asammdf is a fast parser/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 (Travis CI tests done with Python 2.7 and Python >= 3.5)
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
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create new mdf files from scratch
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append new channels
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read unsorted MDF v3 and v4 files
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filter a subset of channels from original mdf file
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cut measurement to specified time interval
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convert to different mdf version
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export to Excel, HDF5, Matlab and CSV
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merge multiple files sharing the same internal structure
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read and save mdf version 4.10 files containing zipped data blocks
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split large data blocks (configurable size) for mdf version 4
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disk space savings by compacting 1-dimensional integer channels (configurable)
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full support (read, append, save) for the following map types (multidimensional array channels):
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mdf version 3 channels with CDBLOCK
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mdf version 4 structure channel composition
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mdf version 4 channel arrays with CNTemplate storage and one of the array types:
- 0 - array
- 1 - scaling axis
- 2 - look-up
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add and extract attachments for mdf version 4
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files are loaded in RAM for fast operations
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handle large files (exceeding the available RAM) using memory = minimum argument
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extract channel data, master channel and extra channel information as Signal objects for unified operations with v3 and v4 files
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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
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for version 3
- functionality related to sample reduction block (but the class is defined)
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for version 4
- handling of bus logging measurements
- handling of unfinished measurements (mdf 4)
- xml schema for TXBLOCK and MDBLOCK
- full support for remaining mdf 4 channel arrays types
- partial conversions
- event blocks
- channels with default X axis
- channels with reference to attachment
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/development/examples.html
http://asammdf.readthedocs.io/en/development
asammdf is available on
pip install asammdf
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
http://asammdf.readthedocs.io/en/development/benchmarks.html