nums.numpy.allclose

nums.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.

This docstring was copied from numpy.allclose.

Some inconsistencies with the NumS version may exist.

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 (BlockArray) – Input arrays to compare.

  • b (BlockArray) – 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 in b in the output array.

Returns

allclose – Returns True if the two arrays are equal within the given tolerance; False otherwise.

Return type

bool

See also

isclose, all, any, equal

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 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.

equal_nan=True not supported.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> nps.allclose(nps.array([1e10,1e-7]), nps.array([1.00001e10,1e-8])).get()  
array(False)
>>> nps.allclose(nps.array([1e10,1e-8]), nps.array([1.00001e10,1e-9])).get()  
array(True)
>>> nps.allclose(nps.array([1e10,1e-8]), nps.array([1.0001e10,1e-9])).get()  
array(False)
>>> nps.allclose(nps.array([1.0, nps.nan]), nps.array([1.0, nps.nan])).get()  
array(False)

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