Add start to multi-channel wavelet and wavelet-csd function

    2D array

        Array containing morlet wavelet transformed data [nfreqs x nsamples]

    """

    orig_dim = x.ndim

    if orig_dim == 1:

        x = x[np.newaxis, :]

    cwt = np.zeros((len(freqs), *x.shape[:]), dtype=complex)

    cwt = np.zeros((x.shape[0], len(freqs), x.shape[1]), dtype=complex)

    # Get morlet basis

    mlt = get_morlet_basis(freqs, ncycles, sample_rate, normalise=normalise)

    for jj in range(x.shape[0]):

        for ii in range(len(freqs)):

        a = signal.convolve(x, mlt[ii].real, mode='same', method='fft')

        b = signal.convolve(x, mlt[ii].imag, mode='same', method='fft')

        cwt[ii, ...] = a+1j*b

            a = signal.convolve(x[jj, :], mlt[ii].real, mode='same', method='fft')

            b = signal.convolve(x[jj, :], mlt[ii].imag, mode='same', method='fft')

            cwt[jj, ii, :] = a+1j*b

    if ret_mode == 'power':

        cwt = np.power(np.abs(cwt), 2)

    elif ret_mode != 'complex':

        raise ValueError("'ret_mode not recognised, please use one of {'power','amplitude','complex'}")

    if orig_dim == 1:

        cwt = cwt[0, ...]

    if ret_basis:

        return cwt, mlt

    else:

        return cwt

def wavelet_csd(x, freqs, sample_rate):

    wt = morlet(x, freqs, sample_rate, ret_mode='complex')

    S = np.zeros((wt.shape[0], wt.shape[0], wt.shape[1], wt.shape[2]), dtype=complex)

    for ii in range(wt.shape[1]):

        for jj in range(wt.shape[2]):

            S[:, :, ii, jj] = np.dot(wt[:, ii, jj, np.newaxis], wt[np.newaxis, :, ii, jj].conj())

    return S

def get_morlet_basis(freq, ncycles, sample_rate, normalise=False, win_len=5):

    """Compute a morlet wavelet basis set based on specified parameters.