pub struct Linear1d<T: Float> { /* private fields */ }Expand description
Piecewise linear 1-D interpolator.
Constructed from sorted (x, y) data. Each query is evaluated via binary
search + linear blend in O(log n).
Implementations§
Source§impl<T: Float> Linear1d<T>
impl<T: Float> Linear1d<T>
Sourcepub fn new(xs: &[T], ys: &[T], extrap: Extrapolate) -> Result<Self>
pub fn new(xs: &[T], ys: &[T], extrap: Extrapolate) -> Result<Self>
Create a new linear interpolator.
§Examples
let xs = [0.0_f64, 1.0, 2.0];
let ys = [0.0_f64, 2.0, 4.0];
let interp = Linear1d::new(&xs, &ys, Extrapolate::Error).unwrap();
let y = interp.eval(0.5).unwrap();
assert!((y - 1.0).abs() < 1e-10);§Errors
xsandysmust have the same length (>= 2).xsmust be strictly increasing.- All values must be finite.
Sourcepub fn eval(&self, x: T) -> Result<T>
pub fn eval(&self, x: T) -> Result<T>
Evaluate the interpolant at a single point.
§Examples
let interp = Linear1d::new(&[0.0_f64, 1.0, 2.0], &[0.0_f64, 1.0, 4.0], Extrapolate::Error).unwrap();
let y = interp.eval(1.5).unwrap();
assert!((y - 2.5).abs() < 1e-10);Sourcepub fn eval_many(&self, xs: &[T]) -> Result<Vec<T>>
pub fn eval_many(&self, xs: &[T]) -> Result<Vec<T>>
Evaluate the interpolant at many points.
§Examples
let interp = Linear1d::new(&[0.0_f64, 1.0, 2.0], &[0.0, 2.0, 4.0], Extrapolate::Error).unwrap();
let ys = interp.eval_many(&[0.5, 1.5]).unwrap();
assert!((ys[0] - 1.0).abs() < 1e-10);
assert!((ys[1] - 3.0).abs() < 1e-10);Trait Implementations§
Auto Trait Implementations§
impl<T> Freeze for Linear1d<T>
impl<T> RefUnwindSafe for Linear1d<T>where
T: RefUnwindSafe,
impl<T> Send for Linear1d<T>
impl<T> Sync for Linear1d<T>
impl<T> Unpin for Linear1d<T>where
T: Unpin,
impl<T> UnsafeUnpin for Linear1d<T>
impl<T> UnwindSafe for Linear1d<T>where
T: UnwindSafe,
Blanket Implementations§
Source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
Source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more