请高手合并输出结果 嵌套块统计
请高手增加一个合并输出结果 ，把第一层的 和嵌套层的输出结果 合并成一个新的输出,原有的保留。;;该程序将生成一份详细说明个人的报告；
;; ;;中的主块和嵌套块，动态块和外部参照的数量
;;选择或整个工程图。 ;;
;;该程序将正确计算嵌套块的数量；
;;嵌套的动态块和嵌套的外部参照；嵌套到任何级别；和;;
;;在每个级别具有相同块的任意数量的实例。 ;;
;;当然，该程序可用作标准块计数器。
;;由于嵌套的块计数数据与;;分开显示
;;主要块计数数据，并且不会显示嵌套报告；
;;如果工程图中没有嵌套块。 ;;
;;该报告将打印到命令行，并分别显示详细信息；
;;分解为;;的主要块和嵌套块的数量；
;;每个块名称的数量，以及总数；
;;与报告相邻的主要块和嵌套块的数量；
;;标题。随后可以将打印的报告提取为;;
;;文本文件或CSV文件。 ;;
;; ------------------------------------------------ ---------------------- ;;
;;作者：Lee Mac，版权所有?2014-www.lee-mac.com ;;
;; ------------------------------------------------ ---------------------- ;;
;;版本1.5-2014-02-02 ;;
(defun c:BlockCount
(
/
_GetBlockCount
_OutputResults
_Main
)
(defun _GetBlockCount;获得块数量
(
selection
/
_Assoc++
_BlockHierarchy
_GetBlockHierarchy
_EffectiveName
_UpdateNestedBlockCount
_IterateSelection
)
(defun _Assoc++ ( key value lst / pair )
(if (setq pair (assoc key lst))
(subst (cons key (+ value (cdr pair))) pair lst)
(cons(cons key value) lst)
)
)
(defun _BlockHierarchy ( blk / alist enx );块层次结构
(while (setq blk (entnext blk))
(if
(and
(= "INSERT" (cdr (assoc 0 (setq enx (entget blk)))))
(/= 1 (cdr (assoc 60 enx)))
)
(setq alist (_Assoc++ (cdr (assoc 2 enx)) 1 alist))
)
)
alist
)
(defun _GetBlockHierarchy ( / block name tree );---获取块层次结构
(while (setq block (tblnext "block" (null block)))
(setq tree
(cons
(cons
(setq name (cdr (assoc 2 block)))
(_BlockHierarchy (tblobjname "block" name))
)
tree
)
)
)
tree
)
(defun _EffectiveName ( ent / blk rep );---有效名称
(if (wcmatch (setq blk (cdr (assoc 2 (entget ent)))) "`**")
(if
(and
(setq rep
(cdadr
(assoc -3
(entget
(cdr
(assoc 330
(entget
(tblobjname "block" blk)
)
)
)
'("AcDbBlockRepBTag")
)
)
)
)
(setq rep (handent (cdr (assoc 1005 rep))))
)
(setq blk (cdr (assoc 2 (entget rep))))
)
)
blk
)
(defun _UpdateNestedBlockCount ( name count tree alist / nests );更新嵌套块计数
(if (setq nests (cdr (assoc name tree)))
(foreach nest nests
(setq alist
(_UpdateNestedBlockCount (car nest) (* count (cdr nest)) tree
(_Assoc++
(_EffectiveName (tblobjname "block" (car nest)))
(* count (cdr nest))
alist
)
)
)
)
alist
)
)
(defun _IterateSelection ( selection blocktree / block idx nested primary );反复选择
(if selection
(repeat (setq idx (sslength selection))
(setq block (ssname selection (setq idx (1- idx)))
primary (_Assoc++ (_EffectiveName block) 1 primary)
nested(_UpdateNestedBlockCount (cdr (assoc 2 (entget block))) 1 blocktree nested)
)
)
)
(list primary nested)
)
(_IterateSelection selection (_GetBlockHierarchy))
)
(defun _OutputResults;---输出结果
(
data
/
_PrintReport
_PrintFile
_PrintOutput
)
(defun _PrintReport(data / _PadBetween _PrintIt);---打印报告
(defun _PadBetween ( s1 s2 ch ln );------------
(
(lambda ( a b c )
(repeat (- ln (length b) (length c)) (setq c (cons a c)))
(vl-list->string (append b c))
)
(ascii ch)
(vl-string->list s1)
(vl-string->list s2)
)
)
(defun _PrintIt ( lst wid );---------
(princ (_PadBetween "\n" "" "=" wid))
(princ "\n 块 计数:")
(princ (_PadBetween "\n" "" "=" wid))
(princ
(_PadBetween
(strcat
"\n 第一级： ("
(itoa (apply '+ (mapcar 'cdr (car lst))))
")"
)
"Count" " " wid
)
)
(princ (_PadBetween "\n" "" "-" wid))
(foreach item
(vl-sort
(car lst)
(function (lambda ( a b ) (< (car a) (car b))))
)
(princ (_PadBetween (strcat "\n " (car item)) (itoa (cdr item)) "." wid))
)
(if (cadr lst)
(progn
(princ (_PadBetween "\n" "" "=" wid))
(princ
(_PadBetween
(strcat
"\n 嵌套块 ("
(itoa (apply '+ (mapcar 'cdr (cadr lst))))
")"
)
"Count" " " wid
)
)
(princ (_PadBetween "\n" "" "-" wid))
(foreach item
(vl-sort
(cadr lst)
(function (lambda ( a b ) (< (car a) (car b))))
)
(princ (_PadBetween (strcat "\n " (car item)) (itoa (cdr item)) "." wid))
)
)
)
(princ (_PadBetween "\n" "" "=" wid))
)
(_PrintIt data 70)
)
(defun _PrintFile( data file / _PrintIt_WriteFile);打印到文件
(defun _PrintIt ( lst del des );----------------------
(princ "块数" des)
(princ
(strcat
"\n第一组块 ("
(itoa (apply '+ (mapcar 'cdr (car lst))))
")"
del
"数量"
)
des
)
(foreach item;---将表中的所有成员以指定变量的身份带入表达式求值
(vl-sort;---根据给定的比较函数来对表中的元素排序
(car lst)
(function (lambda ( a b ) (< (car a) (car b))))
)
(princ (strcat "\n" (car item) del (itoa (cdr item))) des)
)
(if (cadr lst)
(progn
(princ
(strcat
"\n\n嵌套块 ("
(itoa (apply '+ (mapcar 'cdr (cadr lst))))
")"
del
"数量"
)
des
)
(foreach item
(vl-sort
(cadr lst)
(function (lambda ( a b ) (< (car a) (car b))))
)
(princ (strcat "\n" (car item) del (itoa (cdr item))) des)
)
)
)
)
(defun _WriteFile ( data file / desc );---------写入文件
(if file
(if (setq desc (open file "w"))
(progn
(_PrintIt
data;---------------数据
(if (= ".txt" (strcase (vl-filename-extension file) t)) "\t" ",")
desc
)
(close desc)
(startapp "explorer" file)
)
(princ "\n无法写入所选文件.")
)
(princ "\n*Cancel*")
)
)
(_WriteFile data file)
)
(defun _PrintOutput ( data / out );----------------输出
(_PrintReport data)
(textpage)
(initget "TXT CSV")
(cond
( (setq out (getkword "\n输出结果为 <Exit>: "))
(_PrintFile data (getfiled "创建输出文件" "" (strcase out t) 1))
(graphscr)
)
( (graphscr) )
)
)
(_PrintOutput data)
)
;----------------------------------------------------------------
(defun _Main
(
/
*error*
_CountBlocks
)
(defun *error* ( msg );----------------------出错处理
(if (not (wcmatch (strcase msg t) "*break,*cancel*,*exit*"))
(princ (strcat "\nError: " msg))
)
(princ)
)
(defun _CountBlocks ( / allblocks sel );-------------------------标记88
(cond
( (null (setq allblocks (ssget "_X" '((0 . "INSERT")))))
(princ "\n当前图纸中未找到任何块.")
)
( (progn
(setvar 'nomutt 1)
(princ "\n选择要计数的块 <all>: ")
(setq sel
(cond
( (null (setq sel (vl-catch-all-apply 'ssget '(((0 . "INSERT"))))))
allblocks
)
( (null (vl-catch-all-error-p sel))
sel
)
)
)
(setvar 'nomutt 0)
sel
)
(_OutputResults (_GetBlockCount sel))
)
)
(princ)
)
(_CountBlocks)
)
(_Main)
)
;;----------------------------------------------------------------------;;
;;;(princ
;;; (strcat
;;; "\n:: BlockCount.lsp | Version 1.5 | \\U+00A9 Lee Mac "
;;; (menucmd "m=$(edtime,0,yyyy)")
;;; " www.lee-mac.com ::"
;;; "\n:: Type \"BlockCount\" to Invoke ::"
;;; )
;;;)
(princ)
;;----------------------------------------------------------------------;;
;; End of File ;;
;;----------------------------------------------------------------------;;
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不够详细，不能写进cad?无块图层信息，如果是快中快注明一列，同时增加一列给出图块的图层，文字全部一样大，表格全部一样大，图块居中1.1放置的模式2
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