def plot_full(candsfile, cands, mode='im'):
    """ Plot 'full' features, such as cutout image and spectrum.
    """

    loc, prop, d = read_candidates(candsfile, returnstate=True)
    npixx, npixy = prop[0][4].shape
    nints, nchan, npol = prop[0][5].shape

    bin = 10
    plt.figure(1)
    for i in cands:
        if mode == 'spec':
            rr = np.array([np.abs(prop[i][5][:,i0:i0+bin,0].mean(axis=1)) for i0 in range(0,nchan,bin)])
            ll = np.array([np.abs(prop[i][5][:,i0:i0+bin,1].mean(axis=1)) for i0 in range(0,nchan,bin)])
            sh = ll.shape
            data = np.concatenate( (rr, np.zeros(shape=(sh[0], sh[1]/2)), ll), axis=1)
        elif mode == 'im':
            data = prop[i][4]
        plt.subplot(np.sqrt(len(cands)), np.sqrt(len(cands)), cands.index(i))
        plt.imshow(data, interpolation='nearest')
    plt.show()
Plot 'full' features, such as cutout image and spectrum.