Spending some time on this forum and reading around, this strikes me as a recurring mistake of newcomers (me included some time ago of course!): Superfluous type annotations on parameters on function: it looks like people have trouble to understand that these only restrict the applicability of the…

[image] fer2tdt: The “Julia default type” in the function parameters is just an option. Naturally you can use Julia the way you like, but note that because of multiple dispatch, it is idiomatic to write Julia code for which you simply will not know the type of function arguments. But that is…

I allways know the nature of the input data. I allways study which is the best computer representation for the operations to avoid errors on intermediate operations. And I allways have a sample of similar final data. Then you can try the code and results with a Concrete Type. If you don’t know eno…

[image] fer2tdt: I allways know the nature of the input data. Good for you! Again, it is possible to program in Julia with that approach, but you are not reaping the benefits of the language. You might as well just use C. Eg consider nonsense(x, y, z) = √(x + y^2) / z which, as defined, wi…

[image] Tamas_Papp: [image] fer2tdt: I allways know the nature of the input data. Good for you! Again, it is possible to program in Julia with that approach, but you are not reaping the benefits of the language. You might as well just use C. That’s a little too harsh, I think. Someti…

[image] stevengj: That’s a little too harsh, I think. Indeed. Sorry being sarcastic. I was triggered by the “I always know everything”, but should have responded more gently.

Mmmmmm, I don’t think so “strait”, For example in some cases I work with “Natural” numeric data, so for the definition of the functions I use arguments typed with “Unsigned” Abstract types, with this I design functions that are valid for all the “Concrete SubTypes” (“UInt…” ) in which I am interest…

I put type annotations on function parameters, like AbstractString, AbstractArray, etc just as a hint (for myself) on what type does this function expect. Trying not to restrict it too much at same time. Probably, a proper doc string would be a better approach.

Besides the compiler inferring a variable as Any, are other types of type instabilities possible?

Morning. I am going to be polite too, it’s a minimun. This Julia function "always knows everything " because it no needs any types info about args. function savantA(x,y,z) r=sqrt(x+yy)/z println(" savantA:: x’ typ= ", typeof(x), " y’ typ= ", typeof(y), " z’ typ= ", typeof(z), " r’ typ= “, typeof…

About “instabilities”, because someone said that it is better not to specify Types in the function arguments. But at the same time Julia does not automatically handle “the best Type” to represent the inputs, neither the intermediate results of the operations nor even the final result. I understand t…

How is it that new Julia programmers tend to abuse type annotations?

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How is it that new Julia programmers tend to abuse type annotations?

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