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use std::process::exit;
use anyhow::Error;
use argmin::{
core::{CostFunction, Executor, TerminationReason, TerminationStatus},
solver::neldermead::NelderMead,
};
use logger::setup_log;
use pmcore::prelude::*;
fn main() {
let eq = equation::ODE::new(
|x, p, _t, dx, rateiv, _cov| {
// fetch_cov!(cov, t, wt);
fetch_params!(
p, v1, cl1, v2, cl2, popmax, kgs, kks, e50_1s, e50_2s, alpha_s, kgr1, kkr1,
e50_1r1, alpha_r1, kgr2, kkr2, e50_2r2, alpha_r2, init_3, init_4, init_5, h1s, h2s,
h1r1, h2r2
);
// Sec
let e50_2r1 = e50_2s;
let e50_1r2 = e50_1s;
let h2r1 = h2s;
let h1r2 = h1s;
let mut xm0best = 0.0;
dx[0] = rateiv[0] - cl1 * x[0] / v1;
dx[1] = rateiv[1] - cl2 * x[1] / v2;
let xns = x[2];
let xnr1 = x[3];
let xnr2 = x[4];
let e = 1.0 - (xns + xnr1 + xnr2) / popmax;
let mut d1 = x[0] / v1;
let mut d2 = x[1] / v2;
let mut u = d1 / e50_1s;
let mut v = d2 / e50_2s;
let mut w = alpha_s * d1 * d2 / (e50_1s * e50_2s);
let mut h1 = 1.0_f64 / h1s;
let mut h2 = 1.0_f64 / h2s;
let mut xx = (h1 + h2) / 2.0;
if u < 1.0E-5 && v < 1.0E-5 {
xm0best = 0.0;
} else {
if v < 0.0 {
xm0best = u.powf(1.0 / h1);
}
if u < 0.0 {
xm0best = v.powf(1.0 / h2);
}
if v > 0.0 && u > 0.0 {
let start = 0.00001;
let tol = 1.0e-10;
let step = -2.0 * start;
// CALL ELDERY(1,START,XM0BEST1,VALMIN1,TOL,STEP,1000,BESTM0,0,ICONV,NITER,ICNT)
let bm0 = BESTM0 {
u,
v,
w,
h1,
h2,
xx,
};
let (xm0best1, valmin1, iconv) = bm0.get_best(start, step);
if iconv == false {
// Output a message indicating no convergence on the selection of best M0 for s
println!(" NO CONVERGENCE ON SELECTION OF BEST M0 FOR s.");
// Output a message indicating the XP(3) EQ...
println!(" FOR THE XP(3) EQ.... ");
// Output the values of XM0BEST1 and VALMIN1 with formatting
println!(" THE EST. FOR M0 FROM ELDERY WAS {:>20.12}", xm0best1);
println!(" AND THIS GAVE A VALMIN OF {:>20.12}", valmin1);
// Output the values of D1, D2, U, V, W, ALPHA_S, H1, and H2 with formatting
println!(" NOTE THAT D1,D2 = {:>20.12} {:>20.12}", d1, d2);
println!(" U,V = {:>20.12} {:>20.12}", u, v);
println!(" W,ALPHA_S = {:>20.12} {:>20.12}", w, alpha_s);
println!(" H1,H2 = {:>20.12} {:>20.12}", h1, h2);
exit(-1);
}
if valmin1 < 1.0e-10 {
xm0best = xm0best1;
} else {
// CALL FINDM0(U,V,alpha_s,H1,H2,XM0EST)
let xm0est = find_m0(u, v, alpha_s, h1, h2);
if xm0est < 0.0 {
xm0best = xm0best1;
} else {
// START(1) = XM0EST
// STEP(1)= -.2D0*START(1)
// CALL ELDERY(1,START,XM0BEST2,VALMIN2,TOL,STEP,1000,BESTM0,0,ICONV,NITER,ICNT)
let bm0 = BESTM0 {
u,
v,
w,
h1,
h2,
xx,
};
let (xm0best2, valmin2, iconv) = bm0.get_best(xm0est, -2.0 * xm0est);
xm0best = xm0best1;
if valmin2 < valmin1 {
xm0best = xm0best2;
}
if iconv == false {
panic!("NO CONVERGENCE ON SELECTION OF BEST M0 FOR s.");
} //235
} //237
} //240
} //243
}
let xms = xm0best / (xm0best + 1.0);
dx[2] = xns * (kgs * e - kks * xms);
d1 = x[0] / v1;
d2 = x[1] / v2;
u = d1 / e50_1r1;
v = d2 / e50_2r1;
w = alpha_r1 * d1 * d2 / (e50_1r1 * e50_2r1);
h1 = 1.0_f64 / h1r1;
h2 = 1.0_f64 / h2r1;
xx = (h1 + h2) / 2.0;
if u < 1.0e-5 && v < 1.0e-5 {
xm0best = 0.0;
} else {
if v < 0.0 {
xm0best = u.powf(1.0 / h1);
}
if u < 0.0 {
xm0best = v.powf(1.0 / h2);
}
if v > 0.0 && u > 0.0 {
//START(1) = .00001
let tol = 1.0e-10;
// STEP(1)= -.2D0*START(1)
// CALL ELDERY(1,START,XM0BEST1,VALMIN1,TOL,STEP,1000,BESTM0,0,ICONV,NITER,ICNT)
let bm0 = BESTM0 {
u,
v,
w,
h1,
h2,
xx,
};
let (xm0best1, valmin1, iconv) = bm0.get_best(0.00001, -2.0 * 0.00001);
if iconv == false {
panic!("NO CONVERGENCE ON SELECTION OF BEST M0 FOR r1.");
}
if valmin1 < 1.0e-10 {
xm0best = xm0best1;
} else {
// CALL FINDM0(U,V,alpha_r1,H1,H2,XM0EST)
let xm0est = find_m0(u, v, alpha_s, h1, h2);
if xm0est < 0.0 {
xm0best = xm0best1;
} else {
// START(1) = XM0EST
// STEP(1)= -.2D0*START(1)
// CALL ELDERY(1,START,XM0BEST2,VALMIN2,TOL,STEP,1000,BESTM0,0,ICONV,NITER,ICNT)
let bm0 = BESTM0 {
u,
v,
w,
h1,
h2,
xx,
};
let (xm0best2, valmin2, iconv) = bm0.get_best(xm0est, -2.0 * xm0est);
xm0best = xm0best1;
if valmin2 < valmin1 {
xm0best = xm0best2;
}
if iconv == false {
panic!("NO CONVERGENCE ON SELECTION OF BEST M0 FOR r1.");
} //235
} //237
} //240
}
}
let xmr1 = xm0best / (xm0best + 1.0);
dx[3] = xnr1 * (kgr1 * e - kkr1 * xmr1);
d1 = x[0] / v1;
d2 = x[1] / v2;
u = d1 / e50_1r2;
v = d2 / e50_2r2;
w = alpha_r2 * d1 * d2 / (e50_1r2 * e50_2r2);
h1 = 1.0_f64 / h1r2;
h2 = 1.0_f64 / h2r2;
xx = (h1 + h2) / 2.0;
if u < 1.0e-5 && v < 1.0e-5 {
xm0best = 0.0;
} else {
if v < 0.0 {
xm0best = u.powf(1.0 / h1);
}
if u < 0.0 {
xm0best = v.powf(1.0 / h2);
}
if v > 0.0 && u > 0.0 {
//START(1) = .00001
let tol = 1.0e-10;
// STEP(1)= -.2D0*START(1)
// CALL ELDERY(1,START,XM0BEST1,VALMIN1,TOL,STEP,1000,BESTM0,0,ICONV,NITER,ICNT)
let xm0best1 = 0.0;
let valmin1 = 0.0;
let iconv = 0.0;
if iconv == 0.0 {
panic!("NO CONVERGENCE ON SELECTION OF BEST M0 FOR r1.");
}
if valmin1 < 1.0e-10 {
xm0best = xm0best1;
} else {
// CALL FINDM0(U,V,alpha_s,H1,H2,XM0EST)
let xm0est = find_m0(u, v, alpha_s, h1, h2);
if xm0est < 0.0 {
xm0best = xm0best1;
} else {
// START(1) = XM0EST
// STEP(1)= -.2D0*START(1)
// CALL ELDERY(1,START,XM0BEST2,VALMIN2,TOL,STEP,1000,BESTM0,0,ICONV,NITER,ICNT)
let xm0best2 = 0.0;
let valmin2 = 0.0;
let iconv = 0.0;
xm0best = xm0best1;
if valmin2 < valmin1 {
xm0best = xm0best2;
}
if iconv == 0.0 {
panic!("NO CONVERGENCE ON SELECTION OF BEST M0 FOR s.");
} //235
} //237
} //240
} //243
}
let xmr2 = xm0best / (xm0best + 1.0);
dx[4] = xnr2 * (kgr2 * e - kkr2 * xmr2);
},
|_p, _t, _cov| lag! {},
|_p, _t, _cov| fa! {},
|p, t, cov, x| {
fetch_params!(
p, v1, cl1, v2, cl2, popmax, kgs, kks, e50_1s, e50_2s, alpha_s, kgr1, kkr1,
e50_1r1, alpha_r1, kgr2, kkr2, e50_2r2, alpha_r2, init_3, init_4, init_5, h1s, h2s,
h1r1, h2r2
);
fetch_cov!(cov, t, ic_t);
x[0] = 0.0;
x[1] = 0.0;
x[2] = 10.0_f64.powf(ic_t);
x[3] = 10.0_f64.powf(init_4);
x[4] = 10.0_f64.powf(init_5);
},
|x, p, _t, _cov, y| {
fetch_params!(
p, v1, cl1, v2, cl2, popmax, kgs, kks, e50_1s, e50_2s, alpha_s, kgr1, kkr1,
e50_1r1, alpha_r1, kgr2, kkr2, e50_2r2, alpha_r2, init_3, init_4, init_5, h1s, h2s,
h1r1, h2r2
);
y[0] = x[0] / v1;
y[1] = x[1] / v2;
y[2] = (x[2] + x[3] + x[4]).log10();
y[3] = x[3].log10();
y[4] = x[4].log10();
},
(1, 1),
);
let settings = settings::read("examples/drusano/config.toml").unwrap();
setup_log(&settings);
let data = data::read_pmetrics("examples/drusano/drusano.csv").unwrap();
let mut algorithm = dispatch_algorithm(settings, eq, data).unwrap();
let result = algorithm.fit().unwrap();
result.write_outputs().unwrap();
}
struct BESTM0 {
u: f64,
v: f64,
w: f64,
h1: f64,
h2: f64,
xx: f64,
}
impl CostFunction for BESTM0 {
type Param = f64;
type Output = f64;
fn cost(&self, xm0: &Self::Param) -> Result<Self::Output, Error> {
let t1 = self.u / xm0.powf(self.h1);
let t2 = self.v / xm0.powf(self.h2);
let t3 = self.w / xm0.powf(self.xx);
Ok((1.0 - t1 - t2 - t3).powi(2))
}
}
impl BESTM0 {
fn get_best(self, start: f64, step: f64) -> (f64, f64, bool) {
let other_point = start + step;
let solver = NelderMead::new(vec![start, other_point])
.with_sd_tolerance(0.0001)
.unwrap();
let res = Executor::new(self, solver)
.configure(|state| state.max_iters(1000))
// .add_observer(SlogLogger::term(), ObserverMode::Always)
.run()
.unwrap();
let converged = match res.state.termination_status {
TerminationStatus::Terminated(reason) => match reason {
TerminationReason::SolverConverged => true,
_ => false,
},
_ => false,
};
(
res.state.best_param.unwrap(),
res.state.best_cost,
converged,
)
}
}
fn find_m0(ufinal: f64, v: f64, alpha: f64, h1: f64, h2: f64) -> f64 {
let noint = 1000;
let delu = ufinal / (noint as f64);
let mut xm = v.powf(1.0 / h2);
let mut u = 0.0;
let hh = (h1 + h2) / 2.0;
for int in 1..=noint {
let top = 1.0 / xm.powf(h1) + alpha * v / xm.powf(hh);
let b1 = u * h1 / xm.powf(h1 + 1.0);
let b2 = v * h2 / xm.powf(h2 + 1.0);
let b3 = alpha * v * u * hh / xm.powf(hh + 1.0);
let xmp = top / (b1 + b2 + b3);
xm = xm + xmp * delu;
if xm <= 0.0 {
return -1.0; // Greco equation is not solvable
}
u = delu * (int as f64);
}
xm // Return the calculated xm0est
}