def plot_heatmap()

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

• 25 lines of code
• 11 McCabe index (conditional complexity)

def plot_heatmap(heatmap, dimensions, labels=True):

    width = dimensions[0]  # x_bucket_count
    height = dimensions[1]  # y_bucket_count
    max_pause_ms = height * dimensions[3]
    max_time_ms = width * dimensions[2]
    min_time_ms = dimensions[4]
    multipler = max_time_ms / width  # multipler is the size of a bucket for time direction
    # x labels are the time labels
    
    time_labels = [num * multipler + min_time_ms for num in range(1, width + 1)]  # TODO : UPDATE TO BE FASTER

    time_labels_temp = []
    for i in range(len(time_labels)):
        if not i % 2:
            time_labels_temp.append(str(round(time_labels[i], 2)) + " s")
        else:
            time_labels_temp.append("")

    # time_labels = [str(round(label, 2)) + " s" for label in time_labels]
    time_labels = time_labels_temp

    # size of the buckets for ms pause
    multipler = (max_pause_ms) / height
    # y labels are ms pause time labels
    latency_labels = [round((num * multipler), 2) for num in reversed(range(1, height + 1))]
    latency_labels = [str(label) + " ms" for label in latency_labels]

    ## Create a figure, and add data to heatmap. Plot then show heatmap.
    fig, ax = plt.subplots()
    ax.set_title("Latency during runtime.")
    # cmap is the color scheme. To change the heatmap color scheme, use a color scheme from here:
    # https://matplotlib.org/stable/tutorials/colors/colormaps.html
    im = __heatmap_make(heatmap, latency_labels, time_labels, ax=ax, cmap="plasma", cbarlabel="Frequency", fig = fig)

    if labels:
        __annotate_heatmap(im, valfmt="{x}")
    fig.tight_layout()
    return ax