Skip to main content

"Scientific instruments produce proprietary binary data that contains a multitude of primary and metadata. This project aims to create a software that supports the domain scientist in deciphering this data and metadata and supply python scripts that decipher the instrument measurements."

Project description

Pylint MyPy pypi coverage

Documentation on software for deciphering proprietary binary data-files

Scientific instruments produce proprietary binary data that contains a multitude of primary and metadata. This project aims to create a software that supports the domain scientist in deciphering this data and metadata and supply python scripts that decipher the instrument measurements.

MARBLE is open und free software and can be found at a Repository and Documentation.

Contributors

  • Steffen Brinckmann (IEK-2, FZJ) [Principal investigator]
  • Volker Hofmann (IAS-9 and HMC, FZJ)
  • Fiona D.Mello (IAS-9 and HMC, FZJ)

Introduction into proprietary binary data-files and MARBLE

In proprietary binary files, data can be grouped sequentially in sections - like sequential chapters in a book, and can have very different lengths. The sections can have very different lengths: some section only contains the name of the operator while another section contains thousands of temperature values. These files are called binary because they are not human readable but are a list of 1s and 0s and they are called proprietary because the instrument vendor has designed them particularly for this company or even instrument. As such, these files cannot be deciphered manually and MARBLE supports the scientist in this task.

MARBLE reads the proprietary binary files and - with the help of the scientist - outputs a python converter. This python converter can then be used to translate all proprietary binary files from this instrument into an hdf5-file format, which can be easily read by any computer language. The python converter also acts as verification tool: if a binary file A can be converted by this specific converter, then this file A comes from this instrument. This verification ability is helpful in finding files from a particular instrument.

In MARBLE, data in proprietary binary files is grouped into classes:

  • Metadata is data that describes the experiment. Examples are the name of the operator, the instrument vendor, the start time of the experiment. This metadata is commonly stored in key-value-pairs. For instance, "operator" is the key and "Peter Smith" is the value as both form an inseparable pair. Generally, the first parts of proprietary binary files contain lots of metadata.
  • Primary data is the data that the operator wanted to measure and this primary data has a form of a list. Lets say we want to measure the temperature at our house every 1min and store this information; then temperature is a primary data and stored in a long list. Generally, the instrument also saves the time increment after the measurement start and stores these time increments in a separate list, which is also primary data. Primary data can be of two types floating point numbers with normal or high precision.
  • Undefined sections are those sections of the file which the scientist and MARBLE have not identified yet. Some of these sections might be important or unimportant. Unimportant sections are those where the programmer at the instrument vendor was lazy and did included garbage or empty space. These might also be linked to specific languages the instrument vendor used for programming.

MARBLE is semi-automatically deciphering files because it has different methods to automatically identify sections in binary files. For some files, the methods work perfectly while for other files they fail. These failures are where the scientist uses his domain knowledge to augment the automatic methods or corrects MARBLE's mistakes:

  • The scientist always labels data:
    • specifies the key for a value: if he/she reads a name, the key would be "operator"
    • specifies the terms, units and IRI: links to terminology servers
  • Especially primary data has often to be corrected by shifting the starting position or by changing the length. Although these changes feel strange at first, it becomes easier soon after understanding the rule: "different primary data sections generally have the same length".
  • The user identifies which sections are important as these should be saved in the output. Additionally, the user defines how certain he/she is about specific sections.
  • The user can also remove wrongly identified sections and then use other algorithms to identify those.

How to install and run MARBLE

Install MARBLE with

pip install pymarble

To start graphical user interface (GUI)

marbleGUI

How to use the GUI

  1. A dialog opens asking you for a binary file to load.
  2. One row will be automatically filled: everything of the file is unknown. Right-click and select "Automatically identify" depending on your data. A lot will be marked in most cases: click the F5 key to only focus on the non-binary data (see also statusbar).
  3. Now go through the sections and label them
    • by entering a "key" and "unit" where applicable. You can do that directly in the table columns that are grey: click to enter the key, value, probability, importance.
    • if you double click on the start and length column a form will open which gives you more options
    • If you want that the converter uses certain data, ensure that the "important" checkbox is ticked.
    • For keeping track of your own progress, you can use the "certainty". If you are unsure use red, medium-sure is yellow and very sure is green.
  4. Intermediate saving and reloading can be done with the .tags file. Use the F4 and F2 key for convenience.
  5. Once you are done, click the save to save a python converter into the directory of the proprietary binary file that you analysed.
  6. Go into the directory and use python converter to convert all files from this instrument by executing the command "python <converter.py> <proprietary_binary_file.dat>". You can add the "-v" option to the command to make the converter more verbose during translation. If all the conversions are successful, you deciphered this proprietary binary file successfully.

How to use the command line interface (CLI)

The CLI allows you to learn more about proprietary binary files and MARBLE. If you want to become more advanced, follow the tutorials in this section.

marbleCLI

There is a number of tutorials for the CLI:

You can read them and follow those commands. However, you can also just execute them with the argument "m", without the quotation marks. All of these tutorials are in the form of linux scripts, which are used for verification of the code at each development step.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

pymarble-2.2.1-py3-none-any.whl (85.2 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page