DAMASK_EICMD/python/damask/seeds.py

131 lines
4.9 KiB
Python

"""Functionality for generation of seed points for Voronoi or Laguerre tessellation."""
from scipy import spatial as _spatial
import numpy as _np
from . import util
from . import grid_filters
def from_random(size,N_seeds,cells=None,rng_seed=None):
"""
Random seeding in space.
Parameters
----------
size : numpy.ndarray of shape (3)
Physical size of the seeding domain.
N_seeds : int
Number of seeds.
cells : numpy.ndarray of shape (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
A seed to initialize the BitGenerator. Defaults to None.
If None, then fresh, unpredictable entropy will be pulled from the OS.
Returns
-------
coords : numpy.ndarray of shape (N_seeds,3)
Seed coordinates in 3D space.
"""
rng = _np.random.default_rng(rng_seed)
if cells is None:
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
return coords
def from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=True,rng_seed=None):
"""
Seeding in space according to a Poisson disc distribution.
Parameters
----------
size : numpy.ndarray of shape (3)
Physical size of the seeding domain.
N_seeds : int
Number of seeds.
N_candidates : int
Number of candidates to consider for finding best candidate.
distance : float
Minimum acceptable distance to other seeds.
periodic : boolean, optional
Calculate minimum distance for periodically repeated grid.
rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
A seed to initialize the BitGenerator. Defaults to None.
If None, then fresh, unpredictable entropy will be pulled from the OS.
Returns
-------
coords : numpy.ndarray of 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
i = 1
progress = util._ProgressBar(N_seeds+1,'',50)
while i < N_seeds:
candidates = rng.random((N_candidates,3))*_np.broadcast_to(size,(N_candidates,3))
tree = _spatial.cKDTree(coords[:i],boxsize=size) if periodic else \
_spatial.cKDTree(coords[:i])
distances, dev_null = tree.query(candidates)
best = distances.argmax()
if distances[best] > distance: # require minimum separation
coords[i] = candidates[best] # maximum separation to existing point cloud
i += 1
progress.update(i)
return coords
def from_grid(grid,selection=None,invert=False,average=False,periodic=True):
"""
Create seed from existing grid description.
Parameters
----------
grid : damask.Grid
Grid, from which the material IDs are used as seeds.
selection : iterable of integers, optional
Material IDs to consider.
invert : boolean, false
Do not consider the material IDs given in selection. Defaults to False.
average : boolean, optional
Seed corresponds to center of gravity of material ID cloud.
periodic : boolean, optional
Center of gravity with periodic boundaries.
Returns
-------
coords, materials : numpy.ndarray of shape (:,3), numpy.ndarray of shape (:)
Seed coordinates in 3D space, material IDs.
"""
material = grid.material.reshape((-1,1),order='F')
mask = _np.full(grid.cells.prod(),True,dtype=bool) if selection is None else \
_np.isin(material,selection,invert=invert).flatten()
coords = grid_filters.coordinates0_point(grid.cells,grid.size).reshape(-1,3,order='F')
if not average:
return (coords[mask],material[mask])
else:
materials = _np.unique(material[mask])
coords_ = _np.zeros((materials.size,3),dtype=float)
for i,mat in enumerate(materials):
pc = (2*_np.pi*coords[material[:,0]==mat,:]-grid.origin)/grid.size
coords_[i] = grid.origin + grid.size / 2 / _np.pi * (_np.pi +
_np.arctan2(-_np.average(_np.sin(pc),axis=0),
-_np.average(_np.cos(pc),axis=0))) \
if periodic else \
_np.average(coords[material[:,0]==mat,:],axis=0)
return (coords_,materials)