bundles / numpy 2.5.0.dev0+git20251130.2de293a / numpy / isclose
_ArrayFunctionDispatcher
numpy:isclose
source: /dev/numpy/build-install/usr/lib/python3.14/site-packages/numpy/_core/numeric.py :2371
Signature
def isclose ( a , b , rtol = 1e-05 , atol = 1e-08 , equal_nan = False ) Summary
Returns a boolean array where two arrays are element-wise equal within a tolerance.
Extended Summary
The tolerance values are positive, typically very small numbers. The relative difference (rtol * abs(b)) and the absolute difference atol are added together to compare against the absolute difference between a and b.
Parameters
a, b: array_likeInput arrays to compare.
rtol: array_likeThe relative tolerance parameter (see Notes).
atol: array_likeThe absolute tolerance parameter (see Notes).
equal_nan: boolWhether to compare NaN's as equal. If True, NaN's in
awill be considered equal to NaN's inbin the output array.
Returns
y: array_likeReturns a boolean array of where
aandbare equal within the given tolerance. If bothaandbare scalars, returns a single boolean value.
Notes
For finite values, isclose uses the following equation to test whether two floating point values are equivalent.
absolute(a - b) <= (atol + rtol * absolute(b))Unlike the built-in math.isclose, the above equation is not symmetric in a and b -- it assumes b is the reference value -- so that isclose(a, b) might be different from isclose(b, a).
The default value of atol is not appropriate when the reference value b has magnitude smaller than one. For example, it is unlikely that a = 1e-9 and b = 2e-9 should be considered "close", yet isclose(1e-9, 2e-9) is True with default settings. Be sure to select atol for the use case at hand, especially for defining the threshold below which a non-zero value in a will be considered "close" to a very small or zero value in b.
isclose is not defined for non-numeric data types. bool is considered a numeric data-type for this purpose.
Examples
import numpy as np np.isclose([1e10,1e-7], [1.00001e10,1e-8])✓
np.isclose([1e10,1e-8], [1.00001e10,1e-9])
✗np.isclose([1e10,1e-8], [1.0001e10,1e-9])
✓np.isclose([1.0, np.nan], [1.0, np.nan])
✓np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
✗np.isclose([1e-8, 1e-7], [0.0, 0.0])
✓np.isclose([1e-100, 1e-7], [0.0, 0.0], atol=0.0)
✓np.isclose([1e-10, 1e-10], [1e-20, 0.0])
✓np.isclose([1e-10, 1e-10], [1e-20, 0.999999e-10], atol=0.0)
✓See also
- allclose
- math.isclose
Aliases
-
numpy.isclose