I would like to define a certain function that takes a vector as input and does some computations that involving varying just one of the entries. For example, if the input vector is a, I would like to compute f(a) whlie “pretending” that a[1] = 0. One way to do this is b = copy(a); b[1] = 0, f(b), …

[image] maxkapur: let a = a, a[1] = m # do some computations on a @show a end Even if this were valid syntax, it wouldn’t do what you want anyway because let a=a doesn’t change the fact that a points to the same value, so mutating that array will still mutate t…

Are you always changing the same entry b[1] at each iteration? If so, do you need to copy b every time, or can you just reuse the same one?

There is an inner loop in which I am just taking a[1] = m for m in 0:0.1:1, and then an outer loop where I change a[i] for i in 1:length(a). So I can move b = copy(a) to the outside of the inner loop, but this is only a partial solution, because it still requires creating length(a) copies of a in th…

I think you should be more explicit about what kind of computation do you need to do with b. But if it is something that really requires it as a vector, I would create a struct as struct B{T} <: Vector{T} i::Int b::T a1:: Vector{T} end function Base.getindex(x::B,i) if i == x.i return x…

This has proven to be the best solution for the problem at hand. I guess I just expected to be some a built-in way to do this kind of “substitution,” sort of like using filter() instead of storing a new array or the various masking features in NumPy. However, I have no real basis for this expectati…

Is `let` the right tool for this task?

New to Julia

jzr April 22, 2021, 5:55am 7

Masked arrays are discussed in Mask on a 1D array - #2 by rdeits

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Is `let` the right tool for this task?

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