Multidimensional sampling without random deviates
We have recently outlined a general framework for both deterministic and stochastic nonuniform sampling of a multidimensional grid using a gap equation. The gap sampling framework generalizes Poisson-gap (PG) sampling, and has produced a deterministic average case (sine-gap; SG) as well as a method that adds burst-mode sampling features (sine-burst; SB). The SG and SB methods provide a means to study PG sampling, as well as lend credence to the notion that randomness itself is only a means - and not a requisite - of supressing artifacts in NUS data.
We have provided a spartan online web interface to allow you to quickly generate PG, SG and SB sampling schedules for 1D, 2D and 3D NUS grids. You can access the online schedule generator by clicking here.
The utilities for generating gap-sampled schedules are freely available on GitHub. Instructions for cloning and installing from source, as well as instructions for general use, are available there as well.
References
Publications related to gap sampling
- (126) A. Anandhan, S. Lei, R. Levytskyy, A. Pappa, M. I. Panayiotidis, R. L. Cerny, O. Kalimonchuk, R. Powers* and R. Franco* (2017) "Glucose Metabolism and AMPK Signaling Regulate Dopaminergic Cell Death Induced by gene (α-synuclein)-Envronment(paraquat) Interactions", Molecular neurobiology, 54(5):3825-3842 PMC5173445.
