Hi all,
I'm working with an exodus file which has stress (S) as a 6 component
array (obviously a symmetric rank 2 tensor in reality). I want to find
the principal stresses and directions and think a programmable filter is
the best way to do it.
I have tried many variations on the code below without success. Could
someone point me to a similar example?
Thanks,
Pete
My code is:
-----
def process_block(input_block, output_block):
S = input_block.PointData['S']
#SP =
eigenvalue(array([[S[:,0],S[:,3],S[:,5],S[:,3],S[:,1],S[:,4],S[:,5],S[:,4],S[:,2]]]))
#SP =
eigenvalue(array([S[:,0],S[:,3],S[:,5],S[:,3],S[:,1],S[:,4],S[:,5],S[:,4],S[:,2]]))
SP =
eigenvalue(array([[S[:,0],S[:,3],S[:,5]],[S[:,3],S[:,1],S[:,4]],[S[:,5],S[:,4],S[:,2]]]))
output_block.PointData.append(SP, "SP")
from paraview.vtk.dataset_adapter import MultiCompositeDataIterator
iter = MultiCompositeDataIterator([inputs[0], output])
for input_block, output_block in iter:
process_block(input_block, output_block)
------
The error message is:
------
Traceback (most recent call last):
File "<string>", line 21, in <module>
File "<string>", line 16, in RequestData
File "<string>", line 8, in process_block
File
"/usr/lib64/paraview-4.0/site-packages/paraview/vtk/algorithms.py", line
293, in eigenvalue
return _matrix_math_filter(narray, "Eigenvalue")
File
"/usr/lib64/paraview-4.0/site-packages/paraview/vtk/algorithms.py", line
129, in _matrix_math_filter
dataset = narray.DataSet()
AttributeError: 'numpy.ndarray' object has no attribute 'DataSet'
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