91 lines
3.2 KiB
Python
91 lines
3.2 KiB
Python
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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,grid=None,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 periodic field.
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N_seeds : int
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Number of seeds.
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grid : numpy.ndarray of shape (3), optional.
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If given, ensures that all seeds initiate one grain if using a
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standard Voronoi tessellation.
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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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"""
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rng = _np.random.default_rng(seed)
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if grid 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.cell_coord0(grid,size).reshape(-1,3,order='F')
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coords = grid_coords[rng.choice(_np.prod(grid),N_seeds, replace=False)] \
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+ _np.broadcast_to(size/grid,(N_seeds,3))*(rng.random((N_seeds,3))*.5-.25) # wobble without leaving grid
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return (coords,_np.arange(N_seeds,dtype='i'))
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def from_poisson_disc(size,N_seeds,N_candidates,distance,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 periodic field.
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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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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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"""
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rng = _np.random.default_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])
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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,_np.arange(N_seeds,dtype='i'))
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def from_geom(geom,selection=None,invert=False):
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"""
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Create seed from existing geometry description.
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Parameters
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----------
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geom : damask.Geom
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...
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"""
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materials = geom.materials.reshape((-1,1),order='F')
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mask = _np.isin(materials,selection,invert=invert) if selection is not None else \
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_np.full(geom.grid.prod(),True,dtype=bool)
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coords = grid_filters.cell_coord0(geom.grid,geom.size).reshape(-1,3,order='F')
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return (coords[mask],materials[mask])
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