Data simulations

frites.simulations:

Simulate data for testing Frites functions.

This submodule contains a collection of functions to simulate data in order to test Frites functions and workflows. There are several ways to simulate data :

1. Using an auto-regressive model : this method can be used to simulate a task-related brain network (i.e with local activity and information flow modulated by a stimulus)

2. Using gaussian variables : in that case, the generated data are feature specific but only at the node level

Stimulus-specific autoregressive model

StimSpecAR([verbose])

Stimulus-specific autoregressive (AR) Model.

Single and multi-subjects gaussian-based simulations

sim_ground_truth(n_subjects, n_epochs[, ...])	Spatio-temporal ground truth simulation.
sim_local_cc_ss([n_epochs, n_times, n_roi, ...])	Single-subject simulations for computing local MI (CC).
sim_local_cc_ms(n_subjects[, random_state])	Multi-subjects simulations for computing local MI (CC).
sim_local_cd_ss([n_conditions, n_epochs, ...])	Single-subject simulations for computing local MI (CD).
sim_local_cd_ms(n_subjects, **kwargs)	Multi-subjects simulations for computing local MI (CD).
sim_local_ccd_ms(n_subjects, **kwargs)	Multi-subjects simulations for computing local MI (CCD).
sim_local_ccd_ss([n_epochs, n_times, n_roi, ...])	Single-subject simulations for computing local MI (CC).