739 lines
28 KiB
Python
739 lines
28 KiB
Python
import copy
|
|
|
|
import numpy as np
|
|
from numpy.testing import (assert_allclose, assert_almost_equal,
|
|
assert_array_equal, assert_array_almost_equal)
|
|
import pytest
|
|
|
|
from matplotlib import scale
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib.patches as mpatches
|
|
import matplotlib.transforms as mtransforms
|
|
from matplotlib.path import Path
|
|
from matplotlib.testing.decorators import image_comparison
|
|
|
|
|
|
def test_non_affine_caching():
|
|
class AssertingNonAffineTransform(mtransforms.Transform):
|
|
"""
|
|
This transform raises an assertion error when called when it
|
|
shouldn't be and ``self.raise_on_transform`` is True.
|
|
|
|
"""
|
|
input_dims = output_dims = 2
|
|
is_affine = False
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self.raise_on_transform = False
|
|
self.underlying_transform = mtransforms.Affine2D().scale(10, 10)
|
|
|
|
def transform_path_non_affine(self, path):
|
|
assert not self.raise_on_transform, \
|
|
'Invalidated affine part of transform unnecessarily.'
|
|
return self.underlying_transform.transform_path(path)
|
|
transform_path = transform_path_non_affine
|
|
|
|
def transform_non_affine(self, path):
|
|
assert not self.raise_on_transform, \
|
|
'Invalidated affine part of transform unnecessarily.'
|
|
return self.underlying_transform.transform(path)
|
|
transform = transform_non_affine
|
|
|
|
my_trans = AssertingNonAffineTransform()
|
|
ax = plt.axes()
|
|
plt.plot(np.arange(10), transform=my_trans + ax.transData)
|
|
plt.draw()
|
|
# enable the transform to raise an exception if it's non-affine transform
|
|
# method is triggered again.
|
|
my_trans.raise_on_transform = True
|
|
ax.transAxes.invalidate()
|
|
plt.draw()
|
|
|
|
|
|
def test_external_transform_api():
|
|
class ScaledBy:
|
|
def __init__(self, scale_factor):
|
|
self._scale_factor = scale_factor
|
|
|
|
def _as_mpl_transform(self, axes):
|
|
return (mtransforms.Affine2D().scale(self._scale_factor)
|
|
+ axes.transData)
|
|
|
|
ax = plt.axes()
|
|
line, = plt.plot(np.arange(10), transform=ScaledBy(10))
|
|
ax.set_xlim(0, 100)
|
|
ax.set_ylim(0, 100)
|
|
# assert that the top transform of the line is the scale transform.
|
|
assert_allclose(line.get_transform()._a.get_matrix(),
|
|
mtransforms.Affine2D().scale(10).get_matrix())
|
|
|
|
|
|
@image_comparison(['pre_transform_data'],
|
|
tol=0.08, remove_text=True, style='mpl20')
|
|
def test_pre_transform_plotting():
|
|
# a catch-all for as many as possible plot layouts which handle
|
|
# pre-transforming the data NOTE: The axis range is important in this
|
|
# plot. It should be x10 what the data suggests it should be
|
|
|
|
# Remove this line when this test image is regenerated.
|
|
plt.rcParams['pcolormesh.snap'] = False
|
|
|
|
ax = plt.axes()
|
|
times10 = mtransforms.Affine2D().scale(10)
|
|
|
|
ax.contourf(np.arange(48).reshape(6, 8), transform=times10 + ax.transData)
|
|
|
|
ax.pcolormesh(np.linspace(0, 4, 7),
|
|
np.linspace(5.5, 8, 9),
|
|
np.arange(48).reshape(8, 6),
|
|
transform=times10 + ax.transData)
|
|
|
|
ax.scatter(np.linspace(0, 10), np.linspace(10, 0),
|
|
transform=times10 + ax.transData)
|
|
|
|
x = np.linspace(8, 10, 20)
|
|
y = np.linspace(1, 5, 20)
|
|
u = 2*np.sin(x) + np.cos(y[:, np.newaxis])
|
|
v = np.sin(x) - np.cos(y[:, np.newaxis])
|
|
|
|
df = 25. / 30. # Compatibility factor for old test image
|
|
ax.streamplot(x, y, u, v, transform=times10 + ax.transData,
|
|
density=(df, df), linewidth=u**2 + v**2)
|
|
|
|
# reduce the vector data down a bit for barb and quiver plotting
|
|
x, y = x[::3], y[::3]
|
|
u, v = u[::3, ::3], v[::3, ::3]
|
|
|
|
ax.quiver(x, y + 5, u, v, transform=times10 + ax.transData)
|
|
|
|
ax.barbs(x - 3, y + 5, u**2, v**2, transform=times10 + ax.transData)
|
|
|
|
|
|
def test_contour_pre_transform_limits():
|
|
ax = plt.axes()
|
|
xs, ys = np.meshgrid(np.linspace(15, 20, 15), np.linspace(12.4, 12.5, 20))
|
|
ax.contourf(xs, ys, np.log(xs * ys),
|
|
transform=mtransforms.Affine2D().scale(0.1) + ax.transData)
|
|
|
|
expected = np.array([[1.5, 1.24],
|
|
[2., 1.25]])
|
|
assert_almost_equal(expected, ax.dataLim.get_points())
|
|
|
|
|
|
def test_pcolor_pre_transform_limits():
|
|
# Based on test_contour_pre_transform_limits()
|
|
ax = plt.axes()
|
|
xs, ys = np.meshgrid(np.linspace(15, 20, 15), np.linspace(12.4, 12.5, 20))
|
|
ax.pcolor(xs, ys, np.log(xs * ys)[:-1, :-1],
|
|
transform=mtransforms.Affine2D().scale(0.1) + ax.transData)
|
|
|
|
expected = np.array([[1.5, 1.24],
|
|
[2., 1.25]])
|
|
assert_almost_equal(expected, ax.dataLim.get_points())
|
|
|
|
|
|
def test_pcolormesh_pre_transform_limits():
|
|
# Based on test_contour_pre_transform_limits()
|
|
ax = plt.axes()
|
|
xs, ys = np.meshgrid(np.linspace(15, 20, 15), np.linspace(12.4, 12.5, 20))
|
|
ax.pcolormesh(xs, ys, np.log(xs * ys)[:-1, :-1],
|
|
transform=mtransforms.Affine2D().scale(0.1) + ax.transData)
|
|
|
|
expected = np.array([[1.5, 1.24],
|
|
[2., 1.25]])
|
|
assert_almost_equal(expected, ax.dataLim.get_points())
|
|
|
|
|
|
def test_Affine2D_from_values():
|
|
points = np.array([[0, 0],
|
|
[10, 20],
|
|
[-1, 0],
|
|
])
|
|
|
|
t = mtransforms.Affine2D.from_values(1, 0, 0, 0, 0, 0)
|
|
actual = t.transform(points)
|
|
expected = np.array([[0, 0], [10, 0], [-1, 0]])
|
|
assert_almost_equal(actual, expected)
|
|
|
|
t = mtransforms.Affine2D.from_values(0, 2, 0, 0, 0, 0)
|
|
actual = t.transform(points)
|
|
expected = np.array([[0, 0], [0, 20], [0, -2]])
|
|
assert_almost_equal(actual, expected)
|
|
|
|
t = mtransforms.Affine2D.from_values(0, 0, 3, 0, 0, 0)
|
|
actual = t.transform(points)
|
|
expected = np.array([[0, 0], [60, 0], [0, 0]])
|
|
assert_almost_equal(actual, expected)
|
|
|
|
t = mtransforms.Affine2D.from_values(0, 0, 0, 4, 0, 0)
|
|
actual = t.transform(points)
|
|
expected = np.array([[0, 0], [0, 80], [0, 0]])
|
|
assert_almost_equal(actual, expected)
|
|
|
|
t = mtransforms.Affine2D.from_values(0, 0, 0, 0, 5, 0)
|
|
actual = t.transform(points)
|
|
expected = np.array([[5, 0], [5, 0], [5, 0]])
|
|
assert_almost_equal(actual, expected)
|
|
|
|
t = mtransforms.Affine2D.from_values(0, 0, 0, 0, 0, 6)
|
|
actual = t.transform(points)
|
|
expected = np.array([[0, 6], [0, 6], [0, 6]])
|
|
assert_almost_equal(actual, expected)
|
|
|
|
|
|
def test_affine_inverted_invalidated():
|
|
# Ensure that the an affine transform is not declared valid on access
|
|
point = [1.0, 1.0]
|
|
t = mtransforms.Affine2D()
|
|
|
|
assert_almost_equal(point, t.transform(t.inverted().transform(point)))
|
|
# Change and access the transform
|
|
t.translate(1.0, 1.0).get_matrix()
|
|
assert_almost_equal(point, t.transform(t.inverted().transform(point)))
|
|
|
|
|
|
def test_clipping_of_log():
|
|
# issue 804
|
|
path = Path([(0.2, -99), (0.4, -99), (0.4, 20), (0.2, 20), (0.2, -99)],
|
|
closed=True)
|
|
# something like this happens in plotting logarithmic histograms
|
|
trans = mtransforms.BlendedGenericTransform(
|
|
mtransforms.Affine2D(), scale.LogTransform(10, 'clip'))
|
|
tpath = trans.transform_path_non_affine(path)
|
|
result = tpath.iter_segments(trans.get_affine(),
|
|
clip=(0, 0, 100, 100),
|
|
simplify=False)
|
|
tpoints, tcodes = zip(*result)
|
|
assert_allclose(tcodes, path.codes)
|
|
|
|
|
|
class NonAffineForTest(mtransforms.Transform):
|
|
"""
|
|
A class which looks like a non affine transform, but does whatever
|
|
the given transform does (even if it is affine). This is very useful
|
|
for testing NonAffine behaviour with a simple Affine transform.
|
|
|
|
"""
|
|
is_affine = False
|
|
output_dims = 2
|
|
input_dims = 2
|
|
|
|
def __init__(self, real_trans, *args, **kwargs):
|
|
self.real_trans = real_trans
|
|
super().__init__(*args, **kwargs)
|
|
|
|
def transform_non_affine(self, values):
|
|
return self.real_trans.transform(values)
|
|
|
|
def transform_path_non_affine(self, path):
|
|
return self.real_trans.transform_path(path)
|
|
|
|
|
|
class TestBasicTransform:
|
|
def setup_method(self):
|
|
|
|
self.ta1 = mtransforms.Affine2D(shorthand_name='ta1').rotate(np.pi / 2)
|
|
self.ta2 = mtransforms.Affine2D(shorthand_name='ta2').translate(10, 0)
|
|
self.ta3 = mtransforms.Affine2D(shorthand_name='ta3').scale(1, 2)
|
|
|
|
self.tn1 = NonAffineForTest(mtransforms.Affine2D().translate(1, 2),
|
|
shorthand_name='tn1')
|
|
self.tn2 = NonAffineForTest(mtransforms.Affine2D().translate(1, 2),
|
|
shorthand_name='tn2')
|
|
self.tn3 = NonAffineForTest(mtransforms.Affine2D().translate(1, 2),
|
|
shorthand_name='tn3')
|
|
|
|
# creates a transform stack which looks like ((A, (N, A)), A)
|
|
self.stack1 = (self.ta1 + (self.tn1 + self.ta2)) + self.ta3
|
|
# creates a transform stack which looks like (((A, N), A), A)
|
|
self.stack2 = self.ta1 + self.tn1 + self.ta2 + self.ta3
|
|
# creates a transform stack which is a subset of stack2
|
|
self.stack2_subset = self.tn1 + self.ta2 + self.ta3
|
|
|
|
# when in debug, the transform stacks can produce dot images:
|
|
# self.stack1.write_graphviz(file('stack1.dot', 'w'))
|
|
# self.stack2.write_graphviz(file('stack2.dot', 'w'))
|
|
# self.stack2_subset.write_graphviz(file('stack2_subset.dot', 'w'))
|
|
|
|
def test_transform_depth(self):
|
|
assert self.stack1.depth == 4
|
|
assert self.stack2.depth == 4
|
|
assert self.stack2_subset.depth == 3
|
|
|
|
def test_left_to_right_iteration(self):
|
|
stack3 = (self.ta1 + (self.tn1 + (self.ta2 + self.tn2))) + self.ta3
|
|
# stack3.write_graphviz(file('stack3.dot', 'w'))
|
|
|
|
target_transforms = [stack3,
|
|
(self.tn1 + (self.ta2 + self.tn2)) + self.ta3,
|
|
(self.ta2 + self.tn2) + self.ta3,
|
|
self.tn2 + self.ta3,
|
|
self.ta3,
|
|
]
|
|
r = [rh for _, rh in stack3._iter_break_from_left_to_right()]
|
|
assert len(r) == len(target_transforms)
|
|
|
|
for target_stack, stack in zip(target_transforms, r):
|
|
assert target_stack == stack
|
|
|
|
def test_transform_shortcuts(self):
|
|
assert self.stack1 - self.stack2_subset == self.ta1
|
|
assert self.stack2 - self.stack2_subset == self.ta1
|
|
|
|
assert self.stack2_subset - self.stack2 == self.ta1.inverted()
|
|
assert (self.stack2_subset - self.stack2).depth == 1
|
|
|
|
with pytest.raises(ValueError):
|
|
self.stack1 - self.stack2
|
|
|
|
aff1 = self.ta1 + (self.ta2 + self.ta3)
|
|
aff2 = self.ta2 + self.ta3
|
|
|
|
assert aff1 - aff2 == self.ta1
|
|
assert aff1 - self.ta2 == aff1 + self.ta2.inverted()
|
|
|
|
assert self.stack1 - self.ta3 == self.ta1 + (self.tn1 + self.ta2)
|
|
assert self.stack2 - self.ta3 == self.ta1 + self.tn1 + self.ta2
|
|
|
|
assert ((self.ta2 + self.ta3) - self.ta3 + self.ta3 ==
|
|
self.ta2 + self.ta3)
|
|
|
|
def test_contains_branch(self):
|
|
r1 = (self.ta2 + self.ta1)
|
|
r2 = (self.ta2 + self.ta1)
|
|
assert r1 == r2
|
|
assert r1 != self.ta1
|
|
assert r1.contains_branch(r2)
|
|
assert r1.contains_branch(self.ta1)
|
|
assert not r1.contains_branch(self.ta2)
|
|
assert not r1.contains_branch(self.ta2 + self.ta2)
|
|
|
|
assert r1 == r2
|
|
|
|
assert self.stack1.contains_branch(self.ta3)
|
|
assert self.stack2.contains_branch(self.ta3)
|
|
|
|
assert self.stack1.contains_branch(self.stack2_subset)
|
|
assert self.stack2.contains_branch(self.stack2_subset)
|
|
|
|
assert not self.stack2_subset.contains_branch(self.stack1)
|
|
assert not self.stack2_subset.contains_branch(self.stack2)
|
|
|
|
assert self.stack1.contains_branch(self.ta2 + self.ta3)
|
|
assert self.stack2.contains_branch(self.ta2 + self.ta3)
|
|
|
|
assert not self.stack1.contains_branch(self.tn1 + self.ta2)
|
|
|
|
def test_affine_simplification(self):
|
|
# tests that a transform stack only calls as much is absolutely
|
|
# necessary "non-affine" allowing the best possible optimization with
|
|
# complex transformation stacks.
|
|
points = np.array([[0, 0], [10, 20], [np.nan, 1], [-1, 0]],
|
|
dtype=np.float64)
|
|
na_pts = self.stack1.transform_non_affine(points)
|
|
all_pts = self.stack1.transform(points)
|
|
|
|
na_expected = np.array([[1., 2.], [-19., 12.],
|
|
[np.nan, np.nan], [1., 1.]], dtype=np.float64)
|
|
all_expected = np.array([[11., 4.], [-9., 24.],
|
|
[np.nan, np.nan], [11., 2.]],
|
|
dtype=np.float64)
|
|
|
|
# check we have the expected results from doing the affine part only
|
|
assert_array_almost_equal(na_pts, na_expected)
|
|
# check we have the expected results from a full transformation
|
|
assert_array_almost_equal(all_pts, all_expected)
|
|
# check we have the expected results from doing the transformation in
|
|
# two steps
|
|
assert_array_almost_equal(self.stack1.transform_affine(na_pts),
|
|
all_expected)
|
|
# check that getting the affine transformation first, then fully
|
|
# transforming using that yields the same result as before.
|
|
assert_array_almost_equal(self.stack1.get_affine().transform(na_pts),
|
|
all_expected)
|
|
|
|
# check that the affine part of stack1 & stack2 are equivalent
|
|
# (i.e. the optimization is working)
|
|
expected_result = (self.ta2 + self.ta3).get_matrix()
|
|
result = self.stack1.get_affine().get_matrix()
|
|
assert_array_equal(expected_result, result)
|
|
|
|
result = self.stack2.get_affine().get_matrix()
|
|
assert_array_equal(expected_result, result)
|
|
|
|
|
|
class TestTransformPlotInterface:
|
|
def test_line_extent_axes_coords(self):
|
|
# a simple line in axes coordinates
|
|
ax = plt.axes()
|
|
ax.plot([0.1, 1.2, 0.8], [0.9, 0.5, 0.8], transform=ax.transAxes)
|
|
assert_array_equal(ax.dataLim.get_points(),
|
|
np.array([[np.inf, np.inf],
|
|
[-np.inf, -np.inf]]))
|
|
|
|
def test_line_extent_data_coords(self):
|
|
# a simple line in data coordinates
|
|
ax = plt.axes()
|
|
ax.plot([0.1, 1.2, 0.8], [0.9, 0.5, 0.8], transform=ax.transData)
|
|
assert_array_equal(ax.dataLim.get_points(),
|
|
np.array([[0.1, 0.5], [1.2, 0.9]]))
|
|
|
|
def test_line_extent_compound_coords1(self):
|
|
# a simple line in data coordinates in the y component, and in axes
|
|
# coordinates in the x
|
|
ax = plt.axes()
|
|
trans = mtransforms.blended_transform_factory(ax.transAxes,
|
|
ax.transData)
|
|
ax.plot([0.1, 1.2, 0.8], [35, -5, 18], transform=trans)
|
|
assert_array_equal(ax.dataLim.get_points(),
|
|
np.array([[np.inf, -5.],
|
|
[-np.inf, 35.]]))
|
|
|
|
def test_line_extent_predata_transform_coords(self):
|
|
# a simple line in (offset + data) coordinates
|
|
ax = plt.axes()
|
|
trans = mtransforms.Affine2D().scale(10) + ax.transData
|
|
ax.plot([0.1, 1.2, 0.8], [35, -5, 18], transform=trans)
|
|
assert_array_equal(ax.dataLim.get_points(),
|
|
np.array([[1., -50.], [12., 350.]]))
|
|
|
|
def test_line_extent_compound_coords2(self):
|
|
# a simple line in (offset + data) coordinates in the y component, and
|
|
# in axes coordinates in the x
|
|
ax = plt.axes()
|
|
trans = mtransforms.blended_transform_factory(
|
|
ax.transAxes, mtransforms.Affine2D().scale(10) + ax.transData)
|
|
ax.plot([0.1, 1.2, 0.8], [35, -5, 18], transform=trans)
|
|
assert_array_equal(ax.dataLim.get_points(),
|
|
np.array([[np.inf, -50.], [-np.inf, 350.]]))
|
|
|
|
def test_line_extents_affine(self):
|
|
ax = plt.axes()
|
|
offset = mtransforms.Affine2D().translate(10, 10)
|
|
plt.plot(np.arange(10), transform=offset + ax.transData)
|
|
expected_data_lim = np.array([[0., 0.], [9., 9.]]) + 10
|
|
assert_array_almost_equal(ax.dataLim.get_points(), expected_data_lim)
|
|
|
|
def test_line_extents_non_affine(self):
|
|
ax = plt.axes()
|
|
offset = mtransforms.Affine2D().translate(10, 10)
|
|
na_offset = NonAffineForTest(mtransforms.Affine2D().translate(10, 10))
|
|
plt.plot(np.arange(10), transform=offset + na_offset + ax.transData)
|
|
expected_data_lim = np.array([[0., 0.], [9., 9.]]) + 20
|
|
assert_array_almost_equal(ax.dataLim.get_points(), expected_data_lim)
|
|
|
|
def test_pathc_extents_non_affine(self):
|
|
ax = plt.axes()
|
|
offset = mtransforms.Affine2D().translate(10, 10)
|
|
na_offset = NonAffineForTest(mtransforms.Affine2D().translate(10, 10))
|
|
pth = Path(np.array([[0, 0], [0, 10], [10, 10], [10, 0]]))
|
|
patch = mpatches.PathPatch(pth,
|
|
transform=offset + na_offset + ax.transData)
|
|
ax.add_patch(patch)
|
|
expected_data_lim = np.array([[0., 0.], [10., 10.]]) + 20
|
|
assert_array_almost_equal(ax.dataLim.get_points(), expected_data_lim)
|
|
|
|
def test_pathc_extents_affine(self):
|
|
ax = plt.axes()
|
|
offset = mtransforms.Affine2D().translate(10, 10)
|
|
pth = Path(np.array([[0, 0], [0, 10], [10, 10], [10, 0]]))
|
|
patch = mpatches.PathPatch(pth, transform=offset + ax.transData)
|
|
ax.add_patch(patch)
|
|
expected_data_lim = np.array([[0., 0.], [10., 10.]]) + 10
|
|
assert_array_almost_equal(ax.dataLim.get_points(), expected_data_lim)
|
|
|
|
def test_line_extents_for_non_affine_transData(self):
|
|
ax = plt.axes(projection='polar')
|
|
# add 10 to the radius of the data
|
|
offset = mtransforms.Affine2D().translate(0, 10)
|
|
|
|
plt.plot(np.arange(10), transform=offset + ax.transData)
|
|
# the data lim of a polar plot is stored in coordinates
|
|
# before a transData transformation, hence the data limits
|
|
# are not what is being shown on the actual plot.
|
|
expected_data_lim = np.array([[0., 0.], [9., 9.]]) + [0, 10]
|
|
assert_array_almost_equal(ax.dataLim.get_points(), expected_data_lim)
|
|
|
|
|
|
def assert_bbox_eq(bbox1, bbox2):
|
|
assert_array_equal(bbox1.bounds, bbox2.bounds)
|
|
|
|
|
|
def test_bbox_intersection():
|
|
bbox_from_ext = mtransforms.Bbox.from_extents
|
|
inter = mtransforms.Bbox.intersection
|
|
|
|
r1 = bbox_from_ext(0, 0, 1, 1)
|
|
r2 = bbox_from_ext(0.5, 0.5, 1.5, 1.5)
|
|
r3 = bbox_from_ext(0.5, 0, 0.75, 0.75)
|
|
r4 = bbox_from_ext(0.5, 1.5, 1, 2.5)
|
|
r5 = bbox_from_ext(1, 1, 2, 2)
|
|
|
|
# self intersection -> no change
|
|
assert_bbox_eq(inter(r1, r1), r1)
|
|
# simple intersection
|
|
assert_bbox_eq(inter(r1, r2), bbox_from_ext(0.5, 0.5, 1, 1))
|
|
# r3 contains r2
|
|
assert_bbox_eq(inter(r1, r3), r3)
|
|
# no intersection
|
|
assert inter(r1, r4) is None
|
|
# single point
|
|
assert_bbox_eq(inter(r1, r5), bbox_from_ext(1, 1, 1, 1))
|
|
|
|
|
|
def test_bbox_as_strings():
|
|
b = mtransforms.Bbox([[.5, 0], [.75, .75]])
|
|
assert_bbox_eq(b, eval(repr(b), {'Bbox': mtransforms.Bbox}))
|
|
asdict = eval(str(b), {'Bbox': dict})
|
|
for k, v in asdict.items():
|
|
assert getattr(b, k) == v
|
|
fmt = '.1f'
|
|
asdict = eval(format(b, fmt), {'Bbox': dict})
|
|
for k, v in asdict.items():
|
|
assert eval(format(getattr(b, k), fmt)) == v
|
|
|
|
|
|
def test_str_transform():
|
|
# The str here should not be considered as "absolutely stable", and may be
|
|
# reformatted later; this is just a smoketest for __str__.
|
|
assert str(plt.subplot(projection="polar").transData) == """\
|
|
CompositeGenericTransform(
|
|
CompositeGenericTransform(
|
|
CompositeGenericTransform(
|
|
TransformWrapper(
|
|
BlendedAffine2D(
|
|
IdentityTransform(),
|
|
IdentityTransform())),
|
|
CompositeAffine2D(
|
|
Affine2D(
|
|
[[1. 0. 0.]
|
|
[0. 1. 0.]
|
|
[0. 0. 1.]]),
|
|
Affine2D(
|
|
[[1. 0. 0.]
|
|
[0. 1. 0.]
|
|
[0. 0. 1.]]))),
|
|
PolarTransform(
|
|
PolarAxesSubplot(0.125,0.1;0.775x0.8),
|
|
use_rmin=True,
|
|
_apply_theta_transforms=False)),
|
|
CompositeGenericTransform(
|
|
CompositeGenericTransform(
|
|
PolarAffine(
|
|
TransformWrapper(
|
|
BlendedAffine2D(
|
|
IdentityTransform(),
|
|
IdentityTransform())),
|
|
LockableBbox(
|
|
Bbox(x0=0.0, y0=0.0, x1=6.283185307179586, y1=1.0),
|
|
[[-- --]
|
|
[-- --]])),
|
|
BboxTransformFrom(
|
|
_WedgeBbox(
|
|
(0.5, 0.5),
|
|
TransformedBbox(
|
|
Bbox(x0=0.0, y0=0.0, x1=6.283185307179586, y1=1.0),
|
|
CompositeAffine2D(
|
|
Affine2D(
|
|
[[1. 0. 0.]
|
|
[0. 1. 0.]
|
|
[0. 0. 1.]]),
|
|
Affine2D(
|
|
[[1. 0. 0.]
|
|
[0. 1. 0.]
|
|
[0. 0. 1.]]))),
|
|
LockableBbox(
|
|
Bbox(x0=0.0, y0=0.0, x1=6.283185307179586, y1=1.0),
|
|
[[-- --]
|
|
[-- --]])))),
|
|
BboxTransformTo(
|
|
TransformedBbox(
|
|
Bbox(x0=0.125, y0=0.09999999999999998, x1=0.9, y1=0.9),
|
|
BboxTransformTo(
|
|
TransformedBbox(
|
|
Bbox(x0=0.0, y0=0.0, x1=8.0, y1=6.0),
|
|
Affine2D(
|
|
[[80. 0. 0.]
|
|
[ 0. 80. 0.]
|
|
[ 0. 0. 1.]])))))))"""
|
|
|
|
|
|
def test_transform_single_point():
|
|
t = mtransforms.Affine2D()
|
|
r = t.transform_affine((1, 1))
|
|
assert r.shape == (2,)
|
|
|
|
|
|
def test_log_transform():
|
|
# Tests that the last line runs without exception (previously the
|
|
# transform would fail if one of the axes was logarithmic).
|
|
fig, ax = plt.subplots()
|
|
ax.set_yscale('log')
|
|
ax.transData.transform((1, 1))
|
|
|
|
|
|
def test_nan_overlap():
|
|
a = mtransforms.Bbox([[0, 0], [1, 1]])
|
|
b = mtransforms.Bbox([[0, 0], [1, np.nan]])
|
|
assert not a.overlaps(b)
|
|
|
|
|
|
def test_transform_angles():
|
|
t = mtransforms.Affine2D() # Identity transform
|
|
angles = np.array([20, 45, 60])
|
|
points = np.array([[0, 0], [1, 1], [2, 2]])
|
|
|
|
# Identity transform does not change angles
|
|
new_angles = t.transform_angles(angles, points)
|
|
assert_array_almost_equal(angles, new_angles)
|
|
|
|
# points missing a 2nd dimension
|
|
with pytest.raises(ValueError):
|
|
t.transform_angles(angles, points[0:2, 0:1])
|
|
|
|
# Number of angles != Number of points
|
|
with pytest.raises(ValueError):
|
|
t.transform_angles(angles, points[0:2, :])
|
|
|
|
|
|
def test_nonsingular():
|
|
# test for zero-expansion type cases; other cases may be added later
|
|
zero_expansion = np.array([-0.001, 0.001])
|
|
cases = [(0, np.nan), (0, 0), (0, 7.9e-317)]
|
|
for args in cases:
|
|
out = np.array(mtransforms.nonsingular(*args))
|
|
assert_array_equal(out, zero_expansion)
|
|
|
|
|
|
def test_invalid_arguments():
|
|
t = mtransforms.Affine2D()
|
|
# There are two different exceptions, since the wrong number of
|
|
# dimensions is caught when constructing an array_view, and that
|
|
# raises a ValueError, and a wrong shape with a possible number
|
|
# of dimensions is caught by our CALL_CPP macro, which always
|
|
# raises the less precise RuntimeError.
|
|
with pytest.raises(ValueError):
|
|
t.transform(1)
|
|
with pytest.raises(ValueError):
|
|
t.transform([[[1]]])
|
|
with pytest.raises(RuntimeError):
|
|
t.transform([])
|
|
with pytest.raises(RuntimeError):
|
|
t.transform([1])
|
|
with pytest.raises(RuntimeError):
|
|
t.transform([[1]])
|
|
with pytest.raises(RuntimeError):
|
|
t.transform([[1, 2, 3]])
|
|
|
|
|
|
def test_transformed_path():
|
|
points = [(0, 0), (1, 0), (1, 1), (0, 1)]
|
|
path = Path(points, closed=True)
|
|
|
|
trans = mtransforms.Affine2D()
|
|
trans_path = mtransforms.TransformedPath(path, trans)
|
|
assert_allclose(trans_path.get_fully_transformed_path().vertices, points)
|
|
|
|
# Changing the transform should change the result.
|
|
r2 = 1 / np.sqrt(2)
|
|
trans.rotate(np.pi / 4)
|
|
assert_allclose(trans_path.get_fully_transformed_path().vertices,
|
|
[(0, 0), (r2, r2), (0, 2 * r2), (-r2, r2)],
|
|
atol=1e-15)
|
|
|
|
# Changing the path does not change the result (it's cached).
|
|
path.points = [(0, 0)] * 4
|
|
assert_allclose(trans_path.get_fully_transformed_path().vertices,
|
|
[(0, 0), (r2, r2), (0, 2 * r2), (-r2, r2)],
|
|
atol=1e-15)
|
|
|
|
|
|
def test_transformed_patch_path():
|
|
trans = mtransforms.Affine2D()
|
|
patch = mpatches.Wedge((0, 0), 1, 45, 135, transform=trans)
|
|
|
|
tpatch = mtransforms.TransformedPatchPath(patch)
|
|
points = tpatch.get_fully_transformed_path().vertices
|
|
|
|
# Changing the transform should change the result.
|
|
trans.scale(2)
|
|
assert_allclose(tpatch.get_fully_transformed_path().vertices, points * 2)
|
|
|
|
# Changing the path should change the result (and cancel out the scaling
|
|
# from the transform).
|
|
patch.set_radius(0.5)
|
|
assert_allclose(tpatch.get_fully_transformed_path().vertices, points)
|
|
|
|
|
|
@pytest.mark.parametrize('locked_element', ['x0', 'y0', 'x1', 'y1'])
|
|
def test_lockable_bbox(locked_element):
|
|
other_elements = ['x0', 'y0', 'x1', 'y1']
|
|
other_elements.remove(locked_element)
|
|
|
|
orig = mtransforms.Bbox.unit()
|
|
locked = mtransforms.LockableBbox(orig, **{locked_element: 2})
|
|
|
|
# LockableBbox should keep its locked element as specified in __init__.
|
|
assert getattr(locked, locked_element) == 2
|
|
assert getattr(locked, 'locked_' + locked_element) == 2
|
|
for elem in other_elements:
|
|
assert getattr(locked, elem) == getattr(orig, elem)
|
|
|
|
# Changing underlying Bbox should update everything but locked element.
|
|
orig.set_points(orig.get_points() + 10)
|
|
assert getattr(locked, locked_element) == 2
|
|
assert getattr(locked, 'locked_' + locked_element) == 2
|
|
for elem in other_elements:
|
|
assert getattr(locked, elem) == getattr(orig, elem)
|
|
|
|
# Unlocking element should revert values back to the underlying Bbox.
|
|
setattr(locked, 'locked_' + locked_element, None)
|
|
assert getattr(locked, 'locked_' + locked_element) is None
|
|
assert np.all(orig.get_points() == locked.get_points())
|
|
|
|
# Relocking an element should change its value, but not others.
|
|
setattr(locked, 'locked_' + locked_element, 3)
|
|
assert getattr(locked, locked_element) == 3
|
|
assert getattr(locked, 'locked_' + locked_element) == 3
|
|
for elem in other_elements:
|
|
assert getattr(locked, elem) == getattr(orig, elem)
|
|
|
|
|
|
def test_copy():
|
|
a = mtransforms.Affine2D()
|
|
b = mtransforms.Affine2D()
|
|
s = a + b
|
|
# Updating a dependee should invalidate a copy of the dependent.
|
|
s.get_matrix() # resolve it.
|
|
s1 = copy.copy(s)
|
|
assert not s._invalid and not s1._invalid
|
|
a.translate(1, 2)
|
|
assert s._invalid and s1._invalid
|
|
assert (s1.get_matrix() == a.get_matrix()).all()
|
|
# Updating a copy of a dependee shouldn't invalidate a dependent.
|
|
s.get_matrix() # resolve it.
|
|
b1 = copy.copy(b)
|
|
b1.translate(3, 4)
|
|
assert not s._invalid
|
|
assert (s.get_matrix() == a.get_matrix()).all()
|
|
|
|
|
|
def test_deepcopy():
|
|
a = mtransforms.Affine2D()
|
|
b = mtransforms.Affine2D()
|
|
s = a + b
|
|
# Updating a dependee shouldn't invalidate a deepcopy of the dependent.
|
|
s.get_matrix() # resolve it.
|
|
s1 = copy.deepcopy(s)
|
|
assert not s._invalid and not s1._invalid
|
|
a.translate(1, 2)
|
|
assert s._invalid and not s1._invalid
|
|
assert (s1.get_matrix() == mtransforms.Affine2D().get_matrix()).all()
|
|
# Updating a deepcopy of a dependee shouldn't invalidate a dependent.
|
|
s.get_matrix() # resolve it.
|
|
b1 = copy.deepcopy(b)
|
|
b1.translate(3, 4)
|
|
assert not s._invalid
|
|
assert (s.get_matrix() == a.get_matrix()).all()
|