A model can be manipulated and visualized in Jupyter Notebook by calling model.controlpanel(). By default this creates a slider for every constant in the model and gives them automatic ranges, but variables and/or ranges can be changed in the Settings tab or specified in the first argument to controlpanel().
Besides the default behaviour shown above, the control panel can also display custom analyses and plots via the fn_of_sol argument, which accepts a function (or list of functions) that take the solution as their input.
Methods exist to facilitate creating, solving, and plotting the results of a single-variable sweep (see :ref:`Sweeps` for details). Example usage is as follows:
.. literalinclude:: examples/plot_sweep1d.py
Which results in:
