https://github.com/doganulus/python-monitors
A pure Python package to monitor formal specifications over temporal sequences
https://github.com/doganulus/python-monitors
monitoring regular-expressions runtime-verification temporal-logic
Last synced: about 2 months ago
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A pure Python package to monitor formal specifications over temporal sequences
- Host: GitHub
- URL: https://github.com/doganulus/python-monitors
- Owner: doganulus
- License: gpl-3.0
- Created: 2018-11-04T15:44:05.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-10T22:23:41.000Z (over 5 years ago)
- Last Synced: 2025-03-09T14:46:41.011Z (2 months ago)
- Topics: monitoring, regular-expressions, runtime-verification, temporal-logic
- Language: Python
- Size: 43.9 MB
- Stars: 17
- Watchers: 1
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# About python-monitors
`python-monitors` is a pure Python package to monitor formal specifications over temporal sequences. It supports several specification languages such as regular expressions and variants of temporal logic. The usage is fairly easy thanks to Python and allows fast prototyping of applications that monitor temporal sequences using these specifications.
> **WARNING**: This repository is depreciated in favor of the project [Reelay](https://github.com/doganulus/reelay) but will remain as a pure Python solution albeit limited in functionatity and speed. Reelay implements the same runtime monitors and more in C++, which are accessible from Python via bindings.
# Install
The latest release of the package can be installed via `pip` such that
pip install python-monitors
This command will also install dependencies `python-intervals` and `antlr4-python3-runtime`. Alternatively, you can install directly from this repository by running the command
pip install git+https://github.com/doganulus/python-monitors.git
# Use
## MTL over propositions
First generate a monitor from past Metric Temporal Logic (MTL) formula:
from monitors import mtl
my_mtl_monitor = mtl.monitor("always(q -> once[2,4](p))")
Then process a data sequence (over propositions) by updating the monitor and collecting the output at each step:
data = dict(
p = [False, True, False, False, False, False, False, True, False, False, False, False, False, False, False],
q = [False, False, False, False, False, True, False, False, False, False, False, False, False, False, True ]
)
for p, q in zip(data['p'], data['q']):
output = my_mtl_monitor.update(p = p, q = q)
print(my_mtl_monitor.time, output, my_mtl_monitor.states)
## MTL over predicates (also known as STL)
Any Boolean-valued Python function can be used as a predicate in MTL formulas. They are passed to monitor construction via a dictionary as follows:
def my_predicate(x):
return x < 5
# Named parameters should share the same strings in the expression
my_mtl_monitor = mtl.monitor("always[0,5](p(x))", p=my_predicate)
for n in [9, 13, 4, 1, 2, 3,1,1,1,2]:
output = my_mtl_monitor.update(x = n)
print(my_mtl_monitor.time, my_predicate(n), output, my_mtl_monitor.states)
## Regular expressions over propositions and predicates
Regular expressions over propositions and predicates are available in a similar fashion:
from monitors import regexp
def pred1(x):
return x < 5
def pred2(x):
return x > 12
# Named parameters should share the same strings in the expression
my_reg_monitor = regexp.monitor("True*; p1(x); p2(x)+; p1(x)+", p1=pred1, p2=pred2)
for n in [1, 1, 1, 1, 13, 13, 14, 1, 1, 2]:
output = my_reg_monitor.update(x = n)
print(output, my_reg_monitor.states)
# Cite
For MTL monitoring algorithm, please cite [Online Monitoring of Metric Temporal Logic using Sequential Networks](https://arxiv.org/abs/1901.00175). For RE monitoring algorithm, please cite [Sequential Circuits from Regular Expressions Revisited](https://arxiv.org/abs/1801.08979).