A beautiful article about "CartesianIndices" in Julia

[dmm]

julialang.org/blog/2016/02/iteration/

(Caveats and remarks: This article is written for mutable arrays. We do often require immutable arrays these days.

Some of the links to Julia documentation are out of date, see docs.julialang.org/en/v1/manual/arrays/ instead.

X... construction typically "splats" elements of tuple or array X, see the second half of docs.julialang.org/en/v1/manual/functions/#Varargs-Functions section for explanation and examples, starting from the words 'On the flip side, it is often handy to "splat" the values contained in an iterable collection into a function call as individual arguments'; this construction works everywhere, not just in function calls.)

Comments

[dmm]

Why on Earth do I want to understand the internals of "imresize"?

I am using a "warp" technique for wave effects in images, where I change the indices in a discrete way. e.g.

function warp3(x, y, knob) # 0:31
    clip(n, nsize) = max(min(nsize, n), 1)
    (x0, y0) = (clip(convert(Int, floor(x + 10 * ((knob - 16) / 16) * sin(y/4))), xsize), y) # normalize to get +/-        
    img[x0, y0]
end

All this needs to be refactored anyway:

https://github.com/anhinga/julia-notebooks/blob/main/grimoire-team/design-notes.md

But one thing I have noticed is that this cannot be meaningfully differentiated with respect to parameters such as knob. And what if I want to do some gradient descent with respect to knob?

Then I should do better than this. It does not have to be completely smooth, it can have breaks in the derivative, like in ReLU. But it should be a continuous function from knob.