The title would probably be confusing, but I could not make it better than this. I noticed that most programming languages are limited to the alphanumerical set along with the special characters present in a general keyboard. I wondered if this posed a barrier for developers on what characters they were limited to program in, or if it was intentional from the start that these keys would be the most optimal characters for a program to be coded in by a human and was later adopted as a standard for every user. Basically, are the modern keyboards built around programming languages or are programming languages built around these keyboards?

  • cwagner@lemmy.cwagner.me
    18·
    1 year ago

    APL

    From one of the many How to shoot yourself in the foot guides, for APL:

    • You shoot yourself in the foot; then spend all day figuring out how to do it in fewer characters.
    • You hear a gunshot and there’s a hole in your foot, but you don’t remember enough linear algebra to understand what happened.
    • @#&$%&% foot

    A fascinating language to look at, Conway’s Game of Life is simply life ← {⊃1 ⍵ ∨.∧ 3 4 = +/ +⌿ ¯1 0 1 ∘.⊖ ¯1 0 1 ⌽¨ ⊂⍵}, but I have zero interest in ever actually learning it ;)

    There are also keyboards with the proper symbols:

    • fubo@lemmy.world
      3·
      1 year ago

      The core of that Life expression works by taking the array of cells and shifting it in the eight different directions, then summing those arrays to get the population counts.

      I tried translating this into Python — but I’ve never written numpy code before, so this is probably less efficient than it could be. But it does work and you can see a glider move through a few generations.

      The array-shifting logic is in the populations function, with np.roll being the equivalent of the APL rotate operation (written as ⌽ and ⊖ in the original).

      import numpy as np

def alive(popu, cell):
    return int(popu == 3 or (popu == 4 and cell))
alive = np.frompyfunc(alive, 2, 1)

def populations(grid):
    return np.array(
            [ np.roll(r1, shift, 1)
                for shift in [-1, 0, 1]
                for r1 in (np.roll(grid, shift, 0) for shift in [-1, 0, 1]) ]
            ).sum(axis=0)

def nextgen(grid):
    return alive(populations(grid), grid)

grid = np.array([[0, 0, 0, 0, 0, 0],
                 [0, 0, 1, 0, 0, 0],
                 [0, 0, 0, 1, 0, 0],
                 [0, 1, 1, 1, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0]])

for gens in range(5):
    print(grid)
    grid = nextgen(grid)
print(grid)