Research-Stack/5-Applications/scripts/ask_swarm_menger_void_qr_state_machine.py

176 lines
8.4 KiB
Python

#!/usr/bin/env python3
"""
Swarm Query: Menger Void QR Code State Machine Equations
Query the swarm system for their analysis of the Menger void QR code state machine
formalism (MATH_MODEL_MAP 0.4.9).
"""
import json
import uuid
from pathlib import Path
from datetime import datetime
def generate_menger_void_qr_state_machine_request():
"""Generate swarm request for Menger void QR code state machine analysis."""
request = {
"request_id": f"swarm_menger_void_qr_state_machine_{uuid.uuid4().hex[:12]}",
"timestamp": datetime.now().isoformat(),
"query_type": "mathematical_formalism_analysis",
"scope": "menger_void_qr_code_state_machine",
"priority": "P0_CRITICAL",
"description": "Ask the swarm for their analysis of Menger void QR code state machine equations",
"context": {
"insight": "Menger sponge voids used as QR code-like state machines",
"formalism_id": "0.4.9 Menger_Void_QR_Code_State_Machine",
"core_equation": "V_void = {v_i | v_i ∈ MS_removed}",
"state_equation": "S_state = QR_encode(V_void, n_iter)",
"transition_equation": "δ_transition = QR_decode(S_state, position)",
"capacity_equation": "Φ_QR = Σ_i v_i·2^{-i}",
"time_equation": "τ_QR = log₂(n_void)·log₂(d_H)"
},
"integration_points": {
"menger_sponge": "1.1.8 Menger_Sponge_PIST_Surface - Fractal addressing (d_H≈2.7268)",
"negative_pyramid_voids": "1.1.13 Negative_Pyramid_Voids - Anti-resonance from negative heights",
"metacomputation": "1.1.12 Pyramid_Shape_Metacomputation - Shape as metacomputation",
"topology_resonance": "0.4.1 Topology_Resonance_Hierarchy - Resonance across topology levels"
},
"analysis_questions": {
"mathematical_correctness": {
"description": "Assess mathematical correctness of QR encoding/decoding on void patterns",
"questions": [
"Is the QR encoding function well-defined for arbitrary void patterns?",
"Does the QR decoding function correctly extract transition rules?",
"Is the state capacity formula Φ_QR = Σ_i v_i·2^{-i} correct for binary void encoding?",
"Does the transition time formula τ_QR = log₂(n_void)·log₂(d_H) correctly scale with void count and fractal dimension?"
]
},
"feasibility": {
"description": "Assess practical feasibility of implementing Menger void QR state machines",
"questions": [
"Can void patterns be reliably extracted from Menger sponge geometry?",
"What is the computational complexity of QR encoding/decoding on void patterns?",
"How does void pattern resolution affect state machine capacity?",
"What are the memory requirements for storing void-encoded state machines?"
]
},
"qr_code_compatibility": {
"description": "Assess compatibility with existing QR code standards and algorithms",
"questions": [
"Can standard QR code encoding algorithms be applied to void patterns?",
"What modifications are needed for QR decoding on fractal void patterns?",
"Does the fractal nature of void patterns require specialized QR algorithms?",
"Can QR error correction be applied to void-based encoding?"
]
},
"state_machine_properties": {
"description": "Assess state machine properties encoded in void patterns",
"questions": [
"What types of state machines can be encoded in void patterns?",
"How does void pattern complexity affect state machine expressiveness?",
"Can deterministic and non-deterministic state machines be encoded?",
"What is the maximum state count achievable for given iteration depth?"
]
},
"integration_benefits": {
"description": "Assess benefits of integrating with existing Menger sponge and void implementations",
"questions": [
"How does this integrate with existing negative pyramid voids (1.1.13)?",
"What are the synergies with metacomputation (1.1.12)?",
"Does resonance hierarchy (0.4.1) enhance void-based state machine performance?",
"Can this be combined with PIST manifold convergence (1.1.10, 1.1.11)?"
]
}
},
"expected_deliverables": {
"mathematical_validation": "Formal validation of QR encoding/decoding equations",
"feasibility_assessment": "Practical feasibility analysis and implementation requirements",
"qr_compatibility": "QR code compatibility assessment and algorithm modifications",
"state_machine_analysis": "State machine expressiveness and complexity analysis",
"integration_roadmap": "Integration plan with existing Menger sponge and void implementations"
},
"validation_criteria": {
"mathematical_correctness": "All equations must be mathematically sound and well-defined",
"qr_compatibility": "Must be compatible with or extend QR code standards",
"computational_feasibility": "Must be computationally tractable for practical use",
"state_machine_expressiveness": "Must support useful state machine classes",
"integration_compatibility": "Must integrate cleanly with existing implementations"
},
"swarm_response_format": {
"overall_assessment": "High-level summary of thoughts on the formalism",
"detailed_analysis": "Point-by-point analysis of each question category",
"recommendations": "Specific recommendations for implementation or refinement",
"priority_actions": "Immediate next steps if the formalism is viable",
"concerns_or_caveats": "Any concerns or limitations identified"
}
}
return request
def save_request(request, output_path):
"""Save swarm request to file."""
Path(output_path).parent.mkdir(parents=True, exist_ok=True)
with open(output_path, 'w') as f:
json.dump(request, f, indent=2)
return output_path
def main():
"""Generate and save Menger void QR code state machine swarm request."""
print("=" * 70)
print("Swarm Query: Menger Void QR Code State Machine Equations")
print("=" * 70)
# Generate request
request = generate_menger_void_qr_state_machine_request()
# Save request
output_path = "shared-data/data/swarm_requests/swarm_menger_void_qr_state_machine.json"
saved_path = save_request(request, output_path)
print(f"\nRequest generated and saved to: {saved_path}")
print(f"Request ID: {request['request_id']}")
print(f"Priority: {request['priority']}")
print(f"Formalism ID: {request['context']['formalism_id']}")
print("\nCore Insight:")
print(f" {request['context']['insight']}")
print("\nIntegration Points:")
for integration_point, description in request['integration_points'].items():
print(f" - {integration_point}: {description}")
print("\nAnalysis Categories:")
for category, info in request['analysis_questions'].items():
print(f" - {category}: {len(info['questions'])} questions")
print("\nExpected Deliverables:")
for deliverable in request['expected_deliverables'].keys():
print(f" - {deliverable}")
print("\nValidation Criteria:")
for criterion in request['validation_criteria'].keys():
print(f" - {criterion}")
print("\n✅ Swarm query generation completed successfully")
print("\nThis query asks the swarm for their thoughts on:")
print(" - Mathematical correctness of QR encoding/decoding on void patterns")
print(" - Practical feasibility of implementation")
print(" - QR code compatibility and algorithm modifications")
print(" - State machine properties and expressiveness")
print(" - Integration benefits with existing Menger sponge and void implementations")
if __name__ == "__main__":
main()