Skip to main content

LAMMPS trajectory file analysis utility

Project description

Trajan

LAMMPS trajectory analysis utility for LAMMPS, developed by Vasilii Maksimov at the Functional Glasses and Materials Modeling Laboratory (FGM²L) at the University of North Texas, under the supervision of Dr. Jincheng Du.

Trajan is a command-line utility for analyzing molecular dynamics trajectories generated by LAMMPS. It is designed with a focus on disordered systems and oxide glasses, offering specialized tools for calculating density evolution, bond angle distributions, $Q^n$ species distributions, radial distribution functions, and ring size distributions.

🔧 Features

  • Command-line interface with argparse-based subcommands
  • Memory efficient: Batch processing for neighbor list generation in large systems
  • Glass Science Toolkit:
    • Density: Time-dependent density evolution
    • Angle: Bond angle distributions with cutoff support
    • Q-unit: Network connectivity analysis ($Q^n$ distribution)
    • RDF: Partial radial distribution functions and Neutron Total Correlation Functions ($T(r)$)
    • Rings: Primitive and Smallest ring size distribution analysis
  • Experimental comparison: Includes Lorch window broadening for $T(r)$ to match experimental $Q_{max}$
  • Modular design: New analysis methods can be added as handler classes

📦 Installation

Install from PyPI

The package is published on PyPI. You can install the latest stable release with:

pip install lammps-trajan

Install directly from GitHub (bleeding-edge)

For the latest development version:

pip install git+https://github.com/superde1fin/trajan.git

Clone & install locally

For local experimentation or contributing:

git clone https://github.com/superde1fin/trajan.git
cd trajan

# Standard install
pip install .

# OR editable install (auto-reload while editing)
pip install -e .

Requires Python ≥ 3.6, numpy, and scipy.

🥪 Usage

The general syntax involves providing the trajectory file first, followed by the specific analyzer command:

trajan [file] [analyzer] [options]

1. Density Analysis

Calculates the system density over the trajectory. You must provide the atomic masses or element symbols.

Example:

trajan glass_melt.lammpstrj density Si O Na

2. Bond Angle Distribution (angle)

Calculates the distribution of bond angles for a specified triplet of atom types (Type1-Type2-Type3), where Type2 is the central atom.

Example:

trajan glass_melt.lammpstrj angle 1 2 1 -c 1.8 1.8 -b 500

3. Q-unit Distribution (qunit)

Calculates the distribution of $Q^n$ species. The input requires a list of "Former" types and "Connector" types separated by a 0.

Example:

trajan silica.lammpstrj qunit 1 0 2 -c 1.8

4. Radial Distribution Functions (rdf)

Calculates partial Radial Distribution Functions $g(r)$ and the Neutron Total Correlation Function $T(r)$.

Example:

trajan glass.lammpstrj rdf -t Si O Na -br 25.0

5. Ring Size Distribution (rings)

Analyzes the network topology by identifying rings of various sizes. It supports both Smallest and Primitive ring algorithms.

Syntax: [Base Atoms] 0 [Connectors]

Example:

trajan glass.lammpstrj rings 1 0 2 -c 1.8 -m 12 -a p

In this example, Type 1 atoms (e.g., Si) are the base nodes, and Type 2 (e.g., O) are connectors. The analysis looks for Primitive rings up to size 12.

Options:

  • types: Atom types for base and connectors separated by a 0.
  • -c / --cutoffs: Distance thresholds for bonding.
  • -cb / --connector-bonds: Max number of bonds for each connector type (default: 2).
  • -m / --max-size: Maximum ring size to detect (default: 10).
  • -a / --algorithm: Algorithm choice: p for Primitive or s for Smallest rings.
  • '-p' / --paral-mode: Parallelization choice: a for atoms or f for frame based (not yet implemented).

📚 Documentation

All subcommands support -h or --help flags for detailed usage:

trajan rings --help

🧠 Design Overview

The project follows a modular "Handler" architecture:

  • cli.py: Main entry point; handles argument parsing and dispatches to specific handlers.
  • base_handler.py: Core logic for parsing LAMMPS dump files and neighbor searching.
  • handlers/: Contains the logic for specific analyses:
    • density.py: Simple mass/volume calculations.
    • angle.py: Vector algebra for bond angles.
    • qunit.py: Network topology analysis.
    • rdfs.py: Pairwise distances and Fourier transform logic.
    • rings.py: Graph-based ring searching using BFS and shortest path algorithms.

🥪 Development

Install dev dependencies:

pip install .[dev]

🔖 License

GNU General Public License v3.0 (GPLv3). See LICENSE for details.

👤 Author

Vasilii Maksimov University of North Texas ✉️ VasiliiMaksimov@my.unt.edu

🌐 Links

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

lammps_trajan-0.0.5.tar.gz (31.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

lammps_trajan-0.0.5-py3-none-any.whl (37.3 kB view details)

Uploaded Python 3

File details

Details for the file lammps_trajan-0.0.5.tar.gz.

File metadata

  • Download URL: lammps_trajan-0.0.5.tar.gz
  • Upload date:
  • Size: 31.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.3

File hashes

Hashes for lammps_trajan-0.0.5.tar.gz
Algorithm Hash digest
SHA256 6212eef9272200ebb9a5063ac8e311158afa48fc23e3f28354a864ca495ee5c7
MD5 81103a0d97a72bc9110a2ca3bac4083a
BLAKE2b-256 f93ae7ca077e722124e4b997ed123d55455eb566b4521169b090f3139eb3492e

See more details on using hashes here.

File details

Details for the file lammps_trajan-0.0.5-py3-none-any.whl.

File metadata

  • Download URL: lammps_trajan-0.0.5-py3-none-any.whl
  • Upload date:
  • Size: 37.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.3

File hashes

Hashes for lammps_trajan-0.0.5-py3-none-any.whl
Algorithm Hash digest
SHA256 2987604cbc7acbbb7760d529c6a22250b0e686de128afc3277a2e225c4159ec4
MD5 542d5bf173942c0f2949ac2d92f49ec7
BLAKE2b-256 a875a4638ca578584e9e1bae74bbc709341c1b3ec69e97781ed48dbb9818ddef

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page