-
Notifications
You must be signed in to change notification settings - Fork 1
/
AddAttributes.Add_Shape_Attributes_Low_Tool.pyt.xml
2646 lines (2641 loc) · 212 KB
/
AddAttributes.Add_Shape_Attributes_Low_Tool.pyt.xml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<?xml version="1.0"?>
<metadata xml:lang="en"><Esri><CreaDate>20210607</CreaDate><CreaTime>10122200</CreaTime><ArcGISFormat>1.0</ArcGISFormat><SyncOnce>TRUE</SyncOnce><ModDate>20240524</ModDate><ModTime>14014500</ModTime><scaleRange><minScale>150000000</minScale><maxScale>5000</maxScale></scaleRange><ArcGISProfile>ItemDescription</ArcGISProfile></Esri><tool name="Add_Shape_Attributes_Low_Tool" displayname="Add Shape Attributes Low Tool" toolboxalias="AddAttributes" xmlns=""><arcToolboxHelpPath>c:\program files\arcgis\pro\Resources\Help\gp</arcToolboxHelpPath><parameters><param name="inFeatClass" displayname="Input Features" type="Required" direction="Input" datatype="Feature Layer" expression="inFeatClass"><dialogReference><DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-weight:bold;">This is a feature class delineates the bathymetric low features.</SPAN></P></DIV></DIV></DIV></dialogReference><pythonReference><DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-weight:bold;">This is a feature class delineates the bathymetric low features.</SPAN></P></DIV></DIV></DIV></pythonReference></param><param name="bathyRas" displayname="Input Bathymetry Raster" type="Required" direction="Input" datatype="Raster Layer" expression="bathyRas"><dialogReference><DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-weight:bold;">This is the bathymetry raster.</SPAN></P></DIV></DIV></DIV></dialogReference><pythonReference><DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-weight:bold;">This is the bathymetry raster.</SPAN></P></DIV></DIV></DIV></pythonReference></param><param name="tempFolder" displayname="Temporary Folder" type="Required" direction="Input" datatype="Folder" expression="tempFolder"><dialogReference><DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-weight:bold;">This is temporary folder holding some temporal files/data.</SPAN></P></DIV></DIV></DIV></dialogReference><pythonReference><DIV STYLE="text-align:Left;"><DIV><P><SPAN STYLE="font-weight:bold;">This is temporary folder holding some temporal files/data.</SPAN></P></DIV></DIV></pythonReference></param><param name="headFeatClass" displayname="Output Head Features" type="Required" direction="Output" datatype="Feature Class" expression="headFeatClass"><dialogReference><DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-weight:bold;">This is feature class identifies the head point of each bathymetric low feature along the long axis</SPAN><SPAN STYLE="font-weight:bold;">.</SPAN></P></DIV></DIV></DIV></dialogReference><pythonReference><DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-weight:bold;">This is feature class identifies the head point of each bathymetric low feature along the long axis</SPAN><SPAN STYLE="font-weight:bold;">.</SPAN></P></DIV></DIV></DIV></pythonReference></param><param name="footFeatClass" displayname="Output Foot Features" type="Required" direction="Output" datatype="Feature Class" expression="footFeatClass"><dialogReference><DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-weight:bold;">This is feature class identifies the </SPAN><SPAN STYLE="font-weight:bold;">foot </SPAN><SPAN STYLE="font-weight:bold;">point of each bathymetric low feature along the long axis</SPAN><SPAN STYLE="font-weight:bold;">.</SPAN></P></DIV></DIV></DIV></dialogReference><pythonReference><DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-weight:bold;">This is feature class identifies the </SPAN><SPAN STYLE="font-weight:bold;">foot </SPAN><SPAN STYLE="font-weight:bold;">point of each bathymetric low feature along the long axis</SPAN><SPAN STYLE="font-weight:bold;">.</SPAN></P></DIV></DIV></DIV></pythonReference></param><param name="additionalOption" displayname="Calculate additional attributes" type="Required" direction="Input" datatype="Boolean" expression="additionalOption"><dialogReference><DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-weight:bold;">Tick the box if you want to calculate additional shape attributes.</SPAN></P></DIV></DIV></DIV></dialogReference><pythonReference><DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-weight:bold;">Tick the box if you want to calculate additional shape attributes.</SPAN></P></DIV></DIV></DIV></pythonReference></param></parameters><summary><DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This tool add a number of </SPAN><SPAN>shape </SPAN><SPAN>attributes to the input</SPAN><SPAN> bathymetric</SPAN><SPAN> </SPAN><SPAN>low </SPAN><SPAN>feature class. The following attributes are </SPAN><SPAN>calculated </SPAN><SPAN>to describe the </SPAN><SPAN>polygon shape of each bathymetric </SPAN><SPAN>low </SPAN><SPAN>feature</SPAN><SPAN>.</SPAN><SPAN> </SPAN><SPAN STYLE="text-decoration:underline;">Wirth, M.A. Shape Analysis &amp; Measurement</SPAN><SPAN /></P><P><SPAN>1. head_foot_length: the eucli</SPAN><SPAN>d</SPAN><SPAN>ean distance between </SPAN><SPAN>the head and the foot </SPAN><SPAN>of the feature polygon, along the long axis</SPAN><SPAN>.</SPAN></P><P><SPAN>2</SPAN><SPAN>. sinuous_length: the sinuous distance between </SPAN><SPAN>two ends </SPAN><SPAN>of the feature polygon, along the long axis</SPAN><SPAN>.</SPAN></P><P><SPAN>3</SPAN><SPAN>. mean_width: </SPAN><SPAN>the mean width of the feature polygon, calculated from a number of cross-sections perpendicular to the </SPAN><SPAN>orientation </SPAN><SPAN>of the feature polygon</SPAN><SPAN>.</SPAN></P><P><SPAN>4. mean_thickness: </SPAN><SPAN>the mean </SPAN><SPAN>thickness </SPAN><SPAN>of the feature polygon, calculated from a number of cross-sections perpendicular to the </SPAN><SPAN>orientation </SPAN><SPAN>of the feature polygon</SPAN><SPAN>. The thickness of a cross-section is calculated as the depth difference between the deeper end of the cross-section and the deepest point on the bottom of the cross-section.</SPAN></P><P><SPAN>5. mean_width_thickness_ratio: the mean width to thickness ratio of the feature polygon, </SPAN><SPAN>calculated from a number of cross-sections perpendicular to the </SPAN><SPAN>orientation </SPAN><SPAN>of the feature polygon</SPAN><SPAN>. The width to thickness ratio of each cross-section is calculated as width/thickness.</SPAN></P><P><SPAN>6. std_width_thickness_ratio: the standard deviation of width to thickness ratio of the feature polygon, </SPAN><SPAN>calculated from a number of cross-sections perpendicular to the </SPAN><SPAN>orientation </SPAN><SPAN>of the feature polygon</SPAN><SPAN>. The width to thickness ratio of each cross-section is calculated as width/thickness.</SPAN></P><P><SPAN>7. mean_segment_slope: the mean slope gradient of thalweg segments of the feature polygon. The thalweg segments are the line segments linking the deepest points of the </SPAN><SPAN>cross-sections perpendicular to the </SPAN><SPAN>orientation </SPAN><SPAN>of the feature</SPAN><SPAN>.</SPAN></P><P><SPAN>8. width_distance_slope: the slope of the linear regression line between two variables: the widths of cross-sections and the distances of the cross-sections to the head of the feature poly</SPAN><SPAN>g</SPAN><SPAN>on.</SPAN></P><P><SPAN>9. width_distance_correlation: the Pearson correlation coefficient between two variables: the widths of cross-sections and the distances of the cross-sections to the head of the feature poly</SPAN><SPAN>g</SPAN><SPAN>on. </SPAN></P><P><SPAN>10. thick_distance_slope: the slope of the linear regression line between two variables: the thicknesses of cross-sections and the distances of the cross-sections to the head of the feature poly</SPAN><SPAN>g</SPAN><SPAN>on.</SPAN></P><P><SPAN>11. thick_distance_correlation: the Pearson correlation coefficient between two variables: the thicknesses of cross-sections and the distances of the cross-sections to the head of the feature poly</SPAN><SPAN>g</SPAN><SPAN>on. </SPAN></P><P><SPAN>12</SPAN><SPAN>. </SPAN><SPAN>Compactness: Describe how compact the feature polygon is. More complex polygon shape has a lower compactness. It is calculated as 4*</SPAN><SPAN STYLE="font-size:14pt">π</SPAN><SPAN>*</SPAN><SPAN>A/P/P, where A is the area of the polygon, P is the perimeter of the polygon.</SPAN></P><P><SPAN>13</SPAN><SPAN>. Sinuosity: Describe the sinuosity of the feature polygon. Larger the value more sinuo</SPAN><SPAN>u</SPAN><SPAN>s the feature polygon is. It is calculated as </SPAN><SPAN>sinuous_length</SPAN><SPAN>/</SPAN><SPAN>head_foot_length.</SPAN></P><P><SPAN>14</SPAN><SPAN>. LenghWidthRatio: Describe the length to width ratio of the feature polygon. Larger the value more elongate the feature polygon is. It is calculated as </SPAN><SPAN>sinuous_length</SPAN><SPAN>/</SPAN><SPAN>mean_width.</SPAN></P><P><SPAN>15</SPAN><SPAN>. Circularity: Describe how close the feature polygon is to a circle. </SPAN><SPAN>Larger the value closer to a circle the feature polygon is. </SPAN><SPAN>It is calculated as 4*</SPAN><SPAN STYLE="font-size:14pt">π</SPAN><SPAN>/A/Pc/Pc, where Pc is the perimeter of the convex hull polygon that bounds the feature polygon.</SPAN></P><P><SPAN>16</SPAN><SPAN>. Convexity: Describe the convexity of the feature polygon. </SPAN><SPAN>More complex polygon has a lower convexity. </SPAN><SPAN>It is calculated as Pc/P.</SPAN></P><P><SPAN>17</SPAN><SPAN>. Solidity: Describe the solidity of the feature polygon.</SPAN><SPAN> More complex polygon has a lower solidity. </SPAN><SPAN> It is calculated as A/Ac.</SPAN></P><P><SPAN>Important to note that if the "Calculate additional attributes" option is not activated, seven above attributes (4, 5, 6, 8, 9, 10 and 11) will not be calculated. This will save a lot of calculation time.</SPAN></P><P><SPAN /></P><P><SPAN>In addition, a number of intermediate attributes are also calculated.</SPAN></P><P><SPAN>1. rectangle_Length: the length of the bounding rectangle (by width) that bounds the feature polygon</SPAN></P><P><SPAN>2. rectangle_Width: the width of the bounding rectangle (by width) that bounds the feature polygon</SPAN></P><P><SPAN>3. rectangle_Orientation: the orientation of the bounding rectangle (by width) that bounds the feature polygon</SPAN></P><P><SPAN>4. convexhull_Area: the area of the convex hull that bounds the feature polygon</SPAN></P><P><SPAN>5. convexhull_Perimeter: the perimeter of the convex hull that bounds the feature polygon</SPAN></P><P><SPAN /></P><P><SPAN>The key reference for this tool is the following journal article: </SPAN></P><P><SPAN>Huang, Z., Nanson, R., McNeil, M., Wenderlich, M., Gafeira, J., Post, A, Nichol, S., 2023. Rule-based semi-automated tools for mapping seabed morphology from bathymetry data</SPAN><SPAN STYLE="font-style:italic;"><SPAN>, Frontiers in Marine Science</SPAN></SPAN><SPAN><SPAN>, 10, 1236788.</SPAN></SPAN></P></DIV></DIV></DIV></summary><scriptExamples><scriptExample><title>Python script code sample</title><code>import arcpy
from arcpy import env
from arcpy.sa import *
arcpy.CheckOutExtension("Spatial")
# import the python toolbox
arcpy.ImportToolbox("C:/semi_automation_tools/User_Guide/Tools/AddAttributes.pyt")
env.workspace = 'C:/semi_automation_tools/testSampleCode/Gifford.gdb'
env.overwriteOutput = True
# specify input and output parameters of the tool
inFeat = 'test_BL'
inBathy = 'gifford_bathy'
tempFolder = 'C:/semi_automation_tools/temp4'
headFeat = 'test_BL_head'
footFeat = 'test_BL_foot'
# execute the tool
arcpy.AddAttributes.Add_Shape_Attributes_Low_Tool(inFeat,inBathy,tempFolder,headFeat,footFeat)
</code></scriptExample></scriptExamples><scriptExamples><scriptExample><title>Python script for multiprocessing code sample</title><para><DIV STYLE="text-align:Left;"><DIV><P><SPAN>Please use </SPAN><SPAN STYLE="font-weight:bold;">multiprocessing_B</SPAN><SPAN STYLE="font-weight:bold;">L</SPAN><SPAN STYLE="font-weight:bold;">_run.py </SPAN><SPAN>under the Tools folder. The Python script utilises the multiprocessing module to speed up the calculation performance.</SPAN></P></DIV></DIV></para></scriptExample></scriptExamples></tool><dataIdInfo><idCitation><resTitle>Add Shape Attributes Low Tool</resTitle></idCitation><searchKeys><keyword>This tool generates shape attributes for each bathymetric low feature</keyword></searchKeys><idCredit>(c) Commonwealth of Australia (Geoscience Australia) 2024</idCredit><resConst><Consts><useLimit><DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Creative Commons Attribution 4.0 International Licence</SPAN></P></DIV></DIV></DIV></useLimit></Consts></resConst></dataIdInfo><distInfo><distributor><distorFormat><formatName>ArcToolbox Tool</formatName></distorFormat></distributor></distInfo><mdHrLv><ScopeCd value="005"/></mdHrLv><mdDateSt Sync="TRUE">20240524</mdDateSt><Binary><Thumbnail><Data EsriPropertyType="PictureX">/9j/4AAQSkZJRgABAQEAeAB4AAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsK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