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# numpy.testing.assert_allclose

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

  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)`, except that it is stricter: it doesn’t broadcast its operands, and has tighter default tolerance values. It compares the difference between actual and desired to `atol + rtol * abs(desired)`.

  Parameters:
    actual array_like

      Array obtained.

    desired array_like

      Array desired.

    rtol float, optional

      Relative tolerance.

    atol float | np.timedelta64, optional

      Absolute tolerance.

    equal_nan bool, optional.

      If True, NaNs will compare equal.

    err_msg str, optional

      The error message to be printed in case of failure.

    verbose bool, optional

      If True, the conflicting values are appended to the error message.

    strict bool, 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. Note that empty arrays are therefore considered equal to scalars. This behaviour can be disabled by setting `strict==True`.

  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)