def main():
logger.debug("Reading config parameters from file - Start")
cfg = ConfigParserHelper()
print cfg.get('main', 'title')
#Get the config parameters
filename = cfg.get('main','input_filename') #inp file with pmids
source_type = cfg.get('main','source_type') #source type - currently supports only 'Pubmed'
java_path = cfg.get('ner','java_path') #java path to run the MedKATp jar
medkat_conf = cfg.get('ner','medkat_conf_file') #MedKATp config file
txt_input = cfg.get('ner','txt_inp_filepath') #MedKATp input folder with txt files
xmi_output = cfg.get('ner','xmi_out_filepath') #MedKATp output folder with xmi files
enable = cfg.get('ner','enable') #To enable/disable running ner
num_clusters = cfg.get('cluster','num_clusters') #Number of clusters
num_words_per_cluster = cfg.get('cluster','num_words_per_cluster') #Number of top words to be displayed per cluster
logger.info("Reading config parameters - Done")
print("Reading config parameters - Done")
logger.debug("Retrieving pmids from file - Start")
#open a file object and read the lines in the file
with open(filename) as f:
lines = f.read().splitlines()
logger.info("Retrieving pmids from file - Done")
print("Retrieving pmids from file - Done")
logger.debug("Retrieving and storing Pubmed data - Start")
#If the source type is Pubmed, process the list of pmids to get the data from pubmed
if(source_type=='Pubmed'):
pubdata = PubmedData()
pubdata.getPubmedData(lines)
logger.info("Retrieving and storing Pubmed data - Done")
print("Retrieving and storing Pubmed data - Done")
#Call InputHelper to create the input text files for processing
ihelper = InputHelper()
xmi_parser = XMIParser()
#If NER is set to true, prepare input and run the MedKATp pipeline and run the analysis on the terms identified
#Else run the analysis on the complete text
if enable.lower() == 'true':
logger.debug("NER enable set to true")
logger.debug("Creating input and output directories for NER and preparing input - Start")
ihelper.create_input_files(filename, txt_input, xmi_output)
logger.info("Creating input and output directories for NER and preparing input - Done")
print("Creating input and output directories for NER and preparing input - Done")
logger.debug("Running text file processing (NER) on input text - Start")
#start the required environment variables and run a Java process for NER by calling the MedKATp pipeline and block until done
env = dict(os.environ)
env["MEDKATp_INP_CONFIG_FILE"] = medkat_conf
env["CLASSPATH"] = ".:lib/*:MedKATp-20160109.jar:/opt/nlp/MedKATp/resources/"
import subprocess
subprocess.call([java_path, '-Xmx1024m', '-Xms256m', 'nlp.pubmed.ner.inputReader.MedKATRunner'],env=env)
logger.info("Running text file processing (NER) on input text - Done")
print("Running text file processing (NER) on input text - Done")
logger.debug("Running analysis an clustering on NER output - Start")
xmi_parser.parse_xmi_files(xmi_output, num_clusters, num_words_per_cluster)
logger.info("Running analysis an clustering on NER output - Done")
#print("Running analysis an clustering on NER output - Done")
else:
logger.debug("NER enable set to false")
pmids_array = []
text_array = []
logger.debug("Preparing input for analysis and clustering- Start")
input_list = ihelper.create_input(filename)
for key in input_list.keys():
pmids_array.append(key)
text_array.append(input_list[key])
logger.info("Preparing input for analysis and clustering- Done")
print("Preparing input for analysis and clustering- Done")
logger.debug("Running analysis and clustering on text - Start")
xmi_parser.analyze_and_cluster(pmids_array, text_array, num_clusters, num_words_per_cluster)
logger.info("Running analysis and clustering on text - Done")
#Not used currently, for extensibility
class ArticleSentence(Base):
__tablename__ = 'articlesentence'
id = Column(Integer, primary_key=True)
#sentenceID = Column(Integer)
articleID = Column(Integer, ForeignKey('article.PMID'))
startPos = Column(Integer)
endPos = Column(Integer)
#sentIndex = Column(Integer)
class DBHelper:
session = None
def __init__(self):
engine = create_engine(cfg.get('main', 'db_conn_string'))
# create a configured "Session" class
Session = sessionmaker(bind=engine)
# create a Session
self.session = Session()
cfg = ConfigParserHelper()
engine = create_engine(cfg.get('main', 'db_conn_string'))
from sqlalchemy.orm import sessionmaker
session = sessionmaker()
session.configure(bind=engine)
Base.metadata.create_all(engine)
class ArticleSentence(Base):
__tablename__ = 'articlesentence'
id = Column(Integer, primary_key=True)
#sentenceID = Column(Integer)
articleID = Column(Integer, ForeignKey('article.PMID'))
startPos = Column(Integer)
endPos = Column(Integer)
#sentIndex = Column(Integer)
class DBHelper:
session = None
def __init__(self):
engine = create_engine(cfg.get('main', 'db_conn_string'))
# create a configured "Session" class
Session = sessionmaker(bind=engine)
# create a Session
self.session = Session()
cfg = ConfigParserHelper()
engine = create_engine(cfg.get('main', 'db_conn_string'))
from sqlalchemy.orm import sessionmaker
session = sessionmaker()
session.configure(bind=engine)
Base.metadata.create_all(engine)
