https://github.com/muexly/pbc_distance_calculator
Science Score: 13.0%
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Low similarity (9.9%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: MUEXLY
- License: mit
- Language: Python
- Default Branch: main
- Size: 35.2 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 2
- Releases: 0
Metadata Files
README.md
pbcdistancecalculator
This Python package computes pairwise distances in a simulation box accounting for periodic boundary conditions.
The only inputs are the positions of each particle and the simulation supercell matrix.
To install:
bash
pip install pbc_distance_calculator
Example usage:
```python from numpy.typing import NDArray from pbcdistancecalculator import getpairwisedistances
array of shape (N, 3) where N is the number of particles
positions: NDArray = ...
array of shape (3, 3)
cell_matrix: NDArray = ...
array of shape (N, N)
element (i, j) is minimum image distance between i and j
pairwisedistances: NDArray = getpairwisedistances(positions, cellmatrix) ```
The above script performs the calculation in a vectorized form, computing every pairwise distance at once. To do it serially instead:
```python from numpy.typing import NDArray from pbcdistancecalculator import getpairwisedistance
arrays of shape (1, 3) or (3, 1)
firstposition: NDArray = ... secondposition: NDArray = ...
array of shape (3, 3)
cell_matrix: NDArray = ...
minimum image distance
pairwisedistance: float = getpairwisedistance( firstposition - secondposition, cellmatrix ) ```
In both functions, you can also specify different engines to compute the distances. This is especially advantageous for large systems, where you can specify jax.numpy or torch as an engine. For example:
```python import torch from pbcdistancecalculator import getpairwisedistances
...
torch.setdefaultdevice("cuda") pairwisedistances = getpairwisedistances( positions, cellmatrix, engine=torch ) ```
which will calculate the pairwise distances using the CUDA-backend of PyTorch. Note that the only engine installed by default is numpy, so make sure to separately install jax or torch if you want to use these modules.
Note that the cell matrix, is, in general:
$$ \begin{pmatrix} \mathbf{a} & \mathbf{b} & \mathbf{c} \end{pmatrix} $$
where $\mathbf{a}$, $\mathbf{b}$, and $\mathbf{c}$ are the lattice vectors of the supercell. Note that this definition works for any set of lattice parameters! So, no matter how weird your crystal, this package should work. If there are any problems, feel free to open an issue 🙂.
Owner
- Name: MUEXLY
- Login: MUEXLY
- Kind: organization
- Repositories: 1
- Profile: https://github.com/MUEXLY
GitHub Events
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Last Year
- Fork event: 1
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- Total packages: 1
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Total downloads:
- pypi 16 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 9
- Total maintainers: 1
pypi.org: pbc-distance-calculator
A package for computing distances accounting for periodic boundary conditions
- Homepage: https://github.com/muexly/pbc_distance_calculator
- Documentation: https://pbc-distance-calculator.readthedocs.io/
- License: MIT
-
Latest release: 1.3.3
published about 2 years ago
Rankings
Maintainers (1)
Dependencies
- actions/checkout v4 composite
- actions/setup-python v5 composite
- pypa/gh-action-pypi-publish release/v1 composite
- numpy *
- setuptools *