#[test]
#[allow(deprecated)]
fn jeans_instability_isolated() {
use crate::tooling::core::algos::lagrangian::SemiLagrangian;
use crate::tooling::core::algos::uniform::UniformGrid6D;
use crate::tooling::core::init::domain::{Domain, SpatialBoundType, VelocityBoundType};
use crate::tooling::core::integrator::TimeIntegrator as _;
use crate::tooling::core::phasespace::PhaseSpaceRepr as _;
use crate::tooling::core::poisson::fft::FftIsolated;
use crate::tooling::core::time::strang::StrangSplitting;
let lx = 2.0 * std::f64::consts::PI;
let lv = 4.0f64;
let sigma = 1.0f64;
let g = 1.0f64;
let epsilon = 0.05f64; let k = std::f64::consts::PI / lx;
let domain = Domain::builder()
.spatial_extent(lx / 2.0) .velocity_extent(lv)
.spatial_resolution(16)
.velocity_resolution(8)
.t_final(2.0)
.spatial_bc(SpatialBoundType::Isolated)
.velocity_bc(VelocityBoundType::Open)
.build()
.unwrap();
let mut grid = UniformGrid6D::new(domain.clone());
let dx = domain.dx();
let dv = domain.dv();
let [nx1, nx2, nx3, nv1, nv2, nv3] = grid.sizes();
let mut s_norm = 0.0f64;
for iv1 in 0..nv1 {
let v1 = -lv + (iv1 as f64 + 0.5) * dv[0];
for iv2 in 0..nv2 {
let v2 = -lv + (iv2 as f64 + 0.5) * dv[1];
for iv3 in 0..nv3 {
let v3 = -lv + (iv3 as f64 + 0.5) * dv[2];
let v2sq = v1 * v1 + v2 * v2 + v3 * v3;
s_norm += (-v2sq / (2.0 * sigma * sigma)).exp() * dv[0] * dv[1] * dv[2];
}
}
}
let c = 1.0 / s_norm;
for ix1 in 0..nx1 {
let x1 = -(lx / 2.0) + (ix1 as f64 + 0.5) * dx[0];
let perturb = 1.0 + epsilon * (k * x1).cos();
for ix2 in 0..nx2 {
for ix3 in 0..nx3 {
for iv1 in 0..nv1 {
let v1 = -lv + (iv1 as f64 + 0.5) * dv[0];
for iv2 in 0..nv2 {
let v2 = -lv + (iv2 as f64 + 0.5) * dv[1];
for iv3 in 0..nv3 {
let v3 = -lv + (iv3 as f64 + 0.5) * dv[2];
let v2sq = v1 * v1 + v2 * v2 + v3 * v3;
let f = c * (-v2sq / (2.0 * sigma * sigma)).exp() * perturb;
let idx = grid.index([ix1, ix2, ix3], [iv1, iv2, iv3]);
grid.data[idx] = f.max(0.0);
}
}
}
}
}
}
let rho_init = grid.compute_density();
let rho_max_init = rho_init.data.iter().cloned().fold(0.0f64, f64::max);
let rho_min_init = rho_init.data.iter().cloned().fold(f64::MAX, f64::min);
let amp_init = (rho_max_init - rho_min_init).max(1e-30);
let rho0 = rho_init.data.iter().sum::<f64>() / rho_init.data.len() as f64;
let omega_j_sq = 4.0 * std::f64::consts::PI * g * rho0;
let gamma_sq = omega_j_sq - k * k * sigma * sigma;
let gamma_analytic = if gamma_sq > 0.0 { gamma_sq.sqrt() } else { 0.0 };
let poisson = FftIsolated::new(&domain);
let advector = SemiLagrangian::new();
let mut integrator = StrangSplitting::new(g);
let dt = 0.05f64;
let n_steps = 40;
let mut amplitudes = vec![(0.0f64, amp_init)];
for step in 0..n_steps {
integrator
.advance(&mut grid, &poisson, &advector, dt)
.unwrap();
if (step + 1) % 5 == 0 {
let rho = grid.compute_density();
let rho_max = rho.data.iter().cloned().fold(0.0f64, f64::max);
let rho_min = rho.data.iter().cloned().fold(f64::MAX, f64::min);
let amp = rho_max - rho_min;
let t = (step + 1) as f64 * dt;
amplitudes.push((t, amp));
}
}
let rho_final = grid.compute_density();
assert!(
!rho_final.data.iter().any(|x| x.is_nan()),
"Density contains NaN after Jeans-isolated evolution"
);
let rho_max_final = rho_final.data.iter().cloned().fold(0.0f64, f64::max);
let rho_min_final = rho_final.data.iter().cloned().fold(f64::MAX, f64::min);
let amp_final = rho_max_final - rho_min_final;
assert!(
amp_final > amp_init,
"Jeans-isolated: amplitude should grow. init={:.4e}, final={:.4e}",
amp_init,
amp_final
);
let gamma_measured = if amplitudes.len() >= 2 {
let (t0, a0) = amplitudes[0];
let (t_last, a_last) = amplitudes.last().unwrap();
if *a_last > a0 && *t_last > t0 {
(a_last / a0).ln() / (t_last - t0)
} else {
0.0
}
} else {
0.0
};
println!(
"Jeans-isolated: γ_analytic={:.3}, γ_measured={:.3}, amp_init={:.4e}, amp_final={:.4e}",
gamma_analytic, gamma_measured, amp_init, amp_final
);
println!(" Amplitude history: {:?}", amplitudes);
if gamma_analytic > 0.0 && gamma_measured > 0.0 {
let ratio = gamma_measured / gamma_analytic;
println!(" γ_measured/γ_analytic = {:.2}", ratio);
assert!(
ratio > 0.01,
"Measured growth rate is too small relative to analytic"
);
}
}