Merge branch 'grid_filter-flexible-typehints' into 'development'
allow flexible arguments for 1D arguments See merge request damask/DAMASK!494
This commit is contained in:
Daniel Otto de Mentock
commit
731222d099
|
|
@ -8,6 +8,7 @@ with open(_Path(__file__).parent/_Path('VERSION')) as _f:
|
|||
version = _re.sub(r'^v','',_f.readline().strip())
|
||||
__version__ = version
|
||||
|
||||
from . import _typehints # noqa
|
||||
from . import util # noqa
|
||||
from . import seeds # noqa
|
||||
from . import tensor # noqa
|
||||
|
|
|
|||
|
|
@ -3,13 +3,9 @@ import json
|
|||
import functools
|
||||
import colorsys
|
||||
from pathlib import Path
|
||||
from typing import Sequence, Union, TextIO
|
||||
from typing import Union, TextIO
|
||||
|
||||
import numpy as np
|
||||
try:
|
||||
from numpy.typing import ArrayLike
|
||||
except ImportError:
|
||||
ArrayLike = Union[np.ndarray,Sequence[float]] # type: ignore
|
||||
import scipy.interpolate as interp
|
||||
import matplotlib as mpl
|
||||
if os.name == 'posix' and 'DISPLAY' not in os.environ:
|
||||
|
|
@ -18,6 +14,7 @@ import matplotlib.pyplot as plt
|
|||
from matplotlib import cm
|
||||
from PIL import Image
|
||||
|
||||
from ._typehints import FloatSequence, FileHandle
|
||||
from . import util
|
||||
from . import Table
|
||||
|
||||
|
|
@ -82,8 +79,8 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
|
||||
|
||||
@staticmethod
|
||||
def from_range(low: ArrayLike,
|
||||
high: ArrayLike,
|
||||
def from_range(low: FloatSequence,
|
||||
high: FloatSequence,
|
||||
name: str = 'DAMASK colormap',
|
||||
N: int = 256,
|
||||
model: str = 'rgb') -> 'Colormap':
|
||||
|
|
@ -197,7 +194,7 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
|
||||
|
||||
def at(self,
|
||||
fraction : Union[float,Sequence[float]]) -> np.ndarray:
|
||||
fraction : Union[float,FloatSequence]) -> np.ndarray:
|
||||
"""
|
||||
Interpolate color at fraction.
|
||||
|
||||
|
|
@ -229,7 +226,7 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
|
||||
def shade(self,
|
||||
field: np.ndarray,
|
||||
bounds: ArrayLike = None,
|
||||
bounds: FloatSequence = None,
|
||||
gap: float = None) -> Image:
|
||||
"""
|
||||
Generate PIL image of 2D field using colormap.
|
||||
|
|
@ -296,7 +293,7 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
|
||||
|
||||
def _get_file_handle(self,
|
||||
fname: Union[TextIO, str, Path, None],
|
||||
fname: Union[FileHandle, None],
|
||||
suffix: str = '') -> TextIO:
|
||||
"""
|
||||
Provide file handle.
|
||||
|
|
@ -323,7 +320,7 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
return fname
|
||||
|
||||
|
||||
def save_paraview(self, fname: Union[TextIO, str, Path] = None):
|
||||
def save_paraview(self, fname: FileHandle = None):
|
||||
"""
|
||||
Save as JSON file for use in Paraview.
|
||||
|
||||
|
|
@ -350,7 +347,7 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
fhandle.write('\n')
|
||||
|
||||
|
||||
def save_ASCII(self, fname: Union[TextIO, str, Path] = None):
|
||||
def save_ASCII(self, fname: FileHandle = None):
|
||||
"""
|
||||
Save as ASCII file.
|
||||
|
||||
|
|
@ -365,7 +362,7 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
t.save(self._get_file_handle(fname,'.txt'))
|
||||
|
||||
|
||||
def save_GOM(self, fname: Union[TextIO, str, Path] = None):
|
||||
def save_GOM(self, fname: FileHandle = None):
|
||||
"""
|
||||
Save as ASCII file for use in GOM Aramis.
|
||||
|
||||
|
|
@ -385,7 +382,7 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
self._get_file_handle(fname,'.legend').write(GOM_str)
|
||||
|
||||
|
||||
def save_gmsh(self, fname: Union[TextIO, str, Path] = None):
|
||||
def save_gmsh(self, fname: FileHandle = None):
|
||||
"""
|
||||
Save as ASCII file for use in gmsh.
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,11 @@
|
|||
"""Functionality for typehints."""
|
||||
|
||||
from typing import Sequence, Union, TextIO
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
FloatSequence = Union[np.ndarray,Sequence[float]]
|
||||
IntSequence = Union[np.ndarray,Sequence[int]]
|
||||
FileHandle = Union[TextIO, str, Path]
|
||||
|
|
@ -12,21 +12,23 @@ the following operations are required for tensorial data:
|
|||
|
||||
"""
|
||||
|
||||
from typing import Sequence, Tuple, Union
|
||||
from typing import Tuple as _Tuple
|
||||
|
||||
from scipy import spatial as _spatial
|
||||
import numpy as _np
|
||||
|
||||
from ._typehints import FloatSequence as _FloatSequence, IntSequence as _IntSequence
|
||||
|
||||
def _ks(size: _np.ndarray, cells: Union[_np.ndarray,Sequence[int]], first_order: bool = False) -> _np.ndarray:
|
||||
|
||||
def _ks(size: _FloatSequence, cells: _IntSequence, first_order: bool = False) -> _np.ndarray:
|
||||
"""
|
||||
Get wave numbers operator.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
cells : numpy.ndarray of shape (3)
|
||||
cells : sequence of int, len (3)
|
||||
Number of cells.
|
||||
first_order : bool, optional
|
||||
Correction for first order derivatives, defaults to False.
|
||||
|
|
@ -45,20 +47,20 @@ def _ks(size: _np.ndarray, cells: Union[_np.ndarray,Sequence[int]], first_order:
|
|||
return _np.stack(_np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij'), axis=-1)
|
||||
|
||||
|
||||
def curl(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
|
||||
def curl(size: _FloatSequence, f: _np.ndarray) -> _np.ndarray:
|
||||
u"""
|
||||
Calculate curl of a vector or tensor field in Fourier space.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
f : numpy.ndarray of shape (:,:,:,3) or (:,:,:,3,3)
|
||||
f : numpy.ndarray, shape (:,:,:,3) or (:,:,:,3,3)
|
||||
Periodic field of which the curl is calculated.
|
||||
|
||||
Returns
|
||||
-------
|
||||
∇ × f : numpy.ndarray
|
||||
∇ × f : numpy.ndarray, shape (:,:,:,3) or (:,:,:,3,3)
|
||||
Curl of f.
|
||||
|
||||
"""
|
||||
|
|
@ -76,20 +78,20 @@ def curl(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
|
|||
return _np.fft.irfftn(curl_,axes=(0,1,2),s=f.shape[:3])
|
||||
|
||||
|
||||
def divergence(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
|
||||
def divergence(size: _FloatSequence, f: _np.ndarray) -> _np.ndarray:
|
||||
u"""
|
||||
Calculate divergence of a vector or tensor field in Fourier space.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
f : numpy.ndarray of shape (:,:,:,3) or (:,:,:,3,3)
|
||||
f : numpy.ndarray, shape (:,:,:,3) or (:,:,:,3,3)
|
||||
Periodic field of which the divergence is calculated.
|
||||
|
||||
Returns
|
||||
-------
|
||||
∇ · f : numpy.ndarray
|
||||
∇ · f : numpy.ndarray, shape (:,:,:,1) or (:,:,:,3)
|
||||
Divergence of f.
|
||||
|
||||
"""
|
||||
|
|
@ -103,20 +105,20 @@ def divergence(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
|
|||
return _np.fft.irfftn(div_,axes=(0,1,2),s=f.shape[:3])
|
||||
|
||||
|
||||
def gradient(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
|
||||
def gradient(size: _FloatSequence, f: _np.ndarray) -> _np.ndarray:
|
||||
u"""
|
||||
Calculate gradient of a scalar or vector fieldin Fourier space.
|
||||
Calculate gradient of a scalar or vector field in Fourier space.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
f : numpy.ndarray of shape (:,:,:,1) or (:,:,:,3)
|
||||
f : numpy.ndarray, shape (:,:,:,1) or (:,:,:,3)
|
||||
Periodic field of which the gradient is calculated.
|
||||
|
||||
Returns
|
||||
-------
|
||||
∇ f : numpy.ndarray
|
||||
∇ f : numpy.ndarray, shape (:,:,:,3) or (:,:,:,3,3)
|
||||
Divergence of f.
|
||||
|
||||
"""
|
||||
|
|
@ -130,29 +132,30 @@ def gradient(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
|
|||
return _np.fft.irfftn(grad_,axes=(0,1,2),s=f.shape[:3])
|
||||
|
||||
|
||||
def coordinates0_point(cells: Union[ _np.ndarray,Sequence[int]],
|
||||
size: _np.ndarray,
|
||||
origin: _np.ndarray = _np.zeros(3)) -> _np.ndarray:
|
||||
def coordinates0_point(cells: _IntSequence,
|
||||
size: _FloatSequence,
|
||||
origin: _FloatSequence = _np.zeros(3)) -> _np.ndarray:
|
||||
"""
|
||||
Cell center positions (undeformed).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
cells : numpy.ndarray of shape (3)
|
||||
cells : sequence of int, len (3)
|
||||
Number of cells.
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
origin : numpy.ndarray, optional
|
||||
origin : sequence of float, len(3), optional
|
||||
Physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
|
||||
|
||||
Returns
|
||||
-------
|
||||
x_p_0 : numpy.ndarray
|
||||
x_p_0 : numpy.ndarray, shape (:,:,:,3)
|
||||
Undeformed cell center coordinates.
|
||||
|
||||
"""
|
||||
start = origin + size/_np.array(cells)*.5
|
||||
end = origin + size - size/_np.array(cells)*.5
|
||||
size_ = _np.array(size,float)
|
||||
start = origin + size_/_np.array(cells,int)*.5
|
||||
end = origin + size_ - size_/_np.array(cells,int)*.5
|
||||
|
||||
return _np.stack(_np.meshgrid(_np.linspace(start[0],end[0],cells[0]),
|
||||
_np.linspace(start[1],end[1],cells[1]),
|
||||
|
|
@ -160,24 +163,24 @@ def coordinates0_point(cells: Union[ _np.ndarray,Sequence[int]],
|
|||
axis = -1)
|
||||
|
||||
|
||||
def displacement_fluct_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
||||
def displacement_fluct_point(size: _FloatSequence, F: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Cell center displacement field from fluctuation part of the deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
|
||||
Returns
|
||||
-------
|
||||
u_p_fluct : numpy.ndarray
|
||||
u_p_fluct : numpy.ndarray, shape (:,:,:,3)
|
||||
Fluctuating part of the cell center displacements.
|
||||
|
||||
"""
|
||||
integrator = 0.5j*size/_np.pi
|
||||
integrator = 0.5j*_np.array(size,float)/_np.pi
|
||||
|
||||
k_s = _ks(size,F.shape[:3],False)
|
||||
k_s_squared = _np.einsum('...l,...l',k_s,k_s)
|
||||
|
|
@ -192,20 +195,20 @@ def displacement_fluct_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
|||
return _np.fft.irfftn(displacement,axes=(0,1,2),s=F.shape[:3])
|
||||
|
||||
|
||||
def displacement_avg_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
||||
def displacement_avg_point(size: _FloatSequence, F: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Cell center displacement field from average part of the deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
|
||||
Returns
|
||||
-------
|
||||
u_p_avg : numpy.ndarray
|
||||
u_p_avg : numpy.ndarray, shape (:,:,:,3)
|
||||
Average part of the cell center displacements.
|
||||
|
||||
"""
|
||||
|
|
@ -213,42 +216,42 @@ def displacement_avg_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
|||
return _np.einsum('ml,ijkl->ijkm',F_avg - _np.eye(3),coordinates0_point(F.shape[:3],size))
|
||||
|
||||
|
||||
def displacement_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
||||
def displacement_point(size: _FloatSequence, F: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Cell center displacement field from deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
|
||||
Returns
|
||||
-------
|
||||
u_p : numpy.ndarray
|
||||
u_p : numpy.ndarray, shape (:,:,:,3)
|
||||
Cell center displacements.
|
||||
|
||||
"""
|
||||
return displacement_avg_point(size,F) + displacement_fluct_point(size,F)
|
||||
|
||||
|
||||
def coordinates_point(size: _np.ndarray, F: _np.ndarray, origin: _np.ndarray = _np.zeros(3)) -> _np.ndarray:
|
||||
def coordinates_point(size: _FloatSequence, F: _np.ndarray, origin: _FloatSequence = _np.zeros(3)) -> _np.ndarray:
|
||||
"""
|
||||
Cell center positions.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
origin : numpy.ndarray of shape (3), optional
|
||||
origin : sequence of float, len(3), optional
|
||||
Physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
|
||||
|
||||
Returns
|
||||
-------
|
||||
x_p : numpy.ndarray
|
||||
x_p : numpy.ndarray, shape (:,:,:,3)
|
||||
Cell center coordinates.
|
||||
|
||||
"""
|
||||
|
|
@ -256,14 +259,14 @@ def coordinates_point(size: _np.ndarray, F: _np.ndarray, origin: _np.ndarray = _
|
|||
|
||||
|
||||
def cellsSizeOrigin_coordinates0_point(coordinates0: _np.ndarray,
|
||||
ordered: bool = True) -> Tuple[_np.ndarray,_np.ndarray,_np.ndarray]:
|
||||
ordered: bool = True) -> _Tuple[_np.ndarray,_np.ndarray,_np.ndarray]:
|
||||
"""
|
||||
Return grid 'DNA', i.e. cells, size, and origin from 1D array of point positions.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
coordinates0 : numpy.ndarray of shape (:,3)
|
||||
Undeformed cell coordinates.
|
||||
coordinates0 : numpy.ndarray, shape (:,3)
|
||||
Undeformed cell center coordinates.
|
||||
ordered : bool, optional
|
||||
Expect coordinates0 data to be ordered (x fast, z slow).
|
||||
Defaults to True.
|
||||
|
|
@ -277,7 +280,7 @@ def cellsSizeOrigin_coordinates0_point(coordinates0: _np.ndarray,
|
|||
coords = [_np.unique(coordinates0[:,i]) for i in range(3)]
|
||||
mincorner = _np.array(list(map(min,coords)))
|
||||
maxcorner = _np.array(list(map(max,coords)))
|
||||
cells = _np.array(list(map(len,coords)),'i')
|
||||
cells = _np.array(list(map(len,coords)),int)
|
||||
size = cells/_np.maximum(cells-1,1) * (maxcorner-mincorner)
|
||||
delta = size/cells
|
||||
origin = mincorner - delta*.5
|
||||
|
|
@ -305,24 +308,24 @@ def cellsSizeOrigin_coordinates0_point(coordinates0: _np.ndarray,
|
|||
return (cells,size,origin)
|
||||
|
||||
|
||||
def coordinates0_node(cells: Union[_np.ndarray,Sequence[int]],
|
||||
size: _np.ndarray,
|
||||
origin: _np.ndarray = _np.zeros(3)) -> _np.ndarray:
|
||||
def coordinates0_node(cells: _IntSequence,
|
||||
size: _FloatSequence,
|
||||
origin: _FloatSequence = _np.zeros(3)) -> _np.ndarray:
|
||||
"""
|
||||
Nodal positions (undeformed).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
cells : numpy.ndarray of shape (3)
|
||||
cells : sequence of int, len (3)
|
||||
Number of cells.
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
origin : numpy.ndarray of shape (3), optional
|
||||
origin : sequence of float, len(3), optional
|
||||
Physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
|
||||
|
||||
Returns
|
||||
-------
|
||||
x_n_0 : numpy.ndarray
|
||||
x_n_0 : numpy.ndarray, shape (:,:,:,3)
|
||||
Undeformed nodal coordinates.
|
||||
|
||||
"""
|
||||
|
|
@ -332,40 +335,40 @@ def coordinates0_node(cells: Union[_np.ndarray,Sequence[int]],
|
|||
axis = -1)
|
||||
|
||||
|
||||
def displacement_fluct_node(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
||||
def displacement_fluct_node(size: _FloatSequence, F: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Nodal displacement field from fluctuation part of the deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
|
||||
Returns
|
||||
-------
|
||||
u_n_fluct : numpy.ndarray
|
||||
u_n_fluct : numpy.ndarray, shape (:,:,:,3)
|
||||
Fluctuating part of the nodal displacements.
|
||||
|
||||
"""
|
||||
return point_to_node(displacement_fluct_point(size,F))
|
||||
|
||||
|
||||
def displacement_avg_node(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
||||
def displacement_avg_node(size: _FloatSequence, F: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Nodal displacement field from average part of the deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
|
||||
Returns
|
||||
-------
|
||||
u_n_avg : numpy.ndarray
|
||||
u_n_avg : numpy.ndarray, shape (:,:,:,3)
|
||||
Average part of the nodal displacements.
|
||||
|
||||
"""
|
||||
|
|
@ -373,42 +376,42 @@ def displacement_avg_node(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
|||
return _np.einsum('ml,ijkl->ijkm',F_avg - _np.eye(3),coordinates0_node(F.shape[:3],size))
|
||||
|
||||
|
||||
def displacement_node(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
||||
def displacement_node(size: _FloatSequence, F: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Nodal displacement field from deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
|
||||
Returns
|
||||
-------
|
||||
u_p : numpy.ndarray
|
||||
u_p : numpy.ndarray, shape (:,:,:,3)
|
||||
Nodal displacements.
|
||||
|
||||
"""
|
||||
return displacement_avg_node(size,F) + displacement_fluct_node(size,F)
|
||||
|
||||
|
||||
def coordinates_node(size: _np.ndarray, F: _np.ndarray, origin: _np.ndarray = _np.zeros(3)) -> _np.ndarray:
|
||||
def coordinates_node(size: _FloatSequence, F: _np.ndarray, origin: _FloatSequence = _np.zeros(3)) -> _np.ndarray:
|
||||
"""
|
||||
Nodal positions.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
origin : numpy.ndarray of shape (3), optional
|
||||
origin : sequence of float, len(3), optional
|
||||
Physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
|
||||
|
||||
Returns
|
||||
-------
|
||||
x_n : numpy.ndarray
|
||||
x_n : numpy.ndarray, shape (:,:,:,3)
|
||||
Nodal coordinates.
|
||||
|
||||
"""
|
||||
|
|
@ -416,13 +419,13 @@ def coordinates_node(size: _np.ndarray, F: _np.ndarray, origin: _np.ndarray = _n
|
|||
|
||||
|
||||
def cellsSizeOrigin_coordinates0_node(coordinates0: _np.ndarray,
|
||||
ordered: bool = True) -> Tuple[_np.ndarray,_np.ndarray,_np.ndarray]:
|
||||
ordered: bool = True) -> _Tuple[_np.ndarray,_np.ndarray,_np.ndarray]:
|
||||
"""
|
||||
Return grid 'DNA', i.e. cells, size, and origin from 1D array of nodal positions.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
coordinates0 : numpy.ndarray of shape (:,3)
|
||||
coordinates0 : numpy.ndarray, shape (:,3)
|
||||
Undeformed nodal coordinates.
|
||||
ordered : bool, optional
|
||||
Expect coordinates0 data to be ordered (x fast, z slow).
|
||||
|
|
@ -437,7 +440,7 @@ def cellsSizeOrigin_coordinates0_node(coordinates0: _np.ndarray,
|
|||
coords = [_np.unique(coordinates0[:,i]) for i in range(3)]
|
||||
mincorner = _np.array(list(map(min,coords)))
|
||||
maxcorner = _np.array(list(map(max,coords)))
|
||||
cells = _np.array(list(map(len,coords)),'i') - 1
|
||||
cells = _np.array(list(map(len,coords)),int) - 1
|
||||
size = maxcorner-mincorner
|
||||
origin = mincorner
|
||||
|
||||
|
|
@ -463,12 +466,12 @@ def point_to_node(cell_data: _np.ndarray) -> _np.ndarray:
|
|||
|
||||
Parameters
|
||||
----------
|
||||
cell_data : numpy.ndarray of shape (:,:,:,...)
|
||||
cell_data : numpy.ndarray, shape (:,:,:,...)
|
||||
Data defined on the cell centers of a periodic grid.
|
||||
|
||||
Returns
|
||||
-------
|
||||
node_data : numpy.ndarray of shape (:,:,:,...)
|
||||
node_data : numpy.ndarray, shape (:,:,:,...)
|
||||
Data defined on the nodes of a periodic grid.
|
||||
|
||||
"""
|
||||
|
|
@ -485,12 +488,12 @@ def node_to_point(node_data: _np.ndarray) -> _np.ndarray:
|
|||
|
||||
Parameters
|
||||
----------
|
||||
node_data : numpy.ndarray of shape (:,:,:,...)
|
||||
node_data : numpy.ndarray, shape (:,:,:,...)
|
||||
Data defined on the nodes of a periodic grid.
|
||||
|
||||
Returns
|
||||
-------
|
||||
cell_data : numpy.ndarray of shape (:,:,:,...)
|
||||
cell_data : numpy.ndarray, shape (:,:,:,...)
|
||||
Data defined on the cell centers of a periodic grid.
|
||||
|
||||
"""
|
||||
|
|
@ -507,7 +510,7 @@ def coordinates0_valid(coordinates0: _np.ndarray) -> bool:
|
|||
|
||||
Parameters
|
||||
----------
|
||||
coordinates0 : numpy.ndarray
|
||||
coordinates0 : numpy.ndarray, shape (:,3)
|
||||
Array of undeformed cell coordinates.
|
||||
|
||||
Returns
|
||||
|
|
@ -523,17 +526,17 @@ def coordinates0_valid(coordinates0: _np.ndarray) -> bool:
|
|||
return False
|
||||
|
||||
|
||||
def regrid(size: _np.ndarray, F: _np.ndarray, cells: Union[_np.ndarray,Sequence[int]]) -> _np.ndarray:
|
||||
def regrid(size: _FloatSequence, F: _np.ndarray, cells: _IntSequence) -> _np.ndarray:
|
||||
"""
|
||||
Return mapping from coordinates in deformed configuration to a regular grid.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size.
|
||||
F : numpy.ndarray of shape (:,:,:,3,3)
|
||||
F : numpy.ndarray, shape (:,:,:,3,3), shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
cells : numpy.ndarray of shape (3)
|
||||
cells : sequence of int, len (3)
|
||||
Cell count along x,y,z of remapping grid.
|
||||
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ All routines operate on numpy.ndarrays of shape (...,3,3).
|
|||
|
||||
"""
|
||||
|
||||
from typing import Sequence
|
||||
from typing import Sequence as _Sequence
|
||||
|
||||
import numpy as _np
|
||||
|
||||
|
|
@ -243,7 +243,7 @@ def stretch_right(T: _np.ndarray) -> _np.ndarray:
|
|||
return _polar_decomposition(T,'U')[0]
|
||||
|
||||
|
||||
def _polar_decomposition(T: _np.ndarray, requested: Sequence[str]) -> tuple:
|
||||
def _polar_decomposition(T: _np.ndarray, requested: _Sequence[str]) -> tuple:
|
||||
"""
|
||||
Perform singular value decomposition.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,25 +1,27 @@
|
|||
"""Functionality for generation of seed points for Voronoi or Laguerre tessellation."""
|
||||
|
||||
from typing import Sequence,Tuple
|
||||
from typing import Tuple as _Tuple
|
||||
|
||||
from scipy import spatial as _spatial
|
||||
import numpy as _np
|
||||
|
||||
from ._typehints import FloatSequence as _FloatSequence, IntSequence as _IntSequence
|
||||
from . import util as _util
|
||||
from . import grid_filters as _grid_filters
|
||||
|
||||
|
||||
def from_random(size: _np.ndarray, N_seeds: int, cells: _np.ndarray = None, rng_seed=None) -> _np.ndarray:
|
||||
def from_random(size: _FloatSequence, N_seeds: int, cells: _IntSequence = None,
|
||||
rng_seed=None) -> _np.ndarray:
|
||||
"""
|
||||
Place seeds randomly in space.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the seeding domain.
|
||||
N_seeds : int
|
||||
Number of seeds.
|
||||
cells : numpy.ndarray of shape (3), optional.
|
||||
cells : sequence of int, len (3), optional.
|
||||
If given, ensures that each seed results in a grain when a standard Voronoi
|
||||
tessellation is performed using the given grid resolution (i.e. size/cells).
|
||||
rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
|
||||
|
|
@ -28,29 +30,30 @@ def from_random(size: _np.ndarray, N_seeds: int, cells: _np.ndarray = None, rng_
|
|||
|
||||
Returns
|
||||
-------
|
||||
coords : numpy.ndarray of shape (N_seeds,3)
|
||||
coords : numpy.ndarray, shape (N_seeds,3)
|
||||
Seed coordinates in 3D space.
|
||||
|
||||
"""
|
||||
size_ = _np.array(size,float)
|
||||
rng = _np.random.default_rng(rng_seed)
|
||||
if cells is None:
|
||||
coords = rng.random((N_seeds,3)) * size
|
||||
coords = rng.random((N_seeds,3)) * size_
|
||||
else:
|
||||
grid_coords = _grid_filters.coordinates0_point(cells,size).reshape(-1,3,order='F')
|
||||
coords = grid_coords[rng.choice(_np.prod(cells),N_seeds, replace=False)] \
|
||||
+ _np.broadcast_to(size/cells,(N_seeds,3))*(rng.random((N_seeds,3))*.5-.25) # wobble without leaving cells
|
||||
+ _np.broadcast_to(size_/_np.array(cells,int),(N_seeds,3))*(rng.random((N_seeds,3))*.5-.25) # wobble w/o leaving grid
|
||||
|
||||
return coords
|
||||
|
||||
|
||||
def from_Poisson_disc(size: _np.ndarray, N_seeds: int, N_candidates: int, distance: float,
|
||||
def from_Poisson_disc(size: _FloatSequence, N_seeds: int, N_candidates: int, distance: float,
|
||||
periodic: bool = True, rng_seed=None) -> _np.ndarray:
|
||||
"""
|
||||
Place seeds according to a Poisson disc distribution.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the seeding domain.
|
||||
N_seeds : int
|
||||
Number of seeds.
|
||||
|
|
@ -66,13 +69,13 @@ def from_Poisson_disc(size: _np.ndarray, N_seeds: int, N_candidates: int, distan
|
|||
|
||||
Returns
|
||||
-------
|
||||
coords : numpy.ndarray of shape (N_seeds,3)
|
||||
coords : numpy.ndarray, shape (N_seeds,3)
|
||||
Seed coordinates in 3D space.
|
||||
|
||||
"""
|
||||
rng = _np.random.default_rng(rng_seed)
|
||||
coords = _np.empty((N_seeds,3))
|
||||
coords[0] = rng.random(3) * size
|
||||
coords[0] = rng.random(3) * _np.array(size,float)
|
||||
|
||||
s = 1
|
||||
i = 0
|
||||
|
|
@ -96,8 +99,8 @@ def from_Poisson_disc(size: _np.ndarray, N_seeds: int, N_candidates: int, distan
|
|||
return coords
|
||||
|
||||
|
||||
def from_grid(grid, selection: Sequence[int] = None,
|
||||
invert: bool = False, average: bool = False, periodic: bool = True) -> Tuple[_np.ndarray, _np.ndarray]:
|
||||
def from_grid(grid, selection: _IntSequence = None,
|
||||
invert: bool = False, average: bool = False, periodic: bool = True) -> _Tuple[_np.ndarray, _np.ndarray]:
|
||||
"""
|
||||
Create seeds from grid description.
|
||||
|
||||
|
|
@ -105,7 +108,7 @@ def from_grid(grid, selection: Sequence[int] = None,
|
|||
----------
|
||||
grid : damask.Grid
|
||||
Grid from which the material IDs are used as seeds.
|
||||
selection : iterable of integers, optional
|
||||
selection : sequence of int, optional
|
||||
Material IDs to consider.
|
||||
invert : boolean, false
|
||||
Consider all material IDs except those in selection. Defaults to False.
|
||||
|
|
@ -116,7 +119,7 @@ def from_grid(grid, selection: Sequence[int] = None,
|
|||
|
||||
Returns
|
||||
-------
|
||||
coords, materials : numpy.ndarray of shape (:,3), numpy.ndarray of shape (:)
|
||||
coords, materials : numpy.ndarray, shape (:,3); numpy.ndarray, shape (:)
|
||||
Seed coordinates in 3D space, material IDs.
|
||||
|
||||
"""
|
||||
|
|
|
|||
Loading…
Reference in New Issue