Since more than 10 years Go4 is used for the online and as offline analysis. Many histograms, pictures, conditions exist for plotting and computing up to the fragment identification. The FRS Go4 analysis program was written by Sergej Linev from GSI. A good starting point you can find in the last similar experiment.

Figure 1 illustrates the data flow when using the GSI/Hoek-Brown method for estimating the parameters required for a numerical analysis of underground or surface excavations in rock.

The Go4 (GSI Object Oriented On-line-Offline) Analysis Framework has been developed at GSI. It is based on the ROOT system of CERN. Therefore all functionality of ROOT can be used.

The GEF file format was invented by the GSI data analysis group (M.Dahlinger, B.Dechant, H.Döbbeling, H.Göringer) in the 1993/94 timeframe to facilitate exchange of histogram and scatter plot data between the then existing data analysis packages GOOSY, PAW/HBOOK, and SATAN.

Systems must be able to process data, i.e. histograms and event or other data, as produced by DAQ, slow control, simulations and other commonly used systems. This is the GEF format for histograms, the MBS format for event data, and N-tuples for compressed event data.

Jan 8, 2019 · This article presents a novel way to analyze and interpret longitudinal glycemic data, centered on the use of a temporal density glucose histogram together with the novel metrics, glycemic burden (GB) and glycemic severity index (GSI), …

Go4 developed at GSI is an analysis framework with a general purpose non blocking GUI. Go4 is based on ROOT. The GUI is implemented in Qt using GSI's QtROOT interface. Analysis and GUI run in separate tasks communicating through asynchronous object channels. A Go4 analysis may use any ROOT features.

Histogram conventions Histograms are drawn with the following convention: The content Y(i) of the channel X(i) is taken to be valid from the channel-number limit X(i) to X(i+1). Thus the number X(i+1) defines simultaneously the end of the bin (i) and the begin of bin (i+1).

In this study, we propose a novel methodology in order to predict Geological Strength Index (GSI) values of rock outcrops by using computer vision and machine learning methods.

Histogram for the Global severity index (GSI) scores of Basic symptoms inventory (BSI) Data. The bold bar on the left of the histogram indicates proportion of exact zeros. Multilevel...