1pub mod align;
2pub mod bin;
3pub mod bin2d;
4pub mod bindot;
5pub mod binhex;
6pub mod boxplot;
7pub mod calendar;
8#[cfg(feature = "ggpubr")]
9pub mod compare_means;
10pub mod contour;
11pub mod contour_filled;
12#[cfg(feature = "ggpubr")]
13pub mod cor;
14pub mod count;
15pub mod density;
16pub mod density2d;
17pub mod dist;
18pub mod distribution;
19pub mod ecdf;
20pub mod ellipse;
21pub mod function;
22pub(crate) mod gam;
23pub mod identity;
24pub mod loess;
25pub mod marching_squares;
26pub mod qq;
27#[cfg(feature = "regression")]
28pub mod quantile;
29pub mod smooth;
30pub mod sum;
31pub mod summary;
32pub mod summary2d;
33pub mod summary_bin;
34pub mod ydensity;
35
36use crate::aes::{Aes, Aesthetic};
37use crate::data::DataFrame;
38use crate::scale::ScaleSet;
39
40pub trait Stat: Send + Sync {
42 fn compute_group(&self, data: &DataFrame, scales: &ScaleSet) -> DataFrame;
44
45 fn required_aes(&self) -> Vec<Aesthetic>;
47
48 fn default_aes(&self) -> Aes {
50 Aes::default()
51 }
52
53 fn panelwise(&self) -> bool {
58 false
59 }
60
61 fn name(&self) -> &str;
63
64 fn validate(&self, _warnings: &mut Vec<String>) {}
72}
73
74pub(crate) fn bw_nrd0(values: &[f64]) -> f64 {
78 let n = values.len();
79 if n < 2 {
80 return 1.0;
81 }
82 let nf = n as f64;
83 let mean = values.iter().sum::<f64>() / nf;
84 let sd = (values.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / (nf - 1.0)).sqrt();
85 let mut sorted = values.to_vec();
86 sorted.sort_by(|a, b| a.total_cmp(b));
87 let q = |p: f64| {
88 let h = (n - 1) as f64 * p;
89 let lo = h.floor() as usize;
90 let hi = (lo + 1).min(n - 1);
91 sorted[lo] + (h - lo as f64) * (sorted[hi] - sorted[lo])
92 };
93 let iqr = q(0.75) - q(0.25);
94 let mut lo = sd.min(iqr / 1.34);
95 for fallback in [sd, values[0].abs(), 1.0] {
96 if lo > 0.0 && lo.is_finite() {
97 break;
98 }
99 lo = fallback;
100 }
101 0.9 * lo * nf.powf(-0.2)
102}