test a non-parameterized function

.gitignore

@@ -1,3 +1,4 @@
 *.jl.cov
 *.jl.*.cov
 *.jl.mem
+Manifest.toml

Project.toml

@@ -5,12 +5,14 @@ version = "0.1.0"
 [deps]
 DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
 MATLAB = "10e44e05-a98a-55b3-a45b-ba969058deb6"
+ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 
 [compat]
-Reexport = "0.2"
 DiffEqBase = "6.5"
 MATLAB = "0.7"
+ModelingToolkit = "1.4.2"
+Reexport = "0.2"
 julia = "1"
 
 [extras]

README.md

@@ -23,20 +23,19 @@ Pkg.add("MATLABDiffEq")
 
 ## Using MATLABDiffEq.jl
 
-MATLABDiffEq.jl is simply a solver on the DiffEq common interface, so for details see the [DifferentialEquations.jl documentation](https://juliadiffeq.github.io/DiffEqDocs.jl/dev/). However, there are three things to know:
+MATLABDiffEq.jl is simply a solver on the DiffEq common interface, so for details see the [DifferentialEquations.jl documentation](https://juliadiffeq.github.io/DiffEqDocs.jl/dev/).
+However, the only options implemented are those for error calculations
+(`timeseries_error`), `saveat` and tolerances.
 
-1. The only options implemented are those for error calculations (`timeseries_error`), `saveat` and tolerances.
-2. The input function must be defined by a `ParameterizedFunction`
-3. The input function must not use parameters
-
-Note that the algorithms are defined to have the same name as the MATLAB algorithms, but are not exported. Thus to use `ode45`, you would specify the algorithm as `MATLABDiffEq.ode45()`.
+Note that the algorithms are defined to have the same name as the MATLAB algorithms,
+but are not exported. Thus to use `ode45`, you would specify the algorithm as
+`MATLABDiffEq.ode45()`.
 
 ## Example
 
 ```julia
 using MATLABDiffEq, ParameterizedFunctions
 
-
 f = @ode_def LotkaVolterra begin
   dx = 1.5x - x*y
   dy = -3y + x*y
@@ -46,6 +45,16 @@ tspan = (0.0,10.0)
 u0 = [1.0,1.0]
 prob = ODEProblem(f,u0,tspan)
 sol = solve(prob,MATLABDiffEq.ode45())
+
+function lorenz(du,u,p,t)
+   du[1] = 10.0(u[2]-u[1])
+   du[2] = u[1]*(28.0-u[3]) - u[2]
+   du[3] = u[1]*u[2] - (8/3)*u[3]
+end
+u0 = [1.0;0.0;0.0]
+tspan = (0.0,100.0)
+prob = ODEProblem(lorenz,u0,tspan)
+sol = solve(prob,MATLABDiffEq.ode45())
 ```
 
 ## Measuring Overhead
@@ -56,7 +65,7 @@ is done. Thus you can simply call the same ODE function and time it
 directly. This is done by:
 
 ```julia
-@time MATLABDiffEq.eval_string("[t,u] = $(algstr)(f,tspan,u0,options);")
+@time MATLABDiffEq.eval_string("[t,u] = $(algstr)(diffeqf,tspan,u0,options);")
 ```
 
 To be even more pedantic, you can play around in the actual MATLAB
@@ -97,16 +106,16 @@ julia> @time sol = solve(prob,alg);
 julia> @time sol = solve(prob,alg);
   0.065460 seconds (38.84 k allocations: 1.556 MB)
 
-julia> @time MATLABDiffEq.eval_string("[t,u] = $(algstr)(f,tspan,u0,options);")
+julia> @time MATLABDiffEq.eval_string("[t,u] = $(algstr)(diffeqf,tspan,u0,options);")
   0.058249 seconds (11 allocations: 528 bytes)
 
-julia> @time MATLABDiffEq.eval_string("[t,u] = $(algstr)(f,tspan,u0,options);")
+julia> @time MATLABDiffEq.eval_string("[t,u] = $(algstr)(diffeqf,tspan,u0,options);")
   0.060367 seconds (11 allocations: 528 bytes)
 
-julia> @time MATLABDiffEq.eval_string("[t,u] = $(algstr)(f,tspan,u0,options);")
+julia> @time MATLABDiffEq.eval_string("[t,u] = $(algstr)(diffeqf,tspan,u0,options);")
   0.060171 seconds (11 allocations: 528 bytes)
 
-julia> @time MATLABDiffEq.eval_string("[t,u] = $(algstr)(f,tspan,u0,options);")
+julia> @time MATLABDiffEq.eval_string("[t,u] = $(algstr)(diffeqf,tspan,u0,options);")
   0.058928 seconds (11 allocations: 528 bytes)
 ```
 

src/MATLABDiffEq.jl

@@ -2,7 +2,7 @@ module MATLABDiffEq
 
 using Reexport
 @reexport using DiffEqBase
-using MATLAB
+using MATLAB, ModelingToolkit
 
 abstract type MATLABAlgorithm <: DiffEqBase.AbstractODEAlgorithm end
 struct ode23 <: MATLABAlgorithm end
@@ -22,10 +22,6 @@ function DiffEqBase.__solve(
 
     tType = eltype(tupType)
 
-    if !(typeof(prob.f) <: DiffEqBase.AbstractParameterizedFunction)
-      error("Functions must be defined via ParameterizedFunctions.jl to work with this package.")
-    end
-
     if prob.tspan[end]-prob.tspan[1]<tType(0)
         error("final time must be greater than starting time. Aborting.")
     end
@@ -43,15 +39,11 @@ function DiffEqBase.__solve(
         u0 = prob.u0
     end
 
-    strs = [string(f.fex.args[2i].args[2]) for i in 1:length(f.syms)]
-    matstr = ""
-    for i in 1:length(strs)
-      matstr *= strs[i]
-      i < length(strs) && (matstr *= "; ")
-    end
-    matstr = replace(matstr,"["=>"(")
-    matstr = replace(matstr,"]"=>")")
-    matstr = "f = @(t,internal_var___u) ["*matstr*"];"
+	sys = first(modelingtoolkitize(prob))
+
+    matstr = ModelingToolkit.build_function(sys.eqs,sys.dvs,
+										    sys.ps,sys.iv,
+										    target = ModelingToolkit.MATLABTarget())
 
     # Send the variables
     put_variable(get_default_msession(),:tspan,tspan)
@@ -66,7 +58,7 @@ function DiffEqBase.__solve(
     eval_string("options = odeset('RelTol',reltol,'AbsTol',abstol);")
     algstr = string(typeof(alg).name.name)
     #algstr = replace(string(typeof(alg)),"MATLABDiffEq.","")
-    eval_string("[t,u] = $(algstr)(f,tspan,u0,options);")
+    eval_string("[t,u] = $(algstr)(diffeqf,tspan,u0,options);")
     ts = jvector(get_mvariable(:t))
     timeseries_tmp = jarray(get_mvariable(:u))
 

test/runtests.jl

@@ -8,7 +8,16 @@ p = [1.5,1,3,1]
 tspan = (0.0,10.0)
 u0 = [1.0,1.0]
 prob = ODEProblem(f,u0,tspan,p)
+sol = solve(prob,MATLABDiffEq.ode45())
 
+function lorenz(du,u,p,t)
+ du[1] = 10.0(u[2]-u[1])
+ du[2] = u[1]*(28.0-u[3]) - u[2]
+ du[3] = u[1]*u[2] - (8/3)*u[3]
+end
+u0 = [1.0;0.0;0.0]
+tspan = (0.0,100.0)
+prob = ODEProblem(lorenz,u0,tspan)
 sol = solve(prob,MATLABDiffEq.ode45())
 
 algs = [MATLABDiffEq.ode23