old_contents: import os, sys, re
  import numpy as np
  import pandas as pd
  import logging
  from multiprocessing import Pool
  
  from .starmodel import StarModel
  
  
  def get_quantiles(name, rootdir='.', columns=['eep','mass','radius','age','feh','distance','AV'],
                   qs=[0.05,0.16,0.5,0.84,0.95], modelname='mist_starmodel_single',
                   verbose=False, raise_exceptions=False):
      """Returns parameter quantiles for starmodel
      """
  
      modfile = os.path.join(rootdir, name,'{}.h5'.format(modelname))
      try:
          mod = StarModel.load_hdf(modfile)
      except:
          if verbose:
              print('cannnot load starmodel! ({})'.format(modfile))
          if raise_exceptions:
              raise
          return pd.DataFrame()
  
      # Get actual column names
      true_cols = []
      for c1 in mod.samples.columns:
          for c2 in columns:
              if re.search(c2, c1):
                  true_cols.append(c1)
  
      q_df = mod.samples[true_cols].quantile(qs)
  
      df = pd.DataFrame(index=[name])
      for c in true_cols:
          for q in qs:
              col = c + '_{:02.0f}'.format(q*100)
              df.loc[name, col] = q_df.loc[q, c]
  
      return df
  
  class quantile_worker(object):
      def __init__(self, **kwargs):
          self.kwargs = kwargs
  
      def __call__(self, name):
          return get_quantiles(name, **self.kwargs)
  
  def get_summary_df(names=None, pool=None, **kwargs):
  
      if pool is None:
          map_fn = map
      else:
          map_fn = pool.map
  
      worker = quantile_worker(**kwargs)
      dfs = map_fn(worker, names)
  
      df = pd.concat(dfs)
      return df
  
      if filename is None:
          filename = 'summary.h5'
      df.to_hdf(filename, 'df')
      pool.close()
  
      print('Summary dataframe written to {}'.format(filename))
      return df
  
new_contents: import os, sys, re
  import numpy as np
  import pandas as pd
  import logging
  from multiprocessing import Pool
  
  from .starmodel import StarModel, BasicStarModel
  
  
  def get_quantiles(name, rootdir='.', columns=['eep','mass','radius','age','feh','distance','AV'],
                   qs=[0.05,0.16,0.5,0.84,0.95], modelname='mist_starmodel_single',
                   verbose=False, raise_exceptions=False):
      """Returns parameter quantiles for starmodel
      """
  
      modfile = os.path.join(rootdir, name,'{}.h5'.format(modelname))
      try:
          mod = BasicStarModel.load_hdf(modfile)
      except:
          if verbose:
              print('cannnot load starmodel! ({})'.format(modfile))
          if raise_exceptions:
              raise
          return pd.DataFrame()
  
      # Get actual column names
      true_cols = []
      for c1 in mod.samples.columns:
          for c2 in columns:
              if re.search(c2, c1):
                  true_cols.append(c1)
  
      q_df = mod.samples[true_cols].quantile(qs)
  
      df = pd.DataFrame(index=[name])
      for c in true_cols:
          for q in qs:
              col = c + '_{:02.0f}'.format(q*100)
              df.loc[name, col] = q_df.loc[q, c]
  
      return df
  
  class quantile_worker(object):
      def __init__(self, **kwargs):
          self.kwargs = kwargs
  
      def __call__(self, name):
          return get_quantiles(name, **self.kwargs)
  
  def get_summary_df(names=None, pool=None, **kwargs):
  
      if pool is None:
          map_fn = map
      else:
          map_fn = pool.map
  
      worker = quantile_worker(**kwargs)
      dfs = map_fn(worker, names)
  
      df = pd.concat(dfs)
      return df
  
      if filename is None:
          filename = 'summary.h5'
      df.to_hdf(filename, 'df')
      pool.close()
  
      print('Summary dataframe written to {}'.format(filename))
      return df
  
subject: make summary use basicstarmodel