def test_contour_1x1_array():
# github issue 8197
with pytest.raises(TypeError) as excinfo:
contour(plt.gca(), [[0]])
excinfo.match(r'Input z must be at least a 2x2 array.')
with pytest.raises(TypeError) as excinfo:
contour(plt.gca(), [0], [0], [[0]])
excinfo.match(r'Input z must be at least a 2x2 array.')
def test_contour_shape_1d_valid():
x = np.arange(10)
y = np.arange(9)
z = np.random.random((9, 10))
fig = plt.figure()
ax = fig.add_subplot(111)
contour(ax, x, y, z)
def test_contour_empty_levels():
x = np.arange(9)
z = np.random.random((9, 9))
fig, ax = plt.subplots()
with pytest.warns(UserWarning) as record:
contour(ax, x, x, z, levels=[])
assert len(record) == 1
def test_contour_uniform_z():
x = np.arange(9)
z = np.ones((9, 9))
fig, ax = plt.subplots()
with pytest.warns(UserWarning) as record:
contour(ax, x, x, z)
assert len(record) == 1
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 = plt.figure()
ax = fig.add_subplot(111)
contour(ax, xg, yg, z)
def test_contour_shape_mismatch_2():
x = np.arange(10)
y = np.arange(10)
z = np.random.random((9, 10))
fig = plt.figure()
ax = fig.add_subplot(111)
with pytest.raises(TypeError) as excinfo:
contour(ax, x, y, z)
excinfo.match(r'Length of y must be number of rows in z.')
def test_contour_shape_invalid_2():
x = np.random.random((3, 3, 3))
y = np.random.random((3, 3, 3))
z = np.random.random((3, 3, 3))
fig = plt.figure()
ax = fig.add_subplot(111)
with pytest.raises(TypeError) as excinfo:
contour(ax, x, y, z)
excinfo.match(r'Input z must be a 2D array.')
def test_given_colors_levels_and_extends():
_, axes = 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(axes.flatten()):
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 = contourf(ax, data, colors=colors[first_color:last_color],
levels=levels, extend=extend)
else:
# If not filled, we have 4 levels and 4 colors
c = contour(ax, data, colors=colors[:-1],
levels=levels, extend=extend)
plt.colorbar(c, ax=ax)
def test_contour_shape_mismatch_3():
x = np.arange(10)
y = np.arange(10)
xg, yg = np.meshgrid(x, y)
z = np.random.random((9, 10))
fig = plt.figure()
ax = fig.add_subplot(111)
with pytest.raises(TypeError) as excinfo:
contour(ax, xg, y, z)
excinfo.match(r'Number of dimensions of x and y should match.')
with pytest.raises(TypeError) as excinfo:
contour(ax, x, yg, z)
excinfo.match(r'Number of dimensions of x and y should match.')
def test_contour_labels_size_color():
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))
cs = contour(plt.gca(), x, y, z)
pts = np.array([(1.5, 3.0), (1.5, 4.4), (1.5, 6.0)])
plt.clabel(cs, manual=pts, fontsize='small', colors=('r', 'g'))
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))
cs = contour(plt.gca(), x, y, z)
pts = np.array([(1.5, 3.0), (1.5, 4.4), (1.5, 6.0)])
plt.clabel(cs, manual=pts)
def test_contour_shape_mismatch_4():
g = np.random.random((9, 10))
b = np.random.random((9, 9))
z = np.random.random((9, 10))
fig = plt.figure()
ax = fig.add_subplot(111)
with pytest.raises(TypeError) as excinfo:
contour(ax, b, g, z)
excinfo.match(r'Shape of x does not match that of z: found \(9L?, 9L?\) ' +
r'instead of \(9L?, 10L?\)')
with pytest.raises(TypeError) as excinfo:
contour(ax, g, b, z)
excinfo.match(r'Shape of y does not match that of z: found \(9L?, 9L?\) ' +
r'instead of \(9L?, 10L?\)')
def test_circular_contour_warning():
# Check that almost circular contours don't throw a warning
with pytest.warns(None) as record:
x, y = np.meshgrid(np.linspace(-2, 2, 4), np.linspace(-2, 2, 4))
r = np.sqrt(x ** 2 + y ** 2)
plt.figure()
cs = contour(plt.gca(), x, y, r)
plt.clabel(cs)
assert len(record) == 0
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)
contour(plt.gca(), x, y, z)
plt.subplot(222)
contourf(plt.gca(), x, y, z)
x = np.repeat(x[np.newaxis], 20, axis=0)
y = np.repeat(y[:, np.newaxis], 20, axis=1)
plt.subplot(223)
contour(plt.gca(), x, y, z)
plt.subplot(224)
contourf(plt.gca(), x, y, z)
for ax in fig.get_axes():
for label in ax.get_xticklabels():
label.set_ha('right')
label.set_rotation(30)
def get_contour_vertices(x, y, f, lev):
import legacycontour as cntr
c = cntr.contour(x, y, f)
nlist = c.trace(lev, lev, 0)
segs = nlist[:len(nlist) // 2]
N = len(segs[0][:, 0])
xr = [segs[0][ix, 0] for ix in range(N)]
yr = [segs[0][ix, 1] for ix in range(N)]
#Set contour to None if it's found to reach the physical domain
if x.min() >= min(segs[0][:, 0]) or max(segs[0][:, 0]) >= x.max() or \
y.min() >= min(segs[0][:, 1]) or max(segs[0][:, 1]) >= y.max():
return [None, None]
return [xr, yr] # x,y coords of contour points.
def test_contour_badlevel_fmt():
# test funny edge case from
# https://github.com/matplotlib/matplotlib/issues/9742
# User supplied fmt for each level as a dictionary, but
# MPL changed the level to the minimum data value because
# no contours possible.
# This would error out pre
# https://github.com/matplotlib/matplotlib/pull/9743
x = np.arange(9)
z = np.zeros((9, 9))
fig, ax = plt.subplots()
fmt = {1.: '%1.2f'}
with pytest.warns(UserWarning) as record:
cs = contour(ax, x, x, z, levels=[1.])
ax.clabel(cs, fmt=fmt)
assert len(record) == 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 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
# difference of Gaussians
Z = 10.0 * (Z2 - Z1)
fig, ax = plt.subplots(1, 1)
CS = contour(ax, 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)
