Merge pull request #116 from jrevels/jr/arraydepwarn

Array(T, dims...) to Array{T}(dims...) (ref JuliaLang/julia#19989)

src/JLD.jl

@@ -203,7 +203,7 @@ function jldopen(filename::AbstractString, rd::Bool, wr::Bool, cr::Bool, tr::Boo
             if sz < 512
                 error("File size indicates $filename cannot be a Julia data file")
             end
-            magic = Array(UInt8, 512)
+            magic = Vector{UInt8}(512)
             rawfid = open(filename, "r")
             try
                 magic = read!(rawfid, magic)
@@ -393,8 +393,7 @@ function read(obj::JldDataset)
                 T = jldatatype(file(obj), dtype)
             end
             if exists(obj, "dims")
-                dims = a_read(obj.plain, "dims")
-                return Array(T, dims...)
+                return Array{T}(a_read(obj.plain, "dims"))
             else
                 return T[]
             end
@@ -408,7 +407,7 @@ end
 read_scalar{T<:BitsKindOrString}(obj::JldDataset, dtype::HDF5Datatype, ::Type{T}) =
     read(obj.plain, T)
 function read_scalar(obj::JldDataset, dtype::HDF5Datatype, T::Type)
-    buf = Array(UInt8, sizeof(dtype))
+    buf = Vector{UInt8}(sizeof(dtype))
     HDF5.readarray(obj.plain, dtype.id, buf)
     return readas(jlconvert(T, file(obj), pointer(buf)))
 end
@@ -432,7 +431,7 @@ end
     if obj.file.mmaparrays && HDF5.iscontiguous(obj.plain) && dsel_id == HDF5.H5S_ALL
         readmmap(obj.plain, Array{T})
     else
-        out = Array(T, dims)
+        out = Array{T}(dims)
         HDF5.h5d_read(obj.plain.id, dtype.id, dspace_id, dsel_id, HDF5.H5P_DEFAULT, out)
         out
     end
@@ -441,14 +440,14 @@ end
 # Arrays of immutables/bitstypes
 function read_vals(obj::JldDataset, dtype::HDF5Datatype, T::Type, dspace_id::HDF5.Hid,
                    dsel_id::HDF5.Hid, dims::@compat Tuple{Vararg{Int}})
-    out = Array(T, dims)
+    out = Array{T}(dims)
     # Empty objects don't need to be read at all
     T.size == 0 && !T.mutable && return out
 
     # Read from file
     n = prod(dims)
     h5sz = sizeof(dtype)
-    buf = Array(UInt8, h5sz*n)
+    buf = Vector{UInt8}(h5sz*n)
     HDF5.h5d_read(obj.plain.id, dtype.id, dspace_id, dsel_id, HDF5.H5P_DEFAULT, buf)
 
     f = file(obj)
@@ -476,10 +475,10 @@ end
 # Arrays of references
 function read_refs{T}(obj::JldDataset, ::Type{T}, dspace_id::HDF5.Hid, dsel_id::HDF5.Hid,
                       dims::@compat Tuple{Vararg{Int}})
-    refs = Array(HDF5ReferenceObj, dims)
+    refs = Array{HDF5ReferenceObj}(dims)
     HDF5.h5d_read(obj.plain.id, HDF5.H5T_STD_REF_OBJ, dspace_id, dsel_id, HDF5.H5P_DEFAULT, refs)
 
-    out = Array(T, dims)
+    out = Array{T}(dims)
     f = file(obj)
     for i = 1:length(refs)
         if refs[i] != HDF5.HDF5ReferenceObj_NULL
@@ -572,7 +571,7 @@ end
     f = file(parent)
     dtype = h5fieldtype(f, T, true)
     buf = h5convert_array(f, data, dtype, wsession)
-    dims = convert(Array{HDF5.Hsize, 1}, [reverse(size(data))...])
+    dims = convert(Vector{HDF5.Hsize}, [reverse(size(data))...])
     dspace = dataspace(data)
     chunk = HDF5.heuristic_chunk(dtype, size(data))
     dprop, dprop_close = dset_create_properties(parent, sizeof(buf),buf, chunk; kargs...)
@@ -605,7 +604,7 @@ end
 function h5convert_array(f::JldFile, data::Array,
                          dtype::JldDatatype, wsession::JldWriteSession)
     if dtype == JLD_REF_TYPE
-        refs = Array(HDF5ReferenceObj, length(data))
+        refs = Vector{HDF5ReferenceObj}(length(data))
         for i = 1:length(data)
             if isassigned(data, i)
                 refs[i] = write_ref(f, data[i], wsession)
@@ -633,7 +632,7 @@ function _h5convert_vals(f::JldFile, data::Array,
                          dtype::JldDatatype, wsession::JldWriteSession)
     sz = HDF5.h5t_get_size(dtype)
     n = length(data)
-    buf = Array(UInt8, sz*n)
+    buf = Vector{UInt8}(sz*n)
     offset = pointer(buf)
     for i = 1:n
         h5convert!(offset, f, data[i], wsession)
@@ -705,7 +704,7 @@ end
     dtype = h5type(f, T, true)
     gen_h5convert(f, T)
 
-    buf = Array(UInt8, HDF5.h5t_get_size(dtype))
+    buf = Vector{UInt8}(HDF5.h5t_get_size(dtype))
     h5convert!(pointer(buf), file(parent), s, wsession)
 
     dspace = HDF5Dataspace(HDF5.h5s_create(HDF5.H5S_SCALAR))
@@ -821,8 +820,8 @@ end
 
 function convert{K,V,T}(::Type{AssociativeWrapper{K,V,T}}, d::Associative)
     n = length(d)
-    ks = Array(K, n)
-    vs = Array(V, n)
+    ks = Vector{K}(n)
+    vs = Vector{V}(n)
     i = 0
     for (k,v) in d
         ks[i+=1] = k
@@ -1115,7 +1114,7 @@ function full_typename(io::IO, file::JldFile, jltype::DataType)
     end
 end
 function full_typename(file::JldFile, x)
-    io = IOBuffer(Array(UInt8, 64), true, true)
+    io = IOBuffer(Vector{UInt8}(64), true, true)
     truncate(io, 0)
     full_typename(io, file, x)
     String(take!(io))
@@ -1152,7 +1151,7 @@ end
 macro save(filename, vars...)
     if isempty(vars)
         # Save all variables in the current module
-        writeexprs = Array(Expr, 0)
+        writeexprs = Vector{Expr}(0)
         m = current_module()
         for vname in names(m)
             s = string(vname)
@@ -1164,7 +1163,7 @@ macro save(filename, vars...)
             end
         end
     else
-        writeexprs = Array(Expr, length(vars))
+        writeexprs = Vector{Expr}(length(vars))
         for i = 1:length(vars)
             writeexprs[i] = :(write(f, $(string(vars[i])), $(esc(vars[i])), wsession))
         end
@@ -1310,7 +1309,7 @@ export
     translate,
     truncate_module_path
 
-const _runtime_properties = Array(HDF5.HDF5Properties, 1)
+const _runtime_properties = Vector{HDF5.HDF5Properties}(1)
 compact_properties() = _runtime_properties[1]
 
 function __init__()

src/JLD00.jl

@@ -127,7 +127,7 @@ function jldopen(filename::AbstractString, rd::Bool, wr::Bool, cr::Bool, tr::Boo
             if sz < 512
                 error("File size indicates $filename cannot be a Julia data file")
             end
-            magic = Array(UInt8, 512)
+            magic = Vector{UInt8}(512)
             rawfid = open(filename, "r")
             try
                 magic = read!(rawfid, magic)
@@ -447,7 +447,7 @@ function read(obj::JldDataset, T::DataType)
     # Add the parameters
     if exists(obj, "TypeParameters")
         params = a_read(obj.plain, "TypeParameters")
-        p = Array(Any, length(params))
+        p = Vector{Any}(length(params))
         for i = 1:length(params)
             p[i] = eval(current_module(), parse(params[i]))
         end
@@ -483,7 +483,7 @@ end
 # Read an array of references
 function getrefs{T}(obj::JldDataset, ::Type{T})
     refs = read(obj.plain, Array{HDF5ReferenceObj})
-    out = Array(T, size(refs))
+    out = Array{T}(size(refs))
     f = file(obj)
     for i = 1:length(refs)
         if refs[i] != HDF5.HDF5ReferenceObj_NULL
@@ -511,7 +511,7 @@ end
             close(ref)
         end
     else
-        out = Array(T, size(refs))
+        out = Array{T}(size(refs))
         for i = 1:length(refs)
             ref = f[refs[i]]
             try
@@ -643,7 +643,7 @@ end
     grefname = name(gref)
     try
         # Write the items to the reference group
-        refs = Array(HDF5ReferenceObj, size(data)...)
+        refs = Array{HDF5ReferenceObj}(size(data))
         # pad with zeros to keep in order
         nd = ndigits(length(data))
         z = "0"
@@ -690,9 +690,8 @@ end
         return write_composite(parent, name, d)
     end
     n = length(d)
-    T = eltype(d)
-    ks = Array(T[1], n)
-    vs = Array(T[2], n)
+    ks = Vector{keytype(d)}(n)
+    vs = Vector{valtype(d)}(n)
     i = 0
     for (k,v) in d
         ks[i+=1] = k
@@ -743,7 +742,7 @@ end
         if !exists(gtypes, Tname)
             # Write names to a dataset, so that other languages reading this file can
             # at least create a sensible dict
-            nametype = Array(String, 2, length(n))
+            nametype = Matrix{String}(2, length(n))
             t = T.types
             for i = 1:length(n)
                 nametype[1, i] = string(n[i])
@@ -765,7 +764,7 @@ end
         close(gtypes)
     end
     # Write the data
-    v = Array(Any, length(n))
+    v = Vector{Any}(length(n))
     for i = 1:length(v)
         if isdefined(s, n[i])
             v[i] = getfield(s, n[i])
@@ -1027,7 +1026,7 @@ end
 macro save(filename, vars...)
     if isempty(vars)
         # Save all variables in the current module
-        writeexprs = Array(Expr, 0)
+        writeexprs = Vector{Expr}(0)
         m = current_module()
         for vname in names(m)
             s = string(vname)
@@ -1039,7 +1038,7 @@ macro save(filename, vars...)
             end
         end
     else
-        writeexprs = Array(Expr, length(vars))
+        writeexprs = Vector{Expr}(length(vars))
         for i = 1:length(vars)
             writeexprs[i] = :(write(f, $(string(vars[i])), $(esc(vars[i]))))
         end
@@ -1056,8 +1055,8 @@ macro load(filename, vars...)
             filename = eval(current_module(), filename)
         end
         # Load all variables in the top level of the file
-        readexprs = Array(Expr, 0)
-        vars = Array(Expr, 0)
+        readexprs = Vector{Expr}(0)
+        vars = Vector{Expr}(0)
         f = jldopen(filename)
         nms = names(f)
         for n in nms
@@ -1074,7 +1073,7 @@ macro load(filename, vars...)
                          :(close($f))),
                     Symbol[v.args[1] for v in vars]) # "unescape" vars
     else
-        readexprs = Array(Expr, length(vars))
+        readexprs = Vector{Expr}(length(vars))
         for i = 1:length(vars)
             readexprs[i] = :($(esc(vars[i])) = read(f, $(string(vars[i]))))
         end

src/datafile.jl

@@ -40,7 +40,7 @@ read(f::Base.Callable, parent::DataFile, name::ASCIIString...) =
 # Read every variable in the file
 function read(f::DataFile)
     vars = names(f)
-    vals = Array(Any, length(vars))
+    vals = Vector{Any}(length(vars))
     for i = 1:length(vars)
         vals[i] = read(f, vars[i])
     end

src/jld_types.jl

@@ -34,8 +34,8 @@ end
 
 # Get information about the HDF5 types corresponding to Julia types
 function JldTypeInfo(parent::JldFile, types::TypesType, commit::Bool)
-    dtypes = Array(JldDatatype, length(types))
-    offsets = Array(Int, length(types))
+    dtypes = Vector{JldDatatype}(length(types))
+    offsets = Vector{Int}(length(types))
     offset = 0
     for i = 1:length(types)
         dtype = dtypes[i] = h5fieldtype(parent, types[i], commit)
@@ -773,8 +773,8 @@ function reconstruct_type(parent::JldFile, dtype::HDF5Datatype, savedname::Abstr
     else
         # Figure out field names and types
         nfields = HDF5.h5t_get_nmembers(dtype.id)
-        fieldnames = Array(Symbol, nfields)
-        fieldtypes = Array(Type, nfields)
+        fieldnames = Vector{Symbol}(nfields)
+        fieldtypes = Vector{Type}(nfields)
         for i = 1:nfields
             membername = HDF5.h5t_get_member_name(dtype.id, i-1)
             idx = rsearchindex(membername, "_")

test/custom_serialization.jl

@@ -47,7 +47,7 @@ JLD.readas(serdata::MyContainerSerializer) =
 function JLD.writeas{T}(data::MyContainer{T})
     ids = [obj.id for obj in data.objs]
     n = length(data.objs)
-    vectors = Array(T, 5, n)
+    vectors = Matrix{T}(5, n)
     for i = 1:n
         vectors[:,i] = data.objs[i].data
     end

test/jldtests.jl

@@ -73,8 +73,8 @@ typevar_lb = Vector{TypeVar(:U, Integer)}[[1]]
 typevar_ub = Vector{TypeVar(:U, Int, Any)}[[1]]
 typevar_lb_ub = Vector{TypeVar(:U, Int, Real)}[[1]]
 end
-undef = Array{Any}(1)
-undefs = Array{Any}(2, 2)
+undef = Vector{Any}(1)
+undefs = Matrix{Any}(2, 2)
 ms_undef = MyStruct(0)
 # Unexported type:
 cpus = Base.Sys.cpu_info()
@@ -196,17 +196,17 @@ end
 padding_test = PaddingTest[PaddingTest(i, i) for i = 1:8]
 # Empty arrays of various types and sizes
 empty_arr_1 = Int[]
-empty_arr_2 = Array(Int, 56, 0)
+empty_arr_2 = Matrix{Int}(56, 0)
 empty_arr_3 = Any[]
-empty_arr_4 = Array{Any}(0, 97)
+empty_arr_4 = Matrix{Any}(0, 97)
 # Moderately big dataset (which will be mmapped)
 bigdata = [1:10000;]
 # BigFloats and BigInts
 bigints = big(3).^(1:100)
 bigfloats = big(3.2).^(1:100)
 # None
 none = Union{}
-nonearr = Array(Union{}, 5)
+nonearr = Vector{Union{}}(5)
 # nothing/Void
 scalar_nothing = nothing
 vector_nothing = Union{Int,Void}[1,nothing]