code: def lgauss(x, mu, sigma=1.0, logpdf=False):
      
      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
docs: Log10 normal distribution...
  
      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