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Trace Analysis Scripts - Generate Line Graphs and Histograms with Average and Standard Deviation Computations

bspmmTraces_Histogram_Analysis.py

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+#!/usr/bin/env python3
+
+import sys
+import json
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_pdf import PdfPages
+import numpy as np
+
+def extract_data_from_json(json_data):
+    entries = []
+
+    for event in json_data["traceEvents"]:
+        pid = event["pid"]
+        function_name = event["name"].split('<')[0]
+        duration_ms = event["dur"]
+
+        entry_data = {
+            "pid": pid,
+            "function_name": function_name,
+            "duration": duration_ms
+        }
+
+        entries.append(entry_data)
+
+    return entries
+
+def generate_histograms(entries, target_pid):
+    function_histograms = {}  # Create a dictionary to store durations for each function
+
+    for entry in entries:
+        pid = entry["pid"]
+        function_name = entry["function_name"]
+        duration_ms = entry["duration"] / 1000  # Convert ms to sec
+
+        if pid == target_pid:
+            if function_name not in function_histograms:
+                function_histograms[function_name] = []
+
+            function_histograms[function_name].append(duration_ms)
+
+    pdf_filename = f'histograms_for_pid_{target_pid}.pdf'
+    pdf_pages = PdfPages(pdf_filename)
+
+    for function_name, durations in function_histograms.items():
+        plt.figure(figsize=(8, 6))  # Adjust figure size for better visibility
+        n, bins, patches = plt.hist(durations, bins=10, range=(0, max(durations)))  # Create a histogram
+        plt.xlabel('Duration (s)')
+        plt.ylabel('Frequency')
+        plt.title(f'Histogram of Durations for Function {function_name} (PID {target_pid})')
+
+        # Add labels for each bar in the histogram
+        for i, patch in enumerate(patches):
+            x = patch.get_x() + patch.get_width() / 2
+            y = patch.get_height()
+            label = f'{durations[i]:.2f}s'  # Label with exact duration value
+            plt.annotate(label, (x, y), xytext=(0, 5), textcoords='offset points', ha='center')
+
+        pdf_pages.savefig()
+        plt.close()
+
+        # Compute and print average and standard deviation
+        avg_duration = np.mean(durations)
+        std_duration = np.std(durations)
+        print(f"Function: {function_name} (PID {target_pid})")
+        print(f"Average Duration: {avg_duration:.2f} s")
+        print(f"Standard Deviation: {std_duration:.2f} s")
+        print("")
+
+    pdf_pages.close()
+
+def main():
+    if len(sys.argv) != 3:
+        print("Usage: python_script.py function_data.json pid_value")
+        return
+
+    json_file_path = sys.argv[1]
+
+    try:
+        target_pid = int(sys.argv[2])
+    except ValueError:
+        print("Error: Invalid pid_value. Please enter a valid integer.")
+        return
+
+    with open(json_file_path, 'r') as json_file:
+        json_data = json.load(json_file)
+
+    entries = extract_data_from_json(json_data)
+
+    # Check if the target_pid exists in the data
+    pids = set(entry["pid"] for entry in entries)
+    if target_pid not in pids:
+        print(f"Error: PID {target_pid} not found in the data.")
+        return
+
+    generate_histograms(entries, target_pid)
+
+if __name__ == "__main__":
+    main()
+

bspmmTraces_LineGraph_Analysis.py

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+#!/usr/bin/env python3
+
+import sys
+import json
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_pdf import PdfPages
+import numpy as np
+
+def extract_data_from_json(json_data):
+    entries = []
+
+    for event in json_data["traceEvents"]:
+        pid = event["pid"]
+        function_name = event["name"].split('<')[0]
+        duration_ms = event["dur"]
+
+        entry_data = {
+            "pid": pid,
+            "function_name": function_name,
+            "duration": duration_ms
+        }
+
+        entries.append(entry_data)
+
+    return entries
+
+def generate_curves(entries, target_pid):
+    function_histograms = {}  # Create a dictionary to store durations for each function
+
+    for entry in entries:
+        pid = entry["pid"]
+        function_name = entry["function_name"]
+        duration_ms = entry["duration"] / 1000  # Convert ms to sec
+
+        if pid == target_pid:
+            if function_name not in function_histograms:
+                function_histograms[function_name] = []
+
+            function_histograms[function_name].append(duration_ms)
+
+    pdf_filename = f'curves_for_pid_{target_pid}.pdf'
+    pdf_pages = PdfPages(pdf_filename)
+
+    for function_name, durations in function_histograms.items():
+        plt.figure(figsize=(8, 6))  # Adjust figure size for better visibility
+        plt.plot(durations, marker='o', linestyle='-', markersize=5)  # Create a line plot
+        plt.xlabel('Entry')
+        plt.ylabel('Duration (s)')
+        plt.title(f'Curve of Durations for Function {function_name} (PID {target_pid})')
+
+        # Add annotations for each data point
+        for i, duration in enumerate(durations):
+            plt.annotate(f'{duration:.2f}s', (i, duration), textcoords="offset points", xytext=(0,10), ha='center')
+
+        pdf_pages.savefig()
+        plt.close()
+
+        # Compute and print average and standard deviation
+        avg_duration = np.mean(durations)
+        std_duration = np.std(durations)
+        print(f"Function: {function_name} (PID {target_pid})")
+        print(f"Average Duration: {avg_duration:.2f} s")
+        print(f"Standard Deviation: {std_duration:.2f} s")
+        print("")
+
+    pdf_pages.close()
+
+def main():
+    if len(sys.argv) != 3:
+        print("Usage: python_script.py function_data.json pid_value")
+        return
+
+    json_file_path = sys.argv[1]
+
+    try:
+        target_pid = int(sys.argv[2])
+    except ValueError:
+        print("Error: Invalid pid_value. Please enter a valid integer.")
+        return
+
+    with open(json_file_path, 'r') as json_file:
+        json_data = json.load(json_file)
+
+    entries = extract_data_from_json(json_data)
+
+    # Check if the target_pid exists in the data
+    pids = set(entry["pid"] for entry in entries)
+    if target_pid not in pids:
+        print(f"Error: PID {target_pid} not found in the data.")
+        return
+
+    generate_curves(entries, target_pid)
+
+if __name__ == "__main__":
+    main()
+
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