DOC: Inconsistency in the definition of default values in the model UnivariateSpline

[Anselmoo]

My issue is about inconsistency in the definition of default values in the model UnivariateSpline
ttps://github.com/scipy/scipy/blob/4ec4ab8d6ccc1cdb34b84fdcb66fde2cc0210dbf/scipy/interpolate/fitpack2.py#L71

Sometimes the default value is defined in brackets (), sometimes at the end of the docstring.

Reproducing code example:

scipy/scipy/interpolate/fitpack2.py

Lines 80 to 119 in 4ec4ab8

	x : (N,) array_like
	1-D array of independent input data. Must be increasing;
	must be strictly increasing if `s` is 0.
	y : (N,) array_like
	1-D array of dependent input data, of the same length as `x`.
	w : (N,) array_like, optional
	Weights for spline fitting. Must be positive. If None (default),
	weights are all equal.
	bbox : (2,) array_like, optional
	2-sequence specifying the boundary of the approximation interval. If
	None (default), ``bbox=[x[0], x[-1]]``.
	k : int, optional
	Degree of the smoothing spline. Must be 1 <= `k` <= 5.
	Default is `k` = 3, a cubic spline.
	s : float or None, optional
	Positive smoothing factor used to choose the number of knots. Number
	of knots will be increased until the smoothing condition is satisfied::
	sum((w[i] * (y[i]-spl(x[i])))**2, axis=0) <= s
	If None (default), ``s = len(w)`` which should be a good value if
	``1/w[i]`` is an estimate of the standard deviation of ``y[i]``.
	If 0, spline will interpolate through all data points.
	ext : int or str, optional
	Controls the extrapolation mode for elements
	not in the interval defined by the knot sequence.
	* if ext=0 or 'extrapolate', return the extrapolated value.
	* if ext=1 or 'zeros', return 0
	* if ext=2 or 'raise', raise a ValueError
	* if ext=3 of 'const', return the boundary value.
	The default value is 0.
	check_finite : bool, optional
	Whether to check that the input arrays contain only finite numbers.
	Disabling may give a performance gain, but may result in problems
	(crashes, non-termination or non-sensical results) if the inputs
	do contain infinities or NaNs.
	Default is False.

Scipy/Numpy/Python version information:

Current release: 1.6.3