v 0.4.0

Project.toml

@@ -1,26 +1,25 @@
 name = "MetidaBase"
 uuid = "075456b7-4006-432f-9324-2f8453996c49"
 authors = ["PharmCat <[email protected]> and contributors"]
-version = "0.3.2"
+version = "0.4.0"
 
 [deps]
 #StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
-#Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 
 [compat]
 #StatsBase = "0.29, 0.30, 0.31, 0.32, 0.33"
 StatsModels = "0.6"
 Tables = "1"
 PrettyTables = "1"
-#Reexport = "0.2, 1.0, 1.1"
 julia = "1"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 
 [targets]
-test = ["Test", "DataFrames"]
+test = ["Test", "DataFrames", "CSV"]

src/iterators.jl

@@ -5,7 +5,7 @@ end
 skipnonpositive(itr) = SkipNonPositive(itr)
 
 Base.IteratorEltype(::Type{SkipNonPositive{T}}) where {T} = Base.IteratorEltype(T)
-#Base.eltype(::Type{SkipNonPositive{T}}) where {T} = nonmissingtype(eltype(T))
+Base.eltype(::Type{SkipNonPositive{T}}) where {T} = nonmissingtype(eltype(T))
 function Base.iterate(itr::SkipNonPositive, state...)
     y = iterate(itr.x, state...)
     y === nothing && return nothing
@@ -39,7 +39,7 @@ end
 skipnanormissing(itr) = SkipNaNorMissing(itr)
 
 Base.IteratorEltype(::Type{SkipNaNorMissing{T}}) where {T} = Base.IteratorEltype(T)
-#Base.eltype(::Type{SkipNaNorMissing{T}}) where {T} = nonmissingtype(eltype(T))
+Base.eltype(::Type{SkipNaNorMissing{T}}) where {T} = nonmissingtype(eltype(T))
 function Base.iterate(itr::SkipNaNorMissing, state...)
     y = iterate(itr.x, state...)
     y === nothing && return nothing

src/types.jl

@@ -175,6 +175,9 @@ function Base.iterate(d::DataSet, i::Int)
     return Base.iterate(d.ds, i)
 end
 
+function Base.map(f, d::DataSet)
+    DataSet(map(f, getdata(d)))
+end
 ################################################################################
 # BASE.SORT
 ################################################################################
@@ -256,10 +259,15 @@ function uniqueidlist(d::DataSet{T}, list::Symbol) where T <: AbstractIdData
     dl
 end
 
-function subset(d::DataSet, sort::Dict)
+function subset(d::DataSet{T}, sort::Dict) where T <: AbstractIdData
     inds = findall(x-> sort ⊆ x.id, d.ds)
     if length(inds) > 0 return DataSet(d.ds[inds]) end
-    []
+    DataSet(Vector{T}(undef, 0))
+end
+function subset(d::DataSet{T}, sort::Dict) where T <: AbstractIDResult
+    inds = findall(x-> sort ⊆ x.data.id, d.ds)
+    if length(inds) > 0 return DataSet(d.ds[inds]) end
+    DataSet(Vector{T}(undef, 0))
 end
 ################################################################################
 # metida_table from DataSet{AbstractIDResult}

test/runtests.jl

@@ -1,5 +1,5 @@
 using MetidaBase
-using Test, DataFrames
+using Test, DataFrames, CSV
 
 @testset "MetidaBase.jl" begin
     io       = IOBuffer();
@@ -26,6 +26,8 @@ using Test, DataFrames
         @test mt[i, :b] == j[2]
     end
 
+    CSV.write(io, mt)
+
     struct ExampleIDStruct <: MetidaBase.AbstractSubject
         #time
         #obs
@@ -60,6 +62,7 @@ using Test, DataFrames
 
     exrsds = MetidaBase.DataSet(exrsdsv)
 
+
     @test exrsds[:, :r1][1] == 3
     @test exrsds[1, :r1] == 3
 
@@ -77,6 +80,9 @@ using Test, DataFrames
     MetidaBase.uniqueidlist(exidds, :a)
 
     MetidaBase.subset(exidds, Dict(:a => 1))
+    MetidaBase.subset(exrsds, Dict(:a => 1))
+
+    map(identity, exidds)
 
     mt = MetidaBase.metida_table(exrsds)
 
@@ -85,15 +91,25 @@ using Test, DataFrames
     for i in itr1
         @test !MetidaBase.isnanormissing(i)
     end
+    eachindex(itr1)
+    eltype(itr1)
+    keys(itr1)
+    @test length(itr1) == 3
+
     itr2 = MetidaBase.skipnonpositive(v1)
     for i in itr2
         @test MetidaBase.ispositive(i)
     end
+    eachindex(itr2)
+    eltype(itr2)
+    keys(itr2)
+    @test length(itr2) == 2
 
     MetidaBase.sdfromcv(0.4) ≈ 0.38525317015992666
     MetidaBase.varfromcv(0.4) ≈ 0.1484200051182734
     MetidaBase.cvfromvar(0.4) ≈ 0.7013021443295824
     MetidaBase.cvfromsd(0.4) ≈ 0.41654636115540644
 
     smt = MetidaBase.Tables.schema(mt)
+
 end