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This article introduces the knowledge about "how to implement Python add5() unit test". In the actual case operation process, many people will encounter such difficulties. Next, let Xiaobian lead you to learn how to deal with these situations! I hope you can read carefully and learn something!

The TestAdd5 class derives from unittest.TestCase and defines a setUp() method that will be called before each test method is executed. It ensures that certain consistent states can be used for all test methods. This test method calls the assertEqual() and assert_() methods of unittest. If any call fails, the managed test method assumes a failure and moves on to the next test.

Partial functions are a lovely feature of functions. They allow your function to take X arguments, and by fixing some arguments, you get a new function that can only take arguments you didn't specify. For example, the function add() takes two arguments and returns the result. Now if we fix one argument to 5, we get a new function that takes only one argument. The code is as follows:

Traceback (most recent call last): File "test_partial_pickle.py", line 12, in s = pickle.dumps(partial(add, 10)) File "/Library/Frameworks/Python.framework/Versions/ 2.5/lib/python2.5/pickle.py", line 1366, in dumps Pickler(file, protocol).dump(obj) File "/Library/Frameworks/Python.framework/Versions/ 2.5/lib/python2.5/pickle.py", line 224, in dump self.save(obj) File "/Library/Frameworks/Python.framework/Versions/ 2.5/lib/python2.5/pickle.py", line 306, in save rv = reduce(self.proto) File "/Library/Frameworks/Python.framework/Versions/ 2.5/lib/python2.5/copy_reg.py", line 69, in _reduce_ex raise TypeError, "can't pickle %s objects" % base.__ name__ TypeError: can't pickle partial objects

Some functions can be useful when using APIs that require parameters to always be the same. Consider a web application interface that requires a username and password in each method. If you create a partial function with a fixed username and password, it's a great development convenience because you don't have to pass parameters.

At the same time, your code becomes more secure because usernames and passwords don't appear at all calling sites. However, until Python 3.1, some functions still had many unpleasant limitations. Because they can't be encapsulated. Python 3.1 solves this problem, and here's an example:

Traceback (most recent call last): File "test_partial_pickle.py", line 12, in s = pickle.dumps(partial(add, 10)) File "/Library/Frameworks/Python.framework/Versions/ 2.5/lib/python2.5/pickle.py", line 1366, in dumps Pickler(file, protocol).dump(obj) File "/Library/Frameworks/Python.framework/Versions/ 2.5/lib/python2.5/pickle.py", line 224, in dump self.save(obj) File "/Library/Frameworks/Python.framework/Versions/ 2.5/lib/python2.5/pickle.py", line 306, in save rv = reduce(self.proto) File "/Library/Frameworks/Python.framework/Versions/ 2.5/lib/python2.5/copy_reg.py", line 69, in _reduce_ex raise TypeError, "can't pickle %s objects" % base.__ name__ TypeError: can't pickle partial objects

When designing parallel programs using processing modules, people like to encapsulate functions and parts of functions. Since Python 2.6, the processing module has been part of the standard library. ***Python solution for modern multicore machines. In effect, the processing module encapsulates everything passed between processes, so wrappable partial functions increase its expressiveness and the tools available.

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