.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_benchmarks/2d-benchmark.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_benchmarks_2d-benchmark.py>`
        to download the full example code

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

.. _sphx_glr_auto_benchmarks_2d-benchmark.py:


==================================
2D ndsplines vs. scipy.interpolate
==================================

.. GENERATED FROM PYTHON SOURCE LINES 6-116



.. image-sg:: /auto_benchmarks/images/sphx_glr_2d-benchmark_001.png
   :alt: make_interp_spline, spline.__call__
   :srcset: /auto_benchmarks/images/sphx_glr_2d-benchmark_001.png
   :class: sphx-glr-single-img





.. code-block:: Python
   :lineno-start: 7


    import numpy as np
    import gc
    import time

    from scipy import interpolate
    import ndsplines

    import matplotlib.pyplot as plt

    # number of time measurements per input/query size
    n_iter = 10


    def timeit(func, n_iter=1, return_samps=True, **func_kwargs):
        results = np.empty(n_iter, dtype=np.double)
        for i in range(n_iter):
            # gc.collect()

            tstart = time.time()
            func(**func_kwargs)
            delta = time.time() - tstart

            results[i] = delta

        if return_samps:
            return results
        else:
            return np.mean(results)


    def gen_xyz(sizex, sizey):
        x = np.pi * np.linspace(-1, 1, sizex)
        y = np.pi * np.linspace(-1, 1, sizey)
        xx, yy = np.meshgrid(x, y, indexing='ij')
    
        zz = np.sin(xx) * np.cos(yy)
        return x, y, zz


    def gen_xxyy(sizex, sizey):
        x = np.pi * np.linspace(-1, 1, sizex)
        y = np.pi * np.linspace(-1, 1, sizey)
        xx, yy = np.meshgrid(x, y, indexing='ij')
        return xx, yy


    # make_interp_spline timing
    x_sizes = np.logspace(1, 3, 10, dtype=int)
    t_scipy_build = np.empty((2, x_sizes.size))
    t_ndspl_build = np.empty((2, x_sizes.size))
    for i, size in enumerate(x_sizes):
        x, y, z = gen_xyz(size, size)
        t_scipy = 10e3 * timeit(interpolate.RectBivariateSpline, x=x.copy(), y=y.copy(), z=z.copy(), 
                                n_iter=n_iter)
        t_ndspl = 10e3 * timeit(ndsplines.make_interp_spline, x=[x,y], y=z,
                                n_iter=n_iter)
        t_scipy_build[:, i] = np.mean(t_scipy), np.std(t_scipy)
        t_ndspl_build[:, i] = np.mean(t_ndspl), np.std(t_ndspl)

    # spline query timing
    x, y, z = gen_xyz(7, 5)
    xx_sizes = np.logspace(0, 2, 10, dtype=int)
    t_scipy_call = np.empty((2, xx_sizes.size))
    t_ndspl_npy_call = np.empty((2, xx_sizes.size))
    t_ndspl_pyx_call = np.empty((2, xx_sizes.size))
    for i, size in enumerate(xx_sizes):
        xx, yy = gen_xxyy(size, size)
        xxyy = np.stack((xx, yy), axis=-1)
        spl_scipy = interpolate.RectBivariateSpline(x.copy(), y.copy(), z)
        spl_ndspl = ndsplines.make_interp_spline((x,y), z)
        spl_ndspl.allocate_workspace_arrays(size)
        t_scipy = 10e3 * timeit(spl_scipy, x=xx.copy(), y=yy.copy(), grid=False, n_iter=n_iter)
        ndsplines.set_impl('cython')
        t_ndspl_pyx = 10e3 * timeit(spl_ndspl, x=xxyy,
                                n_iter=n_iter)
        ndsplines.set_impl('numpy')
        t_ndspl_npy = 10e3 * timeit(spl_ndspl, x=xxyy,
                                n_iter=n_iter)
        t_scipy_call[:, i] = np.mean(t_scipy), np.std(t_scipy)
        t_ndspl_pyx_call[:, i] = np.mean(t_ndspl_pyx), np.std(t_ndspl_pyx)
        t_ndspl_npy_call[:, i] = np.mean(t_ndspl_npy), np.std(t_ndspl_npy)

    # plot results
    fig, axes = plt.subplots(nrows=2)

    axes[0].errorbar(x_sizes, t_scipy_build[0], capsize=3, yerr=t_scipy_build[1],
                     label='scipy')
    axes[0].errorbar(x_sizes, t_ndspl_build[0], capsize=3, yerr=t_ndspl_build[1],
                     label='ndsplines')
    axes[0].set_title('make_interp_spline')

    axes[1].errorbar(xx_sizes, t_scipy_call[0], capsize=3, yerr=t_scipy_call[1],
                     label='scipy.interpolate')
    axes[1].errorbar(xx_sizes, t_ndspl_npy_call[0], capsize=3, yerr=t_ndspl_npy_call[1],
                     label='ndsplines npy')
    axes[1].errorbar(xx_sizes, t_ndspl_pyx_call[0], capsize=3, yerr=t_ndspl_pyx_call[1],
                     label='ndsplines pyx')
    axes[1].set_title('spline.__call__')

    for ax in axes:
        ax.set_xlabel('input array size')
        ax.set_ylabel('time [ms]')
        ax.set_xscale('log')
        ax.grid()

    axes[-1].legend()
    fig.tight_layout()

    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 7.679 seconds)


.. _sphx_glr_download_auto_benchmarks_2d-benchmark.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 2d-benchmark.ipynb <2d-benchmark.ipynb>`

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

      :download:`Download Python source code: 2d-benchmark.py <2d-benchmark.py>`


.. only:: html

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

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