STLRom is a C++ library with python bindings for Robust online monitoring of Signal Temporal Logic. It computes interval robustness as defined in 'Robust online monitoring of signal temporal logic' by Deshmuk et al, although the algorithm is not exactly the one described in this paper.
The simplest way to install and use STLRom is to use pip:
$ pip install stlrom
The following notebook is formated as a tutorial for stlrom. It can also be accessed on colab.
import stlrom
stl_monitor =stlrom.STLDriver()
s="""
signal x, y # signal names
mux := x[t]>0 # simple predicate
param a=1, b=2, p = -3
muy := a*y[t] + b > p # operation and parameter in predicate
phi1 := alw_[0, 5] mux # always (G works too)
phi2 := ev_[3, 4] (mux) or phi1 # eventually (or F)
phi_until := phi1 until_[0, 1] phi2 # dreaded until
"""
# parse the formulas
stl_monitor.parse_string(s) # or write the above in spec.stl, and use parse_file('spec.stl')
# add data as timed samples
stl_monitor.add_sample([0, 2 , 1 ]) # must be of the format [t, x_val, y_val]
stl_monitor.add_sample([0.5, -3, 2]) # i.e., contain signal value with same order as declared
stl_monitor.add_sample([2.1, 10, 20])
# get the robustness of the formulas at time t0
def test_rob(formula, t0):
rob1 = stl_monitor.get_online_rob(formula, t0)
print("Interval robustness of formula ", formula, " from time ", t0, " is ", rob1)
test_rob("phi1",0)
test_rob("phi2",1)
test_rob("phi_until",1.1)
STLRom is written in C++ with Python bindings using pybind11. Python 3.10 or higher is recommended. To compile, run
cd build
cmake ..
make
In the test folder, test_driver.cpp
is an example of using the C++ library. Go into build/test and run it with
./test_driver
In the build folder, execute
make test
It will run the unit tests python programs from the test
folder.