i-zxl / articles Goto Github PK

PUT /_cluster/settings
{
    "transient" : {
        "cluster.routing.allocation.enable" : "none"
    }
}

ps aux | grep elasticsearch  // 查询es进程
kill -9 esPid

cd es/path
./elasticsearch -d

PUT /_cluster/settings
{
    "transient" : {
        "cluster.routing.allocation.enable" : "all"
    }
}

// 父级元素
position: relative;
width: 100%;
height: 100%;
border-radius: 40px;
box-shadow: 0 0 64px 0 rgba(255,195,90,0.50);

// 子元素动画

 0% {
    -webkit-transform: rotate3d(0, 1, 0, 90deg);
    transform: rotate3d(0, 1, 0, 90deg);
    -webkit-animation-timing-function: ease-in;
    animation-timing-function: ease-in;
  }

  40% {
    -webkit-transform: rotate3d(0, 1, 0, -20deg);
    transform: rotate3d(0, 1, 0, -20deg);
    -webkit-animation-timing-function: ease-in;
    animation-timing-function: ease-in;
  }

  60% {
    -webkit-transform: rotate3d(0, 1, 0, 10deg);
    transform:  rotate3d(0, 1, 0, 10deg);
  }

  80% {
    -webkit-transform:  rotate3d(0, 1, 0, -5deg);
    transform: rotate3d(0, 1, 0, -5deg);
  }

  100% {
    -webkit-transform:  rotate3d(0, 1, 0, 0deg);
    transform: rotate3d(0, 1, 0, 0deg);
  }

/**
 * 
 * 高斯模糊模块
 */

/**
 * -------------------------------------------
 * @param { ImageData } imgData 图片数据
 * @param { Number }    radius  模糊半径
 * @param { Number }    sigma   权重参数
 * @return { ImageData }
 */
export const gaussBlur = function(imgData, radius, sigma) {
  const pixes = imgData.data;
  const width = imgData.width;
  const height = imgData.height;
  const gaussMatrix = [];
  let gaussSum = 0;
  let x;
  let y;
  let r;
  let g;
  let b;
  let a;
  let i;
  let j;
  let k;

  radius = Math.floor(radius) || 3;
  sigma = sigma || radius / 3;

  a = 1 / (Math.sqrt(2 * Math.PI) * sigma);
  b = -1 / (2 * sigma * sigma);

  // 生成高斯矩阵
  for (i = 0, x = -radius; x <= radius; x++, i++) {
    g = a * Math.exp(b * x * x);
    gaussMatrix[i] = g;
    gaussSum += g;
  }

  // x 方向一维高斯运算
  for (y = 0; y < height; y++) {
    for (x = 0; x < width; x++) {
      r = g = b = a = 0;
      gaussSum = 0;
      for (j = -radius; j <= radius; j++) {
        k = x + j;
        if (k >= 0 && k < width) {
          // 确保 k 没超出 x 的范围
          // r,g,b,a 四个一组
          i = (y * width + k) * 4;
          r += pixes[i] * gaussMatrix[j + radius];
          g += pixes[i + 1] * gaussMatrix[j + radius];
          b += pixes[i + 2] * gaussMatrix[j + radius];
          // 计算权和
          gaussSum += gaussMatrix[j + radius];
        }
      }
      i = (y * width + x) * 4;
      // 计算加权均值
      pixes[i] = r / gaussSum;
      pixes[i + 1] = g / gaussSum;
      pixes[i + 2] = b / gaussSum;
    }
  }
  // y 方向一维高斯运算
  for (x = 0; x < width; x++) {
    for (y = 0; y < height; y++) {
      r = g = b = a = 0;
      gaussSum = 0;
      for (j = -radius; j <= radius; j++) {
        k = y + j;
        if (k >= 0 && k < height) {
          // 确保 k 没超出 y 的范围
          i = (k * width + x) * 4;
          r += pixes[i] * gaussMatrix[j + radius];
          g += pixes[i + 1] * gaussMatrix[j + radius];
          b += pixes[i + 2] * gaussMatrix[j + radius];
          gaussSum += gaussMatrix[j + radius];
        }
      }
      i = (y * width + x) * 4;
      pixes[i] = r / gaussSum;
      pixes[i + 1] = g / gaussSum;
      pixes[i + 2] = b / gaussSum;
    }
  }
  return imgData;
};

/**
 * 对远程图片进行高斯模糊，并输出为指定尺寸
 * @deprecated
 * 新的API会在 utils/blur 中提供
 * ------------------------------------
 * @param { String }    URL 图片URL
 * @param { Number }    w   输出宽度
 * @param { Number }    h   输出高度
 * @param { Number }    r   模糊半径
 * @return {Promise<String>}  返回图片的 base64 URL
 */
export const blurG = (URL, w = 16, h = 16, r = 12) => {
  return new Promise((resolve, reject) => {
    const IMG = new Image();
    IMG.crossOrigin = '*';
    IMG.onload = function() {
      const Canvas = document.createElement('CANVAS');
      try {
        Canvas.width = w;
        Canvas.height = h;
        const ctx = Canvas.getContext('2d');
        ctx.fillStyle = '#fff';
        ctx.fillRect(0, 0, w, h);
        ctx.drawImage(IMG, 0, 0, w, h);

        const d = ctx.getImageData(0, 0, w, h);
        const gd = gaussBlur(d, r, 0);
        ctx.putImageData(gd, 0, 0);
        resolve(Canvas.toDataURL());
      } catch (e) {
        reject({});
      }
    };
    IMG.src = URL;
  });
};

const fb = (n) => {
  if (n<=2) {
     return 1;
  } 
  return fb(n-1)+fb(n-2);
}

function f (n) {
  let sum = 1
      n1 = 1,
      n2 = 1;
  for(let i = 3; i <= n; i++) {
    sum = n1 + n2;
    n1 = n2;
    n2 = sum;
  }
  return sum
}

const f = (n) => {
  let n1 = 1, n2 = 1;
  for(let i = 3; i <=n; i++) {
    [n1, n2] = [n2, n1+n2]
  }
  return n2
}

const fb = (function () {
  var memoization = {};
  return function (n) {
    if(n==1 || n==2) {
      return n;
    }
    if (!memoization[n-1]) {
      memoization[n-1] = fb(n-1)
    }
    if (!memoization[n-2]) {
      memoization[n-2] = fb(n-1)
    }
    return memoization[n] = memoization[n-1] + memoization[n-2];
 }
})()

const fb = (n, first=1, second=1) => {
  if (n<=2) {
    return second;
  }
  return fb(n-1, second, second + first)
}