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Tutorials & Learning Guides

Welcome to the Soft Potato Learning Hub! Explore hands-on tutorials covering Soft Potato modules, and scientific Python programming, for students, researchers, and computational scientists. This section is in active development and will be updated continuously.

Chip — Code & Mesh Analyst

Python

Foundational scientific programming tutorials designed for researchers and engineers:

  • Soft Potato on your browser — Run Soft Potato in Google Colab. No Python installation needed!
  • Installing Python on your computer — Installing official Python across operating systems, setting up an isolated virtual environment, and running an analytical chronoamperometry test in IDLE to verify your setup.

Soft Potato Modules

Hands-on guides exploring specific modules in the Soft Potato toolkit:

  • Analytical Module — How to call closed-form electrochemical equations (cottrell, levich, butler_volmer, and secm_approach_curve) across analytical submodules and visualize results.
  • Parameter Fitting — How to use softpotato.analytical.fitting (fit_cottrell, fit_randles_sevcik, fit_secm_approach, and fit_curve) to extract physical parameters from experimental data with statistical confidence.
  • Numerical Solver — Simulating multi-species reaction-diffusion problems with DiffusionProblem(), configuring Dirichlet and Neumann boundary conditions, and extracting concentration profiles and Faradaic currents with SolverResult.
  • Geometry Module — Setting up spatial coordinate systems with softpotato.geometry (planar, cylindrical, and spherical), running spherical diffusion simulations, and visualizing 1D radial profiles, 2D Cartesian cross-sections, and Faradaic currents.
  • Grid Module — Constructing non-uniform spatial discretization meshes with ExponentialMesh in softpotato.grid, coupling with DiffusionProblem(), and comparing near-electrode concentration profiles and Faradaic current transients against Uniform1DMesh for the same number of nodes.