🏆 Programmierwettbewerb 2023 (Mathe-dual e.V.)

🥇 Team π

Samuel Thesing samuel.thesing@rwth-aachen.de @Rankail
Christian Rene Thelen christian.thelen@rwth-aachen.de @cortex359

This repository is one of two repositories we used for conquering the programming competition 2023. It contains our best results (and a few along the way), log files, interesting research, as well as Python scripts to visualize, compare, plot and evaluate the circle packing, run the algorithms in parallel, approximate the score, scrap the webpage with the intermediate score of the other teams, a few other strategic considerations and some miscellaneous shenanigans. cortex359/md.wettbewerb2023

The other repository contains the actual solver, written in C++ as well as scramblers and the code for creating the images and animations. Rankail/md-wettbewerb-2023

Final Score

Place	Team	Bonus	Total Score
1.	pi	100	9025
2.	koeln	50	8794
3.	coca-cola-und-nutella	25	8538
4.	uka		8030
5.	ets		7046
6.	fabio-palmen		5424
7.	horst		5085
8.	bug-prevention		4688
9.	mongulus-und-bongulus		1340
10.	suffkapuff		1220

Mathe-dual e.V.

🇩🇪 Blog Post über die Siegerehrung, Finale Auswertung und Vergleichsübersicht, sowie allgemeine Informationen über den Wettbewerb auf mathe-dual.de.

This Repository

A list with relevant research publications can be found here: RESEARCH.md

An overview over our best results: results/current_best/README.md

The latest comparisons of our results with other teams:

Installation

In order to use the python scripts, some packages need to be installed in your environment.

pip install -r requirements.txt

Web crawler

Usage

Generate relative score tables:

Fetch snapshots with python webcrawler.py and extract current snapshot.zip:

snapshot="snapshot_060323_2300"
teams=( koeln coca-cola-und-nutella uka ets fabio-palmen bug-prevention horst )

for t in $teams; do
  mkdir "web/data/${t}/${snapshot}"
  unzip -d "web/data/${t}/${snapshot}" "web/data/${t}/${snapshot}.zip"
done

Enter the group directory and extract tables:

for t in ${teams}; do
  for i in {01..14} ; do
    pcre2grep -Me '(?:<pre>)([^<]+[\n\s]*)+(?:<\/pre>)' -m1 --output '$1' web/data/${t}/${snapshot}/forest${i}.txt.html >| web/data/${t}/${t}.forest${i}.table
  done
done

parse tables and save relative scores:

{
  for i in {01..14} ; do
    python web/score_webtable.py forest${i} ${teams};
  done
} >| web/relative_score_tables/overview-$(date +'%F_%H-%M').md