replaced the png format with pdf, now all plots will be saved as pdf

measurements/CPU/plot_fig5.py → measurements/CPU/plotResourceUsageDuringThrouputTest.py

@@ -7,17 +7,11 @@
     avg_cpu_sender_zmq = pd.read_csv('sender_usage.csv')['cpu_percent'].mean()
     avg_mem_sender_zmq = pd.read_csv('sender_usage.csv')['memory_mb'].mean()
 
-    # In a real test, you would also measure the receiver. For simplicity, we plot sender.
-    # avg_cpu_receiver_zmq = pd.read_csv('receiver_zmq_usage.csv')['cpu_percent'].mean()
-    # avg_mem_receiver_zmq = pd.read_csv('receiver_zmq_usage.csv')['memory_mb'].mean()
-
-    # Create placeholder data for Mediator until you run the test
     avg_cpu_sender_mediator = 25.5 # Example value
-    avg_mem_sender_mediator = 60.2   # Example value
+    avg_mem_sender_mediator = 60.2 # Example value
 
 except FileNotFoundError:
     print("One or more CSV files not found. Using placeholder data.")
-    # Placeholder data for plotting if you haven't run the experiment yet
     avg_cpu_sender_zmq, avg_mem_sender_zmq = 15.0, 45.0
     avg_cpu_sender_mediator, avg_mem_sender_mediator = 25.5, 60.2
 
@@ -30,13 +24,16 @@
 df_plot = pd.DataFrame(data)
 
 # Create the grouped bar chart
-plt.figure(figsize=(10, 7))
+plt.figure(figsize=(8.27, 11.69))  # A4 size in inches (210mm x 297mm)
 sns.barplot(x='Metric', y='Value', hue='Protocol', data=df_plot, palette={'Mediator': '#F44336', 'ZeroMQ': '#4CAF50'})
 
-plt.title('Figure 5: Resource Utilization During Throughput Test (Sender)', fontsize=16)
-plt.xlabel('Performance Metric', fontsize=12)
-plt.ylabel('Average Usage', fontsize=12)
-plt.legend(title='Protocol')
+plt.xlabel('Performance Metric', fontsize=14)
+plt.ylabel('Average Usage', fontsize=14)
+plt.legend(title='Protocol', fontsize=12, title_fontsize=12)
 plt.grid(axis='y', linestyle='--', alpha=0.7)
 
-plt.show()
+plt.tight_layout()
+
+# Save to PDF
+plt.savefig("resource_utilization.pdf", format="pdf")
+plt.show()

measurements/Latency/latencyDistributionComparison.py

@@ -0,0 +1,47 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+def generate_comparison_plot():
+    """
+    Loads latency data for ZeroMQ and Mediator protocols,
+    and generates a comparative violin plot saved as A4 PDF.
+    """
+    try:
+        df_zmq = pd.read_csv('zeromq_latencies.csv')
+        df_zmq['Protocol'] = 'ZeroMQ'
+
+        df_mediator = pd.read_csv('mediator_latencies.csv')
+        df_mediator['Protocol'] = 'Mediator'
+
+    except FileNotFoundError as e:
+        print("Error: Missing latency CSV file(s). Place 'zeromq_latencies.csv' and 'mediator_latencies.csv' in the same directory.")
+        print(f"Details: {e}")
+        return
+
+    df_combined = pd.concat([df_zmq, df_mediator], ignore_index=True)
+
+    print("Generating plot...")
+    plt.figure(figsize=(8.27, 11.69))  # A4 size in inches
+
+    sns.violinplot(
+        x='Protocol', 
+        y='Latency (ms)', 
+        data=df_combined,
+        palette={'ZeroMQ': '#4CAF50', 'Mediator': '#F44336'}
+    )
+
+    plt.xlabel('Communication Protocol', fontsize=14)
+    plt.ylabel('Round-Trip Latency (ms)', fontsize=14)
+    plt.xticks(fontsize=12)
+    plt.yticks(fontsize=12)
+    plt.grid(True, which='major', linestyle='--', linewidth=0.5, color='grey')
+
+    plt.tight_layout()
+    plt.savefig('latency_comparison.pdf', format='pdf')
+    print("Plot saved as 'latency_comparison.pdf'")
+
+    plt.show()
+
+if __name__ == '__main__':
+    generate_comparison_plot()

measurements/Latency/plot_fig3(2).py → measurements/Latency/latencyDistributionOfZMQ.py

@@ -5,27 +5,35 @@
 # Load the collected data
 try:
     df_zmq = pd.read_csv('zeromq_latencies.csv')
-    df_zmq['Protocol'] = 'ZeroMQ' # Add a column to identify the protocol
+    df_zmq['Protocol'] = 'ZeroMQ'
 except FileNotFoundError:
     print("Error: zeromq_latencies.csv not found. Please run the experiment first.")
     exit()
 
-# It's good practice to filter out extreme outliers if they exist, 
-# for example, values over a certain threshold that might be due to a one-off system lag.
-# For now, we will plot all the data.
-
 # Create the plot
-plt.figure(figsize=(8, 7))
-sns.violinplot(x='Protocol', y='Latency (ms)', data=df_zmq, palette=['#4CAF50'])
+plt.figure(figsize=(8.27, 11.69))  # A4 size in inches
+
+sns.violinplot(
+    x='Protocol',
+    y='Latency (ms)',
+    data=df_zmq,
+    palette=['#4CAF50']
+)
 
 # Add details to the plot
-plt.title('Latency Distribution of ZeroMQ Protocol', fontsize=16)
 plt.xlabel('')
-plt.ylabel('Round-Trip Latency (ms)', fontsize=12)
+plt.ylabel('Round-Trip Latency (ms)', fontsize=14)
 plt.grid(True, linestyle='--', alpha=0.6)
 plt.xticks(fontsize=12)
+plt.yticks(fontsize=12)
+
+plt.tight_layout()
+
+# Save to PDF
+plt.savefig('zmq_latency.pdf', format='pdf')
+print("Plot saved as 'zmq_latency.pdf'")
 
-# Calculate and print statistics to include in your paper
+# Calculate and print stats for your paper
 median_val = df_zmq['Latency (ms)'].median()
 mean_val = df_zmq['Latency (ms)'].mean()
 std_val = df_zmq['Latency (ms)'].std()

measurements/Throughput/maximumThroughputComaprison.py

@@ -0,0 +1,41 @@
+import matplotlib.pyplot as plt
+
+# --- Enter your collected data here ---
+throughput_mediator = 15.7     # messages/sec
+throughput_zmq = 25432.1       # messages/sec
+# -------------------------------------
+
+protocols = ['Mediator', 'ZeroMQ']
+values = [throughput_mediator, throughput_zmq]
+colors = ['#F44336', '#4CAF50']
+
+plt.figure(figsize=(8.27, 11.69))  # A4 size in inches
+
+bars = plt.bar(protocols, values, color=colors)
+
+# Add plot details
+plt.ylabel('Throughput (Messages/Second)', fontsize=14)
+plt.title('Figure 6: Maximum Throughput Comparison', fontsize=18, pad=25)
+plt.xticks(fontsize=12)
+plt.yscale('log')  # log scale for large differences
+plt.grid(axis='y', linestyle='--', alpha=0.7)
+
+# Add text labels on top of the bars
+for bar in bars:
+    yval = bar.get_height()
+    plt.text(
+        bar.get_x() + bar.get_width() / 2.0,
+        yval,
+        f'{yval:,.0f}',
+        va='bottom',
+        ha='center',
+        fontsize=12
+    )
+
+plt.tight_layout()
+
+# Save the figure as PDF
+plt.savefig('throughput_comparison.pdf', format='pdf')
+print("Plot saved as 'throughput_comparison.pdf'")
+
+plt.show()