七、逻辑运算
7.1 布尔型和比较运算
布尔型(boolean)只有两个可选值:true(真) 和 false(假)
Lua 把 false 和 nil 看作是false,其他的都为true(包括0这个值,也是相当于true)
Lua 中也有许多的关系运算符,用于比较大小或比较是否相等,符号及其含义如下表:
![](data:image/png;base64,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**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**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**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**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***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)
我们可以通过以下实例来更加透彻的理解关系运算符的应用:
![](data:image/png;base64,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**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)
下面问题来了,运行以下代码,将会输出什么结果?请自行思考
![](data:image/png;base64,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)
7.2 逻辑运算符
逻辑运算符基于布尔型的值来进行计算,并给出结果,下表列出了 Lua 语言中的常用逻辑运算符:
![](data:image/png;base64,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**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)
我们可以通过以下实例来更加透彻的理解逻辑运算符的应用:
![](data:image/png;base64,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)
下面问题来了,运行以下代码,将会输出什么结果?
![](data:image/png;base64,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)
7.3 检验大小(自测题)
题目:如果已知number变量n,那么如果需要判断n是否符合下面的条件:
3<n≤10</n≤10
以下四行判断代码,正确的是?
(返回true即表示变量n符合要求)
![](data:image/png;base64,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)
八、分支判断
8.1 条件判断
上面一节学习了布尔类型,那么这个需要用到哪里呢?我们需要用它来进行某些判断。
在Lua中,可以使用if语句来进行判断,如下面所举例的代码,可以判断n是否为小于10的数:
![](data:image/png;base64,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)
我们整理一下,实际上if语句就是如下结构:
![](data:image/png;base64,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***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)
下面是你需要完成的事:
已知变量n,请判断n是否为奇数,如果是,请给n的值加上1
如果你觉得有难度,请查看下面的提示:
求出n除以2的余数:n % 2
给n的值加上1:n = n + 1
![](data:image/png;base64,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**//r3Ssx8ANTzUel2/6q9zUr9nbtaMcqxbrV311/W1fu/8XP8rexxds47Ver3erfcw1uXV19X2PfvXdWfr9+xf59b6uFZvn72+eZ/zh82z9qwZuIaBtw+p9R/PB+WsL2PfXdZrf8K3qM8MDI/sNdZkXco847RTpv+ovGfu2CfzU9a+o+tkrK5LXy3reK3XOaM+xvorc47WZU72qO1aP7tHXdPrR3v0sdqu9bFnb/fraF/jj5Pn6DkywMDbh1SIfx5xDwmjfet15jn1fY/W5Hp9TvZImfHeTn/K7HdrXuaPss+te6zqR+vrWK3XvWp/rc/eS8b7WPp7OZt35tp9n94+s0e9dubXfdKXso6p//xngHvunjPAwE8YEFKf+I/7f+KBuYYPBgYYYIABBhj4BANCqpD68L+T+gm/IH5GfwgYYIABBhh4jQEhVUgVUhlggAEGGGCAge0MCKlQbofSifU1J1b33X1ngAEGGNjJgJAqpAqpDDDAAAMMMMDAdgaEVCi3Q7nTKc578a0CAwwwwAADrzEgpAqpQioDDDDAAAMMMLCdASEVyu1QOrG+5sTqvrvvDDDAAAM7GRBShVQhlQEGGGCAAQYY2M6AkArldih3OsV5L75VYIABBhhg4DUGhFQhVUhlgAEGGGCAAQa2MyCkQrkdSifW15xY3Xf3nQEGGGBgJwNCqpAqpDLAAAMMMMAAA9sZEFKh3A7lTqc478W3CgwwwAADDLzGgJAqpAqpDDDAAAMMMMDAdgaEVCi3Q+nE+poTq/vuvjPAAAMM7GRASBVShVQGGGCAAQYYYGA7A0IqlNuh3OkU5734VoEBBhhggIHXGBBShVQhlQEGGGCAAQYY2M6AkArldiidWF9zYnXf3XcGGGCAgZ0MCKlCqpDKAAMMMMAAAwxsZ0BIhXI7lDud4rwX3yowwAADDDDwGgNCqpAqpDLAAAMMMMAAA9sZEFKh3A6lE+trTqzuu/vOAAMMMLCTASFVSBVSGWCAAQYYYICB7QwIqVBuh3KnU5z34lsFBhhggAEGXmNASBVShVQGGGCAAQYYYGA7A0IqlNuhdGJ9zYnVfXffGWCAAQZ2MiCkCqlCKgMMMMAAAwwwsJ0BIRXK7VDudIrzXnyrwAADDDDAwGsMCKlCqpDKAAMMMMAAAwxsZ0BIhXI7lE6srzmxuu/uOwMMMMDATgaEVCFVSGWAAQYYYIABBrYzIKRCuR3KnU5x3otvFRhggAEGGHiNASH1Q0LqP//pTw+H0VtrvzI+1h6tPxobHxq3xvPBMps368v8Xs7mzvr6Ou3XfLC57+47Awww8P4GhNQXh9Q//vhjGh4TgFJ+9Zft3n3q/FU976mOz/r6+GjnlflHZV+fuav+jNdyNnfWV9fU+mzurK+uqfXMTVnHvlJf+fnKnta+/we7Z+gZMsDAFQwIqSWk/vr1+/d4/dSDXQWMBJmU976fse7e1+wauX4v+9yM1/5cf/RlvJd1fq9n/T1l3yPtXDftlLO9M1bLrJ/N7311Xep1ffqeWa4cPfMa9vIHjwEGGGDgpw0IqS8KqUfB4tmhJvs9imu2vvbVeq6RvlHm1cfS/s4y167luN5o9+vO+lZzj/pX+6727/MfaR95emQ/a/wxYoABBhh4tQEhtYTUZz2MM9/GrkJFgkzKr76n2T6zvn6dMWf1GnPrHrWefWpfrfe1mT8rx7rZq8/t+9fxPpb96pzU+9zR3+enPSuzTy2zZ8o69sz6ytMzr2Evf7AYYIABBn7SgJD6gpD6k4FiFo5mfUfoZvNrX61nn9E3e43xWf9qj+yXMvNSZr+M97LOe3Ru3aPW67VW/XXOd9d/0tV3/yz294eQAQYYYODtQ2r990hTr99k9r7ezi/Bqn+MZ6zWR1/Wpsy8Wmaslo+GiRGEEoZST7vun3rmpJ3yaE3m1HI2v/bVetbVvlof47Vd61mbcjaWvpR9v6xNWef1uUdjWT9bM9bNXnXNs+rDSryknna/xqq/z9P2wc8AAwww8A4G3j6kjpucUFhveA+RfU4fz9qj/j7W27f2yPhXwkTCUfYaZQ9btS9jWTcr616zevaoY7Wv1jMnfaPMq4+NduZlrJZZ18u+7t49+jXSXu1T+2s96/r7qf3PqCec1r1mhmZ9dY26P0oMMMAAA+9k4DIhtd/0HiB7u89PezVv1j/rG/us+nONr4SJWUjqfbVd67n+rC9joxzjR6/MyZrZfllf52ZeyjqWvWpZ56U/fSnv3aOu62v7WL9m5o95s1fmP7uceTnb9+z3Yj9/4BhggAEGfsqAkNr+ndRVwJz1z/rGg1v156HOAkbGbpWzIDXryz6zsVlf5vdyNbf213rW177Ueznmpi/rajkbS1/Ke/eo6+q1jvapa2q9rl/11zmP1mdezvY9ek3r/BFigAEGGHi1ASH1BSF1PPRZyDiDYRaGZn3ZazY268v8Xp6ZO5tT+2p97F/btT679hjvr3v3qPveul6dm3pd099LbWf+s8uZld7X289+D/bzx4oBBhhg4KcNXCak9m8vb7VXN7qvy7xZ/6xvzK/9tZ69RvloqKiBKfvN+o7GjuZn3Si/Mq+urfW+bx+7df3Z/Flf9uljvZ15/X2t+lfrV/11n1l9OLhlYTbe+3p7di19/sAwwAADDLyTgcuE1HHTRyDMKw8h7VpmrJZ1PPWMpz3Ko76MjTJral+v3xssRhDKK3ulvQpJdfyonv1SrvbLeC1nc2d9Y03v7+1H9r21xxivr3qNWl/tU/tr/czaOmdVP3IwxvLK+rSzLmXGlf4IMcAAAwxcwcClQuo7PpDvDhirUHV0r86uGfPy6vulv5ezeb0v7bG21rPXUV/GUtY9Rl9vp2/W3/fI9Y/KrLmn/IqBr6y95z2a6w8eAwwwwMBPG3j7kJpvLOu3nD99E13PL+6jBoRMdh61Yx07DDBwdQNvH1Kv/oD8fD6EGGCAAQYYYOATDQip7b/d/4kI/Mw+/BhggAEGGGBgNwNCqpD63//e5244vR8fmAwwwAADDHyuASFVSBVSGWCAAQYYYICB7QwIqVBuh9Kp+XNPzZ69Z88AAwwwEANCqpAqpDLAAAMMMMAAA9sZEFKh3A5lTlBKp2kGGGCAAQY+14CQKqQKqQwwwAADDDDAwHYGhFQot0Pp1Py5p2bP3rNngAEGGIgBIVVIFVIZYIABBhhggIHtDAipUG6HMicopdM0AwwwwAADn2tASBVShVQGGGCAAQYYYGA7A0IqlNuhdGr+3FOzZ+/ZM8AAAwzEgJAqpAqpDDDAAAMMMMDAdgaEVCi3Q5kTlNJpmgEGGGCAgc81IKQKqUIqAwwwwAADDDCwnQEhFcrtUDo1f+6p2bP37BlggAEGYkBIFVKFVAYYYIABBhhgYDsDQiqU26HMCUrpNM0AAwwwwMDnGhBShVQhlQEGGGCAAQYY2M6AkArldiidmj/31OzZe/YMMMAAAzEgpAqpQioDDDDAAAMMMLCdASEVyu1Q5gSldJpmgAEGGGDgcw0IqUKqkMoAAwwwwAADDGxnQEiFcjuUTs2fe2r27D17BhhggIEYEFKFVCGVAQYYYIABBhjYzoCQCuV2KHOCUjpNM8AAAwww8LkGhFQhVUhlgAEGGGCAAQa2MyCkQrkdSqfmzz01e/aePQMMMMBADAipQqqQygADDDDAAAMMbGdASIVyO5Q5QSmdphlggAEGGPhcA0KqkCqkMsAAAwwwwAAD2xkQUqHcDqVT8+eemj17z54BBhhgIAaEVCFVSGWAAQYYYIABBrYzIKQ+gP**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)
8.2 多条件判断
上面一节学习了简单的if语句写法,这一节我们来学习多条件分支语句
在Lua中,可以使用if语句来进行判断,同时可以使用else语句,表示多个分支判断
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqwAAAF6CAYAAADcTQqfAAAgAElEQVR4Ae3d3a7jOLIm0Hyn/f7v1HVX1Td7wAZiEBWHlORtpTIkrwYMUfyTk1z2/ugpnPn17X9WwApYAStgBayAFbACVqDxCvxq/N68NStgBayAFbACVsAKWAEr8C2wQmAFrIAVsAJWwApYASvQegUE1tbb481ZAStgBayAFbACVsAKCKwMWAErYAWsgBWwAlbACrRegX8F1n/++ef7r7/++v7Pf/7jZQ0YYIABBhhggAEGLjUwcujIo/V//wqso9Pff//9/d///t**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)
举个例子,比如有一个数字n:
当它大于等于0、小于5时,输出太小,
当它大于等于5、小于10时,输出适中,
当它大于等于10时,输出太大,
那么代码就像如下这样:
![](data:image/png;base64,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***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***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***zf35tYIABBhhg4PkGhFPhVDhlgAEGGGCAAQbKGBBOYSyD0W70+btRa2yNGWCAAQaqGxBOhVPhlAEGGGCAAQYYKGNAOIWxDMbqOzn359cGBhhggAEGnm9AOBVOhVMGGGCAAQYYYKCMAeEUxjIY7Uafvxu1xtaYAQYYYKC6AeFUOBVOGWCAAQYYYICBMgaEUxjLYKy+k3N/fm1ggAEGGGDg+QaEU+FUOGWAAQYYYIABBsoYEE5hLIPRbvT5u1FrbI0ZYIABBqob+Ec4/euvv4QVgZUBBhhggAEGGGDg5QZaDv3zzz9/+v99tYqWWP1ZAwYYYIABBhhggIFXGmg59O+//+6z6c/Xb2dOrIAVsAJWwApYAStgBazAG1fg/wABY6aK2cuLGQAAAABJRU5ErkJggg==)
注意:else和elseif都是可选的,可有可无,但是end不能省略
下面是你需要完成的事:
已知变量n,请判断n是否为奇数,
如果是,请给n的值加上1
如果不是,请将n的值改为原来的两倍
![](data:image/png;base64,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**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)
8.3 判断三角形合法性(自测题)
你需要使用前面几章的知识,来完成下面的题目
已知三个number类型的变量a、b、c,分别代表三根木棒的长度
请判断,使用这三根木棒,是否可以组成一个三角形(两短边之和大于第三边)
如果可以组成,就打印出true
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqUAAACCCAYAAAB/7rGVAAAQ5UlEQVR4Ae3dzW7kNhYGUL9TXjAv6QfIrjcNdGcRdPfGA2Zyk4sLUmKV5WJJPgPUkOKfVKxj+2MFk3n5/v3727dv37zsAQMMMMAAAwwwwMBDDbQc+***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)
8.4 if的判断依据(自测题)
我们在前面了解到,Lua 把 false 和 nil 看作是false,其他的都为true(包括0这个值,也是相当于true)
那么问题来了,执行下面的代码,将会输出什么?
![](data:image/png;base64,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**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)
九、函数
9.1 初识函数
函数是指一段在一起的、可以做某一件事儿的程序,也叫做子程序。
在前面的内容中,我们已经接触过了函数的调用,这个函数就是前面用到了很多次的print(...)。
调用函数只需要按下面的格式即可:
函数名(参数1,参数2,参数3,......)
为何要使用函数?因为很多事情都是重复性操作,我们使用函数,可以快速完成这些操作
下面我们举一个最简单的函数例子,这个函数没有传入参数、没有返回值
它实现了一个简单的功能,就是输出Hello world!:
![](https://file1.**/web3/M00/05/54/wKgZO2d-VWyAG_LSAAAWBv8dEgs845.png)
这个函数名为hello,我们可以按下面的方法进行调用(执行):
![](https://file1.**/web3/M00/05/54/wKgZO2d-VZSAUMAbAAAbEv18fIY158.png)
这行代码会输出Hello world!。
同时,在Lua中,函数也是一种变量类型,也就是说,hello实际上也是一个变量,里面存储的是一个函数,我们可以用下面的代码来理解:
![](https://file1.**/web3/M00/05/49/wKgZPGd-VcaAErWjAABCTLOb1SU643.png)
因为函数只是个变量,你甚至在一开始可以这样声明hello函数:
![](https://file1.**/web3/M00/05/54/wKgZO2d-VgaABgYeAAAbqq2V5Oc402.png)
下面你需要做一件简单的事情:
新建一个函数变量biu,使其执行后会打印biubiubiu这个字符串
新建一个函数变量pong,使其与biu指向的函数相同
![](https://file1.**/web3/M00/05/4A/wKgZPGd-VimAbmdlAAArvx8pcM0859.png)
9.2 local变量
之前我们创建的变量,都是全局变量,这种变量在代码运行周期从头到尾,都不会被销毁,而且随处都可调用。
但是当我们代码量增加,很多时候大量新建全局变量会导致内存激增,我们需要一种可以临时使用、并且可以自动销毁释放内存资源的变量,要怎么解决呢?
我们可以使用local标志来新建临时变量,使用local创建一个局部变量,与全局变量不同,局部变量只在被声明的那个代码块内有效。
参考下面的代码:
![](https://file1.**/web3/M00/05/4A/wKgZPGd-Vj-ALCviAAAptIddp-M523.png)
上面的代码中,n就是一个局部变量,它只在这个funcion中有效,并且函数运行完后会自动回收这部分的内存。
我们应该尽可能的使用局部变量,以方便lua虚拟机自动回收内存空间,同时减少资源占用提高运行速度。
下面请阅读以下代码,思考一下,正确的输出结果是什么:
![](https://file1.**/web3/M00/05/54/wKgZO2d-VmCAWmG1AAAgtp9gLfA992.png)
9.3 函数参数
在前几章的使用中,我们知道函数是可以传入参数的,如print(123)
那么,我们如何编写可以传入参数的函数呢?可以按下面的模板来写
![](https://file1.**/web3/M00/05/54/wKgZO2d-Vn6AOh9rAAAYJqPSL3g605.png)
这里传入的参数,等价于在函数内部新建了一个local的变量,修改这些数据不会影响外部的数据(除了后面还没有讲到的table等类型)
举个例子,比如下面的函数,可以实现打印出两个传入值的和:
![](https://file1.**/web3/M00/05/4A/wKgZPGd-VpWACGWLAAAgTf9YxBg942.png)
这段代码其实等价于:
![](https://file1.**/web3/M00/05/54/wKgZO2d-VrCAOhZCAAAkCCy8eI4216.png)
下面问题来了,请设计一个函数p,可以按下面的调用方式来打印出物体的密度:
![](https://file1.**/web3/M00/05/54/wKgZO2d-VtKAE-xcAABfTNxhbLc357.png)
9.4 函数返回值
在前面的代码中,我们实现了一个函数,输入变量a、b,函数会自动输出两个数值的和。
但是一般来说,我们的需求远远不止这些,我们可能需要一个如下功能的函数:
执行函数,输入两个值,获取这两个值的和
如果还是按上面几节的内容,我们只会输出这个值,并不能把这个值传递给其他的变量进行后续使用,如何解决这个需求呢?
我们可以使用函数的返回值来实现这个需求,结合上面的需求,我们可以用下面的代码实现:
![](https://file1.**/web3/M00/05/4A/wKgZPGd-VvmAec4bAAAo2R3t4YQ468.png)
这里的return表示返回一个值,并且立刻结束这个函数的运行
同时,和输入值可以有多个一样,返回值也可以有多个
![](https://file1.**/web3/M00/05/54/wKgZO2d-VxWAby2jAAA1huFtYwI555.png)
下面问题来了,请设计一个函数p,可以按下面的调用方式来返回出物体的密度,返回值为number类型:
![](https://file1.**/web3/M00/05/4A/wKgZPGd-V0KAZ2zkAABbxUGtpB8491.png)
9.5 判断三角形合法性2(自测题)
你需要使用前面几章的知识,来完成下面的题目
已知三个number类型的变量,分别代表三根木棒的长度
请判断,使用这三根木棒,是否可以组成一个三角形(两短边之和大于第三边)
请新建一个函数triangle,并可以用如下形式调用(如果可以组成,就返回true):
![](https://file1.**/web3/M00/05/54/wKgZO2d-V3iAUDUTAAAq7Txa7sM642.png)
9.6 返回多个值(自测题)
你需要使用前面几章的知识,来完成下面的题目
已知2个number类型的变量,分别代表一个长方体的长和宽
请计算这个长方形的周长和面积
请新建一个函数rectangle,并可以用如下形式调用:
![](https://file1.**/web3/M00/05/54/wKgZO2d-V5OAI_UVAAA2QLD8r1c631.png)
十、table
10.1 认识数组
数组,使用一个变量名,存储一系列的值
很多语言中都有数组这个概念,在Lua中,我们可以使用table(表)来实现这个功能
在Lua中,table是一个一系列元素的集合,使用大括号进行表示,其中的元素之间以逗号分隔,类似下面的代码:
![](https://file1.**/web3/M00/05/54/wKgZO2d-V7uAGLAfAAAIfZjdE5Q844.png)
我们可以直接使用元素的下标,来访问、或者对该元素进行赋值操作。
在上面的table变量t中,第一个元素的下标是1,第二个是2,以此类推。
我们可以用变量名+中括号,中括号里加上下标,来访问或更改这个元素,如下面的例子:
![](https://file1.**/web3/M00/05/4A/wKgZPGd-V9KAQDXlAABI8y0myvE347.png)
以上就是table最简单的一个例子了,就是当作数组来用(注意,一般语言中的数组基本都为不可变长度,这里的table为可变长度)
下面你需要完成:
新建一个table,名为cards,存入1-10十个数字
将第3个元素与第7个元素交换
将第9个元素与第2个元素交换
增加第11个变量,值为23
![](https://file1.**/web3/M00/05/4A/wKgZPGd-V_qAO6T2AAAPddGV6Xw616.png)
10.2 简单table
上一节里,我们将table来表示数组,实际上,table中可以包括任意类型的数据
比如我们可以在table中放置number和string数据,类似下面的代码:
![](https://file1.**/web3/M00/05/4A/wKgZPGd-WBCAJDhwAAALYk_WR1E480.png)
我们甚至能在里面放function变量
![](https://file1.**/web3/M00/05/4A/wKgZPGd-WCuAAnyCAABD0Fkns_w489.png)
这些table访问每个元素的方式仍然是直接用下标,并且也能用下标来进行修改
下面你需要完成:
新建一个table,名为funcList,并实现以下功能
调用funcList[1](a,b),返回a和b的乘积
调用funcList[2](a,b),返回a减b的差
调用funcList[3](a),返回a的相反数(-a)
![](https://file1.**/web3/M00/05/54/wKgZO2d-WE6ASkd9AABDwWO9LUg924.png)
10.3 table下标
在前两节,我们的table都只是一些简单的List(列表),每个元素的下标都是自动从1排列的
实际上,Lua中,下标可以直接在声明时进行指定,像下面这样:
![](https://file1.**/web3/M00/05/4A/wKgZPGd-WHiAAZ3nAABNq9v09f0016.png)
下面你需要:
新建一个变量t,并按下面的格式声明
下标为1的元素,值为123(number)
下标为13的元素,值为"abc"(string)
下标为666的元素,值为"666"(string)
![](https://file1.**/web3/M00/05/54/wKgZO2d-WJuAUPOwAAAghS8nuo4560.png)
10.4 下标进阶
在上一节,我们学习了如何自定义下标,其实在Lua中,下标也可以是字符串,如下面的例子
![](https://file1.**/web3/M00/05/54/wKgZO2d-WM2ATVmMAABOj-5Rq0I620.png)
可见,在使用string作为下标时,table的灵活性提升了一个数量级。
string作为下标时,也可以动态赋值:
![](https://file1.**/web3/M00/05/4A/wKgZPGd-WPGAQSg9AAAVv9yC7yU151.png)
下面你需要完成:
新建table变量t
下标为apple的元素,值为123(number)
下标为banana的元素,值为"abc"(string)
下标为1@1的元素,值为"666"(string)
![](https://file1.**/web3/M00/05/54/wKgZO2d-WRSAOA4LAAAqwh-XeEo929.png)
10.5 table小测验
下面的代码,将会打印什么?
![](https://file1.**/web3/M00/05/54/wKgZO2d-WTGATlw8AABHjRYiw2A386.png)
10.6 table小测验2
下面的代码,将会打印什么?
![](https://file1.**/web3/M00/05/54/wKgZO2d-WVKAfCKRAABGi48Kcp0236.png)
10.7 Lua全局变量与table
在前面我们知道了,在table中,可以直接用table名[下标]或table名.string下标来访问元素
实际上,在Lua中,所有的全局变量全部被存放在了一个大table中,这个table名为:_G
我们可以用下面的例子来示范:
![](https://file1.**/web3/M00/05/4A/wKgZPGd-WXGAT_R3AAA9JP08xyM928.png)
现在,你明白为什么说万物基于table了吧?
你需要完成下面的任务:
已知有一个全局变量,名为@#$
请新建一个变量result
将@#$变量里的值赋值给result
![](https://file1.**/web3/M00/05/4A/wKgZPGd-WZGAHq8wAAAThg4yP2U001.png)
10.8 table小测试3
请新建一个名为t的table,满足以下要求
t[10]可获得number类型数据100
t.num可获得number类型数据12
t.abc[3]可获得string类型数据abcd
t.a.b.c可获得number类型数据789
![](https://file1.**/web3/M00/05/55/wKgZO2d-WauASxaXAAA2-tDEDc0565.png)
10.9 table.concat
table.concat (table [, sep [, i [, j ] ] ])
将元素是string或者number类型的table,每个元素连接起来变成字符串并返回。
可选参数sep,表示连接间隔符,默认为空。
i和j表示元素起始和结束的下标。
下面是例子:
![](https://file1.**/web3/M00/05/4A/wKgZPGd-Wc6AGZR_AAAhq6cLJt8965.png)
请完成下面的任务:
已知table变量t,
将t中的结果全部连起来
间隔符使用,
并使用print打印出来
![](https://file1.**/web3/M00/05/4A/wKgZPGd-WeSANOHjAAARulRzhyI925.png)
10.10 table删减
table.insert (table, [pos ,] value)
在(数组型)表 table 的 pos 索引位置插入 value,其它元素向后移动到空的地方。pos 的默认值是表的长度加一,即默认是插在表的最后。
table.remove (table [, pos])
在表 table 中删除索引为 pos(pos 只能是 number 型)的元素,并返回这个被删除的元素,它后面所有元素的索引值都会减一。pos 的默认值是表的长度,即默认是删除表的最后一个元素。
下面是例子:
![](https://file1.**/web3/M00/05/4A/wKgZPGd-WgCAHpBDAABphDOe8SQ096.png)
请完成下面的任务:
已知table变量t,
去除t中的第一个元素
然后这时,在t的第三个元素前,加上一个number变量,值为810
![](https://file1.**/web3/M00/05/4A/wKgZPGd-WiSAOqQ8AAAbbp7uj6o962.png)
十一、循环
11.1 while循环
在实际功能实现中,经常会遇到需要循环运行的代码,比如从1到100填充table数据,我们可以直接用循环语句来实现
我们首先来学习while这个循环语法,整体的格式如下:
![](https://file1.**/web3/M00/05/55/wKgZO2d-Wj-AH39qAAAS0MR86aA733.png)
下面举一个例子,我们计算从1加到100的结果:
![](https://file1.**/web3/M00/05/55/wKgZO2d-WleABdl4AAAhoFV4Uq0815.png)
上面的代码,就是当num≤100时,result不断地加num,并且num每次循环后自己加1
理解了上面的代码,我们来完成下面一个简单的任务吧:
已知两个number类型的变量min和max
请计算从min与max之间,所有3的倍数的和
打印出结果
![](https://file1.**/web3/M00/05/4A/wKgZPGd-Wm2AIsixAAAkuxL_tEM122.png)
11.2 for循环
for循环在某些程度上,和while循环很相似,但是for循环可以更加简洁地表达中间累积的量
我们首先来学习for这个循环语法,整体的格式如下:
![](https://file1.**/web3/M00/05/4B/wKgZPGd-WouAJicEAAASyWxV35Q633.png)
其中,步长可以省略,默认为1
临时变量名可以直接在代码区域使用(但不可更改),每次循环会自动加步长值,并且在到达结束值后停止循环
下面举一个例子,我们计算从1加到100的结果:
![](https://file1.**/web3/M00/05/4B/wKgZPGd-WqGATQfqAAAZJYrrFOM875.png)
上面的代码,就是当i≤100时,result不断地加i,并且i每次循环后增加1
理解了上面的代码,我们来完成下面一个简单的任务吧:
已知两个number类型的变量min和max
请计算从min与max之间,所有7的倍数的和
打印出结果
![](https://file1.**/web3/M00/05/4B/wKgZPGd-Wr2ALJeeAAAc7hV0As8965.png)
11.3 中断循环
前面我们学习了循环语句,有些时候循环运行到一半,我们不想再继续运行了,怎么办呢?
我们可以在一个循环体中使用break,来立即结束本次循环,继续运行下面的代码
比如像下面这样,计算1-100相加途中,小于100的最大的和:
![](https://file1.**/web3/M00/05/4B/wKgZPGd-WtWAG4ESAAApDN23A2o912.png)
可以看见,当发现和大于100后,代码立即把result的值还原到了加上当前数字之前的状态,并且调用break语句,立即退出了本次循环
在while中,我们也可以使用break:
![](https://file1.**/web3/M00/05/55/wKgZO2d-WvOAEzoYAAAvXc_nWwY401.png)
我们在这里直接使用了死循环(因为while的继续运行判断依据始终为true),整体逻辑也和之前for的代码一致,当发现和大于100后,代码立即把result的值还原到了加上当前数字之前的状态,并且调用break语句,立即退出了本次循环
现在你需要完成一项任务:
请求出小于变量max的13的倍数的最大值(max大于0)
并将结果打印出来
本题理论上不用循环就能实现,但是为了练习一下技巧,请用for循环来实现
![](https://file1.**/web3/M00/05/4B/wKgZPGd-WwuAVzpRAAAXW4lSi1A561.png)
11.4 循环测试题1(自测题)
前面我们学习了循环语句,我们需要完成下面的任务
我们知道,print函数可以打印一行完整的输出
那么,已知变量a,请打印出下面的结果:
(a为大于0的整数,且需要输出a行数据,数据从1开始,每行与上一行的差为2)
![](https://file1.**/web3/M00/05/4B/wKgZPGd-WyCAXXpKAAAZRk_59SY172.png)
做题区域:
![](https://file1.**/web3/M00/05/4B/wKgZPGd-WzmALjsZAAAYy5z6Muo014.png)
11.5 循环测试题2(自测题)
我们需要完成下面的任务
那么,已知变量a,请打印出下面的结果:
(a为大于0的整数,且需要输出a行数据,第一行为一个,后面每行多一个)
![](https://file1.**/web3/M00/05/4B/wKgZPGd-W1KAXtbCAAAZd5jPmv0653.png)
做题区域:
![](https://file1.**/web3/M00/05/55/wKgZO2d-W2eAQylSAAAY-OybII8110.png)
11.6 循环测试题3(自测题)
我们需要完成下面的任务
那么,已知变量a,请打印出下面的结果:
(a为大于0的整数,且需要输出a行数据,按图示规律输出)
![](https://file1.**/web3/M00/05/4B/wKgZPGd-W36AZXHJAAAyEkhlGDg261.png)
做题区域:
![](https://file1.**/web3/M00/05/4B/wKgZPGd-W5qAPBWsAAAX3NHUCL0725.png)
11.7 循环测试题4(自测题)
有一只猴子,第一天摘了若干个桃子 ,当即吃了一半,但还觉得不过瘾 ,就又多吃了一个。
第2天早上又将剩下的桃子吃掉一半,还是觉得不过瘾,就又多吃了两个。
以后每天早上都吃了前一天剩下的一半加天数个(例如,第5天吃了前一天剩下的一半加5个)。
到第n天早上再想吃的时候,就只剩下一个桃子了。
那么,已知变量a为最后一天的天数,请打印出第一天的桃子数。
如:a为5时,输出114
做题区域:
![](https://file1.**/web3/M00/05/4B/wKgZPGd-W7KAcjbmAAAYA7zsilc439.png)
十二、详解string库
12.1 string.sub
接下来几节会讲解string库的各种接口
![](https://file1.**/web3/M00/05/55/wKgZO2d-W8qADW7tAAAH7bvZgP8737.png)
返回字符串 s 中,从索引 i 到索引 j 之间的子字符串。
i 可以为负数,表示倒数第几个字符。
当 j 缺省时,默认为 -1,也就是字符串 s 的最后位置。
当索引 i 在字符串 s 的位置在索引 j 的后面时,将返回一个空字符串。
下面是例子:
![](https://file1.**/web3/M00/05/55/wKgZO2d-W9-AFOOKAAAtrBoY2kE504.png)
值得注意的是,我们可以使用冒号来简化语法,像下面这样:
![](https://file1.**/web3/M00/05/4B/wKgZPGd-W_eAFrXqAAAbY0TLrig314.png)
请完成下面的任务:
已知字符串变量s,请分别打印出(每种一行):
s从第4个字符开始,到最后的值
s从第1个字符开始,到倒数第3个字符的值
s从倒数第5个字符开始,到倒数第2个字符的值
![](https://file1.**/web3/M00/05/4B/wKgZPGd-XA6AH_yIAAAW71FRH9g532.png)
12.2 string.rep
string.rep(s, n)
返回字符串 s 的 n 次拷贝。
示例代码:
![](https://file1.**/web3/M00/05/4B/wKgZPGd-XCWAZ8_yAAAQ1JDCOOs122.png)
print(string.rep("abc", 3))
--输出结果:
--abcabcabc
请完成下面的任务:
打印一行数据,数据内容为810个114514
![](https://file1.**/web3/M00/05/4B/wKgZPGd-XDiAUhHOAAAKG2FHDmE177.png)
12.3 string.len
string.len(s)
接收一个字符串,返回它的长度。
示例代码:
![](https://file1.**/web3/M00/05/4B/wKgZPGd-XFCAM1U9AAAgHChIcos015.png)
请完成下面的任务:
新建一个变量s,使数据内容为810个114514
并打印出字符串s的长度
![](https://file1.**/web3/M00/05/4B/wKgZPGd-XG-ABzb1AAALGjQqlUY742.png)
12.4 大小写转换
string.lower(s)
接收一个字符串 s,返回一个把所有大写字母变成小写字母的字符串。
string.upper(s)
接收一个字符串 s,返回一个把所有小写字母变成大写字母的字符串。
示例代码:
![](https://file1.**/web3/M00/05/55/wKgZO2d-XIiAD6IFAAAvR_ILD9Q931.png)
请完成下面的任务:
已知一个变量s,打印出全是大写字母的s字符串
![](https://file1.**/web3/M00/05/4B/wKgZPGd-XKCAAIUnAAANZuklA1I173.png)
12.5 string.format
string.format(formatstring, ...)
按照格式化参数formatstring,返回后面...内容的格式化版本。
编写格式化字符串的规则与标准 c 语言中 printf 函数的规则基本相同:
它由常规文本和指示组成,这些指示控制了每个参数应放到格式化结果的什么位置,及如何放入它们。
一个指示由字符%加上一个字母组成,这些字母指定了如何格式化参数,例如d用于十进制数、x用于十六进制数、o用于八进制数、f用于浮点数、s用于字符串等。
示例代码:
![](https://file1.**/web3/M00/05/4B/wKgZPGd-XLmARiI7AAA-wqh8JbQ823.png)
请完成下面的任务:
已知一个变量n,为number类型整数
打印出n:连上n值的字符串
![](https://file1.**/web3/M00/05/55/wKgZO2d-XNaATCXrAAAL3ifrLfs813.png)
12.6 string的本质
这一节我们来讲解字符串的本质
字符串,是用来存储一串字符的,但是它的本质就是一串数字。如何用一串数字来代表一串字符呢?
在计算机中,每一个符号都对应着一个数字,但是在讲解这个知识之前,我们了解一下补充知识:
![](https://file1.**/web3/M00/05/55/wKgZO2d-XOyAMEU4AAAbra103X0874.png)
接下来,你需要了解,每一个符号都对应着一个数字,比如:
0对应着0x30、1对应着0x31 a对应着0x61、b对应着0x62 A对应着0x41、B对应着0x42
上面的编码规则,我们称之为ascii码,具体想了解可以打开下面的网址查看:http://ascii.911cha.com/
当然,1字节最大为0xff,即256,只能存下一部分符号,大部分的中文按某些编码,一个中文占用2或3个字节
计算机如何解析这些数据,我们不需要了解,当你知道了上面的知识后,你应该可以理解下面的描述:
![](https://file1.**/web3/M00/05/4B/wKgZPGd-XRCAXFL3AAARztOWQAI813.png)
同时,lua的字符串中可以保存任何数值,即使是0x00这种不代表任何含义的数,也可以保存
![](https://file1.**/web3/M00/05/4B/wKgZPGd-XSeAYIkQAAAXT6tVaBU227.png)
比如下面的描述:
![](https://file1.**/web3/M00/05/55/wKgZO2d-XUSAYanJAAAqqREpGVA781.png)
下面你需要思考一个问题:一串字符串数据如下,它的实际内容是什么(指人能看见的字符串内容,如abcd)?
0x62,0x61,0x6e,0x61,0x6e,0x61
12.7 string.char
string.char (...)
接收 0 个或更多的整数(整数范围:0~255),返回这些整数所对应的 ASCII 码字符组成的字符串。当参数为空时,默认是一个 0。
如果上一章节有认真学习过了的话,这段话应该是很好理解的。实质上就是把计算机认识的一串数字,变成字符串变量,并且字符串内的数据就是要存的那串数据。
示例代码:
![](https://file1.**/web3/M00/05/4B/wKgZPGd-XVyAOvEkAAAr8WXai88284.png)
请完成下面的任务:
已知一个字符串的每个字符在数组t中按顺序排列
请根据t的值,打印出字符串内容(一行数据)
注:这个字符串存储的不一定是可见的字符
![](https://file1.**/web3/M00/05/4B/wKgZPGd-XYKAfcfwAAAYAgvf5Ic023.png)
12.8 string.byte
string.byte(s [, i [, j ] ])
返回字符 s、s[i + 1]、s[i + 2]、······、s[j] 所对应的 ASCII 码。i 的默认值为 1,即第一个字节,j 的默认值为 i 。
这个函数功能刚好和前面的string.char相反,是提取字符串中实际的数值。
示例代码:
![](https://file1.**/web3/M00/05/55/wKgZO2d-XZaAIt34AAAriniteQA521.png)
请完成下面的任务:
已知字符串s
请把s中代表的数据,全部相加,并打印出来
![](https://file1.**/web3/M00/05/55/wKgZO2d-XauAHbryAAAUB0mtmPk209.png)
12.9 string.find
string.find(s, p [, init [, plain] ])
这个函数会在字符串s中,寻找匹配p字符串的数据。如果成功找到,那么会返回p字符串在s字符串中出现的开始位置和结束位置;如果没找到,那么就返回nil。
第三个参数init默认为1,表示从第几个字符开始匹配,当init为负数时,表示从s字符串的倒数第-init个字符处开始匹配。
第四个参数plain默认为false,当其为true时,只会把p看成一个字符串对待。
可能你会奇怪,第四个参数有什么存在的必要吗?p不是本来就应该是个字符串吗? 实际上,lua中的匹配默认意义是正则匹配,同时,这里的正则与其它语言也有些许不同。
由于篇幅有限,本节和下面的几节涉及匹配内容时,均不会考虑正则的使用方法,Lua正则教程将会在最后几节单独详细地列出来。
第四个参数为true时,便不会使用正则功能。
示例代码:
![](https://file1.**/web3/M00/05/55/wKgZO2d-XcWAOBuAAAA7OcgbmTM773.png)
请完成下面的任务:
已知字符串s,里面有很多相同的字符串
请找出字符串s中,所有字符串awsl的位置
使用print打印结果,结果一行一个
如字符串12awslawslaw,输出3和7
![](https://file1.**/web3/M00/05/55/wKgZO2d-XeaAf05FAAAV9y708vo798.png)
12.10 string.gsub
string.gsub(s, p, r [, n])
将目标字符串s中所有的子串p替换成字符串r。
可选参数n,表示限制替换次数。
返回值有两个,第一个是被替换后的字符串,第二个是替换了多少次。
特别提示:这个函数的目标字符串s,也是支持正则的
下面是例子:
![](https://file1.**/web3/M00/05/4B/wKgZPGd-Xf2ABL02AAAjRyGxJH4180.png)
同样的,我们也可以使用冒号来简化语法,像下面这样:
![](https://file1.**/web3/M00/05/55/wKgZO2d-XheAWIySAAAPT-EdrAU944.png)
请完成下面的任务:
已知字符串变量s,请分别打印出(每种一行):
把字符串s中,前5个a,替换为b
把字符串s中,前3个c,替换为xxx
把结果打印出来,一行数据
![](https://file1.**/web3/M00/05/4B/wKgZPGd-Xi6AKu6sAAAU1Rmw6tY834.png)
十三、跨文件调用
在编写代码时,随着逻辑逐渐复杂,我们的代码量也会变大。虽然有函数可以把一部分代码逻辑封装起来,但是所有代码都放到一个文件里,显然也不是个好办法。
所以我们需要将一些代码放到不同文件中,通过文件来区分这些代码的功能。
比如我们有下面这个函数:
![](https://file1.**/web3/M00/05/4B/wKgZPGd-XkSAZ7GgAAA9HwD6yT8227.png)
我们新建一个文件叫tools.lua,把这个函数放进去,现在,整个文件如下面这样:
tools.lua
![](https://file1.**/web3/M00/05/4B/wKgZPGd-XnSAQQCUAABT62EYdeY543.png)
现在,我们封装的这个函数就能在其他文件里被调用了,具体代码如下:
![](https://file1.**/web3/M00/05/55/wKgZO2d-XpOAA6TMAAAUx2RgZXk961.png)
当调用了require接口后,Lua虚拟机会自动加载你调用的文件,执行文件的内容,然后返回你文件里return的结果。
为了更好地理解这段话,我们可以看下面两个文件,其中run.lua是被运行的那个入口文件
test.lua
![](https://file1.**/web3/M00/05/4B/wKgZPGd-XrCARZUqAABgneUyIoA570.png)
run.lua
![](https://file1.**/web3/M00/05/55/wKgZO2d-Xt6AS5nvAAAsFJqLS3Q094.png)
同时,每个文件最多只会被require一次,如果有多个require,只有第一次会执行。