Improve plotter output, introduce randomness

docs/app/guides/display/plots/page.tsx

@@ -92,18 +92,30 @@ function Functions() {
         <code>minSamplingDepth</code> and <code>maxSamplingDepth</code> props can be tuned.
         Increasing <code>minSamplingDepth</code> can help when you want to ensure more subdivisions
         and improve accuracy, and lowering <code>maxSamplingDepth</code> can help improve
-        performance. These two props should be tuned to meet your needs.
+        performance.
       </p>
 
       <p>
-        Here's an example of a common "stress test" function for plotters, sin(1/x). The top plot
-        has the default sampling depths, while the bottom has <code>minSamplingDepth</code>{" "}
-        increased to <code>15</code>. Neither approach is perfect, but the bottom render is
-        indistinguishable from a perfect plot.
+        Here's an example of a common "stress test" function for plotters, sin(1/x). This function
+        exhibits an infinite oscillation frequency as x approaches 0, requiring theoretically
+        infinite sampling to render perfectly.
+      </p>
+
+      <p>
+        The top plot has the default sampling depths, while the bottom has{" "}
+        <code>minSamplingDepth</code> increased to <code>16</code>. More samples still doesn't
+        render the function perfectly, but it's much closer (at the cost of performance: the bottom
+        plot has nearly 3 megabytes of SVG path data).
       </p>
 
       <CodeAndExample component={<SineStressTest />} source={SineStressTestSource} />
 
+      <p>
+        If you pan this example around, you may see a considerably slow framerate. Interestingly,
+        this slowness is happening in the browser code itself, not in JavaScript (and therefore not
+        in Mafs). It would seem that merely rendering large SVG paths is expensive.
+      </p>
+
       <h3>Vector fields</h3>
 
       <p>

docs/components/guide-examples/plots/sine-stress-test.tsx

@@ -13,9 +13,9 @@ export default function SineStressTest() {
       preserveAspectRatio={false}
     >
       <Coordinates.Cartesian />
-      <Plot.OfX y={(x) => fn(x) + 1.5} />
+      <Plot.OfX y={(x) => fn(x) + 1.5} weight={1} />
       {/* prettier-ignore */}
-      <Plot.OfX y={(x) => fn(x) - 1.5} minSamplingDepth={15} />
+      <Plot.OfX y={(x) => fn(x) - 1.5} minSamplingDepth={16} weight={1} />
     </Mafs>
   )
 }

src/display/Plot/PlotUtils.test.ts

@@ -0,0 +1,23 @@
+import { sample } from "./PlotUtils"
+
+test("sample", () => {
+  const ts: number[] = []
+
+  sample<number>({
+    domain: [0, 8],
+    minDepth: 3,
+    maxDepth: 3,
+    error: () => Number.POSITIVE_INFINITY,
+    fn: (t) => t,
+    lerp: () => 0,
+    onPoint(t) {
+      ts.push(t)
+    },
+    threshold: 0,
+  })
+
+  // Since the sample function deliberately introduces randomness, we round to test.
+  const roundedTs = ts.map(Math.round)
+
+  expect(roundedTs).toEqual([0, 1, 2, 3, 4, 5, 6, 7, 8])
+})

src/display/Plot/PlotUtils.tsx

@@ -5,8 +5,8 @@ interface SampleParams<P> {
   fn: (t: number) => P
   /** A function that computes the error between a real sample function output and a midpoint output */
   error: (real: P, estimate: P) => number
-  /** A function that computes the midpoint of two sample function outputs */
-  midpoint: (p1: P, p2: P) => P
+  /** A function that computes the lerp of two sample function outputs, for purpose of comparing to the function's real output */
+  lerp: (p1: P, p2: P, t: number) => P
   /** A function that is called whenever a point should be part of the sample */
   onPoint: (t: number, p: P) => void
   /** The domain to sample */
@@ -19,64 +19,63 @@ interface SampleParams<P> {
   threshold: number
 }
 
+/**
+ * Cheap psuedo-random hash function to consistently generate randomness when
+ * sampling functions. This return a value between 0.4 and 0.6.
+ */
+function cheapHash(min: number, max: number) {
+  const result = Math.sin(min * 12.9898 + max * 78.233) * 43758.5453
+  return 0.4 + 0.2 * (result - Math.floor(result))
+}
+
 /**
  * A relatively generic internal function which, given a function, domain, and
  * an error function, will recursively subdivide the domain until sampling said
  * function at each point in the domain yields an error less than the supplied
  * threshold. Importantly, this makes no assumptions about the return type of
  * the sampled function.
  */
-function sample<SampledReturnType>({
+export function sample<SampledReturnType>({
   domain,
   minDepth,
   maxDepth,
   threshold,
   fn,
   error,
   onPoint,
-  midpoint,
+  lerp,
 }: SampleParams<SampledReturnType>) {
   const [min, max] = domain
 
   function subdivide(
     min: number,
     max: number,
-    pushLeft: boolean,
-    pushRight: boolean,
     depth: number,
     pMin: SampledReturnType,
     pMax: SampledReturnType,
   ) {
-    const t = 0.5
+    const t = cheapHash(min, max)
     const mid = min + (max - min) * t
     const pMid = fn(mid)
 
-    if (depth < minDepth) {
-      subdivide(min, mid, true, false, depth + 1, pMin, pMid)
-      subdivide(mid, max, false, true, depth + 1, pMid, pMax)
-      return
+    function deepen() {
+      subdivide(min, mid, depth + 1, pMin, pMid)
+      onPoint(mid, pMid)
+      subdivide(mid, max, depth + 1, pMid, pMax)
     }
 
-    if (depth < maxDepth) {
-      const fnMidpoint = midpoint(pMin, pMax)
+    if (depth < minDepth) {
+      deepen()
+    } else if (depth < maxDepth) {
+      const fnMidpoint = lerp(pMin, pMax, t)
       const e = error(pMid, fnMidpoint)
-      if (e > threshold) {
-        subdivide(min, mid, true, false, depth + 1, pMin, pMid)
-        subdivide(mid, max, false, true, depth + 1, pMid, pMax)
-        return
-      }
-    }
-
-    if (pushLeft) {
-      onPoint(min, pMin)
-    }
-    onPoint(mid, pMid)
-    if (pushRight) {
-      onPoint(max, pMax)
+      if (e > threshold) deepen()
     }
   }
 
-  subdivide(min, max, true, true, 0, fn(min), fn(max))
+  onPoint(min, fn(min))
+  subdivide(min, max, 0, fn(min), fn(max))
+  onPoint(max, fn(max))
 }
 
 export function sampleParametric(
@@ -96,7 +95,7 @@ export function sampleParametric(
         result += `${x} ${y} L `
       }
     },
-    midpoint: (p1, p2) => vec.midpoint(p1, p2),
+    lerp: (p1, p2, t) => vec.lerp(p1, p2, t),
     domain,
     minDepth,
     maxDepth,
@@ -141,8 +140,8 @@ export function sampleInequality(
     error: ([realLower, realUpper], [estLower, estUpper]) => {
       return Math.max(vec.squareDist(realLower, estLower), vec.squareDist(realUpper, estUpper))
     },
-    midpoint: ([aLower, aUpper], [bLower, bUpper]) => {
-      return [vec.midpoint(aLower, bLower), vec.midpoint(aUpper, bUpper)]
+    lerp: ([aLower, aUpper], [bLower, bUpper], t) => {
+      return [vec.lerp(aLower, bLower, t), vec.lerp(aUpper, bUpper, t)]
     },
     onPoint: (x, [[, lower], [, upper]]) => {
       // TODO: these inequality operators should reflect the props, perhaps