Creates cube like voxels of different size on a point cloud using the voxels function, and then return a summary_voxels of their features.
voxels_counting(
cloud,
edge_sizes = NULL,
min_size,
length_out = 10,
bootstrap = FALSE,
R = NULL,
progress = TRUE,
parallel = FALSE,
threads = NULL
)A data.table with xyz coordinates of the point clouds in the first three columns.
A positive numeric vector describing the edge length of the different cubes to perform. If NULL, it use edge sizes by default based on the largest range of XYZ and min_size.
A positive numeric vector of length 1 describing the minimum cube edge length to perform. This is required if edge_sizes = NULL.
A positive interger of length 1 indicating the number of different edge lengths to use. This is required if edge_sizes = NULL.
Logical. If TRUE, it computes a bootstrap on the H index calculations. FALSE as default.
A positive integer of length 1 indicating the number of bootstrap replicates. This need to be used if bootstrap = TRUE.
Logical, if TRUE displays a graphical progress bar. TRUE as default.
Logical, if TRUE it uses a parallel processing for the voxelization. FALSE as default.
An integer >= 0 describing the number of threads to use. This need to be used if parallel = TRUE.
A data.table with the summary of the voxels created with their features.
data(pc_tree)
#Applying voxels counting.
voxels_counting(pc_tree, min_size = 2)
#> [1] "Creating voxels"
#>
|
| | 0%
|
|======= | 10%
|
|============== | 20%
|
|===================== | 30%
|
|============================ | 40%
|
|=================================== | 50%
|
|========================================== | 60%
|
|================================================= | 70%
|
|======================================================== | 80%
|
|=============================================================== | 90%
|
|======================================================================| 100%
#> Edge.X Edge.Y Edge.Z N_voxels Volume Surface Density_mean
#> 1: 2.000000 2.000000 2.000000 26 208.0000 52.00000 729.3077
#> 2: 2.273717 2.273717 2.273717 23 270.3565 46.52809 637.8868
#> 3: 2.584894 2.584894 2.584894 18 310.8856 53.45340 630.6469
#> 4: 2.938658 2.938658 2.938658 14 355.2836 69.08568 627.3617
#> 5: 3.340838 3.340838 3.340838 8 298.3020 44.64479 849.4609
#> 6: 3.798059 3.798059 3.798059 8 438.3037 57.70101 657.2501
#> 7: 4.317855 4.317855 4.317855 8 644.0124 74.57549 508.5317
#> 8: 4.908790 4.908790 4.908790 7 827.9828 96.38486 449.6735
#> 9: 5.580598 5.580598 5.580598 4 695.1880 93.42923 608.8672
#> 10: 6.344350 6.344350 6.344350 1 255.3650 40.25077 1884.3862
#> Density_sd H Hmax Equitavility Negentropy
#> 1: 666.6192 2.8071337 3.258097 0.8615870 0.4509629
#> 2: 731.5321 2.5806326 3.135494 0.8230385 0.5548616
#> 3: 861.5751 2.2028562 2.890372 0.7621360 0.6875155
#> 4: 778.8986 1.9014672 2.639057 0.7205100 0.7375901
#> 5: 416.4392 1.9635014 2.079442 0.9442446 0.1159402
#> 6: 408.2369 1.9136861 2.079442 0.9202885 0.1657555
#> 7: 598.9605 1.5854415 2.079442 0.7624362 0.4940001
#> 8: 823.1561 0.9318226 1.945910 0.4788621 1.0140875
#> 9: 1143.8455 0.2341148 1.386294 0.1688781 1.1521796
#> 10: NA 0.0000000 0.000000 NaN 0.0000000
#Voxels counting using bootstrap on the H indexes with 1000 repetitions.
voxels_counting(pc_tree, min_size = 2, bootstrap = TRUE, R = 1000)
#> [1] "Creating voxels"
#>
|
| | 0%
|
|======= | 10%
|
|============== | 20%
|
|===================== | 30%
|
|============================ | 40%
|
|=================================== | 50%
|
|========================================== | 60%
|
|================================================= | 70%
|
|======================================================== | 80%
|
|=============================================================== | 90%
|
|======================================================================| 100%
#> Edge.X Edge.Y Edge.Z N_voxels Volume Surface Density_mean
#> 1: 2.000000 2.000000 2.000000 26 208.0000 52.00000 729.3077
#> 2: 2.273717 2.273717 2.273717 23 270.3565 46.52809 637.8868
#> 3: 2.584894 2.584894 2.584894 18 310.8856 53.45340 630.6469
#> 4: 2.938658 2.938658 2.938658 14 355.2836 69.08568 627.3617
#> 5: 3.340838 3.340838 3.340838 8 298.3020 44.64479 849.4609
#> 6: 3.798059 3.798059 3.798059 8 438.3037 57.70101 657.2501
#> 7: 4.317855 4.317855 4.317855 8 644.0124 74.57549 508.5317
#> 8: 4.908790 4.908790 4.908790 7 827.9828 96.38486 449.6735
#> 9: 5.580598 5.580598 5.580598 4 695.1880 93.42923 608.8672
#> 10: 6.344350 6.344350 6.344350 1 255.3650 40.25077 1884.3862
#> Density_sd H Hmax Equitavility Negentropy H_boot_mean
#> 1: 666.6192 2.8071337 3.258097 0.8615870 0.4509629 2.8037754
#> 2: 731.5321 2.5806326 3.135494 0.8230385 0.5548616 2.5981348
#> 3: 861.5751 2.2028562 2.890372 0.7621360 0.6875155 2.2467749
#> 4: 778.8986 1.9014672 2.639057 0.7205100 0.7375901 1.8931751
#> 5: 416.4392 1.9635014 2.079442 0.9442446 0.1159402 1.9718819
#> 6: 408.2369 1.9136861 2.079442 0.9202885 0.1657555 1.9314591
#> 7: 598.9605 1.5854415 2.079442 0.7624362 0.4940001 1.6645529
#> 8: 823.1561 0.9318226 1.945910 0.4788621 1.0140875 1.1847412
#> 9: 1143.8455 0.2341148 1.386294 0.1688781 1.1521796 0.7345779
#> 10: NA 0.0000000 0.000000 NaN 0.0000000 0.0000000
#> H_boot_sd Equitavility_boot Negentropy_boot
#> 1: 0.11322984 0.8605563 0.4543211
#> 2: 0.11176808 0.8286205 0.5373594
#> 3: 0.12940663 0.7773308 0.6435968
#> 4: 0.22710029 0.7173679 0.7458822
#> 5: 0.04179467 0.9482748 0.1075596
#> 6: 0.06095108 0.9288355 0.1479824
#> 7: 0.18464350 0.8004807 0.4148886
#> 8: 0.33027498 0.6088365 0.7611689
#> 9: 0.49596206 0.5298859 0.6517165
#> 10: 0.00000000 NaN 0.0000000