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Metadata-Version: 1.1
Name: genty
Version: 1.3.0
Summary: Allows you to run a test with multiple data sets
Home-page: https://github.com/box/genty
Author: Box
Author-email: oss@box.com
License:                              Apache License
                       Version 2.0, January 2004
                    http://www.apache.org/licenses/

TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

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Description: genty
        =====
        
        .. image:: http://opensource.box.com/badges/active.svg
            :target: http://opensource.box.com/badges
        
        .. image:: https://travis-ci.org/box/genty.png?branch=master
            :target: https://travis-ci.org/box/genty
        
        .. image:: https://img.shields.io/pypi/v/genty.svg
            :target: https://pypi.python.org/pypi/genty
        
        .. image:: https://img.shields.io/pypi/dm/genty.svg
            :target: https://pypi.python.org/pypi/genty
        
        
        About
        -----
        
        Genty, pronounced "gen-tee", stands for "generate tests". It promotes generative 
        testing, where a single test can execute over a variety of input. Genty makes
        this a breeze.
        
        For example, consider a file sample.py containing both the code under test and
        the tests:
        
        .. code-block:: python
        
            from genty import genty, genty_repeat, genty_dataset
            from unittest import TestCase
        
            # Here's the class under test
            class MyClass(object):
                def add_one(self, x): 
                    return x + 1
        
            # Here's the test code
            @genty
            class MyClassTests(TestCase):
                @genty_dataset(
                    (0, 1),
                    (100000, 100001),
                )
                def test_add_one(self, value, expected_result):
                    actual_result = MyClass().add_one(value)
                    self.assertEqual(expected_result, actual_result)
        
        
        Running the ``MyClassTests`` using the default unittest runner
        
        .. code-block:: console
        
            $ python -m unittest -v sample
            test_add_one(0, 1) (sample.MyClassTests) ... ok
            test_add_one(100000, 100001) (sample.MyClassTests) ... ok
        
            ----------------------------------------------------------------------
            Ran 2 tests in 0.000s
        
            OK
        
        Instead of having to write multiple independent tests for various test cases, 
        code can be refactored and parametrized using genty!
        
        It produces readable tests.
        It produces maintainable tests.
        It produces expressive tests.
        
        Another option is running the same test multiple times. This is useful in stress
        tests or when exercising code looking for race conditions. This particular test
        
        .. code-block:: python
        
            @genty_repeat(3)
            def test_adding_one_to_zero(self):
                self.assertEqual(1, MyClass().add_one(0))
        
        
        would be run 3 times, producing output like
        
        .. code-block:: console
        
            $ python -m unittest -v sample
            test_adding_one() iteration_1 (sample.MyClassTests) ... ok
            test_adding_one() iteration_2 (sample.MyClassTests) ... ok
            test_adding_one() iteration_3 (sample.MyClassTests) ... ok
        
            ----------------------------------------------------------------------
            Ran 3 tests in 0.001s
        
            OK
        
        The 2 techniques can be combined:
        
        .. code-block:: python
        
                @genty_repeat(2)
                @genty_dataset(
                    (0, 1),
                    (100000, 100001),
                )
                def test_add_one(self, value, expected_result):
                    actual_result = MyClass().add_one(value)
                    self.assertEqual(expected_result, actual_result)
                    
        
        There are more options to explore including naming your datasets and ``genty_args``.
        
        .. code-block:: python
         
                @genty_dataset(
                    default_case=(0, 1),
                    limit_case=(999, 1000),
                    error_case=genty_args(-1, -1, is_something=False),
                )
                def test_complex(self, value1, value2, optional_value=None, is_something=True):
                    ...
         
        
        would run 3 tests, producing output like
        
        .. code-block:: console
        
            $ python -m unittest -v sample
            test_complex(default_case) (sample.MyClassTests) ... ok
            test_complex(limit_case) (sample.MyClassTests) ... ok
            test_complex(error_case) (sample.MyClassTests) ... ok
        
            ----------------------------------------------------------------------
            Ran 3 tests in 0.003s
        
            OK
        
        
        The ``@genty_datasets`` can be chained together. This is useful, for example, if there are semantically different datasets
        so keeping them separate would help expressiveness.
        
        
        .. code-block:: python
        
        	@genty_dataset(10, 100)
        	@genty_dataset('first', 'second')
        	def test_composing(self, parameter_value):
        		...
        
        
        would run 4 tests, producing output like
        
        .. code-block:: console
        
            $ python -m unittest -v sample
            test_composing(10) (sample.MyClassTests) ... ok
            test_composing(100) (sample.MyClassTests) ... ok
            test_composing(u'first') (sample.MyClassTests) ... ok
            test_composing(u'second') (sample.MyClassTests) ... ok
        
            ----------------------------------------------------------------------
            Ran 4 tests in 0.000s
        
            OK
        
        
        Sometimes the parameters to a test can't be determined at module load time. For example,
        some test might be based on results from some http request. And first the test needs to
        authenticate, etc. This is supported using the ``@genty_dataprovider`` decorator like so:
        
        
        .. code-block:: python
        
            def setUp(self):
                super(MyClassTests, self).setUp()
                
                # http authentication happens
                # And imagine that _some_function is actually some http request
                self._some_function = lambda x, y: ((x + y), (x - y), (x * y))
        
            @genty_dataset((1000, 100), (100, 1))
            def calculate(self, x_val, y_val):
                # when this is called... we've been authenticated
                return self._some_function(x_val, y_val)
        
            @genty_dataprovider(calculate)
            def test_heavy(self, data1, data2, data3):
                ...
        
        
        would run 4 tests, producing output like
        
        .. code-block:: console
        
        
        	$ python -m unittest -v sample
        	test_heavy_calculate(100, 1) (sample.MyClassTests) ... ok
        	test_heavy_calculate(1000, 100) (sample.MyClassTests) ... ok
        
        	----------------------------------------------------------------------
        	Ran 2 tests in 0.000s
        
        	OK
        
        Notice here how the name of the helper (``calculate``) is added to the names of the 2
        executed test cases.
        
        Like ``@genty_dataset``, ``@genty_dataprovider`` can be chained together.
        
        Enjoy!
        
        Deferred Parameterization
        -------------------------
        
        Parametrized tests where the final parameters are not determined until test
        execution time. It looks like so:
        
        .. code-block:: python
        
            @genty_dataset((1000, 100), (100, 1))
            def calculate(self, x_val, y_val):
                # when this is called... we've been authenticated, perhaps in
                # some Test.setUp() method.
        
                # Let's imagine that _some_function requires that authentication.
                # And it returns a 3-tuple, matching that signature of
                # of the test(s) decorated with this 'calculate' method.
                return self._some_function(x_val, y_val)
        
            @genty_dataprovider(calculate)
            def test_heavy(self, data1, data2, data3):
                ...
        
        The ``calculate()`` method is called 2 times based on the ``@genty_dataset``
        decorator, and each of it's return values define the final parameters that will
        be given to the method ``test_heavy(...)``.
        
        Installation
        ------------
        
        To install, simply:
        
        .. code-block:: console
        
            pip install genty
        
        
        Contributing
        ------------
        
        See `CONTRIBUTING.rst <https://github.com/box/genty/blob/master/CONTRIBUTING.rst>`_.
        
        
        Setup
        ~~~~~
        
        Create a virtual environment and install packages -
        
        .. code-block:: console
        
            mkvirtualenv genty
            pip install -r requirements-dev.txt
        
        
        Testing
        ~~~~~~~
        
        Run all tests using -
        
        .. code-block:: console
        
            tox
        
        The tox tests include code style checks via pep8 and pylint.
        
        The tox tests are configured to run on Python 2.6, 2.7, 3.3, 3.4, 3.5, and
        PyPy 2.6.
        
        
        Copyright and License
        ---------------------
        
        ::
        
         Copyright 2015 Box, Inc. All rights reserved.
        
         Licensed under the Apache License, Version 2.0 (the "License");
         you may not use this file except in compliance with the License.
         You may obtain a copy of the License at
        
            http://www.apache.org/licenses/LICENSE-2.0
        
         Unless required by applicable law or agreed to in writing, software
         distributed under the License is distributed on an "AS IS" BASIS,
         WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
         See the License for the specific language governing permissions and
         limitations under the License.
        
Keywords: genty,tests,generative,unittest
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Software Development
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Operating System :: OS Independent
Classifier: Operating System :: POSIX
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS :: MacOS X