tests/testthat/helper_simulated_data_1536.R
In sathishsrinivasank/pinerrordetector: Detection of Pinning Errors of Robotic Machines used in SGA Yeast Screen
# simulated data using colonyarea data subtypes
j11 <- simulated_data_1536(data_384 = colonyarea$data_subtypes,
in_data_flow = "across",
out_data_flow = "across",
is_plate_coords = FALSE)
j12 <- structure(list(y = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 132,
132, 142, 142, 502, 502, 190, 190, 288, 288, 218, 218, 1002,
1002, 1052, 1052, 860, 860, 980, 980, 672, 672, 643, 643, 820,
820, 1158, 1158, 1153, 1153, 1154, 1154, 1089, 1089, 1195, 1195,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 132, 132, 142, 142, 502,
502, 190, 190, 288, 288, 218, 218, 1002, 1002, 1052, 1052, 860,
860, 980, 980, 672, 672, 643, 643, 820, 820, 1158, 1158, 1153,
1153, 1154, 1154, 1089, 1089, 1195, 1195, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 148, 148, 137, 137, 236, 236, 372, 372, 176, 176,
492, 492, 691, 691, 759, 759, 675, 675, 618, 618, 874, 874, 938,
938, 992, 992, 1173, 1173, 1081, 1081, 1107, 1107, 1137, 1137,
1088, 1088, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 148, 148, 137,
137, 236, 236, 372, 372, 176, 176, 492, 492, 691, 691, 759, 759,
675, 675, 618, 618, 874, 874, 938, 938, 992, 992, 1173, 1173,
1081, 1081, 1107, 1107, 1137, 1137, 1088, 1088, 1074, 1074, 986,
986, 627, 627, 767, 767, 930, 930, 990, 990, 1019, 1019, 618,
618, 605, 605, 603, 603, 964, 964, 807, 807, 720, 720, 870, 870,
671, 671, 653, 653, 1042, 1042, 986, 986, 942, 942, 671, 671,
813, 813, 646, 646, 1070, 1070, 642, 642, 1074, 1074, 986, 986,
627, 627, 767, 767, 930, 930, 990, 990, 1019, 1019, 618, 618,
605, 605, 603, 603, 964, 964, 807, 807, 720, 720, 870, 870, 671,
671, 653, 653, 1042, 1042, 986, 986, 942, 942, 671, 671, 813,
813, 646, 646, 1070, 1070, 642, 642, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 115, 115, 105, 105, 384, 384, 402, 402, 514, 514,
532, 532, 1035, 1035, 841, 841, 690, 690, 862, 862, 970, 970,
683, 683, 1004, 1004, 1094, 1094, 1195, 1195, 1159, 1159, 1090,
1090, 1099, 1099, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 115, 115,
105, 105, 384, 384, 402, 402, 514, 514, 532, 532, 1035, 1035,
841, 841, 690, 690, 862, 862, 970, 970, 683, 683, 1004, 1004,
1094, 1094, 1195, 1195, 1159, 1159, 1090, 1090, 1099, 1099, 563,
563, 348, 348, 203, 203, 341, 341, 591, 591, 515, 515, 165, 165,
255, 255, 337, 337, 180, 180, 488, 488, 194, 194, 384, 384, 160,
160, 432, 432, 534, 534, 252, 252, 482, 482, 297, 297, 432, 432,
520, 520, 497, 497, 400, 400, 230, 230, 563, 563, 348, 348, 203,
203, 341, 341, 591, 591, 515, 515, 165, 165, 255, 255, 337, 337,
180, 180, 488, 488, 194, 194, 384, 384, 160, 160, 432, 432, 534,
534, 252, 252, 482, 482, 297, 297, 432, 432, 520, 520, 497, 497,
400, 400, 230, 230, 517, 517, 0, 0, 522, 522, 296, 296, 152,
152, 0, 0, 581, 581, 327, 327, 230, 230, 129, 129, 265, 265,
411, 411, 176, 176, 0, 0, 353, 353, 190, 190, 184, 184, 611,
611, 239, 239, 336, 336, 517, 517, 101, 101, 427, 427, 507, 507,
517, 517, 0, 0, 522, 522, 296, 296, 152, 152, 0, 0, 581, 581,
327, 327, 230, 230, 129, 129, 265, 265, 411, 411, 176, 176, 0,
0, 353, 353, 190, 190, 184, 184, 611, 611, 239, 239, 336, 336,
517, 517, 101, 101, 427, 427, 507, 507, 205, 205, 368, 368, 445,
445, 225, 225, 545, 545, 231, 231, 595, 595, 326, 326, 561, 561,
420, 420, 393, 393, 526, 526, 206, 206, 377, 377, 340, 340, 284,
284, 362, 362, 259, 259, 506, 506, 557, 557, 238, 238, 204, 204,
276, 276, 512, 512, 205, 205, 368, 368, 445, 445, 225, 225, 545,
545, 231, 231, 595, 595, 326, 326, 561, 561, 420, 420, 393, 393,
526, 526, 206, 206, 377, 377, 340, 340, 284, 284, 362, 362, 259,
259, 506, 506, 557, 557, 238, 238, 204, 204, 276, 276, 512, 512,
0, 0, 0, 0, 0, 0, 707, 707, 1072, 1072, 945, 945, 144, 144, 138,
138, 128, 128, 1085, 1085, 1167, 1167, 1093, 1093, 600, 600,
231, 231, 304, 304, 256, 256, 208, 208, 275, 275, 543, 543, 365,
365, 280, 280, 295, 295, 232, 232, 550, 550, 0, 0, 0, 0, 0, 0,
707, 707, 1072, 1072, 945, 945, 144, 144, 138, 138, 128, 128,
1085, 1085, 1167, 1167, 1093, 1093, 600, 600, 231, 231, 304,
304, 256, 256, 208, 208, 275, 275, 543, 543, 365, 365, 280, 280,
295, 295, 232, 232, 550, 550, 0, 0, 0, 0, 0, 0, 944, 944, 0,
0, 901, 901, 133, 133, 0, 0, 127, 127, 1163, 1163, 0, 0, 1087,
1087, 450, 450, 0, 0, 195, 195, 578, 578, 559, 559, 304, 304,
978, 978, 297, 297, 153, 153, 470, 470, 0, 0, 424, 424, 0, 0,
0, 0, 0, 0, 944, 944, 0, 0, 901, 901, 133, 133, 0, 0, 127, 127,
1163, 1163, 0, 0, 1087, 1087, 450, 450, 0, 0, 195, 195, 578,
578, 559, 559, 304, 304, 978, 978, 297, 297, 153, 153, 470, 470,
0, 0, 424, 424, 0, 0, 0, 0, 0, 0, 720, 720, 638, 638, 921, 921,
134, 134, 108, 108, 137, 137, 1125, 1125, 1094, 1094, 1133, 1133,
507, 507, 164, 164, 579, 579, 585, 585, 459, 459, 189, 189, 308,
308, 251, 251, 485, 485, 407, 407, 303, 303, 327, 327, 0, 0,
0, 0, 0, 0, 720, 720, 638, 638, 921, 921, 134, 134, 108, 108,
137, 137, 1125, 1125, 1094, 1094, 1133, 1133, 507, 507, 164,
164, 579, 579, 585, 585, 459, 459, 189, 189, 308, 308, 251, 251,
485, 485, 407, 407, 303, 303, 327, 327, 892, 892, 737, 737, 664,
664, 871, 871, 704, 704, 1008, 1008, 1013, 1013, 663, 663, 741,
741, 913, 913, 853, 853, 806, 806, 602, 602, 842, 842, 890, 890,
725, 725, 830, 830, 690, 690, 1011, 1011, 935, 935, 976, 976,
779, 779, 839, 839, 1038, 1038, 892, 892, 737, 737, 664, 664,
871, 871, 704, 704, 1008, 1008, 1013, 1013, 663, 663, 741, 741,
913, 913, 853, 853, 806, 806, 602, 602, 842, 842, 890, 890, 725,
725, 830, 830, 690, 690, 1011, 1011, 935, 935, 976, 976, 779,
779, 839, 839, 1038, 1038, 332, 332, 183, 183, 254, 254, 472,
472, 428, 428, 390, 390, 474, 474, 379, 379, 170, 170, 432, 432,
370, 370, 179, 179, 257, 257, 562, 562, 241, 241, 538, 538, 200,
200, 455, 455, 274, 274, 551, 551, 269, 269, 507, 507, 375, 375,
250, 250, 332, 332, 183, 183, 254, 254, 472, 472, 428, 428, 390,
390, 474, 474, 379, 379, 170, 170, 432, 432, 370, 370, 179, 179,
257, 257, 562, 562, 241, 241, 538, 538, 200, 200, 455, 455, 274,
274, 551, 551, 269, 269, 507, 507, 375, 375, 250, 250, 1172,
1172, 1120, 1120, 1148, 1148, 1135, 1135, 1168, 1168, 1146, 1146,
1098, 1098, 1081, 1081, 1173, 1173, 1132, 1132, 1160, 1160, 1104,
1104, 1184, 1184, 1194, 1194, 1137, 1137, 621, 621, 950, 950,
603, 603, 856, 856, 817, 817, 771, 771, 1055, 1055, 959, 959,
851, 851, 1172, 1172, 1120, 1120, 1148, 1148, 1135, 1135, 1168,
1168, 1146, 1146, 1098, 1098, 1081, 1081, 1173, 1173, 1132, 1132,
1160, 1160, 1104, 1104, 1184, 1184, 1194, 1194, 1137, 1137, 621,
621, 950, 950, 603, 603, 856, 856, 817, 817, 771, 771, 1055,
1055, 959, 959, 851, 851, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 859, 859,
994, 994, 1022, 1022, 1031, 1031, 633, 633, 603, 603, 724, 724,
882, 882, 725, 725, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 859, 859, 994,
994, 1022, 1022, 1031, 1031, 633, 633, 603, 603, 724, 724, 882,
882, 725, 725, 928, 928, 927, 927, 915, 915, 707, 707, 889, 889,
998, 998, 913, 913, 755, 755, 688, 688, 613, 613, 879, 879, 702,
702, 1056, 1056, 969, 969, 1021, 1021, 740, 740, 760, 760, 805,
805, 753, 753, 1048, 1048, 978, 978, 1028, 1028, 1056, 1056,
899, 899, 928, 928, 927, 927, 915, 915, 707, 707, 889, 889, 998,
998, 913, 913, 755, 755, 688, 688, 613, 613, 879, 879, 702, 702,
1056, 1056, 969, 969, 1021, 1021, 740, 740, 760, 760, 805, 805,
753, 753, 1048, 1048, 978, 978, 1028, 1028, 1056, 1056, 899,
899, 613, 613, 952, 952, 730, 730, 703, 703, 618, 618, 931, 931,
655, 655, 618, 618, 1027, 1027, 602, 602, 974, 974, 821, 821,
963, 963, 697, 697, 913, 913, 1069, 1069, 1011, 1011, 793, 793,
1075, 1075, 1002, 1002, 772, 772, 876, 876, 687, 687, 849, 849,
613, 613, 952, 952, 730, 730, 703, 703, 618, 618, 931, 931, 655,
655, 618, 618, 1027, 1027, 602, 602, 974, 974, 821, 821, 963,
963, 697, 697, 913, 913, 1069, 1069, 1011, 1011, 793, 793, 1075,
1075, 1002, 1002, 772, 772, 876, 876, 687, 687, 849, 849)),
.Names = "y", row.names = c(NA, -1536L),
class = "data.frame")
sathishsrinivasank/pinerrordetector documentation built on May 7, 2019, 2:55 a.m.
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# simulated data using colonyarea data subtypes
j11 <- simulated_data_1536(data_384 = colonyarea$data_subtypes,
in_data_flow = "across",
out_data_flow = "across",
is_plate_coords = FALSE)
j12 <- structure(list(y = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 132,
132, 142, 142, 502, 502, 190, 190, 288, 288, 218, 218, 1002,
1002, 1052, 1052, 860, 860, 980, 980, 672, 672, 643, 643, 820,
820, 1158, 1158, 1153, 1153, 1154, 1154, 1089, 1089, 1195, 1195,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 132, 132, 142, 142, 502,
502, 190, 190, 288, 288, 218, 218, 1002, 1002, 1052, 1052, 860,
860, 980, 980, 672, 672, 643, 643, 820, 820, 1158, 1158, 1153,
1153, 1154, 1154, 1089, 1089, 1195, 1195, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 148, 148, 137, 137, 236, 236, 372, 372, 176, 176,
492, 492, 691, 691, 759, 759, 675, 675, 618, 618, 874, 874, 938,
938, 992, 992, 1173, 1173, 1081, 1081, 1107, 1107, 1137, 1137,
1088, 1088, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 148, 148, 137,
137, 236, 236, 372, 372, 176, 176, 492, 492, 691, 691, 759, 759,
675, 675, 618, 618, 874, 874, 938, 938, 992, 992, 1173, 1173,
1081, 1081, 1107, 1107, 1137, 1137, 1088, 1088, 1074, 1074, 986,
986, 627, 627, 767, 767, 930, 930, 990, 990, 1019, 1019, 618,
618, 605, 605, 603, 603, 964, 964, 807, 807, 720, 720, 870, 870,
671, 671, 653, 653, 1042, 1042, 986, 986, 942, 942, 671, 671,
813, 813, 646, 646, 1070, 1070, 642, 642, 1074, 1074, 986, 986,
627, 627, 767, 767, 930, 930, 990, 990, 1019, 1019, 618, 618,
605, 605, 603, 603, 964, 964, 807, 807, 720, 720, 870, 870, 671,
671, 653, 653, 1042, 1042, 986, 986, 942, 942, 671, 671, 813,
813, 646, 646, 1070, 1070, 642, 642, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 115, 115, 105, 105, 384, 384, 402, 402, 514, 514,
532, 532, 1035, 1035, 841, 841, 690, 690, 862, 862, 970, 970,
683, 683, 1004, 1004, 1094, 1094, 1195, 1195, 1159, 1159, 1090,
1090, 1099, 1099, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 115, 115,
105, 105, 384, 384, 402, 402, 514, 514, 532, 532, 1035, 1035,
841, 841, 690, 690, 862, 862, 970, 970, 683, 683, 1004, 1004,
1094, 1094, 1195, 1195, 1159, 1159, 1090, 1090, 1099, 1099, 563,
563, 348, 348, 203, 203, 341, 341, 591, 591, 515, 515, 165, 165,
255, 255, 337, 337, 180, 180, 488, 488, 194, 194, 384, 384, 160,
160, 432, 432, 534, 534, 252, 252, 482, 482, 297, 297, 432, 432,
520, 520, 497, 497, 400, 400, 230, 230, 563, 563, 348, 348, 203,
203, 341, 341, 591, 591, 515, 515, 165, 165, 255, 255, 337, 337,
180, 180, 488, 488, 194, 194, 384, 384, 160, 160, 432, 432, 534,
534, 252, 252, 482, 482, 297, 297, 432, 432, 520, 520, 497, 497,
400, 400, 230, 230, 517, 517, 0, 0, 522, 522, 296, 296, 152,
152, 0, 0, 581, 581, 327, 327, 230, 230, 129, 129, 265, 265,
411, 411, 176, 176, 0, 0, 353, 353, 190, 190, 184, 184, 611,
611, 239, 239, 336, 336, 517, 517, 101, 101, 427, 427, 507, 507,
517, 517, 0, 0, 522, 522, 296, 296, 152, 152, 0, 0, 581, 581,
327, 327, 230, 230, 129, 129, 265, 265, 411, 411, 176, 176, 0,
0, 353, 353, 190, 190, 184, 184, 611, 611, 239, 239, 336, 336,
517, 517, 101, 101, 427, 427, 507, 507, 205, 205, 368, 368, 445,
445, 225, 225, 545, 545, 231, 231, 595, 595, 326, 326, 561, 561,
420, 420, 393, 393, 526, 526, 206, 206, 377, 377, 340, 340, 284,
284, 362, 362, 259, 259, 506, 506, 557, 557, 238, 238, 204, 204,
276, 276, 512, 512, 205, 205, 368, 368, 445, 445, 225, 225, 545,
545, 231, 231, 595, 595, 326, 326, 561, 561, 420, 420, 393, 393,
526, 526, 206, 206, 377, 377, 340, 340, 284, 284, 362, 362, 259,
259, 506, 506, 557, 557, 238, 238, 204, 204, 276, 276, 512, 512,
0, 0, 0, 0, 0, 0, 707, 707, 1072, 1072, 945, 945, 144, 144, 138,
138, 128, 128, 1085, 1085, 1167, 1167, 1093, 1093, 600, 600,
231, 231, 304, 304, 256, 256, 208, 208, 275, 275, 543, 543, 365,
365, 280, 280, 295, 295, 232, 232, 550, 550, 0, 0, 0, 0, 0, 0,
707, 707, 1072, 1072, 945, 945, 144, 144, 138, 138, 128, 128,
1085, 1085, 1167, 1167, 1093, 1093, 600, 600, 231, 231, 304,
304, 256, 256, 208, 208, 275, 275, 543, 543, 365, 365, 280, 280,
295, 295, 232, 232, 550, 550, 0, 0, 0, 0, 0, 0, 944, 944, 0,
0, 901, 901, 133, 133, 0, 0, 127, 127, 1163, 1163, 0, 0, 1087,
1087, 450, 450, 0, 0, 195, 195, 578, 578, 559, 559, 304, 304,
978, 978, 297, 297, 153, 153, 470, 470, 0, 0, 424, 424, 0, 0,
0, 0, 0, 0, 944, 944, 0, 0, 901, 901, 133, 133, 0, 0, 127, 127,
1163, 1163, 0, 0, 1087, 1087, 450, 450, 0, 0, 195, 195, 578,
578, 559, 559, 304, 304, 978, 978, 297, 297, 153, 153, 470, 470,
0, 0, 424, 424, 0, 0, 0, 0, 0, 0, 720, 720, 638, 638, 921, 921,
134, 134, 108, 108, 137, 137, 1125, 1125, 1094, 1094, 1133, 1133,
507, 507, 164, 164, 579, 579, 585, 585, 459, 459, 189, 189, 308,
308, 251, 251, 485, 485, 407, 407, 303, 303, 327, 327, 0, 0,
0, 0, 0, 0, 720, 720, 638, 638, 921, 921, 134, 134, 108, 108,
137, 137, 1125, 1125, 1094, 1094, 1133, 1133, 507, 507, 164,
164, 579, 579, 585, 585, 459, 459, 189, 189, 308, 308, 251, 251,
485, 485, 407, 407, 303, 303, 327, 327, 892, 892, 737, 737, 664,
664, 871, 871, 704, 704, 1008, 1008, 1013, 1013, 663, 663, 741,
741, 913, 913, 853, 853, 806, 806, 602, 602, 842, 842, 890, 890,
725, 725, 830, 830, 690, 690, 1011, 1011, 935, 935, 976, 976,
779, 779, 839, 839, 1038, 1038, 892, 892, 737, 737, 664, 664,
871, 871, 704, 704, 1008, 1008, 1013, 1013, 663, 663, 741, 741,
913, 913, 853, 853, 806, 806, 602, 602, 842, 842, 890, 890, 725,
725, 830, 830, 690, 690, 1011, 1011, 935, 935, 976, 976, 779,
779, 839, 839, 1038, 1038, 332, 332, 183, 183, 254, 254, 472,
472, 428, 428, 390, 390, 474, 474, 379, 379, 170, 170, 432, 432,
370, 370, 179, 179, 257, 257, 562, 562, 241, 241, 538, 538, 200,
200, 455, 455, 274, 274, 551, 551, 269, 269, 507, 507, 375, 375,
250, 250, 332, 332, 183, 183, 254, 254, 472, 472, 428, 428, 390,
390, 474, 474, 379, 379, 170, 170, 432, 432, 370, 370, 179, 179,
257, 257, 562, 562, 241, 241, 538, 538, 200, 200, 455, 455, 274,
274, 551, 551, 269, 269, 507, 507, 375, 375, 250, 250, 1172,
1172, 1120, 1120, 1148, 1148, 1135, 1135, 1168, 1168, 1146, 1146,
1098, 1098, 1081, 1081, 1173, 1173, 1132, 1132, 1160, 1160, 1104,
1104, 1184, 1184, 1194, 1194, 1137, 1137, 621, 621, 950, 950,
603, 603, 856, 856, 817, 817, 771, 771, 1055, 1055, 959, 959,
851, 851, 1172, 1172, 1120, 1120, 1148, 1148, 1135, 1135, 1168,
1168, 1146, 1146, 1098, 1098, 1081, 1081, 1173, 1173, 1132, 1132,
1160, 1160, 1104, 1104, 1184, 1184, 1194, 1194, 1137, 1137, 621,
621, 950, 950, 603, 603, 856, 856, 817, 817, 771, 771, 1055,
1055, 959, 959, 851, 851, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 859, 859,
994, 994, 1022, 1022, 1031, 1031, 633, 633, 603, 603, 724, 724,
882, 882, 725, 725, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 859, 859, 994,
994, 1022, 1022, 1031, 1031, 633, 633, 603, 603, 724, 724, 882,
882, 725, 725, 928, 928, 927, 927, 915, 915, 707, 707, 889, 889,
998, 998, 913, 913, 755, 755, 688, 688, 613, 613, 879, 879, 702,
702, 1056, 1056, 969, 969, 1021, 1021, 740, 740, 760, 760, 805,
805, 753, 753, 1048, 1048, 978, 978, 1028, 1028, 1056, 1056,
899, 899, 928, 928, 927, 927, 915, 915, 707, 707, 889, 889, 998,
998, 913, 913, 755, 755, 688, 688, 613, 613, 879, 879, 702, 702,
1056, 1056, 969, 969, 1021, 1021, 740, 740, 760, 760, 805, 805,
753, 753, 1048, 1048, 978, 978, 1028, 1028, 1056, 1056, 899,
899, 613, 613, 952, 952, 730, 730, 703, 703, 618, 618, 931, 931,
655, 655, 618, 618, 1027, 1027, 602, 602, 974, 974, 821, 821,
963, 963, 697, 697, 913, 913, 1069, 1069, 1011, 1011, 793, 793,
1075, 1075, 1002, 1002, 772, 772, 876, 876, 687, 687, 849, 849,
613, 613, 952, 952, 730, 730, 703, 703, 618, 618, 931, 931, 655,
655, 618, 618, 1027, 1027, 602, 602, 974, 974, 821, 821, 963,
963, 697, 697, 913, 913, 1069, 1069, 1011, 1011, 793, 793, 1075,
1075, 1002, 1002, 772, 772, 876, 876, 687, 687, 849, 849)),
.Names = "y", row.names = c(NA, -1536L),
class = "data.frame")
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