accomodate Julia v0.6

OpenMendel · Jun 30, 2017 · a6940da · a6940da
1 parent a9327f1
commit a6940da
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diff --git a/.travis.yml b/.travis.yml
@@ -4,8 +4,8 @@ os:
  - linux
  - osx
 julia:
- - 0.4
  - 0.5
+ - 0.6
  - nightly
 matrix:
  allow_failures:
@@ -23,6 +23,6 @@ after_success:
 notifications:
  email:
  on_success: change
- on_failure: always 
+ on_failure: always
  recipients:
  - [email protected]
diff --git a/README.md b/README.md
@@ -17,7 +17,7 @@ Within Julia, use the package manager to install VarianceComponentModels:
 
  Pkg.clone("https://github.com/OpenMendel/VarianceComponentModels.jl.git")
 
-This package supports Julia v0.4 and v0.5.
+This package supports Julia v0.5 and v0.6.
 
 ## Citation
 

diff --git a/REQUIRE b/REQUIRE
@@ -1,4 +1,4 @@
-julia 0.4
+julia 0.5
 
 Compat
 MathProgBase

diff --git a/appveyor.yml b/appveyor.yml
@@ -1,9 +1,9 @@
 environment:
  matrix:
- - JULIAVERSION: "julialang/bin/winnt/x86/0.4/julia-0.4-latest-win32.exe"
- - JULIAVERSION: "julialang/bin/winnt/x64/0.4/julia-0.4-latest-win64.exe"
- - JULIAVERSION: "julialang/bin/winnt/x86/0.5/julia-0.5-latest-win32.exe"
- - JULIAVERSION: "julialang/bin/winnt/x64/0.5/julia-0.5-latest-win64.exe"
+ - JULIAVERSION: "julialang/bin/winnt/x86/0.4/julia-0.5-latest-win32.exe"
+ - JULIAVERSION: "julialang/bin/winnt/x64/0.4/julia-0.5-latest-win64.exe"
+ - JULIAVERSION: "julialang/bin/winnt/x86/0.5/julia-0.6-latest-win32.exe"
+ - JULIAVERSION: "julialang/bin/winnt/x64/0.5/julia-0.6-latest-win64.exe"
  #- JULIAVERSION: "julianightlies/bin/winnt/x86/julia-latest-win32.exe"
  #- JULIAVERSION: "julianightlies/bin/winnt/x64/julia-latest-win64.exe"
 

diff --git a/src/VarianceComponentModels.jl b/src/VarianceComponentModels.jl
@@ -122,7 +122,7 @@ function TwoVarCompModelRotate{T, BT, ΣT}(
  λ = convert(Vector{T}, F.values)
  Φ = convert(Matrix{T}, F.vectors)
  # correct negative eigenvalues due to roundoff
- map!(x -> max(x, zero(T)), λ)
+ @compat map!(x -> max(x, zero(T)), λ, λ)
  Brot = isempty(vcm.B) ? Array{T}(size(vcm.B)) : vcm.B * Φ
  logdetΣ2 = convert(T, logdet(vcm.Σ[2]))
  TwoVarCompModelRotate{T, BT}(Brot, λ, Φ, logdetΣ2)

diff --git a/src/two_variance_component.jl b/src/two_variance_component.jl
@@ -904,11 +904,11 @@ function mle_fs!{T1<:VarianceComponentModel, T2<:TwoVarCompVariateRotate}(
  Bcov = zeros(T, nmean, nmean)
  Bcov = pinv(fisher_B(vcmodel, vcdatarot, dd.vcaux))
  Bse = similar(vcmodel.B)
- copy!(Bse, sqrt(diag(Bcov)))
+ copy!(Bse, sqrt.(diag(Bcov)))
  Σcov = pinv(fisher_Σ(vcmodel, vcdatarot))
  Σse = (zeros(T, d, d), zeros(T, d, d))
- copy!(Σse[1], sqrt(diag(view(Σcov, 1:d^2, 1:d^2))))
- copy!(Σse[2], sqrt(diag(view(Σcov, d^2+1:2d^2, d^2+1:2d^2))))
+ copy!(Σse[1], sqrt.(diag(view(Σcov, 1:d^2, 1:d^2))))
+ copy!(Σse[2], sqrt.(diag(view(Σcov, d^2+1:2d^2, d^2+1:2d^2))))
 
  # output
  maxlogl, vcmodel, Σse, Σcov, Bse, Bcov
@@ -1105,11 +1105,11 @@ function mle_mm!{T1 <: VarianceComponentModel, T2 <: TwoVarCompVariateRotate}(
  # standard errors
  Bcov = pinv(fisher_B(vcm, vcdatarot, vcaux))
  Bse = similar(vcm.B)
- copy!(Bse, sqrt(diag(Bcov)))
+ copy!(Bse, sqrt.(diag(Bcov)))
  Σcov = pinv(fisher_Σ(vcm, vcdatarot))
  Σse = (zeros(T, d, d), zeros(T, d, d))
- copy!(Σse[1], sqrt(diag(view(Σcov, 1:d^2, 1:d^2))))
- copy!(Σse[2], sqrt(diag(view(Σcov, d^2+1:2d^2, d^2+1:2d^2))))
+ copy!(Σse[1], sqrt.(diag(view(Σcov, 1:d^2, 1:d^2))))
+ copy!(Σse[2], sqrt.(diag(view(Σcov, d^2+1:2d^2, d^2+1:2d^2))))
 
  # output
  logl, vcm, Σse, Σcov, Bse, Bcov
@@ -1240,7 +1240,7 @@ function fit_reml!{
  # standard errors and covariance of mean parameters
  Bcov = inv(fisher_B(vcmodel, vcdatarot, vcaux))
  Bse = similar(vcmodel.B)
- copy!(Bse, sqrt(diag(Bcov)))
+ copy!(Bse, sqrt.(diag(Bcov)))
 
  # output
  logpdf(vcmodel, vcdatarot), vcmodel, Σse, Σcov, Bse, Bcov

diff --git a/test/REQUIRE b/test/REQUIRE
@@ -1 +0,0 @@
-BaseTestNext

diff --git a/test/multivariate_calculus_test.jl b/test/multivariate_calculus_test.jl
@@ -1,7 +1,6 @@
 module MultivariateCalculusTest
 
-using VarianceComponentModels
-using BaseTestNext
+using VarianceComponentModels, Base.Test
 
 srand(123)
 
@@ -102,7 +101,7 @@ end
  dX2[i, j] = vecdot(Y, M[(i-1)*p+1:i*p, (j-1)*r+1:j*r])
  end
  end
- @test_approx_eq_eps vecnorm(dX1 - vec(dX2)) 0.0 1.0e-8
+ @test vecnorm(dX1 - vec(dX2)) < 1e-8
 end
 
 # test duplication

diff --git a/test/two_variance_component_test.jl b/test/two_variance_component_test.jl
@@ -1,7 +1,6 @@
 module TwoVarianceComponentTest
 
-using VarianceComponentModels, MathProgBase, Ipopt
-using BaseTestNext
+using VarianceComponentModels, MathProgBase, Ipopt, Base.Test
 
 srand(123)
 

diff --git a/test/variance_component_models_test.jl b/test/variance_component_models_test.jl
@@ -1,7 +1,6 @@
 module VarianceComponentTypeTest
 
-using VarianceComponentModels, MathProgBase, Ipopt
-using BaseTestNext
+using VarianceComponentModels, MathProgBase, Ipopt, Base.Test
 
 srand(123)