def norm(ctx, x, p=2):
        r"""
        Gives the entrywise `p`-norm of an iterable *x*, i.e. the vector norm
        `\left(\sum_k |x_k|^p\right)^{1/p}`, for any given `1 \le p \le \infty`.

        Special cases:

        If *x* is not iterable, this just returns ``absmax(x)``.

        ``p=1`` gives the sum of absolute values.

        ``p=2`` is the standard Euclidean vector norm.

        ``p=inf`` gives the magnitude of the largest element.

        For *x* a matrix, ``p=2`` is the Frobenius norm.
        For operator matrix norms, use :func:`~mpmath.mnorm` instead.

        You can use the string 'inf' as well as float('inf') or mpf('inf')
        to specify the infinity norm.

        **Examples**

            >>> from mpmath import *
            >>> mp.dps = 15; mp.pretty = False
            >>> x = matrix([-10, 2, 100])
            >>> norm(x, 1)
            mpf('112.0')
            >>> norm(x, 2)
            mpf('100.5186549850325')
            >>> norm(x, inf)
            mpf('100.0')

        """
        try:
            iter(x)
        except TypeError:
            return ctx.absmax(x)
        if type(p) is not int:
            p = ctx.convert(p)
        if p == ctx.inf:
            return max(ctx.absmax(i) for i in x)
        elif p == 1:
            return ctx.fsum(x, absolute=1)
        elif p == 2:
            return ctx.sqrt(ctx.fsum(x, absolute=1, squared=1))
        elif p > 1:
            return ctx.nthroot(ctx.fsum(abs(i)**p for i in x), p)
        else:
            raise ValueError('p has to be >= 1')
dataset/ETHPy150Open fredrik-johansson/mpmath/mpmath/matrices/matrices.py/MatrixMethods.norm/original