This repository contains the accompanying software for the paper "Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes" by Shihao Yang, Samuel W.K. Wong, and S. C. Kou.
User interfaces are available in R, MATLAB, and Python.
For R users: the current version of MAGI is available as a package on CRAN, and may be installed via
install.packages("magi")
Please see https://cran.r-project.org/package=magi for the full documentation of this latest version, which contains a number of enhancements to facilitate ease of usage.
For MATLAB users: Run build.sh to first build the C++ library. Then navigate to MATLABmagi and run MATLAB_build.sh to compile the required MEX files. See README in MATLABmagi for further details.
For Python users: Run build.sh to first build the C++ library. Then navigate to pymagi and run py_build.sh to build the corresponding Python library.
Pre-compiled binaries for C++, R, and Python are also available as a Docker image on Docker Hub: https://hub.docker.com/repository/docker/shihaoyangphd/magi
Inference is performed via the unified function MagiSolver which can be called from R, MATLAB, Python. The basic syntax is
MagiSolver(y, odeModel, control)
where y is the a data matrix, odeModel specifies the functions and parameters of the system, and control passes additional options.
For fully-documented examples and details, please refer to our MAGI software manuscript: https://arxiv.org/abs/2203.06066
See the README in the corresponding subfolders: rmagi (for R), pymagi (for Python), MATLABmagi (for MATLAB).
There, we provide specific examples of how to set up and call MagiSolver in each software environment, and how to supply your own ODE systems and data to the method.
For a full discussion of the method, please see our paper "Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes", PNAS 118 (15), e2020397118 (https://doi.org/10.1073/pnas.2020397118).
For a full discussion of the software package and examples, please see our accompanying manuscript "MAGI: A Package for Inference of Dynamic Systems from Noisy and Sparse Data via Manifold-constrained Gaussian Processes", https://arxiv.org/abs/2203.06066