no need to have this as class method
definition as class method was needed to be visible in multiprocessing
This commit is contained in:
Martin Diehl
parent
b0bb904c89
commit
36c13d2e58
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@ -599,17 +599,6 @@ class Result:
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f['/geometry/T_c'].attrs['VTK_TYPE'].decode())
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@staticmethod
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def _add_absolute(x: Dict[str, Any]) -> Dict[str, Any]:
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return {
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'data': np.abs(x['data']),
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'label': f'|{x["label"]}|',
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'meta': {
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'unit': x['meta']['unit'],
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'description': f"absolute value of {x['label']} ({x['meta']['description']})",
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'creator': 'add_absolute'
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}
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}
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def add_absolute(self, x: str):
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"""
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Add absolute value.
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@ -620,28 +609,20 @@ class Result:
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Name of scalar, vector, or tensor dataset to take absolute value of.
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"""
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self._add_generic_pointwise(self._add_absolute,{'x':x})
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@staticmethod
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def _add_calculation(**kwargs) -> Dict[str, Any]:
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formula = kwargs['formula']
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for d in re.findall(r'#(.*?)#',formula):
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formula = formula.replace(f'#{d}#',f"kwargs['{d}']['data']")
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data = eval(formula)
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if not hasattr(data,'shape') or data.shape[0] != kwargs[d]['data'].shape[0]:
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raise ValueError('"{}" results in invalid shape'.format(kwargs['formula']))
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def absolute(x: Dict[str, Any]) -> Dict[str, Any]:
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return {
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'data': data,
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'label': kwargs['label'],
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'data': np.abs(x['data']),
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'label': f'|{x["label"]}|',
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'meta': {
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'unit': kwargs['unit'],
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'description': f"{kwargs['description']} (formula: {kwargs['formula']})",
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'creator': 'add_calculation'
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'unit': x['meta']['unit'],
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'description': f"absolute value of {x['label']} ({x['meta']['description']})",
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'creator': 'add_absolute'
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}
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}
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self._add_generic_pointwise(absolute,{'x':x})
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def add_calculation(self,
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formula: str,
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name: str,
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@ -690,24 +671,30 @@ class Result:
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... 'Mises equivalent of the Cauchy stress')
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"""
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def calculation(**kwargs) -> Dict[str, Any]:
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formula = kwargs['formula']
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for d in re.findall(r'#(.*?)#',formula):
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formula = formula.replace(f'#{d}#',f"kwargs['{d}']['data']")
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data = eval(formula)
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if not hasattr(data,'shape') or data.shape[0] != kwargs[d]['data'].shape[0]:
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raise ValueError('"{}" results in invalid shape'.format(kwargs['formula']))
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return {
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'data': data,
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'label': kwargs['label'],
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'meta': {
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'unit': kwargs['unit'],
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'description': f"{kwargs['description']} (formula: {kwargs['formula']})",
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'creator': 'add_calculation'
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}
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}
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dataset_mapping = {d:d for d in set(re.findall(r'#(.*?)#',formula))} # datasets used in the formula
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args = {'formula':formula,'label':name,'unit':unit,'description':description}
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self._add_generic_pointwise(self._add_calculation,dataset_mapping,args)
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self._add_generic_pointwise(calculation,dataset_mapping,args)
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@staticmethod
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def _add_stress_Cauchy(P: Dict[str, Any], F: Dict[str, Any]) -> Dict[str, Any]:
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return {
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'data': mechanics.stress_Cauchy(P['data'],F['data']),
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'label': 'sigma',
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'meta': {
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'unit': P['meta']['unit'],
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'description': "Cauchy stress calculated "
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f"from {P['label']} ({P['meta']['description']})"
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f" and {F['label']} ({F['meta']['description']})",
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'creator': 'add_stress_Cauchy'
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}
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}
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def add_stress_Cauchy(self,
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P: str = 'P',
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F: str = 'F'):
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@ -724,20 +711,23 @@ class Result:
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Defaults to 'F'.
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"""
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self._add_generic_pointwise(self._add_stress_Cauchy,{'P':P,'F':F})
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@staticmethod
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def _add_determinant(T: Dict[str, Any]) -> Dict[str, Any]:
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def stress_Cauchy(P: Dict[str, Any], F: Dict[str, Any]) -> Dict[str, Any]:
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return {
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'data': np.linalg.det(T['data']),
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'label': f"det({T['label']})",
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'data': mechanics.stress_Cauchy(P['data'],F['data']),
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'label': 'sigma',
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'meta': {
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'unit': T['meta']['unit'],
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'description': f"determinant of tensor {T['label']} ({T['meta']['description']})",
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'creator': 'add_determinant'
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'unit': P['meta']['unit'],
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'description': "Cauchy stress calculated "
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f"from {P['label']} ({P['meta']['description']})"
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f" and {F['label']} ({F['meta']['description']})",
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'creator': 'add_stress_Cauchy'
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}
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}
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self._add_generic_pointwise(stress_Cauchy,{'P':P,'F':F})
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def add_determinant(self, T: str):
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"""
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Add the determinant of a tensor.
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@ -756,20 +746,21 @@ class Result:
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>>> r.add_determinant('F_p')
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"""
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self._add_generic_pointwise(self._add_determinant,{'T':T})
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@staticmethod
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def _add_deviator(T: Dict[str, Any]) -> Dict[str, Any]:
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def determinant(T: Dict[str, Any]) -> Dict[str, Any]:
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return {
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'data': tensor.deviatoric(T['data']),
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'label': f"s_{T['label']}",
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'data': np.linalg.det(T['data']),
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'label': f"det({T['label']})",
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'meta': {
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'unit': T['meta']['unit'],
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'description': f"deviator of tensor {T['label']} ({T['meta']['description']})",
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'creator': 'add_deviator'
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'description': f"determinant of tensor {T['label']} ({T['meta']['description']})",
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'creator': 'add_determinant'
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}
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}
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self._add_generic_pointwise(determinant,{'T':T})
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def add_deviator(self, T: str):
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"""
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Add the deviatoric part of a tensor.
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@ -788,29 +779,21 @@ class Result:
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>>> r.add_deviator('sigma')
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"""
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self._add_generic_pointwise(self._add_deviator,{'T':T})
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@staticmethod
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def _add_eigenvalue(T_sym: Dict[str, Any], eigenvalue: Literal['max, mid, min']) -> Dict[str, Any]:
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if eigenvalue == 'max':
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label,p = 'maximum',2
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elif eigenvalue == 'mid':
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label,p = 'intermediate',1
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elif eigenvalue == 'min':
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label,p = 'minimum',0
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else:
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raise ValueError(f'invalid eigenvalue: {eigenvalue}')
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def deviator(T: Dict[str, Any]) -> Dict[str, Any]:
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return {
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'data': tensor.eigenvalues(T_sym['data'])[:,p],
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'label': f"lambda_{eigenvalue}({T_sym['label']})",
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'meta' : {
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'unit': T_sym['meta']['unit'],
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'description': f"{label} eigenvalue of {T_sym['label']} ({T_sym['meta']['description']})",
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'creator': 'add_eigenvalue'
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'data': tensor.deviatoric(T['data']),
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'label': f"s_{T['label']}",
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'meta': {
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'unit': T['meta']['unit'],
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'description': f"deviator of tensor {T['label']} ({T['meta']['description']})",
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'creator': 'add_deviator'
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}
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}
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self._add_generic_pointwise(deviator,{'T':T})
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def add_eigenvalue(self,
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T_sym: str,
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eigenvalue: Literal['max', 'mid', 'min'] = 'max'):
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@ -833,11 +816,47 @@ class Result:
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>>> r.add_eigenvalue('sigma','min')
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"""
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self._add_generic_pointwise(self._add_eigenvalue,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
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def eigenval(T_sym: Dict[str, Any], eigenvalue: Literal['max, mid, min']) -> Dict[str, Any]:
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if eigenvalue == 'max':
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label,p = 'maximum',2
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elif eigenvalue == 'mid':
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label,p = 'intermediate',1
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elif eigenvalue == 'min':
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label,p = 'minimum',0
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else:
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raise ValueError(f'invalid eigenvalue: {eigenvalue}')
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return {
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'data': tensor.eigenvalues(T_sym['data'])[:,p],
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'label': f"lambda_{eigenvalue}({T_sym['label']})",
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'meta' : {
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'unit': T_sym['meta']['unit'],
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'description': f"{label} eigenvalue of {T_sym['label']} ({T_sym['meta']['description']})",
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'creator': 'add_eigenvalue'
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}
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}
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self._add_generic_pointwise(eigenval,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
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@staticmethod
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def _add_eigenvector(T_sym: Dict[str, Any], eigenvalue: Literal['max', 'mid', 'min']) -> Dict[str, Any]:
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def add_eigenvector(self,
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T_sym: str,
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eigenvalue: Literal['max', 'mid', 'min'] = 'max'):
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"""
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Add eigenvector of symmetric tensor.
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Parameters
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----------
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T_sym : str
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Name of symmetric tensor dataset.
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eigenvalue : {'max', 'mid', 'min'}, optional
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Eigenvalue to which the eigenvector corresponds.
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Defaults to 'max'.
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"""
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def eigenvector(T_sym: Dict[str, Any], eigenvalue: Literal['max', 'mid', 'min']) -> Dict[str, Any]:
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if eigenvalue == 'max':
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label,p = 'maximum',2
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elif eigenvalue == 'mid':
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@ -857,40 +876,10 @@ class Result:
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'creator': 'add_eigenvector'
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}
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}
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def add_eigenvector(self,
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T_sym: str,
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eigenvalue: Literal['max', 'mid', 'min'] = 'max'):
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"""
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Add eigenvector of symmetric tensor.
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Parameters
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----------
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T_sym : str
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Name of symmetric tensor dataset.
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eigenvalue : {'max', 'mid', 'min'}, optional
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Eigenvalue to which the eigenvector corresponds.
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Defaults to 'max'.
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"""
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self._add_generic_pointwise(self._add_eigenvector,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
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self._add_generic_pointwise(eigenvector,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
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@staticmethod
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def _add_IPF_color(l: FloatSequence, q: Dict[str, Any]) -> Dict[str, Any]:
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m = util.scale_to_coprime(np.array(l))
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lattice = q['meta']['lattice']
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o = Orientation(rotation = q['data'],lattice=lattice)
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return {
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'data': np.uint8(o.IPF_color(l)*255),
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'label': 'IPFcolor_({} {} {})'.format(*m),
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'meta' : {
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'unit': '8-bit RGB',
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'lattice': q['meta']['lattice'],
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'description': 'Inverse Pole Figure (IPF) colors along sample direction ({} {} {})'.format(*m),
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'creator': 'add_IPF_color'
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}
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}
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def add_IPF_color(self,
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l: FloatSequence,
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q: str = 'O'):
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@ -914,20 +903,26 @@ class Result:
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>>> r.add_IPF_color(np.array([0,1,1]))
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"""
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self._add_generic_pointwise(self._add_IPF_color,{'q':q},{'l':l})
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def IPF_color(l: FloatSequence, q: Dict[str, Any]) -> Dict[str, Any]:
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m = util.scale_to_coprime(np.array(l))
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lattice = q['meta']['lattice']
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o = Orientation(rotation = q['data'],lattice=lattice)
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@staticmethod
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def _add_maximum_shear(T_sym: Dict[str, Any]) -> Dict[str, Any]:
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return {
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'data': mechanics.maximum_shear(T_sym['data']),
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'label': f"max_shear({T_sym['label']})",
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'meta': {
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'unit': T_sym['meta']['unit'],
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'description': f"maximum shear component of {T_sym['label']} ({T_sym['meta']['description']})",
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'creator': 'add_maximum_shear'
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'data': np.uint8(o.IPF_color(l)*255),
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'label': 'IPFcolor_({} {} {})'.format(*m),
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'meta' : {
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'unit': '8-bit RGB',
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'lattice': q['meta']['lattice'],
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'description': 'Inverse Pole Figure (IPF) colors along sample direction ({} {} {})'.format(*m),
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'creator': 'add_IPF_color'
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}
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}
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self._add_generic_pointwise(IPF_color,{'q':q},{'l':l})
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def add_maximum_shear(self, T_sym: str):
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"""
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Add maximum shear components of symmetric tensor.
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@ -938,30 +933,20 @@ class Result:
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Name of symmetric tensor dataset.
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"""
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self._add_generic_pointwise(self._add_maximum_shear,{'T_sym':T_sym})
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@staticmethod
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def _add_equivalent_Mises(T_sym: Dict[str, Any], kind: str) -> Dict[str, Any]:
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k = kind
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if k is None:
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if T_sym['meta']['unit'] == '1':
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k = 'strain'
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elif T_sym['meta']['unit'] == 'Pa':
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k = 'stress'
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if k not in ['stress', 'strain']:
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raise ValueError(f'invalid von Mises kind "{kind}"')
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def maximum_shear(T_sym: Dict[str, Any]) -> Dict[str, Any]:
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return {
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'data': (mechanics.equivalent_strain_Mises if k=='strain' else \
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mechanics.equivalent_stress_Mises)(T_sym['data']),
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'label': f"{T_sym['label']}_vM",
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'data': mechanics.maximum_shear(T_sym['data']),
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'label': f"max_shear({T_sym['label']})",
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'meta': {
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'unit': T_sym['meta']['unit'],
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'description': f"Mises equivalent {k} of {T_sym['label']} ({T_sym['meta']['description']})",
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'creator': 'add_Mises'
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'description': f"maximum shear component of {T_sym['label']} ({T_sym['meta']['description']})",
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'creator': 'add_maximum_shear'
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}
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}
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self._add_generic_pointwise(maximum_shear,{'T_sym':T_sym})
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def add_equivalent_Mises(self,
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T_sym: str,
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kind: Optional[str] = None):
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@ -991,11 +976,45 @@ class Result:
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>>> r.add_equivalent_Mises('epsilon_V^0.0(F)')
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"""
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self._add_generic_pointwise(self._add_equivalent_Mises,{'T_sym':T_sym},{'kind':kind})
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def equivalent_Mises(T_sym: Dict[str, Any], kind: str) -> Dict[str, Any]:
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k = kind
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if k is None:
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if T_sym['meta']['unit'] == '1':
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k = 'strain'
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elif T_sym['meta']['unit'] == 'Pa':
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k = 'stress'
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if k not in ['stress', 'strain']:
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raise ValueError(f'invalid von Mises kind "{kind}"')
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return {
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'data': (mechanics.equivalent_strain_Mises if k=='strain' else \
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mechanics.equivalent_stress_Mises)(T_sym['data']),
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'label': f"{T_sym['label']}_vM",
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'meta': {
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'unit': T_sym['meta']['unit'],
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'description': f"Mises equivalent {k} of {T_sym['label']} ({T_sym['meta']['description']})",
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'creator': 'add_Mises'
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}
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}
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self._add_generic_pointwise(equivalent_Mises,{'T_sym':T_sym},{'kind':kind})
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@staticmethod
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def _add_norm(x: Dict[str, Any], ord: Union[int, float, Literal['fro', 'nuc']]) -> Dict[str, Any]:
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def add_norm(self,
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x: str,
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ord: Union[None, int, float, Literal['fro', 'nuc']] = None):
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"""
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Add the norm of a vector or tensor.
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Parameters
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----------
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x : str
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Name of vector or tensor dataset.
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ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional
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Order of the norm. inf means NumPy's inf object. For details refer to numpy.linalg.norm.
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"""
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def norm(x: Dict[str, Any], ord: Union[int, float, Literal['fro', 'nuc']]) -> Dict[str, Any]:
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o = ord
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if len(x['data'].shape) == 2:
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axis: Union[int, Tuple[int, int]] = 1
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@ -1017,36 +1036,10 @@ class Result:
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'creator': 'add_norm'
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}
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}
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def add_norm(self,
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x: str,
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ord: Union[None, int, float, Literal['fro', 'nuc']] = None):
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"""
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Add the norm of a vector or tensor.
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Parameters
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----------
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x : str
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Name of vector or tensor dataset.
|
||||
ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional
|
||||
Order of the norm. inf means NumPy's inf object. For details refer to numpy.linalg.norm.
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_norm,{'x':x},{'ord':ord})
|
||||
self._add_generic_pointwise(norm,{'x':x},{'ord':ord})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_stress_second_Piola_Kirchhoff(P: Dict[str, Any], F: Dict[str, Any]) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': mechanics.stress_second_Piola_Kirchhoff(P['data'],F['data']),
|
||||
'label': 'S',
|
||||
'meta': {
|
||||
'unit': P['meta']['unit'],
|
||||
'description': "second Piola-Kirchhoff stress calculated "
|
||||
f"from {P['label']} ({P['meta']['description']})"
|
||||
f" and {F['label']} ({F['meta']['description']})",
|
||||
'creator': 'add_stress_second_Piola_Kirchhoff'
|
||||
}
|
||||
}
|
||||
def add_stress_second_Piola_Kirchhoff(self,
|
||||
P: str = 'P',
|
||||
F: str = 'F'):
|
||||
|
|
@ -1069,34 +1062,23 @@ class Result:
|
|||
is taken into account.
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_stress_second_Piola_Kirchhoff,{'P':P,'F':F})
|
||||
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_pole(q: Dict[str, Any],
|
||||
uvw: FloatSequence,
|
||||
hkl: FloatSequence,
|
||||
with_symmetry: bool,
|
||||
normalize: bool) -> Dict[str, Any]:
|
||||
c = q['meta']['c/a'] if 'c/a' in q['meta'] else 1
|
||||
brackets = ['[]','()','⟨⟩','{}'][(uvw is None)*1+with_symmetry*2]
|
||||
label = 'p^' + '{}{} {} {}{}'.format(brackets[0],
|
||||
*(uvw if uvw else hkl),
|
||||
brackets[-1],)
|
||||
ori = Orientation(q['data'],lattice=q['meta']['lattice'],a=1,c=c)
|
||||
|
||||
def stress_second_Piola_Kirchhoff(P: Dict[str, Any], F: Dict[str, Any]) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': ori.to_pole(uvw=uvw,hkl=hkl,with_symmetry=with_symmetry,normalize=normalize),
|
||||
'label': label,
|
||||
'meta' : {
|
||||
'unit': '1',
|
||||
'description': f'{"normalized " if normalize else ""}lab frame vector along lattice ' \
|
||||
+ ('direction' if uvw is not None else 'plane') \
|
||||
+ ('s' if with_symmetry else ''),
|
||||
'creator': 'add_pole'
|
||||
'data': mechanics.stress_second_Piola_Kirchhoff(P['data'],F['data']),
|
||||
'label': 'S',
|
||||
'meta': {
|
||||
'unit': P['meta']['unit'],
|
||||
'description': "second Piola-Kirchhoff stress calculated "
|
||||
f"from {P['label']} ({P['meta']['description']})"
|
||||
f" and {F['label']} ({F['meta']['description']})",
|
||||
'creator': 'add_stress_second_Piola_Kirchhoff'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(stress_second_Piola_Kirchhoff,{'P':P,'F':F})
|
||||
|
||||
|
||||
|
||||
def add_pole(self,
|
||||
q: str = 'O',
|
||||
*,
|
||||
|
|
@ -1122,22 +1104,33 @@ class Result:
|
|||
Defaults to True.
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_pole,
|
||||
{'q':q},
|
||||
{'uvw':uvw,'hkl':hkl,'with_symmetry':with_symmetry,'normalize':normalize})
|
||||
def pole(q: Dict[str, Any],
|
||||
uvw: FloatSequence,
|
||||
hkl: FloatSequence,
|
||||
with_symmetry: bool,
|
||||
normalize: bool) -> Dict[str, Any]:
|
||||
c = q['meta']['c/a'] if 'c/a' in q['meta'] else 1
|
||||
brackets = ['[]','()','⟨⟩','{}'][(uvw is None)*1+with_symmetry*2]
|
||||
label = 'p^' + '{}{} {} {}{}'.format(brackets[0],
|
||||
*(uvw if uvw else hkl),
|
||||
brackets[-1],)
|
||||
ori = Orientation(q['data'],lattice=q['meta']['lattice'],a=1,c=c)
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_rotation(F: Dict[str, Any]) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': mechanics.rotation(F['data']).as_matrix(),
|
||||
'label': f"R({F['label']})",
|
||||
'meta': {
|
||||
'unit': F['meta']['unit'],
|
||||
'description': f"rotational part of {F['label']} ({F['meta']['description']})",
|
||||
'creator': 'add_rotation'
|
||||
'data': ori.to_pole(uvw=uvw,hkl=hkl,with_symmetry=with_symmetry,normalize=normalize),
|
||||
'label': label,
|
||||
'meta' : {
|
||||
'unit': '1',
|
||||
'description': f'{"normalized " if normalize else ""}lab frame vector along lattice ' \
|
||||
+ ('direction' if uvw is not None else 'plane') \
|
||||
+ ('s' if with_symmetry else ''),
|
||||
'creator': 'add_pole'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(pole,{'q':q},{'uvw':uvw,'hkl':hkl,'with_symmetry':with_symmetry,'normalize':normalize})
|
||||
|
||||
|
||||
def add_rotation(self, F: str):
|
||||
"""
|
||||
Add rotational part of a deformation gradient.
|
||||
|
|
@ -1156,20 +1149,20 @@ class Result:
|
|||
>>> r.add_rotation('F')
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_rotation,{'F':F})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_spherical(T: Dict[str, Any]) -> Dict[str, Any]:
|
||||
def rotation(F: Dict[str, Any]) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': tensor.spherical(T['data'],False),
|
||||
'label': f"p_{T['label']}",
|
||||
'data': mechanics.rotation(F['data']).as_matrix(),
|
||||
'label': f"R({F['label']})",
|
||||
'meta': {
|
||||
'unit': T['meta']['unit'],
|
||||
'description': f"spherical component of tensor {T['label']} ({T['meta']['description']})",
|
||||
'creator': 'add_spherical'
|
||||
'unit': F['meta']['unit'],
|
||||
'description': f"rotational part of {F['label']} ({F['meta']['description']})",
|
||||
'creator': 'add_rotation'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(rotation,{'F':F})
|
||||
|
||||
|
||||
def add_spherical(self, T: str):
|
||||
"""
|
||||
Add the spherical (hydrostatic) part of a tensor.
|
||||
|
|
@ -1188,22 +1181,20 @@ class Result:
|
|||
>>> r.add_spherical('sigma')
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_spherical,{'T':T})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_strain(F: Dict[str, Any], t: Literal['V', 'U'], m: float) -> Dict[str, Any]:
|
||||
side = 'left' if t == 'V' else 'right'
|
||||
def spherical(T: Dict[str, Any]) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': mechanics.strain(F['data'],t,m),
|
||||
'label': f"epsilon_{t}^{m}({F['label']})",
|
||||
'data': tensor.spherical(T['data'],False),
|
||||
'label': f"p_{T['label']}",
|
||||
'meta': {
|
||||
'unit': F['meta']['unit'],
|
||||
'description': f'Seth-Hill strain tensor of order {m} based on {side} stretch tensor '+\
|
||||
f"of {F['label']} ({F['meta']['description']})",
|
||||
'creator': 'add_strain'
|
||||
'unit': T['meta']['unit'],
|
||||
'description': f"spherical component of tensor {T['label']} ({T['meta']['description']})",
|
||||
'creator': 'add_spherical'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(spherical,{'T':T})
|
||||
|
||||
|
||||
def add_strain(self,
|
||||
F: str = 'F',
|
||||
t: Literal['V', 'U'] = 'V',
|
||||
|
|
@ -1264,21 +1255,22 @@ class Result:
|
|||
| https://de.wikipedia.org/wiki/Verzerrungstensor
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_strain,{'F':F},{'t':t,'m':m})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_stretch_tensor(F: Dict[str, Any], t: str) -> Dict[str, Any]:
|
||||
def strain(F: Dict[str, Any], t: Literal['V', 'U'], m: float) -> Dict[str, Any]:
|
||||
side = 'left' if t == 'V' else 'right'
|
||||
return {
|
||||
'data': (mechanics.stretch_left if t.upper() == 'V' else mechanics.stretch_right)(F['data']),
|
||||
'label': f"{t}({F['label']})",
|
||||
'data': mechanics.strain(F['data'],t,m),
|
||||
'label': f"epsilon_{t}^{m}({F['label']})",
|
||||
'meta': {
|
||||
'unit': F['meta']['unit'],
|
||||
'description': f"{'left' if t.upper() == 'V' else 'right'} stretch tensor "\
|
||||
+f"of {F['label']} ({F['meta']['description']})", # noqa
|
||||
'creator': 'add_stretch_tensor'
|
||||
'description': f'Seth-Hill strain tensor of order {m} based on {side} stretch tensor '+\
|
||||
f"of {F['label']} ({F['meta']['description']})",
|
||||
'creator': 'add_strain'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(strain,{'F':F},{'t':t,'m':m})
|
||||
|
||||
|
||||
def add_stretch_tensor(self,
|
||||
F: str = 'F',
|
||||
t: Literal['V', 'U'] = 'V'):
|
||||
|
|
@ -1294,20 +1286,21 @@ class Result:
|
|||
Defaults to 'V'.
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_stretch_tensor,{'F':F},{'t':t})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_curl(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
|
||||
def stretch_tensor(F: Dict[str, Any], t: str) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': grid_filters.curl(size,f['data']),
|
||||
'label': f"curl({f['label']})",
|
||||
'data': (mechanics.stretch_left if t.upper() == 'V' else mechanics.stretch_right)(F['data']),
|
||||
'label': f"{t}({F['label']})",
|
||||
'meta': {
|
||||
'unit': f['meta']['unit']+'/m',
|
||||
'description': f"curl of {f['label']} ({f['meta']['description']})",
|
||||
'creator': 'add_curl'
|
||||
'unit': F['meta']['unit'],
|
||||
'description': f"{'left' if t.upper() == 'V' else 'right'} stretch tensor "\
|
||||
+f"of {F['label']} ({F['meta']['description']})", # noqa
|
||||
'creator': 'add_stretch_tensor'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(stretch_tensor,{'F':F},{'t':t})
|
||||
|
||||
|
||||
def add_curl(self, f: str):
|
||||
"""
|
||||
Add curl of a field.
|
||||
|
|
@ -1323,20 +1316,20 @@ class Result:
|
|||
i.e. fields resulting from the grid solver.
|
||||
|
||||
"""
|
||||
self._add_generic_grid(self._add_curl,{'f':f},{'size':self.size})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_divergence(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
|
||||
def curl(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': grid_filters.divergence(size,f['data']),
|
||||
'label': f"divergence({f['label']})",
|
||||
'data': grid_filters.curl(size,f['data']),
|
||||
'label': f"curl({f['label']})",
|
||||
'meta': {
|
||||
'unit': f['meta']['unit']+'/m',
|
||||
'description': f"divergence of {f['label']} ({f['meta']['description']})",
|
||||
'creator': 'add_divergence'
|
||||
'description': f"curl of {f['label']} ({f['meta']['description']})",
|
||||
'creator': 'add_curl'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_grid(curl,{'f':f},{'size':self.size})
|
||||
|
||||
|
||||
def add_divergence(self, f: str):
|
||||
"""
|
||||
Add divergence of a field.
|
||||
|
|
@ -1352,21 +1345,20 @@ class Result:
|
|||
i.e. fields resulting from the grid solver.
|
||||
|
||||
"""
|
||||
self._add_generic_grid(self._add_divergence,{'f':f},{'size':self.size})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_gradient(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
|
||||
def divergence(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': grid_filters.gradient(size,f['data'] if len(f['data'].shape) == 4 else \
|
||||
f['data'].reshape(f['data'].shape+(1,))),
|
||||
'label': f"gradient({f['label']})",
|
||||
'data': grid_filters.divergence(size,f['data']),
|
||||
'label': f"divergence({f['label']})",
|
||||
'meta': {
|
||||
'unit': f['meta']['unit']+'/m',
|
||||
'description': f"gradient of {f['label']} ({f['meta']['description']})",
|
||||
'creator': 'add_gradient'
|
||||
'description': f"divergence of {f['label']} ({f['meta']['description']})",
|
||||
'creator': 'add_divergence'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_grid(divergence,{'f':f},{'size':self.size})
|
||||
|
||||
|
||||
def add_gradient(self, f: str):
|
||||
"""
|
||||
Add gradient of a field.
|
||||
|
|
@ -1382,7 +1374,19 @@ class Result:
|
|||
i.e. fields resulting from the grid solver.
|
||||
|
||||
"""
|
||||
self._add_generic_grid(self._add_gradient,{'f':f},{'size':self.size})
|
||||
def gradient(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': grid_filters.gradient(size,f['data'] if len(f['data'].shape) == 4 else \
|
||||
f['data'].reshape(f['data'].shape+(1,))),
|
||||
'label': f"gradient({f['label']})",
|
||||
'meta': {
|
||||
'unit': f['meta']['unit']+'/m',
|
||||
'description': f"gradient of {f['label']} ({f['meta']['description']})",
|
||||
'creator': 'add_gradient'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_grid(gradient,{'f':f},{'size':self.size})
|
||||
|
||||
|
||||
def _add_generic_grid(self,
|
||||
|
|
@ -1444,26 +1448,6 @@ class Result:
|
|||
f'damask.Result.{creator} v{damask.version}'.encode()
|
||||
|
||||
|
||||
def _job_pointwise(self,
|
||||
group: str,
|
||||
callback: Callable,
|
||||
datasets: Dict[str, str],
|
||||
args: Dict[str, str]) -> List[Union[None, Any]]:
|
||||
"""Execute job for _add_generic_pointwise."""
|
||||
try:
|
||||
datasets_in = {}
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
for arg,label in datasets.items():
|
||||
loc = f[group+'/'+label]
|
||||
datasets_in[arg]={'data' :loc[()],
|
||||
'label':label,
|
||||
'meta': {k:(v.decode() if not h5py3 and type(v) is bytes else v) \
|
||||
for k,v in loc.attrs.items()}}
|
||||
r = callback(**datasets_in,**args)
|
||||
return [group,r]
|
||||
except Exception as err:
|
||||
print(f'Error during calculation: {err}.')
|
||||
return [None,None]
|
||||
|
||||
|
||||
def _add_generic_pointwise(self,
|
||||
|
|
@ -1485,6 +1469,27 @@ class Result:
|
|||
Arguments parsed to func.
|
||||
|
||||
"""
|
||||
|
||||
def job_pointwise(group: str,
|
||||
callback: Callable,
|
||||
datasets: Dict[str, str],
|
||||
args: Dict[str, str]) -> List[Union[None, Any]]:
|
||||
"""Execute job for _add_generic_pointwise."""
|
||||
try:
|
||||
datasets_in = {}
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
for arg,label in datasets.items():
|
||||
loc = f[group+'/'+label]
|
||||
datasets_in[arg]={'data' :loc[()],
|
||||
'label':label,
|
||||
'meta': {k:(v.decode() if not h5py3 and type(v) is bytes else v) \
|
||||
for k,v in loc.attrs.items()}}
|
||||
r = callback(**datasets_in,**args)
|
||||
return [group,r]
|
||||
except Exception as err:
|
||||
print(f'Error during calculation: {err}.')
|
||||
return [None,None]
|
||||
|
||||
groups = []
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
for inc in self.visible['increments']:
|
||||
|
|
@ -1498,7 +1503,7 @@ class Result:
|
|||
print('No matching dataset found, no data was added.')
|
||||
return
|
||||
|
||||
default_arg = partial(self._job_pointwise,callback=func,datasets=datasets,args=args)
|
||||
default_arg = partial(job_pointwise,callback=func,datasets=datasets,args=args)
|
||||
|
||||
for grp in util.show_progress(groups):
|
||||
group, result = default_arg(grp) # type: ignore
|
||||
|
|
|
|||
Loading…
Reference in New Issue