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import pandas as pd
import re
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import argparse
def read_compile_log(file_path):
# Regex pattern to match lines with "value: __ ms"
pattern = r'([\w: ]+):\s*([\d.]+(?:e[+-]?\d+)?)ms'
data = []
# Read the file and find matches
with open(file_path, 'r') as file:
for line in file:
match = re.search(pattern, line)
if match:
operation = match.group(1).strip()
try:
value = float(match.group(2))
data.append({'Operation': operation, 'Value': value})
except:
print(f"Invalid Float: {match.group(2)}")
return data
def filter_data(data, quantile):
# Create a DataFrame from the list of dictionaries
df = pd.DataFrame(data)
df['Line Number'] = df.index
df['Operation: Line Number'] = df.apply(
lambda x: x["Operation"] + ": " + str(x['Line Number']), axis=1)
df['Cumulative Sum'] = df['Value'].cumsum()
print(
f"Only included the top {round(1-quantile, 2)}% time-intensive operations"
)
top_operation_time = df['Value'].quantile(quantile)
df = df[df['Value'] >= top_operation_time]
return df
def plot_compile_analysis(df, output_path):
df_sorted_by_time = df.sort_values(by='Value', ascending=False)
df_sorted_by_total_time = df.groupby(
'Operation')['Value'].sum().sort_values(ascending=False).reset_index()
df_sorted_by_avg_time = df.groupby('Operation')['Value'].mean(
).sort_values(ascending=False).reset_index()
df_counts = df['Operation'].value_counts().reset_index()
fig = make_subplots(rows=2,
cols=4,
row_heights=[0.4, 0.6],
specs=[[{
'type': 'bar'
}, {
'type': 'bar'
}, {
'type': 'bar'
}, {
'type': 'bar'
}],
[{
'type': 'scatter',
'colspan': 4
}, None, None, None]],
subplot_titles=('Time Taken per Line',
'Total Time Taken per Operation',
'Avg Time Taken per Operation',
'Number of times Operation called',
'Compile Time Series Graph'),
vertical_spacing=0.25)
fig.add_trace(go.Bar(
x=df_sorted_by_time['Operation: Line Number'],
y=df_sorted_by_time['Value'],
name='Time Taken per Line',
marker=dict(color='red'),
hoverinfo='x+y',
),
row=1,
col=1)
fig.add_trace(go.Bar(x=df_sorted_by_total_time['Operation'],
y=df_sorted_by_total_time['Value'],
name='Total Time Taken per Operation',
marker=dict(color='green'),
hoverinfo='x+y'),
row=1,
col=2)
fig.add_trace(go.Bar(x=df_sorted_by_avg_time['Operation'],
y=df_sorted_by_avg_time['Value'],
name='Avg Time Taken per Operation',
marker=dict(color='green'),
hoverinfo='x+y'),
row=1,
col=3)
fig.add_trace(go.Bar(x=df_counts['Operation'],
y=df_counts['count'],
name='Number of times Operation called',
marker=dict(color='royalblue'),
hoverinfo='x+y'),
row=1,
col=4)
fig.update_xaxes(
row=1,
tickangle=45,
)
fig.add_trace(go.Scatter(
x=list(range(len(df))),
y=df['Cumulative Sum'],
mode='lines+markers',
text=df['Operation: Line Number'],
hoverinfo='text',
name='Cumulative Time',
line=dict(width=2),
marker=dict(size=3),
),
row=2,
col=1)
fig.update_xaxes(
title_text='Operation: Line Number',
tickvals=list(range(len(df))),
ticktext=df['Operation: Line Number'],
row=2,
col=1,
tickangle=45,
)
fig.update_yaxes(title_text='Cumulative Time (ms)', row=2, col=1)
fig.update_layout(
title='Compile Time Analysis',
title_x=0.5,
title_font=dict(size=24, color='darkblue'),
# margin=dict(l=0, r=0, t=100, b=50),
uniformtext_minsize=12,
uniformtext_mode='hide',
height=1200,
template='plotly_white',
showlegend=False)
fig.write_html(output_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--file_path', required=True)
parser.add_argument('--quantile', default=0.95)
parser.add_argument('--output_path', default="compile_analysis.html")
args = parser.parse_args()
data = read_compile_log(args.file_path)
df = filter_data(data, args.quantile)
plot_compile_analysis(df, args.output_path)
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