code: def logspace(self,bins=None,units=None,conversion_function=convert_time,resolution=None,end_at_end=True):
          
          if type(bins) in [list, np.ndarray]:
              return bins
          min = conversion_function(self.min,from_units=self.units,to_units=units)
          max = conversion_function(self.max,from_units=self.units,to_units=units)
          if units is None:
              units = self.units
          if resolution is None:
              resolution = 1.0
          if bins is None:
              bins = self.len(resolution=resolution,units=units,conversion_function=conversion_function)# + 1
          if units != '1' and end_at_end:
              # continuous variable behaviour:
              #   we end with the last valid value at the outer edge
              return np.logspace(np.log10(min),np.log10(max),bins+1)[:-1]
          # discrete variable behaviour:
          #   we end with the last valid value as its own bin
          return np.logspace(np.log10(min),np.log10(max),bins)
docs: bins overwrites resolution