pygeotypes: A Dependency-Light Shape-Catalogue Library with Class-Conditional Conformal Assignment for Physical Response Signals

Many physical systems are characterised through the shape of a response signal: the pressure transient of a fractured reservoir, the drawdown of a hydrogeology pumping test, the temperature curve of a thermal-response test. A recent methodology, GeoTypes (Kamel Targhi et al., 2026, Computational Geosciences), turns an ensemble of such signals into a catalogue of behaviour types by clustering their shapes. Two practical gaps stand between that methodology and routine use. First, the natural building blocks in mid-2026 are awkward to deploy: the maintained k-medoids implementations are either unmaintained (scikit-learn-extra), copyleft (the fast Rust kmedoids, GPL-3), or drag native dependencies that do not run in a browser sandbox (tslearn, aeon). Second, assigning a new signal to a catalogue by nearest medoid gives no guarantee and no way to say 'this shape is unlike anything in the catalogue'. This software note presents pygeotypes, a dependency-light library whose core is pure NumPy/SciPy, so it runs unchanged offline and in the browser (Pyodide), that packages the full GeoTypes pipeline (log-resampling, Bourdet derivative, band-constrained dynamic time warping, PAM k-medoids with silhouette model selection, an exact-JSON catalogue artifact) and adds a class-conditional split-conformal assignment layer. The conformal layer returns, for a new signal, a per-type p-value and the prediction set of behaviour types consistent with it at a requested confidence, and an empty set is an honest out-of-catalogue flag. On a reproducible pressure-transient benchmark the empirical coverage tracks or exceeds the nominal 1-alpha target across the operating range (0.960 at alpha=0.05, 0.938 at 0.10, 0.885 at 0.15, 0.800 at 0.20), the prediction sets stay informative (mean size 1.0 down to 0.7), and every alien shape tested is flagged out-of-catalogue at all confidence levels. The contributions of this note are (i) a reusable, browser-ready implementation of the GeoTypes methodology and (ii) the conformal assignment layer, validated empirically. The underlying shape-catalogue methodology is the contribution of Kamel Targhi et al. (2026), cited throughout. Package on PyPI (pip install pygeotypes). Source and reproducible artifacts (MIT): https://github.com/fsantibanezleal/CAOS_GeoTypes .