AdaptivePy

AdaptivePy performs adaptive sampling on molecular dynamics trajectories using clustering-based state space partitioning and policy-driven seed selection.

Install

pip install adaptivepy-sampling

For Torch-backed policies:

pip install adaptivepy-sampling[torch]

Quick links

• Getting Started — run your first analysis in minutes
• Configuration — YAML options and defaults
• Feature Inputs — supported file formats and layout
• CLI — command-line usage
• Python API — programmatic access
• Policies — seed selection strategies
• API Reference — full module documentation

What it does

1. Load per-trajectory feature arrays (.npy or .pkl)
2. Cluster frames in feature space
3. Apply one or more adaptive policies
4. Select seed frames for new simulations
5. Write metadata, assignments, policy-specific scores, and optional PDB structures

Example

adaptivepy run examples/config.yaml

See the Getting Started guide for a complete walkthrough.