def computeDetectionSize(self, probabilityLevel, confidenceLevel=None):
defectMin = self._defects.getMin()[0]
defectMax = self._defects.getMax()[0]
# compute 'a90'
model = self._buildModel(1. - probabilityLevel)
try:
detectionSize = ot.NumericalPointWithDescription(1, ot.Brent().solve(
model, self._detectionBoxCox, defectMin, defectMax))
except:
raise Exception('The POD model does not contain, for the given ' + \
'defect interval, the wanted probability level.')
description = ['a'+str(int(probabilityLevel*100))]
# compute 'a90_95'
if confidenceLevel is not None:
modelCl = self.getPODCLModel(confidenceLevel)
if not (modelCl([defectMin])[0] <= probabilityLevel <= modelCl([defectMax])[0]):
raise Exception('The POD model at the confidence level does not '+\
'contain, for the given defect interval, the '+\
'wanted probability level.')
detectionSize.add(ot.Brent().solve(modelCl,
probabilityLevel,
defectMin, defectMax))
description.append('a'+str(int(probabilityLevel*100))+'/'\
+str(int(confidenceLevel*100)))
# add description to the NumericalPoint
detectionSize.setDescription(description)
return detectionSize
def _computeDetectionSize(self,
model,
modelCl=None,
probabilityLevel=None,
confidenceLevel=None,
defectMin=None,
defectMax=None):
"""
Compute the detection size for a given probability level.
Parameters
----------
probabilityLevel : float
The probability level for which the defect size is computed.
confidenceLevel : float
The confidence level associated to the given probability level the
defect size is computed. Default is None.
Returns
-------
result : collection of :py:class:`openturns.NumericalPointWithDescription`
A NumericalPointWithDescription containing the detection size
computed at the given probability level and confidence level if provided.
"""
if defectMin is None:
defectMin = self._inputSample.getMin()[0]
if defectMax is None:
defectMax = self._inputSample.getMax()[0]
# compute 'a90'
if not (model([defectMin])[0] <= probabilityLevel <= model([defectMax
])[0]):
raise Exception('The POD model does not contain, for the given ' + \
'defect interval, the wanted probability level.')
detectionSize = ot.NumericalPointWithDescription(
1,
ot.Brent().solve(model, probabilityLevel, defectMin, defectMax))
description = ['a' + str(int(probabilityLevel * 100))]
# compute 'a90_95'
if confidenceLevel is not None:
if not (modelCl([defectMin])[0] <= probabilityLevel <= modelCl(
[defectMax])[0]):
raise Exception('The POD model at the confidence level does not '+\
'contain, for the given defect interval, the '+\
'wanted probability level.')
detectionSize.add(ot.Brent().solve(modelCl, probabilityLevel,
defectMin, defectMax))
description.append('a'+str(int(probabilityLevel*100))+'/'\
+str(int(confidenceLevel*100)))
# add description to the NumericalPoint
detectionSize.setDescription(description)
return detectionSize
def getStandardError(self):
"""
Accessor to the standard error of the estimate.
Returns
-------
stderr : :py:class:`openturns.NumericalPoint`
The standard error of the estimate for the uncensored and censored
(if so) linear regression model.
"""
if self._censored:
stderr = ot.NumericalPointWithDescription([
('Stderr for uncensored case', self._resultsUnc.stderr),
('Stderr for censored case', self._resultsCens.stderr)
])
else:
stderr = ot.NumericalPointWithDescription([
('Stderr for uncensored case', self._resultsUnc.stderr)
])
return stderr
def getSlope(self):
"""
Accessor to the slope of the linear regression model.
Returns
-------
slope : :py:class:`openturns.NumericalPoint`
The slope parameter for the uncensored and censored (if so) linear
regression model.
"""
if self._censored:
slope = ot.NumericalPointWithDescription([
('Slope for uncensored case', self._resultsUnc.slope),
('Slope for censored case', self._resultsCens.slope)
])
else:
slope = ot.NumericalPointWithDescription([
('Slope for uncensored case', self._resultsUnc.slope)
])
return slope
def getIntercept(self):
"""
Accessor to the intercept of the linear regression model.
Returns
-------
intercept : :py:class:`openturns.NumericalPoint`
The intercept parameter for the uncensored and censored (if so) linear
regression model.
"""
if self._censored:
intercept = ot.NumericalPointWithDescription([
('Intercept for uncensored case', self._resultsUnc.intercept),
('Intercept for censored case', self._resultsCens.intercept)
])
else:
intercept = ot.NumericalPointWithDescription([
('Intercept for uncensored case', self._resultsUnc.intercept)
])
return intercept
def _getResultValue(self, test, description):
"""
Generalized accessor method for the R2 or p-values.
Parameters
----------
test : string
name of the keys for the dictionnary.
description : string
name the test to be displayed.
"""
if self._censored:
pValue = ot.NumericalPointWithDescription([
(description + ' for uncensored case',
self._resultsUnc.testResults[test]),
(description + ' for censored case',
self._resultsCens.testResults[test])
])
else:
pValue = ot.NumericalPointWithDescription([
(description + ' for uncensored case',
self._resultsUnc.testResults[test])
])
return pValue
p2[1] = 200.
s1[0] = p2
p3 = ot.NumericalPoint(2, 0.)
p3.setName("Two")
p3[0] = 101.
p3[1] = 201.
s1[1] = p3
p4 = ot.NumericalPoint(2, 0.)
p4.setName("Three")
p4[0] = 102.
p4[1] = 202.
s1[2] = p4
myStudy.add("mySample", s1)
# Add a point with a description
pDesc = ot.NumericalPointWithDescription(p1)
desc = pDesc.getDescription()
desc[0] = "x"
desc[1] = "y"
desc[2] = "z"
pDesc.setDescription(desc)
myStudy.add(pDesc)
# Add a matrix
matrix = ot.Matrix(2, 3)
matrix[0, 0] = 0
matrix[0, 1] = 1
matrix[0, 2] = 2
matrix[1, 0] = 3
matrix[1, 1] = 4
matrix[1, 2] = 5
