in migration_matrix Error in ij[, 1] : incorrect number of dimensions
#1
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pushed possible fix just now.
…On Tue, Nov 21, 2017 at 10:02 PM, Jaime Ashander ***@***.***> wrote:
The state that causes the error is in this gist
<https://gist.github.com/ashander/c560f8b5b3c33591b2ce2ee7d8d75dfe>.
Based on traceback from this is seems the error is entirely in landsim
I can reproduce by running
$ wget https://gist.github.com/ashander/c560f8b5b3c33591b2ce2ee7d8d75dfe/raw/023fb08b8fde330dca192fdbe36d67a495229b78/badpop.rds
then
library(raster)
library(Matrix)
library(landsim)
readRDS('badpop.rds') -> ba
migration_matrix(ba$population, migration=ba$migration, n.chunks=ba$n.chunks)
causes the error
Error in ij[, 1] : incorrect number of dimensions which R says is in this
line of migration_matrix
<https://github.com/petrelharp/landsim/blob/master/R/migration_matrix.R#L97>
but ... dumping the vars in that line prior to the error gives this named
list of the vars, which I store here in bia:
bia <- structure(list(ij = structure(c(782, 782, 782, 782, 782, 783,
783, 783, 783, 783, 784, 784, 784, 800, 741, 781, 742, 743, 783,
742, 782, 743, 744, 784, 743, 783, 744, 760), .Dim = c(14L, 2L
), .Dimnames = list(NULL, c("from", "to"))), both.ij = c(782L,
783L, 784L, 800L), from = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L,
23L, 24L, 25L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L,
58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 75L, 76L, 77L, 78L, 79L,
80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L,
93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L,
105L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L,
124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L,
135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L,
146L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L,
161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L,
172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L,
183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 192L, 193L,
194L, 195L, 196L, 197L, 198L, 199L, 200L, 201L, 202L, 203L, 204L,
205L, 206L, 207L, 208L, 209L, 210L, 211L, 212L, 213L, 214L, 215L,
216L, 217L, 218L, 219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L,
227L, 228L, 229L, 230L, 231L, 232L, 233L, 234L, 235L, 236L, 237L,
238L, 239L, 240L, 242L, 243L, 244L, 245L, 246L, 247L, 248L, 249L,
250L, 251L, 252L, 253L, 254L, 255L, 256L, 257L, 258L, 259L, 260L,
261L, 262L, 263L, 264L, 265L, 266L, 267L, 268L, 269L, 270L, 271L,
272L, 273L, 274L, 275L, 276L, 277L, 278L, 279L, 280L, 283L, 284L,
285L, 286L, 287L, 288L, 289L, 290L, 291L, 292L, 293L, 294L, 295L,
296L, 297L, 298L, 299L, 300L, 301L, 302L, 303L, 304L, 305L, 306L,
307L, 308L, 309L, 310L, 311L, 312L, 313L, 314L, 315L, 316L, 317L,
318L, 319L, 320L, 323L, 324L, 325L, 326L, 327L, 328L, 329L, 330L,
331L, 332L, 333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L,
342L, 343L, 344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L,
353L, 354L, 355L, 356L, 357L, 358L, 359L, 360L, 363L, 364L, 365L,
366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L, 375L, 376L,
377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L, 387L,
388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L, 396L, 397L, 398L,
399L, 400L, 402L, 403L, 404L, 405L, 406L, 407L, 408L, 409L, 410L,
411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L, 420L, 421L,
422L, 423L, 424L, 425L, 426L, 427L, 428L, 429L, 430L, 431L, 432L,
433L, 434L, 435L, 436L, 437L, 438L, 439L, 440L, 441L, 442L, 443L,
444L, 445L, 446L, 447L, 448L, 449L, 450L, 451L, 452L, 453L, 454L,
455L, 456L, 457L, 458L, 459L, 460L, 461L, 462L, 463L, 464L, 465L,
466L, 467L, 468L, 469L, 470L, 471L, 472L, 473L, 474L, 475L, 476L,
477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 485L, 486L, 487L,
488L, 489L, 490L, 491L, 492L, 493L, 494L, 495L, 496L, 497L, 498L,
499L, 500L, 501L, 502L, 503L, 504L, 505L, 506L, 507L, 508L, 509L,
510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L, 519L, 520L,
521L, 522L, 523L, 524L, 525L, 526L, 527L, 528L, 529L, 530L, 531L,
532L, 533L, 534L, 535L, 536L, 537L, 538L, 539L, 540L, 541L, 542L,
543L, 544L, 545L, 546L, 547L, 548L, 549L, 550L, 551L, 553L, 554L,
555L, 556L, 557L, 558L, 559L, 560L, 561L, 562L, 563L, 564L, 565L,
566L, 567L, 568L, 569L, 570L, 571L, 572L, 573L, 574L, 575L, 576L,
577L, 578L, 579L, 580L, 581L, 582L, 583L, 584L, 585L, 586L, 587L,
588L, 596L, 597L, 598L, 599L, 600L, 601L, 602L, 603L, 604L, 605L,
606L, 607L, 608L, 609L, 610L, 611L, 612L, 613L, 614L, 615L, 616L,
617L, 618L, 619L, 620L, 621L, 622L, 623L, 624L, 625L, 626L, 627L,
638L, 639L, 640L, 641L, 642L, 643L, 644L, 645L, 646L, 647L, 648L,
649L, 650L, 651L, 652L, 653L, 654L, 655L, 656L, 657L, 658L, 659L,
660L, 661L, 662L, 663L, 664L, 665L, 666L, 679L, 680L, 681L, 682L,
683L, 684L, 685L, 686L, 687L, 688L, 689L, 690L, 691L, 692L, 693L,
694L, 695L, 696L, 697L, 698L, 699L, 700L, 701L, 702L, 703L, 704L,
705L, 720L, 721L, 722L, 723L, 724L, 725L, 726L, 727L, 728L, 729L,
730L, 731L, 732L, 733L, 734L, 735L, 736L, 737L, 738L, 739L, 740L,
741L, 742L, 743L, 744L, 760L, 761L, 762L, 763L, 764L, 765L, 766L,
767L, 768L, 769L, 770L, 771L, 772L, 773L, 774L, 775L, 776L, 777L,
778L, 779L, 780L, 781L, 782L, 783L, 784L, 800L)), .Names = c("ij",
"both.ij", "from"))
and
with(bia,
match(c(ij[,1],both.ij), from))
produced no error! But this is supposed to be the failing line of code.
Grrrr
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Based on traceback from this is seems the error is entirely in
landsim.The state that causes the error is in this gist and I can reproduce by running
then
causes the error
Error in ij[, 1] : incorrect number of dimensionswhichRsays is in this line of migration_matrix but ... dumping the vars in that line prior to the error givesthis named list of the vars, which I store here in `bia` ...
``` bia <- structure(list(ij = structure(c(782, 782, 782, 782, 782, 783, 783, 783, 783, 783, 784, 784, 784, 800, 741, 781, 742, 743, 783, 742, 782, 743, 744, 784, 743, 783, 744, 760), .Dim = c(14L, 2L ), .Dimnames = list(NULL, c("from", "to"))), both.ij = c(782L, 783L, 784L, 800L), from = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 192L, 193L, 194L, 195L, 196L, 197L, 198L, 199L, 200L, 201L, 202L, 203L, 204L, 205L, 206L, 207L, 208L, 209L, 210L, 211L, 212L, 213L, 214L, 215L, 216L, 217L, 218L, 219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 233L, 234L, 235L, 236L, 237L, 238L, 239L, 240L, 242L, 243L, 244L, 245L, 246L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L, 264L, 265L, 266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L, 276L, 277L, 278L, 279L, 280L, 283L, 284L, 285L, 286L, 287L, 288L, 289L, 290L, 291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L, 299L, 300L, 301L, 302L, 303L, 304L, 305L, 306L, 307L, 308L, 309L, 310L, 311L, 312L, 313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 323L, 324L, 325L, 326L, 327L, 328L, 329L, 330L, 331L, 332L, 333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L, 342L, 343L, 344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L, 353L, 354L, 355L, 356L, 357L, 358L, 359L, 360L, 363L, 364L, 365L, 366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L, 375L, 376L, 377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L, 387L, 388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L, 396L, 397L, 398L, 399L, 400L, 402L, 403L, 404L, 405L, 406L, 407L, 408L, 409L, 410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L, 420L, 421L, 422L, 423L, 424L, 425L, 426L, 427L, 428L, 429L, 430L, 431L, 432L, 433L, 434L, 435L, 436L, 437L, 438L, 439L, 440L, 441L, 442L, 443L, 444L, 445L, 446L, 447L, 448L, 449L, 450L, 451L, 452L, 453L, 454L, 455L, 456L, 457L, 458L, 459L, 460L, 461L, 462L, 463L, 464L, 465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L, 473L, 474L, 475L, 476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 485L, 486L, 487L, 488L, 489L, 490L, 491L, 492L, 493L, 494L, 495L, 496L, 497L, 498L, 499L, 500L, 501L, 502L, 503L, 504L, 505L, 506L, 507L, 508L, 509L, 510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L, 519L, 520L, 521L, 522L, 523L, 524L, 525L, 526L, 527L, 528L, 529L, 530L, 531L, 532L, 533L, 534L, 535L, 536L, 537L, 538L, 539L, 540L, 541L, 542L, 543L, 544L, 545L, 546L, 547L, 548L, 549L, 550L, 551L, 553L, 554L, 555L, 556L, 557L, 558L, 559L, 560L, 561L, 562L, 563L, 564L, 565L, 566L, 567L, 568L, 569L, 570L, 571L, 572L, 573L, 574L, 575L, 576L, 577L, 578L, 579L, 580L, 581L, 582L, 583L, 584L, 585L, 586L, 587L, 588L, 596L, 597L, 598L, 599L, 600L, 601L, 602L, 603L, 604L, 605L, 606L, 607L, 608L, 609L, 610L, 611L, 612L, 613L, 614L, 615L, 616L, 617L, 618L, 619L, 620L, 621L, 622L, 623L, 624L, 625L, 626L, 627L, 638L, 639L, 640L, 641L, 642L, 643L, 644L, 645L, 646L, 647L, 648L, 649L, 650L, 651L, 652L, 653L, 654L, 655L, 656L, 657L, 658L, 659L, 660L, 661L, 662L, 663L, 664L, 665L, 666L, 679L, 680L, 681L, 682L, 683L, 684L, 685L, 686L, 687L, 688L, 689L, 690L, 691L, 692L, 693L, 694L, 695L, 696L, 697L, 698L, 699L, 700L, 701L, 702L, 703L, 704L, 705L, 720L, 721L, 722L, 723L, 724L, 725L, 726L, 727L, 728L, 729L, 730L, 731L, 732L, 733L, 734L, 735L, 736L, 737L, 738L, 739L, 740L, 741L, 742L, 743L, 744L, 760L, 761L, 762L, 763L, 764L, 765L, 766L, 767L, 768L, 769L, 770L, 771L, 772L, 773L, 774L, 775L, 776L, 777L, 778L, 779L, 780L, 781L, 782L, 783L, 784L, 800L)), .Names = c("ij", "both.ij", "from")) ```... but
produces no error! But this is supposed to be the failing line of code. Grrrr
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