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py-data-analysis/.venv/lib/python3.12/site-packages/matplotlib/tests/test_axes.py

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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) == (
"<Axes: "
"label='label', title={'center': 'title'}, xlabel='x', ylabel='y'>")
@check_figures_equal(extensions=['png'])
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(extensions=['png'])
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(extensions=['png'])
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([f'formatter_ticker_{i:03d}.png' for i in range(1, 6)],
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.png"])
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.png"], 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.png'])
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.png'], 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 <gellule.xg@free.fr>
# 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.png'], 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.png'],
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.png'], 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.png'], 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.png'], 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.png'], 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.png'], 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.png'], 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.png'], 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.png'], 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(extensions=['png'])
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.png'], 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.png', 'hist2d.png'], 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.png'], 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.png', 'stackplot_test_image.png'],
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.png'], 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(extensions=['png'])
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 test_stackplot_subfig_legend():
# Smoke test for https://github.com/matplotlib/matplotlib/issues/30158
fig = plt.figure()
subfigs = fig.subfigures(nrows=1, ncols=2)
for _fig in subfigs:
ax = _fig.subplots(nrows=1, ncols=1)
ax.stackplot([3, 4], [[1, 2]], labels=['a'])
fig.legend()
fig.draw_without_rendering()
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(extensions=['png'])
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.png', 'boxplot.png'], 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.png'],
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.png', 'errorbar_mixed.png', 'errorbar_basic.png'])
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.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(extensions=['png'])
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.png'])
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(extensions=['png'])
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, yerr=es)
ax = fig_ref.add_subplot()
ax.errorbar([0, 1], [1, 2], yerr=[4, 5])
ax.errorbar([4], [3], yerr=[6], fmt="C0")
@check_figures_equal()
def test_errorbar_masked_negative(fig_test, fig_ref):
ax = fig_test.add_subplot()
xs = range(5)
mask = np.array([False, False, True, True, False])
ys = np.ma.array([1, 2, 2, 2, 3], mask=mask)
es = np.ma.array([4, 5, -1, -10, 6], mask=mask)
ax.errorbar(xs, ys, yerr=es)
ax = fig_ref.add_subplot()
ax.errorbar([0, 1], [1, 2], yerr=[4, 5])
ax.errorbar([4], [3], yerr=[6], fmt="C0")
@image_comparison(['hist_stacked_stepfilled.png', 'hist_stacked_stepfilled.png'])
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.png'])
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.png'])
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))
with pytest.raises(ValueError):
ax.stem([[y]])
with pytest.raises(ValueError):
ax.stem([[x]], 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.png'])
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.png'])
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.png'])
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.png'])
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)
def test_hist_sequence_type_styles():
facecolor = ('r', 0.5)
edgecolor = [0.5, 0.5, 0.5]
linestyle = (0, (1, 1))
arr = np.random.uniform(size=50)
_, _, bars = plt.hist(arr, facecolor=facecolor, edgecolor=edgecolor,
linestyle=linestyle)
assert mcolors.same_color(bars[0].get_facecolor(), facecolor)
assert mcolors.same_color(bars[0].get_edgecolor(), edgecolor)
assert bars[0].get_linestyle() == linestyle
def test_hist_color_none():
arr = np.random.uniform(size=50)
# No edgecolor is the default but check that it can be explicitly passed.
_, _, bars = plt.hist(arr, facecolor='none', edgecolor='none')
assert bars[0].get_facecolor(), (0, 0, 0, 0)
assert bars[0].get_edgecolor(), (0, 0, 0, 0)
@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.png'], 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.png', 'eventplot.png'], 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.png', 'errorbar_zorder.png'],
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(extensions=['png'])
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(extensions=['png'])
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(extensions=['png'])
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(extensions=['png'])
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(extensions=['png'])
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(extensions=['png'])
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) == '<SecondaryAxis: >'
@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(extensions=['png'])
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])
def test_caps_color():
# Creates a simple plot with error bars and a specified ecolor
x = np.linspace(0, 10, 10)
mpl.rcParams['lines.markeredgecolor'] = 'green'
ecolor = 'red'
fig, ax = plt.subplots()
errorbars = ax.errorbar(x, np.sin(x), yerr=0.1, ecolor=ecolor)
# Tests if the caps have the specified color
for cap in errorbars[2]:
assert mcolors.same_color(cap.get_edgecolor(), ecolor)
def test_caps_no_ecolor():
# Creates a simple plot with error bars without specifying ecolor
x = np.linspace(0, 10, 10)
mpl.rcParams['lines.markeredgecolor'] = 'green'
fig, ax = plt.subplots()
errorbars = ax.errorbar(x, np.sin(x), yerr=0.1)
# Tests if the caps have the default color (blue)
for cap in errorbars[2]:
assert mcolors.same_color(cap.get_edgecolor(), "blue")
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