.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_build/auto_examples/tutorial/09-Vector_output.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_tutorial_09-Vector_output.py>`
        to download the full example code.

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

.. _sphx_glr__build_auto_examples_tutorial_09-Vector_output.py:


Vector outputs
==============

.. GENERATED FROM PYTHON SOURCE LINES 7-9

Pyquickbench can also be used to benchmark functions that return multiple values. This capability corresponds to ``mode = "vector_output"`` in :mod:`pyquickbench`. The following benchmark measures the convergence of quantile estimatores of a uniform random variable towards their theoretical values.
Let us first observe how the values of the naive quantile estimator as returned by ``uniform_quantiles`` evolve with increasing number of simulated random variables:

.. GENERATED FROM PYTHON SOURCE LINES 9-61

.. code-block:: Python



    import pyquickbench

    def uniform_quantiles(n,m):
    
        vec = np.random.random((n+1))
        vec.sort()
    
        return np.array([abs(vec[(n // m)*i]) for i in range(m+1)])
    
    m = 10
    uniform_decile = functools.partial(uniform_quantiles, m=m)
    uniform_decile.__name__ = "uniform_decile"
    
    all_funs = [
        uniform_decile   ,   
    ]

    n_bench = 10
    all_sizes = [m * 2**n for n in range(n_bench)]

    n_repeat = 100
    
    all_values = pyquickbench.run_benchmark(
        all_sizes                   ,
        all_funs                    ,
        n_repeat = n_repeat         ,
        mode = "vector_output"      ,
        StopOnExcept = True         ,
        pooltype = 'process'        ,
    ) 

    plot_intent = {
        pyquickbench.default_ax_name : "points"         ,   
        pyquickbench.repeat_ax_name : "same"            ,   
        pyquickbench.out_ax_name : "curve_color"        ,   
    }

    pyquickbench.plot_benchmark(
        all_values                      ,
        all_sizes                       ,
        all_funs                        ,
        plot_intent = plot_intent       ,
        show = True                     ,
        logy_plot = False               ,
        plot_ylim = (0.,1.)             ,
        alpha = 50./255                 ,
        ylabel = "Quantile estimator"   ,
    )





.. image-sg:: /_build/auto_examples/tutorial/images/sphx_glr_09-Vector_output_001.png
   :alt: 09 Vector output
   :srcset: /_build/auto_examples/tutorial/images/sphx_glr_09-Vector_output_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 89-90

While the above plot hints at a convergence towards specific values, we can be a bit more precise and plot the actual convergence error.

.. GENERATED FROM PYTHON SOURCE LINES 90-134

.. code-block:: Python


    def uniform_quantiles_error(n,m):
    
        vec = np.random.random((n+1))
        vec.sort()
    
        return np.array([abs(vec[(n // m)*i] - i / m) for i in range(m+1)])

    uniform_decile_error = functools.partial(uniform_quantiles_error, m=m)
    uniform_decile_error.__name__ = "uniform_decile_error"

    all_funs = [
        uniform_decile_error   ,   
    ]

    n_repeat = 10000

    all_errors = pyquickbench.run_benchmark(
        all_sizes                   ,
        all_funs                    ,
        n_repeat = n_repeat         ,
        mode = "vector_output"      ,
        StopOnExcept = True         ,
        pooltype = 'process'        ,

    ) 

    plot_intent = {
        pyquickbench.default_ax_name : "points"         ,   
        pyquickbench.fun_ax_name : "curve_color"        ,   
        pyquickbench.repeat_ax_name : "reduction_median",   
        pyquickbench.out_ax_name : "curve_color"        ,   
    }

    pyquickbench.plot_benchmark(
        all_errors                      ,
        all_sizes                       ,
        all_funs                        ,
        plot_intent = plot_intent       ,
        show = True                     ,
        ylabel = "Estimator error"      ,

    )




.. image-sg:: /_build/auto_examples/tutorial/images/sphx_glr_09-Vector_output_002.png
   :alt: 09 Vector output
   :srcset: /_build/auto_examples/tutorial/images/sphx_glr_09-Vector_output_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 135-136

The above plot shows a very distinct behavior for the estimation of the minimum and maximum values compared to the other quantiles. The following plot of convergence order makes this difference even more salient.

.. GENERATED FROM PYTHON SOURCE LINES 136-146

.. code-block:: Python


    pyquickbench.plot_benchmark(
        all_errors                      ,
        all_sizes                       ,
        all_funs                        ,
        plot_intent = plot_intent       ,
        show = True                     ,
        transform = "pol_cvgence_order" ,
        ylabel = "Order of convergence of estimator error"      ,
    )



.. image-sg:: /_build/auto_examples/tutorial/images/sphx_glr_09-Vector_output_003.png
   :alt: 09 Vector output
   :srcset: /_build/auto_examples/tutorial/images/sphx_glr_09-Vector_output_003.png
   :class: sphx-glr-single-img






.. _sphx_glr_download__build_auto_examples_tutorial_09-Vector_output.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 09-Vector_output.ipynb <09-Vector_output.ipynb>`

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

      :download:`Download Python source code: 09-Vector_output.py <09-Vector_output.py>`

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

      :download:`Download zipped: 09-Vector_output.zip <09-Vector_output.zip>`


.. only:: html

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

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