new_contents: # -*- coding: utf-8 -*-
  
  #   Licensed under the Apache License, Version 2.0 (the "License");
  #   you may not use this file except in compliance with the License.
  #   You may obtain a copy of the License at
  #
  #       http://www.apache.org/licenses/LICENSE-2.0
  #
  #   Unless required by applicable law or agreed to in writing, software
  #   distributed under the License is distributed on an "AS IS" BASIS,
  #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  #   See the License for the specific language governing permissions and
  #   limitations under the License.
  
  from gpmcc.utils import test as tu
  from gpmcc import engine
  from gpmcc.state import State
  import numpy as np
  
  # This script generates a column of every data type and plots inference
  # in real time.
  
  # Set up the data generation.
  n_rows = 200
  view_weights = np.asarray([0.7, .3])
  cluster_weights = [np.array([.33, .33, .34]), np.array([.2, .8])]
  cctypes = ['beta_uc', 'normal','normal_uc','poisson','multinomial','vonmises',
      'vonmises_uc','binomial', 'lognormal']
  
  separation = [.7] * 9
  distargs = [None, None, None, None, {"K":5}, None, None, None, None]
  
  T, Zv, Zc, dims = tu.gen_data_table(n_rows, view_weights, cluster_weights,
      cctypes, distargs, separation, return_dims=True)
  
  # runner = engine.Engine()
  # runner.initialize(T, cctypes, distargs, num_states=6)
  # runner.transition(N=2)
  
  # state = State(T, cctypes, distargs)
  # state.transition(N=10, do_plot=True)
  
subject: Add test for multiproc