[latency] filesrc latency compenstation

:Release Notes:
There are maybe an error in latency measurement related to filesrc
module work

:Detailed Notes:
-

:Testing Performed:
-

:QA Notes:
-

:Issues Addressed:
-

latencyAnalysis.py

@@ -2,11 +2,69 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import numpy as np
 import os
+import argparse
 import logging
 
 # Configure logging to show informational messages
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
+prefixImage = 'histograms/'
+
+def parse_args():
+    parser = argparse.ArgumentParser(prog=__file__)
+    parser.add_argument('-c', '--compensate', action="store_true")
+    return parser.parse_args()
+
+cmd_args = None
+def get_args():
+    global cmd_args
+    if cmd_args is None:
+        cmd_args = parse_args()
+    return cmd_args
+
+def plot_latency_data(df):
+    def create_labels(df):
+        """Combines MultiIndex levels (L0-L3) into a single string for notes."""
+        labels = {}
+        for i, index in enumerate(df.index):
+            # Format: L#:value | L#:value | ...
+            label_parts = [f"L{j}:{val}" for j, val in enumerate(index)]
+            labels[i + 1] = " | ".join(label_parts)
+        return labels        
+
+    df = df.head(10)
+    encoder_name = df.index.get_level_values(0)[0]
+    max_notes = create_labels(df)
+
+    bar_width = 0.25
+    num_configs = len(df)
+    r1 = np.arange(num_configs)
+    r2 = [x + bar_width for x in r1]
+    r3 = [x + bar_width for x in r2]
+    fig = plt.figure(figsize=(10, 6), dpi=300)
+    # Create the bars
+    plt.bar(r1, df['max'], color='red', width=bar_width, edgecolor='grey', label='Max Latency')
+    plt.bar(r2, df['avg'], color='blue', width=bar_width, edgecolor='grey', label='Avg Latency')
+    plt.bar(r3, df['median'], color='green', width=bar_width, edgecolor='grey', label='Median Latency')
+
+    # Add labels and ticks
+    plt.xlabel('Индекс конфигурации', fontweight='bold')
+    plt.ylabel('Общая задержка [мс]', fontweight='bold')
+    plt.xticks([r + bar_width for r in range(num_configs)], [str(i + 1) for i in range(num_configs)])
+    plt.title(f'Сравнение производительности {num_configs} лучших конфигураций по задержке для {encoder_name}')
+    plt.legend()
+    plt.grid(axis='y', linestyle='--', alpha=0.6)
+
+    plt.tight_layout()
+    plt.savefig(prefixImage + f'combined_top_configurations_plot_{encoder_name}.png')
+    plt.close()
+
+    # Output Notes (for user interpretation)
+    print("\n--- Notes for Plot (X-Axis Index to Configuration) ---")
+    for index, note in max_notes.items():
+        print(f"Index {index}: {note}")
+
+
 def analyze_latency_data(csv_path: str):
     """
     Analyzes latency data to find the top 10 components (rows) contributing most
@@ -30,7 +88,12 @@ def analyze_latency_data(csv_path: str):
 
     #calculate summary along the rows
     sumDf = df.sum()
-    print(sumDf.info())
+    if get_args().compensate == True:
+        logging.info("Filesrc latency compensation is ON")
+        filesrcData = df.loc["filesrc0"]
+        sumDf -= filesrcData
+    print(sumDf.head())
+    # return
 
     df_summary = sumDf.unstack(level=-1) # or level='Metric' if names are set
 
@@ -61,52 +124,12 @@ def analyze_latency_data(csv_path: str):
     df_common_top_performers = df_summary.loc[common_indices]
     print(df_common_top_performers.head())
 
-    def create_labels(df):
-        """Combines MultiIndex levels (L0-L3) into a single string for notes."""
-        labels = {}
-        for i, index in enumerate(df.index):
-            # Format: L#:value | L#:value | ...
-            label_parts = [f"L{j}:{val}" for j, val in enumerate(index)]
-            labels[i + 1] = " | ".join(label_parts)
-        return labels        
-
-    df_common_top_performers =df_common_top_performers.head(10)
-    encoder_name = df_common_top_performers.index.get_level_values(0)[0]
-    max_notes = create_labels(df_common_top_performers)
-
-    bar_width = 0.25
-    num_configs = len(df_common_top_performers)
-    r1 = np.arange(num_configs)
-    r2 = [x + bar_width for x in r1]
-    r3 = [x + bar_width for x in r2]
-    fig = plt.figure(figsize=(10, 6), dpi=300)
-    # Create the bars
-    plt.bar(r1, df_common_top_performers['max'], color='red', width=bar_width, edgecolor='grey', label='Max Latency')
-    plt.bar(r2, df_common_top_performers['avg'], color='blue', width=bar_width, edgecolor='grey', label='Avg Latency')
-    plt.bar(r3, df_common_top_performers['median'], color='green', width=bar_width, edgecolor='grey', label='Median Latency')
-
-    # Add labels and ticks
-    plt.xlabel('Индекс конфигурации', fontweight='bold')
-    plt.ylabel('Общая задержка [мс]', fontweight='bold')
-    plt.xticks([r + bar_width for r in range(num_configs)], [str(i + 1) for i in range(num_configs)])
-    plt.title(f'Сравнение производительности {num_configs} лучших конфигураций по задержке для {encoder_name}')
-    plt.legend()
-    plt.grid(axis='y', linestyle='--', alpha=0.6)
-
-    plt.tight_layout()
-    plt.savefig(f'combined_top_configurations_plot_{encoder_name}.png')
-    plt.close()
-
-    # Output Notes (for user interpretation)
-    print("\n--- Notes for MAX Plot (X-Axis Index to Configuration) ---")
-    for index, note in max_notes.items():
-        print(f"Index {index}: {note}")
-
-    # Sort 
+    plot_latency_data(df_common_top_performers)
     return
 
 if __name__ == '__main__':
+    parse_args()
     # Set the path to your CSV file here.
-    csv_filename = 'results/latencyDataframenvv4l2h264enc.csv'
+    csv_filename = 'sample/latencyDataframenvh264enc.csv'
     analyze_latency_data(csv_filename)