numpy.testing.utils

assert_(val, msg='') Assert that works in release mode. assert_almost_equal(actual, desired, decimal=7, err_msg='', verbose=True) Raise an assertion if two items are not equal up to desired precision. The test is equivalent to abs(desired-actual) < 0.5 * 10**(-decimal) Given two objects (numbers or ndarrays), check that all elements of these objects are almost equal. An exception is raised at conflicting values. For ndarrays this delegates to assert_array_almost_equal Parameters ---------- actual : number or ndarray The object to check. desired : number or ndarray The expected object. decimal : integer (decimal=7) desired precision err_msg : string The error message to be printed in case of failure. verbose : bool If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- assert_array_almost_equal: compares array_like objects assert_equal: tests objects for equality Examples -------- >>> npt.assert_almost_equal(2.3333333333333, 2.33333334) >>> npt.assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) ... <type 'exceptions.AssertionError'>: Items are not equal: ACTUAL: 2.3333333333333002 DESIRED: 2.3333333399999998 >>> npt.assert_almost_equal(np.array([1.0,2.3333333333333]), np.array([1.0,2.33333334]), decimal=9) ... <type 'exceptions.AssertionError'>: Arrays are not almost equal <BLANKLINE> (mismatch 50.0%) x: array([ 1. , 2.33333333]) y: array([ 1. , 2.33333334]) assert_approx_equal(actual, desired, significant=7, err_msg='', verbose=True) Raise an assertion if two items are not equal up to significant digits. Given two numbers, check that they are approximately equal. Approximately equal is defined as the number of significant digits that agree. Parameters ---------- actual : number The object to check. desired : number The expected object. significant : integer (significant=7) desired precision err_msg : string The error message to be printed in case of failure. verbose : bool If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- assert_almost_equal: compares objects by decimals assert_array_almost_equal: compares array_like objects by decimals assert_equal: tests objects for equality Examples -------- >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, significant=8) >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, significant=8) ... <type 'exceptions.AssertionError'>: Items are not equal to 8 significant digits: ACTUAL: 1.234567e-021 DESIRED: 1.2345672000000001e-021 the evaluated condition that raises the exception is >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) True assert_array_almost_equal(x, y, decimal=6, err_msg='', verbose=True) Raise an assertion if two objects are not equal up to desired precision. The test verifies identical shapes and verifies values with abs(desired-actual) < 0.5 * 10**(-decimal) Given two array_like objects, check that the shape is equal and all elements of these objects are almost equal. An exception is raised at shape mismatch or conflicting values. In contrast to the standard usage in numpy, NaNs are compared like numbers, no assertion is raised if both objects have NaNs in the same positions. Parameters ---------- x : array_like The actual object to check. y : array_like The desired, expected object. decimal : integer (decimal=6) desired precision err_msg : string The error message to be printed in case of failure. verbose : bool If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- assert_almost_equal: simple version for comparing numbers assert_array_equal: tests objects for equality Examples -------- the first assert does not raise an exception >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], [1.0,2.333,np.nan]) >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], [1.0,2.33339,np.nan], decimal=5) ... <type 'exceptions.AssertionError'>: AssertionError: Arrays are not almost equal <BLANKLINE> (mismatch 50.0%) x: array([ 1. , 2.33333, NaN]) y: array([ 1. , 2.33339, NaN]) >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], [1.0,2.33333, 5], decimal=5) <type 'exceptions.ValueError'>: ValueError: Arrays are not almost equal x: array([ 1. , 2.33333, NaN]) y: array([ 1. , 2.33333, 5. ]) assert_array_compare(comparison, x, y, err_msg='', verbose=True, header='') assert_array_equal(x, y, err_msg='', verbose=True) Raise an assertion if two array_like objects are not equal. Given two array_like objects, check that the shape is equal and all elements of these objects are equal. An exception is raised at shape mismatch or conflicting values. In contrast to the standard usage in numpy, NaNs are compared like numbers, no assertion is raised if both objects have NaNs in the same positions. The usual caution for verifying equality with floating point numbers is advised. Parameters ---------- x : array_like The actual object to check. y : array_like The desired, expected object. err_msg : string The error message to be printed in case of failure. verbose : bool If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired objects are not equal. See Also -------- assert_array_almost_equal: test objects for equality up to precision assert_equal: tests objects for equality Examples -------- the first assert does not raise an exception >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], [np.exp(0),2.33333, np.nan]) assert fails with numerical inprecision with floats >>> np.testing.assert_array_equal([1.0,np.pi,np.nan], [1, np.sqrt(np.pi)**2, np.nan]) ... <type 'exceptions.ValueError'>: AssertionError: Arrays are not equal <BLANKLINE> (mismatch 50.0%) x: array([ 1. , 3.14159265, NaN]) y: array([ 1. , 3.14159265, NaN]) use assert_array_almost_equal for these cases instead >>> np.testing.assert_array_almost_equal([1.0,np.pi,np.nan], [1, np.sqrt(np.pi)**2, np.nan], decimal=15) assert_array_less(x, y, err_msg='', verbose=True) Raise an assertion if two array_like objects are not ordered by less than. Given two array_like objects, check that the shape is equal and all elements of the first object are strictly smaller than those of the second object. An exception is raised at shape mismatch or incorrectly ordered values. Shape mismatch does not raise if an object has zero dimension. In contrast to the standard usage in numpy, NaNs are compared, no assertion is raised if both objects have NaNs in the same positions. Parameters ---------- x : array_like The smaller object to check. y : array_like The larger object to compare. err_msg : string The error message to be printed in case of failure. verbose : bool If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired objects are not equal. See Also -------- assert_array_equal: tests objects for equality assert_array_almost_equal: test objects for equality up to precision Examples -------- >>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1.1, 2.0, np.nan]) >>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1, 2.0, np.nan]) ... <type 'exceptions.ValueError'>: Arrays are not less-ordered (mismatch 50.0%) x: array([ 1., 1., NaN]) y: array([ 1., 2., NaN]) >>> np.testing.assert_array_less([1.0, 4.0], 3) ... <type 'exceptions.ValueError'>: Arrays are not less-ordered (mismatch 50.0%) x: array([ 1., 4.]) y: array(3) >>> np.testing.assert_array_less([1.0, 2.0, 3.0], [4]) ... <type 'exceptions.ValueError'>: Arrays are not less-ordered (shapes (3,), (1,) mismatch) x: array([ 1., 2., 3.]) y: array([4]) assert_equal(actual, desired, err_msg='', verbose=True) Raise an assertion if two objects are not equal. Given two objects (lists, tuples, dictionaries or numpy arrays), check that all elements of these objects are equal. An exception is raised at the first conflicting values. Parameters ---------- actual : list, tuple, dict or ndarray The object to check. desired : list, tuple, dict or ndarray The expected object. err_msg : string The error message to be printed in case of failure. verbose : bool If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal. Examples -------- >>> np.testing.assert_equal([4,5], [4,6]) ... <type 'exceptions.AssertionError'>: Items are not equal: item=1 ACTUAL: 5 DESIRED: 6 assert_raises(*args, **kwargs) assert_raises(excecption_class, callable, *args, **kwargs) Fail unless an exception of class exception_class is thrown by callable when invoked with arguments args and keyword arguments kwargs. If a different type of exception is thrown, it will not be caught, and the test case will be deemed to have suffered an error, exactly as for an unexpected exception. assert_string_equal(actual, desired) build_err_msg(arrays, err_msg, header='Items are not equal:', verbose=True, names=('ACTUAL', 'DESIRED')) decorate_methods(cls, decorator, testmatch=None) Apply decorator to all methods in class matching testmatch Parameters ---------- cls : class Class to decorate methods for decorator : function Decorator to apply to methods testmatch : compiled regexp or string to compile to regexp Decorators are applied if testmatch.search(methodname) is not None. Default value is re.compile(r'(?:^|[\b_\.%s-])[Tt]est' % os.sep) (the default for nose) jiffies(_proc_pid_stat='/proc/17578/stat', _load_time=[]) Return number of jiffies (1/100ths of a second) that this process has been scheduled in user mode. See man 5 proc. measure(code_str, times=1, label=None) Return elapsed time for executing code_str in the namespace of the caller for given times. memusage(_proc_pid_stat='/proc/17578/stat')