ASAM MDF measurement data file parser
Project description
asammdf is a fast parser and editor for ASAM (Association for Standardization 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 >= 3.8
Status
Continuous Integration | Coveralls | Codacy | ReadTheDocs |
---|---|---|---|
PyPI | conda-forge |
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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
- experimental 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 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
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 <a href="https://liberapay.com/danielhrisca/donate"><img alt="Donate using Liberapay" src="https://liberapay.com/assets/widgets/donate.svg"></a>
Contributors
Thanks to all who contributed with commits to asammdf:
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 package 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 performance
- python-dateutil : measurement start time handling
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
- PySide6 : 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
- pyqtlet2 : for the GPS window
- isal : for faster zlib compression/decompression
Benchmarks
Project details
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MD5 | 3208d1036c56e1aab60bcbbfc930e14e |
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BLAKE2b-256 | 86f7efe365d4ad201177b6f9415216aaf7fbc38b2f34f11e0a6061efaa9efb0c |
Hashes for asammdf-7.2.0-5-cp38-cp38-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 807fba29d429d0cc8ce4bf6a9f921c3f7bfa4f2263b4ac81dd15720ea422aac0 |
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MD5 | a92de46e25b77b9f6a1ec81e939cc81f |
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BLAKE2b-256 | c7d017d076451c72be8f1a3edb5a5b21b2683f138a95227d4efba663bc59345f |
Hashes for asammdf-7.2.0-5-cp38-cp38-macosx_10_15_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2c6963d52f0e86c64847f8325710344c82e7ced36fcece7ec7ef60e5b230ccb3 |
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MD5 | 76e9f91e25d3abd4c0496a3e469ecccd |
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BLAKE2b-256 | 8c1c804481a6f8e39a974872426cf569a9342e08ea519e33969691ecf6060b51 |
Hashes for asammdf-7.2.0-4-cp310-cp310-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fa22979fc53d69467e2e0bd1664620a40935b4582a8bf7195a4414a1e1991639 |
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MD5 | 948c17f3b7407ca885b30df573e53a08 |
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BLAKE2b-256 | f0bd30da5c8fb627d525cc53c03bd0ab24dea18fea92db7fe1699fb5984ea279 |
Hashes for asammdf-7.2.0-3-cp310-cp310-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c00dc0fd395faa350823e2f37724ce8b3309eed74f55d66654ab7a02f418243b |
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MD5 | ccc2d74e3a1f00d28ced30bf6792f13f |
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BLAKE2b-256 | 1105b68073016178820b06d3e6309cd851b5da05f2db9471ee235efa29b39ee7 |
Hashes for asammdf-7.2.0-2-cp310-cp310-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7f168d22b75401acd82326290764b729ee40bec83dfc4ab1653db0ab31f5c8d8 |
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MD5 | a2c045b2c6fbbf32a36e59d888004f33 |
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BLAKE2b-256 | f73de0fa68d6bdaf2a3bb6bedee76da155cb1b418f934188a4cf08414aa2af4c |
Hashes for asammdf-7.2.0-1-cp311-cp311-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2c0c44f4b4b311cd994519cbd3169880e21076238d891e91c60989dc5386f971 |
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MD5 | 04547b43105d308d36d51249d8255874 |
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BLAKE2b-256 | 9e1fddbe878765bb303650431c511330ef9e9170e5175a868fa22270dc8bdbda |
Hashes for asammdf-7.2.0-1-cp310-cp310-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8660bc4b1f2a1fa4124a4f3ee367a458b45f257eb1ce7f1598f5708499184fa0 |
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MD5 | 86d842818ac57c40dfecff9276a0511c |
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BLAKE2b-256 | 0b7e1453b3d9fc50f52deceba85a57985cb94cb4cafa501d8bd2b6ef44682db9 |
Hashes for asammdf-7.2.0-1-cp310-cp310-macosx_10_15_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0f5ca8c36f06603c1b25b5658c70b2204feb00b8f62f2ba52a208c57c67c2c4b |
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MD5 | 1e515c15ecc51cd3192d8d0a4f789497 |
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BLAKE2b-256 | b597d9f4a6a15e128b96334fc957afe74fac925b3a9a3419871baee83dde0151 |
Hashes for asammdf-7.2.0-1-cp39-cp39-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2c660e5deb06cda5fd91569ad31bf9ae4b3142ca8e8577ed94555c23631a4688 |
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MD5 | 4d9b09d34d7b012fbd9b29c3d797806b |
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BLAKE2b-256 | 0a9091cd5990d0c6bdf795ab3dcab251647d4dbad9685d09e96eb14bfb90adab |
Hashes for asammdf-7.2.0-1-cp39-cp39-macosx_10_15_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4e20561900b5028c3a41a54efd76199f39980e98460bd0a54379c7554f447f08 |
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MD5 | d8bd38e26ab5ab0f710b8165af7b273a |
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BLAKE2b-256 | 2124f7fc51573ed7004285392bff5e22f43e2a7b3b4ac5011b94d2a99af661c8 |
Hashes for asammdf-7.2.0-1-cp38-cp38-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5cd859b9ce9fd6dc4fa0cb6e2e866afd3e6f27155dcbd12faa0230800d671625 |
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MD5 | 8d480189c6999291b26e0425f78b0660 |
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BLAKE2b-256 | ad3335f75c9d21e95f9afda8ac21f14f370038713535a643744d925335fd06da |
Hashes for asammdf-7.2.0-1-cp38-cp38-macosx_10_15_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 431c4ef0410f0bd020d5360b7359fa31b26d96b1433892b3b2d39be979fac7f5 |
|
MD5 | ae7a6f5b0e8ff814165f436f1d928c7a |
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BLAKE2b-256 | 59737e3b98c4669ebe85f5672e926787298d6ffd4721cf5bdcfc966c69c19d68 |