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: []