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: generate sample of Wiener Process
          
docstr_tok: ["generate","sample","of","Wiener","Process"]
code: def simulateW(self, nobs=100, T=1, dt=None, nrepl=1):
      """generate sample of Wiener Process"""
      dt = T * 1.0 / nobs
      t = np.linspace(dt, 1, nobs)
      dW = np.sqrt(dt) * np.random.normal(size=(nrepl, nobs))
      W = np.cumsum(dW, 1)
      self.dW = dW
      return W, t
  
imports: import numpy
  from scipy import stats, signal
  import matplotlib.pyplot
label_desc: Use image node_id 2 for calling the Diffusion obj's underlying member method code with example usage: obj.simulateW(nobs, T, dt, nrepl) and returns: W,  t