When added to an evaluation group, replace numerical(<ftag>) with evaluationGroup(<ftag>).

model.result().numerical().create(<ftag>,"EvalGlobal") creates an evaluation feature with the name <ftag>.

model.result().numerical(<ftag>).getReal() returns the real result, recomputing the feature if necessary. Data is ordered such that one row contains data for all solution numbers. This is identical to (columnwise) when columnwise is false. If columnwise is true, the ordering is the opposite: each column contains the values for all solution numbers.

model.result().numerical(<ftag>).getImag(allocate,columnwise) returns the imaginary part of the complex result, recomputing the feature if necessary. If allocate is true, a zero-valued matrix is allocated even when the result is real. getImag() uses allocate true and columnwise false.

model.result().numerical(<ftag>).isComplex() returns true if the result is complex. The resulting value is a scalar, which true if any of the expressions in an array is complex valued.

model.result().numerical(<ftag>).isComplex(<outersolnum>) returns true if the result is complex for the given outer solution. <outersolnum> is applicable only for parametric sweep solutions and is the index of each outer parametric solution.

model.result().numerical(<ftag>).computeResult() returns the matrix of data that the setResult method adds to a table. The matrix includes data only, not the parameter columns, and it does not use any table-specific settings.

model.result().numerical(<ftag>).setResult() and model.result().numerical(<ftag>).appendResult() evaluate the feature and set or append the result in the table indicated by the table property.

EvalGlobalMatrix, Eval, EvalPoint