Manipulating Data

The smoothening action has two different modes, a rolling average or the Savitzky-Galov filter. The moving average simply works iterating through the data set, and replacing each point by the average of the last few data points that were encountered, this way sharp outliers are ignored but details may be lost. Sudden actual changes in the data may also be detected later when a smoothening has been applied. Another way to smoothen the data is the Savitzky-Galov. The difference with the moving average, is that not a simple average is used for the last data points, but instead a polynomial function is fitted between the data points. This makes it possible to filter the underlaying signal still quite nicely out of very noisy data.

Another way to manipulate the data is by using custom transformations. This field transforms both X and Y coordinate piece by piece according to an equation of choice. For instance, to take the square of all Y-coordinates, simply set Y = Y² on the y-data field, and X = X on the x-data field. Note that the input coordinates are not case-sensitive.