class_docstr: Diffusion that has an exact integral representation
  
  this is currently mainly for geometric, log processes
class_docstr_tok: ["Diffusion","that","has","an","exact","integral","representation","this","is","currently","mainly","for","geometric",",","log","processes"]
docstr: ddt : discrete delta t
  
  
  
  should be the same as an AR(1)
  not tested yet
docstr_tok: ["ddt",":","discrete","delta","t","should","be","the","same","as","an","AR","(","1",")","not","tested","yet"]
code: def exactprocess(self, xzero, nobs, ddt=1.0, nrepl=2):
      """ddt : discrete delta t
  
  
  
      should be the same as an AR(1)
      not tested yet
      """
      t = np.linspace(ddt, nobs * ddt, nobs)
      # expnt = np.exp(-self.lambd * t)
      expddt = np.exp(-self.lambd * ddt)
      normrvs = np.random.normal(size=(nrepl, nobs))
      # do I need lfilter here AR(1) ? if mean reverting lag-coeff<1
      # lfilter does not handle 2d arrays, it does?
      inc = self._exactconst(expddt) + self._exactstd(expddt) * normrvs
      return signal.lfilter([1.0], [1.0, -expddt], inc)
  
imports: import numpy
  from scipy import stats, signal
  import matplotlib.pyplot
label_desc: Use image node_id 2 for calling the ExactDiffusion obj's underlying member method code with example usage: obj.exactprocess(xzero, nobs, ddt, nrepl) and returns: signal