"""DPR pathway optimization – four distinct mechanisms (SPECTRAL A.2)."""
from dataclasses import dataclass
from enum import Enum
from typing import Optional, Tuple
import numpy as np
[docs]
class DPRPathway(Enum):
QUANTUM_ENTANGLEMENT = "quantum_entanglement"
FIELD_DIRECT_ACCESS = "field_direct_access"
CROSS_BRANCH_ISOMORPHISM = "cross_branch_isomorphism"
TEMPORAL_ALIGNMENT = "temporal_alignment"
[docs]
@dataclass
class DPRConfig:
"""Configuration for a DPR pathway."""
pathway: DPRPathway
base_efficiency: float # η₀ (0-1)
decoherence_sensitivity: float # how fast signal degrades with noise
energy_threshold: float # minimum pattern energy [J]
coherence_requirement: float # minimum pattern coherence (0-1)
# Predefined configurations based on theoretical limits
DPR_CONFIGS = {
DPRPathway.QUANTUM_ENTANGLEMENT: DPRConfig(
pathway=DPRPathway.QUANTUM_ENTANGLEMENT,
base_efficiency=0.95,
decoherence_sensitivity=2.0,
energy_threshold=1e-24,
coherence_requirement=0.9,
),
DPRPathway.FIELD_DIRECT_ACCESS: DPRConfig(
pathway=DPRPathway.FIELD_DIRECT_ACCESS,
base_efficiency=0.85,
decoherence_sensitivity=1.0,
energy_threshold=1e-23,
coherence_requirement=0.8,
),
DPRPathway.CROSS_BRANCH_ISOMORPHISM: DPRConfig(
pathway=DPRPathway.CROSS_BRANCH_ISOMORPHISM,
base_efficiency=0.70,
decoherence_sensitivity=0.5,
energy_threshold=1e-22,
coherence_requirement=0.7,
),
DPRPathway.TEMPORAL_ALIGNMENT: DPRConfig(
pathway=DPRPathway.TEMPORAL_ALIGNMENT,
base_efficiency=0.60,
decoherence_sensitivity=1.5,
energy_threshold=1e-23,
coherence_requirement=0.85,
),
}
[docs]
class DPRPathwayOptimizer:
"""
Selects and optimizes the appropriate DPR pathway based on conditions.
"""
def __init__(self):
self.configs = DPR_CONFIGS
self.active_pathway: Optional[DPRPathway] = None
self.current_efficiency = 0.0
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def evaluate_pathway(
self,
pathway: DPRPathway,
pattern_energy: float,
pattern_coherence: float,
environmental_noise: float,
branch_similarity: float = 1.0,
temporal_distance: float = 0.0,
) -> float:
"""
Compute effective efficiency for a given pathway.
η_eff = η₀ * exp(-α * noise) * f(energy) * g(coherence) * h(similarity/distance)
"""
cfg = self.configs[pathway]
# Energy factor: sigmoid threshold
if pattern_energy < cfg.energy_threshold:
energy_factor = np.exp(-(cfg.energy_threshold - pattern_energy) / cfg.energy_threshold)
else:
energy_factor = 1.0
# Coherence factor
if pattern_coherence < cfg.coherence_requirement:
coherence_factor = pattern_coherence / cfg.coherence_requirement
else:
coherence_factor = 1.0
# Noise decoherence
noise_factor = np.exp(-cfg.decoherence_sensitivity * environmental_noise)
# Pathway‑specific factors
if pathway == DPRPathway.CROSS_BRANCH_ISOMORPHISM:
similarity_factor = branch_similarity
elif pathway == DPRPathway.TEMPORAL_ALIGNMENT:
# Temporal distance reduces efficiency
similarity_factor = np.exp(-temporal_distance / 10.0) # 10 sec characteristic
else:
similarity_factor = 1.0
return (
cfg.base_efficiency
* energy_factor
* coherence_factor
* noise_factor
* similarity_factor
)
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def select_optimal_pathway(
self,
pattern_energy: float,
pattern_coherence: float,
environmental_noise: float,
branch_similarity: float = 1.0,
temporal_distance: float = 0.0,
) -> Tuple[DPRPathway, float]:
"""
Select the pathway with highest efficiency.
"""
best_path = None
best_eff = -1.0
for pathway in DPRPathway:
eff = self.evaluate_pathway(
pathway,
pattern_energy,
pattern_coherence,
environmental_noise,
branch_similarity,
temporal_distance,
)
if eff > best_eff:
best_eff = eff
best_path = pathway
self.active_pathway = best_path
self.current_efficiency = best_eff
return best_path, best_eff
[docs]
def simulate_dpr_transfer(
self,
source_pattern: np.ndarray,
pathway: DPRPathway,
efficiency: float,
temporal_shift: float = 0.0,
) -> np.ndarray:
"""
Simulate information transfer via the selected DPR pathway.
"""
# Base transfer is identity (isomorphic mapping)
transferred = source_pattern.copy()
# Apply pathway‑specific distortions
if pathway == DPRPathway.QUANTUM_ENTANGLEMENT:
# Non‑local: no distance decay, but some phase scrambling
phase_noise = np.random.normal(0, 0.1, size=transferred.shape)
transferred = transferred * (1 + 0.05 * phase_noise)
elif pathway == DPRPathway.FIELD_DIRECT_ACCESS:
# Direct field readout: minimal distortion
pass
elif pathway == DPRPathway.CROSS_BRANCH_ISOMORPHISM:
# Requires structural mapping; add small random perturbations
noise = np.random.normal(0, 0.02, size=transferred.shape)
transferred = transferred + noise
elif pathway == DPRPathway.TEMPORAL_ALIGNMENT:
# Temporal shift: pattern is delayed or advanced
if temporal_shift != 0:
# Simplified: add a phase shift in frequency domain
freq = np.fft.fftfreq(len(transferred))
phase_shift = np.exp(2j * np.pi * freq * temporal_shift)
transferred_f = np.fft.fft(transferred)
transferred = np.fft.ifft(transferred_f * phase_shift).real
# Apply efficiency scaling
return transferred * efficiency