numpy.allclose
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numpy.allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)
[source] -
Returns True if two arrays are element-wise equal within a tolerance.
The tolerance values are positive, typically very small numbers. The relative difference (
rtol
* abs(b
)) and the absolute differenceatol
are added together to compare against the absolute difference betweena
andb
.If either array contains one or more NaNs, False is returned. Infs are treated as equal if they are in the same place and of the same sign in both arrays.
Parameters: -
a, b : array_like
-
Input arrays to compare.
-
rtol : float
-
The relative tolerance parameter (see Notes).
-
atol : float
-
The absolute tolerance parameter (see Notes).
-
equal_nan : bool
-
Whether to compare NaN’s as equal. If True, NaN’s in
a
will be considered equal to NaN’s inb
in the output array.New in version 1.10.0.
Returns: -
allclose : bool
-
Returns True if the two arrays are equal within the given tolerance; False otherwise.
Notes
If the following equation is element-wise True, then allclose returns True.
absolute(a
-b
) <= (atol
+rtol
* absolute(b
))The above equation is not symmetric in
a
andb
, so thatallclose(a, b)
might be different fromallclose(b, a)
in some rare cases.The comparison of
a
andb
uses standard broadcasting, which means thata
andb
need not have the same shape in order forallclose(a, b)
to evaluate to True. The same is true forequal
but notarray_equal
.Examples
>>> np.allclose([1e10,1e-7], [1.00001e10,1e-8]) False >>> np.allclose([1e10,1e-8], [1.00001e10,1e-9]) True >>> np.allclose([1e10,1e-8], [1.0001e10,1e-9]) False >>> np.allclose([1.0, np.nan], [1.0, np.nan]) False >>> np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True) True
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Licensed under the 3-clause BSD License.
https://docs.scipy.org/doc/numpy-1.15.4/reference/generated/numpy.allclose.html