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219 lines
7.6 KiB
Markdown
219 lines
7.6 KiB
Markdown
# enry [](https://godoc.org/gopkg.in/src-d/enry.v1) [](https://travis-ci.org/src-d/enry) [](https://codecov.io/gh/src-d/enry)
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File programming language detector and toolbox to ignore binary or vendored files. *enry*, started as a port to _Go_ of the original [linguist](https://github.com/github/linguist) _Ruby_ library, that has an improved *2x performance*.
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Installation
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------------
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The recommended way to install enry is
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```
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go get gopkg.in/src-d/enry.v1/...
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```
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To build enry's CLI you must run
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make build-cli
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this will generate a binary in the project's root directory called `enry`. You can then move this binary to anywhere in your `PATH`.
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Examples
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------------
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```go
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lang, safe := enry.GetLanguageByExtension("foo.go")
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fmt.Println(lang, safe)
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// result: Go true
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lang, safe := enry.GetLanguageByContent("foo.m", []byte("<matlab-code>"))
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fmt.Println(lang, safe)
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// result: Matlab true
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lang, safe := enry.GetLanguageByContent("bar.m", []byte("<objective-c-code>"))
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fmt.Println(lang, safe)
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// result: Objective-C true
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// all strategies together
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lang := enry.GetLanguage("foo.cpp", []byte("<cpp-code>"))
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// result: C++ true
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```
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Note that the returned boolean value `safe` is set either to `true`, if there is only one possible language detected, or to `false` otherwise.
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To get a list of possible languages for a given file, you can use the plural version of the detecting functions.
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```go
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langs := enry.GetLanguages("foo.h", []byte("<cpp-code>"))
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// result: []string{"C", "C++", "Objective-C}
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langs := enry.GetLanguagesByExtension("foo.asc", []byte("<content>"), nil)
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// result: []string{"AGS Script", "AsciiDoc", "Public Key"}
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langs := enry.GetLanguagesByFilename("Gemfile", []byte("<content>"), []string{})
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// result: []string{"Ruby"}
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```
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CLI
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------------
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You can use enry as a command,
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```bash
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$ enry --help
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enry v1.5.0 build: 10-02-2017_14_01_07 commit: 95ef0a6cf3, based on linguist commit: 37979b2
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enry, A simple (and faster) implementation of github/linguist
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usage: enry <path>
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enry [-json] [-breakdown] <path>
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enry [-json] [-breakdown]
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enry [-version]
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```
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and it'll return an output similar to *linguist*'s output,
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```bash
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$ enry
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55.56% Shell
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22.22% Ruby
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11.11% Gnuplot
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11.11% Go
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```
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but not only the output; its flags are also the same as *linguist*'s ones,
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```bash
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$ enry --breakdown
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55.56% Shell
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22.22% Ruby
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11.11% Gnuplot
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11.11% Go
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Gnuplot
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plot-histogram.gp
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Ruby
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linguist-samples.rb
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linguist-total.rb
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Shell
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parse.sh
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plot-histogram.sh
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run-benchmark.sh
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run-slow-benchmark.sh
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run.sh
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Go
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parser/main.go
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```
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even the JSON flag,
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```bash
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$ enry --json
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{"Gnuplot":["plot-histogram.gp"],"Go":["parser/main.go"],"Ruby":["linguist-samples.rb","linguist-total.rb"],"Shell":["parse.sh","plot-histogram.sh","run-benchmark.sh","run-slow-benchmark.sh","run.sh"]}
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```
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Note that even if enry's CLI is compatible with linguist's, its main point is that **_enry doesn't need a git repository to work!_**
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Java bindings
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------------
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Generated Java binidings using a C shared library + JNI are located under [`java`](java)
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Development
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------------
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*enry* re-uses parts of original [linguist](https://github.com/github/linguist) to generate internal data structures. In order to update to the latest upstream and generate the necessary code you must run:
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go generate
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We update enry when changes are done in linguist's master branch on the following files:
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* [languages.yml](https://github.com/github/linguist/blob/master/lib/linguist/languages.yml)
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* [heuristics.rb](https://github.com/github/linguist/blob/master/lib/linguist/heuristics.rb)
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* [vendor.yml](https://github.com/github/linguist/blob/master/lib/linguist/vendor.yml)
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* [documentation.yml](https://github.com/github/linguist/blob/master/lib/linguist/documentation.yml)
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Currently we don't have any procedure established to automatically detect changes in the linguist project and regenerate the code.
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So we update the generated code as needed, without any specific criteria.
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If you want to update *enry* because of changes in linguist, you can run the *go
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generate* command and do a pull request that only contains the changes in
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generated files (those files in the subdirectory [data](data)).
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To run the tests,
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make test
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Divergences from linguist
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------------
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Using [linguist/samples](https://github.com/github/linguist/tree/master/samples)
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as a set for the tests, the following issues were found:
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* With [hello.ms](https://github.com/github/linguist/blob/master/samples/Unix%20Assembly/hello.ms) we can't detect the language (Unix Assembly) because we don't have a matcher in contentMatchers (content.go) for Unix Assembly. Linguist uses this [regexp](https://github.com/github/linguist/blob/master/lib/linguist/heuristics.rb#L300) in its code,
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`elsif /(?<!\S)\.(include|globa?l)\s/.match(data) || /(?<!\/\*)(\A|\n)\s*\.[A-Za-z][_A-Za-z0-9]*:/.match(data.gsub(/"([^\\"]|\\.)*"|'([^\\']|\\.)*'|\\\s*(?:--.*)?\n/, ""))`
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which we can't port.
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* All files for the SQL language fall to the classifier because we don't parse
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this [disambiguator
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expression](https://github.com/github/linguist/blob/master/lib/linguist/heuristics.rb#L433)
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for `*.sql` files right. This expression doesn't comply with the pattern for the
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rest in [heuristics.rb](https://github.com/github/linguist/blob/master/lib/linguist/heuristics.rb).
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Benchmarks
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------------
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Enry's language detection has been compared with Linguist's one. In order to do that, linguist's project directory [*linguist/samples*](https://github.com/github/linguist/tree/master/samples) was used as a set of files to run benchmarks against.
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We got these results:
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The histogram represents the number of files for which spent time in language
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detection was in the range of the time interval indicated in the x axis.
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So you can see that most of the files were detected quicker in enry.
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We found some few cases where enry turns slower than linguist. This is due to
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Golang's regexp engine being slower than Ruby's, which uses the [oniguruma](https://github.com/kkos/oniguruma) library, written in C.
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You can find scripts and additional information (like software and hardware used
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and benchmarks' results per sample file) in [*benchmarks*](benchmarks) directory.
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If you want to reproduce the same benchmarks you can run:
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benchmarks/run.sh
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from the root's project directory and it'll run benchmarks for enry and linguist, parse the output, create csv files and create a histogram (you must have installed [gnuplot](http://gnuplot.info) in your system to get the histogram).
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This can take some time, so to run local benchmarks for a quick check you can either:
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make benchmarks
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to get average times for the main detection function and strategies for the whole samples set or:
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make benchmarks-samples
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if you want to see measures by sample file.
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Why Enry?
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------------
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In the movie [My Fair Lady](https://en.wikipedia.org/wiki/My_Fair_Lady), [Professor Henry Higgins](http://www.imdb.com/character/ch0011719/?ref_=tt_cl_t2) 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.
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`Enry Iggins` is how [Eliza Doolittle](http://www.imdb.com/character/ch0011720/?ref_=tt_cl_t1), [pronounces](https://www.youtube.com/watch?v=pwNKyTktDIE) the name of the Professor during the first half of the movie.
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License
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------------
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Apache License, Version 2.0. See [LICENSE](LICENSE)
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