Fengyang's reshape((@view x[1:6]), (3, 2)) will work well and will be essentially cost-free since reshape creates a view, and a view of a view is still just a view (no copy). Another way to write it is reshape(view(x,1:6), (3, 2)). For example:
function f(t,u,du) x = reshape(view(x,1:6), (3, 2)) # Use x and u[7], u[8], u[9], and u[10] to write directly to du nothing end should be a good way to write the function for Sundials.jl, DifferentialEquations.jl, ODE.jl (after the iterator PR). On Sunday, October 2, 2016 at 5:43:01 AM UTC-7, Alexey Cherkaev wrote: > > I have the model where it is convenient to represent one of the variables > as a matrix. This variable, however, is obtained as a solution of ODEs > (among other variables). I'm using Sundials to solve ODEs and > `Sundials.cvode` requires the ODE RHS function to take a vector. So, it > seems logical to pack the variables into a vector to pass to `cvode` and > unpack them for more convenient use in my code. > > For example, consider `x = fill(1.0, 10)` and the first 6 entries are > actually a matrix of size 3x2, other 4: other variables. So, I can do `s = > reshape(x[1:6], (3,2))`. However, this creates a copy, which I would want > to avoid. And I couldn't find a way to do the view-reshaping by applying > `view()` function or doing `SubArray` directly. For now I settle on to > using indices of the form `(i-1)*M + j +1` to retrieve `s[i,j] = x[(i-1)*M > + j +1]`. But, (1) julia's 1-based arrays make it awkward to use this > notation and (2) matrix (not element-wise) operations are not available. >
