png to svg

tests/README.md

@@ -94,7 +94,7 @@ Firegex 2.5.0 changes the way the threads are assigned to the packets, this is d
 
 The charts are labeled as follows: `[version]-[n_thread]T` eg. `2.5.0-8T` means Firegex version 2.5.0 with 8 threads.
 
-![Firegex Benchmark](results/Benchmark-chart.png)
+![Firegex Benchmark](results/Benchmark-chart.svg)
 
 
 From the benchmark above we can't see the real advantage of multithreading in 2.5.1, we can better see the advantage of multithreading in the chart below where a fake load in filtering is done.
@@ -107,7 +107,7 @@ for (int i=0; i<50000; i++){
 }
 ```
 
-![Firegex Benchmark](results/Benchmark-chart-with-load.png)
+![Firegex Benchmark](results/Benchmark-chart-with-load.svg)
 
 In the chart above we can see that the 2.5.1 version with 8 threads has a better performance than the 2.5.1 version with 1 threads, and we can see it as much as the load increases.
 
@@ -118,15 +118,14 @@ The code used to test matches the following regex with the python re module:
 (?:[a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:\.[a-z0-9!#$%&'*+/=?^_`{|}~-]+)*|"(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21\x23-\x5b\x5d-\x7f]|\\[\x01-\x09\x0b\x0c\x0e-\x7f])*")@(?:(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?|\[(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?|[a-z0-9-]*[a-z0-9]:(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21-\x5a\x53-\x7f]|\\[\x01-\x09\x0b\x0c\x0e-\x7f])+)\])
 ```
 
-![nfproxy benchmarks](results/whisker_nfproxy.png)
+![nfproxy benchmarks](results/whisker_nfproxy.svg)
 
-![nfproxy benchmarks](results/istogramma_nfproxy.png)
+![nfproxy benchmarks](results/istogramma_nfproxy.svg)
 
 # Comparing nfproxy with nfregex
 
 Nfproxy has obviously a worse performance than nfregex, but it is more flexible and can be used in more complex scenarios.
 
-![nfproxy benchmarks](results/whisker_compare.png)
+![nfproxy benchmarks](results/whisker_compare.svg)
-
-![nfproxy benchmarks](results/istrogramma_compare.png)
 
+![nfproxy benchmarks](results/istrogramma_compare.svg)

tests/results_plotter.py

@@ -1,16 +1,15 @@
-
 import matplotlib.pyplot as plt
 import numpy as np
 import csv
 from matplotlib.ticker import MaxNLocator
 from matplotlib import cm
 
-plt.style.use('fivethirtyeight')
+plt.style.use("fivethirtyeight")
 colors = cm.Set1.colors  # Use a different strong color palette
-plt.rcParams['figure.facecolor'] = 'white'
+plt.rcParams["figure.facecolor"] = "white"
-plt.rcParams['axes.edgecolor'] = 'white'
+plt.rcParams["axes.edgecolor"] = "white"
-plt.rcParams['axes.linewidth'] = 1.5
+plt.rcParams["axes.linewidth"] = 1.5
-plt.rcParams['legend.facecolor'] = 'white'
+plt.rcParams["legend.facecolor"] = "white"
 
 files = [
     ("2.5.1 1T", "results/2.5.1-1T.csv"),
@@ -21,30 +20,30 @@ files = [
     ("2.4.0 8T", "results/2.4.0-8T.csv"),
 ]
 
-output = "results/Benchmark-chart.png"
+output = "results/Benchmark-chart.svg"
 
 data_dict = {}
 
 for label, file in files:
-    with open(file, 'r') as csvfile:
+    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()
-ax.set_facecolor('white')
+ax.set_facecolor("white")
 
 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
+        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(prop={"family": "Roboto", "size": 10})
 ax.legend(
     title_fontsize=12,
     loc="upper center",
@@ -54,16 +53,16 @@ ax.legend(
     borderpad=1,
     fontsize=10,
     fancybox=True,
-    ncol=len(data_dict.keys())  # Make the legend horizontal
+    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)
+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.savefig(output, dpi=300, bbox_inches="tight")
 plt.close()
 
 files = [
@@ -71,30 +70,30 @@ files = [
     ("2.5.1 8T", "results/2.5.1-8T-withload.csv"),
 ]
 
-output = "results/Benchmark-chart-with-load.png"
+output = "results/Benchmark-chart-with-load.svg"
 
 data_dict = {}
 
 for label, file in files:
-    with open(file, 'r') as csvfile:
+    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()
-ax.set_facecolor('white')
+ax.set_facecolor("white")
 
 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
+        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(prop={"family": "Roboto", "size": 10})
 ax.legend(
     title_fontsize=12,
     loc="upper center",
@@ -104,12 +103,12 @@ ax.legend(
     borderpad=1,
     fontsize=10,
     fancybox=True,
-    ncol=len(data_dict.keys())
+    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)
+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
@@ -123,7 +122,7 @@ ax.set_ylim(y_min - (y_max - y_min) * 0.1, y_max + (y_max - y_min) * 0.1)
 ax.yaxis.set_major_locator(MaxNLocator(integer=True))
 
 # plt.show()
-plt.savefig(output, dpi=300, bbox_inches='tight')
+plt.savefig(output, dpi=300, bbox_inches="tight")
 plt.close()
 
 files_nfproxy = [
@@ -131,13 +130,13 @@ files_nfproxy = [
     ("NfProxy 8T", "results/comparemark_nfproxy_8T.csv"),
 ]
 
-output_whisker = "results/whisker_nfproxy.png"  
+output_whisker = "results/whisker_nfproxy.svg"
-output_histogram = "results/istogramma_nfproxy.png"
+output_histogram = "results/istogramma_nfproxy.svg"
 
 # Read and process data for nfproxy
 data_nfproxy = {}
 for label, file in files_nfproxy:
-    with open(file, 'r') as csvfile:
+    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]
@@ -146,7 +145,7 @@ for label, file in files_nfproxy:
 
 # Generate whisker plot for nfproxy
 fig, ax = plt.subplots()
-ax.set_facecolor('white')
+ax.set_facecolor("white")
 
 y_max = max([max(data) for data in data_nfproxy.values()])
 y_min = min([min(data) for data in data_nfproxy.values()])
@@ -161,15 +160,17 @@ for i, (label, data) in enumerate(data_nfproxy.items()):
         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
+        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
+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_title("NFProxy Benchmarks", fontweight="bold", fontname="Roboto", pad=20)
 ax.set_ylabel("MB/s", fontname="Roboto", fontsize=12)
 fig.set_size_inches(12, 8)
 
@@ -181,7 +182,7 @@ plt.close()
 average_data = {label: np.mean(data) for label, data in data_nfproxy.items()}
 
 fig, ax = plt.subplots()
-ax.set_facecolor('white')
+ax.set_facecolor("white")
 y_max = max(average_data.values())
 
 bars = ax.bar(
@@ -189,11 +190,13 @@ bars = ax.bar(
     average_data.values(),
     color=[colors[i % len(colors)] for i in range(len(average_data))],
     edgecolor="black",
-    width=0.4  # Make the bars narrower
+    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_yticks(
-ax.set_title("NFProxy Benchmarks", fontweight='bold', fontname="Roboto", pad=20)
+    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)
 
@@ -201,21 +204,21 @@ ax.set_xticklabels(average_data.keys(), fontname="Roboto", fontsize=12)
 for bar in bars:
     height = bar.get_height()
     ax.annotate(
-        f'{height:.2f}',
+        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',
+        ha="center",
-        va='bottom',
+        va="bottom",
         fontsize=10,
-        fontname="Roboto"
+        fontname="Roboto",
     )
 
 fig.set_size_inches(12, 8)
 plt.tight_layout()
 
 # plt.show()
-plt.savefig(output_histogram, dpi=300, bbox_inches='tight')
+plt.savefig(output_histogram, dpi=300, bbox_inches="tight")
 plt.close()
 
 files_nfregex = [
@@ -223,13 +226,13 @@ files_nfregex = [
     ("NfRegex 8T", "results/comparemark_nfregex_8T.csv"),
 ]
 
-output_whisker = "results/whisker_compare.png"  
+output_whisker = "results/whisker_compare.svg"
-output_histogram = "results/istrogramma_compare.png"
+output_histogram = "results/istrogramma_compare.svg"
 
 # Read and process data for nfregex
 data_nfregex = {}
 for label, file in files_nfregex:
-    with open(file, 'r') as csvfile:
+    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]
@@ -241,7 +244,7 @@ combined_data = {**data_nfproxy, **data_nfregex}
 
 # Generate whisker plot for combined data
 fig, ax = plt.subplots()
-ax.set_facecolor('white')
+ax.set_facecolor("white")
 
 y_max = max([max(data) for data in combined_data.values()])
 y_min = min([min(data) for data in combined_data.values()])
@@ -255,64 +258,79 @@ for i, (label, data) in enumerate(combined_data.items()):
         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
+        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
+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_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.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()
-ax.set_facecolor('white')
+ax.set_facecolor("white")
 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],
+    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
+    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_yticks(
-ax.set_title("Combined Benchmarks (NFProxy vs NFRegex)", fontweight='bold', fontname="Roboto", pad=20)
+    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}',
+        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',
+        ha="center",
-        va='bottom',
+        va="bottom",
         fontsize=10,
-        fontname="Roboto"
+        fontname="Roboto",
     )
 
 fig.set_size_inches(14, 8)
 plt.tight_layout()
 
 # plt.show()
-plt.savefig(output_histogram, dpi=300, bbox_inches='tight')
+plt.savefig(output_histogram, dpi=300, bbox_inches="tight")
 plt.close()
-