104 lines
No EOL
3.7 KiB
Rust
104 lines
No EOL
3.7 KiB
Rust
use nalgebra::{*, allocator::Allocator};
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use std::f64::consts::{PI, E};
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/* dynamic matrices */
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pub fn rand_eigval_series(dim: usize, time_res: usize) -> Vec<OVector<Complex<f64>, Dyn>> {
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// initialize the random matrix
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let mut rand_mat = DMatrix::<f64>::from_fn(dim, dim, |j, k| {
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let n = j*dim + k;
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E*((n*n) as f64) % 2.0 - 1.0
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}) * (3.0 / (dim as f64)).sqrt();
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// initialize the rotation step
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let mut rot_step = DMatrix::<f64>::identity(dim, dim);
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let max_freq = 4;
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for n in (0..dim).step_by(2) {
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let ang = PI * ((n % max_freq) as f64) / (time_res as f64);
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let ang_cos = ang.cos();
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let ang_sin = ang.sin();
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rot_step[(n, n)] = ang_cos;
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rot_step[(n+1, n)] = ang_sin;
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rot_step[(n, n+1)] = -ang_sin;
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rot_step[(n+1, n+1)] = ang_cos;
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}
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// find the eigenvalues
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let mut eigval_series = Vec::<OVector<Complex<f64>, Dyn>>::with_capacity(time_res);
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eigval_series.push(rand_mat.complex_eigenvalues());
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for _ in 1..time_res {
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rand_mat = &rot_step * rand_mat;
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eigval_series.push(rand_mat.complex_eigenvalues());
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}
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eigval_series
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}
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/* dynamic single float matrices */
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/*pub fn rand_eigval_series(dim: usize, time_res: usize) -> Vec<OVector<Complex<f32>, Dyn>> {
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// initialize the random matrix
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let mut rand_mat = DMatrix::<f32>::from_fn(dim, dim, |j, k| {
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let n = j*dim + k;
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(E as f32)*((n*n) as f32) % 2.0_f32 - 1.0_f32
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}) * (3.0_f32 / (dim as f32)).sqrt();
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// initialize the rotation step
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let mut rot_step = DMatrix::<f32>::identity(dim, dim);
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let max_freq = 4;
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for n in (0..dim).step_by(2) {
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let ang = (PI as f32) * ((n % max_freq) as f32) / (time_res as f32);
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let ang_cos = ang.cos();
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let ang_sin = ang.sin();
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rot_step[(n, n)] = ang_cos;
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rot_step[(n+1, n)] = ang_sin;
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rot_step[(n, n+1)] = -ang_sin;
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rot_step[(n+1, n+1)] = ang_cos;
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}
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// find the eigenvalues
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let mut eigval_series = Vec::<OVector<Complex<f32>, Dyn>>::with_capacity(time_res);
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eigval_series.push(rand_mat.complex_eigenvalues());
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for _ in 1..time_res {
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rand_mat = &rot_step * rand_mat;
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eigval_series.push(rand_mat.complex_eigenvalues());
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}
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eigval_series
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}*/
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/* static matrices. should only be used when the dimension is really small */
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/*pub fn rand_eigval_series<N>(time_res: usize) -> Vec<OVector<Complex<f64>, N>>
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where
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N: ToTypenum + DimName + DimSub<U1>,
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DefaultAllocator:
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Allocator<N> +
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Allocator<N, N> +
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Allocator<<N as DimSub<U1>>::Output> +
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Allocator<N, <N as DimSub<U1>>::Output>
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{
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// initialize the random matrix
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let dim = N::try_to_usize().unwrap();
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let mut rand_mat = OMatrix::<f64, N, N>::from_fn(|j, k| {
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let n = j*dim + k;
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E*((n*n) as f64) % 2.0 - 1.0
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}) * (3.0 / (dim as f64)).sqrt();
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// initialize the rotation step
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let mut rot_step = OMatrix::<f64, N, N>::identity();
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let max_freq = 4;
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for n in (0..dim).step_by(2) {
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let ang = PI * ((n % max_freq) as f64) / (time_res as f64);
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let ang_cos = ang.cos();
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let ang_sin = ang.sin();
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rot_step[(n, n)] = ang_cos;
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rot_step[(n+1, n)] = ang_sin;
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rot_step[(n, n+1)] = -ang_sin;
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rot_step[(n+1, n+1)] = ang_cos;
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}
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// find the eigenvalues
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let mut eigval_series = Vec::<OVector<Complex<f64>, N>>::with_capacity(time_res);
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eigval_series.push(rand_mat.complex_eigenvalues());
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for _ in 1..time_res {
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rand_mat = &rot_step * rand_mat;
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eigval_series.push(rand_mat.complex_eigenvalues());
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}
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eigval_series
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}*/ |