Merge branch 'seeds-module' into 'development'
Seeds module See merge request damask/DAMASK!236
This commit is contained in:
Philip Eisenlohr
commit
db8f6400f8
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@ -344,6 +344,7 @@ Phenopowerlaw_singleSlip:
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Pytest_grid:
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stage: grid
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script:
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- module load $IntelCompiler $MPICH_Intel $PETSc_MPICH_Intel
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- cd pytest
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- pytest
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except:
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@ -20,6 +20,12 @@ from ._result import Result # noqa
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from ._geom import Geom # noqa
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from ._material import Material # noqa
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from . import solver # noqa
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from . import util # noqa
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from . import seeds # noqa
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from . import grid_filters # noqa
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from . import mechanics # noqa
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# deprecated
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Environment = _
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@ -212,7 +212,7 @@ class Geom:
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return np.argmin(np.sum((np.broadcast_to(point,(len(seeds),3))-seeds)**2,axis=1) - weights)
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@staticmethod
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def from_Laguerre_tessellation(grid,size,seeds,weights,periodic=True):
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def from_Laguerre_tessellation(grid,size,seeds,weights,material=None,periodic=True):
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"""
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Generate geometry from Laguerre tessellation.
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@ -226,6 +226,9 @@ class Geom:
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Position of the seed points in meter. All points need to lay within the box.
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weights : numpy.ndarray of shape (seeds.shape[0])
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Weights of the seeds. Setting all weights to 1.0 gives a standard Voronoi tessellation.
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material : numpy.ndarray of shape (seeds.shape[0]), optional
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Material ID of the seeds. Defaults to None, in which case materials are
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consecutively numbered.
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periodic : Boolean, optional
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Perform a periodic tessellation. Defaults to True.
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@ -245,22 +248,22 @@ class Geom:
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result = pool.map_async(partial(Geom._find_closest_seed,seeds_p,weights_p), [coord for coord in coords])
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pool.close()
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pool.join()
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material = np.array(result.get())
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material_ = np.array(result.get())
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if periodic:
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material = material.reshape(grid*3)
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material = material[grid[0]:grid[0]*2,grid[1]:grid[1]*2,grid[2]:grid[2]*2]%seeds.shape[0]
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material_ = material_.reshape(grid*3)
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material_ = material_[grid[0]:grid[0]*2,grid[1]:grid[1]*2,grid[2]:grid[2]*2]%seeds.shape[0]
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else:
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material = material.reshape(grid)
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material_ = material_.reshape(grid)
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return Geom(material = material+1,
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return Geom(material = material_+1 if material is None else material[material_],
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size = size,
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comments = util.execution_stamp('Geom','from_Laguerre_tessellation'),
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)
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@staticmethod
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def from_Voronoi_tessellation(grid,size,seeds,periodic=True):
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def from_Voronoi_tessellation(grid,size,seeds,material=None,periodic=True):
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"""
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Generate geometry from Voronoi tessellation.
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@ -272,15 +275,18 @@ class Geom:
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Physical size of the geometry in meter.
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seeds : numpy.ndarray of shape (:,3)
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Position of the seed points in meter. All points need to lay within the box.
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material : numpy.ndarray of shape (seeds.shape[0]), optional
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Material ID of the seeds. Defaults to None, in which case materials are
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consecutively numbered.
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periodic : Boolean, optional
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Perform a periodic tessellation. Defaults to True.
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"""
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coords = grid_filters.cell_coord0(grid,size).reshape(-1,3)
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KDTree = spatial.cKDTree(seeds,boxsize=size) if periodic else spatial.cKDTree(seeds)
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devNull,material = KDTree.query(coords)
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devNull,material_ = KDTree.query(coords)
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return Geom(material = material.reshape(grid)+1,
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return Geom(material = (material_+1 if material is None else material[material_]).reshape(grid),
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size = size,
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comments = util.execution_stamp('Geom','from_Voronoi_tessellation'),
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)
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@ -0,0 +1,113 @@
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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 seeding domain.
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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
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def from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=True,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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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],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_geom(geom,selection=None,invert=False,average=False,periodic=True):
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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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Geometry, 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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"""
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material = geom.material.reshape((-1,1),order='F')
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mask = _np.full(geom.grid.prod(),True,dtype=bool) if selection is None else \
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_np.isin(material,selection,invert=invert)
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coords = grid_filters.cell_coord0(geom.grid,geom.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,:]-geom.origin)/geom.size
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coords_[i] = geom.origin + geom.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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@ -83,6 +83,13 @@ class TestGeom:
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with pytest.raises(ValueError):
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Geom.load(tmpdir/'no_materialpoint.vtr')
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def test_invalid_material(self):
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with pytest.raises(TypeError):
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Geom(np.zeros((3,3,3),dtype='complex'),np.ones(3))
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def test_cast_to_int(self):
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g = Geom(np.zeros((3,3,3)),np.ones(3))
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assert g.material.dtype in np.sctypes['int']
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@pytest.mark.parametrize('compress',[True,False])
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def test_compress(self,default,tmpdir,compress):
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@ -324,8 +331,8 @@ class TestGeom:
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size = np.random.random(3) + 1.0
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N_seeds= np.random.randint(10,30)
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seeds = np.random.rand(N_seeds,3) * np.broadcast_to(size,(N_seeds,3))
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Voronoi = Geom.from_Voronoi_tessellation( grid,size,seeds, periodic)
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Laguerre = Geom.from_Laguerre_tessellation(grid,size,seeds,np.ones(N_seeds),periodic)
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Voronoi = Geom.from_Voronoi_tessellation( grid,size,seeds, np.arange(N_seeds)+5,periodic)
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Laguerre = Geom.from_Laguerre_tessellation(grid,size,seeds,np.ones(N_seeds),np.arange(N_seeds)+5,periodic)
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assert geom_equal(Laguerre,Voronoi)
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@ -337,7 +344,7 @@ class TestGeom:
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weights= np.full((N_seeds),-np.inf)
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ms = np.random.randint(1, N_seeds+1)
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weights[ms-1] = np.random.random()
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Laguerre = Geom.from_Laguerre_tessellation(grid,size,seeds,weights,np.random.random()>0.5)
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Laguerre = Geom.from_Laguerre_tessellation(grid,size,seeds,weights,periodic=np.random.random()>0.5)
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assert np.all(Laguerre.material == ms)
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@ -349,7 +356,7 @@ class TestGeom:
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material = np.ones(grid)
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material[:,grid[1]//2:,:] = 2
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if approach == 'Laguerre':
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geom = Geom.from_Laguerre_tessellation(grid,size,seeds,np.ones(2),np.random.random()>0.5)
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geom = Geom.from_Laguerre_tessellation(grid,size,seeds,np.ones(2),periodic=np.random.random()>0.5)
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elif approach == 'Voronoi':
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geom = Geom.from_Voronoi_tessellation(grid,size,seeds, np.random.random()>0.5)
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geom = Geom.from_Voronoi_tessellation(grid,size,seeds, periodic=np.random.random()>0.5)
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assert np.all(geom.material == material)
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@ -0,0 +1,36 @@
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import pytest
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import numpy as np
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from scipy.spatial import cKDTree
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from damask import seeds
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from damask import Geom
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class TestSeeds:
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@pytest.mark.parametrize('grid',[None,np.ones(3,dtype='i')*10])
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def test_from_random(self,grid):
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N_seeds = np.random.randint(30,300)
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size = np.ones(3) + np.random.random(3)
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coords = seeds.from_random(size,N_seeds,grid)
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assert (0<=coords).all() and (coords<size).all()
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@pytest.mark.parametrize('periodic',[True,False])
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def test_from_Poisson_disc(self,periodic):
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N_seeds = np.random.randint(30,300)
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N_candidates = N_seeds//15
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distance = np.random.random()
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size = np.ones(3)*distance*N_seeds
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coords = seeds.from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=periodic)
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min_dists, _ = cKDTree(coords,boxsize=size).query(coords, 2) if periodic else \
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cKDTree(coords).query(coords, 2)
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assert (0<= coords).all() and (coords<size).all() and np.min(min_dists[:,1])>=distance
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def test_from_geom(self):
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grid = np.random.randint(10,20,3)
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N_seeds = np.random.randint(30,300)
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size = np.ones(3) + np.random.random(3)
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coords = seeds.from_random(size,N_seeds,grid)
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geom_1 = Geom.from_Voronoi_tessellation(grid,size,coords)
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coords,material = seeds.from_geom(geom_1)
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geom_2 = Geom.from_Voronoi_tessellation(grid,size,coords,material)
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assert (geom_2.material==geom_1.material).all()
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