Reloading Data into LabOne Modules

LabOne now supports reloading data into the LabOne Modules when using the HDF5 file format.

Starting from LabOne 19.05, data acquired in modules such as the Sweeper, DAQ, Scope and Spectrum Analyzer, can now be reloaded into the same LabOne module. When the data is loaded into the module, it is available along side existing data for further viewing and analysis or for comparison to existing or subsequent data. It can then be exported as graphics, data or text in the same way as with your existing data. Reloading files does not change the settings of the LabOne module, so you are free to load and compare without having to worry about losing your settings (if you reload into a module ensure the X & Y scale matches the reloaded data). Reloading requires saving the data in the HDF5 file format, supported from LabOne 19.05.

Saving and Loading data in HDF5

To save data from the sweeper or DAQ module in HDF5, head to the history tab and select the HDF5 format (see figure 1). You can choose to save selected traces from within your history, or the whole history. Files can also be saved in parallel in other formats such as CSV. Saved files can then be accessed via the LabOne File Manager and downloaded for storage elsewhere.

To load a data set, drag the HDF5 file from the file location on the host computer onto the download area of the history tab as shown in figure 1. This will load the file and prefix the history name with “loaded” so you know which traces have been loaded and which are existing traces. Figure 1 shows the sweeper tab with two traces, one loaded and one existing trace.

The loaded traces are treated like normal measurement data and can also be exported in other formats such as MatLab or CSV as required. The save buttons in the sweeper (circled in orange in figure 1) work as usual to save the trace as a graphics file or text file. Please note, the save text button only saves the most recent trace in the sweeper; if you need to export text files of each of the traces in the history, these should be saved as individual HDF5 files. The loaded HDF5 traces can be used as a reference trace in the sweeper as usual.

 

Figure 1: screenshot of the LabOne sweeper module with annotated History tab showing the HDF5 save field and drag and drop load area.

 

Annotate using the math tools and export as graphics

The loaded traces can be scaled and shifted as usual, and the math tools can also be used to add annotations and analyse your data. The math tools allow you to add markers for peak, trough and tracking, along with many more useful annotations. The final charts can be exported as usual with a single click as vector graphics in a report-ready format.

 

About HDF5

HDF5 is a free binary file format, designed for saving large, structured data. It is widely used in both, academia and private industry. There are HDF5 libraries or plug-ins for almost any programming language or data processing software. As it is a binary format, inspecting a complete HDF5 file requires dedicated software as the freely available HDFview (as shown in figure 2).

When dealing with big and complex data, it is in most cases much more convenient to work with HDF5 instead of the widely used csv file format. We generally encourage our users to use HDF5 as their default file format. Its advantages outweigh the small initial effort required in most cases.

 

Figure 2: Screenshot of a LabOne file opened with HDFview, a software tool for viewing HDF5 files, freely available from the non-profit HDF group.

 

Reloading data improves your workflow

The ability to load previously taken traces improves your workflow by allowing you to select the best traces from your historical data set and include them with existing data in the LabOne module. The graphical toolset of the LabOne modules allow the swift arrangement of multi-trace charts, giving you report-ready vector graphics in seconds.

For further information on loading data back into LabOne, or any other support questions, get in touch.

 

Many thanks to Marco Gähler for implementing this feature and advising on this blog.