jax.numpy.in1d¶

jax.numpy.in1d(ar1, ar2, assume_unique=False, invert=False)[source]¶

Test whether each element of a 1-D array is also present in a second array.

LAX-backend implementation of in1d().

In the JAX version, the assume_unique argument is not referenced.

Original docstring below.

Returns a boolean array the same length as ar1 that is True where an element of ar1 is in ar2 and False otherwise.

We recommend using isin() instead of in1d for new code.

Parameters

ar1 ((M,) array_like) – Input array.
ar2 (array_like) – The values against which to test each value of ar1.
assume_unique (bool, optional) – If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False.
invert (bool, optional) – If True, the values in the returned array are inverted (that is, False where an element of ar1 is in ar2 and True otherwise). Default is False. np.in1d(a, b, invert=True) is equivalent to (but is faster than) np.invert(in1d(a, b)).

Returns

in1d – The values ar1[in1d] are in ar2.

Return type

(M,) ndarray, bool

See also

isin(): Version of this function that preserves the shape of ar1.
numpy.lib.arraysetops(): Module with a number of other functions for performing set operations on arrays.

Notes

in1d can be considered as an element-wise function version of the python keyword in, for 1-D sequences. in1d(a, b) is roughly equivalent to np.array([item in b for item in a]). However, this idea fails if ar2 is a set, or similar (non-sequence) container: As ar2 is converted to an array, in those cases asarray(ar2) is an object array rather than the expected array of contained values.

New in version 1.4.0.

Examples

>>> test = np.array([0, 1, 2, 5, 0])
>>> states = [0, 2]
>>> mask = np.in1d(test, states)
>>> mask
array([ True, False,  True, False,  True])
>>> test[mask]
array([0, 2, 0])
>>> mask = np.in1d(test, states, invert=True)
>>> mask
array([False,  True, False,  True, False])
>>> test[mask]
array([1, 5])