asammdf 4.1.0

ASAM MDF measurement data file parser

asammdf is a fast parser and editor for ASAM (Associtation for Standardisation 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 2.7, and Python >= 3.4

asammdf was tested succesfully on both Linux and Windows

Status

!	Travis CI	Coverage	Codacy	ReadTheDocs
master
development

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 bus logging files

• filter a subset of channels from original mdf file

• cut measurement to specified time interval

• convert to different mdf version

• export to pandas, Excel, 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) using memory = minimum argument

• 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

  • 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 bus logging is implemented with the ability to get signals defined in the attached CAN database (.arxml or .dbc)
  • handling of unfinished measurements (mdf 4): warnings are logged based on the unfinished status flags but no further steps are taken to sanitize the measurement
  • 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 transfered 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 defaukt X axis is ignored and the channel group's master channel is used

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', , memory='minimum')
   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

Documentation

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

Contributing

Please have a look over the contributing guidelines

Contributors

Thanks to all who contributed with commits to asammdf:

• Julien Grave JulienGrv
• Jed Frey jed-frey
• Mihai yahym
• Jack Weinstein jacklev
• Isuru Fernando isuruf
• Felix Kohlgrüber fkohlgrueber
• Stanislav Frolov stanifrolov
• Thomas Kastl kasuteru
• venden venden

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
   # or for anaconda
   conda install -c conda-forge asammdf 

Dependencies

asammdf uses the following libraries

• numpy : the heart that makes all tick
• numexpr : for algebraic and rational channel conversions
• matplotlib : for Signal plotting
• wheel : for installation in virtual environments
• pandas : for DataFrame export
• canmatrix : to handle CAN bus logging measurements

optional dependencies needed for exports

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

other optional dependencies

• chardet : to detect non-standard unicode encodings
• PyQt4 or PyQt5 : for GUI tool
• pyqtgraph : for GUI tool

Benchmarks

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