Dev

[ypriverol]

PR Type

enhancement

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Description

• Introduced a new Python script for benchmarking data analysis and visualization.
• Utilized pandas for data manipulation and seaborn/matplotlib for creating plots.
• Generated and saved visualizations as PNG and SVG files to illustrate average speed metrics.
• Enhanced data presentation by categorizing and ordering benchmark IDs and customizing plot aesthetics.

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Changes walkthrough 📝

	Relevant files
Enhancement	benchmark.py Add benchmark data analysis and visualization script          benchmark/benchmark.py Added a script to load and analyze benchmark data from a CSV file. Implemented data visualization using seaborn and matplotlib. Created multiple plots to display average speed by protocol, file size, and location. Saved plots as PNG and SVG files. +112/-0

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PR Reviewer Guide 🔍

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⏱️ Estimated effort to review: 2 🔵🔵⚪⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Error Handling The script lacks error handling for file operations and data processing, which could lead to unexpected crashes. Code Duplication There's repetition in plot creation and saving logic that could be refactored into a function for better maintainability. Hardcoded Values The script uses hardcoded values for benchmark order and color palette, which might make it less flexible for future changes.

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PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Score
Possible issue	Ensure consistency between the actual file name used and the file name mentioned in print statements The file name in the print statement doesn't match the actual file name used in the plt.savefig() function. This inconsistency could lead to confusion. Ensure that the file names in the code and the print statements are consistent. benchmark/benchmark.py [102-104] +# Define the file name +plot_file_name = 'average_speed_location.png' + # Save the plot -plt.savefig('average_speed_location', dpi=300, bbox_inches='tight') -print("Plot saved as 'file_size_country_plot.png'") +plt.savefig(plot_file_name, dpi=300, bbox_inches='tight') +print(f"Plot saved as '{plot_file_name}'") Apply this suggestion Suggestion importance[1-10]: 9 Why: Correcting the inconsistency between the file name used in plt.savefig() and the print statement is crucial to avoid confusion and ensure accurate feedback to users about saved files.	9
Accessibility	Use a color-blind friendly palette for better accessibility and maintainability The custom color palette is defined using specific color codes. To improve maintainability and readability, consider using a color-blind friendly palette or a built-in seaborn palette. This ensures that your visualizations are accessible to a wider audience and can be easily modified if needed. benchmark/benchmark.py [52-57] -# Define a custom color palette based on the colors in the image -custom_palette = { - 'DE': '#1f77b4', # Blue - 'GB': '#ff7f0e', # Orange - 'EBI': '#2ca02c', # Green - 'US': '#d62728', # Red -} +# Use a color-blind friendly palette +custom_palette = sns.color_palette("colorblind") +# Create a dictionary mapping locations to colors +location_colors = dict(zip(data['Location'].unique(), custom_palette)) + Apply this suggestion Suggestion importance[1-10]: 8 Why: Adopting a color-blind friendly palette improves accessibility for users with color vision deficiencies and enhances the maintainability of the code by using built-in palettes.	8
Best practice	Use a context manager for creating and managing matplotlib figures Consider using a context manager (with plt.figure(...) as fig:) when creating figures. This ensures that the figure is properly closed and resources are released, even if an exception occurs. It's a more robust approach than manually calling plt.close(). benchmark/benchmark.py [22-29] -plt.figure(figsize=(12, 6)) -ax = sns.boxplot(x="Benchmark ID", y="Average Speed (MB/s)", hue="Method", data=data) +with plt.figure(figsize=(12, 6)) as fig: + ax = sns.boxplot(x="Benchmark ID", y="Average Speed (MB/s)", hue="Method", data=data) + + # Customize the plot + plt.title("Average Speed by Protocol and File Size", fontsize=16) + plt.xlabel("File Size (Benchmark ID)", fontsize=12) + plt.ylabel("Average Speed (MB/s)", fontsize=12) + plt.legend(title="Protocol", loc='upper left') -# Customize the plot -plt.title("Average Speed by Protocol and File Size", fontsize=16) -plt.xlabel("File Size (Benchmark ID)", fontsize=12) -plt.ylabel("Average Speed (MB/s)", fontsize=12) -plt.legend(title="Protocol", loc='upper left') - Apply this suggestion Suggestion importance[1-10]: 7 Why: Using a context manager for figure creation ensures proper resource management and reduces the risk of memory leaks, especially in case of exceptions. This is a good practice for robustness and maintainability.	7
Maintainability	Use variables for file names when saving plots to improve flexibility and maintainability Instead of hardcoding the file names for saving plots, consider using variables or parameters. This makes the code more flexible and easier to maintain, especially if you need to change the file names in the future or want to generate multiple plots with different names. benchmark/benchmark.py [38-40] +# Define plot file name +plot_file_name = 'benchmark_plot.png' + # Save the plot to a file -plt.savefig('benchmark_plot.png', dpi=300, bbox_inches='tight') -print("Plot saved as 'benchmark_plot.png'") +plt.savefig(plot_file_name, dpi=300, bbox_inches='tight') +print(f"Plot saved as '{plot_file_name}'") Apply this suggestion Suggestion importance[1-10]: 6 Why: Using variables for file names enhances code flexibility and maintainability, making it easier to update file names or generate multiple plots with different names.	6

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