- xorbits.numpy.ptp(a, axis=None, out=None, keepdims=None)#
Range of values (maximum - minimum) along an axis.
The name of the function comes from the acronym for ‘peak to peak’.
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 than
2**(n-1)-1will be returned as negative values. An example with a work-around is shown below.
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.
New in version 1.15.0(numpy).
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.
ptp – The range of a given array - scalar if array is one-dimensional or a new array holding the result along the given axis
- Return type
ndarray or scalar
>>> 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)
This docstring was copied from numpy.