auto-generated charts

tests/requirements.txt

@@ -1,2 +1,4 @@
 iperf3
 requests
+matplotlib
+numpy

tests/results/test-firegex.csv

@@ -1,52 +0,0 @@
-;2.3.3-1T;2.3.3-8T;2.4.0-1T;2.4.0-8T;2.5.1-1T;2.5.1-8T;;With Load;2.5.1-1T;2.5.1-8T
-0;4090,616;3789,988;4216,05;4203,31;3245,763;4007,679;;0;710,619;1790,382
-1;2211,62;2069,487;4239,598;4283,392;3283,646;3963,986;;1;887,877;1933,881
-2;1165,45;1484,554;2418,527;2383,415;3741,157;4222,243;;2;981,431;1941,564
-3;849,39;956,972;2227,8;2419,701;3691,206;3640,707;;3;1081,412;1926,518
-4;828,635;1052,873;2045,351;2038,823;3365,134;4388,553;;4;1038,514;1945,295
-5;741,537;739,658;2066,161;2038;3691,457;3636,047;;5;1029,805;1734,462
-6;632,721;534,722;2214,416;2160,869;3354,807;3644,611;;6;928,317;2009,994
-7;624,772;638,524;2052,845;2192,641;3526,728;3547,39;;7;1130,938;2007,538
-8;529,234;573,833;2195,199;2216,766;3252,62;3412,162;;8;1165,42;2004,825
-9;469,688;531,658;2186,867;2762,56;3551,086;3632,367;;9;925,632;1848,551
-10;336,33;476,167;2147,534;2160,398;3561,506;3536,655;;10;949,483;1836,558
-11;427,783;443,746;2186,652;2147,886;3525,577;3820,019;;11;1021,973;1977,19
-12;400,662;406,027;2178,036;2146,47;2776,064;3677,177;;12;903,878;1987,207
-13;335,086;385,739;2182,151;2158,101;3541,86;3366,323;;13;1001,53;2007,422
-14;342,042;341,563;2185,324;2154,025;3501,34;3353,031;;14;895,351;1994,914
-15;307,283;318,699;1812,911;1997,694;3692,092;3392,423;;15;1026,722;1982,997
-16;239,694;303,722;2144,689;2028,288;3637,166;3330,368;;16;634,727;1955,828
-17;295,163;284,924;2163,525;2005,373;3617,031;3363,272;;17;744,758;1705,883
-18;285,787;284,336;2073,89;2153,945;3700,092;4027,34;;18;978,59;1983,501
-19;254,402;267,32;2071,682;2190,799;3176,831;3467,982;;19;962,375;1951,311
-20;250,553;202,74;2153,502;2169,302;3368,038;3607,754;;20;997,471;1921,772
-21;227,146;243,849;2144,04;2139,842;3716,577;3767,614;;21;929,785;1956,908
-22;238,747;226,082;2118,517;2155,307;3452,917;3340,544;;22;1200,83;1948,865
-23;234,718;214,348;2141,19;2152,223;3617,604;4086,612;;23;1257,741;1929,387
-24;210,484;216,8;2167,103;2124,155;3651,796;3784,164;;24;772,729;1814,539
-25;210,697;188,98;2168,631;2103,135;3552,053;3496,518;;25;683,913;2084,284
-26;205,943;158,68;2165,555;2148,053;3843,18;3543,808;;26;1188,17;1830,901
-27;202,568;166,556;2158,424;2163,366;3720,406;3453,934;;27;919,961;1946,713
-28;194,341;148,287;2188,376;2122,339;3431,1;3546,188;;28;922,225;1958,238
-29;189,916;149,681;2165,311;2064,701;3578,973;3458,804;;29;1066,286;1906,573
-30;154,228;177,043;2168,158;2134,748;3561,994;3728,609;;30;979,399;1895,341
-31;168,922;175,321;2108,045;1632,533;3524,566;3697,624;;31;978,917;1986,09
-32;173,623;165,312;2121,414;2082,309;3567,537;3698,191;;32;988,415;1943,785
-33;125,431;166,943;2022,533;1878,795;3626,767;3673,973;;33;1061,523;1879,917
-34;162,154;159,026;1888,759;2009,28;3498,361;3690,046;;34;942,85;1946,029
-35;149,865;156,759;2022,837;1987,424;3621,396;3663,799;;35;1045,949;1858,958
-36;150,088;150,216;2015,042;1748,364;3297,839;3540,004;;36;883,941;2009,44
-37;146,085;144,932;1920,401;1725,66;3541,207;3857,604;;37;958,41;1876,749
-38;137,182;146,088;2005,037;1967,877;3560,364;3426,215;;38;989,523;1967,254
-39;138,686;135,897;2028,856;1854,637;3589,746;3704,176;;39;1001,121;1968,595
-40;136,302;136,99;2010,43;1903,963;3686,673;3796,133;;40;1080,079;1846,438
-41;132,707;128,557;1522,342;1987,138;3463,811;3604,623;;41;1151,938;1955,897
-42;100,928;100,307;1525,635;1532,547;3428,408;3650,508;;42;1221,644;1986,446
-43;126,414;103,249;1912,05;1569,27;3753,139;3501,861;;43;991,855;1965,143
-44;125,271;123,49;1920,256;1535,941;3368,89;3685,992;;44;1088,344;1963,016
-45;117,839;120,39;1753,645;1941,715;3324,876;3623,404;;45;973,641;1890,88
-46;89,494;118,055;1476,977;2014,504;3614,895;3728,601;;46;952,35;1998,801
-47;116,939;115;1888,645;2005,794;3245,942;3844,994;;47;1089,644;1682,048
-48;112,517;112,593;1949,103;2022,972;3257,925;3820,046;;48;939,615;2023,688
-49;111,369;109,55;1684,633;1740,836;3200,585;3680,976;;49;1258,419;1982,952
-50;108,568;109,512;1493,935;1726,444;3321,55;3797,432;;50;949,414;1993,641

tests/results_plotter.py

@@ -0,0 +1,308 @@
+
+import matplotlib.pyplot as plt
+import numpy as np
+import csv
+from matplotlib.ticker import MaxNLocator
+from matplotlib import cm
+
+plt.style.use('fivethirtyeight')
+colors = cm.Set1.colors  # Use a different strong color palette
+
+files = [
+    ("2.5.1 1T", "results/2.5.1-1T.csv"),
+    ("2.5.1 8T", "results/2.5.1-8T.csv"),
+    ("2.3.3 1T", "results/2.3.3-1T.csv"),
+    ("2.3.3 8T", "results/2.3.3-8T.csv"),
+    ("2.4.0 1T", "results/2.4.0-1T.csv"),
+    ("2.4.0 8T", "results/2.4.0-8T.csv"),
+]
+
+output = "results/Benchmark-chart.png"
+
+data_dict = {}
+
+for label, file in files:
+    with open(file, 'r') as csvfile:
+        reader = csv.reader(csvfile)
+        data = [list(map(float, row)) for row in reader]
+        data_dict[label] = data
+
+fig, ax = plt.subplots()
+
+for label in data_dict.keys():
+    data = data_dict[label]
+    ax.plot(
+        list(map(lambda d: int(d[0]),data)),
+        list(map(lambda d: d[1],data)),
+        label=label
+    )
+
+ax.set_xlabel("N. of regex", fontname="Roboto", fontsize=12)
+ax.set_ylabel("MB/s", fontname="Roboto", fontsize=12)
+ax.legend(prop={'family': 'Roboto', 'size': 10})
+ax.legend(
+    title_fontsize=12,
+    loc="upper center",
+    bbox_to_anchor=(0.5, -0.1),
+    frameon=True,
+    shadow=True,
+    borderpad=1,
+    fontsize=10,
+    fancybox=True,
+    ncol=len(data_dict.keys())  # Make the legend horizontal
+)
+ax.set_xticks(np.arange(0, max(map(lambda d: int(d[0]), data)), step=3))
+ax.set_yticks(np.arange(0, max(map(lambda d: d[1], data)), step=300))
+plt.subplots_adjust(bottom=0.2)  # Adjust the bottom margin to make space for the legend
+ax.set_title("Firegex benchmark (nfregex)", fontweight='bold', fontname="Roboto", pad=20)
+fig.set_size_inches(12, 8)  # Set the figure size to make the image larger
+
+#plt.show()
+plt.savefig(output, dpi=300, bbox_inches='tight')
+plt.close()
+
+files = [
+    ("2.5.1 1T", "results/2.5.1-1T-withload.csv"),
+    ("2.5.1 8T", "results/2.5.1-8T-withload.csv"),
+]
+
+output = "results/Benchmark-chart-with-load.png"
+
+data_dict = {}
+
+for label, file in files:
+    with open(file, 'r') as csvfile:
+        reader = csv.reader(csvfile)
+        data = [list(map(float, row)) for row in reader]
+        data_dict[label] = data
+
+fig, ax = plt.subplots()
+
+for label in data_dict.keys():
+    data = data_dict[label]
+    ax.plot(
+        list(map(lambda d: int(d[0]), data)),
+        list(map(lambda d: d[1], data)),
+        label=label
+    )
+
+ax.set_xlabel("N. of regex", fontname="Roboto", fontsize=12)
+ax.set_ylabel("MB/s", fontname="Roboto", fontsize=12)
+ax.legend(prop={'family': 'Roboto', 'size': 10})
+ax.legend(
+    title_fontsize=12,
+    loc="upper center",
+    bbox_to_anchor=(0.5, -0.1),
+    frameon=True,
+    shadow=True,
+    borderpad=1,
+    fontsize=10,
+    fancybox=True,
+    ncol=len(data_dict.keys())
+)
+ax.set_xticks(np.arange(0, max(map(lambda d: int(d[0]), data)), step=3))
+ax.set_yticks(np.arange(0, max(map(lambda d: d[1], data)), step=150))
+plt.subplots_adjust(bottom=0.2)
+ax.set_title("Load test firegex (nfregex)", fontweight='bold', fontname="Roboto", pad=20)
+fig.set_size_inches(12, 8)
+
+# Calculate the minimum and maximum y values across all data
+all_y_values = [d[1] for data in data_dict.values() for d in data]
+y_min, y_max = min(all_y_values), max(all_y_values)
+
+# Set the y-axis limits to skip unused parts
+ax.set_ylim(y_min - (y_max - y_min) * 0.1, y_max + (y_max - y_min) * 0.1)
+
+# Ensure y-ticks are integers if applicable
+ax.yaxis.set_major_locator(MaxNLocator(integer=True))
+
+#plt.show()
+plt.savefig(output, dpi=300, bbox_inches='tight')
+plt.close()
+
+files_nfproxy = [
+    ("NfProxy 1T", "results/comparemark_nfproxy_1T.csv"),
+    ("NfProxy 8T", "results/comparemark_nfproxy_8T.csv"),
+]
+
+output_whisker = "results/whisker_nfproxy.png"  
+output_histogram = "results/istogramma_nfproxy.png"
+
+# Read and process data for nfproxy
+data_nfproxy = {}
+for label, file in files_nfproxy:
+    with open(file, 'r') as csvfile:
+        reader = csv.reader(csvfile)
+        next(reader)  # Skip the header
+        data = [(float(row[0]), float(row[1])) for row in reader]
+        data_nfproxy[label+" no filter"] = [ele[0] for ele in data]
+        data_nfproxy[label+" test"] = [ele[1] for ele in data]
+
+# Generate whisker plot for nfproxy
+fig, ax = plt.subplots()
+
+y_max = max([max(data) for data in data_nfproxy.values()])
+y_min = min([min(data) for data in data_nfproxy.values()])
+
+for i, (label, data) in enumerate(data_nfproxy.items()):
+    ax.boxplot(
+        data,
+        positions=[list(data_nfproxy.keys()).index(label)],
+        tick_labels=[label],
+        boxprops=dict(color="black", facecolor=colors[i % len(colors)], linewidth=1.3),
+        whiskerprops=dict(color="black", linewidth=1.3),
+        capprops=dict(color="black", linewidth=1.3),
+        medianprops=dict(color="black", linewidth=1.3),
+        patch_artist=True,  # Enable filling the box with color
+        widths=0.35  # Increase the width of the boxes
+    )
+
+ax.set_yticks(np.arange(0, int(y_max) + 100, step=100))  # Ensure the range includes y_max
+
+# Set the y-axis limits to skip unused parts
+ax.set_ylim(y_min - (y_max - y_min) * 0.1, y_max + (y_max - y_min) * 0.1)
+
+ax.set_title("NFProxy Benchmarks", fontweight='bold', fontname="Roboto", pad=20)
+ax.set_ylabel("MB/s", fontname="Roboto", fontsize=12)
+fig.set_size_inches(12, 8)
+
+#plt.show()
+plt.savefig(output_whisker, dpi=300)
+plt.close()
+
+# Generate bar chart with average data for nfproxy
+average_data = {label: np.mean(data) for label, data in data_nfproxy.items()}
+
+fig, ax = plt.subplots()
+y_max = max(average_data.values())
+
+bars = ax.bar(
+    average_data.keys(),
+    average_data.values(),
+    color=[colors[i % len(colors)] for i in range(len(average_data))],
+    edgecolor="black",
+    width=0.4  # Make the bars narrower
+)
+
+ax.set_yticks(np.arange(0, int(y_max) + 100, step=100))  # Ensure the range includes y_max
+ax.set_title("NFProxy Benchmarks", fontweight='bold', fontname="Roboto", pad=20)
+ax.set_ylabel("Average MB/s", fontname="Roboto", fontsize=12)
+ax.set_xticklabels(average_data.keys(), fontname="Roboto", fontsize=12)
+
+# Annotate bars with their values
+for bar in bars:
+    height = bar.get_height()
+    ax.annotate(
+        f'{height:.2f}',
+        xy=(bar.get_x() + bar.get_width() / 2, height),
+        xytext=(0, 3),  # Offset text above the bar
+        textcoords="offset points",
+        ha='center',
+        va='bottom',
+        fontsize=10,
+        fontname="Roboto"
+    )
+
+fig.set_size_inches(12, 8)
+plt.tight_layout()
+
+#plt.show()
+plt.savefig(output_histogram, dpi=300, bbox_inches='tight')
+plt.close()
+
+files_nfregex = [
+    ("NfRegex 1T", "results/comparemark_nfregex_1T.csv"),
+    ("NfRegex 8T", "results/comparemark_nfregex_8T.csv"),
+]
+
+output_whisker = "results/whisker_compare.png"  
+output_histogram = "results/istrogramma_compare.png"
+
+# Read and process data for nfregex
+data_nfregex = {}
+for label, file in files_nfregex:
+    with open(file, 'r') as csvfile:
+        reader = csv.reader(csvfile)
+        next(reader)  # Skip the header
+        data = [(float(row[0]), float(row[1])) for row in reader]
+        data_nfregex[label + " no filter"] = [ele[0] for ele in data]
+        data_nfregex[label + " test"] = [ele[1] for ele in data]
+
+# Combine nfproxy and nfregex data
+combined_data = {**data_nfproxy, **data_nfregex}
+
+# Generate whisker plot for combined data
+fig, ax = plt.subplots()
+
+y_max = max([max(data) for data in combined_data.values()])
+y_min = min([min(data) for data in combined_data.values()])
+
+for i, (label, data) in enumerate(combined_data.items()):
+    ax.boxplot(
+        data,
+        positions=[list(combined_data.keys()).index(label)],
+        boxprops=dict(color="black", facecolor=colors[i % len(colors)], linewidth=1.3),
+        whiskerprops=dict(color="black", linewidth=1.3),
+        capprops=dict(color="black", linewidth=1.3),
+        medianprops=dict(color="black", linewidth=1.3),
+        patch_artist=True,  # Enable filling the box with color
+        widths=0.6  # Increase the width of the boxes
+    )
+
+ax.set_xticks(range(len(combined_data.keys())))
+ax.set_xticklabels(combined_data.keys(), fontname="Roboto", fontsize=10)
+ax.set_yticks(np.arange(0, int(y_max) + 100, step=250))  # Ensure the range includes y_max
+plt.subplots_adjust(bottom=0.12)
+
+# Set the y-axis limits to skip unused parts
+ax.set_ylim(y_min - (y_max - y_min) * 0.1, y_max + (y_max - y_min) * 0.1)
+
+ax.set_title("Combined Benchmarks (NFProxy vs NFRegex)", fontweight='bold', fontname="Roboto", pad=20)
+ax.set_ylabel("MB/s", fontname="Roboto", fontsize=12)
+fig.set_size_inches(14, 8)
+
+#plt.show()
+plt.savefig(output_whisker, dpi=300, bbox_inches='tight')
+plt.close()
+
+# Generate bar chart with average data for combined data
+average_combined_data = {label: np.mean(data) for label, data in combined_data.items()}
+
+fig, ax = plt.subplots()
+y_max = max(average_combined_data.values())
+
+bars = ax.bar(
+    average_combined_data.keys(),
+    average_combined_data.values(),
+    color=[colors[0 if "nfregex" in ele.lower() else 1] for ele in average_combined_data],
+    edgecolor="black",
+    width=0.4  # Make the bars narrower
+)
+
+ax.set_xticks(range(len(average_combined_data.keys())))
+ax.set_xticklabels(average_combined_data.keys(), fontname="Roboto", fontsize=10)
+ax.set_yticks(np.arange(0, int(y_max) + 100, step=200))  # Ensure the range includes y_max
+ax.set_title("Combined Benchmarks (NFProxy vs NFRegex)", fontweight='bold', fontname="Roboto", pad=20)
+ax.set_ylabel("Average MB/s", fontname="Roboto", fontsize=12)
+
+# Annotate bars with their values
+for bar in bars:
+    height = bar.get_height()
+    ax.annotate(
+        f'{height:.2f}',
+        xy=(bar.get_x() + bar.get_width() / 2, height),
+        xytext=(0, 3),  # Offset text above the bar
+        textcoords="offset points",
+        ha='center',
+        va='bottom',
+        fontsize=10,
+        fontname="Roboto"
+    )
+
+fig.set_size_inches(14, 8)
+plt.tight_layout()
+
+#plt.show()
+plt.savefig(output_histogram, dpi=300, bbox_inches='tight')
+plt.close()
+