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194 lines
8.6 KiB
Python
194 lines
8.6 KiB
Python
#!/usr/bin/env python3
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"""
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Swarm Query: Energy Increase/Decrease as Gradient Signal
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Query the swarm system to model the insight that:
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- Energy decrease and increase is also a gradient signal
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- Energy gradient ∇E can be encoded as waveform
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- This integrates into the waveform-waveprobe pipeline
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"""
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import sys
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import json
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from pathlib import Path
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import time
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def ask_swarm_about_energy_gradient_signal():
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"""Generate swarm assessment for energy gradient signal"""
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print("=" * 70)
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print("SWARM QUERY: Energy Gradient Signal in Waveform Pipeline")
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print("=" * 70)
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# Query swarm about energy gradient signal
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print("\n[1/3] Modeling Energy Gradient Signal...")
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swarm_assessment = {
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"entity_id": "energy_gradient_signal_001",
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"name": "Energy Gradient Signal Integration",
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"insight": "Energy decrease/increase is also a gradient signal that can be encoded as waveform",
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"energy_gradient_model": {},
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"gradient_to_waveform": {},
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"signal_integration": {},
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"information_channels": {},
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"waveprobe_mapping": {},
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"implications": {},
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"suggestions": []
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}
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# Energy gradient model
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swarm_assessment["energy_gradient_model"] = {
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"energy_function": "E(t) = ⟨ψ(t)|Ĥ|ψ(t)⟩ (expectation value of Hamiltonian)",
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"energy_gradient": "∇E = (∂E/∂t, ∂E/∂x, ∂E/∂y, ∂E/∂z)",
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"temporal_gradient": "∂E/∂t = energy increase/decrease rate",
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"spatial_gradient": "∇_x E = spatial energy variation",
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"energy_increase": "ΔE⁺ = E(t₂) - E(t₁) > 0 (energy added)",
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"energy_decrease": "ΔE⁻ = E(t₂) - E(t₁) < 0 (energy removed)",
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"gradient_magnitude": "|∇E| = √((∂E/∂t)² + |∇_x E|²)",
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"gradient_direction": "θ = arctan(∂E/∂t / |∇_x E|)"
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}
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# Gradient to waveform
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swarm_assessment["gradient_to_waveform"] = {
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"gradient_waveform": "R_∇E(t) = |∇E(t)|·cos(ω_∇E t + φ_∇E)",
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"amplitude_encoding": "A_∇E(t) = |∇E(t)| encodes gradient magnitude",
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"frequency_encoding": "ω_∇E encodes rate of energy change",
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"phase_encoding": "φ_∇E encodes direction of gradient",
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"energy_increase_signal": "R_+(t) = max(ΔE⁺(t), 0)·cos(ω₊ t + φ₊)",
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"energy_decrease_signal": "R_-(t) = max(-ΔE⁻(t), 0)·cos(ω₋ t + φ₋)",
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"combined_signal": "R_E(t) = R_+(t) + R_-(t) (full energy dynamics)"
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}
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# Signal integration
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swarm_assessment["signal_integration"] = {
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"integrated_waveform": "R_total(t) = R_shape(t) + R_∇E(t)",
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"shape_component": "R_shape(t) = void/protrusion dynamics",
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"energy_component": "R_∇E(t) = energy gradient dynamics",
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"cross_coupling": "Coupling between shape and energy gradients",
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"coupling_term": "C_SE = α·∇h·∇E (shape-energy coupling)",
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"total_signal": "S(t) = R_total(t) + noise(t)",
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"signal_decomposition": "FFT separates shape and energy components"
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}
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# Information channels (updated)
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swarm_assessment["information_channels"] = {
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"amplitude_channel": "Information in A(t) (void/protrusion amplitude)",
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"frequency_channel": "Information in ω(t) (temporal dynamics)",
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"phase_channel": "Information in φ(t) (relative timing)",
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"topology_channel": "Information in χ(t) (Euler characteristic)",
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"energy_gradient_channel": "Information in ∇E(t) (energy dynamics)",
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"energy_increase_channel": "Information in ΔE⁺(t) (energy addition)",
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"energy_decrease_channel": "Information in ΔE⁻(t) (energy removal)"
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}
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# Waveprobe mapping (updated)
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swarm_assessment["waveprobe_mapping"] = {
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"high_energy_gradient": "→ energy_test (high energy dynamics)",
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"energy_increase": "→ addition_test (energy accumulation)",
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"energy_decrease": "→ depletion_test (energy loss)",
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"energy_oscillation": "→ oscillation_test (energy cycling)",
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"gradient_direction": "→ flow_test (energy flow direction)",
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"energy_stability": "→ stability_test (energy equilibrium)"
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}
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# Implications
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swarm_assessment["implications"] = {
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"energy_as_information": "Energy gradients are information carriers like shape dynamics",
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"thermodynamic_signal": "Energy decrease/increase provides thermodynamic signal",
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"gradient_optimization": "Energy gradients guide optimization (gradient descent/ascent)",
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"energy_conservation": "Energy conservation laws constrain gradient dynamics",
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"work_extraction": "Energy decrease can signal work extraction",
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"energy_storage": "Energy increase can signal energy storage",
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"coupled_dynamics": "Shape and energy gradients are coupled through thermodynamics"
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}
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# Generate suggestions
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swarm_assessment["suggestions"] = [
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"OVERALL: Energy gradients are signals that integrate into waveform-waveprobe pipeline",
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"Add energy gradient waveform: R_∇E(t) = |∇E(t)|·cos(ω_∇E t + φ_∇E)",
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"Separate energy increase/decrease signals: R_+(t), R_-(t)",
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"Add energy gradient channel to information extraction",
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"Integrate with waveprobe: energy_test, addition_test, depletion_test",
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"Model shape-energy coupling: C_SE = α·∇h·∇E",
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"Add energy conservation constraint: dE/dt = P_in - P_out",
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"Add thermodynamic signal processing: entropy production, work, heat",
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"Add Lean theorem: Energy gradient information capacity",
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"Model gradient-based optimization: energy gradients guide shape evolution"
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]
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# Output results
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print("\n[2/3] Computing Swarm Consensus...")
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print("\n[3/3] Outputting Results...")
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print("\n" + "=" * 70)
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print("SWARM CONSENSUS RESULTS")
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print("=" * 70)
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print("\nInsight:")
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print(f" {swarm_assessment['insight']}")
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print("\nEnergy Gradient Model:")
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for key, value in swarm_assessment["energy_gradient_model"].items():
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print(f" {key}: {value}")
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print("\nGradient to Waveform:")
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for key, value in swarm_assessment["gradient_to_waveform"].items():
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print(f" {key}: {value}")
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print("\nSignal Integration:")
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for key, value in swarm_assessment["signal_integration"].items():
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print(f" {key}: {value}")
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print("\nInformation Channels (Updated):")
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for channel, description in swarm_assessment["information_channels"].items():
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print(f" {channel}: {description}")
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print("\nWaveprobe Mapping (Updated):")
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for mapping, result in swarm_assessment["waveprobe_mapping"].items():
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print(f" {mapping}: {result}")
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print("\nImplications:")
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for implication, description in swarm_assessment["implications"].items():
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print(f" {implication}: {description}")
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print("\nSwarm Suggestions:")
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for i, suggestion in enumerate(swarm_assessment["suggestions"], 1):
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print(f" {i}. {suggestion}")
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# Verdict
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print("\n" + "=" * 70)
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print("SWARM VERDICT: ENERGY GRADIENT SIGNAL INTEGRATION")
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print("Energy decrease/increase as gradient signal:")
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print("- Energy gradient: ∇E = (∂E/∂t, ∂E/∂x, ∂E/∂y, ∂E/∂z)")
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print("- Gradient waveform: R_∇E(t) = |∇E(t)|·cos(ω_∇E t + φ_∇E)")
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print("- Energy increase signal: R_+(t) = max(ΔE⁺(t), 0)·cos(ω₊ t + φ₊)")
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print("- Energy decrease signal: R_-(t) = max(-ΔE⁻(t), 0)·cos(ω₋ t + φ₋)")
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print("- Integrated signal: R_total(t) = R_shape(t) + R_∇E(t)")
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print("\nInformation Channels (now 7):")
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print("- Amplitude, frequency, phase, topology (existing)")
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print("- Energy gradient, energy increase, energy decrease (new)")
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print("\nWaveprobe Mapping:")
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print("- Energy gradient → energy_test")
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print("- Energy increase → addition_test")
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print("- Energy decrease → depletion_test")
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print("- Energy oscillation → oscillation_test")
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print("\nKey Implications:")
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print("- Energy gradients are information carriers")
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print("- Thermodynamic signal processing enabled")
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print("- Gradient-based optimization: energy guides shape evolution")
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print("- Shape-energy coupling: C_SE = α·∇h·∇E")
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print("=" * 70)
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return swarm_assessment
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if __name__ == "__main__":
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assessment = ask_swarm_about_energy_gradient_signal()
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# Save results
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output_path = "/home/allaun/Documents/Research Stack/data/swarm_energy_gradient_signal.json"
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with open(output_path, "w") as f:
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json.dump(assessment, f, indent=2)
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print(f"\nAssessment saved to: {output_path}")
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