/usr/share/gocode/src/github.com/alecthomas/chroma/lexers/testdata/dart.actual is in golang-github-alecthomas-chroma-dev 0.4.0+git20180402.51d250f-1.
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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 | import 'dart:async';
import 'dart:html';
import 'dart:math' show Random;
// We changed 5 lines of code to make this sample nicer on
// the web (so that the execution waits for animation frame,
// the number gets updated in the DOM, and the program ends
// after 500 iterations).
main() async {
print('Compute π using the Monte Carlo method.');
var output = querySelector("#output");
await for (var estimate in computePi().take(500)) {
print('π ≅ $estimate');
output.text = estimate.toStringAsFixed(5);
await window.animationFrame;
}
}
/// Generates a stream of increasingly accurate estimates of π.
Stream<double> computePi({int batch: 100000}) async* {
var total = 0;
var count = 0;
while (true) {
var points = generateRandom().take(batch);
var inside = points.where((p) => p.isInsideUnitCircle);
total += batch;
count += inside.length;
var ratio = count / total;
// Area of a circle is A = π⋅r², therefore π = A/r².
// So, when given random points with x ∈ <0,1>,
// y ∈ <0,1>, the ratio of those inside a unit circle
// should approach π / 4. Therefore, the value of π
// should be:
yield ratio * 4;
}
}
Iterable<Point> generateRandom([int seed]) sync* {
final random = new Random(seed);
while (true) {
yield new Point(random.nextDouble(), random.nextDouble());
}
}
class Point {
final double x, y;
const Point(this.x, this.y);
bool get isInsideUnitCircle => x * x + y * y <= 1;
}
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