In depth Statistical analysis of impact of transparent data

[pacu]

Add Statistical Sampling and Complete Implementation

Summary

This PR significantly enhances the compact block analyzer with complete data fetching implementation, statistical sampling strategies, and comprehensive visualization tools.

What Changed

✅ Complete Data Fetching Implementation

• Accurate size measurement: Uses prost::Message::encode() to measure actual protobuf sizes
• Coinbase handling: Includes coinbase transactions in overhead estimates with fixed 79-byte allocation

✅ Statistical Sampling System

• 6 sampling strategies: quick, recommended, thorough, equal, proportional, weighted
• Era-aware sampling: Properly samples across all 6 Zcash network upgrade eras (Sapling through NU6)
• Reproducible results: Fixed random seeds ensure consistent sampling
• Configurable sample sizes: From 1,500 blocks (quick) to 11,000+ blocks (thorough)

✅ Complete Visualization Suite

• Python visualization script with 7 chart types:
  • Distribution histograms with KDE
  • Time series analysis with era markers
  • Era comparison (box plots, violin plots)
  • Correlation analysis
  • Cumulative distribution functions
  • Bandwidth impact projections
  • Heatmaps by era and transaction patterns
• Statistical report generation with:
  • Confidence intervals (95%)
  • Per-era statistics
  • Decision framework recommendations
  • Practical bandwidth calculations

✅ Comprehensive Documentation

• Complete README with all sampling modes
• Quick start guide (QUICKSTART.md)
• Network upgrades reference (NETWORK_UPGRADES.md)
• AI assistance disclaimer (AI_DISCLAIMER.md)
• Project context for future maintenance
• Helper scripts for setup and automation

✅ Repository Structure

• Proper separation: Rust analyzer in analyzer/, Python viz in visualization/
• Build automation scripts
• Example analysis scripts
• CI/CD ready with proper .gitignore

Protocol Analysis Improvements

Network Upgrade Coverage

• Removed pre-Sapling era (not relevant for compact blocks)
• Complete era coverage: Sapling, Blossom, Heartwood, Canopy, NU5, NU6
• Blossom-aware calculations: Properly accounts for 75s block time (vs 150s pre-Blossom)

Size Estimation Accuracy

• Protobuf-compliant calculations: Field tags, length prefixes, nested messages
• Transparent input estimation: OutPoint structure (32-byte txid + index)
• Transparent output estimation: value (uint32) + scriptPubKey (variable)
• Coinbase estimation: Fixed 79-byte overhead per coinbase input

Breaking Changes

None - this is additive only. The original range-based analysis still works:

cargo run --release -- http://127.0.0.1:9067 http://127.0.0.1:8232 range 2400000 2401000 results.csv

New Usage Examples

Statistical Sampling

# Recommended balanced analysis (~5000 blocks, 30 min)
cargo run --release -- http://127.0.0.1:9067 http://127.0.0.1:8232 recommended results.csv

# Quick analysis (~1500 blocks, 15 min)
cargo run --release -- http://127.0.0.1:9067 http://127.0.0.1:8232 quick quick.csv

Visualization

python visualization/visualize.py results.csv --output-dir ./charts

Testing

Tested with:

• Zebrad 1.x (mainnet, fully synced)
• Lightwalletd 0.4.x
• Block ranges across all eras (Sapling through NU6)
• Various sampling strategies verified for statistical validity

Dependencies Added

Rust:

• rand = "0.8" - for statistical sampling

Python:

• pandas >= 2.0.0 - data analysis
• matplotlib >= 3.7.0 - plotting
• seaborn >= 0.12.0 - statistical visualizations
• numpy >= 1.24.0 - numerical operations
• scipy >= 1.10.0 - statistics

Migration Guide

If you were using the first iteration:

1. Fetch updated proto files (now from main branch, not PR):

   ./scripts/fetch_protos.sh

2. Rebuild:

   cd analyzer
   cargo build --release

3. Setup Python environment (new):

   cd visualization
   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

4. Run with new sampling modes or continue using range mode as before

Why These Changes?

The original implementation had placeholder functions that didn't actually fetch data. This PR:

• ✅ Adds statistical rigor (can't analyze 3M blocks individually)
• ✅ Provides decision-making tools (charts, reports, recommendations)
• ✅ Documents everything for future maintainers

Related Issues

Addresses the need for:

• Accurate bandwidth impact analysis of lightwallet-protocol PR In depth Statistical analysis of impact of transparent data  #1
• Statistical sampling to make analysis tractable
• Decision framework for protocol changes

Checklist

• Code compiles and runs
• Tested with real Zebra/lightwalletd
• Documentation updated
• Examples provided
• AI assistance disclosed (see AI_DISCLAIMER.md)

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Note: This tool is for protocol analysis and design decisions. Results should be validated against actual implementation before making final protocol decisions. See AI_DISCLAIMER.md for details on AI assistance in development.