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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

  • filter a subset of channels from original mdf file

  • 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

http://asammdf.readthedocs.io/en/stable

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

  • 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 not loaded into RAM)

  • compression = MDF object created with compression=True/blosc

  • compression bcolz 6 = MDF object created with compression=6

  • noDataLoading = MDF object read with noDataLoading=True

Files used for benchmark: * 183 groups * 36424 channels

Open file

Time [ms]

RAM [MB]

asammdf 2.3.0 mdfv3

1126

294

asammdf 2.3.0 nodata mdfv3

917

123

mdfreader 0.2.5 mdfv3

3743

455

asammdf 2.3.0 mdfv4

2359

348

asammdf 2.3.0 nodata mdfv4

1906

166

mdfreader 0.2.5 mdfv4

43166

577

Save file

Time [ms]

RAM [MB]

asammdf 2.3.0 mdfv3

420

297

asammdf 2.3.0 nodata mdfv3

445

130

mdfreader 0.2.5 mdfv3

20078

1224

asammdf 2.3.0 mdfv4

711

357

asammdf 2.3.0 nodata mdfv4

738

175

mdfreader 0.2.5 mdfv4

17822

1687

Get all channels (36424 calls)

Time [ms]

RAM [MB]

asammdf 2.3.0 mdfv3

777

299

asammdf 2.3.0 nodata mdfv3

18662

132

mdfreader 0.2.5 mdfv3

36

455

asammdf 2.3.0 mdfv4

681

354

asammdf 2.3.0 nodata mdfv4

20439

176

mdfreader 0.2.5 mdfv4

51

578

Python 3 x64

Benchmark environment

  • 3.6.2 (v3.6.2:5fd33b5, Jul 8 2017, 04:57:36) [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 not loaded into RAM)

  • compression = MDF object created with compression=blosc

  • compression bcolz 6 = MDF object created with compression=6

  • noDataLoading = MDF object read with noDataLoading=True

Files used for benchmark: * 183 groups * 36424 channels

Open file

Time [ms]

RAM [MB]

asammdf 2.3.0 mdfv3

1011

379

asammdf 2.3.0 nodata mdfv3

725

198

mdfreader 0.2.5 mdfv3

2973

537

asammdf 2.3.0 mdfv4

1890

464

asammdf 2.3.0 nodata mdfv4

1542

268

mdfreader 0.2.5 mdfv4

32192

748

Save file

Time [ms]

RAM [MB]

asammdf 2.3.0 mdfv3

359

379

asammdf 2.3.0 nodata mdfv3

352

205

mdfreader 0.2.5 mdfv3

21777

1997

asammdf 2.3.0 mdfv4

525

471

asammdf 2.3.0 nodata mdfv4

542

280

mdfreader 0.2.5 mdfv4

19591

2795

Get all channels (36424 calls)

Time [ms]

RAM [MB]

asammdf 2.3.0 mdfv3

589

383

asammdf 2.3.0 nodata mdfv3

8841

209

mdfreader 0.2.5 mdfv3

28

537

asammdf 2.3.0 mdfv4

494

468

asammdf 2.3.0 nodata mdfv4

12330

280

mdfreader 0.2.5 mdfv4

39

748

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