Data Series Operation Settings for Derived Values
These settings are available in the Data Series Operation section for most evaluation types under Derived Values and Evaluation Group nodes.
Normalization
You can use this list to normalize the values within each column of the data table for the evaluation. The following operators are available from the Normalization list. Select:
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None (the default) to not apply any normalization.
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First to normalize with respect to the first value within each column; that is, the first value is set to 1.
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Last to normalize with respect to the last value within each column; that is, the last value is set to 1.
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Maximum absolute value to normalize with respect to the maximum absolute value within each column; that is, the table value that is the maximum absolute value is set to 1. This normalization preserves the sign of negative numbers and the argument of complex-valued numbers.
If the expression that was evaluated has a valid unit, then normalization changes the unit of the expression to dimensionless.
Transformation
In addition to performing the averaging on each solution in a data series (from a parametric or Time Dependent study) an operation can be applied such as the integral or maximum of the averaged quantity for the data series so that the result is, for example, the integral or maximum of the averaged quantity for each step in the data series. The following operators are available from the Transformation list. Select:
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None (the default) to not apply any data series operation.
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Average to evaluate the average of the data series.
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Maximum to evaluate the maximum of the data series. If Maximum is selected, select an option from the Find maximum of list: Real part or Absolute value.
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Minimum to evaluate the minimum of the data series. If Minimum is selected, select an option from the Find minimum of list: Real part or Absolute value.
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Integral to evaluate the integral of the data series.
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RMS (the root mean square or quadratic mean).
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Standard deviation.
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Variance.
For the Average, Integral, RMS, Standard deviation, and Variance operations, there is also a Method list available for choosing one of the following computational methods:
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Auto (the default): The automatic method uses integration whenever it is possible; otherwise, it uses summation.
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Integration: Compute the values using integration (treating the data as samples of a continuous function; see note below).
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Summation: Compute the values using summation of the data values. Summation can be useful when there are parameters on the rows, or different load cases, which you want to compute a linearly sum for rather than an integral.
For the Integral operation, you can also select the Cumulative check box to get cumulative integration of the table values.
The data series operations treat the data points as samples of a continuous function if the rows of a table, when evaluated without a data series operation, come from a transient or parametric solver (not if they come from an outer parametric sweep). The average, integral, standard deviation, variance, and RMS values, when you use integration, are all computed by performing one or more integrations of this continuous function using the trapezoidal method. The results are consistent with viewing the data series as samples of a continuous function but are not consistent with viewing the data points as discrete samples. For such cases, use summation instead.