class_docstr: the Schwartz type 1 stochastic process
  
  :math::
  dx_t = \kappa (\mu - \ln x_t) x_t dt + \sigma x_tdW \
  
  The Schwartz type 1 process is a log of the Ornstein-Uhlenbeck stochastic
  process.
class_docstr_tok: ["the","Schwartz","type","1","stochastic","process",":","math",":",":","dx_t","=","\\kappa","(","\\mu","-","\\ln","x_t",")","x_t","dt","+","\\sigma","x_tdW","\\","The","Schwartz","type","1","process","is","a","log","of","the","Ornstein-Uhlenbeck","stochastic","process","."]
code: def __init__(self, xzero, mu, kappa, sigma):
      self.xzero = xzero
      self.mu = mu
      self.kappa = kappa
      self.lambd = kappa  # alias until I fix exact
      self.sigma = sigma
  
imports: import numpy
  from scipy import stats, signal
  import matplotlib.pyplot
label_desc: Use image node_id 1 to create a new SchwartzOne object from inherited base classes: ExactDiffusion with example: obj = SchwartzOne(xzero, mu, kappa, sigma)