visualization

Visualization Modules

This directory contains visualization scripts for generating plots and graphs based on performance, energy, and sustainability metrics from our study: "Python Under the Microscope: A Comparative Energy Analysis of Execution Methods."

These tools help convert raw measurement data into clear, publication-ready figures, enabling comparative analysis across Python execution methods using energy, time, and carbon emission metrics.

Directory Structure

visualization/
├── visualize_metrics.py         # Core script for generating all charts
└── README.md                    # This file

Module Description

Script	Description
visualize_metrics.py	Command-line tool for generating line charts, bar charts, and scatter plots
matplotlib, pandas	Used for plotting and CSV data handling
argparse	CLI interface to flexibly choose the desired chart

Features

• Plots grouped bar charts comparing energy, time, and carbon across execution methods
• Plots line charts of energy, time, and carbon trends per algorithm
• Plots scatter plot showing relationship between energy and execution time
• Normalizes data where necessary for fair visual comparison
• CLI options to control chart type and input files
• Produces PNG outputs ready for publication or report integration

Setup Instructions

Install required Python libraries if not already available:

pip install matplotlib pandas numpy

Chart Types & CLI Flags

Flag	Description
--evcvt	Generates grouped bar chart (Energy vs Carbon vs Time by method)
--lcpack	Line charts of energy, time, and carbon for each algorithm
--scatter	Scatter plot showing execution time vs energy consumption (--scatter <energy> <time>)
--line_compare	Overlayed line chart comparing energy and time for each method (--line_compare <e> <t>)
--etc_compare	Summary line chart of mean energy/time per method (combined)

Example Usage

1. Grouped Bar Chart

python visualize_metrics.py energy_vs_carbon_vs_time.csv --evcvt --output_file evcvt_bar.png

2. Line Charts of Energy/Time/Carbon (by algorithm)

python visualize_metrics.py --lcpack

3. Scatter Plot (Energy vs Time per Algorithm)

python visualize_metrics.py --scatter energy_com.csv time_com.csv --output_file scatter_energy_time.png

4. Comparative Line Chart: Energy & Time (per method)

python visualize_metrics.py --line_compare energy_com.csv time_com.csv --output_file comparison_linechart.png

Output Examples

• evcvt_bar.png: Grouped bar chart of execution metrics
• line_energy_per_algorithm.png: Energy usage per algorithm
• line_carbon_per_algorithm.png: Carbon emissions by algorithm
• scatter_energy_time.png: Per-algorithm energy vs time scatter plot
• method_metric_comparison_linechart.png: Combined trends across execution methods

Notes on Visualization

• Bar chart normalization: $x_{\text{norm}} = \frac{x}{\max(x)}$
• Line plots include method legends and algorithm-specific breakdowns
• Scatter plots annotate algorithms and color points by method

These visualizations were used in the Results section of our paper to compare execution models.