isfinite(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 finiteness (not infinity and not Not a Number).

The result is returned as a boolean array.

Notes

Not a Number, positive infinity and negative infinity are considered to be non-finite.

NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Also that positive infinity is not equivalent to negative infinity. But infinity is equivalent to positive infinity. Errors result if the second argument is also supplied when x is a scalar input, or if 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 : ndarray, bool

True where x is not positive infinity, negative infinity, or NaN; false otherwise. This is a scalar if x is a scalar.

This docstring was copied from numpy.isfinite.

See Also

isinf

isnan

isneginf

isposinf

Examples

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

>>> np.isfinite(1)  # doctest: +SKIP
True

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

>>> np.isfinite(0)  # doctest: +SKIP
True

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

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

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

>>> np.isfinite(np.inf)  # doctest: +SKIP
False

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

>>> np.isfinite(np.NINF)  # doctest: +SKIP
False

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

>>> np.isfinite([np.log(-1.),1.,np.log(0)])  # doctest: +SKIP
array([False,  True, 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.isfinite(x, y)  # doctest: +SKIP
array([0, 1, 0])

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

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

See :

Back References

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

dask.array.reductions.nanmax dask.array.ufunc.isinf dask.array.reductions.make_arg_reduction.<locals>.wrapped dask.array.ufunc.isnan dask.array.reductions.nansum dask.array.reductions.nanmin

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