Reads and writes MTrackJ Data Files (.mdf). Supports clusters, 2D data, and channel information. If desired, generates unique track identifiers based on cluster and id data from the .mdf file.
First load the package with
library(mdftracks)Reading 3D data:
mdf.file <- system.file("extdata", "example.mdf", package = 'mdftracks')
data <- read.mdf(mdf.file)
#> MTrackJ 1.5.1 Data File
head(data, 10)
#> cluster id time x y z
#> 1.1.1 1 1 1 782 43 1
#> 1.1.2 1 1 2 784 45 1
#> 1.1.3 1 1 3 780 47 1
#> 1.1.4 1 1 4 786 56 1
#> 1.1.5 1 1 5 794 65 1
#> 1.1.6 1 1 6 800 69 1
#> 1.1.7 1 1 7 805 88 1
#> 1.1.8 1 1 8 804 100 1
#> 1.1.9 1 1 9 814 110 1
#> 1.1.10 1 1 10 823 125 1Dropping the z-coordinate for 2D data:
data <- read.mdf(mdf.file, drop.Z = T)
#> MTrackJ 1.5.1 Data File
head(data, 10)
#> cluster id time x y
#> 1.1.1 1 1 1 782 43
#> 1.1.2 1 1 2 784 45
#> 1.1.3 1 1 3 780 47
#> 1.1.4 1 1 4 786 56
#> 1.1.5 1 1 5 794 65
#> 1.1.6 1 1 6 800 69
#> 1.1.7 1 1 7 805 88
#> 1.1.8 1 1 8 804 100
#> 1.1.9 1 1 9 814 110
#> 1.1.10 1 1 10 823 125Writing data in (id, t, x, y, z) format (e.g., from MotilityLab):
library('MotilityLab')
tracks.df <- as.data.frame(TCells)head(tracks.df, 10)#> id t x y z
#> 1 0 0.000 132.521 118.692 8.75
#> 2 0 27.781 133.909 118.700 8.75
#> 3 0 55.484 131.763 118.129 6.25
#> 4 0 83.296 133.161 117.903 6.25
#> 5 0 111.093 131.530 117.894 6.25
#> 6 0 138.906 132.229 117.665 6.25
#> 7 0 166.656 131.763 118.595 6.25
#> 8 0 194.406 131.763 117.663 6.25
#> 9 0 222.078 131.996 117.664 6.25
#> 10 0 249.890 131.763 117.663 6.25write.mdf(head(tracks.df, 10))#> Using the following column mapping:
#> cluster id time x y z channel point
#> NA "id" "t" "x" "y" "z" NA NA
#> Converting factor to numeric in columns: id
#> MTrackJ 1.5.1 Data File
#> Assembly 1
#> Cluster 1
#> Track 0
#> Point 1 132.5209960938 118.6920013428 8.75 0 1
#> Point 2 133.908996582 118.6999969482 8.75 27.7810001373 1
#> Point 3 131.7630004883 118.1289978027 6.25 55.4840011597 1
#> Point 4 133.1609954834 117.9029998779 6.25 83.2959976196 1
#> Point 5 131.5299987793 117.8939971924 6.25 111.0930023193 1
#> Point 6 132.2290039063 117.6650009155 6.25 138.9060058594 1
#> Point 7 131.7630004883 118.5950012207 6.25 166.6560058594 1
#> Point 8 131.7630004883 117.6630020142 6.25 194.4060058594 1
#> Point 9 131.9960021973 117.6640014648 6.25 222.0780029297 1
#> Point 10 131.7630004883 117.6630020142 6.25 249.8899993896 1
#> End of MTrackJ Data FileWriting data with cluster, channel, and point information:
print(mdftracks.example.data)#> cl id p x y z t ch uid
#> 1 1 1 1 187.1 263.2 27.4 1 2 1
#> 2 1 1 3 309.2 264.4 15.8 2 2 1
#> 3 1 2 1 18.4 438.5 28.1 1 2 2
#> 4 1 2 2 142.9 58.6 28.2 2 2 2
#> 5 1 2 5 290.1 197.5 18.8 3 2 2
#> 6 2 1 1 310.1 15.4 5.8 1 2 3
#> 7 2 2 1 99.1 33.5 22.5 1 2 4
#> 8 2 2 2 220.2 396.0 16.4 2 2 4
#> 9 2 3 1 8.4 305.8 30.2 1 2 5
#> 10 2 3 2 84.7 227.7 21.1 2 2 5write.mdf(mdftracks.example.data, cluster.column = 'cl', id.column = 'id',
pos.columns = letters[24:26], channel.column = 'ch',
point.column = "p")#> Using the following column mapping:
#> cluster id time x y z channel point
#> "cl" "id" "id" "x" "y" "z" "ch" "p"
#> MTrackJ 1.5.1 Data File
#> Assembly 1
#> Cluster 1
#> Track 1
#> Point 1 187.1 263.2 27.4 1 2
#> Point 3 309.2 264.4 15.8 1 2
#> Track 2
#> Point 1 18.4 438.5 28.1 2 2
#> Point 2 142.9 58.6 28.2 2 2
#> Point 5 290.1 197.5 18.8 2 2
#> Cluster 2
#> Track 1
#> Point 1 310.1 15.4 5.8 1 2
#> Track 2
#> Point 1 99.1 33.5 22.5 2 2
#> Point 2 220.2 396 16.4 2 2
#> Track 3
#> Point 1 8.4 305.8 30.2 3 2
#> Point 2 84.7 227.7 21.1 3 2
#> End of MTrackJ Data FileFor more information, consult the package documentation.
install.packages('mdftracks')