Statistical model checking of pMC
This prototype is implemented using python 3.6 (we give no guaranties for other versions)
Dependencies:
-
sympy : to represent expressions (to install: sudo pip install sympy)
-
ply : to parse the models (to install: pip install ply)
-
memory-profiler (to install: pip install memory_profiler)
-
matplotlib (to install: pip install matplotlib)
(On a Debian-derived Linux distribution, e.g. Ubuntu, you can install the packages python3-sympy, python3-ply, python3-memory-profiler, and python3-mpltoolkits.basemap)
to execute: run python main.py in a terminal
To execute on different examples, change the last line of main.py to:
- toy() # to perform 10000 simulations of length 100 on example/toy.pm
- toym() # to perform 10000 simulations of length 100 on example/toymult.pm (identical to toy.pm but with 100 parameters)
- zeroconf() # to perform 10000 simulations of length 500 on example/zeroconf.pm
- crowd() # to perform 10000 simulations of length 1000000 on example/crowds.pm
- main() # !! change line 197 and 198 to modify the number and length of runs. In this case, you can also run: python main.py probabilitisc_model_path to parse and execute simulation on the model located at probabilitisc_model_path