A python framework for regular and rule-based stochastic simulations.
Stocal is a framework for stochastic simulation of continuous time Markov processes (also known as Gillespie simulations).
Features of stocal:
- support for reactions of any order
- support for unique and periodic (deterministic) events
- support for time-dependent reaction rates
- support for rules that generate novel reactions on the fly
- support for complex chemical states
Stocal works with python version 2.7 as well as version 3.5.
Running a simple stochastic simulation is straight forward:
import stocal
# Define a stochastic process
process = stocal.Process([
stocal.MassAction({'A': 2}, {'A2': 1}, 1.0),
stocal.MassAction({'A2': 1}, {'A': 2}, 10.0),
stocal.Event({}, {'A': 100}, 0.0, frequency=10.0),
])
# Sample a stochastic trajectory of the process
initial_state = {}
trajectory = process.sample(initial_state, tmax=100) :
for dt, transitions in trajectory:
print trajectory.time, trajectory.state['A2']A tutorial on how to use stocal can be found here.
Various usage examples are provided in stocal/examples.
The package API is thoroughly documented and can be accessed through
pydoc. The behavior of stocal is specified via tests. The test suite
can be run with python setup.py test.
The latest stable release of stocal is available from the python package index:
pip install stocalThe development version can be obtained from github using the following commands:
git clone https://github.com/harfel/stocal.git
cd stocal
git checkout develop
sudo python setup.py installPlease post any issues that might occur with stocal on the github issue tracker. If you are interested in contributing to stocal, pull requests and any other inquiries will be dealt with as soon as possible.
Stocal is distributed under the MIT Software license. (c) 2018 Harold Fellermann