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hub/venv/lib/python3.7/site-packages/matplotlib/tests/test_axes.py

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Add virtual environment to the git repository, this isn't 100% right but it's practical at this development point
2020-06-16 10:34:17 -04:00
from collections import namedtuple
from itertools import product
from distutils.version import LooseVersion
import io
import platform
import datetime
import dateutil.tz as dutz
import numpy as np
from numpy import ma
from cycler import cycler
from decimal import Decimal
import pytest
import warnings
import matplotlib
import matplotlib as mpl
from matplotlib.testing.decorators import (
image_comparison, check_figures_equal, remove_ticks_and_titles)
import matplotlib.pyplot as plt
import matplotlib.markers as mmarkers
import matplotlib.patches as mpatches
import matplotlib.colors as mcolors
import matplotlib.ticker as mticker
import matplotlib.transforms as mtransforms
from numpy.testing import (
assert_allclose, assert_array_equal, assert_array_almost_equal)
from matplotlib import rc_context
from matplotlib.cbook import (
IgnoredKeywordWarning, MatplotlibDeprecationWarning)
# 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.
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'
@image_comparison(['acorr.png'], style='mpl20')
def test_acorr():
# Remove this line when this test image is regenerated.
plt.rcParams['text.kerning_factor'] = 6
np.random.seed(19680801)
n = 512
x = np.random.normal(0, 1, n).cumsum()
fig, ax = plt.subplots()
ax.acorr(x, maxlags=n - 1, label='acorr')
ax.legend()
@image_comparison(['spy.png'], style='mpl20')
def test_spy():
np.random.seed(19680801)
a = np.ones(32 * 32)
a[:16 * 32] = 0
np.random.shuffle(a)
a = np.reshape(a, (32, 32))
fig, ax = plt.subplots()
ax.spy(a)
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)
@image_comparison(['matshow.png'], style='mpl20')
def test_matshow():
np.random.seed(19680801)
a = np.random.rand(32, 32)
fig, ax = plt.subplots()
ax.matshow(a)
@image_comparison(['formatter_ticker_001',
'formatter_ticker_002',
'formatter_ticker_003',
'formatter_ticker_004',
'formatter_ticker_005',
])
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()
@image_comparison(["twin_axis_locators_formatters"])
def test_twin_axis_locators_formatters():
vals = np.linspace(0, 1, num=5, endpoint=True)
locs = np.sin(np.pi * vals / 2.0)
majl = plt.FixedLocator(locs)
minl = plt.FixedLocator([0.1, 0.2, 0.3])
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
ax1.plot([0.1, 100], [0, 1])
ax1.yaxis.set_major_locator(majl)
ax1.yaxis.set_minor_locator(minl)
ax1.yaxis.set_major_formatter(plt.FormatStrFormatter('%08.2lf'))
ax1.yaxis.set_minor_formatter(plt.FixedFormatter(['tricks', 'mind',
'jedi']))
ax1.xaxis.set_major_locator(plt.LinearLocator())
ax1.xaxis.set_minor_locator(plt.FixedLocator([15, 35, 55, 75]))
ax1.xaxis.set_major_formatter(plt.FormatStrFormatter('%05.2lf'))
ax1.xaxis.set_minor_formatter(plt.FixedFormatter(['c', '3', 'p', 'o']))
ax1.twiny()
ax1.twinx()
def test_twinx_cla():
fig, ax = plt.subplots()
ax2 = ax.twinx()
ax3 = ax2.twiny()
plt.draw()
assert not ax2.xaxis.get_visible()
assert not ax2.patch.get_visible()
ax2.cla()
ax3.cla()
assert not ax2.xaxis.get_visible()
assert not ax2.patch.get_visible()
assert ax2.yaxis.get_visible()
assert ax3.xaxis.get_visible()
assert not ax3.patch.get_visible()
assert not ax3.yaxis.get_visible()
assert ax.xaxis.get_visible()
assert ax.patch.get_visible()
assert ax.yaxis.get_visible()
@image_comparison(['twin_autoscale.png'])
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()
# 5. two shared axes. Inverting the master axis should invert the shared
# axes; clearing the master 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 nonmaster should not touch limits
ax0.imshow(img)
ax1.plot(x, np.cos(x))
ax1.cla()
assert ax.yaxis_inverted()
# clean up
plt.close(fig)
@check_figures_equal(extensions=["png"])
def test_minorticks_on_rcParams_both(fig_test, fig_ref):
with matplotlib.rc_context({"xtick.minor.visible": True,
"ytick.minor.visible": True}):
ax_test = fig_test.subplots()
ax_test.plot([0, 1], [0, 1])
ax_ref = fig_ref.subplots()
ax_ref.plot([0, 1], [0, 1])
ax_ref.minorticks_on()
@image_comparison(["autoscale_tiny_range"], remove_text=True)
def test_autoscale_tiny_range():
# github pull #904
fig, axs = plt.subplots(2, 2)
for i, ax in enumerate(axs.flat):
y1 = 10**(-11 - i)
ax.plot([0, 1], [1, 1 + y1])
@pytest.mark.style('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))
@pytest.mark.style('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]))
@pytest.mark.style('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
# "master" 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(['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(111, 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_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
@image_comparison(['polar_axes'], style='default')
def test_polar_annotations():
# you can specify the xypoint and the xytext in different
# positions and coordinate systems, and optionally turn on a
# connecting line and mark the point with a marker. Annotations
# work on polar axes too. In the example below, the xy point is
# in native coordinates (xycoords defaults to 'data'). For a
# polar axes, this is in (theta, radius) space. The text in this
# example is placed in the fractional figure coordinate system.
# Text keyword args like horizontal and vertical alignment are
# respected
# Setup some data
r = np.arange(0.0, 1.0, 0.001)
theta = 2.0 * 2.0 * np.pi * r
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
line, = ax.plot(theta, r, color='#ee8d18', lw=3)
line, = ax.plot((0, 0), (0, 1), color="#0000ff", lw=1)
ind = 800
thisr, thistheta = r[ind], theta[ind]
ax.plot([thistheta], [thisr], 'o')
ax.annotate('a polar annotation',
xy=(thistheta, thisr), # theta, radius
xytext=(0.05, 0.05), # fraction, fraction
textcoords='figure fraction',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='left',
verticalalignment='baseline',
)
ax.tick_params(axis='x', tick1On=True, tick2On=True, direction='out')
@image_comparison(['polar_coords'], style='default', remove_text=True)
def test_polar_coord_annotations():
# You can also use polar notation on a cartesian axes. Here the
# native coordinate system ('data') is cartesian, so you need to
# specify the xycoords and textcoords as 'polar' if you want to
# use (theta, radius)
from matplotlib.patches import Ellipse
el = Ellipse((0, 0), 10, 20, facecolor='r', alpha=0.5)
fig = plt.figure()
ax = fig.add_subplot(111, aspect='equal')
ax.add_artist(el)
el.set_clip_box(ax.bbox)
ax.annotate('the top',
xy=(np.pi/2., 10.), # theta, radius
xytext=(np.pi/3, 20.), # theta, radius
xycoords='polar',
textcoords='polar',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='left',
verticalalignment='baseline',
clip_on=True, # clip to the axes bounding box
)
ax.set_xlim(-20, 20)
ax.set_ylim(-20, 20)
@image_comparison(['polar_alignment.png'])
def test_polar_alignment():
"""
Test that changing the vertical/horizontal alignment of a polar graph
works as expected.
"""
angles = np.arange(0, 360, 90)
grid_values = [0, 0.2, 0.4, 0.6, 0.8, 1]
fig = plt.figure()
rect = [0.1, 0.1, 0.8, 0.8]
horizontal = fig.add_axes(rect, polar=True, label='horizontal')
horizontal.set_thetagrids(angles)
vertical = fig.add_axes(rect, polar=True, label='vertical')
vertical.patch.set_visible(False)
for i in range(2):
fig.axes[i].set_rgrids(
grid_values, angle=angles[i],
horizontalalignment='left', verticalalignment='top')
@image_comparison(['fill_units.png'], savefig_kwarg={'dpi': 60})
def test_fill_units():
from datetime import datetime
import matplotlib.testing.jpl_units as units
units.register()
# generate some data
t = units.Epoch("ET", dt=datetime(2009, 4, 27))
value = 10.0 * units.deg
day = units.Duration("ET", 24.0 * 60.0 * 60.0)
fig = plt.figure()
# Top-Left
ax1 = fig.add_subplot(221)
ax1.plot([t], [value], yunits='deg', color='red')
ax1.fill([733525.0, 733525.0, 733526.0, 733526.0],
[0.0, 0.0, 90.0, 0.0], 'b')
# Top-Right
ax2 = fig.add_subplot(222)
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')
# Bottom-Left
ax3 = fig.add_subplot(223)
ax3.plot([t], [value], yunits='deg', color='red')
ax3.fill([733525.0, 733525.0, 733526.0, 733526.0],
[0 * units.deg, 0 * units.deg, 90 * units.deg, 0 * units.deg],
'b')
# Bottom-Right
ax4 = fig.add_subplot(224)
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()
@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
plt.figure()
plt.subplot(211)
plt.plot([0], [0], 'o')
plt.subplot(212)
plt.plot([1], [1], 'o')
# Reuse testcase from above for a labeled data test
data = {'a': [0], 'b': [1]}
plt.figure()
plt.subplot(211)
plt.plot('a', 'a', 'o', data=data)
plt.subplot(212)
plt.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
time1 = [721964.0]
data1 = [-65.54]
plt.subplot(211)
plt.plot_date(time1, data1, 'o', color='r')
plt.subplot(212)
plt.plot(time1, data1, 'o', color='r')
@image_comparison(['shaped_data'])
def test_shaped_data():
xdata = np.array([[0.53295185, 0.23052951, 0.19057629, 0.66724975,
0.96577916, 0.73136095, 0.60823287, 0.01792100,
0.29744742, 0.27164665],
[0.27980120, 0.25814229, 0.02818193, 0.12966456,
0.57446277, 0.58167607, 0.71028245, 0.69112737,
0.89923072, 0.99072476],
[0.81218578, 0.80464528, 0.76071809, 0.85616314,
0.12757994, 0.94324936, 0.73078663, 0.09658102,
0.60703967, 0.77664978],
[0.28332265, 0.81479711, 0.86985333, 0.43797066,
0.32540082, 0.43819229, 0.92230363, 0.49414252,
0.68168256, 0.05922372],
[0.10721335, 0.93904142, 0.79163075, 0.73232848,
0.90283839, 0.68408046, 0.25502302, 0.95976614,
0.59214115, 0.13663711],
[0.28087456, 0.33127607, 0.15530412, 0.76558121,
0.83389773, 0.03735974, 0.98717738, 0.71432229,
0.54881366, 0.86893953],
[0.77995937, 0.99555600, 0.29688434, 0.15646162,
0.05184800, 0.37161935, 0.12998491, 0.09377296,
0.36882507, 0.36583435],
[0.37851836, 0.05315792, 0.63144617, 0.25003433,
0.69586032, 0.11393988, 0.92362096, 0.88045438,
0.93530252, 0.68275072],
[0.86486596, 0.83236675, 0.82960664, 0.57796630,
0.25724233, 0.84841095, 0.90862812, 0.64414887,
0.35652720, 0.71026066],
[0.01383268, 0.34060930, 0.76084285, 0.70800694,
0.87634056, 0.08213693, 0.54655021, 0.98123181,
0.44080053, 0.86815815]])
y1 = np.arange(10).reshape((1, -1))
y2 = np.arange(10).reshape((-1, 1))
plt.subplot(411)
plt.plot(y1)
plt.subplot(412)
plt.plot(y2)
plt.subplot(413)
with pytest.raises(ValueError):
plt.plot((y1, y2))
plt.subplot(414)
plt.plot(xdata[:, 1], xdata[1, :], 'o')
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(['const_xy'])
def test_const_xy():
plt.subplot(311)
plt.plot(np.arange(10), np.ones(10))
plt.subplot(312)
plt.plot(np.ones(10), np.arange(10))
plt.subplot(313)
plt.plot(np.ones(10), np.ones(10), 'o')
def test_polar_twice():
fig = plt.figure()
plt.polar([1, 2], [.1, .2])
plt.polar([3, 4], [.3, .4])
assert len(fig.axes) == 1, 'More than one polar axes created.'
@check_figures_equal()
def test_polar_wrap(fig_test, fig_ref):
ax = fig_test.add_subplot(projection="polar")
ax.plot(np.deg2rad([179, -179]), [0.2, 0.1])
ax.plot(np.deg2rad([2, -2]), [0.2, 0.1])
ax = fig_ref.add_subplot(projection="polar")
ax.plot(np.deg2rad([179, 181]), [0.2, 0.1])
ax.plot(np.deg2rad([2, 358]), [0.2, 0.1])
@check_figures_equal()
def test_polar_units_1(fig_test, fig_ref):
import matplotlib.testing.jpl_units as units
units.register()
xs = [30.0, 45.0, 60.0, 90.0]
ys = [1.0, 2.0, 3.0, 4.0]
plt.figure(fig_test.number)
plt.polar([x * units.deg for x in xs], ys)
ax = fig_ref.add_subplot(projection="polar")
ax.plot(np.deg2rad(xs), ys)
ax.set(xlabel="deg")
@check_figures_equal()
def test_polar_units_2(fig_test, fig_ref):
import matplotlib.testing.jpl_units as units
units.register()
xs = [30.0, 45.0, 60.0, 90.0]
xs_deg = [x * units.deg for x in xs]
ys = [1.0, 2.0, 3.0, 4.0]
ys_km = [y * units.km for y in ys]
plt.figure(fig_test.number)
# test {theta,r}units.
plt.polar(xs_deg, ys_km, thetaunits="rad", runits="km")
assert isinstance(plt.gca().get_xaxis().get_major_formatter(),
units.UnitDblFormatter)
ax = fig_ref.add_subplot(projection="polar")
ax.plot(np.deg2rad(xs), ys)
ax.xaxis.set_major_formatter(mticker.FuncFormatter("{:.12}".format))
ax.set(xlabel="rad", ylabel="km")
@image_comparison(['polar_rmin'], style='default')
def test_polar_rmin():
r = np.arange(0, 3.0, 0.01)
theta = 2*np.pi*r
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True)
ax.plot(theta, r)
ax.set_rmax(2.0)
ax.set_rmin(0.5)
@image_comparison(['polar_negative_rmin'], style='default')
def test_polar_negative_rmin():
r = np.arange(-3.0, 0.0, 0.01)
theta = 2*np.pi*r
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True)
ax.plot(theta, r)
ax.set_rmax(0.0)
ax.set_rmin(-3.0)
@image_comparison(['polar_rorigin'], style='default')
def test_polar_rorigin():
r = np.arange(0, 3.0, 0.01)
theta = 2*np.pi*r
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True)
ax.plot(theta, r)
ax.set_rmax(2.0)
ax.set_rmin(0.5)
ax.set_rorigin(0.0)
@image_comparison(['polar_invertedylim.png'], style='default')
def test_polar_invertedylim():
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True)
ax.set_ylim(2, 0)
@image_comparison(['polar_invertedylim_rorigin.png'], style='default')
def test_polar_invertedylim_rorigin():
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True)
ax.set_ylim(2, 0)
ax.set_rorigin(3)
@image_comparison(['polar_theta_position'], style='default')
def test_polar_theta_position():
r = np.arange(0, 3.0, 0.01)
theta = 2*np.pi*r
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True)
ax.plot(theta, r)
ax.set_theta_zero_location("NW", 30)
ax.set_theta_direction('clockwise')
@image_comparison(['polar_rlabel_position'], style='default')
def test_polar_rlabel_position():
fig = plt.figure()
ax = fig.add_subplot(111, projection='polar')
ax.set_rlabel_position(315)
ax.tick_params(rotation='auto')
@image_comparison(['polar_theta_wedge'], style='default')
def test_polar_theta_limits():
r = np.arange(0, 3.0, 0.01)
theta = 2*np.pi*r
theta_mins = np.arange(15.0, 361.0, 90.0)
theta_maxs = np.arange(50.0, 361.0, 90.0)
DIRECTIONS = ('out', 'in', 'inout')
fig, axs = plt.subplots(len(theta_mins), len(theta_maxs),
subplot_kw={'polar': True},
figsize=(8, 6))
for i, start in enumerate(theta_mins):
for j, end in enumerate(theta_maxs):
ax = axs[i, j]
ax.plot(theta, r)
if start < end:
ax.set_thetamin(start)
ax.set_thetamax(end)
else:
# Plot with clockwise orientation instead.
ax.set_thetamin(end)
ax.set_thetamax(start)
ax.set_theta_direction('clockwise')
ax.tick_params(tick1On=True, tick2On=True,
direction=DIRECTIONS[i % len(DIRECTIONS)],
rotation='auto')
ax.yaxis.set_tick_params(label2On=True, rotation='auto')
@check_figures_equal(extensions=["png"])
def test_polar_rlim(fig_test, fig_ref):
ax = fig_test.subplots(subplot_kw={'polar': True})
ax.set_rlim(top=10)
ax.set_rlim(bottom=.5)
ax = fig_ref.subplots(subplot_kw={'polar': True})
ax.set_rmax(10.)
ax.set_rmin(.5)
@check_figures_equal(extensions=["png"])
def test_polar_rlim_bottom(fig_test, fig_ref):
ax = fig_test.subplots(subplot_kw={'polar': True})
ax.set_rlim(bottom=[.5, 10])
ax = fig_ref.subplots(subplot_kw={'polar': True})
ax.set_rmax(10.)
ax.set_rmin(.5)
def test_polar_rlim_zero():
ax = plt.figure().add_subplot(projection='polar')
ax.plot(np.arange(10), np.arange(10) + .01)
assert ax.get_ylim()[0] == 0
@image_comparison(['aitoff_proj'], extensions=["png"],
remove_text=True, style='mpl20')
def test_aitoff_proj():
"""
Test aitoff projection ref.:
https://github.com/matplotlib/matplotlib/pull/14451
"""
x = np.linspace(-np.pi, np.pi, 20)
y = np.linspace(-np.pi / 2, np.pi / 2, 20)
X, Y = np.meshgrid(x, y)
fig, ax = plt.subplots(figsize=(8, 4.2),
subplot_kw=dict(projection="aitoff"))
ax.grid()
ax.plot(X.flat, Y.flat, 'o', markersize=4)
@image_comparison(['axvspan_epoch'])
def test_axvspan_epoch():
from datetime import datetime
import matplotlib.testing.jpl_units as units
units.register()
# generate some data
t0 = units.Epoch("ET", dt=datetime(2009, 1, 20))
tf = units.Epoch("ET", dt=datetime(2009, 1, 21))
dt = units.Duration("ET", units.day.convert("sec"))
ax = plt.gca()
plt.axvspan(t0, tf, facecolor="blue", alpha=0.25)
ax.set_xlim(t0 - 5.0*dt, tf + 5.0*dt)
@image_comparison(['axhspan_epoch'])
def test_axhspan_epoch():
from datetime import datetime
import matplotlib.testing.jpl_units as units
units.register()
# generate some data
t0 = units.Epoch("ET", dt=datetime(2009, 1, 20))
tf = units.Epoch("ET", dt=datetime(2009, 1, 21))
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)
@image_comparison(['hexbin_empty.png'], remove_text=True)
def test_hexbin_empty():
# From #3886: creating hexbin from empty dataset raises ValueError
ax = plt.gca()
ax.hexbin([], [])
def test_hexbin_pickable():
# From #1973: Test that picking a hexbin collection works
class FauxMouseEvent:
def __init__(self, x, y):
self.x = x
self.y = y
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)
assert hb.contains(FauxMouseEvent(400, 300))[0]
@image_comparison(['hexbin_log.png'], style='mpl20')
def test_hexbin_log():
# Issue #1636 (and also test log scaled colorbar)
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')
plt.colorbar(h)
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)
@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 = plt.figure()
ax = fig.add_subplot(111)
ax.imshow("r", data=data)
@image_comparison(['imshow_clip'], style='mpl20')
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])
x = c.collections[0]
clipPath = x.get_paths()[0]
clipTransform = x.get_transform()
from matplotlib.transforms import TransformedPath
clip_path = TransformedPath(clipPath, clipTransform)
# Plot the image clipped by the contour
ax.imshow(r, clip_path=clip_path)
@image_comparison(['polycollection_joinstyle'], remove_text=True)
def test_polycollection_joinstyle():
# Bug #2890979 reported by Matthew West
from matplotlib import collections as mcoll
fig, ax = plt.subplots()
verts = np.array([[1, 1], [1, 2], [2, 2], [2, 1]])
c = mcoll.PolyCollection([verts], linewidths=40)
ax.add_collection(c)
ax.set_xbound(0, 3)
ax.set_ybound(0, 3)
@pytest.mark.parametrize(
'x, y1, y2', [
(np.zeros((2, 2)), 3, 3),
(np.arange(0.0, 2, 0.02), np.zeros((2, 2)), 3),
(np.arange(0.0, 2, 0.02), 3, np.zeros((2, 2)))
], ids=[
'2d_x_input',
'2d_y1_input',
'2d_y2_input'
]
)
def test_fill_between_input(x, y1, y2):
fig, ax = plt.subplots()
with pytest.raises(ValueError):
ax.fill_between(x, y1, y2)
@pytest.mark.parametrize(
'y, x1, x2', [
(np.zeros((2, 2)), 3, 3),
(np.arange(0.0, 2, 0.02), np.zeros((2, 2)), 3),
(np.arange(0.0, 2, 0.02), 3, np.zeros((2, 2)))
], ids=[
'2d_y_input',
'2d_x1_input',
'2d_x2_input'
]
)
def test_fill_betweenx_input(y, x1, x2):
fig, ax = plt.subplots()
with pytest.raises(ValueError):
ax.fill_betweenx(y, x1, x2)
@image_comparison(['fill_between_interpolate'], remove_text=True)
def test_fill_between_interpolate():
x = np.arange(0.0, 2, 0.02)
y1 = np.sin(2*np.pi*x)
y2 = 1.2*np.sin(4*np.pi*x)
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
ax1.plot(x, y1, x, y2, color='black')
ax1.fill_between(x, y1, y2, where=y2 >= y1, facecolor='white', hatch='/',
interpolate=True)
ax1.fill_between(x, y1, y2, where=y2 <= y1, facecolor='red',
interpolate=True)
# Test support for masked arrays.
y2 = np.ma.masked_greater(y2, 1.0)
# Test that plotting works for masked arrays with the first element masked
y2[0] = np.ma.masked
ax2.plot(x, y1, x, y2, color='black')
ax2.fill_between(x, y1, y2, where=y2 >= y1, facecolor='green',
interpolate=True)
ax2.fill_between(x, y1, y2, where=y2 <= y1, facecolor='red',
interpolate=True)
@image_comparison(['fill_between_interpolate_decreasing'],
style='mpl20', remove_text=True)
def test_fill_between_interpolate_decreasing():
p = np.array([724.3, 700, 655])
t = np.array([9.4, 7, 2.2])
prof = np.array([7.9, 6.6, 3.8])
fig, ax = plt.subplots(figsize=(9, 9))
ax.plot(t, p, 'tab:red')
ax.plot(prof, p, 'k')
ax.fill_betweenx(p, t, prof, where=prof < t,
facecolor='blue', interpolate=True, alpha=0.4)
ax.fill_betweenx(p, t, prof, where=prof > t,
facecolor='red', interpolate=True, alpha=0.4)
ax.set_xlim(0, 30)
ax.set_ylim(800, 600)
# 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, linthreshx in zip(axs, [20., 2., 1., 0.1, 0.01]):
ax.plot(x, x)
ax.set_xscale('symlog', linthreshx=linthreshx)
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)
plt.pcolor(X, Y, list(Z))
@image_comparison(['pcolormesh'], remove_text=True)
def test_pcolormesh():
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()) / Z.ptp()
# The color array can include masked values:
Zm = ma.masked_where(np.abs(Qz) < 0.5 * np.max(Qz), Z)
fig, (ax1, ax2, ax3) = plt.subplots(1, 3)
ax1.pcolormesh(Qx, Qz, Z, lw=0.5, edgecolors='k')
ax2.pcolormesh(Qx, Qz, Z, lw=2, edgecolors=['b', 'w'])
ax3.pcolormesh(Qx, Qz, Z, shading="gouraud")
@image_comparison(['pcolormesh_alpha'], extensions=["png", "pdf"],
remove_text=True)
def test_pcolormesh_alpha():
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()) / Z.ptp()
vir = plt.get_cmap("viridis", 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, 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, cmap=cmap, shading='flat', zorder=1)
ax4.pcolormesh(Qx, Qy, Z, cmap=cmap, shading='gouraud', zorder=1)
@image_comparison(['pcolormesh_datetime_axis.png'],
remove_text=False, style='mpl20')
def test_pcolormesh_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.pcolormesh(x[:-1], y[:-1], z)
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)
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'],
remove_text=False, 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)
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)
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])
@image_comparison(['canonical'])
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():
from matplotlib import patches
# 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(patches.Ellipse(centre, w, h, alpha=0.3))
ax.add_patch(patches.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():
from matplotlib import patches
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, np.array([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 = patches.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 = patches.Arc((xcenter, ycenter), width, height,
angle=angle, linewidth=2, fill=False, zorder=2)
ax.add_patch(e2)
@image_comparison(['markevery'], remove_text=True)
def test_markevery():
x = np.linspace(0, 10, 100)
y = np.sin(x) * np.sqrt(x/10 + 0.5)
# check marker only plot
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x, y, 'o', label='default')
ax.plot(x, y, 'd', markevery=None, label='mark all')
ax.plot(x, y, 's', markevery=10, label='mark every 10')
ax.plot(x, y, '+', markevery=(5, 20), label='mark every 5 starting at 10')
ax.legend()
@image_comparison(['markevery_line'], remove_text=True)
def test_markevery_line():
x = np.linspace(0, 10, 100)
y = np.sin(x) * np.sqrt(x/10 + 0.5)
# check line/marker combos
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x, y, '-o', label='default')
ax.plot(x, y, '-d', markevery=None, label='mark all')
ax.plot(x, y, '-s', markevery=10, label='mark every 10')
ax.plot(x, y, '-+', markevery=(5, 20), label='mark every 5 starting at 10')
ax.legend()
@image_comparison(['markevery_linear_scales'], remove_text=True)
def test_markevery_linear_scales():
cases = [None,
8,
(30, 8),
[16, 24, 30], [0, -1],
slice(100, 200, 3),
0.1, 0.3, 1.5,
(0.0, 0.1), (0.45, 0.1)]
cols = 3
gs = matplotlib.gridspec.GridSpec(len(cases) // cols + 1, cols)
delta = 0.11
x = np.linspace(0, 10 - 2 * delta, 200) + delta
y = np.sin(x) + 1.0 + delta
for i, case in enumerate(cases):
row = (i // cols)
col = i % cols
plt.subplot(gs[row, col])
plt.title('markevery=%s' % str(case))
plt.plot(x, y, 'o', ls='-', ms=4, markevery=case)
@image_comparison(['markevery_linear_scales_zoomed'], remove_text=True)
def test_markevery_linear_scales_zoomed():
cases = [None,
8,
(30, 8),
[16, 24, 30], [0, -1],
slice(100, 200, 3),
0.1, 0.3, 1.5,
(0.0, 0.1), (0.45, 0.1)]
cols = 3
gs = matplotlib.gridspec.GridSpec(len(cases) // cols + 1, cols)
delta = 0.11
x = np.linspace(0, 10 - 2 * delta, 200) + delta
y = np.sin(x) + 1.0 + delta
for i, case in enumerate(cases):
row = (i // cols)
col = i % cols
plt.subplot(gs[row, col])
plt.title('markevery=%s' % str(case))
plt.plot(x, y, 'o', ls='-', ms=4, markevery=case)
plt.xlim((6, 6.7))
plt.ylim((1.1, 1.7))
@image_comparison(['markevery_log_scales'], remove_text=True)
def test_markevery_log_scales():
cases = [None,
8,
(30, 8),
[16, 24, 30], [0, -1],
slice(100, 200, 3),
0.1, 0.3, 1.5,
(0.0, 0.1), (0.45, 0.1)]
cols = 3
gs = matplotlib.gridspec.GridSpec(len(cases) // cols + 1, cols)
delta = 0.11
x = np.linspace(0, 10 - 2 * delta, 200) + delta
y = np.sin(x) + 1.0 + delta
for i, case in enumerate(cases):
row = (i // cols)
col = i % cols
plt.subplot(gs[row, col])
plt.title('markevery=%s' % str(case))
plt.xscale('log')
plt.yscale('log')
plt.plot(x, y, 'o', ls='-', ms=4, markevery=case)
@image_comparison(['markevery_polar'], style='default', remove_text=True)
def test_markevery_polar():
cases = [None,
8,
(30, 8),
[16, 24, 30], [0, -1],
slice(100, 200, 3),
0.1, 0.3, 1.5,
(0.0, 0.1), (0.45, 0.1)]
cols = 3
gs = matplotlib.gridspec.GridSpec(len(cases) // cols + 1, cols)
r = np.linspace(0, 3.0, 200)
theta = 2 * np.pi * r
for i, case in enumerate(cases):
row = (i // cols)
col = i % cols
plt.subplot(gs[row, col], polar=True)
plt.title('markevery=%s' % str(case))
plt.plot(theta, r, 'o', ls='-', ms=4, markevery=case)
@image_comparison(['marker_edges'], remove_text=True)
def test_marker_edges():
x = np.linspace(0, 1, 10)
fig = plt.figure()
ax = fig.add_subplot(111)
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 = plt.figure()
ax = fig.add_subplot(111)
ax = plt.gca()
ax.bar("a", "b", align='edge', tick_label='0', data=data)
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_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_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)
def test_pandas_minimal_plot(pd):
# smoke test that series and index objcets do not warn
x = pd.Series([1, 2], dtype="float64")
plt.plot(x, x)
plt.plot(x.index, x)
plt.plot(x)
plt.plot(x.index)
@image_comparison(['hist_log'], remove_text=True)
def test_hist_log():
data0 = np.linspace(0, 1, 200)**3
data = np.r_[1-data0, 1+data0]
fig = plt.figure()
ax = fig.add_subplot(111)
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)
@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')
@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('%s/%s' % (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).astype('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
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 = plt.figure()
ax = fig.add_subplot(111)
ax.contourf(x, y, z, 7, hatches=['/', '\\', '//', '-'],
cmap=plt.get_cmap('gray'),
extend='both', alpha=0.5)
@image_comparison(['contour_colorbar'], style='mpl20')
def test_contour_colorbar():
x, y, z = contour_dat()
fig = plt.figure()
ax = fig.add_subplot(111)
cs = ax.contourf(x, y, z, levels=np.arange(-1.8, 1.801, 0.2),
cmap=plt.get_cmap('RdBu'),
vmin=-0.6,
vmax=0.6,
extend='both')
cs1 = ax.contour(x, y, z, levels=np.arange(-2.2, -0.599, 0.2),
colors=['y'],
linestyles='solid',
linewidths=2)
cs2 = ax.contour(x, y, z, levels=np.arange(0.6, 2.2, 0.2),
colors=['c'],
linewidths=2)
cbar = fig.colorbar(cs, ax=ax)
cbar.add_lines(cs1)
cbar.add_lines(cs2, erase=False)
@image_comparison(['hist2d', 'hist2d'], remove_text=True, style='mpl20')
def test_hist2d():
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 = plt.figure()
ax = fig.add_subplot(111)
ax.hist2d(x, y, bins=10, rasterized=True)
# Reuse testcase from above for a labeled data test
data = {"x": x, "y": y}
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist2d("x", "y", bins=10, data=data, rasterized=True)
@image_comparison(['hist2d_transpose'], remove_text=True, style='mpl20')
def test_hist2d_transpose():
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 = plt.figure()
ax = fig.add_subplot(111)
ax.hist2d(x, y, bins=10, rasterized=True)
def test_hist2d_density_normed():
x, y = np.random.random((2, 100))
ax = plt.figure().subplots()
for obj in [ax, plt]:
obj.hist2d(x, y, density=True)
with pytest.warns(MatplotlibDeprecationWarning):
obj.hist2d(x, y, normed=True)
with pytest.warns(MatplotlibDeprecationWarning):
obj.hist2d(x, y, density=True, normed=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])
def test_scatter_size_arg_size(self):
x = np.arange(4)
with pytest.raises(ValueError):
plt.scatter(x, x, x[1:])
with pytest.raises(ValueError):
plt.scatter(x[1:], x[1:], x)
@check_figures_equal(extensions=["png"])
def test_scatter_invalid_color(self, fig_test, fig_ref):
ax = fig_test.subplots()
cmap = plt.get_cmap("viridis", 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 = plt.get_cmap("viridis", 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 plotninfinite=False we plot only 2 points.
ax = fig_test.subplots()
cmap = plt.get_cmap("viridis", 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)
@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():
return 'blue' # currently unused
from matplotlib.axes import Axes
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
}
if re_key is None:
Axes._parse_scatter_color_args(
c=c_case, edgecolors="black", kwargs={}, xsize=xsize,
get_next_color_func=get_next_color)
else:
with pytest.raises(ValueError, match=REGEXP[re_key]):
Axes._parse_scatter_color_args(
c=c_case, edgecolors="black", kwargs={}, xsize=xsize,
get_next_color_func=get_next_color)
def _params(c=None, xsize=2, **kwargs):
edgecolors = kwargs.pop('edgecolors', None)
return (c, edgecolors, kwargs if kwargs is not None else {}, 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():
return 'blue' # currently unused
from matplotlib.axes import Axes
c, colors, _edgecolors = 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():
return 'blue' # currently unused
from matplotlib.axes import Axes
c = kwargs.pop('c', None)
edgecolors = kwargs.pop('edgecolors', None)
_, _, result_edgecolors = \
Axes._parse_scatter_color_args(c, edgecolors, kwargs, xsize=2,
get_next_color_func=get_next_color)
assert result_edgecolors == expected_edgecolors
def test_as_mpl_axes_api():
# tests the _as_mpl_axes api
from matplotlib.projections.polar import PolarAxes
import matplotlib.axes as maxes
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
prj3 = Polar()
# testing axes creation with plt.axes
ax = plt.axes([0, 0, 1, 1], projection=prj)
assert type(ax) == PolarAxes
ax_via_gca = plt.gca(projection=prj)
assert ax_via_gca is ax
plt.close()
# testing axes creation with gca
ax = plt.gca(projection=prj)
assert type(ax) == maxes._subplots.subplot_class_factory(PolarAxes)
ax_via_gca = plt.gca(projection=prj)
assert ax_via_gca is ax
# try getting the axes given a different polar projection
with pytest.warns(UserWarning) as rec:
ax_via_gca = plt.gca(projection=prj2)
assert len(rec) == 1
assert 'Requested projection is different' in str(rec[0].message)
assert ax_via_gca is not ax
assert ax.get_theta_offset() == 0
assert ax_via_gca.get_theta_offset() == np.pi
# try getting the axes given an == (not is) polar projection
with pytest.warns(UserWarning):
ax_via_gca = plt.gca(projection=prj3)
assert len(rec) == 1
assert 'Requested projection is different' in str(rec[0].message)
assert ax_via_gca is ax
plt.close()
# testing axes creation with subplot
ax = plt.subplot(121, projection=prj)
assert type(ax) == maxes._subplots.subplot_class_factory(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)
@image_comparison(['log_scales'])
def test_log_scales():
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(np.log(np.linspace(0.1, 100)))
ax.set_yscale('log', basey=5.5)
ax.invert_yaxis()
ax.set_xscale('log', basex=9.0)
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 = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_xscale('log')
with pytest.warns(UserWarning, match='Attempted to set non-positive'):
ax.set_xlim(-1, 10)
ax.set_yscale('log')
with pytest.warns(UserWarning, match='Attempted to set non-positive'):
ax.set_ylim(-1, 10)
def test_polar_no_data():
plt.subplot(projection="polar")
ax = plt.gca()
assert ax.get_rmin() == 0 and ax.get_rmax() == 1
plt.close("all")
# Used to behave differently (by triggering an autoscale with no data).
plt.polar()
ax = plt.gca()
assert ax.get_rmin() == 0 and ax.get_rmax() == 1
@image_comparison(['stackplot_test_image', 'stackplot_test_image'])
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 labeled data test
data = {"x": x, "y1": y1, "y2": y2, "y3": y3}
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.stackplot("x", "y1", "y2", "y3", data=data)
ax.set_xlim((0, 10))
ax.set_ylim((0, 70))
@image_comparison(['stackplot_test_baseline'], remove_text=True)
def test_stackplot_baseline():
np.random.seed(0)
def layers(n, m):
a = np.zeros((m, n))
for i in range(n):
for j in range(5):
x = 1 / (.1 + np.random.random())
y = 2 * np.random.random() - .5
z = 10 / (.1 + np.random.random())
a[:, i] += x * np.exp(-((np.arange(m) / m - y) * z) ** 2)
return a
d = layers(3, 100)
d[50, :] = 0 # test for fixed weighted wiggle (issue #6313)
fig, axs = plt.subplots(2, 2)
axs[0, 0].stackplot(range(100), d.T, baseline='zero')
axs[0, 1].stackplot(range(100), d.T, baseline='sym')
axs[1, 0].stackplot(range(100), d.T, baseline='wiggle')
axs[1, 1].stackplot(range(100), d.T, baseline='weighted_wiggle')
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('vert', True):
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_precentilewhis.png'],
savefig_kwarg={'dpi': 40},
style='default')
def test_bxp_precentilewhis():
_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(vert=False))
@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(vert=False))
@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)))
@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(['boxplot', 'boxplot'], tol=1.28, style='default')
def test_boxplot():
# Randomness used for bootstrapping.
np.random.seed(937)
x = np.linspace(-7, 7, 140)
x = np.hstack([-25, x, 25])
fig, ax = plt.subplots()
ax.boxplot([x, x], bootstrap=10000, notch=1)
ax.set_ylim((-30, 30))
# Reuse testcase from above for a labeled data test
data = {"x": [x, x]}
fig, ax = plt.subplots()
ax.boxplot("x", bootstrap=10000, notch=1, data=data)
ax.set_ylim((-30, 30))
@image_comparison(['boxplot_sym2.png'], remove_text=True, style='default')
def test_boxplot_sym2():
# Randomness used for bootstrapping.
np.random.seed(937)
x = np.linspace(-7, 7, 140)
x = np.hstack([-25, x, 25])
fig, [ax1, ax2] = plt.subplots(1, 2)
ax1.boxplot([x, x], bootstrap=10000, sym='^')
ax1.set_ylim((-30, 30))
ax2.boxplot([x, x], bootstrap=10000, sym='g')
ax2.set_ylim((-30, 30))
@image_comparison(['boxplot_sym.png'],
remove_text=True,
savefig_kwarg={'dpi': 40},
style='default')
def test_boxplot_sym():
x = np.linspace(-7, 7, 140)
x = np.hstack([-25, x, 25])
fig, ax = plt.subplots()
ax.boxplot([x, x], sym='gs')
ax.set_ylim((-30, 30))
@image_comparison(['boxplot_autorange_false_whiskers.png',
'boxplot_autorange_true_whiskers.png'],
style='default')
def test_boxplot_autorange_whiskers():
# Randomness used for bootstrapping.
np.random.seed(937)
x = np.ones(140)
x = np.hstack([0, x, 2])
fig1, ax1 = plt.subplots()
ax1.boxplot([x, x], bootstrap=10000, notch=1)
ax1.set_ylim((-5, 5))
fig2, ax2 = plt.subplots()
ax2.boxplot([x, x], bootstrap=10000, notch=1, autorange=True)
ax2.set_ylim((-5, 5))
def _rc_test_bxp_helper(ax, rc_dict):
x = np.linspace(-7, 7, 140)
x = np.hstack([-25, x, 25])
with matplotlib.rc_context(rc_dict):
ax.boxplot([x, x])
return ax
@image_comparison(['boxplot_rc_parameters'],
savefig_kwarg={'dpi': 100}, remove_text=True,
tol=1, style='default')
def test_boxplot_rc_parameters():
# Randomness used for bootstrapping.
np.random.seed(937)
fig, ax = plt.subplots(3)
rc_axis0 = {
'boxplot.notch': True,
'boxplot.whiskers': [5, 95],
'boxplot.bootstrap': 10000,
'boxplot.flierprops.color': 'b',
'boxplot.flierprops.marker': 'o',
'boxplot.flierprops.markerfacecolor': 'g',
'boxplot.flierprops.markeredgecolor': 'b',
'boxplot.flierprops.markersize': 5,
'boxplot.flierprops.linestyle': '--',
'boxplot.flierprops.linewidth': 2.0,
'boxplot.boxprops.color': 'r',
'boxplot.boxprops.linewidth': 2.0,
'boxplot.boxprops.linestyle': '--',
'boxplot.capprops.color': 'c',
'boxplot.capprops.linewidth': 2.0,
'boxplot.capprops.linestyle': '--',
'boxplot.medianprops.color': 'k',
'boxplot.medianprops.linewidth': 2.0,
'boxplot.medianprops.linestyle': '--',
}
rc_axis1 = {
'boxplot.vertical': False,
'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 = plt.figure()
ax = fig.add_subplot(111)
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_1():
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])
def test_boxplot_bad_medians_2():
x = np.linspace(-7, 7, 140)
x = np.hstack([-25, x, 25])
fig, ax = plt.subplots()
with pytest.raises(ValueError):
ax.boxplot([x, x], usermedians=[[1, 2], [1, 2]])
def test_boxplot_bad_ci_1():
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]])
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_bad_ci_2():
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], [1]])
@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 i in range(4)]
ax = plt.axes()
ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
showmedians=0)
# Reuse testcase from above for a labeled data test
data = {"d": data}
fig, ax = plt.subplots()
ax = plt.axes()
ax.violinplot("d", positions=range(4), showmeans=0, showextrema=0,
showmedians=0, 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 i in range(4)]
ax.violinplot(data, positions=range(4), showmeans=1, showextrema=0,
showmedians=0)
@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 i in range(4)]
ax.violinplot(data, positions=range(4), showmeans=0, showextrema=1,
showmedians=0)
@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 i in range(4)]
ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
showmedians=1)
@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 i in range(4)]
ax.violinplot(data, positions=range(4), showmeans=1, showextrema=1,
showmedians=1,
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 i in range(4)]
ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
showmedians=0, 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 i in range(4)]
ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
showmedians=0, 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 i in range(4)]
ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
showextrema=0, showmedians=0)
@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 i in range(4)]
ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
showextrema=0, showmedians=1)
@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 i in range(4)]
ax.violinplot(data, positions=range(4), vert=False, showmeans=1,
showextrema=0, showmedians=0)
@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 i in range(4)]
ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
showextrema=1, showmedians=0)
@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 i in range(4)]
ax.violinplot(data, positions=range(4), vert=False, showmeans=1,
showextrema=1, showmedians=1,
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 i in range(4)]
ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
showextrema=0, showmedians=0, 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 i in range(4)]
ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
showextrema=0, showmedians=0, points=200)
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 i 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 i 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]])
def test_manage_xticks():
_, ax = plt.subplots()
ax.set_xlim(0, 4)
old_xlim = ax.get_xlim()
np.random.seed(0)
y1 = np.random.normal(10, 3, 20)
y2 = np.random.normal(3, 1, 20)
ax.boxplot([y1, y2], positions=[1, 2], manage_ticks=False)
new_xlim = ax.get_xlim()
assert_array_equal(old_xlim, new_xlim)
def test_boxplot_not_single():
fig, ax = plt.subplots()
ax.boxplot(np.random.rand(100), positions=[3])
ax.boxplot(np.random.rand(100), positions=[5])
fig.canvas.draw()
assert ax.get_xlim() == (2.5, 5.5)
assert list(ax.get_xticks()) == [3, 5]
assert [t.get_text() for t in ax.get_xticklabels()] == ["3", "5"]
def test_tick_space_size_0():
# allow font size to be zero, which affects ticks when there is
# no other text in the figure.
plt.plot([0, 1], [0, 1])
matplotlib.rcParams.update({'font.size': 0})
b = io.BytesIO()
plt.savefig(b, dpi=80, format='raw')
@image_comparison(['errorbar_basic', 'errorbar_mixed', 'errorbar_basic'])
def test_errorbar():
x = np.arange(0.1, 4, 0.5)
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]
# Try a Nx1 shaped error just to check
with pytest.warns(MatplotlibDeprecationWarning):
ax.errorbar(x, y, yerr=np.reshape(yerr, (len(y), 1)), 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')
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")
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')
def test_errorbar_shape():
fig = plt.figure()
ax = fig.gca()
x = np.arange(0.1, 4, 0.5)
y = np.exp(-x)
yerr1 = 0.1 + 0.2*np.sqrt(x)
yerr = np.vstack((yerr1, 2*yerr1)).T
xerr = 0.1 + yerr
with pytest.raises(ValueError):
ax.errorbar(x, y, yerr=yerr, fmt='o')
with pytest.raises(ValueError):
ax.errorbar(x, y, xerr=xerr, fmt='o')
with pytest.raises(ValueError):
ax.errorbar(x, y, yerr=yerr, xerr=xerr, fmt='o')
@image_comparison(['errorbar_limits'])
def test_errorbar_limits():
x = np.arange(0.5, 5.5, 0.5)
y = np.exp(-x)
xerr = 0.1
yerr = 0.2
ls = 'dotted'
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# standard error bars
plt.errorbar(x, y, xerr=xerr, yerr=yerr, ls=ls, color='blue')
# including upper limits
uplims = np.zeros_like(x)
uplims[[1, 5, 9]] = True
plt.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
plt.errorbar(x, y+1.0, xerr=xerr, yerr=yerr, lolims=lolims, ls=ls,
color='red')
# including upper and lower limits
plt.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
plt.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_errobar_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'))
@image_comparison(['errorbar_with_prop_cycle.png'],
style='mpl20', remove_text=True)
def test_errorbar_with_prop_cycle():
_cycle = cycler(ls=['--', ':'], marker=['s', 's'], mfc=['k', 'w'])
plt.rc("axes", prop_cycle=_cycle)
fig, ax = plt.subplots()
ax.errorbar(x=[2, 4, 10], y=[3, 2, 4], yerr=0.5)
ax.errorbar(x=[2, 4, 10], y=[6, 4, 2], yerr=0.5)
@check_figures_equal()
def test_errorbar_offsets(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
# Using feature in question
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')
@image_comparison(['hist_stacked_stepfilled', 'hist_stacked_stepfilled'])
def test_hist_stacked_stepfilled():
# make some data
d1 = np.linspace(1, 3, 20)
d2 = np.linspace(0, 10, 50)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist((d1, d2), histtype="stepfilled", stacked=True)
# Reuse testcase from above for a labeled data test
data = {"x": (d1, d2)}
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist("x", histtype="stepfilled", stacked=True, data=data)
@image_comparison(['hist_offset'])
def test_hist_offset():
# make some data
d1 = np.linspace(0, 10, 50)
d2 = np.linspace(1, 3, 20)
fig = plt.figure()
ax = fig.add_subplot(111)
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 = plt.figure()
ax = fig.add_subplot(111)
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 = plt.figure()
ax = fig.add_subplot(111)
ax.hist((d1, d2), histtype="step", orientation="horizontal")
@image_comparison(['hist_stacked_weights'])
def test_hist_stacked_weighted():
# make some data
d1 = np.linspace(0, 10, 50)
d2 = np.linspace(1, 3, 20)
w1 = np.linspace(0.01, 3.5, 50)
w2 = np.linspace(0.05, 2., 20)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist((d1, d2), weights=(w1, w2), histtype="stepfilled", stacked=True)
@image_comparison(['stem.png', 'stem.png'], style='mpl20', remove_text=True)
def test_stem():
# Note, we don't use @pytest.mark.parametrize, because in parallel this
# might cause one process result to overwrite another's.
for use_line_collection in [True, False]:
_test_stem(use_line_collection)
def _test_stem(use_line_collection):
x = np.linspace(0.1, 2 * np.pi, 100)
args = (x, np.cos(x))
# Label is a single space to force a legend to be drawn, but to avoid any
# text being drawn
kwargs = dict(linefmt='C2-.', markerfmt='k+', basefmt='C1-.',
label=' ', use_line_collection=use_line_collection)
fig, ax = plt.subplots()
if use_line_collection:
ax.stem(*args, **kwargs)
else:
with pytest.warns(UserWarning):
ax.stem(*args, **kwargs)
ax.legend()
@check_figures_equal(extensions=['png'])
def test_stem_params(fig_test, fig_ref):
x = np.linspace(0, 3.14, 37)
y = np.sin(x)
ax = fig_test.subplots()
ax.stem(x, y, linefmt='grey', use_line_collection=True)
ax = fig_ref.subplots()
with pytest.warns(UserWarning):
ax.stem(x, y, linefmt='grey')
def test_stem_args():
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
x = list(range(10))
y = list(range(10))
# Test the call signatures
ax.stem(y, use_line_collection=True)
ax.stem(x, y, use_line_collection=True)
ax.stem(x, y, 'r--', use_line_collection=True)
ax.stem(x, y, 'r--', basefmt='b--', use_line_collection=True)
def test_stem_dates():
fig, ax = plt.subplots(1, 1)
from dateutil import parser
x = parser.parse("2013-9-28 11:00:00")
y = 100
x1 = parser.parse("2013-9-28 12:00:00")
y1 = 200
ax.stem([x, x1], [y, y1], "*-", use_line_collection=True)
@image_comparison(['hist_stacked_stepfilled_alpha'])
def test_hist_stacked_stepfilled_alpha():
# make some data
d1 = np.linspace(1, 3, 20)
d2 = np.linspace(0, 10, 50)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist((d1, d2), histtype="stepfilled", stacked=True, alpha=0.5)
@image_comparison(['hist_stacked_step'])
def test_hist_stacked_step():
# make some data
d1 = np.linspace(1, 3, 20)
d2 = np.linspace(0, 10, 50)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist((d1, d2), histtype="step", stacked=True)
@image_comparison(['hist_stacked_normed'])
def test_hist_stacked_density():
# make some data
d1 = np.linspace(1, 3, 20)
d2 = np.linspace(0, 10, 50)
fig, ax = plt.subplots()
ax.hist((d1, d2), stacked=True, density=True)
@image_comparison(['hist_step_bottom.png'], remove_text=True)
def test_hist_step_bottom():
# make some data
d1 = np.linspace(1, 3, 20)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(d1, bottom=np.arange(10), histtype="stepfilled")
@image_comparison(['hist_stacked_bar'])
def test_hist_stacked_bar():
# make some data
d = [[100, 100, 100, 100, 200, 320, 450, 80, 20, 600, 310, 800],
[20, 23, 50, 11, 100, 420], [120, 120, 120, 140, 140, 150, 180],
[60, 60, 60, 60, 300, 300, 5, 5, 5, 5, 10, 300],
[555, 555, 555, 30, 30, 30, 30, 30, 100, 100, 100, 100, 30, 30],
[30, 30, 30, 30, 400, 400, 400, 400, 400, 400, 400, 400]]
colors = [(0.5759849696758961, 1.0, 0.0), (0.0, 1.0, 0.350624650815206),
(0.0, 1.0, 0.6549834156005998), (0.0, 0.6569064625276622, 1.0),
(0.28302699607823545, 0.0, 1.0), (0.6849123462299822, 0.0, 1.0)]
labels = ['green', 'orange', ' yellow', 'magenta', 'black']
fig = plt.figure()
ax = fig.add_subplot(111)
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), ncol=1)
def test_hist_emptydata():
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist([[], range(10), range(10)], histtype="step")
def test_hist_labels():
# test singleton labels OK
fig, ax = plt.subplots()
l = ax.hist([0, 1], label=0)
assert l[2][0].get_label() == '0'
l = ax.hist([0, 1], label=[0])
assert l[2][0].get_label() == '0'
l = ax.hist([0, 1], label=None)
assert l[2][0].get_label() == '_nolegend_'
l = ax.hist([0, 1], label='0')
assert l[2][0].get_label() == '0'
l = ax.hist([0, 1], label='00')
assert l[2][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 = plt.figure()
ax = fig.add_subplot(111)
ax.plot(data, 'D', mfc='none', markersize=100)
@image_comparison(['rgba_markers'], remove_text=True)
def test_rgba_markers():
fig, axs = plt.subplots(ncols=2)
rcolors = [(1, 0, 0, 1), (1, 0, 0, 0.5)]
bcolors = [(0, 0, 1, 1), (0, 0, 1, 0.5)]
alphas = [None, 0.2]
kw = dict(ms=100, mew=20)
for i, alpha in enumerate(alphas):
for j, rcolor in enumerate(rcolors):
for k, bcolor in enumerate(bcolors):
axs[i].plot(j+1, k+1, 'o', mfc=bcolor, mec=rcolor,
alpha=alpha, **kw)
axs[i].plot(j+1, k+3, 'x', mec=rcolor, alpha=alpha, **kw)
for ax in axs:
ax.axis([-1, 4, 0, 5])
@image_comparison(['mollweide_grid'], remove_text=True)
def test_mollweide_grid():
# test that both horizontal and vertical gridlines appear on the Mollweide
# projection
fig = plt.figure()
ax = fig.add_subplot(111, 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(111, projection='mollweide')
# set up 1-degree grid in longitude, latitude
lon = np.linspace(-np.pi, np.pi, 360)
lat = np.linspace(-np.pi / 2.0, np.pi / 2.0, 180)
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(111, 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 = plt.figure()
ax = fig.add_subplot(111)
# alpha=.5 markers, solid line
ax.plot(data, '-D', color=[1, 0, 0], mfc=[1, 0, 0, .5],
markersize=20, lw=10)
# everything solid by kwarg
ax.plot(data + 2, '-D', color=[1, 0, 0, .5], mfc=[1, 0, 0, .5],
markersize=20, lw=10,
alpha=1)
# everything alpha=.5 by kwarg
ax.plot(data + 4, '-D', color=[1, 0, 0], mfc=[1, 0, 0],
markersize=20, lw=10,
alpha=.5)
# everything alpha=.5 by colors
ax.plot(data + 6, '-D', color=[1, 0, 0, .5], mfc=[1, 0, 0, .5],
markersize=20, lw=10)
# alpha=.5 line, solid markers
ax.plot(data + 8, '-D', color=[1, 0, 0, .5], mfc=[1, 0, 0],
markersize=20, lw=10)
@image_comparison(['eventplot', 'eventplot'], remove_text=True)
def test_eventplot():
'''
test that eventplot produces the correct output
'''
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(111)
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(111)
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(111)
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 the issue #8193.
'''
data = [[i] for i in range(4)] # 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)
@image_comparison(['test_eventplot_problem_kwargs.png'], remove_text=True)
def test_eventplot_problem_kwargs():
'''
test that 'singular' versions of LineCollection props raise an
IgnoredKeywordWarning 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(111)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
axobj.eventplot(data,
colors=['r', 'b'],
color=['c', 'm'],
linewidths=[2, 1],
linewidth=[1, 2],
linestyles=['solid', 'dashed'],
linestyle=['dashdot', 'dotted'])
# check that three IgnoredKeywordWarnings were raised
assert len(w) == 3
assert all(issubclass(wi.category, IgnoredKeywordWarning) for wi in w)
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, orientation', product(
([[]], [[], [0, 1]], [[0, 1], []]),
('_empty', 'vertical', 'horizontal', None, 'none')))
def test_eventplot_orientation(data, orientation):
"""Introduced when fixing issue #6412."""
opts = {} if orientation == "_empty" else {'orientation': orientation}
fig, ax = plt.subplots(1, 1)
ax.eventplot(data, **opts)
plt.draw()
@image_comparison(['marker_styles.png'], remove_text=True)
def test_marker_styles():
fig = plt.figure()
ax = fig.add_subplot(111)
for y, marker in enumerate(sorted(matplotlib.markers.MarkerStyle.markers,
key=lambda x: str(type(x))+str(x))):
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'])
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 = plt.figure()
ax = fig.add_subplot(111)
ax.plot(data, linestyle='', marker=marker_as_tuple, mfc='k')
ax.plot(data[::-1], linestyle='', marker=marker_as_list, mfc='b')
ax.set_xlim([-1, 10])
ax.set_ylim([-1, 10])
@image_comparison(['vline_hline_zorder', 'errorbar_zorder'],
tol={'aarch64': 0.02}.get(platform.machine(), 0.0))
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")
@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)
@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)
@image_comparison(['step_linestyle', 'step_linestyle'], remove_text=True)
def test_step_linestyle():
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():
from matplotlib import patches
from matplotlib import collections
# First illustrate basic pyplot interface, using defaults where possible.
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
c = patches.Circle((8, 8), radius=4, facecolor='none', edgecolor='green')
# PDF can optimize this one
p1 = 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 = 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.float64(5.5), np.int64(1), np.float64(1.2))
ax.twinx()
assert ax.get_subplotspec().get_geometry() == (5, 1, 0, 0)
@image_comparison(['specgram_freqs.png', 'specgram_freqs_linear.png'],
remove_text=True, tol=0.07, style='default')
def test_specgram_freqs():
'''test axes.specgram in default (psd) mode with sinusoidal stimuli'''
# use former defaults to match existing baseline image
matplotlib.rcParams['image.interpolation'] = 'nearest'
n = 1000
Fs = 10.
fstims1 = [Fs/4, Fs/5, Fs/11]
fstims2 = [Fs/4.7, Fs/5.6, Fs/11.9]
NFFT = int(10 * Fs / min(fstims1 + fstims2))
noverlap = int(NFFT / 2)
pad_to = int(2 ** np.ceil(np.log2(NFFT)))
x = np.arange(0, n, 1/Fs)
y1 = np.zeros(x.size)
y2 = np.zeros(x.size)
for fstim1, fstim2 in zip(fstims1, fstims2):
y1 += np.sin(fstim1 * x * np.pi * 2)
y2 += np.sin(fstim2 * x * np.pi * 2)
y = np.hstack([y1, y2])
fig1 = plt.figure()
fig2 = plt.figure()
ax11 = fig1.add_subplot(3, 1, 1)
ax12 = fig1.add_subplot(3, 1, 2)
ax13 = fig1.add_subplot(3, 1, 3)
ax21 = fig2.add_subplot(3, 1, 1)
ax22 = fig2.add_subplot(3, 1, 2)
ax23 = fig2.add_subplot(3, 1, 3)
ax11.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default')
ax12.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided')
ax13.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided')
ax21.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default',
scale='linear', norm=matplotlib.colors.LogNorm())
ax22.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided',
scale='linear', norm=matplotlib.colors.LogNorm())
ax23.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided',
scale='linear', norm=matplotlib.colors.LogNorm())
@image_comparison(['specgram_noise.png', 'specgram_noise_linear.png'],
remove_text=True, tol=0.01, style='default')
def test_specgram_noise():
'''test axes.specgram in default (psd) mode with noise stimuli'''
# use former defaults to match existing baseline image
matplotlib.rcParams['image.interpolation'] = 'nearest'
np.random.seed(0)
n = 1000
Fs = 10.
NFFT = int(10 * Fs / 11)
noverlap = int(NFFT / 2)
pad_to = int(2 ** np.ceil(np.log2(NFFT)))
y1 = np.random.standard_normal(n)
y2 = np.random.rand(n)
y = np.hstack([y1, y2])
fig1 = plt.figure()
fig2 = plt.figure()
ax11 = fig1.add_subplot(3, 1, 1)
ax12 = fig1.add_subplot(3, 1, 2)
ax13 = fig1.add_subplot(3, 1, 3)
ax21 = fig2.add_subplot(3, 1, 1)
ax22 = fig2.add_subplot(3, 1, 2)
ax23 = fig2.add_subplot(3, 1, 3)
ax11.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default')
ax12.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided')
ax13.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided')
ax21.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default',
scale='linear', norm=matplotlib.colors.LogNorm())
ax22.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided',
scale='linear', norm=matplotlib.colors.LogNorm())
ax23.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided',
scale='linear', norm=matplotlib.colors.LogNorm())
@image_comparison(['specgram_magnitude_freqs.png',
'specgram_magnitude_freqs_linear.png'],
remove_text=True, tol=0.07, style='default')
def test_specgram_magnitude_freqs():
'''test axes.specgram in magnitude mode with sinusoidal stimuli'''
# use former defaults to match existing baseline image
matplotlib.rcParams['image.interpolation'] = 'nearest'
n = 1000
Fs = 10.
fstims1 = [Fs/4, Fs/5, Fs/11]
fstims2 = [Fs/4.7, Fs/5.6, Fs/11.9]
NFFT = int(100 * Fs / min(fstims1 + fstims2))
noverlap = int(NFFT / 2)
pad_to = int(2 ** np.ceil(np.log2(NFFT)))
x = np.arange(0, n, 1/Fs)
y1 = np.zeros(x.size)
y2 = np.zeros(x.size)
for i, (fstim1, fstim2) in enumerate(zip(fstims1, fstims2)):
y1 += np.sin(fstim1 * x * np.pi * 2)
y2 += np.sin(fstim2 * x * np.pi * 2)
y1[-1] = y1[-1]/y1[-1]
y2[-1] = y2[-1]/y2[-1]
y = np.hstack([y1, y2])
fig1 = plt.figure()
fig2 = plt.figure()
ax11 = fig1.add_subplot(3, 1, 1)
ax12 = fig1.add_subplot(3, 1, 2)
ax13 = fig1.add_subplot(3, 1, 3)
ax21 = fig2.add_subplot(3, 1, 1)
ax22 = fig2.add_subplot(3, 1, 2)
ax23 = fig2.add_subplot(3, 1, 3)
ax11.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default', mode='magnitude')
ax12.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided', mode='magnitude')
ax13.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided', mode='magnitude')
ax21.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default', mode='magnitude',
scale='linear', norm=matplotlib.colors.LogNorm())
ax22.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided', mode='magnitude',
scale='linear', norm=matplotlib.colors.LogNorm())
ax23.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided', mode='magnitude',
scale='linear', norm=matplotlib.colors.LogNorm())
@image_comparison(['specgram_magnitude_noise.png',
'specgram_magnitude_noise_linear.png'],
remove_text=True, style='default')
def test_specgram_magnitude_noise():
'''test axes.specgram in magnitude mode with noise stimuli'''
# use former defaults to match existing baseline image
matplotlib.rcParams['image.interpolation'] = 'nearest'
np.random.seed(0)
n = 1000
Fs = 10.
NFFT = int(10 * Fs / 11)
noverlap = int(NFFT / 2)
pad_to = int(2 ** np.ceil(np.log2(NFFT)))
y1 = np.random.standard_normal(n)
y2 = np.random.rand(n)
y = np.hstack([y1, y2])
fig1 = plt.figure()
fig2 = plt.figure()
ax11 = fig1.add_subplot(3, 1, 1)
ax12 = fig1.add_subplot(3, 1, 2)
ax13 = fig1.add_subplot(3, 1, 3)
ax21 = fig2.add_subplot(3, 1, 1)
ax22 = fig2.add_subplot(3, 1, 2)
ax23 = fig2.add_subplot(3, 1, 3)
ax11.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default', mode='magnitude')
ax12.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided', mode='magnitude')
ax13.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided', mode='magnitude')
ax21.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default', mode='magnitude',
scale='linear', norm=matplotlib.colors.LogNorm())
ax22.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided', mode='magnitude',
scale='linear', norm=matplotlib.colors.LogNorm())
ax23.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided', mode='magnitude',
scale='linear', norm=matplotlib.colors.LogNorm())
@image_comparison(['specgram_angle_freqs.png'],
remove_text=True, tol=0.007, style='default')
def test_specgram_angle_freqs():
'''test axes.specgram in angle mode with sinusoidal stimuli'''
# use former defaults to match existing baseline image
matplotlib.rcParams['image.interpolation'] = 'nearest'
n = 1000
Fs = 10.
fstims1 = [Fs/4, Fs/5, Fs/11]
fstims2 = [Fs/4.7, Fs/5.6, Fs/11.9]
NFFT = int(10 * Fs / min(fstims1 + fstims2))
noverlap = int(NFFT / 2)
pad_to = int(2 ** np.ceil(np.log2(NFFT)))
x = np.arange(0, n, 1/Fs)
y1 = np.zeros(x.size)
y2 = np.zeros(x.size)
for i, (fstim1, fstim2) in enumerate(zip(fstims1, fstims2)):
y1 += np.sin(fstim1 * x * np.pi * 2)
y2 += np.sin(fstim2 * x * np.pi * 2)
y1[-1] = y1[-1]/y1[-1]
y2[-1] = y2[-1]/y2[-1]
y = np.hstack([y1, y2])
fig1 = plt.figure()
ax11 = fig1.add_subplot(3, 1, 1)
ax12 = fig1.add_subplot(3, 1, 2)
ax13 = fig1.add_subplot(3, 1, 3)
ax11.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default', mode='angle')
ax12.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided', mode='angle')
ax13.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided', mode='angle')
with pytest.raises(ValueError):
ax11.specgram(y, NFFT=NFFT, Fs=Fs,
noverlap=noverlap, pad_to=pad_to, sides='default',
mode='phase', scale='dB')
with pytest.raises(ValueError):
ax12.specgram(y, NFFT=NFFT, Fs=Fs,
noverlap=noverlap, pad_to=pad_to, sides='onesided',
mode='phase', scale='dB')
with pytest.raises(ValueError):
ax13.specgram(y, NFFT=NFFT, Fs=Fs,
noverlap=noverlap, pad_to=pad_to, sides='twosided',
mode='phase', scale='dB')
@image_comparison(['specgram_angle_noise.png'],
remove_text=True, style='default')
def test_specgram_noise_angle():
'''test axes.specgram in angle mode with noise stimuli'''
# use former defaults to match existing baseline image
matplotlib.rcParams['image.interpolation'] = 'nearest'
np.random.seed(0)
n = 1000
Fs = 10.
NFFT = int(10 * Fs / 11)
noverlap = int(NFFT / 2)
pad_to = int(2 ** np.ceil(np.log2(NFFT)))
y1 = np.random.standard_normal(n)
y2 = np.random.rand(n)
y = np.hstack([y1, y2])
fig1 = plt.figure()
ax11 = fig1.add_subplot(3, 1, 1)
ax12 = fig1.add_subplot(3, 1, 2)
ax13 = fig1.add_subplot(3, 1, 3)
ax11.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default', mode='angle')
ax12.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided', mode='angle')
ax13.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided', mode='angle')
with pytest.raises(ValueError):
ax11.specgram(y, NFFT=NFFT, Fs=Fs,
noverlap=noverlap, pad_to=pad_to, sides='default',
mode='phase', scale='dB')
with pytest.raises(ValueError):
ax12.specgram(y, NFFT=NFFT, Fs=Fs,
noverlap=noverlap, pad_to=pad_to, sides='onesided',
mode='phase', scale='dB')
with pytest.raises(ValueError):
ax13.specgram(y, NFFT=NFFT, Fs=Fs,
noverlap=noverlap, pad_to=pad_to, sides='twosided',
mode='phase', scale='dB')
@image_comparison(['specgram_phase_freqs.png'],
remove_text=True, style='default')
def test_specgram_freqs_phase():
'''test axes.specgram in phase mode with sinusoidal stimuli'''
# use former defaults to match existing baseline image
matplotlib.rcParams['image.interpolation'] = 'nearest'
n = 1000
Fs = 10.
fstims1 = [Fs/4, Fs/5, Fs/11]
fstims2 = [Fs/4.7, Fs/5.6, Fs/11.9]
NFFT = int(10 * Fs / min(fstims1 + fstims2))
noverlap = int(NFFT / 2)
pad_to = int(2 ** np.ceil(np.log2(NFFT)))
x = np.arange(0, n, 1/Fs)
y1 = np.zeros(x.size)
y2 = np.zeros(x.size)
for i, (fstim1, fstim2) in enumerate(zip(fstims1, fstims2)):
y1 += np.sin(fstim1 * x * np.pi * 2)
y2 += np.sin(fstim2 * x * np.pi * 2)
y1[-1] = y1[-1]/y1[-1]
y2[-1] = y2[-1]/y2[-1]
y = np.hstack([y1, y2])
fig1 = plt.figure()
ax11 = fig1.add_subplot(3, 1, 1)
ax12 = fig1.add_subplot(3, 1, 2)
ax13 = fig1.add_subplot(3, 1, 3)
ax11.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default', mode='phase')
ax12.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided', mode='phase')
ax13.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided', mode='phase')
with pytest.raises(ValueError):
ax11.specgram(y, NFFT=NFFT, Fs=Fs,
noverlap=noverlap, pad_to=pad_to, sides='default',
mode='phase', scale='dB')
with pytest.raises(ValueError):
ax12.specgram(y, NFFT=NFFT, Fs=Fs,
noverlap=noverlap, pad_to=pad_to, sides='onesided',
mode='phase', scale='dB')
with pytest.raises(ValueError):
ax13.specgram(y, NFFT=NFFT, Fs=Fs,
noverlap=noverlap, pad_to=pad_to, sides='twosided',
mode='phase', scale='dB')
@image_comparison(['specgram_phase_noise.png'],
remove_text=True, style='default')
def test_specgram_noise_phase():
'''test axes.specgram in phase mode with noise stimuli'''
# use former defaults to match existing baseline image
matplotlib.rcParams['image.interpolation'] = 'nearest'
np.random.seed(0)
n = 1000
Fs = 10.
NFFT = int(10 * Fs / 11)
noverlap = int(NFFT / 2)
pad_to = int(2 ** np.ceil(np.log2(NFFT)))
y1 = np.random.standard_normal(n)
y2 = np.random.rand(n)
y = np.hstack([y1, y2])
fig1 = plt.figure()
ax11 = fig1.add_subplot(3, 1, 1)
ax12 = fig1.add_subplot(3, 1, 2)
ax13 = fig1.add_subplot(3, 1, 3)
ax11.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default', mode='phase')
ax12.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided', mode='phase')
ax13.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided', mode='phase')
with pytest.raises(ValueError):
ax11.specgram(y, NFFT=NFFT, Fs=Fs,
noverlap=noverlap, pad_to=pad_to, sides='default',
mode='phase', scale='dB')
with pytest.raises(ValueError):
ax12.specgram(y, NFFT=NFFT, Fs=Fs,
noverlap=noverlap, pad_to=pad_to, sides='onesided',
mode='phase', scale='dB')
with pytest.raises(ValueError):
ax13.specgram(y, NFFT=NFFT, Fs=Fs,
noverlap=noverlap, pad_to=pad_to, sides='twosided',
mode='phase', scale='dB')
@image_comparison(['psd_freqs.png'], remove_text=True)
def test_psd_freqs():
'''test axes.psd with sinusoidal stimuli'''
n = 10000
Fs = 100.
fstims1 = [Fs/4, Fs/5, Fs/11]
fstims2 = [Fs/4.7, Fs/5.6, Fs/11.9]
NFFT = int(1000 * Fs / min(fstims1 + fstims2))
noverlap = int(NFFT / 2)
pad_to = int(2 ** np.ceil(np.log2(NFFT)))
x = np.arange(0, n, 1/Fs)
y1 = np.zeros(x.size)
y2 = np.zeros(x.size)
for fstim1, fstim2 in zip(fstims1, fstims2):
y1 += np.sin(fstim1 * x * np.pi * 2)
y2 += np.sin(fstim2 * x * np.pi * 2)
y = np.hstack([y1, y2])
fig = plt.figure()
ax1 = fig.add_subplot(3, 1, 1)
ax2 = fig.add_subplot(3, 1, 2)
ax3 = fig.add_subplot(3, 1, 3)
psd1, freqs1 = ax1.psd(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default')
psd2, freqs2 = ax2.psd(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided',
return_line=False)
psd3, freqs3, line3 = ax3.psd(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided',
return_line=True)
ax1.set_xlabel('')
ax2.set_xlabel('')
ax3.set_xlabel('')
ax1.set_ylabel('')
ax2.set_ylabel('')
ax3.set_ylabel('')
@image_comparison(['psd_noise.png'], remove_text=True)
def test_psd_noise():
'''test axes.psd with noise stimuli'''
np.random.seed(0)
n = 10000
Fs = 100.
NFFT = int(1000 * Fs / 11)
noverlap = int(NFFT / 2)
pad_to = int(2 ** np.ceil(np.log2(NFFT)))
y1 = np.random.standard_normal(n)
y2 = np.random.rand(n)
y = np.hstack([y1, y2])
fig = plt.figure()
ax1 = fig.add_subplot(3, 1, 1)
ax2 = fig.add_subplot(3, 1, 2)
ax3 = fig.add_subplot(3, 1, 3)
psd1, freqs1 = ax1.psd(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default')
psd2, freqs2 = ax2.psd(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided',
return_line=False)
psd3, freqs3, line3 = ax3.psd(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided',
return_line=True)
ax1.set_xlabel('')
ax2.set_xlabel('')
ax3.set_xlabel('')
ax1.set_ylabel('')
ax2.set_ylabel('')
ax3.set_ylabel('')
@image_comparison(['csd_freqs.png'], remove_text=True, tol=0.002)
def test_csd_freqs():
'''test axes.csd with sinusoidal stimuli'''
n = 10000
Fs = 100.
fstims1 = [Fs/4, Fs/5, Fs/11]
fstims2 = [Fs/4.7, Fs/5.6, Fs/11.9]
NFFT = int(1000 * Fs / min(fstims1 + fstims2))
noverlap = int(NFFT / 2)
pad_to = int(2 ** np.ceil(np.log2(NFFT)))
x = np.arange(0, n, 1/Fs)
y1 = np.zeros(x.size)
y2 = np.zeros(x.size)
for fstim1, fstim2 in zip(fstims1, fstims2):
y1 += np.sin(fstim1 * x * np.pi * 2)
y2 += np.sin(fstim2 * x * np.pi * 2)
fig = plt.figure()
ax1 = fig.add_subplot(3, 1, 1)
ax2 = fig.add_subplot(3, 1, 2)
ax3 = fig.add_subplot(3, 1, 3)
csd1, freqs1 = ax1.csd(y1, y2, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default')
csd2, freqs2 = ax2.csd(y1, y2, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided',
return_line=False)
csd3, freqs3, line3 = ax3.csd(y1, y2, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided',
return_line=True)
ax1.set_xlabel('')
ax2.set_xlabel('')
ax3.set_xlabel('')
ax1.set_ylabel('')
ax2.set_ylabel('')
ax3.set_ylabel('')
@image_comparison(['csd_noise.png'], remove_text=True)
def test_csd_noise():
'''test axes.csd with noise stimuli'''
np.random.seed(0)
n = 10000
Fs = 100.
NFFT = int(1000 * Fs / 11)
noverlap = int(NFFT / 2)
pad_to = int(2 ** np.ceil(np.log2(NFFT)))
y1 = np.random.standard_normal(n)
y2 = np.random.rand(n)
fig = plt.figure()
ax1 = fig.add_subplot(3, 1, 1)
ax2 = fig.add_subplot(3, 1, 2)
ax3 = fig.add_subplot(3, 1, 3)
csd1, freqs1 = ax1.csd(y1, y2, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='default')
csd2, freqs2 = ax2.csd(y1, y2, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='onesided',
return_line=False)
csd3, freqs3, line3 = ax3.csd(y1, y2, NFFT=NFFT, Fs=Fs, noverlap=noverlap,
pad_to=pad_to, sides='twosided',
return_line=True)
ax1.set_xlabel('')
ax2.set_xlabel('')
ax3.set_xlabel('')
ax1.set_ylabel('')
ax2.set_ylabel('')
ax3.set_ylabel('')
@image_comparison(['magnitude_spectrum_freqs_linear.png',
'magnitude_spectrum_freqs_dB.png'],
remove_text=True)
def test_magnitude_spectrum_freqs():
'''test axes.magnitude_spectrum with sinusoidal stimuli'''
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 = np.zeros(x.size)
for i, fstim1 in enumerate(fstims1):
y += np.sin(fstim1 * x * np.pi * 2) * 10**i
y = y
fig1 = plt.figure()
fig2 = plt.figure()
ax11 = fig1.add_subplot(3, 1, 1)
ax12 = fig1.add_subplot(3, 1, 2)
ax13 = fig1.add_subplot(3, 1, 3)
ax21 = fig2.add_subplot(3, 1, 1)
ax22 = fig2.add_subplot(3, 1, 2)
ax23 = fig2.add_subplot(3, 1, 3)
spec11, freqs11, line11 = ax11.magnitude_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='default')
spec12, freqs12, line12 = ax12.magnitude_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='onesided')
spec13, freqs13, line13 = ax13.magnitude_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='twosided')
spec21, freqs21, line21 = ax21.magnitude_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='default',
scale='dB')
spec22, freqs22, line22 = ax22.magnitude_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='onesided',
scale='dB')
spec23, freqs23, line23 = ax23.magnitude_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='twosided',
scale='dB')
ax11.set_xlabel('')
ax12.set_xlabel('')
ax13.set_xlabel('')
ax11.set_ylabel('')
ax12.set_ylabel('')
ax13.set_ylabel('')
ax21.set_xlabel('')
ax22.set_xlabel('')
ax23.set_xlabel('')
ax21.set_ylabel('')
ax22.set_ylabel('')
ax23.set_ylabel('')
@image_comparison(['magnitude_spectrum_noise_linear.png',
'magnitude_spectrum_noise_dB.png'],
remove_text=True)
def test_magnitude_spectrum_noise():
'''test axes.magnitude_spectrum with noise stimuli'''
np.random.seed(0)
n = 10000
Fs = 100.
NFFT = int(1000 * Fs / 11)
pad_to = int(2 ** np.ceil(np.log2(NFFT)))
y1 = np.random.standard_normal(n)
y2 = np.random.rand(n)
y = np.hstack([y1, y2]) - .5
fig1 = plt.figure()
fig2 = plt.figure()
ax11 = fig1.add_subplot(3, 1, 1)
ax12 = fig1.add_subplot(3, 1, 2)
ax13 = fig1.add_subplot(3, 1, 3)
ax21 = fig2.add_subplot(3, 1, 1)
ax22 = fig2.add_subplot(3, 1, 2)
ax23 = fig2.add_subplot(3, 1, 3)
spec11, freqs11, line11 = ax11.magnitude_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='default')
spec12, freqs12, line12 = ax12.magnitude_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='onesided')
spec13, freqs13, line13 = ax13.magnitude_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='twosided')
spec21, freqs21, line21 = ax21.magnitude_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='default',
scale='dB')
spec22, freqs22, line22 = ax22.magnitude_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='onesided',
scale='dB')
spec23, freqs23, line23 = ax23.magnitude_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='twosided',
scale='dB')
ax11.set_xlabel('')
ax12.set_xlabel('')
ax13.set_xlabel('')
ax11.set_ylabel('')
ax12.set_ylabel('')
ax13.set_ylabel('')
ax21.set_xlabel('')
ax22.set_xlabel('')
ax23.set_xlabel('')
ax21.set_ylabel('')
ax22.set_ylabel('')
ax23.set_ylabel('')
@image_comparison(['angle_spectrum_freqs.png'], remove_text=True)
def test_angle_spectrum_freqs():
'''test axes.angle_spectrum with sinusoidal stimuli'''
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 = np.zeros(x.size)
for i, fstim1 in enumerate(fstims1):
y += np.sin(fstim1 * x * np.pi * 2) * 10**i
y = y
fig = plt.figure()
ax1 = fig.add_subplot(3, 1, 1)
ax2 = fig.add_subplot(3, 1, 2)
ax3 = fig.add_subplot(3, 1, 3)
spec1, freqs1, line1 = ax1.angle_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='default')
spec2, freqs2, line2 = ax2.angle_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='onesided')
spec3, freqs3, line3 = ax3.angle_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='twosided')
ax1.set_xlabel('')
ax2.set_xlabel('')
ax3.set_xlabel('')
ax1.set_ylabel('')
ax2.set_ylabel('')
ax3.set_ylabel('')
@image_comparison(['angle_spectrum_noise.png'], remove_text=True)
def test_angle_spectrum_noise():
'''test axes.angle_spectrum with noise stimuli'''
np.random.seed(0)
n = 10000
Fs = 100.
NFFT = int(1000 * Fs / 11)
pad_to = int(2 ** np.ceil(np.log2(NFFT)))
y1 = np.random.standard_normal(n)
y2 = np.random.rand(n)
y = np.hstack([y1, y2]) - .5
fig = plt.figure()
ax1 = fig.add_subplot(3, 1, 1)
ax2 = fig.add_subplot(3, 1, 2)
ax3 = fig.add_subplot(3, 1, 3)
spec1, freqs1, line1 = ax1.angle_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='default')
spec2, freqs2, line2 = ax2.angle_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='onesided')
spec3, freqs3, line3 = ax3.angle_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='twosided')
ax1.set_xlabel('')
ax2.set_xlabel('')
ax3.set_xlabel('')
ax1.set_ylabel('')
ax2.set_ylabel('')
ax3.set_ylabel('')
@image_comparison(['phase_spectrum_freqs.png'], remove_text=True)
def test_phase_spectrum_freqs():
'''test axes.phase_spectrum with sinusoidal stimuli'''
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 = np.zeros(x.size)
for i, fstim1 in enumerate(fstims1):
y += np.sin(fstim1 * x * np.pi * 2) * 10**i
y = y
fig = plt.figure()
ax1 = fig.add_subplot(3, 1, 1)
ax2 = fig.add_subplot(3, 1, 2)
ax3 = fig.add_subplot(3, 1, 3)
spec1, freqs1, line1 = ax1.phase_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='default')
spec2, freqs2, line2 = ax2.phase_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='onesided')
spec3, freqs3, line3 = ax3.phase_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='twosided')
ax1.set_xlabel('')
ax2.set_xlabel('')
ax3.set_xlabel('')
ax1.set_ylabel('')
ax2.set_ylabel('')
ax3.set_ylabel('')
@image_comparison(['phase_spectrum_noise.png'], remove_text=True)
def test_phase_spectrum_noise():
'''test axes.phase_spectrum with noise stimuli'''
np.random.seed(0)
n = 10000
Fs = 100.
NFFT = int(1000 * Fs / 11)
pad_to = int(2 ** np.ceil(np.log2(NFFT)))
y1 = np.random.standard_normal(n)
y2 = np.random.rand(n)
y = np.hstack([y1, y2]) - .5
fig = plt.figure()
ax1 = fig.add_subplot(3, 1, 1)
ax2 = fig.add_subplot(3, 1, 2)
ax3 = fig.add_subplot(3, 1, 3)
spec1, freqs1, line1 = ax1.phase_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='default')
spec2, freqs2, line2 = ax2.phase_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='onesided')
spec3, freqs3, line3 = ax3.phase_spectrum(y, Fs=Fs, pad_to=pad_to,
sides='twosided')
ax1.set_xlabel('')
ax2.set_xlabel('')
ax3.set_xlabel('')
ax1.set_ylabel('')
ax2.set_ylabel('')
ax3.set_ylabel('')
@image_comparison(['twin_spines.png'], remove_text=True)
def test_twin_spines():
def make_patch_spines_invisible(ax):
ax.set_frame_on(True)
ax.patch.set_visible(False)
for sp in ax.spines.values():
sp.set_visible(False)
fig = plt.figure(figsize=(4, 3))
fig.subplots_adjust(right=0.75)
host = fig.add_subplot(111)
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)
def test_rcparam_grid_minor():
orig_grid = matplotlib.rcParams['axes.grid']
orig_locator = matplotlib.rcParams['axes.grid.which']
matplotlib.rcParams['axes.grid'] = True
values = (
(('both'), (True, True)),
(('major'), (True, False)),
(('minor'), (False, True))
)
for locator, result in values:
matplotlib.rcParams['axes.grid.which'] = locator
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
assert (ax.xaxis._gridOnMajor, ax.xaxis._gridOnMinor) == result
matplotlib.rcParams['axes.grid'] = orig_grid
matplotlib.rcParams['axes.grid.which'] = orig_locator
def test_vline_limit():
fig = plt.figure()
ax = fig.gca()
ax.axvline(0.5)
ax.plot([-0.1, 0, 0.2, 0.1])
(ymin, ymax) = ax.get_ylim()
assert_allclose(ax.get_ylim(), (-.1, .2))
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_polar_not_datalim_adjustable():
ax = plt.figure().add_subplot(projection="polar")
with pytest.raises(ValueError):
ax.set_adjustable("datalim")
@pytest.mark.parametrize('twin', ('x', 'y'))
def test_twin_with_aspect(twin):
fig, ax = plt.subplots()
# test twinx or twiny
ax_twin = getattr(ax, 'twin{}'.format(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(111)
ax.plot(x1, y1)
assert ax.get_xlim() == x1
assert ax.get_ylim() == y1
l = ax.plot(x2, y2)
assert ax.get_xlim() == x2
assert ax.get_ylim() == y2
l[0].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')
@image_comparison(['pie_default.png'])
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'])
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'])
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, 1.5),
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3"))
# Set aspect ratio to be equal so that pie is drawn as a circle.
plt.axis('equal')
@image_comparison(['pie_linewidth_2.png'])
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'])
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'])
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'])
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'])
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()
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"]
@image_comparison(['set_get_ticklabels.png'])
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_xticklabels(('a', 'b', 'c', 'd'))
ax[0].set_yticklabels(('11', '12', '13', '14'))
# 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_xticklabels(ax[0].get_xticklabels())
ax[1].set_yticklabels(ax[0].get_yticklabels())
@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_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)
ax.figure.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_length_one_hist():
fig, ax = plt.subplots()
ax.hist(1)
ax.hist([1])
def test_pathological_hexbin():
# issue #2863
out = io.BytesIO()
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
mylist = [10] * 100
fig, ax = plt.subplots(1, 1)
ax.hexbin(mylist, mylist)
fig.savefig(out)
assert len(w) == 0
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)
ax.yaxis.tick_right()
assert (1, 0.5) == ax.yaxis.offsetText.get_position()
@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):
fig, ax = plt.subplots()
@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() == 'equal'
assert_array_almost_equal(
ax.get_position(original=True).extents,
np.array((0.125, 0.1, 0.9, 0.9)))
assert_array_almost_equal(
ax.get_position(original=False).extents,
np.array((0.2125, 0.1, 0.8125, 0.9)))
def test_no_None():
fig, ax = plt.subplots()
with pytest.raises(ValueError):
plt.plot(None)
with pytest.raises(ValueError):
plt.plot(None, None)
@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_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_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]],
[[1]] * 5,
np.ones(5),
np.ones((2, 5)),
np.ones((5, 1)),
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):
# (n, 1)-shaped error deprecation already tested by test_errorbar.
with mpl.cbook._suppress_matplotlib_deprecation_warning():
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')
@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')
def test_loglog_nonpos():
fig, ax = plt.subplots(3, 3)
x = np.arange(1, 11)
y = x**3
y[7] = -3.
x[4] = -10
for nn, mcx in enumerate(['mask', 'clip', '']):
for mm, mcy in enumerate(['mask', 'clip', '']):
kws = {}
if mcx:
kws['nonposx'] = mcx
if mcy:
kws['nonposy'] = mcy
ax[mm, nn].loglog(x, y**3, lw=2, **kws)
@pytest.mark.style('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))
ax1 = plt.subplot(211)
ax1.set_xlim(-1000, 1000)
ax1.set_ylim(-1000, 1000)
ax1.contour(l, l, t)
ax2 = plt.subplot(212, sharex=ax1, sharey=ax1)
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
@image_comparison(["auto_numticks.png"], style='default')
def test_auto_numticks():
# Make tiny, empty subplots, verify that there are only 3 ticks.
fig, axs = plt.subplots(4, 4)
@image_comparison(["auto_numticks_log.png"], style='default')
def test_auto_numticks_log():
# Verify that there are not too many ticks with a large log range.
fig, ax = plt.subplots()
matplotlib.rcParams['axes.autolimit_mode'] = 'round_numbers'
ax.loglog([1e-20, 1e5], [1e-16, 10])
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()
pp = ax.broken_barh([(datetime.datetime(2018, 11, 9, 0, 0, 0),
datetime.timedelta(hours=1))], [1, 2])
assert pp.get_paths()[0].vertices[0, 0] == 737007.0
assert pp.get_paths()[0].vertices[2, 0] == 737007.0 + 1 / 24
def test_pandas_pcolormesh(pd):
time = pd.date_range('2000-01-01', periods=10)
depth = np.arange(20)
data = np.random.rand(20, 10)
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(np.array(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_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]._color == 'k'
assert axis_1.yaxis.majorTicks[0]._labelsize == 30.0
assert axis_1.yaxis.majorTicks[0]._labelcolor == '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]._grid_color == 'b'
assert axis.majorTicks[0]._grid_linewidth == 5
assert axis.majorTicks[0]._grid_alpha == 0.5
assert axis.majorTicks[0]._grid_linestyle == 'dashdot'
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_ls_ds_conflict():
# Passing the drawstyle with the linestyle is deprecated since 3.1.
# We still need to test this until it's removed from the code.
# But we don't want to see the deprecation warning in the test.
with matplotlib.cbook._suppress_matplotlib_deprecation_warning(), \
pytest.raises(ValueError):
plt.plot(range(32), linestyle='steps-pre:', drawstyle='steps-post')
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'])
def test_date_timezone_x():
# Tests issue 5575
time_index = [datetime.datetime(2016, 2, 22, hour=x,
tzinfo=dutz.gettz('Canada/Eastern'))
for x in range(3)]
# Same Timezone
plt.figure(figsize=(20, 12))
plt.subplot(2, 1, 1)
plt.plot_date(time_index, [3] * 3, tz='Canada/Eastern')
# Different Timezone
plt.subplot(2, 1, 2)
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=dutz.gettz('Canada/Eastern'))
for x in range(3)]
# Same Timezone
plt.figure(figsize=(20, 12))
plt.subplot(2, 1, 1)
plt.plot_date([3] * 3,
time_index, tz='Canada/Eastern', xdate=False, ydate=True)
# Different Timezone
plt.subplot(2, 1, 2)
plt.plot_date([3] * 3, time_index, tz='UTC', xdate=False, ydate=True)
@image_comparison(['date_timezone_x_and_y.png'])
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)
plt.plot_date(time_index, time_index, tz='UTC', ydate=True)
# Different Timezone
plt.subplot(2, 1, 2)
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)
for spine in ax.spines.values():
spine.set_visible(False)
ax.set_axisbelow(setting)
@image_comparison(['titletwiny.png'], style='mpl20')
def test_titletwiny():
# Remove this line when this test image is regenerated.
plt.rcParams['text.kerning_factor'] = 6
# Test that title is put above xlabel if xlabel at top
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')
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.
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.
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
def test_offset_label_color():
# Tests issue 6440
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
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_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
@pytest.mark.style('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('C{}'.format(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('C{}'.format(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('C{}'.format(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)
c_rgb = (0.5, 0.5, 0.5)
ax.scatter(x, y, c=c_rgb)
ax.scatter(x, y, c=[c_rgb] * N)
def test_eventplot_legend():
plt.eventplot([1.0], label='Label')
plt.legend()
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.bar(0, 1, bottom=range(4), width=1, orientation='horizontal')
# 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(111)
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_polar_gridlines():
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
# make all major grid lines lighter, only x grid lines set in 2.1.0
ax.grid(alpha=0.2)
# hide y tick labels, no effect in 2.1.0
plt.setp(ax.yaxis.get_ticklabels(), visible=False)
fig.canvas.draw()
assert ax.xaxis.majorTicks[0].gridline.get_alpha() == .2
assert ax.yaxis.majorTicks[0].gridline.get_alpha() == .2
def test_empty_errorbar_legend():
fig, ax = plt.subplots()
ax.errorbar([], [], xerr=[], label='empty y')
ax.errorbar([], [], yerr=[], label='empty x')
ax.legend()
def test_plot_columns_cycle_deprecation():
with pytest.warns(MatplotlibDeprecationWarning):
plt.plot(np.zeros((2, 2)), np.zeros((2, 3)))
@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)
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]]
rec, connectors = ax.indicate_inset(bounds=rect)
assert connectors is None
fig.canvas.draw()
xx = np.array([[1.5, 2.],
[2.15, 2.5]])
assert np.all(rec.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)
ax.set_aspect(1.)
ax.apply_aspect()
# we need to apply_aspect to make the drawing below work.
# Make the inset_axes... Position axes co-ordinates...
axin1 = ax.inset_axes([0.7, 0.7, 0.35, 0.35])
# redraw the data in the inset axes...
axin1.pcolormesh(x, y, z)
axin1.set_xlim([1.5, 2.15])
axin1.set_ylim([2, 2.5])
axin1.set_aspect(ax.get_aspect())
rec, connectors = ax.indicate_inset_zoom(axin1)
assert len(connectors) == 4
fig.canvas.draw()
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)
@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()
rect, bounds = ax1.indicate_inset([2, 2, 5, 4], ax2)
lower_left, upper_left, lower_right, upper_right = bounds
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_cartopy_backcompat():
import matplotlib
import matplotlib.axes
import matplotlib.axes._subplots
class Dummy(matplotlib.axes.Axes):
...
class DummySubplot(matplotlib.axes.SubplotBase, Dummy):
_axes_class = Dummy
matplotlib.axes._subplots._subplot_classes[Dummy] = DummySubplot
FactoryDummySubplot = matplotlib.axes.subplot_class_factory(Dummy)
assert DummySubplot is FactoryDummySubplot
def test_gettightbbox_ignoreNaN():
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)
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="->"))
def test_deprecated_uppercase_colors():
# Remove after end of deprecation period.
fig, ax = plt.subplots()
with pytest.warns(MatplotlibDeprecationWarning):
ax.plot([1, 2], color="B")
fig.canvas.draw()
@image_comparison(['secondary_xy.png'], style='mpl20')
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)
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')
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 color_boxes(fig, axs):
"""
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([axs.xaxis, axs.yaxis]):
bb = axx.get_tightbbox(renderer)
if bb:
axisr = plt.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 = axs.spines[a].get_window_extent(renderer)
spiner = plt.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 = axs.get_window_extent()
rect2 = plt.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 = axs.get_tightbbox(renderer)
rect2 = plt.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
target = [
None,
None
]
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)
target = [
[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 nn, b in enumerate(bbspines):
targetbb = mtransforms.Bbox.from_bounds(*target[nn])
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)
def test_get_tightbbox_polar():
fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})
fig.canvas.draw()
bb = ax.get_tightbbox(fig.canvas.get_renderer())
assert_allclose(bb.extents,
[107.7778, 29.2778, 539.7847, 450.7222], rtol=1e-03)
@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_datetime_masked():
# make sure that all-masked data falls back to the viewlim
# set in convert.axisinfo....
x = np.array([datetime.datetime(2017, 1, n) for n in range(1, 6)])
y = np.array([1, 2, 3, 4, 5])
m = np.ma.masked_greater(y, 0)
fig, ax = plt.subplots()
ax.plot(x, m)
# these are the default viewlim
assert ax.get_xlim() == (730120.0, 733773.0)
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)
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_invisible_axes():
# 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
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