# Copyright (c) 2019-2023 - for information on the respective copyright owner
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# https://github.com/boschresearch/pylife
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# limitations under the License.
import warnings
from .elementary import Elementary
import numpy as np
import pylife.utils.functions as functions
from pylife.utils.probability_data import ProbabilityFit
[docs]
class Probit(Elementary):
def _specific_analysis(self, wc):
self._inf_groups = self._fd.infinite_zone.groupby('load')
if len(self._inf_groups) < 2:
warnings.warn(UserWarning("Probit needs at least two runout load levels. Falling back to Elementary."))
return wc
wc['TS'], wc['SD'], wc['ND'] = self.__probit_analysis()
return wc
def __probit_rossow_estimation(self):
frac_num = self._inf_groups.fracture.sum().astype(int).to_numpy()
tot_num = self._inf_groups.fracture.count().to_numpy()
fprobs = np.empty_like(frac_num, dtype=np.float64)
c1 = frac_num == 0
w = np.where(c1)
fprobs[w] = 1. - 0.5**(1./tot_num[w])
c2 = frac_num == tot_num
w = np.where(c2)
fprobs[w] = 0.5**(1./tot_num[w])
c3 = np.logical_and(np.logical_not(c1), np.logical_not(c2))
w = np.where(c3)
fprobs[w] = (3*frac_num[w] - 1) / (3*tot_num[w] + 1)
return fprobs, self._inf_groups.load.mean()
def __probit_analysis(self):
if self._fd.num_runouts == 0:
return 1., 0., self._transition_cycles(0.0)
fprobs, load = self.__probit_rossow_estimation()
self._probability_fit = ProbabilityFit(fprobs, load)
TS = functions.std_to_scattering_range(1./self._probability_fit.slope)
SD = 10**(-self._probability_fit.intercept/self._probability_fit.slope)
ND = self._transition_cycles(SD)
return TS, SD, ND
[docs]
def fitter(self):
return self._probability_fit