numpy.testing.assert_allclose#

testing.assert_allclose(actual, desired, rtol=1e-07, atol=0, equal_nan=True, err_msg='', verbose=True, *, strict=False)[source]#

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).

Parameters:

actualarray_like: Array obtained.
desiredarray_like: Array desired.
rtolfloat, optional: Relative tolerance.
atolfloat, optional: Absolute tolerance.
equal_nanbool, optional.: If True, NaNs will compare equal.
err_msgstr, optional: The error message to be printed in case of failure.
verbosebool, optional: If True, the conflicting values are appended to the error message.
strictbool, optional: If True, raise an AssertionError when either the shape or the data type of the arguments does not match. The special handling of scalars mentioned in the Notes section is disabled.

New in version 2.0.0.

Raises:

AssertionError: If actual and desired are not equal up to specified precision.

See also

assert_array_almost_equal_nulp, assert_array_max_ulp

Notes

When one of actual and desired is a scalar and the other is array_like, the function performs the comparison as if the scalar were broadcasted to the shape of the array. This behaviour can be disabled with the strict parameter.

Examples

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

As mentioned in the Notes section, assert_allclose has special handling for scalars. Here, the test checks that the value of numpy.sin is nearly zero at integer multiples of π.

>>> x = np.arange(3) * np.pi
>>> np.testing.assert_allclose(np.sin(x), 0, atol=1e-15)

Use strict to raise an AssertionError when comparing an array with one or more dimensions against a scalar.

>>> np.testing.assert_allclose(np.sin(x), 0, atol=1e-15, strict=True)
Traceback (most recent call last):
    ...
AssertionError:
Not equal to tolerance rtol=1e-07, atol=1e-15

(shapes (3,), () mismatch)
 ACTUAL: array([ 0.000000e+00,  1.224647e-16, -2.449294e-16])
 DESIRED: array(0)

The strict parameter also ensures that the array data types match:

>>> y = np.zeros(3, dtype=np.float32)
>>> np.testing.assert_allclose(np.sin(x), y, atol=1e-15, strict=True)
Traceback (most recent call last):
    ...
AssertionError:
Not equal to tolerance rtol=1e-07, atol=1e-15

(dtypes float64, float32 mismatch)
 ACTUAL: array([ 0.000000e+00,  1.224647e-16, -2.449294e-16])
 DESIRED: array([0., 0., 0.], dtype=float32)