def get_attributes(args):
"""
Gather all data necessary for metrics calculations
"""
# Get publication information
if 'query' in args:
# If we were fed a query, gather the associated bibcodes
bibcodes = get_publications_from_query(args['query'])
elif 'bibcodes' in args:
bibcodes = map(lambda a: a.strip(), args['bibcodes'])
elif 'libid' in args:
# In theory we allow for retrieving bibcodes from private libraries
# Clearly this will currently not be used
bibcodes = get_bibcodes_from_private_library(args['libid'])
# Split the list of bibcodes up in chunks, for parallel processing
biblists = list(chunks(bibcodes,config.METRICS_CHUNK_SIZE))
# Now gather all usage data numbers from the MongoDB 'adsdata' collection,
# keyed on bibcode
ads_data = get_mongo_data(bibcodes=bibcodes)
missing_bibcodes = filter(lambda a: a not in ads_data.keys(), bibcodes)
app.logger.error("Bibcodes found with missing metadata: %s" % ",".join(missing_bibcodes))
bibcodes = filter(lambda a: a not in missing_bibcodes, bibcodes)
# Get precomputed and citation data
metrics_data = get_metrics_data(bibcodes=bibcodes)
# Get the number of citing papers
Nciting = len(list(set(itertools.chain(*map(lambda a: a['citations'], metrics_data.values())))))
Nciting_ref = len(list(set(itertools.chain(*map(lambda a: a['refereed_citations'], metrics_data.values())))))
# The attribute vectors will be used to calculate the metrics
attr_list = make_vectors(bibcodes,ads_data,metrics_data)
# We sort the entries in the attribute list on citation count, which
# will make e.g. the calculation of 'h' trivial
attr_list = sort_list_of_lists(attr_list,2)
return attr_list,Nciting,Nciting_ref
def get_attributes(args):
"""
Gather all data necessary for metrics calculations
"""
solr_url = config.SOLR_URL
max_hits = config.METRICS_MAX_HITS
threads = config.METRICS_THREADS
chunk_size = config.METRICS_CHUNK_SIZE
# Get publication information
if 'query' in args:
# If we were fed a query, gather the associated bibcodes
bibcodes = get_publications_from_query(args['query'])
elif 'bibcodes' in args:
bibcodes = map(lambda a: a.strip(), args['bibcodes'])
elif 'libid' in args:
# In theory we allow for retrieving bibcodes from private libraries
# Clearly this will currently not be used
bibcodes = get_bibcodes_from_private_library(args['libid'])
# Split the list of bibcodes up in chunks, for parallel processing
biblists = list(chunks(bibcodes,chunk_size))
# Gather all publication information into one publication dictionary,
# keyed on bibcode
publication_data = get_publication_data(biblists=biblists)
missing_bibcodes = filter(lambda a: a not in publication_data.keys(), bibcodes)
app.logger.error("Bibcodes found with missing metadata: %s" % ",".join(missing_bibcodes))
bibcodes = filter(lambda a: a not in missing_bibcodes, bibcodes)
# Get citation dictionaries (all, refereed and non-refereed citations in
# separate dictionaries, so that we don't have to figure this out later)
(cit_dict,ref_cit_dict,non_ref_cit_dict) = get_citations(bibcodes=bibcodes, pubdata=publication_data, type='metrics')
# divide by 4 because the values of the dictionary are 4-tuples
# and the flattening removed all structure.
Nciting = len(set([x[0] for v in cit_dict.values() for x in v]))
Nciting_ref = len(set([x[0] for v in ref_cit_dict.values() for x in v]))
# Now gather all usage data numbers from the MongoDB 'adsdata' collection
# This info will get stored in the dictionary 'adsdata', also keyed on bibcode
ads_data = get_mongo_data(bibcodes=bibcodes)
# Generate the list of document attribute vectors and then
# sort this list by citations (descending).
# The attribute vectors will be used to calculate the metrics
attr_list = make_vectors(bibcodes,publication_data,ads_data,cit_dict,ref_cit_dict,non_ref_cit_dict)
# We sort the entries in the attribute list on citation count, which
# will make e.g. the calculation of 'h' trivial
attr_list = sort_list_of_lists(attr_list,2)
return attr_list,Nciting,Nciting_ref
