EIGVALSH

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The EIGVALSH node is based on a numpy or scipy function. The description of that function is as follows: Compute the eigenvalues of a complex Hermitian or real symmetric matrix. Main difference from eigh: the eigenvectors are not computed. Params: a : (..., M, M) array_like A complex- or real-valued matrix whose eigenvalues are to be computed. UPLO : {'L', 'U'} Specifies whether the calculation is done with the lower triangular part of 'a' ('L', default) or the upper triangular part ('U'). Irrespective of this value, only the real parts of the diagonal will be considered in the computation to preserve the notion of a Hermitian matrix. It therefore follows that the imaginary part of the diagonal will always be treated as zero. Returns: out : DataContainer type 'ordered pair', 'scalar', or 'matrix'

Python Code

from flojoy import flojoy, Matrix, Scalar
import numpy as np

import numpy.linalg


@flojoy
def EIGVALSH(
    default: Matrix,
    UPLO: str = "L",
) -> Matrix | Scalar:
    """The EIGVALSH node is based on a numpy or scipy function.

    The description of that function is as follows:

        Compute the eigenvalues of a complex Hermitian or real symmetric matrix.

    Main difference from eigh: the eigenvectors are not computed.

    Parameters
    ----------
    a : (..., M, M) array_like
        A complex- or real-valued matrix whose eigenvalues are to be computed.
    UPLO : {'L', 'U'}, optional
        Specifies whether the calculation is done with the lower triangular
        part of 'a' ('L', default) or the upper triangular part ('U').
        Irrespective of this value, only the real parts of the diagonal will
        be considered in the computation to preserve the notion of a Hermitian matrix.
        It therefore follows that the imaginary part of the diagonal will always
        be treated as zero.

    Returns
    -------
    DataContainer
        type 'ordered pair', 'scalar', or 'matrix'
    """

    result = numpy.linalg.eigvalsh(
        a=default.m,
        UPLO=UPLO,
    )

    if isinstance(result, np.ndarray):
        result = Matrix(m=result)
    else:
        assert isinstance(
            result, np.number | float | int
        ), f"Expected np.number, float or int for result, got {type(result)}"
        result = Scalar(c=float(result))

    return result

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