conspire/math/interpolate/
mod.rs

1#[cfg(test)]
2mod test;
3
4use super::{
5    Derivative, Differentiate, Quantity, Scalar, Tensor, TensorVec, Vector,
6    integrate::{IntegrationError, Times},
7};
8use crate::units::Time;
9use std::ops::{Mul, Sub};
10
11/// Linear interpolation schemes.
12pub struct LinearInterpolation {}
13
14/// One-dimensional interpolation schemes.
15pub trait Interpolate1D<F, T>
16where
17    F: TensorVec<Item = T>,
18    T: Tensor,
19{
20    /// One-dimensional interpolation.
21    fn interpolate_1d(x: &Vector, xp: &Vector, fp: &F) -> F;
22}
23
24/// Solution interpolation schemes.
25pub trait InterpolateSolution<Y, U, V, T = Time>
26where
27    Y: Differentiate<T> + Tensor,
28    for<'a> &'a Y: Mul<Scalar, Output = Y> + Sub<&'a Y, Output = Y>,
29    U: TensorVec<Item = Y>,
30    V: TensorVec<Item = Derivative<Y, T>>,
31{
32    /// Solution interpolation.
33    #[allow(clippy::too_many_arguments)]
34    fn interpolate(
35        &self,
36        time: &Times<T>,
37        tp: &Times<T>,
38        yp: &U,
39        dydtp: &V,
40        k_sol: &[V],
41        function: impl FnMut(Quantity<T>, &Y) -> Result<Derivative<Y, T>, String>,
42    ) -> Result<(U, V), IntegrationError>;
43}
44
45impl<F, T> Interpolate1D<F, T> for LinearInterpolation
46where
47    F: TensorVec<Item = T>,
48    T: Tensor,
49{
50    fn interpolate_1d(x: &Vector, xp: &Vector, fp: &F) -> F {
51        let mut i = 0;
52        x.iter()
53            .map(|x_k| {
54                i = xp.iter().position(|xp_i| xp_i > x_k).unwrap();
55                (fp[i].clone() - &fp[i - 1]) / (xp[i] - xp[i - 1]) * (x_k - xp[i - 1]) + &fp[i - 1]
56            })
57            .collect()
58    }
59}
conspire/math/interpolate/mod.rs

conspire/math/interpolate/
mod.rs
