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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.