Vulkan-native presentation without a second graphics framework.

1use std::f32::consts::PI;
2
3use oa::plot::{Axes, BarStyle, FigureConfig, HeatmapStyle, LineStyle, ScatterStyle, Theme};
4use oa::{Color, Engine};
5
6// ── Style helpers ─────────────────────────────────────────────────────────────
7
8fn line(color: Color, label: &str, width: f32, antialias_samples: u32) -> LineStyle {
9	LineStyle {
10		color,
11		label: label.to_owned(),
12		width,
13		antialias_samples,
14	}
15}
16
17fn points(color: Color, label: &str, radius: f32) -> ScatterStyle {
18	ScatterStyle {
19		color,
20		label: label.to_owned(),
21		radius,
22	}
23}
24
25fn bars(color: Color, label: &str, gap: f32) -> BarStyle {
26	BarStyle {
27		color,
28		label: label.to_owned(),
29		gap,
30	}
31}
32
33fn gradient(a: Color, b: Color, t: f32) -> Color {
34	let t = t.clamp(0.0, 1.0);
35	Color::new(
36		a.r + (b.r - a.r) * t,
37		a.g + (b.g - a.g) * t,
38		a.b + (b.b - a.b) * t,
39		a.a + (b.a - a.a) * t,
40	)
41}
42
43// ── Named colors matching the donor palette ───────────────────────────────────
44
45fn accent() -> Color {
46	Color::new(0.24, 0.65, 1.00, 1.0)
47}
48fn success() -> Color {
49	Color::new(0.35, 0.82, 0.54, 1.0)
50}
51fn purple() -> Color {
52	Color::new(0.75, 0.45, 1.00, 1.0)
53}
54fn cyan() -> Color {
55	Color::new(0.30, 0.87, 0.89, 1.0)
56}
57fn warning() -> Color {
58	Color::new(1.00, 0.80, 0.22, 1.0)
59}
60fn pink() -> Color {
61	Color::new(1.00, 0.55, 0.75, 1.0)
62}
63fn gray() -> Color {
64	Color::new(0.565, 0.565, 0.565, 1.0)
65}
66
67// ── Individual axes builders ──────────────────────────────────────────────────
68
69// [oa-plot-intro-begin]
70fn intro_figure() -> oa::plot::Figure {
71	let mut fig = oa::plot::Figure::new(FigureConfig {
72		title: "oa::plot intro".to_owned(),
73		rows: 1,
74		cols: 2,
75		width: 1280,
76		height: 560,
77		h_spacing: 36,
78		padding: 34,
79		theme: Theme::Dark,
80		..Default::default()
81	});
82	fig.title("One retained figure — Rust and C++ parity");
83
84	let steps = [0.0f32, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0];
85	let train = [1.00f32, 0.78, 0.61, 0.48, 0.37, 0.29, 0.23, 0.19];
86	let validation = [1.04f32, 0.83, 0.66, 0.53, 0.43, 0.35, 0.30, 0.27];
87	let ideal = [0.0f32, 0.25, 0.50, 0.75, 1.0];
88	let confidence = [0.10f32, 0.30, 0.50, 0.70, 0.90];
89	let accuracy = [0.08f32, 0.34, 0.47, 0.74, 0.88];
90
91	let curves = fig.ax(0, 0);
92	curves.title("training curves");
93	curves.x_label("optimizer step");
94	curves.y_label("cross entropy");
95	curves.limits(0.0, 7.0, 0.0, 1.1);
96	curves.plot_xy(&steps, &train, line(accent(), "train", 1.6, 4));
97	curves.plot_xy(&steps, &validation, line(success(), "validation", 1.6, 4));
98
99	let quality = fig.ax(0, 1);
100	quality.title("Calibration");
101	quality.x_label("confidence");
102	quality.y_label("observed accuracy");
103	quality.limits(0.0, 1.0, 0.0, 1.0);
104	quality.plot_xy(&ideal, &ideal, line(gray(), "ideal", 1.35, 4));
105	quality.scatter(&confidence, &accuracy, points(cyan(), "model", 3.5));
106	fig
107}
108// [oa-plot-intro-end]
109
110fn training_plot(ax: &mut Axes) {
111	let n = 72usize;
112	let train: Vec<f32> = (0..n)
113		.map(|i| {
114			let step = i as f32;
115			1.55 * (-step / 18.0).exp() + 0.035 * (step * 0.52).sin() + 0.11
116		})
117		.collect();
118	let validation: Vec<f32> = (0..n)
119		.map(|i| {
120			let step = i as f32;
121			1.45 * (-step / 21.0).exp() + 0.045 * (step * 0.39 + 0.8).sin() + 0.17
122		})
123		.collect();
124	ax.title("training curves");
125	ax.x_label("optimizer step");
126	ax.y_label("cross entropy");
127	ax.plot(&train, line(accent(), "train", 1.35, 4));
128	ax.plot(&validation, line(success(), "validation", 1.35, 4));
129}
130
131fn roc_plot(ax: &mut Axes) {
132	let n = 33usize;
133	let rate: Vec<f32> = (0..n).map(|i| i as f32 / (n - 1) as f32).collect();
134	let vision: Vec<f32> = rate
135		.iter()
136		.map(|&x| (1.0 - (-4.3 * x).exp()).clamp(0.0, 1.0))
137		.collect();
138	let fusion: Vec<f32> = rate
139		.iter()
140		.map(|&x| (1.0 - (-6.7 * x).exp()).clamp(0.0, 1.0))
141		.collect();
142	ax.title("ROC — threshold sweep");
143	ax.x_label("false positive rate");
144	ax.y_label("true positive rate");
145	ax.limits(0.0, 1.0, 0.0, 1.0);
146	ax.plot_xy(&rate, &vision, line(cyan(), "vision AUC 0.88", 1.35, 4));
147	ax.plot_xy(&rate, &fusion, line(success(), "fusion AUC 0.93", 1.35, 4));
148	ax.plot_xy(&rate, &rate, line(gray(), "chance", 1.35, 4));
149}
150
151fn precision_recall_plot(ax: &mut Axes) {
152	let n = 33usize;
153	let recall: Vec<f32> = (0..n).map(|i| i as f32 / (n - 1) as f32).collect();
154	let vision: Vec<f32> = recall
155		.iter()
156		.map(|&x| (0.98 - 0.40 * x.powf(1.6) - 0.025 * (x * 5.0 * PI).sin()).clamp(0.0, 1.0))
157		.collect();
158	let fusion: Vec<f32> = recall
159		.iter()
160		.map(|&x| (0.995 - 0.27 * x.powf(2.1) - 0.015 * (x * 4.0 * PI).sin()).clamp(0.0, 1.0))
161		.collect();
162	ax.title("Precision-recall");
163	ax.x_label("recall");
164	ax.y_label("precision");
165	ax.limits(0.0, 1.0, 0.0, 1.0);
166	ax.plot_xy(&recall, &vision, line(purple(), "vision AP 0.84", 1.35, 4));
167	ax.plot_xy(&recall, &fusion, line(pink(), "fusion AP 0.91", 1.35, 4));
168}
169
170fn score_histogram(ax: &mut Axes) {
171	let scores: Vec<f32> = (0..192usize)
172		.map(|i| {
173			let phase = i as f32;
174			let cluster = if i % 3 == 0 { 0.28 } else { 0.74 };
175			(cluster + 0.12 * (phase * 1.73).sin() + 0.045 * (phase * 0.37).cos()).clamp(0.0, 1.0)
176		})
177		.collect();
178	ax.title("confidence distribution");
179	ax.x_label("model score");
180	ax.y_label("samples");
181	ax.histogram(&scores, 20, bars(accent(), "validation scores", 0.20));
182}
183
184fn calibration_scatter(ax: &mut Axes) {
185	let n = 24usize;
186	let confidence: Vec<f32> = (0..n).map(|i| (i as f32 + 0.5) / n as f32).collect();
187	let accuracy: Vec<f32> = confidence
188		.iter()
189		.map(|&x| (x + 0.055 * (8.0 * PI * x).sin() - 0.025).clamp(0.0, 1.0))
190		.collect();
191	let ideal: Vec<f32> = confidence.clone();
192	ax.title("Calibration");
193	ax.x_label("confidence");
194	ax.y_label("observed accuracy");
195	ax.limits(0.0, 1.0, 0.0, 1.0);
196	ax.plot_xy(&confidence, &ideal, line(gray(), "ideal", 1.35, 4));
197	ax.scatter(&confidence, &accuracy, points(cyan(), "model", 3.5));
198}
199
200fn throughput_bars(ax: &mut Axes) {
201	let throughput = [0.42f32, 0.58, 0.71, 0.86, 0.79, 0.94];
202	ax.title("normalized throughput");
203	ax.x_label("kernel route");
204	ax.y_label("relative peak");
205	ax.limits(-0.5, 5.5, 0.0, 1.0);
206	ax.bar(&throughput, bars(warning(), "device routes", 0.24));
207}
208
209fn confusion_heatmap(ax: &mut Axes) {
210	let confusion = [
211		42.0f32, 2.0, 0.0, 1.0, 0.0, 3.0, 37.0, 2.0, 0.0, 1.0, 0.0, 2.0, 40.0, 3.0, 0.0, 1.0, 0.0, 2.0,
212		38.0, 2.0, 0.0, 1.0, 0.0, 2.0, 43.0,
213	];
214	ax.title("confusion matrix");
215	ax.x_label("predicted class");
216	ax.y_label("reference class");
217	ax.heatmap(
218		&confusion,
219		5,
220		5,
221		HeatmapStyle {
222			colormap: 1,
223			auto_scale: true,
224			show_grid: true,
225			..Default::default()
226		},
227	);
228	ax.grid(false);
229}
230
231fn wireframe(
232	ax: &mut Axes,
233	title: &str,
234	height_fn: impl Fn(f32, f32) -> f32,
235	near: Color,
236	far: Color,
237) {
238	const LINES: i32 = 13;
239	const SAMPLES: i32 = 29;
240	// Row sweeps.
241	for row in 0..LINES {
242		let v = -1.0 + 2.0 * row as f32 / (LINES - 1) as f32;
243		let mut px = vec![0.0f32; SAMPLES as usize];
244		let mut py = vec![0.0f32; SAMPLES as usize];
245		for col in 0..SAMPLES {
246			let u = -1.0 + 2.0 * col as f32 / (SAMPLES - 1) as f32;
247			let z = height_fn(u, v);
248			px[col as usize] = 0.50 + 0.34 * (u - v);
249			py[col as usize] = 0.58 - 0.16 * (u + v) - 0.23 * z;
250		}
251		let t = row as f32 / (LINES - 1) as f32;
252		ax.plot_xy(&px, &py, line(gradient(far, near, t), "", 1.15, 8));
253	}
254	// Column sweeps.
255	for col in 0..LINES {
256		let u = -1.0 + 2.0 * col as f32 / (LINES - 1) as f32;
257		let mut px = vec![0.0f32; SAMPLES as usize];
258		let mut py = vec![0.0f32; SAMPLES as usize];
259		for row in 0..SAMPLES {
260			let v = -1.0 + 2.0 * row as f32 / (SAMPLES - 1) as f32;
261			let z = height_fn(u, v);
262			px[row as usize] = 0.50 + 0.34 * (u - v);
263			py[row as usize] = 0.58 - 0.16 * (u + v) - 0.23 * z;
264		}
265		let t = col as f32 / (LINES - 1) as f32;
266		let mut col_style = line(gradient(far, near, t), "", 1.15, 8);
267		col_style.color.a = 0.78;
268		ax.plot_xy(&px, &py, col_style);
269	}
270	ax.title(title);
271	ax.limits(-0.20, 1.20, 0.02, 1.08);
272	ax.grid(false);
273	ax.legend(false);
274}
275
276fn loss_landscape(ax: &mut Axes) {
277	wireframe(
278		ax,
279		"projected loss landscape",
280		|x, y| {
281			let bowl = 0.42 * (x * x + 0.75 * y * y);
282			bowl + 0.18 * (2.7 * x).sin() * (3.1 * y).cos()
283		},
284		cyan(),
285		purple(),
286	);
287}
288
289fn gradient_basin(ax: &mut Axes) {
290	wireframe(
291		ax,
292		"projected optimizer basin",
293		|x, y| {
294			let radius = (x * x + y * y).sqrt();
295			0.34 * radius + 0.17 * (9.0 * radius).cos() * (-1.8 * radius).exp()
296		},
297		success(),
298		warning(),
299	);
300}
301
302fn gallery(theme: Theme) -> oa::plot::Figure {
303	let mut fig = oa::plot::Figure::new(FigureConfig {
304		title: "OA Plot gallery".to_owned(),
305		rows: 3,
306		cols: 3,
307		width: 1600,
308		height: 1050,
309		h_spacing: 32,
310		v_spacing: 36,
311		padding: 34,
312		theme,
313		..Default::default()
314	});
315	fig.title("OA Plot — GPU-composed ML diagnostics");
316	training_plot(fig.ax(0, 0));
317	roc_plot(fig.ax(0, 1));
318	precision_recall_plot(fig.ax(0, 2));
319	score_histogram(fig.ax(1, 0));
320	calibration_scatter(fig.ax(1, 1));
321	throughput_bars(fig.ax(1, 2));
322	confusion_heatmap(fig.ax(2, 0));
323	loss_landscape(fig.ax(2, 1));
324	gradient_basin(fig.ax(2, 2));
325	fig
326}
327
328fn evaluation_figure() -> oa::plot::Figure {
329	let mut fig = oa::plot::Figure::new(FigureConfig {
330		title: "OA model evaluation".to_owned(),
331		rows: 1,
332		cols: 2,
333		width: 1280,
334		height: 560,
335		h_spacing: 36,
336		padding: 34,
337		..Default::default()
338	});
339	fig.title("Model evaluation — ROC and precision-recall");
340	roc_plot(fig.ax(0, 0));
341	precision_recall_plot(fig.ax(0, 1));
342	fig
343}
344
345fn diagnostics_figure() -> oa::plot::Figure {
346	let mut fig = oa::plot::Figure::new(FigureConfig {
347		title: "OA model diagnostics".to_owned(),
348		rows: 2,
349		cols: 2,
350		width: 1200,
351		height: 900,
352		h_spacing: 32,
353		v_spacing: 36,
354		padding: 34,
355		..Default::default()
356	});
357	fig.title("training and evaluation diagnostics");
358	training_plot(fig.ax(0, 0));
359	score_histogram(fig.ax(0, 1));
360	calibration_scatter(fig.ax(1, 0));
361	confusion_heatmap(fig.ax(1, 1));
362	fig
363}
364
365fn landscapes_figure() -> oa::plot::Figure {
366	let mut fig = oa::plot::Figure::new(FigureConfig {
367		title: "OA projected landscapes".to_owned(),
368		rows: 1,
369		cols: 2,
370		width: 1280,
371		height: 560,
372		h_spacing: 36,
373		padding: 34,
374		..Default::default()
375	});
376	fig.title("scalar-field projections — explicit-X wireframes");
377	loss_landscape(fig.ax(0, 0));
378	gradient_basin(fig.ax(0, 1));
379	fig
380}
381
382// ── Entry point ───────────────────────────────────────────────────────────────
383
384fn usage(program: &str) {
385	eprintln!("usage: {program} [--output DIR] [--headless]");
386}
387
388fn main() -> anyhow::Result<()> {
389	let mut output = std::path::PathBuf::from("artifact/plot");
390	let mut show = true;
391
392	let args: Vec<String> = std::env::args().collect();
393	let mut i = 1;
394	while i < args.len() {
395		match args[i].as_str() {
396			"--headless" => {
397				show = false;
398			}
399			"--output" if i + 1 < args.len() => {
400				i += 1;
401				output = std::path::PathBuf::from(&args[i]);
402			}
403			"--help" => {
404				usage(&args[0]);
405				return Ok(());
406			}
407			other => {
408				eprintln!("unknown argument: {other}");
409				usage(&args[0]);
410				std::process::exit(1);
411			}
412		}
413		i += 1;
414	}
415
416	std::fs::create_dir_all(&output).map_err(|e| {
417		eprintln!("could not create output dir: {e}");
418		e
419	})?;
420
421	let engine = if show {
422		let window = oa::SdlWindow::new("OA Plot presentation probe", 1, 1)?;
423		let extensions = window.required_instance_extensions()?;
424		Engine::builder().instance_extensions(extensions).build()?
425	} else {
426		Engine::new()?
427	};
428
429	let mut dark = gallery(Theme::Dark);
430	let mut light = gallery(Theme::Light);
431	let mut intro = intro_figure();
432	let mut evaluation = evaluation_figure();
433	let mut diagnostics = diagnostics_figure();
434	let mut landscapes = landscapes_figure();
435
436	println!("OA Plot walkthrough artifacts:");
437
438	let save = |fig: &mut oa::plot::Figure, name: &str| -> anyhow::Result<()> {
439		let path = output.join(name);
440		fig.save_to(&engine, path.to_str().unwrap_or(name))?;
441		println!("  {}", path.display());
442		Ok(())
443	};
444
445	save(&mut intro, "oa-plot-intro-dark.png")?;
446	save(&mut dark, "oa-plot-gallery-dark.png")?;
447	save(&mut light, "oa-plot-gallery-light.png")?;
448	save(&mut evaluation, "oa-plot-evaluation-dark.png")?;
449	save(&mut diagnostics, "oa-plot-diagnostics-dark.png")?;
450	save(&mut landscapes, "oa-plot-landscapes-dark.png")?;
451
452	if show {
453		dark.show(&engine)?;
454	}
455	Ok(())
456}
457