Formatting JSON and XML in Gedit

Here’s how to add commands to Gedit to format JSON and XML documents.

  1. Ensure that you have an up-to-date version of Python.  It’s included with nearly every Linux distribution.
  2. Ensure that the External Tools plugin is installed
    1. Click Edit -> Preferences
    2. Select the Plugins tab
    3. Check the box next to External Tools
    4. Click Close
  3. Add the Format JSON command
    1. Click Tools -> Manage External Tools…
    2. Click New (bottom left, looks like a piece of paper with a plus sign)
    3. Enter a name (Format JSON)
    4. Paste this text into the text window on the right
      #! /usr/bin/env python
      import json
      import sys
      j = json.load(sys.stdin)
      print json.dumps(j, sort_keys=True, indent=2)
    5. Set Input to Current document
    6. Set Output to Replace current document
  4. Add the Format XML command
    1. Install lxml (on Ubuntu, sudo apt-get install python-lxml)
      1. Python’s included XML modules either don’t support pretty printing or are buggy
    2. Create a new external tool configuration as above (Format XML)
    3. Paste this text into the text window on the right
      #! /usr/bin/env python
      import sys
      import lxml.etree as etree
      import traceback
      result = ''
      for line in sys.stdin:
        result += line
        x = etree.fromstring(result)
        result = etree.tostring(x, pretty_print=True, xml_declaration=True, encoding=”UTF-8″)
        etype, evalue, etraceback = sys.exc_info()
        traceback.print_exception(etype, evalue, etraceback, file=sys.stderr)
      print result
    4. Set Input to Current document
    5. Set Output to Replace current document

Thanks to Diego Alcorta for improvements that preserve XML declarations and set encoding!

Posted in Python at November 22nd, 2011. 35 Comments.

Python Package Structure with Unit Testing, Injection and Mocking

Python is a language that some people like and use.  I recently was given a small Python application and had to refactor it so that I could implement unit and functional tests.  It took me a while to find the resources and learn how to implement them, so I thought I’d write it up here.

* Please note that this is not a unit testing tutorial.  Those are plentiful and can be found elsewhere.  This describes setting up a professional Python project with a real package structure, injection and tests.

Python Project Structure

Python was conceived as a scripting language.  Unlike compiled languages like C or Java, Python files are designed to be directly runnable.

  • -p someargument < Not ok!
  • -p someargument < Ok!

This is convenient for small scripts, but doesn’t help applications.  Since you are intended to run .py files directly, Python projects should not have source (src) or binary (bin) folders.  Having a source folder makes a program difficult to run.  The root of the Python package structure should be the root of the project.

For the same reason, the test folder should not be separate.  It should be a package in the main project.  Since the root folder is the root package, you won’t have much choice.


  • MyProject
    • model
    • service
    • test
      • unit
        • service
      • functional
        • service

If you’re new to Python, please note that those __init__.pys are required.  They tell Python which folders contain Python files.  This is Python’s way of allowing you to have other folders, such as config, that don’t contain source files.


You should specify exactly one class per file.  It makes your code easier to find and read.

Injection with snake-guice

Injection is essential for unit tests.  In the example above, LDAPUserService may connect to an LDAP server to retrieve information, something we don’t want to happen in unit tests.

I’ve been using snake-guice and highly recommend it.

Install snake-guice, Mock and nose

If you don’t have it, grab a copy of easy install.  Then, $ sudo snake-guice Mock nose.  That installs packages from PyPI, the Python Package Index.  For those used to non-scripting languages, this may not seem like a great way to use external libraries.  Since Python is a scripting language, however, it’s the only way that makes sense.

Create a module and use it to instantiate your application

class ExampleModule:
  def configure(self, binder)
    binder.bind(UserService, to=LDAPUserService)
class ExampleRunner:
  user_service = None
  def __init__(self, user_service):
    self.user_service = user_service
  def main(user_service)
    self.user_service.login("name", "password")
injector = Injector(ExampleModule)
runner = injector.get_instance(ExampleRunner)

When get_instance is called, injector will use the ExampleModule to discover bindings.  Then, it will create an ExampleRunner, passing in arguments specified by @inject.

The setUp method of PyUnit tests should create an injector with a TestExampleModule, which should be configured to return mocks.

class TestExampleModule:
  def user_service = Mock()
  def configure(self, binder)
    binder.bind(UserService, to=user_service)
class ExampleTest(TestCase):
  application = None
  def setUp(self):
    Injector = Injector(TestExampleModule)
    self.application = injector.get_instance(ExampleApplication)
  def test_something(self): = "expected"
    actual ="any")
    self.assertEquals(expected, actual)

Since setUp is called before each test, each test method gets a fresh mock that it can configure any way it likes.

Similarly, a functional test module can be created that connects to a local or testing server.

Mocking with Mock

I recommend using Mock for mocking, which is why I had you install it earlier.  Be careful when searching for it because there appear to be two projects called Mock.

The Mock package is well documented.  Please go to the Mock site for more information.

Running Tests

For running tests, I recommend Nose.  To run your tests, open a command line and change to your root project folder.  Then, nosetests.  Nose will take care of adding the packages you installed earlier, snake-guice and Mock, to the Python path.  It will also find all tests, run them and report the results.  Handy!

$ cd ~/exampleproject
$ nosetests

To run only unit or functional tests, use the -w argument.

$ nosetests -w ./ ./test/unit
$ nosetests -w ./ ./test/functional

Is that all?

To those who have not used injection or mocks for testing, this may seem like a lot of work.  I hope you’ll try it, though, because it’s a one-time setup.  Once these good practices are in place, you’ll find that all of your code organization and testing becomes simple and almost automatic.  If I missed something or if you have any suggestions, please post below.

Posted in Python at February 6th, 2010. 7 Comments.