs[1].get_aspect() == 2 assert axs[1].get_adjustable() == adjustable fig, axs = plt.subplots(nrows=2, sharex=True) axs[0].set_aspect(2, adjustable=adjustable) assert axs[1].get_aspect() == 'auto' def test_shared_with_aspect_2(): # Share 2 axes only with 'box': fig, axs = plt.subplots(nrows=2, sharex=True, sharey=True) axs[0].set_aspect(2, share=True) axs[0].plot([1, 2], [3, 4]) axs[1].plot([3, 4], [1, 2]) plt.draw() # Trigger apply_aspect(). assert axs[0].get_xlim() == axs[1].get_xlim() assert axs[0].get_ylim() == axs[1].get_ylim() def test_shared_with_aspect_3(): # Different aspect ratios: for adjustable in ['box', 'datalim']: fig, axs = plt.subplots(nrows=2, sharey=True) axs[0].set_aspect(2, adjustable=adjustable) axs[1].set_aspect(0.5, adjustable=adjustable) axs[0].plot([1, 2], [3, 4]) axs[1].plot([3, 4], [1, 2]) plt.draw() # Trigger apply_aspect(). assert axs[0].get_xlim() != axs[1].get_xlim() assert axs[0].get_ylim() == axs[1].get_ylim() fig_aspect = fig.bbox_inches.height / fig.bbox_inches.width for ax in axs: p = ax.get_position() box_aspect = p.height / p.width lim_aspect = ax.viewLim.height / ax.viewLim.width expected = fig_aspect * box_aspect / lim_aspect assert round(expected, 4) == round(ax.get_aspect(), 4) def test_shared_aspect_error(): fig, axes = plt.subplots(1, 2, sharex=True, sharey=True) axes[0].axis("equal") with pytest.raises(RuntimeError, match=r"set_aspect\(..., adjustable="): fig.draw_without_rendering() @pytest.mark.parametrize('err, args, kwargs, match', ((TypeError, (1, 2), {}, r"axis\(\) takes from 0 to 1 positional arguments " "but 2 were given"), (ValueError, ('foo', ), {}, "Unrecognized string 'foo' to axis; try 'on' or " "'off'"), (TypeError, ([1, 2], ), {}, "The first argument to axis*"), (TypeError, tuple(), {'foo': None}, r"axis\(\) got an unexpected keyword argument " "'foo'"), )) def test_axis_errors(err, args, kwargs, match): with pytest.raises(err, match=match): plt.axis(*args, **kwargs) def test_axis_method_errors(): ax = plt.gca() with pytest.raises(ValueError, match="unknown value for which: 'foo'"): ax.get_xaxis_transform('foo') with pytest.raises(ValueError, match="unknown value for which: 'foo'"): ax.get_yaxis_transform('foo') with pytest.raises(TypeError, match="Cannot supply both positional and"): ax.set_prop_cycle('foo', label='bar') with pytest.raises(ValueError, match="argument must be among"): ax.set_anchor('foo') with pytest.raises(ValueError, match="scilimits must be a sequence"): ax.ticklabel_format(scilimits=1) with pytest.raises(TypeError, match="Specifying 'loc' is disallowed"): ax.set_xlabel('foo', loc='left', x=1) with pytest.raises(TypeError, match="Specifying 'loc' is disallowed"): ax.set_ylabel('foo', loc='top', y=1) with pytest.raises(TypeError, match="Cannot pass both 'left'"): ax.set_xlim(left=0, xmin=1) with pytest.raises(TypeError, match="Cannot pass both 'right'"): ax.set_xlim(right=0, xmax=1) with pytest.raises(TypeError, match="Cannot pass both 'bottom'"): ax.set_ylim(bottom=0, ymin=1) with pytest.raises(TypeError, match="Cannot pass both 'top'"): ax.set_ylim(top=0, ymax=1) @pytest.mark.parametrize('twin', ('x', 'y')) def test_twin_with_aspect(twin): fig, ax = plt.subplots() # test twinx or twiny ax_twin = getattr(ax, f'twin{twin}')() ax.set_aspect(5) ax_twin.set_aspect(2) assert_array_equal(ax.bbox.extents, ax_twin.bbox.extents) def test_relim_visible_only(): x1 = (0., 10.) y1 = (0., 10.) x2 = (-10., 20.) y2 = (-10., 30.) fig = matplotlib.figure.Figure() ax = fig.add_subplot() ax.plot(x1, y1) assert ax.get_xlim() == x1 assert ax.get_ylim() == y1 line, = ax.plot(x2, y2) assert ax.get_xlim() == x2 assert ax.get_ylim() == y2 line.set_visible(False) assert ax.get_xlim() == x2 assert ax.get_ylim() == y2 ax.relim(visible_only=True) ax.autoscale_view() assert ax.get_xlim() == x1 assert ax.get_ylim() == y1 def test_text_labelsize(): """ tests for issue #1172 """ fig = plt.figure() ax = fig.gca() ax.tick_params(labelsize='large') ax.tick_params(direction='out') # Note: The `pie` image tests were affected by Numpy 2.0 changing promotions # (NEP 50). While the changes were only marginal, tolerances were introduced. # These tolerances could likely go away when numpy 2.0 is the minimum supported # numpy and the images are regenerated. @image_comparison(['pie_default.png'], tol=0.01) def test_pie_default(): # The slices will be ordered and plotted counter-clockwise. labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' sizes = [15, 30, 45, 10] colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral'] explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs') fig1, ax1 = plt.subplots(figsize=(8, 6)) ax1.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90) @image_comparison(['pie_linewidth_0', 'pie_linewidth_0', 'pie_linewidth_0'], extensions=['png'], style='mpl20', tol=0.01) def test_pie_linewidth_0(): # The slices will be ordered and plotted counter-clockwise. labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' sizes = [15, 30, 45, 10] colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral'] explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs') plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90, wedgeprops={'linewidth': 0}) # Set aspect ratio to be equal so that pie is drawn as a circle. plt.axis('equal') # Reuse testcase from above for a labeled data test data = {"l": labels, "s": sizes, "c": colors, "ex": explode} fig = plt.figure() ax = fig.gca() ax.pie("s", explode="ex", labels="l", colors="c", autopct='%1.1f%%', shadow=True, startangle=90, wedgeprops={'linewidth': 0}, data=data) ax.axis('equal') # And again to test the pyplot functions which should also be able to be # called with a data kwarg plt.figure() plt.pie("s", explode="ex", labels="l", colors="c", autopct='%1.1f%%', shadow=True, startangle=90, wedgeprops={'linewidth': 0}, data=data) plt.axis('equal') @image_comparison(['pie_center_radius.png'], style='mpl20', tol=0.01) def test_pie_center_radius(): # The slices will be ordered and plotted counter-clockwise. labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' sizes = [15, 30, 45, 10] colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral'] explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs') plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90, wedgeprops={'linewidth': 0}, center=(1, 2), radius=1.5) plt.annotate("Center point", xy=(1, 2), xytext=(1, 1.3), arrowprops=dict(arrowstyle="->", connectionstyle="arc3"), bbox=dict(boxstyle="square", facecolor="lightgrey")) # Set aspect ratio to be equal so that pie is drawn as a circle. plt.axis('equal') @image_comparison(['pie_linewidth_2.png'], style='mpl20', tol=0.01) def test_pie_linewidth_2(): # The slices will be ordered and plotted counter-clockwise. labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' sizes = [15, 30, 45, 10] colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral'] explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs') plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90, wedgeprops={'linewidth': 2}) # Set aspect ratio to be equal so that pie is drawn as a circle. plt.axis('equal') @image_comparison(['pie_ccw_true.png'], style='mpl20', tol=0.01) def test_pie_ccw_true(): # The slices will be ordered and plotted counter-clockwise. labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' sizes = [15, 30, 45, 10] colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral'] explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs') plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90, counterclock=True) # Set aspect ratio to be equal so that pie is drawn as a circle. plt.axis('equal') @image_comparison(['pie_frame_grid.png'], style='mpl20', tol=0.002) def test_pie_frame_grid(): # The slices will be ordered and plotted counter-clockwise. labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' sizes = [15, 30, 45, 10] colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral'] # only "explode" the 2nd slice (i.e. 'Hogs') explode = (0, 0.1, 0, 0) plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90, wedgeprops={'linewidth': 0}, frame=True, center=(2, 2)) plt.pie(sizes[::-1], explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90, wedgeprops={'linewidth': 0}, frame=True, center=(5, 2)) plt.pie(sizes, explode=explode[::-1], labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90, wedgeprops={'linewidth': 0}, frame=True, center=(3, 5)) # Set aspect ratio to be equal so that pie is drawn as a circle. plt.axis('equal') @image_comparison(['pie_rotatelabels_true.png'], style='mpl20', tol=0.009) def test_pie_rotatelabels_true(): # The slices will be ordered and plotted counter-clockwise. labels = 'Hogwarts', 'Frogs', 'Dogs', 'Logs' sizes = [15, 30, 45, 10] colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral'] explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs') plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90, rotatelabels=True) # Set aspect ratio to be equal so that pie is drawn as a circle. plt.axis('equal') @image_comparison(['pie_no_label.png'], tol=0.01) def test_pie_nolabel_but_legend(): labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' sizes = [15, 30, 45, 10] colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral'] explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs') plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90, labeldistance=None, rotatelabels=True) plt.axis('equal') plt.ylim(-1.2, 1.2) plt.legend() @image_comparison(['pie_shadow.png'], style='mpl20', tol=0.002) def test_pie_shadow(): # Also acts as a test for the shade argument of Shadow sizes = [15, 30, 45, 10] colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral'] explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice _, axes = plt.subplots(2, 2) axes[0][0].pie(sizes, explode=explode, colors=colors, shadow=True, startangle=90, wedgeprops={'linewidth': 0}) axes[0][1].pie(sizes, explode=explode, colors=colors, shadow=False, startangle=90, wedgeprops={'linewidth': 0}) axes[1][0].pie(sizes, explode=explode, colors=colors, shadow={'ox': -0.05, 'oy': -0.05, 'shade': 0.9, 'edgecolor': 'none'}, startangle=90, wedgeprops={'linewidth': 0}) axes[1][1].pie(sizes, explode=explode, colors=colors, shadow={'ox': 0.05, 'linewidth': 2, 'shade': 0.2}, startangle=90, wedgeprops={'linewidth': 0}) def test_pie_textprops(): data = [23, 34, 45] labels = ["Long name 1", "Long name 2", "Long name 3"] textprops = dict(horizontalalignment="center", verticalalignment="top", rotation=90, rotation_mode="anchor", size=12, color="red") _, texts, autopct = plt.gca().pie(data, labels=labels, autopct='%.2f', textprops=textprops) for labels in [texts, autopct]: for tx in labels: assert tx.get_ha() == textprops["horizontalalignment"] assert tx.get_va() == textprops["verticalalignment"] assert tx.get_rotation() == textprops["rotation"] assert tx.get_rotation_mode() == textprops["rotation_mode"] assert tx.get_size() == textprops["size"] assert tx.get_color() == textprops["color"] def test_pie_get_negative_values(): # Test the ValueError raised when feeding negative values into axes.pie fig, ax = plt.subplots() with pytest.raises(ValueError): ax.pie([5, 5, -3], explode=[0, .1, .2]) def test_pie_invalid_explode(): # Test ValueError raised when feeding short explode list to axes.pie fig, ax = plt.subplots() with pytest.raises(ValueError): ax.pie([1, 2, 3], explode=[0.1, 0.1]) def test_pie_invalid_labels(): # Test ValueError raised when feeding short labels list to axes.pie fig, ax = plt.subplots() with pytest.raises(ValueError): ax.pie([1, 2, 3], labels=["One", "Two"]) def test_pie_invalid_radius(): # Test ValueError raised when feeding negative radius to axes.pie fig, ax = plt.subplots() with pytest.raises(ValueError): ax.pie([1, 2, 3], radius=-5) def test_normalize_kwarg_pie(): fig, ax = plt.subplots() x = [0.3, 0.3, 0.1] t1 = ax.pie(x=x, normalize=True) assert abs(t1[0][-1].theta2 - 360.) < 1e-3 t2 = ax.pie(x=x, normalize=False) assert abs(t2[0][-1].theta2 - 360.) > 1e-3 @check_figures_equal() def test_pie_hatch_single(fig_test, fig_ref): x = [0.3, 0.3, 0.1] hatch = '+' fig_test.subplots().pie(x, hatch=hatch) wedges, _ = fig_ref.subplots().pie(x) [w.set_hatch(hatch) for w in wedges] @check_figures_equal() def test_pie_hatch_multi(fig_test, fig_ref): x = [0.3, 0.3, 0.1] hatch = ['/', '+', '.'] fig_test.subplots().pie(x, hatch=hatch) wedges, _ = fig_ref.subplots().pie(x) [w.set_hatch(hp) for w, hp in zip(wedges, hatch)] @image_comparison(['set_get_ticklabels.png'], tol=0 if platform.machine() == 'x86_64' else 0.025) def test_set_get_ticklabels(): # test issue 2246 fig, ax = plt.subplots(2) ha = ['normal', 'set_x/yticklabels'] ax[0].plot(np.arange(10)) ax[0].set_title(ha[0]) ax[1].plot(np.arange(10)) ax[1].set_title(ha[1]) # set ticklabel to 1 plot in normal way ax[0].set_xticks(range(10)) ax[0].set_yticks(range(10)) ax[0].set_xticklabels(['a', 'b', 'c', 'd'] + 6 * ['']) ax[0].set_yticklabels(['11', '12', '13', '14'] + 6 * ['']) # set ticklabel to the other plot, expect the 2 plots have same label # setting pass get_ticklabels return value as ticklabels argument ax[1].set_xticks(ax[0].get_xticks()) ax[1].set_yticks(ax[0].get_yticks()) ax[1].set_xticklabels(ax[0].get_xticklabels()) ax[1].set_yticklabels(ax[0].get_yticklabels()) def test_set_ticks_kwargs_raise_error_without_labels(): """ When labels=None and any kwarg is passed, axis.set_ticks() raises a ValueError. """ fig, ax = plt.subplots() ticks = [1, 2, 3] with pytest.raises(ValueError, match="Incorrect use of keyword argument 'alpha'"): ax.xaxis.set_ticks(ticks, alpha=0.5) @check_figures_equal(extensions=["png"]) def test_set_ticks_with_labels(fig_test, fig_ref): """ Test that these two are identical:: set_xticks(ticks); set_xticklabels(labels, **kwargs) set_xticks(ticks, labels, **kwargs) """ ax = fig_ref.subplots() ax.set_xticks([1, 2, 4, 6]) ax.set_xticklabels(['a', 'b', 'c', 'd'], fontweight='bold') ax.set_yticks([1, 3, 5]) ax.set_yticks([2, 4], minor=True) ax.set_yticklabels(['A', 'B'], minor=True) ax = fig_test.subplots() ax.set_xticks([1, 2, 4, 6], ['a', 'b', 'c', 'd'], fontweight='bold') ax.set_yticks([1, 3, 5]) ax.set_yticks([2, 4], ['A', 'B'], minor=True) def test_xticks_bad_args(): ax = plt.figure().add_subplot() with pytest.raises(TypeError, match='must be a sequence'): ax.set_xticks([2, 9], 3.1) with pytest.raises(ValueError, match='must be 1D'): plt.xticks(np.arange(4).reshape((-1, 1))) with pytest.raises(ValueError, match='must be 1D'): plt.xticks(np.arange(4).reshape((1, -1))) with pytest.raises(ValueError, match='must be 1D'): plt.xticks(np.arange(4).reshape((-1, 1)), labels=range(4)) with pytest.raises(ValueError, match='must be 1D'): plt.xticks(np.arange(4).reshape((1, -1)), labels=range(4)) def test_subsampled_ticklabels(): # test issue 11937 fig, ax = plt.subplots() ax.plot(np.arange(10)) ax.xaxis.set_ticks(np.arange(10) + 0.1) ax.locator_params(nbins=5) ax.xaxis.set_ticklabels([c for c in "bcdefghijk"]) plt.draw() labels = [t.get_text() for t in ax.xaxis.get_ticklabels()] assert labels == ['b', 'd', 'f', 'h', 'j'] def test_mismatched_ticklabels(): fig, ax = plt.subplots() ax.plot(np.arange(10)) ax.xaxis.set_ticks([1.5, 2.5]) with pytest.raises(ValueError): ax.xaxis.set_ticklabels(['a', 'b', 'c']) def test_empty_ticks_fixed_loc(): # Smoke test that [] can be used to unset all tick labels fig, ax = plt.subplots() ax.bar([1, 2], [1, 2]) ax.set_xticks([1, 2]) ax.set_xticklabels([]) @image_comparison(['retain_tick_visibility.png']) def test_retain_tick_visibility(): fig, ax = plt.subplots() plt.plot([0, 1, 2], [0, -1, 4]) plt.setp(ax.get_yticklabels(), visible=False) ax.tick_params(axis="y", which="both", length=0) def test_warn_too_few_labels(): # note that the axis is still using an AutoLocator: fig, ax = plt.subplots() with pytest.warns( UserWarning, match=r'set_ticklabels\(\) should only be used with a fixed number'): ax.set_xticklabels(['0', '0.1']) # note that the axis is still using a FixedLocator: fig, ax = plt.subplots() ax.set_xticks([0, 0.5, 1]) with pytest.raises(ValueError, match='The number of FixedLocator locations'): ax.set_xticklabels(['0', '0.1']) def test_tick_label_update(): # test issue 9397 fig, ax = plt.subplots() # Set up a dummy formatter def formatter_func(x, pos): return "unit value" if x == 1 else "" ax.xaxis.set_major_formatter(plt.FuncFormatter(formatter_func)) # Force some of the x-axis ticks to be outside of the drawn range ax.set_xticks([-1, 0, 1, 2, 3]) ax.set_xlim(-0.5, 2.5) fig.canvas.draw() tick_texts = [tick.get_text() for tick in ax.xaxis.get_ticklabels()] assert tick_texts == ["", "", "unit value", "", ""] @image_comparison(['o_marker_path_snap.png'], savefig_kwarg={'dpi': 72}) def test_o_marker_path_snap(): fig, ax = plt.subplots() ax.margins(.1) for ms in range(1, 15): ax.plot([1, 2, ], np.ones(2) + ms, 'o', ms=ms) for ms in np.linspace(1, 10, 25): ax.plot([3, 4, ], np.ones(2) + ms, 'o', ms=ms) def test_margins(): # test all ways margins can be called data = [1, 10] xmin = 0.0 xmax = len(data) - 1.0 ymin = min(data) ymax = max(data) fig1, ax1 = plt.subplots(1, 1) ax1.plot(data) ax1.margins(1) assert ax1.margins() == (1, 1) assert ax1.get_xlim() == (xmin - (xmax - xmin) * 1, xmax + (xmax - xmin) * 1) assert ax1.get_ylim() == (ymin - (ymax - ymin) * 1, ymax + (ymax - ymin) * 1) fig2, ax2 = plt.subplots(1, 1) ax2.plot(data) ax2.margins(0.5, 2) assert ax2.margins() == (0.5, 2) assert ax2.get_xlim() == (xmin - (xmax - xmin) * 0.5, xmax + (xmax - xmin) * 0.5) assert ax2.get_ylim() == (ymin - (ymax - ymin) * 2, ymax + (ymax - ymin) * 2) fig3, ax3 = plt.subplots(1, 1) ax3.plot(data) ax3.margins(x=-0.2, y=0.5) assert ax3.margins() == (-0.2, 0.5) assert ax3.get_xlim() == (xmin - (xmax - xmin) * -0.2, xmax + (xmax - xmin) * -0.2) assert ax3.get_ylim() == (ymin - (ymax - ymin) * 0.5, ymax + (ymax - ymin) * 0.5) def test_margin_getters(): fig = plt.figure() ax = fig.add_subplot() ax.margins(0.2, 0.3) assert ax.get_xmargin() == 0.2 assert ax.get_ymargin() == 0.3 def test_set_margin_updates_limits(): mpl.style.use("default") fig, ax = plt.subplots() ax.plot([1, 2], [1, 2]) ax.set(xscale="log", xmargin=0) assert ax.get_xlim() == (1, 2) @pytest.mark.parametrize('err, args, kwargs, match', ( (ValueError, (-1,), {}, r'margin must be greater than -0\.5'), (ValueError, (1, -1), {}, r'margin must be greater than -0\.5'), (ValueError, tuple(), {'x': -1}, r'margin must be greater than -0\.5'), (ValueError, tuple(), {'y': -1}, r'margin must be greater than -0\.5'), (TypeError, (1, ), {'x': 1, 'y': 1}, 'Cannot pass both positional and keyword arguments for x and/or y'), (TypeError, (1, ), {'x': 1}, 'Cannot pass both positional and keyword arguments for x and/or y'), (TypeError, (1, 1, 1), {}, 'Must pass a single positional argument'), )) def test_margins_errors(err, args, kwargs, match): with pytest.raises(err, match=match): fig = plt.figure() ax = fig.add_subplot() ax.margins(*args, **kwargs) def test_length_one_hist(): fig, ax = plt.subplots() ax.hist(1) ax.hist([1]) def test_set_xy_bound(): fig = plt.figure() ax = fig.add_subplot() ax.set_xbound(2.0, 3.0) assert ax.get_xbound() == (2.0, 3.0) assert ax.get_xlim() == (2.0, 3.0) ax.set_xbound(upper=4.0) assert ax.get_xbound() == (2.0, 4.0) assert ax.get_xlim() == (2.0, 4.0) ax.set_xbound(lower=3.0) assert ax.get_xbound() == (3.0, 4.0) assert ax.get_xlim() == (3.0, 4.0) ax.set_ybound(2.0, 3.0) assert ax.get_ybound() == (2.0, 3.0) assert ax.get_ylim() == (2.0, 3.0) ax.set_ybound(upper=4.0) assert ax.get_ybound() == (2.0, 4.0) assert ax.get_ylim() == (2.0, 4.0) ax.set_ybound(lower=3.0) assert ax.get_ybound() == (3.0, 4.0) assert ax.get_ylim() == (3.0, 4.0) def test_pathological_hexbin(): # issue #2863 mylist = [10] * 100 fig, ax = plt.subplots(1, 1) ax.hexbin(mylist, mylist) fig.savefig(io.BytesIO()) # Check that no warning is emitted. def test_color_None(): # issue 3855 fig, ax = plt.subplots() ax.plot([1, 2], [1, 2], color=None) def test_color_alias(): # issues 4157 and 4162 fig, ax = plt.subplots() line = ax.plot([0, 1], c='lime')[0] assert 'lime' == line.get_color() def test_numerical_hist_label(): fig, ax = plt.subplots() ax.hist([range(15)] * 5, label=range(5)) ax.legend() def test_unicode_hist_label(): fig, ax = plt.subplots() a = (b'\xe5\xbe\x88\xe6\xbc\x82\xe4\xba\xae, ' + b'r\xc3\xb6m\xc3\xa4n ch\xc3\xa4r\xc3\xa1ct\xc3\xa8rs') b = b'\xd7\xa9\xd7\x9c\xd7\x95\xd7\x9d' labels = [a.decode('utf-8'), 'hi aardvark', b.decode('utf-8'), ] ax.hist([range(15)] * 3, label=labels) ax.legend() def test_move_offsetlabel(): data = np.random.random(10) * 1e-22 fig, ax = plt.subplots() ax.plot(data) fig.canvas.draw() before = ax.yaxis.offsetText.get_position() assert ax.yaxis.offsetText.get_horizontalalignment() == 'left' ax.yaxis.tick_right() fig.canvas.draw() after = ax.yaxis.offsetText.get_position() assert after[0] > before[0] and after[1] == before[1] assert ax.yaxis.offsetText.get_horizontalalignment() == 'right' fig, ax = plt.subplots() ax.plot(data) fig.canvas.draw() before = ax.xaxis.offsetText.get_position() assert ax.xaxis.offsetText.get_verticalalignment() == 'top' ax.xaxis.tick_top() fig.canvas.draw() after = ax.xaxis.offsetText.get_position() assert after[0] == before[0] and after[1] > before[1] assert ax.xaxis.offsetText.get_verticalalignment() == 'bottom' @image_comparison(['rc_spines.png'], savefig_kwarg={'dpi': 40}) def test_rc_spines(): rc_dict = { 'axes.spines.left': False, 'axes.spines.right': False, 'axes.spines.top': False, 'axes.spines.bottom': False} with matplotlib.rc_context(rc_dict): plt.subplots() # create a figure and axes with the spine properties @image_comparison(['rc_grid.png'], savefig_kwarg={'dpi': 40}) def test_rc_grid(): fig = plt.figure() rc_dict0 = { 'axes.grid': True, 'axes.grid.axis': 'both' } rc_dict1 = { 'axes.grid': True, 'axes.grid.axis': 'x' } rc_dict2 = { 'axes.grid': True, 'axes.grid.axis': 'y' } dict_list = [rc_dict0, rc_dict1, rc_dict2] for i, rc_dict in enumerate(dict_list, 1): with matplotlib.rc_context(rc_dict): fig.add_subplot(3, 1, i) def test_rc_tick(): d = {'xtick.bottom': False, 'xtick.top': True, 'ytick.left': True, 'ytick.right': False} with plt.rc_context(rc=d): fig = plt.figure() ax1 = fig.add_subplot(1, 1, 1) xax = ax1.xaxis yax = ax1.yaxis # tick1On bottom/left assert not xax._major_tick_kw['tick1On'] assert xax._major_tick_kw['tick2On'] assert not xax._minor_tick_kw['tick1On'] assert xax._minor_tick_kw['tick2On'] assert yax._major_tick_kw['tick1On'] assert not yax._major_tick_kw['tick2On'] assert yax._minor_tick_kw['tick1On'] assert not yax._minor_tick_kw['tick2On'] def test_rc_major_minor_tick(): d = {'xtick.top': True, 'ytick.right': True, # Enable all ticks 'xtick.bottom': True, 'ytick.left': True, # Selectively disable 'xtick.minor.bottom': False, 'xtick.major.bottom': False, 'ytick.major.left': False, 'ytick.minor.left': False} with plt.rc_context(rc=d): fig = plt.figure() ax1 = fig.add_subplot(1, 1, 1) xax = ax1.xaxis yax = ax1.yaxis # tick1On bottom/left assert not xax._major_tick_kw['tick1On'] assert xax._major_tick_kw['tick2On'] assert not xax._minor_tick_kw['tick1On'] assert xax._minor_tick_kw['tick2On'] assert not yax._major_tick_kw['tick1On'] assert yax._major_tick_kw['tick2On'] assert not yax._minor_tick_kw['tick1On'] assert yax._minor_tick_kw['tick2On'] def test_square_plot(): x = np.arange(4) y = np.array([1., 3., 5., 7.]) fig, ax = plt.subplots() ax.plot(x, y, 'mo') ax.axis('square') xlim, ylim = ax.get_xlim(), ax.get_ylim() assert np.diff(xlim) == np.diff(ylim) assert ax.get_aspect() == 1 assert_array_almost_equal( ax.get_position(original=True).extents, (0.125, 0.1, 0.9, 0.9)) assert_array_almost_equal( ax.get_position(original=False).extents, (0.2125, 0.1, 0.8125, 0.9)) def test_bad_plot_args(): with pytest.raises(ValueError): plt.plot(None) with pytest.raises(ValueError): plt.plot(None, None) with pytest.raises(ValueError): plt.plot(np.zeros((2, 2)), np.zeros((2, 3))) with pytest.raises(ValueError): plt.plot((np.arange(5).reshape((1, -1)), np.arange(5).reshape(-1, 1))) @pytest.mark.parametrize( "xy, cls", [ ((), mpl.image.AxesImage), # (0, N) (((3, 7), (2, 6)), mpl.image.AxesImage), # (xmin, xmax) ((range(5), range(4)), mpl.image.AxesImage), # regular grid (([1, 2, 4, 8, 16], [0, 1, 2, 3]), # irregular grid mpl.image.PcolorImage), ((np.random.random((4, 5)), np.random.random((4, 5))), # 2D coords mpl.collections.QuadMesh), ] ) @pytest.mark.parametrize( "data", [np.arange(12).reshape((3, 4)), np.random.rand(3, 4, 3)] ) def test_pcolorfast(xy, data, cls): fig, ax = plt.subplots() assert type(ax.pcolorfast(*xy, data)) == cls def test_pcolorfast_bad_dims(): fig, ax = plt.subplots() with pytest.raises( TypeError, match=("the given X was 1D and the given Y was 2D")): ax.pcolorfast(np.empty(6), np.empty((4, 7)), np.empty((8, 8))) def test_pcolorfast_regular_xy_incompatible_size(): """ Test that the sizes of X, Y, C are compatible for regularly spaced X, Y. Note that after the regualar-spacing check, pcolorfast may go into the fast "image" mode, where the individual X, Y positions are not used anymore. Therefore, the algorithm had worked with any regularly number of regularly spaced values, but discarded their values. """ fig, ax = plt.subplots() with pytest.raises( ValueError, match=r"Length of X \(5\) must be one larger than the " r"number of columns in C \(20\)"): ax.pcolorfast(np.arange(5), np.arange(11), np.random.rand(10, 20)) with pytest.raises( ValueError, match=r"Length of Y \(5\) must be one larger than the " r"number of rows in C \(10\)"): ax.pcolorfast(np.arange(21), np.arange(5), np.random.rand(10, 20)) def test_shared_scale(): fig, axs = plt.subplots(2, 2, sharex=True, sharey=True) axs[0, 0].set_xscale("log") axs[0, 0].set_yscale("log") for ax in axs.flat: assert ax.get_yscale() == 'log' assert ax.get_xscale() == 'log' axs[1, 1].set_xscale("linear") axs[1, 1].set_yscale("linear") for ax in axs.flat: assert ax.get_yscale() == 'linear' assert ax.get_xscale() == 'linear' def test_shared_bool(): with pytest.raises(TypeError): plt.subplot(sharex=True) with pytest.raises(TypeError): plt.subplot(sharey=True) def test_violin_point_mass(): """Violin plot should handle point mass pdf gracefully.""" plt.violinplot(np.array([0, 0])) def generate_errorbar_inputs(): base_xy = cycler('x', [np.arange(5)]) + cycler('y', [np.ones(5)]) err_cycler = cycler('err', [1, [1, 1, 1, 1, 1], [[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]], np.ones(5), np.ones((2, 5)), None ]) xerr_cy = cycler('xerr', err_cycler) yerr_cy = cycler('yerr', err_cycler) empty = ((cycler('x', [[]]) + cycler('y', [[]])) * cycler('xerr', [[], None]) * cycler('yerr', [[], None])) xerr_only = base_xy * xerr_cy yerr_only = base_xy * yerr_cy both_err = base_xy * yerr_cy * xerr_cy return [*xerr_only, *yerr_only, *both_err, *empty] @pytest.mark.parametrize('kwargs', generate_errorbar_inputs()) def test_errorbar_inputs_shotgun(kwargs): ax = plt.gca() eb = ax.errorbar(**kwargs) eb.remove() @image_comparison(["dash_offset"], remove_text=True) def test_dash_offset(): fig, ax = plt.subplots() x = np.linspace(0, 10) y = np.ones_like(x) for j in range(0, 100, 2): ax.plot(x, j*y, ls=(j, (10, 10)), lw=5, color='k') def test_title_pad(): # check that title padding puts the title in the right # place... fig, ax = plt.subplots() ax.set_title('aardvark', pad=30.) m = ax.titleOffsetTrans.get_matrix() assert m[1, -1] == (30. / 72. * fig.dpi) ax.set_title('aardvark', pad=0.) m = ax.titleOffsetTrans.get_matrix() assert m[1, -1] == 0. # check that it is reverted... ax.set_title('aardvark', pad=None) m = ax.titleOffsetTrans.get_matrix() assert m[1, -1] == (matplotlib.rcParams['axes.titlepad'] / 72. * fig.dpi) def test_title_location_roundtrip(): fig, ax = plt.subplots() # set default title location plt.rcParams['axes.titlelocation'] = 'center' ax.set_title('aardvark') ax.set_title('left', loc='left') ax.set_title('right', loc='right') assert 'left' == ax.get_title(loc='left') assert 'right' == ax.get_title(loc='right') assert 'aardvark' == ax.get_title(loc='center') with pytest.raises(ValueError): ax.get_title(loc='foo') with pytest.raises(ValueError): ax.set_title('fail', loc='foo') @pytest.mark.parametrize('sharex', [True, False]) def test_title_location_shared(sharex): fig, axs = plt.subplots(2, 1, sharex=sharex) axs[0].set_title('A', pad=-40) axs[1].set_title('B', pad=-40) fig.draw_without_rendering() x, y1 = axs[0].title.get_position() x, y2 = axs[1].title.get_position() assert y1 == y2 == 1.0 @image_comparison(["loglog.png"], remove_text=True, tol=0.02) def test_loglog(): fig, ax = plt.subplots() x = np.arange(1, 11) ax.loglog(x, x**3, lw=5) ax.tick_params(length=25, width=2) ax.tick_params(length=15, width=2, which='minor') @image_comparison(["test_loglog_nonpos.png"], remove_text=True, style='mpl20', tol=0 if platform.machine() == 'x86_64' else 0.029) def test_loglog_nonpos(): fig, axs = plt.subplots(3, 3) x = np.arange(1, 11) y = x**3 y[7] = -3. x[4] = -10 for (mcy, mcx), ax in zip(product(['mask', 'clip', ''], repeat=2), axs.flat): if mcx == mcy: if mcx: ax.loglog(x, y**3, lw=2, nonpositive=mcx) else: ax.loglog(x, y**3, lw=2) else: ax.loglog(x, y**3, lw=2) if mcx: ax.set_xscale("log", nonpositive=mcx) if mcy: ax.set_yscale("log", nonpositive=mcy) @mpl.style.context('default') def test_axes_margins(): fig, ax = plt.subplots() ax.plot([0, 1, 2, 3]) assert ax.get_ybound()[0] != 0 fig, ax = plt.subplots() ax.bar([0, 1, 2, 3], [1, 1, 1, 1]) assert ax.get_ybound()[0] == 0 fig, ax = plt.subplots() ax.barh([0, 1, 2, 3], [1, 1, 1, 1]) assert ax.get_xbound()[0] == 0 fig, ax = plt.subplots() ax.pcolor(np.zeros((10, 10))) assert ax.get_xbound() == (0, 10) assert ax.get_ybound() == (0, 10) fig, ax = plt.subplots() ax.pcolorfast(np.zeros((10, 10))) assert ax.get_xbound() == (0, 10) assert ax.get_ybound() == (0, 10) fig, ax = plt.subplots() ax.hist(np.arange(10)) assert ax.get_ybound()[0] == 0 fig, ax = plt.subplots() ax.imshow(np.zeros((10, 10))) assert ax.get_xbound() == (-0.5, 9.5) assert ax.get_ybound() == (-0.5, 9.5) @pytest.fixture(params=['x', 'y']) def shared_axis_remover(request): def _helper_x(ax): ax2 = ax.twinx() ax2.remove() ax.set_xlim(0, 15) r = ax.xaxis.get_major_locator()() assert r[-1] > 14 def _helper_y(ax): ax2 = ax.twiny() ax2.remove() ax.set_ylim(0, 15) r = ax.yaxis.get_major_locator()() assert r[-1] > 14 return {"x": _helper_x, "y": _helper_y}[request.param] @pytest.fixture(params=['gca', 'subplots', 'subplots_shared', 'add_axes']) def shared_axes_generator(request): # test all of the ways to get fig/ax sets if request.param == 'gca': fig = plt.figure() ax = fig.gca() elif request.param == 'subplots': fig, ax = plt.subplots() elif request.param == 'subplots_shared': fig, ax_lst = plt.subplots(2, 2, sharex='all', sharey='all') ax = ax_lst[0][0] elif request.param == 'add_axes': fig = plt.figure() ax = fig.add_axes([.1, .1, .8, .8]) return fig, ax def test_remove_shared_axes(shared_axes_generator, shared_axis_remover): # test all of the ways to get fig/ax sets fig, ax = shared_axes_generator shared_axis_remover(ax) def test_remove_shared_axes_relim(): fig, ax_lst = plt.subplots(2, 2, sharex='all', sharey='all') ax = ax_lst[0][0] orig_xlim = ax_lst[0][1].get_xlim() ax.remove() ax.set_xlim(0, 5) assert_array_equal(ax_lst[0][1].get_xlim(), orig_xlim) def test_shared_axes_autoscale(): l = np.arange(-80, 90, 40) t = np.random.random_sample((l.size, l.size)) fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, sharey=True) ax1.set_xlim(-1000, 1000) ax1.set_ylim(-1000, 1000) ax1.contour(l, l, t) ax2.contour(l, l, t) assert not ax1.get_autoscalex_on() and not ax2.get_autoscalex_on() assert not ax1.get_autoscaley_on() and not ax2.get_autoscaley_on() assert ax1.get_xlim() == ax2.get_xlim() == (-1000, 1000) assert ax1.get_ylim() == ax2.get_ylim() == (-1000, 1000) def test_adjust_numtick_aspect(): fig, ax = plt.subplots() ax.yaxis.get_major_locator().set_params(nbins='auto') ax.set_xlim(0, 1000) ax.set_aspect('equal') fig.canvas.draw() assert len(ax.yaxis.get_major_locator()()) == 2 ax.set_ylim(0, 1000) fig.canvas.draw() assert len(ax.yaxis.get_major_locator()()) > 2 @mpl.style.context("default") def test_auto_numticks(): axs = plt.figure().subplots(4, 4) for ax in axs.flat: # Tiny, empty subplots have only 3 ticks. assert [*ax.get_xticks()] == [*ax.get_yticks()] == [0, 0.5, 1] @mpl.style.context("default") def test_auto_numticks_log(): # Verify that there are not too many ticks with a large log range. fig, ax = plt.subplots() mpl.rcParams['axes.autolimit_mode'] = 'round_numbers' ax.loglog([1e-20, 1e5], [1e-16, 10]) assert (np.log10(ax.get_xticks()) == np.arange(-26, 18, 4)).all() assert (np.log10(ax.get_yticks()) == np.arange(-20, 10, 3)).all() def test_broken_barh_empty(): fig, ax = plt.subplots() ax.broken_barh([], (.1, .5)) def test_broken_barh_timedelta(): """Check that timedelta works as x, dx pair for this method.""" fig, ax = plt.subplots() d0 = datetime.datetime(2018, 11, 9, 0, 0, 0) pp = ax.broken_barh([(d0, datetime.timedelta(hours=1))], [1, 2]) assert pp.get_paths()[0].vertices[0, 0] == mdates.date2num(d0) assert pp.get_paths()[0].vertices[2, 0] == mdates.date2num(d0) + 1 / 24 def test_pandas_pcolormesh(pd): time = pd.date_range('2000-01-01', periods=10) depth = np.arange(20) data = np.random.rand(19, 9) fig, ax = plt.subplots() ax.pcolormesh(time, depth, data) def test_pandas_indexing_dates(pd): dates = np.arange('2005-02', '2005-03', dtype='datetime64[D]') values = np.sin(range(len(dates))) df = pd.DataFrame({'dates': dates, 'values': values}) ax = plt.gca() without_zero_index = df[np.array(df.index) % 2 == 1].copy() ax.plot('dates', 'values', data=without_zero_index) def test_pandas_errorbar_indexing(pd): df = pd.DataFrame(np.random.uniform(size=(5, 4)), columns=['x', 'y', 'xe', 'ye'], index=[1, 2, 3, 4, 5]) fig, ax = plt.subplots() ax.errorbar('x', 'y', xerr='xe', yerr='ye', data=df) def test_pandas_index_shape(pd): df = pd.DataFrame({"XX": [4, 5, 6], "YY": [7, 1, 2]}) fig, ax = plt.subplots() ax.plot(df.index, df['YY']) def test_pandas_indexing_hist(pd): ser_1 = pd.Series(data=[1, 2, 2, 3, 3, 4, 4, 4, 4, 5]) ser_2 = ser_1.iloc[1:] fig, ax = plt.subplots() ax.hist(ser_2) def test_pandas_bar_align_center(pd): # Tests fix for issue 8767 df = pd.DataFrame({'a': range(2), 'b': range(2)}) fig, ax = plt.subplots(1) ax.bar(df.loc[df['a'] == 1, 'b'], df.loc[df['a'] == 1, 'b'], align='center') fig.canvas.draw() def test_axis_get_tick_params(): axis = plt.subplot().yaxis initial_major_style_translated = {**axis.get_tick_params(which='major')} initial_minor_style_translated = {**axis.get_tick_params(which='minor')} translated_major_kw = axis._translate_tick_params( axis._major_tick_kw, reverse=True ) translated_minor_kw = axis._translate_tick_params( axis._minor_tick_kw, reverse=True ) assert translated_major_kw == initial_major_style_translated assert translated_minor_kw == initial_minor_style_translated axis.set_tick_params(labelsize=30, labelcolor='red', direction='out', which='both') new_major_style_translated = {**axis.get_tick_params(which='major')} new_minor_style_translated = {**axis.get_tick_params(which='minor')} new_major_style = axis._translate_tick_params(new_major_style_translated) new_minor_style = axis._translate_tick_params(new_minor_style_translated) assert initial_major_style_translated != new_major_style_translated assert axis._major_tick_kw == new_major_style assert initial_minor_style_translated != new_minor_style_translated assert axis._minor_tick_kw == new_minor_style def test_axis_set_tick_params_labelsize_labelcolor(): # Tests fix for issue 4346 axis_1 = plt.subplot() axis_1.yaxis.set_tick_params(labelsize=30, labelcolor='red', direction='out') # Expected values after setting the ticks assert axis_1.yaxis.majorTicks[0]._size == 4.0 assert axis_1.yaxis.majorTicks[0].tick1line.get_color() == 'k' assert axis_1.yaxis.majorTicks[0].label1.get_size() == 30.0 assert axis_1.yaxis.majorTicks[0].label1.get_color() == 'red' def test_axes_tick_params_gridlines(): # Now treating grid params like other Tick params ax = plt.subplot() ax.tick_params(grid_color='b', grid_linewidth=5, grid_alpha=0.5, grid_linestyle='dashdot') for axis in ax.xaxis, ax.yaxis: assert axis.majorTicks[0].gridline.get_color() == 'b' assert axis.majorTicks[0].gridline.get_linewidth() == 5 assert axis.majorTicks[0].gridline.get_alpha() == 0.5 assert axis.majorTicks[0].gridline.get_linestyle() == '-.' def test_axes_tick_params_ylabelside(): # Tests fix for issue 10267 ax = plt.subplot() ax.tick_params(labelleft=False, labelright=True, which='major') ax.tick_params(labelleft=False, labelright=True, which='minor') # expects left false, right true assert ax.yaxis.majorTicks[0].label1.get_visible() is False assert ax.yaxis.majorTicks[0].label2.get_visible() is True assert ax.yaxis.minorTicks[0].label1.get_visible() is False assert ax.yaxis.minorTicks[0].label2.get_visible() is True def test_axes_tick_params_xlabelside(): # Tests fix for issue 10267 ax = plt.subplot() ax.tick_params(labeltop=True, labelbottom=False, which='major') ax.tick_params(labeltop=True, labelbottom=False, which='minor') # expects top True, bottom False # label1.get_visible() mapped to labelbottom # label2.get_visible() mapped to labeltop assert ax.xaxis.majorTicks[0].label1.get_visible() is False assert ax.xaxis.majorTicks[0].label2.get_visible() is True assert ax.xaxis.minorTicks[0].label1.get_visible() is False assert ax.xaxis.minorTicks[0].label2.get_visible() is True def test_none_kwargs(): ax = plt.figure().subplots() ln, = ax.plot(range(32), linestyle=None) assert ln.get_linestyle() == '-' def test_bar_uint8(): xs = [0, 1, 2, 3] b = plt.bar(np.array(xs, dtype=np.uint8), [2, 3, 4, 5], align="edge") for (patch, x) in zip(b.patches, xs): assert patch.xy[0] == x @image_comparison(['date_timezone_x.png'], tol=1.0) def test_date_timezone_x(): # Tests issue 5575 time_index = [datetime.datetime(2016, 2, 22, hour=x, tzinfo=dateutil.tz.gettz('Canada/Eastern')) for x in range(3)] # Same Timezone plt.figure(figsize=(20, 12)) plt.subplot(2, 1, 1) with pytest.warns(mpl.MatplotlibDeprecationWarning): plt.plot_date(time_index, [3] * 3, tz='Canada/Eastern') # Different Timezone plt.subplot(2, 1, 2) with pytest.warns(mpl.MatplotlibDeprecationWarning): plt.plot_date(time_index, [3] * 3, tz='UTC') @image_comparison(['date_timezone_y.png']) def test_date_timezone_y(): # Tests issue 5575 time_index = [datetime.datetime(2016, 2, 22, hour=x, tzinfo=dateutil.tz.gettz('Canada/Eastern')) for x in range(3)] # Same Timezone plt.figure(figsize=(20, 12)) plt.subplot(2, 1, 1) with pytest.warns(mpl.MatplotlibDeprecationWarning): plt.plot_date([3] * 3, time_index, tz='Canada/Eastern', xdate=False, ydate=True) # Different Timezone plt.subplot(2, 1, 2) with pytest.warns(mpl.MatplotlibDeprecationWarning): plt.plot_date([3] * 3, time_index, tz='UTC', xdate=False, ydate=True) @image_comparison(['date_timezone_x_and_y.png'], tol=1.0) def test_date_timezone_x_and_y(): # Tests issue 5575 UTC = datetime.timezone.utc time_index = [datetime.datetime(2016, 2, 22, hour=x, tzinfo=UTC) for x in range(3)] # Same Timezone plt.figure(figsize=(20, 12)) plt.subplot(2, 1, 1) with pytest.warns(mpl.MatplotlibDeprecationWarning): plt.plot_date(time_index, time_index, tz='UTC', ydate=True) # Different Timezone plt.subplot(2, 1, 2) with pytest.warns(mpl.MatplotlibDeprecationWarning): plt.plot_date(time_index, time_index, tz='US/Eastern', ydate=True) @image_comparison(['axisbelow.png'], remove_text=True) def test_axisbelow(): # Test 'line' setting added in 6287. # Show only grids, not frame or ticks, to make this test # independent of future change to drawing order of those elements. axs = plt.figure().subplots(ncols=3, sharex=True, sharey=True) settings = (False, 'line', True) for ax, setting in zip(axs, settings): ax.plot((0, 10), (0, 10), lw=10, color='m') circ = mpatches.Circle((3, 3), color='r') ax.add_patch(circ) ax.grid(color='c', linestyle='-', linewidth=3) ax.tick_params(top=False, bottom=False, left=False, right=False) ax.spines[:].set_visible(False) ax.set_axisbelow(setting) assert ax.get_axisbelow() == setting def test_titletwiny(): plt.style.use('mpl20') fig, ax = plt.subplots(dpi=72) ax2 = ax.twiny() xlabel2 = ax2.set_xlabel('Xlabel2') title = ax.set_title('Title') fig.canvas.draw() renderer = fig.canvas.get_renderer() # ------- Test that title is put above Xlabel2 (Xlabel2 at top) ---------- bbox_y0_title = title.get_window_extent(renderer).y0 # bottom of title bbox_y1_xlabel2 = xlabel2.get_window_extent(renderer).y1 # top of xlabel2 y_diff = bbox_y0_title - bbox_y1_xlabel2 assert np.isclose(y_diff, 3) def test_titlesetpos(): # Test that title stays put if we set it manually fig, ax = plt.subplots() fig.subplots_adjust(top=0.8) ax2 = ax.twiny() ax.set_xlabel('Xlabel') ax2.set_xlabel('Xlabel2') ax.set_title('Title') pos = (0.5, 1.11) ax.title.set_position(pos) renderer = fig.canvas.get_renderer() ax._update_title_position(renderer) assert ax.title.get_position() == pos def test_title_xticks_top(): # Test that title moves if xticks on top of axes. mpl.rcParams['axes.titley'] = None fig, ax = plt.subplots() ax.xaxis.set_ticks_position('top') ax.set_title('xlabel top') fig.canvas.draw() assert ax.title.get_position()[1] > 1.04 def test_title_xticks_top_both(): # Test that title moves if xticks on top of axes. mpl.rcParams['axes.titley'] = None fig, ax = plt.subplots() ax.tick_params(axis="x", bottom=True, top=True, labelbottom=True, labeltop=True) ax.set_title('xlabel top') fig.canvas.draw() assert ax.title.get_position()[1] > 1.04 @pytest.mark.parametrize( 'left, center', [ ('left', ''), ('', 'center'), ('left', 'center') ], ids=[ 'left title moved', 'center title kept', 'both titles aligned' ] ) def test_title_above_offset(left, center): # Test that title moves if overlaps with yaxis offset text. mpl.rcParams['axes.titley'] = None fig, ax = plt.subplots() ax.set_ylim(1e11) ax.set_title(left, loc='left') ax.set_title(center) fig.draw_without_rendering() if left and not center: assert ax._left_title.get_position()[1] > 1.0 elif not left and center: assert ax.title.get_position()[1] == 1.0 else: yleft = ax._left_title.get_position()[1] ycenter = ax.title.get_position()[1] assert yleft > 1.0 assert ycenter == yleft def test_title_no_move_off_page(): # If an Axes is off the figure (ie. if it is cropped during a save) # make sure that the automatic title repositioning does not get done. mpl.rcParams['axes.titley'] = None fig = plt.figure() ax = fig.add_axes([0.1, -0.5, 0.8, 0.2]) ax.tick_params(axis="x", bottom=True, top=True, labelbottom=True, labeltop=True) tt = ax.set_title('Boo') fig.canvas.draw() assert tt.get_position()[1] == 1.0 def test_title_inset_ax(): # Title should be above any child axes mpl.rcParams['axes.titley'] = None fig, ax = plt.subplots() ax.set_title('Title') fig.draw_without_rendering() assert ax.title.get_position()[1] == 1 ax.inset_axes([0, 1, 1, 0.1]) fig.draw_without_rendering() assert ax.title.get_position()[1] == 1.1 def test_offset_label_color(): # Tests issue 6440 fig, ax = plt.subplots() ax.plot([1.01e9, 1.02e9, 1.03e9]) ax.yaxis.set_tick_params(labelcolor='red') assert ax.yaxis.get_offset_text().get_color() == 'red' def test_offset_text_visible(): fig, ax = plt.subplots() ax.plot([1.01e9, 1.02e9, 1.03e9]) ax.yaxis.set_tick_params(label1On=False, label2On=True) assert ax.yaxis.get_offset_text().get_visible() ax.yaxis.set_tick_params(label2On=False) assert not ax.yaxis.get_offset_text().get_visible() def test_large_offset(): fig, ax = plt.subplots() ax.plot((1 + np.array([0, 1.e-12])) * 1.e27) fig.canvas.draw() def test_barb_units(): fig, ax = plt.subplots() dates = [datetime.datetime(2017, 7, 15, 18, i) for i in range(0, 60, 10)] y = np.linspace(0, 5, len(dates)) u = v = np.linspace(0, 50, len(dates)) ax.barbs(dates, y, u, v) def test_quiver_units(): fig, ax = plt.subplots() dates = [datetime.datetime(2017, 7, 15, 18, i) for i in range(0, 60, 10)] y = np.linspace(0, 5, len(dates)) u = v = np.linspace(0, 50, len(dates)) ax.quiver(dates, y, u, v) def test_bar_color_cycle(): to_rgb = mcolors.to_rgb fig, ax = plt.subplots() for j in range(5): ln, = ax.plot(range(3)) brs = ax.bar(range(3), range(3)) for br in brs: assert to_rgb(ln.get_color()) == to_rgb(br.get_facecolor()) def test_tick_param_label_rotation(): fix, (ax, ax2) = plt.subplots(1, 2) ax.plot([0, 1], [0, 1]) ax2.plot([0, 1], [0, 1]) ax.xaxis.set_tick_params(which='both', rotation=75) ax.yaxis.set_tick_params(which='both', rotation=90) for text in ax.get_xticklabels(which='both'): assert text.get_rotation() == 75 for text in ax.get_yticklabels(which='both'): assert text.get_rotation() == 90 ax2.tick_params(axis='x', labelrotation=53) ax2.tick_params(axis='y', rotation=35) for text in ax2.get_xticklabels(which='major'): assert text.get_rotation() == 53 for text in ax2.get_yticklabels(which='major'): assert text.get_rotation() == 35 @mpl.style.context('default') def test_fillbetween_cycle(): fig, ax = plt.subplots() for j in range(3): cc = ax.fill_between(range(3), range(3)) target = mcolors.to_rgba(f'C{j}') assert tuple(cc.get_facecolors().squeeze()) == tuple(target) for j in range(3, 6): cc = ax.fill_betweenx(range(3), range(3)) target = mcolors.to_rgba(f'C{j}') assert tuple(cc.get_facecolors().squeeze()) == tuple(target) target = mcolors.to_rgba('k') for al in ['facecolor', 'facecolors', 'color']: cc = ax.fill_between(range(3), range(3), **{al: 'k'}) assert tuple(cc.get_facecolors().squeeze()) == tuple(target) edge_target = mcolors.to_rgba('k') for j, el in enumerate(['edgecolor', 'edgecolors'], start=6): cc = ax.fill_between(range(3), range(3), **{el: 'k'}) face_target = mcolors.to_rgba(f'C{j}') assert tuple(cc.get_facecolors().squeeze()) == tuple(face_target) assert tuple(cc.get_edgecolors().squeeze()) == tuple(edge_target) def test_log_margins(): plt.rcParams['axes.autolimit_mode'] = 'data' fig, ax = plt.subplots() margin = 0.05 ax.set_xmargin(margin) ax.semilogx([10, 100], [10, 100]) xlim0, xlim1 = ax.get_xlim() transform = ax.xaxis.get_transform() xlim0t, xlim1t = transform.transform([xlim0, xlim1]) x0t, x1t = transform.transform([10, 100]) delta = (x1t - x0t) * margin assert_allclose([xlim0t + delta, xlim1t - delta], [x0t, x1t]) def test_color_length_mismatch(): N = 5 x, y = np.arange(N), np.arange(N) colors = np.arange(N+1) fig, ax = plt.subplots() with pytest.raises(ValueError): ax.scatter(x, y, c=colors) with pytest.warns(match="argument looks like a single numeric RGB"): ax.scatter(x, y, c=(0.5, 0.5, 0.5)) ax.scatter(x, y, c=[(0.5, 0.5, 0.5)] * N) def test_eventplot_legend(): plt.eventplot([1.0], label='Label') plt.legend() @pytest.mark.parametrize('err, args, kwargs, match', ( (ValueError, [[1]], {'lineoffsets': []}, 'lineoffsets cannot be empty'), (ValueError, [[1]], {'linelengths': []}, 'linelengths cannot be empty'), (ValueError, [[1]], {'linewidths': []}, 'linewidths cannot be empty'), (ValueError, [[1]], {'linestyles': []}, 'linestyles cannot be empty'), (ValueError, [[1]], {'alpha': []}, 'alpha cannot be empty'), (ValueError, [1], {}, 'positions must be one-dimensional'), (ValueError, [[1]], {'lineoffsets': [1, 2]}, 'lineoffsets and positions are unequal sized sequences'), (ValueError, [[1]], {'linelengths': [1, 2]}, 'linelengths and positions are unequal sized sequences'), (ValueError, [[1]], {'linewidths': [1, 2]}, 'linewidths and positions are unequal sized sequences'), (ValueError, [[1]], {'linestyles': [1, 2]}, 'linestyles and positions are unequal sized sequences'), (ValueError, [[1]], {'alpha': [1, 2]}, 'alpha and positions are unequal sized sequences'), (ValueError, [[1]], {'colors': [1, 2]}, 'colors and positions are unequal sized sequences'), )) def test_eventplot_errors(err, args, kwargs, match): with pytest.raises(err, match=match): plt.eventplot(*args, **kwargs) def test_bar_broadcast_args(): fig, ax = plt.subplots() # Check that a bar chart with a single height for all bars works. ax.bar(range(4), 1) # Check that a horizontal chart with one width works. ax.barh(0, 1, left=range(4), height=1) # Check that edgecolor gets broadcast. rect1, rect2 = ax.bar([0, 1], [0, 1], edgecolor=(.1, .2, .3, .4)) assert rect1.get_edgecolor() == rect2.get_edgecolor() == (.1, .2, .3, .4) def test_invalid_axis_limits(): plt.plot([0, 1], [0, 1]) with pytest.raises(ValueError): plt.xlim(np.nan) with pytest.raises(ValueError): plt.xlim(np.inf) with pytest.raises(ValueError): plt.ylim(np.nan) with pytest.raises(ValueError): plt.ylim(np.inf) # Test all 4 combinations of logs/symlogs for minorticks_on() @pytest.mark.parametrize('xscale', ['symlog', 'log']) @pytest.mark.parametrize('yscale', ['symlog', 'log']) def test_minorticks_on(xscale, yscale): ax = plt.subplot() ax.plot([1, 2, 3, 4]) ax.set_xscale(xscale) ax.set_yscale(yscale) ax.minorticks_on() def test_twinx_knows_limits(): fig, ax = plt.subplots() ax.axvspan(1, 2) xtwin = ax.twinx() xtwin.plot([0, 0.5], [1, 2]) # control axis fig2, ax2 = plt.subplots() ax2.axvspan(1, 2) ax2.plot([0, 0.5], [1, 2]) assert_array_equal(xtwin.viewLim.intervalx, ax2.viewLim.intervalx) def test_zero_linewidth(): # Check that setting a zero linewidth doesn't error plt.plot([0, 1], [0, 1], ls='--', lw=0) def test_empty_errorbar_legend(): fig, ax = plt.subplots() ax.errorbar([], [], xerr=[], label='empty y') ax.errorbar([], [], yerr=[], label='empty x') ax.legend() @check_figures_equal(extensions=["png"]) def test_plot_decimal(fig_test, fig_ref): x0 = np.arange(-10, 10, 0.3) y0 = [5.2 * x ** 3 - 2.1 * x ** 2 + 7.34 * x + 4.5 for x in x0] x = [Decimal(i) for i in x0] y = [Decimal(i) for i in y0] # Test image - line plot with Decimal input fig_test.subplots().plot(x, y) # Reference image fig_ref.subplots().plot(x0, y0) # pdf and svg tests fail using travis' old versions of gs and inkscape. @check_figures_equal(extensions=["png"]) def test_markerfacecolor_none_alpha(fig_test, fig_ref): fig_test.subplots().plot(0, "o", mfc="none", alpha=.5) fig_ref.subplots().plot(0, "o", mfc="w", alpha=.5) def test_tick_padding_tightbbox(): """Test that tick padding gets turned off if axis is off""" plt.rcParams["xtick.direction"] = "out" plt.rcParams["ytick.direction"] = "out" fig, ax = plt.subplots() bb = ax.get_tightbbox(fig.canvas.get_renderer()) ax.axis('off') bb2 = ax.get_tightbbox(fig.canvas.get_renderer()) assert bb.x0 < bb2.x0 assert bb.y0 < bb2.y0 def test_inset(): """ Ensure that inset_ax argument is indeed optional """ dx, dy = 0.05, 0.05 # generate 2 2d grids for the x & y bounds y, x = np.mgrid[slice(1, 5 + dy, dy), slice(1, 5 + dx, dx)] z = np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x) fig, ax = plt.subplots() ax.pcolormesh(x, y, z[:-1, :-1]) ax.set_aspect(1.) ax.apply_aspect() # we need to apply_aspect to make the drawing below work. xlim = [1.5, 2.15] ylim = [2, 2.5] rect = [xlim[0], ylim[0], xlim[1] - xlim[0], ylim[1] - ylim[0]] inset = ax.indicate_inset(bounds=rect) assert inset.connectors is None fig.canvas.draw() xx = np.array([[1.5, 2.], [2.15, 2.5]]) assert np.all(inset.rectangle.get_bbox().get_points() == xx) def test_zoom_inset(): dx, dy = 0.05, 0.05 # generate 2 2d grids for the x & y bounds y, x = np.mgrid[slice(1, 5 + dy, dy), slice(1, 5 + dx, dx)] z = np.sin(x)**10 + np.cos(10 + y*x) * np.cos(x) fig, ax = plt.subplots() ax.pcolormesh(x, y, z[:-1, :-1]) ax.set_aspect(1.) ax.apply_aspect() # we need to apply_aspect to make the drawing below work. # Make the inset_axes... Position axes coordinates... axin1 = ax.inset_axes([0.7, 0.7, 0.35, 0.35]) # redraw the data in the inset axes... axin1.pcolormesh(x, y, z[:-1, :-1]) axin1.set_xlim([1.5, 2.15]) axin1.set_ylim([2, 2.5]) axin1.set_aspect(ax.get_aspect()) with pytest.warns(mpl.MatplotlibDeprecationWarning): rec, connectors = ax.indicate_inset_zoom(axin1) fig.canvas.draw() assert len(connectors) == 4 xx = np.array([[1.5, 2.], [2.15, 2.5]]) assert np.all(rec.get_bbox().get_points() == xx) xx = np.array([[0.6325, 0.692308], [0.8425, 0.907692]]) np.testing.assert_allclose( axin1.get_position().get_points(), xx, rtol=1e-4) @image_comparison(['inset_polar.png'], remove_text=True, style='mpl20') def test_inset_polar(): _, ax = plt.subplots() axins = ax.inset_axes([0.5, 0.1, 0.45, 0.45], polar=True) assert isinstance(axins, PolarAxes) r = np.arange(0, 2, 0.01) theta = 2 * np.pi * r ax.plot(theta, r) axins.plot(theta, r) def test_inset_projection(): _, ax = plt.subplots() axins = ax.inset_axes([0.2, 0.2, 0.3, 0.3], projection="hammer") assert isinstance(axins, HammerAxes) def test_inset_subclass(): _, ax = plt.subplots() axins = ax.inset_axes([0.2, 0.2, 0.3, 0.3], axes_class=AA.Axes) assert isinstance(axins, AA.Axes) @pytest.mark.parametrize('x_inverted', [False, True]) @pytest.mark.parametrize('y_inverted', [False, True]) def test_indicate_inset_inverted(x_inverted, y_inverted): """ Test that the inset lines are correctly located with inverted data axes. """ fig, (ax1, ax2) = plt.subplots(1, 2) x = np.arange(10) ax1.plot(x, x, 'o') if x_inverted: ax1.invert_xaxis() if y_inverted: ax1.invert_yaxis() inset = ax1.indicate_inset([2, 2, 5, 4], ax2) lower_left, upper_left, lower_right, upper_right = inset.connectors sign_x = -1 if x_inverted else 1 sign_y = -1 if y_inverted else 1 assert sign_x * (lower_right.xy2[0] - lower_left.xy2[0]) > 0 assert sign_x * (upper_right.xy2[0] - upper_left.xy2[0]) > 0 assert sign_y * (upper_left.xy2[1] - lower_left.xy2[1]) > 0 assert sign_y * (upper_right.xy2[1] - lower_right.xy2[1]) > 0 def test_set_position(): fig, ax = plt.subplots() ax.set_aspect(3.) ax.set_position([0.1, 0.1, 0.4, 0.4], which='both') assert np.allclose(ax.get_position().width, 0.1) ax.set_aspect(2.) ax.set_position([0.1, 0.1, 0.4, 0.4], which='original') assert np.allclose(ax.get_position().width, 0.15) ax.set_aspect(3.) ax.set_position([0.1, 0.1, 0.4, 0.4], which='active') assert np.allclose(ax.get_position().width, 0.1) def test_spines_properbbox_after_zoom(): fig, ax = plt.subplots() bb = ax.spines.bottom.get_window_extent(fig.canvas.get_renderer()) # this is what zoom calls: ax._set_view_from_bbox((320, 320, 500, 500), 'in', None, False, False) bb2 = ax.spines.bottom.get_window_extent(fig.canvas.get_renderer()) np.testing.assert_allclose(bb.get_points(), bb2.get_points(), rtol=1e-6) def test_limits_after_scroll_zoom(): fig, ax = plt.subplots() # xlim = (-0.5, 0.5) ylim = (-1, 2) ax.set_xlim(xlim) ax.set_ylim(ymin=ylim[0], ymax=ylim[1]) # This is what scroll zoom calls: # Zoom with factor 1, small numerical change ax._set_view_from_bbox((200, 200, 1.)) np.testing.assert_allclose(xlim, ax.get_xlim(), atol=1e-16) np.testing.assert_allclose(ylim, ax.get_ylim(), atol=1e-16) # Zoom in ax._set_view_from_bbox((200, 200, 2.)) # Hard-coded values new_xlim = (-0.3790322580645161, 0.12096774193548387) new_ylim = (-0.40625, 1.09375) res_xlim = ax.get_xlim() res_ylim = ax.get_ylim() np.testing.assert_allclose(res_xlim[1] - res_xlim[0], 0.5) np.testing.assert_allclose(res_ylim[1] - res_ylim[0], 1.5) np.testing.assert_allclose(new_xlim, res_xlim, atol=1e-16) np.testing.assert_allclose(new_ylim, res_ylim) # Zoom out, should be same as before, except for numerical issues ax._set_view_from_bbox((200, 200, 0.5)) res_xlim = ax.get_xlim() res_ylim = ax.get_ylim() np.testing.assert_allclose(res_xlim[1] - res_xlim[0], 1) np.testing.assert_allclose(res_ylim[1] - res_ylim[0], 3) np.testing.assert_allclose(xlim, res_xlim, atol=1e-16) np.testing.assert_allclose(ylim, res_ylim, atol=1e-16) def test_gettightbbox_ignore_nan(): fig, ax = plt.subplots() remove_ticks_and_titles(fig) ax.text(np.nan, 1, 'Boo') renderer = fig.canvas.get_renderer() np.testing.assert_allclose(ax.get_tightbbox(renderer).width, 496) def test_scatter_series_non_zero_index(pd): # create non-zero index ids = range(10, 18) x = pd.Series(np.random.uniform(size=8), index=ids) y = pd.Series(np.random.uniform(size=8), index=ids) c = pd.Series([1, 1, 1, 1, 1, 0, 0, 0], index=ids) plt.scatter(x, y, c) def test_scatter_empty_data(): # making sure this does not raise an exception plt.scatter([], []) plt.scatter([], [], s=[], c=[]) @image_comparison(['annotate_across_transforms.png'], style='mpl20', remove_text=True, tol=0 if platform.machine() == 'x86_64' else 0.025) def test_annotate_across_transforms(): x = np.linspace(0, 10, 200) y = np.exp(-x) * np.sin(x) fig, ax = plt.subplots(figsize=(3.39, 3)) ax.plot(x, y) axins = ax.inset_axes([0.4, 0.5, 0.3, 0.3]) axins.set_aspect(0.2) axins.xaxis.set_visible(False) axins.yaxis.set_visible(False) ax.annotate("", xy=(x[150], y[150]), xycoords=ax.transData, xytext=(1, 0), textcoords=axins.transAxes, arrowprops=dict(arrowstyle="->")) class _Translation(mtransforms.Transform): input_dims = 1 output_dims = 1 def __init__(self, dx): self.dx = dx def transform(self, values): return values + self.dx def inverted(self): return _Translation(-self.dx) @image_comparison(['secondary_xy.png'], style='mpl20', tol=0 if platform.machine() == 'x86_64' else 0.027) def test_secondary_xy(): fig, axs = plt.subplots(1, 2, figsize=(10, 5), constrained_layout=True) def invert(x): with np.errstate(divide='ignore'): return 1 / x for nn, ax in enumerate(axs): ax.plot(np.arange(2, 11), np.arange(2, 11)) if nn == 0: secax = ax.secondary_xaxis else: secax = ax.secondary_yaxis secax(0.2, functions=(invert, invert)) secax(0.4, functions=(lambda x: 2 * x, lambda x: x / 2)) secax(0.6, functions=(lambda x: x**2, lambda x: x**(1/2))) secax(0.8) secax("top" if nn == 0 else "right", functions=_Translation(2)) secax(6.25, transform=ax.transData) def test_secondary_fail(): fig, ax = plt.subplots() ax.plot(np.arange(2, 11), np.arange(2, 11)) with pytest.raises(ValueError): ax.secondary_xaxis(0.2, functions=(lambda x: 1 / x)) with pytest.raises(ValueError): ax.secondary_xaxis('right') with pytest.raises(ValueError): ax.secondary_yaxis('bottom') with pytest.raises(TypeError): ax.secondary_xaxis(0.2, transform='error') def test_secondary_resize(): fig, ax = plt.subplots(figsize=(10, 5)) ax.plot(np.arange(2, 11), np.arange(2, 11)) def invert(x): with np.errstate(divide='ignore'): return 1 / x ax.secondary_xaxis('top', functions=(invert, invert)) fig.canvas.draw() fig.set_size_inches((7, 4)) assert_allclose(ax.get_position().extents, [0.125, 0.1, 0.9, 0.9]) def test_secondary_minorloc(): fig, ax = plt.subplots(figsize=(10, 5)) ax.plot(np.arange(2, 11), np.arange(2, 11)) def invert(x): with np.errstate(divide='ignore'): return 1 / x secax = ax.secondary_xaxis('top', functions=(invert, invert)) assert isinstance(secax._axis.get_minor_locator(), mticker.NullLocator) secax.minorticks_on() assert isinstance(secax._axis.get_minor_locator(), mticker.AutoMinorLocator) ax.set_xscale('log') plt.draw() assert isinstance(secax._axis.get_minor_locator(), mticker.LogLocator) ax.set_xscale('linear') plt.draw() assert isinstance(secax._axis.get_minor_locator(), mticker.NullLocator) def test_secondary_formatter(): fig, ax = plt.subplots() ax.set_xscale("log") secax = ax.secondary_xaxis("top") secax.xaxis.set_major_formatter(mticker.ScalarFormatter()) fig.canvas.draw() assert isinstance( secax.xaxis.get_major_formatter(), mticker.ScalarFormatter) def test_secondary_repr(): fig, ax = plt.subplots() secax = ax.secondary_xaxis("top") assert repr(secax) == '' @image_comparison(['axis_options.png'], remove_text=True, style='mpl20') def test_axis_options(): fig, axes = plt.subplots(2, 3) for i, option in enumerate(('scaled', 'tight', 'image')): # Draw a line and a circle fitting within the boundaries of the line # The circle should look like a circle for 'scaled' and 'image' # High/narrow aspect ratio axes[0, i].plot((1, 2), (1, 3.2)) axes[0, i].axis(option) axes[0, i].add_artist(mpatches.Circle((1.5, 1.5), radius=0.5, facecolor='none', edgecolor='k')) # Low/wide aspect ratio axes[1, i].plot((1, 2.25), (1, 1.75)) axes[1, i].axis(option) axes[1, i].add_artist(mpatches.Circle((1.5, 1.25), radius=0.25, facecolor='none', edgecolor='k')) def color_boxes(fig, ax): """ Helper for the tests below that test the extents of various Axes elements """ fig.canvas.draw() renderer = fig.canvas.get_renderer() bbaxis = [] for nn, axx in enumerate([ax.xaxis, ax.yaxis]): bb = axx.get_tightbbox(renderer) if bb: axisr = mpatches.Rectangle( (bb.x0, bb.y0), width=bb.width, height=bb.height, linewidth=0.7, edgecolor='y', facecolor="none", transform=None, zorder=3) fig.add_artist(axisr) bbaxis += [bb] bbspines = [] for nn, a in enumerate(['bottom', 'top', 'left', 'right']): bb = ax.spines[a].get_window_extent(renderer) spiner = mpatches.Rectangle( (bb.x0, bb.y0), width=bb.width, height=bb.height, linewidth=0.7, edgecolor="green", facecolor="none", transform=None, zorder=3) fig.add_artist(spiner) bbspines += [bb] bb = ax.get_window_extent() rect2 = mpatches.Rectangle( (bb.x0, bb.y0), width=bb.width, height=bb.height, linewidth=1.5, edgecolor="magenta", facecolor="none", transform=None, zorder=2) fig.add_artist(rect2) bbax = bb bb2 = ax.get_tightbbox(renderer) rect2 = mpatches.Rectangle( (bb2.x0, bb2.y0), width=bb2.width, height=bb2.height, linewidth=3, edgecolor="red", facecolor="none", transform=None, zorder=1) fig.add_artist(rect2) bbtb = bb2 return bbaxis, bbspines, bbax, bbtb def test_normal_axes(): with rc_context({'_internal.classic_mode': False}): fig, ax = plt.subplots(dpi=200, figsize=(6, 6)) fig.canvas.draw() plt.close(fig) bbaxis, bbspines, bbax, bbtb = color_boxes(fig, ax) # test the axis bboxes target = [ [123.375, 75.88888888888886, 983.25, 33.0], [85.51388888888889, 99.99999999999997, 53.375, 993.0] ] for nn, b in enumerate(bbaxis): targetbb = mtransforms.Bbox.from_bounds(*target[nn]) assert_array_almost_equal(b.bounds, targetbb.bounds, decimal=2) target = [ [150.0, 119.999, 930.0, 11.111], [150.0, 1080.0, 930.0, 0.0], [150.0, 119.9999, 11.111, 960.0], [1068.8888, 119.9999, 11.111, 960.0] ] for nn, b in enumerate(bbspines): targetbb = mtransforms.Bbox.from_bounds(*target[nn]) assert_array_almost_equal(b.bounds, targetbb.bounds, decimal=2) target = [150.0, 119.99999999999997, 930.0, 960.0] targetbb = mtransforms.Bbox.from_bounds(*target) assert_array_almost_equal(bbax.bounds, targetbb.bounds, decimal=2) target = [85.5138, 75.88888, 1021.11, 1017.11] targetbb = mtransforms.Bbox.from_bounds(*target) assert_array_almost_equal(bbtb.bounds, targetbb.bounds, decimal=2) # test that get_position roundtrips to get_window_extent axbb = ax.get_position().transformed(fig.transFigure).bounds assert_array_almost_equal(axbb, ax.get_window_extent().bounds, decimal=2) def test_nodecorator(): with rc_context({'_internal.classic_mode': False}): fig, ax = plt.subplots(dpi=200, figsize=(6, 6)) fig.canvas.draw() ax.set(xticklabels=[], yticklabels=[]) bbaxis, bbspines, bbax, bbtb = color_boxes(fig, ax) # test the axis bboxes for nn, b in enumerate(bbaxis): assert b is None target = [ [150.0, 119.999, 930.0, 11.111], [150.0, 1080.0, 930.0, 0.0], [150.0, 119.9999, 11.111, 960.0], [1068.8888, 119.9999, 11.111, 960.0] ] for nn, b in enumerate(bbspines): targetbb = mtransforms.Bbox.from_bounds(*target[nn]) assert_allclose(b.bounds, targetbb.bounds, atol=1e-2) target = [150.0, 119.99999999999997, 930.0, 960.0] targetbb = mtransforms.Bbox.from_bounds(*target) assert_allclose(bbax.bounds, targetbb.bounds, atol=1e-2) target = [150., 120., 930., 960.] targetbb = mtransforms.Bbox.from_bounds(*target) assert_allclose(bbtb.bounds, targetbb.bounds, atol=1e-2) def test_displaced_spine(): with rc_context({'_internal.classic_mode': False}): fig, ax = plt.subplots(dpi=200, figsize=(6, 6)) ax.set(xticklabels=[], yticklabels=[]) ax.spines.bottom.set_position(('axes', -0.1)) fig.canvas.draw() bbaxis, bbspines, bbax, bbtb = color_boxes(fig, ax) targets = [ [150., 24., 930., 11.111111], [150.0, 1080.0, 930.0, 0.0], [150.0, 119.9999, 11.111, 960.0], [1068.8888, 119.9999, 11.111, 960.0] ] for target, bbspine in zip(targets, bbspines): targetbb = mtransforms.Bbox.from_bounds(*target) assert_allclose(bbspine.bounds, targetbb.bounds, atol=1e-2) target = [150.0, 119.99999999999997, 930.0, 960.0] targetbb = mtransforms.Bbox.from_bounds(*target) assert_allclose(bbax.bounds, targetbb.bounds, atol=1e-2) target = [150., 24., 930., 1056.] targetbb = mtransforms.Bbox.from_bounds(*target) assert_allclose(bbtb.bounds, targetbb.bounds, atol=1e-2) def test_tickdirs(): """ Switch the tickdirs and make sure the bboxes switch with them """ targets = [[[150.0, 120.0, 930.0, 11.1111], [150.0, 120.0, 11.111, 960.0]], [[150.0, 108.8889, 930.0, 11.111111111111114], [138.889, 120, 11.111, 960.0]], [[150.0, 114.44444444444441, 930.0, 11.111111111111114], [144.44444444444446, 119.999, 11.111, 960.0]]] for dnum, dirs in enumerate(['in', 'out', 'inout']): with rc_context({'_internal.classic_mode': False}): fig, ax = plt.subplots(dpi=200, figsize=(6, 6)) ax.tick_params(direction=dirs) fig.canvas.draw() bbaxis, bbspines, bbax, bbtb = color_boxes(fig, ax) for nn, num in enumerate([0, 2]): targetbb = mtransforms.Bbox.from_bounds(*targets[dnum][nn]) assert_allclose( bbspines[num].bounds, targetbb.bounds, atol=1e-2) def test_minor_accountedfor(): with rc_context({'_internal.classic_mode': False}): fig, ax = plt.subplots(dpi=200, figsize=(6, 6)) fig.canvas.draw() ax.tick_params(which='both', direction='out') bbaxis, bbspines, bbax, bbtb = color_boxes(fig, ax) bbaxis, bbspines, bbax, bbtb = color_boxes(fig, ax) targets = [[150.0, 108.88888888888886, 930.0, 11.111111111111114], [138.8889, 119.9999, 11.1111, 960.0]] for n in range(2): targetbb = mtransforms.Bbox.from_bounds(*targets[n]) assert_allclose( bbspines[n * 2].bounds, targetbb.bounds, atol=1e-2) fig, ax = plt.subplots(dpi=200, figsize=(6, 6)) fig.canvas.draw() ax.tick_params(which='both', direction='out') ax.minorticks_on() ax.tick_params(axis='both', which='minor', length=30) fig.canvas.draw() bbaxis, bbspines, bbax, bbtb = color_boxes(fig, ax) targets = [[150.0, 36.66666666666663, 930.0, 83.33333333333334], [66.6667, 120.0, 83.3333, 960.0]] for n in range(2): targetbb = mtransforms.Bbox.from_bounds(*targets[n]) assert_allclose( bbspines[n * 2].bounds, targetbb.bounds, atol=1e-2) @check_figures_equal(extensions=["png"]) def test_axis_bool_arguments(fig_test, fig_ref): # Test if False and "off" give the same fig_test.add_subplot(211).axis(False) fig_ref.add_subplot(211).axis("off") # Test if True after False gives the same as "on" ax = fig_test.add_subplot(212) ax.axis(False) ax.axis(True) fig_ref.add_subplot(212).axis("on") def test_axis_extent_arg(): fig, ax = plt.subplots() xmin = 5 xmax = 10 ymin = 15 ymax = 20 extent = ax.axis([xmin, xmax, ymin, ymax]) # test that the docstring is correct assert tuple(extent) == (xmin, xmax, ymin, ymax) # test that limits were set per the docstring assert (xmin, xmax) == ax.get_xlim() assert (ymin, ymax) == ax.get_ylim() def test_axis_extent_arg2(): # Same as test_axis_extent_arg, but with keyword arguments fig, ax = plt.subplots() xmin = 5 xmax = 10 ymin = 15 ymax = 20 extent = ax.axis(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax) # test that the docstring is correct assert tuple(extent) == (xmin, xmax, ymin, ymax) # test that limits were set per the docstring assert (xmin, xmax) == ax.get_xlim() assert (ymin, ymax) == ax.get_ylim() def test_hist_auto_bins(): _, bins, _ = plt.hist([[1, 2, 3], [3, 4, 5, 6]], bins='auto') assert bins[0] <= 1 assert bins[-1] >= 6 def test_hist_nan_data(): fig, (ax1, ax2) = plt.subplots(2) data = [1, 2, 3] nan_data = data + [np.nan] bins, edges, _ = ax1.hist(data) with np.errstate(invalid='ignore'): nanbins, nanedges, _ = ax2.hist(nan_data) np.testing.assert_allclose(bins, nanbins) np.testing.assert_allclose(edges, nanedges) def test_hist_range_and_density(): _, bins, _ = plt.hist(np.random.rand(10), "auto", range=(0, 1), density=True) assert bins[0] == 0 assert bins[-1] == 1 def test_bar_errbar_zorder(): # Check that the zorder of errorbars is always greater than the bar they # are plotted on fig, ax = plt.subplots() x = [1, 2, 3] barcont = ax.bar(x=x, height=x, yerr=x, capsize=5, zorder=3) data_line, caplines, barlinecols = barcont.errorbar.lines for bar in barcont.patches: for capline in caplines: assert capline.zorder > bar.zorder for barlinecol in barlinecols: assert barlinecol.zorder > bar.zorder def test_set_ticks_inverted(): fig, ax = plt.subplots() ax.invert_xaxis() ax.set_xticks([.3, .7]) assert ax.get_xlim() == (1, 0) ax.set_xticks([-1]) assert ax.get_xlim() == (1, -1) def test_aspect_nonlinear_adjustable_box(): fig = plt.figure(figsize=(10, 10)) # Square. ax = fig.add_subplot() ax.plot([.4, .6], [.4, .6]) # Set minpos to keep logit happy. ax.set(xscale="log", xlim=(1, 10), yscale="logit", ylim=(1/11, 1/1001), aspect=1, adjustable="box") ax.margins(0) pos = fig.transFigure.transform_bbox(ax.get_position()) assert pos.height / pos.width == pytest.approx(2) def test_aspect_nonlinear_adjustable_datalim(): fig = plt.figure(figsize=(10, 10)) # Square. ax = fig.add_axes([.1, .1, .8, .8]) # Square. ax.plot([.4, .6], [.4, .6]) # Set minpos to keep logit happy. ax.set(xscale="log", xlim=(1, 100), yscale="logit", ylim=(1 / 101, 1 / 11), aspect=1, adjustable="datalim") ax.margins(0) ax.apply_aspect() assert ax.get_xlim() == pytest.approx([1*10**(1/2), 100/10**(1/2)]) assert ax.get_ylim() == (1 / 101, 1 / 11) def test_box_aspect(): # Test if axes with box_aspect=1 has same dimensions # as axes with aspect equal and adjustable="box" fig1, ax1 = plt.subplots() axtwin = ax1.twinx() axtwin.plot([12, 344]) ax1.set_box_aspect(1) assert ax1.get_box_aspect() == 1.0 fig2, ax2 = plt.subplots() ax2.margins(0) ax2.plot([0, 2], [6, 8]) ax2.set_aspect("equal", adjustable="box") fig1.canvas.draw() fig2.canvas.draw() bb1 = ax1.get_position() bbt = axtwin.get_position() bb2 = ax2.get_position() assert_array_equal(bb1.extents, bb2.extents) assert_array_equal(bbt.extents, bb2.extents) def test_box_aspect_custom_position(): # Test if axes with custom position and box_aspect # behaves the same independent of the order of setting those. fig1, ax1 = plt.subplots() ax1.set_position([0.1, 0.1, 0.9, 0.2]) fig1.canvas.draw() ax1.set_box_aspect(1.) fig2, ax2 = plt.subplots() ax2.set_box_aspect(1.) fig2.canvas.draw() ax2.set_position([0.1, 0.1, 0.9, 0.2]) fig1.canvas.draw() fig2.canvas.draw() bb1 = ax1.get_position() bb2 = ax2.get_position() assert_array_equal(bb1.extents, bb2.extents) def test_bbox_aspect_axes_init(): # Test that box_aspect can be given to axes init and produces # all equal square axes. fig, axs = plt.subplots(2, 3, subplot_kw=dict(box_aspect=1), constrained_layout=True) fig.canvas.draw() renderer = fig.canvas.get_renderer() sizes = [] for ax in axs.flat: bb = ax.get_window_extent(renderer) sizes.extend([bb.width, bb.height]) assert_allclose(sizes, sizes[0]) def test_set_aspect_negative(): fig, ax = plt.subplots() with pytest.raises(ValueError, match="must be finite and positive"): ax.set_aspect(-1) with pytest.raises(ValueError, match="must be finite and positive"): ax.set_aspect(0) with pytest.raises(ValueError, match="must be finite and positive"): ax.set_aspect(np.inf) with pytest.raises(ValueError, match="must be finite and positive"): ax.set_aspect(-np.inf) def test_redraw_in_frame(): fig, ax = plt.subplots(1, 1) ax.plot([1, 2, 3]) fig.canvas.draw() ax.redraw_in_frame() def test_invisible_axes_events(): # invisible axes should not respond to events... fig, ax = plt.subplots() assert fig.canvas.inaxes((200, 200)) is not None ax.set_visible(False) assert fig.canvas.inaxes((200, 200)) is None def test_xtickcolor_is_not_markercolor(): plt.rcParams['lines.markeredgecolor'] = 'white' ax = plt.axes() ticks = ax.xaxis.get_major_ticks() for tick in ticks: assert tick.tick1line.get_markeredgecolor() != 'white' def test_ytickcolor_is_not_markercolor(): plt.rcParams['lines.markeredgecolor'] = 'white' ax = plt.axes() ticks = ax.yaxis.get_major_ticks() for tick in ticks: assert tick.tick1line.get_markeredgecolor() != 'white' @pytest.mark.parametrize('axis', ('x', 'y')) @pytest.mark.parametrize('auto', (True, False, None)) def test_unautoscale(axis, auto): fig, ax = plt.subplots() x = np.arange(100) y = np.linspace(-.1, .1, 100) ax.scatter(y, x) get_autoscale_on = getattr(ax, f'get_autoscale{axis}_on') set_lim = getattr(ax, f'set_{axis}lim') get_lim = getattr(ax, f'get_{axis}lim') post_auto = get_autoscale_on() if auto is None else auto set_lim((-0.5, 0.5), auto=auto) assert post_auto == get_autoscale_on() fig.canvas.draw() assert_array_equal(get_lim(), (-0.5, 0.5)) @check_figures_equal(extensions=["png"]) def test_polar_interpolation_steps_variable_r(fig_test, fig_ref): l, = fig_test.add_subplot(projection="polar").plot([0, np.pi/2], [1, 2]) l.get_path()._interpolation_steps = 100 fig_ref.add_subplot(projection="polar").plot( np.linspace(0, np.pi/2, 101), np.linspace(1, 2, 101)) @mpl.style.context('default') def test_autoscale_tiny_sticky(): fig, ax = plt.subplots() ax.bar(0, 1e-9) fig.canvas.draw() assert ax.get_ylim() == (0, 1.05e-9) def test_xtickcolor_is_not_xticklabelcolor(): plt.rcParams['xtick.color'] = 'yellow' plt.rcParams['xtick.labelcolor'] = 'blue' ax = plt.axes() ticks = ax.xaxis.get_major_ticks() for tick in ticks: assert tick.tick1line.get_color() == 'yellow' assert tick.label1.get_color() == 'blue' def test_ytickcolor_is_not_yticklabelcolor(): plt.rcParams['ytick.color'] = 'yellow' plt.rcParams['ytick.labelcolor'] = 'blue' ax = plt.axes() ticks = ax.yaxis.get_major_ticks() for tick in ticks: assert tick.tick1line.get_color() == 'yellow' assert tick.label1.get_color() == 'blue' def test_xaxis_offsetText_color(): plt.rcParams['xtick.labelcolor'] = 'blue' ax = plt.axes() assert ax.xaxis.offsetText.get_color() == 'blue' plt.rcParams['xtick.color'] = 'yellow' plt.rcParams['xtick.labelcolor'] = 'inherit' ax = plt.axes() assert ax.xaxis.offsetText.get_color() == 'yellow' def test_yaxis_offsetText_color(): plt.rcParams['ytick.labelcolor'] = 'green' ax = plt.axes() assert ax.yaxis.offsetText.get_color() == 'green' plt.rcParams['ytick.color'] = 'red' plt.rcParams['ytick.labelcolor'] = 'inherit' ax = plt.axes() assert ax.yaxis.offsetText.get_color() == 'red' @pytest.mark.parametrize('size', [size for size in mfont_manager.font_scalings if size is not None] + [8, 10, 12]) @mpl.style.context('default') def test_relative_ticklabel_sizes(size): mpl.rcParams['xtick.labelsize'] = size mpl.rcParams['ytick.labelsize'] = size fig, ax = plt.subplots() fig.canvas.draw() for name, axis in zip(['x', 'y'], [ax.xaxis, ax.yaxis]): for tick in axis.get_major_ticks(): assert tick.label1.get_size() == axis._get_tick_label_size(name) def test_multiplot_autoscale(): fig = plt.figure() ax1, ax2 = fig.subplots(2, 1, sharex='all') ax1.plot([18000, 18250, 18500, 18750], [2, 3, 2, 3]) ax2.axhspan(-5, 5) xlim = ax1.get_xlim() assert np.allclose(xlim, [18000, 18800]) def test_sharing_does_not_link_positions(): fig = plt.figure() ax0 = fig.add_subplot(221) ax1 = fig.add_axes([.6, .6, .3, .3], sharex=ax0) init_pos = ax1.get_position() fig.subplots_adjust(left=0) assert (ax1.get_position().get_points() == init_pos.get_points()).all() @check_figures_equal(extensions=["pdf"]) def test_2dcolor_plot(fig_test, fig_ref): color = np.array([0.1, 0.2, 0.3]) # plot with 1D-color: axs = fig_test.subplots(5) axs[0].plot([1, 2], [1, 2], c=color.reshape(-1)) with pytest.warns(match="argument looks like a single numeric RGB"): axs[1].scatter([1, 2], [1, 2], c=color.reshape(-1)) axs[2].step([1, 2], [1, 2], c=color.reshape(-1)) axs[3].hist(np.arange(10), color=color.reshape(-1)) axs[4].bar(np.arange(10), np.arange(10), color=color.reshape(-1)) # plot with 2D-color: axs = fig_ref.subplots(5) axs[0].plot([1, 2], [1, 2], c=color.reshape((1, -1))) axs[1].scatter([1, 2], [1, 2], c=color.reshape((1, -1))) axs[2].step([1, 2], [1, 2], c=color.reshape((1, -1))) axs[3].hist(np.arange(10), color=color.reshape((1, -1))) axs[4].bar(np.arange(10), np.arange(10), color=color.reshape((1, -1))) @check_figures_equal(extensions=['png']) def test_shared_axes_clear(fig_test, fig_ref): x = np.arange(0.0, 2*np.pi, 0.01) y = np.sin(x) axs = fig_ref.subplots(2, 2, sharex=True, sharey=True) for ax in axs.flat: ax.plot(x, y) axs = fig_test.subplots(2, 2, sharex=True, sharey=True) for ax in axs.flat: ax.clear() ax.plot(x, y) def test_shared_axes_retick(): fig, axs = plt.subplots(2, 2, sharex='all', sharey='all') for ax in axs.flat: ax.plot([0, 2], 'o-') axs[0, 0].set_xticks([-0.5, 0, 1, 1.5]) # should affect all axes xlims for ax in axs.flat: assert ax.get_xlim() == axs[0, 0].get_xlim() axs[0, 0].set_yticks([-0.5, 0, 2, 2.5]) # should affect all axes ylims for ax in axs.flat: assert ax.get_ylim() == axs[0, 0].get_ylim() @pytest.mark.parametrize('ha', ['left', 'center', 'right']) def test_ylabel_ha_with_position(ha): fig = Figure() ax = fig.subplots() ax.set_ylabel("test", y=1, ha=ha) ax.yaxis.set_label_position("right") assert ax.yaxis.label.get_ha() == ha def test_bar_label_location_vertical(): ax = plt.gca() xs, heights = [1, 2], [3, -4] rects = ax.bar(xs, heights) labels = ax.bar_label(rects) assert labels[0].xy == (xs[0], heights[0]) assert labels[0].get_horizontalalignment() == 'center' assert labels[0].get_verticalalignment() == 'bottom' assert labels[1].xy == (xs[1], heights[1]) assert labels[1].get_horizontalalignment() == 'center' assert labels[1].get_verticalalignment() == 'top' def test_bar_label_location_vertical_yinverted(): ax = plt.gca() ax.invert_yaxis() xs, heights = [1, 2], [3, -4] rects = ax.bar(xs, heights) labels = ax.bar_label(rects) assert labels[0].xy == (xs[0], heights[0]) assert labels[0].get_horizontalalignment() == 'center' assert labels[0].get_verticalalignment() == 'top' assert labels[1].xy == (xs[1], heights[1]) assert labels[1].get_horizontalalignment() == 'center' assert labels[1].get_verticalalignment() == 'bottom' def test_bar_label_location_horizontal(): ax = plt.gca() ys, widths = [1, 2], [3, -4] rects = ax.barh(ys, widths) labels = ax.bar_label(rects) assert labels[0].xy == (widths[0], ys[0]) assert labels[0].get_horizontalalignment() == 'left' assert labels[0].get_verticalalignment() == 'center' assert labels[1].xy == (widths[1], ys[1]) assert labels[1].get_horizontalalignment() == 'right' assert labels[1].get_verticalalignment() == 'center' def test_bar_label_location_horizontal_yinverted(): ax = plt.gca() ax.invert_yaxis() ys, widths = [1, 2], [3, -4] rects = ax.barh(ys, widths) labels = ax.bar_label(rects) assert labels[0].xy == (widths[0], ys[0]) assert labels[0].get_horizontalalignment() == 'left' assert labels[0].get_verticalalignment() == 'center' assert labels[1].xy == (widths[1], ys[1]) assert labels[1].get_horizontalalignment() == 'right' assert labels[1].get_verticalalignment() == 'center' def test_bar_label_location_horizontal_xinverted(): ax = plt.gca() ax.invert_xaxis() ys, widths = [1, 2], [3, -4] rects = ax.barh(ys, widths) labels = ax.bar_label(rects) assert labels[0].xy == (widths[0], ys[0]) assert labels[0].get_horizontalalignment() == 'right' assert labels[0].get_verticalalignment() == 'center' assert labels[1].xy == (widths[1], ys[1]) assert labels[1].get_horizontalalignment() == 'left' assert labels[1].get_verticalalignment() == 'center' def test_bar_label_location_horizontal_xyinverted(): ax = plt.gca() ax.invert_xaxis() ax.invert_yaxis() ys, widths = [1, 2], [3, -4] rects = ax.barh(ys, widths) labels = ax.bar_label(rects) assert labels[0].xy == (widths[0], ys[0]) assert labels[0].get_horizontalalignment() == 'right' assert labels[0].get_verticalalignment() == 'center' assert labels[1].xy == (widths[1], ys[1]) assert labels[1].get_horizontalalignment() == 'left' assert labels[1].get_verticalalignment() == 'center' def test_bar_label_location_center(): ax = plt.gca() ys, widths = [1, 2], [3, -4] rects = ax.barh(ys, widths) labels = ax.bar_label(rects, label_type='center') assert labels[0].xy == (0.5, 0.5) assert labels[0].get_horizontalalignment() == 'center' assert labels[0].get_verticalalignment() == 'center' assert labels[1].xy == (0.5, 0.5) assert labels[1].get_horizontalalignment() == 'center' assert labels[1].get_verticalalignment() == 'center' @image_comparison(['test_centered_bar_label_nonlinear.svg']) def test_centered_bar_label_nonlinear(): _, ax = plt.subplots() bar_container = ax.barh(['c', 'b', 'a'], [1_000, 5_000, 7_000]) ax.set_xscale('log') ax.set_xlim(1, None) ax.bar_label(bar_container, label_type='center') ax.set_axis_off() def test_centered_bar_label_label_beyond_limits(): fig, ax = plt.subplots() last = 0 for label, value in zip(['a', 'b', 'c'], [10, 20, 50]): bar_container = ax.barh('col', value, label=label, left=last) ax.bar_label(bar_container, label_type='center') last += value ax.set_xlim(None, 20) fig.draw_without_rendering() def test_bar_label_location_errorbars(): ax = plt.gca() xs, heights = [1, 2], [3, -4] rects = ax.bar(xs, heights, yerr=1) labels = ax.bar_label(rects) assert labels[0].xy == (xs[0], heights[0] + 1) assert labels[0].get_horizontalalignment() == 'center' assert labels[0].get_verticalalignment() == 'bottom' assert labels[1].xy == (xs[1], heights[1] - 1) assert labels[1].get_horizontalalignment() == 'center' assert labels[1].get_verticalalignment() == 'top' @pytest.mark.parametrize('fmt', [ '%.2f', '{:.2f}', '{:.2f}'.format ]) def test_bar_label_fmt(fmt): ax = plt.gca() rects = ax.bar([1, 2], [3, -4]) labels = ax.bar_label(rects, fmt=fmt) assert labels[0].get_text() == '3.00' assert labels[1].get_text() == '-4.00' def test_bar_label_fmt_error(): ax = plt.gca() rects = ax.bar([1, 2], [3, -4]) with pytest.raises(TypeError, match='str or callable'): _ = ax.bar_label(rects, fmt=10) def test_bar_label_labels(): ax = plt.gca() rects = ax.bar([1, 2], [3, -4]) labels = ax.bar_label(rects, labels=['A', 'B']) assert labels[0].get_text() == 'A' assert labels[1].get_text() == 'B' def test_bar_label_nan_ydata(): ax = plt.gca() bars = ax.bar([2, 3], [np.nan, 1]) labels = ax.bar_label(bars) assert [l.get_text() for l in labels] == ['', '1'] assert labels[0].xy == (2, 0) assert labels[0].get_verticalalignment() == 'bottom' def test_bar_label_nan_ydata_inverted(): ax = plt.gca() ax.yaxis_inverted() bars = ax.bar([2, 3], [np.nan, 1]) labels = ax.bar_label(bars) assert [l.get_text() for l in labels] == ['', '1'] assert labels[0].xy == (2, 0) assert labels[0].get_verticalalignment() == 'bottom' def test_nan_barlabels(): fig, ax = plt.subplots() bars = ax.bar([1, 2, 3], [np.nan, 1, 2], yerr=[0.2, 0.4, 0.6]) labels = ax.bar_label(bars) assert [l.get_text() for l in labels] == ['', '1', '2'] assert np.allclose(ax.get_ylim(), (0.0, 3.0)) fig, ax = plt.subplots() bars = ax.bar([1, 2, 3], [0, 1, 2], yerr=[0.2, np.nan, 0.6]) labels = ax.bar_label(bars) assert [l.get_text() for l in labels] == ['0', '1', '2'] assert np.allclose(ax.get_ylim(), (-0.5, 3.0)) fig, ax = plt.subplots() bars = ax.bar([1, 2, 3], [np.nan, 1, 2], yerr=[np.nan, np.nan, 0.6]) labels = ax.bar_label(bars) assert [l.get_text() for l in labels] == ['', '1', '2'] assert np.allclose(ax.get_ylim(), (0.0, 3.0)) def test_patch_bounds(): # PR 19078 fig, ax = plt.subplots() ax.add_patch(mpatches.Wedge((0, -1), 1.05, 60, 120, width=0.1)) bot = 1.9*np.sin(15*np.pi/180)**2 np.testing.assert_array_almost_equal_nulp( np.array((-0.525, -(bot+0.05), 1.05, bot+0.1)), ax.dataLim.bounds, 16) @mpl.style.context('default') def test_warn_ignored_scatter_kwargs(): with pytest.warns(UserWarning, match=r"You passed a edgecolor/edgecolors"): plt.scatter([0], [0], marker="+", s=500, facecolor="r", edgecolor="b") def test_artist_sublists(): fig, ax = plt.subplots() lines = [ax.plot(np.arange(i, i + 5))[0] for i in range(6)] col = ax.scatter(np.arange(5), np.arange(5)) im = ax.imshow(np.zeros((5, 5))) patch = ax.add_patch(mpatches.Rectangle((0, 0), 5, 5)) text = ax.text(0, 0, 'foo') # Get items, which should not be mixed. assert list(ax.collections) == [col] assert list(ax.images) == [im] assert list(ax.lines) == lines assert list(ax.patches) == [patch] assert not ax.tables assert list(ax.texts) == [text] # Get items should work like lists/tuple. assert ax.lines[0] is lines[0] assert ax.lines[-1] is lines[-1] with pytest.raises(IndexError, match='out of range'): ax.lines[len(lines) + 1] # Adding to other lists should produce a regular list. assert ax.lines + [1, 2, 3] == [*lines, 1, 2, 3] assert [1, 2, 3] + ax.lines == [1, 2, 3, *lines] # Adding to other tuples should produce a regular tuples. assert ax.lines + (1, 2, 3) == (*lines, 1, 2, 3) assert (1, 2, 3) + ax.lines == (1, 2, 3, *lines) # Lists should be empty after removing items. col.remove() assert not ax.collections im.remove() assert not ax.images patch.remove() assert not ax.patches assert not ax.tables text.remove() assert not ax.texts for ln in ax.lines: ln.remove() assert len(ax.lines) == 0 def test_empty_line_plots(): # Incompatible nr columns, plot "nothing" x = np.ones(10) y = np.ones((10, 0)) _, ax = plt.subplots() line = ax.plot(x, y) assert len(line) == 0 # Ensure plot([],[]) creates line _, ax = plt.subplots() line = ax.plot([], []) assert len(line) == 1 @pytest.mark.parametrize('fmt, match', ( ("f", r"'f' is not a valid format string \(unrecognized character 'f'\)"), ("o+", r"'o\+' is not a valid format string \(two marker symbols\)"), (":-", r"':-' is not a valid format string \(two linestyle symbols\)"), ("rk", r"'rk' is not a valid format string \(two color symbols\)"), (":o-r", r"':o-r' is not a valid format string \(two linestyle symbols\)"), ("C", r"'C' is not a valid format string \('C' must be followed by a number\)"), (".C", r"'.C' is not a valid format string \('C' must be followed by a number\)"), )) @pytest.mark.parametrize("data", [None, {"string": range(3)}]) def test_plot_format_errors(fmt, match, data): fig, ax = plt.subplots() if data is not None: match = match.replace("not", "neither a data key nor") with pytest.raises(ValueError, match=r"\A" + match + r"\Z"): ax.plot("string", fmt, data=data) def test_plot_format(): fig, ax = plt.subplots() line = ax.plot([1, 2, 3], '1.0') assert line[0].get_color() == (1.0, 1.0, 1.0, 1.0) assert line[0].get_marker() == 'None' fig, ax = plt.subplots() line = ax.plot([1, 2, 3], '1') assert line[0].get_marker() == '1' fig, ax = plt.subplots() line = ax.plot([1, 2], [1, 2], '1.0', "1") fig.canvas.draw() assert line[0].get_color() == (1.0, 1.0, 1.0, 1.0) assert ax.get_yticklabels()[0].get_text() == '1' fig, ax = plt.subplots() line = ax.plot([1, 2], [1, 2], '1', "1.0") fig.canvas.draw() assert line[0].get_marker() == '1' assert ax.get_yticklabels()[0].get_text() == '1.0' fig, ax = plt.subplots() line = ax.plot([1, 2, 3], 'k3') assert line[0].get_marker() == '3' assert line[0].get_color() == 'k' fig, ax = plt.subplots() line = ax.plot([1, 2, 3], '.C12:') assert line[0].get_marker() == '.' assert line[0].get_color() == mcolors.to_rgba('C12') assert line[0].get_linestyle() == ':' def test_automatic_legend(): fig, ax = plt.subplots() ax.plot("a", "b", data={"d": 2}) leg = ax.legend() fig.canvas.draw() assert leg.get_texts()[0].get_text() == 'a' assert ax.get_yticklabels()[0].get_text() == 'a' fig, ax = plt.subplots() ax.plot("a", "b", "c", data={"d": 2}) leg = ax.legend() fig.canvas.draw() assert leg.get_texts()[0].get_text() == 'b' assert ax.get_xticklabels()[0].get_text() == 'a' assert ax.get_yticklabels()[0].get_text() == 'b' def test_plot_errors(): with pytest.raises(TypeError, match=r"plot\(\) got an unexpected keyword"): plt.plot([1, 2, 3], x=1) with pytest.raises(ValueError, match=r"plot\(\) with multiple groups"): plt.plot([1, 2, 3], [1, 2, 3], [2, 3, 4], [2, 3, 4], label=['1', '2']) with pytest.raises(ValueError, match="x and y must have same first"): plt.plot([1, 2, 3], [1]) with pytest.raises(ValueError, match="x and y can be no greater than"): plt.plot(np.ones((2, 2, 2))) with pytest.raises(ValueError, match="Using arbitrary long args with"): plt.plot("a", "b", "c", "d", data={"a": 2}) def test_clim(): ax = plt.figure().add_subplot() for plot_method in [ partial(ax.scatter, range(3), range(3), c=range(3)), partial(ax.imshow, [[0, 1], [2, 3]]), partial(ax.pcolor, [[0, 1], [2, 3]]), partial(ax.pcolormesh, [[0, 1], [2, 3]]), partial(ax.pcolorfast, [[0, 1], [2, 3]]), ]: clim = (7, 8) norm = plot_method(clim=clim).norm assert (norm.vmin, norm.vmax) == clim def test_bezier_autoscale(): # Check that bezier curves autoscale to their curves, and not their # control points verts = [[-1, 0], [0, -1], [1, 0], [1, 0]] codes = [mpath.Path.MOVETO, mpath.Path.CURVE3, mpath.Path.CURVE3, mpath.Path.CLOSEPOLY] p = mpath.Path(verts, codes) fig, ax = plt.subplots() ax.add_patch(mpatches.PathPatch(p)) ax.autoscale() # Bottom ylim should be at the edge of the curve (-0.5), and not include # the control point (at -1) assert ax.get_ylim()[0] == -0.5 def test_small_autoscale(): # Check that paths with small values autoscale correctly #24097. verts = np.array([ [-5.45, 0.00], [-5.45, 0.00], [-5.29, 0.00], [-5.29, 0.00], [-5.13, 0.00], [-5.13, 0.00], [-4.97, 0.00], [-4.97, 0.00], [-4.81, 0.00], [-4.81, 0.00], [-4.65, 0.00], [-4.65, 0.00], [-4.49, 0.00], [-4.49, 0.00], [-4.33, 0.00], [-4.33, 0.00], [-4.17, 0.00], [-4.17, 0.00], [-4.01, 0.00], [-4.01, 0.00], [-3.85, 0.00], [-3.85, 0.00], [-3.69, 0.00], [-3.69, 0.00], [-3.53, 0.00], [-3.53, 0.00], [-3.37, 0.00], [-3.37, 0.00], [-3.21, 0.00], [-3.21, 0.01], [-3.05, 0.01], [-3.05, 0.01], [-2.89, 0.01], [-2.89, 0.01], [-2.73, 0.01], [-2.73, 0.02], [-2.57, 0.02], [-2.57, 0.04], [-2.41, 0.04], [-2.41, 0.04], [-2.25, 0.04], [-2.25, 0.06], [-2.09, 0.06], [-2.09, 0.08], [-1.93, 0.08], [-1.93, 0.10], [-1.77, 0.10], [-1.77, 0.12], [-1.61, 0.12], [-1.61, 0.14], [-1.45, 0.14], [-1.45, 0.17], [-1.30, 0.17], [-1.30, 0.19], [-1.14, 0.19], [-1.14, 0.22], [-0.98, 0.22], [-0.98, 0.25], [-0.82, 0.25], [-0.82, 0.27], [-0.66, 0.27], [-0.66, 0.29], [-0.50, 0.29], [-0.50, 0.30], [-0.34, 0.30], [-0.34, 0.32], [-0.18, 0.32], [-0.18, 0.33], [-0.02, 0.33], [-0.02, 0.32], [0.13, 0.32], [0.13, 0.33], [0.29, 0.33], [0.29, 0.31], [0.45, 0.31], [0.45, 0.30], [0.61, 0.30], [0.61, 0.28], [0.77, 0.28], [0.77, 0.25], [0.93, 0.25], [0.93, 0.22], [1.09, 0.22], [1.09, 0.19], [1.25, 0.19], [1.25, 0.17], [1.41, 0.17], [1.41, 0.15], [1.57, 0.15], [1.57, 0.12], [1.73, 0.12], [1.73, 0.10], [1.89, 0.10], [1.89, 0.08], [2.05, 0.08], [2.05, 0.07], [2.21, 0.07], [2.21, 0.05], [2.37, 0.05], [2.37, 0.04], [2.53, 0.04], [2.53, 0.02], [2.69, 0.02], [2.69, 0.02], [2.85, 0.02], [2.85, 0.01], [3.01, 0.01], [3.01, 0.01], [3.17, 0.01], [3.17, 0.00], [3.33, 0.00], [3.33, 0.00], [3.49, 0.00], [3.49, 0.00], [3.65, 0.00], [3.65, 0.00], [3.81, 0.00], [3.81, 0.00], [3.97, 0.00], [3.97, 0.00], [4.13, 0.00], [4.13, 0.00], [4.29, 0.00], [4.29, 0.00], [4.45, 0.00], [4.45, 0.00], [4.61, 0.00], [4.61, 0.00], [4.77, 0.00], [4.77, 0.00], [4.93, 0.00], [4.93, 0.00], ]) minx = np.min(verts[:, 0]) miny = np.min(verts[:, 1]) maxx = np.max(verts[:, 0]) maxy = np.max(verts[:, 1]) p = mpath.Path(verts) fig, ax = plt.subplots() ax.add_patch(mpatches.PathPatch(p)) ax.autoscale() assert ax.get_xlim()[0] <= minx assert ax.get_xlim()[1] >= maxx assert ax.get_ylim()[0] <= miny assert ax.get_ylim()[1] >= maxy def test_get_xticklabel(): fig, ax = plt.subplots() ax.plot(np.arange(10)) for ind in range(10): assert ax.get_xticklabels()[ind].get_text() == f'{ind}' assert ax.get_yticklabels()[ind].get_text() == f'{ind}' def test_bar_leading_nan(): barx = np.arange(3, dtype=float) barheights = np.array([0.5, 1.5, 2.0]) barstarts = np.array([0.77]*3) barx[0] = np.nan fig, ax = plt.subplots() bars = ax.bar(barx, barheights, bottom=barstarts) hbars = ax.barh(barx, barheights, left=barstarts) for bar_set in (bars, hbars): # the first bar should have a nan in the location nanful, *rest = bar_set assert (~np.isfinite(nanful.xy)).any() assert np.isfinite(nanful.get_width()) for b in rest: assert np.isfinite(b.xy).all() assert np.isfinite(b.get_width()) @check_figures_equal(extensions=["png"]) def test_bar_all_nan(fig_test, fig_ref): mpl.style.use("mpl20") ax_test = fig_test.subplots() ax_ref = fig_ref.subplots() ax_test.bar([np.nan], [np.nan]) ax_test.bar([1], [1]) ax_ref.bar([1], [1]).remove() ax_ref.bar([1], [1]) @image_comparison(["extent_units.png"], style="mpl20") def test_extent_units(): _, axs = plt.subplots(2, 2) date_first = np.datetime64('2020-01-01', 'D') date_last = np.datetime64('2020-01-11', 'D') arr = [[i+j for i in range(10)] for j in range(10)] axs[0, 0].set_title('Date extents on y axis') im = axs[0, 0].imshow(arr, origin='lower', extent=[1, 11, date_first, date_last], cmap=mpl.colormaps["plasma"]) axs[0, 1].set_title('Date extents on x axis (Day of Jan 2020)') im = axs[0, 1].imshow(arr, origin='lower', extent=[date_first, date_last, 1, 11], cmap=mpl.colormaps["plasma"]) axs[0, 1].xaxis.set_major_formatter(mdates.DateFormatter('%d')) im = axs[1, 0].imshow(arr, origin='lower', extent=[date_first, date_last, date_first, date_last], cmap=mpl.colormaps["plasma"]) axs[1, 0].xaxis.set_major_formatter(mdates.DateFormatter('%d')) axs[1, 0].set(xlabel='Day of Jan 2020') im = axs[1, 1].imshow(arr, origin='lower', cmap=mpl.colormaps["plasma"]) im.set_extent([date_last, date_first, date_last, date_first]) axs[1, 1].xaxis.set_major_formatter(mdates.DateFormatter('%d')) axs[1, 1].set(xlabel='Day of Jan 2020') with pytest.raises(TypeError, match=r"set_extent\(\) got an unexpected"): im.set_extent([2, 12, date_first, date_last], clip=False) def test_cla_clears_children_axes_and_fig(): fig, ax = plt.subplots() lines = ax.plot([], [], [], []) img = ax.imshow([[1]]) for art in lines + [img]: assert art.axes is ax assert art.get_figure() is fig ax.clear() for art in lines + [img]: assert art.axes is None assert art.get_figure() is None def test_child_axes_removal(): fig, ax = plt.subplots() marginal = ax.inset_axes([1, 0, .1, 1], sharey=ax) marginal_twin = marginal.twinx() marginal.remove() ax.set(xlim=(-1, 1), ylim=(10, 20)) def test_scatter_color_repr_error(): def get_next_color(): # pragma: no cover return 'blue' # currently unused msg = ( r"'c' argument must be a color, a sequence of colors" r", or a sequence of numbers, not 'red\\n'" ) with pytest.raises(ValueError, match=msg): c = 'red\n' mpl.axes.Axes._parse_scatter_color_args( c, None, kwargs={}, xsize=2, get_next_color_func=get_next_color) def test_zorder_and_explicit_rasterization(): fig, ax = plt.subplots() ax.set_rasterization_zorder(5) ln, = ax.plot(range(5), rasterized=True, zorder=1) with io.BytesIO() as b: fig.savefig(b, format='pdf') @image_comparison(["preset_clip_paths.png"], remove_text=True, style="mpl20", tol=0 if platform.machine() == 'x86_64' else 0.027) def test_preset_clip_paths(): fig, ax = plt.subplots() poly = mpl.patches.Polygon( [[1, 0], [0, 1], [-1, 0], [0, -1]], facecolor="#ddffdd", edgecolor="#00ff00", linewidth=2, alpha=0.5) ax.add_patch(poly) line = mpl.lines.Line2D((-1, 1), (0.5, 0.5), clip_on=True, clip_path=poly) line.set_path_effects([patheffects.withTickedStroke()]) ax.add_artist(line) line = mpl.lines.Line2D((-1, 1), (-0.5, -0.5), color='r', clip_on=True, clip_path=poly) ax.add_artist(line) poly2 = mpl.patches.Polygon( [[-1, 1], [0, 1], [0, -0.25]], facecolor="#beefc0", alpha=0.3, edgecolor="#faded0", linewidth=2, clip_on=True, clip_path=poly) ax.add_artist(poly2) # When text clipping works, the "Annotation" text should be clipped ax.annotate('Annotation', (-0.75, -0.75), xytext=(0.1, 0.75), arrowprops={'color': 'k'}, clip_on=True, clip_path=poly) poly3 = mpl.patches.Polygon( [[0, 0], [0, 0.5], [0.5, 0.5], [0.5, 0]], facecolor="g", edgecolor="y", linewidth=2, alpha=0.3, clip_on=True, clip_path=poly) fig.add_artist(poly3, clip=True) ax.set_xlim(-1, 1) ax.set_ylim(-1, 1) @mpl.style.context('default') def test_rc_axes_label_formatting(): mpl.rcParams['axes.labelcolor'] = 'red' mpl.rcParams['axes.labelsize'] = 20 mpl.rcParams['axes.labelweight'] = 'bold' ax = plt.axes() assert ax.xaxis.label.get_color() == 'red' assert ax.xaxis.label.get_fontsize() == 20 assert ax.xaxis.label.get_fontweight() == 'bold' @check_figures_equal(extensions=["png"]) def test_ecdf(fig_test, fig_ref): data = np.array([0, -np.inf, -np.inf, np.inf, 1, 1, 2]) weights = range(len(data)) axs_test = fig_test.subplots(1, 2) for ax, orientation in zip(axs_test, ["vertical", "horizontal"]): l0 = ax.ecdf(data, orientation=orientation) l1 = ax.ecdf("d", "w", data={"d": np.ma.array(data), "w": weights}, orientation=orientation, complementary=True, compress=True, ls=":") assert len(l0.get_xdata()) == (~np.isnan(data)).sum() + 1 assert len(l1.get_xdata()) == len({*data[~np.isnan(data)]}) + 1 axs_ref = fig_ref.subplots(1, 2) axs_ref[0].plot([-np.inf, -np.inf, -np.inf, 0, 1, 1, 2, np.inf], np.arange(8) / 7, ds="steps-post") axs_ref[0].plot([-np.inf, 0, 1, 2, np.inf, np.inf], np.array([21, 20, 18, 14, 3, 0]) / 21, ds="steps-pre", ls=":") axs_ref[1].plot(np.arange(8) / 7, [-np.inf, -np.inf, -np.inf, 0, 1, 1, 2, np.inf], ds="steps-pre") axs_ref[1].plot(np.array([21, 20, 18, 14, 3, 0]) / 21, [-np.inf, 0, 1, 2, np.inf, np.inf], ds="steps-post", ls=":") def test_ecdf_invalid(): with pytest.raises(ValueError): plt.ecdf([1, np.nan]) with pytest.raises(ValueError): plt.ecdf(np.ma.array([1, 2], mask=[True, False])) def test_fill_between_axes_limits(): fig, ax = plt.subplots() x = np.arange(0, 4 * np.pi, 0.01) y = 0.1*np.sin(x) threshold = 0.075 ax.plot(x, y, color='black') original_lims = (ax.get_xlim(), ax.get_ylim()) ax.axhline(threshold, color='green', lw=2, alpha=0.7) ax.fill_between(x, 0, 1, where=y > threshold, color='green', alpha=0.5, transform=ax.get_xaxis_transform()) assert (ax.get_xlim(), ax.get_ylim()) == original_lims def test_tick_param_labelfont(): fig, ax = plt.subplots() ax.plot([1, 2, 3, 4], [1, 2, 3, 4]) ax.set_xlabel('X label in Impact font', fontname='Impact') ax.set_ylabel('Y label in xkcd script', fontname='xkcd script') ax.tick_params(color='r', labelfontfamily='monospace') plt.title('Title in sans-serif') for text in ax.get_xticklabels(): assert text.get_fontfamily()[0] == 'monospace' def test_set_secondary_axis_color(): fig, ax = plt.subplots() sax = ax.secondary_xaxis("top", color="red") assert mcolors.same_color(sax.spines["bottom"].get_edgecolor(), "red") assert mcolors.same_color(sax.spines["top"].get_edgecolor(), "red") assert mcolors.same_color(sax.xaxis.get_tick_params()["color"], "red") assert mcolors.same_color(sax.xaxis.get_tick_params()["labelcolor"], "red") assert mcolors.same_color(sax.xaxis.label.get_color(), "red") def test_xylim_changed_shared(): fig, axs = plt.subplots(2, sharex=True, sharey=True) events = [] axs[1].callbacks.connect("xlim_changed", events.append) axs[1].callbacks.connect("ylim_changed", events.append) axs[0].set(xlim=[1, 3], ylim=[2, 4]) assert events == [axs[1], axs[1]] @image_comparison(["axhvlinespan_interpolation.png"], style="default") def test_axhvlinespan_interpolation(): ax = plt.figure().add_subplot(projection="polar") ax.set_axis_off() ax.axvline(.1, c="C0") ax.axvspan(.2, .3, fc="C1") ax.axvspan(.4, .5, .1, .2, fc="C2") ax.axhline(1, c="C0", alpha=.5) ax.axhspan(.8, .9, fc="C1", alpha=.5) ax.axhspan(.6, .7, .8, .9, fc="C2", alpha=.5) @check_figures_equal(extensions=["png"]) @pytest.mark.parametrize("which", ("x", "y")) def test_axes_clear_behavior(fig_ref, fig_test, which): """Test that the given tick params are not reset by ax.clear().""" ax_test = fig_test.subplots() ax_ref = fig_ref.subplots() # the following tick params values are chosen to each create a visual difference # from their defaults target = { "direction": "in", "length": 10, "width": 10, "color": "xkcd:wine red", "pad": 0, "labelfontfamily": "serif", "zorder": 7, "labelrotation": 45, "labelcolor": "xkcd:shocking pink", # this overrides color + labelcolor, skip # colors: , "grid_color": "xkcd:fluorescent green", "grid_alpha": 0.5, "grid_linewidth": 3, "grid_linestyle": ":", "bottom": False, "top": True, "left": False, "right": True, "labelbottom": True, "labeltop": True, "labelleft": True, "labelright": True, } ax_ref.tick_params(axis=which, **target) ax_test.tick_params(axis=which, **target) ax_test.clear() ax_ref.grid(True) ax_test.grid(True) @pytest.mark.skipif( sys.version_info[:3] == (3, 13, 0) and sys.version_info.releaselevel != "final", reason="https://github.com/python/cpython/issues/124538", ) def test_axes_clear_reference_cycle(): def assert_not_in_reference_cycle(start): # Breadth first search. Return True if we encounter the starting node to_visit = deque([start]) explored = set() while len(to_visit) > 0: parent = to_visit.popleft() for child in gc.get_referents(parent): if id(child) in explored: continue assert child is not start explored.add(id(child)) to_visit.append(child) fig = Figure() ax = fig.add_subplot() points = np.random.rand(1000) ax.plot(points, points) ax.scatter(points, points) ax_children = ax.get_children() fig.clear() # This should break the reference cycle # Care most about the objects that scale with number of points big_artists = [ a for a in ax_children if isinstance(a, (Line2D, PathCollection)) ] assert len(big_artists) > 0 for big_artist in big_artists: assert_not_in_reference_cycle(big_artist) assert len(ax_children) > 0 for child in ax_children: # Make sure this doesn't raise because the child is already removed. try: child.remove() except NotImplementedError: pass # not implemented is expected for some artists def test_boxplot_tick_labels(): # Test the renamed `tick_labels` parameter. # Test for deprecation of old name `labels`. np.random.seed(19680801) data = np.random.random((10, 3)) fig, axs = plt.subplots(nrows=1, ncols=2, sharey=True) # Should get deprecation warning for `labels` with pytest.warns(mpl.MatplotlibDeprecationWarning, match='has been renamed \'tick_labels\''): axs[0].boxplot(data, labels=['A', 'B', 'C']) assert [l.get_text() for l in axs[0].get_xticklabels()] == ['A', 'B', 'C'] # Test the new tick_labels parameter axs[1].boxplot(data, tick_labels=['A', 'B', 'C']) assert [l.get_text() for l in axs[1].get_xticklabels()] == ['A', 'B', 'C'] @needs_usetex @check_figures_equal() def test_latex_pie_percent(fig_test, fig_ref): data = [20, 10, 70] ax = fig_test.subplots() ax.pie(data, autopct="%1.0f%%", textprops={'usetex': True}) ax1 = fig_ref.subplots() ax1.pie(data, autopct=r"%1.0f\%%", textprops={'usetex': True}) @check_figures_equal(extensions=['png']) def test_violinplot_orientation(fig_test, fig_ref): # Test the `orientation : {'vertical', 'horizontal'}` # parameter and deprecation of `vert: bool`. fig, axs = plt.subplots(nrows=1, ncols=3) np.random.seed(19680801) all_data = [np.random.normal(0, std, 100) for std in range(6, 10)] axs[0].violinplot(all_data) # Default vertical plot. # xticks and yticks should be at their default position. assert all(axs[0].get_xticks() == np.array( [0.5, 1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5])) assert all(axs[0].get_yticks() == np.array( [-30., -20., -10., 0., 10., 20., 30.])) # Horizontal plot using new `orientation` keyword. axs[1].violinplot(all_data, orientation='horizontal') # xticks and yticks should be swapped. assert all(axs[1].get_xticks() == np.array( [-30., -20., -10., 0., 10., 20., 30.])) assert all(axs[1].get_yticks() == np.array( [0.5, 1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5])) plt.close() # Compare images between a figure that # uses vert and one that uses orientation. ax_ref = fig_ref.subplots() with pytest.warns(PendingDeprecationWarning, match='vert: bool'): ax_ref.violinplot(all_data, vert=False) ax_test = fig_test.subplots() ax_test.violinplot(all_data, orientation='horizontal') @check_figures_equal(extensions=['png']) def test_boxplot_orientation(fig_test, fig_ref): # Test the `orientation : {'vertical', 'horizontal'}` # parameter and deprecation of `vert: bool`. fig, axs = plt.subplots(nrows=1, ncols=2) np.random.seed(19680801) all_data = [np.random.normal(0, std, 100) for std in range(6, 10)] axs[0].boxplot(all_data) # Default vertical plot. # xticks and yticks should be at their default position. assert all(axs[0].get_xticks() == np.array( [1, 2, 3, 4])) assert all(axs[0].get_yticks() == np.array( [-30., -20., -10., 0., 10., 20., 30.])) # Horizontal plot using new `orientation` keyword. axs[1].boxplot(all_data, orientation='horizontal') # xticks and yticks should be swapped. assert all(axs[1].get_xticks() == np.array( [-30., -20., -10., 0., 10., 20., 30.])) assert all(axs[1].get_yticks() == np.array( [1, 2, 3, 4])) plt.close() # Deprecation of `vert: bool` keyword and # 'boxplot.vertical' rcparam. with pytest.warns(mpl.MatplotlibDeprecationWarning, match='was deprecated in Matplotlib 3.10'): # Compare images between a figure that # uses vert and one that uses orientation. with mpl.rc_context({'boxplot.vertical': False}): ax_ref = fig_ref.subplots() ax_ref.boxplot(all_data) ax_test = fig_test.subplots() ax_test.boxplot(all_data, orientation='horizontal') @image_comparison(["use_colorizer_keyword.png"], tol=0 if platform.machine() == 'x86_64' else 0.05) def test_use_colorizer_keyword(): # test using the colorizer keyword np.random.seed(0) rand_x = np.random.random(100) rand_y = np.random.random(100) c = np.arange(25, dtype='float32').reshape((5, 5)) fig, axes = plt.subplots(3, 4) norm = mpl.colors.Normalize(4, 20) cl = mpl.colorizer.Colorizer(norm=norm, cmap='RdBu') axes[0, 0].scatter(c, c, c=c, colorizer=cl) axes[0, 1].hexbin(rand_x, rand_y, colorizer=cl, gridsize=(2, 2)) axes[0, 2].imshow(c, colorizer=cl) axes[0, 3].pcolor(c, colorizer=cl) axes[1, 0].pcolormesh(c, colorizer=cl) axes[1, 1].pcolorfast(c, colorizer=cl) # style = image axes[1, 2].pcolorfast((0, 1, 2, 3, 4, 5), (0, 1, 2, 3, 5, 6), c, colorizer=cl) # style = pcolorimage axes[1, 3].pcolorfast(c.T, c, c[:4, :4], colorizer=cl) # style = quadmesh axes[2, 0].contour(c, colorizer=cl) axes[2, 1].contourf(c, colorizer=cl) axes[2, 2].tricontour(c.T.ravel(), c.ravel(), c.ravel(), colorizer=cl) axes[2, 3].tricontourf(c.T.ravel(), c.ravel(), c.ravel(), colorizer=cl) fig.figimage(np.repeat(np.repeat(c, 15, axis=0), 15, axis=1), colorizer=cl) remove_ticks_and_titles(fig) def test_wrong_use_colorizer(): # test using the colorizer keyword and norm or cmap np.random.seed(0) rand_x = np.random.random(100) rand_y = np.random.random(100) c = np.arange(25, dtype='float32').reshape((5, 5)) fig, axes = plt.subplots(3, 4) norm = mpl.colors.Normalize(4, 20) cl = mpl.colorizer.Colorizer(norm=norm, cmap='RdBu') match_str = "The `colorizer` keyword cannot be used simultaneously" kwrds = [{'vmin': 0}, {'vmax': 0}, {'norm': 'log'}, {'cmap': 'viridis'}] for kwrd in kwrds: with pytest.raises(ValueError, match=match_str): axes[0, 0].scatter(c, c, c=c, colorizer=cl, **kwrd) for kwrd in kwrds: with pytest.raises(ValueError, match=match_str): axes[0, 0].scatter(c, c, c=c, colorizer=cl, **kwrd) for kwrd in kwrds: with pytest.raises(ValueError, match=match_str): axes[0, 1].hexbin(rand_x, rand_y, colorizer=cl, gridsize=(2, 2), **kwrd) for kwrd in kwrds: with pytest.raises(ValueError, match=match_str): axes[0, 2].imshow(c, colorizer=cl, **kwrd) for kwrd in kwrds: with pytest.raises(ValueError, match=match_str): axes[0, 3].pcolor(c, colorizer=cl, **kwrd) for kwrd in kwrds: with pytest.raises(ValueError, match=match_str): axes[1, 0].pcolormesh(c, colorizer=cl, **kwrd) for kwrd in kwrds: with pytest.raises(ValueError, match=match_str): axes[1, 1].pcolorfast(c, colorizer=cl, **kwrd) # style = image for kwrd in kwrds: with pytest.raises(ValueError, match=match_str): axes[1, 2].pcolorfast((0, 1, 2, 3, 4, 5), (0, 1, 2, 3, 5, 6), c, colorizer=cl, **kwrd) # style = pcolorimage for kwrd in kwrds: with pytest.raises(ValueError, match=match_str): axes[1, 3].pcolorfast(c.T, c, c[:4, :4], colorizer=cl, **kwrd) # quadmesh for kwrd in kwrds: with pytest.raises(ValueError, match=match_str): axes[2, 0].contour(c, colorizer=cl, **kwrd) for kwrd in kwrds: with pytest.raises(ValueError, match=match_str): axes[2, 1].contourf(c, colorizer=cl, **kwrd) for kwrd in kwrds: with pytest.raises(ValueError, match=match_str): axes[2, 2].tricontour(c.T.ravel(), c.ravel(), c.ravel(), colorizer=cl, **kwrd) for kwrd in kwrds: with pytest.raises(ValueError, match=match_str): axes[2, 3].tricontourf(c.T.ravel(), c.ravel(), c.ravel(), colorizer=cl, **kwrd) for kwrd in kwrds: with pytest.raises(ValueError, match=match_str): fig.figimage(c, colorizer=cl, **kwrd) def test_bar_color_precedence(): # Test the precedence of 'color' and 'facecolor' in bar plots fig, ax = plt.subplots() # case 1: no color specified bars = ax.bar([1, 2, 3], [4, 5, 6]) for bar in bars: assert mcolors.same_color(bar.get_facecolor(), 'blue') # case 2: Only 'color' bars = ax.bar([11, 12, 13], [4, 5, 6], color='red') for bar in bars: assert mcolors.same_color(bar.get_facecolor(), 'red') # case 3: Only 'facecolor' bars = ax.bar([21, 22, 23], [4, 5, 6], facecolor='yellow') for bar in bars: assert mcolors.same_color(bar.get_facecolor(), 'yellow') # case 4: 'facecolor' and 'color' bars = ax.bar([31, 32, 33], [4, 5, 6], color='red', facecolor='green') for bar in bars: assert mcolors.same_color(bar.get_facecolor(), 'green') @check_figures_equal(extensions=['png']) def test_axes_set_position_external_bbox_unchanged(fig_test, fig_ref): # From #29410: Modifying Axes' position also alters the original Bbox # object used for initialization bbox = mtransforms.Bbox([[0.0, 0.0], [1.0, 1.0]]) ax_test = fig_test.add_axes(bbox) ax_test.set_position([0.25, 0.25, 0.5, 0.5]) assert (bbox.x0, bbox.y0, bbox.width, bbox.height) == (0.0, 0.0, 1.0, 1.0) ax_ref = fig_ref.add_axes([0.25, 0.25, 0.5, 0.5])