sawnergy 1.0.9

Toolkit for transforming molecular dynamics (MD) trajectories into rich graph representations

SAWNERGY

A toolkit for transforming molecular dynamics (MD) trajectories into rich graph representations, sampling random and self-avoiding walks, learning node embeddings, and visualizing residue interaction networks (RINs). SAWNERGY keeps the full workflow — from cpptraj output to skip-gram embeddings (node2vec approach) — inside Python, backed by efficient Zarr-based archives and optional GPU acceleration.

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Installation

pip install sawnergy

Optional: For GPU training, install PyTorch separately (e.g., pip install torch). Note: RIN building requires cpptraj (AmberTools). Ensure it is discoverable via $PATH or the CPPTRAJ environment variable. Probably the easiest solution: install AmberTools via Conda, activate the environment, and SAWNERGY will find the cpptraj executable on its own, so just run your code and don't worry about it.

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UPDATES:

v1.0.9 — What’s new:

v1.0.9 — What’s new:

• SGNS_Torch is no longer deprecated.
  • The root cause was weight initialization; it’s fixed.
• SG_Torch and SG_PureML no longer use biases.
  • Affine/Linear layers no longer translate embeddings away from the origin.
• Warm starts for frame embeddings.
  • Each frame initializes from the preceding frame’s representation. This speeds convergence and keeps the basis approximately consistent.
• Alignment function for comparing embeddings from different latent spaces.
  • Based on the Orthogonal Procrustes solution: finds the best-fit orthogonal map between two embedding sets. Orthogonality preserves angles and relative distances, enabling direct comparison across bases.

v1.0.8 — What’s new:

• Temporary deprecation of SGNS_Torch
  • sawnergy.embedding.SGNS_Torch currently produces noisy embeddings in practice. The issue likely stems from weight initialization, although the root cause has not yet been conclusively determined.
  • Action: The class and its __init__ docstring now carry a deprecation notice. Constructing the class emits a DeprecationWarning and logs a warning.
  • Use instead: Prefer SG_Torch (plain Skip-Gram with full softmax) or the PureML backends SGNS_PureML / SG_PureML.
  • Compatibility: No breaking API changes; imports remain stable. PureML backends are unaffected.
• Embedding visualizer update
  • Now you can L2 normalize your embeddings before display.
• Small improvements in the embedding module
  • Improved API with a lot of good defaults in place to ease usage out of the box.
  • Small internal model tweaks.

v1.0.7 — What’s new:

• Added plain Skip-Gram model
  • Now, the user can choose if they want to apply the negative sampling technique (two binary classifiers) or train a single classifier over the vocabulary (full softmax). For more detail, see: node2vec, word2vec, and negative_sampling.
• Set a harsher default for low interaction energies pruning during RIN construction
  • Now we zero out 85% of the lowest interaction energies as opposed to the past 30% default, leading to more meaningful embeddings.
• BUG FIX: Visualizer
  • Previously, the visualizer would silently draw edges of 0 magnitude, meaning they were actually being drawn but were invisible due to full transparency and 0 width. As a result, the displayed image/animation would be very laggy. Now, this was fixed, and given the higher pruning default, the displayed interaction networks are clean and smooth under rotations, dragging, etc.
• New Embedding Visualizer (3D)
  • New lightweight viewer for per-frame embeddings that projects embeddings with PCA to a 3D scatter. Supports the same node coloring semantics, optional node labels, and the same antialiasing/depthshade controls. Works in headless setups using the same backend guard and uses a blocking show=True for scripts.

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Why SAWNERGY?

• Bridge simulations and graph ML: Convert raw MD trajectories into residue interaction networks ready for graph algorithms and downstream machine learning tasks.
• Deterministic, shareable artifacts: Every stage produces compressed Zarr archives that contain both data and metadata so runs can be reproduced, shared, or inspected later.
• High-performance data handling: Heavy arrays live in shared memory during walk sampling to allow parallel processing without serialization overhead; archives are written in chunked, compressed form for fast read/write.
• Flexible objectives & backends: Train Skip-Gram with negative sampling (objective="sgns") or plain Skip-Gram (objective="sg"), using either PureML (default) or PyTorch.
• Visualization out of the box: Plot and animate residue networks without leaving Python, using the data produced by RINBuilder.

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Pipeline at a Glance

MD Trajectory + Topology
          │
          ▼
      RINBuilder 
          │   →  RIN archive (.zip/.zarr) → Visualizer (display/animate RINs)
          ▼
        Walker
          │   →  Walks archive (RW/SAW per frame)
          ▼
       Embedder
          │   →  Embedding archive (frame × vocab × dim)
          ▼
     Downstream ML

Each stage consumes the archive produced by the previous one. Metadata embedded in the archives ensures frame order, node indexing, and RNG seeds stay consistent across the toolchain.

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Small visual example (constructed fully from trajectory and topology files)

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Core Components

sawnergy.rin.RINBuilder

• Wraps the AmberTools cpptraj executable to:
  • compute per-frame electrostatic (EMAP) and van der Waals (VMAP) energy matrices at the atomic level,
  • project atom–atom interactions to residue–residue interactions using compositional masks,
  • prune, symmetrize, remove self-interactions, and L1-normalize the matrices,
  • compute per-residue centers of mass (COM) over the same frames.
• Outputs a compressed Zarr archive with transition matrices, optional pre-normalized energies, COM snapshots, and rich metadata (frame range, pruning quantile, molecule ID, etc.).
• Supports parallel cpptraj execution, batch processing, and keeps temporary stores tidy via ArrayStorage.compress_and_cleanup.

sawnergy.visual.Visualizer

• Opens RIN archives, resolves dataset names from attributes, and renders nodes plus attractive/repulsive edge bundles in 3D using Matplotlib.
• Allows both static frame visualization and trajectory animation.
• Handles backend selection (Agg fallback in headless environments) and offers convenient color palettes via visualizer_util.

sawnergy.walks.Walker

• Attaches to the RIN archive and loads attractive/repulsive transition matrices into shared memory using walker_util.SharedNDArray so multiple processes can sample without copying.
• Samples random walks (RW) and self-avoiding walks (SAW), optionally time-aware, that is, walks move through transition matrices with transition probabilities proportional to cosine similarity between the current and next frame. Randomness is controlled by the seed passed to the class constructor.
• Persists walks as (time, walk_id, length+1) tensors (1-based node indices) alongside metadata such as walk_length, walks_per_node, and RNG scheme.

sawnergy.embedding.Embedder

• Consumes walk archives, generates skip-gram pairs, and normalizes them to 0-based indices.
• Selects skip-gram (SG / SGNS) backends dynamically via model_base="pureml"|"torch" with per-backend overrides supplied through model_kwargs.
• Handles deterministic per-frame seeding and returns the requested embedding kind ("in", "out", or "avg") from embed_frame and embed_all.
• Persists per-frame matrices with rich provenance (walk metadata, objective, hyperparameters, RNG seeds) when embed_all targets an output archive.

Supporting Utilities

• sawnergy.sawnergy_util
  • ArrayStorage: thin wrapper over Zarr v3 with helpers for chunk management, attribute coercion to JSON, and transparent compression to .zip archives.
  • Parallel helpers (elementwise_processor, compose_steps, etc.), temporary file management, logging, and runtime inspection utilities.
• sawnergy.logging_util.configure_logging: configure rotating file/console logging consistently across scripts.

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Archive Layouts

Archive	Key datasets (name → shape, dtype)	Important attributes (root attrs)
RIN	ATTRACTIVE_transitions → (T, N, N), float32 • REPULSIVE_transitions → (T, N, N), float32 (optional) • ATTRACTIVE_energies → (T, N, N), float32 (optional) • REPULSIVE_energies → (T, N, N), float32 (optional) • COM → (T, N, 3), float32	time_created (ISO) • com_name = "COM" • molecule_of_interest (int) • frame_range = (start, end) inclusive • frame_batch_size (int) • prune_low_energies_frac (float in [0,1]) • attractive_transitions_name / repulsive_transitions_name (dataset names or None) • attractive_energies_name / repulsive_energies_name (dataset names or None)
Walks	ATTRACTIVE_RWs → (T, N·num_RWs, L+1), int32 (optional) • REPULSIVE_RWs → (T, N·num_RWs, L+1), int32 (optional) • ATTRACTIVE_SAWs → (T, N·num_SAWs, L+1), int32 (optional) • REPULSIVE_SAWs → (T, N·num_SAWs, L+1), int32 (optional) Note: node IDs are 1-based.	time_created (ISO) • seed (int) • rng_scheme = "SeedSequence.spawn_per_batch_v1" • num_workers (int) • in_parallel (bool) • batch_size_nodes (int) • num_RWs / num_SAWs (ints) • node_count (N) • time_stamp_count (T) • walk_length (L) • walks_per_node (int) • attractive_RWs_name / repulsive_RWs_name / attractive_SAWs_name / repulsive_SAWs_name (dataset names or None) • walks_layout = "time_leading_3d"
Embeddings	FRAME_EMBEDDINGS → (T, N, D), float32	created_at (ISO) • frame_embeddings_name = "FRAME_EMBEDDINGS" • time_stamp_count = T • node_count = N • embedding_dim = D • model_base = "torch" or "pureml" • embedding_kind = `"in"

Notes

• In RIN, T equals the number of frame batches written (i.e., frame_range swept in steps of frame_batch_size). ATTRACTIVE/REPULSIVE_energies are pre-normalized absolute energies (written only when keep_prenormalized_energies=True), whereas ATTRACTIVE/REPULSIVE_transitions are the row-wise L1-normalized versions used for sampling.
• All archives are Zarr v3 groups. ArrayStorage also maintains per-block metadata in root attrs: array_chunk_size_in_block, array_shape_in_block, and array_dtype_in_block (dicts keyed by dataset name). You’ll see these in every archive.
• In Embeddings, alpha and num_negative_samples apply to SGNS only and are ignored for objective="sg".

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Quick Start

from pathlib import Path
from sawnergy.logging_util import configure_logging
from sawnergy.rin import RINBuilder
from sawnergy.walks import Walker
from sawnergy.embedding import Embedder

import logging
configure_logging("./logs", file_level=logging.WARNING, console_level=logging.INFO)

# 1. Build a Residue Interaction Network archive
rin_path = Path("./RIN_demo.zip")
rin_builder = RINBuilder()
rin_builder.build_rin(
    topology_file="system.prmtop",
    trajectory_file="trajectory.nc",
    molecule_of_interest=1,
    frame_range=(1, 100),
    frame_batch_size=10,
    prune_low_energies_frac=0.85,
    output_path=rin_path,
    include_attractive=True,
    include_repulsive=False
)

# 2. Sample walks from the RIN
walker = Walker(rin_path, seed=123)
walks_path = Path("./WALKS_demo.zip")
walker.sample_walks(
    walk_length=16,
    walks_per_node=100,
    saw_frac=0.25,
    include_attractive=True,
    include_repulsive=False,
    time_aware=False,
    output_path=walks_path,
    in_parallel=False
)
walker.close()

# 3. Train embeddings per frame (PyTorch backend)
import torch

embedder = Embedder(walks_path, seed=999)
embeddings_path = embedder.embed_all(
    RIN_type="attr",
    using="merged",
    num_epochs=10,
    negative_sampling=False,
    window_size=4,
    device="cuda" if torch.cuda.is_available() else "cpu",
    model_base="torch",
    output_path="./EMBEDDINGS_demo.zip"
)
print("Embeddings written to", embeddings_path)

For the PureML backend, set model_base="pureml" and pass the optimizer / scheduler classes inside model_kwargs.

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Visualization

from sawnergy.visual import Visualizer

v = Visualizer("./RIN_demo.zip")
v.build_frame(1,
    node_colors="rainbow",
    displayed_nodes="ALL",
    displayed_pairwise_attraction_for_nodes="DISPLAYED_NODES",
    displayed_pairwise_repulsion_for_nodes="DISPLAYED_NODES",
    show_node_labels=True,
    show=True
)

Visualizer lazily loads datasets and works even in headless environments (falls back to the Agg backend).

from sawnergy.embedding import Visualizer

viz = Visualizer("./EMBEDDINGS_demo.zip", normalize_rows=True)
viz.build_frame(1, show=True)

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Advanced Notes

• Time-aware walks: Set time_aware=True, provide stickiness and on_no_options when calling Walker.sample_walks.
• Shared memory lifecycle: Call Walker.close() (or use a context manager) to release shared-memory segments.
• PureML vs PyTorch: Select the backend at call time with model_base="pureml"|"torch" (defaults to "pureml") and pass optimizer / scheduler overrides through model_kwargs.
• ArrayStorage utilities: Use ArrayStorage directly to peek into archives, append arrays, or manage metadata.

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Project Structure

├── sawnergy/
│   ├── rin/           # RINBuilder and cpptraj integration helpers
│   ├── walks/         # Walker class and shared-memory utilities
│   ├── embedding/     # Embedder + SG/SGNS backends (PureML / PyTorch)
│   ├── visual/        # Visualizer and palette utilities
│   │
│   ├── logging_util.py
│   └── sawnergy_util.py
│
└── README.md

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Acknowledgments

SAWNERGY builds on the AmberTools cpptraj ecosystem, NumPy, Matplotlib, Zarr, and PyTorch (for GPU acceleration if necessary; PureML is available by default). Big thanks to the upstream communities whose work makes this toolkit possible.