center

mdtools.statistics.center(x, axis=None, dtype=None, inplace=False)[source]

Center a distribution around its sample mean.

Parameters:

x (array_like) – Input array.
axis (int or None, optional) – The axis along which to compute the sample mean. By default, the flattened input array is used.
dtype (data-type, optional) – The dtype of the output array. This dtype is also used to compute the mean. Note that computing means can be inaccurate when using the floating-point dtype numpy.float32 or less. See numpy.mean() for more details.
inplace (bool, optional) – If True, change the input array inplace if possible (the given dtype must be castable to the dtype of x and x must already be a numpy.ndarray).

Returns:

x_centered (numpy.ndarray) – The centered input distribution: x - np.mean(x).

See also

numpy.mean(): Compute the arithmetic mean along the specified axis
mdtools.statistics.standardize(): Standardize a distribution

Examples

>>> a = np.arange(24).reshape(2,3,4)
>>> mdt.stats.center(a)
array([[[-11.5, -10.5,  -9.5,  -8.5],
        [ -7.5,  -6.5,  -5.5,  -4.5],
        [ -3.5,  -2.5,  -1.5,  -0.5]],

       [[  0.5,   1.5,   2.5,   3.5],
        [  4.5,   5.5,   6.5,   7.5],
        [  8.5,   9.5,  10.5,  11.5]]])
>>> mdt.stats.center(a, axis=0)
array([[[-6., -6., -6., -6.],
        [-6., -6., -6., -6.],
        [-6., -6., -6., -6.]],

       [[ 6.,  6.,  6.,  6.],
        [ 6.,  6.,  6.,  6.],
        [ 6.,  6.,  6.,  6.]]])
>>> mdt.stats.center(a, axis=1)
array([[[-4., -4., -4., -4.],
        [ 0.,  0.,  0.,  0.],
        [ 4.,  4.,  4.,  4.]],

       [[-4., -4., -4., -4.],
        [ 0.,  0.,  0.,  0.],
        [ 4.,  4.,  4.,  4.]]])
>>> mdt.stats.center(a, axis=2)
array([[[-1.5, -0.5,  0.5,  1.5],
        [-1.5, -0.5,  0.5,  1.5],
        [-1.5, -0.5,  0.5,  1.5]],

       [[-1.5, -0.5,  0.5,  1.5],
        [-1.5, -0.5,  0.5,  1.5],
        [-1.5, -0.5,  0.5,  1.5]]])

>>> a = np.ones(3, dtype=np.float64)
>>> a
array([1., 1., 1.])
>>> b = mdt.stats.center(a, inplace=True)
>>> b
array([0., 0., 0.])
>>> b is a
True
>>> b[0] = 1
>>> a
array([1., 0., 0.])