dask.array.isclose

dask.array.isclose¶

dask.array.isclose(arr1, arr2, rtol=1e-05, atol=1e-08, equal_nan=False)[source]¶

Returns a boolean array where two arrays are element-wise equal within a tolerance.

This docstring was copied from numpy.isclose.

Some inconsistencies with the Dask 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.

Warning

The default atol is not appropriate for comparing numbers with magnitudes much smaller than one (see Notes).

Parameters

a, barray_like: Input arrays to compare.
rtolarray_like: The relative tolerance parameter (see Notes).
atolarray_like: The absolute tolerance parameter (see Notes).
equal_nanbool: 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

yarray_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.

See also

allclose
math.isclose

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))

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])  
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])

>>> np.isclose([1e-8, 1e-7], [0.0, 0.0])  
array([ True, False])

>>> np.isclose([1e-100, 1e-7], [0.0, 0.0], atol=0.0)  
array([False, False])

>>> np.isclose([1e-10, 1e-10], [1e-20, 0.0])  
array([ True,  True])

>>> np.isclose([1e-10, 1e-10], [1e-20, 0.999999e-10], atol=0.0)  
array([False,  True])

dask.array.invert

dask.array.iscomplex