maya-metrics: A Stateless Python Library for Affective Neuromorphic Evaluation in Continual Learning SNNs

maya-metrics is the companion evaluation library to the Maya Research Series — the first open-source Python library for measuring internal affective and neuromodulatory dynamics in spiking neural networks (SNNs) engaged in class-incremental continual learning (CIL). It operationalises two confirmed series constants (Bhaya Quiescence Law; Buddhi S-curve Determinism) as programmatic unit tests, and provides stateless pure-function modules for AffectiveMetrics, CrossDimensional, ComplexityMetrics, MaturationIndex, CrossSubstrate, and CLCorrector evaluation. Validated against 11 published preprints across two substrates (SNN + LLM) and one embodied PiCar-X deployment. 16/16 unit tests pass. KARMA_DECAY_RATE = 0.002315.