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Please note that the  triggering files cannot be located on the FTP.

groupImportPattern

Since 2024.01 - Option to specify groups of files that need to be imported together. This can be useful for forecasts spread over several files which should be imported as one forecast i.e. a group of files, in particular if each file is added to the import folder at a different time.

The filenames must contain the forecast date and time. The pattern to define a group is specified in fileNameDateTimePattern, with the expected number of files for one forecast in numberOfFiles. The files will be imported only when all expected files are in the import folder. A time span also has to be specified in waitingTime to define when to consider the forecast as failed and move it to the failed folder. If more than the waiting time has passed between the forecast time and T0, the forecast is considered failed and the files are moved to the failed folder.

This option can be used in combination with the importTriggeringFile option.

For example, for the following configuration:

<groupImportPattern>
	<numberOfFiles>6</numberOfFiles>
    <fileNameDateTimePattern>'timeseries_'MMddHHmm'_?'</fileNameDateTimePattern>
    <waitingTime unit="day" multiplier="1"/>
</groupImportPattern>

with the import folder containing the files: 

• timeseries_01011200_1
• timeseries_01011200_2
• timeseries_01011200_3
• timeseries_01011200_4
• timeseries_01011200_5
• timeseries_01011200_6
• timeseries_01021200_1

The first six files will be imported because six files are matching the pattern "timeseries_01011200_?". But the last file will not be imported yet because it is the only file matching the pattern "timeseries_01021200_?" .

fileNamePatternFilter

Option to filter files that need to be imported based on their filename. This can be useful if there are files in the import folder that should not be imported:

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ID of the IdMap used to convert external parameterId's and locationId's to internal parameter and location Id's. Each of the formats specified will have a unique method of identifying the id in the external format. See section on configuration for Mapping Id's units and flagsof identifying the id in the external format. See section on configuration for Mapping Id's units and flags.

moduleInstanceAware

Since 2023.01. If value is set to true, data will only be imported if the module instance id specified in the config file matches the module instance id of the downloaded data.

useStandardName (since stable build 2012.02)

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For some data formats an external unit is not defined in the file to be imported. This elements The element <externUnit> allows the unit to be specified explicitly. This unit , which is then used in possible to find the corresponding unit conversions as configured in a UnitConversionsFiles (see 02 Unit Conversions).

<externUnit parameterId="P.nwp.fcst" unit="mm" cumulativeSum="false"/>

Attributes of <externUnit>:Attributes;

• parameterId: Id of the parameter for which a unit is specified. This is the internal parameter Id.
• unit: specification of unit. This unit must be available in the UnitConversions specified in the unitConversionsId element.
• cumulativeSum:  if this option is set to "true", then it is possible to receive the parameters as cumulative sums. Cumulative sums are partial sums of a given sequence of numbers. For example, if the sequence is:  {a, b, c, d, ...}, then the cumulative sums are: a, a+b, a+b+c, a+b+c+d, .... . The import module will then calculate the values for the individual time steps {a, b, c, d, ...} by subtracting the cumulative sum of the previous cumulative sum value.
• cumulativeMean: similar to cumulativeSum, if cumulativeMean is set to "true", then it is possible to receive the parameters as cumulative means. Cumulative means are partial means of a given sequence of numbers. Therefore, for a given sequence of numbers s = {x₁,x₂,...,x_n,x_n+1} cumulative means are calculated as follows: CM (x_n+1) = [x_n+1 + n * CM (x_n)] ⁄ n+1. The import module will then calculate the values for the individual time steps {a, b, c, d, ...} by multiplying the cumulative mean with the amount of time steps already processed and then subtracting of the previous value that has been also multiplied with the amount of timestep processed minus 1.

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