This example highlights some applications of the connectivity() filter.

Remove Noisy Isosurfaces#

Use connectivity to remove noisy isosurfaces.

This section is similar to this VTK example.

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')

The plotted mesh is very noisy. We can extract the largest connected isosurface using the 'largest' extraction_mode of the connectivity() filter. Equivalently, 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.camera_position = cpos

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 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.camera_position = cpos

Label Disconnected Regions#

Use connectivity to label all disconnected regions.

This section is similar to this VTK example.

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(

cpos = [(10.5, 12.2, 18.3), (0.0, 0.0, 0.0), (0.0, 1.0, 0.0)]


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.

    clim=[-0.5, 0.5],

Extract a region of interest using the 'point_seed' extraction_mode of the 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 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()

Total running time of the script: (0 minutes 9.338 seconds)

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