isinf(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

Some inconsistencies with the Dask version may exist.

Test element-wise for positive or negative infinity.

Returns a boolean array of the same shape as x, True where x == +/-inf , otherwise False.

Notes

NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754).

Errors result if the second argument is supplied when the first argument is a scalar, or if the first and second arguments have different shapes.

Parameters

x : array_like

Input values

out : ndarray, None, or tuple of ndarray and None, optional

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

where : array_like, optional

This condition is broadcast over the input. At locations where the condition is True, the :None:None:`out` array will be set to the ufunc result. Elsewhere, the :None:None:`out` array will retain its original value. Note that if an uninitialized :None:None:`out` array is created via the default out=None , locations within it where the condition is False will remain uninitialized.

**kwargs :

For other keyword-only arguments, see the ufunc docs <ufuncs.kwargs> .

Returns

y : bool (scalar) or boolean ndarray

True where x is positive or negative infinity, false otherwise. This is a scalar if x is a scalar.

This docstring was copied from numpy.isinf.

See Also

isfinite

isnan

isneginf

isposinf

Examples

This example is valid syntax, but we were not able to check execution

>>> np.isinf(np.inf)  # doctest: +SKIP
True

This example is valid syntax, but we were not able to check execution

>>> np.isinf(np.nan)  # doctest: +SKIP
False

This example is valid syntax, but we were not able to check execution

>>> np.isinf(np.NINF)  # doctest: +SKIP
True

This example is valid syntax, but we were not able to check execution

>>> np.isinf([np.inf, -np.inf, 1.0, np.nan])  # doctest: +SKIP
array([ True,  True, False, False])

This example is valid syntax, but we were not able to check execution

>>> x = np.array([-np.inf, 0., np.inf])  # doctest: +SKIP
... y = np.array([2, 2, 2])  # doctest: +SKIP
... np.isinf(x, y)  # doctest: +SKIP
array([1, 0, 1])

This example is valid syntax, but we were not able to check execution

>>> y  # doctest: +SKIP
array([1, 0, 1])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

dask.array.ufunc.isfinite dask.array.ufunc.isnan

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