To conclude this series, I will leave you with a fast-loading version of the Rust error types diagram that Federico sent me after reading my early posts, rendered from DOT and accessible without Google Drive. This diagram was really helpful for me along the way, big thanks to whoever first made it, so I’m glad I can re-share it in a hopefully improved format.
I hesitate to link there because they have big banners saying “Get FAANG Ready With [our site]” which promotes a software engineering industry culture that I don’t support ?
Today in the penultimate post from the chronicle of teaching myself the Rust programming language by doing programming puzzles from Advent of Code 2020: a hexagonal grid, and another homage to Conway, this time unexpected.
Day 20, Part 2 (Yet Again)
There are two main things that I changed here. First is that I noticed I was passing a lot of the same arguments ( corners , edges , connections ) to the methods that I had, so that was a code smell that indicated that these should be gathered together into a class, which I’ve called Solver .
The second insight was that I don’t actually need to keep track of which tiles are corners, edges, and middles; each tile can only connect in one way, so I only need to find the corners (both for the answer of Part 1, and to pick the top left corner to start assembling the image from.)
Sadly, it doesn’t work
Now that I have the full image, I have to actually solve the Part 2 puzzle: cross-correlate the image with the sea monster matrix.
Unlike NumPy, Rust’s ndarray does not have any built-in facilities for cross-correlation, nor is it provided by any packages that I can find. So I will have to write code to do this, but because what I need is actually a very simple form of cross-correlation, I don’t think it will be so hard.
What I need to do is take a slice of the image at every position, of the size of the sea monster, except where the sea monster would extend outside the boundaries of the image. Then I multiply the slice by the sea monster, and sum all the elements in it, and if that sum is equal to the sum of the elements in the sea monster, then there is a sea monster located there.
I will need to do this operation on each of the eight orientations of the image (rotated four ways, and flipped) until I find one where sea monsters are present. Then to get the answer to the puzzle, I’ll have to subtract the number of sea monsters times the number of pixels in a sea monster, from the sum of the pixels in the image.
First I make sure it produces the right answer for the example data, then I add this to main() :
When trying to connect up the top left corner I get a panic because it is possible to connect it on three sides, not two! This is obviously a bug in my program (the tile wouldn’t have been in the list of corners if it had been able to connect on three sides!) I should investigate and fix this bug, but I am so done with this puzzle. In one last burst, I decide to paper over the bug by only trying the tiles that I already know should connect, replacing the tl_corner_connections code with the following: