The bin size in an FFT is not a setting, but is a result of the interplay between the number of data points and the data-collection rate.<\/p>\n
The number of bins in the FFT scales with the number of data points collected, with an additional power-of-two point number requirement. The FFT algorithm requires 2^n points, where n is an integer. If the number of points collected is not a power of two, the FFT algorithm either has to pad the data or truncate it. Graphical Analysis Pro and Logger Pro<\/i> do both in various situations.<\/p>\n
The number of bins increases as you supply more data points. The number of bins is close to an integer power of two; if a data table would display 1024 points, but the table cuts off at 1000, the number of bins will be 1000. If the data table has 1100 points, you’ll see 1024 bins. If you supply 2500 points, you’ll see the next power of two in bins, or 2048.<\/p>\n
In addition, the maximum frequency of the FFT is half the sample rate.<\/p>\n
These two factors determine the bin size. It is not a setting, but a result of the FFT algorithm.<\/p>\n
We often set up an experiment that uses a graph time range that is shorter than the data collection time. This graph displays the waveform nicely, but a longer data collection takes place so we get the small bin size we are looking for.<\/p>\n