Twk Lausanne Download !!link!! May 2026

import twk.io as tio import twk.preproc as tpre import twk.stats as tstat import twk.vis as tvis

# Verify CUDA availability assert cuda_enabled(), "CUDA not found – install the twk-lausanne[cuda] extra." 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 import twk

# ------------------------------------------------- # 3. Fit a GLM (event‑related design) # ------------------------------------------------- design = tio.load_events(bids_root, task='nback') glm = tstat.GLM() glm.fit(func_clean, design) Example for scaling across a Kubernetes cluster: The

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:

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 |

# ------------------------------------------------- # 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: