R not needed
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@ -136,9 +136,9 @@ 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 = np.dot(U,Vh) # 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.T,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.T) # 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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