allow flexible arguments for 1D arguments

This commit is contained in:
Martin Diehl 2022-01-12 13:18:38 +00:00 committed by Daniel Otto de Mentock
parent dd6e9a016e
commit 410a515afc
6 changed files with 130 additions and 115 deletions

View File

@ -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

View File

@ -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.

View File

@ -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]

View File

@ -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.
"""

View File

@ -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.

View File

@ -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.
"""