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‎doc/conf.py‎

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import numpy as np
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import blosc2
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from blosc2.utils import constructors, elementwise_funcs, reducers
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from blosc2.utils import elementwise_funcs, reducers
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def genbody(f, func_list, lib="blosc2"):
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)
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genbody(f, blosc2_dtypefuncs)
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# GENERATE index_funcs.rst
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blosc2_indexfuncs = sorted(
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[
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"count_nonzero",
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"squeeze",
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"expand_dims",
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"sort",
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"take",
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"take_along_axis",
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"broadcast_to",
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"meshgrid",
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"indices",
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"concat",
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"stack",
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]
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)
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with open("reference/index_funcs.rst", "w") as f:
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f.write(
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"""Indexing and Manipulation Functions and Utilities
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=======================================
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The following functions are useful for performing indexing and other associated operations.
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.. currentmodule:: blosc2
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.. autosummary::
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"""
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)
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genbody(f, blosc2_indexfuncs)
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# GENERATE linear_algebra.rst
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linalg_funcs = [
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name
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)
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genbody(f, sorted(reducers))
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with open("reference/ndarray.rst", "w") as f:
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f.write(
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""".. _NDArray:
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NDArray
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=======
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The multidimensional data array class. Instances may be constructed using the constructor functions in the list below `NDArrayConstructors`_.
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In addition, all the functions from the :ref:`Lazy Functions <lazy_functions>` section can be used with NDArray instances.
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.. currentmodule:: blosc2
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.. autoclass:: NDArray
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:members:
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:inherited-members:
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:exclude-members: get_slice, set_slice, get_slice_numpy, get_oindex_numpy, set_oindex_numpy
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:member-order: groupwise
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:Special Methods:
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.. autosummary::
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__iter__
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__len__
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__getitem__
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__setitem__
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Utility Methods
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---------------
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.. automethod:: __iter__
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.. automethod:: __len__
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.. automethod:: __getitem__
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.. automethod:: __setitem__
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Constructors
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------------
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.. _NDArrayConstructors:
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.. autosummary::
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"""
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)
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genbody(f, sorted(constructors))
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hidden = "_ignore_multiple_size"
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‎doc/getting_started/overview.rst‎

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* DSL kernels for miniexpr-backed UDF authoring and validation (see
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`this tutorial <https://www.blosc.org/python-blosc2/getting_started/tutorials/03.lazyarray-udf-kernels.html>`_).
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* By making use of the simple and open
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`C-Blosc2 format <https://github.com/Blosc/c-blosc2/blob/main/README_FORMAT.rst>`_
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for storing compressed data, Python-Blosc2 facilitates seamless integration with many other
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systems and tools.
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`C-Blosc2 format <https://github.com/Blosc/c-blosc2/blob/main/README_FORMAT.rst>`_
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for storing compressed data, Python-Blosc2 facilitates seamless integration with many other
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systems and tools.
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Python-Blosc2's compute engine
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==============================
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arrays may be stored compressed in memory and then processed chunk-by-chunk;
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both memory footprint and execution time is greatly reduced compared to
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Dask+Zarr, which also uses compression (see the
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`benchmark here <https://github.com/Blosc/python-blosc2/blob/main/bench/ndarray/lazyarray-dask-large.ipynb>`_).
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`larger benchmark here <https://github.com/Blosc/python-blosc2/blob/main/bench/ndarray/lazyarray-dask-large.ipynb>`_).
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.. image:: https://github.com/Blosc/python-blosc2/blob/main/images/lazyarray-dask-large.png?raw=true
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:width: 100%
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1D array stored in memory (or optionally on disk via the ``out=``
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parameter in ``compute()`` or ``sum()`` functions). For a more in-depth look at
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this example, with performance comparisons, see this
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`blog post <https://ironarray.io/blog/compute-bigger>`_.
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`compute-bigger blog post <https://ironarray.io/blog/compute-bigger>`_.
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Hopefully, this overview has provided a good understanding of Python-Blosc2's
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capabilities. To begin your journey with Python-Blosc2, proceed to the

‎doc/reference/additional_funcs.rst‎

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clip
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contains
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endswith
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get_matmul_library
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imag
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lower
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real
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.. autofunction:: blosc2.clip
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.. autofunction:: blosc2.contains
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.. autofunction:: blosc2.endswith
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.. autofunction:: blosc2.get_matmul_library
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.. autofunction:: blosc2.imag
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.. autofunction:: blosc2.lower
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.. autofunction:: blosc2.real

‎doc/reference/classes.rst‎

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CTable
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Column
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NDArray
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Index
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NDField
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LazyArray
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C2Array
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Array
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BatchArray
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VLArray
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SChunk
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DictStore
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TreeStore
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EmbedStore
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BatchArray
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VLArray
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Index
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Ref
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Proxy
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ProxySource
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ctable
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ndarray
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index
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ndfield
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index_class
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lazyarray
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c2array
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array
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embed_store
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batch_array
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vlarray
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ndfield
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ref
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proxy
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proxysource

‎doc/reference/dict_store.rst‎

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.. automethod:: __init__
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Dictionary Interface
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-------------------
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--------------------
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.. automethod:: __getitem__
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.. automethod:: __delitem__

‎doc/reference/embed_store.rst‎

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.. autofunction:: estore_from_cframe
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Dictionary Interface
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-------------------
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--------------------
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.. automethod:: __delitem__

‎doc/reference/index.rst‎

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Index
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=====
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API Reference
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=============
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.. currentmodule:: blosc2
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.. toctree::
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:maxdepth: 2
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.. autoclass:: Index
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:members:
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:member-order: groupwise
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classes
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array_operations
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misc
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low_level
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storage
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save_load
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utilities

‎doc/reference/index_funcs.rst‎

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Indexing and Manipulation Functions and Utilities
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=======================================
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=================================================
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The following functions are useful for performing indexing and other associated operations.
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.. currentmodule:: blosc2
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.. autosummary::
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argsort
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broadcast_to
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concat
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count_nonzero
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expand_dims
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iter_sorted
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meshgrid
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sort
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squeeze
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.. autofunction:: blosc2.argsort
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.. autofunction:: blosc2.broadcast_to
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.. autofunction:: blosc2.concat
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.. autofunction:: blosc2.count_nonzero
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.. autofunction:: blosc2.iter_sorted
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.. autofunction:: blosc2.sort
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.. autofunction:: blosc2.squeeze

‎doc/reference/lazyarray.rst‎

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You can get an object following the LazyArray API with any of the following ways:
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* Any expression that involves one or more NDArray objects. e.g. ``a + b``, where ``a`` and ``b`` are NDArray objects (see `this tutorial <../getting_started/tutorials/03.lazyarray-expressions.html>`_).
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* Any expression that involves one or more NDArray objects. e.g. ``a + b``, where ``a`` and ``b`` are NDArray objects (see `the lazyarray expressions tutorial <../getting_started/tutorials/03.lazyarray-expressions.html>`_).
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* Using the ``lazyexpr`` constructor.
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* Using ``@dsl_kernel`` and ``lazyudf`` for miniexpr-backed DSL kernels (see `this tutorial <../getting_started/tutorials/03.lazyarray-udf-kernels.html>`_).
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* Using ``@dsl_kernel`` and ``lazyudf`` for miniexpr-backed DSL kernels (see `the DSL kernels tutorial <../getting_started/tutorials/03.lazyarray-udf-kernels.html>`_).
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The LazyArray object is a thin wrapper around the expression or user-defined function that allows for lazy computation. This means that the expression is not computed until the ``compute`` or ``__getitem__`` methods are called. The ``compute`` method will return a new NDArray object with the result of the expression evaluation. The ``__getitem__`` method will return an NumPy object instead.
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__getitem__
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Methods
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‎doc/reference/misc.rst‎

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Array,
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BatchArray,
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matmul,
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indices,
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cumulative_sum,
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