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bundles / numpy 2.4.4 / numpy / allclose

_ArrayFunctionDispatcher

numpy:allclose

source: /numpy/_core/numeric.py :2290

Signature

def   allclose ( a ,  b ,  rtol = 1e-05 ,  atol = 1e-08 ,  equal_nan = False )

Summary

Returns True if 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.

NaNs are treated as equal if they are in the same place and if equal_nan=True. 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 : array_like

The relative tolerance parameter (see Notes).

atol : array_like

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 in b in the output array.

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 and b, so that allclose(a, b) might be different from allclose(b, a) in some rare cases.

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 allclose(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.

The comparison of a and b uses standard broadcasting, which means that a and b need not have the same shape in order for allclose(a, b) to evaluate to True. The same is true for equal but not array_equal.

allclose is not defined for non-numeric data types. bool is considered a numeric data-type for this purpose.

Examples

import numpy as np
np.allclose([1e10,1e-7], [1.00001e10,1e-8])
✓
np.allclose([1e10,1e-8], [1.00001e10,1e-9])
✓
np.allclose([1e10,1e-8], [1.0001e10,1e-9])
✓
np.allclose([1.0, np.nan], [1.0, np.nan])
✓
np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
✓

See also

all
any
equal
isclose

Aliases

  • numpy.allclose

Referenced by

This package

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