Merge pull request #42 from ChrisRackauckas/fix-formatting

Apply JuliaFormatter to fix code formatting

Author: ChrisRackauckas

.JuliaFormatter.toml

@@ -0,0 +1,3 @@
+style = "sciml"
+format_markdown = true
+format_docstrings = true

LICENSE.md

@@ -19,4 +19,3 @@ The MATLABDiffEq.jl package is licensed under the MIT "Expat" License:
 > LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 > OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 > SOFTWARE.
-> 

README.md

@@ -37,24 +37,24 @@ but are not exported. Thus to use `ode45`, you would specify the algorithm as
 using MATLABDiffEq, ParameterizedFunctions
 
 f = @ode_def LotkaVolterra begin
-  dx = 1.5x - x*y
-  dy = -3y + x*y
+    dx = 1.5x - x*y
+    dy = -3y + x*y
 end
 
-tspan = (0.0,10.0)
-u0 = [1.0,1.0]
-prob = ODEProblem(f,u0,tspan)
-sol = solve(prob,MATLABDiffEq.ode45())
+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]
+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())
+u0 = [1.0; 0.0; 0.0]
+tspan = (0.0, 100.0)
+prob = ODEProblem(lorenz, u0, tspan)
+sol = solve(prob, MATLABDiffEq.ode45())
 ```
 
 ## Measuring Overhead
@@ -82,28 +82,28 @@ Generally, for long enough problems the overhead is minimal. Example:
 ```julia
 using DiffEqBase, ParameterizedFunctions, MATLABDiffEq
 f = @ode_def_bare RigidBodyBench begin
-  dy1  = -2*y2*y3
-  dy2  = 1.25*y1*y3
-  dy3  = -0.5*y1*y2 + 0.25*sin(t)^2
+    dy1 = -2*y2*y3
+    dy2 = 1.25*y1*y3
+    dy3 = -0.5*y1*y2 + 0.25*sin(t)^2
 end
-prob = ODEProblem(f,[1.0;0.0;0.9],(0.0,100.0))
+prob = ODEProblem(f, [1.0; 0.0; 0.9], (0.0, 100.0))
 alg = MATLABDiffEq.ode45()
 algstr = string(typeof(alg).name.name)
 ```
 
 For this, we get the following:
 
 ```julia
-julia> @time sol = solve(prob,alg);
+julia> @time sol = solve(prob, alg);
   0.063918 seconds (38.84 k allocations: 1.556 MB)
 
-julia> @time sol = solve(prob,alg);
+julia> @time sol = solve(prob, alg);
   0.062600 seconds (38.84 k allocations: 1.556 MB)
 
-julia> @time sol = solve(prob,alg);
+julia> @time sol = solve(prob, alg);
   0.061438 seconds (38.84 k allocations: 1.556 MB)
 
-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)(diffeqf,tspan,u0,options);")
@@ -131,14 +131,14 @@ MATLAB solvers do outperform that of Python and R.
 
 ```julia
 f = @ode_def_bare LotkaVolterra begin
-  dx = a*x - b*x*y
-  dy = -c*y + d*x*y
+    dx = a*x - b*x*y
+    dy = -c*y + d*x*y
 end a b c d
-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,Vern7(),abstol=1/10^14,reltol=1/10^14)
+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, Vern7(), abstol = 1/10^14, reltol = 1/10^14)
 test_sol = TestSolution(sol)
 
 setups = [Dict(:alg=>DP5())
@@ -152,32 +152,29 @@ setups = [Dict(:alg=>DP5())
           Dict(:alg=>SciPyDiffEq.odeint())
           Dict(:alg=>deSolveDiffEq.lsoda())
           Dict(:alg=>deSolveDiffEq.ode45())
-          Dict(:alg=>CVODE_Adams())
-  ]
-
-names = [
-  "Julia: DP5"
-  "Hairer: dopri5"
-  "Julia: Tsit5"
-  "Julia: Vern7"
-  "MATLAB: ode45"
-  "MATLAB: ode113"
-  "SciPy: RK45"
-  "SciPy: LSODA"
-  "SciPy: odeint"
-  "deSolve: lsoda"
-  "deSolve: ode45"
-  "Sundials: Adams"
-  ]
+          Dict(:alg=>CVODE_Adams())]
+
+names = ["Julia: DP5"
+         "Hairer: dopri5"
+         "Julia: Tsit5"
+         "Julia: Vern7"
+         "MATLAB: ode45"
+         "MATLAB: ode113"
+         "SciPy: RK45"
+         "SciPy: LSODA"
+         "SciPy: odeint"
+         "deSolve: lsoda"
+         "deSolve: ode45"
+         "Sundials: Adams"]
 
 abstols = 1.0 ./ 10.0 .^ (6:13)
 reltols = 1.0 ./ 10.0 .^ (3:10)
-wp = WorkPrecisionSet(prob,abstols,reltols,setups;
-                      names = names,
-                      appxsol=test_sol,dense=false,
-                      save_everystep=false,numruns=100,maxiters=10000000,
-                      timeseries_errors=false,verbose=false)
-plot(wp,title="Non-stiff 1: Lotka-Volterra")
+wp = WorkPrecisionSet(prob, abstols, reltols, setups;
+    names = names,
+    appxsol = test_sol, dense = false,
+    save_everystep = false, numruns = 100, maxiters = 10000000,
+    timeseries_errors = false, verbose = false)
+plot(wp, title = "Non-stiff 1: Lotka-Volterra")
 ```
 
 ![](https://user-images.githubusercontent.com/1814174/71537082-ef42ac00-28e4-11ea-9acc-67dfd9990221.png)
@@ -186,12 +183,12 @@ plot(wp,title="Non-stiff 1: Lotka-Volterra")
 
 ```julia
 f = @ode_def_bare RigidBodyBench begin
-  dy1  = -2*y2*y3
-  dy2  = 1.25*y1*y3
-  dy3  = -0.5*y1*y2 + 0.25*sin(t)^2
+    dy1 = -2*y2*y3
+    dy2 = 1.25*y1*y3
+    dy3 = -0.5*y1*y2 + 0.25*sin(t)^2
 end
-prob = ODEProblem(f,[1.0;0.0;0.9],(0.0,100.0))
-sol = solve(prob,Vern7(),abstol=1/10^14,reltol=1/10^14)
+prob = ODEProblem(f, [1.0; 0.0; 0.9], (0.0, 100.0))
+sol = solve(prob, Vern7(), abstol = 1/10^14, reltol = 1/10^14)
 test_sol = TestSolution(sol)
 
 setups = [Dict(:alg=>DP5())
@@ -205,32 +202,29 @@ setups = [Dict(:alg=>DP5())
           Dict(:alg=>SciPyDiffEq.odeint())
           Dict(:alg=>deSolveDiffEq.lsoda())
           Dict(:alg=>deSolveDiffEq.ode45())
-          Dict(:alg=>CVODE_Adams())
-  ]
-
-names = [
-  "Julia: DP5"
-  "Hairer: dopri5"
-  "Julia: Tsit5"
-  "Julia: Vern7"
-  "MATLAB: ode45"
-  "MATLAB: ode113"
-  "SciPy: RK45"
-  "SciPy: LSODA"
-  "SciPy: odeint"
-  "deSolve: lsoda"
-  "deSolve: ode45"
-  "Sundials: Adams"
-  ]
+          Dict(:alg=>CVODE_Adams())]
+
+names = ["Julia: DP5"
+         "Hairer: dopri5"
+         "Julia: Tsit5"
+         "Julia: Vern7"
+         "MATLAB: ode45"
+         "MATLAB: ode113"
+         "SciPy: RK45"
+         "SciPy: LSODA"
+         "SciPy: odeint"
+         "deSolve: lsoda"
+         "deSolve: ode45"
+         "Sundials: Adams"]
 
 abstols = 1.0 ./ 10.0 .^ (6:13)
 reltols = 1.0 ./ 10.0 .^ (3:10)
-wp = WorkPrecisionSet(prob,abstols,reltols,setups;
-                      names = names,
-                      appxsol=test_sol,dense=false,
-                      save_everystep=false,numruns=100,maxiters=10000000,
-                      timeseries_errors=false,verbose=false)
-plot(wp,title="Non-stiff 2: Rigid-Body")
+wp = WorkPrecisionSet(prob, abstols, reltols, setups;
+    names = names,
+    appxsol = test_sol, dense = false,
+    save_everystep = false, numruns = 100, maxiters = 10000000,
+    timeseries_errors = false, verbose = false)
+plot(wp, title = "Non-stiff 2: Rigid-Body")
 ```
 
 ![](https://user-images.githubusercontent.com/1814174/71537083-ef42ac00-28e4-11ea-8dc7-a5dca0518baf.png)
@@ -239,12 +233,12 @@ plot(wp,title="Non-stiff 2: Rigid-Body")
 
 ```julia
 rober = @ode_def begin
-  dy₁ = -k₁*y₁+k₃*y₂*y₃
-  dy₂ =  k₁*y₁-k₂*y₂^2-k₃*y₂*y₃
-  dy₃ =  k₂*y₂^2
+    dy₁ = -k₁*y₁+k₃*y₂*y₃
+    dy₂ = k₁*y₁-k₂*y₂^2-k₃*y₂*y₃
+    dy₃ = k₂*y₂^2
 end k₁ k₂ k₃
-prob = ODEProblem(rober,[1.0,0.0,0.0],(0.0,1e5),[0.04,3e7,1e4])
-sol = solve(prob,CVODE_BDF(),abstol=1/10^14,reltol=1/10^14)
+prob = ODEProblem(rober, [1.0, 0.0, 0.0], (0.0, 1e5), [0.04, 3e7, 1e4])
+sol = solve(prob, CVODE_BDF(), abstol = 1/10^14, reltol = 1/10^14)
 test_sol = TestSolution(sol)
 
 abstols = 1.0 ./ 10.0 .^ (7:8)
@@ -261,30 +255,27 @@ setups = [Dict(:alg=>Rosenbrock23())
           Dict(:alg=>SciPyDiffEq.BDF())
           Dict(:alg=>SciPyDiffEq.odeint())
           Dict(:alg=>deSolveDiffEq.lsoda())
-          Dict(:alg=>CVODE_BDF())
-          ]
-
-names = [
-  "Julia: Rosenbrock23"
-  "Julia: TRBDF2"
-  "Julia: radau"
-  "Hairer: rodas"
-  "Hairer: radau"
-  "MATLAB: ode23s"
-  "MATLAB: ode15s"
-  "SciPy: LSODA"
-  "SciPy: BDF"
-  "SciPy: odeint"
-  "deSolve: lsoda"
-  "Sundials: CVODE"
-  ]
-
-wp = WorkPrecisionSet(prob,abstols,reltols,setups;
-                      names = names,print_names = true,
-                      dense=false,verbose = false,
-                      save_everystep=false,appxsol=test_sol,
-                      maxiters=Int(1e5))
-plot(wp,title="Stiff 1: ROBER", legend=:topleft)
+          Dict(:alg=>CVODE_BDF())]
+
+names = ["Julia: Rosenbrock23"
+         "Julia: TRBDF2"
+         "Julia: radau"
+         "Hairer: rodas"
+         "Hairer: radau"
+         "MATLAB: ode23s"
+         "MATLAB: ode15s"
+         "SciPy: LSODA"
+         "SciPy: BDF"
+         "SciPy: odeint"
+         "deSolve: lsoda"
+         "Sundials: CVODE"]
+
+wp = WorkPrecisionSet(prob, abstols, reltols, setups;
+    names = names, print_names = true,
+    dense = false, verbose = false,
+    save_everystep = false, appxsol = test_sol,
+    maxiters = Int(1e5))
+plot(wp, title = "Stiff 1: ROBER", legend = :topleft)
 ```
 
 ![](https://user-images.githubusercontent.com/1814174/71537080-ef42ac00-28e4-11ea-9abd-37631cd18ad9.png)
@@ -293,23 +284,23 @@ plot(wp,title="Stiff 1: ROBER", legend=:topleft)
 
 ```julia
 f = @ode_def Hires begin
-  dy1 = -1.71*y1 + 0.43*y2 + 8.32*y3 + 0.0007
-  dy2 = 1.71*y1 - 8.75*y2
-  dy3 = -10.03*y3 + 0.43*y4 + 0.035*y5
-  dy4 = 8.32*y2 + 1.71*y3 - 1.12*y4
-  dy5 = -1.745*y5 + 0.43*y6 + 0.43*y7
-  dy6 = -280.0*y6*y8 + 0.69*y4 + 1.71*y5 -
-           0.43*y6 + 0.69*y7
-  dy7 = 280.0*y6*y8 - 1.81*y7
-  dy8 = -280.0*y6*y8 + 1.81*y7
+    dy1 = -1.71*y1 + 0.43*y2 + 8.32*y3 + 0.0007
+    dy2 = 1.71*y1 - 8.75*y2
+    dy3 = -10.03*y3 + 0.43*y4 + 0.035*y5
+    dy4 = 8.32*y2 + 1.71*y3 - 1.12*y4
+    dy5 = -1.745*y5 + 0.43*y6 + 0.43*y7
+    dy6 = -280.0*y6*y8 + 0.69*y4 + 1.71*y5 -
+          0.43*y6 + 0.69*y7
+    dy7 = 280.0*y6*y8 - 1.81*y7
+    dy8 = -280.0*y6*y8 + 1.81*y7
 end
 
 u0 = zeros(8)
 u0[1] = 1
 u0[8] = 0.0057
-prob = ODEProblem(f,u0,(0.0,321.8122))
+prob = ODEProblem(f, u0, (0.0, 321.8122))
 
-sol = solve(prob,Rodas5(),abstol=1/10^14,reltol=1/10^14)
+sol = solve(prob, Rodas5(), abstol = 1/10^14, reltol = 1/10^14)
 test_sol = TestSolution(sol)
 
 abstols = 1.0 ./ 10.0 .^ (5:8)
@@ -326,29 +317,26 @@ setups = [Dict(:alg=>Rosenbrock23())
           Dict(:alg=>SciPyDiffEq.BDF())
           Dict(:alg=>SciPyDiffEq.odeint())
           Dict(:alg=>deSolveDiffEq.lsoda())
-          Dict(:alg=>CVODE_BDF())
-          ]
-
-names = [
-  "Julia: Rosenbrock23"
-  "Julia: TRBDF2"
-  "Julia: radau"
-  "Hairer: rodas"
-  "Hairer: radau"
-  "MATLAB: ode23s"
-  "MATLAB: ode15s"
-  "SciPy: LSODA"
-  "SciPy: BDF"
-  "SciPy: odeint"
-  "deSolve: lsoda"
-  "Sundials: CVODE"
-  ]
-
-wp = WorkPrecisionSet(prob,abstols,reltols,setups;
-                      names = names,print_names = true,
-                      save_everystep=false,appxsol=test_sol,
-                      maxiters=Int(1e5),numruns=100)
-plot(wp,title="Stiff 2: Hires",legend=:topleft)
+          Dict(:alg=>CVODE_BDF())]
+
+names = ["Julia: Rosenbrock23"
+         "Julia: TRBDF2"
+         "Julia: radau"
+         "Hairer: rodas"
+         "Hairer: radau"
+         "MATLAB: ode23s"
+         "MATLAB: ode15s"
+         "SciPy: LSODA"
+         "SciPy: BDF"
+         "SciPy: odeint"
+         "deSolve: lsoda"
+         "Sundials: CVODE"]
+
+wp = WorkPrecisionSet(prob, abstols, reltols, setups;
+    names = names, print_names = true,
+    save_everystep = false, appxsol = test_sol,
+    maxiters = Int(1e5), numruns = 100)
+plot(wp, title = "Stiff 2: Hires", legend = :topleft)
 ```
 
 ![](https://user-images.githubusercontent.com/1814174/71537081-ef42ac00-28e4-11ea-950f-59c762ce9a69.png)