Summarizer Plugin

Some users may have several IDE files from which they need to draw some meaningful insight in a quick, automated fashion. For these users, we recommend using the Summarizer plugin; it will retrieve a handful of representative key metrics (recording duration, min/mean/max values, rms, etc.) from each recording and consolidate them into a single .csv for review.

Another common use case if a user has a very large file that then is split into many small parts.  See Splitting Large Files for how to perform this action in the Lab software.

Note – in order to enable plugins in enDAQ Lab, please read our Enabling Plugins guide.

Video 11: Summarizer Code

Video 12: Summarizer Plugin & Output

Using the Summarizer

Once the summarizer is installed, select Tools → Batch IDE Summarizer:

This should pop up the summarizer’s configuration window, in which you can specify:

• the input files to be summarized
• the output file path in which to write the results
• the type of output file

After selecting the desired options, click Run to execute the program. A progress bar will appear, and on completion will generate a confirmation window:

The output will then be saved to the file specified in the configuration window. The metrics calculated and saved in the output file include:

• IDE filename
• recording device’s serial/part numbers
• sensor channel name
• physical units (m/s²) and type (acceleration)
• recording’s start time/end time/duration
• recording’s UTC start/end times
• mean sampling frequency
• min/mean/max recorded values
• RMS value

For .csv output, the resulting table may look something like this:

Analyzing Resulting CSV File in Excel

The resulting CSV file will have many rows indexed by the file that was used in calculating those metrics. But each file will have upwards of 20 rows because of all the different sensor channels.  A helpful feature in Excel is to utilize the Filter function in the Data tab as shown.  

This could then allow you to plot a particular metric across time and between two different sensors.  An example is provided here of two sensors recording data on a commercial flight that was discussed in a blog article. Using the summarizer tool we can compare the data recorded across these many files.  Here is the resulting Excel file that shows how these plots were generated.