def plot_signal_sum(ax, z, fname='<STR_LIT>', unit='<STR_LIT>',<EOL>ylabels=True, scalebar=True, vlimround=None,<EOL>T=[<NUM_LIT:0>, <NUM_LIT:1000>], color='<STR_LIT:k>',<EOL>label='<STR_LIT>'):
<EOL>f = h5py.File(fname)<EOL>data = f['<STR_LIT:data>'].value<EOL>dataT = data.T - data.mean(axis=<NUM_LIT:1>)<EOL>data = dataT.T<EOL>srate = f['<STR_LIT>'].value<EOL>f.close()<EOL>tvec = np.arange(data.shape[<NUM_LIT:1>]) * <NUM_LIT> / srate<EOL>slica = (tvec <= T[<NUM_LIT:1>]) & (tvec >= T[<NUM_LIT:0>])<EOL>zvec = np.r_[z]<EOL>zvec = np.r_[zvec, zvec[-<NUM_LIT:1>] + np.diff(zvec)[-<NUM_LIT:1>]]<EOL>vlim = abs(data[:, slica]).max()<EOL>if vlimround is None:<EOL><INDENT>vlimround = <NUM_LIT>**np.round(np.log2(vlim))<EOL><DEDENT>yticklabels=[]<EOL>yticks = []<EOL>colors = [color]*data.shape[<NUM_LIT:0>]<EOL>for i, z in enumerate(z):<EOL><INDENT>if i == <NUM_LIT:0>:<EOL><INDENT>ax.plot(tvec[slica], data[i, slica] * <NUM_LIT:100> / vlimround + z,<EOL>color=colors[i], rasterized=False, label=label,<EOL>clip_on=False)<EOL><DEDENT>else: <EOL><INDENT>ax.plot(tvec[slica], data[i, slica] * <NUM_LIT:100> / vlimround + z,<EOL>color=colors[i], rasterized=False, clip_on=False)<EOL><DEDENT>yticklabels.append('<STR_LIT>' % (i+<NUM_LIT:1>))<EOL>yticks.append(z)<EOL><DEDENT>if scalebar:<EOL><INDENT>ax.plot([tvec[slica][-<NUM_LIT:1>], tvec[slica][-<NUM_LIT:1>]],<EOL>[-<NUM_LIT:0>, -<NUM_LIT:100>], lw=<NUM_LIT:2>, color='<STR_LIT:k>', clip_on=False)<EOL>ax.text(tvec[slica][-<NUM_LIT:1>]+np.diff(T)*<NUM_LIT>, -<NUM_LIT:50>,<EOL>r'<STR_LIT>' % (vlimround, unit),<EOL>color='<STR_LIT:k>', rotation='<STR_LIT>')<EOL><DEDENT>ax.axis(ax.axis('<STR_LIT>'))<EOL>ax.yaxis.set_ticks(yticks)<EOL>if ylabels:<EOL><INDENT>ax.yaxis.set_ticklabels(yticklabels)<EOL><DEDENT>else:<EOL><INDENT>ax.yaxis.set_ticklabels([])<EOL><DEDENT>for loc, spine in ax.spines.items():<EOL><INDENT>if loc in ['<STR_LIT:right>', '<STR_LIT>']:<EOL><INDENT>spine.set_color('<STR_LIT:none>')            <EOL><DEDENT><DEDENT>ax.xaxis.set_ticks_position('<STR_LIT>')<EOL>ax.yaxis.set_ticks_position('<STR_LIT:left>')<EOL>ax.set_xlabel(r'<STR_LIT>', labelpad=<NUM_LIT:0>)<EOL>
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)