Add threshold information to the Metrics Dashboard by using
slmetric.config.Threshold
and
slmetric.config.Configuration
objects. You can add thresholds that
define metric data ranges for these three categories:
Compliant — Metric data that is an acceptable range
Warning — Metric data that requires review
Noncompliant — Metric data that requires you to modify your model
Create an slmetric.config.Configuration
object.
Get the default slmetric.config.ThresholdConfiguration
object in
CONF
.
Create an slmetric.config.Threshold
object and add it to the
slmetric.config.ThresholdConfiguration
object. This threshold is
for the mathworks.metrics.SimulinkBlockCount
metric and the
Value
property of the slmetric.metric.Results
object.
By default, the slmetric.config.Threshold
object contains a
slmetric.config.Classification
object that defines metric ranges in
the compliant category. Get the classification object by using the function
getClassifications
on the threshold object
T
.
The Range
property of the classification object is a
slmetric.metric.MetricRange
object. Specify metric values for the
compliant category by using the slmetric.metric.MetricRange
functions
on the range of the classification object.
These values specify that a compliant range is a block count from
5
to 100
. This range does not include the values
5
and 100
.
Specify values for the warning metric range.
These values specify that a warning is a block count between -inf
and 5
. This range does not include -inf
. It does
include 5
.
Specify values for the noncompliant metric range.
These values specify that a block count greater than 100 is noncompliant. This range
includes 100
. It does not include inf
.
Use the validate
method to validate the metric ranges
corresponding to the thresholds in the
slmetric.config.ThresholdConfiguration
object.
If the ranges are not valid, you get an error message. In this example, the ranges
are valid, so the function returns nothing.
Save the changes to the configuration file. Use the
slmetric.config.setActiveConfiguration
function to activate this
configuration for the metric engine to use.
You can now run the Metrics Dashboard with this custom configuration on a
model.