Description and usage

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:

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 Average

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.

PreviousTimeStep

The first input value to be found after each (output) timestep is stored in the output timeseries.

NextTimeStep

The first input value to be found before each (output) timestep is stored in the output timeseries.

Block

The first input value to be found before each (output) timestep is stored in the output timeserie, and repeated until a new value is found.

Linear

The value of each output timestep is calculated, by interpolating the input values just before and after the output timestep. Essentially, the value is found at the point where the line connecting these values, crosses the timestep.

LinearOrClosest

This method is a variation on the previous method. For every output timestep the method will search for input values in a searchwindow of 1 timestep-range before, and 1 timestep range after the output timestep. In this example, that means that for output timestep 02-01-2014 00:00 the method will look for a value in the searchwindow 01-01-2014 00:00 until 02-01-2014 00:00, and for one value in the searchwindow 02-01-2014 00:00 until 03-01-2014 00:00.

If there are input values before and after the output timestep, then these values are interpolated (this is the case for output timestep 02-01-2014 00:00). If there is only one value before or after, this value is stored (this is the case at 03-01-2014 00:00).

In the image below the results from both methods are depicted.

Configuration

A basic configuration of the function is described below.

<?xml version="1.0" encoding="UTF-8"?>
<transformationModule version="1.0" xmlns="http://www.wldelft.nl/fews" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.wldelft.nl/fews http://fews.wldelft.nl/schemas/version1.0/transformationModule.xsd">
	<!--Input time series-->
	<variable>
		<variableId>Q_in</variableId>
		<timeSeriesSet>
			<moduleInstanceId>ImportDatabase</moduleInstanceId>
			<valueType>scalar</valueType>
			<parameterId>Q.obs</parameterId>
			<locationSetId>HydroGauges_HuangChuan</locationSetId>
			<timeSeriesType>external historical</timeSeriesType>
			<timeStep unit="nonequidistant"/>
			<relativeViewPeriod unit="day" start="-16" end="0" startOverrulable="true"/>
			<readWriteMode>add originals</readWriteMode>
		</timeSeriesSet>
	</variable>
	<!--Output time series-->
	<variable>
		<variableId>Q_out</variableId>
		<timeSeriesSet>
			<moduleInstanceId>HuangChuan_Update_Pre</moduleInstanceId>
			<valueType>scalar</valueType>
			<parameterId>Q.obs</parameterId>
			<locationSetId>HydroGauges_HuangChuan</locationSetId>
			<timeSeriesType>external historical</timeSeriesType>
			<timeStep unit="hour" multiplier="6"/>
			<relativeViewPeriod unit="day" start="-16" end="0" startOverrulable="true"/>
			<readWriteMode>add originals</readWriteMode>
		</timeSeriesSet>
	</variable>
	<!--Transformations-->
	<transformation id="nonequidistant to equidistant">
		<sample>
			<nonEquidistant>
				<nonEquidistantInputVariable>
					<variableId>Q_in</variableId>
				</nonEquidistantInputVariable>
				<interpolationType>linear</interpolationType>
				<outputVariable>
					<variableId>Q_out</variableId>
				</outputVariable>
			</nonEquidistant>
		</sample>
	</transformation>
</transformationModule>

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Nonequidistant