SPIKE, EDYNLP and LDYNLP
The SPIKE, LDYNLP and EDYNLP are dynamic EMALP filters used to remove spikes.
LDYNLP and EDYNLP are specific SPIKE cases.
In the images, you can see how the filter makes the value transition smoothly in big variations, keeping the detail of small variations.
To use SPIKE, EDYNL and LDYNLP in Mover, do:
parameter1 is the value we want to filter.
parameter2 is the window where the filter has no effect.
parameter3 is the strength of the spike removal.
parameter4 is the exponential degree.
EDYNLP is a SPIKE filter with parameter4 = 2.
LDYNLP is a SPIKE filter with parameter4 = 1.
The filter acts by applying a stronger EMALP filter for larger variations of value.
So if the value we are filtering goes from 6 to 10, variation is 4, the strength of the filter is calculated based on 4.
If the variation goes from 6 to 56, strength is calculated with the variation of 50.
We could say that the samples used in the EMALP are 4 or 50 depending on the situation.
But using just the sample is to linear, so you can set the degree in parameter4 or/and a multiplier in parameter3.
So we can say that the strength or number of samples used in the EMALP are based in the formula:
Filter strongness = strength x variationdegree
Parameter2 defines a window of variance where the filter doesn't act.
So if window is 10, the filter starts to work above 10 keeping the received value original if under or equal to 10.