reformat with new JuliaFormatter

Author: exaexa

src/DistributedData.jl

@@ -22,9 +22,7 @@ export save_at,
     tmp_symbol
 
 include("io.jl")
-export dstore,
-    dload,
-    dunlink
+export dstore, dload, dunlink
 
 include("tools.jl")
 export dcopy,

src/base.jl

@@ -30,7 +30,7 @@ The symbols are saved in Main module on the corresponding worker. For example,
 `save_at(1, :x, nothing)` _will_ erase your local `x` variable. Beware of name
 collisions.
 """
-function save_at(worker, sym::Symbol, val; mod=Main)
+function save_at(worker, sym::Symbol, val; mod = Main)
     remotecall(() -> Base.eval(mod, :(
         begin
             $sym = $val
@@ -45,7 +45,7 @@ end
 Get a value `val` from a remote `worker`; quoting of `val` works just as with
 `save_at`. Returns a future with the requested value.
 """
-function get_from(worker, val; mod=Main)
+function get_from(worker, val; mod = Main)
     remotecall(() -> Base.eval(mod, :($val)), worker)
 end
 
@@ -162,11 +162,7 @@ end
 
 Same as `dtransform`, but specialized for `Dinfo`.
 """
-function dtransform(
-    dInfo::Dinfo,
-    fn,
-    tgt::Symbol = dInfo.val,
-)::Dinfo
+function dtransform(dInfo::Dinfo, fn, tgt::Symbol = dInfo.val)::Dinfo
     dtransform(dInfo.val, fn, dInfo.workers, tgt)
 end
 
@@ -314,8 +310,7 @@ Call a function `fn` on `workers`, with a single parameter arriving from the
 corresponding position in `arr`.
 """
 function dmap(arr::Vector, fn, workers)
-    fetch.([get_from(w, :($fn($(arr[i]))))
-            for (i, w) in enumerate(workers)])
+    fetch.([get_from(w, :($fn($(arr[i])))) for (i, w) in enumerate(workers)])
 end
 
 """

src/io.jl

@@ -33,10 +33,7 @@ end
 
 Overloaded functionality for `Dinfo`.
 """
-function dstore(
-    dInfo::Dinfo,
-    files = defaultFiles(dInfo.val, dInfo.workers),
-)
+function dstore(dInfo::Dinfo, files = defaultFiles(dInfo.val, dInfo.workers))
     dstore(dInfo.val, dInfo.workers, files)
 end
 
@@ -47,16 +44,12 @@ Import the content of symbol `sym` by each worker specified by `pids` from the
 corresponding filename in `files`.
 """
 function dload(sym::Symbol, pids, files = defaultFiles(sym, pids))
-    dmap(
-        files,
-        (fn) -> Base.eval(Main, :(
-            begin
-                $sym = open($deserialize, $fn)
-                nothing
-            end
-        )),
-        pids,
-    )
+    dmap(files, (fn) -> Base.eval(Main, :(
+        begin
+            $sym = open($deserialize, $fn)
+            nothing
+        end
+    )), pids)
     return Dinfo(sym, pids)
 end
 
@@ -65,10 +58,7 @@ end
 
 Overloaded functionality for `Dinfo`.
 """
-function dload(
-    dInfo::Dinfo,
-    files = defaultFiles(dInfo.val, dInfo.workers),
-)
+function dload(dInfo::Dinfo, files = defaultFiles(dInfo.val, dInfo.workers))
     dload(dInfo.val, dInfo.workers, files)
 end
 
@@ -87,9 +77,6 @@ end
 
 Overloaded functionality for `Dinfo`.
 """
-function dunlink(
-    dInfo::Dinfo,
-    files = defaultFiles(dInfo.val, dInfo.workers),
-)
+function dunlink(dInfo::Dinfo, files = defaultFiles(dInfo.val, dInfo.workers))
     dunlink(dInfo.val, dInfo.workers, files)
 end

src/tools.jl

@@ -13,11 +13,7 @@ end
 
 Reduce dataset to selected columns, optionally save it under a different name.
 """
-function dselect(
-    dInfo::Dinfo,
-    columns::Vector{Int},
-    tgt::Symbol = dInfo.val,
-)::Dinfo
+function dselect(dInfo::Dinfo, columns::Vector{Int}, tgt::Symbol = dInfo.val)::Dinfo
     dtransform(dInfo, mtx -> mtx[:, columns], tgt)
 end
 
@@ -91,10 +87,7 @@ end
 Compute mean and standard deviation of the columns in dataset. Returns a tuple
 with a vector of means in `columns`, and a vector of corresponding sdevs.
 """
-function dstat(
-    dInfo::Dinfo,
-    columns::Vector{Int},
-)::Tuple{Vector{Float64},Vector{Float64}}
+function dstat(dInfo::Dinfo, columns::Vector{Int})::Tuple{Vector{Float64},Vector{Float64}}
 
     sum_squares = x -> sum(x .^ 2)
 
@@ -136,8 +129,7 @@ function dstat_buckets(
         )
 
     # extract the bucketed stats
-    (sums, sqsums, ns) =
-        dmapreduce([dInfo, buckets], get_bucketed_stats, combine_stats)
+    (sums, sqsums, ns) = dmapreduce([dInfo, buckets], get_bucketed_stats, combine_stats)
 
     return (
         sums ./ ns, #means
@@ -285,7 +277,8 @@ less or higher than `targets`.
 """
 function update_extrema(counts, targets, lims, mids)
     broadcast(
-        (cnt, target, lim, mid) -> cnt >= target ? # if the count is too high,
+        (cnt, target, lim, mid) ->
+            cnt >= target ? # if the count is too high,
             (lim[1], mid) : # median is going to be in the lower half
             (mid, lim[2]),  # otherwise in the higher half
         counts,
@@ -313,11 +306,8 @@ function dmedian(dInfo::Dinfo, columns::Vector{Int}; iters = 20)
     target = dmapreduce(dInfo, d -> size(d, 1), +) ./ 2
 
     # current estimation range for the median (tuples of min, max)
-    lims = dmapreduce(
-        dInfo,
-        d -> mapslices(extrema, d[:, columns], dims = 1),
-        reduce_extrema,
-    )
+    lims =
+        dmapreduce(dInfo, d -> mapslices(extrema, d[:, columns], dims = 1), reduce_extrema)
 
     # convert the limits to a simple vector
     lims = cat(lims..., dims = 1)
@@ -368,8 +358,8 @@ function dmedian_buckets(
     get_bucket_extrema =
         (d, b) -> catmapbuckets(
             (_, x) -> length(x) > 0 ? # if there are some elements
-                    extrema(x) : # just take the extrema
-                    (Inf, -Inf), # if not, use backup values
+                extrema(x) : # just take the extrema
+                (Inf, -Inf), # if not, use backup values
             d[:, columns],
             nbuckets,
             b,
@@ -384,21 +374,22 @@ function dmedian_buckets(
         # this counts the elements smaller than mids in buckets
         # (both mids and elements are bucketed and column-sliced into matrices)
         bucketed_count_smaller_than_mids =
-            (d, b) -> vcat(mapbuckets(
-                (bucketID, d) ->
-                    [
-                        count(x -> x < mids[bucketID, colID], d[:, colID])
-                        for (colID, c) in enumerate(columns)
-                    ]',
-                d,
-                nbuckets,
-                b,
-                slicedims = (1, 2),
-            )...)
+            (d, b) -> vcat(
+                mapbuckets(
+                    (bucketID, d) ->
+                        [
+                            count(x -> x < mids[bucketID, colID], d[:, colID]) for
+                            (colID, c) in enumerate(columns)
+                        ]',
+                    d,
+                    nbuckets,
+                    b,
+                    slicedims = (1, 2),
+                )...,
+            )
 
         # gather the counts
-        counts =
-            dmapreduce([dInfo, buckets], bucketed_count_smaller_than_mids, +)
+        counts = dmapreduce([dInfo, buckets], bucketed_count_smaller_than_mids, +)
 
         lims = update_extrema(counts, targets, lims, mids)
     end

test/base.jl

@@ -88,16 +88,18 @@
     @testset "`pmap` on distributed data" begin
         fetch.(save_at.(W, :test, 1234321))
         di = Dinfo(:test, W) # also test the example in docs
-        @test dpmap(x -> :($(di.val) + $x),
-                    WorkerPool(di.workers),
-                    [4321234, 1234, 4321]) == [5555555, 1235555, 1238642]
+        @test dpmap(
+            x -> :($(di.val) + $x),
+            WorkerPool(di.workers),
+            [4321234, 1234, 4321],
+        ) == [5555555, 1235555, 1238642]
         fetch.(remove_from.(W, :test))
     end
 
     @testset "Internal utilities" begin
         @test DistributedData.tmp_symbol(:test) != :test
-        @test DistributedData.tmp_symbol(:test, prefix = "abc",
-                                     suffix = "def") == :abctestdef
+        @test DistributedData.tmp_symbol(:test, prefix = "abc", suffix = "def") ==
+              :abctestdef
         @test DistributedData.tmp_symbol(Dinfo(:test, W)) != :test
     end