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Module « numpy »

Fonction ptp - module numpy

Signature de la fonction ptp

def ptp(a, axis=None, out=None, keepdims=<no value>) 

Description

help(numpy.ptp)

Range of values (maximum - minimum) along an axis.

The name of the function comes from the acronym for 'peak to peak'.

.. warning::
    `ptp` preserves the data type of the array. This means the
    return value for an input of signed integers with n bits
    (e.g. `numpy.int8`, `numpy.int16`, etc) is also a signed integer
    with n bits.  In that case, peak-to-peak values greater than
    ``2**(n-1)-1`` will be returned as negative values. An example
    with a work-around is shown below.

Parameters
----------
a : array_like
    Input values.
axis : None or int or tuple of ints, optional
    Axis along which to find the peaks.  By default, flatten the
    array.  `axis` may be negative, in
    which case it counts from the last to the first axis.
    If this is a tuple of ints, a reduction is performed on multiple
    axes, instead of a single axis or all the axes as before.
out : array_like
    Alternative output array in which to place the result. It must
    have the same shape and buffer length as the expected output,
    but the type of the output values will be cast if necessary.

keepdims : bool, optional
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the input array.

    If the default value is passed, then `keepdims` will not be
    passed through to the `ptp` method of sub-classes of
    `ndarray`, however any non-default value will be.  If the
    sub-class' method does not implement `keepdims` any
    exceptions will be raised.

Returns
-------
ptp : ndarray or scalar
    The range of a given array - `scalar` if array is one-dimensional
    or a new array holding the result along the given axis

Examples
--------
>>> import numpy as np
>>> x = np.array([[4, 9, 2, 10],
...               [6, 9, 7, 12]])

>>> np.ptp(x, axis=1)
array([8, 6])

>>> np.ptp(x, axis=0)
array([2, 0, 5, 2])

>>> np.ptp(x)
10

This example shows that a negative value can be returned when
the input is an array of signed integers.

>>> y = np.array([[1, 127],
...               [0, 127],
...               [-1, 127],
...               [-2, 127]], dtype=np.int8)
>>> np.ptp(y, axis=1)
array([ 126,  127, -128, -127], dtype=int8)

A work-around is to use the `view()` method to view the result as
unsigned integers with the same bit width:

>>> np.ptp(y, axis=1).view(np.uint8)
array([126, 127, 128, 129], dtype=uint8)



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