numpy.isclose()

numpy.isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False) [source]

Returns a boolean array where 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 difference atol are added together to compare against the absolute difference between a and b.

Parameters:

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 in `b` in the output array.
Returns:	y : array_like Returns a boolean array of where `a` and `b` are equal within the given tolerance. If both `a` and `b` are scalars, returns a single boolean value.

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 in b in the output array.

Returns:

y : array_like

Returns a boolean array of where a and b are equal within the given tolerance. If both a and b are scalars, returns a single boolean value.

Notes

New in version 1.7.0.

For finite values, isclose uses the following equation to test whether two floating point values are equivalent.

absolute(a - b) <= (atol + rtol * absolute(b))

The above equation is not symmetric in a and b, so that isclose(a, b) might be different from isclose(b, a) in some rare cases.

Examples

>>> np.isclose([1e10,1e-7], [1.00001e10,1e-8])
array([True, False])
>>> np.isclose([1e10,1e-8], [1.00001e10,1e-9])
array([True, True])
>>> np.isclose([1e10,1e-8], [1.0001e10,1e-9])
array([False, True])
>>> np.isclose([1.0, np.nan], [1.0, np.nan])
array([True, False])
>>> np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
array([True, True])

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.isclose.html

doc_NumPy

2017-01-10 18:14:29

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