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Comment by improbable22 | Hacker News Reader

Julia has really nice abstractions for dealing with this. Things for which you might do such calculations in other languages can often be done in a way generic to the number of dimensions, as well as the initial index, using CartesianIndices & friends. And this should be zero cost.

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Almost all languages above the C level has nice abstractions for this, but you still sometimes need to do it for various reasons. Basically, whenever the indexes don't matter, then 1-indexing and 0-indexing are equivalently good, but when they do matter, then 0-indexing leads to neater calculations.

There are plenty of matrix uses where 1-indexing is more natural. I think the main reason Julia uses it is to be more consistent with Matlab.

OffsetArrays.jl is your friend.

I'm working on a project where indexing naturally runs from -n to n. I kludged the usual index hackery and was so plagued with off-by-one errors that I gave up on it. Then I remembered Tim Holy had a blog post about OffsetArrays.jl and started using that.

Problem solved.

Seriously, the most annoying thing I find about Julia is that the various packages fairly often have "creative" names, that make it difficult discover that they might actually be useful.

YMMV.

> I'm working on a project where indexing naturally runs from -n to n. I kludged the usual index hackery and was so plagued with off-by-one errors that I gave up on it. Then I remembered Tim Holy had a blog post about OffsetArrays.jl and started using that.

Ah, thank you. I've been tinkering around with Julia, and was trying out 2D random walks. Not being able to use a 2 dimensional array and store the position [0, 0] confused me. I ended up starting at X,X (where X > total number of moves) and then got stuck with plotting it nicely, because I wanted the plot axes to show how far it had moved, not X +- how far it had moved.

Abstraction discovery is a hard problem in general. Good names help, but there's probably more we can do. Tooling finding a common pattern that the abstraction eliminates, perhaps?

I guess it could poke you every time you say `strides(A)`?

Here's a recent example of an upgrade, from code with stride calculations to a more abstracted version, which is generic to offsets & number of dimensions:

https://github.com/JuliaLang/julia/pull/37367/files#diff-29c...

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