tlpipe.plot.plot_waterfall.InterpolatedUnivariateSpline¶

class tlpipe.plot.plot_waterfall.InterpolatedUnivariateSpline(x, y, w=None, bbox=[None, None], k=3)[source]¶

One-dimensional interpolating spline for a given set of data points.

Fits a spline y=s(x) of degree k to the provided x, y data. Spline function passes through all provided points. Equivalent to UnivariateSpline with s=0.

Parameters:

x ((N,) array_like) – Input dimension of data points – must be increasing
y ((N,) array_like) – input dimension of data points
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.

See also

UnivariateSpline: Superclass – allows knots to be selected by a smoothing condition
LSQUnivariateSpline: spline for which knots are user-selected
splrep: An older, non object-oriented wrapping of FITPACK

splev, sproot, splint, spalde

BivariateSpline: A similar class for two-dimensional spline interpolation

Notes

The number of data points must be larger than the spline degree k.

Examples

>>> from numpy import linspace,exp
>>> from numpy.random import randn
>>> from scipy.interpolate import InterpolatedUnivariateSpline
>>> x = linspace(-3, 3, 100)
>>> y = exp(-x**2) + randn(100)/10
>>> s = InterpolatedUnivariateSpline(x, y)
>>> xs = linspace(-3, 3, 1000)
>>> ys = s(xs)

xs,ys is now a smoothed, super-sampled version of the noisy gaussian x,y

__init__(x, y, w=None, bbox=[None, None], k=3)[source]¶

Input:

x,y - 1-d sequences of data points (x must be: in strictly ascending order)

Optional input:

w - positive 1-d sequence of weights bbox - 2-sequence specifying the boundary of

the approximation interval. By default, bbox=[x[0],x[-1]]

k=3 - degree of the univariate spline.

Methods

`derivatives`(x)	Return all derivatives of the spline at the point x.
`get_coeffs`()	Return spline coefficients.
`get_knots`()	Return positions of (boundary and interior) knots of the spline.
`get_residual`()	Return weighted sum of squared residuals of the spline approximation: `sum((w[i] * (y[i]-s(x[i])))**2, axis=0)`.
`integral`(a, b)	Return definite integral of the spline between two given points.
`roots`()	Return the zeros of the spline.
`set_smoothing_factor`(s)	Continue spline computation with the given smoothing factor s and with the knots found at the last call.