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
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"),
    ("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()
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
    )

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()
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
    )

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()
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()])

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()
ax.set_facecolor('white')
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()
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()])

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()
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],
    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()