cf.Data.median¶

Data.
median
(axes=None, squeeze=False, mtol=1, inplace=False)[source]¶ Calculate median values.
Calculates the median value or the median values along axes.
See https://ncascms.github.io/cfpython/analysis.html#collapsemethods for mathematical definitions.
..seealso::
mean_of_upper_decile
,percentile
 Parameters
 axes: (sequence of)
int
, optional The axes to be collapsed. By default all axes are collapsed, resulting in output with size 1. Each axis is identified by its integer position. If axes is an empty sequence then the collapse is applied to each scalar element and the result has the same shape as the input data.
 squeeze:
bool
, optional By default, the axes which are collapsed are left in the result as dimensions with size one, so that the result will broadcast correctly against the input array. If set to True then collapsed axes are removed from the data.
 mtol: number, optional
The sample size threshold below which collapsed values are set to missing data. It is defined as a fraction (between 0 and 1 inclusive) of the contributing input data values.
The default of mtol is 1, meaning that a missing datum in the output array occurs whenever all of its contributing input array elements are missing data.
For other values, a missing datum in the output array occurs whenever more than
100*mtol%
of its contributing input array elements are missing data.Note that for nonzero values of mtol, different collapsed elements may have different sample sizes, depending on the distribution of missing data in the input data.
 inplace:
bool
, optional If True then do the operation inplace and return
None
.
 axes: (sequence of)
 Returns
Examples
>>> a = np.ma.arange(12).reshape(4, 3) >>> d = cf.Data(a, 'K') >>> d[1, 1] = cf.masked >>> print(d.array) [[0 1 2]) [3  5] [6 7 8] [9 10 11]] >>> d.median() <CF Data(1, 1): [[6.0]] K>