Information
Transformation: |
LowPass |
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Transformation Group: |
Filter |
Description: |
Low pass filter for discrete functions (e.g. time series). This transformation calculates the following difference equation. y(t) = b0*x(t) + b1*x(t-1) + ... + bM*x(t-M) + a1*y(t-1) + ... + aN*y(t-N). Here x is the input, y is the output, t denotes time, b0 to bM are the feedforward coefficients and a1 to aN are the feedback coefficients. |
Hydrological Information
Purpose and use of Transformation: |
To smooth time series data. In other words to filter out high frequency fluctuations in time series data. |
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Background and Exceptions: |
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Input
Input variable x(t). For each calculation of y(t) the input values x(t) to x(t-M) are required. If one of these input values is NaN, then the output value y(t) will be NaN.
Options
todo
CoefficientSets
todo
Output
Output variable y(t). For each calculation of y(t) the previous output values y(t-1) to y(t-N) are required. When this transformation runs, then the required previous output values from previous runs are retrieved from the database. If one of these previous output values is NaN, then that output value is ignored. Effectively this means that it behaves as if all previous NaN output values would be 0.
Configuration Example
<transformation id="filter low pass"> <filter> <lowPass> <inputVariable> <variableId>input</variableId> </inputVariable> <coefficientSet> <a>0.4</a> <a>0.3</a> <b>0.2</b> <b>0.1</b> </coefficientSet> <outputVariable> <variableId>output</variableId> </outputVariable> </lowPass> </filter> </transformation>
