stretch is symmetric (play it safe here)
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@ -142,17 +142,17 @@ for name in filenames:
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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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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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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.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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V[:,(i+1)%3] /= np.sqrt(np.dot(V[:,(i+1)%3],V[:,(i+1)%3])) # 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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