//! Measure the rotated surface code's logical error rate per round, and fit
//! the code model `P(d) = a·(p/p*)^((d+1)/2)` that resource estimates assume.
//!
//! ```text
//! cargo run --release --example qec_threshold --features quantum_frame -- [SHOTS] [SEED]
//! ```
use wai_quantum::quantum_match::{fit_code_model, memory_experiment};

fn main() {
    let a: Vec<String> = std::env::args().collect();
    let shots: usize = a.get(1).map_or(200_000, |s| s.parse().unwrap());
    let seed: u64 = a.get(2).map_or(1, |s| s.parse().unwrap());
    let mut pts = Vec::new();
    println!("{:>3} {:>7} {:>9} {:>9} {:>12}", "d", "p", "shots", "failures", "per round");
    for d in [3u32, 5, 7] {
        for p in [0.002, 0.003, 0.004, 0.005] {
            // Fewer shots where decoding costs more and failures are plentiful.
            let n = if d == 7 { shots / 4 } else { shots };
            let t = memory_experiment(d, d, p, n, seed).expect("decode");
            println!("{:>3} {:>7} {:>9} {:>9} {:>12.4e}", d, p, t.shots, t.failures, t.per_round());
            pts.push(t);
        }
    }
    let f = fit_code_model(&pts).expect("fit");
    println!("fit: a = {:.4}, p* = {:.5}", f.prefactor, f.threshold);
}