Overlapping Computations
========================

Some array operations require communication of borders between neighboring
blocks.  Example operations include the following:

*  Convolve a filter across an image
*  Sliding sum/mean/max, ...
*  Search for image motifs like a Gaussian blob that might span the border of a
   block
*  Evaluate a partial derivative
*  Play the game of Life_

Dask Array supports these operations by creating a new array where each
block is slightly expanded by the borders of its neighbors.  This costs an
excess copy and the communication of many small chunks, but allows localized
functions to evaluate in an embarrassingly parallel manner.

The main API for these computations is the ``map_overlap`` method defined
below:

.. currentmodule:: dask.array

.. autosummary::
   map_overlap

.. autofunction:: map_overlap


Explanation
-----------

Consider two neighboring blocks in a Dask array:

.. image:: images/unoverlapping-neighbors.png
   :width: 30%
   :alt: un-overlapping neighbors

We extend each block by trading thin nearby slices between arrays:

.. image:: images/overlapping-neighbors.png
   :width: 30%
   :alt: overlapping neighbors

We do this in all directions, including also diagonal interactions with the
overlap function:

.. image:: images/overlapping-blocks.png
   :width: 40%
   :alt: overlapping blocks

.. code-block:: python

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

   >>> x = np.arange(64).reshape((8, 8))
   >>> d = da.from_array(x, chunks=(4, 4))
   >>> d.chunks
   ((4, 4), (4, 4))

   >>> g = da.overlap.overlap(d, depth={0: 2, 1: 1},
   ...                       boundary={0: 100, 1: 'reflect'})
   >>> g.chunks
   ((8, 8), (6, 6))

   >>> np.array(g)
   array([[100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100],
          [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100],
          [  0,   0,   1,   2,   3,   4,   3,   4,   5,   6,   7,   7],
          [  8,   8,   9,  10,  11,  12,  11,  12,  13,  14,  15,  15],
          [ 16,  16,  17,  18,  19,  20,  19,  20,  21,  22,  23,  23],
          [ 24,  24,  25,  26,  27,  28,  27,  28,  29,  30,  31,  31],
          [ 32,  32,  33,  34,  35,  36,  35,  36,  37,  38,  39,  39],
          [ 40,  40,  41,  42,  43,  44,  43,  44,  45,  46,  47,  47],
          [ 16,  16,  17,  18,  19,  20,  19,  20,  21,  22,  23,  23],
          [ 24,  24,  25,  26,  27,  28,  27,  28,  29,  30,  31,  31],
          [ 32,  32,  33,  34,  35,  36,  35,  36,  37,  38,  39,  39],
          [ 40,  40,  41,  42,  43,  44,  43,  44,  45,  46,  47,  47],
          [ 48,  48,  49,  50,  51,  52,  51,  52,  53,  54,  55,  55],
          [ 56,  56,  57,  58,  59,  60,  59,  60,  61,  62,  63,  63],
          [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100],
          [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100]])


Boundaries
----------

With respect to overlapping, you can specify how to handle the boundaries.  Current policies
include the following:

*  ``periodic`` - wrap borders around to the other side
*  ``reflect`` - reflect each border outwards
*  ``any-constant`` - pad the border with this value

An example boundary kind argument might look like the following:

.. code-block:: python

   {0: 'periodic',
    1: 'reflect',
    2: np.nan}

Alternatively, you can use :py:func:`dask.array.pad` for other types of
paddings.


Map a function across blocks
----------------------------

Overlapping goes hand-in-hand with mapping a function across blocks.  This
function can now use the additional information copied over from the neighbors
that is not stored locally in each block:

.. code-block:: python

   >>> from scipy.ndimage.filters import gaussian_filter
   >>> def func(block):
   ...    return gaussian_filter(block, sigma=1)

   >>> filt = g.map_blocks(func)

While in this case we used a SciPy function, any arbitrary function could have been 
used instead. This is a good interaction point with Numba_.

If your function does not preserve the shape of the block, then you will need to
provide a ``chunks`` keyword argument. If your block size is regular, then this
argument can take a block shape of, for example, ``(1000, 1000)``. In case of
irregular block sizes, it must be a tuple with the full chunks shape like
``((1000, 700, 1000), (200, 300))``.

.. code-block:: python

   >>> g.map_blocks(myfunc, chunks=(5, 5))

If your function needs to know the location of the block on which it operates,
you can give your function a keyword argument ``block_id``:

.. code-block:: python

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

This extra keyword argument will be given a tuple that provides the block
location like ``(0, 0)`` for the upper-left block or ``(0, 1)`` for the block
just to the right of that block.


Trim Excess
-----------

After mapping a blocked function, you may want to trim off the borders from each
block by the same amount by which they were expanded.  The function
``trim_internal`` is useful here and takes the same ``depth`` argument
given to ``overlap``:

.. code-block:: python

   >>> x.chunks
   ((10, 10, 10, 10), (10, 10, 10, 10))

   >>> y = da.overlap.trim_internal(x, {0: 2, 1: 1})
   >>> y.chunks
   ((6, 6, 6, 6), (8, 8, 8, 8))


Full Workflow
-------------

And so, a pretty typical overlapping workflow includes ``overlap``, ``map_blocks``
and ``trim_internal``:

.. code-block:: python

   >>> x = ...
   >>> g = da.overlap.overlap(x, depth={0: 2, 1: 2},
   ...                       boundary={0: 'periodic', 1: 'periodic'})
   >>> g2 = g.map_blocks(myfunc)
   >>> result = da.overlap.trim_internal(g2, {0: 2, 1: 2})

.. _Life: https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life
.. _Numba: https://numba.pydata.org/
