This document provides an overview of the source code and data files included in the
"Supplementary_Code" folder. These resources are provided to support the reproducibility
of the methods, data processing pipelines, and analytical results reported in the
manuscript.

The code is organized into six functional modules corresponding to the analysis
workflow described in the study. Detailed instructions for each script can be found
in the individual readme files.

1. Image Color Extraction (Quantification / 24 Colors)
color_extraction.py
  - Purpose: Implements the initial pixel extraction from raw book cover images.
  - Method: Maps raw RGB values to standard CSS4 colors using the CIELAB Delta-E
    2000 perceptual distance formula to ensure visual accuracy. It generates the
    initial color usage ratios for each image.

2. Co-occurrence Network Construction
cooccurrence_network_build.py
  - Purpose: Constructs the weighted color co-occurrence networks.
  - Method: Aggregates the initial color data into 24 representative color clusters.
    It builds weighted edge lists based on the frequency of color pairs appearing
    together within the same image boundaries.


3. Network Metrics Computation
network_metrics_compute.py
  - Purpose: Quantifies the topological properties of the constructed networks.
  - Method: Calculates both node-level features (e.g., Weighted Degree, Betweenness,
    Clustering Coefficient) and global network properties (e.g., Density, Global
    Efficiency) to characterize structural complexity.


4. Community & Centrality Analysis
community_centrality_analysis.py
  - Purpose: Analyzes the modular structure and spatial layout of the networks.
  - Method: Applies the Louvain algorithm for community detection and the
    Fruchterman-Reingold algorithm for force-directed layout calculation. Outputs
    coordinates and community assignments for visualization.


5. Statistical Summary & Visualization
stats_and_figures.py
  - Purpose: Generates the statistical figures and plots used in the manuscript.
  - Method: Visualizes the distribution of color usage across categories and
    performs correlation analysis between different network topological metrics.


6. Generative Simulation Model
simulate_color_pairs.py
  - Purpose: Reproduces the theoretical model validation experiments.
  - Method: Implements a partition-based Gibbs selection model. It simulates
    color-pair usage patterns based on a compatibility matrix (Gamma) and a
    temperature parameter (tau) to verify the observed rank-probability distributions.


Dependencies
The code is written in Python 3. To run these scripts, the following Python
libraries are required:
- numpy, pandas (Data manipulation)
- networkx (Network analysis)
- matplotlib (Visualization)
- colormath (Perceptual color mapping)
- scikit-learn (Data utilities)