JIGSAW is an unstructured mesh generator and tessellation library; designed to generate high-quality triangulations and polyhedral decompositions of general planar, surface and volumetric domains. JIGSAW includes refinement-based algorithms for the construction of new meshes, optimisation-driven techniques for the improvement of existing grids, as well as routines to assemble (restricted) Delaunay tessellations, Voronoi complexes and Power diagrams.
This package provides a Python based scripting interface to the underlying JIGSAW mesh generator, including a range of additional facilities for file I/O, mesh visualisation and post-processing operations.
JIGSAW has been compiled and tested on various 64-bit Linux , Windows and Mac based platforms.
Ensure you have a c++ compiler and the cmake utility installed.
Clone/download + unpack this repository.
python3 setup.py build_external
python3 setup.py install
python3 example.py --IDnumber=0
Note: installation of JIGSAW requires a c++ compiler and the cmake utility. JIGSAW may also be installed as a conda package. See here for details.
See jigsawpy for a description of the various functions available.
setup.py - compile and install JIGSAW's c++ backend using cmake.
example.py - a list of demo programs.
jigsaw.py - cmd-line interface to JIGSAW's backend
libsaw.py - api-lib. interface to JIGSAW's backend
loadmsh.py - load *.msh files.
savemsh.py - save *.msh files.
loadjig.py - load *.jig files.
savejig.py - save *.jig files.
project.py - apply cartographic projection operators to mesh obj.
bisect.py - refine a mesh obj. via bisection.
extrude.py - create a mesh obj. via extrusion.
The following set of example problems are available in example.py:
example: 0; # simple 2-dim. examples to get started
example: 1; # simple 3-dim. examples to get started
example: 2; # frontal-delaunay methods in the plane
example: 3; # frontal-delaunay methods for surfaces
example: 4; # frontal-delaunay methods for volumes
example: 5; # user-defined mesh-spacing constraints
example: 6; # dealing with sharp-features in piecewise smooth domains
example: 7; # dealing with sharp-features in piecewise smooth domains
example: 8; # (re)mesh marching-cubes style outputs
example: 9; # creating prismatic volumes via extrusion
Run python3 example.py --IDnumber=N to call the N-th example. *.vtk output is saved to ../cache and can be visualised with, for example, Paraview.
This program may be freely redistributed under the condition that the copyright notices (including this entire header) are not removed, and no compensation is received through use of the software. Private, research, and institutional use is free. You may distribute modified versions of this code UNDER THE CONDITION THAT THIS CODE AND ANY MODIFICATIONS MADE TO IT IN THE SAME FILE REMAIN UNDER COPYRIGHT OF THE ORIGINAL AUTHOR, BOTH SOURCE AND OBJECT CODE ARE MADE FREELY AVAILABLE WITHOUT CHARGE, AND CLEAR NOTICE IS GIVEN OF THE MODIFICATIONS. Distribution of this code as part of a commercial system is permissible ONLY BY DIRECT ARRANGEMENT WITH THE AUTHOR. (If you are not directly supplying this code to a customer, and you are instead telling them how they can obtain it for free, then you are not required to make any arrangement with me.)
DISCLAIMER: Neither I nor THE CONTRIBUTORS warrant this code in any way whatsoever. This code is provided "as-is" to be used at your own risk.
THE CONTRIBUTORS include:
(a) The University of Sydney
(b) The Massachusetts Institute of Technology
(c) Columbia University
(d) The National Aeronautics & Space Administration
(e) Los Alamos National Laboratory
There are a number of publications that describe the algorithms used in JIGSAW in detail. If you make use of JIGSAW in your work, please consider including a reference to the following:
[1] - Darren Engwirda: Generalised primal-dual grids for unstructured co-volume schemes, J. Comp. Phys., 375, pp. 155-176, https://doi.org/10.1016/j.jcp.2018.07.025, 2018.
[2] - Darren Engwirda, Conforming Restricted Delaunay Mesh Generation for Piecewise Smooth Complexes, Procedia Engineering, 163, pp. 84-96, https://doi.org/10.1016/j.proeng.2016.11.024, 2016.
[3] - Darren Engwirda, Voronoi-based Point-placement for Three-dimensional Delaunay-refinement, Procedia Engineering, 124, pp. 330-342, http://dx.doi.org/10.1016/j.proeng.2015.10.143, 2015.
[4] - Darren Engwirda, David Ivers, Off-centre Steiner points for Delaunay-refinement on curved surfaces, Computer-Aided Design, 72, pp. 157-171, http://dx.doi.org/10.1016/j.cad.2015.10.007, 2016.
[5] - Darren Engwirda, Locally-optimal Delaunay-refinement and optimisation-based mesh generation, Ph.D. Thesis, School of Mathematics and Statistics, The University of Sydney, http://hdl.handle.net/2123/13148, 2014.
