# Copyright (c) 2019-2023 - for information on the respective copyright owner
# see the NOTICE file and/or the repository
# 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.
"""
Utility Functions
=================
A collection of functions frequently used in lifetime estimation
business.
"""
__author__ = "Johannes Mueller"
__maintainer__ = __author__
import numpy as np
[docs]
def scattering_range_to_std(T):
"""Convert a scattering range (``TS`` or ``TN`` in DIN 50100:2016-12) into standard deviation.
Parameters
----------
T : float
inverted scattering range
Returns
-------
std : float
standard deviation corresponding to TS or TN assuming a normal distribution
Notes
-----
Actually ``1/(2*norm.ppf(0.9))*np.log10(T)``
Inverse of ``std_to_scattering_range()``
"""
return 0.39015207303618954*np.log10(T)
[docs]
def std_to_scattering_range(std):
"""Convert a standard deviation into scattering range (``TS`` or ``TN`` in DIN 50100:2016-12).
Parameters
----------
std : float
standard deviation
Returns
-------
T : float
inverted scattering range corresponding to ``std`` assuming a normal distribution
Notes
-----
Actually ``10**(2*norm.ppf(0.9)*std``
Inverse of ``scattering_range_to_std()``
"""
return 10**(2.5631031310892007*std)
[docs]
def rossow_cumfreqs(N):
"""Cumulative frequency estimator according to Rossow.
Parameters
----------
N : int
The sample size of the statistical population
Returns
-------
cumfreqs : numpy.ndarray
The estimated cumulated frequencies of the N samples
Notes
-----
The returned value is the probability that the next taken sample
is below the value of the i-th sample of n sorted samples.
Examples
--------
>>> rossow_cumfreqs(1)
array([0.5])
If we have one sample, the probability that the next sample will
be below it is 0.5.
>>> rossow_cumfreqs(3)
array([0.2, 0.5, 0.8])
If we have three sorted samples, the probability that the next
sample will be
* below the first is 0.2
* below the second is 0.5
* below the third is 0.8
References
----------
*Statistics of Metal Fatigue in Engineering' page 16*
https://books.google.de/books?isbn=3752857722
"""
i = np.arange(1, N+1)
return (3.*i-1.)/(3.*N+1)