import csv

ppelm_s3_key = ''


def process_from_dump(fname=None, delimiter='\t'):
    if fname is None:
        # ToDo Get from S3
        return []
    else:
        with open(fname, 'r') as f:
            csv_reader = csv.reader(f.readlines(), delimiter=delimiter)
            ppelm_json = _get_json_from_entry_rows(csv_reader)
    return ppelm_json


def _get_json_from_entry_rows(row_iter):
    ppelm_json = []
    columns = next(row_iter)
    for entry in row_iter:
        row_dict = {columns[n]: entry[n]
                    for n in range(len(columns))}
        ppelm_json.append(row_dict)
    return ppelm_json

import csv

from .processor import PhosphoELMPRocessor

s3_bucket = 'bigmech'
ppelm_s3_key = ''


def process_from_dump(fname=None, delimiter='\t'):
    if fname is None:
        # ToDo Get from S3
        return []
    else:
        with open(fname, 'r') as f:
            csv_reader = csv.reader(f.readlines(), delimiter=delimiter)
            ppelm_json = _get_json_from_entry_rows(csv_reader)
    return PhosphoELMPRocessor(file_dump_json=ppelm_json)


def _get_json_from_entry_rows(row_iter):
    ppelm_json = []
    columns = next(row_iter)
    for entry in row_iter:
        row_dict = {columns[n]: entry[n]
                    for n in range(len(columns))}
        ppelm_json.append(row_dict)
    return ppelm_json

Return processor w processed statements
Return processor w processed statements

  import csv
  
+ from .processor import PhosphoELMPRocessor
+ 
+ s3_bucket = 'bigmech'
  ppelm_s3_key = ''
  
  
  def process_from_dump(fname=None, delimiter='\t'):
      if fname is None:
          # ToDo Get from S3
          return []
      else:
          with open(fname, 'r') as f:
              csv_reader = csv.reader(f.readlines(), delimiter=delimiter)
              ppelm_json = _get_json_from_entry_rows(csv_reader)
-     return ppelm_json
+     return PhosphoELMPRocessor(file_dump_json=ppelm_json)
  
  
  def _get_json_from_entry_rows(row_iter):
      ppelm_json = []
      columns = next(row_iter)
      for entry in row_iter:
          row_dict = {columns[n]: entry[n]
                      for n in range(len(columns))}
          ppelm_json.append(row_dict)
      return ppelm_json
  
Return processor w processed statements
## Code Before:
import csv

ppelm_s3_key = ''


def process_from_dump(fname=None, delimiter='\t'):
    if fname is None:
        # ToDo Get from S3
        return []
    else:
        with open(fname, 'r') as f:
            csv_reader = csv.reader(f.readlines(), delimiter=delimiter)
            ppelm_json = _get_json_from_entry_rows(csv_reader)
    return ppelm_json


def _get_json_from_entry_rows(row_iter):
    ppelm_json = []
    columns = next(row_iter)
    for entry in row_iter:
        row_dict = {columns[n]: entry[n]
                    for n in range(len(columns))}
        ppelm_json.append(row_dict)
    return ppelm_json

## Instruction:
Return processor w processed statements
## Code After:
import csv

from .processor import PhosphoELMPRocessor

s3_bucket = 'bigmech'
ppelm_s3_key = ''


def process_from_dump(fname=None, delimiter='\t'):
    if fname is None:
        # ToDo Get from S3
        return []
    else:
        with open(fname, 'r') as f:
            csv_reader = csv.reader(f.readlines(), delimiter=delimiter)
            ppelm_json = _get_json_from_entry_rows(csv_reader)
    return PhosphoELMPRocessor(file_dump_json=ppelm_json)


def _get_json_from_entry_rows(row_iter):
    ppelm_json = []
    columns = next(row_iter)
    for entry in row_iter:
        row_dict = {columns[n]: entry[n]
                    for n in range(len(columns))}
        ppelm_json.append(row_dict)
    return ppelm_json
