| Name | lapy JSON |
| Version |
1.1.1
JSON |
| download |
| home_page | None |
| Summary | A package for differential geometry on meshes (Laplace, FEM) |
| upload_time | 2024-08-30 19:26:44 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2020 Deep Medical Imaging Lab (PI Reuter) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
python
laplace
fem
shapedna
brainprint
triangle mesh
tetrahedra mesh
geodesics in heat
mean curvature flow
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://pypi.org/project/lapy/)
# LaPy
LaPy is an open-source Python package for differential geometry on triangle
and tetrahedra meshes. It includes an FEM solver to estimate the Laplace,
Poisson or Heat equations. Further functionality includes the computations
of gradients, divergence, mean-curvature flow, conformal mappings,
geodesics, ShapeDNA (Laplace spectra), and IO and plotting methods.
LaPy is written purely in Python 3 without sacrificing speed as almost all
loops are vectorized, drawing upon efficient and sparse mesh data structures.
## Contents:
- **TriaMesh**: a class for triangle meshes offering various operations, such as
fixing orientation, smoothing, curvature, boundary, quality, normals, and
various efficient mesh datastructures (edges, adjacency matrices). IO from
OFF, VTK and other formats.
- **TetMesh**: a class for tetrahedral meshes (orientation, boundary, IO ...)
- **Solver**: a class for linear FEM computation (Laplace stiffness and mass
matrix, fast and sparse eigenvalue solver, anisotropic Laplace, Poisson)
- **io**: module for IO of vertex functions and eigenvector files
- **diffgeo**: module for gradients, divergence, mean curvature flow, etc.
- **heat**: module for heat kernel and diffusion
- **shapedna**: module for the ShapeDNA descriptor of surfaces and solids
- **plot**: module for interactive visualizations (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 install the latest release of LaPy 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, default) or the faster Cholesky decomposition (from scikit-sparse cholmod, recommended) can be used. 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, as pip currently cannot install it (conda can). scikit-sparse requires numpy and scipy to be installed separately beforehand.
## API Documentation
The API Documentation can be found at https://deep-mi.org/LaPy .
## 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
Shape-DNA [1] introduces the FEM methods and the Laplace spectra for shape analysis, while BrainPrint [2] focusses on medical applications.
For Geodesics please also 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 invite you to check out our lab webpage at https://deep-mi.org
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"description": "[](https://pypi.org/project/lapy/)\n# LaPy\n\nLaPy is an open-source Python package for differential geometry on triangle\nand tetrahedra meshes. It includes an FEM solver to estimate the Laplace,\nPoisson or Heat equations. Further functionality includes the computations\nof gradients, divergence, mean-curvature flow, conformal mappings, \ngeodesics, ShapeDNA (Laplace spectra), and IO and plotting methods. \n\nLaPy is written purely in Python 3 without sacrificing speed as almost all\nloops are vectorized, drawing upon efficient and sparse mesh data structures.\n\n## Contents:\n\n- **TriaMesh**: a class for triangle meshes offering various operations, such as\n fixing orientation, smoothing, curvature, boundary, quality, normals, and\n various efficient mesh datastructures (edges, adjacency matrices). IO from\n OFF, VTK and other formats.\n- **TetMesh**: a class for tetrahedral meshes (orientation, boundary, IO ...)\n- **Solver**: a class for linear FEM computation (Laplace stiffness and mass\n matrix, fast and sparse eigenvalue solver, anisotropic Laplace, Poisson)\n- **io**: module for IO of vertex functions and eigenvector files\n- **diffgeo**: module for gradients, divergence, mean curvature flow, etc.\n- **heat**: module for heat kernel and diffusion\n- **shapedna**: module for the ShapeDNA descriptor of surfaces and solids\n- **plot**: module for interactive visualizations (wrapping plotly)\n\n## Usage:\n\nThe LaPy package is a comprehensive collection of scripts, so we refer to the\n'help' function and docstring of each module / function / class for usage info.\nFor example:\n\n```\nimport lapy as lp\nhelp(lp.TriaMesh)\nhelp(lp.Solver)\n```\n\nIn the `examples` subdirectory, we provide several Jupyter notebooks that\nillustrate prototypical use cases of the toolbox.\n\n## Installation:\n\nUse the following code to install the latest release of LaPy into your local\nPython package directory:\n\n`python3 -m pip install lapy`\n\nUse the following code to install the dev package in editable mode to a location of\nyour choice:\n\n`python3 -m pip install --user --src /my/preferred/location --editable git+https://github.com/Deep-MI/Lapy.git#egg=lapy`\n\nSeveral functions, e.g. the Solver, require a sparse matrix decomposition, for which either the LU decomposition (from scipy sparse, default) or the faster Cholesky decomposition (from scikit-sparse cholmod, recommended) can be used. 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, as pip currently cannot install it (conda can). scikit-sparse requires numpy and scipy to be installed separately beforehand.\n\n## API Documentation\n\nThe API Documentation can be found at https://deep-mi.org/LaPy .\n\n## References:\n\nIf you use this software for a publication please cite both these papers:\n\n**[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\n\n**[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\n\nShape-DNA [1] introduces the FEM methods and the Laplace spectra for shape analysis, while BrainPrint [2] focusses on medical applications.\n\nFor Geodesics please also cite:\n\n[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\n\nFor non-singular mean curvature flow please cite:\n\n[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\u20131754.\nhttps://doi.org/10.1111/j.1467-8659.2012.03179.x\n\nFor conformal mapping please cite:\n\n[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\n\nWe invite you to check out our lab webpage at https://deep-mi.org\n",
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