-
Notifications
You must be signed in to change notification settings - Fork 0
/
Portland water.twb
1324 lines (1323 loc) · 85.5 KB
/
Portland water.twb
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' encoding='utf-8' ?>
<!-- build 20202.20.0525.1210 -->
<workbook original-version='18.1' source-build='2020.2.1 (20202.20.0525.1210)' source-platform='win' version='18.1' xmlns:user='http://www.tableausoftware.com/xml/user'>
<document-format-change-manifest>
<_.fcp.MarkAnimation.true...MarkAnimation />
<_.fcp.ObjectModelEncapsulateLegacy.true...ObjectModelEncapsulateLegacy />
<_.fcp.ObjectModelTableType.true...ObjectModelTableType />
<_.fcp.SchemaViewerObjectModel.true...SchemaViewerObjectModel />
<SheetIdentifierTracking />
<WindowsPersistSimpleIdentifiers />
</document-format-change-manifest>
<preferences>
<preference name='ui.encoding.shelf.height' value='24' />
<preference name='ui.shelf.height' value='26' />
</preferences>
<datasources>
<datasource caption='PortlandWaterLevel2003' inline='true' name='federated.16v67321yye583152wcrb0fbyc7c' version='18.1'>
<connection class='federated'>
<named-connections>
<named-connection caption='PortlandWaterLevel2003' name='textscan.1ojmcp80tsv16c1cbl9ie034m0aa'>
<connection class='textscan' directory='C:/Users/USER/Desktop/DSC/DSC_465 Data Visualization/Homework/Homework_2' filename='PortlandWaterLevel2003.csv' password='' server='' />
</named-connection>
</named-connections>
<_.fcp.ObjectModelEncapsulateLegacy.false...relation connection='textscan.1ojmcp80tsv16c1cbl9ie034m0aa' name='PortlandWaterLevel2003.csv' table='[PortlandWaterLevel2003#csv]' type='table'>
<columns character-set='UTF-8' header='yes' locale='en_IN' separator=','>
<column datatype='integer' name='Station' ordinal='0' />
<column datatype='date' name='Date' ordinal='1' />
<column datatype='datetime' name='Time' ordinal='2' />
<column datatype='real' name='WL' ordinal='3' />
<column datatype='real' name='Sigma' ordinal='4' />
<column datatype='integer' name='I' ordinal='5' />
<column datatype='integer' name='L' ordinal='6' />
</columns>
</_.fcp.ObjectModelEncapsulateLegacy.false...relation>
<_.fcp.ObjectModelEncapsulateLegacy.true...relation connection='textscan.1ojmcp80tsv16c1cbl9ie034m0aa' name='PortlandWaterLevel2003.csv' table='[PortlandWaterLevel2003#csv]' type='table'>
<columns character-set='UTF-8' header='yes' locale='en_IN' separator=','>
<column datatype='integer' name='Station' ordinal='0' />
<column datatype='date' name='Date' ordinal='1' />
<column datatype='datetime' name='Time' ordinal='2' />
<column datatype='real' name='WL' ordinal='3' />
<column datatype='real' name='Sigma' ordinal='4' />
<column datatype='integer' name='I' ordinal='5' />
<column datatype='integer' name='L' ordinal='6' />
</columns>
</_.fcp.ObjectModelEncapsulateLegacy.true...relation>
<metadata-records>
<metadata-record class='capability'>
<remote-name />
<remote-type>0</remote-type>
<parent-name>[PortlandWaterLevel2003.csv]</parent-name>
<remote-alias />
<aggregation>Count</aggregation>
<contains-null>true</contains-null>
<attributes>
<attribute datatype='string' name='character-set'>"UTF-8"</attribute>
<attribute datatype='string' name='collation'>"en_GB"</attribute>
<attribute datatype='string' name='currency'>"Rs"</attribute>
<attribute datatype='string' name='debit-close-char'>""</attribute>
<attribute datatype='string' name='debit-open-char'>""</attribute>
<attribute datatype='string' name='field-delimiter'>","</attribute>
<attribute datatype='string' name='header-row'>"true"</attribute>
<attribute datatype='string' name='locale'>"en_IN"</attribute>
<attribute datatype='string' name='single-char'>""</attribute>
</attributes>
</metadata-record>
<metadata-record class='column'>
<remote-name>Station</remote-name>
<remote-type>20</remote-type>
<local-name>[Station]</local-name>
<parent-name>[PortlandWaterLevel2003.csv]</parent-name>
<remote-alias>Station</remote-alias>
<ordinal>0</ordinal>
<local-type>integer</local-type>
<aggregation>Sum</aggregation>
<contains-null>true</contains-null>
<_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[PortlandWaterLevel2003.csv_0250BAB2450241948BA8D831A78128DE]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
</metadata-record>
<metadata-record class='column'>
<remote-name>Date</remote-name>
<remote-type>133</remote-type>
<local-name>[Date]</local-name>
<parent-name>[PortlandWaterLevel2003.csv]</parent-name>
<remote-alias>Date</remote-alias>
<ordinal>1</ordinal>
<local-type>date</local-type>
<aggregation>Year</aggregation>
<contains-null>true</contains-null>
<_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[PortlandWaterLevel2003.csv_0250BAB2450241948BA8D831A78128DE]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
</metadata-record>
<metadata-record class='column'>
<remote-name>Time</remote-name>
<remote-type>134</remote-type>
<local-name>[Time]</local-name>
<parent-name>[PortlandWaterLevel2003.csv]</parent-name>
<remote-alias>Time</remote-alias>
<ordinal>2</ordinal>
<local-type>datetime</local-type>
<aggregation>Hour</aggregation>
<contains-null>true</contains-null>
<_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[PortlandWaterLevel2003.csv_0250BAB2450241948BA8D831A78128DE]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
</metadata-record>
<metadata-record class='column'>
<remote-name>WL</remote-name>
<remote-type>5</remote-type>
<local-name>[WL]</local-name>
<parent-name>[PortlandWaterLevel2003.csv]</parent-name>
<remote-alias>WL</remote-alias>
<ordinal>3</ordinal>
<local-type>real</local-type>
<aggregation>Sum</aggregation>
<contains-null>true</contains-null>
<_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[PortlandWaterLevel2003.csv_0250BAB2450241948BA8D831A78128DE]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
</metadata-record>
<metadata-record class='column'>
<remote-name>Sigma</remote-name>
<remote-type>5</remote-type>
<local-name>[Sigma]</local-name>
<parent-name>[PortlandWaterLevel2003.csv]</parent-name>
<remote-alias>Sigma</remote-alias>
<ordinal>4</ordinal>
<local-type>real</local-type>
<aggregation>Sum</aggregation>
<contains-null>true</contains-null>
<_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[PortlandWaterLevel2003.csv_0250BAB2450241948BA8D831A78128DE]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
</metadata-record>
<metadata-record class='column'>
<remote-name>I</remote-name>
<remote-type>20</remote-type>
<local-name>[I]</local-name>
<parent-name>[PortlandWaterLevel2003.csv]</parent-name>
<remote-alias>I</remote-alias>
<ordinal>5</ordinal>
<local-type>integer</local-type>
<aggregation>Sum</aggregation>
<contains-null>true</contains-null>
<_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[PortlandWaterLevel2003.csv_0250BAB2450241948BA8D831A78128DE]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
</metadata-record>
<metadata-record class='column'>
<remote-name>L</remote-name>
<remote-type>20</remote-type>
<local-name>[L]</local-name>
<parent-name>[PortlandWaterLevel2003.csv]</parent-name>
<remote-alias>L</remote-alias>
<ordinal>6</ordinal>
<local-type>integer</local-type>
<aggregation>Sum</aggregation>
<contains-null>true</contains-null>
<_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[PortlandWaterLevel2003.csv_0250BAB2450241948BA8D831A78128DE]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
</metadata-record>
</metadata-records>
</connection>
<aliases enabled='yes' />
<column datatype='date' name='[Date]' role='dimension' type='quantitative' />
<column datatype='real' name='[Sigma]' role='measure' type='quantitative' />
<column datatype='real' name='[WL]' role='measure' type='quantitative' />
<_.fcp.ObjectModelTableType.true...column caption='PortlandWaterLevel2003.csv' datatype='table' name='[__tableau_internal_object_id__].[PortlandWaterLevel2003.csv_0250BAB2450241948BA8D831A78128DE]' role='measure' type='quantitative' />
<column-instance column='[Sigma]' derivation='Sum' name='[sum:Sigma:qk]' pivot='key' type='quantitative' />
<column-instance column='[WL]' derivation='Sum' name='[sum:WL:qk]' pivot='key' type='quantitative' />
<column-instance column='[WL]' derivation='Avg' name='[win:avg:WL:qk]' pivot='key' type='quantitative'>
<table-calc aggregation='Avg' from='-2' ordering-type='Rows' to='0' type='WindowTotal' window-options='IncludeCurrent' />
</column-instance>
<column-instance column='[WL]' derivation='Sum' name='[win:sum:WL:qk]' pivot='key' type='quantitative'>
<table-calc aggregation='Avg' from='-2' ordering-type='Rows' to='0' type='WindowTotal' window-options='IncludeCurrent' />
</column-instance>
<layout _.fcp.SchemaViewerObjectModel.false...dim-percentage='0.5' _.fcp.SchemaViewerObjectModel.false...measure-percentage='0.4' dim-ordering='alphabetic' measure-ordering='alphabetic' show-structure='true' />
<style>
<style-rule element='mark'>
<encoding attr='color' field='[:Measure Names]' type='palette'>
<map to='#4e79a7'>
<bucket>"[federated.16v67321yye583152wcrb0fbyc7c].[sum:Sigma:qk]"</bucket>
</map>
<map to='#e15759'>
<bucket>"[federated.16v67321yye583152wcrb0fbyc7c]"</bucket>
</map>
<map to='#f28e2b'>
<bucket>"[federated.16v67321yye583152wcrb0fbyc7c].[sum:WL:qk]"</bucket>
</map>
<map to='#f28e2b'>
<bucket>"[federated.16v67321yye583152wcrb0fbyc7c].[win:avg:WL:qk]"</bucket>
</map>
<map to='#f28e2b'>
<bucket>"[federated.16v67321yye583152wcrb0fbyc7c].[win:sum:WL:qk]"</bucket>
</map>
</encoding>
</style-rule>
</style>
<semantic-values>
<semantic-value key='[Country].[Name]' value='"India"' />
</semantic-values>
<_.fcp.ObjectModelEncapsulateLegacy.true...object-graph>
<objects>
<object caption='PortlandWaterLevel2003.csv' id='PortlandWaterLevel2003.csv_0250BAB2450241948BA8D831A78128DE'>
<properties context=''>
<relation connection='textscan.1ojmcp80tsv16c1cbl9ie034m0aa' name='PortlandWaterLevel2003.csv' table='[PortlandWaterLevel2003#csv]' type='table'>
<columns character-set='UTF-8' header='yes' locale='en_IN' separator=','>
<column datatype='integer' name='Station' ordinal='0' />
<column datatype='date' name='Date' ordinal='1' />
<column datatype='datetime' name='Time' ordinal='2' />
<column datatype='real' name='WL' ordinal='3' />
<column datatype='real' name='Sigma' ordinal='4' />
<column datatype='integer' name='I' ordinal='5' />
<column datatype='integer' name='L' ordinal='6' />
</columns>
</relation>
</properties>
</object>
</objects>
</_.fcp.ObjectModelEncapsulateLegacy.true...object-graph>
</datasource>
</datasources>
<worksheets>
<worksheet name='Section a - Date'>
<layout-options>
<title>
<formatted-text>
<run>Hour vs. Day using WL (Average)</run>
</formatted-text>
</title>
</layout-options>
<table>
<view>
<datasources>
<datasource caption='PortlandWaterLevel2003' name='federated.16v67321yye583152wcrb0fbyc7c' />
</datasources>
<datasource-dependencies datasource='federated.16v67321yye583152wcrb0fbyc7c'>
<column datatype='date' name='[Date]' role='dimension' type='quantitative' />
<column datatype='datetime' name='[Time]' role='dimension' type='ordinal' />
<column datatype='real' name='[WL]' role='measure' type='quantitative' />
<column-instance column='[Date]' derivation='Day' name='[dy:Date:ok]' pivot='key' type='ordinal' />
<column-instance column='[Time]' derivation='Hour' name='[hr:Time:ok]' pivot='key' type='ordinal' />
<column-instance column='[WL]' derivation='Avg' name='[win:avg:WL:qk]' pivot='key' type='quantitative'>
<table-calc aggregation='Avg' from='-2' ordering-type='Rows' to='0' type='WindowTotal' window-options='IncludeCurrent' />
</column-instance>
</datasource-dependencies>
<aggregation value='true' />
</view>
<style>
<style-rule element='mark'>
<encoding attr='color' field='[federated.16v67321yye583152wcrb0fbyc7c].[win:avg:WL:qk]' palette='green_gold_10_0' type='interpolated' />
</style-rule>
</style>
<panes>
<pane selection-relaxation-option='selection-relaxation-allow'>
<view>
<breakdown value='auto' />
</view>
<mark class='Automatic' />
<encodings>
<color column='[federated.16v67321yye583152wcrb0fbyc7c].[win:avg:WL:qk]' />
</encodings>
<style>
<style-rule element='mark'>
<format attr='mark-transparency' value='175' />
<format attr='has-stroke' value='false' />
</style-rule>
</style>
</pane>
</panes>
<rows>[federated.16v67321yye583152wcrb0fbyc7c].[hr:Time:ok]</rows>
<cols>[federated.16v67321yye583152wcrb0fbyc7c].[dy:Date:ok]</cols>
</table>
<simple-id uuid='{AC3CAA2C-3D00-45D4-B389-19E586DE0484}' />
</worksheet>
<worksheet name='Section a - Day'>
<layout-options>
<title>
<formatted-text>
<run>Hour vs. Day of the week using WL (Average)</run>
</formatted-text>
</title>
</layout-options>
<table>
<view>
<datasources>
<datasource caption='PortlandWaterLevel2003' name='federated.16v67321yye583152wcrb0fbyc7c' />
</datasources>
<datasource-dependencies datasource='federated.16v67321yye583152wcrb0fbyc7c'>
<column datatype='date' name='[Date]' role='dimension' type='quantitative' />
<column datatype='real' name='[Sigma]' role='measure' type='quantitative' />
<column datatype='datetime' name='[Time]' role='dimension' type='ordinal' />
<column datatype='real' name='[WL]' role='measure' type='quantitative' />
<column-instance column='[Time]' derivation='Hour' name='[hr:Time:ok]' pivot='key' type='ordinal' />
<column-instance column='[Sigma]' derivation='Sum' name='[sum:Sigma:qk]' pivot='key' type='quantitative' />
<column-instance column='[WL]' derivation='Sum' name='[sum:WL:qk]' pivot='key' type='quantitative' />
<column-instance column='[Date]' derivation='Weekday' name='[wd:Date:ok]' pivot='key' type='ordinal' />
<column-instance column='[WL]' derivation='Avg' name='[win:avg:WL:qk]' pivot='key' type='quantitative'>
<table-calc aggregation='Avg' from='-2' ordering-type='Rows' to='0' type='WindowTotal' window-options='IncludeCurrent' />
</column-instance>
</datasource-dependencies>
<filter class='categorical' column='[federated.16v67321yye583152wcrb0fbyc7c].[:Measure Names]'>
<groupfilter function='union' user:op='manual'>
<groupfilter function='member' level='[:Measure Names]' member='"[federated.16v67321yye583152wcrb0fbyc7c].[sum:Sigma:qk]"' />
<groupfilter function='member' level='[:Measure Names]' member='"[federated.16v67321yye583152wcrb0fbyc7c].[sum:WL:qk]"' />
</groupfilter>
</filter>
<slices>
<column>[federated.16v67321yye583152wcrb0fbyc7c].[:Measure Names]</column>
</slices>
<aggregation value='true' />
</view>
<style>
<style-rule element='mark'>
<encoding attr='color' field='[federated.16v67321yye583152wcrb0fbyc7c].[win:avg:WL:qk]' palette='orange_gold_10_0' type='interpolated' />
</style-rule>
</style>
<panes>
<pane id='5' selection-relaxation-option='selection-relaxation-allow'>
<view>
<breakdown value='auto' />
</view>
<mark class='Automatic' />
<encodings>
<color column='[federated.16v67321yye583152wcrb0fbyc7c].[win:avg:WL:qk]' />
</encodings>
<style>
<style-rule element='mark'>
<format attr='mark-labels-cull' value='true' />
<format attr='mark-labels-show' value='false' />
<format attr='mark-transparency' value='167' />
<format attr='has-stroke' value='false' />
</style-rule>
</style>
</pane>
</panes>
<rows>[federated.16v67321yye583152wcrb0fbyc7c].[hr:Time:ok]</rows>
<cols>[federated.16v67321yye583152wcrb0fbyc7c].[wd:Date:ok]</cols>
</table>
<simple-id uuid='{DFC7391D-C0D1-4674-A468-FFF9D5CBADAC}' />
</worksheet>
<worksheet name='Section a - Month'>
<layout-options>
<title>
<formatted-text>
<run>WL (Average) vs. Month (2003)</run>
</formatted-text>
</title>
</layout-options>
<table>
<view>
<datasources>
<datasource caption='PortlandWaterLevel2003' name='federated.16v67321yye583152wcrb0fbyc7c' />
</datasources>
<datasource-dependencies datasource='federated.16v67321yye583152wcrb0fbyc7c'>
<column datatype='date' name='[Date]' role='dimension' type='quantitative' />
<column datatype='real' name='[WL]' role='measure' type='quantitative' />
<column-instance column='[Date]' derivation='Month' name='[mn:Date:ok]' pivot='key' type='ordinal' />
<column-instance column='[WL]' derivation='Avg' name='[win:avg:WL:qk]' pivot='key' type='quantitative'>
<table-calc aggregation='Avg' from='-2' ordering-type='Rows' to='0' type='WindowTotal' window-options='IncludeCurrent' />
</column-instance>
</datasource-dependencies>
<aggregation value='true' />
</view>
<style>
<style-rule element='axis'>
<encoding attr='space' class='0' domain-expand='false' field='[federated.16v67321yye583152wcrb0fbyc7c].[win:avg:WL:qk]' field-type='quantitative' scope='rows' type='space' />
</style-rule>
<style-rule element='header'>
<format attr='background-color' field='[federated.16v67321yye583152wcrb0fbyc7c].[mn:Date:ok]' value='#ffffff' />
</style-rule>
<style-rule element='label'>
<format attr='text-format' field='[federated.16v67321yye583152wcrb0fbyc7c].[mn:Date:ok]' value='iLLL' />
</style-rule>
</style>
<panes>
<pane selection-relaxation-option='selection-relaxation-allow'>
<view>
<breakdown value='auto' />
</view>
<mark class='Automatic' />
<encodings>
<text column='[federated.16v67321yye583152wcrb0fbyc7c].[win:avg:WL:qk]' />
</encodings>
<style>
<style-rule element='mark'>
<format attr='mark-labels-show' value='true' />
<format attr='mark-labels-cull' value='true' />
<format attr='mark-color' value='#ff1d04' />
<format attr='mark-transparency' value='132' />
<format attr='mark-markers-mode' value='all' />
</style-rule>
</style>
</pane>
</panes>
<rows>[federated.16v67321yye583152wcrb0fbyc7c].[win:avg:WL:qk]</rows>
<cols>[federated.16v67321yye583152wcrb0fbyc7c].[mn:Date:ok]</cols>
</table>
<simple-id uuid='{24784992-831D-4399-8CDA-D3D5A6A93237}' />
</worksheet>
<worksheet name='Section b'>
<layout-options>
<title>
<formatted-text>
<run>Time vs. Date (mmdd) using WL</run>
</formatted-text>
</title>
</layout-options>
<table>
<view>
<datasources>
<datasource caption='PortlandWaterLevel2003' name='federated.16v67321yye583152wcrb0fbyc7c' />
</datasources>
<datasource-dependencies datasource='federated.16v67321yye583152wcrb0fbyc7c'>
<column datatype='date' name='[Date]' role='dimension' type='quantitative' />
<column datatype='datetime' name='[Time]' role='dimension' type='ordinal' />
<column datatype='real' name='[WL]' role='measure' type='quantitative' />
<column-instance column='[Date]' derivation='Day' name='[dy:Date:ok]' pivot='key' type='ordinal' />
<column-instance column='[Time]' derivation='Hour' name='[hr:Time:ok]' pivot='key' type='ordinal' />
<column-instance column='[Date]' derivation='Month' name='[mn:Date:ok]' pivot='key' type='ordinal' />
<column-instance column='[WL]' derivation='Sum' name='[sum:WL:qk]' pivot='key' type='quantitative' />
</datasource-dependencies>
<aggregation value='true' />
</view>
<style>
<style-rule element='cell'>
<format attr='height' field='[federated.16v67321yye583152wcrb0fbyc7c].[dy:Date:ok]' value='5' />
</style-rule>
<style-rule element='label'>
<format attr='display' field='[federated.16v67321yye583152wcrb0fbyc7c].[dy:Date:ok]' value='false' />
</style-rule>
<style-rule element='mark'>
<encoding attr='color' center='1.1000000000000001' field='[federated.16v67321yye583152wcrb0fbyc7c].[sum:WL:qk]' max='2.9619999999999997' min='-0.92600000000000005' palette='red_blue_diverging_10_0' type='interpolated' />
</style-rule>
<style-rule element='legend-title-text'>
<format attr='color' field='[federated.16v67321yye583152wcrb0fbyc7c].[sum:WL:qk]' value='WL Value'>
<formatted-text>
<run>WL Value</run>
</formatted-text>
</format>
</style-rule>
</style>
<panes>
<pane id='1' selection-relaxation-option='selection-relaxation-allow'>
<view>
<breakdown value='auto' />
</view>
<mark class='Line' />
<encodings>
<color column='[federated.16v67321yye583152wcrb0fbyc7c].[sum:WL:qk]' />
</encodings>
<style>
<style-rule element='mark'>
<format attr='mark-transparency' value='198' />
</style-rule>
<style-rule element='pane'>
<format attr='minheight' value='-1' />
<format attr='maxheight' value='-1' />
</style-rule>
</style>
</pane>
</panes>
<rows>([federated.16v67321yye583152wcrb0fbyc7c].[mn:Date:ok] / [federated.16v67321yye583152wcrb0fbyc7c].[dy:Date:ok])</rows>
<cols>[federated.16v67321yye583152wcrb0fbyc7c].[hr:Time:ok]</cols>
</table>
<simple-id uuid='{9BAAC0E5-9AD7-4298-807B-1B32B679058F}' />
</worksheet>
</worksheets>
<windows source-height='30'>
<window class='worksheet' name='Section a - Month'>
<cards>
<edge name='left'>
<strip size='160'>
<card type='pages' />
<card type='filters' />
<card type='marks' />
</strip>
</edge>
<edge name='top'>
<strip size='2147483647'>
<card type='columns' />
</strip>
<strip size='2147483647'>
<card type='rows' />
</strip>
<strip size='31'>
<card type='title' />
</strip>
</edge>
</cards>
<viewpoint>
<highlight>
<color-one-way>
<field>[federated.16v67321yye583152wcrb0fbyc7c].[dy:Date:ok]</field>
<field>[federated.16v67321yye583152wcrb0fbyc7c].[hr:Time:ok]</field>
<field>[federated.16v67321yye583152wcrb0fbyc7c].[tyr:Date:qk]</field>
<field>[federated.16v67321yye583152wcrb0fbyc7c].[yr:Date:ok]</field>
</color-one-way>
</highlight>
</viewpoint>
<simple-id uuid='{277F5A2B-686A-4BB8-BC74-0A836911AC37}' />
</window>
<window class='worksheet' name='Section a - Day'>
<cards>
<edge name='left'>
<strip size='160'>
<card type='pages' />
<card type='filters' />
<card type='marks' />
</strip>
</edge>
<edge name='top'>
<strip size='2147483647'>
<card type='columns' />
</strip>
<strip size='2147483647'>
<card type='rows' />
</strip>
<strip size='31'>
<card type='title' />
</strip>
</edge>
<edge name='right'>
<strip size='100'>
<card pane-specification-id='5' param='[federated.16v67321yye583152wcrb0fbyc7c].[win:avg:WL:qk]' type='color' />
</strip>
</edge>
</cards>
<viewpoint>
<highlight>
<color-one-way>
<field>[federated.16v67321yye583152wcrb0fbyc7c].[:Measure Names]</field>
<field>[federated.16v67321yye583152wcrb0fbyc7c].[dy:Date:ok]</field>
<field>[federated.16v67321yye583152wcrb0fbyc7c].[hr:Time:ok]</field>
<field>[federated.16v67321yye583152wcrb0fbyc7c].[tyr:Date:qk]</field>
<field>[federated.16v67321yye583152wcrb0fbyc7c].[wd:Date:ok]</field>
<field>[federated.16v67321yye583152wcrb0fbyc7c].[win:avg:WL:qk]</field>
</color-one-way>
</highlight>
</viewpoint>
<simple-id uuid='{E509EC70-EF39-469F-A621-4067E923AFC9}' />
</window>
<window class='worksheet' name='Section a - Date'>
<cards>
<edge name='left'>
<strip size='160'>
<card type='pages' />
<card type='filters' />
<card type='marks' />
</strip>
</edge>
<edge name='top'>
<strip size='2147483647'>
<card type='columns' />
</strip>
<strip size='2147483647'>
<card type='rows' />
</strip>
<strip size='31'>
<card type='title' />
</strip>
</edge>
<edge name='right'>
<strip size='160'>
<card pane-specification-id='0' param='[federated.16v67321yye583152wcrb0fbyc7c].[win:avg:WL:qk]' type='color' />
</strip>
</edge>
</cards>
<viewpoint>
<highlight>
<color-one-way>
<field>[federated.16v67321yye583152wcrb0fbyc7c].[hr:Time:ok]</field>
<field>[federated.16v67321yye583152wcrb0fbyc7c].[tyr:Date:qk]</field>
<field>[federated.16v67321yye583152wcrb0fbyc7c].[win:avg:WL:qk]</field>
</color-one-way>
</highlight>
</viewpoint>
<simple-id uuid='{78BCC2C8-D04D-46EF-87B8-996F740FADD2}' />
</window>
<window class='worksheet' maximized='true' name='Section b'>
<cards>
<edge name='left'>
<strip size='160'>
<card type='pages' />
<card type='filters' />
<card type='marks' />
</strip>
</edge>
<edge name='top'>
<strip size='2147483647'>
<card type='columns' />
</strip>
<strip size='2147483647'>
<card type='rows' />
</strip>
<strip size='31'>
<card type='title' />
</strip>
</edge>
<edge name='right'>
<strip size='160'>
<card pane-specification-id='1' param='[federated.16v67321yye583152wcrb0fbyc7c].[sum:WL:qk]' type='color' />
</strip>
</edge>
</cards>
<viewpoint>
<highlight>
<color-one-way>
<field>[federated.16v67321yye583152wcrb0fbyc7c].[hr:Time:ok]</field>
<field>[federated.16v67321yye583152wcrb0fbyc7c].[tyr:Date:qk]</field>
</color-one-way>
</highlight>
</viewpoint>
<simple-id uuid='{932F6936-F598-4636-B3D6-321ABC80C4F6}' />
</window>
</windows>
<thumbnails>
<thumbnail height='192' name='Section a - Date' width='192'>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</thumbnail>
<thumbnail height='192' name='Section a - Day' width='192'>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</thumbnail>
<thumbnail height='192' name='Section a - Month' width='192'>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</thumbnail>
<thumbnail height='192' name='Section b' width='192'>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