Skip to content

nobbi1991/ldfparser

 
 

Repository files navigation

LDF Parser

Workflow Github Pages PyPI version PyPI - Python Version codecov.io Total alerts Language grade: Python GitHub last commit License: MIT

This tool is able parse LIN Description Files, retrieve signal names and frames from them, as well as encoding messages using frame definitions and decoding them.


Disclaimers

The library is still in a pre-release state, therefore features may break between minor versions. For this reason it's recommended that productive environments pin to the exact version of the library and do an integration test or review when updating the version. Breaking changes and how to migrate to the new version will be documented in the changelog and on the Github releases page.

The tool has been written according the LIN standards 1.3, 2.0, 2.1 and 2.2A, but due to errors in the documentation there's no guarantee that the library will be able to parse your LDF. In such cases if possible first verify the LDF with a commercial tool such as Vector LDF Explorer or the tool that was used to create the LDF. If the LDF seems to be correct then open a new issue. I also recommend trying the LDF to JSON conversion mechanism, see if that succeeds.

The LIN standard is now known as ISO 17987 which clears up some of the confusing parts in the 2.2A specification. Since this new standard is not freely available this library won't support the modifications present in ISO 17987. I don't think it's going to a huge problem because the LIN 2.2A released in 2010 has overall better adoption.

The LDF usually contains sensitive information, if you need to open an issue related to the parser then try to provide either an anonymized version with signals and frames obfuscated or just the relevant segments in an example LDF when opening issues.


Installation

You can install this library from PyPI using pip.

pip install ldfparser

Examples

import ldfparser
import binascii

# Load LDF
ldf = ldfparser.parse_ldf(path = "network.ldf")
frame = ldf.get_unconditional_frame('Frame_1')

# Get baudrate from LDF
print(ldf.get_baudrate())

# Encode signal values into frame
message = frame.encode_raw({"Signal_1": 123, "Signal_2": 0})
print(binascii.hexlify(message))
>>> 0x7B00

# Decode message into dictionary of signal names and values
received = bytearray([0x7B, 0x00])
print(frame.decode(received))
>>> {"Signal_1": 123, "Signal_2": 0}

# Encode signal values through converters
message = frame.encode({"MotorRPM": 100, "FanState": "ON"})
print(binascii.hexlify(message))
>>> 0xFE01

More examples can be found in the examples directory.


Documentation

Documentation is published to Github Pages.


Features

  • Semantic validation of LDF files

  • Retrieve header information (version, baudrate)

  • Retrieve Signal and Frame information

  • Retrieve Signal encoding types and use them to convert values

  • Retrieve Node attributes

  • Retrieve schedule table information

  • Command Line Interface

  • Capturing comments

  • Encode and decode standard diagnostic frames

  • Saving LDF object as an .ldf file (experimental)

Known issues / missing features

  • Certain parsing related errors are unintuitive

  • Checksum calculation for frames

  • Token information is not preserved


Development

Install the library locally by running pip install -e .[dev]

Pytest is used for testing, to execute all tests run pytest -m 'not snapshot'

Flake8 is used for linting, run flake8 to print out all linting errors.


Contributors

@c4deszes (Author)


Credits

Inspired by uCAN-LIN LinUSBConverter, specifically the LDF parsing mechanism via Lark. Previously the library included most of the lark file, parsing code and examples, since 0.5.0 they've been completely rewritten to better accomodate the different LIN standards.


License

License: MIT

About

LIN Description File parser written in Python

Resources

License

Code of conduct

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 94.4%
  • Jinja 4.8%
  • Dockerfile 0.8%