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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 corners of reconstructed (combatible) volume element and the vectors calculated by deforming
the initial volume element with the current deformation gradient. the initial volume element with the current deformation gradient.
""" """
sMismatch = np.empty(F.shape[:3])
#--------------------------------------------------------------------------------------------------
# initial positions
delta = size/grid*.5 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))))
#-------------------------------------------------------------------------------------------------- return + np.linalg.norm(nodes[:-1,:-1,:-1] -centres - np.dot(F,delta * np.array((-1,-1,-1))),axis=-1)\
# compare deformed original and deformed positions to actual positions + np.linalg.norm(nodes[+1:,:-1,:-1] -centres - np.dot(F,delta * np.array((+1,-1,-1))),axis=-1)\
for k in range(grid[0]): + np.linalg.norm(nodes[+1:,+1:,:-1] -centres - np.dot(F,delta * np.array((+1,+1,-1))),axis=-1)\
for j in range(grid[1]): + np.linalg.norm(nodes[:-1,+1:,:-1] -centres - np.dot(F,delta * np.array((-1,+1,-1))),axis=-1)\
for i in range(grid[2]): + np.linalg.norm(nodes[:-1,:-1,+1:] -centres - np.dot(F,delta * np.array((-1,-1,+1))),axis=-1)\
sMismatch[k,j,i] = \ + np.linalg.norm(nodes[+1:,:-1,+1:] -centres - np.dot(F,delta * np.array((+1,-1,+1))),axis=-1)\
+ 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[+1:,+1:,+1:] -centres - np.dot(F,delta * np.array((+1,+1,+1))),axis=-1)\
+ 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[:-1,+1:,+1:] -centres - np.dot(F,delta * np.array((-1,+1,+1))),axis=-1)
+ 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
# -------------------------------------------------------------------- # --------------------------------------------------------------------