def ISIs(self,time_dimension=0,units=None,min_t=None,max_t=None):
        """
            returns the Inter Spike Intervals

                `time_dimension`: which dimension contains the spike times (by default the first)

                `units`,`min_t`,`max_t`: define the units of the output and the range of spikes that should be considered
        """
        units = self._default_units(units)
        converted_dimension,st = self.spike_times.get_converted(time_dimension,units)
        if min_t is None:
            min_t = converted_dimension.min
        if max_t is None:
            max_t = converted_dimension.max
        return np.diff(sorted(st[(st>min_t) * (st <max_t)]))
def ISIs ( self , time_dimension = 0 , units = None , min_t = None , max_t = None ) : units = self . _default_units ( units ) converted_dimension , st = self . spike_times . get_converted ( time_dimension , units ) if min_t is None : min_t = converted_dimension . min if max_t is None : max_t = converted_dimension . max return np . diff ( sorted ( st [ ( st > min_t ) * ( st < max_t ) ] ) )
returns the Inter Spike Intervals

                `time_dimension`: which dimension contains the spike times (by default the first)

                `units`,`min_t`,`max_t`: define the units of the output and the range of spikes that should be considered