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STYLE: Use np.array instead of np.r_ and import Poly3DCollection

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Josh Warner (Mac)
2013-09-02 15:31:51 -05:00
parent 1174d6fbc5
commit abc7a16a80
2 changed files with 56 additions and 56 deletions
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doc/examples/plot_marching_cubes.py
+3 -3
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@@ -23,7 +23,7 @@ voxel spacing is not equal for every spatial dimension, through use of the
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import mpl_toolkits.mplot3d as a3
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from skimage import measure
from skimage.draw import ellipsoid
@@ -41,8 +41,8 @@ verts, faces = measure.marching_cubes(ellip_double, 0)
fig = plt.figure(figsize=(10, 12))
ax = fig.add_subplot(111, projection='3d')
# Fancy indexing: `verts[faces]` to generate a Poly3DCollection
mesh = a3.art3d.Poly3DCollection(verts[faces])
# Fancy indexing: `verts[faces]` to generate a collection of triangles
mesh = Poly3DCollection(verts[faces])
ax.add_collection3d(mesh)
ax.set_xlabel("x-axis: a = 6 per ellipsoid")
skimage/draw/tests/test_draw3d.py
+53 -53
View File
@@ -1,5 +1,5 @@
from numpy.testing import assert_array_equal
import numpy as np
from numpy.testing import assert_array_equal, assert_allclose
from skimage.draw import ellipsoid, ellipsoid_stats
@@ -9,35 +9,35 @@ def test_ellipsoid_bool():
test_anisotropic = ellipsoid(2, 2, 4, sampling=(1., 1., 2.))
test_anisotropic = test_anisotropic[1:-1, 1:-1, 1:-1]
expected = np.r_[[[[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]],
expected = np.array([[[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]],
[[0, 0, 1, 0, 0],
[0, 1, 1, 1, 0],
[1, 1, 1, 1, 1],
[0, 1, 1, 1, 0],
[0, 0, 1, 0, 0]],
[[0, 0, 1, 0, 0],
[0, 1, 1, 1, 0],
[1, 1, 1, 1, 1],
[0, 1, 1, 1, 0],
[0, 0, 1, 0, 0]],
[[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]]]
[[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]])
assert_array_equal(test, expected.astype(bool))
assert_array_equal(test_anisotropic, expected.astype(bool))
@@ -49,38 +49,38 @@ def test_ellipsoid_levelset():
levelset=True)
test_anisotropic = test_anisotropic[1:-1, 1:-1, 1:-1]
expected = np.r_[[[[ 2. , 1.25, 1. , 1.25, 2. ],
[ 1.25, 0.5 , 0.25, 0.5 , 1.25],
[ 1. , 0.25, 0. , 0.25, 1. ],
[ 1.25, 0.5 , 0.25, 0.5 , 1.25],
[ 2. , 1.25, 1. , 1.25, 2. ]],
expected = np.array([[[ 2. , 1.25, 1. , 1.25, 2. ],
[ 1.25, 0.5 , 0.25, 0.5 , 1.25],
[ 1. , 0.25, 0. , 0.25, 1. ],
[ 1.25, 0.5 , 0.25, 0.5 , 1.25],
[ 2. , 1.25, 1. , 1.25, 2. ]],
[[ 1.25, 0.5 , 0.25, 0.5 , 1.25],
[ 0.5 , -0.25, -0.5 , -0.25, 0.5 ],
[ 0.25, -0.5 , -0.75, -0.5 , 0.25],
[ 0.5 , -0.25, -0.5 , -0.25, 0.5 ],
[ 1.25, 0.5 , 0.25, 0.5 , 1.25]],
[[ 1.25, 0.5 , 0.25, 0.5 , 1.25],
[ 0.5 , -0.25, -0.5 , -0.25, 0.5 ],
[ 0.25, -0.5 , -0.75, -0.5 , 0.25],
[ 0.5 , -0.25, -0.5 , -0.25, 0.5 ],
[ 1.25, 0.5 , 0.25, 0.5 , 1.25]],
[[ 1. , 0.25, 0. , 0.25, 1. ],
[ 0.25, -0.5 , -0.75, -0.5 , 0.25],
[ 0. , -0.75, -1. , -0.75, 0. ],
[ 0.25, -0.5 , -0.75, -0.5 , 0.25],
[ 1. , 0.25, 0. , 0.25, 1. ]],
[[ 1. , 0.25, 0. , 0.25, 1. ],
[ 0.25, -0.5 , -0.75, -0.5 , 0.25],
[ 0. , -0.75, -1. , -0.75, 0. ],
[ 0.25, -0.5 , -0.75, -0.5 , 0.25],
[ 1. , 0.25, 0. , 0.25, 1. ]],
[[ 1.25, 0.5 , 0.25, 0.5 , 1.25],
[ 0.5 , -0.25, -0.5 , -0.25, 0.5 ],
[ 0.25, -0.5 , -0.75, -0.5 , 0.25],
[ 0.5 , -0.25, -0.5 , -0.25, 0.5 ],
[ 1.25, 0.5 , 0.25, 0.5 , 1.25]],
[[ 1.25, 0.5 , 0.25, 0.5 , 1.25],
[ 0.5 , -0.25, -0.5 , -0.25, 0.5 ],
[ 0.25, -0.5 , -0.75, -0.5 , 0.25],
[ 0.5 , -0.25, -0.5 , -0.25, 0.5 ],
[ 1.25, 0.5 , 0.25, 0.5 , 1.25]],
[[ 2. , 1.25, 1. , 1.25, 2. ],
[ 1.25, 0.5 , 0.25, 0.5 , 1.25],
[ 1. , 0.25, 0. , 0.25, 1. ],
[ 1.25, 0.5 , 0.25, 0.5 , 1.25],
[ 2. , 1.25, 1. , 1.25, 2. ]]]]
[[ 2. , 1.25, 1. , 1.25, 2. ],
[ 1.25, 0.5 , 0.25, 0.5 , 1.25],
[ 1. , 0.25, 0. , 0.25, 1. ],
[ 1.25, 0.5 , 0.25, 0.5 , 1.25],
[ 2. , 1.25, 1. , 1.25, 2. ]]])
assert_array_equal(test, expected)
assert_array_equal(test_anisotropic, expected)
assert_allclose(test, expected)
assert_allclose(test_anisotropic, expected)
def test_ellipsoid_stats():