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_Data_Analytics (P06 Local)

Paper-Specific Data Analysis for The Grace Function

Ring 2 — Canonical Grounding

Ring 3 — Framework Connections

PURPOSE

This folder contains local analytics — data analysis and statistical methods specific to P06: The Grace Function.

For cross-paper or vault-wide analytics, see:
📍 01_CANONICAL/_GLOBAL_ANALYTICS/

STRUCTURE

📁 datasets/

Raw data and simulation outputs specific to P06

Examples:

  • [Paper-specific measurements]
  • [Simulation outputs]
  • [Experimental data]
  • [Validation datasets]

📁 methods/

Statistical methods and parameters used in P06 analysis

Files:

  • statistical_methods.md — Statistical approaches for P06 predictions
  • simulation_parameters.md — Parameters for P06 simulations

WHAT GOES HERE (LOCAL)

✅ Data unique to P06:

  • Paper-specific field measurements
  • Collapse timing data
  • Coherence calculations
  • Operator eigenvalues
  • Function simulations

✅ Analysis methods for P06:

  • Statistical validation of P06 predictions
  • Error analysis for P06 experiments
  • Simulation convergence tests for P06 models

✅ P06-specific comparisons:

  • P06 vs existing theories (single-paper comparison)

WHAT GOES IN GLOBAL

❌ Cross-paper analysis:

  • P06 vs other papers → _GLOBAL_ANALYTICS/cross_paper_analysis/

❌ Vault-wide metrics:

  • Symbol usage across all papers → _GLOBAL_ANALYTICS/vault_wide_metrics/

❌ Shared datasets:

  • Data used in multiple papers → _GLOBAL_ANALYTICS/master_datasets/

WORKFLOW

1. Generate Local Data

# Run P06-specific simulations
cd ../_Python/
python P07_simulation.py
 
# Output goes to:
# _Data_Analytics/datasets/

2. Analyze Locally

# Analyze P06 data
import pandas as pd
data = pd.read_csv('datasets/results.csv')
analyze_data(data)

3. Export to Global (if needed)

# If comparing with other papers:
cp datasets/results.csv ../../_GLOBAL_ANALYTICS/master_datasets/P07_results.csv

INTEGRATION WITH PYTHON

Python scripts in ../_Python/ should:

  • Read from: datasets/ (existing data)
  • Write to: datasets/ (new simulation results)
  • Document in: methods/ (how analysis was done)

REPRODUCIBILITY CHECKLIST

For academic credibility, this folder must contain:

  • Raw datasets (or links to external repositories)
  • Simulation parameters (all variables documented)
  • Statistical methods (clearly described)
  • Error analysis (uncertainties quantified)
  • Replication instructions (step-by-step)

This is P06’s local analytics hub. Keep it focused on this paper only.

Canonical Hub: CANONICAL_INDEX

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