Slam is an open source python package dedicated to the representation of neuroanatomical surfaces stemming from MRI data in the form of triangular meshes and to their processing and analysis. Slam is an extension of Trimesh, an open source python package dedicated to triangular meshes processing.
Look at the doc for a complete overview of available features!
io
: read/write gifti (and nifti) file format
generate_parametric_surfaces
: generation of parametric surfaces with random sampling
geodesics
: geodesic distance computation using tvb-gdist and networkx
differential_geometry
: several implementations of graph Laplacian (conformal, authalic, FEM...), texture Gradient
mapping
: several types of mapping between the mesh and a sphere, a disc...
distortion
: distortion measures between two meshes, for quantitative analysis of mapping properties
remeshing
: projection of vertex-level information between two meshes based on their spherical representation
topology
: mesh surgery (boundary indentification, large hole closing)
vertex_voronoi
: compute the voronoi of each vertex of a mesh, usefull for numerous applications
texture
: a class to manage properly vertex-level information.
plot
: extension of pyglet and visbrain viewers to visualize slam objects
The very first thing to do before contributing is to read our Code of conduct.
We are using a github project to organize the code development and maintenance: https://github.com/orgs/brain-slam/projects/1
If you are interested in contributing, please first have a look at it and contact us by creating a new issue.
conda create -q -n slam python=3.8
conda activate slam
This creates an empty conda virtual environment with Python 3.8 and basic packages (e.g. pip, setuptools) and make it the default python environment.
git clone https://github.com/<username>/slam
pip install -e .['dev']
git remote add upstream https://github.com/brain-slam/slam.git
You are now ready to modify slam code and submit a pull request
These dependencies, whether mandatory or optional, are managed automatically and transparently for the user during the installation phase and are listed here for the sake of completeness.
In order to work fine, slam requires:
a Python 3.8 installation
setuptools
pip
numpy
scipy
cython
trimesh
nibabel
tvb-gdist is recommended for geodesic distance/shortest paths computations
All contributions are of course much welcome! In addition to the global thank you to all contributors to this project, a special big thanks to:
. https://github.com/alexpron and https://github.com/davidmeunier79 for their precious help for setting up continuous integration tools.
. https://github.com/EtienneCmb for his help regarding visualization and Visbrain (https://github.com/EtienneCmb/visbrain).
. https://github.com/aymanesouani for his implementation of a very nice curvature estimation technique.
. https://github.com/Anthys for implementing the curvature decomposition and many unitests
. to be continued...
Thanks goes to these wonderful people (emoji key):
alexpron 🚧 📆 🤔 💻 |
JulienLefevreMars 💻 📖 💡 🤔 ⚠️ |
Tianqi SONG 💻 💡 🤔 |
Etienne Combrisson 💻 🔧 |
This project follows the all-contributors specification. Contributions of any kind welcome!