.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_build/auto_examples/benchmarks/numpy_array_init.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download__build_auto_examples_benchmarks_numpy_array_init.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr__build_auto_examples_benchmarks_numpy_array_init.py:


Numpy array initialization
==========================

.. GENERATED FROM PYTHON SOURCE LINES 7-8

This benchmark compares the execution time of several :class:`numpy:numpy.ndarray` initialization routines.

.. GENERATED FROM PYTHON SOURCE LINES 8-45

.. code-block:: Python



    import numpy as np
    import pyquickbench

    dtypes_dict = {
        "float32" : np.float32,
        "float64" : np.float64,
    }

    def zeros(n, real_dtype):
        np.zeros((n), dtype=dtypes_dict[real_dtype])
    
    def ones(n, real_dtype):
        np.ones((n), dtype=dtypes_dict[real_dtype])
    
    def empty(n, real_dtype):
        np.empty((n), dtype=dtypes_dict[real_dtype])
    
    def full(n, real_dtype):
        np.full((n), 0., dtype=dtypes_dict[real_dtype])

    all_funs = [
        zeros   ,
        ones    ,
        empty   ,
        full    ,
    ]

    all_args = {
        "n" : np.array([2**n for n in range(0, 30)]),
        "real_dtype" : ["float32", "float64"],
    }

    def setup(n, real_dtype):
        return {'n': n, 'real_dtype':real_dtype}








.. GENERATED FROM PYTHON SOURCE LINES 84-109

.. code-block:: Python


    all_timings = pyquickbench.run_benchmark(
        all_args                ,
        all_funs                ,
        setup = setup           ,
        filename = filename     ,
        StopOnExcept = True     ,
        ShowProgress = True     ,

    )

    plot_intent = {
        "n" : 'points'                              ,
        "real_dtype" : 'curve_linestyle'            ,
        pyquickbench.fun_ax_name :  'curve_color'   ,
    }

    pyquickbench.plot_benchmark(
        all_timings                             ,
        all_args                                ,
        all_funs                                ,
        plot_intent = plot_intent               ,
        show = True                             ,
    )




.. image-sg:: /_build/auto_examples/benchmarks/images/sphx_glr_numpy_array_init_001.png
   :alt: numpy array init
   :srcset: /_build/auto_examples/benchmarks/images/sphx_glr_numpy_array_init_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 110-116

While these measurement seem surprizing, they are explained in `the numpy documentation <https://numpy.org/doc/stable/benchmarking.html>`_ :


      Be mindful that large arrays created with ``np.empty`` or ``np.zeros`` might not be allocated in physical memory until the memory is accessed. [...] One can force pagefaults to occur in the setup phase either by calling ``np.ones`` or ``arr.fill(value)`` after creating the array.




.. _sphx_glr_download__build_auto_examples_benchmarks_numpy_array_init.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: numpy_array_init.ipynb <numpy_array_init.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: numpy_array_init.py <numpy_array_init.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: numpy_array_init.zip <numpy_array_init.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_