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


  • create new mdf files from scratch

  • append new channels

  • read unsorted MDF v2, v3 and v4 files

  • filter a subset of channels from original mdf file

  • cut measurement to specified time interval

  • convert to different mdf version

  • export to Excel, HDF5, Matlab and CSV

  • merge multiple files sharing the same internal structure

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

  • disk space savings by compacting 1-dimensional integer channels (configurable)

  • 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

  • files are loaded in RAM for fast operations

  • handle large files (exceeding the available RAM) 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
    • usually a measurement will have channels from different sources at different rates
    • the Signal class facilitates operations with such channels

Major features not implemented (yet)

  • for version 3

    • functionality related to sample reduction block (but the class is defined)
  • for version 4

    • handling of bus logging measurements
    • handling of unfinnished measurements (mdf 4)
    • full support for remaining mdf 4 channel arrays types
    • xml schema for TXBLOCK and MDBLOCK
    • partial conversions
    • event blocks
    • channels with default X axis
    • chanenls with reference to attachment


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

optional dependencies needed for exports

  • h5py : for HDF5 export
  • xlsxwriter : for Excel export
  • scipy : for Matlab .mat export

other optional dependencies

  • chardet : to detect non-standard unicode encodings


asammdf is available on

pip install asammdf