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grid assemble + corrected grid.scale

This commit is contained in:
Martin Diehl 2022-11-08 18:52:08 +00:00 committed by Philip Eisenlohr
parent 507e1a2f05
commit 4f0db64e15
10 changed files with 220 additions and 66 deletions
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98
python/damask/_grid.py
View File

@ -10,7 +10,7 @@ from pathlib import Path
import numpy as np
import pandas as pd
import h5py
from scipy import ndimage, spatial
from scipy import ndimage, spatial, interpolate
from . import VTK
from . import util
@ -41,7 +41,7 @@ class Grid:
Parameters
----------
material : numpy.ndarray, shape (:,:,:)
material : numpy.ndarray of int, shape (:,:,:)
Material indices. The shape of the material array defines
the number of cells.
size : sequence of float, len (3)
@ -50,7 +50,7 @@ class Grid:
Coordinates of grid origin in meter. Defaults to [0.0,0.0,0.0].
initial_conditions : dictionary, optional
Labels and values of the inital conditions at each material point.
comments : str or iterable of str, optional
comments : str or sequence of str, optional
Additional, human-readable information, e.g. history of operations.
"""
@ -183,7 +183,7 @@ class Grid:
@comments.setter
def comments(self,
comments: Union[str, Sequence[str]]):
self._comments = [str(c) for c in comments] if isinstance(comments,list) else [str(comments)]
self._comments = [str(c) for c in comments] if isinstance(comments,Sequence) else [str(comments)]
@property
@ -427,7 +427,7 @@ class Grid:
coordinates : str
Label of the vector column containing the spatial coordinates.
Need to be ordered (1./x fast, 3./z slow).
labels : (list of) str
labels : str or sequence of str
Label(s) of the columns containing the material definition.
Each unique combination of values results in one material ID.
@ -474,7 +474,7 @@ class Grid:
Number of cells in x,y,z direction.
size : sequence of float, len (3)
Physical size of the grid in meter.
seeds : numpy.ndarray, shape (:,3)
seeds : numpy.ndarray of float, shape (:,3)
Position of the seed points in meter. All points need to lay within the box.
weights : sequence of float, len (seeds.shape[0])
Weights of the seeds. Setting all weights to 1.0 gives a standard Voronoi tessellation.
@ -531,7 +531,7 @@ class Grid:
Number of cells in x,y,z direction.
size : sequence of float, len (3)
Physical size of the grid in meter.
seeds : numpy.ndarray, shape (:,3)
seeds : numpy.ndarray of float, shape (:,3)
Position of the seed points in meter. All points need to lay within the box.
material : sequence of int, len (seeds.shape[0]), optional
Material ID of the seeds.
@ -940,17 +940,14 @@ class Grid:
def scale(self,
cells: IntSequence,
periodic: bool = True) -> 'Grid':
cells: IntSequence) -> 'Grid':
"""
Scale grid to new cells.
Scale grid to new cell count.
Parameters
----------
cells : sequence of int, len (3)
Number of cells in x,y,z direction.
periodic : bool, optional
Assume grid to be periodic. Defaults to True.
Returns
-------
@ -963,7 +960,11 @@ class Grid:
>>> import numpy as np
>>> import damask
>>> g = damask.Grid(np.zeros([32]*3,int),np.ones(3)*1e-4)
>>> (g := damask.Grid(np.zeros([32]*3,int),np.ones(3)*1e-4))
cells: 32 × 32 × 32
size: 0.0001 × 0.0001 × 0.0001 m³
origin: 0.0 0.0 0.0 m
# materials: 1
>>> g.scale(g.cells*2)
cells : 64 x 64 x 64
size : 0.0001 x 0.0001 x 0.0001 m³
@ -971,20 +972,49 @@ class Grid:
# materials: 1
"""
return Grid(material = ndimage.interpolation.zoom(
self.material,
cells/self.cells,
output=self.material.dtype,
order=0,
mode='wrap' if periodic else 'nearest',
prefilter=False
),
options = ('nearest',False,None)
orig = tuple(map(np.linspace,self.origin + self.size/self.cells*.5,
self.origin + self.size - self.size/self.cells*.5,self.cells))
new = grid_filters.coordinates0_point(cells,self.size,self.origin)
return Grid(material = interpolate.RegularGridInterpolator(orig,self.material,*options)(new).astype(int),
size = self.size,
origin = self.origin,
initial_conditions = {k: interpolate.RegularGridInterpolator(orig,v,*options)(new)
for k,v in self.initial_conditions.items()},
comments = self.comments+[util.execution_stamp('Grid','scale')],
)
def assemble(self,
idx: np.ndarray) -> 'Grid':
"""
Assemble new grid from index map.
Parameters
----------
idx : numpy.ndarray of int, shape (:,:,:) or (:,:,:,3)
Grid of flat indices or coordinate indices.
Returns
-------
updated : damask.Grid
Updated grid-based geometry.
Cell count of resulting grid matches shape of index map.
"""
cells = idx.shape[:3]
flat = (idx if len(idx.shape)==3 else grid_filters.ravel_index(idx)).flatten(order='F')
ic = {k: v.flatten(order='F')[flat].reshape(cells,order='F') for k,v in self.initial_conditions.items()}
return Grid(material = self.material.flatten(order='F')[flat].reshape(cells,order='F'),
size = self.size,
origin = self.origin,
initial_conditions = ic,
comments = self.comments+[util.execution_stamp('Grid','assemble')],
)
def renumber(self) -> 'Grid':
"""
Renumber sorted material indices as 0,...,N-1.
@ -1113,13 +1143,13 @@ class Grid:
selection_ = None if selection is None else \
set(self.material.flatten()) - set(util.aslist(selection)) if invert_selection else \
set(self.material.flatten()) & set(util.aslist(selection))
material = ndimage.filters.generic_filter(
self.material,
most_frequent,
footprint=footprint,
mode='wrap' if periodic else 'nearest',
extra_keywords=dict(selection=selection_,rng=rng),
).astype(self.material.dtype)
material = ndimage.generic_filter(
self.material,
most_frequent,
footprint=footprint,
mode='wrap' if periodic else 'nearest',
extra_keywords=dict(selection=selection_,rng=rng),
).astype(self.material.dtype)
return Grid(material = material,
size = self.size,
origin = self.origin,
@ -1269,12 +1299,12 @@ class Grid:
selection_ = None if selection is None else \
set(self.material.flatten()) - set(util.aslist(selection)) if invert_selection else \
set(self.material.flatten()) & set(util.aslist(selection))
mask = ndimage.filters.generic_filter(self.material,
tainted_neighborhood,
footprint=footprint,
mode='wrap' if periodic else 'nearest',
extra_keywords=dict(selection=selection_),
)
mask = ndimage.generic_filter(self.material,
tainted_neighborhood,
footprint=footprint,
mode='wrap' if periodic else 'nearest',
extra_keywords=dict(selection=selection_),
)
return Grid(material = np.where(mask, self.material + offset_,self.material),
size = self.size,

118
python/damask/grid_filters.py
View File

@ -541,28 +541,118 @@ def coordinates0_valid(coordinates0: _np.ndarray) -> bool:
return False
def unravel_index(idx: _np.ndarray) -> _np.ndarray:
"""
Convert flat indices to coordinate indices.
Parameters
----------
idx : numpy.ndarray, shape (:,:,:)
Grid of flat indices.
Returns
-------
unravelled : numpy.ndarray, shape (:,:,:,3)
Grid of coordinate indices.
Examples
--------
Unravel a linearly increasing sequence of material indices on a 3 × 2 × 1 grid.
>>> import numpy as np
>>> import damask
>>> seq = np.arange(6).reshape((3,2,1),order='F')
>>> (coord_idx := damask.grid_filters.unravel_index(seq))
array([[[[0, 0, 0]],
[[0, 1, 0]]],
[[[1, 0, 0]],
[[1, 1, 0]]],
[[[2, 0, 0]],
[[2, 1, 0]]]])
>>> coord_idx[1,1,0]
array([1, 1, 0])
"""
cells = idx.shape
idx_ = _np.expand_dims(idx,3)
return _np.block([ idx_ %cells[0],
(idx_//cells[0]) %cells[1],
((idx_//cells[0])//cells[1])%cells[2]])
def ravel_index(idx: _np.ndarray) -> _np.ndarray:
"""
Convert coordinate indices to flat indices.
Parameters
----------
idx : numpy.ndarray, shape (:,:,:,3)
Grid of coordinate indices.
Returns
-------
ravelled : numpy.ndarray, shape (:,:,:)
Grid of flat indices.
Examples
--------
Ravel a reversed sequence of coordinate indices on a 2 × 2 × 1 grid.
>>> import numpy as np
>>> import damask
>>> (rev := np.array([[1,1,0],[0,1,0],[1,0,0],[0,0,0]]).reshape((2,2,1,3)))
array([[[[1, 1, 0]],
[[0, 1, 0]]],
[[[1, 0, 0]],
[[0, 0, 0]]]])
>>> (flat_idx := damask.grid_filters.ravel_index(rev))
array([[[3],
[2]],
[[1],
[0]]])
"""
cells = idx.shape[:3]
return idx[:,:,:,0] \
+ idx[:,:,:,1]*cells[0] \
+ idx[:,:,:,2]*cells[0]*cells[1]
def regrid(size: _FloatSequence,
F: _np.ndarray,
cells: _IntSequence) -> _np.ndarray:
"""
Return mapping from coordinates in deformed configuration to a regular grid.
Map a deformed grid A back to a rectilinear grid B.
The size of grid B is chosen as the average deformed size of grid A.
Parameters
----------
size : sequence of float, len (3)
Physical size.
F : numpy.ndarray, shape (:,:,:,3,3), shape (:,:,:,3,3)
Deformation gradient field.
Physical size of grid A.
F : numpy.ndarray, shape (:,:,:,3,3)
Deformation gradient field on grid A.
cells : sequence of int, len (3)
Cell count along x,y,z of remapping grid.
Cell count along x,y,z of grid B.
Returns
-------
idx : numpy.ndarray of int, shape (cells)
Flat index of closest point on deformed grid A for each point on grid B.
"""
c = coordinates_point(size,F)
outer = _np.dot(_np.average(F,axis=(0,1,2)),size)
for d in range(3):
c[_np.where(c[:,:,:,d]<0)] += outer[d]
c[_np.where(c[:,:,:,d]>outer[d])] -= outer[d]
tree = _spatial.cKDTree(c.reshape(-1,3),boxsize=outer)
return tree.query(coordinates0_point(cells,outer))[1].flatten()
box = _np.dot(_np.average(F,axis=(0,1,2)),size)
c = coordinates_point(size,F)%box
tree = _spatial.cKDTree(c.reshape((-1,3),order='F'),boxsize=box)
return tree.query(coordinates0_point(cells,box))[1]

4
python/tests/reference/Grid/scale_grid_10-10-10.vti vendored
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@ -3,7 +3,7 @@
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4
python/tests/reference/Grid/scale_grid_10-11-10.vti vendored
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@ -3,7 +3,7 @@
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@ -11,7 +11,7 @@
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<CellData>
<DataArray type="Int64" Name="material" format="binary" RangeMin="1" RangeMax="41">
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4
python/tests/reference/Grid/scale_grid_10-13-10.vti vendored
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@ -3,7 +3,7 @@
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4
python/tests/reference/Grid/scale_grid_10-20-2.vti vendored
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@ -3,7 +3,7 @@
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4
python/tests/reference/Grid/scale_grid_5-4-20.vti vendored
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@ -3,7 +3,7 @@
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4
python/tests/reference/Grid/scale_grid_8-10-12.vti vendored
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@ -3,7 +3,7 @@
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<CellData>
<DataArray type="Int64" Name="material" format="binary" RangeMin="1" RangeMax="41">
AQAAAACAAAAAHgAAyAAAAA==eF7t2LcBAkEUxFDu8N5776H/BglQBwpvNnmRskn+FrX/K2KMsWKWWMcGNrGFbexgF23fwz4OcIgjHOMEp2j7Gc5xgUtc4Ro3uEXb73CPBzziCc94wSva/oZ3fOATX/jGD37R9nY/trf7sb3dj+3tfmxv92N7ux/b2/3Y3u7H9nY/trf7sb3dj+3tfmxv92N7ux/b2/3YvowxxhhjjJXS/gPa3t4htrd3iO3tHWJ7e4fY3u7H9nY/trf7sb3dj+3tfmz/A1V7KtE=
AQAAAACAAAAAHgAAyQAAAA==eF7t17cRw0AUxFCR8t5776X+G1QgdIAZRnvJi5Bt8K+o/V8RY4wxxsossY4NbGIL29jBLtq+h30c4BBHOMYJTtH2M5zjApe4wjVucIu23+EeD3jEE57xgle0/Q3v+MAnvvCNH/yi7e1+bG/3Y3u7H9vb/dje7sf2dj+2t/uxvd2P7e1+bG/3Y/syxhhjjJVr/8G2t3eg7e0daHt7B9re3oG2t/uxvd2P7e1+bG/3Y3u7H9vb/dje7sf2dj+2t/uxvd2P7X9ILyox
</DataArray>
</CellData>
</Piece>

8
python/tests/test_Grid.py
View File

@ -212,6 +212,14 @@ class TestGrid:
assert default == modified.renumber()
def test_assemble(self):
cells = np.random.randint(8,16,3)
N = cells.prod()
g = Grid(np.arange(N).reshape(cells),np.ones(3))
idx = np.random.randint(0,N,N).reshape(cells)
assert (idx == g.assemble(idx).material).all
def test_substitute(self,default):
offset = np.random.randint(1,500)
modified = Grid(default.material + offset,

38
python/tests/test_grid_filters.py
View File

@ -144,16 +144,15 @@ class TestGridFilters:
def test_regrid_identity(self):
size = np.random.random(3) # noqa
cells = np.random.randint(8,32,(3))
F = np.broadcast_to(np.eye(3), tuple(cells)+(3,3))
assert all(grid_filters.regrid(size,F,cells) == np.arange(cells.prod()))
F = np.broadcast_to(np.eye(3), (*cells,3,3))
assert (grid_filters.regrid(size,F,cells).flatten() == np.arange(cells.prod())).all
def test_regrid_double_cells(self):
size = np.random.random(3) # noqa
cells = np.random.randint(8,32,(3))
g = Grid.from_Voronoi_tessellation(cells,size,seeds.from_random(size,10))
F = np.broadcast_to(np.eye(3), tuple(cells)+(3,3))
assert all(g.scale(cells*2).material.flatten() ==
g.material.flatten()[grid_filters.regrid(size,F,cells*2)])
F = np.broadcast_to(np.eye(3), (*cells,3,3))
assert g.scale(cells*2) == g.assemble(grid_filters.regrid(size,F,cells*2))
@pytest.mark.parametrize('differential_operator',[grid_filters.curl,
grid_filters.divergence,
@ -319,7 +318,6 @@ class TestGridFilters:
]
@pytest.mark.parametrize('field_def,div_def',div_test_data)
def test_div(self,field_def,div_def):
size = np.random.random(3)+1.0
cells = np.random.randint(8,32,(3))
@ -336,3 +334,31 @@ class TestGridFilters:
div=div.reshape(tuple(cells))
assert np.allclose(div,grid_filters.divergence(size,field))
def test_ravel_index(self):
cells = np.random.randint(8,32,(3))
indices = np.block(np.meshgrid(np.arange(cells[0]),
np.arange(cells[1]),
np.arange(cells[2]),indexing='ij')).reshape(tuple(cells)+(3,),order='F')
x,y,z = map(np.random.randint,cells)
assert grid_filters.ravel_index(indices)[x,y,z] == np.arange(0,np.product(cells)).reshape(cells,order='F')[x,y,z]
def test_unravel_index(self):
cells = np.random.randint(8,32,(3))
indices = np.arange(np.prod(cells)).reshape(cells,order='F')
x,y,z = map(np.random.randint,cells)
assert np.all(grid_filters.unravel_index(indices)[x,y,z] == [x,y,z])
def test_ravel_unravel_index(self):
cells = np.random.randint(8,32,(3))
indices = np.random.randint(0,np.prod(cells),cells).reshape(cells)
assert np.all(indices==grid_filters.ravel_index(grid_filters.unravel_index(indices)))
def test_unravel_ravel_index(self):
cells = np.hstack([np.random.randint(8,32,(3)),1])
indices = np.block([np.random.randint(0,cells[0],cells),
np.random.randint(0,cells[1],cells),
np.random.randint(0,cells[2],cells)])
assert np.all(indices==grid_filters.unravel_index(grid_filters.ravel_index(indices)))