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Introduction

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

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 pandas, 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 files, 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 base
    • a measurement will usually 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: the sample reduction blocks are simply ignored

  • for version 4

    • functionality related to sample reduction block: the sample 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): 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 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

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
  • fastparquet : 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 (preferably the latest develop branch code)
  • 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

Installation

asammdf is available on

pip install asammdf
# 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.

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

## Contributors Thanks to all who contributed with commits to asammdf: