from __future__ import absolute_import, division, print_function

from bisect import bisect
from collections import Iterable, Mapping
from collections import Iterator
from functools import partial, wraps
from itertools import product
import math
from numbers import Number
import operator
from operator import add, getitem, mul
import os
import sys
import traceback
import pickle
from threading import Lock
import uuid
import warnings

try:
    from cytoolz.curried import (partition, concat, pluck, join, first,
                                 groupby, valmap, accumulate, assoc)

except ImportError:
    from toolz.curried import (partition, concat, pluck, join, first,
                               groupby, valmap, accumulate, assoc)
from toolz import pipe, map, reduce
import numpy as np

from . import chunk
from .slicing import slice_array, replace_ellipsis
from ..base import Base, tokenize, normalize_token
from ..context import _globals
from ..utils import (homogeneous_deepmap, ndeepmap, ignoring, concrete,
                     is_integer, IndexCallable, funcname, derived_from,
                     SerializableLock, ensure_dict, package_of)
from ..compatibility import unicode, long, getargspec, zip_longest, apply
from ..delayed import to_task_dask
from .. import threaded, core
from .. import sharedict
from ..sharedict import ShareDict


def getter(a, b, asarray=True, lock=None):
    if isinstance(b, tuple) and any(x is None for x in b):
        b2 = tuple(x for x in b if x is not None)
        b3 = tuple(None if x is None else slice(None, None)
                   for x in b if not isinstance(x, (int, long)))
        return getter(a, b2, asarray=asarray, lock=lock)[b3]

    if lock:
        lock.acquire()
    try:
        c = a[b]
        if asarray:
            c = np.asarray(c)
    finally:
        if lock:
            lock.release()
    return c


def getter_nofancy(a, b, asarray=True, lock=None):
    """ A simple wrapper around ``getter``.

    Used to indicate to the optimization passes that the backend doesn't
    support fancy indexing.
    """
    return getter(a, b, asarray=asarray, lock=lock)


def getter_inline(a, b, asarray=True, lock=None):
    """ A getter function that optimizations feel comfortable inlining

    Slicing operations with this function may be inlined into a graph, such as
    in the following rewrite

    **Before**

    >>> a = x[:10]  # doctest: +SKIP
    >>> b = a + 1  # doctest: +SKIP
    >>> c = a * 2  # doctest: +SKIP

    **After**

    >>> b = x[:10] + 1  # doctest: +SKIP
    >>> c = x[:10] * 2  # doctest: +SKIP

    This inlining can be relevant to operations when running off of disk.
    """
    return getter(a, b, asarray=asarray, lock=lock)


from .optimization import optimize, fuse_slice


def slices_from_chunks(chunks):
    """ Translate chunks tuple to a set of slices in product order

    >>> slices_from_chunks(((2, 2), (3, 3, 3)))  # doctest: +NORMALIZE_WHITESPACE
     [(slice(0, 2, None), slice(0, 3, None)),
      (slice(0, 2, None), slice(3, 6, None)),
      (slice(0, 2, None), slice(6, 9, None)),
      (slice(2, 4, None), slice(0, 3, None)),
      (slice(2, 4, None), slice(3, 6, None)),
      (slice(2, 4, None), slice(6, 9, None))]
    """
    cumdims = [list(accumulate(add, (0,) + bds[:-1])) for bds in chunks]
    shapes = product(*chunks)
    starts = product(*cumdims)
    return [tuple(slice(s, s + dim) for s, dim in zip(start, shape))
            for start, shape in zip(starts, shapes)]


def getem(arr, chunks, getitem=getter, shape=None, out_name=None, lock=False,
          asarray=True):
    """ Dask getting various chunks from an array-like

    >>> getem('X', chunks=(2, 3), shape=(4, 6))  # doctest: +SKIP
    {('X', 0, 0): (getter, 'X', (slice(0, 2), slice(0, 3))),
     ('X', 1, 0): (getter, 'X', (slice(2, 4), slice(0, 3))),
     ('X', 1, 1): (getter, 'X', (slice(2, 4), slice(3, 6))),
     ('X', 0, 1): (getter, 'X', (slice(0, 2), slice(3, 6)))}

    >>> getem('X', chunks=((2, 2), (3, 3)))  # doctest: +SKIP
    {('X', 0, 0): (getter, 'X', (slice(0, 2), slice(0, 3))),
     ('X', 1, 0): (getter, 'X', (slice(2, 4), slice(0, 3))),
     ('X', 1, 1): (getter, 'X', (slice(2, 4), slice(3, 6))),
     ('X', 0, 1): (getter, 'X', (slice(0, 2), slice(3, 6)))}
    """
    out_name = out_name or arr
    chunks = normalize_chunks(chunks, shape)

    keys = list(product([out_name], *[range(len(bds)) for bds in chunks]))
    slices = slices_from_chunks(chunks)

    if not asarray or lock:
        values = [(getitem, arr, x, asarray, lock) for x in slices]
    else:
        # Common case, drop extra parameters
        values = [(getitem, arr, x) for x in slices]

    return dict(zip(keys, values))


def dotmany(A, B, leftfunc=None, rightfunc=None, **kwargs):
    """ Dot product of many aligned chunks

    >>> x = np.array([[1, 2], [1, 2]])
    >>> y = np.array([[10, 20], [10, 20]])
    >>> dotmany([x, x, x], [y, y, y])
    array([[ 90, 180],
           [ 90, 180]])

    Optionally pass in functions to apply to the left and right chunks

    >>> dotmany([x, x, x], [y, y, y], rightfunc=np.transpose)
    array([[150, 150],
           [150, 150]])
    """
    if leftfunc:
        A = map(leftfunc, A)
    if rightfunc:
        B = map(rightfunc, B)
    return sum(map(partial(np.dot, **kwargs), A, B))


def lol_tuples(head, ind, values, dummies):
    """ List of list of tuple keys

    Parameters
    ----------

    head : tuple
        The known tuple so far
    ind : Iterable
        An iterable of indices not yet covered
    values : dict
        Known values for non-dummy indices
    dummies : dict
        Ranges of values for dummy indices

    Examples
    --------

    >>> lol_tuples(('x',), 'ij', {'i': 1, 'j': 0}, {})
    ('x', 1, 0)

    >>> lol_tuples(('x',), 'ij', {'i': 1}, {'j': range(3)})
    [('x', 1, 0), ('x', 1, 1), ('x', 1, 2)]

    >>> lol_tuples(('x',), 'ij', {'i': 1}, {'j': range(3)})
    [('x', 1, 0), ('x', 1, 1), ('x', 1, 2)]

    >>> lol_tuples(('x',), 'ijk', {'i': 1}, {'j': [0, 1, 2], 'k': [0, 1]}) # doctest: +NORMALIZE_WHITESPACE
    [[('x', 1, 0, 0), ('x', 1, 0, 1)],
     [('x', 1, 1, 0), ('x', 1, 1, 1)],
     [('x', 1, 2, 0), ('x', 1, 2, 1)]]
    """
    if not ind:
        return head
    if ind[0] not in dummies:
        return lol_tuples(head + (values[ind[0]],), ind[1:], values, dummies)
    else:
        return [lol_tuples(head + (v,), ind[1:], values, dummies)
                for v in dummies[ind[0]]]


def zero_broadcast_dimensions(lol, nblocks):
    """

    >>> lol = [('x', 1, 0), ('x', 1, 1), ('x', 1, 2)]
    >>> nblocks = (4, 1, 2)  # note singleton dimension in second place
    >>> lol = [[('x', 1, 0, 0), ('x', 1, 0, 1)],
    ...        [('x', 1, 1, 0), ('x', 1, 1, 1)],
    ...        [('x', 1, 2, 0), ('x', 1, 2, 1)]]

    >>> zero_broadcast_dimensions(lol, nblocks)  # doctest: +NORMALIZE_WHITESPACE
    [[('x', 1, 0, 0), ('x', 1, 0, 1)],
     [('x', 1, 0, 0), ('x', 1, 0, 1)],
     [('x', 1, 0, 0), ('x', 1, 0, 1)]]

    See Also
    --------
    lol_tuples
    """
    f = lambda t: (t[0],) + tuple(0 if d == 1 else i for i, d in zip(t[1:], nblocks))
    return homogeneous_deepmap(f, lol)


def broadcast_dimensions(argpairs, numblocks, sentinels=(1, (1,)),
                         consolidate=None):
    """ Find block dimensions from arguments

    Parameters
    ----------
    argpairs: iterable
        name, ijk index pairs
    numblocks: dict
        maps {name: number of blocks}
    sentinels: iterable (optional)
        values for singleton dimensions
    consolidate: func (optional)
        use this to reduce each set of common blocks into a smaller set

    Examples
    --------
    >>> argpairs = [('x', 'ij'), ('y', 'ji')]
    >>> numblocks = {'x': (2, 3), 'y': (3, 2)}
    >>> broadcast_dimensions(argpairs, numblocks)
    {'i': 2, 'j': 3}

    Supports numpy broadcasting rules

    >>> argpairs = [('x', 'ij'), ('y', 'ij')]
    >>> numblocks = {'x': (2, 1), 'y': (1, 3)}
    >>> broadcast_dimensions(argpairs, numblocks)
    {'i': 2, 'j': 3}

    Works in other contexts too

    >>> argpairs = [('x', 'ij'), ('y', 'ij')]
    >>> d = {'x': ('Hello', 1), 'y': (1, (2, 3))}
    >>> broadcast_dimensions(argpairs, d)
    {'i': 'Hello', 'j': (2, 3)}
    """
    # List like [('i', 2), ('j', 1), ('i', 1), ('j', 2)]
    L = concat([zip(inds, dims) for (x, inds), (x, dims)
                in join(first, argpairs, first, numblocks.items())])

    g = groupby(0, L)
    g = dict((k, set([d for i, d in v])) for k, v in g.items())

    g2 = dict((k, v - set(sentinels) if len(v) > 1 else v) for k, v in g.items())

    if consolidate:
        return valmap(consolidate, g2)

    if g2 and not set(map(len, g2.values())) == set([1]):
        raise ValueError("Shapes do not align %s" % g)

    return valmap(first, g2)


def top(func, output, out_indices, *arrind_pairs, **kwargs):
    """ Tensor operation

    Applies a function, ``func``, across blocks from many different input
    dasks.  We arrange the pattern with which those blocks interact with sets
    of matching indices.  E.g.::

        top(func, 'z', 'i', 'x', 'i', 'y', 'i')

    yield an embarrassingly parallel communication pattern and is read as

        $$ z_i = func(x_i, y_i) $$

    More complex patterns may emerge, including multiple indices::

        top(func, 'z', 'ij', 'x', 'ij', 'y', 'ji')

        $$ z_{ij} = func(x_{ij}, y_{ji}) $$

    Indices missing in the output but present in the inputs results in many
    inputs being sent to one function (see examples).

    Examples
    --------

    Simple embarrassing map operation

    >>> inc = lambda x: x + 1
    >>> top(inc, 'z', 'ij', 'x', 'ij', numblocks={'x': (2, 2)})  # doctest: +SKIP
    {('z', 0, 0): (inc, ('x', 0, 0)),
     ('z', 0, 1): (inc, ('x', 0, 1)),
     ('z', 1, 0): (inc, ('x', 1, 0)),
     ('z', 1, 1): (inc, ('x', 1, 1))}

    Simple operation on two datasets

    >>> add = lambda x, y: x + y
    >>> top(add, 'z', 'ij', 'x', 'ij', 'y', 'ij', numblocks={'x': (2, 2),
    ...                                                      'y': (2, 2)})  # doctest: +SKIP
    {('z', 0, 0): (add, ('x', 0, 0), ('y', 0, 0)),
     ('z', 0, 1): (add, ('x', 0, 1), ('y', 0, 1)),
     ('z', 1, 0): (add, ('x', 1, 0), ('y', 1, 0)),
     ('z', 1, 1): (add, ('x', 1, 1), ('y', 1, 1))}

    Operation that flips one of the datasets

    >>> addT = lambda x, y: x + y.T  # Transpose each chunk
    >>> #                                        z_ij ~ x_ij y_ji
    >>> #               ..         ..         .. notice swap
    >>> top(addT, 'z', 'ij', 'x', 'ij', 'y', 'ji', numblocks={'x': (2, 2),
    ...                                                       'y': (2, 2)})  # doctest: +SKIP
    {('z', 0, 0): (add, ('x', 0, 0), ('y', 0, 0)),
     ('z', 0, 1): (add, ('x', 0, 1), ('y', 1, 0)),
     ('z', 1, 0): (add, ('x', 1, 0), ('y', 0, 1)),
     ('z', 1, 1): (add, ('x', 1, 1), ('y', 1, 1))}

    Dot product with contraction over ``j`` index.  Yields list arguments

    >>> top(dotmany, 'z', 'ik', 'x', 'ij', 'y', 'jk', numblocks={'x': (2, 2),
    ...                                                          'y': (2, 2)})  # doctest: +SKIP
    {('z', 0, 0): (dotmany, [('x', 0, 0), ('x', 0, 1)],
                            [('y', 0, 0), ('y', 1, 0)]),
     ('z', 0, 1): (dotmany, [('x', 0, 0), ('x', 0, 1)],
                            [('y', 0, 1), ('y', 1, 1)]),
     ('z', 1, 0): (dotmany, [('x', 1, 0), ('x', 1, 1)],
                            [('y', 0, 0), ('y', 1, 0)]),
     ('z', 1, 1): (dotmany, [('x', 1, 0), ('x', 1, 1)],
                            [('y', 0, 1), ('y', 1, 1)])}

    Pass ``concatenate=True`` to concatenate arrays ahead of time

    >>> top(f, 'z', 'i', 'x', 'ij', 'y', 'ij', concatenate=True,
    ...     numblocks={'x': (2, 2), 'y': (2, 2,)})  # doctest: +SKIP
    {('z', 0): (f, (concatenate_axes, [('x', 0, 0), ('x', 0, 1)], (1,)),
                   (concatenate_axes, [('y', 0, 0), ('y', 0, 1)], (1,)))
     ('z', 1): (f, (concatenate_axes, [('x', 1, 0), ('x', 1, 1)], (1,)),
                   (concatenate_axes, [('y', 1, 0), ('y', 1, 1)], (1,)))}

    Supports Broadcasting rules

    >>> top(add, 'z', 'ij', 'x', 'ij', 'y', 'ij', numblocks={'x': (1, 2),
    ...                                                      'y': (2, 2)})  # doctest: +SKIP
    {('z', 0, 0): (add, ('x', 0, 0), ('y', 0, 0)),
     ('z', 0, 1): (add, ('x', 0, 1), ('y', 0, 1)),
     ('z', 1, 0): (add, ('x', 0, 0), ('y', 1, 0)),
     ('z', 1, 1): (add, ('x', 0, 1), ('y', 1, 1))}

    Support keyword arguments with apply

    >>> def f(a, b=0): return a + b
    >>> top(f, 'z', 'i', 'x', 'i', numblocks={'x': (2,)}, b=10)  # doctest: +SKIP
    {('z', 0): (apply, f, [('x', 0)], {'b': 10}),
     ('z', 1): (apply, f, [('x', 1)], {'b': 10})}

    See Also
    --------
    atop
    """
    numblocks = kwargs.pop('numblocks')
    concatenate = kwargs.pop('concatenate', None)
    new_axes = kwargs.pop('new_axes', {})
    argpairs = list(partition(2, arrind_pairs))

    assert set(numblocks) == set(pluck(0, argpairs))

    all_indices = pipe(argpairs, pluck(1), concat, set)
    dummy_indices = all_indices - set(out_indices)

    # Dictionary mapping {i: 3, j: 4, ...} for i, j, ... the dimensions
    dims = broadcast_dimensions(argpairs, numblocks)
    for k in new_axes:
        dims[k] = 1

    # (0, 0), (0, 1), (0, 2), (1, 0), ...
    keytups = list(product(*[range(dims[i]) for i in out_indices]))
    # {i: 0, j: 0}, {i: 0, j: 1}, ...
    keydicts = [dict(zip(out_indices, tup)) for tup in keytups]

    # {j: [1, 2, 3], ...}  For j a dummy index of dimension 3
    dummies = dict((i, list(range(dims[i]))) for i in dummy_indices)

    # Create argument lists
    valtups = []
    for kd in keydicts:
        args = []
        for arg, ind in argpairs:
            tups = lol_tuples((arg,), ind, kd, dummies)
            if any(nb == 1 for nb in numblocks[arg]):
                tups2 = zero_broadcast_dimensions(tups, numblocks[arg])
            else:
                tups2 = tups
            if concatenate and isinstance(tups2, list):
                axes = [n for n, i in enumerate(ind) if i in dummies]
                tups2 = (concatenate_axes, tups2, axes)
            args.append(tups2)
        valtups.append(args)

    if not kwargs:  # will not be used in an apply, should be a tuple
        valtups = [tuple(vt) for vt in valtups]

    # Add heads to tuples
    keys = [(output,) + kt for kt in keytups]

    dsk = {}
    # Unpack delayed objects in kwargs
    if kwargs:
        task, dsk2 = to_task_dask(kwargs)
        if dsk2:
            dsk.update(ensure_dict(dsk2))
            kwargs2 = task
        else:
            kwargs2 = kwargs
        vals = [(apply, func, vt, kwargs2) for vt in valtups]
    else:
        vals = [(func,) + vt for vt in valtups]

    dsk.update(dict(zip(keys, vals)))

    return dsk


def _concatenate2(arrays, axes=[]):
    """ Recursively Concatenate nested lists of arrays along axes

    Each entry in axes corresponds to each level of the nested list.  The
    length of axes should correspond to the level of nesting of arrays.

    >>> x = np.array([[1, 2], [3, 4]])
    >>> _concatenate2([x, x], axes=[0])
    array([[1, 2],
           [3, 4],
           [1, 2],
           [3, 4]])

    >>> _concatenate2([x, x], axes=[1])
    array([[1, 2, 1, 2],
           [3, 4, 3, 4]])

    >>> _concatenate2([[x, x], [x, x]], axes=[0, 1])
    array([[1, 2, 1, 2],
           [3, 4, 3, 4],
           [1, 2, 1, 2],
           [3, 4, 3, 4]])

    Supports Iterators
    >>> _concatenate2(iter([x, x]), axes=[1])
    array([[1, 2, 1, 2],
           [3, 4, 3, 4]])
    """
    if isinstance(arrays, Iterator):
        arrays = list(arrays)
    if not isinstance(arrays, (list, tuple)):
        return arrays
    if len(axes) > 1:
        arrays = [_concatenate2(a, axes=axes[1:]) for a in arrays]
    module = package_of(type(max(arrays, key=lambda x: x.__array_priority__))) or np
    return module.concatenate(arrays, axis=axes[0])


def apply_infer_dtype(func, args, kwargs, funcname, suggest_dtype=True):
    args = [np.ones((1,) * x.ndim, dtype=x.dtype)
            if isinstance(x, Array) else x for x in args]
    try:
        with np.errstate(all='ignore'):
            o = func(*args, **kwargs)
    except Exception as e:
        exc_type, exc_value, exc_traceback = sys.exc_info()
        tb = ''.join(traceback.format_tb(exc_traceback))
        suggest = ("Please specify the dtype explicitly using the "
                   "`dtype` kwarg.\n\n") if suggest_dtype else ""
        msg = ("`dtype` inference failed in `{0}`.\n\n"
               "{1}"
               "Original error is below:\n"
               "------------------------\n"
               "{2}\n\n"
               "Traceback:\n"
               "---------\n"
               "{3}").format(funcname, suggest, repr(e), tb)
    else:
        msg = None
    if msg is not None:
        raise ValueError(msg)
    return o.dtype


def map_blocks(func, *args, **kwargs):
    """ Map a function across all blocks of a dask array.

    Parameters
    ----------
    func : callable
        Function to apply to every block in the array.
    args : dask arrays or constants
    dtype : np.dtype, optional
        The ``dtype`` of the output array. It is recommended to provide this.
        If not provided, will be inferred by applying the function to a small
        set of fake data.
    chunks : tuple, optional
        Chunk shape of resulting blocks if the function does not preserve
        shape. If not provided, the resulting array is assumed to have the same
        block structure as the first input array.
    drop_axis : number or iterable, optional
        Dimensions lost by the function.
    new_axis : number or iterable, optional
        New dimensions created by the function. Note that these are applied
        after ``drop_axis`` (if present).
    token : string, optional
        The key prefix to use for the output array. If not provided, will be
        determined from the function name.
    name : string, optional
        The key name to use for the output array. Note that this fully
        specifies the output key name, and must be unique. If not provided,
        will be determined by a hash of the arguments.
    **kwargs :
        Other keyword arguments to pass to function. Values must be constants
        (not dask.arrays)

    Examples
    --------
    >>> import dask.array as da
    >>> x = da.arange(6, chunks=3)

    >>> x.map_blocks(lambda x: x * 2).compute()
    array([ 0,  2,  4,  6,  8, 10])

    The ``da.map_blocks`` function can also accept multiple arrays.

    >>> d = da.arange(5, chunks=2)
    >>> e = da.arange(5, chunks=2)

    >>> f = map_blocks(lambda a, b: a + b**2, d, e)
    >>> f.compute()
    array([ 0,  2,  6, 12, 20])

    If the function changes shape of the blocks then you must provide chunks
    explicitly.

    >>> y = x.map_blocks(lambda x: x[::2], chunks=((2, 2),))

    You have a bit of freedom in specifying chunks.  If all of the output chunk
    sizes are the same, you can provide just that chunk size as a single tuple.

    >>> a = da.arange(18, chunks=(6,))
    >>> b = a.map_blocks(lambda x: x[:3], chunks=(3,))

    If the function changes the dimension of the blocks you must specify the
    created or destroyed dimensions.

    >>> b = a.map_blocks(lambda x: x[None, :, None], chunks=(1, 6, 1),
    ...                  new_axis=[0, 2])

    Map_blocks aligns blocks by block positions without regard to shape. In the
    following example we have two arrays with the same number of blocks but
    with different shape and chunk sizes.

    >>> x = da.arange(1000, chunks=(100,))
    >>> y = da.arange(100, chunks=(10,))

    The relevant attribute to match is numblocks.

    >>> x.numblocks
    (10,)
    >>> y.numblocks
    (10,)

    If these match (up to broadcasting rules) then we can map arbitrary
    functions across blocks

    >>> def func(a, b):
    ...     return np.array([a.max(), b.max()])

    >>> da.map_blocks(func, x, y, chunks=(2,), dtype='i8')
    dask.array<func, shape=(20,), dtype=int64, chunksize=(2,)>

    >>> _.compute()
    array([ 99,   9, 199,  19, 299,  29, 399,  39, 499,  49, 599,  59, 699,
            69, 799,  79, 899,  89, 999,  99])

    Your block function can learn where in the array it is if it supports a
    ``block_id`` keyword argument.  This will receive entries like (2, 0, 1),
    the position of the block in the dask array.

    >>> def func(block, block_id=None):
    ...     pass

    You may specify the key name prefix of the resulting task in the graph with
    the optional ``token`` keyword argument.

    >>> x.map_blocks(lambda x: x + 1, token='increment')  # doctest: +SKIP
    dask.array<increment, shape=(100,), dtype=int64, chunksize=(10,)>
    """
    if not callable(func):
        msg = ("First argument must be callable function, not %s\n"
               "Usage:   da.map_blocks(function, x)\n"
               "   or:   da.map_blocks(function, x, y, z)")
        raise TypeError(msg % type(func).__name__)
    name = kwargs.pop('name', None)
    token = kwargs.pop('token', None)
    if not name:
        name = '%s-%s' % (token or funcname(func),
                          tokenize(token or func, args, **kwargs))
    dtype = kwargs.pop('dtype', None)
    chunks = kwargs.pop('chunks', None)
    drop_axis = kwargs.pop('drop_axis', [])
    new_axis = kwargs.pop('new_axis', [])
    if isinstance(drop_axis, Number):
        drop_axis = [drop_axis]
    if isinstance(new_axis, Number):
        new_axis = [new_axis]

    arrs = [a for a in args if isinstance(a, Array)]
    other = [(i, a) for i, a in enumerate(args) if not isinstance(a, Array)]

    argpairs = [(a.name, tuple(range(a.ndim))[::-1]) for a in arrs]
    numblocks = {a.name: a.numblocks for a in arrs}
    arginds = list(concat(argpairs))
    out_ind = tuple(range(max(a.ndim for a in arrs)))[::-1]

    try:
        spec = getargspec(func)
        block_id = ('block_id' in spec.args or
                    'block_id' in getattr(spec, 'kwonly_args', ()))
    except Exception:
        block_id = False

    if block_id:
        kwargs['block_id'] = '__dummy__'

    if other:
        dsk = top(partial_by_order, name, out_ind, *arginds,
                  numblocks=numblocks, function=func, other=other,
                  **kwargs)
    else:
        dsk = top(func, name, out_ind, *arginds, numblocks=numblocks,
                  **kwargs)

    # If func has block_id as an argument, add it to the kwargs for each call
    if block_id:
        for k in dsk.keys():
            dsk[k] = dsk[k][:-1] + (assoc(dsk[k][-1], 'block_id', k[1:]),)

    if dtype is None:
        if block_id:
            kwargs2 = assoc(kwargs, 'block_id', first(dsk.keys())[1:])
        else:
            kwargs2 = kwargs
        dtype = apply_infer_dtype(func, args, kwargs2, 'map_blocks')

    if len(arrs) == 1:
        numblocks = list(arrs[0].numblocks)
    else:
        dims = broadcast_dimensions(argpairs, numblocks)
        numblocks = [b for (_, b) in sorted(dims.items(), reverse=True)]

    if drop_axis:
        if any(numblocks[i] > 1 for i in drop_axis):
            raise ValueError("Can't drop an axis with more than 1 block. "
                             "Please use `atop` instead.")
        dsk = dict((tuple(k for i, k in enumerate(k)
                          if i - 1 not in drop_axis), v)
                   for k, v in dsk.items())
        numblocks = [n for i, n in enumerate(numblocks) if i not in drop_axis]
    if new_axis:
        new_axis = sorted(new_axis)
        for i in new_axis:
            if not 0 <= i <= len(numblocks):
                ndim = len(numblocks)
                raise ValueError("Can't add axis %d when current "
                                 "axis are %r. Missing axis: "
                                 "%r" % (i, list(range(ndim)),
                                         list(range(ndim, i))))
            numblocks.insert(i, 1)
        dsk, old_dsk = dict(), dsk
        for key in old_dsk:
            new_key = list(key)
            for i in new_axis:
                new_key.insert(i + 1, 0)
            dsk[tuple(new_key)] = old_dsk[key]

    if chunks:
        if len(chunks) != len(numblocks):
            raise ValueError("Provided chunks have {0} dims, expected {1} "
                             "dims.".format(len(chunks), len(numblocks)))
        chunks2 = []
        for i, (c, nb) in enumerate(zip(chunks, numblocks)):
            if isinstance(c, tuple):
                if not len(c) == nb:
                    raise ValueError("Dimension {0} has {1} blocks, "
                                     "chunks specified with "
                                     "{2} blocks".format(i, nb, len(c)))
                chunks2.append(c)
            else:
                chunks2.append(nb * (c,))
    else:
        if len(arrs) == 1:
            chunks2 = list(arrs[0].chunks)
        else:
            try:
                chunks2 = list(broadcast_chunks(*[a.chunks for a in arrs]))
            except Exception:
                raise ValueError("Arrays in `map_blocks` don't align, can't "
                                 "infer output chunks. Please provide "
                                 "`chunks` kwarg.")
        if drop_axis:
            chunks2 = [c for (i, c) in enumerate(chunks2) if i not in drop_axis]
        if new_axis:
            for i in sorted(new_axis):
                chunks2.insert(i, (1,))

    chunks = tuple(chunks2)

    return Array(sharedict.merge((name, dsk), *[a.dask for a in arrs]),
                 name, chunks, dtype)


def broadcast_chunks(*chunkss):
    """ Construct a chunks tuple that broadcasts many chunks tuples

    >>> a = ((5, 5),)
    >>> b = ((5, 5),)
    >>> broadcast_chunks(a, b)
    ((5, 5),)

    >>> a = ((10, 10, 10), (5, 5),)
    >>> b = ((5, 5),)
    >>> broadcast_chunks(a, b)
    ((10, 10, 10), (5, 5))

    >>> a = ((10, 10, 10), (5, 5),)
    >>> b = ((1,), (5, 5),)
    >>> broadcast_chunks(a, b)
    ((10, 10, 10), (5, 5))

    >>> a = ((10, 10, 10), (5, 5),)
    >>> b = ((3, 3,), (5, 5),)
    >>> broadcast_chunks(a, b)
    Traceback (most recent call last):
        ...
    ValueError: Chunks do not align: [(10, 10, 10), (3, 3)]
    """
    if len(chunkss) == 1:
        return chunkss[0]
    n = max(map(len, chunkss))
    chunkss2 = [((1,),) * (n - len(c)) + c for c in chunkss]
    result = []
    for i in range(n):
        step1 = [c[i] for c in chunkss2]
        if all(c == (1,) for c in step1):
            step2 = step1
        else:
            step2 = [c for c in step1 if c != (1,)]
        if len(set(step2)) != 1:
            raise ValueError("Chunks do not align: %s" % str(step2))
        result.append(step2[0])
    return tuple(result)


def store(sources, targets, lock=True, regions=None, compute=True, **kwargs):
    """ Store dask arrays in array-like objects, overwrite data in target

    This stores dask arrays into object that supports numpy-style setitem
    indexing.  It stores values chunk by chunk so that it does not have to
    fill up memory.  For best performance you can align the block size of
    the storage target with the block size of your array.

    If your data fits in memory then you may prefer calling
    ``np.array(myarray)`` instead.

    Parameters
    ----------

    sources: Array or iterable of Arrays
    targets: array-like or iterable of array-likes
        These should support setitem syntax ``target[10:20] = ...``
    lock: boolean or threading.Lock, optional
        Whether or not to lock the data stores while storing.
        Pass True (lock each file individually), False (don't lock) or a
        particular ``threading.Lock`` object to be shared among all writes.
    regions: tuple of slices or iterable of tuple of slices
        Each ``region`` tuple in ``regions`` should be such that
        ``target[region].shape = source.shape``
        for the corresponding source and target in sources and targets, respectively.
    compute: boolean, optional
        If true compute immediately, return ``dask.delayed.Delayed`` otherwise

    Examples
    --------
    >>> x = ...  # doctest: +SKIP

    >>> import h5py  # doctest: +SKIP
    >>> f = h5py.File('myfile.hdf5')  # doctest: +SKIP
    >>> dset = f.create_dataset('/data', shape=x.shape,
    ...                                  chunks=x.chunks,
    ...                                  dtype='f8')  # doctest: +SKIP

    >>> store(x, dset)  # doctest: +SKIP

    Alternatively store many arrays at the same time

    >>> store([x, y, z], [dset1, dset2, dset3])  # doctest: +SKIP
    """
    if isinstance(sources, Array):
        sources = [sources]
        targets = [targets]

    if any(not isinstance(s, Array) for s in sources):
        raise ValueError("All sources must be dask array objects")

    if len(sources) != len(targets):
        raise ValueError("Different number of sources [%d] and targets [%d]"
                         % (len(sources), len(targets)))

    if isinstance(regions, tuple) or regions is None:
        regions = [regions]

    if len(sources) > 1 and len(regions) == 1:
        regions *= len(sources)

    if len(sources) != len(regions):
        raise ValueError("Different number of sources [%d] and targets [%d] than regions [%d]"
                         % (len(sources), len(targets), len(regions)))

    updates = {}
    keys = []
    for tgt, src, reg in zip(targets, sources, regions):
        # if out is a delayed object update dictionary accordingly
        try:
            dsk = {}
            dsk.update(tgt.dask)
            tgt = tgt.key
        except AttributeError:
            dsk = {}

        update = insert_to_ooc(tgt, src, lock=lock, region=reg)
        keys.extend(update)

        update.update(dsk)
        updates.update(update)

    name = 'store-' + tokenize(*keys)
    dsk = sharedict.merge((name, updates), *[src.dask for src in sources])
    if compute:
        Array._get(dsk, keys, **kwargs)
    else:
        from ..delayed import Delayed
        dsk.update({name: keys})
        return Delayed(name, dsk)


def blockdims_from_blockshape(shape, chunks):
    """

    >>> blockdims_from_blockshape((10, 10), (4, 3))
    ((4, 4, 2), (3, 3, 3, 1))
    >>> blockdims_from_blockshape((10, 0), (4, 0))
    ((4, 4, 2), (0,))
    """
    if chunks is None:
        raise TypeError("Must supply chunks= keyword argument")
    if shape is None:
        raise TypeError("Must supply shape= keyword argument")
    if np.isnan(sum(shape)) or np.isnan(sum(chunks)):
        raise ValueError("Array chunk sizes are unknown. shape: %s, chunks: %s"
                         % (shape, chunks))
    if not all(map(is_integer, chunks)):
        raise ValueError("chunks can only contain integers.")
    if not all(map(is_integer, shape)):
        raise ValueError("shape can only contain integers.")
    shape = tuple(map(int, shape))
    chunks = tuple(map(int, chunks))
    return tuple(((bd,) * (d // bd) + ((d % bd,) if d % bd else ())
                 if d else (0,))
                 for d, bd in zip(shape, chunks))


def finalize(results):
    if not results:
        return concatenate3(results)
    results2 = results
    while isinstance(results2, (tuple, list)):
        if len(results2) > 1:
            return concatenate3(results)
        else:
            results2 = results2[0]
    return unpack_singleton(results)


CHUNKS_NONE_ERROR_MESSAGE = """
You must specify a chunks= keyword argument.
This specifies the chunksize of your array blocks.

See the following documentation page for details:
  http://dask.pydata.org/en/latest/array-creation.html#chunks
""".strip()


class Array(Base):
    """ Parallel Dask Array

    A parallel nd-array comprised of many numpy arrays arranged in a grid.

    This constructor is for advanced uses only.  For normal use see the
    ``da.from_array`` function.

    Parameters
    ----------

    dask : dict
        Task dependency graph
    name : string
        Name of array in dask
    shape : tuple of ints
        Shape of the entire array
    chunks: iterable of tuples
        block sizes along each dimension

    See Also
    --------
    dask.array.from_array
    """
    __slots__ = 'dask', '_name', '_cached_keys', '_chunks', 'dtype'

    _optimize = staticmethod(optimize)
    _default_get = staticmethod(threaded.get)
    _finalize = staticmethod(finalize)

    def __new__(cls, dask, name, chunks, dtype, shape=None):
        self = super(Array, cls).__new__(cls)
        assert isinstance(dask, Mapping)
        if not isinstance(dask, ShareDict):
            s = ShareDict()
            s.update_with_key(dask, key=name)
            dask = s
        self.dask = dask
        self.name = name
        self._chunks = normalize_chunks(chunks, shape)
        if self._chunks is None:
            raise ValueError(CHUNKS_NONE_ERROR_MESSAGE)
        if dtype is None:
            raise ValueError("You must specify the dtype of the array")
        self.dtype = np.dtype(dtype)

        for plugin in _globals.get('array_plugins', ()):
            result = plugin(self)
            if result is not None:
                self = result

        return self

    def __reduce__(self):
        return (Array, (self.dask, self.name, self.chunks, self.dtype))

    @property
    def numblocks(self):
        return tuple(map(len, self.chunks))

    @property
    def npartitions(self):
        return reduce(mul, self.numblocks, 1)

    @property
    def shape(self):
        return tuple(map(sum, self.chunks))

    def _get_chunks(self):
        return self._chunks

    def _set_chunks(self, chunks):
        raise TypeError("Can not set chunks directly\n\n"
                        "Please use the rechunk method instead:\n"
                        "  x.rechunk(%s)" % str(chunks))

    chunks = property(_get_chunks, _set_chunks, "chunks property")

    def __len__(self):
        return sum(self.chunks[0])

    def __array_ufunc__(self, numpy_ufunc, method, *inputs, **kwargs):
        out = kwargs.get('out', ())
        for x in inputs + out:
            if not isinstance(x, (np.ndarray, Number, Array)):
                return NotImplemented

        if method == '__call__':
            if numpy_ufunc.signature is not None:
                return NotImplemented
            if numpy_ufunc.nout > 1:
                from . import ufunc
                try:
                    da_ufunc = getattr(ufunc, numpy_ufunc.__name__)
                except AttributeError:
                    return NotImplemented
                return da_ufunc(*inputs, **kwargs)
            else:
                return elemwise(numpy_ufunc, *inputs, **kwargs)
        elif method == 'outer':
            from . import ufunc
            try:
                da_ufunc = getattr(ufunc, numpy_ufunc.__name__)
            except AttributeError:
                return NotImplemented
            return da_ufunc.outer(*inputs, **kwargs)
        else:
            return NotImplemented

    def __repr__(self):
        """

        >>> import dask.array as da
        >>> da.ones((10, 10), chunks=(5, 5), dtype='i4')
        dask.array<..., shape=(10, 10), dtype=int32, chunksize=(5, 5)>
        """
        chunksize = str(tuple(c[0] for c in self.chunks))
        name = self.name.rsplit('-', 1)[0]
        return ("dask.array<%s, shape=%s, dtype=%s, chunksize=%s>" %
                (name, self.shape, self.dtype, chunksize))

    @property
    def ndim(self):
        return len(self.shape)

    @property
    def size(self):
        """ Number of elements in array """
        return reduce(mul, self.shape, 1)

    @property
    def nbytes(self):
        """ Number of bytes in array """
        return self.size * self.dtype.itemsize

    @property
    def itemsize(self):
        """ Length of one array element in bytes """
        return self.dtype.itemsize

    @property
    def name(self):
        return self._name

    @name.setter
    def name(self, val):
        self._name = val
        # Clear the key cache when the name is reset
        self._cached_keys = None

    def _keys(self, *args):
        if not args:
            if self._cached_keys is not None:
                return self._cached_keys

        if not self.chunks:
            return [(self.name,)]
        ind = len(args)
        if ind + 1 == self.ndim:
            result = [(self.name,) + args + (i,)
                      for i in range(self.numblocks[ind])]
        else:
            result = [self._keys(*(args + (i,)))
                      for i in range(self.numblocks[ind])]
        if not args:
            self._cached_keys = result
        return result

    __array_priority__ = 11  # higher than numpy.ndarray and numpy.matrix

    def __array__(self, dtype=None, **kwargs):
        x = self.compute()
        if dtype and x.dtype != dtype:
            x = x.astype(dtype)
        if not isinstance(x, np.ndarray):
            x = np.array(x)
        return x

    @property
    def _elemwise(self):
        return elemwise

    @wraps(store)
    def store(self, target, **kwargs):
        return store([self], [target], **kwargs)

    def to_hdf5(self, filename, datapath, **kwargs):
        """ Store array in HDF5 file

        >>> x.to_hdf5('myfile.hdf5', '/x')  # doctest: +SKIP

        Optionally provide arguments as though to ``h5py.File.create_dataset``

        >>> x.to_hdf5('myfile.hdf5', '/x', compression='lzf', shuffle=True)  # doctest: +SKIP

        See Also
        --------
        da.store
        h5py.File.create_dataset
        """
        return to_hdf5(filename, datapath, self, **kwargs)

    def to_dask_dataframe(self, columns=None):
        """ Convert dask Array to dask Dataframe

        Parameters
        ----------
        columns: list or string
            list of column names if DataFrame, single string if Series

        See Also
        --------
        dask.dataframe.from_dask_array
        """
        from ..dataframe import from_dask_array
        return from_dask_array(self, columns=columns)

    def __int__(self):
        return int(self.compute())

    def __bool__(self):
        return bool(self.compute())

    __nonzero__ = __bool__  # python 2

    def __float__(self):
        return float(self.compute())

    def __complex__(self):
        return complex(self.compute())

    def __setitem__(self, key, value):
        from .routines import where
        if isinstance(key, Array):
            if isinstance(value, Array) and value.ndim > 1:
                raise ValueError('boolean index array should have 1 dimension')
            y = where(key, value, self)
            self.dtype = y.dtype
            self.dask = y.dask
            self.name = y.name
            return self
        else:
            raise NotImplementedError("Item assignment with %s not supported"
                                      % type(key))

    def __getitem__(self, index):
        out = 'getitem-' + tokenize(self, index)

        # Field access, e.g. x['a'] or x[['a', 'b']]
        if (isinstance(index, (str, unicode)) or
                (isinstance(index, list) and index and
                 all(isinstance(i, (str, unicode)) for i in index))):
            if isinstance(index, (str, unicode)):
                dt = self.dtype[index]
            else:
                dt = np.dtype([(name, self.dtype[name]) for name in index])

            if dt.shape:
                new_axis = list(range(self.ndim, self.ndim + len(dt.shape)))
                chunks = self.chunks + tuple((i,) for i in dt.shape)
                return self.map_blocks(getitem, index, dtype=dt.base, name=out,
                                       chunks=chunks, new_axis=new_axis)
            else:
                return self.map_blocks(getitem, index, dtype=dt, name=out)

        # Slicing
        if isinstance(index, Array):
            return slice_with_dask_array(self, index)

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

        if any(isinstance(i, Array) for i in index):
            raise NotImplementedError("Indexing with a dask Array")

        if all(isinstance(i, slice) and i == slice(None) for i in index):
            return self

        dsk, chunks = slice_array(out, self.name, self.chunks, index)

        dsk2 = sharedict.merge(self.dask, (out, dsk))

        return Array(dsk2, out, chunks, dtype=self.dtype)

    def _vindex(self, key):
        if not isinstance(key, tuple):
            key = (key,)
        if any(k is None for k in key):
            raise IndexError(
                "vindex does not support indexing with None (np.newaxis), "
                "got {}".format(key))
        if all(isinstance(k, slice) for k in key):
            raise IndexError(
                "vindex requires at least one non-slice to vectorize over. "
                "Use normal slicing instead when only using slices. Got: {}"
                .format(key))
        return _vindex(self, *key)

    @property
    def vindex(self):
        """Vectorized indexing with broadcasting.

        This is equivalent to numpy's advanced indexing, using arrays that are
        broadcast against each other. This allows for pointwise indexing:

        >>> x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
        >>> x = from_array(x, chunks=2)
        >>> x.vindex[[0, 1, 2], [0, 1, 2]].compute()
        array([1, 5, 9])

        Mixed basic/advanced indexing with slices/arrays is also supported. The
        order of dimensions in the result follows those proposed for
        ndarray.vindex [1]_: the subspace spanned by arrays is followed by all
        slices.

        Note: ``vindex`` provides more general functionality than standard
        indexing, but it also has fewer optimizations and can be significantly
        slower.

        _[1]: https://github.com/numpy/numpy/pull/6256
        """
        return IndexCallable(self._vindex)

    @derived_from(np.ndarray)
    def dot(self, other):
        from .routines import tensordot
        return tensordot(self, other,
                         axes=((self.ndim - 1,), (other.ndim - 2,)))

    @property
    def A(self):
        return self

    @property
    def T(self):
        return self.transpose()

    @derived_from(np.ndarray)
    def transpose(self, *axes):
        from .routines import transpose
        if not axes:
            axes = None
        elif len(axes) == 1 and isinstance(axes[0], Iterable):
            axes = axes[0]
        return transpose(self, axes=axes)

    @derived_from(np.ndarray)
    def ravel(self):
        from .routines import ravel
        return ravel(self)

    flatten = ravel

    @derived_from(np.ndarray)
    def choose(self, choices):
        from .routines import choose
        return choose(self, choices)

    @derived_from(np.ndarray)
    def reshape(self, *shape):
        from .reshape import reshape
        if len(shape) == 1 and not isinstance(shape[0], Number):
            shape = shape[0]
        return reshape(self, shape)

    def topk(self, k):
        """The top k elements of an array.

        See ``da.topk`` for docstring"""
        from .routines import topk
        return topk(k, self)

    def astype(self, dtype, **kwargs):
        """Copy of the array, cast to a specified type.

        Parameters
        ----------
        dtype : str or dtype
            Typecode or data-type to which the array is cast.
        casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
            Controls what kind of data casting may occur. Defaults to 'unsafe'
            for backwards compatibility.

            * 'no' means the data types should not be cast at all.
            * 'equiv' means only byte-order changes are allowed.
            * 'safe' means only casts which can preserve values are allowed.
            * 'same_kind' means only safe casts or casts within a kind,
                like float64 to float32, are allowed.
            * 'unsafe' means any data conversions may be done.
        copy : bool, optional
            By default, astype always returns a newly allocated array. If this
            is set to False and the `dtype` requirement is satisfied, the input
            array is returned instead of a copy.
        """
        # Scalars don't take `casting` or `copy` kwargs - as such we only pass
        # them to `map_blocks` if specified by user (different than defaults).
        extra = set(kwargs) - {'casting', 'copy'}
        if extra:
            raise TypeError("astype does not take the following keyword "
                            "arguments: {0!s}".format(list(extra)))
        casting = kwargs.get('casting', 'unsafe')
        copy = kwargs.get('copy', True)
        dtype = np.dtype(dtype)
        if self.dtype == dtype:
            return self
        elif not np.can_cast(self.dtype, dtype, casting=casting):
            raise TypeError("Cannot cast array from {0!r} to {1!r}"
                            " according to the rule "
                            "{2!r}".format(self.dtype, dtype, casting))
        name = 'astype-' + tokenize(self, dtype, casting, copy)
        return self.map_blocks(chunk.astype, dtype=dtype, name=name,
                               astype_dtype=dtype, **kwargs)

    def __abs__(self):
        return elemwise(operator.abs, self)

    def __add__(self, other):
        return elemwise(operator.add, self, other)

    def __radd__(self, other):
        return elemwise(operator.add, other, self)

    def __and__(self, other):
        return elemwise(operator.and_, self, other)

    def __rand__(self, other):
        return elemwise(operator.and_, other, self)

    def __div__(self, other):
        return elemwise(operator.div, self, other)

    def __rdiv__(self, other):
        return elemwise(operator.div, other, self)

    def __eq__(self, other):
        return elemwise(operator.eq, self, other)

    def __gt__(self, other):
        return elemwise(operator.gt, self, other)

    def __ge__(self, other):
        return elemwise(operator.ge, self, other)

    def __invert__(self):
        return elemwise(operator.invert, self)

    def __lshift__(self, other):
        return elemwise(operator.lshift, self, other)

    def __rlshift__(self, other):
        return elemwise(operator.lshift, other, self)

    def __lt__(self, other):
        return elemwise(operator.lt, self, other)

    def __le__(self, other):
        return elemwise(operator.le, self, other)

    def __mod__(self, other):
        return elemwise(operator.mod, self, other)

    def __rmod__(self, other):
        return elemwise(operator.mod, other, self)

    def __mul__(self, other):
        return elemwise(operator.mul, self, other)

    def __rmul__(self, other):
        return elemwise(operator.mul, other, self)

    def __ne__(self, other):
        return elemwise(operator.ne, self, other)

    def __neg__(self):
        return elemwise(operator.neg, self)

    def __or__(self, other):
        return elemwise(operator.or_, self, other)

    def __pos__(self):
        return self

    def __ror__(self, other):
        return elemwise(operator.or_, other, self)

    def __pow__(self, other):
        return elemwise(operator.pow, self, other)

    def __rpow__(self, other):
        return elemwise(operator.pow, other, self)

    def __rshift__(self, other):
        return elemwise(operator.rshift, self, other)

    def __rrshift__(self, other):
        return elemwise(operator.rshift, other, self)

    def __sub__(self, other):
        return elemwise(operator.sub, self, other)

    def __rsub__(self, other):
        return elemwise(operator.sub, other, self)

    def __truediv__(self, other):
        return elemwise(operator.truediv, self, other)

    def __rtruediv__(self, other):
        return elemwise(operator.truediv, other, self)

    def __floordiv__(self, other):
        return elemwise(operator.floordiv, self, other)

    def __rfloordiv__(self, other):
        return elemwise(operator.floordiv, other, self)

    def __xor__(self, other):
        return elemwise(operator.xor, self, other)

    def __rxor__(self, other):
        return elemwise(operator.xor, other, self)

    def __matmul__(self, other):
        from .routines import tensordot
        if not hasattr(other, 'ndim'):
            other = np.asarray(other)  # account for array-like RHS
        if other.ndim > 2:
            msg = ('The matrix multiplication operator (@) is not yet '
                   'implemented for higher-dimensional Dask arrays. Try '
                   '`dask.array.tensordot` and see the discussion at '
                   'https://github.com/dask/dask/pull/2349 for details.')
            raise NotImplementedError(msg)
        return tensordot(self, other, axes=((self.ndim - 1,),
                                            (other.ndim - 2,)))

    def __rmatmul__(self, other):
        from .routines import tensordot
        if not hasattr(other, 'ndim'):
            other = np.asarray(other)  # account for array-like on LHS
        if self.ndim > 2:
            msg = ('The matrix multiplication operator (@) is not yet '
                   'implemented for higher-dimensional Dask arrays. Try '
                   '`dask.array.tensordot` and see the discussion at '
                   'https://github.com/dask/dask/pull/2349 for details.')
            raise NotImplementedError(msg)
        return tensordot(other, self, axes=((other.ndim - 1,),
                                            (self.ndim - 2,)))

    @derived_from(np.ndarray)
    def any(self, axis=None, keepdims=False, split_every=None, out=None):
        from .reductions import any
        return any(self, axis=axis, keepdims=keepdims, split_every=split_every,
                   out=out)

    @derived_from(np.ndarray)
    def all(self, axis=None, keepdims=False, split_every=None, out=None):
        from .reductions import all
        return all(self, axis=axis, keepdims=keepdims, split_every=split_every,
                   out=out)

    @derived_from(np.ndarray)
    def min(self, axis=None, keepdims=False, split_every=None, out=None):
        from .reductions import min
        return min(self, axis=axis, keepdims=keepdims, split_every=split_every,
                   out=out)

    @derived_from(np.ndarray)
    def max(self, axis=None, keepdims=False, split_every=None, out=None):
        from .reductions import max
        return max(self, axis=axis, keepdims=keepdims, split_every=split_every,
                   out=out)

    @derived_from(np.ndarray)
    def argmin(self, axis=None, split_every=None, out=None):
        from .reductions import argmin
        return argmin(self, axis=axis, split_every=split_every, out=out)

    @derived_from(np.ndarray)
    def argmax(self, axis=None, split_every=None, out=None):
        from .reductions import argmax
        return argmax(self, axis=axis, split_every=split_every, out=out)

    @derived_from(np.ndarray)
    def sum(self, axis=None, dtype=None, keepdims=False, split_every=None,
            out=None):
        from .reductions import sum
        return sum(self, axis=axis, dtype=dtype, keepdims=keepdims,
                   split_every=split_every, out=out)

    @derived_from(np.ndarray)
    def prod(self, axis=None, dtype=None, keepdims=False, split_every=None,
             out=None):
        from .reductions import prod
        return prod(self, axis=axis, dtype=dtype, keepdims=keepdims,
                    split_every=split_every, out=out)

    @derived_from(np.ndarray)
    def mean(self, axis=None, dtype=None, keepdims=False, split_every=None,
             out=None):
        from .reductions import mean
        return mean(self, axis=axis, dtype=dtype, keepdims=keepdims,
                    split_every=split_every, out=out)

    @derived_from(np.ndarray)
    def std(self, axis=None, dtype=None, keepdims=False, ddof=0,
            split_every=None, out=None):
        from .reductions import std
        return std(self, axis=axis, dtype=dtype, keepdims=keepdims, ddof=ddof,
                   split_every=split_every, out=out)

    @derived_from(np.ndarray)
    def var(self, axis=None, dtype=None, keepdims=False, ddof=0,
            split_every=None, out=None):
        from .reductions import var
        return var(self, axis=axis, dtype=dtype, keepdims=keepdims, ddof=ddof,
                   split_every=split_every, out=out)

    def moment(self, order, axis=None, dtype=None, keepdims=False, ddof=0,
               split_every=None, out=None):
        """Calculate the nth centralized moment.

        Parameters
        ----------
        order : int
            Order of the moment that is returned, must be >= 2.
        axis : int, optional
            Axis along which the central moment is computed. The default is to
            compute the moment of the flattened array.
        dtype : data-type, optional
            Type to use in computing the moment. For arrays of integer type the
            default is float64; for arrays of float types it is the same as the
            array type.
        keepdims : bool, optional
            If this is set to True, the axes which are reduced are left in the
            result as dimensions with size one. With this option, the result
            will broadcast correctly against the original array.
        ddof : int, optional
            "Delta Degrees of Freedom": the divisor used in the calculation is
            N - ddof, where N represents the number of elements. By default
            ddof is zero.

        Returns
        -------
        moment : ndarray

        References
        ----------
        .. [1] Pebay, Philippe (2008), "Formulas for Robust, One-Pass Parallel
        Computation of Covariances and Arbitrary-Order Statistical Moments"
        (PDF), Technical Report SAND2008-6212, Sandia National Laboratories

        """

        from .reductions import moment
        return moment(self, order, axis=axis, dtype=dtype, keepdims=keepdims,
                      ddof=ddof, split_every=split_every, out=out)

    def vnorm(self, ord=None, axis=None, keepdims=False, split_every=None,
              out=None):
        """ Vector norm """
        from .reductions import vnorm
        return vnorm(self, ord=ord, axis=axis, keepdims=keepdims,
                     split_every=split_every, out=out)

    @wraps(map_blocks)
    def map_blocks(self, func, *args, **kwargs):
        return map_blocks(func, self, *args, **kwargs)

    def map_overlap(self, func, depth, boundary=None, trim=True, **kwargs):
        """ Map a function over blocks of the array with some overlap

        We share neighboring zones between blocks of the array, then map a
        function, then trim away the neighboring strips.

        Parameters
        ----------

        func: function
            The function to apply to each extended block
        depth: int, tuple, or dict
            The number of cells that each block should share with its neighbors
            If a tuple or dict this can be different per axis
        boundary: str, tuple, dict
            how to handle the boundaries.  Values include 'reflect',
            'periodic', 'nearest', 'none', or any constant value like 0 or
            np.nan
        trim: bool
            Whether or not to trim the excess after the map function.  Set this
            to false if your mapping function does this for you.
        **kwargs:
            Other keyword arguments valid in ``map_blocks``

        Examples
        --------

        >>> x = np.array([1, 1, 2, 3, 3, 3, 2, 1, 1])
        >>> x = from_array(x, chunks=5)
        >>> def derivative(x):
        ...     return x - np.roll(x, 1)

        >>> y = x.map_overlap(derivative, depth=1, boundary=0)
        >>> y.compute()
        array([ 1,  0,  1,  1,  0,  0, -1, -1,  0])

        >>> import dask.array as da
        >>> x = np.arange(16).reshape((4, 4))
        >>> d = da.from_array(x, chunks=(2, 2))
        >>> d.map_overlap(lambda x: x + x.size, depth=1).compute()
        array([[16, 17, 18, 19],
               [20, 21, 22, 23],
               [24, 25, 26, 27],
               [28, 29, 30, 31]])

        >>> func = lambda x: x + x.size
        >>> depth = {0: 1, 1: 1}
        >>> boundary = {0: 'reflect', 1: 'none'}
        >>> d.map_overlap(func, depth, boundary).compute()  # doctest: +NORMALIZE_WHITESPACE
        array([[12,  13,  14,  15],
               [16,  17,  18,  19],
               [20,  21,  22,  23],
               [24,  25,  26,  27]])
        """
        from .ghost import map_overlap
        return map_overlap(self, func, depth, boundary, trim, **kwargs)

    def cumsum(self, axis, dtype=None, out=None):
        """ See da.cumsum for docstring """
        from .reductions import cumsum
        return cumsum(self, axis, dtype, out=out)

    def cumprod(self, axis, dtype=None, out=None):
        """ See da.cumprod for docstring """
        from .reductions import cumprod
        return cumprod(self, axis, dtype, out=out)

    @derived_from(np.ndarray)
    def squeeze(self):
        from .routines import squeeze
        return squeeze(self)

    def rechunk(self, chunks, threshold=None, block_size_limit=None):
        """ See da.rechunk for docstring """
        from . import rechunk   # avoid circular import
        return rechunk(self, chunks, threshold, block_size_limit)

    @property
    def real(self):
        from .ufunc import real
        return real(self)

    @property
    def imag(self):
        from .ufunc import imag
        return imag(self)

    def conj(self):
        from .ufunc import conj
        return conj(self)

    @derived_from(np.ndarray)
    def clip(self, min=None, max=None):
        from .ufunc import clip
        return clip(self, min, max)

    def view(self, dtype, order='C'):
        """ Get a view of the array as a new data type

        Parameters
        ----------
        dtype:
            The dtype by which to view the array
        order: string
            'C' or 'F' (Fortran) ordering

        This reinterprets the bytes of the array under a new dtype.  If that
        dtype does not have the same size as the original array then the shape
        will change.

        Beware that both numpy and dask.array can behave oddly when taking
        shape-changing views of arrays under Fortran ordering.  Under some
        versions of NumPy this function will fail when taking shape-changing
        views of Fortran ordered arrays if the first dimension has chunks of
        size one.
        """
        dtype = np.dtype(dtype)
        mult = self.dtype.itemsize / dtype.itemsize

        if order == 'C':
            ascontiguousarray = np.ascontiguousarray
            chunks = self.chunks[:-1] + (tuple(ensure_int(c * mult)
                                         for c in self.chunks[-1]),)
        elif order == 'F':
            ascontiguousarray = np.asfortranarray
            chunks = ((tuple(ensure_int(c * mult) for c in self.chunks[0]), ) +
                      self.chunks[1:])
        else:
            raise ValueError("Order must be one of 'C' or 'F'")

        out = elemwise(ascontiguousarray, self, dtype=self.dtype)
        out = elemwise(np.ndarray.view, out, dtype, dtype=dtype)
        out._chunks = chunks
        return out

    @derived_from(np.ndarray)
    def swapaxes(self, axis1, axis2):
        from .routines import swapaxes
        return swapaxes(self, axis1, axis2)

    @derived_from(np.ndarray)
    def round(self, decimals=0):
        from .routines import round
        return round(self, decimals=decimals)

    def copy(self):
        """
        Copy array.  This is a no-op for dask.arrays, which are immutable
        """
        return Array(self.dask, self.name, self.chunks, self.dtype)

    def __deepcopy__(self, memo):
        c = self.copy()
        memo[id(self)] = c
        return c

    def to_delayed(self):
        """ Convert Array into dask Delayed objects

        Returns an array of values, one value per chunk.

        See Also
        --------
        dask.array.from_delayed
        """
        from ..delayed import Delayed
        dsk = self._optimize(self.dask, self._keys())
        L = ndeepmap(self.ndim, lambda k: Delayed(k, dsk), self._keys())
        return np.array(L, dtype=object)

    @derived_from(np.ndarray)
    def repeat(self, repeats, axis=None):
        from .creation import repeat
        return repeat(self, repeats, axis=axis)

    @derived_from(np.ndarray)
    def nonzero(self):
        from .routines import nonzero
        return nonzero(self)


def ensure_int(f):
    i = int(f)
    if i != f:
        raise ValueError("Could not coerce %f to integer" % f)
    return i


normalize_token.register(Array, lambda a: a.name)


def normalize_chunks(chunks, shape=None):
    """ Normalize chunks to tuple of tuples

    >>> normalize_chunks((2, 2), shape=(5, 6))
    ((2, 2, 1), (2, 2, 2))

    >>> normalize_chunks(((2, 2, 1), (2, 2, 2)), shape=(4, 6))  # Idempotent
    ((2, 2, 1), (2, 2, 2))

    >>> normalize_chunks([[2, 2], [3, 3]])  # Cleans up lists to tuples
    ((2, 2), (3, 3))

    >>> normalize_chunks(10, shape=(30, 5))  # Supports integer inputs
    ((10, 10, 10), (5,))

    >>> normalize_chunks((), shape=(0, 0))  #  respects null dimensions
    ((0,), (0,))
    """
    if chunks is None:
        raise ValueError(CHUNKS_NONE_ERROR_MESSAGE)
    if type(chunks) is not tuple:
        if type(chunks) is list:
            chunks = tuple(chunks)
        if isinstance(chunks, Number):
            chunks = (chunks,) * len(shape)
    if not chunks and shape and all(s == 0 for s in shape):
        chunks = ((0,),) * len(shape)

    if shape and len(chunks) != len(shape):
        if not (len(shape) == 1 and sum(chunks) == shape[0]):
            raise ValueError(
                "Chunks and shape must be of the same length/dimension. "
                "Got chunks=%s, shape=%s" % (chunks, shape))

    if shape is not None:
        chunks = tuple(c if c is not None else s for c, s in zip(chunks, shape))

    if chunks and shape is not None:
        chunks = sum((blockdims_from_blockshape((s,), (c,))
                      if not isinstance(c, (tuple, list)) else (c,)
                      for s, c in zip(shape, chunks)), ())
    for c in chunks:
        if not c:
            raise ValueError("Empty tuples are not allowed in chunks. Express "
                             "zero length dimensions with 0(s) in chunks")

    return tuple(tuple(int(x) if not math.isnan(x) else x for x in c) for c in chunks)


def from_array(x, chunks, name=None, lock=False, asarray=True, fancy=True,
               getitem=None):
    """ Create dask array from something that looks like an array

    Input must have a ``.shape`` and support numpy-style slicing.

    Parameters
    ----------
    x : array_like
    chunks : int, tuple
        How to chunk the array. Must be one of the following forms:
        - A blocksize like 1000.
        - A blockshape like (1000, 1000).
        - Explicit sizes of all blocks along all dimensions
          like ((1000, 1000, 500), (400, 400)).
    name : str, optional
        The key name to use for the array. Defaults to a hash of ``x``.
        Use ``name=False`` to generate a random name instead of hashing (fast)
    lock : bool or Lock, optional
        If ``x`` doesn't support concurrent reads then provide a lock here, or
        pass in True to have dask.array create one for you.
    asarray : bool, optional
        If True (default), then chunks will be converted to instances of
        ``ndarray``. Set to False to pass passed chunks through unchanged.
    fancy : bool, optional
        If ``x`` doesn't support fancy indexing (e.g. indexing with lists or
        arrays) then set to False. Default is True.

    Examples
    --------

    >>> x = h5py.File('...')['/data/path']  # doctest: +SKIP
    >>> a = da.from_array(x, chunks=(1000, 1000))  # doctest: +SKIP

    If your underlying datastore does not support concurrent reads then include
    the ``lock=True`` keyword argument or ``lock=mylock`` if you want multiple
    arrays to coordinate around the same lock.

    >>> a = da.from_array(x, chunks=(1000, 1000), lock=True)  # doctest: +SKIP
    """
    chunks = normalize_chunks(chunks, x.shape)
    if len(chunks) != len(x.shape):
        raise ValueError("Input array has %d dimensions but the supplied "
                         "chunks has only %d dimensions" %
                         (len(x.shape), len(chunks)))
    if tuple(map(sum, chunks)) != x.shape:
        raise ValueError("Chunks do not add up to shape. "
                         "Got chunks=%s, shape=%s" % (chunks, x.shape))
    if name in (None, True):
        token = tokenize(x, chunks)
        original_name = 'array-original-' + token
        name = name or 'array-' + token
    elif name is False:
        original_name = name = 'array-' + str(uuid.uuid1())
    else:
        original_name = name
    if lock is True:
        lock = SerializableLock()

    if getitem is None:
        getitem = getter if fancy else getter_nofancy

    dsk = getem(original_name, chunks, getitem=getitem, shape=x.shape,
                out_name=name, lock=lock, asarray=asarray)
    dsk[original_name] = x

    return Array(dsk, name, chunks, dtype=x.dtype)


def from_delayed(value, shape, dtype, name=None):
    """ Create a dask array from a dask delayed value

    This routine is useful for constructing dask arrays in an ad-hoc fashion
    using dask delayed, particularly when combined with stack and concatenate.

    The dask array will consist of a single chunk.

    Examples
    --------
    >>> from dask import delayed
    >>> value = delayed(np.ones)(5)
    >>> array = from_delayed(value, (5,), float)
    >>> array
    dask.array<from-value, shape=(5,), dtype=float64, chunksize=(5,)>
    >>> array.compute()
    array([ 1.,  1.,  1.,  1.,  1.])
    """
    from dask.delayed import delayed, Delayed
    if not isinstance(value, Delayed) and hasattr(value, 'key'):
        value = delayed(value)
    name = name or 'from-value-' + tokenize(value, shape, dtype)
    dsk = {(name,) + (0,) * len(shape): value.key}
    chunks = tuple((d,) for d in shape)
    return Array(sharedict.merge(value.dask, (name, dsk)), name, chunks, dtype)


def from_func(func, shape, dtype=None, name=None, args=(), kwargs={}):
    """ Create dask array in a single block by calling a function

    Calling the provided function with func(*args, **kwargs) should return a
    NumPy array of the indicated shape and dtype.

    Examples
    --------

    >>> a = from_func(np.arange, (3,), dtype='i8', args=(3,))
    >>> a.compute()
    array([0, 1, 2])

    This works particularly well when coupled with dask.array functions like
    concatenate and stack:

    >>> arrays = [from_func(np.array, (), dtype='i8', args=(n,)) for n in range(5)]
    >>> stack(arrays).compute()
    array([0, 1, 2, 3, 4])
    """
    name = name or 'from_func-' + tokenize(func, shape, dtype, args, kwargs)
    if args or kwargs:
        func = partial(func, *args, **kwargs)
    dsk = {(name,) + (0,) * len(shape): (func,)}
    chunks = tuple((i,) for i in shape)
    return Array(dsk, name, chunks, dtype)


def common_blockdim(blockdims):
    """ Find the common block dimensions from the list of block dimensions

    Currently only implements the simplest possible heuristic: the common
    block-dimension is the only one that does not span fully span a dimension.
    This is a conservative choice that allows us to avoid potentially very
    expensive rechunking.

    Assumes that each element of the input block dimensions has all the same
    sum (i.e., that they correspond to dimensions of the same size).

    Examples
    --------
    >>> common_blockdim([(3,), (2, 1)])
    (2, 1)
    >>> common_blockdim([(1, 2), (2, 1)])
    (1, 1, 1)
    >>> common_blockdim([(2, 2), (3, 1)])  # doctest: +SKIP
    Traceback (most recent call last):
        ...
    ValueError: Chunks do not align
    """
    if not any(blockdims):
        return ()
    non_trivial_dims = set([d for d in blockdims if len(d) > 1])
    if len(non_trivial_dims) == 1:
        return first(non_trivial_dims)
    if len(non_trivial_dims) == 0:
        return max(blockdims, key=first)

    if np.isnan(sum(map(sum, blockdims))):
        raise ValueError("Arrays chunk sizes are unknown: %s", blockdims)

    if len(set(map(sum, non_trivial_dims))) > 1:
        raise ValueError("Chunks do not add up to same value", blockdims)

    # We have multiple non-trivial chunks on this axis
    # e.g. (5, 2) and (4, 3)

    # We create a single chunk tuple with the same total length
    # that evenly divides both, e.g. (4, 1, 2)

    # To accomplish this we walk down all chunk tuples together, finding the
    # smallest element, adding it to the output, and subtracting it from all
    # other elements and remove the element itself.  We stop once we have
    # burned through all of the chunk tuples.
    # For efficiency's sake we reverse the lists so that we can pop off the end
    rchunks = [list(ntd)[::-1] for ntd in non_trivial_dims]
    total = sum(first(non_trivial_dims))
    i = 0

    out = []
    while i < total:
        m = min(c[-1] for c in rchunks)
        out.append(m)
        for c in rchunks:
            c[-1] -= m
            if c[-1] == 0:
                c.pop()
        i += m

    return tuple(out)


def unify_chunks(*args, **kwargs):
    """
    Unify chunks across a sequence of arrays

    Parameters
    ----------
    *args: sequence of Array, index pairs
        Sequence like (x, 'ij', y, 'jk', z, 'i')

    Examples
    --------
    >>> import dask.array as da
    >>> x = da.ones(10, chunks=((5, 2, 3),))
    >>> y = da.ones(10, chunks=((2, 3, 5),))
    >>> chunkss, arrays = unify_chunks(x, 'i', y, 'i')
    >>> chunkss
    {'i': (2, 3, 2, 3)}

    >>> x = da.ones((100, 10), chunks=(20, 5))
    >>> y = da.ones((10, 100), chunks=(4, 50))
    >>> chunkss, arrays = unify_chunks(x, 'ij', y, 'jk')
    >>> chunkss  # doctest: +SKIP
    {'k': (50, 50), 'i': (20, 20, 20, 20, 20), 'j': (4, 1, 3, 2)}

    Returns
    -------
    chunkss : dict
        Map like {index: chunks}.
    arrays : list
        List of rechunked arrays.

    See Also
    --------
    common_blockdim
    """
    args = [asarray(a) if i % 2 == 0 else a for i, a in enumerate(args)]
    warn = kwargs.get('warn', True)
    arginds = list(partition(2, args)) # [x, ij, y, jk] -> [(x, ij), (y, jk)]
    arrays, inds = zip(*arginds)
    if all(ind == inds[0] for ind in inds) and all(a.chunks == arrays[0].chunks for a in arrays):
        return dict(zip(inds[0], arrays[0].chunks)), arrays

    nameinds = [(a.name, i) for a, i in arginds]
    blockdim_dict = dict((a.name, a.chunks) for a, _ in arginds)

    chunkss = broadcast_dimensions(nameinds, blockdim_dict,
                                   consolidate=common_blockdim)
    max_parts = max(arg.npartitions for arg in args[::2])
    nparts = np.prod(list(map(len, chunkss.values())))

    if warn and nparts and nparts >= max_parts * 10:
        warnings.warn("Increasing number of chunks by factor of %d" %
                      (nparts / max_parts))

    arrays = []
    for a, i in arginds:
        chunks = tuple(chunkss[j] if a.shape[n] > 1 else a.shape[n]
                       if not np.isnan(sum(chunkss[j])) else None
                       for n, j in enumerate(i))
        if chunks != a.chunks and all(a.chunks):
            arrays.append(a.rechunk(chunks))
        else:
            arrays.append(a)
    return chunkss, arrays


def atop(func, out_ind, *args, **kwargs):
    """ Tensor operation: Generalized inner and outer products

    A broad class of blocked algorithms and patterns can be specified with a
    concise multi-index notation.  The ``atop`` function applies an in-memory
    function across multiple blocks of multiple inputs in a variety of ways.
    Many dask.array operations are special cases of atop including elementwise,
    broadcasting, reductions, tensordot, and transpose.

    Parameters
    ----------
    func : callable
        Function to apply to individual tuples of blocks
    out_ind : iterable
        Block pattern of the output, something like 'ijk' or (1, 2, 3)
    *args : sequence of Array, index pairs
        Sequence like (x, 'ij', y, 'jk', z, 'i')
    **kwargs : dict
        Extra keyword arguments to pass to function
    dtype : np.dtype
        Datatype of resulting array.
    concatenate : bool, keyword only
        If true concatenate arrays along dummy indices, else provide lists
    adjust_chunks : dict
        Dictionary mapping index to function to be applied to chunk sizes
    new_axes : dict, keyword only
        New indexes and their dimension lengths

    Examples
    --------
    2D embarrassingly parallel operation from two arrays, x, and y.

    >>> z = atop(operator.add, 'ij', x, 'ij', y, 'ij', dtype='f8')  # z = x + y  # doctest: +SKIP

    Outer product multiplying x by y, two 1-d vectors

    >>> z = atop(operator.mul, 'ij', x, 'i', y, 'j', dtype='f8')  # doctest: +SKIP

    z = x.T

    >>> z = atop(np.transpose, 'ji', x, 'ij', dtype=x.dtype)  # doctest: +SKIP

    The transpose case above is illustrative because it does same transposition
    both on each in-memory block by calling ``np.transpose`` and on the order
    of the blocks themselves, by switching the order of the index ``ij -> ji``.

    We can compose these same patterns with more variables and more complex
    in-memory functions

    z = X + Y.T

    >>> z = atop(lambda x, y: x + y.T, 'ij', x, 'ij', y, 'ji', dtype='f8')  # doctest: +SKIP

    Any index, like ``i`` missing from the output index is interpreted as a
    contraction (note that this differs from Einstein convention; repeated
    indices do not imply contraction.)  In the case of a contraction the passed
    function should expect an iterable of blocks on any array that holds that
    index.  To receive arrays concatenated along contracted dimensions instead
    pass ``concatenate=True``.

    Inner product multiplying x by y, two 1-d vectors

    >>> def sequence_dot(x_blocks, y_blocks):
    ...     result = 0
    ...     for x, y in zip(x_blocks, y_blocks):
    ...         result += x.dot(y)
    ...     return result

    >>> z = atop(sequence_dot, '', x, 'i', y, 'i', dtype='f8')  # doctest: +SKIP

    Add new single-chunk dimensions with the ``new_axes=`` keyword, including
    the length of the new dimension.  New dimensions will always be in a single
    chunk.

    >>> def f(x):
    ...     return x[:, None] * np.ones((1, 5))

    >>> z = atop(f, 'az', x, 'a', new_axes={'z': 5}, dtype=x.dtype)  # doctest: +SKIP

    If the applied function changes the size of each chunk you can specify this
    with a ``adjust_chunks={...}`` dictionary holding a function for each index
    that modifies the dimension size in that index.

    >>> def double(x):
    ...     return np.concatenate([x, x])

    >>> y = atop(double, 'ij', x, 'ij',
    ...          adjust_chunks={'i': lambda n: 2 * n}, dtype=x.dtype)  # doctest: +SKIP

    See Also
    --------
    top - dict formulation of this function, contains most logic
    """
    out = kwargs.pop('name', None)      # May be None at this point
    token = kwargs.pop('token', None)
    dtype = kwargs.pop('dtype', None)
    adjust_chunks = kwargs.pop('adjust_chunks', None)
    new_axes = kwargs.get('new_axes', {})

    if dtype is None:
        raise ValueError("Must specify dtype of output array")

    chunkss, arrays = unify_chunks(*args)
    for k, v in new_axes.items():
        chunkss[k] = (v,)
    arginds = list(zip(arrays, args[1::2]))

    numblocks = dict([(a.name, a.numblocks) for a, _ in arginds])
    argindsstr = list(concat([(a.name, ind) for a, ind in arginds]))
    # Finish up the name
    if not out:
        out = '%s-%s' % (token or funcname(func).strip('_'),
                         tokenize(func, out_ind, argindsstr, dtype, **kwargs))

    dsk = top(func, out, out_ind, *argindsstr, numblocks=numblocks, **kwargs)
    dsks = [a.dask for a, _ in arginds]

    chunks = [chunkss[i] for i in out_ind]
    if adjust_chunks:
        for i, ind in enumerate(out_ind):
            if ind in adjust_chunks:
                if callable(adjust_chunks[ind]):
                    chunks[i] = tuple(map(adjust_chunks[ind], chunks[i]))
                elif isinstance(adjust_chunks[ind], int):
                    chunks[i] = tuple(adjust_chunks[ind] for _ in chunks[i])
                elif isinstance(adjust_chunks[ind], (tuple, list)):
                    chunks[i] = tuple(adjust_chunks[ind])
                else:
                    raise NotImplementedError(
                        "adjust_chunks values must be callable, int, or tuple")
    chunks = tuple(chunks)

    return Array(sharedict.merge((out, dsk), *dsks), out, chunks, dtype=dtype)


def unpack_singleton(x):
    """

    >>> unpack_singleton([[[[1]]]])
    1
    >>> unpack_singleton(np.array(np.datetime64('2000-01-01')))
    array(datetime.date(2000, 1, 1), dtype='datetime64[D]')
    """
    while isinstance(x, (list, tuple)):
        try:
            x = x[0]
        except (IndexError, TypeError, KeyError):
            break
    return x


def concatenate(seq, axis=0, allow_unknown_chunksizes=False):
    """
    Concatenate arrays along an existing axis

    Given a sequence of dask Arrays form a new dask Array by stacking them
    along an existing dimension (axis=0 by default)

    Parameters
    ----------
    seq: list of dask.arrays
    axis: int
        Dimension along which to align all of the arrays
    allow_unknown_chunksizes: bool
        Allow unknown chunksizes, such as come from converting from dask
        dataframes.  Dask.array is unable to verify that chunks line up.  If
        data comes from differently aligned sources then this can cause
        unexpected results.

    Examples
    --------

    Create slices

    >>> import dask.array as da
    >>> import numpy as np

    >>> data = [from_array(np.ones((4, 4)), chunks=(2, 2))
    ...          for i in range(3)]

    >>> x = da.concatenate(data, axis=0)
    >>> x.shape
    (12, 4)

    >>> da.concatenate(data, axis=1).shape
    (4, 12)

    Result is a new dask Array

    See Also
    --------
    stack
    """
    n = len(seq)
    ndim = len(seq[0].shape)

    if axis < 0:
        axis = ndim + axis
    if axis >= ndim:
        msg = ("Axis must be less than than number of dimensions"
               "\nData has %d dimensions, but got axis=%d")
        raise ValueError(msg % (ndim, axis))

    if n == 1:
        return seq[0]

    if (not allow_unknown_chunksizes and
        not all(i == axis or all(x.shape[i] == seq[0].shape[i] for x in seq)
                for i in range(ndim))):
        if any(map(np.isnan, seq[0].shape)):
            raise ValueError("Tried to concatenate arrays with unknown"
                             " shape %s.  To force concatenation pass"
                             " allow_unknown_chunksizes=True."
                             % str(seq[0].shape))
        raise ValueError("Shapes do not align: %s", [x.shape for x in seq])

    inds = [list(range(ndim)) for i in range(n)]
    for i, ind in enumerate(inds):
        ind[axis] = -(i + 1)

    uc_args = list(concat(zip(seq, inds)))
    _, seq = unify_chunks(*uc_args, warn=False)

    bds = [a.chunks for a in seq]

    chunks = (seq[0].chunks[:axis] + (sum([bd[axis] for bd in bds], ()), ) +
              seq[0].chunks[axis + 1:])

    cum_dims = [0] + list(accumulate(add, [len(a.chunks[axis]) for a in seq]))

    dt = reduce(np.promote_types, [a.dtype for a in seq])
    seq = [x.astype(dt) for x in seq]

    names = [a.name for a in seq]

    name = 'concatenate-' + tokenize(names, axis)
    keys = list(product([name], *[range(len(bd)) for bd in chunks]))

    values = [(names[bisect(cum_dims, key[axis + 1]) - 1],) + key[1:axis + 1] +
              (key[axis + 1] - cum_dims[bisect(cum_dims, key[axis + 1]) - 1], ) +
              key[axis + 2:] for key in keys]

    dsk = dict(zip(keys, values))
    dsk2 = sharedict.merge((name, dsk), * [a.dask for a in seq])

    return Array(dsk2, name, chunks, dtype=dt)


def insert_to_ooc(out, arr, lock=True, region=None):
    if lock is True:
        lock = Lock()

    def store(out, x, index, lock, region):
        if lock:
            lock.acquire()
        try:
            if region is None:
                out[index] = np.asanyarray(x)
            else:
                out[fuse_slice(region, index)] = np.asanyarray(x)
        finally:
            if lock:
                lock.release()

        return None

    slices = slices_from_chunks(arr.chunks)

    name = 'store-%s' % arr.name
    dsk = dict(((name,) + t[1:], (store, out, t, slc, lock, region))
               for t, slc in zip(core.flatten(arr._keys()), slices))

    return dsk


def asarray(array):
    """Coerce argument into a dask array

    Examples
    --------
    >>> x = np.arange(3)
    >>> asarray(x)
    dask.array<array, shape=(3,), dtype=int64, chunksize=(3,)>
    """
    if isinstance(array, Array):
        return array
    if not isinstance(getattr(array, 'shape', None), Iterable):
        array = np.asarray(array)
    return from_array(array, chunks=array.shape, getitem=getter_inline)


def asanyarray(array):
    """Coerce argument into a dask array.

    Subclasses of `np.ndarray` will be passed through as chunks unchanged.

    Examples
    --------
    >>> x = np.arange(3)
    >>> asanyarray(x)
    dask.array<array, shape=(3,), dtype=int64, chunksize=(3,)>
    """
    if isinstance(array, Array):
        return array
    if not isinstance(getattr(array, 'shape', None), Iterable):
        array = np.asanyarray(array)
    return from_array(array, chunks=array.shape, getitem=getter_inline,
                      asarray=False)


def partial_by_order(*args, **kwargs):
    """

    >>> partial_by_order(5, function=add, other=[(1, 10)])
    15
    """
    function = kwargs.pop('function')
    other = kwargs.pop('other')
    args2 = list(args)
    for i, arg in other:
        args2.insert(i, arg)
    return function(*args2, **kwargs)


def is_scalar_for_elemwise(arg):
    """

    >>> is_scalar_for_elemwise(42)
    True
    >>> is_scalar_for_elemwise('foo')
    True
    >>> is_scalar_for_elemwise(True)
    True
    >>> is_scalar_for_elemwise(np.array(42))
    True
    >>> is_scalar_for_elemwise([1, 2, 3])
    True
    >>> is_scalar_for_elemwise(np.array([1, 2, 3]))
    False
    >>> is_scalar_for_elemwise(from_array(np.array(0), chunks=()))
    False
    >>> is_scalar_for_elemwise(np.dtype('i4'))
    True
    """
    return (np.isscalar(arg) or
            not isinstance(getattr(arg, 'shape', None), Iterable) or
            isinstance(arg, np.dtype) or
            (isinstance(arg, np.ndarray) and arg.ndim == 0))


def broadcast_shapes(*shapes):
    """
    Determines output shape from broadcasting arrays.

    Parameters
    ----------
    shapes : tuples
        The shapes of the arguments.

    Returns
    -------
    output_shape : tuple

    Raises
    ------
    ValueError
        If the input shapes cannot be successfully broadcast together.
    """
    if len(shapes) == 1:
        return shapes[0]
    out = []
    for sizes in zip_longest(*map(reversed, shapes), fillvalue=-1):
        dim = max(sizes)
        if any(i != -1 and i != 1 and i != dim and not np.isnan(i) for i in sizes):
            raise ValueError("operands could not be broadcast together with "
                             "shapes {0}".format(' '.join(map(str, shapes))))
        out.append(dim)
    return tuple(reversed(out))


def elemwise(op, *args, **kwargs):
    """ Apply elementwise function across arguments

    Respects broadcasting rules

    Examples
    --------
    >>> elemwise(add, x, y)  # doctest: +SKIP
    >>> elemwise(sin, x)  # doctest: +SKIP

    See Also
    --------
    atop
    """
    out = kwargs.pop('out', None)
    if not set(['name', 'dtype']).issuperset(kwargs):
        msg = "%s does not take the following keyword arguments %s"
        raise TypeError(msg % (op.__name__, str(sorted(set(kwargs) - set(['name', 'dtype'])))))

    args = [np.asarray(a) if isinstance(a, (list, tuple)) else a for a in args]

    shapes = [getattr(arg, 'shape', ()) for arg in args]
    shapes = [s if isinstance(s, Iterable) else () for s in shapes]
    out_ndim = len(broadcast_shapes(*shapes))   # Raises ValueError if dimensions mismatch
    expr_inds = tuple(range(out_ndim))[::-1]

    arrays = [a for a in args if not is_scalar_for_elemwise(a)]
    other = [(i, a) for i, a in enumerate(args) if is_scalar_for_elemwise(a)]

    need_enforce_dtype = False
    if 'dtype' in kwargs:
        dt = kwargs['dtype']
    else:
        # We follow NumPy's rules for dtype promotion, which special cases
        # scalars and 0d ndarrays (which it considers equivalent) by using
        # their values to compute the result dtype:
        # https://github.com/numpy/numpy/issues/6240
        # We don't inspect the values of 0d dask arrays, because these could
        # hold potentially very expensive calculations. Instead, we treat
        # them just like other arrays, and if necessary cast the result of op
        # to match.
        vals = [np.empty((1,) * max(1, a.ndim), dtype=a.dtype)
                if not is_scalar_for_elemwise(a) else a
                for a in args]
        dt = apply_infer_dtype(op, vals, {}, 'elemwise', suggest_dtype=False)
        need_enforce_dtype = any(not is_scalar_for_elemwise(a) and a.ndim == 0 for a in args)

    name = kwargs.get('name', None) or '%s-%s' % (funcname(op),
                                                  tokenize(op, dt, *args))

    atop_kwargs = dict(dtype=dt, name=name, token=funcname(op).strip('_'))
    if other:
        atop_kwargs['function'] = op
        atop_kwargs['other'] = other
        op = partial_by_order
    if need_enforce_dtype:
        atop_kwargs['enforce_dtype'] = dt
        atop_kwargs['enforce_dtype_function'] = op
        op = _enforce_dtype
    result = atop(op, expr_inds,
                  *concat((a, tuple(range(a.ndim)[::-1])) for a in arrays),
                  **atop_kwargs)

    return handle_out(out, result)


def handle_out(out, result):
    """ Handle out paramters

    If out is a dask.array then this overwrites the contents of that array with
    the result
    """
    if isinstance(out, tuple):
        if len(out) == 1:
            out = out[0]
        elif len(out) > 1:
            raise NotImplementedError("The out parameter is not fully supported")
        else:
            out = None
    if isinstance(out, Array):
        if out.shape != result.shape:
            raise NotImplementedError(
                "Mismatched shapes between result and out parameter. "
                "out=%s, result=%s" % (str(out.shape), str(result.shape)))
        out._chunks = result.chunks
        out.dask = result.dask
        out.dtype = result.dtype
        out.name = result.name
    elif out is not None:
        msg = ("The out parameter is not fully supported."
               " Received type %s, expected Dask Array" % type(out).__name__)
        raise NotImplementedError(msg)
    else:
        return result


def _enforce_dtype(*args, **kwargs):
    """Calls a function and converts its result to the given dtype.

    The parameters have deliberately been given unwieldy names to avoid
    clashes with keyword arguments consumed by atop or partial_by_order.

    A dtype of `object` is treated as a special case and not enforced,
    because it is used as a dummy value in some places when the result will
    not be a block in an Array.

    Parameters
    ----------
    enforce_dtype : dtype
        Result dtype
    enforce_dtype_function : callable
        The wrapped function, which will be passed the remaining arguments
    """
    dtype = kwargs.pop('enforce_dtype')
    function = kwargs.pop('enforce_dtype_function')

    result = function(*args, **kwargs)
    if dtype != object:
        if np.isscalar(result):
            # scalar astype method doesn't take the keyword arguments, so
            # have to convert via 0-dimensional array and back.
            result = result[...].astype(dtype, casting='same_kind', copy=False)[()]
        else:
            result = result.astype(dtype, casting='same_kind', copy=False)
    return result


@wraps(chunk.broadcast_to)
def broadcast_to(x, shape):
    x = asarray(x)
    shape = tuple(shape)

    if x.shape == shape:
        return x

    ndim_new = len(shape) - x.ndim
    if ndim_new < 0 or any(new != old
                           for new, old in zip(shape[ndim_new:], x.shape)
                           if old != 1):
        raise ValueError('cannot broadcast shape %s to shape %s'
                         % (x.shape, shape))

    name = 'broadcast_to-' + tokenize(x, shape)
    chunks = (tuple((s,) for s in shape[:ndim_new]) +
              tuple(bd if old > 1 else (new,)
              for bd, old, new in zip(x.chunks, x.shape, shape[ndim_new:])))
    dsk = dict(((name,) + (0,) * ndim_new + key[1:],
                (chunk.broadcast_to, key, shape[:ndim_new] +
                 tuple(bd[i] for i, bd in zip(key[1:], chunks[ndim_new:]))))
               for key in core.flatten(x._keys()))
    return Array(sharedict.merge((name, dsk), x.dask), name, chunks, dtype=x.dtype)


def offset_func(func, offset, *args):
    """  Offsets inputs by offset

    >>> double = lambda x: x * 2
    >>> f = offset_func(double, (10,))
    >>> f(1)
    22
    >>> f(300)
    620
    """
    def _offset(*args):
        args2 = list(map(add, args, offset))
        return func(*args2)

    with ignoring(Exception):
        _offset.__name__ = 'offset_' + func.__name__

    return _offset


def chunks_from_arrays(arrays):
    """ Chunks tuple from nested list of arrays

    >>> x = np.array([1, 2])
    >>> chunks_from_arrays([x, x])
    ((2, 2),)

    >>> x = np.array([[1, 2]])
    >>> chunks_from_arrays([[x], [x]])
    ((1, 1), (2,))

    >>> x = np.array([[1, 2]])
    >>> chunks_from_arrays([[x, x]])
    ((1,), (2, 2))

    >>> chunks_from_arrays([1, 1])
    ((1, 1),)
    """
    if not arrays:
        return ()
    result = []
    dim = 0

    def shape(x):
        try:
            return x.shape
        except AttributeError:
            return (1,)

    while isinstance(arrays, (list, tuple)):
        result.append(tuple([shape(deepfirst(a))[dim] for a in arrays]))
        arrays = arrays[0]
        dim += 1
    return tuple(result)


def deepfirst(seq):
    """ First element in a nested list

    >>> deepfirst([[[1, 2], [3, 4]], [5, 6], [7, 8]])
    1
    """
    if not isinstance(seq, (list, tuple)):
        return seq
    else:
        return deepfirst(seq[0])


def ndimlist(seq):
    if not isinstance(seq, (list, tuple)):
        return 0
    elif not seq:
        return 1
    else:
        return 1 + ndimlist(seq[0])


def shapelist(a):
    """ Get the shape of nested list """
    if type(a) is list:
        return tuple([len(a)] + list(shapelist(a[0])))
    else:
        return ()


def reshapelist(shape, seq):
    """ Reshape iterator to nested shape

    >>> reshapelist((2, 3), range(6))
    [[0, 1, 2], [3, 4, 5]]
    """
    if len(shape) == 1:
        return list(seq)
    else:
        n = int(len(seq) / shape[0])
        return [reshapelist(shape[1:], part) for part in partition(n, seq)]


def transposelist(arrays, axes, extradims=0):
    """ Permute axes of nested list

    >>> transposelist([[1,1,1],[1,1,1]], [2,1])
    [[[1, 1], [1, 1], [1, 1]]]

    >>> transposelist([[1,1,1],[1,1,1]], [2,1], extradims=1)
    [[[[1], [1]], [[1], [1]], [[1], [1]]]]
    """
    if len(axes) != ndimlist(arrays):
        raise ValueError("Length of axes should equal depth of nested arrays")
    if extradims < 0:
        raise ValueError("`newdims` should be positive")
    if len(axes) > len(set(axes)):
        raise ValueError("`axes` should be unique")

    ndim = max(axes) + 1
    shape = shapelist(arrays)
    newshape = [shape[axes.index(i)] if i in axes else 1 for i in range(ndim + extradims)]

    result = list(core.flatten(arrays))
    return reshapelist(newshape, result)


def stack(seq, axis=0):
    """
    Stack arrays along a new axis

    Given a sequence of dask Arrays form a new dask Array by stacking them
    along a new dimension (axis=0 by default)

    Examples
    --------

    Create slices

    >>> import dask.array as da
    >>> import numpy as np

    >>> data = [from_array(np.ones((4, 4)), chunks=(2, 2))
    ...          for i in range(3)]

    >>> x = da.stack(data, axis=0)
    >>> x.shape
    (3, 4, 4)

    >>> da.stack(data, axis=1).shape
    (4, 3, 4)

    >>> da.stack(data, axis=-1).shape
    (4, 4, 3)

    Result is a new dask Array

    See Also
    --------
    concatenate
    """
    n = len(seq)
    ndim = len(seq[0].shape)
    if axis < 0:
        axis = ndim + axis + 1
    if axis > ndim:
        raise ValueError("Axis must not be greater than number of dimensions"
                         "\nData has %d dimensions, but got axis=%d" %
                         (ndim, axis))
    if not all(x.shape == seq[0].shape for x in seq):
        raise ValueError("Stacked arrays must have the same shape. Got %s",
                         [x.shape for x in seq])

    ind = list(range(ndim))
    uc_args = list(concat((x, ind) for x in seq))
    _, seq = unify_chunks(*uc_args)

    dt = reduce(np.promote_types, [a.dtype for a in seq])
    seq = [x.astype(dt) for x in seq]

    assert len(set(a.chunks for a in seq)) == 1  # same chunks
    chunks = (seq[0].chunks[:axis] + ((1,) * n,) + seq[0].chunks[axis:])

    names = [a.name for a in seq]
    name = 'stack-' + tokenize(names, axis)
    keys = list(product([name], *[range(len(bd)) for bd in chunks]))

    inputs = [(names[key[axis + 1]], ) + key[1:axis + 1] + key[axis + 2:]
              for key in keys]
    values = [(getitem, inp, (slice(None, None, None),) * axis +
              (None, ) + (slice(None, None, None), ) * (ndim - axis))
              for inp in inputs]

    dsk = dict(zip(keys, values))
    dsk2 = sharedict.merge((name, dsk), *[a.dask for a in seq])

    return Array(dsk2, name, chunks, dtype=dt)


def concatenate3(arrays):
    """ Recursive np.concatenate

    Input should be a nested list of numpy arrays arranged in the order they
    should appear in the array itself.  Each array should have the same number
    of dimensions as the desired output and the nesting of the lists.

    >>> x = np.array([[1, 2]])
    >>> concatenate3([[x, x, x], [x, x, x]])
    array([[1, 2, 1, 2, 1, 2],
           [1, 2, 1, 2, 1, 2]])

    >>> concatenate3([[x, x], [x, x], [x, x]])
    array([[1, 2, 1, 2],
           [1, 2, 1, 2],
           [1, 2, 1, 2]])
    """
    arrays = concrete(arrays)
    if not arrays:
        return np.empty(0)

    advanced = max(core.flatten(arrays, container=(list, tuple)),
                   key=lambda x: getattr(x, '__array_priority__', 0))
    module = package_of(type(advanced)) or np
    if module is not np and hasattr(module, 'concatenate'):
        x = unpack_singleton(arrays)
        return _concatenate2(arrays, axes=list(range(x.ndim)))

    ndim = ndimlist(arrays)
    if not ndim:
        return arrays
    chunks = chunks_from_arrays(arrays)
    shape = tuple(map(sum, chunks))

    def dtype(x):
        try:
            return x.dtype
        except AttributeError:
            return type(x)

    result = np.empty(shape=shape, dtype=dtype(deepfirst(arrays)))

    for (idx, arr) in zip(slices_from_chunks(chunks), core.flatten(arrays)):
        if hasattr(arr, 'ndim'):
            while arr.ndim < ndim:
                arr = arr[None, ...]
        result[idx] = arr

    return result


def concatenate_axes(arrays, axes):
    """ Recursively call np.concatenate along axes """
    if len(axes) != ndimlist(arrays):
        raise ValueError("Length of axes should equal depth of nested arrays")

    extradims = max(0, deepfirst(arrays).ndim - (max(axes) + 1))
    return concatenate3(transposelist(arrays, axes, extradims=extradims))


def to_hdf5(filename, *args, **kwargs):
    """ Store arrays in HDF5 file

    This saves several dask arrays into several datapaths in an HDF5 file.
    It creates the necessary datasets and handles clean file opening/closing.

    >>> da.to_hdf5('myfile.hdf5', '/x', x)  # doctest: +SKIP

    or

    >>> da.to_hdf5('myfile.hdf5', {'/x': x, '/y': y})  # doctest: +SKIP

    Optionally provide arguments as though to ``h5py.File.create_dataset``

    >>> da.to_hdf5('myfile.hdf5', '/x', x, compression='lzf', shuffle=True)  # doctest: +SKIP

    This can also be used as a method on a single Array

    >>> x.to_hdf5('myfile.hdf5', '/x')  # doctest: +SKIP

    See Also
    --------
    da.store
    h5py.File.create_dataset
    """
    if len(args) == 1 and isinstance(args[0], dict):
        data = args[0]
    elif (len(args) == 2 and
          isinstance(args[0], str) and
          isinstance(args[1], Array)):
        data = {args[0]: args[1]}
    else:
        raise ValueError("Please provide {'/data/path': array} dictionary")

    chunks = kwargs.pop('chunks', True)

    import h5py
    with h5py.File(filename) as f:
        dsets = [f.require_dataset(dp, shape=x.shape, dtype=x.dtype,
                                   chunks=tuple([c[0] for c in x.chunks])
                                   if chunks is True else chunks, **kwargs)
                 for dp, x in data.items()]
        store(list(data.values()), dsets)


def interleave_none(a, b):
    """

    >>> interleave_none([0, None, 2, None], [1, 3])
    (0, 1, 2, 3)
    """
    result = []
    i = j = 0
    n = len(a) + len(b)
    while i + j < n:
        if a[i] is not None:
            result.append(a[i])
            i += 1
        else:
            result.append(b[j])
            i += 1
            j += 1
    return tuple(result)


def keyname(name, i, okey):
    """

    >>> keyname('x', 3, [None, None, 0, 2])
    ('x', 3, 0, 2)
    """
    return (name, i) + tuple(k for k in okey if k is not None)


def _vindex(x, *indexes):
    """Point wise indexing with broadcasting.

    >>> x = np.arange(56).reshape((7, 8))
    >>> x
    array([[ 0,  1,  2,  3,  4,  5,  6,  7],
           [ 8,  9, 10, 11, 12, 13, 14, 15],
           [16, 17, 18, 19, 20, 21, 22, 23],
           [24, 25, 26, 27, 28, 29, 30, 31],
           [32, 33, 34, 35, 36, 37, 38, 39],
           [40, 41, 42, 43, 44, 45, 46, 47],
           [48, 49, 50, 51, 52, 53, 54, 55]])

    >>> d = from_array(x, chunks=(3, 4))
    >>> result = _vindex(d, [0, 1, 6, 0], [0, 1, 0, 7])
    >>> result.compute()
    array([ 0,  9, 48,  7])
    """
    indexes = replace_ellipsis(x.ndim, indexes)
    partial_slices = {i: ind for i, ind in enumerate(indexes)
                      if isinstance(ind, slice) and ind != slice(None)}
    if partial_slices:
        key = tuple(partial_slices.get(i, slice(None))
                    for i in range(len(indexes)))
        x = x[key]

    array_indexes = {i: np.asarray(ind) for i, ind in enumerate(indexes)
                     if not isinstance(ind, slice)}
    if any(ind.dtype.kind == 'b' for ind in array_indexes.values()):
        raise IndexError('vindex does not support indexing with boolean arrays')

    try:
        broadcast_indexes = np.broadcast_arrays(*array_indexes.values())
    except ValueError:
        # note: error message exactly matches numpy
        shapes_str = ' '.join(str(a.shape) for a in array_indexes.values())
        raise IndexError('shape mismatch: indexing arrays could not be '
                         'broadcast together with shapes ' + shapes_str)
    broadcast_shape = broadcast_indexes[0].shape

    lookup = dict(zip(array_indexes, broadcast_indexes))
    flat_indexes = [lookup[i].ravel().tolist() if i in lookup else None
                    for i in range(len(indexes))]
    flat_indexes.extend([None] * (x.ndim - len(flat_indexes)))
    result_1d = _vindex_1d(x, *flat_indexes)
    return result_1d.reshape(broadcast_shape + result_1d.shape[1:])


def _vindex_1d(x, *indexes):
    """Point wise indexing with only 1D lists and full slices."""
    indexes = [list(index) if index is not None else index for index in indexes]
    bounds = [list(accumulate(add, (0,) + c)) for c in x.chunks]
    bounds2 = [b for i, b in zip(indexes, bounds) if i is not None]
    axis = _get_axis(indexes)
    token = tokenize(x, indexes)
    out_name = 'vindex-merge-' + token

    points = list()
    for i, idx in enumerate(zip(*[i for i in indexes if i is not None])):
        block_idx = [np.searchsorted(b, ind, 'right') - 1
                     for b, ind in zip(bounds2, idx)]
        inblock_idx = [ind - bounds2[k][j]
                       for k, (ind, j) in enumerate(zip(idx, block_idx))]
        points.append((i, tuple(block_idx), tuple(inblock_idx)))

    chunks = [c for i, c in zip(indexes, x.chunks) if i is None]
    chunks.insert(0, (len(points),) if points else (0,))
    chunks = tuple(chunks)

    if points:
        per_block = groupby(1, points)
        per_block = dict((k, v) for k, v in per_block.items() if v)

        other_blocks = list(product(*[list(range(len(c))) if i is None else [None]
                                    for i, c in zip(indexes, x.chunks)]))

        full_slices = [slice(None, None) if i is None else None for i in indexes]

        name = 'vindex-slice-' + token
        dsk = dict((keyname(name, i, okey),
                    (_vindex_transpose,
                    (_vindex_slice, (x.name,) + interleave_none(okey, key),
                     interleave_none(full_slices, list(zip(*pluck(2, per_block[key]))))),
                     axis))
                   for i, key in enumerate(per_block)
                   for okey in other_blocks)

        dsk.update((keyname('vindex-merge-' + token, 0, okey),
                   (_vindex_merge,
                    [list(pluck(0, per_block[key])) for key in per_block],
                    [keyname(name, i, okey) for i in range(len(per_block))]))
                   for okey in other_blocks)

        return Array(sharedict.merge(x.dask, (out_name, dsk)), out_name, chunks,
                     x.dtype)

    # output has a zero dimension, just create a new zero-shape array with the
    # same dtype
    from .wrap import empty
    return empty(tuple(map(sum, chunks)), chunks=chunks, dtype=x.dtype,
                 name=out_name)


def _get_axis(indexes):
    """ Get axis along which point-wise slicing results lie

    This is mostly a hack because I can't figure out NumPy's rule on this and
    can't be bothered to go reading.

    >>> _get_axis([[1, 2], None, [1, 2], None])
    0
    >>> _get_axis([None, [1, 2], [1, 2], None])
    1
    >>> _get_axis([None, None, [1, 2], [1, 2]])
    2
    """
    ndim = len(indexes)
    indexes = [slice(None, None) if i is None else [0] for i in indexes]
    x = np.empty((2,) * ndim)
    x2 = x[tuple(indexes)]
    return x2.shape.index(1)


def _vindex_slice(block, points):
    """ Pull out point-wise slices from block """
    points = [p if isinstance(p, slice) else list(p) for p in points]
    return block[tuple(points)]


def _vindex_transpose(block, axis):
    """ Rotate block so that points are on the first dimension """
    axes = [axis] + list(range(axis)) + list(range(axis + 1, block.ndim))
    return block.transpose(axes)


def _vindex_merge(locations, values):
    """

    >>> locations = [0], [2, 1]
    >>> values = [np.array([[1, 2, 3]]),
    ...           np.array([[10, 20, 30], [40, 50, 60]])]

    >>> _vindex_merge(locations, values)
    array([[ 1,  2,  3],
           [40, 50, 60],
           [10, 20, 30]])
    """
    locations = list(map(list, locations))
    values = list(values)

    n = sum(map(len, locations))

    shape = list(values[0].shape)
    shape[0] = n
    shape = tuple(shape)

    dtype = values[0].dtype

    x = np.empty(shape, dtype=dtype)

    ind = [slice(None, None) for i in range(x.ndim)]
    for loc, val in zip(locations, values):
        ind[0] = loc
        x[tuple(ind)] = val

    return x


def to_npy_stack(dirname, x, axis=0):
    """ Write dask array to a stack of .npy files

    This partitions the dask.array along one axis and stores each block along
    that axis as a single .npy file in the specified directory

    Examples
    --------

    >>> x = da.ones((5, 10, 10), chunks=(2, 4, 4))  # doctest: +SKIP
    >>> da.to_npy_stack('data/', x, axis=0)  # doctest: +SKIP

        $ tree data/
        data/
        |-- 0.npy
        |-- 1.npy
        |-- 2.npy
        |-- info

    The ``.npy`` files store numpy arrays for ``x[0:2], x[2:4], and x[4:5]``
    respectively, as is specified by the chunk size along the zeroth axis.  The
    info file stores the dtype, chunks, and axis information of the array.

    You can load these stacks with the ``da.from_npy_stack`` function.

    >>> y = da.from_npy_stack('data/')  # doctest: +SKIP

    See Also
    --------
    from_npy_stack
    """

    chunks = tuple((c if i == axis else (sum(c),))
                   for i, c in enumerate(x.chunks))
    xx = x.rechunk(chunks)

    if not os.path.exists(dirname):
        os.path.mkdir(dirname)

    meta = {'chunks': chunks, 'dtype': x.dtype, 'axis': axis}

    with open(os.path.join(dirname, 'info'), 'wb') as f:
        pickle.dump(meta, f)

    name = 'to-npy-stack-' + str(uuid.uuid1())
    dsk = dict(((name, i), (np.save, os.path.join(dirname, '%d.npy' % i), key))
               for i, key in enumerate(core.flatten(xx._keys())))

    Array._get(sharedict.merge(dsk, xx.dask), list(dsk))


def from_npy_stack(dirname, mmap_mode='r'):
    """ Load dask array from stack of npy files

    See ``da.to_npy_stack`` for docstring

    Parameters
    ----------
    dirname: string
        Directory of .npy files
    mmap_mode: (None or 'r')
        Read data in memory map mode
    """
    with open(os.path.join(dirname, 'info'), 'rb') as f:
        info = pickle.load(f)

    dtype = info['dtype']
    chunks = info['chunks']
    axis = info['axis']

    name = 'from-npy-stack-%s' % dirname
    keys = list(product([name], *[range(len(c)) for c in chunks]))
    values = [(np.load, os.path.join(dirname, '%d.npy' % i), mmap_mode)
              for i in range(len(chunks[axis]))]
    dsk = dict(zip(keys, values))

    return Array(dsk, name, chunks, dtype)


def slice_with_dask_array(x, index):
    y = elemwise(getitem, x, index, dtype=x.dtype)

    name = 'getitem-' + tokenize(x, index)

    dsk = {(name, i): k
           for i, k in enumerate(core.flatten(y._keys()))}
    chunks = ((np.nan,) * y.npartitions,)

    return Array(sharedict.merge(y.dask, (name, dsk)), name, chunks, x.dtype)
