rename 'transliterations' to 'keywords'

keywords/src/wikidata_search.py

@@ -0,0 +1,95 @@
+# generate a list of wikidata entities related to keywords
+from os import path
+from sys import stdout
+from wikidata_api_calls import search_wikidata, get_wikidata_api
+import csv
+from itertools import chain
+
+class Wikidata_ResultSet:
+    def __init__(self):
+        self.results = []
+
+    def extend(self, term, results):
+        self.results.append(
+            (Wikidata_Result(term, result, i)
+             for i, result in enumerate(results))
+        )
+
+    def to_csv(self, outfile=None, mode='w'):
+        if outfile is None:
+            of = stdout
+
+        else:
+            if path.exists(outfile) and mode != 'w':
+                of = open(outfile,'a',newline='')
+            else:
+                of = open(outfile,'w',newline='')
+        writer = csv.writer(of)
+        writer.writerow(Wikidata_Result.__slots__)
+        writer.writerows(map(Wikidata_Result.to_list, chain(* self.results)))
+
+
+class Wikidata_Result:
+    # store unique entities found in the search results, the position in the search result, and the date
+    __slots__=['search_term','entityid','pageid','search_position','timestamp']
+
+    def __init__(self,
+                 term,
+                 search_result,
+                 position):
+
+        self.search_term = term.strip()
+        self.entityid = search_result['title']
+        self.pageid = int(search_result['pageid'])
+        self.search_position = int(position)
+        self.timestamp = search_result['timestamp']
+
+    def to_list(self):
+        return [self.search_term,
+                self.entityid,
+                self.pageid,
+                self.search_position,
+                self.timestamp]
+    
+def run_wikidata_searches(terms):
+    api = get_wikidata_api()
+    resultset = Wikidata_ResultSet()
+    for term in terms:
+        search_results = search_wikidata(api, term)
+        resultset.extend(term, search_results)
+    return resultset
+
+def read_google_trends_files(terms_files):
+    def _read_file(infile):
+        return csv.DictReader(open(infile,'r',newline=''))
+
+    for row in chain(* [_read_file(terms_file) for terms_file in terms_files]):
+        yield row['query']
+
+
+def trawl_google_trends(terms_files, outfile = None, mode='w'):
+    terms = list(read_google_trends_files(terms_files))
+    resultset = run_wikidata_searches(terms)
+    resultset.to_csv(outfile, mode)
+
+def trawl_base_terms(infiles, outfile = None, mode='w'):
+    terms = list(chain(* (open(infile,'r') for infile in infiles)))
+    resultset = run_wikidata_searches(terms)
+    resultset.to_csv(outfile, mode)
+
+    ## search each of the base terms in wikidata
+
+    # store unique entities found in the search results, the position in the search result, and the date
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser("Search wikidata for entities related to a set of terms.")
+    parser.add_argument('inputs', type=str, nargs='+', help='one or more files to read')
+    parser.add_argument('--use-gtrends', action='store_true', help = 'toggle whether the input is the output from google trends')
+    parser.add_argument('--output', type=str, help='an output file. defaults to stdout')
+    parser.add_argument('--overwrite', action='store_true', help = 'overwrite existing output files instead of appending')
+    args = parser.parse_args()
+    if args.use_gtrends:
+        trawl_google_trends(args.inputs, args.output)
+    else:
+        trawl_base_terms(args.inputs, args.output)