Description Usage Arguments Details Author(s) References See Also Examples
The circles found from applying Alt_Alg
to one trajectory are used to find the residence times of another trajectory passing through these circles.
1 2 | Alt_Alg_mini(Circles_name, t_centers, X_centers, Y_centers, Path_name, t, X, Y, R,
s = 10, m = 500, save = 'n')
|
Circles_name |
name of the trajectory used to find the circles |
t_centers |
array of times when the positions were recorded |
X_centers |
array of the x-coordinates of the circles' centres |
Y_centers |
array of the y-coordinates of the circles' centres |
Path_name |
name of the trajectory that the residence times are found from |
t |
array of the times that the positions are recorded at |
X |
array of the x-coordinates describing the trajectory |
Y |
array of the y-coordinates describing the trajectory |
R |
radius value to use |
s |
number of time steps between checks for entrances and exits |
m |
estimate of the maximum number of crossings across all circles |
save |
if |
This functions works in a similar way to Alt_Alg
, but the circles are found from one trajectory and are applied to another. The results are stored in a csv file 'Circles_name'_multi_'Path_name'_UD_alt_R'R'.csv and the crossing times are stored in 'Circles_name'_multi_'Path_name'_M_alt_R'R'.csv
Rhys Munden <rdmunden1@sheffield.ac.uk>
Munden, R., Borger , L., Wilson, R.P., Redcliffe, J., Loison, A., Garel, M. and Potts, J.P. in review. Making sense of ultra-high-resolution movement data: an algorithm for inferring sites of interest.
See also Alt_Alg
for how to apply the algorithm to continuous data. Alt_Alg_discont
perform Alt_Alg_mini
on all trjectories, then combining
combines the results from each application of Alt_Alg_mini
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ##Find the current working directory
wd = getwd()
##Set the working directory as the temporary one
setwd(tempdir())
##Load the data
data(OU_14)
t=unlist(OU_14["t"])
X=unlist(OU_14["X"])
Y=unlist(OU_14["Y"])
##Number of path sections
n=5
##Number of recorded locations
N = length(t)
##A list of arrays of the time recoding for the 3 of the path segments
t_all = list(t[seq(1,floor(N/n))], t[seq(floor(N/n)*2,floor(N/n)*3)],
t[seq(floor(N/n)*4,floor(N/n)*5)])
##A list of arrays of the x-coordinates for the 3 of the path segments
X_all = list(X[seq(1,floor(N/n))], X[seq(floor(N/n)*2,floor(N/n)*3)],
X[seq(floor(N/n)*4,floor(N/n)*5)])
##A list of arrays of the y-coordinates for the 3 of the path segments
Y_all = list(Y[seq(1,floor(N/n))], Y[seq(floor(N/n)*2,floor(N/n)*3)],
Y[seq(floor(N/n)*4,floor(N/n)*5)])
##Calculates the residence time for one particular path segment
Alt_Alg("OU_14.1",unlist(t_all[1]),unlist(X_all[1]),unlist(Y_all[1]),0.3,first='y',save='y')
##Load the data of the circles found from Alt_Alg
df = read.csv(paste("OU_14.1","_UD_alt_R",0.3,".csv",sep=''))
t_centers = unlist(df[1])
X_centers = unlist(df[2])
Y_centers = unlist(df[3])
##Calculates the residence time from path segment 3, using circles from path segment 1
Alt_Alg_mini("OU14.1", t_centers, X_centers, Y_centers, "OU_14.3", unlist(t_all[2]),
unlist(X_all[2]), unlist(Y_all[2]), 0.3,save='y')
##Reset the original working directory
setwd(wd)
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