Merge branch '125-python-typehints' into 'development'
first few type hints for Python See merge request damask/DAMASK!450
This commit is contained in:
Daniel Otto de Mentock
commit
8e08af31e5
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@ -81,7 +81,7 @@ checkout:
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- release
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###################################################################################################
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processing:
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pytest:
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stage: python
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script:
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- cd $DAMASKROOT/python
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@ -91,6 +91,15 @@ processing:
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- master
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- release
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mypy:
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stage: python
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script:
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- cd $DAMASKROOT/python
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- mypy -m damask
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except:
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- master
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- release
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###################################################################################################
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compile_grid_Intel:
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@ -14,8 +14,8 @@ from . import tensor # noqa
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from . import mechanics # noqa
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from . import solver # noqa
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from . import grid_filters # noqa
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#Modules that contain only one class (of the same name), are prefixed by a '_'.
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#For example, '_colormap' containsa class called 'Colormap' which is imported as 'damask.Colormap'.
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# Modules that contain only one class (of the same name), are prefixed by a '_'.
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# For example, '_colormap' contains a class called 'Colormap' which is imported as 'damask.Colormap'.
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from ._rotation import Rotation # noqa
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from ._crystal import Crystal # noqa
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from ._orientation import Orientation # noqa
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@ -125,7 +125,7 @@ class Orientation(Rotation,Crystal):
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"""Create deep copy."""
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dup = copy.deepcopy(self)
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if rotation is not None:
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dup.quaternion = Orientation(rotation,family='cubic').quaternion
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dup.quaternion = Rotation(rotation).quaternion
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return dup
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copy = __copy__
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@ -1,3 +1,5 @@
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import copy
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import numpy as np
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from . import tensor
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@ -85,9 +87,12 @@ class Rotation:
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+ str(self.quaternion)
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def __copy__(self,**kwargs):
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def __copy__(self,rotation=None):
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"""Create deep copy."""
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return self.__class__(rotation=kwargs['rotation'] if 'rotation' in kwargs else self.quaternion)
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dup = copy.deepcopy(self)
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if rotation is not None:
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dup.quaternion = Rotation(rotation).quaternion
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return dup
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copy = __copy__
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@ -11,10 +11,14 @@ the following operations are required for tensorial data:
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- D1 = D3.reshape(cells+(-1,)).reshape(-1,9,order='F')
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"""
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from typing import Sequence, Tuple, Union
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from scipy import spatial as _spatial
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import numpy as _np
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def _ks(size,cells,first_order=False):
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def _ks(size: _np.ndarray, cells: Union[_np.ndarray,Sequence[int]], first_order: bool = False) -> _np.ndarray:
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"""
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Get wave numbers operator.
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@ -41,7 +45,7 @@ def _ks(size,cells,first_order=False):
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return _np.stack(_np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij'), axis=-1)
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def curl(size,f):
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def curl(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
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u"""
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Calculate curl of a vector or tensor field in Fourier space.
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@ -72,7 +76,7 @@ def curl(size,f):
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return _np.fft.irfftn(curl_,axes=(0,1,2),s=f.shape[:3])
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def divergence(size,f):
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def divergence(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
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u"""
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Calculate divergence of a vector or tensor field in Fourier space.
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@ -99,7 +103,7 @@ def divergence(size,f):
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return _np.fft.irfftn(div_,axes=(0,1,2),s=f.shape[:3])
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def gradient(size,f):
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def gradient(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
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u"""
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Calculate gradient of a scalar or vector fieldin Fourier space.
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@ -126,7 +130,9 @@ def gradient(size,f):
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return _np.fft.irfftn(grad_,axes=(0,1,2),s=f.shape[:3])
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def coordinates0_point(cells,size,origin=_np.zeros(3)):
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def coordinates0_point(cells: Union[ _np.ndarray,Sequence[int]],
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size: _np.ndarray,
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origin: _np.ndarray = _np.zeros(3)) -> _np.ndarray:
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"""
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Cell center positions (undeformed).
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@ -145,8 +151,8 @@ def coordinates0_point(cells,size,origin=_np.zeros(3)):
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Undeformed cell center coordinates.
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"""
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start = origin + size/cells*.5
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end = origin + size - size/cells*.5
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start = origin + size/_np.array(cells)*.5
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end = origin + size - size/_np.array(cells)*.5
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return _np.stack(_np.meshgrid(_np.linspace(start[0],end[0],cells[0]),
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_np.linspace(start[1],end[1],cells[1]),
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@ -154,7 +160,7 @@ def coordinates0_point(cells,size,origin=_np.zeros(3)):
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axis = -1)
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def displacement_fluct_point(size,F):
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def displacement_fluct_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
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"""
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Cell center displacement field from fluctuation part of the deformation gradient field.
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@ -186,7 +192,7 @@ def displacement_fluct_point(size,F):
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return _np.fft.irfftn(displacement,axes=(0,1,2),s=F.shape[:3])
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def displacement_avg_point(size,F):
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def displacement_avg_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
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"""
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Cell center displacement field from average part of the deformation gradient field.
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@ -207,7 +213,7 @@ def displacement_avg_point(size,F):
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return _np.einsum('ml,ijkl->ijkm',F_avg - _np.eye(3),coordinates0_point(F.shape[:3],size))
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def displacement_point(size,F):
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def displacement_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
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"""
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Cell center displacement field from deformation gradient field.
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@ -227,7 +233,7 @@ def displacement_point(size,F):
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return displacement_avg_point(size,F) + displacement_fluct_point(size,F)
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def coordinates_point(size,F,origin=_np.zeros(3)):
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def coordinates_point(size: _np.ndarray, F: _np.ndarray, origin: _np.ndarray = _np.zeros(3)) -> _np.ndarray:
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"""
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Cell center positions.
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@ -249,7 +255,8 @@ def coordinates_point(size,F,origin=_np.zeros(3)):
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return coordinates0_point(F.shape[:3],size,origin) + displacement_point(size,F)
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def cellsSizeOrigin_coordinates0_point(coordinates0,ordered=True):
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def cellsSizeOrigin_coordinates0_point(coordinates0: _np.ndarray,
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ordered: bool = True) -> Tuple[_np.ndarray,_np.ndarray,_np.ndarray]:
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"""
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Return grid 'DNA', i.e. cells, size, and origin from 1D array of point positions.
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@ -292,13 +299,15 @@ def cellsSizeOrigin_coordinates0_point(coordinates0,ordered=True):
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raise ValueError('Regular cell spacing violated.')
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if ordered and not _np.allclose(coordinates0.reshape(tuple(cells)+(3,),order='F'),
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coordinates0_point(cells,size,origin),atol=atol):
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coordinates0_point(list(cells),size,origin),atol=atol):
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raise ValueError('Input data is not ordered (x fast, z slow).')
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return (cells,size,origin)
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def coordinates0_node(cells,size,origin=_np.zeros(3)):
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def coordinates0_node(cells: Union[_np.ndarray,Sequence[int]],
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size: _np.ndarray,
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origin: _np.ndarray = _np.zeros(3)) -> _np.ndarray:
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"""
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Nodal positions (undeformed).
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@ -323,7 +332,7 @@ def coordinates0_node(cells,size,origin=_np.zeros(3)):
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axis = -1)
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def displacement_fluct_node(size,F):
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def displacement_fluct_node(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
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"""
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Nodal displacement field from fluctuation part of the deformation gradient field.
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@ -343,7 +352,7 @@ def displacement_fluct_node(size,F):
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return point_to_node(displacement_fluct_point(size,F))
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def displacement_avg_node(size,F):
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def displacement_avg_node(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
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"""
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Nodal displacement field from average part of the deformation gradient field.
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@ -364,7 +373,7 @@ def displacement_avg_node(size,F):
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return _np.einsum('ml,ijkl->ijkm',F_avg - _np.eye(3),coordinates0_node(F.shape[:3],size))
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def displacement_node(size,F):
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def displacement_node(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
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"""
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Nodal displacement field from deformation gradient field.
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@ -384,7 +393,7 @@ def displacement_node(size,F):
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return displacement_avg_node(size,F) + displacement_fluct_node(size,F)
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def coordinates_node(size,F,origin=_np.zeros(3)):
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def coordinates_node(size: _np.ndarray, F: _np.ndarray, origin: _np.ndarray = _np.zeros(3)) -> _np.ndarray:
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"""
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Nodal positions.
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@ -406,7 +415,8 @@ def coordinates_node(size,F,origin=_np.zeros(3)):
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return coordinates0_node(F.shape[:3],size,origin) + displacement_node(size,F)
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def cellsSizeOrigin_coordinates0_node(coordinates0,ordered=True):
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def cellsSizeOrigin_coordinates0_node(coordinates0: _np.ndarray,
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ordered: bool = True) -> Tuple[_np.ndarray,_np.ndarray,_np.ndarray]:
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"""
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Return grid 'DNA', i.e. cells, size, and origin from 1D array of nodal positions.
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@ -441,13 +451,13 @@ def cellsSizeOrigin_coordinates0_node(coordinates0,ordered=True):
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raise ValueError('Regular cell spacing violated.')
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if ordered and not _np.allclose(coordinates0.reshape(tuple(cells+1)+(3,),order='F'),
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coordinates0_node(cells,size,origin),atol=atol):
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coordinates0_node(list(cells),size,origin),atol=atol):
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raise ValueError('Input data is not ordered (x fast, z slow).')
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return (cells,size,origin)
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def point_to_node(cell_data):
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def point_to_node(cell_data: _np.ndarray) -> _np.ndarray:
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"""
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Interpolate periodic point data to nodal data.
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@ -469,7 +479,7 @@ def point_to_node(cell_data):
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return _np.pad(n,((0,1),(0,1),(0,1))+((0,0),)*len(cell_data.shape[3:]),mode='wrap')
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def node_to_point(node_data):
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def node_to_point(node_data: _np.ndarray) -> _np.ndarray:
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"""
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Interpolate periodic nodal data to point data.
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@ -491,7 +501,7 @@ def node_to_point(node_data):
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return c[1:,1:,1:]
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def coordinates0_valid(coordinates0):
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def coordinates0_valid(coordinates0: _np.ndarray) -> bool:
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"""
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Check whether coordinates form a regular grid.
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@ -513,7 +523,7 @@ def coordinates0_valid(coordinates0):
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return False
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def regrid(size,F,cells):
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def regrid(size: _np.ndarray, F: _np.ndarray, cells: Union[_np.ndarray,Sequence[int]]) -> _np.ndarray:
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"""
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Return mapping from coordinates in deformed configuration to a regular grid.
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@ -5,13 +5,15 @@ All routines operate on numpy.ndarrays of shape (...,3,3).
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"""
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from . import tensor as _tensor
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from . import _rotation
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from typing import Sequence
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import numpy as _np
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from . import tensor as _tensor
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from . import _rotation
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def deformation_Cauchy_Green_left(F):
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def deformation_Cauchy_Green_left(F: _np.ndarray) -> _np.ndarray:
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"""
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Calculate left Cauchy-Green deformation tensor (Finger deformation tensor).
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@ -29,7 +31,7 @@ def deformation_Cauchy_Green_left(F):
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return _np.matmul(F,_tensor.transpose(F))
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def deformation_Cauchy_Green_right(F):
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def deformation_Cauchy_Green_right(F: _np.ndarray) -> _np.ndarray:
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"""
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Calculate right Cauchy-Green deformation tensor.
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@ -47,7 +49,7 @@ def deformation_Cauchy_Green_right(F):
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return _np.matmul(_tensor.transpose(F),F)
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def equivalent_strain_Mises(epsilon):
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def equivalent_strain_Mises(epsilon: _np.ndarray) -> _np.ndarray:
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"""
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Calculate the Mises equivalent of a strain tensor.
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@ -65,7 +67,7 @@ def equivalent_strain_Mises(epsilon):
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return _equivalent_Mises(epsilon,2.0/3.0)
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def equivalent_stress_Mises(sigma):
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def equivalent_stress_Mises(sigma: _np.ndarray) -> _np.ndarray:
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"""
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Calculate the Mises equivalent of a stress tensor.
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@ -83,7 +85,7 @@ def equivalent_stress_Mises(sigma):
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return _equivalent_Mises(sigma,3.0/2.0)
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def maximum_shear(T_sym):
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def maximum_shear(T_sym: _np.ndarray) -> _np.ndarray:
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"""
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Calculate the maximum shear component of a symmetric tensor.
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@ -102,7 +104,7 @@ def maximum_shear(T_sym):
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return (w[...,0] - w[...,2])*0.5
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def rotation(T):
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def rotation(T: _np.ndarray) -> _rotation.Rotation:
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"""
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Calculate the rotational part of a tensor.
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@ -120,7 +122,7 @@ def rotation(T):
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return _rotation.Rotation.from_matrix(_polar_decomposition(T,'R')[0])
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def strain(F,t,m):
|
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def strain(F: _np.ndarray, t: str, m: float) -> _np.ndarray:
|
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"""
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Calculate strain tensor (Seth–Hill family).
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@ -160,7 +162,7 @@ def strain(F,t,m):
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return eps
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def stress_Cauchy(P,F):
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def stress_Cauchy(P: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
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"""
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Calculate the Cauchy stress (true stress).
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@ -182,7 +184,7 @@ def stress_Cauchy(P,F):
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return _tensor.symmetric(_np.einsum('...,...ij,...kj',1.0/_np.linalg.det(F),P,F))
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def stress_second_Piola_Kirchhoff(P,F):
|
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def stress_second_Piola_Kirchhoff(P: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
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"""
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Calculate the second Piola-Kirchhoff stress.
|
||||
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||||
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@ -205,7 +207,7 @@ def stress_second_Piola_Kirchhoff(P,F):
|
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return _tensor.symmetric(_np.einsum('...ij,...jk',_np.linalg.inv(F),P))
|
||||
|
||||
|
||||
def stretch_left(T):
|
||||
def stretch_left(T: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Calculate left stretch of a tensor.
|
||||
|
||||
|
|
@ -223,7 +225,7 @@ def stretch_left(T):
|
|||
return _polar_decomposition(T,'V')[0]
|
||||
|
||||
|
||||
def stretch_right(T):
|
||||
def stretch_right(T: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
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Calculate right stretch of a tensor.
|
||||
|
||||
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|
@ -241,7 +243,7 @@ def stretch_right(T):
|
|||
return _polar_decomposition(T,'U')[0]
|
||||
|
||||
|
||||
def _polar_decomposition(T,requested):
|
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def _polar_decomposition(T: _np.ndarray, requested: Sequence[str]) -> tuple:
|
||||
"""
|
||||
Perform singular value decomposition.
|
||||
|
||||
|
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@ -257,21 +259,21 @@ def _polar_decomposition(T,requested):
|
|||
u, _, vh = _np.linalg.svd(T)
|
||||
R = _np.einsum('...ij,...jk',u,vh)
|
||||
|
||||
output = ()
|
||||
output = []
|
||||
if 'R' in requested:
|
||||
output+=(R,)
|
||||
output+=[R]
|
||||
if 'V' in requested:
|
||||
output+=(_np.einsum('...ij,...kj',T,R),)
|
||||
output+=[_np.einsum('...ij,...kj',T,R)]
|
||||
if 'U' in requested:
|
||||
output+=(_np.einsum('...ji,...jk',R,T),)
|
||||
output+=[_np.einsum('...ji,...jk',R,T)]
|
||||
|
||||
if len(output) == 0:
|
||||
raise ValueError('output needs to be out of V, R, U')
|
||||
|
||||
return output
|
||||
return tuple(output)
|
||||
|
||||
|
||||
def _equivalent_Mises(T_sym,s):
|
||||
def _equivalent_Mises(T_sym: _np.ndarray, s: float) -> _np.ndarray:
|
||||
"""
|
||||
Base equation for Mises equivalent of a stress or strain tensor.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,7 @@
|
|||
"""Functionality for generation of seed points for Voronoi or Laguerre tessellation."""
|
||||
|
||||
from typing import Sequence,Tuple
|
||||
|
||||
from scipy import spatial as _spatial
|
||||
import numpy as _np
|
||||
|
||||
|
|
@ -7,7 +9,7 @@ from . import util as _util
|
|||
from . import grid_filters as _grid_filters
|
||||
|
||||
|
||||
def from_random(size,N_seeds,cells=None,rng_seed=None):
|
||||
def from_random(size: _np.ndarray, N_seeds: int, cells: _np.ndarray = None, rng_seed=None) -> _np.ndarray:
|
||||
"""
|
||||
Place seeds randomly in space.
|
||||
|
||||
|
|
@ -41,7 +43,8 @@ def from_random(size,N_seeds,cells=None,rng_seed=None):
|
|||
return coords
|
||||
|
||||
|
||||
def from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=True,rng_seed=None):
|
||||
def from_Poisson_disc(size: _np.ndarray, N_seeds: int, N_candidates: int, distance: float,
|
||||
periodic: bool = True, rng_seed=None) -> _np.ndarray:
|
||||
"""
|
||||
Place seeds according to a Poisson disc distribution.
|
||||
|
||||
|
|
@ -75,18 +78,17 @@ def from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=True,rng_seed=
|
|||
i = 0
|
||||
progress = _util._ProgressBar(N_seeds+1,'',50)
|
||||
while s < N_seeds:
|
||||
i += 1
|
||||
candidates = rng.random((N_candidates,3))*_np.broadcast_to(size,(N_candidates,3))
|
||||
tree = _spatial.cKDTree(coords[:s],boxsize=size) if periodic else \
|
||||
_spatial.cKDTree(coords[:s])
|
||||
distances = tree.query(candidates)[0]
|
||||
best = distances.argmax()
|
||||
if distances[best] > distance: # require minimum separation
|
||||
i = 0
|
||||
coords[s] = candidates[best] # maximum separation to existing point cloud
|
||||
s += 1
|
||||
progress.update(s)
|
||||
i = 0
|
||||
else:
|
||||
i += 1
|
||||
|
||||
if i == 100:
|
||||
raise ValueError('Seeding not possible')
|
||||
|
|
@ -94,22 +96,23 @@ def from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=True,rng_seed=
|
|||
return coords
|
||||
|
||||
|
||||
def from_grid(grid,selection=None,invert=False,average=False,periodic=True):
|
||||
def from_grid(grid, selection: Sequence[int] = None,
|
||||
invert: bool = False, average: bool = False, periodic: bool = True) -> Tuple[_np.ndarray, _np.ndarray]:
|
||||
"""
|
||||
Create seeds from grid description.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
grid : damask.Grid
|
||||
Grid, from which the material IDs are used as seeds.
|
||||
Grid from which the material IDs are used as seeds.
|
||||
selection : iterable of integers, optional
|
||||
Material IDs to consider.
|
||||
invert : boolean, false
|
||||
Do not consider the material IDs given in selection. Defaults to False.
|
||||
Consider all material IDs except those in selection. Defaults to False.
|
||||
average : boolean, optional
|
||||
Seed corresponds to center of gravity of material ID cloud.
|
||||
periodic : boolean, optional
|
||||
Center of gravity with periodic boundaries.
|
||||
Center of gravity accounts for periodic boundaries.
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ All routines operate on numpy.ndarrays of shape (...,3,3).
|
|||
import numpy as _np
|
||||
|
||||
|
||||
def deviatoric(T):
|
||||
def deviatoric(T: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Calculate deviatoric part of a tensor.
|
||||
|
||||
|
|
@ -26,7 +26,7 @@ def deviatoric(T):
|
|||
return T - spherical(T,tensor=True)
|
||||
|
||||
|
||||
def eigenvalues(T_sym):
|
||||
def eigenvalues(T_sym: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Eigenvalues, i.e. principal components, of a symmetric tensor.
|
||||
|
||||
|
|
@ -45,7 +45,7 @@ def eigenvalues(T_sym):
|
|||
return _np.linalg.eigvalsh(symmetric(T_sym))
|
||||
|
||||
|
||||
def eigenvectors(T_sym,RHS=False):
|
||||
def eigenvectors(T_sym: _np.ndarray, RHS: bool = False) -> _np.ndarray:
|
||||
"""
|
||||
Eigenvectors of a symmetric tensor.
|
||||
|
||||
|
|
@ -70,7 +70,7 @@ def eigenvectors(T_sym,RHS=False):
|
|||
return v
|
||||
|
||||
|
||||
def spherical(T,tensor=True):
|
||||
def spherical(T: _np.ndarray, tensor: bool = True) -> _np.ndarray:
|
||||
"""
|
||||
Calculate spherical part of a tensor.
|
||||
|
||||
|
|
@ -92,7 +92,7 @@ def spherical(T,tensor=True):
|
|||
return _np.einsum('...jk,...',_np.eye(3),sph) if tensor else sph
|
||||
|
||||
|
||||
def symmetric(T):
|
||||
def symmetric(T: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Symmetrize tensor.
|
||||
|
||||
|
|
@ -110,7 +110,7 @@ def symmetric(T):
|
|||
return (T+transpose(T))*0.5
|
||||
|
||||
|
||||
def transpose(T):
|
||||
def transpose(T: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Transpose tensor.
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,14 @@
|
|||
[mypy-scipy.*]
|
||||
ignore_missing_imports = True
|
||||
[mypy-h5py.*]
|
||||
ignore_missing_imports = True
|
||||
[mypy-vtk.*]
|
||||
ignore_missing_imports = True
|
||||
[mypy-PIL.*]
|
||||
ignore_missing_imports = True
|
||||
[mypy-matplotlib.*]
|
||||
ignore_missing_imports = True
|
||||
[mypy-pandas.*]
|
||||
ignore_missing_imports = True
|
||||
[mypy-wx.*]
|
||||
ignore_missing_imports = True
|
||||
Loading…
Reference in New Issue