numpy.testing._private.utils

Assert that works in release mode. Accepts callable msg to allow deferring evaluation until failure.

The Python built-in assert does not work when executing code in optimized mode (the -O flag) - no byte-code is generated for it.

For documentation on usage, refer to the Python documentation.

Raises an AssertionError if two objects are not equal up to desired tolerance.

Given two array_like objects, check that their shapes and all elements are equal (but see the Notes for the special handling of a scalar). An exception is raised if the shapes mismatch or any values conflict. 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 test is equivalent to allclose(actual, desired, rtol, atol) (note that allclose has different default values). It compares the difference between actual and desired to atol + rtol * abs(desired).

>>> x = [1e-5, 1e-3, 1e-1]
>>> y = np.arccos(np.cos(x))
>>> np.testing.assert_allclose(x, y, rtol=1e-5, atol=0)

Raises an AssertionError if two items are not equal up to desired precision.

The test verifies that the elements of actual and desired satisfy.

abs(desired-actual) < float64(1.5 * 10**(-decimal))

That is a looser test than originally documented, but agrees with what the actual implementation in assert_array_almost_equal did up to rounding vagaries. An exception is raised at conflicting values. For ndarrays this delegates to assert_array_almost_equal

>>> from numpy.testing import assert_almost_equal
>>> assert_almost_equal(2.3333333333333, 2.33333334)
>>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
Traceback (most recent call last):
    ...
AssertionError:
Arrays are not almost equal to 10 decimals
 ACTUAL: 2.3333333333333
 DESIRED: 2.33333334

>>> assert_almost_equal(np.array([1.0,2.3333333333333]),
...                     np.array([1.0,2.33333334]), decimal=9)
Traceback (most recent call last):
    ...
AssertionError:
Arrays are not almost equal to 9 decimals
<BLANKLINE>
Mismatched elements: 1 / 2 (50%)
Max absolute difference: 6.66669964e-09
Max relative difference: 2.85715698e-09
 x: array([1.         , 2.333333333])
 y: array([1.        , 2.33333334])

Raises an AssertionError 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.

>>> 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)
Traceback (most recent call last):
    ...
AssertionError:
Items are not equal to 8 significant digits:
 ACTUAL: 1.234567e-21
 DESIRED: 1.2345672e-21

>>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
True

Raises an AssertionError if two objects are not equal up to desired precision.

The test verifies identical shapes and that the elements of actual and desired satisfy.

abs(desired-actual) < 1.5 * 10**(-decimal)

That is a looser test than originally documented, but agrees with what the actual implementation did up to rounding vagaries. 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.

>>> 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)
Traceback (most recent call last):
    ...
AssertionError:
Arrays are not almost equal to 5 decimals
<BLANKLINE>
Mismatched elements: 1 / 3 (33.3%)
Max absolute difference: 6.e-05
Max relative difference: 2.57136612e-05
 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)
Traceback (most recent call last):
    ...
AssertionError:
Arrays are not almost equal to 5 decimals
<BLANKLINE>
x and y nan location mismatch:
 x: array([1.     , 2.33333,     nan])
 y: array([1.     , 2.33333, 5.     ])

Compare two arrays relatively to their spacing.

This is a relatively robust method to compare two arrays whose amplitude is variable.

abs(x - y) <= nulp * spacing(maximum(abs(x), abs(y)))

>>> x = np.array([1., 1e-10, 1e-20])
>>> eps = np.finfo(x.dtype).eps
>>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x)

>>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x)
Traceback (most recent call last):
  ...
AssertionError: X and Y are not equal to 1 ULP (max is 2)

Undocumented

Raises an AssertionError 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 (but see the Notes for the special handling of a scalar). 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.

>>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
...                               [np.exp(0),2.33333, np.nan])

>>> np.testing.assert_array_equal([1.0,np.pi,np.nan],
...                               [1, np.sqrt(np.pi)**2, np.nan])
Traceback (most recent call last):
    ...
AssertionError:
Arrays are not equal
<BLANKLINE>
Mismatched elements: 1 / 3 (33.3%)
Max absolute difference: 4.4408921e-16
Max relative difference: 1.41357986e-16
 x: array([1.      , 3.141593,      nan])
 y: array([1.      , 3.141593,      nan])

>>> np.testing.assert_allclose([1.0,np.pi,np.nan],
...                            [1, np.sqrt(np.pi)**2, np.nan],
...                            rtol=1e-10, atol=0)

>>> x = np.full((2, 5), fill_value=3)
>>> np.testing.assert_array_equal(x, 3)

>>> np.testing.assert_array_equal(x, 3, strict=True)
Traceback (most recent call last):
    ...
AssertionError:
Arrays are not equal
<BLANKLINE>
(shapes (2, 5), () mismatch)
 x: array([[3, 3, 3, 3, 3],
       [3, 3, 3, 3, 3]])
 y: array(3)

>>> x = np.array([2, 2, 2])
>>> y = np.array([2., 2., 2.], dtype=np.float32)
>>> np.testing.assert_array_equal(x, y, strict=True)
Traceback (most recent call last):
    ...
AssertionError:
Arrays are not equal
<BLANKLINE>
(dtypes int64, float32 mismatch)
 x: array([2, 2, 2])
 y: array([2., 2., 2.], dtype=float32)

Raises an AssertionError 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.

>>> 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])
Traceback (most recent call last):
    ...
AssertionError:
Arrays are not less-ordered
<BLANKLINE>
Mismatched elements: 1 / 3 (33.3%)
Max absolute difference: 1.
Max relative difference: 0.5
 x: array([ 1.,  1., nan])
 y: array([ 1.,  2., nan])

>>> np.testing.assert_array_less([1.0, 4.0], 3)
Traceback (most recent call last):
    ...
AssertionError:
Arrays are not less-ordered
<BLANKLINE>
Mismatched elements: 1 / 2 (50%)
Max absolute difference: 2.
Max relative difference: 0.66666667
 x: array([1., 4.])
 y: array(3)

>>> np.testing.assert_array_less([1.0, 2.0, 3.0], [4])
Traceback (most recent call last):
    ...
AssertionError:
Arrays are not less-ordered
<BLANKLINE>
(shapes (3,), (1,) mismatch)
 x: array([1., 2., 3.])
 y: array([4])

Check that all items of arrays differ in at most N Units in the Last Place.

>>> a = np.linspace(0., 1., 100)
>>> res = np.testing.assert_array_max_ulp(a, np.arcsin(np.sin(a)))

Raises an AssertionError if two objects are not equal.

Given two objects (scalars, lists, tuples, dictionaries or numpy arrays), check that all elements of these objects are equal. An exception is raised at the first conflicting values.

When one of actual and desired is a scalar and the other is array_like, the function checks that each element of the array_like object is equal to the scalar.

This function handles NaN comparisons as if NaN was a "normal" number. That is, AssertionError is not raised if both objects have NaNs in the same positions. This is in contrast to the IEEE standard on NaNs, which says that NaN compared to anything must return False.

>>> np.testing.assert_equal([4,5], [4,6])
Traceback (most recent call last):
    ...
AssertionError:
Items are not equal:
item=1
 ACTUAL: 5
 DESIRED: 6

>>> np.testing.assert_equal(np.array([1.0, 2.0, np.nan]), [1, 2, np.nan])

Fail if the given callable produces any reference cycles.

If called with all arguments omitted, may be used as a context manager:

with assert_no_gc_cycles():

do_something()

Fail if the given callable produces any warnings.

If called with all arguments omitted, may be used as a context manager:

with assert_no_warnings():

do_something()

The ability to be used as a context manager is new in NumPy v1.11.0.

assert_raises(exception_class, callable, *args, **kwargs) assert_raises(exception_class)

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.

Alternatively, assert_raises can be used as a context manager:

>>> from numpy.testing import assert_raises
>>> with assert_raises(ZeroDivisionError):
...     1 / 0

is equivalent to

>>> def div(x, y):
...     return x / y
>>> assert_raises(ZeroDivisionError, div, 1, 0)

assert_raises_regex(exception_class, expected_regexp, callable, *args,

**kwargs)

assert_raises_regex(exception_class, expected_regexp)

Fail unless an exception of class exception_class and with message that matches expected_regexp is thrown by callable when invoked with arguments args and keyword arguments kwargs.

Alternatively, can be used as a context manager like assert_raises.

Test if two strings are equal.

If the given strings are equal, assert_string_equal does nothing. If they are not equal, an AssertionError is raised, and the diff between the strings is shown.

>>> np.testing.assert_string_equal('abc', 'abc')
>>> np.testing.assert_string_equal('abc', 'abcd')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
...
AssertionError: Differences in strings:
- abc+ abcd?    +

Fail unless the given callable throws the specified warning.

A warning of class warning_class should be thrown by the callable when invoked with arguments args and keyword arguments kwargs. If a different type of warning is thrown, it will not be caught.

If called with all arguments other than the warning class omitted, may be used as a context manager:

with assert_warns(SomeWarning):

do_something()

The ability to be used as a context manager is new in NumPy v1.11.0.

>>> import warnings
>>> def deprecated_func(num):
...     warnings.warn("Please upgrade", DeprecationWarning)
...     return num*num
>>> with np.testing.assert_warns(DeprecationWarning):
...     assert deprecated_func(4) == 16
>>> # or passing a func
>>> ret = np.testing.assert_warns(DeprecationWarning, deprecated_func, 4)
>>> assert ret == 16

Break reference cycles by calling gc.collect Objects can call other objects' methods (for instance, another object's

__del__) inside their own __del__. On PyPy, the interpreter only runs

between calls to gc.collect, so multiple calls are needed to completely release all cycles.

Undocumented

Check whether free_bytes amount of memory is currently free. Returns: None if enough memory available, otherwise error message

Apply a decorator to all methods in a class matching a regular expression.

The given decorator is applied to all public methods of cls that are matched by the regular expression testmatch (testmatch.search(methodname)). Methods that are private, i.e. start with an underscore, are ignored.

Undocumented

like isfinite, but always raise an error if type not supported instead of returning a TypeError object.

like isinf, but always raise an error if type not supported instead of returning a TypeError object.

like isnan, but always raise an error if type not supported instead of returning a TypeError object.

Return the signed-magnitude interpretation of the binary representation of x.

Return number of jiffies elapsed.

Return number of jiffies (1/100ths of a second) that this process has been scheduled in user mode. See man 5 proc.

Return elapsed time for executing code in the namespace of the caller.

The supplied code string is compiled with the Python builtin compile. The precision of the timing is 10 milli-seconds. If the code will execute fast on this timescale, it can be executed many times to get reasonable timing accuracy.

>>> times = 10
>>> etime = np.testing.measure('for i in range(1000): np.sqrt(i**2)', times=times)
>>> print("Time for a single execution : ", etime / times, "s")  # doctest: +SKIP
Time for a single execution :  0.005 s

Undocumented

For each item in x and y, return the number of representable floating points between them.

Test if two objects are equal, and print an error message if test fails.

The test is performed with actual == desired.

>>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 1])
>>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 2])
Traceback (most recent call last):
...
AssertionError: Test XYZ of func xyz failed
ACTUAL:
[0, 1]
DESIRED:
[0, 2]

Decorator to check for raised exceptions.

The decorated test function must raise one of the passed exceptions to pass. If you want to test many assertions about exceptions in a single test, you may want to use assert_raises instead.

@raises(TypeError, ValueError)
def test_raises_type_error():
    raise TypeError("This test passes")

@raises(Exception)
def test_that_fails_by_passing():
    pass

Decorator to skip a test if not enough memory is available

Run doctests found in the given file.

By default rundocs raises an AssertionError on failure.

>>> np.lib.test(doctests=True)  # doctest: +SKIP

Undocumented

Context manager to provide a temporary test folder.

All arguments are passed as this to the underlying tempfile.mkdtemp function.

Context manager for temporary files.

Context manager that returns the path to a closed temporary file. Its parameters are the same as for tempfile.mkstemp and are passed directly to that function. The underlying file is removed when the context is exited, so it should be closed at that time.

Windows does not allow a temporary file to be opened if it is already open, so the underlying file must be closed after opening before it can be opened again.