allow flexible arguments for 1D arguments
This commit is contained in:
Martin Diehl
Daniel Otto de Mentock
parent
dd6e9a016e
commit
410a515afc
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@ -8,6 +8,7 @@ with open(_Path(__file__).parent/_Path('VERSION')) as _f:
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version = _re.sub(r'^v','',_f.readline().strip())
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__version__ = version
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from . import _typehints # noqa
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from . import util # noqa
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from . import seeds # noqa
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from . import tensor # noqa
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@ -3,13 +3,9 @@ import json
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import functools
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import colorsys
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from pathlib import Path
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from typing import Sequence, Union, TextIO
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from typing import Union, TextIO
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import numpy as np
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try:
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from numpy.typing import ArrayLike
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except ImportError:
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ArrayLike = Union[np.ndarray,Sequence[float]] # type: ignore
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import scipy.interpolate as interp
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import matplotlib as mpl
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if os.name == 'posix' and 'DISPLAY' not in os.environ:
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@ -18,6 +14,7 @@ import matplotlib.pyplot as plt
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from matplotlib import cm
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from PIL import Image
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from ._typehints import FloatSequence, FileHandle
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from . import util
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from . import Table
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@ -82,8 +79,8 @@ class Colormap(mpl.colors.ListedColormap):
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@staticmethod
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def from_range(low: ArrayLike,
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high: ArrayLike,
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def from_range(low: FloatSequence,
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high: FloatSequence,
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name: str = 'DAMASK colormap',
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N: int = 256,
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model: str = 'rgb') -> 'Colormap':
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@ -197,7 +194,7 @@ class Colormap(mpl.colors.ListedColormap):
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def at(self,
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fraction : Union[float,Sequence[float]]) -> np.ndarray:
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fraction : Union[float,FloatSequence]) -> np.ndarray:
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"""
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Interpolate color at fraction.
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@ -229,7 +226,7 @@ class Colormap(mpl.colors.ListedColormap):
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def shade(self,
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field: np.ndarray,
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bounds: ArrayLike = None,
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bounds: FloatSequence = None,
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gap: float = None) -> Image:
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"""
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Generate PIL image of 2D field using colormap.
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@ -296,7 +293,7 @@ class Colormap(mpl.colors.ListedColormap):
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def _get_file_handle(self,
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fname: Union[TextIO, str, Path, None],
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fname: Union[FileHandle, None],
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suffix: str = '') -> TextIO:
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"""
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Provide file handle.
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@ -323,7 +320,7 @@ class Colormap(mpl.colors.ListedColormap):
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return fname
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def save_paraview(self, fname: Union[TextIO, str, Path] = None):
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def save_paraview(self, fname: FileHandle = None):
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"""
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Save as JSON file for use in Paraview.
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@ -350,7 +347,7 @@ class Colormap(mpl.colors.ListedColormap):
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fhandle.write('\n')
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def save_ASCII(self, fname: Union[TextIO, str, Path] = None):
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def save_ASCII(self, fname: FileHandle = None):
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"""
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Save as ASCII file.
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@ -365,7 +362,7 @@ class Colormap(mpl.colors.ListedColormap):
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t.save(self._get_file_handle(fname,'.txt'))
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def save_GOM(self, fname: Union[TextIO, str, Path] = None):
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def save_GOM(self, fname: FileHandle = None):
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"""
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Save as ASCII file for use in GOM Aramis.
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@ -385,7 +382,7 @@ class Colormap(mpl.colors.ListedColormap):
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self._get_file_handle(fname,'.legend').write(GOM_str)
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def save_gmsh(self, fname: Union[TextIO, str, Path] = None):
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def save_gmsh(self, fname: FileHandle = None):
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"""
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Save as ASCII file for use in gmsh.
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@ -0,0 +1,11 @@
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"""Functionality for typehints."""
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from typing import Sequence, Union, TextIO
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from pathlib import Path
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import numpy as np
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FloatSequence = Union[np.ndarray,Sequence[float]]
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IntSequence = Union[np.ndarray,Sequence[int]]
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FileHandle = Union[TextIO, str, Path]
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@ -12,21 +12,23 @@ the following operations are required for tensorial data:
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"""
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from typing import Sequence, Tuple, Union
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from typing import Tuple as _Tuple
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from scipy import spatial as _spatial
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import numpy as _np
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from ._typehints import FloatSequence as _FloatSequence, IntSequence as _IntSequence
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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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def _ks(size: _FloatSequence, cells: _IntSequence, first_order: bool = False) -> _np.ndarray:
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"""
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Get wave numbers operator.
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Parameters
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----------
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size : numpy.ndarray of shape (3)
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size : sequence of float, len (3)
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Physical size of the periodic field.
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cells : numpy.ndarray of shape (3)
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cells : sequence of int, len (3)
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Number of cells.
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first_order : bool, optional
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Correction for first order derivatives, defaults to False.
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@ -45,20 +47,20 @@ def _ks(size: _np.ndarray, cells: Union[_np.ndarray,Sequence[int]], first_order:
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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: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
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def curl(size: _FloatSequence, 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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Parameters
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----------
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size : numpy.ndarray of shape (3)
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size : sequence of float, len (3)
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Physical size of the periodic field.
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f : numpy.ndarray of shape (:,:,:,3) or (:,:,:,3,3)
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f : numpy.ndarray, shape (:,:,:,3) or (:,:,:,3,3)
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Periodic field of which the curl is calculated.
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Returns
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-------
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∇ × f : numpy.ndarray
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∇ × f : numpy.ndarray, shape (:,:,:,3) or (:,:,:,3,3)
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Curl of f.
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"""
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@ -76,20 +78,20 @@ def curl(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
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return _np.fft.irfftn(curl_,axes=(0,1,2),s=f.shape[:3])
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def divergence(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
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def divergence(size: _FloatSequence, 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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Parameters
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----------
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size : numpy.ndarray of shape (3)
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size : sequence of float, len (3)
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Physical size of the periodic field.
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f : numpy.ndarray of shape (:,:,:,3) or (:,:,:,3,3)
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f : numpy.ndarray, shape (:,:,:,3) or (:,:,:,3,3)
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Periodic field of which the divergence is calculated.
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Returns
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-------
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∇ · f : numpy.ndarray
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∇ · f : numpy.ndarray, shape (:,:,:,1) or (:,:,:,3)
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Divergence of f.
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"""
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@ -103,20 +105,20 @@ def divergence(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
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return _np.fft.irfftn(div_,axes=(0,1,2),s=f.shape[:3])
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def gradient(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
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def gradient(size: _FloatSequence, 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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Calculate gradient of a scalar or vector field in Fourier space.
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Parameters
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----------
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size : numpy.ndarray of shape (3)
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size : sequence of float, len (3)
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Physical size of the periodic field.
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f : numpy.ndarray of shape (:,:,:,1) or (:,:,:,3)
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f : numpy.ndarray, shape (:,:,:,1) or (:,:,:,3)
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Periodic field of which the gradient is calculated.
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Returns
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-------
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∇ f : numpy.ndarray
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∇ f : numpy.ndarray, shape (:,:,:,3) or (:,:,:,3,3)
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Divergence of f.
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"""
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@ -130,29 +132,30 @@ def gradient(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
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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: 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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def coordinates0_point(cells: _IntSequence,
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size: _FloatSequence,
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origin: _FloatSequence = _np.zeros(3)) -> _np.ndarray:
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"""
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Cell center positions (undeformed).
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Parameters
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----------
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cells : numpy.ndarray of shape (3)
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cells : sequence of int, len (3)
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Number of cells.
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size : numpy.ndarray of shape (3)
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size : sequence of float, len (3)
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Physical size of the periodic field.
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origin : numpy.ndarray, optional
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origin : sequence of float, len(3), optional
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Physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
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Returns
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-------
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x_p_0 : numpy.ndarray
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x_p_0 : numpy.ndarray, shape (:,:,:,3)
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Undeformed cell center coordinates.
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"""
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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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size_ = _np.array(size,float)
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start = origin + size_/_np.array(cells,int)*.5
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end = origin + size_ - size_/_np.array(cells,int)*.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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@ -160,24 +163,24 @@ def coordinates0_point(cells: Union[ _np.ndarray,Sequence[int]],
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axis = -1)
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def displacement_fluct_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
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def displacement_fluct_point(size: _FloatSequence, 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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Parameters
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----------
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size : numpy.ndarray of shape (3)
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size : sequence of float, len (3)
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Physical size of the periodic field.
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F : numpy.ndarray
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F : numpy.ndarray, shape (:,:,:,3,3)
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Deformation gradient field.
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Returns
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-------
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u_p_fluct : numpy.ndarray
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u_p_fluct : numpy.ndarray, shape (:,:,:,3)
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Fluctuating part of the cell center displacements.
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"""
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integrator = 0.5j*size/_np.pi
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integrator = 0.5j*_np.array(size,float)/_np.pi
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k_s = _ks(size,F.shape[:3],False)
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k_s_squared = _np.einsum('...l,...l',k_s,k_s)
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@ -192,20 +195,20 @@ def displacement_fluct_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
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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: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
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def displacement_avg_point(size: _FloatSequence, 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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Parameters
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----------
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size : numpy.ndarray of shape (3)
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size : sequence of float, len (3)
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Physical size of the periodic field.
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F : numpy.ndarray
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F : numpy.ndarray, shape (:,:,:,3,3)
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Deformation gradient field.
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Returns
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-------
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u_p_avg : numpy.ndarray
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u_p_avg : numpy.ndarray, shape (:,:,:,3)
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Average part of the cell center displacements.
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"""
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@ -213,42 +216,42 @@ def displacement_avg_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
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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: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
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def displacement_point(size: _FloatSequence, 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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Parameters
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----------
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size : numpy.ndarray of shape (3)
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size : sequence of float, len (3)
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Physical size of the periodic field.
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F : numpy.ndarray
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F : numpy.ndarray, shape (:,:,:,3,3)
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Deformation gradient field.
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Returns
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-------
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u_p : numpy.ndarray
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u_p : numpy.ndarray, shape (:,:,:,3)
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Cell center displacements.
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"""
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return displacement_avg_point(size,F) + displacement_fluct_point(size,F)
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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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def coordinates_point(size: _FloatSequence, F: _np.ndarray, origin: _FloatSequence = _np.zeros(3)) -> _np.ndarray:
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"""
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Cell center positions.
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Parameters
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----------
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size : numpy.ndarray of shape (3)
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size : sequence of float, len (3)
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Physical size of the periodic field.
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F : numpy.ndarray
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F : numpy.ndarray, shape (:,:,:,3,3)
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Deformation gradient field.
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origin : numpy.ndarray of shape (3), optional
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origin : sequence of float, len(3), optional
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Physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
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Returns
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-------
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x_p : numpy.ndarray
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x_p : numpy.ndarray, shape (:,:,:,3)
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Cell center coordinates.
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"""
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@ -256,14 +259,14 @@ def coordinates_point(size: _np.ndarray, F: _np.ndarray, origin: _np.ndarray = _
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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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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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Parameters
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----------
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coordinates0 : numpy.ndarray of shape (:,3)
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Undeformed cell coordinates.
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coordinates0 : numpy.ndarray, shape (:,3)
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Undeformed cell center coordinates.
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ordered : bool, optional
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Expect coordinates0 data to be ordered (x fast, z slow).
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Defaults to True.
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@ -277,7 +280,7 @@ def cellsSizeOrigin_coordinates0_point(coordinates0: _np.ndarray,
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coords = [_np.unique(coordinates0[:,i]) for i in range(3)]
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mincorner = _np.array(list(map(min,coords)))
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maxcorner = _np.array(list(map(max,coords)))
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cells = _np.array(list(map(len,coords)),'i')
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cells = _np.array(list(map(len,coords)),int)
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size = cells/_np.maximum(cells-1,1) * (maxcorner-mincorner)
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delta = size/cells
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origin = mincorner - delta*.5
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@ -305,24 +308,24 @@ def cellsSizeOrigin_coordinates0_point(coordinates0: _np.ndarray,
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return (cells,size,origin)
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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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def coordinates0_node(cells: _IntSequence,
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size: _FloatSequence,
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origin: _FloatSequence = _np.zeros(3)) -> _np.ndarray:
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"""
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Nodal positions (undeformed).
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Parameters
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----------
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cells : numpy.ndarray of shape (3)
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cells : sequence of int, len (3)
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Number of cells.
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size : numpy.ndarray of shape (3)
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size : sequence of float, len (3)
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Physical size of the periodic field.
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origin : numpy.ndarray of shape (3), optional
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origin : sequence of float, len(3), optional
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Physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
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Returns
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-------
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x_n_0 : numpy.ndarray
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x_n_0 : numpy.ndarray, shape (:,:,:,3)
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Undeformed nodal coordinates.
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"""
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@ -332,40 +335,40 @@ def coordinates0_node(cells: Union[_np.ndarray,Sequence[int]],
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axis = -1)
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def displacement_fluct_node(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
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def displacement_fluct_node(size: _FloatSequence, F: _np.ndarray) -> _np.ndarray:
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"""
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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