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

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import datetime
import platform
import re
from unittest import mock
import contourpy
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_almost_equal_nulp
import matplotlib as mpl
from matplotlib import pyplot as plt, rc_context, ticker
from matplotlib.colors import LogNorm, same_color
import matplotlib.patches as mpatches
from matplotlib.testing.decorators import check_figures_equal, image_comparison
import pytest
def test_contour_shape_1d_valid():
x = np.arange(10)
y = np.arange(9)
z = np.random.random((9, 10))
fig, ax = plt.subplots()
ax.contour(x, y, z)
def test_contour_shape_2d_valid():
x = np.arange(10)
y = np.arange(9)
xg, yg = np.meshgrid(x, y)
z = np.random.random((9, 10))
fig, ax = plt.subplots()
ax.contour(xg, yg, z)
@pytest.mark.parametrize("args, message", [
((np.arange(9), np.arange(9), np.empty((9, 10))),
'Length of x (9) must match number of columns in z (10)'),
((np.arange(10), np.arange(10), np.empty((9, 10))),
'Length of y (10) must match number of rows in z (9)'),
((np.empty((10, 10)), np.arange(10), np.empty((9, 10))),
'Number of dimensions of x (2) and y (1) do not match'),
((np.arange(10), np.empty((10, 10)), np.empty((9, 10))),
'Number of dimensions of x (1) and y (2) do not match'),
((np.empty((9, 9)), np.empty((9, 10)), np.empty((9, 10))),
'Shapes of x (9, 9) and z (9, 10) do not match'),
((np.empty((9, 10)), np.empty((9, 9)), np.empty((9, 10))),
'Shapes of y (9, 9) and z (9, 10) do not match'),
((np.empty((3, 3, 3)), np.empty((3, 3, 3)), np.empty((9, 10))),
'Inputs x and y must be 1D or 2D, not 3D'),
((np.empty((3, 3, 3)), np.empty((3, 3, 3)), np.empty((3, 3, 3))),
'Input z must be 2D, not 3D'),
(([[0]],), # github issue 8197
'Input z must be at least a (2, 2) shaped array, but has shape (1, 1)'),
(([0], [0], [[0]]),
'Input z must be at least a (2, 2) shaped array, but has shape (1, 1)'),
])
def test_contour_shape_error(args, message):
fig, ax = plt.subplots()
with pytest.raises(TypeError, match=re.escape(message)):
ax.contour(*args)
def test_contour_no_valid_levels():
fig, ax = plt.subplots()
# no warning for empty levels.
ax.contour(np.random.rand(9, 9), levels=[])
# no warning if levels is given and is not within the range of z.
cs = ax.contour(np.arange(81).reshape((9, 9)), levels=[100])
# ... and if fmt is given.
ax.clabel(cs, fmt={100: '%1.2f'})
# no warning if z is uniform.
ax.contour(np.ones((9, 9)))
def test_contour_Nlevels():
# A scalar levels arg or kwarg should trigger auto level generation.
# https://github.com/matplotlib/matplotlib/issues/11913
z = np.arange(12).reshape((3, 4))
fig, ax = plt.subplots()
cs1 = ax.contour(z, 5)
assert len(cs1.levels) > 1
cs2 = ax.contour(z, levels=5)
assert (cs1.levels == cs2.levels).all()
@check_figures_equal(extensions=['png'])
def test_contour_set_paths(fig_test, fig_ref):
cs_test = fig_test.subplots().contour([[0, 1], [1, 2]])
cs_ref = fig_ref.subplots().contour([[1, 0], [2, 1]])
cs_test.set_paths(cs_ref.get_paths())
@image_comparison(['contour_manual_labels'], remove_text=True, style='mpl20', tol=0.26)
def test_contour_manual_labels():
x, y = np.meshgrid(np.arange(0, 10), np.arange(0, 10))
z = np.max(np.dstack([abs(x), abs(y)]), 2)
plt.figure(figsize=(6, 2), dpi=200)
cs = plt.contour(x, y, z)
pts = np.array([(1.0, 3.0), (1.0, 4.4), (1.0, 6.0)])
plt.clabel(cs, manual=pts)
pts = np.array([(2.0, 3.0), (2.0, 4.4), (2.0, 6.0)])
plt.clabel(cs, manual=pts, fontsize='small', colors=('r', 'g'))
def test_contour_manual_moveto():
x = np.linspace(-10, 10)
y = np.linspace(-10, 10)
X, Y = np.meshgrid(x, y)
Z = X**2 * 1 / Y**2 - 1
contours = plt.contour(X, Y, Z, levels=[0, 100])
# This point lies on the `MOVETO` line for the 100 contour
# but is actually closest to the 0 contour
point = (1.3, 1)
clabels = plt.clabel(contours, manual=[point])
# Ensure that the 0 contour was chosen, not the 100 contour
assert clabels[0].get_text() == "0"
@image_comparison(['contour_disconnected_segments'],
remove_text=True, style='mpl20', extensions=['png'])
def test_contour_label_with_disconnected_segments():
x, y = np.mgrid[-1:1:21j, -1:1:21j]
z = 1 / np.sqrt(0.01 + (x + 0.3) ** 2 + y ** 2)
z += 1 / np.sqrt(0.01 + (x - 0.3) ** 2 + y ** 2)
plt.figure()
cs = plt.contour(x, y, z, levels=[7])
cs.clabel(manual=[(0.2, 0.1)])
@image_comparison(['contour_manual_colors_and_levels.png'], remove_text=True,
tol=0 if platform.machine() == 'x86_64' else 0.018)
def test_given_colors_levels_and_extends():
# Remove this line when this test image is regenerated.
plt.rcParams['pcolormesh.snap'] = False
_, axs = plt.subplots(2, 4)
data = np.arange(12).reshape(3, 4)
colors = ['red', 'yellow', 'pink', 'blue', 'black']
levels = [2, 4, 8, 10]
for i, ax in enumerate(axs.flat):
filled = i % 2 == 0.
extend = ['neither', 'min', 'max', 'both'][i // 2]
if filled:
# If filled, we have 3 colors with no extension,
# 4 colors with one extension, and 5 colors with both extensions
first_color = 1 if extend in ['max', 'neither'] else None
last_color = -1 if extend in ['min', 'neither'] else None
c = ax.contourf(data, colors=colors[first_color:last_color],
levels=levels, extend=extend)
else:
# If not filled, we have 4 levels and 4 colors
c = ax.contour(data, colors=colors[:-1],
levels=levels, extend=extend)
plt.colorbar(c, ax=ax)
@image_comparison(['contourf_hatch_colors'],
remove_text=True, style='mpl20', extensions=['png'])
def test_hatch_colors():
fig, ax = plt.subplots()
cf = ax.contourf([[0, 1], [1, 2]], hatches=['-', '/', '\\', '//'], cmap='gray')
cf.set_edgecolors(["blue", "grey", "yellow", "red"])
@pytest.mark.parametrize('color, extend', [('darkred', 'neither'),
('darkred', 'both'),
(('r', 0.5), 'neither'),
((0.1, 0.2, 0.5, 0.3), 'neither')])
def test_single_color_and_extend(color, extend):
z = [[0, 1], [1, 2]]
_, ax = plt.subplots()
levels = [0.5, 0.75, 1, 1.25, 1.5]
cs = ax.contour(z, levels=levels, colors=color, extend=extend)
for c in cs.get_edgecolors():
assert same_color(c, color)
@image_comparison(['contour_log_locator.svg'], style='mpl20', remove_text=False)
def test_log_locator_levels():
fig, ax = plt.subplots()
N = 100
x = np.linspace(-3.0, 3.0, N)
y = np.linspace(-2.0, 2.0, N)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X * 10)**2 - (Y * 10)**2)
data = Z1 + 50 * Z2
c = ax.contourf(data, locator=ticker.LogLocator())
assert_array_almost_equal(c.levels, np.power(10.0, np.arange(-6, 3)))
cb = fig.colorbar(c, ax=ax)
assert_array_almost_equal(cb.ax.get_yticks(), c.levels)
@image_comparison(['contour_datetime_axis.png'], style='mpl20')
def test_contour_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(20)])
y = np.arange(20)
z1, z2 = np.meshgrid(np.arange(20), np.arange(20))
z = z1 * z2
plt.subplot(221)
plt.contour(x, y, z)
plt.subplot(222)
plt.contourf(x, y, z)
x = np.repeat(x[np.newaxis], 20, axis=0)
y = np.repeat(y[:, np.newaxis], 20, axis=1)
plt.subplot(223)
plt.contour(x, y, z)
plt.subplot(224)
plt.contourf(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(['contour_test_label_transforms.png'],
remove_text=True, style='mpl20', tol=1.1)
def test_labels():
# Adapted from pylab_examples example code: contour_demo.py
# see issues #2475, #2843, and #2818 for explanation
delta = 0.025
x = np.arange(-3.0, 3.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-(X**2 + Y**2) / 2) / (2 * np.pi)
Z2 = (np.exp(-(((X - 1) / 1.5)**2 + ((Y - 1) / 0.5)**2) / 2) /
(2 * np.pi * 0.5 * 1.5))
# difference of Gaussians
Z = 10.0 * (Z2 - Z1)
fig, ax = plt.subplots(1, 1)
CS = ax.contour(X, Y, Z)
disp_units = [(216, 177), (359, 290), (521, 406)]
data_units = [(-2, .5), (0, -1.5), (2.8, 1)]
CS.clabel()
for x, y in data_units:
CS.add_label_near(x, y, inline=True, transform=None)
for x, y in disp_units:
CS.add_label_near(x, y, inline=True, transform=False)
def test_label_contour_start():
# Set up data and figure/axes that result in automatic labelling adding the
# label to the start of a contour
_, ax = plt.subplots(dpi=100)
lats = lons = np.linspace(-np.pi / 2, np.pi / 2, 50)
lons, lats = np.meshgrid(lons, lats)
wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons)
mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2)
data = wave + mean
cs = ax.contour(lons, lats, data)
with mock.patch.object(
cs, '_split_path_and_get_label_rotation',
wraps=cs._split_path_and_get_label_rotation) as mocked_splitter:
# Smoke test that we can add the labels
cs.clabel(fontsize=9)
# Verify at least one label was added to the start of a contour. I.e. the
# splitting method was called with idx=0 at least once.
idxs = [cargs[0][1] for cargs in mocked_splitter.call_args_list]
assert 0 in idxs
@image_comparison(['contour_corner_mask_False.png', 'contour_corner_mask_True.png'],
remove_text=True, tol=1.88)
def test_corner_mask():
n = 60
mask_level = 0.95
noise_amp = 1.0
np.random.seed([1])
x, y = np.meshgrid(np.linspace(0, 2.0, n), np.linspace(0, 2.0, n))
z = np.cos(7*x)*np.sin(8*y) + noise_amp*np.random.rand(n, n)
mask = np.random.rand(n, n) >= mask_level
z = np.ma.array(z, mask=mask)
for corner_mask in [False, True]:
plt.figure()
plt.contourf(z, corner_mask=corner_mask)
def test_contourf_decreasing_levels():
# github issue 5477.
z = [[0.1, 0.3], [0.5, 0.7]]
plt.figure()
with pytest.raises(ValueError):
plt.contourf(z, [1.0, 0.0])
def test_contourf_symmetric_locator():
# github issue 7271
z = np.arange(12).reshape((3, 4))
locator = plt.MaxNLocator(nbins=4, symmetric=True)
cs = plt.contourf(z, locator=locator)
assert_array_almost_equal(cs.levels, np.linspace(-12, 12, 5))
def test_circular_contour_warning():
# Check that almost circular contours don't throw a warning
x, y = np.meshgrid(np.linspace(-2, 2, 4), np.linspace(-2, 2, 4))
r = np.hypot(x, y)
plt.figure()
cs = plt.contour(x, y, r)
plt.clabel(cs)
@pytest.mark.parametrize("use_clabeltext, contour_zorder, clabel_zorder",
[(True, 123, 1234), (False, 123, 1234),
(True, 123, None), (False, 123, None)])
def test_clabel_zorder(use_clabeltext, contour_zorder, clabel_zorder):
x, y = np.meshgrid(np.arange(0, 10), np.arange(0, 10))
z = np.max(np.dstack([abs(x), abs(y)]), 2)
fig, (ax1, ax2) = plt.subplots(ncols=2)
cs = ax1.contour(x, y, z, zorder=contour_zorder)
cs_filled = ax2.contourf(x, y, z, zorder=contour_zorder)
clabels1 = cs.clabel(zorder=clabel_zorder, use_clabeltext=use_clabeltext)
clabels2 = cs_filled.clabel(zorder=clabel_zorder,
use_clabeltext=use_clabeltext)
if clabel_zorder is None:
expected_clabel_zorder = 2+contour_zorder
else:
expected_clabel_zorder = clabel_zorder
for clabel in clabels1:
assert clabel.get_zorder() == expected_clabel_zorder
for clabel in clabels2:
assert clabel.get_zorder() == expected_clabel_zorder
def test_clabel_with_large_spacing():
# When the inline spacing is large relative to the contour, it may cause the
# entire contour to be removed. In current implementation, one line segment is
# retained between the identified points.
# This behavior may be worth reconsidering, but check to be sure we do not produce
# an invalid path, which results in an error at clabel call time.
# see gh-27045 for more information
x = y = np.arange(-3.0, 3.01, 0.05)
X, Y = np.meshgrid(x, y)
Z = np.exp(-X**2 - Y**2)
fig, ax = plt.subplots()
contourset = ax.contour(X, Y, Z, levels=[0.01, 0.2, .5, .8])
ax.clabel(contourset, inline_spacing=100)
# tol because ticks happen to fall on pixel boundaries so small
# floating point changes in tick location flip which pixel gets
# the tick.
@image_comparison(['contour_log_extension.png'],
remove_text=True, style='mpl20',
tol=1.444)
def test_contourf_log_extension():
# Remove this line when this test image is regenerated.
plt.rcParams['pcolormesh.snap'] = False
# Test that contourf with lognorm is extended correctly
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 5))
fig.subplots_adjust(left=0.05, right=0.95)
# make data set with large range e.g. between 1e-8 and 1e10
data_exp = np.linspace(-7.5, 9.5, 1200)
data = np.power(10, data_exp).reshape(30, 40)
# make manual levels e.g. between 1e-4 and 1e-6
levels_exp = np.arange(-4., 7.)
levels = np.power(10., levels_exp)
# original data
c1 = ax1.contourf(data,
norm=LogNorm(vmin=data.min(), vmax=data.max()))
# just show data in levels
c2 = ax2.contourf(data, levels=levels,
norm=LogNorm(vmin=levels.min(), vmax=levels.max()),
extend='neither')
# extend data from levels
c3 = ax3.contourf(data, levels=levels,
norm=LogNorm(vmin=levels.min(), vmax=levels.max()),
extend='both')
cb = plt.colorbar(c1, ax=ax1)
assert cb.ax.get_ylim() == (1e-8, 1e10)
cb = plt.colorbar(c2, ax=ax2)
assert_array_almost_equal_nulp(cb.ax.get_ylim(), np.array((1e-4, 1e6)))
cb = plt.colorbar(c3, ax=ax3)
@image_comparison(['contour_addlines.png'], remove_text=True, style='mpl20',
tol=0.03 if platform.machine() == 'x86_64' else 0.15)
# tolerance is because image changed minutely when tick finding on
# colorbars was cleaned up...
def test_contour_addlines():
# Remove this line when this test image is regenerated.
plt.rcParams['pcolormesh.snap'] = False
fig, ax = plt.subplots()
np.random.seed(19680812)
X = np.random.rand(10, 10)*10000
pcm = ax.pcolormesh(X)
# add 1000 to make colors visible...
cont = ax.contour(X+1000)
cb = fig.colorbar(pcm)
cb.add_lines(cont)
assert_array_almost_equal(cb.ax.get_ylim(), [114.3091, 9972.30735], 3)
@image_comparison(baseline_images=['contour_uneven'],
extensions=['png'], remove_text=True, style='mpl20')
def test_contour_uneven():
# Remove this line when this test image is regenerated.
plt.rcParams['pcolormesh.snap'] = False
z = np.arange(24).reshape(4, 6)
fig, axs = plt.subplots(1, 2)
ax = axs[0]
cs = ax.contourf(z, levels=[2, 4, 6, 10, 20])
fig.colorbar(cs, ax=ax, spacing='proportional')
ax = axs[1]
cs = ax.contourf(z, levels=[2, 4, 6, 10, 20])
fig.colorbar(cs, ax=ax, spacing='uniform')
@pytest.mark.parametrize(
"rc_lines_linewidth, rc_contour_linewidth, call_linewidths, expected", [
(1.23, None, None, 1.23),
(1.23, 4.24, None, 4.24),
(1.23, 4.24, 5.02, 5.02)
])
def test_contour_linewidth(
rc_lines_linewidth, rc_contour_linewidth, call_linewidths, expected):
with rc_context(rc={"lines.linewidth": rc_lines_linewidth,
"contour.linewidth": rc_contour_linewidth}):
fig, ax = plt.subplots()
X = np.arange(4*3).reshape(4, 3)
cs = ax.contour(X, linewidths=call_linewidths)
assert cs.get_linewidths()[0] == expected
@pytest.mark.backend("pdf")
def test_label_nonagg():
# This should not crash even if the canvas doesn't have a get_renderer().
plt.clabel(plt.contour([[1, 2], [3, 4]]))
@image_comparison(baseline_images=['contour_closed_line_loop'],
extensions=['png'], remove_text=True)
def test_contour_closed_line_loop():
# github issue 19568.
z = [[0, 0, 0], [0, 2, 0], [0, 0, 0], [2, 1, 2]]
fig, ax = plt.subplots(figsize=(2, 2))
ax.contour(z, [0.5], linewidths=[20], alpha=0.7)
ax.set_xlim(-0.1, 2.1)
ax.set_ylim(-0.1, 3.1)
def test_quadcontourset_reuse():
# If QuadContourSet returned from one contour(f) call is passed as first
# argument to another the underlying C++ contour generator will be reused.
x, y = np.meshgrid([0.0, 1.0], [0.0, 1.0])
z = x + y
fig, ax = plt.subplots()
qcs1 = ax.contourf(x, y, z)
qcs2 = ax.contour(x, y, z)
assert qcs2._contour_generator != qcs1._contour_generator
qcs3 = ax.contour(qcs1, z)
assert qcs3._contour_generator == qcs1._contour_generator
@image_comparison(baseline_images=['contour_manual'],
extensions=['png'], remove_text=True, tol=0.89)
def test_contour_manual():
# Manually specifying contour lines/polygons to plot.
from matplotlib.contour import ContourSet
fig, ax = plt.subplots(figsize=(4, 4))
cmap = 'viridis'
# Segments only (no 'kind' codes).
lines0 = [[[2, 0], [1, 2], [1, 3]]] # Single line.
lines1 = [[[3, 0], [3, 2]], [[3, 3], [3, 4]]] # Two lines.
filled01 = [[[0, 0], [0, 4], [1, 3], [1, 2], [2, 0]]]
filled12 = [[[2, 0], [3, 0], [3, 2], [1, 3], [1, 2]], # Two polygons.
[[1, 4], [3, 4], [3, 3]]]
ContourSet(ax, [0, 1, 2], [filled01, filled12], filled=True, cmap=cmap)
ContourSet(ax, [1, 2], [lines0, lines1], linewidths=3, colors=['r', 'k'])
# Segments and kind codes (1 = MOVETO, 2 = LINETO, 79 = CLOSEPOLY).
segs = [[[4, 0], [7, 0], [7, 3], [4, 3], [4, 0],
[5, 1], [5, 2], [6, 2], [6, 1], [5, 1]]]
kinds = [[1, 2, 2, 2, 79, 1, 2, 2, 2, 79]] # Polygon containing hole.
ContourSet(ax, [2, 3], [segs], [kinds], filled=True, cmap=cmap)
ContourSet(ax, [2], [segs], [kinds], colors='k', linewidths=3)
@image_comparison(baseline_images=['contour_line_start_on_corner_edge'],
extensions=['png'], remove_text=True)
def test_contour_line_start_on_corner_edge():
fig, ax = plt.subplots(figsize=(6, 5))
x, y = np.meshgrid([0, 1, 2, 3, 4], [0, 1, 2])
z = 1.2 - (x - 2)**2 + (y - 1)**2
mask = np.zeros_like(z, dtype=bool)
mask[1, 1] = mask[1, 3] = True
z = np.ma.array(z, mask=mask)
filled = ax.contourf(x, y, z, corner_mask=True)
cbar = fig.colorbar(filled)
lines = ax.contour(x, y, z, corner_mask=True, colors='k')
cbar.add_lines(lines)
def test_find_nearest_contour():
xy = np.indices((15, 15))
img = np.exp(-np.pi * (np.sum((xy - 5)**2, 0)/5.**2))
cs = plt.contour(img, 10)
nearest_contour = cs.find_nearest_contour(1, 1, pixel=False)
expected_nearest = (1, 0, 33, 1.965966, 1.965966, 1.866183)
assert_array_almost_equal(nearest_contour, expected_nearest)
nearest_contour = cs.find_nearest_contour(8, 1, pixel=False)
expected_nearest = (1, 0, 5, 7.550173, 1.587542, 0.547550)
assert_array_almost_equal(nearest_contour, expected_nearest)
nearest_contour = cs.find_nearest_contour(2, 5, pixel=False)
expected_nearest = (3, 0, 21, 1.884384, 5.023335, 0.013911)
assert_array_almost_equal(nearest_contour, expected_nearest)
nearest_contour = cs.find_nearest_contour(2, 5, indices=(5, 7), pixel=False)
expected_nearest = (5, 0, 16, 2.628202, 5.0, 0.394638)
assert_array_almost_equal(nearest_contour, expected_nearest)
def test_find_nearest_contour_no_filled():
xy = np.indices((15, 15))
img = np.exp(-np.pi * (np.sum((xy - 5)**2, 0)/5.**2))
cs = plt.contourf(img, 10)
with pytest.raises(ValueError, match="Method does not support filled contours"):
cs.find_nearest_contour(1, 1, pixel=False)
with pytest.raises(ValueError, match="Method does not support filled contours"):
cs.find_nearest_contour(1, 10, indices=(5, 7), pixel=False)
with pytest.raises(ValueError, match="Method does not support filled contours"):
cs.find_nearest_contour(2, 5, indices=(2, 7), pixel=True)
@mpl.style.context("default")
def test_contour_autolabel_beyond_powerlimits():
ax = plt.figure().add_subplot()
cs = plt.contour(np.geomspace(1e-6, 1e-4, 100).reshape(10, 10),
levels=[.25e-5, 1e-5, 4e-5])
ax.clabel(cs)
# Currently, the exponent is missing, but that may be fixed in the future.
assert {text.get_text() for text in ax.texts} == {"0.25", "1.00", "4.00"}
def test_contourf_legend_elements():
from matplotlib.patches import Rectangle
x = np.arange(1, 10)
y = x.reshape(-1, 1)
h = x * y
cs = plt.contourf(h, levels=[10, 30, 50],
colors=['#FFFF00', '#FF00FF', '#00FFFF'],
extend='both')
cs.cmap.set_over('red')
cs.cmap.set_under('blue')
cs.changed()
artists, labels = cs.legend_elements()
assert labels == ['$x \\leq -1e+250s$',
'$10.0 < x \\leq 30.0$',
'$30.0 < x \\leq 50.0$',
'$x > 1e+250s$']
expected_colors = ('blue', '#FFFF00', '#FF00FF', 'red')
assert all(isinstance(a, Rectangle) for a in artists)
assert all(same_color(a.get_facecolor(), c)
for a, c in zip(artists, expected_colors))
def test_contour_legend_elements():
x = np.arange(1, 10)
y = x.reshape(-1, 1)
h = x * y
colors = ['blue', '#00FF00', 'red']
cs = plt.contour(h, levels=[10, 30, 50],
colors=colors,
extend='both')
artists, labels = cs.legend_elements()
assert labels == ['$x = 10.0$', '$x = 30.0$', '$x = 50.0$']
assert all(isinstance(a, mpl.lines.Line2D) for a in artists)
assert all(same_color(a.get_color(), c)
for a, c in zip(artists, colors))
@pytest.mark.parametrize(
"algorithm, klass",
[('mpl2005', contourpy.Mpl2005ContourGenerator),
('mpl2014', contourpy.Mpl2014ContourGenerator),
('serial', contourpy.SerialContourGenerator),
('threaded', contourpy.ThreadedContourGenerator),
('invalid', None)])
def test_algorithm_name(algorithm, klass):
z = np.array([[1.0, 2.0], [3.0, 4.0]])
if klass is not None:
cs = plt.contourf(z, algorithm=algorithm)
assert isinstance(cs._contour_generator, klass)
else:
with pytest.raises(ValueError):
plt.contourf(z, algorithm=algorithm)
@pytest.mark.parametrize(
"algorithm", ['mpl2005', 'mpl2014', 'serial', 'threaded'])
def test_algorithm_supports_corner_mask(algorithm):
z = np.array([[1.0, 2.0], [3.0, 4.0]])
# All algorithms support corner_mask=False
plt.contourf(z, algorithm=algorithm, corner_mask=False)
# Only some algorithms support corner_mask=True
if algorithm != 'mpl2005':
plt.contourf(z, algorithm=algorithm, corner_mask=True)
else:
with pytest.raises(ValueError):
plt.contourf(z, algorithm=algorithm, corner_mask=True)
@image_comparison(baseline_images=['contour_all_algorithms'],
extensions=['png'], remove_text=True, tol=0.06)
def test_all_algorithms():
algorithms = ['mpl2005', 'mpl2014', 'serial', 'threaded']
rng = np.random.default_rng(2981)
x, y = np.meshgrid(np.linspace(0.0, 1.0, 10), np.linspace(0.0, 1.0, 6))
z = np.sin(15*x)*np.cos(10*y) + rng.normal(scale=0.5, size=(6, 10))
mask = np.zeros_like(z, dtype=bool)
mask[3, 7] = True
z = np.ma.array(z, mask=mask)
_, axs = plt.subplots(2, 2)
for ax, algorithm in zip(axs.ravel(), algorithms):
ax.contourf(x, y, z, algorithm=algorithm)
ax.contour(x, y, z, algorithm=algorithm, colors='k')
ax.set_title(algorithm)
def test_subfigure_clabel():
# Smoke test for gh#23173
delta = 0.025
x = np.arange(-3.0, 3.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-(X**2) - Y**2)
Z2 = np.exp(-((X - 1) ** 2) - (Y - 1) ** 2)
Z = (Z1 - Z2) * 2
fig = plt.figure()
figs = fig.subfigures(nrows=1, ncols=2)
for f in figs:
ax = f.subplots()
CS = ax.contour(X, Y, Z)
ax.clabel(CS, inline=True, fontsize=10)
ax.set_title("Simplest default with labels")
@pytest.mark.parametrize(
"style", ['solid', 'dashed', 'dashdot', 'dotted'])
def test_linestyles(style):
delta = 0.025
x = np.arange(-3.0, 3.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2
# Positive contour defaults to solid
fig1, ax1 = plt.subplots()
CS1 = ax1.contour(X, Y, Z, 6, colors='k')
ax1.clabel(CS1, fontsize=9, inline=True)
ax1.set_title('Single color - positive contours solid (default)')
assert CS1.linestyles is None # default
# Change linestyles using linestyles kwarg
fig2, ax2 = plt.subplots()
CS2 = ax2.contour(X, Y, Z, 6, colors='k', linestyles=style)
ax2.clabel(CS2, fontsize=9, inline=True)
ax2.set_title(f'Single color - positive contours {style}')
assert CS2.linestyles == style
# Ensure linestyles do not change when negative_linestyles is defined
fig3, ax3 = plt.subplots()
CS3 = ax3.contour(X, Y, Z, 6, colors='k', linestyles=style,
negative_linestyles='dashdot')
ax3.clabel(CS3, fontsize=9, inline=True)
ax3.set_title(f'Single color - positive contours {style}')
assert CS3.linestyles == style
@pytest.mark.parametrize(
"style", ['solid', 'dashed', 'dashdot', 'dotted'])
def test_negative_linestyles(style):
delta = 0.025
x = np.arange(-3.0, 3.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2
# Negative contour defaults to dashed
fig1, ax1 = plt.subplots()
CS1 = ax1.contour(X, Y, Z, 6, colors='k')
ax1.clabel(CS1, fontsize=9, inline=True)
ax1.set_title('Single color - negative contours dashed (default)')
assert CS1.negative_linestyles == 'dashed' # default
# Change negative_linestyles using rcParams
plt.rcParams['contour.negative_linestyle'] = style
fig2, ax2 = plt.subplots()
CS2 = ax2.contour(X, Y, Z, 6, colors='k')
ax2.clabel(CS2, fontsize=9, inline=True)
ax2.set_title(f'Single color - negative contours {style}'
'(using rcParams)')
assert CS2.negative_linestyles == style
# Change negative_linestyles using negative_linestyles kwarg
fig3, ax3 = plt.subplots()
CS3 = ax3.contour(X, Y, Z, 6, colors='k', negative_linestyles=style)
ax3.clabel(CS3, fontsize=9, inline=True)
ax3.set_title(f'Single color - negative contours {style}')
assert CS3.negative_linestyles == style
# Ensure negative_linestyles do not change when linestyles is defined
fig4, ax4 = plt.subplots()
CS4 = ax4.contour(X, Y, Z, 6, colors='k', linestyles='dashdot',
negative_linestyles=style)
ax4.clabel(CS4, fontsize=9, inline=True)
ax4.set_title(f'Single color - negative contours {style}')
assert CS4.negative_linestyles == style
def test_contour_remove():
ax = plt.figure().add_subplot()
orig_children = ax.get_children()
cs = ax.contour(np.arange(16).reshape((4, 4)))
cs.clabel()
assert ax.get_children() != orig_children
cs.remove()
assert ax.get_children() == orig_children
def test_contour_no_args():
fig, ax = plt.subplots()
data = [[0, 1], [1, 0]]
with pytest.raises(TypeError, match=r"contour\(\) takes from 1 to 4"):
ax.contour(Z=data)
def test_contour_clip_path():
fig, ax = plt.subplots()
data = [[0, 1], [1, 0]]
circle = mpatches.Circle([0.5, 0.5], 0.5, transform=ax.transAxes)
cs = ax.contour(data, clip_path=circle)
assert cs.get_clip_path() is not None
def test_bool_autolevel():
x, y = np.random.rand(2, 9)
z = (np.arange(9) % 2).reshape((3, 3)).astype(bool)
m = [[False, False, False], [False, True, False], [False, False, False]]
assert plt.contour(z.tolist()).levels.tolist() == [.5]
assert plt.contour(z).levels.tolist() == [.5]
assert plt.contour(np.ma.array(z, mask=m)).levels.tolist() == [.5]
assert plt.contourf(z.tolist()).levels.tolist() == [0, .5, 1]
assert plt.contourf(z).levels.tolist() == [0, .5, 1]
assert plt.contourf(np.ma.array(z, mask=m)).levels.tolist() == [0, .5, 1]
z = z.ravel()
assert plt.tricontour(x, y, z.tolist()).levels.tolist() == [.5]
assert plt.tricontour(x, y, z).levels.tolist() == [.5]
assert plt.tricontourf(x, y, z.tolist()).levels.tolist() == [0, .5, 1]
assert plt.tricontourf(x, y, z).levels.tolist() == [0, .5, 1]
def test_all_nan():
x = np.array([[np.nan, np.nan], [np.nan, np.nan]])
assert_array_almost_equal(plt.contour(x).levels,
[-1e-13, -7.5e-14, -5e-14, -2.4e-14, 0.0,
2.4e-14, 5e-14, 7.5e-14, 1e-13])
def test_allsegs_allkinds():
x, y = np.meshgrid(np.arange(0, 10, 2), np.arange(0, 10, 2))
z = np.sin(x) * np.cos(y)
cs = plt.contour(x, y, z, levels=[0, 0.5])
# Expect two levels, the first with 5 segments and the second with 4.
for result in [cs.allsegs, cs.allkinds]:
assert len(result) == 2
assert len(result[0]) == 5
assert len(result[1]) == 4
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