Merge pull request #403 from K3D-tools/devel

docs + tests review
K3D-tools · Dec 18, 2022 · fa53000 · fa53000
2 parents 61d7652 + 6a584d1
commit fa53000
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diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -11,11 +11,10 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
 import os
+import shutil
 import sys
 import time
 from source.k3d_directives.plot import K3D_Plot
-import shutil
-import json
 
 sys.path.insert(0, os.path.abspath('../..'))
 
@@ -29,12 +28,8 @@
 # The full version, including alpha/beta/rc tags
 here = os.path.dirname(__file__)
 repo = os.path.join(here, '..', '..')
-_version_py = os.path.join(repo, 'package.json')
 version_ns = {}
 
-with open(_version_py) as f:
-    version = json.load(f)["version"]
-
 # -- General configuration ---------------------------------------------------
 
 # Add any Sphinx extension module names here, as strings. They can be
@@ -65,7 +60,6 @@
 # This pattern also affects html_static_path and html_extra_path.
 exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
 
-
 # -- Options for HTML output -------------------------------------------------
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for

diff --git a/docs/source/gallery/api/thumbnails/marching_cubes_sinus_cube_thumbnail.py b/docs/source/gallery/api/thumbnails/marching_cubes_sinus_cube_thumbnail.py
@@ -1,14 +1,16 @@
-import k3d
 import numpy as np
-from numpy import sin
+
+import k3d
 from k3d.headless import k3d_remote, get_headless_driver
 
+sin = np.sin
+
 
 def generate():
     t = np.linspace(-5, 5, 100, dtype=np.float32)
     x, y, z = np.meshgrid(t, t, t, indexing='ij')
 
-    scalars = sin(x*y + x*z + y*z) + sin(x*y) + sin(y*z) + sin(x*z) - 1
+    scalars = sin(x * y + x * z + y * z) + sin(x * y) + sin(y * z) + sin(x * z) - 1
 
     marching = k3d.marching_cubes(scalars, level=0.0,
                                   color=0x0e2763,

diff --git a/docs/source/gallery/showcase/plots/lorenz_system_plot.py b/docs/source/gallery/showcase/plots/lorenz_system_plot.py
@@ -1,10 +1,11 @@
-import k3d
 import numpy as np
+
+import k3d
 from k3d.colormaps import paraview_color_maps
 
 
 def generate():
-    def lorenz(x, y, z, s=10, r=28, b=8/3):
+    def lorenz(x, y, z, s=10, r=28, b=8 / 3):
         x_dot = s * (y - x)
         y_dot = r * x - y - x * z
         z_dot = x * y - b * z

diff --git a/docs/source/gallery/showcase/plots/menger_sponge_plot.py b/docs/source/gallery/showcase/plots/menger_sponge_plot.py
@@ -1,6 +1,7 @@
-import k3d
 import numpy as np
 
+import k3d
+
 
 def generate():
     def iterate(length, x, y, z):

diff --git a/docs/source/gallery/showcase/plots/orbits_plot.py b/docs/source/gallery/showcase/plots/orbits_plot.py
@@ -1,5 +1,6 @@
-import k3d
 import numpy as np
+
+import k3d
 from k3d.colormaps import paraview_color_maps
 
 
@@ -9,90 +10,90 @@ def generate():
 
     bodies_count = 40
 
-    bodies = np.array([[0.0000000e+00,  0.0000000e+00,  0.0000000e+00,  0.0000000e+00,
-                        0.0000000e+00,  0.0000000e+00,  1.0000000e+06],
-                      [-4.9679345e-01,  2.6570296e-01,  3.4227931e-01, -2.2651915e-02,
-                       -2.5265724e-03, -9.3185175e-03,  6.3181415e+02],
-                      [3.7021518e-02, -3.3532095e-01, -1.3006118e-01,  3.8169832e-03,
-                       -1.7639263e-02,  1.7560443e-02,  1.2228839e+03],
-                      [3.2473081e-01, -3.8958952e-01,  3.6065096e-01, -1.6591853e-02,
-                       2.2979354e-02,  1.5027565e-02,  1.1379092e+03],
-                      [1.9925886e-01, -4.3243438e-02,  2.3790616e-01, -2.4283929e-02,
-                       2.2036737e-02,  1.5822455e-02,  7.3010986e+02],
-                      [-4.4143024e-01, -7.1139961e-02,  1.7319494e-01, -1.1633939e-02,
-                       5.0242455e-03, -5.0497951e-04,  6.7444598e+02],
-                      [7.8688264e-02, -1.7087817e-01, -1.5081182e-01, -2.4320260e-03,
-                       7.0646438e-03, -1.7999319e-02,  9.7800610e+02],
-                      [-5.9594750e-02, -7.2615296e-02,  4.3256253e-01, -1.0310315e-02,
-                       -1.0246425e-02,  1.0331392e-02,  5.5932916e+02],
-                      [-2.2181371e-01,  1.2450159e-02, -3.5403877e-02,  2.4978900e-02,
-                       -9.8985955e-03, -1.0434589e-02,  1.2175408e+03],
-                      [2.1475327e-01,  4.1201133e-01, -3.1412601e-02, -1.1963538e-02,
-                       1.0723472e-03, -3.5631433e-03,  1.1424460e+03],
-                      [4.2195350e-01,  3.7973666e-01, -1.8134981e-02, -1.9921964e-02,
-                       -7.1983635e-03,  7.6964349e-03,  1.4089318e+03],
-                      [2.1617496e-01, -1.9794500e-01, -8.7558389e-02, -1.6835531e-02,
-                       -1.5182316e-02, -1.3301859e-02,  5.5457037e+02],
-                      [-4.1794181e-02, -1.9101912e-01,  1.8947464e-01,  1.4356637e-02,
-                       -1.5364028e-02,  1.2453362e-02,  1.0287977e+03],
-                      [2.2971267e-01, -2.8791130e-03, -2.1326435e-01, -7.8020454e-03,
-                       2.5177689e-03, -9.7344117e-03,  1.0474579e+03],
-                      [2.6785791e-02,  1.6838884e-01,  2.1399486e-01,  2.4175057e-02,
-                       2.4402628e-02, -1.3445861e-02,  1.0599727e+03],
-                      [3.1909162e-01,  3.1714398e-01,  2.0338118e-02, -2.4140883e-02,
-                       1.5961260e-02, -2.0101408e-02,  9.4045221e+02],
-                      [-3.3931443e-01, -2.2721624e-01,  9.7239554e-02,  3.6300363e-03,
-                       -1.9278442e-02,  1.9151056e-02,  6.7030249e+02],
-                      [6.6103995e-02, -4.8539740e-01,  1.7521322e-01,  1.8680961e-03,
-                       9.0755373e-03,  1.3850155e-02,  9.4479297e+02],
-                      [-1.6933033e-01,  5.7348192e-02,  2.0398575e-01, -1.0792896e-02,
-                       -1.6141150e-02,  8.3085243e-03,  1.4722698e+03],
-                      [3.5467851e-01,  2.5269783e-01, -4.7175428e-01, -2.1373475e-02,
-                       4.0807934e-03,  2.4820814e-02,  8.4746240e+02],
-                      [-2.7038407e-01, -4.8006400e-01, -3.2617545e-01, -2.1090398e-02,
-                       1.7032122e-02,  1.7789362e-02,  6.2347046e+02],
-                      [-4.8136929e-01, -3.3214843e-01, -1.8433192e-01, -8.0333194e-03,
-                       -1.6524477e-02,  6.1281892e-03,  9.0508881e+02],
-                      [5.4457009e-02,  1.7607331e-02,  6.7196190e-02, -1.7219210e-02,
-                       1.9105632e-02,  2.4653585e-03,  5.7763861e+02],
-                      [-1.3687915e-01,  8.4126353e-02, -1.3019252e-01, -1.8430030e-02,
-                       2.0881360e-02, -2.4473562e-03,  5.5547150e+02],
-                      [1.0026491e-01,  2.7884889e-01, -3.1183958e-03,  6.0113161e-03,
-                       -1.4223135e-02, -1.8654644e-02,  5.5057245e+02],
-                      [2.2953439e-01, -2.0373765e-01,  1.9156224e-01, -3.1318248e-03,
-                       -2.5028258e-03,  8.5311802e-03,  6.9054175e+02],
-                      [-4.9273950e-01,  2.5772792e-01,  4.5453101e-01, -1.1930334e-02,
-                       -1.0238136e-02,  3.0950904e-03,  5.3005304e+02],
-                      [3.5183370e-02,  4.1218692e-01, -3.8023493e-01,  1.6193924e-02,
-                       8.6651091e-03, -5.8665574e-03,  8.8095959e+02],
-                      [-3.0682594e-01, -1.1159393e-01,  3.8180280e-01, -1.8235832e-02,
-                       2.0600855e-02, -5.5592731e-03,  1.2845226e+03],
-                      [1.3181090e-01, -1.3926461e-01,  3.5556364e-01,  1.8209113e-02,
-                       -7.5311526e-03, -2.0307558e-02,  9.4135608e+02],
-                      [2.9862744e-01,  2.6593310e-01,  8.9805841e-02,  2.3497565e-02,
-                       -2.3358732e-03,  6.1519952e-03,  6.3521173e+02],
-                      [1.8002808e-02, -3.1908777e-01, -1.4509407e-01,  1.7548909e-02,
-                       1.0903591e-02, -2.2118099e-02,  1.2389504e+03],
-                      [-4.0650040e-02, -2.2764242e-01, -7.5831264e-02, -5.7892441e-03,
-                       3.0585080e-03, -2.3829598e-02,  1.1236339e+03],
-                      [-2.4570310e-01,  2.0286041e-01,  1.2090033e-01, -1.4608744e-02,
-                       1.2567866e-02, -2.4784094e-02,  9.3537274e+02],
-                      [3.9212245e-01, -1.8226206e-02, -3.4510234e-01,  2.7927459e-04,
-                       -1.9444963e-02,  6.7013353e-03,  6.1505707e+02],
-                      [-4.4596395e-01, -4.6554554e-01, -3.3184126e-01,  1.9790804e-02,
-                       -2.9474200e-04,  1.0445378e-02,  6.7410992e+02],
-                      [-4.7785598e-01, -4.4018179e-01,  3.1628680e-01,  3.2713532e-03,
-                       1.2704453e-02,  1.4309299e-02,  7.2144165e+02],
-                      [1.7605799e-01,  4.6154976e-01,  3.6913526e-01, -2.0895386e-02,
-                       -2.3292074e-02,  4.9127610e-03,  6.6594043e+02],
-                      [1.8955290e-02, -3.2265896e-01,  4.2756569e-01, -1.1221913e-02,
-                       -2.0262299e-02,  1.1423305e-02,  9.2574188e+02],
-                      [1.0150486e-01, -2.8043854e-01, -3.0282909e-01,  1.2297285e-02,
-                       1.9709280e-02,  1.1570200e-02,  1.3365149e+03]]).astype(np.float32)
+    bodies = np.array([[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
+                        0.0000000e+00, 0.0000000e+00, 1.0000000e+06],
+                       [-4.9679345e-01, 2.6570296e-01, 3.4227931e-01, -2.2651915e-02,
+                        -2.5265724e-03, -9.3185175e-03, 6.3181415e+02],
+                       [3.7021518e-02, -3.3532095e-01, -1.3006118e-01, 3.8169832e-03,
+                        -1.7639263e-02, 1.7560443e-02, 1.2228839e+03],
+                       [3.2473081e-01, -3.8958952e-01, 3.6065096e-01, -1.6591853e-02,
+                        2.2979354e-02, 1.5027565e-02, 1.1379092e+03],
+                       [1.9925886e-01, -4.3243438e-02, 2.3790616e-01, -2.4283929e-02,
+                        2.2036737e-02, 1.5822455e-02, 7.3010986e+02],
+                       [-4.4143024e-01, -7.1139961e-02, 1.7319494e-01, -1.1633939e-02,
+                        5.0242455e-03, -5.0497951e-04, 6.7444598e+02],
+                       [7.8688264e-02, -1.7087817e-01, -1.5081182e-01, -2.4320260e-03,
+                        7.0646438e-03, -1.7999319e-02, 9.7800610e+02],
+                       [-5.9594750e-02, -7.2615296e-02, 4.3256253e-01, -1.0310315e-02,
+                        -1.0246425e-02, 1.0331392e-02, 5.5932916e+02],
+                       [-2.2181371e-01, 1.2450159e-02, -3.5403877e-02, 2.4978900e-02,
+                        -9.8985955e-03, -1.0434589e-02, 1.2175408e+03],
+                       [2.1475327e-01, 4.1201133e-01, -3.1412601e-02, -1.1963538e-02,
+                        1.0723472e-03, -3.5631433e-03, 1.1424460e+03],
+                       [4.2195350e-01, 3.7973666e-01, -1.8134981e-02, -1.9921964e-02,
+                        -7.1983635e-03, 7.6964349e-03, 1.4089318e+03],
+                       [2.1617496e-01, -1.9794500e-01, -8.7558389e-02, -1.6835531e-02,
+                        -1.5182316e-02, -1.3301859e-02, 5.5457037e+02],
+                       [-4.1794181e-02, -1.9101912e-01, 1.8947464e-01, 1.4356637e-02,
+                        -1.5364028e-02, 1.2453362e-02, 1.0287977e+03],
+                       [2.2971267e-01, -2.8791130e-03, -2.1326435e-01, -7.8020454e-03,
+                        2.5177689e-03, -9.7344117e-03, 1.0474579e+03],
+                       [2.6785791e-02, 1.6838884e-01, 2.1399486e-01, 2.4175057e-02,
+                        2.4402628e-02, -1.3445861e-02, 1.0599727e+03],
+                       [3.1909162e-01, 3.1714398e-01, 2.0338118e-02, -2.4140883e-02,
+                        1.5961260e-02, -2.0101408e-02, 9.4045221e+02],
+                       [-3.3931443e-01, -2.2721624e-01, 9.7239554e-02, 3.6300363e-03,
+                        -1.9278442e-02, 1.9151056e-02, 6.7030249e+02],
+                       [6.6103995e-02, -4.8539740e-01, 1.7521322e-01, 1.8680961e-03,
+                        9.0755373e-03, 1.3850155e-02, 9.4479297e+02],
+                       [-1.6933033e-01, 5.7348192e-02, 2.0398575e-01, -1.0792896e-02,
+                        -1.6141150e-02, 8.3085243e-03, 1.4722698e+03],
+                       [3.5467851e-01, 2.5269783e-01, -4.7175428e-01, -2.1373475e-02,
+                        4.0807934e-03, 2.4820814e-02, 8.4746240e+02],
+                       [-2.7038407e-01, -4.8006400e-01, -3.2617545e-01, -2.1090398e-02,
+                        1.7032122e-02, 1.7789362e-02, 6.2347046e+02],
+                       [-4.8136929e-01, -3.3214843e-01, -1.8433192e-01, -8.0333194e-03,
+                        -1.6524477e-02, 6.1281892e-03, 9.0508881e+02],
+                       [5.4457009e-02, 1.7607331e-02, 6.7196190e-02, -1.7219210e-02,
+                        1.9105632e-02, 2.4653585e-03, 5.7763861e+02],
+                       [-1.3687915e-01, 8.4126353e-02, -1.3019252e-01, -1.8430030e-02,
+                        2.0881360e-02, -2.4473562e-03, 5.5547150e+02],
+                       [1.0026491e-01, 2.7884889e-01, -3.1183958e-03, 6.0113161e-03,
+                        -1.4223135e-02, -1.8654644e-02, 5.5057245e+02],
+                       [2.2953439e-01, -2.0373765e-01, 1.9156224e-01, -3.1318248e-03,
+                        -2.5028258e-03, 8.5311802e-03, 6.9054175e+02],
+                       [-4.9273950e-01, 2.5772792e-01, 4.5453101e-01, -1.1930334e-02,
+                        -1.0238136e-02, 3.0950904e-03, 5.3005304e+02],
+                       [3.5183370e-02, 4.1218692e-01, -3.8023493e-01, 1.6193924e-02,
+                        8.6651091e-03, -5.8665574e-03, 8.8095959e+02],
+                       [-3.0682594e-01, -1.1159393e-01, 3.8180280e-01, -1.8235832e-02,
+                        2.0600855e-02, -5.5592731e-03, 1.2845226e+03],
+                       [1.3181090e-01, -1.3926461e-01, 3.5556364e-01, 1.8209113e-02,
+                        -7.5311526e-03, -2.0307558e-02, 9.4135608e+02],
+                       [2.9862744e-01, 2.6593310e-01, 8.9805841e-02, 2.3497565e-02,
+                        -2.3358732e-03, 6.1519952e-03, 6.3521173e+02],
+                       [1.8002808e-02, -3.1908777e-01, -1.4509407e-01, 1.7548909e-02,
+                        1.0903591e-02, -2.2118099e-02, 1.2389504e+03],
+                       [-4.0650040e-02, -2.2764242e-01, -7.5831264e-02, -5.7892441e-03,
+                        3.0585080e-03, -2.3829598e-02, 1.1236339e+03],
+                       [-2.4570310e-01, 2.0286041e-01, 1.2090033e-01, -1.4608744e-02,
+                        1.2567866e-02, -2.4784094e-02, 9.3537274e+02],
+                       [3.9212245e-01, -1.8226206e-02, -3.4510234e-01, 2.7927459e-04,
+                        -1.9444963e-02, 6.7013353e-03, 6.1505707e+02],
+                       [-4.4596395e-01, -4.6554554e-01, -3.3184126e-01, 1.9790804e-02,
+                        -2.9474200e-04, 1.0445378e-02, 6.7410992e+02],
+                       [-4.7785598e-01, -4.4018179e-01, 3.1628680e-01, 3.2713532e-03,
+                        1.2704453e-02, 1.4309299e-02, 7.2144165e+02],
+                       [1.7605799e-01, 4.6154976e-01, 3.6913526e-01, -2.0895386e-02,
+                        -2.3292074e-02, 4.9127610e-03, 6.6594043e+02],
+                       [1.8955290e-02, -3.2265896e-01, 4.2756569e-01, -1.1221913e-02,
+                        -2.0262299e-02, 1.1423305e-02, 9.2574188e+02],
+                       [1.0150486e-01, -2.8043854e-01, -3.0282909e-01, 1.2297285e-02,
+                        1.9709280e-02, 1.1570200e-02, 1.3365149e+03]]).astype(np.float32)
 
     for i in range(1, bodies_count):
         bodies[i, 0:3] = (
-            bodies[i, 0:3] / np.linalg.norm(bodies[i, 0:3])) * 0.5
+                                 bodies[i, 0:3] / np.linalg.norm(bodies[i, 0:3])) * 0.5
 
     points = k3d.points(bodies[:, 0:3],
                         point_size=0.03,

diff --git a/docs/source/gallery/showcase/plots/point_cloud_plot.py b/docs/source/gallery/showcase/plots/point_cloud_plot.py
@@ -1,7 +1,7 @@
+import numpy as np
 import os
 
 import k3d
-import numpy as np
 
 
 def generate():

diff --git a/docs/source/gallery/showcase/plots/segmentation_plot.py b/docs/source/gallery/showcase/plots/segmentation_plot.py
@@ -1,7 +1,7 @@
+import numpy as np
 import os
 
 import k3d
-import numpy as np
 
 
 def generate():

diff --git a/docs/source/gallery/showcase/plots/streamlines_plot.py b/docs/source/gallery/showcase/plots/streamlines_plot.py
@@ -1,8 +1,7 @@
+import numpy as np
 import os
 
 import k3d
-import numpy as np
-from k3d import matplotlib_color_maps
 
 
 def generate():
@@ -19,7 +18,7 @@ def generate():
     plt_streamlines = k3d.line(lines,
                                width=0.00007,
                                attribute=v,
-                               color_map=matplotlib_color_maps.Inferno,
+                               color_map=k3d.matplotlib_color_maps.Inferno,
                                color_range=[0, 0.5],
                                shader='mesh')
 

diff --git a/docs/source/gallery/showcase/plots/turbine_cfd_plot.py b/docs/source/gallery/showcase/plots/turbine_cfd_plot.py
@@ -1,11 +1,8 @@
+import numpy as np
 import os
+import pyvista as pv
 
 import k3d
-import numpy as np
-import pyvista as pv
-from k3d.colormaps import paraview_color_maps
-from k3d.helpers import map_colors
-from numpy.linalg import norm
 
 
 def generate():
@@ -30,8 +27,8 @@ def generate():
     plot += plt_vectors
 
     plot.camera = [3.0792, 14.6017, -8.8171,
-                  -0.9959, 0.5287, -0.2337,
-                  1, 0, 0]
+                   -0.9959, 0.5287, -0.2337,
+                   1, 0, 0]
 
     plot.snapshot_type = 'inline'
     return plot.get_snapshot()
diff --git a/docs/source/gallery/showcase/plots/vtk_cutter_plot.py b/docs/source/gallery/showcase/plots/vtk_cutter_plot.py
@@ -1,11 +1,10 @@
+import numpy as np
 import os
+import vtk
 
 import k3d
-import numpy as np
-import vtk
-from ipywidgets import FloatSlider, interact
-from k3d.colormaps import paraview_color_maps
-from vtk.util import numpy_support
+
+numpy_support = vtk.util.numpy_support
 
 
 def generate():
@@ -59,10 +58,8 @@ def get_mesh_data(poly_data, color_attribute=('Umag', 0.0, 0.1)):
         if color_attribute is not None:
             attribute = numpy_support.vtk_to_numpy(
                 poly_data.GetPointData().GetArray(color_attribute[0]))
-            color_range = color_attribute[1:3]
         else:
             attribute = []
-            color_range = []
 
         vertices = numpy_support.vtk_to_numpy(poly_data.GetPoints().GetData())
         indices = numpy_support.vtk_to_numpy(
@@ -72,12 +69,6 @@ def get_mesh_data(poly_data, color_attribute=('Umag', 0.0, 0.1)):
                 np.array(indices, np.uint32),
                 np.array(attribute, np.float32))
 
-    def clipping_plane_to_vtkPlane(clipping_plane):
-        vtk_n = -np.array(clipping_plane[:3])
-        vtk_o = clipping_plane[3] * vtk_n
-
-        return (vtk_o, vtk_n)
-
     vtk_n = np.array([0., .3, 0.])
     vtk_o = np.array([0.04984861, 20.03934663, 0.04888905])
 
@@ -89,7 +80,7 @@ def clipping_plane_to_vtkPlane(clipping_plane):
             vtk_o, vtk_n
         ),
         color_attribute=('Umag', 0.0, 0.32),
-        color_map=paraview_color_maps.Cool_to_Warm,
+        color_map=k3d.paraview_color_maps.Cool_to_Warm,
         side='double')
 
     plt_vtk.flat_shading = True

diff --git a/docs/source/gallery/showcase/thumbnails/lorenz_system_thumbnail.py b/docs/source/gallery/showcase/thumbnails/lorenz_system_thumbnail.py
@@ -1,11 +1,12 @@
-import k3d
 import numpy as np
+
+import k3d
 from k3d.colormaps import paraview_color_maps
 from k3d.headless import k3d_remote, get_headless_driver
 
 
 def generate():
-    def lorenz(x, y, z, s=10, r=28, b=8/3):
+    def lorenz(x, y, z, s=10, r=28, b=8 / 3):
         x_dot = s * (y - x)
         y_dot = r * x - y - x * z
         z_dot = x * y - b * z
@@ -36,7 +37,7 @@ def lorenz(x, y, z, s=10, r=28, b=8/3):
                     label_color=0xf0f0f0,
                     grid_visible=False,
                     menu_visibility=False,
-                    axes_helper = 0)
+                    axes_helper=0)
     plot += plt_line
 
     plot.camera = [10.1646, -36.8108, -0.4144,