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 >= 3.6 (for Python 2.7, 3.4 and 3.5 see the 4.x.y releases)

Status

!	Travis CI	Appveyor	CoverAlls	Codacy	ReadTheDocs
master

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
- experiemental 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 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
- 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 pacakge 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 peformance

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

Benchmarks

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

zl475505/asammdf