slmetric.metric.Result

Metric data for specified model component and metric algorithm

Description

An slmetric.metric.Result object contains the metric data for a specified model component and metric algorithm.

Creation

Syntax

metric_result = slmetric.metric.Result

Description

metric_result = slmetric.metric.Result creates a handle to a metric results object.

Alternatively, if you collect results in an slmetric.metric.ResultCollection object, the Results property of the collection object returns the collected slmetric.metric.Result objects in an array.

Properties

ID — Numeric identifier

integer

This property is read-only.

Unique numeric identifier for the metric result object, returned as an integer.

Data Types: uint64

ComponentID — Component identifier

character vector

Unique identifier of the component object for which the metric is calculated, specified as a character vector. Use ComponentID to trace the generated result object to the analyzed component. Set the ComponentID or ComponentPath properties by using the algorithm method.

Example: 'sldemo_mdlref_basic'

Data Types: char

ComponentPath — Component path

character vector

Component path for which metric is calculated, specified as a character vector. Use ComponentPath as an alternative to setting the ComponentID property. The metric engine converts the ComponentPath to a ComponentID. Set the ComponentID or ComponentPath properties by using the algorithm method.

Example: 'vdp/More Info/Model Info/EmptySubsystem'

Data Types: char

MetricID — Metric identifier

character vector

Metric identifier for Model Metrics or custom model metrics that you create, specified as a character vector. You can get metric identifiers by calling slmetric.metric.getAvailableMetrics.

Example: 'mathworks.metrics.SimulinkBlockCount'

Data Types: char

Value — Metric value

double

Metric scalar value generated by the algorithm for the metric specified by MetricID and the component specified by ComponentID, specified as a double.

If the algorithm does not specify a metric scalar value, the value of Value is NaN. For example, suppose you collect metric data for a model that contains a Stateflow Chart. For the StateflowChartObjectCount metric, the Value property of the model slmetric.metric.Result object is NaN because the model itself cannot have Stateflow objects. The AggregatedValue property of the model slmetric.metric.Result object contains the total number of Stateflow objects in the chart.

Data Types: double

AggregatedValue — Aggregated metric value

double

This property is read-only.

Metric value aggregated across the model hierarchy, returned as a double. The metric engine implicitly aggregates the metric values based on the AggregationMode. If the Value property is NaN for all components, the AggregatedValue is zero.

Data Types: double

Measures — Metric measures

double array

Metric measures, specified by the metric algorithm, specified as a double array. Metric measures contain detailed information about the metric value. For example, for a metric that counts the number of blocks per subsystem, you can specify measures that contain the number of virtual and nonvirtual blocks. The metric value is the sum of the virtual and nonvirtual block count.

Set this property by using the slmetric.metric.Metric.algorithm method.

Data Types: double

AggregatedMeasures — Aggregated metric measures

double array

This property is read-only.

Metric measures value aggregated across the model hierarchy, returned as a double array. The metric engine implicitly aggregates the metric measure values based on the AggregationMode.

Data Types: double

Details — Metric result details

array of slmetric.metric.ResultDetail objects

Details about what the metric engine counts for the Value property, specified as an array of slmetric.metric.ResultDetail objects.

Category — Metric data category based on thresholding criteria

'Compliant' | 'NonCompliant' | 'Warning' | 'Uncategorized'

This property is read-only.

Metric data category, returned as one of these four categories:

• Compliant — Metric data that is in an acceptable range.

• Warning — Metric data that requires review.

• NonCompliant — Metric data that requires you to modify your model.

• Uncategorized — Metric data that does not have threshold values set.

Classifications — Metric data category and thresholding criteria

slmetric.config.ResultClassification object

Metric data category and the ranges that correspond to each category, specified as an slmetric.config.ResultClassification object. This property is empty if no threshold values are set.

UserData — User data

character vector

User data optionally provided by the metric algorithm, specified as a character vector.

Data Types: char

Examples

Collect and Access Metric Data for One Metric

This example shows how to collect and access metric data for the model sldemo_mdlref_basic.

Create an slmetric.Engine object and set the root in the model for analysis.

metric_engine = slmetric.Engine();

% Include referenced models and libraries in the analysis,
%   these properties are on by default
metric_engine.ModelReferencesSimulationMode = 'AllModes';
metric_engine.AnalyzeLibraries = 1;

setAnalysisRoot(metric_engine, 'Root', 'sldemo_mdlref_basic')

Collect model metric data.

execute(metric_engine, 'mathworks.metrics.ExplicitIOCount');

Return the model metric data as an array of slmetric.metric.ResultCollection objects and assign it to res_col.

res_col = getMetrics(metric_engine, 'mathworks.metrics.ExplicitIOCount');

Display the results for the mathworks.metrics.ExplicitIOCount metric.

for n=1:length(res_col)
    if res_col(n).Status == 0
        result = res_col(n).Results;
        
        for m=1:length(result)
            disp(['MetricID: ',result(m).MetricID]);
            disp(['  ComponentPath: ',result(m).ComponentPath]);
            disp(['  Value: ', num2str(result(m).Value)]);
            disp(['  AggregatedValue: ', num2str(result(m).AggregatedValue)]);
            disp(['  Measures: ', num2str(result(m).Measures)]);
            disp(['  AggregatedMeasures: ', num2str(result(m).AggregatedMeasures)]);
        end
    else
        disp(['No results for:', result(n).MetricID]);
    end
    disp(' ');
end

MetricID: mathworks.metrics.ExplicitIOCount

  ComponentPath: sldemo_mdlref_basic

  Value: 3

  AggregatedValue: 4

  Measures: 0  3

  AggregatedMeasures: 3  3

MetricID: mathworks.metrics.ExplicitIOCount

  ComponentPath: sldemo_mdlref_counter

  Value: 4

  AggregatedValue: 4

  Measures: 3  1

  AggregatedMeasures: 3  1

For ComponentPath: sldemo_mdlref_basic, the value is 3 because there are three outputs. The three outputs are in the second element of the Measures array. The slmetric.metric.AggregationMode is Max, so the AggregatedValue is 4, which is the number of inputs and outputs to sldemo_mdlref_counter. The AggregatedMeasures array contains the maximum number of inputs and outputs for a component or subcomponent.