morphe-metrics: A Stateless Python Library for Morphogenetic Computing Evaluation

morphe-metrics is a stateless, open-source Python library that standardises the evaluation of internal computational dynamics in morphogenetic neural networks used for biological wound repair simulation, addressing the recurring problem of researchers independently reimplementing the same telemetry and measurement scripts from scratch. The library provides six architecture-agnostic modules — AffectiveMetrics, CrossDimensional, ComplexityMetrics, MaturationIndex, CrossSubstrate, and CLCorrector — that together capture the temporal evolution of signals such as synaptic protection, developmental consolidation gates, second-order plasticity history, metabolic budgets, and cross-dimensional affective interactions, all accepting raw CSV batch input without requiring knowledge of underlying SNN architecture. A companion corrector module (CLCorrector) enforces oracle-consistent class-incremental continual learning metrics, complementing the external outcome standardisation already established by cl-metrics (Swaminathan, 2026j) with a corresponding standard for internal neuromodulatory state. This work is part of the Maya-Morphe Series — morphogenetic neural networks for biological wound repair simulation — in which the Bhaya Quiescence Law applies in modified form, with dynamic-topology substrates exhibiting elevated β during active wound repair phases, a behaviour that is both biologically predicted and empirically validated within this series. Series: Part of the Maya-Morphe Series — morphogenetic neural networks for biological wound repair simulation. Bhaya Quiescence Law applies in modified form: dynamic-topology substrates show elevated β during active wound repair — biologically predicted and validated. Links: GitHub Repository (private — to request access: email research@nexuslearninglabs.in with subject Code Access Request — morphe-metrics and your research context) | Full Series Index — venky2099.github.io Nexus Learning Labs, Bengaluru · UDYAM-KR-02-0122422 · BHASKAR IN-0526-9452JSORCID: 0000-0002-3315-7907 · VAIRAGYA_DECAY_RATE = 0.002315 — an ORCID-derived provenance mark, not an experimental parameter. Each paper's disclosure block states whether it reached a result in that paper.Canary: MayaNexusVS2026NLL_Bengaluru