long[] dataset = Longs.toArray(SIXTEEN_SQUARES_LONGS);
    assertThat(Quantiles.scale(10).index(1).compute(dataset))
        .isWithin(ALLOWED_ERROR)
        .of(SIXTEEN_SQUARES_DECILE_1);
    assertThat(dataset).asList().isEqualTo(SIXTEEN_SQUARES_LONGS);
  }

  public void testScale_index_compute_intVarargs() {
    int[] dataset = Ints.toArray(SIXTEEN_SQUARES_INTEGERS);

    long[] dataset = Longs.toArray(SIXTEEN_SQUARES_LONGS);
    assertThat(Quantiles.scale(10).index(1).compute(dataset))
        .isWithin(ALLOWED_ERROR)
        .of(SIXTEEN_SQUARES_DECILE_1);
    assertThat(dataset).asList().isEqualTo(SIXTEEN_SQUARES_LONGS);
  }

  public void testScale_index_compute_intVarargs() {
    int[] dataset = Ints.toArray(SIXTEEN_SQUARES_INTEGERS);

   *
   * <p>This is guaranteed to return zero if the dataset contains only exactly one finite value. It
   * is not guaranteed to return zero when the dataset consists of the same value multiple times,
   * due to numerical errors. However, it is guaranteed never to return a negative result.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

      for (int j = 0; j < datasetSize; j++) {
        datasets[i][j] = rng.nextDouble();
      }
    }
  }

  private double[] dataset(int i) {
    // We must test on a fresh clone of the dataset each time. Doing sorts and quickselects on a
    // dataset which is already sorted or partially sorted is cheating.
    return datasets[i & 0xFF].clone();
  }

  @Benchmark
  double median(int reps) {

   *
   * <p>This is guaranteed to return zero if the dataset contains only exactly one finite value. It
   * is not guaranteed to return zero when the dataset consists of the same value multiple times,
   * due to numerical errors. However, it is guaranteed never to return a negative result.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

   *
   * <p>This is guaranteed to return zero if the dataset contains a single pair of finite values. It
   * is not guaranteed to return zero when the dataset consists of the same pair of values multiple
   * times, due to numerical errors.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

   *
   * <p>This is guaranteed to return zero if the dataset contains a single pair of finite values. It
   * is not guaranteed to return zero when the dataset consists of the same pair of values multiple
   * times, due to numerical errors.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

     *
     * @param dataset the dataset to do the calculation on, which must be non-empty, and which will
     *     be arbitrarily reordered by this method call
     * @return the quantile value
     */
    public double computeInPlace(double... dataset) {
      checkArgument(dataset.length > 0, "Cannot calculate quantiles of an empty dataset");
      if (containsNaN(dataset)) {

* **WebSocket**-Unterstützung.
* Hintergrundaufgaben im selben Prozess.
* Ereignisse beim Starten und Herunterfahren.
* Testclient baut auf HTTPX auf.
* **CORS**, GZip, statische Dateien, Responses streamen.
* **Sitzungs- und Cookie**-Unterstützung.
* 100 % Testabdeckung.
* 100 % Typen annotierte Codebasis.

## Pydantic's Merkmale

pkg syscall (netbsd-arm64-cgo), type BpfHdr struct
pkg syscall (netbsd-arm64-cgo), type BpfHdr struct, Caplen uint32
pkg syscall (netbsd-arm64-cgo), type BpfHdr struct, Datalen uint32
pkg syscall (netbsd-arm64-cgo), type BpfHdr struct, Hdrlen uint16
pkg syscall (netbsd-arm64-cgo), type BpfHdr struct, Pad_cgo_0 [6]uint8
pkg syscall (netbsd-arm64-cgo), type BpfHdr struct, Tstamp BpfTimeval