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This transformation is used to convert a non equidistant time series to an equidistant time series. The value at this time step will derived from sampling the equidistant time series. It is possible to configure how the equidistant time series should be sampled.
The available options are:
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options
- maxGapLength (Only implemented for linear and block interpolation.)
- ignoreMissing / validationRule
maxGaplength: Gaps equal to or smaller than maxGapLength will be filled with sampled values. Gaps larger than maxGapLength will not be filled. If maxGapLength is not defined, then all gaps will be filled with sampled values.
ignoreMissing: if true, then missing values are ignored. If false, then an output value will be missing if one or more of the corresponding input values are missing or unreliable. Default is true.
Examples
Below examples are shown. Each example uses the same non-equidistant input timeseries, but a different sampling method is applied. The output timeseries has timesteps of 1 day.
Accumulate
Accumulates the values, weighted to the timestep, to the wanted output timestep.
Time Weighted AverageTimeWeightedAverage
Calculates the average value while taking the time between different values into account
Zero
Every input value that fits an output timestep is stored in the output timeseries. In this case that is only the last value, at 06-01-2014. All other values in the output timeseries are set to zero.
Missing
Every input value that fits an output timestep is stored in the output timeseries. In this case that is only the last value, at 06-01-2014. All other values in the output timeseries are set to missing.
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