Merge remote-tracking branch 'origin/development' into even-more-HDF5-postprocessing
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commit
de316f1afe
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@ -13,7 +13,7 @@ scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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scriptID = ' '.join([scriptName,damask.version])
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def operator(stretch,strain,eigenvalues):
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def operator(stretch,strain,eigenvalues):
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"""Albrecht Bertram: Elasticity and Plasticity of Large Deformations An Introduction (3rd Edition, 2012), p. 102"""
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"""Albrecht Bertram: Elasticity and Plasticity of Large Deformations An Introduction (3rd Edition, 2012), p. 102."""
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return {
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return {
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'V#ln': np.log(eigenvalues) ,
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'V#ln': np.log(eigenvalues) ,
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'U#ln': np.log(eigenvalues) ,
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'U#ln': np.log(eigenvalues) ,
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@ -88,7 +88,7 @@ for name in filenames:
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try:
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try:
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table = damask.ASCIItable(name = name,
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table = damask.ASCIItable(name = name,
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buffered = False)
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buffered = False)
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except: continue
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except IOError: continue
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damask.util.report(scriptName,name)
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damask.util.report(scriptName,name)
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# ------------------------------------------ read header ------------------------------------------
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# ------------------------------------------ read header ------------------------------------------
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@ -136,23 +136,19 @@ for name in filenames:
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for column in items['tensor']['column']: # loop over all requested defgrads
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for column in items['tensor']['column']: # loop over all requested defgrads
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F = np.array(list(map(float,table.data[column:column+items['tensor']['dim']])),'d').reshape(items['tensor']['shape'])
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F = np.array(list(map(float,table.data[column:column+items['tensor']['dim']])),'d').reshape(items['tensor']['shape'])
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(U,S,Vh) = np.linalg.svd(F) # singular value decomposition
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(U,S,Vh) = np.linalg.svd(F) # singular value decomposition
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R_inv = np.linalg.inv(np.dot(U,Vh)) # inverse rotation of polar decomposition
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R_inv = np.dot(U,Vh).T # rotation of polar decomposition
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stretch['U'] = np.dot(R_inv,F) # F = RU
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stretch['U'] = np.dot(R_inv,F) # F = RU
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stretch['V'] = np.dot(F,R_inv) # F = VR
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stretch['V'] = np.dot(F,R_inv) # F = VR
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for theStretch in stretches:
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for theStretch in stretches:
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stretch[theStretch] = np.where(abs(stretch[theStretch]) < 1e-12, 0, stretch[theStretch]) # kill nasty noisy data
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stretch[theStretch] = np.where(abs(stretch[theStretch]) < 1e-12, 0, stretch[theStretch]) # kill nasty noisy data
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(D,V) = np.linalg.eig(stretch[theStretch]) # eigen decomposition (of symmetric matrix)
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(D,V) = np.linalg.eigh((stretch[theStretch]+stretch[theStretch].T)*0.5) # eigen decomposition (of symmetric(ed) matrix)
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neg = np.where(D < 0.0) # find negative eigenvalues ...
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neg = np.where(D < 0.0) # find negative eigenvalues ...
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D[neg] *= -1. # ... flip value ...
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D[neg] *= -1. # ... flip value ...
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V[:,neg] *= -1. # ... and vector
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V[:,neg] *= -1. # ... and vector
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for i,eigval in enumerate(D):
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if np.dot(V[:,i],V[:,(i+1)%3]) != 0.0: # check each vector for orthogonality
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V[:,(i+1)%3] = np.cross(V[:,(i+2)%3],V[:,i]) # correct next vector
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V[:,(i+1)%3] /= np.sqrt(np.dot(V[:,(i+1)%3],V[:,(i+1)%3].conj())) # and renormalize (hyperphobic?)
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for theStrain in strains:
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for theStrain in strains:
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d = operator(theStretch,theStrain,D) # operate on eigenvalues of U or V
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d = operator(theStretch,theStrain,D) # operate on eigenvalues of U or V
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eps = (np.dot(V,np.dot(np.diag(d),V.T)).real).reshape(9) # build tensor back from eigenvalue/vector basis
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eps = np.dot(V,np.dot(np.diag(d),V.T)).reshape(9) # build tensor back from eigenvalue/vector basis
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table.data_append(list(eps))
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table.data_append(list(eps))
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