Path 1: 3 calls (0.75)

→ word 'appel' (1) 'wheel' (1) 'accept' (1)

→ possibilities ['False', 'None', 'True', 'and', 'as', 'assert', 'async', 'await', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'f...

→ n 3 (3)

→ cutoff 0.6 (3)

↩ return ['apple', 'ape'] (1) ['while'] (1) ['except'] (1)

13 run 2 not run

1def get_close_matches(word, possibilities, n=3, cutoff=0.6):
2    """Use SequenceMatcher to return list of the best "good enough" matches.
3
4    word is a sequence for which close matches are desired (typically a
5    string).
6
7    possibilities is a list of sequences against which to match word
8    (typically a list of strings).
9
10    Optional arg n (default 3) is the maximum number of close matches to
11    return.  n must be > 0.
12
13    Optional arg cutoff (default 0.6) is a float in [0, 1].  Possibilities
14    that don't score at least that similar to word are ignored.
15
16    The best (no more than n) matches among the possibilities are returned
17    in a list, sorted by similarity score, most similar first.
18
19    >>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"])
20    ['apple', 'ape']
21    >>> import keyword as _keyword
22    >>> get_close_matches("wheel", _keyword.kwlist)
23    ['while']
24    >>> get_close_matches("Apple", _keyword.kwlist)
25    []
26    >>> get_close_matches("accept", _keyword.kwlist)
27    ['except']
28    """
29
30    if not n >  0:
31        raise ValueError("n must be > 0: %r" % (n,))
32    if not 0.0 <= cutoff <= 1.0:
33        raise ValueError("cutoff must be in [0.0, 1.0]: %r" % (cutoff,))
34    result = []
35    s = SequenceMatcher()
36    s.set_seq2(word)
37    for x in possibilities:
38        s.set_seq1(x)
39        if s.real_quick_ratio() >= cutoff and \
40           s.quick_ratio() >= cutoff and \
41           s.ratio() >= cutoff:
42            result.append((s.ratio(), x))
43
44    # Move the best scorers to head of list
45    result = _nlargest(n, result)
46    # Strip scores for the best n matches
47    return [x for score, x in result]
            

Path 2: 1 calls (0.25)

→ word 'Apple' (1)

→ possibilities ['False', 'None', 'True', 'and', 'as', 'assert', 'async', 'await', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'f...

→ n 3 (1)

→ cutoff 0.6 (1)

↩ return [] (1)

11 run 4 not run

1def get_close_matches(word, possibilities, n=3, cutoff=0.6):
2    """Use SequenceMatcher to return list of the best "good enough" matches.
3
4    word is a sequence for which close matches are desired (typically a
5    string).
6
7    possibilities is a list of sequences against which to match word
8    (typically a list of strings).
9
10    Optional arg n (default 3) is the maximum number of close matches to
11    return.  n must be > 0.
12
13    Optional arg cutoff (default 0.6) is a float in [0, 1].  Possibilities
14    that don't score at least that similar to word are ignored.
15
16    The best (no more than n) matches among the possibilities are returned
17    in a list, sorted by similarity score, most similar first.
18
19    >>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"])
20    ['apple', 'ape']
21    >>> import keyword as _keyword
22    >>> get_close_matches("wheel", _keyword.kwlist)
23    ['while']
24    >>> get_close_matches("Apple", _keyword.kwlist)
25    []
26    >>> get_close_matches("accept", _keyword.kwlist)
27    ['except']
28    """
29
30    if not n >  0:
31        raise ValueError("n must be > 0: %r" % (n,))
32    if not 0.0 <= cutoff <= 1.0:
33        raise ValueError("cutoff must be in [0.0, 1.0]: %r" % (cutoff,))
34    result = []
35    s = SequenceMatcher()
36    s.set_seq2(word)
37    for x in possibilities:
38        s.set_seq1(x)
39        if s.real_quick_ratio() >= cutoff and \
40           s.quick_ratio() >= cutoff and \
41           s.ratio() >= cutoff:
42            result.append((s.ratio(), x))
43
44    # Move the best scorers to head of list
45    result = _nlargest(n, result)
46    # Strip scores for the best n matches
47    return [x for score, x in result]