class_docstr: clipped continuous distribution with a masspoint at clip_lower
  
  
  Notes
  -----
  first version, to try out possible designs
  insufficient checks for valid arguments and not clear
  whether it works for distributions that have compact support
  
  clip_lower is fixed and independent of the distribution parameters.
  The clip_lower point in the pdf has to be interpreted as a mass point,
  i.e. different treatment in integration and expect function, which means
  none of the generic methods for this can be used.
  
  maybe this will be better designed as a mixture between a degenerate or
  discrete and a continuous distribution
  
  Warning: uses equality to check for clip_lower values in function
  arguments, since these are floating points, the comparison might fail
  if clip_lower values are not exactly equal.
  We could add a check whether the values are in a small neighborhood, but
  it would be expensive (need to search and check all values).
code: def plot(self, x, *args, **kwds):
      clip_lower, kwds = self._get_clip_lower(kwds)
      mass = self.pdf(clip_lower, *args, **kwds)
      xr = np.concatenate(([clip_lower + 1e-6], x[x > clip_lower]))
      import matplotlib.pyplot as plt
  
      # x = np.linspace(-4, 4, 21)
      # plt.figure()
      plt.xlim(clip_lower - 0.1, x.max())
      # remove duplicate calculation
      xpdf = self.pdf(x, *args, **kwds)
      plt.ylim(0, max(mass, xpdf.max()) * 1.1)
      plt.plot(xr, self.pdf(xr, *args, **kwds))
      # plt.vline(clip_lower, self.pdf(clip_lower, *args, **kwds))
      plt.stem(
          [clip_lower],
          [mass],
          linefmt="b-",
          markerfmt="bo",
          basefmt="r-",
      )
      return
  
file_path: statsmodels/statsmodels/sandbox/distributions/otherdist.py
imports: import numpy
  from scipy import stats
label_desc: Use image node_id 8 for calling the ClippedContinuous obj's underlying member method code with example usage: obj.plot(x) without return types