scotch implements algorithms for stochastic, continuous-time chains, or Markov processes. We are currently in alpha release; contributions are very welcome. For pull requests or bug reports, find us on Github. The core developers are Quentin Caudron and Ruthie Birger.


  • Quick-Start : dive straight into describing stochastic processes and simulating them with scotch.
  • Model Specifications : the full specs for scotch model files.
  • Scotch API and Docs : docs for model building, simulating, sampling, and plotting scotch systems.
  • Examples : a series of example scotch models that work out of the box.


Currently implemented :

  • interactive, text-based "wizard" for full model specification
  • Gillespie's (SSA) algorithm
  • tau-leaping algorithm
  • a quick-plot method for single realisations
  • repeat trajectory sampling and bootstrapping confidence intervals
  • complete event tracking

For the future :

  • Gibson-Bruck algorithm
  • adaptive timestepping in tau-leaping
  • parameter inference

Bug Reports and Contributing

Please let us know about bugs as issues on Github. Pull requests are also absolutely welcome !



  • Python >= 2.7
  • Numpy
  • Scipy (for model.sample() and model.plotsamples())
  • Matplotlib (for model.plot() and model.plotsamples())


  • Seaborn

Fork me on GitHub