consistent layout for grid data
has now always the shape ([x,y,z,...]) with x fastest. For conversion from or to linear layout ([x*y*z,...]), e.g. storage in ASCII table, reshape needs to have the 'F' option. Credits to Vitesh and Fran for pointing this out.
This commit is contained in:
Martin Diehl
parent
bce5ed62d5
commit
9979eb58f4
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@ -49,9 +49,10 @@ for name in filenames:
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for label in options.labels:
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field = table.get(label)
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shape = (3,) if np.prod(field.shape)//np.prod(grid) == 3 else (3,3) # vector or tensor
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field = field.reshape(np.append(grid[::-1],shape))
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field = field.reshape(tuple(grid)+(-1,),order='F').reshape(tuple(grid)+shape)
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curl = damask.grid_filters.curl(size,field)
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table.add('curlFFT({})'.format(label),
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damask.grid_filters.curl(size[::-1],field).reshape(-1,np.prod(shape)),
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curl.reshape(tuple(grid)+(-1,)).reshape(-1,np.prod(shape),order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.to_ASCII(sys.stdout if name is None else name)
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@ -51,23 +51,23 @@ for name in filenames:
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table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
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grid,size,origin = damask.grid_filters.cell_coord0_gridSizeOrigin(table.get(options.pos))
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F = table.get(options.f).reshape(np.append(grid[::-1],(3,3)))
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F = table.get(options.f).reshape(tuple(grid)+(-1,),order='F').reshape(tuple(grid)+(3,3))
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if options.nodal:
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table = damask.Table(damask.grid_filters.node_coord0(grid[::-1],size[::-1]).reshape(-1,3),
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table = damask.Table(damask.grid_filters.node_coord0(grid,size).reshape(-1,3,order='F'),
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{'pos':(3,)})
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table.add('avg({}).{}'.format(options.f,options.pos),
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damask.grid_filters.node_displacement_avg(size[::-1],F).reshape(-1,3),
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damask.grid_filters.node_displacement_avg(size,F).reshape(-1,3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.add('fluct({}).{}'.format(options.f,options.pos),
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damask.grid_filters.node_displacement_fluct(size[::-1],F).reshape(-1,3),
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damask.grid_filters.node_displacement_fluct(size,F).reshape(-1,3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.to_ASCII(sys.stdout if name is None else os.path.splitext(name)[0]+'_nodal.txt')
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else:
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table.add('avg({}).{}'.format(options.f,options.pos),
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damask.grid_filters.cell_displacement_avg(size[::-1],F).reshape(-1,3),
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damask.grid_filters.cell_displacement_avg(size,F).reshape(-1,3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.add('fluct({}).{}'.format(options.f,options.pos),
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damask.grid_filters.cell_displacement_fluct(size[::-1],F).reshape(-1,3),
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damask.grid_filters.cell_displacement_fluct(size,F).reshape(-1,3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.to_ASCII(sys.stdout if name is None else name)
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@ -49,9 +49,10 @@ for name in filenames:
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for label in options.labels:
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field = table.get(label)
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shape = (3,) if np.prod(field.shape)//np.prod(grid) == 3 else (3,3) # vector or tensor
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field = field.reshape(np.append(grid[::-1],shape))
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field = field.reshape(tuple(grid)+(-1,),order='F').reshape(tuple(grid)+shape)
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div = damask.grid_filters.divergence(size,field)
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table.add('divFFT({})'.format(label),
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damask.grid_filters.divergence(size[::-1],field).reshape(-1,np.prod(shape)//3),
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div.reshape(tuple(grid)+(-1,)).reshape(-1,np.prod(shape)//3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.to_ASCII(sys.stdout if name is None else name)
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@ -49,9 +49,10 @@ for name in filenames:
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for label in options.labels:
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field = table.get(label)
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shape = (1,) if np.prod(field.shape)//np.prod(grid) == 1 else (3,) # scalar or vector
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field = field.reshape(np.append(grid[::-1],shape))
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field = field.reshape(tuple(grid)+(-1,),order='F').reshape(tuple(grid)+shape)
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grad = damask.grid_filters.gradient(size,field)
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table.add('gradFFT({})'.format(label),
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damask.grid_filters.gradient(size[::-1],field).reshape(-1,np.prod(shape)*3),
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grad.reshape(tuple(grid)+(-1,)).reshape(-1,np.prod(shape)*3,order='F'),
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scriptID+' '+' '.join(sys.argv[1:]))
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table.to_ASCII(sys.stdout if name is None else name)
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@ -357,7 +357,6 @@ class Geom:
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seeds_p = np.vstack((seeds_p-np.array([0.,size[1],0.]),seeds_p,seeds_p+np.array([0.,size[1],0.])))
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seeds_p = np.vstack((seeds_p-np.array([0.,0.,size[2]]),seeds_p,seeds_p+np.array([0.,0.,size[2]])))
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coords = grid_filters.cell_coord0(grid*3,size*3,-size).reshape(-1,3,order='F')
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else:
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weights_p = weights.flatten()
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seeds_p = seeds
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@ -370,10 +369,10 @@ class Geom:
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microstructure = np.array(result.get())
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if periodic:
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microstructure = microstructure.reshape(grid*3)
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microstructure = microstructure.reshape(grid*3,order='F')
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microstructure = microstructure[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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microstructure = microstructure.reshape(grid)
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microstructure = microstructure.reshape(grid,order='F')
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#comments = 'geom.py:from_Laguerre_tessellation v{}'.format(version)
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return Geom(microstructure+1,size,homogenization=1)
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@ -401,7 +400,7 @@ class Geom:
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devNull,microstructure = KDTree.query(coords)
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#comments = 'geom.py:from_Voronoi_tessellation v{}'.format(version)
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return Geom(microstructure.reshape(grid)+1,size,homogenization=1)
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return Geom(microstructure.reshape(grid,order='F')+1,size,homogenization=1)
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def to_file(self,fname,pack=None):
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@ -7,7 +7,7 @@ def _ks(size,grid,first_order=False):
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Parameters
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----------
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size : numpy.ndarray
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size : numpy.ndarray of shape (3)
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physical size of the periodic field.
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"""
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@ -19,8 +19,7 @@ def _ks(size,grid,first_order=False):
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k_si = _np.arange(grid[2]//2+1)/size[2]
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kk, kj, ki = _np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij')
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return _np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3)
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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,field):
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@ -29,7 +28,7 @@ def curl(size,field):
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Parameters
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----------
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size : numpy.ndarray
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size : numpy.ndarray of shape (3)
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physical size of the periodic field.
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"""
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@ -53,7 +52,7 @@ def divergence(size,field):
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Parameters
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----------
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size : numpy.ndarray
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size : numpy.ndarray of shape (3)
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physical size of the periodic field.
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"""
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@ -73,7 +72,7 @@ def gradient(size,field):
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Parameters
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----------
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size : numpy.ndarray
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size : numpy.ndarray of shape (3)
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physical size of the periodic field.
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"""
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@ -93,9 +92,9 @@ def cell_coord0(grid,size,origin=_np.zeros(3)):
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Parameters
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----------
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grid : numpy.ndarray
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grid : numpy.ndarray of shape (3)
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number of grid points.
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size : numpy.ndarray
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size : numpy.ndarray of shape (3)
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physical size of the periodic field.
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origin : numpy.ndarray, optional
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physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
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@ -103,7 +102,11 @@ def cell_coord0(grid,size,origin=_np.zeros(3)):
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"""
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start = origin + size/grid*.5
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end = origin + size - size/grid*.5
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return _np.mgrid[start[0]:end[0]:grid[0]*1j,start[1]:end[1]:grid[1]*1j,start[2]:end[2]:grid[2]*1j].T
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return _np.stack(_np.meshgrid(_np.linspace(start[0],end[0],grid[0]),
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_np.linspace(start[1],end[1],grid[1]),
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_np.linspace(start[2],end[2],grid[2]),indexing = 'ij'),
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axis = -1)
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def cell_displacement_fluct(size,F):
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@ -112,7 +115,7 @@ def cell_displacement_fluct(size,F):
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Parameters
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----------
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size : numpy.ndarray
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size : numpy.ndarray of shape (3)
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physical size of the periodic field.
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F : numpy.ndarray
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deformation gradient field.
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@ -139,14 +142,14 @@ def cell_displacement_avg(size,F):
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Parameters
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----------
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size : numpy.ndarray
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size : numpy.ndarray of shape (3)
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physical size of the periodic field.
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F : numpy.ndarray
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deformation gradient field.
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"""
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F_avg = _np.average(F,axis=(0,1,2))
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return _np.einsum('ml,ijkl->ijkm',F_avg - _np.eye(3),cell_coord0(F.shape[:3][::-1],size))
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return _np.einsum('ml,ijkl->ijkm',F_avg - _np.eye(3),cell_coord0(F.shape[:3],size))
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def cell_displacement(size,F):
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@ -155,7 +158,7 @@ def cell_displacement(size,F):
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Parameters
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----------
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size : numpy.ndarray
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size : numpy.ndarray of shape (3)
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physical size of the periodic field.
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F : numpy.ndarray
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deformation gradient field.
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@ -170,30 +173,30 @@ def cell_coord(size,F,origin=_np.zeros(3)):
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Parameters
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----------
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size : numpy.ndarray
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size : numpy.ndarray of shape (3)
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physical size of the periodic field.
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F : numpy.ndarray
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deformation gradient field.
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origin : numpy.ndarray, optional
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origin : numpy.ndarray of shape (3), optional
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physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
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"""
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return cell_coord0(F.shape[:3][::-1],size,origin) + cell_displacement(size,F)
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return cell_coord0(F.shape[:3],size,origin) + cell_displacement(size,F)
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def cell_coord0_gridSizeOrigin(coord0,ordered=True):
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"""
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Return grid 'DNA', i.e. grid, size, and origin from array of cell positions.
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Return grid 'DNA', i.e. grid, size, and origin from 1D array of cell positions.
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Parameters
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----------
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coord0 : numpy.ndarray
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array of undeformed cell coordinates.
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coord0 : numpy.ndarray of shape (:,3)
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undeformed cell coordinates.
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ordered : bool, optional
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expect coord0 data to be ordered (x fast, z slow).
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"""
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coords = [_np.unique(coord0[:,i]) for i in range(3)]
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coords = [_np.unique(coord0[:,i]) for i in range(3)] # _np.unique(coord0, axis=1)
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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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grid = _np.array(list(map(len,coords)),'i')
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@ -216,7 +219,7 @@ def cell_coord0_gridSizeOrigin(coord0,ordered=True):
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_np.allclose(coords[2],_np.linspace(start[2],end[2],grid[2])):
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raise ValueError('Regular grid spacing violated.')
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if ordered and not _np.allclose(coord0.reshape(tuple(grid[::-1])+(3,)),cell_coord0(grid,size,origin)):
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if ordered and not _np.allclose(coord0.reshape(tuple(grid)+(3,),order='F'),cell_coord0(grid,size,origin)):
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raise ValueError('Input data is not a regular grid.')
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return (grid,size,origin)
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@ -241,17 +244,18 @@ def node_coord0(grid,size,origin=_np.zeros(3)):
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Parameters
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----------
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grid : numpy.ndarray
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grid : numpy.ndarray of shape (3)
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number of grid points.
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size : numpy.ndarray
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size : numpy.ndarray of shape (3)
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physical size of the periodic field.
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origin : numpy.ndarray, optional
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origin : numpy.ndarray of shape (3), optional
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physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
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"""
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return _np.mgrid[origin[0]:size[0]+origin[0]:(grid[0]+1)*1j,
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origin[1]:size[1]+origin[1]:(grid[1]+1)*1j,
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origin[2]:size[2]+origin[2]:(grid[2]+1)*1j].T
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return _np.stack(_np.meshgrid(_np.linspace(origin[0],size[0]+origin[0],grid[0]+1),
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_np.linspace(origin[1],size[1]+origin[1],grid[1]+1),
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_np.linspace(origin[2],size[2]+origin[2],grid[2]+1),indexing = 'ij'),
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axis = -1)
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def node_displacement_fluct(size,F):
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@ -260,7 +264,7 @@ def node_displacement_fluct(size,F):
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Parameters
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----------
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size : numpy.ndarray
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size : numpy.ndarray of shape (3)
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physical size of the periodic field.
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F : numpy.ndarray
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deformation gradient field.
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@ -275,14 +279,14 @@ def node_displacement_avg(size,F):
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Parameters
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----------
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size : numpy.ndarray
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size : numpy.ndarray of shape (3)
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physical size of the periodic field.
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F : numpy.ndarray
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deformation gradient field.
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"""
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F_avg = _np.average(F,axis=(0,1,2))
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return _np.einsum('ml,ijkl->ijkm',F_avg - _np.eye(3),node_coord0(F.shape[:3][::-1],size))
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return _np.einsum('ml,ijkl->ijkm',F_avg - _np.eye(3),node_coord0(F.shape[:3],size))
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def node_displacement(size,F):
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@ -291,7 +295,7 @@ def node_displacement(size,F):
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Parameters
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----------
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size : numpy.ndarray
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size : numpy.ndarray of shape (3)
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physical size of the periodic field.
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F : numpy.ndarray
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deformation gradient field.
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@ -306,15 +310,15 @@ def node_coord(size,F,origin=_np.zeros(3)):
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Parameters
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----------
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size : numpy.ndarray
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size : numpy.ndarray of shape (3)
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physical size of the periodic field.
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F : numpy.ndarray
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deformation gradient field.
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origin : numpy.ndarray, optional
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origin : numpy.ndarray of shape (3), optional
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physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
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"""
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return node_coord0(F.shape[:3][::-1],size,origin) + node_displacement(size,F)
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return node_coord0(F.shape[:3],size,origin) + node_displacement(size,F)
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def cell_2_node(cell_data):
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@ -335,19 +339,19 @@ def node_2_cell(node_data):
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return c[:-1,:-1,:-1]
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def node_coord0_gridSizeOrigin(coord0,ordered=False):
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def node_coord0_gridSizeOrigin(coord0,ordered=True):
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"""
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Return grid 'DNA', i.e. grid, size, and origin from array of nodal positions.
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Return grid 'DNA', i.e. grid, size, and origin from 1D array of nodal positions.
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Parameters
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----------
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coord0 : numpy.ndarray
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array of undeformed nodal coordinates.
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coord0 : numpy.ndarray of shape (:,3)
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undeformed nodal coordinates.
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ordered : bool, optional
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expect coord0 data to be ordered (x fast, z slow).
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"""
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coords = [_np.unique(coord0[:,i]) for i in range(3)]
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coords = [_np.unique(coord0[:,i]) for i in range(3)] # _np.unique(coord0, axis=1)
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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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grid = _np.array(list(map(len,coords)),'i') - 1
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@ -362,7 +366,7 @@ def node_coord0_gridSizeOrigin(coord0,ordered=False):
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_np.allclose(coords[2],_np.linspace(mincorner[2],maxcorner[2],grid[2]+1)):
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raise ValueError('Regular grid spacing violated.')
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if ordered and not _np.allclose(coord0.reshape(tuple((grid+1)[::-1])+(3,)),node_coord0(grid,size,origin)):
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if ordered and not _np.allclose(coord0.reshape(tuple(grid+1)+(3,),order='F'),node_coord0(grid,size,origin)):
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raise ValueError('Input data is not a regular grid.')
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return (grid,size,origin)
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@ -374,7 +378,7 @@ def regrid(size,F,new_grid):
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Parameters
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----------
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size : numpy.ndarray
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size : numpy.ndarray of shape (3)
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physical size
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F : numpy.ndarray
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deformation gradient field
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@ -382,7 +386,7 @@ def regrid(size,F,new_grid):
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new grid for undeformed coordinates
|
||||
|
||||
"""
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||||
c = cell_coord0(F.shape[:3][::-1],size) \
|
||||
c = cell_coord0(F.shape[:3],size) \
|
||||
+ cell_displacement_avg(size,F) \
|
||||
+ cell_displacement_fluct(size,F)
|
||||
|
||||
|
|
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|
|
@ -4,18 +4,18 @@ import numpy as np
|
|||
from damask import grid_filters
|
||||
|
||||
class TestGridFilters:
|
||||
|
||||
|
||||
def test_cell_coord0(self):
|
||||
size = np.random.random(3)
|
||||
grid = np.random.randint(8,32,(3))
|
||||
coord = grid_filters.cell_coord0(grid,size)
|
||||
assert np.allclose(coord[0,0,0],size/grid*.5) and coord.shape == tuple(grid[::-1]) + (3,)
|
||||
assert np.allclose(coord[0,0,0],size/grid*.5) and coord.shape == tuple(grid) + (3,)
|
||||
|
||||
def test_node_coord0(self):
|
||||
size = np.random.random(3)
|
||||
grid = np.random.randint(8,32,(3))
|
||||
coord = grid_filters.node_coord0(grid,size)
|
||||
assert np.allclose(coord[-1,-1,-1],size) and coord.shape == tuple(grid[::-1]+1) + (3,)
|
||||
assert np.allclose(coord[-1,-1,-1],size) and coord.shape == tuple(grid+1) + (3,)
|
||||
|
||||
def test_coord0(self):
|
||||
size = np.random.random(3)
|
||||
|
|
@ -31,7 +31,7 @@ class TestGridFilters:
|
|||
size = np.random.random(3)
|
||||
origin = np.random.random(3)
|
||||
coord0 = eval('grid_filters.{}_coord0(grid,size,origin)'.format(mode)) # noqa
|
||||
_grid,_size,_origin = eval('grid_filters.{}_coord0_gridSizeOrigin(coord0.reshape(-1,3))'.format(mode))
|
||||
_grid,_size,_origin = eval('grid_filters.{}_coord0_gridSizeOrigin(coord0.reshape(-1,3,order="F"))'.format(mode))
|
||||
assert np.allclose(grid,_grid) and np.allclose(size,_size) and np.allclose(origin,_origin)
|
||||
|
||||
def test_displacement_fluct_equivalence(self):
|
||||
|
|
@ -57,9 +57,9 @@ class TestGridFilters:
|
|||
shifted = eval('grid_filters.{}_coord0(grid,size,origin)'.format(mode))
|
||||
unshifted = eval('grid_filters.{}_coord0(grid,size)'.format(mode))
|
||||
if mode == 'cell':
|
||||
assert np.allclose(shifted,unshifted+np.broadcast_to(origin,tuple(grid[::-1]) +(3,)))
|
||||
assert np.allclose(shifted,unshifted+np.broadcast_to(origin,tuple(grid) +(3,)))
|
||||
elif mode == 'node':
|
||||
assert np.allclose(shifted,unshifted+np.broadcast_to(origin,tuple(grid[::-1]+1)+(3,)))
|
||||
assert np.allclose(shifted,unshifted+np.broadcast_to(origin,tuple(grid+1)+(3,)))
|
||||
|
||||
@pytest.mark.parametrize('function',[grid_filters.cell_displacement_avg,
|
||||
grid_filters.node_displacement_avg])
|
||||
|
|
@ -83,5 +83,5 @@ class TestGridFilters:
|
|||
def test_regrid(self):
|
||||
size = np.random.random(3)
|
||||
grid = np.random.randint(8,32,(3))
|
||||
F = np.broadcast_to(np.eye(3), tuple(grid[::-1])+(3,3))
|
||||
F = np.broadcast_to(np.eye(3), tuple(grid)+(3,3))
|
||||
assert all(grid_filters.regrid(size,F,grid) == np.arange(grid.prod()))
|
||||
|
|
|
|||
Loading…
Reference in New Issue