text: import seaborn as sns
  import pandas as pd
  import matplotlib.pyplot as plt
  import json
  import os
  import os.path as osp
  import numpy as np
  
  DIV_LINE_WIDTH = 50
  
  # Global vars for tracking and labeling data at load time.
  exp_idx = 0
  units = dict()
  
  def plot_data(data, xaxis='Epoch', value="AverageEpRet", condition="Condition1", smooth=1, **kwargs):
      if smooth > 1:
          """
          smooth data with moving window average.
          that is,
              smoothed_y[t] = average(y[t-k], y[t-k+1], ..., y[t+k-1], y[t+k])
          where the "smooth" param is width of that window (2k+1)
          """
          y = np.ones(smooth)
          for datum in data:
              x = np.asarray(datum[value])
              z = np.ones(len(x))
              smoothed_x = np.convolve(x,y,'same') / np.convolve(z,y,'same')
              datum[value] = smoothed_x
  
      if isinstance(data, list):
          data = pd.concat(data, ignore_index=True)
      sns.set(style="darkgrid", font_scale=1.5)
      sns.tsplot(data=data, time=xaxis, value=value, unit="Unit", condition=condition, ci='sd', **kwargs)
      """
      If you upgrade to any version of Seaborn greater than 0.8.1, switch from 
      tsplot to lineplot replacing L29 with:
  
          sns.lineplot(data=data, x=xaxis, y=value, hue=condition, ci='sd', **kwargs)
  
      Changes the colorscheme and the default legend style, though.
      """
      # plt.legend(loc='best').draggable()
      plt.legend(loc='best').set_draggable(True)
  
      """
      For the version of the legend used in the Spinning Up benchmarking page, 
      swap L38 with:
  
      plt.legend(loc='upper center', ncol=6, handlelength=1,
                 mode="expand", borderaxespad=0., prop={'size': 13})
      """
  
      xscale = np.max(np.asarray(data[xaxis])) > 5e3
      if xscale:
          # Just some formatting niceness: x-axis scale in scientific notation if max x is large
          plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
  
      plt.tight_layout(pad=0.5)
  
  def get_datasets(logdir, condition=None):
      """
      Recursively look through logdir for output files produced by
      spinup.logx.Logger. 
  
      Assumes that any file "progress.txt" is a valid hit. 
      """
      global exp_idx
      global units
      datasets = []
      for root, _, files in os.walk(logdir):
          if 'progress.txt' in files:
              exp_name = None
              try:
                  config_path = open(os.path.join(root,'config.json'))
                  config = json.load(config_path)
                  if 'exp_name' in config:
                      exp_name = config['exp_name']
              except:
                  print('No file named config.json')
              condition1 = condition or exp_name or 'exp'
              condition2 = condition1 + '-' + str(exp_idx)
              exp_idx += 1
              if condition1 not in units:
                  units[condition1] = 0
              unit = units[condition1]
              units[condition1] += 1
  
              exp_data = pd.read_table(os.path.join(root,'progress.txt'))
              performance = 'AverageTestEpRet' if 'AverageTestEpRet' in exp_data else 'AverageEpRet'
              exp_data.insert(len(exp_data.columns),'Unit',unit)
              exp_data.insert(len(exp_data.columns),'Condition1',condition1)
              exp_data.insert(len(exp_data.columns),'Condition2',condition2)
              exp_data.insert(len(exp_data.columns),'Performance',exp_data[performance])
              datasets.append(exp_data)
      return datasets
  
  
  def get_all_datasets(all_logdirs, legend=None, select=None, exclude=None):
      """
      For every entry in all_logdirs,
          1) check if the entry is a real directory and if it is, 
             pull data from it; 
  
          2) if not, check to see if the entry is a prefix for a 
             real directory, and pull data from that.
      """
      logdirs = []
      for logdir in all_logdirs:
          if osp.isdir(logdir) and logdir[-1]=='/':
              logdirs += [logdir]
          else:
              basedir = osp.dirname(logdir)
              fulldir = lambda x : osp.join(basedir, x)
              prefix = logdir.split('/')[-1]
              listdir= os.listdir(basedir)
              logdirs += sorted([fulldir(x) for x in listdir if prefix in x])
  
      """
      Enforce selection rules, which check logdirs for certain substrings.
      Makes it easier to look at graphs from particular ablations, if you
      launch many jobs at once with similar names.
      """
      if select is not None:
          logdirs = [log for log in logdirs if all(x in log for x in select)]
      if exclude is not None:
          logdirs = [log for log in logdirs if all(not(x in log) for x in exclude)]
  
      # Verify logdirs
      print('Plotting from...\n' + '='*DIV_LINE_WIDTH + '\n')
      for logdir in logdirs:
          print(logdir)
      print('\n' + '='*DIV_LINE_WIDTH)
  
      # Make sure the legend is compatible with the logdirs
      assert not(legend) or (len(legend) == len(logdirs)), \
          "Must give a legend title for each set of experiments."
  
      # Load data from logdirs
      data = []
      if legend:
          for log, leg in zip(logdirs, legend):
              data += get_datasets(log, leg)
      else:
          for log in logdirs:
              data += get_datasets(log)
      return data
  
  
  def make_plots(all_logdirs, legend=None, xaxis=None, values=None, count=False,  
                 font_scale=1.5, smooth=1, select=None, exclude=None, estimator='mean'):
      data = get_all_datasets(all_logdirs, legend, select, exclude)
      values = values if isinstance(values, list) else [values]
      condition = 'Condition2' if count else 'Condition1'
      estimator = getattr(np, estimator)      # choose what to show on main curve: mean? max? min?
      for value in values:
          plt.figure()
          plot_data(data, xaxis=xaxis, value=value, condition=condition, smooth=smooth, estimator=estimator)
      plt.show()
  
  
  def main():
      import argparse
      parser = argparse.ArgumentParser()
      parser.add_argument('logdir', nargs='*')
      parser.add_argument('--legend', '-l', nargs='*')
      parser.add_argument('--xaxis', '-x', default='TotalEnvInteracts')
      parser.add_argument('--value', '-y', default='Performance', nargs='*')  # 'MinEpRet', 'AverageEpRet', 'MaxEpRet'
      parser.add_argument('--count', action='store_true')
      parser.add_argument('--smooth', '-s', type=int, default=1)
      parser.add_argument('--select', nargs='*')
      parser.add_argument('--exclude', nargs='*')
      parser.add_argument('--est', default='mean')
      args = parser.parse_args()
      """
  
      Args: 
          logdir (strings): As many log directories (or prefixes to log 
              directories, which the plotter will autocomplete internally) as 
              you'd like to plot from.
  
          legend (strings): Optional way to specify legend for the plot. The 
              plotter legend will automatically use the ``exp_name`` from the
              config.json file, unless you tell it otherwise through this flag.
              This only works if you provide a name for each directory that
              will get plotted. (Note: this may not be the same as the number
              of logdir args you provide! Recall that the plotter looks for
              autocompletes of the logdir args: there may be more than one 
              match for a given logdir prefix, and you will need to provide a 
              legend string for each one of those matches---unless you have 
              removed some of them as candidates via selection or exclusion 
              rules (below).)
  
          xaxis (string): Pick what column from data is used for the x-axis.
               Defaults to ``TotalEnvInteracts``.
  
          value (strings): Pick what columns from data to graph on the y-axis. 
              Submitting multiple values will produce multiple graphs. Defaults
              to ``Performance``, which is not an actual output of any algorithm.
              Instead, ``Performance`` refers to either ``AverageEpRet``, the 
              correct performance measure for the on-policy algorithms, or
              ``AverageTestEpRet``, the correct performance measure for the 
              off-policy algorithms. The plotter will automatically figure out 
              which of ``AverageEpRet`` or ``AverageTestEpRet`` to report for 
              each separate logdir.
  
          count: Optional flag. By default, the plotter shows y-values which
              are averaged across all results that share an ``exp_name``, 
              which is typically a set of identical experiments that only vary
              in random seed. But if you'd like to see all of those curves 
              separately, use the ``--count`` flag.
  
          smooth (int): Smooth data by averaging it over a fixed window. This 
              parameter says how wide the averaging window will be.
  
          select (strings): Optional selection rule: the plotter will only show
              curves from logdirs that contain all of these substrings.
  
          exclude (strings): Optional exclusion rule: plotter will only show 
              curves from logdirs that do not contain these substrings.
  
      """
  
      make_plots(args.logdir, args.legend, args.xaxis, args.value, args.count, 
                 smooth=args.smooth, select=args.select, exclude=args.exclude,
                 estimator=args.est)
  
  if __name__ == "__main__":
      main()