advent-tools

Python interface to conveniently download and interact with advent data with a high-level-wrapper.

Installation

A Virtual Environment is strongly recommended to install the library

# Linux/macOS
python3 -m pip install -U advent-tools

# Windows
py -3 -m pip install -U advent-tools

Quickstart

First we need to get the session cookie (🍪), in order to do that

1. Open your broswer's Devtools (Chrome / Firefox guide)
2. Head on over to Application tab, then to Cookies
3. Find and copy session cookie value
4. set AOC_SESSION=<session-cookie-value> environment variable (make sure terminal restart doesn't reset the value)
   OR
   Enter the following command in your terminal which will create an .env file storing your session cookie.

# Same for all Windows, Linux and macOS
echo AOC_SESSION=<session-cookie-value> > .env
# Make sure to replace <session-cookie-value> with your cookie value

Make sure version control system ignores your token container in case you are using one, cause your token is sensitive and can be used for log-ins.

Usage

year and day can be given as kwargs when subclassing advent.Advent or can be provided using it's __getitem__ behaviour like Advent[year:day]

from advent import Advent


class Solution(Advent, year=2020, day=3):
    def __init__(self, data: str) -> None:
        self.data = ...

    def part_1(self):
        ...

    def part_2(self):
        ...

Example 1:

from advent import Advent


class Solution(Advent, year=2022, day=2):
    PLAY = {"A": ["Z", "X", "Y"], "B": ["X", "Y", "Z"], "C": ["Y", "Z", "X"]}

    def __init__(self, data: str) -> None:
        self.data = [line.split() for line in data.splitlines()]

    def part_1(self):
        return sum(self.PLAY[oppo].index(me) * 3 + ord(me) - 87 for oppo, me in self.data)

    def part_2(self):
        return sum((fate := ord(res) - 88) * 3 + ord(self.PLAY[oppo][fate]) - 87 for oppo, res in self.data)

Example 2:

from heapq import nlargest
from advent import Advent

class Solution(Advent[2022:1]):
    def __init__(self, data: str) -> None:
        self.max, *self.top = nlargest(3, [sum(map(int, chunk.split())) for chunk in data.split("\n\n")])

    def part_1(self):
        return self.max

    def part_2(self):
        return self.max + sum(self.top)