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https://github.com/gobanos/cargo-aoc


https://github.com/gobanos/cargo-aoc

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README 

        
# Cargo Advent of Code Helper



`cargo-aoc` is a simple CLI tool that aims to be a helper for the [Advent of Code](https://adventofcode.com). 



Implement your solution. Let us handle the rest.



# Features

* Input downloading 

* Running your solution 

* Automatic benchmarking of your solution using [Criterion](https://github.com/japaric/criterion.rs)



# Getting started



## Install `cargo aoc`



`cargo-aoc` is hosted as a binary on crates.io.

Boot a terminal and install the program using `cargo install cargo-aoc`



## Setting up the CLI



You will need to find your session token for the AoC in order for cargo-aoc to work. Thankfully, finding your token is easy since it is stored in your Browser's cookies. Open up the devtools of your browser, and then :



* Firefox: "Storage" tab, Cookies, and copy the "Value" field of the `session` cookie.

* Google Chrome / Chromium: "Application" tab, Cookies, and copy the "Value" field of the `session` cookie.



Once you have it, simply run : `cargo aoc credentials {token}`



You're now ready to start coding ! 



NOTE: If for some reason your token has changed, dont forget to change it back. 



`cargo aoc credentials` will show the currently stored user token



## Setting up the project



In order for `cargo-aoc` to work properly, you have to set the project up correctly. 



If you get lost during the process, you can take [this example repository of AoC 2015](https://github.com/gobanos/advent-of-code-2015) as a template.



First, you must add a dependency on `aoc-runner` and `aoc-runner-derive` in your `Cargo.toml`.

At the end of the `src/lib.rs`, you will have to use the macro `aoc_lib!{ year = XXXX }`, where XXXX is the

year of the AoC puzzles being solved.



When implementing a solution for a day, you have to provide functions and tag them accordingly.

A function is either a **solver** or a **generator**. 



Those two types of functions are being executed and benchmarked seperately. Lets have a closer look : 



### Generator functions



Generators allows you to provide a custom type to the solver functions. Sometimes in AoC, you have to parse 

an input and extract a logical structure out of it, before you can actually solve the problem. 



Generator functions are tagged `#[aoc_generator(dayX)]`.



Because examples are worth a thousand words, lets take a look at [Year 2015, Day 2](https://adventofcode.com/2015/day/2) : 



From the puzzle's description, we know that `[we] have a list of the dimensions (length l, width w, and height h) of each present`, each present on one line, represented like so: `{L}x{W}x{H}`.



We might want to first parse the input and extract logical `Gift` structs out of it, like: 



```

pub struct Gift {

    l: u32,

    w: u32,

    h: u32

}

``` 



In @Gobanos' reference implementation, we can see that he instead chose to settle for a custom type :

`type Gift = (u32, u32, u32);`.



Thus, writing a generator for `Gift`s is fairly simple: 



```

#[aoc_generator(day2)]

pub fn input_generator(input: &str) -> Vec {

    input

        .lines()

        .map(|l| {

            let mut gift = l.trim().split('x').map(|d| d.parse().unwrap());

            (

                gift.next().unwrap(),

                gift.next().unwrap(),

                gift.next().unwrap(),

            )

        }).collect()

}

``` 



As you can see, generators take a `&str` (or a `&[u8]`) type as an input, and outputs any type that you want, so you can then use it in `solver` functions.



[link to doc](https://docs.rs/aoc-runner-derive/latest/aoc_runner_derive/attr.aoc_generator.html)



### Solver functions 



Solver functions are typically your algorithms, they take any input type provided by a generator, and return any type that you want to use, provided that it implements the `Display` trait.



Solver functions are tagged `#[aoc(day2, part1)]`. 

Optionally, you can have multiple implementation for the same part of a day. You must then use a name to tag them correctly, for example : `#[aoc(day2, part1, for_loop)]`. 



Following with the previous example, implementing a solver for the part one could be done like this :



```

#[aoc(day2, part1)]

pub fn solve_part1(input: &[Gift]) -> u32 {

    input

        .iter()

        .map(|&(l, w, h)| {

            let (s1, s2) = smallest_side((l, w, h));

            2 * l * w + 2 * w * h + 2 * h * l + s1 * s2

        })

        .sum()

}

``` 



Notice how we're taking the `Gift`s generated previously, and using Rust's iterators to solve the problem efficiently, all the while keeping the code maintainable. 



The output of this particular solver is an `u32`, which of course implements `Display`.

When running your solution using `cargo aoc`, said result will then get printed in the console, along with other informations about execution time.



[link to doc](https://docs.rs/aoc-runner-derive/latest/aoc_runner_derive/attr.aoc.html)



# Downloading your input manually



`cargo aoc input` will download an input and store it in `input/{year}/day{day}.txt`. 



Please note that by default, we're taking today's date as the argument. Of course, you can change this using : `cargo aoc input -d {day} -y {year}`



# Running your solution



`cargo aoc` will run the latest implemented day, downloading your input beforehand. It will show you the result, and a short summary of how well it did perform.



Example output on my Chromebook, running [@Gobanos' AOC2015](https://github.com/gobanos/advent-of-code-2015) : 

```

[olivier@olivier-pc advent-of-code-2015]$ cargo aoc

    Finished dev [unoptimized + debuginfo] target(s) in 0.12s

   Compiling aoc-autobuild v0.1.0 (/home/olivier/Workspace/Rust/advent-of-code-2015/target/aoc/aoc-autobuild)

    Finished release [optimized] target(s) in 0.87s

     Running `target/release/aoc-autobuild`

AOC 2015

Day 5 - Part 1 : 238

        generator: 18.122µs,

        runner: 420.958µs



Day 5 - Part 2 : 69

        generator: 5.499µs,

        runner: 1.142373ms

```



If you want to run an older puzzle, or only a specific part, specify those using `cargo aoc -d {day} -p {part}`.



# Benchmarking your solution



Benchmarking is powered by [Criterion](https://github.com/japaric/criterion.rs). Use `cargo aoc bench` to launch the benchmarks, just like you would use `cargo aoc`.



Benchmarks for each days are then generated in `target/aoc/aoc-autobench/target/criterion`.



You can open the benchmark automatically in your Browser afterwards, using `cargo aoc bench -o` 



Soon(tm), you will also be able to use our (free) online platform, to compare your results with those of the community.



------



Happy Advent of Code !