adaptivepy-sampling 0.1.2

Adaptive sampling on MD trajectories via clustering and policy-driven seed selection

AdaptivePy

Adaptive sampling for molecular dynamics trajectories

Clustering-based state space partitioning and policy-driven seed selection for MD workflows.

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Overview

AdaptivePy helps you identify under-sampled regions of conformational space and select seed frames for new simulations. It loads per-trajectory feature arrays, clusters frames, applies adaptive policies, and writes reproducible metadata and optional PDB structures.

Full documentation: https://hnadeem2.github.io/AdaptivePy/

Input	Feature arrays (.npy / .pkl), optional coordinate trajectories
Clustering	KMeans, MiniBatch KMeans, regular-space
Policies	Least counts, random (extensible)
Output	Seeds, cluster assignments, model, logs, optional PDBs

Installation

pip install adaptivepy-sampling

For development:

git clone https://github.com/hnadeem2/AdaptivePy.git
cd AdaptivePy
pip install -e ".[dev,docs]"

Quick start

1. Prepare features — one file per trajectory, shape (n_frames, n_features):

   features/
   ├── traj_0.npy
   └── traj_1.pkl
   

2. Configure — edit examples/config.yaml (or create your own).

3. Run:

   adaptivepy run examples/config.yaml
   

See the Getting Started guide for a complete walkthrough.

CLI

adaptivepy run config.yaml       # run adaptive sampling
adaptivepy validate config.yaml  # validate inputs only
adaptivepy list-policies         # list available policies

Python API

from adaptivepy import run_adaptive_sampling

results = run_adaptive_sampling("config.yaml")

Documentation

Guide	Description
Getting Started	First run in minutes
Configuration	YAML options and defaults
Feature Inputs	File formats and layout
Policies	Seed selection strategies
API Reference	Module documentation

License

MIT