vecarr_to_arr changes

SciML · Jun 16, 2017 · 62f9a56 · 62f9a56
1 parent 62d556e
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Showing 3 changed files with 7 additions and 7 deletions.
diff --git a/docs/src/analysis/parameter_estimation.md b/docs/src/analysis/parameter_estimation.md
@@ -132,10 +132,10 @@ sol = solve(prob,Tsit5())
 t = collect(linspace(0,10,200))
 randomized = [(sol(t[i]) + .01randn(2)) for i in 1:length(t)]
 using RecursiveArrayTools
-data = vecvec_to_mat(randomized)
+data = vecarr_to_arr(randomized)
 ```
 
-Here we used `vecvec_to_mat` from [RecursiveArrayTools.jl](https://github.com/ChrisRackauckas/RecursiveArrayTools.jl)
+Here we used `vecarr_to_arr` from [RecursiveArrayTools.jl](https://github.com/ChrisRackauckas/RecursiveArrayTools.jl)
 to turn the result of an ODE into a matrix.
 
 If we plot the solution with the parameter at `a=1.42`, we get the following:
@@ -253,7 +253,7 @@ sol = solve(prob,Tsit5())
 
 t = collect(linspace(0,10,200))
 randomized = [(sol(t[i]) + .01randn(2)) for i in 1:length(t)]
-data = vecvec_to_mat(randomized)
+data = vecarr_to_arr(randomized)
 
 obj = build_loss_objective(prob,Tsit5(),L2DistLoss(t,data),maxiters=10000)
 ```

diff --git a/docs/src/analysis/sensitivity.md b/docs/src/analysis/sensitivity.md
@@ -78,7 +78,7 @@ Therefore, the solution to the ODE are the first `n` components of the solution.
 This means we can grab the matrix of solution values like:
 
 ```julia
-x = vecvec_to_mat([sol[i][1:sol.prob.indvars] for i in 1:length(sol)])
+x = vecarr_to_arr([sol[i][1:sol.prob.indvars] for i in 1:length(sol)])
 ```
 
 Since each sensitivity is a vector of derivatives for each function, the sensitivities
@@ -97,11 +97,11 @@ is available in this case, including interpolations and plot recipes (the recipe
 will plot the expanded system).
 
 Since the closure returns a vector of vectors, it can be helpful to use
-`vecvec_to_mat` from [RecursiveArrayTools.jl](https://github.com/ChrisRackauckas/RecursiveArrayTools.jl)
+`vecarr_to_arr` from [RecursiveArrayTools.jl](https://github.com/ChrisRackauckas/RecursiveArrayTools.jl)
 in order to plot the solution.
 
 ```julia
-plot(sol.t,vecvec_to_mat(da),lw=3)
+plot(sol.t,vecarr_to_arr(da),lw=3)
 ```
 
 ![Sensitivity Solution](../assets/sensitivityplot.png)

diff --git a/docs/src/features/low_dep.md b/docs/src/features/low_dep.md
@@ -53,7 +53,7 @@ sol = solve(prob,Tsit5())
 t = collect(linspace(0,10,200))
 randomized = [(sol(t[i]) + .01randn(2)) for i in 1:length(t)]
 using RecursiveArrayTools
-data = vecvec_to_mat(randomized)
+data = vecarr_to_arr(randomized)
 cost_function = build_loss_objective(prob,t,data,Tsit5(),maxiters=10000)
 ```