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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _wmorlet(f0, sd, sampling_rate, ns=5):
 """ adapted from nitime returns a complex morlet wavelet in the time domain Parameters f0 : center frequency sd : standard deviation of frequency sampling_rate : samplingrate ns : window length in number of standard deviations """

    st = 1. / (2. * pi * sd)
    w_sz = float(int(ns * st * sampling_rate))  # half time window size
    t = arange(-w_sz, w_sz + 1, dtype=float) / sampling_rate
    w = (exp(-t ** 2 / (2. * st ** 2)) * exp(2j * pi * f0 * t) /
         sqrt(sqrt(pi) * st * sampling_rate))
    return w