-
numpy.testing.assert_almost_equal(actual, desired, decimal=7, err_msg='', verbose=True)
[source] -
Raises an AssertionError if two items are not equal up to desired precision.
Note
It is recommended to use one of
assert_allclose
,assert_array_almost_equal_nulp
orassert_array_max_ulp
instead of this function for more consistent floating point comparisons.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 : array_like
The object to check.
desired : array_like
The expected object.
decimal : int, optional
Desired precision, default is 7.
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.
Raises: AssertionError
If actual and desired are not equal up to specified precision.
See also
-
assert_allclose
- Compare two array_like objects for equality with desired relative and/or absolute precision.
assert_array_almost_equal_nulp
,assert_array_max_ulp
,assert_equal
Examples
12345678>>>
import
numpy.testing as npt
>>> 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
123456789>>> 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
(mismatch
50.0
%
)
x: array([
1.
,
2.33333333
])
y: array([
1.
,
2.33333334
])
-
numpy.testing.assert_almost_equal()

2025-01-10 15:47:30
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