modified match1 to make 177(i0) right

Data/tables/angle.csv

@@ -11,13 +11,7 @@ inter_no,move_no,angle_code
 176,4,359180
 176,5,270356
 176,8,180356
-177,1,090180
-177,2,268090
-177,3,179270
 177,4,000180
-177,5,268000
-177,6,090270
-177,7,000090
 177,8,179000
 178,1,090180
 178,2,270090

Data/tables/angle_new.csv

@@ -1,49 +0,0 @@
-inter_no,move_no,angle_code
-175,1,090180
-175,2,270090
-175,3,179270
-175,4,001180
-175,5,270000
-175,6,090270
-175,7,001090
-175,8,179000
-176,3,180270
-176,4,359180
-176,5,270356
-176,8,180356
-177,1,090180
-177,2,268090
-177,3,179270
-177,4,000180
-177,5,268000
-177,6,090270
-177,7,000090
-177,8,179000
-178,1,090180
-178,2,270090
-178,3,180270
-178,4,000180
-178,5,270000
-178,6,090270
-178,7,000090
-178,8,180000
-201,1,090180
-201,2,270090
-201,3,180270
-201,4,000180
-201,5,270000
-201,6,090270
-201,7,000090
-201,8,180000
-202,2,270090
-202,6,090270
-206,4,000180
-206,8,180000
-210,1,090180
-210,2,270090
-210,3,180270
-210,4,000180
-210,5,270000
-210,6,090270
-210,7,000090
-210,8,180000

Data/tables/plan.csv

@@ -1,32 +1,32 @@
 inter_no,start_hour,start_minute,dura_A1,dura_A2,dura_A3,dura_A4,dura_A5,dura_A6,dura_A7,dura_A8,dura_B1,dura_B2,dura_B3,dura_B4,dura_B5,dura_B6,dura_B7,dura_B8,cycle,offset,yellow_A1,yellow_B1,yellow_A2,yellow_B2,yellow_A3,yellow_B3,yellow_A4,yellow_B4,yellow_A5,yellow_B5,yellow_A6,yellow_B6,yellow_A7,yellow_B7,yellow_A8,yellow_B8,red_A1,red_B1,red_A2,red_B2,red_A3,red_B3,red_A4,red_B4,red_A5,red_B5,red_A6,red_B6,red_A7,red_B7,red_A8,red_B8
-175,00,00,37,39,55,29,0,0,0,0,37,39,25,59,0,0,0,0,160,57,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-175,07,00,40,42,55,33,0,0,0,0,40,42,29,59,0,0,0,0,170,40,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-175,09,00,43,45,55,37,0,0,0,0,43,45,33,59,0,0,0,0,180,28,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-175,18,30,46,48,55,41,0,0,0,0,46,48,37,59,0,0,0,0,190,18,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-176,00,00,37,73,40,0,0,0,0,0,37,73,40,0,0,0,0,0,150,131,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-176,07,00,37,93,40,0,0,0,0,0,37,93,40,0,0,0,0,0,170,153,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-176,09,00,37,103,40,0,0,0,0,0,37,103,40,0,0,0,0,0,180,169,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-176,18,30,37,113,40,0,0,0,0,0,37,113,40,0,0,0,0,0,190,185,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-177,00,00,36,20,68,26,0,0,0,0,36,20,68,26,0,0,0,0,150,35,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2
-177,07,00,40,25,71,34,0,0,0,0,40,25,71,34,0,0,0,0,170,33,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2
-177,09,00,43,27,70,40,0,0,0,0,43,27,70,40,0,0,0,0,180,41,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2
-177,18,30,45,32,77,36,0,0,0,0,45,32,77,36,0,0,0,0,190,49,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2
-178,00,00,38,39,40,23,0,0,0,0,38,39,40,23,0,0,0,0,140,50,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-178,07,00,38,39,42,41,0,0,0,0,38,39,62,21,0,0,0,0,160,90,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-178,09,00,38,39,43,50,0,0,0,0,38,39,71,22,0,0,0,0,170,80,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-178,18,30,38,39,44,59,0,0,0,0,38,39,80,23,0,0,0,0,180,75,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+175,00,00,37,39,55,29,0,0,0,0,37,39,25,59,0,0,0,0,160,57,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2
+175,07,00,40,42,55,33,0,0,0,0,40,42,29,59,0,0,0,0,170,40,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2
+175,09,00,43,45,55,37,0,0,0,0,43,45,33,59,0,0,0,0,180,28,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2
+175,18,30,46,48,55,41,0,0,0,0,46,48,37,59,0,0,0,0,190,18,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2
+176,00,00,37,73,40,0,0,0,0,0,37,73,40,0,0,0,0,0,150,131,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+176,07,00,37,93,40,0,0,0,0,0,37,93,40,0,0,0,0,0,170,153,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+176,09,00,37,103,40,0,0,0,0,0,37,103,40,0,0,0,0,0,180,169,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+176,18,30,37,113,40,0,0,0,0,0,37,113,40,0,0,0,0,0,190,185,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+177,00,00,36,20,68,26,0,0,0,0,36,20,68,26,0,0,0,0,150,35,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+177,07,00,40,25,71,34,0,0,0,0,40,25,71,34,0,0,0,0,170,33,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+177,09,00,43,27,70,40,0,0,0,0,43,27,70,40,0,0,0,0,180,41,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+177,18,30,45,32,77,36,0,0,0,0,45,32,77,36,0,0,0,0,190,49,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+178,00,00,38,39,40,23,0,0,0,0,38,39,40,23,0,0,0,0,140,50,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+178,07,00,38,39,42,41,0,0,0,0,38,39,62,21,0,0,0,0,160,90,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+178,09,00,38,39,43,50,0,0,0,0,38,39,71,22,0,0,0,0,170,80,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+178,18,30,38,39,44,59,0,0,0,0,38,39,80,23,0,0,0,0,180,75,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
 201,00,00,24,24,17,58,17,0,0,0,24,24,17,58,17,0,0,0,140,133,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
 201,07,00,30,36,18,58,18,0,0,0,30,36,18,58,18,0,0,0,160,132,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
 201,09,00,33,36,25,58,18,0,0,0,33,36,25,58,18,0,0,0,170,134,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
 201,18,30,36,50,18,58,18,0,0,0,36,50,18,58,18,0,0,0,180,137,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-202,00,00,39,101,0,0,0,0,0,0,39,101,0,0,0,0,0,0,140,103,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-202,07,00,46,114,0,0,0,0,0,0,46,114,0,0,0,0,0,0,160,103,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-202,09,00,46,114,0,0,0,0,0,0,46,114,0,0,0,0,0,0,160,103,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-202,18,30,48,122,0,0,0,0,0,0,48,122,0,0,0,0,0,0,170,103,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-206,00,00,33,35,26,26,0,0,0,0,33,35,26,26,0,0,0,0,120,10,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-206,07,00,44,44,26,26,0,0,0,0,44,44,26,26,0,0,0,0,140,7,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-206,09,00,45,53,26,26,0,0,0,0,45,53,26,26,0,0,0,0,150,17,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-206,18,30,46,62,26,26,0,0,0,0,46,62,26,26,0,0,0,0,160,10,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+202,00,00,39,101,0,0,0,0,0,0,39,101,0,0,0,0,0,0,140,103,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+202,07,00,46,114,0,0,0,0,0,0,46,114,0,0,0,0,0,0,160,103,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+202,09,00,46,114,0,0,0,0,0,0,46,114,0,0,0,0,0,0,160,103,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+202,18,30,48,122,0,0,0,0,0,0,48,122,0,0,0,0,0,0,170,103,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+206,00,00,33,35,26,26,0,0,0,0,33,35,26,26,0,0,0,0,120,10,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+206,07,00,44,44,26,26,0,0,0,0,44,44,26,26,0,0,0,0,140,7,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+206,09,00,45,53,26,26,0,0,0,0,45,53,26,26,0,0,0,0,150,17,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+206,18,30,46,62,26,26,0,0,0,0,46,62,26,26,0,0,0,0,160,10,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
 210,00,00,43,29,56,22,0,0,0,0,24,48,56,22,0,0,0,0,150,115,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
 210,07,00,43,39,65,23,0,0,0,0,24,58,65,23,0,0,0,0,170,131,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
 210,09,00,43,43,70,24,0,0,0,0,28,58,70,24,0,0,0,0,180,137,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1

Intermediates/match1.csv

@@ -1,33 +1,31 @@
-,inter_no,phas_A,phas_B,move_A,move_B
-0,175,1,1,8,4
-1,175,2,2,7,3
-2,175,3,3,6,1
-3,175,3,4,6,2
-4,175,4,4,5,2
-5,176,1,1,8,4
-6,176,2,2,8,3
-7,176,3,3,5,18
-8,177,1,1,8,4
-9,177,2,2,7,3
-10,177,3,3,17,18
-11,177,4,4,5,1
-12,178,1,1,8,4
-13,178,2,2,7,3
-14,178,3,3,5,2
-15,178,4,4,6,1
-16,201,1,1,8,3
-17,201,2,2,5,2
-18,201,3,3,6,2
-19,201,4,4,6,1
-20,201,5,5,7,4
-21,202,1,1,6,2
-22,202,2,2,17,18
-23,206,1,1,8,4
-24,206,2,2,17,18
-25,206,3,3,8,4
-26,206,4,4,17,18
-27,210,1,1,6,18
-28,210,1,2,6,2
-29,210,2,2,5,2
-30,210,3,3,7,4
-31,210,4,4,8,3
+inter_no,phas_A,phas_B,move_A,move_B
+175,1,1,8,4
+175,2,2,7,3
+175,3,3,6,1
+175,3,4,6,2
+175,4,4,5,2
+176,1,1,8,4
+176,2,2,8,3
+176,3,3,5,18
+177,1,1,8,4
+177,2,2,17,18
+178,1,1,8,4
+178,2,2,7,3
+178,3,3,5,2
+178,4,4,6,1
+201,1,1,8,3
+201,2,2,5,2
+201,3,3,6,2
+201,4,4,6,1
+201,5,5,7,4
+202,1,1,6,2
+202,2,2,17,18
+206,1,1,8,4
+206,2,2,17,18
+206,3,3,8,4
+206,4,4,17,18
+210,1,1,6,18
+210,1,2,6,2
+210,2,2,5,2
+210,3,3,7,4
+210,4,4,8,3

Intermediates/match6.csv

@@ -21,97 +21,89 @@
 19,176,i1,2,B,3,-571542810_01,571543469_01,grrrrrGgrr
 20,176,i1,3,A,5,571543469_02,-571542797_02.99,grrrrrrgGG
 21,176,i1,3,B,18,,,grrrrrrgrr
-22,177,i2,1,A,8,-571542809_01,571542811_01,rrgrGGG
-23,177,i2,1,B,4,571542811_02,571542809_01,GGgrrrr
-24,177,i2,2,A,7,571542811_02,571542107_01,rrgrrrr
-25,177,i2,2,B,3,-571542809_01,571542809_01,rrgrrrr
-26,177,i2,3,A,17,,,rrgrrrr
-27,177,i2,3,B,18,,,rrgrrrr
-28,177,i2,4,A,5,-571542809_01,571542811_01,rrgrGGG
-29,177,i2,4,B,1,571542107_02,571542809_01,rrgrrrr
-30,177,u20,1,A,8,,,ggrggg
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Intermediates/matching.csv

@@ -18,60 +18,49 @@
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+54,210,i6,3,571500585_02,571500535_01,grrrgrrrrgrrgrrr
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+57,210,i6,6,-571542115_01,571500535_01,grrrgGGGrgrrgrrr
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Intermediates/modify_match1.py

@@ -0,0 +1,9 @@
+import pandas as pd
+match1 = pd.read_csv('Intermediates/match1.csv')
+print(match1)
+# match1 = match1.drop(columns='Unnamed: 0')
+match1 = match1.drop(index=[9, 11])
+match1.loc[10, ['phas_A', 'phas_B']] = 2
+match1 = match1.reset_index(drop=True)
+match1.to_csv('Intermediates/match1.csv', index=0)
+print(match1)

Intermediates/node2init.json

@@ -1 +1 @@
-{"c30": ["r", "r", "r", "r", "r", "r"], "i0": ["g", "r", "r", "r", "g", "r", "r", "r", "g", "r", "r", "r", "r", "r", "g", "r", "r"], "i1": ["g", "r", "r", "r", "r", "r", "r", "g", "r", "r"], "i2": ["r", "r", "g", "r", "r", "r", "r"], "i3": ["g", "r", "r", "r", "r", "g", "r", "r", "r", "r", "g", "r", "r", "r", "r", "g", "r", "r", "r", "r"], "i6": ["g", "r", "r", "r", "g", "r", "r", "r", "r", "g", "r", "r", "g", "r", "r", "r"], "i7": ["r", "r", "r", "g", "g", "r", "r"], "i8": ["g", "r", "r", "r", "r", "r", "r", "r", "g", "r", "r", "r", "g", "r", "r", "r"], "i9": ["r", "r", "r", "r"], "u00": ["g", "g", "g", "g", "r", "g", "g", "g", "g"], "u20": ["g", "g", "r", "g", "g", "g"], "u30": ["g", "g", "g", "g", "r", "g", "g", "g"], "u31": ["g", "g", "g", "g", "r", "g", "g", "g"], "u32": ["g", "g", "g", "g", "g", "g", "g", "r"], "u60": ["g", "g", "g", "g", "g", "g", "g", "g", "r"]}
+{"c30": ["r", "r", "r", "r", "r", "r"], "i0": ["g", "r", "r", "r", "g", "r", "r", "r", "g", "r", "r", "r", "r", "r", "g", "r", "r"], "i1": ["g", "r", "r", "r", "r", "r", "r", "g", "r", "r"], "i2": ["r", "r", "g", "g", "r", "r", "r"], "i3": ["g", "r", "r", "r", "r", "g", "r", "r", "r", "r", "g", "r", "r", "r", "r", "g", "r", "r", "r", "r"], "i6": ["g", "r", "r", "r", "g", "r", "r", "r", "r", "g", "r", "r", "g", "r", "r", "r"], "i7": ["r", "r", "r", "g", "g", "r", "r"], "i8": ["g", "r", "r", "r", "r", "r", "r", "r", "g", "r", "r", "r", "g", "r", "r", "r"], "i9": ["r", "r", "r", "r"], "u00": ["g", "g", "g", "g", "r", "g", "g", "g", "g"], "u20": ["g", "g", "g", "g", "g", "g"], "u30": ["g", "g", "g", "g", "r", "g", "g", "g"], "u31": ["g", "g", "g", "g", "r", "g", "g", "g"], "u32": ["g", "g", "g", "g", "g", "g", "g", "r"], "u60": ["g", "g", "g", "g", "g", "g", "g", "g", "r"]}

Scripts/preprocess_daily.py

@@ -268,7 +268,7 @@ class DailyPreprocessor():
                 self.match1.append(pd.DataFrame({'inter_no':[inter_no], 'phas_A':[phas_A], 'phas_B':[phas_B],
                                             'move_A':[frequent_pair[0]], 'move_B':[frequent_pair[1]]}))
             self.match1 = pd.concat(self.match1).reset_index(drop=True)
-            self.match1.to_csv(os.path.join(self.path_intermediates, 'match1.csv'))
+            self.match1.to_csv(os.path.join(self.path_intermediates, 'match1.csv'), index=0)
         else:
             self.match1 = pd.read_csv(os.path.join(self.path_intermediates, 'match1.csv'))