ASAM MDF measurement data file parser

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  • License: GNU Lesser General Public License v3 or later (LGPLv3+) (LGPLv3+)
  • Author: Daniel Hrisca
  • Tags read, reader, edit, editor, parse, parser, asam, mdf, measurement
  • Requires: Python >=3.10
  • Provides-Extra: decode, export, export-matlab-v5, gui, encryption, symbolic-math, filesystem
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Project description

logo of asammdf

asammdf is a fast parser and editor for ASAM (Association for Standardization of Automation and Measuring Systems) MDF (Measurement Data Format) files.

asammdf supports MDF versions 2 (.dat), 3 (.mdf) and 4 (.mf4).

asammdf works on Python >= 3.10

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Status

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PyPI conda-forge
PyPI version conda-forge version

Project goals

The main goals for this library are:

  • to be faster than the other Python based mdf libraries
  • to have clean and easy to understand code base
  • to have minimal 3-rd party dependencies

Features

  • create new mdf files from scratch

  • append new channels

  • read unsorted MDF v3 and v4 files

  • read CAN and LIN bus logging files

  • extract CAN and LIN signals from anonymous bus logging measurements

  • filter a subset of channels from original mdf file

  • cut measurement to specified time interval

  • convert to different mdf version

  • export to HDF5, Matlab (v7.3), CSV and parquet

  • merge multiple files sharing the same internal structure

  • read and save mdf version 4.10 files containing zipped data blocks

  • space optimizations for saved files (no duplicated blocks)

  • split large data blocks (configurable size) for mdf version 4

  • full support (read, append, save) for the following map types (multidimensional array channels):

    • mdf version 3 channels with CDBLOCK

    • mdf version 4 structure channel composition

    • mdf version 4 channel arrays with CNTemplate storage and one of the array types:

      • 0 - array
      • 1 - scaling axis
      • 2 - look-up

  • add and extract attachments for mdf version 4

  • handle large files (for example merging two fileas, each with 14000 channels and 5GB size, on a RaspberryPi)

  • extract channel data, master channel and extra channel information as Signal objects for unified operations with v3 and v4 files

  • time domain operation using the Signal class

    • Pandas data frames are good if all the channels have the same time based
    • a measurement will usually have channels from different sources at different rates
    • the Signal class facilitates operations with such channels

  • graphical interface to visualize channels and perform operations with the files

Major features not implemented (yet)

  • for version 3

    • functionality related to sample reduction block: the samples reduction blocks are simply ignored

  • for version 4

    • experimental support for MDF v4.20 column oriented storage
    • functionality related to sample reduction block: the samples reduction blocks are simply ignored
    • handling of channel hierarchy: channel hierarchy is ignored
    • full handling of bus logging measurements: currently only CAN and LIN bus logging are implemented with the ability to get signals defined in the attached CAN/LIN database (.arxml or .dbc). Signals can also be extracted from an anonymous bus logging measurement by providing a CAN or LIN database (.dbc or .arxml)
    • handling of unfinished measurements (mdf 4): finalization is attempted when the file is loaded, however the not all the finalization steps are supported
    • full support for remaining mdf 4 channel arrays types
    • xml schema for MDBLOCK: most metadata stored in the comment blocks will not be available
    • full handling of event blocks: events are transferred to the new files (in case of calling methods that return new MDF objects) but no new events can be created
    • channels with default X axis: the default X axis is ignored and the channel group's master channel is used
    • attachment encryption/decryption using user provided encryption/decryption functions; this is not part of the MDF v4 spec and is only supported by this library

Usage

from asammdf import MDF

mdf = MDF('sample.mdf')
speed = mdf.get('WheelSpeed')
speed.plot()

important_signals = ['WheelSpeed', 'VehicleSpeed', 'VehicleAcceleration']
# get short measurement with a subset of channels from 10s to 12s
short = mdf.filter(important_signals).cut(start=10, stop=12)

# convert to version 4.10 and save to disk
short.convert('4.10').save('important signals.mf4')

# plot some channels from a huge file
efficient = MDF('huge.mf4')
for signal in efficient.select(['Sensor1', 'Voltage3']):
    signal.plot()

Check the examples folder for extended usage demo, or the documentation http://asammdf.readthedocs.io/en/master/examples.html

https://canlogger.csselectronics.com/canedge-getting-started/ce3/log-file-tools/asammdf-gui/

Documentation

http://asammdf.readthedocs.io/en/master

And a nicely written tutorial on the CSS Electronics site

Contributing & Support

Please have a look over the contributing guidelines

If you enjoy this library please consider making a donation to the numpy project or to danielhrisca using liberapay.

Donate using Liberapay

Contributors

Thanks to all who contributed with commits to asammdf:

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Installation

asammdf is available on

pip install asammdf
# for the GUI
pip install asammdf[gui]
# or for anaconda
conda install -c conda-forge asammdf

In case a wheel is not present for you OS/Python versions and you lack the proper compiler setup to compile the c-extension code, then you can simply copy-paste the package code to your site-packages. In this way the python fallback code will be used instead of the compiled c-extension code.

Dependencies

asammdf uses the following libraries

  • numpy : the heart that makes all tick
  • numexpr : for algebraic and rational channel conversions
  • wheel : for installation in virtual environments
  • pandas : for DataFrame export
  • canmatrix : to handle CAN/LIN bus logging measurements
  • natsort
  • lxml : for canmatrix arxml support
  • lz4 : to speed up the disk IO performance
  • python-dateutil : measurement start time handling

optional dependencies needed for exports

  • h5py : for HDF5 export
  • hdf5storage : for Matlab v7.3 .mat export
  • pyarrow : for parquet export
  • scipy: for Matlab v4 and v5 .mat export

other optional dependencies

  • PySide6 : for GUI tool
  • pyqtgraph : for GUI tool and Signal plotting
  • matplotlib : as fallback for Signal plotting
  • faust-cchardet : to detect non-standard Unicode encodings
  • chardet : to detect non-standard Unicode encodings
  • pyqtlet2 : for the GPS window
  • isal : for faster zlib compression/decompression
  • fsspec : access files stored in the cloud

Benchmarks

http://asammdf.readthedocs.io/en/master/benchmarks.html

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