6.2. pyopus.design.wc
— Worst case performance computation¶
Worst case analysis (PyOPUS subsystem name: WC)
Computes the worst case performance of a circuit. Statistical parameters are assumed to be independent with zero mean and standard deviation equal to 1.

class
pyopus.design.wc.
WorstCase
(heads, analyses, measures, corners, statParamDesc, opParamDesc, fixedParams={}, variables={}, debug=0, beta=3.0, sigmaBox=None, linearWc=True, alternating=True, maxPass=20, wcStepTol=0.01, stepTol=0.01, constrTol=0.01, angleTol=15, stepScaling=4, perturbationScaling=64, maximalStopperStep=0.5, evaluatorOptions={}, sensOptions={}, screenOptions={'contribThreshold': 0.01, 'cumulativeThreshold': 0.25, 'squared': True, 'useSens': False}, opOptimizerOptions={}, optimizerOptions={}, spawnerLevel=1)[source]¶ Performs worst case analysis.
See
PerformanceEvaluator
for details on heads, analyses, measures, and variables.corners is the dictionary of corner definitions, exactly one corner for every head. These are prototype corners and do not specify any operating or statistical parameters.
Performance measures that are vectors can also be a subject of worst case analysis.
Operating parameters are specified with the opParamDesc dictionary. This dictionary has parameter name for key and stores parameter property dictionaries. A parameter property dictionary contains entries
lo
for lowe bound,hi
for upper bound, andinit
for the nominal value
Statistical parameters are specified with the statParamDesc dictionary. Parameter name is the key and parameter property dictonaries are the values. A parameter property dictionary contains entries
distrib
is a probability density distribution (seepyopus.evaluator.distrib.Distribution
class for details on how to specify a probability density distribution).
Statistical parameters are assumed to be uncorrelated.
Fixed parameters are given by fixedParams  a dictionary with parameter name for key and parameter value for value. Alternatively the value can be a dictionary in which case the
init
member specifies the parameter value.If fixedParams is a list the members of this list must be dictionaries describing parameters. The set of fixed parameters is obtained by merging the information from these dictionaries.
Setting debug to a value greater than 0 turns on debug messages. The verbosity is proportional to the specified number.
beta is the maximal distance from the origin in the space of statistical parameters. If it is a vector or a list the worst cases corresponding to all values are computed.
sigmaBox is the box constraint on normalized statistical parameters (i.e. normalized to N(0,1)). If not specified it is set to 10 or 2x beta, whichever is greater.
If linearWc is set to
True
the initial point for statistical parameters is chosen using linear worst case analysis. Otherwise the initial point is at the origin.If alternating is set to
True
the worst case is computed by alternating optimizations in the operating and statistical parameter space.maxPass is the maximum number of algorithm passes (main optimization runs). For
None
the number of passes is not limited.wcStepTol specifies the step tolerance for the optimization in the space of operating parameters.
stepTol is the step tolerance for stopping the main optimization algorithm.
constrTol is the constraint violation tolerance for stopping the algorithm.
angleTol is the gradient angle tolerance in degrees for stopping the algorithm.
stepScaling is the divider of the lohi range for computing the problem scaling.
perturbationScaling is the divider for the lohi range for computing the perturbation used in sensitivity computation. The lohi range for statistical parameters is equal to 2x sigmaBox.
maximalStopperStep is the maximal step for which the main optimization stopper is invoked to check the alignment of gradients.
evaluatorOptions specifies the option overrides for the circuit evaluator.
sensOptions specifies the option overrides for the sensitivity analysis.
screenOptions specifies the option overrides for the screening.
opOptimizerOptions specifies the option overrides for the optimizer used in the space of operating parameters.
optimizerOptions specifies the option overrides for the main optimizer.
This is a callable object with an optional argument specifying which worst cases to compute. If given, the argument must be a list of entries. Every entry is either
a tuple of the form (
name
,``type``), wherename
is the measure name andtype
islower
orupper
a string specifying the measure name. In this case the type of comuted worst case is given by the presence of the
lower
and theupper
entries in the measure’s description.
If no argument is specified, all worst cases corresponding to lower/upper bounds of all measures are computed. These bounds are specified as the
lower
and theupper
member of the measurement description dictionary.The results are stored in the
results
member. They are represented as a list of dictionaries with the following members:name
 name of the performance measurecomponent
 index of the performance measure’s componentNone
for scalar performance measuresbeta
 targeted distance of statistical parameterstype
lower
orupper
passes
 number of algorithm passesevals
 number of evaluationsstatus
OK
orFAILED
nominal
 nominal performancenominal_wcop
 performance at nominal statistical parameters and initial worst case operating parameterslinwc
 performance at the linearized worst case pointwc
 worst case performance (at the worst case point)dist
 distance of worst case point from the origin of the statistical parametersop
 operating parameter values at the worst case pointstat
 statistical parameter values at the worst case pointmodules
 input file modules from the corner where the measure was evaluatedhead
 name of the head used for evaluating this measure
Status FAILED means that the algorithm failed to converge in maxPass passes.
Objects of this type store the number of analyses performed during the last call to the object in a dictionary stored in the
analysisCount
member.The return value of a call to an object of this class is a tuple holding the results structure and the analysisCount dictionary.

formatResults
(results=None, nMeasureName=10, nResult=15, nPrec=5, nEvalPrec=4, details=False)[source]¶ Formats the results as a string.
results is the results structure. If not given the
results
member is used.nMeasureName specifies the formatting width for the performance measure name.
nResult and nPrec specify the formatting width and the number of significant digits for the performance measure values.
nEvalPrec is the number of spaces reserved for the evaluations count.
If details is
True
the nominal performance and the performace at the linear worst case are also formatted.