from __future__ import division, absolute_import, print_function

import functools
import sys
import math

import numpy.core.numeric as _nx
from numpy.core.numeric import (
    asarray, ScalarType, array, alltrue, cumprod, arange, ndim
    )
from numpy.core.numerictypes import find_common_type, issubdtype

import numpy.matrixlib as matrixlib
from .function_base import diff
from numpy.core.multiarray import ravel_multi_index, unravel_index
from numpy.core.overrides import set_module
from numpy.core import overrides, linspace
from numpy.lib.stride_tricks import as_strided


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


__all__ = [
    'ravel_multi_index', 'unravel_index', 'mgrid', 'ogrid', 'r_', 'c_',
    's_', 'index_exp', 'ix_', 'ndenumerate', 'ndindex', 'fill_diagonal',
    'diag_indices', 'diag_indices_from'
    ]


def _ix__dispatcher(*args):
    return args


@array_function_dispatch(_ix__dispatcher)
def ix_(*args):
    """
    Construct an open mesh from multiple sequences.

    This function takes N 1-D sequences and returns N outputs with N
    dimensions each, such that the shape is 1 in all but one dimension
    and the dimension with the non-unit shape value cycles through all
    N dimensions.

    Using `ix_` one can quickly construct index arrays that will index
    the cross product. ``a[np.ix_([1,3],[2,5])]`` returns the array
    ``[[a[1,2] a[1,5]], [a[3,2] a[3,5]]]``.

    Parameters
    ----------
    args : 1-D sequences
        Each sequence should be of integer or boolean type.
        Boolean sequences will be interpreted as boolean masks for the
        corresponding dimension (equivalent to passing in
        ``np.nonzero(boolean_sequence)``).

    Returns
    -------
    out : tuple of ndarrays
        N arrays with N dimensions each, with N the number of input
        sequences. Together these arrays form an open mesh.

    See Also
    --------
    ogrid, mgrid, meshgrid

    Examples
    --------
    >>> a = np.arange(10).reshape(2, 5)
    >>> a
    array([[0, 1, 2, 3, 4],
           [5, 6, 7, 8, 9]])
    >>> ixgrid = np.ix_([0, 1], [2, 4])
    >>> ixgrid
    (array([[0],
           [1]]), array([[2, 4]]))
    >>> ixgrid[0].shape, ixgrid[1].shape
    ((2, 1), (1, 2))
    >>> a[ixgrid]
    array([[2, 4],
           [7, 9]])

    >>> ixgrid = np.ix_([True, True], [2, 4])
    >>> a[ixgrid]
    array([[2, 4],
           [7, 9]])
    >>> ixgrid = np.ix_([True, True], [False, False, True, False, True])
    >>> a[ixgrid]
    array([[2, 4],
           [7, 9]])

    """
    out = []
    nd = len(args)
    for k, new in enumerate(args):
        new = asarray(new)
        if new.ndim != 1:
            raise ValueError("Cross index must be 1 dimensional")
        if new.size == 0:
            # Explicitly type empty arrays to avoid float default
            new = new.astype(_nx.intp)
        if issubdtype(new.dtype, _nx.bool_):
            new, = new.nonzero()
        new = new.reshape((1,)*k + (new.size,) + (1,)*(nd-k-1))
        out.append(new)
    return tuple(out)

class nd_grid(object):
    """
    Construct a multi-dimensional "meshgrid".

    ``grid = nd_grid()`` creates an instance which will return a mesh-grid
    when indexed.  The dimension and number of the output arrays are equal
    to the number of indexing dimensions.  If the step length is not a
    complex number, then the stop is not inclusive.

    However, if the step length is a **complex number** (e.g. 5j), then the
    integer part of its magnitude is interpreted as specifying the
    number of points to create between the start and stop values, where
    the stop value **is inclusive**.

    If instantiated with an argument of ``sparse=True``, the mesh-grid is
    open (or not fleshed out) so that only one-dimension of each returned
    argument is greater than 1.

    Parameters
    ----------
    sparse : bool, optional
        Whether the grid is sparse or not. Default is False.

    Notes
    -----
    Two instances of `nd_grid` are made available in the NumPy namespace,
    `mgrid` and `ogrid`, approximately defined as::

        mgrid = nd_grid(sparse=False)
        ogrid = nd_grid(sparse=True)

    Users should use these pre-defined instances instead of using `nd_grid`
    directly.
    """

    def __init__(self, sparse=False):
        self.sparse = sparse

    def __getitem__(self, key):
        try:
            size = []
            typ = int
            for k in range(len(key)):
                step = key[k].step
                start = key[k].start
                if start is None:
                    start = 0
                if step is None:
                    step = 1
                if isinstance(step, complex):
                    size.append(int(abs(step)))
                    typ = float
                else:
                    size.append(
                        int(math.ceil((key[k].stop - start)/(step*1.0))))
                if (isinstance(step, float) or
                        isinstance(start, float) or
                        isinstance(key[k].stop, float)):
                    typ = float
            if self.sparse:
                nn = [_nx.arange(_x, dtype=_t)
                        for _x, _t in zip(size, (typ,)*len(size))]
            else:
                nn = _nx.indices(size, typ)
            for k in range(len(size)):
                step = key[k].step
                start = key[k].start
                if start is None:
                    start = 0
                if step is None:
                    step = 1
                if isinstance(step, complex):
                    step = int(abs(step))
                    if step != 1:
                        step = (key[k].stop - start)/float(step-1)
                nn[k] = (nn[k]*step+start)
            if self.sparse:
                slobj = [_nx.newaxis]*len(size)
                for k in range(len(size)):
                    slobj[k] = slice(None, None)
                    nn[k] = nn[k][tuple(slobj)]
                    slobj[k] = _nx.newaxis
            return nn
        except (IndexError, TypeError):
            step = key.step
            stop = key.stop
            start = key.start
            if start is None:
                start = 0
            if isinstance(step, complex):
                step = abs(step)
                length = int(step)
                if step != 1:
                    step = (key.stop-start)/float(step-1)
                stop = key.stop + step
                return _nx.arange(0, length, 1, float)*step + start
            else:
                return _nx.arange(start, stop, step)


class MGridClass(nd_grid):
    """
    `nd_grid` instance which returns a dense multi-dimensional "meshgrid".

    An instance of `numpy.lib.index_tricks.nd_grid` which returns an dense
    (or fleshed out) mesh-grid when indexed, so that each returned argument
    has the same shape.  The dimensions and number of the output arrays are
    equal to the number of indexing dimensions.  If the step length is not a
    complex number, then the stop is not inclusive.

    However, if the step length is a **complex number** (e.g. 5j), then
    the integer part of its magnitude is interpreted as specifying the
    number of points to create between the start and stop values, where
    the stop value **is inclusive**.

    Returns
    ----------
    mesh-grid `ndarrays` all of the same dimensions

    See Also
    --------
    numpy.lib.index_tricks.nd_grid : class of `ogrid` and `mgrid` objects
    ogrid : like mgrid but returns open (not fleshed out) mesh grids
    r_ : array concatenator

    Examples
    --------
    >>> np.mgrid[0:5,0:5]
    array([[[0, 0, 0, 0, 0],
            [1, 1, 1, 1, 1],
            [2, 2, 2, 2, 2],
            [3, 3, 3, 3, 3],
            [4, 4, 4, 4, 4]],
           [[0, 1, 2, 3, 4],
            [0, 1, 2, 3, 4],
            [0, 1, 2, 3, 4],
            [0, 1, 2, 3, 4],
            [0, 1, 2, 3, 4]]])
    >>> np.mgrid[-1:1:5j]
    array([-1. , -0.5,  0. ,  0.5,  1. ])

    """
    def __init__(self):
        super(MGridClass, self).__init__(sparse=False)

mgrid = MGridClass()

class OGridClass(nd_grid):
    """
    `nd_grid` instance which returns an open multi-dimensional "meshgrid".

    An instance of `numpy.lib.index_tricks.nd_grid` which returns an open
    (i.e. not fleshed out) mesh-grid when indexed, so that only one dimension
    of each returned array is greater than 1.  The dimension and number of the
    output arrays are equal to the number of indexing dimensions.  If the step
    length is not a complex number, then the stop is not inclusive.

    However, if the step length is a **complex number** (e.g. 5j), then
    the integer part of its magnitude is interpreted as specifying the
    number of points to create between the start and stop values, where
    the stop value **is inclusive**.

    Returns
    ----------
    mesh-grid `ndarrays` with only one dimension :math:`\\neq 1`

    See Also
    --------
    np.lib.index_tricks.nd_grid : class of `ogrid` and `mgrid` objects
    mgrid : like `ogrid` but returns dense (or fleshed out) mesh grids
    r_ : array concatenator

    Examples
    --------
    >>> from numpy import ogrid
    >>> ogrid[-1:1:5j]
    array([-1. , -0.5,  0. ,  0.5,  1. ])
    >>> ogrid[0:5,0:5]
    [array([[0],
            [1],
            [2],
            [3],
            [4]]), array([[0, 1, 2, 3, 4]])]

    """
    def __init__(self):
        super(OGridClass, self).__init__(sparse=True)

ogrid = OGridClass()


class AxisConcatenator(object):
    """
    Translates slice objects to concatenation along an axis.

    For detailed documentation on usage, see `r_`.
    """
    # allow ma.mr_ to override this
    concatenate = staticmethod(_nx.concatenate)
    makemat = staticmethod(matrixlib.matrix)

    def __init__(self, axis=0, matrix=False, ndmin=1, trans1d=-1):
        self.axis = axis
        self.matrix = matrix
        self.trans1d = trans1d
        self.ndmin = ndmin

    def __getitem__(self, key):
        # handle matrix builder syntax
        if isinstance(key, str):
            frame = sys._getframe().f_back
            mymat = matrixlib.bmat(key, frame.f_globals, frame.f_locals)
            return mymat

        if not isinstance(key, tuple):
            key = (key,)

        # copy attributes, since they can be overridden in the first argument
        trans1d = self.trans1d
        ndmin = self.ndmin
        matrix = self.matrix
        axis = self.axis

        objs = []
        scalars = []
        arraytypes = []
        scalartypes = []

        for k, item in enumerate(key):
            scalar = False
            if isinstance(item, slice):
                step = item.step
                start = item.start
                stop = item.stop
                if start is None:
                    start = 0
                if step is None: