Fixed some indexing and printing issues

import cPickle as pickle

import warnings

from multiprocessing import Pool

import itertools

from functools import partial

import pandas as pd

def merge_dicts(*dict_args):

  '''

  """

  Given any number of dicts, shallow copy and merge into a new dict,

  precedence goes to key value pairs in latter dicts.

  '''

  """

  result = {}

  for dictionary in dict_args:

    if isinstance(dictionary, list):

      print("dictionary[:100]")

      print(dictionary[:100])

    result.update(dictionary)

  return result

  all_mols_dict = merge_dicts(*all_mols)

  return all_mols_dict

def mols_to_dict(mols, id_prefix, log_every_n=2000):

def mols_to_dict(mols, id_prefix, log_every_n=5000):

  print("About to process molecules")

  mol_dict = {}

  for ind, mol in enumerate(mols):

  return mol_dict

def get_target_names(targets_dir):

  """Read list of targets in collection from disk."""

  targets = [target for target in os.listdir(targets_dir)

             if "pkl.gz" in target]

  # Remove the .pkl.gz

  return [os.path.splitext(os.path.splitext(target)[0])[0]

          for target in targets]

  return [remove_extensions(target) for target in targets]

def process_target(target, targets_dir, overwrite):

  if "pkl.gz" not in target:

    process_target_partial = partial(

        process_target, targets_dir=targets_dir, overwrite=overwrite)

    csv_files = worker_pool.map(process_target_partial, targets)

    csv_files = [csv_file for csv_file in csv_files if csv_file is not None]

  return csv_files

def remove_extensions(target_name):

def target_to_csv(targets_dir, df, target_name, log_every_n=50000,

                  overwrite=False):

  csv_file = os.path.join(

      targets_dir, remove_extensions(target_name) + ".csv")

  """Converts the data in a target dataframe to a csv."""

  target = remove_extensions(target_name) 

  csv_file = os.path.join(targets_dir, target + ".csv")

  if not overwrite and os.path.isfile(csv_file):

    return csv_file

  target_names = get_target_names(targets_dir) 

  data_df = pd.DataFrame(columns=(["mol_id"] + target_names))

  data_df["mol_id"] = df["mol_id"]

  data_targets = df["target"]

  #data_targets = df["target"]

  def get_outcome(row):

    if row["outcome"] == "active":

      return "1"

    else:

      return "" 

  data_outcomes = df.apply(get_outcome, axis=1)

  for ind, (target, outcome) in enumerate(zip(data_targets, data_outcomes)):

  for ind, outcome in enumerate(data_outcomes):

    data_df.set_value(ind, target, outcome)

    for other_target in target_names:

      if other_target != target:

        data_df.set_value(ind, other_target, "")

  #iterator = data_df.iterrows()

  data_df.fillna("")

  with open(csv_file, "wb") as f:

    data_df.to_csv(f)

  return csv_file

  #return data_df.to_dict("records")

def join_datapoints(old_record, new_record, target_names):

  """Merge two datapoints together."""

  return out_record

def merge_mol_data_dicts(mol_dict, csv_files, target_names):

  """Merge data from target and molecule listings."""

  print("len(mol_dict) = %d" % len(mol_dict))

  num_missing = 0

  merged_data = {}

  csv_files = process_targets(targets_dir, overwrite, worker_pool)

  merged_dict = merge_mol_data_dicts(mol_dict, csv_files, target_names)

  print("merged_dict.values()[0]")

  print(merged_dict.values()[0])

  merged_df = pd.DataFrame(merged_dict.values())

  print("merged_df.iterrows().next()[1]")

  print(merged_df.iterrows().next()[1])

  merged_df.fillna("")

  with open(out, "wb") as f:

    merged_df.to_csv(f)

  #write_csv(targets_dir, merged_dict, out)

    print("out")

    print(out)

    merged_df.to_csv(f, index=False)

def main():

  args = parse_args()