array_equal

Documentation

treetensor.numpy.array_equal(a1, a2, *args, **kwargs)[source]

Numpy Version Related

This documentation is based on numpy.array_equal in numpy v1.24.4. Its arguments’ arrangements depend on the version of numpy you installed.

If some arguments listed here are not working properly, please check your numpy’s version with the following command and find its documentation.

1
python -c 'import numpy as np;print(np.__version__)'

The arguments and keyword arguments supported in numpy v1.24.4 is listed below.

Description From Numpy v1.24

True if two arrays have the same shape and elements, False otherwise.

Parameters

a1, a2 : array_like

Input arrays.

equal_nan : bool

Whether to compare NaN’s as equal. If the dtype of a1 and a2 is complex, values will be considered equal if either the real or the imaginary component of a given value is nan.

New in version 1.19.0.

Returns

b : bool

Returns True if the arrays are equal.

See Also

allclose: Returns True if two arrays are element-wise equal within a

tolerance.

array_equiv: Returns True if input arrays are shape consistent and all

elements equal.

Examples

>>> np.array_equal([1, 2], [1, 2])
True
>>> np.array_equal(np.array([1, 2]), np.array([1, 2]))
True
>>> np.array_equal([1, 2], [1, 2, 3])
False
>>> np.array_equal([1, 2], [1, 4])
False
>>> a = np.array([1, np.nan])
>>> np.array_equal(a, a)
False
>>> np.array_equal(a, a, equal_nan=True)
True

When equal_nan is True, complex values with nan components are considered equal if either the real or the imaginary components are nan.

>>> a = np.array([1 + 1j])
>>> b = a.copy()
>>> a.real = np.nan
>>> b.imag = np.nan
>>> np.array_equal(a, b, equal_nan=True)
True