[analysis] mean values for non-unique config runs
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vendored
@ -1,3 +1,7 @@
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#project ignore:
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plots/
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results/
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# ---> Python
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# Byte-compiled / optimized / DLL files
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__pycache__/
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@ -2,20 +2,23 @@ import pandas as pd
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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import re
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import argparse
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import logging
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# Configure logging to show informational messages
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logging.basicConfig(level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s')
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def parse_args():
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parser = argparse.ArgumentParser(prog=__file__)
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parser.add_argument('-c', '--compensate', action="store_true")
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parser.add_argument('--latency-csv',
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type=str,
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parser.add_argument('--latency-csv',
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type=str,
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default='sample/latencyDataframenvh264enc.csv',
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help='Path to the latency results CSV file.')
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parser.add_argument('-pd','--plot-dir',
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parser.add_argument('-pd', '--plot-dir',
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type=str,
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default='plots/',
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help='Path to directory in which resulted plots should be saved')
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@ -25,13 +28,17 @@ def parse_args():
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help='Path to directory in which resulted csv data should be saved')
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return parser.parse_args()
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cmd_args = None
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def get_args():
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global cmd_args
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if cmd_args is None:
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cmd_args = parse_args()
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return cmd_args
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def plot_latency_data(df):
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def create_labels(df):
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"""Combines MultiIndex levels (L0-L3) into a single string for notes."""
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@ -40,7 +47,7 @@ def plot_latency_data(df):
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# Format: L#:value | L#:value | ...
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label_parts = [f"L{j}:{val}" for j, val in enumerate(index)]
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labels[i + 1] = " | ".join(label_parts)
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return labels
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return labels
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df = df.head(10)
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encoder_name = df.index.get_level_values(0)[0]
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@ -53,20 +60,26 @@ def plot_latency_data(df):
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r3 = [x + bar_width for x in r2]
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fig = plt.figure(figsize=(10, 6), dpi=300)
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# Create the bars
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plt.bar(r1, df['max'], color='red', width=bar_width, edgecolor='grey', label='Max Latency')
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plt.bar(r2, df['avg'], color='blue', width=bar_width, edgecolor='grey', label='Avg Latency')
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plt.bar(r3, df['median'], color='green', width=bar_width, edgecolor='grey', label='Median Latency')
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plt.bar(r1, df['max'], color='red', width=bar_width,
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edgecolor='grey', label='Max Latency')
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plt.bar(r2, df['avg'], color='blue', width=bar_width,
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edgecolor='grey', label='Avg Latency')
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plt.bar(r3, df['median'], color='green', width=bar_width,
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edgecolor='grey', label='Median Latency')
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# Add labels and ticks
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plt.xlabel('Индекс конфигурации', fontweight='bold')
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plt.ylabel('Общая задержка [мс]', fontweight='bold')
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plt.xticks([r + bar_width for r in range(num_configs)], [str(i + 1) for i in range(num_configs)])
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plt.title(f'Сравнение производительности {num_configs} лучших конфигураций по задержке для {encoder_name}')
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plt.xticks([r + bar_width for r in range(num_configs)],
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[str(i + 1) for i in range(num_configs)])
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plt.title(
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f'Сравнение производительности {num_configs} лучших конфигураций по задержке для {encoder_name}')
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plt.legend()
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plt.grid(axis='y', linestyle='--', alpha=0.6)
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plt.tight_layout()
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plt.savefig(get_args().plot_dir + f'combined_top_configurations_plot_{encoder_name}.png')
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plt.savefig(get_args().plot_dir +
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f'combined_top_configurations_plot_{encoder_name}.png')
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plt.close()
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# Output Notes (for user interpretation)
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@ -74,6 +87,7 @@ def plot_latency_data(df):
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for index, note in max_notes.items():
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print(f"Index {index}: {note}")
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def plot_start_latency(df):
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fig = plt.figure(figsize=(10, 6), dpi=300)
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r1 = np.arange(len(df))
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@ -86,6 +100,7 @@ def plot_start_latency(df):
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plt.savefig(get_args().plot_dir + f"start_latency_{encoder_name}.png")
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plt.close()
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def analyze_latency_data(csv_path: str):
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"""
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Analyzes latency data to find the top 10 components (rows) contributing most
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@ -96,8 +111,9 @@ def analyze_latency_data(csv_path: str):
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"""
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# --- 1. Load Data with Multi-level Headers ---
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try:
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df = pd.read_csv(csv_path, header=[0,1, 2, 3, 4], index_col=0)
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logging.info(f"Successfully loaded '{csv_path}' with multi-level headers. Shape: {df.shape}")
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df = pd.read_csv(csv_path, header=[0, 1, 2, 3, 4], index_col=0)
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logging.info(
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f"Successfully loaded '{csv_path}' with multi-level headers. Shape: {df.shape}")
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if df.index.name == 'Unnamed: 0':
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df.index.name = 'component'
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except FileNotFoundError:
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@ -107,22 +123,50 @@ def analyze_latency_data(csv_path: str):
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logging.error(f"An error occurred while reading the CSV file: {e}")
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return
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#calculate summary along the rows
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# calculate summary along the rows
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sumDf = df.sum()
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if get_args().compensate == True:
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logging.info("Filesrc latency compensation is ON")
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logging.info("Filesrc and rawvideoparse latency compensation is ON")
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filesrcData = df.loc["filesrc0"]
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rawvideoparseData = df.loc["rawvideoparse0"]
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sumDf -= filesrcData
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print(sumDf.head())
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# return
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sumDf -= rawvideoparseData
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logging.debug(f"\n{sumDf.head()}")
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df_summary = sumDf.unstack(level=-1) # or level='Metric' if names are set
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# calculate mean accross non-unique runs:
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def get_base_metric(metric):
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"""Strips suffixes like '.1' or '.2' from the metric name."""
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return re.sub(r'\.\d+$', '', str(metric))
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metric_level_values = sumDf.index.get_level_values(-1)
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base_metrics_key = metric_level_values.map(get_base_metric)
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config_levels = list(range(sumDf.index.nlevels - 1)
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) # This gives [0, 1, 2, 3]
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grouping_keys = sumDf.index.droplevel(config_levels) # type: ignore
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grouping_keys = [
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sumDf.index.get_level_values(i) for i in config_levels
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] + [base_metrics_key]
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# 3. Perform Grouping and Mean Calculation
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# This command groups all entries that share the same (Config + Base Metric),
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# collapsing (avg, avg.1, avg.2) into a single average.
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averaged_sumDf = sumDf.groupby(grouping_keys).mean()
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logging.info(f"\n{averaged_sumDf.head(10)}")
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sumDf = averaged_sumDf
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df_summary = sumDf.unstack(level=-1) # or level='Metric' if names are set
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# 2. Sort the resulting DataFrame by the desired metric column.
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df_sorted_by_max = df_summary.sort_values(by='max', ascending=True)
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df_sorted_by_avg = df_summary.sort_values(by='avg', ascending=True)
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df_sorted_by_median = df_summary.sort_values(by='median', ascending=True)
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df_sorted_by_max = df_summary.sort_values(
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by='max', ascending=True) # type: ignore
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df_sorted_by_avg = df_summary.sort_values(
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by='avg', ascending=True) # type: ignore
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df_sorted_by_median = df_summary.sort_values(
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by='median', ascending=True) # type: ignore
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print("SORTED BY MAX")
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print(df_sorted_by_max)
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print("---------------")
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@ -139,7 +183,8 @@ def analyze_latency_data(csv_path: str):
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# 2. Find the intersection (common elements) of the three sets of indices
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# max is main index because it is commonly introduces the largest amount of latency to the stream
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common_indices = max_indices.intersection(avg_indices).intersection(median_indices)
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common_indices = max_indices.intersection(
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avg_indices).intersection(median_indices)
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# 3. Filter the original summary DataFrame (df_summary) using the common indices
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df_common_top_performers = df_summary.loc[common_indices]
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@ -156,8 +201,8 @@ def analyze_latency_data(csv_path: str):
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top_10_df.to_csv(get_args().csv_dir + f"{encoder_name}.csv")
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return
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if __name__ == '__main__':
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os.makedirs(get_args().csv_dir, exist_ok=True)
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os.makedirs(get_args().plot_dir, exist_ok=True)
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analyze_latency_data(get_args().latency_csv)
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@ -4,19 +4,22 @@ import numpy as np
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import logging
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import argparse
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import os
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import re
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# Configure logging to show informational messages
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logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
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logging.basicConfig(level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s')
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def parse_args():
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parser = argparse.ArgumentParser(prog=__file__)
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parser.add_argument(
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'--quality-csv',
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type=str,
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'--quality-csv',
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type=str,
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default='sample/qualityResultsnvh264enc.csv',
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help='Path to the quality results CSV file.'
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)
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parser.add_argument('-pd','--plot-dir',
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parser.add_argument('-pd', '--plot-dir',
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type=str,
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default='plots/',
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help='Path to directory in which resulted plots should be saved')
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@ -26,21 +29,25 @@ def parse_args():
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help='Path to directory in which resulted csv data should be saved')
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return parser.parse_args()
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cmd_args = None
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def get_args():
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global cmd_args
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if cmd_args is None:
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cmd_args = parse_args()
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return cmd_args
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def plot_top_configurations(df: pd.DataFrame, file_name: str, title: str):
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"""
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Draws a bar plot comparing PSNR and SSIM for the top 10 video configurations.
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The plot uses a primary Y-axis for PSNR and a secondary Y-axis for SSIM
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due to their different value ranges. The X-axis uses simple numerical indices,
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with detailed configuration notes printed separately below the plot.
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Args:
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df: DataFrame containing the top configurations, must have 'PSNR' and 'SSIM' columns.
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file_name: Name of the file to which plot would be saved.
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@ -48,50 +55,50 @@ def plot_top_configurations(df: pd.DataFrame, file_name: str, title: str):
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"""
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# Use the top 10 rows for plotting
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plot_df = df.head(10).copy()
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if plot_df.empty:
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logging.warning("DataFrame is empty, cannot generate plot.")
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return
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# Create the index for the x-axis (0 to 9 for bar plotting)
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config_indices = np.arange(len(plot_df))
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# 1. Create simple numerical labels for the X-axis (1 to 10)
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x_labels_simple = [str(i + 1) for i in config_indices]
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# 2. Generate notes mapping index to configuration details (similar to the template)
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quality_notes = {}
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for i, row in plot_df.iterrows():
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# Format: Index: encoder | profile | video | parameters
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note_parts = [
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row['encoder'],
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row['profile'],
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row['video'],
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row['encoder'],
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row['profile'],
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row['video'],
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row['parameters']
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]
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quality_notes[len(quality_notes) + 1] = " | ".join(note_parts)
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# 3. Setup the figure and the primary axis (ax1)
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fig, ax1 = plt.subplots(figsize=(12, 6))
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# Define bar width and positions
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bar_width = 0.35
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# 4. Plot PSNR on the primary axis (left)
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bar1 = ax1.bar(config_indices - bar_width/2, plot_df['PSNR'], bar_width,
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bar1 = ax1.bar(config_indices - bar_width/2, plot_df['PSNR'], bar_width,
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label='PSNR (dB)', color='Blue', edgecolor='grey')
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ax1.set_xlabel('Configuration Index', fontsize=12) # Simplified X-label
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ax1.set_xlabel('Configuration Index', fontsize=12) # Simplified X-label
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ax1.set_ylabel('PSNR (dB)', color='Black', fontsize=12)
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ax1.tick_params(axis='y', labelcolor='Black')
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ax1.set_xticks(config_indices)
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# Use simple numerical labels for the X-axis
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ax1.set_xticklabels(x_labels_simple, fontsize=10)
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ax1.set_xticklabels(x_labels_simple, fontsize=10)
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# Add PSNR value labels above the bars
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for rect in bar1:
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height = rect.get_height()
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ax1.annotate(f'PSNR={height:.2f}',
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xy=(rect.get_x() + rect.get_width() / 2, height / 1.5 ),
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xy=(rect.get_x() + rect.get_width() / 2, height / 1.5),
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xytext=(0, 0), # 3 points vertical offset
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textcoords="offset points", transform_rotates_text=True,
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rotation=90,
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@ -99,18 +106,18 @@ def plot_top_configurations(df: pd.DataFrame, file_name: str, title: str):
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# 5. Create a secondary axis (ax2) for SSIM (twinx)
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ax2 = ax1.twinx()
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# 6. Plot SSIM on the secondary axis (right)
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bar2 = ax2.bar(config_indices + bar_width/2, plot_df['SSIM'], bar_width,
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bar2 = ax2.bar(config_indices + bar_width/2, plot_df['SSIM'], bar_width,
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label='SSIM', color='Red', edgecolor='grey')
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ax2.set_ylabel('SSIM (Structural Similarity)', color='Black', fontsize=12)
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ax2.tick_params(axis='y', labelcolor='Black')
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# Add SSIM value labels above the bars
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for rect in bar2:
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height = rect.get_height()
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ax2.annotate(f'SSIM={height:.4f}',
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xy=(rect.get_x() + rect.get_width() / 2, height / 1.5 ),
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xy=(rect.get_x() + rect.get_width() / 2, height / 1.5),
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xytext=(0, 0), # 3 points vertical offset
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textcoords="offset points", transform_rotates_text=True,
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rotation=90,
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@ -118,12 +125,13 @@ def plot_top_configurations(df: pd.DataFrame, file_name: str, title: str):
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# 7. Final Plot appearance
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fig.suptitle(title)
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fig.tight_layout(rect=[0, 0.03, 1, 0.95])
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fig.tight_layout(rect={0.0, 0.03, 1.0, 0.95}) # type: ignore
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# Combine legends from both axes
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lines1, labels1 = ax1.get_legend_handles_labels()
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lines2, labels2 = ax2.get_legend_handles_labels()
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ax1.legend(lines1 + lines2, labels1 + labels2, bbox_to_anchor=(0.6, 1.1), ncol=2)
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ax1.legend(lines1 + lines2, labels1 + labels2,
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bbox_to_anchor=(0.6, 1.1), ncol=2)
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plt.grid(axis='y', linestyle='--', alpha=0.7)
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plt.savefig(f'{file_name}.png')
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@ -133,11 +141,13 @@ def plot_top_configurations(df: pd.DataFrame, file_name: str, title: str):
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for index, note in quality_notes.items():
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print(f"Index {index}: {note}")
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def analyze_quality_report(csv_path: str):
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# --- 1. Load Data with Multi-level Headers ---
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try:
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df = pd.read_csv(csv_path, header=[0, 1, 2, 3, 4], index_col=0)
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logging.info(f"Successfully loaded '{csv_path}' with multi-level headers. Shape: {df.shape}")
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logging.info(
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f"Successfully loaded '{csv_path}' with multi-level headers. Shape: {df.shape}")
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if df.index.name == 'Unnamed: 0':
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df.index.name = 'component'
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except FileNotFoundError:
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@ -146,16 +156,45 @@ def analyze_quality_report(csv_path: str):
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except Exception as e:
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logging.error(f"An error occurred while reading the CSV file: {e}")
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return
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# Get row with average results
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avgDf = df.loc["Average"]
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logging.info(f"\n{avgDf.head(10)}")
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# calculate mean accross non-unique runs:
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def get_base_metric(metric):
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"""Strips suffixes like '.1' or '.2' from the metric name."""
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return re.sub(r'\.\d+$', '', str(metric))
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metric_level_values = avgDf.index.get_level_values(-1)
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base_metrics_key = metric_level_values.map(get_base_metric)
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config_levels = list(range(avgDf.index.nlevels - 1)
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) # This gives [0, 1, 2, 3]
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grouping_keys = avgDf.index.droplevel(config_levels) # type: ignore
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grouping_keys = [
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avgDf.index.get_level_values(i) for i in config_levels
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] + [base_metrics_key]
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# 3. Perform Grouping and Mean Calculation
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# This command groups all entries that share the same (Config + Base Metric),
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# collapsing (avg, avg.1, avg.2) into a single average.
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averaged_sumDf = avgDf.groupby(grouping_keys).mean()
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logging.info(f"\n{averaged_sumDf.head(10)}")
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avgDf = averaged_sumDf
|
||||
|
||||
logging.info(f"\n{avgDf.head(10)}")
|
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|
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avgDf = avgDf.unstack(level=-1)
|
||||
|
||||
encoder_name = avgDf.index.get_level_values(0)[0]
|
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logging.debug(f"encoder_name={encoder_name}")
|
||||
|
||||
dfPSNRsorted = avgDf.sort_values(by="PSNR", ascending=False)
|
||||
dfSSIMsorted = avgDf.sort_values(by="SSIM", ascending=False)
|
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dfPSNRsorted = avgDf.sort_values(
|
||||
by="PSNR", ascending=False) # type: ignore
|
||||
dfSSIMsorted = avgDf.sort_values(
|
||||
by="SSIM", ascending=False) # type: ignore
|
||||
|
||||
indexPSNR = dfPSNRsorted.index
|
||||
indexSSIM = dfSSIMsorted.index
|
||||
@ -168,53 +207,65 @@ def analyze_quality_report(csv_path: str):
|
||||
# Convert the MultiIndex (encoder, profile, video, parameters) into columns
|
||||
df_quality_results = intersectedDf.reset_index()
|
||||
# Rename the columns to match the latency report's structure
|
||||
df_quality_results.columns = ['encoder', 'profile', 'video', 'parameters', 'PSNR', 'SSIM']
|
||||
logging.debug(f"Prepared quality results dataframe columns: {df_quality_results.columns.tolist()}")
|
||||
|
||||
df_quality_results.columns = [
|
||||
'encoder', 'profile', 'video', 'parameters', 'PSNR', 'SSIM']
|
||||
logging.debug(
|
||||
f"Prepared quality results dataframe columns: {df_quality_results.columns.tolist()}")
|
||||
|
||||
# Now intersected with latency report
|
||||
latency_df = pd.read_csv(f'results/{encoder_name}.csv')
|
||||
columns = {'Unnamed: 0': 'encoder', 'Unnamed: 1': 'profile', 'Unnamed: 2': 'video', 'Unnamed: 3': 'parameters'}
|
||||
columns = {'Unnamed: 0': 'encoder', 'Unnamed: 1': 'profile',
|
||||
'Unnamed: 2': 'video', 'Unnamed: 3': 'parameters'}
|
||||
latency_df.rename(columns=columns, inplace=True)
|
||||
logging.debug(f"\n{latency_df.head()}")
|
||||
|
||||
# --- 4. Merge Quality and Latency Reports ---
|
||||
# --- 4. Merge Quality and Latency Reports ---
|
||||
# Use an inner merge on the four identifier columns to combine the data.
|
||||
merge_keys = ['encoder', 'profile', 'video', 'parameters']
|
||||
merged_df = pd.merge(
|
||||
df_quality_results,
|
||||
latency_df,
|
||||
on=merge_keys,
|
||||
how='inner' # Only keep records present in both (i.e., the top quality configurations)
|
||||
# Only keep records present in both (i.e., the top quality configurations)
|
||||
how='inner'
|
||||
)
|
||||
|
||||
logging.info("=" * 70)
|
||||
logging.info("--- Intersected Quality (PSNR/SSIM) and Latency Report ---")
|
||||
logging.info(f"Number of common configuration entries found: {len(merged_df)}")
|
||||
logging.info(
|
||||
f"Number of common configuration entries found: {len(merged_df)}")
|
||||
logging.info("=" * 70)
|
||||
|
||||
# Prepare for display
|
||||
merged_df_display = merged_df.sort_values(by='PSNR', ascending=False)
|
||||
|
||||
|
||||
# Select and display key metrics
|
||||
display_columns = [
|
||||
'encoder', 'profile', 'video', 'parameters',
|
||||
'PSNR', 'SSIM', # Quality metrics
|
||||
'avg', 'max', 'median', 'std' # Latency metrics (assuming these are in the latency report)
|
||||
'encoder', 'profile', 'video', 'parameters',
|
||||
'PSNR', 'SSIM', # Quality metrics
|
||||
# Latency metrics (assuming these are in the latency report)
|
||||
'avg', 'max', 'median', 'std'
|
||||
]
|
||||
|
||||
final_cols = [col for col in display_columns if col in merged_df_display.columns]
|
||||
|
||||
final_cols = [
|
||||
col for col in display_columns if col in merged_df_display.columns]
|
||||
|
||||
print(f"\n{merged_df_display[final_cols].to_string()}")
|
||||
|
||||
plot_top_configurations(merged_df_display, get_args().plot_dir + f"top_quality_configurations_by_latency_{encoder_name}", f"Результаты качества для 10 лучших конфигураций по задержкам для {encoder_name}")
|
||||
plot_top_configurations(merged_df_display,
|
||||
get_args().plot_dir +
|
||||
f"top_quality_configurations_by_latency_{encoder_name}",
|
||||
f"Результаты качества для 10 лучших конфигураций по задержкам для {encoder_name}")
|
||||
|
||||
plot_top_configurations(df_quality_results, get_args().plot_dir + f"top_quality_configurations_{encoder_name}", f"10 лучших конфигураций по PSNR и SSIM для {encoder_name}")
|
||||
plot_top_configurations(df_quality_results,
|
||||
get_args().plot_dir +
|
||||
f"top_quality_configurations_{encoder_name}",
|
||||
f"10 лучших конфигураций по PSNR и SSIM для {encoder_name}")
|
||||
|
||||
return
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
os.makedirs(get_args().csv_dir, exist_ok=True)
|
||||
os.makedirs(get_args().plot_dir, exist_ok=True)
|
||||
analyze_quality_report(get_args().quality_csv)
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user