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Module « scipy.stats »

Fonction tmax - module scipy.stats

Signature de la fonction tmax

def tmax(a, upperlimit=None, axis=0, inclusive=True, nan_policy='propagate', *, keepdims=False) 

Description

help(scipy.stats.tmax)

    


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.

Parameters
----------
a : array_like
    Array of values.
upperlimit : None 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.
axis : int or None, default: 0
    If an int, the axis of the input along which to compute the statistic.
    The statistic of each axis-slice (e.g. row) of the input will appear in a
    corresponding element of the output.
    If ``None``, the input will be raveled before computing the statistic.
inclusive : {True, False}, optional
    This flag determines whether values exactly equal to the upper limit
    are included.  The default value is True.
nan_policy : {'propagate', 'omit', 'raise'}
    Defines how to handle input NaNs.
    
    - ``propagate``: if a NaN is present in the axis slice (e.g. row) along
      which the  statistic is computed, the corresponding entry of the output
      will be NaN.
    - ``omit``: NaNs will be omitted when performing the calculation.
      If insufficient data remains in the axis slice along which the
      statistic is computed, the corresponding entry of the output will be
      NaN.
    - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
keepdims : bool, default: False
    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.

Returns
-------
tmax : float, int or ndarray
    Trimmed maximum.

Notes
-----

Beginning in SciPy 1.9, ``np.matrix`` inputs (not recommended for new
code) are converted to ``np.ndarray`` before the calculation is performed. In
this case, the output will be a scalar or ``np.ndarray`` of appropriate shape
rather than a 2D ``np.matrix``. Similarly, while masked elements of masked
arrays are ignored, the output will be a scalar or ``np.ndarray`` rather than a
masked array with ``mask=False``.

Examples
--------
>>> import numpy as np
>>> from scipy import stats
>>> x = np.arange(20)
>>> stats.tmax(x)
19

>>> stats.tmax(x, 13)
13

>>> stats.tmax(x, 13, inclusive=False)
12

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