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DAMASK_EICMD
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vectorized

This commit is contained in:
Martin Diehl 2020-11-23 21:23:45 +01:00
parent 4e94118cfb
commit da654b7c91
1 changed files with 8 additions and 27 deletions
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35
processing/post/addCompatibilityMismatch.py
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@ -22,35 +22,16 @@ def shapeMismatch(size,F,nodes,centres):
the corners of reconstructed (combatible) volume element and the vectors calculated by deforming
the initial volume element with the current deformation gradient.
"""
sMismatch = np.empty(F.shape[:3])
#--------------------------------------------------------------------------------------------------
# initial positions
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[0]):
for j in range(grid[1]):
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+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, 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 ,j+1,i+1,0:3] - centres[k,j,i,0:3] - np.dot(F[k,j,i,:,:], coordsInitial[7,0:3]))
return sMismatch
return + np.linalg.norm(nodes[:-1,:-1,:-1] -centres - np.dot(F,delta * np.array((-1,-1,-1))),axis=-1)\
+ np.linalg.norm(nodes[+1:,:-1,:-1] -centres - np.dot(F,delta * np.array((+1,-1,-1))),axis=-1)\
+ np.linalg.norm(nodes[+1:,+1:,:-1] -centres - np.dot(F,delta * np.array((+1,+1,-1))),axis=-1)\
+ np.linalg.norm(nodes[:-1,+1:,:-1] -centres - np.dot(F,delta * np.array((-1,+1,-1))),axis=-1)\
+ np.linalg.norm(nodes[:-1,:-1,+1:] -centres - np.dot(F,delta * np.array((-1,-1,+1))),axis=-1)\
+ np.linalg.norm(nodes[+1:,:-1,+1:] -centres - np.dot(F,delta * np.array((+1,-1,+1))),axis=-1)\
+ np.linalg.norm(nodes[+1:,+1:,+1:] -centres - np.dot(F,delta * np.array((+1,+1,+1))),axis=-1)\
+ np.linalg.norm(nodes[:-1,+1:,+1:] -centres - np.dot(F,delta * np.array((-1,+1,+1))),axis=-1)
# --------------------------------------------------------------------