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

.. only:: html

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

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

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

.. _sphx_glr__auto_examples_skip_performance_paper.py:


Code to measure performance for tabel in publication.

.. GENERATED FROM PYTHON SOURCE LINES 4-165

.. code-block:: Python


    import time

    import numpy as np
    import pandas as pd
    import scipy
    import splinebox

    # Number of times each task is repeated for more reliable results
    n_repetitions = 1000
    # Number of knots
    M = 100
    # Dimensionality
    ndim = 3

    closed = True

    # Number of evaluation points
    nt = 1000

    results_creation = []
    results_evaluation = []
    results_fitting = []

    for repetition in range(n_repetitions + 1):
        knots = np.random.rand(M, ndim)

        # Measure splinebox creation time
        start = time.perf_counter_ns()
        spline = splinebox.Spline(M, splinebox.B3(), closed=closed)
        spline.knots = knots
        stop = time.perf_counter_ns()

        if repetition != 0:
            results_creation.append(
                [
                    "splinebox",
                    (stop - start) / 1000,
                ]
            )

        # Measure splinebox evaluation time
        t = np.linspace(0, M if closed else M - 1, nt)
        start_eval = time.perf_counter_ns()
        spline(t)
        stop_eval = time.perf_counter_ns()

        if repetition != 0:
            results_evaluation.append(
                [
                    "splinebox",
                    (stop_eval - start_eval) / 1000,
                ]
            )

        # Measure splinebox fitting time

        data = np.random.rand(M * 10, ndim)

        start = time.perf_counter_ns()
        spline = splinebox.Spline(M, splinebox.B3(), closed=closed)
        spline.fit(data)
        stop = time.perf_counter_ns()

        if repetition != 0:
            results_fitting.append(
                [
                    "splinebox",
                    (stop - start) / 1000,
                ]
            )

        # Measure scipy spline creation time
        if closed:
            start = time.perf_counter_ns()
            t_knots = np.arange(M + 1)
            knots_periodic = np.concatenate([knots, knots[0][np.newaxis, :]], axis=0)
            spline = scipy.interpolate.make_interp_spline(t_knots, knots_periodic, k=3, bc_type="periodic")
            stop = time.perf_counter_ns()
        else:
            start = time.perf_counter_ns()
            t_knots = np.arange(M)
            spline = scipy.interpolate.make_interp_spline(t_knots, knots, k=3, bc_type="natural")
            stop = time.perf_counter_ns()

        if repetition != 0:
            results_creation.append(
                [
                    "scipy",
                    (stop - start) / 1000,
                ]
            )

        # Measure scipy spline evaluation time
        t = np.linspace(0, M if closed else M - 1, nt)
        start_eval = time.perf_counter_ns()
        spline(t)
        stop_eval = time.perf_counter_ns()
        if repetition != 0:
            results_evaluation.append(
                [
                    "scipy",
                    (stop_eval - start_eval) / 1000,
                ]
            )

        # Measure scipy fitting time
        # The `splprep` is declared as legacy by scipy.
        # However, neither `make_splprep` nor `make_lsq_spline` seem to be able to do the same thing.
        k = 3
        if closed:
            start = time.perf_counter_ns()
            N = len(data)
            t = np.arange(-k, M + k + 1)
            u = np.linspace(0, M, N + 1)[:-1]
            tck, u = scipy.interpolate.splprep(data.T, k=k, u=u, t=t, task=-1, s=0, per=N)
            t, c, k = tck
            c = np.stack(c, axis=-1)
            spline = scipy.interpolate.BSpline(t, c, k)
            stop = time.perf_counter_ns()

        else:
            start = time.perf_counter_ns()
            N = len(data)
            t = np.arange(-k, M + k)
            u = np.linspace(0, M - 1, N)
            tck, u = scipy.interpolate.splprep(data.T, k=k, u=u, t=t, task=-1, s=0)
            t, c, k = tck
            c = np.stack(c, axis=-1)
            spline = scipy.interpolate.BSpline(t, c, k)
            stop = time.perf_counter_ns()

        if repetition != 0:
            results_fitting.append(
                [
                    "scipy",
                    (stop - start) / 1000,
                ]
            )

    creation_df = pd.DataFrame(results_creation, columns=["Package", "time [micros]"])
    creation_df = creation_df.groupby("Package", as_index=False).agg(["mean", "std"])
    print()
    print("##########################################")
    print("Creation")
    print("##########################################")
    print(creation_df)
    evaluation_df = pd.DataFrame(results_evaluation, columns=["Package", "time [micros]"])
    evaluation_df = evaluation_df.groupby("Package", as_index=False).agg(["mean", "std"])
    print()
    print("##########################################")
    print("Evaluation")
    print("##########################################")
    print(evaluation_df)
    fitting_df = pd.DataFrame(results_fitting, columns=["Package", "time [micros]"])
    fitting_df = fitting_df.groupby("Package", as_index=False).agg(["mean", "std"])
    print()
    print("##########################################")
    print("Fitting")
    print("##########################################")
    print(fitting_df)


.. _sphx_glr_download__auto_examples_skip_performance_paper.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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