.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_performance_comparison_with_scipy.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_performance_comparison_with_scipy.py: Performance Comparison: splinebox vs scipy ------------------------------------------ In this example, we will compare the performance of ``splinebox`` against ``scipy`` in three tasks: #. Spline creation #. Spline evaluation #. Data fitting We will measure the time required for each task across different numbers of knots, spline dimensionalities, and whether the spline is open or closed. .. GENERATED FROM PYTHON SOURCE LINES 13-28 .. code-block:: Python import time import matplotlib.pyplot as plt import numpy as np import pandas as pd import scipy import seaborn as sns import splinebox # Number of times each task is repeated for more reliable results n_repetitions = 10 # Number of knots Ms = np.arange(10, 101, 10) .. GENERATED FROM PYTHON SOURCE LINES 29-32 Spline Creation and Evaluation ------------------------------ First, we compare the time taken to create splines and evaluate them. We repeat the measurements 11 times, discarding the first due to numba's Just-In-Time (JIT) compilation overhead, which is cached for future runs. .. GENERATED FROM PYTHON SOURCE LINES 32-122 .. code-block:: Python results_creation = [] results_evaluation = [] # Evaluate both open and closed splines for closed in [True, False]: # Dimensionalities: 1D, 2D, 3D, 10D for ndim in (1, 2, 3, 10): for M in Ms: 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", f"{ndim}D", M, "closed" if closed else "open", stop - start, ] ) # Measure spline evaluation time for different number of parameter values (nt) for nt in [10, 100, 1000, 10000]: 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", f"{ndim}D", M, nt, "closed" if closed else "open", stop - start, ] ) # 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", f"{ndim}D", M, "closed" if closed else "open", stop - start, ] ) # Measure scipy spline evaluation time for nt in [10, 100, 1000, 10000]: 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", f"{ndim}D", M, nt, "closed" if closed else "open", stop - start, ] ) .. GENERATED FROM PYTHON SOURCE LINES 123-126 Plotting Spline Creation Time ----------------------------- Now, that we have collected all of the times, we can turn the results into a data frame and plot the results using seaborn. .. GENERATED FROM PYTHON SOURCE LINES 126-144 .. code-block:: Python # Convert results to DataFrame and plot the creation times df = pd.DataFrame(results_creation, columns=["Package", "Dimensionality", "Number of knots", "closed", "time [ns]"]) for periodicity in ["closed", "open"]: fig, ax = plt.subplots() ax.set(yscale="log") sns.set_palette(sns.color_palette(["#228b18", "#0053a6"])) sns.lineplot( x="Number of knots", y="time [ns]", hue="Package", style="Dimensionality", data=df.loc[df["closed"] == periodicity], ax=ax, ) plt.title(f"Creation of {periodicity} splines") plt.show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/images/sphx_glr_plot_performance_comparison_with_scipy_001.png :alt: Creation of closed splines :srcset: /auto_examples/images/sphx_glr_plot_performance_comparison_with_scipy_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/images/sphx_glr_plot_performance_comparison_with_scipy_002.png :alt: Creation of open splines :srcset: /auto_examples/images/sphx_glr_plot_performance_comparison_with_scipy_002.png :class: sphx-glr-multi-img .. GENERATED FROM PYTHON SOURCE LINES 145-147 The results show that ``splinbox`` outperforms ``scipy`` in all condition for the task of creating a spline from a given set of knots. .. GENERATED FROM PYTHON SOURCE LINES 149-151 Plotting Spline Evaluation Time ------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 151-172 .. code-block:: Python # Convert evaluation results to DataFrame and plot df = pd.DataFrame( results_evaluation, columns=["Package", "Dimensionality", "Number of knots", "Number of parameter values", "closed", "time [ns]"], ) for periodicity in ["closed", "open"]: fig, ax = plt.subplots(figsize=(7, 7)) ax.set(yscale="log") sns.set_palette(sns.color_palette(["#228b18", "#0053a6"])) sns.lineplot( x="Number of knots", y="time [ns]", hue="Package", style="Number of parameter values", data=df.loc[df["closed"] == periodicity], ax=ax, ) plt.title(f"Evaluation of {periodicity} splines") plt.show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/images/sphx_glr_plot_performance_comparison_with_scipy_003.png :alt: Evaluation of closed splines :srcset: /auto_examples/images/sphx_glr_plot_performance_comparison_with_scipy_003.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/images/sphx_glr_plot_performance_comparison_with_scipy_004.png :alt: Evaluation of open splines :srcset: /auto_examples/images/sphx_glr_plot_performance_comparison_with_scipy_004.png :class: sphx-glr-multi-img .. GENERATED FROM PYTHON SOURCE LINES 173-178 Once again, ``splinebox`` outperforms ``scipy``, in all conditions for the spline evaluation task. Data Fitting ------------ Now, we compare the time it takes for ``splinebox`` and ``scipy`` to fit data, i.e., determine control points for a spline with M knots using least-squares fitting. .. GENERATED FROM PYTHON SOURCE LINES 178-233 .. code-block:: Python results_fitting = [] for closed in [True, False]: for ndim in (1, 2, 3, 10): for M in Ms: for repetition in range(n_repetitions + 1): data = np.random.rand(M * 10, ndim) # Measure splinebox fitting time 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", f"{ndim}D", M, "closed" if closed else "open", stop - start, ] ) # Measure scipy fitting time 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) 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) stop = time.perf_counter_ns() if repetition != 0: results_fitting.append( [ "scipy", f"{ndim}D", M, "closed" if closed else "open", stop - start, ] ) .. GENERATED FROM PYTHON SOURCE LINES 234-236 Plotting Fitting Time --------------------- .. GENERATED FROM PYTHON SOURCE LINES 236-255 .. code-block:: Python df = pd.DataFrame( results_fitting, columns=["Package", "Dimensionality", "Number of knots", "closed", "time [ns]"], ) for periodicity in ["closed", "open"]: fig, ax = plt.subplots(figsize=(7, 7)) ax.set(yscale="log") sns.set_palette(sns.color_palette(["#228b18", "#0053a6"])) sns.lineplot( x="Number of knots", y="time [ns]", hue="Package", style="Dimensionality", data=df.loc[df["closed"] == periodicity], ax=ax, ) plt.title(f"Fitting of {periodicity} splines") plt.show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/images/sphx_glr_plot_performance_comparison_with_scipy_005.png :alt: Fitting of closed splines :srcset: /auto_examples/images/sphx_glr_plot_performance_comparison_with_scipy_005.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/images/sphx_glr_plot_performance_comparison_with_scipy_006.png :alt: Fitting of open splines :srcset: /auto_examples/images/sphx_glr_plot_performance_comparison_with_scipy_006.png :class: sphx-glr-multi-img .. GENERATED FROM PYTHON SOURCE LINES 256-258 In the fitting task ``splinebox`` is outperformed by ``scipy``'s ``splprep``, but is competetive for splines with relatively few knots (i.e. < 20). .. GENERATED FROM PYTHON SOURCE LINES 260-265 Results Summary --------------- #. **Spline Creation:** ``splinebox`` consistently outperforms ``scipy`` across all dimensions for both open and closed splines. #. **Spline Evaluation:** Similarly, ``splinebox`` is faster in evaluating splines, especially as the number of parameter values increases. #. **Data Fitting:** While ``scipy`` has an edge in fitting tasks, particularly for splines with more than 20 knots, ``splinebox`` remains competitive for smaller splines. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 19.604 seconds) .. _sphx_glr_download_auto_examples_plot_performance_comparison_with_scipy.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_performance_comparison_with_scipy.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_performance_comparison_with_scipy.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_performance_comparison_with_scipy.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_