kuva 0.1.3

Scientific plotting library in Rust with various backends.
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151

//! 2D histogram documentation examples.
//!
//! Generates canonical SVG outputs used in the kuva documentation.
//! Run with:
//!
//! ```bash
//! cargo run --example histogram2d
//! ```
//!
//! SVGs are written to `docs/src/assets/histogram2d/`.

use rand::SeedableRng;
use rand_distr::{Distribution, Normal};
use kuva::plot::Histogram2D;
use kuva::plot::histogram2d::ColorMap;
use kuva::backend::svg::SvgBackend;
use kuva::render::render::render_multiple;
use kuva::render::layout::Layout;
use kuva::render::plots::Plot;

const OUT: &str = "docs/src/assets/histogram2d";

// ── Data helpers ──────────────────────────────────────────────────────────────

/// Independent bivariate Gaussian samples.
fn bivariate(n: usize, mx: f64, my: f64, sx: f64, sy: f64, seed: u64) -> Vec<(f64, f64)> {
    let mut rng = rand::rngs::SmallRng::seed_from_u64(seed);
    let dx = Normal::new(mx, sx).unwrap();
    let dy = Normal::new(my, sy).unwrap();
    (0..n).map(|_| (dx.sample(&mut rng), dy.sample(&mut rng))).collect()
}

/// Correlated bivariate Gaussian: x ~ N(mx, sx), y = rho*z1 + sqrt(1-rho²)*z2
/// scaled to N(my, sy) — Pearson r ≈ rho.
fn correlated(n: usize, mx: f64, my: f64, sx: f64, sy: f64, rho: f64, seed: u64) -> Vec<(f64, f64)> {
    let mut rng = rand::rngs::SmallRng::seed_from_u64(seed);
    let std = Normal::new(0.0_f64, 1.0).unwrap();
    let k = (1.0 - rho * rho).sqrt();
    (0..n).map(|_| {
        let z1 = std.sample(&mut rng);
        let z2 = std.sample(&mut rng);
        let x = mx + sx * z1;
        let y = my + sy * (rho * z1 + k * z2);
        (x, y)
    }).collect()
}

// ── Examples ──────────────────────────────────────────────────────────────────

fn main() {
    std::fs::create_dir_all(OUT).expect("could not create docs/src/assets/histogram2d");

    basic();
    correlation();
    bimodal();
    bin_resolution();

    println!("Histogram2D SVGs written to {OUT}/");
}

/// Basic 2D histogram — single Gaussian cluster, Viridis colormap.
///
/// `with_data` bins 5 000 scatter points into a 30×30 grid over the range
/// [0, 30) × [0, 30). Points outside the range are silently discarded.
/// A colorbar labeled "Count" is added automatically.
fn basic() {
    let data = bivariate(5_000, 15.0, 15.0, 3.0, 3.0, 1);

    let hist = Histogram2D::new()
        .with_data(data, (0.0, 30.0), (0.0, 30.0), 30, 30);

    let plots = vec![Plot::Histogram2d(hist)];
    let layout = Layout::auto_from_plots(&plots)
        .with_title("2D Histogram — Viridis")
        .with_x_label("X")
        .with_y_label("Y");

    let svg = SvgBackend.render_scene(&render_multiple(plots, layout));
    std::fs::write(format!("{OUT}/basic.svg"), svg).unwrap();
}

/// Correlated data with Pearson r annotation.
///
/// `with_correlation()` overlays the Pearson correlation coefficient in the
/// top-right corner. Here rho ≈ 0.85 produces a clear diagonal density ridge.
fn correlation() {
    let data = correlated(4_000, 10.0, 10.0, 2.0, 2.0, 0.85, 2);

    let hist = Histogram2D::new()
        .with_data(data, (0.0, 20.0), (0.0, 20.0), 25, 25)
        .with_correlation();

    let plots = vec![Plot::Histogram2d(hist)];
    let layout = Layout::auto_from_plots(&plots)
        .with_title("Correlated Variables (r ≈ 0.85)")
        .with_x_label("X")
        .with_y_label("Y");

    let svg = SvgBackend.render_scene(&render_multiple(plots, layout));
    std::fs::write(format!("{OUT}/correlation.svg"), svg).unwrap();
}

/// Bimodal distribution — two clusters, Inferno colormap.
///
/// `ColorMap::Inferno` uses a dark-to-bright yellow-orange scheme that makes
/// high-density regions stand out against a near-black background.
fn bimodal() {
    let mut data = bivariate(3_000,  9.0,  9.0, 2.0, 2.0, 3);
    data.extend(bivariate(3_000, 21.0, 21.0, 2.0, 2.0, 4));

    let hist = Histogram2D::new()
        .with_data(data, (0.0, 30.0), (0.0, 30.0), 30, 30)
        .with_color_map(ColorMap::Inferno);

    let plots = vec![Plot::Histogram2d(hist)];
    let layout = Layout::auto_from_plots(&plots)
        .with_title("Bimodal — Inferno")
        .with_x_label("X")
        .with_y_label("Y");

    let svg = SvgBackend.render_scene(&render_multiple(plots, layout));
    std::fs::write(format!("{OUT}/bimodal.svg"), svg).unwrap();
}

/// Effect of bin resolution — coarse (10×10, Grayscale) vs fine (50×50, Viridis).
///
/// Coarse binning uses `ColorMap::Grayscale` (white → black) to show overall
/// shape; fine binning uses `ColorMap::Viridis` to reveal finer density
/// structure with a perceptually uniform colour scale.
fn bin_resolution() {
    let data = bivariate(8_000, 15.0, 15.0, 3.5, 3.5, 5);

    let configs: &[(usize, &str, ColorMap, &str)] = &[
        (10, "coarse", ColorMap::Grayscale, "Grayscale — 10×10 bins"),
        (50, "fine",   ColorMap::Viridis,   "Viridis — 50×50 bins"),
    ];
    for &(bins, name, ref cmap, title) in configs {
        let hist = Histogram2D::new()
            .with_data(data.clone(), (0.0, 30.0), (0.0, 30.0), bins, bins)
            .with_color_map(cmap.clone());

        let plots = vec![Plot::Histogram2d(hist)];
        let layout = Layout::auto_from_plots(&plots)
            .with_title(title)
            .with_x_label("X")
            .with_y_label("Y");

        let svg = SvgBackend.render_scene(&render_multiple(plots, layout));
        std::fs::write(format!("{OUT}/bins_{name}.svg"), svg).unwrap();
    }
}