Installation and resources

The easiest way to install fnyzer is to use pip (the standard tool for installing Python packages). Execute the following line in a shell:

$ pip install fnyzer

Requirements

In order to solve the optimization problems associated with the flexible nets, fnyzer requires a solver supported by pyomo. For untimed and steady state analysis of FNs, the GLPK solver can be used. GLPK can be straightforwardly installed as a Debian package, e.g. in Ubuntu:

$ apt-get install glpk-utils

Windows users can follow the instructions in this this link to install GLPK. In general, the set of constraints associated with the FNs include quadratic constraints, and hence, fnyzer requires a solver that can handle such constraints. fnyzer has been mainly tested with Gurobi and CPLEX (both solvers offer free academic licenses).

Warning

GLPK does not support quadratic constraints and will not be able to solve some optimization problems.

The installation of at least one solver in your system is necessary to run fnyzer. You can check that a solver is properly installed by executing these commands:

$ cplex
Welcome to IBM(R) ILOG(R) CPLEX(R) Interactive Optimizer 12.6.1.0
...
$ gurobi.sh
Gurobi Interactive Shell (linux64), Version 6.0.0
...

Testing your installation

Download the file fnexamples.py in your working directory. If you have CPLEX in your system, execute the following line:

$ fnyzer fnexamples net0cplex

If you have Gurobi in your system, execute the following line:

$ fnyzer fnexamples net0gurobi

If you have GLPK in your system, execute the following line:

$ fnyzer fnexamples net0glpk

After the execution, the files net0.xls and net0.pkl should be in your working directory.

Resources

fnyzer is hosted at Bitbucket:

Queries

Queries can be sent to fnyzer@unizar.es

Bibliography

Flexible Nets: A modeling formalism for dynamic systems with uncertain parameters; J. Júlvez, S. G. Oliver; Discrete Event Dynamic Systems: Theory and Applications, 2019. https://doi.org/10.1007/s10626-019-00287-9

Modeling, analyzing and controlling hybrid systems by Guarded Flexible Nets; J. Júlvez, S. G. Oliver; Nonlinear Analysis: Hybrid Systems, 2019. https://doi.org/10.1016/j.nahs.2018.11.004

Steady State Analysis of Flexible Nets; J. Júlvez, S. G. Oliver; IEEE Transactions on Automatic Control, 2019. https://doi.org/10.1109/TAC.2019.2931836

Handling variability and incompleteness of biological data by flexible nets: a case study for Wilson disease; J. Júlvez, D. Dikicioglu, S. G. Oliver; npj Systems Biology and Applications, 2018. https://doi.org/10.1038/s41540-017-0044-x