anchor.diff:  
   (**
   - Because zeros are introduced, the adjacent signals are going to receive a high correlation!
  -- In this example the correlation coefficients in general drop to the half intensity in respect to the default transform above because a zero value is introduced between every data point (minDistance = 0.5).
  +- In this example the correlation coefficients in general drop to a reduced intensity because a zero values are introduced between every data point (minDistance = minimal spacing / 2.). So here a zero padding makes no sense. The Temperature wont drop to zero between two measurements.
   - s = scale
   - f = frequency [days]
   *)
  
positive.code: 
  **Three regions can be defined where padding points could be introduced:**
  
   1. In the beginning and end of the data set artificial data points have to be added to analyse the start- and end-regions of the data. 
   
   2. If the data is not measured in discrete intervals, the region between two adjacent values have to be padded to ensure sufficient coverage for convolution.
   
  
negative.code: ///take random data point y-values when huge gaps are between data points
  let hugeGapPadMethod = Padding.HugeGapPaddingMethod.Random
  
  ///padd the start and end of the signal with random data points
  let borderPadMethod = Padding.BorderPaddingMethod.Random
  
  ///the maximal distance that is allowed between data points is the average spacing
  let minDistance = 
      Padding.HelperFunctions.getMedianSpacing data (-)
  