Surface anaLysis And Modeling (Slam)

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.

Main Features

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

Prerequisites

We highly recommend to rely on a (conda) virtual environment as provided by miniconda. See miniconda installation instructions if you do not already have one. Then create a virtual environment by typing the following lines in a terminal:

  conda create -q -n slam python=3.6
  conda activate slam

This creates an empty conda virtual environment with Python 3.6 and basic packages (e.g. pip, setuptools) and make it the default python environment.

User installation

Clone the slam Github repository
```
git clone https://github.com/gauzias
```
Move to slam folder and type the following command in terminal
```
 pip install .['full'] 
```
Try example scripts located in examples folder

Contributing to slam code

Installation

Create an account on Github if you do not already have one
Sign in Github and fork the slam Github repository
Clone your personal slam fork in your current local directory
```
git clone https://github.com/<username>/slam
```
Perform a full slam installation in editable mode
```
 pip install -e .['full']
```

Set upstream repository to keep your clone up-to-date

 git remote add upstream https://github.com/gauzias/slam.git

You are now ready to modify slam code and submit a pull request

Dependencies

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.