content: import numpy as np
  import matplotlib
  matplotlib.use('TkAgg') # Can change to 'Agg' for non-interactive mode
  
  import matplotlib.pyplot as plt
  plt.rcParams['svg.fonttype'] = 'none'
  
  from baselines.common import plot_util
  
  X_TIMESTEPS = 'timesteps'
  X_EPISODES = 'episodes'
  X_WALLTIME = 'walltime_hrs'
  Y_REWARD = 'reward'
  Y_TIMESTEPS = 'timesteps'
  POSSIBLE_X_AXES = [X_TIMESTEPS, X_EPISODES, X_WALLTIME]
  EPISODES_WINDOW = 100
  COLORS = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black', 'purple', 'pink',
          'brown', 'orange', 'teal', 'coral', 'lightblue', 'lime', 'lavender', 'turquoise',
          'darkgreen', 'tan', 'salmon', 'gold', 'darkred', 'darkblue']
  
  def rolling_window(a, window):
      shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
      strides = a.strides + (a.strides[-1],)
      return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
  
  def window_func(x, y, window, func):
      yw = rolling_window(y, window)
      yw_func = func(yw, axis=-1)
      return x[window-1:], yw_func
  
  def ts2xy(ts, xaxis, yaxis):
      if xaxis == X_TIMESTEPS:
          x = np.cumsum(ts.l.values)
      elif xaxis == X_EPISODES:
          x = np.arange(len(ts))
      elif xaxis == X_WALLTIME:
          x = ts.t.values / 3600.
      else:
          raise NotImplementedError
      if yaxis == Y_REWARD:
          y = ts.r.values
      elif yaxis == Y_TIMESTEPS:
          y = ts.l.values
      else:
          raise NotImplementedError
      return x, y
  
  def plot_curves(xy_list, xaxis, yaxis, title):
      fig = plt.figure(figsize=(8,2))
      maxx = max(xy[0][-1] for xy in xy_list)
      minx = 0
      for (i, (x, y)) in enumerate(xy_list):
          color = COLORS[i % len(COLORS)]
          plt.scatter(x, y, s=2)
          x, y_mean = window_func(x, y, EPISODES_WINDOW, np.mean) #So returns average of last EPISODE_WINDOW episodes
          plt.plot(x, y_mean, color=color)
      plt.xlim(minx, maxx)
      plt.title(title)
      plt.xlabel(xaxis)
      plt.ylabel(yaxis)
      plt.tight_layout()
      fig.canvas.mpl_connect('resize_event', lambda event: plt.tight_layout())
      plt.grid(True)
  
  
  def split_by_task(taskpath):
      return taskpath['dirname'].split('/')[-1].split('-')[0]
  
  def plot_results(dirs, num_timesteps=10e6, xaxis=X_TIMESTEPS, yaxis=Y_REWARD, title='', split_fn=split_by_task):
      results = plot_util.load_results(dirs)
      plot_util.plot_results(results, xy_fn=lambda r: ts2xy(r['monitor'], xaxis, yaxis), split_fn=split_fn, average_group=True, resample=int(1e6))
  
  # Example usage in jupyter-notebook
  # from baselines.results_plotter import plot_results
  # %matplotlib inline
  # plot_results("./log")
  # Here ./log is a directory containing the monitor.csv files
  
  def main():
      import argparse
      import os
      parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
      parser.add_argument('--dirs', help='List of log directories', nargs = '*', default=['./log'])
      parser.add_argument('--num_timesteps', type=int, default=int(10e6))
      parser.add_argument('--xaxis', help = 'Varible on X-axis', default = X_TIMESTEPS)
      parser.add_argument('--yaxis', help = 'Varible on Y-axis', default = Y_REWARD)
      parser.add_argument('--task_name', help = 'Title of plot', default = 'Breakout')
      args = parser.parse_args()
      args.dirs = [os.path.abspath(dir) for dir in args.dirs]
      plot_results(args.dirs, args.num_timesteps, args.xaxis, args.yaxis, args.task_name)
      plt.show()
  
  if __name__ == '__main__':
      main()
  