advent-of-code-ocr VS advent_of_code

In past years, I’ve created Advent of Code OCR for Python to convert ASCII letter art from these problems into plain text you can copy and paste.

Python: advent-of-code-ocr module by /u/bsoyka (original post)

The OCR is based on the number of active pixels for each column. The letter "E" has 6 lit pixels in the first column, 3 pixels in the 2nd and 3rd column and 2 pixels in the last column. By looking at the character list (thanks bsoyka on github!) I could craft a lookup table. The four integers will be shifted and added together to get a single integer. The 6,3,3,2 is transformed to 6<<0 + 3<<2 + 3<<4 + 2<<6 = 194 (the bits overlap, I know, but there are no collisions). The index of 194 in this magic list is 4. By adding 65 (ascii value of 'A') I can get the actual character with chr().

Here's the relevant extract from my (recursive) solution. Full code on GitHub.

Like others, I used lcm. Here's an extract of the solution, omitting parsing and main(). Full solution on GitHub.

Solutions to parts 1 and 2 nearly identical and pretty much worked first time. Nothing clever here. This was far simpler than I thought it would be. I probably spent most time trying to think of a mathematical way of getting all points between p1 and p2. In the end, I just constructed two ranges. Full code here.

Full code on github.

The core of my solution below using numpy (because I'm learning it!). See github for full code.

Python 3 solution using numpy. I got held up because I assume (but know better) that numpy arrays are [x,y] instead of [row, col], which is [y,x].

Below is the new solution, which works for part 1 and 2. The full code is on GitHub.

These are the key functions. The whole code is on GitHub

Part 2 (extract shown below) was a matter of starting with the low points found in part 1 then recursively looking around for relevant points. Full code on GitHub

Code is on GitHub.