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DAMASK_EICMD
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handle min/mid/max eigenvalues/vectors separately 

storing as matrix/vector is not favorable for paraview and in many
cases, either min or max are of interest only
This commit is contained in:
Martin Diehl 2020-05-27 17:36:30 +02:00
parent addc4c36d1
commit 31f72aa912
2 changed files with 49 additions and 29 deletions
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56
python/damask/_result.py
View File

@ -568,9 +568,8 @@ class Result:
'label': 'sigma',
'meta': {
'Unit': P['meta']['Unit'],
'Description': 'Cauchy stress calculated from {} ({}) '.format(P['label'],
P['meta']['Description'])+\
'and {} ({})'.format(F['label'],F['meta']['Description']),
'Description': 'Cauchy stress calculated from {} ({}) and {} ({})'\
.format(P['label'],P['meta']['Description'],F['label'],F['meta']['Description']),
'Creator': inspect.stack()[0][3][1:]
}
}
@ -638,17 +637,24 @@ class Result:
@staticmethod
def _add_eigenvalue(T_sym):
def _add_eigenvalue(T_sym,eigenvalue):
if eigenvalue == 'max':
label,p = 'Maximum',2
elif eigenvalue == 'mid':
label,p = 'Intermediate',1
elif eigenvalue == 'min':
label,p = 'Minimum',0
return {
'data': mechanics.eigenvalues(T_sym['data']),
'label': 'lambda({})'.format(T_sym['label']),
'data': mechanics.eigenvalues(T_sym['data'])[:,p],
'label': 'lambda_{}({})'.format(eigenvalue,T_sym['label']),
'meta' : {
'Unit': T_sym['meta']['Unit'],
'Description': 'Eigenvalues of {} ({})'.format(T_sym['label'],T_sym['meta']['Description']),
'Description': '{} eigenvalue of {} ({})'.format(label,T_sym['label'],T_sym['meta']['Description']),
'Creator': inspect.stack()[0][3][1:]
}
}
def add_eigenvalues(self,T_sym):
def add_eigenvalue(self,T_sym,eigenvalue='max'):
"""
Add eigenvalues of symmetric tensor.
@ -656,33 +662,46 @@ class Result:
----------
T_sym : str
Label of symmetric tensor dataset.
eigenvalue : str, optional
Eigenvalue. Select from ‘max’, ‘mid’, ‘min’. Defaults to ‘max’.
"""
self._add_generic_pointwise(self._add_eigenvalue,{'T_sym':T_sym})
self._add_generic_pointwise(self._add_eigenvalue,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
@staticmethod
def _add_eigenvector(T_sym):
def _add_eigenvector(T_sym,eigenvalue):
if eigenvalue == 'max':
label,p = 'maximum',2
elif eigenvalue == 'mid':
label,p = 'intermediate',1
elif eigenvalue == 'min':
label,p = 'minimum',0
print('p',eigenvalue)
return {
'data': mechanics.eigenvectors(T_sym['data']),
'label': 'v({})'.format(T_sym['label']),
'data': mechanics.eigenvectors(T_sym['data'])[:,p],
'label': 'v_{}({})'.format(eigenvalue,T_sym['label']),
'meta' : {
'Unit': '1',
'Description': 'Eigenvectors of {} ({})'.format(T_sym['label'],T_sym['meta']['Description']),
'Description': 'Eigenvector corresponding to {} eigenvalue of {} ({})'\
.format(label,T_sym['label'],T_sym['meta']['Description']),
'Creator': inspect.stack()[0][3][1:]
}
}
def add_eigenvectors(self,T_sym):
def add_eigenvector(self,T_sym,eigenvalue='max'):
"""
Add eigenvectors of symmetric tensor.
Add eigenvector of symmetric tensor.
Parameters
----------
T_sym : str
Label of symmetric tensor dataset.
eigenvalue : str, optional
Eigenvalue to which the eigenvector corresponds. Select from
‘max’, ‘mid’, ‘min’. Defaults to ‘max’.
"""
self._add_generic_pointwise(self._add_eigenvector,{'T_sym':T_sym})
self._add_generic_pointwise(self._add_eigenvector,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
@staticmethod
@ -819,9 +838,8 @@ class Result:
'label': 'S',
'meta': {
'Unit': P['meta']['Unit'],
'Description': '2. Kirchhoff stress calculated from {} ({}) '.format(P['label'],
P['meta']['Description'])+\
'and {} ({})'.format(F['label'],F['meta']['Description']),
'Description': '2. Piola-Kirchhoff stress calculated from {} ({}) and {} ({})'\
.format(P['label'],P['meta']['Description'],F['label'],F['meta']['Description']),
'Creator': inspect.stack()[0][3][1:]
}
}

20
python/tests/test_Result.py
View File

@ -132,21 +132,23 @@ class TestResult:
in_file = default.read_dataset(loc['s_P'],0)
assert np.allclose(in_memory,in_file)
def test_add_eigenvalues(self,default):
@pytest.mark.parametrize('eigenvalue,function',[('max',np.amax),('min',np.amin)])
def test_add_eigenvalue(self,default,eigenvalue,function):
default.add_Cauchy('P','F')
default.add_eigenvalues('sigma')
default.add_eigenvalue('sigma',eigenvalue)
loc = {'sigma' :default.get_dataset_location('sigma'),
'lambda(sigma)':default.get_dataset_location('lambda(sigma)')}
in_memory = mechanics.eigenvalues(default.read_dataset(loc['sigma'],0))
in_file = default.read_dataset(loc['lambda(sigma)'],0)
'lambda':default.get_dataset_location('lambda_{}(sigma)'.format(eigenvalue))}
in_memory = function(mechanics.eigenvalues(default.read_dataset(loc['sigma'],0)),axis=1,keepdims=True)
in_file = default.read_dataset(loc['lambda'],0)
assert np.allclose(in_memory,in_file)
def test_add_eigenvectors(self,default):
@pytest.mark.parametrize('eigenvalue,idx',[('max',2),('mid',1),('min',0)])
def test_add_eigenvector(self,default,eigenvalue,idx):
default.add_Cauchy('P','F')
default.add_eigenvectors('sigma')
default.add_eigenvector('sigma',eigenvalue)
loc = {'sigma' :default.get_dataset_location('sigma'),
'v(sigma)':default.get_dataset_location('v(sigma)')}
in_memory = mechanics.eigenvectors(default.read_dataset(loc['sigma'],0))
'v(sigma)':default.get_dataset_location('v_{}(sigma)'.format(eigenvalue))}
in_memory = mechanics.eigenvectors(default.read_dataset(loc['sigma'],0))[:,idx]
in_file = default.read_dataset(loc['v(sigma)'],0)
assert np.allclose(in_memory,in_file)