#!/usr/bin/env python3
"""
Swarm Query: Energy Increase/Decrease as Gradient Signal

Query the swarm system to model the insight that:
- Energy decrease and increase is also a gradient signal
- Energy gradient ∇E can be encoded as waveform
- This integrates into the waveform-waveprobe pipeline
"""

import sys
import json
from pathlib import Path
import time


def ask_swarm_about_energy_gradient_signal():
    """Generate swarm assessment for energy gradient signal"""
    print("=" * 70)
    print("SWARM QUERY: Energy Gradient Signal in Waveform Pipeline")
    print("=" * 70)
    
    # Query swarm about energy gradient signal
    print("\n[1/3] Modeling Energy Gradient Signal...")
    
    swarm_assessment = {
        "entity_id": "energy_gradient_signal_001",
        "name": "Energy Gradient Signal Integration",
        "insight": "Energy decrease/increase is also a gradient signal that can be encoded as waveform",
        "energy_gradient_model": {},
        "gradient_to_waveform": {},
        "signal_integration": {},
        "information_channels": {},
        "waveprobe_mapping": {},
        "implications": {},
        "suggestions": []
    }
    
    # Energy gradient model
    swarm_assessment["energy_gradient_model"] = {
        "energy_function": "E(t) = ⟨ψ(t)|Ĥ|ψ(t)⟩ (expectation value of Hamiltonian)",
        "energy_gradient": "∇E = (∂E/∂t, ∂E/∂x, ∂E/∂y, ∂E/∂z)",
        "temporal_gradient": "∂E/∂t = energy increase/decrease rate",
        "spatial_gradient": "∇_x E = spatial energy variation",
        "energy_increase": "ΔE⁺ = E(t₂) - E(t₁) > 0 (energy added)",
        "energy_decrease": "ΔE⁻ = E(t₂) - E(t₁) < 0 (energy removed)",
        "gradient_magnitude": "|∇E| = √((∂E/∂t)² + |∇_x E|²)",
        "gradient_direction": "θ = arctan(∂E/∂t / |∇_x E|)"
    }
    
    # Gradient to waveform
    swarm_assessment["gradient_to_waveform"] = {
        "gradient_waveform": "R_∇E(t) = |∇E(t)|·cos(ω_∇E t + φ_∇E)",
        "amplitude_encoding": "A_∇E(t) = |∇E(t)| encodes gradient magnitude",
        "frequency_encoding": "ω_∇E encodes rate of energy change",
        "phase_encoding": "φ_∇E encodes direction of gradient",
        "energy_increase_signal": "R_+(t) = max(ΔE⁺(t), 0)·cos(ω₊ t + φ₊)",
        "energy_decrease_signal": "R_-(t) = max(-ΔE⁻(t), 0)·cos(ω₋ t + φ₋)",
        "combined_signal": "R_E(t) = R_+(t) + R_-(t) (full energy dynamics)"
    }
    
    # Signal integration
    swarm_assessment["signal_integration"] = {
        "integrated_waveform": "R_total(t) = R_shape(t) + R_∇E(t)",
        "shape_component": "R_shape(t) = void/protrusion dynamics",
        "energy_component": "R_∇E(t) = energy gradient dynamics",
        "cross_coupling": "Coupling between shape and energy gradients",
        "coupling_term": "C_SE = α·∇h·∇E (shape-energy coupling)",
        "total_signal": "S(t) = R_total(t) + noise(t)",
        "signal_decomposition": "FFT separates shape and energy components"
    }
    
    # Information channels (updated)
    swarm_assessment["information_channels"] = {
        "amplitude_channel": "Information in A(t) (void/protrusion amplitude)",
        "frequency_channel": "Information in ω(t) (temporal dynamics)",
        "phase_channel": "Information in φ(t) (relative timing)",
        "topology_channel": "Information in χ(t) (Euler characteristic)",
        "energy_gradient_channel": "Information in ∇E(t) (energy dynamics)",
        "energy_increase_channel": "Information in ΔE⁺(t) (energy addition)",
        "energy_decrease_channel": "Information in ΔE⁻(t) (energy removal)"
    }
    
    # Waveprobe mapping (updated)
    swarm_assessment["waveprobe_mapping"] = {
        "high_energy_gradient": "→ energy_test (high energy dynamics)",
        "energy_increase": "→ addition_test (energy accumulation)",
        "energy_decrease": "→ depletion_test (energy loss)",
        "energy_oscillation": "→ oscillation_test (energy cycling)",
        "gradient_direction": "→ flow_test (energy flow direction)",
        "energy_stability": "→ stability_test (energy equilibrium)"
    }
    
    # Implications
    swarm_assessment["implications"] = {
        "energy_as_information": "Energy gradients are information carriers like shape dynamics",
        "thermodynamic_signal": "Energy decrease/increase provides thermodynamic signal",
        "gradient_optimization": "Energy gradients guide optimization (gradient descent/ascent)",
        "energy_conservation": "Energy conservation laws constrain gradient dynamics",
        "work_extraction": "Energy decrease can signal work extraction",
        "energy_storage": "Energy increase can signal energy storage",
        "coupled_dynamics": "Shape and energy gradients are coupled through thermodynamics"
    }
    
    # Generate suggestions
    swarm_assessment["suggestions"] = [
        "OVERALL: Energy gradients are signals that integrate into waveform-waveprobe pipeline",
        "Add energy gradient waveform: R_∇E(t) = |∇E(t)|·cos(ω_∇E t + φ_∇E)",
        "Separate energy increase/decrease signals: R_+(t), R_-(t)",
        "Add energy gradient channel to information extraction",
        "Integrate with waveprobe: energy_test, addition_test, depletion_test",
        "Model shape-energy coupling: C_SE = α·∇h·∇E",
        "Add energy conservation constraint: dE/dt = P_in - P_out",
        "Add thermodynamic signal processing: entropy production, work, heat",
        "Add Lean theorem: Energy gradient information capacity",
        "Model gradient-based optimization: energy gradients guide shape evolution"
    ]
    
    # Output results
    print("\n[2/3] Computing Swarm Consensus...")
    
    print("\n[3/3] Outputting Results...")
    
    print("\n" + "=" * 70)
    print("SWARM CONSENSUS RESULTS")
    print("=" * 70)
    
    print("\nInsight:")
    print(f"  {swarm_assessment['insight']}")
    
    print("\nEnergy Gradient Model:")
    for key, value in swarm_assessment["energy_gradient_model"].items():
        print(f"  {key}: {value}")
    
    print("\nGradient to Waveform:")
    for key, value in swarm_assessment["gradient_to_waveform"].items():
        print(f"  {key}: {value}")
    
    print("\nSignal Integration:")
    for key, value in swarm_assessment["signal_integration"].items():
        print(f"  {key}: {value}")
    
    print("\nInformation Channels (Updated):")
    for channel, description in swarm_assessment["information_channels"].items():
        print(f"  {channel}: {description}")
    
    print("\nWaveprobe Mapping (Updated):")
    for mapping, result in swarm_assessment["waveprobe_mapping"].items():
        print(f"  {mapping}: {result}")
    
    print("\nImplications:")
    for implication, description in swarm_assessment["implications"].items():
        print(f"  {implication}: {description}")
    
    print("\nSwarm Suggestions:")
    for i, suggestion in enumerate(swarm_assessment["suggestions"], 1):
        print(f"  {i}. {suggestion}")
    
    # Verdict
    print("\n" + "=" * 70)
    print("SWARM VERDICT: ENERGY GRADIENT SIGNAL INTEGRATION")
    print("Energy decrease/increase as gradient signal:")
    print("- Energy gradient: ∇E = (∂E/∂t, ∂E/∂x, ∂E/∂y, ∂E/∂z)")
    print("- Gradient waveform: R_∇E(t) = |∇E(t)|·cos(ω_∇E t + φ_∇E)")
    print("- Energy increase signal: R_+(t) = max(ΔE⁺(t), 0)·cos(ω₊ t + φ₊)")
    print("- Energy decrease signal: R_-(t) = max(-ΔE⁻(t), 0)·cos(ω₋ t + φ₋)")
    print("- Integrated signal: R_total(t) = R_shape(t) + R_∇E(t)")
    print("\nInformation Channels (now 7):")
    print("- Amplitude, frequency, phase, topology (existing)")
    print("- Energy gradient, energy increase, energy decrease (new)")
    print("\nWaveprobe Mapping:")
    print("- Energy gradient → energy_test")
    print("- Energy increase → addition_test")
    print("- Energy decrease → depletion_test")
    print("- Energy oscillation → oscillation_test")
    print("\nKey Implications:")
    print("- Energy gradients are information carriers")
    print("- Thermodynamic signal processing enabled")
    print("- Gradient-based optimization: energy guides shape evolution")
    print("- Shape-energy coupling: C_SE = α·∇h·∇E")
    print("=" * 70)
    
    return swarm_assessment


if __name__ == "__main__":
    assessment = ask_swarm_about_energy_gradient_signal()
    
    # Save results
    output_path = "/home/allaun/Documents/Research Stack/data/swarm_energy_gradient_signal.json"
    with open(output_path, "w") as f:
        json.dump(assessment, f, indent=2)
    
    print(f"\nAssessment saved to: {output_path}")