residual juliaformatting

Author: exaexa

docs/make.jl

@@ -1,10 +1,13 @@
 using Documenter, DistributedData
 
-makedocs(modules = [DistributedData],
+makedocs(
+    modules = [DistributedData],
     clean = false,
-    format = Documenter.HTML(prettyurls = !("local" in ARGS),
-             canonical = "https://lcsb-biocore.github.io/DistributedData.jl/stable/",
-             assets = ["assets/logo.ico"]),
+    format = Documenter.HTML(
+        prettyurls = !("local" in ARGS),
+        canonical = "https://lcsb-biocore.github.io/DistributedData.jl/stable/",
+        assets = ["assets/logo.ico"],
+    ),
     sitename = "DistributedData.jl",
     authors = "The developers of DistributedData.jl",
     linkcheck = !("skiplinks" in ARGS),
@@ -20,5 +23,5 @@ deploydocs(
     target = "build",
     branch = "gh-pages",
     devbranch = "develop",
-    push_preview = true
+    push_preview = true,
 )

docs/slurm-example/run-analysis.jl

@@ -1,14 +1,14 @@
-using  Distributed, ClusterManagers, DistributedData
+using Distributed, ClusterManagers, DistributedData
 
 # read the number of available workers from  environment and start the worker processes
-n_workers = parse(Int , ENV["SLURM_NTASKS"])
-addprocs_slurm(n_workers , topology =:master_worker)
+n_workers = parse(Int, ENV["SLURM_NTASKS"])
+addprocs_slurm(n_workers, topology = :master_worker)
 
 # load the required packages on all workers
 @everywhere using DistributedData
 
 # generate a random dataset on all workers
-dataset = dtransform((), _ -> randn(10000,10000), workers(), :myData)
+dataset = dtransform((), _ -> randn(10000, 10000), workers(), :myData)
 
 # for demonstration, sum the whole dataset
 totalResult = dmapreduce(dataset, sum, +)

src/base.jl

@@ -1,3 +1,4 @@
+
 """
     save_at(worker, sym, val)