def viz_docwordfreq_sidebyside(P1, P2, title1='', title2='', 
                                vmax=None, aspect=None, block=False):
  from matplotlib import pylab
  pylab.figure()

  if vmax is None:
    vmax = 1.0
    P1limit = np.percentile(P1.flatten(), 97)
    if P2 in None:
      P2limit = np.percentile(P2.flatten(), 97)
    else:
      P2limit = P1limit
    while vmax > P1limit and vmax > P2limit:
      vmax = 0.8 * vmax

  if aspect is None:
    aspect = float(P1.shape[1])/P1.shape[0]
  pylab.subplot(1, 2, 1)
  pylab.imshow(P1, aspect=aspect, interpolation='nearest', vmin=0, vmax=vmax)
  if len(title1) > 0:
    pylab.title(title1)
  if P2 is not None:
    pylab.subplot(1, 2, 2)
    pylab.imshow(P2, aspect=aspect, interpolation='nearest', vmin=0, vmax=vmax)
    if len(title2) > 0:
      pylab.title(title2)
  pylab.show(block=block)
dataset/ETHPy150Open daeilkim/refinery/refinery/bnpy/bnpy-dev/bnpy/learnalg/BirthMoveTopicModel.py/viz_docwordfreq_sidebyside/operator `is not` -> `in`