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16
Doc/glossary.rst
View File

@ -194,7 +194,7 @@ Glossary
An object exposing a file-oriented API (with methods such as
:meth:`read()` or :meth:`write()`) to an underlying resource. Depending
on the way it was created, a file object can mediate access to a real
on-disk file or to another other type of storage or communication device
on-disk file or to another type of storage or communication device
(for example standard input/output, in-memory buffers, sockets, pipes,
etc.). File objects are also called :dfn:`file-like objects` or
:dfn:`streams`.
@ -523,6 +523,20 @@ Glossary
definition), or pass several arguments as a list to a function. See
:term:`argument`.
provisional package
A provisional package is one which has been deliberately excluded from the
standard library's backwards compatibility guarantees. While major
changes to such packages are not expected, as long as they are marked
provisional, backwards incompatible changes (up to and including removal
of the package) may occur if deemed necessary by core developers. Such
changes will not be made gratuitously -- they will occur only if serious
flaws are uncovered that were missed prior to the inclusion of the
package.
This process allows the standard library to continue to evolve over time,
without locking in problematic design errors for extended periods of time.
See :pep:`411` for more details.
Python 3000
Nickname for the Python 3.x release line (coined long ago when the release
of version 3 was something in the distant future.) This is also

4
Doc/library/development.rst
View File

@ -20,10 +20,6 @@ The list of modules described in this chapter is:
doctest.rst
unittest.rst
unittest.mock.rst
unittest.mock-patch.rst
unittest.mock-magicmethods.rst
unittest.mock-helpers.rst
unittest.mock-getting-started.rst
unittest.mock-examples.rst
2to3.rst
test.rst

9
Doc/library/syslog.rst
View File

@ -78,11 +78,14 @@ Priority levels (high to low):
Facilities:
:const:`LOG_KERN`, :const:`LOG_USER`, :const:`LOG_MAIL`, :const:`LOG_DAEMON`,
:const:`LOG_AUTH`, :const:`LOG_LPR`, :const:`LOG_NEWS`, :const:`LOG_UUCP`,
:const:`LOG_CRON` and :const:`LOG_LOCAL0` to :const:`LOG_LOCAL7`.
:const:`LOG_CRON`, :const:`LOG_SYSLOG`, :const:`LOG_LOCAL0` to
:const:`LOG_LOCAL7`, and, if defined in ``<syslog.h>``,
:const:`LOG_AUTHPRIV`.
Log options:
:const:`LOG_PID`, :const:`LOG_CONS`, :const:`LOG_NDELAY`, :const:`LOG_NOWAIT`
and :const:`LOG_PERROR` if defined in ``<syslog.h>``.
:const:`LOG_PID`, :const:`LOG_CONS`, :const:`LOG_NDELAY`, and, if defined
in ``<syslog.h>``, :const:`LOG_ODELAY`, :const:`LOG_NOWAIT`, and
:const:`LOG_PERROR`.
Examples

7
Doc/library/time.rst
View File

@ -143,12 +143,14 @@ The module defines the following functions and data items:
.. versionadded:: 3.3
.. function:: clock_gettime(clk_id)
Return the time of the specified clock *clk_id*.
.. versionadded:: 3.3
.. data:: CLOCK_REALTIME
System-wide real-time clock. Setting this clock requires appropriate
@ -156,6 +158,7 @@ The module defines the following functions and data items:
.. versionadded:: 3.3
.. data:: CLOCK_MONOTONIC
Clock that cannot be set and represents monotonic time since some
@ -163,6 +166,7 @@ The module defines the following functions and data items:
.. versionadded:: 3.3
.. data:: CLOCK_MONOTONIC_RAW
Similar to :data:`CLOCK_MONOTONIC`, but provides access to a raw
@ -172,18 +176,21 @@ The module defines the following functions and data items:
.. versionadded:: 3.3
.. data:: CLOCK_PROCESS_CPUTIME_ID
High-resolution per-process timer from the CPU.
.. versionadded:: 3.3
.. data:: CLOCK_THREAD_CPUTIME_ID
Thread-specific CPU-time clock.
.. versionadded:: 3.3
.. function:: ctime([secs])
Convert a time expressed in seconds since the epoch to a string representing

425
Doc/library/unittest.mock-examples.rst
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@ -1,18 +1,427 @@
.. _further-examples:
:mod:`unittest.mock` --- getting started
========================================
:mod:`unittest.mock` --- further examples
=========================================
.. module:: unittest.mock
:synopsis: Mock object library.
.. moduleauthor:: Michael Foord <michael@python.org>
.. currentmodule:: unittest.mock
.. versionadded:: 3.3
Here are some more examples for some slightly more advanced scenarios than in
the :ref:`getting started <getting-started>` guide.
.. _getting-started:
Using Mock
----------
Mock Patching Methods
~~~~~~~~~~~~~~~~~~~~~
Common uses for :class:`Mock` objects include:
* Patching methods
* Recording method calls on objects
You might want to replace a method on an object to check that
it is called with the correct arguments by another part of the system:
>>> real = SomeClass()
>>> real.method = MagicMock(name='method')
>>> real.method(3, 4, 5, key='value')
<MagicMock name='method()' id='...'>
Once our mock has been used (`real.method` in this example) it has methods
and attributes that allow you to make assertions about how it has been used.
.. note::
In most of these examples the :class:`Mock` and :class:`MagicMock` classes
are interchangeable. As the `MagicMock` is the more capable class it makes
a sensible one to use by default.
Once the mock has been called its :attr:`~Mock.called` attribute is set to
`True`. More importantly we can use the :meth:`~Mock.assert_called_with` or
:meth`~Mock.assert_called_once_with` method to check that it was called with
the correct arguments.
This example tests that calling `ProductionClass().method` results in a call to
the `something` method:
>>> class ProductionClass(object):
... def method(self):
... self.something(1, 2, 3)
... def something(self, a, b, c):
... pass
...
>>> real = ProductionClass()
>>> real.something = MagicMock()
>>> real.method()
>>> real.something.assert_called_once_with(1, 2, 3)
Mock for Method Calls on an Object
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In the last example we patched a method directly on an object to check that it
was called correctly. Another common use case is to pass an object into a
method (or some part of the system under test) and then check that it is used
in the correct way.
The simple `ProductionClass` below has a `closer` method. If it is called with
an object then it calls `close` on it.
>>> class ProductionClass(object):
... def closer(self, something):
... something.close()
...
So to test it we need to pass in an object with a `close` method and check
that it was called correctly.
>>> real = ProductionClass()
>>> mock = Mock()
>>> real.closer(mock)
>>> mock.close.assert_called_with()
We don't have to do any work to provide the 'close' method on our mock.
Accessing close creates it. So, if 'close' hasn't already been called then
accessing it in the test will create it, but :meth:`~Mock.assert_called_with`
will raise a failure exception.
Mocking Classes
~~~~~~~~~~~~~~~
A common use case is to mock out classes instantiated by your code under test.
When you patch a class, then that class is replaced with a mock. Instances
are created by *calling the class*. This means you access the "mock instance"
by looking at the return value of the mocked class.
In the example below we have a function `some_function` that instantiates `Foo`
and calls a method on it. The call to `patch` replaces the class `Foo` with a
mock. The `Foo` instance is the result of calling the mock, so it is configured
by modify the mock :attr:`~Mock.return_value`.
>>> def some_function():
... instance = module.Foo()
... return instance.method()
...
>>> with patch('module.Foo') as mock:
... instance = mock.return_value
... instance.method.return_value = 'the result'
... result = some_function()
... assert result == 'the result'
Naming your mocks
~~~~~~~~~~~~~~~~~
It can be useful to give your mocks a name. The name is shown in the repr of
the mock and can be helpful when the mock appears in test failure messages. The
name is also propagated to attributes or methods of the mock:
>>> mock = MagicMock(name='foo')
>>> mock
<MagicMock name='foo' id='...'>
>>> mock.method
<MagicMock name='foo.method' id='...'>
Tracking all Calls
~~~~~~~~~~~~~~~~~~
Often you want to track more than a single call to a method. The
:attr:`~Mock.mock_calls` attribute records all calls
to child attributes of the mock - and also to their children.
>>> mock = MagicMock()
>>> mock.method()
<MagicMock name='mock.method()' id='...'>
>>> mock.attribute.method(10, x=53)
<MagicMock name='mock.attribute.method()' id='...'>
>>> mock.mock_calls
[call.method(), call.attribute.method(10, x=53)]
If you make an assertion about `mock_calls` and any unexpected methods
have been called, then the assertion will fail. This is useful because as well
as asserting that the calls you expected have been made, you are also checking
that they were made in the right order and with no additional calls:
You use the :data:`call` object to construct lists for comparing with
`mock_calls`:
>>> expected = [call.method(), call.attribute.method(10, x=53)]
>>> mock.mock_calls == expected
True
Setting Return Values and Attributes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Setting the return values on a mock object is trivially easy:
>>> mock = Mock()
>>> mock.return_value = 3
>>> mock()
3
Of course you can do the same for methods on the mock:
>>> mock = Mock()
>>> mock.method.return_value = 3
>>> mock.method()
3
The return value can also be set in the constructor:
>>> mock = Mock(return_value=3)
>>> mock()
3
If you need an attribute setting on your mock, just do it:
>>> mock = Mock()
>>> mock.x = 3
>>> mock.x
3
Sometimes you want to mock up a more complex situation, like for example
`mock.connection.cursor().execute("SELECT 1")`. If we wanted this call to
return a list, then we have to configure the result of the nested call.
We can use :data:`call` to construct the set of calls in a "chained call" like
this for easy assertion afterwards:
>>> mock = Mock()
>>> cursor = mock.connection.cursor.return_value
>>> cursor.execute.return_value = ['foo']
>>> mock.connection.cursor().execute("SELECT 1")
['foo']
>>> expected = call.connection.cursor().execute("SELECT 1").call_list()
>>> mock.mock_calls
[call.connection.cursor(), call.connection.cursor().execute('SELECT 1')]
>>> mock.mock_calls == expected
True
It is the call to `.call_list()` that turns our call object into a list of
calls representing the chained calls.
Raising exceptions with mocks
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A useful attribute is :attr:`~Mock.side_effect`. If you set this to an
exception class or instance then the exception will be raised when the mock
is called.
>>> mock = Mock(side_effect=Exception('Boom!'))
>>> mock()
Traceback (most recent call last):
...
Exception: Boom!
Side effect functions and iterables
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
`side_effect` can also be set to a function or an iterable. The use case for
`side_effect` as an iterable is where your mock is going to be called several
times, and you want each call to return a different value. When you set
`side_effect` to an iterable every call to the mock returns the next value
from the iterable:
>>> mock = MagicMock(side_effect=[4, 5, 6])
>>> mock()
4
>>> mock()
5
>>> mock()
6
For more advanced use cases, like dynamically varying the return values
depending on what the mock is called with, `side_effect` can be a function.
The function will be called with the same arguments as the mock. Whatever the
function returns is what the call returns:
>>> vals = {(1, 2): 1, (2, 3): 2}
>>> def side_effect(*args):
... return vals[args]
...
>>> mock = MagicMock(side_effect=side_effect)
>>> mock(1, 2)
1
>>> mock(2, 3)
2
Creating a Mock from an Existing Object
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
One problem with over use of mocking is that it couples your tests to the
implementation of your mocks rather than your real code. Suppose you have a
class that implements `some_method`. In a test for another class, you
provide a mock of this object that *also* provides `some_method`. If later
you refactor the first class, so that it no longer has `some_method` - then
your tests will continue to pass even though your code is now broken!
`Mock` allows you to provide an object as a specification for the mock,
using the `spec` keyword argument. Accessing methods / attributes on the
mock that don't exist on your specification object will immediately raise an
attribute error. If you change the implementation of your specification, then
tests that use that class will start failing immediately without you having to
instantiate the class in those tests.
>>> mock = Mock(spec=SomeClass)
>>> mock.old_method()
Traceback (most recent call last):
...
AttributeError: object has no attribute 'old_method'
If you want a stronger form of specification that prevents the setting
of arbitrary attributes as well as the getting of them then you can use
`spec_set` instead of `spec`.
Patch Decorators
----------------
.. note::
With `patch` it matters that you patch objects in the namespace where they
are looked up. This is normally straightforward, but for a quick guide
read :ref:`where to patch <where-to-patch>`.
A common need in tests is to patch a class attribute or a module attribute,
for example patching a builtin or patching a class in a module to test that it
is instantiated. Modules and classes are effectively global, so patching on
them has to be undone after the test or the patch will persist into other
tests and cause hard to diagnose problems.
mock provides three convenient decorators for this: `patch`, `patch.object` and
`patch.dict`. `patch` takes a single string, of the form
`package.module.Class.attribute` to specify the attribute you are patching. It
also optionally takes a value that you want the attribute (or class or
whatever) to be replaced with. 'patch.object' takes an object and the name of
the attribute you would like patched, plus optionally the value to patch it
with.
`patch.object`:
>>> original = SomeClass.attribute
>>> @patch.object(SomeClass, 'attribute', sentinel.attribute)
... def test():
... assert SomeClass.attribute == sentinel.attribute
...
>>> test()
>>> assert SomeClass.attribute == original
>>> @patch('package.module.attribute', sentinel.attribute)
... def test():
... from package.module import attribute
... assert attribute is sentinel.attribute
...
>>> test()
If you are patching a module (including `__builtin__`) then use `patch`
instead of `patch.object`:
>>> mock = MagicMock(return_value = sentinel.file_handle)
>>> with patch('__builtin__.open', mock):
... handle = open('filename', 'r')
...
>>> mock.assert_called_with('filename', 'r')
>>> assert handle == sentinel.file_handle, "incorrect file handle returned"
The module name can be 'dotted', in the form `package.module` if needed:
>>> @patch('package.module.ClassName.attribute', sentinel.attribute)
... def test():
... from package.module import ClassName
... assert ClassName.attribute == sentinel.attribute
...
>>> test()
A nice pattern is to actually decorate test methods themselves:
>>> class MyTest(unittest2.TestCase):
... @patch.object(SomeClass, 'attribute', sentinel.attribute)
... def test_something(self):
... self.assertEqual(SomeClass.attribute, sentinel.attribute)
...
>>> original = SomeClass.attribute
>>> MyTest('test_something').test_something()
>>> assert SomeClass.attribute == original
If you want to patch with a Mock, you can use `patch` with only one argument
(or `patch.object` with two arguments). The mock will be created for you and
passed into the test function / method:
>>> class MyTest(unittest2.TestCase):
... @patch.object(SomeClass, 'static_method')
... def test_something(self, mock_method):
... SomeClass.static_method()
... mock_method.assert_called_with()
...
>>> MyTest('test_something').test_something()
You can stack up multiple patch decorators using this pattern:
>>> class MyTest(unittest2.TestCase):
... @patch('package.module.ClassName1')
... @patch('package.module.ClassName2')
... def test_something(self, MockClass2, MockClass1):
... self.assertTrue(package.module.ClassName1 is MockClass1)
... self.assertTrue(package.module.ClassName2 is MockClass2)
...
>>> MyTest('test_something').test_something()
When you nest patch decorators the mocks are passed in to the decorated
function in the same order they applied (the normal *python* order that
decorators are applied). This means from the bottom up, so in the example
above the mock for `test_module.ClassName2` is passed in first.
There is also :func:`patch.dict` for setting values in a dictionary just
during a scope and restoring the dictionary to its original state when the test
ends:
>>> foo = {'key': 'value'}
>>> original = foo.copy()
>>> with patch.dict(foo, {'newkey': 'newvalue'}, clear=True):
... assert foo == {'newkey': 'newvalue'}
...
>>> assert foo == original
`patch`, `patch.object` and `patch.dict` can all be used as context managers.
Where you use `patch` to create a mock for you, you can get a reference to the
mock using the "as" form of the with statement:
>>> class ProductionClass(object):
... def method(self):
... pass
...
>>> with patch.object(ProductionClass, 'method') as mock_method:
... mock_method.return_value = None
... real = ProductionClass()
... real.method(1, 2, 3)
...
>>> mock_method.assert_called_with(1, 2, 3)
As an alternative `patch`, `patch.object` and `patch.dict` can be used as
class decorators. When used in this way it is the same as applying the
decorator indvidually to every method whose name starts with "test".
.. _further-examples:
Further Examples
================
Here are some more examples for some slightly more advanced scenarios.
Mocking chained calls

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Doc/library/unittest.mock-getting-started.rst
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@ -1,419 +0,0 @@
:mod:`unittest.mock` --- getting started
========================================
.. module:: unittest.mock
:synopsis: Mock object library.
.. moduleauthor:: Michael Foord <michael@python.org>
.. currentmodule:: unittest.mock
.. versionadded:: 3.3
.. _getting-started:
Using Mock
----------
Mock Patching Methods
~~~~~~~~~~~~~~~~~~~~~
Common uses for :class:`Mock` objects include:
* Patching methods
* Recording method calls on objects
You might want to replace a method on an object to check that
it is called with the correct arguments by another part of the system:
>>> real = SomeClass()
>>> real.method = MagicMock(name='method')
>>> real.method(3, 4, 5, key='value')
<MagicMock name='method()' id='...'>
Once our mock has been used (`real.method` in this example) it has methods
and attributes that allow you to make assertions about how it has been used.
.. note::
In most of these examples the :class:`Mock` and :class:`MagicMock` classes
are interchangeable. As the `MagicMock` is the more capable class it makes
a sensible one to use by default.
Once the mock has been called its :attr:`~Mock.called` attribute is set to
`True`. More importantly we can use the :meth:`~Mock.assert_called_with` or
:meth`~Mock.assert_called_once_with` method to check that it was called with
the correct arguments.
This example tests that calling `ProductionClass().method` results in a call to
the `something` method:
>>> class ProductionClass(object):
... def method(self):
... self.something(1, 2, 3)
... def something(self, a, b, c):
... pass
...
>>> real = ProductionClass()
>>> real.something = MagicMock()
>>> real.method()
>>> real.something.assert_called_once_with(1, 2, 3)
Mock for Method Calls on an Object
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In the last example we patched a method directly on an object to check that it
was called correctly. Another common use case is to pass an object into a
method (or some part of the system under test) and then check that it is used
in the correct way.
The simple `ProductionClass` below has a `closer` method. If it is called with
an object then it calls `close` on it.
>>> class ProductionClass(object):
... def closer(self, something):
... something.close()
...
So to test it we need to pass in an object with a `close` method and check
that it was called correctly.
>>> real = ProductionClass()
>>> mock = Mock()
>>> real.closer(mock)
>>> mock.close.assert_called_with()
We don't have to do any work to provide the 'close' method on our mock.
Accessing close creates it. So, if 'close' hasn't already been called then
accessing it in the test will create it, but :meth:`~Mock.assert_called_with`
will raise a failure exception.
Mocking Classes
~~~~~~~~~~~~~~~
A common use case is to mock out classes instantiated by your code under test.
When you patch a class, then that class is replaced with a mock. Instances
are created by *calling the class*. This means you access the "mock instance"
by looking at the return value of the mocked class.
In the example below we have a function `some_function` that instantiates `Foo`
and calls a method on it. The call to `patch` replaces the class `Foo` with a
mock. The `Foo` instance is the result of calling the mock, so it is configured
by modify the mock :attr:`~Mock.return_value`.
>>> def some_function():
... instance = module.Foo()
... return instance.method()
...
>>> with patch('module.Foo') as mock:
... instance = mock.return_value
... instance.method.return_value = 'the result'
... result = some_function()
... assert result == 'the result'
Naming your mocks
~~~~~~~~~~~~~~~~~
It can be useful to give your mocks a name. The name is shown in the repr of
the mock and can be helpful when the mock appears in test failure messages. The
name is also propagated to attributes or methods of the mock:
>>> mock = MagicMock(name='foo')
>>> mock
<MagicMock name='foo' id='...'>
>>> mock.method
<MagicMock name='foo.method' id='...'>
Tracking all Calls
~~~~~~~~~~~~~~~~~~
Often you want to track more than a single call to a method. The
:attr:`~Mock.mock_calls` attribute records all calls
to child attributes of the mock - and also to their children.
>>> mock = MagicMock()
>>> mock.method()
<MagicMock name='mock.method()' id='...'>
>>> mock.attribute.method(10, x=53)
<MagicMock name='mock.attribute.method()' id='...'>
>>> mock.mock_calls
[call.method(), call.attribute.method(10, x=53)]
If you make an assertion about `mock_calls` and any unexpected methods
have been called, then the assertion will fail. This is useful because as well
as asserting that the calls you expected have been made, you are also checking
that they were made in the right order and with no additional calls:
You use the :data:`call` object to construct lists for comparing with
`mock_calls`:
>>> expected = [call.method(), call.attribute.method(10, x=53)]
>>> mock.mock_calls == expected
True
Setting Return Values and Attributes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Setting the return values on a mock object is trivially easy:
>>> mock = Mock()
>>> mock.return_value = 3
>>> mock()
3
Of course you can do the same for methods on the mock:
>>> mock = Mock()
>>> mock.method.return_value = 3
>>> mock.method()
3
The return value can also be set in the constructor:
>>> mock = Mock(return_value=3)
>>> mock()
3
If you need an attribute setting on your mock, just do it:
>>> mock = Mock()
>>> mock.x = 3
>>> mock.x
3
Sometimes you want to mock up a more complex situation, like for example
`mock.connection.cursor().execute("SELECT 1")`. If we wanted this call to
return a list, then we have to configure the result of the nested call.
We can use :data:`call` to construct the set of calls in a "chained call" like
this for easy assertion afterwards:
>>> mock = Mock()
>>> cursor = mock.connection.cursor.return_value
>>> cursor.execute.return_value = ['foo']
>>> mock.connection.cursor().execute("SELECT 1")
['foo']
>>> expected = call.connection.cursor().execute("SELECT 1").call_list()
>>> mock.mock_calls
[call.connection.cursor(), call.connection.cursor().execute('SELECT 1')]
>>> mock.mock_calls == expected
True
It is the call to `.call_list()` that turns our call object into a list of
calls representing the chained calls.
Raising exceptions with mocks
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A useful attribute is :attr:`~Mock.side_effect`. If you set this to an
exception class or instance then the exception will be raised when the mock
is called.
>>> mock = Mock(side_effect=Exception('Boom!'))
>>> mock()
Traceback (most recent call last):
...
Exception: Boom!
Side effect functions and iterables
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
`side_effect` can also be set to a function or an iterable. The use case for
`side_effect` as an iterable is where your mock is going to be called several
times, and you want each call to return a different value. When you set
`side_effect` to an iterable every call to the mock returns the next value
from the iterable:
>>> mock = MagicMock(side_effect=[4, 5, 6])
>>> mock()
4
>>> mock()
5
>>> mock()
6
For more advanced use cases, like dynamically varying the return values
depending on what the mock is called with, `side_effect` can be a function.
The function will be called with the same arguments as the mock. Whatever the
function returns is what the call returns:
>>> vals = {(1, 2): 1, (2, 3): 2}
>>> def side_effect(*args):
... return vals[args]
...
>>> mock = MagicMock(side_effect=side_effect)
>>> mock(1, 2)
1
>>> mock(2, 3)
2
Creating a Mock from an Existing Object
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
One problem with over use of mocking is that it couples your tests to the
implementation of your mocks rather than your real code. Suppose you have a
class that implements `some_method`. In a test for another class, you
provide a mock of this object that *also* provides `some_method`. If later
you refactor the first class, so that it no longer has `some_method` - then
your tests will continue to pass even though your code is now broken!
`Mock` allows you to provide an object as a specification for the mock,
using the `spec` keyword argument. Accessing methods / attributes on the
mock that don't exist on your specification object will immediately raise an
attribute error. If you change the implementation of your specification, then
tests that use that class will start failing immediately without you having to
instantiate the class in those tests.
>>> mock = Mock(spec=SomeClass)
>>> mock.old_method()
Traceback (most recent call last):
...
AttributeError: object has no attribute 'old_method'
If you want a stronger form of specification that prevents the setting
of arbitrary attributes as well as the getting of them then you can use
`spec_set` instead of `spec`.
Patch Decorators
----------------
.. note::
With `patch` it matters that you patch objects in the namespace where they
are looked up. This is normally straightforward, but for a quick guide
read :ref:`where to patch <where-to-patch>`.
A common need in tests is to patch a class attribute or a module attribute,
for example patching a builtin or patching a class in a module to test that it
is instantiated. Modules and classes are effectively global, so patching on
them has to be undone after the test or the patch will persist into other
tests and cause hard to diagnose problems.
mock provides three convenient decorators for this: `patch`, `patch.object` and
`patch.dict`. `patch` takes a single string, of the form
`package.module.Class.attribute` to specify the attribute you are patching. It
also optionally takes a value that you want the attribute (or class or
whatever) to be replaced with. 'patch.object' takes an object and the name of
the attribute you would like patched, plus optionally the value to patch it
with.
`patch.object`:
>>> original = SomeClass.attribute
>>> @patch.object(SomeClass, 'attribute', sentinel.attribute)
... def test():
... assert SomeClass.attribute == sentinel.attribute
...
>>> test()
>>> assert SomeClass.attribute == original
>>> @patch('package.module.attribute', sentinel.attribute)
... def test():
... from package.module import attribute
... assert attribute is sentinel.attribute
...
>>> test()
If you are patching a module (including `__builtin__`) then use `patch`
instead of `patch.object`:
>>> mock = MagicMock(return_value = sentinel.file_handle)
>>> with patch('__builtin__.open', mock):
... handle = open('filename', 'r')
...
>>> mock.assert_called_with('filename', 'r')
>>> assert handle == sentinel.file_handle, "incorrect file handle returned"
The module name can be 'dotted', in the form `package.module` if needed:
>>> @patch('package.module.ClassName.attribute', sentinel.attribute)
... def test():
... from package.module import ClassName
... assert ClassName.attribute == sentinel.attribute
...
>>> test()
A nice pattern is to actually decorate test methods themselves:
>>> class MyTest(unittest2.TestCase):
... @patch.object(SomeClass, 'attribute', sentinel.attribute)
... def test_something(self):
... self.assertEqual(SomeClass.attribute, sentinel.attribute)
...
>>> original = SomeClass.attribute
>>> MyTest('test_something').test_something()
>>> assert SomeClass.attribute == original
If you want to patch with a Mock, you can use `patch` with only one argument
(or `patch.object` with two arguments). The mock will be created for you and
passed into the test function / method:
>>> class MyTest(unittest2.TestCase):
... @patch.object(SomeClass, 'static_method')
... def test_something(self, mock_method):
... SomeClass.static_method()
... mock_method.assert_called_with()
...
>>> MyTest('test_something').test_something()
You can stack up multiple patch decorators using this pattern:
>>> class MyTest(unittest2.TestCase):
... @patch('package.module.ClassName1')
... @patch('package.module.ClassName2')
... def test_something(self, MockClass2, MockClass1):
... self.assertTrue(package.module.ClassName1 is MockClass1)
... self.assertTrue(package.module.ClassName2 is MockClass2)
...
>>> MyTest('test_something').test_something()
When you nest patch decorators the mocks are passed in to the decorated
function in the same order they applied (the normal *python* order that
decorators are applied). This means from the bottom up, so in the example
above the mock for `test_module.ClassName2` is passed in first.
There is also :func:`patch.dict` for setting values in a dictionary just
during a scope and restoring the dictionary to its original state when the test
ends:
>>> foo = {'key': 'value'}
>>> original = foo.copy()
>>> with patch.dict(foo, {'newkey': 'newvalue'}, clear=True):
... assert foo == {'newkey': 'newvalue'}
...
>>> assert foo == original
`patch`, `patch.object` and `patch.dict` can all be used as context managers.
Where you use `patch` to create a mock for you, you can get a reference to the
mock using the "as" form of the with statement:
>>> class ProductionClass(object):
... def method(self):
... pass
...
>>> with patch.object(ProductionClass, 'method') as mock_method:
... mock_method.return_value = None
... real = ProductionClass()
... real.method(1, 2, 3)
...
>>> mock_method.assert_called_with(1, 2, 3)
As an alternative `patch`, `patch.object` and `patch.dict` can be used as
class decorators. When used in this way it is the same as applying the
decorator indvidually to every method whose name starts with "test".
For some more advanced examples, see the :ref:`further-examples` page.

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:mod:`unittest.mock` --- helpers
================================
.. module:: unittest.mock
:synopsis: Mock object library.
.. moduleauthor:: Michael Foord <michael@python.org>
.. currentmodule:: unittest.mock
.. versionadded:: 3.3
sentinel
--------
.. data:: sentinel
The ``sentinel`` object provides a convenient way of providing unique
objects for your tests.
Attributes are created on demand when you access them by name. Accessing
the same attribute will always return the same object. The objects
returned have a sensible repr so that test failure messages are readable.
Sometimes when testing you need to test that a specific object is passed as an
argument to another method, or returned. It can be common to create named
sentinel objects to test this. `sentinel` provides a convenient way of
creating and testing the identity of objects like this.
In this example we monkey patch `method` to return `sentinel.some_object`:
>>> real = ProductionClass()
>>> real.method = Mock(name="method")
>>> real.method.return_value = sentinel.some_object
>>> result = real.method()
>>> assert result is sentinel.some_object
>>> sentinel.some_object
sentinel.some_object
DEFAULT
-------
.. data:: DEFAULT
The `DEFAULT` object is a pre-created sentinel (actually
`sentinel.DEFAULT`). It can be used by :attr:`~Mock.side_effect`
functions to indicate that the normal return value should be used.
call
----
.. function:: call(*args, **kwargs)
`call` is a helper object for making simpler assertions, for comparing
with :attr:`~Mock.call_args`, :attr:`~Mock.call_args_list`,
:attr:`~Mock.mock_calls` and:attr: `~Mock.method_calls`. `call` can also be
used with :meth:`~Mock.assert_has_calls`.
>>> m = MagicMock(return_value=None)
>>> m(1, 2, a='foo', b='bar')
>>> m()
>>> m.call_args_list == [call(1, 2, a='foo', b='bar'), call()]
True
.. method:: call.call_list()
For a call object that represents multiple calls, `call_list`
returns a list of all the intermediate calls as well as the
final call.
`call_list` is particularly useful for making assertions on "chained calls". A
chained call is multiple calls on a single line of code. This results in
multiple entries in :attr:`~Mock.mock_calls` on a mock. Manually constructing
the sequence of calls can be tedious.
:meth:`~call.call_list` can construct the sequence of calls from the same
chained call:
>>> m = MagicMock()
>>> m(1).method(arg='foo').other('bar')(2.0)
<MagicMock name='mock().method().other()()' id='...'>
>>> kall = call(1).method(arg='foo').other('bar')(2.0)
>>> kall.call_list()
[call(1),
call().method(arg='foo'),
call().method().other('bar'),
call().method().other()(2.0)]
>>> m.mock_calls == kall.call_list()
True
.. _calls-as-tuples:
A `call` object is either a tuple of (positional args, keyword args) or
(name, positional args, keyword args) depending on how it was constructed. When
you construct them yourself this isn't particularly interesting, but the `call`
objects that are in the :attr:`Mock.call_args`, :attr:`Mock.call_args_list` and
:attr:`Mock.mock_calls` attributes can be introspected to get at the individual
arguments they contain.
The `call` objects in :attr:`Mock.call_args` and :attr:`Mock.call_args_list`
are two-tuples of (positional args, keyword args) whereas the `call` objects
in :attr:`Mock.mock_calls`, along with ones you construct yourself, are
three-tuples of (name, positional args, keyword args).
You can use their "tupleness" to pull out the individual arguments for more
complex introspection and assertions. The positional arguments are a tuple
(an empty tuple if there are no positional arguments) and the keyword
arguments are a dictionary:
>>> m = MagicMock(return_value=None)
>>> m(1, 2, 3, arg='one', arg2='two')
>>> kall = m.call_args
>>> args, kwargs = kall
>>> args
(1, 2, 3)
>>> kwargs
{'arg2': 'two', 'arg': 'one'}
>>> args is kall[0]
True
>>> kwargs is kall[1]
True
>>> m = MagicMock()
>>> m.foo(4, 5, 6, arg='two', arg2='three')
<MagicMock name='mock.foo()' id='...'>
>>> kall = m.mock_calls[0]
>>> name, args, kwargs = kall
>>> name
'foo'
>>> args
(4, 5, 6)
>>> kwargs
{'arg2': 'three', 'arg': 'two'}
>>> name is m.mock_calls[0][0]
True
create_autospec
---------------
.. function:: create_autospec(spec, spec_set=False, instance=False, **kwargs)
Create a mock object using another object as a spec. Attributes on the
mock will use the corresponding attribute on the `spec` object as their
spec.
Functions or methods being mocked will have their arguments checked to
ensure that they are called with the correct signature.
If `spec_set` is `True` then attempting to set attributes that don't exist
on the spec object will raise an `AttributeError`.
If a class is used as a spec then the return value of the mock (the
instance of the class) will have the same spec. You can use a class as the
spec for an instance object by passing `instance=True`. The returned mock
will only be callable if instances of the mock are callable.
`create_autospec` also takes arbitrary keyword arguments that are passed to
the constructor of the created mock.
See :ref:`auto-speccing` for examples of how to use auto-speccing with
`create_autospec` and the `autospec` argument to :func:`patch`.
ANY
---
.. data:: ANY
Sometimes you may need to make assertions about *some* of the arguments in a
call to mock, but either not care about some of the arguments or want to pull
them individually out of :attr:`~Mock.call_args` and make more complex
assertions on them.
To ignore certain arguments you can pass in objects that compare equal to
*everything*. Calls to :meth:`~Mock.assert_called_with` and
:meth:`~Mock.assert_called_once_with` will then succeed no matter what was
passed in.
>>> mock = Mock(return_value=None)
>>> mock('foo', bar=object())
>>> mock.assert_called_once_with('foo', bar=ANY)
`ANY` can also be used in comparisons with call lists like
:attr:`~Mock.mock_calls`:
>>> m = MagicMock(return_value=None)
>>> m(1)
>>> m(1, 2)
>>> m(object())
>>> m.mock_calls == [call(1), call(1, 2), ANY]
True
FILTER_DIR
----------
.. data:: FILTER_DIR
`FILTER_DIR` is a module level variable that controls the way mock objects
respond to `dir` (only for Python 2.6 or more recent). The default is `True`,
which uses the filtering described below, to only show useful members. If you
dislike this filtering, or need to switch it off for diagnostic purposes, then
set `mock.FILTER_DIR = False`.
With filtering on, `dir(some_mock)` shows only useful attributes and will
include any dynamically created attributes that wouldn't normally be shown.
If the mock was created with a `spec` (or `autospec` of course) then all the
attributes from the original are shown, even if they haven't been accessed
yet:
>>> dir(Mock())
['assert_any_call',
'assert_called_once_with',
'assert_called_with',
'assert_has_calls',
'attach_mock',
...
>>> from urllib import request
>>> dir(Mock(spec=request))
['AbstractBasicAuthHandler',
'AbstractDigestAuthHandler',
'AbstractHTTPHandler',
'BaseHandler',
...
Many of the not-very-useful (private to `Mock` rather than the thing being
mocked) underscore and double underscore prefixed attributes have been
filtered from the result of calling `dir` on a `Mock`. If you dislike this
behaviour you can switch it off by setting the module level switch
`FILTER_DIR`:
>>> from unittest import mock
>>> mock.FILTER_DIR = False
>>> dir(mock.Mock())
['_NonCallableMock__get_return_value',
'_NonCallableMock__get_side_effect',
'_NonCallableMock__return_value_doc',
'_NonCallableMock__set_return_value',
'_NonCallableMock__set_side_effect',
'__call__',
'__class__',
...
Alternatively you can just use `vars(my_mock)` (instance members) and
`dir(type(my_mock))` (type members) to bypass the filtering irrespective of
`mock.FILTER_DIR`.
mock_open
---------
.. function:: mock_open(mock=None, read_data=None)
A helper function to create a mock to replace the use of `open`. It works
for `open` called directly or used as a context manager.
The `mock` argument is the mock object to configure. If `None` (the
default) then a `MagicMock` will be created for you, with the API limited
to methods or attributes available on standard file handles.
`read_data` is a string for the `read` method of the file handle to return.
This is an empty string by default.
Using `open` as a context manager is a great way to ensure your file handles
are closed properly and is becoming common::
with open('/some/path', 'w') as f:
f.write('something')
The issue is that even if you mock out the call to `open` it is the
*returned object* that is used as a context manager (and has `__enter__` and
`__exit__` called).
Mocking context managers with a :class:`MagicMock` is common enough and fiddly
enough that a helper function is useful.
>>> m = mock_open()
>>> with patch('__main__.open', m, create=True):
... with open('foo', 'w') as h:
... h.write('some stuff')
...
>>> m.mock_calls
[call('foo', 'w'),
call().__enter__(),
call().write('some stuff'),
call().__exit__(None, None, None)]
>>> m.assert_called_once_with('foo', 'w')
>>> handle = m()
>>> handle.write.assert_called_once_with('some stuff')
And for reading files:
>>> with patch('__main__.open', mock_open(read_data='bibble'), create=True) as m:
... with open('foo') as h:
... result = h.read()
...
>>> m.assert_called_once_with('foo')
>>> assert result == 'bibble'
.. _auto-speccing:
Autospeccing
------------
Autospeccing is based on the existing `spec` feature of mock. It limits the
api of mocks to the api of an original object (the spec), but it is recursive
(implemented lazily) so that attributes of mocks only have the same api as
the attributes of the spec. In addition mocked functions / methods have the
same call signature as the original so they raise a `TypeError` if they are
called incorrectly.
Before I explain how auto-speccing works, here's why it is needed.
`Mock` is a very powerful and flexible object, but it suffers from two flaws
when used to mock out objects from a system under test. One of these flaws is
specific to the `Mock` api and the other is a more general problem with using
mock objects.
First the problem specific to `Mock`. `Mock` has two assert methods that are
extremely handy: :meth:`~Mock.assert_called_with` and
:meth:`~Mock.assert_called_once_with`.
>>> mock = Mock(name='Thing', return_value=None)
>>> mock(1, 2, 3)
>>> mock.assert_called_once_with(1, 2, 3)
>>> mock(1, 2, 3)
>>> mock.assert_called_once_with(1, 2, 3)
Traceback (most recent call last):
...
AssertionError: Expected to be called once. Called 2 times.
Because mocks auto-create attributes on demand, and allow you to call them
with arbitrary arguments, if you misspell one of these assert methods then
your assertion is gone:
.. code-block:: pycon
>>> mock = Mock(name='Thing', return_value=None)
>>> mock(1, 2, 3)
>>> mock.assret_called_once_with(4, 5, 6)
Your tests can pass silently and incorrectly because of the typo.
The second issue is more general to mocking. If you refactor some of your
code, rename members and so on, any tests for code that is still using the
*old api* but uses mocks instead of the real objects will still pass. This
means your tests can all pass even though your code is broken.
Note that this is another reason why you need integration tests as well as
unit tests. Testing everything in isolation is all fine and dandy, but if you
don't test how your units are "wired together" there is still lots of room
for bugs that tests might have caught.
`mock` already provides a feature to help with this, called speccing. If you
use a class or instance as the `spec` for a mock then you can only access
attributes on the mock that exist on the real class:
>>> from urllib import request
>>> mock = Mock(spec=request.Request)
>>> mock.assret_called_with
Traceback (most recent call last):
...
AttributeError: Mock object has no attribute 'assret_called_with'
The spec only applies to the mock itself, so we still have the same issue
with any methods on the mock:
.. code-block:: pycon
>>> mock.has_data()
<mock.Mock object at 0x...>
>>> mock.has_data.assret_called_with()
Auto-speccing solves this problem. You can either pass `autospec=True` to
`patch` / `patch.object` or use the `create_autospec` function to create a
mock with a spec. If you use the `autospec=True` argument to `patch` then the
object that is being replaced will be used as the spec object. Because the
speccing is done "lazily" (the spec is created as attributes on the mock are
accessed) you can use it with very complex or deeply nested objects (like
modules that import modules that import modules) without a big performance
hit.
Here's an example of it in use:
>>> from urllib import request
>>> patcher = patch('__main__.request', autospec=True)
>>> mock_request = patcher.start()
>>> request is mock_request
True
>>> mock_request.Request
<MagicMock name='request.Request' spec='Request' id='...'>
You can see that `request.Request` has a spec. `request.Request` takes two
arguments in the constructor (one of which is `self`). Here's what happens if
we try to call it incorrectly:
>>> req = request.Request()
Traceback (most recent call last):
...
TypeError: <lambda>() takes at least 2 arguments (1 given)
The spec also applies to instantiated classes (i.e. the return value of
specced mocks):
>>> req = request.Request('foo')
>>> req
<NonCallableMagicMock name='request.Request()' spec='Request' id='...'>
`Request` objects are not callable, so the return value of instantiating our
mocked out `request.Request` is a non-callable mock. With the spec in place
any typos in our asserts will raise the correct error:
>>> req.add_header('spam', 'eggs')
<MagicMock name='request.Request().add_header()' id='...'>
>>> req.add_header.assret_called_with
Traceback (most recent call last):
...
AttributeError: Mock object has no attribute 'assret_called_with'
>>> req.add_header.assert_called_with('spam', 'eggs')
In many cases you will just be able to add `autospec=True` to your existing
`patch` calls and then be protected against bugs due to typos and api
changes.
As well as using `autospec` through `patch` there is a
:func:`create_autospec` for creating autospecced mocks directly:
>>> from urllib import request
>>> mock_request = create_autospec(request)
>>> mock_request.Request('foo', 'bar')
<NonCallableMagicMock name='mock.Request()' spec='Request' id='...'>
This isn't without caveats and limitations however, which is why it is not
the default behaviour. In order to know what attributes are available on the
spec object, autospec has to introspect (access attributes) the spec. As you
traverse attributes on the mock a corresponding traversal of the original
object is happening under the hood. If any of your specced objects have
properties or descriptors that can trigger code execution then you may not be
able to use autospec. On the other hand it is much better to design your
objects so that introspection is safe [#]_.
A more serious problem is that it is common for instance attributes to be
created in the `__init__` method and not to exist on the class at all.
`autospec` can't know about any dynamically created attributes and restricts
the api to visible attributes.
>>> class Something(object):
... def __init__(self):
... self.a = 33
...
>>> with patch('__main__.Something', autospec=True):
... thing = Something()
... thing.a
...
Traceback (most recent call last):
...
AttributeError: Mock object has no attribute 'a'
There are a few different ways of resolving this problem. The easiest, but
not necessarily the least annoying, way is to simply set the required
attributes on the mock after creation. Just because `autospec` doesn't allow
you to fetch attributes that don't exist on the spec it doesn't prevent you
setting them:
>>> with patch('__main__.Something', autospec=True):
... thing = Something()
... thing.a = 33
...
There is a more aggressive version of both `spec` and `autospec` that *does*
prevent you setting non-existent attributes. This is useful if you want to
ensure your code only *sets* valid attributes too, but obviously it prevents
this particular scenario:
>>> with patch('__main__.Something', autospec=True, spec_set=True):
... thing = Something()
... thing.a = 33
...
Traceback (most recent call last):
...
AttributeError: Mock object has no attribute 'a'
Probably the best way of solving the problem is to add class attributes as
default values for instance members initialised in `__init__`. Note that if
you are only setting default attributes in `__init__` then providing them via
class attributes (shared between instances of course) is faster too. e.g.
.. code-block:: python
class Something(object):
a = 33
This brings up another issue. It is relatively common to provide a default
value of `None` for members that will later be an object of a different type.
`None` would be useless as a spec because it wouldn't let you access *any*
attributes or methods on it. As `None` is *never* going to be useful as a
spec, and probably indicates a member that will normally of some other type,
`autospec` doesn't use a spec for members that are set to `None`. These will
just be ordinary mocks (well - `MagicMocks`):
>>> class Something(object):
... member = None
...
>>> mock = create_autospec(Something)
>>> mock.member.foo.bar.baz()
<MagicMock name='mock.member.foo.bar.baz()' id='...'>
If modifying your production classes to add defaults isn't to your liking
then there are more options. One of these is simply to use an instance as the
spec rather than the class. The other is to create a subclass of the
production class and add the defaults to the subclass without affecting the
production class. Both of these require you to use an alternative object as
the spec. Thankfully `patch` supports this - you can simply pass the
alternative object as the `autospec` argument:
>>> class Something(object):
... def __init__(self):
... self.a = 33
...
>>> class SomethingForTest(Something):
... a = 33
...
>>> p = patch('__main__.Something', autospec=SomethingForTest)
>>> mock = p.start()
>>> mock.a
<NonCallableMagicMock name='Something.a' spec='int' id='...'>
.. [#] This only applies to classes or already instantiated objects. Calling
a mocked class to create a mock instance *does not* create a real instance.
It is only attribute lookups - along with calls to `dir` - that are done.

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:mod:`unittest.mock` --- MagicMock and magic method support
===========================================================
.. module:: unittest.mock
:synopsis: Mock object library.
.. moduleauthor:: Michael Foord <michael@python.org>
.. currentmodule:: unittest.mock
.. versionadded:: 3.3
.. _magic-methods:
Mocking Magic Methods
---------------------
:class:`Mock` supports mocking the Python protocol methods, also known as
"magic methods". This allows mock objects to replace containers or other
objects that implement Python protocols.
Because magic methods are looked up differently from normal methods [#]_, this
support has been specially implemented. This means that only specific magic
methods are supported. The supported list includes *almost* all of them. If
there are any missing that you need please let us know.
You mock magic methods by setting the method you are interested in to a function
or a mock instance. If you are using a function then it *must* take ``self`` as
the first argument [#]_.
>>> def __str__(self):
... return 'fooble'
...
>>> mock = Mock()
>>> mock.__str__ = __str__
>>> str(mock)
'fooble'
>>> mock = Mock()
>>> mock.__str__ = Mock()
>>> mock.__str__.return_value = 'fooble'
>>> str(mock)
'fooble'
>>> mock = Mock()
>>> mock.__iter__ = Mock(return_value=iter([]))
>>> list(mock)
[]
One use case for this is for mocking objects used as context managers in a
`with` statement:
>>> mock = Mock()
>>> mock.__enter__ = Mock(return_value='foo')
>>> mock.__exit__ = Mock(return_value=False)
>>> with mock as m:
... assert m == 'foo'
...
>>> mock.__enter__.assert_called_with()
>>> mock.__exit__.assert_called_with(None, None, None)
Calls to magic methods do not appear in :attr:`~Mock.method_calls`, but they
are recorded in :attr:`~Mock.mock_calls`.
.. note::
If you use the `spec` keyword argument to create a mock then attempting to
set a magic method that isn't in the spec will raise an `AttributeError`.
The full list of supported magic methods is:
* ``__hash__``, ``__sizeof__``, ``__repr__`` and ``__str__``
* ``__dir__``, ``__format__`` and ``__subclasses__``
* ``__floor__``, ``__trunc__`` and ``__ceil__``
* Comparisons: ``__cmp__``, ``__lt__``, ``__gt__``, ``__le__``, ``__ge__``,
``__eq__`` and ``__ne__``
* Container methods: ``__getitem__``, ``__setitem__``, ``__delitem__``,
``__contains__``, ``__len__``, ``__iter__``, ``__getslice__``,
``__setslice__``, ``__reversed__`` and ``__missing__``
* Context manager: ``__enter__`` and ``__exit__``
* Unary numeric methods: ``__neg__``, ``__pos__`` and ``__invert__``
* The numeric methods (including right hand and in-place variants):
``__add__``, ``__sub__``, ``__mul__``, ``__div__``,
``__floordiv__``, ``__mod__``, ``__divmod__``, ``__lshift__``,
``__rshift__``, ``__and__``, ``__xor__``, ``__or__``, and ``__pow__``
* Numeric conversion methods: ``__complex__``, ``__int__``, ``__float__``,
``__index__`` and ``__coerce__``
* Descriptor methods: ``__get__``, ``__set__`` and ``__delete__``
* Pickling: ``__reduce__``, ``__reduce_ex__``, ``__getinitargs__``,
``__getnewargs__``, ``__getstate__`` and ``__setstate__``
The following methods exist but are *not* supported as they are either in use
by mock, can't be set dynamically, or can cause problems:
* ``__getattr__``, ``__setattr__``, ``__init__`` and ``__new__``
* ``__prepare__``, ``__instancecheck__``, ``__subclasscheck__``, ``__del__``
Magic Mock
----------
There are two `MagicMock` variants: `MagicMock` and `NonCallableMagicMock`.
.. class:: MagicMock(*args, **kw)
``MagicMock`` is a subclass of :class:`Mock` with default implementations
of most of the magic methods. You can use ``MagicMock`` without having to
configure the magic methods yourself.
The constructor parameters have the same meaning as for :class:`Mock`.
If you use the `spec` or `spec_set` arguments then *only* magic methods
that exist in the spec will be created.
.. class:: NonCallableMagicMock(*args, **kw)
A non-callable version of `MagicMock`.
The constructor parameters have the same meaning as for
:class:`MagicMock`, with the exception of `return_value` and
`side_effect` which have no meaning on a non-callable mock.
The magic methods are setup with `MagicMock` objects, so you can configure them
and use them in the usual way:
>>> mock = MagicMock()
>>> mock[3] = 'fish'
>>> mock.__setitem__.assert_called_with(3, 'fish')
>>> mock.__getitem__.return_value = 'result'
>>> mock[2]
'result'
By default many of the protocol methods are required to return objects of a
specific type. These methods are preconfigured with a default return value, so
that they can be used without you having to do anything if you aren't interested
in the return value. You can still *set* the return value manually if you want
to change the default.
Methods and their defaults:
* ``__lt__``: NotImplemented
* ``__gt__``: NotImplemented
* ``__le__``: NotImplemented
* ``__ge__``: NotImplemented
* ``__int__`` : 1
* ``__contains__`` : False
* ``__len__`` : 1
* ``__iter__`` : iter([])
* ``__exit__`` : False
* ``__complex__`` : 1j
* ``__float__`` : 1.0
* ``__bool__`` : True
* ``__index__`` : 1
* ``__hash__`` : default hash for the mock
* ``__str__`` : default str for the mock
* ``__sizeof__``: default sizeof for the mock
For example:
>>> mock = MagicMock()
>>> int(mock)
1
>>> len(mock)
0
>>> list(mock)
[]
>>> object() in mock
False
The two equality method, `__eq__` and `__ne__`, are special.
They do the default equality comparison on identity, using a side
effect, unless you change their return value to return something else:
>>> MagicMock() == 3
False
>>> MagicMock() != 3
True
>>> mock = MagicMock()
>>> mock.__eq__.return_value = True
>>> mock == 3
True
The return value of `MagicMock.__iter__` can be any iterable object and isn't
required to be an iterator:
>>> mock = MagicMock()
>>> mock.__iter__.return_value = ['a', 'b', 'c']
>>> list(mock)
['a', 'b', 'c']
>>> list(mock)
['a', 'b', 'c']
If the return value *is* an iterator, then iterating over it once will consume
it and subsequent iterations will result in an empty list:
>>> mock.__iter__.return_value = iter(['a', 'b', 'c'])
>>> list(mock)
['a', 'b', 'c']
>>> list(mock)
[]
``MagicMock`` has all of the supported magic methods configured except for some
of the obscure and obsolete ones. You can still set these up if you want.
Magic methods that are supported but not setup by default in ``MagicMock`` are:
* ``__subclasses__``
* ``__dir__``
* ``__format__``
* ``__get__``, ``__set__`` and ``__delete__``
* ``__reversed__`` and ``__missing__``
* ``__reduce__``, ``__reduce_ex__``, ``__getinitargs__``, ``__getnewargs__``,
``__getstate__`` and ``__setstate__``
* ``__getformat__`` and ``__setformat__``
.. [#] Magic methods *should* be looked up on the class rather than the
instance. Different versions of Python are inconsistent about applying this
rule. The supported protocol methods should work with all supported versions
of Python.
.. [#] The function is basically hooked up to the class, but each ``Mock``
instance is kept isolated from the others.

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@ -1,538 +0,0 @@
:mod:`unittest.mock` --- the patchers
=====================================
.. module:: unittest.mock
:synopsis: Mock object library.
.. moduleauthor:: Michael Foord <michael@python.org>
.. currentmodule:: unittest.mock
.. versionadded:: 3.3
The patch decorators are used for patching objects only within the scope of
the function they decorate. They automatically handle the unpatching for you,
even if exceptions are raised. All of these functions can also be used in with
statements or as class decorators.
patch
-----
.. note::
`patch` is straightforward to use. The key is to do the patching in the
right namespace. See the section `where to patch`_.
.. function:: patch(target, new=DEFAULT, spec=None, create=False, spec_set=None, autospec=None, new_callable=None, **kwargs)
`patch` acts as a function decorator, class decorator or a context
manager. Inside the body of the function or with statement, the `target`
(specified in the form `'package.module.ClassName'`) is patched
with a `new` object. When the function/with statement exits the patch is
undone.
The `target` is imported and the specified attribute patched with the new
object, so it must be importable from the environment you are calling the
decorator from. The target is imported when the decorated function is
executed, not at decoration time.
If `new` is omitted, then a new `MagicMock` is created and passed in as an
extra argument to the decorated function.
The `spec` and `spec_set` keyword arguments are passed to the `MagicMock`
if patch is creating one for you.
In addition you can pass `spec=True` or `spec_set=True`, which causes
patch to pass in the object being mocked as the spec/spec_set object.
`new_callable` allows you to specify a different class, or callable object,
that will be called to create the `new` object. By default `MagicMock` is
used.
A more powerful form of `spec` is `autospec`. If you set `autospec=True`
then the mock with be created with a spec from the object being replaced.
All attributes of the mock will also have the spec of the corresponding
attribute of the object being replaced. Methods and functions being mocked
will have their arguments checked and will raise a `TypeError` if they are
called with the wrong signature. For mocks
replacing a class, their return value (the 'instance') will have the same
spec as the class. See the :func:`create_autospec` function and
:ref:`auto-speccing`.
Instead of `autospec=True` you can pass `autospec=some_object` to use an
arbitrary object as the spec instead of the one being replaced.
By default `patch` will fail to replace attributes that don't exist. If
you pass in `create=True`, and the attribute doesn't exist, patch will
create the attribute for you when the patched function is called, and
delete it again afterwards. This is useful for writing tests against
attributes that your production code creates at runtime. It is off by by
default because it can be dangerous. With it switched on you can write
passing tests against APIs that don't actually exist!
Patch can be used as a `TestCase` class decorator. It works by
decorating each test method in the class. This reduces the boilerplate
code when your test methods share a common patchings set. `patch` finds
tests by looking for method names that start with `patch.TEST_PREFIX`.
By default this is `test`, which matches the way `unittest` finds tests.
You can specify an alternative prefix by setting `patch.TEST_PREFIX`.
Patch can be used as a context manager, with the with statement. Here the
patching applies to the indented block after the with statement. If you
use "as" then the patched object will be bound to the name after the
"as"; very useful if `patch` is creating a mock object for you.
`patch` takes arbitrary keyword arguments. These will be passed to
the `Mock` (or `new_callable`) on construction.
`patch.dict(...)`, `patch.multiple(...)` and `patch.object(...)` are
available for alternate use-cases.
Patching a class replaces the class with a `MagicMock` *instance*. If the
class is instantiated in the code under test then it will be the
:attr:`~Mock.return_value` of the mock that will be used.
If the class is instantiated multiple times you could use
:attr:`~Mock.side_effect` to return a new mock each time. Alternatively you
can set the `return_value` to be anything you want.
To configure return values on methods of *instances* on the patched class
you must do this on the `return_value`. For example:
>>> class Class(object):
... def method(self):
... pass
...
>>> with patch('__main__.Class') as MockClass:
... instance = MockClass.return_value
... instance.method.return_value = 'foo'
... assert Class() is instance
... assert Class().method() == 'foo'
...
If you use `spec` or `spec_set` and `patch` is replacing a *class*, then the
return value of the created mock will have the same spec.
>>> Original = Class
>>> patcher = patch('__main__.Class', spec=True)
>>> MockClass = patcher.start()
>>> instance = MockClass()
>>> assert isinstance(instance, Original)
>>> patcher.stop()
The `new_callable` argument is useful where you want to use an alternative
class to the default :class:`MagicMock` for the created mock. For example, if
you wanted a :class:`NonCallableMock` to be used:
>>> thing = object()
>>> with patch('__main__.thing', new_callable=NonCallableMock) as mock_thing:
... assert thing is mock_thing
... thing()
...
Traceback (most recent call last):
...
TypeError: 'NonCallableMock' object is not callable
Another use case might be to replace an object with a `StringIO` instance:
>>> from StringIO import StringIO
>>> def foo():
... print 'Something'
...
>>> @patch('sys.stdout', new_callable=StringIO)
... def test(mock_stdout):
... foo()
... assert mock_stdout.getvalue() == 'Something\n'
...
>>> test()
When `patch` is creating a mock for you, it is common that the first thing
you need to do is to configure the mock. Some of that configuration can be done
in the call to patch. Any arbitrary keywords you pass into the call will be
used to set attributes on the created mock:
>>> patcher = patch('__main__.thing', first='one', second='two')
>>> mock_thing = patcher.start()
>>> mock_thing.first
'one'
>>> mock_thing.second
'two'
As well as attributes on the created mock attributes, like the
:attr:`~Mock.return_value` and :attr:`~Mock.side_effect`, of child mocks can
also be configured. These aren't syntactically valid to pass in directly as
keyword arguments, but a dictionary with these as keys can still be expanded
into a `patch` call using `**`:
>>> config = {'method.return_value': 3, 'other.side_effect': KeyError}
>>> patcher = patch('__main__.thing', **config)
>>> mock_thing = patcher.start()
>>> mock_thing.method()
3
>>> mock_thing.other()
Traceback (most recent call last):
...
KeyError
patch.object
------------
.. function:: patch.object(target, attribute, new=DEFAULT, spec=None, create=False, spec_set=None, autospec=None, new_callable=None, **kwargs)
patch the named member (`attribute`) on an object (`target`) with a mock
object.
`patch.object` can be used as a decorator, class decorator or a context
manager. Arguments `new`, `spec`, `create`, `spec_set`, `autospec` and
`new_callable` have the same meaning as for `patch`. Like `patch`,
`patch.object` takes arbitrary keyword arguments for configuring the mock
object it creates.
When used as a class decorator `patch.object` honours `patch.TEST_PREFIX`
for choosing which methods to wrap.
You can either call `patch.object` with three arguments or two arguments. The
three argument form takes the object to be patched, the attribute name and the
object to replace the attribute with.
When calling with the two argument form you omit the replacement object, and a
mock is created for you and passed in as an extra argument to the decorated
function:
>>> @patch.object(SomeClass, 'class_method')
... def test(mock_method):
... SomeClass.class_method(3)
... mock_method.assert_called_with(3)
...
>>> test()
`spec`, `create` and the other arguments to `patch.object` have the same
meaning as they do for `patch`.
patch.dict
----------
.. function:: patch.dict(in_dict, values=(), clear=False, **kwargs)
Patch a dictionary, or dictionary like object, and restore the dictionary
to its original state after the test.
`in_dict` can be a dictionary or a mapping like container. If it is a
mapping then it must at least support getting, setting and deleting items
plus iterating over keys.
`in_dict` can also be a string specifying the name of the dictionary, which
will then be fetched by importing it.
`values` can be a dictionary of values to set in the dictionary. `values`
can also be an iterable of `(key, value)` pairs.
If `clear` is True then the dictionary will be cleared before the new
values are set.
`patch.dict` can also be called with arbitrary keyword arguments to set
values in the dictionary.
`patch.dict` can be used as a context manager, decorator or class
decorator. When used as a class decorator `patch.dict` honours
`patch.TEST_PREFIX` for choosing which methods to wrap.
`patch.dict` can be used to add members to a dictionary, or simply let a test
change a dictionary, and ensure the dictionary is restored when the test
ends.
>>> foo = {}
>>> with patch.dict(foo, {'newkey': 'newvalue'}):
... assert foo == {'newkey': 'newvalue'}
...
>>> assert foo == {}
>>> import os
>>> with patch.dict('os.environ', {'newkey': 'newvalue'}):
... print os.environ['newkey']
...
newvalue
>>> assert 'newkey' not in os.environ
Keywords can be used in the `patch.dict` call to set values in the dictionary:
>>> mymodule = MagicMock()
>>> mymodule.function.return_value = 'fish'
>>> with patch.dict('sys.modules', mymodule=mymodule):
... import mymodule
... mymodule.function('some', 'args')
...
'fish'
`patch.dict` can be used with dictionary like objects that aren't actually
dictionaries. At the very minimum they must support item getting, setting,
deleting and either iteration or membership test. This corresponds to the
magic methods `__getitem__`, `__setitem__`, `__delitem__` and either
`__iter__` or `__contains__`.
>>> class Container(object):
... def __init__(self):
... self.values = {}
... def __getitem__(self, name):
... return self.values[name]
... def __setitem__(self, name, value):
... self.values[name] = value
... def __delitem__(self, name):
... del self.values[name]
... def __iter__(self):
... return iter(self.values)
...
>>> thing = Container()
>>> thing['one'] = 1
>>> with patch.dict(thing, one=2, two=3):
... assert thing['one'] == 2
... assert thing['two'] == 3
...
>>> assert thing['one'] == 1
>>> assert list(thing) == ['one']
patch.multiple
--------------
.. function:: patch.multiple(target, spec=None, create=False, spec_set=None, autospec=None, new_callable=None, **kwargs)
Perform multiple patches in a single call. It takes the object to be
patched (either as an object or a string to fetch the object by importing)
and keyword arguments for the patches::
with patch.multiple(settings, FIRST_PATCH='one', SECOND_PATCH='two'):
...
Use :data:`DEFAULT` as the value if you want `patch.multiple` to create
mocks for you. In this case the created mocks are passed into a decorated
function by keyword, and a dictionary is returned when `patch.multiple` is
used as a context manager.
`patch.multiple` can be used as a decorator, class decorator or a context
manager. The arguments `spec`, `spec_set`, `create`, `autospec` and
`new_callable` have the same meaning as for `patch`. These arguments will
be applied to *all* patches done by `patch.multiple`.
When used as a class decorator `patch.multiple` honours `patch.TEST_PREFIX`
for choosing which methods to wrap.
If you want `patch.multiple` to create mocks for you, then you can use
:data:`DEFAULT` as the value. If you use `patch.multiple` as a decorator
then the created mocks are passed into the decorated function by keyword.
>>> thing = object()
>>> other = object()
>>> @patch.multiple('__main__', thing=DEFAULT, other=DEFAULT)
... def test_function(thing, other):
... assert isinstance(thing, MagicMock)
... assert isinstance(other, MagicMock)
...
>>> test_function()
`patch.multiple` can be nested with other `patch` decorators, but put arguments
passed by keyword *after* any of the standard arguments created by `patch`:
>>> @patch('sys.exit')
... @patch.multiple('__main__', thing=DEFAULT, other=DEFAULT)
... def test_function(mock_exit, other, thing):
... assert 'other' in repr(other)
... assert 'thing' in repr(thing)
... assert 'exit' in repr(mock_exit)
...
>>> test_function()
If `patch.multiple` is used as a context manager, the value returned by the
context manger is a dictionary where created mocks are keyed by name:
>>> with patch.multiple('__main__', thing=DEFAULT, other=DEFAULT) as values:
... assert 'other' in repr(values['other'])
... assert 'thing' in repr(values['thing'])
... assert values['thing'] is thing
... assert values['other'] is other
...
.. _start-and-stop:
patch methods: start and stop
-----------------------------
All the patchers have `start` and `stop` methods. These make it simpler to do
patching in `setUp` methods or where you want to do multiple patches without
nesting decorators or with statements.
To use them call `patch`, `patch.object` or `patch.dict` as normal and keep a
reference to the returned `patcher` object. You can then call `start` to put
the patch in place and `stop` to undo it.
If you are using `patch` to create a mock for you then it will be returned by
the call to `patcher.start`.
>>> patcher = patch('package.module.ClassName')
>>> from package import module
>>> original = module.ClassName
>>> new_mock = patcher.start()
>>> assert module.ClassName is not original
>>> assert module.ClassName is new_mock
>>> patcher.stop()
>>> assert module.ClassName is original
>>> assert module.ClassName is not new_mock
A typical use case for this might be for doing multiple patches in the `setUp`
method of a `TestCase`:
>>> class MyTest(TestCase):
... def setUp(self):
... self.patcher1 = patch('package.module.Class1')
... self.patcher2 = patch('package.module.Class2')
... self.MockClass1 = self.patcher1.start()
... self.MockClass2 = self.patcher2.start()
...
... def tearDown(self):
... self.patcher1.stop()
... self.patcher2.stop()
...
... def test_something(self):
... assert package.module.Class1 is self.MockClass1
... assert package.module.Class2 is self.MockClass2
...
>>> MyTest('test_something').run()
.. caution::
If you use this technique you must ensure that the patching is "undone" by
calling `stop`. This can be fiddlier than you might think, because if an
exception is raised in the ``setUp`` then ``tearDown`` is not called.
:meth:`unittest.TestCase.addCleanup` makes this easier:
>>> class MyTest(TestCase):
... def setUp(self):
... patcher = patch('package.module.Class')
... self.MockClass = patcher.start()
... self.addCleanup(patcher.stop)
...
... def test_something(self):
... assert package.module.Class is self.MockClass
...
As an added bonus you no longer need to keep a reference to the `patcher`
object.
In fact `start` and `stop` are just aliases for the context manager
`__enter__` and `__exit__` methods.
TEST_PREFIX
-----------
All of the patchers can be used as class decorators. When used in this way
they wrap every test method on the class. The patchers recognise methods that
start with `test` as being test methods. This is the same way that the
:class:`unittest.TestLoader` finds test methods by default.
It is possible that you want to use a different prefix for your tests. You can
inform the patchers of the different prefix by setting `patch.TEST_PREFIX`:
>>> patch.TEST_PREFIX = 'foo'
>>> value = 3
>>>
>>> @patch('__main__.value', 'not three')
... class Thing(object):
... def foo_one(self):
... print value
... def foo_two(self):
... print value
...
>>>
>>> Thing().foo_one()
not three
>>> Thing().foo_two()
not three
>>> value
3
Nesting Patch Decorators
------------------------
If you want to perform multiple patches then you can simply stack up the
decorators.
You can stack up multiple patch decorators using this pattern:
>>> @patch.object(SomeClass, 'class_method')
... @patch.object(SomeClass, 'static_method')
... def test(mock1, mock2):
... assert SomeClass.static_method is mock1
... assert SomeClass.class_method is mock2
... SomeClass.static_method('foo')
... SomeClass.class_method('bar')
... return mock1, mock2
...
>>> mock1, mock2 = test()
>>> mock1.assert_called_once_with('foo')
>>> mock2.assert_called_once_with('bar')
Note that the decorators are applied from the bottom upwards. This is the
standard way that Python applies decorators. The order of the created mocks
passed into your test function matches this order.
.. _where-to-patch:
Where to patch
--------------
`patch` works by (temporarily) changing the object that a *name* points to with
another one. There can be many names pointing to any individual object, so
for patching to work you must ensure that you patch the name used by the system
under test.
The basic principle is that you patch where an object is *looked up*, which
is not necessarily the same place as where it is defined. A couple of
examples will help to clarify this.
Imagine we have a project that we want to test with the following structure::
a.py
-> Defines SomeClass
b.py
-> from a import SomeClass
-> some_function instantiates SomeClass
Now we want to test `some_function` but we want to mock out `SomeClass` using
`patch`. The problem is that when we import module b, which we will have to
do then it imports `SomeClass` from module a. If we use `patch` to mock out
`a.SomeClass` then it will have no effect on our test; module b already has a
reference to the *real* `SomeClass` and it looks like our patching had no
effect.
The key is to patch out `SomeClass` where it is used (or where it is looked up
). In this case `some_function` will actually look up `SomeClass` in module b,
where we have imported it. The patching should look like::
@patch('b.SomeClass')
However, consider the alternative scenario where instead of `from a import
SomeClass` module b does `import a` and `some_function` uses `a.SomeClass`. Both
of these import forms are common. In this case the class we want to patch is
being looked up on the a module and so we have to patch `a.SomeClass` instead::
@patch('a.SomeClass')
Patching Descriptors and Proxy Objects
--------------------------------------
Both patch_ and patch.object_ correctly patch and restore descriptors: class
methods, static methods and properties. You should patch these on the *class*
rather than an instance. They also work with *some* objects
that proxy attribute access, like the `django setttings object
<http://www.voidspace.org.uk/python/weblog/arch_d7_2010_12_04.shtml#e1198>`_.

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@ -5,65 +5,40 @@
:synopsis: Implementation of the ElementTree API.
.. moduleauthor:: Fredrik Lundh <fredrik@pythonware.com>
**Source code:** :source:`Lib/xml/etree/ElementTree.py`
--------------
The :class:`Element` type is a flexible container object, designed to store
hierarchical data structures in memory. The type can be described as a cross
between a list and a dictionary.
Each element has a number of properties associated with it:
* a tag which is a string identifying what kind of data this element represents
(the element type, in other words).
* a number of attributes, stored in a Python dictionary.
* a text string.
* an optional tail string.
* a number of child elements, stored in a Python sequence
To create an element instance, use the :class:`Element` constructor or the
:func:`SubElement` factory function.
The :class:`ElementTree` class can be used to wrap an element structure, and
convert it from and to XML.
See http://effbot.org/zone/element-index.htm for tutorials and links to other
docs.
.. versionchanged:: 3.2
The ElementTree API is updated to 1.3. For more information, see
`Introducing ElementTree 1.3
<http://effbot.org/zone/elementtree-13-intro.htm>`_.
The :mod:`xml.etree.ElementTree` module implements a simple and efficient API
for parsing and creating XML data.
.. versionchanged:: 3.3
This module will use a fast implementation whenever available.
The :mod:`xml.etree.cElementTree` module is deprecated.
Tutorial
--------
.. _elementtree-xpath:
This is a short tutorial for using :mod:`xml.etree.ElementTree` (``ET`` in
short). The goal is to demonstrate some of the building blocks and basic
concepts of the module.
XPath support
-------------
XML tree and elements
^^^^^^^^^^^^^^^^^^^^^
This module provides limited support for
`XPath expressions <http://www.w3.org/TR/xpath>`_ for locating elements in a
tree. The goal is to support a small subset of the abbreviated syntax; a full
XPath engine is outside the scope of the module.
XML is an inherently hierarchical data format, and the most natural way to
represent it is with a tree. ``ET`` has two classes for this purpose -
:class:`ElementTree` represents the whole XML document as a tree, and
:class:`Element` represents a single node in this tree. Interactions with
the whole document (reading and writing to/from files) are usually done
on the :class:`ElementTree` level. Interactions with a single XML element
and its sub-elements are done on the :class:`Element` level.
Example
^^^^^^^
.. _elementtree-parsing-xml:
Here's an example that demonstrates some of the XPath capabilities of the
module::
Parsing XML
^^^^^^^^^^^
import xml.etree.ElementTree as ET
We'll be using the following XML document contained in a Python string as the
sample data for this section::
xml = r'''<?xml version="1.0"?>
countrydata = r'''<?xml version="1.0"?>
<data>
<country name="Liechtenshtein">
<rank>1</rank>
@ -88,23 +63,121 @@ module::
</data>
'''
tree = ET.fromstring(xml)
First, import the module and parse the data::
import xml.etree.ElementTree as ET
root = ET.fromstring(countrydata)
:func:`fromstring` parses XML from a string directly into an :class:`Element`,
which is the root element of the parsed tree. Other parsing functions may
create an :class:`ElementTree`. Make sure to check the documentation to be
sure.
As an :class:`Element`, ``root`` has a tag and a dictionary of attributes::
>>> root.tag
'data'
>>> root.attrib
{}
It also has children nodes over which we can iterate::
>>> for child in root:
... print(child.tag, child.attrib)
...
country {'name': 'Liechtenshtein'}
country {'name': 'Singapore'}
country {'name': 'Panama'}
Children are nested, and we can access specific child nodes by index::
>>> root[0][1].text
'2008'
Finding interesting elements
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
:class:`Element` has some useful methods that help iterate recursively over all
the sub-tree below it (its children, their children, and so on). For example,
:meth:`Element.iter`::
>>> for neighbor in root.iter('neighbor'):
... print(neighbor.attrib)
...
{'name': 'Austria', 'direction': 'E'}
{'name': 'Switzerland', 'direction': 'W'}
{'name': 'Malaysia', 'direction': 'N'}
{'name': 'Costa Rica', 'direction': 'W'}
{'name': 'Colombia', 'direction': 'E'}
More sophisticated specification of which elements to look for is possible by
using :ref:`XPath <elementtree-xpath>`.
Building XML documents
^^^^^^^^^^^^^^^^^^^^^^
``ET`` provides a simple way to build XML documents and write them to files.
The :meth:`ElementTree.write` method serves this purpose.
Once created, an :class:`Element` object may be manipulated by directly changing
its fields (such as :attr:`Element.text`), adding and modifying attributes
(:meth:`Element.set` method), as well as adding new children (for example
with :meth:`Element.append`).
The :func:`SubElement` function also provides a convenient way to create new
sub-elements for a given element::
>>> a = ET.Element('a')
>>> b = ET.SubElement(a, 'b')
>>> c = ET.SubElement(a, 'c')
>>> d = ET.SubElement(c, 'd')
>>> ET.dump(a)
<a><b /><c><d /></c></a>
Additional resources
^^^^^^^^^^^^^^^^^^^^
See http://effbot.org/zone/element-index.htm for tutorials and links to other
docs.
.. _elementtree-xpath:
XPath support
-------------
This module provides limited support for
`XPath expressions <http://www.w3.org/TR/xpath>`_ for locating elements in a
tree. The goal is to support a small subset of the abbreviated syntax; a full
XPath engine is outside the scope of the module.
Example
^^^^^^^
Here's an example that demonstrates some of the XPath capabilities of the
module. We'll be using the ``countrydata`` XML document from the
:ref:`Parsing XML <elementtree-parsing-xml>` section::
import xml.etree.ElementTree as ET
root = ET.fromstring(countrydata)
# Top-level elements
tree.findall(".")
root.findall(".")
# All 'neighbor' grand-children of 'country' children of the top-level
# elements
tree.findall("./country/neighbor")
root.findall("./country/neighbor")
# Nodes with name='Singapore' that have a 'year' child
tree.findall(".//year/..[@name='Singapore']")
root.findall(".//year/..[@name='Singapore']")
# 'year' nodes that are children of nodes with name='Singapore'
tree.findall(".//*[@name='Singapore']/year")
root.findall(".//*[@name='Singapore']/year")
# All 'neighbor' nodes that are the second child of their parent
tree.findall(".//neighbor[2]")
root.findall(".//neighbor[2]")
Supported XPath syntax
^^^^^^^^^^^^^^^^^^^^^^

3
Lib/idlelib/NEWS.txt
View File

@ -3,6 +3,9 @@ What's New in IDLE 3.3?
- IDLE can be launched as `python -m idlelib`
- Issue #14409: IDLE doesn't not execute commands from shell,
error with default keybinding for Return. (Patch by Roger Serwy)
- Issue #3573: IDLE hangs when passing invalid command line args
(directory(ies) instead of file(s)).

2
Lib/idlelib/configHandler.py
View File

@ -596,7 +596,7 @@ class IdleConf:
'<<replace>>': ['<Control-h>'],
'<<goto-line>>': ['<Alt-g>'],
'<<smart-backspace>>': ['<Key-BackSpace>'],
'<<newline-and-indent>>': ['<Key-Return> <Key-KP_Enter>'],
'<<newline-and-indent>>': ['<Key-Return>', '<Key-KP_Enter>'],
'<<smart-indent>>': ['<Key-Tab>'],
'<<indent-region>>': ['<Control-Key-bracketright>'],
'<<dedent-region>>': ['<Control-Key-bracketleft>'],

15
Lib/logging/handlers.py
View File

@ -559,11 +559,16 @@ class SocketHandler(logging.Handler):
"""
ei = record.exc_info
if ei:
dummy = self.format(record) # just to get traceback text into record.exc_text
record.exc_info = None # to avoid Unpickleable error
s = pickle.dumps(record.__dict__, 1)
if ei:
record.exc_info = ei # for next handler
# just to get traceback text into record.exc_text ...
dummy = self.format(record)
# See issue #14436: If msg or args are objects, they may not be
# available on the receiving end. So we convert the msg % args
# to a string, save it as msg and zap the args.
d = dict(record.__dict__)
d['msg'] = record.getMessage()
d['args'] = None
d['exc_info'] = None
s = pickle.dumps(d, 1)
slen = struct.pack(">L", len(s))
return slen + s

1
Lib/test/test_smtplib.py
View File

@ -9,6 +9,7 @@ import re
import sys
import time
import select
import errno
import unittest
from test import support, mock_socket

21
Lib/unittest/mock.py
View File

@ -1352,17 +1352,20 @@ def patch(
"""
`patch` acts as a function decorator, class decorator or a context
manager. Inside the body of the function or with statement, the `target`
(specified in the form `'package.module.ClassName'`) is patched
with a `new` object. When the function/with statement exits the patch is
undone.
is patched with a `new` object. When the function/with statement exits
the patch is undone.
The `target` is imported and the specified attribute patched with the new
object, so it must be importable from the environment you are calling the
decorator from. The target is imported when the decorated function is
executed, not at decoration time.
If `new` is omitted, then the target is replaced with a
`MagicMock`. If `patch` is used as a decorator and `new` is
omitted, the created mock is passed in as an extra argument to the
decorated function. If `patch` is used as a context manager the created
mock is returned by the context manager.
If `new` is omitted, then a new `MagicMock` is created and passed in as an
extra argument to the decorated function.
`target` should be a string in the form `'package.module.ClassName'`. The
`target` is imported and the specified object replaced with the `new`
object, so the `target` must be importable from the environment you are
calling `patch` from. The target is imported when the decorated function
is executed, not at decoration time.
The `spec` and `spec_set` keyword arguments are passed to the `MagicMock`
if patch is creating one for you.

1
Misc/ACKS
View File

@ -832,6 +832,7 @@ Terry Reedy
Gareth Rees
Steve Reeves
Lennart Regebro
Federico Reghenzani
Ofir Reichenberg
Sean Reifschneider
Michael P. Reilly

8
Misc/NEWS
View File

@ -34,6 +34,12 @@ Core and Builtins
Library
-------
- Issue #14409: IDLE doesn't not execute commands from shell,
error with default keybinding for Return. (Patch by Roger Serwy)
- Issue #14416: syslog now defines the LOG_ODELAY and LOG_AUTHPRIV constants
if they are defined in <syslog.h>.
- IDLE can be launched as python -m idlelib
- Issue #14295: Add unittest.mock
@ -193,6 +199,8 @@ Extension Modules
Tests
-----
- Issue #14442: Add missing errno import in test_smtplib.
- Issue #8315: (partial fix) python -m unittest test.test_email now works.

8
Modules/syslogmodule.c
View File

@ -291,6 +291,9 @@ PyInit_syslog(void)
PyModule_AddIntConstant(m, "LOG_PID", LOG_PID);
PyModule_AddIntConstant(m, "LOG_CONS", LOG_CONS);
PyModule_AddIntConstant(m, "LOG_NDELAY", LOG_NDELAY);
#ifdef LOG_ODELAY
PyModule_AddIntConstant(m, "LOG_ODELAY", LOG_ODELAY);
#endif
#ifdef LOG_NOWAIT
PyModule_AddIntConstant(m, "LOG_NOWAIT", LOG_NOWAIT);
#endif
@ -331,5 +334,10 @@ PyInit_syslog(void)
PyModule_AddIntConstant(m, "LOG_CRON", LOG_CRON);
PyModule_AddIntConstant(m, "LOG_UUCP", LOG_UUCP);
PyModule_AddIntConstant(m, "LOG_NEWS", LOG_NEWS);
#ifdef LOG_AUTHPRIV
PyModule_AddIntConstant(m, "LOG_AUTHPRIV", LOG_AUTHPRIV);
#endif
return m;
}

158
Objects/floatobject.c
View File

@ -16,53 +16,16 @@
/* Special free list
Since some Python programs can spend much of their time allocating
and deallocating floats, these operations should be very fast.
Therefore we use a dedicated allocation scheme with a much lower
overhead (in space and time) than straight malloc(): a simple
dedicated free list, filled when necessary with memory from malloc().
block_list is a singly-linked list of all PyFloatBlocks ever allocated,
linked via their next members. PyFloatBlocks are never returned to the
system before shutdown (PyFloat_Fini).
free_list is a singly-linked list of available PyFloatObjects, linked
via abuse of their ob_type members.
*/
#define BLOCK_SIZE 1000 /* 1K less typical malloc overhead */
#define BHEAD_SIZE 8 /* Enough for a 64-bit pointer */
#define N_FLOATOBJECTS ((BLOCK_SIZE - BHEAD_SIZE) / sizeof(PyFloatObject))
struct _floatblock {
struct _floatblock *next;
PyFloatObject objects[N_FLOATOBJECTS];
};
typedef struct _floatblock PyFloatBlock;
static PyFloatBlock *block_list = NULL;
#ifndef PyFloat_MAXFREELIST
#define PyFloat_MAXFREELIST 100
#endif
static int numfree = 0;
static PyFloatObject *free_list = NULL;
static PyFloatObject *
fill_free_list(void)
{
PyFloatObject *p, *q;
/* XXX Float blocks escape the object heap. Use PyObject_MALLOC ??? */
p = (PyFloatObject *) PyMem_MALLOC(sizeof(PyFloatBlock));
if (p == NULL)
return (PyFloatObject *) PyErr_NoMemory();
((PyFloatBlock *)p)->next = block_list;
block_list = (PyFloatBlock *)p;
p = &((PyFloatBlock *)p)->objects[0];
q = p + N_FLOATOBJECTS;
while (--q > p)
Py_TYPE(q) = (struct _typeobject *)(q-1);
Py_TYPE(q) = NULL;
return p + N_FLOATOBJECTS - 1;
}
double
PyFloat_GetMax(void)
{
@ -151,14 +114,16 @@ PyFloat_GetInfo(void)
PyObject *
PyFloat_FromDouble(double fval)
{
register PyFloatObject *op;
if (free_list == NULL) {
if ((free_list = fill_free_list()) == NULL)
return NULL;
register PyFloatObject *op = free_list;
if (op != NULL) {
free_list = (PyFloatObject *) Py_TYPE(op);
numfree--;
} else {
op = (PyFloatObject*) PyObject_MALLOC(sizeof(PyFloatObject));
if (!op)
return PyErr_NoMemory();
}
/* Inline PyObject_New */
op = free_list;
free_list = (PyFloatObject *)Py_TYPE(op);
PyObject_INIT(op, &PyFloat_Type);
op->ob_fval = fval;
return (PyObject *) op;
@ -217,6 +182,11 @@ static void
float_dealloc(PyFloatObject *op)
{
if (PyFloat_CheckExact(op)) {
if (numfree >= PyFloat_MAXFREELIST) {
PyObject_FREE(op);
return;
}
numfree++;
Py_TYPE(op) = (struct _typeobject *)free_list;
free_list = op;
}
@ -1932,96 +1902,22 @@ _PyFloat_Init(void)
int
PyFloat_ClearFreeList(void)
{
PyFloatObject *p;
PyFloatBlock *list, *next;
int i;
int u; /* remaining unfreed floats per block */
int freelist_size = 0;
list = block_list;
block_list = NULL;
free_list = NULL;
while (list != NULL) {
u = 0;
for (i = 0, p = &list->objects[0];
i < N_FLOATOBJECTS;
i++, p++) {
if (PyFloat_CheckExact(p) && Py_REFCNT(p) != 0)
u++;
}
next = list->next;
if (u) {
list->next = block_list;
block_list = list;
for (i = 0, p = &list->objects[0];
i < N_FLOATOBJECTS;
i++, p++) {
if (!PyFloat_CheckExact(p) ||
Py_REFCNT(p) == 0) {
Py_TYPE(p) = (struct _typeobject *)
free_list;
free_list = p;
}
}
}
else {
PyMem_FREE(list);
}
freelist_size += u;
list = next;
PyFloatObject *f = free_list, *next;
int i = numfree;
while (f) {
next = (PyFloatObject*) Py_TYPE(f);
PyObject_FREE(f);
f = next;
}
return freelist_size;
free_list = NULL;
numfree = 0;
return i;
}
void
PyFloat_Fini(void)
{
PyFloatObject *p;
PyFloatBlock *list;
int i;
int u; /* total unfreed floats per block */
u = PyFloat_ClearFreeList();
if (!Py_VerboseFlag)
return;
fprintf(stderr, "# cleanup floats");
if (!u) {
fprintf(stderr, "\n");
}
else {
fprintf(stderr,
": %d unfreed float%s\n",
u, u == 1 ? "" : "s");
}
if (Py_VerboseFlag > 1) {
list = block_list;
while (list != NULL) {
for (i = 0, p = &list->objects[0];
i < N_FLOATOBJECTS;
i++, p++) {
if (PyFloat_CheckExact(p) &&
Py_REFCNT(p) != 0) {
char *buf = PyOS_double_to_string(
PyFloat_AS_DOUBLE(p), 'r',
0, 0, NULL);
if (buf) {
/* XXX(twouters) cast
refcount to long
until %zd is
universally
available
*/
fprintf(stderr,
"# <float at %p, refcnt=%ld, val=%s>\n",
p, (long)Py_REFCNT(p), buf);
PyMem_Free(buf);
}
}
}
list = list->next;
}
}
(void)PyFloat_ClearFreeList();
}
/*----------------------------------------------------------------------------