Crop cooling paths by time and temperature
Arguments
- x
an object of class
"HeFTy"(output ofread_hefty())- time
numeric. two column vector of the desired time range.
- temperature
numeric. two column vector of the desired temperature range.
Value
object of class "HeFTy" (output of read_hefty())
Examples
# example data
data(tT_paths)
crop_paths(tT_paths, time = c(-Inf, 300), temperature = c(-Inf, 200))
#> $paths
#> # A tibble: 1,465 × 5
#> segment time temperature Fit Comp_GOF
#> <chr> <dbl> <dbl> <fct> <dbl>
#> 1 1 40 66.5 Good 0.889
#> 2 1 37.9 66.0 Good 0.889
#> 3 1 37.8 58.4 Good 0.889
#> 4 1 34.9 54.2 Good 0.889
#> 5 1 29.3 35.1 Good 0.889
#> 6 1 0 24.5 Good 0.889
#> 7 10 40 48.9 Good 0.671
#> 8 10 27.4 29.0 Good 0.671
#> 9 10 11.4 25.5 Good 0.671
#> 10 10 0 23.7 Good 0.671
#> # ℹ 1,455 more rows
#>
#> $constraints
#> constraint max_time min_time max_temp min_temp max_good_time min_good_time
#> 1 1 3400 2500 600 300 3394.5917 2527.9325
#> 2 2 420 390 20 0 419.4643 390.6450
#> 3 3 390 300 250 0 389.1371 306.0568
#> 4 4 145 100 250 0 143.6673 100.3994
#> 5 5 0 0 25 22 0.0000 0.0000
#> mean_good_time sd_good_time max_good_temp min_good_temp mean_good_temp
#> 1 2984.6547 270.378762 593.89919 314.88849330 464.782407
#> 2 404.6355 8.705273 19.94702 0.06272746 9.908724
#> 3 345.7136 25.505943 246.82226 2.06590750 129.707408
#> 4 125.5710 11.823954 242.00596 74.48304650 120.179502
#> 5 0.0000 0.000000 24.93335 22.01530751 23.443949
#> sd_good_temp max_acc_time min_acc_time mean_acc_time sd_acc_time max_acc_temp
#> 1 87.4152469 3390.4289 2502.4600 2944.8438 261.56548 598.04071
#> 2 6.1742180 419.8625 390.1400 405.4543 8.69456 19.97769
#> 3 70.9521957 389.9840 300.0337 344.0218 25.27657 247.57047
#> 4 41.7146341 144.9557 100.0757 122.5955 12.49893 249.52373
#> 5 0.8797127 0.0000 0.0000 0.0000 0.00000 24.99147
#> min_acc_temp mean_acc_temp sd_acc_temp
#> 1 301.2327312 451.36462 85.6931366
#> 2 0.2631528 10.14937 5.7615345
#> 3 0.9634828 129.33594 70.5193661
#> 4 64.4460620 139.54144 49.2159598
#> 5 22.0173362 23.63394 0.8662358
#>
#> $weighted_mean_path
#> time temperature
#> 1 2960.16921 455.812425
#> 2 2627.83220 397.183056
#> 3 2293.61770 343.018567
#> 4 1986.10059 292.617437
#> 5 1649.65971 240.959932
#> 6 1357.79092 184.533090
#> 7 1010.81742 126.588291
#> 8 689.76015 64.875455
#> 9 404.84572 9.864213
#> 10 397.71951 25.764745
#> 11 388.34676 41.712565
#> 12 381.08391 55.273139
#> 13 373.46480 71.377998
#> 14 366.09287 85.913719
#> 15 358.63442 100.511002
#> 16 351.48380 116.296938
#> 17 345.01425 131.622703
#> 18 316.02643 131.889002
#> 19 287.47253 131.759535
#> 20 261.43578 130.658033
#> 21 232.22510 130.672051
#> 22 203.82099 130.608529
#> 23 175.59248 129.570122
#> 24 151.24504 130.098876
#> 25 124.75598 129.217728
#> 26 114.09291 111.347133
#> 27 102.08167 94.456106
#> 28 84.22129 82.855368
#> 29 65.46133 70.190954
#> 30 47.51716 59.207906
#> 31 28.72815 47.998102
#> 32 13.14855 36.583111
#> 33 0.00000 23.564927
#>
#> $summary
#> grain mean sd min max
#> 1 good AFT Lm (<b5>m) 11.91381 0.3385423 11.17598 12.61495
#> 2 good AFT age (Ma) 97.10919 3.3983183 91.25161 103.43879
#> 3 good 112-73-2a corr. age (Ma) 43.67936 4.2493954 34.68526 52.60130
#> 4 good 112-73-3a corr. age (Ma) 43.13571 4.3059370 33.98576 52.21119
#> 5 good 112-73-4a corr. age (Ma) 36.97330 6.0464201 25.28206 49.20111
#> 6 good 112-73-5a corr. age (Ma) 48.60404 3.8672977 39.50322 57.92818
#> 7 acc AFT Lm (<b5>m) 12.41629 0.5669847 10.71820 13.38772
#> 8 acc AFT age (Ma) 92.63882 7.0138344 82.44131 112.45117
#> 9 acc 112-73-2a corr. age (Ma) 49.17848 8.8319324 27.06320 63.77134
#> 10 acc 112-73-3a corr. age (Ma) 48.56021 8.8235832 26.76311 63.33728
#> 11 acc 112-73-4a corr. age (Ma) 42.07558 9.6831207 18.15850 60.46109
#> 12 acc 112-73-5a corr. age (Ma) 53.85368 8.4278438 29.71129 66.94035
#>
#> attr(,"class")
#> [1] "list" "HeFTy"