grass.benchmark.plots — GRASS Python Library documentation

# Source code for grass.benchmark.plots

    # MODULE:    grass.benchmark
    #
    # AUTHOR(S): Vaclav Petras <wenzeslaus gmail com>
    #
    # PURPOSE:   Benchmarking for GRASS GIS modules
    #
    # COPYRIGHT: (C) 2021 Vaclav Petras, and by the GRASS Development Team
    #
    #            This program is free software under the GNU General Public
    #            License (>=v2). Read the file COPYING that comes with GRASS
    #            for details.

    """Plotting functionality for benchmark results"""

    def get_pyplot(to_file):
        """Get pyplot from matplotlib

        Lazy import to easily run code importing this function on limited installations.
        Only actual call to this function requires matplotlib.

        The *to_file* parameter can be set to True to avoid tkinter dependency
        if the interactive show method is not needed.
        """
        import matplotlib  # pylint: disable=import-outside-toplevel

        if to_file:
            backend = "agg"
        else:
            backend = None
        if backend:
            matplotlib.use(backend)

        import matplotlib.pyplot as plt  # pylint: disable=import-outside-toplevel

        return plt

    def nprocs_plot(results, filename=None, title=None):
        """Plot results from a multiple nprocs (thread) benchmarks.

        *results* is a list of individual results from separate benchmarks.
        One result is required to have attributes: *nprocs*, *times*, *label*.
        The *nprocs* attribute is a list of all processing elements
        (cores, threads, processes) used in the benchmark.
        The *times* attribute is a list of corresponding times for each value
        from the *nprocs* list.
        The *label* attribute identifies the benchmark in the legend.

        Optionally, result can have an *all_times* attribute which is a list
        of lists. One sublist is all times recorded for each value of nprocs.

        Each result can come with a different list of nprocs, i.e., benchmarks
        which used different values for nprocs can be combined in one plot.
        """
        plt = get_pyplot(to_file=bool(filename))
        axes = plt.gca()

        x_ticks = set()  # gather x values
        for result in results:
            x = result.nprocs
            x_ticks.update(x)
            plt.plot(x, result.times, label=result.label)
            if hasattr(result, "all_times"):
                mins = [min(i) for i in result.all_times]
                maxes = [max(i) for i in result.all_times]
                plt.fill_between(x, mins, maxes, color="gray", alpha=0.3)
        plt.legend()
        # If there is not many x values, show ticks for each, but use default
        # ticks when there is a lot of x values.
        if len(x_ticks) < 10:
            axes.set(xticks=sorted(x_ticks))
        else:
            from matplotlib.ticker import (  # pylint: disable=import-outside-toplevel
                MaxNLocator,
            )

            axes.xaxis.set_major_locator(MaxNLocator(integer=True))
        plt.xlabel("Number of processing elements (cores, threads, processes)")
        plt.ylabel("Time [s]")
        if title:
            plt.title(title)
        else:
            plt.title("Execution time by processing elements")
        if filename:
            plt.savefig(filename)
        else:
            plt.show()

    def num_cells_plot(results, filename=None, title=None, show_resolution=False):
        """Plot results from a multiple raster grid size benchmarks.

        *results* is a list of individual results from separate benchmarks
        with one result being similar to the :func:`nprocs_plot` function.
        The result is required to have *times* and *label* attributes
        and may have an *all_times* attribute.
        Further, it is required to have *cells* attribute, or,
        when ``show_resolution=True``, it needs to have a *resolutions* attribute.

        Each result can come with a different list of nprocs, i.e., benchmarks
        which used different values for nprocs can be combined in one plot.
        """
        plt = get_pyplot(to_file=bool(filename))
        axes = plt.gca()
        if show_resolution:
            axes.invert_xaxis()

        x_ticks = set()
        for result in results:
            if show_resolution:
                x = result.resolutions
            else:
                x = result.cells
            x_ticks.update(x)
            plt.plot(x, result.times, label=result.label)
            if hasattr(result, "all_times"):
                mins = [min(i) for i in result.all_times]
                maxes = [max(i) for i in result.all_times]
                plt.fill_between(x, mins, maxes, color="gray", alpha=0.3)

        plt.legend()
        axes.set(xticks=sorted(x_ticks))
        if not show_resolution:
            axes.ticklabel_format(axis="x", style="scientific", scilimits=(0, 0))
        if show_resolution:
            plt.xlabel("Resolution [map units]")
        else:
            plt.xlabel("Number of cells")
        plt.ylabel("Time [s]")
        if title:
            plt.title(title)
        elif show_resolution:
            plt.title("Execution time by resolution")
        else:
            plt.title("Execution time by cell count")
        if filename:
            plt.savefig(filename)
        else:
            plt.show()

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