dask.array.allclose
dask.array.allclose¶
- dask.array.allclose(arr1, arr2, 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 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).
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, 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.
New in version 1.10.0.
- Returns
- allclosebool
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 fromallclose(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
andb = 2e-9
should be considered “close”, yetallclose(1e-9, 2e-9)
isTrue
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]) False
>>> np.allclose([1e10,1e-8], [1.00001e10,1e-9]) True
>>> np.allclose([1e10,1e-8], [1.0001e10,1e-9]) False
>>> np.allclose([1.0, np.nan], [1.0, np.nan]) False
>>> np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True) True