309 lines
9.8 KiB
Python
309 lines
9.8 KiB
Python
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import matplotlib.pyplot as plt
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import numpy as np
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import csv
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from matplotlib.ticker import MaxNLocator
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from matplotlib import cm
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plt.style.use('fivethirtyeight')
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colors = cm.Set1.colors # Use a different strong color palette
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files = [
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("2.5.1 1T", "results/2.5.1-1T.csv"),
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("2.5.1 8T", "results/2.5.1-8T.csv"),
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("2.3.3 1T", "results/2.3.3-1T.csv"),
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("2.3.3 8T", "results/2.3.3-8T.csv"),
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("2.4.0 1T", "results/2.4.0-1T.csv"),
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("2.4.0 8T", "results/2.4.0-8T.csv"),
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]
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output = "results/Benchmark-chart.png"
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data_dict = {}
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for label, file in files:
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with open(file, 'r') as csvfile:
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reader = csv.reader(csvfile)
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data = [list(map(float, row)) for row in reader]
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data_dict[label] = data
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fig, ax = plt.subplots()
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for label in data_dict.keys():
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data = data_dict[label]
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ax.plot(
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list(map(lambda d: int(d[0]),data)),
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list(map(lambda d: d[1],data)),
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label=label
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)
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ax.set_xlabel("N. of regex", fontname="Roboto", fontsize=12)
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ax.set_ylabel("MB/s", fontname="Roboto", fontsize=12)
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ax.legend(prop={'family': 'Roboto', 'size': 10})
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ax.legend(
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title_fontsize=12,
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loc="upper center",
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bbox_to_anchor=(0.5, -0.1),
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frameon=True,
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shadow=True,
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borderpad=1,
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fontsize=10,
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fancybox=True,
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ncol=len(data_dict.keys()) # Make the legend horizontal
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)
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ax.set_xticks(np.arange(0, max(map(lambda d: int(d[0]), data)), step=3))
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ax.set_yticks(np.arange(0, max(map(lambda d: d[1], data)), step=300))
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plt.subplots_adjust(bottom=0.2) # Adjust the bottom margin to make space for the legend
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ax.set_title("Firegex benchmark (nfregex)", fontweight='bold', fontname="Roboto", pad=20)
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fig.set_size_inches(12, 8) # Set the figure size to make the image larger
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#plt.show()
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plt.savefig(output, dpi=300, bbox_inches='tight')
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plt.close()
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files = [
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("2.5.1 1T", "results/2.5.1-1T-withload.csv"),
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("2.5.1 8T", "results/2.5.1-8T-withload.csv"),
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]
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output = "results/Benchmark-chart-with-load.png"
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data_dict = {}
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for label, file in files:
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with open(file, 'r') as csvfile:
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reader = csv.reader(csvfile)
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data = [list(map(float, row)) for row in reader]
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data_dict[label] = data
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fig, ax = plt.subplots()
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for label in data_dict.keys():
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data = data_dict[label]
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ax.plot(
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list(map(lambda d: int(d[0]), data)),
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list(map(lambda d: d[1], data)),
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label=label
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)
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ax.set_xlabel("N. of regex", fontname="Roboto", fontsize=12)
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ax.set_ylabel("MB/s", fontname="Roboto", fontsize=12)
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ax.legend(prop={'family': 'Roboto', 'size': 10})
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ax.legend(
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title_fontsize=12,
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loc="upper center",
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bbox_to_anchor=(0.5, -0.1),
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frameon=True,
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shadow=True,
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borderpad=1,
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fontsize=10,
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fancybox=True,
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ncol=len(data_dict.keys())
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)
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ax.set_xticks(np.arange(0, max(map(lambda d: int(d[0]), data)), step=3))
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ax.set_yticks(np.arange(0, max(map(lambda d: d[1], data)), step=150))
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plt.subplots_adjust(bottom=0.2)
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ax.set_title("Load test firegex (nfregex)", fontweight='bold', fontname="Roboto", pad=20)
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fig.set_size_inches(12, 8)
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# Calculate the minimum and maximum y values across all data
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all_y_values = [d[1] for data in data_dict.values() for d in data]
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y_min, y_max = min(all_y_values), max(all_y_values)
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# Set the y-axis limits to skip unused parts
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ax.set_ylim(y_min - (y_max - y_min) * 0.1, y_max + (y_max - y_min) * 0.1)
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# Ensure y-ticks are integers if applicable
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ax.yaxis.set_major_locator(MaxNLocator(integer=True))
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#plt.show()
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plt.savefig(output, dpi=300, bbox_inches='tight')
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plt.close()
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files_nfproxy = [
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("NfProxy 1T", "results/comparemark_nfproxy_1T.csv"),
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("NfProxy 8T", "results/comparemark_nfproxy_8T.csv"),
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]
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output_whisker = "results/whisker_nfproxy.png"
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output_histogram = "results/istogramma_nfproxy.png"
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# Read and process data for nfproxy
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data_nfproxy = {}
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for label, file in files_nfproxy:
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with open(file, 'r') as csvfile:
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reader = csv.reader(csvfile)
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next(reader) # Skip the header
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data = [(float(row[0]), float(row[1])) for row in reader]
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data_nfproxy[label+" no filter"] = [ele[0] for ele in data]
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data_nfproxy[label+" test"] = [ele[1] for ele in data]
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# Generate whisker plot for nfproxy
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fig, ax = plt.subplots()
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y_max = max([max(data) for data in data_nfproxy.values()])
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y_min = min([min(data) for data in data_nfproxy.values()])
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for i, (label, data) in enumerate(data_nfproxy.items()):
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ax.boxplot(
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data,
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positions=[list(data_nfproxy.keys()).index(label)],
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tick_labels=[label],
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boxprops=dict(color="black", facecolor=colors[i % len(colors)], linewidth=1.3),
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whiskerprops=dict(color="black", linewidth=1.3),
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capprops=dict(color="black", linewidth=1.3),
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medianprops=dict(color="black", linewidth=1.3),
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patch_artist=True, # Enable filling the box with color
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widths=0.35 # Increase the width of the boxes
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)
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ax.set_yticks(np.arange(0, int(y_max) + 100, step=100)) # Ensure the range includes y_max
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# Set the y-axis limits to skip unused parts
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ax.set_ylim(y_min - (y_max - y_min) * 0.1, y_max + (y_max - y_min) * 0.1)
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ax.set_title("NFProxy Benchmarks", fontweight='bold', fontname="Roboto", pad=20)
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ax.set_ylabel("MB/s", fontname="Roboto", fontsize=12)
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fig.set_size_inches(12, 8)
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#plt.show()
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plt.savefig(output_whisker, dpi=300)
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plt.close()
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# Generate bar chart with average data for nfproxy
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average_data = {label: np.mean(data) for label, data in data_nfproxy.items()}
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fig, ax = plt.subplots()
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y_max = max(average_data.values())
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bars = ax.bar(
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average_data.keys(),
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average_data.values(),
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color=[colors[i % len(colors)] for i in range(len(average_data))],
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edgecolor="black",
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width=0.4 # Make the bars narrower
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)
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ax.set_yticks(np.arange(0, int(y_max) + 100, step=100)) # Ensure the range includes y_max
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ax.set_title("NFProxy Benchmarks", fontweight='bold', fontname="Roboto", pad=20)
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ax.set_ylabel("Average MB/s", fontname="Roboto", fontsize=12)
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ax.set_xticklabels(average_data.keys(), fontname="Roboto", fontsize=12)
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# Annotate bars with their values
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for bar in bars:
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height = bar.get_height()
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ax.annotate(
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f'{height:.2f}',
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xy=(bar.get_x() + bar.get_width() / 2, height),
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xytext=(0, 3), # Offset text above the bar
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textcoords="offset points",
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ha='center',
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va='bottom',
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fontsize=10,
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fontname="Roboto"
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)
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fig.set_size_inches(12, 8)
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plt.tight_layout()
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#plt.show()
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plt.savefig(output_histogram, dpi=300, bbox_inches='tight')
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plt.close()
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files_nfregex = [
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("NfRegex 1T", "results/comparemark_nfregex_1T.csv"),
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("NfRegex 8T", "results/comparemark_nfregex_8T.csv"),
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]
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output_whisker = "results/whisker_compare.png"
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output_histogram = "results/istrogramma_compare.png"
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# Read and process data for nfregex
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data_nfregex = {}
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for label, file in files_nfregex:
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with open(file, 'r') as csvfile:
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reader = csv.reader(csvfile)
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next(reader) # Skip the header
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data = [(float(row[0]), float(row[1])) for row in reader]
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data_nfregex[label + " no filter"] = [ele[0] for ele in data]
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data_nfregex[label + " test"] = [ele[1] for ele in data]
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# Combine nfproxy and nfregex data
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combined_data = {**data_nfproxy, **data_nfregex}
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# Generate whisker plot for combined data
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fig, ax = plt.subplots()
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y_max = max([max(data) for data in combined_data.values()])
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y_min = min([min(data) for data in combined_data.values()])
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for i, (label, data) in enumerate(combined_data.items()):
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ax.boxplot(
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data,
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positions=[list(combined_data.keys()).index(label)],
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boxprops=dict(color="black", facecolor=colors[i % len(colors)], linewidth=1.3),
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whiskerprops=dict(color="black", linewidth=1.3),
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capprops=dict(color="black", linewidth=1.3),
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medianprops=dict(color="black", linewidth=1.3),
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patch_artist=True, # Enable filling the box with color
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widths=0.6 # Increase the width of the boxes
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)
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ax.set_xticks(range(len(combined_data.keys())))
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ax.set_xticklabels(combined_data.keys(), fontname="Roboto", fontsize=10)
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ax.set_yticks(np.arange(0, int(y_max) + 100, step=250)) # Ensure the range includes y_max
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plt.subplots_adjust(bottom=0.12)
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# Set the y-axis limits to skip unused parts
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ax.set_ylim(y_min - (y_max - y_min) * 0.1, y_max + (y_max - y_min) * 0.1)
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ax.set_title("Combined Benchmarks (NFProxy vs NFRegex)", fontweight='bold', fontname="Roboto", pad=20)
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ax.set_ylabel("MB/s", fontname="Roboto", fontsize=12)
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fig.set_size_inches(14, 8)
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#plt.show()
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plt.savefig(output_whisker, dpi=300, bbox_inches='tight')
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plt.close()
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# Generate bar chart with average data for combined data
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average_combined_data = {label: np.mean(data) for label, data in combined_data.items()}
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fig, ax = plt.subplots()
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y_max = max(average_combined_data.values())
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bars = ax.bar(
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average_combined_data.keys(),
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average_combined_data.values(),
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color=[colors[0 if "nfregex" in ele.lower() else 1] for ele in average_combined_data],
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edgecolor="black",
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width=0.4 # Make the bars narrower
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)
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ax.set_xticks(range(len(average_combined_data.keys())))
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ax.set_xticklabels(average_combined_data.keys(), fontname="Roboto", fontsize=10)
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ax.set_yticks(np.arange(0, int(y_max) + 100, step=200)) # Ensure the range includes y_max
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ax.set_title("Combined Benchmarks (NFProxy vs NFRegex)", fontweight='bold', fontname="Roboto", pad=20)
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ax.set_ylabel("Average MB/s", fontname="Roboto", fontsize=12)
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# Annotate bars with their values
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for bar in bars:
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height = bar.get_height()
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ax.annotate(
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f'{height:.2f}',
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xy=(bar.get_x() + bar.get_width() / 2, height),
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xytext=(0, 3), # Offset text above the bar
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textcoords="offset points",
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ha='center',
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va='bottom',
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fontsize=10,
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fontname="Roboto"
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)
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fig.set_size_inches(14, 8)
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plt.tight_layout()
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#plt.show()
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plt.savefig(output_histogram, dpi=300, bbox_inches='tight')
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plt.close()
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