Twk Lausanne Download //free\\ 👑

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.

from twk.distributed import RayExecutor

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: twk lausanne download

git clone https://github.com/epfl-twk/twk-lausanne.git cd twk-lausanne # Optionally check out the latest tag, e.g., v2.0.3 git checkout tags/v2.0.3 # Install in editable mode python -m pip install -e . The repository includes ; you can run the test suite with: pytest -n auto Below is a minimal example

# Activate the environment conda activate twk-lausanne Source Code (Git) If you prefer to develop

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 |