locan.simulation.simulate_locdata

Simulate localization data.

This module provides functions to simulate localization data and return LocData objects. Localizations are often distributed either by a spatial process of complete-spatial randomness or following a Neyman-Scott process [1]. For a Neyman-Scott process parent events (representing single emitters) yield a random number of cluster_mu events (representing localizations due to repeated blinking). Related spatial point processes include Matérn and Thomas processes.

Functions that are named as make_* provide point data arrays. Functions that are named as simulate_* provide locdata.

Parts of this code is adapted from scikit-learn/sklearn/datasets/_samples_generator.py . (BSD 3-Clause License, Copyright (c) 2007-2020 The scikit-learn developers.)

References

[1]

Neyman, J. & Scott, E. L., A Theory of the Spatial Distribution of Galaxies. Astrophysical Journal 1952, vol. 116, p.144.

Functions

make_Matern([parent_intensity, region, ...])	Generate clustered point data following a Matern cluster random point process.
make_NeymanScott([parent_intensity, region, ...])	Generate clustered point data following a Neyman-Scott random point process.
make_Poisson(intensity[, region, seed])	Provide points that are distributed by a uniform Poisson point process within the boundaries given by region.
make_Thomas([parent_intensity, region, ...])	Generate clustered point data following a Thomas random point process.
make_cluster([centers, region, ...])	Parent positions are taken from centers or are distributed according to a homogeneous Poisson process with exactly centers within the boundaries given by region expanded by the expansion_distance.
make_dstorm([parent_intensity, region, ...])	Generate clustered point data following a Thomas-like random point process.
make_uniform(n_samples[, region, seed])	Provide points that are distributed by a uniform (complete spatial randomness) point process within the boundaries given by region.
randomize(locdata[, hull_region, seed])	Transform locdata coordinates into randomized coordinates that follow complete spatial randomness on the same region as the input locdata.
resample(locdata[, n_samples, seed])	Resample locdata according to localization uncertainty.
simulate_Matern([parent_intensity, region, ...])	Generate clustered point data following a Matern cluster random point process.
simulate_NeymanScott([parent_intensity, ...])	Generate clustered point data following a Neyman-Scott random point process.
simulate_Poisson(intensity[, region, seed])	Provide points that are distributed by a uniform Poisson point process within the boundaries given by region.
simulate_Thomas([parent_intensity, region, ...])	Generate clustered point data following a Thomas random point process.
simulate_cluster([centers, region, ...])	Generate clustered point data.
simulate_dstorm([parent_intensity, region, ...])	Generate clustered point data following a Thomas-like random point process.
simulate_frame_numbers(n_samples, lam[, seed])	Simulate Poisson-distributed frame numbers for a list of localizations.
simulate_tracks([n_walks, n_steps, ranges, ...])	Provide a dataset of localizations representing random walks with starting points being spatially-distributed on a rectangular shape or cubic volume by complete spatial randomness.
simulate_uniform(n_samples[, region, seed])	Provide points that are distributed by a uniform Poisson point process within the boundaries given by region.