131 lines
4.9 KiB
Python
131 lines
4.9 KiB
Python
"""Functionality for generation of seed points for Voronoi or Laguerre tessellation."""
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from scipy import spatial as _spatial
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import numpy as _np
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from . import util
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from . import grid_filters
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def from_random(size,N_seeds,cells=None,rng_seed=None):
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"""
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Random seeding in space.
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Parameters
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----------
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size : numpy.ndarray of shape (3)
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Physical size of the seeding domain.
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N_seeds : int
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Number of seeds.
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cells : numpy.ndarray of shape (3), optional.
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If given, ensures that each seed results in a grain when a standard Voronoi
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tessellation is performed using the given grid resolution (i.e. size/cells).
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rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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A seed to initialize the BitGenerator. Defaults to None.
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If None, then fresh, unpredictable entropy will be pulled from the OS.
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Returns
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-------
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coords : numpy.ndarray of shape (N_seeds,3)
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Seed coordinates in 3D space.
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"""
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rng = _np.random.default_rng(rng_seed)
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if cells is None:
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coords = rng.random((N_seeds,3)) * size
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else:
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grid_coords = grid_filters.coordinates0_point(cells,size).reshape(-1,3,order='F')
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coords = grid_coords[rng.choice(_np.prod(cells),N_seeds, replace=False)] \
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+ _np.broadcast_to(size/cells,(N_seeds,3))*(rng.random((N_seeds,3))*.5-.25) # wobble without leaving cells
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return coords
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def from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=True,rng_seed=None):
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"""
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Seeding in space according to a Poisson disc distribution.
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Parameters
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----------
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size : numpy.ndarray of shape (3)
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Physical size of the seeding domain.
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N_seeds : int
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Number of seeds.
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N_candidates : int
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Number of candidates to consider for finding best candidate.
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distance : float
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Minimum acceptable distance to other seeds.
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periodic : boolean, optional
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Calculate minimum distance for periodically repeated grid.
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rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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A seed to initialize the BitGenerator. Defaults to None.
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If None, then fresh, unpredictable entropy will be pulled from the OS.
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Returns
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-------
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coords : numpy.ndarray of shape (N_seeds,3)
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Seed coordinates in 3D space.
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"""
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rng = _np.random.default_rng(rng_seed)
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coords = _np.empty((N_seeds,3))
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coords[0] = rng.random(3) * size
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i = 1
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progress = util._ProgressBar(N_seeds+1,'',50)
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while i < N_seeds:
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candidates = rng.random((N_candidates,3))*_np.broadcast_to(size,(N_candidates,3))
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tree = _spatial.cKDTree(coords[:i],boxsize=size) if periodic else \
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_spatial.cKDTree(coords[:i])
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distances, dev_null = tree.query(candidates)
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best = distances.argmax()
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if distances[best] > distance: # require minimum separation
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coords[i] = candidates[best] # maximum separation to existing point cloud
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i += 1
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progress.update(i)
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return coords
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def from_grid(grid,selection=None,invert=False,average=False,periodic=True):
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"""
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Create seed from existing grid description.
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Parameters
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----------
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grid : damask.Grid
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Grid, from which the material IDs are used as seeds.
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selection : iterable of integers, optional
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Material IDs to consider.
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invert : boolean, false
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Do not consider the material IDs given in selection. Defaults to False.
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average : boolean, optional
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Seed corresponds to center of gravity of material ID cloud.
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periodic : boolean, optional
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Center of gravity with periodic boundaries.
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Returns
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-------
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coords, materials : numpy.ndarray of shape (:,3), numpy.ndarray of shape (:)
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Seed coordinates in 3D space, material IDs.
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"""
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material = grid.material.reshape((-1,1),order='F')
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mask = _np.full(grid.cells.prod(),True,dtype=bool) if selection is None else \
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_np.isin(material,selection,invert=invert).flatten()
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coords = grid_filters.coordinates0_point(grid.cells,grid.size).reshape(-1,3,order='F')
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if not average:
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return (coords[mask],material[mask])
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else:
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materials = _np.unique(material[mask])
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coords_ = _np.zeros((materials.size,3),dtype=float)
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for i,mat in enumerate(materials):
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pc = (2*_np.pi*coords[material[:,0]==mat,:]-grid.origin)/grid.size
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coords_[i] = grid.origin + grid.size / 2 / _np.pi * (_np.pi +
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_np.arctan2(-_np.average(_np.sin(pc),axis=0),
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-_np.average(_np.cos(pc),axis=0))) \
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if periodic else \
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_np.average(coords[material[:,0]==mat,:],axis=0)
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return (coords_,materials)
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