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style: apply rustfmt drift across the crate

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This commit is contained in:
swaits
2026-05-05 11:40:14 -06:00
parent 84cee3f29e
commit 4a59041d1a
60 changed files with 1520 additions and 608 deletions
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examples/jiggly_tuning.rs
+55 -16
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@@ -152,7 +152,10 @@ impl JigglyTuning {
let mut rng = StdRng::seed_from_u64(day_seed);
let mut expire = s + rt;
// Boot press at workday start: user presses to begin cycle 1.
let mut o = DayOutcome { presses: 1, ..Default::default() };
let mut o = DayOutcome {
presses: 1,
..Default::default()
};
// Allow the loop to extend past the larger of (workday end, last
// possible cycle end given any in-loop expire bumps). Cap at one
// extra cycle's worth so a long string of presses can't blow the
@@ -348,7 +351,11 @@ fn print_header() {
}
fn print_row(label: &str, r: &Row) {
let prefix = if label.is_empty() { String::new() } else { format!("{label} ") };
let prefix = if label.is_empty() {
String::new()
} else {
format!("{label} ")
};
println!(
"{}{:<6} {:>3} {:>3} {:>3} {:>9} {:>9} {:>7.2}/d {:>8} {:>6.1}%",
prefix,
@@ -440,7 +447,11 @@ fn main() {
println!("=== Pareto front (sorted by lunch sleep, descending) ===");
print_header();
rows.sort_by(|a, b| b.lunch.partial_cmp(&a.lunch).unwrap_or(std::cmp::Ordering::Equal));
rows.sort_by(|a, b| {
b.lunch
.partial_cmp(&a.lunch)
.unwrap_or(std::cmp::Ordering::Equal)
});
for r in rows.iter().take(15) {
print_row("", r);
}
@@ -507,8 +518,12 @@ fn main() {
let shipping_candidate_idx = candidates.len();
candidates.push(("shipping default".to_string(), shipping_row.clone()));
let scores =
compute_weighted_scores(&candidates.iter().map(|(_, r)| r.clone()).collect::<Vec<_>>());
let scores = compute_weighted_scores(
&candidates
.iter()
.map(|(_, r)| r.clone())
.collect::<Vec<_>>(),
);
let mut ranked: Vec<(usize, f64)> = scores.iter().copied().enumerate().collect();
ranked.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
@@ -529,7 +544,10 @@ fn main() {
" balance bonus: min(yellow_width, red_width), saturates at {:.0} min",
BALANCE_SATURATION_MIN,
);
println!(" candidate set: {} Pareto-front rows + 1 shipping default", rows.len());
println!(
" candidate set: {} Pareto-front rows + 1 shipping default",
rows.len()
);
println!();
println!("{:>4} {:>5} source", "rank", "score");
print_header();
@@ -548,7 +566,10 @@ fn main() {
.map(|p| p + 1)
.unwrap_or(0);
let max_work = candidates.iter().map(|(_, r)| r.work_fail).fold(0.0, f64::max);
let max_work = candidates
.iter()
.map(|(_, r)| r.work_fail)
.fold(0.0, f64::max);
println!("=== RECOMMENDED PICK ({top_label}) ===");
println!(
@@ -591,9 +612,7 @@ fn main() {
" • {:.2} button presses/day total — {}",
top.presses, press_note,
);
println!(
" (counts: boot + 13:00 retap + warning-phase reactions + death-restarts)"
);
println!(" (counts: boot + 13:00 retap + warning-phase reactions + death-restarts)");
println!(
" • warning phases: yellow {} min, red {} min, fast-red {} min (balance score {:.2})",
yellow_w,
@@ -629,12 +648,24 @@ fn main() {
/// phases, computed as `min(YA - RA, RA - FRA)` saturated at
/// `BALANCE_SATURATION_MIN`.
fn compute_weighted_scores(rows: &[Row]) -> Vec<f64> {
let work_min = rows.iter().map(|r| r.work_fail).fold(f64::INFINITY, f64::min);
let work_max = rows.iter().map(|r| r.work_fail).fold(f64::NEG_INFINITY, f64::max);
let work_min = rows
.iter()
.map(|r| r.work_fail)
.fold(f64::INFINITY, f64::min);
let work_max = rows
.iter()
.map(|r| r.work_fail)
.fold(f64::NEG_INFINITY, f64::max);
let lunch_min = rows.iter().map(|r| r.lunch).fold(f64::INFINITY, f64::min);
let lunch_max = rows.iter().map(|r| r.lunch).fold(f64::NEG_INFINITY, f64::max);
let lunch_max = rows
.iter()
.map(|r| r.lunch)
.fold(f64::NEG_INFINITY, f64::max);
let after_min = rows.iter().map(|r| r.after).fold(f64::INFINITY, f64::min);
let after_max = rows.iter().map(|r| r.after).fold(f64::NEG_INFINITY, f64::max);
let after_max = rows
.iter()
.map(|r| r.after)
.fold(f64::NEG_INFINITY, f64::max);
rows.iter()
.map(|r| {
@@ -676,12 +707,20 @@ fn balance_score_for(r: &Row) -> f64 {
/// Normalize a minimize-direction value to `[0, 1]` (best→1, worst→0).
fn norm_min(v: f64, lo: f64, hi: f64) -> f64 {
if (hi - lo).abs() < 1e-12 { 1.0 } else { (hi - v) / (hi - lo) }
if (hi - lo).abs() < 1e-12 {
1.0
} else {
(hi - v) / (hi - lo)
}
}
/// Normalize a maximize-direction value to `[0, 1]` (best→1, worst→0).
fn norm_max(v: f64, lo: f64, hi: f64) -> f64 {
if (hi - lo).abs() < 1e-12 { 1.0 } else { (v - lo) / (hi - lo) }
if (hi - lo).abs() < 1e-12 {
1.0
} else {
(v - lo) / (hi - lo)
}
}
/// Render `worst / best` as e.g. "7.5×" for the recommendation rationale.