G

Intro

For duplication, we look at places in code where there are 6 or more lines of code that are exactly the same.
Before duplication is calculated, the code is cleaned to remove empty lines, comments, and frequently duplicated constructs such as imports.
You should aim at having as little as possible (<5%) of duplicated code as high-level of duplication can lead to maintenance difficulties, poor factoring, and logical contradictions.

Learn more...

Duplication Overall

48% duplication:

6,985 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
3,394 duplicated lines

177 duplicates

Duplication per Extension

Duplication per Component (primary)

Duplication Between Components (50+ lines)

Download: SVG DOT (open online Graphviz editor)

From Component --> To Component	Duplicated Lines	File Pairs	Details
experiments/overlap/augmentations (28%) --> imagenet_c_bar (64%)	1491	7 file pairs	details...
experiments/overlap (8%) --> notebook_utils (73%)	216	1 file pair	details...
experiments/overlap/augmentations (4%) --> imagenet_c_bar/utils (100%)	210	3 file pairs	details...

Open 3D force graph...

Show more details on duplication between components...

Longest Duplicates

The list of 20 longest duplicates.

See data for all 177 duplicates...

Size	#	Folders	Files	Lines	Code
108	x 2	experiments/overlap notebook_utils	wideresnet.py wideresnet.py	12:142 (100%) 13:143 (100%)	view
93	x 2	experiments experiments	severity_scan.py severity_scan_imagenet.py	22:122 (86%) 22:122 (84%)	view
56	x 2	experiments experiments	train_imagenet.py train_imagenet_jsd.py	20:80 (72%) 20:80 (70%)	view
55	x 2	experiments/overlap/augmentations imagenet_c_bar	base.py base.py	22:83 (85%) 22:83 (85%)	view
55	x 2	experiments/overlap/augmentations/utils imagenet_c_bar/utils	noise.py perlin_noise.py	8:80 (100%) 8:80 (100%)	view
55	x 2	experiments/overlap/augmentations imagenet_c_bar	distortion.py corrupt.py	24:96 (18%) 732:803 (6%)	view
46	x 2	experiments/overlap experiments/overlap	train_net.py train_net_jsd.py	33:82 (41%) 37:86 (33%)	view
39	x 2	experiments/overlap/augmentations imagenet_c_bar	color.py corrupt.py	133:180 (30%) 548:595 (4%)	view
37	x 2	experiments/overlap/augmentations imagenet_c_bar	distortion.py corrupt.py	263:310 (12%) 862:908 (4%)	view
37	x 2	experiments/overlap/augmentations experiments/overlap/augmentations	pil.py pil.py	480:519 (8%) 535:574 (8%)	view
35	x 2	experiments/overlap experiments/overlap	datasets.py datasets.py	74:115 (5%) 330:371 (5%)	view
34	x 2	experiments/overlap/augmentations imagenet_c_bar	additive_noise.py corrupt.py	243:286 (10%) 227:267 (3%)	view
33	x 2	experiments/overlap/augmentations imagenet_c_bar	pil.py corrupt.py	385:421 (7%) 1037:1073 (3%)	view
32	x 2	experiments/overlap/augmentations imagenet_c_bar	color.py corrupt.py	67:104 (25%) 484:521 (3%)	view
32	x 2	experiments/overlap/augmentations imagenet_c_bar	additive_noise.py corrupt.py	303:341 (9%) 280:318 (3%)	view
30	x 2	experiments/overlap/augmentations/utils imagenet_c_bar/utils	converters.py converters.py	10:44 (100%) 10:44 (100%)	view
29	x 2	experiments/overlap/augmentations imagenet_c_bar	obscure.py corrupt.py	134:169 (17%) 648:679 (3%)	view
27	x 2	experiments/overlap/augmentations imagenet_c_bar	pil.py corrupt.py	562:592 (6%) 999:1029 (3%)	view
27	x 2	experiments/overlap/augmentations imagenet_c_bar	distortion.py corrupt.py	161:197 (9%) 815:851 (3%)	view
25	x 2	experiments/overlap/augmentations imagenet_c_bar	additive_noise.py corrupt.py	81:111 (7%) 66:96 (2%)	view

Duplicated Units

The list of top 20 duplicated units.

See data for all 43 unit duplicates...

Size	#	Folders	Files	Lines	Code
36	x 2	experiments/overlap/augmentations imagenet_c_bar	color.py corrupt.py	0:0 0:0	view
30	x 2	experiments/overlap/augmentations experiments/overlap/augmentations	pil.py pil.py	0:0 0:0	view
33	x 2	experiments/overlap notebook_utils	wideresnet.py wideresnet.py	0:0 0:0	view
28	x 2	experiments/overlap/augmentations imagenet_c_bar	base.py base.py	0:0 0:0	view
25	x 2	experiments/overlap/augmentations imagenet_c_bar	distortion.py corrupt.py	0:0 0:0	view
25	x 2	experiments/overlap/augmentations/utils imagenet_c_bar/utils	noise.py perlin_noise.py	0:0 0:0	view
24	x 2	experiments/overlap/augmentations imagenet_c_bar	additive_noise.py corrupt.py	0:0 0:0	view
23	x 2	experiments/overlap/augmentations imagenet_c_bar	additive_noise.py corrupt.py	0:0 0:0	view
23	x 2	experiments/overlap notebook_utils	wideresnet.py wideresnet.py	0:0 0:0	view
21	x 4	experiments experiments experiments experiments	train_imagenet_jsd.py severity_scan_imagenet.py severity_scan.py train_imagenet.py	0:0 0:0 0:0 0:0	view
20	x 2	experiments/overlap/augmentations imagenet_c_bar	additive_noise.py corrupt.py	0:0 0:0	view
19	x 2	experiments/overlap experiments/overlap	datasets.py datasets.py	0:0 0:0	view
19	x 2	experiments/overlap/augmentations imagenet_c_bar	color.py corrupt.py	0:0 0:0	view
18	x 2	experiments/tools experiments	get_target_error.py sample_datasets.py	0:0 0:0	view
18	x 2	experiments/overlap/augmentations/utils imagenet_c_bar/utils	noise.py perlin_noise.py	0:0 0:0	view
18	x 2	experiments/overlap/augmentations imagenet_c_bar	additive_noise.py corrupt.py	0:0 0:0	view
18	x 2	experiments/overlap/augmentations imagenet_c_bar	pil.py corrupt.py	0:0 0:0	view
18	x 2	experiments/overlap/augmentations imagenet_c_bar	pil.py corrupt.py	0:0 0:0	view
17	x 2	experiments/overlap/augmentations imagenet_c_bar	additive_noise.py corrupt.py	0:0 0:0	view
15	x 2	experiments/overlap/augmentations/utils imagenet_c_bar/utils	image.py image.py	0:0 0:0	view