mstlo-python

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Python bindings for mstlo (mistletoe), a Rust library for online monitoring of Signal Temporal Logic (STL) specifications. The package exposes the Rust monitoring engine through a Pythonic API, so STL formulas can be parsed and evaluated over streaming signals from Python applications and notebooks at comparable performance.

  • PyPI package: mstlo-python
  • Import name: mstlo_python
  • Rust core crate: mstlo

Features

  • Unified semantics interface: delayed qualitative, delayed quantitative, eager qualitative and Robust Satisfaction Intervals (RoSI) behind a single Monitor class.
  • Dense-time signals: samples may arrive at arbitrary, irregular timestamps, per signal, with zero-order hold or linear interpolation between them.
  • High performance: evaluation runs in the Rust engine; benchmarks show throughput exceeding existing state-of-the-art tools.

Installation

pip install mstlo-python

Usage

A formula is parsed from the DSL syntax, then a monitor is fed samples as (signal, value, timestamp), with timestamps in seconds. This monitors G[0, 10](x > 5) ("x stays above 5 for the next ten seconds") under RoSI semantics:

import mstlo_python as mstlo

# Parse formula using the DSL syntax
phi = mstlo.parse_formula("G[0, 10](x > 5)")

# Create a monitor using the selected semantics
monitor = mstlo.Monitor(phi, semantics="Rosi")

# Update the monitor with streaming data (signal_name, value, timestamp)
output = monitor.update("x", 6.0, 0.5)
print(f"Verdicts: {output.verdicts()}")
# Verdicts: [(0.5, (-inf, 1.0))]  # (timestamp, (lower_bound, upper_bound) for robustness)

output = monitor.update("x", 3.0, 1.2)
print(f"Verdicts: {output.verdicts()}")
# Verdicts: [(0.5, (-inf, -2.0)), (1.2, (-inf, -2.0))]  # early indication of violation

The semantics are selected with semantics= ("DelayedQualitative", "DelayedQuantitative", "EagerQualitative" or "Rosi"), the interpolation between samples with signal_interpolation= ("ZeroOrderHold" or "Linear"), and the values signals hold before their first sample with init_signals={"x": 0.0}.

Batch updates

Whole traces can be fed at once with update_batch, either signal-major (a dict of (value, timestamp) lists) or flat (an iterable of (signal, value, timestamp) tuples):

monitor = mstlo.Monitor(mstlo.parse_formula("x > 10.0"), semantics="Rosi")

output = monitor.update_batch({"x": [(5.0, 0.0), (15.0, 1.0), (8.0, 2.0)]})
output = monitor.update_batch([("x", 12.0, 3.0), ("x", 9.0, 4.0)])

More examples, including a notebook comparing the semantics, are in mstlo-python/examples.

Documentation

Building from Source

Prerequisites: Rust (stable toolchain), Python 3.9+ and maturin.

git clone https://github.com/INTO-CPS-Association/mstlo.git
cd mstlo/mstlo-python
pip install maturin pytest
maturin develop
pytest

License

This package is distributed under the INTO-CPS Association Public License (ICAPL) with GPL v3 as a supported subsidiary mode. See LICENSE for the full terms and ICA-USAGE-MODE.txt for the selected usage mode in this distribution.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.2.2)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for AKThomsen from gravatar.com AKThomsen Avatar for Voggo from gravatar.com Voggo

Credits

Author: Andreas Kaag Thomsen

License

GNU General Public License v3 (GPLv3) (INTO-CPS Association Public License v1.0)

Requires

Python >=3.8

Provides Extra

docs

Tags

signal temporal logic stl monitoring runtime verification online monitoring temporal logic

Classifiers

Report project as malware

Metadata

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.2.2)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for AKThomsen from gravatar.com AKThomsen Avatar for Voggo from gravatar.com Voggo

Credits

Author: Andreas Kaag Thomsen

License

GNU General Public License v3 (GPLv3) (INTO-CPS Association Public License v1.0)

Requires

Python >=3.8

Provides Extra

docs

Tags

signal temporal logic stl monitoring runtime verification online monitoring temporal logic

Classifiers

Report project as malware

Release files for mstlo-python 0.2.0

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Source distribution for mstlo-python 0.2.0
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mstlo_python-0.2.0.tar.gz 193.3 kB Details

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Table of built distributions (wheels) for mstlo-python 0.2.0
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mstlo_python-0.2.0-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl 625.6 kB CPython 3.9 CPython 3.9 Linux glibc 2.17+ ARMv7l Details
mstlo_python-0.2.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 526.0 kB CPython 3.9 CPython 3.9 Linux glibc 2.17+ ARM64 Details
mstlo_python-0.2.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl 663.7 kB CPython 3.9 CPython 3.9 Linux glibc 2.5+ x86-32 Details
mstlo_python-0.2.0-cp38-cp38-musllinux_1_2_x86_64.whl 769.0 kB CPython 3.8 CPython 3.8 Linux musl 1.2+ x86-64 Details
mstlo_python-0.2.0-cp38-cp38-musllinux_1_2_i686.whl 868.7 kB CPython 3.8 CPython 3.8 Linux musl 1.2+ x86-32 Details
mstlo_python-0.2.0-cp38-cp38-musllinux_1_2_armv7l.whl 903.8 kB CPython 3.8 CPython 3.8 Linux musl 1.2+ ARMv7l Details
mstlo_python-0.2.0-cp38-cp38-musllinux_1_2_aarch64.whl 704.5 kB CPython 3.8 CPython 3.8 Linux musl 1.2+ ARM64 Details
mstlo_python-0.2.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl 622.9 kB CPython 3.8 CPython 3.8 Linux glibc 2.17+ IBM System/390x Details
mstlo_python-0.2.0-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl 589.0 kB CPython 3.8 CPython 3.8 Linux glibc 2.17+ PowerPC 64-le Details
mstlo_python-0.2.0-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl 625.7 kB CPython 3.8 CPython 3.8 Linux glibc 2.17+ ARMv7l Details
mstlo_python-0.2.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 526.4 kB CPython 3.8 CPython 3.8 Linux glibc 2.17+ ARM64 Details

Total release size: 70.7 MB

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0.2.2

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0.2.0 This release

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