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alt_std

The Zig languages is deliberately postponing serious development of the standard library until the language has stabilized. As a result, the current standard library largely contains only the things needed for development of the core infrastructure. This library is a parking place for useful functionality that will likely qualify for inclusion in a standard library.

Documentation

Online docs are built automatically from the master branch.

Usage

The modules in this library do not have any dependencies except the Zig standard library itself. You can include this library in your project with zigmod by:

  1. Run zigmod init in your project root
  2. Add a dependency to zigmod.yml: 
    dependencies:
    - src: git https://github.com/jwhear/zig_alt_std
  3. Run zigmod fetch
  4. Add deps handling to your executable/library in build.zig: 
    const deps = @import("./deps.zig");

// Further down, before exe.install()
deps.addAllTo(exe);
  5. In your code, import with @import("alt_std")

Modules

alt_std.algorithm

Contains the functions expected in an algorithm module (ala C++ STL/D/etc). All functions are constrained to operate on slices as the language has not settled on a standardized interface for iterators or ranges. Note that the current std.mem module includes a number of things that would normally go in this module (e.g. startsWith, count, etc.) and no effort is made to replace/copy these.

alt_std.allocators

Contains allocators that are either useful on their own or as building blocks of more sophisticated composite allocators. These should complement and work with those already in std.heap.

alt_std.levenshtein

Algorithms for computing the Levenshtein edit distance between two strings. Most users should use alt_std.levenshtein.distance for the most flexible and general-purpose implementation. If you don't need custom edit costs and can guarantee your strings will be 64 bytes or less in length (a reasonable limitation for human languages), the alt_std.levenshtein.distance64 does not allocate, cannot fail, and is ~13x faster than most implementations. To benchmark on your hardware, run:

zig build run-bench-levenshtein -Doptimize=ReleaseFast -Dtarget=native -Dcpu=native

Note that alt_std.levenshtein.distance automatically dispatches to the fast distance64 implementation when possible.

Contribution

Bug Fixes

If you find a bug, please do report it. While I will respond to issues on this project, the ideal way to report an issue is to make a merge request with a minimal, failing test case as part of the function's test suite. If you also have a fix for the code in question, great!

New Features

I consider new functionality with the following criteria:

  1. Is the functionality commonly part of a language's standard library? Do C, C++, D, Rust, or Go include it?
  2. Does the functionality introduce dependencies? If so, the bar for inclusion is very high.
  3. Is the proposed code well documented and tested? Would it look at home in Zig's standard library?