tegoryAxes\":[{\"id\":\"CategoryAxis-1\",\"type\":\"category\",\"position\":\"bottom\",\"show\":true,\"style\":{},\"scale\":{\"type\":\"linear\"},\"labels\":{\"show\":true,\"truncate\":100},\"title\":{}}],\"valueAxes\":[{\"id\":\"ValueAxis-1\",\"name\":\"LeftAxis-1\",\"type\":\"value\",\"position\":\"left\",\"show\":true,\"style\":{},\"scale\":{\"type\":\"linear\",\"mode\":\"normal\"},\"labels\":{\"show\":true,\"rotate\":0,\"filter\":false,\"truncate\":100},\"title\":{\"text\":\"Query Count\"}}]...

            "barAlignment": 0,
            "drawStyle": "line",
            "fillOpacity": 10,
            "gradientMode": "none",
            "hideFrom": {
              "legend": false,
              "tooltip": false,
              "viz": false
            },
            "insertNulls": false,
            "lineInterpolation": "linear",
            "lineWidth": 1,
            "pointSize": 5,

    }
    return this;
  }

  /**
   * Adds each member of {@code elements} as a candidate for the top {@code k} elements. This
   * operation takes amortized linear time in the length of {@code elements}.
   *
   * <p>If all input data to this {@code TopKSelector} is in a single {@code Iterable}, prefer

    return ensureInUnitRange(sumOfProductsOfDeltas / Math.sqrt(productOfSumsOfSquaresOfDeltas));
  }

  /**
   * Returns a linear transformation giving the best fit to the data according to <a
   * href="http://mathworld.wolfram.com/LeastSquaresFitting.html">Ordinary Least Squares linear
   * regression</a> of {@code y} as a function of {@code x}. The count must be greater than one, and

    return ensureInUnitRange(sumOfProductsOfDeltas / Math.sqrt(productOfSumsOfSquaresOfDeltas));
  }

  /**
   * Returns a linear transformation giving the best fit to the data according to <a
   * href="http://mathworld.wolfram.com/LeastSquaresFitting.html">Ordinary Least Squares linear
   * regression</a> of {@code y} as a function of {@code x}. The count must be greater than one, and

    checkNotNull(presentBehavior);
    checkNotNull(absentBehavior);
    if (!(list instanceof RandomAccess)) {
      list = new ArrayList<>(list);
    }
    // TODO(lowasser): benchmark when it's best to do a linear search

    int lower = 0;
    int upper = list.size() - 1;

    while (lower <= upper) {
      int middle = (lower + upper) >>> 1;
      int c = comparator.compare(key, list.get(middle));

    checkNotNull(presentBehavior);
    checkNotNull(absentBehavior);
    if (!(list instanceof RandomAccess)) {
      list = new ArrayList<>(list);
    }
    // TODO(lowasser): benchmark when it's best to do a linear search

    int lower = 0;
    int upper = list.size() - 1;

    while (lower <= upper) {
      int middle = (lower + upper) >>> 1;
      int c = comparator.compare(key, list.get(middle));

				[this, 6],
			]);
			this._idPool = [];   // unused ids that have been garbage collected
			this.exited = false; // whether the Go program has exited

			// Pass command line arguments and environment variables to WebAssembly by writing them to the linear memory.
			let offset = 4096;

			const strPtr = (str) => {
				const ptr = offset;
				const bytes = encoder.encode(str + "\0");

 * described by <a
 * href="http://en.wikipedia.org/wiki/Quantile#Estimating_the_quantiles_of_a_population">
 * wikipedia</a> as providing "Linear interpolation of the modes for the order statistics for the
 * uniform distribution on [0,1]."
 *
 * <h3>Handling of non-finite values</h3>
 *

     *
     * In theory, we can quickly determine the size of any Collection. However, thanks to
     * regrettable implementations like our own Sets.filter, Collection.size() is sometimes a
     * linear-time operation, and it can even have side effects. Thus, we limit the special case to
     * List, which is _even more likely_ to have size() implemented to be fast and side-effect-free.
     *