My Ideal Array Language

Not op, but I assume it means that there's rank polymorphism (i.e. data can be of arbitrary dimensions, and there's support for things like functions working on both N-dimensions, without having to specify n, or maybe setting constraints on n), and that the polymorphism can be used on the programmer side (so it's not limited to just a handful of language builtins) through the oop equivalent of subclasses and interfaces.

  schema do
    input do
      array :regions do
        float :tax_rate
        array :offices do
          float :col_adjustment
          array :employees do
            float :salary
            float :rating
          end
        end
      end
    end

    trait :high_performer, input.regions.offices.employees.rating > 4.5
    
    value :bonus do
      on high_performer, input.regions.offices.employees.salary * 0.25
      base input.regions.offices.employees.salary * 0.10
    end
    
    value :final_pay,
      (input.regions.offices.employees.salary + bonus * input.regions.offices.col_adjustment) *
      (1 - input.regions.tax_rate)
  end

  result = schema.from(nested_data)[:final_pay]
  # => [[[91_000, 63_700], [58_500]], [[71_225]]]

The programmer can define functions that operate on matrices without having to be explicit about the number of dimensions and possibly (types of data, size of data, or length).

Example 1: A function that can take as input a 4x2x8 matrix or a 3x7 matrix.

Example 2: A function that can take as input a 4x2x8 matrix and a 3x7 matrix and output a third matrix.

game math libraries often have this (and glsl gpu shader language), like "2 * vec3(1,2,3)" results in "vec3(2,4,6)"

There are other cases like adding vectors to matrices and so on, but in the end this logic is defined in some custom add operator overload on a class or object in the language.

(I had no idea what it meant either until i searched for examples..)

Well, do you know how it works? Don't judge a book by its cover and all. Although none of these are entirely aiming for elegance. The first is code golf and the other two have some performance hacks that I doubt are even good any more, but replacing ∧≢⥊ with ∧⌜ in the last gets you something decent (personally I'm more in the "utilitarian code is never art" camp, but I'd have no reason to direct that at any specific language).

The double-struck characters have disappeared from the second and third lines creating a fun puzzle. Original post https://www.ashermancinelli.com/csblog/2022-5-2-BQN-reflecti... has the answers.

When the junior programmers start saying "Turing complete" or the academics build a DSL in Julia that uses RegEx to parse Logic Symbols and stuffs the result in variables that use ancient characters that don't appear on your keyboard, it's a sure sign of imminent progress. Bonus if the PhD with nine years of schooling and five months of PHP experience at Facebook starts using emoji in commit messages.

This type of syntax allows rapid iteration when looking at different implementations and experimenting with array problems. It should be thought of more as math notation than general programming.

Array Programming is an acquired taste, but once you do, solutions can be extremely simple, both to write and to explain.

Think about using matrix to describe geometric transformations instead of using standard functions.

It's giving APL: https://en.wikipedia.org/wiki/APL_(programming_language)

I see it as beautiful the same way Galadriel would be beautiful as the Dark Queen. Utterly captivating and powerful, and yet something that should never be.

"Rank" means the number of dimensions of an array.

So "rank polymorphism" means being able to write expressions that work correctly regardless of how many dims the arrays involved have.

For example, in numpy you can write a function that handles both lists and matrices automatically, by taking advantage of broadcasting. (The J language takes this idea a lot further -- numpy is a fairly minimal implementation of the idea.)

It's just like polymorphism, only stinkier

If I understand correctly what is meant by rank polymorphism, it is not just about speed, but about ergonomics.

Taking examples I am familiar w/, it is key that you can add a scalar 1 to a rank 2 array in numpy/matllab without having to explicitly create a rank 2 array of 1s, and numpy somehow generalizes that (broadcasting). I understand other array programming languages have more advanced/generic versions of broadcasting, but I am not super familiar w/ them