Fix depwarns

Author: YingboMa

REQUIRE

@@ -3,3 +3,4 @@ ParameterizedFunctions 2.0.0
 DiffEqBase 3.0.3
 DiffEqOperators
 DiffEqBiological
+Reexport

src/DiffEqProblemLibrary.jl

@@ -2,22 +2,20 @@ __precompile__(true)
 
 module DiffEqProblemLibrary
 
-const file = @__FILE__
-
 module ODEProblemLibrary
-importodeproblems() = @eval ODEProblemLibrary include(joinpath(@__DIR__, "ode/ode_premade_problems.jl"))
+importodeproblems() = @eval include(joinpath(@__DIR__, "ode/ode_premade_problems.jl"))
 end # module
 module DAEProblemLibrary
-importdaeproblems() = @eval DAEProblemLibrary include(joinpath(@__DIR__, "dae_premade_problems.jl"))
+importdaeproblems() = @eval include(joinpath(@__DIR__, "dae_premade_problems.jl"))
 end # module
 module DDEProblemLibrary
-importddeproblems() = @eval DDEProblemLibrary include(joinpath(@__DIR__, "dde_premade_problems.jl"))
+importddeproblems() = @eval include(joinpath(@__DIR__, "dde_premade_problems.jl"))
 end # module
 module SDEProblemLibrary
-importsdeproblems() = @eval SDEProblemLibrary include(joinpath(@__DIR__, "sde_premade_problems.jl"))
+importsdeproblems() = @eval include(joinpath(@__DIR__, "sde_premade_problems.jl"))
 end # module
 module JumpProblemLibrary
-importjumpproblems() = @eval JumpProblemLibrary include(joinpath(@__DIR__, "src/jump_premade_problems.jl"))
+importjumpproblems() = @eval include(joinpath(@__DIR__, "src/jump_premade_problems.jl"))
 end # module
 
 end # module

src/ode/brusselator_prob.jl

@@ -13,7 +13,7 @@ function brusselator_2d_loop(du, u, p, t)
   @inbounds begin
     A, B, α, xyd, dx, N = p
     α = α/dx^2
-    for I in CartesianRange((N, N))
+    for I in CartesianIndices((N, N))
       x = xyd[I[1]]
       y = xyd[I[2]]
       i = I[1]
@@ -25,7 +25,7 @@ function brusselator_2d_loop(du, u, p, t)
       du[i,j,1] = α*(u[im1,j,1] + u[ip1,j,1] + u[i,jp1,1] + u[i,jm1,1] - 4u[i,j,1]) +
       B + u[i,j,1]^2*u[i,j,2] - (A + 1)*u[i,j,1] + brusselator_f(x, y, t)
     end
-    for I in CartesianRange((N, N))
+    for I in CartesianIndices((N, N))
       i = I[1]
       j = I[2]
       ip1 = limit(i+1, N)
@@ -40,17 +40,17 @@ end
 function init_brusselator_2d(xyd)
   N = length(xyd)
   u = zeros(N, N, 2)
-  for I in CartesianRange((N, N))
+  for I in CartesianIndices((N, N))
     x = xyd[I[1]]
     y = xyd[I[2]]
     u[I,1] = 22*(y*(1-y))^(3/2)
     u[I,2] = 27*(x*(1-x))^(3/2)
   end
   u
 end
-xyd_brusselator = linspace(0,1,32)
+xyd_brusselator = range(0,stop=1,length=32)
 
-doc"""
+@doc @doc doc"""
 2D Brusselator
 
 ```math
@@ -120,15 +120,15 @@ function brusselator_1d(du, u_, p, t)
 end
 function init_brusselator_1d(N)
   u = zeros(N, 2)
-  x = linspace(0, 1, N)
+  x = range(0, stop=1, length=N)
   for i in 1:N
     u[i, 1] = 1 + sin(2pi*x[i])
     u[i, 2] = 3.
   end
   u
 end
 
-doc"""
+@doc @doc doc"""
 1D Brusselator
 
 ```math

src/ode/filament_prob.jl

@@ -35,14 +35,14 @@ struct NoHydroProjectionCache <: AbstractInextensibilityCache
     J         :: Matrix{T}
     P         :: Matrix{T}
     J_JT      :: Matrix{T}
-    J_JT_LDLT :: Base.LinAlg.LDLt{T, SymTridiagonal{T}}
+    J_JT_LDLT :: LinearAlgebra.LDLt{T, SymTridiagonal{T}}
     P0        :: Matrix{T}
 
     NoHydroProjectionCache(N::Int) = new(
         zeros(N, 3*(N+1)),          # J
         zeros(3*(N+1), 3*(N+1)),    # P
         zeros(N,N),                 # J_JT
-        Base.LinAlg.LDLt{T,SymTridiagonal{T}}(SymTridiagonal(zeros(N), zeros(N-1))),
+        LinearAlgebra.LDLt{T,SymTridiagonal{T}}(SymTridiagonal(zeros(N), zeros(N-1))),
         zeros(N, 3*(N+1))
     )
 end
@@ -66,7 +66,7 @@ function FilamentCache(N=20; Cm=32, ω=200, Solver=SolverDiffEq)
 end
 function stiffness_matrix!(f::AbstractFilamentCache)
     N, μ, A = f.N, f.μ, f.A
-    A[:] = eye(3*(N+1))
+    A[:] = Matrix{Float64}(I, 3*(N+1), 3*(N+1))
     for i in 1 : 3
         A[i,i] =    1
         A[i,3+i] = -2
@@ -94,7 +94,7 @@ function stiffness_matrix!(f::AbstractFilamentCache)
             A[3*j+i,3*(j+2)+i] =  1
         end
     end
-    scale!(A, -μ^4)
+    rmul!(A, -μ^4)
     nothing
 end
 function update_separate_coordinates!(f::AbstractFilamentCache, r)
@@ -126,7 +126,7 @@ end
 function initialize!(initial_conf_type::Symbol, f::AbstractFilamentCache)
     N, x, y, z = f.N, f.x, f.y, f.z
     if initial_conf_type == :StraightX
-        x[:] = linspace(0, 1, N+1)
+        x[:] = range(0, stop=1, length=N+1)
         y[:] = 0 .* x
         z[:] = 0 .* x
     else
@@ -192,14 +192,14 @@ function projection!(f::FilamentCache)
 end
 
 function subtract_from_identity!(A)
-    scale!(-1, A)
+    rmul!(-1, A)
     @inbounds for i in 1 : size(A,1)
         A[i,i] += 1
     end
     nothing
 end
 
-function LDLt_inplace!{T<:Real}(L::Base.LinAlg.LDLt{T,SymTridiagonal{T}}, A::Matrix{T})
+function LDLt_inplace!(L::LinearAlgebra.LDLt{T,SymTridiagonal{T}}, A::Matrix{T}) where {T<:Real}
     n = size(A,1)
     dv, ev = L.data.dv, L.data.ev
     @inbounds for (i,d) in enumerate(diagind(A))

src/ode/ode_premade_problems.jl

@@ -1,4 +1,5 @@
-using DiffEqBase, ParameterizedFunctions, DiffEqOperators, Random, LinearAlgebra
+using DiffEqBase, ParameterizedFunctions, DiffEqOperators, Random, LinearAlgebra,
+      Markdown
 
 srand(100)
 

src/ode/pollution_prob.jl

@@ -188,7 +188,7 @@ u0[8]  = 0.3
 u0[9]  = 0.01
 u0[17] = 0.007
 
-doc"""
+@doc doc"""
 [Pollution Problem](http://nbviewer.jupyter.org/github/JuliaDiffEq/DiffEqBenchmarks.jl/blob/master/StiffODE/Pollution.ipynb) (Stiff)
 
 This IVP is a stiff system of 20 non-linear Ordinary Differential Equations. It is in the form of ``\\frac{dy}{dt}=f(y), \\quad y(0)=y0,`` with

src/sde_premade_problems.jl

@@ -14,7 +14,7 @@ f = (u,p,t) -> 1.01u
 σ = (u,p,t) -> 0.87u
 (ff::typeof(f))(::Type{Val{:analytic}},u0,p,t,W) = u0.*exp.(0.63155t+0.87W)
 
-doc"""
+@doc doc"""
 ```math
 du_t = βudt + αudW_t
 ```
@@ -43,7 +43,7 @@ end
   end
 end
 (ff::typeof(f))(::Type{Val{:analytic}},u0,p,t,W) = u0.*exp.(0.63155*t+0.87*W)
-doc"""
+@doc doc"""
 8 linear SDEs (as a 4x2 matrix):
 
 ```math
@@ -73,7 +73,7 @@ prob_sde_2Dlinear_stratonovich = SDEProblem(f,σ,ones(4,2)/2,(0.0,1.0))
 f = (u,p,t) -> -.25*u*(1-u^2)
 σ = (u,p,t) -> .5*(1-u^2)
 (ff::typeof(f))(::Type{Val{:analytic}},u0,p,t,W) = ((1+u0).*exp.(W)+u0-1)./((1+u0).*exp.(W)+1-u0)
-doc"""
+@doc doc"""
 ```math
 du_t = \\frac{1}{4}u(1-u^2)dt + \\frac{1}{2}(1-u^2)dW_t
 ```
@@ -89,7 +89,7 @@ prob_sde_cubic = SDEProblem(f,σ,1/2,(0.0,1.0))
 f = (u,p,t) -> -0.01*sin.(u).*cos.(u).^3
 σ = (u,p,t) -> 0.1*cos.(u).^2
 (ff::typeof(f))(::Type{Val{:analytic}},u0,p,t,W) = atan.(0.1*W + tan.(u0))
-doc"""
+@doc doc"""
 ```math
 du_t = -\\frac{1}{100}\sin(u)\cos^3(u)dt + \\frac{1}{10}\cos^{2}(u_t) dW_t
 ```
@@ -107,7 +107,7 @@ f = (u,p,t) -> p[2]./sqrt.(1+t) - u./(2*(1+t))
 p = (0.1,0.05)
 (ff::typeof(f))(::Type{Val{:analytic}},u0,p,t,W) = u0./sqrt.(1+t) + p[2]*(t+p[1]*W)./sqrt.(1+t)
 
-doc"""
+@doc doc"""
 Additive noise problem
 
 ```math
@@ -137,7 +137,7 @@ end
 end
 (ff::typeof(f))(::Type{Val{:analytic}},u0,p,t,W) = u0./sqrt(1+t) + sde_wave_βvec.*(t+sde_wave_αvec.*W)./sqrt(1+t)
 
-doc"""
+@doc doc"""
 A multiple dimension extension of `additiveSDEExample`
 
 """
@@ -154,7 +154,7 @@ end σ ρ β
     du[i] = 3.0 #Additive
   end
 end
-doc"""
+@doc doc"""
 Lorenz Attractor with additive noise
 
 ```math
@@ -173,7 +173,7 @@ prob_sde_lorenz = SDEProblem(f,σ,ones(3),(0.0,10.0),(10.0,28.0,2.66))
 f = (u,p,t) -> (1/3)*u^(1/3) + 6*u^(2/3)
 σ = (u,p,t) -> u^(2/3)
 (ff::typeof(f))(::Type{Val{:analytic}},u0,p,t,W) = (2t + 1 + W/3)^3
-doc"""
+@doc doc"""
 Runge–Kutta methods for numerical solution of stochastic differential equations
 Tocino and Ardanuy
 """
@@ -346,7 +346,7 @@ function stiff_quad_f_strat(::Type{Val{:analytic}},u0,p,t,W)
     (tmp*exp_tmp + u0 - 1)/(tmp*exp_tmp - u0 + 1)
 end
 
-doc"""
+@doc doc"""
 The composite Euler method for stiff stochastic
 differential equations
 
@@ -365,7 +365,7 @@ Higher α or β is stiff, with α being deterministic stiffness and
 """
 prob_sde_stiffquadito = SDEProblem(stiff_quad_f_ito,stiff_quad_g,0.5,(0.0,3.0),(1.0,1.0))
 
-doc"""
+@doc doc"""
 The composite Euler method for stiff stochastic
 differential equations
 
@@ -384,7 +384,7 @@ Higher α or β is stiff, with α being deterministic stiffness and
 """
 prob_sde_stiffquadstrat = SDEProblem(stiff_quad_f_strat,stiff_quad_g,0.5,(0.0,3.0),(1.0,1.0))
 
-doc"""
+@doc doc"""
 Stochastic Heat Equation with scalar multiplicative noise
 
 S-ROCK: CHEBYSHEV METHODS FOR STIFF STOCHASTIC