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array-split-0.5.2
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مشاهده بیشترتوضیحات
Python package for splitting arrays into sub-arrays (i.e. rectangular-tiling and rectangular-domain-decomposition), similar to ``numpy.array_split``.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | array-split-0.5.2 |
| نام | array-split |
| نسخه کتابخانه | 0.5.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Shane J. Latham |
| ایمیل نویسنده | array.split@gmail.com |
| آدرس صفحه اصلی | http://github.com/array-split/array_split |
| آدرس اینترنتی | https://pypi.org/project/array-split/ |
| مجوز | MIT |
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The `array_split <http://array-split.readthedocs.io/en/latest>`_ python package is
an enhancement to existing
`numpy.ndarray <http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html>`_ functions,
such as
`numpy.array_split <http://docs.scipy.org/doc/numpy/reference/generated/numpy.array_split.html>`_,
`skimage.util.view_as_blocks <http://scikit-image.org/docs/0.13.x/api/skimage.util.html#view-as-blocks>`_
and
`skimage.util.view_as_windows <http://scikit-image.org/docs/0.13.x/api/skimage.util.html#view-as-windows>`_,
which sub-divide a multi-dimensional array into a number of multi-dimensional sub-arrays (slices).
Example application areas include:
**Parallel Processing**
A large (dense) array is partitioned into smaller sub-arrays which can be
processed concurrently by multiple processes
(`multiprocessing <https://docs.python.org/3/library/multiprocessing.html>`_
or `mpi4py <http://pythonhosted.org/mpi4py/>`_) or other memory-limited hardware
(e.g. GPGPU using `pyopencl <https://mathema.tician.de/software/pyopencl/>`_,
`pycuda <https://mathema.tician.de/software/pycuda/>`_, etc).
For GPGPU, it is necessary for sub-array not to exceed the GPU memory and
desirable for the sub-array shape to be a multiple of the *work-group*
(`OpenCL <https://en.wikipedia.org/wiki/OpenCL>`_)
or *thread-block* (`CUDA <https://en.wikipedia.org/wiki/CUDA>`_) size.
**File I/O**
A large (dense) array is partitioned into smaller sub-arrays which can be
written to individual files
(as, for example, a
`HDF5 Virtual Dataset <https://support.hdfgroup.org/HDF5/docNewFeatures/NewFeaturesVirtualDatasetDocs.html>`_).
It is often desirable for the individual files not to exceed a specified number
of (Giga) bytes and, for `HDF5 <https://support.hdfgroup.org/HDF5/>`_, it is desirable
to have the individual file sub-array shape a multiple of
the `chunk shape <https://support.hdfgroup.org/HDF5/doc1.8/Advanced/Chunking/index.html>`_.
Similarly, `out of core <https://en.wikipedia.org/wiki/Out-of-core_algorithm>`_
algorithms for large dense arrays often involve processing the entire data-set as
a series of *in-core* sub-arrays. Again, it is desirable for the individual sub-array shape
to be a multiple of the
`chunk shape <https://support.hdfgroup.org/HDF5/doc1.8/Advanced/Chunking/index.html>`_.
The `array_split <http://array-split.readthedocs.io/en/latest>`_ package provides the
means to partition an array (or array shape) using any of the following criteria:
- Per-axis indices indicating the *cut* positions.
- Per-axis number of sub-arrays.
- Total number of sub-arrays (with optional per-axis *number of sections* constraints).
- Specific sub-array shape.
- Specification of *halo* (*ghost*) elements for sub-arrays.
- Arbitrary *start index* for the shape to be partitioned.
- Maximum number of bytes for a sub-array with constraints:
- sub-arrays are an even multiple of a specified sub-tile shape
- upper limit on the per-axis sub-array shape
Quick Start Example
===================
>>> from array_split import array_split, shape_split
>>> import numpy as np
>>>
>>> ary = np.arange(0, 4*9)
>>>
>>> array_split(ary, 4) # 1D split into 4 sections (like numpy.array_split)
[array([0, 1, 2, 3, 4, 5, 6, 7, 8]),
array([ 9, 10, 11, 12, 13, 14, 15, 16, 17]),
array([18, 19, 20, 21, 22, 23, 24, 25, 26]),
array([27, 28, 29, 30, 31, 32, 33, 34, 35])]
>>>
>>> shape_split(ary.shape, 4) # 1D split into 4 parts, returns slice objects
array([(slice(0, 9, None),), (slice(9, 18, None),), (slice(18, 27, None),), (slice(27, 36, None),)],
dtype=[('0', 'O')])
>>>
>>> ary = ary.reshape(4, 9) # Make ary 2D
>>> split = shape_split(ary.shape, axis=(2, 3)) # 2D split into 2*3=6 sections
>>> split.shape
(2, 3)
>>> split
array([[(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))]],
dtype=[('0', 'O'), ('1', 'O')])
>>> sub_arys = [ary[tup] for tup in split.flatten()] # Create sub-array views from slice tuples.
>>> sub_arys
[array([[ 0, 1, 2], [ 9, 10, 11]]),
array([[ 3, 4, 5], [12, 13, 14]]),
array([[ 6, 7, 8], [15, 16, 17]]),
array([[18, 19, 20], [27, 28, 29]]),
array([[21, 22, 23], [30, 31, 32]]),
array([[24, 25, 26], [33, 34, 35]])]
Latest sphinx documentation (including more examples)
at http://array-split.readthedocs.io/en/latest/.
نیازمندی
| مقدار | نام |
|---|---|
| >=1.6 | numpy |
نحوه نصب
نصب پکیج whl array-split-0.5.2:
pip install array-split-0.5.2.whl
نصب پکیج tar.gz array-split-0.5.2:
pip install array-split-0.5.2.tar.gz