Jupyter notebooks for Dyalog APL

Jupyter notebooks for Dyalog APL#

What is this and how do I use it?#

Please see our wiki.

Where is the language kernel?#

A kernel for using these notebooks is available here.

Contributing#

Feel free to contribute additional notebooks to our collection through emailing notebooks@dyalog.com or submitting pull requests.

AOC 2024#

AOC1

n←,⎕CSV'/Users/jeremy/Desktop/aoc-1.txt'⍬4
aoc1←{(⊣/,⊢/)¨(∊∘⎕D¨⍵)/¨⍵}

  1. Nested Arrays

Summary: Nested arrays allow APL to handle more complex data structures by permitting arrays to contain other arrays as elements, rather than just simple scalar values.

Example:

      words ← 'apple' 'banana' 'cherry' 'date' 'elderberry'
      nested ← ⊂¨words
      nested
┌─────┬──────┬──────┬────┬──────────┐
│apple│banana│cherry│date│elderberry│
└─────┴──────┴──────┴────┴──────────┘
      ≢¨nested
5 6 6 4 10

  1. Dfns (Direct Functions)

Summary: Dfns provide a concise way to define functions using curly braces, with ⍵ representing the right argument and ⍺ the left argument.

Example:

      splitOnComma ← {⍵⊂⍨1,1↓0=⍵∊','}
      splitOnComma 'apple,banana,cherry'
┌─────┬──────┬──────┐
│apple│banana│cherry│
└─────┴──────┴──────┘

  1. Trains

Summary: Trains allow the composition of functions without explicit argument passing, creating more readable and concise code.

Example (Fork):

      average ← {(+⌿⍵)÷≢⍵}  ⍝ Dfn version
      average 1 2 3 4 5
3
      averageTrain ← +⌿ ÷ ≢  ⍝ Train version
      averageTrain 1 2 3 4 5
3

Example (Atop):

      countUnique ← ≢∘∪
      countUnique 1 2 2 3 3 3 4 4 4 4
4

  1. Leading Axis Theory

Summary: Leading Axis Theory involves working along the first (leading) axis of arrays and using the rank operator to apply functions to specific sub-arrays.

Example:

      matrix ← 3 3 ⍴ ⍳9
      matrix
1 2 3
4 5 6
7 8 9
      +⌿matrix  ⍝ Sum along the leading axis
12 15 18
      averageTrain⌿⍤1 matrix  ⍝ Average of each column
4 5 6
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