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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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76
python/damask/_result.py
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@ -22,7 +22,7 @@ from . import grid_filters
from . import mechanics
from . import tensor
from . import util
from ._typehints import FloatSequence, IntSequence
from ._typehints import FloatSequence, IntSequence, DADF5Dataset
h5py3 = h5py.__version__[0] == '3'
@ -36,7 +36,7 @@ def _read(dataset: h5py._hl.dataset.Dataset) -> np.ndarray:
return np.array(dataset,dtype=dtype)
def _match(requested,
existing: h5py._hl.base.KeysViewHDF5) -> List[Any]:
existing: h5py._hl.base.KeysViewHDF5) -> List[str]:
"""Find matches among two sets of labels."""
def flatten_list(list_of_lists):
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.
"""
def absolute(x: Dict[str, Any]) -> Dict[str, Any]:
def absolute(x: DADF5Dataset) -> DADF5Dataset:
return {
'data': np.abs(x['data']),
'label': f'|{x["label"]}|',
@ -671,7 +671,7 @@ class Result:
... 'Mises equivalent of the Cauchy stress')
"""
def calculation(**kwargs) -> Dict[str, Any]:
def calculation(**kwargs) -> DADF5Dataset:
formula = kwargs['formula']
for d in re.findall(r'#(.*?)#',formula):
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 {
'data': mechanics.stress_Cauchy(P['data'],F['data']),
'label': 'sigma',
@ -747,7 +747,7 @@ class Result:
"""
def determinant(T: Dict[str, Any]) -> Dict[str, Any]:
def determinant(T: DADF5Dataset) -> DADF5Dataset:
return {
'data': np.linalg.det(T['data']),
'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 {
'data': tensor.deviatoric(T['data']),
'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':
label,p = 'maximum',2
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':
label,p = 'maximum',2
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))
lattice = q['meta']['lattice']
o = Orientation(rotation = q['data'],lattice=lattice)
return {
'data': np.uint8(o.IPF_color(l)*255),
'data': (o.IPF_color(l)*255).astype(np.uint8),
'label': 'IPFcolor_({} {} {})'.format(*m),
'meta' : {
'unit': '8-bit RGB',
@ -933,7 +933,7 @@ class Result:
Name of symmetric tensor dataset.
"""
def maximum_shear(T_sym: Dict[str, Any]) -> Dict[str, Any]:
def maximum_shear(T_sym: DADF5Dataset) -> DADF5Dataset:
return {
'data': mechanics.maximum_shear(T_sym['data']),
'label': f"max_shear({T_sym['label']})",
@ -976,7 +976,7 @@ class Result:
>>> 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
if k is None:
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.
"""
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
if len(x['data'].shape) == 2:
axis: Union[int, Tuple[int, int]] = 1
@ -1062,7 +1062,7 @@ class Result:
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 {
'data': mechanics.stress_second_Piola_Kirchhoff(P['data'],F['data']),
'label': 'S',
@ -1104,12 +1104,11 @@ class Result:
Defaults to True.
"""
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
def pole(q: DADF5Dataset,
uvw: FloatSequence, hkl: FloatSequence,
with_symmetry: bool,
normalize: bool) -> DADF5Dataset:
c = q['meta']['c/a'] if 'c/a' in q['meta'] else 1.0
brackets = ['[]','()','⟨⟩','{}'][(uvw is None)*1+with_symmetry*2]
label = 'p^' + '{}{} {} {}{}'.format(brackets[0],
*(uvw if uvw else hkl),
@ -1149,7 +1148,7 @@ class Result:
>>> r.add_rotation('F')
"""
def rotation(F: Dict[str, Any]) -> Dict[str, Any]:
def rotation(F: DADF5Dataset) -> DADF5Dataset:
return {
'data': mechanics.rotation(F['data']).as_matrix(),
'label': f"R({F['label']})",
@ -1181,7 +1180,7 @@ class Result:
>>> r.add_spherical('sigma')
"""
def spherical(T: Dict[str, Any]) -> Dict[str, Any]:
def spherical(T: DADF5Dataset) -> DADF5Dataset:
return {
'data': tensor.spherical(T['data'],False),
'label': f"p_{T['label']}",
@ -1255,14 +1254,14 @@ class Result:
| 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'
return {
'data': mechanics.strain(F['data'],t,m),
'label': f"epsilon_{t}^{m}({F['label']})",
'meta': {
'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']})",
'creator': 'add_strain'
}
@ -1286,14 +1285,14 @@ class Result:
Defaults to 'V'.
"""
def stretch_tensor(F: Dict[str, Any], t: str) -> Dict[str, Any]:
def stretch_tensor(F: DADF5Dataset, t: str) -> DADF5Dataset:
return {
'data': (mechanics.stretch_left if t.upper() == 'V' else mechanics.stretch_right)(F['data']),
'label': f"{t}({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
'description': f"{'left' if t.upper() == 'V' else 'right'} stretch tensor "
f"of {F['label']} ({F['meta']['description']})", # noqa
'creator': 'add_stretch_tensor'
}
}
@ -1316,7 +1315,7 @@ class Result:
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 {
'data': grid_filters.curl(size,f['data']),
'label': f"curl({f['label']})",
@ -1345,7 +1344,7 @@ class Result:
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 {
'data': grid_filters.divergence(size,f['data']),
'label': f"divergence({f['label']})",
@ -1374,7 +1373,7 @@ class Result:
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 {
'data': grid_filters.gradient(size,f['data'] if len(f['data'].shape) == 4 else \
f['data'].reshape(f['data'].shape+(1,))),
@ -1390,7 +1389,7 @@ class Result:
def _add_generic_grid(self,
func: Callable,
func: Callable[..., DADF5Dataset],
datasets: Dict[str, str],
args: Dict[str, str] = {},
constituents = None):
@ -1441,7 +1440,7 @@ class Result:
now.strftime('%Y-%m-%d %H:%M:%S%z').encode()
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 \
h5_dataset.attrs['creator'].decode()
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,
func: Callable,
datasets: Dict[str, Any],
func: Callable[..., DADF5Dataset],
datasets: Dict[str, str],
args: Dict[str, Any] = {}):
"""
General function to add pointwise data.
@ -1471,9 +1470,9 @@ class Result:
"""
def job_pointwise(group: str,
callback: Callable,
callback: Callable[..., DADF5Dataset],
datasets: Dict[str, str],
args: Dict[str, str]) -> Union[None, Any]:
args: Dict[str, str]) -> Union[None, DADF5Dataset]:
try:
datasets_in = {}
with h5py.File(self.fname,'r') as f:
@ -1561,7 +1560,7 @@ class Result:
def get(self,
output: Union[str, List[str]] = '*',
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.
@ -1930,6 +1929,7 @@ class Result:
v.save(vtk_dir/f'{self.fname.stem}_inc{inc.split(prefix_inc)[-1].zfill(N_digits)}',
parallel=parallel)
def export_DADF5(self,
fname,
output: Union[str, List[str]] = '*',

14
python/damask/_typehints.py
View File

@ -1,6 +1,6 @@
"""Functionality for typehints."""
from typing import Sequence, Union, Literal, TextIO
from typing import Sequence, Union, TypedDict, Literal, TextIO
from pathlib import Path
import numpy as np
@ -16,3 +16,15 @@ CrystalKinematics = Literal['slip', 'twin']
NumpyRngSeed = Union[int, IntSequence, np.random.SeedSequence, np.random.Generator]
# BitGenerator does not exists in older numpy versions
#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})