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Parallel computation

The computationally most intense functions qProb.3d(), n.sim.cond.3d() and reproduce.track.3d() of the package are also implemented in a parallel version. On Unix systems this is done using a fork cluster. On Windows systems PSOCK cluster is used.

Definition of start conditions and parameters:

Get movement characteristics (P) from the example trajectory and simulate a Unconditional Eprircal Random Walk (UERW) in order to extract the attraction term (Q):

P <- get.track.densities.3d(niclas, heightDistEllipsoid = TRUE, DEM = dem)
uerw <- sim.uncond.3d(sim.locs*f, start = c(niclas$x[1], niclas$y[1], niclas$z[1]), 
                      a0 = a0, g0 = g0, densities = P)

The parallel version of the qProb.3d() function can be accessed by setting the parameter parallel = TRUE:

Q <- qProb.3d(uerw, sim.locs, parallel = TRUE)
cerwList <- reproduce.track.3d(n.sim = 100, niclas, DEM = dem, parallel = TRUE)

And also for n.sim.uncond.3d():

cerwList <- n.sim.cond.3d(n.sim = 100, sim.locs, start=start, end=end,a0 = a0, g0 = g0,
                      densities=P, qProbs=Q, DEM = dem, parallel = TRUE)

Alternativly the number of nodes in the cluster can be specified by passing a number to the function: parallel = 4. In this case a fork or PSOCK cluster with 4 nodes will be used. The maximum number of nodes is not allowed to be larger than the number of available cores (Hyper Threading included).

cerwList <- n.sim.cond.3d(n.sim = 100, sim.locs, start=start, end=end,a0 = a0, g0 = g0,
                      densities=P, qProbs=Q, DEM = dem, parallel = 4)

Note: If only a few tracks are simulated and the track length is short sim.locs < 30, then it is faster in many cases to stay with the single core version of the function, especially on Windows systems, where setting up clusters takes some time.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.