dti = DTI(gpu=True) dti.fit(dataset.dwi, bvals=dataset.bval, bvecs=dataset.bvec) fa_map = dti.fa() tvis.plot_volume(fa_map, cmap='viridis') TWK Lausanne ships a Ray‑based distributed executor . Example for scaling across a Kubernetes cluster:
# ------------------------------------------------- # 3. Fit a GLM (event‑related design) # ------------------------------------------------- design = tio.load_events(bids_root, task='nback') glm = tstat.GLM() glm.fit(func_clean, design) twk lausanne download
pipeline_json = preproc.to_json() tvis.save_dashboard(pipeline_json, out="my_analysis.json") 6.1. GPU‑Accelerated Diffusion Tensor Imaging from twk.diffusion import DTI, cuda_enabled dti = DTI(gpu=True) dti
singularity pull docker://epfl/twk-lausanne:2.0 singularity exec twk-lausanne_2.0.sif twk-dashboard These containers embed all optional dependencies (CUDA, neuroimaging libraries, JupyterLab) and are . 4.4. Source Code (Git) If you prefer to develop on the bleeding edge: GPU‑Accelerated Diffusion Tensor Imaging from twk
| Domain | Typical Use‑Cases | |--------|-------------------| | | Pre‑processing, statistical modelling, and visualisation of MRI, fMRI, and diffusion data. | | Computational Neuroscience | Large‑scale network simulations, dynamic causal modelling, and brain‑computer‑interface prototyping. | | Data‑Science & Machine Learning | Pipelines for feature extraction, classification, and clustering of high‑dimensional neuro‑datasets. | | Education & Training | Interactive notebooks, tutorials, and teaching modules for graduate‑level courses in brain science. |