Description

This library was written as part of CMSC858D - Algorithms, Data Structures and Inference for High-throughput Genomics Class Homework.

The official version of the library is hosted on github at this repository, while crates.io version is hosted at this page.

Installation

You can download the source code from github and build the project using cargo build or add it to your project as a dependency from crates.io.

Usage

Below are some ways to instantiate and use given constructs.

BitVector

use bvrs::BitVec;

let size = 16;
let b1 = BitVec::new_with_random(size);
let b2 = BitVec::new_with_zeros(size);
let b3 = BitVec::new_with_vec(vec![0b10010001, 0b10000001]);
let b4 = BitVec::new(size); // uses new with zeros
let b5 = b1 + b2;
let b6 = b1.incr();
let b7 = b1.concat(&b2);
let b8 = b1.extract(0, 7);
let bit = b1.get(2);

Rank Support

use bvrs::BitVec;
use bvrs::RankSupport;

let size = 16;
let b1 = BitVec::new_with_random(size);
let r = RankSupport::new(&b);
let index = 3;
let res = r.rank1(index);
let res_prime = RankSupport::dummy_rank(&b, index);
r.save("example.txt");
let r2 = RankSupport::load("example.txt".to_owned()).unwrap();

Select Support

use bvrs::BitVec;
use bvrs::RankSupport;
use bvrs::SelectSupport;

let b = BitVec::new_with_random(size);
let r = RankSupport::new(&b);
let s = SelectSupport::new(Cow::Borrowed(&r));
let select = s.select1(3);

Sparse Array

use bvrs::SparseArray;

let size = 32;
let mut sa: SparseArray<String> = SparseArray::new(size);
sa.append("alp".to_owned(), 3);
let res = sa.get_at_index(3).unwrap();
assert_eq!(*res, "alp".to_owned());