Using AMICI’s C++ interface
The various import functions in of the Python interface and Matlab interface translate models defined in different formats into C++ code. These generated model libraries, together with the AMICI base library can be used in any C++ application for model simulation and sensitivity analysis. This section will give a short overview over the generated files and provide a brief introduction of how this code can be included in other applications. Further details are available in the C++ API reference.
AMICI-generated C++ model files
The content of a model source directory looks something like this (given MODEL_NAME=model_steadystate):
CMakeLists.txt main.cpp model_steadystate_deltaqB.cpp model_steadystate_deltaqB.h [... many more files model_steadystate_*.(cpp|h|md5|o) ] wrapfunctions.cpp wrapfunctions.h model_steadystate.h
These files provide the implementation of a model-specific subclass of
CMakeLists.txt file can be used to build the
model library using CMake.
main.cpp contains a simple scaffold for running a model simulation from C++.
See next section for more details on these files.
Running a model simulation
AMICI’s public API is mostly available through
amici/amici.h. This is the only header file
that needs to be included for basic usage. All functions there are declared within the amici namespace.
amici/hdf5.h and amici/serialization.h may be handy for specific use cases.
The former provides some functions for reading and writing
HDF5 files, latter for serialization
All model-specific functions are defined in the namespace
The main function for running an AMICI simulation is
amici::runAmiciSimulation(). This function requires
an instance of a
amici::Modelsubclass as generated during model import. For the example model_steadystate the respective class is provided as
model_steadystate.hin output directory for the given model.
amici::ExpDatainstance, which contains any experimental data (e.g. measurements, noise model parameters or model inputs) to evaluate residuals or an objective function.
This function returns a
amici::ReturnData object, which contains
all simulation results.
For running simulations for multiple experimental conditions
provides an alternative entry point. If AMICI (and your application)
have been compiled with OpenMP support (see installation guide), this allows
for running those simulations in parallel.
A scaffold for a standalone simulation program is automatically generated
during model import in
main.cpp in the model output directory. This program
shows how to use the above-mentioned classes, how to obtain the simulation
results, and may provide a starting point for your own simulation code.
Working with multiple or anonymous models
AMICI model import generates a
amici::Model subclass for the
specific model, based on the name used during import. One the one hand, this
allows you to use multiple models with different names within a single
application. On the other hand, this requires you to know the name of the
model, which can be inconvenient in some cases.
When working with a single model, the
wrapfunctions.h file generated during
model import can be used to avoid specifying model names explicitly. It defines
amici::generic_model::getModel(), that returns an instance of
the model class by a generic name.
wrapfunctions.h files from different
models in a single application is not possible. When using multiple models,
explicit names have to be used or the different model libraries need to be
loaded dynamically at runtime.
Parameter estimation for AMICI models in high-performance computing environments
To perform parameter estimation for large or otherwise computationally demanding AMICI models from C++ in a high-performance computing environment, you may find the parPE library helpful. parPE allows for the private or shared memory parallel evaluation of a cost function requiring multiple simulations of the same model with different inputs. It provides interfaces to different optimizers, such as Ipopt.