Fast Python reader and editor for ASAM MDF / MF4 (Measurement Data Format) files
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Latest commit e5204b1 Jan 16, 2019

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


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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


  • 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


from asammdf import MDF

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

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['Sensor1', 'Voltage3']):

Check the examples folder for extended usage demo, or the documentation


Contributing & Support

Please have a look over the contributing guidelines

If you enjoy this library please consider making a donation to the numpy project.


Thanks to all who contributed with commits to asammdf:


asammdf is available on

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


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 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

  • cChardet : to detect non-standard unicode encodings
  • PyQt4 or PyQt5 : for GUI tool
  • pyqtgraph : for GUI tool and Signal plotting
  • matplotlib : as fallback for Signal plotting