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Comment: Allow nulls: This functionality allows the aggregation to ignore/show any null values. It works with the repeating date range filter and all aggregation methods.

Transforms allow you to modify (or transform) the view of the data. Once generated, they can be used as a template for another action, such as applying to custom charts. Transforms are a form of data manipulation. They are centrally managed using the Transforms Manager and are saved with the project.

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Figure 1. Results Manager, single result data manipulation

  


The same two methods of data manipulation apply in a custom chart. However, any transforms that are applied to single results remain applied to those results in the custom chart. You can then apply transforms to the custom chart, and these will affect all results in the custom chart, regardless of what other transforms were applied to the individual results (Figure 2). For example, if you had observed flow at a daily time step, and the rest of your model was at a monthly time step, you could load the observed flow time series and aggregate it to a monthly time step using an aggregator transform. Then, you could add it to a custom chart with modelled flow, and investigate the low flows by applying a number filter transform to the custom chart. Note that for custom charts, while the Data tab lists the results in the custom chart, it does not indicate whether a transform has been applied to those individual results.

Figure 2. Results Manager, complex data manipulation

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Creating transforms

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Figure 9. Repeating range transform


Allow nulls:

This functionality allows the aggregation to ignore/show any null values. It works with the repeating date range filter and all aggregation methods.

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