original_docstring: 
      Split the data, write it to csvs, create new csvs with just
      SMILES and no properties, and rename the existing csvs.
  
      Args:
        summary_path (str): path to the JSON file that summarizes
          all of the information about the species, apart from their
          conformers.
        csv_folder (str): path to the folder in which we will save our
          csv files with the SMILES, properties and training splits.
        cp_folder (str): path to the ChemProp folder on your computer
        props (list[str]): list of property names that you want to predict
        split_sizes (list[float]): list of the form [train_split_size, val_split_size,
          test_split_size].
        split_type (str): how to split the data. Options can be found in the Chemprop
          script `split_data.py`. A good choice is usually `scaffold_balanced`, which splits
          in such a way that similar scaffolds are in the same split. 
        max_specs (int): maximum number of species allowed in dataset
        max_atoms (int): Maximum number of atoms allowed in a species
        dataset_type (str): type of problem, e.g. "classification" or 
          "regression".
        seed (int): random seed for split
      Returns:
        None
      
docstring: Split the data, write it to csvs, create new csvs with just
  SMILES and no properties, and rename the existing csvs.
docstring_tokens: Split the data write it to csvs create new csvs with just SMILES and no properties and rename the existing csvs .
code: def main(summary_path,
           csv_folder,
           cp_folder,
           props,
           split_sizes,
           split_type,
           max_specs,
           max_atoms,
           dataset_type,
           seed,
           **kwargs):
      """
      Split the data, write it to csvs, create new csvs with just
      SMILES and no properties, and rename the existing csvs.
  
      Args:
        summary_path (str): path to the JSON file that summarizes
          all of the information about the species, apart from their
          conformers.
        csv_folder (str): path to the folder in which we will save our
          csv files with the SMILES, properties and training splits.
        cp_folder (str): path to the ChemProp folder on your computer
        props (list[str]): list of property names that you want to predict
        split_sizes (list[float]): list of the form [train_split_size, val_split_size,
          test_split_size].
        split_type (str): how to split the data. Options can be found in the Chemprop
          script `split_data.py`. A good choice is usually `scaffold_balanced`, which splits
          in such a way that similar scaffolds are in the same split. 
        max_specs (int): maximum number of species allowed in dataset
        max_atoms (int): Maximum number of atoms allowed in a species
        dataset_type (str): type of problem, e.g. "classification" or 
          "regression".
        seed (int): random seed for split
      Returns:
        None
      """
  
      make_split(summary_path=summary_path,
                 csv_folder=csv_folder,
                 cp_folder=cp_folder,
                 props=props,
                 split_sizes=split_sizes,
                 split_type=split_type,
                 max_specs=max_specs,
                 max_atoms=max_atoms,
                 dataset_type=dataset_type,
                 seed=seed)
  
      add_just_smiles(csv_folder)
      rename_csvs(csv_folder)
  
      summarize(csv_folder, dataset_type)
code_tokens: def main ( summary_path , csv_folder , cp_folder , props , split_sizes , split_type , max_specs , max_atoms , dataset_type , seed , ** kwargs ) : make_split ( summary_path = summary_path , csv_folder = csv_folder , cp_folder = cp_folder , props = props , split_sizes = split_sizes , split_type = split_type , max_specs = max_specs , max_atoms = max_atoms , dataset_type = dataset_type , seed = seed ) add_just_smiles ( csv_folder ) rename_csvs ( csv_folder ) summarize ( csv_folder , dataset_type )
short_docstring: Split the data, write it to csvs, create new csvs with just
  SMILES and no properties, and rename the existing csvs.
short_docstring_tokens: Split the data write it to csvs create new csvs with just SMILES and no properties and rename the existing csvs .
comment: """
      Split the data, write it to csvs, create new csvs with just
      SMILES and no properties, and rename the existing csvs.
  
      Args:
        summary_path (str): path to the JSON file that summarizes
          all of the information about the species, apart from their
          conformers.
        csv_folder (str): path to the folder in which we will save our
          csv files with the SMILES, properties and training splits.
        cp_folder (str): path to the ChemProp folder on your computer
        props (list[str]): list of property names that you want to predict
        split_sizes (list[float]): list of the form [train_split_size, val_split_size,
          test_split_size].
        split_type (str): how to split the data. Options can be found in the Chemprop
          script `split_data.py`. A good choice is usually `scaffold_balanced`, which splits
          in such a way that similar scaffolds are in the same split. 
        max_specs (int): maximum number of species allowed in dataset
        max_atoms (int): Maximum number of atoms allowed in a species
        dataset_type (str): type of problem, e.g. "classification" or 
          "regression".
        seed (int): random seed for split
      Returns:
        None
      """