AdaptivePy 1.0.0 — First stable release

AdaptivePy 1.0.0

The first stable release of AdaptivePy — adaptive sampling for molecular dynamics trajectories via clustering-based state-space partitioning and policy-driven seed selection. This marks a commitment to a stable public API under semantic versioning.

Highlights

• Seven built-in policies
  • least_counts — target under-sampled clusters
  • random — baseline random cluster sampling
  • fast — feature-directed exploration/exploitation (Zimmerman & Bowman 2015)
  • ma_reap — multi-agent REAP with learned CV weights and agent stakes (Kleiman & Shukla 2022)
  • knn_as — k-nearest-neighbors adaptive sampling over cluster representatives (Rovers et al. 2025)
  • maxent_vampnet — entropy-based frame selection via VAMPNet, no clustering required (Kleiman & Shukla 2023)
  • ts_dar — OOD-score frame selection via TS-DAR hyperspherical embeddings, no clustering required (Liu et al. 2025)
• Metapolicy ensembles — combine multiple policies with majority_polling or per-policy allocation.
• Flexible clustering — KMeans, MiniBatch KMeans, and regular-space, with clustering automatically skipped for frame-level-only runs.
• Reproducible outputs — seeds, cluster assignments, fitted model, per-policy score sidecars, logs, a config snapshot, and optional PDB export.
• CLI + Python API — adaptivepy run/validate/list-policies plus run_adaptive_sampling(...).
• Full documentation — https://shuklagroup.github.io/AdaptivePy/

Installation

pip install adaptivepy-sampling