update plot result

data/draw.py

+import random
+
+import matplotlib.pyplot as plt
+import ast, sys, os, json
+import numpy as np
+import scipy.stats
+
+NUM_FILE = 1
+
+
+def draw_bar(number_pc_measured, included_string):
+    start_name = "measurement-" + number_pc_measured + "-"
+    directory = os.getcwd()
+
+    y_values = []
+    # Iterate over files in the directory
+    for filename in os.listdir(directory):
+        # Check if the file starts with "measure" and ends with ".txt"
+        if filename.startswith(start_name) and filename.endswith(".txt") and included_string in filename:
+            with open(filename, 'r') as file:
+                contents = file.read()
+            data = ast.literal_eval(contents)
+
+            # Now you can work with the array as needed
+            if len(data) != int(number_pc_measured):
+                print(f"Array from {filename}:")
+            else:
+                y_values.append(data[-1][0] - data[0][0])
+    x_values = list(map(int, range(1, len(y_values) + 1)))
+    plt.bar(x_values, y_values)
+    # Add labels and title
+    plt.xlabel('Run time')
+    plt.xticks(x_values)
+    # plt.yscale('log')
+    plt.ylim(0)
+    plt.ylabel('Time (s)')
+    plt.title('Infected PC Time each run')
+
+
+def draw_bar_special(number_pc_measured, included_string):
+    start_name = "measurement-" + number_pc_measured + "-"
+    directory = os.getcwd()
+
+    y_values = []
+    y_second_values = []
+    # Iterate over files in the directory
+    for filename in os.listdir(directory):
+        # Check if the file starts with "measure" and ends with ".txt"
+        if filename.startswith(start_name) and filename.endswith(".txt") and included_string in filename:
+            with open(filename, 'r') as file:
+                contents = file.read()
+            data = ast.literal_eval(contents)
+
+            # Now you can work with the array as needed
+            if len(data) != int(number_pc_measured):
+                print(f"Array from {filename}:")
+            else:
+                y_values.append(data[-1][0] - data[0][0])
+                y_second_values.append(data[1][0] - data[0][0])
+    x_values = list(map(int, range(1, len(y_values) + 1)))
+
+    # Plot the blue bars on top of the red bars
+    plt.bar(x_values, y_values, label='Time for ' + number_pc_measured + 'th PC infection')
+    # Plot the red bars
+    if number_pc_measured != '2':
+        plt.bar(x_values, y_second_values, color='red', label='Time for 2nd PC infection')
+
+    # Add labels and title
+    plt.xlabel('Run time', fontsize=18)
+    plt.xticks(x_values, fontsize=18)
+    plt.yticks(fontsize=18)
+    # plt.yscale('log')
+    plt.ylim(0)
+    plt.ylabel('Time (s)', fontsize=18)
+    # plt.title('Infected PC Time each run')
+    plt.legend(fontsize=18)
+
+
+def plot_graph(file_path):
+    # Read the contents of the file
+    with open(file_path, 'r') as file:
+        contents = file.read()
+    data = ast.literal_eval(contents)
+
+    # Extract x and y values from the data
+    ip_values = [entry[1] for entry in data]
+    x_values = [entry[0] for entry in data]
+    first_x = x_values[0]
+    x_values = [i - first_x for i in x_values]
+    y_values = list(map(int, range(1, len(data) + 1)))
+
+    if True:
+        plt.xlim(min(x_values), max(x_values) + 50)
+        # plt.xticks(x_values)
+        plt.xticks(list(plt.xticks()[0]) + [x_values[-1]], list(plt.xticks()[0]) + [round(x_values[-1])], fontsize=18)
+        # plt.xticks(rotation=45)
+    else:
+        plt.xscale('log')
+        x_values = x_values[1:]
+        y_values = y_values[1:]
+        ip_values = ip_values[1:]
+
+    # Plot the graph with lines connecting the points
+    plt.ylabel('Infected PC number', fontsize=18)
+    plt.xlabel('Time (s)', fontsize=18)
+    # plt.title('Infected PC over time')
+    plt.plot(x_values, y_values, marker='o', markersize=5, label='IP address')
+
+    last_x = x_values[-1]
+    last_y = y_values[-1]
+    plt.axvline(x=last_x, linestyle='--', color='gray')
+    # plt.axhline(y=last_y, linestyle='--', color='gray')
+    for i in range(len(x_values)):
+        plt.text(x_values[i], y_values[i], f'{ip_values[i]}', ha='right', va='bottom', fontsize=14)
+    plt.subplots_adjust(left=0.045, right=0.975, top=0.995, bottom=0.070)
+
+    plt.yticks(fontsize=18)
+    # plt.gca().yaxis.set_major_locator(MultipleLocator(10))  # Set major ticks every 10 units
+    plt.legend(loc='upper left', fontsize=18)
+
+
+def plot_graph_with_infector(file_path):
+    # Read the contents of the file
+    with open(file_path, 'r') as file:
+        contents = file.read()
+    data = ast.literal_eval(contents)
+
+    temp_path = file_path.split("-")
+    infector_path = "infector" + "-" + temp_path[1] + "-" + temp_path[2] + "-" + temp_path[3]
+    with open(infector_path, 'r') as file:
+        contents = file.read()
+    infector_data = ast.literal_eval(contents)
+    # Extract x and y values from the data
+    infector_ips = [entry.split(",") for entry in infector_data]
+    map_point = {}
+    y_values_infector = [0]
+
+    # Extract x and y values from the data
+    ip_values = [entry[1] for entry in data]
+    x_values = [entry[0] for entry in data]
+    first_x = x_values[0]
+    x_values = [i - first_x for i in x_values]
+    y_values = list(map(int, range(1, len(data) + 1)))
+
+    for ip in ip_values:
+        is_exist = False
+        temp = y_values_infector[-1]
+        for infector_ip in infector_ips:
+            if ip == infector_ip[0]:
+                if infector_ip[1] in map_point:
+                    y_values_infector.append(temp)
+                else:
+                    y_values_infector.append(temp + 1)
+                    map_point[infector_ip[1]] = 1
+                is_exist = True
+                break
+        if (not is_exist) and (ip != '10.0.0.1'):
+            print(ip)
+            if '10.0.0.1' in map_point:
+                y_values_infector.append(temp)
+            else:
+                y_values_infector.append(temp + 1)
+                map_point['10.0.0.1'] = 1
+
+    # print(y_values_infector)
+
+    if True:
+        plt.xlim(min(x_values), max(x_values) + 50)
+        # plt.xticks(x_values)
+        # plt.xticks(list(plt.xticks()[0]) + [x_values[-1]], list(plt.xticks()[0]) + [round(x_values[-1])], fontsize=18)
+        # plt.xticks(rotation=45)
+    else:
+        plt.xscale('log')
+        x_values = x_values[1:]
+        y_values = y_values[1:]
+        ip_values = ip_values[1:]
+
+    # Plot the graph with lines connecting the points
+    plt.ylabel('Number of PCs', fontsize=18)
+    plt.xlabel('Time (s)', fontsize=18)
+    # plt.title('Infected PC over time')
+    plt.plot(x_values, y_values, marker='o', markersize=5, label='Infected PC')
+
+    y_values_safe = [(len(y_values) - item) for item in y_values]
+    plt.plot(x_values, y_values_safe, marker='o', markersize=5, label='Safe PC')
+
+    plt.plot(x_values, y_values_infector, marker='o', markersize=5, label='Infector PC')
+
+    last_x = x_values[-1]
+    last_y = y_values[-1]
+    plt.axvline(x=last_x, linestyle='--', color='gray')
+    # plt.axhline(y=last_y, linestyle='--', color='gray')
+    for i in range(len(x_values)):
+        plt.text(x_values[i], y_values[i], f'{ip_values[i]}', ha='right', va='bottom', fontsize=14)
+    plt.subplots_adjust(left=0.045, right=0.975, top=0.995, bottom=0.070)
+    plt.ylim(0)
+
+    plt.yticks(fontsize=18)
+    # plt.gca().yaxis.set_major_locator(MultipleLocator(10))  # Set major ticks every 10 units
+    plt.legend(loc='upper left', fontsize=18)
+
+
+def plot_graph_with_infector_average(number_pc):
+    y_values_infector_total = []
+    x_values_total = []
+    x_values_min = []
+    x_values_max = []
+    standard_deviation = []
+
+    for i in range(11, 14):
+        file_path = "measurement-" + number_pc + "-" + str(i) + "-SUB.txt"
+        # Read the contents of the file
+        with open(file_path, 'r') as file:
+            contents = file.read()
+        data = ast.literal_eval(contents)
+
+        infector_path = "infector" + "-" + number_pc + "-" + str(i) + "-SUB.txt"
+        with open(infector_path, 'r') as file:
+            contents = file.read()
+        infector_data = ast.literal_eval(contents)
+        # Extract x and y values from the data
+        infector_ips = [entry.split(",") for entry in infector_data]
+        map_point = {}
+        y_values_infector = [0]
+
+        # Extract x and y values from the data
+        ip_values = [entry[1] for entry in data]
+        x_values = [entry[0] for entry in data]
+        first_x = x_values[0]
+        x_values = [i - first_x for i in x_values]
+        y_values = list(map(int, range(1, len(data) + 1)))
+
+        for ip in ip_values:
+            is_exist = False
+            temp = y_values_infector[-1]
+            for infector_ip in infector_ips:
+                if ip == infector_ip[0]:
+                    if infector_ip[1] in map_point:
+                        y_values_infector.append(temp)
+                    else:
+                        y_values_infector.append(temp + 1)
+                        map_point[infector_ip[1]] = 1
+                    is_exist = True
+                    break
+            if (not is_exist) and (ip != '10.0.0.1'):
+                print(ip)
+                if '10.0.0.1' in map_point:
+                    y_values_infector.append(temp)
+                else:
+                    y_values_infector.append(temp + 1)
+                    map_point['10.0.0.1'] = 1
+
+        if len(x_values_total) != len(x_values):
+            for i in range(len(x_values)):
+                x_values_min.append(10000)
+                x_values_max.append(-1)
+                x_values_total.append(0)
+
+        for i in range(len(x_values)):
+            x_values_total[i] += x_values[i]
+            if x_values_min[i] > x_values[i]:
+                x_values_min[i] = x_values[i]
+            if x_values_max[i] < x_values[i]:
+                x_values_max[i] = x_values[i]
+
+        # if len(y_values_infector_total) != len(y_values_infector):
+        #    for i in range(len(y_values_infector)):
+        #        y_values_infector_total[i] = 0
+
+        # for i in range(len(y_values_infector)):
+        #    y_values_infector_total[i] += y_values_infector[i]
+
+    # print(y_values_infector)
+
+    for i in range(len(x_values_total)):
+        x_values_total[i] = x_values_total[i] / 3.0
+
+    if True:
+        plt.xlim(min(x_values), max(x_values) + 50)
+        # plt.xticks(x_values)
+        # plt.xticks(list(plt.xticks()[0]) + [x_values[-1]], list(plt.xticks()[0]) + [round(x_values[-1])], fontsize=18)
+        # plt.xticks(rotation=45)
+    else:
+        plt.xscale('log')
+        x_values = x_values[1:]
+        y_values = y_values[1:]
+        ip_values = ip_values[1:]
+
+    # Plot the graph with lines connecting the points
+    plt.ylabel('Number of PCs', fontsize=18)
+    plt.xlabel('Time (s)', fontsize=18)
+    # plt.title('Infected PC over time')
+    # plt.plot(x_values, y_values, marker='o', markersize=5, label='Infected PC')
+
+    y_values_safe = [(len(y_values) - item) for item in y_values]
+    # plt.plot(x_values_total, y_values_safe, marker='o', markersize=5, label='Average Time Safe PC')
+
+    plt.plot(x_values_total, y_values_infector, marker='o', markersize=5, label='Infector PC')
+
+    plt.plot(x_values_total, y_values, marker='o', markersize=5, label='Average Time Infected PC')
+    plt.plot(x_values_max, y_values, linestyle='dashed', markersize=5, label='Upper Time Infected PC')
+    plt.plot(x_values_min, y_values, linestyle='dashed', markersize=5, label='Lower Time Infected PC')
+
+    # last_x = x_values[-1]
+    # last_y = y_values[-1]
+    a = [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800]
+    for i in a:
+        plt.axvline(x=i, linestyle='--', color='gray')
+    # plt.axhline(y=last_y, linestyle='--', color='gray')
+    # for i in range(len(x_values)):
+    #    plt.text(x_values[i], y_values[i], f'{ip_values[i]}', ha='right', va='bottom', fontsize=14)
+    plt.subplots_adjust(left=0.045, right=0.975, top=0.995, bottom=0.070)
+    plt.ylim(0)
+
+    plt.yticks(fontsize=18)
+    # plt.gca().yaxis.set_major_locator(MultipleLocator(10))  # Set major ticks every 10 units
+    plt.legend(loc='upper left', fontsize=18)
+
+
+def draw_plot(number_pc_measured, malware_name):
+    global NUM_FILE
+
+    # pc_number_values = list(map(int, range(1, int(number_pc_measured) + 1)))
+    pc_number_values = [1] + list(range(10, int(number_pc_measured) + 1, 10))
+    # print(pc_number_values)
+    x_values = [[] for _ in range(int(number_pc_measured))]
+    for i in range(1, NUM_FILE + 1):
+        file_path = "measurement-" + number_pc_measured + "-" + malware_name + "-" + str(i) + ".txt"
+        # Read the contents of the file
+        with open(file_path, 'r') as file:
+            contents = file.read()
+        data = ast.literal_eval(contents)
+
+        ip_values = [entry[1] for entry in data]
+        time_values = [entry[0] for entry in data]
+        first_time = time_values[0]
+        time_values = [i - first_time for i in time_values]
+        for i in range(len(time_values)):
+            x_values[i].append(time_values[i])
+        # pc_number_values = list(map(int, range(1, int(number_pc_measured) + 1)))
+
+    print(x_values)
+    # boxplot = plt.boxplot(x_values, vert=False, showfliers=False)
+
+    lower_bounds = []
+    upper_bounds = []
+    average = []
+    for x in x_values:
+        average.append(np.mean(x))
+        lower_bounds.append(min(x))
+        upper_bounds.append(max(x))
+
+    # plt.plot(lower_bounds, [i for i in range(int(number_pc_measured))], linestyle='dashed', label='Lower Bound')
+    # plt.plot(upper_bounds, [i for i in range(int(number_pc_measured))], linestyle='dashed', label='Upper Bound')
+    # plt.plot(average, [i for i in range(int(number_pc_measured))], label='Average')
+    # plt.legend(fontsize=18)
+    # plt.fill_betweenx(range(len(average)), lower_bounds, upper_bounds, color='gray', alpha=0.3)
+    plt.errorbar(average, [i for i in range(int(number_pc_measured))],
+                 xerr=[[average[i] - lower_bounds[i] for i in range(len(lower_bounds))], [upper_bounds[i] - average[i] for i in range(len(upper_bounds))]], color='orange',
+                 capsize=7, linestyle='', fmt='o', ecolor='black', label='Average')
+
+    printMean(x_values[-1])
+    # print(f"Confidence interval: {mean} and {margin_of_error}")
+    plt.ylabel('Number of PCs', fontsize=18)
+    plt.xlabel('Time (s)', fontsize=18)
+    plt.subplots_adjust(left=0.045, right=0.995, top=0.995, bottom=0.070)
+    # plt.ylim(1)
+    plt.xticks(fontsize=18)
+    plt.yticks(pc_number_values, [str(i) for i in pc_number_values], fontsize=18)
+
+
+def draw_whisker_plot(number_pc_measured, malware_name):
+    global NUM_FILE
+
+    # pc_number_values = list(map(int, range(1, int(number_pc_measured) + 1)))
+    pc_number_values = [1] + list(range(10, int(number_pc_measured) + 1, 10))
+    # print(pc_number_values)
+    x_values = [[] for _ in range(int(number_pc_measured))]
+    for i in range(1, NUM_FILE + 1):
+        file_path = "measurement-" + number_pc_measured + "-" + malware_name + "-" + str(i) + ".txt"
+        # Read the contents of the file
+        with open(file_path, 'r') as file:
+            contents = file.read()
+        data = ast.literal_eval(contents)
+
+        ip_values = [entry[1] for entry in data]
+        time_values = [entry[0] for entry in data]
+        first_time = time_values[0]
+        time_values = [i - first_time for i in time_values]
+        for i in range(len(time_values)):
+            x_values[i].append(time_values[i])
+        # pc_number_values = list(map(int, range(1, int(number_pc_measured) + 1)))
+
+    print(x_values)
+    boxplot = plt.boxplot(x_values, vert=False, showfliers=False)
+
+    printMean(x_values[-1])
+
+    # print(f"Confidence interval: {mean} and {margin_of_error}")
+
+    plt.ylabel('Number of PCs', fontsize=18)
+    plt.xlabel('Time (s)', fontsize=18)
+    plt.subplots_adjust(left=0.045, right=0.995, top=0.995, bottom=0.070)
+    # plt.ylim(1)
+    plt.xticks(fontsize=18)
+    plt.yticks(pc_number_values, [str(i) for i in pc_number_values], fontsize=18)
+
+
+def mean_confidence_interval(data, confidence=0.95):
+    a = 1.0 * np.array(data)
+    n = len(a)
+    m, se = np.mean(a), scipy.stats.sem(a)
+    h = se * scipy.stats.t.ppf((1 + confidence) / 2., n - 1)
+    return m, m - h, m + h, h
+
+
+def draw_confidence_inter_time(number_pc_measured, malware_name):
+    global NUM_FILE
+    num_time_period = 100
+    Num = int(2500 / num_time_period)
+    # pc_number_values = list(map(int, range(1, int(number_pc_measured) + 1)))
+    period_number_values = list(range(num_time_period, num_time_period * Num + num_time_period, num_time_period))
+    # print(pc_number_values)
+    x_values = [[] for _ in range(int(Num))]
+    for i in range(1, NUM_FILE + 1):
+        file_path = "measurement-" + number_pc_measured + "-" + malware_name + "-" + str(i) + ".txt"
+        # Read the contents of the file
+        with open(file_path, 'r') as file:
+            contents = file.read()
+        data = ast.literal_eval(contents)
+
+        ip_values = [entry[1] for entry in data]
+        time_values = [entry[0] for entry in data]
+        first_time = time_values[0]
+        time_values = [i - first_time for i in time_values]
+
+        temp_values = [0 for _ in range(int(Num))]
+        for each_time in time_values:
+            period_time = int(each_time // num_time_period)
+            print(period_time, each_time, num_time_period)
+            temp_values[period_time] += 1
+        for i in range(Num):
+            x_values[i].append(temp_values[i])
+
+        # pc_number_values = list(map(int, range(1, int(number_pc_measured) + 1)))
+    print(x_values)
+
+    means = []
+    lower_bounds = []
+    upper_bounds = []
+    errors = []
+    for x in x_values:
+        mean, lower_bound, upper_bound, error = mean_confidence_interval(x)
+        errors.append(error)
+        means.append(mean)
+        lower_bounds.append(lower_bound)
+        upper_bounds.append(upper_bound)
+
+    temp_value = [i for i in range(1, Num + 1)]
+    plt.plot(temp_value, means, marker='o', label='Average')
+    plt.errorbar(temp_value, means, yerr=errors, fmt='o', color='k', capsize=10)
+    # plt.plot(temp_value, lower_bounds, linestyle='dashed', label='Lower Bound')
+    # plt.plot(temp_value, upper_bounds, linestyle='dashed', label='Upper Bound')
+    # plt.fill_between(range(len(x_values)), lower_bounds, upper_bounds, color='gray', alpha=0.3)
+
+    # plt.boxplot(x_values, showfliers=False)
+    plt.ylabel('Number of PCs', fontsize=18)
+    plt.xlabel('Each ' + str(num_time_period) + '-second Period', fontsize=18)
+    plt.subplots_adjust(left=0.045, right=0.995, top=0.995, bottom=0.070)
+    # plt.ylim(1)
+    plt.yticks(fontsize=18)
+    plt.xticks([i for i in range(1, Num + 1)], [str(i) for i in period_number_values], fontsize=15)
+    plt.legend(fontsize=18)
+
+
+def draw_whisker_plot_inter_time(number_pc_measured, malware_name):
+    global NUM_FILE
+
+    num_time_period = 50
+    Num = int(1800 / num_time_period)
+    # pc_number_values = list(map(int, range(1, int(number_pc_measured) + 1)))
+    period_number_values = list(range(num_time_period, num_time_period * Num + num_time_period, num_time_period))
+    # print(pc_number_values)
+    x_values = [[] for _ in range(int(Num))]
+    for i in range(1, NUM_FILE + 1):
+        file_path = "measurement-" + number_pc_measured + "-" + malware_name + "-" + str(i) + ".txt"
+        # Read the contents of the file
+        with open(file_path, 'r') as file:
+            contents = file.read()
+        data = ast.literal_eval(contents)
+
+        ip_values = [entry[1] for entry in data]
+        time_values = [entry[0] for entry in data]
+        first_time = time_values[0]
+        time_values = [i - first_time for i in time_values]
+
+        temp_values = [0 for _ in range(int(Num))]
+        for each_time in time_values:
+            period_time = int(each_time // num_time_period)
+            print(period_time, each_time, num_time_period)
+            temp_values[period_time] += 1
+        for i in range(Num):
+            x_values[i].append(temp_values[i])
+
+        # pc_number_values = list(map(int, range(1, int(number_pc_measured) + 1)))
+    print(x_values)
+    plt.boxplot(x_values, showfliers=False)
+    plt.ylabel('Number of PCs', fontsize=18)
+    plt.xlabel('Each ' + number_pc_measured + '-second Period', fontsize=18)
+    plt.subplots_adjust(left=0.045, right=0.995, top=0.995, bottom=0.070)
+    # plt.ylim(1)
+    plt.yticks(fontsize=18)
+    plt.xticks([i for i in range(1, Num + 1)], [str(i) for i in period_number_values], fontsize=15)
+
+
+def draw_confidence_ip_distance(number_pc_measured, malware_name):
+    global NUM_FILE
+
+    x_values = [[] for _ in range(1, int(number_pc_measured))]
+    for i in range(1, NUM_FILE + 1):
+        map_distance = {}
+        infector_path = "infector" + "-" + number_pc_measured + "-" + malware_name + "-" + str(i) + ".txt"
+        with open(infector_path, 'r') as file:
+            contents = file.read()
+        data = ast.literal_eval(contents)
+        victim_infector_ips = [entry.split(",") for entry in data]
+        for ips in victim_infector_ips:
+            distance = abs(int(ips[0].split(".")[3]) - int(ips[1].split(".")[3]))
+            if distance in map_distance:
+                map_distance[distance] += 1
+            else:
+                map_distance[distance] = 1
+
+        for i in range(len(x_values)):
+            if (i + 1) in map_distance:
+                x_values[i].append(map_distance[i + 1])
+            else:
+                x_values[i].append(0)
+
+    y_values = [str(i) for i in range(1, int(number_pc_measured))]
+    print(y_values)
+    print(x_values)
+
+    means = []
+    lower_bounds = []
+    upper_bounds = []
+    errors = []
+    totalx = [[0] for i in range(int(number_pc_measured) - 1)]
+    for i in range(len(x_values)):
+        totalx[i] = sum(x_values[i])
+        # mean, lower_bound, upper_bound, error = mean_confidence_interval(x)
+        # print(mean, lower_bound, upper_bound, error)
+        # errors.append(error)
+        # means.append(mean)
+        # lower_bounds.append(lower_bound)
+        # upper_bounds.append(upper_bound)
+
+    # plt.plot(y_values, means, marker='o', label='Average')
+    # plt.bar(y_values, means)
+    # plt.errorbar(y_values, means, yerr=errors, fmt = 'o', color = 'k', capsize=10)
+    plt.bar(y_values, totalx)
+    # plt.plot(y_values, lower_bounds, linestyle='dashed', label='Lower Bound')
+    # plt.plot(y_values, upper_bounds, linestyle='dashed', label='Upper Bound')
+    # plt.fill_between(range(len(x_values)), lower_bounds, upper_bounds, color='gray', alpha=0.3)
+
+    plt.ylabel('Occurences', fontsize=18)
+    plt.xlabel('Distance', fontsize=18)
+    plt.subplots_adjust(left=0.03, right=0.995, top=0.985, bottom=0.070)
+
+    locs, labels = plt.yticks()
+    yint = [int(each) for each in locs]
+    plt.yticks(yint, fontsize=18)
+    plt.xticks(fontsize=18)
+    # plt.legend(fontsize=18)
+
+
+def draw_whisker_plot_ip_distance(number_pc_measured, malware_name):
+    global NUM_FILE
+
+    x_values = [[] for _ in range(1, int(number_pc_measured))]
+    for i in range(1, NUM_FILE + 1):
+        map_distance = {}
+        infector_path = "infector" + "-" + number_pc_measured + "-" + malware_name + "-" + str(i) + ".txt"
+        with open(infector_path, 'r') as file:
+            contents = file.read()
+        data = ast.literal_eval(contents)
+        victim_infector_ips = [entry.split(",") for entry in data]
+        for ips in victim_infector_ips:
+            distance = abs(int(ips[0].split(".")[3]) - int(ips[1].split(".")[3]))
+            if distance in map_distance:
+                map_distance[distance] += 1
+            else:
+                map_distance[distance] = 1
+
+        for i in range(len(x_values)):
+            if (i + 1) in map_distance:
+                x_values[i].append(map_distance[i + 1])
+            else:
+                x_values[i].append(0)
+
+    y_values = [i for i in range(1, int(number_pc_measured))]
+    print(y_values)
+    print(x_values)
+    plt.boxplot(x_values, showfliers=False)
+    plt.ylabel('Occurences', fontsize=18)
+    plt.xlabel('Distance', fontsize=18)
+    plt.subplots_adjust(left=0.03, right=0.995, top=0.995, bottom=0.070)
+
+    locs, labels = plt.yticks()
+    yint = [int(each) for each in locs]
+    plt.yticks(yint, fontsize=18)
+    plt.xticks(y_values, fontsize=18)
+
+
+def draw_confidence_infector(number_pc_measured, malware_name):
+    global NUM_FILE
+
+    pc_number_values = list(map(int, range(1, int(number_pc_measured) + 1)))
+    x_values = [[] for _ in range(int(number_pc_measured))]
+    x_infector_values = [[] for _ in range(int(number_pc_measured))]
+
+    for i in range(1, NUM_FILE + 1):
+        infector_map = {}
+        infecting_map = {}
+
+        file_path = "infecting" + "-" + number_pc_measured + "-" + malware_name + "-" + str(i) + ".txt"
+        with open(file_path, 'r') as file:
+            infecting_map = json.load(file)
+
+        file_path = "infector" + "-" + number_pc_measured + "-" + malware_name + "-" + str(i) + ".txt"
+        with open(file_path, 'r') as file:
+            contents = file.read()
+        data = ast.literal_eval(contents)
+        for entry in data:
+            victim_infector_ip = entry.split(",")
+            if victim_infector_ip[0] not in infector_map:
+                infector_map[victim_infector_ip[0]] = victim_infector_ip[1]
+            else:
+                print(victim_infector_ip[0])
+                print("error")
+                input()
+
+        file_path = "measurement-" + number_pc_measured + "-" + malware_name + "-" + str(i) + ".txt"
+        with open(file_path, 'r') as file:
+            contents = file.read()
+        data = ast.literal_eval(contents)
+        ip_values = [entry[1] for entry in data]
+        time_values = [entry[0] for entry in data]
+        first_time = time_values[0]
+        time_values = [i - first_time for i in time_values]
+
+        map_point_infector = {}
+        number_infector = 0
+        for i in range(len(ip_values)):
+            ip = ip_values[i]
+            if ip in infecting_map:
+                number_infecting = len(infecting_map[ip])
+            else:
+                if ip != "10.0.0.1":
+                    print("1", ip)
+                number_infecting = 0
+            if ip in infector_map:
+                if infector_map[ip] not in map_point_infector:
+                    map_point_infector[infector_map[ip]] = 1
+                    number_infector += 1
+
+                if infector_map[ip] in infecting_map[ip]:
+                    number_infecting -= 1
+                else:
+                    pass
+                    # print("3",ip)
+            elif ip != "10.0.0.1":
+                print("2", ip)
+            x_values[i].append(number_infecting)
+            x_infector_values[i].append(number_infector)
+
+    print(x_values)
+    print(x_infector_values)
+
+    means = []
+    lower_bounds = []
+    upper_bounds = []
+    errors = []
+    for x in x_infector_values:
+        mean, lower_bound, upper_bound, error = mean_confidence_interval(x)
+        errors.append(error)
+        means.append(mean)
+        lower_bounds.append(lower_bound)
+        upper_bounds.append(upper_bound)
+    plt.plot(pc_number_values, means, marker='o', label='Average Number of Effective Infectors')
+    plt.errorbar(pc_number_values, means, yerr=errors, fmt='o', color='black', capsize=7)
+
+    means = []
+    lower_bounds = []
+    upper_bounds = []
+    errors = []
+    for x in x_values:
+        mean, lower_bound, upper_bound, error = mean_confidence_interval(x)
+        errors.append(error)
+        means.append(mean)
+        lower_bounds.append(lower_bound)
+        upper_bounds.append(upper_bound)
+    plt.plot(pc_number_values, means, marker='o', label='Average Number of Attempted Infectors')
+    plt.errorbar(pc_number_values, means, yerr=errors, fmt='o', color='green', capsize=5)
+
+    # plt.boxplot(x_values, positions=[x - 0.25 for x in pc_number_values],widths=0.4,patch_artist=True, showfliers=False, boxprops=dict(facecolor="skyblue"),medianprops=dict(color="red"))
+    # plt.boxplot(x_infector_values, positions=[x + 0.25 for x in pc_number_values],widths=0.4, patch_artist=True,showfliers=False, boxprops=dict(facecolor="lightgreen"))
+    plt.ylabel('Number of Infectors', fontsize=18)
+    plt.xlabel('Infection Number', fontsize=18)
+    plt.subplots_adjust(left=0.04, right=0.995, top=0.995, bottom=0.070)
+    # plt.ylim(1)
+    plt.yticks(fontsize=18)
+    plt.xticks(pc_number_values, [str(i) for i in pc_number_values], fontsize=18)
+
+    # legend_handles = [plt.Rectangle((0,0),1,1,fc="skyblue", edgecolor = 'black'), plt.Rectangle((0,0),1,1,fc="lightgreen", edgecolor = 'black')]
+    # legend_labels = ['Number of Attempted Infectors', 'Number of Effective Infectors']
+    plt.legend(fontsize=18)
+
+
+def draw_whisker_plot_infector(number_pc_measured, malware_name):
+    global NUM_FILE
+
+    pc_number_values = list(map(int, range(1, int(number_pc_measured) + 1)))
+    x_values = [[] for _ in range(int(number_pc_measured))]
+    x_infector_values = [[] for _ in range(int(number_pc_measured))]
+
+    for i in range(1, NUM_FILE + 1):
+        infector_map = {}
+        infecting_map = {}
+
+        file_path = "infecting" + "-" + number_pc_measured + "-" + malware_name + "-" + str(i) + ".txt"
+        with open(file_path, 'r') as file:
+            infecting_map = json.load(file)
+
+        file_path = "infector" + "-" + number_pc_measured + "-" + malware_name + "-" + str(i) + ".txt"
+        with open(file_path, 'r') as file:
+            contents = file.read()
+        data = ast.literal_eval(contents)
+        for entry in data:
+            victim_infector_ip = entry.split(",")
+            if victim_infector_ip[0] not in infector_map:
+                infector_map[victim_infector_ip[0]] = victim_infector_ip[1]
+            else:
+                print(victim_infector_ip[0])
+                print("error")
+                input()
+
+        file_path = "measurement-" + number_pc_measured + "-" + malware_name + "-" + str(i) + ".txt"
+        with open(file_path, 'r') as file:
+            contents = file.read()
+        data = ast.literal_eval(contents)
+        ip_values = [entry[1] for entry in data]
+        time_values = [entry[0] for entry in data]
+        first_time = time_values[0]
+        time_values = [i - first_time for i in time_values]
+
+        map_point_infector = {}
+        number_infector = 0
+        for i in range(len(ip_values)):
+            ip = ip_values[i]
+            if ip in infecting_map:
+                number_infecting = len(infecting_map[ip])
+            else:
+                if ip != "10.0.0.1":
+                    print("1", ip)
+                number_infecting = 0
+            if ip in infector_map:
+                if infector_map[ip] not in map_point_infector:
+                    map_point_infector[infector_map[ip]] = 1
+                    number_infector += 1
+
+                if infector_map[ip] in infecting_map[ip]:
+                    number_infecting -= 1
+                else:
+                    pass
+                    # print("3",ip)
+            elif ip != "10.0.0.1":
+                print("2", ip)
+            x_values[i].append(number_infecting)
+            x_infector_values[i].append(number_infector)
+
+    print(x_values)
+    print(x_infector_values)
+
+    plt.boxplot(x_values, positions=[x - 0.25 for x in pc_number_values], widths=0.4, patch_artist=True, showfliers=False, boxprops=dict(facecolor="skyblue"),
+                medianprops=dict(color="red"))
+    plt.boxplot(x_infector_values, positions=[x + 0.25 for x in pc_number_values], widths=0.4, patch_artist=True, showfliers=False, boxprops=dict(facecolor="lightgreen"))
+    plt.ylabel('Number of Infectors', fontsize=18)
+    plt.xlabel('Infection Number', fontsize=18)
+    plt.subplots_adjust(left=0.04, right=0.995, top=0.995, bottom=0.070)
+    # plt.ylim(1)
+    plt.yticks(fontsize=18)
+    plt.xticks(pc_number_values, [str(i) for i in pc_number_values], fontsize=18)
+
+    legend_handles = [plt.Rectangle((0, 0), 1, 1, fc="skyblue", edgecolor='black'), plt.Rectangle((0, 0), 1, 1, fc="lightgreen", edgecolor='black')]
+    legend_labels = ['Number of Attempted Infectors', 'Number of Effective Infectors']
+    plt.legend(legend_handles, legend_labels, fontsize=18)
+
+
+def printMean(x):
+    mean, lower_bound, upper_bound, error = mean_confidence_interval(x)
+    print("Mean:", mean)
+    print("Lower Bound:", lower_bound)
+    print("Upper Bound:", upper_bound)
+    print("Error:", error)
+
+
+def read_data():
+    array_def_infect = []
+    array_def_notif = []
+    array_nodef_infect = []
+
+    for i in range(1, 2):
+        file_path = "measurement-" + str(i) + ".txt"
+        # Read the contents of the file
+        with open(file_path, 'r') as file:
+            contents = file.read()
+        data = ast.literal_eval(contents)
+        array_def_infect.append(data)
+        print(data)
+
+        file_path = "deploy_notif-" + str(i) + ".txt"
+        # Read the contents of the file
+        with open(file_path, 'r') as file:
+            contents = file.read()
+        data = ast.literal_eval(contents)
+        array_def_notif.append(data)
+        print(data)
+
+        file_path = "measurement-infect-" + str(i) + ".txt"
+        # Read the contents of the file
+        with open(file_path, 'r') as file:
+            contents = file.read()
+        data = ast.literal_eval(contents)
+        array_nodef_infect.append(data)
+        print(data)
+
+    return array_def_infect, array_def_notif, array_nodef_infect
+
+
+def plot(number_pc):
+    array_def_infect, array_def_notif, array_nodef_infect = read_data()
+
+    pc_number_values = [1] + list(range(10, int(number_pc) + 1, 10))
+    x_values = [[] for _ in range(int(number_pc))]
+    for item in array_nodef_infect:
+        ip_values = [entry[1] for entry in item]
+        time_values = [entry[0] for entry in item]
+        first_time = time_values[0]
+        time_values = [i - first_time for i in time_values]
+        for i in range(len(time_values)):
+            x_values[i].append(time_values[i])
+
+    # print(x_values)
+
+    lower_bounds = []
+    upper_bounds = []
+    average = []
+    for x in x_values:
+        average.append(np.mean(x))
+        lower_bounds.append(min(x))
+        upper_bounds.append(max(x))
+
+    # plt.plot(lower_bounds, [i for i in range(int(number_pc))], linestyle='dashed', label='Lower Bound')
+    # plt.plot(upper_bounds, [i for i in range(int(number_pc))], linestyle='dashed', label='Upper Bound')
+    plt.plot(average, [i for i in range(int(number_pc))])
+    # plt.fill_betweenx(range(len(average)), lower_bounds, upper_bounds, color='gray', alpha=0.3)
+    plt.errorbar(average, [i for i in range(int(number_pc))],
+                 xerr=[[average[i] - lower_bounds[i] for i in range(len(lower_bounds))], [upper_bounds[i] - average[i] for i in range(len(upper_bounds))]], color='orange',
+                 capsize=7, linestyle='', fmt='o', ecolor='black', label='Average')
+
+    printMean(x_values[-1])
+    # print(f"Confidence interval: {mean} and {margin_of_error}")
+    plt.ylabel('Number of PCs', fontsize=18)
+    plt.xlabel('Time (s)', fontsize=18)
+    plt.subplots_adjust(left=0.045, right=0.995, top=0.995, bottom=0.070)
+    # plt.ylim(1)
+    plt.xticks(fontsize=18)
+    plt.yticks(pc_number_values, [str(i) for i in pc_number_values], fontsize=18)
+
+    attack_stop_time = array_def_notif[0][0][2] - array_def_infect[0][0][0]
+    plt.axvline(x=attack_stop_time, color='red', linestyle='--', label="Attack Stopped")
+    plt.legend(fontsize=18)
+
+
+# file_path = 'measurement-1.txt'  # Update this with your file path
+
+# if len(sys.argv) > 2:
+# func_id = sys.argv[1]
+
+
+# if func_id == "1":
+#     file_path = sys.argv[2]
+#     plot_graph(file_path)
+# elif func_id == "2":
+#     number_pc_measured = sys.argv[2]
+#     malware_name = sys.argv[3]
+#     # draw_whisker_plot(number_pc_measured, malware_name)
+#     draw_plot(number_pc_measured, malware_name)
+# elif func_id == "3":
+#     number_pc_measured = sys.argv[2]
+#     malware_name = sys.argv[3]
+#     # draw_whisker_plot_infector(number_pc_measured, malware_name)
+#     draw_confidence_infector(number_pc_measured, malware_name)
+# elif func_id == "4":
+#     number_pc_measured = sys.argv[2]
+#     malware_name = sys.argv[3]
+#     # draw_whisker_plot_inter_time(number_pc_measured, malware_name)
+#     draw_confidence_inter_time(number_pc_measured, malware_name)
+# elif func_id == "5":
+#     number_pc_measured = sys.argv[2]
+#     malware_name = sys.argv[3]
+#     # draw_whisker_plot_ip_distance(number_pc_measured, malware_name)
+#     draw_confidence_ip_distance(number_pc_measured, malware_name)
+
+
+# draw_bar(number_pc_measured, included_string)
+#    draw_bar_special(number_pc_measured, included_string)
+# elif func_id == "3":
+#    file_path = sys.argv[2]
+#    plot_graph_with_infector(file_path)
+# elif func_id == "4":
+#    number_pc = sys.argv[2]
+#    plot_graph_with_infector_average(number_pc)
+
+if __name__ == "__main__":
+    plot(12)
+    plt.get_current_fig_manager().window.showMaximized()
+    plt.show()