Source code for

from __future__ import absolute_import, print_function, unicode_literals
from builtins import dict, str
import os
import argparse
import stat
import random
import sys
import re
import tempfile
import shutil
import subprocess
import glob
import json
import pickle
import functools
import signal
import time
import multiprocessing as mp
from datetime import datetime
from collections import Counter
from platform import system
import logging
from indra import get_config

logger = logging.getLogger('')

from indra.sources import reach
from indra.literature import pmc_client, s3_client, get_full_text, \
from indra.sources.sparser import api as sparser

def make_parser():
    parser = argparse.ArgumentParser(
        description=('Apply NLP readers to the content available for a list of '
        '-r', '--reader',
        choices=['reach', 'sparser', 'all'],
        help='Choose which reader(s) to use.'
        '-u', '--upload_json',
        help=('Option to simply upload previously read json files. Overrides -r '
              'option, so no reading will be done.')
        help='Option to force reading of the full text.'
        help='Option to force the reader to reread everything.'
        '-n', '--num_cores',
        help='Select the number of cores you want to use.'
        '-v', '--verbose',
        help='Show more output to screen.'
        '-m', '--messy',
        help='Choose to not clean up after run.'
        '-s', '--start_index',
        help='Select the first pmid in the list to start reading.',
        '-e', '--end_index',
        help='Select the last pmid in the list to read.',
        help=('Select a random sample of the pmids provided. -s/--start_index '
              'will be ingored, and -e/--end_index will set the number of '
              'samples to take.')
        '-o', '--outdir',
        help=('The output directory where stuff is written. This is only a '
              'temporary directory when reading. By default this will be the'
        help='The name of this job.'
        help=('Path to a file containing a list of line separated pmids for the '
              'articles to be read.')
    return parser

# Version 1: If JSON is not available, get content and store;
#       assume force_read is False
# Version 1.5: If JSON is not available, get content and store;
#       check for force_read
# Version 2: If JSON is available, return JSON or process
# it and return statements (process it?)

# LOADING -- the following are methods to load the content to be read.

[docs]def join_json_files(prefix): """Join different REACH output JSON files into a single JSON object. The output of REACH is broken into three files that need to be joined before processing. Specifically, there will be three files of the form: `<prefix>.uaz.<subcategory>.json`. Parameters ---------- prefix : str The absolute path up to the extensions that reach will add. Returns ------- json_obj : dict The result of joining the files, keyed by the three subcategories. """ try: with open(prefix + '.uaz.entities.json', 'rt') as f: entities = json.load(f) with open(prefix + '', 'rt') as f: events = json.load(f) with open(prefix + '.uaz.sentences.json', 'rt') as f: sentences = json.load(f) except IOError as e: logger.error( 'Failed to open JSON files for %s; REACH error?' % prefix ) logger.exception(e) return None return {'events': events, 'entities': entities, 'sentences': sentences}
def download_from_s3(pmid, reader='all', input_dir=None, reader_version=None, force_read=False, force_fulltext=False):'Downloading %s from S3, force_read=%s, force_fulltext=%s ' 'reader_version=%s') % (pmid, force_read, force_fulltext, reader_version)) if input_dir is None: raise ValueError('input_dir must be defined') # First define the text retrieval function def get_text(): # Add timeout here for PubMed time.sleep(0.5) # full_pmid = s3_client.check_pmid(pmid) # Look for the full text content, content_type = s3_client.get_upload_content( pmid, force_fulltext_lookup=force_fulltext ) content_path = None # Write the contents to a file if content_type is None or content is None: # No content found on S3, skipping content_source = 'content_not_found' elif content_type == 'pmc_oa_xml': content_source = 'pmc_oa_xml' content_path = os.path.join(input_dir, '%s.nxml' % pmid) elif content_type == 'pmc_auth_xml': content_source = 'pmc_auth_xml' content_path = os.path.join(input_dir, '%s.nxml' % pmid) elif content_type == 'pmc_oa_txt': content_source = 'pmc_oa_txt' content_path = os.path.join(input_dir, '%s.txt' % pmid) elif content_type == 'elsevier_xml': content = elsevier_client.extract_text(content) # Couldn't get text from Elsevier XML if content is None: content_source = 'elsevier_extract_text_failure' else: content_source = 'elsevier_xml' content_path = os.path.join(input_dir, '%s.txt' % pmid) elif content_type == 'txt': content_source = 'txt' content_path = os.path.join(input_dir, '%s.txt' % pmid) elif content_type == 'abstract': content_source = 'abstract' content_path = os.path.join(input_dir, '%s.txt' % pmid) # Unhandled content type, skipping else: content_source = 'unhandled_content_type_%s' % content_type # If we got content, write the content to a file with the appropriate # extension if content_path: with open(content_path, 'wb') as f: # The XML string is Unicode enc = content.encode('utf-8') f.write(enc) # Return dict of results for this PMID result = {pmid: {'content_source': content_source, 'content_path': content_path}} return result # If we're forcing a read regardless of whether there is cached REACH # output, then we download the text content if force_read or reader_version is None: return get_text() # If not, look for REACH JSON on S3 reader_version_s3, read_source_text = \ s3_client.get_reader_metadata(reader, pmid) # Found it, same version, no need to get text if (reader_version_s3 is not None and reader_version_s3 == reader_version): result = {pmid: { 'reader_version': reader_version_s3, 'reach_source_text': read_source_text }} # Found it, different version, get the text else: result = get_text() result[pmid].update({'reader_version': reader_version_s3, 'reach_source_text': read_source_text}) return result def get_content_to_read(pmid_list, start_index, end_index, tmp_dir, num_cores, force_fulltext, force_read, reader, reader_version): if end_index is None or end_index > len(pmid_list): end_index = len(pmid_list) pmids_in_range = pmid_list[start_index:end_index] # Create the temp directories for input and output base_dir = tempfile.mkdtemp(prefix='read_%s_to_%s_' % (start_index, end_index), dir=tmp_dir) # Make the temp directory writeable by REACH os.chmod(base_dir, stat.S_IRWXO | stat.S_IRWXU | stat.S_IRWXG) input_dir = os.path.join(base_dir, 'input') output_dir = os.path.join(base_dir, 'output') os.makedirs(input_dir) os.makedirs(output_dir) download_from_s3_func = functools.partial( download_from_s3, input_dir=input_dir, reader=reader, reader_version=reader_version, force_read=force_read, force_fulltext=force_fulltext ) if num_cores > 1: # Get content using a multiprocessing pool'Creating multiprocessing pool with %d cpus' % num_cores) pool = mp.Pool(num_cores)'Getting content for PMIDs in parallel') res =, pmids_in_range) pool.close() # Wait for procs to end.'Multiprocessing pool closed.') pool.join()'Multiprocessing pool joined.') else: res = list(map(download_from_s3_func, pmids_in_range)) # Combine the results into a single dict pmid_results = { pmid: results for pmid_dict in res for pmid, results in pmid_dict.items() } # Tabulate and log content results here pmids_read = { pmid: result for pmid, result in pmid_results.items() if result.get('reader_version') == reader_version } pmids_unread = { pmid: pmid_results[pmid] for pmid in set(pmid_results.keys()).difference(set(pmids_read.keys())) } '%d / %d papers already read with %s %s' % (len(pmids_read), len(pmid_results), reader, reader_version) ) num_found = len([ pmid for pmid in pmids_unread if pmids_unread[pmid].get('content_path') is not None ]) 'Retrieved content for %d / %d papers to be read' % (num_found, len(pmids_unread)) ) # Tabulate sources and log in sorted order content_source_list = [ pmid_dict.get('content_source') for pmid_dict in pmids_unread.values() ] content_source_counter = Counter(content_source_list) content_source_list = [ (source, count) for source, count in content_source_counter.items() ] content_source_list.sort(key=lambda x: x[1], reverse=True) if content_source_list:'Content sources:') for source, count in content_source_list:'%s: %d' % (source, count)) # Save text sources'Saving text sources...') text_source_file = os.path.join(base_dir, 'content_types.pkl') with open(text_source_file, 'wb') as f: pickle.dump(pmids_unread, f, protocol=4)'Text sources saved.') return base_dir, input_dir, output_dir, pmids_read, pmids_unread, num_found #============================================================================== # SPARSER -- The following are methods to process content with sparser. #============================================================================== def _timeout_handler(signum, frame): raise Exception('Timeout')
[docs]def read_pmid(pmid, source, cont_path, sparser_version, outbuf=None, cleanup=True): "Run sparser on a single pmid." signal.signal(signal.SIGALRM, _timeout_handler) signal.alarm(60) try: if (source is 'content_not_found' or source.startswith('unhandled_content_type') or source.endswith('failure')):'No content read for %s.' % pmid) return # No real content here. if cont_path.endswith('.nxml') and source.startswith('pmc'): new_fname = 'PMC%s%d.nxml' % (pmid, mp.current_process().pid) os.rename(cont_path, new_fname) try: sp = sparser.process_nxml_file( new_fname, outbuf=outbuf, cleanup=cleanup ) finally: if cleanup and os.path.exists(new_fname): os.remove(new_fname) elif cont_path.endswith('.txt'): content_str = '' with open(cont_path, 'r') as f: content_str = sp = sparser.process_text( content_str, outbuf=outbuf, cleanup=cleanup ) signal.alarm(0) except Exception as e: logger.error('Failed to process data for %s.' % pmid) logger.exception(e) signal.alarm(0) return if sp is None: logger.error('Failed to run sparser on pmid: %s.' % pmid) return # At this point, we rewrite the PMID in the Evidence of Sparser # Statements according to the actual PMID that was read. sp.set_statements_pmid(pmid) s3_client.put_reader_output('sparser', sp.json_stmts, pmid, sparser_version, source) return sp.statements
[docs]def get_stmts(pmids_unread, cleanup=True, sparser_version=None): "Run sparser on the pmids in pmids_unread." if sparser_version is None: sparser_version = sparser.get_version() stmts = {} now = outbuf_fname = 'sparser_%s_%s.log' % ( now.strftime('%Y%m%d-%H%M%S'), mp.current_process().pid, ) outbuf = open(outbuf_fname, 'wb') try: for pmid, result in pmids_unread.items():'Reading %s' % pmid) source = result['content_source'] cont_path = result['content_path'] outbuf.write(('\nReading pmid %s from %s located at %s.\n' % ( pmid, source, cont_path )).encode('utf-8')) outbuf.flush() some_stmts = read_pmid(pmid, source, cont_path, sparser_version, outbuf, cleanup) if some_stmts is not None: stmts[pmid] = some_stmts else: continue # We didn't get any new statements. except KeyboardInterrupt as e: logger.exception(e)'Caught keyboard interrupt...stopping. \n' 'Results so far will be pickled unless ' 'Keyboard interupt is hit again.') finally: outbuf.close() print("Sparser logs may be found in %s" % outbuf_fname) return stmts
def get_stmts_from_cache(pmid): json_str = s3_client.get_reader_json_str('sparser', pmid) stmts = [] if json_str is not None: stmts = sparser.process_json_dict(json.loads(json_str)).statements return {pmid: stmts}
[docs]def run_sparser(pmid_list, tmp_dir, num_cores, start_index, end_index, force_read, force_fulltext, cleanup=True, verbose=True): 'Run the sparser reader on the pmids in pmid_list.' reader_version = sparser.get_version() _, _, _, pmids_read, pmids_unread, _ =\ get_content_to_read( pmid_list, start_index, end_index, tmp_dir, num_cores, force_fulltext, force_read, 'sparser', reader_version )'Adjusting num cores to length of pmid_list.') num_cores = min(len(pmid_list), num_cores)'Adjusted...') if num_cores is 1: stmts = get_stmts(pmids_unread, cleanup=cleanup) stmts.update({pmid: get_stmts_from_cache(pmid)[pmid] for pmid in pmids_read.keys()}) elif num_cores > 1:"Starting a pool with %d cores." % num_cores) pool = mp.Pool(num_cores) pmids_to_read = list(pmids_unread.keys()) N = len(pmids_unread) dn = int(N/num_cores)"Breaking pmids into batches.") batches = [] for i in range(num_cores): batches.append({ k: pmids_unread[k] for k in pmids_to_read[i*dn:min((i+1)*dn, N)] }) get_stmts_func = functools.partial( get_stmts, cleanup=cleanup, sparser_version=reader_version )"Mapping get_stmts onto pool.") unread_res =, batches)'len(unread_res)=%d' % len(unread_res)) read_res =, pmids_read.keys())'len(read_res)=%d' % len(read_res)) pool.close()'Multiprocessing pool closed.') pool.join()'Multiprocessing pool joined.') stmts = { pmid: stmt_list for res_dict in unread_res + read_res for pmid, stmt_list in res_dict.items() }'len(stmts)=%d' % len(stmts)) return (stmts, pmids_unread)
#============================================================================== # REACH -- The following are methods to process content with reach. #============================================================================== REACH_CONF_FMT_FNAME = os.path.join(os.path.dirname(__file__), '../readers/reach/reach_conf_fmt.txt') REACH_MEM = 5 # GB MEM_BUFFER = 2 # GB def process_reach_str(reach_json_str, pmid): if reach_json_str is None: raise ValueError('reach_json_str cannot be None') # Run the REACH processor on the JSON try: reach_proc = reach.process_json_str(reach_json_str, citation=pmid) # If there's a problem, skip it except Exception as e: print("Exception processing %s" % pmid) print(e) return [] return reach_proc.statements def process_reach_from_s3(pmid): reach_json_str = s3_client.get_reader_json_str('reach', pmid) if reach_json_str is None: return [] else: return {pmid: process_reach_str(reach_json_str, pmid)} def upload_reach_readings(pmid, source_type, reader_version, output_dir=None):'Uploading reach result for %s for %s.' % (source_type, pmid)) # The prefixes should be PMIDs prefix_with_path = os.path.join(output_dir, pmid) full_json = join_json_files(prefix_with_path) # Check that all parts of the JSON could be assembled if full_json is None: logger.error('REACH output missing JSON for %s' % pmid) return {pmid: []} # Upload the REACH output to S3 s3_client.put_reader_output('reach', full_json, pmid, reader_version, source_type) return full_json def upload_process_pmid(pmid_json_tpl): pmid, full_json = pmid_json_tpl # Process the REACH output with INDRA # Convert the JSON object into a string first so that a series of string # replacements can happen in the REACH processor reach_json_str = json.dumps(full_json) return {pmid: process_reach_str(reach_json_str, pmid)} def upload_process_reach_files(output_dir, pmid_info_dict, reader_version, num_cores): # At this point, we have a directory full of JSON files # Collect all the prefixes into a set, then iterate over the prefixes # Collect prefixes json_files = glob.glob(os.path.join(output_dir, '*.json')) json_prefixes = set([]) for json_file in json_files: filename = os.path.basename(json_file) prefix = filename.split('.')[0] json_prefixes.add(prefix) # Make a list with PMID and source_text info"Uploading reading results for reach.") pmid_json_tuples = [] for json_prefix in json_prefixes: try: full_json = upload_reach_readings( json_prefix, pmid_info_dict[json_prefix].get('content_source'), reader_version, output_dir ) pmid_json_tuples.append((json_prefix, full_json)) except Exception as e: logger.error("Caught an exception while trying to upload reach " "reading results onto s3 for %s." % json_prefix) logger.exception(e) # Create a multiprocessing pool'Creating a multiprocessing pool with %d cores' % num_cores) # Get a multiprocessing pool. pool = mp.Pool(num_cores)'Processing local REACH JSON files') res =, pmid_json_tuples) stmts_by_pmid = { pmid: stmts for res_dict in res for pmid, stmts in res_dict.items() } pool.close()'Multiprocessing pool closed.') pool.join()'Multiprocessing pool joined.') """'Uploaded REACH JSON for %d files to S3 (%d failures)' % (num_uploaded, num_failures)) failures_file = os.path.join(output_dir, 'failures.txt') with open(failures_file, 'wt') as f: for fail in failures: f.write('%s\n' % fail) """ return stmts_by_pmid
[docs]def run_reach(pmid_list, base_dir, num_cores, start_index, end_index, force_read, force_fulltext, cleanup=False, verbose=True): """Run reach on a list of pmids."""'Running REACH with force_read=%s' % force_read)'Running REACH with force_fulltext=%s' % force_fulltext) # Get the path to the REACH JAR path_to_reach = get_config('REACHPATH') if path_to_reach is None or not os.path.exists(path_to_reach): logger.warning( 'Reach path not set or invalid. Check REACHPATH environment var.' ) return {}, {}'Using REACH jar at: %s' % path_to_reach) # Get the REACH version reach_version = get_config('REACH_VERSION') if reach_version is None:'REACH version not set in REACH_VERSION') m = re.match('reach-(.*?)\.jar', os.path.basename(path_to_reach)) reach_version = re.sub('-SNAP.*?$', '', m.groups()[0])'Using REACH version: %s' % reach_version) tmp_dir, _, output_dir, pmids_read, pmids_unread, num_found =\ get_content_to_read( pmid_list, start_index, end_index, base_dir, num_cores, force_fulltext, force_read, 'reach', reach_version ) stmts = {} mem_tot = get_mem_total() if mem_tot is not None and mem_tot <= REACH_MEM + MEM_BUFFER: logger.error( "Too little memory to run reach. At least %s required." % REACH_MEM + MEM_BUFFER )"REACH not run.") elif len(pmids_unread) > 0 and num_found > 0: # Create the REACH configuration file with open(REACH_CONF_FMT_FNAME, 'r') as fmt_file: conf_file_path = os.path.join(tmp_dir, 'indra.conf') with open(conf_file_path, 'w') as conf_file: conf_file.write(, num_cores=num_cores, loglevel='INFO') ) # Run REACH!"Beginning reach.") args = ['java', '-Xmx24000m', '-Dconfig.file=%s' % conf_file_path, '-jar', path_to_reach] p = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE) if verbose: for line in iter(p.stdout.readline, b''): p_out, p_err = p.communicate() if p.returncode: logger.error('Problem running REACH:') logger.error('Stdout: %s' % p_out.decode('utf-8')) logger.error('Stderr: %s' % p_err.decode('utf-8')) raise Exception('REACH crashed') # Process JSON files from local file system, process to INDRA # Statements and upload to S3 some_stmts = upload_process_reach_files( output_dir, pmids_unread, reach_version, num_cores ) stmts.update(some_stmts) # Delete the tmp directory if desired if cleanup: shutil.rmtree(tmp_dir) # Create a new multiprocessing pool for processing the REACH JSON # files previously cached on S3'Creating multiprocessing pool with %d cpus' % num_cores) pool = mp.Pool(num_cores) # Download and process the JSON files on S3'Processing REACH JSON from S3 in parallel') res =, pmids_read.keys()) pool.close()'Multiprocessing pool closed.') pool.join()'Multiprocessing pool joined.') s3_stmts = { pmid: stmt_list for res_dict in res for pmid, stmt_list in res_dict.items() } stmts.update(s3_stmts) # Save the list of PMIDs with no content found on S3/literature client ''' content_not_found_file = os.path.join(tmp_dir, 'content_not_found.txt') with open(content_not_found_file, 'wt') as f: for c in content_not_found: f.write('%s\n' % c) ''' return stmts, pmids_unread
#============================================================================== # MAIN -- the main script #============================================================================== READER_DICT = {'reach': run_reach, 'sparser': run_sparser} def get_mem_total(): if system() == 'Linux': with open('/proc/meminfo', 'r') as f: lines = f.readlines() tot_entry = [line for line in lines if line.startswith('MemTotal')][0] ret = int(tot_entry.split(':')[1].replace('kB', '').strip())/10**6 else: ret = None return ret def get_proc_num(): if system() == 'Linux': with open('/proc/cpuinfo', 'r') as f: ret = len([ line for line in f.readlines() if line.startswith('processor') ]) else: ret = None return ret def main(): parser = make_parser() args = parser.parse_args() now = # Set some variables if args.upload_json: args.readers = 'none' out_dir = args.out_dir if out_dir is None: out_dir = args.basename + '_out' made_outdir = False if not os.path.exists(out_dir): os.mkdir(out_dir) made_outdir = True ret = None available_cores = get_proc_num() if args.num_cores >= available_cores: msg = ("You requested %d cores, but only %d available.\n" % (args.num_cores, available_cores) + "Are you sure you want to proceed? [y/N] > ") if sys.version_info.major > 2: resp = input(msg) else: resp = raw_input(msg) if resp.lower() not in ['y', 'yes']:"Aborting...") return try: # Option -u: just upload previously read JSON files if args.upload_json: with open(args.pmid_list_file, 'rb') as f: text_sources = pickle.load(f) stmts = upload_process_reach_files( args.out_dir, text_sources, args.num_cores ) pickle_file = '%s_stmts.pkl' % args.basename with open(pickle_file, 'wb') as f: pickle.dump(stmts, f, protocol=4) sys.exit() # Option -r <reader>: actually read the content. # Load the list of PMIDs from the given file with open(args.pmid_list_file) as f: pmid_list = [line.strip('\n') for line in f.readlines()] if args.end_index is None: args.end_index = len(pmid_list) if args.shuffle: pmid_list = random.sample(pmid_list, args.end_index) # Do the reading if 'all' in args.readers: readers = list(READER_DICT.keys()) else: readers = args.readers[:] stmts = {} for reader in readers: run_reader = READER_DICT[reader] some_stmts, _ = run_reader( pmid_list, out_dir, args.num_cores, args.start_index, args.end_index, args.force_read, args.force_fulltext, cleanup=args.cleanup, verbose=args.verbose ) stmts[reader] = some_stmts N_tot = sum([ len(stmts[reader][pmid]) for reader in readers for pmid in stmts[reader].keys() ])'Collected a total of %s statements.' % N_tot) for reader in readers: N = sum([len(l) for l in stmts[reader].values()]) '%s accumulated %d statements.' % (reader.capitalize(), N) ) # Pickle the statements if args.end_index is None: args.end_index = 'end' pickle_file = '%s_stmts_%s-%s.pkl' % ( args.basename, args.start_index, args.end_index ) with open(pickle_file, 'wb') as f: pickle.dump(stmts, f, protocol=4) ret = pickle_file finally: time_taken = - now print('This run took', time_taken) time_file = os.path.join(os.path.dirname(__file__), 'time_data.txt') with open(time_file, 'a') as f: f.write('Started run at %s with args %s lasting %s.\n' % (now, str(args), time_taken)) if made_outdir and args.cleanup: shutil.rmtree(out_dir) return ret if __name__ == '__main__': main()