mapcar 图解。lisp 基础知识图解系列。
本帖最后由 vitalgg 于 2022-6-3 05:24 编辑mapcar
最常使用的是 mapcar ,接受一个函数以及一个或多个列表,并返回把函数应用至每个列表的元素的结果,直到有的列表没有元素为止。
mapcar 后跟的 函数 需要几个参数,再后面就需要几个 作为参数的列表,如下面的代码,函数 需要两个参数,那么后面就需要跟两个表,参数表A和参数表B
返回结果也是一个表。
(defun 函数 (参数A 参数B) ...) ;; 函数 定义,有两个参数
(mapcar '函数 参数表A 参数表B) ;; 返回 结果表
apply
autolisp 的 apply 接受两个参数 ,第一个是 quote 的 函数本体(类型为函数的符号或 lambda 匿名函数,第二个是一个表,这个表内元素个数须与函数所需的参数一致。
(defun fun (para1 para2 para3) ...) ;; 假定定义的 fun 有 3 个参数。
(apply 'fun '(para1 para2 para3)) ;; apply fun 后面的表中 元素也需要 3 个, 与 fun 所需的参数一致。
;;等价于
(fun para1 para2 para3)
全文: https://gitee.com/atlisp/atlisp- ... B%E9%98%B6/lisp.org
@lisp开源项目: https://gitee.com/atlisp
可以mpacar和 apply放在一起讲,相似和不同,可以更好的理解两个函数的用法,mapcar是遍历参数去执行函数,例如(mapcar '(lambda(x y)(+ x y)) '(1 2 3)'(4 5 6)),返回的是(1+4 2+5 3+6)也就是(5 7 9)。而apply是将所有的参数去执行函数,例如(apply '+ '(1 2 3 4 5 6)),返回的是1+2+3+4+5+6也就是21。其它用法大神来补充。 本帖最后由 e2002 于 2022-5-31 21:24 编辑
mapcar 你可以理解为一台可变加工程序(也就是mapcar的那个函数参数)的食品加工机,投入的是一份份的原料,例如:若干个(比如100个,后面的其他原料数量也是一样的100份)面粉团,若干份糖,若干份巧克力,若干个鸡蛋,通过使用的的“做面包函数”这个特定程序,这台机器最后做出来了若干个(这里就是100个)面包。
如果换成“做包子”程序,输入各种做包子的“原料”(就是那些参数列表),最后出来的就是一批包子。
本帖最后由 wzg356 于 2022-5-31 23:06 编辑
(mapcar fun lst1 lst2....)r的执行过程及结果理解
也确实是这样
结果理解
即依次把表lst1 lst2....的原子作为函数fun的参数1 参数2....
并依次执行(fun 参数1 参数2....)
并以表的形式顺序列出执行结果
直观描述结果就是
(mapcar fun lst1 lst2....)
===
(list
(fun (nth 0 lst1)(nth 0 lst2).....)
(fun (nth 1 lst1)(nth 1 lst2).....)
......依次类推
次数为lst1 lst2....的最小长度
)
执行(fun 参数1 参数2....)次数/结果表取决于lst1 lst2....的最小长度
即取决于lst1 lst2....能得到几组合法的fun参数1 参数2....
能执行的前提是lst1 lst2....的原子符合fun相应位置参数规则(fun 参数1 参数2....)
过程理解
(mapcar fun lst1 lst2....)
执行过程如下
=====
(setq lst nil)
(repeat (setq n(min (length lst1)(length lst2)....))
(setqn(1- n))
(setq lst(cons (fun(nth n lst1)(nth n lst2)....) lst))
)
-------例如------------------
(setq lst1(list -3 2 0 -2.35))
(setq lst2(list 1 2 3 4 5))
(mapcar 'abs lst1)
===
(repeat (setq lst nil n(min (length lst1)))
(setqn(1- n) lst(cons (abs(nth n lst1)) lst))
)
例1
(mapcar 'abs lst1)
===
(repeat (setq lst nil n(min (length lst1)))
(setqn(1- n) lst(cons (abs(nth n lst1)) lst))
)
例2
(mapcar '+ lst1 lst2)
===
(repeat (setq lst nil n(min (length lst1)(length lst2)))
(setqn(1- n) lst(cons (+ (nth n lst1) (nth n lst1)) lst))
)
函数加一个计数器k验证执行过程
(setq k 0)
(mapcar '(lambda(x)(setq k(1+ k))(abs x)) lst1)
!k看看
===
(repeat (setq k 0 lst nil n(min (length lst1)))
(setqk(1+ k) n(1- n) lst(cons (abs(nth n lst1)) lst))
)
!k看看
这部分感觉讲的不够详细!大佬加油! 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py6zvOItpqlkDQND0HR7YtZ5/5gP3T9CZPQSNsk9B2d9jedZp6Qt9T/2foWUFNc6LH49+X+by4/jS99aafo+3PgvcvHZctTIBi7cvuES7jyn3oaJTD4QgbyYo0DemZZ55hdHQ0cJU9dN2ImXESHrpoP9FRrcrKyrDphulKZhphPB6PhzNnzrBr1y7KyspwOByBohvJMBLFaKMhxujg6OhoYJt169YxMDAQmJZp7Mec+BiMEZ5ICakxyhmt/HosiRx/X19f3FEWYwqdcR8w4/iMtUnmEbz+/v6gqYotLS2BhMScCJj/bawNM7/GGG01j6iFllpPp/10pXv86XI6nbS0tAS1HTpKbdi8eXPQ81aUnheZivLgCod3mopkHLrO2l0ddFhZIWPOd5QCnq+jnyjN2pFgovU8OEMSLYDChsmiF4HCGp0w2381PL82uEDGrFSOA5hjTrQA5kNxksU2HC8W+wthFPOU0/dccXWxqUBGEbGvc0Yzk+fMiRaAs5DVocU2Evg8qLtIb5WTmprqwKPNA+6mPlpTrnwwzO7eqwxt8++zrRRX/Wm6tqdSAMFXdGN/PRzZNtnHmt+Pszul/dlpLv0jgHMkzaIRQzT19tHECv/xruKId5T6/aZiFHUX6fWvhzLek21HoH5/yOcWYbuamkWU7rariEkh+171t7OtAi++RNscX9ETN6vev9Scys/nr3ESrgfnzsW8oTHA5zNmxLzHFkyOcNnBGLUwF8OINv0r9HctLS2sXLmSY8eOBZ5rb2+nvLw8aK2MsZDeOHk3F8Po7u4OJAfxVFVVBV1Zt0KkaoTpWLduHStXruTjjz8OFJBIp4iGOeE1KtkZo4OhKisrcTgcgdLbEL6GzzyFzzzyBb7Pora2lt27d6e8Xi7R4zdXB9y8eXNCozC1tbU4HI7ACF5nZyfz5s2LmlSElk8399Fc2GHz5s2BODDfsDn09Va1n6pstr9u3bqwmDCS1nil35VoiUQWPqflo3doOTkMy37BxsUWtVI0jgsHQ+NRfl8Gs5f7/jlxAkYafI+bEaaYF/u/a757a3K7Ef+Mivza1JOoZNzp9LX77Xl/uz/JrbtDe9+7ye933ORPn/p+Lq78AUGnOPE+D/AVPAg5+e1pXoEHKF2UYjLjglPmkZOeJbR5wLX6RvIn+HV/p94FnraQEZueJWmNlNnli386gHEWzUlvP94jq0yfSyEfnPLvtwLgDttfGQLPiqD3YHDfjzniBXfVrcBzFYvGAQenPjC/V3NpzrVRQT+r3r9Utc+cSbqrttpmJlYIxxjhsprH4wmUgzdPrYo0gjQ6Osrnn38edJIaeiPeAwcOsGHDhqCqdg0NDYFtNmzYECip7fV6GRkZSbhE+rPPPhuU2GWTcXzmqXtGArF79+6o0wePHj0a9P7FUl1dzY0bNwBfImSs1XG5XGFFT/r7++nu7sblclFdXR0xWQCCSve7XC7Wr18fVI0w0QqDqR6/IbQ6YCKjMObpoUY1RfNIi7Ge6cyZM1y6dClmRUNzgmUcs3Evq2ijfla0n45sty8i1oqYI1zxXgesWyBRURTrrB4K3P6Rqusw9l6MDZ+fHFWa/SbMjrBJfhncfg9G/PspavWNbk2cgBux9p0g83TF22dgzvLk9zF6fIzfHwfI5yf/6hvdGusbo+f4RNr98753kz/6L0pdPn+X554OP8GL93nE4yq9Tcj4XoKdK+GDkDP5L4Yc4B5nKSR1kl9XNQTeCt5Na0pj5gyOFwFDlBYBKVfVC02OYHDfcmr2+X+ouMFqF3h+HzpCVMjVIcA9Qh1z6QEGr/r6U7//PESaYphjrHn/UncxL4//k5/PaxOp/T+6r6CAixat/Yo05SzaqELoleZI20WrJpbISXHo9KtEfxetf0BgBCa0n9Eq8EUTWjUv0jRC8++NESRjBO7o0aOB9W2GgYGBwBojY/rdwMAAR48eZf369UGfTawCGeYpgIZ4UwqN15gLJhjvn/E7czXCaMe5e/duGhoaAs+FTv2y6vhDp5jFY07EjWQCCBS08Hq9nDlzhnPnzrF58+aw4hfmIhcQfpsD83seelyhIzlWtJ+ObLcvItaKmGyt+eky4AJea2tkRO/ED/3/uJ6LRSyC3be6SKOlJrj1D+v2FreEt2X8IzMJn/DfYVGpjd15WC0dxwW4mvrojXfbop4l1HxRRNf+Qer39+FbOhVlbZcA0OUfmdp0715ShSz+d0EBxwpyaew7t1VXV9Pe3h72fKzELVIiZj4Zjcc8FTJasmOU2A6temgklQMDA/T391NVVRXW19AT/2ROip955hnANy0w2pQ/8/4iVVoM7Y+5/8Yoj5GoWXH8yZ70m9dsGVUDQ9txuVyBtUOh68yqqqro7u4OSjhCS+gnujbNqvZTle32RcR6YWcAizfuZdMyuHDIuiIZvqvSCYymzPd1KJGE69sGuJ1mv6ar+J+HUcwg9OQ7uLiFFZaWjgKlXJ3iJ/i+0cQ4UzfT9UURXmAo0SImgwt4tca/PshfLKN+fx+laRdBsV5G3r8EdM2cyZ/y82m6e5cn4xSx+HzGDNpmzrRsRGu6SGQ0LlRoMpHs2pFEynXHG6mz6yQ32VLiya4dSzQhtfP4zX2IdxPgaCOxoe9TqiXYrWo/2X1nov1kxIsjJXUiiSvo6OgIfmb4JC2Nh7F0UGu8CC9DrC65w75vw6efBabjzYfCMrh3GXjeXxHQbAgm8E05nLMDbodf/EzOZV9ilw/klfp+DlQizAVf3mOMQorJZ+4PgS9NlQjTEefzgNuUugDPIptHOfwjVB4nyZ3b+9Yq1dcP80LFAgYT6WOgsmIpbTVLordn9XZ+Sx/xJ5V2ToEbnM0Q/rVZPUlW7RtcwKvboGv/oH9NXoxponUjlibcicjI+5egi3l5/I/CQlZNTPDkgwcsm5hgqT/xupSXx7m8PC7OmMF/ximGISIiIlNfQWNjo/2tfOs/CXwkylqfD+FOg+830dZiAXAZxk74k6HlvqqCQc7DSJIJ2H3/8rTCBt8jt9zn1gi4nOB6eR6vJFn1MKp4n4fBtMYnvEx4+upa/aNne5Iv5z24bxGe+rPU77/IVXOyU3eRrkX/ElYko67KmP85RFXdEnqiZEdWb+dziyonMJJsUpmsubx7xIG7/ixd22OX1K9r7aOqP3gEq+KFYVw4OBJYFxYhqY1wO4CkDT7GKS/U1/+dun3xE9XMvX/JOZ2fz2mAKTZFUBXFRERErJOhs4S5vPulA/fCq2yfszzivZ3GGgBzWfXzMHIs/H5a996DEW+CZeITMN4MeaH3zfp3KI5QXj7zJvjk1+PMDb1v1u9g9Y5CilPeb7zPYy7N2yro2j9IU+8QvuU/pbTVrGJR12nqw/YXj3HSPsj+XtMwlLeCbTWpVr+bS3NNtW9dWaCP+O/VFJ5k9PSX0uT2jUT1xzhjt3o7AOb7RoJiltq3yOC+5dR8cI2u/SH3sCL4nmY9zatY1BW6tit8lG5w33LaSvtoCqzrihwH4ev7hiY/l7B7nxWy79VV0HU6wmcX4T3K4PsnIiIiYqUZ1dXVKd89s6Ojg8RHxm7RWn0W98gKagZ00pR9+jwyw/8+/7OCbX/OzbLquW16vn9//etfs90FEZGM+PGPf5ztLojYKoMrt32jKTjP0lWeazednY70eWRCXeVZ3Dg48un0SRSspPdPREREHmYZXWwweGk5bY/00bTwNK3j1TRfz2TrcvJ2SP3GT3/EvU+hhAecVG1He/zXc9wD1nOb9aan187KxO23H24V5edpcoLns8hTj6ey/Px8JlK8p5eIyMMiX4WEZBrIeE3inoFq2kYcgEZTRCSaO7zgHMX75appeVHm0UcfzXYXRERsN3euljHI1JeVMlo9A8tzqqqYiOSaQvb9uZp92e5Gljz++OMAfPPNNxrhEpEpJz8/n0cffRSn05ntrojYbmrVLBYRmQJmzJhBSUkJJSUl2e6KiIiIpCHj0whFRERERESmA41sTSMqyiAiIiIikjlBI1uLN+6lo6ODjo69bFxsX6N1lX0qNy6KA8lJFeXnbY1Lxb3kIsW9TEd2x326cr1/kpjJZGvxRn651v71AXWVfTQ5HZwaLrS9rSml4hpdvX309l6kLt62C2dT1z6PV0yPuhdzq7xqtDiYtQOcncGP4udj7KgMHvNvV1Rmb59z3x22d/XR23WNimx35aHlq4LoWnjalj9wins7KO7Tp7h/+Cju02dv3Kcv1/snifJPI1zMxl+upWT4JIfOPcOmtTa1Nv/itL1vTtoGZzMEuOJt5y7ilZdnZqBDabAwDmatByON/IEbxi+nt7+iVpg9H75tILN3Hqu7SG/TEODgyLbl7MvUHXynW7tx+aogXq3so2nhX9k+bOF3leI+SF1rH01u0xPeCra9av/Nq6dbu4lR3Gci7iu2n2d//WjwkxmIg+nWbuJsjHtL5Hr/JFF5AIs3/pK1JcOc/I/DDNnW1C1anxqCkRXT8r45lvEW8UWMX5ct9ydaI3c4seMmv/c/eo7nSvno2HFwux1GGnyPRK7j3D4GxpF970mza8/7/vBmlDFiWeWkLd3+q13L9AyswMMo9U9bddVYcW9W19pHEyuoqan2PbZV4HUNst/mq/TTrd1kKe7tNbhv+WQM1FRTU7MCTwbiYLq1myzr495aud4/iS/PmD44fPI/OHzFvoYqyq/ixsGRK7qBXWpmMeSNt00+c/0zQccGvmc09sZZYXkcXIYb/j/W6V7lzLxbtO4f5tS2amqaM/n/xXRrNxVzaf6sFB4Z5DULTsgU98F6mkNiYHABe444wDXIa3HnSatd+yjuM2su7x5xgGuYFzJ6Fj3d2o3H2ri3Xq73T+IpMKYPttiZaXGL1xaOwsiKiEOgRa0wG/j2E5hT63vuu7eA/z555ckY5p+1A+YsD9/HxAm48d7kz8WdUOh/3Uz/vwG4Djeb4V7I6wtehnm1pidCtku0XWO7iRNwwwvOhsnf3emEsQ/D95GYQva9muZNXhfOpm5HIcVM8Fn7GJ986Xva8WIxtdX5vtGwX3/HKDP5eXsRLu7ypx3fs7C9aHL6YmCbVMSOg2RE+jyivr/PB38OodsaU0nM5nTCHOOH8zDSnkInA1PlJnnaqmkO3NF7Ls01EYLKErdo7T2LMWvJe2QVr+4z/i+wr92waSNBU0bsPF4bXHfieWoI9+O34Ho6J4uK++C4j2zwahGkfYkoVtzb127suLevXVso7jMa9/5ecTWteXXJx70V7aYS91a0awvL4t4mud4/ialgbckwJ1sOY2eqxZzvKAU8X8cIkPmTiRbA7DeDfz37Zbj9HlHl18JjBCc+4PsSDW1nXmtwIlVsTsZM2xW/HL6/RNvlh/BYbfBThQ1w90Pr54YHkiWT4upiXqk2frrLn3aMk/yFwJk81x6y/stZyOoXv09tWmIicWCxaElyYQPcH7Lx6mjdRXqrnNTULJl8qrWPpqY+Wkn0D3CqhtndC6e2VdM86O9L02m6SPQPcCrusL3rNPUuB0e2VU+uwaq7SNf2Oza2a6e59I+A2zlCHXNJ+SNT3CcU9xWLxtNsPLW4T6/d1OM+/eO1i+I+aanGfcU1dteP4j3y49Tf51TiPu12U4x7S47XLhbFvW1yvX8SS4Hd0wcBKBrHhYNT8f62nIeRY/DYm75FsHc64TuXb8Qp/4e+TW63hycrxqhU/k+g4L3wUavv3vJ/yRpXvOZDYRncu+z7cja+FoKulJVBsWkxc7Lt5i8naHTMSOhmPg+3Ux7dyg7vezf5owfK/ts8nnsaiit/gON4CqNbicZBAsyfR8Rk2S/ffwUz0gikkZ6ON8M4BF0RTXvBdM8SakK+DXuaV1DVe5bSRXeI3mMLuOCUuehEzxLaqoZoWn2Din02LUyu+zv1LvC0hRS76FnCq3a0lyFf/NMBjLNoDpDq1XnFfQJxf4vX6keBUvpTPYtIKe7TbDfluLfgeG2kuE9SMnEfOgLmWUFNOhejEt0GRQsAABYGSURBVI17K9tNJu6tPl4bWRL3Nsr1/kl0BSVrd9ERofrg2l0drGWYky07007GKooS+7b97pjph+vw3YdwL0IZ2LApf7H2+ZbpataHcKfB97VnVDKa6b8KNnEiZErCZRgLuQqWTLsA35pGz8YaYCzxlyZl9PgYvz8OkM9P/rWYp5ww1jdmSVEMI9ECuHz+Ls89nXqlw0TjwEoT14H5vhFIZ+1kPNxOZZqIRVylt7E12fKW8EHImeUXQw5wj7MUbEm26qqGwFvBuzl44piOwfEiYIjSIlL+46a494ke93fY3uWbAuU98i+pX7FNOu7Tbze1uLfoeG2kuLdOWNz3LKGmxxgB840Q9famUZU10bi3sN2k4t7q47WRFXFvp1zvn0RX0NjYGPTE4o172bUWS5IsO0Sab52M+/4v41D34hSfSLrd8xkuHW6LCW79I9t9SM/tdsg3fXaz3/StDwT7y/2GlXrOunEWVWBDtnWHRaVW71PS8TDFfV3raepdgGeFTdNNI8d9+u2mFvf2H+/09TDFvU8h+15dQWnvWepfu8U+SwsHxfq+T6fddL7v7TxekdxVkIlGfNm4BVe5TKVazVP+khlxyvO/PjS5Koh1AysL2hUL4yBJgWkjBE9BmbPDriue5vns5qt3wYuYM2lpqW/KUs4tSs5xvqvzDobSCFvFffS4D5ygelbYUqEyWtzb3W402Wo3WYr7ZKT7fe+rNGzl34XEvu+tbzcx2Wo3Pivi3k653j+JLi8jrYwX4WWU1SXW3QH7vjEFuAyKE0x4ilonv3jv+hOmu+d9/82vDblzfRk8tsOadnPGl/f8UxnzmetfAxepuIZtbIiDeIpaYZbp57EG+Nb/mTM/+tWGmRGmrybuNqUuwLMoR6ZJ+K9Eepw2TVkq5INTNpXzrbtIb28fvb0XiVkh2+rt/JY+MgoUcTWdKRuK+4gqtp/3JR7eCrbZknhEjnvr2k0u7pNqV3Gfkoch7sNU3GC1C7xDs+Jvm5AEv+9TbjfN7/tY7eZ63Od6/yRnhSVbVw7vpLHR4imE385mCHA9kuYg/oeTNz6c/SY4O8H55uTC10gC23VOjk5NnJicTmC+SWJhw+S2QftNod3cdJ9bI75/uV6exyvt8zKXaEFCcVDcOfkZGImx+XOZleR24KtI6TQ9jGpVE5+EFFMxfc5BsRAh6U6Ie2TyC7TiGl1ZGtXyTVtycORd+66kD+5b5Lvp4v6QPxr+6lSpqqsyrm4MURXjr5HV2/ncosoJjKSZpCruw+I+UDI64VLRyYsU91a3m2jcJ9uu4n5qxn24W7TuH8TlrWCPRVNKE/u+T6/d1L/vY7eb63Gf6/2T3JWRaYQwl3e/dOBeeJXtc5andc+NsbegwJzoXIeb/w7FCSY/QQUzIHCTxLA1WdfhpmnKQbrt5oYJPvn1OHND75v1O1i9o5Bi29u3Lg4SNd4MeRGqV0W7R0vY55ySuTRvq6Br/yBNvUM0AVBKW80qFnWdpt60Zfg8/1Hq9/f5tymlrWZJEl+sviuO9fWD7O81nc55K9hWE3yCZ2274LuHVrVvv4Fjxj9davLdT7bdnv5SmtxDxKvaZvV2AMwfwY0VpasV98Fxb1TiA1whsRp4jR1xb3W7kFjcJ9+u4j41uR33xnTD4Fd72qrDKhkmJtG4t7pdSCzuk2831+M+1/snuWtGdXX1g1Rf3NHRQWiBjehu0Vp9FvfICmoG7A8WY652WHIlWZbZOBBJjT9O/1nBtj9bMfKiuJeHgeJepiOr495qud4/iScza7YA4yoXzrN0lWduDrfkGsWB5L66yrO4cXDkU6v+sCnuJfcp7mU6sj7urZXr/ZP4MphsweCl5bSNgGvhaVrTKN8uDzfFgeSyivLzNDnB85m1U58U95LLFPcyHdkV91bJ9f5JYjKabAH0DFTTNuJgcmmqTEeKA8lNd3jBOYr3y1U0X7d+74p7yU2Ke5mO7I379OV6/yRRGVyzJSIiIiIiMn1kfGRLRERERERkOpjyydYTL25KrqR56SbWvL2TJ+zqkIiIiIiITAsZus9W9nz1F1jz9k4uvbGXr/zPFb+4iUeP/ydFO39DmTPy6yp3buKbvYcYMz9Zuok1O34GIwt4JMrrYvnnH/8/Pjr+RfIvFBERERGRh07Bxr0drC2J8JsLh2h856OMd8hyQ4e49On7IcnTz6h8+2dcbn+JE0NxXh/B9d+9xN+Set0LrHh7PeN/md6J1snbt7PdBREREZGHztpZs7LdBUmRb2RrqiRWUXzz9TXg78GjVJ8eSzJhAob+zrf8LMVe/IPxFBI7ERERERF5OE35aYQAY8d/xYlsd0JERERERKaVaZFsRfT0Vmrf3hr995/+lhMdH2SuPyIiIiIiMqVMzWSrdBNrdtTxSNCTZxkwFcmImkz5i2BcP5FIovUCK97eSqQlb5CjBTGM42v/VfLTKNOk+cYiIiIiMp34kq1lm+jo2BR4cvhkCzsPX7G2JSMBijdiZMV2Q4f46I1DgR+faHyf8q//72SiFUPxT3/GI0mt5wpJ4gJ9e4JLuZZoAQz9J9dH6ijb8T7zczEZFBERERGZIvIO72yksdH0OHSBkrW76Ni7kcUWNvRErX+k6Wl3zHtYWb1dJI8+voB/fn2VJxrfp7bxBdNvXmDpz+FyQqNaD6sv+Nvelzj1x2s88vPfhBy/iIiIiIhYJXwa4Ufv0OLay661a3lxzWGsKlL41YWzVD69Aj71xBxhsnq7cEspKoFvL3zBV8df4iv3TmrfXs/l9l/xj5+up+TTYymVg09KxGmOZv7RMqu3Mz0zdvxXnLi2k9qNW6l92x0+OiciIiIiImnJ3Jotz15OeLKwXZhFFDnPMmy81rOXE56l/Gjn+6x2nmXgjQyMaoVMc8zYdqE8eznh8a07q3z73yjKwjouEREREZGpKmKyVbqgBLjAX6birbdK/4U5n3o4G/TkIoqcMPzHryh/+33Kk1rLtILKt9+nMuz5sxG2zWULKKt9gb+pAqOIiIiIiCXCkq01r3ewaRlcOPQOUzHXeqL2Z1w/YR4FWsqPdm6Fwy9x1gP8Bdbs+A21jyda+j1agYyfUVQKRBopyoFphMH9iPJ7ERERERFJWcHejo7g0uXDJ2lpPIzFtQgTP7G3ejsz907Kv36Hj4wEKFAG/SXOGs8NHeKjw09Qu3ErK9wf+BKwqH2INEpG/Gl9OTCNsPjFf2P1zxfofmIiIiIiIjYp2NnYmJGGin/6M//IywrKX1zKV1Gm6Vm9nVEQg6/hCTz+6YFL+dHO31BGD6fe+BVjoS/x7OXUgp380BvnoIYO8VFHnG1y0BON71P5dI7eB0xEREREZIrIy1RDY8ff4fKI79+PPL4oM9uVbmLN26/D717iE16ncqObJ1jKj3b6njux91B4ohXY/96pWSyidBPlT1/jcvtLSrRERERERGyUuWqEfMH4MOCE4Quxpq1ZsZ1/5Mp5jctGhb2hX3Hi+AusePs3vmmTCRSD8E21Y3IfACygbMf7lMV8ZSRnGU76NTZItXKhiIiIiIgkJXPJVukmyp8GRnr4IuY6KCu2+4K//a4HXIdCRqc+4Ky/tHvxi/9G7dtbY/d5pIdTb5hGv0r/hTlcC0m+EvECK95eH71ghoiIiIiITDkZSLZ893EqwbdG6ETUqWsWbzcUmmgFGzv+K04cT/gg0nSVL9p/xZgSLRERERGRaWNGdXX1g1Rf3NHRQWOGCmyIiIiIiIg8TDJWIENERERERGQ6UbIlIiIiIiJiAyVbIiIiIiIiNlCyJSIiIiIiYoNAsrV44146OjomH6+vsa3Ruso+usrv2Lb/XDPdjleyq6L8vK3xpniWTFI8y1RidzxnylQ5DpFMKIA1vN6xiWVc4FDjTj6yucG6yj6anA6O/Fdh0POzdsCc5cHb3umEsQ+j7KgMHnsT8oHv3oLxy7Z0N23RjjfIwtnU7Sik2PTUWN8YPccnbO+fTDV3eME5iuuR03SxilcvxYi7FDyc8XyH7V2nqaeCba8uYFDtPkQUzzKV2BvPmTNVjkMkMwo27t3EsguHaHzH7jQLmH+RJid4PlvOvm/T29Ws9b5EC+AH7vSTraJWmD0fvm2A2+ntalIix+su4pWXZ1rVYvbUXaS3aQhwcGTbcvbZfIZXsf08++tHg5/02n9yma12E1fIvj9Xc7Wyj6aFf2X7cPr/rwUonm2jeI5G8WwJxfPUj+eMmirHIZIZBc+UDHPyPzKQaHGL1qeGYGQFzdfDf3u7fTLJKe6EeNdJbh+D2ct9Cdf3njS79rwv0bJW7OM1lC33/yEfucOJX3/HaPRNc1PFNbr2D+LyrKDNM0STOzPNDu5bTs0+8zO3aO09y/4ubP3Dmq12k9UzsIKq6rPUP32ND/5sRb8Uz3ZSPMemeE6R4nmaxHN2TJXjELFbXgnX8V6xv6GK8qu4cXDkylxrdngZbjTASENuTiFM7HjzmVvi+9fYwPcP3x9ybtG6f5hT26qpabboc03ZXN494gDXMC9UTId245lL82el8Mggr1lwIUHxnGmK52CK5+QpnqdLPGfPVDkOEXsVMHyNoTWv07FpmenpCxxqfMfC9Vu3eG3hKIysSH/6YDJru54HZ0P0bY2pg2ZzOmGO8cN5GGlPpZcWHW9grcAEn7WP8cmXvqcdLxZTW51vuto6k5+3F+HiLn/a8T0L24twGftI54psYOrJJE9bNc09xk9zaa5ZHvaytMVtN5YirqZ6eS1b7drluhPPU0O4H78F19M52Zoi8ew/ltbesxgX+L1HVvHqPmMc3aZ4jttuLOnGVbbatYHiOUzYtLmgKXP2xXPsdmNJL66y1a4tLIvnLJsqxyFiowJK1rLrp4dobHzH/9RiNu7dxaaOvbhadnLYilGvOd9RCni+ztz/iJGSMoDCBrg/ZPNoWJzjDfwxNimuLuaVauOnu/xpxzjJd3Emz7WHrC9wFrL6xe+TX8xdd5HeKic1NUsmn2rto6mpj1YSTUBSkGq7FdfYXT+K98iPSalr2WrXVnPpHwG3c4Q65qbev6kQzwAMs7sXTm2rpnkQf3LtX+CdUAKSqhTatSSustWuXRTPk/xFUFwOjmyrnlyDVXeRru13bIznFNtNO66y1a6dLIrnrJsqxyFinwKGT9ISVBzjCof/4yTP7FrLM+7FHL5iQbZVNI4LB6fG099Vomu78v0jVhMn4MZ7k8/P2jFZWGO8GcYhaATMkgIZFh5vKrzv3eSPHij7b/N47mkorvwBjuNJXj3tWUJNyLdmT/MKqnrPUrroDvFX1aUomXZDR6I8K6hJ9SQjW+3a7It/OoBxFs0BUr2KPxXiGcAFp8zFAXqW0FY1RNPqG1Tss3G9QaLtWh1X2WrXRopnv7q/U+8CT1tIsYueJbxqcX9TbtfKuMpWuzazJJ5zwFQ5DhG7FFCygFIgKKW64uU6sGxB2G9SUlGU+b9qE9eB+ZBfC87ayfLwt1OaFpiceMc7enyM3x8HyOcn/1rMU07rSgkbf8gBLp+/y3NPW19Jy1V6G9uSrWTa7VlCTY8xEuW78tnba321rWy1a4XB8SJgiNIiUv4jOGXi2VvCByGfzxdDDnCPsxTsS7YSbdfquMpWuzZSPPvUVQ2Bt4J3MzyMkFS7FsZVttq1mxXxnAumynGI2CUP5uNaHPLsYhfzgQt/yUSVQnvcbofvTFWmZr8Jzk7fY1b2umWzCW79w7q91bX20dtrfkyu/bBTau0Wsu/VFXgYpf61Ww9VuxKNtfEc3TiLsrJ4Pla7dsZVttqd7qyK5zssKrViP5lsN524yla7IiLWKDg5XMLaX27Es/OwfwxrMRt/uZaS4ZNYVRHed9Uj86NbgWmCBE85nLPD3hGubB2vdczz481XA4MX2+deu7MY8pJC/7LVrv18V/EdDKURjg9/PEe3tHQUKM344vnE2rU+rrLVrlUUzw+zbMXV1I7nXDBVjkPELnmHd7ZwkrXs6uigo6ODjo5drL1+iMZA8mWB8SK8jLK65I5Ve4yrqDV4BGusAb497/9hPhREed3M5y1o3Krj/fIeYwDkM/eHvqciLd623m1KXYBnUYanXaTZbsUNVrvAO5Ts2KWN7dZd9I+SXaQu1j6s3s5v6SOjQBFX05na8dDHczT+K+YeZ4YXdSfYbsrxbGO7iudJWYvnQj44lY1y5mm2m3I829juVIhnO/qXreMQmaLy4AqHdzbS2Gh6vGPx9MFvZzMEuB6JXnqiuHNymp8xAlXYED71L9HtwFfG3Wl6GNUJJz6Be+bGP4Q7Efbl3GHf8SbmPrdGfP9yvTyPV9rnZfbE1D0y+UVbcY2uDE0jTK3dW7TuH8TlrWBPqouhbWi3rspYqD1EVYy/WlZvZ/StygmMpJlMTJV4DlHX6h/NfDez5YoTa9eCeLahXcWzWfbieXDfIt/UuP0hJ8P+6ny512568WxXu1Minm3oX7aOQ2SqijbAY7G5vPulA/fCq2yfszzte20lYrwZ8iJUK4x2T66xt6DgzclKhemx6ngn+OTX48wNvS/L72D1jkKKLelrJHNp3lZB1/5BmnqHaAKglLaaVSzqOk29acu61j6agjKSUer39/m3KaWtZkkSX8CJtmtM+wt+taetOqyiYLbb7ekvpck9BJTSH6NvVm8HwPwR3Fhxy4WHPZ59V8br6wfZ32sauvRWsK0muAqhtfGcaLtWx7N97SqezbIVz+C7h1a1L14D31n4Ku81T/7VszaeE23X6ni2r92pEc9T5zhEpqoZ1dXVD1J9cUdHB42NjQlufYvW6rO4R1ZQMzAd/qecbscrucUff/+sYNufrShrrniWbFI8y1RidTxny1Q5DhF75WWuKd/VRJxn6SrP3Nqt7Jluxyu5pK7yLG4cHPnUqj+AimfJHsWzTCXWx3N2TJXjELFbBpMtGLy0nLYRcC08Tev8TLacHdPteCU3VJSfp8kJns+snbKreJZsUDzLVGJXPGfaVDkOkUzIaLIF0DNQTduIg8mSFFPbdDteybY7vOAcxfvlKpqvx986WYpnySzFs0wl9sZz5kyV4xDJjAyu2RIREREREZk+Mj6yJSIiIiIiMh0o2RIREREREbGBki0REREREREbFHR0dET95fDJFnYevpLB7oiIiIiIiEwNBZEKXCzeuJdda+GcR4mWiIiIiIhIKiJMI1zDi2tL4MIf0KCWiIiIiIhIasKSrcUbf8Ey4MJfPspCd0RERERERKaGkGRrMe5nSmD4JMeVa4mIiIiIiKQsONla8yK+GYSH0QxCERERERGR1JmSrcVs/MUyjWqJiIiIiIhYYDLZ0qiWiIiIiIiIZfzJln9UiwuoLoaIiIiIiEj6fMmWf1Rr+ORxlGuJiIiIiIikL888qvUH3VhLRERERETEEnka1RIREREREbFeAR+9Q6OyLBEREREREUvlxd9EREREREREkqVkS0RERERExAZKtkRERERERGygZEtERERERMQGSrZERERERERsoGRLRERERETEBkq2REREREREbPD/Ayj6iRMwuUkmAAAAAElFTkSuQmCC
学习一下知识 学习一下!支持! 学习了~~~~~ 回帖是一种美德
@lisp 开源项目文档: https://gitee.com/atlisp/atlisp-docs
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