About

Swagger.py is a Python library for using Swagger defined API's.

Swagger itself is best described on the Swagger home page:

Swagger is a specification and complete framework implementation for describing, producing, consuming, and visualizing RESTful web services.

The Swagger specification defines how API's may be described using Swagger.

Usage

Install swagger.py using the setup.py script.

$ sudo ./setup.py install

This installs the swagger.py libraries, and a swagger-codegen tool for generating code from a set of Swagger API docs.

swagger-codegen

Inspired by the original swagger-codegen project, templates are written using Mustache templates (Pystache, specifically). There are several important differences.

• The model that is fed into the mustache templates is almost identical to Swagger's API resource listing and API declaration model. The differences are listed below.
• The templates themselves are completely self contained, with the logic to enrich the model being specified in translate.py in the same directory as the *.mustache files.

Data model

The data model presented by the swagger_model module is nearly identical to the original Swagger API resource listing and API declaration. This means that if you add extra custom metadata to your docs (such as a _author or _copyright field), they will carry forward into the object model. I recommend prefixing custom fields with an underscore, to avoid collisions with future versions of Swagger.

There are a few meaningful differences.

• Resource listing
  • The file and base_dir fields have been added, referencing the original .json file.
  • The objects in a resource_listing's api array contains a field api_declaration, which is the processed result from the referenced API doc.
• API declaration
  • A file field has been added, referencing the original .json file.
  • The model field was changed from an object to an array, so it can be better referenced from a mustache template.
  • Similarly, a model's properties field was changed from an object to an array.

Development

The code is documented using Epydoc, which allows IntelliJ IDEA to do a better job at inferring types for autocompletion.

To keep things isolated, I also recommend installing (and using) virtualenv. Some scripts are provided to help keep the environments manageable

$ sudo pip install virtualenv
$ ./make-env.sh
$ . activate-env.sh

Setuptools is used for building.

$ ./setup.py develop   # prep for development (install deps, launchers, etc.)
$ ./setup.py nosetests # run unit tests
$ ./setup.py bdist_egg # build distributable

Nose is used for unit testing, with the coverage plugin installed to generated code coverage reports. Pass --with-coverage to generate the code coverage report. HTML versions of the reports are put in cover/index.html.

License

Copyright (c) 2013, Digium, Inc. All rights reserved.

Swagger.py is licensed with a BSD 3-Clause License.