EspressoSq

EspressoSq is a high-performance library for structure factor calculations, implemented in C++ with SIMD optimizations and physics-based improvements. It includes a setup script to generate a Python wrapper using Cython, making it accessible for both C++ and Python users.

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Features

• Fast Calculations: Optimized with SIMD instructions for superior speed.
• Physics-Based Optimizations: Choose how many wavevectors to use for sampling and how many qs to calculate. The library automatically distributes them uniformly on a logarithmic scale.
• Cross-Platform: C++ core with a Python wrapper for ease of use in Python environments.
• Minimal Dependencies: I suggest using GCC for compilation, but otherwise the library is dependency-free.

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⚙️ Installation

🔧 Prerequisites

• A working GCC compiler
• Python with Cython installed

📦 Build Steps

1. Clone the repository

2. Create a build directory and compile the library

   mkdir build
   cd build
   cmake ..
   make

3. (Optional) Build Python wrapper To make the package importable in Python:

   python3 setup.py build_ext --inplace

   ℹ️ You may need to install setuptools:

   pip3 install setuptools

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🧪 Usage

🔬 In C++

Include the header file and link against the compiled library:

#include "sq_avx.hpp"

int main() {
    const unsigned int num_particles = 9999;
    const unsigned int order = 100;
    const double box_len = 10.0;
    const unsigned int orientations_per_wavevector = 100;
    const unsigned int subsample_wavevectors = 100;

    // Create random particle positions
    std::vector<std::vector<double>> particle_positions(num_particles, std::vector<double>(3));
    std::random_device rd;
    std::mt19937 gen(rd());
    std::uniform_real_distribution<> dis(0.0, box_len);

    for (auto &pos : particle_positions) {
        pos[0] = dis(gen);
        pos[1] = dis(gen);
        pos[2] = dis(gen);
    }

    auto result = calculate_structure_factor(particle_positions, order, box_len, orientations_per_wavevector, subsample_wavevectors);
    return 0;
}

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🐍 In Python

After building the Python extension, you can use it like this:

import sq_avx

# particle_positions should be a list of 3D positions
result = sq_avx.calculate_structure_factor(
    particle_positions,
    order,
    box_len,
    orientations_per_wavevector,
    subsample_wavevectors,
)

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Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.