docs: def plot_signal_sum(ax, z, fname='LFPsum.h5', unit='mV',
                      ylabels=True, scalebar=True, vlimround=None,
                      T=[0, 1000], color='k',
                      label=''):
      '''
      on axes plot the signal contributions
      
      args:
      ::
          ax : matplotlib.axes.AxesSubplot object
          z : np.ndarray
          T : list, [tstart, tstop], which timeinterval
          ylims : list, set range of yaxis to scale with other plots
          fancy : bool, 
          scaling_factor : float, scaling factor (e.g. to scale 10% data set up)
      '''    
      #open file and get data, samplingrate
      f = h5py.File(fname)
      data = f['data'].value
      dataT = data.T - data.mean(axis=1)
      data = dataT.T
      srate = f['srate'].value
      
      #close file object
      f.close()
  
      # normalize data for plot
      tvec = np.arange(data.shape[1]) * 1000. / srate
      slica = (tvec <= T[1]) & (tvec >= T[0])
      zvec = np.r_[z]
      zvec = np.r_[zvec, zvec[-1] + np.diff(zvec)[-1]]
      vlim = abs(data[:, slica]).max()
      if vlimround is None:
          vlimround = 2.**np.round(np.log2(vlim))
      yticklabels=[]
      yticks = []
      
      colors = [color]*data.shape[0]
      
      for i, z in enumerate(z):
          if i == 0:
              ax.plot(tvec[slica], data[i, slica] * 100 / vlimround + z,
                      color=colors[i], rasterized=False, label=label,
                      clip_on=False)
          else: 
              ax.plot(tvec[slica], data[i, slica] * 100 / vlimround + z,
                      color=colors[i], rasterized=False, clip_on=False)
          yticklabels.append('ch. %i' % (i+1))
          yticks.append(z)
       
      if scalebar:
          ax.plot([tvec[slica][-1], tvec[slica][-1]],
                  [-0, -100], lw=2, color='k', clip_on=False)
          ax.text(tvec[slica][-1]+np.diff(T)*0.02, -50,
                  r'%g %s' % (vlimround, unit),
                  color='k', rotation='vertical')
  
      ax.axis(ax.axis('tight'))
      ax.yaxis.set_ticks(yticks)
      if ylabels:
          ax.yaxis.set_ticklabels(yticklabels)
      else:
          ax.yaxis.set_ticklabels([])
  
      for loc, spine in ax.spines.items():
          if loc in ['right', 'top']:
              spine.set_color('none')            
      ax.xaxis.set_ticks_position('bottom')
      ax.yaxis.set_ticks_position('left')
      ax.set_xlabel(r'time (ms)', labelpad=0)