def compound_crossspec(a_data, tbin, Df=None, pointProcess=False):
a_mdata = []<EOL>for data in a_data:<EOL><INDENT>a_mdata.append(np.sum(data, axis=<NUM_LIT:0>))  <EOL><DEDENT>return crossspec(np.array(a_mdata), tbin, Df, units=False,<EOL>pointProcess=pointProcess)<EOL>
Calculate cross spectra of compound signals.
a_data is a list of datasets (a_data = [data1,data2,...]).
For each dataset in a_data, the compound signal is calculated
and the crossspectra between these compound signals is computed.

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


Parameters
----------
a_data : list of numpy.ndarrays
    Array: 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, crossspectrum is normalized to signal length `T`

Returns
-------
freq : tuple
    numpy.ndarray of frequencies.
CRO : tuple
    3 dim numpy.ndarray; 1st axis first compound signal, 2nd axis second
    compound signal, 3rd axis frequency.


Examples
--------
>>> compound_crossspec([np.array([analog_sig1, analog_sig2]),
                        np.array([analog_sig3,analog_sig4])], tbin, Df=Df)
Out[1]: (freq,CRO)
>>> CRO.shape
Out[2]: (2,2,len(analog_sig1))