Python HoverTool.renderers方法代码示例

# 需要导入模块: from bokeh.models import HoverTool [as 别名]
# 或者: from bokeh.models.HoverTool import renderers [as 别名]
def plot_merged_benchmark(savefile, benchmarks, title, xaxis_type, yaxis_type,
    save_prefix=""):

    # Sort the benchmarks by device name
    bmarks_sorted = sorted(benchmarks, key=lambda k: k['extra_data']['AF_DEVICE'])
    benchmarks = bmarks_sorted

    # configure the colors
    colors = unique_colors()
    assigned_colors = dict()

    # configure the hover box
    hover = HoverTool(
        tooltips = [
            ("Device", "@device"),
            ("Benchmark", "@benchmark"),
            ("Backend", "@platform"),
            ("OS", "@os"),
            ("(x,y)", "(@x,@y)")
        ])

    # configure the plot title and axis labels, use CDN for the data source
    #bplt.output_file(save_prefix + savefile + ".html", title=title, mode='cdn')
    bplt.output_file(save_prefix + savefile + ".html", title=title)
    plot = bplt.figure(title=title, tools=[hover,'save,box_zoom,resize,reset'])
    xlabel = ""
    ylabel = ""
    legend_location = "top_right"

    # plot images/second vs. data size
    scatter_renderers = list()
    for benchmark in benchmarks:
        bmark_name = benchmark['benchmark_name']

        # Look up the color
        device = benchmark['extra_data']['AF_DEVICE']
        platform = benchmark['extra_data']['AF_PLATFORM']
        operating_system = benchmark['extra_data']['AF_OS']
#        key = device
        key = bmark_name + device + platform
        if key in assigned_colors:
            color = assigned_colors[key]
        else:
            color = colors.next()
            assigned_colors[key] = color

        # extract benchmarks
        x,xlabel,legend_location = format_data(benchmark, xaxis_type)
        y,ylabel,legend_location = format_data(benchmark, yaxis_type)
        # get the device name, override if necessary
        if 'AF_LABEL' in benchmark['extra_data'].keys():
            device = benchmark['extra_data']['AF_LABEL']

        source = bplt.ColumnDataSource(
            data = dict(x=x,y=y,
                device=[device]*len(x),
                benchmark=[bmark_name]*len(x),
                platform=[platform]*len(x),
                os=[operating_system]*len(x),
            ))

        # Generate the legend, automatically add the platform if needed
#        legend = device
        legend = device + " (" + platform + ") " + bmark_name

        # generate the plot
        plot.line(x,y, legend=legend, color=color, line_width=2)
        sr = plot.scatter('x', 'y', source=source, legend=legend, color=color,
            fill_color="white", size=8)
        scatter_renderers.append(sr)

    hover = plot.select(HoverTool)
    hover.renderers = scatter_renderers

    plot.xaxis.axis_label = xlabel
    plot.yaxis.axis_label = ylabel
    plot.legend.location = legend_location

    # save the plot
    bplt.save(plot)

# 需要导入模块: from bokeh.models import HoverTool [as 别名]
# 或者: from bokeh.models.HoverTool import renderers [as 别名]
def plot_benchmark(savefile, benchmarks, title, xaxis_type, yaxis_type,
    save_prefix=""):

    show_backends = False
    show_os = False

    # Determine the number of backends
    backends = list_recordTable_attribute(benchmarks, 'AF_PLATFORM')
    if len(backends) > 1:
        show_backends = True

    operating_sys = list_recordTable_attribute(benchmarks, 'AF_OS')
    if len(operating_sys) > 1:
        show_os = True

    # Sort the benchmarks by device name
    bmarks_sorted = sorted(benchmarks, key=lambda k: k['extra_data']['AF_DEVICE'])
    benchmarks = bmarks_sorted

    # configure the colors
    colors = unique_colors()

    # configure the hover box
    hover = HoverTool(
        tooltips = [
            ("Device", "@device"),
            ("Backend", "@platform"),
            ("OS", "@os"),
            ("(x,y)", "(@x,@y)")
        ])

    # configure the plot title and axis labels, use CDN for the data source
    #bplt.output_file(save_prefix + savefile + ".html", title=title, mode='cdn')
    bplt.output_file(save_prefix + savefile + ".html", title=title)
    plot = bplt.figure(title=title, tools=[hover,'save,box_zoom,resize,reset'])
    xlabel = ""
    ylabel = ""
    legend_location = "top_right"

    # plot images/second vs. data size
    scatter_renderers = list()
    for benchmark in benchmarks:
        # get the color we will use for this plot
        color = colors.next()

        # extract benchmarks
        x,xlabel,legend_location = format_data(benchmark, xaxis_type)
        y,ylabel,legend_location = format_data(benchmark, yaxis_type)
        platform = benchmark['extra_data']['AF_PLATFORM']
        # get the device name, override if necessary
        device = benchmark['extra_data']['AF_DEVICE']
        operating_system = benchmark['extra_data']['AF_OS']
        if 'AF_LABEL' in benchmark['extra_data'].keys():
            device = benchmark['extra_data']['AF_LABEL']

        source = bplt.ColumnDataSource(
            data = dict(x=x,y=y,
                device=[device]*len(x),
                platform=[platform]*len(x),
                os=[operating_system]*len(x),
            ))

        # Generate the legend, automatically add the platform if needed
        legend = device
        if show_os or show_backends:
            legend += "( "
        if show_os:
            legend += operating_system + " "
        if show_backends:
            legend += platform + " "
        if show_os or show_backends:
            legend += ")"

        # generate the plot
        plot.line(x,y, legend=legend, color=color, line_width=2)
        sr = plot.scatter('x', 'y', source=source, legend=legend, color=color,
            fill_color="white", size=8)
        scatter_renderers.append(sr)

    hover = plot.select(HoverTool)
    hover.renderers = scatter_renderers

    plot.xaxis.axis_label = xlabel
    plot.yaxis.axis_label = ylabel
    plot.legend.location = legend_location

    # save the plot
    bplt.save(plot)