numpy.testing.assert_allclose#

testing.assert_allclose(actual, desired, rtol=1e-07, atol=0, equal_nan=True, err_msg='', verbose=True)[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).

New in version 1.5.0.

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.

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 checks that each element of the array_like object is equal to the scalar.

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)