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Fixes for 0.6 #235

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Feb 17, 2017
Merged

Fixes for 0.6 #235

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91d3ce9
Updates for Julia 0.6
andreasnoack Feb 10, 2017
3041382
Remove Compat code
andreasnoack Feb 12, 2017
6fe6e13
Fix broadcast warnings and remove unused operator groups form operato…
andreasnoack Feb 12, 2017
1c423b9
Use iteration instead of indexing into indices in setindex to avoid
andreasnoack Feb 12, 2017
af188c7
Adjust CI
andreasnoack Feb 12, 2017
4dbce68
Reenable precompilation
andreasnoack Feb 12, 2017
a2bb139
Use @inline instead of @generate when possible
andreasnoack Feb 12, 2017
1416606
Now, erf, erfc, and digamma are in SpecialFunctions instead of Base
andreasnoack Feb 12, 2017
0c9184c
Intialize arrays with all NAs. Flip bits _unsafe_getindex.
andreasnoack Feb 13, 2017
a38a6f6
Define linearindexing for Type{T} instead of T
andreasnoack Feb 13, 2017
a75d440
Update ! -> .! and remove commented out tests
andreasnoack Feb 14, 2017
ed30192
Reenable Compat. Necessary for old @nsplat and @ngenerate macros
andreasnoack Feb 14, 2017
937d396
Make broadcast work for scalar Strings
andreasnoack Feb 16, 2017
9f68def
Update syntax for indexing traits
andreasnoack Feb 17, 2017
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2 changes: 1 addition & 1 deletion src/dataarray.jl
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Expand Up @@ -424,7 +424,7 @@ end
#'
#' dv = @data [1, 2, NA, 4]
#' v = dropna(dv)
dropna(dv::DataVector) = copy(dv.data[!dv.na]) # -> Vector
dropna(dv::DataVector) = dv.data[.!dv.na] # -> Vector

#' @description
#'
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2 changes: 1 addition & 1 deletion src/pooleddataarray.jl
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Expand Up @@ -92,7 +92,7 @@ end
function PooledDataArray{T,R<:Integer,N}(d::AbstractArray{T, N},
m::AbstractArray{Bool, N},
r::Type{R} = DEFAULT_POOLED_REF_TYPE)
pool = convert(Array, unique(d[!m]))
pool = convert(Array, unique(d[.!m]))
if method_exists(isless, (T, T))
sort!(pool)
end
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2 changes: 0 additions & 2 deletions test/broadcast.jl
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Expand Up @@ -20,7 +20,6 @@ rb = 1:5
@test broadcast!(+, DataArray(Int, 2, 2), [1, 0], [1 4]) == [2 5; 1 4]
@test broadcast!(+, DataArray(Int, 2), [1, 0], [1, 4]) == [2, 4]
@test broadcast!(+, DataArray(Int, 2), [1, 0], 2) == [3, 2]
# @test broadcast!(abs, @data([-1, -2])) == @data([1, 2])
for arr in (identity, as_dataarray, as_pda, as_dataarray_bigfloat, as_pda_bigfloat)
@test broadcast(+, arr(eye(2)), arr([1, 4])) == [2 1; 4 5]
@test broadcast(+, arr(eye(2)), arr([1 4])) == [2 4; 1 5]
Expand Down Expand Up @@ -123,7 +122,6 @@ rt = Base.return_types(broadcast!, (typeof(+), DataArray{Float64, 3}, Array{Floa
@test isequal(broadcast(|, @data([NA, false]), @data([NA true false])), @data([NA true NA; NA true false]))

# Test map!
# @test_throws DimensionMismatch map!(+, DataArray(Float64, 2, 2), @data([1 2]), @data([1 2]))
@test map!(+, DataArray(Float64, 2), @data([1, 2]), @data([1, 2])) == @data([2, 4])
x = @data([-1, -2])
@test map!(abs, x, x) == @data([1, 2])
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42 changes: 21 additions & 21 deletions test/datamatrix.jl
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Expand Up @@ -44,19 +44,19 @@ module TestDataMatrix
b[1, 1] = NA
res = a * b[1:1, :]
@assert all(isna(res[:, 1]))
@assert all(!isna(res[:, 2]))
@assert all(!isna(res[:, 3]))
@assert all(.!(isna(res[:, 2])))
@assert all(.!(isna(res[:, 3])))
res = a * b[2:2, :]
@assert all(!isna(res))
@assert all(.!(isna(res)))

#
# DataMatrix w NA's * DataVector
#

res = b * a
@assert isna(res[1])
@assert !isna(res[2])
@assert !isna(res[3])
@assert .!(isna(res[2]))
@assert .!(isna(res[3]))

#
# DataMatrix * DataMatrix
Expand All @@ -71,11 +71,11 @@ module TestDataMatrix
@assert isna(res[1, 2])
@assert isna(res[1, 3])
@assert isna(res[2, 1])
@assert !isna(res[2, 2])
@assert !isna(res[2, 3])
@assert .!(isna(res[2, 2]))
@assert .!(isna(res[2, 3]))
@assert isna(res[3, 1])
@assert !isna(res[3, 2])
@assert !isna(res[3, 3])
@assert .!(isna(res[3, 2]))
@assert .!(isna(res[3, 3]))

res = b * @data eye(3)
# 3x3 Float64 DataMatrix:
Expand All @@ -85,12 +85,12 @@ module TestDataMatrix
@assert isna(res[1, 1])
@assert isna(res[1, 2])
@assert isna(res[1, 3])
@assert !isna(res[2, 1])
@assert !isna(res[2, 2])
@assert !isna(res[2, 3])
@assert !isna(res[3, 1])
@assert !isna(res[3, 2])
@assert !isna(res[3, 3])
@assert .!(isna(res[2, 1]))
@assert .!(isna(res[2, 2]))
@assert .!(isna(res[2, 3]))
@assert .!(isna(res[3, 1]))
@assert .!(isna(res[3, 2]))
@assert .!(isna(res[3, 3]))

res = (@data eye(3)) * b
# julia> dataeye(3) * b
Expand All @@ -99,14 +99,14 @@ module TestDataMatrix
# NA 1.0 0.0
# NA 0.0 1.0
@assert isna(res[1, 1])
@assert !isna(res[1, 2])
@assert !isna(res[1, 3])
@assert .!(isna(res[1, 2]))
@assert .!(isna(res[1, 3]))
@assert isna(res[2, 1])
@assert !isna(res[2, 2])
@assert !isna(res[2, 3])
@assert .!(isna(res[2, 2]))
@assert .!(isna(res[2, 3]))
@assert isna(res[3, 1])
@assert !isna(res[3, 2])
@assert !isna(res[3, 3])
@assert .!(isna(res[3, 2]))
@assert .!(isna(res[3, 3]))

# Test row operations
dm = @data eye(6, 2)
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6 changes: 3 additions & 3 deletions test/reduce.jl
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Expand Up @@ -142,11 +142,11 @@ da2 = DataArray(randn(128))
@same_behavior mean(da1, weights(da2.data); skipna=true) mean(da1.data, weights(da2.data))

da1[1:3:end] = NA
@same_behavior mean(da1, weights(da2); skipna=true) mean(dropna(da1), weights(da2.data[!da1.na]))
@same_behavior mean(da1, weights(da2.data); skipna=true) mean(dropna(da1), weights(da2.data[!da1.na]))
@same_behavior mean(da1, weights(da2); skipna=true) mean(dropna(da1), weights(da2.data[(!).(da1.na)]))
@same_behavior mean(da1, weights(da2.data); skipna=true) mean(dropna(da1), weights(da2.data[(!).(da1.na)]))

da2[1:2:end] = NA
keep = !da1.na .& !da2.na
keep = .!da1.na .& .!da2.na
@test isna(mean(da1, weights(da2)))
@same_behavior mean(da1, weights(da2); skipna=true) mean(da1.data[keep], weights(da2.data[keep]))
end
2 changes: 1 addition & 1 deletion test/reducedim.jl
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Expand Up @@ -109,7 +109,7 @@ macro test_da_approx_eq(da1, da2)
v2 = $(esc(da2))
na = isna(v1)
@test na == isna(v2)
defined = !na
defined = (!).(na)
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.!na should work here as you've written it elsewhere, right?

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Nope. Apparently, this form is required in macros. I tried the other version first.

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That's... bizarre. Maybe file a Base issue?

if any(defined)
@test isapprox(v1[defined], v2[defined], nans = true)
end
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2 changes: 1 addition & 1 deletion test/sort.jl
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Expand Up @@ -17,7 +17,7 @@ for T in (Float64, BigFloat)
nna = sum(na)
a = Vector{T}(n)
ra = randn(n-nna)
a[!na] = ra
a[.!na] = ra
for da in (DataArray(a, na), PooledDataArray(a, na), (pda = PooledDataArray(a, na); setlevels!(pda, shuffle!(pda.pool))))
@test isequal(sort(da), [DataArray(sort(dropna(da))); DataArray(T, nna)])
@test isequal(sort(da; lt=(x,y)->isless(x,y)), [DataArray(sort(dropna(da))); DataArray(T, nna)])
Expand Down