mercurial.spectral.dpr_pathways module

DPR pathway optimization – four distinct mechanisms (SPECTRAL A.2).

class mercurial.spectral.dpr_pathways.DPRConfig(pathway: DPRPathway, base_efficiency: float, decoherence_sensitivity: float, energy_threshold: float, coherence_requirement: float)[source]

Bases: object

Configuration for a DPR pathway.

base_efficiency: float

coherence_requirement: float

decoherence_sensitivity: float

energy_threshold: float

pathway: DPRPathway

class mercurial.spectral.dpr_pathways.DPRPathway(value)[source]

Bases: Enum

An enumeration.

CROSS_BRANCH_ISOMORPHISM = 'cross_branch_isomorphism'

FIELD_DIRECT_ACCESS = 'field_direct_access'

QUANTUM_ENTANGLEMENT = 'quantum_entanglement'

TEMPORAL_ALIGNMENT = 'temporal_alignment'

class mercurial.spectral.dpr_pathways.DPRPathwayOptimizer[source]

Bases: object

Selects and optimizes the appropriate DPR pathway based on conditions.

Methods

`evaluate_pathway`(pathway, pattern_energy, ...)	Compute effective efficiency for a given pathway.
`select_optimal_pathway`(pattern_energy, ...)	Select the pathway with highest efficiency.
`simulate_dpr_transfer`(source_pattern, ...[, ...])	Simulate information transfer via the selected DPR pathway.

evaluate_pathway(pathway: DPRPathway, pattern_energy: float, pattern_coherence: float, environmental_noise: float, branch_similarity: float = 1.0, temporal_distance: float = 0.0) → float[source]

Compute effective efficiency for a given pathway.

η_eff = η₀ * exp(-α * noise) * f(energy) * g(coherence) * h(similarity/distance)

select_optimal_pathway(pattern_energy: float, pattern_coherence: float, environmental_noise: float, branch_similarity: float = 1.0, temporal_distance: float = 0.0) → Tuple[DPRPathway, float][source]: Select the pathway with highest efficiency.

simulate_dpr_transfer(source_pattern: ndarray, pathway: DPRPathway, efficiency: float, temporal_shift: float = 0.0) → ndarray[source]: Simulate information transfer via the selected DPR pathway.