def lucvergpop(request, lucverg_metadata):
conn = TessMongoConnection('localhost', 27017, None, None, 'lucvergtest')
for metadata in lucverg_metadata:
text = Text.json_decode(metadata)
tessfile = TessFile(text.path, metadata=text)
conn.insert(text)
tokens, tags, features = \
LatinTokenizer(conn).tokenize(
tessfile.read(), text=tessfile.metadata)
feature_cache = {
(f.feature, f.token): f
for f in conn.find(Feature.collection, language=text.language)
}
features_for_insert = []
features_for_update = []
for f in features:
if (f.feature, f.token) not in feature_cache:
features_for_insert.append(f)
feature_cache[(f.feature, f.token)] = f
else:
f.id = feature_cache[(f.feature, f.token)].id
features_for_update.append(f)
conn.insert(features_for_insert)
conn.update(features_for_update)
unitizer = Unitizer()
lines, _ = unitizer.unitize(tokens, tags, tessfile.metadata)
conn.insert_nocheck(lines)
yield conn
obliterate(conn)
def resultsdb():
conn = TessMongoConnection('localhost', 27017, None, None, 'resultdb')
results_id = uuid.uuid4()
search_result = Search(results_id=results_id,
search_type=NORMAL_SEARCH,
status=Search.DONE)
conn.insert(search_result)
conn.insert([_create_match(search_result) for _ in range(100)])
yield conn
for coll_name in conn.connection.list_collection_names():
conn.connection.drop_collection(coll_name)
def main():
"""Perform Tesserae search and display the top 10 results"""
args = parse_args()
if args.password:
password = getpass(prompt='Tesserae MongoDB Password: '******'-', ' ')
source_title = args.source_title.lower().replace('-', ' ')
source = TextOptions(text=connection.find('texts',
author=source_author,
title=source_title)[0],
unit_type=args.source_unit)
target_author = args.target_author.lower().replace('_', ' ')
target_title = args.target_title.lower().replace('_', ' ')
target = TextOptions(text=connection.find('texts',
author=target_author,
title=target_title)[0],
unit_type=args.target_unit)
start = time.time()
stopword_indices = create_stoplist(
connection,
args.n_stopwords,
args.feature,
source.text.language,
basis='corpus' if args.stopword_basis == 'corpus' else
[source.text.id, target.text.id])
stopword_tokens = get_stoplist_tokens(connection, stopword_indices,
args.feature, source.text.language)
parameters = {
'source': {
'object_id': str(source.text.id),
'units': source.unit_type
},
'target': {
'object_id': str(target.text.id),
'units': target.unit_type
},
'method': {
'name': SparseMatrixSearch.matcher_type,
'feature': args.feature,
'stopwords': stopword_tokens,
'freq_basis': args.freq_basis,
'max_distance': args.max_distance,
'distance_basis': args.distance_basis
}
}
results_id = check_cache(connection, parameters['source'],
parameters['target'], parameters['method'])
if results_id:
print('Cached results found.')
search = connection.find(Search.collection,
results_id=results_id,
search_type=NORMAL_SEARCH)[0]
else:
search = Search(results_id=uuid.uuid4().hex,
search_type=NORMAL_SEARCH,
parameters=parameters)
connection.insert(search)
search_params = {
'source': source,
'target': target,
'feature': parameters['method']['feature'],
'stopwords': parameters['method']['stopwords'],
'freq_basis': parameters['method']['freq_basis'],
'max_distance': parameters['method']['max_distance'],
'distance_basis': parameters['method']['distance_basis'],
'min_score': 0
}
_run_search(connection, search, SparseMatrixSearch.matcher_type,
search_params)
matches = get_results(connection, search.id, PageOptions())
end = time.time() - start
matches.sort(key=lambda x: x['score'], reverse=True)
print(f'Search found {len(matches)} matches in {end}s.')
display_count = 10 if len(matches) >= 10 else len(matches)
print(f'The Top {display_count} Matches')
print('------------------')
print()
print("Result\tScore\tSource Locus\tTarget Locus\tShared")
for i, m in enumerate(matches[:10]):
shared = m['matched_features']
print(f'{i}.\t{m["score"]}\t{m["source_tag"]}\t{m["target_tag"]}\t'
f'{[t for t in shared]}')