def plot_percentile_intervals()

in src/graphing/plotting.py [0:0]

• 60 lines of code
• 27 McCabe index (conditional complexity)

def plot_percentile_intervals(
    gc_event_dataframes,
    group_by=None,
    filter_by=None,
    labels=None,
    colors=None,
    plot=None,
    column="Duration_milliseconds",
    interval_duration = 0,
    percentiles = [99.99, 90, 50],
    column_timing = None,
    line_graph = False, 
    different_colors = None
    ):
    if not interval_duration:
        print("No interval length provided. Abort.")
        return

    # Filter and group, collecting colors and labels
    timestamp_groups, datapoint_groups, labels, colors, _ = filter_and_group(gc_event_dataframes, 
                                                                             group_by,
                                                                             filter_by, 
                                                                             labels, 
                                                                             column, 
                                                                             colors,
                                                                             column_timing)
    if not line_graph and different_colors == None:
        different_colors = True
    if different_colors:
        from filter_and_group import get_colors_and_alphas
        colors, _ = get_colors_and_alphas(len(colors) * len(percentiles))
    # # if no plot is passed in, create a new plot
    if not plot:
        f, plot = plt.subplots()
    
    number_of_buckets, min_time_duration, _ = get_buckets_and_range(timestamp_groups, interval_duration)
    # Determine the spacing along the X axis for the data
    x_alignment = [idx * interval_duration + min_time_duration for idx in range(number_of_buckets)]
    if len(timestamp_groups) > len(labels):
        print("Not enough labels to plot")
    if len(datapoint_groups) > len(colors):
        print("Not enough colors to plot")
    # For each group, determine the percentile, and plot an independent line for that percentile
    for group, (timestamps, dataset) in enumerate(zip(timestamp_groups, datapoint_groups)):
        
        # First, group into buckets based on time interverals.
        buckets = group_into_buckets([time - min_time_duration for time in timestamps], dataset, number_of_buckets, interval_duration)
        buckets_of_percentiles = map_get_percentiles(buckets, percentiles)
        
        # Collect the percentiles for the i-th group, percentile 0.
        single_line = [buckets_of_percentiles[i][0] for i in range(len(buckets_of_percentiles))] 
        
        # Plot the first line based on the first percentile, with a label
        if line_graph:
            plot.plot(x_alignment, single_line, label = labels[group], color = colors[group]) # changed 
        else:

            plot.scatter(x_alignment, single_line, label = labels[group], color = colors[group]) # changed 
        
        # Plot the rest of the percentiles, with decreasing alpha (opacity) values per line
        for idx in range(1, len(percentiles)):
            if different_colors:
                single_line = [buckets_of_percentiles[i][idx] for i in range(len(buckets_of_percentiles))]
                if line_graph:
                    plot.plot(x_alignment, single_line, color = colors [group + idx], alpha = 1 - 0.15 * idx )
                else:
                    plot.scatter(x_alignment, single_line, color = colors [group + idx ], alpha = 1 - 0.15 * idx ) 
            else:
                single_line = [buckets_of_percentiles[i][idx] for i in range(len(buckets_of_percentiles))]
                if line_graph:
                    plot.plot(x_alignment, single_line, color = colors [group], alpha = 1 - 0.15 * idx )
                else:
                    plot.scatter(x_alignment, single_line, color = colors [group], alpha = 1 - 0.15 * idx ) 
    
    # Add styling to the plot. Add legend, and x axis correct titles
    plot.legend(bbox_to_anchor=(1.05, 1), loc="upper left")
    return plot