What | nameofinstance.xml |
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Description | Configuration for the new version of the transformation module |
schema location |
Entry in ModuleDescriptors
<moduleDescriptor id="TransformationModule">
<description>Transformation Module</description>
<className>nl.wldelft.fews.system.plugin.transformationmodule.TransformationModule</className>
</moduleDescriptor>
Contents
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The Transformation module is a general-purpose module that allows for generic transformation and manipulation of time series data. The module may be configured to provide for simple arithmetic manipulation, time interval transformation, shifting the series in time etc, as well as for applying specific hydro-meteorological transformations such as stage discharge relationships etc.
A new An improvement version of the FEWS Transformation Module is currently under construction. The new version is much more easy to configure than the old version. The new version uses a new schema for configuration, which is explained below (TODO). Currently only a few of the old transformations are available in the new transformation module. All of the old transformations will be made available in the new module, as well as several new transformations.
...
This list will be continuously updated to show the current status of the transformations available in the new transformation module.
Group | Transformation | Code completed? | Test completed? | Comment | |
---|---|---|---|---|---|
adjust | adjustStage | | | New feature | |
adjust | adjustQ | | | New feature | |
adjust | | | New feature | ||
adjust | | | New feature | ||
adjust | adjustTide | | | New feature | |
aggregation | instantaneous | | |
| |
aggregation | accumulative | | |
| |
aggregation | instantaneousToMean | | |
| |
aggregation | meanToMean | | |
| |
aggregation | seasonal | | |
| |
altitude | zero degree altitude | | |
| |
altitude | elevation lapse | | |
| |
conditional | multiple | | | This allows definition of multiple rule groups, combined with logical operators (from the current lookup module) | |
conversion | datum | | | (New feature) This includes an enumeration option called direction local/global | |
disaggregation | accumulative | | |
| |
disaggregation | instantaneous | | |
| |
disaggregation | meanToInstantaneous | | |
| |
disaggregation | meanToMean | | |
| |
disaggregation | weights | | |
| |
discharge stage | power | | | Uses the same coefficient sets as stage discharge power. | |
discharge stage | table | | | Uses the same coefficient sets (for table entries) as stage discharge table. | |
evaporation | penmanNorthEast | | | should be renamed to the specific type of penman equation | |
filter | moving window | | | contains options average/maximum/minimum | |
filter | block | | | contains options average/maximum/minimum | |
interpolation serial | block | | |
| |
interpolation serial | default | | |
| |
interpolation serial | extrapolate base | | | Extrapolates by adding future values that start at the last data value and linearly approach a given base value. | |
interpolation serial | extrapolate constant | | | Extrapolates by filling the gap between the start of the input time series and the first data value or by filling the gap between the last data value and the end of the input time series or by filling both of these gaps. | |
interpolation serial | extrapolate exponential | | | New feature: Extrapolates by adding future values that start at the last data value and exponentially approach a given base value. | |
interpolation serial | extrapolate persistence | | | Extrapolates by adding future values that start with the last moving average value and exponentially approach zero. This can be used together with the filterMoving average and userSimple transformations to reproduce the old originalAndPersistence and movingAverageAndPersistence transformations. | |
interpolation serial | extrapolate trend | | | Should contain option on how to extrapolate: persistence trend/running mean trend | |
interpolation serial | linear | | |
| |
interpolation spatial | average | | |
| |
interpolation spatial | bilinear | |
| |
|
interpolation spatial | closest distance | | |
| |
interpolation spatial | kriging | | |
| |
interpolation spatial | sum | | |
| |
interpolation spatial | inputAverageTimesOutputArea | | |
| |
interpolation spatial | inverse distance | | | (Uses Fortran 90 code) | |
interpolation spatial | sum | | |
| |
interpolation spatial | triangulation | | | (Uses Fortran 90 code) Used to be called Renka & Cline | |
interpolation spatial | weighted | | | This is the old weighted average approach used for rainfall. | |
lookup | simple | | | should be called oneDimensional. From the current lookup module | |
lookup | twoDimensionalLookup | | | should be called twoDimensional. New feature | |
lookup | twoDimensionalTable | | | From the current lookup module | |
merge | simple | | | merge data hierarchy | |
merge | toggle | | |
| |
merge | mean | | | New feature | |
merge | synoptic | | | This needs a true false element to indicate if the smaller or the larger quantities prevail (new feature) | |
merge | forecasts | | | equivalent to sampleForecasts - this is a new feature. | |
moisture | soil moisture | | | unit test not yet available | |
moisture | optical depth | | | unit test not yet available | |
precipitation | rainfallEvent | | | writes median value of event (Enumeration of 2 to indicate where to write the data) | |
precipitation | rainfallEventLength | | | writes length of event (Enumeration of 2 to indicate where to write the data) | |
precipitation | tipping bucket | | |
| |
profile | timeSeries | | | Converts a profile to a time series. | |
profile | harmonics | | | Converts an input profile consisting of harmonic components (a Fourier series) to a time series. | |
review | stageReview | | | New feature | |
review | tidal balanceReview | | | New feature | |
sample | historical | | |
| |
sample | forecasts | | | This samples the first n values from a forecast - somewhat similar to the merge-forecast function | |
sample | equidistant | | | converts an equidistant series to another equidistant series or to a non-equidistant series | |
sample | non-equidistant | | | converts a non-equidistant series to another non-equidistant series or to an equidistant series | |
selection | peaks | | |
| |
selection | independent peaks | | | previously called selectWithinHorizontalgaps | |
selection | lows | | |
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selection | independent lows | | |
| |
selection | maximum | | |
| |
selection | minimum | | |
| |
stage discharge | power | | | Uses the same coefficient sets as discharge stage power. | |
stage discharge | power backwater | | | Backwater correction (new feature). | |
stage discharge | power unsteady | | | Unsteady flow (new feature). | |
stage discharge | parabolic | | | New feature | |
stage discharge | table | | | Uses the same coefficient sets (for table entries) as discharge stage table. | |
statisticsSummary | sum, max, etc. | | | Input is one time series. Output is one value. All values in the input series within the transformation run period are used to get a statistic output value. This covers the case in the old transformations for which isBlockFunction is false and no season is specified. | |
statisticsSerial | sum, max, etc. | | | These are aggregation functions. Input is one time series. Output is one time series. For each output time all input values within a certain 'aggregation period' are used to get a statistic output value. This is used to get e.g. the mean temperature per day for every day in the input time series. This covers the case in the old transformations for which isBlockFunction is true. Together with the (still to implement) repeating timeStep types this also covers the case in the old transformations for which isBlockFunction is false, a season is specified and seasonal is true. The aggregation periods of different output times can overlap, therefore it is also possible to calculate e.g. moving average. | |
statisticsPeriodical | sum, max, etc. | | | These are the seasonal stats - used to define e.g. mean monthly temp. This is e.g. used in climatology to get e.g. the mean temperature in January over the last 100 years. This covers the case in the old transformations for which isBlockFunction is false, a season is specified and seasonal is false. | |
statisticsEnsemble | sum, max, etc. | | | Input is an ensemble with multiple time series. Output is one time series. Per time step the ensemble values for that time step are used to get a statistic output value. | |
statisticsSpatial | sum, max, etc. | | | New feature. Input is a grid time series. Output is a scalar time series. Per time step all grid cell values for that time step are used to get a statistic output value. | |
statisticsChildrenLocations | sum, max, etc. | | | New feature. Input is multiple time series. Output is multiple time series. For each output series (with a parent location) the corresponding child locations are searched in the input series and used to get a statistic output value, per time step. | |
structure | general weir fixed height | | |
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structure | general weir variable height | | |
| |
structure | flatV weir | | | Enumeration to indicate simple or crest tapping | |
structure | flatV weir backwater | | | New feature | |
structure | crump weir | | | Enumeration to indicate simple or crest tapping | |
structure | crump weir backwater | | | New feature | |
structure | | |
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structure | | |
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structure | | |
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structure | | |
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time shift | constant | | |
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time shift | variable | | |
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time shift | length | | |
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user | | | free format expression | ||
user | | | This can be used for periodic output - the range can be defined, e.g 0-1, 0-360 or 0-100 |
Configuration details (TODO)
When available as configuration on the file system, the name of an XML file for configuring an instance of the transformation module called for example TransformHBV_Inputs may be:
TransformHBV_Inputs 1.00 default.xml
TransformHBV_Inputs | File name for the TransformHBV_Inputs configuration. |
1.00 | Version number |
default | Flag to indicate the version is the default configuration (otherwise omitted). |
The configuration for the transformation module consists of two parts: transformation configuration files in the Config/ModuleConfigFiles directory and coefficient set configuration files in the Config/CoefficientSetsFiles directory.
In a transformation configuration file one or more transformations can be configured. See List of transformations for the available types of transformation. Some transformations require coefficient sets in which given coefficients are defined. For a given transformation that requires a coefficient set there are different ways of defining the coefficient set in the configuration. One way is to specify an embedded coefficient set in the transformation configuration itself. Another way is to put a reference in the transformation configuration. This reference consists of the name of a separate coefficient set configuration file and the id of a coefficient set in that file.
Both the transformations and coefficient sets can be configured to be time dependent. This can be used for instance to define a given coefficient value to be 3 from 1 January 2008 to 1 January 2009, and to be 4 from 1 January 2009 onwards. This can be done by defining multiple periodCoefficientSets, each one with a different period, as in the following xml example.
No Format |
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<periodCoefficientSet>
<period>
<startDateTime date="2008-01-01" time="00:00:00"/>
<endDateTime date="2009-01-01" time="00:00:00"/>
</period>
<structure>
<pumpFixedDischarge>
<discharge>3</discharge>
</pumpFixedDischarge>
</structure>
</periodCoefficientSet>
<periodCoefficientSet>
<period>
<validAfterDateTime date="2009-01-01"/>
</period>
<structure>
<pumpFixedDischarge>
<discharge>4</discharge>
</pumpFixedDischarge>
</structure>
</periodCoefficientSet>
|
If a date is specified without a time, then the time is assumed to be 00:00:00, so <validAfterDateTime date="2009-01-01"/> is the same as <validAfterDateTime date="2009-01-01" time="00:00:00"/>. To specify dates and times in a particular time zone use the optional time zone element at the beginning of a transformations or a coefficient sets configuration file, e.g. <timeZone>GMT+5:00</timeZone>. Then all dates and times in that configuration file are in the defined time zone. If no time zone is defined, then dates and times are in GMT. Note: 2008-06-20 11:33:00 in time zone GMT+5:00 is physically the same time as 2008-06-20 06:33:00 in GMT.
If for a given transformation there are different coefficientSets configured for different periods in time, then the following rule is used. The start of a period is always inclusive. The end of a period is exclusive if another period follows without a gap in between, otherwise the end of the period is inclusive. If for example there are three periodCoefficientSets defined (A, B and C), each with a different period, as in the following xml example. Then at 2002-01-01 00:00:00 periodCoefficientSet A is valid. At 2003-01-01 00:00:00 periodCoefficientSet B is valid since the start of the period is inclusive. At 2004-01-01 00:00:00 periodCoefficientSet B is still valid, since there is a gap after 2004-01-01 00:00:00. At 2011-01-01 00:00:00 periodCoefficientSet C is valid, since no other periods follow (the period of C is the last period in time that is defined). This same rule applies to time-dependent transformations.
...
also several new transformations are added.
Configuration
When available as configuration on the file system, the name of an XML file for configuring an instance of the transformation module called for example TransformHBV_Inputs may be:
TransformHBV_Inputs 1.00 default.xml.
TransformHBV_Inputs | File name for the TransformHBV_Inputs configuration. |
1.00 | Version number |
default | Flag to indicate the version is the default configuration (otherwise omitted). |
The configuration for the transformation module consists of two parts: transformation configuration files in the Config/ModuleConfigFiles directory and coefficient set configuration files in the Config/CoefficientSetsFiles directory.
In a transformation configuration file one or more transformations can be configured. Some transformations require coefficient sets in which given coefficients are defined. For a given transformation that requires a coefficient set there are different ways of defining the coefficient set in the configuration. One way is to specify an embedded coefficient set in the transformation configuration itself. Another way is to put a reference in the transformation configuration. This reference consists of the name of a separate coefficient set configuration file and the id of a coefficient set in that file.
Both the transformations and coefficient sets can be configured to be time dependent. This can be used for instance to define a given coefficient value to be 3 from 1 January 2008 to 1 January 2009, and to be 4 from 1 January 2009 onwards. This can be done by defining multiple periodCoefficientSets, each one with a different period, as in the following xml example.
Code Block | ||||
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<periodCoefficientSet>
<period>
<startDateTime date="2008-01-01" time="00:00:00"/>
<endDateTime date="2009-01-01" time="00:00:00"/>
</period>
<structure>
<pumpFixedDischarge>
<discharge>3</discharge>
</pumpFixedDischarge>
</structure>
</periodCoefficientSet>
<periodCoefficientSet>
<period>
<validAfterDateTime date="2009-01-01"/>
</period>
<structure>
<pumpFixedDischarge>
<discharge>4</discharge>
</pumpFixedDischarge>
</structure>
</periodCoefficientSet>
|
If a date is specified without a time, then the time is assumed to be 00:00:00, so <validAfterDateTime date="2009-01-01"/> is the same as <validAfterDateTime date="2009-01-01" time="00:00:00"/>. To specify dates and times in a particular time zone use the optional time zone element at the beginning of a transformations or a coefficient sets configuration file, e.g. <timeZone>GMT+5:00</timeZone>. Then all dates and times in that configuration file are in the defined time zone. If no time zone is defined, then dates and times are in GMT. Note: 2008-06-20 11:33:00 in time zone GMT+5:00 is physically the same time as 2008-06-20 06:33:00 in GMT.
If for a given transformation there are different coefficientSets configured for different periods in time, then the following rule is used. The start of a period is always inclusive. The end of a period is exclusive if another period follows without a gap in between, otherwise the end of the period is inclusive. If for example there are three periodCoefficientSets defined (A, B and C), each with a different period, as in the following xml example. Then at 2002-01-01 00:00:00 periodCoefficientSet A is valid. At 2003-01-01 00:00:00 periodCoefficientSet B is valid since the start of the period is inclusive. At 2004-01-01 00:00:00 periodCoefficientSet B is still valid, since there is a gap after 2004-01-01 00:00:00. At 2011-01-01 00:00:00 periodCoefficientSet C is valid, since no other periods follow (the period of C is the last period in time that is defined). This same rule applies to time-dependent transformations.
Code Block | ||||
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| ||||
<periodCoefficientSet>
<!-- periodCoefficientSet A -->
<period>
<startDateTime date="2002-01-01" time="00:00:00"/>
<endDateTime date="2003-01-01" time="00:00:00"/>
</period>
...
</periodCoefficientSet>
<periodCoefficientSet>
<!-- periodCoefficientSet B -->
<period>
<startDateTime date="2003-01-01" time="00:00:00"/>
<endDateTime date="2004-01-01" time="00:00:00"/>
</period>
...
</periodCoefficientSet>
<periodCoefficientSet>
<!-- periodCoefficientSet C -->
<period>
<startDateTime date="2010-01-01" time="00:00:00"/>
<endDateTime date="2011-01-01" time="00:00:00"/>
</period>
...
</periodCoefficientSet>
|
Validation rules
The concept of the validation rules was introduced as a solution for a common problem in operational situations when using aggregation transformations. When for example an aggregation was done over an entire year a single missing value in the input values would cause that the yearly average was also a missing value.
The validation rules provide a solution for these types of situations. It allows to configure in which cases an output value should be computed although the input contains missing values and/or doubtful values.
The validation rules are optional in the configuration and can be used to define the outputflag and the custom flagsource of the output value based on the number of missing values/unreliables values and/or the number of doubtful values in the used input values per aggregation timestep. The available output flags are reliable, doubtful and missing.
With these rules it is possible to define for example that the output of the transformation is reliable if less than 10% of the input is unreliable and/or missing and that if this percentage is above 10% that in that case the output should be a missing value.
It is important to note that input values which are missing and input values which are marked as unreliable are treated the same. Both are seen as missing values by the validation rules.
Below the configuration of the basic example which was described above.
Code Block | ||
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<validationRule>
<inputMissingPercentage>10</inputMissingPercentage>
<outputValueFlag>reliable</outputValueFlag>
</validationRule>
<validationRule>
<inputMissingPercentage>100</inputMissingPercentage>
<outputValueFlag>missing</outputValueFlag>
</validationRule> |
The configured validation rules are applied in the following way. The first validation rules are applied first. In the example above the first rule is that if 10% or less of the input is missing (or unreliable) that the output flag will be set to reliable. If the input doesn't meet the criteria for the first rule the transformation module will try to apply the second rule. In this case the second rule will always apply because a percentage of 100% is configured.
Configuring a rule with a percentage of 100% is a recommended way of configuring the validation rules. By default if validation rules are configured and none of the configured rules are valid the output will be set to missing. Which means that in this case the second rule of 100% was not necessary because it is also the default hard-coded behaviour of the system.
But for the users of the system it is more understandable if the behaviour of the aggregation is configured instead of a hard-coded fallback mechanism in the software.
To explain the validation rules a bit more a more difficult example will explained. Let's say that we would like configure our aggregation in such a way that the following rules are applied:
1 if the percentage of missing and/or unreliable values is less than 15% the output should be reliable.
2 if the percentage of missing values is less than 40% the output should be doubtful.
3 in all other cases the output should be a missing value.
Below shows a configuration example in which the rules above are implemented.
Code Block | ||
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<validationRule>
<inputMissingPercentage>15</inputMissingPercentage>
<outputValueFlag>reliable</outputValueFlag>
</validationRule>
<validationRule>
<inputMissingPercentage>40</inputMissingPercentage>
<outputValueFlag>doubtful</outputValueFlag>
</validationRule>
<validationRule>
<inputMissingPercentage>100</inputMissingPercentage>
<outputValueFlag>missing</outputValueFlag>
</validationRule>
|
The example shows that in total 3 validation rules were needed. The first rule checks if less than 15% of the input is missing/unreliable. If this is not the case than it will be checked if the second rule can be applied. The second rule states that if less than 40% of the input is missing that in that case the output flag should be set to doubtful. The last rule takes care of all the other situations. Note that it has a percentage configured of 100%. Which means that this rule will be applied. However because 2 rules are defined above this rule FEWS will always try to apply these rules first before applying this rule.
In some cases one would like to differ between situations in which the outputflag is the same. In the example above if all of the input values were reliable the output is marked as reliable. But if for example 10% of the input values were unreliable the output is also marked as reliable.
It would be nice if the user of the system would be able to see in the GUI of FEWS why the input was marked reliable. Were there missing values in the input or not? Is the output based on a few missing values?
To make this possible the concept of the custom flag source was added to the validation rules. In addition to configuring an output flag it is also possible to configure a custom flag source. In the table of the Timeseriesdialog the custom flag source can be made visible by pressing ctrl + shift + j. This will make a new column in the table visible in which the custom flag source ids are shown. In the graph itself it also possible to make the custom flag sources visible by pressing ctrl + alt + v. To use the custom flagsources a file CustomFlagSources.xml should be added to the RegionConfig directory. For details see 27 CustomFlagSources In this file the custom flag sources should be defined. By configuring several rules which has the same outputflag but a different custom flagsource it is possible to make a difference between situations in which the outputflag is the same.
Below an example in which the output is reliable when there are no missing values in the input and when the percentage if missing values is less than 15%. However in the first case the output doesn't get a custom flagsource assigned while in the second case the output gets a custom flagsource assigned which is visible in the GUI to indicate that a output value was calculated but that missing values were found in the input.
Code Block | ||
---|---|---|
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<validationRule>
<inputMissingPercentage>0</inputMissingPercentage>
<outputValueFlag>reliable</outputValueFlag>
</validationRule>
<validationRule>
<inputMissingPercentage>15</inputMissingPercentage>
<outputValueFlag>reliable</outputValueFlag>
<outputCustomFlagSourceId>CA</outputCustomFlagSourceId>
</validationRule>
<validationRule>
<inputMissingPercentage>40</inputMissingPercentage>
<outputValueFlag>doubtful</outputValueFlag>
</validationRule>
<validationRule>
<inputMissingPercentage>100</inputMissingPercentage>
<outputValueFlag>missing</outputValueFlag>
</validationRule>
|
Finally it is also possible to define validation rules based on the number of doubtful values in the input. It is important to note that missing values and unrealiable values found in the input are also counted as doubtful values. It is even possible to define validation rules based on a combination of an allowed percentage of unreliable/missing values and doubtful values. The sequence of applying the rules is also in this case the order in which the rules are configured. The first rule which applies to the
current situation is used.
Let's say for example that we also want rules to be defined for the doubtful input values. For example when only a small number of input values are doubtful we still want the output to be reliable. Otherwise we would like to have the output to be doubtful but with an custom flag source which give us an indication of how many of the input values were doubtful.
Below a configuration example
Code Block | ||
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<validationRule>
<inputDoubtfulPercentage>10</inputDoubtfulPercentage>
<inputMissingPercentage>0</inputMissingPercentage>
<outputValueFlag>reliable</outputValueFlag>
</validationRule>
<validationRule>
<inputDoubtfulPercentage>30</inputDoubtfulPercentage>
<inputMissingPercentage>0</inputMissingPercentage>
<outputValueFlag>doubtful</outputValueFlag>
<outputCustomFlagSourceId>D1</outputCustomFlagSourceId>
</validationRule>
<validationRule>
<inputDoubtfulPercentage>60</inputDoubtfulPercentage>
<inputMissingPercentage>0</inputMissingPercentage>
<outputValueFlag>doubtful</outputValueFlag>
<outputCustomFlagSourceId>D2</outputCustomFlagSourceId>
</validationRule>
<validationRule>
<inputDoubtfulPercentage>100</inputDoubtfulPercentage>
<inputMissingPercentage>0</inputMissingPercentage>
<outputValueFlag>doubtful</outputValueFlag>
<outputCustomFlagSourceId>D3</outputCustomFlagSourceId>
</validationRule>
<validationRule>
<inputMissingPercentage>15</inputMissingPercentage>
<outputValueFlag>reliable</outputValueFlag>
<outputCustomFlagSourceId>CA</outputCustomFlagSourceId>
</validationRule>
<validationRule>
<inputMissingPercentage>40</inputMissingPercentage>
<outputValueFlag>doubtful</outputValueFlag>
</validationRule>
<validationRule>
<inputMissingPercentage>100</inputMissingPercentage>
<outputValueFlag>missing</outputValueFlag>
</validationRule> |
The explanation above gave an good idea of the possibilities of the use of the validation rules.
In the examples above the inputMissingValuePercentage and the inputDoubtfulPercentage was configured hard-coded in the configuration file. However it is also possible to make a reference to an attribute of a location. To reference to an attribute the referenced attribute should be placed within @.
Code Block | ||
---|---|---|
| ||
<inputMissingPercentage>@MV@</inputMissingPercentage>
|
For example to reference the attribute MV for the inputMissingValuePercentage the configuration should be like.
To explain the concept of the validation rules more the table below shows the input time series and the output time series of an aggregation accumulative tranformation which uses the validation rules which are shown above in the last eexample.
Time | Input value | input flag | Output value | output flag | custom flagsource |
---|---|---|---|---|---|
1-1-2012 00:15 | |||||
1-1-2012 00:30 | 1 | ||||
1-1-2012 00:45 | 1 | ||||
1-1-2012 01:00 | 1 | 3 | doubtful | - | |
1-1-2012 01:15 | |||||
1-1-2012 01:30 | 1 | ||||
1-1-2012 01:45 | |||||
1-1-2012 02:00 | 1 | NaN | - | - | |
1-1-2012 02:15 | 1 | ||||
1-1-2012 02:30 | 1 | doubtful | |||
1-1-2012 02:45 | 1 | ||||
1-1-2012 03:00 | 1 | 4 | doubtful | D1 | |
1-1-2012 03:15 | 1 | ||||
1-1-2012 03:30 | 1 | ||||
1-1-2012 03:45 | 1 | ||||
1-1-2012 04:00 | 1 | 4 | reliable |
The first output value is set to doubtful. Because in this case the total percentage of missing values is 25%. Which means that the following rule is applied.
Code Block | ||
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| ||
<validationRule>
<inputMissingPercentage>40</inputMissingPercentage>
<outputValueFlag>doubtful</outputValueFlag>
</validationRule>
|
The second output value is a missing value because in this case the percentage of missing values is equal to 50%. This means that in this case the following rule will be appplied.
Code Block | ||
---|---|---|
| ||
<validationRule>
<inputMissingPercentage>100</inputMissingPercentage>
<outputValueFlag>missing</outputValueFlag>
</validationRule>
|
The third output value is set to doubtful. The input doesn't contain missing values but has a single doubtful input value. The percentage of doubtful values in the input is therefore 25% which means that the following rule will be applied.
Code Block | ||
---|---|---|
| ||
<validationRule>
<inputDoubtfulPercentage>30</inputDoubtfulPercentage>
<inputMissingPercentage>0</inputMissingPercentage>
<outputValueFlag>doubtful</outputValueFlag>
<outputCustomFlagSourceId>D1</outputCustomFlagSourceId>
</validationRule>
|
the fourth and last output value has set to reliable with no output flag. In this case all in the input values are reliable. In this case the first rule
is applied (in the case when all of the input values are reliable the first rule always applies).
Manual Edits
Since FEWS 2017.02 it is possible to configure if manual edits should be preserved. This setting applies to all transformations that are configured. The default is false. For an example configuration see:
Code Block | ||
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| ||
<transformationModule xmlns="http://www.wldelft.nl/fews" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://www.wldelft.nl/fews https://fewsdocs.deltares.nl/schemas/version1.0/transformationModule.xsd" version="1.0">
<preserveManualEdits>true</preserveManualEdits> |
Description
Since 2019.02 an optional field description is available. This field can be configured per transformation, and the text will be shown in the workflow tree asmouse ower label (tooltip). This can be used with any type of transformation.
Example:
Code Block | ||||
---|---|---|---|---|
| ||||
<transformation id="merge">
<merge>
<simple>
<inputVariable>
<variableId>Wiski</variableId>
</inputVariable>
<inputVariable>
<variableId>Server</variableId>
</inputVariable>
<fillGapConstant>0</fillGapConstant>
<outputVariable>
<variableId>merge1</variableId>
</outputVariable>
</simple>
</merge>
<description>transformation description</description>
</transformation> |
Copying comments, flags and flag sources from input to output
<TODO>
Preventing previously calculated values to be overwritten with missings
Delft-FEWS processes data in moving windows compared to the timezero of the workflow. These workflows can be run several times per day. In certain conditions this could lead a transformation to calculate a missing value for a datetime for which earlier a correct value was calculated. This can lead to warnings such as:
Existing value overwritten by missing
Suspicious write action. Long time series written with only changes at the start and at the end. If this happens often this will explode the database.
This mechanism is illustrated with the image below, which shows a water level for which an average per day is calculated. The box shows the moving window (relativeViewPeriod). The image shows the daily values (red dots) calculated in the first run.
In a later run (illustrated below), there are not sufficient values to calculate a daily value for 24 December. So this run will return a missing value for 24 December (while calculating a new value for 27 December.
If these transformations would directly write to the same output timeserie (in this case with timeseriesType “external historical”), the later run would cause the average water level for December 24 to be overwritten with a missing value. The log would include warnings about this!
To prevent this, the output timeserie should be of timeserieType “temporary”, and next be merged (merge / simple transformation) with the final timeserie. This way, missing values will not overwrite previously calculated values.
Run transformations for a set of selected locations
In some cases it is usefull to run a transformation only for a specific set of locations. For example when the entire workflow has already ran and there is only a change at a specific location. This situation can occur, for example, when a water level has been editted or when the configuration is changed.
In this case the workflow can skip the calculations for the unchanged locations. The main benefit of this approach is that it saves a lot of processing time.
This functionality is now available in FEWS.However it is important to understand that this functionality cannot be used in all workflows. The functionality can be applied for transformations only. It cannot be used for running models or secondary validations. When a transformation is started for a location selection than the transformation will only start when the location of one of the input time series is selected. When a transformation has created output for a location which was not selected by the user than this location whill be added to the selection.
It is possible to run a workflow for a selected set of locations from the IFD, the task dialog and the manual forecast dialog. By default workflows cannot be run for a selected set of locations. To enable this the option allowSelection should be set to true in the workflowdescriptor of the workflow. Below an example.
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<workflowDescriptor id="FillRelations" forecast="false" visible="true"autoApprove="false">
<description>Met deze taak worden de gaten groter dan 2 uur gevuld dmv. relaties.</description>
<allowSelection>true</allowSelection>
<schedulingAllowed>true</schedulingAllowed>
</workflowDescriptor>
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When a node in the IFD is selected with a workflow which has the allowSelection option set true, the GUI will look like this:
In the property dialog below the tree with the nodes two selection boxes will appear.
The first checkbox will enable the option to run a workflow for a specific set of locations. The second checkbox will enable to run the workflow for specified period.
In the taskrun dialog an additinal checkbox will appear.
Which locations should the user select?
The transformation will run for the selected locations. If one the input timeseries is selected in the filters or in the map the transformation will run.
This means that the user should select the locations which are changed. This can be a change in the data or a change in the configuration.
This can be explained with the use of a simple example. Lets say we have a system which has a workflow which consists of a user simple function which estimates the water level at location B by simply copying the water level at location A to location B.
After the copying a set of statistical transformations are run to compute statistics.
The user edits the water level at location A and want to recompute the water level at location B. However the workflow which does this, is configured to do similar estimates at another 500 locations. In this case the user should select location A.
When the run starts the majority of the calculations are skipped except when the water level for location will be recalculated because in this case location A which is one of the input time series is selected. When this calculation is done, FEWS will
remember that location B is now also changed and will add location B to the list of selected locations. When the statistical transformations are started after the water level at location B is recomputed the statistics for location B are also recalculated because in this case the transformation which recomputed the statistics for location B has a input time series with location B and because location B was added to the list of selected locations, the statistical transformations which calculates statistics for location B will also be started.
This functionality cannot be used for spatial transformations. Before enabling this option for a workflow, the configurator should check if the workflow contains spatial transformations.
In addition to the above, this functionality can only be used for non-forecast workflows. Typically this functionality should be used for pre-processing of post-processing.
Therefore it is by default not possible to run a workflow for a specific location selection. This is only possible when in the workflowdescriptors the option allowSelection is to true. This option should only be set to true when the configurator has checked that the workflow is suitable for running for a specific location.
Steps to follow when implementing selection specific calculations
The following steps should be followed when this functionality is implemented.
1) Decide in which situations this functionality is needed
2) Make a list of the workflows which need to run in this type of situations
3) Ensure that the workflow only consists of transformations for which this functionality can be used.
4) Move transformations or other parts of the workflow which are suitable for this type of operations to another workflow
5) Set the option allowSelection to true in the workflow descriptor for the workflow which can be used for selection specific calculations
6) When the workflows will be started from the taskrun dialog of the manual forecast dialog no additional configuration is needed. These displays are available in almost every FEWS system. However when the IFD will be used for this. the following additional steps should be taken.
Implement selecion specific calculations for IFD
First step is to create a topology.xml to configure the content of the tree from which the workflows should be started.
Detailed informations about configuring the topology.xml can be found at 24 Topology
The following steps should be done when using the IFD for selection specific calculations.
- first create the tree structure by creating nodes in the topology.xml,
- add workflows to the nodes.
- add dependencies to the nodes by configuring the previous nodes
- by default leaf nodes will run locally and not at the server. This is not desired in this case Therefore the option localRun should be set to false for the leafnodes.
Below an example (part of the topology.xml)
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<nodes id="HDSR"><nodes id="oppervlaktewaterstand"><relativePeriod unit="week" start="-52" end="0" />
<node id="vul gaten kleiner dan 2 uur">
<previousNodeId>secondary validatie</previousNodeId>
<workflowId>FillGap2H_WerkOpvlWater</workflowId>
<filterId>Fillgap</filterId>
<localRun>false</localRun>
</node>
<node id="vul gaten groter dan 2 uur">
<previousNodeId>vul gaten kleiner dan 2 uur</previousNodeId>
<workflowId>FillRelations</workflowId>
<localRun>false</localRun>
</node>
<node id="berekening debieten">
<previousNodeId>vul gaten groter dan 2 uur</previousNodeId>
<workflowId>DebietBerekening</workflowId>
<localRun>false</localRun>
</node>
<node id="sample werkfilterdata nonequi naar 15min">
<previousNodeId>berekening debieten</previousNodeId>
<workflowId>SampleRuwNaar15M</workflowId>
<localRun>false</localRun>
</node>
<node id="langsprofielen berekenen">
<previousNodeId>sample werkfilterdata nonequi naar 15min</previousNodeId>
<workflowId>Langsprofiel</workflowId>
<localRun>false</localRun>
</node>
<node id="aggregatie van kwartier naar uur">
<previousNodeId>langsprofielen berekenen</previousNodeId>
<workflowId>AggregeerWerkOpvlWater</workflowId>
<localRun>false</localRun>
</node>
<node id="Peilbesluit evaluatie">
<previousNodeId>aggregatie van kwartier naar uur</previousNodeId>
<workflowId>PeilbesluitEvaluatie</workflowId>
<localRun>false</localRun>
</node>
<node id="export LIZARD">
<previousNodeId>Peilbesluit evaluatie</previousNodeId>
<workflowId>ExportCIW</workflowId>
<localRun>false</localRun>
</node>
<node id="export WIS-REPORTS">
<previousNodeId>export LIZARD</previousNodeId>
<workflowId>ExportCIW</workflowId>
<localRun>false</localRun>
</node>
</nodes>
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second step is to add the following line the explorer.xml to add the IFD tool window to the system.
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<explorerTask name="Forecasts">
<predefinedDisplay>topology tree</predefinedDisplay>
<toolbarTask>false</toolbarTask>
<menubarTask>false</menubarTask>
<toolWindow>true</toolWindow>
<loadAtStartup>true</loadAtStartup>
</explorerTask>
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Trim Output
A boolean option <trimOutput> is available within transformations. When true, missing values at the start and end of the output will be removed before writing the data to the database. This can prevent existing values to be overwritten with missings.
Forecast Loop
For some transformations it is possible to define a forecast loop by configuring a <forecastLoopSearchPeriod>. This means that the transformation will be run for each forecast found within that period.
This will only work when the <inputVariable> and <outputVariable> are external forecasts. The output variable will get the same external forecast time as the input time series.
When this is configured in combination with a locationSet it will try to run the transformation for the maximum number of forecasts available for the locations. If for a location some forecasts are unavailable a warning will be logged and those transformation runs will be skipped for that forecast and location combination.
List of all available transformations
For the most recent development version see the xsd schema at https://fewsdocs.deltares.nl/schemas/version1.0/transformationTypes.xsd
Available since stable build 2014.01: