Twk Lausanne Download ((hot)) May 2026

| 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. |

# ------------------------------------------------- # 2. Preprocess functional runs # ------------------------------------------------- preproc = tpre.Pipeline() preproc.add_step('realign', reference='mean') preproc.add_step('slice_time_correction', method='interleaved') preproc.add_step('denoise', method='ica_aroma') func_clean = preproc.apply(dataset.func)

The name Lausanne reflects both the geographic origin and the project’s commitment to the . 3. Core Architecture 3.1. Modules | Module | Description | Key Dependencies | |--------|-------------|-------------------| | twk.io | Unified I/O handling (BIDS, NIfTI, DICOM, HDF5). | nibabel, pydicom | | twk.preproc | Pre‑processing pipelines (realignment, slice‑timing, denoising). | Nilearn, scikit‑image | | twk.stats | Classical (GLM) and Bayesian statistical tools. | statsmodels, pymc3 | | twk.ml | Machine‑learning wrappers (feature selection, model evaluation). | scikit‑learn, torch, tensorflow | | twk.vis | Interactive visualisation (3‑D brain surfaces, connectomes). | plotly, pyvista | | twk.sim | Neural‑network simulation (spiking, rate‑based). | Brian2, NEST | | twk.dashboard | Web‑based GUI built on Dash for workflow orchestration. | dash, flask | twk lausanne download

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:

# Activate the environment conda activate twk-lausanne | | Education & Training | Interactive notebooks,

python -m pip install "twk-lausanne[cuda]" Pre‑built images are published on Docker Hub:

# ------------------------------------------------- # 3. Fit a GLM (event‑related design) # ------------------------------------------------- design = tio.load_events(bids_root, task='nback') glm = tstat.GLM() glm.fit(func_clean, design) flask | dti = DTI(gpu=True) dti.fit(dataset.dwi

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: