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Module « numpy.matlib »

Fonction heaviside - module numpy.matlib

Signature de la fonction heaviside

def heaviside(*args, **kwargs) 

Description

help(numpy.matlib.heaviside)

heaviside(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])

Compute the Heaviside step function.

The Heaviside step function [1]_ is defined as::

                          0   if x1 < 0
    heaviside(x1, x2) =  x2   if x1 == 0
                          1   if x1 > 0

where `x2` is often taken to be 0.5, but 0 and 1 are also sometimes used.

Parameters
----------
x1 : array_like
    Input values.
x2 : array_like
    The value of the function when x1 is 0.
    If ``x1.shape != x2.shape``, they must be broadcastable to a common
    shape (which becomes the shape of the output).
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 `out` array will be set to the ufunc result.
    Elsewhere, the `out` array will retain its original value.
    Note that if an uninitialized `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
    :ref:`ufunc docs <ufuncs.kwargs>`.

Returns
-------
out : ndarray or scalar
    The output array, element-wise Heaviside step function of `x1`.
    This is a scalar if both `x1` and `x2` are scalars.

References
----------
.. [1] Wikipedia, "Heaviside step function",
       https://en.wikipedia.org/wiki/Heaviside_step_function

Examples
--------
>>> import numpy as np
>>> np.heaviside([-1.5, 0, 2.0], 0.5)
array([ 0. ,  0.5,  1. ])
>>> np.heaviside([-1.5, 0, 2.0], 1)
array([ 0.,  1.,  1.])

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