lammpskit 1.2.1

A Python toolkit for advanced LAMMPS data processing and scientific simulation analysis.

LAMMPSKit

LAMMPSKit is a comprehensive Python toolkit for molecular dynamics (MD) simulation analysis with LAMMPS, specialized for electrochemical memory device characterization and filament analysis. It provides advanced post-processing capabilities for HfTaO-based resistive random access memory (ReRAM) devices, including filament formation tracking, charge redistribution analysis, and temporal evolution characterization.

🚀 Features

• 🔬 Specialized Analysis: Advanced filament evolution tracking for electrochemical memory devices
• 📊 Comprehensive Visualization: Publication-ready scientific plotting with customizable styling
• ⚡ High Performance: Optimized for large trajectory analysis (>1M atoms, >10K frames)
• 🧪 OVITO Integration: Advanced cluster analysis and structural characterization
• 📐 Robust Data Processing: Memory-efficient streaming for multi-gigabyte datasets
• 🎯 Modular Design: Separate I/O, plotting, and analysis components for flexibility
• 🔧 Configuration Management: Centralized validation and parameter management
• ✅ Complete Testing: 96% code coverage with visual regression testing

📦 Installation

From PyPI (Recommended)

pip install lammpskit

For Development

git clone https://github.com/simantalahkar/lammpskit.git
cd lammpskit

# Primary method (modern pip with optional dependencies)
pip install -e .[dev]

# Alternative method (if above fails)
pip install -e . && pip install -r requirements-dev.txt

📋 Requirements

Runtime Dependencies:

• Python 3.12+
• numpy ≥2.3.1
• matplotlib ≥3.10.3
• ovito ≥3.12.4

Development Dependencies:

• All runtime requirements
• Testing: pytest, pytest-cov, pytest-mpl
• Documentation: sphinx, sphinx-rtd-theme
• Code Quality: black, flake8, isort, mypy
• Build Tools: build, twine, setuptools

Function Summary Table

Core I/O Functions (lammpskit.io.lammps_readers)

Function	Purpose
read_structure_info	Parse trajectory metadata (timestep, atom count, box dims)
read_coordinates	Load coordinates and cell info from trajectory files
read_displacement_data	Parse processed displacement data with robust error handling

Analysis Functions (lammpskit.ecellmodel.filament_layer_analysis)

Function	Purpose
analyze_clusters	OVITO-based cluster analysis and filament property extraction
track_filament_evolution	Track filament connectivity, gap, and size over time
plot_atomic_distribution	Analyze and plot atomic distributions by element type
plot_atomic_charge_distribution	Analyze and plot atomic charge distributions
plot_displacement_comparison	Compare displacement data across multiple cases
plot_displacement_timeseries	Plot time series of displacement data with customization

Plotting Utilities (lammpskit.plotting)

Function	Purpose
plot_multiple_cases	General scientific plotting utility with flexible styling
timeseries_plots.*	Centralized timeseries plotting with font and style control

Data Processing (lammpskit.ecellmodel.data_processing)

Function	Purpose
Various validation functions	Centralized input validation and error handling

Example Workflows (usage/ecellmodel/)

Script	Purpose
run_analysis.py	Complete workflow demonstrating 4 main analysis types

Quick Start

Using the Example Workflow

LAMMPSKit v1.2.1 includes a comprehensive example workflow that demonstrates all major package capabilities:

# Clone the repository and navigate to the usage example
git clone https://github.com/simantalahkar/lammpskit.git
cd lammpskit/usage/ecellmodel

# Run the complete analysis workflow
python run_analysis.py

This workflow demonstrates four main analysis types:

1. Filament Evolution Tracking - Monitor filament connectivity and structural changes over time
2. Displacement Analysis - Compare atomic displacements across different species (Hf, O, Ta)
3. Charge Distribution Analysis - Analyze local charge distributions in electrochemical systems
4. Atomic Distribution Analysis - Study atomic distributions under different applied voltages

Generated plots and analysis results are saved to usage/ecellmodel/output/.

Package Structure

LAMMPSKit v1.2.1 features a modular architecture:

lammpskit/
├── io/                    # Data reading and I/O operations
├── plotting/              # Visualization utilities and timeseries plots
├── ecellmodel/           # Electrochemical analysis functions
└── config.py             # Centralized configuration management

Docker Image

An official Docker image for lammpskit is available on Docker Hub. Using the Docker container provides a portable, reproducible environment with all dependencies pre-installed for running and testing lammpskit anywhere Docker is supported.

How to Use

1. Install Docker on your system.
   See Get Docker for instructions.

2. Pull the latest image:

   docker pull simantalahkar/lammpskit:latest
   

   Or pull a specific version:

   docker pull simantalahkar/lammpskit:1.2.1
   

3. Run the container with your local data mounted as a volume:

   docker run -it -v /path/to/your/data:/data simantalahkar/lammpskit:latest
   

   This starts a bash shell in the container. Your local data is accessible at /data.

4. Use the installed Python package:

   python
   >>> import lammpskit
   # ...your analysis code...
   

5. Copy custom scripts or files into the container (from another terminal):

   docker cp /path/to/local/script.py <container_id>:/home/lammpsuser/
   

   You can also install additional Python packages inside the container:

   pip install <package-name>
   

6. Exit the container after analysis:

   exit
   

   The container will remain on your system for future use.
   To re-enter the container:

   docker start <container_id>
   docker exec -it <container_id> bash
   

   To delete the container completely:

   docker rm <container_id>
   

Installation (PyPI)

For end users (runtime):

pip install lammpskit

For development and testing:

# Method 1: Using optional dependencies (recommended)
pip install -e .[dev]

# Method 2: Manual installation (fallback)
pip install -e .
pip install -r requirements-dev.txt

Development and Testing Environment

All runtime dependencies are listed in requirements.txt. Development and test dependencies are available in multiple formats:

• [project.optional-dependencies] in pyproject.toml (modern standard)
• requirements-dev.txt (traditional method)
• extras_require in setup.py (legacy compatibility)

To set up a development environment and run tests locally:

# Recommended approach with fallback
pip install -e .[dev] || (pip install -e . && pip install -r requirements-dev.txt)
pytest

Tests are not shipped with the PyPI package, but are available in the source repository for development and validation.

Test Coverage & Visual Regression Testing

LAMMPSKit v1.2.1 includes extensive test coverage with over 270 test functions and 90+ baseline images for visual regression testing. The test suite uses a centralized baseline approach for consistent and maintainable visual regression testing.

Test Organization

• tests/test_io.py - I/O function validation
• tests/test_plotting.py - General plotting utilities
• tests/test_timeseries_plots.py - Timeseries plotting functions
• tests/test_centralized_font_control.py - Font and styling consistency
• tests/test_ecellmodel/ - Analysis function validation (subdirectory)
• tests/test_config.py - Configuration management
• tests/baseline/ - Centralized baseline images for all visual tests

Running Tests Locally

# Install with development dependencies
pip install .[dev]

# Run all tests
pytest

# Run with visual regression testing
pytest --mpl

# Generate new baseline images (when plots change intentionally)
pytest --mpl-generate-path=tests/baseline tests/

# Run tests with coverage
pytest --cov=lammpskit --cov-report=html

Visual Regression Testing Architecture

Centralized Baseline Directory Structure:

tests/
├── baseline/                    # All baseline images (centralized)
├── test_*.py                   # Root level tests → "baseline"
├── test_ecellmodel/            # Subdirectory tests → "../baseline"
│   └── test_*.py              # Use relative paths to centralized location
└── conftest.py                # Shared pytest configuration

Key Benefits:

• Cross-platform compatibility: Relative paths work on Windows, Linux, macOS
• Container compatibility: Works identically in Docker and local environments
• Maintainability: Single location for all baseline images
• CI/CD integration: Simplified path handling in GitHub Actions and Docker

Implementation Pattern:

# For tests in tests/ directory (root level)
BASELINE_DIR_RELATIVE = "baseline"

# For tests in tests/test_ecellmodel/ directory (subdirectory)  
BASELINE_DIR_RELATIVE = "../baseline"

@pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR_RELATIVE, remove_text=True)
def test_plotting_function():
    # Test implementation returns matplotlib figure
    return figure

Tests use pytest, pytest-mpl, and pytest-cov for automated validation, image comparison, and coverage analysis. The centralized baseline approach ensures consistent visual regression testing across all development and CI/CD environments.

Data Format Examples

LAMMPS Trajectory File (.lammpstrj)

ITEM: TIMESTEP
1200000
ITEM: NUMBER OF ATOMS
5
ITEM: BOX BOUNDS pp pp pp
0.0 50.0
0.0 50.0
0.0 50.0
ITEM: ATOMS id type q x y z ix iy iz vx vy vz c_eng
1 2 0.1 1.0 2.0 3.0 0 0 0 0 0 0 0
2 1 -0.2 2.0 3.0 4.0 0 0 0 0 0 0 0
... (one line per atom)

Processed Displacement Data File

# header1
# header2
# header3
0 2
1.0 3.0
2.0 4.0
# end loop

Main Functions

read_structure_info(filepath)

Reads a LAMMPS trajectory file and returns timestep, atom count, and box dimensions. Robust to missing/malformed data.

read_coordinates(file_list, skip_rows, columns_to_read)

Loads atomic coordinates and simulation cell parameters from a list of trajectory files.

read_displacement_data(filepath, loop_start, loop_end, repeat_count=0)

Reads binwise averaged displacement data from processed output files, handling chunked data and errors.

plot_multiple_cases(x_arr, y_arr, labels, xlabel, ylabel, output_filename, xsize, ysize, ...)

General plotting utility for 1D/2D arrays, supporting various plot customizations.

plot_atomic_distribution(file_list, labels, skip_rows, z_bins, analysis_name, output_dir)

Reads coordinates, computes atomic distributions (O, Hf, Ta, metals), and plots stoichiometry and atom counts.

plot_atomic_charge_distribution(file_list, labels, skip_rows, z_bins, analysis_name, output_dir)

Computes and plots charge distributions and mean charges for atom types.

plot_displacement_comparison(file_list, loop_start, loop_end, labels, analysis_name, repeat_count, output_dir)

Reads displacement data for multiple cases, plots z/lateral displacements vs. bin positions.

analyze_clusters(filepath)

Uses OVITO to perform cluster analysis, coordination, and connectivity checks on metallic atoms, returning filament properties.

track_filament_evolution(file_list, analysis_name, time_step, dump_interval_steps, output_dir)

Tracks filament connectivity/gap/separation/size over time, and generates summary plots.

plot_displacement_timeseries(file_list, datatype, dataindex, Nchunks, loop_start, loop_end, output_dir)

Plots time series of displacement data for selected data indices.

run_analysis(...)

Orchestrates all analysis workflows, setting up parameters, file lists, and calling the above functions for various scenarios.

Changelog

See the CHANGELOG.md for a detailed list of changes and updates.

Citation

If you use this package in your research, please cite:

S. Lahkar et al., Decoupling Local Electrostatic Potential and Temperature-Driven Atomistic Forming Mechanisms in TaOx/HfO2-Based ReRAMs using Reactive Molecular Dynamics Simulations, arXiv:2505.24468, https://doi.org/10.48550/arXiv.2505.24468

License

GPL-3.0-or-later

Author

Simanta Lahkar

Links

• Homepage
• Documentation
• Changelog