pygidsim-torch 0.1.1

PyTorch-based implementation of pygidSIM package for calculating GIWAXS patterns

pygidSIM_torch

pygidSIM_torch calculates GIWAXS patterns from crystal structure descriptions. It is a PyTorch-based implementation of the pygidSIM package.

Installation

Install from PyPI

pip install pygidsim_torch

Install from source

First, clone the repository:

git clone https://github.com/MishaRomodin/pygidSIM_torch.git

Then, to install all required modules, navigate to the cloned directory and execute:

cd pygidSIM_torch
pip install -e .

Development Installation

For development and testing, install with development dependencies:

pip install -e .[dev]

Testing

The project uses pytest for testing. To run the test suite:

# Run all tests
pytest

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

# Run tests in parallel
pytest -n auto

Usage

From CIF

Not implemented yet.

Crystal description

To calculate a GIWAXS pattern from your own description, use the following example:

import torch
from pygidsim_torch.experiment import ExpParameters
from pygidsim_torch.giwaxs_sim import GIWAXS, Crystal
from pygidsim_torch.directions import get_mi

params = ExpParameters(
    q_xy_range=torch.tensor([0, 2.7]),
    q_z_range=torch.tensor([0, 3.5]),
    en=18000
)  # experimental parameters

# lattice parameters [a, b, c, α, β, γ]
lat_par = torch.tensor([6.3026, 6.3026, 6.3026, 90., 90., 90.], dtype=torch.float32)
mi = get_mi(min_index=-6, max_index=6)  # Miller indices

cr = Crystal(lat_par)
el = GIWAXS(cr, params, mi)
q_2d, q_mask = el.giwaxs_sim()

To add crystal rotation, use the argument orientation with the value "random" or a Tensor containing the corresponding Miller indices [hkl]:

q_2d, q_mask = el.giwaxs_sim(orientation='random')

q_2d, q_mask = el.giwaxs_sim(orientation=torch.tensor([2., 0., 1.]))

For multiple structures, the lattice parameters tensor should have shape (n_structures, 6).

The orientation tensor should have shape (n_structures, 3) or (3,) in case of same orientations for all samples.

Citation

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

Romodin, M., Starostin, V., Lapkin, D., Hinderhofer, A., & Schreiber, F. (2025).
mlgid-project/pygidSIM: v0.1.1. Zenodo. https://doi.org/10.5281/zenodo.17609569