from __future__ import division, print_function, absolute_import

import numpy as np
from numpy.linalg import LinAlgError
from .blas import get_blas_funcs
from .lapack import get_lapack_funcs

__all__ = ['LinAlgError', 'norm']


def norm(a, ord=None):
    """
    Matrix or vector norm.

    This function is able to return one of seven different matrix norms,
    or one of an infinite number of vector norms (described below), depending
    on the value of the ``ord`` parameter.

    Parameters
    ----------
    a : (M,) or (M, N) array_like
        Input array.
    ord : {non-zero int, inf, -inf, 'fro'}, optional
        Order of the norm (see table under ``Notes``). inf means numpy's
        `inf` object.

    Returns
    -------
    norm : float
        Norm of the matrix or vector.

    Notes
    -----
    For values of ``ord <= 0``, the result is, strictly speaking, not a
    mathematical 'norm', but it may still be useful for various numerical
    purposes.

    The following norms can be calculated:

    =====  ============================  ==========================
    ord    norm for matrices             norm for vectors
    =====  ============================  ==========================
    None   Frobenius norm                2-norm
    'fro'  Frobenius norm                --
    inf    max(sum(abs(x), axis=1))      max(abs(x))
    -inf   min(sum(abs(x), axis=1))      min(abs(x))
    0      --                            sum(x != 0)
    1      max(sum(abs(x), axis=0))      as below
    -1     min(sum(abs(x), axis=0))      as below
    2      2-norm (largest sing. value)  as below
    -2     smallest singular value       as below
    other  --                            sum(abs(x)**ord)**(1./ord)
    =====  ============================  ==========================

    The Frobenius norm is given by [1]_:

        :math:`||A||_F = [\\sum_{i,j} abs(a_{i,j})^2]^{1/2}`

    References
    ----------
    .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
           Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15

    Examples
    --------
    >>> from scipy.linalg import norm
    >>> a = np.arange(9) - 4
    >>> a
    array([-4, -3, -2, -1,  0,  1,  2,  3,  4])
    >>> b = a.reshape((3, 3))
    >>> b
    array([[-4, -3, -2],
           [-1,  0,  1],
           [ 2,  3,  4]])

    >>> norm(a)
    7.745966692414834
    >>> norm(b)
    7.745966692414834
    >>> norm(b, 'fro')
    7.745966692414834
    >>> norm(a, np.inf)
    4
    >>> norm(b, np.inf)
    9
    >>> norm(a, -np.inf)
    0
    >>> norm(b, -np.inf)
    2

    >>> norm(a, 1)
    20
    >>> norm(b, 1)
    7
    >>> norm(a, -1)
    -4.6566128774142013e-010
    >>> norm(b, -1)
    6
    >>> norm(a, 2)
    7.745966692414834
    >>> norm(b, 2)
    7.3484692283495345

    >>> norm(a, -2)
    nan
    >>> norm(b, -2)
    1.8570331885190563e-016
    >>> norm(a, 3)
    5.8480354764257312
    >>> norm(a, -3)
    nan

    """
    # Differs from numpy only in non-finite handling and the use of blas.
    a = np.asarray_chkfinite(a)
    if a.dtype.char in 'fdFD':
        if ord in (None, 2) and (a.ndim == 1):
            # use blas for fast and stable euclidean norm
            nrm2 = get_blas_funcs('nrm2', dtype=a.dtype)
            return nrm2(a)

        if a.ndim == 2:
            # Use lapack for a couple fast matrix norms.
            # For some reason the *lange frobenius norm is slow.
            lange_args = None
            if ord == 1:
                if np.isfortran(a):
                    lange_args = '1', a
                elif np.isfortran(a.T):
                    lange_args = 'i', a.T
            elif ord == np.inf:
                if np.isfortran(a):
                    lange_args = 'i', a
                elif np.isfortran(a.T):
                    lange_args = '1', a.T
            if lange_args:
                lange = get_lapack_funcs('lange', dtype=a.dtype)
                return lange(*lange_args)

    return np.linalg.norm(a, ord=ord)


def _datacopied(arr, original):
    """
    Strict check for `arr` not sharing any data with `original`,
    under the assumption that arr = asarray(original)

    """
    if arr is original:
        return False
    if not isinstance(original, np.ndarray) and hasattr(original, '__array__'):
        return False
    return arr.base is None