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 >= 3.7 (for Python 2.7, 3.4 and 3.5 see the 4.x.y releases)

Status

Continuous Integration	Coveralls	Codacy	ReadTheDocs

PyPI	conda-forge

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 (v4, v5 and 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/log-file-tools/asammdf-api/

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

Contributors

Thanks to all who contributed with commits to asammdf:

• Julien Grave JulienGrv
• Jed Frey jed-frey
• Mihai yahym
• Jack Weinstein jackjweinstein
• Isuru Fernando isuruf
• Felix Kohlgrüber fkohlgrueber
• Stanislav Frolov stanifrolov
• Thomas Kastl kasuteru
• venden venden
• Marat K. kopytjuk
• freakatzz freakatzz
• Martin Falch MartinF
• dxpke dxpke
• Nick James driftregion
• tobiasandorfer tobiasandorfer

Installation

asammdf is available on

• github: https://github.com/danielhrisca/asammdf/
• PyPI: https://pypi.org/project/asammdf/
• conda-forge: https://anaconda.org/conda-forge/asammdf

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

optional dependencies needed for exports

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

other optional dependencies

• PyQt5 : for GUI tool
• pyqtgraph : for GUI tool and Signal plotting
• matplotlib : as fallback for Signal plotting
• cChardet : to detect non-standard Unicode encodings
• chardet : to detect non-standard Unicode encodings
• pyqtlet : for GPS window
• isal : for faster zlib compression/decompression

Benchmarks

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