Path 1: 1021 calls (0.98)

[] (1021) 

1def _compute_sims(self) -> list[tuple[int, set[LinesChunkLimits_T]]]:
2        """Compute similarities in appended files."""
3        no_duplicates: dict[int, list[set[LinesChunkLimits_T]]] = defaultdict(list)
4
5        for commonality in self._iter_sims():
6            num = commonality.cmn_lines_nb
7            lineset1 = commonality.fst_lset
8            start_line_1 = commonality.fst_file_start
9            end_line_1 = commonality.fst_file_end
10            lineset2 = commonality.snd_lset
11            start_line_2 = commonality.snd_file_start
12            end_line_2 = commonality.snd_file_end
13
14            duplicate = no_duplicates[num]
15            couples: set[LinesChunkLimits_T]
16            for couples in duplicate:
17                if (lineset1, start_line_1, end_line_1) in couples or (
18                    lineset2,
19                    start_line_2,
20                    end_line_2,
21                ) in couples:
22                    break
23            else:
24                duplicate.append(
25                    {
26                        (lineset1, start_line_1, end_line_1),
27                        (lineset2, start_line_2, end_line_2),
28                    }
29                )
30        sims: list[tuple[int, set[LinesChunkLimits_T]]] = []
31        ensembles: list[set[LinesChunkLimits_T]]
32        for num, ensembles in no_duplicates.items():
33            cpls: set[LinesChunkLimits_T]
34            for cpls in ensembles:
35                sims.append((num, cpls))
36        sims.sort()
37        sims.reverse()
38        return sims

Path 2: 14 calls (0.01)

list (14) 

1def _compute_sims(self) -> list[tuple[int, set[LinesChunkLimits_T]]]:
2        """Compute similarities in appended files."""
3        no_duplicates: dict[int, list[set[LinesChunkLimits_T]]] = defaultdict(list)
4
5        for commonality in self._iter_sims():
6            num = commonality.cmn_lines_nb
7            lineset1 = commonality.fst_lset
8            start_line_1 = commonality.fst_file_start
9            end_line_1 = commonality.fst_file_end
10            lineset2 = commonality.snd_lset
11            start_line_2 = commonality.snd_file_start
12            end_line_2 = commonality.snd_file_end
13
14            duplicate = no_duplicates[num]
15            couples: set[LinesChunkLimits_T]
16            for couples in duplicate:
17                if (lineset1, start_line_1, end_line_1) in couples or (
18                    lineset2,
19                    start_line_2,
20                    end_line_2,
21                ) in couples:
22                    break
23            else:
24                duplicate.append(
25                    {
26                        (lineset1, start_line_1, end_line_1),
27                        (lineset2, start_line_2, end_line_2),
28                    }
29                )
30        sims: list[tuple[int, set[LinesChunkLimits_T]]] = []
31        ensembles: list[set[LinesChunkLimits_T]]
32        for num, ensembles in no_duplicates.items():
33            cpls: set[LinesChunkLimits_T]
34            for cpls in ensembles:
35                sims.append((num, cpls))
36        sims.sort()
37        sims.reverse()
38        return sims

Path 3: 1 calls (0.0)

list (1) 

1def _compute_sims(self) -> list[tuple[int, set[LinesChunkLimits_T]]]:
2        """Compute similarities in appended files."""
3        no_duplicates: dict[int, list[set[LinesChunkLimits_T]]] = defaultdict(list)
4
5        for commonality in self._iter_sims():
6            num = commonality.cmn_lines_nb
7            lineset1 = commonality.fst_lset
8            start_line_1 = commonality.fst_file_start
9            end_line_1 = commonality.fst_file_end
10            lineset2 = commonality.snd_lset
11            start_line_2 = commonality.snd_file_start
12            end_line_2 = commonality.snd_file_end
13
14            duplicate = no_duplicates[num]
15            couples: set[LinesChunkLimits_T]
16            for couples in duplicate:
17                if (lineset1, start_line_1, end_line_1) in couples or (
18                    lineset2,
19                    start_line_2,
20                    end_line_2,
21                ) in couples:
22                    break
23            else:
24                duplicate.append(
25                    {
26                        (lineset1, start_line_1, end_line_1),
27                        (lineset2, start_line_2, end_line_2),
28                    }
29                )
30        sims: list[tuple[int, set[LinesChunkLimits_T]]] = []
31        ensembles: list[set[LinesChunkLimits_T]]
32        for num, ensembles in no_duplicates.items():
33            cpls: set[LinesChunkLimits_T]
34            for cpls in ensembles:
35                sims.append((num, cpls))
36        sims.sort()
37        sims.reverse()
38        return sims

Path 4: 1 calls (0.0)

list (1) 

1def _compute_sims(self) -> list[tuple[int, set[LinesChunkLimits_T]]]:
2        """Compute similarities in appended files."""
3        no_duplicates: dict[int, list[set[LinesChunkLimits_T]]] = defaultdict(list)
4
5        for commonality in self._iter_sims():
6            num = commonality.cmn_lines_nb
7            lineset1 = commonality.fst_lset
8            start_line_1 = commonality.fst_file_start
9            end_line_1 = commonality.fst_file_end
10            lineset2 = commonality.snd_lset
11            start_line_2 = commonality.snd_file_start
12            end_line_2 = commonality.snd_file_end
13
14            duplicate = no_duplicates[num]
15            couples: set[LinesChunkLimits_T]
16            for couples in duplicate:
17                if (lineset1, start_line_1, end_line_1) in couples or (
18                    lineset2,
19                    start_line_2,
20                    end_line_2,
21                ) in couples:
22                    break
23            else:
24                duplicate.append(
25                    {
26                        (lineset1, start_line_1, end_line_1),
27                        (lineset2, start_line_2, end_line_2),
28                    }
29                )
30        sims: list[tuple[int, set[LinesChunkLimits_T]]] = []
31        ensembles: list[set[LinesChunkLimits_T]]
32        for num, ensembles in no_duplicates.items():
33            cpls: set[LinesChunkLimits_T]
34            for cpls in ensembles:
35                sims.append((num, cpls))
36        sims.sort()
37        sims.reverse()
38        return sims

Path 5: 1 calls (0.0)

list (1) 

1def _compute_sims(self) -> list[tuple[int, set[LinesChunkLimits_T]]]:
2        """Compute similarities in appended files."""
3        no_duplicates: dict[int, list[set[LinesChunkLimits_T]]] = defaultdict(list)
4
5        for commonality in self._iter_sims():
6            num = commonality.cmn_lines_nb
7            lineset1 = commonality.fst_lset
8            start_line_1 = commonality.fst_file_start
9            end_line_1 = commonality.fst_file_end
10            lineset2 = commonality.snd_lset
11            start_line_2 = commonality.snd_file_start
12            end_line_2 = commonality.snd_file_end
13
14            duplicate = no_duplicates[num]
15            couples: set[LinesChunkLimits_T]
16            for couples in duplicate:
17                if (lineset1, start_line_1, end_line_1) in couples or (
18                    lineset2,
19                    start_line_2,
20                    end_line_2,
21                ) in couples:
22                    break
23            else:
24                duplicate.append(
25                    {
26                        (lineset1, start_line_1, end_line_1),
27                        (lineset2, start_line_2, end_line_2),
28                    }
29                )
30        sims: list[tuple[int, set[LinesChunkLimits_T]]] = []
31        ensembles: list[set[LinesChunkLimits_T]]
32        for num, ensembles in no_duplicates.items():
33            cpls: set[LinesChunkLimits_T]
34            for cpls in ensembles:
35                sims.append((num, cpls))
36        sims.sort()
37        sims.reverse()
38        return sims