Lausanne Download Free: Twk

from twk.distributed import RayExecutor

pytest -n auto Below is a minimal example that demonstrates a typical end‑to‑end analysis: loading a BIDS dataset, preprocessing, statistical modelling, and visualising results. twk lausanne download

import twk.io as tio import twk.preproc as tpre import twk.stats as tstat import twk.vis as tvis 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. | | | Data‑Science & Machine Learning | Pipelines

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 |

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:

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