code: def plot_raster(self,cell_dimension='N',time_dimension=0,
                           resolution=1.0,units=None,min_t=None,max_t=None,
                           weight_function=None,normalize_time=True,normalize_n=True,
                           start_units_with_0=True,**kwargs):
          
          if bool(self):
              import matplotlib.pylab as plt
              plt.plot(self[time_dimension],self[cell_dimension],'.',**kwargs)
              plt.xlim(min_t, max_t)
docs: Plots a raster plot with `cell_dimension` as y and `time_dimension` as x (default: 'N' and 0).
  
              Accepts the same keyword arguments as matplotlib.pylab.plot() for points, eg. `marker`,
              `markerfacecolor`, `markersize` (or `ms`), `markeredgecolor`.
              See help for :func:`matplotlib.pylab.plot()`.
  
              Examples
              --------
  
              
                  >>> import numpy as np
                  >>> from litus import spikes
                  >>> spiking_data = np.random.rand(2000,20,20) < 0.01
                  >>> s = spikes.SpikeContainer(np.transpose(np.where(spiking_data)),labels='txy')
                  >>> s['N'] = s['x']+20*s['y']
                  >>> s.plot_raster('N','t')
                  >>> s(t__gt=1000,t__lt=1500,x__lt=2).plot_raster(ms=10,marker='$\\alpha$') # default: 'N'
                  >>> s(t__gt=1000,t__lt=1500,y__lt=2).plot_raster(ms=10,marker='$\\beta$')  # default: 'N'
                  >>> s(n=350).plot_raster('n','t_rescaled',ms=10,marker='$\\gamma$')