8b9190bb86
:Release Notes: - :Detailed Notes: - :Testing Performed: - :QA Notes: - :Issues Addressed: -
113 lines
4.5 KiB
Python
113 lines
4.5 KiB
Python
import pandas as pd
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import os
|
|
import logging
|
|
|
|
# Configure logging to show informational messages
|
|
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
|
|
|
|
def analyze_latency_data(csv_path: str):
|
|
"""
|
|
Analyzes latency data to find the top 10 components (rows) contributing most
|
|
to latency, and plots histograms of their summed avg, median, and max latencies.
|
|
|
|
Args:
|
|
csv_path (str): The path to the input CSV file.
|
|
"""
|
|
# --- 1. Load Data with Multi-level Headers ---
|
|
try:
|
|
df = pd.read_csv(csv_path, header=[0,1, 2, 3, 4], index_col=0)
|
|
logging.info(f"Successfully loaded '{csv_path}' with multi-level headers. Shape: {df.shape}")
|
|
if df.index.name == 'Unnamed: 0':
|
|
df.index.name = 'component'
|
|
except FileNotFoundError:
|
|
logging.error(f"Error: The file '{csv_path}' was not found.")
|
|
return
|
|
except Exception as e:
|
|
logging.error(f"An error occurred while reading the CSV file: {e}")
|
|
return
|
|
|
|
#calculate summary along the rows
|
|
sumDf = df.sum()
|
|
print(sumDf.info())
|
|
|
|
df_summary = sumDf.unstack(level=-1) # or level='Metric' if names are set
|
|
|
|
# 2. Sort the resulting DataFrame by the desired metric column.
|
|
df_sorted_by_max = df_summary.sort_values(by='max', ascending=True)
|
|
df_sorted_by_avg = df_summary.sort_values(by='avg', ascending=True)
|
|
df_sorted_by_median = df_summary.sort_values(by='median', ascending=True)
|
|
|
|
print("SORTED BY MAX")
|
|
print(df_sorted_by_max)
|
|
print("---------------")
|
|
print("SORTED BY AVERAGE")
|
|
print(df_sorted_by_avg)
|
|
print("---------------")
|
|
print("SORTED BY MEDIAN")
|
|
print(df_sorted_by_median)
|
|
|
|
# 1. Get the indices (configurations) for each top 10 list
|
|
max_indices = df_sorted_by_max.index
|
|
avg_indices = df_sorted_by_avg.index
|
|
median_indices = df_sorted_by_median.index
|
|
|
|
# 2. Find the intersection (common elements) of the three sets of indices
|
|
# max is main index because it is commonly introduces the largest amount of latency to the stream
|
|
common_indices = max_indices.intersection(avg_indices).intersection(median_indices)
|
|
|
|
# 3. Filter the original summary DataFrame (df_summary) using the common indices
|
|
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
|
|
return
|
|
|
|
if __name__ == '__main__':
|
|
# Set the path to your CSV file here.
|
|
csv_filename = 'results/latencyDataframenvv4l2h264enc.csv'
|
|
analyze_latency_data(csv_filename)
|
|
|