format code and add label to hover data (d62631c1) · Commits · IVAS Codec Public Collaboration / IVAS Codec

ci/process_long_term_logs.py

+94 −61

Original line number	Diff line number	Diff line
		@@ -11,13 +11,13 @@ def read_csv_files(root_dir):
		csv_data = {}
		for subdir, dirs, files in os.walk(root_dir):
		for file in files:
		if file.endswith('.csv'):
		if file.endswith(".csv"):
		file_path = os.path.join(subdir, file)
		try:
		df = pd.read_csv(file_path)
		csv_data[file_path] = df
		except Exception as e:
		print(f'Failed to read {file_path}: {e}')
		print(f"Failed to read {file_path}: {e}")
		exit(-1)
		return csv_data

		@@ -28,11 +28,10 @@ def parse_csv_data(csv_data):
		cols_to_keep = ["testcase", "format", "MLD", "MAX_ABS_DIFF"]
		parsed_data = {}
		for key, df in csv_data.items():
		cols = [
		col for col in cols_to_keep if col in df.columns]
		cols = [col for col in cols_to_keep if col in df.columns]
		date = os.path.basename(os.path.dirname(key))
		new_df = df[cols].copy()
		new_df['date'] = date
		new_df["date"] = date
		parsed_data[key] = new_df

		# concatenate all dataframe in the dictionary
		@@ -44,73 +43,102 @@ def plot_data(df, output_filename):
		"""plot max values for 'MLD' and 'MAX_ABS_DIFF' data and save
		to html file."""
		# Convert 'date' to datetime
		df['date'] = pd.to_datetime(df['date'], errors='coerce')
		df['MLD'] = pd.to_numeric(df['MLD'], errors='coerce')
		df['MAX_ABS_DIFF'] = pd.to_numeric(df['MAX_ABS_DIFF'], errors='coerce')
		df["date"] = pd.to_datetime(df["date"], errors="coerce")
		df["MLD"] = pd.to_numeric(df["MLD"], errors="coerce")
		df["MAX_ABS_DIFF"] = pd.to_numeric(df["MAX_ABS_DIFF"], errors="coerce")

		# Drop rows with NaT and NaN
		clean_df = df.dropna(subset=['date', 'MLD', 'MAX_ABS_DIFF'])
		clean_df = df.dropna(subset=["date", "MLD", "MAX_ABS_DIFF"])

		# Group by 'format' and 'date' to get rows with max 'MLD' per group
		max_mld = clean_df.groupby(['format', 'date']).apply(
		lambda x: x.loc[x['MLD'].idxmax()]).reset_index(drop=True)
		max_mld = (
		clean_df.groupby(["format", "date"])
		.apply(lambda x: x.loc[x["MLD"].idxmax()])
		.reset_index(drop=True)
		)

		# Group by 'format' and 'date' to get rows with max 'MAX_ABS_DIFF' per
		# group
		max_diff = clean_df.groupby(['format', 'date']).apply(
		lambda x: x.loc[x['MAX_ABS_DIFF'].idxmax()]).reset_index(drop=True)
		max_abs_diff = (
		clean_df.groupby(["format", "date"])
		.apply(lambda x: x.loc[x["MAX_ABS_DIFF"].idxmax()])
		.reset_index(drop=True)
		)

		formats = sorted(clean_df['format'].unique())
		formats = sorted(clean_df["format"].unique())

		fig = make_subplots(
		rows=5, cols=2,
		rows=5,
		cols=2,
		specs=[[{"secondary_y": True}] * 2] * 5,
		subplot_titles=[f'{i}' for i in formats],
		shared_xaxes='columns'
		subplot_titles=[f"{i}" for i in formats],
		shared_xaxes="columns",
		)

		for i, fmt in enumerate(formats):
		row = i // 2 + 1
		col = i % 2 + 1

		data_mld = max_mld[max_mld['format'] == fmt].sort_values('date')
		data_diff = max_diff[max_diff['format'] == fmt].sort_values('date')
		data_mld = max_mld[max_mld["format"] == fmt].sort_values("date")
		data_diff = max_abs_diff[max_abs_diff["format"]
		== fmt].sort_values("date")

		# Add max 'MLD' to primary y-axis
		fig.add_trace(
		go.Scatter(
		x=data_mld['date'], y=data_mld['MLD'], mode='lines+markers',
		name=f' {fmt} - Max MLD',
		hovertext=[f"Testcase: {tc}<br>MAX_ABS_DIFF: {diff}<br>Format:"
		x=data_mld["date"],
		y=data_mld["MLD"],
		mode="lines+markers",
		name=f" {fmt} - Max MLD",
		hovertext=[
		f"Testcase: {tc}<br>MLD: {mld:.4f}<br>MAX_ABS_DIFF:"
		f"{abs_diff}<br>Format:"
		f" {format}<br>Date: {date.date()}"
		for tc, diff, format, date in zip(
		data_mld['testcase'], data_mld['MAX_ABS_DIFF'],
		data_mld['format'], data_mld['date'])],
		hoverinfo='text+y'
		for tc, mld, abs_diff, format, date in zip(
		data_mld["testcase"],
		data_mld["MLD"],
		data_mld["MAX_ABS_DIFF"],
		data_mld["format"],
		data_mld["date"],
		)
		],
		hoverinfo="text",
		),
		row=row, col=col, secondary_y=False
		row=row,
		col=col,
		secondary_y=False,
		)

		# Add max 'MAX_ABS_DIFF' to secondary y-axis
		fig.add_trace(
		go.Scatter(
		x=data_diff['date'], y=data_diff['MAX_ABS_DIFF'],
		mode='lines+markers',
		name=f'{fmt} - Max MAX_ABS_DIFF',
		hovertext=[f"Testcase: {tc}<br>MLD: {mld:.4f}<br>Format:"
		x=data_diff["date"],
		y=data_diff["MAX_ABS_DIFF"],
		mode="lines+markers",
		name=f"{fmt} - Max MAX_ABS_DIFF",
		hovertext=[
		f"Testcase: {tc}<br>MLD: {mld:.4f}<br>MAX_ABS_DIFF:"
		f" {abs_diff:.4f}<br>Format:"
		f" {format}<br>Date: {date.date()}"
		for tc, mld, format, date in zip(
		data_diff['testcase'], data_diff['MLD'],
		data_mld['format'], data_diff['date'])],
		hoverinfo='text+y'
		for tc, mld, abs_diff, format, date in zip(
		data_diff["testcase"],
		data_diff["MLD"],
		data_diff["MAX_ABS_DIFF"],
		data_diff["format"],
		data_diff["date"],
		)
		],
		hoverinfo="text",
		),
		row=row, col=col, secondary_y=True
		row=row,
		col=col,
		secondary_y=True,
		)

		fig.update_layout(
		title_text='Long-term regression: max MLD and max MAX_ABS_DIFF',
		legend=dict(x=1, y=1, orientation='v'),
		hovermode='x unified'
		title_text="Long-term regression: max MLD and max MAX_ABS_DIFF",
		legend=dict(x=1, y=1, orientation="v"),
		hovermode="x unified",
		)

		fig.update_xaxes(automargin=True)
		@@ -120,26 +148,31 @@ def plot_data(df, output_filename):
		for i in range(10):
		yaxis_num = i * 2 + 1
		yaxis2_num = yaxis_num + 1
		fig['layout'][f'yaxis{yaxis_num}'].update(
		title='Max MLD', titlefont=dict(color='blue'),
		tickfont=dict(color='blue'))
		fig['layout'][f'yaxis{yaxis2_num}'].update(
		title='Max MAX_ABS_DIFF', titlefont=dict(color='green'),
		tickfont=dict(color='green'))
		fig["layout"][f"yaxis{yaxis_num}"].update(
		title="Max MLD", titlefont=dict(color="blue"), tickfont=dict(color="blue")
		)
		fig["layout"][f"yaxis{yaxis2_num}"].update(
		title="Max MAX_ABS_DIFF",
		titlefont=dict(color="green"),
		tickfont=dict(color="green"),
		)

		# Save to html
		fig.write_html(output_filename)


		if __name__ == '__main__':
		parser = argparse.ArgumentParser(
		description='Plot long term logs')
		if __name__ == "__main__":
		parser = argparse.ArgumentParser(description="Plot long term logs")
		parser.add_argument(
		'root_dir', type=str, help='Root directory containing subdirectories'
		' with CSV log files')
		"root_dir",
		type=str,
		help="Root directory containing subdirectories" " with CSV log files",
		)
		parser.add_argument(
		'output_filename', type=str, help='Filename of the generated plot. e.g'
		'. long_term_regression.html')
		"output_filename",
		type=str,
		help="Filename of the generated plot. e.g" ". long_term_regression.html",
		)
		args = parser.parse_args()

		csv_data = read_csv_files(args.root_dir)