copying only upper triangular values of sympy Matrix into array from numpy.triu()?
numpy, python, sympy
Solution
Give a numpy array, this is an easy operation using `numpy.triu_indices(N, k=0)` where N is the size of the square array:
In [28]: B = np.array([[4, 0, 3], [2, 4, -2], [-2, -3, 7]])
In [29]: B[np.triu_indices(B.shape[0])]
Out[29]: array([ 4, 0, 3, 4, -2, 7])
The `B.shape[0]` is just there in case you don't want to hard code the size of the array (3).
Given a sympy Matrix, this isn't as easy, but close enough. Just convert to a numpy array and make sure you change the `dtype` from `object`. This should work well if your matrices are reasonably sized. If they get really big, you might want to rethink this.
In [36]: A = sp.Matrix([[4, 0, 3], [2, 4, -2], [-2, -3, 7]])
# you can change the dtype of the new array to match the first array
# e.g., .astype(int), .astype(sp.Symbol)
# or you can just leave the default (dtype=object)
In [37]: C = np.array(A) #.astype(new_dtype)
In [38]: C[np.triu_indices(C.shape[0])]
Out[38]: array([ 4, 0, 3, 4, -2, 7])
To get them into just a plain list, do
In [39]: C[np.triu_indices(C.shape[0])].tolist()
Out[39]: [4, 0, 3, 4, -2, 7]
Problem
I have a square matrix A (could be any size) and I want to take the upper triangular part and place those values in an array without the values below the center diagonal (k=0). ``` A = sympy.Matrix([[ 4, 0, 3], [ 2, 4, -2], [-2, -3, 7]]) ``` using A_upper = numpy.triu(A) gets me to ``` A_Upper = sympy.Matrix([[ 4, 0, 3], [ 0, 4, -2], [ 0, 0, 7]]) ``` but from here how would I copy only the upper triangular elements into a simply array? Such as: ``` [4, 0, 3, 4, -2, 7] ``` I was going to just iterate though and copy all non-zero elements, however zero's in the upper triangular are allowed.