1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
|
"""
.. _mesh_quality_example:
Computing Mesh Quality
~~~~~~~~~~~~~~~~~~~~~~
Leverage powerful VTK algorithms for computing mesh quality.
Here we will use the :func:`~pyvista.DataObjectFilters.cell_quality` filter
to compute the cell qualities. The following quality measures are available
for various cell types:
.. include:: /api/core/cell_quality/cell_quality_measures_table.rst
"""
# sphinx_gallery_thumbnail_number = 2
from __future__ import annotations
import pyvista as pv
from pyvista import examples
# %%
# Triangle Cell Quality
# ---------------------
# Load a :class:`~pyvista.PolyData` mesh and :meth:`~pyvista.PolyDataFilters.decimate`
# it to show coarse :attr:`~pyvista.CellType.TRIANGLE` cells for the example.
# Here we use :meth:`~pyvista.examples.downloads.download_cow`.
mesh = examples.download_cow().triangulate().decimate(0.7)
# %%
# Compute some valid measures for triangle cells.
measures = ['area', 'shape', 'min_angle', 'max_angle']
qual = mesh.cell_quality(measures)
# %%
# Plot the meshes in subplots for comparison. We define a custom method
# for adding each mesh to each subplot.
def add_mesh(plotter, mesh, *, scalars=None, cmap='bwr', show_edges=True):
# Create a copy to avoid reusing the same mesh in different plots
copied = mesh.copy(deep=False)
plotter.add_mesh(copied, scalars=scalars, cmap=cmap, show_edges=show_edges)
plotter.view_xy()
pl = pv.Plotter(shape=(2, 2))
pl.link_views()
pl.subplot(0, 0)
add_mesh(pl, qual, scalars=measures[0])
pl.subplot(0, 1)
add_mesh(pl, qual, scalars=measures[1])
pl.subplot(1, 0)
add_mesh(pl, qual, scalars=measures[2])
pl.subplot(1, 1)
add_mesh(pl, qual, scalars=measures[3])
pl.show()
# %%
# Visualize Acceptable Range
# ==========================
# The previous plots show the full range of cell quality values present in the mesh.
# However, it may be more useful to show the `acceptable` range of values instead.
# Get the acceptable range for the ``shape`` quality measure using
# :func:`~pyvista.cell_quality_info`.
info = pv.cell_quality_info('TRIANGLE', 'shape')
print(info)
# %%
# Plot the shape quality measure again but this time we color the cells based on
# the acceptable range for the measure. Cells outside of this range are saturated
# as blue or red and may be considered to be "poor" quality cells.
qual.plot(
scalars='shape',
clim=info.acceptable_range,
cmap='bwr',
below_color='blue',
above_color='red',
cpos='xy',
zoom=1.5,
show_axes=False,
)
# %%
# Use :meth:`~pyvista.DataSetFilters.extract_values` to extract the "poor" quality
# cells outside the acceptable range.
unacceptable = qual.extract_values(scalars='shape', ranges=info.acceptable_range, invert=True)
# %%
# Plot the unacceptable cells along with the original mesh as wireframe for context.
pl = pv.Plotter()
pl.add_mesh(mesh, style='wireframe', color='light gray')
pl.add_mesh(unacceptable, color='lime')
pl.view_xy()
pl.camera.zoom(1.5)
pl.show()
# %%
# Tetrahedron Cell Quality
# ------------------------
# Load a mesh with :attr:`~pyvista.CellType.TETRA` cells. Here we use
# :meth:`~pyvista.examples.downloads.download_letter_a`.
mesh = examples.download_letter_a()
# %%
# Plot some valid quality measures for tetrahedral cells.
measures = ['volume', 'collapse_ratio', 'jacobian', 'scaled_jacobian']
qual = mesh.cell_quality(measures)
pl = pv.Plotter(shape=(2, 2))
pl.link_views()
pl.subplot(0, 0)
add_mesh(pl, qual, scalars=measures[0])
pl.subplot(0, 1)
add_mesh(pl, qual, scalars=measures[1])
pl.subplot(1, 0)
add_mesh(pl, qual, scalars=measures[2])
pl.subplot(1, 1)
add_mesh(pl, qual, scalars=measures[3])
pl.show()
# %%
# .. tags:: filter
|
