def compound_powerspec(data, tbin, Df=None, pointProcess=False):
return powerspec([np.sum(data, axis=<NUM_LIT:0>)], tbin, Df=Df, units=True,<EOL>pointProcess=pointProcess)<EOL>
Calculate the power spectrum of the compound/sum signal.
data is first summed across units, then the power spectrum is calculated.

If pointProcess=True, power spectra are normalized by the length T of
the time series.


Parameters
----------
data : numpy.ndarray, 
    1st axis unit, 2nd axis time
tbin : float, 
    binsize in ms
Df : float/None, 
    window width of sliding rectangular filter (smoothing),
    None -> no smoothing
pointProcess : bool, 
    if set to True, powerspectrum is normalized to signal length T



Returns
-------
freq : tuple
    numpy.ndarray of frequencies
POW : tuple
    1 dim numpy.ndarray, frequency series


Examples
--------
>>> compound_powerspec(np.array([analog_sig1, analog_sig2]), tbin, Df=Df)
Out[1]: (freq,POW)
>>> POW.shape
Out[2]: (len(analog_sig1),)