Enhanced Data Visualization for Execution Time Analysis

• Day: 2023-09-12
• Time: 18:00 to 18:35
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Data Visualization, Python, Matplotlib, Seaborn, Execution Time

Description

Session Goal

The session aimed to enhance data visualization techniques to effectively analyze and compare execution times of various methods across different data structures using Python libraries.

Key Activities

• Adapted plots to visualize median execution times using Matplotlib and Seaborn.
• Implemented Python code for plotting sparse and dense datasets, ensuring consistent styling for comparability.
• Adjusted plot sizes and evaluated execution times to decide on the use of shared or independent y-axes.
• Addressed code execution issues by reloading and preprocessing data, and resolved library import oversights.
• Requested file uploads for processing and plotting, emphasizing the need for CSV files.
• Developed logic to determine when to use shared versus independent y-axes based on data analysis.
• Updated Python code to adjust y-axis scales according to performance metrics.
• Debugged y-axis behavior in Matplotlib, focusing on the sharey parameter and ax.set_ylim() function.
• Customized y-axis behavior in plotting loops to track maximum y-values for shared y-axis methods.

Achievements

• Successfully visualized performance metrics for various graph methods on sparse and dense graphs.
• Enhanced understanding of y-axis customization and control in Matplotlib subplots.

Pending Tasks

• Further refinement of y-axis logic to improve clarity in performance comparisons.
• Integration of additional datasets once CSV files are provided.

Evidence

• source_file=2023-09-12.sessions.jsonl, line_number=1, event_count=0, session_id=ad5f42b63d96e0ea6b4b4c61143b270cc6d1281911981a316c36371c12af690d
• event_ids: []