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
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
|
"""
.. _connectivity_example:
Connectivity
~~~~~~~~~~~~
This example highlights some applications of the :func:`~pyvista.DataSetFilters.connectivity`
filter.
"""
# %%
# Remove Noisy Isosurfaces
# ~~~~~~~~~~~~~~~~~~~~~~~~
#
# Use connectivity to remove noisy isosurfaces.
#
# This section is similar to `this VTK example <https://kitware.github.io/vtk-examples/site/Python/VisualizationAlgorithms/PineRootConnectivity/>`__.
# sphinx_gallery_thumbnail_number = 2
from __future__ import annotations
import numpy as np
import pyvista as pv
from pyvista import examples
# %%
# Load a dataset with noisy isosurfaces.
pine_roots = examples.download_pine_roots()
# Plot the raw data
cpos = [(40.6018, -280.533, 47.0172), (40.6018, 37.2813, 50.1953), (0.0, 0.0, 1.0)]
p = pv.Plotter()
p.add_mesh(pine_roots, color='#965434')
p.add_mesh(pine_roots.outline())
p.show(cpos=cpos)
# %%
# The plotted mesh is very noisy. We can extract the largest connected
# isosurface using the ``'largest'`` ``extraction_mode`` of the
# :func:`~pyvista.DataSetFilters.connectivity` filter. Equivalently,
# :func:`~pyvista.DataSetFilters.extract_largest` may also be used.
# Grab the largest connected volume present
largest = pine_roots.connectivity('largest')
# or: largest = mesh.extract_largest()
p = pv.Plotter()
p.add_mesh(largest, color='#965434')
p.add_mesh(pine_roots.outline())
p.camera_position = cpos
p.show()
# %%
# Extract Small Regions
# ~~~~~~~~~~~~~~~~~~~~~
#
# Use connectivity to extract the smaller 'noisy' regions that were
# removed in the remove noisy isosurfaces example above.
#
# First, get a list of all region ids.
all_regions = pine_roots.connectivity('all')
region_ids = np.unique(all_regions['RegionId'])
# %%
# Since the region IDs are sorted in descending order (by cell count),
# we can extract all regions *except* for the largest one using the
# ``'specified'`` ``extraction_mode`` of the :func:`~pyvista.DataSetFilters.connectivity`
# filter.
noise_region_ids = region_ids[1::] # All region ids except '0'
noise = pine_roots.connectivity('specified', noise_region_ids)
# %%
# Plot the noisy regions. For context, also plot the largest region.
p = pv.Plotter()
p.add_mesh(noise, cmap='glasbey', categories=True)
p.add_mesh(largest, color='lightgray', opacity=0.1)
p.add_mesh(pine_roots.outline())
p.camera_position = cpos
p.show()
# %%
# Label Disconnected Regions
# ~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# Use connectivity to label all disconnected regions.
#
# This section is similar to `this VTK example <https://examples.vtk.org/site/Cxx/PolyData/ColorDisconnectedRegionsDemo/>`__.
#
# First, load a dataset with disconnected regions.
mesh = examples.download_foot_bones()
# %%
# Extract all regions.
conn = mesh.connectivity('all')
# %%
# Plot the labeled regions.
# Format scalar bar text for integer values.
scalar_bar_args = dict(
fmt='%.f',
)
cpos = [(10.5, 12.2, 18.3), (0.0, 0.0, 0.0), (0.0, 1.0, 0.0)]
conn.plot(
categories=True,
cmap='glasbey',
scalar_bar_args=scalar_bar_args,
cpos=cpos,
)
# %%
# Extract Regions From Seed Points
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# Use connectivity to extract regions of interest using scalar data and
# seed points.
#
# First, create a dataset with salient features. Here, we create hills
# and use curvature to define its peaks and valleys.
mesh = pv.ParametricRandomHills()
mesh['Curvature'] = mesh.curvature()
# %%
# Visualize the peaks and valleys.
# Peaks have large positive curvature (i.e. are convex).
# Valleys have large negative curvature (i.e. are concave).
# Flat regions have curvature close to zero.
mesh.plot(
clim=[-0.5, 0.5],
cmap='bwr',
below_color='blue',
above_color='red',
)
# %%
# Extract a region of interest using the
# ``'point_seed'`` ``extraction_mode`` of the :func:`~pyvista.DataSetFilters.connectivity`
# filter. Let's extract the steepest peak using a seed point where the
# curvature is maximized.
# Get seed point
peak_point_id = np.argmax(mesh['Curvature'])
# Define a scalar range of the region to extract
data_min, data_max = mesh.get_data_range()
peak_range = [0.2, data_max] # Peak if curvature > 0.2
peak_mesh = mesh.connectivity('point_seed', peak_point_id, scalar_range=peak_range)
# %%
# Let's also extract the closest valley to the steepest peak using the
# ``'closest'`` ``extraction_mode`` of the :func:`~pyvista.DataSetFilters.connectivity`
# filter.
valley_range = [data_min, -0.2] # Valley if curvature < -0.2
peak_point = mesh.points[peak_point_id]
valley_mesh = mesh.connectivity('closest', peak_point, scalar_range=valley_range)
# %%
# Plot extracted regions.
p = pv.Plotter()
_ = p.add_mesh(mesh, style='wireframe', color='lightgray')
_ = p.add_mesh(peak_mesh, color='red', label='Steepest Peak')
_ = p.add_mesh(valley_mesh, color='blue', label='Closest Valley')
_ = p.add_legend()
p.show()
# %%
# .. tags:: filter
|
