Using AMICI’s Python interface
In the following we will give a detailed overview how to specify models in Python and how to call the generated simulation files.
This document provides an overview of different interfaces to import models in AMICI. Further examples are available in the AMICI repository in the python/examples directory.
Status of SBML support in Python-AMICI
The following SBML test suite tags are currently supported (i.e., at least one test case with the respective test passes; tag descriptions):
In addition, we currently plan to add support for the following features (see corresponding issues for details and progress):
Algebraic rules (#760)
However, the following features are unlikely to be supported:
any SBML extensions
factorial(), ceil(), floor(), due to incompatibility with symbolic sensitivity computations
delay() due to missing SUNDIALS solver support
events with delays, events with non-persistent triggers
A basic tutorial on how to import and simulate SBML models is available in the Getting Started notebook, while a more detailed example including customized import and sensitivity computation is available in the Example Steadystate notebook.
Importing plain ODEs
The AMICI Python interface does not currently support direct import of ODEs. However, it is straightforward to encode them as RateRules in an SBML model. The yaml2sbml package may come in handy, as it facilitates generating SBML models from a YAML-based specification of an ODE model. Besides the SBML model it can also create PEtab files.
We also plan to implement support for the Simulation Experiment Description Markup Language (SED-ML).
OpenMP support for parallelized simulation for multiple experimental conditions
AMICI can be built with OpenMP support, which allows to parallelize model simulations for multiple experimental conditions.
On Linux and OSX this is enabled by default. This can be verified using:
import amici amici.compiledWithOpenMP()
If not already enabled by default, you can enable OpenMP support by setting
the environment variables
AMICI_LDFLAGS to the
correct OpenMP flags of your compiler and linker, respectively. This has to be
done for both AMICI package installation and model compilation. When using
gcc on Linux, this would be:
# on your shell: AMICI_CXXFLAGS=-fopenmp AMICI_LDFLAGS=-fopenmp pip3 install amici
# in python, before model compilation: import os os.environ['AMICI_CXXFLAGS'] = '-fopenmp' os.environ['AMICI_LDFLAGS'] = '-fopenmp'