Function rusty_fitpack::splrep

pub fn splrep(
    x: Vec<f64>, 
    y: Vec<f64>, 
    w: Option<Vec<f64>>, 
    xb: Option<f64>, 
    xe: Option<f64>, 
    k: Option<usize>, 
    task: Option<i8>, 
    s: Option<f64>, 
    t: Option<Vec<f64>>, 
    full_output: Option<bool>, 
    per: Option<bool>, 
    quiet: Option<bool>
) -> (Vec<f64>, Vec<f64>, usize)

Find the B-spline representation of a 1-D curve. Given the set of data points $(x(i), y(i))$ determine a smooth spline approximation of degree k on the interval $xb <= x <= xe$.

Example

Simple example of spline interpolation

use rusty_fitpack::splrep;
let x = vec![0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0];
let y = vec![0.0, 1.0, 4.0, 9.0, 16.0, 25.0, 36.0, 49.0, 64.0];

let (t, c, k) = splrep(x, y, None, None, None, None, None, None, None, None, None, None);

Parameters

---

x, y : The data points defining a curve $y = f(x)$.

w : Strictly positive Vec<f64> of weights the same length as x and y. The weights are used in computing the weighted least-squares spline fit. If the errors in the y values have standard-deviation given by the vector d, then w should be 1/d. Default is vec![1.0; x.len()].

xb, xe : The interval to fit. If None, these default to x[0] and x[-1] respectively.

k : The degree of the spline fit. It is recommended to use cubic splines. Even values of k should be avoided especially with small s values. 1 <= k <= 5

task : {0, -1} If task==0 find t and c for a given smoothing factor, s.
If task=-1 find the weighted least square spline for a given set of knots, t. These should be interior knots as knots on the ends will be added automatically.

s : A smoothing condition. The amount of smoothness is determined by satisfying the conditions: sum((w * (y - g)).powi(2),axis=0) <= s where g(x) is the smoothed interpolation of (x,y). The user can use s to control the tradeoff between closeness and smoothness of fit. Larger s means more smoothing while smaller values of s indicate less smoothing. Recommended values of s depend on the weights, w. If the weights represent the inverse of the standard-deviation of y, then a good s value should be found in the range (m-(2*m).sqrt(),m+(2*m).sqrt()) where m is the number of datapoints in x, y, and w. default : s=m-(2*m).sqrt() if weights are supplied. s = 0.0 (interpolating) if no weights are supplied.

t : The knots needed for task=-1. If given then task is automatically set to -1.

full_output Should be None. Feature is not implemented yet.

per : Should be None. Periodic spline approximations are not supported yet.

quiet: Should be None. Feature is not implemented yet.