numpy.ma.ptp¶

numpy.ma.
ptp
(obj, axis=None, out=None, fill_value=None, keepdims=<no value>)[source]¶ Return (maximum  minimum) along the given dimension (i.e. peaktopeak value).
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, peaktopeak values greater than2**(n1)1
will be returned as negative values. An example with a workaround is shown below. Parameters
 axis{None, int}, optional
Axis along which to find the peaks. If None (default) the flattened array is used.
 out{None, array_like}, optional
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 will be cast if necessary.
 fill_value{var}, optional
Value used to fill in the masked values.
 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 array.
 Returns
 ptpndarray.
A new array holding the result, unless
out
was specified, in which case a reference toout
is returned.
Examples
>>> x = np.ma.MaskedArray([[4, 9, 2, 10], ... [6, 9, 7, 12]])
>>> x.ptp(axis=1) masked_array(data=[8, 6], mask=False, fill_value=999999)
>>> x.ptp(axis=0) masked_array(data=[2, 0, 5, 2], mask=False, fill_value=999999)
>>> x.ptp() 10
This example shows that a negative value can be returned when the input is an array of signed integers.
>>> y = np.ma.MaskedArray([[1, 127], ... [0, 127], ... [1, 127], ... [2, 127]], dtype=np.int8) >>> y.ptp(axis=1) masked_array(data=[ 126, 127, 128, 127], mask=False, fill_value=999999, dtype=int8)
A workaround is to use the view() method to view the result as unsigned integers with the same bit width:
>>> y.ptp(axis=1).view(np.uint8) masked_array(data=[126, 127, 128, 129], mask=False, fill_value=999999, dtype=uint8)