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# https://github.com/boschresearch/pylife
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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__author__ = "Johannes Mueller"
__maintainer__ = __author__
import pandas as pd
import numpy as np
from pylife import signal
[docs]@pd.api.extensions.register_dataframe_accessor("voigt")
class StressTensorVoigtAccessor(signal.PylifeSignal):
'''DataFrame accessor class for Voigt noted stress tensors
Raises
------
AttributeError
if at least one of the needed columns is missing.
Notes
-----
Base class to access :class:`pandas.DataFrame` objects containing
Voigt noted stress tensors. The stress tensor components are assumed
to be in the columns `S11`, `S22`, `S33`, `S12`, `S13`, `S23`.
See also
--------
:func:`pandas.api.extensions.register_dataframe_accessor()`
Examples
--------
For an example see :class:`equistress.StressTensorEquistressAccessor`.
'''
def _validate(self, obj, validator):
validator.fail_if_key_missing(obj, ['S11', 'S22', 'S33', 'S12', 'S13', 'S23'])
[docs]@pd.api.extensions.register_dataframe_accessor("cyclic_stress")
class CyclicStressAccessor(signal.PylifeSignal):
'''DataFrame accessor class for cyclic stress data
Raises
------
AttributeError
if there is no stress amplitide `sigma_a` key in the data
Notes
-----
Base class to access :class:`pandas.DataFrame` objects containing
cyclic stress data consisting of at least a stress amplitude using
the key `sigma_a`. Optionally an value for the stress ratio `R` or
for meanstress `sigma_m` can be supplied. If neither `R` nor
`sigma_a` are supplied a mean stress of zero i.e. `R=-1` is assumed.
Todo
----
Handle also input data with lower and upper stress.
'''
def _validate(self, obj, validator):
if 'sigma_a' in obj.columns and 'R' in obj.columns:
obj['sigma_m'] = obj['sigma_a']*(1.+obj.R)/(1.-obj.R)
obj.loc[obj['R'] == -np.inf, 'sigma_m'] = -obj.sigma_a
return True
if 'sigma_a' in obj.columns and 'sigma_m' in obj.columns:
return True
if 'sigma_a' in obj.columns:
obj['sigma_m'] = np.zeros_like(obj['sigma_a'].to_numpy())
else:
validator.fail_if_key_missing(obj, ['sigma_m', 'sigma_a'])
return True
[docs] def constant_R(self, R):
''' Sets `sigma_m` in a way that `R` is a constant value.
Parameters
----------
R : float
The value for `R`.
Returns
-------
the accessed DataFrame : :class:`pandas.DataFrame`
'''
self._obj['sigma_m'] = self._obj['sigma_a']*(1.+R)/(1.-R)
return self._obj