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DAMASK_EICMD
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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 2020-04-20 12:41:03 +02:00
parent bce5ed62d5
commit 9979eb58f4
7 changed files with 75 additions and 69 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
processing/post/addCurl.py
View File

@ -49,9 +49,10 @@ for name in filenames:
for label in options.labels: for label in options.labels:
field = table.get(label) field = table.get(label)
shape = (3,) if np.prod(field.shape)//np.prod(grid) == 3 else (3,3) # vector or tensor shape = (3,) if np.prod(field.shape)//np.prod(grid) == 3 else (3,3) # vector or tensor
field = field.reshape(np.append(grid[::-1],shape)) field = field.reshape(tuple(grid)+(-1,),order='F').reshape(tuple(grid)+shape)
curl = damask.grid_filters.curl(size,field)
table.add('curlFFT({})'.format(label), table.add('curlFFT({})'.format(label),
damask.grid_filters.curl(size[::-1],field).reshape(-1,np.prod(shape)), curl.reshape(tuple(grid)+(-1,)).reshape(-1,np.prod(shape),order='F'),
scriptID+' '+' '.join(sys.argv[1:])) scriptID+' '+' '.join(sys.argv[1:]))
table.to_ASCII(sys.stdout if name is None else name) table.to_ASCII(sys.stdout if name is None else name)

12
processing/post/addDisplacement.py
View File

@ -52,22 +52,22 @@ for name in filenames:
table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name) table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
grid,size,origin = damask.grid_filters.cell_coord0_gridSizeOrigin(table.get(options.pos)) grid,size,origin = damask.grid_filters.cell_coord0_gridSizeOrigin(table.get(options.pos))
F = table.get(options.f).reshape(np.append(grid[::-1],(3,3))) F = table.get(options.f).reshape(tuple(grid)+(-1,),order='F').reshape(tuple(grid)+(3,3))
if options.nodal: if options.nodal:
table = damask.Table(damask.grid_filters.node_coord0(grid[::-1],size[::-1]).reshape(-1,3), table = damask.Table(damask.grid_filters.node_coord0(grid,size).reshape(-1,3,order='F'),
{'pos':(3,)}) {'pos':(3,)})
table.add('avg({}).{}'.format(options.f,options.pos), table.add('avg({}).{}'.format(options.f,options.pos),
damask.grid_filters.node_displacement_avg(size[::-1],F).reshape(-1,3), damask.grid_filters.node_displacement_avg(size,F).reshape(-1,3,order='F'),
scriptID+' '+' '.join(sys.argv[1:])) scriptID+' '+' '.join(sys.argv[1:]))
table.add('fluct({}).{}'.format(options.f,options.pos), table.add('fluct({}).{}'.format(options.f,options.pos),
damask.grid_filters.node_displacement_fluct(size[::-1],F).reshape(-1,3), damask.grid_filters.node_displacement_fluct(size,F).reshape(-1,3,order='F'),
scriptID+' '+' '.join(sys.argv[1:])) scriptID+' '+' '.join(sys.argv[1:]))
table.to_ASCII(sys.stdout if name is None else os.path.splitext(name)[0]+'_nodal.txt') table.to_ASCII(sys.stdout if name is None else os.path.splitext(name)[0]+'_nodal.txt')
else: else:
table.add('avg({}).{}'.format(options.f,options.pos), table.add('avg({}).{}'.format(options.f,options.pos),
damask.grid_filters.cell_displacement_avg(size[::-1],F).reshape(-1,3), damask.grid_filters.cell_displacement_avg(size,F).reshape(-1,3,order='F'),
scriptID+' '+' '.join(sys.argv[1:])) scriptID+' '+' '.join(sys.argv[1:]))
table.add('fluct({}).{}'.format(options.f,options.pos), table.add('fluct({}).{}'.format(options.f,options.pos),
damask.grid_filters.cell_displacement_fluct(size[::-1],F).reshape(-1,3), damask.grid_filters.cell_displacement_fluct(size,F).reshape(-1,3,order='F'),
scriptID+' '+' '.join(sys.argv[1:])) scriptID+' '+' '.join(sys.argv[1:]))
table.to_ASCII(sys.stdout if name is None else name) table.to_ASCII(sys.stdout if name is None else name)

5
processing/post/addDivergence.py
View File

@ -49,9 +49,10 @@ for name in filenames:
for label in options.labels: for label in options.labels:
field = table.get(label) field = table.get(label)
shape = (3,) if np.prod(field.shape)//np.prod(grid) == 3 else (3,3) # vector or tensor shape = (3,) if np.prod(field.shape)//np.prod(grid) == 3 else (3,3) # vector or tensor
field = field.reshape(np.append(grid[::-1],shape)) field = field.reshape(tuple(grid)+(-1,),order='F').reshape(tuple(grid)+shape)
div = damask.grid_filters.divergence(size,field)
table.add('divFFT({})'.format(label), table.add('divFFT({})'.format(label),
damask.grid_filters.divergence(size[::-1],field).reshape(-1,np.prod(shape)//3), div.reshape(tuple(grid)+(-1,)).reshape(-1,np.prod(shape)//3,order='F'),
scriptID+' '+' '.join(sys.argv[1:])) scriptID+' '+' '.join(sys.argv[1:]))
table.to_ASCII(sys.stdout if name is None else name) table.to_ASCII(sys.stdout if name is None else name)

5
processing/post/addGradient.py
View File

@ -49,9 +49,10 @@ for name in filenames:
for label in options.labels: for label in options.labels:
field = table.get(label) field = table.get(label)
shape = (1,) if np.prod(field.shape)//np.prod(grid) == 1 else (3,) # scalar or vector shape = (1,) if np.prod(field.shape)//np.prod(grid) == 1 else (3,) # scalar or vector
field = field.reshape(np.append(grid[::-1],shape)) field = field.reshape(tuple(grid)+(-1,),order='F').reshape(tuple(grid)+shape)
grad = damask.grid_filters.gradient(size,field)
table.add('gradFFT({})'.format(label), table.add('gradFFT({})'.format(label),
damask.grid_filters.gradient(size[::-1],field).reshape(-1,np.prod(shape)*3), grad.reshape(tuple(grid)+(-1,)).reshape(-1,np.prod(shape)*3,order='F'),
scriptID+' '+' '.join(sys.argv[1:])) scriptID+' '+' '.join(sys.argv[1:]))
table.to_ASCII(sys.stdout if name is None else name) table.to_ASCII(sys.stdout if name is None else name)

7
python/damask/_geom.py
View File

@ -357,7 +357,6 @@ class Geom:
seeds_p = np.vstack((seeds_p-np.array([0.,size[1],0.]),seeds_p,seeds_p+np.array([0.,size[1],0.]))) seeds_p = np.vstack((seeds_p-np.array([0.,size[1],0.]),seeds_p,seeds_p+np.array([0.,size[1],0.])))
seeds_p = np.vstack((seeds_p-np.array([0.,0.,size[2]]),seeds_p,seeds_p+np.array([0.,0.,size[2]]))) seeds_p = np.vstack((seeds_p-np.array([0.,0.,size[2]]),seeds_p,seeds_p+np.array([0.,0.,size[2]])))
coords = grid_filters.cell_coord0(grid*3,size*3,-size).reshape(-1,3,order='F') coords = grid_filters.cell_coord0(grid*3,size*3,-size).reshape(-1,3,order='F')
else: else:
weights_p = weights.flatten() weights_p = weights.flatten()
seeds_p = seeds seeds_p = seeds
@ -370,10 +369,10 @@ class Geom:
microstructure = np.array(result.get()) microstructure = np.array(result.get())
if periodic: if periodic:
microstructure = microstructure.reshape(grid*3) microstructure = microstructure.reshape(grid*3,order='F')
microstructure = microstructure[grid[0]:grid[0]*2,grid[1]:grid[1]*2,grid[2]:grid[2]*2]%seeds.shape[0] microstructure = microstructure[grid[0]:grid[0]*2,grid[1]:grid[1]*2,grid[2]:grid[2]*2]%seeds.shape[0]
else: else:
microstructure = microstructure.reshape(grid) microstructure = microstructure.reshape(grid,order='F')
#comments = 'geom.py:from_Laguerre_tessellation v{}'.format(version) #comments = 'geom.py:from_Laguerre_tessellation v{}'.format(version)
return Geom(microstructure+1,size,homogenization=1) return Geom(microstructure+1,size,homogenization=1)
@ -401,7 +400,7 @@ class Geom:
devNull,microstructure = KDTree.query(coords) devNull,microstructure = KDTree.query(coords)
#comments = 'geom.py:from_Voronoi_tessellation v{}'.format(version) #comments = 'geom.py:from_Voronoi_tessellation v{}'.format(version)
return Geom(microstructure.reshape(grid)+1,size,homogenization=1) return Geom(microstructure.reshape(grid,order='F')+1,size,homogenization=1)
def to_file(self,fname,pack=None): def to_file(self,fname,pack=None):

88
python/damask/grid_filters.py
View File

@ -7,7 +7,7 @@ def _ks(size,grid,first_order=False):
Parameters Parameters
---------- ----------
size : numpy.ndarray size : numpy.ndarray of shape (3)
physical size of the periodic field. physical size of the periodic field.
""" """
@ -19,8 +19,7 @@ def _ks(size,grid,first_order=False):
k_si = _np.arange(grid[2]//2+1)/size[2] k_si = _np.arange(grid[2]//2+1)/size[2]
kk, kj, ki = _np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij') return _np.stack(_np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij'), axis=-1)
return _np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3)
def curl(size,field): def curl(size,field):
@ -29,7 +28,7 @@ def curl(size,field):
Parameters Parameters
---------- ----------
size : numpy.ndarray size : numpy.ndarray of shape (3)
physical size of the periodic field. physical size of the periodic field.
""" """
@ -53,7 +52,7 @@ def divergence(size,field):
Parameters Parameters
---------- ----------
size : numpy.ndarray size : numpy.ndarray of shape (3)
physical size of the periodic field. physical size of the periodic field.
""" """
@ -73,7 +72,7 @@ def gradient(size,field):
Parameters Parameters
---------- ----------
size : numpy.ndarray size : numpy.ndarray of shape (3)
physical size of the periodic field. physical size of the periodic field.
""" """
@ -93,9 +92,9 @@ def cell_coord0(grid,size,origin=_np.zeros(3)):
Parameters Parameters
---------- ----------
grid : numpy.ndarray grid : numpy.ndarray of shape (3)
number of grid points. number of grid points.
size : numpy.ndarray size : numpy.ndarray of shape (3)
physical size of the periodic field. physical size of the periodic field.
origin : numpy.ndarray, optional origin : numpy.ndarray, optional
physical origin of the periodic field. Defaults to [0.0,0.0,0.0]. physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
@ -103,7 +102,11 @@ def cell_coord0(grid,size,origin=_np.zeros(3)):
""" """
start = origin + size/grid*.5 start = origin + size/grid*.5
end = origin + size - size/grid*.5 end = origin + size - size/grid*.5
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
return _np.stack(_np.meshgrid(_np.linspace(start[0],end[0],grid[0]),
_np.linspace(start[1],end[1],grid[1]),
_np.linspace(start[2],end[2],grid[2]),indexing = 'ij'),
axis = -1)
def cell_displacement_fluct(size,F): def cell_displacement_fluct(size,F):
@ -112,7 +115,7 @@ def cell_displacement_fluct(size,F):
Parameters Parameters
---------- ----------
size : numpy.ndarray size : numpy.ndarray of shape (3)
physical size of the periodic field. physical size of the periodic field.
F : numpy.ndarray F : numpy.ndarray
deformation gradient field. deformation gradient field.
@ -139,14 +142,14 @@ def cell_displacement_avg(size,F):
Parameters Parameters
---------- ----------
size : numpy.ndarray size : numpy.ndarray of shape (3)
physical size of the periodic field. physical size of the periodic field.
F : numpy.ndarray F : numpy.ndarray
deformation gradient field. deformation gradient field.
""" """
F_avg = _np.average(F,axis=(0,1,2)) F_avg = _np.average(F,axis=(0,1,2))
return _np.einsum('ml,ijkl->ijkm',F_avg - _np.eye(3),cell_coord0(F.shape[:3][::-1],size)) return _np.einsum('ml,ijkl->ijkm',F_avg - _np.eye(3),cell_coord0(F.shape[:3],size))
def cell_displacement(size,F): def cell_displacement(size,F):
@ -155,7 +158,7 @@ def cell_displacement(size,F):
Parameters Parameters
---------- ----------
size : numpy.ndarray size : numpy.ndarray of shape (3)
physical size of the periodic field. physical size of the periodic field.
F : numpy.ndarray F : numpy.ndarray
deformation gradient field. deformation gradient field.
@ -170,30 +173,30 @@ def cell_coord(size,F,origin=_np.zeros(3)):
Parameters Parameters
---------- ----------
size : numpy.ndarray size : numpy.ndarray of shape (3)
physical size of the periodic field. physical size of the periodic field.
F : numpy.ndarray F : numpy.ndarray
deformation gradient field. deformation gradient field.
origin : numpy.ndarray, optional origin : numpy.ndarray of shape (3), optional
physical origin of the periodic field. Defaults to [0.0,0.0,0.0]. physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
""" """
return cell_coord0(F.shape[:3][::-1],size,origin) + cell_displacement(size,F) return cell_coord0(F.shape[:3],size,origin) + cell_displacement(size,F)
def cell_coord0_gridSizeOrigin(coord0,ordered=True): def cell_coord0_gridSizeOrigin(coord0,ordered=True):
""" """
Return grid 'DNA', i.e. grid, size, and origin from array of cell positions. Return grid 'DNA', i.e. grid, size, and origin from 1D array of cell positions.
Parameters Parameters
---------- ----------
coord0 : numpy.ndarray coord0 : numpy.ndarray of shape (:,3)
array of undeformed cell coordinates. undeformed cell coordinates.
ordered : bool, optional ordered : bool, optional
expect coord0 data to be ordered (x fast, z slow). expect coord0 data to be ordered (x fast, z slow).
""" """
coords = [_np.unique(coord0[:,i]) for i in range(3)] coords = [_np.unique(coord0[:,i]) for i in range(3)] # _np.unique(coord0, axis=1)
mincorner = _np.array(list(map(min,coords))) mincorner = _np.array(list(map(min,coords)))
maxcorner = _np.array(list(map(max,coords))) maxcorner = _np.array(list(map(max,coords)))
grid = _np.array(list(map(len,coords)),'i') grid = _np.array(list(map(len,coords)),'i')
@ -216,7 +219,7 @@ def cell_coord0_gridSizeOrigin(coord0,ordered=True):
_np.allclose(coords[2],_np.linspace(start[2],end[2],grid[2])): _np.allclose(coords[2],_np.linspace(start[2],end[2],grid[2])):
raise ValueError('Regular grid spacing violated.') raise ValueError('Regular grid spacing violated.')
if ordered and not _np.allclose(coord0.reshape(tuple(grid[::-1])+(3,)),cell_coord0(grid,size,origin)): if ordered and not _np.allclose(coord0.reshape(tuple(grid)+(3,),order='F'),cell_coord0(grid,size,origin)):
raise ValueError('Input data is not a regular grid.') raise ValueError('Input data is not a regular grid.')
return (grid,size,origin) return (grid,size,origin)
@ -241,17 +244,18 @@ def node_coord0(grid,size,origin=_np.zeros(3)):
Parameters Parameters
---------- ----------
grid : numpy.ndarray grid : numpy.ndarray of shape (3)
number of grid points. number of grid points.
size : numpy.ndarray size : numpy.ndarray of shape (3)
physical size of the periodic field. physical size of the periodic field.
origin : numpy.ndarray, optional origin : numpy.ndarray of shape (3), optional
physical origin of the periodic field. Defaults to [0.0,0.0,0.0]. physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
""" """
return _np.mgrid[origin[0]:size[0]+origin[0]:(grid[0]+1)*1j, return _np.stack(_np.meshgrid(_np.linspace(origin[0],size[0]+origin[0],grid[0]+1),
origin[1]:size[1]+origin[1]:(grid[1]+1)*1j, _np.linspace(origin[1],size[1]+origin[1],grid[1]+1),
origin[2]:size[2]+origin[2]:(grid[2]+1)*1j].T _np.linspace(origin[2],size[2]+origin[2],grid[2]+1),indexing = 'ij'),
axis = -1)
def node_displacement_fluct(size,F): def node_displacement_fluct(size,F):
@ -260,7 +264,7 @@ def node_displacement_fluct(size,F):
Parameters Parameters
---------- ----------
size : numpy.ndarray size : numpy.ndarray of shape (3)
physical size of the periodic field. physical size of the periodic field.
F : numpy.ndarray F : numpy.ndarray
deformation gradient field. deformation gradient field.
@ -275,14 +279,14 @@ def node_displacement_avg(size,F):
Parameters Parameters
---------- ----------
size : numpy.ndarray size : numpy.ndarray of shape (3)
physical size of the periodic field. physical size of the periodic field.
F : numpy.ndarray F : numpy.ndarray
deformation gradient field. deformation gradient field.
""" """
F_avg = _np.average(F,axis=(0,1,2)) F_avg = _np.average(F,axis=(0,1,2))
return _np.einsum('ml,ijkl->ijkm',F_avg - _np.eye(3),node_coord0(F.shape[:3][::-1],size)) return _np.einsum('ml,ijkl->ijkm',F_avg - _np.eye(3),node_coord0(F.shape[:3],size))
def node_displacement(size,F): def node_displacement(size,F):
@ -291,7 +295,7 @@ def node_displacement(size,F):
Parameters Parameters
---------- ----------
size : numpy.ndarray size : numpy.ndarray of shape (3)
physical size of the periodic field. physical size of the periodic field.
F : numpy.ndarray F : numpy.ndarray
deformation gradient field. deformation gradient field.
@ -306,15 +310,15 @@ def node_coord(size,F,origin=_np.zeros(3)):
Parameters Parameters
---------- ----------
size : numpy.ndarray size : numpy.ndarray of shape (3)
physical size of the periodic field. physical size of the periodic field.
F : numpy.ndarray F : numpy.ndarray
deformation gradient field. deformation gradient field.
origin : numpy.ndarray, optional origin : numpy.ndarray of shape (3), optional
physical origin of the periodic field. Defaults to [0.0,0.0,0.0]. physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
""" """
return node_coord0(F.shape[:3][::-1],size,origin) + node_displacement(size,F) return node_coord0(F.shape[:3],size,origin) + node_displacement(size,F)
def cell_2_node(cell_data): def cell_2_node(cell_data):
@ -335,19 +339,19 @@ def node_2_cell(node_data):
return c[:-1,:-1,:-1] return c[:-1,:-1,:-1]
def node_coord0_gridSizeOrigin(coord0,ordered=False): def node_coord0_gridSizeOrigin(coord0,ordered=True):
""" """
Return grid 'DNA', i.e. grid, size, and origin from array of nodal positions. Return grid 'DNA', i.e. grid, size, and origin from 1D array of nodal positions.
Parameters Parameters
---------- ----------
coord0 : numpy.ndarray coord0 : numpy.ndarray of shape (:,3)
array of undeformed nodal coordinates. undeformed nodal coordinates.
ordered : bool, optional ordered : bool, optional
expect coord0 data to be ordered (x fast, z slow). expect coord0 data to be ordered (x fast, z slow).
""" """
coords = [_np.unique(coord0[:,i]) for i in range(3)] coords = [_np.unique(coord0[:,i]) for i in range(3)] # _np.unique(coord0, axis=1)
mincorner = _np.array(list(map(min,coords))) mincorner = _np.array(list(map(min,coords)))
maxcorner = _np.array(list(map(max,coords))) maxcorner = _np.array(list(map(max,coords)))
grid = _np.array(list(map(len,coords)),'i') - 1 grid = _np.array(list(map(len,coords)),'i') - 1
@ -362,7 +366,7 @@ def node_coord0_gridSizeOrigin(coord0,ordered=False):
_np.allclose(coords[2],_np.linspace(mincorner[2],maxcorner[2],grid[2]+1)): _np.allclose(coords[2],_np.linspace(mincorner[2],maxcorner[2],grid[2]+1)):
raise ValueError('Regular grid spacing violated.') raise ValueError('Regular grid spacing violated.')
if ordered and not _np.allclose(coord0.reshape(tuple((grid+1)[::-1])+(3,)),node_coord0(grid,size,origin)): if ordered and not _np.allclose(coord0.reshape(tuple(grid+1)+(3,),order='F'),node_coord0(grid,size,origin)):
raise ValueError('Input data is not a regular grid.') raise ValueError('Input data is not a regular grid.')
return (grid,size,origin) return (grid,size,origin)
@ -374,7 +378,7 @@ def regrid(size,F,new_grid):
Parameters Parameters
---------- ----------
size : numpy.ndarray size : numpy.ndarray of shape (3)
physical size physical size
F : numpy.ndarray F : numpy.ndarray
deformation gradient field deformation gradient field
@ -382,7 +386,7 @@ def regrid(size,F,new_grid):
new grid for undeformed coordinates new grid for undeformed coordinates
""" """
c = cell_coord0(F.shape[:3][::-1],size) \ c = cell_coord0(F.shape[:3],size) \
+ cell_displacement_avg(size,F) \ + cell_displacement_avg(size,F) \
+ cell_displacement_fluct(size,F) + cell_displacement_fluct(size,F)

12
python/tests/test_grid_filters.py
View File

@ -9,13 +9,13 @@ class TestGridFilters:
size = np.random.random(3) size = np.random.random(3)
grid = np.random.randint(8,32,(3)) grid = np.random.randint(8,32,(3))
coord = grid_filters.cell_coord0(grid,size) 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): def test_node_coord0(self):
size = np.random.random(3) size = np.random.random(3)
grid = np.random.randint(8,32,(3)) grid = np.random.randint(8,32,(3))
coord = grid_filters.node_coord0(grid,size) 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): def test_coord0(self):
size = np.random.random(3) size = np.random.random(3)
@ -31,7 +31,7 @@ class TestGridFilters:
size = np.random.random(3) size = np.random.random(3)
origin = np.random.random(3) origin = np.random.random(3)
coord0 = eval('grid_filters.{}_coord0(grid,size,origin)'.format(mode)) # noqa 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) assert np.allclose(grid,_grid) and np.allclose(size,_size) and np.allclose(origin,_origin)
def test_displacement_fluct_equivalence(self): def test_displacement_fluct_equivalence(self):
@ -57,9 +57,9 @@ class TestGridFilters:
shifted = eval('grid_filters.{}_coord0(grid,size,origin)'.format(mode)) shifted = eval('grid_filters.{}_coord0(grid,size,origin)'.format(mode))
unshifted = eval('grid_filters.{}_coord0(grid,size)'.format(mode)) unshifted = eval('grid_filters.{}_coord0(grid,size)'.format(mode))
if mode == 'cell': 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': 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, @pytest.mark.parametrize('function',[grid_filters.cell_displacement_avg,
grid_filters.node_displacement_avg]) grid_filters.node_displacement_avg])
@ -83,5 +83,5 @@ class TestGridFilters:
def test_regrid(self): def test_regrid(self):
size = np.random.random(3) size = np.random.random(3)
grid = np.random.randint(8,32,(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())) assert all(grid_filters.regrid(size,F,grid) == np.arange(grid.prod()))