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[research] changed encoder max with avg for latency calc

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This commit is contained in:
Artur Mukhamadiev 2025-10-14 19:28:17 +03:00
parent f8385dbc42
commit 75274ee2be
2 changed files with 111 additions and 51 deletions
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153
latencyAnalysis.py
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@ -30,6 +30,7 @@ def parse_args():
cmd_args = None
encoder_name = "default"
def get_args():
@ -40,6 +41,8 @@ def get_args():
def plot_latency_data(df):
global encoder_name
def create_labels(df_slice):
labels = {}
for i, index in enumerate(df_slice.index):
@ -78,8 +81,6 @@ def plot_latency_data(df):
yerr_upper_median = df_top_n[max_median_key] - mean_median_values
yerr_median = np.array(
[yerr_lower_median.values, yerr_upper_median.values])
encoder_name = df_top_n.index.get_level_values(0)[0]
max_notes = create_labels(df_top_n)
bar_width = 0.25
@ -92,7 +93,17 @@ def plot_latency_data(df):
fig = plt.figure(figsize=(12, 7), dpi=300)
plt.bar(
def add_annotation(bar):
for rect in bar:
height = rect.get_height()
plt.annotate(f'{height:.2f} мс',
xy=(rect.get_x() + rect.get_width() / 2, height / 2),
xytext=(0, 0), # 3 points vertical offset
textcoords="offset points", transform_rotates_text=True,
rotation=90,
ha='center', va='bottom', fontsize=10, color='White')
bar1 = plt.bar(
r_max_orig,
df_top_n[mean_max_key],
yerr=yerr_max_orig,
@ -102,8 +113,9 @@ def plot_latency_data(df):
edgecolor='grey',
label='Максимальная задержка'
)
add_annotation(bar1)
plt.bar(
bar2 = plt.bar(
r_avg,
df_top_n[mean_avg_key],
yerr=yerr_avg,
@ -113,8 +125,9 @@ def plot_latency_data(df):
edgecolor='grey',
label='Средняя задержка'
)
add_annotation(bar2)
plt.bar(
bar3 = plt.bar(
r_median,
df_top_n[mean_median_key],
yerr=yerr_median,
@ -124,6 +137,7 @@ def plot_latency_data(df):
edgecolor='grey',
label='Медианная задержка'
)
add_annotation(bar3)
plt.xlabel('Индекс конфигурации', fontweight='bold')
plt.ylabel('Общая задержка [мс]', fontweight='bold')
@ -146,12 +160,13 @@ def plot_latency_data(df):
def plot_start_latency(df):
global encoder_name
fig = plt.figure(figsize=(10, 6), dpi=300)
r1 = np.arange(len(df))
mean_col = ('mean', 'max')
min_col = ('left', 'max')
max_col = ('right', 'max')
mean_col = ('mean', 'avg')
min_col = ('left', 'avg')
max_col = ('right', 'avg')
mean_values = df[mean_col]
min_values = df[min_col]
@ -173,13 +188,77 @@ def plot_start_latency(df):
)
plt.xlabel('Индекс конфигурации', fontweight='bold')
plt.ylabel('Общая задержка [мс]', fontweight='bold')
encoder_name = df.index.get_level_values(0)[0]
plt.title(f"Результаты стартовой задержки для {encoder_name}")
plt.tight_layout()
plt.savefig(get_args().plot_dir + f"start_latency_{encoder_name}.png")
plt.close()
def get_base_metric(metric):
"""Strips suffixes like '.1' or '.2' from the metric name."""
return re.sub(r'\.\d+$', '', str(metric))
def compensate(func):
def wrapper(df: pd.DataFrame, group: pd.DataFrame) -> pd.DataFrame:
logging.debug("Inside transpose decorator")
res_df = func(df, group)
if get_args().compensate == True:
logging.info(
"Filesrc and rawvideoparse latency compensation is ON")
res_df = res_df.drop('filesrc0', axis=0)
res_df = res_df.drop('rawvideoparse0', axis=0)
return res_df
return wrapper
def log_result(func):
def wrapper(*args, **kwargs):
res = func(*args, **kwargs)
logging.info(f"\n{res}")
return res
return wrapper
@compensate
def transpose_and_set(df: pd.DataFrame, group: pd.DataFrame) -> pd.DataFrame:
new_column_index_data = group.index
res_df = group.T
res_df.columns = pd.MultiIndex.from_tuples(
new_column_index_data,
names=df.columns.names
)
return res_df
@log_result
def do_sum_and_change(df: pd.DataFrame):
global encoder_name
idx = pd.IndexSlice
# some shit, to be fair, so here we are trying to replace value with correct one,
# so we will have zero pain in ass on data plotting
mean_max_values = df.loc[f"{encoder_name}0",
idx[:, :, :, :, 'max']] # type: ignore
logging.info(mean_max_values.values)
df.loc[f"{encoder_name}0", idx[:, :, :, :, 'avg']
] = mean_max_values.values # type: ignore
# we want to change recorded encoder avg latency with max latency to get full pipeline latency
res_df = df.sum()
return res_df
def get_grouping_keys(df: pd.DataFrame):
metric_level_values = df.columns.get_level_values(-1)
base_metrics_key = metric_level_values.map(get_base_metric)
config_levels_to_group = list(range(df.columns.nlevels - 1))
grouping_keys = [
df.columns.get_level_values(i) for i in config_levels_to_group
] + [base_metrics_key]
return grouping_keys
def analyze_latency_data(csv_path: str):
"""
Analyzes latency data to find the top 10 components (rows) contributing most
@ -188,6 +267,7 @@ def analyze_latency_data(csv_path: str):
Args:
csv_path (str): The path to the input CSV file.
"""
global encoder_name
try:
df = pd.read_csv(csv_path, header=[0, 1, 2, 3, 4], index_col=0)
@ -201,45 +281,23 @@ def analyze_latency_data(csv_path: str):
except Exception as e:
logging.error(f"An error occurred while reading the CSV file: {e}")
return
encoder_name = df.columns.get_level_values(0)[0]
logging.info(f"encoder name={encoder_name}")
grouping_keys = get_grouping_keys(df)
sumDf = df.sum()
if get_args().compensate == True:
logging.info("Filesrc and rawvideoparse latency compensation is ON")
filesrcData = df.loc["filesrc0"]
rawvideoparseData = df.loc["rawvideoparse0"]
sumDf -= filesrcData
sumDf -= rawvideoparseData
logging.debug(f"\n{sumDf.head()}")
mean_df = transpose_and_set(df, df.T.groupby(grouping_keys).mean())
min_df = transpose_and_set(df, df.T.groupby(grouping_keys).min())
max_df = transpose_and_set(df, df.T.groupby(grouping_keys).max())
def get_base_metric(metric):
"""Strips suffixes like '.1' or '.2' from the metric name."""
return re.sub(r'\.\d+$', '', str(metric))
logging.info(f"\n{mean_df}")
metric_level_values = sumDf.index.get_level_values(-1)
base_metrics_key = metric_level_values.map(get_base_metric)
config_levels = list(range(sumDf.index.nlevels - 1))
grouping_keys = sumDf.index.droplevel(config_levels) # type: ignore
grouping_keys = [
sumDf.index.get_level_values(i) for i in config_levels
] + [base_metrics_key]
# 3. Perform Grouping and Mean Calculation
# This command groups all entries that share the same (Config + Base Metric),
# collapsing (avg, avg.1, avg.2) into a single average.
sumDfgrouping = sumDf.groupby(grouping_keys)
averaged_sumDf = sumDfgrouping.mean()
max_sumDf = sumDfgrouping.max()
min_sumDf = sumDfgrouping.min()
logging.debug(f"\n{max_sumDf.head(10)}")
logging.debug(f"\n{min_sumDf.head(10)}")
logging.info(f"\n{averaged_sumDf.head(10)}")
# at this stage our dataframe is summarized no per element data is accessible
mean_sumDf = do_sum_and_change(mean_df)
min_sumDf = do_sum_and_change(min_df)
max_sumDf = do_sum_and_change(max_df)
merged_sumDf = pd.concat(
[min_sumDf, averaged_sumDf, max_sumDf],
[min_sumDf, mean_sumDf, max_sumDf],
axis=1,
keys=['left', 'mean', 'right']
)
@ -269,13 +327,12 @@ def analyze_latency_data(csv_path: str):
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)
# avg is main index because it is commonly introduces the largest amount of latency to the stream
common_indices = avg_indices.intersection(
max_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]
encoder_name = df_common_top_performers.index.get_level_values(0)[0]
print(df_common_top_performers.head())
@ -284,7 +341,7 @@ def analyze_latency_data(csv_path: str):
plot_start_latency(df_common_top_performers)
# 4. Save top performers to csv
top_10_df = df_common_top_performers.head(10)
top_10_df = df_common_top_performers.head(10)["mean"]
top_10_df.to_csv(get_args().csv_dir + f"{encoder_name}.csv")
return

9
qualityAnalysis.py
View File

@ -79,7 +79,7 @@ def plot_top_configurations(df: pd.DataFrame, file_name: str, title: str):
quality_notes[len(quality_notes) + 1] = " | ".join(note_parts)
# 3. Setup the figure and the primary axis (ax1)
fig, ax1 = plt.subplots(figsize=(12, 6))
fig, ax1 = plt.subplots(figsize=(10, 6), dpi=300)
# Define bar width and positions
bar_width = 0.35
@ -125,7 +125,8 @@ def plot_top_configurations(df: pd.DataFrame, file_name: str, title: str):
# 7. Final Plot appearance
fig.suptitle(title)
fig.tight_layout(rect={0.0, 0.03, 1.0, 0.95}) # type: ignore
rect = tuple([0.0, 0.0, 1.0, 0.95])
fig.tight_layout(rect=rect) # type: ignore
# Combine legends from both axes
lines1, labels1 = ax1.get_legend_handles_labels()
@ -214,9 +215,11 @@ def analyze_quality_report(csv_path: str):
# Now intersected with latency report
latency_df = pd.read_csv(f'results/{encoder_name}.csv')
logging.info(latency_df.head())
columns = {'Unnamed: 0': 'encoder', 'Unnamed: 1': 'profile',
'Unnamed: 2': 'video', 'Unnamed: 3': 'parameters'}
latency_df.rename(columns=columns, inplace=True)
latency_df.rename(columns=columns, inplace=True) # type: ignore
logging.debug(f"\n{latency_df.head()}")
# --- 4. Merge Quality and Latency Reports ---