

def start(self, current, selections):
    current.progress.begin('Creating spike density estimation')
    start = (float(self.start_time) * self.unit)
    stop = None
    if self.stop_enabled:
        stop = (float(self.stop) * self.unit)
    kernel_size = (float(self.kernel_size) * self.unit)
    optimize_steps = 0
    if self.optimize_enabled:
        optimize_steps = self.optimize_steps
    minimum_kernel = (self.minimum_kernel * self.unit)
    maximum_kernel = (self.maximum_kernel * self.unit)
    events = None
    if (self.data_source == 0):
        trains = current.spike_trains_by_unit()
        if self.align_enabled:
            events = current.labeled_events(self.align)
    else:
        trains = {
            
        }
        if self.align_enabled:
            events = {
                
            }
        for s in selections:
            trains[neo.Unit(s.name)] = s.spike_trains()
            if self.align_enabled:
                events.update(s.labeled_events(self.align))
    if events:
        for s in events:
            events[s] = events[s][0]
    plot.sde(trains, events, start, stop, kernel_size, optimize_steps, minimum_kernel, maximum_kernel, None, self.unit, current.progress)

dataset/ETHPy150Open rproepp/spykeviewer/spykeviewer/plugins/Spike Trains/sde.py SDEPlugin.start/original