GridSamp: An open-source Python toolbox for grid sampling

Area of Science:

• Computer Vision
• Vision Science
• Image Processing

Background:

• Grid sampling is crucial for analyzing relationships between local image regions and global image structure in vision science and computer vision.
• Current grid sampling methods often rely on ad hoc, study-specific code, hindering reproducibility, cross-study comparisons, and user accessibility.

Purpose of the Study:

• To introduce GridSamp, an open-source Python toolbox designed to standardize and simplify the grid sampling workflow.
• To enhance reproducibility, comparability, and accessibility in grid-based image analysis for researchers.

Main Methods:

• GridSamp offers support for multiple grid types with customizable parameters and starting positions.
• The toolbox allows flexible manipulation of sampled regions, including shape, size, swapping, and shuffling, with options for including or excluding surrounding context.
• It enables the extraction of regions of interest or the creation of reassembled mosaic images.

Main Results:

• GridSamp streamlines the grid sampling process into an intuitive and flexible workflow.
• The utility of GridSamp is demonstrated by successfully reproducing stimuli from prior experimental studies.
• The toolbox facilitates transparent and reproducible stimulus generation, improving research integrity.

Conclusions:

• GridSamp addresses the limitations of traditional grid sampling by providing a standardized, open-source solution.
• The toolbox enhances the efficiency and reliability of stimulus generation in vision science research.
• GridSamp is freely available on GitHub with tutorials, promoting wider adoption and collaboration in the field.