model.result().numerical().create(<ftag>,"EvalPoint") creates a point evaluation feature with the name <ftag>. Point evaluations can be performed both on points in a geometry or on cut points.

model.result().numerical(<ftag>).getReal() returns the real result, recomputing the feature if necessary. Data is ordered such that there is one row per point, with one row containing 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 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() evaluates the feature and set or append the result in the table indicated by the table property.