Download [portable]: Twk Lausanne

# ------------------------------------------------- # 4. Threshold and visualise the contrast # ------------------------------------------------- contrast = glm.contrast('2back > 0back') thresholded = tstat.threshold(contrast, p=0.05, method='fdr') tvis.plot_brain(thresholded, surface='fsaverage', cmap='cold_hot') The same pipeline can be that the web dashboard can execute without writing any code:

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: # ------------------------------------------------- # 4