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https://github.com/croach/Flask-Fixtures

A simple library for adding database fixtures for unit tests using nothing but JSON or YAML.
https://github.com/croach/Flask-Fixtures

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A simple library for adding database fixtures for unit tests using nothing but JSON or YAML.

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README 

        
Flask-Fixtures

==============



A simple library that allows you to add database fixtures for your unit

tests using nothing but JSON or YAML.



Installation

------------



Installing Flask-Fixtures is simple, just do a typical pip install like

so:



::



    pip install flask-fixtures



    If you are going to use JSON as your data serialization format, you

    should also consider installing the dateutil package since it will

    add much more powerful and flexible parsing of dates and times.



To install the library from source simply download the source code, or

check it out if you have git installed on your system, then just run the

install command.



::



    git clone https://github.com/croach/Flask-Fixtures.git

    cd /path/to/flask-fixtures

    python setup.py install



Setup

-----



To setup the library, you simply need to tell Flask-Fixtures where it

can find the fixtures files for your tests. Fixtures can reside anywhere

on the file system, but by default, Flask-Fixtures looks for these files

in a directory called ``fixtures`` in your app's root directory. To add

more directories to the list to be searched, just add an attribute

called ``FIXTURES_DIRS`` to your app's config object. This attribute

should be a list of strings, where each string is a path to a fixtures

directory. Absolute paths are added as is, but reltative paths will be

relative to your app's root directory.



Once you have configured the extension, you can begin adding fixtures

for your tests.



Adding Fixtures

---------------



To add a set of fixtures, you simply add any number of JSON or YAML

files describing the individual fixtures to be added to your test

database into one of the directories you specified in the

``FIXTURES_DIRS`` attribute, or into the default fixtures directory. As

an example, I'm going to assume we have a Flask application with the

following directory structure.



::



    /myapp

        __init__.py

        config.py

        models.py

        /fixtures

            authors.json



The ``__init__.py`` file will be responsible for creating our Flask

application object.



.. code:: python



    # myapp/__init__.py



    from flask import Flask



    app = Flask(__name__)



The ``config.py`` object holds our test configuration file.



.. code:: python



    # myapp/config.py



    class TestConfig(object):

        SQLALCHEMY_DATABASE_URI = 'sqlite://'

        testing = True

        debug = True



And, finally, inside of the ``models.py`` files we have the following

database models.



.. code:: python



    # myapp/models.py



    from flask_sqlalchemy import SQLAlchemy



    from myapp import app



    db = SQLAlchemy(app)



    class Author(db.Model):

        id = db.Column(db.Integer, primary_key=True)

        first_name = db.Column(db.String(30))

        last_name = db.Column(db.String(30))



    class Book(db.Model):

        id = db.Column(db.Integer, primary_key=True)

        title = db.Column(db.String(200))

        author_id = db.Column(db.Integer, db.ForeignKey('author.id'))

        author = db.relationship('Author', backref='books')



Given the model classes above, if we wanted to mock up some data for our

database, we could do so in single file, or we could even split our

fixtures into multiple files each corresponding to a single model class.

For this simple example, we'll go with one file that we'll call

``authors.json``.



A fixtures file contains a list of objects. Each object contains a key

called ``records`` that holds another list of objects each representing

either a row in a table, or an instance of a model. If you wish to work

with tables, you'll need to specify the name of the table with the

``table`` key. If you'd prefer to work with models, specify the

fully-qualified class name of the model using the ``model`` key. Once

you've specified the table or model you want to work with, you'll need

to specify the data associated with each table row, or model instance.

Each object in the ``records`` list will hold the data for a single row

or model. The example below is the JSON for a single author record and a

few books associated with that author. Create a file called

``myapp/fixtures/authors.json`` and copy and paste the fixtures JSON

below into that file.



.. code:: json



    [

        {

            "table": "author",

            "records": [{

                "id": 1,

                "first_name": "William",

                "last_name": "Gibson",

            }]

        },

        {

            "model": "myapp.models.Book",

            "records": [{

                "title": "Neuromancer",

                "author_id": 1

            },

            {

                "title": "Count Zero",

                "author_id": 1

            },

            {

                "title": "Mona Lisa Overdrive",

                "author_id": 1

            }]

        }

    ]



Another option, if you have `PyYAML `__ installed,

is to write your fixtures using the YAML syntax instead of JSON.

Personally, I prefer to use YAML; I find its syntax is easier to read,

and I find the ability to add comments to my fixtures to be invaluable.



If you'd prefer to use YAML, I've added a version of the authors.json

file written in YAML below. Just copy and paste it into a file called

``myapp/fixtures/authors.yaml`` in place of creating the JSON file

above.



.. code:: yaml



    - table: author

      records:

        - id: 1

          first_name: William

          last_name: Gibson



    - model: myapp.models.Book

      records:

        - title: Neuromancer

          author_id: 1

          published_date: 1984-07-01

        - title: Count Zero

          author_id: 1

          published_date: 1986-03-01

        - title: Neuromancer

          author_id: 1

          published_date: 1988-10-01



After reading over the previous section, you might be asking yourself

why the library supports two methods for adding records to the database.

There are a few good reasons for supporting both tables and models when

creating fixtures. Using tables is faster, since we can take advantage

of SQLAlchemy's bulk insert to add several records at once. However, to

do so, you must first make sure that the records list is homegenous.

**In other words, every object in the ``records`` list must have the

same set of key/value pairs, otherwise the bulk insert will not work.**

Using models, however, allows you to have a heterogenous list of record

objects.



The other reason you may want to use models instead of tables is that

you'll be able to take advantage of any python-level defaults, checks,

etc. that you have setup on the model. Using a table, bypasses the model

completely and inserts the data directly into the database, which means

you'll need to think on a lower level when creating table-based

fixtures.



Usage

-----



To use Flask-Fixtures in your unit tests, you'll need to make sure your

test class inherits from ``FixturesMixin`` and that you've specified a

list of fixtures files to load. The sample code below shows how to do

each these steps.



First, make sure the app that you're testing is initialized with the proper

configuration. Then import and initialize the ``FixturesMixin`` class, create

a new test class, and inherit from ``FixturesMixin``. Now you just need to

tell Flask-Fixtures which fixtures files to use for your tests. You can do so

by setting the ``fixtures`` class variable. Doing so will setup and tear down

fixtures between each test. To persist fixtures across tests, i.e., to setup

fixtures only when the class is first created and tear them down after all

tests have finished executing, you'll need to set the ``persist_fixtures``

variable to True. The ``fixtures`` variable should be set to a list of

strings, each of which is the name of a fixtures file to load. Flask-Fixtures

will then search the default fixtures directory followed by each directory in

the ``FIXTURES_DIRS`` config variable, in order, for a file matching each name

in the list and load each into the test database.



.. code:: python



    # myapp/fixtures/test_fixtures.py



    import unittest



    from myapp import app

    from myapp.models import db, Book, Author



    from flask_fixtures import FixturesMixin



    # Configure the app with the testing configuration

    app.config.from_object('myapp.config.TestConfig')



    # Make sure to inherit from the FixturesMixin class

    class TestFoo(unittest.TestCase, FixturesMixin):



        # Specify the fixtures file(s) you want to load.

        # Change the list below to ['authors.yaml'] if you created your fixtures

        # file using YAML instead of JSON.

        fixtures = ['authors.json']



        # Specify the Flask app and db we want to use for this set of tests

        app = app

        db = db



        # Your tests go here



        def test_authors(self):

            authors = Author.query.all()

            assert len(authors) == Author.query.count() == 1

            assert len(authors[0].books) == 3



        def test_books(self):

            books = Book.query.all()

            assert len(books) == Book.query.count() == 3

            gibson = Author.query.filter(Author.last_name=='Gibson').one()

            for book in books:

                assert book.author == gibson



Examples

--------



To see the library in action, you can find a simple Flask application

and set of unit tests matching the ones in the example above in the

``tests/myapp`` directory. To run these examples yourself, just follow

the directions below for "Contributing to Flask-Fixtures".



Contributing to Flask-Fixtures

------------------------------



Currently, Flask-Fixtures supports python versions 2.6 and 2.7 and the

py.test, nose, and unittest (included in the python standard library)

libraries. To contribute bug fixes and features to Flask-Fixtures,

you'll need to make sure that any code you contribute does not break any

of the existing unit tests in any of these environments.



To run unit tests in all six of the supported environments, I suggest

you install `tox `__ and simply run the

``tox`` command. If, however, you insist on running things by hand,

you'll need to create a virtualenv for both python 2.6 and python 2.7.

Then, install nose and py.test in each virtualenv. Finally, you can run

the tests with the commands in the table below.



+------------+-------------------------------------------------------+

| Library    | Command                                               |

+============+=======================================================+

| py.test    | py.test                                               |

+------------+-------------------------------------------------------+

| nose       | nosetests                                             |

+------------+-------------------------------------------------------+

| unittest   | python -m unittest discover --start-directory tests   |

+------------+-------------------------------------------------------+