In my 14 years of LabVIEW programming, I think I've only twice needed to use a 3D Array.

I wanted to brag about the latest one, and see how others have used them.

In this case, I was working on a LabVIEW driver for a company which makes custom DAQ equipment. This particular device has an oversample mode, so that their DLL spits out data in a 1-D stream as follows:

1a 1b 1c 2a 2b 2c 1a 1b 1c 2a 2b 2c ...

where in this case: Xa, Xb, Xc are oversamples of channel X.

After fighting with some very complicated preinitialization and insert/replace ideas, I realized that this was a 3D array of data.

M = # repeats
N = # of channels
O = # of oversamples

I reshaped the 1D array to a 3D with inputs: M, N, O, then did a double nested indexing for loop with a Mean function at the centre.

Worked like a charm!

Anyone else regularly using 3+ dimension arrays?

Ben

Not recently, but in years past I quite commonly used four and five dimensional arrays as the test case matrices for automatic storage triggering for data acquisition in wind turbine test projects. (For each bin of wind speed, For each bin of wind direction, For each bin of windspeed turbulence, For each bin of wind direction turbulence, For each of a number of possible turbine controller states, store up to so many ten minute samples of high bandwidth structural loading data.-- One five dimensional array for the number of samples desired, another for the number of samples obtained.)

But recently, I've been able to handle everything with two or less dimensions.

Best Regards, Louis.

I remember using a 4D one once (it had a changing parameter and each time it was changed it had several runs with each run recording more than one value). My boss also did a 5D once, but I don't remember the details.

Multi dimensional arrays works fantastic most of the occasion. I never tried using 4D or 5D but have used 3D for generating simultaneous outputs using a AO card.

In one of my DAQ systems I transfer data packages
from the DAQ loop to a TCP-server loop
via a functional global used as FIFO buffer.
Each data package is a 2D array, AI channels x samples,
therefore the buffer uses a 3D array where the
third dimension is the position of the 2D package
in the buffer.
Simple and efficient.

G