If you also wanted this custom transformer to provide a single feature with just the LengthAverage attribute, which of the following methods would work?

If you also wanted this custom transformer to provide a single feature with just the LengthAverage attribute, which of the following methods would work?

• A. Add a connection between the StatisticsCalculator:Summary port and a new custom transformer output object named Summary.
• B. Add a connection between the StatisticsCalculator:Summary port and the Output port.
• C. Add a Sampler after the StatisticsCalculator.
• D. Add a Tester after the StatisticsCalculator testing for values of LengthAverage under a certain threshold provided by a user parameter.

Answer: (A)

The key idea is to emit a single, self-contained feature that represents the computed statistic, so you can downstream reference just LengthAverage. The StatisticsCalculator’s Summary port already produces a single feature that contains the various summary statistics as attributes, including LengthAverage. By routing that port into a dedicated output object on the custom transformer (named Summary), you create a focused output that represents the summary results as one feature. Inside the transformer you can trim away the other attributes if you want, leaving only LengthAverage for downstream use. Using the general Output port wouldn’t give you a single, isolated summary feature and would require extra steps to filter or remove other attributes; a Sampler won’t create a single summary feature, and a Tester changes which features pass through rather than producing the summarized feature you need.

The key idea is to emit a single, self-contained feature that represents the computed statistic, so you can downstream reference just LengthAverage. The StatisticsCalculator’s Summary port already produces a single feature that contains the various summary statistics as attributes, including LengthAverage. By routing that port into a dedicated output object on the custom transformer (named Summary), you create a focused output that represents the summary results as one feature. Inside the transformer you can trim away the other attributes if you want, leaving only LengthAverage for downstream use. Using the general Output port wouldn’t give you a single, isolated summary feature and would require extra steps to filter or remove other attributes; a Sampler won’t create a single summary feature, and a Tester changes which features pass through rather than producing the summarized feature you need.