"""Module containing non-deprecated functions borrowed from Numeric.

"""
import functools
import types
import warnings

import numpy as np
from . import multiarray as mu
from . import overrides
from . import umath as um
from . import numerictypes as nt
from ._asarray import asarray, array, asanyarray
from .multiarray import concatenate
from . import _methods

_dt_ = nt.sctype2char

# functions that are methods
__all__ = [
    'alen', 'all', 'alltrue', 'amax', 'amin', 'any', 'argmax',
    'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip',
    'compress', 'cumprod', 'cumproduct', 'cumsum', 'diagonal', 'mean',
    'ndim', 'nonzero', 'partition', 'prod', 'product', 'ptp', 'put',
    'ravel', 'repeat', 'reshape', 'resize', 'round_',
    'searchsorted', 'shape', 'size', 'sometrue', 'sort', 'squeeze',
    'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var',
]

_gentype = types.GeneratorType
# save away Python sum
_sum_ = sum

array_function_dispatch = functools.partial(
    overrides.array_function_dispatch, module='numpy')


# functions that are now methods
def _wrapit(obj, method, *args, **kwds):
    try:
        wrap = obj.__array_wrap__
    except AttributeError:
        wrap = None
    result = getattr(asarray(obj), method)(*args, **kwds)
    if wrap:
        if not isinstance(result, mu.ndarray):
            result = asarray(result)
        result = wrap(result)
    return result


def _wrapfunc(obj, method, *args, **kwds):
    bound = getattr(obj, method, None)
    if bound is None:
        return _wrapit(obj, method, *args, **kwds)

    try:
        return bound(*args, **kwds)
    except TypeError:
        # A TypeError occurs if the object does have such a method in its
        # class, but its signature is not identical to that of NumPy's. This
        # situation has occurred in the case of a downstream library like
        # 'pandas'.
        #
        # Call _wrapit from within the except clause to ensure a potential
        # exception has a traceback chain.
        return _wrapit(obj, method, *args, **kwds)


def _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs):
    passkwargs = {k: v for k, v in kwargs.items()
                  if v is not np._NoValue}

    if type(obj) is not mu.ndarray:
        try:
            reduction = getattr(obj, method)
        except AttributeError:
            pass
        else:
            # This branch is needed for reductions like any which don't
            # support a dtype.
            if dtype is not None:
                return reduction(axis=axis, dtype=dtype, out=out, **passkwargs)
            else:
                return reduction(axis=axis, out=out, **passkwargs)

    return ufunc.reduce(obj, axis, dtype, out, **passkwargs)


def _take_dispatcher(a, indices, axis=None, out=None, mode=None):
    return (a, out)


@array_function_dispatch(_take_dispatcher)
def take(a, indices, axis=None, out=None, mode='raise'):
    """
    Take elements from an array along an axis.

    When axis is not None, this function does the same thing as "fancy"
    indexing (indexing arrays using arrays); however, it can be easier to use
    if you need elements along a given axis. A call such as
    ``np.take(arr, indices, axis=3)`` is equivalent to
    ``arr[:,:,:,indices,...]``.

    Explained without fancy indexing, this is equivalent to the following use
    of `ndindex`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of
    indices::

        Ni, Nk = a.shape[:axis], a.shape[axis+1:]
        Nj = indices.shape
        for ii in ndindex(Ni):
            for jj in ndindex(Nj):
                for kk in ndindex(Nk):
                    out[ii + jj + kk] = a[ii + (indices[jj],) + kk]

    Parameters
    ----------
    a : array_like (Ni..., M, Nk...)
        The source array.
    indices : array_like (Nj...)
        The indices of the values to extract.

        .. versionadded:: 1.8.0

        Also allow scalars for indices.
    axis : int, optional
        The axis over which to select values. By default, the flattened
        input array is used.
    out : ndarray, optional (Ni..., Nj..., Nk...)
        If provided, the result will be placed in this array. It should
        be of the appropriate shape and dtype. Note that `out` is always
        buffered if `mode='raise'`; use other modes for better performance.
    mode : {'raise', 'wrap', 'clip'}, optional
        Specifies how out-of-bounds indices will behave.

        * 'raise' -- raise an error (default)
        * 'wrap' -- wrap around
        * 'clip' -- clip to the range

        'clip' mode means that all indices that are too large are replaced
        by the index that addresses the last element along that axis. Note
        that this disables indexing with negative numbers.

    Returns
    -------
    out : ndarray (Ni..., Nj..., Nk...)
        The returned array has the same type as `a`.

    See Also
    --------
    compress : Take elements using a boolean mask
    ndarray.take : equivalent method
    take_along_axis : Take elements by matching the array and the index arrays

    Notes
    -----

    By eliminating the inner loop in the description above, and using `s_` to
    build simple slice objects, `take` can be expressed  in terms of applying
    fancy indexing to each 1-d slice::

        Ni, Nk = a.shape[:axis], a.shape[axis+1:]
        for ii in ndindex(Ni):
            for kk in ndindex(Nj):
                out[ii + s_[...,] + kk] = a[ii + s_[:,] + kk][indices]

    For this reason, it is equivalent to (but faster than) the following use
    of `apply_along_axis`::

        out = np.apply_along_axis(lambda a_1d: a_1d[indices], axis, a)

    Examples
    --------
    >>> a = [4, 3, 5, 7, 6, 8]
    >>> indices = [0, 1, 4]
    >>> np.take(a, indices)
    array([4, 3, 6])

    In this example if `a` is an ndarray, "fancy" indexing can be used.

    >>> a = np.array(a)
    >>> a[indices]
    array([4, 3, 6])

    If `indices` is not one dimensional, the output also has these dimensions.

    >>> np.take(a, [[0, 1], [2, 3]])
    array([[4, 3],
           [5, 7]])
    """
    return _wrapfunc(a, 'take', indices, axis=axis, out=out, mode=mode)


def _reshape_dispatcher(a, newshape, order=None):
    return (a,)


# not deprecated --- copy if necessary, view otherwise
@array_function_dispatch(_reshape_dispatcher)
def reshape(a, newshape, order='C'):
    """
    Gives a new shape to an array without changing its data.

    Parameters
    ----------
    a : array_like
        Array to be reshaped.
    newshape : int or tuple of ints
        The new shape should be compatible with the original shape. If
        an integer, then the result will be a 1-D array of that length.
        One shape dimension can be -1. In this case, the value is
        inferred from the length of the array and remaining dimensions.
    order : {'C', 'F', 'A'}, optional
        Read the elements of `a` using this index order, and place the
        elements into the reshaped array using this index order.  'C'
        means to read / write the elements using C-like index order,
        with the last axis index changing fastest, back to the first
        axis index changing slowest. 'F' means to read / write the
        elements using Fortran-like index order, with the first index
        changing fastest, and the last index changing slowest. Note that
        the 'C' and 'F' options take no account of the memory layout of
        the underlying array, and only refer to the order of indexing.
        'A' means to read / write the elements in Fortran-like index
        order if `a` is Fortran *contiguous* in memory, C-like order
        otherwise.

    Returns
    -------
    reshaped_array : ndarray
        This will be a new view object if possible; otherwise, it will
        be a copy.  Note there is no guarantee of the *memory layout* (C- or
        Fortran- contiguous) of the returned array.

    See Also
    --------
    ndarray.reshape : Equivalent method.

    Notes
    -----
    It is not always possible to change the shape of an array without
    copying the data. If you want an error to be raised when the data is copied,
    you should assign the new shape to the shape attribute of the array::

     >>> a = np.zeros((10, 2))

     # A transpose makes the array non-contiguous
     >>> b = a.T

     # Taking a view makes it possible to modify the shape without modifying
     # the initial object.
     >>> c = b.view()
     >>> c.shape = (20)
     Traceback (most recent call last):
        ...
     AttributeError: Incompatible shape for in-place modification. Use
     `.reshape()` to make a copy with the desired shape.

    The `order` keyword gives the index ordering both for *fetching* the values
    from `a`, and then *placing* the values into the output array.
    For example, let's say you have an array:

    >>> a = np.arange(6).reshape((3, 2))
    >>> a
    array([[0, 1],
           [2, 3],
           [4, 5]])

    You can think of reshaping as first raveling the array (using the given
    index order), then inserting the elements from the raveled array into the
    new array using the same kind of index ordering as was used for the
    raveling.

    >>> np.reshape(a, (2, 3)) # C-like index ordering
    array([[0, 1, 2],
           [3, 4, 5]])
    >>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
    array([[0, 1, 2],
           [3, 4, 5]])
    >>> np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
    array([[0, 4, 3],
           [2, 1, 5]])
    >>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
    array([[0, 4, 3],
           [2, 1, 5]])

    Examples
    --------
    >>> a = np.array([[1,2,3], [4,5,6]])
    >>> np.reshape(a, 6)
    array([1, 2, 3, 4, 5, 6])
    >>> np.reshape(a, 6, order='F')
    array([1, 4, 2, 5, 3, 6])

    >>> np.reshape(a, (3,-1))       # the unspecified value is inferred to be 2
    array([[1, 2],
           [3, 4],
           [5, 6]])
    """
    return _wrapfunc(a, 'reshape', newshape, order=order)


def _choose_dispatcher(a, choices, out=None, mode=None):
    yield a
    yield from choices
    yield out


@array_function_dispatch(_choose_dispatcher)
def choose(a, choices, out=None, mode='raise'):
    """
    Construct an array from an index array and a set of arrays to choose from.

    First of all, if confused or uncertain, definitely look at the Examples -
    in its full generality, this function is less simple than it might
    seem from the following code description (below ndi =
    `numpy.lib.index_tricks`):

    ``np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])``.

    But this omits some subtleties.  Here is a fully general summary:

    Given an "index" array (`a`) of integers and a sequence of `n` arrays
    (`choices`), `a` and each choice array are first broadcast, as necessary,
    to arrays of a common shape; calling these *Ba* and *Bchoices[i], i =
    0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape``
    for each `i`.  Then, a new array with shape ``Ba.shape`` is created as
    follows:

    * if ``mode=raise`` (the default), then, first of all, each element of
      `a` (and thus `Ba`) must be in the range `[0, n-1]`; now, suppose that
      `i` (in that range) is the value at the `(j0, j1, ..., jm)` position
      in `Ba` - then the value at the same position in the new array is the
      value in `Bchoices[i]` at that same position;

    * if ``mode=wrap``, values in `a` (and thus `Ba`) may be any (signed)
      integer; modular arithmetic is used to map integers outside the range
      `[0, n-1]` back into that range; and then the new array is constructed
      as above;

    * if ``mode=clip``, values in `a` (and thus `Ba`) may be any (signed)
      integer; negative integers are mapped to 0; values greater than `n-1`
      are mapped to `n-1`; and then the new array is constructed as above.

    Parameters
    ----------