}
		worst := mmuCurve.Examples(test.window, 2)
		// Which exact windows are returned is unspecified
		// (and depends on the exact banding), so we just
		// check that we got the right number with the right
		// utilizations.
		if len(worst) != len(test.worst) {
			t.Errorf("for %s window, want worst %v, got %v", test.window, test.worst, worst)
		} else {
			for i := range worst {

package main

func main() {
	pv := []int{3, 4, 5}
	if pv[1] != 9 {
		pv = append(pv, 9)
	}
	tryit := func() bool {
		lpv := len(pv)
		if lpv == 101 {
			return false
		}
		if worst := pv[pv[1]&1]; worst != 101 {
			return true
		}
		return false
	}()
	if tryit {
		println(pv[0])
	}

         })
         .done(function(worst) {
            details.text('Lowest mutator utilization in ' + niceDuration(windowNS) + ' windows:');
            for (var i = 0; i < worst.length; i++) {
              details.append($('<br>'));
              var text = worst[i].MutatorUtil.toFixed(3) + ' at time ' + niceDuration(worst[i].Time);
              details.append($('<a/>').text(text).attr('href', worst[i].URL));
            }
         });

		acc.mmu = mu
	}
	acc.bound = acc.mmu

	if acc.nWorst == 0 {
		// If the minimum has reached zero, it can't go any
		// lower, so we can stop early.
		return mu == 0
	}

	// Consider adding this window to the n worst.
	if len(acc.wHeap) < acc.nWorst || mu < acc.wHeap[0].MutatorUtil {
		// This window is lower than the K'th worst window.
		//
		// Check if there's any overlapping window

 * offering expected O(n + k log k) performance (worst case O(n log k)) for n calls to {@link
 * #offer} and a call to {@link #topK}, with O(k) memory. In comparison, quickselect has the same
 * asymptotics but requires O(n) memory, and a {@code PriorityQueue} implementation takes O(n log
 * k). In benchmarks, this implementation performs at least as well as either implementation, and
 * degrades more gracefully for worst-case input.
 *

    assertEquals("Correct string should be delivered.", EVENT, events.get(0));
  }

  /**
   * An {@link Executor} wanna-be that simply records the tasks it's given. Arguably the Worst
   * Executor Ever.
   *
   * @author cbiffle
   */
  public static class FakeExecutor implements Executor {
    List<Runnable> tasks = Lists.newArrayList();

    @Override

	Error   *apiservercel.Error
	// If true, the compiled expression contains a reference to the identifier "oldSelf".
	UsesOldSelf bool
	// Represents the worst-case cost of the compiled expression in terms of CEL's cost units, as used by cel.EstimateCost.
	MaxCost uint64
	// MaxCardinality represents the worse case number of times this validation rule could be invoked if contained under an
	// unbounded map or list in an OpenAPIv3 schema.
	MaxCardinality uint64

 * offering expected O(n + k log k) performance (worst case O(n log k)) for n calls to {@link
 * #offer} and a call to {@link #topK}, with O(k) memory. In comparison, quickselect has the same
 * asymptotics but requires O(n) memory, and a {@code PriorityQueue} implementation takes O(n log
 * k). In benchmarks, this implementation performs at least as well as either implementation, and
 * degrades more gracefully for worst-case input.
 *

		default:
			// This may happen as new metrics get added.
			//
			// The safest thing to do here is to simply log it somewhere
			// as something to look into, but ignore it for now.
			// In the worst case, you might temporarily miss out on a new metric.
			fmt.Printf("%s: unexpected metric Kind: %v\n", name, value.Kind())
		}
	}
}

func medianBucket(h *metrics.Float64Histogram) float64 {
	total := uint64(0)

	constValue.reset(OpInvalid)
	return lattice{bottom, nil}
}

func (t *worklist) visitValue(val *Value) {
	if !possibleConst(val) {
		// fast fail for always worst Values, i.e. there is no lowering happen
		// on them, their lattices must be initially worse Bottom.
		return
	}

	oldLt := t.getLatticeCell(val)
	defer func() {
		// re-visit all uses of value if its lattice is changed
		newLt := t.getLatticeCell(val)