2015-12-30

Python Patch Instance Method

Python mock patch instance method and check call arguments Or is there something better in general that I can do to patch instance methods. Publié par.

Method Objects There are some useful functions that are useful for working with method objects. PyTypeObject PyMethod_Type This instance of PyTypeObject.

Mock is now part of the Python standard library, available as unittest.mock in Python 3.3 onwards. This package contains a rolling backport of the standard library.

There are also generator expressions and more advanced uses of generators, but we aren t concerned about them here. A very good introduction to generators and how powerful they are is: Generator Tricks for Systems Programmers.

26.5.3.4. Applying the same patch to every test method В

If you want several patches in place for multiple test methods the obvious way is to apply the patch decorators to every method. This can feel like unnecessary repetition. For Python 2.6 or more recent you can use

patch

in all its various forms as a class decorator. This applies the patches to all test methods on the class. A test method is identified by methods whose names start with

test

patch mymodule.SomeClass class MyTest TestCase : def test_one self, MockSomeClass : self.assertIs mymodule.SomeClass, MockSomeClass def test_two self, MockSomeClass : self.assertIs mymodule.SomeClass, MockSomeClass def not_a_test self : return something MyTest test_one. test_one MyTest test_two. test_two MyTest test_two. not_a_test something

An alternative way of managing patches is to use the patch methods: start and stop. These allow you to move the patching into your

setUp

tearDown

methods.

class MyTest TestCase : def setUp self : self.patcher patch mymodule.foo self.mock_foo self.patcher.start def test_foo self : self.assertIs mymodule.foo, self.mock_foo def tearDown self : self.patcher.stop MyTest test_foo. run

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. unittest.TestCase.addCleanup makes this easier:

class MyTest TestCase : def setUp self : patcher patch mymodule.foo self.addCleanup patcher.stop self.mock_foo patcher.start def test_foo self : self.assertIs mymodule.foo, self.mock_foo MyTest test_foo. run

26.5.3.5. Mocking Unbound Methods В

Whilst writing tests today I needed to patch an unbound method patching the method on the class rather than on the instance. I needed self to be passed in as the first argument because I want to make asserts about which objects were calling this particular method. The issue is that you can t patch with a mock for this, because if you replace an unbound method with a mock it doesn t become a bound method when fetched from the instance, and so it doesn t get self passed in. The workaround is to patch the unbound method with a real function instead. The patch decorator makes it so simple to patch out methods with a mock that having to create a real function becomes a nuisance.

If you pass

autospec True

to patch then it does the patching with a real function object. This function object has the same signature as the one it is replacing, but delegates to a mock under the hood. You still get your mock auto-created in exactly the same way as before. What it means though, is that if you use it to patch out an unbound method on a class the mocked function will be turned into a bound method if it is fetched from an instance. It will have

self

passed in as the first argument, which is exactly what I wanted:

class Foo: def foo self : pass with patch.object Foo, foo, autospec True as mock_foo: mock_foo.return_value foo foo Foo foo.foo foo mock_foo.assert_called_once_with foo

If we don t use

then the unbound method is patched out with a Mock instance instead, and isn t called with

26.5.3.6. Checking multiple calls with mock В

mock has a nice API for making assertions about how your mock objects are used.

mock Mock mock.foo_bar.return_value None mock.foo_bar baz, spam eggs mock.foo_bar.assert_called_with baz, spam eggs

If your mock is only being called once you can use the assert_called_once_with method that also asserts that the call_count is one.

mock.foo_bar.assert_called_once_with baz, spam eggs mock.foo_bar mock.foo_bar.assert_called_once_with baz, spam eggs Traceback most recent call last : AssertionError: Expected to be called once. Called 2 times.

Both

and

assert_called_once_with

make assertions about the most recent call. If your mock is going to be called several times, and you want to make assertions about all those calls you can use call_args_list :

mock Mock return_value None mock 1, 2, 3 mock 4, 5, 6 mock mock.call_args_list call 1, 2, 3, call 4, 5, 6, call

The call helper makes it easy to make assertions about these calls. You can build up a list of expected calls and compare it to

call_args_list

. This looks remarkably similar to the repr of the

expected call 1, 2, 3, call 4, 5, 6, call mock.call_args_list expected True

26.5.3.7. Coping with mutable arguments В

Another situation is rare, but can bite you, is when your mock is called with mutable arguments.

call_args

store references to the arguments. If the arguments are mutated by the code under test then you can no longer make assertions about what the values were when the mock was called.

Here s some example code that shows the problem. Imagine the following functions defined in mymodule :

def frob val : pass def grob val : First frob and then clear val frob val val.clear

When we try to test that

grob

frob

with the correct argument look what happens:

with patch mymodule.frob as mock_frob: val set 6 mymodule.grob val val set mock_frob.assert_called_with set 6 Traceback most recent call last : AssertionError: Expected: set 6, , Called with: set, ,

One possibility would be for mock to copy the arguments you pass in. This could then cause problems if you do assertions that rely on object identity for equality.

Here s one solution that uses the side_effect functionality. If you provide a

side_effect

function for a mock then

will be called with the same args as the mock. This gives us an opportunity to copy the arguments and store them for later assertions. In this example I m using another mock to store the arguments so that I can use the mock methods for doing the assertion. Again a helper function sets this up for me.

from copy import deepcopy from unittest.mock import Mock, patch, DEFAULT def copy_call_args mock : new_mock Mock def side_effect args, kwargs : args deepcopy args kwargs deepcopy kwargs new_mock args, kwargs return DEFAULT mock.side_effect side_effect return new_mock with patch mymodule.frob as mock_frob: new_mock copy_call_args mock_frob val set 6 mymodule.grob val new_mock.assert_called_with set 6 new_mock.call_args call set 6

copy_call_args

is called with the mock that will be called. It returns a new mock that we do the assertion on. The

function makes a copy of the args and calls our

new_mock

with the copy.

Note

If your mock is only going to be used once there is an easier way of checking arguments at the point they are called. You can simply do the checking inside a

function.

def side_effect arg : assert arg set 6 mock Mock side_effect side_effect mock set 6 mock set Traceback most recent call last : AssertionError

An alternative approach is to create a subclass of

Mock

MagicMock

that copies using copy.deepcopy the arguments. Here s an example implementation:

from copy import deepcopy class CopyingMock MagicMock : def __call__ self, args, kwargs : args deepcopy args kwargs deepcopy kwargs return super CopyingMock, self. __call__ args, kwargs c CopyingMock return_value None arg set c arg arg.add 1 c.assert_called_with set c.assert_called_with arg Traceback most recent call last : AssertionError: Expected call: mock set 1 Actual call: mock set c.foo

When you subclass.

One of the first things that should stick out is that we re using the mock.patch method decorator to instance method, org.uk/python/mock.

26.5. unittest.mock mock object library В

New in version 3.3.

unittest.mock is a library for testing in Python. It allows you to replace parts of your system under test with mock objects and make assertions about how they have been used.

unittest.mock provides a core Mock class removing the need to create a host of stubs throughout your test suite. After performing an action, you can make assertions about which methods / attributes were used and arguments they were called with. You can also specify return values and set needed attributes in the normal way.

Additionally, mock provides a patch decorator that handles patching module and class level attributes within the scope of a test, along with sentinel for creating unique objects. See the quick guide for some examples of how to use Mock, MagicMock and patch .

Mock is very easy to use and is designed for use with unittest. Mock is based on the action - assertion pattern instead of record - replay used by many mocking frameworks.

There is a backport of unittest.mock for earlier versions of Python, available as mock on PyPI.

Source code: Lib/unittest/mock.py

26.5.1. Quick Guide В

Mock and MagicMock objects create all attributes and methods as you access them and store details of how they have been used. You can configure them, to specify return values or limit what attributes are available, and then make assertions about how they have been used:

from unittest.mock import MagicMock thing ProductionClass thing.method MagicMock return_value 3 thing.method 3, 4, 5, key value 3 thing.method.assert_called_with 3, 4, 5, key value

side_effect allows you to perform side effects, including raising an exception when a mock is called:

mock Mock side_effect KeyError foo mock Traceback most recent call last : KeyError: foo

values a : 1, b : 2, c : 3 def side_effect arg : return values arg mock.side_effect side_effect mock a, mock b, mock c 1, 2, 3 mock.side_effect 5, 4, 3, 2, 1 mock, mock, mock 5, 4, 3

Mock has many other ways you can configure it and control its behaviour. For example the spec argument configures the mock to take its specification from another object. Attempting to access attributes or methods on the mock that don t exist on the spec will fail with an AttributeError.

The patch decorator / context manager makes it easy to mock classes or objects in a module under test. The object you specify will be replaced with a mock or other object during the test and restored when the test ends:

from unittest.mock import patch patch module.ClassName2 patch module.ClassName1 def test MockClass1, MockClass2 : module.ClassName1 module.ClassName2 assert MockClass1 is module.ClassName1 assert MockClass2 is module.ClassName2 assert MockClass1.called assert MockClass2.called test

Note

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 module.ClassName1 is passed in first.

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 where to patch.

As well as a decorator patch can be used as a context manager in a with statement:

with patch.object ProductionClass, method, return_value None as mock_method: thing ProductionClass thing.method 1, 2, 3 mock_method.assert_called_once_with 1, 2, 3

There is also 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

Mock supports the mocking of Python magic methods. The easiest way of using magic methods is with the MagicMock class. It allows you to do things like:

mock MagicMock mock.__str__.return_value foobarbaz str mock foobarbaz mock.__str__.assert_called_with

Mock allows you to assign functions or other Mock instances to magic methods and they will be called appropriately. The MagicMock class is just a Mock variant that has all of the magic methods pre-created for you well, all the useful ones anyway.

The following is an example of using magic methods with the ordinary Mock class:

mock Mock mock.__str__ Mock return_value wheeeeee str mock wheeeeee

For ensuring that the mock objects in your tests have the same api as the objects they are replacing, you can use auto-speccing. Auto-speccing can be done through the autospec argument to patch, or the create_autospec function. Auto-speccing creates mock objects that have the same attributes and methods as the objects they are replacing, and any functions and methods including constructors have the same call signature as the real object.

This ensures that your mocks will fail in the same way as your production code if they are used incorrectly:

from unittest.mock import create_autospec def function a, b, c : pass mock_function create_autospec function, return_value fishy mock_function 1, 2, 3 fishy mock_function.assert_called_once_with 1, 2, 3 mock_function wrong arguments Traceback most recent call last : TypeError: takes exactly 3 arguments 1 given

create_autospec can also be used on classes, where it copies the signature of the __init__ method, and on callable objects where it copies the signature of the __call__ method.

26.5.2. The Mock Class В

Mock is a flexible mock object intended to replace the use of stubs and test doubles throughout your code. Mocks are callable and create attributes as new mocks when you access them 1 . Accessing the same attribute will always return the same mock. Mocks record how you use them, allowing you to make assertions about what your code has done to them.

MagicMock is a subclass of Mock with all the magic methods pre-created and ready to use. There are also non-callable variants, useful when you are mocking out objects that aren t callable: NonCallableMock and NonCallableMagicMock

The patch decorators makes it easy to temporarily replace classes in a particular module with a Mock object. By default patch will create a MagicMock for you. You can specify an alternative class of Mock using the new_callable argument to patch .

class unittest.mock.Mock spec None, side_effect None, return_value DEFAULT, wraps None, name None, spec_set None, unsafe False, kwargs В

Create a new Mock object. Mock takes several optional arguments that specify the behaviour of the Mock object:

spec: This can be either a list of strings or an existing object a class or instance that acts as the specification for the mock object. If you pass in an object then a list of strings is formed by calling dir on the object excluding unsupported magic attributes and methods. Accessing any attribute not in this list will raise an AttributeError.

If spec is an object rather than a list of strings then __class__ returns the class of the spec object. This allows mocks to pass isinstance tests.

spec_set: A stricter variant of spec. If used, attempting to set or get an attribute on the mock that isn t on the object passed as spec_set will raise an AttributeError.

side_effect: A function to be called whenever the Mock is called. See the side_effect attribute. Useful for raising exceptions or dynamically changing return values. The function is called with the same arguments as the mock, and unless it returns DEFAULT, the return value of this function is used as the return value.

Alternatively side_effect can be an exception class or instance. In this case the exception will be raised when the mock is called.

If side_effect is an iterable then each call to the mock will return the next value from the iterable.

A side_effect can be cleared by setting it to None.

return_value: The value returned when the mock is called. By default this is a new Mock created on first access. See the return_value attribute.

unsafe: By default if any attribute starts with assert or assret will raise an AttributeError. Passing unsafe True will allow access to these attributes.

New in version 3.5.

wraps: Item for the mock object to wrap. If wraps is not None then calling the Mock will pass the call through to the wrapped object returning the real result. Attribute access on the mock will return a Mock object that wraps the corresponding attribute of the wrapped object so attempting to access an attribute that doesn t exist will raise an AttributeError .

If the mock has an explicit return_value set then calls are not passed to the wrapped object and the return_value is returned instead.

name: If the mock has a name then it will be used in the repr of the mock. This can be useful for debugging. The name is propagated to child mocks.

Mocks can also be called with arbitrary keyword arguments. These will be used to set attributes on the mock after it is created. See the configure_mock method for details.

assert_called_with args, kwargs В

This method is a convenient way of asserting that calls are made in a particular way:

mock Mock mock.method 1, 2, 3, test wow mock.method.assert_called_with 1, 2, 3, test wow

assert_called_once_with args, kwargs В

Assert that the mock was called exactly once and with the specified arguments.

mock Mock return_value None mock foo, bar baz mock.assert_called_once_with foo, bar baz mock foo, bar baz mock.assert_called_once_with foo, bar baz Traceback most recent call last : AssertionError: Expected mock to be called once. Called 2 times.

assert_any_call args, kwargs В

assert the mock has been called with the specified arguments.

The assert passes if the mock has ever been called, unlike assert_called_with and assert_called_once_with that only pass if the call is the most recent one.

mock Mock return_value None mock 1, 2, arg thing mock some, thing, else mock.assert_any_call 1, 2, arg thing

assert_has_calls calls, any_order False В

assert the mock has been called with the specified calls. The mock_calls list is checked for the calls.

If any_order is false the default then the calls must be sequential. There can be extra calls before or after the specified calls.

If any_order is true then the calls can be in any order, but they must all appear in mock_calls.

mock Mock return_value None mock 1 mock 2 mock 3 mock 4 calls call 2, call 3 mock.assert_has_calls calls calls call 4, call 2, call 3 mock.assert_has_calls calls, any_order True

assert_not_called args, kwargs В

Assert the mock was never called.

m Mock m.hello.assert_not_called obj m.hello m.hello.assert_not_called Traceback most recent call last : AssertionError: Expected hello to not have been called. Called 1 times.

The reset_mock method resets all the call attributes on a mock object:

mock Mock return_value None mock hello mock.called True mock.reset_mock mock.called False

This can be useful where you want to make a series of assertions that reuse the same object. Note that reset_mock doesn t clear the return value, side_effect or any child attributes you have set using normal assignment. Child mocks and the return value mock if any are reset as well.

mock_add_spec spec, spec_set False В

Add a spec to a mock. spec can either be an object or a list of strings. Only attributes on the spec can be fetched as attributes from the mock.

If spec_set is true then only attributes on the spec can be set.

attach_mock mock, attribute В

Attach a mock as an attribute of this one, replacing its name and parent. Calls to the attached mock will be recorded in the method_calls and mock_calls attributes of this one.

configure_mock kwargs В

Set attributes on the mock through keyword arguments.

Attributes plus return values and side effects can be set on child mocks using standard dot notation and unpacking a dictionary in the method call:

mock Mock attrs method.return_value : 3, other.side_effect : KeyError mock.configure_mock attrs mock.method 3 mock.other Traceback most recent call last : KeyError

The same thing can be achieved in the constructor call to mocks:

attrs method.return_value : 3, other.side_effect : KeyError mock Mock some_attribute eggs, attrs mock.some_attribute eggs mock.method 3 mock.other Traceback most recent call last : KeyError

configure_mock exists to make it easier to do configuration after the mock has been created.

__dir__ В

Mock objects limit the results of dir some_mock to useful results. For mocks with a spec this includes all the permitted attributes for the mock.

See FILTER_DIR for what this filtering does, and how to switch it off.

_get_child_mock kw В

Create the child mocks for attributes and return value. By default child mocks will be the same type as the parent. Subclasses of Mock may want to override this to customize the way child mocks are made.

For non-callable mocks the callable variant will be used rather than any custom subclass.

A boolean representing whether or not the mock object has been called:

mock Mock return_value None mock.called False mock mock.called True

An integer telling you how many times the mock object has been called:

mock Mock return_value None mock.call_count 0 mock mock mock.call_count 2

Set this to configure the value returned by calling the mock:

mock Mock mock.return_value fish mock fish

The default return value is a mock object and you can configure it in the normal way:

mock Mock mock.return_value.attribute sentinel.Attribute mock.return_value mock.return_value.assert_called_with

return_value can also be set in the constructor:

mock Mock return_value 3 mock.return_value 3 mock 3

side_effect В

This can either be a function to be called when the mock is called, an iterable or an exception class or instance to be raised.

If you pass in a function it will be called with same arguments as the mock and unless the function returns the DEFAULT singleton the call to the mock will then return whatever the function returns. If the function returns DEFAULT then the mock will return its normal value from the return_value .

If you pass in an iterable, it is used to retrieve an iterator which must yield a value on every call. This value can either be an exception instance to be raised, or a value to be returned from the call to the mock DEFAULT handling is identical to the function case.

An example of a mock that raises an exception to test exception handling of an API :

mock Mock mock.side_effect Exception Boom. mock Traceback most recent call last : Exception: Boom.

Using side_effect to return a sequence of values:

mock Mock mock.side_effect 3, 2, 1 mock, mock, mock 3, 2, 1

Using a callable:

mock Mock return_value 3 def side_effect args, kwargs : return DEFAULT mock.side_effect side_effect mock 3

side_effect can be set in the constructor. Here s an example that adds one to the value the mock is called with and returns it:

side_effect lambda value: value 1 mock Mock side_effect side_effect mock 3 4 mock -8 -7

Setting side_effect to None clears it:

m Mock side_effect KeyError, return_value 3 m Traceback most recent call last : KeyError m.side_effect None m 3

call_args В

This is either None if the mock hasn t been called, or the arguments that the mock was last called with. This will be in the form of a tuple: the first member is any ordered arguments the mock was called with or an empty tuple and the second member is any keyword arguments or an empty dictionary.

mock Mock return_value None print mock.call_args None mock mock.call_args call mock.call_args True mock 3, 4 mock.call_args call 3, 4 mock.call_args 3, 4, True mock 3, 4, 5, key fish, next w00t. mock.call_args call 3, 4, 5, key fish, next w00t.

call_args, along with members of the lists call_args_list, method_calls and mock_calls are call objects. These are tuples, so they can be unpacked to get at the individual arguments and make more complex assertions. See calls as tuples.

call_args_list В

This is a list of all the calls made to the mock object in sequence so the length of the list is the number of times it has been called. Before any calls have been made it is an empty list. The call object can be used for conveniently constructing lists of calls to compare with call_args_list.

mock Mock return_value None mock mock 3, 4 mock key fish, next w00t. mock.call_args_list call, call 3, 4, call key fish, next w00t. expected , 3, 4, , key : fish, next : w00t., mock.call_args_list expected True

Members of call_args_list are call objects. These can be unpacked as tuples to get at the individual arguments. See calls as tuples.

method_calls В

As well as tracking calls to themselves, mocks also track calls to methods and attributes, and their methods and attributes:

mock Mock mock.method mock.property.method.attribute mock.method_calls call.method, call.property.method.attribute

Members of method_calls are call objects. These can be unpacked as tuples to get at the individual arguments. See calls as tuples.

mock_calls В

mock_calls records all calls to the mock object, its methods, magic methods and return value mocks.

mock MagicMock result mock 1, 2, 3 mock.first a 3 mock.second int mock 1 result 1 expected call 1, 2, 3, call.first a 3, call.second, call.__int__, call 1 mock.mock_calls expected True

Members of mock_calls are call objects. These can be unpacked as tuples to get at the individual arguments. See calls as tuples.

__class__ В

Normally the __class__ attribute of an object will return its type. For a mock object with a spec, __class__ returns the spec class instead. This allows mock objects to pass isinstance tests for the object they are replacing / masquerading as:

mock Mock spec 3 isinstance mock, int True

__class__ is assignable to, this allows a mock to pass an isinstance check without forcing you to use a spec:

mock Mock mock.__class__ dict isinstance mock, dict True

class unittest.mock.NonCallableMock spec None, wraps None, name None, spec_set None, kwargs В

A non-callable version of Mock. The constructor parameters have the same meaning of Mock, with the exception of return_value and side_effect which have no meaning on a non-callable mock.

Mock objects that use a class or an instance as a spec or spec_set are able to pass isinstance tests:

mock Mock spec SomeClass isinstance mock, SomeClass True mock Mock spec_set SomeClass isinstance mock, SomeClass True

The Mock classes have support for mocking magic methods. See magic methods for the full details.

The mock classes and the patch decorators all take arbitrary keyword arguments for configuration. For the patch decorators the keywords are passed to the constructor of the mock being created. The keyword arguments are for configuring attributes of the mock:

m MagicMock attribute 3, other fish m.attribute 3 m.other fish

The return value and side effect of child mocks can be set in the same way, using dotted notation. As you can t use dotted names directly in a call you have to create a dictionary and unpack it using :

A callable mock which was created with a spec or a spec_set will introspect the specification object s signature when matching calls to the mock. Therefore, it can match the actual call s arguments regardless of whether they were passed positionally or by name:

def f a, b, c : pass mock Mock spec f mock 1, 2, c 3 mock.assert_called_with 1, 2, 3 mock.assert_called_with a 1, b 2, c 3

This applies to assert_called_with , assert_called_once_with , assert_has_calls and assert_any_call . When Autospeccing, it will also apply to method calls on the mock object.

Changed in version 3.4: Added signature introspection on specced and autospecced mock objects.

class unittest.mock.PropertyMock args, kwargs В

A mock intended to be used as a property, or other descriptor, on a class. PropertyMock provides __get__ and __set__ methods so you can specify a return value when it is fetched.

Fetching a PropertyMock instance from an object calls the mock, with no args. Setting it calls the mock with the value being set.

class Foo: property def foo self : return something foo.setter def foo self, value : pass with patch __main__.Foo.foo, new_callable PropertyMock as mock_foo: mock_foo.return_value mockity-mock this_foo Foo print this_foo.foo this_foo.foo 6 mockity-mock mock_foo.mock_calls call, call 6

Because of the way mock attributes are stored you can t directly attach a PropertyMock to a mock object. Instead you can attach it to the mock type object:

m MagicMock p PropertyMock return_value 3 type m. foo p m.foo 3 p.assert_called_once_with

26.5.2.1. Calling В

Mock objects are callable. The call will return the value set as the return_value attribute. The default return value is a new Mock object; it is created the first time the return value is accessed either explicitly or by calling the Mock - but it is stored and the same one returned each time.

Calls made to the object will be recorded in the attributes like call_args and call_args_list.

If side_effect is set then it will be called after the call has been recorded, so if side_effect raises an exception the call is still recorded.

The simplest way to make a mock raise an exception when called is to make side_effect an exception class or instance:

m MagicMock side_effect IndexError m 1, 2, 3 Traceback most recent call last : IndexError m.mock_calls call 1, 2, 3 m.side_effect KeyError Bang. m two, three, four Traceback most recent call last : KeyError: Bang. m.mock_calls call 1, 2, 3, call two, three, four

If side_effect is a function then whatever that function returns is what calls to the mock return. The side_effect function is called with the same arguments as the mock. This allows you to vary the return value of the call dynamically, based on the input:

def side_effect value : return value 1 m MagicMock side_effect side_effect m 1 2 m 2 3 m.mock_calls call 1, call 2

If you want the mock to still return the default return value a new mock, or any set return value, then there are two ways of doing this. Either return mock.return_value from inside side_effect, or return DEFAULT :

m MagicMock def side_effect args, kwargs : return m.return_value m.side_effect side_effect m.return_value 3 m 3 def side_effect args, kwargs : return DEFAULT m.side_effect side_effect m 3

To remove a side_effect, and return to the default behaviour, set the side_effect to None:

m MagicMock return_value 6 def side_effect args, kwargs : return 3 m.side_effect side_effect m 3 m.side_effect None m 6

The side_effect can also be any iterable object. Repeated calls to the mock will return values from the iterable until the iterable is exhausted and a StopIteration is raised :

m MagicMock side_effect 1, 2, 3 m 1 m 2 m 3 m Traceback most recent call last : StopIteration

If any members of the iterable are exceptions they will be raised instead of returned:

iterable 33, ValueError, 66 m MagicMock side_effect iterable m 33 m Traceback most recent call last : ValueError m 66

26.5.2.2. Deleting Attributes В

Mock objects create attributes on demand. This allows them to pretend to be objects of any type.

You may want a mock object to return False to a hasattr call, or raise an AttributeError when an attribute is fetched. You can do this by providing an object as a spec for a mock, but that isn t always convenient.

You block attributes by deleting them. Once deleted, accessing an attribute will raise an AttributeError.

mock MagicMock hasattr mock, m True del mock.m hasattr mock, m False del mock.f mock.f Traceback most recent call last : AttributeError: f

26.5.2.3. Mock names and the name attribute В

Since name is an argument to the Mock constructor, if you want your mock object to have a name attribute you can t just pass it in at creation time. There are two alternatives. One option is to use configure_mock :

mock MagicMock mock.configure_mock name my_name mock.name my_name

A simpler option is to simply set the name attribute after mock creation:

mock MagicMock mock.name foo

26.5.2.4. Attaching Mocks as Attributes В

When you attach a mock as an attribute of another mock or as the return value it becomes a child of that mock. Calls to the child are recorded in the method_calls and mock_calls attributes of the parent. This is useful for configuring child mocks and then attaching them to the parent, or for attaching mocks to a parent that records all calls to the children and allows you to make assertions about the order of calls between mocks:

parent MagicMock child1 MagicMock return_value None child2 MagicMock return_value None parent.child1 child1 parent.child2 child2 child1 1 child2 2 parent.mock_calls call.child1 1, call.child2 2

The exception to this is if the mock has a name. This allows you to prevent the parenting if for some reason you don t want it to happen.

mock MagicMock not_a_child MagicMock name not-a-child mock.attribute not_a_child mock.attribute mock.mock_calls

Mocks created for you by patch are automatically given names. To attach mocks that have names to a parent you use the attach_mock method:

thing1 object thing2 object parent MagicMock with patch __main__.thing1, return_value None as child1: with patch __main__.thing2, return_value None as child2: parent.attach_mock child1, child1 parent.attach_mock child2, child2 child1 one child2 two parent.mock_calls call.child1 one, call.child2 two

The only exceptions are magic methods and attributes those that have leading and trailing double underscores. Mock doesn t create these but instead raises an AttributeError. This is because the interpreter will often implicitly request these methods, and gets very confused to get a new Mock object when it expects a magic method. If you need magic method support see magic methods.

26.5.3. The patchers В

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.

26.5.3.1. patch В

patch is straightforward to use. The key is to do the patching in the right namespace. See the section where to patch.

unittest.mock.patch target, new DEFAULT, spec None, create False, spec_set None, autospec None, new_callable None, kwargs В

patch acts as a function decorator, class decorator or a context manager. Inside the body of the function or with statement, the target is patched with a new object. When the function/with statement exits the patch is undone.

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.

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.

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 will 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 create_autospec function and Autospeccing.

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 default because it can be dangerous. With it switched on you can write passing tests against APIs that don t actually exist.

Changed in version 3.5: If you are patching builtins in a module then you don t need to pass create True, it will be added by default.

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.

patch as function decorator, creating the mock for you and passing it into the decorated function:

patch __main__.SomeClass def function normal_argument, mock_class : print mock_class is SomeClass function None True

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 return_value of the mock that will be used.

If the class is instantiated multiple times you could use 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: 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 MagicMock for the created mock. For example, if you wanted a 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 an io.StringIO instance:

from io 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 return_value and 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 target, attribute, new DEFAULT, spec None, create False, spec_set None, autospec None, new_callable None, kwargs В

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 .

26.5.3.3. patch.dict В

patch.dict in_dict, values, clear False, kwargs В

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: 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

26.5.3.4. patch.multiple В

patch.multiple target, spec None, create False, spec_set None, autospec None, new_callable None, kwargs В

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 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 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

26.5.3.5. patch methods: start and stop В

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. 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.

It is also possible to stop all patches which have been started by using patch.stopall .

Stop all active patches. Only stops patches started with start.

26.5.3.6. patch builtins В

You can patch any builtins within a module. The following example patches builtin ord :

patch __main__.ord def test mock_ord : mock_ord.return_value 101 print ord c test 101

26.5.3.7. TEST_PREFIX В

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 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: def foo_one self : print value def foo_two self : print value Thing. foo_one not three Thing. foo_two not three value 3

26.5.3.8. Nesting Patch Decorators В

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.

26.5.3.9. Where to patch В

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 in the module and so we have to patch a.SomeClass instead:

patch a.SomeClass

26.5.3.10. Patching Descriptors and Proxy Objects В

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 settings object.

26.5.4. MagicMock and magic method support В

26.5.4.1. Mocking Magic Methods В

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 2 , 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 3 .

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 method_calls, but they are recorded in mock_calls.

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__

Comparisons: __lt__, __gt__, __le__, __ge__, __eq__ and __ne__

Container methods: __getitem__, __setitem__, __delitem__, __contains__, __len__, __iter__, __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__, __matmul__, __div__, __truediv__, __floordiv__, __mod__, __divmod__, __lshift__, __rshift__, __and__, __xor__, __or__, and __pow__

Numeric conversion methods: __complex__, __int__, __float__ and __index__

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__

There are two MagicMock variants: MagicMock and NonCallableMagicMock.

class unittest.mock.MagicMock args, kw В

MagicMock is a subclass of 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 Mock.

If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

class unittest.mock.NonCallableMagicMock args, kw В

A non-callable version of MagicMock.

The constructor parameters have the same meaning as for 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.

For Python 2.6 or more recent you can use patch If we don t use autospec True then the unbound method is patched out with a Mock instance instead.

Class methods monkey patching in Python is really easy, we want each monkey-patch not to The monkey-patching of the instance is done on itself by a method.

Patching Mocks in Python. I explain the mock patching mechanism and describe four types of patch The __str__ method returns a MagicMock instance, the.

Patch finds tests by looking for method names that start with patch.TEST_PREFIX. instance. method. return_value This is the standard way that Python applies.
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Video driver ATI Radeon 200M, X300, X600, FireGL V3200 for Windows Vista - ThinkPad R51e, R52, T43, T43p, Z60m.

Radeon X600; ATI Radeon X600 drivers for Windows 7. Free drivers for ATI Radeon X600 for Windows 7. Found 4 files. Please select the driver to download.

Latest driver for Radeon X600.

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4 April 2012 :11

Hi TomsHardware

I have bought a radeon x600 and as it s quite old I can t find the latest drivers. I have found drivers from 2006 but I m sure that this card has newer drivers than that being that it was released 2004

Anyone know where I can get the most up to date drivers.

Btw, it s a Dell ATI Radeon X600 256mb Pro.

Thanks.

Windows driver: ati mobility radeon x600 drivers update - windows 7 drivers, ati mobility radeon x600 drivers update for Windows Vista Enterprise.

Drivers for windows 7, Update driver: RADEON X600 256MB HyperMemory X600 256MB HyperMemory Driver for Windows 7 Service radeon x1550 64.

Description: Catalyst Software Suite for ATI Radeon X600 The Catalyst Software Suite 9. 3 revision 10. 2 contains the following: Radeon display driver 8. 591.

Download AMD Radeon Software drivers for Radeon, FirePro, APU, desktops, laptops

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For use with Microsoft Windows operating systems equipped with AMD Radeon discrete desktop/mobile and APU graphics.

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For Linux systems, Apple Boot Camp systems or other AMD graphics products such as AMD FirePro and Embedded graphics, please select your driver using the selection menus below.

Download AMD Radeon Software drivers for Radeon, FirePro, APU, desktops, laptops Automatically Detect and Install Your Driver. For use Windows 7 32-b it.

2015-12-30

Serial Dilution Lab Food Coloring

Serial Dilution Spectronic-20 Activity Name: In this lab we will use a Spectronic-20 to measure the concentration of solutions food coloring.

Lab: Vinegar Serial Dilution Key Concept: Effect of Concentration on Reaction Rate 2. Add one drop of food coloring. Stir. We found that blue made.

Students will perform a serial dilution of food coloring to create highly diluted Define serial dilution; food coloring in the remaining wells and record.

Cuvette

Preliminary questions

1. If you are given a solution and test it to be pH 4, this is considered to be

a. Acidic

b. Neutral

c. Basic

2. If the solution is then diluted by adding water, the condition in the solution will become

a. More acidic d. Less basic

b. Less acidic e. No change

c. Move basic

3. You are given a solution and your tests show that it transmits 60 of the light that strikes it. If the solution is then diluted, the transmission of light

a. increases perhaps to 70

b. decreases perhaps to 50

c. doesn t change

Procedure

Connect the pH Sensor and the Colorimeter to the interface and launch the data collection software. Calibrate the Colorimeter.

Change the data collection mode to Events w/ Entry. The entry name will be Concentration and the units will be .

Obtain a 100-mL sample of stock solution in the beaker.

Pour a small amount from the beaker into a cuvette until it is 0.75 full. Place the cuvette in the Colorimeter. Be sure to wipe any spill on the side of the cuvette off so you don t contaminate the Colorimeter.

Pour 50.0 mL of the sample into the graduated cylinder, leaving about 50 mL in the beaker.

Rinse and dry the pH sensor bulb, then place the sensor in the remaining liquid in the beaker.

Begin data collection. When the readings are steady, click on KEEP. When asked, the concentration will be 100. Do not stop data collection at this point.

Empty the rest of the beaker into the WASTE and rinse the beaker. Pour the contents of the graduated cylinder into the beaker and then add an additional 50.0 mL of distilled water. Stir the contents to mix well. The concentration will now be half of what it was before. Rinse and dry the cuvette.

Follow steps 4-8, each time diluting the solution by half 50, 25, 12.5, 6.25, etc.. After 6 dilutions, stop data collection. Rinse and dry your cuvette, beaker and graduate cylinder and return materials to their designated locations. Move on to Analysis.

Analysis

Were the answers you gave in the preliminary section upheld during the lab. Explain any discrepancies that arose.

You should have 2 graphs on the screen, pH and Transmission. Focus on the graph of Transmission first.

How does the transmission of light through your sample change as the concentration changes. During the lab you reduced the concentration but the graph reverses that pattern Is there a mathematical relationship between the two. Test your thinking by doing a curve fit for Transmission vs. Concentration. Report your results. Does this relationship make sense to you. Explain.

Observe the graph to pH vs. Concentration. Describe the graph. Is it the same shape as your Transmission graph. If the pH increases, what does this say about the acidity of the sample, or if it decreases, what does that mean. Describe in words the affect on the acidity of your sample produced by diluting it.

What would happen to the pH if you continued diluting the solution a very large number of times.

Use the curve fitting capability in Logger Pro to determine the mathematical relationship between pH and concentration. By definition, pH involves logarithms, so you may wish to model the graph using a log function. Report your best result and interpret the results.

The data collection system could be LabPro, LabQuest Mini, or 2 Go. Links with a computer running Logger Pro or a LabQuest by itself or a TI graphing calculator and appropriate interface. You need 2 analog inputs to carry out the lab as described.

TEACHER NOTE

In this lab, the author envisions using a mildly acidic solution including some yellow food coloring. Dilution will reduce the concentration of yellow as well as diluting the acid. Variations can be made using different colors, basic solutions or different acids. Finally, one could use absorption rather than transmission in this study, yielding a different mathematical relationship from that obtained with transmission.

This lab was suggested by a participant in an on-site workshop the author conducted in 2010. It was reviewed by a chemist and deemed to have merit. If you use this lab, please forward your results and other feedback to the author for fine tuning. Thank you.

And Serial Dilution Lab 1 pipette full or dropping bottle of food coloring Continue the serial dilution by taking one drop from well 2 and placing.

Color the first circle the color of the container with food coloring or describe the color in the circle. Worksheet on serial dilution.

I. Title: Serial Dilution Lab II. Purpose: Place one drop of food coloring in well numbered 1; add 9 drops of water to the food coloring.

Associated Unit: Serial Dilution – the Solution to Pollution. food coloring in vial 1 should be calculating a diluted concentration of 0.00005 drops/mL.

Dilution Lab Directions _____ Place a Serial Dilution Template under your SEPUP tray _____ Put 10 drops of 10 red food coloring into small Cup 1 from the bottle.

2015-12-30

Patch Codes Nz

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