Python Environment

TI-Toolbox runs inside a container that wraps around SimNIBS. simnibs_python is SimNIBS’s own bundled interpreter, and it is where the tit package is installed — every simulation, optimization and analysis runs under it.

Where you type Python — the Notebooks page, or docker exec into the container from your own terminal — is shown on the Scripting page. A run page’s Terminal tab is a read-only job log, not a shell. Nothing is installed on your host: the science needs SimNIBS, which is in the image.

If you want to add a package for a new feature, follow the steps below.

Environment Management

Task	Command
Check location	which simnibs_python
List installed packages	simnibs_python -m pip list
Install package	simnibs_python -m pip install <package>

Key Points

• Containerized Setup: The environment is defined in container/blueprint/Dockerfile.ti-toolbox, with scientific dependency constraints in container/blueprint/runtime-constraints.txt. Scientific preparation uses SimNIBS 4.6 with NumPy 2.3.5. Montage rendering runs pinned standalone Blender 4.4.3 in a background process with its own Python environment; do not install bpy into SimNIBS or downgrade scientific NumPy to satisfy Blender. See Blender Integration. See the full-net montage memory guidance before allocating container resources.

• Script executions: Inside the container, run scripts with simnibs_python script.py, not python.

• Installs do not survive the container: simnibs_python -m pip install <package> installs into the running container only, and closing the app stops and removes that container — so the package is gone the next time you open the project, not only after an image rebuild. To keep it, add it to the Dockerfile and rebuild (container/blueprint/build.sh), or install it at the top of the notebook that needs it.

The tit imports and the simnibs_python -m tit.<module> config.json runners are listed on the Scripting page, which owns them.