Added distributed example (#172)

docs/src/examples/Time Mean.md

@@ -6,3 +6,60 @@ c = Cube()
 citaly = c[var = ["air_temperature_2m", "evaporation"], region="Italy", time=2001:2003]
 mapslices(mean ∘ skipmissing, c, dims="Time")
 ````
+
+## Distributed calculations
+
+### Local machine
+
+It is possible to distribute the calculations over multiple process. The following code does a time mean over all grid points using multiple CPU over a local machine.
+
+````julia
+using Distributed
+addprocs(2)
+
+@everywhere using Pkg
+@everywhere Pkg.activate(".")
+@everywhere using ESDL
+@everywhere using Statistics
+
+@everywhere function mymean(output, pixel)
+       output = mean(pixel)
+end
+
+c = Cube()
+tair = subsetcube(c,variable="air_temperature_2m", time=2001:2016)
+tair_c = map(t->t-273.15, tair)
+
+indims = InDims(TimeAxis)
+outdims = OutDims()
+
+resultcube = mapCube(mymean, tair_c, indims=indims, outdims=outdims)
+````
+
+In the last example, `mapCube` was used to map the `mymean` function. `mapslices` is a convenient function that can replace `mapCube`, where you can omit defining an extra function with the output argument as an input (e.g. `mymean`). It is possible to simply use `mapslice`
+
+```julia
+resultcube = mapslices(mean ∘ skipmissing, c, dims="time")
+```
+
+### SLURM cluster
+
+It is also possible to distribute easily the workload on a cluster, with little modification to the code. The following code does a time mean over all grid points using multiple CPU over a SLURM cluster. To do so, we use the `ClusterManagers` package.
+
+```julia
+using Distributed
+using ClusterManagers
+
+addprocs(SlurmManager(10))
+
+@everywhere using Pkg
+@everywhere Pkg.activate(".")
+@everywhere using ESDL
+@everywhere using Statistics
+
+inpath="zg1000_AERday_CanESM5_esm-hist_r6i1p1f1_gn_18500101-20141231.nc"
+
+c = NetCDFCube(inpath, "zg1000")
+
+resultcube = mapslices(mean ∘ skipmissing, c, dims="time")
+```