223031012
/
DAMASK_EICMD
mirror of https://github.com/achalhp/DAMASK_EICMD.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge branch 'fix-grid-convention' into 'development' 

Fix grid convention

See merge request damask/DAMASK!160
This commit is contained in:
Francisco Jose Gallardo Basile 2020-04-23 19:02:23 +02:00
parent 289808875c 936ce6a160
commit 898cf42aca
15 changed files with 209 additions and 150 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
processing/post/DADF5_postResults.py
View File

@ -33,7 +33,7 @@ for filename in options.filenames:
results = damask.Result(filename)
if not results.structured: continue
coords = damask.grid_filters.cell_coord0(results.grid,results.size,results.origin)
coords = damask.grid_filters.cell_coord0(results.grid,results.size,results.origin).reshape(-1,3,order='F')
N_digits = int(np.floor(np.log10(int(results.increments[-1][3:]))))+1
N_digits = 5 # hack to keep test intact

75
processing/post/addCompatibilityMismatch.py
View File

@ -16,8 +16,8 @@ scriptID = ' '.join([scriptName,damask.version])
def volTetrahedron(coords):
"""
Return the volume of the tetrahedron with given vertices or sides.
Ifvertices are given they must be in a NumPy array with shape (4,3): the
If vertices are given they must be in a NumPy array with shape (4,3): the
position vectors of the 4 vertices in 3 dimensions; if the six sides are
given, they must be an array of length 6. If both are given, the sides
will be used in the calculation.
@ -62,19 +62,18 @@ def volTetrahedron(coords):
def volumeMismatch(size,F,nodes):
"""
Calculates the volume mismatch.
volume mismatch is defined as the difference between volume of reconstructed
volume mismatch is defined as the difference between volume of reconstructed
(compatible) cube and determinant of deformation gradient at Fourier point.
"""
coords = np.empty([8,3])
vMismatch = np.empty(grid[::-1])
volInitial = size.prod()/grid.prod()
vMismatch = np.empty(F.shape[:3])
#--------------------------------------------------------------------------------------------------
# calculate actual volume and volume resulting from deformation gradient
for k in range(grid[2]):
for k in range(grid[0]):
for j in range(grid[1]):
for i in range(grid[0]):
for i in range(grid[2]):
coords[0,0:3] = nodes[k, j, i ,0:3]
coords[1,0:3] = nodes[k ,j, i+1,0:3]
coords[2,0:3] = nodes[k ,j+1,i+1,0:3]
@ -91,47 +90,45 @@ def volumeMismatch(size,F,nodes):
+ abs(volTetrahedron([coords[6,0:3],coords[4,0:3],coords[1,0:3],coords[5,0:3]])) \
+ abs(volTetrahedron([coords[6,0:3],coords[4,0:3],coords[1,0:3],coords[0,0:3]]))) \
/np.linalg.det(F[k,j,i,0:3,0:3])
return vMismatch/volInitial
return vMismatch/(size.prod()/grid.prod())
def shapeMismatch(size,F,nodes,centres):
"""
Routine to calculate the shape mismatch.
shape mismatch is defined as difference between the vectors from the central point to
the corners of reconstructed (combatible) volume element and the vectors calculated by deforming
the initial volume element with the current deformation gradient.
"""
coordsInitial = np.empty([8,3])
sMismatch = np.empty(grid[::-1])
sMismatch = np.empty(F.shape[:3])
#--------------------------------------------------------------------------------------------------
# initial positions
coordsInitial[0,0:3] = [-size[0]/grid[0],-size[1]/grid[1],-size[2]/grid[2]]
coordsInitial[1,0:3] = [+size[0]/grid[0],-size[1]/grid[1],-size[2]/grid[2]]
coordsInitial[2,0:3] = [+size[0]/grid[0],+size[1]/grid[1],-size[2]/grid[2]]
coordsInitial[3,0:3] = [-size[0]/grid[0],+size[1]/grid[1],-size[2]/grid[2]]
coordsInitial[4,0:3] = [-size[0]/grid[0],-size[1]/grid[1],+size[2]/grid[2]]
coordsInitial[5,0:3] = [+size[0]/grid[0],-size[1]/grid[1],+size[2]/grid[2]]
coordsInitial[6,0:3] = [+size[0]/grid[0],+size[1]/grid[1],+size[2]/grid[2]]
coordsInitial[7,0:3] = [-size[0]/grid[0],+size[1]/grid[1],+size[2]/grid[2]]
coordsInitial = coordsInitial/2.0
delta = size/grid*.5
coordsInitial = np.vstack((delta * np.array((-1,-1,-1)),
delta * np.array((+1,-1,-1)),
delta * np.array((+1,+1,-1)),
delta * np.array((-1,+1,-1)),
delta * np.array((-1,-1,+1)),
delta * np.array((+1,-1,+1)),
delta * np.array((+1,+1,+1)),
delta * np.array((-1,+1,+1))))
#--------------------------------------------------------------------------------------------------
# compare deformed original and deformed positions to actual positions
for k in range(grid[2]):
for k in range(grid[0]):
for j in range(grid[1]):
for i in range(grid[0]):
for i in range(grid[2]):
sMismatch[k,j,i] = \
+ np.linalg.norm(nodes[k, j, i ,0:3] - centres[k,j,i,0:3] - np.dot(F[k,j,i,:,:], coordsInitial[0,0:3]))\
+ np.linalg.norm(nodes[k, j, i+1,0:3] - centres[k,j,i,0:3] - np.dot(F[k,j,i,:,:], coordsInitial[1,0:3]))\
+ np.linalg.norm(nodes[k, j+1,i+1,0:3] - centres[k,j,i,0:3] - np.dot(F[k,j,i,:,:], coordsInitial[2,0:3]))\
+ np.linalg.norm(nodes[k+1,j, i ,0:3] - centres[k,j,i,0:3] - np.dot(F[k,j,i,:,:], coordsInitial[1,0:3]))\
+ np.linalg.norm(nodes[k+1,j+1,i ,0:3] - centres[k,j,i,0:3] - np.dot(F[k,j,i,:,:], coordsInitial[2,0:3]))\
+ np.linalg.norm(nodes[k, j+1,i ,0:3] - centres[k,j,i,0:3] - np.dot(F[k,j,i,:,:], coordsInitial[3,0:3]))\
+ np.linalg.norm(nodes[k+1,j, i ,0:3] - centres[k,j,i,0:3] - np.dot(F[k,j,i,:,:], coordsInitial[4,0:3]))\
+ np.linalg.norm(nodes[k, j, i+1,0:3] - centres[k,j,i,0:3] - np.dot(F[k,j,i,:,:], coordsInitial[4,0:3]))\
+ np.linalg.norm(nodes[k+1,j, i+1,0:3] - centres[k,j,i,0:3] - np.dot(F[k,j,i,:,:], coordsInitial[5,0:3]))\
+ np.linalg.norm(nodes[k+1,j+1,i+1,0:3] - centres[k,j,i,0:3] - np.dot(F[k,j,i,:,:], coordsInitial[6,0:3]))\
+ np.linalg.norm(nodes[k+1,j+1,i ,0:3] - centres[k,j,i,0:3] - np.dot(F[k,j,i,:,:], coordsInitial[7,0:3]))
+ np.linalg.norm(nodes[k ,j+1,i+1,0:3] - centres[k,j,i,0:3] - np.dot(F[k,j,i,:,:], coordsInitial[7,0:3]))
return sMismatch
@ -174,24 +171,24 @@ if filenames == []: filenames = [None]
for name in filenames:
damask.util.report(scriptName,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))
F = table.get(options.defgrad).reshape(grid[2],grid[1],grid[0],3,3)
F = table.get(options.defgrad).reshape(tuple(grid)+(-1,),order='F').reshape(tuple(grid)+(3,3))
nodes = damask.grid_filters.node_coord(size,F)
if options.shape:
centers = damask.grid_filters.cell_coord(size,F)
shapeMismatch = shapeMismatch( size,table.get(options.defgrad).reshape(grid[2],grid[1],grid[0],3,3),nodes,centers)
shapeMismatch = shapeMismatch(size,F,nodes,centers)
table.add('shapeMismatch(({}))'.format(options.defgrad),
shapeMismatch.reshape(-1,1),
shapeMismatch.reshape(-1,1,order='F'),
scriptID+' '+' '.join(sys.argv[1:]))
if options.volume:
volumeMismatch = volumeMismatch(size,table.get(options.defgrad).reshape(grid[2],grid[1],grid[0],3,3),nodes)
volumeMismatch = volumeMismatch(size,F,nodes)
table.add('volMismatch(({}))'.format(options.defgrad),
volumeMismatch.reshape(-1,1),
volumeMismatch.reshape(-1,1,order='F'),
scriptID+' '+' '.join(sys.argv[1:]))
table.to_ASCII(sys.stdout if name is None else name)

7
processing/post/addCurl.py
View File

@ -49,9 +49,10 @@ for name in filenames:
for label in options.labels:
field = table.get(label)
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),
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:]))
table.to_ASCII(sys.stdout if name is None else name)

16
processing/post/addDisplacement.py
View File

@ -5,8 +5,6 @@ import sys
from io import StringIO
from optparse import OptionParser
import numpy as np
import damask
@ -51,23 +49,23 @@ for name in filenames:
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))
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:
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,)})
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:]))
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:]))
table.to_ASCII(sys.stdout if name is None else os.path.splitext(name)[0]+'_nodal.txt')
else:
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:]))
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:]))
table.to_ASCII(sys.stdout if name is None else name)

7
processing/post/addDivergence.py
View File

@ -49,9 +49,10 @@ for name in filenames:
for label in options.labels:
field = table.get(label)
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),
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:]))
table.to_ASCII(sys.stdout if name is None else name)

7
processing/post/addGradient.py
View File

@ -49,9 +49,10 @@ for name in filenames:
for label in options.labels:
field = table.get(label)
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')
grad = damask.grid_filters.gradient(size,field)
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:]))
table.to_ASCII(sys.stdout if name is None else name)

6
processing/post/averageDown.py
View File

@ -61,7 +61,7 @@ if any(shift != 0): prefix += 'shift{:+}{:+}{:+}_'.format(*shift)
for name in filenames:
damask.util.report(scriptName,name)
table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
if (options.grid is None or options.size is None):
@ -87,11 +87,11 @@ for name in filenames:
origin = list(-(packing//2)) + [0])\
[::packing[0],::packing[1],::packing[2],:].reshape((packedGrid.prod(),-1),order = 'F')
table = damask.Table(averagedDown,table.shapes,table.comments)
coords = damask.grid_filters.cell_coord0(packedGrid,size,shift/packedGrid*size+origin)
table.set(options.pos, coords.reshape(-1,3))
table.set(options.pos, coords.reshape(-1,3,order='F'))
outname = os.path.join(os.path.dirname(name),prefix+os.path.basename(name))

6
processing/post/blowUp.py
View File

@ -59,13 +59,13 @@ for name in filenames:
packing = np.array(options.packing,'i')
outSize = grid*packing
data = table.data.values.reshape(tuple(grid)+(-1,))
blownUp = ndimage.interpolation.zoom(data,tuple(packing)+(1,),order=0,mode='nearest').reshape(outSize.prod(),-1)
data = table.data.values.reshape(tuple(grid)+(-1,),order='F')
blownUp = ndimage.interpolation.zoom(data,tuple(packing)+(1,),order=0,mode='nearest').reshape(outSize.prod(),-1,order='F')
table = damask.Table(blownUp,table.shapes,table.comments)
coords = damask.grid_filters.cell_coord0(outSize,size,origin)
table.set(options.pos,coords.reshape(-1,3))
table.set(options.pos,coords.reshape(-1,3,order='F'))
table.set('elem',np.arange(1,outSize.prod()+1))
outname = os.path.join(os.path.dirname(name),prefix+os.path.basename(name))

12
processing/pre/geom_fromVoronoiTessellation.py
View File

@ -24,22 +24,22 @@ def findClosestSeed(seeds, weights, point):
def Laguerre_tessellation(grid, size, seeds, weights, origin = np.zeros(3), periodic = True, cpus = 2):
if periodic:
weights_p = np.tile(weights,27).flatten(order='F') # Laguerre weights (1,2,3,1,2,3,...,1,2,3)
weights_p = np.tile(weights.squeeze(),27) # Laguerre weights (1,2,3,1,2,3,...,1,2,3)
seeds_p = np.vstack((seeds -np.array([size[0],0.,0.]),seeds, seeds +np.array([size[0],0.,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]])))
coords = damask.grid_filters.cell_coord0(grid*3,size*3,-origin-size).reshape(-1,3,order='F')
coords = damask.grid_filters.cell_coord0(grid*3,size*3,-origin-size).reshape(-1,3)
else:
weights_p = weights.flatten()
weights_p = weights.squeeze()
seeds_p = seeds
coords = damask.grid_filters.cell_coord0(grid,size,-origin).reshape(-1,3,order='F')
coords = damask.grid_filters.cell_coord0(grid,size,-origin).reshape(-1,3)
if cpus > 1:
pool = multiprocessing.Pool(processes = cpus)
result = pool.map_async(partial(findClosestSeed,seeds_p,weights_p), [coord for coord in coords])
pool.close()
pool.join()
closest_seed = np.array(result.get())
closest_seed = np.array(result.get()).reshape(-1,3)
else:
closest_seed= np.array([findClosestSeed(seeds_p,weights_p,coord) for coord in coords])
@ -52,7 +52,7 @@ def Laguerre_tessellation(grid, size, seeds, weights, origin = np.zeros(3), peri
def Voronoi_tessellation(grid, size, seeds, origin = np.zeros(3), periodic = True):
coords = damask.grid_filters.cell_coord0(grid,size,-origin).reshape(-1,3,order='F')
coords = damask.grid_filters.cell_coord0(grid,size,-origin).reshape(-1,3)
KDTree = spatial.cKDTree(seeds,boxsize=size) if periodic else spatial.cKDTree(seeds)
devNull,closest_seed = KDTree.query(coords)

8
processing/pre/seeds_fromGeom.py
View File

@ -45,7 +45,7 @@ options.blacklist = [int(i) for i in options.blacklist]
for name in filenames:
damask.util.report(scriptName,name)
geom = damask.Geom.from_file(StringIO(''.join(sys.stdin.read())) if name is None else name)
microstructure = geom.get_microstructure().reshape((-1,1),order='F')
@ -53,9 +53,9 @@ for name in filenames:
np.full(geom.grid.prod(),True,dtype=bool),
np.in1d(microstructure,options.blacklist,invert=True) if options.blacklist else \
np.full(geom.grid.prod(),True,dtype=bool))
seeds = damask.grid_filters.cell_coord0(geom.grid,geom.size).reshape(-1,3)
seeds = damask.grid_filters.cell_coord0(geom.grid,geom.size).reshape(-1,3,order='F')
comments = geom.comments \
+ [scriptID + ' ' + ' '.join(sys.argv[1:]),
'grid\ta {}\tb {}\tc {}'.format(*geom.grid),

2
processing/pre/seeds_fromRandom.py
View File

@ -128,7 +128,7 @@ for name in filenames:
if not options.selective:
coords = damask.grid_filters.cell_coord0(grid,size).reshape(-1,3)
coords = damask.grid_filters.cell_coord0(grid,size).reshape(-1,3,order='F')
seeds = coords[np.random.choice(np.prod(grid), options.N, replace=False)] \
+ np.broadcast_to(size/grid,(options.N,3))*(np.random.rand(options.N,3)*.5-.25) # wobble without leaving grid
else:

16
python/damask/_geom.py
View File

@ -322,11 +322,10 @@ class Geom:
if i != grid.prod():
raise TypeError('Invalid file: expected {} entries, found {}'.format(grid.prod(),i))
microstructure = microstructure.reshape(grid,order='F')
if not np.any(np.mod(microstructure.flatten(),1) != 0.0): # no float present
if not np.any(np.mod(microstructure,1) != 0.0): # no float present
microstructure = microstructure.astype('int')
return Geom(microstructure.reshape(grid),size,origin,homogenization,comments)
return Geom(microstructure.reshape(grid,order='F'),size,origin,homogenization,comments)
@staticmethod
@ -352,16 +351,15 @@ class Geom:
"""
if periodic:
weights_p = np.tile(weights,27).flatten(order='F') # Laguerre weights (1,2,3,1,2,3,...,1,2,3)
weights_p = np.tile(weights,27) # Laguerre weights (1,2,3,1,2,3,...,1,2,3)
seeds_p = np.vstack((seeds -np.array([size[0],0.,0.]),seeds, seeds +np.array([size[0],0.,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]])))
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)
else:
weights_p = weights.flatten()
weights_p = weights
seeds_p = seeds
coords = grid_filters.cell_coord0(grid,size).reshape(-1,3,order='F')
coords = grid_filters.cell_coord0(grid,size).reshape(-1,3)
pool = multiprocessing.Pool(processes = int(Environment().options['DAMASK_NUM_THREADS']))
result = pool.map_async(partial(Geom._find_closest_seed,seeds_p,weights_p), [coord for coord in coords])
@ -396,7 +394,7 @@ class Geom:
perform a periodic tessellation. Defaults to True.
"""
coords = grid_filters.cell_coord0(grid,size).reshape(-1,3,order='F')
coords = grid_filters.cell_coord0(grid,size).reshape(-1,3)
KDTree = spatial.cKDTree(seeds,boxsize=size) if periodic else spatial.cKDTree(seeds)
devNull,microstructure = KDTree.query(coords)

16
python/damask/_result.py
View File

@ -111,7 +111,7 @@ class Result:
select from 'set', 'add', and 'del'
what : str
attribute to change (must be from self.selection)
datasets : list of str or Boolean
datasets : list of str or bool
name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
@ -203,7 +203,7 @@ class Result:
----------
what : str
attribute to change (must be from self.selection)
datasets : list of str or Boolean
datasets : list of str or bool
name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
@ -219,7 +219,7 @@ class Result:
----------
what : str
attribute to change (must be from self.selection)
datasets : list of str or Boolean
datasets : list of str or bool
name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
@ -235,7 +235,7 @@ class Result:
----------
what : str
attribute to change (must be from self.selection)
datasets : list of str or Boolean
datasets : list of str or bool
name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
@ -262,10 +262,10 @@ class Result:
datasets : iterable or str
component : int
homogenization component to consider for constituent data
tagged : Boolean
tagged : bool
tag Table.column name with '#component'
defaults to False
split : Boolean
split : bool
split Table by increment and return dictionary of Tables
defaults to True
@ -326,7 +326,7 @@ class Result:
Parameters
----------
datasets : iterable or str or Boolean
datasets : iterable or str or bool
Examples
--------
@ -460,7 +460,7 @@ class Result:
def cell_coordinates(self):
"""Return initial coordinates of the cell centers."""
if self.structured:
return grid_filters.cell_coord0(self.grid,self.size,self.origin).reshape(-1,3)
return grid_filters.cell_coord0(self.grid,self.size,self.origin).reshape(-1,3,order='F')
else:
with h5py.File(self.fname,'r') as f:
return f['geometry/x_c'][()]

130
python/damask/grid_filters.py
View File

@ -1,3 +1,17 @@
"""
Filters for operations on regular grids.
Notes
-----
The grids are defined as (x,y,z,...) where x is fastest and z is slowest.
This convention is consistent with the geom file format.
When converting to/from a plain list (e.g. storage in ASCII table),
the following operations are required for tensorial data:
D3 = D1.reshape(grid+(-1,),order='F').reshape(grid+(3,3))
D1 = D3.reshape(grid+(-1,)).reshape(-1,9,order='F')
"""
from scipy import spatial as _spatial
import numpy as _np
@ -7,8 +21,12 @@ def _ks(size,grid,first_order=False):
Parameters
----------
size : numpy.ndarray
size : numpy.ndarray of shape (3)
physical size of the periodic field.
grid : numpy.ndarray of shape (3)
number of grid points.
first_order : bool, optional
correction for first order derivatives, defaults to False.
"""
k_sk = _np.where(_np.arange(grid[0])>grid[0]//2,_np.arange(grid[0])-grid[0],_np.arange(grid[0]))/size[0]
@ -19,8 +37,7 @@ def _ks(size,grid,first_order=False):
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.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3)
return _np.stack(_np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij'), axis=-1)
def curl(size,field):
@ -29,8 +46,10 @@ def curl(size,field):
Parameters
----------
size : numpy.ndarray
size : numpy.ndarray of shape (3)
physical size of the periodic field.
field : numpy.ndarray of shape (:,:,:,3) or (:,:,:,3,3)
periodic field of which the curl is calculated.
"""
n = _np.prod(field.shape[3:])
@ -53,8 +72,10 @@ def divergence(size,field):
Parameters
----------
size : numpy.ndarray
size : numpy.ndarray of shape (3)
physical size of the periodic field.
field : numpy.ndarray of shape (:,:,:,3) or (:,:,:,3,3)
periodic field of which the divergence is calculated.
"""
n = _np.prod(field.shape[3:])
@ -69,12 +90,14 @@ def divergence(size,field):
def gradient(size,field):
"""
Calculate gradient of a vector or scalar field in Fourier space.
Calculate gradient of a scalar or vector field in Fourier space.
Parameters
----------
size : numpy.ndarray
size : numpy.ndarray of shape (3)
physical size of the periodic field.
field : numpy.ndarray of shape (:,:,:,1) or (:,:,:,3)
periodic field of which the gradient is calculated.
"""
n = _np.prod(field.shape[3:])
@ -93,9 +116,9 @@ def cell_coord0(grid,size,origin=_np.zeros(3)):
Parameters
----------
grid : numpy.ndarray
grid : numpy.ndarray of shape (3)
number of grid points.
size : numpy.ndarray
size : numpy.ndarray of shape (3)
physical size of the periodic field.
origin : numpy.ndarray, optional
physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
@ -103,7 +126,11 @@ def cell_coord0(grid,size,origin=_np.zeros(3)):
"""
start = origin + 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):
@ -112,7 +139,7 @@ def cell_displacement_fluct(size,F):
Parameters
----------
size : numpy.ndarray
size : numpy.ndarray of shape (3)
physical size of the periodic field.
F : numpy.ndarray
deformation gradient field.
@ -139,14 +166,14 @@ def cell_displacement_avg(size,F):
Parameters
----------
size : numpy.ndarray
size : numpy.ndarray of shape (3)
physical size of the periodic field.
F : numpy.ndarray
deformation gradient field.
"""
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):
@ -155,7 +182,7 @@ def cell_displacement(size,F):
Parameters
----------
size : numpy.ndarray
size : numpy.ndarray of shape (3)
physical size of the periodic field.
F : numpy.ndarray
deformation gradient field.
@ -170,25 +197,25 @@ def cell_coord(size,F,origin=_np.zeros(3)):
Parameters
----------
size : numpy.ndarray
size : numpy.ndarray of shape (3)
physical size of the periodic field.
F : numpy.ndarray
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].
"""
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):
"""
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
----------
coord0 : numpy.ndarray
array of undeformed cell coordinates.
coord0 : numpy.ndarray of shape (:,3)
undeformed cell coordinates.
ordered : bool, optional
expect coord0 data to be ordered (x fast, z slow).
@ -211,13 +238,13 @@ def cell_coord0_gridSizeOrigin(coord0,ordered=True):
start = origin + delta*.5
end = origin - delta*.5 + size
if not _np.allclose(coords[0],_np.linspace(start[0],end[0],grid[0])) and \
_np.allclose(coords[1],_np.linspace(start[1],end[1],grid[1])) and \
_np.allclose(coords[2],_np.linspace(start[2],end[2],grid[2])):
if not (_np.allclose(coords[0],_np.linspace(start[0],end[0],grid[0])) and \
_np.allclose(coords[1],_np.linspace(start[1],end[1],grid[1])) and \
_np.allclose(coords[2],_np.linspace(start[2],end[2],grid[2]))):
raise ValueError('Regular grid spacing violated.')
if ordered and not _np.allclose(coord0.reshape(tuple(grid[::-1])+(3,)),cell_coord0(grid,size,origin)):
raise ValueError('Input data is not a regular grid.')
if ordered and not _np.allclose(coord0.reshape(tuple(grid)+(3,),order='F'),cell_coord0(grid,size,origin)):
raise ValueError('Input data is not ordered (x fast, z slow).')
return (grid,size,origin)
@ -241,17 +268,18 @@ def node_coord0(grid,size,origin=_np.zeros(3)):
Parameters
----------
grid : numpy.ndarray
grid : numpy.ndarray of shape (3)
number of grid points.
size : numpy.ndarray
size : numpy.ndarray of shape (3)
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].
"""
return _np.mgrid[origin[0]:size[0]+origin[0]:(grid[0]+1)*1j,
origin[1]:size[1]+origin[1]:(grid[1]+1)*1j,
origin[2]:size[2]+origin[2]:(grid[2]+1)*1j].T
return _np.stack(_np.meshgrid(_np.linspace(origin[0],size[0]+origin[0],grid[0]+1),
_np.linspace(origin[1],size[1]+origin[1],grid[1]+1),
_np.linspace(origin[2],size[2]+origin[2],grid[2]+1),indexing = 'ij'),
axis = -1)
def node_displacement_fluct(size,F):
@ -260,7 +288,7 @@ def node_displacement_fluct(size,F):
Parameters
----------
size : numpy.ndarray
size : numpy.ndarray of shape (3)
physical size of the periodic field.
F : numpy.ndarray
deformation gradient field.
@ -275,14 +303,14 @@ def node_displacement_avg(size,F):
Parameters
----------
size : numpy.ndarray
size : numpy.ndarray of shape (3)
physical size of the periodic field.
F : numpy.ndarray
deformation gradient field.
"""
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):
@ -291,7 +319,7 @@ def node_displacement(size,F):
Parameters
----------
size : numpy.ndarray
size : numpy.ndarray of shape (3)
physical size of the periodic field.
F : numpy.ndarray
deformation gradient field.
@ -306,15 +334,15 @@ def node_coord(size,F,origin=_np.zeros(3)):
Parameters
----------
size : numpy.ndarray
size : numpy.ndarray of shape (3)
physical size of the periodic field.
F : numpy.ndarray
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].
"""
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):
@ -335,14 +363,14 @@ def node_2_cell(node_data):
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
----------
coord0 : numpy.ndarray
array of undeformed nodal coordinates.
coord0 : numpy.ndarray of shape (:,3)
undeformed nodal coordinates.
ordered : bool, optional
expect coord0 data to be ordered (x fast, z slow).
@ -357,13 +385,13 @@ def node_coord0_gridSizeOrigin(coord0,ordered=False):
if (grid+1).prod() != len(coord0):
raise ValueError('Data count {} does not match grid {}.'.format(len(coord0),grid))
if not _np.allclose(coords[0],_np.linspace(mincorner[0],maxcorner[0],grid[0]+1)) and \
_np.allclose(coords[1],_np.linspace(mincorner[1],maxcorner[1],grid[1]+1)) and \
_np.allclose(coords[2],_np.linspace(mincorner[2],maxcorner[2],grid[2]+1)):
if not (_np.allclose(coords[0],_np.linspace(mincorner[0],maxcorner[0],grid[0]+1)) and \
_np.allclose(coords[1],_np.linspace(mincorner[1],maxcorner[1],grid[1]+1)) and \
_np.allclose(coords[2],_np.linspace(mincorner[2],maxcorner[2],grid[2]+1))):
raise ValueError('Regular grid spacing violated.')
if ordered and not _np.allclose(coord0.reshape(tuple((grid+1)[::-1])+(3,)),node_coord0(grid,size,origin)):
raise ValueError('Input data is not a regular grid.')
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 ordered (x fast, z slow).')
return (grid,size,origin)
@ -374,15 +402,15 @@ def regrid(size,F,new_grid):
Parameters
----------
size : numpy.ndarray
size : numpy.ndarray of shape (3)
physical size
F : numpy.ndarray
F : numpy.ndarray of shape (:,:,:,3,3)
deformation gradient field
new_grid : numpy.ndarray
new_grid : numpy.ndarray of shape (3)
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_fluct(size,F)

49
python/tests/test_grid_filters.py
View File

@ -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])
@ -80,8 +80,43 @@ class TestGridFilters:
F = np.broadcast_to(np.random.random((3,3)), tuple(grid)+(3,3))
assert np.allclose(function(size,F),0.0)
@pytest.mark.parametrize('function',[grid_filters.coord0_check,
grid_filters.node_coord0_gridSizeOrigin,
grid_filters.cell_coord0_gridSizeOrigin])
def test_invalid_coordinates(self,function):
invalid_coordinates = np.random.random((np.random.randint(12,52),3))
with pytest.raises(ValueError):
function(invalid_coordinates)
@pytest.mark.parametrize('function',[grid_filters.node_coord0_gridSizeOrigin,
grid_filters.cell_coord0_gridSizeOrigin])
def test_uneven_spaced_coordinates(self,function):
start = np.random.random(3)
end = np.random.random(3)*10. + start
grid = np.random.randint(8,32,(3))
uneven = np.stack(np.meshgrid(np.logspace(start[0],end[0],grid[0]),
np.logspace(start[1],end[1],grid[1]),
np.logspace(start[2],end[2],grid[2]),indexing = 'ij'),
axis = -1).reshape((grid.prod(),3),order='F')
with pytest.raises(ValueError):
function(uneven)
@pytest.mark.parametrize('mode',[True,False])
@pytest.mark.parametrize('function',[grid_filters.node_coord0_gridSizeOrigin,
grid_filters.cell_coord0_gridSizeOrigin])
def test_unordered_coordinates(self,function,mode):
origin = np.random.random(3)
size = np.random.random(3)*10.+origin
grid = np.random.randint(8,32,(3))
unordered = grid_filters.node_coord0(grid,size,origin).reshape(-1,3)
if mode:
with pytest.raises(ValueError):
function(unordered,mode)
else:
function(unordered,mode)
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()))