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png to svg

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This commit is contained in:
Domingo Dirutigliano
2025-06-16 22:44:23 +02:00
parent c8398dbd39
commit d019e504fd
14 changed files with 11352 additions and 76 deletions
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152
tests/results_plotter.py
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@@ -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['axes.edgecolor'] = 'white'
plt.rcParams['axes.linewidth'] = 1.5
plt.rcParams['legend.facecolor'] = 'white'
plt.rcParams["figure.facecolor"] = "white"
plt.rcParams["axes.edgecolor"] = "white"
plt.rcParams["axes.linewidth"] = 1.5
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
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(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.show()
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
@@ -122,8 +121,8 @@ 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.show()
plt.savefig(output, dpi=300, bbox_inches="tight")
plt.close()
files_nfproxy = [
@@ -131,22 +130,22 @@ files_nfproxy = [
("NfProxy 8T", "results/comparemark_nfproxy_8T.csv"),
]
output_whisker = "results/whisker_nfproxy.png"
output_histogram = "results/istogramma_nfproxy.png"
output_whisker = "results/whisker_nfproxy.svg"
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]
data_nfproxy[label+" no filter"] = [ele[0] for ele in data]
data_nfproxy[label+" test"] = [ele[1] for ele in data]
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()
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()])
@@ -161,19 +160,21 @@ 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)
#plt.show()
# plt.show()
plt.savefig(output_whisker, dpi=300)
plt.close()
@@ -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_title("NFProxy Benchmarks", fontweight='bold', fontname="Roboto", pad=20)
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)
@@ -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',
va='bottom',
ha="center",
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.show()
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_histogram = "results/istrogramma_compare.png"
output_whisker = "results/whisker_compare.svg"
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.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()
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_title("Combined Benchmarks (NFProxy vs NFRegex)", fontweight='bold', fontname="Roboto", pad=20)
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}',
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',
ha="center",
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.show()
plt.savefig(output_histogram, dpi=300, bbox_inches="tight")
plt.close()