Hi Andy,
ok - try to do the job with a programmable filter:
importnumpyasnp
fromnumpyimportlinalg asLA
fromparaview.numpy_supportimportvtk_to_numpy
input0 =inputs[0]
# Test NumPy
w, v =LA.eig(np.diag((1, 2, 3)))
printw
printv
npArr =vtk_to_numpy(input0.PointData["val"])
The test wit
Hi,
The best way to do this would be in a programmable filter. I'm guessing
numpy has some method to compute eigenvalues for non-symmetric matrices.
Beyond that adding the capability to the Python calculator or some other
filter would be needed.
On Tue, Jun 13, 2017 at 3:35 AM, Stefan Melber w
Hi Andy,
i think i found the reason: i need the computation of (unsymmetrical)
eigenvalues for lambda2 - however it seems that the function
"eingenvalue" calculates only symmetric ones (see
https://www.paraview.org/ParaView/Doc/Nightly/www/py-doc/paraview.vtk.numpy_interface.algorithms.html).
Hi Andy,
unfortunately my comparison values (correct lambda2) does not have an
results in between to compare with. Regarding the gradients i am sure
they are ok. For this dataset i calculated other stuff based on the
gradients and thats ok. Maybe the error is in the syntax or the usage of
the
Hi,
Maybe compute each portion manually and check that against the correct
values. Also, verifying the gradient calculation is correct is another
thing to look at. If the grid isn't specified properly then the gradient
operation will likely be wrong.
On Fri, Jun 9, 2017 at 8:46 AM, Stefan Melber
