ASAM MDF measurement data file parser
Project description
asammdf is a fast parser/editor for ASAM (Associtation for Standardisation of Automation and Measuring Systems) MDF (Measurement Data Format) files.
asammdf supports both MDF version 3 and 4 formats.
asammdf works on Python 2.7, and Python >= 3.4
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
read sorted and unsorted MDF v3 and v4 files
files are loaded in RAM for fast operations
for low memory computers or for large data files there is the option to load only the metadata and leave the raw channel data (the samples) unread; this of course will mean slower channel data access speed
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
usually a measuremetn will have channels from different sources at different rates
the Signal class facilitates operations with such channels
remove data group by index or by specifing a channel name inside the target data group
create new mdf files from scratch
append new channels
convert to different mdf version
add and extract attachments
mdf 4.10 zipped blocks
mdf 4 structure channels
Major features still not implemented
functionality related to sample reduction block (but the class is defined)
mdf 3 channel dependency save and append (only reading is implemented)
handling of unfinnished measurements (mdf 4)
mdf 4 channel arrays
xml schema for TXBLOCK and MDBLOCK
Usage
Check the examples folder for extended usage demo.
Documentation
Installation
asammdf is available on
Dependencies
asammdf uses the following libraries
numpy : the heart that makes all tick
numexpr : for algebraic and rational channel conversions
blosc : optionally used for in memmory raw channel data compression
matplotlib : for Signal plotting
pandas : for DataFrame export
Benchmarks
Python 3 x86
Benchmark environment
3.6.1 (v3.6.1:69c0db5, Mar 21 2017, 17:54:52) [MSC v.1900 32 bit (Intel)]
Windows-10-10.0.14393-SP0
Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
16GB installed RAM
Notations used in the results
nodata = MDF object created with load_measured_data=False (raw channel data no loaded into RAM)
compression = MDF object created with compression=True (raw channel data loaded into RAM and compressed)
noconvert = MDF object created with convertAfterRead=False
Files used for benchmark: * 183 groups * 36424 channels
Open file |
Time [ms] |
RAM [MB] |
|---|---|---|
asammdf 2.1.0 mdfv3 |
1031 |
284 |
asammdf 2.1.0 compression mdfv3 |
1259 |
192 |
asammdf 2.1.0 nodata mdfv3 |
584 |
114 |
mdfreader 0.2.5 mdfv3 |
3809 |
455 |
mdfreader 0.2.5 no convert mdfv3 |
3498 |
321 |
asammdf 2.1.0 mdfv4 |
2109 |
341 |
asammdf 2.1.0 compression mdfv4 |
2405 |
239 |
asammdf 2.1.0 nodata mdfv4 |
1686 |
159 |
mdfreader 0.2.5 mdfv4 |
44400 |
578 |
mdfreader 0.2.5 noconvert mdfv4 |
43867 |
449 |
Save file |
Time [ms] |
RAM [MB] |
|---|---|---|
asammdf 2.1.0 mdfv3 |
713 |
286 |
asammdf 2.1.0 compression mdfv3 |
926 |
194 |
mdfreader 0.2.5 mdfv3 |
19862 |
1226 |
asammdf 2.1.0 mdfv4 |
1109 |
347 |
asammdf 2.1.0 compression mdfv4 |
1267 |
246 |
mdfreader 0.2.5 mdfv4 |
17518 |
1656 |
Get all channels (36424 calls) |
Time [ms] |
RAM [MB] |
|---|---|---|
asammdf 2.1.0 mdfv3 |
3943 |
295 |
asammdf 2.1.0 compression mdfv3 |
29682 |
203 |
asammdf 2.1.0 nodata mdfv3 |
23215 |
129 |
mdfreader 0.2.5 mdfv3 |
38 |
455 |
asammdf 2.1.0 mdfv4 |
3227 |
351 |
asammdf 2.1.0 compression mdfv4 |
26070 |
250 |
asammdf 2.1.0 nodata mdfv4 |
21619 |
171 |
mdfreader 0.2.5 mdfv4 |
51 |
578 |
Python 3 x64
Benchmark environment
3.6.1 (v3.6.1:69c0db5, Mar 21 2017, 18:41:36) [MSC v.1900 64 bit (AMD64)]
Windows-10-10.0.14393-SP0
Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
16GB installed RAM
Notations used in the results
nodata = MDF object created with load_measured_data=False (raw channel data no loaded into RAM)
compression = MDF object created with compression=True (raw channel data loaded into RAM and compressed)
noconvert = MDF object created with convertAfterRead=False
Files used for benchmark: * 183 groups * 36424 channels
Open file |
Time [ms] |
RAM [MB] |
|---|---|---|
asammdf 2.1.0 mdfv3 |
801 |
352 |
asammdf 2.1.0 compression mdfv3 |
946 |
278 |
asammdf 2.1.0 nodata mdfv3 |
490 |
172 |
mdfreader 0.2.5 mdfv3 |
2962 |
525 |
mdfreader 0.2.5 no convert mdfv3 |
2740 |
392 |
asammdf 2.1.0 mdfv4 |
1674 |
440 |
asammdf 2.1.0 compression mdfv4 |
1916 |
343 |
asammdf 2.1.0 nodata mdfv4 |
1360 |
245 |
mdfreader 0.2.5 mdfv4 |
31915 |
737 |
mdfreader 0.2.5 noconvert mdfv4 |
31425 |
607 |
Save file |
Time [ms] |
RAM [MB] |
|---|---|---|
asammdf 2.1.0 mdfv3 |
575 |
353 |
asammdf 2.1.0 compression mdfv3 |
705 |
276 |
mdfreader 0.2.5 mdfv3 |
21591 |
1985 |
asammdf 2.1.0 mdfv4 |
913 |
447 |
asammdf 2.1.0 compression mdfv4 |
1160 |
352 |
mdfreader 0.2.5 mdfv4 |
18666 |
2782 |
Get all channels (36424 calls) |
Time [ms] |
RAM [MB] |
|---|---|---|
asammdf 2.1.0 mdfv3 |
2835 |
363 |
asammdf 2.1.0 compression mdfv3 |
18188 |
287 |
asammdf 2.1.0 nodata mdfv3 |
11926 |
188 |
mdfreader 0.2.5 mdfv3 |
29 |
525 |
asammdf 2.1.0 mdfv4 |
2338 |
450 |
asammdf 2.1.0 compression mdfv4 |
15566 |
355 |
asammdf 2.1.0 nodata mdfv4 |
12598 |
260 |
mdfreader 0.2.5 mdfv4 |
39 |
737 |
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