def test_compare_false_positive():
# This two completely different fingerprints initially have a relatively good score.
# 'compare' handles these cases by checking difference between median and max value.
fp1 = _load_fp("edgecase_4/sample-1.log")
fp2 = _load_fp("edgecase_4/sample-2.log")
assert fingerprints.compare(fp1, fp2) == 0.0
def test_compare_requires_fingerprints_to_be_at_least_10_seconds_long():
fp1 = _load_fp("edgecase_3/sample-1.log")
fp2 = _load_fp("edgecase_3/sample-2.log")
assert fingerprints.compare(fp1, fp2) == 0.0
def test_compare_returns_immediately_for_score_greater_than_confidence_score():
fp1 = _load_fp("edgecase_2/sample-1.log")
fp2 = _load_fp("edgecase_2/sample-2.log")
assert fingerprints.compare(fp1, fp2) > fingerprints.CONFIDENCE_SCORE
def test_compare_add_correction_for_short_fingerprints():
fp1 = _load_fp("edgecase_1/sample-1.log")
fp2 = _load_fp("edgecase_1/sample-2.log")
assert fingerprints.compare(fp1, fp2) == 0.0
def test_compare(a, b, score):
fp1 = _load_fp(a)
fp2 = _load_fp(b)
assert round(fingerprints.compare(fp1, fp2), 2) == score
def _compare(pair: Tuple[List[int], List[int]]) -> float:
"""Just a wrapper for fingerprints.compare, that unpack its first argument"""
return fingerprints.compare(*pair)