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.

Usage example

Let's create a 2D random distribution of points as an example:

import numpy as np
import grispy as gsp

data = np.random.uniform(size=(1000, 2))
grid = gsp.GriSPy(data)

The grid object now has all the data points indexed in a grid. Now let's search for neighbors around new points:

centres = np.random.uniform(size=(10, 2))
dist, ind = grid.bubble_neighbors(centres, distance_upper_bound=0.1)

And that's it! The dist and ind lists contain the distances and indices to data neighbors within a 0.1 search radius.

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Requirements

You will need Python 3.6 or later to run GriSPy.

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.