Skip to main content
Join the official 2020 Python Developers SurveyStart the survey!

Mesh optimization/smoothing

Project description

optimesh

CircleCI codecov Code style: black smooth PyPi Version GitHub stars

Several mesh smoothing/optimization methods with one simple interface. optimesh

  • is fast,
  • preserves submeshes,
  • only works for triangular meshes (for now; upvote this issue if you're interested in tetrahedral mesh smoothing), and
  • supports all mesh formats that meshio can handle.

Install with

pip3 install optimesh --user

Example call:

optimesh in.e out.vtk --method cvt-uniform-lloyd -n 50

Output: terminal-screenshot

The left hand-side graph shows the distribution of angles (the grid line is at the optimal 60 degrees). The right hand-side graph shows the distribution of simplex quality, where quality is twice the ratio of circumcircle and incircle radius.

All command-line options are documented at

optimesh -h

disk-step0

The following examples show the various algorithms at work, all starting from the same randomly generated disk mesh above. The cell coloring indicates quality; dark blue is bad, yellow is good.

CVT (centroidal Voronoi tesselation)

cvt-uniform-lloyd2 cvt-uniform-qnb cvt-uniform-qnf-09
uniform-density relaxed Lloyd's algorithm (--method lloyd --omega 2.0) uniform-density quasi-Newton iteration (block-diagonal Hessian, --method cvt-uniform-qnb) uniform-density quasi-Newton iteration (full Hessian, --method cvt-uniform-qnf --omega 0.9)

Centroidal Voronoi tessellation smoothing (Du et al.) is one of the oldest and most reliable approaches. optimesh provides classical Lloyd smoothing as well as several variants that provide faster convergence.

The method cvt-uniform-qnf provides updates closest to the actual Newton updates, but is unstable. Set the relaxation parameter --omega to someting smaller than 1 to stabilize.

CPT (centroidal patch tesselation)

cpt-cp cpt-uniform-fp cpt-uniform-qn
density-preserving linear solve (Laplacian smoothing, --method cpt-dp) uniform-density fixed-point iteration (--method cpt-uniform-fp) uniform-density quasi-Newton (--method cpt-uniform-qn)

A smoothing method suggested by Chen and Holst, mimicking CVT but much more easily implemented. The density-preserving variant leads to the exact same equation system as Laplacian smoothing, so CPT smoothing can be thought of as a generalization.

The uniform-density variants are implemented classically as a fixed-point iteration and as a quasi-Newton method. The latter typically converges faster.

ODT (optimal Delaunay tesselation)

odt-dp-fp odt-uniform-fp odt-uniform-bfgs
density-preserving fixed-point iteration (--method odt-dp-fp) uniform-density fixed-point iteration (--method odt-uniform-fp) uniform-density BFGS (--method odt-uniform-bfgs)

Optimal Delaunay Triangulation (ODT) as suggested by Chen and Holst. Typically superior to CPT, but also more expensive to compute.

Implemented once classically as a fixed-point iteration, once as a nonlinear optimization method. The latter typically leads to better results.

Which method is best?

From practical experiments, it seems that the CVT smoothing variants, e.g.,

optimesh in.vtk out.vtk -m cvt-uniform-qnf --omega 0.9 -n 50

give very satisfactory results. Here is a comparison of all uniform-density methods applied to the random circle mesh seen above:

(Mesh quality is twice the ratio of incircle and circumcircle radius, with the maximum being 1.)

Access from Python

All optimesh functions can also be accessed from Python directly, for example:

import optimesh

X, cells = optimesh.odt.fixed_point_uniform(X, cells, 1.0e-2, 100, verbosity=1)

Installation

optimesh is available from the Python Package Index, so simply do

pip3 install --upgrade --user optimesh

to install or upgrade.

Relevant publications

Testing

To run the optimesh unit tests, check out this repository and type

pytest

License

optimesh is published under the MIT license.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for optimesh, version 0.4.5
Filename, size File type Python version Upload date Hashes
Filename, size optimesh-0.4.5-py2.py3-none-any.whl (23.3 kB) File type Wheel Python version py2.py3 Upload date Hashes View
Filename, size optimesh-0.4.5.tar.gz (22.3 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page