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
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
conda-forge: https://anaconda.org/conda-forge/asammdf
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: