def test_simple_search (self):
try:
search_index = SearchIndex(self.context)
hits, searcher = search_index.search('test')
for i, doc in hits:
foo, bar = doc.get('uid'), hits.score(i)
if searcher:
searcher.close()
except Exception, e:
self.fail('Error searching: %s' % e)
def test_simple_search(self):
try:
search_index = SearchIndex(self.context)
hits, searcher = search_index.search('test')
for i, doc in hits:
foo, bar = doc.get('uid'), hits.score(i)
if searcher:
searcher.close()
except Exception, e:
self.fail('Error searching: %s' % e)
config = canary.context.CanaryConfig()
config.read_file(options.config)
context = canary.context.Context(config)
source_catalog = context.get_source_catalog()
ctm = source_catalog.get_complete_mapping()
if not args:
print usage
sys.exit(0)
query_str = options.boolean.join(
[' "%s" [%s] ' % (term, options.field) for term in args])
#print query_str.strip()
search_index = SearchIndex(context)
hit_list = []
hits, searcher = search_index.search(query_str)
for i, doc in hits:
hit_list.append(doc.get('uid'))
searcher.close()
output = []
for id in hit_list:
rec = QueuedRecord(context, int(id))
if options.locations:
study = Study(context, rec.study_id)
for loc in study.locations:
out = []
out.extend((id, loc.uid, loc.study_id, loc.feature_id))
feature = Feature(uid=loc.feature_id)
(p_exp[0], p_coh[0], p_csec[0], p_desc[0], precords[0], pstudies[0]),
('Experimental', 'Cohort', 'Cross-Sectional', 'Descriptive',
'# Studies Total', '# Records w/Linkage'))
## legend((p_exp[0], p_desc[0], p_agg[0], p_csec[0], p_coh[0],
## p_cc[0], p_dm[0], precords[0], pstudies[0]),
## ('Experimental', 'Descriptive', 'Aggregate',
## 'Cross-Sectional', 'Cohort', 'Case-Control', 'Disease Model',
#savefig(('%s' % token.replace(' ', '_')))
show()
cla()
if __name__ == '__main__':
context = Context()
search_index = SearchIndex(context)
searches = (
'Lead [exposure]',
'Hantavirus [exposure]',
'peromyscus [species]',
'Michigan [location]',
'DDT [exposure]',
'2003 [year]',
'cats and dogs',
'"Burger J" [author]',
'cross-sectional [methodology]',
'case-control [methodology] and cattle [species]',
'disease-model [methodology]',
'"age factors" [risk_factors]',
'Sarin [exposure]',
'Arsenic [exposure]',
def run(search_index):
while True:
print
print "Hit enter with no input to quit."
command = raw_input("Query: ")
if command == '':
return
#query_parser = QueryParser('all', analyzer)
#query_parser.setOperator(query_parser.DEFAULT_OPERATOR_AND)
#query = QueryParser.parse(unicode(command), 'all', analyzer)
#query = query_parser.parseQuery(unicode(command))
#print "[Parsed Query: %s]" % query
#hits = searcher.search(query)
#print "%s total matching documents." % hits.length()
hits, searcher = search_index.search(command)
for i, doc in hits:
print doc.get('uid'), hits.score(i)
searcher.close()
if __name__ == '__main__':
context = canary.context.Context()
#directory = FSDirectory.getDirectory(context.config.search_index_dir, False)
#searcher = IndexSearcher(directory)
#analyzer = StandardAnalyzer()
#run(searcher, analyzer)
search_index = SearchIndex(context)
run(search_index)
'Cross-Sectional', 'Descriptive',
'# Studies Total', '# Records w/Linkage'))
## legend((p_exp[0], p_desc[0], p_agg[0], p_csec[0], p_coh[0],
## p_cc[0], p_dm[0], precords[0], pstudies[0]),
## ('Experimental', 'Descriptive', 'Aggregate',
## 'Cross-Sectional', 'Cohort', 'Case-Control', 'Disease Model',
#savefig(('%s' % token.replace(' ', '_')))
show()
cla()
if __name__ == '__main__':
context = Context()
search_index = SearchIndex(context)
searches = (
'Lead [exposure]',
'Hantavirus [exposure]',
'peromyscus [species]',
'Michigan [location]',
'DDT [exposure]',
'2003 [year]',
'cats and dogs',
'"Burger J" [author]',
'cross-sectional [methodology]',
'case-control [methodology] and cattle [species]',
'disease-model [methodology]',
'"age factors" [risk_factors]',
'Sarin [exposure]',
'Arsenic [exposure]',
