# scipy.linalg.issymmetric#

scipy.linalg.issymmetric(a, atol=None, rtol=None)#

Check if a square 2D array is symmetric.

Parameters
andarray

Input array of size (N, N).

atolfloat, optional

Absolute error bound

rtolfloat, optional

Relative error bound

Returns
symbool

Returns True if the array symmetric.

Raises
TypeError

If the dtype of the array is not supported, in particular, NumPy float16, float128 and complex256 dtypes for exact comparisons.

`ishermitian`

Check if a square 2D array is Hermitian

Notes

For square empty arrays the result is returned True by convention. Complex valued arrays are tested for symmetricity and not for being Hermitian (see examples)

The diagonal of the array is not scanned. Thus if there are infs, NaNs or similar problematic entries on the diagonal, they will be ignored. However, `numpy.inf` will be treated as a number, that is to say ```[[1, inf], [inf, 2]]``` will return `True`. On the other hand `numpy.NaN` is never symmetric, say, `[[1, nan], [nan, 2]]` will return `False`.

When `atol` and/or `rtol` are set to , then the comparison is performed by `numpy.allclose` and the tolerance values are passed to it. Otherwise an exact comparison against zero is performed by internal functions. Hence performance can improve or degrade depending on the size and dtype of the array. If one of `atol` or `rtol` given the other one is automatically set to zero.

Examples

```>>> from scipy.linalg import issymmetric
>>> A = np.arange(9).reshape(3, 3)
>>> A = A + A.T
>>> issymmetric(A)
True
>>> Ac = np.array([[1. + 1.j, 3.j], [3.j, 2.]])
>>> issymmetric(Ac)  # not Hermitian but symmetric
True
```