Dear all,
I have the following code to extract the square root of a symmetric
second-order tensor.
from sympy import symbols, Matrix, mpmathimport numpy as np
F11, F12, F13, F21, F22, F23, F31, F32, F33 = symbols('F11, F12, F13, F21, F22,
F23, F31, F32, F33', real=True)
F = np.array([[F11, F
Hi,
mpmath is for multi-precision math, not symbolic math. It's expecting
numbers, not symbols. I don't think there's a square root routine for
symbolic matrices implemented.
Cheers,
Tim.
On 24 Nov 2014, at 14:05, Yuxiang Wang wrote:
Dear all,
I have the following code to extract the squ
Hi Tim,
Thank you for your response!
And thanks for letting me know that symbolic square root matrix routine was
not implemented.
I will try to do the following:
1) Get eigenvectors first;
2) Take square root of the eigenvalues;
3) Re-construct the square root matrix from the original eigenve
It seems sqrt(Matrix) (where sqrt is sympy.sqrt) is unevaluated, but
if you do Matrix(B)**(S(1)/2) it tries to evaluate it (but it hangs).
You shouldn't use NumPy if you are dealing with symbolic matrices.
There is no advantage to using NumPy at that point, vs. sympy.Matrix.
Aaron Meurer
On Mon,
Hi Aaron,
Thank you for the response!
I tried to use numpy.ndarray because I got other orders of tensors, so I
wanted to be consistent. But it seems that Matrix object can be
higher-order too, so I'll try to use them. Thanks for the tip!
Shawn
On Monday, November 24, 2014 3:22:34 PM UTC-5, Aa
To take the sqrt of each element in B, use B.applyfunc(sqrt).
On Monday, November 24, 2014 5:30:48 PM UTC-6, Yuxiang Wang wrote:
>
> Hi Aaron,
>
> Thank you for the response!
>
> I tried to use numpy.ndarray because I got other orders of tensors, so I
> wanted to be consistent. But it seems that
Chris,
Thanks for responding! I was actually doing the square-root of the matrix,
rather than the element-wise. But thanks still!
-Shawn
On Tuesday, November 25, 2014 3:33:43 PM UTC-5, Chris Smith wrote:
>
> To take the sqrt of each element in B, use B.applyfunc(sqrt).
>
> On Monday, November 2
