《NumPy Beginner's Guide》笔记Chapter8

# -*- coding: utf-8 -*-import numpy as np__author__ = 'ZengDong'#日期 =  15:04"""    Some programmers test only in production. If you are not one of them you're    probably familiar with the concept of unit testing. Unit tests are automated    tests written by a programmer to test his or her code. These tests could, for    example, test a function or part of a function in isolation. Only a small unit of    code is tested by each test. The benefits are increased confidence in the quality    of the code, reproducible tests, and as a side effect, more clear code.    Python has good support for unit testing. Additionally, NumPy adds the numpy.    testing package to that for NumPy code unit testing.""""""    【Assert functions】    For integers, comparison is a trivial operation, but for floating-point numbers it is not because of the inexact representation by computers.    The numpy.testing package has a number of utility functions that test whether a    precondition is true or not, taking into account the problem of floating-point comparisons:    函数：    assert_almost_equal Raises an exception if two numbers are not equal up to a                               specified precision    assert_approx_equal Raises an exception if two numbers are not equal up to a                               certain significance    assert_array_almost_equal Raises an exception if two arrays are not equal up to a                               specified precision    assert_array_equal Raises an exception if two arrays are not equal    assert_array_less Raises an exception if two arrays do not have the same                             shape and the elements of the first array are strictly less than the elements of the second array    assert_equal Raises an exception if two objects are not equal    assert_raises Fails if a specified exception is not raised by a callable invoked with defined arguments    assert_warns Fails if a specified warning is not thrown    assert_string_equal Asserts that two strings are equal    assert_allclose Raise an assertion if two objects are not equal up to desired tolerance""""""    1. assert_almost_equal"""#Imagine that you have two numbers that are almost equal. Let's use the assert_almost_ equal function to check whether they are equal#call the function with low precisionprint("Decimal 6", np.testing.assert_almost_equal(0.123456789, 0.123456780, decimal=7))#输出：Decimal 6 None  因为没有exception#call the function with higher precision(8diecmal)#print("Decimal 7", np.testing.assert_almost_equal(0.123456789, 0.123456780, decimal=8))   #输出：raise AssertionError(_build_err_msg())"""    2. Approximately equal arrays    abs(actual - expected) >= 10**-(significant - 1)"""#The assert_approx_equal function raises an exception if two numbers are not equal up to a certain number of significant digits.print("Significance 8", np.testing.assert_approx_equal(0.123456789, 0.123456780, significant=8)) #输出：('Significance 8', None)#significance 9  : exception"""    3. Almost equal arrays    assert_array_almost_equal:raises an exception if two arrays are not equal up to a specified precision.    |expected - actual| < 0.5 10-decimal"""print("Decimal 8", np.testing.assert_array_almost_equal([0, 0.123456789], [0, 0.123456780], decimal=8))"""    4. Equal arrays    assert_array_equal:  raises an exception if two arrays are not equal        The shapes of the arrays have to be equal and the elements of each array must be equal."""print("Pass", np.testing.assert_allclose([0, 0.123456789, np.nan], [0, 0.123456780, np.nan], rtol=1e-7, atol=0))#assert_array_equal  Fail"""    5. Ordering arrays    The assert_array_less function raises an exception if two arrays do not have the    same shape and the elements of the first array are strictly less than the elements of the second array."""print("Pass", np.testing.assert_array_less([0, 0.123456789, np.nan], [1, 0.23456780, np.nan]))  #Pass None"""    6. Objects comparison    The assert_equal function raises an exception if two objects are not equal. The objects do    not have to be NumPy arrays, they can also be lists, tuples, or dictionaries.""""""    7. String comparison    The assert_string_equal function asserts that two strings are equal.    大小写敏感"""print("Pass", np.testing.assert_string_equal("Numpy", "Numpy"))print("888888888888888888888888888888888888888888888888888888888888888888888888888888888888888888888")"""    8. Floating point comparisons    The representation of floating-point numbers in computers is not exact. This leads to issues    when comparing floating-point numbers. The assert_array_almost_equal_nulp and    assert_array_max_ulp NumPy functions provide consistent floating-point comparisons."""#Let's see the assert_array_almost_equal_nulp function in action:#determine the machine epsilon with the finfo functioneps = np.finfo(float).epsprint("EPS", eps)   #输出：('EPS', 2.2204460492503131e-16)#Compare two almost equal floats: Compare 1.0 with 1 + epsilon (eps) using the assert_almost_equal_nulp functionprint("1", np.testing.assert_array_almost_equal_nulp(1.0, 1.0 + eps))#print("2", np.testing.assert_array_almost_equal_nulp(1.0, 1.0 + 2*eps))print("99999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999")"""    9. Unit tests    Unit tests are automated tests, which test a small piece of code, usually a function or method."""#we start by writing the factorial functiondef factorial(n):    if n == 0:        return 1    if n < 0:        raise ValueError, "Unexcepted negative value"    return np.arange(1, n+1).cumprod()#now we will write the unit test, Let's write a class that will contain the unit tests.#It's extends the TestCase class from the unittest module which is part of standard Pythonimport unittestclass FactorialTest(unittest.TestCase):    def test_factorial(self):    #Test for the factorial of 3 that should pass.        self.assertEqual(6, factorial(3)[-1])        np.testing.assert_equal(np.array([1, 2, 6]), factorial(3))    def test_zero(self):    #Test for the factorial of 0 that should pass        self.assertEqual(1, factorial(0))    def test_negative(self):        #Test for the factorial of negative numbers that should fail        #It should throw a ValueError, but we expect IndexError        self.assertRaises(IndexError, factorial(-10))    if __name__ == "__main__":        unittest.main()print("10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 ")"""    10. Nose tests decorators    numpy.testing.decorators.deprecated             Filters deprecation warnings when running tests.    numpy.testing.decorators.knownfailureif         Raises KnownFailureTest exception based on a condition.    numpy.testing.decorators.setastest              Marks a function as being a test or not being a test.    numpy.testing.decorators.skipif                 Raises SkipTest exception based on a condition.    numpy.testing.decorators.slow                   Labels test functions or methods as slow."""