def getPODCLModel(self, confidenceLevel=0.95):
"""
Accessor to the POD model at a given confidence level.
Parameters
----------
confidenceLevel : float
The confidence level the POD must be computed. Default is 0.95
Returns
-------
PODModelCl : :py:class:`openturns.Function`
The function which computes the probability of detection for a given
defect value at the confidence level given as parameter.
"""
if self._resDistFact is None:
# Berens Binomial
PODfunction = self._PODbinomialModelCl(self._residuals,
self._linearModel,
confidenceLevel)
else:
# Linear regression model + gaussian residuals or + bootstrap
def PODfunction(x):
samplePODDef = ot.Sample(self._simulationSize, 1)
for i in range(self._simulationSize):
samplePODDef[i] = [self._PODcollDict[i](x[0])]
return samplePODDef.computeQuantilePerComponent(
1. - confidenceLevel)
PODmodelCl = ot.PythonFunction(1, 1, PODfunction)
return PODmodelCl
def _exec_sample_ipyparallel(func, n, p):
"""Return a function that executes a sample in parallel using ipyparallel.
Parameters
----------
func : Function or callable
A callable python object, usually a function. The function should take
an input vector as argument and return an output vector.
n_cpus : int
Number of CPUs on which to distribute the function calls.
Returns
-------
_exec_sample : Function or callable
The parallelized function.
"""
import ipyparallel as ipp
rc = ipp.Client()
return ot.PythonFunction(func_sample=lambda X: rc[:].map_sync(func, X),
n=func.getInputDimension(),
p=func.getOutputDimension())
def __init__(self, dijkpaal):
self.dijkpaal = dijkpaal
self.data_dijkpaal = self.data1[dijkpaal]
#Sellmeijer model deterministische waarden
self.h = self.data_dijkpaal['Algemene gegevens',
'Waterstand bij norm [m NAP]']
self.gamma_sat = self.data_dijkpaal[
'Berekening Opbarsten',
'Volumegewicht deklaag verzadigd [kN/m3]'] #volumetric weight of saturated sand
#Sellmeijer model stochastische waarden
self.i_ch = self.data_dijkpaal[
'Berekening Heave',
'Kritieke heave gradient'] #critical heave gradient mu
self.D = self.data_dijkpaal[
'Piping gegevens', 'Dikte zandlaag [m]'] #upper sand cover layer
self.D_std = 0
self.D_cover = self.data_dijkpaal[
'Piping gegevens', 'Dikte deklaag [m]'] #aquifer cover layer
self.D_cover_std = 0
self.d70 = self.data_dijkpaal['Piping gegevens',
'Korreldiameter [m]'] #grain size
self.d70_std = 0
self.h_exit = self.data_dijkpaal[
'Piping gegevens', 'Kwelslootpeil [m NAP]'] #seepage ditch level
self.h_exit_std = 0
self.k = self.data_dijkpaal[
'Piping gegevens',
'Doorlatendheid zandlaag [m/s]'] #permeability sand
self.k_std = 0
self.L = self.data_dijkpaal[
'Piping gegevens', 'Aanwezige kwelweglengte [m]'] #seepage length
self.L_std = 0
self.Z_function = ot.PythonFunction(9, 1, self.Z)
