lapy 0.6.0

A package for differential geometry on meshes (Laplace, FEM)

LaPy

LaPy is a package to compute spectral features (Laplace-Beltrami operator) on tetrahedral and triangle meshes. It is written purely in python 3 without sacrificing speed as almost all loops are vectorized, drawing upon efficient and sparse mesh data structures. It is basically a port of the C++ ShapeDNA project with extended differential geometry capabilities.

Contents:

• TriaMesh: a class for triangle meshes offering various operations, such as fixing orientation, smoothing, curvature, boundary, quality, normals, and various efficient mesh datastructure (edges, adjacency matrices)
• TetMesh: a class for tetrahedral meshes (orientation, boundary ...)
• TriaIO, TetIO: for both tets and trias from off, vtk, etc. formats
• FuncIO: import/export vertex functions and eigenvector files
• Solver: a class for linear FEM computation (Laplace stiffness and mass matrix, fast and sparse eigenvalue solver, anisotropic Laplace, Poisson)
• DiffGeo: compute gradients, divergence, mean curvature flow, etc.
• Heat: for heat kernel and diffusion
• ShapeDNA: compute the ShapeDNA descriptor of surfaces and solids
• Plot: functions for interactive visualization (wrapping plotly)

Usage:

The LaPy package is a comprehensive collection of scripts, so we refer to the 'help' function and docstring of each module / function / class for usage info. For example:

import lapy as lp
help(lp.TriaMesh)
help(lp.Solver)

In the examples subdirectory, we provide several Jupyter notebooks that illustrate prototypical use cases of the toolbox.

Installation:

Use the following code to download, build and install a package from this repository into your local Python package directory:

python3 -m pip install lapy

Use the following code to install the dev package in editable mode to a location of your choice:

python3 -m pip install --user --src /my/preferred/location --editable git+https://github.com/Deep-MI/Lapy.git#egg=lapy

Several functions, e.g. the Solver, require a sparse matrix decomposition, for which either the LU decomposition (from scipy sparse) or the faster Cholesky decomposition (from scikit-sparse cholmod) can be used. The default is to use the LU decomposition. If the parameter flag use_cholmod is True, the code will try to import cholmod from the scikit-sparse package. If this fails, an error will be thrown. If you would like to use cholmod, you need to install scikit-sparse separately. It cannot be listed among LaPy's dependencies as that causes errors with pip. scikit-sparse requires numpy and scipy to be installed separately beforehand.

References:

If you use this software for a publication please cite both these papers:

[1] Laplace-Beltrami spectra as 'Shape-DNA' of surfaces and solids. Reuter M, Wolter F-E, Peinecke N. Computer-Aided Design. 2006;38(4):342-366. http://dx.doi.org/10.1016/j.cad.2005.10.011

[2] BrainPrint: a discriminative characterization of brain morphology. Wachinger C, Golland P, Kremen W, Fischl B, Reuter M. Neuroimage. 2015;109:232-48. http://dx.doi.org/10.1016/j.neuroimage.2015.01.032 http://www.ncbi.nlm.nih.gov/pubmed/25613439

[1] introduces the FEM methods and the Laplace spectra for shape analysis, while [2] focusses on medical applications.

For Geodesics please cite:

[3] Crane K, Weischedel C, Wardetzky M. Geodesics in heat: A new approach to computing distance based on heat flow. ACM Transactions on Graphics. https://doi.org/10.1145/2516971.2516977

For non-singular mean curvature flow please cite:

[4] Kazhdan M, Solomon J, Ben-Chen M. 2012. Can Mean-Curvature Flow be Modified to be Non-singular? Comput. Graph. Forum 31, 5, 1745–1754. https://doi.org/10.1111/j.1467-8659.2012.03179.x

For conformal mapping please cite:

[5] Choi PT, Lam KC, Lui LM. FLASH: Fast Landmark Aligned Spherical Harmonic Parameterization for Genus-0 Closed Brain Surfaces. SIAM Journal on Imaging Sciences, vol. 8, no. 1, pp. 67-94, 2015. https://doi.org/10.1137/130950008

We also invite you to check out our lab webpage at https://deep-mi.org