Mirrors
/
gitea
mirror of https://github.com/go-gitea/gitea.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
gitea/vendor/github.com/src-d/enry/v2
History
…
..
data
…
internal/tokenizer
…
regex
…
.gitignore
…
.travis.yml
…
CONTRIBUTING.md
…
DCO
…
LICENSE
…
MAINTAINERS
…
Makefile
…
README.md
…
classifier.go
…
common.go
…
enry.go
…
go.mod
…
go.sum
…
utils.go
…

README.md

enry GoDoc Build Status codecov

File programming language detector and toolbox to ignore binary or vendored files. enry, started as a port to Go of the original linguist Ruby library, that has an improved 2x performance.

Installation

The recommended way to install enry is to either download a release or

go get github.com/src-d/enry/cmd/enry

This project is now part of source{d} Engine, which provides the simplest way to get started with a single command. Visit sourced.tech/engine for more information.

Examples

lang, safe := enry.GetLanguageByExtension("foo.go")
fmt.Println(lang, safe)
// result: Go true

lang, safe := enry.GetLanguageByContent("foo.m", []byte("<matlab-code>"))
fmt.Println(lang, safe)
// result: Matlab true

lang, safe := enry.GetLanguageByContent("bar.m", []byte("<objective-c-code>"))
fmt.Println(lang, safe)
// result: Objective-C true

// all strategies together
lang := enry.GetLanguage("foo.cpp", []byte("<cpp-code>"))
// result: C++ true

Note that the returned boolean value safe is set either to true, if there is only one possible language detected, or to false otherwise.

To get a list of possible languages for a given file, you can use the plural version of the detecting functions.

langs := enry.GetLanguages("foo.h",  []byte("<cpp-code>"))
// result: []string{"C", "C++", "Objective-C}

langs := enry.GetLanguagesByExtension("foo.asc", []byte("<content>"), nil)
// result: []string{"AGS Script", "AsciiDoc", "Public Key"}

langs := enry.GetLanguagesByFilename("Gemfile", []byte("<content>"), []string{})
// result: []string{"Ruby"}

CLI

You can use enry as a command,

$ enry --help
enry v2.0.0 build: 05-08-2019_20_40_35 commit: 6ccf0b6, based on linguist commit: e456098
enry, A simple (and faster) implementation of github/linguist
usage: enry [-mode=(file|line|byte)] [-prog] <path>
        enry [-mode=(file|line|byte)] [-prog] [-json] [-breakdown] <path>
        enry [-mode=(file|line|byte)] [-prog] [-json] [-breakdown]
        enry [-version]

and on repository root, it'll return an output similar to linguist's output,

$ enry
97.71%	Go
1.60%	C
0.31%	Shell
0.22%	Java
0.07%	Ruby
0.05%	Makefile
0.04%	Scala
0.01%	Gnuplot

but not only the output; its flags are also the same as linguist's ones,

$ enry --breakdown
97.71%	Go
1.60%	C
0.31%	Shell
0.22%	Java
0.07%	Ruby
0.05%	Makefile
0.04%	Scala
0.01%	Gnuplot

Scala
java/build.sbt
java/project/plugins.sbt

Java
java/src/main/java/tech/sourced/enry/Enry.java
java/src/main/java/tech/sourced/enry/GoUtils.java
java/src/main/java/tech/sourced/enry/Guess.java
java/src/test/java/tech/sourced/enry/EnryTest.java

Makefile
Makefile
java/Makefile

Go
benchmark_test.go

even the JSON flag,

$ enry --json | jq .
{
  "C": [
    "internal/tokenizer/flex/lex.linguist_yy.c",
    "internal/tokenizer/flex/lex.linguist_yy.h",
    "internal/tokenizer/flex/linguist.h",
    "python/_c_enry.c",
    "python/enry.c"
  ],
  "Gnuplot": [
    "benchmarks/plot-histogram.gp"
  ],
  "Go": [
    "benchmark_test.go",

Note that enry's CLI doesn't need a git repository to work, which is intentionally different from the linguist.

Java bindings

Generated Java bindings using a C shared library and JNI are available under java and published on Maven at tech.sourced:enry-java for macOS and linux.

Python bindings

Generated Python bindings using a C shared library and cffi are not available yet and are WIP under src-d/enry#154.

Divergences from linguist

The enry library is based on the data from github/linguist version v7.5.1.

As opposed to linguist, enry CLI tool does not require a full Git repository in the filesystem in order to report languages.

Parsing linguist/samples the following enry results are different from linguist:

In all the cases above that have an issue number - we plan to update enry to match Linguist behavior.

Benchmarks

Enry's language detection has been compared with Linguist's one. In order to do that, Linguist's project directory linguist/samples was used as a set of files to run benchmarks against.

We got these results:

histogram

The histogram shows the number of files detected (y-axis) per time interval bucket (x-axis). As one can see, most of the files were detected faster by enry.

We found few cases where enry turns slower than linguist due to Go regexp engine being slower than Ruby's, based on oniguruma library, written in C.

See instructions for running enry with oniguruma.

Why Enry?

In the movie My Fair Lady, Professor Henry Higgins is one of the main characters. Henry is a linguist and at the very beginning of the movie enjoys guessing the origin of people based on their accent.

"Enry Iggins" is how Eliza Doolittle, pronounces the name of the Professor during the first half of the movie.

Development

To build enry's CLI run:

make build

this will generate a binary in the project's root directory called enry.

To run the tests:

make test

Sync with github/linguist upstream

enry re-uses parts of the original github/linguist to generate internal data structures. In order to update to the latest release of linguist do:

$ git clone https://github.com/github/linguist.git .linguist
$ cd .linguist; git checkout <release-tag>; cd ..

# put the new release's commit sha in the generator_test.go (to re-generate .gold test fixtures)
# https://github.com/src-d/enry/blob/13d3d66d37a87f23a013246a1b0678c9ee3d524b/internal/code-generator/generator/generator_test.go#L18

$ make code-generate

To stay in sync, enry needs to be updated when a new release of the linguist includes changes to any of the following files:

There is no automation for detecting the changes in the linguist project, so this process above has to be done manually from time to time.

When submitting a pull request syncing up to a new release, please make sure it only contains the changes in the generated files (in data subdirectory).

Separating all the necessary "manual" code changes to a different PR that includes some background description and an update to the documentation on "divergences from linguist" is very much appreciated as it simplifies the maintenance (review/release notes/etc).

Misc

Benchmark

All benchmark scripts are in benchmarks directory.

Dependencies

As benchmarks depend on Ruby and Github-Linguist gem make sure you have:

  • Ruby (e.g using rbenv), bundler installed
  • Docker
  • native dependencies installed
  • Build the gem cd .linguist && bundle install && rake build_gem && cd -
  • Install it gem install --no-rdoc --no-ri --local .linguist/github-linguist-*.gem

Quick benchmark

To run quicker benchmarks you can either:

make benchmarks

to get average times for the main detection function and strategies for the whole samples set or:

make benchmarks-samples

if you want to see measures per sample file.

Full benchmark

If you want to reproduce the same benchmarks as reported above:

  • Make sure all dependencies are installed
  • Install gnuplot (in order to plot the histogram)
  • Run ENRY_TEST_REPO="$PWD/.linguist" benchmarks/run.sh (takes ~15h)

It will run the benchmarks for enry and linguist, parse the output, create csv files and plot the histogram.

Faster regexp engine (optional)

Oniguruma is CRuby's regular expression engine. It is very fast and performs better than the one built into Go runtime. enry supports swapping between those two engines thanks to rubex project. The typical overall speedup from using Oniguruma is 1.5-2x. However, it requires CGo and the external shared library. On macOS with Homebrew, it is:

brew install oniguruma

On Ubuntu, it is

sudo apt install libonig-dev

To build enry with Oniguruma regexps use the oniguruma build tag

go get -v -t --tags oniguruma ./...

and then rebuild the project.

License

Apache License, Version 2.0. See LICENSE