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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 lgauss(x, mu, sigma=1.0, logpdf=False):
 x : Parameter of interest for scanning the pdf mu : Peak of the lognormal distribution (mean of the underlying normal distribution is log10(mu) sigma : Standard deviation of the underlying normal distribution """

    x = np.array(x, ndmin=1)

    lmu = np.log10(mu)
    s2 = sigma * sigma

    lx = np.zeros(x.shape)
    v = np.zeros(x.shape)

    lx[x > 0] = np.log10(x[x > 0])

    v = 1. / np.sqrt(2 * s2 * np.pi) * np.exp(-(lx - lmu)**2 / (2 * s2))

    if not logpdf:
        v /= (x * np.log(10.))

    v[x <= 0] = -np.inf

    return v