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
- create new mdf files from scratch
- append new channels
- read unsorted MDF v3 and v4 files
- read CAN bus logging files
- filter a subset of channels from original mdf file
- cut measurement to specified time interval
- convert to different mdf version
- export to pandas, Excel, HDF5, Matlab (v4, v5 and v7.3) and CSV
- 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) using memory = minimum argument
- 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
for version 3
- functionality related to sample reduction block
for version 4
- functionality related to sample reduction block
- handling of channel hierarchy
- full handling of bus logging measurements
- handling of unfinished measurements (mdf 4)
- full support for remaining mdf 4 channel arrays types
- xml schema for MDBLOCK
- full handling of event blocks
- channels with default X axis
- chanenls with reference to attachment
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
- canmatrix : to handle CAN bus logging measurements
optional dependencies needed for exports
- h5py : for HDF5 export
- xlsxwriter : for Excel export
- scipy : for Matlab v4 and v5 .mat export
- hdf5storage : for Matlab v7.3 .mat export
other optional dependencies
- chardet : to detect non-standard unicode encodings
- PyQt5 : for GUI tool
- pyqtgraph : for GUI tool
asammdf is available on
- github: https://github.com/danielhrisca/asammdf/
- PyPI: https://pypi.org/project/asammdf/
- conda-forge: https://anaconda.org/conda-forge/asammdf