Set of tools to process raw instrument data according to a dataschema into a standardised form called datagram, annotated with metadata, provenance information, timestamps, units, and uncertainties. Developed by the Materials for Energy Conversion at Empa - Materials Science and Technology.

Capabilities:

• Parsing tabulated data using CSV parsing functionality, including Bronkhorst and DryCal output formats. Columns can be post-processed using any linear combinations of raw and processed data using the calibration functionality.
• Parsing chromatography data from gas and liquid chromatography, including several Agilent, Masshunter, and Fusion formats. If a calibration file is provided, the traces are automatically integrated using built-in integration routines.
• Parsing reflection coefficient traces from network analysers. The raw data can be fitted to obtain the quality factor and central frequency using several algorithms.
• Parsing potentiostat files for electrochemistry applications. Supports BioLogic file formats.

Features:

• timezone-aware timestamping using Unix timestamps
• automatic uncertainty determination using data contained in the raw files, instrument specification, or last significant digit
• uncertainty propagation to derived quantities
• tagging of data with units
• extensive dataschema and datagram validation using provided specifications
• mandatory metadata (such as provenance) is enforced

The full list of capabilities and features is listed in the project documentation.

Installation:

The released versions of yadg are available on the Python Package Index (PyPI) under yadg. Those can be installed using:

    pip install yadg

If you wish to install the current development version as an editable installation, check out the master branch using git, and install yadg as an editable package using pip:

   git clone [email protected]:dgbowl/yadg.git
   cd yadg
   pip install -e .

Additional targets yadg[testing] and yadg[docs] are available and can be specified in the above commands, if testing and/or documentation capabilities are required.

Contributors:

• Peter Kraus
• Nicolas Vetsch

Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957189. The project is part of BATTERY 2030+, the large-scale European research initiative for inventing the sustainable batteries of the future.