flopscope.

flopscope.numpy.median

fnp.median(a, axis=None, out=None, overwrite_input=False, keepdims=False)[flopscope source][numpy source]

Compute the median along the specified axis.

Adapted from NumPy docs np.median

Areacore
Typecounted
NumPy Refnp.median
Cost
numel(input)\text{numel}(\text{input})
Flopscope Context

Median of array elements (sorts internally).

Returns the median of the array elements.

Parameters

a:array_like

Input array or object that can be converted to an array.

axis:{int, sequence of int, None}, optional

Axis or axes along which the medians are computed. The default, axis=None, will compute the median along a flattened version of the array. If a sequence of axes, the array is first flattened along the given axes, then the median is computed along the resulting flattened axis.

out:ndarray, 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 (of the output) will be cast if necessary.

overwrite_input:bool, optional

If True, then allow use of memory of input array a for calculations. The input array will be modified by the call to median. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it will probably be fully or partially sorted. Default is False. If overwrite_input is True and a is not already an ndarray, an error will be raised.

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 original arr.

Returns

median:ndarray

A new array holding the result. If the input contains integers or floats smaller than float64, then the output data-type is flops.float64. Otherwise, the data-type of the output is the same as that of the input. If out is specified, that array is returned instead.

See also

Notes

Given a vector V of length N, the median of V is the middle value of a sorted copy of V, V_sorted - i e., V_sorted[(N-1)/2], when N is odd, and the average of the two middle values of V_sorted when N is even.

Examples

>>> import flopscope.numpy as fnp
>>> a = flops.array([[10, 7, 4], [3, 2, 1]])
>>> a
array([[10,  7,  4],
       [ 3,  2,  1]])
>>> flops.median(a)
flops.float64(3.5)
>>> flops.median(a, axis=0)
array([6.5, 4.5, 2.5])
>>> flops.median(a, axis=1)
array([7.,  2.])
>>> flops.median(a, axis=(0, 1))
flops.float64(3.5)
>>> m = flops.median(a, axis=0)
>>> out = flops.zeros_like(m)
>>> flops.median(a, axis=0, out=m)
array([6.5,  4.5,  2.5])
>>> m
array([6.5,  4.5,  2.5])
>>> b = a.copy()
>>> flops.median(b, axis=1, overwrite_input=True)
array([7.,  2.])
>>> assert not flops.all(a==b)
>>> b = a.copy()
>>> flops.median(b, axis=None, overwrite_input=True)
flops.float64(3.5)
>>> assert not flops.all(a==b)