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Merge branch 'result-typehints' into 'development' 

more specific typehints

See merge request damask/DAMASK!825
This commit is contained in:
Daniel Otto de Mentock 2023-10-06 08:43:37 +00:00
parent 05e675bbdb 983a376f45
commit 4f2c726b95
2 changed files with 51 additions and 39 deletions
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74
python/damask/_result.py
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@ -22,7 +22,7 @@ from . import grid_filters
from . import mechanics from . import mechanics
from . import tensor from . import tensor
from . import util from . import util
from ._typehints import FloatSequence, IntSequence from ._typehints import FloatSequence, IntSequence, DADF5Dataset
h5py3 = h5py.__version__[0] == '3' h5py3 = h5py.__version__[0] == '3'
@ -36,7 +36,7 @@ def _read(dataset: h5py._hl.dataset.Dataset) -> np.ndarray:
return np.array(dataset,dtype=dtype) return np.array(dataset,dtype=dtype)
def _match(requested, def _match(requested,
existing: h5py._hl.base.KeysViewHDF5) -> List[Any]: existing: h5py._hl.base.KeysViewHDF5) -> List[str]:
"""Find matches among two sets of labels.""" """Find matches among two sets of labels."""
def flatten_list(list_of_lists): def flatten_list(list_of_lists):
return [e for e_ in list_of_lists for e in e_] return [e for e_ in list_of_lists for e in e_]
@ -609,7 +609,7 @@ class Result:
Name of scalar, vector, or tensor dataset to take absolute value of. Name of scalar, vector, or tensor dataset to take absolute value of.
""" """
def absolute(x: Dict[str, Any]) -> Dict[str, Any]: def absolute(x: DADF5Dataset) -> DADF5Dataset:
return { return {
'data': np.abs(x['data']), 'data': np.abs(x['data']),
'label': f'|{x["label"]}|', 'label': f'|{x["label"]}|',
@ -671,7 +671,7 @@ class Result:
... 'Mises equivalent of the Cauchy stress') ... 'Mises equivalent of the Cauchy stress')
""" """
def calculation(**kwargs) -> Dict[str, Any]: def calculation(**kwargs) -> DADF5Dataset:
formula = kwargs['formula'] formula = kwargs['formula']
for d in re.findall(r'#(.*?)#',formula): for d in re.findall(r'#(.*?)#',formula):
formula = formula.replace(f'#{d}#',f"kwargs['{d}']['data']") formula = formula.replace(f'#{d}#',f"kwargs['{d}']['data']")
@ -712,7 +712,7 @@ class Result:
""" """
def stress_Cauchy(P: Dict[str, Any], F: Dict[str, Any]) -> Dict[str, Any]: def stress_Cauchy(P: DADF5Dataset, F: DADF5Dataset) -> DADF5Dataset:
return { return {
'data': mechanics.stress_Cauchy(P['data'],F['data']), 'data': mechanics.stress_Cauchy(P['data'],F['data']),
'label': 'sigma', 'label': 'sigma',
@ -747,7 +747,7 @@ class Result:
""" """
def determinant(T: Dict[str, Any]) -> Dict[str, Any]: def determinant(T: DADF5Dataset) -> DADF5Dataset:
return { return {
'data': np.linalg.det(T['data']), 'data': np.linalg.det(T['data']),
'label': f"det({T['label']})", 'label': f"det({T['label']})",
@ -780,7 +780,7 @@ class Result:
""" """
def deviator(T: Dict[str, Any]) -> Dict[str, Any]: def deviator(T: DADF5Dataset) -> DADF5Dataset:
return { return {
'data': tensor.deviatoric(T['data']), 'data': tensor.deviatoric(T['data']),
'label': f"s_{T['label']}", 'label': f"s_{T['label']}",
@ -817,7 +817,7 @@ class Result:
""" """
def eigenval(T_sym: Dict[str, Any], eigenvalue: Literal['max, mid, min']) -> Dict[str, Any]: def eigenval(T_sym: DADF5Dataset, eigenvalue: Literal['max', 'mid', 'min']) -> DADF5Dataset:
if eigenvalue == 'max': if eigenvalue == 'max':
label,p = 'maximum',2 label,p = 'maximum',2
elif eigenvalue == 'mid': elif eigenvalue == 'mid':
@ -856,7 +856,7 @@ class Result:
""" """
def eigenvector(T_sym: Dict[str, Any], eigenvalue: Literal['max', 'mid', 'min']) -> Dict[str, Any]: def eigenvector(T_sym: DADF5Dataset, eigenvalue: Literal['max', 'mid', 'min']) -> DADF5Dataset:
if eigenvalue == 'max': if eigenvalue == 'max':
label,p = 'maximum',2 label,p = 'maximum',2
elif eigenvalue == 'mid': elif eigenvalue == 'mid':
@ -904,13 +904,13 @@ class Result:
""" """
def IPF_color(l: FloatSequence, q: Dict[str, Any]) -> Dict[str, Any]: def IPF_color(l: FloatSequence, q: DADF5Dataset) -> DADF5Dataset:
m = util.scale_to_coprime(np.array(l)) m = util.scale_to_coprime(np.array(l))
lattice = q['meta']['lattice'] lattice = q['meta']['lattice']
o = Orientation(rotation = q['data'],lattice=lattice) o = Orientation(rotation = q['data'],lattice=lattice)
return { return {
'data': np.uint8(o.IPF_color(l)*255), 'data': (o.IPF_color(l)*255).astype(np.uint8),
'label': 'IPFcolor_({} {} {})'.format(*m), 'label': 'IPFcolor_({} {} {})'.format(*m),
'meta' : { 'meta' : {
'unit': '8-bit RGB', 'unit': '8-bit RGB',
@ -933,7 +933,7 @@ class Result:
Name of symmetric tensor dataset. Name of symmetric tensor dataset.
""" """
def maximum_shear(T_sym: Dict[str, Any]) -> Dict[str, Any]: def maximum_shear(T_sym: DADF5Dataset) -> DADF5Dataset:
return { return {
'data': mechanics.maximum_shear(T_sym['data']), 'data': mechanics.maximum_shear(T_sym['data']),
'label': f"max_shear({T_sym['label']})", 'label': f"max_shear({T_sym['label']})",
@ -976,7 +976,7 @@ class Result:
>>> r.add_equivalent_Mises('epsilon_V^0.0(F)') >>> r.add_equivalent_Mises('epsilon_V^0.0(F)')
""" """
def equivalent_Mises(T_sym: Dict[str, Any], kind: str) -> Dict[str, Any]: def equivalent_Mises(T_sym: DADF5Dataset, kind: str) -> DADF5Dataset:
k = kind k = kind
if k is None: if k is None:
if T_sym['meta']['unit'] == '1': if T_sym['meta']['unit'] == '1':
@ -1014,7 +1014,7 @@ class Result:
Order of the norm. inf means NumPy's inf object. For details refer to numpy.linalg.norm. Order of the norm. inf means NumPy's inf object. For details refer to numpy.linalg.norm.
""" """
def norm(x: Dict[str, Any], ord: Union[int, float, Literal['fro', 'nuc']]) -> Dict[str, Any]: def norm(x: DADF5Dataset, ord: Union[int, float, Literal['fro', 'nuc']]) -> DADF5Dataset:
o = ord o = ord
if len(x['data'].shape) == 2: if len(x['data'].shape) == 2:
axis: Union[int, Tuple[int, int]] = 1 axis: Union[int, Tuple[int, int]] = 1
@ -1062,7 +1062,7 @@ class Result:
is taken into account. is taken into account.
""" """
def stress_second_Piola_Kirchhoff(P: Dict[str, Any], F: Dict[str, Any]) -> Dict[str, Any]: def stress_second_Piola_Kirchhoff(P: DADF5Dataset, F: DADF5Dataset) -> DADF5Dataset:
return { return {
'data': mechanics.stress_second_Piola_Kirchhoff(P['data'],F['data']), 'data': mechanics.stress_second_Piola_Kirchhoff(P['data'],F['data']),
'label': 'S', 'label': 'S',
@ -1104,12 +1104,11 @@ class Result:
Defaults to True. Defaults to True.
""" """
def pole(q: Dict[str, Any], def pole(q: DADF5Dataset,
uvw: FloatSequence, uvw: FloatSequence, hkl: FloatSequence,
hkl: FloatSequence,
with_symmetry: bool, with_symmetry: bool,
normalize: bool) -> Dict[str, Any]: normalize: bool) -> DADF5Dataset:
c = q['meta']['c/a'] if 'c/a' in q['meta'] else 1 c = q['meta']['c/a'] if 'c/a' in q['meta'] else 1.0
brackets = ['[]','()','⟨⟩','{}'][(uvw is None)*1+with_symmetry*2] brackets = ['[]','()','⟨⟩','{}'][(uvw is None)*1+with_symmetry*2]
label = 'p^' + '{}{} {} {}{}'.format(brackets[0], label = 'p^' + '{}{} {} {}{}'.format(brackets[0],
*(uvw if uvw else hkl), *(uvw if uvw else hkl),
@ -1149,7 +1148,7 @@ class Result:
>>> r.add_rotation('F') >>> r.add_rotation('F')
""" """
def rotation(F: Dict[str, Any]) -> Dict[str, Any]: def rotation(F: DADF5Dataset) -> DADF5Dataset:
return { return {
'data': mechanics.rotation(F['data']).as_matrix(), 'data': mechanics.rotation(F['data']).as_matrix(),
'label': f"R({F['label']})", 'label': f"R({F['label']})",
@ -1181,7 +1180,7 @@ class Result:
>>> r.add_spherical('sigma') >>> r.add_spherical('sigma')
""" """
def spherical(T: Dict[str, Any]) -> Dict[str, Any]: def spherical(T: DADF5Dataset) -> DADF5Dataset:
return { return {
'data': tensor.spherical(T['data'],False), 'data': tensor.spherical(T['data'],False),
'label': f"p_{T['label']}", 'label': f"p_{T['label']}",
@ -1255,14 +1254,14 @@ class Result:
| https://de.wikipedia.org/wiki/Verzerrungstensor | https://de.wikipedia.org/wiki/Verzerrungstensor
""" """
def strain(F: Dict[str, Any], t: Literal['V', 'U'], m: float) -> Dict[str, Any]: def strain(F: DADF5Dataset, t: Literal['V', 'U'], m: float) -> DADF5Dataset:
side = 'left' if t == 'V' else 'right' side = 'left' if t == 'V' else 'right'
return { return {
'data': mechanics.strain(F['data'],t,m), 'data': mechanics.strain(F['data'],t,m),
'label': f"epsilon_{t}^{m}({F['label']})", 'label': f"epsilon_{t}^{m}({F['label']})",
'meta': { 'meta': {
'unit': F['meta']['unit'], 'unit': F['meta']['unit'],
'description': f'Seth-Hill strain tensor of order {m} based on {side} stretch tensor '+\ 'description': f'Seth-Hill strain tensor of order {m} based on {side} stretch tensor '
f"of {F['label']} ({F['meta']['description']})", f"of {F['label']} ({F['meta']['description']})",
'creator': 'add_strain' 'creator': 'add_strain'
} }
@ -1286,14 +1285,14 @@ class Result:
Defaults to 'V'. Defaults to 'V'.
""" """
def stretch_tensor(F: Dict[str, Any], t: str) -> Dict[str, Any]: def stretch_tensor(F: DADF5Dataset, t: str) -> DADF5Dataset:
return { return {
'data': (mechanics.stretch_left if t.upper() == 'V' else mechanics.stretch_right)(F['data']), 'data': (mechanics.stretch_left if t.upper() == 'V' else mechanics.stretch_right)(F['data']),
'label': f"{t}({F['label']})", 'label': f"{t}({F['label']})",
'meta': { 'meta': {
'unit': F['meta']['unit'], 'unit': F['meta']['unit'],
'description': f"{'left' if t.upper() == 'V' else 'right'} stretch tensor "\ 'description': f"{'left' if t.upper() == 'V' else 'right'} stretch tensor "
+f"of {F['label']} ({F['meta']['description']})", # noqa f"of {F['label']} ({F['meta']['description']})", # noqa
'creator': 'add_stretch_tensor' 'creator': 'add_stretch_tensor'
} }
} }
@ -1316,7 +1315,7 @@ class Result:
i.e. fields resulting from the grid solver. i.e. fields resulting from the grid solver.
""" """
def curl(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]: def curl(f: DADF5Dataset, size: np.ndarray) -> DADF5Dataset:
return { return {
'data': grid_filters.curl(size,f['data']), 'data': grid_filters.curl(size,f['data']),
'label': f"curl({f['label']})", 'label': f"curl({f['label']})",
@ -1345,7 +1344,7 @@ class Result:
i.e. fields resulting from the grid solver. i.e. fields resulting from the grid solver.
""" """
def divergence(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]: def divergence(f: DADF5Dataset, size: np.ndarray) -> DADF5Dataset:
return { return {
'data': grid_filters.divergence(size,f['data']), 'data': grid_filters.divergence(size,f['data']),
'label': f"divergence({f['label']})", 'label': f"divergence({f['label']})",
@ -1374,7 +1373,7 @@ class Result:
i.e. fields resulting from the grid solver. i.e. fields resulting from the grid solver.
""" """
def gradient(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]: def gradient(f: DADF5Dataset, size: np.ndarray) -> DADF5Dataset:
return { return {
'data': grid_filters.gradient(size,f['data'] if len(f['data'].shape) == 4 else \ 'data': grid_filters.gradient(size,f['data'] if len(f['data'].shape) == 4 else \
f['data'].reshape(f['data'].shape+(1,))), f['data'].reshape(f['data'].shape+(1,))),
@ -1390,7 +1389,7 @@ class Result:
def _add_generic_grid(self, def _add_generic_grid(self,
func: Callable, func: Callable[..., DADF5Dataset],
datasets: Dict[str, str], datasets: Dict[str, str],
args: Dict[str, str] = {}, args: Dict[str, str] = {},
constituents = None): constituents = None):
@ -1441,7 +1440,7 @@ class Result:
now.strftime('%Y-%m-%d %H:%M:%S%z').encode() now.strftime('%Y-%m-%d %H:%M:%S%z').encode()
for l,v in r['meta'].items(): for l,v in r['meta'].items():
h5_dataset.attrs[l.lower()]=v if h5py3 else v.encode() h5_dataset.attrs[l.lower()]=v.encode() if not h5py3 and type(v) is str else v
creator = h5_dataset.attrs['creator'] if h5py3 else \ creator = h5_dataset.attrs['creator'] if h5py3 else \
h5_dataset.attrs['creator'].decode() h5_dataset.attrs['creator'].decode()
h5_dataset.attrs['creator'] = f'damask.Result.{creator} v{damask.version}' if h5py3 else \ h5_dataset.attrs['creator'] = f'damask.Result.{creator} v{damask.version}' if h5py3 else \
@ -1451,8 +1450,8 @@ class Result:
def _add_generic_pointwise(self, def _add_generic_pointwise(self,
func: Callable, func: Callable[..., DADF5Dataset],
datasets: Dict[str, Any], datasets: Dict[str, str],
args: Dict[str, Any] = {}): args: Dict[str, Any] = {}):
""" """
General function to add pointwise data. General function to add pointwise data.
@ -1471,9 +1470,9 @@ class Result:
""" """
def job_pointwise(group: str, def job_pointwise(group: str,
callback: Callable, callback: Callable[..., DADF5Dataset],
datasets: Dict[str, str], datasets: Dict[str, str],
args: Dict[str, str]) -> Union[None, Any]: args: Dict[str, str]) -> Union[None, DADF5Dataset]:
try: try:
datasets_in = {} datasets_in = {}
with h5py.File(self.fname,'r') as f: with h5py.File(self.fname,'r') as f:
@ -1561,7 +1560,7 @@ class Result:
def get(self, def get(self,
output: Union[str, List[str]] = '*', output: Union[str, List[str]] = '*',
flatten: bool = True, flatten: bool = True,
prune: bool = True) -> Optional[Dict[str,Any]]: prune: bool = True) -> Union[None,Dict[str,Any]]:
""" """
Collect data per phase/homogenization reflecting the group/folder structure in the DADF5 file. Collect data per phase/homogenization reflecting the group/folder structure in the DADF5 file.
@ -1930,6 +1929,7 @@ class Result:
v.save(vtk_dir/f'{self.fname.stem}_inc{inc.split(prefix_inc)[-1].zfill(N_digits)}', v.save(vtk_dir/f'{self.fname.stem}_inc{inc.split(prefix_inc)[-1].zfill(N_digits)}',
parallel=parallel) parallel=parallel)
def export_DADF5(self, def export_DADF5(self,
fname, fname,
output: Union[str, List[str]] = '*', output: Union[str, List[str]] = '*',

14
python/damask/_typehints.py
View File

@ -1,6 +1,6 @@
"""Functionality for typehints.""" """Functionality for typehints."""
from typing import Sequence, Union, Literal, TextIO from typing import Sequence, Union, TypedDict, Literal, TextIO
from pathlib import Path from pathlib import Path
import numpy as np import numpy as np
@ -16,3 +16,15 @@ CrystalKinematics = Literal['slip', 'twin']
NumpyRngSeed = Union[int, IntSequence, np.random.SeedSequence, np.random.Generator] NumpyRngSeed = Union[int, IntSequence, np.random.SeedSequence, np.random.Generator]
# BitGenerator does not exists in older numpy versions # BitGenerator does not exists in older numpy versions
#NumpyRngSeed = Union[int, IntSequence, np.random.SeedSequence, np.random.BitGenerator, np.random.Generator] #NumpyRngSeed = Union[int, IntSequence, np.random.SeedSequence, np.random.BitGenerator, np.random.Generator]
# https://peps.python.org/pep-0655/
# Metadata = TypedDict('Metadata', {'unit': str, 'description': str, 'creator': str, 'lattice': NotRequired[str]})
_Metadata = TypedDict('_Metadata', {'lattice': str, 'c/a': float}, total=False)
class Metadata(_Metadata):
unit: str
description: str
creator: str
DADF5Dataset = TypedDict('DADF5Dataset', {'data': np.ndarray, 'label': str, 'meta': Metadata})