numpy.ptp
-
numpy.ptp(a, axis=None, out=None, keepdims=<no value>)
[source] -
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.np.int8
,np.int16
, etc) is also a signed integer with n bits. In that case, peak-to-peak values greater than2**(n-1)-1
will be returned as negative values. An example with a work-around is shown below.- Parameters
-
-
aarray_like
-
Input values.
-
axisNone 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.New in version 1.15.0.
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.
-
outarray_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.
-
keepdimsbool, 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 theptp
method of sub-classes ofndarray
, however any non-default value will be. If the sub-class’ method does not implementkeepdims
any exceptions will be raised.
-
- Returns
-
-
ptpndarray
-
A new array holding the result, unless
out
was specified, in which case a reference toout
is returned.
-
Examples
>>> 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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https://numpy.org/doc/1.19/reference/generated/numpy.ptp.html