Thread: Release of the Quality Assurance Report Generator (QuARG)

Started: 2020-11-18 14:15:55
Last activity: 2020-11-18 14:15:55
Topics: DMC Software

We are pleased to announce the release of QuARG, the Quality Assurance Report Generator, available through the iris-edu GitHub repository:
This utility creates a Quality Assurance report, intended for network operators who want and need to have an understanding of the health of the stations in their network. The report calls attention to underperforming or broken stations so that time and resources can be prioritized as they are allocated for improving the quality of the network.

QuARG is a python-based utility that walks the user through the process of creating a quality assurance report. This process follows 4 broad steps:

It utilizes MUSTANG ( metrics available through our web services, or alternately metrics generated using ISPAQ (using ISPAQ 3.0, available soon) (, to find and highlight potential problems in the data by flagging days that exceed user configurable threshold values. By using the pre-computed metrics, it reduces the amount of time that an analyst has to spend scanning the data for problems. It can also find issues that would otherwise go undetected by the eye.

Users then analyze the list of potential issues to determine if these are data quality problems that should be included in the report. QuARG makes it easy to keep track of which issues have been investigated, keep notes on what the analyst has found, and link to a slew of QA tools, such as waveform plots, metric plots, and Probability Density Function (PDF) plots, to make it easier to understand the problem.

From there, users create tickets that describe the problem. Tickets can be created in QuARG, or in an external ticketing system if the analysts have one that they already use.

These tickets, which track problems as they arise and can be updated when they are fixed, are then used to create a nicely formatted HTML quality assurance report.

Full documentation can be read at

Software Requirements:
- Linux or macOS operating system
- Anaconda ( or Miniconda (

For questions or comments: dmc_qa<at>
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