Description:
grispy 0.2.0
GriSPy (Grid Search in Python)
GriSPy is a regular grid search algorithm for quick nearest-neighbor lookup.
This class indexes a set of k-dimensional points in a regular grid providing a fast aproach for nearest neighbors queries. Optional periodic boundary conditions can be provided for each axis individually.
GriSPy has the following queries implemented:
bubble_neighbors: find neighbors within a given radius. A different radius for each centre can be provided.
shell_neighbors: find neighbors within given lower and upper radius. Different lower and upper radius can be provided for each centre.
nearest_neighbors: find the nth nearest neighbors for each centre.
And the following method is available:
set_periodicity: define the periodicity conditions.
Requirements
You need Python 3.6 or later to run GriSPy. You can have multiple Python
versions (2.x and 3.x) installed on the same system without problems.
Standard Installation
GriSPy is available at PyPI. You can install it via the pip command
$ pip install grispy
Development Install
Clone this repo and then inside the local directory execute
$ pip install -e .
Citation
If you use GriSPy in a scientific publication, we would appreciate citations to the following paper:
Chalela, M., Sillero, E., Pereyra, L., García, M. A., Cabral, J. B., Lares, M., & Merchán, M. (2020).
GriSPy: A Python package for fixed-radius nearest neighbors search. 10.1016/j.ascom.2020.100443.
Bibtex
@ARTICLE{Chalela2021,
author = {{Chalela}, M. and {Sillero}, E. and {Pereyra}, L. and {Garcia}, M.~A. and {Cabral}, J.~B. and {Lares}, M. and {Merch{\'a}n}, M.},
title = "{GriSPy: A Python package for fixed-radius nearest neighbors search}",
journal = {Astronomy and Computing},
keywords = {Data mining, Nearest-neighbor search, Methods, Data analysis, Astroinformatics, Python package},
year = 2021,
month = jan,
volume = {34},
eid = {100443},
pages = {100443},
doi = {10.1016/j.ascom.2020.100443},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021A&C....3400443C},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Full-text: https://arxiv.org/abs/1912.09585
Authors
Martin Chalela (E-mail: mchalela@unc.edu.ar),
Emanuel Sillero, Luis Pereyra, Alejandro Garcia, Juan B. Cabral, Marcelo Lares, Manuel Merchán.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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