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test_axes.py
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test_axes.py
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from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import xrange
from itertools import chain
import io
from nose.tools import assert_equal, assert_raises, assert_false, assert_true
import datetime
import numpy as np
from numpy import ma
from numpy import arange
from cycler import cycler
import warnings
import matplotlib
from matplotlib.testing.decorators import image_comparison, cleanup
import matplotlib.pyplot as plt
import matplotlib.markers as mmarkers
from numpy.testing import assert_allclose, assert_array_equal
import warnings
from matplotlib.cbook import IgnoredKeywordWarning
# 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 nose runs
# the tests with multiple threads.
@image_comparison(baseline_images=['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)]
fig = plt.figure()
ax = plt.subplot(111)
ax.set_xlabel("x-label 001")
fig = plt.figure()
ax = plt.subplot(111)
ax.set_xlabel("x-label 001")
ax.plot(xdata, ydata1, color='blue', xunits="sec")
fig = plt.figure()
ax = plt.subplot(111)
ax.set_xlabel("x-label 001")
ax.plot(xdata, ydata1, color='blue', xunits="sec")
ax.set_xlabel("x-label 003")
fig = plt.figure()
ax = plt.subplot(111)
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
fig = plt.figure()
ax = plt.subplot(111)
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(baseline_images=["formatter_large_small"])
def test_formatter_large_small():
# github issue #617, pull #619
fig, ax = plt.subplots(1)
x = [0.500000001, 0.500000002]
y = [1e64, 1.1e64]
ax.plot(x, y)
@image_comparison(baseline_images=["twin_axis_locaters_formatters"])
def test_twin_axis_locaters_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']))
ax2 = ax1.twiny()
ax3 = ax1.twinx()
@cleanup
def test_twinx_cla():
fig, ax = plt.subplots()
ax2 = ax.twinx()
ax3 = ax2.twiny()
plt.draw()
assert_false(ax2.xaxis.get_visible())
assert_false(ax2.patch.get_visible())
ax2.cla()
ax3.cla()
assert_false(ax2.xaxis.get_visible())
assert_false(ax2.patch.get_visible())
assert_true(ax2.yaxis.get_visible())
assert_true(ax3.xaxis.get_visible())
assert_false(ax3.patch.get_visible())
assert_false(ax3.yaxis.get_visible())
assert_true(ax.xaxis.get_visible())
assert_true(ax.patch.get_visible())
assert_true(ax.yaxis.get_visible())
@image_comparison(baseline_images=["minorticks_on_rcParams_both"], extensions=['png'])
def test_minorticks_on_rcParams_both():
fig = plt.figure()
matplotlib.rcParams['xtick.minor.visible'] = True
matplotlib.rcParams['ytick.minor.visible'] = True
plt.plot([0, 1], [0, 1])
plt.axis([0, 1, 0, 1])
@image_comparison(baseline_images=["autoscale_tiny_range"], remove_text=True)
def test_autoscale_tiny_range():
# github pull #904
fig, ax = plt.subplots(2, 2)
ax = ax.flatten()
for i in xrange(4):
y1 = 10**(-11 - i)
ax[i].plot([0, 1], [1, 1 + y1])
@image_comparison(baseline_images=['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(baseline_images=['polar_axes'])
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',
)
@image_comparison(baseline_images=['polar_coords'],
remove_text=True)
def test_polar_coord_annotations():
# You can also use polar notation on a catesian 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(baseline_images=['fill_units'], extensions=['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(baseline_images=['single_point', 'single_point'])
def test_single_point():
# Issue #1796: don't let lines.marker affect the grid
matplotlib.rcParams['lines.marker'] = 'o'
matplotlib.rcParams['axes.grid'] = True
fig = 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]}
fig = plt.figure()
plt.subplot(211)
plt.plot('a', 'a', 'o', data=data)
plt.subplot(212)
plt.plot('b','b', 'o', data=data)
@image_comparison(baseline_images=['single_date'])
def test_single_date():
time1 = [721964.0]
data1 = [-65.54]
fig = plt.figure()
plt.subplot(211)
plt.plot_date(time1, data1, 'o', color='r')
plt.subplot(212)
plt.plot(time1, data1, 'o', color='r')
@image_comparison(baseline_images=['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)
y1.shape = 1, 10
y2 = np.arange(10)
y2.shape = 10, 1
fig = plt.figure()
plt.subplot(411)
plt.plot(y1)
plt.subplot(412)
plt.plot(y2)
plt.subplot(413)
assert_raises(ValueError, plt.plot, (y1, y2))
plt.subplot(414)
plt.plot(xdata[:, 1], xdata[1, :], 'o')
@image_comparison(baseline_images=['const_xy'])
def test_const_xy():
fig = plt.figure()
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')
@image_comparison(baseline_images=['polar_wrap_180',
'polar_wrap_360',
])
def test_polar_wrap():
D2R = np.pi / 180.0
fig = plt.figure()
plt.subplot(111, polar=True)
plt.polar([179*D2R, -179*D2R], [0.2, 0.1], "b.-")
plt.polar([179*D2R, 181*D2R], [0.2, 0.1], "g.-")
plt.rgrids([0.05, 0.1, 0.15, 0.2, 0.25, 0.3])
assert len(fig.axes) == 1, 'More than one polar axes created.'
fig = plt.figure()
plt.subplot(111, polar=True)
plt.polar([2*D2R, -2*D2R], [0.2, 0.1], "b.-")
plt.polar([2*D2R, 358*D2R], [0.2, 0.1], "g.-")
plt.polar([358*D2R, 2*D2R], [0.2, 0.1], "r.-")
plt.rgrids([0.05, 0.1, 0.15, 0.2, 0.25, 0.3])
@image_comparison(baseline_images=['polar_units', 'polar_units_2'])
def test_polar_units():
import matplotlib.testing.jpl_units as units
from nose.tools import assert_true
units.register()
pi = np.pi
deg = units.UnitDbl(1.0, "deg")
km = units.UnitDbl(1.0, "km")
x1 = [pi/6.0, pi/4.0, pi/3.0, pi/2.0]
x2 = [30.0*deg, 45.0*deg, 60.0*deg, 90.0*deg]
y1 = [1.0, 2.0, 3.0, 4.0]
y2 = [4.0, 3.0, 2.0, 1.0]
fig = plt.figure()
plt.polar(x2, y1, color="blue")
# polar(x2, y1, color = "red", xunits="rad")
# polar(x2, y2, color = "green")
fig = plt.figure()
# make sure runits and theta units work
y1 = [y*km for y in y1]
plt.polar(x2, y1, color="blue", thetaunits="rad", runits="km")
assert_true(isinstance(plt.gca().get_xaxis().get_major_formatter(), units.UnitDblFormatter))
@image_comparison(baseline_images=['polar_rmin'])
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(baseline_images=['polar_theta_position'])
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")
ax.set_theta_direction('clockwise')
@image_comparison(baseline_images=['polar_rlabel_position'])
def test_polar_rlabel_position():
fig = plt.figure()
ax = fig.add_subplot(111, projection='polar')
ax.set_rlabel_position(315)
@image_comparison(baseline_images=['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"))
fig = plt.figure()
plt.axvspan(t0, tf, facecolor="blue", alpha=0.25)
ax = plt.gca()
ax.set_xlim(t0 - 5.0*dt, tf + 5.0*dt)
@image_comparison(baseline_images=['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"))
fig = plt.figure()
plt.axhspan(t0, tf, facecolor="blue", alpha=0.25)
ax = plt.gca()
ax.set_ylim(t0 - 5.0*dt, tf + 5.0*dt)
@image_comparison(baseline_images=['hexbin_extent', 'hexbin_extent'],
remove_text=True, extensions=['png'])
def test_hexbin_extent():
# this test exposes sf bug 2856228
fig = plt.figure()
ax = fig.add_subplot(111)
data = np.arange(2000.)/2000.
data.shape = 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 = plt.figure()
ax = fig.add_subplot(111)
ax.hexbin("x", "y", extent=[.1, .3, .6, .7], data=data)
@image_comparison(baseline_images=['hexbin_empty'], remove_text=True,
extensions=['png'])
def test_hexbin_empty():
# From #3886: creating hexbin from empty dataset raises ValueError
ax = plt.gca()
ax.hexbin([], [])
@cleanup
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 = plt.figure()
ax = fig.add_subplot(111)
data = np.arange(200.)/200.
data.shape = 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(baseline_images=['hexbin_log'],
remove_text=True,
extensions=['png'])
def test_hexbin_log():
# Issue #1636
fig = plt.figure()
np.random.seed(0)
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)
ax = fig.add_subplot(111)
ax.hexbin(x, y, yscale='log')
@cleanup
def test_inverted_limits():
# Test gh:1553
# Calling invert_xaxis prior to plotting should not disable autoscaling
# while still maintaining the inverted direction
fig = plt.figure()
ax = fig.gca()
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 = plt.figure()
ax = fig.gca()
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)
plt.close()
@image_comparison(baseline_images=['nonfinite_limits'])
def test_nonfinite_limits():
x = np.arange(0., np.e, 0.01)
olderr = np.seterr(divide='ignore') # silence divide by zero warning from log(0)
try:
y = np.log(x)
finally:
np.seterr(**olderr)
x[len(x)//2] = np.nan
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x, y)
@image_comparison(baseline_images=['imshow', 'imshow'],
remove_text=True)
def test_imshow():
# 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 = plt.figure()
ax = fig.add_subplot(111)
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(baseline_images=['imshow_clip'])
def test_imshow_clip():
# As originally reported by Gellule Xg <gellule.xg@free.fr>
# 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 = plt.figure()
ax = fig.add_subplot(111)
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(baseline_images=['polycollection_joinstyle'],
remove_text=True)
def test_polycollection_joinstyle():
# Bug #2890979 reported by Matthew West
from matplotlib import collections as mcoll
fig = plt.figure()
ax = fig.add_subplot(111)
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)
@image_comparison(baseline_images=['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 = plt.figure()
ax = fig.add_subplot(211)
ax.plot(x, y1, x, y2, color='black')
ax.fill_between(x, y1, y2, where=y2 >= y1, facecolor='white', hatch='/', interpolate=True)
ax.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
ax1 = fig.add_subplot(212, sharex=ax)
ax1.plot(x, y1, x, y2, color='black')
ax1.fill_between(x, y1, y2, where=y2 >= y1, facecolor='green', interpolate=True)
ax1.fill_between(x, y1, y2, where=y2 <= y1, facecolor='red', interpolate=True)
@image_comparison(baseline_images=['symlog'])
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 = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x, y)
ax.set_yscale('symlog')
ax.set_xscale = ('linear')
ax.set_ylim(-1, 10000000)
@image_comparison(baseline_images=['symlog2'],
remove_text=True)
def test_symlog2():
# Numbers from -50 to 50, with 0.1 as step
x = np.arange(-50, 50, 0.001)
fig = plt.figure()
ax = fig.add_subplot(511)
# Plots a simple linear function 'f(x) = x'
ax.plot(x, x)
ax.set_xscale('symlog', linthreshx=20.0)
ax.grid(True)
ax = fig.add_subplot(512)
# Plots a simple linear function 'f(x) = x'
ax.plot(x, x)
ax.set_xscale('symlog', linthreshx=2.0)
ax.grid(True)
ax = fig.add_subplot(513)
# Plots a simple linear function 'f(x) = x'
ax.plot(x, x)
ax.set_xscale('symlog', linthreshx=1.0)
ax.grid(True)
ax = fig.add_subplot(514)
# Plots a simple linear function 'f(x) = x'
ax.plot(x, x)
ax.set_xscale('symlog', linthreshx=0.1)
ax.grid(True)
ax = fig.add_subplot(515)
# Plots a simple linear function 'f(x) = x'
ax.plot(x, x)
ax.set_xscale('symlog', linthreshx=0.01)
ax.grid(True)
ax.set_ylim(-0.1, 0.1)
@cleanup
def test_pcolorargs():
# 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(baseline_images=['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.sqrt(X**2 + Y**2)/5
Z = (Z - Z.min()) / (Z.max() - Z.min())
# The color array can include masked values:
Zm = ma.masked_where(np.fabs(Qz) < 0.5*np.amax(Qz), Z)
fig = plt.figure()
ax = fig.add_subplot(131)
ax.pcolormesh(Qx, Qz, Z, lw=0.5, edgecolors='k')
ax = fig.add_subplot(132)
ax.pcolormesh(Qx, Qz, Z, lw=2, edgecolors=['b', 'w'])
ax = fig.add_subplot(133)
ax.pcolormesh(Qx, Qz, Z, shading="gouraud")
@image_comparison(baseline_images=['pcolormesh_datetime_axis'],
extensions=['png'], remove_text=False)
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(baseline_images=['pcolor_datetime_axis'],
extensions=['png'], remove_text=False)
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)
@cleanup
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.sqrt(X**2 + Y**2)/5
_, ax = plt.subplots()
assert_raises(TypeError, ax.pcolormesh, y, x, Z)
assert_raises(TypeError, ax.pcolormesh, X, Y, Z.T)
assert_raises(TypeError, ax.pcolormesh, x, y, Z[:-1, :-1],
shading="gouraud")
assert_raises(TypeError, ax.pcolormesh, X, Y, Z[:-1, :-1],
shading="gouraud")
@image_comparison(baseline_images=['canonical'])
def test_canonical():
fig, ax = plt.subplots()
ax.plot([1, 2, 3])
@image_comparison(baseline_images=['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.arange(0.0, 360.0, 1.0)*np.pi/180.0
x = width/2. * np.cos(theta)
y = height/2. * np.sin(theta)
rtheta = angle*np.pi/180.
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(baseline_images=['units_strings'])
def test_units_strings():
# Make sure passing in sequences of strings doesn't cause the unit
# conversion registry to recurse infinitely
Id = ['50', '100', '150', '200', '250']
pout = ['0', '7.4', '11.4', '14.2', '16.3']
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(Id, pout)
@image_comparison(baseline_images=['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(baseline_images=['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(baseline_images=['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(baseline_images=['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(baseline_images=['markevery_log_scales'],
remove_text=True)
def test_markevery_log_scales():
cases = [None,
8,