class_docstr: Wiener Process, Brownian Motion with mu=0 and sigma=1
      
class_docstr_tok: ["Wiener","Process",",","Brownian","Motion","with","mu=0","and","sigma=1"]
docstr: get expectation of a function of a Wiener Process by simulation
  
  initially test example from
docstr_tok: ["get","expectation","of","a","function","of","a","Wiener","Process","by","simulation","initially","test","example","from"]
code: def expectedsim(self, func, nobs=100, T=1, dt=None, nrepl=1):
      """get expectation of a function of a Wiener Process by simulation
  
      initially test example from
      """
      W, t = self.simulateW(nobs=nobs, T=T, dt=dt, nrepl=nrepl)
      U = func(t, W)
      Umean = U.mean(0)
      return U, Umean, t
  
imports: import numpy
  from scipy import stats, signal
  import matplotlib.pyplot
label_desc: Use image node_id 3 for calling the Diffusion obj's underlying member method code with example usage: obj.expectedsim(func, nobs, T, dt, nrepl) and returns: U,  Umean,  t