Create Dask Arrays
==================

You can load or store Dask arrays from a variety of common sources like HDF5,
NetCDF, `Zarr`_, or any format that supports NumPy-style slicing.

.. currentmodule:: dask.array

.. autosummary::
   from_array
   from_delayed
   from_npy_stack
   from_zarr
   stack
   concatenate

NumPy Slicing
-------------

.. autosummary::
   from_array

Many storage formats have Python projects that expose storage using NumPy
slicing syntax.  These include HDF5, NetCDF, BColz, Zarr, GRIB, etc.  For
example, we can load a Dask array from an HDF5 file using `h5py <https://www.h5py.org/>`_:

.. code-block:: Python

   >>> import h5py
   >>> f = h5py.File('myfile.hdf5') # HDF5 file
   >>> d = f['/data/path']          # Pointer on on-disk array
   >>> d.shape                      # d can be very large
   (1000000, 1000000)

   >>> x = d[:5, :5]                # We slice to get numpy arrays

Given an object like ``d`` above that has ``dtype`` and ``shape`` properties
and that supports NumPy style slicing, we can construct a lazy Dask array:

.. code-block:: Python

   >>> import dask.array as da
   >>> x = da.from_array(d, chunks=(1000, 1000))

This process is entirely lazy.  Neither creating the h5py object nor wrapping
it with ``da.from_array`` have loaded any data.


Random Data
-----------

For experimentation or benchmarking it is common to create arrays of random
data.  The ``dask.array.random`` module implements most of the functions in the
``numpy.random`` module.  We list some common functions below but for a full
list see the :doc:`Array API <array-api>`:

.. autosummary::
   random.binomial
   random.normal
   random.poisson
   random.random

.. code-block:: python

   >>> import dask.array as da
   >>> x = da.random.random((10000, 10000), chunks=(1000, 1000))


Concatenation and Stacking
--------------------------

.. autosummary::
   stack
   concatenate

Often we store data in several different locations and want to stitch them together:

.. code-block:: Python

    dask_arrays = []
    for fn in filenames:
        f = h5py.File(fn)
        d = f['/data']
        array = da.from_array(d, chunks=(1000, 1000))
        dask_arrays.append(array)

    x = da.concatenate(dask_arrays, axis=0)  # concatenate arrays along first axis

For more information, see :doc:`concatenation and stacking <array-stack>` docs.


Using ``dask.delayed``
----------------------

.. autosummary::
   from_delayed
   stack
   concatenate

Sometimes NumPy-style data resides in formats that do not support NumPy-style
slicing.  We can still construct Dask arrays around this data if we have a
Python function that can generate pieces of the full array if we use
:doc:`dask.delayed <delayed>`.  Dask delayed lets us delay a single function
call that would create a NumPy array.  We can then wrap this delayed object
with ``da.from_delayed``, providing a dtype and shape to produce a
single-chunked Dask array.  Furthermore, we can use ``stack`` or ``concatenate`` from
before to construct a larger lazy array.

As an example, consider loading a stack of images using ``skimage.io.imread``:

.. code-block:: python

    import skimage.io
    import dask.array as da
    import dask

    imread = dask.delayed(skimage.io.imread, pure=True)  # Lazy version of imread

    filenames = sorted(glob.glob('*.jpg'))

    lazy_images = [imread(path) for path in filenames]   # Lazily evaluate imread on each path
    sample = lazy_images[0].compute()  # load the first image (assume rest are same shape/dtype)

    arrays = [da.from_delayed(lazy_image,           # Construct a small Dask array
                              dtype=sample.dtype,   # for every lazy value
                              shape=sample.shape)
              for lazy_image in lazy_images]

    stack = da.stack(arrays, axis=0)                # Stack all small Dask arrays into one

See :doc:`documentation on using dask.delayed with collections<delayed-collections>`.

Often it is substantially faster to use ``da.map_blocks`` rather than ``da.stack``

.. code-block:: python

    import glob
    import skimage.io
    import numpy as np
    import dask.array as da

    filenames = sorted(glob.glob('*.jpg'))

    def read_one_image(block_id, filenames=filenames, axis=0):
        # a function that reads in one chunk of data
        path = filenames[block_id[axis]]
        image = skimage.io.imread(path)
        return np.expand_dims(image, axis=axis)

    # load the first image (assume rest are same shape/dtype)
    sample = skimage.io.imread(filenames[0])

    stack = da.map_blocks(
        read_one_image,
        dtype=sample.dtype,
        chunks=((1,) * len(filenames),  *sample.shape)
    )


From Dask DataFrame
-------------------

There are several ways to create a Dask array from a Dask DataFrame. Dask DataFrames have a ``to_dask_array`` method:

.. code-block:: python

   >>> df = dask.dataframes.from_pandas(...)
   >>> df.to_dask_array()
   dask.array<values, shape=(nan, 3), dtype=float64, chunksize=(nan, 3), chunktype=numpy.ndarray>

This mirrors the `to_numpy
<https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html>`_
function in Pandas. The ``values`` attribute is also supported:

.. code-block:: python

   >>> df.values
   dask.array<values, shape=(nan, 3), dtype=float64, chunksize=(nan, 3), chunktype=numpy.ndarray>

However, these arrays do not have known chunk sizes because dask.dataframe does
not track the number of rows in each partition. This means that some operations
like slicing will not operate correctly.

The chunk sizes can be computed:

.. code-block:: python

   >>> df.to_dask_array(lengths=True)
   dask.array<array, shape=(100, 3), dtype=float64, chunksize=(50, 3), chunktype=numpy.ndarray>

Specifying ``lengths=True`` triggers immediate computation of the chunk sizes.
This enables downstream computations that rely on having known chunk sizes
(e.g., slicing).

The Dask DataFrame ``to_records`` method also returns a Dask Array, but does not compute the shape
information:

.. code-block:: python

   >>> df.to_records()
   dask.array<to_records, shape=(nan,), dtype=(numpy.record, [('index', '<i8'), ('x', '<f8'), ('y', '<f8'), ('z', '<f8')]), chunksize=(nan,), chunktype=numpy.ndarray>

If you have a function that converts a Pandas DataFrame into a NumPy array,
then calling ``map_partitions`` with that function on a Dask DataFrame will
produce a Dask array:

.. code-block:: python

   >>> df.map_partitions(np.asarray)
   dask.array<asarray, shape=(nan, 3), dtype=float64, chunksize=(nan, 3), chunktype=numpy.ndarray>


Interactions with NumPy arrays
------------------------------

Dask array operations will automatically convert NumPy arrays into single-chunk
dask arrays:

.. code-block:: python

   >>> x = da.sum(np.ones(5))
   >>> x.compute()
   5

When NumPy and Dask arrays interact, the result will be a Dask array.  Automatic
rechunking rules will generally slice the NumPy array into the appropriate Dask
chunk shape:

.. code-block:: python

   >>> x = da.ones(10, chunks=(5,))
   >>> y = np.ones(10)
   >>> z = x + y
   >>> z
   dask.array<add, shape=(10,), dtype=float64, chunksize=(5,), chunktype=numpy.ndarray>

These interactions work not just for NumPy arrays but for any object that has
shape and dtype attributes and implements NumPy slicing syntax.

Memory mapping
--------------

Memory mapping can be a highly effective method to access raw binary data since
it has nearly zero overhead if the data is already in the file system cache. For
the threaded scheduler, creating a Dask array from a raw binary file can be as simple as
:code:`a = da.from_array(np.memmap(filename, shape=shape, dtype=dtype, mode='r'))`.

For multiprocessing or distributed schedulers, the memory map for each array
chunk should be created on the correct worker process and not on the main
process to avoid data transfer through the cluster. This can be achieved by
wrapping the function that creates the memory map using :code:`dask.delayed`.

.. code-block:: python

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


   def mmap_load_chunk(filename, shape, dtype, offset, sl):
       '''
       Memory map the given file with overall shape and dtype and return a slice
       specified by :code:`sl`.

       Parameters
       ----------

       filename : str
       shape : tuple
           Total shape of the data in the file
       dtype:
           NumPy dtype of the data in the file
       offset : int
           Skip :code:`offset` bytes from the beginning of the file.
       sl:
           Object that can be used for indexing or slicing a NumPy array to
           extract a chunk

       Returns
       -------

       numpy.memmap or numpy.ndarray
           View into memory map created by indexing with :code:`sl`,
           or NumPy ndarray in case no view can be created using :code:`sl`.
       '''
       data = np.memmap(filename, mode='r', shape=shape, dtype=dtype, offset=offset)
       return data[sl]


   def mmap_dask_array(filename, shape, dtype, offset=0, blocksize=5):
       '''
       Create a Dask array from raw binary data in :code:`filename`
       by memory mapping.

       This method is particularly effective if the file is already
       in the file system cache and if arbitrary smaller subsets are
       to be extracted from the Dask array without optimizing its
       chunking scheme.

       It may perform poorly on Windows if the file is not in the file
       system cache. On Linux it performs well under most circumstances.

       Parameters
       ----------

       filename : str
       shape : tuple
           Total shape of the data in the file
       dtype:
           NumPy dtype of the data in the file
       offset : int, optional
           Skip :code:`offset` bytes from the beginning of the file.
       blocksize : int, optional
           Chunk size for the outermost axis. The other axes remain unchunked.

       Returns
       -------

       dask.array.Array
           Dask array matching :code:`shape` and :code:`dtype`, backed by
           memory-mapped chunks.
       '''
       load = dask.delayed(mmap_load_chunk)
       chunks = []
       for index in range(0, shape[0], blocksize):
           # Truncate the last chunk if necessary
           chunk_size = min(blocksize, shape[0] - index)
           chunk = dask.array.from_delayed(
               load(
                   filename,
                   shape=shape,
                   dtype=dtype,
                   offset=offset,
                   sl=slice(index, index + chunk_size)
               ),
               shape=(chunk_size, ) + shape[1:],
               dtype=dtype
           )
           chunks.append(chunk)
       return da.concatenate(chunks, axis=0)

   x = mmap_dask_array(
       filename='testfile-50-50-100-100-float32.raw',
       shape=(50, 50, 100, 100),
       dtype=np.float32
   )


Chunks
------

See :doc:`documentation on Array Chunks <array-chunks>` for more information.


Store Dask Arrays
=================

.. autosummary::
   store
   to_hdf5
   to_npy_stack
   to_zarr
   compute

In Memory
---------

.. autosummary::
   compute

If you have a small amount of data, you can call ``np.array`` or ``.compute()``
on your Dask array to turn in to a normal NumPy array:

.. code-block:: Python

   >>> x = da.arange(6, chunks=3)
   >>> y = x**2
   >>> np.array(y)
   array([0, 1, 4, 9, 16, 25])

   >>> y.compute()
   array([0, 1, 4, 9, 16, 25])


NumPy style slicing
-------------------

.. autosummary::
   store

You can store Dask arrays in any object that supports NumPy-style slice
assignment like ``h5py.Dataset``:

.. code-block:: Python

   >>> import h5py
   >>> f = h5py.File('myfile.hdf5')
   >>> d = f.require_dataset('/data', shape=x.shape, dtype=x.dtype)
   >>> da.store(x, d)

Also, you can store several arrays in one computation by passing lists of sources and
destinations:

.. code-block:: Python

   >>> da.store([array1, array2], [output1, output2])  # doctest: +SKIP

HDF5
----

.. autosummary::
   to_hdf5

HDF5 is sufficiently common that there is a special function ``to_hdf5`` to
store data into HDF5 files using ``h5py``:

.. code-block:: Python

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

You can store several arrays in one computation with the function
``da.to_hdf5`` by passing in a dictionary:

.. code-block:: Python

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


Zarr
----

The `Zarr`_ format is a chunk-wise binary array
storage file format with a good selection of encoding and compression options.
Due to each chunk being stored in a separate file, it is ideal for parallel
access in both reading and writing (for the latter, if the Dask array
chunks are aligned with the target). Furthermore, storage in
:doc:`remote data services <how-to/connect-to-remote-data>` such as S3 and GCS is
supported.

For example, to save data to a local zarr dataset you would do:

.. code-block:: Python

   >>> arr.to_zarr('output.zarr')

or to save to a particular bucket on S3:

.. code-block:: Python

   >>> arr.to_zarr('s3://mybucket/output.zarr', storage_option={'key': 'mykey',
                   'secret': 'mysecret'})

or your own custom zarr Array:

.. code-block:: Python

   >>> z = zarr.create((10,), dtype=float, store=zarr.ZipStore("output.zarr"))
   >>> arr.to_zarr(z)

To retrieve those data, you would do ``da.from_zarr`` with exactly the same arguments. The
chunking of the resultant Dask array is defined by how the files were saved, unless
otherwise specified.


TileDB
------

`TileDB <https://docs.tiledb.io>`_  is a binary array format and storage manager with
tunable chunking, layout, and compression options. The TileDB storage manager library
includes support for scalable storage backends such as S3 API compatible object stores
and HDFS, with automatic scaling, and supports multi-threaded and multi-process
reads (consistent) and writes (eventually-consistent).

To save data to a local TileDB array:

.. code-block:: Python

  >>> arr.to_tiledb('output.tdb')

or to save to a bucket on S3:

.. code-block:: python

  >>> arr.to_tiledb('s3://mybucket/output.tdb',
                    storage_options={'vfs.s3.aws_access_key_id': 'mykey',
                                     'vfs.s3.aws_secret_access_key': 'mysecret'})

Files may be retrieved by running `da.from_tiledb` with the same URI, and any
necessary arguments.


Intermediate storage
--------------------

.. autosummary::
   store

In some cases, one may wish to store an intermediate result in long term
storage. This differs from ``persist``, which is mainly used to manage
intermediate results within Dask that don't necessarily have longevity.
Also it differs from storing final results as these mark the end of the Dask
graph. Thus intermediate results are easier to reuse without reloading data.
Intermediate storage is mainly useful in cases where the data is needed
outside of Dask (e.g. on disk, in a database, in the cloud, etc.). It can
be useful as a checkpoint for long running or error-prone computations.

The intermediate storage use case differs from the typical storage use case as
a Dask Array is returned to the user that represents the result of that
storage operation. This is typically done by setting the ``store`` function's
``return_stored`` flag to ``True``.

.. code-block:: python

   x.store()  # stores data, returns nothing
   x = x.store(return_stored=True)  # stores data, returns new dask array backed by that data

The user can then decide whether the
storage operation happens immediately (by setting the ``compute`` flag to
``True``) or later (by setting the ``compute`` flag to ``False``). In all
other ways, this behaves the same as a normal call to ``store``. Some examples
are shown below.

.. code-block:: Python

   >>> import dask.array as da
   >>> import zarr as zr
   >>> c = (2, 2)
   >>> d = da.ones((10, 11), chunks=c)
   >>> z1 = zr.open_array('lazy.zarr', shape=d.shape, dtype=d.dtype, chunks=c)
   >>> z2 = zr.open_array('eager.zarr', shape=d.shape, dtype=d.dtype, chunks=c)
   >>> d1 = d.store(z1, compute=False, return_stored=True)
   >>> d2 = d.store(z2, compute=True, return_stored=True)

This can be combined with any other storage strategies either noted above, in
the docs or for any specialized storage types.


Plugins
=======

We can run arbitrary user-defined functions on Dask arrays whenever they are
constructed.  This allows us to build a variety of custom behaviors that improve
debugging, user warning, etc.  You can register a list of functions to run on
all Dask arrays to the global ``array_plugins=`` value:

.. code-block:: python

   >>> def f(x):
   ...     print(x.nbytes)

   >>> with dask.config.set(array_plugins=[f]):
   ...     x = da.ones((10, 1), chunks=(5, 1))
   ...     y = x.dot(x.T)
   80
   80
   800
   800

If the plugin function returns None, then the input Dask array will be returned
without change.  If the plugin function returns something else, then that value
will be the result of the constructor.

Examples
--------

Automatically compute
~~~~~~~~~~~~~~~~~~~~~

We may wish to turn some Dask array code into normal NumPy code.  This is
useful, for example, to track down errors immediately that would otherwise be
hidden by Dask's lazy semantics:

.. code-block:: python

   >>> with dask.config.set(array_plugins=[lambda x: x.compute()]):
   ...     x = da.arange(5, chunks=2)

   >>> x  # this was automatically converted into a numpy array
   array([0, 1, 2, 3, 4])

Warn on large chunks
~~~~~~~~~~~~~~~~~~~~

We may wish to warn users if they are creating chunks that are too large:

.. code-block:: python

   def warn_on_large_chunks(x):
       shapes = list(itertools.product(*x.chunks))
       nbytes = [x.dtype.itemsize * np.prod(shape) for shape in shapes]
       if any(nb > 1e9 for nb in nbytes):
           warnings.warn("Array contains very large chunks")

   with dask.config.set(array_plugins=[warn_on_large_chunks]):
       ...

Combine
~~~~~~~

You can also combine these plugins into a list.  They will run one after the
other, chaining results through them:

.. code-block:: python

   with dask.config.set(array_plugins=[warn_on_large_chunks, lambda x: x.compute()]):
       ...


.. _Zarr: https://zarr.readthedocs.io/en/stable/