one more...

clients/pe_indexer.py

@@ -3,8 +3,8 @@
 import os
 import pprint
 
-def main():
 
+def main():
 
     parser = argparse.ArgumentParser()
     parser.add_argument('-p', '--port', type=int, default=4242, help='port used by workbench server')
@@ -18,9 +18,10 @@ def main():
     for filename in file_list:
 
         # Skip OS generated files
-        if '.DS_Store' in filename: continue
+        if '.DS_Store' in filename: 
+            continue
 
-        with open(filename,'rb') as file:
+        with open(filename, 'rb') as file:
             md5 = c.store_sample(filename, file.read(), 'pe')
 
             # Index the strings and features output (notice we can ask for any worker output)
@@ -37,7 +38,7 @@ def main():
     # Now actually do something interesing with our ELS index
     # ES Facets are kewl (http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-facets.html)
     facet_query = '{"facets" : {"tag" : {"terms" : {"field" : "string_list"}}}}'
-    results = c.search('strings',facet_query)
+    results = c.search('strings', facet_query)
     try:
         print '\nQuery: %s' % facet_query
         print 'Number of hits: %d' % results['hits']['total']
@@ -46,15 +47,16 @@ def main():
     except TypeError:
         print 'Probably using a Stub Indexer, if you want an ELS Indexer see the readme'
 
-
     # Fuzzy is kewl (http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl-fuzzy-query.html)
-    fuzzy_query = '{"fields":["md5","sparse_features.imported_symbols"],"query": {"fuzzy" : {"sparse_features.imported_symbols" : "loadlibrary"}}}'
-    results = c.search('pe_features',fuzzy_query)
+    fuzzy_query = '{"fields":["md5","sparse_features.imported_symbols"],' \
+        '"query": {"fuzzy" : {"sparse_features.imported_symbols" : "loadlibrary"}}}'
+    results = c.search('pe_features', fuzzy_query)
     try:
         print '\nQuery: %s' % fuzzy_query
         print 'Number of hits: %d' % results['hits']['total']
         print 'Max Score: %f' % results['hits']['max_score']
-        pprint.pprint([ (hit['fields']['md5'], hit['fields']['sparse_features.imported_symbols']) for hit in results['hits']['hits'] ])
+        pprint.pprint([(hit['fields']['md5'], hit['fields']['sparse_features.imported_symbols']) 
+            for hit in results['hits']['hits']])
     except TypeError:
         print 'Probably using a Stub Indexer, if you want an ELS Indexer see the readme'