b9819977a5
Includes prior-session patches (carry forward so the app compiles): - crates/gpui/build.rs: cross-compile manifest fix - crates/gpui/src/platform.rs: PlatformWindow::activate_with_token trait method - crates/gpui/src/window.rs: Window::activate_with_token public API - crates/gpui_linux/src/linux/wayland/window.rs: WaylandWindow::activate_with_token + activate() keyboard-serial fix Plus the focus-serial fix: - serial.rs: SerialKind::KeyboardEnter - client.rs: store wl_keyboard.enter serial; latest_serial_of() Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
75 lines
3.4 KiB
Python
Executable File
75 lines
3.4 KiB
Python
Executable File
#!/usr/bin/env python3
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# Required dependencies for this script:
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#
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# pandas: For data manipulation and analysis.
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# matplotlib: For creating static, interactive, and animated visualizations in Python.
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# seaborn: For making statistical graphics in Python, based on matplotlib.
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# To install these dependencies, use the following pip command:
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# pip install pandas matplotlib seaborn
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# This script is designed to parse log files for performance measurements and create histograms of these measurements.
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# It expects log files to contain lines with measurements in the format "measurement: timeunit" where timeunit can be in milliseconds (ms) or microseconds (µs).
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# Lines that do not contain a colon ':' are skipped.
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# The script takes one or more file paths as command-line arguments, parses each log file, and then combines the data into a single DataFrame.
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# It then converts all time measurements into milliseconds, discards the original time and unit columns, and creates histograms for each unique measurement type.
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# The histograms display the distribution of times for each measurement, separated by log file, and normalized to show density rather than count.
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# To use this script, run it from the command line with the log file paths as arguments, like so:
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# python this_script.py log1.txt log2.txt ...
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# The script will then parse the provided log files and display the histograms for each type of measurement found.
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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import sys
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def parse_log_file(file_path):
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data = {'measurement': [], 'time': [], 'unit': [], 'log_file': []}
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with open(file_path, 'r') as file:
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for line in file:
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if ':' not in line:
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continue
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parts = line.strip().split(': ')
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if len(parts) != 2:
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continue
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measurement, time_with_unit = parts[0], parts[1]
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if 'ms' in time_with_unit:
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time, unit = time_with_unit[:-2], 'ms'
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elif 'µs' in time_with_unit:
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time, unit = time_with_unit[:-2], 'µs'
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else:
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# Print an error message if we can't parse the line and then continue with rest.
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print(f'Error: Invalid time unit in line "{line.strip()}". Skipping.', file=sys.stderr)
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continue
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data['measurement'].append(measurement)
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data['time'].append(float(time))
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data['unit'].append(unit)
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data['log_file'].append(file_path.split('/')[-1])
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return pd.DataFrame(data)
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def create_histograms(df, measurement):
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filtered_df = df[df['measurement'] == measurement]
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plt.figure(figsize=(12, 6))
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sns.histplot(data=filtered_df, x='time_ms', hue='log_file', element='step', stat='density', common_norm=False, palette='bright')
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plt.title(f'Histogram of {measurement}')
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plt.xlabel('Time (ms)')
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plt.ylabel('Density')
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plt.grid(True)
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plt.xlim(filtered_df['time_ms'].quantile(0.01), filtered_df['time_ms'].quantile(0.99))
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plt.show()
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file_paths = sys.argv[1:]
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dfs = [parse_log_file(path) for path in file_paths]
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combined_df = pd.concat(dfs, ignore_index=True)
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combined_df['time_ms'] = combined_df.apply(lambda row: row['time'] if row['unit'] == 'ms' else row['time'] / 1000, axis=1)
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combined_df.drop(['time', 'unit'], axis=1, inplace=True)
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measurement_types = combined_df['measurement'].unique()
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for measurement in measurement_types:
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create_histograms(combined_df, measurement)
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