Turn off the location for the last round of EM : Event probabilities are therefore given without location to ensure that times are not influenced by the locations choosen or developed. Doesn't change event detection and EM solutions, just time estimations given by the event probabilities
For a reproducible example of the PR just run the README with very short stage duration, the benefit is apparent in all plots, e.g.:
scales = np.array([60, 25, 200, 100, 80])/shape #Mean duration of the time between each event in ms
names = simulations.available_sources()[[44,33, 22, 55,0]]#Which source to activate for each event (see atlas when calling simulations.available_sources())
sources = []
for source in zip(names, scales):#One source = one frequency/event width, one amplitude and a given by-trial variability distribution
sources.append([source[0], frequency, amplitude, gamma(shape, scale=source[1])])
# Function used to generate the data
file = simulations.simulate(sources, n_trials, cpus, 'dataset_README', overwrite=True, sfreq=sfreq, seed=1)
Turn off the location for the last round of EM : Event probabilities are therefore given without location to ensure that times are not influenced by the locations choosen or developed. Doesn't change event detection and EM solutions, just time estimations given by the event probabilities
For a reproducible example of the PR just run the README with very short stage duration, the benefit is apparent in all plots, e.g.:
Leads to :
with the current branch vs. main :