Grapgh entire data set from "read from measurement file"

[McMillinRacing]

Hello all,

     I am having trouble graphing the entire data set pulled from a "read from measuremet file" sub-VI. I have tried several configurations of loops. I understand that I am only pulling a defined "segment size" at a time from the RFMF sub-VI, the problem is the graph is only showing one segment at a time.

If I use no loop at all, I only see the first segment, which works out to bhe the first second of data collected. If I put the RFMF sub-VI in a while loop and the graph outside the loop, there is a long pause, then the graph displays the final segment of the data file (the last second). If I put the RFMF and graph inside the loop, I can watch the graph change as it graphs one set of data at a time, then eventually ends, showing just the last set of data.

Could someone pleas let me know what i am missing? (I attached my current VI as well as a sample data set (taken with a CDAQ 9133, with analog and strain modules connected)

Thanks a ton,

Max

[Wolleee]

You  need to use a chart instead of a graph.  The graph will always overwrite previous data with most recent data set.  Depending on the size of the data set the chart can be very memory intensive, so you can limit the history of the chart if necessary.

[edit]:

Graphs vs Charts

[McMillinRacing]

Yes, I see what you mean with a chart being able to plot all data (while the VI is running). I edited my VI to include a chart, which shows all the data. But, I like the mixed signal graph because it gives me one x-scale for all the graphs. So that if I am concerned with one section of data, I am able to zoom in on that particular section and can view multiple plots in the same x-scale region. Is there a way to append one segment onto the next so that I can still view all the data in one graph?

Thanks

[Wolleee]

Off the top of my head, you can always keep a running storage of all the data, using Shift Registers and then with every update it will plot all the data. 

Caution with this, because you can consume large amounts of memory, depending on your dataset size.