scipy.stats.mstats.

tmax#

scipy.stats.mstats.tmax(a, upperlimit=None, axis=0, inclusive=True)[source]#

Compute the trimmed maximum

This function computes the maximum value of an array along a given axis, while ignoring values larger than a specified upper limit.

Deprecated since version 2.0.0: scipy.stats.mstats.tmax is deprecated as of SciPy 2.0.0 and will be removed, along with the scipy.stats.mstats namespace, in SciPy 2.4.0. For similar functionality, use scipy.stats.tmax with MArray(s) instead of NumPy masked array(s).

Parameters:

aarray_like: array of values
upperlimitNone or float, optional: Values in the input array greater than the given limit will be ignored. When upperlimit is None, then all values are used. The default value is None.
axisint or None, optional: Axis along which to operate. Default is 0. If None, compute over the whole array a.
inclusive{True, False}, optional: This flag determines whether values exactly equal to the upper limit are included. The default value is True.

Returns:

tmaxfloat, int or ndarray

Notes

For more details on tmax, see scipy.stats.tmax.

Examples

>>> import numpy as np
>>> from scipy.stats import mstats
>>> a = np.array([[6, 8, 3, 0],
...               [3, 9, 1, 2],
...               [8, 7, 8, 2],
...               [5, 6, 0, 2],
...               [4, 5, 5, 2]])
...
...
>>> mstats.tmax(a, 4)
masked_array(data=[4, --, 3, 2],
             mask=[False,  True, False, False],
       fill_value=999999)