original_docstring: 
      Apply transformation to quantities in the dataset. For example,
      if a requested property is log_<actual property>, then create
      this requested property by taking logs in the dataset.
      Args:
         props (list[str]): list of property names that you want to predict
         summary_dic (dict): dictionary of the form {smiles: sub_dic},
          where `sub_dic` is a dictionary with all the species properties
          apart from its conformers.
      Returns:
        None
      
docstring: Apply transformation to quantities in the dataset. For example,
  if a requested property is log_, then create
  this requested property by taking logs in the dataset.
docstring_tokens: Apply transformation to quantities in the dataset . For example if a requested property is log_ then create this requested property by taking logs in the dataset .
code: def apply_transfs(props, summary_dic):
      """
      Apply transformation to quantities in the dataset. For example,
      if a requested property is log_<actual property>, then create
      this requested property by taking logs in the dataset.
      Args:
         props (list[str]): list of property names that you want to predict
         summary_dic (dict): dictionary of the form {smiles: sub_dic},
          where `sub_dic` is a dictionary with all the species properties
          apart from its conformers.
      Returns:
        None
      """
  
      for prop in props:
          prop_present = any([prop in sub_dic for sub_dic
                              in summary_dic.values()])
          if prop_present:
              continue
  
          if prop.startswith("log_"):
              base_prop = prop.split("log_")[-1]
  
              def transf(x): return np.log(x)
  
          else:
              raise Exception((f"{prop} is not in the summary "
                               "dictionary and doesn't have a prefix "
                               "corresponding to a known transformation, "
                               "such as log."))
  
          base_present = any([base_prop in sub_dic for sub_dic
                              in summary_dic.values()])
          if not base_present:
              raise Exception((f"{base_prop} is not in the summary "
                               "dictionary."))
  
          # update summary dictionary with transformed keys
  
          for smiles, sub_dic in summary_dic.items():
              if base_prop in sub_dic:
                  sub_dic.update({prop: transf(sub_dic[base_prop])})
code_tokens: def apply_transfs ( props , summary_dic ) : for prop in props : prop_present = any ( [ prop in sub_dic for sub_dic in summary_dic . values ( ) ] ) if prop_present : continue if prop . startswith ( "log_" ) : base_prop = prop . split ( "log_" ) [ - 1 ] def transf ( x ) : return np . log ( x ) else : raise Exception ( ( f"{prop} is not in the summary " "dictionary and doesn't have a prefix " "corresponding to a known transformation, " "such as log." ) ) base_present = any ( [ base_prop in sub_dic for sub_dic in summary_dic . values ( ) ] ) if not base_present : raise Exception ( ( f"{base_prop} is not in the summary " "dictionary." ) ) for smiles , sub_dic in summary_dic . items ( ) : if base_prop in sub_dic : sub_dic . update ( { prop : transf ( sub_dic [ base_prop ] ) } )
short_docstring: Apply transformation to quantities in the dataset.
short_docstring_tokens: Apply transformation to quantities in the dataset .
comment: """
      Apply transformation to quantities in the dataset. For example,
      if a requested property is log_<actual property>, then create
      this requested property by taking logs in the dataset.
      Args:
         props (list[str]): list of property names that you want to predict
         summary_dic (dict): dictionary of the form {smiles: sub_dic},
          where `sub_dic` is a dictionary with all the species properties
          apart from its conformers.
      Returns:
        None
      """$SEP$# update summary dictionary with transformed keys