用同步的方式执行jQuery异步动画

在编写jQuery动画时，通过回调函数进行动画队列的编排，但当回调过多，往往会出现这样的代码：

 $(".box1").fadeIn(1000,function(){
    $(".box2").fadeIn(1000,function(){
       $(".box3").hide(1000,function(){
           // todo xxx
        })
     })
 })

那么是否可以将这种层次的回调以链式的写法完成呢？

如：f(fn1).f(fn2).f(fn3)... 表示fn1执行完成后fn2，fn2执行完成后执行fn3，...

下面逐步介绍实现方式：

1、确定结构：

为实现链式调用，需要f()的返回值一个包含其本身的对象

function f(n){
   var obj = {};
   obj.f = f;
   return obj;
}

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这样就构造好了链式调用的基本结构。

尝试加入参数：

function f(n){
   console.log(n);
   var obj = {};
   obj.f = function(m){return f(m);};
   return obj;
}

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" alt="" />

与我们预想的结果相同，但如结果所见，f调用()会立即将参数代入执行。现在增加一个需求，希望f().f().f()...仅表示一个序列，真正的执行需要我下达其他命令后才发生。于是我们需要在f()的obj中加入一个函数run()，此函数用来下达执行指令，修改代码如下：

 function f(n){
    var obj = {};
    obj.f = function(m){return f(m);};
    obj.run = function(){
        console.log(n)
    }
    return obj;
 }

aaarticlea/png;base64,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" alt="" />

可以看到，在我们原先的代码并没有输出结果，而当我们执行run()后会怎样呢？

aaarticlea/png;base64,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" alt="" />

可以看到仅输出了第三次结果，这是因为run()并没有保存前两次f()参数的值，我们期望的将三次f()一并执行，实际上仅仅执行了第三次。那么如何解决这个问题呢，换句话说：如何在run()中保存所有之前获得的f()的参数呢？

2、保存参数：

    我们知道，函数通过参数获得函数外部变量的值，这里我们就通过这种方式保存f()的所有参数，代码修改如下：

 function f(n,fn){
     var obj = {};
     obj.arr = fn===undefined?[]:[fn];
     obj.arr.push(n);
     obj.f = function(m){return f(m,n);};
     obj.run = function(){
         $.each(obj.arr,function(i,fn){
             fn();
          });
     }
     return obj;
  }

f()中添加obj.arr数组，用来保存所有传入的参数，而run()统一执行所有传入的参数，f()的第二个参数为上一次传入的参数，这样上一次执行所需的参数就被保存了下来。我们将传入的参数换成函数，用如下代码测试：

aaarticlea/png;base64,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" alt="" />

可以看到这正是我们想要的结果。

aaarticlea/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeUAAABOCAIAAACR/pmsAAAGVUlEQVR4nO3dz4rkRBzA8XqjvIE+gAfxAWpBPQjKgmcvsoLSdRbmCebgsVhYz8ssHkSUQSPDsDAMOy6iMgd3dsW/Ux6SrlSq0vnTXZnpX+b7YQ5NTzpVyS/5dVUlXVEOACCBuu0KAABGIV8DgAxxvr6+dt8//TNzIdZpm3mVuzPalc6ZwnVWrTROFa686UrlV21mzLrCtBcr2u8kC0xAuG8P4a70h1uuOF8/+/nvdz756cl3L7dbXWmcUk7p1ptaDyywE7vNmVYaZ9af0V2VsWbGs9cUTqkpR5J1Sm1zRoWb6ZwzZuO/ov92LtC5fsI96BbCXbpCtfY84V6GjvGQR09+f/vj8+PTbVJ2tXfCr7XStI7UdIGpbI5vTNNuXKRHanc7JZ9ot4xhpp/AzUdsx4kUrTDd5MESCfdINxxuq+virG42lnAvQEe+vnr170efP3v3wdPz539MXV0act2bI7aw+xdm3JQonU5qtYATOG0xRbvOn9X1+pNNjhYYUyXCvakONxluU9SvwzcJ9wJ0X288PX/11oc/3v/s9NfLv8auqWrBKadUMJ4V7qxogdIVVSfR1r1Fq11hnG6vQYfdurSIpOdodb2AKesiTNVHC3pV6XGZHiXhkafbXcvBIupeoa9Y0jl1acMkWSBlNm1mWES7nv2bGR3Z6ZhAXx+ZcO99uF07KRPuBdiQr8+u3nj/+L0H5S+X0649xl+wyZdbNITqe23VC13FeP0p35sLY9B3sgWXTXThSudKU78TriGOaFJJq5tDKq7DiCLijue6e1iaZrXhCew/2D+G2L+ZddF6woEbLVCaOINMbnAR7n0Kt29ldy5wZ8MtWke+fvHynw8+Ld+8/+3ZxdXU1U3qMaUR9f+tXnT2Unsimvb+fBHhCROfJ70DmlEdRhVhW62hpsLBoRPWp7oeFTdeEj2b6RtcRXv39o+H7D6gSbirFe5huKNk7Qj3InTk6y++vHjt3lff/HC5xerSiMajZl1HmFbdEW2ulgQ7vYpoeF1iTEuk1SFtf+V2dpf8wRTXYWQRQcXGN7hc6ey2DS6VnMBpyyK6lB+fz5tT1XCVfNUI9x6EO7xRxNj2O8kmxx/ZbDHhlivO12cXL16/9/jR4+eT1xSMqXV3w9MF1g0TrZ3S9eCUKZsXnUOBVfMkHOEKB7DSES6lgxe+suuDadO17OZoTuowWISOWk/JGvwCzfEXboWN96E/P/370YBm2AYJm2zNSRjtqK77bVsn8OAdu4R7j8PtS6w/QriXIs7X/127r49/y1zIHt9Rv4msH1CkZ7g38s6H+PzcBeGeGeEeNPcNP7eF36MDgAzkawCQgXwNADKQrwFABvI1AMiQP1/LutTe4xamphxUXYuftQgA+yp/vl7a1JT+NlKb/MvXymz4bG7+5v/5igCwt/Ln6wVMdRZ+pJn9IOg07P7T3i1EGz5HEQD2Gfm6u4i09dozp8wWU1NuoX+2JgCLN0O+XujUlD2Tb0yamrLaRm3rmttxM0+mk4EwJALcNZnz9TKnprTxVGq7Tk3ZnjJicOZJ5zryNe1r4K6ZcTxkMVNT+uWzPVrJxtP09M88Wcn+LA8AssyYr5cxNWVahwxTUyb5un/myfpDutUkZzAEuGvmvN64iKkpm0kd108t2nFqSl+BZghlcObJNb25DgAW767/Xma/pqYctJdzVwK4GfweHQBkIF8DgAzkawCQgXwNADKQrwFABnUCAJCA9jUAyEC+BgAZyNcAIAP5GgBkIF8DgAzkawCQIWe+tnql1EqpA6b6BIDs8uXr8qE2J9WLQh9lWy0AwDmXM1/bQ1Wn6SNNvgaA3GYYv7aHzNEMANnlztdNKxsAkFPWfE2yBoDZZMzXzbC1NQ+5QwQA8sqWr0tzoNSq/qOVDQC58XsZAJCBfA0AMpCvAUAG8jUAyEC+BgAZeH4jAMhA+xoAZCBfA4AM5GsAkIF8DQAykK8BQAbyNQDIkDFfn5hipdRKFUzOBwD55X9+Y2kOeL4MAGQ3w3iIf/AuACCf/Pm6NIeGAREAyC1vvj4xBePXADCLWcZDCp4vAwC55cvX9rCoh62PNE1sAMhthvv51AHj1wCQHb+XAQAZyNcAIAP5GgBkIF8DgAzkawCQgXwNADLwvF0AkIH2NQDIQL4GABnI1wAgw/9uU1ZNxWOvZwAAAABJRU5ErkJggg==" alt="" />

再次向后追加，希望输出1 2 3，但实际只输出后两次结果，原因在于f()的第二个参数仅保存了上一次传入的参数，而不是前几次的

aaarticlea/png;base64,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" alt="" />

f()的第二个参数为数组，用来保存前几次已传入的参数，修改代码如下：

function f(n,ar){
   var obj = {};
   obj.arr = ar===undefined?[]:ar;
   obj.arr.push(n);
   obj.f = function(m){return f(m,obj.arr);};
   obj.run = function(){
      $.each(obj.arr,function(i,fn){
         fn();
      });
  }
 return obj;
}

aaarticlea/png;base64,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" alt="" />

再次执行之前的代码，已符合预期。

3、执行回调

f(
    function () {
       $(".box1").fadeIn(1000);
    }
  ).f(
     function () {
        $(".box2").fadeIn(1000);
    }
 ).run();

执行上述代码，我们希望看到的是box1用1秒淡入，这个动画执行完后，box2开始执行1秒淡入，但实际上，这两个动画几乎是同时执行，所以接下来的关键是如何执行回调。修改测试代码如下：

f(
    function (cb1) {
       $(".box1").fadeIn(1000,cb1);
    }
  ).f(
     function (cb2) {
        $(".box2").fadeIn(1000,cb2);
    }
 ).run();

我们看到，cb1和cb2分别为box1和box2执行动画的回调函数，也就是说，我们希望达到这样的效果：在cb1中，执行box2的动画，在cb2中执行后面的动画（如果有），即：需要在cb中下达执行下一个f()中函数的指令。而run()仅仅是控制f()当前携带函数的执行，修改代码如下：

function f(n,ar){
   var obj = {};
   obj.arr = ar===undefined?[]:ar;
   obj.arr.push(n);
   obj.f = function(m){return f(m,obj.arr);};
   obj.run = function(){
     var fn = obj.arr.shift();
     if(typeof fn === "function") fn(callback);
   }
   function callback(){
      obj.run();
   }
   return obj;
}

f(
    function (cb) {
       $(".box1").fadeIn(1000,cb);
    }
  ).f(
     function (cb) {
        $(".box2").fadeIn(1000,cb);
    }
 ).run();

再次执行以上代码进行测试，实现box1执行完成后执行box2，目标实现