Compare Field Across Mesh Regions#

Here is some velocity data from a glacier modelling simulation that is compared across nodes in the simulation. We have simplified the mesh to have the simulation node value already on the mesh.

This was originally posted to pyvista/pyvista-support#83.

The modeling results are courtesy of Urruty Benoit and are from the Elmer/Ice simulation software.

from __future__ import annotations

import numpy as np



import pyvista as pv
from pyvista import examples

# Load the sample data
mesh = examples.download_antarctica_velocity()
mesh["magnitude"] = np.linalg.norm(mesh["ssavelocity"], axis=1)
mesh
HeaderData Arrays
PolyDataInformation
N Cells1106948
N Points557470
N Strips0
X Bounds-2.506e+06, 2.743e+06
Y Bounds-2.143e+06, 2.240e+06
Z Bounds0.000e+00, 0.000e+00
N Arrays3
NameFieldTypeN CompMinMax
ssavelocityPointsfloat643-4.341e+039.677e+03
node_valuePointsint6410.000e+002.300e+01
magnitudePointsfloat6416.649e-031.013e+04


Here is a helper to extract regions of the mesh based on the simulation node.

def extract_node(node):
    idx = mesh["node_value"] == node
    return mesh.extract_points(idx)
p = pv.Plotter()
p.add_mesh(mesh, scalars="node_value")
for node in np.unique(mesh["node_value"]):
    loc = extract_node(node).center
    p.add_point_labels(loc, [f"Node {node}"])
p.show(cpos="xy")
antarctica compare
vel_dargs = dict(scalars="magnitude", clim=[1e-3, 1e4], cmap='Blues', log_scale=True)

mesh.plot(cpos="xy", **vel_dargs)
antarctica compare
a = extract_node(12)
b = extract_node(20)
pl = pv.Plotter()
pl.add_mesh(a, **vel_dargs)
pl.add_mesh(b, **vel_dargs)
pl.show(cpos='xy')
antarctica compare

plot vectors without mesh

pl = pv.Plotter()
pl.add_mesh(a.glyph(orient="ssavelocity", factor=20), **vel_dargs)
pl.add_mesh(b.glyph(orient="ssavelocity", factor=20), **vel_dargs)
pl.camera_position = [
    (-1114684.6969340036, 293863.65389149904, 752186.603224546),
    (-1114684.6969340036, 293863.65389149904, 0.0),
    (0.0, 1.0, 0.0),
]
pl.show()
antarctica compare

Compare directions. Normalize them so we can get a reasonable direction comparison.

flow_a = a.point_data['ssavelocity'].copy()
flow_a /= np.linalg.norm(flow_a, axis=1).reshape(-1, 1)
flow_b = b.point_data['ssavelocity'].copy()
flow_b /= np.linalg.norm(flow_b, axis=1).reshape(-1, 1)


# plot normalized vectors
pl = pv.Plotter()
pl.add_arrows(a.points, flow_a, mag=10000, color='b', label='flow_a')
pl.add_arrows(b.points, flow_b, mag=10000, color='r', label='flow_b')
pl.add_legend()
pl.camera_position = [
    (-1044239.3240694795, 354805.0268606294, 484178.24825854995),
    (-1044239.3240694795, 354805.0268606294, 0.0),
    (0.0, 1.0, 0.0),
]
pl.show()
antarctica compare

flow_a that agrees with the mean flow path of flow_b

agree = flow_a.dot(flow_b.mean(0))

pl = pv.Plotter()
pl.add_mesh(a, scalars=agree, cmap='bwr', scalar_bar_args={'title': 'Flow agreement with block b'})
pl.add_mesh(b, color='w')
pl.show(cpos='xy')
antarctica compare
agree = flow_b.dot(flow_a.mean(0))

pl = pv.Plotter()
pl.add_mesh(a, color='w')
pl.add_mesh(b, scalars=agree, cmap='bwr', scalar_bar_args={'title': 'Flow agreement with block a'})
pl.show(cpos='xy')
antarctica compare

Total running time of the script: (0 minutes 30.646 seconds)

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