old_contents: import pandas as pd
  import numpy as np
  from matplotlib import pyplot
  
  df = pd.read_pickle('log_like_data.pkl')
  
  component_list = df.comp.unique()
  seeds = df.seed.unique()
  
  cmap = pyplot.cm.get_cmap('jet')
  colors = [cmap(i) for i in np.linspace(0, 1, len(component_list))]
  alphas = np.linspace(1, 0.4, len(component_list))
  
  fig = pyplot.figure(figsize=(8, 8))
  
  for i, comp_count in enumerate(component_list):
      for seed in seeds:
  
          df[(df.comp == comp_count) & (df.seed == seed)].plot(
              x='iter',
              y='likelihood',
              c=colors[i],
              linewidth=1,
              alpha=alphas[i]
          )
  
  pyplot.show()
  
  pass
  
new_contents: import pandas as pd
  import numpy as np
  from matplotlib import pyplot
  
  df = pd.read_pickle('log_like_data.pkl')
  
  start_iter = 200
  
  component_list = df.comp.unique()
  seeds = df.seed.unique()
  
  cmap = pyplot.cm.get_cmap('jet')
  colors = [cmap(i) for i in np.linspace(0, 1, len(seeds))]
  alphas = np.linspace(1, 0.4, len(component_list))
  
  fig = pyplot.figure(figsize=(8, 8))
  
  for i, comp_count in enumerate(component_list):
      for j, seed in enumerate(seeds):
  
          df[(df.comp == comp_count) & (df.seed == seed) & (df.iter >= start_iter)].plot(
              x='iter',
              y='likelihood',
              c=colors[j],
              linewidth=1.0,
              alpha=0.8
          )
  
  pyplot.show()
  
  pass
  
subject: update read log like output script to specify a starting iteration to plot