Data and code for Distribution-dependent transfer growth and matching tolerance in a random harmonic chain
Description
Companion data and reproducible code for “Distribution-dependent transfer growth and matching tolerance in a random harmonic chain” by Abhimanyu Singh. Includes the original ternary-law simulation source and 24 replicate summaries; the fresh paired binary/ternary comparison with 24 replicate files; frozen protocols; statistical analysis and numerical controls; deterministic figure-generation code; and symbolic and high-precision checks for the structured-mismatch derivation. The primary comparison frequency is 0.07; 0.05 is descriptive. The original 0.10 diagnostic discrepancy is retained. All reported chain simulations use exact matching; no chain simulation tests mismatch. This archive contains simulated data and mathematical computations, not experimental measurements. Finite numerical checks do not certify the theorem’s uniform remainder. See README.md for contents and SHA256SUMS for file integrity.
Files
Steps to reproduce
Extract 01_Data_and_Code_UPLOAD_TO_MENDELEY.zip and work from the pla_data_code directory. Read README.md and ENVIRONMENT.json. Run python experiments/r206/comparison_v1/analyze_comparison.py experiments/r206/comparison_v1 to analyze the stored paired results. Run python figures/r206/plot_coefficient_reportlab.py with ReportLab installed to reproduce the coefficient figure. In theory/mismatch32, derive.py uses SymPy and check_grid.py uses mpmath. Recorded versions are SymPy 1.14.0 and mpmath 1.3.0. Simulation sources and frozen protocols are included for full reruns; stored replicate summaries permit analysis without repeating long simulations.