def test_orientation():
path = pypathlib.ClosedPath([[0.1, 1.0], [1.1, 1.1], [1.0, 0.0], [0.0, 0.0]])
ref = 1.045
assert abs(path.area - ref) < 1.0e-12 * ref
assert not path.positive_orientation
assert all(path.is_convex_node)
def test_closest_points():
path = pypathlib.ClosedPath(
[[0.0, 0.0], [1.0, 0.0], [0.9, 0.5], [1.0, 1.0], [0.0, 1.0]]
)
closest_points = path.closest_points(
[
[0.2, 0.1],
[0.5, 0.5],
[1.0, 0.5 + 1.0e-12],
[0.0, 1.1],
[-0.1, 1.1],
[1.0, 1.0],
]
)
ref = np.array(
[
[0.2, 0.0],
[0.9, 0.5],
[9.0384615384615385e-01, 5.1923076923076927e-01],
[0.0, 1.0],
[0.0, 1.0],
[1.0, 1.0],
]
)
assert np.all(np.abs(closest_points - ref) < 1.0e-12)
def test_concave_counterclock():
path = pypathlib.ClosedPath(
[[0.1, 1.0], [1.1, 1.1], [0.9, 0.5], [1.0, 0.0], [0.0, 0.0]]
)
ref = 0.965
assert abs(path.area - ref) < 1.0e-12 * ref
assert not path.positive_orientation
assert np.array_equal(path.is_convex_node, [True, True, False, True, True])
def test_inside():
path = pypathlib.ClosedPath(
[[0.0, 0.0], [1.0, 0.0], [0.9, 0.5], [1.0, 1.0], [0.0, 1.0]]
)
contains_points = path.contains_points(
[[0.2, 0.1], [0.5, 0.5], [1.0, 0.5], [0.0, 1.1], [-0.1, 1.1], [1.0, 1.0]]
)
assert numpy.array_equal(contains_points, [True, True, False, False, False, True])
def test_distance():
path = pypathlib.ClosedPath(
[[0.0, 0.0], [1.0, 0.0], [0.9, 0.5], [1.0, 1.0], [0.0, 1.0]]
)
dist = path.distance(
[[0.2, 0.1], [0.5, 0.5], [1.0, 0.5], [0.0, 1.1], [-0.1, 1.1], [1.0, 1.0]]
)
ref = numpy.array([0.1, 0.4, numpy.sqrt(1.0 / 104.0), 0.1, numpy.sqrt(2) / 10, 0.0])
assert numpy.all(numpy.abs(dist - ref) < 1.0e-12)
def test_squared_distance():
path = pypathlib.ClosedPath(
[[0.0, 0.0], [1.0, 0.0], [0.9, 0.5], [1.0, 1.0], [0.0, 1.0]]
)
dist = path.squared_distance(
[[0.2, 0.1], [0.5, 0.5], [1.0, 0.5], [0.0, 1.1], [-0.1, 1.1], [1.0, 1.0]]
)
ref = numpy.array([0.01, 0.16, 1.0 / 104.0, 0.01, 0.02, 0.0])
assert numpy.all(numpy.abs(dist - ref) < 1.0e-12)
def test_signed_distance():
path = pypathlib.ClosedPath(
[[0.0, 0.0], [1.0, 0.0], [0.9, 0.5], [1.0, 1.0], [0.0, 1.0]]
)
dist = path.signed_distance(
[[0.2, 0.1], [0.5, 0.5], [1.0, 0.5], [0.0, 1.1], [-0.1, 1.1], [1.0, 1.0]]
)
print(dist)
ref = np.array([-0.1, -0.4, np.sqrt(1.0 / 104.0), 0.1, np.sqrt(2) / 10, 0.0])
assert np.all(np.abs(dist - ref) < 1.0e-12)
def test_sharp_angle():
path = pypathlib.ClosedPath(
[
[0.0, 0.0],
[1.0, 0.0],
[1.0, 0.45],
[0.6, 0.5],
[1.0, 0.55],
[1.0, 1.0],
[0.0, 1.0],
]
)
contains_points = path.contains_points([[0.5, 0.4], [0.5, 0.6]])
assert numpy.all(contains_points)
dist = path.signed_squared_distance([[0.5, 0.4], [0.5, 0.6]])
ref = numpy.array([-0.02, -0.02])
assert numpy.all(numpy.abs(dist - ref) < 1.0e-12)
def test_project_distance():
path = pypathlib.ClosedPath(
[
[0.0, 0.0],
[1.5, 0.4],
[1.0, 1.0],
]
)
closest_points = path.closest_points(
[
[0.5, 0.1],
[0.5, 0.2],
[0.5, 0.3],
[0.5, 0.4],
[0.5, 0.5],
]
)
# closest_points = np.array([4.9170124481327798e-01, 1.3112033195020747e-01])
# closest_points = np.array([4.9170124481327804e-01, 1.3112033195020747e-01])
# the projected point should be _on_ the polygon
dist = path.distance(closest_points)
assert np.all(dist < 1.0e-12)
def test_speed(n=3):
path_pts = [[0, 0], [0, 1], [1, 1], [1, 0]]
path0 = path.Path(path_pts)
path1 = pypathlib.ClosedPath(path_pts)
def _mpl_path(pts):
return path0.contains_points(pts)
def _pypathlib_contains_points(pts):
return path1.contains_points(pts)
numpy.random.seed(0)
perfplot.show(
setup=lambda n: numpy.random.rand(n, 2),
kernels=[_mpl_path, _pypathlib_contains_points],
n_range=[2 ** k for k in range(n)],
labels=["matplotlib.path.contains_points", "pypathlib.contains_points"],
logx=True,
logy=True,
xlabel="num points",
)
return
def test_show():
path = pypathlib.ClosedPath([[0.0, 0.0], [1.0, 0.0], [1.1, 1.1], [0.1, 1.0]])
path.show()
def benchmark():
path_pts = [[0, 0], [0, 1], [1, 1], [1, 0]]
path1 = pypathlib.ClosedPath(path_pts)
pts = np.random.rand(5000000, 2)
path1.contains_points(pts)
