A generic framework for ode models, specifically compartmental type problems.
This package depends on:
dask matplotlib enum34 pandas python-dateutil numpy scipy sympy
and they should be installed if not already available. Alternatively, the easier way to use a minimal (and isolated) setup is to use conda and create a new environment via:
conda env create -f conda-env.yml
Installation of this package can be performed via:
$ python setup.py install
and tested via:
$ python setup.py test
A reduced form of the documentation may be found on ReadTheDocs.
You may get the full documentation, including the lengthy examples by locally building the documentation found in the folder:
$ doc
Note that building the documentation can be extremely slow depending on the setup of the system. Further details can be found at it's own read me:
$ doc/README.rst
Please be aware that if the module tests fails, then the documentation for the package will not compile.
Please be aware that there may be redundant files within the package as it is under active development.
Thomas Finnie ([email protected])
Edwin Tye
Hannah Williams
Jonty Carruthers
Martin Grunnill
0.1.7 Add Approximate Bayesian Computation (ABC) as a method of fitting to data
0.1.6 Bugfix scipy API, pickling, print to logging and simulation
0.1.5 Remove auto-simplification for much faster startup
0.1.4 Much faster Tau leap for stochastic simulations
0.1.3 Defaults to python built-in unittest and more in sync with conda