From 7fec6872a1a65ba4efc05b2d8b5f87f20b0852f3 Mon Sep 17 00:00:00 2001 From: stepushovgs Date: Sat, 14 Feb 2026 11:41:46 +0300 Subject: [PATCH 01/23] [0] initial commit --- stepushovgs/427 | 1 + 1 file changed, 1 insertion(+) create mode 100644 stepushovgs/427 diff --git a/stepushovgs/427 b/stepushovgs/427 new file mode 100644 index 0000000..d2a1e59 --- /dev/null +++ b/stepushovgs/427 @@ -0,0 +1 @@ +427 From 5e85fa060bc48fdb95036c808ce51aca35103054 Mon Sep 17 00:00:00 2001 From: GordStep Date: Thu, 16 Apr 2026 22:21:02 +0300 Subject: [PATCH 02/23] init lab1 --- stepushovgs/data-structures/sorce/main.c | 8 ++++++++ stepushovgs/data-structures/sorce/main.o | Bin 0 -> 15896 bytes 2 files changed, 8 insertions(+) create mode 100644 stepushovgs/data-structures/sorce/main.c create mode 100755 stepushovgs/data-structures/sorce/main.o diff --git a/stepushovgs/data-structures/sorce/main.c b/stepushovgs/data-structures/sorce/main.c new file mode 100644 index 0000000..35f3250 --- /dev/null +++ b/stepushovgs/data-structures/sorce/main.c @@ -0,0 +1,8 @@ +#include + +int main() +{ + printf("hello world!\n"); + + return 0; +} diff --git a/stepushovgs/data-structures/sorce/main.o b/stepushovgs/data-structures/sorce/main.o new file mode 100755 index 0000000000000000000000000000000000000000..1537b41b16258c4ed8b18538f9742fd5f4a4e49b GIT binary patch literal 15896 zcmeHOU2Ggz6~4Pk8kfdD%@1`FXr?HE6yc5SBvwL6vX0|q9OcI(P6UE5ti2n1(R$bH zj$6C5B2u6t3K4+^RQgc0Z-o~k)Q9lqD5d33!WA!35vm0Za)ClB3UWZo@}0Ton~aBb zk@^5a%#~)od%kVz*=xha?g8I>c^;@G8eKrwtHU z9=7L*F_IIULu^+&V$@DtnC-CovwIe!o3`?xBJD&JyE(O+Q@aqgy*Wk3Q(_{=pvE(f zn^7i-D7uma<+$qLgcQeYz)GLn6mM_Av3wkr4#UOvPTutM@1?y#wOgwd$8ocD<6o}t zld$7ZP?dKD4v!Ps-ctS7sr^RO?g(PJ7A;6O96EN&eE0O_Ex&qg{jc|4p8DB0-f}Pg z;>E3%ZKdLH&yH=Sd`GERo}B8K+SSw1vm;ZhWV(ZUPwHZQ@9J!%P_A)xL}u1S#-R{F zslSna9=sFLW45*g-wysmREpxnq+c_hhf5vtYJRTjd*iuc8O+F3&O2Ky=Ss!L3r33N z!h6i2BLn;UyzWeQW@nh+zQcF}Lr1-Qp;{O%*8DjQiCYKV6@7&%e}kJqSa6=TAY` zxBMLE)=gRDL>`l}H|Og0y4ZKUHE`k8&$$+)I02gHCY(e%CbxzZx7=%g z+UH(dSmvf)c3-{YuSEsTT0v8N?riI(19$DkzjJ#!P;4f*pK{Od`F#di_rguT*}b@@ z1$=$o4N&tVxR{q$EC-P~bN#Nr@FQ!^-j$`kH%Fm+3U%NrW?H1SSLWZiQ?FkY$Io0h zVNAm)NTDIWr?1zp156YLT3d$)YQciN4rN5>AdJB@sv> zkVGJfKoWr@0!ajt2qY0mB9KJje<1=~m&kR9d>@jvj*?bc2p2v!t30IZ8P}`K^^O}= z=DSz$P6m$a9_4*a63hR3r(O~HvMw^@+Qd)kixt--UeWURi89h93+6sWSyw7)orUnM zW@cX3IdZKe_4!^Z>l`ILV8P6*BjmTLOuuJT4~sy9#QOeGx7PzQP{n87;QAKydhjJU zkQryZwg&R=wEVWp^joX{j|10_#~bd^^{V8#PW4b<-^ZP;cr$+3>Dq}!xm`|oXLnC$ zcjsqN}Ihm?OV#>;;TqR(*;F-V7T zPMP(lJ!@t5)9HX;8;)aJYRr?!NIkrd$X^Kb*M#eU$-jV1Bmcj@1pmXJo;GvEK_-?O z)$R3XH~xq^dAWC5AwH>X|&$^{RoIdr3jt~_;%BjjgZK8gKtz% zANV%Y9>$qghebc#Z1{X3KdAcg`p zH7%Y7zZ(6U)$tSjM}?wGs?TvktM91(Zhb!T`S&b%@h7hTJ@5{VBlr^d;18|{C)wA* zuTJAB7#q*#{qAF*=laQ;N>UB$ERTukvwSn zi&SM%e`Z8{E0pu%ndcom(R;Mt>pynD!MjOsMBpXL4uyu>4i@iqSnQhw(wIzb zdfd+q1O2MSG3Lq@zmOR%PiBTEi`Xer%$rQHj2#dYCSb>MwK0>)PnS`HMZaoG9xYUB z#Y%aR;GwKqDCNXJa}ypj75clsR7_cB*~c zf2B-U{{}CA{v6new-2NOmi3#Vc;qzIa)0`JWB}nvx z@|Tc_+B<3&A{7<2IBe#rn0;0ih-+dUMtfHK^_V?>e~Fy;Q$Ozix742TH;!s5>d&E) zwy#0~nP+f_i?ruFg}6DYjC{QP7h?9i9BV*)!iwRU{YT;l$VBh|tBx%pW~~^WeH=&cz*baf&wXn%+dv|1wGf`!F7Y->qV{}Wn^$`$w6q!85z{b^ z+H;=R)M@qUC##`dD8pDd`p Date: Thu, 16 Apr 2026 22:23:45 +0300 Subject: [PATCH 03/23] add gitignore --- stepushovgs/data-structures/.gitignore | 1 + 1 file changed, 1 insertion(+) create mode 100644 stepushovgs/data-structures/.gitignore diff --git a/stepushovgs/data-structures/.gitignore b/stepushovgs/data-structures/.gitignore new file mode 100644 index 0000000..5761abc --- /dev/null +++ b/stepushovgs/data-structures/.gitignore @@ -0,0 +1 @@ +*.o From c9947a713f5d84b801006d46e429216e1b3dd44a Mon Sep 17 00:00:00 2001 From: GordStep Date: Thu, 16 Apr 2026 22:27:29 +0300 Subject: [PATCH 04/23] move gitignore --- stepushovgs/{data-structures => }/.gitignore | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename stepushovgs/{data-structures => }/.gitignore (100%) diff --git a/stepushovgs/data-structures/.gitignore b/stepushovgs/.gitignore similarity index 100% rename from stepushovgs/data-structures/.gitignore rename to stepushovgs/.gitignore From d9d935c0bfc0b29da5f8ff6e9ef1e0becae75414 Mon Sep 17 00:00:00 2001 From: GordStep Date: Sun, 19 Apr 2026 00:31:51 +0300 Subject: [PATCH 05/23] implementing a linked list - insert - delete - find - listAll --- .../data-structures/sorce/linked_list.c | 141 ++++++++++++++++++ .../data-structures/sorce/linked_list.h | 29 ++++ stepushovgs/data-structures/sorce/main.c | 40 ++++- stepushovgs/data-structures/sorce/main.exe | Bin 0 -> 63350 bytes stepushovgs/data-structures/sorce/main.o | Bin 15896 -> 61346 bytes 5 files changed, 208 insertions(+), 2 deletions(-) create mode 100644 stepushovgs/data-structures/sorce/linked_list.c create mode 100644 stepushovgs/data-structures/sorce/linked_list.h create mode 100644 stepushovgs/data-structures/sorce/main.exe diff --git a/stepushovgs/data-structures/sorce/linked_list.c b/stepushovgs/data-structures/sorce/linked_list.c new file mode 100644 index 0000000..04231ba --- /dev/null +++ b/stepushovgs/data-structures/sorce/linked_list.c @@ -0,0 +1,141 @@ +#include +#include +#include + +#include "linked_list.h" + +int getListNodeLength(Node* HEAD) +{ + int len = 0; + + Node* current = HEAD; + while (current != NULL) + { + len++; + current = current->next; + } + + return len; +} + +// Добавление в конец +Node* insert(Node* head, char name[NAME_BUFF_SIZE], char phone[PHONE_BUFF_SIZE], int show) +{ + Node* newNode = (Node*)malloc(sizeof(Node)); + + strcpy_s(newNode->name_, NAME_BUFF_SIZE, name); + strcpy_s(newNode->phone_, PHONE_BUFF_SIZE, phone); + newNode->next = NULL; + + printf("Data: %s %s\n", name, phone); + printf("New Data: %s %s\n", newNode->name_, newNode->phone_); + + if (head == NULL) +{ + printf("\nNew list\n"); + head = newNode; + return newNode; + } + + Node* last = head; + int ind = 0; + while (last->next != NULL) + { + if (show == 1) + printf("%d \n", ind++); + last = last->next; + } + + last->next = newNode; + return head; +} + +char* find(Node* HEAD, char target_name[NAME_BUFF_SIZE]) +{ + Node* current = HEAD; + + while (current != NULL) + { + + if (strcmp(target_name, current->name_) == 0) + { + return current->phone_; + } + + current = current->next; + } + return NULL; +} + +// Вывод всех элементов +void printAllNodes(Node* head) +{ + Node* current = head; + int ind = 0; + + while (current != NULL) + { + printf("Ind: %d\nName: %s\nPhone: %s\n", ind++, current->name_, current->phone_); + current = current->next; + } +} + +Node* deleteNode(Node* HEAD, char target_name[NAME_BUFF_SIZE]) +{ + Node* privious = HEAD; + Node* current = HEAD->next; + + while (current != NULL) + { + if (strcmp(target_name, current->name_) == 0) + { + privious->next = current->next; + break; + } + + privious = current; + current = current->next; + } + + return HEAD; +} + +Node* listAll(Node* HEAD) +{ + if (HEAD == NULL) + { + return NULL; + } + + int len = getListNodeLength(HEAD); + Node* current = HEAD; + + Node* list = (Node*)malloc(len * sizeof(Node)); + + int ind = 0; + while (current != NULL) + { + list[ind++] = *current; + current = current->next; + } + + return list; +} + +void printNode(Node node) +{ + printf("%s ", node.name_); + printf("%s\n", node.phone_); +} + +void printListNode(Node* list, int length) +{ + printf("\n\n%d\n", length); + for (int i = 0; i < length; i++) + { + printNode(list[i]); + } +} + + + diff --git a/stepushovgs/data-structures/sorce/linked_list.h b/stepushovgs/data-structures/sorce/linked_list.h new file mode 100644 index 0000000..7fb4422 --- /dev/null +++ b/stepushovgs/data-structures/sorce/linked_list.h @@ -0,0 +1,29 @@ +#define NAME_BUFF_SIZE 50 +#define PHONE_BUFF_SIZE 12+1 // +1 for end symbol + +typedef struct Node +{ + char name_[NAME_BUFF_SIZE]; + char phone_[PHONE_BUFF_SIZE]; + struct Node* next; +} Node; + +typedef struct LinkedListPhoneNumbers { + Node* HEAD; +} LinkedListPhoneNumbers; + +Node* insert(Node* head, char name[NAME_BUFF_SIZE], char phone[PHONE_BUFF_SIZE], int show); +void printAllNodes(Node* head); +void printNode(Node node); + +char* find(Node* HEAD, char target_name[NAME_BUFF_SIZE]); + + +Node* deleteNode(Node* HEAD, char target_name[NAME_BUFF_SIZE]); + + +Node* listAll(Node* HEAD); + +void printListNode(Node list[], int length); + +int getListNodeLength(Node* HEAD); diff --git a/stepushovgs/data-structures/sorce/main.c b/stepushovgs/data-structures/sorce/main.c index 35f3250..8b73cdf 100644 --- a/stepushovgs/data-structures/sorce/main.c +++ b/stepushovgs/data-structures/sorce/main.c @@ -1,8 +1,44 @@ -#include +#include +#include +#include + +#include "linked_list.h" + +#define NAME_BUFF_SIZE 50 +#define PHONE_BUFF_SIZE 12+1 // +1 for end symbol int main() { - printf("hello world!\n"); + Node* list = NULL; + char phone[] = "1234"; + for (int i = 0; i < 12; i++) + { + char num[3]; + sprintf_s(num, 3, "%d", i); + + char name[] = "name "; + strcat_s(name, 9, num); + printf("%d %s %s\n", i, name, phone); + list = insert(list, name, phone, 0); + } + char test_name[] = "name 20"; + char test_phone[] = "phone 343"; + + list = insert(list, test_name, test_phone, 1); + + printAllNodes(list); + + printf("\n%s\n", find(list, test_name)); + + strcpy_s(test_name, NAME_BUFF_SIZE, "name 10"); + list = deleteNode(list, test_name); + + printAllNodes(list); + + Node* listNodes = listAll(list); + printListNode(listNodes, getListNodeLength(list)); + + free(listNodes); return 0; } diff --git a/stepushovgs/data-structures/sorce/main.exe b/stepushovgs/data-structures/sorce/main.exe new file mode 100644 index 0000000000000000000000000000000000000000..b7fdfffcdb2767e2cf9dfcbabe1fdaa84f31e48e GIT binary patch literal 63350 zcmeHw3w&JFdFL5PW6Mv;fWbTp*IbYt-Px`~S}4 zKIYDhuu9o&m;2M`ob#RUe6RDJ?>z3EJGHw;SUzK{0G~5w7#l-Mmxuq){jVR@OD}t2 zDSLLocUO+NYQDR&sVkh&V)35Nc(7Y*3r3?oNo`L^i}yyga8#?TZ`8Vb+C%G>ELpfp zk`DM7YyZ?T_QtP&5oU45{)d~fX?7)Bl$W=TT?3@c@yTOMFGflqh~}dIln0#TrprwQ zx&o-3$JqBTbwRII6f_Q&ZIKs5R6>;!BJf5Tdu)No^8Yc$-jSqvd7QCUp2_@gGQe0# zhN{soGIo*+o}m(;b;(d)66y5S_(uYf?$E<}X<)ssJ(vsvI8GHFd`Py9_*gCvs@KH@ zV&8mJKqgj*PZ>Uz%L5AQC5+p#6_rHq27D}+hv>xwJ#Guwgd6@w3lg%x^Q`L=c#FxY z`tc#c+wrkn9>Uv`NDye0Y%)SQ=haVq!-5{~lTqL-#HR`$%jF?@$q1)MrejC&Aw2?A zFfR|cODNLQCi`$4b%fV|kLB_};`JMZVK|8iR1#j31qoRX;cdK@0}m}jGKtSQ`0T{T za(M`^Yy*dUjsJ(zM6Zh%jDH@&+q{{V57GZ(iT5!MGX8l8Z_8F*_S1h&;zc;f_~#+K z@={*@B>gXycu@{A{&@)RS|Q(EK;a{dqr; z8T4tDgS90icfQQQ1E;j-Nx#&Kh-f4Jfn%CcQF`2W8nXrS54kU@Wb8O2w@Gcozd_&W zk&z+aX`q*8LZ7N2bh=VYGo#;kY6KPj8b64@VB|lgT7~^{FOtj zW_Wq%9yA$VzLO{JTX<6YjDN6ZmF62fT01yF^gN)DChb43f|%}$Me7wvfcCG@U!s9G zSLzj@LB8)pZm=Dr1c(~sOP#Zb!yW_dI1k!W3y-^hdN<=9KXA`!)_WW`G_T?_D_LdlTSK!vI5WpMbejrBw92>A&w74JczUY7n6p zA_x~nq0d_QgthR~n@AAf3{;sZ`a0{_R=X>?kc<7+pMxZUhdej<+h{1w_+97)3>b{S zA-0Vcb_(}Cfs3&;z5*yIprk;q=CA-QI1C)Esjb`Q)f-9Ml6neuZ` z{{%>2i2cEM(R1TdWdsAENu-lQo`?ngLJsPmw4%E?B^kdO{R5}`{^7_f&tR>Fs8=%N z(+0*}{wMrLC-bS-zes&x4E%*-5Rkp~Tl@p%5)pw^)OqW_0Zg&(3IFhU-$I^nQe+1T zR7X=gt^kCHL)3J%Y5(vgc_b_$@_rx8cj5?A=;8TXj;X$M6v3@nf)^_xrv0uHB*akd zBs_*#3_+FwghXIo@6(DN_F-J66GN*B|8deoD%(5_H&oX1-(hhfnlgrbm^O~4esno$ zNt46K$jHk@mw8x6$GW1+4stph#$WCjv6P$1{^exbWdTfn|h@~$*(3t|B(9! zs392_fXcW0A{Ha1E;9@w+qb4(od?9@z8SL0ao;aCY}{1# zitm@mzC)0=9>Kq%=668)g>oPc`@ZKL`~kY|1&nm=OTU1XkNZwUgWaKy_@PP{-H7IX!KOu}AgCTfO#zd-$oAskHxxrq9JWl8A?BZEI-;r1koGKKNbXO z#7Kg_;ev?(LcY?8ajp=l?4t7EOX!6OtY-;w3zu*CD^wiz{g||)?TygxX&9G!QE(#k zk8=VhgLzcuZg4d9im0P-1GhMuqICp#@z{Y{SXSkq*5u9?z1YhA7_Q7O*?(~K+G4rZ zKqC}=(^JJXo=`%_^LPj%)(UUYuo^g4Y>XDv*%w&P!+TijS};W`5)Yr|6?Rp>@%zHV zP&Y8X5%n6?FF`|LiqD(%$2}f^p-i&|S}V}wk8hzKn1E$d?_MI+91egAs0}Z9z^wgy zsvWqenDzc1`rsZ^kfHvL`hc4Wnj)RL4gX2!8R@Q5YXgc z6+EZd4BRurdcQx$4Pje)a;j@ z`v8KMALA1YX<9<_1BiSZK6xJY;r_ulNW0RDkR7hrNcl3j8TCd9O-haa`%XPXW*B~E zRX?S$5ue#XXvyDg;$8Zy3n_k{MlGA_r4j1Tl7|M3&)!nc+EM@Dorg_DYmcBjW-3}c zj`9gp(OTI0&XZEPNrW%7Kbl&D5Ngc4C3g?Ni|#u{;zINhHNQ;JD)QdTA^VWnq$DUm zFb-?GyExb#6t$RwdjFPtB4%9K9ENsZPJ?#{E8Ejpm7eKct&}a0yfILLb($RfHUCh- z1krgDhJc?;orjvAqlOQ+f{8N|1Kl&205GEd5na&2XU3zcGrvA_#y$eHA@}uhJ}irl zyVoK|p+D6Le8Qs@0SU+lrHrbEzzB(Q%L(39#2Z_yyyOy;%25()?SXq%7v z@KFs>624L_Czl`K%w?(q0)7D#oS(+ffN!R7>T>R3Vi;gZ$vK-3JTfiepiP3>TWM-@CM>{EqJC=9B?l_(rig$q%5Q56=WkdlQFlIiWrqGAt* zH*!VAebfn*i4{|ZVuILT_N*WQj`FBI?0y~u&~91fs?xWMmfgLo1pkw(Fpd34PE8Z% z%UQRm1ZF59GnBv#=HP(Qtm{R&V8<~-sBw*hl7{n)!Esu(gJRMB-aNTlcUPd>$-rvvHJP1rT zp|LktVAWj$Z-4896FslUM_h;8q{jHl>e3bc8?py=RFM1C8Xu%d(CQ#FM} z2VYY8JUTX-DL`>*J~AF;AjnvE77GbOj>C|cFqS-;8x20U9ph!m$FiZhu#|@@G@pcK z<3qJGSh9Qh-p!AF{=H-w80I1r7%%Wuvm1(&&Oba2o!u9JG`5?A?xo0x*wbo6E;_P< zK1}Z1m;5}EMJv|{Ix{Ld*bijL`$BfS${|wdkFq1s)Cp(Vp9Mp>KB95+O!eRoe6(Pt z9)yY^9AKT9fC_x_a^DC7d=OO9)M5?SIHUAa$X0U+t&EbZVL^b7rmlclC^EyU&N^{xOS+23ws<=X;K^(8BG}4ph@!ML;&c(r@E;i;X-PYINJ9U z=FJ?F#3R~g{0rb}Xt0SI2%W~JuEO?wj4%L{+F|*ae+^$gCjS+VH&rk~qiU#@B)6F7 zB|paL8cq_PMwKUNnh+WisnusdcF_0wz&)?uw7+x6_xcO8P=crb?ayg-K9Hg#q7#5q z?)Xb2qER_W2hvdUTHlGg7s}lDC$Y$B9;+FuJ&7F*&X!(>q=Pux@DDbv(l7;lYlio; z*-6PZbkP1@^h?rKLwA%fHH zN#aweBzTPA7ntxu32y-$!__xUX{i{waD&7AZ|LH>5 z$|G|PEnmbF_jyRnKp`OqPgxdkv>u`RB?G4dGUi}O)2!j&ohAMu_b7;y zv)zbDBW^FOK)0U6Ldi!szDdf_D#(|j9Ic9AlcD8Z)NUB}BLKv7)3(PbJ5WQ}&vM-B zjO>GdNZHSD=zx)Z@IJ~u$|>xl>@cU0PuYh#?rlc)!Dh;0GYc6Wfp#MM;7tTAVUBn=f=>Pt4# zR|tuD%^+%M4~H8{PeWPBWoj*#Jb5LTnh;I$=O~ijwK)z= ziI~C|_cGx-6xH53PkI^0|4-*W=P%CC&)l(2<3Fs%ZIIJ_I7XWLyokn`-RCI_*_`e} zWHY%>yN$HfeQrjLbe~SFd8F`J+=r7V_u(0f`|vwtm-&a>U)lp55N)toS^kHV7jf*& zYcj5LA2Z{ZjIW{{LXX^yf8qt~*O<72`4(;)!YZ^d!$HOTG0u7y=*|o^7t`G{RMEO` zFhKkLO~jM0E&l=!iqL)F8xJ?P#>pkens1Nm8wES3efVE_Sf-xGLdTznFu zq4D1Y6{j77lQB%(Kjl?8I&Ow^SU_N}*Skf?zl{0gTsXC^WzFy!_Fi56b>3%QpWZ9dK0pwrfI5Ka`TAPm;J z+8rMrz;`#I%=owrV+NDLVKa zNJ%FyMGw9za(P7u|3c*+#!bbCJny~;Q`G}kQhLt*HEF4%{-DHK#X{m6gZeOd=KT*ux2By-^V z4P*u14V>@P(iFMyRADLsrchg~xupB^`TNJe7%>(daz5A0RREdSyc;XWMIFI%-inoo zv}u8#jMK^_g*@Gd?ZEBVq;cFuCJ~2sHs0t9pkvh_Z}%396|Oo%7Nk7q6@7hzvQ#OU z%uHvX3wK=OlJedqf_XMc@!SCB z(~pq*(YWJx7Pu?k2DG#ZddPic8m;HBBliwb7GScE5I+-I9B3H2dag z$&ZjFhdirR!;BiHhjS1{a5qYr@e2Pycd^Ur5msA%E0ZmU+*b=Z4LiPoImy($fcKm= zk}Lh*rkh?Aqo`|P|NCw2wgH^c{(lEF?qCD=q-Yj`9&8HF#E@UD`t`)o)Md2Uwch9A zdxs(S{emCG6bh87a|A!>zF!6zsxi8W-irCX;Y#?&(E78<1IO&uRmy?`wX z_8+$f{M+dHRsV3!sul4I{rA1aO9Km-e=X1P+Wo)uuQ}=mu;q61Xq<0jGVNbd^AVG< zV8m8;1r0iWqC=1VhnYc}QP^A-esjc9xg|L8Cf8{gv8 z5fuEqgjuFIbvs&$B8*8mrL>SpT@JAF9mI4g!I{-x-Am%kma6A7&aA#p;?Fg`|6_vR z2bKONEcmJP4PG|&c_FQQzr@d$Hl*Qil=zp+^c$@Hr-{h~}i zBh&k38kgxFo@N@abl#9^#Pq<&Y^`P)w-^95tKjkQdK$pw=(~&wj9fs{^YBSguK$Rh zyxD`nQ%jHeXc#nz1qjLZIM5BRHU}oW>aC$4(}}Th!8Jd;a#%pSO@6;Z;?SmwF4e@E zCm1@RQ>CksTvdpg_N^n~8N zf8*K>>)Im`rt9%gXE>1z#q|z+k0+#uqa8hfwudso)3~l~XRF(_)d~D9WDDsZe|Bs` zsl=v~Bnhmg!AQA8;A`4k{v>mwZ3+o^SHCh;;C(n3}fdyN*5S3{Hq!*6} ze<_sdkr7c2r1^R5(#(G4jd%8ThoVWXy{9{f?=Xep@t(L=QdwW?t*%?cjOs)<+SwZk z#>2@2T1jK|4%G3heqSiw5$V~l#e>n#kZNAP)7MZ{Q-9kU76`_Z;b26I^du5mPluKW zcSgeB85oLW$l;Qo~oVl07|IqB|IgAjiLm z6^fw}>V(?0lIFTfB}H>|Z?tE>)Q-!ocL$SQP_|?Z?=5J)I#IH^^BQe+=Ne5biHDN= zf{|?~q}jg~cG$mm|CTcP(pNH!#|Ars;Ye>hbe%x-wzoq9xJOLu3MRB@j~4C@c81m_ zLT$-#Pn2oe&Tu^08;soA8;T#`jUj6XYz~jub08T?Xu+fwG^nWVHrfT^JxS0~E%%3$ zU0NHybf%T|t?t7fLsPoO5~1Gq9vBPX2MQ8z`r=x$r>!TVq0bToSlzx(sh6wWo^TTV zc2~%XPtpSHWCH1-drxnYdYe1NHCi$l?+hhjIaFPv9SDaa?NmSqXs~iG2*;El%BsQ- zo$ep-*!{RQt<2K=9`>`vTSt8we&MZld`}wvBWdLcIo?!1J)2e@ODlgftz4WI@1IDk zA4w~hq{&;FRxVE~d(z6|Y4l3d^kHf8Tz!q*96Y5xnPswmlwd>q=^k!6?FS;uq@~Cg zB2OuO=lVS4DSZT=a^xvJbuMGOk*9PTkJ5A@PwD#e@g*MQDXqn)ANc^%Tk&}q`BtQD z_>3SQLwW%o$$JcW4e3Ym86_N~%kil0F@ht#6Q2p>DLr@*V?RNj(l_u)Ay4UMoNBy- zJf%AkpPybAe#r1U6s8S<2F#fh8;c}jJBYLIV5x)+}o z_xZMqd+2Ax~*xIqZNu_K0i{pVP>Du7j`P^9=l91nJN48ABfN zo&5tog#+xv%VGgnL7uxH-xY8b=DQ0wQjW0&ly4CE*tOy&*-gcaHRD5mMrkITDBuSr z4yCR|1@1+zb67fkzP`BU^D9XIO;8Oae3oHMxOtwC4Y`3Z^db_ zz%%8oaxE%Y4H>p`8J4*pSeC!QwTw{-GFoiK^Hy3LB@e4zRlOCa@Gf4|eDOo`+4+6* z*g0Kpw$#ce-40*PBEO`&%>0DB0q67W{n4S$B*A>dkIF~IzQWxDz# zd_<;)W%`^GCd{J!lwi^O{S$Xt&{0anYPPxpG=hupGqq~F3V5L^oUHy((qnN zE1yg&pOWPQdXT|#EtTa&{m+7Zu`nXKLDAOY{eNCos$n@tjRjg0R z^6zB26n;V1B{JP2(*~LDmT9|81vX<*S-wlA2W2`U)6dCN;eXh#!V>@4`t>STWhfF# zhAMDkiqplof37)}G*)eo5`=~3ARhVp!!Gd z#!#|3+QqrF`}*2KF*-Y}!f{O~&i>5Z7zu@9?7Qx!NCND4(&-)hty`XH%Ckv4exxA$ zl#_Z`*JHXKitY=?aR|yTV0t2y)T5#OI=wK9tza+_e{a!+tOIsn>>@_Tx;<@Ji7~yc zC)yG2Om@XX!FD>$jD!eEBzhB}xID?lo&~T9JrN7F(FwCIbb$3d1>K?Uw%7rBSreB- z$9W_uyUo%*#?A+yP+vHS`9N{Rv92Bsq6_v@3*cOens`zV;$>Dqyn^XG-NbMUK^a~( zkh;>@I%8XezQI^bPacScSh;8clXkOf&Ac9B)dDjd#ldK}U5^Fn4h7w+Ftpjpf_r-6 zNgQMfX+w<8)PoUWlMHvpiAm5T$&S63O;(4-5>1sJ^cw z77s_09jwSvlMpp}f<1}XA4GZ+U5x!Ti}fZGjJ?dz6Z`h`cCat9L^6)kW}vYz@qBj- zdsUu`9U$mo%11)*fiF`o+C~VYkbMrHTYL?5zM4%N`CT25I1e;}vGCgN#M=Gg=vq2* zwDiK-$cD8W)|N`-65v`tY3;f~!I-VV4Nls^6Kz;nBRKKMKW$j;z@bkXPgr;#Jj;g4 z`hYi=Pa17`tZt|MFlgucq|xB6Vx=&O_URu-8#q*_wGsR9`p^O&pandL3t(3~+CMrn zIy!n}6b0;YYmiKirbeenXGRPEWa3McUz+|BJ5+o~J5+keb0~1A^-%0k|DlmXqlb z5FGbqyl{L9{1W^Z_!*Ht1pbGyz$!@U#)%dQ?4o}*2UraN)*sB(-?vQQQLLE1qpn%2 z*tkjFh}7102jgvB+oHI49u1eE5@Iq6>a zTJao;wzebMvo?_ow(VU@cjK`s!1Dh0@^_q{&%PmL*^M;MHLtBJ7-xBf^CLaDQ_lbO zMromkrv}UDM=37gl-hfG_h4b2=PER+r3?kC zs-OUmdIXcKuy7M>xgf7VNN3;+Iov`HD&&Xh{x2B{E9CZYTPVT)3F>{s^=c7}-h#TO z<~pC=SmF0oHrM#9uzCqF4GFoAfGg4WxmbdR2XczN4msxlN2lR5b=_s+&leMmEo^QQ}wC zcz1A38=Jf}HEh0#QCq*Wg%w)LzS{bR+x6P&##(PvgOVmHPGu zZyk^}ZDb2`V{9p7i*jQ$){~HpY;hWzx2D;`iY-a2hWxx5Xl!X(oxQ1E>#Jo&v!dwL zwY7M0tl>Lc-!w`M)d5(u#@kfA)5n&XxZaA2=Gtb!eR_R!(~kP;x*fW=0ZN@?;shG% zYwCA2`}F2Us)qbNcCK0N3sgI*&oisZ_%%K++AKGL0legcv_r?S^Gz_(uYn7rmR(?i zE9;xLEAR>vtg!0?>_QW&kO95Oga*8odZWL-0e;zBSJ6~mU&k)C*EaZ?nj7jG*(D~p zs=m2FzquKnQ&U}A-Nb&+tn$|2fiJzOdWXMBuc@yOG_p(0N;pQ-ZQceb(%`G|!6(UV z+kJJus_F_1V0M{_T2m2_4y&2K1|OuYt*@-Ex}B{w!DPwmx{69)1-smYR@XK8cK90f z$||(*@!n}>4|)qNb^mt+m6pH?egl zL?~nrWZP@2+4|flO}i@Un(*djLxPNSY8u%F3rgk9%?O&!br@?kY@=CGkIA6AiU(+P zeq$ZmWY$T8S793Pu`(0xYY9-O^KNfsn@uR$!C%dtq^cUG-(s(7v{Y@iS5;Lt_0~FD%7?l|@)U3^XW;Gw|fkuCG)pk~8f`w<5ZNDA!0*%Y+78q)WSxwYJ070S|*y_Y_C*JW$i8YTG_N*G_A2$s;08G)~po@ZP-{| zRqNF&{CJYLk=3PFZ`{1aQeB^3y?Mh%OLf4k-np@|3K6TanNKVt>Nn_q1lB+$yVb-a zr-$oQRA46c>6p^$DtEF5v);S21tMe{7mYa(?L(u*)bsLoI8uA;3`9)!ps3N}|i-HVv_AUj-a%mjglQV`AK4!yx);Bz8Lx(n;Lu zz-YS7I%9m)td~oO+p3%VIy#Z*W|g<*Ht+3?SlnXetz*53m1Ox={OE)c>qP{oNeSJ{ zf@Vddzq+bv1KVRlcKK+^YH5NS=z>>WC2KS5F{9PiV*tvaO;a*!H><0>m@vfPWFZr5 zSv=^sHB>kGSch53tya51-&yU|5#2h?n!0+LeFEO<1}_|wb(uA4MNZoev9=C7E5%w4 zgMijqe8v7T3zAgiRzAhBz0MkcHJW1888NG*)?{PWZGwd-*VM~}Xd{c7bu_A4D*QXP zvmQGTYL~K@2_t1i*re%@ecY@u{F*{4R$5dYH>>6D0amZ97K0sYJ8YYpytkmY6SE@{ zXMvnFk%e8jJF#99tor{}X`Fp#6%X5uO%-gv2_x+rDjHay395mMumf_>NjrAip7S4F zw-(F&rMCflB3i83%K@^c++a!+4l3V{O) zJh@#LyXa!K4tbIYK`gI$zRDZBb(WV`pjXlEdczi$SCEf8GOBUP|FFn@{7OoPOUw8>#|B<(fEn0NrqI{eVZ<>T|DK z>AK#1?aFzsbD@{ZRedgH++}EW-O9Y{7hSt@S@pR$U3dc?*_>C#b^tw}&=*#po3{d| zN6TER3(s9N;#%~mYhM0k7hhI<0S*(3z^NKk7A$91q20psiP$30;?PAaA=cuRtoq#b z5bMM1{j)#|NV##Ai$_K=v|aBmT|x<+1mc6tW&CjX7L@alFUsqr8vYOGTk5PZ{~hMK z{PND*OM7qW>@3|@bNS_YE;OzLF44Y$+GL`g|3lPhU{s0^r(e)WZ11FEJLO{Z4@e(g z|4tU5^IZC_1Ny5^TRgvTR9c(v3(%#ySltisE-k)4&%1O*e|~gn@wj(s;Y$MV!~g#= zEwFCgdhsU?;Ma>sN!Euqm2X+!cAyi_39M^m4cF~P1{euE4-xFddA&|wSlUvy8{ZvW zE544jn;+%t-KgBZyH9+p310(T%K@7w4+0X-rsJnFbGNT89lY<)Bftu8Vc< ziL9j?SqbFL%KtYUXkAAbH?26gjLCGvJuZ z!7rn}RN`RyVY#rTme*e)y$K(xp2uOw@xK*{5?McRG z!J(Cv2h00xni8D@z!{x|&Yu9MbQU^a2Tt)UINt}(B!tN&&%3}0%tGf}bdzTmnYU6` z&O)aaIMcJxX#-B{EOb5r9Bmey$AL3B3$JH@GcgO!OTa0e1?Mf`oPv>^5%}PX@y?+QX!T;oR)5Ls>0di= zrXik?kmdq2RH%`AA8|(wj}rKHg@{sHmu8X&Xt-3L3Yp1oL1%<+C6aG(t2Ash@g%cc6_IAEeLqQNBJDVPIU>D9`0Cps1aGOP5` zQ09Fh9^kYgD!oPk6|t9k&VDI*z7MdFXFs0DYzxNQ=O#}n=m>c-*iilAJ8-hi1xlX3 zH{`(!$!N1_io?JELm*)vE$7}2_+NEsh0bIzOhJd27;VAN1WtP8q-(vVihSiQ- zrIZ`KVV}Qo}cCuhFMjc~$N*dN~u092c#|xX>(I zL{`m%mBzTp5Hp2d6#<)GXkfX7%r%uQxp1UEFGo}1&wWwx#Xr-d4d3AYlRNf$z<;*! zH4YqzgnrLHzE&IZw9&UDZD()qw>%SuJjkS7!iLq}a+?k9ND^n4XI7jFZ^`U9yGnE6 z$o|@F*ryvWy)*lYBvWrGjJ=ofZuIFjpqP1`-~r9q`ZT8v<@nOTL5!~}kh5QEEzyq} zv6i6oQ2V9EMHg^rbmqIV&llT(BmFtqMPL85m`M50C_)nnl4~vDLA}sDgRjcDJE!il zzkG%~{ML`&6^g|0>N+mf*~k+x_fkXjkh{=h44-4w!|dza97^1jQu@YrEHM50CD4}nf57mJJ)hLK*smJ@%E>4GvU-5 zI2XbxzJaZ_lg_OMP6=@Guzhjj&?&FD%7N35EroO6q%at30M02CO}*{}q!`8&-fLei zlXuYd2ynCvwpY2c(F;bF-eR{6(Ngk!+K>n5s_NANmLB{Us3GLh()JevhIGZ~-m4M1 zoqZ~#R{dq__16%{?!jlHSFxcNjY|8aJp43ps0Z?0+578t;0O=T-d|4x$5`{@y%Lt~ zAZL6Pbk~$T9I_b@zYRib)qoAM6Oa+SL(gfk$AKrV1aLN7mI>zx1Lw2Ad0XPxz2$QT z&NIMyRLew1@KL=Z~W_|Ng-b=qyVf0QnJlNv7hF&X4_xC!QGK>^C0H3P>3oQpAhow0@Dowyo-b37x?e>ZU2fb(ufoRbDlKX4+eGw~99oPGM|7+`jP zRgu3HO{k~raZJ*YV~`$#B&W%UBYm|7L_}`XZ7 z2}BFdS&WNZHYAECq&)PjNWLp(GO^@NN*p>%%y&_Irk=B3zmzz|0Oz|tX|I%}wjxznzZii=_aIzHmM@r0g?d*vH9gZa$_t2?0X=UV>Eg``s{8=6 z$#TMm8gkUT_KSRjJVN+}A35@V`$hdly(Ic;p_2&e=*~zh#1#92>~}`!mv4og#g0|J zFpE!ObF(vsd)lSe2_uj+>sXfa=_*4Wddkvx>lBwqy@E>gdwP5pOr0`&QIY0ja_o=| zsU~A=k?|*|%rgGak3x}0h?5FrESHKuj{!%-AMQmK40C0^5jdhRxbEVuT5>rdU%tgA zxzFd;UEq*BUEo6=U|womykEK>GjuODnaG^dee^x(?y>7GUp6L`S8pB*Ts8Z=BXCIf zU62AqE!X?4`|F18)Tj1K=}u2F&DQVrhVDe;65C|i!(XwN;NzXN5xsc^5#Y#su!g`P zYfxOW2MVV(Ks4=oPa{{3f=R;~8R*CrBaM{Vtf3p$5bxvT2g6bGE|>@Q8PB0RsZa-; z00NPSek6~nJ6Tah2|qWL!5*|?!I(?O`3)4inYU^%TgnSaM1Z1-x?qGwI#lYPL*%F;^KqU7k=ES|# z!JS5~jaONpgZo_$?xSJEBM$Ce z4(=lk?w>Qc%Qf3qfFmN3bKlfOKY<3{GX19`+0`4}ixV@Gm-L@;rG-5ZqqP~04S&vr z^Iq0$!oxR?f@UE;VviZ%K{!R|vRiym@^(uQp^_PpZvtMd-$CTx> z<7|y7-*Bz86G6$L*I7jyTHkQX8_|X)9~-2McI!6CmQx785+c3jM2VQO&o0(!4(^2x z?xhayzjJV(m=pI^4(@cn$);&pxr6&sbHqvAPtJ*ZiG%wJ2X~Ky`y~$UlXK#Jjf1=9 z;O=*D-{Rn&niKa@2lo;O_ke?YgM<6@oVb@cxR*M(w>Y@(c5t7W6Zdil_i_jKRtNWX zle>&{c?grU#d&k&syJ^M-O_v68aZ+^#c;Vdf4}1V!-!Xf_~eT7rpzirT4a{G$8+ee z;^7CVS6P?puk0~=vq$xEcATv@DBo~tu#)|9=yg_cejH}!mN(-3L$IL@Qg#F$V1sN~ z>WH+3%3IEg`!NUi5eN4%2lq=H+>7VLolfX%dToBf!F}Ap{b~pI6?5YL9S8SO2lo>W z?qv?{+MKvgIJh5naG!8+uXAuOnG^Td9o%W}Y3qwJx|?BxY}xJLPQN<_w6okv(Fw1O z^5!uI_elr$b_e(JIdT7qgZsFHd&K zZGBNT-lzcAGcu+pEqSzK5vm(`uvMIbXUId0qRwD zSoK$Sf8Iyyeia`?QV*I9X8Oi@-IzPVM&qabAuDJ>La%SqGcrRBey z!}7}OOghTzEWD)GjeLM!7l5&a<>?+j6tG+>(wZ_Wud~Q3b^mz|-IdpUfO?gUsQ${~ zbzQ2Lv*T<{D&KJ7(ZsaNq1Rb?onKK_9<@23>3hohh^OI*ZKG>n_4MK(@7w>YWczud;sCU)eosbBpTb>^NI%lyA6J zrum$CMs4VIR$jM4QC1$cxkSmMAY}oig@SB(-1Iu>Er*r2oE7&=9NbGC+*=&npLTFR zGAHhugL}Dyd#i){5eN6NIdNa*;O=p7?{aV-Gr3Ejr_LZY_T+XMn2*-Xocp2kj9EjLxlRWawVbqjRJ{fXS>8^Z! zbD^@Bf|QjiJ1fYRedf53{(MsT^I37HXYsjbD$1K7tQ}HT;^0ni+>os#_sKbNpKx%W zQRfm$o-)nBeW!zaYEIn00IDOC8*2=EVJ&gZrd| zd!d87=HSk*PCvtcFFrr+;C|A;#;I7Sy`w<8CF$eca2lwY3+)L)foxZbY z>(k9g9NbSjxF2?KFP#(j!w&9;9o#1z+(#VT%jd-Xkc0cEgZl{w_db)mT!Fj>NVava zT78*x)auK^OU@VO+v>nH?NV$0alj!Y*6L{pC_3iTOgQp6G-}QJJ;!;bDYIIAS!9;F zPv+2FtpPqjy~@f}gv`FO+FYz=^XxcVf1-SYKf!Ldwfe5muMOSts%7=YNW8gLJrVt9 z3q(7h(EL;5ffwg!d!0>3ADbsiZ@r1<#o; z#6KJ43P2`7ghqkDSr3TncLBKtkO>1v{f_)diLimdxf3{QS0W%WK6tT1p6?Q!Vk_hy34#m6rOc-}M6|g zKP+Z?!vCaJXiYX2c0NNmwpK;>VokuXGx55LzG{O@$lM+3Zj0dy1bqf~d?9?_p56{7 zp5@ad<&8uKMihF*dXxO!S>kC!g;N0>&BiMX2(80K?{o(vcm)pL0&4Km=}YOgO~$wQ zg%v#tte%XmGb97$^96O{QkU|^fU4W=($c3iA4~SZw3dk1#X$2uF3CK4I2lAMj9tUL9 zM)`+;Oxt9B6Oak0U~2IWAOTP{>1g<}>=7WCkZb7cv%oX;+75^&WfAi10z}b;4*{|F#hZYrJB9-1 z9E2JgO{QmV1Z3LQUpE0V3Q}fYbOX|FYxM{q;~;6`JOv1|;XDtBdfrBG|0h6Nfn&D% zH6R`v_X51f%VT&ZXtFEl#l#?Bj>;{7s51~jc_$#HHaqtMqRv|c&ZhxM*?4^pkcUCs zl;=1g5YWQwCxB3&5)DD;6dnyr2VNGsO8H1hHotwm!8^u%b1f&k1`ozK`426w*N zf%mo=y{yy1M#fdb7rZv06|Kw#FQN=bVB%YKn#5@Yjyl^Bt@Z%2!a^A%C73=6V!$c4 zS?pdw)QO^?GX#j;qn;ti)k-gD(MFU_UnR+p0;ko`lprUm6#}c-gZ~>4dpyj)KzOCV z(L3m^SZD;3n&WF3aApi^5X~z9>9=W721v2Ze|&%x+Ptm>ki#|!KL&_8)fc*d0T6ZG zA|QVO2($6}CP554lRQ5rh(Ve6avLJH4QCoSejAgulQU1%%E{K}vET14P|f!;V9~cX!lA=O2ON zv1u_4NS96K-vL4=z$WD-G(p*Lt{@05(>JbnKxzPKv9-Db5cMt;p+y%UPk@f8=_dgZ z-w0D9We5x=#GS<@Jt>zl*|_bXA%OL(dHUJn9Zu!1ELvpNU|N&3XDu%5kS;Aq_9{Y zAnJ5ZKpp{P)X;+Hd>N2Fq$c+_05NSyIHv&#*jk-$yi5mL@OQ7lz0$60fHP^c#`S>M zW5O+fi0`$LH9F!UiU{n8P182usHZ-J7M}%V0(im`4xzLz)TMXC>077k+CzJKJM~~Z z80`!tR52Xw=u!FLo;~r7FcxE7!GzvMKXOIy zZl~XX!#%)#!AQ700M^~1WGL`=pH1tQqJ7raDoPd2>jZVZg=!9<9)_r@aOHh7U9i($-QXe2O1e15&E z!CQ;pefRCCuEQ#*Jru{!`y?<<#K6@%qCmqhHRyJ@1g4`a)$h9NF6-c@BivQ`zJz#l zj-JpX%wFQh;;W2$X6D383)32lH@c()`3oE@L?o6;OW(C&YiB4~1O4i-%BcxOJCj|3 zGRiZ3*OqWJ5sD{8IndU)K@S63))9`jD;UZfHgP4qkq9+Q7_B#ywud5!F9fxqZ>3*H z0kVO2EpJX(im#-oq!O55t+)5c0iw6lulCs>{CE76wawsU{RwFD(lA2bU7>brYVlo1 zJ9#vP;1xkP6E{l>S&WBbIJVI7?rYR`2m2cF#=dY*G=Y44FTI(VO8WkII2qi7pYp@o z7C|PYV>ub_2;*mBBnGKLNT>;Q_rwn{XenNZ3=g9tGd&if-z(JPz0qX2J0ze%5@|nT zJ1K=90C9}4hUU7aYWQ)W(brsAkKb*tsjm>fdrd!Nub)i=9UF;ectUM3+2xCy6G=Q6 zPC#lvVFPF(1~rZlEpOj7z>OGL+*C$b)-y+INe3@C)!WE6oT?~sW!c`)0p9=ocXoiF z>szYV%HR0WchXdfAY#T9y^6+% zPkIBh7q=g%qsd>{YDXw3@v3^_-u8C*h{@Zmtmr}4r&03th2R>%3HU6$s}prSQRQ;g z(Td)9JQPiG1cPZP+S{#%ySek?{dMtR{D4j`!Z^!C2rWqXVk7fP#h-DQ9JXlvY)i;P3 zfMQoDNIz92Sp;KYy*r`f*I9LXwH>-=A5D#s4f+PX6jOg=DA^qC;w!~=pITv6g(F~( zuCOl!Ni6%cE~KN8MDffNRjjZq0IY{Q34^aHxP5u3QuH$}0ue})unS+*>M^ydHNp&r zNKG)2MBw5fZEaN)w^*-B90&YSWjF}^zKO+}az${FBxl`xumqc7(Aqh{I+0 zSRsGoeuDEHyJ#5pSX1c~#kfOShoNdAj+abHgy$L~mu{_bqGr#iImT?0dlZQ-a4hiA z3q1JnC4AJf;9^aY1pEZSyjKk+QUoVlx{l`$({$AJ3Ox}EwP6}>Gu$la$QF^rbOMaM znAK&31?gJm5r?M+q`YaGOMxcq(<4!SW9KpSD1Y$KF!Y!NS=yO5fm zV0(?2EW99-EpAXp89~DcR+gY-h%O~DwvB0`nM-)-n*nSRu%wzLZpqk3${J!oT;X}` z@?cYyV`=?b6AJDNWf@hvZF0%v0O^`Jyw60)?!3xTgupRFV00T|L{GvN97rVk!L1zq zAvQLKDP)iMq6lRUMRRRng?I5gzdYPxV@V5H+OVnE%#U`BqFO-boMmj@0xkG3;D=!t zDsugd^;Kt-_b0_eIRPM~(u9tsa4^AVx@`WKK94Dvq!@!$D(wT5Z-L9&dvO|)wp^eCopoI>6%85e8o@AqiwK$M&_@|k?b+q@ErO-64MID=JC6Wl zoypC(Z*)uU*`0?UV``Unx2=-W+M5dyhCo!yr#5kHM8SX}q!Lqh(7v{|2&N~qgM^ja zB%YzHJsz2Zp0D-P`HS+fwAmjs(Oz(;9?Iy@Hmg&IV_hEVLyI6ys^OkJdPi@RIwXxQ z905m5$}!r~f&}zOC~7ko>-N6L>F;9hG)AM@=eEtLd5|J|UEIA(>yoquTiQUkkhVvB z;{-*}cJ!v%GnQRN)^M-3q{^4n@Zy~Uk?0gzFp`gd8053<{pg87-9F~2gR6~c%@MY8 z&v-c+av?>u$=PdST2?ATpKiWjOAE$4c0qss{R$va5K$pT#fFR{KP@_NI&CrtQX$vM z+u@A16+VyDmI;vlV%2Ocb19sYME0du8g0X0)Tygz!^1-whz2zVEhL8J2=X7K5p{N1CLsRLB{O?U{OFj9Y=QvoA)4j+xfviXy{Nm~n!ewsN9#EpbSi z-3)e@HVj}r60=63Y_qFv9#K?G8C4YE&kPtnY2Wju$6_(I!gNY&TkAxy{-h(=NU&3E zzsQ8NMyLDR=pMbTYi}ZnQ)eoq1#~%4z~rXOz*&rKAo>uE#1bp>HjJ3b$EXydQ$*RR z*o+!u&QMz1o|YS_UYr8T#kpL*QB&Ir+tQGpE#c!xI=4}kYrST5v9qt^Co1^s!^^m9n{N+SIF@HGLQIe*im27DnJ7uS$&UU>mPseTP zxp7(a%^PbE1=~L6bQC7?tE5YZ&1;$PyrOV6z&;4%* z)hn-ibtQXg$#>VBbOpb=roA_o_9RpNJ*jY?ClXF1`ZJ#WQBP_h;fW9d%Y*|rJHLC*B_4pJqrj;Y54@7g(f64<+ za?|Ce0$m~0E@JFkNf-3$M8U(svMut0h)SqZLImCzWA`i(S^huC*b9<0FHbPm$us%? z4TTsN+g>~R9Ajs>;Px6(uFgaUGsupA0{=)L(j9u3FArE(cZD-y04J!zix0_GjgRT_ zqPjXI5YG|~$izzVslmr|c|l=AtpIR4b|O#owhNg0?OYF8N!F~K826z@)F+ubece8WRp?KIj3Z;zM}6Kmc)h32)QQym0(7BvbfY zj!!E-rprrswHrC)-}3)Zn&@@$g8t7-cw4sc@^SiKF7diKNdM<0ysg`KIY9qC60eto z^nYH$+g`)V57Pe{iT7C!(*Jo0?`9$2eL&$OjO2^!C_Li@o)z*@>9^ei9f6Vm5g2JI z4~%qF1V{Xy`jMup(R*Lx;NkP0S4h9yLzNh#f#KIZdPU6{|NEFNSRm_uwT`hfjNB%- z1OG<+?~jgV{qF<4CLem1qXUuUR^~^4`1~j;0%!a)gmuP0MIM~>PepqISr!=ap9^H^ z<1Ix#2RQ`RJCsbXLETK?R)20_32I2FY5yy^VlL?aehO3EBqZJg5zrP{e-7zcu$rLO zfo#)Rz$OCOj>(xo*}qwe%9+6M12b&k1e$F`vn(5)bqy?iCdwZB z5f}oo6aZn7S7;RFDsEuR$1UkQSpvYT_&|VDxecfY9Cd&GA1_>>;vb{(v5Aa3_YmN@ z7ZFK7>{bv%C35Se{8t>oC=84gq2;G!%LZ!j)HTEl97o)r!f;26>wuUglFNl4i-9)1 z^cCJ$zrG&WPvT!7d&L>TyL_ZPcTkkSsLCrbYmE5c8Ghg$c6jM4G==02tl^~VeaaYR zVDx1)APp*3@yf?#f?D?R>^Q%xh2#JwC8Y|{RP2h`lxh}IQIfc8t5jt1Ub zsb_!&JM=?tu-!um5T`&d_ji~;0jmY<3=i7VOV7Cf=6=RKe)xg+*}%t9?dDDVC*ext zPzEhJ#^FjQ1CN)Zw#Q2l7J#zX5`9th@wL{gML_4yabYs|kMJwVaR}rCBWSszk8;^7 zK7%CdZpZ(|k+YEO6?h6X&AK}PdTe4~Y1aP^z%39s>-GznJ2&G&(f>~1;n!(E>4VV& z5w3s;!bMT2Fc+>d7aqKw1o6*8mD#dCXWcuR_GOlGvETg~ND?^fy)6)-p)?zCp%*Y< zFaon|2QBOr?!y8XV`-uSC^?|yK(6f1Cw44z0d2{SWw1hK-Slp%$$IY@o?UrpG3PTK z;Gm&NkiZc8gNd@2C#Gu&20~Lvr?TF-3H>S#8k#bryE!EpzuE%B=L3PG@k;MVlP56J zQI++3h9_KsV}Vms#nkKjJzQrw@Ry#1fb8Ah;2$Vgi3p^k&bwa+m}1?rz|qxzi9F%t z$PN^!PUXs|JBc_%wWG}hj$T_t!V=<732~&@i6cm%hZl1>rU&gPf?K%+FIPg$1YBoH zh-}joJcd}@0T#muiNL(o?ayz|<#aYqyw*KxDtD0mx0C&A0!LRocr!xs6;)4=_OH?K zNCFt+z|8;#vny6p-O($00Sk=$ATZ)Sa2W8BrWxObHv#WfaR2Ouj+r2N;fT9x9b-)( zb{P=v0Xk@2(TEU?;D2<*jilW0>)Y?X_uUOJ0fOjWB*CNZom3iePgfyEeydx_uO`Ak z*8L`GNX8|g@|OWIm#p~VAOZg>Q^qY2xUlw)EMw?*Wf0lE#yrQqg2pWOtKUIOIbIMh zDjG*7U)@B)@nHWf^rp#*CZLH;sP|C)3cdb*qyCK73otl7P|gMxQIqM-_>aEv&rUB# z4#U^$rIMb9L;YA3sE0#dj&7N-xxA9d8UK64V0r^J2_6k>B0FdO?;Q=fMmpYuH5PA2 zI69r{yc$vzVE4L%g*W#&h86bM3eM{|njo3GyyEP`2ZJ&_9i}Zs4f@ zC%%z4;mxnoh$P1wg$2*}-${h~qTOfwIS+6;egeFA$W*lN5vFWJk6f4x9`#S7{vGo9 zqbt&&*feqqh>-ilRYbmtS3}GhIbwX6TxrB0$S)~-GDYq|-2uk`1vP3^c)8od^Tnn7 zWtwJ%uYUrBvtrTlJmrTcuxN1~<6!qwq82VS@V(Jd;V`S$VpwCMtFH8>i%|ee^Ht_dw_&wayvHW=d z!Uf`MtkXPM_kn|aMkzbvz85(%b*=~aQod0VkgG1$)&)jDkV{DLzLI!jsZ^9%fl?_- zV(uJ%prR=Aar}1;+>O}ef!mW`NQ1w58Jw&R0$z6;R40EO_Rp41Uk?-vYcaD>P~&5e zx`LaQaM0%Gx1!C>{@H;yr(I^HC{Xc{A?Fh@8CWBk6dZCDXBOc<8-Q4L={A#eoV18^ zvcKB`51&6mzJ`z%DqAm!+&r~8V{Q$!#fN?Z=r93|>tAZJ-$P865!^5wWeK2{D#mwTcwMpU!;_wc~PM$k0@ zN*?S_Mka_VD3(3q!}^AgTQ?nh-0Xk9?1_F6P|7KwoPuw059gbWRp^Cs1gRvBAV{&Z zw93z}*jRue)*gri6q$CSM^`K@gyuRcruN())6Rmr6$>@*a^J`)X!CX|zg?isr;n~) zf=L2Svf^JCz*5#b+A-lfSwoiQlh_v#!ZC>OktcCV(eSAIna#A)ogDc# z8RxHlLo1bchG)HHkLEy|=S#{S{h`S37(!b1=zml-rDczvR{0{@2F_x!%%&G3<3$F7 zbI@!e+cb;$#>dxUfvkIAm@EUs zT#bT<7e-D)@$YZqw#$7Vp*ic`1=3hPVn2tBh&`QpI?9m}0 zL*Abx3ux~;Tc0I`{;DuSAR8!$vwWohhH!mEf8R_*Q3z@9H zB5)(o;Clv2F}XZ2wYcob`eIlG*<}wBmsv8^z%@MQEkTwdBKA0?$hv7d;_E4nv`k0J z?4>Md=BB;KVFOcw&O~4mJEAFMr^vFXVXny$|4RgbkzNwV0p#O2d%pnkIrtqm5V!g( zQZK*zXNVan5_g&yNMXtgKdC_-+I{U;d{qKvCYD2hnRlb25k`_j2JFhrVv$?o12X(X zg~lHCR(zDb3{RoVHQQ3%pdCSvovhp4tBHzvTYppDKHqp&`he1jUnU zhX_pmChJcIR&ec5|L#9iP59ZZdePeE#fINA>p=BGI(iAS_A z1eQ$l2DI*lxk;y!)1SoJQmoQMWFaAsuK3;tzE;ir5{@@rGD@Q=+eDI^%yT+`ak`F^ zgr`yEInk8@&s)0o0vM0@-yVM8?b{FaWc_dRH7Pv(dw)mM%5aXhd}jft+?mrPqFy;d z2TV}&X8+mym&)A4gIMHroD620&S7nhz2e)DbT1?gjI>vJFa`YOEbnKdlQI!>(4hhJ zOU8$PhYk$SmeSgxJahH52CC#=Cw+X=NPaSgfiu0!fWu2b`&8}-!D(GD@hMaiJW246 z8SqjG-vc;?tAB>Z0Ufkels$43@tFsw^FwO%ov+EdpN#Vz`(rfuXWc_6@lbXMncSLX zWWM+FPQ=3#Wr0(<5=_b^xd-73fpxD3j@DOt;Np5kV8nd`=$vt{!GF3Cw%)>B5FC~K zH=elPK_a8W^sjLObp`{4gd91acfB5xF$YVU4iEm_TNTKml7T9v+u?JahLGBF~=6eWOf2 zV-+h)xxS?H+2Qjw=4gh+QWb(ft<5p13HANu81chXMG#jWSzIY1L3x&~h5LDS)Ya|s z(7IM)x)9OiQO?Bidg z>=!t6SkFHGFlC?O6b@4MD5p?N*(W&eU3&KM4$5Lz2N|A(ULyPW?F5Af0(zsKeS95d zq2o~5qgPPYO$d*zLY9YxSKwkS=U)lcXzPH5m22P{K1@Z;ji^yG=zj+)VX(4C|1)Lb z+8pV^q{8?Q01L7-LUKPPzqFI(HE*LvHG-y!>?4XSA?akN6X-t(EJ@QK%l&f;=^KQ^ zyyh@!Xb*=2;x9v4$z^&ympt{%V+_BE%Kdi=WFsJ))44eCW9gXw5&mWk`&gnix zHlO>nTS%MT=N8mR_vyi!M+#rSeK>h?AD%I}4?pp~E|7J9uNyib+F-MC#Tm+rICkMJ z8P~awVZSHimyEBX9YT-XjsM=O*sn2hN8$FIdq!VCu$i%)Xa`#^U#+fh!Z;i#f@ z-$;n|`w`;F*OqS{BA08pawbcW{Ick#Hv zG>tEmNJZ5QDc=qrhYSO{466z$kaZ6Ni56Zw*c^5H^fq5VC@kdu3gf?`YabGQS;5!{r zir&fjPOr?73s0Bk(qIa;#hOdHKllIt_M=gK(ILl!$5;iBd5ya(a$GbMY|dLN5|K77 z@RJEzndFeC`zqbIosyCDZY7SsGcq<<-o7apD_q>#;D^0}ly`O6pHEVjDg~3- z>8$`jw&hrs-;9k44mwc`iH(uB6dN24Pt!^C?DWTn{qMPG>9qYN02s?0!zrArtPZiO1XoA|vp>B!lkQB=!Y*NW%;*^YM#PU8uVi z0>-8ui>1IgZ=-IY6AXDyu^gRo1NFs2my5dP{4jvTn7)jv@jX9{O=$-KhY8d-n2EUSkKN=7014CAS!18Vg2j zbt`Dl@e>`ol`kHWAmjapFjNdRs9`;^n{FU_`3+pq^aOAR*LHx69=LZ#EGhno$c%g| z_YAi_cy^${J&F-Ow*DEhdlGp1y2X31dOmv>Vh87Yz+B7QPK}?qtTv+?(;}JvEH3QucQSoT zrl)24qD-HY=@)pKZ+z1ELN0>ofRE88k7=C3;e(a%aCkWlSQvY2ZID-A0gN}?)%`y`Y1t&9ewvZe@iFC`UO|~==vwLKytTyzFP92O&DE@ zr8!S9biQ6kWr`){%Z`slk>wiQY~?p*D^J)mgw#Wv@4nqZsXXP=(@$TwrgoK8X}Wk8tUy&@Elv) zg+GAsXKFMq`mVx|6IIPycKcf!f-QHgW1(;=6AQ;Z z@&0t$)8Fk$$9fX6?ijw?5Q%!KLcx}{HcqL%KT{B^UB_aqy%%4Hh^7bP84tcHk?HUE zr2E40ICA`p644|op-!~RQ`OO2ucYWm97yyZlG<^(wZ3qs7s^(xBP-LlE7qo~*7n@w zS=+PDD(AHY|Qbs1$7xi?9WATAh^cI2W>*|68=%b{k zH=On)`aQ9}a8Go7IvUBu`V-9K*&Rz|2Ey?>2coINyfI|$hRxBp`wwTLX-_!g3F}l8 zYrS14)t><^)$&j*)9Z=gOCg?`!L@_fGkBD)$#is}s~^U~^ZH@pO-@h&Q$ z13a+u00<|QAj+!34>1sw0c0BTTK%}wRu0+x9?iU1Sm~F&-d;Wtvf_Jf^v7-GNjcsW zgO_aOleY3VZRK)XJU(WtAGMVSZTbz_%13PFQCoS!Mz6-E53|Yht54eOagDV+W}~;O zuI?636-LRfyVl=@Q8KuFtG2av{Xis@S)c4r#|AMHdT;XVABbTD??3F>nTkewGoH3^ z0^u=zlP9%pooC~g>P^+SK}*;FYxHQor)zAh)y@KHT`LF+&HZ1+{a+S{|8uwgsP-cZ zd}M(+Er7F~kKx0HL|TJ{WPwA8RY)78b^Q-V3N#rS=S$af-*;4_MR66wc2#@N%ydyswxpE1HgT3*4}>&R1@#%B_FO6#v; z?1#vQkbWMY9P*SdzM8T3kf(G5KI~!0kMvf2mLrcC!hVR)YUC+JEMXqxDcy!I%~TN% z()hKE)gqrn`h9#t$W!_aoU+`5Jf&F=^hKW1xA4gzPiY7nn<3;WeGi`}kY_jq{VYDm zkf-!%e2ybOh7|J>8%Lhf&-^Z9-$cF>sq033PZW7dPvi48@|1>e!q*Ftr}WQ5RDv_sj8lM{EDg9y%W49qs>38r6AW!LS z8?k0Zp3>jh1fIxuBK;G5;>eF9t=P=iAo3oh_u+E{`5~l7@p&3~?C}`(b?jN>DLseJ zapb3v?yUt4z%Ae^DRP$-yF#wgVt45# z$}zTt@~t9YvqjugyS<#T4t&U8Db0tI0Q^yjL#b<7iF=vra%QIwxPMvkqDP8qtbAS2 zj(?VwY(f2gN4-fdE^kW6$n#QON8XIHsQ|u$n{A=!nPCd=nq?i=99hh+99+aM?{%}4 z#Y0#rrk0zL zXY37G{u`P8RHpCC^h)>yU7KWjn@k0iu?AUgmFZ_>+H1r6s2_#Je%t%;ldk$`JerBt z;lvfErSUd-e%R#l<8(7+2eWUx8dA|{Ft$GxP90`Pigraa!Eid`r{i44{)oy={apj` zXaFZI@u-in|4J~NCPw6uC1cMNHzvR>9LHIooxyv>!D#rP1I%WNgZ<$y$rn_=* zIugB{OP7Bz5>3+CV*`$RqAB*T+->n_G|9f}ZjYzIem9-|vER7mnW#M5#N$W`!cR%5 zM>Q>}Y0<>NSPF-z>|;zzM>AR?dPt)e;;;$^6Y=+wT*bO!2ga^ubS&H-!Ags1k^V$? ztS8f(iiW%BG&CM1D3KUQM^o}780&q&>a=t+8le+vP3QpYc}x1DeUap0dKn9sL&JF{ zD7%f)LB_5GpXgvLgV?S(;#gQqgwX|us0DC7j+#_P3*%)qK)iuzJl)K2i$ED(HITZ} z**q=oxxV3KQp+4pM)86f-U24=V>cUlEy@}NW-Nh&)mWF74AUJ5x)q^ovzvwY_op)K zE+(XnGCGqF$Ay){Bwwt5zt%mFh+s9z^^NrRaWk_bQSKI{VwBREu2??|41|PsusfNG zB{JQt%u$mTHCmcIi?27w2hzQay~fZ32lo$jv*%bklfo%As@a!$K6#k(Cnz6}qR0P? za*0S^lEfTa*yj#^YqLMNc@w`&0}`u2C!CC}?@O;g6ick9BTAFi*T*-m-?+X;B3A*| z{IRv`jfRt!2DdqB3(vD)VSabwkw03nx`0C;8&6pDAUw%}$_9bAkdKYFJchT@egd=$ zeQfQyTbNsqp{@Djz=z}Xm>aNV9@tWh_hf^68CLzte5w~aVYSs@L%BPMfwQ% zTVsJrNa|)T-kC0v+Ts9f0iYF&YiRH?fk&}o@vi0$Pu-@?@D`gQz4=5K zrF6Kr8%bs$5#7-fO+-_%h-ZC|`fJ(bS+73bbRU)OrLPyyp?KDJC;Hc?GvUaA^>nu% zn+7cZf2VlYmBsAeNm=eeTI5<3=?$k?QR(7%Kkmfye*+0x2;yl3Ea6b&HflJaXmPq1 z>qd_it;EeMO9ZyaFSeqTmvBm5{R8{4@Gf$d>eW()5>-`Ff=5Zh8CF`l8Ma(fR3fC) z@r4|2AqN$TV|0I+42AV@S1b}uv!6h{8(rTngVFb(uDzq#ueH?${Pi6{zZn+twYE3< zf?BJ8msal&`SGwwbKPF%GPP>+YXz{13S%{dw7QmNJpa<(W~N(QxJ5qn5(B;7-`1`* z_=1oIa%v4Nt=g_&9djEPZK!Gs1smH*nKrh_s8QlK1bw@>rfu!MV2~{~Fq&F+?_s5; zvcIXNb+6Xc*w*B0uM23R!4dT3ql=1pwr+!$MH*|NDY+FD4+ zHn!Y`<_mV1Sg{qhYRJ#4fyP$a>a0z*CVvwvn-@iEY-+-bBX!^5`r0V9Hilr$ps&4g zx1U{R;QH$7I+{8F_iHU3?YmkUn|EoxRw#A3ffH(N3AXI&@M|4yR1Nw4Y?V>%4>dZf zR~yx2{Gi{5Hdh$H5MByG+M(mvl?IsT2jRk~Wgj!Z^(`Gc6}Z9xE9{mKyUKtnWI(Sr zpdnwq))r`KgW8#VE%l8Jd)XQTOqOhHuB-RgvFi#eYz?X22>2!*VHY-dv=+b}mu`@Xv7cD#92 zmmn{lU>nLkZLHZ|y=luD9|PlV3kK1*k+{c!U?*{}1Ec*e^NjHsqh2l{?rLlgXy`qP{oNeSJ{!bU|~ps}HSBinC4_W5bb+S3j< z&;+mMdKNM2F{3rLU;xUXO;a-KGO8PVm@vfPWKjcbT0CfXwKlf88@n=)?{PWXMlw#2V3Mqw2dWH@oVvVJQNYS*x&0V8Ea*re%@ea@)S z{hC55R$5e@GOFe70amYX6oVaWJ8YZUeRrU@)AJ(|XMvnCkcC~iJFx)+tor{>X`F*b z6%X5O?RD&s0VC~O>sr~M0SdxJ*kQTnq#e6u&-wSRJIm$%($|VT5iQoN=@cVwKl- z>#V4#L~Eek^~SBNsH7NoY?ATlpvQof>M*ehFDlmoYJcw0A(kC|xuQ(G4ID`tz@>CE zNlRvM;PAWU>#?;j`3-D!6^=xfF^J7)++ZUHpvE?=q=OPot0$L{uo{B#CU+vU{2A=< zU2G9Gzkj(8|CbaG$*0yAtw-?z2~64^Wk<6Q{=$PxKo01*)qzh(B=vzd<}|vLUv4`_>JS z!##Mept_B<-f|Bzz)0hHiEt0j>oxj9)YjU2@ZHh%;_FWL@S}XK50!`R85G}|!q*Jf zbHL_J_XzroZXNjAJe#U(x2ms5{dOAe{}QP`z^cezMgJmrI9x(|Icus>h2hBYBAP= z&p+au{5>uV?C4+=R@5)?v=}`-hSeKB#Y{X6Chh?X2p&A58-?^r7>Prr1NdOcXM*4% zGDi21MJr_35dFjDVjk=pL|*(~q8G-H!zwWkj%&K;CHeW0W)w@nGG8jDB6gb7+M?nabe7FWE}a z!S^F{2qFFgpC>uQuKPBs=2t1`hvtxwkZ4cRIS&r4tfuC{ISd@{JUD*>oXL4`{v0^t z^Wgj^;0(d+g)C2c{Tetk^Uzs^Zkn2h&NkqT%|oXNIHU96M93wuMVd?I-viFXJanD` z&d@wK^hxM&Q*1Y}&~fm-;nDg1`i}_#^Pt zFBMXWM=h6!vX)Ez>LCFst`xhT!2hLG)Gzf5iUgotcCo7y|0y~emx{(c0Lf^4xQA9y z^l;~Xyp}y)1FKUL5jmK$3YahLaw7{Vi}jATpm- zL!1t2u@c6`#@%!|t?GPMNLkf`cEYM569th~RwY{st9qO`@^>jaQ6sEMbHH*7p873J z0+LaRU54kI5r2Woa(#4LJw@TU$mF<$nm~cX;Lpa8|hftOG z()bYG`$+;jF7n>aIlNc->howRd=(4VJnlLHy+RPd+3#QAJd<+YruP?zzJ*qw&)e#Y zx}@&_XB1KjEhr`!U9Bwdf(ApV;k}Hf@P(HxMksa4BM2)W6Ec&J{VghNu~+W5!p0-8vqM=4&k}HNI2ECFnMY~N63@MhU%Apfm3KK zD|t@o^5Df(wCOO!;olP>kg$(E)}bLTrwy&KhvP`!@S-4OCaCpNHXH-Epv>}@kC&sL zgl~|KSuf=qGXUp{8-%TN|10E0U(ol2=o>4B@=7o7MfipnbS_@6JpCno^?&Q~;HQ?; z14%;~`gYS`mlj6MgyMX33&?$pP3RCbXDgtzklEf}Z&EeCt_4nVo+z;yI6urA6TYX& zu-k$2%e*)O#c9KTKn3-jajCh<3($Nv90yLp@g;wqnkZ8w5IapO85jM6b>+T&`^akw z;?%XaSMCY=jw!2(2X_r3$W;rRedBZC$X^E^0=|gGG-6>e(pQPU-LuRk#tj`ZhWqMaCDg9-5kF~g&E z-$1w^4s*p`FY4#>jS1ig-zYr3mVl9vCqmz*vz)`@*rcogbf>gM=Un9B1xQm zqx0g_`S6ki(~O^wPTjttxo~8EU8UQn4=-0U`idy3x8Zqtf7xS;;OVsCO`wPXVY)75 zyw7PvIld-9Ta2$8$c-$QT1(WT2I|Ad=v>Ttsd3=}ju;o#^(tYj`C<)lq(5hR>1(1U z6Di%3XikD$oImIBRXKOhsk`hi`h6B555EPV^+w}KydsTDbt&?MAn$zR%aA9J%u=3D z>+*<~{;lV47Br013C5OC-Re`rl>?F;(ifVjQ{oNReMoGm)e3E;ey7iXJ}a}qcq+~zB^ekN{j0p~nY zLyNx#WPF}kcqQC+Vh$W69KCn?;K3%()%C)!sgW_P_7Sw4dgU1@a>nlj5iyz! zIaE%`^PDaZmg-t}a$}7l5Q_9><4h-)$3B{#ArYBGVfk?cH>Y0KlNG{JdUYZ{-<&e1 zUUKI$3+Y6EPF zL*YM3bWOzUKD$A$h8aK4il=bJhm`U>=IYx2<%dOQ2{ zYZzcwf4vl6O@cGT%0HH+uaaX3UyTbPO#jtPJq0?#R|y9J)O0D|_#5B|-{=k>h?)>& z_If!KxVPhhQNh)US1oZ?09@?C5;V_so5Wekaqhu?lFYc24L#SBor#Wm63v7oPw+Yc znr~dpWkaGEkn+$o1I4Z+fgIN!i9=_JB8EH1SdYY^dA`{7po0bhQW6In0pJAi-%3aI z#n1GZ-p{eC~pMBZk-sNbo#MZf=vwNjSIhBUJY z8+wqV-nCxj8{`qfHv-6!_ggROFX|=HUrU`tP)B!0#^F|CUvP(&07}ZvztZh2cC7Nn zN_-Mqn4JsHr=#Fia4gFmYbo48j6r(ZPJcTQmq)#-Nc6isJ_{9{GW$?r^D#Mg$c9vt zcby>D0CUPL<4+t!MEoHSv0f_v1c4*s5BDMqlIn{qfn(BLynRV7C*(_-*!cDH$6kR$ zdi8>jZC?I>bsyAqr%`XcljLib9m?((HnLV5LuD$fn`>n?Ce_k9osL`|292_ICy zAJKI$-0xi%q5EcCcM|qljBexV=dWT*^YKpFh~9h%I71g<4S_?}ptxiW6i#bc7;_QS zJ)nyj1yAeN$U{f27?T%ajXK>L;=OJBU^rpi1)Bi*(K&P{!T%$0W?(9^R^BJv@x-k*g1TR5HDryj9-NAG`~&}Z*p+o?%+;w!^*wY!9BSk?llhX zUI%xYAz8z&69N9Du0n0P3>ZqpcOa_)SWM3LIKq#ruH&g_{|_ixXkyNY!mqF%KW zql8}c-+HO>6;i#N@BCSg%I(TGTx+aEP;%&XN#p%I1mhCw?(aLempixz9Ne!oqK)kL6AR)_hd@@{D;(TI4(^*A+{YKhy~M%Y-??5Id<8eh_e6H1GGQB>~HKB!3h1R@64YmP{3$gE26zfJEBW}&D>$Rvm`UvhAt zaB!#ldXkq|XKsDO!M%Jz+)p~VpLKAj&9ap{JxL%TQum4lasQ@+`=o<=se}8A4(^@> zai4H-Kj+|H?%@8agL~D2xWDe;KIPzE;oyGS;4bI)-Dp*4epkClW42Oj8`F#?d$2}n z0bjFU>g42uTH6eQ0r#Id);5OBYHeeZS?ZpeLw7YQKSaH1J*vM7uUod{R4*6C*>+a> zhUi!3F=&pSJL)5GGgzB%t{=6lr zdbu#pwz%?*IecE%>yo_At0*fE-x5;tC`j!QrG1iq` zV7lb!c6y!JGo|jUN($?)#`}k;S8Y=DSK+a|WxML-!Z_Pzm2b>3%jkMt(un(}qO8X9 zmQp2;g4BAI77DVp+u?O<=CHi-I)je#IukGHb)z4m*O9Ny=XHk6YL+p{EWK{S9J(v7 z`w;c24XFMq>``0FRWBFD**2kk!}ST%cobQ+ru{=*cY2X6zRYQQW`{7Z#wk0kwLl8v zdSTX}J^~5T<$TJ5aIBYump(2gfQXGY$s_Mvj~Nom`>^x`HunwX^IK-s-dsUyYm}W8 zWb1BYTu6UDru_MmxF2_LpK)-na&WJAa35O`_b~_eoP)c^!F{WP`-uf{f7Zc$%E7(D z!M)nSeSAUOk2$#0y+4a5)RsHA)4Oyej_mi73*!E?gZrd|d#QsvJs@P|KCvL~qYmz8 z9o%UkTCA}(hX`Qhes)3JpKx%WaB!c+sI_u`!@+%WLEMixxSw=zpK)-1)xrJTg1A5G z;6Co)o^x>jvV;57g18SkxSw!vpK@@2#=$+eAnx}&xQ{uwpL1|O;^02BAnt<>?#CS5 zCmr0A4(_uH;+}DEA9Zj)>)^h};4W7nCjgmm&2P|At1lBT8MTZzcY&$xQfvMRz#$~o z>NW%v9rI~E95KW4FSqcqJja^fkXfz1OfpN|U!FsEwFdYQ^{U;jB4pu})fU>~2;J$w z^^%%ydqMdIe~R0NBX$3-t~*{ktKOc6w|J_jg4bd7MHkTIu4)?~V_447w?y;y;D_i5 zB$%({FV*g5fstzMxY^yL!$-nLj~oK1@b%~lhP9e&es7^{VpKi z0c1kQQNO+2St;ZaIDZWswJQ;j89?Zwl7L(Qq}RfIC0+>9g?iFa;OqcIt(pX6FCg@^ z7-=dXzXym%mj_^W1Q6;zgU*)#8MNShm2hy8Sc1+gfOJ|Yp97?vM^O)OV8b}QXGy*P zN3{9};HWuHKuYknDyvm10GR@JA+!3Wckwg}QKoAPa5~Yk7cMAyngF4%svD4Qg5bh1 zl&$XP5RVDx89>OJj8-oKVwL%IKvdL{dco}Ky*)ynDd3#dEvDfs==3gLvZ!G(Hyn0U z*9%qb8iH_(DG93p8Pe$x&dq>?bgL4i2@v(h8^K)zq{h;N2|z}HOZ5WhAwaBpeIAe* zi#%Tegf1a-U$ifh#6$Gkb?)jnT6K%Q=nKd3(iixc zPFbU`VK=qY&pDf9F2dJe#wux~C|=hR#A-46iVQBJm$w4aX|ds*fLMJjL#-^{@&q8I z78`yQ5VejHHv9`fCM=r1NjMf>zX0SVi#+A{Qec$@=Ndq~z%hEK29T2$y>cXxas-3tjVHvB4Zj#+rU0*E?G5xmX=LT^KW*s_=3 z2jrv$vJzhgRr?FkY8@a$vW0-`0Hnsks}&HhE^~N4e&-e~DlB@Xfin))2KRpnh{0Xc z@QywFf(>29$o(2{tdP@yh-dJW|9lq^^@O&N@Hc?WSR}k0e$fd+R4;Hg5X2(k4nRhA zEr@FnkfcTCdjJWc#)zv&02u?00eKn_s~>(95D$O`_tSu=d!t1xBFBz6pI3VQ95_R0 zWmt@U_Q$Xos#qDmc&b*OLa%E8>9xpH1&DQ|G)M>rue3%25OIOK3%`Ja@oB!=;?Zal z0CjpGc>O-Law`cn@fx$}1c1iK{UUG(V(5Mx5D)BZSnNeWtP)NDqE5dBueSlI0v)3j zy%uN8LV30PN|S+84ahl*C)`f0pq!8pGg^#R1*4Wxa0hTIEtDgGSmSdV5Q@)6?>r62 zD4H3O?T-NoS#bUW5Ve;PdYuEr173#3-T`D>htRK7h2zlO;;a7)I71d*7XTU6TOq@) z#A}fUb-l1kNksaROnf^=`B)Wj)Sg<%vjY%oUjzYB_x=RVeSl0taYOe%1Y`fJ|7p|9e2hla|Upe+|f4i~sxsAm=Q+itxsuCoGz700hiUyy^j=J|zu=7VUr- zD-eL$en2|WUt(0!sxo<4OZQeP2;n>doFh7Sz8-`3j_bXw(Go>QQNkCzzJyliESmm1 zKqf7EeH)No6CI3;u-)g+0Y^Lws_gS)K&C8|e-4P%e^y@!JL|moIvkV@&m=k50VkyE zMG!9_6h@8y+6#y^`W*lSjUcBU5e76y(_!G`ED~k`@p5^T7S94QZt<3{0Wxcm zX9AF87RXzGOn{N$hl}wZrZEezs{oncJ*aq90%EmK9UwD$E8eFO(9!#X?63zor52gH z04Ya}u@?OtL7;^p&r^WR>XeDjaX?O5w0H@S8q@%de;_a6pk&za2Y{$2^9c{73d{va zK}xpr0HW@QVMQ(989Ro0gHAJWm_>^{fCMZucLOqM>5BnCW-Kyii4HEqH=cJuUIIjY zdr`>z8X)RD9s)86$T83{B>a0oj#&EZ=YSaf6&;Lah_lC{g&PwlQ8eUP1Bm)gjo?*H zld1)$0g!QBFaE`0)aNYR?*>lNVvWxNBA&zdfG46vJemNGrPag0nFL$I6P^ZS%tGgR zK&C9Mz7EI`96kqM?I|}dgs@GOaf1M!g9Qn zraIcIb*Je2n$=y={R2H(I2BIxMANDmOLX_Ee0cx-K#bXJ*k%a;}%Z!8HXi2fY za3b6j?FuK8tT&w2BJ@K&^xkp$9V^_sI~b0~x0&l72FVzsmcNe06(ebt>A8ACIh7RKZKj98N~T+tN_eGB${KtD%Vi zUK1WWi1$zmRvI>Z2b^4NNI=fO9{lU)2C1IJ06abt46^LMWF?i{L{z7<-eHa)!!|5pN8c4=t z5qOc7Ok&JnXrwVj`~j_@)z^gIuJ-S0Y{r_WE1JSjl%z3E#K6_M6F|eS3}{xk1lrM+ z>i6AupLy`p(QBo4FfHEtqNTMsvzGXAcBNj=jGVS_Yb=qDrZS=0W~@v?k+zK*)AnuL z%xU@Jae}5rrF{2-jgxq7iyg?{bz|Zvu}qA$u72sDS{MED7gRPN{I|fAwnlI;|BNho zNerRqzGxRUHTjOCojl}0@QB0A#Ld(~7E{qAjs!HkHyO2k;lVb%?Jd@yNF$#*KyS6B zl6EK+%Y^si=fLo$K9GrOSkA?|W6>^!L23{ZYNCDpslyC9iI?f3hw1Q1OGfE8&$QG) zA`|P23TT)_I)vCtO5w*N93I}<(cIn$KMJ+^JL+5T+t0z4I`P}N^kdlCr8LkmA$Oq1 zo5Go1f655tsc-ng%RmZ^*VJ}SBL9twf10}9#+h6I9^Y@aqC0d zO;Z^Lu`#Z-hFGF2*LOgcb}!gslyt*!r>AZ0kk0ClYRb` zglSnImJ1q9tYMnQyJGNg1iVbQrrtip-!wB92H9FmqO;|bKTiLJMy6^_e{Bug^+fp^ z06%PzW`dIm0?I-#owO!Huox)TFst#U43Cc0a={>joKfWOA~K5NR|>ao*|K>HWYG!_ zOoUPD7fl>B!Wyyt@NE1)5KSeb@y(mGFk}?|!?h(_U}EUg$fJb*umPTnilzE#mY}bB zi)9T4JXo85KO3(O4|f?X!pWG{m)7tLj~cxe4c&8)rl$BtZKGC$skbef=}7eQm0Fiy zt#BG*aj-{MSeIfXmUUVc(#Zoslc-{aV*+43+Cx3fXLW909;y`myo)dd(hTgv7nNF4 ztt#~}gCPPH;l(yyt9BhVG2J{C!HYo;-eX#L$} zU#vE0RfaVPnOTHEg!zGZREwqQ1Q<{&I=!|vg-FEl!=LR>Y3YIe>BE@j`?Q02p*zhS zyvwLFaC=IbLYS&P8pp!94wD8J5OF=y2p5>!8iumwnQ?avi|lax&VeZAA6s`EWKb^A z9qU01hRg7=LjK161m`JM(J<_>rqL*hafdVyL)AhY<`|L)&s784=z61P)Er~9nR^t8 zE^sWc(F;8I@FjG@wBTXw@ihDd!F+&J!=rjOweWlNS~?kxVCIhKo;BxS7Qw^lcMP|r z*-M1dR(aHW=!ESoj;m{KS~^P;li|d&24Ngten>@tK7@W~BaBgKQ1ZPbLZoxyAM6iz z1;zZ}0~tLU*+K>NjUFfT_+*Mhy6938eWzv<%~&-{cMM@&j#U(!J5*p zQGD0CW$vNDV~#$B%l4MlkgdJ3&|ru@O?(Oy$1@ZGC@d;5Wd|LMMB{U=;RCS@#v{C%{u0(gB$g^%n_^Rp62Fg z$b}TqX3ky{bFoqp`tW6AFi`u1v#O`#S%wo>2N>ZgdZSC@Fs`45rCx|3IHSRPl*OIR`L zvFxKGRIxhNm$MYkNh0eK%SK!G7j?QR+VJpT1JR(yz@=Ljg%>6H76lfkZNL$_T-<<+ zoK`7F6fl^Id!&hdc|y*hZ^QWV)r?(u(J6GyxAs;P84kGglTzDqiO#RYVPs)5SY28- zfcfal9EA$au9kU3Q88pxQGnlx*L%{sx3kA$F}7lK8fsbK#Ifk4qt1A^M{Kjm;Iyfr zyT<4qEz)}+ox$lY6>I@rP88@&!)4$s`gRY!f<|Ipm4CZM%;cj;3ehPdAD+qNT#Gcb8_&5b6k9llL~{m2d(?;V t=3mBg>*U=q=2*Np+sBTvK`P{`wy9S+t9|S7{{ge|<#7N2 literal 15896 zcmeHOU2Ggz6~4Pk8kfdD%@1`FXr?HE6yc5SBvwL6vX0|q9OcI(P6UE5ti2n1(R$bH zj$6C5B2u6t3K4+^RQgc0Z-o~k)Q9lqD5d33!WA!35vm0Za)ClB3UWZo@}0Ton~aBb zk@^5a%#~)od%kVz*=xha?g8I>c^;@G8eKrwtHU z9=7L*F_IIULu^+&V$@DtnC-CovwIe!o3`?xBJD&JyE(O+Q@aqgy*Wk3Q(_{=pvE(f zn^7i-D7uma<+$qLgcQeYz)GLn6mM_Av3wkr4#UOvPTutM@1?y#wOgwd$8ocD<6o}t zld$7ZP?dKD4v!Ps-ctS7sr^RO?g(PJ7A;6O96EN&eE0O_Ex&qg{jc|4p8DB0-f}Pg z;>E3%ZKdLH&yH=Sd`GERo}B8K+SSw1vm;ZhWV(ZUPwHZQ@9J!%P_A)xL}u1S#-R{F zslSna9=sFLW45*g-wysmREpxnq+c_hhf5vtYJRTjd*iuc8O+F3&O2Ky=Ss!L3r33N z!h6i2BLn;UyzWeQW@nh+zQcF}Lr1-Qp;{O%*8DjQiCYKV6@7&%e}kJqSa6=TAY` zxBMLE)=gRDL>`l}H|Og0y4ZKUHE`k8&$$+)I02gHCY(e%CbxzZx7=%g z+UH(dSmvf)c3-{YuSEsTT0v8N?riI(19$DkzjJ#!P;4f*pK{Od`F#di_rguT*}b@@ z1$=$o4N&tVxR{q$EC-P~bN#Nr@FQ!^-j$`kH%Fm+3U%NrW?H1SSLWZiQ?FkY$Io0h zVNAm)NTDIWr?1zp156YLT3d$)YQciN4rN5>AdJB@sv> zkVGJfKoWr@0!ajt2qY0mB9KJje<1=~m&kR9d>@jvj*?bc2p2v!t30IZ8P}`K^^O}= z=DSz$P6m$a9_4*a63hR3r(O~HvMw^@+Qd)kixt--UeWURi89h93+6sWSyw7)orUnM zW@cX3IdZKe_4!^Z>l`ILV8P6*BjmTLOuuJT4~sy9#QOeGx7PzQP{n87;QAKydhjJU zkQryZwg&R=wEVWp^joX{j|10_#~bd^^{V8#PW4b<-^ZP;cr$+3>Dq}!xm`|oXLnC$ zcjsqN}Ihm?OV#>;;TqR(*;F-V7T zPMP(lJ!@t5)9HX;8;)aJYRr?!NIkrd$X^Kb*M#eU$-jV1Bmcj@1pmXJo;GvEK_-?O z)$R3XH~xq^dAWC5AwH>X|&$^{RoIdr3jt~_;%BjjgZK8gKtz% zANV%Y9>$qghebc#Z1{X3KdAcg`p zH7%Y7zZ(6U)$tSjM}?wGs?TvktM91(Zhb!T`S&b%@h7hTJ@5{VBlr^d;18|{C)wA* zuTJAB7#q*#{qAF*=laQ;N>UB$ERTukvwSn zi&SM%e`Z8{E0pu%ndcom(R;Mt>pynD!MjOsMBpXL4uyu>4i@iqSnQhw(wIzb zdfd+q1O2MSG3Lq@zmOR%PiBTEi`Xer%$rQHj2#dYCSb>MwK0>)PnS`HMZaoG9xYUB z#Y%aR;GwKqDCNXJa}ypj75clsR7_cB*~c zf2B-U{{}CA{v6new-2NOmi3#Vc;qzIa)0`JWB}nvx z@|Tc_+B<3&A{7<2IBe#rn0;0ih-+dUMtfHK^_V?>e~Fy;Q$Ozix742TH;!s5>d&E) zwy#0~nP+f_i?ruFg}6DYjC{QP7h?9i9BV*)!iwRU{YT;l$VBh|tBx%pW~~^WeH=&cz*baf&wXn%+dv|1wGf`!F7Y->qV{}Wn^$`$w6q!85z{b^ z+H;=R)M@qUC##`dD8pDd`p Date: Sun, 19 Apr 2026 00:33:46 +0300 Subject: [PATCH 06/23] update gitignore for c --- .gitignore | 55 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 55 insertions(+) diff --git a/.gitignore b/.gitignore index 5d381cc..5cef1d9 100644 --- a/.gitignore +++ b/.gitignore @@ -160,3 +160,58 @@ cython_debug/ # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ +# Prerequisites +*.d + +# Object files +*.o +*.ko +*.obj +*.elf + +# Linker output +*.ilk +*.map +*.exp + +# Precompiled Headers +*.gch +*.pch + +# Libraries +*.lib +*.a +*.la +*.lo + +# Shared objects (inc. Windows DLLs) +*.dll +*.so +*.so.* +*.dylib + +# Executables +*.exe +*.out +*.app +*.i*86 +*.x86_64 +*.hex + +# Debug files +*.dSYM/ +*.su +*.idb +*.pdb + +# Kernel Module Compile Results +*.mod* +*.cmd +.tmp_versions/ +modules.order +Module.symvers +Mkfile.old +dkms.conf + +# debug information files +*.dwo From 62e1b34fd03468a5ba2b26530f91312059213089 Mon Sep 17 00:00:00 2001 From: GordStep Date: Mon, 20 Apr 2026 11:03:49 +0300 Subject: [PATCH 07/23] Setup gitignore for data-structure --- stepushovgs/data-structures/.gitignore | 2 ++ stepushovgs/data-structures/sorce/main.exe | Bin 63350 -> 0 bytes stepushovgs/data-structures/sorce/main.o | Bin 61346 -> 0 bytes 3 files changed, 2 insertions(+) create mode 100644 stepushovgs/data-structures/.gitignore delete mode 100644 stepushovgs/data-structures/sorce/main.exe delete mode 100755 stepushovgs/data-structures/sorce/main.o diff --git a/stepushovgs/data-structures/.gitignore b/stepushovgs/data-structures/.gitignore new file mode 100644 index 0000000..9fb6852 --- /dev/null +++ b/stepushovgs/data-structures/.gitignore @@ -0,0 +1,2 @@ +.exe +.o diff --git a/stepushovgs/data-structures/sorce/main.exe b/stepushovgs/data-structures/sorce/main.exe deleted file mode 100644 index b7fdfffcdb2767e2cf9dfcbabe1fdaa84f31e48e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 63350 zcmeHw3w&JFdFL5PW6Mv;fWbTp*IbYt-Px`~S}4 zKIYDhuu9o&m;2M`ob#RUe6RDJ?>z3EJGHw;SUzK{0G~5w7#l-Mmxuq){jVR@OD}t2 zDSLLocUO+NYQDR&sVkh&V)35Nc(7Y*3r3?oNo`L^i}yyga8#?TZ`8Vb+C%G>ELpfp zk`DM7YyZ?T_QtP&5oU45{)d~fX?7)Bl$W=TT?3@c@yTOMFGflqh~}dIln0#TrprwQ zx&o-3$JqBTbwRII6f_Q&ZIKs5R6>;!BJf5Tdu)No^8Yc$-jSqvd7QCUp2_@gGQe0# zhN{soGIo*+o}m(;b;(d)66y5S_(uYf?$E<}X<)ssJ(vsvI8GHFd`Py9_*gCvs@KH@ zV&8mJKqgj*PZ>Uz%L5AQC5+p#6_rHq27D}+hv>xwJ#Guwgd6@w3lg%x^Q`L=c#FxY z`tc#c+wrkn9>Uv`NDye0Y%)SQ=haVq!-5{~lTqL-#HR`$%jF?@$q1)MrejC&Aw2?A zFfR|cODNLQCi`$4b%fV|kLB_};`JMZVK|8iR1#j31qoRX;cdK@0}m}jGKtSQ`0T{T za(M`^Yy*dUjsJ(zM6Zh%jDH@&+q{{V57GZ(iT5!MGX8l8Z_8F*_S1h&;zc;f_~#+K z@={*@B>gXycu@{A{&@)RS|Q(EK;a{dqr; z8T4tDgS90icfQQQ1E;j-Nx#&Kh-f4Jfn%CcQF`2W8nXrS54kU@Wb8O2w@Gcozd_&W zk&z+aX`q*8LZ7N2bh=VYGo#;kY6KPj8b64@VB|lgT7~^{FOtj zW_Wq%9yA$VzLO{JTX<6YjDN6ZmF62fT01yF^gN)DChb43f|%}$Me7wvfcCG@U!s9G zSLzj@LB8)pZm=Dr1c(~sOP#Zb!yW_dI1k!W3y-^hdN<=9KXA`!)_WW`G_T?_D_LdlTSK!vI5WpMbejrBw92>A&w74JczUY7n6p zA_x~nq0d_QgthR~n@AAf3{;sZ`a0{_R=X>?kc<7+pMxZUhdej<+h{1w_+97)3>b{S zA-0Vcb_(}Cfs3&;z5*yIprk;q=CA-QI1C)Esjb`Q)f-9Ml6neuZ` z{{%>2i2cEM(R1TdWdsAENu-lQo`?ngLJsPmw4%E?B^kdO{R5}`{^7_f&tR>Fs8=%N z(+0*}{wMrLC-bS-zes&x4E%*-5Rkp~Tl@p%5)pw^)OqW_0Zg&(3IFhU-$I^nQe+1T zR7X=gt^kCHL)3J%Y5(vgc_b_$@_rx8cj5?A=;8TXj;X$M6v3@nf)^_xrv0uHB*akd zBs_*#3_+FwghXIo@6(DN_F-J66GN*B|8deoD%(5_H&oX1-(hhfnlgrbm^O~4esno$ zNt46K$jHk@mw8x6$GW1+4stph#$WCjv6P$1{^exbWdTfn|h@~$*(3t|B(9! zs392_fXcW0A{Ha1E;9@w+qb4(od?9@z8SL0ao;aCY}{1# zitm@mzC)0=9>Kq%=668)g>oPc`@ZKL`~kY|1&nm=OTU1XkNZwUgWaKy_@PP{-H7IX!KOu}AgCTfO#zd-$oAskHxxrq9JWl8A?BZEI-;r1koGKKNbXO z#7Kg_;ev?(LcY?8ajp=l?4t7EOX!6OtY-;w3zu*CD^wiz{g||)?TygxX&9G!QE(#k zk8=VhgLzcuZg4d9im0P-1GhMuqICp#@z{Y{SXSkq*5u9?z1YhA7_Q7O*?(~K+G4rZ zKqC}=(^JJXo=`%_^LPj%)(UUYuo^g4Y>XDv*%w&P!+TijS};W`5)Yr|6?Rp>@%zHV zP&Y8X5%n6?FF`|LiqD(%$2}f^p-i&|S}V}wk8hzKn1E$d?_MI+91egAs0}Z9z^wgy zsvWqenDzc1`rsZ^kfHvL`hc4Wnj)RL4gX2!8R@Q5YXgc z6+EZd4BRurdcQx$4Pje)a;j@ z`v8KMALA1YX<9<_1BiSZK6xJY;r_ulNW0RDkR7hrNcl3j8TCd9O-haa`%XPXW*B~E zRX?S$5ue#XXvyDg;$8Zy3n_k{MlGA_r4j1Tl7|M3&)!nc+EM@Dorg_DYmcBjW-3}c zj`9gp(OTI0&XZEPNrW%7Kbl&D5Ngc4C3g?Ni|#u{;zINhHNQ;JD)QdTA^VWnq$DUm zFb-?GyExb#6t$RwdjFPtB4%9K9ENsZPJ?#{E8Ejpm7eKct&}a0yfILLb($RfHUCh- z1krgDhJc?;orjvAqlOQ+f{8N|1Kl&205GEd5na&2XU3zcGrvA_#y$eHA@}uhJ}irl zyVoK|p+D6Le8Qs@0SU+lrHrbEzzB(Q%L(39#2Z_yyyOy;%25()?SXq%7v z@KFs>624L_Czl`K%w?(q0)7D#oS(+ffN!R7>T>R3Vi;gZ$vK-3JTfiepiP3>TWM-@CM>{EqJC=9B?l_(rig$q%5Q56=WkdlQFlIiWrqGAt* zH*!VAebfn*i4{|ZVuILT_N*WQj`FBI?0y~u&~91fs?xWMmfgLo1pkw(Fpd34PE8Z% z%UQRm1ZF59GnBv#=HP(Qtm{R&V8<~-sBw*hl7{n)!Esu(gJRMB-aNTlcUPd>$-rvvHJP1rT zp|LktVAWj$Z-4896FslUM_h;8q{jHl>e3bc8?py=RFM1C8Xu%d(CQ#FM} z2VYY8JUTX-DL`>*J~AF;AjnvE77GbOj>C|cFqS-;8x20U9ph!m$FiZhu#|@@G@pcK z<3qJGSh9Qh-p!AF{=H-w80I1r7%%Wuvm1(&&Oba2o!u9JG`5?A?xo0x*wbo6E;_P< zK1}Z1m;5}EMJv|{Ix{Ld*bijL`$BfS${|wdkFq1s)Cp(Vp9Mp>KB95+O!eRoe6(Pt z9)yY^9AKT9fC_x_a^DC7d=OO9)M5?SIHUAa$X0U+t&EbZVL^b7rmlclC^EyU&N^{xOS+23ws<=X;K^(8BG}4ph@!ML;&c(r@E;i;X-PYINJ9U z=FJ?F#3R~g{0rb}Xt0SI2%W~JuEO?wj4%L{+F|*ae+^$gCjS+VH&rk~qiU#@B)6F7 zB|paL8cq_PMwKUNnh+WisnusdcF_0wz&)?uw7+x6_xcO8P=crb?ayg-K9Hg#q7#5q z?)Xb2qER_W2hvdUTHlGg7s}lDC$Y$B9;+FuJ&7F*&X!(>q=Pux@DDbv(l7;lYlio; z*-6PZbkP1@^h?rKLwA%fHH zN#aweBzTPA7ntxu32y-$!__xUX{i{waD&7AZ|LH>5 z$|G|PEnmbF_jyRnKp`OqPgxdkv>u`RB?G4dGUi}O)2!j&ohAMu_b7;y zv)zbDBW^FOK)0U6Ldi!szDdf_D#(|j9Ic9AlcD8Z)NUB}BLKv7)3(PbJ5WQ}&vM-B zjO>GdNZHSD=zx)Z@IJ~u$|>xl>@cU0PuYh#?rlc)!Dh;0GYc6Wfp#MM;7tTAVUBn=f=>Pt4# zR|tuD%^+%M4~H8{PeWPBWoj*#Jb5LTnh;I$=O~ijwK)z= ziI~C|_cGx-6xH53PkI^0|4-*W=P%CC&)l(2<3Fs%ZIIJ_I7XWLyokn`-RCI_*_`e} zWHY%>yN$HfeQrjLbe~SFd8F`J+=r7V_u(0f`|vwtm-&a>U)lp55N)toS^kHV7jf*& zYcj5LA2Z{ZjIW{{LXX^yf8qt~*O<72`4(;)!YZ^d!$HOTG0u7y=*|o^7t`G{RMEO` zFhKkLO~jM0E&l=!iqL)F8xJ?P#>pkens1Nm8wES3efVE_Sf-xGLdTznFu zq4D1Y6{j77lQB%(Kjl?8I&Ow^SU_N}*Skf?zl{0gTsXC^WzFy!_Fi56b>3%QpWZ9dK0pwrfI5Ka`TAPm;J z+8rMrz;`#I%=owrV+NDLVKa zNJ%FyMGw9za(P7u|3c*+#!bbCJny~;Q`G}kQhLt*HEF4%{-DHK#X{m6gZeOd=KT*ux2By-^V z4P*u14V>@P(iFMyRADLsrchg~xupB^`TNJe7%>(daz5A0RREdSyc;XWMIFI%-inoo zv}u8#jMK^_g*@Gd?ZEBVq;cFuCJ~2sHs0t9pkvh_Z}%396|Oo%7Nk7q6@7hzvQ#OU z%uHvX3wK=OlJedqf_XMc@!SCB z(~pq*(YWJx7Pu?k2DG#ZddPic8m;HBBliwb7GScE5I+-I9B3H2dag z$&ZjFhdirR!;BiHhjS1{a5qYr@e2Pycd^Ur5msA%E0ZmU+*b=Z4LiPoImy($fcKm= zk}Lh*rkh?Aqo`|P|NCw2wgH^c{(lEF?qCD=q-Yj`9&8HF#E@UD`t`)o)Md2Uwch9A zdxs(S{emCG6bh87a|A!>zF!6zsxi8W-irCX;Y#?&(E78<1IO&uRmy?`wX z_8+$f{M+dHRsV3!sul4I{rA1aO9Km-e=X1P+Wo)uuQ}=mu;q61Xq<0jGVNbd^AVG< zV8m8;1r0iWqC=1VhnYc}QP^A-esjc9xg|L8Cf8{gv8 z5fuEqgjuFIbvs&$B8*8mrL>SpT@JAF9mI4g!I{-x-Am%kma6A7&aA#p;?Fg`|6_vR z2bKONEcmJP4PG|&c_FQQzr@d$Hl*Qil=zp+^c$@Hr-{h~}i zBh&k38kgxFo@N@abl#9^#Pq<&Y^`P)w-^95tKjkQdK$pw=(~&wj9fs{^YBSguK$Rh zyxD`nQ%jHeXc#nz1qjLZIM5BRHU}oW>aC$4(}}Th!8Jd;a#%pSO@6;Z;?SmwF4e@E zCm1@RQ>CksTvdpg_N^n~8N zf8*K>>)Im`rt9%gXE>1z#q|z+k0+#uqa8hfwudso)3~l~XRF(_)d~D9WDDsZe|Bs` zsl=v~Bnhmg!AQA8;A`4k{v>mwZ3+o^SHCh;;C(n3}fdyN*5S3{Hq!*6} ze<_sdkr7c2r1^R5(#(G4jd%8ThoVWXy{9{f?=Xep@t(L=QdwW?t*%?cjOs)<+SwZk z#>2@2T1jK|4%G3heqSiw5$V~l#e>n#kZNAP)7MZ{Q-9kU76`_Z;b26I^du5mPluKW zcSgeB85oLW$l;Qo~oVl07|IqB|IgAjiLm z6^fw}>V(?0lIFTfB}H>|Z?tE>)Q-!ocL$SQP_|?Z?=5J)I#IH^^BQe+=Ne5biHDN= zf{|?~q}jg~cG$mm|CTcP(pNH!#|Ars;Ye>hbe%x-wzoq9xJOLu3MRB@j~4C@c81m_ zLT$-#Pn2oe&Tu^08;soA8;T#`jUj6XYz~jub08T?Xu+fwG^nWVHrfT^JxS0~E%%3$ zU0NHybf%T|t?t7fLsPoO5~1Gq9vBPX2MQ8z`r=x$r>!TVq0bToSlzx(sh6wWo^TTV zc2~%XPtpSHWCH1-drxnYdYe1NHCi$l?+hhjIaFPv9SDaa?NmSqXs~iG2*;El%BsQ- zo$ep-*!{RQt<2K=9`>`vTSt8we&MZld`}wvBWdLcIo?!1J)2e@ODlgftz4WI@1IDk zA4w~hq{&;FRxVE~d(z6|Y4l3d^kHf8Tz!q*96Y5xnPswmlwd>q=^k!6?FS;uq@~Cg zB2OuO=lVS4DSZT=a^xvJbuMGOk*9PTkJ5A@PwD#e@g*MQDXqn)ANc^%Tk&}q`BtQD z_>3SQLwW%o$$JcW4e3Ym86_N~%kil0F@ht#6Q2p>DLr@*V?RNj(l_u)Ay4UMoNBy- zJf%AkpPybAe#r1U6s8S<2F#fh8;c}jJBYLIV5x)+}o z_xZMqd+2Ax~*xIqZNu_K0i{pVP>Du7j`P^9=l91nJN48ABfN zo&5tog#+xv%VGgnL7uxH-xY8b=DQ0wQjW0&ly4CE*tOy&*-gcaHRD5mMrkITDBuSr z4yCR|1@1+zb67fkzP`BU^D9XIO;8Oae3oHMxOtwC4Y`3Z^db_ zz%%8oaxE%Y4H>p`8J4*pSeC!QwTw{-GFoiK^Hy3LB@e4zRlOCa@Gf4|eDOo`+4+6* z*g0Kpw$#ce-40*PBEO`&%>0DB0q67W{n4S$B*A>dkIF~IzQWxDz# zd_<;)W%`^GCd{J!lwi^O{S$Xt&{0anYPPxpG=hupGqq~F3V5L^oUHy((qnN zE1yg&pOWPQdXT|#EtTa&{m+7Zu`nXKLDAOY{eNCos$n@tjRjg0R z^6zB26n;V1B{JP2(*~LDmT9|81vX<*S-wlA2W2`U)6dCN;eXh#!V>@4`t>STWhfF# zhAMDkiqplof37)}G*)eo5`=~3ARhVp!!Gd z#!#|3+QqrF`}*2KF*-Y}!f{O~&i>5Z7zu@9?7Qx!NCND4(&-)hty`XH%Ckv4exxA$ zl#_Z`*JHXKitY=?aR|yTV0t2y)T5#OI=wK9tza+_e{a!+tOIsn>>@_Tx;<@Ji7~yc zC)yG2Om@XX!FD>$jD!eEBzhB}xID?lo&~T9JrN7F(FwCIbb$3d1>K?Uw%7rBSreB- z$9W_uyUo%*#?A+yP+vHS`9N{Rv92Bsq6_v@3*cOens`zV;$>Dqyn^XG-NbMUK^a~( zkh;>@I%8XezQI^bPacScSh;8clXkOf&Ac9B)dDjd#ldK}U5^Fn4h7w+Ftpjpf_r-6 zNgQMfX+w<8)PoUWlMHvpiAm5T$&S63O;(4-5>1sJ^cw z77s_09jwSvlMpp}f<1}XA4GZ+U5x!Ti}fZGjJ?dz6Z`h`cCat9L^6)kW}vYz@qBj- zdsUu`9U$mo%11)*fiF`o+C~VYkbMrHTYL?5zM4%N`CT25I1e;}vGCgN#M=Gg=vq2* zwDiK-$cD8W)|N`-65v`tY3;f~!I-VV4Nls^6Kz;nBRKKMKW$j;z@bkXPgr;#Jj;g4 z`hYi=Pa17`tZt|MFlgucq|xB6Vx=&O_URu-8#q*_wGsR9`p^O&pandL3t(3~+CMrn zIy!n}6b0;YYmiKirbeenXGRPEWa3McUz+|BJ5+o~J5+keb0~1A^-%0k|DlmXqlb z5FGbqyl{L9{1W^Z_!*Ht1pbGyz$!@U#)%dQ?4o}*2UraN)*sB(-?vQQQLLE1qpn%2 z*tkjFh}7102jgvB+oHI49u1eE5@Iq6>a zTJao;wzebMvo?_ow(VU@cjK`s!1Dh0@^_q{&%PmL*^M;MHLtBJ7-xBf^CLaDQ_lbO zMromkrv}UDM=37gl-hfG_h4b2=PER+r3?kC zs-OUmdIXcKuy7M>xgf7VNN3;+Iov`HD&&Xh{x2B{E9CZYTPVT)3F>{s^=c7}-h#TO z<~pC=SmF0oHrM#9uzCqF4GFoAfGg4WxmbdR2XczN4msxlN2lR5b=_s+&leMmEo^QQ}wC zcz1A38=Jf}HEh0#QCq*Wg%w)LzS{bR+x6P&##(PvgOVmHPGu zZyk^}ZDb2`V{9p7i*jQ$){~HpY;hWzx2D;`iY-a2hWxx5Xl!X(oxQ1E>#Jo&v!dwL zwY7M0tl>Lc-!w`M)d5(u#@kfA)5n&XxZaA2=Gtb!eR_R!(~kP;x*fW=0ZN@?;shG% zYwCA2`}F2Us)qbNcCK0N3sgI*&oisZ_%%K++AKGL0legcv_r?S^Gz_(uYn7rmR(?i zE9;xLEAR>vtg!0?>_QW&kO95Oga*8odZWL-0e;zBSJ6~mU&k)C*EaZ?nj7jG*(D~p zs=m2FzquKnQ&U}A-Nb&+tn$|2fiJzOdWXMBuc@yOG_p(0N;pQ-ZQceb(%`G|!6(UV z+kJJus_F_1V0M{_T2m2_4y&2K1|OuYt*@-Ex}B{w!DPwmx{69)1-smYR@XK8cK90f z$||(*@!n}>4|)qNb^mt+m6pH?egl zL?~nrWZP@2+4|flO}i@Un(*djLxPNSY8u%F3rgk9%?O&!br@?kY@=CGkIA6AiU(+P zeq$ZmWY$T8S793Pu`(0xYY9-O^KNfsn@uR$!C%dtq^cUG-(s(7v{Y@iS5;Lt_0~FD%7?l|@)U3^XW;Gw|fkuCG)pk~8f`w<5ZNDA!0*%Y+78q)WSxwYJ070S|*y_Y_C*JW$i8YTG_N*G_A2$s;08G)~po@ZP-{| zRqNF&{CJYLk=3PFZ`{1aQeB^3y?Mh%OLf4k-np@|3K6TanNKVt>Nn_q1lB+$yVb-a zr-$oQRA46c>6p^$DtEF5v);S21tMe{7mYa(?L(u*)bsLoI8uA;3`9)!ps3N}|i-HVv_AUj-a%mjglQV`AK4!yx);Bz8Lx(n;Lu zz-YS7I%9m)td~oO+p3%VIy#Z*W|g<*Ht+3?SlnXetz*53m1Ox={OE)c>qP{oNeSJ{ zf@Vddzq+bv1KVRlcKK+^YH5NS=z>>WC2KS5F{9PiV*tvaO;a*!H><0>m@vfPWFZr5 zSv=^sHB>kGSch53tya51-&yU|5#2h?n!0+LeFEO<1}_|wb(uA4MNZoev9=C7E5%w4 zgMijqe8v7T3zAgiRzAhBz0MkcHJW1888NG*)?{PWZGwd-*VM~}Xd{c7bu_A4D*QXP zvmQGTYL~K@2_t1i*re%@ecY@u{F*{4R$5dYH>>6D0amZ97K0sYJ8YYpytkmY6SE@{ zXMvnFk%e8jJF#99tor{}X`Fp#6%X5uO%-gv2_x+rDjHay395mMumf_>NjrAip7S4F zw-(F&rMCflB3i83%K@^c++a!+4l3V{O) zJh@#LyXa!K4tbIYK`gI$zRDZBb(WV`pjXlEdczi$SCEf8GOBUP|FFn@{7OoPOUw8>#|B<(fEn0NrqI{eVZ<>T|DK z>AK#1?aFzsbD@{ZRedgH++}EW-O9Y{7hSt@S@pR$U3dc?*_>C#b^tw}&=*#po3{d| zN6TER3(s9N;#%~mYhM0k7hhI<0S*(3z^NKk7A$91q20psiP$30;?PAaA=cuRtoq#b z5bMM1{j)#|NV##Ai$_K=v|aBmT|x<+1mc6tW&CjX7L@alFUsqr8vYOGTk5PZ{~hMK z{PND*OM7qW>@3|@bNS_YE;OzLF44Y$+GL`g|3lPhU{s0^r(e)WZ11FEJLO{Z4@e(g z|4tU5^IZC_1Ny5^TRgvTR9c(v3(%#ySltisE-k)4&%1O*e|~gn@wj(s;Y$MV!~g#= zEwFCgdhsU?;Ma>sN!Euqm2X+!cAyi_39M^m4cF~P1{euE4-xFddA&|wSlUvy8{ZvW zE544jn;+%t-KgBZyH9+p310(T%K@7w4+0X-rsJnFbGNT89lY<)Bftu8Vc< ziL9j?SqbFL%KtYUXkAAbH?26gjLCGvJuZ z!7rn}RN`RyVY#rTme*e)y$K(xp2uOw@xK*{5?McRG z!J(Cv2h00xni8D@z!{x|&Yu9MbQU^a2Tt)UINt}(B!tN&&%3}0%tGf}bdzTmnYU6` z&O)aaIMcJxX#-B{EOb5r9Bmey$AL3B3$JH@GcgO!OTa0e1?Mf`oPv>^5%}PX@y?+QX!T;oR)5Ls>0di= zrXik?kmdq2RH%`AA8|(wj}rKHg@{sHmu8X&Xt-3L3Yp1oL1%<+C6aG(t2Ash@g%cc6_IAEeLqQNBJDVPIU>D9`0Cps1aGOP5` zQ09Fh9^kYgD!oPk6|t9k&VDI*z7MdFXFs0DYzxNQ=O#}n=m>c-*iilAJ8-hi1xlX3 zH{`(!$!N1_io?JELm*)vE$7}2_+NEsh0bIzOhJd27;VAN1WtP8q-(vVihSiQ- zrIZ`KVV}Qo}cCuhFMjc~$N*dN~u092c#|xX>(I zL{`m%mBzTp5Hp2d6#<)GXkfX7%r%uQxp1UEFGo}1&wWwx#Xr-d4d3AYlRNf$z<;*! zH4YqzgnrLHzE&IZw9&UDZD()qw>%SuJjkS7!iLq}a+?k9ND^n4XI7jFZ^`U9yGnE6 z$o|@F*ryvWy)*lYBvWrGjJ=ofZuIFjpqP1`-~r9q`ZT8v<@nOTL5!~}kh5QEEzyq} zv6i6oQ2V9EMHg^rbmqIV&llT(BmFtqMPL85m`M50C_)nnl4~vDLA}sDgRjcDJE!il zzkG%~{ML`&6^g|0>N+mf*~k+x_fkXjkh{=h44-4w!|dza97^1jQu@YrEHM50CD4}nf57mJJ)hLK*smJ@%E>4GvU-5 zI2XbxzJaZ_lg_OMP6=@Guzhjj&?&FD%7N35EroO6q%at30M02CO}*{}q!`8&-fLei zlXuYd2ynCvwpY2c(F;bF-eR{6(Ngk!+K>n5s_NANmLB{Us3GLh()JevhIGZ~-m4M1 zoqZ~#R{dq__16%{?!jlHSFxcNjY|8aJp43ps0Z?0+578t;0O=T-d|4x$5`{@y%Lt~ zAZL6Pbk~$T9I_b@zYRib)qoAM6Oa+SL(gfk$AKrV1aLN7mI>zx1Lw2Ad0XPxz2$QT z&NIMyRLew1@KL=Z~W_|Ng-b=qyVf0QnJlNv7hF&X4_xC!QGK>^C0H3P>3oQpAhow0@Dowyo-b37x?e>ZU2fb(ufoRbDlKX4+eGw~99oPGM|7+`jP zRgu3HO{k~raZJ*YV~`$#B&W%UBYm|7L_}`XZ7 z2}BFdS&WNZHYAECq&)PjNWLp(GO^@NN*p>%%y&_Irk=B3zmzz|0Oz|tX|I%}wjxznzZii=_aIzHmM@r0g?d*vH9gZa$_t2?0X=UV>Eg``s{8=6 z$#TMm8gkUT_KSRjJVN+}A35@V`$hdly(Ic;p_2&e=*~zh#1#92>~}`!mv4og#g0|J zFpE!ObF(vsd)lSe2_uj+>sXfa=_*4Wddkvx>lBwqy@E>gdwP5pOr0`&QIY0ja_o=| zsU~A=k?|*|%rgGak3x}0h?5FrESHKuj{!%-AMQmK40C0^5jdhRxbEVuT5>rdU%tgA zxzFd;UEq*BUEo6=U|womykEK>GjuODnaG^dee^x(?y>7GUp6L`S8pB*Ts8Z=BXCIf zU62AqE!X?4`|F18)Tj1K=}u2F&DQVrhVDe;65C|i!(XwN;NzXN5xsc^5#Y#su!g`P zYfxOW2MVV(Ks4=oPa{{3f=R;~8R*CrBaM{Vtf3p$5bxvT2g6bGE|>@Q8PB0RsZa-; z00NPSek6~nJ6Tah2|qWL!5*|?!I(?O`3)4inYU^%TgnSaM1Z1-x?qGwI#lYPL*%F;^KqU7k=ES|# z!JS5~jaONpgZo_$?xSJEBM$Ce z4(=lk?w>Qc%Qf3qfFmN3bKlfOKY<3{GX19`+0`4}ixV@Gm-L@;rG-5ZqqP~04S&vr z^Iq0$!oxR?f@UE;VviZ%K{!R|vRiym@^(uQp^_PpZvtMd-$CTx> z<7|y7-*Bz86G6$L*I7jyTHkQX8_|X)9~-2McI!6CmQx785+c3jM2VQO&o0(!4(^2x z?xhayzjJV(m=pI^4(@cn$);&pxr6&sbHqvAPtJ*ZiG%wJ2X~Ky`y~$UlXK#Jjf1=9 z;O=*D-{Rn&niKa@2lo;O_ke?YgM<6@oVb@cxR*M(w>Y@(c5t7W6Zdil_i_jKRtNWX zle>&{c?grU#d&k&syJ^M-O_v68aZ+^#c;Vdf4}1V!-!Xf_~eT7rpzirT4a{G$8+ee z;^7CVS6P?puk0~=vq$xEcATv@DBo~tu#)|9=yg_cejH}!mN(-3L$IL@Qg#F$V1sN~ z>WH+3%3IEg`!NUi5eN4%2lq=H+>7VLolfX%dToBf!F}Ap{b~pI6?5YL9S8SO2lo>W z?qv?{+MKvgIJh5naG!8+uXAuOnG^Td9o%W}Y3qwJx|?BxY}xJLPQN<_w6okv(Fw1O z^5!uI_elr$b_e(JIdT7qgZsFHd&K zZGBNT-lzcAGcu+pEqSzK5vm(`uvMIbXUId0qRwD zSoK$Sf8Iyyeia`?QV*I9X8Oi@-IzPVM&qabAuDJ>La%SqGcrRBey z!}7}OOghTzEWD)GjeLM!7l5&a<>?+j6tG+>(wZ_Wud~Q3b^mz|-IdpUfO?gUsQ${~ zbzQ2Lv*T<{D&KJ7(ZsaNq1Rb?onKK_9<@23>3hohh^OI*ZKG>n_4MK(@7w>YWczud;sCU)eosbBpTb>^NI%lyA6J zrum$CMs4VIR$jM4QC1$cxkSmMAY}oig@SB(-1Iu>Er*r2oE7&=9NbGC+*=&npLTFR zGAHhugL}Dyd#i){5eN6NIdNa*;O=p7?{aV-Gr3Ejr_LZY_T+XMn2*-Xocp2kj9EjLxlRWawVbqjRJ{fXS>8^Z! zbD^@Bf|QjiJ1fYRedf53{(MsT^I37HXYsjbD$1K7tQ}HT;^0ni+>os#_sKbNpKx%W zQRfm$o-)nBeW!zaYEIn00IDOC8*2=EVJ&gZrd| zd!d87=HSk*PCvtcFFrr+;C|A;#;I7Sy`w<8CF$eca2lwY3+)L)foxZbY z>(k9g9NbSjxF2?KFP#(j!w&9;9o#1z+(#VT%jd-Xkc0cEgZl{w_db)mT!Fj>NVava zT78*x)auK^OU@VO+v>nH?NV$0alj!Y*6L{pC_3iTOgQp6G-}QJJ;!;bDYIIAS!9;F zPv+2FtpPqjy~@f}gv`FO+FYz=^XxcVf1-SYKf!Ldwfe5muMOSts%7=YNW8gLJrVt9 z3q(7h(EL;5ffwg!d!0>3ADbsiZ@r1<#o; z#6KJ43P2`7ghqkDSr3TncLBKtkO>1v{f_)diLimdxf3{QS0W%WK6tT1p6?Q!Vk_hy34#m6rOc-}M6|g zKP+Z?!vCaJXiYX2c0NNmwpK;>VokuXGx55LzG{O@$lM+3Zj0dy1bqf~d?9?_p56{7 zp5@ad<&8uKMihF*dXxO!S>kC!g;N0>&BiMX2(80K?{o(vcm)pL0&4Km=}YOgO~$wQ zg%v#tte%XmGb97$^96O{QkU|^fU4W=($c3iA4~SZw3dk1#X$2uF3CK4I2lAMj9tUL9 zM)`+;Oxt9B6Oak0U~2IWAOTP{>1g<}>=7WCkZb7cv%oX;+75^&WfAi10z}b;4*{|F#hZYrJB9-1 z9E2JgO{QmV1Z3LQUpE0V3Q}fYbOX|FYxM{q;~;6`JOv1|;XDtBdfrBG|0h6Nfn&D% zH6R`v_X51f%VT&ZXtFEl#l#?Bj>;{7s51~jc_$#HHaqtMqRv|c&ZhxM*?4^pkcUCs zl;=1g5YWQwCxB3&5)DD;6dnyr2VNGsO8H1hHotwm!8^u%b1f&k1`ozK`426w*N zf%mo=y{yy1M#fdb7rZv06|Kw#FQN=bVB%YKn#5@Yjyl^Bt@Z%2!a^A%C73=6V!$c4 zS?pdw)QO^?GX#j;qn;ti)k-gD(MFU_UnR+p0;ko`lprUm6#}c-gZ~>4dpyj)KzOCV z(L3m^SZD;3n&WF3aApi^5X~z9>9=W721v2Ze|&%x+Ptm>ki#|!KL&_8)fc*d0T6ZG zA|QVO2($6}CP554lRQ5rh(Ve6avLJH4QCoSejAgulQU1%%E{K}vET14P|f!;V9~cX!lA=O2ON zv1u_4NS96K-vL4=z$WD-G(p*Lt{@05(>JbnKxzPKv9-Db5cMt;p+y%UPk@f8=_dgZ z-w0D9We5x=#GS<@Jt>zl*|_bXA%OL(dHUJn9Zu!1ELvpNU|N&3XDu%5kS;Aq_9{Y zAnJ5ZKpp{P)X;+Hd>N2Fq$c+_05NSyIHv&#*jk-$yi5mL@OQ7lz0$60fHP^c#`S>M zW5O+fi0`$LH9F!UiU{n8P182usHZ-J7M}%V0(im`4xzLz)TMXC>077k+CzJKJM~~Z z80`!tR52Xw=u!FLo;~r7FcxE7!GzvMKXOIy zZl~XX!#%)#!AQ700M^~1WGL`=pH1tQqJ7raDoPd2>jZVZg=!9<9)_r@aOHh7U9i($-QXe2O1e15&E z!CQ;pefRCCuEQ#*Jru{!`y?<<#K6@%qCmqhHRyJ@1g4`a)$h9NF6-c@BivQ`zJz#l zj-JpX%wFQh;;W2$X6D383)32lH@c()`3oE@L?o6;OW(C&YiB4~1O4i-%BcxOJCj|3 zGRiZ3*OqWJ5sD{8IndU)K@S63))9`jD;UZfHgP4qkq9+Q7_B#ywud5!F9fxqZ>3*H z0kVO2EpJX(im#-oq!O55t+)5c0iw6lulCs>{CE76wawsU{RwFD(lA2bU7>brYVlo1 zJ9#vP;1xkP6E{l>S&WBbIJVI7?rYR`2m2cF#=dY*G=Y44FTI(VO8WkII2qi7pYp@o z7C|PYV>ub_2;*mBBnGKLNT>;Q_rwn{XenNZ3=g9tGd&if-z(JPz0qX2J0ze%5@|nT zJ1K=90C9}4hUU7aYWQ)W(brsAkKb*tsjm>fdrd!Nub)i=9UF;ectUM3+2xCy6G=Q6 zPC#lvVFPF(1~rZlEpOj7z>OGL+*C$b)-y+INe3@C)!WE6oT?~sW!c`)0p9=ocXoiF z>szYV%HR0WchXdfAY#T9y^6+% zPkIBh7q=g%qsd>{YDXw3@v3^_-u8C*h{@Zmtmr}4r&03th2R>%3HU6$s}prSQRQ;g z(Td)9JQPiG1cPZP+S{#%ySek?{dMtR{D4j`!Z^!C2rWqXVk7fP#h-DQ9JXlvY)i;P3 zfMQoDNIz92Sp;KYy*r`f*I9LXwH>-=A5D#s4f+PX6jOg=DA^qC;w!~=pITv6g(F~( zuCOl!Ni6%cE~KN8MDffNRjjZq0IY{Q34^aHxP5u3QuH$}0ue})unS+*>M^ydHNp&r zNKG)2MBw5fZEaN)w^*-B90&YSWjF}^zKO+}az${FBxl`xumqc7(Aqh{I+0 zSRsGoeuDEHyJ#5pSX1c~#kfOShoNdAj+abHgy$L~mu{_bqGr#iImT?0dlZQ-a4hiA z3q1JnC4AJf;9^aY1pEZSyjKk+QUoVlx{l`$({$AJ3Ox}EwP6}>Gu$la$QF^rbOMaM znAK&31?gJm5r?M+q`YaGOMxcq(<4!SW9KpSD1Y$KF!Y!NS=yO5fm zV0(?2EW99-EpAXp89~DcR+gY-h%O~DwvB0`nM-)-n*nSRu%wzLZpqk3${J!oT;X}` z@?cYyV`=?b6AJDNWf@hvZF0%v0O^`Jyw60)?!3xTgupRFV00T|L{GvN97rVk!L1zq zAvQLKDP)iMq6lRUMRRRng?I5gzdYPxV@V5H+OVnE%#U`BqFO-boMmj@0xkG3;D=!t zDsugd^;Kt-_b0_eIRPM~(u9tsa4^AVx@`WKK94Dvq!@!$D(wT5Z-L9&dvO|)wp^eCopoI>6%85e8o@AqiwK$M&_@|k?b+q@ErO-64MID=JC6Wl zoypC(Z*)uU*`0?UV``Unx2=-W+M5dyhCo!yr#5kHM8SX}q!Lqh(7v{|2&N~qgM^ja zB%YzHJsz2Zp0D-P`HS+fwAmjs(Oz(;9?Iy@Hmg&IV_hEVLyI6ys^OkJdPi@RIwXxQ z905m5$}!r~f&}zOC~7ko>-N6L>F;9hG)AM@=eEtLd5|J|UEIA(>yoquTiQUkkhVvB z;{-*}cJ!v%GnQRN)^M-3q{^4n@Zy~Uk?0gzFp`gd8053<{pg87-9F~2gR6~c%@MY8 z&v-c+av?>u$=PdST2?ATpKiWjOAE$4c0qss{R$va5K$pT#fFR{KP@_NI&CrtQX$vM z+u@A16+VyDmI;vlV%2Ocb19sYME0du8g0X0)Tygz!^1-whz2zVEhL8J2=X7K5p{N1CLsRLB{O?U{OFj9Y=QvoA)4j+xfviXy{Nm~n!ewsN9#EpbSi z-3)e@HVj}r60=63Y_qFv9#K?G8C4YE&kPtnY2Wju$6_(I!gNY&TkAxy{-h(=NU&3E zzsQ8NMyLDR=pMbTYi}ZnQ)eoq1#~%4z~rXOz*&rKAo>uE#1bp>HjJ3b$EXydQ$*RR z*o+!u&QMz1o|YS_UYr8T#kpL*QB&Ir+tQGpE#c!xI=4}kYrST5v9qt^Co1^s!^^m9n{N+SIF@HGLQIe*im27DnJ7uS$&UU>mPseTP zxp7(a%^PbE1=~L6bQC7?tE5YZ&1;$PyrOV6z&;4%* z)hn-ibtQXg$#>VBbOpb=roA_o_9RpNJ*jY?ClXF1`ZJ#WQBP_h;fW9d%Y*|rJHLC*B_4pJqrj;Y54@7g(f64<+ za?|Ce0$m~0E@JFkNf-3$M8U(svMut0h)SqZLImCzWA`i(S^huC*b9<0FHbPm$us%? z4TTsN+g>~R9Ajs>;Px6(uFgaUGsupA0{=)L(j9u3FArE(cZD-y04J!zix0_GjgRT_ zqPjXI5YG|~$izzVslmr|c|l=AtpIR4b|O#owhNg0?OYF8N!F~K826z@)F+ubece8WRp?KIj3Z;zM}6Kmc)h32)QQym0(7BvbfY zj!!E-rprrswHrC)-}3)Zn&@@$g8t7-cw4sc@^SiKF7diKNdM<0ysg`KIY9qC60eto z^nYH$+g`)V57Pe{iT7C!(*Jo0?`9$2eL&$OjO2^!C_Li@o)z*@>9^ei9f6Vm5g2JI z4~%qF1V{Xy`jMup(R*Lx;NkP0S4h9yLzNh#f#KIZdPU6{|NEFNSRm_uwT`hfjNB%- z1OG<+?~jgV{qF<4CLem1qXUuUR^~^4`1~j;0%!a)gmuP0MIM~>PepqISr!=ap9^H^ z<1Ix#2RQ`RJCsbXLETK?R)20_32I2FY5yy^VlL?aehO3EBqZJg5zrP{e-7zcu$rLO zfo#)Rz$OCOj>(xo*}qwe%9+6M12b&k1e$F`vn(5)bqy?iCdwZB z5f}oo6aZn7S7;RFDsEuR$1UkQSpvYT_&|VDxecfY9Cd&GA1_>>;vb{(v5Aa3_YmN@ z7ZFK7>{bv%C35Se{8t>oC=84gq2;G!%LZ!j)HTEl97o)r!f;26>wuUglFNl4i-9)1 z^cCJ$zrG&WPvT!7d&L>TyL_ZPcTkkSsLCrbYmE5c8Ghg$c6jM4G==02tl^~VeaaYR zVDx1)APp*3@yf?#f?D?R>^Q%xh2#JwC8Y|{RP2h`lxh}IQIfc8t5jt1Ub zsb_!&JM=?tu-!um5T`&d_ji~;0jmY<3=i7VOV7Cf=6=RKe)xg+*}%t9?dDDVC*ext zPzEhJ#^FjQ1CN)Zw#Q2l7J#zX5`9th@wL{gML_4yabYs|kMJwVaR}rCBWSszk8;^7 zK7%CdZpZ(|k+YEO6?h6X&AK}PdTe4~Y1aP^z%39s>-GznJ2&G&(f>~1;n!(E>4VV& z5w3s;!bMT2Fc+>d7aqKw1o6*8mD#dCXWcuR_GOlGvETg~ND?^fy)6)-p)?zCp%*Y< zFaon|2QBOr?!y8XV`-uSC^?|yK(6f1Cw44z0d2{SWw1hK-Slp%$$IY@o?UrpG3PTK z;Gm&NkiZc8gNd@2C#Gu&20~Lvr?TF-3H>S#8k#bryE!EpzuE%B=L3PG@k;MVlP56J zQI++3h9_KsV}Vms#nkKjJzQrw@Ry#1fb8Ah;2$Vgi3p^k&bwa+m}1?rz|qxzi9F%t z$PN^!PUXs|JBc_%wWG}hj$T_t!V=<732~&@i6cm%hZl1>rU&gPf?K%+FIPg$1YBoH zh-}joJcd}@0T#muiNL(o?ayz|<#aYqyw*KxDtD0mx0C&A0!LRocr!xs6;)4=_OH?K zNCFt+z|8;#vny6p-O($00Sk=$ATZ)Sa2W8BrWxObHv#WfaR2Ouj+r2N;fT9x9b-)( zb{P=v0Xk@2(TEU?;D2<*jilW0>)Y?X_uUOJ0fOjWB*CNZom3iePgfyEeydx_uO`Ak z*8L`GNX8|g@|OWIm#p~VAOZg>Q^qY2xUlw)EMw?*Wf0lE#yrQqg2pWOtKUIOIbIMh zDjG*7U)@B)@nHWf^rp#*CZLH;sP|C)3cdb*qyCK73otl7P|gMxQIqM-_>aEv&rUB# z4#U^$rIMb9L;YA3sE0#dj&7N-xxA9d8UK64V0r^J2_6k>B0FdO?;Q=fMmpYuH5PA2 zI69r{yc$vzVE4L%g*W#&h86bM3eM{|njo3GyyEP`2ZJ&_9i}Zs4f@ zC%%z4;mxnoh$P1wg$2*}-${h~qTOfwIS+6;egeFA$W*lN5vFWJk6f4x9`#S7{vGo9 zqbt&&*feqqh>-ilRYbmtS3}GhIbwX6TxrB0$S)~-GDYq|-2uk`1vP3^c)8od^Tnn7 zWtwJ%uYUrBvtrTlJmrTcuxN1~<6!qwq82VS@V(Jd;V`S$VpwCMtFH8>i%|ee^Ht_dw_&wayvHW=d z!Uf`MtkXPM_kn|aMkzbvz85(%b*=~aQod0VkgG1$)&)jDkV{DLzLI!jsZ^9%fl?_- zV(uJ%prR=Aar}1;+>O}ef!mW`NQ1w58Jw&R0$z6;R40EO_Rp41Uk?-vYcaD>P~&5e zx`LaQaM0%Gx1!C>{@H;yr(I^HC{Xc{A?Fh@8CWBk6dZCDXBOc<8-Q4L={A#eoV18^ zvcKB`51&6mzJ`z%DqAm!+&r~8V{Q$!#fN?Z=r93|>tAZJ-$P865!^5wWeK2{D#mwTcwMpU!;_wc~PM$k0@ zN*?S_Mka_VD3(3q!}^AgTQ?nh-0Xk9?1_F6P|7KwoPuw059gbWRp^Cs1gRvBAV{&Z zw93z}*jRue)*gri6q$CSM^`K@gyuRcruN())6Rmr6$>@*a^J`)X!CX|zg?isr;n~) zf=L2Svf^JCz*5#b+A-lfSwoiQlh_v#!ZC>OktcCV(eSAIna#A)ogDc# z8RxHlLo1bchG)HHkLEy|=S#{S{h`S37(!b1=zml-rDczvR{0{@2F_x!%%&G3<3$F7 zbI@!e+cb;$#>dxUfvkIAm@EUs zT#bT<7e-D)@$YZqw#$7Vp*ic`1=3hPVn2tBh&`QpI?9m}0 zL*Abx3ux~;Tc0I`{;DuSAR8!$vwWohhH!mEf8R_*Q3z@9H zB5)(o;Clv2F}XZ2wYcob`eIlG*<}wBmsv8^z%@MQEkTwdBKA0?$hv7d;_E4nv`k0J z?4>Md=BB;KVFOcw&O~4mJEAFMr^vFXVXny$|4RgbkzNwV0p#O2d%pnkIrtqm5V!g( zQZK*zXNVan5_g&yNMXtgKdC_-+I{U;d{qKvCYD2hnRlb25k`_j2JFhrVv$?o12X(X zg~lHCR(zDb3{RoVHQQ3%pdCSvovhp4tBHzvTYppDKHqp&`he1jUnU zhX_pmChJcIR&ec5|L#9iP59ZZdePeE#fINA>p=BGI(iAS_A z1eQ$l2DI*lxk;y!)1SoJQmoQMWFaAsuK3;tzE;ir5{@@rGD@Q=+eDI^%yT+`ak`F^ zgr`yEInk8@&s)0o0vM0@-yVM8?b{FaWc_dRH7Pv(dw)mM%5aXhd}jft+?mrPqFy;d z2TV}&X8+mym&)A4gIMHroD620&S7nhz2e)DbT1?gjI>vJFa`YOEbnKdlQI!>(4hhJ zOU8$PhYk$SmeSgxJahH52CC#=Cw+X=NPaSgfiu0!fWu2b`&8}-!D(GD@hMaiJW246 z8SqjG-vc;?tAB>Z0Ufkels$43@tFsw^FwO%ov+EdpN#Vz`(rfuXWc_6@lbXMncSLX zWWM+FPQ=3#Wr0(<5=_b^xd-73fpxD3j@DOt;Np5kV8nd`=$vt{!GF3Cw%)>B5FC~K zH=elPK_a8W^sjLObp`{4gd91acfB5xF$YVU4iEm_TNTKml7T9v+u?JahLGBF~=6eWOf2 zV-+h)xxS?H+2Qjw=4gh+QWb(ft<5p13HANu81chXMG#jWSzIY1L3x&~h5LDS)Ya|s z(7IM)x)9OiQO?Bidg z>=!t6SkFHGFlC?O6b@4MD5p?N*(W&eU3&KM4$5Lz2N|A(ULyPW?F5Af0(zsKeS95d zq2o~5qgPPYO$d*zLY9YxSKwkS=U)lcXzPH5m22P{K1@Z;ji^yG=zj+)VX(4C|1)Lb z+8pV^q{8?Q01L7-LUKPPzqFI(HE*LvHG-y!>?4XSA?akN6X-t(EJ@QK%l&f;=^KQ^ zyyh@!Xb*=2;x9v4$z^&ympt{%V+_BE%Kdi=WFsJ))44eCW9gXw5&mWk`&gnix zHlO>nTS%MT=N8mR_vyi!M+#rSeK>h?AD%I}4?pp~E|7J9uNyib+F-MC#Tm+rICkMJ z8P~awVZSHimyEBX9YT-XjsM=O*sn2hN8$FIdq!VCu$i%)Xa`#^U#+fh!Z;i#f@ z-$;n|`w`;F*OqS{BA08pawbcW{Ick#Hv zG>tEmNJZ5QDc=qrhYSO{466z$kaZ6Ni56Zw*c^5H^fq5VC@kdu3gf?`YabGQS;5!{r zir&fjPOr?73s0Bk(qIa;#hOdHKllIt_M=gK(ILl!$5;iBd5ya(a$GbMY|dLN5|K77 z@RJEzndFeC`zqbIosyCDZY7SsGcq<<-o7apD_q>#;D^0}ly`O6pHEVjDg~3- z>8$`jw&hrs-;9k44mwc`iH(uB6dN24Pt!^C?DWTn{qMPG>9qYN02s?0!zrArtPZiO1XoA|vp>B!lkQB=!Y*NW%;*^YM#PU8uVi z0>-8ui>1IgZ=-IY6AXDyu^gRo1NFs2my5dP{4jvTn7)jv@jX9{O=$-KhY8d-n2EUSkKN=7014CAS!18Vg2j zbt`Dl@e>`ol`kHWAmjapFjNdRs9`;^n{FU_`3+pq^aOAR*LHx69=LZ#EGhno$c%g| z_YAi_cy^${J&F-Ow*DEhdlGp1y2X31dOmv>Vh87Yz+B7QPK}?qtTv+?(;}JvEH3QucQSoT zrl)24qD-HY=@)pKZ+z1ELN0>ofRE88k7=C3;e(a%aCkWlSQvY2ZID-A0gN}?)%`y`Y1t&9ewvZe@iFC`UO|~==vwLKytTyzFP92O&DE@ zr8!S9biQ6kWr`){%Z`slk>wiQY~?p*D^J)mgw#Wv@4nqZsXXP=(@$TwrgoK8X}Wk8tUy&@Elv) zg+GAsXKFMq`mVx|6IIPycKcf!f-QHgW1(;=6AQ;Z z@&0t$)8Fk$$9fX6?ijw?5Q%!KLcx}{HcqL%KT{B^UB_aqy%%4Hh^7bP84tcHk?HUE zr2E40ICA`p644|op-!~RQ`OO2ucYWm97yyZlG<^(wZ3qs7s^(xBP-LlE7qo~*7n@w zS=+PDD(AHY|Qbs1$7xi?9WATAh^cI2W>*|68=%b{k zH=On)`aQ9}a8Go7IvUBu`V-9K*&Rz|2Ey?>2coINyfI|$hRxBp`wwTLX-_!g3F}l8 zYrS14)t><^)$&j*)9Z=gOCg?`!L@_fGkBD)$#is}s~^U~^ZH@pO-@h&Q$ z13a+u00<|QAj+!34>1sw0c0BTTK%}wRu0+x9?iU1Sm~F&-d;Wtvf_Jf^v7-GNjcsW zgO_aOleY3VZRK)XJU(WtAGMVSZTbz_%13PFQCoS!Mz6-E53|Yht54eOagDV+W}~;O zuI?636-LRfyVl=@Q8KuFtG2av{Xis@S)c4r#|AMHdT;XVABbTD??3F>nTkewGoH3^ z0^u=zlP9%pooC~g>P^+SK}*;FYxHQor)zAh)y@KHT`LF+&HZ1+{a+S{|8uwgsP-cZ zd}M(+Er7F~kKx0HL|TJ{WPwA8RY)78b^Q-V3N#rS=S$af-*;4_MR66wc2#@N%ydyswxpE1HgT3*4}>&R1@#%B_FO6#v; z?1#vQkbWMY9P*SdzM8T3kf(G5KI~!0kMvf2mLrcC!hVR)YUC+JEMXqxDcy!I%~TN% z()hKE)gqrn`h9#t$W!_aoU+`5Jf&F=^hKW1xA4gzPiY7nn<3;WeGi`}kY_jq{VYDm zkf-!%e2ybOh7|J>8%Lhf&-^Z9-$cF>sq033PZW7dPvi48@|1>e!q*Ftr}WQ5RDv_sj8lM{EDg9y%W49qs>38r6AW!LS z8?k0Zp3>jh1fIxuBK;G5;>eF9t=P=iAo3oh_u+E{`5~l7@p&3~?C}`(b?jN>DLseJ zapb3v?yUt4z%Ae^DRP$-yF#wgVt45# z$}zTt@~t9YvqjugyS<#T4t&U8Db0tI0Q^yjL#b<7iF=vra%QIwxPMvkqDP8qtbAS2 zj(?VwY(f2gN4-fdE^kW6$n#QON8XIHsQ|u$n{A=!nPCd=nq?i=99hh+99+aM?{%}4 z#Y0#rrk0zL zXY37G{u`P8RHpCC^h)>yU7KWjn@k0iu?AUgmFZ_>+H1r6s2_#Je%t%;ldk$`JerBt z;lvfErSUd-e%R#l<8(7+2eWUx8dA|{Ft$GxP90`Pigraa!Eid`r{i44{)oy={apj` zXaFZI@u-in|4J~NCPw6uC1cMNHzvR>9LHIooxyv>!D#rP1I%WNgZ<$y$rn_=* zIugB{OP7Bz5>3+CV*`$RqAB*T+->n_G|9f}ZjYzIem9-|vER7mnW#M5#N$W`!cR%5 zM>Q>}Y0<>NSPF-z>|;zzM>AR?dPt)e;;;$^6Y=+wT*bO!2ga^ubS&H-!Ags1k^V$? ztS8f(iiW%BG&CM1D3KUQM^o}780&q&>a=t+8le+vP3QpYc}x1DeUap0dKn9sL&JF{ zD7%f)LB_5GpXgvLgV?S(;#gQqgwX|us0DC7j+#_P3*%)qK)iuzJl)K2i$ED(HITZ} z**q=oxxV3KQp+4pM)86f-U24=V>cUlEy@}NW-Nh&)mWF74AUJ5x)q^ovzvwY_op)K zE+(XnGCGqF$Ay){Bwwt5zt%mFh+s9z^^NrRaWk_bQSKI{VwBREu2??|41|PsusfNG zB{JQt%u$mTHCmcIi?27w2hzQay~fZ32lo$jv*%bklfo%As@a!$K6#k(Cnz6}qR0P? za*0S^lEfTa*yj#^YqLMNc@w`&0}`u2C!CC}?@O;g6ick9BTAFi*T*-m-?+X;B3A*| z{IRv`jfRt!2DdqB3(vD)VSabwkw03nx`0C;8&6pDAUw%}$_9bAkdKYFJchT@egd=$ zeQfQyTbNsqp{@Djz=z}Xm>aNV9@tWh_hf^68CLzte5w~aVYSs@L%BPMfwQ% zTVsJrNa|)T-kC0v+Ts9f0iYF&YiRH?fk&}o@vi0$Pu-@?@D`gQz4=5K zrF6Kr8%bs$5#7-fO+-_%h-ZC|`fJ(bS+73bbRU)OrLPyyp?KDJC;Hc?GvUaA^>nu% zn+7cZf2VlYmBsAeNm=eeTI5<3=?$k?QR(7%Kkmfye*+0x2;yl3Ea6b&HflJaXmPq1 z>qd_it;EeMO9ZyaFSeqTmvBm5{R8{4@Gf$d>eW()5>-`Ff=5Zh8CF`l8Ma(fR3fC) z@r4|2AqN$TV|0I+42AV@S1b}uv!6h{8(rTngVFb(uDzq#ueH?${Pi6{zZn+twYE3< zf?BJ8msal&`SGwwbKPF%GPP>+YXz{13S%{dw7QmNJpa<(W~N(QxJ5qn5(B;7-`1`* z_=1oIa%v4Nt=g_&9djEPZK!Gs1smH*nKrh_s8QlK1bw@>rfu!MV2~{~Fq&F+?_s5; zvcIXNb+6Xc*w*B0uM23R!4dT3ql=1pwr+!$MH*|NDY+FD4+ zHn!Y`<_mV1Sg{qhYRJ#4fyP$a>a0z*CVvwvn-@iEY-+-bBX!^5`r0V9Hilr$ps&4g zx1U{R;QH$7I+{8F_iHU3?YmkUn|EoxRw#A3ffH(N3AXI&@M|4yR1Nw4Y?V>%4>dZf zR~yx2{Gi{5Hdh$H5MByG+M(mvl?IsT2jRk~Wgj!Z^(`Gc6}Z9xE9{mKyUKtnWI(Sr zpdnwq))r`KgW8#VE%l8Jd)XQTOqOhHuB-RgvFi#eYz?X22>2!*VHY-dv=+b}mu`@Xv7cD#92 zmmn{lU>nLkZLHZ|y=luD9|PlV3kK1*k+{c!U?*{}1Ec*e^NjHsqh2l{?rLlgXy`qP{oNeSJ{!bU|~ps}HSBinC4_W5bb+S3j< z&;+mMdKNM2F{3rLU;xUXO;a-KGO8PVm@vfPWKjcbT0CfXwKlf88@n=)?{PWXMlw#2V3Mqw2dWH@oVvVJQNYS*x&0V8Ea*re%@ea@)S z{hC55R$5e@GOFe70amYX6oVaWJ8YZUeRrU@)AJ(|XMvnCkcC~iJFx)+tor{>X`F*b z6%X5O?RD&s0VC~O>sr~M0SdxJ*kQTnq#e6u&-wSRJIm$%($|VT5iQoN=@cVwKl- z>#V4#L~Eek^~SBNsH7NoY?ATlpvQof>M*ehFDlmoYJcw0A(kC|xuQ(G4ID`tz@>CE zNlRvM;PAWU>#?;j`3-D!6^=xfF^J7)++ZUHpvE?=q=OPot0$L{uo{B#CU+vU{2A=< zU2G9Gzkj(8|CbaG$*0yAtw-?z2~64^Wk<6Q{=$PxKo01*)qzh(B=vzd<}|vLUv4`_>JS z!##Mept_B<-f|Bzz)0hHiEt0j>oxj9)YjU2@ZHh%;_FWL@S}XK50!`R85G}|!q*Jf zbHL_J_XzroZXNjAJe#U(x2ms5{dOAe{}QP`z^cezMgJmrI9x(|Icus>h2hBYBAP= z&p+au{5>uV?C4+=R@5)?v=}`-hSeKB#Y{X6Chh?X2p&A58-?^r7>Prr1NdOcXM*4% zGDi21MJr_35dFjDVjk=pL|*(~q8G-H!zwWkj%&K;CHeW0W)w@nGG8jDB6gb7+M?nabe7FWE}a z!S^F{2qFFgpC>uQuKPBs=2t1`hvtxwkZ4cRIS&r4tfuC{ISd@{JUD*>oXL4`{v0^t z^Wgj^;0(d+g)C2c{Tetk^Uzs^Zkn2h&NkqT%|oXNIHU96M93wuMVd?I-viFXJanD` z&d@wK^hxM&Q*1Y}&~fm-;nDg1`i}_#^Pt zFBMXWM=h6!vX)Ez>LCFst`xhT!2hLG)Gzf5iUgotcCo7y|0y~emx{(c0Lf^4xQA9y z^l;~Xyp}y)1FKUL5jmK$3YahLaw7{Vi}jATpm- zL!1t2u@c6`#@%!|t?GPMNLkf`cEYM569th~RwY{st9qO`@^>jaQ6sEMbHH*7p873J z0+LaRU54kI5r2Woa(#4LJw@TU$mF<$nm~cX;Lpa8|hftOG z()bYG`$+;jF7n>aIlNc->howRd=(4VJnlLHy+RPd+3#QAJd<+YruP?zzJ*qw&)e#Y zx}@&_XB1KjEhr`!U9Bwdf(ApV;k}Hf@P(HxMksa4BM2)W6Ec&J{VghNu~+W5!p0-8vqM=4&k}HNI2ECFnMY~N63@MhU%Apfm3KK zD|t@o^5Df(wCOO!;olP>kg$(E)}bLTrwy&KhvP`!@S-4OCaCpNHXH-Epv>}@kC&sL zgl~|KSuf=qGXUp{8-%TN|10E0U(ol2=o>4B@=7o7MfipnbS_@6JpCno^?&Q~;HQ?; z14%;~`gYS`mlj6MgyMX33&?$pP3RCbXDgtzklEf}Z&EeCt_4nVo+z;yI6urA6TYX& zu-k$2%e*)O#c9KTKn3-jajCh<3($Nv90yLp@g;wqnkZ8w5IapO85jM6b>+T&`^akw z;?%XaSMCY=jw!2(2X_r3$W;rRedBZC$X^E^0=|gGG-6>e(pQPU-LuRk#tj`ZhWqMaCDg9-5kF~g&E z-$1w^4s*p`FY4#>jS1ig-zYr3mVl9vCqmz*vz)`@*rcogbf>gM=Un9B1xQm zqx0g_`S6ki(~O^wPTjttxo~8EU8UQn4=-0U`idy3x8Zqtf7xS;;OVsCO`wPXVY)75 zyw7PvIld-9Ta2$8$c-$QT1(WT2I|Ad=v>Ttsd3=}ju;o#^(tYj`C<)lq(5hR>1(1U z6Di%3XikD$oImIBRXKOhsk`hi`h6B555EPV^+w}KydsTDbt&?MAn$zR%aA9J%u=3D z>+*<~{;lV47Br013C5OC-Re`rl>?F;(ifVjQ{oNReMoGm)e3E;ey7iXJ}a}qcq+~zB^ekN{j0p~nY zLyNx#WPF}kcqQC+Vh$W69KCn?;K3%()%C)!sgW_P_7Sw4dgU1@a>nlj5iyz! zIaE%`^PDaZmg-t}a$}7l5Q_9><4h-)$3B{#ArYBGVfk?cH>Y0KlNG{JdUYZ{-<&e1 zUUKI$3+Y6EPF zL*YM3bWOzUKD$A$h8aK4il=bJhm`U>=IYx2<%dOQ2{ zYZzcwf4vl6O@cGT%0HH+uaaX3UyTbPO#jtPJq0?#R|y9J)O0D|_#5B|-{=k>h?)>& z_If!KxVPhhQNh)US1oZ?09@?C5;V_so5Wekaqhu?lFYc24L#SBor#Wm63v7oPw+Yc znr~dpWkaGEkn+$o1I4Z+fgIN!i9=_JB8EH1SdYY^dA`{7po0bhQW6In0pJAi-%3aI z#n1GZ-p{eC~pMBZk-sNbo#MZf=vwNjSIhBUJY z8+wqV-nCxj8{`qfHv-6!_ggROFX|=HUrU`tP)B!0#^F|CUvP(&07}ZvztZh2cC7Nn zN_-Mqn4JsHr=#Fia4gFmYbo48j6r(ZPJcTQmq)#-Nc6isJ_{9{GW$?r^D#Mg$c9vt zcby>D0CUPL<4+t!MEoHSv0f_v1c4*s5BDMqlIn{qfn(BLynRV7C*(_-*!cDH$6kR$ zdi8>jZC?I>bsyAqr%`XcljLib9m?((HnLV5LuD$fn`>n?Ce_k9osL`|292_ICy zAJKI$-0xi%q5EcCcM|qljBexV=dWT*^YKpFh~9h%I71g<4S_?}ptxiW6i#bc7;_QS zJ)nyj1yAeN$U{f27?T%ajXK>L;=OJBU^rpi1)Bi*(K&P{!T%$0W?(9^R^BJv@x-k*g1TR5HDryj9-NAG`~&}Z*p+o?%+;w!^*wY!9BSk?llhX zUI%xYAz8z&69N9Du0n0P3>ZqpcOa_)SWM3LIKq#ruH&g_{|_ixXkyNY!mqF%KW zql8}c-+HO>6;i#N@BCSg%I(TGTx+aEP;%&XN#p%I1mhCw?(aLempixz9Ne!oqK)kL6AR)_hd@@{D;(TI4(^*A+{YKhy~M%Y-??5Id<8eh_e6H1GGQB>~HKB!3h1R@64YmP{3$gE26zfJEBW}&D>$Rvm`UvhAt zaB!#ldXkq|XKsDO!M%Jz+)p~VpLKAj&9ap{JxL%TQum4lasQ@+`=o<=se}8A4(^@> zai4H-Kj+|H?%@8agL~D2xWDe;KIPzE;oyGS;4bI)-Dp*4epkClW42Oj8`F#?d$2}n z0bjFU>g42uTH6eQ0r#Id);5OBYHeeZS?ZpeLw7YQKSaH1J*vM7uUod{R4*6C*>+a> zhUi!3F=&pSJL)5GGgzB%t{=6lr zdbu#pwz%?*IecE%>yo_At0*fE-x5;tC`j!QrG1iq` zV7lb!c6y!JGo|jUN($?)#`}k;S8Y=DSK+a|WxML-!Z_Pzm2b>3%jkMt(un(}qO8X9 zmQp2;g4BAI77DVp+u?O<=CHi-I)je#IukGHb)z4m*O9Ny=XHk6YL+p{EWK{S9J(v7 z`w;c24XFMq>``0FRWBFD**2kk!}ST%cobQ+ru{=*cY2X6zRYQQW`{7Z#wk0kwLl8v zdSTX}J^~5T<$TJ5aIBYump(2gfQXGY$s_Mvj~Nom`>^x`HunwX^IK-s-dsUyYm}W8 zWb1BYTu6UDru_MmxF2_LpK)-na&WJAa35O`_b~_eoP)c^!F{WP`-uf{f7Zc$%E7(D z!M)nSeSAUOk2$#0y+4a5)RsHA)4Oyej_mi73*!E?gZrd|d#QsvJs@P|KCvL~qYmz8 z9o%UkTCA}(hX`Qhes)3JpKx%WaB!c+sI_u`!@+%WLEMixxSw=zpK)-1)xrJTg1A5G z;6Co)o^x>jvV;57g18SkxSw!vpK@@2#=$+eAnx}&xQ{uwpL1|O;^02BAnt<>?#CS5 zCmr0A4(_uH;+}DEA9Zj)>)^h};4W7nCjgmm&2P|At1lBT8MTZzcY&$xQfvMRz#$~o z>NW%v9rI~E95KW4FSqcqJja^fkXfz1OfpN|U!FsEwFdYQ^{U;jB4pu})fU>~2;J$w z^^%%ydqMdIe~R0NBX$3-t~*{ktKOc6w|J_jg4bd7MHkTIu4)?~V_447w?y;y;D_i5 zB$%({FV*g5fstzMxY^yL!$-nLj~oK1@b%~lhP9e&es7^{VpKi z0c1kQQNO+2St;ZaIDZWswJQ;j89?Zwl7L(Qq}RfIC0+>9g?iFa;OqcIt(pX6FCg@^ z7-=dXzXym%mj_^W1Q6;zgU*)#8MNShm2hy8Sc1+gfOJ|Yp97?vM^O)OV8b}QXGy*P zN3{9};HWuHKuYknDyvm10GR@JA+!3Wckwg}QKoAPa5~Yk7cMAyngF4%svD4Qg5bh1 zl&$XP5RVDx89>OJj8-oKVwL%IKvdL{dco}Ky*)ynDd3#dEvDfs==3gLvZ!G(Hyn0U z*9%qb8iH_(DG93p8Pe$x&dq>?bgL4i2@v(h8^K)zq{h;N2|z}HOZ5WhAwaBpeIAe* zi#%Tegf1a-U$ifh#6$Gkb?)jnT6K%Q=nKd3(iixc zPFbU`VK=qY&pDf9F2dJe#wux~C|=hR#A-46iVQBJm$w4aX|ds*fLMJjL#-^{@&q8I z78`yQ5VejHHv9`fCM=r1NjMf>zX0SVi#+A{Qec$@=Ndq~z%hEK29T2$y>cXxas-3tjVHvB4Zj#+rU0*E?G5xmX=LT^KW*s_=3 z2jrv$vJzhgRr?FkY8@a$vW0-`0Hnsks}&HhE^~N4e&-e~DlB@Xfin))2KRpnh{0Xc z@QywFf(>29$o(2{tdP@yh-dJW|9lq^^@O&N@Hc?WSR}k0e$fd+R4;Hg5X2(k4nRhA zEr@FnkfcTCdjJWc#)zv&02u?00eKn_s~>(95D$O`_tSu=d!t1xBFBz6pI3VQ95_R0 zWmt@U_Q$Xos#qDmc&b*OLa%E8>9xpH1&DQ|G)M>rue3%25OIOK3%`Ja@oB!=;?Zal z0CjpGc>O-Law`cn@fx$}1c1iK{UUG(V(5Mx5D)BZSnNeWtP)NDqE5dBueSlI0v)3j zy%uN8LV30PN|S+84ahl*C)`f0pq!8pGg^#R1*4Wxa0hTIEtDgGSmSdV5Q@)6?>r62 zD4H3O?T-NoS#bUW5Ve;PdYuEr173#3-T`D>htRK7h2zlO;;a7)I71d*7XTU6TOq@) z#A}fUb-l1kNksaROnf^=`B)Wj)Sg<%vjY%oUjzYB_x=RVeSl0taYOe%1Y`fJ|7p|9e2hla|Upe+|f4i~sxsAm=Q+itxsuCoGz700hiUyy^j=J|zu=7VUr- zD-eL$en2|WUt(0!sxo<4OZQeP2;n>doFh7Sz8-`3j_bXw(Go>QQNkCzzJyliESmm1 zKqf7EeH)No6CI3;u-)g+0Y^Lws_gS)K&C8|e-4P%e^y@!JL|moIvkV@&m=k50VkyE zMG!9_6h@8y+6#y^`W*lSjUcBU5e76y(_!G`ED~k`@p5^T7S94QZt<3{0Wxcm zX9AF87RXzGOn{N$hl}wZrZEezs{oncJ*aq90%EmK9UwD$E8eFO(9!#X?63zor52gH z04Ya}u@?OtL7;^p&r^WR>XeDjaX?O5w0H@S8q@%de;_a6pk&za2Y{$2^9c{73d{va zK}xpr0HW@QVMQ(989Ro0gHAJWm_>^{fCMZucLOqM>5BnCW-Kyii4HEqH=cJuUIIjY zdr`>z8X)RD9s)86$T83{B>a0oj#&EZ=YSaf6&;Lah_lC{g&PwlQ8eUP1Bm)gjo?*H zld1)$0g!QBFaE`0)aNYR?*>lNVvWxNBA&zdfG46vJemNGrPag0nFL$I6P^ZS%tGgR zK&C9Mz7EI`96kqM?I|}dgs@GOaf1M!g9Qn zraIcIb*Je2n$=y={R2H(I2BIxMANDmOLX_Ee0cx-K#bXJ*k%a;}%Z!8HXi2fY za3b6j?FuK8tT&w2BJ@K&^xkp$9V^_sI~b0~x0&l72FVzsmcNe06(ebt>A8ACIh7RKZKj98N~T+tN_eGB${KtD%Vi zUK1WWi1$zmRvI>Z2b^4NNI=fO9{lU)2C1IJ06abt46^LMWF?i{L{z7<-eHa)!!|5pN8c4=t z5qOc7Ok&JnXrwVj`~j_@)z^gIuJ-S0Y{r_WE1JSjl%z3E#K6_M6F|eS3}{xk1lrM+ z>i6AupLy`p(QBo4FfHEtqNTMsvzGXAcBNj=jGVS_Yb=qDrZS=0W~@v?k+zK*)AnuL z%xU@Jae}5rrF{2-jgxq7iyg?{bz|Zvu}qA$u72sDS{MED7gRPN{I|fAwnlI;|BNho zNerRqzGxRUHTjOCojl}0@QB0A#Ld(~7E{qAjs!HkHyO2k;lVb%?Jd@yNF$#*KyS6B zl6EK+%Y^si=fLo$K9GrOSkA?|W6>^!L23{ZYNCDpslyC9iI?f3hw1Q1OGfE8&$QG) zA`|P23TT)_I)vCtO5w*N93I}<(cIn$KMJ+^JL+5T+t0z4I`P}N^kdlCr8LkmA$Oq1 zo5Go1f655tsc-ng%RmZ^*VJ}SBL9twf10}9#+h6I9^Y@aqC0d zO;Z^Lu`#Z-hFGF2*LOgcb}!gslyt*!r>AZ0kk0ClYRb` zglSnImJ1q9tYMnQyJGNg1iVbQrrtip-!wB92H9FmqO;|bKTiLJMy6^_e{Bug^+fp^ z06%PzW`dIm0?I-#owO!Huox)TFst#U43Cc0a={>joKfWOA~K5NR|>ao*|K>HWYG!_ zOoUPD7fl>B!Wyyt@NE1)5KSeb@y(mGFk}?|!?h(_U}EUg$fJb*umPTnilzE#mY}bB zi)9T4JXo85KO3(O4|f?X!pWG{m)7tLj~cxe4c&8)rl$BtZKGC$skbef=}7eQm0Fiy zt#BG*aj-{MSeIfXmUUVc(#Zoslc-{aV*+43+Cx3fXLW909;y`myo)dd(hTgv7nNF4 ztt#~}gCPPH;l(yyt9BhVG2J{C!HYo;-eX#L$} zU#vE0RfaVPnOTHEg!zGZREwqQ1Q<{&I=!|vg-FEl!=LR>Y3YIe>BE@j`?Q02p*zhS zyvwLFaC=IbLYS&P8pp!94wD8J5OF=y2p5>!8iumwnQ?avi|lax&VeZAA6s`EWKb^A z9qU01hRg7=LjK161m`JM(J<_>rqL*hafdVyL)AhY<`|L)&s784=z61P)Er~9nR^t8 zE^sWc(F;8I@FjG@wBTXw@ihDd!F+&J!=rjOweWlNS~?kxVCIhKo;BxS7Qw^lcMP|r z*-M1dR(aHW=!ESoj;m{KS~^P;li|d&24Ngten>@tK7@W~BaBgKQ1ZPbLZoxyAM6iz z1;zZ}0~tLU*+K>NjUFfT_+*Mhy6938eWzv<%~&-{cMM@&j#U(!J5*p zQGD0CW$vNDV~#$B%l4MlkgdJ3&|ru@O?(Oy$1@ZGC@d;5Wd|LMMB{U=;RCS@#v{C%{u0(gB$g^%n_^Rp62Fg z$b}TqX3ky{bFoqp`tW6AFi`u1v#O`#S%wo>2N>ZgdZSC@Fs`45rCx|3IHSRPl*OIR`L zvFxKGRIxhNm$MYkNh0eK%SK!G7j?QR+VJpT1JR(yz@=Ljg%>6H76lfkZNL$_T-<<+ zoK`7F6fl^Id!&hdc|y*hZ^QWV)r?(u(J6GyxAs;P84kGglTzDqiO#RYVPs)5SY28- zfcfal9EA$au9kU3Q88pxQGnlx*L%{sx3kA$F}7lK8fsbK#Ifk4qt1A^M{Kjm;Iyfr zyT<4qEz)}+ox$lY6>I@rP88@&!)4$s`gRY!f<|Ipm4CZM%;cj;3ehPdAD+qNT#Gcb8_&5b6k9llL~{m2d(?;V t=3mBg>*U=q=2*Np+sBTvK`P{`wy9S+t9|S7{{ge|<#7N2 From b638d6c169ae6b144261ede7b27b46c7af691ee2 Mon Sep 17 00:00:00 2001 From: GordStep Date: Thu, 30 Apr 2026 16:11:36 +0300 Subject: [PATCH 08/23] update bin tree --- stepushovgs/data-structures/sorce/main_tree.c | 129 ++++++++++++++++++ 1 file changed, 129 insertions(+) create mode 100644 stepushovgs/data-structures/sorce/main_tree.c diff --git a/stepushovgs/data-structures/sorce/main_tree.c b/stepushovgs/data-structures/sorce/main_tree.c new file mode 100644 index 0000000..9890883 --- /dev/null +++ b/stepushovgs/data-structures/sorce/main_tree.c @@ -0,0 +1,129 @@ +#include +#include +#include +#include "linked_list.h" + +/* +3. Двоичное дерево поиска +Узел — словарь: `{'name': 'Имя', 'phone': '123', 'left': None, 'right': None}.` + +Функции: + +def bst_insert(root, name, phone) — рекурсивно или итеративно вставляет, возвращает новый корень (если корень меняется). + +def bst_find(root, name) — поиск. + +def bst_delete(root, name) — удаление, возвращает новый корень. + +def bst_list_all(root) — центрированный обход (рекурсивно собирает записи в отсортированном порядке). +*/ + +typedef struct bst_node +{ + char name[NAME_BUFF_SIZE]; + char phone[PHONE_BUFF_SIZE]; + + struct bst_node* right; + struct bst_node* left; +}bst_node; + +bst_node* create_bst_node(char name[NAME_BUFF_SIZE], char phone[PHONE_BUFF_SIZE]) +{ + bst_node* node = (bst_node*)malloc(sizeof(bst_node)); + + strcpy(node->name, name); + strcpy(node->phone, phone); + + node->left = NULL; + node->right = NULL; + + return node; +} + +void print_bst(bst_node node) +{ + printf("name: %s phone: %s\n", node.name, node.phone); +} + +void bst_inorder_traversal(bst_node* HEAD) +{ + if (HEAD != NULL) + { + bst_inorder_traversal(HEAD->left); + print_bst(*HEAD); + bst_inorder_traversal(HEAD->right); + } +} + +bst_node* bst_search(bst_node* HEAD, char target_name[NAME_BUFF_SIZE]) +{ + /* + Node search(x : Node, k : T): + if x == null or k == x.key + return x + if k < x.key + return search(x.left, k) + else + return search(x.right, k) + */ + + if ((HEAD == NULL) || (strcmp(HEAD->name, target_name) == 0)) + { + return HEAD; + } + if (strcmp(HEAD->name, target_name) > 0) + { + return bst_search(HEAD->left, target_name); + } + else + { + return bst_search(HEAD->right, target_name); + } +} + +bst_node* bst_insert(bst_node* HEAD, char name[NAME_BUFF_SIZE], char phone[PHONE_BUFF_SIZE]) +{ + /* + Node insert(x : Node, z : T): // x — корень поддерева, z — вставляемый ключ + if x == null + return Node(z) // подвесим Node с key = z + else if z < x.key + x.left = insert(x.left, z) + else if z > x.key + x.right = insert(x.right, z) + return x + */ + + if (HEAD == NULL) + { + return create_bst_node(name, phone); + } + else if (strcmp(HEAD->name, name) > 0) + { + HEAD->left = bst_insert(HEAD->left, name, phone); + } + else if (strcpy(HEAD->name, name) < 0) + { + HEAD->right = bst_insert(HEAD->right, name, phone); + } + return HEAD; +} + +int main() +{ + printf("hello world!\n"); + + //bst_node* head = create_bst_node("name", "phone"); + bst_node* head = NULL; + char name[] = "name0"; + + head = bst_insert(head, name, "phone"); + + print_bst(*head); + head = bst_insert(head, "name1", "phone1"); + head = bst_insert(head, "name2", "phone2"); + head = bst_insert(head, "name3", "phone3"); + + bst_inorder_traversal(head); + return 0; +} From 0e7a03e78472f26d448a09eea79f23919b66408e Mon Sep 17 00:00:00 2001 From: GordStep Date: Thu, 7 May 2026 14:28:06 +0300 Subject: [PATCH 09/23] =?UTF-8?q?=D0=9E=D0=B1=D0=BD=D0=BE=D0=B2=D0=BB?= =?UTF-8?q?=D0=B5=D0=BD=D0=B8=D0=B5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Изменение структуры лабораторной работы - Завершение библитеки для Бинарного дерева поиска --- .../sorce/bin_search_tree/bst.c | 208 ++++++++++++++++++ .../sorce/bin_search_tree/bst.h | 37 ++++ .../sorce/bin_search_tree/queue.c | 42 ++++ .../sorce/bin_search_tree/queue.h | 17 ++ .../sorce/{ => linked_list}/linked_list.c | 43 +++- .../sorce/{ => linked_list}/linked_list.h | 0 stepushovgs/data-structures/sorce/main.c | 2 +- stepushovgs/data-structures/sorce/main_tree.c | 171 ++++++-------- stepushovgs/data-structures/sorce/swap.c | 40 ++++ 9 files changed, 443 insertions(+), 117 deletions(-) create mode 100644 stepushovgs/data-structures/sorce/bin_search_tree/bst.c create mode 100644 stepushovgs/data-structures/sorce/bin_search_tree/bst.h create mode 100644 stepushovgs/data-structures/sorce/bin_search_tree/queue.c create mode 100644 stepushovgs/data-structures/sorce/bin_search_tree/queue.h rename stepushovgs/data-structures/sorce/{ => linked_list}/linked_list.c (61%) rename stepushovgs/data-structures/sorce/{ => linked_list}/linked_list.h (100%) create mode 100644 stepushovgs/data-structures/sorce/swap.c diff --git a/stepushovgs/data-structures/sorce/bin_search_tree/bst.c b/stepushovgs/data-structures/sorce/bin_search_tree/bst.c new file mode 100644 index 0000000..80e06f1 --- /dev/null +++ b/stepushovgs/data-structures/sorce/bin_search_tree/bst.c @@ -0,0 +1,208 @@ +#include +#include +#include + +#include "bst.h" +#include "queue.h" +/* +3. Двоичное дерево поиска +Узел — словарь: `{'val': '123', 'left': None, 'right': None}.` + +Функции: + +def bst_insert(root, name, phone) — рекурсивно или итеративно вставляет, возвращает новый корень (если корень меняется). + +def bst_find(root, name) — поиск. + +def bst_delete(root, name) — удаление, возвращает новый корень. + +def bst_list_all(root) — центрированный обход (рекурсивно собирает записи в отсортированном порядке). +*/ + + + +bst_node* create_bst_node(char name[NAME_LEN], char phone[PHONE_LEN]) +{ + bst_node* node = (bst_node*)malloc(sizeof(bst_node)); + + strcpy(node->name, name); + strcpy(node->phone, phone); + + node->left = NULL; + node->right = NULL; + + return node; +} + +bst_node* bst_minimum(bst_node* node) +{ + if (node->left == NULL) + return node; + return bst_minimum(node->left); +} + +bst_node* bst_maximum(bst_node* node) +{ + if (node->right == NULL) + return node; + return bst_maximum(node->right); +} + +void print_bst(bst_node node) +{ + //printf("value: %d\n", node.value); + + printf("name: %s, phone: %s\n", node.name, node.phone); +} + +void bst_inorder_traversal(bst_node* HEAD) +{ + if (HEAD != NULL) + { + bst_inorder_traversal(HEAD->left); + print_bst(*HEAD); + bst_inorder_traversal(HEAD->right); + } +} + +void bst_preorder_traversal(bst_node* HEAD) +{ + if (HEAD != NULL) + { + print_bst(*HEAD); + bst_preorder_traversal(HEAD->left); + bst_preorder_traversal(HEAD->right); + } +} + +bst_node* bst_search(bst_node* HEAD, char target_name[NAME_LEN]) +{ + /* + Node search(x : Node, k : T): + if x == null or k == x.key + return x + if k < x.key + return search(x.left, k) + else + return search(x.right, k) + */ + + if ((HEAD == NULL) || strcmp(HEAD->name, target_name) == 0) + { + return HEAD; + } + if (strcmp(target_name, HEAD->name) < 0) + { + return bst_search(HEAD->left, target_name); + } + else + { + return bst_search(HEAD->right, target_name); + } +} + +bst_node* bst_insert(bst_node* HEAD, char name[NAME_LEN], char phone[PHONE_LEN]) +{ + /* + Node insert(x : Node, z : T): // x — корень поддерева, z — вставляемый ключ + if x == null + return Node(z) // подвесим Node с key = z + else if z < x.key + x.left = insert(x.left, z) + else if z > x.key + x.right = insert(x.right, z) + return x + */ + + if (HEAD == NULL) + { + return create_bst_node(name, phone); + } + else if (strcmp(name, HEAD->name) < 0) + { + HEAD->left = bst_insert(HEAD->left, name, phone); + } + else if (strcmp(name, HEAD->name) > 0) + { + HEAD->right = bst_insert(HEAD->right, name, phone); + } + return HEAD; +} + +bst_node* bst_delete(bst_node* root, char target_name[NAME_LEN]) +{ // корень поддерева, удаляемый ключ + if (root == NULL) + return root; + + if (strcmp(target_name, root->name) < 0) + root->left = bst_delete(root->left, target_name); + else if (strcmp(target_name, root->name) > 0) + root->right = bst_delete(root->right, target_name); + else { + if (root->left != NULL && root->right != NULL) + { + strcpy(root->name, bst_minimum(root->right)->name); + strcpy(root->phone, bst_minimum(root->right)->phone); + + root->right = bst_delete(root->right, root->name); + } + else + { + bst_node* temp = root; + if (root->left != NULL) + root = root->left; + else + root = root->right; + free(temp); + } + } + return root; +} + +void delete_bst(bst_node* root) +{ + if (root == NULL) + return; + else + { + delete_bst(root->left); + delete_bst(root->right); + free(root); + } +} + +void printTree(bst_node* node, int depth) { + + if (node == NULL) return; + + printTree(node->right, depth + 1); + + for (int i = 0; i < depth; i++) + printf("\t"); + //printf("%d\n", node->value); + print_bst(*node); + + printTree(node->left, depth + 1); +} + +void treeLevelTraversal(bst_node* node) { + if (!node) return; + + Queue q; + Queue* hq = &q; + queueInit(hq); + + queuePush(hq, node); + while(!queueEmpty(hq)) + { + bst_node* hn = queuePop(hq); + //printf("%d\n", hn->value); + print_bst(*hn); + + if(hn->left) + queuePush(hq, hn->left); + if(hn->right) + queuePush(hq, hn->right); + + }; +}; diff --git a/stepushovgs/data-structures/sorce/bin_search_tree/bst.h b/stepushovgs/data-structures/sorce/bin_search_tree/bst.h new file mode 100644 index 0000000..1bf960d --- /dev/null +++ b/stepushovgs/data-structures/sorce/bin_search_tree/bst.h @@ -0,0 +1,37 @@ +#define NAME_LEN 20 +#define PHONE_LEN 20 + +typedef struct bst_node +{ + //int value; + + char name[NAME_LEN]; + char phone[PHONE_LEN]; + + struct bst_node* right; + struct bst_node* left; +}bst_node; + +bst_node* create_bst_node(char name[NAME_LEN], char phone[PHONE_LEN]); + +bst_node* bst_minimum(bst_node* node); + +bst_node* bst_maximum(bst_node* node); + +void print_bst(bst_node node); + +void bst_inorder_traversal(bst_node* HEAD); + +void bst_preorder_traversal(bst_node* HEAD); + +bst_node* bst_search(bst_node* HEAD, char target_name[NAME_LEN]); + +bst_node* bst_insert(bst_node* HEAD, char name[NAME_LEN], char phone[PHONE_LEN]); + +bst_node* bst_delete(bst_node* root, char target_name[NAME_LEN]); + +void treeLevelTraversal(bst_node* node); + +void printTree(bst_node* node, int depth); + +void delete_bst(bst_node* root); diff --git a/stepushovgs/data-structures/sorce/bin_search_tree/queue.c b/stepushovgs/data-structures/sorce/bin_search_tree/queue.c new file mode 100644 index 0000000..adea3cb --- /dev/null +++ b/stepushovgs/data-structures/sorce/bin_search_tree/queue.c @@ -0,0 +1,42 @@ +#include + +#include "queue.h" + +int queueEmpty(Queue* q) +{ + return (q->head == q->tail); +} + +int size(Queue* q) +{ + if (q->head > q->tail) + return QUEUE_MAX_LENGTH - q->head + q->tail; + else + return q->tail - q->head; +} + +void queuePush(Queue* q, void* ptr) +{ + if (size(q) != QUEUE_MAX_LENGTH) + { + q->p[q->tail] = ptr; + q->tail = (q->tail + 1) % QUEUE_MAX_LENGTH; + } +}; + +void queueInit(Queue* q) +{ + q->head = 0; + q->tail = 0; +} + +void* queuePop(Queue* q) +{ + if (queueEmpty(q)) + return NULL; + void* x = q->p[q->head]; + q->head = (q->head + 1) % QUEUE_MAX_LENGTH; + + return x; +}; + diff --git a/stepushovgs/data-structures/sorce/bin_search_tree/queue.h b/stepushovgs/data-structures/sorce/bin_search_tree/queue.h new file mode 100644 index 0000000..5bbdfce --- /dev/null +++ b/stepushovgs/data-structures/sorce/bin_search_tree/queue.h @@ -0,0 +1,17 @@ +#define QUEUE_MAX_LENGTH 100 + +typedef struct Queue { + void* p[QUEUE_MAX_LENGTH]; + unsigned int head; + unsigned int tail; +} Queue; + +int queueEmpty(Queue* q); + +void queuePush(Queue* q, void* p); + +void* queuePop(Queue* q); + +void queueInit(Queue* q); + +int size(Queue* q); diff --git a/stepushovgs/data-structures/sorce/linked_list.c b/stepushovgs/data-structures/sorce/linked_list/linked_list.c similarity index 61% rename from stepushovgs/data-structures/sorce/linked_list.c rename to stepushovgs/data-structures/sorce/linked_list/linked_list.c index 04231ba..0eeb9e7 100644 --- a/stepushovgs/data-structures/sorce/linked_list.c +++ b/stepushovgs/data-structures/sorce/linked_list/linked_list.c @@ -1,9 +1,27 @@ +#include #include #include #include #include "linked_list.h" +/* +Связный список (LinkedListPhoneBook) + +Узел представляется словарём: `{'name': 'Имя', 'phone': '123', 'next': None}.` + +Функции: + +def ll_insert(head, name, phone) — проходит до конца (или сразу добавляет в конец) и возвращает новую голову (если вставка в начало) или изменяет список по ссылке. Удобнее возвращать новую голову, если вставка может быть в начало. + +def ll_find(head, name) — ищет узел, возвращает телефон или None. + +def ll_delete(head, name) — удаляет узел, возвращает новую голову. + +def ll_list_all(head) — собирает все записи в список и сортирует (сортировка вынесена отдельно). +*/ + + int getListNodeLength(Node* HEAD) { int len = 0; @@ -82,21 +100,28 @@ void printAllNodes(Node* head) Node* deleteNode(Node* HEAD, char target_name[NAME_BUFF_SIZE]) { - Node* privious = HEAD; - Node* current = HEAD->next; + Node* previous = NULL; + Node* current = HEAD; - while (current != NULL) + if (current != NULL && strcmp(target_name, current->name_) == 0) { - if (strcmp(target_name, current->name_) == 0) - { - privious->next = current->next; - break; - } + HEAD = current->next; + free(current); - privious = current; + return HEAD; + } + + while (current != NULL && strcmp(target_name, current->name_) == 0) + { + previous = current; current = current->next; } + if (current == NULL) return HEAD; + + previous->next = current->next; + free(current); + return HEAD; } diff --git a/stepushovgs/data-structures/sorce/linked_list.h b/stepushovgs/data-structures/sorce/linked_list/linked_list.h similarity index 100% rename from stepushovgs/data-structures/sorce/linked_list.h rename to stepushovgs/data-structures/sorce/linked_list/linked_list.h diff --git a/stepushovgs/data-structures/sorce/main.c b/stepushovgs/data-structures/sorce/main.c index 8b73cdf..15b3a51 100644 --- a/stepushovgs/data-structures/sorce/main.c +++ b/stepushovgs/data-structures/sorce/main.c @@ -2,7 +2,7 @@ #include #include -#include "linked_list.h" +#include "linked_list/linked_list.h" #define NAME_BUFF_SIZE 50 #define PHONE_BUFF_SIZE 12+1 // +1 for end symbol diff --git a/stepushovgs/data-structures/sorce/main_tree.c b/stepushovgs/data-structures/sorce/main_tree.c index 9890883..9b264a7 100644 --- a/stepushovgs/data-structures/sorce/main_tree.c +++ b/stepushovgs/data-structures/sorce/main_tree.c @@ -1,113 +1,29 @@ #include #include -#include -#include "linked_list.h" -/* -3. Двоичное дерево поиска -Узел — словарь: `{'name': 'Имя', 'phone': '123', 'left': None, 'right': None}.` +#include "bin_search_tree/bst.h" -Функции: +#define COUNT_NUMBERS 64 -def bst_insert(root, name, phone) — рекурсивно или итеративно вставляет, возвращает новый корень (если корень меняется). - -def bst_find(root, name) — поиск. - -def bst_delete(root, name) — удаление, возвращает новый корень. - -def bst_list_all(root) — центрированный обход (рекурсивно собирает записи в отсортированном порядке). -*/ - -typedef struct bst_node -{ - char name[NAME_BUFF_SIZE]; - char phone[PHONE_BUFF_SIZE]; - - struct bst_node* right; - struct bst_node* left; -}bst_node; - -bst_node* create_bst_node(char name[NAME_BUFF_SIZE], char phone[PHONE_BUFF_SIZE]) +int isInArr(int arr[], int len, int target) { - bst_node* node = (bst_node*)malloc(sizeof(bst_node)); + for (int i = 0; i < len; i++) + { + if (arr[i] == target) return 1; + } - strcpy(node->name, name); - strcpy(node->phone, phone); - - node->left = NULL; - node->right = NULL; - - return node; + return 0; } -void print_bst(bst_node node) +char get_dozen(int number) { - printf("name: %s phone: %s\n", node.name, node.phone); + return (char)'0' + number % 10; +} +char get_units(int number) +{ + return (char)'0' + number / 10; } -void bst_inorder_traversal(bst_node* HEAD) -{ - if (HEAD != NULL) - { - bst_inorder_traversal(HEAD->left); - print_bst(*HEAD); - bst_inorder_traversal(HEAD->right); - } -} - -bst_node* bst_search(bst_node* HEAD, char target_name[NAME_BUFF_SIZE]) -{ - /* - Node search(x : Node, k : T): - if x == null or k == x.key - return x - if k < x.key - return search(x.left, k) - else - return search(x.right, k) - */ - - if ((HEAD == NULL) || (strcmp(HEAD->name, target_name) == 0)) - { - return HEAD; - } - if (strcmp(HEAD->name, target_name) > 0) - { - return bst_search(HEAD->left, target_name); - } - else - { - return bst_search(HEAD->right, target_name); - } -} - -bst_node* bst_insert(bst_node* HEAD, char name[NAME_BUFF_SIZE], char phone[PHONE_BUFF_SIZE]) -{ - /* - Node insert(x : Node, z : T): // x — корень поддерева, z — вставляемый ключ - if x == null - return Node(z) // подвесим Node с key = z - else if z < x.key - x.left = insert(x.left, z) - else if z > x.key - x.right = insert(x.right, z) - return x - */ - - if (HEAD == NULL) - { - return create_bst_node(name, phone); - } - else if (strcmp(HEAD->name, name) > 0) - { - HEAD->left = bst_insert(HEAD->left, name, phone); - } - else if (strcpy(HEAD->name, name) < 0) - { - HEAD->right = bst_insert(HEAD->right, name, phone); - } - return HEAD; -} int main() { @@ -115,15 +31,56 @@ int main() //bst_node* head = create_bst_node("name", "phone"); bst_node* head = NULL; - char name[] = "name0"; - - head = bst_insert(head, name, "phone"); - - print_bst(*head); - head = bst_insert(head, "name1", "phone1"); - head = bst_insert(head, "name2", "phone2"); - head = bst_insert(head, "name3", "phone3"); - + + int arr[COUNT_NUMBERS] = {0}; + char name[NAME_LEN] = "name_xx"; + char phone[PHONE_LEN] = "phone_xx"; + int temp = 0; + for (int i = 0; i < COUNT_NUMBERS; i++) + { + do + { + temp = rand() % 100; + } + while (isInArr(arr, i - 1, temp)); + + arr[i] = temp; + + name[5] = get_dozen(temp); + name[6] = get_units(temp); + + phone[6] = get_dozen(temp); + phone[7] = get_units(temp); + + + head = bst_insert(head, name, phone); + printf("%d ", arr[i]); + } + + printf("\n\ninorder traversal: \n"); bst_inorder_traversal(head); + + printf("\n\npreorder traversal: \n"); + bst_preorder_traversal(head); + + char tar_name[NAME_LEN] = "name_44"; + + printf("\n\nУдаляем элемент с значением %s:\n", tar_name); + + head = bst_delete(head, tar_name); + + bst_inorder_traversal(head); + + + printf("\n\nВывод дерева:\n"); + + printTree(head, 0); + + printf("\n\nОбход в ширину:\n"); + treeLevelTraversal(head); + + + + delete_bst(head); return 0; } diff --git a/stepushovgs/data-structures/sorce/swap.c b/stepushovgs/data-structures/sorce/swap.c new file mode 100644 index 0000000..3a6cc08 --- /dev/null +++ b/stepushovgs/data-structures/sorce/swap.c @@ -0,0 +1,40 @@ +#include + +int Partition_Hoa(int arr[], int l, int r) +{ + int p = arr[(l + r) / 2]; + int i = l; + int j = r; + + while (1) + { + // #print(p) + while (arr[i] <= p) i++; + while (arr[j] > p) j--; + + if (i >= j) return j; + + swap(arr[i], arr[j]); + i++; + j--; + } +} +void QuickSort(int arr[], int l, int r) +{ + if (l < r): + { + int s = Partition_Hoa(arr, l, r); + QuickSort(arr, l, s-1); + QuickSort(arr, s+1, r); + } +} + + +int main() +{ + int arr[] = {2, 56, 10, 5, 2, 6, 9, 6, 3, 923, 3, 2, 1}; + + + + return 0; +} From d616bfe1fbe154a11a085d58390001cdda16a417 Mon Sep 17 00:00:00 2001 From: GordStep Date: Sat, 9 May 2026 19:27:57 +0300 Subject: [PATCH 10/23] =?UTF-8?q?=D0=A0=D0=B5=D0=B0=D0=BB=D0=B8=D0=B7?= =?UTF-8?q?=D0=B0=D1=86=D0=B8=D1=8F=20bst=20=D0=BF=D0=B5=D1=80=D0=B5=D0=BF?= =?UTF-8?q?=D0=B8=D1=81=D0=B0=D0=BD=D0=B0=20=D0=BD=D0=B0=20Go?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../sorce/bin_search_tree/bst.go | 168 ++++++++++++++++++ .../bst.c | 0 .../bst.h | 0 .../queue.c | 0 .../queue.h | 0 .../sorce/hash_table/hash_table.c | 1 + 6 files changed, 169 insertions(+) create mode 100644 stepushovgs/data-structures/sorce/bin_search_tree/bst.go rename stepushovgs/data-structures/sorce/{bin_search_tree => bin_search_tree_C}/bst.c (100%) rename stepushovgs/data-structures/sorce/{bin_search_tree => bin_search_tree_C}/bst.h (100%) rename stepushovgs/data-structures/sorce/{bin_search_tree => bin_search_tree_C}/queue.c (100%) rename stepushovgs/data-structures/sorce/{bin_search_tree => bin_search_tree_C}/queue.h (100%) create mode 100644 stepushovgs/data-structures/sorce/hash_table/hash_table.c diff --git a/stepushovgs/data-structures/sorce/bin_search_tree/bst.go b/stepushovgs/data-structures/sorce/bin_search_tree/bst.go new file mode 100644 index 0000000..78d60b8 --- /dev/null +++ b/stepushovgs/data-structures/sorce/bin_search_tree/bst.go @@ -0,0 +1,168 @@ +package binsearchtree + +import ( + "fmt" +) + +type BinSearchTree struct { + name string + phone string + + left *BinSearchTree + right *BinSearchTree +} + +func NewBinSearchTree(name, phone string) *BinSearchTree { + return &BinSearchTree{ + name: name, + phone: phone, + left: nil, + right: nil, + } +} + +func (bst *BinSearchTree) Minimum() *BinSearchTree { + if bst.left == nil { + return bst + } + return bst.left.Minimum() +} +func (bst *BinSearchTree) Maximum() *BinSearchTree { + if bst.right == nil { + return bst + } + return bst.right.Maximum() +} + +func (node *BinSearchTree) PrintNode() { + fmt.Printf("Имя: %s, Телефон: %s", node.name, node.phone) +} + +func (node *BinSearchTree) ToString() string { + if node == nil { + return "nil" + } + return fmt.Sprintf("Имя: %s, Телефон: %s", node.name, node.phone) +} + +func (bst *BinSearchTree) BstInorderTraversal() { + if bst != nil { + bst.left.BstInorderTraversal() + bst.PrintNode() + bst.right.BstInorderTraversal() + } +} + +func (bst *BinSearchTree) BstPreorderTraversal() { + if bst != nil { + bst.PrintNode() + bst.left.BstPreorderTraversal() + bst.right.BstPreorderTraversal() + } +} + +// Search +// Возвращает строковое представление узла +func (bst *BinSearchTree) Search(targetName string) (string, bool) { + node, ok := bst.search(targetName) + if ok { + return node.ToString(), true + } + return "", false +} + +/* + Node search(x : Node, k : T): + if x == null or k == x.key + return x + if k < x.key + return search(x.left, k) + else + return search(x.right, k) +*/ +// Приватная вспомогательная функция поиска +func (node *BinSearchTree) search(targetName string) (*BinSearchTree, bool) { + if node == nil { + return nil, false + } + if node.name == targetName { + return node, true + } + if targetName < node.name { + return node.left.search(targetName) + } + return node.right.search(targetName) +} + +// func (bst *BinSearchTree) Search(targetName string) (string, bool) { +/* + Node search(x : Node, k : T): + if x == null or k == x.key + return x + if k < x.key + return search(x.left, k) + else + return search(x.right, k) +*/ +// targetNode, ok := bst.head.search(targetName) + +// return targetNode.ToString(), ok +// } + +func (node *BinSearchTree) Insert(name, phone string) *BinSearchTree { + if node == nil { + return NewBinSearchTree(name, phone) + } else if name < node.name { + node.left.Insert(name, phone) + } else if name > node.name { + node.right.Insert(name, phone) + } else { + node.phone = phone // Заменяем существующее значение + } + return node +} + +// Delete удаляет узел по имени. +// Возвращает нового потомка для родительского узла. +func (root *BinSearchTree) Delete(targetName string) *BinSearchTree { + if root == nil { + return nil + } + + if targetName < root.name { + root.left = root.left.Delete(targetName) + } else if targetName > root.name { + root.right = root.right.Delete(targetName) + } else { + if root.left != nil && root.right != nil { + temp := root.right.Minimum() + root.name = root.right.Minimum().name + root.phone = root.right.Minimum().phone + // strcpy(root->name, bst_minimum(root->right)->name); + // strcpy(root->phone, bst_minimum(root->right)->phone); + + root.right = root.right.Delete(temp.name) + } else { + if root.left != nil { + root = root.left + } else { + root = root.right + } + } + } + return root +} + +func (bst *BinSearchTree) PrintAll(depth int) { + if bst == nil { + return + } + + bst.right.PrintAll(depth + 1) + + for i := 0; i < depth; i++ { + fmt.Printf("\t") + } + bst.PrintNode() + bst.left.PrintAll(depth + 1) +} diff --git a/stepushovgs/data-structures/sorce/bin_search_tree/bst.c b/stepushovgs/data-structures/sorce/bin_search_tree_C/bst.c similarity index 100% rename from stepushovgs/data-structures/sorce/bin_search_tree/bst.c rename to stepushovgs/data-structures/sorce/bin_search_tree_C/bst.c diff --git a/stepushovgs/data-structures/sorce/bin_search_tree/bst.h b/stepushovgs/data-structures/sorce/bin_search_tree_C/bst.h similarity index 100% rename from stepushovgs/data-structures/sorce/bin_search_tree/bst.h rename to stepushovgs/data-structures/sorce/bin_search_tree_C/bst.h diff --git a/stepushovgs/data-structures/sorce/bin_search_tree/queue.c b/stepushovgs/data-structures/sorce/bin_search_tree_C/queue.c similarity index 100% rename from stepushovgs/data-structures/sorce/bin_search_tree/queue.c rename to stepushovgs/data-structures/sorce/bin_search_tree_C/queue.c diff --git a/stepushovgs/data-structures/sorce/bin_search_tree/queue.h b/stepushovgs/data-structures/sorce/bin_search_tree_C/queue.h similarity index 100% rename from stepushovgs/data-structures/sorce/bin_search_tree/queue.h rename to stepushovgs/data-structures/sorce/bin_search_tree_C/queue.h diff --git a/stepushovgs/data-structures/sorce/hash_table/hash_table.c b/stepushovgs/data-structures/sorce/hash_table/hash_table.c new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/stepushovgs/data-structures/sorce/hash_table/hash_table.c @@ -0,0 +1 @@ + From 9b92dcc2066a6606e97f2ddabd5cddb045b7cb49 Mon Sep 17 00:00:00 2001 From: GordStep Date: Sat, 9 May 2026 20:02:11 +0300 Subject: [PATCH 11/23] =?UTF-8?q?=D0=9F=D0=B5=D1=80=D0=B5=D0=BF=D0=B8?= =?UTF-8?q?=D1=81=D0=B0=D0=BD=20=D1=82=D0=B5=D1=81=D1=82=20=D0=B4=D0=BB?= =?UTF-8?q?=D1=8F=20BST=20=D1=81=20C=20=D0=BD=D0=B0=20Go?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../sorce/bin_search_tree/bst.go | 55 ++++++++---- .../sorce/bin_search_tree/main.go | 84 +++++++++++++++++++ 2 files changed, 123 insertions(+), 16 deletions(-) create mode 100644 stepushovgs/data-structures/sorce/bin_search_tree/main.go diff --git a/stepushovgs/data-structures/sorce/bin_search_tree/bst.go b/stepushovgs/data-structures/sorce/bin_search_tree/bst.go index 78d60b8..fdc8527 100644 --- a/stepushovgs/data-structures/sorce/bin_search_tree/bst.go +++ b/stepushovgs/data-structures/sorce/bin_search_tree/bst.go @@ -1,4 +1,4 @@ -package binsearchtree +package main import ( "fmt" @@ -49,6 +49,7 @@ func (bst *BinSearchTree) BstInorderTraversal() { if bst != nil { bst.left.BstInorderTraversal() bst.PrintNode() + fmt.Println() bst.right.BstInorderTraversal() } } @@ -113,9 +114,9 @@ func (node *BinSearchTree) Insert(name, phone string) *BinSearchTree { if node == nil { return NewBinSearchTree(name, phone) } else if name < node.name { - node.left.Insert(name, phone) + node.left = node.left.Insert(name, phone) } else if name > node.name { - node.right.Insert(name, phone) + node.right = node.right.Insert(name, phone) } else { node.phone = phone // Заменяем существующее значение } @@ -124,6 +125,28 @@ func (node *BinSearchTree) Insert(name, phone string) *BinSearchTree { // Delete удаляет узел по имени. // Возвращает нового потомка для родительского узла. + +/* +Node delete(root : Node, z : T): // корень поддерева, удаляемый ключ + if root == null + return root + if z < root.key + root.left = delete(root.left, z) + else if z > root.key + root.right = delete(root.right, z) + else if root.left != null and root.right != null + root.key = minimum(root.right).key + root.right = delete(root.right, root.key) + else + if root.left != null + root = root.left + else if root.right != null + root = root.right + else + root = null + return root +*/ + func (root *BinSearchTree) Delete(targetName string) *BinSearchTree { if root == nil { return nil @@ -133,21 +156,21 @@ func (root *BinSearchTree) Delete(targetName string) *BinSearchTree { root.left = root.left.Delete(targetName) } else if targetName > root.name { root.right = root.right.Delete(targetName) - } else { - if root.left != nil && root.right != nil { - temp := root.right.Minimum() - root.name = root.right.Minimum().name - root.phone = root.right.Minimum().phone - // strcpy(root->name, bst_minimum(root->right)->name); - // strcpy(root->phone, bst_minimum(root->right)->phone); + } else if root.left != nil && root.right != nil { + temp := root.right.Minimum() + root.name = root.right.Minimum().name + root.phone = root.right.Minimum().phone - root.right = root.right.Delete(temp.name) + // strcpy(root->name, bst_minimum(root->right)->name); + // strcpy(root->phone, bst_minimum(root->right)->phone); + root.right = root.right.Delete(temp.name) + } else { + if root.left != nil { + return root.left + } else if root.right != nil { + return root.right } else { - if root.left != nil { - root = root.left - } else { - root = root.right - } + return nil } } return root diff --git a/stepushovgs/data-structures/sorce/bin_search_tree/main.go b/stepushovgs/data-structures/sorce/bin_search_tree/main.go new file mode 100644 index 0000000..07ff05e --- /dev/null +++ b/stepushovgs/data-structures/sorce/bin_search_tree/main.go @@ -0,0 +1,84 @@ +package main + +import ( + "fmt" + "math/rand" +) + +const ( + countNumbers = 64 +) + +// isInArr проверяет, содержится ли target в срезе arr[:len] +func isInArr(arr []int, length int, target int) bool { + for i := 0; i < length; i++ { + if arr[i] == target { + return true + } + } + return false +} + +func main() { + fmt.Println("hello world!") + + var head *BinSearchTree = nil + + arr := make([]int, countNumbers) + + var temp int + for i := 0; i < countNumbers; i++ { + // Генерируем уникальное случайное число + for { + temp = rand.Intn(100) // 0 до 99 + if !isInArr(arr, i, temp) { + break + } + } + + arr[i] = temp + + nameStr := fmt.Sprintf("name_%02d", temp) + phoneStr := fmt.Sprintf("phone_%02d", temp) + + head = head.Insert(nameStr, phoneStr) + fmt.Printf("%d ", arr[i]) + } + + fmt.Printf("\n\nКоличество узлов: %d\n", countNodes(head)) + + fmt.Println("\ninorder traversal:") + head.BstInorderTraversal() + + tarName := "name_44" + + fmt.Printf("\nПоиск '%s' перед удалением: ", tarName) + if found, ok := head.Search(tarName); ok { + fmt.Printf("Найден: %s\n", found) + } else { + fmt.Printf("НЕ найден!\n") + } + + fmt.Printf("\nУдаляем элемент с значением %s:\n", tarName) + + head = head.Delete(tarName) + + fmt.Printf("\nКоличество узлов после удаления: %d\n", countNodes(head)) + + fmt.Printf("Поиск '%s' после удаления: ", tarName) + if found, ok := head.Search(tarName); ok { + fmt.Printf("ОШИБКА! Все еще существует: %s\n", found) + } else { + fmt.Printf("Успешно удален\n") + } + + fmt.Println("\ninorder traversal after delete:") + head.BstInorderTraversal() +} + +func countNodes(root *BinSearchTree) int { + if root == nil { + return 0 + } + return 1 + countNodes(root.left) + countNodes(root.right) +} From 7e045c71e0c6f7f9f01d67598ed407c1b7396157 Mon Sep 17 00:00:00 2001 From: GordStep Date: Sat, 9 May 2026 21:11:45 +0300 Subject: [PATCH 12/23] =?UTF-8?q?=D0=9F=D0=B5=D1=80=D0=B5=D0=BF=D0=B8?= =?UTF-8?q?=D1=81=D0=B0=D0=BD=20LinkedLIst=20=D0=BD=D0=B0=20Go?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../sorce/bin_search_tree/bst.go | 7 + .../sorce/bin_search_tree/main.go | 11 +- .../sorce/linked_list/linked_list.go | 133 ++++++++++++++++++ .../data-structures/sorce/linked_list/main.go | 83 +++++++++++ .../sorce/linked_list/q_sort_ll.go | 37 +++++ .../linked_list.c | 0 .../linked_list.h | 0 7 files changed, 270 insertions(+), 1 deletion(-) create mode 100644 stepushovgs/data-structures/sorce/linked_list/linked_list.go create mode 100644 stepushovgs/data-structures/sorce/linked_list/main.go create mode 100644 stepushovgs/data-structures/sorce/linked_list/q_sort_ll.go rename stepushovgs/data-structures/sorce/{linked_list => linked_list_c}/linked_list.c (100%) rename stepushovgs/data-structures/sorce/{linked_list => linked_list_c}/linked_list.h (100%) diff --git a/stepushovgs/data-structures/sorce/bin_search_tree/bst.go b/stepushovgs/data-structures/sorce/bin_search_tree/bst.go index fdc8527..a909c0d 100644 --- a/stepushovgs/data-structures/sorce/bin_search_tree/bst.go +++ b/stepushovgs/data-structures/sorce/bin_search_tree/bst.go @@ -21,6 +21,13 @@ func NewBinSearchTree(name, phone string) *BinSearchTree { } } +func (bst *BinSearchTree) Len() int { + if bst == nil { + return 0 + } + return 1 + bst.left.Len() + bst.right.Len() +} + func (bst *BinSearchTree) Minimum() *BinSearchTree { if bst.left == nil { return bst diff --git a/stepushovgs/data-structures/sorce/bin_search_tree/main.go b/stepushovgs/data-structures/sorce/bin_search_tree/main.go index 07ff05e..5ef8e0d 100644 --- a/stepushovgs/data-structures/sorce/bin_search_tree/main.go +++ b/stepushovgs/data-structures/sorce/bin_search_tree/main.go @@ -1,14 +1,21 @@ package main import ( + "bufio" "fmt" "math/rand" + "os" ) const ( countNumbers = 64 ) +func pressEnterToContinue() { + fmt.Print("Нажмите Enter для продолжения...") + bufio.NewReader(os.Stdin).ReadBytes('\n') +} + // isInArr проверяет, содержится ли target в срезе arr[:len] func isInArr(arr []int, length int, target int) bool { for i := 0; i < length; i++ { @@ -45,7 +52,9 @@ func main() { fmt.Printf("%d ", arr[i]) } - fmt.Printf("\n\nКоличество узлов: %d\n", countNodes(head)) + fmt.Printf("\n\nКоличество узлов: %d\n", head.Len()) + + pressEnterToContinue() fmt.Println("\ninorder traversal:") head.BstInorderTraversal() diff --git a/stepushovgs/data-structures/sorce/linked_list/linked_list.go b/stepushovgs/data-structures/sorce/linked_list/linked_list.go new file mode 100644 index 0000000..db20023 --- /dev/null +++ b/stepushovgs/data-structures/sorce/linked_list/linked_list.go @@ -0,0 +1,133 @@ +package main + +import ( + "fmt" +) + +/* +Связный список (LinkedListPhoneBook) + +Узел представляется словарём: `{'name': 'Имя', 'phone': '123', 'next': None}.` + +Функции: + +def ll_insert(head, name, phone) — проходит до конца (или сразу добавляет в конец) и возвращает новую голову (если вставка в начало) или изменяет список по ссылке. Удобнее возвращать новую голову, если вставка может быть в начало. + +def ll_find(head, name) — ищет узел, возвращает телефон или None. + +def ll_delete(head, name) — удаляет узел, возвращает новую голову. + +def ll_list_all(head) — собирает все записи в список и сортирует (сортировка вынесена отдельно). +*/ + +type LinkedList struct { + name string + phone string + + next *LinkedList +} + +func NewLinkedList(name, phone string) *LinkedList { + return &LinkedList{ + name: name, + phone: phone, + next: nil, + } +} + +func (ll *LinkedList) ToString() string { + return fmt.Sprintf("Имя: %s, Телефон: %s", ll.name, ll.phone) +} + +func (ll *LinkedList) Len() int { + + if ll == nil { + return 0 + } + len := 0 + + current := ll + for current != nil { + len++ + current = current.next + } + + return len +} + +func (ll *LinkedList) Insert(name, phone string) *LinkedList { + newNode := NewLinkedList(name, phone) + + if ll == nil { + return newNode + } + + current := ll + for current.next != nil { + current = current.next + } + current.next = newNode + return ll +} + +func (ll *LinkedList) Search(targetName string) (string, bool) { + current := ll + + for current != nil { + if current.name == targetName { + return current.phone, true + } + + current = current.next + } + return "", false +} + +func (ll *LinkedList) PrintAll() { + current := ll + index := 0 + + for current != nil { + fmt.Printf("[%d] %s\n", index, current.ToString()) + index++ + current = current.next + } +} + +func (ll *LinkedList) Delete(targetName string) (*LinkedList, bool) { + + if ll == nil { + return nil, false + } + if ll.name == targetName { + return ll.next, true + } + + prev := ll + current := ll.next + + for current != nil { + if current.name == targetName { + prev.next = current.next + } + prev = current + current = current.next + } + + return ll, false +} + +func (ll *LinkedList) listAll() []LinkedList { + current := ll + + listLL := make([]LinkedList, ll.Len()) + ind := 0 + for current != nil { + listLL[ind] = *current + ind++ + current = current.next + } + + listLL = QSortElementsHT(listLL, 0, len(listLL)-1) + return listLL +} diff --git a/stepushovgs/data-structures/sorce/linked_list/main.go b/stepushovgs/data-structures/sorce/linked_list/main.go new file mode 100644 index 0000000..a77923a --- /dev/null +++ b/stepushovgs/data-structures/sorce/linked_list/main.go @@ -0,0 +1,83 @@ +package main + +import ( + "bufio" + "fmt" + "math/rand" + "os" +) + +const countNumbers = 64 + +func isInArr(arr []int, length int, target int) bool { + for i := 0; i < length; i++ { + if arr[i] == target { + return true + } + } + return false +} + +func pressEnterToContinue() { + fmt.Print("Нажмите Enter для продолжения...") + bufio.NewReader(os.Stdin).ReadBytes('\n') +} + +func main() { + fmt.Println("hello world!") + + var head *LinkedList = nil + + arr := make([]int, countNumbers) + + var temp int + for i := 0; i < countNumbers; i++ { + // Генерируем уникальное случайное число + for { + temp = rand.Intn(100) // 0 до 99 + if !isInArr(arr, i, temp) { + break + } + } + + arr[i] = temp + + nameStr := fmt.Sprintf("name_%02d", temp) + phoneStr := fmt.Sprintf("phone_%02d", temp) + + head = head.Insert(nameStr, phoneStr) + fmt.Printf("%d ", arr[i]) + // pressEnterToContinue() + } + pressEnterToContinue() + + fmt.Printf("\n\nКоличество узлов: %d\n", head.Len()) + + fmt.Println("\ninorder traversal:") + pressEnterToContinue() + + head.PrintAll() + pressEnterToContinue() + + tarName := "name_44" + + fmt.Printf("\nПоиск '%s' перед удалением: ", tarName) + if found, ok := head.Search(tarName); ok { + fmt.Printf("Найден: %s\n", found) + } else { + fmt.Printf("НЕ найден!\n") + } + + fmt.Printf("\nУдаляем элемент с значением %s:\n", tarName) + + head, _ = head.Delete(tarName) + + fmt.Printf("\nКоличество узлов после удаления: %d\n", head.Len()) + + fmt.Printf("Поиск '%s' после удаления: ", tarName) + if found, ok := head.Search(tarName); ok { + fmt.Printf("ОШИБКА! Все еще существует: %s\n", found) + } else { + fmt.Printf("Успешно удален\n") + } +} diff --git a/stepushovgs/data-structures/sorce/linked_list/q_sort_ll.go b/stepushovgs/data-structures/sorce/linked_list/q_sort_ll.go new file mode 100644 index 0000000..2fd6c0c --- /dev/null +++ b/stepushovgs/data-structures/sorce/linked_list/q_sort_ll.go @@ -0,0 +1,37 @@ +package main + +func QSortElementsHT(arr []LinkedList, l, r int) []LinkedList { + if l < r { + s := Partition_Hoa(arr, l, r) + arr = QSortElementsHT(arr, l, s) + arr = QSortElementsHT(arr, s+1, r) + } + return arr +} + +func Partition_Hoa(arr []LinkedList, l, r int) int { + p := arr[(l+r)/2].name + i := l - 1 + j := r + 1 + + for { + for { + i++ + if arr[i].name >= p { + break + } + } + for { + j-- + if arr[j].name <= p { + break + } + } + + if i >= j { + return j + } + + arr[i], arr[j] = arr[j], arr[i] + } +} diff --git a/stepushovgs/data-structures/sorce/linked_list/linked_list.c b/stepushovgs/data-structures/sorce/linked_list_c/linked_list.c similarity index 100% rename from stepushovgs/data-structures/sorce/linked_list/linked_list.c rename to stepushovgs/data-structures/sorce/linked_list_c/linked_list.c diff --git a/stepushovgs/data-structures/sorce/linked_list/linked_list.h b/stepushovgs/data-structures/sorce/linked_list_c/linked_list.h similarity index 100% rename from stepushovgs/data-structures/sorce/linked_list/linked_list.h rename to stepushovgs/data-structures/sorce/linked_list_c/linked_list.h From 1b21f97e28bbfb3f1ed9eab6fd1ce300bdf55e44 Mon Sep 17 00:00:00 2001 From: GordStep Date: Sat, 9 May 2026 22:34:03 +0300 Subject: [PATCH 13/23] =?UTF-8?q?=D0=98=D0=B7=D0=BC=D0=B5=D0=BD=D0=B5?= =?UTF-8?q?=D0=BD=D0=B8=D0=B5=20=D0=B8=D0=B5=D1=80=D0=B0=D1=80=D1=85=D0=B8?= =?UTF-8?q?=D0=B8=20=D0=BF=D1=80=D0=BE=D0=B5=D0=BA=D1=82=D0=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - test/ -- папка с файлами для тестов каждой структуры - pkg/ -- папка с реализациями и вспомогательными модулями --- .../sorce/hash_table/hash_table.c | 1 - .../{sorce => source}/bin_search_tree_C/bst.c | 0 .../{sorce => source}/bin_search_tree_C/bst.h | 0 .../bin_search_tree_C/queue.c | 0 .../bin_search_tree_C/queue.h | 0 .../linked_list_c/linked_list.c | 0 .../linked_list_c/linked_list.h | 0 .../{sorce => source/old_c}/main.c | 0 .../{sorce => source/old_c}/main_tree.c | 0 .../{sorce => source/old_c}/swap.c | 0 .../pkg/bin_search_tree/bin_search_tree.go} | 43 ++-- .../source/pkg/data_struct/data_structure.go | 19 ++ .../source/pkg/data_struct/qsort.go | 37 +++ .../source/pkg/gen_data/data_generator.go | 41 +++ .../source/pkg/hash_table/hash_string.go | 15 ++ .../source/pkg/hash_table/hash_table.go | 242 ++++++++++++++++++ .../pkg/hash_table/qsort.go} | 6 +- .../pkg}/linked_list/linked_list.go | 35 ++- .../tests/test_bst}/main.go | 17 +- .../source/tests/test_ht/main.go | 71 +++++ .../tests/test_ll}/main.go | 8 +- 21 files changed, 479 insertions(+), 56 deletions(-) delete mode 100644 stepushovgs/data-structures/sorce/hash_table/hash_table.c rename stepushovgs/data-structures/{sorce => source}/bin_search_tree_C/bst.c (100%) rename stepushovgs/data-structures/{sorce => source}/bin_search_tree_C/bst.h (100%) rename stepushovgs/data-structures/{sorce => source}/bin_search_tree_C/queue.c (100%) rename stepushovgs/data-structures/{sorce => source}/bin_search_tree_C/queue.h (100%) rename stepushovgs/data-structures/{sorce => source}/linked_list_c/linked_list.c (100%) rename stepushovgs/data-structures/{sorce => source}/linked_list_c/linked_list.h (100%) rename stepushovgs/data-structures/{sorce => source/old_c}/main.c (100%) rename stepushovgs/data-structures/{sorce => source/old_c}/main_tree.c (100%) rename stepushovgs/data-structures/{sorce => source/old_c}/swap.c (100%) rename stepushovgs/data-structures/{sorce/bin_search_tree/bst.go => source/pkg/bin_search_tree/bin_search_tree.go} (80%) create mode 100644 stepushovgs/data-structures/source/pkg/data_struct/data_structure.go create mode 100644 stepushovgs/data-structures/source/pkg/data_struct/qsort.go create mode 100644 stepushovgs/data-structures/source/pkg/gen_data/data_generator.go create mode 100644 stepushovgs/data-structures/source/pkg/hash_table/hash_string.go create mode 100644 stepushovgs/data-structures/source/pkg/hash_table/hash_table.go rename stepushovgs/data-structures/{sorce/linked_list/q_sort_ll.go => source/pkg/hash_table/qsort.go} (74%) rename stepushovgs/data-structures/{sorce => source/pkg}/linked_list/linked_list.go (77%) rename stepushovgs/data-structures/{sorce/bin_search_tree => source/tests/test_bst}/main.go (88%) create mode 100644 stepushovgs/data-structures/source/tests/test_ht/main.go rename stepushovgs/data-structures/{sorce/linked_list => source/tests/test_ll}/main.go (90%) diff --git a/stepushovgs/data-structures/sorce/hash_table/hash_table.c b/stepushovgs/data-structures/sorce/hash_table/hash_table.c deleted file mode 100644 index 8b13789..0000000 --- a/stepushovgs/data-structures/sorce/hash_table/hash_table.c +++ /dev/null @@ -1 +0,0 @@ - diff --git a/stepushovgs/data-structures/sorce/bin_search_tree_C/bst.c b/stepushovgs/data-structures/source/bin_search_tree_C/bst.c similarity index 100% rename from stepushovgs/data-structures/sorce/bin_search_tree_C/bst.c rename to stepushovgs/data-structures/source/bin_search_tree_C/bst.c diff --git a/stepushovgs/data-structures/sorce/bin_search_tree_C/bst.h b/stepushovgs/data-structures/source/bin_search_tree_C/bst.h similarity index 100% rename from stepushovgs/data-structures/sorce/bin_search_tree_C/bst.h rename to stepushovgs/data-structures/source/bin_search_tree_C/bst.h diff --git a/stepushovgs/data-structures/sorce/bin_search_tree_C/queue.c b/stepushovgs/data-structures/source/bin_search_tree_C/queue.c similarity index 100% rename from stepushovgs/data-structures/sorce/bin_search_tree_C/queue.c rename to stepushovgs/data-structures/source/bin_search_tree_C/queue.c diff --git a/stepushovgs/data-structures/sorce/bin_search_tree_C/queue.h b/stepushovgs/data-structures/source/bin_search_tree_C/queue.h similarity index 100% rename from stepushovgs/data-structures/sorce/bin_search_tree_C/queue.h rename to stepushovgs/data-structures/source/bin_search_tree_C/queue.h diff --git a/stepushovgs/data-structures/sorce/linked_list_c/linked_list.c b/stepushovgs/data-structures/source/linked_list_c/linked_list.c similarity index 100% rename from stepushovgs/data-structures/sorce/linked_list_c/linked_list.c rename to stepushovgs/data-structures/source/linked_list_c/linked_list.c diff --git a/stepushovgs/data-structures/sorce/linked_list_c/linked_list.h b/stepushovgs/data-structures/source/linked_list_c/linked_list.h similarity index 100% rename from stepushovgs/data-structures/sorce/linked_list_c/linked_list.h rename to stepushovgs/data-structures/source/linked_list_c/linked_list.h diff --git a/stepushovgs/data-structures/sorce/main.c b/stepushovgs/data-structures/source/old_c/main.c similarity index 100% rename from stepushovgs/data-structures/sorce/main.c rename to stepushovgs/data-structures/source/old_c/main.c diff --git a/stepushovgs/data-structures/sorce/main_tree.c b/stepushovgs/data-structures/source/old_c/main_tree.c similarity index 100% rename from stepushovgs/data-structures/sorce/main_tree.c rename to stepushovgs/data-structures/source/old_c/main_tree.c diff --git a/stepushovgs/data-structures/sorce/swap.c b/stepushovgs/data-structures/source/old_c/swap.c similarity index 100% rename from stepushovgs/data-structures/sorce/swap.c rename to stepushovgs/data-structures/source/old_c/swap.c diff --git a/stepushovgs/data-structures/sorce/bin_search_tree/bst.go b/stepushovgs/data-structures/source/pkg/bin_search_tree/bin_search_tree.go similarity index 80% rename from stepushovgs/data-structures/sorce/bin_search_tree/bst.go rename to stepushovgs/data-structures/source/pkg/bin_search_tree/bin_search_tree.go index a909c0d..b3a653a 100644 --- a/stepushovgs/data-structures/sorce/bin_search_tree/bst.go +++ b/stepushovgs/data-structures/source/pkg/bin_search_tree/bin_search_tree.go @@ -1,21 +1,20 @@ -package main +package bin_search_tree import ( "fmt" + ds "source/pkg/data_struct" ) type BinSearchTree struct { - name string - phone string + data ds.MyData left *BinSearchTree right *BinSearchTree } -func NewBinSearchTree(name, phone string) *BinSearchTree { +func NewBinSearchTree(data ds.MyData) *BinSearchTree { return &BinSearchTree{ - name: name, - phone: phone, + data: data, left: nil, right: nil, } @@ -42,14 +41,14 @@ func (bst *BinSearchTree) Maximum() *BinSearchTree { } func (node *BinSearchTree) PrintNode() { - fmt.Printf("Имя: %s, Телефон: %s", node.name, node.phone) + fmt.Print(node.data.ToString()) } func (node *BinSearchTree) ToString() string { if node == nil { return "nil" } - return fmt.Sprintf("Имя: %s, Телефон: %s", node.name, node.phone) + return node.data.ToString() } func (bst *BinSearchTree) BstInorderTraversal() { @@ -93,10 +92,10 @@ func (node *BinSearchTree) search(targetName string) (*BinSearchTree, bool) { if node == nil { return nil, false } - if node.name == targetName { + if node.data.Name == targetName { return node, true } - if targetName < node.name { + if targetName < node.data.Name { return node.left.search(targetName) } return node.right.search(targetName) @@ -117,15 +116,15 @@ func (node *BinSearchTree) search(targetName string) (*BinSearchTree, bool) { // return targetNode.ToString(), ok // } -func (node *BinSearchTree) Insert(name, phone string) *BinSearchTree { +func (node *BinSearchTree) Insert(data ds.MyData) *BinSearchTree { if node == nil { - return NewBinSearchTree(name, phone) - } else if name < node.name { - node.left = node.left.Insert(name, phone) - } else if name > node.name { - node.right = node.right.Insert(name, phone) + return NewBinSearchTree(data) + } else if data.Name < node.data.Name { + node.left = node.left.Insert(data) + } else if data.Name > node.data.Name { + node.right = node.right.Insert(data) } else { - node.phone = phone // Заменяем существующее значение + node.data.Phone = data.Phone // Заменяем существующее значение } return node } @@ -159,18 +158,18 @@ func (root *BinSearchTree) Delete(targetName string) *BinSearchTree { return nil } - if targetName < root.name { + if targetName < root.data.Name { root.left = root.left.Delete(targetName) - } else if targetName > root.name { + } else if targetName > root.data.Name { root.right = root.right.Delete(targetName) } else if root.left != nil && root.right != nil { temp := root.right.Minimum() - root.name = root.right.Minimum().name - root.phone = root.right.Minimum().phone + root.data.Name = root.right.Minimum().data.Name + root.data.Phone = root.right.Minimum().data.Phone // strcpy(root->name, bst_minimum(root->right)->name); // strcpy(root->phone, bst_minimum(root->right)->phone); - root.right = root.right.Delete(temp.name) + root.right = root.right.Delete(temp.data.Name) } else { if root.left != nil { return root.left diff --git a/stepushovgs/data-structures/source/pkg/data_struct/data_structure.go b/stepushovgs/data-structures/source/pkg/data_struct/data_structure.go new file mode 100644 index 0000000..cbceb85 --- /dev/null +++ b/stepushovgs/data-structures/source/pkg/data_struct/data_structure.go @@ -0,0 +1,19 @@ +package data_struct + +import "fmt" + +type MyData struct { + Name string + Phone string +} + +func NewData(name, phone string) *MyData { + return &MyData{ + Name: name, + Phone: phone, + } +} + +func (d *MyData) ToString() string { + return fmt.Sprintf("Имя: %s, Телефон: %s", d.Name, d.Phone) +} diff --git a/stepushovgs/data-structures/source/pkg/data_struct/qsort.go b/stepushovgs/data-structures/source/pkg/data_struct/qsort.go new file mode 100644 index 0000000..00e0c01 --- /dev/null +++ b/stepushovgs/data-structures/source/pkg/data_struct/qsort.go @@ -0,0 +1,37 @@ +package data_struct + +func QSort(arr []MyData, l, r int) []MyData { + if l < r { + s := Partition_Hoa(arr, l, r) + arr = QSort(arr, l, s) + arr = QSort(arr, s+1, r) + } + return arr +} + +func Partition_Hoa(arr []MyData, l, r int) int { + p := arr[(l+r)/2].Name + i := l - 1 + j := r + 1 + + for { + for { + i++ + if arr[i].Name >= p { + break + } + } + for { + j-- + if arr[j].Name <= p { + break + } + } + + if i >= j { + return j + } + + arr[i], arr[j] = arr[j], arr[i] + } +} diff --git a/stepushovgs/data-structures/source/pkg/gen_data/data_generator.go b/stepushovgs/data-structures/source/pkg/gen_data/data_generator.go new file mode 100644 index 0000000..31de838 --- /dev/null +++ b/stepushovgs/data-structures/source/pkg/gen_data/data_generator.go @@ -0,0 +1,41 @@ +package gendata + +import ( + "fmt" + "math/rand" + ds "source/pkg/data_struct" +) + +const ( + MAX_USER_IND = 10000 + PHONE_LEN = 11 +) + +func genRandomPhone() string { + phone := "" + for i := 0; i < PHONE_LEN; i++ { + phone += fmt.Sprintf("%d", rand.Intn(10)) + } + + return phone +} + +func RecordsShuffled(count int) []ds.MyData { + data := make([]ds.MyData, count) + number := 0 + for i := 0; i < count; i++ { + number = rand.Intn(MAX_USER_IND) + data[i].Name = fmt.Sprintf("User_%05d", number) + data[i].Phone = genRandomPhone() + } + + return data +} + +func RecordsSorted(count int) []ds.MyData { + data := RecordsShuffled(count) + + data = ds.QSort(data, 0, len(data)-1) + + return data +} diff --git a/stepushovgs/data-structures/source/pkg/hash_table/hash_string.go b/stepushovgs/data-structures/source/pkg/hash_table/hash_string.go new file mode 100644 index 0000000..0efa5bb --- /dev/null +++ b/stepushovgs/data-structures/source/pkg/hash_table/hash_string.go @@ -0,0 +1,15 @@ +package hash_table + +func GetHashString(str string) int { + hash := 0 + + for _, ch := range str { + hash = (hash << 5) - hash + int(ch) + } + + if hash < 0 { + hash = -hash + } + + return hash +} diff --git a/stepushovgs/data-structures/source/pkg/hash_table/hash_table.go b/stepushovgs/data-structures/source/pkg/hash_table/hash_table.go new file mode 100644 index 0000000..7696bd6 --- /dev/null +++ b/stepushovgs/data-structures/source/pkg/hash_table/hash_table.go @@ -0,0 +1,242 @@ +package hash_table + +import ( + "bufio" + "fmt" + "os" +) + +// HashTable - хеш-таблица с цепочками +type HashTable struct { + buckets []*bucket + size int + capacity int + loadFactor float64 +} + +type bucket struct { + head *elementHT +} + +type elementHT struct { + name string + phone string + next *elementHT +} + +// NewHashTable - создает новую хеш-таблицу +func NewHashTable(capacity int, loadFactor float64) *HashTable { + + buckets := make([]*bucket, capacity) + + for i := 0; i < capacity; i++ { + buckets[i] = &bucket{} + } + + return &HashTable{ + buckets: buckets, + size: 0, + capacity: capacity, + loadFactor: loadFactor, + } +} + +// func (h HashTable) getIndex(name string) int { +// return GetHashString(name) % h.size +// } + +// func (h HashTable) Add(name string) { + +// } + +func (ht *HashTable) GetIndex(name string) int { + hash := GetHashString(name) + return hash % ht.capacity +} + +// func (ht *HashTable) getIndex(hash int) int { +// return hash % ht.capacity +// } + +func (h *HashTable) Put(name string, phone string) { + + if h.size >= int(float64(h.capacity)*h.loadFactor) { + h.resize() + } + + ind := h.GetIndex(name) + + buck := h.buckets[ind] + + current := buck.head + + for current != nil { + if current.name == name { + current.phone = phone + return + } + + current = current.next + } + + newHead := &elementHT{ + name: name, + phone: phone, + next: buck.head, + } + + buck.head = newHead + h.size++ +} + +func (h *HashTable) Get(name string) (phone string, status bool) { + ind := h.GetIndex(name) + + buck := h.buckets[ind] + + current := buck.head + + for current != nil { + if current.name == name { + return current.phone, true + } + + current = current.next + } + + return "", false +} + +func pressEnterToContinue() { + fmt.Print("Нажмите Enter для продолжения...") + bufio.NewReader(os.Stdin).ReadBytes('\n') +} + +// resize - увеличивает размер таблицы +func (ht *HashTable) resize() { + + // fmt.Printf("Resize table!\n elements: %d(%.3f%%)\n old capacity: %d\n new capacity: %d\n", ht.size, float64(ht.size)/float64(ht.capacity), ht.capacity, 2*ht.capacity) + + // ht.Print() + + // pressEnterToContinue() + + newCapacity := ht.capacity * 2 + newHT := NewHashTable(newCapacity, ht.loadFactor) + + for _, b := range ht.buckets { + current := b.head + for current != nil { + newHT.Put(current.name, current.phone) + current = current.next + } + } + + ht.buckets = newHT.buckets + ht.capacity = newCapacity +} + +func (ht *HashTable) Remove(name string) bool { + ind := ht.GetIndex(name) + + buck := ht.buckets[ind] + + if buck.head == nil { + return false + } + + if buck.head.name == name { + buck.head = buck.head.next + ht.size-- + return true + } + + prev := buck.head + current := buck.head.next + + for current != nil { + if current.name == name { + prev.next = current.next + ht.size-- + return true + } + prev = current + current = current.next + } + + return false +} + +func (ht *HashTable) Contains(name string) bool { + _, ok := ht.Get(name) + return ok +} + +func (elem *elementHT) ToString() string { + if elem == nil { + return "nil" + } + + return fmt.Sprintf("Имя: %s, Телефон: %s", elem.name, elem.phone) +} + +func (ht *HashTable) Print() { + for ind := 0; ind < ht.capacity; ind++ { + buck := ht.buckets[ind] + current := buck.head + + bucketsStr := "" + + for current != nil { + bucketsStr += " --> " + current.ToString() + current = current.next + } + fmt.Printf("[%d]: %s\n", ind, bucketsStr) + } +} + +func (ht *HashTable) listAll() []elementHT { + data := make([]elementHT, ht.size) + + index := 0 + + for ind := 0; ind < ht.capacity; ind++ { + buck := ht.buckets[ind] + current := buck.head + + for current != nil { + data[index] = elementHT{ + name: current.name, + phone: current.phone, + } + index++ + // fmt.Println(current.name, current.phone) + current = current.next + } + } + + return data +} + +func (ht *HashTable) ListAll() []elementHT { + // fmt.Printf("Size: %d, Capacity: %d\n", ht.size, ht.capacity) + data := ht.listAll() + + data = QSortElementsHT(data, 0, len(data)-1) + + // for i, el := range data { + // fmt.Printf("[%d]: \"%s\", %d\n", i, el.name, el.phone) + // } + return data +} + +// func (ht *HashTable) PrintMostPopularnames(phone int) { +// // fmt.Printf("Size: %d, Capacity: %d\n", ht.size, ht.capacity) +// data := ht.listAll() + +// data = QSortElementsHT(data, 0, len(data)-1) + +// for i := 0; i < phone; i++ { +// fmt.Printf("[%d]: %3d : %s\n", i, ht.GetIndex(data[len(data)-i-1].name), data[len(data)-i-1].ToString()) +// } +// } diff --git a/stepushovgs/data-structures/sorce/linked_list/q_sort_ll.go b/stepushovgs/data-structures/source/pkg/hash_table/qsort.go similarity index 74% rename from stepushovgs/data-structures/sorce/linked_list/q_sort_ll.go rename to stepushovgs/data-structures/source/pkg/hash_table/qsort.go index 2fd6c0c..fb01c32 100644 --- a/stepushovgs/data-structures/sorce/linked_list/q_sort_ll.go +++ b/stepushovgs/data-structures/source/pkg/hash_table/qsort.go @@ -1,6 +1,6 @@ -package main +package hash_table -func QSortElementsHT(arr []LinkedList, l, r int) []LinkedList { +func QSortElementsHT(arr []elementHT, l, r int) []elementHT { if l < r { s := Partition_Hoa(arr, l, r) arr = QSortElementsHT(arr, l, s) @@ -9,7 +9,7 @@ func QSortElementsHT(arr []LinkedList, l, r int) []LinkedList { return arr } -func Partition_Hoa(arr []LinkedList, l, r int) int { +func Partition_Hoa(arr []elementHT, l, r int) int { p := arr[(l+r)/2].name i := l - 1 j := r + 1 diff --git a/stepushovgs/data-structures/sorce/linked_list/linked_list.go b/stepushovgs/data-structures/source/pkg/linked_list/linked_list.go similarity index 77% rename from stepushovgs/data-structures/sorce/linked_list/linked_list.go rename to stepushovgs/data-structures/source/pkg/linked_list/linked_list.go index db20023..9d33735 100644 --- a/stepushovgs/data-structures/sorce/linked_list/linked_list.go +++ b/stepushovgs/data-structures/source/pkg/linked_list/linked_list.go @@ -1,7 +1,8 @@ -package main +package linked_list import ( "fmt" + ds "source/pkg/data_struct" ) /* @@ -21,22 +22,20 @@ def ll_list_all(head) — собирает все записи в список */ type LinkedList struct { - name string - phone string + data ds.MyData next *LinkedList } -func NewLinkedList(name, phone string) *LinkedList { +func NewLinkedList(data ds.MyData) *LinkedList { return &LinkedList{ - name: name, - phone: phone, - next: nil, + data: data, + next: nil, } } func (ll *LinkedList) ToString() string { - return fmt.Sprintf("Имя: %s, Телефон: %s", ll.name, ll.phone) + return ll.data.ToString() } func (ll *LinkedList) Len() int { @@ -55,8 +54,8 @@ func (ll *LinkedList) Len() int { return len } -func (ll *LinkedList) Insert(name, phone string) *LinkedList { - newNode := NewLinkedList(name, phone) +func (ll *LinkedList) Insert(data ds.MyData) *LinkedList { + newNode := NewLinkedList(data) if ll == nil { return newNode @@ -74,8 +73,8 @@ func (ll *LinkedList) Search(targetName string) (string, bool) { current := ll for current != nil { - if current.name == targetName { - return current.phone, true + if current.data.Name == targetName { + return current.data.Phone, true } current = current.next @@ -99,7 +98,7 @@ func (ll *LinkedList) Delete(targetName string) (*LinkedList, bool) { if ll == nil { return nil, false } - if ll.name == targetName { + if ll.data.Name == targetName { return ll.next, true } @@ -107,7 +106,7 @@ func (ll *LinkedList) Delete(targetName string) (*LinkedList, bool) { current := ll.next for current != nil { - if current.name == targetName { + if current.data.Name == targetName { prev.next = current.next } prev = current @@ -117,17 +116,17 @@ func (ll *LinkedList) Delete(targetName string) (*LinkedList, bool) { return ll, false } -func (ll *LinkedList) listAll() []LinkedList { +func (ll *LinkedList) listAll() []ds.MyData { current := ll - listLL := make([]LinkedList, ll.Len()) + listLL := make([]ds.MyData, ll.Len()) ind := 0 for current != nil { - listLL[ind] = *current + listLL[ind] = current.data ind++ current = current.next } - listLL = QSortElementsHT(listLL, 0, len(listLL)-1) + listLL = ds.QSort(listLL, 0, len(listLL)-1) return listLL } diff --git a/stepushovgs/data-structures/sorce/bin_search_tree/main.go b/stepushovgs/data-structures/source/tests/test_bst/main.go similarity index 88% rename from stepushovgs/data-structures/sorce/bin_search_tree/main.go rename to stepushovgs/data-structures/source/tests/test_bst/main.go index 5ef8e0d..197b5dd 100644 --- a/stepushovgs/data-structures/sorce/bin_search_tree/main.go +++ b/stepushovgs/data-structures/source/tests/test_bst/main.go @@ -5,6 +5,8 @@ import ( "fmt" "math/rand" "os" + bst "source/pkg/bin_search_tree" + ds "source/pkg/data_struct" ) const ( @@ -29,7 +31,7 @@ func isInArr(arr []int, length int, target int) bool { func main() { fmt.Println("hello world!") - var head *BinSearchTree = nil + var head *bst.BinSearchTree = nil arr := make([]int, countNumbers) @@ -48,7 +50,9 @@ func main() { nameStr := fmt.Sprintf("name_%02d", temp) phoneStr := fmt.Sprintf("phone_%02d", temp) - head = head.Insert(nameStr, phoneStr) + data := ds.NewData(nameStr, phoneStr) + + head = head.Insert(*data) fmt.Printf("%d ", arr[i]) } @@ -72,7 +76,7 @@ func main() { head = head.Delete(tarName) - fmt.Printf("\nКоличество узлов после удаления: %d\n", countNodes(head)) + fmt.Printf("\nКоличество узлов после удаления: %d\n", head.Len()) fmt.Printf("Поиск '%s' после удаления: ", tarName) if found, ok := head.Search(tarName); ok { @@ -84,10 +88,3 @@ func main() { fmt.Println("\ninorder traversal after delete:") head.BstInorderTraversal() } - -func countNodes(root *BinSearchTree) int { - if root == nil { - return 0 - } - return 1 + countNodes(root.left) + countNodes(root.right) -} diff --git a/stepushovgs/data-structures/source/tests/test_ht/main.go b/stepushovgs/data-structures/source/tests/test_ht/main.go new file mode 100644 index 0000000..4891571 --- /dev/null +++ b/stepushovgs/data-structures/source/tests/test_ht/main.go @@ -0,0 +1,71 @@ +package main + +import ( + "fmt" + + // hash_table "hash-table-task/hash-table" + + ht "source/pkg/hash_table" +) + +/* + +1. Сконструировать и реализовать свою хеш таблицу + +- изначальный размер 8, коэф-т загрузки 0.75 + +- Преобразование подаваемого данного в индекс с помощью хеш функции(в ручну) пример: полиномиальный хеш + +- Коллизии обрабатываются методом цепочек, каждая корзина таблицы - список в котором хранятся пары значений key-value + +- При превышении коэф-та загрузки происходит перехеширование таблицы, размер увеличивается вдвое, все пары заново вставляются в таблицу. + +2. Читаем текстовый файл, разбивает на слова, приводим к нижнему регистру, подсчитываем повторения каждого слова: key - слово, value - кол-во повторений + +- На вывод 10 самых встречающихся слов, для каждого слова выводим: ind(hash), key, value +- Текст - первая глава, первые три стиха Евгений Онегин + +*/ + +func main() { + // Чтение всего файла + + // const filePath = "../data/onegin.txt" + // // const filePath = "../data/onegin_full.txt" + + // data, err := os.ReadFile(filePath) + // text := string(data) + // if err != nil { + // fmt.Println("Ошибка чтения файла:", err) + // return + // } + // fmt.Println(text) + + // text = strings.ToLower(text) + + // // Разбиение на слова (разделители: пробелы и переводы строк) + // re := regexp.MustCompile(`[\p{L}\p{N}-]+`) + // words := re.FindAllString(text, -1) + + // fmt.Printf("Найдено слов: %d\n", len(words)) + // for i, word := range words { + // fmt.Printf("Слово %d: %s\n", i+1, word) + // } + + hashTable := ht.NewHashTable(8, 0.95) + + for i, word := range words { + fmt.Printf("%d : %s\n", i, word) + hashTable.Put(word, 1) + } + + fmt.Println("\nХеш таблица текста: ") + hashTable.Print() + + // fmt.Println("Отсортированные ячейки таблицы: ") + // hashTable.PrintSort() + + fmt.Println("\nСамые часто встречающиеся слова: ") + + // hashTable.PrintMostPopularWords(10) +} diff --git a/stepushovgs/data-structures/sorce/linked_list/main.go b/stepushovgs/data-structures/source/tests/test_ll/main.go similarity index 90% rename from stepushovgs/data-structures/sorce/linked_list/main.go rename to stepushovgs/data-structures/source/tests/test_ll/main.go index a77923a..20c7824 100644 --- a/stepushovgs/data-structures/sorce/linked_list/main.go +++ b/stepushovgs/data-structures/source/tests/test_ll/main.go @@ -5,6 +5,9 @@ import ( "fmt" "math/rand" "os" + ds "source/pkg/data_struct" + ll "source/pkg/linked_list" + // dg "source/pkg/gendata" ) const countNumbers = 64 @@ -26,7 +29,7 @@ func pressEnterToContinue() { func main() { fmt.Println("hello world!") - var head *LinkedList = nil + var head *ll.LinkedList = nil arr := make([]int, countNumbers) @@ -44,8 +47,9 @@ func main() { nameStr := fmt.Sprintf("name_%02d", temp) phoneStr := fmt.Sprintf("phone_%02d", temp) + data := ds.NewData(nameStr, phoneStr) - head = head.Insert(nameStr, phoneStr) + head = head.Insert(*data) fmt.Printf("%d ", arr[i]) // pressEnterToContinue() } From 4a214a2843889c8207a462160afa323ce8d075a9 Mon Sep 17 00:00:00 2001 From: GordStep Date: Sun, 10 May 2026 12:30:21 +0300 Subject: [PATCH 14/23] =?UTF-8?q?=D0=98=D0=B7=D0=BC=D0=B5=D0=BD=D0=B5?= =?UTF-8?q?=D0=BD=D0=B8=D0=B5=20=D0=B8=D0=B5=D1=80=D0=B0=D1=80=D1=85=D0=B8?= =?UTF-8?q?=D0=B8=20=20-=20=D0=B4=D0=BE=D0=B1=D0=B0=D0=B2=D0=B5=D0=BB?= =?UTF-8?q?=D0=B5=D0=BD=D0=B8=D0=B5=20=D0=B2=20=D0=BE=D1=82=D0=BB=D1=81?= =?UTF-8?q?=D0=B5=D0=B6=D0=B8=D0=B2=D0=B0=D0=BD=D0=B8=D0=B5=20go.mod=20=20?= =?UTF-8?q?-=20=D0=BF=D0=B5=D1=80=D0=B5=D0=BD=D0=BE=D1=81=20=D1=85=D0=B5?= =?UTF-8?q?=D1=88=20=D1=82=D0=B0=D0=B1=D0=BB=D0=B8=D1=86=D1=8B=20=D0=BD?= =?UTF-8?q?=D0=B0=20=D0=BE=D0=B1=D1=89=D0=B8=D0=B9=20=D1=84=D0=BE=D1=80?= =?UTF-8?q?=D0=BC=D0=B0=D1=82=20=D1=85=D1=80=D0=B0=D0=BD=D0=B5=D0=BD=D0=B8?= =?UTF-8?q?=D1=8F=20=D0=B4=D0=B0=D0=BD=D0=BD=D1=8B=D1=85=20=20-=20=D1=83?= =?UTF-8?q?=D0=B4=D0=B0=D0=BB=D0=B5=D0=BD=D0=B8=D0=B5=20=D0=BB=D0=B8=D1=88?= =?UTF-8?q?=D0=BD=D0=B8=D1=85?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 217 --------------- stepushovgs/.gitignore | 255 ++++++++++++++++++ stepushovgs/data-structures/.gitignore | 2 - stepushovgs/data-structures/source/go.mod | 3 + .../source/pkg/data_struct/data_structure.go | 6 + .../source/pkg/gen_data/data_generator.go | 2 +- .../source/pkg/resulter/resulter.go | 36 +++ .../bin_search_tree/bin_search_tree.go | 0 .../hash_table/hash_string.go | 0 .../{ => structures}/hash_table/hash_table.go | 44 ++- .../pkg/{ => structures}/hash_table/qsort.go | 0 .../linked_list/linked_list.go | 18 ++ .../source/tests/test_bst/main.go | 2 +- .../source/tests/test_ht/main.go | 28 +- .../source/tests/test_ll/main.go | 182 +++++++++---- 15 files changed, 492 insertions(+), 303 deletions(-) delete mode 100644 .gitignore delete mode 100644 stepushovgs/data-structures/.gitignore create mode 100644 stepushovgs/data-structures/source/go.mod create mode 100644 stepushovgs/data-structures/source/pkg/resulter/resulter.go rename stepushovgs/data-structures/source/pkg/{ => structures}/bin_search_tree/bin_search_tree.go (100%) rename stepushovgs/data-structures/source/pkg/{ => structures}/hash_table/hash_string.go (100%) rename stepushovgs/data-structures/source/pkg/{ => structures}/hash_table/hash_table.go (84%) rename stepushovgs/data-structures/source/pkg/{ => structures}/hash_table/qsort.go (100%) rename stepushovgs/data-structures/source/pkg/{ => structures}/linked_list/linked_list.go (90%) diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 5cef1d9..0000000 --- a/.gitignore +++ /dev/null @@ -1,217 +0,0 @@ -# ---> Python -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# poetry -# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -#poetry.lock - -# pdm -# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. -#pdm.lock -# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it -# in version control. -# https://pdm.fming.dev/#use-with-ide -.pdm.toml - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ - -# Prerequisites -*.d - -# Object files -*.o -*.ko -*.obj -*.elf - -# Linker output -*.ilk -*.map -*.exp - -# Precompiled Headers -*.gch -*.pch - -# Libraries -*.lib -*.a -*.la -*.lo - -# Shared objects (inc. Windows DLLs) -*.dll -*.so -*.so.* -*.dylib - -# Executables -*.exe -*.out -*.app -*.i*86 -*.x86_64 -*.hex - -# Debug files -*.dSYM/ -*.su -*.idb -*.pdb - -# Kernel Module Compile Results -*.mod* -*.cmd -.tmp_versions/ -modules.order -Module.symvers -Mkfile.old -dkms.conf - -# debug information files -*.dwo diff --git a/stepushovgs/.gitignore b/stepushovgs/.gitignore index 5761abc..09b1f43 100644 --- a/stepushovgs/.gitignore +++ b/stepushovgs/.gitignore @@ -1 +1,256 @@ +# ---> Python +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# Prerequisites +*.d + +# Object files *.o +*.ko +*.obj +*.elf + +# Linker output +*.ilk +*.map +*.exp + +# Precompiled Headers +*.gch +*.pch + +# Libraries +*.lib +*.a +*.la +*.lo + +# Shared objects (inc. Windows DLLs) +*.dll +*.so +*.so.* +*.dylib + +# Executables +*.exe +*.out +*.app +*.i*86 +*.x86_64 +*.hex + +# Debug files +*.dSYM/ +*.su +*.idb +*.pdb + +# Kernel Module Compile Results +*.mod* +*.cmd +.tmp_versions/ +modules.order +Module.symvers +Mkfile.old +dkms.conf + +# debug information files +*.dwo + +################################# +############## Go ############### +################################# + +# If you prefer the allow list template instead of the deny list, see community template: +# https://github.com/github/gitignore/blob/main/community/Golang/Go.AllowList.gitignore +# +# Binaries for programs and plugins +*.exe +*.exe~ +*.dll +*.so +*.dylib + +# Test binary, built with `go test -c` +*.test + +# Code coverage profiles and other test artifacts +*.out +coverage.* +*.coverprofile +profile.cov + +# Dependency directories (remove the comment below to include it) +# vendor/ + +# Go workspace file +go.work +go.work.sum + +# env file +.env + +# Editor/IDE +# .idea/ +.vscode/ + +!go.mod \ No newline at end of file diff --git a/stepushovgs/data-structures/.gitignore b/stepushovgs/data-structures/.gitignore deleted file mode 100644 index 9fb6852..0000000 --- a/stepushovgs/data-structures/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -.exe -.o diff --git a/stepushovgs/data-structures/source/go.mod b/stepushovgs/data-structures/source/go.mod new file mode 100644 index 0000000..3367dd1 --- /dev/null +++ b/stepushovgs/data-structures/source/go.mod @@ -0,0 +1,3 @@ +module source + +go 1.26.3 diff --git a/stepushovgs/data-structures/source/pkg/data_struct/data_structure.go b/stepushovgs/data-structures/source/pkg/data_struct/data_structure.go index cbceb85..059eed7 100644 --- a/stepushovgs/data-structures/source/pkg/data_struct/data_structure.go +++ b/stepushovgs/data-structures/source/pkg/data_struct/data_structure.go @@ -17,3 +17,9 @@ func NewData(name, phone string) *MyData { func (d *MyData) ToString() string { return fmt.Sprintf("Имя: %s, Телефон: %s", d.Name, d.Phone) } + +func PrintList(list []MyData) { + for _, el := range list { + fmt.Printf("%s\n", el.ToString()) + } +} diff --git a/stepushovgs/data-structures/source/pkg/gen_data/data_generator.go b/stepushovgs/data-structures/source/pkg/gen_data/data_generator.go index 31de838..f13f1f5 100644 --- a/stepushovgs/data-structures/source/pkg/gen_data/data_generator.go +++ b/stepushovgs/data-structures/source/pkg/gen_data/data_generator.go @@ -1,4 +1,4 @@ -package gendata +package gen_data import ( "fmt" diff --git a/stepushovgs/data-structures/source/pkg/resulter/resulter.go b/stepushovgs/data-structures/source/pkg/resulter/resulter.go new file mode 100644 index 0000000..5dd6c38 --- /dev/null +++ b/stepushovgs/data-structures/source/pkg/resulter/resulter.go @@ -0,0 +1,36 @@ +package resulter + +import ( + "encoding/csv" + "fmt" + "os" + "path/filepath" +) + +type BenchmarkResult struct { + Structure string + Mode string + Operation string + Time float64 +} + +func (b *BenchmarkResult) ToString() string { + return fmt.Sprintf("%s %s %s %f", b.Structure, b.Mode, b.Operation, b.Time) +} + +// AppendRaw дописывает произвольные строки в CSV +func AppendRaw(rows [][]string) error { + + filename := filepath.Join("results", "benchmarks.csv") + + file, err := os.OpenFile(filename, os.O_APPEND|os.O_CREATE|os.O_WRONLY, 0644) + if err != nil { + return err + } + defer file.Close() + + writer := csv.NewWriter(file) + defer writer.Flush() + + return writer.WriteAll(rows) // WriteAll пишет всё сразу +} diff --git a/stepushovgs/data-structures/source/pkg/bin_search_tree/bin_search_tree.go b/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go similarity index 100% rename from stepushovgs/data-structures/source/pkg/bin_search_tree/bin_search_tree.go rename to stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go diff --git a/stepushovgs/data-structures/source/pkg/hash_table/hash_string.go b/stepushovgs/data-structures/source/pkg/structures/hash_table/hash_string.go similarity index 100% rename from stepushovgs/data-structures/source/pkg/hash_table/hash_string.go rename to stepushovgs/data-structures/source/pkg/structures/hash_table/hash_string.go diff --git a/stepushovgs/data-structures/source/pkg/hash_table/hash_table.go b/stepushovgs/data-structures/source/pkg/structures/hash_table/hash_table.go similarity index 84% rename from stepushovgs/data-structures/source/pkg/hash_table/hash_table.go rename to stepushovgs/data-structures/source/pkg/structures/hash_table/hash_table.go index 7696bd6..b4ef466 100644 --- a/stepushovgs/data-structures/source/pkg/hash_table/hash_table.go +++ b/stepushovgs/data-structures/source/pkg/structures/hash_table/hash_table.go @@ -4,6 +4,7 @@ import ( "bufio" "fmt" "os" + ds "source/pkg/data_struct" ) // HashTable - хеш-таблица с цепочками @@ -19,9 +20,8 @@ type bucket struct { } type elementHT struct { - name string - phone string - next *elementHT + data ds.MyData + next *elementHT } // NewHashTable - создает новую хеш-таблицу @@ -58,21 +58,21 @@ func (ht *HashTable) GetIndex(name string) int { // return hash % ht.capacity // } -func (h *HashTable) Put(name string, phone string) { +func (h *HashTable) Insert(new ds.MyData) { if h.size >= int(float64(h.capacity)*h.loadFactor) { h.resize() } - ind := h.GetIndex(name) + ind := h.GetIndex(new.Name) buck := h.buckets[ind] current := buck.head for current != nil { - if current.name == name { - current.phone = phone + if current.data.Name == new.Name { + current.data.Phone = new.Phone return } @@ -80,9 +80,8 @@ func (h *HashTable) Put(name string, phone string) { } newHead := &elementHT{ - name: name, - phone: phone, - next: buck.head, + data: new, + next: buck.head, } buck.head = newHead @@ -97,8 +96,8 @@ func (h *HashTable) Get(name string) (phone string, status bool) { current := buck.head for current != nil { - if current.name == name { - return current.phone, true + if current.data.Name == name { + return current.data.Phone, true } current = current.next @@ -127,7 +126,7 @@ func (ht *HashTable) resize() { for _, b := range ht.buckets { current := b.head for current != nil { - newHT.Put(current.name, current.phone) + newHT.Insert(current.data) current = current.next } } @@ -145,7 +144,7 @@ func (ht *HashTable) Remove(name string) bool { return false } - if buck.head.name == name { + if buck.head.data.Name == name { buck.head = buck.head.next ht.size-- return true @@ -155,7 +154,7 @@ func (ht *HashTable) Remove(name string) bool { current := buck.head.next for current != nil { - if current.name == name { + if current.data.Name == name { prev.next = current.next ht.size-- return true @@ -177,7 +176,7 @@ func (elem *elementHT) ToString() string { return "nil" } - return fmt.Sprintf("Имя: %s, Телефон: %s", elem.name, elem.phone) + return elem.ToString() } func (ht *HashTable) Print() { @@ -195,8 +194,8 @@ func (ht *HashTable) Print() { } } -func (ht *HashTable) listAll() []elementHT { - data := make([]elementHT, ht.size) +func (ht *HashTable) listAll() []ds.MyData { + data := make([]ds.MyData, ht.size) index := 0 @@ -205,10 +204,7 @@ func (ht *HashTable) listAll() []elementHT { current := buck.head for current != nil { - data[index] = elementHT{ - name: current.name, - phone: current.phone, - } + data[index] = current.data index++ // fmt.Println(current.name, current.phone) current = current.next @@ -218,11 +214,11 @@ func (ht *HashTable) listAll() []elementHT { return data } -func (ht *HashTable) ListAll() []elementHT { +func (ht *HashTable) ListAll() []ds.MyData { // fmt.Printf("Size: %d, Capacity: %d\n", ht.size, ht.capacity) data := ht.listAll() - data = QSortElementsHT(data, 0, len(data)-1) + data = ds.QSort(data, 0, len(data)-1) // for i, el := range data { // fmt.Printf("[%d]: \"%s\", %d\n", i, el.name, el.phone) diff --git a/stepushovgs/data-structures/source/pkg/hash_table/qsort.go b/stepushovgs/data-structures/source/pkg/structures/hash_table/qsort.go similarity index 100% rename from stepushovgs/data-structures/source/pkg/hash_table/qsort.go rename to stepushovgs/data-structures/source/pkg/structures/hash_table/qsort.go diff --git a/stepushovgs/data-structures/source/pkg/linked_list/linked_list.go b/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go similarity index 90% rename from stepushovgs/data-structures/source/pkg/linked_list/linked_list.go rename to stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go index 9d33735..85ef19f 100644 --- a/stepushovgs/data-structures/source/pkg/linked_list/linked_list.go +++ b/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go @@ -130,3 +130,21 @@ func (ll *LinkedList) listAll() []ds.MyData { listLL = ds.QSort(listLL, 0, len(listLL)-1) return listLL } + +func (ll *LinkedList) GetByInd(ind int) (ds.MyData, bool) { + if ind >= ll.Len() { + return ds.MyData{}, false + } + + index := 0 + current := ll + for current != nil { + if index == ind { + return current.data, true + } + current = current.next + index++ + } + + return ds.MyData{}, false +} diff --git a/stepushovgs/data-structures/source/tests/test_bst/main.go b/stepushovgs/data-structures/source/tests/test_bst/main.go index 197b5dd..a16d473 100644 --- a/stepushovgs/data-structures/source/tests/test_bst/main.go +++ b/stepushovgs/data-structures/source/tests/test_bst/main.go @@ -5,8 +5,8 @@ import ( "fmt" "math/rand" "os" - bst "source/pkg/bin_search_tree" ds "source/pkg/data_struct" + bst "source/pkg/structures/bin_search_tree" ) const ( diff --git a/stepushovgs/data-structures/source/tests/test_ht/main.go b/stepushovgs/data-structures/source/tests/test_ht/main.go index 4891571..6443a3b 100644 --- a/stepushovgs/data-structures/source/tests/test_ht/main.go +++ b/stepushovgs/data-structures/source/tests/test_ht/main.go @@ -5,7 +5,8 @@ import ( // hash_table "hash-table-task/hash-table" - ht "source/pkg/hash_table" + ds "source/pkg/data_struct" + ht "source/pkg/structures/hash_table" ) /* @@ -28,6 +29,9 @@ import ( */ func main() { + fmt.Println("hello world") + hashTable := ht.NewHashTable(8, 0.75) + hashTable.Insert(*ds.NewData("User_0", "Phone_0")) // Чтение всего файла // const filePath = "../data/onegin.txt" @@ -52,20 +56,20 @@ func main() { // fmt.Printf("Слово %d: %s\n", i+1, word) // } - hashTable := ht.NewHashTable(8, 0.95) + // hashTable := ht.NewHashTable(8, 0.95) - for i, word := range words { - fmt.Printf("%d : %s\n", i, word) - hashTable.Put(word, 1) - } + // for i, word := range words { + // fmt.Printf("%d : %s\n", i, word) + // hashTable.Put(word, 1) + // } - fmt.Println("\nХеш таблица текста: ") - hashTable.Print() + // fmt.Println("\nХеш таблица текста: ") + // hashTable.Print() - // fmt.Println("Отсортированные ячейки таблицы: ") - // hashTable.PrintSort() + // // fmt.Println("Отсортированные ячейки таблицы: ") + // // hashTable.PrintSort() - fmt.Println("\nСамые часто встречающиеся слова: ") + // fmt.Println("\nСамые часто встречающиеся слова: ") - // hashTable.PrintMostPopularWords(10) + // // hashTable.PrintMostPopularWords(10) } diff --git a/stepushovgs/data-structures/source/tests/test_ll/main.go b/stepushovgs/data-structures/source/tests/test_ll/main.go index 20c7824..88d48c6 100644 --- a/stepushovgs/data-structures/source/tests/test_ll/main.go +++ b/stepushovgs/data-structures/source/tests/test_ll/main.go @@ -5,12 +5,19 @@ import ( "fmt" "math/rand" "os" - ds "source/pkg/data_struct" - ll "source/pkg/linked_list" - // dg "source/pkg/gendata" + dg "source/pkg/gen_data" + rs "source/pkg/resulter" + ll "source/pkg/structures/linked_list" + "time" ) -const countNumbers = 64 +const ( + countUsers = 10000 + countRepeat = 5 + countRandomSearch = 10000 + countNotExitstSearch = 10 + countDeletes = 50 +) func isInArr(arr []int, length int, target int) bool { for i := 0; i < length; i++ { @@ -21,6 +28,13 @@ func isInArr(arr []int, length int, target int) bool { return false } +func Razdelitel() { + for i := 0; i < 20; i++ { + fmt.Print("-") + } + fmt.Println() +} + func pressEnterToContinue() { fmt.Print("Нажмите Enter для продолжения...") bufio.NewReader(os.Stdin).ReadBytes('\n') @@ -29,59 +43,135 @@ func pressEnterToContinue() { func main() { fmt.Println("hello world!") + results := make([]rs.BenchmarkResult, 0, countUsers) + averageInsertTime := 0. + + Razdelitel() + fmt.Println("Тестирование вставки:") var head *ll.LinkedList = nil - arr := make([]int, countNumbers) + for testNum := 0; testNum < countRepeat; testNum++ { + head = nil - var temp int - for i := 0; i < countNumbers; i++ { - // Генерируем уникальное случайное число - for { - temp = rand.Intn(100) // 0 до 99 - if !isInArr(arr, i, temp) { - break - } + testData := dg.RecordsShuffled(countUsers) + + start := time.Now() + + for i := 0; i < countUsers; i++ { + head.Insert(testData[i]) } - arr[i] = temp + elapsed := time.Since(start).Seconds() + averageInsertTime += elapsed - nameStr := fmt.Sprintf("name_%02d", temp) - phoneStr := fmt.Sprintf("phone_%02d", temp) - data := ds.NewData(nameStr, phoneStr) - - head = head.Insert(*data) - fmt.Printf("%d ", arr[i]) - // pressEnterToContinue() + results = append(results, rs.BenchmarkResult{ + Structure: "Связный список", Mode: "Случайный", Operation: "Вставка", Time: elapsed, + }) } - pressEnterToContinue() - - fmt.Printf("\n\nКоличество узлов: %d\n", head.Len()) - - fmt.Println("\ninorder traversal:") - pressEnterToContinue() - - head.PrintAll() - pressEnterToContinue() - - tarName := "name_44" - - fmt.Printf("\nПоиск '%s' перед удалением: ", tarName) - if found, ok := head.Search(tarName); ok { - fmt.Printf("Найден: %s\n", found) - } else { - fmt.Printf("НЕ найден!\n") + averageInsertTime /= countRepeat + results = append(results, rs.BenchmarkResult{ + Structure: "Связный список", Mode: "Случайный", Operation: "Вставка", Time: averageInsertTime, + }) + for i := 0; i < 6; i++ { + fmt.Println(results[i].ToString()) } - fmt.Printf("\nУдаляем элемент с значением %s:\n", tarName) + Razdelitel() + // fmt.Println("Тестирование Поиска:") + // // results = make([]rs.BenchmarkResult, 0, countUsers) + // averageSearchTime := 0. - head, _ = head.Delete(tarName) + // for testNum := 0; testNum < countRepeat; testNum++ { + // // var head *ll.LinkedList = nil + // // head = dg.RecordsShuffled(countUsers) - fmt.Printf("\nКоличество узлов после удаления: %d\n", head.Len()) + // testData := make([]ds.MyData, countRandomSearch) - fmt.Printf("Поиск '%s' после удаления: ", tarName) - if found, ok := head.Search(tarName); ok { - fmt.Printf("ОШИБКА! Все еще существует: %s\n", found) - } else { - fmt.Printf("Успешно удален\n") + // for i := 0; i < countRandomSearch; i++ { + // // randInd := rand.Intn(countUsers) + // randInd := rand.Intn(1000) + 9000 + // testData[i], _ = head.GetByInd(randInd) + // // fmt.Println(randInd) + // } + + // start := time.Now() + + // for i := 0; i < countRandomSearch; i++ { + // head.Search(testData[i].Name) + // } + + // elapsed := time.Since(start).Seconds() + // averageSearchTime += elapsed + + // results = append(results, rs.BenchmarkResult{ + // Structure: "Связный список", Mode: "Случайный", Operation: "Поиск", Time: elapsed, + // }) + // } + // averageSearchTime /= countRepeat + // results = append(results, rs.BenchmarkResult{ + // Structure: "Связный список", Mode: "Случайный", Operation: "Поиск", Time: averageSearchTime, + // }) + // for i := 0; i < len(results); i++ { + // fmt.Println(results[i].ToString()) + // } + + resultsS := runSearchBenchmark(head) + for i := 0; i < len(resultsS); i++ { + fmt.Println(resultsS[i].ToString()) } + // rs.AppendRaw(results) +} + +func runSearchBenchmark(head *ll.LinkedList) []rs.BenchmarkResult { + fmt.Println("\n=== Тестирование Поиска ===") + + const countRandomSearch = 1000 // уменьшим для поиска + + var results []rs.BenchmarkResult + var totalTime float64 + + // Предварительно собираем имена для поиска + names := make([]string, countUsers) + for i := 0; i < countUsers; i++ { + data, found := head.GetByInd(i) + if found { + names[i] = data.Name + } + } + + for testNum := 0; testNum < countRepeat; testNum++ { + // Выбираем случайные имена + searchNames := make([]string, countRandomSearch) + for i := 0; i < countRandomSearch; i++ { + searchNames[i] = names[rand.Intn(countUsers)] + } + + start := time.Now() + + for _, name := range searchNames { + el, ok := head.Search(name) + if ok { + fmt.Println(el) + } + } + elapsed := time.Since(start).Seconds() + + totalTime += elapsed + + fmt.Printf(" Тест %d: %.6f сек (%.2f мкс/оп)\n", + testNum+1, elapsed, + elapsed/float64(countRandomSearch)*1_000_000) + } + + avgTime := totalTime / float64(countRepeat) + fmt.Printf("Среднее: %.6f сек\n", avgTime) + + results = append(results, rs.BenchmarkResult{ + Structure: "Связный список", + Mode: "Случайный", + Operation: "Поиск", + Time: avgTime, + }) + + return results } From b9c44211275defe3aead9106e5fa2e0408352cc8 Mon Sep 17 00:00:00 2001 From: GordStep Date: Sun, 10 May 2026 12:33:22 +0300 Subject: [PATCH 15/23] =?UTF-8?q?=D1=83=D0=B4=D0=B0=D0=BB=D0=B5=D0=BD?= =?UTF-8?q?=D0=B8=D0=B5=20=D1=81=D0=BE=D1=80=D1=82=D0=B8=D1=80=D0=BE=D0=B2?= =?UTF-8?q?=D0=BA=D0=B8=20=D0=B4=D0=BB=D1=8F=20=D1=85=D0=B5=D1=88=D1=82?= =?UTF-8?q?=D0=B0=D0=B1=D0=BB=D0=B8=D1=86=D1=8B=20(=D1=80=D0=B5=D0=B0?= =?UTF-8?q?=D0=BB=D0=B8=D0=B7=D0=BE=D0=B2=D0=B0=D0=BD=D0=B0=20=D0=B2=20MyD?= =?UTF-8?q?ata)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../source/pkg/structures/hash_table/qsort.go | 37 ------------------- 1 file changed, 37 deletions(-) delete mode 100644 stepushovgs/data-structures/source/pkg/structures/hash_table/qsort.go diff --git a/stepushovgs/data-structures/source/pkg/structures/hash_table/qsort.go b/stepushovgs/data-structures/source/pkg/structures/hash_table/qsort.go deleted file mode 100644 index fb01c32..0000000 --- a/stepushovgs/data-structures/source/pkg/structures/hash_table/qsort.go +++ /dev/null @@ -1,37 +0,0 @@ -package hash_table - -func QSortElementsHT(arr []elementHT, l, r int) []elementHT { - if l < r { - s := Partition_Hoa(arr, l, r) - arr = QSortElementsHT(arr, l, s) - arr = QSortElementsHT(arr, s+1, r) - } - return arr -} - -func Partition_Hoa(arr []elementHT, l, r int) int { - p := arr[(l+r)/2].name - i := l - 1 - j := r + 1 - - for { - for { - i++ - if arr[i].name >= p { - break - } - } - for { - j-- - if arr[j].name <= p { - break - } - } - - if i >= j { - return j - } - - arr[i], arr[j] = arr[j], arr[i] - } -} From bc6ece83d0d8ef134d7afdc36ce2fad030b6f725 Mon Sep 17 00:00:00 2001 From: GordStep Date: Sun, 10 May 2026 19:16:18 +0300 Subject: [PATCH 16/23] =?UTF-8?q?=D0=9D=D0=B0=D1=87=D0=B0=D0=BB=D0=BE=20?= =?UTF-8?q?=D0=BD=D0=B0=D0=BF=D0=B8=D1=81=D0=B0=D0=BD=D0=B8=D1=8F=20=D1=82?= =?UTF-8?q?=D0=B5=D1=81=D1=82=D0=BE=D0=B2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../pkg/{resulter => csv_writer}/resulter.go | 13 +- .../pkg/structures/linked_list/linked_list.go | 4 +- .../source/tests/benchmark/main.go | 152 ++++++++++++++++++ .../source/tests/test_ll/main.go | 8 +- 4 files changed, 167 insertions(+), 10 deletions(-) rename stepushovgs/data-structures/source/pkg/{resulter => csv_writer}/resulter.go (69%) create mode 100644 stepushovgs/data-structures/source/tests/benchmark/main.go diff --git a/stepushovgs/data-structures/source/pkg/resulter/resulter.go b/stepushovgs/data-structures/source/pkg/csv_writer/resulter.go similarity index 69% rename from stepushovgs/data-structures/source/pkg/resulter/resulter.go rename to stepushovgs/data-structures/source/pkg/csv_writer/resulter.go index 5dd6c38..ed67788 100644 --- a/stepushovgs/data-structures/source/pkg/resulter/resulter.go +++ b/stepushovgs/data-structures/source/pkg/csv_writer/resulter.go @@ -1,4 +1,4 @@ -package resulter +package csvwriter import ( "encoding/csv" @@ -17,9 +17,12 @@ type BenchmarkResult struct { func (b *BenchmarkResult) ToString() string { return fmt.Sprintf("%s %s %s %f", b.Structure, b.Mode, b.Operation, b.Time) } +func (b *BenchmarkResult) ToStrings() []string { + return []string{b.Structure, b.Mode, b.Operation, fmt.Sprintf("%d", b.Time)} +} // AppendRaw дописывает произвольные строки в CSV -func AppendRaw(rows [][]string) error { +func AppendRaw(results []BenchmarkResult) error { filename := filepath.Join("results", "benchmarks.csv") @@ -32,5 +35,11 @@ func AppendRaw(rows [][]string) error { writer := csv.NewWriter(file) defer writer.Flush() + rows := make([][]string, len(results)) + + for i, res := range results { + rows[i] = res.ToStrings() + } + return writer.WriteAll(rows) // WriteAll пишет всё сразу } diff --git a/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go b/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go index 85ef19f..eb1467c 100644 --- a/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go +++ b/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go @@ -99,7 +99,9 @@ func (ll *LinkedList) Delete(targetName string) (*LinkedList, bool) { return nil, false } if ll.data.Name == targetName { - return ll.next, true + ll.data = ll.next.data + ll.next = ll.next.next + return ll, true } prev := ll diff --git a/stepushovgs/data-structures/source/tests/benchmark/main.go b/stepushovgs/data-structures/source/tests/benchmark/main.go new file mode 100644 index 0000000..cd0e575 --- /dev/null +++ b/stepushovgs/data-structures/source/tests/benchmark/main.go @@ -0,0 +1,152 @@ +package benchmark + +import ( + "fmt" + "math/rand" + csvwriter "source/pkg/csv_writer" + ds "source/pkg/data_struct" + dg "source/pkg/gen_data" + ll "source/pkg/structures/linked_list" + + // csv "source/pkg/csv_ri" + + "time" +) + +const ( + countUsers = 10_000 + countRepeat = 5 + countRandomSearch = 100 + countNotExitstSearch = 10 + countDeletes = 50 +) + +type TestData struct { + Items []ds.MyData // все записи + ItemsSorted []ds.MyData // все записи отсортированные + Existing []ds.MyData // для поиска (существующие) + NonExisting []ds.MyData // для поиска (несуществующие) + ToDelete []ds.MyData // для удаления +} + +type DataStructure interface { + Insert(data ds.MyData) + Search(name string) *ds.MyData + Delete(name string) bool + Size() int +} + +func uniqueElements(data []ds.MyData) []ds.MyData { + res := make([]ds.MyData, 0, len(data)) + isUnique := true + for _, el := range data { + for _, resEl := range res { + if el == resEl { + isUnique = false + break + } + } + if isUnique { + res = append(res, el) + } + } + + return res +} + +func GenerateTestData() TestData { + items := dg.RecordsShuffled(countUsers) + itemsSort := ds.QSort(items, 0, len(items)-1) + + uniqueItems := uniqueElements(items) + existing := make([]ds.MyData, countRandomSearch) + // notExisting := [countNotExitstSearch]ds.MyData{} + notExisting := make([]ds.MyData, countNotExitstSearch) + toDelete := make([]ds.MyData, countDeletes) + + countUniq := len(uniqueItems) + for i := 0; i < countUniq; i++ { + // randInd := rand.Intn(countUsers) + randInd := rand.Intn(countUniq) + existing[i] = uniqueItems[randInd] + // fmt.Println(randInd) + } + + for i := 0; i < countUniq; i++ { + // randInd := rand.Intn(countUsers) + randInd := rand.Intn(10) + name := fmt.Sprintf("User_%d", randInd) + notExisting[i] = *ds.NewData(name, "") + // fmt.Println(randInd) + } + + return TestData{ + Items: items, + ItemsSorted: itemsSort, + Existing: existing, + NonExisting: notExisting, + ToDelete: toDelete, + } +} + +func testOneInsert(structure DataStructure, data []ds.MyData) float64 { + start := time.Now() + + for _, item := range data { + structure.Insert(item) + } + + return time.Since(start).Seconds() +} + +func testRepInsert(structure DataStructure, data []ds.MyData, nameStruct, mode string) { + BenchRes := make([]csvwriter.BenchmarkResult, 0) + + allTestTime := time.Now() + averageTime := 0. + + // Тест Слчайной вставки + + for i := 0; i < countRepeat; i++ { + + head := structure + resTime := testOneInsert(head, data) + + averageTime += resTime + BenchRes = append(BenchRes, csvwriter.BenchmarkResult{ + Structure: nameStruct, + Mode: mode, + Operation: "Вставка", + Time: resTime, + }) + } + + averageTime = time.Since(allTestTime).Seconds() / countRepeat + + BenchRes = append(BenchRes, csvwriter.BenchmarkResult{ + Structure: nameStruct, + Mode: mode, + Operation: "Вставка", + Time: averageTime, + }) + + csvwriter.AppendRaw(BenchRes) +} + +func Test(nameStruct string, structure DataStructure, data TestData) { + // BenchRes := make([]csvwriter.BenchmarkResult, 0) + + // allTestTime := time.Now() + + testRepInsert(structure, data.Items, nameStruct, "Случайный") + testRepInsert(structure, data.ItemsSorted, nameStruct, "Отсортированный") + +} + +func main() { + testData := GenerateTestData() + + var head_ll *ll.LinkedList = nil + + Test("Связный список", head_ll, testData) +} diff --git a/stepushovgs/data-structures/source/tests/test_ll/main.go b/stepushovgs/data-structures/source/tests/test_ll/main.go index 88d48c6..b13d382 100644 --- a/stepushovgs/data-structures/source/tests/test_ll/main.go +++ b/stepushovgs/data-structures/source/tests/test_ll/main.go @@ -11,13 +11,7 @@ import ( "time" ) -const ( - countUsers = 10000 - countRepeat = 5 - countRandomSearch = 10000 - countNotExitstSearch = 10 - countDeletes = 50 -) + func isInArr(arr []int, length int, target int) bool { for i := 0; i < length; i++ { From b3688d3ed9107eb9a2178e492e81fe5fa6b5a5fa Mon Sep 17 00:00:00 2001 From: GordStep Date: Wed, 13 May 2026 21:25:29 +0300 Subject: [PATCH 17/23] =?UTF-8?q?=D0=98=D0=B7=D0=BC=D0=B5=D0=BD=D0=B8?= =?UTF-8?q?=D0=BB=20=D1=80=D0=B0=D0=B1=D0=BE=D1=82=D1=83=20=D1=81=D0=BE=20?= =?UTF-8?q?=D1=81=D1=82=D1=80=D1=83=D0=BA=D1=82=D1=83=D1=80=D0=B0=D0=BC?= =?UTF-8?q?=D0=B8,=20=D1=87=D1=82=D0=BE=D0=B1=D1=8B=20=D0=BE=D0=BD=D0=B8?= =?UTF-8?q?=20=D0=BF=D0=BE=D1=85=D0=BE=D0=B4=D0=B8=D0=BB=D0=B8=20=D0=BF?= =?UTF-8?q?=D0=BE=D0=B4=20=D0=B8=D0=BD=D1=82=D0=B5=D1=80=D1=84=D0=B5=D0=B9?= =?UTF-8?q?=D1=81=D1=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../bin_search_tree/bin_search_tree.go | 191 +++++++++++------- .../pkg/structures/hash_table/hash_table.go | 20 +- .../pkg/structures/linked_list/linked_list.go | 71 ++++--- .../source/tests/benchmark/main.go | 11 +- 4 files changed, 177 insertions(+), 116 deletions(-) diff --git a/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go b/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go index b3a653a..f647ddb 100644 --- a/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go +++ b/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go @@ -6,14 +6,18 @@ import ( ) type BinSearchTree struct { - data ds.MyData - - left *BinSearchTree - right *BinSearchTree + root *BSTree } -func NewBinSearchTree(data ds.MyData) *BinSearchTree { - return &BinSearchTree{ +type BSTree struct { + data ds.MyData + + left *BSTree + right *BSTree +} + +func NewBinSearchTree(data ds.MyData) *BSTree { + return &BSTree{ data: data, left: nil, right: nil, @@ -21,30 +25,42 @@ func NewBinSearchTree(data ds.MyData) *BinSearchTree { } func (bst *BinSearchTree) Len() int { + return bst.root.Len() +} + +func (bst *BSTree) Len() int { if bst == nil { return 0 } return 1 + bst.left.Len() + bst.right.Len() } -func (bst *BinSearchTree) Minimum() *BinSearchTree { - if bst.left == nil { - return bst - } - return bst.left.Minimum() -} -func (bst *BinSearchTree) Maximum() *BinSearchTree { - if bst.right == nil { - return bst - } - return bst.right.Maximum() +func (bst *BinSearchTree) Minimum() *BSTree { + return bst.root.Minimum() } -func (node *BinSearchTree) PrintNode() { +func (root *BSTree) Minimum() *BSTree { + if root.left == nil { + return root + } + return root.left.Minimum() +} + +func (bst *BinSearchTree) Maximum() *BSTree { + return bst.root.Maximum() +} +func (root *BSTree) Maximum() *BSTree { + if root.right == nil { + return root + } + return root.right.Maximum() +} + +func (node *BSTree) PrintNode() { fmt.Print(node.data.ToString()) } -func (node *BinSearchTree) ToString() string { +func (node *BSTree) ToString() string { if node == nil { return "nil" } @@ -52,28 +68,37 @@ func (node *BinSearchTree) ToString() string { } func (bst *BinSearchTree) BstInorderTraversal() { - if bst != nil { - bst.left.BstInorderTraversal() - bst.PrintNode() + bst.root.BstInorderTraversal() +} + +func (root *BSTree) BstInorderTraversal() { + if root != nil { + root.left.BstInorderTraversal() + root.PrintNode() fmt.Println() - bst.right.BstInorderTraversal() + root.right.BstInorderTraversal() } } func (bst *BinSearchTree) BstPreorderTraversal() { - if bst != nil { - bst.PrintNode() - bst.left.BstPreorderTraversal() - bst.right.BstPreorderTraversal() + bst.root.BstPreorderTraversal() +} + +func (root *BSTree) BstPreorderTraversal() { + if root != nil { + root.PrintNode() + fmt.Println() + root.left.BstPreorderTraversal() + root.right.BstPreorderTraversal() } } // Search -// Возвращает строковое представление узла +// Возвращает номер телефона по имени func (bst *BinSearchTree) Search(targetName string) (string, bool) { - node, ok := bst.search(targetName) + node, ok := bst.root.search(targetName) if ok { - return node.ToString(), true + return node.data.Phone, true } return "", false } @@ -88,7 +113,7 @@ func (bst *BinSearchTree) Search(targetName string) (string, bool) { return search(x.right, k) */ // Приватная вспомогательная функция поиска -func (node *BinSearchTree) search(targetName string) (*BinSearchTree, bool) { +func (node *BSTree) search(targetName string) (*BSTree, bool) { if node == nil { return nil, false } @@ -101,32 +126,38 @@ func (node *BinSearchTree) search(targetName string) (*BinSearchTree, bool) { return node.right.search(targetName) } -// func (bst *BinSearchTree) Search(targetName string) (string, bool) { -/* - Node search(x : Node, k : T): - if x == null or k == x.key - return x - if k < x.key - return search(x.left, k) - else - return search(x.right, k) -*/ -// targetNode, ok := bst.head.search(targetName) - -// return targetNode.ToString(), ok +// func (node *BinSearchTree) Insert(data ds.MyData) *BinSearchTree { +// if node == nil { +// return NewBinSearchTree(data) +// } else if data.Name < node.data.Name { +// node.left = node.left.Insert(data) +// } else if data.Name > node.data.Name { +// node.right = node.right.Insert(data) +// } else { +// node.data.Phone = data.Phone // Заменяем существующее значение +// } +// return node // } -func (node *BinSearchTree) Insert(data ds.MyData) *BinSearchTree { - if node == nil { - return NewBinSearchTree(data) - } else if data.Name < node.data.Name { - node.left = node.left.Insert(data) - } else if data.Name > node.data.Name { - node.right = node.right.Insert(data) - } else { - node.data.Phone = data.Phone // Заменяем существующее значение +func (bst *BinSearchTree) Insert(data ds.MyData) { + bst.root = bst.root.insert(data) +} + +func (root *BSTree) insert(data ds.MyData) *BSTree { + if root == nil { + return &BSTree{ + data: data, + } } - return node + + if data.Name < root.data.Name { + root.left = root.left.insert(data) + } else if data.Name > root.data.Name { + root.right = root.right.insert(data) + } else { + root.data.Phone = data.Phone + } + return root } // Delete удаляет узел по имени. @@ -153,45 +184,59 @@ Node delete(root : Node, z : T): // корень поддерев return root */ -func (root *BinSearchTree) Delete(targetName string) *BinSearchTree { +func (bst *BinSearchTree) Delete(targetName string) bool { + if bst.root == nil { + return false + } + bst.root = bst.root.delete(targetName) + return true +} + +func (root *BSTree) delete(targetName string) *BSTree { if root == nil { return nil } if targetName < root.data.Name { - root.left = root.left.Delete(targetName) + root.left = root.left.delete(targetName) } else if targetName > root.data.Name { - root.right = root.right.Delete(targetName) - } else if root.left != nil && root.right != nil { - temp := root.right.Minimum() - root.data.Name = root.right.Minimum().data.Name - root.data.Phone = root.right.Minimum().data.Phone - - // strcpy(root->name, bst_minimum(root->right)->name); - // strcpy(root->phone, bst_minimum(root->right)->phone); - root.right = root.right.Delete(temp.data.Name) + root.right = root.right.delete(targetName) } else { - if root.left != nil { - return root.left - } else if root.right != nil { + // Нашли узел для удаления + + // Случай 1: нет левого потомка + if root.left == nil { return root.right - } else { - return nil } + + // Случай 2: нет правого потомка + if root.right == nil { + return root.left + } + + // Случай 3: оба потомка есть + successor := root.right.Minimum() + root.data = successor.data // Копируем все данные сразу + root.right = root.right.delete(successor.data.Name) } + return root } -func (bst *BinSearchTree) PrintAll(depth int) { +func (bst *BinSearchTree) PrintAll() { + bst.root.printAll(0) +} + +func (bst *BSTree) printAll(depth int) { if bst == nil { return } - bst.right.PrintAll(depth + 1) + bst.right.printAll(depth + 1) for i := 0; i < depth; i++ { fmt.Printf("\t") } bst.PrintNode() - bst.left.PrintAll(depth + 1) + bst.left.printAll(depth + 1) } diff --git a/stepushovgs/data-structures/source/pkg/structures/hash_table/hash_table.go b/stepushovgs/data-structures/source/pkg/structures/hash_table/hash_table.go index b4ef466..fa566b2 100644 --- a/stepushovgs/data-structures/source/pkg/structures/hash_table/hash_table.go +++ b/stepushovgs/data-structures/source/pkg/structures/hash_table/hash_table.go @@ -1,9 +1,7 @@ package hash_table import ( - "bufio" "fmt" - "os" ds "source/pkg/data_struct" ) @@ -54,6 +52,10 @@ func (ht *HashTable) GetIndex(name string) int { return hash % ht.capacity } +func (ht *HashTable) Len() int { + return ht.size +} + // func (ht *HashTable) getIndex(hash int) int { // return hash % ht.capacity // } @@ -88,7 +90,7 @@ func (h *HashTable) Insert(new ds.MyData) { h.size++ } -func (h *HashTable) Get(name string) (phone string, status bool) { +func (h *HashTable) Search(name string) (phone string, status bool) { ind := h.GetIndex(name) buck := h.buckets[ind] @@ -106,10 +108,10 @@ func (h *HashTable) Get(name string) (phone string, status bool) { return "", false } -func pressEnterToContinue() { - fmt.Print("Нажмите Enter для продолжения...") - bufio.NewReader(os.Stdin).ReadBytes('\n') -} +// func pressEnterToContinue() { +// fmt.Print("Нажмите Enter для продолжения...") +// bufio.NewReader(os.Stdin).ReadBytes('\n') +// } // resize - увеличивает размер таблицы func (ht *HashTable) resize() { @@ -135,7 +137,7 @@ func (ht *HashTable) resize() { ht.capacity = newCapacity } -func (ht *HashTable) Remove(name string) bool { +func (ht *HashTable) Delete(name string) bool { ind := ht.GetIndex(name) buck := ht.buckets[ind] @@ -167,7 +169,7 @@ func (ht *HashTable) Remove(name string) bool { } func (ht *HashTable) Contains(name string) bool { - _, ok := ht.Get(name) + _, ok := ht.Search(name) return ok } diff --git a/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go b/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go index eb1467c..82317b8 100644 --- a/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go +++ b/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go @@ -22,19 +22,26 @@ def ll_list_all(head) — собирает все записи в список */ type LinkedList struct { - data ds.MyData - - next *LinkedList + head *LList } -func NewLinkedList(data ds.MyData) *LinkedList { - return &LinkedList{ +type LList struct { + data ds.MyData + + next *LList +} + +func NewLinkedList(data ds.MyData) *LList { + return &LList{ data: data, next: nil, } } -func (ll *LinkedList) ToString() string { +func (ll *LList) ToString() string { + if ll == nil { + return "nil" + } return ll.data.ToString() } @@ -45,7 +52,7 @@ func (ll *LinkedList) Len() int { } len := 0 - current := ll + current := ll.head for current != nil { len++ current = current.next @@ -54,23 +61,23 @@ func (ll *LinkedList) Len() int { return len } -func (ll *LinkedList) Insert(data ds.MyData) *LinkedList { +func (ll *LinkedList) Insert(data ds.MyData) { newNode := NewLinkedList(data) - if ll == nil { - return newNode + if ll.head == nil { + ll.head = newNode + return } - current := ll + current := ll.head for current.next != nil { current = current.next } current.next = newNode - return ll } func (ll *LinkedList) Search(targetName string) (string, bool) { - current := ll + current := ll.head for current != nil { if current.data.Name == targetName { @@ -83,7 +90,7 @@ func (ll *LinkedList) Search(targetName string) (string, bool) { } func (ll *LinkedList) PrintAll() { - current := ll + current := ll.head index := 0 for current != nil { @@ -93,33 +100,33 @@ func (ll *LinkedList) PrintAll() { } } -func (ll *LinkedList) Delete(targetName string) (*LinkedList, bool) { - - if ll == nil { - return nil, false - } - if ll.data.Name == targetName { - ll.data = ll.next.data - ll.next = ll.next.next - return ll, true +func (ll *LinkedList) Delete(targetName string) bool { + if ll.head == nil { + return false } - prev := ll - current := ll.next + // Особый случай: удаление головы списка + if ll.head.data.Name == targetName { + // Сдвигаем данные и указатель + *ll.head = *ll.head.next + return true + } - for current != nil { - if current.data.Name == targetName { - prev.next = current.next + // Стандартное удаление из середины/конца + current := ll.head + for current.next != nil { + if current.next.data.Name == targetName { + current.next = current.next.next + return true } - prev = current current = current.next } - return ll, false + return false } func (ll *LinkedList) listAll() []ds.MyData { - current := ll + current := ll.head listLL := make([]ds.MyData, ll.Len()) ind := 0 @@ -139,7 +146,7 @@ func (ll *LinkedList) GetByInd(ind int) (ds.MyData, bool) { } index := 0 - current := ll + current := ll.head for current != nil { if index == ind { return current.data, true diff --git a/stepushovgs/data-structures/source/tests/benchmark/main.go b/stepushovgs/data-structures/source/tests/benchmark/main.go index cd0e575..0140589 100644 --- a/stepushovgs/data-structures/source/tests/benchmark/main.go +++ b/stepushovgs/data-structures/source/tests/benchmark/main.go @@ -6,6 +6,8 @@ import ( csvwriter "source/pkg/csv_writer" ds "source/pkg/data_struct" dg "source/pkg/gen_data" + bst "source/pkg/structures/bin_search_tree" + ht "source/pkg/structures/hash_table" ll "source/pkg/structures/linked_list" // csv "source/pkg/csv_ri" @@ -31,9 +33,9 @@ type TestData struct { type DataStructure interface { Insert(data ds.MyData) - Search(name string) *ds.MyData + Search(name string) (string, bool) Delete(name string) bool - Size() int + Len() int } func uniqueElements(data []ds.MyData) []ds.MyData { @@ -89,6 +91,7 @@ func GenerateTestData() TestData { } } +// Тест вставки массива данных (один раз) func testOneInsert(structure DataStructure, data []ds.MyData) float64 { start := time.Now() @@ -147,6 +150,10 @@ func main() { testData := GenerateTestData() var head_ll *ll.LinkedList = nil + var head_ht *ht.HashTable = nil + var head_bst *bst.BinSearchTree = nil Test("Связный список", head_ll, testData) + Test("Связный список", head_ht, testData) + Test("Связный список", head_bst, testData) } From 6e259a4770666ad6f3abda5ad8c5c660d0e59f03 Mon Sep 17 00:00:00 2001 From: GordStep Date: Wed, 13 May 2026 22:03:54 +0300 Subject: [PATCH 18/23] =?UTF-8?q?=D0=98=D1=81=D0=BF=D1=80=D0=B0=D0=B2?= =?UTF-8?q?=D0=BB=D0=B5=D0=BD=D0=BD=D1=8B=20=D0=BE=D1=88=D0=B8=D0=B1=D0=BA?= =?UTF-8?q?=D0=B8=20=D0=B8=20=D0=BD=D0=B0=D0=BF=D0=B8=D1=81=D0=B0=D0=BD?= =?UTF-8?q?=D1=8B=20=D0=BC=D0=B8=D0=BD=D0=B8=20=D1=82=D0=B5=D1=81=D1=82?= =?UTF-8?q?=D1=8B=20=D0=B4=D0=BB=D1=8F=20=D0=B2=D1=81=D0=B5=D1=85=20=D1=81?= =?UTF-8?q?=D1=82=D1=80=D1=83=D0=BA=D1=82=D1=83=D1=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../bin_search_tree/bin_search_tree.go | 6 +- .../pkg/structures/hash_table/hash_table.go | 2 +- .../pkg/structures/linked_list/linked_list.go | 8 +- .../source/tests/benchmark/main.go | 18 +-- .../source/tests/test_bst/main.go | 60 ++------ .../source/tests/test_ht/main.go | 19 ++- .../source/tests/test_ll/main.go | 143 ++---------------- 7 files changed, 60 insertions(+), 196 deletions(-) diff --git a/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go b/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go index f647ddb..2154865 100644 --- a/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go +++ b/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go @@ -16,7 +16,11 @@ type BSTree struct { right *BSTree } -func NewBinSearchTree(data ds.MyData) *BSTree { +func NewBinSearchTree() *BinSearchTree { + return &BinSearchTree{} +} + +func newBinSearchTree(data ds.MyData) *BSTree { return &BSTree{ data: data, left: nil, diff --git a/stepushovgs/data-structures/source/pkg/structures/hash_table/hash_table.go b/stepushovgs/data-structures/source/pkg/structures/hash_table/hash_table.go index fa566b2..32fed46 100644 --- a/stepushovgs/data-structures/source/pkg/structures/hash_table/hash_table.go +++ b/stepushovgs/data-structures/source/pkg/structures/hash_table/hash_table.go @@ -178,7 +178,7 @@ func (elem *elementHT) ToString() string { return "nil" } - return elem.ToString() + return elem.data.ToString() } func (ht *HashTable) Print() { diff --git a/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go b/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go index 82317b8..b383414 100644 --- a/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go +++ b/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go @@ -31,7 +31,11 @@ type LList struct { next *LList } -func NewLinkedList(data ds.MyData) *LList { +func NewLinkedList() *LinkedList { + return &LinkedList{} +} + +func newLinkedList(data ds.MyData) *LList { return &LList{ data: data, next: nil, @@ -62,7 +66,7 @@ func (ll *LinkedList) Len() int { } func (ll *LinkedList) Insert(data ds.MyData) { - newNode := NewLinkedList(data) + newNode := newLinkedList(data) if ll.head == nil { ll.head = newNode diff --git a/stepushovgs/data-structures/source/tests/benchmark/main.go b/stepushovgs/data-structures/source/tests/benchmark/main.go index 0140589..1bc241e 100644 --- a/stepushovgs/data-structures/source/tests/benchmark/main.go +++ b/stepushovgs/data-structures/source/tests/benchmark/main.go @@ -1,4 +1,4 @@ -package benchmark +package main import ( "fmt" @@ -67,14 +67,14 @@ func GenerateTestData() TestData { toDelete := make([]ds.MyData, countDeletes) countUniq := len(uniqueItems) - for i := 0; i < countUniq; i++ { + for i := 0; i < countRandomSearch; i++ { // randInd := rand.Intn(countUsers) randInd := rand.Intn(countUniq) existing[i] = uniqueItems[randInd] // fmt.Println(randInd) } - for i := 0; i < countUniq; i++ { + for i := 0; i < countNotExitstSearch; i++ { // randInd := rand.Intn(countUsers) randInd := rand.Intn(10) name := fmt.Sprintf("User_%d", randInd) @@ -102,7 +102,7 @@ func testOneInsert(structure DataStructure, data []ds.MyData) float64 { return time.Since(start).Seconds() } -func testRepInsert(structure DataStructure, data []ds.MyData, nameStruct, mode string) { +func TestInsert(structure DataStructure, data []ds.MyData, nameStruct, mode string) { BenchRes := make([]csvwriter.BenchmarkResult, 0) allTestTime := time.Now() @@ -141,19 +141,19 @@ func Test(nameStruct string, structure DataStructure, data TestData) { // allTestTime := time.Now() - testRepInsert(structure, data.Items, nameStruct, "Случайный") - testRepInsert(structure, data.ItemsSorted, nameStruct, "Отсортированный") + TestInsert(structure, data.Items, nameStruct, "Случайный") + TestInsert(structure, data.ItemsSorted, nameStruct, "Отсортированный") } func main() { testData := GenerateTestData() - var head_ll *ll.LinkedList = nil + head_ll := &ll.LinkedList{} var head_ht *ht.HashTable = nil var head_bst *bst.BinSearchTree = nil Test("Связный список", head_ll, testData) - Test("Связный список", head_ht, testData) - Test("Связный список", head_bst, testData) + Test("Хеш таблица", head_ht, testData) + Test("Бинарное дерево поиска", head_bst, testData) } diff --git a/stepushovgs/data-structures/source/tests/test_bst/main.go b/stepushovgs/data-structures/source/tests/test_bst/main.go index a16d473..f5e5ccb 100644 --- a/stepushovgs/data-structures/source/tests/test_bst/main.go +++ b/stepushovgs/data-structures/source/tests/test_bst/main.go @@ -3,7 +3,6 @@ package main import ( "bufio" "fmt" - "math/rand" "os" ds "source/pkg/data_struct" bst "source/pkg/structures/bin_search_tree" @@ -31,60 +30,21 @@ func isInArr(arr []int, length int, target int) bool { func main() { fmt.Println("hello world!") - var head *bst.BinSearchTree = nil + head := bst.NewBinSearchTree() - arr := make([]int, countNumbers) - - var temp int - for i := 0; i < countNumbers; i++ { - // Генерируем уникальное случайное число - for { - temp = rand.Intn(100) // 0 до 99 - if !isInArr(arr, i, temp) { - break - } - } - - arr[i] = temp - - nameStr := fmt.Sprintf("name_%02d", temp) - phoneStr := fmt.Sprintf("phone_%02d", temp) - - data := ds.NewData(nameStr, phoneStr) - - head = head.Insert(*data) - fmt.Printf("%d ", arr[i]) + for i := 1; i <= 20; i++ { + name := fmt.Sprintf("User_%02d", i) + phone := fmt.Sprintf("Phone_%02d", i) + head.Insert(*ds.NewData(name, phone)) } - fmt.Printf("\n\nКоличество узлов: %d\n", head.Len()) - - pressEnterToContinue() - - fmt.Println("\ninorder traversal:") head.BstInorderTraversal() - tarName := "name_44" + head.Delete("User_05") + fmt.Println("Удаляем User_05") - fmt.Printf("\nПоиск '%s' перед удалением: ", tarName) - if found, ok := head.Search(tarName); ok { - fmt.Printf("Найден: %s\n", found) - } else { - fmt.Printf("НЕ найден!\n") - } - - fmt.Printf("\nУдаляем элемент с значением %s:\n", tarName) - - head = head.Delete(tarName) - - fmt.Printf("\nКоличество узлов после удаления: %d\n", head.Len()) - - fmt.Printf("Поиск '%s' после удаления: ", tarName) - if found, ok := head.Search(tarName); ok { - fmt.Printf("ОШИБКА! Все еще существует: %s\n", found) - } else { - fmt.Printf("Успешно удален\n") - } - - fmt.Println("\ninorder traversal after delete:") head.BstInorderTraversal() + + fmt.Println(head.Search("User_07")) + } diff --git a/stepushovgs/data-structures/source/tests/test_ht/main.go b/stepushovgs/data-structures/source/tests/test_ht/main.go index 6443a3b..2920570 100644 --- a/stepushovgs/data-structures/source/tests/test_ht/main.go +++ b/stepushovgs/data-structures/source/tests/test_ht/main.go @@ -30,8 +30,23 @@ import ( func main() { fmt.Println("hello world") - hashTable := ht.NewHashTable(8, 0.75) - hashTable.Insert(*ds.NewData("User_0", "Phone_0")) + head := ht.NewHashTable(8, 0.75) + + for i := 1; i <= 40; i++ { + name := fmt.Sprintf("User_%02d", i) + phone := fmt.Sprintf("Phone_%02d", i) + head.Insert(*ds.NewData(name, phone)) + } + + head.Print() + + head.Delete("User_05") + fmt.Println("Удаляем User_05") + + head.Print() + + fmt.Println(head.Search("User_07")) + // Чтение всего файла // const filePath = "../data/onegin.txt" diff --git a/stepushovgs/data-structures/source/tests/test_ll/main.go b/stepushovgs/data-structures/source/tests/test_ll/main.go index b13d382..3420389 100644 --- a/stepushovgs/data-structures/source/tests/test_ll/main.go +++ b/stepushovgs/data-structures/source/tests/test_ll/main.go @@ -3,16 +3,13 @@ package main import ( "bufio" "fmt" - "math/rand" "os" - dg "source/pkg/gen_data" - rs "source/pkg/resulter" + ds "source/pkg/data_struct" + + // rs "source/pkg/resulter" ll "source/pkg/structures/linked_list" - "time" ) - - func isInArr(arr []int, length int, target int) bool { for i := 0; i < length; i++ { if arr[i] == target { @@ -37,135 +34,19 @@ func pressEnterToContinue() { func main() { fmt.Println("hello world!") - results := make([]rs.BenchmarkResult, 0, countUsers) - averageInsertTime := 0. + head := ll.NewLinkedList() - Razdelitel() - fmt.Println("Тестирование вставки:") - var head *ll.LinkedList = nil - - for testNum := 0; testNum < countRepeat; testNum++ { - head = nil - - testData := dg.RecordsShuffled(countUsers) - - start := time.Now() - - for i := 0; i < countUsers; i++ { - head.Insert(testData[i]) - } - - elapsed := time.Since(start).Seconds() - averageInsertTime += elapsed - - results = append(results, rs.BenchmarkResult{ - Structure: "Связный список", Mode: "Случайный", Operation: "Вставка", Time: elapsed, - }) - } - averageInsertTime /= countRepeat - results = append(results, rs.BenchmarkResult{ - Structure: "Связный список", Mode: "Случайный", Operation: "Вставка", Time: averageInsertTime, - }) - for i := 0; i < 6; i++ { - fmt.Println(results[i].ToString()) + for i := 1; i <= 20; i++ { + name := fmt.Sprintf("User_%02d", i) + phone := fmt.Sprintf("Phone_%02d", i) + head.Insert(*ds.NewData(name, phone)) } - Razdelitel() - // fmt.Println("Тестирование Поиска:") - // // results = make([]rs.BenchmarkResult, 0, countUsers) - // averageSearchTime := 0. + head.PrintAll() - // for testNum := 0; testNum < countRepeat; testNum++ { - // // var head *ll.LinkedList = nil - // // head = dg.RecordsShuffled(countUsers) + head.Delete("User_05") - // testData := make([]ds.MyData, countRandomSearch) + head.PrintAll() - // for i := 0; i < countRandomSearch; i++ { - // // randInd := rand.Intn(countUsers) - // randInd := rand.Intn(1000) + 9000 - // testData[i], _ = head.GetByInd(randInd) - // // fmt.Println(randInd) - // } - - // start := time.Now() - - // for i := 0; i < countRandomSearch; i++ { - // head.Search(testData[i].Name) - // } - - // elapsed := time.Since(start).Seconds() - // averageSearchTime += elapsed - - // results = append(results, rs.BenchmarkResult{ - // Structure: "Связный список", Mode: "Случайный", Operation: "Поиск", Time: elapsed, - // }) - // } - // averageSearchTime /= countRepeat - // results = append(results, rs.BenchmarkResult{ - // Structure: "Связный список", Mode: "Случайный", Operation: "Поиск", Time: averageSearchTime, - // }) - // for i := 0; i < len(results); i++ { - // fmt.Println(results[i].ToString()) - // } - - resultsS := runSearchBenchmark(head) - for i := 0; i < len(resultsS); i++ { - fmt.Println(resultsS[i].ToString()) - } - // rs.AppendRaw(results) -} - -func runSearchBenchmark(head *ll.LinkedList) []rs.BenchmarkResult { - fmt.Println("\n=== Тестирование Поиска ===") - - const countRandomSearch = 1000 // уменьшим для поиска - - var results []rs.BenchmarkResult - var totalTime float64 - - // Предварительно собираем имена для поиска - names := make([]string, countUsers) - for i := 0; i < countUsers; i++ { - data, found := head.GetByInd(i) - if found { - names[i] = data.Name - } - } - - for testNum := 0; testNum < countRepeat; testNum++ { - // Выбираем случайные имена - searchNames := make([]string, countRandomSearch) - for i := 0; i < countRandomSearch; i++ { - searchNames[i] = names[rand.Intn(countUsers)] - } - - start := time.Now() - - for _, name := range searchNames { - el, ok := head.Search(name) - if ok { - fmt.Println(el) - } - } - elapsed := time.Since(start).Seconds() - - totalTime += elapsed - - fmt.Printf(" Тест %d: %.6f сек (%.2f мкс/оп)\n", - testNum+1, elapsed, - elapsed/float64(countRandomSearch)*1_000_000) - } - - avgTime := totalTime / float64(countRepeat) - fmt.Printf("Среднее: %.6f сек\n", avgTime) - - results = append(results, rs.BenchmarkResult{ - Structure: "Связный список", - Mode: "Случайный", - Operation: "Поиск", - Time: avgTime, - }) - - return results + fmt.Println(head.Search("User_07")) } From 62795e88ba38db11361db63d1b7a6a1cfb8089cf Mon Sep 17 00:00:00 2001 From: GordStep Date: Wed, 13 May 2026 22:51:35 +0300 Subject: [PATCH 19/23] =?UTF-8?q?=D0=9E=D0=B1=D0=BD=D0=BE=D0=B2=D0=BB?= =?UTF-8?q?=D0=B5=D0=BD=D0=B8=D0=B5=20csvwriter?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Функция создания пустого файла - Разобрался с директорией для сохранения файла --- .../csv_writer/{resulter.go => csv_writer.go} | 38 +++++++++++++++- .../source/results/benchmarks.csv | 37 ++++++++++++++++ .../source/tests/benchmark/main.go | 44 ++++++++++++++----- .../source/tests/test_csv_writer/main.go | 19 ++++++++ 4 files changed, 125 insertions(+), 13 deletions(-) rename stepushovgs/data-structures/source/pkg/csv_writer/{resulter.go => csv_writer.go} (50%) create mode 100644 stepushovgs/data-structures/source/results/benchmarks.csv create mode 100644 stepushovgs/data-structures/source/tests/test_csv_writer/main.go diff --git a/stepushovgs/data-structures/source/pkg/csv_writer/resulter.go b/stepushovgs/data-structures/source/pkg/csv_writer/csv_writer.go similarity index 50% rename from stepushovgs/data-structures/source/pkg/csv_writer/resulter.go rename to stepushovgs/data-structures/source/pkg/csv_writer/csv_writer.go index ed67788..713b668 100644 --- a/stepushovgs/data-structures/source/pkg/csv_writer/resulter.go +++ b/stepushovgs/data-structures/source/pkg/csv_writer/csv_writer.go @@ -18,7 +18,26 @@ func (b *BenchmarkResult) ToString() string { return fmt.Sprintf("%s %s %s %f", b.Structure, b.Mode, b.Operation, b.Time) } func (b *BenchmarkResult) ToStrings() []string { - return []string{b.Structure, b.Mode, b.Operation, fmt.Sprintf("%d", b.Time)} + return []string{b.Structure, b.Mode, b.Operation, fmt.Sprintf("%f", b.Time)} +} + +// Создаём пустой csv файл с заголовками +func CreateEmptyCSV(dir, name string) error { + filename := filepath.Join(dir, name) + + file, err := os.Create(filename) + + if err != nil { + return err + } + defer file.Close() + + writer := csv.NewWriter(file) + defer writer.Flush() + header := []string{"Structure", "Mode", "Operation", "Time"} + writer.Write(header) + + return writer.Error() } // AppendRaw дописывает произвольные строки в CSV @@ -26,6 +45,14 @@ func AppendRaw(results []BenchmarkResult) error { filename := filepath.Join("results", "benchmarks.csv") + fileExists := true + isEmpty := true + if info, err := os.Stat(filename); err == nil { + isEmpty = info.Size() == 0 + } else if os.IsNotExist(err) { + fileExists = false + } + file, err := os.OpenFile(filename, os.O_APPEND|os.O_CREATE|os.O_WRONLY, 0644) if err != nil { return err @@ -35,10 +62,19 @@ func AppendRaw(results []BenchmarkResult) error { writer := csv.NewWriter(file) defer writer.Flush() + // Если файл новый или пустой, записываем заголовки + if !fileExists || isEmpty { + header := []string{"Structure", "Mode", "Operation", "Time"} + if err := writer.Write(header); err != nil { + return fmt.Errorf("не удалось записать заголовки: %w", err) + } + } + rows := make([][]string, len(results)) for i, res := range results { rows[i] = res.ToStrings() + // fmt.Println(res.ToStrings()) } return writer.WriteAll(rows) // WriteAll пишет всё сразу diff --git a/stepushovgs/data-structures/source/results/benchmarks.csv b/stepushovgs/data-structures/source/results/benchmarks.csv new file mode 100644 index 0000000..3f611b3 --- /dev/null +++ b/stepushovgs/data-structures/source/results/benchmarks.csv @@ -0,0 +1,37 @@ +Structure,Mode,Operation,Time +Связный список,Случайный,Вставка,0.047607 +Связный список,Случайный,Вставка,0.050350 +Связный список,Случайный,Вставка,0.049572 +Связный список,Случайный,Вставка,0.049258 +Связный список,Случайный,Вставка,0.048659 +Связный список,Случайный,Вставка,0.049089 +Связный список,Отсортированный,Вставка,0.047619 +Связный список,Отсортированный,Вставка,0.047478 +Связный список,Отсортированный,Вставка,0.048357 +Связный список,Отсортированный,Вставка,0.048174 +Связный список,Отсортированный,Вставка,0.048270 +Связный список,Отсортированный,Вставка,0.047980 +Хеш таблица,Случайный,Вставка,0.002014 +Хеш таблица,Случайный,Вставка,0.002013 +Хеш таблица,Случайный,Вставка,0.002008 +Хеш таблица,Случайный,Вставка,0.001003 +Хеш таблица,Случайный,Вставка,0.002505 +Хеш таблица,Случайный,Вставка,0.001908 +Хеш таблица,Отсортированный,Вставка,0.001514 +Хеш таблица,Отсортированный,Вставка,0.001504 +Хеш таблица,Отсортированный,Вставка,0.002012 +Хеш таблица,Отсортированный,Вставка,0.001003 +Хеш таблица,Отсортированный,Вставка,0.002506 +Хеш таблица,Отсортированный,Вставка,0.001708 +Бинарное дерево поиска,Случайный,Вставка,0.318901 +Бинарное дерево поиска,Случайный,Вставка,0.320504 +Бинарное дерево поиска,Случайный,Вставка,0.316685 +Бинарное дерево поиска,Случайный,Вставка,0.315862 +Бинарное дерево поиска,Случайный,Вставка,0.320947 +Бинарное дерево поиска,Случайный,Вставка,0.318580 +Бинарное дерево поиска,Отсортированный,Вставка,0.313718 +Бинарное дерево поиска,Отсортированный,Вставка,0.318131 +Бинарное дерево поиска,Отсортированный,Вставка,0.322564 +Бинарное дерево поиска,Отсортированный,Вставка,0.315526 +Бинарное дерево поиска,Отсортированный,Вставка,0.314289 +Бинарное дерево поиска,Отсортированный,Вставка,0.316846 diff --git a/stepushovgs/data-structures/source/tests/benchmark/main.go b/stepushovgs/data-structures/source/tests/benchmark/main.go index 1bc241e..3cd60da 100644 --- a/stepushovgs/data-structures/source/tests/benchmark/main.go +++ b/stepushovgs/data-structures/source/tests/benchmark/main.go @@ -38,6 +38,21 @@ type DataStructure interface { Len() int } +// Создатели структур +type StructureFactory func() DataStructure + +func NewLinkedList() DataStructure { + return ll.NewLinkedList() +} + +func NewHashTable() DataStructure { + return ht.NewHashTable(256, 0.75) +} + +func NewBinSearchTree() DataStructure { + return bst.NewBinSearchTree() +} + func uniqueElements(data []ds.MyData) []ds.MyData { res := make([]ds.MyData, 0, len(data)) isUnique := true @@ -102,7 +117,7 @@ func testOneInsert(structure DataStructure, data []ds.MyData) float64 { return time.Since(start).Seconds() } -func TestInsert(structure DataStructure, data []ds.MyData, nameStruct, mode string) { +func TestInsert(factory StructureFactory, data []ds.MyData, nameStruct, mode string) { BenchRes := make([]csvwriter.BenchmarkResult, 0) allTestTime := time.Now() @@ -111,8 +126,8 @@ func TestInsert(structure DataStructure, data []ds.MyData, nameStruct, mode stri // Тест Слчайной вставки for i := 0; i < countRepeat; i++ { + head := factory() - head := structure resTime := testOneInsert(head, data) averageTime += resTime @@ -122,6 +137,8 @@ func TestInsert(structure DataStructure, data []ds.MyData, nameStruct, mode stri Operation: "Вставка", Time: resTime, }) + fmt.Printf("%s | Вставка | %s | Время: %f\n", nameStruct, mode, resTime) + // fmt.Println(BenchRes) } averageTime = time.Since(allTestTime).Seconds() / countRepeat @@ -136,24 +153,27 @@ func TestInsert(structure DataStructure, data []ds.MyData, nameStruct, mode stri csvwriter.AppendRaw(BenchRes) } -func Test(nameStruct string, structure DataStructure, data TestData) { +func Test(nameStruct string, factory StructureFactory, data TestData) { + // BenchRes := make([]csvwriter.BenchmarkResult, 0) // allTestTime := time.Now() - - TestInsert(structure, data.Items, nameStruct, "Случайный") - TestInsert(structure, data.ItemsSorted, nameStruct, "Отсортированный") + TestInsert(factory, data.Items, nameStruct, "Случайный") + TestInsert(factory, data.ItemsSorted, nameStruct, "Отсортированный") } func main() { testData := GenerateTestData() + csvwriter.CreateEmptyCSV("results", "benchmarks.csv") - head_ll := &ll.LinkedList{} - var head_ht *ht.HashTable = nil - var head_bst *bst.BinSearchTree = nil + // head_ll := &ll.LinkedList{} + // head_ht := ht.NewHashTable(256, 0.75) + // head_bst := bst.NewBinSearchTree() - Test("Связный список", head_ll, testData) - Test("Хеш таблица", head_ht, testData) - Test("Бинарное дерево поиска", head_bst, testData) + fmt.Println("============= Начало тестов =============") + + Test("Связный список", NewLinkedList, testData) + Test("Хеш таблица", NewHashTable, testData) + Test("Бинарное дерево поиска", NewBinSearchTree, testData) } diff --git a/stepushovgs/data-structures/source/tests/test_csv_writer/main.go b/stepushovgs/data-structures/source/tests/test_csv_writer/main.go new file mode 100644 index 0000000..db06963 --- /dev/null +++ b/stepushovgs/data-structures/source/tests/test_csv_writer/main.go @@ -0,0 +1,19 @@ +package main + +import ( + "fmt" + csvwriter "source/pkg/csv_writer" +) + +func main() { + // Простой способ + results := []csvwriter.BenchmarkResult{ + {Structure: "HashTable", Mode: "Chaining", Operation: "Insert", Time: 0.001234}, + {Structure: "LinkedList", Mode: "Singly", Operation: "Search", Time: 0.005678}, + {Structure: "BSTree", Mode: "Recursive", Operation: "Delete", Time: 0.003456}, + } + + if err := csvwriter.AppendRaw(results); err != nil { + fmt.Printf("Ошибка: %v\n", err) + } +} From 6554e2ada19df4cd0d81a7c3521d248b60ed8b20 Mon Sep 17 00:00:00 2001 From: GordStep Date: Thu, 14 May 2026 01:16:37 +0300 Subject: [PATCH 20/23] =?UTF-8?q?=D0=BE=D1=84=D0=BE=D1=80=D0=BC=D0=BB?= =?UTF-8?q?=D0=B5=D0=BD=D0=B8=D0=B5=20=D0=B3=D1=80=D0=B0=D1=84=D0=B8=D0=BA?= =?UTF-8?q?=D0=BE=D0=B2,=20=D0=BF=D0=BE=20=D0=BF=D0=BE=D0=BB=D1=83=D1=87?= =?UTF-8?q?=D0=B5=D0=BD=D0=BD=D1=8B=D0=BC=20=D0=B4=D0=B0=D0=BD=D0=BD=D1=8B?= =?UTF-8?q?=D0=BC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../source/docs/img/delete.pdf | Bin 0 -> 33129 bytes .../source/docs/img/insert.pdf | Bin 0 -> 33049 bytes .../source/docs/img/search.pdf | Bin 0 -> 32716 bytes .../data-structures/source/docs/main.ipynb | 749 ++++++++++++++++++ .../bin_search_tree/bin_search_tree.go | 6 + .../pkg/structures/hash_table/hash_table.go | 6 + .../pkg/structures/linked_list/linked_list.go | 6 + .../source/results/benchmarks.csv | 234 +++++- .../source/tests/benchmark/main.go | 151 +++- 9 files changed, 1079 insertions(+), 73 deletions(-) create mode 100644 stepushovgs/data-structures/source/docs/img/delete.pdf create mode 100644 stepushovgs/data-structures/source/docs/img/insert.pdf create mode 100644 stepushovgs/data-structures/source/docs/img/search.pdf create mode 100644 stepushovgs/data-structures/source/docs/main.ipynb diff --git a/stepushovgs/data-structures/source/docs/img/delete.pdf b/stepushovgs/data-structures/source/docs/img/delete.pdf new file mode 100644 index 0000000000000000000000000000000000000000..9ba8b4ac11fe9dd9f9f7243b3443afed9832faae GIT binary patch literal 33129 zcmb@u1#lcqvbJlHC5xFEEoK;5%*>1yBR_kb%%z-yE8Yi;!N))y9aBUQo|L z&(hk2kX}yD#K@kI>HPo&LLMGyBP+w-2Xg$66GW`790-~J&jIvmvijym1`dSm|5_Dx zaS&B>&~q>%Wck+yIXwplBReZX*59kp^opi>hGteKglzxbYiDhsXyibs`F>d;k#|#! zTpS4L#ewf82>km~@ZX=}gxdeZ9LE2B1HbM5ZM~h*yLJCEpI*Vp-rCX5;QfAn+y8!j zX(K~3JppT%_bW2I{{aAubW99v9PF%wEKE#vtPH=uX}>>=_d9%V#-0%HFFX0ItgPSH z7=NGhKcC9)-T!%vQbtxL4yJ^R|Gu<{ndSS96Vi)VzCR5iBLizgqu=)1JJ=cN0ij(o zPPL_@ahqb><|~SF;VcUWp}g=tQq(ITFc%DZxr8{BW zo+Fu#>vlga`g{iY{PIA|;d$lsm^>ZrvvVnFd!Rb1gl;4;n(#q z508RxaYTV9`joxZpoUW!bn$z8>+P;BN^2PsNGNz61zQ*UQwrA3qisnHl3YMbJ+z+9 zsx+CD+EDoB_IAnVxcdP-#ENc(y&>*ylsNS~j@y9Az^x zn%&PodDg<5g~FR)L&QEivE=57{#;^f3_`cZtri4~lbvn`tWn(wQ73j`IJM_uk^TEt6fmhI0YUD~ zFh5FuPN5ERk%HsO-Tn$&?}H6{{ruf0+8}1qD=uV%{dKcv|LyHqHk;t}dG&7HeLn?> zIAfi*h>%#aSS7%1EjC{#qd4ljzuPe7ivh{98(QL?2xbx>=|aIcbE`P1VUrM zL(I4^&GzsnMNvL##kFyvRnNI^5Btq0w@N^`+P%Fm`1Og?{mOy0HY6-7NFvbgMtF^h+;9?yYOANH5y;pm9icd|%Xo$dn` zd`f4$f$`!(&y~}9?i@=w)VC8~tB2)#T z#N0C$SgLMT{Y0>De9cF?DIJZflpqI|q452@=T|K~Qy>*vw~zEy&6nGY4Ho3_|4Y%XkBH%R}1L zm%Fy*6!V+ni*}|*-xMM>n!kFrkGw$;wKNgexq|tsd|Jx~SW8_3tf6>W$17n=;{$gw zh#8u%$9HOO#=&u+tYr*G=^c9QRG7@Em?Q*5is4i3j9mJ? zwsNS^>h$q1D8)U}t4pbOB{2DbiN&N7?7QUVlG7T})WM{ymvdBXxq0S?hy{G*bu&V@ zU1BVCBlS*f%OkFX@^jkW||7}YcGf*JJK^%4@f{>LtvnDFiG?LE8#W}9S4YZHpVcsze_9{J zx(PWOPW}R3%q4%24~xLUJzUOerk=I5l=lNNo6{`KQs>yLh-UMVAHT-SQ^V~$C!kQ3 zQsg6kr@=9|M}q?v);K)8AXODr6%_StRXA+BMl8-PtX!ym5CZ~k<-Ax$oGwbbljJbg@x<^$hyshd^JIaCxKPd~$qCtmV&-V3X5) z!G4=lVpS?p1j=FbzuA6lvc>Dj{F1Z~RZ-eG0#IoI$9dtyHdx{4)GZ1a1H$sa@y3u> z6D=d0G&ui2b}w`Yw*Fq%Mlj0^;YwXY-G4IU%g~*1GFai_XuKTC=#Plk(-N{5DJy%L z=@w|>t4%9k?6f_$R8<9eue_j&n0;msTl?r@*udVdgW2E{%GO*n-fls?ra%Bjw!!uo z_{6m&gSWbjzK)cpu^GFB@D%{qt5lqgY4u29b$uIa_9)*70KTUVx?JT1(6J zO96C_4J#S>YWleR_F=Jdl>3+0xy0!DVG_=GL#bm<$WI%{KW z!%;f2Ic`FuUcnffi!#o&TG4I2t%dfqU@Hs3ZCqOsSC-ZIVpf1SlD15kQ;>dG-<*jqM(RPE$ov2){`B8Zjbrs<(X9(4+r&?0O7RNH@|Q&=5Q zS#uRE0T{D>$x~#Q%ffs%OGn8naf4_xH^U)YC2K$ZqHD1}6c$*CH#x>!sjZbPbti*Q zqBoaJDBG9S?WksRc})WOv=AzFLtGC2kzc==kv4saxnK@ zaJntm3gT*#dOC_qs*T@k2^5Jh=ezczxXTwWr!-#pnt^9(*KDh#b0cz9v6+IT7)lq; z8q&eWQN7BqYAr215?rEr@!7)NDazVr9kR=BY)yb(5JndATIP5*(L+2Dw_RpS85QOb zyRWcH7cra66XeKpR!eHh&?22#{FM`6RZUK2<}He0jmgRbB*ybhWFCv7!{HZ?=2=*0 zYrOqRK(;e?q<)x3|4EhjM3zPNO=&9E<~++Cn1H|*` zp;hVpSt?{f|8olUfbW5;dj7kyi7?v?6YtyqWo+pqlbolH#=EhJq+nmOK9=MKZ!WPk zLaPc$#dV13m?a_1iC4Jhzc1`ZcN5f}KgDVm=O#vH7mt6AD)i__RB~X@e@rL6Bbkt< zYeO+c1D7U)IAXNede1q+|CMv>f{f#Ekd07h`(qV2cT25Rs>sF5a+^wcVb+Zo`2A=w z+a_Nu(%MBVC$8p9GjXHeU~=j}CZ+!dSw0jERtPlT;7q6P9J0U6=-*%Qy5TdWT)dX) z)W*Nldl;olPk3gq$&`lBvY!4J<$+s0`AusWXvDTYTRNR7WeT)OJcex~=Y`LxFZMmQ zEdLPv@`WM#!fZjzx?TE98;XsMz%F-;$7ch`j5vaikS_;wbMgOgGfSOShA@c|0B~>v*-@duWF8JLY2lY-DPeA=hwQlUCE%c0(GJJ{ z13B+dA;fuKTAhO@uwJejlW~Ib8qRWye!V^4T_s&WEvqGgkC|P3D4H~nj#r&g=X>YL zltI$OoB?_Q?L-{6kcw1nsT5}Kz}bDMTs zlak{-WV#Me^d_TJj?=*U zg4ilvKVdKZOiF;TX`^=nTq4sQAxl9 z@dp0P_$qVYjq+BPw}IRxq?d&n(cV?pA`V@CNpxk;_*u-o>_TNB&M#4o0>XKVX)%+^ z0?<~X$4S|EBR~OR;6{9OluvB`T^4$-Itg_pkCReOGQL{f7?W<(jfN6)#s=6X0Zn(A zddejUO_u_H8B3t8Pzkk}I2z~24*(-x;h|Ium@g@iQl+&Nuaa20G^zVZIju5V5of2Y zsQFs{YbN2a^syq&Pxz>thO#k_K`xaHqK6gq*Hzxv*hh}{Hbav*g~MM8$%@kCA&!*1 zgWqP9xRrh=n#)-acyp8F4DQECV^@fGy)VH7r>vCsbmCn=ptE>a7-XWBys;#m@<|Db zJS$2=nX>Q_ZI?2PGE*74Y-mm9BISTe13%f$(XGr-OWKxZR>c-aCTiJ(97a}=;&P^1 zy|ti$;p$h7BOKaoKFJiy8tApTAS)UT_t{2i?c*R9J)RS?r}?~%_r?3&*g75!g-YX* z(puYS33eb?KC>cPVIApQzZcc2GE8Cbg3f)4!17`%`SQ_`rcSxlVM6z^W3f)l%%hIx zX{3lo7xlUWdd13&D$hlV6$R~0NYbN>g-ZRt_bWn7 z+(wCattPpr#@b2qo=GuEd?|Qt! zp$vCr#jgMbG=$emiHW;P!l=(AhLl8G1Mqh-CtExe=Y1HIIJ1{YRh2nE zeN}zfv(1i20Hllz=(Hlw5_=?)HneWF)fCe=t%o#oDC?wp8^4)x87J zRk?n-k7E!9pST@>R<(br($e|h9&}uNiCT_qY=KDfTkEhKYe$swTZ>MlZbcOC@~`-RjpiEPdHgcZ!U_!)O{ zi7QX~+}y`kr)Q5>pDD(yD=meRh7x;Z-MOB!>@b~yg;cO%$oaI z#8J8}fF)~=+v`uFFAv{RcrUWEhrPl8I(NXF_E&mA#=fE2S_$K4J3wtbJjELWUF2X~glJ+}pPWVkFCG0dmdWUFGXpFwA9L%d`YPx% z&bsM-ZdDKiZQiK$+O}Hr5J0tHcyZq9`il-)n-rOl2`-_lip5D$2o_QK+FVGzOU_X3 z3Jotn3~4JQ!LVO5$5dcQpJXysuM(Z*Ecyc2a6qeZWdkO4%RxyP44na!4NFZ#Ad8s1 zDtmy%yZwX!FwUy~-|lDn4W|F&)qkPy|1fNNMMr&y{{zECtnGk=^aB3^#k7o!%&dg8 z%pB~5j4S}wcM1$(c~=Sjzkx3^3*f&?f>mKC7Mv04$r&}^t4z8}fw=`i&4agUPK)j! zB;hAIdi6_ZqQ>F7632n8nj<6xdFoKIY)RV?gS2}rE1~t|Un3diw#v7Jm78u6@r^`ejX37fxxP&l zIA1{{?mRxmw#;#-2L*>Kh5&1ryYRXtT=r{7t~Yg%u?2;)8>LywiGx|JlVVKN*KHeH zM~k=i2@KyNrvKpL-;)i0)5T1Hf8b(i1<9yB2B_tIR9qrHpB=75k(z^+Hz&&+im_p< z2h0|{Urf8qAIdMI9O-@$96BeTwl;r{S4D^K@CG|nM zYdWJWlVRf%;Sd7F*T_G&=T5#wJ61JTsN-8CHM#5ds}iZ!8PlEOVB4ZmQ`K?6BFl-G zRIJUTJImvj>NR^HXC<96h#6b7@VS1M-ueb3T@|`nMzbQ=b|Y<@uY+9U90U;v>iV$9 zps(fWK364kj(`KhM5TjfMJ{WIOs8);}s*|qmw z#QMb*B;398@gE%adphWEI*f&xV*F#Cq`Rtp9Tn%u7p=sH_FBz&&GgY9X}e}G zmYP^I`lV&_LYULSXZ!Wz^>qz()Kj6(Q(onCH~A)~hwI00LN_VN@HEUCsSU(q0VH`s z(H%}tLVSbI+4GXhiOSbXsxvzr_{zs!YyNUcPzj?j2jnD)vHr7ov*Q#P7k_Bm2FBVa4T#=dCHj{BAQ@D?%HiWT0 ziN;W72W6%mivc<{TQXoq-!Ve3kw}DTr2>b2X}bEp`7f@+*ix!5StVd2WOm`Y>U_D^ zrEjMP;0ph!2$C-mr-44lW@R5JibR!-w)zzip58IPj41L6Y4Q2at1DS$AH?jm8Je${sGs z+v5x7UEv@^+n{m}?#r0nm)>9A^wxV&4dbprnw;HDuMfwA?8`@}#EYG-#i3?_8rRTk zAo`oYMSPH2F4+tQ0y&sW*}Y;Ba8K2(ydY(%8$YNv$r}1_d^#2GiC`1wwRlB=vO|Kb z%U&x0cPr$=3cF%~RYiKKsjFKrdTcMonJ+!ob%uVxpiuM-Vps^o`K-bY?tu^y&n}R z_~N*Duy(Lq@%Il)pG86zc*MC+js3+ov!8n{sXDOo>2!FrQS{Nc2mW9ay z3T6`z2VfI1Bm)-&&67{4z=^O%a4%BRk=@nNcwqv$w5KMPlhg*6!L=&mWru>HL#MM; zN&-EV%lWD?KP8y7!u6NSk4~<%Bb@6O7}AmC-UCT!aG2zzf!}=&rEb1OJlcw4t-z|A z81B;4pb7DzJm`pDzCm4r3|Hn7$E{GKg0SoY8=7~S1~-$R)KNGD-9Oloktv|7}&WX!5HO0@?+*`aU>%~bw0O5>#^))^RRX2!cUD1mYN zn0~3GDoIC5cUywQev1lY2n=pX8Hdi{T zx2ougtDeS1Pm{^bFKKI!Sxay4hB}CIkpv~DOV;irA_6w^z~w`V{M|VxLn;JyTD&wF zj&I19j`zG|q8`)F-?D9hX`@l6It@A}w}Une*LQv@=2(LldHaFi1&&r}%CUC~@1wzT zG_$vMIPr&UjN8|q1u=5mGr9Hw^5mbJMX^ZPr=u)0dH#7I>EP#$wEGY44%z$k!~E2K zUcETin?bls_DnXjOG&~N&CFC z$ksGPdK@p*Sjfqf|rVxsj zG!sIe)C9FSBV9()=)A`(0tK&fm;Wsz=HG=Tf3;&{`Uf2ww?b1a7$fXR19TE0&*Ra> z=op04+aXAd2T~QXDrpu)0qYaj=h$1X>TJ$1$9&-iydy&syu#45)XTG0&( zYon%>+xiu=W%-4w?y%L+Y8uJizEp5)UrTUHq+v_}z(s{M%Vkw)2i5swFLa*vf!w}GX(C{WU;r~8DIY_JtIH{QTV@IGs)4O-G~e(+U9 zg@PNsQI*uQ((CB$!M-s)kTzWxA5|UBgst)BLWDY1d`zpkR?e!Zk&(P4*0!sx6w+QN zR~g}$DpGH0xP zu9ESqmm`)1*^EQtn47-r3vdb)RJKJbDV{~xYqk~a+A)S9W2yWVNh8&odgGtNIICz^ zjh{Lpt_ZFzKWd@M4=m(!9~@uN^|nuhE%Yma+BwdqO?`U?QA4XB`h!;fcPZ5WzX2^P zGuwafx}+1t!8D~722ixyBzx^3_{G!$Wb!Voh){-)+#nhCKpi*+PxrZzQtDwg-mw@>hScR^-4|w z`u!~iEFt>=3xVz~_Jf{L9ABTl`_J=Ey`)BYw^N;<{el zifh+8nE=ZNn88;Q@&Z-9rn(61cq_ooAXsMT0YYyfylXY;DAJ|SrrM>Fe|m&A{ao?N zhDPGY6!HlgNIv|fS+d+>pn};Cu_^h6N_iqBmsZU^So4#@<}?fzdENtWX1$u{b>O)` zPw6UlY|ysP$t`y}WH6nElY}eCIkxjSPF@Yx83<)hi;(8^j)i|(k+Q-mJlRKlk0|tM ztg0`FY~G{|jY)|LP(Z6}Tc%PESi|p&utj|);1$a3ox;bjYy|yD0AwuWML>2%eXz8# zC0t)gb62&|{LhlGhOGoQM9bWoF4u}dp9-`#@TH?$?2BbN9f(Lj*&8~LbWL|x49@5k zynq`*&CuA(pF1$FUN0b6uhV#cQ0MP*xW5_6m|0o>yEw{uXSLO&rjw6W4R^ac)CCV8DL)EtK7}db8R5&| zj`TS@P$2x2qRvh-VC{#c!b4N!%9i={wU}upeORvlkOfRD*FIS(^)80`Nw-HvdPLbzzJsn6MCOyvSc@<>V*^=sy8tM;cdkO?heNBf8D z+yT&-+d3d*cX*{S@WRtJ`*@##gdimH503tywDhlJKJ5P>4E@f0-p68szr)a^0u)j9 zRqeksA1OB@4Dn9Pqr>I7+KxhmmQvhZ3MCSa}+i-B3)0RiiY3}K+rd*VcWd)uYZkfKl6U1_9Q2h_QHN!QQ9xNDh&_oAIA*k`Hd@!wf1X@} zo=MwQb%E@+yK$d0Cx~Gsm=y_XEUAkb1%!Cwi&|#RX4*3;=%0Ytm4j{xdgn3N;iy|)@Mo!b9_F=_iSgTW1>2nm= z_yq74<&LyF@oR}A>^DdRaRoKwo9Adg$#dMWodv-zr3Vy8FAl$WCADkAm0XOGRUFnT zPOv_F=jOu59}b6bFB^@$q?y@+AC1$yX1~%O(U*(Y zuVjU}8GkLN_--*D&g#%$1ld(KvigZChU<2+16i_1UQM_TuZ;V9|Jj{(&^!$q{cdi- z`7Z(C%BMbSe?HcR%%lEMvoCcYoj(*~jm$i)cVw^c?-Pvmw8j5HVSk(4vam7#_fW#E zBpF5X4)fQJlk<=MNLxF5x8z=@nbA7OPCAjN1>iyx@iM2(Q zy>EGeibrP)J?EPoMDh}7w8dek@Vs#j!eD?9%4kISfvjw!huW;w=-$V-T2J$qO3u0$ zZQcMq(thUC9OgRlg{q9IdKiyO#V}*$7KX^-QS# zN?VR-$j>wl{rDJ%9JxdXuBVOh;*XM*Psl-#ied-KTY#S&JV$!aw57!&Mozc0SK_My zJ5J$tTvzsN)rUS|s|of63j~h8<-o9lI;3{0%RI$(mO&F=iE~)gq~}}ud#j42@lxME zqZ526>^&557h5*P7v}S@c=#4fW1GVcao4}Z7A9m6eD8~%TMf%~p?U0@W^8ZacC=i! zd3^hNJ8o6~82esRDbj{`SvG|C+08iau}Jzn7DmNF;SDx>|Ly7wV*$N4=?|j%+pL(4 zh2tNIs@#GRD%}}PUErRK;<)(w`eWNuw$5^%YKdXm2M~kt5sT4_D^Qn+3F;hMz(tjB zIJ4-2OUF2~K0-R8 z!sHVT_C+tZ?L>cC*w?5B={WC0%OR$jF6UEMpX>U_eFPac39lU6M5}Xzc2oUaVZn8? zL7#EesKEePOSM2)1X+5k)k!y-7K}?AtHsB?G5v;b`4V+P)k%HnEb2%?48~8F$fRnS zgUHOAnwd4&`WjkRmszMfNS7)rg9=%8Eh<^{cIih$XwG7x(15zKVj^gHZfkOim)I_( zpZ3n5jk>gOi3a@)hap~C^MnMa0|{HNa=dK1)u8=eVw?D7Y@*8dcfYfKax3S+fQt)- z1OU*!brE{y*5i*Xtry?x;})_q#-~mq)`m-DIYLxvbE?7BRbZFw^5>Iy*0aOnE9wgB zn>)r)lJ;$&ASQ4`%j2)$qehbww%2*c^(1k~r~A5_SY;FcR44NK)>ljeDf?`#A=|LJ z(v0`wY<-U}`WAg_RqrO=OAuKMM>J6n023T@s7^@(Pxkm)%X4ky((!s+(m5fkX`0O+ zSjR*of>);uKu@-^{V6_$RJ|F~H+Qgk8@b8J3?tfI)}0?_X2h%M~S z=<7(f^^txmM`tPQ>&(nllG!029~P&S=$a*hbU2KPc-OarYLQvu@XH;$?@Kx(_<|}B6V1uPY1I;U47s_c@omf&~99H;TY>ay~+SqjcTf8`MI7CLv?qk zt@W_?=H7OaOn%f)AY-qqOYTYtS67oF1+-sJ>E*hr zh1@$3e_3%gE1U@zyC2`$kObtb6(S_EF)6wvWn0f>YR2g`q2KE%CuFjvgnhx1_rck= zLWZ~Q9PpZrUJmtmkf7XS0ui!7ZSiw zBNp2rCd-`_W`bMXI{REf_(Rk?)anM|nX4mP*BgZLpeXYXI{EA1oq_ScM@?x(ajSPe z@ebaveL?58PPswC@TcB%C8yvg1qB3JI4-xBMV8LMGFFL!aFPw$w{CXn4VA=FI7BYo zHK9^zNFBiG=HyfI5yuF4YH@%+C|U-b+_%wKj!0j)w-CvkyRY^HBjgs30*2q{`)ye^ z=+lql9s-Os_xh$|Ey-xihPciJFkHx;_}N;NG)trc29g(^7)CF>+)m!4*1VY4WKVm{ z3(V2o_V>tVi3r_PokuqzzvFRR88Bmk=TO-Z*EO;{r_jrpOBUd(($+%)>}|4g(hBKn z`;tb13)t-eR+Xq!{KZVaq?LRfI`V|GJM?{CtgTh2iVBX06+y5 zk*ul{Q1XWY40KNj5R&&U8$tD0T|4#h!YBMA*O_gQqBJAfuCWJO;Nhyf0!1%(IZE~f zwC+$3Kdgu@#6S%Wh+}6b=&T$+xVFE2(4opN|AQ?4Iz|5{;e$I~Jc^76_BZ_`071ay zm`K)WyLWs8a;GcNDj=C{Q!+#&U|n8)*t_(swk`e2L5_6k>X0%@##vtm!&&YbzJ0G# zU%vY3%O}gI6Ix$dm?N8>!VDuH!nn!2{d) z`D(6oBAQC22>+jISq~b>N-=7uoatd{Il(m8^iERDJpwBE1A_HG9WenSt_Ow*CLPEN z<*cqo(g&r>+t!)Gg9cTP?Fj zjE(S7cD!UGIj8mL?5F4&)>2gPF^9d=dt-T;tcsGdwM3{iM0#t3mIt*-2M?B9Ldd1a zr)R#08cGRJSqxeDb;HBnB_kR*Mu0DxH#R_lGxGY$TC2@xc%t>*LdC1c-f2wCltTZoei|vV%e0=^t~tY75`4; zp^j%LL*}oX8Br=?;1J`8wvt0hJRaAqx==B&NAKw zWf~DJwi9f|%stfF?CbI)bXb6;bO#uz%xN|3Ot@6Kyou=bQ6kzcw#XpyDib!qj zSlUly+sNs25DYDBPLQ^Lw#dP?78K%mQNQ^Q%^Ou&M40{aXr{5DN5V>D5Cip%86X$Dk))*UozqL@zZH(NXQ=tBud1^?P1KLEs9grCWMFz zo*`(AzYLgH>BMxk`O!AW`F^@k~~FC7z>LZ4p|(3c*^1P9SnXx*}~h zMsf7j_6fLkm&6R=kAL1 zyq2zW^eoyVbTT_4eJmAD;K$Cc?}N{~(K#RuGTs<%4S3N8Kmk(8|R-5WOX8T!XLujwp=*y#A(xtkm)jU7D*AE3zttBT?khDtU7X#rw zNf(6qnlD-48&x+Y4YhK2c?lkUJv+X8d;n_z@g4nx=l(h^W&Q`1ORNf#RtAi)%N>db zpb%x*M%fg6jN!Mh%S8FAgua;K1!hdqi5`be51!A$yc0bBGR{dWd}A>axso8;A?4ub zF1{KFid?YZkxrlHUsAM00cwB)e>jp3GT*~CQ%%-;A}C$M&+(leG4p#ic(_gaG2N-9 zPA^=o;h!DuM0O6Dvnl40o#R>1O1~((gXT4n{)!DZIGmD=fLJA*h;*i9JkSpuoH^VW zkSRy@Tu~<8u@MBOqy%J8}PTzw&YF zjMZlU#JPmMe<}G+cSKR^b@?Eoxs52q*dFU7XnH|(#D&q{@oYWnEs(jIRv1JtBpZw4 z6~VRT5}1OB*2=o`1Et&~wxsnFmrCL8NVCF$1C5rxJnDFcTjchtTMU_i9O-dZ#SLxL z?5)yE`gyU(?VUG_XlV5x9Q(KFC^PdvcqLiiG5C9rCv@itO@}z-)3I+xIZS@HzK51Y zWBd&yIh=~uWD)cB>D)QWh@Id&BG0L3LYYCXrtwm0l1E$gem#DV2f?wYCTzt$kF8Zw zRT*=lzxaIWiifSRwa<^%sxO(t`YP<1xDl3)FYw_#O}o81reVZ$)?~aW{A)>^h_VFF zTRBv>DtJaEq+(u${oFM)6tmwr^^l(DsE@u-MAUXZ_(+-$405GlF;P(Ihc#$k$fKaWI3*N=8c%~`o?-lCafq>bmRgaAis z<(@zo4N8T5xN!~&1GJPi^$jaI)vK>~E$U$5k9PHyNW-*nRLXZvR8F~<4`6m^lge3q z#|m>}jyx&&>Pt+imO zQxos~p~tuU$^g95DURyxqpG@U$D43~QjnS| zDU5I#X!;7)?8q2?S-Gp!5~g@Mz`!(s>!H@1Pl8qR$g(|HJczF}9T^5KQn0wkH@`w* zlWD0avi397p%FmQewN1fc{;5a`YQ8ttbcHFOkCc7V-xovFBl@K?Dz+aU$XZk3ENHX zWB?>rwq96mygoF-BbM+=o_WyDivGccMu^SEM#|oKqoB#mf}e2SP8Huy7=*W)$Vdx> zx}0=w7z2L3lI^5>G|c4L20Qq*0Ta=uBp+R=&$c5mk>J>^qEer}BqHQC4=WUX7OFR* zE%i&NPTRm5KP-2>n2ui6mPXC57PMPUaYln1bWl4{1C=sLT|@ll4N=D2X4Yu|;Y9x!=$r3w(Flu!mJ`5g82V*H7_`NO8F<&y zp^uM`n@YZ_*V1^5_O+x@(i`b|4RN#R8uJEGFFoX`_WmYhSgObmb=+a@HR1^Hk!MO@ zGP=pmq-EV?AS?uboiZ~_?rETRlKl{I)Z{2E3NL}=@`Q6dwDGon!|K@HCjiy%K=^~U z{x&gVWBDfmKnY;?ff4HX4vnh|m+jV_DPBDGxg(O)TH&N~3+$6G0RqnLRj1I>UA&Cu zvi_pPNM&?k!*0F5O?q@aE8?}yF~dNV_8yuOdJjrf)z~~!v|0PbY?nj?%Rp#|o>xja z%}3RhRXd;|@2FCJ@-F4E8EXm*pZSJKm*u8^S5wjzEd^wTx=Ixsz-%R$Mm`elnmCXYd zZxam6=ev9G8Os84ic*feEW;9%U1-nW-PFrV47cjTIW?J(3}PJ>*mj2!k`ItnSmG0B zfs;OAag-LXCQVH1P*>4*rWAS8W+3x3T$P)><9@vGcRf~QvCgV{rQ@WsbW_0GL*zzMX3 zithl6zCK~kY2Q1g=J@eZO)ASQioMzn zKH1{75>m{+;rlk|)sgEu^ocMLhj3IvITzPg4WzmBDs#JW5aU`XD#7#p=}om zmQ)H>=g7B7;FTk9mo^Vo9i$6ol=G!yRFFdfqm{Y0A?=&)h7`(~WZP#nRTGVHksaL& z1w4LqkFbtU(aksUL*EZNfIpe`oC{F+8JMV=g$V>Tr*rdxN)eiquhh%5cc2hAMXO3- z&7at1)UgCmddMU_OxceCV%GV5eXQaMDdCxnLq=6unAtccxtta4EBt{oBV!LAPHUCp z?MunG?VVw9=v!Q5kx4n3mPhK2c+74~igNuh8Mw*sh`)=SC~!d7?uYMPbdGLhpVvZh z<{M~@T`hyRUWDo)giP(}CG#M(NF{561cjG%FqP8tVG0TKs%NS$Me>_^3OmHvol8+F zAwxLd2n(w+$SSQ_5au}D(&E`s0KS9r>ieDIjeqUOFW)jwVOE^=3N@Tg6S+qLCN5 z!U+|f#KW_XOm18Ct~C|y+~(FT$Y+u=7D9>TF}5;E&BnIreJWPbyRx!|)DZ#tK&}4a z1jnDS>D0KbE2||%^E3g@wqB1YSt?i*&;52}YNL&JRbCKAB;=i z!LFU)@8QcEDlR`L_>`+w;&6A<8`~23`Em1c4?;AX!=+{|Ewr<+N6qs_88Ae%&>vDABaXsd8PrOGIDm{v9kLFGZn=qRy7KIE|A0rYdosu4%n%DnE(aeK7-fusZHRFX zhUyy^jwfBS4Zj%ZRuztwtgXZ=IY(BfujOQMSZLC$-%xGBNY?|^a$huj{l+ zC9`LuGrgQYKAaUYe?z*CgtmvNY-?tE3hQNm#`H~{qNbu+vmZ3!>m z^ug2X6=daS2lM}y%zOR&-!1%NdH?0!e;paIDz?}PGQu8nLx>`F#O+;Z(*@Re?I#d`+k5~)$#jb?k6Okl~-uVA1@5{rf>b}R{NO4K2kg==RnCZ@g zA;}Oj&!Kd2Nrr2tNTm=m%T!Uyln8|eQ&EacWeTZes=Op3Q-1qgl&SaA^LxJkz4v*} zJ?E@Fuf6u#Ypt{QUdZ??(KgGImnwgtc+xy4%w!e2S~SRf5BMhA;5SCs4~OM+O(Hfs z^m&yLF`P~#@@Fzn98tLD-3LE>Ci>Vz8ZuqO-cvpv##a#=%PBy>zL-JzUWlzH|ceg z`IkkSrS?_F!ylNc&_?U1T%vF><%A42z@v&EUplWFrz}dpL)1PL?;Jac_Wg0f(@#gG z**|h{LAQOdx5*h)}WUEYm+4g_Xo=aW%Y1o`3-5H$icT)5|S?(wCU@BcCo#HDNfx{K||Ke^gb{?(f8u$uRU+yNYY zd1DyPp2!5s1h+>(uW}}b;aT70j@|p7=y!YE7;Jsz+xU6c-Sy|Tvo}A;;4E9`c-EWQ z%Rsm{LOQbM_LWCm@TQTi8J1I9Nx=f6rLXgwkJS|iz7SWGhVQ5j<*IywiizIYtDZ!| zf?&gY>=m`RTnbl!XC(;dDG9J~G$I%a*WJnC`rC8F$ERVhj}7y!7}0J_v*!_XonmN# zMbnun=Np`Y>#BE<#cY|jW!Xm#m`?h{X=qR6#&R(RNcmi3Ej`>B-)a0^w!g%HQG)nR z{UV%xchiZ;^Qew3eEQXcxn66i#5|i`hIjg6MO?F)$F{VO;_QepA=xtgQ?ogSi86CA-BS) z_Sm-JL9gHj_Lzek`L=uOJImRNU&>8ZB=?ccX!l=r2{o7zdvQh2yR3qB)2%7Jm!J43 z88(4kMdO2<$iVZ4?-49=k@h5u%@Lm;@O22}b<8{!v+$nVW{nyMHon^&MN;BFR6C$7 zkeXdrc?))d(iP+!G`8D@`}?c>ZJ%xkh-*dGx^dPFZF@^A*IQJ)Cq86<&>9F?YKL6h zz)bvuy#Y!U2S2}1lR2HgX7o%}D<0&q{q$W1CBd#L9(7kB( zaRm%Eo?TfoC^33xQ@VJ{;3)i6O2xaH?()+|ihYRkGREfJR~U@Qf)~l_GxY3T?d;bD zVQv#Ip6~n|np`!UAQpAeCFD&@l8@@WW``PM!%#ePV*V{ok^927hpOF52e^F2B+QLQ zzQvno1b(mM4IO?KG;p1rd{fab!gdqMpxub+f{$ypQ4azyNGmkk9V1#RkdtDO_#E7C8{$G9guG*js0e)9ilK;$Ti(P|FXySm9-1aG6tnWJ45A+{x&PDaS7ZsnI*v!&|6)C9*SXxYEI)TddmzKQ%0>3 zF8(_BfeYxDy2s24q}d9%Y?1lmLii#KG1fVe-C&>Is~V-7oIwrQvI;`+`>-h&dAeSQ z>Z%Yva~*v3PrY69(4c?eP*;Zc^V>FO6P4aJe%k)}by2~Q?FF2qwB5#;+40?5dv5G< zGQj4&DpikF`F>om{chF%R*IO$7Za}y+g@za%^nuIE%mrhJG;5(oafG3LAqz6Ig+Es zSLSZEwj7GN7`v4s{*QV?!M)TY(ZSiU9gL*v@#im3#*7n*_f1T1nHj-wmyAM#e&|TP zY`mU5(?vP#pn$eCk`C6)>oVG|z(XdYwLAvl>#*lz=V$HzX=4DIVXeP3+&@kT3i;Zt}RVT-lFxGU<7uYmmWG)7m0wgYy^urx0kMEzMRz{qh@rx~%+o zdu!{N&QsZg4SWaq?fFE!hMQCc_u%3!m`=D>BvgFdo$u$N=MKA2eoGYOe{hd#4oKby+8N&-iTqDAPo0n}DNLz76r4^!bQ4iX}UIB@>LF%&a$8(`7ll z6)&Y=qqxbc?1%9sUYYBAIu)uu|XZDu{!?UhNbu{ zb9pnmt@uXw_Awd@?3;{|0o!a+#i4d7k&iJqsA( zLOmhAvoGb9-gUQMY_``8EVwTbP_8U*Vl&e(Odrfo&>&+io{nqoo{jP0ZJ+)|TQSZa zo+B%VtaYpn`(`&LbK$j^ylu!2AF;7dBitIBu-wg|M)YPe9ztg?`})|w=&hp7&`Z`7 z%mcImG?q6WLv zg0e{Y_&+d$h-*LEM9arCjrQNK!o+~6H!#@QI$P#`PwljZz28N|S&{=!1s=6clJcO+ z%_rmri|44%4iT%30&$ba%w#U~)~*}UHLf0d)^?MLZzs1$%$fVmh0mPDE-2#VKfYm~ zYIx}}hGon#WKbGXP;c`*o&TIRzzj91PnW}duIHE#q5kry)>q8etubxgld-(4XYX!P zyBe`OdHR;8C?|fKIJt@wl_`dKy$?m$vyO7pMpAQoi zG|!)&;oOIoVL!lTix9Hr&b@lsNI18!kLjXt>;<=&f0)JUXE=7Y zm}V)tsX$#b`#^(`ixwg#>duv370)*M$`*yb^Mj@u2hzvCg-CQA6}rK3NKJyssoL3C zV=va(Q7+)0MtR`JtQS#K$bixhWY$eQcJQlPPJ2_Wk7=#e!zth5!GEN`n16MhHG6w1 zwLo=^^PIrauV03S#li9?+g-&q@3YX#(}V4<7jw%;>}AKjB%J3m=}J#)(J<6LRwhPs zKlLakKey)kF0qeOqvPXXK&wUQ`UIJQCsON`aLhK%ja#xZ`Df%G+nBzbj$QBmX_ymB zZhDw^u8FQ!>2O3O1MU7e-=--W3(N<_{{2$)U7ts31+@g%Sg?azOHKJB3t6PRqaq*J z=XUuq`wkJL>giE+>?ey8a}GSp|5X1t(mlafd*s>UvteyPCjOE;O@heZIi@>PC!E4h z8D+FPz14lJs_!Ths7H%>rx)br^+|fD?&U|7hBQq1yXO9TsV>9eAJ_uMeHixY4GNxb{zD=35Wb9#li7_3*~dfwiwH($3+;!rKg`HBRh&wa=P?I_@|mG#Duy3F(P z@oD<{vR1F~;gKmireqzHHAcc3It=Ddb_HUiBTmK|9 zhCV6pz01|7xg_);kK>)f?1I6#GJ>l4xKZ5&(&s!*GB2qh?BwCn8pAZD9N%a~vfS;< ztu-1qs=vx~itW1ctj0R3m)P2J{d}u|`y%Sz>Dj(m^4DfznR&3^tAeAC6*;_esY-Of`4w$B|}F*~;KeTEr- zG4G;m*j^XyD;1esc{DX}wG8r?+kMm%(7%?FR z2;{-B3YEhW!pu5y8p`%W|Kc<*J9Fu@U0z9v75V9$BTXUanvc@72MbdU80k!$`$Vx# z&nntWfPZh2y85Ps~jH8mLUc23Z>vrw2TBohbA+m9^ZCv*Z)gpU~7ky*DRB z6{qa}SGeedh@4bDDerG8kxVj8yCP^~?vMgoOoV2z``AYg&Gn{_y|R9+Ez0+ub7rW( z4m0*ciQi0a?l0AAxoa#h#I24Lihvofl{t-`r12k8rOK5#Wi%u z`_2xycp&?m*ny1)p3&XW*e-~U(aO8vV34I_XH>;uVq|mv8dtPK@7{Rd{f7)u9O`WO z{*@w`EJxE`XgOPc8#lbwCE$5Edhg()O%G}d{p%YOtdoV?@;Kc3)Pu|VA~JciS`58! zpPV)HdSdC;baR-&v4O7G>8)23oBemU{r)jlYVYo`-%u`^e5v#fyN;0c50Crbe|IyH zqikyL`gk#C7j$~xO}Ufk!4#=$7@x=8?xw+{%{OCq>zG}$NpqzX%BUkN}sV(+q+3O6hZ*+romt!fhw9^shh|Sl}_)aOr zy-02yZmpNwX;A!W4MJH%RY7C_;1dTn>bW~u$)h+N?6;+|<$%nG{{9NWT-K?Ar|f60 zo=FKVr9903mf4Pbnk!jWH+TC&U{q=Ln_d67yBn*2E_CB^BnBy^({1iD`YLE?I-dVv zG!O0d_wAF$Emsp=4v$MUU%IbOZyjzQl3{5cH(WJ}bf`q!zUwSWzU;_#q@zF>~>+A!7wG!Tgismvq68|9QPUwj()_|v{yZdE;d^#0ba3VBH!YWjlsdb z+aCt|lV^8R9@jW`3cj~GaP#Uql=F^#!?>oWG!Ukk6sBdV1KYdqj(mjR z1ZC7p=*ixI3_g+Ftx@3g%fL)U@yy+mP3$~H0&=7DZ=$Y-ABeNMTWo&wf=N)zo-*Su z^S6>&rB|-#vztp?+KKo0Oo8LlCbu}cpkK{_8f7-90Y^lwZp-FN8-hyOZqUc#x4MIQSBHF_JX0wQ@TO93nv~+fs)}RP-)C?qNj-P%-r{ z*aoPyC)v1y_Wlvx9u&CrWD3OamCf#jqz-w*^$(s=O^c#{Htu52yk z9c}E%pz@M>Ze#~T=s|hZd_|h~G{9i8EVi=STDhJAA@xtIy)N=3uw|Jm;;sFig z!3KI9kcV*K*gQN~ssn{VTXC>RGz_W}4uypikvJ-x`VQhkcRV;@1`8y1Y93I2;6fr$ zAYkDI2mE_2G!zEip?9bvixq?tV*s@;-q8r`d>DXJ8x6gK{Gewlq5vGEg+cKlIGTtA z7iJz=3oxiMP#APk^9IOTL^id2sPn0froySnf{;r9n^BV zJVUuc7nB-YKhcgvEc6c!2UT%CODg_BA}D(*enFz2)dGfo2$xj+;qb6uRRH*+zJRtt znIWMI{HRERiiL88A}pqW>br<^0Hfx#n89K^>K&qjg-%*|Cn7+;06|oWfT&{rfknzSzQ;}H&4y;!~nIOnifnAYv!>?WDrp- zy&%GX-ogFX0ntm-I=5-6!44-!dQlHf=vV@Y|`Z=->E=vOrKZ=)e3z-<02BIaKrf`zufB4Ym~ zBAECW?aIFgEZFcG1_cwK;E z;K&6c&;SQdLuO$>DauH3O9yvH7YA{YwXH4S8ekfx9%~Kya8($@E*5%pbr|553ni@% zg9glR$^#^VqSj@N@&L&I%hlTTgxK1G{I#k)91)JD63`m$1Iz7!_yZBw>h=M%IB4S9 z<>3K`U%>CG@_?g9AT#_X9S$(8-<1d2@H@OfJpFfg!Grew4ljUFQuj!%)jk5?rrm9O}4QE`9#5qwolDaRP7-N`Sn6E*g)~NI--Cb?$_W`UWRUw~)N;X-zEyUe_;u#C1i>P30csQ z@4#B}fUOl|HvviyH1SZKI=cho09DRh5MXgjF{c0=GeZ_4VPM8_vZuJ&+giXq&=N?5 z1YQh&)XmMwRZ3FQ;b)ix#l=PhCJF30R_>O-(eu(|PFB`%3zDTBC}uGW5S3bhEyYn0 za*r*nD1}0xa0o0CgFqsIS|N%+3n36fB1_~;FBh^k4Cqlr7*Grsen8j80=dN+{u2ht ziqH@4xBx>Vp~J_Q!@#yuNSjy|55U00yaGle9ELgzXjaFhrmXtbk$Bkn{}we$@qw2g30R7%(oZ>~ml= zS=C2KJa7TDGA$ktsrSpvAb^5b!ib1fxJMGPkPUTNTChNQRbK)p^{Z(OiCTp}6cP*h zw_2Vbk_fCUD_|((>amZ4?0PHX5y3I6D`047|MN;1eia`AqXV(3E`aP;;tI7!U!p+w zt||iyokOysTr7Ikm_cCyQdh1BolLD+qP}nwrx*rJDG`X+qP}n#?5@^oVs7#Z>r8)b*Iu*&(pmd ztM~f%>a~A+Jt3445Tc}|VgM&BUBxMB2B*cL!Lime1Lxqtp;mCUF~p(f*LBdfur|h_ zmeDmfw8x?Q+(8b9iwoS)%Ha2oEdT2OA!{oK9Qyy+fm%gc&&*KY0f+fttAZ{L!txHf z4u&`k|Jop<>)>E$XNAM~dlj5o-bB~H)XEr#>EEq(*81{>4mj$cmlY8DG{w-x0f$=D z^3w#qe}D4-`%@H0^M9E``+slXx81+3w=?{-?qB9p%Ng2RJKE`g-p_CQKd&!oXke<# zXYKNNMVik)R#sXnI%XyoW=0$aIyx#wn&00vKRe^|4nI$0kHh*eJ9(|FtUuRie;@O| zd*yfYf4Wh^(8}1s1c&zDmliU$_`Go(Y9Wiy-ViXB{RZNI&PouRHJxJ&w(rg#)a z16=z=MY2Ai0A+RP1V6M>Iv7~=7dznH=v~;sdOdzS9ng>uulC~<)9>yr&s8OwdsTq* z>t%*WVc0`%(WOgC+8%Ee)*D`LdnFSS9nU>ShA&5F+*0qanCtIu z0kJ{7MLF#Y3Q`P@Zn(_fQrVntdt7-vbUY5GmKOj%Uan4VuqQUMw7rCoGcbf6ci3MK zeqRwcz1@THZT}Vsr|C7irxQ74UH7z32^!aqt>ewc>xtd@efvUQ{x-8o<9)6M8-pEu zDi4DRy~Bo|V(w*|C2?%t%I7;G-Ak{#fH6+0XKpLyGf4lT9(0Pw7&{^>_b{1FC$plr z?dK5J;3+}Wjt;3G+URn(QwW0ltxG7AdRs0*ks7OW%K?zE=`iewU62aeKv(tiu$ubW zJ|6<>xXnh`g)YS}0><0vc8*?A^GG#ta-xpH`cypZO&AV4FO=BC@&_IPweXGFT+Uw3 zRjfDnVI!bM#)D0Aq*UwoFlC(yl?0V;8vD&_sb5bqm#%+3kM*143^N9L&?}mo2_%D+ zy4_2xn9*#OA~E%ynLti)xx>iU6*!`ho$`kudaMCyIpxWP@9scf(%5G5iv}p{^o#@< zK21I~9?t#0t#Uyaxtg6FnkIe30%53FY4qv!Sn1hV zg68U*XlmiQxN(B)9s@0tV$vZ>ye{eT0y>4D3xz>b%ZQDkO5?#Kd6-)pRJGR#*+?Xi zHoRaL%{yU2oT2meos6}BxNu_A<2#?t!}8e)baI`pC`JD{lCO2oNjP5-KW_|MpVU1` zB+~b*ch@=IS4nT5VZRLYxTY27*l(}>9WF0hSsNdZTYcIcPxtBshm~KoU*BHceEVK- z36u4T*JJv$(uX2Vf!w>j_~ooC@yTcwNh8{Xx5b7|IaN+E`qx?A_w6*EY*1f7l)};^ z&cTtt7w;td4P}mv;|E5bDO6?G6uFmwXaBA;HmJ(lXECnM6f;8cO(`U%Dwp!!Eu%qr zqfL@Q=Bl^J##>=!@Wtt(y7SQ5y)do;HU~>mx3h|V{ zC_Z*{oD{>I>)oy1WtK_I935~rAkZ5B{9zF83?7m}h5&l?Fh59Z=Gxw&IY_m1%`izX zKs}NrTQAxZiH@31Pf46{x1fzZMraOX`JJR_mQes4QM?$bZKQGz_E(88g5F&%voLCsUV%|t)T>XWh7QFocuv^#Q zJNPpr*0o3uh_swwH0dt5I$XPAhLE?urf;0EDDL!~e4QDSKzbBQIzc)7+JR_%g0HZ! zy_k{Eq|iekKa!9RQ&0OK!vipVZ6jDyPv;;<@F%J3vVlsy?N2=>4rw&X*{Pl7z1j5H zCctWG+|O_4p6q6>_S8mqf-q9dlLt5uJk_4C#f$P@lF>~aHK$OV7fa7ltz}Y8GDT9f;1C6`M(WMyRE<2y37p-TbjQ=5GYU{T+bb6rK(+l=YY1 zo^DU7t&h_uGyE*|BRxF`10?9W;avFh0!@!m8n_?ux)KJiG+^VbsIISw5xYMcAEDtQ zckKe*e^zQ zbxZ~P<12DAf3a_tKkMHa-z60p70qNU<&gJ3n*CNAqqN8TnakhnAv(K`*9{Gq*{Xx0bwlg@jur~~l13m< zZ>GZx4OglZV82EUaA(*mI~dYsXy6i9Kw4_9xfs!N37wC&^jIMN7?4e~gEbGjQP}W{5q%-JEoLEtc&2EV ziG1bgCq=bfBdRqcli(~1iCvFGnB^rI=B);a=SpkcuOm_|H5m7Zwn`0Y*n|1Z-dMYc zgdB00y8HaDfC}Fpipr&-alSE6=Jh`MQAemH3H*{FsY}MR`}$<2ptr&C^(uOI>HM}m zj%s}Dp>+mT(vg zX=Gj7JH+Ie!OWh2n@*95B`cinCJjMinu3s7><-d4T{{+w1;2sGzTviEiUiSxIhm2f z-(sOj*Te4GfoIL)Uo+9FWQob7F(&t>*TIe#*Cs;Z8v?cD)uR6pIat51lGEGAE{$#+ zQ&y>;DOMLXs=;o5xt7v%N8-uh*!dk*BNf{YAHQpN45kUGu0+lP?>wF~@&OV;6Qf*# zK%@?*QV)m z9Z`~?$}3UBImiT=|60XGS6JWnn=g&+N(7vM?ufou6={7!U`>C#+^^(UXiiugUR}RA z*$C|oYgdZ?mxpo%g%e`DoiMvI_S84oiApx>;6#jPVhHuT<#2amzHxt^Vj`&9F=yT4Se_5E?TCAg+W`NDHgc0s6RCfg{Fr< zBlzwk2Q{O@Y>tg3gob~Kh?FEusiucSqaB|VO~vQ-9ve$=jR11`?jq3d)LeKP3701< z)J*@`A7W`0b`_`u85^OTfExG!E-xD^&|H3#OvBC-6-%Q%nG}kUD~bAZS=apl-V2LL z(Pdil3rhk@en)JGan4U3f!818Pme>6lOSD)WZ>Eh>WkydidvxIf-+ca;vZxPDvg2R z&nF}sWD8+3xXxD8(#MOk2?|5W0;`t!`jlMOOFxSC#$uwDFH{z1+Oto)xm}Zf`6D?> ziH-}-3WYs^wfz3FxF$bz_|SOWpga-l9Kc)oGG=foF6HX{<(>S)AlnlI zvQ@VfCad1-CxO!(kHKBHz9l}*9fU2VZ5eP2>MKj7>cAUFpYlt zb^g8xX@B$@?EXcadTFFL8mkP60Cnn_{s>Vn>U<3`iTO7_)vaI>`T2YMkR5NskR9K; ztaJIDLLii;Rmh|TPE84hDRtDX0Xg%d^Z7{ouvX{_+k72-Z;!V`s;|lvBAwg)QYH2f zbW<=$Y*bt`G+J6br_(@@F!Mmrq*@%8kh#|7VVIUr=zWnYa{Tcvw^4HEhbjHe2Z(@4 zE(KfmZ95;rZ8pP#)M`;D6W3L9`l>)P$HKGujF^3*Ne_I>-J?@vAL&( z3Il!hYUFp3a@4B&MZnTV-PS$IH5~Yqs73h=Tl90pppRWCIu1LWo5aAKE3Zr*0mgfF9Wfmwd5Ej0C7L-*$!vX*Mu99t>MX-Xbh*BNFrWY~(V#0whvl{RFZ zr@>{*3Nem#rovQlt3@?R6vc=$bNQBt&C9ieOV{CK3>PnE@{;Cr`4Pn>l8lU`DUN|# z@Z|%=k|Qc`oE1!z*ZC`B#gbthd!jXFGk%YvTtFQaOh{yQ=k3c1!42j4@M4k{bBUqL z;PiEqML*>4DvCK1ipyveV9dyH6?c^9^7-z(lq}VDxk$&0B_SvbsBX;Uf9WN@e4Y%) zQCFsx#Y@ev_?zNSsW<0G>Eh!eGk>`_H|O_-tYu_UjAWL~63MLpSw84XBv;nYizO2< z6+U04>T}uE<;~r)>f1`tMd3~*>9YcghDAg2f=0wnWb3nRg<{fW7Yazlk{;wZicC_< zX=d()=+gP}BN?R@75p`WYPxD@=#)D=;>n~{;OqJP%*c}3jMg)g&7@i?pYO{z`?{mg z<-?_}Xc_yn1Jy^+>z1$-2NqA`sazG9$`pl}P701m7;TFiPeT@#N2T`5Lgk$|z`5%3 z0+WIzJ&hZ{q?2^0vchmODl2p33m+d0__(%lahw3w+IotTz8b9- zcXp3UCig5`$J4vZ^2yWdt+_+wE`k;7$J2HEo-jKoE{j8h*tYJHYMpy~A2m zEkLcjf+UiR82Dgw3bGyfGZqi8{igJ1r=j*m} zcqhd4*W1jFSt$S((d64}1;L27o5b2J5 z!1Dgp@Cls~)mB6EsdFjeDbB$lZR1w+ni%DaCs{1d%J@jScVb7-xfx$%%-QWyCU@XN z*SR?BwAj5Rbk>$f{gUNb3fO*m z3lWNyD|DvBj-j=dlojZQ*kH;oKA*?6Zw&0JKlg}s%>6e}g6ksp&7i&sf`tph()iQb z?zou_0OO@;)!+E@-^lLYAoPFu-f!l|NJCHeA3FL!VDR@wbsWwAY@_>4NdG@@ke-$K zlL-D7GRP?}?gT^&w(^3A(WP^9<(gkn5qA5&BF1ur$J=uW_fs^B&XDpoFHnH;X?kj+ zgF$?ck;9Z8#aTjWh~6RJ0zoku-%Px9&51zHAtkrRf;7*)32_Z~VCC@kKBs`tjUgMG zw5z)i*4=p&IP@H@WfI<+=LI#0XNd%3a+xDn^3u%5SF2~ATaPZdh*0Pmn$sLX-^Nv^X(eetRaxim#s*t5*hYwZS45QZ{Ur!dgrQlI$cOPw)25|n6 zKJ~eLf+8Zd$zEXgp1*&PtKf_tumEQ}7Uha{mmckoYQ&X zmJNrLYBx4O689{mI6>g)5LuAqxY*LLNo&%FsN6d*Fsw$I_5auXbicXwf4urHa{eC# zPA%`K=kPx)UC7$b5{H`aU!0kemX@9ohmxL!8HbjEmGKiIvod_Dg!o&d5)IQwuC1Pa;0=HQ0KKI!GEhSM5`vVy zQL@(|t{L*15Ht^f(6*^f5nwA{egx`o`(AQ(xY=qr6ZSWPm?E-8eYotr)2YLYW8i9+ zX0v{UL2h3oh6y9$?W5lv0M<I4 z2LEBtDro)%YvnN(zWg{ zlL!_+3UNq;pAh4pWYra~(BPkkE{oT@K>5Ddx@23m^mgsna9DVTeeCgR$v5w7yPida z#AVR^U`6DbVR;BrSjW97){$41cy2MqPzeH8$9zs6i6oL3q*V4@6u>d&6F~& zRx8mQ{AP#1CNNt$WthrMiKjI%&`ghcuU`z|_BHF|VsDT&4jI=+$QXRJ)Dq*Oxa)Bq zKKg0!rwDl`YCU&aBBNV#SVaH1(!llv&|wVyyTV^j4zjzlJZgZQi%lK z$Lkl6z)I#n=;(h>j{l9EfZ?Cy1e9^&kp!Rd=fM@6YBKg|Y5XRhAyshVt>LDdsz?k6 zh!-|mnj*~Ps~d;W}Xw$n@HjT2tkz(Y+93KqoKm^6&XFp-oMV zU)=SU{O|#%dA(mwjk3xH+FMxF!|e5T+SA+Uf{9yFjd8eA;#Fb|wP^{Wa-VOo<-E#W z{vf0OT}|*;WJE{z-@_57TvH4XE#yc8cp?th^YPW_7>Luy5kRyDTot@BK_+oN;|m8# z%)M827F(!eo?ruJqE*F!dQJL%nYv{{mE$U<@FuafVbkh;{i^AT>|#}S=vqiMh4@}y z3b3`W1+WF&FbW^*RfRRfbyY|Q+2wODcz`aaG*l$qw%sDr{N!jd6f-MxaQb_p0JrIN zo&Q(jSh{>WF_!%8eqPLrdzu3*{bc_pve_;u*x>J(AY@mYw24rs(5{zQDnE!ANirhb z2A1a|0SR7S9^;dZ84DG7f1L091x@Nf1;R+7(2ZhUVK@{R5RwduP0 zs_bwfXpK1^EYPXsV^YnrdQn9V59cMezEfo-m-;@js)74b%SGVW@GTCxKF_!`l}h0j znp|=atVQ}p|vS3bWn~<|D>|h~! zAU8)q3|CCrBsj!FREJyhW<(cv6Cbh=L{VZ6qZ)S;J9Z+oR8o$=$8u0iBS7B_A1Ni- z$zT^_tz?sEd=*Kf*$bfJTXYj&K=**H3QbL-TYSc<*WaSg5Pa}sF~Hr$e$X?LMdyV* zhf`(crOdfL`inj;2|2o@PrX}2*m7;zL*3wmS6;?P+5P^8vu!z2r2?zIQr$6;&e9eU zJiuXb$INmLU(W@TPPsN9Kxk`oL;|28uX z)3ObfvbYK^t?K)bW~T+ssmKhn+=t%ux;3vGmX~}zC2MFgfjd5@_nc{lz}x+q-FY+bh@*`$MH%vNHS1$1+4HtZOzd14z1p{|z@c>`b?OM;&(`d1c9o3m{{^STGd>rd;W_Gg%dn?as95X-Si|X<2-~&PI`y9&c?OJ22i2&V?UsRClfCaNM5v0_ zg0*gLW-Yl5jF=8f86A-RE_T5T-&|#`k|PQM0#tLva8I`!MF?|Ho2=W2rIIm3vgdM* zSq>1me+VL?Cdg&tUl?)SFmjd(b^s|F%~+kROr0&q#KVWVBy+6UiB*diZoi4okHN1V z*E~=0O_t@3=^_wvIqgSL)Y9;qS7N&+RPogqVZ~9c`~>6EFHR2Ryx}kq_tMdrYl_)@ z=+Ri+Tjm?RF||_cwZ69@Xj5*!ccC;Iiy5XyjlMVgjY>v{yYcrT;$P+qVT=w9hM--g zBWvHtqB-s-JK)88WK{&~FiSaq^Ix_pR(UG-raDYA5(-!v!h5c`e`d5-7Ci?##N;nn7BPl)+>;fqZ-y;FRf$7l^ zrP+B0Ci*mfodGx)z|O6{eBdj`7kgLihknD}GQUpLZL+Kbiz`G-DqHY*-=skNw*bR! z7CX7u%}W3peV7nhL((sVrJFtErmcpLKEBnu>i1+a*1bpz`rjk$XTQy(Y~WofODU^{ za=BETLmAW)m2+PbsI9Lz8VOzoXh6oG$kIBKkm+0_ZNm*-{a}>UopGln=QGDog}6hK zjm!+Ekb+R#FV*5$9ML1FrEtbWnQ3ih`BpAEOlGuNO=Qg6E7P&sdnd6fH)vujuQZpE zzYsd3l&i#D^-4uLiQDkqeij~LiWA>I&1+;cG&#jHzFLQ}9M*uBZU+4MITk)*nF?4} z6Zy>_Au|t$1v>@74v;$^D=BE6;IL_1gF%FdYIncbR}FHU*zKgQ^wp~G`-H7J5IZUW z6mQF+K?Qkm?R1w}vg;g;I+g<4u&{B@&$Q38%9q4R{37{|{iUGyNXT7e#RN-`$HV;T zXAp&LHZ#aW|1wjkfPT=U@AsT)NRBJ@6W3HDdvmwrmC~(~`}g~CtNP~{L~jP6HrVUZ zAxsiCquA#{$;%iBC3CqC$gG2pn+xQ{@4bnC5Y^v?xJ(Qz|43Bj=CojG&Pb|!kA%c0 zMYp$K+g`G?R&te#4N|`V=#P(>k6zsXx`a=VXWOz~Rr!X|3op8KjMMAEq|si3&3BPT z_`HB7M)3?T;Y0hu_+U+7u$of<;RHaed&Kq&3rV1eq`ik5f1^OV>gBYZ=uZvRiF}fb z^**u~qMPkkzx|_%C=3gx`b&r(aRC!-!L8Y8CQ-R{2^_j5&#J!O>MO{ z>1NY{e2s3k^t?Z&*YGn>tWKahu`i856;6Q0=;a!oKqX@ko_DYM=#?RW^uStJCUwXU=X7hIOpnuz!iC4-dvixB07vnd#au#H$*bq=wR-~U@I9@sRSR>0DMUQ$I z1&p+DDU-0ZVPct%AXS=dDo}M5XvKTHdHA07%#c{}+WdNEjcbsjQ3i7c{dzV60WS$I>bxL!Z|iYP#(U#-=o8`f5vG2fi6AF+f#qVBEg-9&q_ zBZ{DKC+b;&_{SWolT(3{JalTgZVg>J-cO1l9hPCt2A}iB7{+ zZ$d?afyUiXEKqH<2tHc z=7$$RK{vYbAD)K4&ASY&9M#MOXz##9Sp2l9ctnYG`y{Ez(rr2+jCFE$t@2mbQqP`tcd3|(?@<*WBGJZr zSE_tJ(BeYjId`1Ilb-eB37PBalDgub#q)*f*&35*jHUm3uePa9>=#g z#aZ)I3SbhL=;U1zv#jSc)MIs7wP-4Z%6AyFSkjJ~N7vMuy!4uiUBg~l-q=Q=8z96Z< zDHHY%vATnK<><)L_68t5EX??WPX0P(r=k7tQBzW0)asK@v`moS`U1{xpK$_&VoiT& zi%&yO^7HYvuw3t~2rXX#rLW;xLWwtM-n-eQHB{nBpyRr5)`Uo;!gW~AG$)-AjW~uw zlZ&$Y10tnE$^0CRVTtgC`Un=!e$cVU9w9P+<}>(7-EYgVNu72a`}D(5eZOxe#)6Q- zbco}U57~vtiI=HGLA_Yw$3W8J3(e@Ym)q%w#JU&#y7XC(S-#nKw}XA6Ib0k!W#`c? z&|jFGR{Hd)z}aMWcy)~ougTOhX5#r+%9Qn>toAmU*{KCowS9@BmWycZKddSd$#{$C z&LtIm9XfIa>=>9vtH!^uGQt*|OVT93#Aaml*${-fu*o|v}q`H!)~j8etBpyN17S zES;;|f9++{WNPl}zJW$KfaV%Vu#B8}be+CQs8BUeLu|Fk95FJ)LfG|^j$oV7rLv!< zYFJNJ!a^DLPV0@~YO*R!%+e4dQxoc~4O|)2Bp5tgb_phuAex!|6=ER4M`k``?$-?s z`H%#w?-&leWY*XK229KCCvB~=knV}pdzUvGGF1Zt{&G%F;lFS_*V_|)Ijpa#Xa$Hut9ywSl;mmo&0S|PS5lY!iS)|sNLsSvE_l>#ZT4Z^g%+A zENJMXg10*^J-0rBhliWVaBxCihw5UW%@)gXgd&^Ourtnb$+Gu+|KM@kfkA79Ba$c8zRg8C*LAafZOC2pRF-iI(LeWvPjmYmt_AOoM~K@u@gx@p&dgfl0XPb$m~E( z|16=yTMaPS@xp$yGW9!UX;_GZ%cPRYoOs~!fir&V`Nb?G{Hqj^L-Lj{Ux`cF`-r1> zY07b3WGYD^;NLQ!4zSWFDe#G&2gHg+MeQNXqRfkuRVD;*^It(IjMxXvs` zU6f$hl(8KmwfTz^h+ea_CLV6?Akwb0#{S^$zfH60{?VkY47(Kw4&(}J0JThrhmTDr zv7)BiN1c-lC}La`rtxa8*lQ%CEljJ90HjsO1S|`vE8JFNBuigyAD>%yar6+@IEk0g zB7ao|=4omgeNuu&EDsjGaoZvuUp?e;BgJSyU2k0qb8qV-UfDrdd_@q27}P|!@hjxc z#G0w_;JohBb&+F^M2$xHBC-8a7w=`SZ z2*iX55EQD3y>%8)+cX>-h1K2m@$cGt$M6L@Sg(Y(x!gMZ7=*?l*ofF)=A*Ak>r$6m|~c2%8UOyB_ww{zgjNdAWRb&bEO)@{!*KFAw%domBaj2EE z%ZvY5=i=ny`3a~2z<2Zyp8M;xl>Q&on=s0WTj|q6u5`#B0)mug8D zu_3cuUq0 zW>sN5^oPRlAoM+IGf`)J#0AqfAc^brh+f#Y!Nh3Ni|$S-aeCuu4I^=Q5ZXPW&mvxc zcaCE~Dq&Z22h43EIFAX_Kbn>f2U)|Jh;XK)J=6;roITnck&-$)*3P8}n{Q%lc$m7TdgpJ=QbL#%(arNu;1*6TusdF)N|8mlU_K3X3 z`^sT>a~p23kv-~Z;LM`%hzqU1n zOmXWs4yA&8G@6{iaW~4xqF4Tw96uo`v-3b;gIS- zIQDPTQF{7+@JKR#V(`y5p1^}AI2G)WPsf2N=`hj5#y(Oeh0#xdq%bmW<0X{)mrLhJ zLuTw>uv}-J@um7X>PE{ci5_iH2lZGz9@rW~$WT((w;Ri*R^{-O&hs~)z3);?ve zRqPqVdP>Y07~vLP$s!=VImH(N8qENsoS`+H4K6W+BkZ@1FQAX)5d(m_=3H-b7{pqp z4fz#<)}ESQA2)l7Y95kR)3}k|AiB0(+Oo>|{%ht(0lA4gLJl7f#O??@6H|K<%nyij^1u!gp*Vr6kEtJL0l_|L zG-#_p*mQs}beWgGz|t;L`VIrB>8dx=mN0w8`jLr6=0QzY z8SB5sbbYTIwbc`&7q~JgEXjEpnVlwA_ZH3=BWS!_!(nwKSL_LZP$QK)fEs5Z)<;TS zSKYLdQNGc^Y*7UYd$y~ugd3)WB2#>5B6G^Qegd*XnpDi>Igy(mbL2|KQeCD?A-Yth zPPHrO!*!$5mrE*EqAYlLFKONQ6}J$h19NI*h9{85ssFlrw+CGP^-wl4_|awDvR5qToZ&e3iuV zc{!^X(vg}P>mQsP6P5Mf+`>4_4FZWQJt>3mOY)w?XS&Oo`~k|5r5josrw0!6j4HUA zYZkb>s&{y$7HqS*nY@45$ZtHm=qH%FTg9^*3gWFIG|~d1DkGT_N`sZBU_0p^1u=QC z$qam}Plx+0(MMZ?#C9Y)0vN4TSmGOd0!&Wxuv{UDK)oSliC=tm>L%*=QMv2YOw^jD zBw}7Qzuj80GZNIGgUYE2pxEiW^`=Zfs?L;*g$fk6mMk~PIhd$l=#Hv~;L7Qq0ao*- zFl|mV;|!l*f`2sl-7lypn599B3Cn5->Jl9;siwFHrp zn`ycYv2)+m7W5|)y9qBOrQM`J%=ypH=xHYR z)xLKUmI*nkvlJAD6@zkkLOCAUcw2vE4|#PBiAVBZ9zM?zD3o=$kJ#4Ep^|r@-<C-jdizWmG}KUcJ9fT2wtF z?5)iS%|N8)K9bY-9)zl@u?4y))Ap;mF0pWifskNbujF!yuga@yc9sU*qYCv&d!#3( zjL8r@W}C)c7F+&ZO^G*@#GvV_N>xy-rmH~|vKi+` zrSvBHy;wi4>5^8g&NWPXZ(AwhnOZK9>b1I*f3M%-WF+LCn9p2Rjy?R>XW93MGwAbDkw+vUF; z;4Hb3;sCB=fLXAIS4j)&*gyr(;%jS=GL%xZ=&#E}MLHA;TL7Hvm~H#@L#_@J1wT_8wYAxM=a&nBK*hNxZAEJS&b zDuhd}UvZ>AemAbXPOfYwAsINU{gbU%dk`Kx=lbzHJ`p0VHegTO=R z(;3hCA97Ox3Cfu$mVjndZa!ei0`sz!x*7Hk#G)ohRmqHb6MM8;7ObQmQi)H~_G7Hk z8$7-~R&h9_&~!$@qsk2QOe~We&hqvZ{+6>NV^3esY8B(`ONe&togvYwTU?~!3E1dX zM(U2aOz(>ebNo1&MLtN^!Oh3LWrPw(p{ zaly1mB&h=ghLv{El~D7b2=Mi)W+*R5@S1oEI>g#tN{}kRgE-#_3M$h`E3BL2WINqc zV%ib2{sQFI^E<;F1%BO7N&IS6AgG{6%I1$Z9u2hj8rzNh)aLaL6athr{09~MZ3;+F z{}1MAKL;_92A}h^zl-KyuUVx<%d}=!z1bIYkqOnKzVw&tlF~-KqT_Jwv2Tkl#?*iv zM`ZoF2vcR)HyALEo{PSyRPmPZm#9F_KNP)kr!~INqiWWC9qm624bwEiv3Dp)R=X1K zEq}kpBqad%#0@#PJAG}-{%4olr-*PP+d6OxaP|`{`y7)@xwq5U9Q_;?8 zX59jRAueSukWe0NE0x%6WSiEfWEHh1Ep0#^{zK1FqklNwaSAex9HVu0t+;T3;)k=X z*BSBcGoCj5zT0q5ruwtwWpL6?dmFISJVDpo%~K~pWqyb~L3j0>79Y+S=PfIH;2AZ@ zjDWTPp=z5m@^})(2WuS=r0a=hwrm9=Qk_^^qA6+Z7p3Jiq{D9aP1D;a<)hW1;Xbuv z=3Sg`t&5m$R{59jFIiXYpDv{xk8b#bOaC^oW1#y7!3zjH?W&P5 z+}-rSw1(rpV`Z-6vd!IWFt`CE8~30;k0!PzUuhe-7{6d&uQH55mbRf12)(|e&5fr%Ak{X*@~gTvH5Z zON649yXdAH&p!7rnAF;*^IXm}F`z^;(2)U=M#yLe$d5pXzOiALk~KTfO95_GVW>%( z3f$uJgmrovPUc4i#?5*S)yA|`Jzy=TcJ~fM6LR+2PJ3ih`^H)`D|zF?nZXNp1RHQj z`zVUGrY2{QUiKGsKUIlqDyr4{0pm}v^vd-HZu=sQZb5~gJiXolR;N1X{~(#a4gDBC ze{c0)M@EeDEw=o$kSClV!mu5&`&XJ&0X5#}J|5*?xI+klyQ#%huO9%DM-KM5RD?Ll zSQ^DO_THL>J;JDx8lfDo5_)alfQ+_Dn$%yj9|2ul3PPx1YoXXb z|HF(YEPf`n5p8k_`J%pOTJJj@X1?=-2B*OKyrdy+Gw#zOPVNoxMP&K<6Ag^zzDoxy z1DeehuWyl?4*xIreH<;{II|W&Ly$=xZx1JxpERJ{lRybMnvaJYSkUAjsg-v^sJGUm z_g-9y06RrM+naV1)yEsK&2EyBguM9$Vph;=WEQ>=BvuT|g#bMmS|$)OzbZk9O>{%C zPJ>%Gv(1-rDkqdF13;sn{_WBMn2B!(BBANR9*?010S5uBJwkwWfx*tqw7 zD-j4F*fiu%^`P$5I3Ic2tnJPFf6DvvaH_iR@i$UjQp!-Ku3mGv^JGXeMCQ4qWVj^r ztPGVx$UHaDWX@P#c3VF0Q@ZNnJ9iud|ryE}7 zdNj}c{Irb74}$sJ+mkKf?Ef%*7(+^_xJ6}WRorc@GCPL7R5T249QS?b;Zpe>Gip#9 z-*fHqrUK5?_vt}6mF7xf6?0Lofqse&RY&7s$C2}SoQx+MPUk5z&Ivi`O^HS{$#6_j z+hvbUF|a0*Xe(oCYRs?Gb$q;fO?^-D)@UY6c446>p@J;Fu1#g1U-%eo;HoxomwCz@ ztaGP8Bt3<7rf;X{|6s#N@ zFWdl^@KwEJ@cANlB7S)Ce8BxSZOcY{EV=V|E7>SCl)Setqjv0iBDrJ-8>&F<-S=Qq zCO!sOF{<_nF#h~h9XaPUAMm7<9sE2Lz@cUSx|{XHKRFr^zSU|Su$p%ToPN6oa=&8P z`@-YxCOKXG`W4c4>0R(n>=ZumOsCiN&T!i+@5WDi?r*%v%+~TKmA!O>?FA1;cU{5$ zFp2P*?CVcB;LT&(Q%$G08wc`@m%PqvIaOceUn?pn0pD32%u(?S9TmB|UnPNx3CT)u z-M`o5S_xbho)IsYE6>Nes}V_Gu;E@7$KP&ao}LZ+JuMjTM(ypzwz!?Z)XN3un>3%F zcDTdNzoB|3S;UfIM}~Fy(9tQ+SXGV5oEQ#zKXK1!mXhOLab1VMOAQw5(u+cO;Xz2sp2C1YPXx!2#oQesa2ST)rbf+)vdFqhNMH? z6rD>CYLmD?b73pD(%0>;e9S33uJLup z=&*ZW16$OQ%{ga91?Tryc{@Jd;S*I4uXARv8QIZ8E!|&Ov@b5`V8A*ISsp=LS_n+~ zgVh@Hz~JSQq3xU$dQhfF_SSVtV3R*$Hc}d_Y89n=A@w=l&!8qIsWY}bufU~n?rAv; zHj!CTJS;YTZ%c~kt>JO_t6Sx7YkJGhohb4o$w(eH>b*{_QI(Ess?@mke=ui-;NNk9~_X zO7;I<&mBBk6)<$0jhrrL6=u1`Shqu;;j*VwwSFIxz)vkO*BiCBjxQrgA8#7m`DJ2! z_Vh?--6261t4?zx&Ki{e-7rGm+3UNEhThatg}kCxRK4bF&c`&uu`OLaCY|?TUp3lV zWpjK;s{$`9R5Uq0^nR9aLBUHJw%3(YH#WrvJqmhD7gp}y(~q5>n}MICb93n#m^o|6 zrQ}dBN_ekhFT4lge#r4EQSx5h+4(#UQUrf16c5H2%BTaekGe`1gTSPipn@S&_n#nzI z+GgO@1ZMMWZH)H@r}wlUEG|a$z1u4~Ke?5u8OOg)pO#0t7W;(7{=xUzxJYG2&R=gJ z#s(#K$RFW_UO`A5sb$_WpiMS3ZO>t!_`7SMsGv13@yFY-{)xii_j+V+0nH$$iPLz$ ztr8q@u6E^v4nN4Jhu-4U`;W=tzx`5b)olGgfvxu%ubx^XaTY zhgSLxoDV(VyDN7dBj-sVdLP)a%hE7AJa5%Un^@Nv7I4|=r7C4LzM?fvbv#P1KE}aY z4_Cg7Y0^4nm@mPa&tZwm6BQs7Vo5O$SJ-q9XuqnFPiGHk$dr;5h&zD070uQCI#^4Q z_=)4ltAAcvwTuk=7L0VKdc4TCxNt?jr|~26>(_<(Cz$iujgy5Br)9?VZtuIZ$6gng z`>I4GM)CV;{*L>V2ixpKR6iTIZ`x72MJsbuAY1(DfJSCZ-$l3Gb^J6{d$SPZhp*44 zx3wONijLWCC;E>{L;i!L6OnPrNw<#y{c%W{!P7v8@Z0XYl^PPW z49^~YSE{$kiWvD!6M$~r|u*5`uevMrrfsd@9$p}S6WLRfr$=>apj~ZzJm#_2 z)q`{IT9~uFyIE?(OXq8vy}U~fsytqY7t5m@OOFKl4?aV|0k0MWDkv+b-qNC!h9q0w zeSYdSZzKJ^CCCnGYJRIa-%KgaGyYItcyZxuBU?Cabm{wRbpj6yG{Q>y<~oSGv}A?? zBRXmVtlr88c3;zs**7tlH_kAb)XryXmS;iwCUGh3ja>0gZ$$jzXR{lP4rwtR-%b#h zwUFClR{G=cRc^`K`?}+qca>?mR8B^{kb2 z5z8%qVbwI-QypxZk2&XWaoiX48XkXXP`j}s&V~PR2;!(T$5C8B=Q*6FZ?9enA;U<< zkY+of(WT=nl?m=mYVnX|rg8b%R2rIrp?K>3;?F+bqO%}+shaWlRgAb@^1xU5jc}l! zk=|ODa7*mDKPWQS?+lhe6!R%mlr^&&>J+35=f$g%aVF0v)P(1vJh?k&zEPJ?u!Uwx zNu%m)>q5R+eU-fYT13V&=!d7s*NSg;%>}e%bKtp-Q8a!^?aRPFZbaWdyPi@L9cNdG9h67)gxfWpWx-F zRQr;!D{~@mzEn(2A_abO!w#z_IfUb!984kcexXLU1i!Yy1MS%rwmh&m@AaBO_c1nb zKBLLgAZx{$;GZa|sN379*a{g3kf`3d`1rWpV6MOV0d_lKQZV?y<=GVb?-56xLpZ9b z6L$RlsYue+e^+{PdDl$hO?t0{8_YUA6JDq<^b?Uud=+W+Qgm~hw(Cw04FARTAhXz@ zPm)d?FB@M>FHxkX898*5CAOmY{;fk%ZrW3yWHY;_?@iB4gE;2~^*1WmwcNok_gR;? z^Cw@*0PaAq<-vEXv**K2Xv2yu($iyq%o{|$POyGWb-_!NLGE3_Lu0GkCU1kZU(v0dwxU&b;L`9>Rppe4Z$V<+Ck5{8I(A5m#IDrU zSYs{H)mg^pn{4;UhfzDMuz(J&QY*>U9N z$GI1qY5L`lhlSHoAB^>Gp0+T-zLy(3C{Ek`X`Gs0oqwHAsPOB#DSs5R441K$;{huV zuRUekDTGowH?D&FXmV!WhD+g}>K`Y1Cwb~lR6V^A(jH*oi`Z=tK>ogKrYmXEKJ=`9 zYKMJ~)>9=NTS^AT zj*3^Sbt!uv_mMo)S(a3C?+O2>O&>;l@J=7CuU2l1oV0rp3R5fZcnD)T*?8Vh%zc;O zPFTnweBXY4NxYB!D`a$_cCphXYs3CLt#+|vb;b_Y#gGFoBd!=*KAY_*gsjPiHnfQ0$={iFKmEO*8cL7(zh0w$Mcpip9M$J zCgi?zyzxB87&FXed#@lfe>k?3sAM#uUw_&7Q!YE1+c-bO>v&0xUb1|accdIyI{R8% zjq081FOpp%d#+d2m`C)J+FEa4DiY%96z0Av_9>7-Qaw*(IL9+9sXk%SjL+48wPz@j zs)+Z%^DEvq$86lq6w|Y0`gD$xZ@Z-r^4=meN6!`Y)=u;~OcPl@b*e}0+{W_>cKEYV zx7{Y;mndnqVrE|AzW=Z4EbU=DI~Ge$SmI3p|cWnKI)|c|>$3)iloS&k9n) zE4cg>y7y67RuYf6$2Y}r2Fd0Ae<|I}S?7aK{CY0XAH?G@c)T9+TsgCB78WRIYV~y_Z9PYX_y^$w`AF3) z4iv)$Xhrb0vgzE)ed>Y!VjVMfGn$MI@_T~&nS=r{l{v;N6O4sdLKkFh?Ak-=U%qa&Cs;cuiRyzZ*l1+N2E>v{y6W0$Mn#URPA1o?JD7f((7W5s=XNb}|L~J7kLn71UpB^@CknRb?s6Vb2`n85OXJRH)$_>q zn$vTCX6oFWK1yfXKvQJj;~v3k{oVPXZNrSxvyqL1qc$~vwJ2KNUqop2E^T)?+ga-)Gh+bUX>CEpJYmJ{bQ&gMU7JAdQ+ zt-uny$C=;KI`GeP5T*6=*_ZtzN;2Q<`NzfOu*#nNUO!0Vl`Oe8alVcXTM+UOr z4-Y2J3EMrbap>ZIXQrHfV*}b@=Ydi7PjN}bzKUO;VpvY?Gdw2AmjBALUsL!m-o1=% zV_NKA_QVoh&_0d#LLV6zPH6VIR|yFkBnM7qP0?sRe8T;4qjU2Mp3K06fDSs%G<|LV z&qFm^Zd|&BI^7?6bttqXd`6D?>4A#Odt2uQn_qX$GaB@s{JxRv3)cad*8|^PZ%H}Q zt=j2f_-I{1eO*c7D=#DIw#nUzZkGhs8LxHRsXu$~8!iEiDF)dYDawQGQGZV+jDON@ z+)Uuve!o;6A>py`z!c)y`T!nnnpE3+jGMsy6Tlg{8{o$cfcQ{(T+zI5P0Xnw^!;)MvvjF)p!r4_GYD7(FWVObr@{`H-P`gdChR9L8N(l~E~YdZs#h&b+JQO_lla zfo*vgSzCCdw5kJxgDa$JivvtK!|w;Nlriq>hDd+vDQXc}UAeZiuj* zcR1&2TbikP*o(hXR4Sf}Xc!yKNNUj70fu6@cHS>jaN`l9F3%CLrm|@7;aaef2Y=9RQh_8 zN8{R20sidj*D{ohKZpayPUMBNgR%;T<+b+8s_JT!9WBj8wd`z+ZJ~&Oqm#4jNn=Mq z0HI>Mc#g*5U`k!3r~|CgjgEu<;_)Kn;+12qZaz3_g$*NZuBt=Yi4^{Yr8rlv`*4SQgp(98b;_%WbNa_UAO<9`C*jiYV zLE{nH&SV=s=tR}nozg2f3Wdin&-k?DmL7!@fD`tAaKQe5X+SCp4Tb>+USSZyIrxP^ zzXSq-0>==EaOi;U6966>z?YYS;Qw-fon;s9L*<})AR0>tmVlW2oO(rF5u!YqinM6f&v!e1&w5uc@op^zxR7Etmh1#k{g zg$}4RIDWDngNk_5HS>V zfIkY8plTti5W-RksJ%-}2QUhyB?3!$lwU{+76)ndF9`|g0tQhe0+Nb_0|rUN@`C^j zc1l@D64o4x61PqrK$?EC>Zc$qlW3^!6>VMY--0wR2m{o2N?9;9tV?4_$RMFuen5f& zU4!3W8!az)+tuKdeV4)1vY>&%BSD#H7E}-p@>#*X1x<>BJQi?8(Y$~vxPUDvV;p2i zf;$w|i-W8bZ~^o-oDvcQ=f4y`FfS>0DUb#I55$B*YEkn8zEdz3v^pio04P9;9z3@T zcPJAI#eo8M7G?$T;+#SRnkaxGq~KBJ4_sRRj#`u!Rj|`D6h8(crGOGOwQ;d^v=KEnx3mQ61|C!L4i2>VZ_44}z_?hyoj`j5_4}K0K*9fxF9guP-|+=#O`z7-ua5}S`l125 zW*mq>`~8kDL<~qX`I~lvc^msXz7U~l_IKsLH2FKe02>5YpzHI6NP>e!2kZBb1m?Nl z@r4BX@H@VMxe`nm>(>W%zQ?WBOqAzprJ4G__K73HMTsDpClSihxfEr_pnxp>k4^P$ z!ekgP!WrZzrQ{Kxm6YYlpPH;5#H;kq6;eCwS9ybND3SEm{1Ne4Evf7~FP*a5y}mQA z!kQkbT;4g*k|$VsLsPW1-i4U@vccZeIVhJoxw&=7{r1%l`0hyQ>6{}x3eud6*%v)n zW%&(-P@DXFz#^6Md8UNp&1Ji91$&zs$j+_xvnZHkZ>BIY-t7sSb%o~O(2vc%q}2`Kru)cEnV<=?=Q~Df<($tC`k~+ zd+e?4oUJWQ;I0@k6jF>J0zc{OZ0{tFK-m1eCuZkpAp}E!z>k@WDM-w`yqLY2Io!n9 z)CyFyL;|2vDzLP(m4nh*3(AS3k!U;;hr%LJC>$EU7l{!-A_at&*_ZB)WOEpBxJWSI zK`#CQryK|D7IXMd7-TC#e{kDH7zTv_#?(p}nuv!iniY5e1~Fh2j09Ozt6`95vl<36 zTd#zHE6B-Mfrmt5AS+}g42LCvL0JhyqCxOs6%2=kX8#pHsRL|E0Xa}B=>d;=bscB|2C`gN;Gqd9C{VT%hF_1T7-0OZ#6#k+AXc#&MuJ?s z74(pJ$n{+fBLV|;6^wv|thSY9iAcy2Uj@TM35Qm~KrCks?@2_+l3js^0tt#%*9(%* zK-Ta|JQNPHx>v%`m^C~_0Rdc72L=nm4XesxNRTtVk{;;pYM!F7M94#0i3gsnHF!8M zn5*&dXb@XkRThtjyrY%$2@yV+z!2TcgBcFNz literal 0 HcmV?d00001 diff --git a/stepushovgs/data-structures/source/docs/img/search.pdf b/stepushovgs/data-structures/source/docs/img/search.pdf new file mode 100644 index 0000000000000000000000000000000000000000..34e6c9bed2a9836cdf1fe4c798d13486253bcd09 GIT binary patch literal 32716 zcmb@t18^tZ7PmXmBoo`V&50(qZQHhOTNB&%#I|kQPQJ{+{m#ATtvYYjok~~z)7-0j z{r2j$pS_w$nqQELj+zmisB9Itv;~|FmloGb*A$$C1D8hL#o7RuhEK;{$K1*YmquF0 z$iNPl{__A?TrMtf155qi2eSUh34&IZ_P7lHIDkf3O4rmt&mNcMpQ{4S_Cj*@I`#&* zjQ`vqtz&O*U~7rX^m`SYM$TAA-^9`gm-%0NZLRd=4D4~$KAYti{4~YD*&dfh#NyKg z-hch_{p(i*SK~j-q5JO}_-*%Z>un7_t^23>G_nSERt~m$pZD|I{?GO$4D?NOc&(g2 zTcrK`V`HPErl)0QWnsc)q^GB5qW%4!#^=NMyu;7S*x|DM(@q{sORLW{y5Hyg=TrH; z``?dI+`!Vv-WZqeUrh^|n19|lE{&l1=hNUf(6iDv_-((Py{&k~1V{#6(#-l(!p0 zG9E(H`F2I=a|jC_pH+6gWnFN4zKpe2PINxM9i82-40L97KY_kJ+20@FTpk?Qv3B-k zNs&ZtaAm1NP(RDE?JR)4nT1Xn&~S79EPp?vDV%XCY}Dcz+~~SJwbQ=%LV88 z=q{R|L7kt=WlWxM$lDrC@-6kd{q2qmk9%lezW;o7bMyqahuizajn;%!XDCVrhv4H3 z$?L^u!%w`d9ygY+X){sYyVqiOs7i(<0ixeQEh~6heXkN--yfe|?>F9_7Q8Uh!oH^O zT;llD%;7fyW2s$b?~e6b@%9w~2E^MfBac;Bn^))c8mKi4^fst-)DNbWUxgJ;n^Neg z!GTxQE1QwwolE=IKqT)W*G93^`rL}w%lh5RFnM{odU16yPliy0cvLXOmbs`97mm=o z+&XQvshL_)XNKoTXo*2RUgVf8s%ST1)2ttpQ`RDqhhp}vq%le z038wqRjs9iLkfuXDS54=n{`id_#?_)dPZHIk~*U{NgD^9p1PBLa|A3Z-49xTA5slH z*nm3kX?1;n0>+rMgaA3Ob*gF%n67(B3trC7@Z=_Cs92Da!`T)Is3EIpcE8Intn}%d zSRi^4#MN=6i!x)bxv3898qDl?w|0G5v4*yPzg*jV;eLI(4Z5LurJ49}c)x6&vEpju zkj9H0nIq_*b=@}B6tDRXs*fCnt97U=V!4HnnK53VW3gy!Fx&-i*m;OzWW{rLF#~<5D{8rf zPpL7{tA$|eaPP~hVhf2X6}!LJrOMhS@v%-FGe||wTn+`{nQqAE&JF@q{<->5IwwOx znW&t4Dr+bzUiDwtXTcN~wQ75r#&d@pcu2>i;W^lXW^$Y4s_IxuCPKn|PT74<{1gJa zk(q4&_snwtyq|`3e6FewOXaRob)pUcCIW~5_f(>0!RbiY%yZsSle)TZgtkQZrbDq% z8dt5sJTo}i4KOak2nAV$1dkTZH&-`aA9sZlR`0iK4;us!c)X$3o}ItoeJS$m(bX=e zbSu~O2WEZRg|QPT@4J!N6}@jW3Gm+N+@z4%^Ru0{zc9F{KiQ+bfGCEsr>>6GCq@VE z#fm#JvroWB6)abkEX`siu4*R<^b0%3h}5!Knd1)#uX>cQ$#tKio}PWTqUrD>nc7*B z^{V+E^KiCl;Pco#T0CNtI#Q!k8_r;h9oaG;D|Yb4i7+Z+@9iMM)wOr$O!h4E>w64> zZaY6h4J;hE81rN*Wv#>Lc?xSW05coskE(HoQQ~Kv(K_J(6_TlbNFQqSgj8H=b8W{7 ze{Fayh*ns4itGXLsVLp~kaXG;qSRzOBI`Z^zVi6j`SmuVpQV=0MNNwEzGc`90^Ff0 zB(~jUNyfT@h#JGTZZTmS%W3s6BK+eRF{reW`$56})pK$MD#mdF$l3{t+m)zPX&IU2vQzS+IbNzszcs7@Xl8foTTM zYlhZGCq>3F$;&8}$P2C$G7r0E_TZ0fi!Xy13U0!+zttW{d2wlt=Xa{mRjKYb2Bsn6D~}{S-PATv6;FQ@tlPOeOlI+(TJ^JTv@$kL-5KzWDO`Q_qSM zxpaPyjvX_$4Rn*Yi+NO?@;5(F{vbX%m0qE9Jdmwb)1Mzzjk4OD&38Fu9YM^m<%SjoNc(!|noErZVAs65K zJCh@gCDfC(qU4lmOWCcpp0mRYPS?H$t5C|`M4j=ks<8n+I6B&P{JN03wp@b1{HRFo zY@@n^I=DuQFl>cUdP-jeD$w0@n;q@9vWmb;MHr`em#U*`-%G=Y^s+rUOcY8>Wh5CV z)_yKqNSnS|9OGPpPM6?=mlT9!l0mOY@s9Kf%58X&MFPc*NTEJxD zpS|SRqt$STy*rhwYH}41@)UC~&roZ4z8Xj;B-GtMjn1OBm|dMfonpFA_3T#;%NdrN z9nz33c{Q_|ORZB0wF2c2$`SLL2Ol59Bq|=anYjQLG3qWVf`aRN6Bj7tmuRP|whG8D zMXr?7#VDi?0hbzAb(j8LkW)_noh7bV;+*Jk<;636 zK!gopIZCOh_SSn57I)W+?TIy^2P^o}tdoP=V-_NWW{P(u6&q%FRP^&N_1oqa ziFJeg@THQ;s=?Pqf-I?tJ?7eqvx~_zRbg5qIF)sqt47N_5XD53AzSjkP1M{MbWufa z(o%fYI;EUlb?TWT?NV>-HAG$(zb74lR=aOVWd;osTqJYuwR?dNiOc|X+$F#~<;Y9k zPyOn6!En0Yf$3~`wkO}9yW7x5A4IK`I|zVAQJcb7IhXFxEo-GfEoiZ3HQ3J0`91aJ zXvXEJ$ue8Rh{nT??{PzI1Vdpd6ZhamIZ1;MJO^`ZA*C2{y9u_!I2R1vIzb>mV zhy*BP0=fgnKPE@0Y1M4w#=SXDP;jC&3-Ettsm=29ZsTM2nrl z05K;?SfOBE@XR8T4X?-u=9T;!#bH9h3}rE1#LScPx|5m!?JgN0m*Gh$+WL?kVA*qO zn}>db=}FZVx}HrXW{N;uyC^B1le9T^ewCY${t9c41wQrqQ>Z@j5;*&QG4l&jNY*$e zuhYIYClih&R?R9v{F4haJ$$oZdWI2230H$6J~ooeP|TYwO3+E0q=zI|f}Or#W^zH} zRn6v5kSe+PcD}l|VhIGjyJqlq9m2ubzOeCb3n2k{#~fQ(UE(lhkpk;WUHLbCp|XDI z+^CgM`6Qrezu4vSKwSk#fr=FEK*E9zWN3Z@-!C|3+nl+G`2=tlJ5W<>(!I+S@(L#p zQfg#j*&lJG9fo2i*{2j1iEM9KjmpY76y+P3X7Bkym%S!hq1u_6u*_-qT~I-)Tdwq4 zO7@zSQrq5_;3Cy}hgZ6kg0~Z*(C`%E1o& zousHY?g%hb+cZrmhyqK`RKmQ^vCb#{wqKSkCIiz`PiBo& z5ueeP=OyXzd8xTxrHRsJ#5-E`G(e@T5-6@qmpvVtZ%PR@=gc$*va>xPGiiVenKRGnuydDlCvi#|IZ2Acz|w-KA%m4w1S@BXrtS!LR14@B~UIj#Zar>UGRq^jXm*BT|B3+tP6Umyp8w&?8w>sg`=wt@M@u9FG z!}ze+gZYrPZw$&PpjODXr#y1EJw*=mNapS07%Wfp-%i10AlUMA`*a@wD~7g2wNo{K zNuO;t>%^hdN$fzH$Z5Xst8ELDXqxzEXGR4TH|N z$**5d)l{)q6rmmCK9oamEpx~4E&BgT~yy^wF@{3Mx=?D}Ars_fJIi0{)A zLjI5|8K`sF1-#p3*Jg-gpBjL=?3-0>!Ko#HWt%o3n;?jedEL8HYZx@FM^y48 z)3ZQc;}uIzkMJGZZBgPd5ayPvJ+S;b5}jG?De<97$ZpPMSq{hunrqN5Pr}n}O+*(j zZKUzn*!jjh4nz`P5!8C1xhF!$k2b#zO;@}<&R0jI6nO}X>q>c_E_X|%sd9yEJWL~0 zW7ZSxO!YGP7!f2@LgT30K4wg2&vMpvPe9XS9L{cvZ;(n8T;{I?ZNHd1BedcxqpcLD z$}i*GqcBn&d|PWKEX7}Ur1Sk$NX@#=GRe8_=c<#8c9aooicaq40oN-BtwLYFTubjy z#q9+WtOs9f9>c+Lz1W_*$m)x~CcI9@osc@6QtEbY>_&!4e7>bo5uO0_Z|O)z?nu{y z8^^!nM?I5YGlmCcstY#j86mKSJz`TF=oM46lpu$45H_#&_^4wL$jIpnqAP>jHbKC} z6v>C!U{Z^bugXB4l3q#65bIa4K~2CeF%9)kHi)+?oWjR`ua8`=$O!QdfsDfzi+yIH zV)=>+o_U0ugggeL;Gn8wUJ{)vzj;*cT(>yYCP~}aXKb>xliO&ZVQaFQle%$ev{w0DlTw&t*6yKIiP`n@ zlleHTxmc3il@mG@I0s?8B7QxAoUcM>-AEGojQA{i2 z2+=g4^;V+^bC*vI(-nd`Ddd_J|2|M|Fh60p)tYUvw3U_tfkoAbQDJ22(VkvNZjOR> znW?-?3o}Wp_ZQ7}ey`*tInpg7smEM;am*CpSNj?5#_Y0K(|(~!2hEc&7xJJ*+%X|X zW;RV}SA2A)SsjE|nqY8y9kjxsOCklmn&%wC@5P2YjPu*S7Js&4ObKIm>TXC?7WOEZ<U4wlhU2VxPhS2DOIot#)WHY{!>H7>pMmg!jqH3k?8ZL3ew z;7csKr4}tQbk)j=WeacZQ4f?Y?U@7&w8VNj43C$I@3DP12LbjECd>5Zkj=GlbXywa z6b_^q#w!OVD!7i={TxliPKFF=o-xsE?D9Sbq;EtLpPl<9MG1;rQ9~||S9UqP%4&g! zCAgd&6>#3!!r&iV#>0{?n%K}jiHvWFKoS$0tL%cj^RquH-A-ITF_+-=r#oee+`gVx zos~WeMHaq53`Vx=^TS6_guAb~L`<%5BwBURq7W}1!LWM*E6R%ddPAUc=sem&1^GrnPdvccsqY?n##tco z08>3xYO14GL#8tj>$TyE-`0O@!uDI%=~`p=M(C_$ylk9D^2+EKs8^UU7YZt@757Hw z`R01oJ4*2p#L91C_%F)!FLwA3B=#GVF|yLL{2M6#gQfl6sD`WY?``zIq2m9Cr!lax zd_uJU0n<3;#2kU>z*b%mF}t*Hu3QQ#E5mNzS43Hl@OgSJ;eLu_(Hl^`<_GdqJxxzd zbTW$VF>#nMpgM^w4l&plm?J186PSv%tvM3P+Nb38n3LtZH6yO!4XhmA-sct)xiaS9 zkacwz!MZt(0*9W%wNAoY{dhqO`msccIl0V{Cvj=&?W5VV&#g-zZPJyROh!qDK62ng zqQe&s9Ba|`W62HfN9G-m!lT6%Le*gA{8SNZ=?*WNlqqJfAAzn6bZg^)!qAXniT17IQUb}Z@@+b#q89r^ZZC=c@?V$;zX))?RtE8P-E zGvmhO3<7ZJDEn9jo&DXcMQO#DYiG4KpZ7S^mfNLeK{U8&llpu6TJ4iR`J(>SWt+=6 zHMj;LJT5&{d{;H2qZ~gZtir7+ze<<1zVZzcmcKCEc5u9Yzgmtl?~pFRWQtA|GUG+m zl||zrmFkT(koY|-8Ez1GIz$#E1s;wRY|@(4AsYA23k;iKX2XBGpZ+%r{A+r`5dF0!)0_)T&2Va;yp(cUwFW_PzfseuTsz@44L_7P3}n_G!@jmrJ~u6Oc{!f=2$mVrSB` zM+tcMfdQKwFgQ1zufphyNU4o*Y|de}2)k`?4{tonV6MnKB5Z^V#wdV=kmO{6W;clgZ23tC}Av(_Ij*%I47 zAtb6pw#q11`P%O!Z1OeXYn=i?0svi}_GxvModa>w1&Vgfp80H(A)Kw{kDywn-djLV z=nH@m_BjwOa}+mbDJ>QEStxRc#)rdIEa-L~O2RUbROsB{-Sz#TB^guqhgL;uS!Yeg zEfphwt&n}It?5=g6-XncKK>vKDJi@4dI;aRx&eiHbUOJ9hy9*_`I8P~WMKRoeKJn0 z0*ele|DYY58MgQ-xnaRBpVTkuP0B8`1I=f&vZ#B(bOijxZ3}U{*`kkGYha%3z8N3; zge&o(`ao0BDdJ5%;i;oeElw?Mv^;gs2upN0yl{c?f@I4+0x1G&T3I%~S8t10C)?=U0JZ{`ALP%Vy zi}45+C73)Jg)}e)XXo0D#^H>izLr|!#&{pnkTI_^4>9K&a8y~NJ(8(HMA&sLci!zxiygQ-|7;mu34%XZ1(Pdk& z+I&&ZklQ|MrgZ3Rtm#Dpx<*|!-e0G3L_qlIF!|FsgmRpr57HKrBku8OnN%=%k*h&q zXJe{LRwV&56vF;YB2xjmKp^`@eQ^k9SPCM?f-ehy*dHB_Hpya<&ot?j92gJn6Xc|% z!Mmv-aYF=fXiQJ6Bq|TC0IOHUNeu;ohsMwrSoM#b@Bf_=(*Ngt@}I~F82?UAKoutzN%$Fm9$dhwCu5(M#&6;oQw1j88gII( zi$!sPcwnQYD8r0j9tMvZwAk3RMl9w`?3AMnvW!~q>~AJlXg5ZP1F}f^>r!-L7SWN- zR!(^c^%j9~(~<%_diJ65_W~YNL71#`H}pbwTWFA2c9fO)*KH~8j;t*1co6(_m{tqI zzNb`hvGMq6C6=jmcWjW&U-a88N}iN28HmNi4`8m(myYVJ$$4O^q;k+uW^nR|+1R1h z(b%~n4q{v-f=O!;bvOzN0?j^gcoV{BJLRN{2P4jilqNy_81kX!zATw&K=t)AZy%uF zY|^Yr1<%Rtqzc9KS(uJKQRRl;dE#+~qLQC>=-tNps<#rw;H43U|0N6M{=H{Wm>Bay zx?_MiX=wspMhr$;q{vh1*G*;rqWln)9!eho_ z{R7qjG|3Zu{QJ_urMnb?5XLS^{i{0p%yU9|%c+5wU0!NLTdEulh9_bS=;Xy+!98y# zw26t)i<|C}F9F~*kJrnoVOIG-M=P6Jn4RuUM|wMbFiC5w5iVCsymG9879C+!-t!HP ztY?MuUxcIooPhr$GNPyd-{FW;wmAlf4sxUsJQ0`c`S@yd48-x{2q4-Wt{Ps6Fq5Q! z>4k$d=H9a=>wBm}zCa^ZqGjcPT5bA%xtc{nwZkfv&?bqMLG$W;!>Y-O%wlzS=vqh( zrPy9y3b2)rIj}k0Fe)$GRizcvjcz_P46jUVKw(TPG{N!jd6bl;^4TsZ*kI&L5b~={x(A9W_e4D+IRTOg)8^|3&KG8j( z)?HU$mFzDBtgz;T`F|;T8`p5GUQ|=S!+DCX?^IjLroKirjS?-55#)Z?{2jA4@RiA_C`9%mbiTfznvpDrK@|bmh^3q#+L%viiKmHpSkYy zcMRZDuz@Qno`czMvEl35HH08ytU8aN6mLtp^UI-|le4YCPMHvq1J;lkHB;iTDE#3z zIKHaoWtRY1=vxA|dy++!^6?6wj8uvD-&$e#UA*xpUtLTL%>P>}67eFj>c2yd132|1 zL2up`2iDeQ&hxt(w{-oPz=II)kJxW_vM%-|?w$5w&23tA$7y1e`qy3~3uaU{3Ax)s z_U0l7vU7waa3y5T0z*HD>+x#e4C&);;zJgKC`-*?RN`)8$4;b|N-GHVSPx2Q`59W^ zBPAt%G1|shDOx8QT}9Gr^a3dR6yL-b(m&v+K+}-wl$^2Y_P6RW1|Rq@2Dmxf4SGbf zYQM1Oaw@O9lsh#DurNFT8Zg7nVTdpgAs2_ar%+L5Jzu(_*vZ+9-l4sLXtUo5! zUfLpt2RJP0oLSE0?YUsquFwJm2yJVLNB}h8QOhr`@71cjb*Y#1H-CZ{d^aL4Q2Nna zAC4Yp$#ypglo4_W(_0AbQiC`Ocg?@0d@b*n7Op`vU%a}hn(#H5c!Co22lnzDQEst? ztVy}>v`k}_44%Amo7z64>1kn0Dhi_v_n{YqPVMW4#U*b~=^A=W;Ewm{J!cwd5VfkK zs0+X)y3;sDUM<=M09j8fzuN7tnO|y=g6tVI(N}EuNaPu`YIay=FT%#A!~|I|3(G7U z`cijD{p=;kqCO+wN(Gi*0w?dxIQ@xi@M!Q$Y+02JK@x`MP<V^G*%oq`a2ceEQY0@nLV5qTuN(*;j$Bmryw@18hmmkv=DT5|}A*imXIErhZ6r zEF?LOEXi~2V*1&%Vd?%OMs%G`sr^O*EfZ{9thQC`SoC(v-oxw>Q? zb`ENoO^{;sO5gP`fMsvJo6SWH&vn>WA_!Q+*Qwy))y0=tKM_SPeYa-m6XR;4>! zk~z+1a^Uztb-Nnlv#jPrxPVvNFM|Q&`AHQ@SCNStlw98uSPa~U77 zSk@bj$Cu|#qg&1Qin5uh3j`BN;O+j*?);f|#L=dh;tV;s+I3UGuI5bghQqeJp<^Fq z2VlBVr*%tE-S0hy+hZcsMR{p_z!>dgJR~|N5 zCkHqLIKdHr;ppG>VSh3VF|z!PF!Vd~`J^Fyzr)a^EErzaP2ImTA8}U$6p>%3$44vk zb)AJUD=V%nR^`81+i_yB=Si$71-o8CfgdFnSi@(dW44ys-|+bs>ZCUf6c zgisx?32W8T!d7}47%?4|GCClIEPBBL-%@R+oGStW0#tj%cu&6^MFewDm#ou-t(-AL zy61e2RRIvVe+VM1D!^szR}^vGIC7Q>b^s|7%~X@DM3W=S{DT*3N%~mh7j_+fxZNfJ zA10q#T+2M=HyPGD=8Hhcxe2DHY)%f8{NXSVx3bZg zYs%Su=+Ri6Tb3K$G1W4hwZ69@XcKPUcfmAT^BLwQ^}aW|jVdOHyYcs8l5DeuFedv( z1JJIrk+pB+(H!@ao$z8kGRgw=SY@2q{TB}!feVyKG<&%Pm*>0!RWE&3em|HRGmiU5 zP1x(dI(;cd8<~CC=*-$UIKUa}X^;C0h5aW*{Ub>cGsFK5C7kkNk(8eZc7cqQ_mPnB zz~tzN%JjSw3uBs~ULPC`VCPm(F7TD(i=7M3L%%_9x$iHuZSt%G^D9IwY8&u*pQJ#7 zw*Z4}R$JNE%}W4UJ(v(W1F|nfWt%+|CT#|f-aa)tYWL*QR=r3IddLxWv)|@XH}J2N zB$ZS`xtuG{q4XPwE4VKSRoB;>3 zA?}c5A~VA&B_UMz%QQI_M|276D4p<8XWH6Wzg36~lN+wq5F4@dN_VdI-ifcu4jS9Y zDbA%7EQHP|<|%SlzfzM;;x!`M&B8-WaT55Yd5&y`CZ~AB*Jx8!!0PkR&wxKa$HGS} zQv>U0puG7ZWai_t;-nzh0&*8%Ck4$D9yV{QGYS(^@9vlQs6vjDxSrIPy;}AmPuQpd zv7-S%@w6W5S5gGmO?R0lyUfw5VatCX7BcGjnf7^AxzafCY*J*LFNM8Ff^Nbq#@GTs z+|8bT22tANuz)=DFEfYo>jgdfAm`RVa$KpMxTG4|nYkXXlx>~dzu%8rHay25dNB&N z!(NvSVUfBT#y%HGT*g2sn#q1ZW*vOoT%ar>_a^>@sQxs>WoBgkTcWBkqXSEGLQ>&< zBqBK}zPc`*`0kkiSH}(W3n;9h#ZUDr(du+dupg5{<+IzUsH%j!YUQU~d{?t(I z$R~+duOssz`q?h0GZ*jMhKK_gNoG;c9Ge8oOPCI0-CO~_4U<9daiz#Xe<^e207w`q z8q2jwSL;@kYYfY!=lwC=#-I73_53x7eQAs;aQw7}FW2yd${B<33|nd$wdlI4>Xz4; zh?;QMimQXNnYOKpnGLpS$3sX?!Xe;n^<~9);4++6#3XMqU2s!&PNW81>X>+gzWT!; zZ*6(}d@}*KZ8tfd*4@hBzHc$jJd)Ot6$g9SOy689SW%#2LqOTskbZXIdgeA@k1TH# zKk8x@GSS7QOv2WMiDo*0RBL=!hN`bbFWKYCC-7)sfy9>6;?p&Ch$SQJ+eCnkXN{7< zUd2X?BE;>ecbD!-WR*$taWk^a!k<#X^ZeOYObIIWYNaaGxVGAY_2y*th%NLHb#K|= zD$CC2UV#lc$pKI4PvoWl>h-nGqM_P%d@_agSHp} z*J*h<6U6j#^M&*DgboDC)Y(e_|YDpebvRJa5hZ% zaeRAIj4fZe5GH|{Ud}l&%W6JDEmo%)`B6t9K7%`sZj2YlIKfN3dAVgSH*c2(j5Sul`S(ejCP3nzZBCCw~LA{l1wP zb0SKUA&yI46lY>b9_ChgwGwgvfuzM3+Rx!*ss>OtJL}Ax)I#dIzQj?BMf47T%PK^2 zo?`lQ33(s;&OClwM&{A#@h@ylu!ZLmvC<)dRnJA zFiHE@O@KN~F28iK!Y2G8HW;koBGn?8Z_x)^p`mKJ0)(#jSWEVK)gKU#zN`u@MuQCw zh@fZ1Yp$L=xpaJd(In5W_zPM5af<$T!Ut!ZNF)&+{^;;w!I>sc zB@1V=Lq;0~gLZxMW&hfv#->!8l^E{Y#XfnI=zBvM#PRJV^zbU?5AZciIiRu$x*Y1q8+F1jRzS+7k3YZ*`70c| zguibroh#pe?ftI7+|t#31C4M1%{7o<5jpedGJTU!sbZFf*k+zNVrYPkuz z+HRVej+-%6q zw;LMrAqiH`Asl$gw5bscn2y_5%1U`5-2yU~c3X378j9ZsQ^DE!`BUSO;CH1i$_A zwtrh(G3IcO|xSg@*zymco9nE0djvs4$Lqffy{*ccbhqk7QeVN%qUdoyJohJCX1e+%a$@4TPYA z%n8)+%Mv`iRR@C|FX}ffSG!Y^f`vG^Oe&qsjR&q6IODULU(7-xxJnT|q-g!}m87(z zk0gqRwgS&tx{3?}{w)LQ06UF}l7RSmK(s_e#16tV%B(n9d4eCW;1z_@kbS_kS~I$< z-IqqfSrO*D5{`YO7GFsM@oSdm#KX-UMA~)M*k8E&Pt$Drzcnc<&29;T3%SA;KqDRE z?roh(qM+gWQST@XiWnD#WwhEW`Wne(1JkC>4`~@P0m};N0=Lx^$=X-f$Lrc%5y4WFCyjDGYk;HB(j60X)r4mEjf68YXP8E^oy`5Dk)=>qMVgJdD zSMaEWSAL>a5}C?KqTWOTKJRwT5wZB}z`(gQuO;F9vwVx+nrH@EUCSwI4(S;@iRF7m zZ>OEc)(edBgyPPBKX1lAO@tX}|3(%kAuAHu`va`?0Eq(tyl9UzM$k_9 zw=^4@2*iX55LD`ky>(Vln>1W&`PJQyabzvsWB5XC>{lY2JZ^11Od_KY97LQi3=N{j z!;D{4!+p~TL^AO!aM|v?6uU6*Ygos0Rqn!$F1!cJLSj?_!SO=i@`nq- zQ97xR$y3yS!rH*eLZDFML4}!Z# z3|S-#@J?}zNTuuwZh(2sgy%6~dPmbz;UH_c6A?~SbcebDgR@7QBU1T=WwwR`4PZSLnQ#96P$&{`VQmLdTo&mi`2%n zTaHj+6jRdnjYF|;Z=^-`(4JCVR|auB-8Euo%{7{cSDNr7v+|BAa_(OKE$y<{{rFC6=)=_muk-{_J|pBVh}jmQ7s0Zt7&YxF;#~tUyLk+U>k;}$1vAT>Q!B1o%W!2qA zz{N4=-inADfoDDOJFFDW>vj(Ly&{%D38AoO zVLxXrCCS{+?>ca=^U@?K8=eyuV>3&ARG?{0mPUobjKQJCo2~R4#$QNqK8!PYb9y{% zmLsUw;-3ltfi8B%U61yygxMR}UCAIIy*b7g0~*c%qn@ERpA9ZCh9m5^jW1w~-5|QOUfg?lW?pb-qxe4mgW?Y^ zvw&dlG+OjkARKx?82Zf1Y_PP;l)l428v2^ev?Z)w(S8&X;kgkVF%SYYN0b(tB)_uO zBIBD34W@>#F@qc*yt4E$I6MWwkfBeT;KYFnfX;(n>elSgk5RVb8V=RdB;pP~-{^&E$@`*H1vUNRtYgKTc%l#~iqlu~nAo zQ;08BXi{wp`|w<;^<{pPFPJo=&C-mWH-6LCvBV@z~oTa0{w-P{xsubWceG#Jxp>Uzo#ZX--qr$^Q-&` zFUU+7gEBv~1_IB;bgGdYwGR`LR0lUm^W;W#4iJ@GG~$d{0Z9mr!S0H7R)! zG+rgJye1tUOZ3(fC$$-gjsQk)6B7T%o&b~EGAvs}%HLo>Rq7jGle&pEepKOd zH50X_A%U1*!)Lpe?1Th0Xs>*#3@Ca!Z?!2Mkg7c;ZLSQ(ttrDzdJZPy8@i+7F0gXC zr;pvTDMXjs!ZgDxkl+^$ewPgu1+z40K4DP~Rs;q*;zFK1y_@JlLdsPV#EkF! zjDdD?UlsWmQMsUl8f#%uSP3YH2b9B+wU^ZgTIbFI4xmOS%wK5hPZKj{#=jE)O)0gz z$#g+{ImTu%sv^V0&PT}920uUaa*Dp{`Y^8sA(^Gfp~4a$ai`M@=5T1O~$yqw25O)u00_)5kjdNds|+l;m1Xz zthxd8Pu8-V==T!6xaLbb(R!CKt-WoZRUBltT$!9vo_D?fm_+BH%%B zO+o%-n8dG8LGM|I3dvDMzE%5)&=j&iAodZSe6UedymIdwF#^oN;yQt%ZckZqI`+>f zSj)dE%cVMsWisD?M{Ryil#{&oQT^UbHR`36nGRltc%T+Di9f8!NIYU&UXOWu&;Iz_I-dw3)_(UD_nb$4*HYmrirc3c8bFz2QDO! zG)jlumjm1-S2A3{bu3V*4ogPYvoFqY544eN5@e7hw^3;EM=asv@>sB3o_#rd5s&Br z&O~^&A!}U(kYZVt2RA5R=)d*-MQ8ZaSdfABZ&cqZ#InbpeO6K0Rr*3TiM|q6XR0wV z`E0z;EJV2bg&0Es-^S30p{R3P@1OM701krFQiOOXi=KsnW@?vnLTTMygI`%AeSy(==xzbC*nf znzkEbi{AL*<82v-O9oAE7(A-P$iU1x$>AhtSLtUlJ2Lk4<*ZI2&aRYr$Ib~7gQnG4 z3ZC#g{mMxFF_+1GNl~sZDlI4R1AeyfsVpmq%|Y1y)vwXbtjjvE@A-P_V>c_nZC4>W zFu~LNI!RnGt>Q^)0D)m;o%E$NKT!F3dsQ-&mLqtKJp}AyZ7;>iymxWnYA;Fz)LQ7)8%TUsNf3iTjCHq7)p8T)EL1UFcG`XuOX0pN58M7~|U6 z7bdG-iS<^zUt@9QD&NgZGVD=O)Mo+gL)Z;ot_)>#+{r=Fh+LMyfrwI4gwNURLj(3=XOryYTTU{$D zTA=iIvhh44xqZghg5P%??#WbpmbeT~+UaNqmYgT-n!9=W1yEHGVn^6rGpEUmJH~m- z#vXV^12V&}#ZRQt?u0U)MESv14+QCQqLCw0iHKA$+MZ}aR`*45ISuKs+ilb2_DShz zb!fOx^_XQB_gmW{maApK<@-z475k@4>BgfQ|H7qz8rU(?|BZ3!C)l;+`#pU5K*Zz$ z1fF(LPZ;iQ{$O6ib=$Et({|qGZqXmy0FsG&(3?jWU6ZS_30#a{uxn5r#w7L>-PTU% zw|<2x)NQhrJ>9(ax$MQ>))Ns2<24=%BQseyF#m;Hc9MuiG@}}`T~XwEg6`l)*H04c z)iWl^2s3A3DTFt~ie}Acn6rDUSV_EzzXUWoysQ%!@9^NU{lQxq-NUSmN{crmX`5<{ zj--ob;g=bSOqEzU&=Ku1i5fZ(TMlagmuKuBA}MVP9Am>qt$hw#)koUhaEs5Zj;q%y zeb*aApSfk5O)<_~&RXL=ZAWd=NzvhSl9CR zp_FHW>12UW+vW#Wi8cn5SQo_IpmPRntRTyoz?b7m!fd^j_B z;f`6LDU?!awdq~R^7(36MfJHYBxC&OPz z=1)UE#?SAq{^!VuNv_p~j}G#L6GRBMGj{(S8cv*w|!hqpE%Q2Km(A;A71W`%0FpAxhH`Vay1?gH?W~8K2odh z1krA-M(;hj5&?FKfwnhoCu)v2U|U=zB8hkk3PmlU*T~I%B1kP6mx};;Fg1-Kq_e9) zNQ`wtu}_0rIdjaGajPbjr~*Kvx9}WI0l%^gDG-XZlO5508>ST`Z7yAzgu_n(W9qSc z!6zhwOkT@(v0_lVb<`V(BaqA*V{wb!&4{lHk?#&3lHE!#GnG8vkIOe)D;$5SNhh28 z&O4lp+oC266elB`W`zM+5q3I>8{5{M7A%`2NCAdQVIIL^lla8o`R?lED`lkRA>6%YWe(H;tyAkG5?J+K~6a~+s_Hb&otWI*ZIg<%8;Hc z{i6{8mpwotSoP8$JB#`B!6&3{LC$1~Imuu}bKXEJD$0OjtaS7$Bg>!z0fnwXa_ccd z4~_^Dyq#{e>oe5|l(5D8kZHmz{3UdG7=j^dxU`O}eO4tM8!#-n3_h~efEkFkUwzAn zz8jNb^?>OZxF=YQrs(IHwL;F@C%^5Oce*g+Idy_x2keiCGS^as-U(po=LYSpK)M&r z9y3yz(S?CKdj(&l_YAKSN_5k)R5NEf-WD`U#m|n5Ivjz~+eaTChqEW12?$4v%;NZi zLN~nwmk+lzg|u3L2pMgP7NP;*|CIOT;Z$|s<8MiE4W*JYb@iGm+<7u28A9ecL%O&m zmusd-BSPjem88rO3Js>BD9Mm1M9EZnNs2P$yU#_LdOtnC=l9?1d5&|=I&1A=?RC~( zYu&T=x}DAQx5tRLcimoZ8}^$~ySuSX9)~e66oT_D8c$C+-QX5p^=vCe%$9XiCN+G( z%u-ohyjd!g%W3DoZr-Pn>tH1J3dStnDr^vs2rGLQjttX`}@8 z{b;WeehnvAZ@-ttSnjTu;AhQ;tz6Giv$ygAul%UimrcWio`H2-Q3uxvZ1&P~lBJ4Y z%DJjQ>7$r3>`!zK)}Io4enr=-w49Ue=7es;2LW1|jelq1*dRB`|GdFlB!_G`m29y- z?BjibcA?z%sV8C-5fguaykn_t@Ig7-77j!ABhEzc`(E1Z5*4ug$lRTK|OjNWET6;B!*g}+QH ze^cFEcIt4EH%U&~$h`Xsvmr$|nzANMm+E3iT@`@6MT$P(@iF*ns*u!1-md? zHnM)3A?pQimuH4ONP;kf$aHtq?i!)YWJA1VaQo-6(WzrYp*5{$0J;ndwRR5oQm!uD;2WrhF<+1;d1QdXBqxAUsu_FA=40 zw_YM@-;{fBM>{6$W%+0CIk-#y<)Ai-$bmsDQjCeL3cc?*YeMUEYTH7J2NY%3T(XMT;JofdPMmV>r)|Z?rw?e6XkiL2=#k>b zj0w4nZ?{=9ePStYzzj9yc}^b8pK20o-_lU};OJ!TzGL=%FUK(Jrk=<6ta5qB^xo!N zM9fSnMBs4UCICz|Q+C-aXPUy-D$)AQbSHIrw}tTc0k) z)Uq{)d+hIyzM_KWysJOnjP#Bb2EQ|)_=so+af}_q`>mJaiF2ow_dETd92e-{FKs2tmYuhu%op^}v!wrBBDQ3!R!-R`K5Rf^Vt8Zira7CiXpZm*zJMRNfN9V}d=;#tO#H}m@Z~?Xc1=Trz6C>_Xx;OUR*x{&;%Y9j*7Nh*_m~h*j%Ka@gG4)Tz zo@+NfXVb|V7P%$)s81`aspp)>_8MWvs@>U$QKKs}87<9bQPD9QY2yE=)#cw!J{%dC z1>4F(em3^B;Y8FJiFD7{J3$ki9&{(oiZ;Be&CVvpgS# zgwb>#gmcNi=)-*-ICJdFht)&-`cB;w+y8a@%}On)Db~mLKM3WCcEx@_<|AABW0iDj z?(P-DU}0!&UbMlg`TuDI>T5}DDX0JP8+xj=>{we%%ju4jS%Y-~2L!1CJ3WURRfPB8 z<1AQ@yOdunf4?j5jJvKI>_XX1`IH-Y)$AePTPXtLf~yD0n4>yaI|3BrIkY$>Tl_T7 z$h^Ap^{^#>_a=6YEjM(TPrSLkHMnYPpPHXT{FomM{y;*Xg`rLa7W$H@*Gz=){b@?6;;M8q(4_=~=#& zN}Tsa)t>O;!l`<$aHi=y*J;HAwprxz zlWB~MeFGO6_DVkfki=|5JfUtS;8!u^ao!7G zR8iKr#Z<2#bujOuIt6F(WK3h%bd)!L+vHb<@-eQ^Y#CWpjYCbySGzCL7hZ|U*#`aa z7W?vHgjbyn$J-Qa$YdJjE^_v=k2m#sZzV&T?o|!poOUJ7FM}$vhV5Pxe6n86e3~5g zJQm?kb+58e#(IOdSBKAd$G4Bm?j~LXYpa)TEP`7YL7A6)!XE@d#I+u>F$l0uVtn^2 zvoa%V_4T*6OqaUdRXwFnJrj*QOSb1LC!jYWX!k2!y@PIWc#L{)-D$N>DE9kNQ|Sx6 zHLHenjGhfuwPvsiZ0B{4I(@IHpvp8ym;g5{gTlxtu*_ zJG{NOTIR_Ly@A zIe@hKt=pM19*LV@TzU_?o-i&NyzjzPs^hl^6W0))XAB9O{{C1b?cl#HBc;4!^6E8~ z6A6i%^}5DRpgyyVMJ5YXq`#11o?g3Af1+>jFTT53#l}6-%#wKdi^VKbMHQ8qorV7NJa_)&y9~e`=tp4i)#TLaa0{le zBAbki*dH^-k*^Y{uU4NuAVJbzqAvQpB1! zC-JhOXih;NYqV(01=py5*u`oYT$J|+hQF(@;q%Py7C69?Vu@hOSJTKkP$%N7iHwT4 zeWh2~qm`+mS-$W5po#i{)UmHY5}ijxZfrACl^}7ebktW<#X8!{gnUzI_s_8Fh7}et zqxJmRb>fd6{Op?D)>z|hQlt4`!l!8PAE{5~pIxR+yG|zOtGF*`41S^OIRCQz$#Pe5 zje8sna!g>kYjjT82vugxQ^IK`ow4MU=4$4eM@pnf-Y4!wl&97_-z4^NtGB-&^lPyQ zUUN};;IZTyMLfGrQ~id_bipaPM>Zx6lQC=DJ`8i?D2)$t&owgkDjo_8XJ*(R>(e-4 zV}X6A(7#`jsq^C~gRrLX3Lor%uh)Kgzw={@`vdK679j<#b5roTa)t|;xc;PM=vl?U zn(6kizmId=7~xrOdx`he^YqBAW}Q!;2#?5_Uw{1CR6|yM+YU)#&ApPZ*&eC*rf3A( zZ1fm)8+ys#!*jPiVZ){MHwGzdzxr+LlRbKIZT{VQKGi-ag}^aAYJFh3%a2}mCCcv2 z<00%DYHvt(8C;!-lH@8DZjuQ(%W17;XUStT{#;@cX)ujytXs<0X?7`XkZZQNO~7Go$lDjeUjnU`j3}C z+28!@;f7;5D2Kjt-|f5+4`QcZA1Vzra%osW3|8=L(0>$V43~3I5CCfhFF)eix)Y^x zYE%vP!Q%LgJ)hD)&wd>49v7%NT=nQ|NNa$xFJilK0Oi}Z$&Tc4$Iz38X>E>OI*(NJ z9Hjkq8PIQZ16(~nNDaMcc&}WSf-QT~)PFbGc{udl7QZoX=DoUuq8NEcpQwu(&)v%2 z#eJYmwwEQB++Wp?m?Y$cX=u%1IYZR*%DKCC7SHb@_dI{2kr`H+Mt zySA*l5|!jzl)__YE|s#wGa=k=1R1qO=2s+T-Uw&}>sP3-=P?-x?(o zyXq0GHm!a8Z?&qlC<1-@}l4dvTmzx+@XG?ktC zOXXV53ZH1=S8%!hDZ3QbHOZ7fm-ln!C&$nV{I>(We z=bBEIU&jn?b_#i1j@&!=knMg=fp2a7MeD1gt-0G=`_uwU`@+)sGn);(Zk?Dm@O*6P z+L$rS>`=#8ozc(np!L1@@{=?_?_ut*Dln9%eJKmn` zS@|7acam<$yR(Mt7{ulBwz;aaY6(o4-aKm8WZYCCi8gFcRYLs8X5nd&Dd<6{>={lK z8QygWkLF@L|Gss? zs5vp-`OuhT)1`ZAOxB^)pfpSK*x|}ilzj#A)*UAV<+20M;j-cSg1_R<6iyQ#pcExT zrZ5W}XVkv;kpIIP*T$y;S%C=wZOq!~hPwWr2CCT- z&nKad^+sMA2rUVpRA6|tuOjRA`sx10R~<9##@$E0t>OF3w-0vWu5Y)Gv;ryV`NSZ6 zxF+F6P07`lC(LDA#gR%=u6vUu zG^fICF>bMWcR6j2o8K?=P7CUbd7r6$kP@6T7{*^%m077^dHh-Qv~@?t>niI*eH-%5 zZD|sa(Rmga99$vuyg0y;Hw^E+#cEH9U!7$CYPe+PLm9Ks4LjVr!Ew$|c0G<&iG6jgC{ag{$pb_N9? z)W~z!XdDiPfanw`E|$)=j;=IkI2y9Jb>P1?IfUu~ewqf^9%QOnvH00C{&gEf&;n46 zI@#6PmY&8&0twcHK{X+NUV*I=p$2M!>^dNm4`lFx)HjL_AO{VUd*@fqpLq}rp2TT3|y8!81f z9--?>u{VG&)XARoUcpf)Jbqzvqj?wB1Wo{6+y93H_WxQ2q^Qtf7;xanAR@R1XBc!Q z5D0WQhDd}%2lSi(@X!FhkOqSP%K@8z2GK$FKxNDySOS_}4*~F&06cgCkYobbiU1F+ zB0LUUgO8q|XV9W190~)2+Jr~r;3O2D4yWG(Tbp8V(sCYVm0nyK9fuBquUefu8C%}F+0q~1{1NsIPhJp_8 zM`sdLEmSImFi!!scb@40MlWf;fO$On8IppzL0WnyAwju-L3D|Lq+<4fK@zd>AON$S zP76uGvSUu-R;U9=(@$3Y6ods54b{D>XW`;vhd3VA3@&4sv3^j6he$pbGjE zy6VM2wk1G=^fo=X3a)>te;gEeoQKTL0>JL17d5B(fz-@mKobK!S_9B$RXl#KJ&@K1 zFEJf$c8;O@RG>MtQv~=Pf&L7dHGo#6Uqh1#90(M21Lgt{2Zj{>Lf2dgyuGvC|Mh&K zt0sq9IPLvRQiqV21BD_i$#4`@FhUOfTQo2!{X)b177Zx@=EYx#*ndF;-!K0{#Qh5* zm<0hMQi2HDxS;-30xq;b)q&!hW4}5IY_B}aVRbYdHOF{$40w6xpjbF+ zmg@+0unRMN_`w{B61TK>b8xm7CtKUv0u=)0WcnV@V62xtgLDVI;1%e=42qtsX&D_5 z10eWdz*nFHVnN?Oa;5r!lz?E?Z|F!sZO`^>S$$xo0){R?E7S*mQ2-VZT@shm0hLZ? z=Sp-qIMCF;p~J&L_g8Kw&`?0d{)P@H@!#=<0QxugiY{v>(2PKJuUsDysP5nKg$Oj< z@AyK*fUJqXX(yPQfra>+`k<-wcXVJX{2gDw)CsK2mH9#>!NG!nmFpvcx$Jj*A%Q;p zjxS*6c`zwJ_5FI4=zvmXd12F?sI~rG zV3A1q3`fGD#Qohp;zy}+j~g4{C9f~sn7px@1Z$v zapz}qBqI*kT0!9w;N?J*1U+xC8;AzbZOJ(!6~B;k@*wvw6c`c((Hlo9&6R3v0e8np zppX&-G58TzS4S5~1j7F3GYOir%}y8sgmJ9gEI}^eg=CIa)^H25r5&hdz6gLyufUe( zpa5lH7FCc$Bhh#y4uwUcP{5Dajl_r`ks>=6OlO|X6l)l8oJcU>3C{fi=NboW6KnWS z7-SDZe{hF67zTxetdK=8G!X+_wWTmD63p~V@bHlJwiE`^(&38a_8aF9j1 z1djlWr^PTd0SS3&i}28RB4lALfnkVC>p&C1fGoj7;?TrpJirkk7jIE{NIV*{441(0 z%f=E(KtopIVmty7viujrh*;!OULlDj$md&(2S7_>cw~{BmuI%7twYzZC?2YDON->-h)NWcMG3\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
StructureModeOperationTime
0Связный списокСлучайныйВставка0.047621
1Связный списокСлучайныйПоиск0.003505
2Связный списокСлучайныйУдаление0.005502
3Связный списокСлучайныйВставка0.048707
4Связный списокСлучайныйПоиск0.003505
...............
193Бинарное дерево поискаОтсортированныйПоиск0.007040
194Бинарное дерево поискаОтсортированныйУдаление0.013611
195Бинарное дерево поискаОтсортированныйВставка (среднее)0.303134
196Бинарное дерево поискаОтсортированныйПоиск (среднее)0.007562
197Бинарное дерево поискаОтсортированныйУдаление (среднее)0.013591
\n", + "

198 rows × 4 columns

\n", + "" + ], + "text/plain": [ + " Structure Mode Operation Time\n", + "0 Связный список Случайный Вставка 0.047621\n", + "1 Связный список Случайный Поиск 0.003505\n", + "2 Связный список Случайный Удаление 0.005502\n", + "3 Связный список Случайный Вставка 0.048707\n", + "4 Связный список Случайный Поиск 0.003505\n", + ".. ... ... ... ...\n", + "193 Бинарное дерево поиска Отсортированный Поиск 0.007040\n", + "194 Бинарное дерево поиска Отсортированный Удаление 0.013611\n", + "195 Бинарное дерево поиска Отсортированный Вставка (среднее) 0.303134\n", + "196 Бинарное дерево поиска Отсортированный Поиск (среднее) 0.007562\n", + "197 Бинарное дерево поиска Отсортированный Удаление (среднее) 0.013591\n", + "\n", + "[198 rows x 4 columns]" + ] + }, + "execution_count": 226, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "csv_path = \"../results/benchmarks.csv\"\n", + "\n", + "data = pd.read_csv(csv_path)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "id": "a3737f45", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.003486), np.float64(0.048099), np.float64(0.006181))" + ] + }, + "execution_count": 227, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Получение данных для Связного списка\n", + "\n", + "ll_random_insert = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_random_insert_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ll_random_search = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_random_search_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ll_random_delete = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_random_delete_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "ll_random_search_average, ll_random_insert_average, ll_random_delete_average" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "id": "5434d260", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.003512), np.float64(0.048028), np.float64(0.005869))" + ] + }, + "execution_count": 228, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Получение данных для Связного списка\n", + "\n", + "ll_sorted_insert = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_sorted_insert_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ll_sorted_search = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_sorted_search_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ll_sorted_delete = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_sorted_delete_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "ll_sorted_search_average, ll_sorted_insert_average, ll_sorted_delete_average" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "id": "3deed9a5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.0), np.float64(0.002007), np.float64(0.0))" + ] + }, + "execution_count": 229, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Получение данных для хеш таблицы\n", + "ht_random_insert = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_random_insert_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_random_search = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_random_search_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_random_delete = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_random_delete_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_random_delete_average, ht_random_insert_average, ht_random_search_average" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "id": "490e5c46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.00015), np.float64(0.001794), np.float64(5e-05))" + ] + }, + "execution_count": 230, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Получение данных для хеш таблицы\n", + "ht_sorted_insert = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_sorted_insert_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_sorted_search = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_sorted_search_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_sorted_delete = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_sorted_delete_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_sorted_delete_average, ht_sorted_insert_average, ht_sorted_search_average" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "id": "9d7274ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.013959), np.float64(0.301662), np.float64(0.007347))" + ] + }, + "execution_count": 231, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Получение данных для дерева\n", + "bst_random_insert = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_random_insert_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_random_search = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_random_search_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_random_delete = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_random_delete_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_random_delete_average, bst_random_insert_average, bst_random_search_average" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "id": "92a545c9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.013591), np.float64(0.303134), np.float64(0.007562))" + ] + }, + "execution_count": 232, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Получение данных для дерева\n", + "bst_sorted_insert = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_sorted_insert_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_sorted_search = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_sorted_search_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_sorted_delete = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_sorted_delete_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_sorted_delete_average, bst_sorted_insert_average, bst_sorted_search_average" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "id": "b2e93d6e", + "metadata": {}, + "outputs": [], + "source": [ + "countUsers = 10_000\n", + "countRepeat = 10\n", + "countRandomSearch = 200\n", + "countNotExitstSearch = 100\n", + "countDeletes = 500\n", + "\n", + "\n", + "ll_col = 'blue'\n", + "ht_col = 'orange'\n", + "bst_col = 'green'\n", + "\n", + "iterations = range(countRepeat)" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "id": "208784a5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Создание двух графиков рядом\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# ============= Левый график: случайные данные =============\n", + "ax1.set_title(\"Вставка случайных данных\")\n", + "ax1.set_ylabel('Время, с')\n", + "ax1.set_xlabel('Повторения')\n", + "ax1.set_xticks(iterations)\n", + "\n", + "# Связный список\n", + "ax1.scatter(iterations, ll_random_insert, label='связный список', color=ll_col)\n", + "ax1.axhline(y=ll_random_insert_average, color=ll_col, linewidth=1, \n", + " linestyle='--', alpha=0.7)\n", + "\n", + "# Хеш таблица\n", + "\n", + "ax1.scatter(iterations, ht_random_insert, label='хеш таблица', color=ht_col)\n", + "ax1.axhline(y=ht_random_insert_average, color=ht_col, linewidth=1, \n", + " linestyle='--', alpha=0.7)\n", + "\n", + "# Дерево\n", + "\n", + "ax1.scatter(iterations, bst_random_insert, label='дерево', color=bst_col)\n", + "ax1.axhline(y=bst_random_insert_average, color=bst_col, linewidth=1, \n", + " linestyle='--', alpha=0.7)\n", + "\n", + "ax1.legend(loc='best')\n", + "ax1.grid(True, alpha=0.3)\n", + "\n", + "# ============= Правый график: отсортированные данные =============\n", + "ax2.set_title(\"Вставка отсортированных данных\")\n", + "ax2.set_ylabel('Время, с')\n", + "ax2.set_xlabel('Повторения')\n", + "ax2.set_xticks(iterations)\n", + "# ax2.set_xticklabels(range(1, 6))\n", + "\n", + "# Связный список\n", + "ax2.scatter(iterations, ll_sorted_insert, label='связный список', color=ll_col)\n", + "ax2.axhline(y=ll_sorted_insert_average, color=ll_col, linewidth=1, \n", + " linestyle='--', alpha=0.5)\n", + "\n", + "# Хеш таблица\n", + "ax2.scatter(iterations, ht_sorted_insert, label='хеш таблица', color=ht_col)\n", + "ax2.axhline(y=ht_sorted_insert_average, color=ht_col, linewidth=1, \n", + " linestyle='--', alpha=0.5)\n", + "\n", + "# Дерево\n", + "ax2.scatter(iterations, bst_sorted_insert, label='дерево', color=bst_col)\n", + "ax2.axhline(y=bst_sorted_insert_average, color=bst_col, linewidth=1, \n", + " linestyle='--', alpha=0.5)\n", + "\n", + "ax2.legend(loc='best')\n", + "ax2.grid(True, alpha=0.3)\n", + "\n", + "# Общая настройка\n", + "plt.suptitle(f'Сравнение производительности вставки в структуры данных (N = {countUsers})', \n", + " fontsize=14)\n", + "plt.tight_layout()\n", + "plt.savefig('./img/insert.pdf',\n", + " format='pdf',\n", + " dpi=300,\n", + " bbox_inches='tight', \n", + " pad_inches=0.1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "id": "7de42c9d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Создание двух графиков рядом\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# ============= Левый график: случайные данные =============\n", + "ax1.set_title(\"Поиск в случайных данных\")\n", + "ax1.set_ylabel('Время, с')\n", + "ax1.set_xlabel('Повторения')\n", + "ax1.set_xticks(iterations)\n", + "# ax1.set_xticklabels(range(1, 6))\n", + "\n", + "ax1.scatter(iterations, ll_random_search, label='связный список', color=ll_col)\n", + "ax1.axhline(y=ll_random_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.scatter(iterations, ht_random_search, label='хеш таблица', color=ht_col)\n", + "ax1.axhline(y=ht_random_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.scatter(iterations, bst_random_search, label='дерево', color=bst_col)\n", + "ax1.axhline(y=bst_random_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.legend()\n", + "ax1.grid(True, alpha=0.3)\n", + "\n", + "# ============= Правый график: отсортированные данные =============\n", + "ax2.set_title(\"Поиск в отсортированных данных\")\n", + "ax2.set_ylabel('Время, с')\n", + "ax2.set_xlabel('Повторения')\n", + "ax2.set_xticks(iterations)\n", + "# ax2.set_xticklabels(range(1, 6))\n", + "\n", + "ax2.scatter(iterations, ll_sorted_search, label='связный список', color=ll_col)\n", + "ax2.axhline(y=ll_sorted_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.scatter(iterations, ht_sorted_search, label='хеш таблица', color=ht_col)\n", + "ax2.axhline(y=ht_sorted_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.scatter(iterations, bst_sorted_search, label='дерево', color=bst_col)\n", + "ax2.axhline(y=bst_sorted_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.legend()\n", + "ax2.grid(True, alpha=0.3)\n", + "\n", + "# Общий заголовок\n", + "plt.suptitle(f'Сравнение времени поиска в структурах данных (N = {countRandomSearch} + {countNotExitstSearch})', fontsize=14)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('./img/search.pdf', \n", + " format='pdf',\n", + " dpi=300,\n", + " bbox_inches='tight', # обрезает лишние поля\n", + " pad_inches=0.1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "id": "0fd42f30", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABWEAAAJKCAYAAABNpYc9AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAA+3pJREFUeJzs3QecE9Xax/GH3psISi/SLIjlWhEpKqLXK4qI6FXs9apgoVkARUWsoGLvDQuo98WCXUGsYBcLvQtIB+nk/fzP3hMm2WQ3u2xCNvl9/axLJjOTOTOT2WeeOaVEKBQKGQAAAAAAAAAgKUomZ7UAAAAAAAAAACEJCwAAAAAAAABJRBIWAAAAAAAAAJKIJCwAAAAAAAAAJBFJWAAAAAAAAABIIpKwAAAAAAAAAJBEJGEBAAAAAAAAIIlIwgIAAAAAAABAEpGEBQAAAAAAAIAkIgkLAAAAAMXMxo0b7ZprrnG/kb+HH37Yvvvuu529GQCALEYSFgCw0yxbtsxuuOEG69Spk51++ul24YUX2n/+8x/75ptv3PuaBgAAcrvsssvs3//+t5UrV84WL15sZ555ph177LFWokQJ23PPPW39+vUR83/88cd21FFHWenSpa1q1ap24oknuuV2hoULF9pBBx1kb731lq1cudK9fvrpp+3iiy/ONe/zzz/vYoO7777bvf/KK6/kmmfLli02aNAgGzhwoN1555129tln2w8//BAxj2KMYcOG2YIFC5JaNgAA4ikRCoVCcd8FACBJ3nvvPXfz2KdPH1eTp3z58m765s2b7ZZbbrFFixbZY489ZvyZAgAg0n//+1/75Zdf7LrrrouYPnv2bDv//PPto48+smuvvdYlJKNpmaOPPto9AN1ZtJ1NmjSJmLbXXnvZ+PHjrUGDBuFpL7zwgr300ks2btw493rbtm3WpUsXu+CCC6xHjx4RCdYWLVpY37593eulS5dau3bt3HLNmzcPz6cErPbL6NGjU1BKAAAikYQFAKTcJ598Yp07d7aRI0fapZdeGnOebt262euvv04SFgCAgA0bNtihhx5qn376qVWrVi1XclN/Y19++WV7//337csvv7R//OMfEfOoxmmHDh2scePGKd7yyO1U4rRRo0au5u5hhx3mavKWLVs2onZrw4YN7YknnrDjjjsuIgF9+eWX25w5c6xkyZL2888/2wEHHOBa11SpUiU831VXXeVq+r744osRn33RRRe5WsAnnHBCikoLAEAOuiMAAKTU33//bWeccYa1atUqbgJWlKDVjRkAANhOCdYjjjgiVwI26JFHHrEKFSq4WrFqYZKO2rZta48//rhr9XLeeedFJGBl4sSJrlXMvvvuGzG9TZs2Nn/+fPviiy/c61dffdUlc4MJWD/fG2+8kav8SuDecccdSSsXAADxkIQFAKTUU0895W6q1BVBXtQcMfrGCyhO1GwWiKba/Turhv/O/OytW7fulM8tThLdR2qir+4E8qIapOr/9Mcff7Thw4dbcfTtt9+63zVq1IiYXrNmTffb9x+v+aLn8fOpX1x12xCk2OLXX3+1efPmJXHrAQDIjSQsACCl3n77bfdbTSnz42uqfPjhh3bwwQe7GypNu/XWW+3mm2+2K6+80jUnjB7tWDeyGgX5nnvucf3Ldu3aNfy53kMPPeQGBVFt2/79+7sBP/T7n//8p2vaKGvWrHGfV69ePatYsaL7TCWQx44da8ccc4xb9qyzzrJ33303vN533nnHzj33XLv99tttyJAhbp1qOipjxoxx69dy6svugw8+sN9//90GDBjgmlSqPzxts5Ik+q3XGkBFg43ohlFNTDXYiJbXelT7x/vqq6/snHPOcfvmtttuc4OYaLCTvKxevdqVaffdd7dddtnFbbP6D1Rfe6qVtGLFikLtf7/9qm101113ud9PPvlk+P0HHnjADjzwQFdL67PPPsu1XSqLanjp+IwaNSo8XQO3qPa0BnPT8VJ5tV/kt99+sxtvvNFKlSrl+v/T+6Lt0GtN1/tTp051TXR13LQftf06LmqyqvJrsBodb5Vz1apVcfedmvhq4DjfjNaXY+3atdaxY0c3UM7VV18dTqroWNx0001u3dp32i9qiqvl1TWHkiqe9rdqgutYqIwaaGbTpk25tkGD0+g8VNcdWp+2Wc2LK1eu7MqqprqiZXUOqWmu9ouO75tvvlngczy4j/fee2+3b/3x1Lka3Me+KbE+r1evXu4zNH+/fv3cOnVsH3zwwTzPz0mTJrlBeDS/+q5UGXVu65g9+uijlijVmFMZ1AR56NChdu+999rkyZPde/qO7rPPPu7Y6Ec18fbbbz+XtNK5pu+f6Dho39apU8fVzrv++uvdPtW1RftMZfX9Tep4abm6devac889F94O9VWpspx66qnu2ASvB2qGrfPSN7XWNLUW8OVU03E1x1ZSTTUb9f7JJ5/sztl//etf7v2CfLauOfpe6FzRuas+QgcPHuze13mybt26iH04bdo0GzFihDuGel/7U99HT4k+ne/af2XKlLH/+7//C18PdW3T+aJtT4TOze7du7sy6Zjre6C+Q7Wt2udz585182kb77vvPrdNmn7SSSe572WQv45qG1RmXW913dX1N3gd1b7TuaWalLvttpvbVq1f1y015de8ut5p/bo+6d+apvf0tyaavs+6LmgeXf9UY1MDUKlcmqYEqbZFtF3+OhBrwKlo+j7rmB1++OEJDdyl+fR3UN/fdPPnn3+6fa3rhPqG7927d/jvpfgkqe8z3tPfjuD7+h09T6z5ghSDqN9cAABSSn3CAgCQKnvttZeqYYV+/fXXAi23adOmUPXq1UP77bdf6M8//wxPf+ONN0IVK1YMffXVV+Fpd999d6ht27ahLVu2uNdz584NVapUKTRx4sSIdT7xxBNuW7Zt2xaedtNNN4Xq1KkTWrt2bXjaWWed5dYX9P7777tlZ8yYEZ72yiuvhNq1a+e21evdu3fovPPOC7/+8MMP3XLTpk2LWF+9evVCN954Y8Q0vW7YsGHEtHfeecctr/V4n332WWjvvfcOrV69Ojzt3nvvDR111FGhRKh8Rx55ZMS0Tp06hU488cRC7f/LL7881Ldv3/DrrVu3hg444IDQs88+G572+OOPh0466aTQGWeckWt7nnnmmVCjRo3c8fGWLl0aat68eej7778PT/vuu+9CtWrVCi1btiw8rUGDBqHrr78+Yn16relBOm7ajx999FF42vr1693nan8kQudX48aNQ1dddVXEdK1z2LBhMZcZMGBA+N8zZ87MdSwXLVrkzoXguXrCCSeELrzwwpjr07LBMmjbdQ56Orf/9a9/hUaNGhWetm7dOvcZn376aYHPcdG+HDRoUMS00047Ldc+VlnLli0bWr58ecT2aJ3BY5sXnTua/8knn4zYbyVLlgy99tpr+S4/f/78UN26dUMvvPBCxHdD57KOt77vWp+n4x/8Hp5zzjnhf99xxx2h0qVLR+zLjRs3hg455JBQ9+7dIz73zDPPDO2///4R0/QdOv744yOm+etBcB8PGTIkdN1110VcR84991y3L2TWrFkR5432qd4v6GfreqX16LM8ratbt26hww8/3JXNO/DAA0MjR44Mv77rrrvctTy4jfLAAw+EypUr5665snLlylCHDh3cOVcQf/zxR8Q50qtXr4jzWq644opQz549w6+/+eabUIUKFULTp0+PmE/HU+d7kK6/0d890Wf8+9//jpj22GOPuXn9/g+el3ovHv/3ZfPmzRHTTz311NC+++4bnv7222+7vxOJ+vHHH0O77rpr3Pd1fjz11FPh1/pbq2Oi77f/W6f3NV8idC3SdybRn/vuuy/iOx/PggUL3HH1f6f99y34XTr//PPdPozmryP+urjHHnuE2rdvn2s+XRs1X/D77+lv1DXXXJPQPgAAoKhQExYAUCyodpVqR2owDdVU8lQTrXXr1q6WW5BqS/naaeraQDWxogfnUO0oCfY9q1pwqgk4Y8aMiPl8jTjPv/brUO0d1T5VLVhtq6fakuqCwdcs8/PHWp9/L/i5wW1bvnx5uMZkcN5LLrnETjnllIj+8PS5qlHpayXmJfpzfF96qvFX0P0/ZcoUVzMy2N+v1q+aZ/fff394mmpNqiboa6+95srlqfZt9erVc5VRtRRVC1HbFTxWqp2rkbODn5XovpVguVWrsFKlSrnmjUdlUI1hHZNgn4Off/65q4EWiz8ng58d/Dz1max1/fXXX+Fpxx9/vD3//PMx16dlg8tHv9b+VS1W1Z70VONVtSd1nApyjgfnDe431ejUdyZ6vp9++smdk8FmwrHKnJdYx0m1InUuBs/PeFS7rn79+q4fak+1fn1NfNUCjx6hPbhtqgHu1apVyx2/4Lmtmp86N7UP1Pekp++Daoj/8MMP4Wkff/yxqy0a67P8b9XUbdasmattHryOaHvzOodVs7ign63yiGpGe1qXak2q5mawprJqdPua1f6c1LUluha8roGqQa2asuqOQ+vS91PnXEHoeho8H7VdscqvGrqeaqXqfFMt7uj5CnJuJzJvvOt49LpizaO+WnXNUw1qfd9V89fXKk90QCu1XEiUalSr9YBqlgdbFiRKfc/26dMn4Z8rrrgiZtcA0VRb+5lnnnHXUU81sfVdiv5uR3eh4V8Hp8fqZiPWfJ62UfsSAIBUih85AACQBGrSq5v3JUuWuJvDvCxdujScKPBiDdZ17LHHuhvamTNnWtOmTV2yQYlJNTPVTVbt2rVd03Ld8OZF76ubAjUZVRPlglDzUG3v999/H9E8Vf3RqcmrPl8Jvh2hhIaacwcTctOnT3fdJyg5Gd0sVs22YzVjz4+SLUreqWl5tPz2/+uvv+6mqfuH4M21mr2rWXCQXu+///6u2bSaofrldCMeTevVTXt0GQ855BDXhcCO0vqVyFKXDwWhpLuScGpCrvNGSTod83jblF/yUeevukbQ+aREnJKNSnRpnbEoYZvXeaVyqUmuHgQEKSlWFPtt1qxZrlm1umDQiOvRCVA1oVfz7/z6rywINRFX02N1VZCXjRs3usRo9Hw6v/w5pm4O8qKEYn7fgaOOOsqd6/rOqEm8tGvXzlq2bOkGHPLJbiVC1eQ93nFUtw06zrG+dwXZzoJ+dnR59txzT/fgSuVRUk10Dupao+4BVFZ/XYl1TdXxUZ+buv6o2X7woU2i1FWKfxgTj7pH0EMbddmwbNky97dCyeL8rvM7m5J/uuap+wElkbVPE30o4a+liSQ5g9Qtjv4eqrsKPUhLV+qmQcaPH+/OY3VBIbquBh9K+Ide/tqn+YIPuOLNF6R9qH0JAEAqkYQFAKRUly5dXKJNfesdeeSRec6rhKj6+suPakiKkldKYmndqnmpvvZ006mkQXQt2OibeSVsPv3003B/otE3xepXUvN5SkgEKansa4gpKRmkPhRjJSqCtZmUdMhvX/Ts2TPX6M/+c3XDGp1sUiI6Ub582g7dAKvmVLCGXKL732+PllW/qPlRbVjtb5+EVRIqeoRs0XpVxugyxSqjjn/wWEX3Exkr6aw+LdUnpvqfLAjVstTxViJISVjValPSPZ5YSewgJZBUi1nJA9USU2JJyc1gn7pBqmGthwzxaL8pCZvIuZDfOR5N26hjpz5WlTCOpsSs+qFUzUT1oarvlmorFsZ7773nEm5KWmmQHSUVlWjMixJz+l7nlWxWrdO85Pe+r+m46667uvM/SLWPlfjUOaW+gjVPvOOva5WSY1pGiWOfzC3sdhbks+N9p4Pl0YMG1fhWLUUlWPVwS9sci85ZfbauR7HOi0Ro/fkNzKh+Z3VeK9Hsa+LrQVUsuq4Fz+1g7ftoekgYnPfrr7/O87xUf8/6Hur7o/5XNehkfklV9Xer77lqfBbmYUhBjqUogfnEE0+4GtXaZ/r7uDOpprMewKkFR/CY+RrTeqApehgg2r/BpLxvWaLrr58vVr+v0fMF7awB6gAA2Y0kLAAgpVRzUDddSoqqVmdegjVf8uIHiFFNSSXxlMBQc+t4TcJ9jVnP1/ZSbaFx48a55vVKhARr6uomzs8nqjEZbNqpzw5uS5Ca5UbflKsZu2oFe8Gb/miqhaaaZ2oaHd18Mq/PFdUMC9ZIjSdYPiW+VYtRycSXX345XBspkf0f3J7oZt6xtkXJACVg1YRfiYV4A7ZpvYmWUesIHislSeINSqPaU37QqMJSwlnJBNUK1aA98QYgUlJQNVvzoocG2hdKNsaqCRh97irJGmwyH2u/afAZJUyjv0/R+y2/czyaum/Q/Hl9T3WOKwms2rJqlq5zItZDifyo1qAGxxIlY9u3b++SfGr+Ho9qumnb8hugbkf57iPUzUGQBtFT8lmDLakmvJJz8egBgJKOOk91PumY+u9SYRTks2PRd80ndlWbWgOYqXZ2rMSouqLQvvYDIynxre+ByqFuINRFSUG7I1Dt/tNOOy3u+/pMva+HRRqYLlaST+eJf0CkRGfw3NZ1VF0/xKIa+sF59RBk9OjR+Z6XepijJKy6gYmuFR5NDziUONR+08BdeuCSKD2I84MmFoQegOi7p5q3+nuiRHAiJkyY4K5JidI1RQ8B8uoyQeeIrnH6HeQfRvrvkgZwFH2/gtdD/7DvgAMOCM8X3TWGn08P9WK1bFEiPvqhJgAAyUafsACAlFJCT83pdZP9+OOPx51PfRL6G7Cg6Js2PwK3Em+NGjVyteRUg0tNYYM0+rUXHDU8mpK3qoUTq0lwXnTzrSSWatNGUxP6vGpe5UXJBDUlV7PeeEkuNaOP9blqIq/9URhKLKjGcnRyIL/970f/jrU9sZpDq5moEjU6F9SHZbxuIJSsVTInetR2HSs1uy4sJV+VBE6k1m5e54xqo6pLhlg1rjzVCNW8eVECR30wBhMO0eeuEii+lqISPz7RFG+/Kdmq/iCjxWuenghtp2q25ldDU8k4JX50fKOT8oWlxJWa3+s7kVdzYiV/VTNdycNokydPdttWGNHfAdWG1D6OThrqnNDxVi1pHS/1PxuPrw2pc0j7SV0T6OFNYRXks6PLo2uzanX68uhY63xs27ZtzHNSfQ4Ha82qDHqgpVrSSshF90WbHz0Y0Tbl1d+qzmddG4PXee2vP//80/1bv3VcUknXP33fnn322TxbNqhsqqGsh12+drEedhXkcwqThPXHZo899nDHNFFqsaJaz4n+9O3bN98+a/UwSg+colse6G+AHljq74jonNP1Tdf+ICVcdU771jTqXkQPDvzxD86n74F/QBCkfRh8EAoAQCqQhAUApJwSKKppqht11RqMTgK89dZbLkmiWkaxmqAGb3BVu061A9XU0t+gKrH3xx9/hOdR02U1kVUCT8uqaa4ocRJNgwnp5szXsPE399FNF32CxP9WbRs1GVc/itp2TzeGag7sb0r9/LHWF5100WslmVSjzTc/jf5c8QlMJV09LaftyK9Jb7z9oNpPuhnW4FcF2f9KoqrWnGo7BxMFqo0ZXJdulv0Ns2rMqcZesFmutilYRiXFlTyITo7fd999duaZZ0bss0T3rajP0hYtWuQ5b36ULFLNQ9WWUxPjWNRUVgnxYJLZb2dw/6sWngaF89ugRJMfJE7nr2p2ah9oPaqBqfeDTZOj95uSkKp9ft1117l5PT0IUdkLco4HpyvJp+4xgtOi51OC+OSTT3b7xCdV4q2zIOemllViVUng/Gppq4a5voP+/PTrVKIs+B33tE9j9S0ZFHyIo5r3qo2pJt7R3ZD4c1u1CIMJzOiyiO9jVcdXDz6UJIvXtN73c5nfdub32bHKo+1RCwXVgvfdkeic1DVMtU+DtaQ1KJ7OSV1r9G/R4HtKsCmRrCS4yqKBqIKDluVH5fa1Sz2dm8FzQa0UdH0KXuf1/dP3Ivo6X9BzO5F5Y52XOh7qXkD9c+taFn1s/Ty6VqimqP5mqBxKjOr8SXSQKF2vVD7VrI9Xy1a18mPRMdmRh1ZFSTVzg90Eqca2WiXo74cG0vTfByVq1R1P8Djowabm9Yl6za/zNTiYnP6+qGudeN8jdTuRyN9HAACKEt0RAAB2CtVg+vXXX11NRCVb1fRWN66qOaV+YzXCcixKKqmJtOZTLUjdaCnZ6GvZKdmqJK76MFTiQM0NVZNGSRclFtTsV0ksJQt8k1ElOTWfanOpBq76MVSCWDe6I0eOdDWqdGOvZdX3oG60/UBHqvmjWmsqj5LLSp6ohqFq2Kg8aobrm18rSeCXU1JMN+MaiETboZplSmiqxo5qCCk5rddKuOgGVDV9NI+vPaym4EqKqJmxbiRVq0/bp0GQfP+P+XX3oJte7X9fY0yfqRtcJVV1bFRzMzqBk9/+F9XwUg1JJVKUFNGNsvrv9CPUDx8+3DWJ1f7QDbcSdervVTWWlKhU4kZl1Y22klxqcq7jo/2udWu92r9KbujfSsQFl1NCV8kGHRvtJ9Uy03TtD43aroSoP/ZKDiuhqBpVuoFXjSsldtRUWedgfoMDeTqWqj0YXQNM+0h9puq46fzT9nh+ZHedL0qiq8agElZqCq3uKnRc1UeszkHtB+0/dbGhsqmfSjVj9ueWP1fff/99d95rPykpofNLCUid71pex0PHWLVtlQQpyDmuY+z3sWqj6hhee+217njqXA3uYyXplFxSM2IlrLT96nfXr1Pl1MOXSy+9NO4+VaLVz6/161zTNn7zzTduW5SEya9vTJ0n2lcqg7ZJXTloe9Tdhu9GQd8xfQ+U7FbCVklJnd+q3R1MVAfXqWuE5vn2229dGXyfpNGUmFVNdV3TokVfD3TM9f1S1w1KMOq7MXfuXHctU/NsfS+1H/zgcTpW+t6r+xWftEr0s4PUN6fOdyUF9RBK30XVZvRdVShBqISf+m9WE3YdS12PdH3TtimhqGOi81zduahVgL/2+GbselCi77HWEa/2tK43Or9Ue1HXQdXEFyVF9VBITct1bVRCX60k9D1Xf7D6vupYah/oPNc1QWXR/tF3y5+buqbre6p96ve7v47q+6/kpI6nkpjaH/puqaaq749Zx1nntvjllUT056W2W98pnff6vuscF50behCi7dS1XPPpgZLvRkT7SF1maIA3bZ+2M6+a+do3hx12mPtbFawJrP2jbdTfIG2Pzg3tL3XdEd1Xs45prL63U6lr167uOqz9o/Lq/Nb2Bh+qif7G6VzU9Vjfe30/dB5Fz6frt4631qHrk85lDUYWawBQnVPaP/4YAQCQKiVC9EoOACgmlPzQDbaSE0g99n98Sj4pURzdz6KSR0ryK5GjBGB0n7hKlmjgMO1XJYcKOuBOulKiV2XJlPKIEkZKphUkdFYSWsk9JYZSLb/PVjJXCTnVmtzRZtnaJ/rJb0CqvCgJqWS/+niN7qtT69ZDDiXZ9MCuoN3FZNp3QsdVNTmV+EbB6WGbkuy6PgMAkEp0RwAAAFBAqlmnWm3BWpuxBrpR7WolYdV0PtYAaeo6wyef4jUvLo6UjMukBGyi1NxciTHVMve1XaNr7GXiZ+tY70gCVpQIVk3QWIMlaf0ayEr9zBa2P9RM+k6oVruv9Y6CUzc2qnEMAECqkYQFABQbajbs+0NE6rH/I5tOa0AvNedXTVY16Y5FTYTjvRek/nL9iN9IT/7cz+s7oNqJ6lZCTcWVLNSAfEoepkJBPzuR8qSSulzIj7p3UZ+z2U4Pb9TdgrpsQcGoxYG61FD3MAAApBrdEQAA0p5GwlYNLyW+dtttN9dvou+nD8nH/reYNVzVLYPv+zde36a64Ve/rPlRn5CqKRccnAzpQ32katA99Vupvp+vvPLKiP44g03ONa++J0r0qG9U9eGbCgX5bPWnq/5O1U+v+hdVv5zRg2GlmgZy08CKRTVfNlDNYTWrjx5AEfFri5922mmuJmwiSX8AAIoaSVgAAAAAKGY2bNjgBp3T4Hh5DeaF7YN36aFDIq0TAABIBpKwAAAAAAAAAJBE9AkLAAAAAAAAAElEEhYAAAAAAAAAkogkLAAAAAAAAAAkEUlYAAAAAAAAAEgikrAAAAAAAAAAkEQkYQEAAAAAAAAgiUjCAgAAAAAAAEASkYQFAAAAAAAAgCQiCQsAAAAAAAAASUQSFgAAAAAAAACSiCQsAAAAAAAAACQRSVgAAAAAAAAASCKSsAAAAAAAAACQRCRhAQAAAAAAACCJSMICAAAAAAAAQBKRhAUAAAAAAACAJCIJCwAAAAAAAABJRBIWAAAAAAAAAJKIJCwAAAAAAAAAJBFJWAAAAAAAAABIIpKwAAAAAAAAAJBEJGEBAAAAAAAAIIlIwgIAAAAAAABAEpGEBQAAAAAAAIAkIgkLAAAAAAAAAElEEhYAAAAAAAAAkogkLAAAAAAAAAAkEUlYAAAAAAAAAEgikrAAAAAAAAAAkEQkYYGd6PXXX7cTTzzRqlSpYiVKlHA/RxxxhL322mu55v3yyy/tgAMOCM93+OGH25QpU3bKdgMAAGDnIH4EAKB4KhEKhUI7eyOAbPfLL7/YQQcdZBs3brSZM2dao0aN4s576KGH2iWXXGLnnHNOSrcRKIhZs2bZuHHj7MILL7QKFSrYunXr7LHHHrNu3bpZw4YNd/bmAQBQ7BE/AgBQvFATFkgDe++9t9100022bds2u+iii+LOt3nzZitXrhwBNNLeNddcY71797YnnnjCvX788cftqquusquvvnpnbxoAABmB+BEAgOKFJCyQJpScOvDAA+29996zp59+OuY8qll4/PHHp3zbgILab7/9rGLFiq55pBx88MGuRuy+++67szcNAICMQfwIAEDxQXcEQBr54YcfXLOyypUr29SpU2333XePeF/9fz300ENWr169nbaNKL50uVd/cKmgWjmjRo2yTz75xDZs2OA++4QTTrBLL700ZdtQXPZVNmB/AkDyED8CSEfEf0WHfZk5qAkLpJE2bdpYv379bMWKFfaf//wn4r2FCxe65mTxAmjVcjj33HNd4D1kyBC75ZZbbMCAAVaqVCkrW7asaxr+2WefhefXv++//3679dZb7corr7TjjjvOPv7445jrnjZtmtuuunXruj4+te6hQ4faIYcc4v4YHHPMMXbfffdFLDN69Gjr2rWrDRw40CXeevXq5cog6h9Uzef22msvt7z6Kbvtttvce7pJ0LZoum9mt2bNGre+nj17uum77LKLK5u2S0k+NXMvWbKka2qnZvCTJk0Kb8eqVavctLPOOstuuOEGO/XUU+2uu+5yf8gSoXKqFqcfzEJlv/nmm916zjzzTJs7d64VxPr1612Z/vnPf1qfPn3suuuus5EjR9r8+fMj5nv//ffdOVCjRg27/vrr3edquTp16rhtOf/8890x//HHH93+1XHWdP3b72ftt2OPPTa87RMnTnTHXce7TJky7rzQNnz66ac2e/Zsty06xpr/9NNPtxdeeCG8Pepr7uGHH3bHqX///m69jzzySMQ2B4+rtkfHTMf2rbfesqOOOsq91nGKd1yrV6/u1v3rr7/aF1984Wr36JgGj6ve07HXftEyWlbriPbNN9+4ZfzyWpeW1z647LLL3DQt365dO/e9Ep3jmrbbbru5fb9o0aKEjqnKoXOrZcuW7rzQsdLPYYcdFj6/Vd7gflL/uHfccYfdeOONdtJJJ1nfvn3d9CB9F7p06eLWUb9+ffcZOrbvvPOOXXzxxRHfBfUL6PeZjqveU9NUzatl9DkNGjSIOLbR30Ntr/8eBi1ZsiRiee1zLa9zX5/tz0n1Rfjbb7+5Zf7v//7PSpcubeXLl3ffE20bACA5iB8zP35cu3at+1usfTJ48GD39/7aa6+15cuXh+dRmXTczjjjDHcsfblbtWrl9oeOpfaB3gt67rnnXLyhc0dx2J133mlfffVVxDw6h+6++24Xf+rzFbdcfvnlNm/evPA8f/31lzsmjRs3dg8EFBPo+GtbdY517NjRDSoX1KFDB2vRooWbb9CgQe481Tar/2J9juJHn3gq6LH0fSZrLALFKppX8+nn3//+t4tlJ0yYEHN/K17Usdd5qP2u7Vc8G6RYqHv37m57tN06vjrO2vbOnTvHXPerr75q9957r5v37LPPdsdKMXZQMM7Td1DbqzgqOgbWufD222/nivNULn0fZNiwYeF4VOehvrfRMaaW8zFmNJ0Lfnn9VvlE55Mf8E8t3fxxnT59evheQtuhcytRybgWRe+z0047zZ1HupboM3St0HS1FHjwwQdz7bM999zTnYeaP3j+6ZzSPtf9W/B4+XuOWHGvjr1icL/P/fLaR7oX8IMn6jsoW7duDW+HWhLqGCFDqCYsgPSxYcOG0J577qkIL/Tqq6+Gp992222h559/Pt/lr7322ojXDRo0CB1++OER037++edQuXLlQg8//HB42scffxwqXbp06M0334y77uuvvz60devW8OvHHnvMbecff/yRa7799tsvtHr16vC0e+65J9SiRYvQunXrwtMeffRRt/x7770XsbzWp+laf9CmTZvc9H//+98R09evXx/aZZddQm3bto2YvmLFitDee+8dGjp0aHjaxo0bQ//4xz/cNiZK26fPfeKJJ8LTtB+0Xxs3bhz6+++/E1qPyn7YYYeFevTo4critW/f3m1nLAMHDox4rbLXrVs313wXXnhhqFSpUqHly5dHTJ87d677zOBxk0MPPdRNj/V5KuvmzZvD07Zs2RKqX79+xH5fvHhxaLfddgv1798/1zr8cX3//fcjpj/zzDN5Htczzjgj17q0j6OPq2heLRPcj7Fo+YMPPjjX9BdffNEt/8ADD0R8L/Td+/PPP0OF8cgjj4SmTZsWfq3zJdb53bNnz1CTJk3cuSja18ccc4zbVu3rWN+F6667Ltfn6TyI/i74Msc6R7SO6GOb1/cw3vLR5/tvv/0WqlKlSqh79+7hadu2bQvtscceoUmTJuW5TgBA0SB+zNz4Uduj/fLkk09GTNc+b968eWj+/PnhY3HJJZeE358xY4b7/OA2v/TSS6HBgweHX1955ZW5Yp8bbrghVLFixfB6VXbFKYMGDYr4/MmTJ4caNWoU+vHHHyOmDxgwwH3u2WefHXEsv/rqq1CFChVCt9xyS3jaUUcdFf4cOeussyJiFcUTe+21V6GPpfb36aefHipbtmzo6quvDsde8sYbb7hzV/Fp0OOPPx468MADQ8uWLQtP07Hq0KFDaNiwYRHz+u3RdkfvA31Xvvvuu/A0fW8qVaoU+v7778PThgwZEqpZs2bEPvAUy0V/B30MXK9evbhxWvT3yp+H0XF5XjFmrOXffvvtiOn6Tuq81LYE9+upp54aGjlyZKiwknEtinff4K8lwfg9WGa9H+t4x7oOxDtesZbv169frvcuuugi957K5uk+5bTTTsv3fgfFCzVhgTSjJ2gaxEhPc/WE2T/hHjt2rJ188sn5Lh/dTEHrUa3HINWU0DQ1Ew8+idYTez25j0fLaH2earr56Z5qVeqJpJ6QVqlSJTxdT4XnzJljTz75ZMT6opcPvvbrz2+6npSq+Xv0dD05Vi0+PQH19CRVNSFVm2HZsmVxyxrrc4Nl17/1dF01SKOfYMejp+lqMvjoo49GlHnlypV5ng9BKmP0/hI9HdYT0+j+4PQUXk9wg9vu16t9Ec1PC+5L1d6Vv//+Ozytdu3arjaE9qNqZ+R3nJYuXerKHT09OH+scmla9Pz5LRM9n57QR9OT6AsuuMA9Vfa1UUaMGGHjx493NWELY9OmTRH71O/z6G3U/tS8W7Zsca9VPtUo+fzzz10N6KLYN/Hm95+X6GfEmi96f6oGsGpUjBkzxtWAFdVM0P5UrQsAQPIRP2Zu/Kgajzq+qiUYpFZVqkWqmrWeajcGPyu63MH3VXtRNZH1E4x9VINYtQ8Vq4jiSNUq1e8g9UWsrqZOOeWUcKsiHxeIavQFj6XOE7XSUQ1AX6OxdevWEbW0o7dZ56VqlRb2WGp92kcqi+YJxmmqca19qFra/lj8/PPPbn9rXtW29RT7qFaoWo2p5m309kTH2artvXHjxoja1YqXVZPa71e/j3QfoLipIPFcQWLjZMV/GvvhpZdectvvvytqida8eXNXM7WwknEtKqp9E29+/15h96WoZeQ+++xj5513nrtH0H5VjVrVlM1vvSheSMICaUiJCzUJWrx4sQsE1SRGzRD0x64oaLCk1atXu8RdUNOmTe3PP//coXX75i/RyRc/KFMwcCkKSvzsv//+Vq1atYjpCiDVTEh/jKP/UPrAaEeaSKvPtVdeecXd6KhZXH6UeFMSsm3btrm29fvvv3dB345QcxoFH9r/waZyH374YUTwGgxwggFzXtSkTInK1157Ldf5osSvmp/lRdujLhXUPCydKNhRkyk1BbvnnntcUyA1LyosNVXSvsrPG2+84ZrvBb/P2peyo9+/nUXN+tSsToGjbup086obMwBA6hA/Zl78qHjh5Zdfds2SY1FcqX0zZcoU161TfnFM1apVXXcBooel6jZI3QQEabq6Y2jSpIkrrxKE2h/RiUb/+Wrm7x/CBsVKNOkBvuJC3/Q7kQFb85sn3rFMdHuUFNUDDB8bKj6Otb+VdFYyXN0y5EUPQLQP1eWVYkxPcbASsUrCe9r/6iqhuMZ/Srg/8MADrrxqrq/fwS64kiWZ16KdReeCvuu6J1QlEXU3cvvtt5OAzUC5U/gA0oL6uFJA8+yzz7oknYKC/OipYPCpbV6UhFM/OepLSH/Eatas6frx2VHqo1TrVp+X0U8yFcw1a9Ys1zIvvviiffnllxHlSMSMGTNc0Km+e6ITfCqbgij9IdYfsCAFlEcffXTE0/lEqC8orU8JJtVYVJ+ewdoH+QXdelqrvj2TRTU0evTo4UZIVj9MOm/UT1ysTtxVE0LnmBKou+66q5um7Qv27RWkdajc//3vf12NFJ0v3333XULbpWD1nHPOiXjyH8tPP/2U61gp+ZvXDYWfXwGvaqYceeSRLiEY6wl1NP8EXzcWuilRH047Quet+pxKhPa1vns6Rvrs4JP8WFRjJHrf6AYpHr0XPX+wH61Y/PdQyeQFCxa4frB0c5LoNUUDsenGVMsk2p8uAKBoET9mVvyomoVKWvpYLVqtWrXc76+//trV4MyP9q1iMvn2229dAlC1XmPNJ6oBq7ggkc9Xjdj8+OOo4y3RtXtjyWuevI5lIqK3R33hKoaMldDVPtH5rrLGi2GVZP3ggw9cPKrvYqx4VElztVTTe4oBgy3NoikOjj4P9Vn5PdAIxqP51bj2MabidPVRqjLqHPE1mvOjeVVmJZz9/iusZF+Lovelzp1EvruJ8sdLtbK1nGq06nuhvo0ToQczumardvbw4cOTet+InYckLJAmFAjpRwMQqKmMatRp4CMly/RUvn379gkl+pQ4yY8CBF3cFUSoeZdftz4/r+RJIoMR+KfkapaS6AiO+qOtWpyemmgpQM2LAmEFN0r85LUdauYUbE62I9RkyQeu+uOqZJOevutGR0FUXlRj1C9XEIkOACFqbqiandonSsKqa4J4T6PV/EnNv9S0TB3g61jpCWy8RKmaCKq2qJrhaRk9ldX68+twX82w/GAA+dViiXWs1D1AXoLz62azU6dO7inym2++GbPGRjTtL9WwePfdd+2jjz5yyxeWjnEi57y2TR34q0sEPelWzRWd8xqQIq8n/tH7RgOlxaObh+j5lehVzehEvofqPkI1WVW7QdeJRLpoUFJbCW0NPKCboeiBPwAARY/4MbPjR991Ubz4UeUJzlfQuCW/uLSoP9+fC0Uxynt+x7Iw26Ny5HW+6jNj7Yvg+aKuDPT90MBVelCgJubyxx9/uHNWSUbFcL4VlGLreFQRIfo81IBnecXUSsb72s6iefVwI5EYUw8gVEtblTgUgwe/X3lRLK04Wl2v6UFDIjH4zrgWRe9L3cvou5jId9dTGRM9Xs8884x7cKPzNK/lohOxGrhL911qZVbYbtKQvuiOAEgTCpb1xyT4R1KJNCVC1B9WIsGKnqqqWVB+1B+T+gjTH9dgcB4MOmI9SUwkwFJzIK0nXo3K/GpDJkrJRf2RU+IwFv0B13uqtRmLtrEwAaun4EJ9DqlmaPRIxLFoZFolLnWDEC+o84FsMDguSBJWT551bPXUVglJnTN5Nc1SLQwFruqbyzd72WOPPXLNp24U9L5qtKo2gm8WE9w21UQNjtDrp+kGY0drmCZqv/32c4GjRimN1SwumrZfzTXVfF79gml/KPlYGEpwxupjN1ZwqWS5RoRW0K0ErN+WoKKoVbQjVLNFiVQ90dfvROiJvZIAugHWDUi8EYcBAEWH+DGz40cl9yRebTzVrg3OVxBKnClhFR1/BlvVqDak4r6i+nwlIkUJymQfy8Jsj8qh+DtWPKgEpfrpTKSs6p5JNVyPP/54d85oH+t7qeThuHHjwgnYRL4/qaRjrS4FtE19+vRJaBldP9TtmmJvdYGihGNhJftalGpKoqpbCt1HRd8nxaLzTl116D5O3WecddZZBboXRPFAEhZIEwog1HREQainphVqlpFIkOabTsVrLhSkP/5KWKnj9KBgwKn+sPSH4IknnnCBgwKSRIJOdcSuAFOJwGhqzqS+QXeUnugqAIre/qBKlSq5gZfUTC3WAArqtyjYhK0wfN9S8QL1INV00B9Sfab6aot28803u+n6o6s+Q0WDeCl5WxAKStSsTEnFRJp4JULni6irg6BgudWMK7p5lJK2SsYVRW2HRPkaJf6mIC9KLqrrAtWG1XmuJKqCpcIEO/q+JDLwiRLE+h7ltS/9+na2guxLDaiiAFzJZV0DVHNAtT0SHbwEAFA4xI+ZHT8qKaymzPFqPqqFi2otqvl7Qen8UHJbsUk0Jbd0LPQwXw9YVeZYyVp9vro06N69e673Yu0/1QBVnBrdl2cyjmWi26Pa44qfRQ/z/fpjxTo6lxMddErHWd8t7WP186mKGCeeeGLEoLv6/gVrbqZD/KdrirYxkfhP86j7AyUZ//GPf7im+GoJFRyQLFHJuBalA8XTqj2d3xgamkeVQ1SZQbWl1WWazkNVckBmIQkLpAk1TVLTbj8ggf6odevWzT3lVUCYFwW4elqpmhCxLujRtQcUqOvpYDAY+fjjj93nKHDXH0EFwqrFqEBUfwD0hyC6qbYf2Ck4wJM6m7/33ntd02r10+MpaFEAHQxc/HLR2+dfRw8c5V+rmYaacke/Fz2//ogpIFAZgp8xa9Ys18eWmt8kIt4AViqn74s1EZpfA2ipWUuw/yf1R6Qnz2rColqp6jtICS2dD3pqnl85gxQIKxmodSUy2EG0WPve39gFbzoUJKmWpOg8UmDhm8v4ZRXI+r7CgtPjHddYtVw0LVZ5/bRgkzDV2FCtXZXd3wxoPt28RVONIfVf5fevbmA1OrBuRPJryhhNiXP1j6tzLchvW7BcChij96W2UTc6Onbx9mVB9028+YPrDM4f3F7/byXRFYT7m3g/X/T+1HfptNNOi7hB1s23bsKV1C5oFxwAgMQRP2Z+/Kj9qXXdeeeduZpSa5s00Fes5t8+1lRMGYsqB+j80Tmg9XiqsaeuhfwDY9WMVLJLLaaCD6oVRyluUnPuWAOTqsVPsM97nQ8qixKf8WrC5rfNBT2W0Q/fffdgoq4CVHNT3Vipiy5RrUUd/+uuuy6i9q8eavTr18/1O6vvV/T2RFOTfCVetX8VS6lfY7V+Up+pwX2o2FMDvvnvVPA45hXP5RUbF/R7ER2nKamqrkx8S7Z48Z8qjxx33HGuiy3fr7CSiIp1dWwK0rosWdeiHd03ic7vp0XvS90j6FxQy4QWLVrE3Zdar8quPopVOUR0XuheSveG+Y3rgOKFPmGBNKE/9uprU00rfH9E6gsmv0SanjYqgaOO1KMHBdIfUA2woyesesKr0U/1R1Gj3CugVaJKQbv+cOqiryBEf0z9H1SNDKon4WquraeSqjUo+uOmJ52a7oNINbfRH15RoKz+j9TvqJJweoquP65qFqc+btQUR01V/PLaFj0x1Q2DOiNXEy1R+RUUqqmRaof6Jubq70fLKHhUszUFUQsXLnR/7K+44go79dRTXY0APYFWgKj1KDGppj8qq8qSaBNrPdn1/ZIqsaQ/+KoJoIEKtB+0Tf/6178SfhKqZjbad2o+qCZeGtxBf2z1B1ZUM0CBp/ahblp85/SqaapgW8dayUYFdXqarhqv0ZRYLOhIyOq0X81f/FNjnScKGJQw1g2R9qXOUTUR0jYroFQQrWBH82h+lUnz+OOqRKeeSOv4+aZ3eR1XlU3nkMqm5l6qbaKbHdWk9cdV57hGsFW/qqIEoTri1/yqiavzUH0uqb8s3ZRqeQVEOv9VM1M3pyqnajNoH33++efhG1f1oSdaXrWQlViMtX89DUKlfqi0z3QO6FwJ8ueNvgc6D/v27etqs+hmRTdg+l4q6Ffg5YNW7T8dC52fCsx9OdWnlK99oYSvrhNaXoMR6IZAyU6dj/oOq8wK8lQTWjcKClT1vXr++ecjjq2+/6qB7Y+LbpbUn5cCQ410rGOumwUlhPUZOh6i/aLvk0YJ1nbqeqJt0371TdeeeuopVy4dU/UndtJJJ6WsWwoAyCbEj5kfPypO1N92lV1dJyk2VKJUST09yFff/kEaoFU1VH0/8IpV9De5UaNGuZqYK6bS32zFEXqIrf2uGE8JXz/AkhKsipv0cLZnz54uLlAZFJ+pT85YXVn5GE3nmZJhSpYpBlOcceihh0bMp21TnKiknj9Wil8UZ+hc8w+wlcAt6LGM7iJA55ZaPinBqnh78uTJufogVbymz9R6tD9UTq1b37VgjV/tN789KpeOuWJOxb7qfkr70J/bOmZKQmserUOVMvR9VTzWuXPncLdYSnSrlqfOW18e7Ud9/3S8dS4pNtR31N8LaD/df//94RhefcJquu4pdG3wtcv12Uo6K/bTue1jTF07fKJTn6n9qe+YYjd9t3yLOO1vJQMVJysu1Dw6rkrS+75jdc4phtT5oeOsiiX63Lz6Pk7mtUiJcB0nvw90/qpcOr803oXKHn0t0vb42uE653Ut0WfpM/21x8flOjaKy/We9p2uUdo/Oud17HT/pmue5lMc7j9P57KW1+fp/k4xtu5d1Jevjpv2g/aHtlvzadtUI137oDCVbJBeSoToZAIothSYKjBSskt/xH3/kkG6cKvGooJTBQR6GofMpqBDAyr5vluxnX9CXdgBA4IUpCkRq++WAr9Yo8EqOaxarwqIFXwl0s9VcaHwQfsz1qjKAID0RfyIZFJSSQ+ClbwKDhC1syheUzKRtEfRXkOKIv7jWpSzD3Rfksru27Bz0R0BUIyp2bJ/chrrj5YoMaQn+HpKGStJhOJNT6v1VFdBiSjgVU0GErCxKcgpigSsqMaGAkL12xvvu6XvpWoI66l4rL6AizMFiyRgAaD4IX4EsCOKKv7jWpSzL0nAZheSsEAxpqROojXrdHEPjsSJzKC+htRvl5r+qFaimpSr+RSST0/mEx04TU2lMi0JCwAonogfkUz59eua7duD7bgWIRuRhAWKMfXZlOjgAOL760HmUJ9H6jtIfXeqny/1LZRXv0soOurfNVEKGlVDGQCAnY34EcmgfmpVq1HN/0X/VndMO4v62FQTd41hIOqr2G8b0gPXImQj+oQFAAAAAAAAgCSiJiwAAAAAAAAAJBFJWAAAAAAAAABIoswbXi4NaHCchQsXWpUqVRjpDgAAIEHqJWvNmjVWt25dK1mSugI7A3EsAABAcmJYkrBJoMC1QYMGO3szAAAAiqV58+ZZ/fr1d/ZmZCXiWAAAgOTEsCRhk0A1B/zOT/Yo5aqtsHTpUqtVq1ZW1BihvJktm8qbTWUVypvZKG9mS2V5V69e7RKAPpZC6hHHJkc2lVUob2ajvJktm8qbTWUVyrvzY1iSsEngm24pcE1F8Lphwwb3OdnyJaK8mSubyptNZRXKm9kob2bbGeWlGfzOQxybHNlUVqG8mY3yZrZsKm82lVUo786PYTN/rwMAAAAAAADATkQSFgAAAAAAAACSiCQsAAAAAAAAACQRSVgAAAAAAAAASCKSsAAAAAAAAACQRCRhAQAAAAAAACCJSMICAAAAAAAAQBKRhAUAAAAAAACAJCIJCwAAAAAAAABJRBIWAAAAAAAAAJKIJCwAAAAAAAAAJBFJWAAAAAAAAABIotLJXDmAwtu6batNmDPBli5earXW17IjGx1ppUqW2tmbBQAAgAIgpgMAAEISFkhDr/36mvUe39sWrl5oB1Y90KasnmJ1q9a1kV1GWrc9u+3szQMAAEACiOkAAIBHdwRAGgbr3V/pbvNXz4+YvmD1Ajdd7wMAACC9EdMBAIAgkrBAmjVXU22JkIVyveen9Rnfx80HAACA9ERMBwAAopGEBdLIxLkTc9WWiA7a562e5+YDAABAeiKmAwCkEz30+3TOpzZh9gT3m4eAOwd9wgJpZNGaRUU6HwAAAFKPmA4AkC7onzx9UBMWSCN1qtQp0vkAAACQesR0AIB0QP/k6YUkLJBG2jVsZ/Wr1rcSViLm+5reoGoDNx8AAADSEzEdAGBno3/y9EMSFkgjpUqWck0CJDpo969HdBnh5gMAAOln3Lhx1q9fP3v00Uetf//+9uKLL+a7zOeff25XXXWVPfLIIzZ48GAbMWJEzPk2bNhgd999t1122WX5rjMUCtkJJ5xgCxYsKFQ5sGOI6QAAOxv9k6cf+oQF0oz6ZBnTY0y4zxZPtSkUrNNnCwAA6WnSpEl22223uaRqiRI5ibauXbtayZIlrWfPnjGXmTlzpp177rn2ww8/WPny5d203r172/Dhw10SV1avXm233367lSpVysaOHWsHH3xwvtvy0EMP2VtvvWWbN28u0jIiccR0AICdif7J0w9JWCANKSjv2rKrTZgzwZYuXmq1dqtlRzY6ktoSAACksUGDBlmPHj3CCVg5++yzbeDAgXGTsLfeeqt16dIlnID1y3Tq1MmuvPJKq1ChglWtWtUld2XixPxrq/z++++2ePHiIikTdgwxHQBgZ6F/8vRDEhZIUwrO2zdqb0sqLLHatWu7WjQAACA9rV+/3iZMmOASp0FNmjSxP/74w9V4bdq0aa7lxo8f77oviF5m1apV9sUXX7hkbEFs2bLFdYGg2rU333xzvvNv3LjR/XiqdSvbtm1zP8mk9avbhGR/zs6m7gfaNWhnS8svtVq1arnXmV7mbDm2HuXNbJQ3c2V6WdvWb2sNqzZ0g3Cp64GSVtL9DdJv0b/VOkPzZeI+2JbC45voZ5CEBQAAAHaQkqxKgFaqVClieuXKlcO1U6OTsOvWrbOFCxfmuUxBk7DqV/biiy+2TZs2JTT/sGHD7Kabbso1fenSpa4P2mTfsCjZrBukTH/YnE1lFcqb2ShvZsum8mZDWe894l67/bPbw0nXZhWbRQzMNeCIAbbsr2WWibal8PiuWbMmoflIwgIAAAA7aMWKFe536dKR4bV/7d/f0WXyMmXKFKtXr57VrVvXZs+endAy6irh6quvjqgJ26BBA1djU90gJPvmSF036LMy9eY3G8sqlDezUd7Mlk3lzYaynlT7JLNKZle9e1W4f/JvV39r9arWs3uOvcdOanWSZaptKTy+wW6l8kISFgAAANhBvh9Y1bYI8q+jpxd2mXhUa/Xdd9+16667rkDbXa5cOfcTTTcrqbgh1T5I1WftbNlUVqG8mY3yZrZsKm82lLXbXt2sa6vs7J88Vcc30fWThAUAAAB2ULVq1dzv6G4AfH+r/v0dXSaehx56yP7zn/8UYssBAECmY8yZ9EASFsXG1m1btz+5WZ89T24AIJ1xbQZyqL/XUqVKhQe28tQXmTRv3jzXMur7tU6dOgVaJt6gYJMnT7bFixeHpy1ZssT9Hj58uOue4MYbbyxEqQAAAFBUSMKiWHjt19es9/jerg+TA6seaFNWT7G6VevayC4jrdue3Xb25gFAVuLaDGxXsWJFO+KII2z69OkR06dNm2YNGza0Fi1axFyuc+fOMZfR+tq2bZvQZ1eoUMFeeOGFiGmffPKJPfXUU9a/f39r3LhxgcsDAACAokX9YxSLm/zur3S3+avnR0xfsHqBm673AQCpxbUZyG3w4ME2ZswY27JlS3ja6NGjbejQoa5PsqlTp1qbNm3sww8/DL8/YMAA9zo4qq6W0XTVlI01yIR+8rN169bw/AAAANnWWu/TOZ/ahNkT3G+9TgfUhEVa0xdFtaxClntgCk0rYSWsz/g+1rVlV5q/AkCKcG0GYuvYsaMNGjTI+vbtay1btrSZM2faKaecYr169XLvr1u3zubMmWNr164NL9OqVSt7+umnXdK1devWtmjRImvUqJH169cvYt1DhgyxlStXum4HfvvtN/cZ9erVsz59+uTaDm2DasJK7969rVOnTnbVVVclvfxANqJbHqB44rubuV5L49Z6JGGR1ibOnZirllX0zf681fPcfB0ad0jptgFAtuLaDMTXtWtX9xPLQQcd5BKp0dSNgX7yMnDgQCtbtqyNGDHCQqGQ+wnWuA26/vrrXdJWg26oJuzmzZsLWRoAxfVGH0WDRF1m4rub+a31QhaykoHG/7613pgeY3bqMaY7AqS1RWsWFel8AIAdx7UZSL1y5cq5Lg1Ev5VgVVI23rx+1GP91msARStbu+VJ1ya+yaBj2HhkYzv62aPtri/ucr/1OlOPbbYc32z97maDrfm01hO11tuZ5zVJWKS1OlXqFOl8AIAdx7UZAJDNisONfjJkU1IyGxN12XB8s/W7my0mFqC13s5CdwRIa+0atrP6Veu7P3axLpTqd1Dvaz4AQGpwbQYy38zlM63Klirh15XLVrbdKu9mm7Zusnmr5uWaf49d9nC/dV3YsGVDxHu1K9W2KuWq2KoNq+yvv/8KT1dXCeont7bVtm2hbTZrxaxc621UvZGVLlna1az/e/PfEe/VrFjTqpevbms3rbXFaxdHvFe2VFlrUK1BTllWzHTdNwTpPc2zZN0SW7Nx+6BoonVq3es3r7eFaxZGvKdmyI2rN3b/nr1ydq4b9bpV6lqFMhVs2d/LbOWGlRFl3bh+oytrrH2o2s1NazR1/9Z7midI+17HQOvUuoMqlqnoHnpt2bbF5qyck2sfNqnRxEqWKOnKojIF7VpxV6tWvprbB9oXQeVLl7d6Veu5f89YPiPXev0+1L7XMQiqVq6a+61jtnhd5LEpU6qMNazWMO4+1Gfqs3Wu6JwJqlquqtWqVMs2btmY60Y7v324e+XdrVLZSrZi/Qpbvn55xHuarvfj7UOtV+v35/eX87/M9flbQ1vDfxP9jb6SV6fufao7/3Qexju//1z7p63btC7ivV0q7GI1KtRw0/V+Qc5v/Q0uV7qcLV231FZvXB3xno63jrvKofLEO7/nrpprm7du78rk3Rnv2n/e/k/OvrYStmHbBldO7YdTXjnFnjjxCTtv//OK9Boh+j7pe5XKa4TOySvfuTLiePryBhN1nZt2znV+F/YaIdoH2hc74xrxxm9vuOMY6/gq6fzcyc/ZofUPLbJrhM5tneM6ZtEtp5J5jdB5HZwePLbB7+5LP78UUd6CXiOCtD3aLn0X9Z2MtQ9TdY3Q36Jlq5ZZtV2qWYWyFYr0GiE6z3S+6Rqra21QMuKI6GvE94u+tyD3jY26Porma1C1QZHGEWtWR8YS8ZCERVrTl1z9sujCrz8GQf71iC4j6JcHAFKIazOQ+QZ8OMDKVCwTft2hUQe75vBr3M19n3dzDwY27vRx7ve9X95rvy/7PeK9qw+92jo26Wifzf3MHp7ycHi6bmhaVGlhdzW5y91wxVrv8yc/724IH//2cft64dcR752///l2UquT7Ps/v7fhk4ZHvNe0elMbedxI9+9r3rvG3TgHjTp+lLvJ1432+zPfj3iv+57d7ez9zrbpy6fbdR9dF/FezQo17emTnnb/HvLJEFu2PjLZcVun26z1bq3tzT/etDG/joko66G1DrW9Gu3lbpajy6qb7NdPe939+67P77KZKyNvxvu37W9HNDzCPpn9iT3x3RMR7x1c92C7sf2N7kY81j58ufvL7qb44ckP23d/fhfx3iUHXmL/bPFPm7xwst3z5T0R77Ws2dLu6nyX+3es9T56wqPuhvv5H5+3T+bkDATnnbbXaXb0bkfbb3/9ZjdNuCnivTqV69ij/3rU/fv6j67PlQC485g7rdWurVxS6L+//zfiveObHW+XHnSpS6JEb1OF0hXslVNfcf8e9tkwl0gJuqHdDXZI/UPsg5kf2LM/PhvxXtsGbW3AEQNcAitWWV/r8ZpLDD3w9QP289KfcyUmZO3WtbkeTGrfKAmr8y/Wep/q+pRLdjz9/dM2ad6kiPd67dvLLfvzkp/tlom3RLyn5MGD/3zQ/XvABwNs/ZbI5PqIY0e4hMaYqWPs7elvR7ynQTMvOOACl9zq+37fiPeUKHqh2wvu37dMuMUWrV0UPn8/nPVhxLxzN8yNKO/V715tZ7c5u0ivEbL/7vvbzR1vTuk1QmVYsGb7MVY5o8ur8+uVqa/Y67/lfG939BohxzQ9xq485MqUXyNe7Paiqx2aV3mvevcqO7jeweFucXb0GnH6PqfbGa3PcNeIwZ8MTtk14uRWJ0dMi1VWufuLu8MJ5sJcI4KuOPgK67xHZ/fw5v6v7494b59a+9iwo4el7Bqh7/KmjZtsVM1R1nzX5kV2jfBu6nCTHVDnABs/fbyN/nl0xHvJiCOirxH6TgbpuG613LWaNd+Hsz8s0jhi89+J9b9fIhQrLYwdsnr1aqtWrZqtWrXKqlatmtTP0pOMJUuWWO3atcN9f2VLx9m6KOomP5M7zs6W45uN5c2msgrlzUxcmzP7+O6M8qYyhkLex+C7Wd9ZlaopqAm7ap3t22RfPcHJ/Jqwaza6JOyW0JasqAm7Zc0Wq1yjcsbWhD3z9TPD72vwl/2q7GffrfkuIpnzSvdXMqImbHR59cB1n8r72M9rf44o78dnf2yHNzi82NeEHff7OLvqvavyLe/TXZ92ic/iXhN2zqo5dtSzR+VbXiW8g7VDi2tN2I7PdCxwWTOqJuyyZdamSZuMrAk7fdl0a/9M+/D+0PE9sMqB9u2ab22bbcvZxsp17JOzP3FlKeqasPs32T/fGJYkbBKQhE3ByJS7ZcfIlNl0fLOtvNlUVqG8mYtrc2YfXyEJm12IY5Mjm8qaDeXV3z71lem75VES1j+M1I2+75ZnVu9ZGfE3cfRPo+2M184Iv44ub7BG5emtT7fiTrVJg4m6eOVV0rlD4w5W3GXT8c227262XZuD/TmHk7D/O74+yT6mx5ikVBZJNH7KzL2OjKSLYPtG7e3Ixke635l4UQSA4oZrMwAgW7vlkWzolifbBuT0fd9HH1tP09XcO1P6vs+m45tt391s1G3Pbi7RGuxOQvSdTlYCtiBIwgIAABSgBsWncz61CbMnuN+ZPnputpUXADLlRr8oZVtSMtsSddl2fLPpu5utuu3ZzWb3nm0f9PrArj3sWvdbtZvT4dgyMBcAIHXN1ddnR3N1ZE8fuHWr1nU3aukQ1BW1bCsvABSUroUaxCbTu+XJxgE5faLO/x0MJuoyre/7bD2+2fDdzWal/tdab0mF9Op+gSQsACBpSOIg0/qX8v2HeepTTNMzreZEtpUXADLtRr+oZVNSMhsTddl4fLPlu4v0krZJ2HHjxtnEiROtWbNmNmPGDGvTpo2dccb2zqJj+fzzz+3VV1+1Vq1a2cKFC61GjRrWp0+fXPNt2LDBRo0a5db74IMP5rlOjVv2r3/9yx555BGrVy+yujoAID6SOMik2ty6KQmOmutpmmqJ9Bnfx92oZcKNWbaVFwCQmGxKSmZjoi4bjy+QammZhJ00aZLddtttLqlaokRO9feuXbu6C17Pnj1jLjNz5kw799xz7YcffrDy5cu7ab1797bhw4db//79w6OV3X777VaqVCkbO3asHXzwwfluy0MPPWRvvfWWbd68uUjLCACZjCQOMsnEuRNt/ur5cd/XOT1v9Tw3XyaMkpxt5QUAJC6bkpLZiOMLJFdafqMGDRpkPXr0CCdg5eyzz7bBgwfHXebWW2+1Ll26hBOwfplhw4bZ+vXr3euqVau65O7QoUPdBSU/v//+uy1evHiHywMA2aYgSRwg3S1as6hI50t32VZeAAAAICtrwiphOmHCBLvyyisjpjdp0sT++OMPV+O1adOmuZYbP3689evXL9cyq1atsi+++MI6depUoO3YsmWLvfjii6527c0335znvBs3bnQ/nmrcyrZt29xPMmn96jIh2Z+TLihvZsum8mZ6WRetXhTRBYH+rdqvwWl+vkzcB5l+fLOtvLtX3j2h81nzZcI+2JnlzYT9h8QwaGPm4tgCAFBMkrBKsioBWqlSpYjplStXDtdOjU7Crlu3zvUBm9cyBU3Cqg/Yiy++2DZt2pTvvKpte9NNN+WaPmXGFKtUefs2VSpTyWpVrGWbtm6yhWu3d3btNa7W2P1etG6RbdyyPakru1bY1SqXrWyrN6625RuWh6dvC22zzX9vdje/Grhw7uq5udZbv0p9K12ytC1et9jWb8mpFezVKF/DqpWrZus2r7Olfy+NeK9MqTJWr3JOP7hzVs/J+YyAupXrWtlSZe2v9X/Z2k1rI96rWq6q7VJ+F/d5+twgBWENqjRw/563Zp4L1IJ2q7SbVShdwZVT5Q2qWLqildlUxu2fP//+M+I91ZxuVLWR+/eCtQts89bILiS073UMVm1cZSs2rIh4T5+nz92ybYvNX5O79l7Dqg2tZImS9ue6P23Dlg0R76mcKq/2gfZFULnS5axOpTru37NXzc61Xr8Pte91DIJ0XKqVrWZ/LvvTZq2a5T7f0/HUcY23D3evtLuVL13elq1fZms2rYl4T+eRzqeNWzfaorWLCrQPa1esbRXLVLSVG1fayg0rI97TdL0fbx9qvVp/rPO7ZoWaVqVsFVu1YZXNXjrbqqyqEi6v34c6/3Qexju/l/y9xP7e/HfEe9XLV7fq5aq76Xq/IOd3ncp1rFypcjHPb22rtlnngs6JeOe39oP2R6x9uOzvZTZ/2fyIsibjGiE6F3RO6HqRqmvEprWbbP8q+7uybd622bbZNtut7G62V+W9XC3Y0vqvZGmrsqWKfT3t6yK7RvjzO9Y+TOU1Qvt6zZo17vhqe5J1jdDxiXV+p/oa4cu7x6Y9rHK5ykm7Rmhbtc2x9mEyrxHlN5a3I3Y9InxeKCHZpEIT9+9N2zbZVtvqjkW5jeXc+VwU1wh9ls7FoFRdI8qFykWUt3yJ8tasYrNwWcWXd9q8aUUaR6xbG3meIzMxaGPm4tgCAFCMkrArVuQE/KVLR26af+3f39Fl8jJlyhQ3CFfdunVt9uzcN8XRBg4caFdffXVETdgGDRrYHd/dYWUqlglP79Cog1192NWu+d4tn96Saz3/1/P/3O8737/Tfl/2e8R7Vx16lXWs39G+mfaNPTLlke1vhMyaV21ud+57p23YusFu+Tj3ep876TmrVr6aPTrhUft6YWSy47z9zrOTGpxkn839zO6YckfEe01rNLURx45w/77kk0ty3Sg+cNwDVrtabXv565ft/ZnvR7x3yp6n2NltzrafFv9kt0yJ3CbdmD7V9Sn37/5f9M91Q31rx1utUe1G9s4P79jYX8dGvHdUk6OsZ+Oetr7sertlYuR6dZP9Wo/X3L9v++42m7liZsT7/Q7vZ0fUO8I+/+1ze/L7JyPeO7juwXbDkTe4JOAtn+Tehy+d8pK7KR71ySj77s/vIt67+MCL7Z8N/mm/zP7F7p1yb8R7LWu2tDuPudP9+4KPLsi13kf++YjVrlLbnv/ieftkzicR7/Xcu6f7+WnZT3b/lPtdkj2YIHzkhJzz4OpJV+dKRN1x9B3WcNeG9n/f/Z/93+8555V3XLPj7NJ/XGozls/IdR4q0fRy95fdv2+afJNrLh50fbvr7ZDah9inUz+15358LuK9tg3aWv+2/e2vv/+KuQ/HnjrW3ZCP+HCE/bz054j3Lj/ocutcv7N9N/07u+fne6xc2XLh8u5Tax+77ajbXLLnwo8vzLXeJ0980natuKs9NekpmzRvUsR7Z+17lp3a4FT7asFXduuUWyPea1C1gY06fpT79xUTrsiVfLy3873WYJcGNnbyWHtn+jsR753Y8kS7YP8L7Le/frNbJkSWVcm2509+3v37xq9vzJXEGtJ+iDWu3dje//F9e/bnZyPKmpRrhJntv/v+dlOHm1yiKVXXCCVbFoUW2eI1i10CVuZumGurtqwK9wmrxGXd3evaoE8GFdk14pimx9gVB19hc1fNzbUPU3qNCJlt3LTRHd+L/5G8a8QZDc+wbxd9m+s6m/JrxP/KO6TTEGtau2nSrhHfz/jeHpjyQMR7qbpGrCuzzn766yf3WuevTF071SUldU4fWOVAu+3b24rsGvHBTx/YS7+8FPFeKq8RwfKWslIuQf7j2h/D32df3qKOI/RgGZmNQRszF8cWAIC8lQhFV0tIg0G5jjjiCPvoo4+sY8eOETVk99hjD3v++eft3//+d8QyCxYssPr169uTTz7pug8INmnTIFy33HKLXX/99RHLdOjQwRo3bmxPP/10xPQNGzbYPffcY9ddd517rSSsujWYNWuWmz8RSsJWq1bNvpv1nVWpWiU8XTVQdqu8m6vBMm9VZHJL9thlj5zyrF6Qq7Zl7Uq1rUq5nJqCuoENlnHdqnW2b5N9XSJn1opZudbbqHojl3zQTVt0LaCaFWu6mkCqgbJ4bWSNVdVOaVAtp7aOkhXRp4re0zxL1i2xNRsja1JpnVr3+s3rbeGahblqATWunrMvZ6+cnauGVt0qda1CmQqupmB0TSrVArJ1ZtVrVrcFaxZEvKcbRN3wifav9nOQ9r2OgdapdQcpeVKnSh13gzhnZe6aVE1qNHG13FQWlSlIN/dKYGkfaF9E1y6qVzWnFpASGtH8PtS+j65tWaNCDVdDa/aC2ba1wtaITtGVqGhYrWHcfajP1GfrXNE5E50AqFWplqslFd1nZ377UE1PK5WtZCvWr7Dl6yNrUmm63o+3D7VerT/W+a3t0XatXL/Sfpv7m9WsWTNcXr8Pdf5FJ82C5/efa/+0dZsia1DtUmEXtx81Xe8X5PyuX7W+q2G3dN3SXAksHW8dd5VD5Yl3fisRGF3bUueZzre/1v1l0+dPjyhrMq4Rou+Tvleq5ZbKa8TkhZPt9LGnh5Ou+1Tex35e+3N4sK6xPca6hF9RXiO0D7QvYu3DVF4jdG1etmyZO761K9dO2jVC57iOWXTfnKm+Rvjy7t1ob6tSvkrSrhH6Luo7GWsfpuIa8e6Md23ohKHueBxQ5QD7bs13rkzXH3m9HbvHsUV6jdD+034MSvU1Iljef1T9h01ePdmVVw8kfHmLOo5Ys3qN7d9kf9edlPryR+r5ODYZx0DXosYjG4evLUrU+dqSSvDrb4X+/s7qPSvjmq/rOrlkSeYOdJPNxzYbjm80ypvZsqm82VRWobw7P35KuyTszz//bK1bt3Z9vB577PYbml9//dX22msvGzdunJ1wwgkRy6xdu9aqVKliDz/8sOtCINi/bMWKFe3++++3yy+/PKEk7L333mvnnXee23k7moRNxQ0EX6LMRnkzV7aUNVazRCXMRnQZkdG1YbLl+GZjeSP6Otwt8/s6THV5UxlDIfXH4JPZn1jHZ7ZXsohO1Hkfn/2xdWjcwTJJpl8ns/nYZsPxjUZ5M1s2lTebyiqUd+fHT2nXHYH6e1XtVT+4laeCSPPmzXMto75f69SpU6BlYlHSdvLkybZ48faaHDpgMnz4cNc9wY033liIUgFAdlKitWvLrlmVtEJm07nbvlF7W1IhOwLYbCsvkiu61v6Ozof0wbEFACB/aZeEVc1VdUcwffr0iOnTpk2zhg0bWosWLWIu17lz55jLaH1t27ZN6LMrVKhgL7zwQsS0Tz75xJ566inr379/wjVhAQDbkcQBAPjuNopyPqQPji0AAPlLyzvhwYMH25gxY2zLlu0DOIwePdqGDh3q+oubOnWqtWnTxj788MPw+wMGDHCvNTpzcBlNV03ZWNWS9ZOfrVtz+tFLZF4AAAAAsbVr2M71C+oHt4um6RoUT/OheOHYAgBQDGvCigbkGjRokPXt29datmzpBuU65ZRTrFevXu79devW2Zw5c1xfsF6rVq1c/65KuqpP2UWLFlmjRo2sX79+EeseMmSIrVy50nU78Ntvv7nPqFevnvXp0yfXdmgbVBNWevfubZ06dbKrrrrK0rKftvU08UXxxvkMAEBm09/1kV1GWvdXuudK1vnX6jOcv//FD8cWAIBimoSVrl27up9YDjroIJdIjaZuDPSTl4EDB1rZsmVtxIgRbpRe/QRr3AZdf/31LmmrprOqCbt5c+QIxuk22E3dqnVd8JPJg90gM3E+AwCQHfR3fUyPMeG/+55qUWb6oI2ZjmMLAEAxTcImS7ly5cL/VtcG+lFSNr95lYgNvt7ZCSs9ZQ5ZyI086i1YvcBNV/BDkIPigvMZAIDswqCNmYtjCwBAMesTFnk32dbTZSWsovlpfcb3cfMB6Y7zGQCA7B608cjGR7rfJOkyB8cWAIDYSMIWMxPnTrT5q+fHfV+Jq3mr57n5gHTH+QwAAAAAALIBSdhiZtGaRUU6H7AzcT4DAAAAAIBsQBK2mKlTpU6RzgfsTJzPAAAAAAAgG5CELWbaNWznRhgtYSVivq/pDao2cPMB6Y7zGQAAAAAAZAOSsMWMOrYf2WWk+3d04sq/HtFlBB3go1jgfAYAAAAAANmAJGwx1G3PbjamxxirV7VexHTVKNR0vQ8UF5zPAAAAAAAg05Xe2RuAwlFiqmvLrjZhzgRbunip1dqtlh3Z6EhqDKJY4nwGAAAAAACZjCRsMaYEVftG7W1JhSVWu3ZtK1mSis0ovjifAQAAAABApiLLAQAAAAAAAABJRE1YAACK0NZtW7d3rbGerjUAAAAAACRhAQAoMq/9+pr1Ht/bFq5eaAdWPdCmrJ5idavWtZFdRjLIHAAAAABkMbojAACgiBKw3V/pbvNXz4+YvmD1Ajdd7wMAAAAAshNJWAAAiqALAtWADVko13t+Wp/xfdx8AAAAAIDsQxIWAIAdNHHuxFw1YKMTsfNWz3PzAQAAAACyD33CAkCKMXBT5lm0ZlGRzgcAAAAAyCwkYQEghRi4KTPVqVKnSOcDAAAAAGQWuiMAgBRh4KbM1a5hO6tftb6VsBIx39f0BlUbuPkAAAAAANmHJCwApAADN2U2dSeh2swSnYj1r0d0GUG3EwAAAACQpeiOAADSbOCmDo07pHTbUDTUncSYHmPC3U14qiGrBCzdTQDZYdy4cTZx4kRr1qyZzZgxw9q0aWNnnHFGnst8/vnn9uqrr1qrVq1s4cKFVqNGDevTp0+u+TZs2GCjRo1y633wwQdzvf/RRx/Zxx9/bOvWrbNff/3VDjnkEBswYICVL1++SMsIAACAgiMJCwApwMBN2UGJ1q4tu24feG03Bl4DssmkSZPstttuc0nVEiVyasF37drVSpYsaT179oy5zMyZM+3cc8+1H374IZws7d27tw0fPtz69+/vXq9evdpuv/12K1WqlI0dO9YOPvjgXOv58ssvbf78+TZ06NDwMoceeqh99dVX9s477ySx1AAAAEgE3REAQAowcFP2UMK1faP2dmTjI91vErBA9hg0aJD16NEjnICVs88+2wYPHhx3mVtvvdW6dOkSUVtVywwbNszWr1/vXletWtUld5VgrV27dsz1qGbs448/bhs3bgwvc8UVV9j48eNdghYAAAA7F0lYAEgBBm4CgMymhOmECROsadOmEdObNGlif/zxh6vxGouSpLGWWbVqlX3xxRcJf36tWrVcVwZbtmyJWI/MmTOngKUBAABAUaM7AgBI4cBN3V/pzsBNAJCBlGRVArRSpUoR0ytXrux+//7777mSreq7VYnTvJbp1KlTQp9/9913u5/obZK99tor7nKqOetrz/puDGTbtm3uJ5m0/lAolPTPSQfZVFahvJmN8ma2bCpvNpVVKG/yJPoZJGEBIEUYuAkAMteKFSvc79KlI8Nr/9q/v6PLFMQzzzxjRx99tLVu3TruPOr24Kabbso1fenSpW4gsGTfsKjGr26Q1G9uJsumsgrlzWyUN7NlU3mzqaxCeZNnzZo1Cc1HEhYAUoiBmwAgM/l+YBXoB/nX0dMLu0yinnzySXdD8NZbb+U538CBA+3qq6+OqAnboEED172B+pVN9s2R9oE+K9NvBrOprEJ5MxvlzWzZVN5sKqtQ3uQJ9u2fF5KwALCTBm5aUmGJG2AlG/4AAkCmq1atmvu9adOmiOm+qb9/f0eXScSUKVPskUcesY8++sh23XXXPOctV66c+4mmv02p+Pukm6NUfdbOlk1lFcqb2ShvZsum8mZTWYXyJkei68+OvQ4AAAAkkfp7LVWqVLhPVU/N4KR58+a5llHfr3Xq1CnQMvmZNWuW3XHHHfbuu+/a7rvv7vqpXb58eYHXAwAAgKJFEhYAAADYQRUrVrQjjjjCpk+fHjF92rRp1rBhQ2vRokXM5Tp37hxzGa2vbdu2BdqGZcuW2T333GNPP/20Va9e3U378ccf7bvvvitweQAAAFC0SMICAAAARWDw4ME2ZswYV/vUGz16tA0dOtQ1h5s6daq1adPGPvzww/D7AwYMcK+DAzpoGU1XTdlY/ZvFGoFXg2idc845tvfee9vLL7/sErHqF1a1Ylu1apWU8gIAACBx9AkLAAAAFIGOHTvaoEGDrG/fvtayZUubOXOmnXLKKdarVy/3/rp162zOnDm2du3a8DJKkCphqqRr69atbdGiRdaoUSPr169fxLqHDBliK1eutMmTJ9tvv/3mPqNevXrWp08f937v3r3tzTffdD9BVapUsZdeeikl5QcAAEB8JGEBAACAItK1a1f3E8tBBx3kEqnR1I2BfvIycOBAK1u2rI0YMcJCoZD7Cda41UBc+gEAAEB6IgkLAAAApLly5cqF/62uDfSjpCwAAACKB/qEBQAAAAAAAIAkIgkLAAAAAAAAAElEEhYAAAAAAAAAkogkLAAAAAAAAAAkEUlYAAAAAAAAAEgikrAAAAAAAAAAkESlLU2NGzfOJk6caM2aNbMZM2ZYmzZt7Iwzzshzmc8//9xeffVVa9WqlS1cuNBq1Khhffr0yTXfhg0bbNSoUW69Dz74YK73P/roI/v4449t3bp19uuvv9ohhxxiAwYMsPLlyxdpGQEAAAAAAABkvrRMwk6aNMluu+02l1QtUaKEm9a1a1crWbKk9ezZM+YyM2fOtHPPPdd++OGHcLK0d+/eNnz4cOvfv797vXr1arv99tutVKlSNnbsWDv44INzrefLL7+0+fPn29ChQ8PLHHroofbVV1/ZO++8k8RSAwAAAAAAAMhEadkdwaBBg6xHjx7hBKycffbZNnjw4LjL3HrrrdalS5eI2qpaZtiwYbZ+/Xr3umrVqi65qwRr7dq1Y65HNWMff/xx27hxY3iZK664wsaPH+8StAAAAAAAAABQrJOwSphOmDDBmjZtGjG9SZMm9scff7gar7EoSRprmVWrVtkXX3yR8OfXqlXLdWWwZcuWiPXInDlzClgaAAAAAAAAANku7bojUJJVCdBKlSpFTK9cubL7/fvvv+dKtqrvViVO81qmU6dOCX3+3Xff7X6it0n22muvmMuo1qyvOeu7MJBt27a5n2TS+kOhUNI/J11Q3syWTeXNprIK5c1slDezpbK82bJPAQAAkH3SLgm7YsUK97t06chN86/9+zu6TEE888wzdvTRR1vr1q1jvq8uD2666aZc05cuXeoGAUv2zYpq++rmSH3mZjrKm9myqbzZVFahvJmN8ma2VJZ3zZo1SV0/AAAAsLOkXRLW9wOrQD/Iv46eXthlEvXkk0+6G4K33nor7jwDBw60q6++OqImbIMGDVzXBupTNtk3Riq/PitbbgQpb+bKpvJmU1mF8mY2ypvZUlneYN/+AAAAQCZJuyRstWrV3O9NmzZFTPfN/f37O7pMIqZMmWKPPPKIffTRR7brrrvGna9cuXLuJ5puVFJxc6Ybo1R9VjqgvJktm8qbTWUVypvZKG9mS1V5s2V/AgAAIPukXaSr/l5LlSoV7lfVUzM4ad68ea5l1PdrnTp1CrRMfmbNmmV33HGHvfvuu7b77ru7fmqXL19e4PUAAAAAAAAAyG5pl4StWLGiHXHEETZ9+vSI6dOmTbOGDRtaixYtYi7XuXPnmMtofW3bti3QNixbtszuuecee/rpp6169epu2o8//mjfffddgcsDAAAAAAAAILulXRJWBg8ebGPGjHG1T73Ro0fb0KFDXXO4qVOnWps2bezDDz8Mvz9gwAD3Ojigg5bRdNWUjdW/WawReDWQ1jnnnGN77723vfzyyy4Rq35hVSu2VatWSSkvAAAAAAAAgMyVdn3CSseOHW3QoEHWt29fa9mypc2cOdNOOeUU69Wrl3t/3bp1NmfOHFu7dm14GSVIlTBV0rV169a2aNEia9SokfXr1y9i3UOGDLGVK1fa5MmT7bfffnOfUa9ePevTp497v3fv3vbmm2+6n6AqVarYSy+9lJLyAwAAAAAAAMgcaZmEla5du7qfWA466CCXSI2mbgz0k5eBAwda2bJlbcSIERYKhdxPsMatBuLSDwAAAAAAAABkdBI2WcqVKxf+t7o20I+SsgAAAAAAAACQNX3CAgAAAAAAAECmIAkLAAAAAAAAAElEEhYAAAAAAAAAkogkLAAAAAAAAAAkEUlYAAAAAAAAAEgikrAAAAAAAAAAkEQkYQEAAAAAAAAgiUjCAgAAAAAAAEASkYQFAAAAAAAAgCQiCQsAAAAAAAAASUQSFgAAAAAAAACSiCQsAAAAAAAAACQRSVgAAAAAAAAASCKSsAAAAAAAAACQRCRhAQAAAAAAACCJSMICAAAAAAAAQBKRhAUAAAAAAACAJCIJCwAAAAAAAABJRBIWAAAAAAAAAJKIJCwAAAAAAAAAJBFJWAAAAAAAAABIIpKwAAAAAAAAAJBEJGEBAAAAAAAAIIlKJ3PlAAAAQDYZN26cTZw40Zo1a2YzZsywNm3a2BlnnJHnMp9//rm9+uqr1qpVK1u4cKHVqFHD+vTpk2u+DRs22KhRo9x6H3zwwVzvL1q0yG6//XZr3ry5m3fx4sU2ZMgQq1SpUpGWEQAAAAVHEhYAAAAoApMmTbLbbrvNJVVLlCjhpnXt2tVKlixpPXv2jLnMzJkz7dxzz7UffvjBypcv76b17t3bhg8fbv3793evV69e7ZKrpUqVsrFjx9rBBx+caz2bN2+2zp0720svvWR77723m/b6669b9+7d7Z133kliqQEAAJAIuiMAAAAAisCgQYOsR48e4QSsnH322TZ48OC4y9x6663WpUuXcALWLzNs2DBbv369e121alWX3B06dKjVrl075nqUfFWy1ydg5cQTT7Qvv/zSvvjiiyIqIQAAAAqLmrAAAADADlLCdMKECXbllVdGTG/SpIn98ccfrsZr06ZNcy03fvx469evX65lVq1a5ZKnnTp1SujztZ7o9avmbMOGDV1N2MMOOyzmchs3bnQ/nmrdyrZt29xPMmn9oVAo6Z+TDrKprEJ5MxvlzWzZVN5sKqtQ3uRJ9DNIwgIAAAA7SEnWLVu25Op/tXLlyu7377//nitJum7dOtcHbF7LJJqEVaK3ZcuWuaZrXVpPPKpxe9NNN+WavnTpUtevbLJvWJRs1g2SavFmsmwqq1DezEZ5M1s2lTebyiqUN3nWrFmT0HwkYQEAAIAdtGLFCve7dOnI8Nq/9u/v6DJ5fX70evy68lrPwIED7eqrr46oCdugQQOrVauW6wYh2TdH6rpBn5XpN4PZVFahvJmN8ma2bCpvNpVVKG/yBLuVygtJWAAAAGAH+X5gVdsiyL+Onl7YZfL6/Fjza1pe6ylXrpz7iaablVTcoGm7U/VZO1s2lVUob2ajvJktm8qbTWUVypscia4/O/Y6AAAAkETVqlVzvzdt2hQx3fe36t/f0WXy+vzo9fh1FWQ9AAAASA6SsAAAAMAOUn+vGgjLD2zlqS8yad68ecz+WuvUqVOgZeJp0aJFrvX4dRVkPQAAAEgOkrAAAADADqpYsaIdccQRNn369Ijp06ZNs4YNG7okaSydO3eOuYzW17Zt24Q/P9Z6VDN2zpw5dswxxxSoLAAAACh6JGEBAACAIjB48GAbM2aMbdmyJTxt9OjRNnToUNcn2dSpU61Nmzb24Ycfht8fMGCAex0cVVfLaLpqysYaZEI/0Xr27Olq4k6ePDk87Y033rDDDz/cOnXqVMQlBQAAQEExMBcAAABQBDp27GiDBg2yvn37WsuWLW3mzJl2yimnWK9evdz769atczVT165dG16mVatW9vTTT7uka+vWrW3RokXWqFEj69evX8S6hwwZYitXrnRJ1t9++819Rr169axPnz7hUXnfe+89GzZsmH3++eeuFuy8efPs9ddfT/FeAAAAQCwkYQEAAIAi0rVrV/cTy0EHHeQSqdHUjYF+8jJw4EArW7asjRgxwkKhkPsJ1riV+vXr26hRo3awBAAAAEgGkrAAAABAmitXrlz43+raQD9KygIAAKB4oE9YAAAAAAAAAEgikrAAAAAAAAAAkI3dEYwbN84mTpxozZo1sxkzZriRZM8444w8l9EgBK+++qob4GDhwoVWo0aN8GAFQRs2bHD9ZWm9Dz74YK73NSDC7bffbs2bN3fzLl682A2GUKlSpSItIwAAAAAAAIDMl5ZJ2EmTJtltt93mkqrq70o0wEHJkiWtZ8+eMZfR6LPnnnuu/fDDD250WOndu7cNHz7c+vfv716vXr3aJVdLlSplY8eOtYMPPjjXejZv3mydO3e2l156yfbee283TaPKdu/e3d55550klhoAAAAAAABAJkrL7ggGDRpkPXr0CCdg5eyzz7bBgwfHXebWW2+1Ll26hBOwfplhw4bZ+vXr3euqVau65O7QoUOtdu3aMdej5KuSvT4BKyeeeKJ9+eWX9sUXXxRRCQEAAAAAAABki7RLwiphOmHCBGvatGnE9CZNmtgff/zharzGMn78+JjLrFq1qkDJ01jrUc3Zhg0bUhMWAAAAAAAAQPHvjkBJ1i1btuTqf7Vy5cru9++//54rSbpu3TrXB2xey3Tq1Cmhz1eit2XLlrmma11aTywbN250P566PZBt27a5n2TS+kOhUNI/J11Q3syWTeXNprIK5c1slDezpbK82bJPAQAAkH3SLgm7YsUK97t06chN86/9+zu6TF6fH70ev65461GXBzfddFOu6UuXLnUDeyX7ZkW1fXVzpG4UMh3lzWzZVN5sKqtQ3sxGeTNbKsu7Zs2apK4fAAAA2FnSLgnr+4FVoB/kX0dPL+wyeX1+rPk1Ld56Bg4caFdffXVETdgGDRpYrVq1XD+0yb4x0jbrs7LlRpDyZq5sKm82lVUob2ajvJktleUN9u0PAAAAZJK0S8JWq1bN/d60aVPEdN/c37+/o8vk9fnR6/HrijeYV7ly5dxPNN2opOLmTDdGqfqsdEB5M1s2lTebyiqUN7NR3syWqvJmy/4EAABA9km7SFf9vWogLN+vqqdmcNK8efOY/bXWqVOnQMvE06JFi1zr8esqyHoAAAAAAAAAIC2TsBUrVrQjjjjCpk+fHjF92rRp1rBhQ5ckjaVz584xl9H62rZtm/Dnx1qPasbOmTPHjjnmmAKVBQAAAAAAAADSLgkrgwcPtjFjxtiWLVvC00aPHm1Dhw51zeGmTp1qbdq0sQ8//DD8/oABA9zr4IAOWkbTVVM2Vv9msUbg7dmzp6uJO3ny5PC0N954ww4//HDr1KlTEZcUAAAAAAAAQKZLuz5hpWPHjjZo0CDr27evtWzZ0mbOnGmnnHKK9erVy72/bt06VzN17dq14WVatWplTz/9tEu6tm7d2hYtWmSNGjWyfv36Rax7yJAhtnLlSpdk/e2339xn1KtXz/r06RMeEOK9996zYcOG2eeff+5qwc6bN89ef/31FO8FAAAAAAAAAJkgLZOw0rVrV/cTy0EHHeQSqdHUjYF+8jJw4EArW7asjRgxwkKhkPsJ1riV+vXr26hRo3awBAAAAAAAAACQxknYZClXrlz43+raQD9KygIAAAAAAABA1vQJCwAAAAAAAACZgiQsAAAAAAAAACQRSVgAAAAAAAAASCKSsAAAAAAAAACQRCRhAQAAAAAAACCJSMICAAAAAAAAQBKRhAUAAAAAAACAJCIJCwAAAAAAAABJRBIWAAAAAAAAAJKIJCwAAAAAAAAAJBFJWAAAAAAAAABIIpKwAAAAAAAAAJBEJGEBAAAAAAAAIIlIwgIAAAAAAABAEpGEBQAAAAAAAIAkIgkLAAAAAAAAAOmahP3xxx/tiy++iJi2Zs0au+uuu2zVqlU7um0AAABAUhDHAgAAIJVKF3bBDz/80E466STbsmWLrV+/Pjy9SpUq1q1bN7vhhhusd+/e1qxZs6LaVgAAstrWrVtt8+bNlm62bdvmtmvDhg1WsmTmN7KhvAVXpkwZK1WqlKUL4lgAAFIrHeNYYrrMtq0Iylu6dGkXw5YoUWLnJmG/+uorGz16tG3cuDHXe02bNrURI0ZY37597Z577tnRbQQAIKuFQiH7888/beXKlZau26cgR7UIiypASWeUt3CqV69uu+++e1rsM+JYAABSI53jWGK6zBYqovIqCVu7dm2rVq3aDu+3Qidh9QU64YQT8txIFRYAAOwYH7jqj3/FihXTLmhSgKMahXpSnG7blgyUt+DL//3337ZkyRL3uk6dOrazEccCAJAa6RzHEtNltlARxLBafvXq1bZo0SLXempH49hCJ2EXL16c7zwzZswo7OoBAMD/mm75wLVmzZqWjgjoMltRlLdChQrutxKxOpd3dtcExLEAACRfusexxHSZLVRE5VV3VeXKlbO//vprh+PYQncCoQI899xzcd9XM66qVasWdvUAAMAs3HeWag4AxZk/h9OhPzjiWAAAko84FpmiUqVKLqm7o3FsoWvC3nrrrXbIIYfYk08+acccc4zVrVvXbdCcOXPszTfftPnz59s333yzQxsHAAByZMPTamS2dDqHiWMBAMjOGAAojJ0+MFe9evXss88+swsvvNCNIKsNUvAqhx12mE2cONEaNGhQJBsJAAAAFBXiWAAAAKRaoZOw0rhxY3v//fddn1nff/+96+9jn332sb322qvothAAAAAoYsSxAAAAKDZJWG+PPfZwPwAAAEBxQhwLAACAYpOEBQAAQI7Fixdb69at7YMPPrB9993XDj30UDvjjDPsyiuv3NmbhhQYN26c686gWbNmrpZtmzZt3PHPy+eff26vvvqqtWrVyhYuXGg1atSwPn36RMzz66+/2sMPP+zmWbFihW3atMl1paARf73Zs2fba6+9ZmXLlrUNGzbYsmXL7JprrrFdd901aeUFAACZgzg2uUjCAgCQhbZuNZs40WzRIrM6dczatTMrVWpnb1VmWL9+va1evdolyUT/1g8y36RJk+y2225zSVU/gEPXrl2tZMmS1rNnz5jLzJw5084991z74YcfrHz58m5a7969bfjw4da/f3/3euXKlXb88ce7wcJ8QvXee++1K664wh566CH3eu3atfbYY4+5Qce8efPm2fnnn2///e9/k152AABShTg2eYhjk6tkktcPAADSzGuvqT9Ms44dzVRBT7/1WtNRNH2N/t///Z+9/vrrrjajEmz9+vXb2ZuFFBg0aJD16NEjYgTds88+2wYPHhx3GSVNu3TpEk7A+mWGDRvmboTk/vvvd7VRgjVae/Xq5ZKu8+fPd69V+7ZKlSoR69bgYqopq5qzAABkAuLY5CKOTS6SsAAAZBEFqN27m/0vbxO2YEHO9FQGsNu2bbMhQ4bYf/7zH1er75577rG5c+faqlWr7I477rAyZcrYRRddZE888YTdfPPNdvrpp9uSJUvCy//444923nnnuQTVk08+addee61Lfl1//fUuMXXWWWfZyJEj7bnnnnO1Bc855xxXuzCnvAtcYkzzX3755a6p94cffmidO3e2mjVr2qhRo2zjxo3u9y677OKm6/2gvOa/++677YgjjnBNt7TN5cqVc2X1CbN4xowZ47b7zjvvtBEjRthnn33mpj/11FPWpEkTa9++vT3++OPuR2XR9l922WX2yy+/2Mcff+y2R4m6Bx980P7++29XbjVtP/bYYyO2X++rhqT2uT7np59+Cr+n2pbal48++qg7DtqHnv8MlfGBBx5wtSRUk1PboW3z25uNlDCdMGGCNW3aNGK6jtsff/zh9lMs48ePj7mMvgdffPFF3Hl03lWqVMnee+8991rnmM6bTz75JDyPBhvTeqpXr15k5QQAYGchjiWOLWgcq/2bVnFsCEVu1apVIe1a/U62rVu3hhYtWuR+ZwPKm9myqbzZVFahvIW3fv360NSpU93vHbVlSyhUv34opL/+sX5KlAiFGjTIma8gtm3bFtq0aZP7XRB9+vQJ/ec//wm/Puecc0KnnXZa+HXDhg1DH3/8cfj1scceGzrrrLPcvzdu3BiqXbt26LHHHgu/P3PmTPf312/HlVdeGdoSKIz2YZUqVULvv/9+eLs1/6xZs8LzPPXUU6H27dtHbOeRRx7ppscq75NPPhlq165dnvO/9tpr7nPy8/rrr4f+8Y9/hDZs2OBeP/HEE6G6deuG3+/Vq1do8ODBEctEb7+2J7j9S5cuDe2yyy4R2zNixIjQv/71r/D5qXUeeuih7t8//fST2waVz9P71113XXi/6jNURu/BBx8MPfLII0VyLqcyhipqP//8s9t2f355f/zxh5v+9ttv51pm7dq17r3geSza/5qufSu77rpr6Prrr8+1vM6Pfv36uX/rXD/ssMNCJUqUCJ133nnuvOjdu3foq6++ynO7db5pf/ufefPmuc9esWKFO0eS+bN58+bQwoUL3e9kf9bO/smmslLenb89lJfypkt5161bF/rll19Cf//9t4sjduRn8+Ztofr1FbttixPHbgs1aJAzX0HWq5iyMNujv7GXXXZZ+LWPY/1rxbEfffRR+LWPY/Vv/e1VHPvoo4+G358xY4b7+6v9ptdXXHGFOwb+fe1DxbHvvfeee635NL/iXz+PjwOD26mYTdNjlVexpuLYvOYfO3ZsOL7O60fxrmJIxXl6/fjjj7s4xb+vOHbQoEERy0Rvv7YnuP1LlixxcWxwe+69914Xxyru0WutU3Gs/v3jjz+6bQiW8cYbbwzdcMMNEZ+hMvrXo0aNCj388MP5lk/7X+eyzulY57ripkRiWPqEBQAgS6jvrLweYCuEnTcvZ74OHZK7LXqCryfTwSfX//rXv1ytAS/YpHvdunWudoH6xRTVMtCT+bp16+aa3/8O1uAUDZJUtWpVN2BS9Przktd8ek/9fcabf9GiRe6JfCIGDhzoahyotoEcdNBBblClRLYj3jyqHdCiRYvwa9VyUM2J0aNHh7e7Y8eOrumZqPbFcccdF3Ec/v3vf9vee+/talrUqVPHfYb/HK1/n332saOPPtqynW/yHxwoK/g6VpcAiS6j39Hz+Pn8PKVKlbJ3333XDZ6hGjX60Xfg4IMPznO71e3BTTfdlGv60qVL3eBeya5FpO+ynidEf48yTTaVVShvZqO8ma0oy7t582a3vi1btrifHfHppyVs/vz4KaxQqISLYz/5ZIu1b698WP5URrUaKUhc6ONY1aT89ttvw+Xy8ZN/rfVp3XqtOHbOnDmuRqdea+BMxbG77bZbeH6/Hf63aqNq+/z7v//+u4tjFbMF92Vw32pfB5fx/DGILq9e63e8+RXHfvXVV+HPyS+OVayoeETz7r///q47A7+cPmvb/9YbFNx+zRPcftV0bd68eXg5xbGqkavawZqmnyOPPNIaNmzo3r/uuutcTVdfJq1L3UQdcMABrlby7rvv7qb5z73vvvtcjHvUUUflWz69r8/TsQvGyd6aNWssESRhAQDIEhq8oCjn2xFqZq0gcI899ghP69atW6751AxbTbnV5FrJw9NOO81Nr1WrlmvW9c4774QTs/FoHn2emhh9+umn4YRjsOmU72szXjMkP12Bl4K3Cy64wKpVq5bn5ypQU7MuNZdSoisvf/31l/32228R+0Mj0+qnsJSEU+AZLJOS3rqxCn6Oml/pxzfT0k1EUP369d1NlPahP0Y6dkrcqbmZ9im237z54N7zr6OnF2QZf6MUzd+seOojVjcfuunRoF0a4Es3Ty+88EKeN01XX311+LUG31BfsvqO6WYvmfQdUdn0WZme2MimsgrlzWyUN7MVZXn1ME/JKT00jPUwsSACLfnzma+UFfSjYiXV8qIH7IqFWrZsGS7Xqaeemmu+999/31UC0O9gHKuH2opjFd+qEoL49UTvJx/HavBPdTkUjOHkjTfeCMexfmDQ4Dr0WtN1LBXHarvVhYK6NNK0WPNrun4eeeQRu/DCC12z/ryOn+JYJYmVMPXzKQmrH8+vs3SMB89+WnB7FMdqUFOV3S/3/fffuzhWFQz8Mp06dQqvSzGp7guCn6EEreJYHbOTTz7ZrUvnuPrkv+uuu9w+TeTc1DxaVt03BPvx92JNi7keSxL1FfHll1+6DTnkkEPcFxgAAOw8Gj22KOfbEQp+gr/j0YBFHTp0cE+vL7nkEps6dWq41p4SS2PHjrUTTjjBJQp9DddoSirqR6PPH3744S6xGwwKu3fvHpGYnT59eq51qF8s9cUlSlip5qfvrzMe1fTVYAaqEVBU+yNR2k96Yq/BnAryOf4pf6xlgjUEtI+0/xXnaYCoKVOmJBx8FgeFiWN9Ut6PJuyp1kbw/cIso9/R8/j5/DxKwOq1T6jqfFd/a7rp002HzvNYVPPa174O8jdLyRa82ct02VRWobyZjfJmtqIqr0+qBVvRFFag8VM+8+mzEpvX1wSVgmxf8GFpXssp/lQce/HFF7s4Vn23RsexSsIG49jo9SmpqB/9XW/btm2uOFbJXx/HalklfaPX0a5dOxfHansVJ6gVlOLYeGXXa9X0VZ+1Po7Nq5yJ7o8SMc6D4DT/W/tJyeI2bdpEzJPf5/jar8GH3H4Zrc+vx8exFSpUcIOhJhLH+mXjfS8S/a4k5QqiDoaVAVdnuOp8d9y4cS4AVFVmAACwc7Rrp1qNCiJiv6/pDRrkzJdsaiKtYEW1P4OC3RNEU5CqgQ30BFwUCClg+vnnn92o9NFJpu+++y7itQI51fB7+umnd2jblVhVsKZBBOJRjQN1+h9dWyGe2rVru4GXoveHH+ChIJSIe/bZZ13NhWjqOqBy5cq5PkdlUbL1sMMOc58ZNHv2bBeAH3rooeFpqvmhmgV9+/Z1NQJuuOEGyxSFjWN1/LSfVJM0yB8/rTOajoVqw+S3jGp8RM/j5/PzaAAKPawI1urRcdF3QwOGAQBQnBHHFk0cq4fnkydPJo5tuHPi2KQkYX3fDDpo6l9B2XP1MfbKK68k4+MAAEAC9CDbd5MaHcD61yNG5MyXbHpir1oBGk02OLq8H+ldoptfq98tBUy+ibSa2evp9euvvx7RN6yn/jHV/Mhbvny5e/L9j3/8I2L9wc+J1+Q7ejs0UqsSdMG+tYLz//nnn2502IK47bbbXCJNo8F6ekqvYNOvN7gtsWqz6n0FpldddVXMGgIVK1a0G2+80dXS9dut9fz3v/91NxPaBjVrC26D+n1V03YF/v4z/LJaRjcD6nZBTe0yQWHjWO1b1ZiOrkk9bdo0d94G++YNUvcBsZbR+lTjJd488+bNczcqvj9eHRd9h6KpNjQt0gAAxR1xbNHEsUruZlMce//997sxF9Imjg0liUYE00hu2SiVI/tqFDZGHM9clDdzZVNZhfIWXiIjyhfU2LGhUP36kSPKNmiQM70wNGKoRnPX74LQqKYasfTMM88MjRgxIjRy5MjQmjVr3Oiiw4YNC5UuXTp00UUXhR566KHQNddcE+rQoYMb9VT69+8fqlixYmi//fYLzZ49241WqlFk9fdXo6RqhPcFCxa4+W6++WY3Kupxxx0Xuv32293yel/TNP/FF1/sRrZ/99133WdoFFZti0auvfvuu0PVq1cPnXjiiW47brrpJreeSZMmudHuO3bsGKpWrVrorrvucvNruRo1arjpen/atGmhSy65xH2Oyjp37tw898lLL70UOumkk0LDhw93++T3339305966qlQkyZNQgcccEBozJgxbj3XXXedW69G5lX5P/jgg9DRRx8dqlmzZujBBx90o7dqHdqeY445xo2m6913332hU0891ZVPo8zqfPU+++wzt9+1DdrvOhZ+lFn/GdonWrem6/zUZ2ia1rUj53IqY6hkxLEaBVnHKLiszpdnnnnG/VvHad9993X70fv1119De+yxR2j16tXhaZdeeqk7bz2NDtyoUSN33no6Puedd1749bPPPutGHtb3ytNIvmeccUZo8eLFBSo7cWzRy6ayCuXNbJQ3s2VTHFvYGLa4xrFDhgwJdenSJfTll1+G3nnnnYyPY6+++urQrbfeGj6XkxnHJho/ldD/UpPuzR5qLqb+uVTtOhUDGmhUPVX/zob+aShvZsum8mZTWYXy7tiABrNmzXJPq4uyz009AJ44MWcQLvUBq6Zbha054EcxVYf1O9rfV6L0BDuRvlaTYWeUd2cqqvImci6nMoZKFtXG0CAPauo2c+ZM91uDs4kGhTjmmGPsmWeesa5du4aXUW2Y0aNHu4HY1O2Banr069cvYn+rn1/V5tA8K1eutLVr17oRgsuWLRsxeIc+X9ceTVetHA2+EauGTTzEscmRTWUVypvZKG9my6Y4dmfFdDsrjiWGTd65nGj8VOiBuTQym28iFY8CSjXLAgAA6UVxX4cOVmztrAQsMkMy41glV4MJ1qCDDjrIJVCj6XPy+6y99trLHnrooTzn8YPQAQCQyYhjUVwVOgn7/PPP5xu8qv+HwiZhNQjCxIkT3QhxGt1NnRCfccYZeS6jzoNfffVVa9WqlS1cuND1c9GnT5+IeTTK2sMPP+zmWbFihRtpVp3wKjMe7Lj3tddeczUIlO1etmyZXXPNNbbrrrsWqiwAAABIH8mOYwEAAIAiS8I+8sgj9tZbb0UkL4NU5XfBggX5PrGPVztBHeoqqeqrDKtGgarC9+zZM+Yyau6l0Yp/+OGHcNXg3r172/Dhw61///7utWoeHH/88a4pmE+oqiNlDTbht1NNux577DG79dZbIwY+UDMyNe8CAABA8ZbMOBYAAAAo0iSsRnjt0aNHRDVq9X/V4X91whW8qgZBYQwaNMitO9hng0aNGzhwYNwkrJKmXbp0ieibQct06tTJjYRWoUIF14+WRogN1mjt1auX7bbbbm7U2/r167vat1WqVIlYt0ZRU5CumrOqXQsAAIDiK5lxLAAAAFCkSdgzzzzTNeOP7sB58ODBEZ0NF5QGEJgwYYJLnAap89s//vjD1Xht2rRpruXGjx/vBjCIXkad4n7xxRcuGat5Dj744Ih5atasaZUqVbL33nvPzjvvPCtXrpzdeeedduihh4YDcZVD66levXqBywMAAID0kqw4FgAAACjyJGysjoTfffdda9y4sesWQFRztaCUZFXtAyVGgypXrux+//7777mSsOvWrXN9wOa1jJKwSuJ27Ngx12dqPs0j7du3d6PYan6V48Ybb7QRI0a47hHijaa2ceNG9xMcFc0H8/pJJq1fI74l+3PSBeXNbNlU3mwqq1DeHV+X/0lXftvSeRuLEuUt3Dr89yLedyNV14hkxbEAAABAkSdh16xZE/F68+bNLkl52WWX2U8//WR33XWXLV68OFfT/vyoyb/bsKg+uvxr/35hltHvWH1/+a4GfFCuIFyDgD355JPuZ+TIkblq0AYNGzbMbrrpplzTly5d6gb2SibdrKiWrm5q1GdupqO8mS2byptNZRXKW3j6+6r16QGlftKRyulrDcZ7YJlJKG/h6PzVuawBT8uUKZNQfJksyYpjAQAAgCJPwqpW6QcffOCa7C9fvtwlIi+99FJXS7Vbt26uC4CKFSvahx9+WKD1+uA+uqZFXjUwEl1G88VaPrp2kQbm6ty5s/Xp08cN2qUBvr766qu4fYOppsTVV18dURNW/cjWqlXLqlatasmkmxmVS5+VLYkNypu5sqm82VRWobyFp4d5ShjpgWG8QYTSRbzEWqaivAWj81ffB3UFFezDPyje9KKWrDgWAAAAiKfQd3PnnHOOS1T6BKgGtdLgWApYP//8c/fenDlzCrzeatWqud+bNm2KmO6b+/v3C7OMfkfP4+fz8ygBq9c+qfrDDz/Y8OHDXR9hJ598snXv3j3X8upHVj/RdKORimSDjkGqPisdUN7Mlk3lzaayCuUtHC2vdfmfdKQHmX7b0nUbixLlLRx/Duf1vUjV9SFZcSwAAAAQT6Ej3RNOOMFefPFFO+6449yAVgpYFbhKs2bN3AizhakFqhoI6hLA96vqqVmnNG/ePGafrnXq1Ml3GY2EGz2Pn8/P8+ijj9pFF10UUetDAzcMGjTIDRgGAACA4i1ZcSwAAAAQzw61a+zZs6f7iaVhw4Z2+eWXR9SeSIQC4COOOMKmT58eMX3atGlunUqkxqIaC7GW0fratm0bnkfNy4LmzZvnar4effTR7rW2d/369bnWv++++9rPP/+ccDkAAACQvpIRxwIAAABFXhM2kYET+vbtW6jAVU3/x4wZEzEAyejRo23o0KFufVOnTrU2bdpE9NM1YMAA9zq4XVpG01VTVv7zn//Y77//bvPnz4+YRzUg9thjD/da/b9ef/314QEoRElZbc/FF19c4LIAAAAgvSQzjgUAAACKtCbsHXfc4ZKiebn99tvttttuK/C6O3bs6Jr/K/ht2bKlzZw500455RTr1auXe3/dunWun661a9eGl2nVqpU9/fTTLunaunVrW7RokTVq1Mj69esXnkcDpLz99tuuzy/Ns3LlSreOhx56KDzPWWedZbvuuqtL2NauXdvKli3rkrB33nmnew0AAJCXFStW2EcffeRijhkzZrim7UgvyYxjAQAAiivi2DRNwj777LOudkC8kZo3b95sL7zwQqGD165du7qfWA466CCXQI2mbgz0k5e99torIukai/oH0w8AABlr21azpRPN1i8yq1DHrFY7s5KldvZWFWuzZ8+2K6+80vUvqljl0EMPJYmXppIdxwIAgCQiji1yxLFpnoRVDdKJEyfGfV/B65IlSwq7egAAkCzzXjOb0tvs7+3d81jF+mYHjjRr0G1nblmxpYfDarVz7bXX2muvvRY3uYf0QBwLAEAxRRxb5IhjU6fQe1bZ8XfffddKlSrlao02bdo01zx9+vTZ0e0DAABFHbhO7K6hKCOn/70gZ3q7MQSwhaAuke666y7XpRLSH3EsUklDTUyYYLZ0qbpHMzvySLNSVNgCgIIjjk0K4thiMDCX+mpVVeVLLrnEfv31V7vvvvvcIFd///13eJ7zzz+/qLYTAAAURdMt1RyIDlyd/02b0idnviR75513rFOnTq5J+COPPOKmqf919cWu5JcfRHPatGluYMxRo0a5gTuff/55N10DbWr0ei1/ww03uCZUmmeXXXaxo48+2t5//303YGfnzp2tZs2a7r3ggJ9BQ4YMsRNOOMF1V3TiiSfabrvtZg8//LBdc801bht9zUh99hNPPOG28+qrr47om3758uXWoEED1ze9yqP+RNXnaPRnajvUvEvzPPbYY7bnnnva/vvvb2PHjrUFCxa4MqpMirF++eUX1yfXMcccEy7Dxo0b7cEHH3SvNV3lVBx2xRVXuOW0vN93kydPtmeeecZ9zkUXXWQffPBBko5m8UMci1R57TWzxo3Njj7a7K67cn7rtaYDAAqAOLZI4ti7777bDUhPHLuThIrQsmXLQk8++WTo/vvvD02YMCGUrVatWqUrgPudbFu3bg0tWrTI/c4GlDezZVN5s6msQnkLb/369aGpU6e63zvsz49DoRcs/x/NVwDbtm0Lbdq0yf0uiLVr14aaNWsWev75593rl156KTRmzJjw+2vWrAm1aNEitGDBgvC0ww47LPT111+7f8+cOdP9vQ3u5yOPPDL0xBNPhF8rLmnfvn2e2zFs2DD3WfLUU0+5dXg33HCD+/3II4+E9txzz3B5+/fvHzrnnHPC8w0YMMC9v3z58vC0Z599NtSrV69cn6fP8PT+oEGDwq+1bpVp1qxZEfNElyG6nH5fBNWvXz/04osvhmO0XXbZJTR9+vRQQRX2+BbmXE5lDBWNODYHcWzRGTs2FCpRIhTSV7Nkya2hgw5a5H5rmn70fqbK9GMbjfJmNsqbuXHsjsQ4xTGO9eUdOHBg6Nxzz834OHZbEcWwiZzLicZPha4JG4uy9q1bt3aZ7C5dutixxx5blKsHAAA7QoMXFOV8O6hSpUruCbee1E+dOtVmzZrl+qPy9MRcT+Xr1q0bnqYaARowSfTEXEqW3B7OaFr06/zUqVPHKleuHHOZZs2aud966n/OOeeEp2sg0AlqX/w/6j/rwAMPtBo1aoSnnXbaafbSSy/Zt99+G/F5wfXr39Gvg/773//G7JcrkXJedtll1qZNm3CM1rx5c9cMH7ERx6KouyDo3Vt3lLnf89PU44XmAwAkgDi2yOLYdu3a2aeffhp+TRybOkXS2+7ixYvtueeec/1IqLr18ccfby+++KL985//LIrVAwCAoqDRY4tyviJw+OGH27nnnusSXoohgr755hvXVErxhRcKhaxRo0ZFug1nn312vu/tu+++rnnX/fffb2XKlLG5c+fa1kD2ZNOmTVavXr2IZdUkbdddd3VB7gEHHFDg7VJzLO0TBcozZswo8PIDBw609957z95++2237atXr47YZuQgjkUyaNy3/7WojEmJ2Hnzcubr0CGVWwYAxRRxbJHEsUqKLly4kDi2uCVh1afE//3f/9lTTz3lBjZQXxDnnXeenXnmmVa7dm03z/fff2/77bdfUW4vAAAorFrtckaP1eAFMfvTKpHzvuZLoVatWlm5cuVcn5wKZL0NGzbY7rvvHvHkfmdRAKj+ql5//XVXW1L9dClR56kGQqwn/epLK7o/LQXg+dm2bZsLlG+55ZZwjYmCUJ9b3bp1cwNO3XPPPS5xrH7AkIM4Fsm2aFHRzgcAWY84dofjWNV43Wuvveyzzz4L908rxLGpU+juCFStWR0MN2nSxL766iv78ccf3SAVPnCV6667rqi2EwAA7KiSpcwOHPm/F9HNfv73+sAROfOlyB9//GHr16+3V1991fr27Wtz5swJv6cn5xq4IFp0s6hUUJMobZ9qEvjg0NOgAwcffLDNnDkzYpl169bZX3/9ZYcddljE9HgDKwRp0IILL7zQBZ2FoRsBJRFHjBgRXoffZm1vtiOORbLVqVO08wFA1iOOLTTi2AyoCavqyxp5TdWrH3jggVwHRQHt9OnTi2IbAQBAUWnQzazdmJzRZf8OtJVVzQEFrno/RRRDDBs2zD3ZVr9Ql156qastoFqm/vXjjz/uXh911FFuGSXL1KRJzaISeRIvic7nn9zHml+1GapXrx5+/dNPP4WDUI0Gq9Fk27dv70a3bayhz81c87N//etfrv/YO+64w72nADiY6Iv+LP9a/V75frwKUiZNV79a2t6qVataqVKlwk3ulyxZ4rZZ25vtiGORbO3amdWvr+tD7H5h1f2d3td8AIAEEccWSRyr7SCOLWZJWDXZeuSRR/I8IdXpMAAASDMKUOt1NVs6MWfwAvWdpaZbKaw58Oyzz9rw4cNtzZo1tmLFCqtZs6aLHT755BM3EECfPn2sbdu2rh+qG2+80Q2CpWBMHfOrqZcGQBg5Mqc2xO233+4CSwWeqnEwduxYF7zJO++84xKmasp05ZVXxmxqJUuXLrVXXnnFNc365Zdf7M4777QjjzzSDjnkEPe+Bia46667XBCoz1Ew/eWXX7qmXfpRsPnxxx+7eerXr+9qD6jfqpdfftkt37JlS/v6669d8/fBgwe7aeqHVM3B9Hnq4kCf5WMrDQ6l6UoWaptUhvvuu8/V3tRADwqe1aTMl9P3taUg+owzznD76IcffrDevXu7z1ZAq5uE/v372yWXXGLZjjgWyab7Rl2iunfPSbgG+dcjRuTMBwAoAOLYQsexf/75p4tPjznmGDfIFXFs6pUIFSStHqCTSAc1L8GMfzZRh8HVqlWzVatWuS9aMumLqicCehoRHF0uU1HezJZN5c2msgrlLTwFJxptVc2my5cvb+lIoYSeTCswTGQUV7+Piuu5UJjypoJuAlTjQnHI0KFD0668iZzLqYqhiGPjI44tWq+9Zta7t2pfb7MDD1xiU6bUtnr1SroEbLfUVdhKuWw4tkGUN7NR3syNYwsb4xTXODZdY9hkxbFFWd78zuVE46dCnzU+cNWGqK8GZalF2XKNAifZGLgCAID8FcfANd3VqFHDNROLV0sC2xHHIlWUaJ092+yDD8yuvTbn96xZmZ2ABYBMRxxb9GpkSRy7Q2eOqk3XqVPHDjzwQOvXr5+bpj4blB2+5pprXAfFAIBIW7eaffqpamLl/NZrACgq9erV29mbUCwQxyJV1OVA+/ZK/uf8pgsCAACyM44tdBL2lltucf1dqLrwtGnT3MhvXo8ePVwGW31kAAAimyWqr/Ojjza7666c33qt6QBQFC644IKdvQlpjzgWAAAg/VyQ4XFsoev5asTY8ePHh1+XLVs24v3dd9/d9YkAAMihRKsG6FBP3MEWLBrgUdPHjKF5IgCkAnEsAAAAik1N2MaqupUPP9IZAGQ7dTmggTliDYXop/XpQ9cEAJAKxLEAAAAoNknYqVOnulHGgqOOBc2bN8/9AADMJk40mz8//vu6hOqSqfkAAMlFHAskD33fAwBQxEnY4447zjp16uSacv31118ueNXP3LlzXf9ahx9+uPVWtS8AgC1aVLTzAQAKjzgWSA76vgcAIAl9wp577rkuUD3hhBPCtQeuv/5697tMmTL2wAMP2NH6qwsAsDp1inY+AEDhEccCRY++7wEASFISVgYPHmwnn3yyPfvss/brr79ayZIlbd9997XzzjvP9thjjx1ZNQBkFA28Xb9+zo1IrH5hS5TIeT8wQDeANKTv75o1ZmrJXrq0WZUqOd9fFD/EsUDq+r7XdVJ933ftalaq1M7YQgAAinkSVhSs3qW2JgCAuHTDMXJkTk2Q6ISNfz1iBDcmQDpbsSKn7+ZNm8wqVTJbt86sbFmzBg3MatTY2VuHwiCOBVLf932HDqncMgAAMqBPWO/jjz+2f//737b//vvbAQcc4GoPfPPNN0WzdQCQQdQET03x6tWLnK4asDTRA9I/ATtjRk4CNkivNV3vo/ghjgWKBn3fAwCQ5Jqw11xzjd17773u39WqVXO/v//+e9esa9iwYda3b98dWT0AZBwlWtUUTyMGL11qVquW2ZFHUgMWqbd121abOHeiLVqzyOpUqWPtGrazUiU5EfOqwZUXvV+9Ol0TFCfEsUDRoe97AKlEHIusS8I+8sgj9vLLL9t9993nahDU+F87vGXLltnTTz9td955p+211172z3/+syi3FwCKPSVc27c3W7LErHbtyMErgFR47dfXrPf43jZ/9fa2o/Wr1reRXUZatz2pkh1t7drcNWCj6X3Npz5ikf6IY4GiRd/3AFKFOBbFWaFv/UePHu2aa11++eXhwFVq1qzpahZ89dVX9vDDDxfVdgJZOcDBp5/m1JjUb70GgKIIXLu/0j0icJUFqxe46XofkfJLwBZ0Pux8xLFAcvq+F/q+B5AsxLHI2pqw++yzj9XJoz1Jo0aNrGXLloVdPZDVXnstZ4TZhQvNDjzQbMoUs7p1c4Jb+g0FsCNNt1RzIGS5qylpWgkrYX3G97GuLbumrEnXli1b7MEHH7SXXnrJzj33XAuFQjZo0CBr3bq19evXzzp27Gg33XSTlStXznbddVf7/fff7ZZbbrEyZcrYOeecY++++67179/frat06dIusaYR73v06BH+jGnTprnBlzQI05IlS6x58+Z25pln2q+//mojR450tSL1vpqka3kl4vQZJf9XTf2nn76xxx9/2Ro2bGFr1660MmXK2gUX/CdXWTRIF4oH4lggeX3f+xjWUw1YJWCJYQHsCOLYwsWxmqbWPy1atLAVK1a4z77qqqtSsn9QhElYHbj8lI26G/njjz/cgQeQdwK2e/ecplzBZupq3qXpDOAEoLDUd1Z0zYHoAHbe6nluvg6NUzN8tQLOK6+80ipWrGgXXHCBm/biiy+6JuLHHHOMCxKVEOvTp497T7UTFaw+8MADbr5DDz3UBaePPfaYe3/t2rWuGbkCz+7du7vXJ5xwghuAqa6eZpnZ4Ycf7hJsBx10kFuXglfVfpRevXpZrVq1rFmzZm6Qpp9//tn69r3MHn74cwuFcmKfxx4bYvffP8TOO++WcDkU8lSunJJdhiJAHAskB33fA0gW4tjCxbGXXXaZff755y72UZJ48ODBLlE8dOjQlOwjFFF3BHvvvbd98skncd//4osvrEmTJhHTyLYDeVOXA6o9EKsvLT9N12+6JgBQGBq8oCjnK0qbN2/ONW3BggUuSFUQ6nXp0sVeeOGF8Ovy5ctb27Ztw68rV67sguCBAwe616NGjbIGDRqEA1fp3LlzeB0lotrNzpw50/1WACzXX3+9HXfccda06fakXZcu/7bHHrvT/vrrz/C0Bg0YlKs4IY4Fkt/3vZKv+k0CFkBRII4tfBwbfPh8+umn2/Dhw+3PP7fHsSgGNWFVjfq2226zww47zFWtDlq+fLnrS0sHW0GsbNiwwT766KMd32Igg02caDZ/fv4jdGu+Dql5uAcgg2j02KKcryj5JlNB3333nWvmNX78eFfTQLZu3WqdOnVywW682oxt2rRxT/mXLl3qmmCpFoEGW/JUC0C1EoL0/po1a+ydd96xzz77zDUjE9U8UDyjbkP32CPnGly7dn3bsmWz/fTTF3bssd1cAjbQrSiKAeJYAACKF+LYwsexQfXr13efrxhHXR+gmCRhn3vuOfv7779dteZYlNHXAffWr19vmxixAsjTokVFOx8ABLVr2M6NHqvBC2L1p6W+tPS+5kslBZcVKlTINV2JL1ENgurVq4enn3/++XmuT8GpD4i1jt133931u5UX//5FF13kgmPVQFDzLwXP27Ztc+8p0arNWLw453Xt2ltMMS41YIsf4lgAAIoX4tgdi2M9/1rvoRh1R7Dbbru56tWzZs1K6EdVnY844oii3Xogw+QxRkih5gOAIA1SMLLLyHCgGuRfj+gyImWDGXgffvihdYhRvV+1FFVzQLUWg77//vuIgNIHq963335re+65pxuYoF27drmW9/PEolqR6mtLfWX5bZg7d274fSVcly2bbaVKlbKOHQ8lAVtMEccCKCrqJuzTT3P6wdXvTO82LNvKi/RBHBs5T2HiWJk9OyeOVX+0KEZJWI30VqVKlQItc/nllxf244Cs0K5dzgiy8W7qNV3NXjUfABRGtz272ZgeY6xe1XoR01VzQNP1fiqpduEvv/xiDRs2jJiugLRevXp29dVXu/60PDXjeuONNyKafQVrLK5cudIef/xxu/vuu93rSy+91D3pV4Ds/fjjjzZjxozw58QKbNVnqKjJuj5PtSa9ESNG2BVXXOH66ELxlMw4dty4cW79jz76qBswQwNv5Ec1ctXnrAbXUBNEnWPRNApy79697aGHHnLn5ZAhQ2LWYtH5q/NeIyVr1ORXX301wRICKMyAuo0bmx19tNldd+X81mtNz0TZVl6kH+LYHY9j77//fjeYGHFsMeuOQKO9FdSpp55a2I8DsoIGLhg5Uk0Wcidi/WvdlzHAAYAdoQC1a8uubvRYDV6gvrPUdCvVNQdeeeUVu/fee+2AAw5wo8WKagb89ttv9uyzz1qlSpXs9ttvd8kkJcA0UJL6sPrPf/4TsR4FkQpW9VR/ypQprl8sDVogVatWtU8//dRuvPFGmzBhgnu9yy672LnnnmtTp051SSrR8qo98PXXX1vt2rXtvvvuc9MPOeQQe+KJJ1yCbI899rAlS5a43wqqUXwlK46dNGmSu+FRUtUPltG1a1d3s9WzZ8+Yy2gQDZ2PP/zwg+sGQZRs1aAZSuL6m7Ljjz/e9Q236667umn67uhhgJKy3gcffGA33XSTvfXWW+5cHzNmjJ199tl27LHHutcAio4Sj4rZlQMJdge5YEHO9DFjzLqlNh+UVNlWXqQv4tjCx7GLFy+2pk2b2oABA1K6rxAQKiLTp08PXXfddaErrrgi9M4774Sy2apVq/Q4wv1Otq1bt4YWLVrkfmeDbCnv2LGhUP36oVDJkltDBx20yP1u0CBneibLluObbWUVylt469evD02dOtX9Tlfbtm0Lbdq0yf0uiPbt24e++OKL0MaNGyOma11ff/11qEOHDgmt46mnngoVh/IWV0VV3kTO5VTGUMmIYzt16hS65557IqaNHTs21KJFi7jLnHfeeaErr7wyYtqUKVNC1apVC/3999/u9c033xw68cQTI+b566+/QqVKlQrNmzfPvV6+fHmoVq1aoUmTJoXn+emnn0LXX399aPPmzQmXgTg2ObKprNlQ3i1bcmL1nJRkZMyu1yVKhFzsrvkyQbaVN9vO52yOY3ckximOcSwxbPLO5UTjp4RrwqovrD59+rjR1mrVquWqRV9zzTXuPWXk9XRegxaoOvSoUaPsggsucE2qABScniJ37ZrT19LSpWa1apkdeSQ1YAFkFvWfFas/Ko0We9BBB7mBBfKjGgexmmIBqY5jtbzWpSZ+Qar58scff7gar6p9Ek2jJqv7guhlVq1a5UYu1vdA8xx88MER86ivONWyee+99+y8886zp556yjVZVP9v3j777GO33HJLntu9ceNG9+OtXr06/N2KHsyjqPnvb7I/Jx1kU1mzobyK0Rcu3F4jtGTJbVaiRMj9DtYQ1Xzt21uxl23lzbbzOZnl9evyP+nIb1dBt699+/aupmn0suoH9h//+Id17Ngx33X6v7Wp3DeFLW9xFSqi8vpzOF58lOj3JaEkrJpAaTACBY+yZs0aFywuXbrU9VulZk4KaLt06eJOuHfffdf1Y6FOhM8888yClg3A/7omUBCzZIlG4I5s9gMAxZ36xIqVkApq3ry5GxXWN9GO7oNLsch3331n69ats7JlyxaqiTkyX6riWK1fSVAlRoMqV67sfmtgjehzXufuwoUL81xGSVglcXUzF03z+QE71BWBmhqqKwINuqHE6vTp023o0KHhLgxiGTZsmOvCIJr2jx/ZOVl0w6Jks25qgv3jZaJsKms2lFeVJA48cPvrEiW2WbNmq3SbrnqiEfMpli/usq282XY+J7O8an6v9envY6x+zHc2lVExqfhuhBKhZRo1apRnmfQ3f+3atXHj2IEDB7o4VvOoK4IzzjjD0rW8xVWoCMurY61zedmyZa7CSDTFl0WWhNUTdH3I2LFjXSColb/wwgt26623us6GTzrpJLvjjjvCG6Iv2sUXX2wPPvggSVgAAJCLgs1evXrlOU+8PjRFSVcNXhRrACNgZ8SxK1ascL+VyA3yr/37hVlGv6Pn8fP5eZR4VdJU3w0/iNgzzzxjxxxzjH311Vdueiy6CQz2cayasOqfTonpZPcjq5sZ3RTpszI9sZFNZc2G8qqV2pQp21/n1AgtYd9+W8u2bSsZMZ8qUxR32VbebDufk1le/V3S3139vYr1dyxdxEqq5UVlUb+secmrcoCWV3+uvk/XdC9vcVemCMqrY6bvg1oixUqsx5oWcz2JzPTRRx/ZZ5995j5MqlWr5gYK2H///V3Q9tNPP0VklVVABa4tW7ZMvEQAAABIK2q5pQf7quihe6cqVXIPHJnuUhXH+nVEN3fLqxlcostovljLB5t3qoaGErJ+MA857rjj7JxzznGDh8RLKGsgD/1E041GMpMNqpgycaJqzulGv6QdeWTJjO92Sccx2fs1nWRyedVNWN26OU3w/VczFCrhEpL60Ve7fv2c+TKh+NlW3mw7n5NZXi2vdfmfdKO/oX670nH7ihrlLTx/Dsf7XiT6XUkoCataAj5wDVKQd+SRR8YsjLLALVq0SGgjAEA3Y/SBC8SXLf02IX2oguW8eWoyZ6bW8uvWqQayRvE1q1Gj+JzDqYpjldz1TQyDfH+r/v3CLKPf0fP4+fw81atXz9WtgS+3+pYtaOs09d6gpLunHhJ22y3nfNB5EW2PPXJ+K0kT3YuBasZpXatWmf31l9m775oNHWq2eLHZvvuWtJ9+MqtTx+y668yOPTZy2UaNch4ALFpk9vffke+peNWrm61dm7OuIH+u+rJEn356T/Oo6XR0C0KtU+tevz6nH8wgxSaNG+f8e/bsnPglSImqChXMli1TVxjbp6uruI0bS7p9EWsf6jT0vVX4712Q9r2OgdapdQdVrJiz//SwZM4cy6VJk5zEmMqiMgWppwqdQtoH0c3IVamnXr2cf8+YkXu9fh9q3+sYBPnTXccs+tioQlLDhvH3oT5Tn61zRedMkCpnK07UV2T+/ILtw913z7mW6dq2fHnke5qu9+PtQ61X6w+e3wMHmgUHOd+6VQ9Lcv6t3xp8/M8/c8qj1//rESXm+a35dI0N2mWXnGutpuv9gpzfSojq2Yri6v918xxxbHTcVQ6VJ975PXeuWgZsf8+XV/tBn7dhQ6mI8g4ZkrN8UVwjgvR90vdK36FZs+Lvw2ReI7Rdy5aVct8TfZeScY0Q7YN0uEZoX/vyant25Boh2gcbNkTuYB0z/ei94Hnmy+qfDeq7Hn1sdFy0vVouev9q/+saozJE76PgevWe78JT+0Xr0XJaXq+jexnw69W2BLpRD9N6tf7gej2/3lhlVTlUnvzWG6usee1Dv95Y+1Bl0/bEW2+i+zDWsUlkH26LsV7xFUnz2oex1pvfPvS50VjrLcg+1DmseXRt1LZExxEJ9kaQWBI2r6q7Df1f0BiqBCO3LJTM4NXTSbRuXU5AtzP/MKUieBV/b6F9GB00ZFrwqqBL+1HHTMsGH6xkSvDqffFFTmCn9/bZp5T9/HPO8brxxpwByjIleBWdZzrftP9mz94ezCXrGpEOwavW++GHKnMpd0w0tozWm6nBq/bV119vL+/xx+eci4W9RuhvsD531aq/rUSJCmkZvIq2UevMhuDV7wvJ1OBV1z1dw6Jp3TpX9TdIf08Ksg91Dmue+fPLuP0QK45INIAtiFTFser7Td1s+IGtPPWr5/s4jtWna506dfJdRgnh6Hn8fH6evffe27755puYie/C1GRSAim46zp0MNNYZro+9umTe/5x43J+33uv+rKNfE+9HahL288+Mxs8ONisuYQtXVox/LdNCR71O6nrrff88zl/dx9/POfaGnT++WYnnWT2/fdmw4dHvqfrvW/lqe2OPu9Hjco5j196yez99yPf697d7OyzzaZPz0kMB+lv19NP5/xbSafovxe33WbWurXZm2+ajRmzfbpqDR56aHnba6+cmCR6H+p79PrrOf++667cMU///mZHHGH2ySdmTzwR+Z7+ripmUrwT69i8/HLO37GHHzb77rvI9y65xOyf/zSbPNnsnnsi31NlcG2LxFrvo4/mHCsdI21X0GmnmR19tNlvv5lFdzmsZbSsXH997jjrzjvNWrUye+MNs//+N/I9/U299NKcGDZ6mxQbvPJKzr+HDcsdB9xwg5nG0PngA7Nnn418r23bnHNeMUCssr72Ws734YEHzMWpns5XxQWK69auLeOOs67Fe+9t9s47OdugbdH5F2u9Tz2VE1PqnJo0KfI99dhz6qk5nxc9vp6unw8+mPNvbXf0/Yl66lHcqHPw7bcj31NsfcEFOTFW376R7+m6/sILOf/WZyqGiz42+l7p+zp3bpWI8voeV3b0GqHzNGj//c1uvjkn9o213lRcI957r4Rt2lTFypZVTbjkXCPkmGPMNLbjzr5G6Lj68ur7tiPXiNq1y7jv+IIFf1vZstvjWMXG2lc6rtH3Avqu+XsBnWvRcZbu0/R91/c1+l5A50K8ewH9KfT3Uzq3fSwVCpVyx1XXJt0LaHujj5um+3uBePdpWodimujvo09k6x4h+l5A5VB59Oc61nq1H7Q/dH8XnS/Q/tN+1OdFf1cVX+keT3S9DMaFKq/ObX139b2Nzhfo3i9evkCxpL+f0mdGx366T1NeQOuMzhcorNr9f/mCWGX1YZLuOaPvdXWfpuuT9oHuz4N07up+SmWMtV5/v6p9GJ0v0PVX+QLdA0fnCxSv+zBR6924cZ0tWlTCHnusjNs30XFE9L6Ip0QogWoJJ598sr3uv/FRhg8f7pp0xdKtWzd7TX+xCmHcuHE2ceJEa9asmc2YMcPatGmTb0fFn3/+ub366qvWqlUrN8hBjRo13Ei4Qb/++qs9/PDDbh412VKtghtuuCFX/yTffvutPfbYY26AAwXvdevWtVP1VzABCpJVK+HYY1dZmTJVcwWvOlkvuihWmXN+X3tt/D9Mb70V+YdJh69Fi3V2110VbcOGki7gifeHSTUO4v1h0h+8vP4wnXxy/D9M990XP3hVzYa8/jCdc078P0zPPBMdvCrRsc3atVti27bVtuefz2n6EusPU+/e8f8wKaCL94dJF4tYFUX8H6ZBg+IHrx9/nPcfpn/9K37wevfduYPX009Xf4jb7IMPltt999WMqKkTDF7V1Uy84FXBSLzgVTfReQWvl10WP3h99dX4wasubrG6x/HBq5KtweBV3wd/M6ZO/qtW3WSrVilDUSJ87mlZXdS6dYsfvN5+e/zg9auv8g5ee/SIH7w+9FD84FU3FnkFr/qeR/9B1I3IAQfoe7nNnn12k5UtWy58bJNxjQgGr/rjkuprhAK2887Tdytk1aptdMe2fPkSbv/df3/RXiOCwauSR8HaKam6Ruh8U1CtJ6W+vDVrlnDfV/2BL+w14tVXF1mdOiutRo3aVqqUEhYl3DHzQUN0QKfvmk/u6nscHbwqAFLgpeAo+vqhAEnHQMFF9Pmr4NUHIwqKtwcbOU2ia9cuYZUqlYgZFGv/KAjVMtEPL0Tr1foVAEUHXtoebZcSdNHng8qh8ijwipU01H7Q/tA+in7IoCBTPwrIogO64D7UeiOTmiGrVWuLVaxY2pYvL5ErcajrgIJilSM6oPNPznPvwxzaR9pXsYJiBbUKinXTEH3DIv6GRcctOiGt66S/sYgOinXtV3Cr88Rf97VPtkeIIatQYYutX69YyTfpytkenYM6F2PtQ90AKBjXuTFr1t+2fPkS++KL6jZhQp24ccTmzavt3XerueRiUfVHmso4tkOHDnbssce6fla95557zsWa6rM1Vq1bdReg7gAeeeSR8DTFwBoobPHixS5RO2TIEFebVYOGefPmzXNJZA2+pXhVXQ5ceOGFLr71SddFixa5GHb06NF59rEcK4797rtVVqXK9mNQFA8Kde4pWeO/F/q7v+++f9lPP+0a7ldS1zzFRL41TKZUJlAfixs3/mV77bWrbdlSMgtqwmogniVWuXJtW7w48iFAplUm0PX++++32Zw5S6106Vp28MHbu9bw+zCTKhNovW++qfIus112qRkub6ZWJsipCZszGI9aF+j6mvk1YbeXt3btkjt8jZg2bZFt2LDSatWqbeXLV3R/C7Wf/EPc6HsIldU/xFU5Yz2w9pUJoh86a7p/YB2rkkLu9YZcdz45fdbmbJevGRtrvVom1oNwrddXJojeXpXTVyaIV9b81hurrIXbhznlrVBB/ZuWKKJ9mLusee3DbTHWK8GKHgVZb977UN0RbLEyZUrb5s3bWyskug/LlMnZX8uXr3bxUcmS1a18+Zw4NndN2NW2//75x7AJJWFPPPFE+7//+7+Y72kgA40wG0vXrl3tv9EZoARMmjTJrr32WpdU9cGq1nX66afHDSA1Iq36vfrhhx/CHeL27t3bBZ4+uNbouOr/S7UE/Cix9957rxtx9iFlWv5Ho8tqlFiNLqudN2bMGDdyrgLZRG4Ikhm85q4Ju83WrfvL9t1X5SmZsTVhtzdb22b/+McSmzy5tu22W0mXFPHN1jKzJuw2mz17iW3dWjuiFkumBK/a7vbtI2/G9tlnmf38c83wSKs6LtoWFT9Tgledb3/9tc2mT98ezEmmBa9KwOphgqbHOrZjx6q/wswJXpW8PeUU/1mR5dVnP/ec2aGHFu4a8eefIVu27E/bunVlxH7SqaPjGisY8c8Wtc2xAhnNo+ViBV5at5aJFYzEXm/I/T0qU0Z9JKmPuNiBV7z1iq9xF2t7Ey1rrIAur7IWfh8qoNtmpUqpL7yc8iZa1vyOTV7blKx9GL1efTciryMhK1t2m23apO/u9iSiroP6LiZ6bEqVqm7lyu0eju1i14RNLIAtiFTGsR9//LGLYTUQln/Af/zxx7v4VQPRTZ061cWz99xzjx111FHu/d9++81OOOEEN0Kyr3172WWXuRqyNyrQcSOOL7WDDjrI9W1bX3+g/rftv//+uz3xv6dGukk45JBDXMJXiWfRgB9KwGq5RAdE8XFsUR4DT8lVPTAMDu5z4IFLbMqU2hGD++jBth5MZhJdI5csWWK1a0fGdJmK8mY2ypvZirq8Sjn9+eefLh+TbrRtKq/vuzbTUd7CUUsnfR8UH8VbT6LxU0JJWK1AtUD1wdF+/PFH23fffXNN37p1q6s9EGsk2PwoKFUwetVVV4WnaV2qVaBgM5bzzz/f1RQIji6n2qwaBVfJ0woVKtjQoUNt8uTJEQG1nvDsttturnaCglptrwZieOONN+zwww938/z888/20ksvuVoIiQSwyQxes/EPgiqhqGatztRgsO4TOaoJF6uGZCbI9OPLzVjmHlslY5RI9Yn+6GPrB3BQUjgT+v5NVXn1t1Ujt6ebYI2JTDyfs628qs2tGveezue9915mv/xSM+LarFrcJ5yQ2DrVqihWHJmKGCrVcazizE8++cTFk6okoN+KU0UVAY455hh75plnXJLXU5JUydLWrVu7uLVixYouORwM9JXAvf/++908upFdu3ati03L+ioprqndX245nZs6T9evX2/Dhg2L2R/tzohjR482CzZsi/d3/8UXcx7iZZJM/7sfjfJmNsqb2ZJV3nSMYzM9potGeQtOOUDFkPklcRONnxJ6JK4g7ym1+Y3j6+j2q/9TmEyzgsUJEybYlWpPGtCkSRNXY1XBrPrcijZ+/PhcNRm0jHaAmm8pGat5Dlab0gAdDA1i8N5779l5553nyqmaBIcddlh4nn322cduiW7LjJQlNtRsONajgpzadTlN6nUfkwmJnGwT3dR5R+dD+tCI19E1rYN8v0eaLxMS7KkqrwKARBJZqZZTC7aMa4mSLQFdJpdXNVyDNb6VKNt11zI2Z075iESZ5vP90KazVMaxouRqMMEapNqssWoCHXHEEe4nL3vttVdEy61Y1NLrySeftHQV7Ou1KOYDABQf6RjHZnpMF43y7nwJJWEbN25sb775Zq4RV/MLeNX8q6CUZFUSNPqzVMtVVBM2Ogm7bt061wdsXssoCaskbsdgtbvAfL6GrboiUL9a6opAtWM16qz62lItWt+FQTTN40exFT9wgg64fpJJ6/dVrDPRhAk5TZX990U3giVKhNxvT02zNZ+atWeaTD++6r4geC2MdXz9fJm2CzL92Cpxnsix1XyZsAuyrbzZdj5nW3nVx7e6vNHfV98KJXg++5rdmq+od0Ey9mkq41jkrV27nHPHn1vR/Lml+QAgXSoF6V5TXZSpS7cjj6TyT6bItmObbeVNVwklYTXaqp6+F1RhlvHNvqKb/fvXsZqFJbqMfsfqTkDT/DxKvG7YsME17br88svdNDUZU9Mx9e8VbPLlqZmX+pCNpv67tK5k0s2KavvqZjBdMvtFSRcIjTrqqQ++Zs1W/W8QmJIR80X3x5oJMv34alCizp23978Z6/jq2Yfmy7Tjm+nHVsctke+u5suEY5tt5c228zkby6vRqzXgYbzzWQMxRvedXBTWRHcyXwRSGccib7rZU89h6mYquqKxf62BMbkpBJAu3eKpVaYqBSnO02DCGptA17FM7Q4vW2Tbsc228qZz0jmhJKxqgRZGYZbzTb+iu6r1r2N1YZvoMpov1vKa5qerFq4Ssp2VGfofDfilkWs16uyZMYbFVl+1V2to8kBN2AYNGlitWrVS0iesyqXPysQbQX1ZdIHwcmrhlLBvv60V0SRS82kwnkyT6cdXLrzQrEeP7Tf6/vj6G/1XXsmpCZtpMv3Yqsm9BvIK1qQLfnd9bSfNlw5/DHdUtpU3287nbCzvSSfl/Fb3/AsXbj+f69Uraffcs/39ouYHVy1KqYxjkT/d7Kk/f38z6OkaqQRspt4MAijO45Jsn65YT9MzeVySTJdtxzbbypvuSeeEkrD77bdfoVZemOX8wAGbooat9s39Yw0skOgy+h09j5/Pz1O9evVczdXUb6yob9lYSdhy5cq5n2i6MUvFzZluBFP1WammpxX6sgSbrYVCGom6ZERiQ/NlYPEz/viKLoJKtPqLpD++utHP9JuxTD62KpJq0ukPu6iFsT+2SrDr+6xEjh/NvbjLtvJm2/mcreXV9Vddm+bUIlDSuaQdeWTJpD5ISMb+TGUci8KcW+lVQwUAGJckc2Xbsc228haHpHPa3Tmov1d11uz7VfXU7E+aN28es0/XOnXq5LtMixYtcs3j5/PzqMla9Ih9vpZsJt9opXuzNaHZWubSRXD2bPXJnDMit35rFPlMTsBmU22nevUip+vByc7+45cM2VZeZAf9fVWf60qQ6Td/b/+/vTsBj6q8Hj9+ZhKyL2wB2XeCKNCK4IIbS1FxSStWkf6Kivq3Py2Clk1ZFQFBBS2tYKviUsWFYv2hqFitgqAWUHEBwyYIgoDIkrAEwsz/Oe/kTmYmk7CYm8zc+/08zzzD3GUy585w77nnvvd9UVn4bQFwwoCriC9u+27dFu/RYxSdlRaddbmYbglbldLS0szosDoYVqi1a9dK06ZNTSE1Gu0+INo6+n7ddOSIkmW0NWuozZs3m5awvXr1CnY9MG/ePHOroVV01b5dlfU+qFrctuaukzHtL1O7luCahzO4rbWT2+IFAABwGh1ItTKXiyex2o9mZXHbd+u2eBefQNFZu4mrDjFZ5hg3bpzMnTvX9M9qmTNnjumbS2//W7VqlXTq1Enefffd4PyRI0ea16EDOug6Ol1byqrbb79d8vPzZUvIt6LLDBw4UFq1amVeX3XVVdK6dWt57bXXgstoX7BnnXWWXG3dZ4oqR0tJIH65rbWT2+IFAABwkgYNKne5eLqNu3lzEW2f9tBDgWd9rdOdwm3frdvi3RYHReeYawmrunfvLmPHjpVhw4ZJbm6ubNiwQfr27SsDBgww8/fv3y+bNm2SwsLC4Drt2rWTp59+2hRdO3ToINu2bZNmzZrJ8OHDg8voABoLFiyQiRMnmmX27Nlj3mPmzJnBZRITE+Xtt9826y1dutS0iD148KCZpvNQfWgpCQAAAACw0/nnB+66DB2XJJQ1Loku5xSx3o9mZXHbd+u2eBvEQdE5ZquKeXl55hFNly5dTAE1knZjoI+KtG/fPqzoGk3dunXlqaeeOsFPDAAAAAAAnDAuiRYf3TAuiZsGb3Lbd+u2eM+Pg6IzbQnjfGf5wQeBPlv0uTo7FwYAuBPHIgAA4DRuGnDVbYM3uem7dVu8CXEwsHvMtoTFsW8XsAaq6txZZMUKkYYNAz84J/0nAgDELo5FAADAqdwy4Go89KNZ2dzy3box3qtifGB3irBxyC39tQAAYhfHIgAA4HRuGJckHvrRtIMbvlu3xntVDBedHbzZnelY/bUo7a+F20EBAHbhWAQAAOCsfjQjb9+26PQmTZwzeBPcIaGk6KzFV32OhQKsoggbZ9zWXwsAIPZwLAIAAHCGeOhHE3AKirBxxo39tQAAYgvHIgAAAOdw0+BNQHWiT9g449b+WgAAsYNjEQAAgLPEcj+agFNQhI3T/lp04JNoffHp7QI6n/5aAAB24VgEAADgPG4avAmoDvyXijP01wIAqG4ciwAAAADgxFCEjUP01wIAqG4ciwAAAADg+NEdQZyivxYAQHXjWAQAAAAAx4cibByjvxYAQHXjWAQAAAAAx8apEgAAAAAAAADYiCIsAAAAAAAAANiIIiwAAAAAAAAA2IgiLAAAAAAAAADYiCIsAAAAAAAAANiIIiwAAAAAAAAA2IgiLAAAAAAAAADYiCIsAAAAAAAAANiIIiwAAAAAAAAA2IgiLAAAAAAAAADYiCIsAAAAAAAAANiIIiwAAAAAAAAA2IgiLAAAAAAAAADYiCIsAAAAAAAAANiIIiwAAAAAAAAA2IgiLAAAAAAAAADYiCIsAAAAAAAAANiIIiwAAAAAAAAA2IgiLAAAAAAAAADYiCIsAAAAAAAAANiIIiwAAAAAAAAA2IgiLAAAAAAAAADYKNHONwcAAADcZP78+bJ48WJp3bq1rF+/Xjp16iT9+/evcJ2lS5fKK6+8Iu3atZOtW7dKrVq1ZMiQIWHLrF69WmbNmmWW2b17txw+fFhGjx4tiYnR03m/3y9XXHGFPP7449KoUaNKjREAAAAnjiIsAAAAUAmWLFkikyZNMkVVj8djpuXl5YnX65V+/fpFXWfDhg1y4403ysqVKyUlJcVMGzx4sEyZMkVGjBhhXu/Zs0f69Okjy5Ytk7p165pp06dPl0GDBsnMmTOjvq9Of+ONN+TIkSM2RQsAAIATQXcEAAAAQCUYO3asXHPNNcECrLr++utl3Lhx5a4zceJEueSSS4IFWGudyZMny8GDB83rGTNmSMeOHYMFWDVgwAD5+9//Llu2bCnznvn5+bJ9+/ZKjAwAAAA/F0VYAAAA4GfSgumiRYukZcuWYdNbtGgha9asMS1eo3nrrbeirrN371756KOPyl2mTp06kp6eLgsXLgybXlxcLC+88IJpXQsAAIDYQXcEAAAAwM+kRVYtgGphNFRGRkawdWpkIXX//v2mD9iK1unRo4cp4nbv3r3M39TldJlQ2gfsrbfeavqMPR5FRUXmYdm3b5959vl85mEnfX/tu9buvxML3BSrIl5nI15nc1O8bopVEa99jvdvUIQFAAAAfiYdLEtFDpRlvbbmn8w6+hxtAC6dFvq+K1asMINwNWzYUDZu3Hhcn1u7Pbj33nvLTN+5c6ccOnRI7D5h0Ra/eoKk/eY6mZtiVcTrbMTrbG6K102xKuK1T0FBwXEtRxEWAAAA+JmsfmA10Q9lvY6cfiLr6HLR1tdp1nQtmL799ttyzz33nNDnvvvuu+Wuu+4KawnbpEkTycnJkaysLLH75Ehj07/l9JNBN8WqiNfZiNfZ3BSvm2JVxGuf0L7947IIO3/+fFm8eLG0bt1a1q9fL506dZL+/ftXuI6ORPvKK69Iu3btzK1dtWrVkiFDhoQts3r1apk1a5ZZRlsO6K1ao0ePjtq6QGlie8UVV5hbu7RlAQAAABApOzvbPEd2A2Dd6m/NP5l19Dla9wK6nLXMzJkz5fbbbz/hz52cnGwekfRkpSpO0PTkqKr+VnVzU6yKeJ2NeJ3NTfG6KVZFvPY43vePySLskiVLZNKkSaaoarUQyMvLM0H169ev3H64dACClStXBivQgwcPlilTpsiIESPM6z179kifPn1k2bJlwdFlp0+fLoMGDTKJazQ6/Y033pAjR47YFC0AAADinfb3mpCQEOxT1aK3wak2bdpE7dO1QYMGx1ynbdu2ZZaxltNldFCw5cuXy/bt24PzduzYYZ41F9buCcaMGVMpcQIAAODkxGQRduzYsXLNNdcEC7Dq+uuvN7dLlVeEnThxolxyySVhTYB1HR3M4I477pDU1FSZMWOGdOzYMViAVQMGDJD69evLqFGjpHHjxmHvqQMdhCazAAAAQDRpaWly3nnnybp168Kmr127Vpo2bWoKqdH07t076jr6ft26dQsu89FHH4Uts3nzZtMStlevXibPff7558Pmv//++zJ79mzTGKF58+aVFCUAAAAcU4TVK/mLFi0yhdNQLVq0MCPDaovXyJFl1VtvvSXDhw8vs462ENCkVYuxukzXrl3DlqlTp44ZkXbhwoUycODA4HQd3faFF14wrWvvu+++Cj8zo8pWHeJ1NjfF66ZYFfE6G/E6WyyOLBurxo0bJ0OHDpVhw4YFu7qaM2eOTJgwwTQuWLVqlVx33XUybdo06dmzp5k/cuRIufzyy82ADpmZmcF1dLq2lFXazcDTTz8tW7ZsCTYa0GU0d23VqlXUz3L06FFHbFMAAACniLkirBZZtQCqhdFQVhKqrVMji7D79+83fcBWtI4WYbWI27179zJ/U5fTZUJpH7C33npr1P63IjGqbNUhXmdzU7xuilURr7MRr7PF4siysUrzTL2jS4uwubm5Jq/t27evufPKylk3bdokhYWFwXV0nAItsGrRtUOHDrJt2zZp1qxZWOMCHVBiwYIF5s4vXUa72NL3KK87Lf0M2hLW6p5L8+A777zT9vgBAAAQR0VYHSxLRQ6UZb225p/MOvocbQAunRb6vitWrDCDcGn/WRs3bjzmZ2ZU2apDvM7mpnjdFKsiXmcjXmeLxZFlY5mOY6CPaLp06WIKqJG0GwN9VKR9+/blFl0jaTdb48ePN9+Xfn+MbQAAAFD9Yq4Ia/UDq60tQlmvI6efyDq6XLT1dZo1XVuuvv3223LPPfcc92dmVNmqRbzO5qZ43RSrIl5nI15ni7WRZVGx0LxUt2m0PBUAAABVK+Yy3ezsbPMc2Q2A1eeqNf9k1tHnaN0L6HLWMtrCQPvdAgAAAAAAAABHtoTV/l4TEhKCg1tZtC8y1aZNm6h9ujZo0OCY6+iotJHLWMvpMjoo2PLly2X79u3BeTt27DDPU6ZMMd0TjBkzplLiBAAAAAAAAOAOMVeETUtLM31irVu3Lmz62rVrpWnTpqaQGk3v3r2jrqPv161bt+AyH330UdgymzdvNi1he/XqJampqfL888+HzddBDWbPni0jRoyQ5s2bV1KUAAAAAAAAANwi5rojUOPGjZO5c+dKcXFxcNqcOXNkwoQJpk+yVatWSadOneTdd98NztcRZfV16Ki6uo5O15aySrsZyM/Ply1btoQtM3DgQGnVqlXUz3L06FHzrIMaAAAAAAAAAEDct4RV3bt3l7Fjx8qwYcMkNzdXNmzYIH379pUBAwaY+fv375dNmzZJYWFhcJ127drJ008/bYquHTp0kG3btkmzZs1k+PDhwWV0VN8FCxbIxIkTzTI6Oq2+R3kjzepn0JawavDgwdKjRw+58847bY8fAAAAAAAAgHPEZBFW5eXlmUc0Xbp0MQXUSNqNgT4q0r59+3KLrpFGjRol48ePN6PKakvYI0eOHOenBwAAAAAAAIAYL8LGguTk5OC/tRAb+hoAAAAAAAAA4rZPWAAAAAAAAABwCoqwAAAAAAAAAGAjirAAAAAAAAAAYCOKsAAAAAAAAABgI4qwAAAAAAAAAGAjirAAAAAAAAAAYCOKsAAAAAAAAABgI4qwAAAAAAAAAGAjirAAAAAAAAAAYCOKsAAAAAAAAABgI4qwAAAAAAAAAGAjirAAAAAAAAAAYCOKsAAAAAAAAABgI4qwAAAAAAAAAGAjirAAAAAAAAAAYCOKsAAAAAAAAABgI4qwAAAAAAAAAGAjirAAAAAAAAAAYCOKsAAAAAAAAABgI4qwAAAAAAAAAGAjirAAAAAAAAAAYCOKsAAAAAAAAABgI4qwAAAAAAAAAGAjirAAAAAAAAAAYCOKsAAAAAAAAABgI4qwAAAAAAAAAGAjirAAAAAAAAAAYCOKsAAAAAAAAABgI4qwAAAAAAAAAGAjirAAAAAAAAAAYCOKsAAAAAAAAABgI4qwAAAAAAAAAGAjirAAAAAAAAAAYCOKsAAAAAAAAABgI4qwAAAAAAAAAGAjirAAAAAAAAAAYKNEO98cAAAAcJP58+fL4sWLpXXr1rJ+/Xrp1KmT9O/fv8J1li5dKq+88oq0a9dOtm7dKrVq1ZIhQ4aELbN69WqZNWuWWWb37t1y+PBhGT16tCQmlqbz7733nvznP/+R/fv3m+XPOussGTlypKSkpNgWLwAAAI4PRVgAAACgEixZskQmTZpkiqoej8dMy8vLE6/XK/369Yu6zoYNG+TGG2+UlStXBoulgwcPlilTpsiIESPM6z179kifPn1k2bJlUrduXTNt+vTpMmjQIJk5c6Z5/fHHH8uWLVtkwoQJ5vW+ffvk7LPPlk8++UTefPPNKokfAAAA5aM7AgAAAKASjB07Vq655ppgAVZdf/31Mm7cuHLXmThxolxyySVhrVV1ncmTJ8vBgwfN6xkzZkjHjh2DBVg1YMAA+fvf/24Kr+qxxx6TJ554QoqKiszrrKwsU6R96623TIEWAAAA1StmW8JyKxcAAADihRZMFy1aJHfccUfY9BYtWsiaNWtMi9eWLVuWWU+LpMOHDy+zzt69e+Wjjz6SHj16mGW6du0atkydOnUkPT1dFi5cKAMHDpScnByTCxcXF0tycnLwfdSmTZtMq9hotGhrFW6tFrTK5/OZh530/f1+v+1/Jxa4KVZFvM5GvM7mpnjdFKsiXvsc79+IySIst3IBAAAgnmguqgVQLYyGysjIMM/5+fllirB6wV8bDlS0jhZhtYjbvXv3Mn9Tl9Nl1MMPP2wekZ9JtW/fvtzPrS1u77333jLTd+7cKYcOHRK7T1i02KwnSJrnO5mbYlXE62zE62xuitdNsSritU9BQUH8FmHLu5Xr7rvvLrcIW96tXJq4aouE1NTUcm/lql+/vowaNUoaN25sbuXauHGjXHvttaYVgXUr12233WYKtOW1IgAAAIB76R1WKvTuqtDX1vyTWUefI5exlov2vpZnnnlGevXqJR06dCh3Gc2v77rrruBrbYDQpEkT07JW82C7T44039e/5fSTQTfFqojX2YjX2dwUr5tiVcRrn+O9cz7mirDxeisXAAAA3MtqPKCtLUJZryOnn8g6uly09XVatOnqqaeeMq0y3njjjQo/t+a7Vs4bSk9WquIETWOrqr9V3dwUqyJeZyNeZ3NTvG6KVRGvPY73/WOuCBuPt3LRl1bVIV5nc1O8bopVEa+zEa+zxWJ/WrEoOzvbPOt4A6GsHNGafzLr6HPkMtZy0d53xYoV8vjjj5txDkLvAAMAAED1ibkibDzeykVfWlWHeJ3NTfG6KVZFvM5GvM4Wi/1pxSJtJJCQkBC8GG/RbafatGkTtSFAgwYNjrlO27ZtyyxjLRf5vt9++61MnTpV3n77balZs6Zp3KDr1q5duxKiBAAAgGOKsPF4Kxd9aVUd4nU2N8XrplgV8Tob8TpbLPanFYvS0tLkvPPOk3Xr1oVNX7t2rTRt2tQUUqPp3bt31HX0/bp16xZcRrvXCrV582bTElYbClh27dol06ZNk6efftqMh6C++OIL09igZ8+elRYrAAAAHFCEjcdbuehLq2oRr7O5KV43xaqI19mI19lirT+tWDVu3DgZOnSoDBs2LHj31Zw5c2TChAlmG65atUquu+46Uyi1iqIjR46Uyy+/3Fz0z8zMDK6j062utW6//XZTWN2yZYsZSNZaRsczaNWqlXmtd1/dcMMNctlll8lLL70ULKDruAeRXW0BAACg6sVcEZZbuQAAABCPdOyBsWPHmiJsbm6uGVegb9++MmDAgOA4BjrQa2FhYXCddu3amQKrFl2166tt27ZJs2bNwgac1VbICxYskIkTJ5pl9uzZY95j5syZwWUGDx4sr7/+unmE0sLuiy++WCXxAwAAII6KsNzKBQAAgHiVl5dnHtF06dLFFFAjae6rj4roALGhRddIeveWPgAAABCbvLF6K9fcuXNN61NL5K1cnTp1knfffTc4X1sP6OvQAR2i3cqVn59vbuUKXSbarVynnXaauZVLC7HaL6y2itWWCgAAAABw3HxHRXZ8ILJ9UeBZXwMAANeJuZawilu5AAAAAMS9zfNEVgwWObBVJKGzyNcrRNIainR+VKTJVdX96QAAgNuLsIpbuQAAAADEdQF28dUi4g+/AfHA94Hp58+lEAsAgIvEZHcEAAAAABC3tMsBbQFrCrCRSqatGELXBAAAuAhFWAAAAACoTDsXixwoHYeiLL/Igc2B5QAAgCtQhAUAAACAynRwW+UuBwAA4h5FWAAAAACoTKkNKnc5AAAQ9yjCAgAAAEBlyjlfJK2xiHjKWcAjktYksBwAAHAFirAAAAAAUJm8CSKdHy15EVmILXnd+ZHAcgAAwBUowgIAAABAZWtylcj5c0XSGoVP1xayOl3nAwAA10is7g8AAAAAAI6khdZGeSI7Fols3ylSP0ek3gW0gAUAwIUowgIAAACAXbTgWu9CEdkhUq+eiJebEQEAcCMyAAAAAAAAAACwEUVYAAAAAAAAALARRVgAAAAAAAAAsBFFWAAAAAAAAACwEUVYAAAAAAAAALARRVgAAAAAAAAAsBFFWAAAAAAAAACwEUVYAAAAAAAAALARRVgAAAAAAAAAsBFFWAAAAAAAAACwEUVYAAAAAAAAALARRVgAAAAAAAAAsBFFWAAAAAAAAACwEUVYAAAAAAAAALARRVgAAAAAAAAAsBFFWAAAAAAAAACwEUVYAAAAAAAAALARRVgAAAAAAAAAsBFFWAAAAAAAAACwEUVYAAAAAAAAALARRVgAAAAAAAAAsBFFWAAAAAAAAACwEUVYAAAAAAAAALARRVgAAAAAAAAAsBFFWAAAAAAAAACwEUVYAAAAAAAAALARRVgAAAAAAAAAsBFFWAAAAAAAAACwEUVYAAAAAAAAALARRVgAAAAAAAAAsBFFWAAAAAAAAACwUaLEqPnz58vixYuldevWsn79eunUqZP079+/wnWWLl0qr7zyirRr1062bt0qtWrVkiFDhoQts3r1apk1a5ZZZvfu3XL48GEZPXq0JCaWbopt27bJAw88IG3atJFDhw7J9u3bZfz48ZKeni4xxXdUZMcike07RSRHpN4FIt4EcSziJV6ncFOsiniJ10mI19nxVgLH5LAFG0Q8maWvEzNEUuuLHD0scmBz2eUzWwWeD3wvcvRQ+LyUeiI1MkUO7xUp+rH0t7Xrv+L9qVBEckVyzhM58F3Z901vJuJNFDm4TaT4QPi85DoiSTVFjhSKHNoePs+bJJLeJPDvwg0ifn/4/LQmIglJIod2iBwpCJ+n76nvXXxQ5ODW8HmeBJGM5iXvu1HEfzR8fmpDkcRUkaJdIof3lE73+cR7uEhE6kXfhh6PSEbLwL/3bxbxHY7YhvVFamQE3lPfO1RimkhqAxFfscj+TZFbUCSjhYjHK3Jgq8jRg+HzkuuKJGUHtoFui1AJKSJpjQL/Llhf9n2tbXhwu0hxYUisR0X2rRb5qUjkaKZIRqvw/Ya3hkh60/K3of5N/duHfhQ5sjd8Xo0skZQckaNFIge2nNg2TD1FJDFd5PBukaKfwufpdJ1f7jZsGXj/aL/vpNoiuz8Xz9ZNIgcSRep0LY3X2ob6+9PfYbm/7x9EiveHz0uuLZJUKzBd55/Q77uxSEKyyKGdIkf2RWzDbJGUuoE4NJ7yft/7vxPxHYnYhg1EvMki378uCT9ovLVL463sfYQlIVUkraGI3ydS+G3V7yMObBP54V1J2PVTabwpdSp/H6F0G6RU8z5Ct9Wu/5bG27CPSErtyt1HKP1t629cvzP97kJV1T5Cv7/QWBtfKZKUVfn7CP08+rn0/6L+n4y2DatiH1Fy3DXx+nJFTuklcvinyt9H6O9Nt59ux7BtWMX7iJJ4ZZ/uj08RyWwj4iuyL48oiMgl4qkIu2TJEpk0aZJJSD36QxaRvLw88Xq90q9fv6jrbNiwQW688UZZuXKlpKSkmGmDBw+WKVOmyIgRI8zrPXv2SJ8+fWTZsmVSt25dM2369OkyaNAgmTlzpnl95MgR6d27t7z44oty2mmnmWmvvvqqXH311fLmm29KzNg8T2TF4MCOMqGzyNcrAj+8zo+KNLlKHId4idcp3BSrIl7idRLidXa8lcBROeznI0XSa5S+rn+RyKl/Ejm8S2RFeIHYuGh+4Pmb6SL78sPnnXqXSP3uIjs/FFk7S+TQNpE9X4vHd0jSPA1F1vwQOLnVk7eUBuHrnvuPQJFw3ROBk6lQrW4SafJrUwCTVVPKnhCf+Wjg35/+KXDiHKrLXwMn+ZteFNn2Tvi8pleLtLxepHCdyOf3lD1hO+fpwL+/HF+22PGLSSI1O5gilXw3NzjZI35JST9bpHF7kUM/lN2GepJ9wauBf69+qOzJePsRIvXOE9n+vsj6J8PnaVGow5jAiXi07+a8lwInxetmifz0Wfi8Nn8QaXSZyE/LRVZPC5+XlStyxkOBf0d737P+FvjONv4j8LlUyXdrTnQTzhQ5ujxQtKt5Wul3q+voumrlqLIFgF8+KJLdTmTLv0S2vBY+r1EfkTb/GyiuRH4mLWyd93Lg36smB4osoU4fLVL3LJEf/i2y4dnweTndRE4bGShgRYv1gnkinhoia/4isuer0ukarxZkin6SGt7W4vGtEfGmlMZb83SRX0wW8RdHf9+zZweKHRueFtm5JHxeywEiTX8b+Htf3R8+TwsDXR4r/b+qxaRQnR8JFDQ2zxX5fkH4vMZ5Iq1vDhS3PhsWPk8LRd2eD/xb/2Zkcaz+hSL5j5oiSaZkiWft3tJ4m11XefuIULV/KdLxvkBRJtr72rmP0P8XHw8Uz5G9kinZpfHq9jtzRqXuI4wGvxLJvaP69hF6zP/+dbNvDsar8XT9W6DYVxn7CEvz60Sa9xfZ943IF+PC51XFPuK/t4hs/yAi1hyRrrMCRd/K2keo3EEiDXqL/PixSP6M8HlVtY/Q30vJcVfjlbX7AsXfBpeW/X/+c/YRHe8VqX2GyLa3RDbOCZ9XmXnEsfYRIXmGx/tLkVUrAxcptBAbmmdUZh6xP6IgXQ6P3x9Zzq1+PXv2lMsvv1zuvPPO4LR58+bJ3XffLfn5EV9GiZtuukkyMjLk0UdLNpJup08/lR49ephWAampqTJhwgRZvny5vPZa6X/UXbt2Sf369WXjxo3SuHFjee655+Shhx4yibDl6NGjJuFdsGCBnHPOOcf8/Pv27ZPs7GzZu3evZGVliS0nRYuvFhG/+MQrOxI6S72jK8QrJV/l+XOddXJEvMTrlHjdFKsiXuIl3vhVTfHankPZLN5z2LDvYMtnkpVlQ0vYjc+LLL/dTPaJR370dpS6vi/FK77Asmf+VaTBxY5rCevz+eTHvUVSt3F78erJthNbwm57O+y73ZFwptQ7urx0v2F9t05pCRsWr1d2en8hOb7PwuPVYoZTWsJGfL+7vKdLHd9XpfGe9aRIq4HOaQmrBdgl15UcB6PEe/4/SwpYDmkJqwXNxX3Ny7LxekTOeU6k7tnOaAlrcpwKYj376UDR1SktYdc8Vs7/XeUve9yN95aw2yKORd7OUs/3afQ8oxLziH37CiS78S+PmcPGXEvYgwcPyqJFi+SOO+4Im96iRQtZs2aNaS3QsmXJzifEW2+9JcOHDy+zjm6Ajz76yCSyukzXrl3DlqlTp465RWvhwoUycOBAs0zk+yckJEjTpk1NK4LjTWBto02q9QqVtfMPU7LT0Op/ozxn3C5IvCGIN665KVZFvCGIN+4Rr7PjrSSOy2EzW4pkRjmJ0BMO60QpGuuEPBo9AdMWikEe8XmSwl7LqgdEWt9a9relJ3bl0cKDPspjFS6i0RM7fUT9vKkVx2qdiEajJ3b6sPh84ju44/i2oXXSF42eLOojGj1Rr/C7aVj+PD251Ud5KnpfPan21S3z3ZY++8v/bivahloE0Ec0WmA86W1YK/A4qW3YqHQ/GRaviF+LE9HiVVqcqXAbnlL+PC3+VPg7rOj3nRN4RKPFnwq3YUkRLGq8HjnqSQmPV1t9trj+5+0j9IKAPqLRCwkVbsNK3EdovO9eFHIcjBKvHgevzKu8fUSoqt5HBI/7Uk682iL1bpErvy3/uH+sfYToIwot2J3sNjyZfcTxxPrF6PJjPd59RDRaiNVHNHbtIyr8v+ur+Lh7IvuISFog1kc0CTbuI9Kblz0WldyZFHwdLd7KyCP8ERe84qUIqwlqcXFxmb6rtIWA0lYEkQnm/v37Tf9ZFa2jCawmwN27dy/zN3U5q3WCLpObm1vhMpGKiorMI7QFgfLtXS8+f8gXqV9qys+s/G9dUHI1whOs7Hv8R8zV1+BOQ6/4aNPvOmfFfwsCvYoREa9fE3bz2hMer16Zs7sFgcZSbEMLAt32pv+dTyLi9Yrf7ysb73eviDS95thXB/UqkV4titUWBGHxBr5Xr7+obLzaH2HOBbHTgkC/7+QTvDp4IrHWOadyrw7q/6fUKm5BEIw3sG+KHu/3gVs3tK+4eG9BoP0YagujkH1VeLyesvvmk9lHVHYLgpPdR+jvNGLfXBpvyfEoMt54bkGgtxlFHov8/vKPvSezjzieFgSVkUcczz6izLGogmOv9hVXiXmEb1/E7zyOxGMOW2Eeq0VDX0mrkcpi9h26r/SW5jnmtxUyVrD+9nS5eheKk+i2NPuNyt6mscJt3y3xEi/xxic3xaqIV6oq3uM9vsdcEVYHGlChgwyEvrbmn8w6+hy5jLXciSwTafLkyXLvvfeWmX542Z/kcHrpex2uda4caPoH8RZtl6xvIvrW0P6+OgX6HclcO0kSDoSfjB9oeqscrtVNkjcukVQJvXriEa8Uy46EM8ytTTV9Jet9NT14ZW3vaX8Rf2KWpH/7qNTY93nY+x5seJ0U5VwqNfZ8Iumb/ho272hqUyloG+hbpOYXg8qcxO/LnSS+lMaStvkJSfppUdi8Q/Uul0MNrpHEwtWSsT7iKnGNWrK3feCWu+xVd4vnSPh2LWx1txRnnCop216WlM3PaGczwXlFnmzZ421tTvazfRtLV9J4U+rJno6zA9twzQRJOBg+uMP+ZrfLkZpnSfLONyV1a3j/JEeyfiH7W9wlnuJ9kv31HyXSntNniSSkScaGaZJYEN7Hy8FGA6Sobi9J2r1E0r57PHwbprWSgjaBPm5qrgw0iQ/bhu0eFF9yfbNe0u6lJYWfQLyHPHXkgLeuFHlqS7FPWyaUnrz6Vj8h+1L06qxI9tdDxRNxe0dB6zFyNL2NpG59XpJ3vh02r6hOTznY+HpJOLBRMteODZvnT0iRvacH+t/Jyh8n3kPhBfT9zYfIkewzJHnHfEnd9kr4NszuIvubDxLP4V2SvfrOstuww5OmMJSxfqokFpacDJbEe8BbXw57sqWGf5+k+n+UYimNt9iTKoU6IIxvq9T8suw23HvqdPEn1ZH0jX+VGnuXhc072OC3UlTvCqmx91NJ3/hI2DxfSkPZl/tAYBt+dZd4IgoWBW3uk6NpzSV1yzOSvOvd8G2Yc7EcbPg7Sdi/VjLXTQjfhokZsve0QP87Wd+MFm/RjrBYC72NpNiTLsm+XWViPezJkgPbd4q3KP/k9xE//ltSvw/vw6g483QpbDlc5OgBqflVlG1oxz6iaJfs83YQnydZ0nzbJSniuz3kqS2HvHUlcctXkrFn2snvI3a8HjbvcO0L5ECTm8V7aItk5Uf00eVJsG8fUe/3ps/MDN/3kujXQp8nGO9Bb44UeWqabZAWsm8+qX1EiEP1fy2HTrlKEgu+kIwNJf1xlfAl15N97R6ybx+xfadkeeqK128VsgPx7vbmylH9fft/klTfj2HHopPaR5Q40HigHK5zkSTtel/StjwVNq84I1cKW40yxUzb9hFf/0U8Ecfe/d4G4vd4Jd23XZL9JRcDSuI9qX1EicKWQ6U4s6Ok/DBPUrb/K2xepeQRx7OPCDkWqd3eVubYm+77XpL8Ib+lr6bLwf1HKjWPOLw/or+tOBKPOWxFeezOnTvN4F6VSo/n2r9wCS3s7/W2NkfB4C2+1nIScWE7zunJmbZu1kKs9hHsOG77bomXeK3liDe+uClWRbxSVfEWxOvAXNYgBpFd1Vqvo3Vhe7zr6HLR1tdpJ7JMJO3n66677gprQdCkSRNJ6vKwJGWVtoRNqpEhGaaFVk2RmuEnKqpeZsmtUBn3lGnBkmS1YCnuJvLd1OB0rej7JFHqHdU+LkJObk6/M9j6KMdqBZQ5uEwrtySrlVut7iIN2od/IG+SpKaXfKazZ5RpwVLXasGSdbPIkWvD3zeppmTpe9fOFMmJiNWTIPUySt43bXKZk7LaViu37OtEsmuLfDwgOC/RXyg1fetK+jDxhcdb9+zS900fU6aVW5LVyq1mnkjT88LnJaZJemo9EV9tkcwo301G05JbYO4q0xI2yWoJW6uXSMOO4SsmpEhqmrUNy75v6Ta8VeTI7wKtj0ri1YJNxtHvZKe3kyRKQXi8p94sKfWsbfhQmW1Yx2rllv17kVZXhn/eGlmSaVq5ZYvUifxuPCHb8N6y29BqCVvzKpFmF0Vsw/TSbZgdbRs2LGlFNzy8JezHAyTJp61uAq2sfvKeJqn+r4PxmsJV/RyRnIYi6WXft/T3fXuZVm5JViu32ueLnNImfEVvkqRYv++u08r8vutYLWGzbhA5Et7vYVJStmSaVm5ZInUr+H2n3x/eEvbjAVL7GLFmaKx1ck9+H1HzMpEmZ0V8N6mSpt+NtnLLqGgbVuI+YtcnUtf8lgO39ui+ap+naTBejTXr6CaRxveJtMs7+X1Ey0vDP2+NzNL9bK2Kft+VvI/QlrCrVwQL6qHxZvv2lt7idPqzZVrCntA+IvQzJdWSLP2N1z5XpF7Eut4apb9vW/YROSJfl7amtOKt7dPvV7eBv8yx6KT2EdZnslrC1rpEpHHn8HkJKZKm21B/f3btI077o8hHvw+LN923TXJ8n0c99p7UPqJEbaslbPY1Ii1+Ff55KyOPOJ59RMixyGxD30oRT6Kk+X8oc+xNslrCVlIekWRawp4h8Sgec9iK8ticnBwb+uXVfYfuK0v/L+kWyDF5bMhvS4+FVp7joCKsfke6XR1ZhHXdd0u8xEu88clNsSri9VRRvNbgqnFXhNWBANThw+Enx9ZtUtb8k1lHnyOXsZY7nmXqlfMlJScnm0ckb3Yr8UZLXnVExeyIk71QGRX08aInO3rybG4TDJzo+j01zA8q2JG0Fo90tMHIPj3SK+hbIzkr8ChPVuvy56Vp/yTl9FGSlC6SVEGsWRX0rZGaE4jji7vD4tWbIjXWCuPNbFb++6bUDjyi0VsnK/pu0huXPy9Zbz8tp38SVdH7pjUo7X8tLF5N2L1l49VREK0EvsJtWC/wiMabWvFnqnAb1gk8TmYbhv6+w+LVnaLXtJwsE2+9CwLfb4XfTQX9nCVlBh4n9fuuoA8jb9oxtmFze2KtaB+RUivwiP6Bj7ENK3EfUea37I8Sr47I+auK+5Q81j5CH9Ecaz9b2fsIjVf72is3Xil/33wi+4hokjJ+xn72JPcR+juNOBaVxlvSv1R58Z7IPiJSsnZ1U04/Z8qufUSzfiIrR4YfizyeYx97T2QfESm1buARzc/JI45nHxHl/+8xj72VlEd4PcfXn1YsiscctsI81uut/GKh2XeE7iv11xTYb4TtO8yx0HmFSrPfsGO7xgK3fbfES7zEG5/cFKsiXqmqeI/32B5zW1n7ytJBBKz+qCx6+45q06ZN1L6uGjRocMx12rZtW2YZa7kTWaZa6clOZ2v03NAOhkNed37EOQNlEG8I4o1rbopVEW8I4o17xOvseCsJOexx4LflXG77bok3BPHGPTfF66ZYFfHGXLwxV4RNS0uT8847T9atWxc2fe3atWZ0V00wo+ndu3fUdfT9unXrVu4ymzdvNi0EevXqVe4y2qpg06ZN8qtfhd8WWG2aXCVy/tyyo8ZpRV+n63wnId4A4o1/bopVEW8A8ToD8To73kpADnuc+G05l9u+W+INIF5ncFO8bopVEW9MxevxV9RJVDX5z3/+I0OHDpVPPvkkOMBAnz59pF+/fjJgwABZtWqVXHfddTJt2jTp2bOnmf/NN9/I5ZdfLp999plkZgZuKbzttttM64IxY8YEBxjo0qWLfPjhh9K4ceC28qlTp5oRY5988knzWgcgOOOMM+TZZ5+VM88800x7+eWX5fHHH5d33w0fnKc82gpBbwnTlgeV35dWCN9R8e1YJDu275R69XPEa93G7FTES7xO4aZYFfESr5MQr63xVlkOZZN4z2EVeax9fcLu2LHDdA3hyO4IXPzdEi/xOoqb4nVTrIp4JRZy2JgswqrXXntN3n//fcnNzZUNGzaY55tuusnMW7Zsmbmi/8wzz0heXumALpqYzpkzRzp06CDbtm0zLQiGDx8eHPRAafI7Y8YMs8yePXuksLBQxo8fL0lJScFltmzZYkaK1b+pLQi0pcGECROOOxGtyhMIVyV0xCtO56Z43RSrIl5nI15nq8p4470IG+85rCKPtYebYlXE62zE62xuitdNsSritU/cF2HjGcmrfYjX2dwUr5tiVcTrbMTrbLGYwMI+5LH2cFOsinidjXidzU3xuilWRbzVnz85f6sDAAAAAAAAQDWiCAsAAAAAAAAANqIICwAAAAAAAAA2oggLAAAAAAAAADaiCAsAAAAAAAAANqIICwAAAAAAAAA2oggLAAAAAAAAADaiCAsAAAAAAAAANqIICwAAAAAAAAA2oggLAAAAAAAAADaiCAsAAAAAAAAANkq0883dyu/3m+d9+/bZ/rd8Pp8UFBRISkqKeL3Or6kTr7O5KV43xaqI19mI19mqMl4rd7JyKVQ98lh7uClWRbzORrzO5qZ43RSrIt7qz2EpwtpAv2TVpEmT6v4oAAAAcZlLZWdnV/fHcCXyWAAAAHtyWI+fpga2VNu3bt0qmZmZ4vF4bK+2a5K8efNmycrKEqcjXmdzU7xuilURr7MRr7NVZbyalmry2rBhQ1e00IhF5LH2cFOsinidjXidzU3xuilWRbzVn8PSEtYGusEbN25cpX9Tf1Bu+E9kIV5nc1O8bopVEa+zEa+zVVW8tICtXuSx9nJTrIp4nY14nc1N8bopVkW81ZfD0sQAAAAAAAAAAGxEERYAAAAAAAAAbEQRNs4lJyfLuHHjzLMbEK+zuSleN8WqiNfZiNfZ3BYvqo6bfltuilURr7MRr7O5KV43xaqIt/oxMBcAAAAAAAAA2IiWsAAAAAAAAABgI4qwAAAAAAAAAGAjirAAAAAAAAAAYKNEO98cAABVVFQkBQUFUlhYKCkpKZKZmSlpaWni8Xiq+6PhZ9q1a5f5frWL+dBu5tPT06VWrVrV+tkAAAB+DnJYZyOPRVWjCBvH5s+fL4sXL5bWrVvL+vXrpVOnTtK/f39xsu3bt8uIESOkd+/ejo718OHD8te//tUc8Lds2WK+XytuJzpy5IjMmzdPdu7caWL/5JNP5MILL5TbbrtNnG7NmjUyevRoefnll8Wp9DfcpEmT4Guv1yu/+c1vZObMmZKTkyNOowmcxvbtt99Ko0aNxOfzyaWXXiqnnnqqOI3ul6ZOnRp13oMPPihDhw4Vp3njjTdk7dq15uTrp59+Mr/tm2++WZzq2WeflaVLl0rbtm3NseiKK66QSy65pLo/FhyAPNa5sbopj3VzDuuGPNZtOawijw0gj41/z8ZoDksRNk4tWbJEJk2aZH5U1lW4vLw8c2Do16+fOM3nn38uL730krka9cwzz8hFF10kTqY7/euvv14aN25sXr/zzjsmcX3hhRfkuuuuE6cZM2aMfPXVVyaJTUpKMolsgwYNTDI7ZMgQcaqjR4/KDTfcYGJ2suLiYpkyZYp07tzZJHIdO3aU+vXri1Pdcsst0qpVK/P/WPXt29fsq+fOnStOc/DgQfnnP/8Z9hvWE9K//e1vMnjwYHGaN998UxITE8P2S3qi8sQTTzgygf3zn/8szz//vPn9JiQkmO/2F7/4hWRlZcm5555b3R8PcYw8ljzWKdyaw7olj3VbDqvIY8ljneDPsZzD+hGXevTo4Z82bVrYtH/+85/+tm3b+p1Of7azZ8/2O9WhQ4f8tWvX9j/wwANh07t27erPzc31O9HgwYP9TZs29RcWFgan1a9f33/FFVf4nWzGjBn+m266yX/hhRf6nezbb7919P/ZUP/4xz/Mftjn8wWnPfHEE/558+b5nejBBx8sM23ChAn+/Px8vxNdc801/m3btoVN27dvn//KK6/0O01BQYE/LS3NP3ny5LDpf/rTn/wXX3xxtX0uOAN5rHOPiW7LY92aw7olj3VTDqvIY8ljnaAgxnNYBuaK0ys2ixYtkpYtW4ZNb9GihbklZMOGDdX22VA5V1z1Co3eHhD5/W7atEmc6JFHHjGxad87at++ffLjjz/KOeecI0716aefmpYSVisROIO2lujTp09YP2E33XSTuXXNie68886w1x9++KGccsop5rYfJ0pOTjatu7T/MMtnn31mWsY4zddffy0HDhyQevXqhU3XWxPfe+8908oLOBnksc7mtjzWjTmsIo91JvJY8lgn+DrGc1i6I4hDmpxqgmMd7C0ZGRnmOT8/v0xii/ih36v2wRPte2/fvr24wcSJE+X888937G1chw4dkgULFpg+tL788ktxg9WrV8ujjz5qTsz0tswzzjjDJAFOsmPHDvN93njjjSZWvbVJ/982a9ZM/vjHP4oT6e09Fr3N57HHHjO3mzqVJut6Yp2bm2tOVPS2Jo132rRp4jQ6+IjS2y9DaUM+/a7XrVvnmmMSKhd5rLO5PY91eg7rxjzWDTmsIo8lj3WKlBjPYSnCxqHdu3ebZ+3PI5T12poPZ13NWbZsmfzjH/8QJ9N+0v7973+bFgXah0tqaqo4kR7gb7/9dnELTeL0IGj1raQn33qFuWbNmqYPQKfYuHGjeX7rrbdM33BWgaF79+7mauzw4cPFyXQQFh24wcl++ctfmlYS2qm/9p3VsGFDeffdd80oyU5z+umnmxZOOihJqC+++MI879mzp5o+GeIdeaz7uCGPdUsO67Y81i05rCKPJY91itNjPIelO4I4ZN0eEOhWqpT1OnI64pse+PXq47Bhw+R3v/udOJleVX7uuedk/Pjx0qFDBzOQg9N88MEHZgRoHZzDLfQAb3Xub51o9+zZU0aOHClOoom50iuroS28NKGbMGGCuQXXyYNzPPzww+Z7dTK9vVYHL3j11Vfl/vvvN8UiTWj/7//+T5zYOuTJJ580A3Hs3bs3mLzqiZhy8kAssBd5rLu4JY91Qw7rxjzWLTmsIo8lj3WKhBjPYSnCxqHs7GzzHNmXRVFRUdh8OIMe5M8880yZOnWquEWPHj2kXbt2Jll30gFf+wlbsWKF4w/wxyMnJ0e++eYbKSgoEKfQVhGqefPmYdPr1KkjhYWFZvRkp1q4cKG5vUdPVpxKC0PXXHONaQmit5qOGjVKVq1aZW7r0v7S9PZMp9HRzOfMmSMzZswwD70tUWNXTZo0qe6PhzhFHusubstjnZrDKvJY5+awijyWPNZJesdwDksRNg5pP1la3dcDYSiryt+mTZtq+mSobLNmzTKdg1tXYLdv3y5Oo7/bq666yrQeiBzAYefOnebg4BTaEfh3331nTkisxxtvvGEOCvpv7V/LaTRBbdq0qbm6HO1k27rq7gStW7c2V1Y1iQtlteryep17yNUWPzpAh5PpvkhvLw3tq1JPVPS2vdq1aztqXxVKW3Rpv3+DBg2SX//617J+/XrTSqZ+/frV/dEQp8hj3cPpeaybclg35rFuymEVeSx5rNN0iNEclj5h45D22XHeeeeZDoVDrV271hwonDqan9vMnz/fHAj/8Ic/BKc9++yz5nYuJ9GRkPWWCO1A+/e//31wuo7aqLcsavLuFLrz10eoG264wdzy88ADD4gT6W9Y44vcL+mgHdoZvJNuZ9NYtXWI1aeWRU/EtGWX9k/k5FGSIwfZcRo9CYnWqkm/91NPPVXq1q0rTvPKK6+YfrNuueWWsNYikaMJAyeCPNYd3JDHuimHdWMe66YcVpHHksc6ySsxnMM693KGw40bN870cRF6BU6bW2t/LVZfW05kjXAXOdKd03zyySemHxO94vj000+bx+OPP25OUJxG+6G5+OKLw/pb2rx5s+k0XK9aNWrUSJxM+yBy8u85OTnZ9AWnt7pY9MT7/ffflz//+c/iNNoXnLYEsW5R0+9XBzfQfpd0WziVjqgbOciO0+jJh7be0xYDoZYvX25GDtbikdO8/PLL8tprr4UNxqKtFHUwB+DnII917nHfTXms23NYp+exbsthFXmsc7ktj305hnNYj5/e7+OW/qj0IJCbm2tuA9Fn7c/DifSK3FNPPWUOfJqka4fwl112mXTp0qXMFdl4p7fn6e0getUxkiYC2qeJEzsJnzlzpjnQ6y0w2t9U3759ZeDAgY49Gfv888/Nb1l/1/v37zctRa688kq56KKLxGm03z/9fvXqq966p/+Phw4dKmeddZY4kY6OrCefrVq1MqNyXnjhhXLjjTeKk/32t781CdxDDz0kTqYd+k+fPt0MZJCRkWFaFejta3qVXRNbp8nPz5eXXnrJnGBv27bNxHrPPfdU+4AGcAbyWPJYJ3BjDuumPNZtOawij3UuN+Wx+TGcw1KERVzQ/zya3OgVKk1o9Ger0/RRo0aN6v54wAnT37P+jrWViD6slgT8ngEAcBbyWDgNeSwAnByKsAAAAAAAAABgI/qEBQAAAAAAAAAbUYQFAAAAAAAAABtRhAUAAAAAAAAAG1GEBQAAAAAAAAAbUYQFAAAAAAAAABtRhAUAAAAAAAAAG1GEBQAAAAAAAAAbJdr55gAQj7766isZMWKEfPnll7J582ZJTEyUnj17SkpKSthyPp9PPvzwQ9m9e7dkZ2dL165d5fe//715AAAAAFWNPBYAYpfH7/f7q/tDAEAsWrVqlZx22mnSrVs3k6RGM2bMGLn//vvlsccek//93/+t8s8IAAAARCKPBYDYQ3cEAFCOtLQ086wtCMqTkJBgnlNTU6vscwEAAAAVIY8FgNhDERYAAAAAAAAAbEQRFgAAAAAAAABsxMBcAGCDw4cPy0MPPSRbt26V+vXry65du8zz0KFDpUaNGmaZZ599Vp5//nlZuHCh6a/rkksukeLiYvn000+ladOmMnnyZMnMzJSNGzdKixYt5OqrrzZ9e/33v/+VN998Uy699FIziMKyZctkwYIFEtrF93vvvSfPPPOMWe/IkSPm7w8fPlxatmxp5utgDTfddJP5fI0aNTKf9eWXXxav1yurV6+WTp06yfjx4yU9PT0srqVLl8qDDz4o7dq1k/3798uBAwfM61q1asnXX38ts2fPlhkzZphlBw0aJDfffLNs2rTJxPrCCy+YuG644Qa566675PXXXzfT9LPr37v22mvl7rvvlocffthM1+1w2WWXmenWIBEHDx6UqVOnSn5+vrRu3doMMrFnzx7z+Rs3bmyWGzlypNluAAAAOHHkseSxAGyiA3MBAMr69ttvNRv0X3jhheUuM27cOLPM7Nmzg9OKi4v9l156qX/q1Klhyz7wwAP+Pn36mPmWNWvWmPWfeuqp4LRDhw75W7Zs6f/Nb34T/Bx5eXnB+e+9955Z55133glO69SpU/Dfzz33nP/ss8/2FxQUBKfl5+eb9/zyyy/DPudFF13kr1mzpv/hhx8OTj98+LD/V7/6lXmPgwcPBqcvXLjQf8opp/g3bdoUnHb//ff7e/fuHRZnt27d/Oeee27YNH1P/cyjRo0Km7527Voz/YknngibPmXKFDNd54e6+OKL/c2aNTPbKFTjxo3LvDcAAIBbkceSxwKIPXRHAACVbPr06bJy5Ur505/+FDZdWw+sWLFCHnnkkeA0qzWBx+MJTktOTpYOHTrIBx98EJzWq1ev4L+tZUMHWujevbt53rx5s/y///f/ZNy4cZKRkRGc37ZtW7nqqqvkd7/7XbClgQ7G0KxZM3MVXq/oh34mvYr/8ccfy8SJE820oqIiufHGG+V//ud/TCsAi/4tbQGxZMmS4DT9XFZckXFGDg5hvbYGhlDfffedaUEQufzOnTvl7bfflnPPPddso1C6fkUDTwAAAODYyGPJYwHYhyIsAFSyv/zlL9K5c2dzS1RkgtWlS5fgbU7l0URw0aJFMmnSJPNak8tWrVpVuE7Hjh3N8xNPPGFuddLbuyKdffbZ8sUXX4QlxSoyEVSaPOvjySefNK/feecd+f77783nD5WTkyNNmjSRTz75RCqDz+czcd96661l5mkyro+ffvqpUv4WAAAAwpHHnjzyWADHwuUWAKhE2meV9h1lXdGPVKdOHTNfE7DatWsHp7/xxhvyww8/mATx/fffl1dffVUuvPBCM++UU04x/WZVRK/uK+1/SlsYhL536N+2lrnooouOGYv2u6V9bu3evVtWrVoVTGI3bNgQttwZZ5xR5u9pK4AHHnhATqb1xS233GL+bqTU1FR59NFHTR9dmoBb2wcAAAA/H3lsAHksALtQhAWASqQDEqjQwQUiBzoIXc6iHfdrR/+qoKBALr74Yrn88svlnnvuOeG/r39bH6G3hlX0t49F38dqDdGvXz/p2bPnMdfRW710YIFQOlhBRTSp1s+trS+iJa9q4MCBZvCHOXPmmMESdCCE008/3QxqAAAAgJNHHhtAHgvALnRHAACVqF69eubWph07dkSdr/1B6Xx9lEdHRL399ttl1KhRZsTVE6Gjzlp/J9rfDl3mWNatW2eS0Jo1awZvE9O+uqLRkWt/Dr317G9/+1tYn17lyc3NNdu3sLDQtDjQ0W/1MwIAAODkkceeHPJYAMeLIiwAVCK92q5XtpctW1YmodNBAbTPKR0EIPLqfrRblipKFsujV9i1z67QAQYseutTixYtpHfv3mHT9ep7ZIsHHXjh66+/lttuu8287tGjh7Rp08YMXhBpy5Ytx+wf7Fgee+wx08Igsv+xaHRAiKeeekrmzZtn+vECAADAz0cee3LIYwEcL4qwAFDBVe3Q52gOHDhQZpmxY8dKu3btzMiuoTQ5036nxowZU+GV96NHj8rjjz9u+r7Ky8sr93MdOnSozLz27dub5E4/g/aBZfnvf/8rr732mrz44otlRnzV27tCk099Xx0RV28t05FwlY7YqrdO6aiub775Zti6kydPNv1fhcYUGZf1urzpOtqtjnB7rOWfffZZ08rgwQcflHPOOSdsm53o7WkAAABORR5LHgsg9tAnLABE0CvnegvVypUrg4nfBRdcYBJSvdVIzZw50ySDixcvNq91+ddff1369+9vEjHt+F+TOv23JqHbt283iaUmf9YorjpiqyaESq+I621TmgwvX77c3Jb04YcfmsEMLDrQgd7WpX9HjRgxQv7zn/+YBPfcc88NLvfHP/7RjEKrCaWury0XNOHVkWr1Fqhot55pf1TDhg0zrQ908ALtx2vIkCHmtUX7uPr4449NYqxJsA5ioC0PNMHVW890xFqNST+/Tr/jjjvkD3/4g3z77bcye/bsYPKpiab2s/Wvf/0rGP/cuXNN8jl69Gi577775IUXXgjGcu2115r+u/TWNh34QengD1arCH0PHQhC31vfQ/sfy8rKqtTfBAAAQDwgjyWPBRC7PP7yet0GADieDqKgSfHGjRsl1mnSG5pMAwAAwL3IYwHEG7ojAADEBRJXAAAAxCPyWACKIiwAuJje3hWtTy4AAAAglpHHAog3FGEBwKX9hfXp08f0B6b9fHXr1s30ZwUAAADEMvJYAPGKPmEBAAAAAAAAwEa0hAUAAAAAAAAAG1GEBQAAAAAAAAAbUYQFAAAAAAAAABtRhAUAAAAAAAAAG1GEBQAAAAAAAAAbUYQFAAAAAAAAABtRhAUAAAAAAAAAG1GEBQAAAAAAAACxz/8HWrVnhWHOHHgAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Создание двух графиков рядом\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# ============= Левый график: случайные данные =============\n", + "ax1.set_title(\"Удаление в случайных данных\")\n", + "ax1.set_ylabel('Время, с')\n", + "ax1.set_xlabel('Повторения')\n", + "ax1.set_xticks(iterations)\n", + "# ax1.set_xticklabels(range(1, 6))\n", + "\n", + "ax1.scatter(iterations, ll_random_delete, label='связный список', color=ll_col)\n", + "ax1.axhline(y=ll_random_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.scatter(iterations, ht_random_delete, label='хеш таблица', color=ht_col)\n", + "ax1.axhline(y=ht_random_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.scatter(iterations, bst_random_delete, label='дерево', color=bst_col)\n", + "ax1.axhline(y=bst_random_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.legend()\n", + "ax1.grid(True, alpha=0.3)\n", + "\n", + "# ============= Правый график: отсортированные данные =============\n", + "ax2.set_title(\"Удаление в отсортированных данных\")\n", + "ax2.set_ylabel('Время, с')\n", + "ax2.set_xlabel('Повторения')\n", + "ax2.set_xticks(iterations)\n", + "# ax2.set_xticklabels(range(1, 6))\n", + "\n", + "ax2.scatter(iterations, ll_sorted_delete, label='связный список', color=ll_col)\n", + "ax2.axhline(y=ll_sorted_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.scatter(iterations, ht_sorted_delete, label='хеш таблица', color=ht_col)\n", + "ax2.axhline(y=ht_sorted_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.scatter(iterations, bst_sorted_delete, label='дерево', color=bst_col)\n", + "ax2.axhline(y=bst_sorted_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.legend()\n", + "ax2.grid(True, alpha=0.3)\n", + "\n", + "# Общий заголовок\n", + "plt.suptitle(f'Сравнение времени удаления в структурах данных (N = {countDeletes})', fontsize=14)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('./img/delete.pdf', \n", + " format='pdf',\n", + " dpi=300,\n", + " bbox_inches='tight', \n", + " pad_inches=0.1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9eca6493", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go b/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go index 2154865..4291039 100644 --- a/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go +++ b/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go @@ -164,6 +164,12 @@ func (root *BSTree) insert(data ds.MyData) *BSTree { return root } +func (bst *BinSearchTree) InsertAll(data []ds.MyData) { + for _, el := range data { + bst.Insert(el) + } +} + // Delete удаляет узел по имени. // Возвращает нового потомка для родительского узла. diff --git a/stepushovgs/data-structures/source/pkg/structures/hash_table/hash_table.go b/stepushovgs/data-structures/source/pkg/structures/hash_table/hash_table.go index 32fed46..129c223 100644 --- a/stepushovgs/data-structures/source/pkg/structures/hash_table/hash_table.go +++ b/stepushovgs/data-structures/source/pkg/structures/hash_table/hash_table.go @@ -90,6 +90,12 @@ func (h *HashTable) Insert(new ds.MyData) { h.size++ } +func (ht *HashTable) InsertAll(data []ds.MyData) { + for _, el := range data { + ht.Insert(el) + } +} + func (h *HashTable) Search(name string) (phone string, status bool) { ind := h.GetIndex(name) diff --git a/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go b/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go index b383414..c2e8bbb 100644 --- a/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go +++ b/stepushovgs/data-structures/source/pkg/structures/linked_list/linked_list.go @@ -80,6 +80,12 @@ func (ll *LinkedList) Insert(data ds.MyData) { current.next = newNode } +func (ll *LinkedList) InsertAll(data []ds.MyData) { + for _, el := range data { + ll.Insert(el) + } +} + func (ll *LinkedList) Search(targetName string) (string, bool) { current := ll.head diff --git a/stepushovgs/data-structures/source/results/benchmarks.csv b/stepushovgs/data-structures/source/results/benchmarks.csv index 3f611b3..1e4bbad 100644 --- a/stepushovgs/data-structures/source/results/benchmarks.csv +++ b/stepushovgs/data-structures/source/results/benchmarks.csv @@ -1,37 +1,199 @@ Structure,Mode,Operation,Time -Связный список,Случайный,Вставка,0.047607 -Связный список,Случайный,Вставка,0.050350 -Связный список,Случайный,Вставка,0.049572 -Связный список,Случайный,Вставка,0.049258 -Связный список,Случайный,Вставка,0.048659 -Связный список,Случайный,Вставка,0.049089 -Связный список,Отсортированный,Вставка,0.047619 -Связный список,Отсортированный,Вставка,0.047478 -Связный список,Отсортированный,Вставка,0.048357 -Связный список,Отсортированный,Вставка,0.048174 -Связный список,Отсортированный,Вставка,0.048270 -Связный список,Отсортированный,Вставка,0.047980 -Хеш таблица,Случайный,Вставка,0.002014 -Хеш таблица,Случайный,Вставка,0.002013 -Хеш таблица,Случайный,Вставка,0.002008 -Хеш таблица,Случайный,Вставка,0.001003 -Хеш таблица,Случайный,Вставка,0.002505 -Хеш таблица,Случайный,Вставка,0.001908 -Хеш таблица,Отсортированный,Вставка,0.001514 -Хеш таблица,Отсортированный,Вставка,0.001504 -Хеш таблица,Отсортированный,Вставка,0.002012 -Хеш таблица,Отсортированный,Вставка,0.001003 -Хеш таблица,Отсортированный,Вставка,0.002506 -Хеш таблица,Отсортированный,Вставка,0.001708 -Бинарное дерево поиска,Случайный,Вставка,0.318901 -Бинарное дерево поиска,Случайный,Вставка,0.320504 -Бинарное дерево поиска,Случайный,Вставка,0.316685 -Бинарное дерево поиска,Случайный,Вставка,0.315862 -Бинарное дерево поиска,Случайный,Вставка,0.320947 -Бинарное дерево поиска,Случайный,Вставка,0.318580 -Бинарное дерево поиска,Отсортированный,Вставка,0.313718 -Бинарное дерево поиска,Отсортированный,Вставка,0.318131 -Бинарное дерево поиска,Отсортированный,Вставка,0.322564 -Бинарное дерево поиска,Отсортированный,Вставка,0.315526 -Бинарное дерево поиска,Отсортированный,Вставка,0.314289 -Бинарное дерево поиска,Отсортированный,Вставка,0.316846 +Связный список,Случайный,Вставка,0.047621 +Связный список,Случайный,Поиск,0.003505 +Связный список,Случайный,Удаление,0.005502 +Связный список,Случайный,Вставка,0.048707 +Связный список,Случайный,Поиск,0.003505 +Связный список,Случайный,Удаление,0.006007 +Связный список,Случайный,Вставка,0.048086 +Связный список,Случайный,Поиск,0.003546 +Связный список,Случайный,Удаление,0.006606 +Связный список,Случайный,Вставка,0.049133 +Связный список,Случайный,Поиск,0.003018 +Связный список,Случайный,Удаление,0.006084 +Связный список,Случайный,Вставка,0.048328 +Связный список,Случайный,Поиск,0.003524 +Связный список,Случайный,Удаление,0.007066 +Связный список,Случайный,Вставка,0.047057 +Связный список,Случайный,Поиск,0.003518 +Связный список,Случайный,Удаление,0.006054 +Связный список,Случайный,Вставка,0.047979 +Связный список,Случайный,Поиск,0.003604 +Связный список,Случайный,Удаление,0.006039 +Связный список,Случайный,Вставка,0.047694 +Связный список,Случайный,Поиск,0.004520 +Связный список,Случайный,Удаление,0.005522 +Связный список,Случайный,Вставка,0.047864 +Связный список,Случайный,Поиск,0.003514 +Связный список,Случайный,Удаление,0.006814 +Связный список,Случайный,Вставка,0.048523 +Связный список,Случайный,Поиск,0.002611 +Связный список,Случайный,Удаление,0.006122 +Связный список,Случайный,Вставка (среднее),0.048099 +Связный список,Случайный,Поиск (среднее),0.003486 +Связный список,Случайный,Удаление (среднее),0.006181 +Связный список,Отсортированный,Вставка,0.046193 +Связный список,Отсортированный,Поиск,0.004520 +Связный список,Отсортированный,Удаление,0.005769 +Связный список,Отсортированный,Вставка,0.048112 +Связный список,Отсортированный,Поиск,0.003001 +Связный список,Отсортированный,Удаление,0.007026 +Связный список,Отсортированный,Вставка,0.047699 +Связный список,Отсортированный,Поиск,0.003503 +Связный список,Отсортированный,Удаление,0.006515 +Связный список,Отсортированный,Вставка,0.047867 +Связный список,Отсортированный,Поиск,0.003509 +Связный список,Отсортированный,Удаление,0.006001 +Связный список,Отсортированный,Вставка,0.047622 +Связный список,Отсортированный,Поиск,0.003001 +Связный список,Отсортированный,Удаление,0.006008 +Связный список,Отсортированный,Вставка,0.047737 +Связный список,Отсортированный,Поиск,0.003546 +Связный список,Отсортированный,Удаление,0.005514 +Связный список,Отсортированный,Вставка,0.048361 +Связный список,Отсортированный,Поиск,0.003012 +Связный список,Отсортированный,Удаление,0.005328 +Связный список,Отсортированный,Вставка,0.049622 +Связный список,Отсортированный,Поиск,0.003510 +Связный список,Отсортированный,Удаление,0.005522 +Связный список,Отсортированный,Вставка,0.048137 +Связный список,Отсортированный,Поиск,0.003518 +Связный список,Отсортированный,Удаление,0.005531 +Связный список,Отсортированный,Вставка,0.048927 +Связный список,Отсортированный,Поиск,0.004004 +Связный список,Отсортированный,Удаление,0.005470 +Связный список,Отсортированный,Вставка (среднее),0.048028 +Связный список,Отсортированный,Поиск (среднее),0.003512 +Связный список,Отсортированный,Удаление (среднее),0.005869 +Хеш таблица,Случайный,Вставка,0.001504 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.002047 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.002010 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.002005 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.002513 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.001505 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.002529 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.001504 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.002023 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.002429 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка (среднее),0.002007 +Хеш таблица,Случайный,Поиск (среднее),0.000000 +Хеш таблица,Случайный,Удаление (среднее),0.000000 +Хеш таблица,Отсортированный,Вставка,0.002005 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.001282 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.001504 +Хеш таблица,Отсортированный,Вставка,0.001015 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.002016 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.001505 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.002016 +Хеш таблица,Отсортированный,Поиск,0.000502 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.002009 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.001068 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.002516 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.002505 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка (среднее),0.001794 +Хеш таблица,Отсортированный,Поиск (среднее),0.000050 +Хеш таблица,Отсортированный,Удаление (среднее),0.000150 +Бинарное дерево поиска,Случайный,Вставка,0.304277 +Бинарное дерево поиска,Случайный,Поиск,0.007550 +Бинарное дерево поиска,Случайный,Удаление,0.013564 +Бинарное дерево поиска,Случайный,Вставка,0.297918 +Бинарное дерево поиска,Случайный,Поиск,0.007167 +Бинарное дерево поиска,Случайный,Удаление,0.014539 +Бинарное дерево поиска,Случайный,Вставка,0.300777 +Бинарное дерево поиска,Случайный,Поиск,0.008009 +Бинарное дерево поиска,Случайный,Удаление,0.015144 +Бинарное дерево поиска,Случайный,Вставка,0.307342 +Бинарное дерево поиска,Случайный,Поиск,0.008307 +Бинарное дерево поиска,Случайный,Удаление,0.013577 +Бинарное дерево поиска,Случайный,Вставка,0.301149 +Бинарное дерево поиска,Случайный,Поиск,0.007792 +Бинарное дерево поиска,Случайный,Удаление,0.012897 +Бинарное дерево поиска,Случайный,Вставка,0.304480 +Бинарное дерево поиска,Случайный,Поиск,0.006521 +Бинарное дерево поиска,Случайный,Удаление,0.014053 +Бинарное дерево поиска,Случайный,Вставка,0.299029 +Бинарное дерево поиска,Случайный,Поиск,0.007552 +Бинарное дерево поиска,Случайный,Удаление,0.013073 +Бинарное дерево поиска,Случайный,Вставка,0.304265 +Бинарное дерево поиска,Случайный,Поиск,0.006518 +Бинарное дерево поиска,Случайный,Удаление,0.014555 +Бинарное дерево поиска,Случайный,Вставка,0.297193 +Бинарное дерево поиска,Случайный,Поиск,0.007529 +Бинарное дерево поиска,Случайный,Удаление,0.014092 +Бинарное дерево поиска,Случайный,Вставка,0.300190 +Бинарное дерево поиска,Случайный,Поиск,0.006525 +Бинарное дерево поиска,Случайный,Удаление,0.014096 +Бинарное дерево поиска,Случайный,Вставка (среднее),0.301662 +Бинарное дерево поиска,Случайный,Поиск (среднее),0.007347 +Бинарное дерево поиска,Случайный,Удаление (среднее),0.013959 +Бинарное дерево поиска,Отсортированный,Вставка,0.317393 +Бинарное дерево поиска,Отсортированный,Поиск,0.007554 +Бинарное дерево поиска,Отсортированный,Удаление,0.013053 +Бинарное дерево поиска,Отсортированный,Вставка,0.301527 +Бинарное дерево поиска,Отсортированный,Поиск,0.006503 +Бинарное дерево поиска,Отсортированный,Удаление,0.014265 +Бинарное дерево поиска,Отсортированный,Вставка,0.300625 +Бинарное дерево поиска,Отсортированный,Поиск,0.008043 +Бинарное дерево поиска,Отсортированный,Удаление,0.013068 +Бинарное дерево поиска,Отсортированный,Вставка,0.303297 +Бинарное дерево поиска,Отсортированный,Поиск,0.007039 +Бинарное дерево поиска,Отсортированный,Удаление,0.013601 +Бинарное дерево поиска,Отсортированный,Вставка,0.300072 +Бинарное дерево поиска,Отсортированный,Поиск,0.008539 +Бинарное дерево поиска,Отсортированный,Удаление,0.013535 +Бинарное дерево поиска,Отсортированный,Вставка,0.298420 +Бинарное дерево поиска,Отсортированный,Поиск,0.007256 +Бинарное дерево поиска,Отсортированный,Удаление,0.013550 +Бинарное дерево поиска,Отсортированный,Вставка,0.298652 +Бинарное дерево поиска,Отсортированный,Поиск,0.008040 +Бинарное дерево поиска,Отсортированный,Удаление,0.013532 +Бинарное дерево поиска,Отсортированный,Вставка,0.299859 +Бинарное дерево поиска,Отсортированный,Поиск,0.007577 +Бинарное дерево поиска,Отсортированный,Удаление,0.013575 +Бинарное дерево поиска,Отсортированный,Вставка,0.307201 +Бинарное дерево поиска,Отсортированный,Поиск,0.008025 +Бинарное дерево поиска,Отсортированный,Удаление,0.014125 +Бинарное дерево поиска,Отсортированный,Вставка,0.304290 +Бинарное дерево поиска,Отсортированный,Поиск,0.007040 +Бинарное дерево поиска,Отсортированный,Удаление,0.013611 +Бинарное дерево поиска,Отсортированный,Вставка (среднее),0.303134 +Бинарное дерево поиска,Отсортированный,Поиск (среднее),0.007562 +Бинарное дерево поиска,Отсортированный,Удаление (среднее),0.013591 diff --git a/stepushovgs/data-structures/source/tests/benchmark/main.go b/stepushovgs/data-structures/source/tests/benchmark/main.go index 3cd60da..36bbd71 100644 --- a/stepushovgs/data-structures/source/tests/benchmark/main.go +++ b/stepushovgs/data-structures/source/tests/benchmark/main.go @@ -17,22 +17,23 @@ import ( const ( countUsers = 10_000 - countRepeat = 5 - countRandomSearch = 100 - countNotExitstSearch = 10 - countDeletes = 50 + countRepeat = 10 + countRandomSearch = 200 + countNotExitstSearch = 100 + countDeletes = 500 ) type TestData struct { Items []ds.MyData // все записи ItemsSorted []ds.MyData // все записи отсортированные - Existing []ds.MyData // для поиска (существующие) - NonExisting []ds.MyData // для поиска (несуществующие) + Search []ds.MyData // для поиска (существующие и несуществующие) ToDelete []ds.MyData // для удаления + UniqueItems []ds.MyData // Уникальные элементы для тестов } type DataStructure interface { Insert(data ds.MyData) + InsertAll(data []ds.MyData) Search(name string) (string, bool) Delete(name string) bool Len() int @@ -97,17 +98,35 @@ func GenerateTestData() TestData { // fmt.Println(randInd) } + for _, el := range notExisting { + existing = append(existing, el) + } + + // toDelete = make([]ds.MyData, countDeletes) + usedIndices := make(map[int]bool) + for i := 0; i < countDeletes; i++ { + var randInd int + for { + randInd = rand.Intn(countUniq) + if !usedIndices[randInd] { + usedIndices[randInd] = true + break + } + } + toDelete[i] = uniqueItems[randInd] + } + return TestData{ Items: items, ItemsSorted: itemsSort, - Existing: existing, - NonExisting: notExisting, + Search: existing, ToDelete: toDelete, + UniqueItems: uniqueItems, } } // Тест вставки массива данных (один раз) -func testOneInsert(structure DataStructure, data []ds.MyData) float64 { +func testOnesInsert(structure DataStructure, data []ds.MyData) float64 { start := time.Now() for _, item := range data { @@ -117,63 +136,121 @@ func testOneInsert(structure DataStructure, data []ds.MyData) float64 { return time.Since(start).Seconds() } -func TestInsert(factory StructureFactory, data []ds.MyData, nameStruct, mode string) { - BenchRes := make([]csvwriter.BenchmarkResult, 0) +// Тест поиска массива данных (один раз) +func testOnesSearch(structure DataStructure, data []ds.MyData) float64 { + start := time.Now() - allTestTime := time.Now() - averageTime := 0. + for _, item := range data { + structure.Search(item.Name) + // p, ok := structure.Search(item.Name) + // fmt.Println(p, item.Phone, ok) + } - // Тест Слчайной вставки + return time.Since(start).Seconds() +} - for i := 0; i < countRepeat; i++ { - head := factory() +// Тест удаления массива данных (один раз) +func testOnesDelete(structure DataStructure, data []ds.MyData) float64 { + start := time.Now() - resTime := testOneInsert(head, data) + for _, item := range data { + structure.Delete(item.Name) + } + + return time.Since(start).Seconds() +} + +func testForData(nameStruct, mode string, factory StructureFactory, data_insert, data_search, data_delete []ds.MyData) { + BenchRes := make([]csvwriter.BenchmarkResult, 0, countRepeat*3+3) // Массив строк отчёта + + averageTimeInsert := 0. + averageTimeSearch := 0. + averageTimeDelete := 0. + + for iteration := 0; iteration < countRepeat; iteration++ { + + structure := factory() + + insertTime := testOnesInsert(structure, data_insert) + averageTimeInsert += insertTime - averageTime += resTime BenchRes = append(BenchRes, csvwriter.BenchmarkResult{ Structure: nameStruct, Mode: mode, Operation: "Вставка", - Time: resTime, + Time: insertTime, }) - fmt.Printf("%s | Вставка | %s | Время: %f\n", nameStruct, mode, resTime) - // fmt.Println(BenchRes) + + searchTime := testOnesSearch(structure, data_search) + averageTimeSearch += searchTime + + BenchRes = append(BenchRes, csvwriter.BenchmarkResult{ + Structure: nameStruct, + Mode: mode, + Operation: "Поиск", + Time: searchTime, + }) + + deleteTime := testOnesDelete(structure, data_delete) + averageTimeDelete += deleteTime + + BenchRes = append(BenchRes, csvwriter.BenchmarkResult{ + Structure: nameStruct, + Mode: mode, + Operation: "Удаление", + Time: deleteTime, + }) + fmt.Printf("%s | Вставка | %s | Время: %f\n", nameStruct, mode, insertTime) + fmt.Printf("%s | Поиск | %s | Время: %f\n", nameStruct, mode, searchTime) + fmt.Printf("%s | Удаление | %s | Время: %.9f\n", nameStruct, mode, deleteTime) } - averageTime = time.Since(allTestTime).Seconds() / countRepeat + averageTimeInsert /= countRepeat + averageTimeSearch /= countRepeat + averageTimeDelete /= countRepeat BenchRes = append(BenchRes, csvwriter.BenchmarkResult{ Structure: nameStruct, Mode: mode, - Operation: "Вставка", - Time: averageTime, + Operation: "Вставка (среднее)", + Time: averageTimeInsert, }) + BenchRes = append(BenchRes, csvwriter.BenchmarkResult{ + Structure: nameStruct, + Mode: mode, + Operation: "Поиск (среднее)", + Time: averageTimeSearch, + }) + BenchRes = append(BenchRes, csvwriter.BenchmarkResult{ + Structure: nameStruct, + Mode: mode, + Operation: "Удаление (среднее)", + Time: averageTimeDelete, + }) + + fmt.Printf("%s | Вставка | %s | Время (среднее): %f\n", nameStruct, mode, averageTimeInsert) + fmt.Printf("%s | Поиск | %s | Время (среднее): %f\n", nameStruct, mode, averageTimeSearch) + fmt.Printf("%s | Удаление | %s | Время (среднее): %f\n", nameStruct, mode, averageTimeDelete) csvwriter.AppendRaw(BenchRes) } -func Test(nameStruct string, factory StructureFactory, data TestData) { +func Test(nameStruct string, factory StructureFactory) { + data := GenerateTestData() - // BenchRes := make([]csvwriter.BenchmarkResult, 0) + testForData(nameStruct, "Случайный", factory, data.Items, data.Search, data.ToDelete) - // allTestTime := time.Now() - TestInsert(factory, data.Items, nameStruct, "Случайный") - TestInsert(factory, data.ItemsSorted, nameStruct, "Отсортированный") + testForData(nameStruct, "Отсортированный", factory, data.ItemsSorted, data.Search, data.ToDelete) } func main() { - testData := GenerateTestData() - csvwriter.CreateEmptyCSV("results", "benchmarks.csv") - // head_ll := &ll.LinkedList{} - // head_ht := ht.NewHashTable(256, 0.75) - // head_bst := bst.NewBinSearchTree() + csvwriter.CreateEmptyCSV("results", "benchmarks.csv") fmt.Println("============= Начало тестов =============") - Test("Связный список", NewLinkedList, testData) - Test("Хеш таблица", NewHashTable, testData) - Test("Бинарное дерево поиска", NewBinSearchTree, testData) + Test("Связный список", NewLinkedList) + Test("Хеш таблица", NewHashTable) + Test("Бинарное дерево поиска", NewBinSearchTree) } From a939b762734b04301ac47481b0221ee04464136f Mon Sep 17 00:00:00 2001 From: GordStep Date: Thu, 14 May 2026 02:10:03 +0300 Subject: [PATCH 21/23] =?UTF-8?q?=D0=A3=D0=B2=D0=B5=D0=BB=D0=B8=D1=87?= =?UTF-8?q?=D0=B5=D0=BD=D0=B8=D0=B5=20=D0=B2=D1=8B=D0=B1=D0=BE=D1=80=D0=BA?= =?UTF-8?q?=D0=B8,=20=D0=B8=D1=81=D0=BF=D1=80=D0=B0=D0=B2=D0=BB=D0=B5?= =?UTF-8?q?=D0=BD=D0=B8=D0=B5=20=D1=81=D0=BE=D1=80=D1=82=D0=B8=D1=80=D0=BE?= =?UTF-8?q?=D0=B2=D0=BA=D0=B8=20=D0=B8=20=D1=80=D0=B0=D0=B1=D0=BE=D1=82?= =?UTF-8?q?=D1=8B=20=D0=B4=D0=B5=D1=80=D0=B5=D0=B2=D0=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../source/docs/img/delete.pdf | Bin 33129 -> 34086 bytes .../source/docs/img/insert.pdf | Bin 33049 -> 34147 bytes .../source/docs/img/search.pdf | Bin 32716 -> 33695 bytes .../data-structures/source/docs/main.ipynb | 749 ----------------- .../data-structures/source/docs/report.md | 0 .../source/docs/src/main.ipynb | 754 ++++++++++++++++++ .../source/pkg/data_struct/qsort.go | 11 +- .../source/pkg/gen_data/data_generator.go | 6 + .../bin_search_tree/bin_search_tree.go | 35 + .../source/results/benchmarks.csv | 502 ++++++++---- .../source/tests/benchmark/main.go | 37 +- 11 files changed, 1176 insertions(+), 918 deletions(-) delete mode 100644 stepushovgs/data-structures/source/docs/main.ipynb create mode 100644 stepushovgs/data-structures/source/docs/report.md create mode 100644 stepushovgs/data-structures/source/docs/src/main.ipynb diff --git a/stepushovgs/data-structures/source/docs/img/delete.pdf b/stepushovgs/data-structures/source/docs/img/delete.pdf index 9ba8b4ac11fe9dd9f9f7243b3443afed9832faae..9be44496303e755e4c883d3d86f3399911c5f1b6 100644 GIT binary patch delta 7311 zcmZu!c|4Tu*DqyXda{-^We8z5GbRm5$k+)(h%6D3$-doD$Wk))EwT%deT%GFvWKxR zne4Lf%RBvk&-=X3hxhvH{+#PN*Y|tA=bZal9MWI58Ir1SS@|LdJwffEH>N2_mbgg?d)xIMmRZ$UHu`}9lDOmKVB zhqMMr#^Ol+3WwxnMD2N=?lldoJ9FRp^)QIH=9C+Cd~Bx5l9-yD}10qaIXmN+AzBkyKHUDLwU2hhQY?`)dG7YoD znL#cNE=2=;cDQIwME7ImZ2MJs=o?O4un8rhU**Nnx8N9PbK>{Z| ze?Jz%%IEqUKhtdG|4gq1OaFpzzQn_GZ_mS}u?<5f%ATYDNW(0u5h#!wwLRKuue2t{ z3DthHI%<^KY;%IwSqBU-UbwNEOeFh|Pef2MeE^geh--^>SW(?pWRy~o`YgjN|h=KwB`c_x{VoLx#cp_|OzZ{(W>bCp#lPt8vj2YDIbd(PLgV zx*xXSFO zMWJBLrnBXZ<+YKa^T|)1AHtx6`_zpu2wV3~Qxhj5DQakMxLv`%J)go@{{75k-k@|YoG-+6!LA6KfkemB8OLGDBk`rB6iNUET_Ve>`(u8%5xvU+v$b+`mov zo}Nq=)2+RS)d)!zXC=tee%fzH<5VA-xk;d`SP7=EZ$)Xd>uFp_5oadIDRl^8P(L3H z`S+jmbaA5kRt5TazaW`g-uile5UWF#Fwtz=mJNGhV-i4J;Vhhfl!j;71Y>2r4fECG z1c}NtPCWV-9Hu_sZ0(QDb@*RLo#d7%T>nl2$At?UQwcJ+n?(IF0>bkeMNMSxmZhL+l&s+=4K>v=^-`oHfOPAAQF zT9$sUwpq-RZt*+%y%@Khc(gB8@Nn~BlZDkf>OZsc}s03DXGHrJF@tbmAGWvuUiM+u4`~}cOBPu5ys0+f8 z=R^|j=ER4U#rr54JU!UB;+|BPz8CfRsZWqSte*K0Y^I;;lY=Mt(b+!Z?s(nF$n3!D zKF!}G6hjuDX30dO{Z4RCqgg0a*sQpL-)y>ZR)Q&4?j88W;;~sIq_+V=47YA(KD=%g z(achy|A+}XczExtFa=OyAhCE`K7C96ZU;xG(SH*~d_m`z8da z2<~{UiUvJVN=QF^Db1{YPB}?Uxmtg+F>JnbngKa;2dj*D2?!SHn;A# zRmLZa!H!|Yb3?vu!-qkgl!U?s+uSCZeG|}duK2^22MN3tR()(DpN0I&y%xCcqo88#r0&%4WF|S6Z1fv!%))H?o zQGG|2_eM)1hTH4|jf(O|(nr$d#7kvUIk8iTi^CW17yex7OcB2M;}@dR`m-r3c|*eM z1ump%r_;}r#Nh3izitnvGcu@5YsO00m#mm%a#i4h&~1_c#YChj)Wc&Wr`CaDSy1w4 z>_HaFI%lM>w5zP!k2+x6sjByEvXf3dQ&&cJiX2RO`6Dcz;%Gb zi@-10;5NtrFX-1qtdX$q3fY0;fpl<v};nrPzB`0I&a!`UgjvY{a;KD_s)MdNb^a)o0qhWVlV$FYLP5yXRP&P6(8&M ziIw7%fp-)ilczp3YJOo9(rrvwbk^N$8>7!L>R0hC+^o`KvCn#_!EzrnOEa%u(*)Rl zWEbiatnLAf=EY#m;f@DS7qE4gqi5n%1novKLX)`$UQ^28r=Vq<9q*#+98dPWi(Ln9 zR6AjucUWVGhw8XnYCD!`#e}ycK#|TaW|BsRK;hE){7EJ^7q;s1VuS3hKy-cK3vJqP zT;*!}kO_h7K#0Kd>eoK!xpq+{pKZT>-KKVx<*AO?t#q#fkuApSXG^Epme(zXopKZ; zd_xH(YJ5X)Le>nJtkpzoiaZ&6%{qNY%kOG{0juTE>x_8kL4@2#M?TdYDX7v_74$^9d+!mfrc8VUN#$>s})9Ckf%7wxf+cEX?Q zWI|aiva$5Pd;HvPL~hO7Kd4Oi-)Z$AM=y9C-Sr}nqmgPX5KpobP5sGO64p|vJ zVLC%+yNj>cq=7!@^yzqEaI-1JsVw{p%1cxaq@Ati_LV=1PwPMHM(R%`$9-(oMVFL3 z?{oR65LmWlc##Ez`4u>$|HaSCHVa@+D^^-~CdIKms(+ErYx70OTt%0|@9F+?7M?hq z01;HdKr5=C{B&T$v@bAiZr) zo8p<3qV|VQT(>w?utDt<9+9udU$s+ICC__0fu{6mI8ml$I*qG0Bm}tdd3L}Ky5H)K zU82#1zQC~Jqq?d^zer@uh->jJIgZcOEAE$Az0N6sz8(d98s!}X+1b8RX}8y7x}nb? z4iOF3IbZiAJiEsptB<=Hx|rQ2++%N&*dUIZ{$_cGu0DW1Ih^ZFdcY(rNVbWAwwI6k z@dVLz>a$toeezi@O75UYUI!{*^W`)%!P|e8N*0{wR5TR8W^JO>z&7Ts@Rzh`gRyhC z-`lqx%}a)rDIw}((TaL^GFO{#)64sLcNXOha;GpR?feR-u9Di3uIR{Xvdw72k8{+B zv(!dRBcDcpnK+iCMwTn9<{ZZ>C2L)}HV>{PGJdTJ-cJJ!o^6Xm?_Z7u<^|a9+%0S7 z4;GfbzxUf5F6A5$B~olHdR+BmtKz6Vttp-#wO7Q@ziegZ;mrB6YTh|n#6NA6`Aryy zzEy{BKNI~SGb6dGV5*7L>KT{-_U&h5q-%L9(=^2vHb2V4wd2~=6nm@M`i!+p5+NM) zOAI@nMSP!D%Zg;Nv?yrYvuMG;{5XAcOIfL~MlZ4-YJnR^pWTG~Br#+Q=@pOZD! zCQ&L&yNLE(=hmT65W6ZW{Kd^BEmPs)-C*)9qq{qd^|5h1OCwAouYjsGpCr|gluZ9_ z(m0wN(PoDdPV@cWq_1YpHmyke~eKuP}t(A9Sp#_o#y>n1VrQA+ktv`z~!L^Y=}D zmAC&p%(k`Pf37t~x{Mf>>N+;vqVf00xkfde^F?r#L^#kDcQ@`XD$wmx?uW0W|Fh3f z@-X!G2-Bzd47wViyd#_hKS>I{E}{)JVVmW=Or?u|(O0hWky8#GAMSWPpHkQ7Ju+>C zzTA14I!e~kqK;pe1n)p6G zDwg}b!h4R~b>hd7Hjqss_jefdcE`HEkW!Xm3T7ubp z(03_xHG@%Q%>>{PzV@I#eb(P3+f0b?B*vCNC7LhZt?Bx#_uNvbM&|MrGHcaK1MQ63 z8$Xk*V%Uo?7L@R4n_WELRl=?2gNeJ1i5QIoT(|+-=G2rqp;@6oHtp5e2jvIDA?}9!b zTleGuWvP0U>0Xl5Kjtdei8w;xceNo3Q>RCm*fK|dojvHGv~Kn%!~Un=&Qr(o3yi%w z58Et6E)Q+?m#i7P%CvX|y-(&*OmIB3SNZvcuEEINX+3Lmk%Aay=#@0EQVQ#pE!#gMTJ{ebCQTN3-P z{$X%=Mm)XXy`F$8)C@YfI)``Dsch_##fyW~7mWf!u1s}`MsDaSR%^eq`fbbmn3}Eg zPJA{->-7y(IUFLb?J$`LiSX}Jg?d_jzzza_j|`y-|mUsbUWX5b3Raie#24BP@nl%GdUR zA(0==C$o>Y3l+c{%0Zj}?!=gt(5_9OU3L$bk?3%Y?U>+tXtVj+|uUsl~ zN5fr_D}8uDKmkMqUIANV#&0N4l`b~S)KZ-;7I#$J6P2|YihVNvq%Edmo|_jE zAg=C)d3`2(x=ZtBJ3j&4sUa*Eck9lND`6O>`ZVrb0pDn278N3u(reM*hlbi{GHTKB z9MoJS|L&y^u!w6nE|(-$6m6mNiu0bBiAXHOVWx88=3Cj&>BEWxZg&UvR0q@nO{ngz zfqC%_Yo)&HS)!}!)$0cPs=3i!P1*t^Qtl3w3^VsEl`;DA&+h>oR;BW9%gRlP)D(n4-BAE%ozJhCzc*gw{qg1krI* zbV(K)i0T|nO=>;dMIL({%>b|=o1=r_+_O%!-#?)R%M0$r%8905<*O}NOQ22tVHRa4 zE7yYXSzapij!hWh-Xc8mC+994Sk-BKe<6>${jCeOLwy2++h??>bn3*j2uN~xxbqYv zYGb+1GBHyhH2yX8(B2X$#rPxBcY*TvBzg0-smq0>8E+P4+_5}%hTnigON5kCKRY!m zs8=owT|868XDWLiG>#w33bgi8bhwbhKV&mAr`KqAuIeeFhEQ2N%I&u66Tqvw_*zqP z$TrVa)b;J8pfLJrsif43eG~qc9}WOi=&{3!b>c^7UWPc0s;w3c2@$Ra4_-;_|LlX^a{;? zKAukl@oc6y$^r1_nc*Cu}4jggD6-X5+zo9sxs$hlP6Z0SFlUBrpst zAptw#41+*mCrQH~u+zX01pE&OY5fx%3?hL-owPwA;(suJe;OoFoHANQnQ-_6MJ|kTBROXCxANQdY1840bXSumlSFuM7WsA4(j2 z@*D&VMx9Iv1PmbHCxJ;yLr&WyPSp$q3WuCj1_A@0sv}b3P$%mM0*CzM@;)d@|5N!S73S%JgF;S82!+BCr$C@6*vW2!!XT%M6^8gvEB>kf zf8>Y4P_TbX^QV%aa5(g2j!C%4)8|m)r^twtel#akg`Q03AN`@?$Wy%yML_>%_%DJ8 x*uPun4?i#zfjZr?NZ2W0*dNyaN5%nXVr65Fqq!A!!y8Kb2aE;;l2enX`5%Ty`=$T@ delta 6332 zcmZuzcR1GV+i#GvS14OX2=^Z1w&S+5iCaR5+fMeEB$AS?aFadCp4n7JMp+rzD?6jx z=GF83-sktcyw`F4aUJJ%p6BO0Kj-HVN-2CHn1yN@KwJ^5I@S5-WS*cWZnJpK6 zIlSWx^GR6-NMTIl0m}j0UDj4y9$@o#bE|s_dis#K=y1-F1&H0|#jCDCEHdli-l1Gq zbRB#TmdEOjek~&%>M%!htLhFz4xEQ>>vNpHwx$LXf%>teuETvMK#IWTyXT$LM>DO} zN>}IGU3URU;TGSSXJ}g(JKS>JUD=!!X4zql8YQT=(h&BuJ_b;Pa$=HXTjf-UfV`X0i{@kC~*a13#s*U_32O&|BmhjI~-g$CubRt62 z8~4ptgI<|aEoq6yr*l@bCb(F$WR{H3n?9X197-H`BRPUQaOs}l$D(r<-3g+R zv$Mchtw)v4Xo4~w1AAS@DeV8P}+H56!XE$Zr|X(=&QyP6^6V14Ghz07iU8GbMv$4w#w}U+rb;u z{af4&x{Bas5bgr^rCSrrG1^xu8X<1cMI%528LhEfy_8p-$lu|1rfb&hosNAvrK<(a^d z=h3>f$0Ros86fva4I3s~ig%>Cp<#bF#u>9yLLmZZlxSVmQe~9=Udb;R+Mm&&TS8CM z@RltzVnMXz=S%DGT)F$454sbzyHl<4tj2N_hnut^QP(!znT@u#{rG{tX2h>E97E^I||=uW=XsrRlLCFE4pwULX~|D>gK$WuZStHa9I*}su9lC6&0 z|2I;PODZbZnY3^oGb*$tFC5*#>{gq4h3`MT09q_UD(I(PA1L1EeCuXxk>Dj^(apd7d)7;U~qDB(u;r__hnAcn!9Z$lTNER25O19n;&(ZL7 zTtfECw*j7Qg1dLKGwrg?FDNaC(~9sF6Yplbu-MKm$wW4@a*F!Y)jzO_%r*%!l6%Oe z4FdX#4LukESX78%Vnk*tme%qiZ5vB z8-z~e_>LR6IQLx>G7VZJfVH8^fgt5z13-@*r|ipE&}kv}uH0I77W;B=O$< zdh%}UjPplD#szNM^`=)gk>o|4DO84S0h$<8UFe*fZQMxDCw#`GcsR?om_eBKdTQK5 ze%AXn3JlOoJ%$oBQP$P6hD}R^y!ZvKGVLyK|Dc5SdQ;qSp5o4w7ls$VzUrBN4B$Q4 zp+{Y2z&Ta!MyCb3DrK6fzIMTqC^)O|6B9UzJi)oF&c9D8XqS$W8vS-)=}@mVY_jjQ zUGQ%vwRRSn%X~?~V7v^|?zVKhM6gWKjJPEwjWl7v-kr%xzK~Q0+SC!4YiLun$2oU@ zQ<`@|EsRkoO7nbz*4tlADXH?#2`9%(HH)^^MF2V0-e*vzdK20 zXe9LJm61@}{>072cS!{AB?onjb}`qF&U%msLJ(!yTiMjFoh{uz`%b0^UoO4Ju_lQ1 zjxWj;o>69z#z$xKb|Gi@AE@*gsR;-1j;!|!AyU&G%+qE_=au%#6C0FZrC-Y25W`J@zRXzkx?#_zKxnfL?1(yEt~m-h?KlwA!JC2Ce1&H)MfB32qLmtV`l z=MkwsWLicwQT`PAtCAVLOyOfW*EB!xHmJXPJ~cHp%bo#sj3uLQ=oOoNx5lwY%aaw+ z)Fx`s{`Ne3eS8R?Re2HBP_Y}O74moS`!37+YC#)`n46g2-n3S59zfK>U3V5?Pn{I@ zkXDSFsjZc0wzL+&95WNjmcY30)cH$6ofmu-y|d1^uQKws?y;$ebAic6zI1_~k>?LT zehPTm!|+SM=^XeF!??t8XS?oZ&5jU`xYI0pL7k`8VKi>x?v0b2J`_OBoIKL29zC?~YQm}9x@DSU#g?>YYg zNw&o8pXrn|Fv+$&xQ!uUXejMHS(3QT8#~;hP1cQxT^WvIUmqjSx8mqbeZgB7IO@$8 zCB4gCuUzS*rj`>b6e_$ZOe}m(S!)!|N~F|$cJC>OhOOYcQhoujE*D4>O6nDV5%)07 zQu(9z^j?{+K@M+AYdHI$q#nY>!k?FV{XvZhOD*~C5AP2@0|(HuH&q4CzR=KEPV@}4 z@;4E=o$WT&F;rMcwyis$Pn)#rLRqrwZc&b`#j%w8Jwtpb20CkPg-5kG&-0BVb_4e$ zhSb?dQ3;Kz20BE^)WPjg{{W4kLg8;jY(h3p@i#4mZ#YT)yU4?}!EISs-KMdz)6ER> z6hAQ2Qj1CJWlM1woc{)8N1nZe#yw`M%@xqqPv%UvLO}&j-Jkiqd6oC@A<3$H@I^e@ zm4xPdeEkDKHvs=iv!%&dV~yG#^CC0l%_Mh?+j}Qt1|n}hg+_1o5+Y`KD4E3&UJ+it z5KsSrEvmvSqXXft%erZhMJ#i&HR+M7hH(-jYO*8<_ML--Cs?|BdNHw>E(@&NP_?U=c4f-j?YFHA^%MxY1Nb_gtrb_7nC+hC zjqaUE*~*t<6f{#bl6P?FFTCVG@>cmz3eugs;gOb{-Oaq@6<*A*U^;mQsmveEl%XQ| zMnJ)!01*DrNk?>+i1ShGyZc-QH?G+n!J`Hz{f=&=Pr-di+r?!LU4BvWSiklglYsq# zkb^|V5E@Kaxh;_JK!f7CO*{i3$CI??fekC)2w&~;O;g*k){wvg_OBh#0u$p{)h%_7 zYi9kiTpFJUjSmeR*Eg?`?F~Fr{mqj{asgqn#|^}o+>{u8WBy{2dDS}rv01whJ)^zghCv9y>K?hN}u1pTVdv5l0=i@DZO|}T8W%V!OAq{ zIeVItj^WSHzNIH<>_d zyaJ&Wca>FtBfYRXH*-O5=*EHHB-?ZrVG4k^I;U4uN0ARG(98P{qxP-LXqT`~!-%+c z1(ecuzJ2^Ho3CkdD8eIyn5qP0-rm8LlqdE@{CqZmBC`Vv9E0a!_lbsfM2ND=V8bv2 zV~7gkT~5B~{sqJNG_gziJWCodjU%1zRLA4bS%7fsSqEPnX8w6&;Kj~9kHgFefa9rc z&$y6r;bV6~tmbZnqdy&4(%hwFWlzRDd_ZpY$jhh7U0<&I+^&!f9ENwD(d}+>#9uWI z6#gjF?Ldgs+D&oPmF1GqIQzdBD%6+PH=LDC>GK-tWWGs|>0oJqw#JMGcm=sB=Gqh* zn=VGKFB$;L*Eshl1o$giv;R*I!}Q~Fqba69UB?%Hmz%o_(e%L?9hV!;d;TurLQY!H z#1O24Twhi-qD~2C6GPLF&G$$@-pGZmE@7WrxY~VYq*pM2(Mh0c<;Y{0Vvf_a|fqINQmJ;`<_;6mTWuQKRZ;fu3fhhNQK!FH31> z#^d+pHdFkAS;jN8c|1kpZ>&7U#vyKX^t@upWO=P&=QHsS#>L}>>swvc6F$%V@ck`q zEjYNTUT?y&Wwz5U8XvU}a!gbsHFoH^@Y0*D=xefZ8{QHz)|cCpsA`ROj9#Ry0QjY- z6Uwsv7wz)S1RdN2g{|2PC^^)s-K=47bd;TueC2)JoGc-N^Fm&6w%;N??~MM;i(*AP zvDL1y+2m1*3TgWFmef>^iht4!)fZY&L@T37NX#|bnOCA)E8sRy+2$mH_{mzMsGD`pX2>3 zO{bXnhL{!a!x^cS6wa_X^W`ie^4{{S7gl?yU%)FdN)lTNxE-OFzn0Ivq;cZfv5Q*B zl1M*roaa5@<^GwHIWF*|GTWgGBNq0k{P%L24i@^O*A%NP;-RRuVx*PpJ!%(YJ%-i> z_ylz$jIP|It;Uvhxv1^)a?(sVnl0f+KX=XAq2I+Kqh(Dr>XRy#zM5hesBDgdLwrdN z!A#5roEU)gh-4ppsQwdRI4VsWr*DuqBQS>6Hk4Lj$@A8^H(bEimAu}q=?z3Yjy4b= zeKZLfOcb(3*W{Rq3s(G=6(BYD(Kzb$a847gf{PQ;?GcY<(B}c39~eP{C*N~2RP{ydu!YJ)>sF=T z1bg)Q@*|veweK9}t@K9C=kDf5JXTm}ydhYT2GV@ZN7@8Gx3ZCTH+&a$ob*|YxWe;; zOuno)w@DXtfS{vZ9Z8+{+7C1xngPjcDJ^pmYFG2I4aYmEc^NLaEsAZx;~!xgLdc?x zf!p_0M0J;P7;eKE%HQcK42d-8Qs_e7F=;$2P8<|$)+?9cn_eKQw^qHGyhF&JL?tr5 z+tFkuWN8m48k9N6nOlt9HCkX5nUYqC6D%el?SE>2!^o?zLd|6Hsk<3wiErZK=+Shz0sF zqL02-!M#t%kfdG2m)M4p?@X8M?5htci`1$Nt?54t2T6=-dOAf7DOf*>1;qg89cIRb zFWW3Dqg89#D(TBcEPj}U&U~h7`Lc&$URSORC8cPz*txGWl{eLmd6FKtVQhlU`N0XS zNzIuirn&(8Gu}s33~ddB$RBuFBZbji9-fAr5aCtjRvpFaCmb^@U#ooXBdxWfFb?u-x+&Lr%GJMx@*)ilLxmsbLQaDh_kwQB}kaF_%dnaeyL!}yZi zE$U&UnFa%7*u$fLevc%Rb=VZ=)sCmB@xGCDO_VJ-gnenqme4~xO#&qL+pk899CDd{ zraSBJ4J@wd(lA>E`62{3-(Y%K8YT?3Z*oSt?*5|mCOWrFN7T429a`%6<*>NA zXW+no4xRVo0%OKq33TEa(JeiBQhn^Zp9p}ZtEIMoSt6s^22ILuZpC+mpWi!!mx)d3 zKl)AASPOi4EC=16*Bt6?9&AT|6iB*=&E)zn2ZhGCGhR;@i-gxX@+|`s4$sn4{&pj` zw6}0^b+$Ca|2a9_dq56`aDlk~9AIb!y10)e@IEOR42K{iO#u}E41 z1qYps1;JsElf{7GDD=NF;0o18L1PXkTQVsDbV@^D2nu>qLtrTMKfwRN{WAkF^z_WZ zFc|V=cVHMAc~XF2I23lOXgCse$`1j7pVSo?frJ33K#<2UCv*TtBH^bJqK=VI`k_Im z=m(?W;FI|vAkhDg6Z?-UAt1zQv4J6{D1?CFe_r^X_1{=98X4&yzyO?%Uo)O)S diff --git a/stepushovgs/data-structures/source/docs/img/insert.pdf b/stepushovgs/data-structures/source/docs/img/insert.pdf index 220627995f946a82fc9770c080d7ed70fe38070f..e1fba2ad36535f6f740b01f4fab2ae516728a0f1 100644 GIT binary patch delta 7545 zcmZWsby!qgw--=QQW^yXrKD$u2?iNLxqZ>Iqy3_vZ1k{BObw zy|*|I{I+E(n@9vLNV0EO&@@W2RE-$44+!T(mb7j$8|uDn+gMigE^;J*S*phvKSgt~ z_byQIT-(E^Zym2k{hkl{^L-sBb+owaf3$nN#T#%;E5$zeoxm9!jGV@?5(34p1>!yX zCN*sCJ-x{;wU!VMJls(T{u5n^9|c}%%x9VbmpFl_k@Qi%iC*;UWM4@9ul{#Q{jQa4 zR4Y_ScKKZ&xf*19nM9B2l>#6UPJT~rJ+Bv%Uj?aePR4(CnV^>2= zW#To_p-4Dj;U0PT?ZWkkZ(_i#Plb1#ZoI=>d#2X58-PvDyhEDhT#}o3GXVoBkN3+- zrZ~ggngM*AOc(c-eYG=U!l9f0K9SSJ)V%*YLZ5%cvH>PdI zM0*+he)M!m?jft6lUsJ3?e)wB32nFQGS}xKQFyL~7Ka6yRVL=7Egy=elDZNb zFd83n!1r7^wCY(;zOwFo|4y`lLtV(YQ0CwP0$bKG-dyON7mhutA(WCyqblQ;HbzQP#?t%; zZA2s%YQmrtNw)TFtod$+Pb5roDIDH_*5Xv6R(to0i29NB5na%^?+#TXd}P;X4HO$9 zW#))`-4~KJ$25w#UyAtFC zwlDe%Gu%hGCf`&jaw?C}yU>4&kJl{lve0j(YIHgag)fqhY0oVK?iuIcETU4?GtkxaMa_%8AFSkTBmj8x2$4^|madjS3KTY>VJ6z4V` zzMh3>*@s<F# z9N=;ZL8)3tuJh?Z)KCl9a{|WuiE0^q;d30{h4litQIz`Q7WBdghk{rui@PkpCD46UsXmBTCT-lw zyC4(OaP_)3^f@N&g<=Zz8=UnLiVWnJePX+X+B+<~24HW)QhRWBmM@coNxyK$NZ3<6 zvA!ofk%PWTdTQSl{hg#l_k8D@6agEYlTB6IMbU>YG63)YQerazFzz_lc@~p|931oA zVUM=cPyY>hSx&F7mvs!>cPeG&^$K6*=N1mksm{_#@NboL5l%Fl6g2 zo!&^tqtjw8694-At#P2n0++Vt{-P2_H1&1nL*sW5xak{=PVcU|YecMQsiR&NisPx)Zjri~s-rs7#O>Zn%+lNY zp|t%PiVFH)-pco=9{Ayloz2~3HgKAH@MUo4s|f3Uo9-9uy~UgAzlCine;AYm48Xje z$FO-2CvR6wxm7$h!5qv0w<4_KzNGoL>`%|W+j5-hr)9a_hrL##D*+GsO@$YWEb#<> z6FZT7XA^rQr{*e)8J_f2ga*HRFEM-4(2p2GkZ00xNQ|lEUc)=oA zyO^a2dH#d6IxtZWq7JPB7o4cxrmTVEwrdzyOeOi>Hk5!yWD> zKCm%13OvfgXcyTfZYOD}jR|MF^=_tHFgWjV3T``e>~4)cFUCL!kTee~ine8` z*yd5gS@kPN#BmVGp+>EKvkw*xlRSP=vg6kY_^=4v`w>(~sDVVKp?kuX4EOt<2vd;acY4>Y(V7 zAi86aqX?24qn$_RgS85CqL=pe$IyAA2bq0?vRygGv?}x3au+ld3{aIl4Ri3 z&&ouVjTqp;CoVz)i{@S!-_PBAEdD48u@c z#&B~Wo8Q&?Rh9%&`J$hMPRD6_6Z*#r{Ygb`^|}I?kT6K>7id+9+kvj4X-bf8Z0-bw ztQh5MSg^Fz*S(T-I{!HvRu9uwNr%g&V^f&>5R-ZDqR-a_t0M+e#h-}dzYJDUZv!4F z>rt1T3J<=l(`1ougMO%McpZrcx6!O3wqrHS+_@)lIV%g9%O7ojx&(av_4xz4EPFy~ zdd>~Pe1;i-6%qKcM+{=B>ED~D=Mh;dKtiL|6L2bFdz_2E*Y(yXD-j(x2Q^HeyO` zC>?o!iJihh3yvf0K$*<<>wYiO4EUYIjQ2^(vVY@K(%i*QFO2H&b)>v?SllU?dIdx$ z3@LwmSa;FU1=7}hm6*~S$DNs^6#kn@rXsqkxWr?4I(yrG2&PYt+mm5FVg>S|9m1>u zzp+nuCY4Kbm_A1h#Fo9-OD&{}j(N9$)pp9}XofjDoZJqO$|jZzuaeg;&c5TInjL3A zejJ1g%(x@(U7ydt347{2tr?(IqQIEnkE0-@7Os^VvF3{JIh>d)K7YCLD_zLXx>zN6 z*kTXu%3}*vOf-uhwP~M9$pFx`k(l8n=X{O6zW1g@2SgfrBiBoUay=inY=fA(7}p+~ zv1Z>gwkNGH@)3OYHT-KQ=DMm!Y2gartzhp^n^xP8RZd={?CNh|%O7aEqeJz28XWzg zVY5FFl!F{SCQSvc6lyhjl4P78o?#CZ-|v(b-UQ#;rV%hclDBK#(XIi?noNW=TQsoh zx~R`jJwx_bU-oiHE}XDC%o- zO`Bd_ZGgG_dR$cQxzn%>EQ!CL?dnhQGC)<9f;Qhj+kgB9HGna^v}F{PCGr(t7;K!T z=lN^Um&I#8%Y9aA#y@~gvv1OVpQmYTTK|Ud*Xx2Wu1MY;%N|;5N~Z~a1LJ8lpyKq< zeO><5^T?$T&sI|v`XC27a)*X0^)}h9Q9a22F=~JBzXGqkB2cOv7f=4m8}h9$g+mO;3VL z3f8#4a0O3!x1fTC1bC3c(wgxFREG^(xjya&#tUHIQFihA%e0ZK0MPwUpn#mQ?0d+akv2%fmsty=emR3OIz2W7R&eJBjf5yxIYk&u_G7!iP%&9JPNZ26LAQc z%Hw8M#SFh9Q@h*Cfejjvec$ce9!7hR!jO00sD|1qnp2(Y`6Y(m1?E5eR4k>XyLO2Z zRr;H}ALYv4;1qkuQCksp;q9tKAI%sE^@sWo`!&VYLIJ#qW_`67@z?(4qSU1rMf}Xg zyDO%ioU7LWPUYJK^l$~07e+s1LG(z4#NojN>q>YjPIUHznZypE$+C|M)W5fQ6_j1t zF@YGqO~ZbHeZ3*lysa&y%UGH63u05I_CDq1;-L9TlKCD9*lP8Vhzqv+!gpx-8&?NJ zS46yO3f`6*i?KWyk|RmE@<1vKRTt=CA~YJDH`fm2Sh209VzN4-5i?uN0=(R}KNTqd zIocr))D|{*Gs-Q-eu0%p?@7R5p81qgR`*p}Jk^+SV5+m^8z7(Njl2||A#_f9T*)8W z%i$fXT7BKT~=baCfVxoi5qsxnQ-7uZAug?cWbQo_n7Qb2ri zh))zK`JX}gsr%kH+nCOul-+PMxPO}EoHY9lNmz_Va=vN05XV2Ii>x^VS z+M|Nse{O|*bZlF2n!`+1A62V-l$#T^c=`%x@4`JbYJY@9}%+@jg1Un z?$!Hqc_%s}9g}&Dq{hBPtcO1%QnB=n0%jU!qOQ3AWV|(;YXQBTF#SbNYGa>7?96!j zQnD_MNhV9jGTSjU3h1E=huuU&P64X_0)!sfpR{AFes>D69+F16kb}4Z)KmR=3;MDR z?CYI=_ouUrBFdSGHOLRABFs#~KVc5t33r=81*Zt;=`VKa7uA$$zMUG|87t%PiK6PC z^OH)`&#bsnaerSfA`urH0$1wO^aRl+p$BNa4OOEG-n9fx0q^ngTSYEvHw^b=#$!p` zihQ?&KDNkjEdk3lOS6dEXl9iboxXnK2gSup+;@8`Z{mp^-n(G&jCRl8(m_t=Ohe22 zp?Xur**wgTD*6U%^L2Q)dRD@yY@nn=k>#N-$?Q?;(_`g~aMp5pq=3USo9j+AHBnE< z`rt@jvbB-#5^MaDaW8A~IZkI)+75$@kyoD3*7U$_XaL(L{6Wc;Xn(rPGBGdF?4%{- z1zG|wUoEZctG;1hrfZrmY6a&+NX zrXZ7acb#Ou0hDF_dF0z{NHAxAyovUv9iO8uaQODH(Y|u=!Ed8yf+#drQTWbPk6EWL zi~Vn!(MX8hR()A$;*>X^%Ts?t$Evzs`X$eFUgN7zrkOhzDO{@-RO53*km#0WB|LjNr^D-M`FJiYS+Ys}0Bk&d zr&L@dVZYOfjvZS)9w=p$?@r?I3$2(BYRq99@zOes@eE2eQJCr~b4b7>j3UuMwbWQ7 z^LW9guvtxU$6BISq(hlYbppwzW!?b4@3=TaP{mjMz3GwatXQA!j{K?qk_Lc*%spgau?dJQ}`&v41eTnfNr`7SaLNgup zrFSmueTg|uOQ~$icL(-Ek?38-(~mw0%T8<7hIyZOppPpJA`jC|DjBn?TU-Qd_GWF3 z%?~~nEYW7Uf%|OY(WX{V7^Zuhl z#EJj0;v#W3;jng^dqPOM5nsM+bz_9zy_3?bqjTq3w#5To#g7bNVQ!oZ%A!c}^-Q#X zae+0}&!SXX{kIu3pUF$&qgiD6`afatj|=gms19q77Mm>=xv`f*x-cxUb=CgfBx_!)f~yUwsR@D< zTDqC*c!Rb!NZ7^){faUgW4Txo{NDG^^Qsd-u-9_0z}4-+1Ij)Q8xe1f#I>wB~X8wsr{dvyR+Xubf z4}eQLWm1tuN8M>X5?SVMt)e7&Y!OaJNLq@Pa$g5>Cq-rKI9 z1Dv^fgcU;GDeRiMNE%DMF)Kq^5j~%{>UZ$b^euHC4~FQMZfPqp@MX}RcV64Nt&U}J z=T~k*q*vSHB>S<&gJrA*=`JgV{&5QqK4ADdL)Oq5^@1yuAxx?B{^t)g;hD;2t-RxP zsERovu_FWk{j^4I>uTfS>27Q3@^|NEOaDX`eZs1^1P?bL8zU?Il2o44%=>(_( z5GY)X1N3)sxN?yHvwFMyF?(7$T;Pi;zw@%&}j#sDc$Ms2RmCP2p9r@&)NzB!_PO|X}6wL z41qw;H5UYegq;Hx1)sAYA_^D#Z(QL2?1zXV&)E-wLZRnaL*dAOTlm)o2o#Ap>kI@2 zIrrN^U{Kiq6a3eI2n=!F8aU*C82*bOT=d)@0f8gWl?j3nJqIj$%I07F;puMa;B4zo WE|D}70-=Dw#SrA&-13?V}QslV`k5UtYdTdma;?krl^w-t>p@%emT-)kX~BrTYvl;iw)$pp^xK7e{GM>YM2zBj2h zBuZj$Bl{F-yfewkpPs{BSbVxj*Is3>k=ari$?$Zs9a9un zhyT;pV<{H6qc^TIK}SnPeSHo4&EM<}zWtEbIzHt5Fr76;kj5Q(?tV4g^Y_8-eQPc- zkiV@ZpnAAxe!>zSy1WR~{km5K>`#2#?)kQ4Pq#%2u9wQPW?>-2MUXMG%-~QV@nJ0m zc^^jfwBURGJdzaYQr_Fmet+RCabNq#N6w!dY_9y|>YK^22vT9r;!xRJkUsqS8k4{zIZRTf3a=ZnkEg`Bh3gxzhK0=d&Tl5a(=m* zH_10HsGTOn*pTO*y9`lL(?J9U`UlE=ka-_YHP(s=w;StHLz@|NX3Yc=91D)-_apqm z`;}Q68nhl;0PL9U{wvB~-jA_-wx071i!q%X8V{kS%cPZNY@*h)Ale?q=Mm{g`7{2PZ|2> zZd4c9UHkSL3`l%>m$tYusg%Fe-}FoERQjb=&atiM`#?{d19vAP6bI9Ha#Ttsf9=1k zG2sZFea(by{c(@FUoL=FxAK)2i}0#q1S4*W)ZF_e=E>p$%?8*#Tk%$i-a>O%m>uCW zp*qhZ*g^KhGuW=6y7GL(WWZV#GSNhzO2w~pkW0l5FR)*-!y|4ilrjIE?v2*|Hu~w_ zw^}ueCqNhD^xBq5=c5EpvC=F#z{8)w4(#HBe)h23eoy$m{px4mWM3bEZXo>lHsX4X zi+@iq?j~s23iMqkwd!KY`H&qcThw|>%FA+6v6IAm0`iN&%6(I_Y&&4%3=+fsNV{+i zhFaST5&@rc1(m4c2F_8*lipI?*)!pW(A;M73t3Q;)>#ka|V{p@Ehm*~*u zRf61@iqFx0``p{gGALg0Q8!ngLMQ02<)Q!VV!}9Hm^C(--D?$8iekN%0n4nqmYI7^ z*~w&<&G?BGPlB>E?o-`oHSGw4zkD-wl`QkgM)Je@#~I&68uW$>z2uym5eF)^uH-Ta zQM>&`zFxZ!r6L%!@{R`bjbAjW|-;A4;?#sXaFTIFb)MhASsniAYt4=x%+ zWoWEXGG8rRNDc1D?&;+VjrpNhmRnvBP;yoJs!>n-9dzscUK3nFS}*E(v>n}$S{WLaNYM@Rq&{8n}ee-y$dzv$B>%7# zgnZjCe23x3UG!9p3{^&6IGc78eI*aJ5FT;VT0stn!mPkwZ z=mJtUQhnqV;DEL5Ap&-AbIG`SA&eus^#@9A81K= zP|RjY-G!S>$&PU60#iaFNX4mOGo260oU?Z_saN^W}33ayhUf6cQZx3-_^PB z(hA4g%2);S+4vJsTdc_reYey_N%=NuF;NcW^b)zUQ5=77It8sS#?xPzH9A^HQ_ z50E<-q_Vyt(ezk;Y+rSD-EOeX_z~sGXR=-2W z=*7qH_Gsv17O|lL7~5HIfrj-CyLf3Y6+q7wn_(TjXAqsMGxMlZF;$0E$GmpHMb^PM z+tc}PWbiLegeKJW2(Fjx7AO)cBrh*k|0I*7k(~)fYKR+}Zti-~)$OLTn}mtbkGqro zxZR^8F4Q;wu@r-iKq|9BWYg9qO_dOiW>vp6WPH)ya5Z z0X1BHRV`O-?Ld&w+o4#PfJu5Bfk`YGe|DpurZ5`FeJCFh47oUiNQ%3?o%NYNFEnud zK%3-tn?yw+_cW&--}g-s$46=u&d;ij5yzVP2z|%f&KTw|C$+(o?_w=_9!ZuQS5Y-K zeV?|a?!4E{w=g^B)zRVik~6X2_CC-~)9F7`a%Vm!^);;%{1@Uy7vt0D^{xe}U77v1 z)u(9_KGgU%Pd^rB50FSnP5P?W(?>l|^Yu*p%6(L1jE&4W_~b^JS?+o7>GuVAjE49r z%A{le(wkphNt+kj<^L+!c$unyqLVB^wo6=?HM{47$=js9R1{Z~jN=n;p>+*lSL$up zQ>)0T;BOCs(^{Uo2|Zs=(T%`)ZTzfbLKHsyO4Ca1F2UDte{ z@$n~}3@n{fxXs%0aL<(@*ar6C;dY#&{jAy36hfx;E7Ex#A$n`qdHOHBl@DasG-UFt zpCi~u!t3Kkq6G&F7Lg(o-=2xS1u*YeT@gs-zfnTiW{oK44Pnl0Q=!0#_m$7Tfy|9* z1ET9oO||L14rjN-W22Mu)M+bs@l7Fb9yW`Xj&<}Z^aM&yx5B!;E{|#O6?JHBAToBV zQjuXt?Y+}wumEj*{gTXEKCX^w_#IH%mvmL{uelbt_%vphpx0cgEElU<0ce=G&t`iQ z)H>Di>w0UZSF&?u9LdzvfWTT0^Ir>fKL=Q+73zA;ucAfX+laaTeK+873;*|^w*{ma!9+9T^Mk&exRZ! z{u;v!r}T{fi~|!@6X)lw6vevxX3%D7zXo1c)T8pg_Xf_KNYft7k#&PKZ$kY|p@$Kd z$Aao?e#Nv`eKE>wX_eMyw~s3>waHeijO{j+_Sl$8Yu*tGl<{i3%4!Ovb7A@TnihL# zYnUpFF!y=a5LHCRog46%Rq&8bw8f08pJ>~`uM$1I6+zyGDC`gE^dp_VQYqK)WR8RO z=c^8v$<-Fqgn|(98vnR#-F&l#AJ|Z?IOo7ifjbIe=w`(wygKXX(E{)bzoQRK^gQy+Fs%7v!6Y!xCphFqn2vScy2>!uqzWFr}U0;aZg49q4#$l;O0za`l#rpmL~0MGn~ddtb!K! z4RNE{&6(0PL0jnv3w6?+ z6*k2NvR*U&kP5miAeSORoYPh(7nY)8)2xtV!BHussmF}faX$FeE(V)+#mofe1PuT* z5(Af83TsV}&50cW%U!k|tx;@;hrN!YRm2Mjw@XDJZMSmu&24yN-NK?+a>(D_zC=hZ zZh>{P9ii?S<5h|i9GbsCX6MT@Hy)?P8bIafU6`&*GV|kLlf)A)#`w14gqmP>hR8&# zu$MPo^GLRJizO+wu}mQ~tJM_`vMjSv%w!M5mI zauRq`HI3n;;5&2SLb}7d9uW(GUBtq}%A7Ubg?v($nn~)E5m_@0c)tlNF!u@L^lfc8 z#v|%I%^P>TB_Du0N*2FlD0;JTzD=vhg9_SD%OoW(HwZR2zp*+%8bdowN&_;N*`R*m zS`x+SOki(~F~TS&)@{dP)|S)Se7-W0r1-v?^Qd1qQLWD_ADtt3mu9r9*DsJ3im6ZQ zI6H_~EL`)38fH-i6e4` zUeP|b^+XuHe&DF9=`@6oE-cbSAV6tep>qr+p9(d*jOb1&$&urfu*%N`k9Bw66v`$Q7K8O@9ff)H7Yv8+ z3Ohjt&c=&!px#1FDp6bUpN_iEt&4@%_M?u;ya7$Y6YrwTzm@du z3OaO^?+SVgPya2IuclqRuE$iUi4)=0M`{^nI0odiYvXmhvR>bRqgdWK@I#rjIkL z4GK?u=OC%B8H^ISPhN9rn?a~@qHWDc9x#pB5l{ zU1xjFjx;d!18!cI#^9fLv69Msl#|FG1Fc&1U6sadAOmvTy;gTNN&#|2xFT?(G8QN ze9xMC^Q`S@s*XWFW~+TZ;1*w`tR%1)X4c3a&ij;36jc-7^(C2EM=o}DyQK$JgdanE zlHk;LnM;n7T7lhTe0|b;KQ_Cb-`6bNYyE0%@o;oscf6@#{%7j1jfn+X7cu|!!PMDASI}C+FQmI|92HtGlTgp>ZsUkl<;X zF|%#9CCG9vP&od^L~mCnklYZq=BN-8_hWl3TR!wx)0#SZKyBBVCT9*;HRk*yj*2^6 zGtEc7W@YparHADWkio%mS-N9FpH=q?o%<~|-^rbhuXWd1T>NOVH5Tk#&9z*}PSo1d z7Wou#TMy&tCLhUnS;Xy9+~VIV2$&6P?c*O=7kHhxTJbSqu;IM@*uWi1I~Q9IPpqAl z^Y4|bwId}2$_?iJy+D9r(DLW3yLJ~L5CjyNkO`>YgTN%g+~D7X+l8C*p9K_!gdxwy zkf<{;Gz@k&hJc@qNg@)oAD}KE;F6Gps0aK20**L?iblatqr$)tBCr2u2Lr>-Ai%&# z@M(51FbYW&{(Jme84Ln}{Kf$O3_>Mgu+tz=NeBdeDjAAI{y!GLP^WXj2sj#YDi@4E zBmao~#wQLW9Ddp>FcKvxc{*7V4o94hp}^471c`!wm-_ekw_-3Fa)vY*4WMAB?Sc?{ z<1{b?jE0?wK@hOhq#;n)Keqk}3?X*W84MU4{7=IW_5Yh81cpYORtF&pK5Z)mj)I)I zkl4j%&LN2PJmmre2?hU&@sIxyBocbM;UJRGGra&ofgyj??jL_4D8w22At*HZ%zZ>{ zr^^HdLjcHWTZ#P6HXN~APb-EJ?K({cN@Q~y7z!nJ(kU09Pz36KaDo5W4~3%6*bjxl z;AdFF5a>TG{A~jihC-bo11Fk&8W;{k{FmV0{zKu&v(_LW|I6?Xf(YoDFM=rKOqrm> mYMus$LEwL$^Tb*?+_%G0$|PJ5fl@&bC^V&jfU>p<<-Y*&K^N)( diff --git a/stepushovgs/data-structures/source/docs/img/search.pdf b/stepushovgs/data-structures/source/docs/img/search.pdf index 34e6c9bed2a9836cdf1fe4c798d13486253bcd09..1999353f3042c900855b651faf0736bc386fd614 100644 GIT binary patch delta 7194 zcmZuzcOcd6-!37$kYk0A3gPT?>|`fokIYE+p2zqW8QC*R2-&-kt;i;O@0}wva_sl? zywC6VJTLF}uX8^4xv%T`T=(bxo~!fpVoD%EaqLAvzv$*?2k#U^NCYx^F>EB26eTkhwRsZFZEYsfuoD3K4Jm$%twdzo4DUwkF4w$)Y_;G z*?wvP?2f&wS(jtWrcYMwaqF80LZXqehkYx{iF*DY8!u zJ!IolT+&KY;dM**)V&oQw1sZQzp7YJwH#BWg%@gei$&PfKj3%g5E%V6;_9F>LjUFC zS4Rn2D-!mY?#8~hSC1&@+y_cl9=)@FSVqGme)s#=xG!zPK`P~6(ZqM03gS3zvdJr@ zxBya*2lKfX)?4Lk_Vk)FE**$suLe-TsrgbT&MEfR1$f`2HYH`DG2P6G?0rFQ>e+r! z@sDsLQaNS6vawQ|sC8m1b9=hGM}zcXEWIP8q<8B$wZEiU!oMkwgH~*d1BkW02Gey0 zII8C^C>ph~eC7E`sPNFXbwtk14*{Pc=T5B-0>HV2!?nP_Iev2F>0O2 z%G#dt?l$X0fMeY@(ns0tTktpOyCONMCY+ODgFQ1mn%b8WRFo_~*n~^sIcfB-MoLMx z$W$@sr+(EaCgk^#t|XXt9&hDwBjlBirv|;Tm zQyIS<_-FBHsU;5(C{fYnMB?T=GkRMq+tdO#BDa@@Kvo2R^xRt11mBl-x!@OP8R_O! ztt=Qu<=oQoy##~O7NK#!qC1eK>pvPl-MAid;G1bN!7&*&gjcIOt~9y=fW{>n2;1Dxb66}$@1UmI`oMC; zSRcc3?O0`+<|TS9N7aN!2dhWm(}WnofiB6cD#VKDVfjpCbydqKInyRf-~xT~`@v zw37`-%UdcyTjh*ilQ+b&6jpY_$X%8nJxG)nR4RHkG({=6vB6-#|61~1%|l4)eM&u< zI{KpK1p;lWk`V2l_;>8|6f8+)tC5qvITl3{F>6nyU(P`sPf1`c3-TAi-5%fS>hOv>pz#h8VbO*Lw8;Q6@->Ms;-&)Ks z1dwVQ&o;@k-UB^8Ra~o44Y|1baB$Mli$m+?wm&DsAm~vS1&XlnUdHIUI2(Psut;ss zF2jt(EfI+b%J{;(Avd@C4jS+;p#(*ypQ|N%xdb-joge4q8B`@F=Qj4|-3ThgtR1|G z%2X5;d9L<(+h4qEGOGVYPk+>gI7@FLW&xl}T8_Z~OUQmX3EkXP}w{{IkjMdqi%s>G1FfFW2QLZc8s|YYEW&u zS>o1qVbN_u-TZ-o*-*Zx&2X=Q2(G8*YM_8`WxUecMV0=|M{nKlaD1V}eAkyVV-*7W zdsO*vxZVg8RsHRfp+}8k(acKonSWaOW~fg@xqs-wP%fb(flO{wNBh8UQI~DzfpK)M zN)aC@@7uO|Qc0_k>%zCx?l!^Qfs$CRd)H@4Aa)^j1(+v+GoON!J2zTRCfk>1yr{H4 z^90&%CRZzWYZyX|dP5+tl07t@*o=ojI`6{E-OqUjo>+wN4zng<+CYI9R!kIEAtAoE z>-U_-Dkn#3dU!)q{3_1ZihIUk1v`9_Z;TT}#HPuH9R6?<79g1kq)q0M``M6kup1~T z6Rj}!&eE_x!Z4V5M{DyECviI$w2}9O@R;Bzi>}>-(1+lJ4wrr^ZTRHM7T^iEA4`ky zy^R=NDGo^D?`L69fH1gjLpakm9CdkulW)6INOjL$4r!?GIXWSToSnSkf?UK7#5{B3yhE|HMgb8ATrfF`F4=~nnR%2}- zplZ8n){^iezu>+Nx-o|`rXsf+P@mmbY2reg^^vX4`zakfdsv%(^ZIHp6IYMW;_v-P z_Q>9G$IEIOzE^0trM(CG?#OkAy&V_tP&bSq+P|5~olB)0o{Gr5JWLb=WP8`yvQjIQ zX(;YKNC&0b)JHL8gVKoy_V+1IoHT5-_`;w`2c@TMvXLoETZszdVPDDMR>h<1DK$Kp zojzZCu6&!toOqH<+<};6j-{XEEkf!iUdC}Fi@E)#~I*hAyEr+tUS=Gcu zf$(dYy9Ghl6jp>md}SyAJ4pAfEjoL^XR%s&+4s$JX@A10E@aD>T=M%|Q%pGqD=73q z6$+HTQLk|)HA}3CY&F(JHBh*I zDLUg<^ubNCL}*+oovg82@J3Gk4s@dqnJTYwXEHj1Xdkg zIpzi)2Qt>s$nV{jBvYRZutWt2!VQ(gT+bJZESscpkt3=i6MBSxGxGs+>`948rwI;g z`6*CxPXAbvPI*CNLu+OOxkq9-V`|gaAWF05{1i@ZrH5T>r73xEcVYd>Y*UL{J1}@ z<<&P-xP6bGZR%pcr)*<6wardcSQ89Yb|uB5bHK?=WIRpA-KUD0DK@l=sC#h>bdZsm z8UHW_XEDAH9PNbV2g#M(eOD2&sybZTZ$i(oEA0hzSjVk`XDyp&3`U{pg%}kr0v6s% zVBy25vwJy0whP2@yw*_nSjk0o8J=Oqh|{v`e&uRS#s*Be1lhw&boG8d(+b0R^LiJn zR@>M-6p^n@fBbuxwM6{z``3U4h0Nzc9=bc2-FZ5|$es2TB8-HSLqtD#DNcCcr{gZy zN=!^v2F_Dcc1tR zhA-8I0r#Z*0vuLCr8!^c>jVV&BjkLG0JTph*w<`i%1`j4OzGW#USk)bR zG+6J>9)>*QqjDgac)QSD**M3vk~?dpITqJ269!oHJ*kW?C8lA-*r&17+`i_)4+evPkvwXgH0Jy zK>xdtAe4u-ce8?(Kua5L{au7OF>~+}$bIr-DrXj|yz)D_H}STo zQ%Hy0o(VT9^5g(hJ%PRehAm zQkZ??W>-x4>ABtT0<=y1@+wb@8A$G|3JT)UK=dv?fPmvV!-{PwKE3xgJh?E0(WFUv zoJJQ?2d$H<`j0eTnJm<1N=(!8YxAcsGoLGOWIa0?ht{fej{bDvUPOpa<@er->B9|olG(`?`c zRNw{7K5?+%*;{$@r_nyiTl*Zp{?iWMRqup$QO>w-zFD52t}iq;SiA+4!}zeRhw;Vm z?1k!ypK1)^;)};Z51xo%~H&hA$(j3Tgy$x<#j%`7(C{83{@Z zR;_QqwkH!1NJ{g9E-_1u&o=2Np@3fIMaw-3K^t=Eop07$mG^9-He|cTO0yBj!)&69 zZL3kE@AuS)jjNyiut?M<6T|59>W+Z+eMa&X$se0I5tTRabJF-J= z;x>isd;hLhx5l9ncySr>uy|!qYw1po`zs5Ua?j?p@>fBZxQsjs=!^NbkBb4FL{NG2 z5}Ea=uxLjfX+__SqybSARZkIJr;QtT2m|58_)t5-lt^4YWX6U}Hb{SY-`4^4oUd$s z^BpTUu_D1$!)?xEreBPIS^Y}N(MKbURJSuO>^rtGg?O=^q~vK1D^UZ) zYtO;w8c0dV`Z0-mpcuruWgsOaT6}?>G4^FjO)pJANuLZ z%kOj^dBeDfGFO^~Nt3UqT=SBT+u@au8}R23?&&Y+X!2gto7_) zHC(-FpSRwsyXH@`xk&)MmpEI7Bdt#Nj}MfyK2f*C%~cjn`wQ4ly$7b*FcNd1W1rj) zt@ymA%JM?2B`0l2mCr(`?vZ1!={^1F58b*LhggQPJ^RQi+}>7R+v1k!vh6WKOG35C z-o>sULfn{+VRdh-KNLN$uEKi|#O7~h{PKR^H1p57D_5p9cEr=t>AGDNa(Ubj?#=b? z-DrtWaOvx4zpshAU<~x~Ueio?AYB1|g>x`Qa!A}(w%RqO#MDxILEh_L^BUdx-cgVEyqP(2E zTI_1VHngOIRsov)TaKd3uv}Jy3PZU-P`-w%Ys?ZVBCAy6H4S}bhO!*&au;A)`TAoz zPnN0t>jZWIh=m!K!W^N~LY7hIgVlWxzQtcFC@&qR_P}egS? zt%;9Be!!>v#myGNJ7&B-9i(iH{h;>l4W zAbzqQBy>10&XT?doUAQ=ybBxXV@sIGSJag57UFQ`mY-{XBZbZ7B{DXZk>LDX5UQDE z8iHBRSzQT9#W2Ssur7Nw#aMhiNhX(#`Ml1eyPIv%RblxXGQAh{A*)dQ!yKe|1}!HJ z6k|2TVz$k8NyVCO--vLr%%5UI9t>SF4R_bnd>3uI$h&IS%b7i}K&rpn$M#D*9)8d_ zEi@(9Qc$;k*B=TQYi2{6??}FNT`L(Yx=y(6R$I5*qwfal3Lladciz=;o77>X1@iJ&k88qjpviOPuzzi}Sw{40OypZ~?K*2OiylXVp_!}6 zkB43gRxgJqmo-+sK(}7&Lwr;0(t>u?kWYN2FcD$1h5J>JRd(FQ}c|o1UiAeW481qri=qX!Q)4 zf9#F;C{gbm$maDlE9cfR(_SXOM-*oJ2dm=2An>$W^_T|vW3Md+?D_F~El*#HH}3)6 z0M*08lUlc?10CxnX;Q?5__{K^OvZzJ`XKt0BRt^ED{?d<`ymriLd%BJj1C zZVWpi5(-De>|kyJNGKG3=7%saTo8J$hJv3J4TJn?;V=FFZ3G5^BF^DK5a6={VGtDZ z4+roE;=eAS!LYN$P!t+^&KVR8d6on!2u7dX3r0f!>iFLrU?d9m$A!OZfB5ka3JN)^ z5R5{eI}$tvaRJ%_y1q}A#fD*YP}%h5qj~2L7=h3d2v~nL&cW1d-?NI~TB5ZbT{J5^^tbgIiLM20^+c1rg~E$s=7#BPj?-cY|~{hzJ7G-2wuF z)Q9gIL^g@5$pkR)r$9-n;Kz&yS8zfy0@z}nVhCQ+!VZ&H=?F_TE`#=N z>SBL~wP<@bj$gF>1WkXTjYY|)wwKzKoo$sm0u^0}1-l_xU(Fx}+T;X<6`JOp%-f2- zrG#-yG-YA@P_KQt3T2-|B_sg20zwU_FF}wXk3wW*zRNTAtX{awzVl3-wwX0g^4kpH zoxUvb(a$u~F&+LQZW=8CODksTVe;bfYWDS&!=V}7{N}oPY!#IOEhVLs;@x{s7bYax zrF0$`jE`0=`r#JDy=Xn~z@8Rx6qS0h^odocK;S*?G$W-zmDaf>&mUw_9sn&yR8G zzE~07-Y#=4uJZd@{yQ;nBK3o(@FPUwcLf!uc^SV_&cYh^gN&>qulB+3Y$uWr4V++M z=BUzNCH~D_$qnZ=t|vciNF2_0re;rsPmlKle(|324qiB(@6--k3)KlIGDr4~0HBU9 zVk+O#6)g#MZb8I<27c0QBKYB_Ud^GEQ5`8OmqRxxF8Fpdvq`O-pn3BnLyXo0Px(B< zkf_B1t(OYK)h5`Ls=(6A+=SWRDd$OTAo)+xknJG#(8510IS6>LD{aA3uRIgBQ%lL( zp0<-s_0Jqy-2A@4&aP0;I<*= z7S(BD+zN#JEYn^}HW^tJV5VO0dz;26X|6I0*3x0nFqM)Pb53n{7KcbIh9+bG8(HA{ z>7#MEn5)+B@d8(c@+XcrO(jip(a zFU=Ukb=X3~ul{B~8N_@Bb5o$<&q#HizkzVkId-^za$7xwKVh=J;(fUPQly*{ihqzI z49J`)&K>{8@P6`ntVDj;@LthEJ8r}U&pE>@*wlXi3Ku{Cdl5695LxH3@sIO6x# z>`Sksn!cP~+l1aygR-{>JI2t@qmi;JXM&V{G7fJXWrUiS4qkKqQEGY|L1|boPFYGu zP9Tf=lE78w*tZ>z$+?Nb3jV8j0MW<(#{lRnm-c(cG1Nii#q%I0;USO3bEiSy=M)Tt zwPfy))K0mfFvHQHB_{ z?~BX;Jvg6B1Dv@Rlf2qIyhO2Pm$MUq$o%#8IeyuS0wAP6kkR-IRx(MZw;0~r*CxMj zy9RJ(ytk@IW_sx^(#1H{8|M#tm@fgS2AFOUcZ#Rbk-!wAhKh0XjEs+29*D*kmI-Tr zA2$JJ`)+Q}(0OmgEohj()8}cH;!LCe2o^iuK{1^Ftc9eg3hN(~mCzYeAa4 z(Ipr^ePrF8SE=dnrdt*ji?pVV{~=erQffpPwyrrnbA%KXXV+i26peZzeoc3?wbmS*->M| z?6L;zYP!{e-?fFgQh^TUSam<$k0~|cyEjq5@ibjYLxMqS=ZlhQH1-gYR_ePU<>GyK zV7jI|PP;Lc9T)%AN_lRob!+v5plf@jbud@kdxHTytw=vFCWGhp;)XRAFEnld5`_=k3~QVm=2Ein=E?94iH?_qm7V8>u)Rq26fjlG%Tto) z8=U$$VX0_#YPrs|d*^E|3qfw?TY65CDFxBqc1e{PfHU+~)FVs|!Tf@VV_VKe>5Ukg z$KJVrG>#x@Svg;Q()@OZ$1aM!Pm9B5`WN`j<|>z!9V{6$+RdZ!04460s*z5m30k%G zp)qm(+;xvy<8JEX#8EbwE0hukBh}O=;*Y z1~+@^ZaZ)ZO53H(IhRL`>t9^G7Nx(vyo_VHMcjr*{9I&tnz=!ldxxej#55=2&c$_w zM9u0#@tb2|)E5#iK&h*aOGn%7O+oR6aiPo|b~m~)@_`~hqorl#dnukgtevt7LKT*= zd}tzPg?ihR)gB}7E?);O(~SoPuO6@j2_Sxc)G!)HnyWAxn9lF$$y5F)C>m&ra)v!r zmHXgzr($=fTyYI5kjKrvG~Grywm6;Rz^+HqRVbEd6Bk$?0epWaHJr03uU z$Ea>(jNLH6heaWdvxHz;Ccuh^-D9LKCpCvtPm8qnouH;+O>tko2bJuTIHJ#=@CQo| zGn=_XXQ2>0{Y|cKlg_OpkD7I=+Kg!?t;bd*tWv$vORreLIhrQU*jCL=b+IaHAl?}~UT~>lL`u^|nIYjf-WQ8W_cOTfq zNMr=)nU z`_tX_IQf4P>O71p%{GeX9=xzEqK}t|s()X96u z3&q$QNhZf-R_&I~b_ZyIE#@ zLs-SOi9%Jijo%8|oFS`L%``~C488ezM!-&+sebVDWIZxe z;$bs!fYzKVTwlXMuSjA3%??4l(}YzrXzxasWrS(ij;|_hiL}N163P!0&S}T4?cVdF zWp@K$WAb8J>|fqzsz~2!B2K`~0O$Iyu za(cRrvCxjok^wKiMoj58ig!i% zTkGxNM4a!lxxUk1f33IbQSlVYf7{sjPry7gj+?sICHL)Gf~}R?y1?c6t?=^vW`i{P zRw7s=x+yUS?vB9}tzKrbu_Vy(%fppAm3Y$ju>E*Xi{}prQ_r2zI@Rf&q0$omn;b=gfiHfQ738i4cx@PgXICEtL8f-&}}?Zu|VZ^@6dTyf!mBuY{d8;x~GdQiC=h8 z|2Kq%kSd_F3C5Ottkn7)Tna>j;gpX_+{T`+`jhSp)%zFhg+4%OAHV-yEM-69GNE$o z6^T%n{immZr`wc_A#-AHRa5`=%qSCKtXMYjw7-QXWy8Pfd}j1qnU@4lov7ng2`%@E zRK1$rtu%%T)KZ!0U(uRiHl8Qz1bEOaXsy$wj#k*s;~iV_oY#0Xh=kpuahOA{=>S~J<| zr9%e-lvIo)A7~QTRTGj&WUB_*((}lICzXgcAMGkCu^Sa)i3iEXk=-3%D&^|cHYp+> zSAEB(FV;rRgVCbJQ}h$PG_don0&Oa;5v>+bT_kuhj5t zXt6uY4Dy?WJYJNSX58*zOc23PM=u{u@z~=GnE!GLC-EdNmnm#<{+gN?B4Z$EJ3RJ{ zo;N?>ptC>et0VQYSHL&!Hgmt3|6juLekl{vnvl?_#^@u?#O&1xPgh>+7a8hublSr7 z>AD2HNM+wo=Y^Wi1nH8*+rMtNcMkEn>gFOcVT;u z#wQ)Pu=8IAPM0RRv&h$sie%T_ql_cIn|A4%iQFi&f9bf{l@I0dX8`dRA>3j&HY}Yg z$L3ZF8vBEL$EaN9jwI(~nf=qv;;p>j-Jw}0gk7QaM&cCjAaC8LT;6^8A@JV18G4U> zVvUsF6IWeX&if4^t$@Li9dTe2>3Q{dZ&kvMEMu+D%w*kOks|YvAvQ$2M!51_!?*C2 zhZhNRJ?rQ!(hxi#k4edzp+VE#{b&#N(b7}fBeLk6EbY(w){9i?BZ^*OO>foWj{0$< z&Xi%2b2FYs&0sM(zt_&~hcv^lCEl;l>m(=)HMHp}HTpB0R_==1`SuUm;iqg)Rkh3n zf0K_^j`E26(bsS|#yn(AU{D15A@uL{(CQ(>0z;sY zSKEQ{T{i@b4|UBDFeDUtbp|lxrQm<%xm5K(9)_VVfh(H9P<+>%14BdM*Ak*J(0>f; z@1&qG4C0#JFbo>|PbdC+sxT}9d37)_EEaZEM>rG-`)BZ968NP(ug1b*@ar$sUumv1 z2Zvz+%+-$Ja5&#p`QdQ%b!)@1m}~nGmm9B=AduJOhyQ1sz<=exH0\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
StructureModeOperationTime
0Связный списокСлучайныйВставка0.047621
1Связный списокСлучайныйПоиск0.003505
2Связный списокСлучайныйУдаление0.005502
3Связный списокСлучайныйВставка0.048707
4Связный списокСлучайныйПоиск0.003505
...............
193Бинарное дерево поискаОтсортированныйПоиск0.007040
194Бинарное дерево поискаОтсортированныйУдаление0.013611
195Бинарное дерево поискаОтсортированныйВставка (среднее)0.303134
196Бинарное дерево поискаОтсортированныйПоиск (среднее)0.007562
197Бинарное дерево поискаОтсортированныйУдаление (среднее)0.013591
\n", - "

198 rows × 4 columns

\n", - "" - ], - "text/plain": [ - " Structure Mode Operation Time\n", - "0 Связный список Случайный Вставка 0.047621\n", - "1 Связный список Случайный Поиск 0.003505\n", - "2 Связный список Случайный Удаление 0.005502\n", - "3 Связный список Случайный Вставка 0.048707\n", - "4 Связный список Случайный Поиск 0.003505\n", - ".. ... ... ... ...\n", - "193 Бинарное дерево поиска Отсортированный Поиск 0.007040\n", - "194 Бинарное дерево поиска Отсортированный Удаление 0.013611\n", - "195 Бинарное дерево поиска Отсортированный Вставка (среднее) 0.303134\n", - "196 Бинарное дерево поиска Отсортированный Поиск (среднее) 0.007562\n", - "197 Бинарное дерево поиска Отсортированный Удаление (среднее) 0.013591\n", - "\n", - "[198 rows x 4 columns]" - ] - }, - "execution_count": 226, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "csv_path = \"../results/benchmarks.csv\"\n", - "\n", - "data = pd.read_csv(csv_path)\n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": 227, - "id": "a3737f45", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(np.float64(0.003486), np.float64(0.048099), np.float64(0.006181))" - ] - }, - "execution_count": 227, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Получение данных для Связного списка\n", - "\n", - "ll_random_insert = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n", - " 'Time'\n", - " ].tolist()\n", - "ll_random_insert_average = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ll_random_search = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n", - " 'Time'\n", - " ].tolist()\n", - "ll_random_search_average = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ll_random_delete = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n", - " 'Time'\n", - " ].tolist()\n", - "ll_random_delete_average = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "ll_random_search_average, ll_random_insert_average, ll_random_delete_average" - ] - }, - { - "cell_type": "code", - "execution_count": 228, - "id": "5434d260", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(np.float64(0.003512), np.float64(0.048028), np.float64(0.005869))" - ] - }, - "execution_count": 228, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Получение данных для Связного списка\n", - "\n", - "ll_sorted_insert = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n", - " 'Time'\n", - " ].tolist()\n", - "ll_sorted_insert_average = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ll_sorted_search = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n", - " 'Time'\n", - " ].tolist()\n", - "ll_sorted_search_average = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ll_sorted_delete = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n", - " 'Time'\n", - " ].tolist()\n", - "ll_sorted_delete_average = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "ll_sorted_search_average, ll_sorted_insert_average, ll_sorted_delete_average" - ] - }, - { - "cell_type": "code", - "execution_count": 229, - "id": "3deed9a5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(np.float64(0.0), np.float64(0.002007), np.float64(0.0))" - ] - }, - "execution_count": 229, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Получение данных для хеш таблицы\n", - "ht_random_insert = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n", - " 'Time'\n", - " ].tolist()\n", - "ht_random_insert_average = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ht_random_search = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n", - " 'Time'\n", - " ].tolist()\n", - "ht_random_search_average = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ht_random_delete = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n", - " 'Time'\n", - " ].tolist()\n", - "ht_random_delete_average = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ht_random_delete_average, ht_random_insert_average, ht_random_search_average" - ] - }, - { - "cell_type": "code", - "execution_count": 230, - "id": "490e5c46", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(np.float64(0.00015), np.float64(0.001794), np.float64(5e-05))" - ] - }, - "execution_count": 230, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Получение данных для хеш таблицы\n", - "ht_sorted_insert = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n", - " 'Time'\n", - " ].tolist()\n", - "ht_sorted_insert_average = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ht_sorted_search = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n", - " 'Time'\n", - " ].tolist()\n", - "ht_sorted_search_average = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ht_sorted_delete = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n", - " 'Time'\n", - " ].tolist()\n", - "ht_sorted_delete_average = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ht_sorted_delete_average, ht_sorted_insert_average, ht_sorted_search_average" - ] - }, - { - "cell_type": "code", - "execution_count": 231, - "id": "9d7274ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(np.float64(0.013959), np.float64(0.301662), np.float64(0.007347))" - ] - }, - "execution_count": 231, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Получение данных для дерева\n", - "bst_random_insert = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n", - " 'Time'\n", - " ].tolist()\n", - "bst_random_insert_average = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "bst_random_search = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n", - " 'Time'\n", - " ].tolist()\n", - "bst_random_search_average = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "bst_random_delete = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n", - " 'Time'\n", - " ].tolist()\n", - "bst_random_delete_average = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "bst_random_delete_average, bst_random_insert_average, bst_random_search_average" - ] - }, - { - "cell_type": "code", - "execution_count": 232, - "id": "92a545c9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(np.float64(0.013591), np.float64(0.303134), np.float64(0.007562))" - ] - }, - "execution_count": 232, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Получение данных для дерева\n", - "bst_sorted_insert = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n", - " 'Time'\n", - " ].tolist()\n", - "bst_sorted_insert_average = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "bst_sorted_search = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n", - " 'Time'\n", - " ].tolist()\n", - "bst_sorted_search_average = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "bst_sorted_delete = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n", - " 'Time'\n", - " ].tolist()\n", - "bst_sorted_delete_average = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "bst_sorted_delete_average, bst_sorted_insert_average, bst_sorted_search_average" - ] - }, - { - "cell_type": "code", - "execution_count": 233, - "id": "b2e93d6e", - "metadata": {}, - "outputs": [], - "source": [ - "countUsers = 10_000\n", - "countRepeat = 10\n", - "countRandomSearch = 200\n", - "countNotExitstSearch = 100\n", - "countDeletes = 500\n", - "\n", - "\n", - "ll_col = 'blue'\n", - "ht_col = 'orange'\n", - "bst_col = 'green'\n", - "\n", - "iterations = range(countRepeat)" - ] - }, - { - "cell_type": "code", - "execution_count": 234, - "id": "208784a5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Создание двух графиков рядом\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", - "\n", - "# ============= Левый график: случайные данные =============\n", - "ax1.set_title(\"Вставка случайных данных\")\n", - "ax1.set_ylabel('Время, с')\n", - "ax1.set_xlabel('Повторения')\n", - "ax1.set_xticks(iterations)\n", - "\n", - "# Связный список\n", - "ax1.scatter(iterations, ll_random_insert, label='связный список', color=ll_col)\n", - "ax1.axhline(y=ll_random_insert_average, color=ll_col, linewidth=1, \n", - " linestyle='--', alpha=0.7)\n", - "\n", - "# Хеш таблица\n", - "\n", - "ax1.scatter(iterations, ht_random_insert, label='хеш таблица', color=ht_col)\n", - "ax1.axhline(y=ht_random_insert_average, color=ht_col, linewidth=1, \n", - " linestyle='--', alpha=0.7)\n", - "\n", - "# Дерево\n", - "\n", - "ax1.scatter(iterations, bst_random_insert, label='дерево', color=bst_col)\n", - "ax1.axhline(y=bst_random_insert_average, color=bst_col, linewidth=1, \n", - " linestyle='--', alpha=0.7)\n", - "\n", - "ax1.legend(loc='best')\n", - "ax1.grid(True, alpha=0.3)\n", - "\n", - "# ============= Правый график: отсортированные данные =============\n", - "ax2.set_title(\"Вставка отсортированных данных\")\n", - "ax2.set_ylabel('Время, с')\n", - "ax2.set_xlabel('Повторения')\n", - "ax2.set_xticks(iterations)\n", - "# ax2.set_xticklabels(range(1, 6))\n", - "\n", - "# Связный список\n", - "ax2.scatter(iterations, ll_sorted_insert, label='связный список', color=ll_col)\n", - "ax2.axhline(y=ll_sorted_insert_average, color=ll_col, linewidth=1, \n", - " linestyle='--', alpha=0.5)\n", - "\n", - "# Хеш таблица\n", - "ax2.scatter(iterations, ht_sorted_insert, label='хеш таблица', color=ht_col)\n", - "ax2.axhline(y=ht_sorted_insert_average, color=ht_col, linewidth=1, \n", - " linestyle='--', alpha=0.5)\n", - "\n", - "# Дерево\n", - "ax2.scatter(iterations, bst_sorted_insert, label='дерево', color=bst_col)\n", - "ax2.axhline(y=bst_sorted_insert_average, color=bst_col, linewidth=1, \n", - " linestyle='--', alpha=0.5)\n", - "\n", - "ax2.legend(loc='best')\n", - "ax2.grid(True, alpha=0.3)\n", - "\n", - "# Общая настройка\n", - "plt.suptitle(f'Сравнение производительности вставки в структуры данных (N = {countUsers})', \n", - " fontsize=14)\n", - "plt.tight_layout()\n", - "plt.savefig('./img/insert.pdf',\n", - " format='pdf',\n", - " dpi=300,\n", - " bbox_inches='tight', \n", - " pad_inches=0.1)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 235, - "id": "7de42c9d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Создание двух графиков рядом\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", - "\n", - "# ============= Левый график: случайные данные =============\n", - "ax1.set_title(\"Поиск в случайных данных\")\n", - "ax1.set_ylabel('Время, с')\n", - "ax1.set_xlabel('Повторения')\n", - "ax1.set_xticks(iterations)\n", - "# ax1.set_xticklabels(range(1, 6))\n", - "\n", - "ax1.scatter(iterations, ll_random_search, label='связный список', color=ll_col)\n", - "ax1.axhline(y=ll_random_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax1.scatter(iterations, ht_random_search, label='хеш таблица', color=ht_col)\n", - "ax1.axhline(y=ht_random_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax1.scatter(iterations, bst_random_search, label='дерево', color=bst_col)\n", - "ax1.axhline(y=bst_random_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax1.legend()\n", - "ax1.grid(True, alpha=0.3)\n", - "\n", - "# ============= Правый график: отсортированные данные =============\n", - "ax2.set_title(\"Поиск в отсортированных данных\")\n", - "ax2.set_ylabel('Время, с')\n", - "ax2.set_xlabel('Повторения')\n", - "ax2.set_xticks(iterations)\n", - "# ax2.set_xticklabels(range(1, 6))\n", - "\n", - "ax2.scatter(iterations, ll_sorted_search, label='связный список', color=ll_col)\n", - "ax2.axhline(y=ll_sorted_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax2.scatter(iterations, ht_sorted_search, label='хеш таблица', color=ht_col)\n", - "ax2.axhline(y=ht_sorted_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax2.scatter(iterations, bst_sorted_search, label='дерево', color=bst_col)\n", - "ax2.axhline(y=bst_sorted_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax2.legend()\n", - "ax2.grid(True, alpha=0.3)\n", - "\n", - "# Общий заголовок\n", - "plt.suptitle(f'Сравнение времени поиска в структурах данных (N = {countRandomSearch} + {countNotExitstSearch})', fontsize=14)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig('./img/search.pdf', \n", - " format='pdf',\n", - " dpi=300,\n", - " bbox_inches='tight', # обрезает лишние поля\n", - " pad_inches=0.1)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 236, - "id": "0fd42f30", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Создание двух графиков рядом\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", - "\n", - "# ============= Левый график: случайные данные =============\n", - "ax1.set_title(\"Удаление в случайных данных\")\n", - "ax1.set_ylabel('Время, с')\n", - "ax1.set_xlabel('Повторения')\n", - "ax1.set_xticks(iterations)\n", - "# ax1.set_xticklabels(range(1, 6))\n", - "\n", - "ax1.scatter(iterations, ll_random_delete, label='связный список', color=ll_col)\n", - "ax1.axhline(y=ll_random_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax1.scatter(iterations, ht_random_delete, label='хеш таблица', color=ht_col)\n", - "ax1.axhline(y=ht_random_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax1.scatter(iterations, bst_random_delete, label='дерево', color=bst_col)\n", - "ax1.axhline(y=bst_random_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax1.legend()\n", - "ax1.grid(True, alpha=0.3)\n", - "\n", - "# ============= Правый график: отсортированные данные =============\n", - "ax2.set_title(\"Удаление в отсортированных данных\")\n", - "ax2.set_ylabel('Время, с')\n", - "ax2.set_xlabel('Повторения')\n", - "ax2.set_xticks(iterations)\n", - "# ax2.set_xticklabels(range(1, 6))\n", - "\n", - "ax2.scatter(iterations, ll_sorted_delete, label='связный список', color=ll_col)\n", - "ax2.axhline(y=ll_sorted_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax2.scatter(iterations, ht_sorted_delete, label='хеш таблица', color=ht_col)\n", - "ax2.axhline(y=ht_sorted_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax2.scatter(iterations, bst_sorted_delete, label='дерево', color=bst_col)\n", - "ax2.axhline(y=bst_sorted_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax2.legend()\n", - "ax2.grid(True, alpha=0.3)\n", - "\n", - "# Общий заголовок\n", - "plt.suptitle(f'Сравнение времени удаления в структурах данных (N = {countDeletes})', fontsize=14)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig('./img/delete.pdf', \n", - " format='pdf',\n", - " dpi=300,\n", - " bbox_inches='tight', \n", - " pad_inches=0.1)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9eca6493", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/stepushovgs/data-structures/source/docs/report.md b/stepushovgs/data-structures/source/docs/report.md new file mode 100644 index 0000000..e69de29 diff --git a/stepushovgs/data-structures/source/docs/src/main.ipynb b/stepushovgs/data-structures/source/docs/src/main.ipynb new file mode 100644 index 0000000..dbea58c --- /dev/null +++ b/stepushovgs/data-structures/source/docs/src/main.ipynb @@ -0,0 +1,754 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 31, + "id": "e631810e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "12fa3ed1", + "metadata": {}, + "outputs": [], + "source": [ + "# CMU Serif\n", + "plt.rcParams['font.family'] = 'CMU Serif'\n", + "plt.rcParams['mathtext.fontset'] = 'cm'\n", + "plt.rcParams['font.size'] = 14\n", + "plt.rcParams['axes.titlesize'] = 16\n", + "plt.rcParams['axes.labelsize'] = 15\n", + "plt.rcParams['xtick.labelsize'] = 13\n", + "plt.rcParams['ytick.labelsize'] = 13\n", + "plt.rcParams['legend.fontsize'] = 12" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "c691c40e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
StructureModeOperationTime
0Связный списокСлучайныйВставка0.047666
1Связный списокСлучайныйПоиск0.002502
2Связный списокСлучайныйУдаление0.004514
3Связный списокСлучайныйВставка0.048082
4Связный списокСлучайныйПоиск0.003015
...............
193Бинарное дерево поискаОтсортированныйПоиск0.007098
194Бинарное дерево поискаОтсортированныйУдаление0.026717
195Бинарное дерево поискаОтсортированныйВставка (среднее)0.317356
196Бинарное дерево поискаОтсортированныйПоиск (среднее)0.007994
197Бинарное дерево поискаОтсортированныйУдаление (среднее)0.025775
\n", + "

198 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " Structure Mode Operation Time\n", + "0 Связный список Случайный Вставка 0.047666\n", + "1 Связный список Случайный Поиск 0.002502\n", + "2 Связный список Случайный Удаление 0.004514\n", + "3 Связный список Случайный Вставка 0.048082\n", + "4 Связный список Случайный Поиск 0.003015\n", + ".. ... ... ... ...\n", + "193 Бинарное дерево поиска Отсортированный Поиск 0.007098\n", + "194 Бинарное дерево поиска Отсортированный Удаление 0.026717\n", + "195 Бинарное дерево поиска Отсортированный Вставка (среднее) 0.317356\n", + "196 Бинарное дерево поиска Отсортированный Поиск (среднее) 0.007994\n", + "197 Бинарное дерево поиска Отсортированный Удаление (среднее) 0.025775\n", + "\n", + "[198 rows x 4 columns]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "csv_path = \"../../results/benchmarks.csv\"\n", + "\n", + "data = pd.read_csv(csv_path)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "a3737f45", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.002936), np.float64(0.047986), np.float64(0.004647))" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Получение данных для Связного списка\n", + "\n", + "ll_random_insert = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_random_insert_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ll_random_search = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_random_search_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ll_random_delete = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_random_delete_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "ll_random_search_average, ll_random_insert_average, ll_random_delete_average" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "5434d260", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.003821), np.float64(0.048337), np.float64(0.005586))" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Получение данных для Связного списка\n", + "\n", + "ll_sorted_insert = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_sorted_insert_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ll_sorted_search = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_sorted_search_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ll_sorted_delete = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_sorted_delete_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "ll_sorted_search_average, ll_sorted_insert_average, ll_sorted_delete_average" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "3deed9a5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.0), np.float64(0.002009), np.float64(0.0))" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Получение данных для хеш таблицы\n", + "ht_random_insert = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_random_insert_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_random_search = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_random_search_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_random_delete = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_random_delete_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_random_delete_average, ht_random_insert_average, ht_random_search_average" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "490e5c46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.00015), np.float64(0.001666), np.float64(0.0))" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Получение данных для хеш таблицы\n", + "ht_sorted_insert = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_sorted_insert_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_sorted_search = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_sorted_search_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_sorted_delete = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_sorted_delete_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_sorted_delete_average, ht_sorted_insert_average, ht_sorted_search_average" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "9d7274ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.000101), np.float64(0.002153), np.float64(0.0))" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Получение данных для дерева\n", + "bst_random_insert = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_random_insert_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_random_search = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_random_search_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_random_delete = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_random_delete_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_random_delete_average, bst_random_insert_average, bst_random_search_average" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "92a545c9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.025775), np.float64(0.317356), np.float64(0.007994))" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Получение данных для дерева\n", + "bst_sorted_insert = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_sorted_insert_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_sorted_search = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_sorted_search_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_sorted_delete = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_sorted_delete_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_sorted_delete_average, bst_sorted_insert_average, bst_sorted_search_average" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2e93d6e", + "metadata": {}, + "outputs": [], + "source": [ + "# countUsers = 10_000\n", + "# countRepeat = 10\n", + "# countRandomSearch = 200\n", + "# countNotExitstSearch = 100\n", + "# countDeletes = 500\n", + "\n", + "countUsers = 20_000\n", + "countRepeat = 20\n", + "countRandomSearch = 1000\n", + "countNotExitstSearch = 500\n", + "countDeletes = 1000\n", + "\n", + "ll_col = 'blue'\n", + "ht_col = 'orange'\n", + "bst_col = 'green'\n", + "\n", + "iterations = range(countRepeat)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "208784a5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Создание двух графиков рядом\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# ============= Левый график: случайные данные =============\n", + "ax1.set_title(\"Вставка случайных данных\")\n", + "ax1.set_ylabel('Время, с')\n", + "ax1.set_xlabel('Повторения')\n", + "ax1.set_xticks(iterations)\n", + "\n", + "# Связный список\n", + "ax1.scatter(iterations, ll_random_insert, label='связный список', color=ll_col)\n", + "ax1.axhline(y=ll_random_insert_average, color=ll_col, linewidth=1, \n", + " linestyle='--', alpha=0.7)\n", + "\n", + "# Хеш таблица\n", + "\n", + "ax1.scatter(iterations, ht_random_insert, label='хеш таблица', color=ht_col)\n", + "ax1.axhline(y=ht_random_insert_average, color=ht_col, linewidth=1, \n", + " linestyle='--', alpha=0.7)\n", + "\n", + "# Дерево\n", + "\n", + "ax1.scatter(iterations, bst_random_insert, label='дерево', color=bst_col)\n", + "ax1.axhline(y=bst_random_insert_average, color=bst_col, linewidth=1, \n", + " linestyle='--', alpha=0.7)\n", + "\n", + "ax1.legend(loc='best')\n", + "ax1.grid(True, alpha=0.3)\n", + "\n", + "# ============= Правый график: отсортированные данные =============\n", + "ax2.set_title(\"Вставка отсортированных данных\")\n", + "ax2.set_ylabel('Время, с')\n", + "ax2.set_xlabel('Повторения')\n", + "ax2.set_xticks(iterations)\n", + "# ax2.set_xticklabels(range(1, 6))\n", + "\n", + "# Связный список\n", + "ax2.scatter(iterations, ll_sorted_insert, label='связный список', color=ll_col)\n", + "ax2.axhline(y=ll_sorted_insert_average, color=ll_col, linewidth=1, \n", + " linestyle='--', alpha=0.5)\n", + "\n", + "# Хеш таблица\n", + "ax2.scatter(iterations, ht_sorted_insert, label='хеш таблица', color=ht_col)\n", + "ax2.axhline(y=ht_sorted_insert_average, color=ht_col, linewidth=1, \n", + " linestyle='--', alpha=0.5)\n", + "\n", + "# Дерево\n", + "ax2.scatter(iterations, bst_sorted_insert, label='дерево', color=bst_col)\n", + "ax2.axhline(y=bst_sorted_insert_average, color=bst_col, linewidth=1, \n", + " linestyle='--', alpha=0.5)\n", + "\n", + "ax2.legend(loc='best')\n", + "ax2.grid(True, alpha=0.3)\n", + "\n", + "# Общая настройка\n", + "plt.suptitle(f'Сравнение производительности вставки в структуры данных (N = {countUsers})', \n", + " fontsize=14)\n", + "plt.tight_layout()\n", + "plt.savefig('../img/insert.pdf',\n", + " format='pdf',\n", + " dpi=300,\n", + " bbox_inches='tight', \n", + " pad_inches=0.1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "7de42c9d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABWIAAAJKCAYAAACmkjw+AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAA8TVJREFUeJzs3QeYE1XbxvFnl97rgnQEEURRFLHRERErvojYXgGxvCoiiIiiCKJiRQF7Q+xYEPWzYUVBREXsgqA0adJ7ZzffdZ914iSbrWx2s8n/5xVDpmXOzGT2zDNnnpMUCAQCBgAAAAAAAACImuToLRoAAAAAAAAAIARiAQAAAAAAACDKCMQCAAAAAAAAQJQRiAUAAAAAAACAKCMQCwAAAAAAAABRRiAWAAAAAAAAAKKMQCwAAAAAAAAARBmBWAAAAAAAAACIMgKxAAAAAAAAABBlBGIBAAAAIEwgELDhw4fbmjVrCntVECP27Nlj119/ve3evbuwVwUAUEQVL+wVAAAUPevXr7exY8faV199ZTVr1rTy5ctbyZIlrW/fvta6dWs7//zzbdKkSYW9mgAA5NmIESPsuOOOsxo1alhaWpr17t3bdu7caVOmTHHDfv75Z/c30PPrr7/a0KFD7bPPPnOfO3fubPfee68ddthhhbL+06dPt6eeesqSkpJs/vz57u/z6NGjrVKlSiHTTZ061d544w075JBDbMmSJXbwwQfb1VdfnWF5+ru/bNkyq1Onjv3222920UUXWadOnSxWDBgwwNU/TjjhhAzjtm3b5vZnhQoVrEyZMm57jBw50ho2bBgy3YIFC+y+++6zJk2a2JYtW9z+vv32261s2bJuvOo6Kre2j7YtAAC5lRTQrV4AAHLoo48+sgsvvNAGDRpk1113nZUuXdoN37t3r91xxx22atUqd3HCnxcAQFH1ww8/2JNPPmmPPfZYhnEKPn7xxRd29tln2+uvv55hvOYrXry49evXzwrLd999ZxMnTrQHH3zQihUrZtu3b7cOHTpYamqqzZw5MxhYVDmGDBnibqyWKFHCDdN6Kyirlp8eBXBXrlxpjzzyiPusAGWbNm3soYcecu95tXbtWktJSdmvsm7cuNFuu+02GzdunE2bNs06duyYYZqTTjrJLr/8cjvnnHPcZwVizzjjDPv666+tatWqwZvMxxxzjAukN2jQwA17/vnn7dVXX7X33nsvZHkPPPCANWrUyM4666z9WncAQOIhNQEAIMc+//xzO/30090Fz8033xwMwoou4EaNGmXr1q0r1HUEAGB/DRw40N1sjEQBzf/97382efJke+uttzKMV4tSBekK0/33329jxoxxQVgpV66c+xv9448/ukCxR8FWBV69IKxX9ltvvdU2bdrkPuvvugKx/layalXap0+fkGBtXjz66KP7Nb9atUZqvev3/vvv2y+//GI9e/YMDmvatKkdeuihbht57rnnHjv66KODQVjRjedvvvnG3YT20/7Xd+/YsWO/1h8AkHgIxAIAckQXGxdccIE1a9bMrrzyykynGz9+vHsMEgCAokitRCtWrGgHHXRQptMoaFe3bl276qqrggHLWPJ///d/rhWon9IseDdVZfHixTZ79mw7/PDDQ6ZTKgXlQPVagb7zzju2b98+9/ff74gjjrBZs2bZihUr8rye+/v0jILLL730knXv3j3TadSitUWLFhnqJlp/f4tmTRe+LRTI1vYIb/mswPaJJ55or7zyyn6tPwAg8RCIBQDkiB5xVNoBtQ7JSr169TJcyABAQVFgR/k8E+279cg58mcbKbDXpUuXLKdRoFZpC/R3UY/2xxq1yNUj+37Kb+r3/fffu/cqVapkCD4qj6yCtN50lStXzhDIrFatmnv3potVWv/wMnrr/+eff7pAutIS/PXXX5lOF6mMygH88ssvR229AQDxiUAsACBH9Gifv0VNVtQ5iXz66acu35ouYjRMjzYqrcE111zjUhwoB1/4RfLjjz/ucq8p36xauHjf69GFrzoc0QXhDTfc4B6/1Ptpp53mOkqRrVu3uu9ThyLKg6fv1MWyOiNRCyHNq842Pvzww+ByP/jgA7v44ovt7rvvdo9kapm7du1y4/T4qZav+Xr16mWffPKJyy934403WnJysjVv3tyts4Iwetdn5QdUxyDz5s1zrY/0CKfm13L8LWv0yKM6OdO2ufPOO61///7Ztq5SByIq0wEHHOBy22md1bnIpZde6h4x9S6+c7v9vfXXY556XFPvzzzzTHD8ww8/bK1atXKPpH755ZcZ1ktl0cW79o+XR1CUV1CtqNX7uPaXyqvtIr///rvdcsst7sJfnaNovGg99FnDNX7u3Ln28ccfu/2m7aj1135ZvXq1K7+CItrfKufmzZsz3XbqYEaP0mq5egRV5dRL86l1lIZrvB5j9Wh/qdWbptE+HTZsmOv4xaNtpM5htF56pFfbQRf+1157rRumcU8//XRweu0fLU/HhFpzqcxe5z6aV/tH8x1//PE2YcIEN1zHr45l7UsFfZSTOae/mZxsiyOPPNJtB23LHj16uGXltKVaVuV59tln3WO+2oc6/nS8qnMcfc/gwYNdR3/qHEi/Je1vfdaxddNNN7nHfpXPUdtR+S2937Y6zlGHO3opJ6T/+NTvUb9xdaLkP170u9PxIj/99JMbpptGWifRMan9pHJr+2q88kyq1aOWceCBB+bquzVOjz3ruFRrQh37Ko86e9JvVHkx/XQcax51hqRtoXLrHOPRNtIytDytm/c495w5c1zLvPr167ttnJPHpGfMmGGXXXaZO9513tB+1/4PP/Zzcmz5j309qv3tt9+6gNUVV1wRcuzn5pwcfk7QOoYHULVNTz31VDefbg5qPh03KpeGKWiolpqiMmiY/nY98cQTOTqmde7MSd5TnYfOO+889zv1jvlYof0Qfo73Pus8Lup4S/xphjw6z3vj9Z7ZNP7lxKqcrH9Ot4Wfjinl21XrYQAAckyddQEAkJ3mzZsrKhOYN29erubbs2dPoHLlyoGWLVsG/v777+Dwt956K1C2bNnAN998Exx2//33B9q0aRPYt2+f+/zXX38FypUrF5gxY0bIMidMmODWJS0tLThs1KhRgVq1agW2bdsWHHbRRRe55fl9/PHHbt6FCxcGh7322muBdu3auXX1DBw4MNCvX7/g508//dTN98cff4Qsr06dOoFbbrklZJg+169fP2TYBx984ObXcjxffvll4NBDDw1s2bIlOGzs2LGBE088MZATKl/79u1DhnXu3Dlw5pln5mn7X3311YHrr78++Dk1NTVw1FFHBZ5//vngsKeffjpw1llnBS644IIM6/Pcc88FGjRo4PaPZ+3atYEmTZoEfvzxx+CwH374IZCSkhJYv359cFi9evUCN998c8jy9FnD/bTftB0/++yz4LCdO3e679X2yCktd+TIkSHDRowYkeH7xowZE+jRo4fbFp4333wzcMwxx7jvzeqY1G9Fw/zbQ/v6sMMOC4wePTo47PXXX3f7YunSpe6zvit8PpX3jDPOCKxZsyZk/XL6m8luW6jsnk2bNrlj5oEHHsh23uzKM3HixMC0adOC4zp06BCyn2699dbA4sWLg79Dlfumm24Kjtf21PY/4YQTArt37w4OHz58uDuG9u7dG7I+p556asgw73jx/+6effZZ99veunVrcFj//v1DPodv/759++b6u7/99lu3nN69e4cMHzRokPtNbNiwITise/fugaFDhwY/T548OVCjRo2QaeTdd991y/R+t1pux44dA6tXrw7khs4L/vNWpGM/t+djfxn17/BtmJtzcmbnBL/MzslDhgwJ1KxZM3henTt3buDss88OliM7mk/L9f8tCec/d+g3Wa1atUDjxo0DO3bscMN0zPuP+6z89NNP7ryf09e4cePcvsiLPn36BCpWrBhYuXKl+3z77be7snq/QT/9DTvppJPcv/U3SefYcIsWLXLz+3//uRV+Hs4rbW+tS6TtXqxYMVf2cM8884ybZ+bMme641r91zgqn33CJEiUifq/OBb/88ku+lAEAkBhoEQsAiCp1AKJWkmeeeabVrFkzOFytq5SzTb0Y++nRQOWiE7VYU6uy8Ef/1PJM/I9JtmzZ0rWwWrhwYch0apnq5332lqFWr2qFqtaw/s5Kzj//fJeOwWuJ500faXneOP/3+tdtw4YN7lFX/3JErcbU67Za1/m/V62x1AI0O+HfI2rlphZvud3+almnVn3+/L9avloAq1dsj1qpqdXZlClTXLn8rSLVCi28jGohWatWLbde/n2llp3+3HqZ7atI21b85VbrSrUKDJ82u20XTsv0D9cjq2oZrZab/uHqJVst/PS9ma2XWkh5HeL451ULTD0Cq1aPHrVsVstl77Fhb3rvXbke1eLvzTffjNi7eE5+M9ltC//21PGiFqD+4ygzOSmPWkWGf59H03m8sqllt0frpdaYat3o79RH0+h7/T2ZqwWpWvP6j6Pwbal9oo6H1IJRrW89Sqfi/5zVeub0u73yqLWtf7haeyqnpn4b/nLqePOoteeaNWtcq14/tezV+Uota9X6Va1Z1fq8Ro0alhs67/nXKfzYz8v52L+88PNsbs7JWU2b3fd6x2Tt2rXddlKaCJ2/1PO912lVdtT6WMeuzik5of2s1sz626N9m1s69gYNGpTjl1rda1/kllrp65yrl87JfpFav2uYf3hm02Q2LtbkdP1zsi38lMpAxwwAADmVee0GAAAfPU6s4KCCA+EddoTTY7fhAaNIHXidfPLJ7hHVRYsWuXx2CngpOKlHwXVho+CCHjPP7nFbjVfKAvWIrE41ckOPs2p91ZO0HmH17Ny50wU99P05vSDPjAJJQ4cOtRdffDE4TEEXpVJQgNL/vd7jrnv27Mn19yxdutQFSCMFA7Lb/gryiR499gcsFHBUqgU/fdajzC+88IILCnjz6XHqcFqugiLhZTz22GPdY9b7S8tXKgSv85n8pICFHokOL79omAJSSmMRiffovR4191NnMFpff9Cpbdu2Nm3atIjLUZBNj2brce1IgaS8/mayot7BdSyp473sZFce/Ts8L6WfjqPwAGj4sXrIIYe4wJOObQWiROkOlMPzqaeeCnbS89prr7ntEYmCKEp3oLQiSv8QKddjVvydHuX2u8PLo/QMSouh8njpDXQcK4XHgw8+6IL4mkYi7UcFXzW/vluPxUc6PrOj9CbejZPMROPYKgg63vTb1OP3On/pN6p0CDmleSLlCc3Kf//7X/ed2p/aJ7FGZdI6KpXEKaecEhzu/fa8YLuf0p94f/s0XWbTSHZ/IzWvUlxE+rs2ffp09zcynOoE6iA0P+Rm/bPbFuF0rGj7AgCQUwRiAQA50q1bNxds+/rrr619+/ZZTqugqHJpZsdrlaP8iLro0rLVAlOt65SHU4GnrFr26aJXQQv1cO3lFw1vVbV8+fKQXI7+VmeiwLLXAk2BST/lpgynC1kvSOIFNLLbFrow97d69X9vu3btXG5FPwU/csorn9Zj6tSpLo+kv0VhTre/tz6at1SpUtnOq1ax2t5eIFaB60gBNy1XZQwvU6Qyav/795U+Z0XBQuXjVO5NBSvzm1oDei1EI+UM1PdHohasCrCrR/Vwagmp4GROqPWmWigreKHfhH/beHL7m8mMt+11POnmhFp75iQfdHblyarXea/1bE6PVR2nfjpW1YJcuRu1HG2nzIIlyr+s3KwKdCrA7AV0c7qe4eNz892Zlcd/fCuoq5siulnToUOH4HdEohyWauWp4HF4OXJKgVW1es5KXs7HOZHdOTl8HTStzi/a1rrRdskll2R7jtLNQuX61f72OpTKjUg3rrKjm03e+ilY7n/CojCpVbDyN+tGRHhg02tZ6z354adh3jlM00W62eXNF+lc56dWy7oZGYkCn/q7FU1a/8zK6K2/t8+z2xbhikJrYABAbCEQCwDIET26r1YruhDP7ILKk9MLULUAE7WY1IW2Hvk+44wzXMc/kXgtZz1eEEKPj6vzFj1q/9Zbb4W02NXFkz9YoYtJf0dS+m7/uoRfwIYHdhXMUevgnAQf1DGKAjR6rDn80cWsvlfUEjMnj9L6y6fg93XXXeda8qqlYnhLw6y2v399wgM0kdZFARoFYb/66qtgRziRaLk5LaOW4d9X6rRMLUEzu3hX5z9qZRUt3uPeuggPD6SrZWCkx8EVLFInOerUKbNlZtcZm0cBfHVgpNbl2qdqyea/WZCX30xm/Ntej63rxos6K/I6s8pMbsqzP3QMhQdD1SJUN0V0c0QBMK91aiQDBgxw6Tl0w0LHrYKYeiQ8r3Lz3ZmVRx1XeWlBdDNGLcy9IKyf1/me10pTgR91cqXfu85HuhnhTzuSE+pUS7/hzOzP+TirG1k5OSdndlwqpcSJJ57oyh6esiGcUkfouNRNIP2GlO4lp6lL9Fv3tnluqMO0u+66y3VyqE7echpc1NMYuomWUzrf6tygltk5oSckNL1/f+v79Bv3Ou3StvXTOUCtPI866ij3WdNpH6llqP/vu3cDz5suVmn9IwX8tf4K0lavXt191jYN3xbedJmVUSl6wv8+AACQFXLEAgByREE9tdbS4/T+HuDDKZejd3HnF6lX4Q8++MBdaOviRz24q8WbHssPb3HnUaAiMwoYKMVAbnP0KX+lAgNqVRuphZM/D2pu6EJWOWavueaaiOMVzNUj3ZG+9+2333bbIy+Uj1Itl5977rlcbX+lddAFfqT1UWvCcGr9p9ZVOhbUC31mKSF08a9AU3grI+0rtQLMKwVgFVDLSevdvFLOT1GgK1LwJFIqBgVgbr755kyXqe2sIJj3SKw/gKuWtH5e6gYFTBRIVKs2L/Ah+/ubyYxaXCp3sfZ7dsdhbsqTU+HHqs45akV57rnnhgxXC2wF2ZTvVcFvPa6fGW9bKuCp7anWrAo25lVuvju8PDr2v/vuu2B5lMZBwVX/fvTvQ/2+/MegWvQqR6weuVcwWTfJcmvx4sXBQHAk0Tq29oeCZQo8f/bZZ1kel9qWaiWv7aPzk27m6KZNTul8qH2Wl+NDQWvdwFDgN6eUM1utjnP60o3HnAZhlZtcT4uEB91nzpwZ8ndI5+jwm4g6D+jvquhd51rlmQ2fTn/vc3KzpzCp/L/88kuG85TW379tvL9XfppHx1tmNy4UtPffnAUAIDsEYgEAOaY8iWpxqgtBtfwJDzDocWYFGLp27ZphXgVl/I/xK/+mWlQpkCG6sFRwb8GCBcFpFKBQa0AF8TSv12pFLSnD6SJLF0T+Vitq0Rr+2KCG+d8VUFGrNuVr1Lr7W6xt27YtmIbAmz7S8rxx/mFqTaSAnPe4Y/j3ihfEVODVo/m0HjlprRdpOyjfnlp+6eI+N9tfgVQFLtTq2d8aTEEP/7L+/vtv9/IenVZuTH+uV62Tv4wKjDdu3DhDgFyPDCtnoX+b5XTbilqHHnzwwVlOm5VI0+uC2z9Mx5IeydY28W9rBbmVrkDBnvD1UqBPAQz/MP8yNY9aNoa3mFVHad4j/t70/nyKyjmrQIi2mbcuufnN5OU40rzZdQqUk/KEb+NIORj9/AE+bQu1wFeL4EiP6muYgr5qjRhJpG2pGwBKK6HOnCLxgjXZrWd23+3RDSz/sa3fgjq98lpMejle/ftRrT51fIfvR51/9btVK3udW5599ln3G81pWgDvvBOehkXr5z8OcnNsRTo3Rjr2c3pOzuq41Dqolb1aR0fat1q2jhelz9FxqRbbOhbVelgB85zQ+Uz7dN68eRHHq2WlAtmRaJ9o+0bzBlFOKReyzhu6kaE0DXrpiQndhPCn11GKGZ3T/IFntX5VKgMv17vSO+i49bdcVq5g/VajkRYmL7zfbXiw1Us9pLzk/nOLgqsK0vs7GlTdRvvdv++1bTRv+E0J0Y0xbQcFuwEAyClSEwAAckUXI7pIUYtEBVx1UawLVz1i7rXci0QXQrqI03RqEaZgngKO3mPwuuBTIFc9iSsAqkf9lEdRvV0rCKOAjy4ElRtRwQdRoFPTqeWWWuLqMWpdSClIoFZj6nRIQQPNe/nll7te4NVKVXTxpVZlKo8CzGrBpBaAatmi8qhzF+/R2kmTJgXn08WpWibqQl3roRZiCmoq+KagnQLU+qyLQeWHVatJTeO1ItaF8apVq+zCCy90wVZdLGv91ImQAhu6kM8u9YMei9f2V/lE36mghAI02jcK4qhVVm62vyhYoQCHWtUqMKu8fk2bNg3mFVRrT+U+1PZQ0PY///mPe/RXraV0UasLVpVVLYl1Ua9Al/aPtruWreVq+ypwon+rlbV/PgV1lXtV+0bbSekVNFzbQ60PFfTy9r2CTwqyKODy6KOPulZMChopIKhjMLOOiMK/T/tZ3+ftN//3KdWFjgkdg8r7qH2uILW2ofKoerljtZ+940PLVktaTeMFuf3bQ8EhtUbTcaRjQ2k0FDzSNlZwRKkevNbMyhGrYKBa2alHdgWm9Ei218maWnPn5DeTGd280HrrpoNaR+sYVktu3RzQPlLQK7sO1bIrj0e/Wx2fCoYp+KHfrrZvpI6N1IGX9qNukmgddYxdf/31EVN1qCMvPc6v31M4tQz3jhe1iNQjx1qv+fPnu5sVKrvOFdp+CozqcXZtc7WSE51DtG3UMZe2dW6+269jx46uPArOKYinbabH8b2csvpt6hjSNtS5SPtc8+g3oP2n4LuOPX2Pfnv6jeq8pvm1PTW9to+OEf2uIj2RIDrHaXvoxo/OVdq2/hs1KqvOefo9K3VDTo4t/7Gv41TnVPGOYe/Y1+8pp+dkBYG941LroONSy1Bnkfq96iaBhj/xxBNuPpVdLWW1HXSDR+O1n70WjPpN6Xys9VZuap0fwjuTDKcUCJov/Maegph6akB/c5RuRstSq3A//QZ0fizsYKxatet8pkBsOP9NMP2+tN2UAkIBRQWZVe7wzud0fOp3rOH6u6/l6u9Qp06drDDpPKuWutrvomNYfxf0O9B526MO8XTc6aW/MzqedD7136xSwFl/W/T3QL8z3RjV31vdAIlEx4jODYW9rwEARUtSgAzjAIAoU/BNAYLMephHdLH9URQoOKmgjgJBOX3UV8E3BXV1k6CgZffdXodYCmgrsLo/VF3XK6d5TiNRsFLBJXUA5+Wb9VNwUcFNtRb2biIUJQqU6kZWXjraCqcAnW7wKHiH6FKw3/90QW7pyRzdpNFL+95r3a1XtAOkCsTrxpF+WwAA5BSpCQAAAFBkqBWmcsaKOulTp1Lx/t0KMO1PEFY0v1rCRgrCijr8Usv1nHa2GGtUvvwIwopaQas1qYLSiK7MnqLJKQVb9fSGt+/1rs/RDsKqpaxuHkVq1Q8AQFYIxAIAok6PhUbK24aCwfZHUZBVjkePUgnoEfh3333XpU9QSpLjjz++QNYvt9+dk/IUJD1OnpNApVIQJDptJz2Gr/ypiC5/CpOiRDdllHJE6TIAAMgNArEAgKhR7kjlz1OuPz3qyuN7BYvtj6JCOVzVmZAoB7OX2zWcctbqUeZNmza5PLnKCV1QcvPd6gBQ6UBEHSQpt3Jh8+fLzIq33olOHTQpyKZcw4Cf8kgr33NBtsYHAMQPcsQCAAAAQIS8s8oDrA6g1DkgoJy0OibUOSiddAEA8oJALAAAAAAAAABEGakJAAAAAAAAACDKCMQCAAAAAAAAQJQRiAUAAAAAAACAKCMQCwAAAAAAAABRRiAWAAAAAAAAAKKMQCwAAAAAAAAARBmBWAAAAAAAAACIMgKxAAAAAAAAABBlBGIBAAAAAAAAIMoIxAIAAAAAAABAlBGIBQAAAAAAAIAoIxALAAAAAAAAAFFGIBYAAAAAAAAAooxALAAAAAAAAABEGYFYAAAAAAAAAIgyArEAAAAAAAAAEGUEYgEAAAAAAAAgygjEAgAAAAAAAECUEYgFAAAAAAAAgCgjEAsAAAAAAAAAUUYgFgAAAAAAAACijEAsAAAAAAAAAEQZgVgAAAAAAAAAiDICsQAAAAAAAAAQZQRiAQAAAAAAACDKCMQCAAAAAAAAQJQRiAUAAAAAAACAKCMQCwAAAAAAAABRRiAWAAAAAAAAAKKseLS/AEDh+fnnn23YsGG2YMEC+/PPP92w1q1bW+3atTNMu2PHDvv0008tLS3Nqlevbq1atbLzzjvP+vbtWwhrDgAAgGijrggAQMFKCgQCgQL+TgAFTBXncuXKuX/v2bPHSpQoEXG6tm3b2syZM+29996zU089tYDXEkXB4sWL7Z133rHLLrvMypQpY9u3b7ennnrKevToYfXr1y/s1QMAAHlAXREAgIJBagIgAZQtWzb478wq1lK8ePEM0wN+1113nQ0cONAmTJjgPj/99NN27bXX2uDBgwt71QAAQB5RVwQAoGAQiAUA5FjLli3dxZdaxMgxxxzjWsYefvjhhb1qAAAAAADENHLEAkARpwwzSUlJBfJdw4cPtypVqtjtt99uu3btct89ZswYu/LKK60oKMhtlQjYngAAAPGLul7+YVvCQ4tYADn+w6HH0Xv37m0jRoywG264wS6//HKbO3ducBrlCx01apQ1b97c/ZE54YQT7L777nPjHnzwQevcubMbfuihh7rptm7dGpx3+fLldskll1ivXr3cY+4jR460J5980gX75LHHHrNu3bq5+bX8W2+9NTj/Lbfc4oZXrVrVBQT37t2bbXkUSFRrTm8977jjDrvtttvsnHPOsf/+97/2119/5Wr77Ny505XptNNOs0GDBtlNN91k48ePd+Xy+/jjj61///4umHnzzTe779V8tWrVcuuibaAcrOo8Q2UpVqyYG65/r1y50i1D5T755JOD6z5jxgz78ssv7ZprrnGPE5YsWdKtwxdffGFLlixx66JONzT9+eefby+99FJwfRYtWmSPP/643XnnnW6farlPPPFEyDr796vWZ9KkSXbccce5/HAnnnii+5ycnByyXzVMHXjoOytXruyWPW/ePJs1a5bbv6VKlXIvpTpQrjmNu/HGG9120TyaV8sIN3v2bDePN7+Wpfm1Da666io3TPO3a9cueBwMHTrUDatZs6bb9qtWrcrRPlU5FHhu2rSpOy60r/Q6/vjj3fK0DVRe/3ZSvtx7773XHZNnnXWWXX/99W64n/9Yrlu3rvsO7dsPPvjA/ve//wWPZW2P3377LbjNtF81Tr87Tat59D316tUL2bfhv0Otr/ZvuDVr1oTMr22u+XXs67u9Y7JBgwb2+++/u3n+7//+zz2WWrp0afc70boBACDUFbO2bds2tx76fq27yjBkyBDbsGFDcJrPP//c1bUuuOACt/5eXapZs2Zueyg9lMqgcX4vvPCCK7vqOdru2qbffPNNyDQq8/333++2ob5fdZSrr77ali1bFpxm3bp1rn7TsGFDK1++vPv7r3qU1vXiiy+2Tp062Ztvvhmy3I4dO9rBBx/sptN+r1OnjlvnK664wn2P6ope8MlfP/TqOn/88Ycrt1JdqT7prx+qHqTO49QPgeolmlbT6XXhhRe6euv06dMjbm/VDS+66CLXGZ22u9ZfdVc/1Xt69uzp1kfrrf2r/ax179q1a8Rlv/766zZ27Fg3bZ8+fdy+Un3az1+nU/1N66s6U3h9V8fC+++/n6FOp3LpeJa77rorWPfUcTh69OgMx7vm8+qT4XQsePPrXeUTHU9HHXWUG66n27z9qk77vOsGrYeOrZzSNYy28wEHHOCOUX2XyqtjWttBx6+uWTz690MPPeTKpOuYU045xaZNmxayzPBtdu6557rjSOcSfYfOFRqunNWPPvpohm12yCGHuONQ0/uPPx1T2uY6r/j3l3d9EamOq32v+ra3zb35tY1U79dwvfQblNTU1OB66OlB7SPAUWddAOKffu7Z/eQ7dOjgppk2bVqGcX369AlcfPHFgX379gWHLV68ONCoUaPAJ598EjLtk08+6Zbz8ccfhwx/9dVX3fCnnnoqZPi8efMCNWvWDIwfPz44bPny5YHatWsH+vfvHxy2YMECN7+W7zdw4MDARRddFFi3bl0gNz766CO3vAkTJgSHpaamBk444YRAw4YNAzt27MjRcrZv3x44/vjjA7169Qrs2bMnZHseeuihEecZNmxYyOcLL7zQlTfcZZddFihWrFhgw4YNIcP/+usv951aX7/jjjvODY/0fSrr3r17g8O0L+vWreu+27N69Wq3L2644YYMy8hsvz733HMR96u2hYZfcMEFGZalbdymTZsMwzWt5vFvx0g0/zHHHJNh+Msvv+zmf/jhh4PDfv3118AhhxwS+PvvvwN58cQTTwT++OOP4GcdL/oOHT9+5513XuDAAw8M7N69233Wtj7ppJPcuvp/N/5j+aabbsrwfToO/PvEX+ZIx4iWEb5v/fsrfD0zmz/8eP/9998DFSpUCPTs2TM4LC0tLdC4cePAzJkzs1wmAKDooa4Yvbrixo0bAy1btgw888wzIcPffffdQJMmTVxZRNv1iiuuCI5fuHCh+/6bb745OOyVV14JjBw5Mvj5mmuuyVDPGT58eKBs2bLB5apuojrJiBEjQr7/u+++CzRo0CDw888/hwy/8cYb3fdqn27ZsiU4/JtvvgmUKVMmcMcddwSHnXjiicHvEW1nf71EdYfmzZtnqB+G13V27twZqFq1aob6obb3+eefHyhZsmRg8ODBwXqWvPXWW4HixYu7uqjf008/HWjVqlVg/fr1wWHaVx07dgzcddddIdN666P1Dt8GpUqVCvzwww/BYY8//nigXLlygR9//DE47NZbbw1Uq1YtZBt4dHzqWIlU361Tp06mdTIdx5GOw/DfS1b1yUjzv//++xmuYXRcal382/Wcc84J+a3l1pAhQ0I+16tXL8N2UP1c21fb1KPjX/tTv4ucXiN45xJ/Xd1f5vBzgbe//b+p7PZXpPmHDh2aYdzll1/uxqlsHl2TnHvuudle2yCx0CIWQLbUQlJ3EHX3V3c0Pbpbrju5apngv5vvdfLgdeggGu/dpfQPV71fdyYPOuggdyfUozuIajng5y3Xe09LS3N3HBs3bmzPP/+8VatWLVfl8paju+8e/Vt33tWSNPzudmZ0p/2nn35yrTL8HVxs2rQp03l0t9VP2yRS5xi6c6xt8eyzz4YM1x163d31r7u3XN1xDucN8297teL1ekr21KhRw7WUULqBzLa/fxlr16515Q4f7p8+Urk0LHz67OYJn05378PpLvWll17q7jh7LVXGjRtnU6dOdS1i80K9R/u3qbfNw9dR21PT7tu3z31W+dTa5KuvvnItofNj22Q2vfd9Of2OSNOFb0+1BFZri8mTJ7uWsKJWC9qeapEBAICHumLWtA1UR1NrQT89SaXWpGph61ErR/93hW8P/3i1YlRLYr389Ry1BNZ+UL1EVGdU61K9+7Vq1cpOP/10O/vss0NaCasOINp2FSpUCA5XC2G1clZLQK9lY4sWLVxr0szWWa0B1bo0u3qLWk9qf4UP1/K0jVQWTeOvk3Xv3t1tw8suuyy4L3799Ve3vTWtWt16VM9R61A9KaYWuOHrE16nPvbYY2337t2uZa5Hx5uOR2+7ettIdX7VkXJTd8tNPThadT31+/DKK6+49VerU+/psyZNmoT81nIr/PF7bdvwddy4caMb5rVo91pX6xhTa/RobZvMpvfG5XVbip6GPOyww6xfv37uekDbVS1r1WI2u+UisRCIBZAtBeX0iFelSpUyjGvTpo2rOE+cODHT+VVhUYVNQcVwqggpiKnHSfz0uIj+eD388MMRl6mKkB4BOfroo23AgAGWX/T43GuvveYe1VKZs6PgmwKR2g7h2+fHH390lcH9ocdtVClRQCy9sUq6Tz/9NKRS66/45ORxO9EjZwpWTpkyJWR4o0aN3MWNHk/LitZH6RX0+FgsUSVIj1TpUbEHHnjAHSc6nvJKjzJpW2Xnrbfeco/3+XuS1raUv//+24oiPfanx+5UodTFntIZ6IINAAA/6oqZU93g1VdfdY8oR6LtozLOmTPHpXPKrs5SsWJFF+AW3RxVuiClDPDT8M2bN9uBBx7ogokKEirAFR5s9L5fj/x7N139IgWbdMNe+8sLmuekw9bsptFN3yOPPDLi8ZOT9VFg9Omnnw7WA1UXjrS9FXhWQFwpGrKi41XbUKmuVJ/0qM6r40qBeI+2v9ImFNW6noLu+g2pvLqZond/6q1oUce/W7ZsyfCbV925qG5LHQv6rev6T41ClHrk7rvvJgiLDOisC0C2FRHlCurSpUvE8SkpKe7922+/zXQZqhApoOO/e+z5/vvv3btyZYbLLJm5WiCoRYFahWoa5eraH8oXpT/4CjKp5aJyfPpbJmRXGded3Ejrn1+U+1QtST766COXq0kB3iOOOCLi9lErCeUDVRC1evXqbpjWz5//y0/LULnffvttW7p0qWsp8sMPP+RovVSJ7du3b8T96vfLL7+4SoifAsBZXWh40+v4W79+vbVv394dQ5HuXofz7u7rgkMXK8rztD90x155qXJC21q5vrSP9N3+u/yRqDVJ+LbRhVNmNC58en+urUhefvll+/rrr11AecWKFS5Xli5a/K1EsvLII4+4u/maJ6f5dQEAiYO6YtbUwlCBS69eltX2yUnnpyqP6l/etlEQ0N8K2T+dqCWs6gA5+X61jM2OWiaL+jOQ8Fa+kWQ1zcKFC10QWnk983JzP3x9lBtX9cVIQV1tE9V1Ix2LXn1VgdZPPvnE1T1Vp45U91TgXMeWxqm+53+6LJzqvOF1N31XVtQAw1/3zK7ltVef1O9HOUtVRh0jXsvm7GhalVlBZ2/77U+9Oad1TO0P5YBV3VlBWa23ziVZCd+WOnZy8tvNKW9/qXW25lPLVv0ulOs4J3RzRucztdK+5557onqNiKKLQCyALHmPWeuPUSS6y+6fLpwCOJpXjzL7HwPyqOVlVsuP5LPPPnN37T/88EP3qJru3KpzqrzSI01ehVbroYCT7szrETZVrrKSl/UXf+vW7PznP/9xLTwVEFMgVmkKMrtTrcej9HiYHj1TonxVcHR3NrNgqZLjq9WoWrJoHt2x1fKzS8yvx7S8TgMi7Vc/PbLmPe7kUaqArPinV1BTnXfoDvO7774bsTVHOG0vtb7QMaLjRfPnlfZxTno41bop0b/SI+guuFq16EJQHVdk1RogfNuo87TM6KIifHoFe9VCOjOqVKtVtZdKQi1a1fJBFyE5SdegwLaC2uqgQBdJ4R2EAAASG3XFilHdPlnRtsluu+T393t12Pzo/V3frWCn6rh5Fb4+KkdW9Wx9Z6Rt4a+vKq3BM8884zqz0g1tPW4uCxYscPUqBRpVX/OefFI9OjNqeBBed1MnaFnVnxWQ91o9i6ZVh7A5qU+qNbBaa6vRhurbXh0wO6o363ejlGu62ZCT+nZmjVR00z87CngrWKn6qLZ1hw4dgjcXsrrxH74tdd2i32JOfrselTGn++u5555zN5l0nGY1X3gwVp156RpLT5blNT0a4hepCQBkSXfJ9ccjszuJahngVV4i3RHVHy/1FJrdo0oKWEUSqXWgHpNWkEu9ZuoPuIKPXmuJ/aVKh/ISqYWoep7NjnqxVfAys/VXZc+r4PorzbkJxOqutPJx6Y6ugpKqaGb16JZaaKhCq0f8vMdilBstnFIqaLxatqqlgvfYjH/d1MrFn9PNG6YLj/1taZpTLVu2dBVK9Wga6bG5cFp/HXN6lF65w7Q9FIDMCwU5I+XcjVTpVMD8pJNOcpVxHZ/euvhld5e/IH7PCqbqbr/ec0J389UDtlr/6MIks96JAQCJibpi1rxy52X7ZEfbRkGr8Lqmf9uoVaTqePn1/QpGioKU+0sNCxT0UqOBvApfH5VDde1IdT8FKZXOIidl1TGklq5KiaH6nLaxGkQogPjOO+8Eg7Dh9b3CrutpX+vGg9YppzcfVLdTujXVs2fMmOGCjnmlZSndRXZ0bfPGG2+4YLEXhI21bSkKpCpFha6Zwq+JItFxp7QdumZTKo2LLrooV9d9SAwEYgFkSUE/BcF05zZSBU4t8dRiTi0DwikgpYpqVnfMdYdRCfiVXzMSfXdW9OiH5ldFWx0T5Acv/5Qe1c+OWkHoD6we/V69enWG8bfddpsbrj/GXhmV50wB3NxQZUWPnSmwmJNHwHJClUhR2gM/f7n1mFf441MK3Coglx8tIXLKa23iXSxkRQFGPd6oVrETJkxwgVRVovJSCXrppZdcgDU7ChKrBUZW29JbXmHLzbb84osvXMVcAWZ12qBWBWoJonQRAAAIdcWsqXWgHmvOrAWkto9aL+pR+NxSIFhPPakeEk4BLt3E18173VBVXTVSwFbfr/QGPXv2zDAu0t97tQRVnTRSPt/c0PZQQFQdQ+VUZuujXP6qK/uPh0jbW/Ua1ddy2hGV9rOOaW1j5f3UzYAzzzwzpNNdBXf9LThjoa6nwLbWMSd1PU2jVAgKNCqfsh7L19NP/k7KckrbQtsrszQY4dchamwRvv/9v6lY2JZe3VmtqLPrP0PT6KaSGi+o1bRSpek4VKMGwI9ALJAA/HmLFFTJjPJHhU8vulN9xhlnuMdk/I+4K0inXnL1GLsev/B4nUWpgublnfIP93cmpdae6vBA+YzCe3JVi01/YMubz1tPr4KkyoPumKqSmZvH1jLr1Eo9/nq5WXNC06tTLT324t92ylmku9J6xEWtU5VfSNtf20t31MPXJatOtlRBVkBQy8pJpwjhvP3m/w51iiCqmHtUeVJrSa+yqwqH9ziNN68quNntV//nSGkRNCxSeb1h/v2o1hw6FlR27yJB0/mPA48e21KOK2/7Kr+WehLWBYoqRbmh4Lny5apS6uetm79cqkiGb0utoy6AtO8y25a53TaZTe9fpn96//p6/1YgXZVzrxVPpN+Vl9NOF63qkM2jwLbu9Cuwndt0HACA2EVdMbp1RT1JpGXdd999GR6rXrx4sStfpEfBve2c2T5RYwAFuNXyUcvxqOWeUgp520YtJBXw0lNS/hvTqjOpjqRHuyN1TKpAuT/fvYJKKouCn5m1iM1unb1tqse3lc4pN/Vh3Wz3UlWI0gaoBafSVyk1l6j1oup8N910U8iNAbW+Hjp0qMtD26NHjwzrE06P5yv4qu2repM6PlMra+VQ9W9D1TOPPfbYYJDYvx+zqrtlVQ8Onyc3dT3R8a6bDt7Ta5nV9dRY5JRTTnGptbw8wwokql6rfZObJ8q0X3QcqgV6OK1feJl0HaLfpD+4Pm3aNCtXrpzLF6t1Dt+Wedk2OZ3eGxa+LXU9oGNBqb10QyezbanlquzKWazGIKLjQtdNug7Mrk8HJJgAgLj1888/B7p37x5o1qyZagvupX9r2HfffeemSU1NDfznP/8JnHDCCcFpatSoETjttNMCzz33XHBZmu6RRx4JnH322YEBAwYELrnkksB5550X+P7774PTbN26NTBs2LDg93Xu3Dlw1113uXF33nln4Oijjw6ug6bbvHlzcN4lS5YE+vTpE+jSpUvgqquuClxzzTWBKVOmBMePHz8+cNJJJ7n5mzRpErjhhhuC8+vf3rq3a9cu8O6772a7bW6++eZAq1at3Dxt27Z1n4cMGRI45ZRTAm3atAn83//9X6629bZt2wLDhw9333/ppZcGrr322sB9990X2Ldvnxv/8ccfB0488UQ37uWXXw7O99NPP7lpK1Wq5NalX79+gbfeeivid0yYMCEwadKkXK3XwoULA9ddd13ggAMOcMvX/ps4caIbt3fvXrdfjj322MDgwYMDI0eOdJ93794duPrqqwOHHXZYYPTo0Rn2a6dOnYL79bbbbgtuR/9+1bGj79LwihUrBgYNGhT45ZdfAjNmzHD7t3jx4oESJUq47/niiy/csap9rmk1zxVXXOH2Sf/+/QPt27cPDBw4MPD3338HZs6c6cZp3mLFigUuu+yywLRp0wKvvvpqoEOHDm7esmXLuuk8p556qhuelJQUOP/88zPdvp6vvvrK7acyZcoEevXq5dbD//LK27Jly8D1118fWLNmjZtv8uTJ7tjRvNqW2hYrVqwIvPjii4H69eu7cmvb+I/l2rVru2UsW7bMHXP6DWh45cqV3T7R8aFtpu2kMmtc37593bSaR8ds+L7V/tIyDz74YDdcx7TWW/tAv+uePXsGfvzxx8CqVavcd9SqVctNp3FPP/104IcffnDl1j7S8LFjxwa3zb333hvyW7v//vtzdTwCAGILdcWCqytu2rQpMHTo0MCFF17oto/eVc/R3+NwH374oZvW+/7q1au7v+P+v8l+zzzzjCuXlqnpVN7169eHTLNr1y5Xr9PfeH2/tqXqFH/++WeG5ak+oe9VHUTL0r64/PLLA2eddVZg1qxZGaZX3VH1YNXLSpUq5ebt1q2b22aqV3j89cOaNWsGbrrppsC8efMCH330kVsXDffXDz2qV2ncb7/95uq1Wp+LL77YHV9z586NuE20TNV5rrzySne8aN1ff/31DNtNw7XsevXqufXVsi+44AJXz/PX5UX1p9NPPz3Qo0ePwC233OKmVZ1Tv6MDDzzQbX+VV8eG6vReefT92paqC2v/VKhQIaTerzqd9nedOnWCv4tx48a57xwxYkTwODjmmGPc94rqYKqTa3jz5s3duqv+p3VQ3fnNN9900916663B+Y844ojAjTfe6H4Xqq9Wq1bNDdfv3aNrFq8+3qhRI7dM/+8wEu0f/XZVJw2vN+t3mpyc7OqVOj6866C1a9e6Y6pjx47uOFC5dHzoHKHfm66bXnvtNbfNVF/1rpV0LvKmU1n0W9dwnRceeOCB4PpoW2m46sOaTtNrPm9/a1trmy9dutTtg969ewevWzS91knrq+skLVfH+AsvvBCcX+dA7UvNP2rUqOA5rWHDhoENGza49dB+9erj2uf6vatuDyTpf4UdDAYAZE93q9XJkpfLFf/y7l7ntWMBPz2OpY5D9Cij7mpH6jlW+cXU+lUtf1588cUc5cIqKlQt0PaM1AMzAACIf2qpq1RYamXr7zSqsKhupnyyhC7yj1qw5kddT8tRa2o9laZWo14/CX5KB6En7lS3Vr8KamEaT7QNdA1SkGnbULSRmgAAYtCyZctcL7+qrIgqwno0nyBsZKr85EcQ1ntMSxVF5fGNFIQVVTK7devmek+NlBu4KFMlkiAsAABA/Mqvup5Sb3md40YKworq0+rcTCkyMqtbF/VtSRAWuUEgFgBikPIRKbfXH3/84VonKkfXgAEDCnu1EoLu2ue0M7UTTjgh7gKxAAAgsWWX5zXR1wf/Uj04p0+GKVipgCyQ6AjEAkAMUuJ8dWChDgGU+F6J873e7hFd6tk4p1SZVEtlAACAok6dfKl1o1IBiP6tNEyF5ffff3ePu6tzMDn++OOD64bYoI7M2rZtm+PpO3bsGNX1AYoCcsQCAAAAAAAAQJTRIhYAAAAAAAAAooxALAAAAAAAAABEWfx1WVdEqPOdlStXWoUKFehhDwAAIAJl0Nq6davVrl3bkpNpP1AYqLMCAADkX52VQGwhUYW2Xr16hb0aAAAAMW/ZsmVWt27dwl6NhESdFQAAIP/qrARiC4laFXg7Kdo9oaslw9q1ay0lJSXuW5MkUlkTrbyJVNZEKy9ljV+JVF7KGh1btmxxQUCv3oSCR501OhKprIlWXsoavxKpvIlU1kQrL2Ut/DorgdhC4j3apQptQVRqd+3a5b4nEX5oiVLWRCtvIpU10cpLWeNXIpWXskYXj8QXHuqs0ZFIZU208lLW+JVI5U2ksiZaeSlr4ddZ43urAwAAAAAAAEAMIBALAAAAAAAAAFFGIBYAAAAAAAAAooxALAAAAAAAAABEGZ11FRGpqam2d+/ePCco1rxKUpwIyZgTpaxFobzFixe3YsWK0ckKAAAAACSw/YlpJPp1dX6irIUf0yAQG+MCgYD9/ffftmnTpv1ahg7ArVu3xn1ALJHKWlTKq5NWjRo1rFKlSjG7jgAAAACA2IxpRFtRuK7OL5S18GMaBGJjnHfC0k4vW7Zsnna6Dr59+/a5SH4i/NASpayxXl5v3bZs2WKrVq2ynTt3Wq1atQp7tQAAAAAARSimkcjX1fmNshZ+TINAbIw33fdOWNWqVcvzcvihxa+iUN4KFSpYqVKlbN26de5Y1t0kAAAAAEB8y6+YRrQVhevq/EJZCz+mEd8JIYo4L3+K7hoBRVm5cuXcSTBWcwIBAAAAAPIXMQ3Ei3L5GNMgEFsExPtdCsQ/jmEAAAAASExcD6KoS8rHY5hALAAAAAAAAABEGYFYAAAAAAAAAIgyOusCAADYD6mpZtOnm61da5aSYta+vRn9EgIAAMQ+5f3cumer7du7z4qnFbcKJSuQSgFRRSAWAAAgj6ZMMRs40GzlSrNWrczmzDGrXdts/HizHj0Ke+0AAIiO1LRUm750uq1dvdZSdqZY+wbtrVgydyFRtGzcudGWbVlme1L3WLli5Wx76nYrWayk1atYz6qUqWLxhqBzbCA1AZCPVq9ebTVq1LCff/7ZfT7uuOPswQcfLOzVAgBEKQjbs6fZ8uWhw1esSB+u8QAAxJsp86ZYw/ENrcvzXWzMrDHuXZ81HChKQdiFGxe6IKyfPmu4xscTleeXNb/YgvULbPX21e5dn8PLSUwj+gjEJugjlJ9/bjZpUvq7PiN/7Ny507Zs2WJ79qSfzPVvvQAA8UV/O9USNhDIOM4bNmgQf2MBAPFFwdaer/W05VtC70Ku2LLCDScYi6IQ01DLULWEzYrGa7pECzoT04g+ArEJRq1zGjY069TJ7IIL0t/1mVY7+aNhw4b2f//3f/bmm2/aoEGD7OKLL7ahQ4cW9moBAPLZjBkZW8L6qd6+bFn6dAAAxEs6goFTB1rAMganvGGDpg5y0wGxHNPYtmdbhqBkOI3XdEVdboPOxDSij0BsAomlRyjT0tLs1ltvtf79+9vYsWPtgQcesL/++ss2b95s9957r5UoUcIuv/xymzBhgt122212/vnn25o1a4Lzq5l8v3797KGHHrJnnnnGhgwZ4nKb3HzzzbZ8+XLr3bu3jR8/3l544QV77LHHrG/fvnbPPff8U94VNnLkSDf91VdfbfPmzbNPP/3UunbtatWqVbNHHnnEdu/e7d6rVq3qhmu8X1bT33///da2bVu75ppr3DqXKlXKlVXrlZXJkyfbRRddZPfdd5+NGzfOvvzySzd84sSJduCBB1qHDh3s6aefdi+VRet/1VVX2W+//WbTpk1z61O9enV79NFHbceOHa7cVapUsZNPPjlk/TX+kksucdtc3/PLL78Ex82ePdttyyeffNLtB21Dj/cdKuPDDz/s7pAtWrTIrYfWzVtfAEgEq1bl73QAAMS6GX/NyNASNjwYq4COpgNiOaaRXRA2J9NFO6ah2EC+xDSqVbXLe11u3874NmT99fnq86+2Lod2sRcnvGjrt66Py5jGQw89FHsxjQAKxebNm3W7wb1nZufOnYG5c+e69/2RlpYW2LlzT6Bu3bRAehudjK+kpECgXr1AYN++QIEYNGhQoH///sHPffv2DZx77rnBz/Xr1w9MmzYt+Pnkk08OXHTRRe7fu3fvDtSoUSPw1FNPBccvWrTIbc/U1NTAnj17AgMGDAjs8xVG27BChQqBjz/+OLhNNP3ixYuD00ycODHQoUOHkPVs3769Gx7JM888E2jXrl2W00+ZMsV9T3befPPNwNFHHx3YtWuX+zxhwoRA7dq1g+N79+4dGDlyZMg8Wq7KrfKqPFof//qvXbs2ULVq1ZD1GTduXOCMM85w20m0zOOOO879+5dffnHroOV5br311sAtt9wSUmaV0fPoo48GnnjiiWzLlx/HstZ51apVwXWPd4lUXsoav+K5vPoT5f87mpycGmjdepV79w/3/SmLGwW5X3NSX0L87IN4PmckclkTrbzxXNaXf345YLda8JV8a3Kg9QOt3bt/uKaLR/G8b6NV1vyKaYgu7+vWDeRLTGPLri2B2Stmh7zm/j03wzBNV9AxDV3byzXXXJMvMY3j2x4fGPHAiIhlveWBWwJHHnuk+/e67esKJaax2Lf++R3T0DZSPGP48OFRj2nkpr5UvGDCvShsX36ZZMuXJ+XoEcqOHaO7Lrp7o7sS/rsWZ5xxhrtj5PH33Ld9+3Z3Z+nUU091n3WHSXdlaqtb6rDpvXfdOfIvY+HChVaxYkU76KCDMiw/K1lNp3HJycmZTr9q1Sp3NyYnhg0b5u426U6TtG7d2q677rocrUdm0+jO0MEHHxz8rDtiums2adKk4Hp36tTJPXoguvN2yimnhOyHCy+80Jo3b+7uUh1wwAHuO7zv0fIPO+ww69KlS47KCADxpF07s7p101tgREofplOlxms6AADiQa0KtfJ1OiBaaaGyi2mUL1neShYrmWWLV43XdIUZ0/DLa0yjWFKxTMcl6b/kpGB54zGmcf7559sRRxzhWi7HSkyDQGyCiKVHKGfNmmWpqanWuHHj4LAePXpkmG7q1Km2YMEC++ijj9yP7dxzz3XDU1JS3I/pgw8+CJ7IMqNp9H1qYv7FF18Ef6D+pvNq+i6ZNUP3hq9fv969X3HFFVauXLksv1ePKahZv5rL33XXXVlOu27dOvv9999DtkeLFi3cK6/0aEOvXr1CyqQ/Ejrh+79Hze/18prp66TlV7duXdu7d6/bhv/5z3/cMO27UaNGuccNtE0BIBEVK6YKcvpjcOH1Su/zuHHp0wEAEA/a1W9ndSvWdR1zRcoTq6COxms6IJZjGgrE1atYz3VUlRmNzyx4WJRiGsWSi9kv36UHjDdv3OzeL+p7kVnprIPOxDSih0BsgqhVK3+n2x/6QfvfM9OtWzfr2LGjy6ui4OfcuXPdj0Veeukle+ONN+z00093PyzvrlA4/Qj1+umnn+yEE05wJ8IjjzwyOL5nz54hJ7I///wzwzKUG0X5WER3dLRO+hFnRXfHlNS6WA6uwHO6PXJK22nfvn12+OGH5+p7NE/4OO+zxvm3kbZ/6dKlXS7eOXPmuH8DQKJRfXvyZLOBA81Wrvx3uFrCKggboT4OAECRpYDO+G7jredrPV3Q1c/7PK7bODcdEOsxjSplqlhja+zyGvtbxiooqSCsxsdLTOPEDifasacf6/49dtRY69ujrz31f09lGXQmphE9dNaVINq2DVjduoEMrXY8Gl6vXsE8QnnMMce4ZuS6Y+Lnb9YfTs38leBadz9EJwn9UH799VcbMWKEO/n4/fDDDyGf1RS9Xr169uyzz+7XuutE9N1337lk0pn56quvXPJn/12arNSoUcMaNWqUYXt4ib5zQ031n3/+ebvssssyjFOT+/Lly2f4HpVFJ6fjjz/efaffkiVL3In3uOOOCw5r2rSp1a9f366//nqXCHz48OG5WkcAiCcKti5ZYvbJJ2ZDhqS/L15MEBYAEJ96HNLDJveabHUq1gkZrpawGq7xQDTTQuVnTEPB1hY1WtjB1Q62muVqund9zioIWxRjGuVKlrPGVRq7IPMZ555hv/70qy2av8iKJxe3MsXLZCgvMY3oIhCbIHQTQ61zpLAfodTdmv/973+uZ0HPzp07XXN9T3re5n99//337oeinCii5ul9+vSxN998MySviufDDz90zc89GzZscHc9jj766JDl+78n/DszWw/12qce/zROTdrDp//7779dT4G5ceedd7pe/dQzoEd3aHSS8ZbrX5dId4A0Xieka6+9NuIjFGXLlrVbbrnF3dny1lvLefvtt90fEa3DW2+9FbIOypkycOBAd8L3vsObV/Poj4AeV/j4449zVV4AiCf626knotq3T38nHQEAIJ4p2Lpk4BL7pPcnNuT4Ie598cDFBGFRIGmh8jumoWvnCiUrWIVSFdx7TnKZFsWYhhd03rx4s1WuUtnat2xvdSrUsaRAUlzHNB566CGXuzaWYhqkJkjQRyj9Sa4L4xFK/RjUJF8/bp1I9CPTiWzTpk32+OOP28qVK10CZv0IdbJRU/F3333XTXfjjTe6+ZW0WXdp1Gxd84jyrqjVqpqX6weqXK579uxx8yt5tL5v+fLl9sQTT7jp77nnHhswYIBLtq27LrqD9eCDD7p10Y/x559/difGXbt2uWTaX3/9tXsUQHeIXnjhBTf9/fffb1dffbVbpj63bNnS5XFp0qSJGye6w6U7Ot6PP5yXK0adY+kujhJcn3322e7OjU4MM2bMcHfK1DRfd9+88iqnidZ17dq19vLLL7vpp0yZ4k7oTz31lFt/Da9Tp46ddNJJNnToUCtTpozLR6M7Qjrx9OvXzy3r2GOPtQkTJriTnu58qbx61zzy6aefumVpPZQ4/Morr3R3q7Q85W7Rth80aFABHD0AgMKieuv06WZr1yq/WXrwmcAzACQepR/o0KCDrSmzxrWGC+/EGIiGRIlpKHYQzZjGe+++5/LGvvjiizER0+jfv7/rGCy/YxqrV692LXW1vWMpppEUiBQyR7YUdd+6dat7aRNWqFDBRfqLF89ZbHvLli1WqVIl10zbuyMSTj+UxYsXu9aX+5OvQuunfBhaN/3odRGlngSVxFr5U9R0vyhdROnuRWZ5SsLLGu+KSnnz41jWb05/OBKlopdI5aWs8SuRypsIZZ0yxcuHm2atWq2xOXNqWO3aya51SrQufHJSX0J0FeQ+SITfUSKWNdHKS1njVyKVN7/Kml8xjXD5HdMo6OvqrGIa0VZUYgixVtbsjuXc1JditkXsO++84yLmSli8cOFClw/jggsuyHIetVJ8/fXXrVmzZu7ugx4hD49mz5s3z0XeNc3GjRvdnQXlg/AHULUc5ZhQc+Zly5a5OxqjR4+2mjVrBqc5+eST7RMlgvuH7mQ8/PDDLkIf6/R779jRiqzCOmEBAJCoFIRV6jLdvvdfj61YkT5crVPIiwsAAAoCMQ0UZTEZiJ05c6bL7aCAqBe17t69u7sTc95550WcZ9GiRe6RdPUk50WnldtSzbRvuOEG91kB1VNPPdVmz55t1atXd8OU00PNuB977DH3WU2U+/bt65ozt/sny7O+W4HXH3/8Mfh9anqudVRT5wYNGliLFi2ivFUAAAAKnlqdqCVspGeoNExVNd337t69aD1hAwAAABS0mGxPr9wTys/gbzqs/BDK15AZtVjt1q1bSBNhzXPXXXe5pMmiHBzKR+EFYUV5N5R3Qjk2vGDtunXrXM5NzyGHHOICvEqO7FG+i9atW9vpp59OEBYAAMQtPfrnz8MWKRi7bFn6dAAAAACKUCBWQdPp06e7hLp+ysOwYMEC1/I1EnWgFGke5WeYNWtWptNUq1bNJT/2erdr27atC7j28D1fp9QIXhJlAACARKL8a/k5HQAAAJCoYi41gQKtSqar4KifOsKS+fPnZwimbt++3eWEzWqezp07u0Bup06dMnynptM0kfzwww8uV616mQtP1Kse6DSvWs9qve+9997gd4ZTT2x6+RP5ekmw9YpEw5Vc2HvtD2/+ROibLZHKWlTK6x3DWR3v2fF+D3mdv6hJpPJS1viVSOWN57IecEBoXtjk5DRLSgq49/Dp8rv48bg9EVlqWqpNXzrd1q5eayk7U6x9g/auV3YUfexbAABiOBCrDrTE33mW/7M3Pi/z6D18Gm+68OV+99139tZbb9kbb7xhjz76qB1//PEh49WRl/LVqkWt3HLLLXbRRRdlCNh6lCJh1KhRGYYriKugbiR79+51FyAKTOuVV7owVK98kgi94iVKWYtSeXX86lhev369lShRIk/L0Pxq4a4yx3svpYlWXsoavxKpvPFc1qZNzbp2NVu/Pv1zUlKaHXTQZv0VskAgvazK+qTp1qzJ3+/eunVr/i4QMWnKvCk2cOpAW7llpbWq2MrmbJljtSvWtvHdxluPQ+gFrihj3wIAEOOBWC+YFN66L6tWfzmdR9NFmj9Si1N1xqXX9ddf7zrt+vjjj4MdesmDDz4YMr3y095xxx32zTff2LHHHpvhO4YNG2aDBw8OaRFbr149S0lJsYoVK0bcFgrQ6gJEgeJIAeTcymsArChKpLIWhfLq+FVgQjcu/Hmccxvk0G9Yv5l4C3Ikenkpa/xKpPLGe1kvu8ysV69/A7FmSfb99ynBQOxrr6W3iM1vef2bgaIVqOv5Wk+F9S3ZlzVtxZYVbvjkXpMJ2BVR7FsAAIpAILZSpUrufc+ePSHDvcf6vfF5mUfv4dN400VarjfPVVddZVdeeaV16dLFzj777IjT6cJLZs+eHTEQq8699Aqni7XMLtg0XBd13iuvFGT25o/lVpP5IZHKWpTK6x3DWR3vOV3O/i6jKEmk8lLW+JVI5Y3nsip1voKtAwearVypvz9JlpaWbHXqJNu4cenjoyEetyVCH1lXa0kF6sJpWJIl2aCpg6x70+48yl7EsG8BAIgs5mq3yv9arFixYA5Vjx73kyZNmmSYR3lZa9Wqle086nArfBpvOm8a5Xnt06dP8HFvr9Mv+fzzz937NddcY0cccUTEoK/SCQAAAMQbBVuXLDH75BOzIUPS3xcvjl4QFvFvxl8zbPmW5ZmOV8Bu2ZZlbjoULexbAACKSCC2bNmy1rZtW/vzzz9Dhv/xxx9Wv359F0yNpGvXrhHn0fLatGmT6TTLli1zQVS1dpXnnnvOXnvtNdu2bVtwGuW1lNq1a7t3tapt3759yHIW60rELGJnYAAAAPGgWDGzDh3MVA3Suz4DebVq66p8nQ6xg30LFO0W7V8s/cKmL5nu3vUZQBwHYmXkyJE2efLkkA6qJk2aZLfffrt77G/u3LmuReqnn34aHH/jjTe6z/5OHTSPhqvFrPTv39/mz59vy5cvD5mmX79+1rhxY/e5b9++9sgjj4SkKtA0DRs2tCuuuMJ91vSnnHJKSF445Y9V+oKWLVtGbbsAAAAA8aJWhVr5Ol1RkQhBjkTdt0A85HZuOL6hdXm+i42ZNca967OGA4jTHLFeq9IRI0a4jrKaNm1qixYtcrlZe/fu7cZv377dli5dGtJqtVmzZvbss8+6wGuLFi1s1apV1qBBAxs6dGhIHtf333/fRo8e7abZtGmTW4a/Ey5958svv2zDhw93OV0XLlzoWsJOnDjRqlSp4qY55phjXNBXgWHl6FRw96STTrJrr722QLcTYs/GjRvts88+c8eZjh0vnQUAAABCtavfzupWrOs6b4qUS1R5RDVe08ULBTOUO3XllpXWqmIrm7NljtWuWNvGdxsfVx1XJeK+BYo6OtiDENNI0ECsdO/e3b0iad26tQuihlNKA72y0rx585DAayQXXHBBtut34oknuleRpLvua2eY7VxlVqaWWUo7M5Lk75clS5a43MFfffWVOz6PO+44u/POOwt7tQAAQAx45513bMaMGXbQQQe5ixo92ZVdfVN1itdff901Nli5cqVrEDBo0KCQaebNm2ePP/64m0YXTkqfpcYExYsXD6mjTJkyxUqWLGm7du1yKbeuu+46q169uhU2ddKkAKQu8BWY8/M+j+s2Lm46c0qkIEei7VugqCtyHewR08h3xDQKTswGYhEly6aYzRlotsOXPL9sXbNW483qxUfFr6DppoBabA8ZMsRd6PgvfgAAQGKbOXOmu5DRhY1SbIkaGyQnJ9t5550XcR49DXbxxRfbTz/9ZKVLl3bDBg4caPfcc4/dcMMNwfrHqaeearNnzw4GVceOHWsDBgwINjrQk19PPfWUexrM3z/CJZdcYm+//bbFAgUeFYD0Wol61FpSgbp4CUwWuSBHPkiUfQskWgd7HRt2tEJFTCPfEdMoWGzdRKIT1pfnuNNoiB0rzGb0NGs3mRNXHiglxpgxY+ioDQAAZKB0W7169QoGYaVPnz42bNiwTAOxCpx269YtGIT15uncubNrrVKmTBl76KGH7PDDDw9p2ao0XjVr1rSbb77Z6tat61rhVqhQIWTZ9erVcxdYakHrpd0qbArIKQA5fel0W7t6raXUTLH2DdrHTUCyyAU58lEi7FsgHhSZDvYU01DsgphGviKmUbBisrMuREEg1WyOHmfLeBc+OEzjC6CzgA8++MBdSOiC5IknnnDD7rvvPvfInB658zpT++OPP+x///uf6zxNHbi9+OKLbrhy8l599dVufj1+pyb0mqZq1aouV+8nn3zicvh27drVqlWr5sb5O37zu/XWW+300093LUfOPPNMd/GiR/z0yJ7WUfbu3eu+e8KECW491SLFn594w4YN7qJG+YlVnrvvvtvlDw7/Tq2HmvdrGrVOOeSQQ+zII4+0N954w1asWOHKqDLpAuu3335zeVlUHq8Mu3fvtkcffdR91vCPP/7YPZKobaZWNZrf23bfffedPffcc+57Lr/8crdNAABAwdq5c6dNnz7dGjVqFDL8wAMPtAULFriWr5FMnTo14jybN2+2WbNmZTqN6gjlypWzjz76yH1Wfwequ/jzu6WmprrlVK5c2WKJAnMdGnSw9g3bu/d4C9QVmSBHFMT7vgXiQZHoYE+xCrWEjfOYRpcuXdy1fjRjGoMHD46pmMaAAQPcfIkS06BFbIJIWvulJe3M/C68O3HtWJaeZ6VmdO/Cn3LKKda+fXtr2bKllS9f3g2rX7++TZo0yTWHF50UdDKZNm2a6yxNTjjhBNd5m/KV6KSiH/Jtt93mgpD9+/e31157zc4//3x34lJLDz16p1xpGpcZtTR55ZVX3HqodYkuTK644go37pZbbnHv6qht3LhxNnfuXPdZrUx0YnnmmWeCJzWd8PToodey5IUXXnCP/enE4dF66OKob9++7vOXX35pDRs2DJZZJ1CVRydFDT/00EPd/Fq+V4arrrrKXn31VVdOnbjUWZwCwzqZjRo1Kvhd//nPf+zee+91rWe0/CZNmti3335rjRs3zsc9CQAAsqJAqy5i9Pffz6v/6EIsPJiqTmmVEzareXRhpUBupJYrmk7TSIcOHVzdSdMr1YHqNqrTKFWCv4Wuny6S9PJs2bLFvS9cv9DK7yn/7/eULG81y9e0Pal7bNnmZRmW07hq42D+0137doWMq1GuhlUoVcE279ps63asCw5PC6TZjm07XCtf/XvxxsUZltugcgMrnlzcBSx37N0RMq5a2WpWuXRl27Znm63etjpkXMliJa1epXru34s2LnJ1KD+N0zRrtq+xrbu3hozTMrXsnXt32sqt/z5iLwoqNqzc0P17yaYlLgWBX+0Kta1MiTJWpniZkFyp+ve+tH0uV6w/XUFqINX+WPeH2z+NqqQfG9q+2s5+2vbaB5t2bbL1O9aHjCtboqwLlmj5SzctzbAND6xyoCUnJbuyqEx+1ctWt0qlK7ltoG3hV7p4aatTsY7798INCzMs19uG2vbaB36VSlVy23zb7m22envovilRrITVr1Q/022o79R361jRMeNXsVRFSymXYrv37c7Q4ji7bXhA+QOsXMlytnHnRtuwc0PIOA3X+My2oZar5Uc6vquVqebKumnnJlu/c33EbajxOg4zO77/3va3bd+zPWRc1TJVrUqZKm64xufm+FY6iFLFS9na7Wtty+7037RH+1v7XeVQeTI7vv/a/JftTd0bMr5muZruu9ZtX2ebd4fum2icI0S/J/2uCuMcUTypuK3dsdY2r9/sfkP5eY7Q71i/Zz9tA22LSNswv88RB5Q7wGqVr+W2i5fHem/aXneu8v7Tb+Kw6odZWlpajs8RqXtT3XGze+9uF6TUemu5Wkb4dtJ+034NP840j/aPrZluSf50BJnENPas+tTSarQPWa72Z/g20im5VLFS7p8ap+9NtdSQc5P2s/aZtpXfiV1PtHbt2rkgZMnSJW3X3l12QO0D7LkXnrP/9PiP+71t3brVTjv9NJv60dRgTKNj+47WuEljO+6Y42zgtQNdTOOmW25yMY1LLr8keK2vmIbOaz0X97Rdu3e5cfsC+yw5kOz2hX8bFitRzJ594VmrXLGyi29s2rzJ+l6SHnMYNXKUW7cJT0+wRx56xAVHtdxbht9iVw+42p54Kj2IrLiJYhqfT//cKlaq6Ia9/OLL1vfivjbx2YnBfaP1UHkv6n2Rm+aL6V9YowMbWY8ePdw2vPHmG11Mo/+A/tagYQNrdkgzF9PQ8jWvjq1+l/WzSa9MsnPOPceVU/tG0z/88MM2bPgwt1yts2IaSsl0/oXn22lnnmYtmrewGTNnWKPGjax4seJWLKlYxH2jfaZ9JyrrvtR9Ifu1ZPGS6dswda8rk5/KqWMm0nG4Z98et67a7n+u/zPDOaJSUiXLKQKxiWJXDu+uK9l1AdDFhX6Q+sHq5LV48WJ398WjE5LuyHgnLNHdoJdeeskFYr2LB52wPBoW/jk7tWrVCl7YhM+jDjVEd3y84KnohOsP7iqHyjHHHBPyeN+5555rl156qQuSHnXUURGXr3+Hf/ZT7rZIuVlyUk4FbNURiOiumgKxyk1HIBYAgIKjx/8l/O+599kbn5d59B6pnuClHZBixYrZhx9+6DoG0w1kvcaPH+/qLZm56667Qm7ueq774DorXubf7zuh9gl2xRFXuKDa9dOvzzD986c8797vnHWnLdwUGrT73+H/szZ12tgnSz+x5+emTye6QGtSvokNLzvcdqXusv6fZLyZ/nDnh13wbfyc8fbjmh9Dxp3f7Hw75cBT7JtV39gjPz4SMq5+xfp2R5s73L8HfDggQzDkzrZ3Wt0Kde3pX5626cunh4w7vdHp1qtpL5u3fp7d9e1dIeOqlK5i4zuNd/8eNm2YbdwVuk+HHTPMDql2iC1ZvcSql6oeDAToIlAXi0dVPMp2pe2ypbuWukDDc3Oes+fnPO8uBCeePNFNe/vM2+2vLX+FLLd/y/52bK1j7YPFH9ik3yeFjGtZo6UNbjXYBdyu/uzqDNvw8S6Pu0DMA7MfsF/X/Royrnfz3talQRebuWKmPfFz+gW6p3Hlxjby+JHp3/9Bxn1zX/v7XGDuiZ+esK9WfhUyrnvj7tY5pbP9vPZnu3/O/SHjapStYWM6jHH/HvLpkAxB3FuOu8WaVGliL817yT5c8mHIuBPrn2h9Du1jSzYvsRFfjcgQFHrypCfdv0fOGGkrt4UGyAYdNciOqnmUvbPwHXt9wesh41of0NoGHDnABVKv/fzaDGWd0HWCu9C/95t7bf6G9BsfnosPvdhaVmhpPy770Sb+lr4PPU2rNrWbj73ZXeD3/yjjNhzbcawL5D7ywyM2++/ZIePOOfgcO6PxGfb96u9t3PfjQsbVLl/b7m53t/v34I8HZwhs3nbCbdawUkN77rfn7NO/Pg0Zd3LDk+3CQy60Pzb+Ybd/fXvIOAXyHj3xUffv4V8MtzU7QgNv17W6zuqXqG+f/fGZvb0wNO90NM4RokDg0NZDXZC1oM8RtcvVtkm/TLLZG2aH3FjJj3PEa/Nfs3cXvRsyrn3d9nZpi0tt+dbldtOXN4WMi8Y5onbZ2rZj1w5rXKaxCzrvSN1hFYpXCN4sqlW2lk39bWquzhEppVLssiaXWYltJaxBiQbud6Pg+ra9ob/zyqUqu22l/bp6x+oMwbF6FepZ0vblOQpgrd/wq20tlR78rlCyglUvU912p+7OcA7QdXTDiunTKYCu87P/2rpm2ZruXLlp96YM+61ciXLu3PXkU0+69EM1D6ppP/3+k10+8HJ300LLVQqhGrVq2L5y+9wwad2utT33/HN29FFH2/bd6TdbtH89CrC697173T7VTSIFE735tR20PXRDYPve9PlLVippG1I3WGBnepBQQURv+sq1K7t/H3jYgXbRRRe5m8MK3Dc9qqm9euOrwenemPKGHdP6GEstmRocdszJx7hGar0v622dju/kziurtq9y6+RNs2PfDheQ1HJXbF0RDIrq30mbk+y7z75zsQsN9+YRbWvdVNJ8Oh96+8Y/jVoSq3GabqrsLrbb6h1Yz9797F07q/pZllImxZ2fdAyv3xV6o0E3PnVjwW2HLX+59fPv1/oV6rvfj24kaP39qpau6m4catuGn++KpRZz21edng74LOM54qaWob/RrBCITRSlc/gIgXocLCBq4arWGSeffLJrsu+njifUKla5Sjz6ATVo0CBf10EtRrMbp/xrat6vE6nu4qmpvB7r8+juTp066Xf+PJpOrTm++OKLkEBsTuk7tE0U9FXvyrmlvHN6LPH99993667WLP51BgAA0edV/MNbTXifw4fnZh5NF2l+DfMP1yN9upmtRyX16J9uEn/zzTfu5nZmdQg9neNRHUI3x+8/5X4rXyG0RWyN8jWscmple+SM0ICG1Khaw73f1OmmTFu7nVbxNDu28bGhLWI377AaNWq4lkqRluu1dhvYdmCmrd06Ve5kzes1DxmnIGeNSunr9NDpD2XaIvbS4y61c3efGzLOa+1WoUoFe6Rm6DrpYq5G5fTl3nXyXZm2druw/IVWsmxJG/DBADdcQRyV96etP1mapbfIeeikh+ykRielj09KshpV0pd7S+dbMm3t1r1id2vbpG3IOAUOalSoYVXTqkbchvWr1HdB4MHtB2faIrZLpS52eIPDMwQ2a1RMX6dIy/W24f+O/59duOfCkHG6sE3dlmp1KtexR2qHzqvAjLdvxpwyJtMWsReVv8jObHFmxBaxlapWskeqhS7Xvw1HdRmVaYvYHhV6WMemoU8EariO78y2Ye0qtd3yh3YYGrFF7O4tu+3k+ifb0Y2OjrgNdfxldXz3b9M/0xax7Sq3syZ1mmR6fD9w6gOZtojtW66v9TiiR8QWsRWrVrRHUjI/vu/oekfEFrHbN223c+qeY12bdw0ZF41zhOj3pONbv5+CPkcoOHl+i/OtT4U+EVvE7s854vzy59sph54SsUVs5WqV7ZEqmR/f+XmO+HjRx3b3l3fbqi2r7LDyh9nWfVvdsm5ud7M7P+X2HKEWsXvW73E3C0qXLO3Wu3r56u67w7eT9lv5YuWtVIlSGcqqFpBWrq7lRLWqh1mVf1rZe8vVjUmv5f2/C7b05Wo/VKxte/ftDX72t4itklzF7Qs/BRe1XD15okZbl593uf0699dgbvcSxUvY999/b3t37rUv3v4iOF/FkhWtygFV3E3T8qX/eTrYt146DrXsEiXSnxTQcaXfrjeN15ozpXyKVUur5oYN/N/AYMBa86rM3vTeuAbHN7Cjmhzl0hckF092Tw8nBZKC0+3bu8/llq9erro7z3gU0/h59s92UruTrGyxsla/eH13jvPmU0DabYvixYOt/aVOhTqWvDXZli9d7p6G1hNC/nKWKl7KKpSu4ObTvtH04dtCTyIrpvHBhx9YjZQatnv7bqtSqoqbxmsRWym5kpUrFfoEkfaZtx81rVrE+vertw11fsqsRWyFYhWsdIl/8/TLnt17bPn65S6tQqRzBC1ikUEgpa0FytS1pJ0rMsmpkpTe02BKuwJdr2bNmrn8ZUpLoKCsZ9euXXbAAQeEtEQtLApm6sLlzTffdEFZ5VlT6gGPWtRGapGiuzvhOVUiXTCF010WBX3vuOOOTC+SsqLHCdXSWI86PvDAA+4krlwwAACgYFWqVCl409bPe/TfG5+XefQePo03nTeNgrD67AVWf/rpJ/eYn3Kw6ZG/nj3V4Uko1cv0Cte4WmOrWDH9cUW/0smlrUn10KCQX73K6Y/6RlKlbBX38teB1qSucRd1emW13DqVQm+C+1UsXdG9MnNQtfSnniI5oMIB7hWJLvaalMp8nRpVDU0z4aeL5quOucote+DUgbZyy8r0Rz0tzV28jus2znVsFUmDKpk3RKhatqp7RVIyuWSW27BupcyDGpXKVHKvzGS13FoVMzbscPt2+xorX6q8VSxTMU/bUBfNekVSpmSZLNcpq21YrVw198rLNox0fLuybl1jlctUtqrlIu8byWq5CgplRsELvfJyfNesUNO9IilbsmyW69SwSnrLwfCy7kja4YI3CuxFkp/nCD89Ol/Q5wiVN6VsitWoViPkCcX8OkfolZdtmJ/nCH3+39H/y7aDvZyeI3Rdv3jTYhdc9baZSzOQSapoBdcyzSOtdAOKWezIOqZRstaJZmHLUDBX2zEzLgAaSA8ohj9xqiCeP5AX+o1Jdtihh1npUqXtzclvZohp6Ancyy65LOK8+hsg/oCf99SsW98SpYPB4PCgYGbb0M1r6fNmFtNo0aKFyz/76qRXg9MppqG4gb7Pe6xfUvelWiAtvUWpt2+0Lbz59NlbXwVXPVrGE48/EYxpeOUJCZYmF/933n+C7940qrsoVaU/pvH8c8+75fqXk9W+Ea1TMSsWcb8qIJuZiMdh6r9PJ0c6R3ipnHKCzroSRVIxs1beIyzhj7L/81njCzB5vnKbqROL119/3a6//npbuvTf/EtqCerlN/PTXaWCpsf8tX4Kwoo/b5qST+vxvvDONpTfbd26dXb88ceHDM8swbaf8r1edtll7mSTFwpq//jjjy4HnLcMb521vgAAoGDoAkItPcIr58pJL0odFE4XQ7pwy26egw8+OGKlX9N50zz55JOugwuP6gXqFGTEiBGuEzEUPAVblwxcYp/0/sSGHD/EvS8euDjTICwAFLSY7WBP69EqPc0DMY3cIaYRWwjEJpJ6PczaTTYrG3Z3UHeVNFzjC4jSDigHmS4OlCP2yiuvdK1fvYTT+qwfuO7UeH7++efgY/o5aVmam+lE3x1pet3J8vcsrPXwTj7qGVC5bdVKVj0depRS4YwzzrC2bdu6TrN04lMeXPeoXSbr5n3WxZOXnzY3ZfKGa33VYkUXfrJ69Wpbs2ZNet6WFaEJ+AEAQPSULVvW1QX+/PPPkOFKP6SOShVMjUSpBCLNo+W1adMm02n0qKEuVNT5hVc30AViOF2IpaREbnmFBA5yAECsI6aRLzGNX375hZhGISI1QaLRialOd7O1M9I75lJOWKUjKMAK4PPPP+8ei1MvgupMQjk2dBLTD1+dXCmHmS4ylF9VvfuqxYZ+hOp0Sk39586d6zqakLvvvtudUHTC0d0mdZy1Y8cO11x86tSp7gSjpuzXXHNNxPQBsnbtWnvttdfsxRdfdL0I3nfffS6PybHHpucjeuWVV2zMmDHux68Tw0knnWQzZ850Tfv10klm2rRpbhrlVdGdI+VjVY+Hot6Kv/32W5s4caJ7FFCU2uDLL79036dHA/RdTzyRnux83rx5brh6TNY6qQwPPvigS1atTsx08lY5dXLS+ngXYTp5qjMObSM9eqgccPpuNZ9XaoIbbrjBJdsGAAAFR3/7hwwZ4lqieHURtfS4/fbb3d9o1WvUQ7LqKyeeeGLwb7oeyVNdqUKFCsF5NNzrZFQdh+oiSXnlVf/wpunXr1+wc07VBZRjTZ10eRczCsxOnjzZxo4dWyjbAwCA/RLnMY033njDXevLBx98kO8xDX2P6htff/11TMQ0xAtOJ0pMIymQm/A68o0eJVP+Lj0+FinfVjCfyuLFduCBBwaTPueF14tdpLwYhUE//Eg5dfJDrJU12rzy6g+ATkw6rnRhF2vy41h2+bbWrHF34KJ1/MSSRCovZY1fiVReylp49aWi4O2333YXZ7qY0KN/er/kkkuCHZTqJu9zzz1n3bt3D86jixsFVnVxs2rVKtcadujQoSH1G13IKa+8ptm0aZO7CLz11ltdp6EeXcTp+7W/NFyBWAVxa9fOPAdlYe0DfkfxK5HKS1njVyKVN7/Kml8xjWjLbRwhmjGNaIvVmMnGjRvt6aefzteYRn6WNbtjOTf1JVrEosAV1RNWLKtSpYpraTNq1KjCXhUAABBGAVZ/kNWvdevWLogaTo8C6pWV5s2bux6Qs3LKKae4FwAAyB/ENPJflQSKaXD0AHGkTp3MewcFAAAAAACIVXUSIKZBIBaII5deemlhrwIAAAAAAECuXZoAMQ0CsQAAAAAAAAAQZQRiAQAAAAAAACDKCMQCAAAAAAAAQJQRiAUAAAAAAACAKCMQCwAAAAAAAABRRiAWAAAAAAAAAKKMQCwAAAAAAAAARFnxaH8BAAAAAABAUZWalmrTl063tavXWsrOFGvfoL0VSy5W2KsFoAgiEAsAAAAAABDBlHlTbODUgbZyy0prVbGVzdkyx2pXrG3ju423Hof0KOzVA1DEEIhN0Lt5M/6aYau2rrJaFWpZu/rtuJsHAAAAAEBYELbnaz0tYAFL9mV2XLFlhRs+uddkgrGFgJgGijICsQl6N2/5luXBYXUr1uVuHgAAAAAAvmCfrp0VhA2nYUmWZIOmDrLuTbsTBCxAxDRQ1BGITbAT1jmvn5PhD0lh3c3bt2+fPfroo/bKK6/YxRdfbIFAwEaMGGEtWrSwoUOHWqdOnWzUqFFWqlQpq169us2fP9/uuOMOK1GihPXt29c+/PBDu+GGG9yyihcvbrNnz7azzjrLevT4twx//PGHjRkzxg4//HBbs2aNNWnSxP773//avHnzbPz48fbEE0+48ZUqVXLzV6tWzX1HcnL63U4Ne/XVV+3ggw+2TZs2uXUZOHBggW0jAAAAAEDBU4tLf7AvnK6rl21Z5qbr2LBjga5bovK3UI7XmMZ//vMf69WrV9RiGiVLlrT+/fsX2DZCRgRiE+hu3qAPB8XU3TydaK655horW7asXXrppW7Yyy+/bBdeeKGddNJJdu2111qDBg1s0KBBbtzjjz/uTlIPP/ywm+64445zJ6WnnnrKjd+2bZs1b97cnfzOPfdc9/n000+3adOmWe3atd00J5xwgjVt2tRat27tlqWT1nXXXefG9e7d21JSUuyggw6yfv362a+//mpXXXWVffXVV+5EKTqJ6sR62223Fcg2AgAAAAAUPD32np/TIf5aKEcrpqEgas+ePaMS07j11lvdS8FaFI5/k5wgrn257Msc380raHv37s0wbMWKFe7kpJOPp1u3bvbSSy8FP5cuXdratGkT/Fy+fHm75JJL7JZbbnGfH3nkEatXr17whCVdu3YNLiMpKSnkOxctWuTedeKTm2++2U455ZTgCUt0Qr377rvt77//zpeyAwAAAABij3KP5ud0KLgWykU5pqGA7rBhw6Ia07jvvvuIaRQiWsQmiFXbYvduntdk3u+HH35wzfynTp3q7jJJamqqde7c2Z3k/CcSvyOOOMLd3Vm7dq1rgq87SM8++2xwvFrL6o6Un8Zv3brVPvjgA/vyyy/dYwSiu046afnVrVvXff+sWbPcIwMAAAAAgPijDqCUe1SPvUdqhakWmBqv6ZDYLZTzO6YxcuTIAolp+NM6ouAQiE0QtcrH5t08nVTKlCmTYfiuXbvcu+4eVa5cOThcLV6zopOSdyLUMg444ACXeyUr3vjLL7/cnRR190nN/3XSTEtLC5nW+6xxAAAAAID4pMfb1QGUco8q6OrnfR7XbRwddSV4C2ViGsgtUhMkiLb12rq7deF/QDwaXq9ivQK/m/fpp59ax44ZE5sff/zx7q6Rkln7/fjjjyEnEu8k5fn++++tWbNmLkF1u3btMszvTROJEmgr34pywHrr8Ndff4VMs2TJEitWrJjL5QIAAAAAiF/q+EkdQNWpWCdkuK6tC7pjqETntVBOhJjGIYccQkwjjhGITRC6Szfu5HHu37FyN2/Pnj3222+/Wf369UOG60RUp04dGzx4sMup4lEz/rfeeiuk2b+a2nvUA+CECRPs3nvvdZ+vvPJKd5dHJ0bPzz//bAsXLgx+T6QT2qGHHur+feedd7rv27FjR3D8uHHjbODAgS5PCwAAAAAgvinYumTgEvuk9yc25Pgh7n3xwMUEYQuphXK8xzSefvppu//++6Ma0xgwYAAxjUJEaoIEvJunngb9Sa51V0knrIL8Q/Laa6/Z2LFj7aijjnI9B4ruCv3+++/2/PPPW7ly5VynWGPGjLGrr77aDjzwQJfHpH///iHL0clDJynd0ZkzZ45NnDjRNcWXihUr2hdffOE675o+fbr7XLVqVbv44ott7ty5Nn58+klc8+vO0bfffms1atSwBx980A0/9thjXWBXPR02btzY1qxZ496HDh1aYNsJAAAAAFC4FNzr0KCDrSmzxl0zRsoJiuhLhJiG8r2qQ65oxjQUIEbhSQpECqEj6rZs2WKVKlWyzZs3ux9TJMoHsnjxYveDVW96eaVdrLsoahavXvVS01JdT4JKYq38KWq6X9B5bdR0XyclnbRKliwZHK4Tk5rqK9jpvzOU2TKUC8WfLyW8rPGuqJQ3P45l/VHTH45EqfgkUnkpa/xKpPJS1sKrL8HiZh/wO4pfiVReyhq/Eqm8+VXW/IpphMvvmEZerqujFdOItqISQ4i1smZ3LOemvkSL2ASkE1THhhlzmBQknXAi5SRRz4GtW7cOtmrN7o8D9xEQj1JTzaZPN1u71iwlxax9e7Ni9AEAAAAQ0xQcmr50uq1dvdZSdqZY+wbt6cgJiAJiGijKCMSiwCkvSqNGjbKcpkmTJu6OQ6Q7DcrDortLP/zwg23fvt3dfbrwwgujuMZAwZkyxWzgQLOVK81atTKbM8esdm0zPXXSgzRUAAAAMWnKvCnucemVW1Zaq4qtbM6WOVa7Ym2X05JcokB8IaaB/UEgFgVOuU969+6d5TTnnXdepuN0klKCab2AeAvC9uypRyjM/E8ArViRPnzyZIKxRRktnQEAiN8gbM/XelrAApbs6w97xZYVbrhyWhKMBeIHMQ3sj/hObAIARShIp5awkZ5M8YYNGpQ+HYpmkL1hQ7MuXczGjEl/12cNBwAARTsdgVrCKggbzhs2aOogNx0AAARiASAGzJhhtvzfjj8jBmOXLUufDkWzpXP4/vVaOhOMBQCg6FKHQf7e2yMFY5dtWeamAwCAQGwRQPJmFHUcw9lbtSp/p0NsSOSWzirTF1+kp2PQezyWEQAA9dqen9MB8YjrQRR1gXw8hgnExjD1tic7duwo7FUB9osSkCclJQWPaWRUq1b+TofYkKgtnUnFAABIFLUq1MrX6YB4QkwD8WJ7PsY06KwrxhNAV65c2dasWeM+ly1b1u34vETu9+3bZ8WLF8/T/EWFAhrbt/9b1nLlkiyOixvz+9Zbty1btriXjmUd04isXTuzunXTH1ePdLNNu1fjNV08ifcOrBKxpTOdzgEAEkm7+u2sbsW6rmOuSHlikyzJjdd0QKLJr5hGIl9X5zfKWvgxDQKxMe6AAw5w796JKy908KSlpVlycnLc/tB0g23DBgV1AlayZJrt2ZNsxYolWdWqOtlb3CoK+1Ynqlq1almlSpUKe1Vims7n48enB6rCd6X3WZ1qxlOQUgE7Pba/cqVZq1Zmc+aY1a6dvh3iJVCXaC2ds0vFoGNZqRi6d4+vYxkAkLiKJRez8d3GW8/Xerqgq5/3eVy3cW46IBHlR0wj2orCdXV+SZSyBgIB252621JTU11MolSxUnkub37HNGI2EPvOO+/YjBkz7KCDDrKFCxfaEUccYRdccEGW83z11Vf2+uuvW7NmzWzlypVWpUoVG6QrPp958+bZ448/7qbZuHGj7dmzx4YPH+4i5P7l/Pbbb675/LJly2zTpk02evRoq1mzZnCaVatW2d13321NmjSxXbt22erVq+3WW2+1cuXK5et20IGiHV6jRg3bu3dvnpahH9n69eutWrVq7scWbz766N8L/+TkNDv00PX222/VLBBIL6uCOl27WlyK9X2r35VOWvF8gs9PCj6qtaAXnPSoJayCsPESnEykVpOJ1tI5N6kYOnYsyDUDACB6ehzSwyb3mmwDpw60lVv+rcSpJayCsBoPJKr8iGkk+nV1fkqEsn608CO7c8adtmbbGju0/KH227bfrEb5GnZTu5usa+OuhR7TiMlA7MyZM+3OO+90AVGvsN27d3cHyXnnnRdxnkWLFtnFF19sP/30k5UuXdoNGzhwoN1zzz12ww03uM8KqJ566qk2e/Zsq169uhs2duxYGzBggD322GPu86+//mp9+/a1CRMmWLt/roz13SeffLL9+OOP7rNOHl27drVXXnnFDj30UDfszTfftJ49e9oHH3wQlW2iHZ/XJtD6oSmPhbZLvP3Q1Pqqf/9/L/wViK1evYQtXVra0tJ0h8fs6qvNFi+Oz9ZX8bxvE5WCj2otGM+P6ydSq8lEa+mciKkYAAAQBVu7N+1u05dOt7Wr11pKzRRr36A9LWGBfIhpRFsiXVfHe1mnzJtiPSf3dKliki3Zqherbku3L7Ul25fYWZPPcjfNCvvmWExu9REjRlivXr1CIs59+vSxkSNHZjqPWqx269YtGIT15rnrrrts586d7vNDDz1khx9+eDAIK71797annnrKlv8TyVOwdt26dbZWEZB/HHLIIS7Au0HPvpu5AKwOWC8IK2eeeaZ9/fXXNmvWrHzbDsheonaEg/im+kmHDukBWL3HaH0lzxLtd+u1dK5TJ3S4WsLGS8vfRE3FAACAn4KuHRp0sPYN27t3grAAUHBS01LdkwmR8nV7wwZNHeSmK0wxF4hV0HT69OnWqFGjkOEHHnigLViwwLV8jWTq1KkR59m8eXMwOBppGjXHVjqBj/R8u5m1bdvWBVx7+K6MlRrh4IMPtqpKOJrJcnRnp379+lFrEYvIaH0FFD2J+LvVn5QlS8w++cRsyJD0d7XUj6cgrD8VQ2ZP7mh4vXrxk4oBAAAAQGyY8dcMW74l8xY/CsYu27LMTVeYYi41gQKt6pUsPNdq+fLl3fv8+fMzBEG3b9/ucsJmNU/nzp1dILdTp04ZvlPTaZpIfvjhB5erVqkHPFpO06ZNc7Wc3bt3u5dHPa55zcL1iiYt30vIHG+U99vfml6pCZKSAu49fLo4LH5c79tELmu8lzdRf7cKQrZrl2Zr1wYsJUVljq/yicqkVAy9enmf/923/lQM8Vj2eP7NFmZZE2F7AgAAYP+t2roqX6dLmECsOtASf+dZ/s/e+LzMo/fwabzpwpf73Xff2VtvvWVvvPGGPfroo3b88ceHfF9Ol+NRioRRo0ZlGK4UCOrsK9oXMWoZrIumeMsBoni4OuJav/7fi/6DDtqcfq/jn866lIlC08VwJ415Fs/7NpHLGu/lTeTfbTzvV88JJ5i99prZU0+Zbdjw776tVi3ZLr00fXy87ddE2beFUdatW7dGdfkAAACID7Uq1MrX6RImEOvlhVXl3s/7HD48N/Noukjza1j48KOPPtq9rr/+etdp18cffxzs0Cs3y/EMGzbMBg8eHNIitl69epaSkmIVK1a0aF8waZ31XfF4cXjZZaGtr8yS7PvvU4IBHQUE1LIuHsX7vk3UsiZCeRP1dxvv+9Vz1llmZ5yhPL9ptm5dklWvnmLt2iXHXb7jRNy3BV1Wf+5/AAAAIDPt6rezuhXr2ootKyLmiU2yJDde0xWmmAvEVqpUyb3v2bMnZLj3WL83Pi/z6D18Gm+6SMv15rnqqqvsyiuvtC5dutjZZ5+d5XJq1KgRcTmlSpVyr3C6gCmICzZdMBXUdxU05VhU0Ea9sK9cqYB4kqWlJVudOsnuEdh4y8GYSPs2kcsa7+VN5N9tPO9XPxWvY0e1fk2yGjXiv7yJtG8LsqyJsC0BAACw/9RB4vhu463naz1d0NXP+zyu27hC70gx5mq3yv+qjq+8HKoePQInTZo0iZibtVatWtnOow63wqfxpvOmuffee61Pnz6Wmpoa0umXfP755zleDgpWonSEA8QTfrcAAAAAgPzS45AeNrnXZKtTsU7IcLWE1XCNL2wx1yK2bNmy1rZtW/vzzz9Dhv/xxx9Wv359FwSNpGvXrhHn0fLatGkTnGbWrFkh0yxbtsy1ZFVrV3nuuedch2EPPvhgsJXs+n8SGdauXTu4HOV89VML2aVLl9pJJ520n1sAeaVHXjt0SM89qIbJNKIBYh+/WwAAAABAflGwtXvT7jZ96XRbu3qtpdRMsfYN2hd6S1hPTF7yjhw50iZPnmz79u0LDps0aZLdfvvt7lG4uXPn2hFHHGGffvppcPyNN97oPvs7ddA8Gq4Ws9K/f3+bP3++LV++PGSafv36WePGjd3nvn372iOPPBKSqkDTNGzY0K644gr3+bzzznOtdtWhl0cde51wwgnWuXPnqG0XAAAAAAAAAJlT0LVDgw7WvmF79x4rQdiYbBErnTp1shEjRriOspo2bepaqCo3a+/evd347du3u9an27ZtC87TrFkze/bZZ13gtUWLFrZq1Spr0KCBDR06NDiNOpV4//33bfTo0W6aTZs2uWV4nXCJvvPll1+24cOHu5yuCxcudC1hJ06caFWqVAl2HPHRRx+5VrFfffWVaw2rlrVvvvlmrsu6aJFZhQr/flbMuGZNtbBVa92M0/8TL7YVK8x27Qodp9ZkWpYyMqxb9+/wtDRts2Q3Xv/Wo7/hGjQwK17cbNUqsx07QsdVq2ZWubKZNvfq1aHjSpY0q1fv37KE91WmcZpGrd3COz7WMrXsnTvTc0SGt5Jr2DD933p02ZcpwlHj5DJl0ntd37QptKy7d6eXNdI2VL9ujRql/1vjwlP9attrH2iZXo/unrJlzWrVMtP9gaVLw7egUlikt+ZTWVQmP/UAr9i+tkF4b+Hqh6TOP63mFy7MuFxvG2rb+w55x7tfoH0Wvm9KlDCrXz/zbajv1HfrWPkni0eQ+o9LSdG2NPPdt8jRNlQHS+XKmW3cqB7TQ8dpuMZntg21XC0/0vGtY0WUFSR833jbUMefjsPMju+//9ZvIXRc1apm+mlruMbn5viuW1f5n83Wrk1fr/B9o/2ucqg8mR3ff/1ltndv6HgdZyrTxo1J7pjxtxKNxjlC9HvS76owzhFa7rp1yRnKGo1zhGgbFNY5Qv7+O2NZo3WO0LGtY1z7TPuuMM4Rq1YVCylvtM4RWh+tl36L+k0Wxjli3brQskbzHKHjTdtP29GvIM4ROg7Xrw8ta7TOEZmk8AcAAACKpJgMxEr37t3dK5LWrVu7IGo4pTTQKyvNmzcPCbxGcsEFF2S7fnXr1nUtZ/fXjTemXwx71LHJddelX+APGpRx+nfeSX8fO9Zs/vzQcYMHK4ht9uWXZo8//u9wdYJz8MFlbcyY9IuuSMt98cX0i52nnzb79tvQcZdckt4D9o8/mt1zT8aL4vHj0/+t9fY1Yna0iXSh/8orZh9/HDquZ0+zPn3MlFHippsyXrQ9+2z6v2+9NWPA4847zVq0MHv3XbPJk0PLetxxpa158/QL5vCy6iLRi5dre4RfkN9wg44j5QM2mzAhdNwxx5jdckv6xXikbfjqq+kXxtr2P/wQOk6NqU87zUyNqB94IHRc06bp6yKRlvvkk+kX3dpH/6QpDjr3XDNl1fj9d7NRo0LHaR7NKzffnDEIcN99uoGh1txmb78dOu7UU82uvDI9wBK+TroYVydLogwd4Rf6w4ebHXtser7P558PHacsITrm9fONVNYpU9J/Dw8/bPbrr6Hj+vc3a9nS7Ouv048rv8MOS18XHX+RljtxYnrAQ8fUzJmh43R/55xz0r/vjjtCxyk48Oij6f/WeocH2NWplIIaOgbffz90nE5fl16aHuC6/vrQcQoWvfRS+r/1neEBMu1LlXXatFL2/vtJLvAUzXOEHHmk2W23Fc45QsGqt94qbV9/HVrWaJwjRBlkrrmmcM4RCgg+/3xZW7AgtKzROkecf77+pqWfI0aOLPhzxN13J9nChRWsZMl/yxutc8SAAUodlH6OeOihgj9HDBuWZJs3h5Y1mueIo44ymzpVT+2EjiuIc8S11ybZnj2hZY3WOeLuuzOWAQAAACiqkgKB8LYHKAjq7EvpD374YbNVqFAxyi1i02z79nV2+OHVXTaK+G4Rm2a7d6+z5s2r2759yQnQIjbN9u1bY+XL17DVq5PjvEVsmu3atcZKl65h69cnJ0CL2DSbP3+tFS+eEtJreHy2iE2zuXPXWalS1UPKGp8tYtPs55/XWblyoWWN1xaxS5em2apV661atWrB8sZri9g//0yzdetCyxq/LWLTXP58f1mj1yJ2i6WkVHIdolbUDkah1VkLYh+oHrdmzRqrUaNGyDkyHiVSWROtvJQ1fiVSeROprIlWXspa+PUlArGFhEptdCRSWROtvIlU1kQrL2WNX4lUXspa9OtLiIw6a3QkUlkTrbyUNX4lUnkTqayJVl7KWvj1pfje6gAAAAAAAAAQAwjEAgAAAAAAAECUEYgFAAAAAAAAgCgjEAsAAAAAAAAAUUYgFgAAAAAAAACijEAsAAAAAAAAAEQZgVgAAAAAAAAAiDICsQAAAAAAAAAQZQRiAQAAAAAAACDKCMQCAAAAAAAAQJQRiAUAAAAAAACAKCMQCwAAAAAAAABRRiAWAAAAAAAAAKKMQCwAAAAAAAAARBmBWAAAAAAAAACIMgKxAAAAAAAAABBlBGIBAAAAAAAAIMoIxAIAAAAAAABAlBGIBQAAAAAAAIAoIxALAAAAAAAAAFFGIBYAAAAAAAAAooxALAAAAAAAAABEGYFYAAAAAAAAAIgyArEAAAAAAAAAEGUEYgEAAAAAAAAgygjEAgAAAAAAAECUEYgFAAAAAAAAgCgjEAsAAAAAAAAAUUYgFgAAAAAAAACijEAsAAAAAAAAAEQZgVgAAAAAAAAAiDICsQAAAAAAAAAQZQRiAQAAAAAAACDKCMQCAAAAAAAAQJQRiAUAAAAAAACAKCse7S8AAAAAEtk777xjM2bMsIMOOsgWLlxoRxxxhF1wwQVZzvPVV1/Z66+/bs2aNbOVK1dalSpVbNCgQSHTzJs3zx5//HE3zcaNG23Pnj02fPhwK148tIr//fff21NPPWWNGze2EiVKWO3ate2cc86JSlkBAACQOQKxAAAAQJTMnDnT7rzzThdYTUpKcsO6d+9uycnJdt5550WcZ9GiRXbxxRfbTz/9ZKVLl3bDBg4caPfcc4/dcMMN7vOmTZvs1FNPtdmzZ1v16tXdsLFjx9qAAQPsscceCy7rk08+sVGjRtl7771nFStWtMmTJ1ufPn3s5JNPdp8BAABQcEhNAAAAAETJiBEjrFevXsEgrCgQOnLkyEznGT16tHXr1i0YhPXmueuuu2znzp3u80MPPWSHH354MAgrvXv3di1fly9f7j6rlaxa3iqA6wVd1Xr22muvtbJly0alvAAAAMgcgVgAAAAgChQ0nT59ujVq1Chk+IEHHmgLFixwLV8jmTp1asR5Nm/ebLNmzcp0mmrVqlm5cuXso48+cp8nTpxo+/bts+OPPz44zWGHHWZ33HFHhvQFAAAAiD5qYHmUmppqW7Zssa1bt1qxYsWsQoUKruKrfwPIX6mpZtOnm61da5aSYta+vRk/NQBArFOgVYFQ1RH9ypcv797nz5+fIZi6fft2lxM2q3k6d+7sArmdOnXK8J2aTtN4aQmUF1ZpCZYsWWK7d++2P//8026//faQlrR+mkYvj+q7kpaW5l7RpOUHAoGof08sSKSyJlp5KWv8SqTyJlJZE628lDU6cvMdMRuILcxODT777DObNm2aqwhr+mOPPdZuvPHGkMfDNL8qsp5WrVrZk08+aUcddZTFEgJYKOqmTFFePLOVK/U7M5szx6x2bbPx48169CjstQMAIHOqa0p461Pvszc+L/PoPVKrVg3zplHwddeuXVayZEm7+uqr3bDnnnvOTjrpJPvmm2/c8HBKf6CcsuHWrl3rlhXtixi1+tVFk3LoxrNEKmuilZeyxq9EKm8ilTXRyktZo0ONNIt0ILYwOzX4+uuvXV4ttRTwWgEcd9xxrrL6wQcfBL/vrLPOsv/85z9umU2aNHGvWEMAC0WdjuGePc0CATP/eXPFivThkydzLAMAYpdXj9UFgJ/3OXx4bubRdJHm1zBvuFrjKijbtWvX4PhTTjnF+vbta6+99pr997//zTD/sGHDbPDgwcHPqgvXq1fPUlJSot65ly6YVC59VyJcHCZKWROtvJQ1fiVSeROprIlWXsoaHf6Gm0UyEJtZpwaqGGYWiM2sUwM9unXNNddYmTJlMu3UoGbNmnbzzTdb3bp17dFHH3WtB84991wrVaqUq3AqUHvVVVe5IK2CsqLHxU444QSLVQSwUNSpNbduJES4xnTDdHpQg/fu3WnlDQCITZUqVXLvegLLz3v03xufl3n0Hj6NN503TeXKlTOkOFAeWVGu2UiBWNV/9QqnC5iCuGBT/b+gvquwJVJZE628lDV+JVJ5E6msiVZeypr/crP8mNvqhd2pgSLlSmugFgT+5cjSpUstHgJYogCWpgNi1YwZZv90+hyRjuVly9KnAwAgFqneqf4DvDyrHtVPJdITVcrxWqtWrWznOfjggzNM403nTXPooYfa3r17Q8Z7rWUT4eILAAAg1sRci9jC7tTg/vvvd6/wdZLmzZsHhyklwbhx46xq1ar2119/uXwQapWbWQ+0BdnxgXLCKh2BV79OTlZzbOXESAtpGavpOnSwuJJIiafjvbyrVoW25o50HHvTxWHx43rfhqOs8SuRyktZo/ddRVnZsmWtbdu2If0KyB9//GH169d3wdRIlEog0jxaXps2bYLTeI0NPMuWLXP1zS5dugTTEEyZMsVtRy/wqlyv4i0HAAAACRyILexODSJRpwaq0LZo0SI4TIHXK664IpgK4aKLLrLrr7/e5ZyNpCA7PlD9WjlhPUlJaXbQQWpFoZxhySHTrVljcSWREk/He3mVQSQnx7Gmi7fjON73bTjKGr8SqbyUtfA7PohVI0eOtCFDhrh6olcPnTRpkuuPQI/LzZ07184//3x74IEH7MQTT3Tj1Uns6aef7spfoUKF4Dwa7jU06N+/vz377LOubwOl1/Km6devnzVu3Nh97tGjh+sv4e2333Z9G4hyw6oj2p7KVQUAAIDEDsQWdqcG4Z555hlXCX7vvfdChk+YMCHks/LTquMDVbTr1KlTqB0fpKSkd8zlSW9BmGTff59iaWnJIdPVqGFxJZEST8d7eTt2NFu9Or31dnqu49DjWD97XXdqunjMERvP+zYcZY1fiVReylr4HR/EKj2Npf4PFIht2rSpe9Lq7LPPdv0UeE92Kf3Vtm3bgvM0a9bMBVkVeFVDgFWrVlmDBg1s6NChwWm0/d9//333RJam0dNaWobXAa0o8Pvhhx+6+dQJrvad0oBpWGZPcQEAACB6Yq4GVtidGvjNmTPHnnjiCfvss89COviKRJVhpVT44YcfIgZiC7Ljg/btzWrX/jeAJYFAkgte+QNYmi4erxUTKfF0PJdXxVEDc6/Bjp5O9Y5jtYjVsf3AA2YlSljcitd9GwlljV+JVF7Kmv/iZVt2797dvSJp3bq1C6KGU0oDvbKitFn+wGskqsOqYQEAAAAKX8zVbgu7UwPP4sWL7d5773UtBg444AAXZN2wYUOmlWkv6BveIUJhUOvA8ePT//1PY+Eg7/O4cfHZihDxpUcPs8mTzcLvbehGgoZrPAAAAAAAQFGQnGidGoRPE96pgaxfv97l6dIjYZUrV3bDfv75Z9fa1WsFotYL4YHbkiVLxkzHBwSwEC90rC5ZYvbJJ2ZDhqS/L17MMQwAAAAAAIqWmAvEep0aTJ482bVC9YR3anDEEUfYp59+GhyvHFr67O/UIVKnBvPnz3edGvin8XdqoI6zlOv10EMPtVdffdUFY/U4l1rHKl+XXHbZZS4nrEfzKGes1q9GDCVdJYCFeKHW2x06pKfT0DutuQEAAAAAQFETczliC7tTg4EDB9q7777rXn7qsfaVV15x/z7ttNNcoFhpC5Rzdt68ea6TrosuushiNYClXuUVI46TVGsAAAAAAABAkRKTgdjC7NRAnXPplZ2eXg9CAAAAAAAAAJAN2kcCAAAAAAAAQJQRiAUAAAAAAACAKCMQCwAAAAAAAABRRiAWAAAAAAAAAKKMQCwAAAAAAAAARBmBWAAAAAAAAACIMgKxAAAAAAAAABBlBGIBAAAAAAAAIMoIxAIAAAAAAABAlBGIBQAAAAAAAIAoIxALAAAAAAAAAFFGIBYAAAAAAAAAooxALAAAAAAAAABEGYFYAAAAAAAAAIgyArEAAAAAAAAAEGUEYgEAAAAAAAAgygjEAgAAAAAAAECUEYgFAAAAAAAAgCgjEAsAAAAAAAAAUUYgFgAAAAAAAACijEAsAAAAAAAAAEQZgVgAAAAAAAAAiDICsQAAAAAAAAAQZQRiAQAAAAAAACDKCMQCAAAAAAAAQJQRiAUAAAAAAACAKCMQCwAAAAAAAABRRiAWAAAAAAAAAKKMQCwAAAAAAAAAxHIg9ueff7ZZs2aFDNu6dauNGTPGNm/evL/rBgAAABQY6rYAAACIyUDsp59+am3atLHOnTuHDK9QoYL16NHDhg8fbn/++Wd+rCMAAAAQVdRtAQAAELOB2G+++cYmTZpkL774YoZxjRo1snHjxtmjjz66v+sHAAAARB11WwAAAERb8bzOuGnTJjv99NMzHV+sWDFLS0vL6+IBAACAAkPdFgAAADHbInb16tXZTrNw4cK8Lh4AAAAoMNRtAQAAELOB2KSkJHvhhRcyHa/HtypWrJjXxQMAAAAFhrotAAAAYjY1wejRo+3YY4+1Z555xk466SSrXbu2BQIBW7p0qb377ru2fPlymz17dv6uLQAAABAF1G0BAAAQs4HYOnXq2JdffmmXXXaZ60VWrQhUWZXjjz/eZsyYYfXq1cvPdQUAAACigrotAAAAYjYQKw0bNrSPP/7Y5cv68ccfLTU11Q477DBr3rx5/q0hAAAAUACo2wIAACBmA7Gexo0buxcAAABQ1FG3BQAAQEx11gUAAAAAAAAAyBkCsQAAAAAAAAAQZQRiAQAAAAAAAKAo5IgFUPBSU82mTzdbu9YsJcWsfXuzYsUKe60AAAAAAABQpAKx77zzjs2YMcMOOugg13PtEUccYRdccEGW83z11Vf2+uuvW7NmzWzlypVWpUoVGzRoUMg08+bNs8cff9xNs3HjRtuzZ48NHz7cihf/d1N89tlnNm3aNNu+fbub/thjj7Ubb7zRSpcunavlANEyZYrZwIFmK1eatWplNmeOWe3aZuPHm/XoUdhrBwAAAAAAgHAxGTWcOXOm3XnnnS6wmpSU5IZ1797dkpOT7bzzzos4z6JFi+ziiy+2n376KRgwHThwoN1zzz12ww03uM+bNm2yU0891WbPnm3Vq1d3w8aOHWsDBgywxx57zH3++uuvbfny5Xb77be7z1u2bLHjjjvOvvnmG/vggw9yvBwgmkHYnj3NAgGzZF9ykRUr0odPnkwwFgAAAAAAINbEZI7YESNGWK9evYJBWOnTp4+NHDky03lGjx5t3bp1C2m1qnnuuusu27lzp/v80EMP2eGHHx4Mnkrv3r3tqaeecsFXefTRR+3pp5+23bt3u88VK1Z0AdapU6e6IG1OlwNEKx2BWsIqCBvOG6ZG4JoOAAAAAAAACRCIVUBy8uTJ9u6779paJbHMIQVNp0+fbo0aNQoZfuCBB9qCBQtcy9dIFCiNNM/mzZtt1qxZmU5TrVo1K1eunH300Ufuc0pKiktrsG/fvpDlyNKlS3O8HCAaZszQbyvz8QrGLluWPh0AACj8ui0AAAAQ1dQEP//8s8urWrNmTXvvvfdcvlcFMa+44gqrVatWlvMq0KogqIKafuXLl3fv8+fPzxAEVS5XBU+zmqdz584ukNupU6cM36npNI3cf//97hW+TtK8eXP3npPlhFMLW6+VrZfyQNLS0twrmrT8QCAQ9e+JBfFe1lWrQtMRJCenWVJSwL2HTxdvmyDe920il5eyxq9EKi9ljd53xYL9qdsCAAAAUQ3EesHFqlWr2qGHHupe6sxK+VOVtzUr6vjKrVhYp1feZ298XubRe6TOtDQs0nI9zz33nHXp0sVatGiR5+UoRcKoUaMyDFeLil27dlk0aV+oZbAumpRnN57Fe1mVDUOdc3mSktLsoIM2qy2sBQLJIdOtWWNxJd73bSKXl7LGr0QqL2WNjq1bt1os2J+6LQAAABDVQGzLli1dgLFs2bLBYSVLlsxRRdXLC6vKvZ/3OXx4bubRdJHm17BIw+WZZ55xFwFq/eD/vtwuZ9iwYTZ48OCQFrH16tVzqRCUhzaadOGgddZ3JcLFYTyXtWNHs9Wr0zvmSu+sSy2Fkuz771MsLS3Z9FOoWzd9umLFLK7E+75N5PJS1viVSOWlrNHhz/1fmPanbgsAAABENRAreQ0uVqpUyb2rlYGf91i/Nz4v8+g9fBpvukjLnTNnjj3xxBP22WefhXTMldvlSKlSpdwrnC5gCuKCTRdMBfVdhS2ey6oijR1r1rNn+mc9sRkIJLkgrFrEKjj7wANmJUpYXIrnfZvo5aWs8SuRyktZ818sbcto3zgHAABA/Mtz7XbmzJnZTvPll1/mernK/1qsWLFgDlWPHoGTJk2aRMzNqvxc2c1z8MEHZ5jGmy58uYsXL7Z7773XPvzwQzvggANc3toNGzbkejlAfuvRw2zyZLM6dUKHqyWshms8AACIjbotAAAAsN+B2BdffDHbaV566aVcL1ePfLVt29b+/PPPkOF//PGH1a9f3wVBI+natWvEebS8Nm3aZDrNsmXLXEtW5YD1rF+/3h544AF79tlnrXLlysFOGn744YdcLQeIFgVblywx++QTsyFD0t8XLyYICwBArNVtkTOpqWZffGE2fXr6uz4DAADEmzwHYvXIvgKjasEa6aVxTz75ZJ6WPXLkSJs8ebJrheqZNGmS3X777e5RuLlz59oRRxxhn376aXD8jTfe6D77O3XQPBquFrPSv39/mz9/vi1fvjxkmn79+lnjxo3dZ3Wc1bdvX9cJw6uvvuqCscoTq9axzZo1y/FygGhTDtgOHczat09/j7ecsAAAFKRo1m2RtSlTzBo2NFN7hjFj0t/1WcMBAADiSZ5zxKplaq9evVwaAc/nn39uHdVLkJkLoua11UCnTp1sxIgRdv3111vTpk1t0aJFdvbZZ1vv3r3d+O3bt9vSpUtt27ZtwXkUJFXQVIHXFi1a2KpVq6xBgwY2dOjQ4DTqVOL999+30aNHu2k2bdrklqEebz3qdOHdd991L78KFSrYK6+8kuPlAAAAoOiIZt0WmVOwVbnv0zsh/Xe4OibVcNIuAQCAeJLnQOx///tfGz58eIZedNWa1ZO6H88Ude/e3b0iad26tQt+hlNKA72y0rx58ywDpmoNoVd2slsOAAAAio5o122RkTbnwIHpQdhwGpaUZDZokK4LePIHAAAkeGoCf2sBjzq2mjhxYvDzsGHD8r5mAAAAQAGhblvwZsww82X6ihiMXbYsfToAAICEDsT6c7HK3r17Xf7Wq666ygYPHuxaEKxevTo/1hEAAACIKuq2BW/VqvydDgAAIG5TEyxYsMA++eQTlzdrw4YNdtddd9mVV17pOjPo0aOHzZo1y8qWLRvSoRYAAAAQi6jbFrxatfJ3OgAAgLgNxPbt29e6du3qWgpI3bp1XedVqqB+9dVXbpw61AIAAABiHXXbgteunbZzesdckfLEaldovKYDAABI6NQEp59+ur388st2yimnWL9+/VwFVRVVOeigg1wvsxUrVszPdQUAAACigrptwVNa3vHj0//9T/w7yPs8bhwddQEAgPiR5xaxct5557lXJPXr17err77aAoFAsGUBAAAAEKuo2xa8Hj3MJk82GzjQbOXKf4erJayCsBoPAABgiR6IVYcGFSpUyHKa66+/nooqAAAAYh5128KjYGv37mbTp5utXWuWkmLWvj0tYQEAQPzJc2qCe++9N9tp7r777rwuHgAAACgw1G0Ll4KuHTqkB2D1ThAWAADEozy3iH3++eddi4DixSMvYu/evfbSSy/ZnXfeuT/rBwAAAEQddVsAAADEbCB227ZtNmPGjEzHq7K6Zs2avC4eAAAAKDDUbQEAABCzgVj1JPvhhx9asWLFXO+yjRo1yjDNoEGD9nf9AAAAgKijbgsAAICYDcQ2bdrUvVJTU23q1Kn27rvvWkpKinXv3t3Kli3rprnkkkvyc10BAACAqKBuCwAAgJgNxHrUauC0005z/96wYYO9+uqrtn37djviiCOsXbt2+bGOAAAAQIGgbgsAAIBoSc7PhVWtWtVatGhh8+bNs27dutnJJ5+cn4sHAAAACgx1WwAAAMRcIHb16tU2ZswYO+yww6xNmza2cuVKe/nll+29997Lj8UDAAAABYa6LQAAAGIqNYF6jv2///s/mzhxouvY4JBDDrF+/frZf//7X6tRo4ab5scff7SWLVvm5/oCAAAA+Y66LQAAAGI2EHvQQQe5fFnnn3++ffPNN3bUUUdlmOamm26y999/f3/XEQAAAIgq6rYAAACI2UCsHtE688wzbdu2bfbwww+HjNu3b5+rwP7555/5sY4AAABAVFG3BQAAQMwGYvWo1hNPPJHpeFViu3btmtfFAwAAAAWGui0AAABiNhB74YUXZjm+fPnydvvtt+d18QAAAECBoW4LAMhMaqrZ9Olma9eapaSYtW9vVqxYYa8VkDscx7EhOa8zttceM7Ndu3a5jgt++ukn9zk1NdVmz57t/n3iiSfm13oCAAAAUUPdFgV5IfzFF+kXw3rX53iWaOVF/JkyxaxhQ7MuXczGjEl/12cNB4qKRDuOU2P4b0+eA7Fy9913W61ataxVq1Y2dOhQN6xYsWK2ePFiu+6662znzp35tZ4AAABAVEWrbvvOO++45T355JN2ww032Msvv5ztPF999ZVde+21Ll3CyJEjbdy4cRmmmTdvng0cONAee+wxu/POO+3WW291+WwzEwgE7PTTT7cVK1bkqRzYf4l2IZxo5UX80bHas6fZ8uWhw3Ua1XCOZRQFiXYcT4nxvz15Tk1wxx132JdffmlPP/20HXnkkSEVyl69erlWBffcc4+rEAIAAACxLFp125kzZ7ogqQKrSUlJblj37t0tOTnZzjvvvIjzLFq0yC6++GLXKrd06dJumAKu+n4FcmXTpk126qmnuta61atXd8PGjh1rAwYMcIHZSDT8vffes7179+aqDMjfC+FAwCw5OeOF8OTJZj16WNxItPIi/qgF3cCB6cdwOA3TKX3QIJ3TebwbsSvRjuMpReBvT55bxKrX2KlTp9rZZ59tjRo1spIlS4aMP+CAA2zLli35sY4AAABAVEWrbjtixAgXyPWCsNKnTx/XyjUzo0ePtm7dugWDsN48d911V7BV7kMPPWSHH354MAgrvXv3tqeeesqWhzd5MbP58+fb6tWrc73+KJgLYdGFcCw9Ork/Eq28iE8zZmRsQRh+LC9blj4dEKsS6ThOLSJ/e/LcIrah2vVmQzm2AAAAgFgXjbqtgqbTp0+3a665JmT4gQceaAsWLHAtXxX0DaeAsJcawT/P5s2bbdasWda5c2c3zTHHHBMyTbVq1axcuXL20UcfWb9+/YLDla5ALXzVyva2227Lcp13797tXh4v+JyWluZe0aTlK31CtL+nMChH3cqV/7bOSU5Os6SkgHv3t9bRdB06WJGXaOVNlOM40cq6alVoi7pIx7E3Xbxtgnjft4lU3kQ6jqcX4t+e3Bw7eQ7Ezp0711XqihdPX4QOWr9ly5a5FwAAABDrolG3VaBVy1Rw1K98+fLBVqrhgdjt27fbypUrs5xHgVgFcjt16pThOzWdpvFTntn//e9/tmfPnmzXWa1uR40alWH42rVro97IQhcxCjZr2yt1QzxRD9WtWv37OSkpzQ46aLOONAsEkkOmW7PGirxEK2+iHMeJVlY9cJCT41jTcRwXbfFc3kQ6jtcW4t+erVu3Rj8Qe8opp7hK4E033WRHH320O2D1UgVVd+FVgZs4cWJeFw8AAAAUmGjUbTdu3OjeveCux/vsjc/LPHoPn8abzr/cOXPmWJ06dax27dq2ZMmSbNd52LBhNnjw4JAWsfXq1bOUlBSrWLGiRftCWCkc9F3xdiGckqJ98e/n9NY5Sfb99ymWlpYcMl2NGlbkJVp5E+U4TrSyduxopowuakGXnm8y9DhWxpm6ddOni4fcmom0bxOpvIl0HKcU4t8efzqpqAVi9WjTX3/95Xpe9VoM3Hzzze69RIkS9vDDD1sXdU0GAAAAxLho1G29vLDhrWu9z+HDczOPpos0vxdAFrVg/fDDD11wOadKlSrlXuF0YVoQF6cqV0F9V0Fq396sdu1/L4QlEEhyF4b+C2FNFw9FT7TyJspxnGhlVZHGjk3v4Ef05LF3HKt1nY7tBx7Q3wiLS/G8bxOpvIl0HLcvxL89uTlu9uur1cnA999/b9dee63rUEA9t954443222+/2aWXXro/iwYAAAAKVH7XbStVquTew1MCeDlYvfF5mUfvkVINaDpvmscee8z69++f6/VG/lMro/Hj0//t67ct5PO4cUW/NVKilhfxS72rq5f1OnVChyuYEwu9rwM5kSjHcbEi8rcnzy1iPeqtdcyYMfmzNgAAAEAhys+6rfK/FitWLNjhlUd56KRJkyYRc7zWqlUr23kOPvjgDNN402kadRT23Xff2Wo9j/iPNf8kRLvnnntcqoJbbrklX8qJ3F0Iq0dndSbivxDWhWG8XAgnankRv3Ssdu+e3sGPckvqsWa1qCvsYA6QG4lyHPcoAn979jsQO23aNHv66addBwdqyt2yZUu78sorrXXr1vmzhgAAAEAByc+6bdmyZa1t27b2559/hgz/448/rH79+i6YGknXrl0jzqPltWnTJjjNrFmzQqZRPlu1iFUKhTJlythLL70UMv7zzz93eW5vuOEGa9iwYa7Lg/2XKBfCiVpexC8ds+plXfezlFsyzp5eR4JIlOO4R4z/7dmvzX7dddfZiSeeaJMmTXLJ/xcvXmzPPvusHX/88Xbffffl31oCAAAAURaNuq3SHUyePNn27dsXHKbl33777S7Qq4DvEUccYZ9++mlwvNIh6LO/B17No+FqMStKOTB//nxbvnx5yDT9+vWzxo0bR1yX1NTUYKckKPwLYV0U6j1WLgyjJdHKCwAofMVi+G9PnlvEPvHEE/bqq6/agw8+aBdeeKFVqVLFDV+/fr2rsKqy2rx5czvttNPyc30BAACAfBetum2nTp1sxIgRdv3111vTpk1t0aJFdvbZZ1vv3r3d+O3bt9vSpUtt27ZtwXmaNWvmvlOB1xYtWtiqVausQYMGNnTo0OA06tn5/ffft9GjR7tpNm3a5JahvLCRaB3UIlYGDhxonTt3drlwAQAAUHCSApG6W82Bjh07urvuymEViSqUV199tb3zzjv7u45xSTm91JGC8nhVrFgxqt+lVg/KCVajRo246wEwkcuaaOVNpLImWnkpa/xKpPJS1qJfX4r3uq1SFpQoUcLtM+3DvXv3WqlSpXK8D374YbNVqPDvPlDD3Jo11amYUiJknM9rlKuei3ftCh2nxyErVFA+W7N16/4drvXavn2dHX54dffg3uLFGZfboIFZ8eJmq1aZ7dgROq5aNbPKlc0U0/alxnVKljSrVy/934sW/dubskfjNI0e1/Q1RHa0TC17587QfHOiFjZeloclS9TqOHS8em8uU0YBfbNNm0LLunv3OmvevLrt25ecYRuqU5FGjdL/rXHh/bJp22sfaJlatl/ZsmY6jNUIe+nS8C1oduCB6Y+jqiwqk1/16uoILn0b/JNSOKh06X87elm4MONyvW2obe+7r+BUqpRm+/atsfLla9jq1aHnDfXUXb9+5ttQ36nv1rHyT6rkIJ0W9Mip+rLzNQ7P0TY84ACzcuXMNm4027AhdJyGa3xm21DL1fIjHd/VqqXZrl1rrHTpGrZ+fXLEbajjT8dhZsf333/rJk3ouKpVzXR/SMM1PjfHt3Ij6ueuR3TDU0trf2u/qxwqT2bH919/me3dGzq+Zs0027ZtjRUvXsM2bw4tazTOEaLfk35Xathf0OeI4sXTbO7cdVaqVPWQv3/ROEeItoG2RaRtGO1zhFma/fzzOitXLrSs0TpH6NjWMa59pn1X0OeIpUvTbNWq9VatWrVgeaN1jtD6aL30W9RvsqDPEX/+mWbr1oWWNVrnCB1nOt60/bQdC/ocsXBhmruh7i9rtM4RlSptsZSUnNVZ89wi9rDDDsu0oiq6a6+7/gAAAECsi/e6rT/oqouRnARh/W68Mf1i2NOxo1I5pF/gDxqUcXovXj12rNn8+aHjBg9WS2GzL780e/zxf4cHAkl28MFlTX2l6aIr0nJffDH9ovDpp82+/TZ03CWXmJ11ltmPP6pDsowXxV5PylpvX6YI55FH0i/0X3nF7OOPQ8f17GnWp48uXs1uuinjRduzz6b/+9ZbMwY87rzTrEULs3ffTe88xF/W444rbc2bp18wh5dVF4lvvpn+b22P8AvyG24wa9tWeX/NJkwIHXfMMWbqh00X45G24auvpl8Ya9v/8EPouCuuMFOj7+++M3vggdBxOvy9fuwiLffJJ9MvurWP/ml8HXTuuWZdupj9/rvZqFGh4zSP5pWbb84YBFBWkGbNzN56y+ztt0PHnXqq2ZVXpgdYwtdJF+OvvZb+77vuynihP3y42bHHmn3yidnzz4eOUypmHfMKYkUq65Qp6b+Hhx82+/XX0HH9+5u1bGn29dfpx5XfYYelr4uOv0jLnTgxPeChY2rmzNBxakR/zjnp33fHHaHjFBx49NH0f2u9wwPs6qBGQQ0dg++/HzpOeRQvvTQ9wHX99aHjFEvwUk3rO8MDZCNHpgdwpk5NP678onGOkCOPNLvttsI5R6isb71V2r7+OimkR/ZonCPkpJPMrrmmcM4RCgg+/3xZW7AgtKzROkecf77ZBReknyN0XBX0OeLuu5Ns4cIKVrLkv+WN1jliwADleU8/Rzz0UMGfI4YNS7LNm0PLGq1zhM73Rx2Vfo6YNKngzxHXXptke/aEljVa54i777bot4jVo0xjtUWycNNNN9mdOrP8Y8GCBZl2SpBoaBEbHYlU1kQrbyKVNdHKS1njVyKVl7IW/foSddvIaBFLi1gPLWL/RYvYdLSITUeL2HS0iP0XLWLT0SI2nwOx6k32oIMOco9xRaJeXH/99Ve77LLLgsOUU+u9997Ly9fFHQKx0ZFIZU208iZSWROtvJQ1fiVSeSlr0a8vUbeNjDprdCRSWROtvJQ1fiVSeROprIlWXspa+PWlPKcmUC+tahGgXmTDH23asGGDffPNN3bKKae4Sqvs2rXLPvvss7x+HQAAABA11G0BAAAQbXkOxL7wwgu2Y8cO++qrryKOL126tE2bNi34eefOnbYnvL08AAAAEAOo2wIAACBmA7E1a9a0L7/80iooMUMOdejQIa9fBwAAAEQNdVsAAADEbCB26NChuaqoytVXX53XrwMAAACihrotAOScOkyaPj29cx91PtS+fXrHPQCArOU5W+2FF16Y63nOOeccixfq42zr1q22YsUK++uvv2zjxo22N7yLOAAAABQJiV63BYCcmjIlvaf0Ll3MxoxJf9dnDQcARKlFbLiFCxfaM88844KTp556qnXr1m2/lvfOO+/YjBkzXO+1WvYRRxxhF1xwQZbzKKfX66+/bs2aNbOVK1dalSpVbNCgQSHTzJs3zx5//HE3jYKnyu01fPhwK148dFPs27fPXnrpJXv77bdtSoS/KJdffrnrXddTp04du/fee7NdRwAAAMS+/K7bAkA80KVxz55qmGTm74R8xYr04ZMnm/XoUZhrCABxEoj9+++/XVDzgw8+sJSUFLvyyivtuuuuc+OmT5/uKqjqtEAtRR955BG79NJL7YknnsjTSs2cOdP1WqvAalJSkhvWvXt3S05OtvPOOy/iPIsWLbKLL77YfvrpJ9eZggwcONDuueceu+GGG9znTZs2ufWcPXu2Va9e3Q0bO3asDRgwwB577LHgshSYTUtLc4HgYpk8X3HAAQfYDz/8YMuXL7datWrZkUce6dYPAAAAsa8g67YAEC/pCAYOTA/ChtMwXbqrHVT37qQpAID9CsQqgNm2bVsX7BS1DFAerbVr19rIkSOtT58+rgKrlgJqWfrhhx+61qLt2rWz//73v5ZbI0aMsF69egWDsKLvGDZsWKaB2NGjR7vv94Kw3jydO3e2a665xsqUKWMPPfSQHX744cEgrPTu3dt1znDzzTdb3bp13bA77rjDvfft29eWLFkS8fsUoG3ZsqV7AQAAoOgo6LotAMSDGTPMli/PfLyCscuWpU/XsWNBrhmQd+Q7RkHLURNOBSZLlChhb7zxhnucXzlR1WL10UcfdZXSs846yxYsWOBalSrY+dtvv7kKrMbnlloeqBVCo0aNQoYfeOCB7ju8CnO4qVOnRpxn8+bNNmvWrEynqVatmpUrV84++uijXK8rAAAAip6CrNsCQLxYtSp/pwMKG/mOEbMtYj/77DP78ssvXdBSKlWq5B731+P4gwcPtl9++SWk9aoqtqqoNm3aNNcrpECr8rMqOOpXvnx59z5//vwMwdTt27e7nLBZzaOWsapQd+rUKcN3ajpNkxtKXTBhwgTXMnbLli0uJYJa5SplQSS7d+92L4/m8ZajVzRp+XqsLtrfEwsSqayJVt5EKmuilZeyxq9EKi9ljd53RUNB1m0BJI54b1lXq1b+TgcUJvIdI6YDseqIyquo+nXt2tXat28fUlH1KEXAwQcfnOsVUqsEt2JhnWd5n73xeZlH7+HTeNNFWm52OnbsaI0bN3b/VlD29NNPt2+//TZirti77rrLRo0alWG4HoHbtWuXRfsiRi2DddEU73lsE6msiVbeRCpropWXssavRCovZY0OpQyIhoKs2wJInKCO8qeuXGnWqpXZnDlmtWubjR8fP8Gcdu3MlM1PgapIeWJ16tR4TQfEMvIdI+YDsWoFkJn69etnOq5ChQq5XiGv4qvKvZ/3OXx4bubRdJHm17BIw7Ny2223hXxWDjF14vD666/bueeem2F65bdVCwt/i9h69eq5/GMVK1a0aF8wqez6rkS4OEyUsiZaeROprIlWXsoavxKpvJQ1Ovy5//NTQdZtAcS/RGlZp4CUAssqU/j9Ku/zuHEErhD7yHeMmA/EZhWkjNRiYH/o0TDZs2dPyHDvsX5vfF7m0Xv4NN50kZabG7oYkdmzZ0cMxJYqVcq9wukCpiAu2LSfCuq7ClsilTXRyptIZU208lLW+JVI5aWs+S9ayy/Iui2A+JZoLesUUFZg2Wv961FLWAVh4yHgjPhHvmMUphzVblP11yUPldWs5suM8r96eVf99AicNGnSJGKO11q1amU7jx4nC5/Gmy7ScjMzZswY11pCuWnDg7579+7N8XIAAABQ8AqybgsgvuWmZV28ULB1yRKzTz4xGzIk/X3xYoKwKDrId4yYbxH7+eef2yWXXOICpOF+/vln+/PPPyNWVKcrU3kulS1b1tq2bZthmX/88YcLfmaWm0s5vSLNo+W1adMmOM2sWbNCplm2bJkLonZR93g5tG3bNrfMkiVLBoct1l8eM9cpGAAAAGJXQdZtAcS3RG1Zp9Nnhw5ma9aY1agRmpIBiHXkO0bMB2IVeJw4cWKm49VBVX4+2jVy5EgbMmSIXX/99cHOtSZNmmS33367W+bcuXPt/PPPtwceeMBOPPFEN/7GG290nWWpUwcvf5fm0XC1mJX+/fvbs88+a8uXL7e6+lX9M02/fv2CnW6F50CL1Ftv37597YsvvgjJL/bII4/YqaeeameeeWaeygwAAICCUdB1WwDxi5Z1QNFDvmPEfCC2YcOG9u6771q5cuVyVcHNa1CyU6dONmLECBeIbdq0qS1atMjOPvts6927txuvlABLly513+Fp1qyZC7Iq8NqiRQtbtWqVNWjQwIYOHRqSx/X999+30aNHu2k2bdrklvHYY4+FfP+4ceNcsPaDDz6wHTt22LXXXutyyN56663B7bF+/XrXYZcq5GoNq9QIDz30EBV0AACAGFfQdVsA8YuWdUDRRL5jxHQg9tBDD7XmzZvneuF5mcfTvXt394qkdevWLogaTikN9MpuncIDr+Guuuoq1xL3vvvuc4FVdegQ3slXq1at3AsAAABFS2HUbQHEJ1rWAUWXgq0KOynz0Nq1arxn1r49v1dEV44yuSglQF7kdb7Cptyv6qXXa92q91KlShX2agEAACAfJFrdFkDBtKyrUyd0uFrWaTgt64DYz3esAKzeCcIiJlrEtmzZMk8Lz+t8AAAAQLRQtwWQ32hZBwDIt0AsAAAAAADIvmXdmjVmNWqYJefo+VMAQCLhTwMAAAAAAAAARBmBWAAAAAAAAACIMgKxAAAAAAAAABBlBGIBAAAAAAAAIMoIxAIAAAAAAABAlBGIBQAAAAAAAIAoIxALAAAAAAAAAFFGIBYAAAAAAAAAoqx4tL8AAAAAAAAAsS811Wz6dLO1a81SUszatzcrVqyw1wqIH7SIBQAAAAAASHBTppg1bGjWpYvZmDHp7/qs4QDyB4FYAAAAAACABKZga8+eZsuXhw5fsSJ9OMFYIH8QiAUAAAAAAEjgdAQDB5oFAhnHecMGDUqfDvj/9u4EPIoqW+D4yUZCyAKEHcK+iSwK4gIoiwqICjPiQ8Q3CC4zPtEBHVlUVpFFVNBhRnBXZhRHGNRBhUFBZZNFVBTBsCMBhIiELGQhSb/v3NhNV6eDICmSVP1/39dfp6tuVffpdCqnTt+6F+eGQiwAAAAAAIBLrVpVtCdsYDF2//7CdgDODYVYAAAAAAAAlzp0qGTbASgehVgAAAAAAACXql27ZNsBKB6FWAAAAAAAAJe68kqRevVEQkKCr9fliYmF7QCcGwqxAAAAAAAALhUWJvLss4U/BxZjvY+feaawHYBzQyEWAAAAAADAxW66SWThQpG6da3LtaesLtf1AM5deAnsAwAAAAAAAOWYFlv79RNZuVIkJUWkenWRq66iJyxQkijEAgAAAAAAwBRdu3YVOXJEpEYNkVCuowZKFH9SAAAAAAAAAGAzCrEAAAAAAAAAYDMKsQAAAAAAAABgMwqxAAAAAAAAAGAzCrEAAAAAAAAAYDMKsQAAAAAAAABgMwqxAAAAAAAAAGAzCrEAAAAAAAAAYDMKsQAAAAAAAABgMwqxAAAAAAAAAGAzCrEAAAAAAAAAYDMKsQAAAAAAAABgMwqxAAAAAAAAAGAzCrEAAAAAAAAAYDMKsQAAAAAAAABgMwqxAAAAAAAAAGAzCrEAAAAAAAAAYDMKsQAAAAAAAABgMwqxAAAAAAAAAGAzCrEAAAAAAAAAYLNwKaMWL14sq1atkqZNm8quXbukXbt2MmjQoNNus3btWlmwYIG0bNlSDh48KFWqVJERI0ZY2mzbtk3mzp1r2hw7dkxyc3Nl7NixEh5ufSvy8vLkjTfekPfee08WLVpU5LnS0tJk8uTJUqdOHQkNDZWdO3fKuHHjpEaNGiX0DgAAAAAAAABwijJZiF2zZo1MnTrVFFZDQkLMsn79+pmC58CBA4Nus3v3bhk6dKhs3rxZoqKizLLhw4fLE088IaNHjzaPU1NTpU+fPrJx40apVq2aWTZr1iy5//77Zc6cOb59aWG2oKDAFILDwsKCPt+AAQPMdtdff715/OWXX8q1114rX3zxhURERJTwOwIAAAAAAACgPCuTQxOMHz/eFDq9RVh1++23y4QJE4rdZsqUKdK7d29fEda7zbRp0yQrK8s8nj17trRt29ZXhFWDBw+WF198UZKTk33LHn/8cVMIbtKkSdDn0gLthg0bzPN5tW/fXjwejyxcuPAcIgcAAAAAAADgRGWuEKtF05UrV0rjxo0tyxs1aiTbt283PV+DWbp0adBtjh8/Lp9//nmxbRISEqRSpUqybNmyM36Nup8GDRoU6S2rz7dkyZIz3g8AAAAAAAAAdyhzQxNooVXHZ9XiqL+YmBhzn5SUVKSYmpmZacaEPd02PXr0MIXc7t27F3lObadtzpTuJ/C5fm0/OTk55uY/xqzSIRD0Zifdv/bWtft5ygI3xeq2eN0Uq9viJVbnclO8xGrfcwEAAABOUeYKsTqBlgqcPMv72Lv+t2yj94FtvO2C7fd0r/Fs96NDJEyaNKnI8pSUFMnOzha7T2K0Z7CeNOk4u07mpljdFq+bYnVbvMTqXG6Kl1jtkZ6ebuv+AQAAAFcXYr3jwmpy78/7OHD52Wyj7YJtr8uCLT/dazzb/Tz88MPy4IMPWnrEJiYmSvXq1SUuLk7sPmHS16zP5YaTQ7fE6rZ43RSr2+IlVudyU7zEag//sf8BAACA8q7MFWLj4+PNfW5urmW597J+7/rfso3eB7bxtgu239O9xgMHDpzVfiIjI80tkJ7AnI8TNj1hOl/PVdrcFKvb4nVTrG6Ll1idy03xEmvJc8N7CQAAAPcoc9mtjv+qk2B5x1D10kvgVLNmzYKOzVq7du1f3aZ58+ZF2njbBdtvcUpqPwAAAAAAAADcocwVYqOjo6VLly6yc+dOy/IdO3ZI/fr1TRE0mJ49ewbdRvfXuXPnYtvs37/f9GS95pprzvg16n727dtnJhULfL5rr732jPcDAAAAAAAAwB3KXCFWTZgwQRYuXGgpdM6fP18mT55sLoXbunWrtGvXTpYvX+5bP2bMGPPYf1IH3UaXa49ZNWzYMElKSpLk5GRLmzvuuEOaNGkSdAy0YLP1duvWTS699FJ57733fMs2bNhghh647bbbSuhdAAAAAAAAAOAUZW6MWNW9e3cZP368jBw5Ulq0aCG7d++W/v37y+DBg836zMxM0yM1IyPDt03Lli3ltddeM4XXNm3ayKFDh6RBgwYyatQoXxudVOLDDz+UKVOmmDapqalmH3PmzLE8/zPPPGOKtUuWLJETJ07IAw88YMZ+nThxoq/NokWLZNKkSbJ3716JiIiQbdu2yccffxx0HFgAAAAAAAAA7lYmC7GqX79+5hZMx44dTRE1kA5poLfTadWqVZHCa6B7771XwsPD5cknnzQ9cD0eT5FJvipXriyzZs06o1gAAAAAAAAAuFuZLcSWpgoVKlgeazGWnq4AAAAAAAAAHDVGLAAAAAAAAAA4CYVYAAAAAAAAALAZhVgAAAAAAAAAsBljxAIAAAA2Wrx4saxatUqaNm0qu3btknbt2smgQYNOu83atWtlwYIF0rJlSzl48KBUqVJFRowYYWmzbds2mTt3rmlz7NgxM7ns2LFjzaSzXitWrJBPPvlEMjMzTfvLLrtMxowZI1FRUbbFCwAAgOAoxAIAAAA2WbNmjUydOtUUVnUCWNWvXz8JDQ2VgQMHBt1m9+7dMnToUNm8ebOvYDp8+HB54oknZPTo0eZxamqq9OnTRzZu3CjVqlUzy2bNmiX333+/zJkzxzxet26dJCcny+TJk83jtLQ0ufzyy2X9+vWyZMmS8xI/AAAATmFoAgAAAMAm48ePlwEDBviKsOr222+XCRMmFLvNlClTpHfv3pZeq7rNtGnTJCsryzyePXu2tG3b1leEVYMHD5YXX3zRFF/Vc889Jy+99JLk5OSYx3FxcaZQu3TpUlOkBQAAwPlFIRYAAACwgRZNV65cKY0bN7Ysb9SokWzfvt30fA1GC6XBtjl+/Lh8/vnnxbZJSEiQSpUqybJly8zj6tWrm2EN8vLyLPtR+/btK6EoAQAAcKYYmgAAAACwgRZatQiqxVF/MTEx5j4pKalIMVXHctXi6em26dGjhynkdu/evchzajtto55++mlzC3xNqlWrVkFfs/ae9fag9Q5noAoKCszNTrp/j8dj+/OUBW6K1W3xEqtzuSleN8XqtniJ1R5n8xwUYgEAAAAb6ARayn/yLP/H3vW/ZRu9D2zjbRdsv16vv/66XHPNNdKmTZug63X4g0mTJhVZnpKSItnZ2WL3SYz2+tWTJh1D18ncFKvb4iVW53JTvG6K1W3xEqs90tPTz7gthVgAAADABt5xYfUEwJ/3ceDys9lG2wXbXpcFW65eeeUVc6LwwQcfFPuaH374YXnwwQctPWITExPNMAc6xqzdJ0walz6XG04O3RKr2+IlVudyU7xuitVt8RKrPfzH9f81FGIBAAAAG8THx5v73Nxcy3Lvpf/e9b9lG70PbONtF2y/mzZtkueff15WrFhhmeArUGRkpLkF0hOY83HCpidM5+u5SpubYnVbvMTqXG6K102xui1eYi15Z7N/57/rAAAAQCnQ8V/DwsJ846x66WVyqlmzZkHHeK1du/avbtO8efMibbztAve7Z88emTFjhvz3v/+VWrVqmXFrf/755xKIEAAAAGeDQiwAAABgg+joaOnSpYvs3LnTsnzHjh1Sv359U0wNpmfPnkG30f117ty52Db79+83PWJ1DFivo0ePysyZM+W1116TypUrm2XffPONfPXVVyUWJwAAAM4MhVgAAADAJhMmTJCFCxeaXqhe8+fPl8mTJ5vL5bZu3Srt2rWT5cuX+9aPGTPGPPaf+EG30eXaY1YNGzZMkpKSJDk52dLmjjvukCZNmpjHOrnWkCFD5MILL5R//etfphir48Rq79iWLVuep3cAAAAAXowRCwAAANike/fuMn78eBk5cqS0aNFCdu/eLf3795fBgweb9ZmZmbJv3z7JyMjwbaNFUi2aauG1TZs2cujQIWnQoIGMGjXK10Ynnvjwww9lypQppk1qaqrZx5w5c3xthg8fLu+//765+YuNjZW33nrrvMQPAACAUyjEAgAAADbq16+fuQXTsWNHU0QNpEMa6O10WrVqZSm8BtLJufQGAACAsoGhCQAAAAAAAADAZhRiAQAAAAAAAMBmFGIBAAAAAAAAwGYUYgEAAAAAAADAZhRiAQAAAAAAAMBmFGIBAAAAAAAAwGYUYgEAAAAAAADAZhRiAQAAAAAAAMBmFGIBAAAAAAAAwGYUYgEAAAAAAADAZhRiAQAAAAAAAMBmFGIBAAAAAAAAwGYUYgEAAAAAAADAZhRiAQAAAAAAAMBmFGIBAAAAAAAAwGYUYgEAAAAAAADAZhRiAQAAAAAAAMBmFGIBAAAAAAAAwGYUYgEAAAAAAADAZhRiAQAAAAAAAMBmFGIBAAAAAAAAwGYUYgEAAAAAAADAZhRiAQAAAAAAAMBmFGIBAAAAAAAAwGbhdj+BU3k8HsnIyJC0tDTJz8+X2NhYiYmJkYiIiNJ+aQAAAAAAAADKmDJbiF28eLGsWrVKmjZtKrt27ZJ27drJoEGDTrvN2rVrZcGCBdKyZUs5ePCgVKlSRUaMGGFps23bNpk7d65pc+zYMcnNzZWxY8dKePipt+LQoUMyffp0adasmWRnZ8vhw4dl4sSJUqlSJV+bP/7xj/LSSy/5HtetW1dmzJjxq68RAAAAAAAAgPuUyULsmjVrZOrUqaawGhISYpb169dPQkNDZeDAgUG32b17twwdOlQ2b94sUVFRZtnw4cPliSeekNGjR5vHqamp0qdPH9m4caNUq1bNLJs1a5bcf//9MmfOHPP45MmT0rNnT3nrrbfkwgsvNMveeecdufnmm2XJkiW+56tVq5Z89dVXkpycLLVr15aLL77YvD4AAAAAAAAACFQmK4fjx4+XAQMG+Iqw6vbbb5cJEyYUu82UKVOkd+/eviKsd5tp06ZJVlaWeTx79mxp27atrwirBg8eLC+++KIpqCotwGpB1VuEVX379pV169bJ559/7lsWFhYmF110kdxwww3SoUMHirAAAAAAAAAAilXmqodaNF25cqU0btzYsrxRo0ayfft20/M1mKVLlwbd5vjx474CarA2CQkJZsiBZcuWFdtGi67169e39IgFAAAAAAAAgHI7NIEWWvPy8izjsSqdCEslJSUVKZRmZmaaMWFPt02PHj1MIbd79+5FnlPbaRulbVq0aHHaNqqgoEBefvllU6TVCbt0SATtlatDFgSTk5Njbl66jXc/erOT7l8nF7P7ecoCN8XqtnjdFKvb4iVW53JTvMRq33MBAAAATlHmCrE6gZbynzzL/7F3/W/ZRu8D23jbnU0br27dukmTJk3Mz1qU1WEKNmzYEHSYAh0iYdKkSUWWp6SkmAnB7D6J0Z7BetLk9CEU3BSr2+J1U6xui5dYnctN8RKrPdLT023dPwAAAODqQqx3XFhN7v15HwcuP5tttF2w7XXZ2bRRjz32mGW9jk971113yYIFC+SWW24psv3DDz8sDz74oKVHbGJiolSvXl3i4uLE7hMmjUufyw0nh26J1W3xuilWt8VLrM7lpniJ1R7+Y/8DAAAA5V2ZK8TGx8eb+9zcXMty72X93vW/ZRu9D2zjbXcmbWrUqFHs69aTEbVx48aghdjIyEhzC6QnMOfjhE1PmM7Xc5U2N8XqtnjdFKvb4iVW53JTvMRa8tzwXgIAAMA9ylx2q+O/esdd9aeXwKlmzZoFHb+1du3av7pN8+bNi7TxtjubNk899ZSZvEvHpg0s+p48efI3RA0AAAAAAADAycpcITY6Olq6dOkiO3futCzfsWOHKX5qoTSYnj17Bt1G99e5c+di2+zfv98UUa+55ppi22gP2X379sm1115rHmdkZJh9VqhQwddmz5495l4nBQMAAAAAAACAMl2IVRMmTJCFCxdKXl6eb9n8+fNl8uTJ5lK4rVu3Srt27WT58uW+9WPGjDGP/Sd10G10ufaYVcOGDZOkpCRJTk62tLnjjjt8k24NHDjQ9Mj94osvfG3effdd6dSpk6/IOmTIEDMmbEREhK/N3//+d+nTp4/07dvXtvcFAAAAAAAAQPlU5saIVd27d5fx48fLyJEjpUWLFrJ7927p37+/DB482KzXIQG0h6r2TPVq2bKlvPbaa6bw2qZNGzl06JA0aNBARo0aZRnH9cMPP5QpU6aYNqmpqWYfc+bMsUwKsWzZMpk2bZqsXbvW9IbVXrPvvPOOr03Dhg3l6NGjZsIuLQxrb1gdGmH27Nm+icMAAAAAAAAAoEwXYlW/fv3MLZiOHTuaImogHdJAb6fTqlUrS+E1mHr16pkerqfToUMHcwMAAAAAAACAcjk0AQAAAAAAAAA4CYVYAAAAAAAAALAZhVgAAAAAAAAAsBmFWAAAAAAAAACwGYVYAAAAAAAAALAZhVgAAAAAAAAAsBmFWAAAAAAAAACwGYVYAAAAAAAAALAZhVgAAAAAAAAAsBmFWAAAAAAAAACwGYVYAAAAAAAAALAZhVgAAAAAAAAAsBmFWAAAAAAAAACwGYVYAAAAAAAAALAZhVgAAAAAAAAAsBmFWAAAAAAAAACwGYVYAAAAAAAAALAZhVgAAAAAAAAAsBmFWAAAAAAAAACwGYVYAAAAAAAAALAZhVgAAAAAAAAAsBmFWAAAAAAAAACwGYVYAAAAAAAAALAZhVgAAAAAAAAAsBmFWAAAAAAAAACwGYVYAAAAAAAAALAZhVgAAAAAAAAAsBmFWAAAAAAAAACwGYVYAAAAAAAAALAZhVgAAAAAAAAAsBmFWAAAAAAAAACwGYVYAAAAAAAAALAZhVgAAAAAAAAAsBmFWAAAAAAAAACwGYVYAAAAAAAAALAZhVgAAAAAAAAAsBmFWAAAAAAAAACwGYVYAAAAAAAAALAZhVgAAAAAAAAAsBmFWAAAAAAAAACwGYVYAAAAAAAAALAZhVgAAAAAAAAAsBmFWAAAAAAAAACwWbiUUYsXL5ZVq1ZJ06ZNZdeuXdKuXTsZNGjQabdZu3atLFiwQFq2bCkHDx6UKlWqyIgRIyxttm3bJnPnzjVtjh07Jrm5uTJ27FgJDz/1Vhw6dEimT58uzZo1k+zsbDl8+LBMnDhRKlWq5GuTlpYmkydPljp16khoaKjs3LlTxo0bJzVq1JCyJD8vV1Zufk5Sjh6X6gnxclW7eyUsvII4kZtidVu8borVbfESqzNjdVu8xOrMWN2U15YJBfkiR1aKHE4RkeoiNa4SCQ0TR3JTrG6Ll1jFsdwUr5tidVu8xCplgqcMWr16tefyyy/3FBQU+Jb17dvXM3/+/GK32bVrl6d58+aerKws37I///nPnunTp/seHzt2zNOwYUNPSkqKb9nMmTM999xzj+9xbm6up3Xr1p4tW7b4li1atMjTu3dvy/P16tXL8/777/seb9q0ydO2bVuz/Zk4fvy4R99+vbfLv1eO9NSbEuYJnRjq6Tizo7nXx7rcadwUq9vidVOsbouXWJ0Zq9viJVZ7Yz0f+ZLdykNeW+q/gx/+7fG8U8+T/0ao59BbHc29PjbLncZNsbotXmJ1Zqxui9dNsbotXmL12Bnr2eRLZXJogvHjx8uAAQMkJCTEt+z222+XCRMmFLvNlClTpHfv3hIVFWXZZtq0aZKVlWUez549W9q2bSvVqlXztRk8eLC8+OKLkpycbB6/9dZbpofrhRde6GvTt29fWbdunXz++efmsfZo2LBhg3k+r/bt22tRWxYuXChlwaJVo+TmFU9K8sl8y/IDJ/PNcl3vFG6K1W3xuilWt8VLrM6M1W3xEqszY3VbXlvq9i8SWXWzyInC1+xz4kDhcl3vFG6K1W3xEqszY3VbvG6K1W3xEquUpVjLXCFWk8uVK1dK48aNLcsbNWok27dvl927dwfdbunSpUG3OX78uC/RDNYmISHBXJq1bNmyYtuEhYVJ/fr1ZcmSJb42DRo0MMsDn8/bprQvGxy+aqZoOT6Qd9mIVTNNu/LOTbG6LV43xeq2eInVmbG6LV5idWasbsxrS/2ywU3D/T5J/n5ZtmlEYbvyzk2xui1eYnVmrG6L102xui1eYi1zsZa5MWI1Ic3LyysyblVMTIy5T0pKKpJQZmZmmrGzTrdNjx49TMLbvXv3Is+p7bSN0jYtWrT41TbBxtXybxMoJyfH3PzHmFW7fvhUYmJP7SsmurrUTGgtubkZsv/wxiL7aZJY+PoPHN4k2bmF+/CqUaWFxMbUkf+unyJhnnxp8MtvV/tfxMoJCTV19wJJ1OWefFn02QNyUdObTJsGta+Q8PAoOZTytZzIPmbZb0J8Y6kc10AyMn+Uwz9vs6yrEBEtibUuMz/vTv7U9Ar2l1izo1SoECNHjm6V9BOHLesqx9aThMrNJCvrZzn402bLurDQCGlYt4v5ee+B1ZJfcNKyvk61dlKxYlVZsm5SkVgrebJMrOFSILUDYtXeKI3rdTNt9/+4XnJPnrDst2bVCySmUi1JTdsnR49bT46io6pI7eoXSV5etuw7VLQXSaO6V0poaLgcTPlSsrKPW9ZVq9xU4mMTJT3joBw5Zv2MRFWIk7o1O5ifd+3/pMh+ve/h4aNbZPU3z1vizcjXmEMkWn9P3r/mX+Lt2HKQ1K99RbHvYd0aF0tUZGX56ViSHM84aFkXV6mWVK96geTkpEnykU2Wdb/2HtZKuFAqRdeQY2l75Ofjey3rKlVMkFrV2hb7Hjau21VCQkPN53vd1tcssR7/Jda4EJH4MGusl7caYt5DT0GB7D7wWZH9ej/fP/70jWRmHbWsqxrfUKrENZLME0fkx6PfndXnu16NDhIZGScpP2+TtMwfLeviY+pItSotJDsnVQ4c+arYz/cPhz6Xk3nZ8vXORb54U/JEciVUoj05Uj/c79+IJ18+WDtObugy7ZyOEcfT98tPqTst6ypGxUud6u2loCBP9hxYVex7WBLHCP9YD+XpUSnU/N0Gxqp/3306TT6nY8TR1B2Smm79NjQ2uqbUSGgV9D0s6WOEf6z78/Tbz1BzPA6MVY/bva+YcM7HiIwTOgbSKVVi60vVyk3kRNZPcuinby3rIsKjSvwYofGGFuT/8v9GpE6YSGVJs8T708l8M8Zo2yY3ntMxIvDzXb1KM4mLqSdpGcmScmxH0PewJI8RGmuBX6z1g8T64y+xtmpw7TkfI/zVrtZGoitWk59Td8mx9B8s60oijwg8RmisefneWAtMjIGxHvgl1ub1rirRPCK+YtGcrDwpD3ntmeasBcd3SYGn8DUYETEiUTVF8nNFTuwvuqPYJqd6oORbP8MSVUMkIlbk4IeF600Gp5+uEAnxnDT/F3yfLu3Rsne+SELh50QqNRAJDRfJOiSSZz1GS2SCSIXKIiczRLKteaeEVhCplFj4c8ZuHZ/Nuj46USSsgkj2EZGT6dZ1uk/dd16WSJb1eCghYSIxDX/Z715zTLeoWEckvKLIgcVBYs0rPlbtQR3zy2cjc79IQcAXHfre6+8gN1Ukx3rckvBokYq1RQryRDL3SRExjURCQgtj0Zgs72E1kQrxhe+Bvhf+wqJEousW/py+q+h+fe/hYZEflxeJ1yMh5t67zBdv9S4ileoX/x7qc+pz5/wkkmvNsSUiTiSqukh+TtHeT7/2HlasJRJeSST3mEjOzwHvYaXC9cW+h40L968xHvnszGOt0bUwHv386ecwkO/z/aNIXqZ1XWRVkQpVCpfr+rP6fNcTCYsUyU4ROWk99pvft/7e9e/UxFHM5zvzB5GU1Wcea62rz/0Yob9v/b37078n/bvyFIhk7DnNe1gCx4ij6wPiDf3l7zYgXv37rtv33I4R+nesf8/+9D3Q9yLYe1jSx4igsZ4sGqset+tcf+7HCP0d+NPPtn7G9Xemvzt/oRElf4zwxVuYx2mcoZ6covHqGKOV257bMaLI57t64evSv0X9mwz2HpbkMeJsYo1rdW7HiIDzCfM508+bvn96rPUXEVPyxwhLrJ7TxxrbvETziIK8eCm3hVidaED5TzLg/9i7/rdso/eBbbztSrpNIL2UbNKkSUWWj/5gqEREneqY3KnWxXJn13mScmyLjFl2a5H2L99SeCL91JK7ZWe69Y/gjxfdJ5e1+JNs2fuZ1A14fVXliLSPay9hnlwJObnFLHvh69elwpa3zc/P3LhUYqPrypyP/yxfHbUm5re2HCjXtHtUNu14XZ77cqZlXYOYGjL++uXm5wffvVXy9R+in8lXvyp1ql0i81aNls8ObrCsu6FhL/n9ZU/J9v0fyBNrx1hfb4UYefL3hSfhE96/XX7OtR6oR3eaLs0Tr5cNO5YWibVu2HETa6ykSVbuTkusYSGh8sKAwoLOE0vukn0Z1n8e97Z/UDo0Gyofb54p879/y7Lu4oQWct81CyX9xAEZsXigBJr9u08lOjJB/rbsPvn2mPUfz+BWt0vXNg/J+qSX5YWv/2ZZ1zS2rjzcZ6n5ecS7Rfc7ved8qV6ltbzy2UPyafJaS7w1oqtKdHRTqSY/SWruqZNxjXfJrmUytW9hQe2RxbdJ+knrwevRK2dJ4zrXyILPJ8jSH6zFnavrdZFBnefID4dXy6RP/8+yLiosQv5+85fm5ykfDJUDJ6z/2Id3fFjaNh4kS76aIQu3W7v9X1q9tfypx3z5OW2njFxSNNa5/ddLRHi0PLPsHvnmpyRLrBdF15JK0U2lthyUlNwfLbGuS14nI3t9ICfzTgR9D5+87h2pGtdUXlzxgGxIKfz8e93c/Ca57uJJ8s3uN+XZjdMs6+pGJ8hjN35qfh79n/+V7HzrP5YJ3eZI/Zpd5M01Y2V58mrLut71u8v/XPFX2X3wY5my6gHLutiIKHnmpsICyWMfDJHDWamSm5fli7dVdKIclerSMPyYnDypx4dTx4hPv39XLm3+wDkdIz77drbM2/q6ZV2bKk1kRM935UTO0aDvYUkeI/xjbRrdVNIlTuqGHpLccGus+vd9SdP7z+kY8c76yfL+3v9a1nWtc6kMvvJlOfjTFzJu+VDLupI+RvjHWqdia8kPqSBVZY9kB8Sqx+32TY6c8zFi7Y/Wgt7vmtwgN14yTb7b92+ZuW6iZV3NipVL/Bih8TasECE1Iy4yj6vkb5Gsk3uljl+8raPrmYme/nPk3I4R36daC5B3tL5bOl/4Z1nz3Vx5ZcuLlnUtK9cv8WOExto6KkrqhrUxjyue3CzZAbE2j25sYn1j77kfI/w9ePlEubBBf1n8xVR5d9f7lnUlkUcEHiM01kuiY+Rw6AUS7jlp8ojAWBOjW5lYl28r2Txi9GV/l/KsPOS1Z5qz5m78i+RWOrWv3Cqd5ET9eyQ057DEfT+ySPvUdvPMfeyOqRJ2wpoXnaj/J8mt0lki966RihLntyZEQiVPjoS1l1BPnlQu+GW7LbMKT470i9kL/yae8DiptOdZiUj72rLfrDq3Sk716yQidb1U2mf97ORXrC/pzR83P1f+5v4iJ/JpLaZKQVQ9id7/klT4eaVlXXaNGyS79gAJz9gmMbusuYInooocb/Ws+Tl+68MSctL6vmY0eVjyYi6QqN0fSVRArB4JNbGGe7IlrmCfNdaQMElt+2rhe7h9soRlWY95mQ2GycnKl0lkyhKpeHC+Zd3JuIsks9GDEpKXJvHf3SeBUlvPFQmLlpjdMyU83XrMy6o7WHKqXSMVjq2R6B+et76H0U0kvVnhkBqVNw8rst+0lk9KQWRNs12FHxfr13an9huSIKmhTSXckymxBX7FkC2zpCBuqaS1fKrwPfzuIQnJs/5PT286TvIrNZOKB9+QyBTr//SchKslq97tEnZir8TuGG9Z5wmLkuOtXzA/xyVNkNBsa4Ess+EIORnfXiKPLJaKhxZY38P4jpLZ8H4JyT0q8dseKPoetnnZFIdids2Q8KNrLbFmhtYysVbwHJdKBX7FkC2zJC9hvWQ0edQUKyp/W/Q9PH7BLPFUSJBKe/8uEcetX6Zl1f4fyalxo0Qc/1Iq7X3Gsq4gqo6ktZhe+B5ueVBCAooW6c0ek/zohlIx+XWJPLrc+h5W7yVZdW6TsMwdErtzsvU9DI+R4xc+V/gefj9WQtO2WWJND61nYo0q+EkqevzOCbbMktyfdp77MeKnj6XigcJ2XnmxrSWj8SiR/BNSeUuQ97AkjxGmgFkYb1poA8kLiZJQT77kSaylx1327o8kO+LycztGHHpboo5Y/6fnVr1KTiTeJaHZyRKX9Ig10JI+RvjFmhraRApCwiXCc6JIrFl710hOeMdzP0Yc07+bU7Jr/k6ya90k4enfSMzuwuOBV0FkjZI/RuQcFU9IZTke2rTwPSzYJxU9P1nizQytIycPp0jkkUXndozIsOZFJ+rdIbkJ3aTC0U8lOvkVy7q8mBYlf4zIOSoFIdUkLbSwYBpfsKtIrOmh9SX/cIpUTH7t3I4ROdZzp4zGD0lebFuJ+nGRRB1+17IutyTyiMBjRM5RyQupLRmhWszOl8oFu4vEejy0sXgOp0ilPUtKNI84Wifgb/Q0QnSgWClD1qxZI126dJEVK1ZYvuXXHgVNmjSRf/7zn3LbbbdZtjlw4IDUq1dPXnnlFRk69NRJdUFBgbn86vHHH5dHH31UIiIi5JFHHimSXOrlWb169TJjaumMsp06dZLXX7cWKa666iqJjIyUjz76SK699lozK+1nn1l71ei4XHq52I4d1p44xfUuSExMlC+/fa/Ee8Qu/XyS3PvpY5aUtnH0BbIyLUkKJK+wR6yeeF5+b7nvEfvh2nFy32dTLbE2rNhKVqd/bxL52gGxOqFH7Mh1hQc6lZEfKk1iL5GtaV9IQvipk1eN1wk9Yv1jPZ4fKs1jL5Ht6V9IfJg1Vqf0iPXGW9gjNlw6x7aSfVlbzHd5Xn+9cpQjesR6Yy3sERsuXWJbyt6srZZY/9b1EUf0iPXGWtgjNlyuimshu09ss8T6XLfxjukR+9Dnz0ly/qkesRdUaiY7TuzwxftTnsjiG2Y5okfsiM+fkx/zT/WIbRYQ6495IktumOWIHrF/XvucHCnw9ogNlWbR1lgP5Iksu2GWLT1ia9ZMNJfkx8X5F7HKh/KQ155pznrshy8lLq6ke8R+ILKy36kYJVSOhraRhIItEip+JziXz3NAj9j/iKy6KSDWtpJQ8G3wWJ3QI3bdYEu8KWEdpHr+Jgn1+/9n4nVCj9gzjdUJPWLPNFan9Ii1xBv2yzHqG2u8Vy5yRo/YIrG2/uUY5RfrVe85p0esifdU7+6fQy+UqgXfWePtsdwZPWLPNFYn9Ihd541Vs9TQ4mMt4R6xaXnxUqVazTPKWctcIXbLli3Spk0bM6aVJpFe27Ztk1atWsnixYvlhhtusGyTkZEhsbGxMnfuXPnTn/5kGZcrOjraTGZw3333mckM7r77bvNNv7+aNWvKH/7wB3nqqafkkksuMUnr/PnWb40uu+wyk4TqZFw333yzSZIDJzm45ZZbZNeuXfLFF1/8apya1MbHx9tyYqFjtDWcEW0uE9Rfrl5K2CGug2xK2yQFv3TKrhcRJntGnZCw8ApSnrkpVrfF66ZY3RYvsTozVrfFS6znJ1Y786XzoTzktaX6O9Ax2v7T8JcTu8ITpiNhHaSGr6gTUlhA6rtHJNQ6N0O546ZY3RYvsTozVrfF66ZY3RYvscr5iPVs8qUyN1mXjpOl3/Z7x6Py0mCUJpPBxrmqXbv2r27TvHnzIm287Uq6TWnSk6Bnr3zQ/Hxqfl6xPH7mygfL/Ymh22J1W7xuitVt8RKrM2N1W7zE6sxY3ZjXlio9CepQeMlusZ+uDs+U/xNDt8XqtniJ1Zmxui1eN8XqtniJtczFWuYKsfpNv17CtXOn9bJZvdxfL7XShDKYnj17Bt1G99e5c+di2+zfv99cfnXNNdcU20aHIdi3b58ZksDbRh/r5AuBz+dtU9puunKGLOwxUupGWD9g2mNFl+t6p3BTrG6L102xui1eYnVmrG6Ll1idGavb8tpSl3iTyJULT13O6qU9VnS5rncKN8XqtniJ1Zmxui1eN8XqtniJVcpSrGVuaAL1ySefyEMPPSTr16/3TTDQp08fGThwoBmHdevWrXLrrbfKzJkz5eqrrzbrv//+e3Np11dffWUu51L33nuv6VEwbtw48zglJUU6duwoq1evNmNvqRkzZphZY19++WXzODs7W9q3by/z5s0zl3Opt99+W55//nlZvvzUoMU6W+2wYcOkf//+5vGGDRtkyJAh5vl1zK2ycqmdXk6osxjrBBrVE+Llqnb3OrbHiptidVu8borVbfESqzNjdVu8xGpfrOV9aILykteWid9BQb4UHFkpRw6nSI2a1SW0xlWl3mPFNm6K1W3xEqs4lpvidVOsbouXWMUuZ5MvlclCrHrvvffk008/lRYtWpgJDfT+zjvvNOs2btxovsXXiQf69Ts1wL8mojoGlo7FdejQIdNrYNSoUWbyFS9NdnVsLW2TmppqxuGaOHGiVKhw6iQiOTnZjLelz6m9BrR3weTJky1vpm6rkyNo4quTJehYX5oY16lTp8ydWOjkDkeOHJEaNWpIaGiZ6wRdotwUq9vidVOsbouXWJ3LTfESqz2cUIgtD3nt6ZCz2sNNsbotXmJ1LjfF66ZY3RYvsdrDEYVYpyOptYebYnVbvG6K1W3xEqtzuSleYrWHUwqx5Rk5qz3cFKvb4iVW53JTvG6K1W3xEqs9yvVkXQAAAAAAAADgNBRiAQAAAAAAAMBmFGIBAAAAAAAAwGYUYgEAAAAAAADAZhRiAQAAAAAAAMBmFGIBAAAAAAAAwGYUYgEAAAAAAADAZhRiAQAAAAAAAMBmFGIBAAAAAAAAwGYUYgEAAAAAAADAZhRiAQAAAAAAAMBm4XY/AYLzeDzmPi0tzfbnKigokPT0dImKipLQUGfX3t0Uq9vidVOsbouXWJ3LTfESqz28eZI3b8L5R85qDzfF6rZ4idW53BSvm2J1W7zEWvo5K4XYUqIfBpWYmFjaLwUAAKDM503x8fGl/TJciZwVAACg5HLWEA9dDEqtMn/w4EGJjY2VkJAQ2yvzmjzv379f4uLixMncFKvb4nVTrG6Ll1idy03xEqs9NE3VhLZOnTqO77VRVpGz2sNNsbotXmJ1LjfF66ZY3RYvsZZ+zkqP2FKiv5h69eqd1+fUD57T/9DcGKvb4nVTrG6Ll1idy03xEmvJoyds6SJntZebYnVbvMTqXG6K102xui1eYi29nJWuBQAAAAAAAABgMwqxAAAAAAAAAGAzCrEuEBkZKRMmTDD3TuemWN0Wr5tidVu8xOpcboqXWIFz56bPlptidVu8xOpcborXTbG6LV5iLX1M1gUAAAAAAAAANqNHLAAAAAAAAADYjEIsAAAAAAAAANiMQiwAAAAAAAAA2Czc7icAAKA4OTk5kp6eLhkZGRIVFSWxsbESHR0tISEhpf3ScI6OHj1qfr86FL3/cPSVKlWSKlWqlOprAwAAOBvkrM5FzorzjUKswy1evFhWrVolTZs2lV27dkm7du1k0KBB4lSHDx+W0aNHS8+ePR0dZ25urvz97383yUBycrL53XrjdpqTJ0/KokWLJCUlxcS9fv166dq1q9x7773iBtu3b5exY8fK22+/LU6jn93ExETf49DQUPn9738vc+bMkerVq4sTaXKn8e3Zs0fq1q0rBQUFct1118kFF1wgTqLHoxkzZgRd9+STT8pDDz0kTvLBBx/Ijh07zMnYzz//bD7Xd911lzjVvHnzZO3atdK8eXPz/+fGG2+U3r17l/bLQjlHzupM5KzuyFmdnK+6MWd1S76qyFnJWUsDhVgHW7NmjUydOtV88Lzf1PXr18/84xg4cKA4yddffy3/+te/zDdWr7/+unTr1k2cTP8p3H777VKvXj3z+KOPPjIJ7Ztvvim33nqrOMm4ceNky5YtJrGtUKGCSW5r165tEtwRI0aIk+Xn58uQIUNM3E6Ul5cnTzzxhHTo0MEkeG3btpWaNWuKk919993SpEkT8zes+vfvb47RCxcuFCfJysqSf//735bPrp6gvvDCCzJ8+HBxkiVLlkh4eLjleKQnLy+99JIjE9u//vWv8sYbb5jPbVhYmPm9XnTRRRIXFyedOnUq7ZeHcoqc1bnIWZ2fszo9X3VjzuqWfFWRs5KzlgoPHKtHjx6emTNnWpb9+9//9jRv3tzjZPqxfvXVVz1OlZ2d7alatapn+vTpluWXXnqpp0WLFh6nGT58uKd+/fqejIwM37KaNWt6brzxRo/TzZ4923PnnXd6unbt6nGiPXv2OPpvNdA///lPc/wtKCjwLXvppZc8ixYt8jjNk08+WWTZ5MmTPUlJSR6nGTBggOfQoUOWZWlpaZ6+fft6nCY9Pd0THR3tmTZtmmX5X/7yF0+vXr1K7XWh/CNndSZyVnfkrE7PV92Ws7opX1XkrOSspYHJuhxKv9lZuXKlNG7c2LK8UaNG5tKR3bt3l9prw7l/I6vf4uilBIG/23379onTPPPMMyYuHaNHpaWlyU8//SRXXHGFONmXX35pelF4e5Cg/NOeFH369LGMJXbnnXeaS9uc5oEHHrA8Xr16tdSqVctcFuQ0kZGRpreXji/m9dVXX5neMk7z3XffyYkTJ6RGjRqW5XrZ4ooVK0yvL+BskbM6Fzmr83NW8lXncVO+qshZyVlLA0MTOJQmrZr8eBMBr5iYGHOflJRUJOFF+aC/Ux2vJ9jvvFWrVuJ0U6ZMkSuvvNLRl3hlZ2fLhx9+aMba+vbbb8XJtm3bJs8++6w5UdPLNdu3b28SBKc5cuSI+V0OHTrUxKuXP+nfbIMGDeS+++4Tp9HLf7z0MqDnnnvOXIbq1AReT7JbtGhhTl70kieNdebMmeI0OjmJ0ssy/WnHPv0979y50xX/h1CyyFmdi5zV2Tmrm/JVt+SsbstXFTkrOWtpoBDrUMeOHTP3OgaIP+9j73o45xufjRs3yj//+U9xKh1H7eOPPzY9DXSsl4oVK4pTaQIwbNgwcTpN7vSfo3f8JT0R12+fK1eubMYGdJK9e/ea+6VLl5qx47wFh+7du5tva0eNGiVOpZO06AQPTnXxxReb3hM68L+Or1WnTh1Zvny5mUnZaVq3bm16PemkJf6++eYbc5+amlpKrwzlGTmru5CzOodb8lU35axuzlcVOatztC7jOStDEziU91KCwuGnTvE+DlyO8kuTAv2GcuTIkXLbbbeJU+k3zv/4xz9k4sSJ0qZNGzPZgxN99tlnZqZoncTD6fSfv3cSAO9J99VXXy1jxowRp9GEXek3r/69vjTZmzx5srk016kTeDz99NPm9+pUesmtTnLwzjvvyOOPP26KRpro/uc//xEn9hp5+eWXzWQdx48f9yW0enKmnDxRC+xDzuoe5KzO4aZ81U05q1vzVUXO6ixhZTxnpRDrUPHx8eY+cOyLnJwcy3qUf5oAXHLJJTJjxgxxgx49ekjLli1NAu+0ZEDHEtu0aZOjE4BfU716dfn+++8lPT1dnER7TKiGDRtalickJEhGRoaZZdmJli1bZi7/0RMYJ9IC0YABA0wPEb389NFHH5WtW7eay750PDW9bNNpdLbz+fPny+zZs81NL1nU2FViYmJpvzyUQ+Ss7kHO6gzkq87NWd2arypyVnLW84lCrEPpWFr6LYD+o/Tn/TagWbNmpfTKUJLmzp1rBhP3fkN7+PBhcRL9vN50002mV0HgJA8pKSnmn4eT6MDhP/zwgzlR8d4++OAD809Df9ZxuJxCk9b69eubb56DnXh7v5F3iqZNm5pvXjXB8+ft6RUa6sx/x9oLSCfxcCo9Buklp/7jV+rJi17SV7VqVccdo7y0h5eOCXj//ffL7373O9m1a5fpPVOzZs3Sfmkoh8hZ3YGc1Tn/D9yUr7otZ3VrvqrIWZ1zjCoPOStjxDqUjvPRpUsXMwixvx07dph/JE6cBdBtFi9ebP5R3nPPPb5l8+bNM5d7OYXOlqyXTuhg23/4wx98y3WmR72UURN6J9F/DnrzN2TIEHNp0PTp08VJ9LOrcQUei3RSDx043mmXumm82nPEO/aWl56caW8vHcfIqbMpB07A4yR6YhKsl5P+vi+44AKpVq2aOM2CBQvMuFp33323pRdJ4KzDwJkiZ3U+clZn5axuylfdlrO6NV9V5KzkrOeTc7/SgEyYMMGMieH/LZ12zdbxXbzjcTmNd1a8wNnxnGb9+vVmzBP9VvK1114zt+eff96ctDiJjlnTq1cvy5hM+/fvN4OM67dadevWFafT8Yqc+HmOjIw048Tp5TBeehL+6aefyl//+ldxIh0rTnuJeC9h09+tToSgYzTp++FEOvtu4AQ8TqInJNqTT3sT+Pviiy/MDMNaRHKat99+W9577z3LZC3aY1EnfQB+K3JW5yJndUfO6tR81Y05qxvzVUXOSs56PoV4GAHf0fSDp/8kWrRoYS4X0XsdA8Rp9Fu7V155xfxT1MRdB4+//vrrpWPHjkW+sS3v9NI9vWxEv5kMpEmCjn/itEHF58yZY5IAvUxGx6Tq37+/3HHHHY49OVNff/21+Szr5zozM9P0Iunbt69069ZNnELHA9TfrX47q5f06d/vQw89JJdddpk4lc6irCekTZo0MbN4du3aVYYOHSpO9T//8z8muXvqqafEqXTQ/1mzZpkJD2JiYkyPA720Tb9914TXaZKSkuRf//qXOeE+dOiQifWRRx4p9UkPUP6Rs5KzlnduzFndkK+6MWd1W76qyFnJWc8nCrFwBP3j0qRHv8XSREc/1rpMbxEREaX98oCzpp9n/RxrDxK9eXsa8HkGAKD8ImeFk5CvAsDZoxALAAAAAAAAADZjjFgAAAAAAAAAsBmFWAAAAAAAAACwGYVYAAAAAAAAALAZhVgAAAAAAAAAsBmFWAAAAAAAAACwGYVYAAAAAAAAALAZhVgAAAAAAAAAsFm43U8AAE6yZcsWGT16tHz77beyf/9+CQ8Pl6uvvlqioqIs7QoKCmT16tVy7NgxiY+Pl0svvVT+8Ic/mBsAAABgJ3JWACibQjwej6e0XwQAlDdbt26VCy+8UDp37myS12DGjRsnjz/+uDz33HPyf//3f+f9NQIAAMDdyFkBoGxhaAIA+A2io6PNvfYuKE5YWJi5r1ix4nl7XQAAAIAXOSsAlC0UYgEAAAAAAADAZhRiAQAAAAAAAMBmTNYFAOdZbm6uPPXUU3Lw4EGpWbOmHD161Nw/9NBDEhERYdrMmzdP3njjDVm2bJkZ06t3796Sl5cnX375pdSvX1+mTZsmsbGxsnfvXmnUqJHcfPPNZvyvDRs2yJIlS+S6664zky1s3LhRPvzwQ/EfDnzFihXy+uuvm+1Onjxpnn/UqFHSuHFjs14ndbjzzjvN66tbt655rW+//baEhobKtm3bpF27djJx4kSpVKmSJa61a9fKk08+KS1btpTMzEw5ceKEeVylShX57rvv5NVXX5XZs2ebtvfff7/cddddsm/fPhPrm2++aeIaMmSIPPjgg/L++++bZfra9fluueUWefjhh+Xpp582y/V9uP76681y72QSWVlZMmPGDElKSpKmTZuayShSU1PN669Xr55pN2bMGPO+AQAA4PTIWclZAdhAJ+sCAJydPXv2aJbo6dq1a7FtJkyYYNq8+uqrvmV5eXme6667zjNjxgxL2+nTp3v69Olj1ntt377dbP/KK6/4lmVnZ3saN27s+f3vf+97Hf369fOtX7Fihdnmo48+8i1r166d7+d//OMfnssvv9yTnp7uW5aUlGT2+e2331peZ7du3TyVK1f2PP30077lubm5nmuvvdbsIysry7d82bJlnlq1ann27dvnW/b44497evbsaYmzc+fOnk6dOlmW6T71NT/66KOW5Tt27DDLX3rpJcvyJ554wizX9f569erladCggXmP/NWrV6/IvgEAANyAnJWcFUDZwtAEAHAezZo1SzZv3ix/+ctfLMu1Z8GmTZvkmWee8S3z9jQICQnxLYuMjJQ2bdrIZ5995lt2zTXX+H72tvWfkKF79+7mfv/+/fLHP/5RJkyYIDExMb71zZs3l5tuukluu+02Xy8EnbShQYMG5ht6/bbf/zXpN/zr1q2TKVOmmGU5OTkydOhQ+d///V/TQ8BLn0t7R6xZs8a3TF+XN67AOAMnkfA+9k4goX744QfTuyCwfUpKivz3v/+VTp06mffIn25/ugkqAAAAYEXOSs4KwB4UYgHgPPrb3/4mHTp0MJdMBSZeHTt29F0GVRxNEFeuXClTp041jzXpbNKkyWm3adu2rbl/6aWXzKVQevlXoMsvv1y++eYbS7KsAhNEpUm13l5++WXz+KOPPpIDBw6Y1++vevXqkpiYKOvXr5eSUFBQYOL+05/+VGSdJul6+/nnn0vkuQAAANyMnPW3I2cFcDp83QIA54mOa6XjS3m/7Q+UkJBg1mtiVrVqVd/yDz74QH788UeTOH766afyzjvvSNeuXc26WrVqmbG1Tke/+Vc6RpX2PvDft/9ze9t069btV2PRsbl0XK5jx47J1q1bfcnt7t27Le3at29f5Pm0h8D06dPlt/TMuPvuu83zBqpYsaI8++yzZhwvTcy97w8AAADODjlrIXJWAHagEAsA54lOXKD8JyEInBDBv52XDvCvEwKo9PR06dWrl9xwww3yyCOPnPXz63Przf/SsdM996/R/Xh7SgwcOFCuvvrqX91GLwXTCQj86aQGp6PJtr5u7ZkRLKlVd9xxh5kkYv78+WZSBZ0woXXr1mbyAwAAAJwZctZC5KwA7MDQBABwntSoUcNc+nTkyJGg63XMKF2vt+Lo7KnDhg2TRx991MzOejZ0hlrv8wR7bv82v2bnzp0mOa1cubLvMjIdzysYneX2XOilaS+88IJl3K/itGjRwry/GRkZpjeCzpSrrxEAAABnhpz1tyFnBXAmKMQCwHmi38Trt94bN24skujp5AE6LpVOFhD4zX+wS5pOl0QWR79913G9/Cci8NJLoxo1aiQ9e/a0LNdv5gN7Q+gEDd99953ce++95nGPHj2kWbNmZpKDQMnJyb86htivee6550zvg8AxyoLRiSNeeeUVWbRokRnrCwAAAGeHnPW3IWcFcCYoxALAb/zG2/8+mBMnThRpM378eGnZsqWZBdafJm06NtW4ceNO+618fn6+PP/882Z8rH79+hX7urKzs4usa9WqlUn69DXoOFleGzZskPfee0/eeuutIrPD6uVf/kmp7ldnz9VLz3TWXKWzu+qlVToD7JIlSyzbTps2zYyR5R9TYFzex8Ut15lxdTbcX2s/b9480wPhySeflCuuuMLynp3t5WsAAABOQM5KzgqgbGGMWAA4C/qtul5itXnzZl9CeNVVV5lEVS9FUnPmzDFJ4qpVq8xjbf/+++/LoEGDTIKmEwRosqc/a3J6+PBhk3BqUuid8VVnd9VEUem35XpZlSbJX3zxhblsafXq1WbSAy+dEEEv+9LnUaNHj5ZPPvnEJL6dOnXytbvvvvvMjLWaaOr22qtBE2Gd1VYvkQp2aZqOWTVy5EjTM0EnOdCxvkaMGGEee+k4WOvWrTMJsybHOtmB9krQxFcvTdPZbTUmff26/M9//rPcc889smfPHnn11Vd9SakmoDoW17vvvuuLf+HChSYpHTt2rDz22GPy5ptv+mK55ZZbzBhfeumbThChdJIIb48J3YdOGKH71n3oGGVxcXEl+pkAAAAoa8hZyVkBlE0hnuJG4AYAuJpOtqDJ8t69e6Ws02TYP8kGAACAO5CzAihPGJoAAFDukdACAACgrCNnBUAhFgAQlF7+FWzcLgAAAKCsIGcFUJ5QiAUAFBlTrE+fPmbMMB0LrHPnzmbMKwAAAKCsIGcFUB4xRiwAAAAAAAAA2IwesQAAAAAAAABgMwqxAAAAAAAAAGAzCrEAAAAAAAAAYDMKsQAAAAAAAABgMwqxAAAAAAAAAGAzCrEAAAAAAAAAYDMKsQAAAAAAAABgMwqxAAAAAAAAACD2+n+5bbK9s9iDaQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Создание двух графиков рядом\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# ============= Левый график: случайные данные =============\n", + "ax1.set_title(\"Поиск в случайных данных\")\n", + "ax1.set_ylabel('Время, с')\n", + "ax1.set_xlabel('Повторения')\n", + "ax1.set_xticks(iterations)\n", + "# ax1.set_xticklabels(range(1, 6))\n", + "\n", + "ax1.scatter(iterations, ll_random_search, label='связный список', color=ll_col)\n", + "ax1.axhline(y=ll_random_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.scatter(iterations, ht_random_search, label='хеш таблица', color=ht_col)\n", + "ax1.axhline(y=ht_random_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.scatter(iterations, bst_random_search, label='дерево', color=bst_col)\n", + "ax1.axhline(y=bst_random_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.legend()\n", + "ax1.grid(True, alpha=0.3)\n", + "\n", + "# ============= Правый график: отсортированные данные =============\n", + "ax2.set_title(\"Поиск в отсортированных данных\")\n", + "ax2.set_ylabel('Время, с')\n", + "ax2.set_xlabel('Повторения')\n", + "ax2.set_xticks(iterations)\n", + "# ax2.set_xticklabels(range(1, 6))\n", + "\n", + "ax2.scatter(iterations, ll_sorted_search, label='связный список', color=ll_col)\n", + "ax2.axhline(y=ll_sorted_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.scatter(iterations, ht_sorted_search, label='хеш таблица', color=ht_col)\n", + "ax2.axhline(y=ht_sorted_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.scatter(iterations, bst_sorted_search, label='дерево', color=bst_col)\n", + "ax2.axhline(y=bst_sorted_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.legend()\n", + "ax2.grid(True, alpha=0.3)\n", + "\n", + "# Общий заголовок\n", + "plt.suptitle(f'Сравнение времени поиска в структурах данных (N = {countRandomSearch} + {countNotExitstSearch})', fontsize=14)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('../img/search.pdf', \n", + " format='pdf',\n", + " dpi=300,\n", + " bbox_inches='tight', # обрезает лишние поля\n", + " pad_inches=0.1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "0fd42f30", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Создание двух графиков рядом\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# ============= Левый график: случайные данные =============\n", + "ax1.set_title(\"Удаление в случайных данных\")\n", + "ax1.set_ylabel('Время, с')\n", + "ax1.set_xlabel('Повторения')\n", + "ax1.set_xticks(iterations)\n", + "# ax1.set_xticklabels(range(1, 6))\n", + "\n", + "ax1.scatter(iterations, ll_random_delete, label='связный список', color=ll_col)\n", + "ax1.axhline(y=ll_random_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.scatter(iterations, ht_random_delete, label='хеш таблица', color=ht_col)\n", + "ax1.axhline(y=ht_random_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.scatter(iterations, bst_random_delete, label='дерево', color=bst_col)\n", + "ax1.axhline(y=bst_random_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.legend()\n", + "ax1.grid(True, alpha=0.3)\n", + "\n", + "# ============= Правый график: отсортированные данные =============\n", + "ax2.set_title(\"Удаление в отсортированных данных\")\n", + "ax2.set_ylabel('Время, с')\n", + "ax2.set_xlabel('Повторения')\n", + "ax2.set_xticks(iterations)\n", + "# ax2.set_xticklabels(range(1, 6))\n", + "\n", + "ax2.scatter(iterations, ll_sorted_delete, label='связный список', color=ll_col)\n", + "ax2.axhline(y=ll_sorted_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.scatter(iterations, ht_sorted_delete, label='хеш таблица', color=ht_col)\n", + "ax2.axhline(y=ht_sorted_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.scatter(iterations, bst_sorted_delete, label='дерево', color=bst_col)\n", + "ax2.axhline(y=bst_sorted_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.legend()\n", + "ax2.grid(True, alpha=0.3)\n", + "\n", + "# Общий заголовок\n", + "plt.suptitle(f'Сравнение времени удаления в структурах данных (N = {countDeletes})', fontsize=14)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('../img/delete.pdf', \n", + " format='pdf',\n", + " dpi=300,\n", + " bbox_inches='tight', \n", + " pad_inches=0.1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9eca6493", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/stepushovgs/data-structures/source/pkg/data_struct/qsort.go b/stepushovgs/data-structures/source/pkg/data_struct/qsort.go index 00e0c01..0ae848a 100644 --- a/stepushovgs/data-structures/source/pkg/data_struct/qsort.go +++ b/stepushovgs/data-structures/source/pkg/data_struct/qsort.go @@ -1,10 +1,17 @@ package data_struct func QSort(arr []MyData, l, r int) []MyData { + result := make([]MyData, len(arr)) + copy(result, arr) + qSort(result, l, r) + return result +} + +func qSort(arr []MyData, l, r int) []MyData { if l < r { s := Partition_Hoa(arr, l, r) - arr = QSort(arr, l, s) - arr = QSort(arr, s+1, r) + arr = qSort(arr, l, s) + arr = qSort(arr, s+1, r) } return arr } diff --git a/stepushovgs/data-structures/source/pkg/gen_data/data_generator.go b/stepushovgs/data-structures/source/pkg/gen_data/data_generator.go index f13f1f5..2ebffd5 100644 --- a/stepushovgs/data-structures/source/pkg/gen_data/data_generator.go +++ b/stepushovgs/data-structures/source/pkg/gen_data/data_generator.go @@ -29,6 +29,12 @@ func RecordsShuffled(count int) []ds.MyData { data[i].Phone = genRandomPhone() } + // Перемешиваем (Fisher-Yates shuffle) + for i := len(data) - 1; i > 0; i-- { + j := rand.Intn(i + 1) + data[i], data[j] = data[j], data[i] + } + return data } diff --git a/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go b/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go index 4291039..4266d3c 100644 --- a/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go +++ b/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go @@ -44,6 +44,9 @@ func (bst *BinSearchTree) Minimum() *BSTree { } func (root *BSTree) Minimum() *BSTree { + if root == nil { + return nil + } if root.left == nil { return root } @@ -54,6 +57,9 @@ func (bst *BinSearchTree) Maximum() *BSTree { return bst.root.Maximum() } func (root *BSTree) Maximum() *BSTree { + if root == nil { + return nil + } if root.right == nil { return root } @@ -194,10 +200,39 @@ Node delete(root : Node, z : T): // корень поддерев return root */ +func (bst *BinSearchTree) Height() int { + if bst.root == nil { + return 0 + } + return bst.root.height() +} + +// height возвращает высоту поддерева (для BSTree) +func (node *BSTree) height() int { + if node == nil { + return 0 + } + + leftHeight := node.left.height() + rightHeight := node.right.height() + + // Высота = 1 (текущий узел) + максимум из высот поддеревьев + if leftHeight > rightHeight { + return leftHeight + 1 + } + return rightHeight + 1 +} + func (bst *BinSearchTree) Delete(targetName string) bool { if bst.root == nil { return false } + + _, found := bst.Search(targetName) + if !found { + return false + } + bst.root = bst.root.delete(targetName) return true } diff --git a/stepushovgs/data-structures/source/results/benchmarks.csv b/stepushovgs/data-structures/source/results/benchmarks.csv index 1e4bbad..dcb6c95 100644 --- a/stepushovgs/data-structures/source/results/benchmarks.csv +++ b/stepushovgs/data-structures/source/results/benchmarks.csv @@ -1,199 +1,379 @@ Structure,Mode,Operation,Time -Связный список,Случайный,Вставка,0.047621 -Связный список,Случайный,Поиск,0.003505 -Связный список,Случайный,Удаление,0.005502 -Связный список,Случайный,Вставка,0.048707 -Связный список,Случайный,Поиск,0.003505 -Связный список,Случайный,Удаление,0.006007 -Связный список,Случайный,Вставка,0.048086 -Связный список,Случайный,Поиск,0.003546 -Связный список,Случайный,Удаление,0.006606 -Связный список,Случайный,Вставка,0.049133 -Связный список,Случайный,Поиск,0.003018 -Связный список,Случайный,Удаление,0.006084 -Связный список,Случайный,Вставка,0.048328 -Связный список,Случайный,Поиск,0.003524 -Связный список,Случайный,Удаление,0.007066 -Связный список,Случайный,Вставка,0.047057 -Связный список,Случайный,Поиск,0.003518 -Связный список,Случайный,Удаление,0.006054 -Связный список,Случайный,Вставка,0.047979 -Связный список,Случайный,Поиск,0.003604 -Связный список,Случайный,Удаление,0.006039 -Связный список,Случайный,Вставка,0.047694 -Связный список,Случайный,Поиск,0.004520 -Связный список,Случайный,Удаление,0.005522 -Связный список,Случайный,Вставка,0.047864 -Связный список,Случайный,Поиск,0.003514 -Связный список,Случайный,Удаление,0.006814 -Связный список,Случайный,Вставка,0.048523 -Связный список,Случайный,Поиск,0.002611 -Связный список,Случайный,Удаление,0.006122 -Связный список,Случайный,Вставка (среднее),0.048099 -Связный список,Случайный,Поиск (среднее),0.003486 -Связный список,Случайный,Удаление (среднее),0.006181 -Связный список,Отсортированный,Вставка,0.046193 -Связный список,Отсортированный,Поиск,0.004520 -Связный список,Отсортированный,Удаление,0.005769 -Связный список,Отсортированный,Вставка,0.048112 -Связный список,Отсортированный,Поиск,0.003001 -Связный список,Отсортированный,Удаление,0.007026 -Связный список,Отсортированный,Вставка,0.047699 -Связный список,Отсортированный,Поиск,0.003503 -Связный список,Отсортированный,Удаление,0.006515 -Связный список,Отсортированный,Вставка,0.047867 -Связный список,Отсортированный,Поиск,0.003509 -Связный список,Отсортированный,Удаление,0.006001 -Связный список,Отсортированный,Вставка,0.047622 -Связный список,Отсортированный,Поиск,0.003001 -Связный список,Отсортированный,Удаление,0.006008 -Связный список,Отсортированный,Вставка,0.047737 -Связный список,Отсортированный,Поиск,0.003546 -Связный список,Отсортированный,Удаление,0.005514 -Связный список,Отсортированный,Вставка,0.048361 -Связный список,Отсортированный,Поиск,0.003012 -Связный список,Отсортированный,Удаление,0.005328 -Связный список,Отсортированный,Вставка,0.049622 -Связный список,Отсортированный,Поиск,0.003510 -Связный список,Отсортированный,Удаление,0.005522 -Связный список,Отсортированный,Вставка,0.048137 -Связный список,Отсортированный,Поиск,0.003518 -Связный список,Отсортированный,Удаление,0.005531 -Связный список,Отсортированный,Вставка,0.048927 -Связный список,Отсортированный,Поиск,0.004004 -Связный список,Отсортированный,Удаление,0.005470 -Связный список,Отсортированный,Вставка (среднее),0.048028 -Связный список,Отсортированный,Поиск (среднее),0.003512 -Связный список,Отсортированный,Удаление (среднее),0.005869 -Хеш таблица,Случайный,Вставка,0.001504 +Связный список,Случайный,Вставка,0.193393 +Связный список,Случайный,Поиск,0.023572 +Связный список,Случайный,Удаление,0.013566 +Связный список,Случайный,Вставка,0.193837 +Связный список,Случайный,Поиск,0.023969 +Связный список,Случайный,Удаление,0.013580 +Связный список,Случайный,Вставка,0.190963 +Связный список,Случайный,Поиск,0.023944 +Связный список,Случайный,Удаление,0.013505 +Связный список,Случайный,Вставка,0.194440 +Связный список,Случайный,Поиск,0.022179 +Связный список,Случайный,Удаление,0.015040 +Связный список,Случайный,Вставка,0.191873 +Связный список,Случайный,Поиск,0.023604 +Связный список,Случайный,Удаление,0.014556 +Связный список,Случайный,Вставка,0.191788 +Связный список,Случайный,Поиск,0.023570 +Связный список,Случайный,Удаление,0.013037 +Связный список,Случайный,Вставка,0.192362 +Связный список,Случайный,Поиск,0.023050 +Связный список,Случайный,Удаление,0.013997 +Связный список,Случайный,Вставка,0.191223 +Связный список,Случайный,Поиск,0.023778 +Связный список,Случайный,Удаление,0.013582 +Связный список,Случайный,Вставка,0.191326 +Связный список,Случайный,Поиск,0.023035 +Связный список,Случайный,Удаление,0.013183 +Связный список,Случайный,Вставка,0.191919 +Связный список,Случайный,Поиск,0.023584 +Связный список,Случайный,Удаление,0.013988 +Связный список,Случайный,Вставка,0.190009 +Связный список,Случайный,Поиск,0.022013 +Связный список,Случайный,Удаление,0.014679 +Связный список,Случайный,Вставка,0.191010 +Связный список,Случайный,Поиск,0.024724 +Связный список,Случайный,Удаление,0.015116 +Связный список,Случайный,Вставка,0.192876 +Связный список,Случайный,Поиск,0.023690 +Связный список,Случайный,Удаление,0.021114 +Связный список,Случайный,Вставка,0.202786 +Связный список,Случайный,Поиск,0.023466 +Связный список,Случайный,Удаление,0.014063 +Связный список,Случайный,Вставка,0.191339 +Связный список,Случайный,Поиск,0.022909 +Связный список,Случайный,Удаление,0.014661 +Связный список,Случайный,Вставка,0.189898 +Связный список,Случайный,Поиск,0.023056 +Связный список,Случайный,Удаление,0.013592 +Связный список,Случайный,Вставка,0.195372 +Связный список,Случайный,Поиск,0.023198 +Связный список,Случайный,Удаление,0.013819 +Связный список,Случайный,Вставка,0.191713 +Связный список,Случайный,Поиск,0.023542 +Связный список,Случайный,Удаление,0.012883 +Связный список,Случайный,Вставка,0.190514 +Связный список,Случайный,Поиск,0.023745 +Связный список,Случайный,Удаление,0.015012 +Связный список,Случайный,Вставка,0.189447 +Связный список,Случайный,Поиск,0.023544 +Связный список,Случайный,Удаление,0.014063 +Связный список,Случайный,Вставка (среднее),0.192404 +Связный список,Случайный,Поиск (среднее),0.023409 +Связный список,Случайный,Удаление (среднее),0.014352 +Связный список,Отсортированный,Вставка,0.193078 +Связный список,Отсортированный,Поиск,0.033527 +Связный список,Отсортированный,Удаление,0.022597 +Связный список,Отсортированный,Вставка,0.190167 +Связный список,Отсортированный,Поиск,0.032657 +Связный список,Отсортированный,Удаление,0.022773 +Связный список,Отсортированный,Вставка,0.191660 +Связный список,Отсортированный,Поиск,0.034012 +Связный список,Отсортированный,Удаление,0.022709 +Связный список,Отсортированный,Вставка,0.191970 +Связный список,Отсортированный,Поиск,0.032553 +Связный список,Отсортированный,Удаление,0.022828 +Связный список,Отсортированный,Вставка,0.194583 +Связный список,Отсортированный,Поиск,0.034762 +Связный список,Отсортированный,Удаление,0.022843 +Связный список,Отсортированный,Вставка,0.192641 +Связный список,Отсортированный,Поиск,0.033173 +Связный список,Отсортированный,Удаление,0.022690 +Связный список,Отсортированный,Вставка,0.194789 +Связный список,Отсортированный,Поиск,0.033526 +Связный список,Отсортированный,Удаление,0.022985 +Связный список,Отсортированный,Вставка,0.191488 +Связный список,Отсортированный,Поиск,0.032518 +Связный список,Отсортированный,Удаление,0.023373 +Связный список,Отсортированный,Вставка,0.203929 +Связный список,Отсортированный,Поиск,0.034133 +Связный список,Отсортированный,Удаление,0.023423 +Связный список,Отсортированный,Вставка,0.193756 +Связный список,Отсортированный,Поиск,0.033023 +Связный список,Отсортированный,Удаление,0.022845 +Связный список,Отсортированный,Вставка,0.195554 +Связный список,Отсортированный,Поиск,0.032817 +Связный список,Отсортированный,Удаление,0.023482 +Связный список,Отсортированный,Вставка,0.193150 +Связный список,Отсортированный,Поиск,0.033271 +Связный список,Отсортированный,Удаление,0.022587 +Связный список,Отсортированный,Вставка,0.192696 +Связный список,Отсортированный,Поиск,0.033454 +Связный список,Отсортированный,Удаление,0.022527 +Связный список,Отсортированный,Вставка,0.192065 +Связный список,Отсортированный,Поиск,0.033382 +Связный список,Отсортированный,Удаление,0.022786 +Связный список,Отсортированный,Вставка,0.191702 +Связный список,Отсортированный,Поиск,0.031664 +Связный список,Отсортированный,Удаление,0.023819 +Связный список,Отсортированный,Вставка,0.194042 +Связный список,Отсортированный,Поиск,0.032380 +Связный список,Отсортированный,Удаление,0.023181 +Связный список,Отсортированный,Вставка,0.190397 +Связный список,Отсортированный,Поиск,0.033168 +Связный список,Отсортированный,Удаление,0.022893 +Связный список,Отсортированный,Вставка,0.191636 +Связный список,Отсортированный,Поиск,0.032367 +Связный список,Отсортированный,Удаление,0.022448 +Связный список,Отсортированный,Вставка,0.193131 +Связный список,Отсортированный,Поиск,0.032905 +Связный список,Отсортированный,Удаление,0.022932 +Связный список,Отсортированный,Вставка,0.191676 +Связный список,Отсортированный,Поиск,0.032851 +Связный список,Отсортированный,Удаление,0.022315 +Связный список,Отсортированный,Вставка (среднее),0.193206 +Связный список,Отсортированный,Поиск (среднее),0.033107 +Связный список,Отсортированный,Удаление (среднее),0.022902 +Хеш таблица,Случайный,Вставка,0.002119 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002047 +Хеш таблица,Случайный,Вставка,0.003507 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002010 +Хеш таблица,Случайный,Вставка,0.002563 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002005 +Хеш таблица,Случайный,Вставка,0.003023 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002513 +Хеш таблица,Случайный,Вставка,0.003513 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.001505 +Хеш таблица,Случайный,Вставка,0.002516 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002529 +Хеш таблица,Случайный,Вставка,0.003369 +Хеш таблица,Случайный,Поиск,0.001002 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.002091 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.001504 +Хеш таблица,Случайный,Вставка,0.003654 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002023 +Хеш таблица,Случайный,Вставка,0.003102 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002429 +Хеш таблица,Случайный,Вставка,0.003008 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка (среднее),0.002007 -Хеш таблица,Случайный,Поиск (среднее),0.000000 +Хеш таблица,Случайный,Вставка,0.003997 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.001580 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.005478 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.002015 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.003410 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.002158 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.003586 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.003123 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.003011 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка (среднее),0.003041 +Хеш таблица,Случайный,Поиск (среднее),0.000050 Хеш таблица,Случайный,Удаление (среднее),0.000000 -Хеш таблица,Отсортированный,Вставка,0.002005 +Хеш таблица,Отсортированный,Вставка,0.003208 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.001282 -Хеш таблица,Отсортированный,Поиск,0.000000 -Хеш таблица,Отсортированный,Удаление,0.001504 -Хеш таблица,Отсортированный,Вставка,0.001015 +Хеш таблица,Отсортированный,Вставка,0.003095 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002016 +Хеш таблица,Отсортированный,Вставка,0.002048 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.001505 +Хеш таблица,Отсортированный,Вставка,0.003991 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002016 -Хеш таблица,Отсортированный,Поиск,0.000502 -Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002009 +Хеш таблица,Отсортированный,Вставка,0.002006 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.001068 +Хеш таблица,Отсортированный,Вставка,0.003505 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002516 +Хеш таблица,Отсортированный,Вставка,0.003013 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002505 +Хеш таблица,Отсортированный,Вставка,0.002423 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.001102 +Хеш таблица,Отсортированный,Вставка,0.003121 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000503 +Хеш таблица,Отсортированный,Вставка,0.002511 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка (среднее),0.001794 -Хеш таблица,Отсортированный,Поиск (среднее),0.000050 -Хеш таблица,Отсортированный,Удаление (среднее),0.000150 -Бинарное дерево поиска,Случайный,Вставка,0.304277 -Бинарное дерево поиска,Случайный,Поиск,0.007550 -Бинарное дерево поиска,Случайный,Удаление,0.013564 -Бинарное дерево поиска,Случайный,Вставка,0.297918 -Бинарное дерево поиска,Случайный,Поиск,0.007167 -Бинарное дерево поиска,Случайный,Удаление,0.014539 -Бинарное дерево поиска,Случайный,Вставка,0.300777 -Бинарное дерево поиска,Случайный,Поиск,0.008009 -Бинарное дерево поиска,Случайный,Удаление,0.015144 -Бинарное дерево поиска,Случайный,Вставка,0.307342 -Бинарное дерево поиска,Случайный,Поиск,0.008307 -Бинарное дерево поиска,Случайный,Удаление,0.013577 -Бинарное дерево поиска,Случайный,Вставка,0.301149 -Бинарное дерево поиска,Случайный,Поиск,0.007792 -Бинарное дерево поиска,Случайный,Удаление,0.012897 -Бинарное дерево поиска,Случайный,Вставка,0.304480 -Бинарное дерево поиска,Случайный,Поиск,0.006521 -Бинарное дерево поиска,Случайный,Удаление,0.014053 -Бинарное дерево поиска,Случайный,Вставка,0.299029 -Бинарное дерево поиска,Случайный,Поиск,0.007552 -Бинарное дерево поиска,Случайный,Удаление,0.013073 -Бинарное дерево поиска,Случайный,Вставка,0.304265 -Бинарное дерево поиска,Случайный,Поиск,0.006518 -Бинарное дерево поиска,Случайный,Удаление,0.014555 -Бинарное дерево поиска,Случайный,Вставка,0.297193 -Бинарное дерево поиска,Случайный,Поиск,0.007529 -Бинарное дерево поиска,Случайный,Удаление,0.014092 -Бинарное дерево поиска,Случайный,Вставка,0.300190 -Бинарное дерево поиска,Случайный,Поиск,0.006525 -Бинарное дерево поиска,Случайный,Удаление,0.014096 -Бинарное дерево поиска,Случайный,Вставка (среднее),0.301662 -Бинарное дерево поиска,Случайный,Поиск (среднее),0.007347 -Бинарное дерево поиска,Случайный,Удаление (среднее),0.013959 -Бинарное дерево поиска,Отсортированный,Вставка,0.317393 -Бинарное дерево поиска,Отсортированный,Поиск,0.007554 -Бинарное дерево поиска,Отсортированный,Удаление,0.013053 -Бинарное дерево поиска,Отсортированный,Вставка,0.301527 -Бинарное дерево поиска,Отсортированный,Поиск,0.006503 -Бинарное дерево поиска,Отсортированный,Удаление,0.014265 -Бинарное дерево поиска,Отсортированный,Вставка,0.300625 -Бинарное дерево поиска,Отсортированный,Поиск,0.008043 -Бинарное дерево поиска,Отсортированный,Удаление,0.013068 -Бинарное дерево поиска,Отсортированный,Вставка,0.303297 -Бинарное дерево поиска,Отсортированный,Поиск,0.007039 -Бинарное дерево поиска,Отсортированный,Удаление,0.013601 -Бинарное дерево поиска,Отсортированный,Вставка,0.300072 -Бинарное дерево поиска,Отсортированный,Поиск,0.008539 -Бинарное дерево поиска,Отсортированный,Удаление,0.013535 -Бинарное дерево поиска,Отсортированный,Вставка,0.298420 -Бинарное дерево поиска,Отсортированный,Поиск,0.007256 -Бинарное дерево поиска,Отсортированный,Удаление,0.013550 -Бинарное дерево поиска,Отсортированный,Вставка,0.298652 -Бинарное дерево поиска,Отсортированный,Поиск,0.008040 -Бинарное дерево поиска,Отсортированный,Удаление,0.013532 -Бинарное дерево поиска,Отсортированный,Вставка,0.299859 -Бинарное дерево поиска,Отсортированный,Поиск,0.007577 -Бинарное дерево поиска,Отсортированный,Удаление,0.013575 -Бинарное дерево поиска,Отсортированный,Вставка,0.307201 -Бинарное дерево поиска,Отсортированный,Поиск,0.008025 -Бинарное дерево поиска,Отсортированный,Удаление,0.014125 -Бинарное дерево поиска,Отсортированный,Вставка,0.304290 -Бинарное дерево поиска,Отсортированный,Поиск,0.007040 -Бинарное дерево поиска,Отсортированный,Удаление,0.013611 -Бинарное дерево поиска,Отсортированный,Вставка (среднее),0.303134 -Бинарное дерево поиска,Отсортированный,Поиск (среднее),0.007562 -Бинарное дерево поиска,Отсортированный,Удаление (среднее),0.013591 +Хеш таблица,Отсортированный,Вставка,0.003011 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.002003 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.001341 +Хеш таблица,Отсортированный,Вставка,0.001513 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.004485 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.002740 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.002310 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.002506 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.002515 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.003073 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.004169 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка (среднее),0.002862 +Хеш таблица,Отсортированный,Поиск (среднее),0.000000 +Хеш таблица,Отсортированный,Удаление (среднее),0.000147 +Бинарное дерево поиска,Случайный,Вставка,0.004626 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.001014 +Бинарное дерево поиска,Случайный,Вставка,0.004910 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.005058 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.001009 +Бинарное дерево поиска,Случайный,Вставка,0.004521 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.005904 +Бинарное дерево поиска,Случайный,Поиск,0.001025 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.005221 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.001066 +Бинарное дерево поиска,Случайный,Вставка,0.005843 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.007343 +Бинарное дерево поиска,Случайный,Поиск,0.000503 +Бинарное дерево поиска,Случайный,Удаление,0.000516 +Бинарное дерево поиска,Случайный,Вставка,0.006171 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.000512 +Бинарное дерево поиска,Случайный,Вставка,0.005454 +Бинарное дерево поиска,Случайный,Поиск,0.001009 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.006885 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.001451 +Бинарное дерево поиска,Случайный,Вставка,0.006402 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.001009 +Бинарное дерево поиска,Случайный,Вставка,0.004521 +Бинарное дерево поиска,Случайный,Поиск,0.001055 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.005170 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.001503 +Бинарное дерево поиска,Случайный,Вставка,0.005615 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.001001 +Бинарное дерево поиска,Случайный,Вставка,0.005535 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.005566 +Бинарное дерево поиска,Случайный,Поиск,0.001004 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.004022 +Бинарное дерево поиска,Случайный,Поиск,0.001010 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.004053 +Бинарное дерево поиска,Случайный,Поиск,0.001079 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.004512 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка (среднее),0.005367 +Бинарное дерево поиска,Случайный,Поиск (среднее),0.000334 +Бинарное дерево поиска,Случайный,Удаление (среднее),0.000454 +Бинарное дерево поиска,Отсортированный,Вставка,0.901140 +Бинарное дерево поиска,Отсортированный,Поиск,0.055198 +Бинарное дерево поиска,Отсортированный,Удаление,0.068970 +Бинарное дерево поиска,Отсортированный,Вставка,0.901221 +Бинарное дерево поиска,Отсортированный,Поиск,0.055432 +Бинарное дерево поиска,Отсортированный,Удаление,0.067588 +Бинарное дерево поиска,Отсортированный,Вставка,0.875497 +Бинарное дерево поиска,Отсортированный,Поиск,0.055297 +Бинарное дерево поиска,Отсортированный,Удаление,0.068742 +Бинарное дерево поиска,Отсортированный,Вставка,0.894420 +Бинарное дерево поиска,Отсортированный,Поиск,0.054808 +Бинарное дерево поиска,Отсортированный,Удаление,0.068112 +Бинарное дерево поиска,Отсортированный,Вставка,0.879010 +Бинарное дерево поиска,Отсортированный,Поиск,0.054503 +Бинарное дерево поиска,Отсортированный,Удаление,0.068600 +Бинарное дерево поиска,Отсортированный,Вставка,0.873702 +Бинарное дерево поиска,Отсортированный,Поиск,0.055239 +Бинарное дерево поиска,Отсортированный,Удаление,0.067484 +Бинарное дерево поиска,Отсортированный,Вставка,0.889047 +Бинарное дерево поиска,Отсортированный,Поиск,0.066586 +Бинарное дерево поиска,Отсортированный,Удаление,0.071171 +Бинарное дерево поиска,Отсортированный,Вставка,0.881977 +Бинарное дерево поиска,Отсортированный,Поиск,0.059766 +Бинарное дерево поиска,Отсортированный,Удаление,0.069783 +Бинарное дерево поиска,Отсортированный,Вставка,0.890244 +Бинарное дерево поиска,Отсортированный,Поиск,0.054863 +Бинарное дерево поиска,Отсортированный,Удаление,0.069397 +Бинарное дерево поиска,Отсортированный,Вставка,0.871035 +Бинарное дерево поиска,Отсортированный,Поиск,0.055970 +Бинарное дерево поиска,Отсортированный,Удаление,0.065886 +Бинарное дерево поиска,Отсортированный,Вставка,0.873882 +Бинарное дерево поиска,Отсортированный,Поиск,0.056181 +Бинарное дерево поиска,Отсортированный,Удаление,0.069140 +Бинарное дерево поиска,Отсортированный,Вставка,0.932258 +Бинарное дерево поиска,Отсортированный,Поиск,0.055037 +Бинарное дерево поиска,Отсортированный,Удаление,0.066576 +Бинарное дерево поиска,Отсортированный,Вставка,0.875622 +Бинарное дерево поиска,Отсортированный,Поиск,0.055679 +Бинарное дерево поиска,Отсортированный,Удаление,0.068214 +Бинарное дерево поиска,Отсортированный,Вставка,0.871718 +Бинарное дерево поиска,Отсортированный,Поиск,0.055748 +Бинарное дерево поиска,Отсортированный,Удаление,0.067876 +Бинарное дерево поиска,Отсортированный,Вставка,0.872458 +Бинарное дерево поиска,Отсортированный,Поиск,0.054689 +Бинарное дерево поиска,Отсортированный,Удаление,0.068765 +Бинарное дерево поиска,Отсортированный,Вставка,0.871178 +Бинарное дерево поиска,Отсортированный,Поиск,0.054816 +Бинарное дерево поиска,Отсортированный,Удаление,0.067832 +Бинарное дерево поиска,Отсортированный,Вставка,0.876907 +Бинарное дерево поиска,Отсортированный,Поиск,0.054184 +Бинарное дерево поиска,Отсортированный,Удаление,0.068164 +Бинарное дерево поиска,Отсортированный,Вставка,0.868001 +Бинарное дерево поиска,Отсортированный,Поиск,0.054167 +Бинарное дерево поиска,Отсортированный,Удаление,0.068493 +Бинарное дерево поиска,Отсортированный,Вставка,0.879394 +Бинарное дерево поиска,Отсортированный,Поиск,0.058173 +Бинарное дерево поиска,Отсортированный,Удаление,0.068654 +Бинарное дерево поиска,Отсортированный,Вставка,0.868870 +Бинарное дерево поиска,Отсортированный,Поиск,0.055234 +Бинарное дерево поиска,Отсортированный,Удаление,0.068269 +Бинарное дерево поиска,Отсортированный,Вставка (среднее),0.882379 +Бинарное дерево поиска,Отсортированный,Поиск (среднее),0.056078 +Бинарное дерево поиска,Отсортированный,Удаление (среднее),0.068386 diff --git a/stepushovgs/data-structures/source/tests/benchmark/main.go b/stepushovgs/data-structures/source/tests/benchmark/main.go index 36bbd71..b2e804e 100644 --- a/stepushovgs/data-structures/source/tests/benchmark/main.go +++ b/stepushovgs/data-structures/source/tests/benchmark/main.go @@ -16,11 +16,11 @@ import ( ) const ( - countUsers = 10_000 - countRepeat = 10 - countRandomSearch = 200 - countNotExitstSearch = 100 - countDeletes = 500 + countUsers = 20_000 + countRepeat = 20 + countRandomSearch = 1000 + countNotExitstSearch = 500 + countDeletes = 1000 ) type TestData struct { @@ -56,8 +56,9 @@ func NewBinSearchTree() DataStructure { func uniqueElements(data []ds.MyData) []ds.MyData { res := make([]ds.MyData, 0, len(data)) - isUnique := true + for _, el := range data { + isUnique := true for _, resEl := range res { if el == resEl { isUnique = false @@ -74,6 +75,7 @@ func uniqueElements(data []ds.MyData) []ds.MyData { func GenerateTestData() TestData { items := dg.RecordsShuffled(countUsers) + // fmt.Println("isSorted:", isSorted(items)) itemsSort := ds.QSort(items, 0, len(items)-1) uniqueItems := uniqueElements(items) @@ -174,6 +176,14 @@ func testForData(nameStruct, mode string, factory StructureFactory, data_insert, insertTime := testOnesInsert(structure, data_insert) averageTimeInsert += insertTime + // Отладочная информация для бинарного дерева (проверка на вырождение) + if bst, ok := structure.(*bst.BinSearchTree); ok { + fmt.Printf( + "Высота дерева: %d, элементов: %d\n", + bst.Height(), bst.Len(), + ) + } + BenchRes = append(BenchRes, csvwriter.BenchmarkResult{ Structure: nameStruct, Mode: mode, @@ -235,9 +245,21 @@ func testForData(nameStruct, mode string, factory StructureFactory, data_insert, csvwriter.AppendRaw(BenchRes) } +func isSorted(data []ds.MyData) bool { + for i := 0; i < len(data)-1; i++ { + if data[i].Name > data[i+1].Name { + return false + } + } + return true +} + func Test(nameStruct string, factory StructureFactory) { data := GenerateTestData() + // fmt.Println("items", isSorted(data.Items)) + // fmt.Println("items sort", isSorted(data.ItemsSorted)) + testForData(nameStruct, "Случайный", factory, data.Items, data.Search, data.ToDelete) testForData(nameStruct, "Отсортированный", factory, data.ItemsSorted, data.Search, data.ToDelete) @@ -253,4 +275,7 @@ func main() { Test("Связный список", NewLinkedList) Test("Хеш таблица", NewHashTable) Test("Бинарное дерево поиска", NewBinSearchTree) + + // fmt.Println("User_0001" < "User_00100") + // fmt.Println(isSorted(dg.RecordsShuffled(10000))) } From 845db5b195a2e440e58715b3408db72dc2c4e28e Mon Sep 17 00:00:00 2001 From: GordStep Date: Thu, 14 May 2026 14:51:03 +0300 Subject: [PATCH 22/23] =?UTF-8?q?=D0=9D=D0=B0=D0=BF=D0=B8=D1=81=D0=B0?= =?UTF-8?q?=D0=BD=20=D0=BE=D1=82=D1=87=D1=91=D1=82=20=D0=BF=D0=BE=20=D0=BB?= =?UTF-8?q?=D0=B0=D0=B1=D0=BE=D1=80=D0=B0=D1=82=D0=BE=D1=80=D0=BD=D0=BE?= =?UTF-8?q?=D0=B9=20=D1=80=D0=B0=D0=B1=D0=BE=D1=82=D0=B5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- stepushovgs/.gitignore | 8 +- .../{source => }/docs/img/delete.pdf | Bin .../{source => }/docs/img/insert.pdf | Bin .../{source => }/docs/img/search.pdf | Bin .../data-structures/docs/src/main.ipynb | 754 ++++++++++++++++++ stepushovgs/data-structures/docs/Отчёт.md | 122 +++ .../data-structures/source/docs/report.md | 0 .../source/docs/src/main.ipynb | 754 ------------------ .../source/results/benchmarks.csv | 616 +++++++------- .../source/tests/benchmark/main.go | 9 +- 10 files changed, 1199 insertions(+), 1064 deletions(-) rename stepushovgs/data-structures/{source => }/docs/img/delete.pdf (100%) rename stepushovgs/data-structures/{source => }/docs/img/insert.pdf (100%) rename stepushovgs/data-structures/{source => }/docs/img/search.pdf (100%) create mode 100644 stepushovgs/data-structures/docs/src/main.ipynb create mode 100644 stepushovgs/data-structures/docs/Отчёт.md delete mode 100644 stepushovgs/data-structures/source/docs/report.md delete mode 100644 stepushovgs/data-structures/source/docs/src/main.ipynb diff --git a/stepushovgs/.gitignore b/stepushovgs/.gitignore index 09b1f43..b4f7484 100644 --- a/stepushovgs/.gitignore +++ b/stepushovgs/.gitignore @@ -253,4 +253,10 @@ go.work.sum # .idea/ .vscode/ -!go.mod \ No newline at end of file +!go.mod + +################################# +########### Obsidian ############ +################################# + +.obsidian/ \ No newline at end of file diff --git a/stepushovgs/data-structures/source/docs/img/delete.pdf b/stepushovgs/data-structures/docs/img/delete.pdf similarity index 100% rename from stepushovgs/data-structures/source/docs/img/delete.pdf rename to stepushovgs/data-structures/docs/img/delete.pdf diff --git a/stepushovgs/data-structures/source/docs/img/insert.pdf b/stepushovgs/data-structures/docs/img/insert.pdf similarity index 100% rename from stepushovgs/data-structures/source/docs/img/insert.pdf rename to stepushovgs/data-structures/docs/img/insert.pdf diff --git a/stepushovgs/data-structures/source/docs/img/search.pdf b/stepushovgs/data-structures/docs/img/search.pdf similarity index 100% rename from stepushovgs/data-structures/source/docs/img/search.pdf rename to stepushovgs/data-structures/docs/img/search.pdf diff --git a/stepushovgs/data-structures/docs/src/main.ipynb b/stepushovgs/data-structures/docs/src/main.ipynb new file mode 100644 index 0000000..56e1992 --- /dev/null +++ b/stepushovgs/data-structures/docs/src/main.ipynb @@ -0,0 +1,754 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e631810e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "12fa3ed1", + "metadata": {}, + "outputs": [], + "source": [ + "# CMU Serif\n", + "plt.rcParams['font.family'] = 'CMU Serif'\n", + "plt.rcParams['mathtext.fontset'] = 'cm'\n", + "plt.rcParams['font.size'] = 14\n", + "plt.rcParams['axes.titlesize'] = 16\n", + "plt.rcParams['axes.labelsize'] = 15\n", + "plt.rcParams['xtick.labelsize'] = 13\n", + "plt.rcParams['ytick.labelsize'] = 13\n", + "plt.rcParams['legend.fontsize'] = 12" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c691c40e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
StructureModeOperationTime
0Связный списокСлучайныйВставка0.193393
1Связный списокСлучайныйПоиск0.023572
2Связный списокСлучайныйУдаление0.013566
3Связный списокСлучайныйВставка0.193837
4Связный списокСлучайныйПоиск0.023969
...............
373Бинарное дерево поискаОтсортированныйПоиск0.055234
374Бинарное дерево поискаОтсортированныйУдаление0.068269
375Бинарное дерево поискаОтсортированныйВставка (среднее)0.882379
376Бинарное дерево поискаОтсортированныйПоиск (среднее)0.056078
377Бинарное дерево поискаОтсортированныйУдаление (среднее)0.068386
\n", + "

378 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " Structure Mode Operation Time\n", + "0 Связный список Случайный Вставка 0.193393\n", + "1 Связный список Случайный Поиск 0.023572\n", + "2 Связный список Случайный Удаление 0.013566\n", + "3 Связный список Случайный Вставка 0.193837\n", + "4 Связный список Случайный Поиск 0.023969\n", + ".. ... ... ... ...\n", + "373 Бинарное дерево поиска Отсортированный Поиск 0.055234\n", + "374 Бинарное дерево поиска Отсортированный Удаление 0.068269\n", + "375 Бинарное дерево поиска Отсортированный Вставка (среднее) 0.882379\n", + "376 Бинарное дерево поиска Отсортированный Поиск (среднее) 0.056078\n", + "377 Бинарное дерево поиска Отсортированный Удаление (среднее) 0.068386\n", + "\n", + "[378 rows x 4 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "csv_path = \"../../source/results/benchmarks.csv\"\n", + "\n", + "data = pd.read_csv(csv_path)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "a3737f45", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.023409), np.float64(0.192404), np.float64(0.014352))" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Получение данных для Связного списка\n", + "\n", + "ll_random_insert = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_random_insert_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ll_random_search = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_random_search_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ll_random_delete = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_random_delete_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "ll_random_search_average, ll_random_insert_average, ll_random_delete_average" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "5434d260", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.033107), np.float64(0.193206), np.float64(0.022902))" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Получение данных для Связного списка\n", + "\n", + "ll_sorted_insert = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_sorted_insert_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ll_sorted_search = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_sorted_search_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ll_sorted_delete = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "ll_sorted_delete_average = data.loc[\n", + " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "ll_sorted_search_average, ll_sorted_insert_average, ll_sorted_delete_average" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "3deed9a5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.0), np.float64(0.003041), np.float64(5e-05))" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Получение данных для хеш таблицы\n", + "ht_random_insert = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_random_insert_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_random_search = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_random_search_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_random_delete = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_random_delete_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_random_delete_average, ht_random_insert_average, ht_random_search_average" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "490e5c46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.000147), np.float64(0.002862), np.float64(0.0))" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Получение данных для хеш таблицы\n", + "ht_sorted_insert = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_sorted_insert_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_sorted_search = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_sorted_search_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_sorted_delete = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "ht_sorted_delete_average = data.loc[\n", + " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "ht_sorted_delete_average, ht_sorted_insert_average, ht_sorted_search_average" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "9d7274ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.000454), np.float64(0.005367), np.float64(0.000334))" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Получение данных для дерева\n", + "bst_random_insert = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_random_insert_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_random_search = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_random_search_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_random_delete = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_random_delete_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_random_delete_average, bst_random_insert_average, bst_random_search_average" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "92a545c9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.068386), np.float64(0.882379), np.float64(0.056078))" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Получение данных для дерева\n", + "bst_sorted_insert = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_sorted_insert_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_sorted_search = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_sorted_search_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_sorted_delete = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n", + " 'Time'\n", + " ].tolist()\n", + "bst_sorted_delete_average = data.loc[\n", + " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n", + " 'Time'\n", + " ].iloc[0]\n", + "\n", + "bst_sorted_delete_average, bst_sorted_insert_average, bst_sorted_search_average" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "b2e93d6e", + "metadata": {}, + "outputs": [], + "source": [ + "# countUsers = 10_000\n", + "# countRepeat = 10\n", + "# countRandomSearch = 200\n", + "# countNotExitstSearch = 100\n", + "# countDeletes = 500\n", + "\n", + "countUsers = 20_000\n", + "countRepeat = 20\n", + "countRandomSearch = 1000\n", + "countNotExitstSearch = 500\n", + "countDeletes = 1000\n", + "\n", + "ll_col = 'blue'\n", + "ht_col = 'orange'\n", + "bst_col = 'green'\n", + "\n", + "iterations = range(countRepeat)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "208784a5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Создание двух графиков рядом\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# ============= Левый график: случайные данные =============\n", + "ax1.set_title(\"Вставка случайных данных\")\n", + "ax1.set_ylabel('Время, с')\n", + "ax1.set_xlabel('Повторения')\n", + "ax1.set_xticks(iterations)\n", + "\n", + "# Связный список\n", + "ax1.scatter(iterations, ll_random_insert, label='связный список', color=ll_col)\n", + "ax1.axhline(y=ll_random_insert_average, color=ll_col, linewidth=1, \n", + " linestyle='--', alpha=0.7)\n", + "\n", + "# Хеш таблица\n", + "\n", + "ax1.scatter(iterations, ht_random_insert, label='хеш таблица', color=ht_col)\n", + "ax1.axhline(y=ht_random_insert_average, color=ht_col, linewidth=1, \n", + " linestyle='--', alpha=0.7)\n", + "\n", + "# Дерево\n", + "\n", + "ax1.scatter(iterations, bst_random_insert, label='дерево', color=bst_col)\n", + "ax1.axhline(y=bst_random_insert_average, color=bst_col, linewidth=1, \n", + " linestyle='--', alpha=0.7)\n", + "\n", + "ax1.legend(loc='best')\n", + "ax1.grid(True, alpha=0.3)\n", + "\n", + "# ============= Правый график: отсортированные данные =============\n", + "ax2.set_title(\"Вставка отсортированных данных\")\n", + "ax2.set_ylabel('Время, с')\n", + "ax2.set_xlabel('Повторения')\n", + "ax2.set_xticks(iterations)\n", + "# ax2.set_xticklabels(range(1, 6))\n", + "\n", + "# Связный список\n", + "ax2.scatter(iterations, ll_sorted_insert, label='связный список', color=ll_col)\n", + "ax2.axhline(y=ll_sorted_insert_average, color=ll_col, linewidth=1, \n", + " linestyle='--', alpha=0.5)\n", + "\n", + "# Хеш таблица\n", + "ax2.scatter(iterations, ht_sorted_insert, label='хеш таблица', color=ht_col)\n", + "ax2.axhline(y=ht_sorted_insert_average, color=ht_col, linewidth=1, \n", + " linestyle='--', alpha=0.5)\n", + "\n", + "# Дерево\n", + "ax2.scatter(iterations, bst_sorted_insert, label='дерево', color=bst_col)\n", + "ax2.axhline(y=bst_sorted_insert_average, color=bst_col, linewidth=1, \n", + " linestyle='--', alpha=0.5)\n", + "\n", + "ax2.legend(loc='best')\n", + "ax2.grid(True, alpha=0.3)\n", + "\n", + "# Общая настройка\n", + "plt.suptitle(f'Сравнение производительности вставки в структуры данных (N = {countUsers})', \n", + " fontsize=14)\n", + "plt.tight_layout()\n", + "plt.savefig('../img/insert.pdf',\n", + " format='pdf',\n", + " dpi=300,\n", + " bbox_inches='tight', \n", + " pad_inches=0.1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "7de42c9d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Создание двух графиков рядом\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# ============= Левый график: случайные данные =============\n", + "ax1.set_title(\"Поиск в случайных данных\")\n", + "ax1.set_ylabel('Время, с')\n", + "ax1.set_xlabel('Повторения')\n", + "ax1.set_xticks(iterations)\n", + "# ax1.set_xticklabels(range(1, 6))\n", + "\n", + "ax1.scatter(iterations, ll_random_search, label='связный список', color=ll_col)\n", + "ax1.axhline(y=ll_random_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.scatter(iterations, ht_random_search, label='хеш таблица', color=ht_col)\n", + "ax1.axhline(y=ht_random_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.scatter(iterations, bst_random_search, label='дерево', color=bst_col)\n", + "ax1.axhline(y=bst_random_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.legend()\n", + "ax1.grid(True, alpha=0.3)\n", + "\n", + "# ============= Правый график: отсортированные данные =============\n", + "ax2.set_title(\"Поиск в отсортированных данных\")\n", + "ax2.set_ylabel('Время, с')\n", + "ax2.set_xlabel('Повторения')\n", + "ax2.set_xticks(iterations)\n", + "# ax2.set_xticklabels(range(1, 6))\n", + "\n", + "ax2.scatter(iterations, ll_sorted_search, label='связный список', color=ll_col)\n", + "ax2.axhline(y=ll_sorted_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.scatter(iterations, ht_sorted_search, label='хеш таблица', color=ht_col)\n", + "ax2.axhline(y=ht_sorted_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.scatter(iterations, bst_sorted_search, label='дерево', color=bst_col)\n", + "ax2.axhline(y=bst_sorted_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.legend()\n", + "ax2.grid(True, alpha=0.3)\n", + "\n", + "# Общий заголовок\n", + "plt.suptitle(f'Сравнение времени поиска в структурах данных (N = {countRandomSearch} + {countNotExitstSearch})', fontsize=14)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('../img/search.pdf', \n", + " format='pdf',\n", + " dpi=300,\n", + " bbox_inches='tight', # обрезает лишние поля\n", + " pad_inches=0.1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "0fd42f30", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Создание двух графиков рядом\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# ============= Левый график: случайные данные =============\n", + "ax1.set_title(\"Удаление в случайных данных\")\n", + "ax1.set_ylabel('Время, с')\n", + "ax1.set_xlabel('Повторения')\n", + "ax1.set_xticks(iterations)\n", + "# ax1.set_xticklabels(range(1, 6))\n", + "\n", + "ax1.scatter(iterations, ll_random_delete, label='связный список', color=ll_col)\n", + "ax1.axhline(y=ll_random_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.scatter(iterations, ht_random_delete, label='хеш таблица', color=ht_col)\n", + "ax1.axhline(y=ht_random_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.scatter(iterations, bst_random_delete, label='дерево', color=bst_col)\n", + "ax1.axhline(y=bst_random_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax1.legend()\n", + "ax1.grid(True, alpha=0.3)\n", + "\n", + "# ============= Правый график: отсортированные данные =============\n", + "ax2.set_title(\"Удаление в отсортированных данных\")\n", + "ax2.set_ylabel('Время, с')\n", + "ax2.set_xlabel('Повторения')\n", + "ax2.set_xticks(iterations)\n", + "# ax2.set_xticklabels(range(1, 6))\n", + "\n", + "ax2.scatter(iterations, ll_sorted_delete, label='связный список', color=ll_col)\n", + "ax2.axhline(y=ll_sorted_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.scatter(iterations, ht_sorted_delete, label='хеш таблица', color=ht_col)\n", + "ax2.axhline(y=ht_sorted_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.scatter(iterations, bst_sorted_delete, label='дерево', color=bst_col)\n", + "ax2.axhline(y=bst_sorted_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", + "\n", + "ax2.legend()\n", + "ax2.grid(True, alpha=0.3)\n", + "\n", + "# Общий заголовок\n", + "plt.suptitle(f'Сравнение времени удаления в структурах данных (N = {countDeletes})', fontsize=14)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('../img/delete.pdf', \n", + " format='pdf',\n", + " dpi=300,\n", + " bbox_inches='tight', \n", + " pad_inches=0.1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9eca6493", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/stepushovgs/data-structures/docs/Отчёт.md b/stepushovgs/data-structures/docs/Отчёт.md new file mode 100644 index 0000000..3227081 --- /dev/null +++ b/stepushovgs/data-structures/docs/Отчёт.md @@ -0,0 +1,122 @@ +## Практические графики +### Информация о тестировании +- Общее число записей: 20000 +- Каждый замер повторялся: 20 раз +- Количество существующих записей для случайного поиска: 1000 +- Количество несуществующих записей для поиска: 500 +- Количество элементов для удаления: 1000 + +![[insert.pdf]] +**Тестирование вставки (рис. 1)** + +![[search.pdf]] +**Тестирование поиска (рис. 2)** + +![[delete.pdf]] +**Тестирование удаления (рис. 3)** +## Анализ результатов + +### Как порядок входных данных влияет на скорость вставки в BST (деградация до O(n) на отсортированных данных)? + +По определению, при вставке отсортированных данных, структура бинарного дерева поиска вырождается в связный список. +Для визуализации этого в тесте выводятся высота и количество элементов в дереве: +Для случайных данных вывод выглядит примерно так: +``` +Высота дерева: 28, элементов: 8634 +``` +Для сортированных данных же: +``` +Высота дерева: 8634, элементов: 8634 +``` +Заметим, что при случайных данных скорость вставки в бинарное дерево почти лишь немного уступает по скорости хеш-таблице. При сортированных данных из-за рекурсивной реализации вставки бинарное дерево проигрывает связному списку(который имеет линейную сложность вставки) + +### Почему хеш-таблица почти не чувствительна к порядку. +Хеш-таблица не чувствительна к порядку данных, так как использует для распределения элементов хеш значения данных (сложность операции одинакова для любых однотипных данных) и после производит вставку в связный список(в моей реализации проходит по списку и вставляет данные в конец). Поэтому хеш-таблица ни на одном из этапов не сравнивает данные, следовательно их порядок не влияет на скорость. + +### Почему связный список всегда медленен при поиске. +Операция поиска в связном списке имеет линейную сложность $O(n)$ не зависимо от порядка данных, что можно видеть на графике (см. рис. 2). Для бинарного дерева поиска эта сложность в лучшем случае $O(\log(N))$, а в худшем $O(N)$. Для хеш-таблицы сложность вставки $O(1)$, с хорошей хеш-функцией и низким заполнением. + +### Как удаление работает в каждой структуре. +#### Связный список +Находим элемент перед удаляем элементом, и заменяем его поле `next` на `next.next`, то есть теперь он указывает на элемент, который идёт после удаляемого элемента +``` Go +current := ll.head + +for current.next != nil { + if current.next.data.Name == targetName { + current.next = current.next.next + return true + } + current = current.next +} +``` + +#### Бинарное дерево поиска +После того, как мы нашли узел, который необходимо удалить, у нас возможны три случая. + +Случай 1: У удаляемого узла нет правого ребенка. +В этом случае мы просто перемещаем левого ребенка (3) на место удаляемого узла(5). В результате дерево будет выглядеть так: +``` +Удаляем элемент со значением 5 +ДО УДАЛЕНИЯ: ПОСЛЕ УДАЛЕНИЯ: + + [8] [8] + / \ / \ + [5] [10] [3] [10] + / / \ + [3] [1] [4] + / \ +[1] [4] +``` + + +Случай 2: У удаляемого узла есть только правый ребенок, у которого, в свою очередь нет левого ребенка. +В этом случае нам надо переместить правого ребенка(8) удаляемого узла (5) на его место. +``` +Удаляем элемент со значением 5 +До удаления: После удаления: + + [10] [10] + / \ / \ + [5] [12] [8] [12] + / \ / \ + [1] [8] [1] [9] + \ + [9] +``` + + +Случай 3: У удаляемого узла есть первый ребенок, у которого есть левый ребенок. +В этом случае место удаляемого узла занимает крайний левый ребенок правого ребенка удаляемого узла. +Давайте посмотрим, почему это так. Мы знаем о поддереве, начинающемся с удаляемого узла следующее: + +- Все значения справа от него больше или равны значению самого узла. +- Наименьшее значение правого поддерева — крайнее левое. + +Мы должны поместить на место удаляемого узел со значением, меньшим или равным любому узлу справа от него. Для этого нам необходимо найти наименьшее значение в правом поддереве. Поэтому мы берем крайний левый узел правого поддерева. + +``` +Удаляем элемент со значением 5 +До удаления: После удаления: + + [10] [10] + / \ / \ + [5] [12] [7] [12] + / \ / \ + [1] [9] [1] [9] + / / + [7] [8] + \ + [8] +``` + +#### Хеш-таблица +Находим индекс элемента в таблица, далее производим удаление элемента в связном списке, который соответствует этому индексу. + + +# Вывод +Мы реализовали и протестировали три различные структуры хранения данных: связный список, бинарное дерево поиска и хеш-таблица. Сравнили скорость операций вставки, удаления и поиска для каждой структуры. +Если не важен порядок хранения и извлечения данных, то хеш-таблица лучший выбор для быстрых вставки, удаления и поиска. +Если нужно хранить данные с возможностью быстрого отсортированного обхода, то стоит выбрать бинарное дерево поиска. +Если нужно хранить данные в порядке поступления(например очередь), то стоит выбрать связный список. + diff --git a/stepushovgs/data-structures/source/docs/report.md b/stepushovgs/data-structures/source/docs/report.md deleted file mode 100644 index e69de29..0000000 diff --git a/stepushovgs/data-structures/source/docs/src/main.ipynb b/stepushovgs/data-structures/source/docs/src/main.ipynb deleted file mode 100644 index dbea58c..0000000 --- a/stepushovgs/data-structures/source/docs/src/main.ipynb +++ /dev/null @@ -1,754 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 31, - "id": "e631810e", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "12fa3ed1", - "metadata": {}, - "outputs": [], - "source": [ - "# CMU Serif\n", - "plt.rcParams['font.family'] = 'CMU Serif'\n", - "plt.rcParams['mathtext.fontset'] = 'cm'\n", - "plt.rcParams['font.size'] = 14\n", - "plt.rcParams['axes.titlesize'] = 16\n", - "plt.rcParams['axes.labelsize'] = 15\n", - "plt.rcParams['xtick.labelsize'] = 13\n", - "plt.rcParams['ytick.labelsize'] = 13\n", - "plt.rcParams['legend.fontsize'] = 12" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "c691c40e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
StructureModeOperationTime
0Связный списокСлучайныйВставка0.047666
1Связный списокСлучайныйПоиск0.002502
2Связный списокСлучайныйУдаление0.004514
3Связный списокСлучайныйВставка0.048082
4Связный списокСлучайныйПоиск0.003015
...............
193Бинарное дерево поискаОтсортированныйПоиск0.007098
194Бинарное дерево поискаОтсортированныйУдаление0.026717
195Бинарное дерево поискаОтсортированныйВставка (среднее)0.317356
196Бинарное дерево поискаОтсортированныйПоиск (среднее)0.007994
197Бинарное дерево поискаОтсортированныйУдаление (среднее)0.025775
\n", - "

198 rows × 4 columns

\n", - "
" - ], - "text/plain": [ - " Structure Mode Operation Time\n", - "0 Связный список Случайный Вставка 0.047666\n", - "1 Связный список Случайный Поиск 0.002502\n", - "2 Связный список Случайный Удаление 0.004514\n", - "3 Связный список Случайный Вставка 0.048082\n", - "4 Связный список Случайный Поиск 0.003015\n", - ".. ... ... ... ...\n", - "193 Бинарное дерево поиска Отсортированный Поиск 0.007098\n", - "194 Бинарное дерево поиска Отсортированный Удаление 0.026717\n", - "195 Бинарное дерево поиска Отсортированный Вставка (среднее) 0.317356\n", - "196 Бинарное дерево поиска Отсортированный Поиск (среднее) 0.007994\n", - "197 Бинарное дерево поиска Отсортированный Удаление (среднее) 0.025775\n", - "\n", - "[198 rows x 4 columns]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "csv_path = \"../../results/benchmarks.csv\"\n", - "\n", - "data = pd.read_csv(csv_path)\n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "a3737f45", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(np.float64(0.002936), np.float64(0.047986), np.float64(0.004647))" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Получение данных для Связного списка\n", - "\n", - "ll_random_insert = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n", - " 'Time'\n", - " ].tolist()\n", - "ll_random_insert_average = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ll_random_search = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n", - " 'Time'\n", - " ].tolist()\n", - "ll_random_search_average = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ll_random_delete = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n", - " 'Time'\n", - " ].tolist()\n", - "ll_random_delete_average = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "ll_random_search_average, ll_random_insert_average, ll_random_delete_average" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "5434d260", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(np.float64(0.003821), np.float64(0.048337), np.float64(0.005586))" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Получение данных для Связного списка\n", - "\n", - "ll_sorted_insert = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n", - " 'Time'\n", - " ].tolist()\n", - "ll_sorted_insert_average = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ll_sorted_search = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n", - " 'Time'\n", - " ].tolist()\n", - "ll_sorted_search_average = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ll_sorted_delete = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n", - " 'Time'\n", - " ].tolist()\n", - "ll_sorted_delete_average = data.loc[\n", - " (data['Structure'] == 'Связный список') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "ll_sorted_search_average, ll_sorted_insert_average, ll_sorted_delete_average" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "3deed9a5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(np.float64(0.0), np.float64(0.002009), np.float64(0.0))" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Получение данных для хеш таблицы\n", - "ht_random_insert = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n", - " 'Time'\n", - " ].tolist()\n", - "ht_random_insert_average = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ht_random_search = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n", - " 'Time'\n", - " ].tolist()\n", - "ht_random_search_average = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ht_random_delete = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n", - " 'Time'\n", - " ].tolist()\n", - "ht_random_delete_average = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ht_random_delete_average, ht_random_insert_average, ht_random_search_average" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "490e5c46", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(np.float64(0.00015), np.float64(0.001666), np.float64(0.0))" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Получение данных для хеш таблицы\n", - "ht_sorted_insert = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n", - " 'Time'\n", - " ].tolist()\n", - "ht_sorted_insert_average = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ht_sorted_search = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n", - " 'Time'\n", - " ].tolist()\n", - "ht_sorted_search_average = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ht_sorted_delete = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n", - " 'Time'\n", - " ].tolist()\n", - "ht_sorted_delete_average = data.loc[\n", - " (data['Structure'] == 'Хеш таблица') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "ht_sorted_delete_average, ht_sorted_insert_average, ht_sorted_search_average" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "9d7274ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(np.float64(0.000101), np.float64(0.002153), np.float64(0.0))" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Получение данных для дерева\n", - "bst_random_insert = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n", - " 'Time'\n", - " ].tolist()\n", - "bst_random_insert_average = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "bst_random_search = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n", - " 'Time'\n", - " ].tolist()\n", - "bst_random_search_average = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "bst_random_delete = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n", - " 'Time'\n", - " ].tolist()\n", - "bst_random_delete_average = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "bst_random_delete_average, bst_random_insert_average, bst_random_search_average" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "92a545c9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(np.float64(0.025775), np.float64(0.317356), np.float64(0.007994))" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Получение данных для дерева\n", - "bst_sorted_insert = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n", - " 'Time'\n", - " ].tolist()\n", - "bst_sorted_insert_average = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "bst_sorted_search = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n", - " 'Time'\n", - " ].tolist()\n", - "bst_sorted_search_average = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "bst_sorted_delete = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n", - " 'Time'\n", - " ].tolist()\n", - "bst_sorted_delete_average = data.loc[\n", - " (data['Structure'] == 'Бинарное дерево поиска') & (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n", - " 'Time'\n", - " ].iloc[0]\n", - "\n", - "bst_sorted_delete_average, bst_sorted_insert_average, bst_sorted_search_average" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b2e93d6e", - "metadata": {}, - "outputs": [], - "source": [ - "# countUsers = 10_000\n", - "# countRepeat = 10\n", - "# countRandomSearch = 200\n", - "# countNotExitstSearch = 100\n", - "# countDeletes = 500\n", - "\n", - "countUsers = 20_000\n", - "countRepeat = 20\n", - "countRandomSearch = 1000\n", - "countNotExitstSearch = 500\n", - "countDeletes = 1000\n", - "\n", - "ll_col = 'blue'\n", - "ht_col = 'orange'\n", - "bst_col = 'green'\n", - "\n", - "iterations = range(countRepeat)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "208784a5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Создание двух графиков рядом\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", - "\n", - "# ============= Левый график: случайные данные =============\n", - "ax1.set_title(\"Вставка случайных данных\")\n", - "ax1.set_ylabel('Время, с')\n", - "ax1.set_xlabel('Повторения')\n", - "ax1.set_xticks(iterations)\n", - "\n", - "# Связный список\n", - "ax1.scatter(iterations, ll_random_insert, label='связный список', color=ll_col)\n", - "ax1.axhline(y=ll_random_insert_average, color=ll_col, linewidth=1, \n", - " linestyle='--', alpha=0.7)\n", - "\n", - "# Хеш таблица\n", - "\n", - "ax1.scatter(iterations, ht_random_insert, label='хеш таблица', color=ht_col)\n", - "ax1.axhline(y=ht_random_insert_average, color=ht_col, linewidth=1, \n", - " linestyle='--', alpha=0.7)\n", - "\n", - "# Дерево\n", - "\n", - "ax1.scatter(iterations, bst_random_insert, label='дерево', color=bst_col)\n", - "ax1.axhline(y=bst_random_insert_average, color=bst_col, linewidth=1, \n", - " linestyle='--', alpha=0.7)\n", - "\n", - "ax1.legend(loc='best')\n", - "ax1.grid(True, alpha=0.3)\n", - "\n", - "# ============= Правый график: отсортированные данные =============\n", - "ax2.set_title(\"Вставка отсортированных данных\")\n", - "ax2.set_ylabel('Время, с')\n", - "ax2.set_xlabel('Повторения')\n", - "ax2.set_xticks(iterations)\n", - "# ax2.set_xticklabels(range(1, 6))\n", - "\n", - "# Связный список\n", - "ax2.scatter(iterations, ll_sorted_insert, label='связный список', color=ll_col)\n", - "ax2.axhline(y=ll_sorted_insert_average, color=ll_col, linewidth=1, \n", - " linestyle='--', alpha=0.5)\n", - "\n", - "# Хеш таблица\n", - "ax2.scatter(iterations, ht_sorted_insert, label='хеш таблица', color=ht_col)\n", - "ax2.axhline(y=ht_sorted_insert_average, color=ht_col, linewidth=1, \n", - " linestyle='--', alpha=0.5)\n", - "\n", - "# Дерево\n", - "ax2.scatter(iterations, bst_sorted_insert, label='дерево', color=bst_col)\n", - "ax2.axhline(y=bst_sorted_insert_average, color=bst_col, linewidth=1, \n", - " linestyle='--', alpha=0.5)\n", - "\n", - "ax2.legend(loc='best')\n", - "ax2.grid(True, alpha=0.3)\n", - "\n", - "# Общая настройка\n", - "plt.suptitle(f'Сравнение производительности вставки в структуры данных (N = {countUsers})', \n", - " fontsize=14)\n", - "plt.tight_layout()\n", - "plt.savefig('../img/insert.pdf',\n", - " format='pdf',\n", - " dpi=300,\n", - " bbox_inches='tight', \n", - " pad_inches=0.1)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "7de42c9d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Создание двух графиков рядом\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", - "\n", - "# ============= Левый график: случайные данные =============\n", - "ax1.set_title(\"Поиск в случайных данных\")\n", - "ax1.set_ylabel('Время, с')\n", - "ax1.set_xlabel('Повторения')\n", - "ax1.set_xticks(iterations)\n", - "# ax1.set_xticklabels(range(1, 6))\n", - "\n", - "ax1.scatter(iterations, ll_random_search, label='связный список', color=ll_col)\n", - "ax1.axhline(y=ll_random_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax1.scatter(iterations, ht_random_search, label='хеш таблица', color=ht_col)\n", - "ax1.axhline(y=ht_random_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax1.scatter(iterations, bst_random_search, label='дерево', color=bst_col)\n", - "ax1.axhline(y=bst_random_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax1.legend()\n", - "ax1.grid(True, alpha=0.3)\n", - "\n", - "# ============= Правый график: отсортированные данные =============\n", - "ax2.set_title(\"Поиск в отсортированных данных\")\n", - "ax2.set_ylabel('Время, с')\n", - "ax2.set_xlabel('Повторения')\n", - "ax2.set_xticks(iterations)\n", - "# ax2.set_xticklabels(range(1, 6))\n", - "\n", - "ax2.scatter(iterations, ll_sorted_search, label='связный список', color=ll_col)\n", - "ax2.axhline(y=ll_sorted_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax2.scatter(iterations, ht_sorted_search, label='хеш таблица', color=ht_col)\n", - "ax2.axhline(y=ht_sorted_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax2.scatter(iterations, bst_sorted_search, label='дерево', color=bst_col)\n", - "ax2.axhline(y=bst_sorted_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax2.legend()\n", - "ax2.grid(True, alpha=0.3)\n", - "\n", - "# Общий заголовок\n", - "plt.suptitle(f'Сравнение времени поиска в структурах данных (N = {countRandomSearch} + {countNotExitstSearch})', fontsize=14)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig('../img/search.pdf', \n", - " format='pdf',\n", - " dpi=300,\n", - " bbox_inches='tight', # обрезает лишние поля\n", - " pad_inches=0.1)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "0fd42f30", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Создание двух графиков рядом\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", - "\n", - "# ============= Левый график: случайные данные =============\n", - "ax1.set_title(\"Удаление в случайных данных\")\n", - "ax1.set_ylabel('Время, с')\n", - "ax1.set_xlabel('Повторения')\n", - "ax1.set_xticks(iterations)\n", - "# ax1.set_xticklabels(range(1, 6))\n", - "\n", - "ax1.scatter(iterations, ll_random_delete, label='связный список', color=ll_col)\n", - "ax1.axhline(y=ll_random_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax1.scatter(iterations, ht_random_delete, label='хеш таблица', color=ht_col)\n", - "ax1.axhline(y=ht_random_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax1.scatter(iterations, bst_random_delete, label='дерево', color=bst_col)\n", - "ax1.axhline(y=bst_random_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax1.legend()\n", - "ax1.grid(True, alpha=0.3)\n", - "\n", - "# ============= Правый график: отсортированные данные =============\n", - "ax2.set_title(\"Удаление в отсортированных данных\")\n", - "ax2.set_ylabel('Время, с')\n", - "ax2.set_xlabel('Повторения')\n", - "ax2.set_xticks(iterations)\n", - "# ax2.set_xticklabels(range(1, 6))\n", - "\n", - "ax2.scatter(iterations, ll_sorted_delete, label='связный список', color=ll_col)\n", - "ax2.axhline(y=ll_sorted_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax2.scatter(iterations, ht_sorted_delete, label='хеш таблица', color=ht_col)\n", - "ax2.axhline(y=ht_sorted_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax2.scatter(iterations, bst_sorted_delete, label='дерево', color=bst_col)\n", - "ax2.axhline(y=bst_sorted_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n", - "\n", - "ax2.legend()\n", - "ax2.grid(True, alpha=0.3)\n", - "\n", - "# Общий заголовок\n", - "plt.suptitle(f'Сравнение времени удаления в структурах данных (N = {countDeletes})', fontsize=14)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig('../img/delete.pdf', \n", - " format='pdf',\n", - " dpi=300,\n", - " bbox_inches='tight', \n", - " pad_inches=0.1)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9eca6493", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/stepushovgs/data-structures/source/results/benchmarks.csv b/stepushovgs/data-structures/source/results/benchmarks.csv index dcb6c95..ae7cd89 100644 --- a/stepushovgs/data-structures/source/results/benchmarks.csv +++ b/stepushovgs/data-structures/source/results/benchmarks.csv @@ -1,379 +1,379 @@ Structure,Mode,Operation,Time -Связный список,Случайный,Вставка,0.193393 -Связный список,Случайный,Поиск,0.023572 +Связный список,Случайный,Вставка,0.194844 +Связный список,Случайный,Поиск,0.023535 +Связный список,Случайный,Удаление,0.015070 +Связный список,Случайный,Вставка,0.195476 +Связный список,Случайный,Поиск,0.024576 +Связный список,Случайный,Удаление,0.013662 +Связный список,Случайный,Вставка,0.196501 +Связный список,Случайный,Поиск,0.027028 +Связный список,Случайный,Удаление,0.018409 +Связный список,Случайный,Вставка,0.194328 +Связный список,Случайный,Поиск,0.024307 +Связный список,Случайный,Удаление,0.014566 +Связный список,Случайный,Вставка,0.192402 +Связный список,Случайный,Поиск,0.024709 +Связный список,Случайный,Удаление,0.012597 +Связный список,Случайный,Вставка,0.195884 +Связный список,Случайный,Поиск,0.024688 +Связный список,Случайный,Удаление,0.014169 +Связный список,Случайный,Вставка,0.193099 +Связный список,Случайный,Поиск,0.024969 +Связный список,Случайный,Удаление,0.014594 +Связный список,Случайный,Вставка,0.190199 +Связный список,Случайный,Поиск,0.024101 +Связный список,Случайный,Удаление,0.014087 +Связный список,Случайный,Вставка,0.194119 +Связный список,Случайный,Поиск,0.024697 +Связный список,Случайный,Удаление,0.015095 +Связный список,Случайный,Вставка,0.192847 +Связный список,Случайный,Поиск,0.024285 +Связный список,Случайный,Удаление,0.014059 +Связный список,Случайный,Вставка,0.191560 +Связный список,Случайный,Поиск,0.024087 +Связный список,Случайный,Удаление,0.013595 +Связный список,Случайный,Вставка,0.192786 +Связный список,Случайный,Поиск,0.023233 +Связный список,Случайный,Удаление,0.014077 +Связный список,Случайный,Вставка,0.197807 +Связный список,Случайный,Поиск,0.024156 +Связный список,Случайный,Удаление,0.015051 +Связный список,Случайный,Вставка,0.191335 +Связный список,Случайный,Поиск,0.024676 +Связный список,Случайный,Удаление,0.013941 +Связный список,Случайный,Вставка,0.195091 +Связный список,Случайный,Поиск,0.024660 Связный список,Случайный,Удаление,0.013566 -Связный список,Случайный,Вставка,0.193837 -Связный список,Случайный,Поиск,0.023969 -Связный список,Случайный,Удаление,0.013580 -Связный список,Случайный,Вставка,0.190963 -Связный список,Случайный,Поиск,0.023944 -Связный список,Случайный,Удаление,0.013505 -Связный список,Случайный,Вставка,0.194440 -Связный список,Случайный,Поиск,0.022179 -Связный список,Случайный,Удаление,0.015040 -Связный список,Случайный,Вставка,0.191873 -Связный список,Случайный,Поиск,0.023604 -Связный список,Случайный,Удаление,0.014556 -Связный список,Случайный,Вставка,0.191788 -Связный список,Случайный,Поиск,0.023570 -Связный список,Случайный,Удаление,0.013037 -Связный список,Случайный,Вставка,0.192362 -Связный список,Случайный,Поиск,0.023050 -Связный список,Случайный,Удаление,0.013997 -Связный список,Случайный,Вставка,0.191223 -Связный список,Случайный,Поиск,0.023778 -Связный список,Случайный,Удаление,0.013582 -Связный список,Случайный,Вставка,0.191326 -Связный список,Случайный,Поиск,0.023035 -Связный список,Случайный,Удаление,0.013183 -Связный список,Случайный,Вставка,0.191919 -Связный список,Случайный,Поиск,0.023584 -Связный список,Случайный,Удаление,0.013988 -Связный список,Случайный,Вставка,0.190009 -Связный список,Случайный,Поиск,0.022013 -Связный список,Случайный,Удаление,0.014679 -Связный список,Случайный,Вставка,0.191010 -Связный список,Случайный,Поиск,0.024724 -Связный список,Случайный,Удаление,0.015116 -Связный список,Случайный,Вставка,0.192876 -Связный список,Случайный,Поиск,0.023690 -Связный список,Случайный,Удаление,0.021114 -Связный список,Случайный,Вставка,0.202786 -Связный список,Случайный,Поиск,0.023466 -Связный список,Случайный,Удаление,0.014063 -Связный список,Случайный,Вставка,0.191339 -Связный список,Случайный,Поиск,0.022909 -Связный список,Случайный,Удаление,0.014661 -Связный список,Случайный,Вставка,0.189898 -Связный список,Случайный,Поиск,0.023056 -Связный список,Случайный,Удаление,0.013592 -Связный список,Случайный,Вставка,0.195372 -Связный список,Случайный,Поиск,0.023198 -Связный список,Случайный,Удаление,0.013819 -Связный список,Случайный,Вставка,0.191713 -Связный список,Случайный,Поиск,0.023542 -Связный список,Случайный,Удаление,0.012883 -Связный список,Случайный,Вставка,0.190514 -Связный список,Случайный,Поиск,0.023745 -Связный список,Случайный,Удаление,0.015012 -Связный список,Случайный,Вставка,0.189447 -Связный список,Случайный,Поиск,0.023544 -Связный список,Случайный,Удаление,0.014063 -Связный список,Случайный,Вставка (среднее),0.192404 -Связный список,Случайный,Поиск (среднее),0.023409 -Связный список,Случайный,Удаление (среднее),0.014352 -Связный список,Отсортированный,Вставка,0.193078 -Связный список,Отсортированный,Поиск,0.033527 -Связный список,Отсортированный,Удаление,0.022597 -Связный список,Отсортированный,Вставка,0.190167 -Связный список,Отсортированный,Поиск,0.032657 -Связный список,Отсортированный,Удаление,0.022773 -Связный список,Отсортированный,Вставка,0.191660 -Связный список,Отсортированный,Поиск,0.034012 -Связный список,Отсортированный,Удаление,0.022709 -Связный список,Отсортированный,Вставка,0.191970 -Связный список,Отсортированный,Поиск,0.032553 -Связный список,Отсортированный,Удаление,0.022828 -Связный список,Отсортированный,Вставка,0.194583 -Связный список,Отсортированный,Поиск,0.034762 -Связный список,Отсортированный,Удаление,0.022843 -Связный список,Отсортированный,Вставка,0.192641 +Связный список,Случайный,Вставка,0.196118 +Связный список,Случайный,Поиск,0.024149 +Связный список,Случайный,Удаление,0.014061 +Связный список,Случайный,Вставка,0.192031 +Связный список,Случайный,Поиск,0.024358 +Связный список,Случайный,Удаление,0.013602 +Связный список,Случайный,Вставка,0.192075 +Связный список,Случайный,Поиск,0.024104 +Связный список,Случайный,Удаление,0.013106 +Связный список,Случайный,Вставка,0.192208 +Связный список,Случайный,Поиск,0.023780 +Связный список,Случайный,Удаление,0.014568 +Связный список,Случайный,Вставка,0.193080 +Связный список,Случайный,Поиск,0.024706 +Связный список,Случайный,Удаление,0.013573 +Связный список,Случайный,Вставка (среднее),0.193690 +Связный список,Случайный,Поиск (среднее),0.024440 +Связный список,Случайный,Удаление (среднее),0.014272 +Связный список,Отсортированный,Вставка,0.193077 +Связный список,Отсортированный,Поиск,0.033712 +Связный список,Отсортированный,Удаление,0.023180 +Связный список,Отсортированный,Вставка,0.191460 Связный список,Отсортированный,Поиск,0.033173 -Связный список,Отсортированный,Удаление,0.022690 -Связный список,Отсортированный,Вставка,0.194789 -Связный список,Отсортированный,Поиск,0.033526 -Связный список,Отсортированный,Удаление,0.022985 -Связный список,Отсортированный,Вставка,0.191488 -Связный список,Отсортированный,Поиск,0.032518 -Связный список,Отсортированный,Удаление,0.023373 -Связный список,Отсортированный,Вставка,0.203929 -Связный список,Отсортированный,Поиск,0.034133 -Связный список,Отсортированный,Удаление,0.023423 -Связный список,Отсортированный,Вставка,0.193756 -Связный список,Отсортированный,Поиск,0.033023 -Связный список,Отсортированный,Удаление,0.022845 -Связный список,Отсортированный,Вставка,0.195554 -Связный список,Отсортированный,Поиск,0.032817 -Связный список,Отсортированный,Удаление,0.023482 -Связный список,Отсортированный,Вставка,0.193150 -Связный список,Отсортированный,Поиск,0.033271 -Связный список,Отсортированный,Удаление,0.022587 -Связный список,Отсортированный,Вставка,0.192696 -Связный список,Отсортированный,Поиск,0.033454 -Связный список,Отсортированный,Удаление,0.022527 -Связный список,Отсортированный,Вставка,0.192065 -Связный список,Отсортированный,Поиск,0.033382 -Связный список,Отсортированный,Удаление,0.022786 -Связный список,Отсортированный,Вставка,0.191702 -Связный список,Отсортированный,Поиск,0.031664 -Связный список,Отсортированный,Удаление,0.023819 -Связный список,Отсортированный,Вставка,0.194042 -Связный список,Отсортированный,Поиск,0.032380 -Связный список,Отсортированный,Удаление,0.023181 -Связный список,Отсортированный,Вставка,0.190397 -Связный список,Отсортированный,Поиск,0.033168 -Связный список,Отсортированный,Удаление,0.022893 -Связный список,Отсортированный,Вставка,0.191636 -Связный список,Отсортированный,Поиск,0.032367 -Связный список,Отсортированный,Удаление,0.022448 -Связный список,Отсортированный,Вставка,0.193131 -Связный список,Отсортированный,Поиск,0.032905 -Связный список,Отсортированный,Удаление,0.022932 -Связный список,Отсортированный,Вставка,0.191676 -Связный список,Отсортированный,Поиск,0.032851 -Связный список,Отсортированный,Удаление,0.022315 -Связный список,Отсортированный,Вставка (среднее),0.193206 -Связный список,Отсортированный,Поиск (среднее),0.033107 -Связный список,Отсортированный,Удаление (среднее),0.022902 -Хеш таблица,Случайный,Вставка,0.002119 +Связный список,Отсортированный,Удаление,0.024734 +Связный список,Отсортированный,Вставка,0.193809 +Связный список,Отсортированный,Поиск,0.034748 +Связный список,Отсортированный,Удаление,0.022083 +Связный список,Отсортированный,Вставка,0.198505 +Связный список,Отсортированный,Поиск,0.034239 +Связный список,Отсортированный,Удаление,0.022935 +Связный список,Отсортированный,Вставка,0.192402 +Связный список,Отсортированный,Поиск,0.036666 +Связный список,Отсортированный,Удаление,0.022155 +Связный список,Отсортированный,Вставка,0.198979 +Связный список,Отсортированный,Поиск,0.033534 +Связный список,Отсортированный,Удаление,0.024246 +Связный список,Отсортированный,Вставка,0.196372 +Связный список,Отсортированный,Поиск,0.033731 +Связный список,Отсортированный,Удаление,0.023694 +Связный список,Отсортированный,Вставка,0.209801 +Связный список,Отсортированный,Поиск,0.057158 +Связный список,Отсортированный,Удаление,0.027851 +Связный список,Отсортированный,Вставка,0.194425 +Связный список,Отсортированный,Поиск,0.034020 +Связный список,Отсортированный,Удаление,0.021955 +Связный список,Отсортированный,Вставка,0.195977 +Связный список,Отсортированный,Поиск,0.033676 +Связный список,Отсортированный,Удаление,0.022606 +Связный список,Отсортированный,Вставка,0.191872 +Связный список,Отсортированный,Поиск,0.032348 +Связный список,Отсортированный,Удаление,0.023074 +Связный список,Отсортированный,Вставка,0.195098 +Связный список,Отсортированный,Поиск,0.032630 +Связный список,Отсортированный,Удаление,0.023753 +Связный список,Отсортированный,Вставка,0.194327 +Связный список,Отсортированный,Поиск,0.032106 +Связный список,Отсортированный,Удаление,0.023585 +Связный список,Отсортированный,Вставка,0.190991 +Связный список,Отсортированный,Поиск,0.031614 +Связный список,Отсортированный,Удаление,0.022668 +Связный список,Отсортированный,Вставка,0.193774 +Связный список,Отсортированный,Поиск,0.033153 +Связный список,Отсортированный,Удаление,0.022583 +Связный список,Отсортированный,Вставка,0.206674 +Связный список,Отсортированный,Поиск,0.033185 +Связный список,Отсортированный,Удаление,0.023165 +Связный список,Отсортированный,Вставка,0.191183 +Связный список,Отсортированный,Поиск,0.032613 +Связный список,Отсортированный,Удаление,0.022630 +Связный список,Отсортированный,Вставка,0.190937 +Связный список,Отсортированный,Поиск,0.033334 +Связный список,Отсортированный,Удаление,0.022092 +Связный список,Отсортированный,Вставка,0.191349 +Связный список,Отсортированный,Поиск,0.036153 +Связный список,Отсортированный,Удаление,0.022576 +Связный список,Отсортированный,Вставка,0.195836 +Связный список,Отсортированный,Поиск,0.032643 +Связный список,Отсортированный,Удаление,0.022599 +Связный список,Отсортированный,Вставка (среднее),0.195342 +Связный список,Отсортированный,Поиск (среднее),0.034722 +Связный список,Отсортированный,Удаление (среднее),0.023208 +Хеш таблица,Случайный,Вставка,0.002008 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003507 +Хеш таблица,Случайный,Вставка,0.004030 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002563 +Хеш таблица,Случайный,Вставка,0.002006 +Хеш таблица,Случайный,Поиск,0.001004 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.002157 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003023 +Хеш таблица,Случайный,Вставка,0.003016 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003513 +Хеш таблица,Случайный,Вставка,0.002017 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002516 +Хеш таблица,Случайный,Вставка,0.003028 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003369 -Хеш таблица,Случайный,Поиск,0.001002 -Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002091 +Хеш таблица,Случайный,Вставка,0.002514 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003654 +Хеш таблица,Случайный,Вставка,0.002955 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003102 +Хеш таблица,Случайный,Вставка,0.002034 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003008 +Хеш таблица,Случайный,Вставка,0.002006 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003997 +Хеш таблица,Случайный,Вставка,0.004015 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.001580 +Хеш таблица,Случайный,Вставка,0.002255 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.005478 +Хеш таблица,Случайный,Вставка,0.003012 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.002006 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.002301 +Хеш таблица,Случайный,Поиск,0.000503 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.002568 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.002006 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка,0.003140 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 Хеш таблица,Случайный,Вставка,0.002015 -Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Поиск,0.001009 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003410 -Хеш таблица,Случайный,Поиск,0.000000 -Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002158 -Хеш таблица,Случайный,Поиск,0.000000 -Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003586 -Хеш таблица,Случайный,Поиск,0.000000 -Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003123 -Хеш таблица,Случайный,Поиск,0.000000 -Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003011 -Хеш таблица,Случайный,Поиск,0.000000 -Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка (среднее),0.003041 -Хеш таблица,Случайный,Поиск (среднее),0.000050 +Хеш таблица,Случайный,Вставка (среднее),0.002554 +Хеш таблица,Случайный,Поиск (среднее),0.000126 Хеш таблица,Случайный,Удаление (среднее),0.000000 -Хеш таблица,Отсортированный,Вставка,0.003208 +Хеш таблица,Отсортированный,Вставка,0.001504 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.003095 +Хеш таблица,Отсортированный,Вставка,0.003014 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002048 +Хеш таблица,Отсортированный,Вставка,0.002020 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.003991 +Хеш таблица,Отсортированный,Вставка,0.001503 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.002023 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000503 +Хеш таблица,Отсортированный,Вставка,0.001509 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.002282 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.002008 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.003019 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.001504 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.002523 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 Хеш таблица,Отсортированный,Вставка,0.002006 +Хеш таблица,Отсортированный,Поиск,0.001000 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.001503 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.001148 +Хеш таблица,Отсортированный,Вставка,0.001504 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.003505 +Хеш таблица,Отсортированный,Вставка,0.002013 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.003013 +Хеш таблица,Отсортированный,Вставка,0.001002 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002423 -Хеш таблица,Отсортированный,Поиск,0.000000 -Хеш таблица,Отсортированный,Удаление,0.001102 -Хеш таблица,Отсортированный,Вставка,0.003121 -Хеш таблица,Отсортированный,Поиск,0.000000 -Хеш таблица,Отсортированный,Удаление,0.000503 -Хеш таблица,Отсортированный,Вставка,0.002511 +Хеш таблица,Отсортированный,Вставка,0.003522 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.003011 +Хеш таблица,Отсортированный,Вставка,0.001008 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002003 -Хеш таблица,Отсортированный,Поиск,0.000000 -Хеш таблица,Отсортированный,Удаление,0.001341 -Хеш таблица,Отсортированный,Вставка,0.001513 +Хеш таблица,Отсортированный,Вставка,0.002546 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.004485 +Хеш таблица,Отсортированный,Вставка,0.001001 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002740 -Хеш таблица,Отсортированный,Поиск,0.000000 -Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002310 -Хеш таблица,Отсортированный,Поиск,0.000000 -Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002506 -Хеш таблица,Отсортированный,Поиск,0.000000 -Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002515 -Хеш таблица,Отсортированный,Поиск,0.000000 -Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.003073 -Хеш таблица,Отсортированный,Поиск,0.000000 -Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.004169 -Хеш таблица,Отсортированный,Поиск,0.000000 -Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка (среднее),0.002862 -Хеш таблица,Отсортированный,Поиск (среднее),0.000000 -Хеш таблица,Отсортированный,Удаление (среднее),0.000147 -Бинарное дерево поиска,Случайный,Вставка,0.004626 -Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.001014 -Бинарное дерево поиска,Случайный,Вставка,0.004910 +Хеш таблица,Отсортированный,Вставка (среднее),0.001951 +Хеш таблица,Отсортированный,Поиск (среднее),0.000050 +Хеш таблица,Отсортированный,Удаление (среднее),0.000083 +Бинарное дерево поиска,Случайный,Вставка,0.003507 +Бинарное дерево поиска,Случайный,Поиск,0.001002 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.004530 Бинарное дерево поиска,Случайный,Поиск,0.000000 Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.005058 -Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.001009 -Бинарное дерево поиска,Случайный,Вставка,0.004521 +Бинарное дерево поиска,Случайный,Вставка,0.004086 Бинарное дерево поиска,Случайный,Поиск,0.000000 Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.005904 -Бинарное дерево поиска,Случайный,Поиск,0.001025 -Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.005221 -Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.001066 -Бинарное дерево поиска,Случайный,Вставка,0.005843 +Бинарное дерево поиска,Случайный,Вставка,0.004518 Бинарное дерево поиска,Случайный,Поиск,0.000000 Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.007343 -Бинарное дерево поиска,Случайный,Поиск,0.000503 -Бинарное дерево поиска,Случайный,Удаление,0.000516 -Бинарное дерево поиска,Случайный,Вставка,0.006171 +Бинарное дерево поиска,Случайный,Вставка,0.005591 Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.000512 -Бинарное дерево поиска,Случайный,Вставка,0.005454 -Бинарное дерево поиска,Случайный,Поиск,0.001009 Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.006885 -Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.001451 -Бинарное дерево поиска,Случайный,Вставка,0.006402 -Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.001009 -Бинарное дерево поиска,Случайный,Вставка,0.004521 -Бинарное дерево поиска,Случайный,Поиск,0.001055 -Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.005170 +Бинарное дерево поиска,Случайный,Вставка,0.004526 Бинарное дерево поиска,Случайный,Поиск,0.000000 Бинарное дерево поиска,Случайный,Удаление,0.001503 -Бинарное дерево поиска,Случайный,Вставка,0.005615 +Бинарное дерево поиска,Случайный,Вставка,0.004028 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.004618 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.001503 +Бинарное дерево поиска,Случайный,Вставка,0.006937 +Бинарное дерево поиска,Случайный,Поиск,0.001400 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.006121 Бинарное дерево поиска,Случайный,Поиск,0.000000 Бинарное дерево поиска,Случайный,Удаление,0.001001 -Бинарное дерево поиска,Случайный,Вставка,0.005535 +Бинарное дерево поиска,Случайный,Вставка,0.007520 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.001006 +Бинарное дерево поиска,Случайный,Вставка,0.005534 +Бинарное дерево поиска,Случайный,Поиск,0.001001 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.003024 Бинарное дерево поиска,Случайный,Поиск,0.000000 Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.005566 -Бинарное дерево поиска,Случайный,Поиск,0.001004 -Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.004022 -Бинарное дерево поиска,Случайный,Поиск,0.001010 -Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.004053 -Бинарное дерево поиска,Случайный,Поиск,0.001079 -Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.004512 +Бинарное дерево поиска,Случайный,Вставка,0.007068 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.001003 +Бинарное дерево поиска,Случайный,Вставка,0.004552 Бинарное дерево поиска,Случайный,Поиск,0.000000 Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка (среднее),0.005367 -Бинарное дерево поиска,Случайный,Поиск (среднее),0.000334 -Бинарное дерево поиска,Случайный,Удаление (среднее),0.000454 -Бинарное дерево поиска,Отсортированный,Вставка,0.901140 -Бинарное дерево поиска,Отсортированный,Поиск,0.055198 -Бинарное дерево поиска,Отсортированный,Удаление,0.068970 -Бинарное дерево поиска,Отсортированный,Вставка,0.901221 -Бинарное дерево поиска,Отсортированный,Поиск,0.055432 -Бинарное дерево поиска,Отсортированный,Удаление,0.067588 -Бинарное дерево поиска,Отсортированный,Вставка,0.875497 -Бинарное дерево поиска,Отсортированный,Поиск,0.055297 -Бинарное дерево поиска,Отсортированный,Удаление,0.068742 -Бинарное дерево поиска,Отсортированный,Вставка,0.894420 -Бинарное дерево поиска,Отсортированный,Поиск,0.054808 -Бинарное дерево поиска,Отсортированный,Удаление,0.068112 -Бинарное дерево поиска,Отсортированный,Вставка,0.879010 -Бинарное дерево поиска,Отсортированный,Поиск,0.054503 -Бинарное дерево поиска,Отсортированный,Удаление,0.068600 -Бинарное дерево поиска,Отсортированный,Вставка,0.873702 -Бинарное дерево поиска,Отсортированный,Поиск,0.055239 -Бинарное дерево поиска,Отсортированный,Удаление,0.067484 -Бинарное дерево поиска,Отсортированный,Вставка,0.889047 -Бинарное дерево поиска,Отсортированный,Поиск,0.066586 -Бинарное дерево поиска,Отсортированный,Удаление,0.071171 -Бинарное дерево поиска,Отсортированный,Вставка,0.881977 -Бинарное дерево поиска,Отсортированный,Поиск,0.059766 -Бинарное дерево поиска,Отсортированный,Удаление,0.069783 -Бинарное дерево поиска,Отсортированный,Вставка,0.890244 -Бинарное дерево поиска,Отсортированный,Поиск,0.054863 -Бинарное дерево поиска,Отсортированный,Удаление,0.069397 -Бинарное дерево поиска,Отсортированный,Вставка,0.871035 -Бинарное дерево поиска,Отсортированный,Поиск,0.055970 -Бинарное дерево поиска,Отсортированный,Удаление,0.065886 -Бинарное дерево поиска,Отсортированный,Вставка,0.873882 -Бинарное дерево поиска,Отсортированный,Поиск,0.056181 -Бинарное дерево поиска,Отсортированный,Удаление,0.069140 -Бинарное дерево поиска,Отсортированный,Вставка,0.932258 -Бинарное дерево поиска,Отсортированный,Поиск,0.055037 -Бинарное дерево поиска,Отсортированный,Удаление,0.066576 -Бинарное дерево поиска,Отсортированный,Вставка,0.875622 -Бинарное дерево поиска,Отсортированный,Поиск,0.055679 -Бинарное дерево поиска,Отсортированный,Удаление,0.068214 -Бинарное дерево поиска,Отсортированный,Вставка,0.871718 -Бинарное дерево поиска,Отсортированный,Поиск,0.055748 -Бинарное дерево поиска,Отсортированный,Удаление,0.067876 -Бинарное дерево поиска,Отсортированный,Вставка,0.872458 -Бинарное дерево поиска,Отсортированный,Поиск,0.054689 -Бинарное дерево поиска,Отсортированный,Удаление,0.068765 -Бинарное дерево поиска,Отсортированный,Вставка,0.871178 -Бинарное дерево поиска,Отсортированный,Поиск,0.054816 -Бинарное дерево поиска,Отсортированный,Удаление,0.067832 -Бинарное дерево поиска,Отсортированный,Вставка,0.876907 -Бинарное дерево поиска,Отсортированный,Поиск,0.054184 -Бинарное дерево поиска,Отсортированный,Удаление,0.068164 -Бинарное дерево поиска,Отсортированный,Вставка,0.868001 -Бинарное дерево поиска,Отсортированный,Поиск,0.054167 -Бинарное дерево поиска,Отсортированный,Удаление,0.068493 -Бинарное дерево поиска,Отсортированный,Вставка,0.879394 -Бинарное дерево поиска,Отсортированный,Поиск,0.058173 -Бинарное дерево поиска,Отсортированный,Удаление,0.068654 -Бинарное дерево поиска,Отсортированный,Вставка,0.868870 -Бинарное дерево поиска,Отсортированный,Поиск,0.055234 -Бинарное дерево поиска,Отсортированный,Удаление,0.068269 -Бинарное дерево поиска,Отсортированный,Вставка (среднее),0.882379 -Бинарное дерево поиска,Отсортированный,Поиск (среднее),0.056078 -Бинарное дерево поиска,Отсортированный,Удаление (среднее),0.068386 +Бинарное дерево поиска,Случайный,Вставка,0.004518 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.003011 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.004291 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.004562 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.001002 +Бинарное дерево поиска,Случайный,Вставка,0.003511 +Бинарное дерево поиска,Случайный,Поиск,0.001003 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка (среднее),0.004803 +Бинарное дерево поиска,Случайный,Поиск (среднее),0.000220 +Бинарное дерево поиска,Случайный,Удаление (среднее),0.000351 +Бинарное дерево поиска,Отсортированный,Вставка,0.900417 +Бинарное дерево поиска,Отсортированный,Поиск,0.052424 +Бинарное дерево поиска,Отсортированный,Удаление,0.061880 +Бинарное дерево поиска,Отсортированный,Вставка,0.889120 +Бинарное дерево поиска,Отсортированный,Поиск,0.053632 +Бинарное дерево поиска,Отсортированный,Удаление,0.062189 +Бинарное дерево поиска,Отсортированный,Вставка,0.881248 +Бинарное дерево поиска,Отсортированный,Поиск,0.055152 +Бинарное дерево поиска,Отсортированный,Удаление,0.061669 +Бинарное дерево поиска,Отсортированный,Вставка,0.885030 +Бинарное дерево поиска,Отсортированный,Поиск,0.055543 +Бинарное дерево поиска,Отсортированный,Удаление,0.062420 +Бинарное дерево поиска,Отсортированный,Вставка,0.891156 +Бинарное дерево поиска,Отсортированный,Поиск,0.056153 +Бинарное дерево поиска,Отсортированный,Удаление,0.062845 +Бинарное дерево поиска,Отсортированный,Вставка,0.891750 +Бинарное дерево поиска,Отсортированный,Поиск,0.058122 +Бинарное дерево поиска,Отсортированный,Удаление,0.062453 +Бинарное дерево поиска,Отсортированный,Вставка,0.888773 +Бинарное дерево поиска,Отсортированный,Поиск,0.055758 +Бинарное дерево поиска,Отсортированный,Удаление,0.061916 +Бинарное дерево поиска,Отсортированный,Вставка,0.888226 +Бинарное дерево поиска,Отсортированный,Поиск,0.051592 +Бинарное дерево поиска,Отсортированный,Удаление,0.063321 +Бинарное дерево поиска,Отсортированный,Вставка,0.880372 +Бинарное дерево поиска,Отсортированный,Поиск,0.053502 +Бинарное дерево поиска,Отсортированный,Удаление,0.063089 +Бинарное дерево поиска,Отсортированный,Вставка,0.889972 +Бинарное дерево поиска,Отсортированный,Поиск,0.055111 +Бинарное дерево поиска,Отсортированный,Удаление,0.062310 +Бинарное дерево поиска,Отсортированный,Вставка,0.882075 +Бинарное дерево поиска,Отсортированный,Поиск,0.055492 +Бинарное дерево поиска,Отсортированный,Удаление,0.062256 +Бинарное дерево поиска,Отсортированный,Вставка,0.884940 +Бинарное дерево поиска,Отсортированный,Поиск,0.056025 +Бинарное дерево поиска,Отсортированный,Удаление,0.062441 +Бинарное дерево поиска,Отсортированный,Вставка,0.881076 +Бинарное дерево поиска,Отсортированный,Поиск,0.051258 +Бинарное дерево поиска,Отсортированный,Удаление,0.061709 +Бинарное дерево поиска,Отсортированный,Вставка,0.882319 +Бинарное дерево поиска,Отсортированный,Поиск,0.053783 +Бинарное дерево поиска,Отсортированный,Удаление,0.061596 +Бинарное дерево поиска,Отсортированный,Вставка,0.890215 +Бинарное дерево поиска,Отсортированный,Поиск,0.051424 +Бинарное дерево поиска,Отсортированный,Удаление,0.063194 +Бинарное дерево поиска,Отсортированный,Вставка,0.886962 +Бинарное дерево поиска,Отсортированный,Поиск,0.054314 +Бинарное дерево поиска,Отсортированный,Удаление,0.062138 +Бинарное дерево поиска,Отсортированный,Вставка,0.887173 +Бинарное дерево поиска,Отсортированный,Поиск,0.055819 +Бинарное дерево поиска,Отсортированный,Удаление,0.064069 +Бинарное дерево поиска,Отсортированный,Вставка,0.884293 +Бинарное дерево поиска,Отсортированный,Поиск,0.052575 +Бинарное дерево поиска,Отсортированный,Удаление,0.063521 +Бинарное дерево поиска,Отсортированный,Вставка,0.888483 +Бинарное дерево поиска,Отсортированный,Поиск,0.053261 +Бинарное дерево поиска,Отсортированный,Удаление,0.062347 +Бинарное дерево поиска,Отсортированный,Вставка,0.889441 +Бинарное дерево поиска,Отсортированный,Поиск,0.050554 +Бинарное дерево поиска,Отсортированный,Удаление,0.062955 +Бинарное дерево поиска,Отсортированный,Вставка (среднее),0.887152 +Бинарное дерево поиска,Отсортированный,Поиск (среднее),0.054075 +Бинарное дерево поиска,Отсортированный,Удаление (среднее),0.062516 diff --git a/stepushovgs/data-structures/source/tests/benchmark/main.go b/stepushovgs/data-structures/source/tests/benchmark/main.go index b2e804e..5993dbb 100644 --- a/stepushovgs/data-structures/source/tests/benchmark/main.go +++ b/stepushovgs/data-structures/source/tests/benchmark/main.go @@ -142,12 +142,19 @@ func testOnesInsert(structure DataStructure, data []ds.MyData) float64 { func testOnesSearch(structure DataStructure, data []ds.MyData) float64 { start := time.Now() + // flag := true + for _, item := range data { structure.Search(item.Name) // p, ok := structure.Search(item.Name) - // fmt.Println(p, item.Phone, ok) + + // if flag { + // flag = ((p == item.Phone) == ok) + // } } + // fmt.Println(flag) + return time.Since(start).Seconds() } From 0df719334b033b613578126cb3f60bf038bf5c81 Mon Sep 17 00:00:00 2001 From: GordStep Date: Thu, 14 May 2026 15:01:23 +0300 Subject: [PATCH 23/23] =?UTF-8?q?=D0=B4=D0=BE=D0=BF=D0=BE=D0=BB=D0=BD?= =?UTF-8?q?=D0=B5=D0=BD=D0=B8=D0=B5=20=D0=BE=D1=82=D1=87=D1=91=D1=82=D0=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../data-structures/docs/img/delete.pdf | Bin 34086 -> 34082 bytes .../data-structures/docs/img/insert.pdf | Bin 34147 -> 34185 bytes .../data-structures/docs/img/search.pdf | Bin 33695 -> 33638 bytes .../data-structures/docs/src/main.ipynb | 98 +-- stepushovgs/data-structures/docs/Отчёт.md | 2 +- .../source/results/benchmarks.csv | 588 +++++++++--------- 6 files changed, 344 insertions(+), 344 deletions(-) diff --git a/stepushovgs/data-structures/docs/img/delete.pdf b/stepushovgs/data-structures/docs/img/delete.pdf index 9be44496303e755e4c883d3d86f3399911c5f1b6..c93434bfcd1dcee1f2bcbe6ff486562fc3d6bdd1 100644 GIT binary patch delta 7299 zcmZu#2RPM#`~Jx?t*mrLLz;$YWm0RsM9EQauOOH&M4+rZpUqA7M&8(b zLZuPrOiZl)>@>;MDD`aNYIfy<{r5AMlEt*m#pdy3pefC$Z}*rSKqZr&?k-1{rgUJh ziJl&+os9sLO4A(xMpE~r@=PejOYr^4CFxUwOxJ9Uq9_hwo~_3WU&T-<`f!cy@}1YQ zj;}hu1y||x4_0TS^=bDAcWKN+Cq;CD(}klS%zXf|;p91=5@czXpLr7yo9io7d2T8iYOh&-Z6BaJv#Bu z1nt<9(B>~7<m%_owJBvJrMDdGgt5bFc6f@yHB%SUuXe-{?A5b( zU)m;dJ%qR17$a3!-&ucti*T`swuiimQ6Ec$ zt#|{zJzq4OpB}C>NOpF(^YdUck%snpaGU9RNmWVrw%GXiobBm)lIy1~M9TB9p>3L@ zzZ2F;+ie0rQC;QT%2>GcS|=AyNFDXny;=k~Lh#1%EF^Vvv~||3&0gx*&P*TPqATg- z7~3sdyZ(M~iBzxoKHM9|Ylo5`8kP;Q{LmV|E3kQQt8e69m6!BWxWBMUBb$h~Z?AA0 z0iHtJ3y*QoRH{G7IB0B*YmM$1`fgm?eEZiR+E$i+DLvb1aDIH|71m4KGXnzb4po{lR)|>a#MrKT zMFrQcS8`+^~GNKYMQ^15XmHEjV=n%3e_OL<6j=yyWZG^ zqTngf2pOp5zN$IctV2e&TU64~E0Uh9z=U_gRJ7#t2$@K`EZwCw6SM=;k?=Jr+AJif zHg95PcJXPw`ZtNrV^dP6kxDjE($W4$G`wM$YdPfXVy+G}IvYNZI8qtl@j6Jrl;hvW z`7rxbEbsLwG>y)w!y{H^8ILOi*&9ibOqXrYMT9r#TJR|sNYxJwbo>ZS{GvabekF<# ze(*0=F2zl#4U9DEz}WiN27D#^C%Of_JU9QPbN=>wi8mZ2@~OqdGuG`?T!eC zr!is=247pgB-`y5Ukc^b?ZV5%q~lJn^jT75Cu=N3#O#0`;)Ey*=gF>4b^xzStondO zgx{D>Q)%((%3XbpBP2zFn*O+;IEE}|qHT5p?g^Q9wsvv+Aq0oULAk~-mZG2+?JMyi zo>Y0BC6P?hHkL)f4>CIY7V@`*uQD;wwhH@VLk!4cRDSbu^yd_gi!!bMl^N=a{!5xg z11RJlY_=pLa~WWhw#iI(uKS+ch607lJQ0;*PWh)SfU6tpME!9A{VK)q)F+}YShfpI zTqmmUL0;v1!_uzWh>b*G8OJ8&+6E8OeGhRE<4Ly06*0eGgVQ8(UkC~_waa4tdO{` zzmQ)w;G&Yu1+#Iz>+IsRK6Wd9XsfMOVTQk`qAVvTB|F&wzUFI_+it$VWU$9)rnowW z_UO6m+*88f6vW_(4%I`m(YTp>joe!U36cFghS}{IHnwiNZ>86N)M`0;u<7i(|Gg;2 zP^>Ea33=w5q-!U~32*moEg|20md%Qj@!KFm!1ODv7MX!T0+NR@?{7aGS7sU){AlCm zJ&I4AWkNSMFi$0p)oIS0m)Xiyyz#+0SgD2AJ$`|App~J9nHg^!pBitZK{k?LqRKD7 zOnmM8mlL}5EbM}plbU{K7~6vc-WMY`l3su#^m!FR zJx!SIeOvka{#qSn@C_fu*gld{e5KwWw}GKe6Oy;m(#gR(&>`!+wBFVwAI&DXg!-F1 zcmgckKXVdD%D!49&67KlkD7{!FsVePJ#REjT$n1!NlnbbzGaW;Kpkee+ueqwe$z13 z{E$Ar-@+f9{du8&Yr(29t(W#A6hcFqiUeFArrs>GK)o&sbfD(Au_g3fOe479vlmb= zI(z2%M6}js!dm}>I2>*((#HMykYr1tRl6)Cm)&nwMfK6nghZ^$y8zxhz^s6xR!#h} zE@H5-)N5)xy~fsO@Ro{VOAafl){>j~S_|Q2((A1~sNOgZem={sdztZ4hVqf0{4E`` z^&P229-7NHZkIad$&jp4k)PYDZigU4 z5m5DR#B(bvVMOK!M}`Q@(Y@E>4sW_;sak=FsRy{F?aA;S9*|nCa=SKc=eZNLg`yUR zPd@#S9+)hNlw*4w%q6fr(>GEo(?mGssOTRSmLw40`$5#qLSI8nz=G_O2Nq!1tQ>HL zHw-ffRnO3tsDKlDH$J77M|Ot(G}TKR!n#E@c`~?quISQmE@HNQ0w-%-o%ZKlQvN1H zQEqSD_FCVP*z`*=x-`3nrfzUKib|F=x#Sp5(%HVH;uhdV*pNIgNk1uoboW8g&8j5x zPB}coJH1uR+%{8#{cMUo`vhcH8tr#8o$a(;OWIiQTMon|_P;=~>}n)eLf35N>^_Mw z!g#-gQ3wmZe5r$71Bah3k>`cwQqV?xvM}I>sDS1&%ZL0XLxN{Mz;AH zTz!|xXHlwR_>Gx^txZHMlV|=LcRcJHB$Mns>kN~3~Srbakl%EP0EE=eQ4 z&V*UCmL^--lC7;KlY=0@8&-3AAQEFRsw7qPvra?m?NQ+y;#?C=pPEslxdyn_RjuXd zo5PD=&tSeib)hr1_zHjHcXN9am{;R14=tHa`~;#@M?*bq7%%+nB!i82fh3`!_&o zvRb_coiW`o%iOEmz@UnoHk6sYc#D3|4s;WAn`S>~$R8vObYgR~BS`D5ecy<~g^g;T`GV4t>jZ}RmYjkwE6Wa)1N&anf+3y zH50A+^;HWo)a*f{-h}E)DXYSpmE|@g6h;YGJyPDk6=Aft1Kz*aZ;%OHf_hNf{oGXw z2!2mMZ~n!Qk0NQ+u3okA$tPXDzbhEbHBvy(fK#{thI~@jz>v3+dZ2%&lEK1x)wE10 z?z;Gp!H;}9ibZvgLaR&s2=1bfY-AO-VIylblJ7#4Zh{p#17ts{S?E5HE6^yLtY_K# z`NOHj2$fuzau+b<(mv>JQ9l~h;~V_qA=z@P)@NIT?Qb1N;rq4n!J5r(p%Yw91tNRV za+!W}>S4Q4Y>H#om9Af5KNJtLx||Nni);)`a|$Y@5t9>CatDS7Y@xkTuGPv)ZhEpmubEPB3G!yz6F?@DL{y4GR{8Kuuk)w~ zL`$XMA1fVgByd@fR|q*h6Z)2>!YFb{Y3)W5{yr~VLrx}hxn^1^4|U)E+j;uta_&$K z^^~XbhD$IJV3aGieVb*vDMm>8`nWMz|IVeVkDMRW1<kn(7C*LKsH6Woqgo?RR*) z+YR;~(}+K59<@v>M!f{0gJrb9Y3YyC=n zl|lvJBxA%&z(d&J#_2R~!7_P4kyt3^IXD*oOT3H)`hvjADzT#8ry%Es?6EUQ>niKB8xD02TEy0l_ z$E$bewf7{u39FUMS@YEYF`AL97ih5{v^F<2@AIA`{)7zhY)*`I3 zHy%Y=gm1rd_tlMHCRCaneyar+F5)=g8o5PQJyo*$VYlO0BOz8hSJ}(b$9gRK7=(@i zlrHPpmXgtTN||&87H=Klp~HG4oM{&PtNX41@S^Jn4r66XWO`<$_Y-zoQHOK^kciZXxhE22Y<{Y<$gXI(4B4z{AM)%EVKI%kco4Cv{Fg*N_i%Qf3G>U z7`1GxbLFMyV`hY&M?~S!nN#`71jo9|!*I~c1)6w-RU`N5+Q(X#&hm9sc%ZZ#C)}eh zk~zJ%=Cmqn%aFjlP_*`7*xkiGHO}41S`*s|*Ov_BR6|R?#%c4Wq=tSM{PFt)3yaK#> z{1|tP88NJl+bFt#lTD6&!UIvOe2l}H#IeVYS!XjfvNoN+dmDbnMWD4~hGa__51M;E z`pnsECl~8iuNWwR(dAIi`&?eqgvz>^wcIVw55wqEZLh!7eEdzW0>D{BzxsSx0Zc+H zff>{mROi16K7>^HXZ{ST=k27s(o})_Z7g&JX(eTJTl~Non}`dm1Q<_f@0j6C;6r3} zSy8_$mzDA1ptQC!NUI^^s)o{CMbjD$9?pc7h75k{ocq!S196!wZ!T*N#j(OJZMaDo z-Bu5d&duOg@Bs>(g9>)j3hrx#aI1bgd3-cQwz<^J{VbE;<6qB_ zn1a!RQ5Pwrtm9XaJaM{~MbWM!(Yh1VG+uWX%1Exq-Qb?!yKBf*&t1wxA8+TIK|Zu@ zVAWO@%pdZ&tKI4lvVPThZp7KTHZShoq^d}X9mrr6pf_muDKO6J;eMt-pIdxqUF&Ow z%e$LjczzfcmID8VCCuj6Sbg0eXJub48~k0ZMWQcZ)95+HU~sy_z-fYWssd>5Xc0#UB&3BR(SDP<}7=VlASm9jZG zp;)V|n@&s9^`feIQIoHk^j5}v+E<+GwQ?4lZN&4~nPctmT;Zt3P?s9iTI}2O_7cpX zWZqRidKZc%3jKS_tatHfYy1dHlm&x}%)=<+m%KHOo5y10O zC+FWcYJ#)et^3>(pFh^x9@-E#aCjGXl7votKi*qt5mQh0qNez)b$Vodx4)Y|p7u*i z=$-P?&%23>AS6;edwQ|9->$)_!$^c>bL#YR)NZa!NU4MvoRc{bW%hlprFhfTkTYk8 z^5&T3LUbo~cfxAVD793Ge$E4JSBU%n>pq8XM_U^$n``Liu@gk2Heyyy3iIi>Gef`UTzEl&M* z3pfx>*jR-skWoKCv$s?^Db^tOSTTLtIhWk)MWbJaLRE}Vjz#Thxa(5(?y;hR6EFQd zxA_r~;5;f6B#1zs%Y=f# z(DRI;VCY3uFdPQHkSmD%2lY?4PzV_I4+iij2oZ#VVCRw{f?(u%U7xBPC$Z4$p03^)k6w^&c6r7jpO`0z#upb z2At~{r!@FH88BE7bY2B81O~nkgMuzB5g3X=0sP)TFzn*o!2&So`R>31 zNXYpO1j8Z7-v$5FgCn39-a|k@=XMJKff4Wva{>#3K^IsHB0v`kB5|zGDG0$$;Jo}0 z5Dam?9s~@+jq}(1|N9=|+B+{R1OmPwAp`>ZeV6{Tzz`(#{B45Zrg;Gv3Wxtu75M!D z07XL1NdtnwV2}%BU~u@w_mBb?Iu?LjkRS5@j1zze;MV`VgmB1z82%;*hn~ASAP@wO rym)1C<<5@@ic{;qFrGRZTcOPyiA6+M_@maakSh>k4h|_5Y2yC_$FS5) delta 7302 zcmZu!c|4Tu*DsZwo~&h8h7e{mV`4}`#!eVQ_N>Wd-|i@6DH;0~*@eiyMb<3Y!`PQh zcG=1L>V2Q*`Modi{nzz5*LBYKd(QV<_vc(GK_nSLBn1)Yfrt4#We(m+k07Clx67a$ zSFe^A3{6y5@1JWWIWU^##wxHa9>EDpV!n3@O(ud`25L5&0| z-S7D^7`mq;55V1SNO|4UJsbfNRiqNez0Y$47y7FD_zA6eAti}xUw5baTFqggN19UB z0%UQvHE(q&;`9XeetXJRr%K357IlPg%|5D;I$k@7-WU;?>*aYtd$fN3c!&PzaPQvk z&oQf`_W8$LxZj{yeph?X`-q-cHECLIo+SL*>`qdrJ#gHSP`kINk??Ki1!y*;Bl&%L zBP4Tav|yE0{2HR}B4^LKy4BtJZ@jt~#2a(6&3YbYQzdcCk3oiOZ{26Du%pmQE7PY4 zqQgAWJIMcTTbKz!a~OnS0Iw~TgU*2nGh&(?p(^k6x97}%I^;B%g&><}Y@1EPEpcX$ zD?`gM0FON`Mg!6FSSiP04IcKI4HsfeMi@|jKKwN#78>>1=OT;0=S;N>LYbeyM#DRR zMKJNW{ld?-SOq-QZN<_&=UFKA^xE6=bZu(KPz$r<>Y1pUMK=M3vSW5f+Z|Ol#CU4Qh?+b_sGWz#`;v#W<$sQ}L(}s*zELNMN-D{6qH4Dtj zCxt%qod!kwH>uyQ>oiwUQ>=Q)dGkqgzsyi&qZ+P2TKt351Bv=^)|(dV&hmrb%&ObZ zCyR><(+n*^+$F#9Rt&a>aQp1PNIIk)Do6-RRp8x6$FQ-`;j^2Tyr5QOH%%UMGgB7< zFGMXdo8zQ7Z$u)p<4u>r^jxon?y%#LmxIUDWFmk*Un7zK(Dl$sADlmumAD<5b-3Ec ziFwGhwPu`mp*GNW7t9dNcg#SNP{W|SktRJ(P7YC7O=IF?>ti$&X-!V3IoRIZb}3x% znmAHB*s-q_Kb${c-5A*b5^4_*=N|#JpT$)MxOfVEUiYIlzo*`ReQCR(P5nB9zb2`? z4YT&P50=|jn%cJ@WnmbD4(V4jx-4kZH$y?3j3TY2y5-J=eZxzriNW=kMY>{4XZCP z?1Br%x&9XC+>D-pA+f+_>i_GR`^qp97xQvof^7c|**h8% z8BCAX9#%axMU;skL-ldLF`Z3qc=k4dta3Gk)S(Tf#iFZzIaQQ_AgkCZfIvhn&sLf?y`cYC)pejvX`h^5ztP;$kbBF-gZWZh^5fJ_ZG9@%%(3 zN@q^J%Z}5ZZnq7@*{K4Md-C+UG(6XF~IsX$DI3X1Ej6M>F z=vEOrzN0!Wd(j->r_XOLiw^lyyu8Vb2W^oTMhz^rK2|$B4mEHKj}Cm>HJC}B>#{8S zRAal8FV*UQ^lK@8C+TQkq}J1YBT4FDH&tjv@zLSd!4@NvP3B*dD{B581QXSa);FIn zc-)z@H3(+^DE%VwZuS^J|Cq94*dRF3Ae&vz7sYO0UA1{gViO!6H|o|lWR9B0DQ^>U zN81@$qM;d1wx#HwOyU;0Z&0$dl8CQDh_GOZPY$gf&Q}vjJ-k`UJn|>C)^Wez*L0QO zck(6|N!FebH1h5BUx^23^eP9{u+eXI`tzD{Ts8cN7mB*f@a#FzO(`rZ6{G{gUCNCj z+{;Y}FHi7Q(0_8U$>ot;l(856>4|T!1FV7J5NxKG=9`Nr_*2_G<>-9XMaSUC?J>jK zED%eQkZws&sr8nBPrXGTOwg>Pk=JadX-jj6r`^aLX5C!VK}^L7TLmB zsAobC9Xh=KMUWJz)E8U2BbTu)cdwH*%<{cx(USQzUmSj;+QFu}h2%q+@dIN5R0wxG zUrmXgEF)wbzK~*2yP%Y;s{A$8uG%iHF#Kt&IDyd69Dl$N@?tAm8PDrsy(d&pTMOEs zi(jgk7j+YO_#o@KOwn54Ba7-WqVEqP??zJR)ehJw5A{0$1-Qi7Bg!KET+D3-?3C~+ zBCumv$^5Wi`^aH%7a5^w(Jrr9dfyl{k|+A8^% zEBa!@5hLvmL59TzqZy;=vZ7@&X>8c(q@|H74~l-QcBKm5{{9nDW%J3D=~83jt3`IC zX_xbl)TEG|*uU-!Wzf+o&uGMnIh3v%XR%k}g3;~b0O@3uDb&+*G`G%?bcJ91N8CX+ z$|iTTzpT5w$BEj=nl|wwCHFo<>OA(#c5aDUlj#xR=PcZKJWXx-_=jd!C3*fBA7@Ot zU0r+w-=JHg^lG4qWtGVC+KUUMiy|VLg+@>x2VbULFr-mOa`Hjs)BXNK-xBzB`*F@xt#ah4~0d zjv(9$H_S$u*5%)_M>PB2Y8;|%6Fz!&T|YB8P~568OA6$|?E!N6_$@$oWw^j^*Ko(= zy|+Ybm$3}v@A*K)V478Rv6gx?hi7h9b&LK2psQBlzv$tXyhEHHE~;vRBxYZ|mK(;G z{ZxL*^MJt(Pqi*72$e@(srRAk;AV(MJN!lOc>m%j{dC`yd-=&bD3*#3!e&|LD(+BC z>oXov|NhA9pBW^4AjrslPEoPA`{u79#MXu2N#$DyHk!tj3JAk4t<3e_?2=>MevvYq z67ZJvL(25~CXLT@0y<5JOD;NF?c+4Lh6BoeMO)RHj1Jk4)EOUO<|r5RYMTMO4=e)x z{58FR;erUPCBo_8$s)G?TFh)hD!=_0MqnyW-+NjK`~4+!Y9?-I02D1>ESREqcV(`rDACW^4nj8+J=da&z*Vhv z3>y>J4+IE|FMsZ{UFZ;2^xg3v&}r^aUYYKU+s^PV6xyb{dA@9#d1b>=&^cFL%rA^k zs>(C`I&@v1-bPipw%CiV&#cRDtm6LrWz~S8G)bzFVBU!xXN^T?%J8a2-xA!Tc>rVX zd-Ympc;9g3^?HS`&Gz4`mSTUuooVj=P<%z?5;U6d81%E^Bc^VH$O5gx)V+wiQg$(a z(3z&k4_PJ&5*@$y1%$)$iute~5Lt9qi|N{aa+B9}#1)KzQzgT3IVdg(Am ziySP?uU>!mTT$B!4iBp`0(RRx331$H*M7K2ST=2xx0eN&+hi%ac{SwOY^wyRo(;T}m%C>#L9hTp?0 z(~-Eyng}Y{`B*^-&B&Ecg%2Pif~w!!Ff=$ey=;u2Amd7qc91sHhe(C=K*#LN-f->V z^F1XuY|}ySwfnWbF}S(Zk~Bu%MWto(hf*$9^ZQC4L}&D#_8|49Q{q3g>7YwXpY^+b zkPj-~Hn_rw!Tb!G)%)!4ZI=zOq?afzK9ywM8PmH$?Y;FpbiT6N@z>121q&}6j*kec zq@@y;S9&ry?wrEwL8o(_Z1?6eE9dg?)X>j4W5g8muj-}3HCw+#;|<(?VX*EFn{CPL zYH`OSXLboTWo&QJ&Y)>sN;Z^fxpvdqEipcJe7-%fiypALYoBB| zsmC{>U{YVb9d#>_kY)W;v+8k`$@_vl=*v;y$1(09kiFeoREOA z4bamZGSFR8;ZHNRS~~~h!@l`sgmkM&qn{z&#uh|-x^>>Tp6Xy#*O0k>RVe}z%gvy)HC%#7yB-G^H%cz=tTFJluN}Tubpgw^-ku+;+Ve_ z)|B~ORsy3g(^c7N%-mcsE9`mb<=?jLllsn3|B7nqb*U@@v#eCHlj7{j+MUgbCc z472SW51wg`p9CX@r@4*Kw5tC-a;}rlptV-*?FLQJM;u z6^dvXON)A5ofCNflUbUIxr#pmC!$*OOtsioUnwx&>~9!hJ+;l!h$`;=PV^M!RYx*t z!S>J2$CC$&evj&>lbyp(_Ng{GL*UMga^(pSy@=t@3699Vah~|IUV( znh**^F-Ua#toP1|Uh1-;d?&R3LvwX>gTah3kFzzW-Bdn&6@c@&x85!VJwlUn$Lg(N zIo`>?W6eu>7Dpg;3&e7l;xFIG00}e-1ll!1qvJn-nc5^r5i&K1`ui7Bu2u0sihe=JHO)zogP666`&_gMmoR0T++Z_C)Gb8GgR4Mep0fJ$6Ut zi%f>=CKQq)r{8QEf8KnEe@PL=c)=*{AaPR{4|oTIwp7+f$II-d`6kqFEYf>$+OgJ_ zc3K=DQ+HA-4WCQZ6>)C3^oA-#r5f)Q(AM(Q;q$4*2@TPcoHG|nvHK}U0lPcB(I9&w z|22bBhm_A|q%G?RU|ct@l~Y;{Tm+K2lvkx@yxtWUvly)z9)6v^iEsUDTmn;tqWMKK z02fsSw*}Sce8y&Ts>Zw{FSq{C{f*x4qe9F$dzW$biO^A<>$Z% zxFR!whW~zVAQuI#Hm=_B?MxaoOH|3y5XBY4z)-H~F5#$6U4*xF1t6cim0M z!Dzm^g{pu-pom z9*@)@uQC(!f-t=#>!nPT$8ykc+lA<*2kQ7+a~ZuQDn0#2eWF>~?afyoSEKPv?;*<;sz6oT?Gx#7jKcp9ZL>AUJ4 z3hWspi+u8+5?}4LWMPAcj-!~|$ZFy0EEOS_o$4hAX zvAN4yKS~|*Sf&LxjPnbbqcVR%f$9vA5&G7e43YSwy58vQtuU@eM34&9-m)~5u-AZT=A9guR{YZGzo?9L@sJR ziud=`_prztx2}~YRTgif^Gour&4k1j<1y2@@e6It=!_ACLHB!udn$u!fCf}YVsJrp z(?+rXX14I!M$Lx)zDiz9ce57X2`LZ9lgq}Ka!a!zB3$(`hUxQdxC60;JE}oJ@J+8f zsa_%|0@wYpXGjU}K3Sozx!!H?Cf3MV<_C3Zgdk~48(Q**Nv3|IZ=~jC3k1=5TXu zpQmS_IB5E7_>qGpQj+d_mfs@Tuc=EdH>R%@m1VwOlJ>xI+8cZY4lNOqiUTYZOrSp5 zaCFIRHIJ#x1JDG1JUht7Tfy;iD(|rE?7VK1{e|i$gjzyX$ry+GZ{I*}m8Dl2;=^|N zZo+PFrkr-({JL9p*vaj^_WaAUeJb8%t;?K`$pBjwNw`=asE~lzGwY_zIEp2UNXm``(6?9qc^N9GxP~s*D0j3|_ zX;8iO`|CV#-mLIEl+yf(*)vC+xv|}!mA#2IB?QU^=K8Y`ML`ioJy%~^kb% zi=OrcBVb~u$$$}JqW{+hoD>-egPn0kB9W&n2Nr|DPG|0=QSj+=kQ3{P z?0>$06NZ2ha53Pt5dwppCIf+pflpTf0)<1)*kIr@t#Tq9>U66>;4t_<7{EV$27#Z| z9U=;coz4y-ih`Z)GYA5T`X}JO@eoM(nR7@ei`Anes#bA2|W2DDuqRh9aQQg{&H;VMeX5c9*?ODrYU6* zf3AAV#HVkbo~?EMvE0L=84bB${f$XGbwVB#w}8 zwj3#UErJ2xA@D@t%6nc;kRMH8^xiWpP#l$%*;^*NLb;L(_U_zgx8M?2vODwquXswS z1kCq2!SCe>gT>?yIfBn z7AhIzVhQcFN_8uzi+JXz{k1VZzu(>ZV)C?$T?vl@L(W@qAuPu(9$N)Jyg?HQ znZf)?%bF2CR?DQS59eMVZ9S_xDvB4K3gSvsyi(rFxT9?|ZNoB+oC)rC1G5M2+p1V1 zVs%|hn??n>O2)sjaJ~G^`;p5A^y{Nw&t=pt`Dda)?YXlPo3*-^&Wz)uf^4)$m_8PWP|$gEt9&x}!_ZnYdurK!QDA<`|nw&s9y+vWhd3uQTm~X_?7{U|S*zh zduw8=A!X|5c)NFfje(idObI3U_C~kCKn8e+t@3FnHT6^0+R)^)#XlnN2q*?#QF&-k z?i|BMNgxzcu=B_pK*WPy+6VQ`Xaec;Ld<{7#D%mL3^d*KWJC5hT1MVV=Q)TE>MM;< zle}aY+oF6i>{eD#|B{4LdcZp>6m{pzi~K5|6qyU=UWHSbg!`*|aS+05L=3cM$XQ`L zQVas~{K2f;%1i>yqF<51iXldpRU)Q>&uPOm;^QsYk82Af+1Trbye;}Q?*BZZ<#jMT=yW`6rGU7FJS z;`2O>TLR^qpnkM_XH?up_sl3h4N|5r+3V_}v3$=KP4m?wLT4k@1Z(~pHdBj2`>+<) zH?m{e*F|Fi?8i1ZyHh_1Xeev)~EEZzQ)%BQ32No$71u} zzt%(E@p1N8ZmYTCy#B$&&4)>Lx_7!r&82I$ZuLxvo4@g8(wA7YdPkDb4W(9U&_h;G zvJ%9{+1HfQaj+U&9QAi5WWN>aKk2B$i&QPR-|C2@A``g1E-H!E1TU0#_Qo=BjM+NiQ^eT^Pv|H7p4CQ{7>FA!7E|R_kzH%KS$&BcI(^a6#RQ)pAZ|#*z$6pENJ5N@tYW1d!D&qLqqC>Wva1Wd|*YdX9 zX}G74dnkq}UO$%IfAyqXp`o^@ULY~l+xB5~Ue8Y|Y#<14S}>Dw$1y*hLb%7nxV);#WQ3JzOw+LH`Gckbbm%!9?O33UeCt^&rTy_19VE)= zv$>WDy36>$s;e9wV3t-AK89(URSqPtfZ*OZCeQmvBtz)`=f9aoi0=MfXzzLGQhdJy z)=r8RW&c-?uszz3wM=C`#4NWYgi}A|xw`o<|63Ab$~Zbi&|+hp4sSYA%-W*0)Vx`u z#Q)1QA&ey*n)Ir+=ls%gy!&F0pZv?$Ch3u>Aj{3X_UN2(+q<_PptQmx`7deo*8MmN z+REF$N~19Z==0@Td|+Yh{&llEZOJ>BN74OqQ_Gc1+Mq4QSdY~ornXi$^@I!%6wyo9 zK9)w*n=6{ITE1JW707y3;+vJ}R6A{(8fveXX=x2hi109E$9WHq4-?kp}vGX)cjd*wBu ziRKmDWk3)$de240H}9*z$^rREZpZ*3G4>&7Ret%?4gL^S$$q_#uGi9;_Z8=ZpUVj@ z8pyR?S|;4$>F;JJnE!4AjSo#G8xx&p2iRh%IW&%93RU1{vG0QzuRrQpyQvr5loP#o zpZ?LpJzyvjvqJzG=ibyjl+{-p&bz{w6E0S3@YQfE@_ZP>1x^r8h5Fw3Y`bQ))t!yI zt>+54ES(=ezi*w?zd5&5>ufY@sG+NY{r<(kx2Q;dsnjW#Io4VTFq&QNi`*dJv3ajl zEuOZ(G}MAAvU~%lGeP6A=+@y!UzW-!X{9r^T7Xu4&4zLBxibwdpG#`qNgSrRD3c+c z25Grs^G1qhgSJyG8=hG{*z}X^cqpoT{%t7>0$p10@dv}{C37BaW zU3^wjY*Bdj4cMFMLr($<10T65k8LVk_@26t zZVV%P*sO!)&y$Mt!iTk5{ji)z6|ajs{k3~d6%c{1o)}w8?ool6FGJN-x3s8DouN(d z!OV2abMZXp6plY%D|F9D-CxFR?}{Cw%mHi`*pmN2F3`k^y6!e?$>(X<3Fi!--xAoG zk^D}?<%WJ}2Hl9$EC_qPJ)Dzugj+cZrD3EG4@o!GoF~)C9Tbdv4ooRb=_aRpR)b^q zxLRsg7Zh`hvuFnA4=9dtxiy4gja*&d@!um%w%@%E?YZj&`s%yM28)-DzVP5TfMj)_ zG5Vg+3TZ|9$=KA;iw;dYsri@*Pc~)7=oqW8LSOpDnI|JwQaH)HHgD(9z+67g=Dbx? zt`3Llfn+Xik3!>KLzUwdvTTfOJ@Em~3oejv6|{dx3fwnTQW(YCediI8{spX^`l9Pn z(OH(EyxtBfw=(Qz#=Sa%aYQ^LurFg=Nxafkvk~vA#;1F3`JN(Pc4*1Fa1Ar3&p_Si ztmPZU-TV5<%0TtzDu)Esi-qntjn$IX*aVHq`g1yTs}|jLHZWHY_O-96vdoP(MHVRk zu>;hezDm6!!R=?OF4fvtP2)#yQO&1LKLhB4HLd)&vT&O5xIycBhCECiz;mGn;k@Ba zD1NY6^K(YNVzT8^EHaJJ(g-5^GcY04Pv$Jaygsae)F@sXzY-WumFpmdc`EE6 zR!+ckTl9RQ*ckdHP@TxA(UCgP=;j^18!f0~%66TJ3Pem?$d=@j0ldRweocuwi%ZOZ z(_x~DUeVWI`7lLKx12^rXxj8#*yBxN+L`s9(UtCM<@E^ANHGRlXgS{Q zd3Bpzl*>TccoYwjugzMxc-|Hq@A81pd5Ec>nT&HeXa(%Z2jo_|1|@tSm9VDZEmhr3 z^6es*hL>JhQ-!Yl(!g0?&{s+|^}0-EHH{~bgR;cEwCr73xfEBPf0`4d9}Uf6zRVju z_p#sVTbg|Ka|`exB_Ig(*zx9yI^IxFm6YVmZaQIcJ6nu%$AfD|+XKO1KIHkZrsm6L~5 z`EBQ+V7@*Um&t3(1p6MIRTb#x%PUkpDs?l#yf;J;M4_;pO00xDB|q^04KgBV}O@uaj>sp+Lke6L(od8UVCr6z}85mUQTKd2m$Cgh28obldVyq0` zChJ&E4$vr()rWD)g{JAM4Mqj1UFSa15iO1jR(J!}L7n}CrBNzzLQH8St%D6alte6b zcPg|&-NK7DH-D1neHFrA_hRzZaA_h4R1u9qJ}S0p+_G#bEXB3JEV z<93f++E~?U7|XbSXjH#kJL~C|R>Y~V`7+dc?$)c}a!{j#oYs>-;ulo4^vj>P^B+{f zPlYEV%5StgFfy>j(mHRuo49S$r6(>(*z}kE{+0reCgI_f%lzppPs1k{eXxx?h5B}l zd9`*#gMlV}(ms$D<;$=KT`PXvNTI37oXhfpHPn~k?|lzw_dLV?a$(}D%a6TA6AW|pB=@+RlB z%lM|R_@Y(upK;c1+8yA0HmW$SG9a&BCVWU*ZQOR)kO`V4Jnbm~ao}JQ05{_|C(Z`^ zHW)+N65jPmg~((u)=FZO1lz|9{74u5YcBTo9@~OmC?NsQ3ME?zFU0+OD85b}sz2QD z3Q$`q^qThMcWd?1KcAS|QZ|FvVl=WV-WSY`~^fJj`Bw1(lW^<$r1WIwm}(_&TOA8TMjhN|BP|~pg)d_?;rA`C2|YB)_fPA0efh`z z&oWcTc;xU~MWp2MObk73L^lw4psf`#Gh{N_Ectl4#hWu`)MCTB8c~yVOe%mEsQ?L^ z^FJQ>2*i=>%uyO;^Z(d4N~s*XX*-(NLgK)P&4(GLS`4$K&7_%D{3_R!cVX#E_1I+w z%hBfg;+KlK$6m;i+WHG-p4z)zD`AohwFNUD_XK`%y$5j76CsbLg73_jAFN8tG_1`& z$EectzRnM-ziRGjGZwc+?*BPUvUXT)Ea~|bOR9h73WW{b! z-^1<8-9=}#>fCp_D$>9us2t5PpX$d_8uXnKU4>cD#$wY-IibfU2AsT{#A!~9h7SVb zja9>5tH7ihA3zTQMA@$Hex*u<6di+}UF<`Dn(+m^MHMH~SS{PZBfS2&Efa?IjLP~0qF{r2Dp_O0Rj{S>0-e*Eg00>g~@lwL29 zi)Zds`0@H3q}4Iby>|%N8B_is5mU!=cB&LW&x=7;S&VaeW}9!h$7+AJZ;~*gGBFkx z9?mxi_}5@qkg4AZ?v|*iR4GFX$3>3f4I(*BO3{vEoe~vj1OXpQnFVtOe`(s`Al#%m zwtsIOgZ}H?8DzczM=R`^%|{90MxM%i1L_+w3-km$m1~4~PE@&@osq#n%u_8%z#=Dc zaEpx)4fPa$IuK@_lYa9T8v%bUY@!$~e2y%cbd5Nb$7HM#I&)5zbpCle!>j73?)*^R z&1W?V4IcoA(W9Ypv7ubOUk$!leD&XuwDrYkU#l2mqr{GKtkJ!c2?_#!^98yP^Sx6y zvyNa79eC}PrD&l0*zVeZN(=BdvxRocAnl=m;{Z}jL)3Y_@i^1s5Y3wPB-uV@;8_3S zZA|uXt=HH<;1KBk3-2Q5x(M=htG9y^UMAcj^`@&vN0sn`A2TT%;H8y@ey*fPKYzW3$k$?0P87fHocUzD^Le?Ca!Xuh0BxCpcpVnZChrP3T_+G|TTpf$-=g1>$r;w-ve5xh6v^H@wgMe_CXEMl9Gl^1t2dgXH^ zLkU{EXg|Q{o@lA>B?A7Kp8cRA_K7eq)^6O%fJGF?CgNXrPKX*m{3xuYvcA@7SsPE& zYg6P`WUQZV7BU9dTr)6C%fH$PaS6kPVX44O`wqJto+A7Cxyi&ygs<`69a6EP4IVKy%wnwA; z?QWU=-xxMj6{j-HcZA%<*%|c;HzsI?tfJ?h=GL9LbTAg57OHL1>yqljE3fiyZGJu@ zh9bv&YIxa(LKDwqd%ixz*LPI#7Eg|)t5OGQ<9Lez(!2fa(wfessmYY8>o?J&s~G%O z%Jt=ghwqi_0o?{a-xWc0qC&EqCS8Nip?RC-BsnORsk8ECFVE6$5LmxKX=^@P_}c9^ zUQ?!B%iVf#{r5^fpObx>oicLYm^*Is*;|tC*ly?Mc#!;MH3OsPoAcgyPNbWIBri*gWGh!H`cRZ4~vwH=m-|)oq_OPBIP!2(7-T)p={!kvvSJiJFHaw!h zHXY0H7Y6@K9~^Zd>{;S3iZi)mTn>wMGJXs3E{+rxS|mqC=+imACC?u&+HE+b1SRpj zvT61qV%C-K`^RvAK6rqB6(Xmi_;YSo@l~IBK6AyVkR_@yA=|5dG0W@x9u->! z(Uv;`Hfk95#m$YZUi2VETlZvR;B52=C5asqRTFa0?nAxOEhG|nA6 zN(cfBMj)b-0UZYf7a-07K4pVIAklhH zQRGOlY&7nv2q1S75(Y-eorHuzps-VZ5cuiqA+qpO)F3F7+({ZxDB|B3!2iot4vstt z0+oXz5GR76vZwQhq7aaiv0x+;DtjUpEDJ{cj}7=Ett>+Bq)=cvFa&loSPmfvIcY;d zkS7U}AmAr6hJazGiUuJ$0w?8yK;X!ezz`@{_OuNtdy+H+2LD^u|EdasgHHp)5wO3z z1^7>^k@^sIGCK$YajHKc2o&NJX(SAFQhri{pK3{nEDZj?7$p56va-mN4F{2fo$3V$ z3IhM1+L0;;K_O4c4<%LZ1CFqf|NS0dtsI@~urvw^m!ic2AT+X21dXt; Jx}FBj{{eQwSKO{k7L;=bU$Z-e<4RiHRlp_LQgsa}m%;w7vV3 zokMypF#&k6qZs@rx(YuAywse}G6ybm0a2qFWBikS=vOJekc3|YZ<7bys@TYWC<$X_ zl0P#2a*|bBAw7CL2i{_CGs4vRb({nr|0ML{EIyUDWvjiSFh0PM5>}K51P_{*twGfU z>@FQ=L!h#~DpST_K|0rdYF|C>kJie>iS^Jt0dVD4wSNjnR`uKy&)^rAqk7OiEKE>U zze6^)^0F}xHQhr)>5ci5@Aow1mOTNnuf}SrB9%+eMqLBeiR%Gngu;zEiDYi`K318p z@#;$^?+TL9rry%f%407Q+&+-o6`vP06}VlI>EOm&Bfkfm9^;<)+XXM_s*k zV?DhY)q>*4TK3Ki89Bg!?0$*A*m^q<2<+6k&u^s6?-5C9#DTNu6f;`JM{A!%fWLIF zHj~eGTnGv89z%A!S&hR%CKtR}X+Cx^V*sftoTM%}6nZIcX}m5D8a!K*m|d7ZBo3OC+L52hG34@T{=9B>1{%W@N@ znFQDZGKi8pxIrS_fJiwmxs8yrWLtq59Q(7o*EF_1KUIyzHEpddY|L6{0um=Zgnfeh zicg#L5w7V^xGm-F>jwc;jYg{e^!csrjM~W1IMvDl4Eu!vDOw@ zmry&`4q8qucQo_eSoWCI9N;QJIAn0Gr*yRQffprwq{AX7$%7V2{iZunUU2)8zX<(3 zgj))`VzF~YjQ)jzoBVv{fmeioE7zbin8|&Sw2X^ZB@*A$m3g3aI(`uP=TDyPP|K5X zGvv0v331Cnvv(J{S41-2isQe`-)l)N_aIU!TOn9=Q2jpi-=_inB4xR2vp- zu5$q<`$_7V{NZz)-$nETc~BGv6P9!$2ZusfYs)*#za`QA)oDJ6m!|DJDZ3z3vv7_2 z*L1n29Yx}b4I5kyl1lVsSA1f7ggZJdy#`@#!qR$ix0kPwfk{4d#Yj4kKe4$hGMS5J zCpoq6s=-cjl1G6HJ9)qc*Hm-$c5(Eft1Q6xA4+V70LBC7HqUIDn2Te&Gve8P`qRH5 zugL57_py$H`%k5;yjtO}`qavaIn`Nqs?(H8UB4eZd_6XJgX=0v0ETRvrPUwpe0bW- zMWSDyzBLWjTH?~zJf2s3^>`o3vWb|lLdH;?yw3mj?G0(*diMa#)<7I1VpRt%gR zM{$&}-9!SoI^4_+2IsfeJTxO#v^7vKiX`xqYd1;U%`{M5=@RyDBxmUy{7^c6jm3on zFK!li)(rme#m?q!G8sBg-~T+c^F@?(zg_RM&E6ur#%~cjiXVoh0fR8FXEAJ^L@C>q z)9#f|O)&>wftwN5ai7!uTlZ&X-)=cg5702*>c_Iw>Pf0rXyZod86BNUwfC>6Jyoy5h;V_!@F*-z(olvEh@Qh0p`<;iShdtPc| zFP$t%-11VAWeZVe(U)kg|J)QTD69|Ws(p~nhNF1|FiLcsb%Ye!J*D*GobR7X)e4GuNpEewJ9B?8Q zALiV@T;P$8djT&52?~7y+r->)c^us%F&?K1?2oBzG}FeyHKi=VilgnAE4O*oan=JW z5pkRsl20ISL}N{_Q6`#(CU!blGJV{@(3G< zcNOpS9>a$Wa6`B|rA6 z`p%l82ea1>x^(fUsm8005Px7B=H<<`R+<0vfy@nPQ}$2SsU%WCj<58??U^Gjfoy)) z8djMTNfe5I61tpb=uGJzDGnqTyEo_wWl#Neg?mvJLv7q3Lxb5!_C_ir9|TH22_{!sV_kWG#QN`{^3+<=3b89C93qX&JdU2=kfd z09I7+#~u-gv36i@o{m>^sSpW`T2I8OhV5}J{$AH_zjb`&(Mw-JZ+_t!G97b!6wT7a z0C{*aEpc7(fepWA>?{_rlJ~1@y6?i!YEWvJ$DtLMBH*5TtJ+{{Q^lAut+8zM-DM7P zM{PKcq!VR2KcM%$Tr1#rG85h>Iosj2PiafH0G$Y`)7OdO=3z;fP})@xtq7#z&0+l| zCs#;&%QYejZyZlnvU2!uM%l{f>XK5=k(r!rhhdlj8E#LO>4+7`k9G{R0sO{4-kwq^ z%Vqo&H5gm|d@rqtHah0*0#?U4hqDFd@9 zf_FoKz$Wae_l#D6cBvvm!2ph&kXE!-X3Ux=vFCVVq4ey<%C8JzKbsQO;1SC`v>UG- zR4K_kVa&E;IyDnO*F|DRmRt%n`}^OS6(3yG)E~WG8kG0=QR_B{v72G-kvVJ5O%n%_ zN@E|P_%GpKx-i$(Jj;q!_-_V#huXH;eW-T!BH_?@4O@Os-4h+E-`nWq2MwG3fuI=T z>@{sJY$I2%&6gtOdLNHHPTgzxlJu-a-?A2vZGTAlsB6SYqe@(HS|!Q)`k?w zyi~8bsjVeX`tC2=Acj)h(}HlT-#=z`Gzd0$yihc6yQEIBY4)+%rnATWYm=-y_pnX5 zSwoU@X>bAx^UUp&09*6o)#c1d2C6w_)$qY~Ca((|3Op}G%_JX)+$C?Q%QI_!d94xV z`s-0~#p9jEZD2{_-E8+js+S?EstmOGE`I;fYt$gd=<=3vRJQ0Bd{MATy8h!|L%z&j z``I3|(qH`pXtnyM9QJve$7c*~hp z&JKO=+b+NlR=4a9xuP%xaL=&$Fn?z;mSb!$w}o-Ue8!QQ$Ii3cGpPAVaB1Nh&u8x7 zY4284(6AscazsWep^);hQ9IAa!;maT3VClH=Y*F~uV(|-n;ku4)%%>R9V0a1xklQ( zrbSx)al}D|3fwqTZ&jO}uzClJPkmwZo6V(ihF z_|ew3U`DImu5OPiSsBt6Y4jp{VazXgNLIJkmcR-Y7S$Zv==zh-L()t10o)W!w|LjO z>f%M9_J&Nl-P<0;1sbJcTzfsYkG9{XXR8pKU*WMV{I0K3GzWR9QWT}bw9Kgf?5U}} zBEBNu29?tG783F+a4ykaeqiTf7F!DeIg>jNGE; zxWKXA7-`Yo9@1^1Lh%`~DO-1s;z~)-d=>G0uOw`>W?0k}+jHSNwBog!BceMZK`j+; z$AiUK9SqA8r(V4;9fqn8^fVP73(lYG0CKI_R?{%qozaM|TTFs{Jhwg;s{A?HAq&(I zF?~J8BhGPw^`ichfT4VgY31ymYczPuag)F_7pd1k0rhJIX*_-CoXmu>KeUh2Nwi#8 zBG1SJ84_T1Oz}83;srXwzSXNORq7{cLZ;mPWK#uU|22;0ub&ZvzcVnFHN zgYr|4y>E6gT|dcy6w?)znXbRiDfw%fHKej7#`C=B`gzr$CYUJatL?GwXwJhuO8C8J z*2ss)c7>;Vm`EF<>Qs*Ma-$YcUjZH6xF<`o9x<~FA{??FwGDrX_}8G`+`R2)1gUK5 zVED$}*VMoKH&<^dOKqc*4i{^mZo#)mU{0hO;QwLO+(?q)i#_}!YM!dOk?G5`dT%cO zM0d1vD!+-u#P=fW;m?RPEM1eJxu&_88}6SSZ;a+zL2o9_evy&bIwTXhFr2=WY|3I% zNHefZ_Vi7H`skt&cd?LDfSSJmLXR9yIxyD1y98MeNupfIKs*5IslmJ@UHJy~)y{y& z(^&>lm8_&%MGRVPL1t~m390`Uj482Q_3^( z);y@Vzg{k)5f>Z-R~pjwh0vy<2Wb5bHRDRYwL~pJ?}>?<#jff%jP_(FVu{_0eYb-? zv?^>Z0n4>Zvxr-0Ce>Em{sEKwB_&HdclxT>@kEaAT(Nit`)6-xA*XYuk=4CW{ppe% zUZ#hY{X=yHx_n!`D`AwjP?F)uicr@Sjwp?p@d^ewYlQ+*&@tZjx-)fc)DzNvIFgTa zZS=e3nt)W?i`oLt(^-|K)9_N{)h9Hyy>MG=2b@MWY!nc4klDaZLo2~jf?|HB?=Z*yM&X$Q5&Z9<%oGn@nElUNMCE00#eltu+UBhfX`+Zm4Gi|l2+9^82g?(YncXx(QSn;C2> z%srkG9k-OwS3D=bE2~=SWx|nYdEbC>^nnuj1e>gCV?N!eVp^Bn+M$3_QqfxakTSwa zB`|lo+=DyjzFQwX5=|R@Ax{HH$ff6|{t8z@E0@kME31a?Usq__e2Xg3&mkMf9$r3H z)AKSYjn{pJuQ{1v>RKdst6oq`$Q4DRTbGsb92s1WuVxeyxUr-urVWFziG-ap3DLy; zE@xT}Y|TWVv~ht4vEwhaQURzbmu=Kb`!MElP@1XYba%O9A|`PRi3Vz<$0M003O7Z} zYfC!UlDr}v%Ux>{i8rnC2L*g5BGUy&yuM~$b>-+! z%57dsV^g^^xF3o{?;@Ul@JU>DUb8XE|Hun{RAm@>m|8?qXHgX6keTOJMlS|_w|%M(1S&IaIz@NVyRbOqy0+?Z76>h zr_mU^$)x^7R+;Y`?ih~;WeGJYn# z9p&Hb;Jl^+_!b7Py!MKBCo7=J;8xw8}AJ&%c)J|V~O#5-#^W(O#;DQ%YA~^wuhGEb2(NpGNuVdq0i#g)(VaxA2MM_kA_$UzY+9K<4N8zfzd6w_I;o$m5EpM3? zDgQAx|E|H2@lT~tf9mS;atjXB4hhK(zPfRjvM8y~b6f+b==%ftE>znGeLeSq%ev*# zkr$78(t9PdE!^A0i1FBBoUXJ+k_~;n4&XDlDm_&~%+Ix7drZ`zb0W@B_Dl|5TRE%0 z)dt)a)y)oUdGO6y_xcgvqOwI>qqmNtq_vGQt;_^dxzou2za#|7*%Q6D-7p8ZaQBKR zhP+kWHFK3Rk$!Dnj_|Wm5F89j?f}#QC|sNq^w)>ejg#zOUm!??81#G%4nI>9 z1A)ZA=W8&?`5Hp(Ozjj14*RQ?93OI%7zRfsSBCHc(6dy;KuG9WDq>(T^4vNw^gJvW z0Xjz+jD&;EP!WSb!2iYo{>uY#DC8^%L>vM}oY@RPAkO&$K|&E{_nxX1N1oXWLV#fZ zQv?389|41&H4ubABK~F8e>Djt4uhUE6$yr%B?v+yz-JYML6CE#!62j5&nkvMpy$E`fgoY$fW^S)?1zZK#sAYz;Gcg0G2}V>Ay6pv9BU{X z`M(x|KoBSran>0K407(;L10kWe}MnS1warO;=DC*$UhAKAqW>ccOxKh}z(( zzGoeN^ZlOh`JHo^zuvi@=en=^zMkuSKlhUsdg)ilrQ*1YfO?T_mHW@VqTktXY{hM_ z%P<<<*OGkxf|$wog2;;C%V@D^kc~Wq4v$Z%!5@|m7pY80U|fK?MyA<1x`O*`F`;^& zZR>_C{Er$Y8&3Z$!!6iOcfCW0cB}RPMD;B7{@(PHw=z?c-OApmK!BZ!p~3%d+ziD? zqRy8h_T8dXpp95S#$Uk*KSo%bWHkpaasn@UlfQA?ooy3Rb^7%9cw=!Y0*WZ_eQVqv z{aS#>y&^qZO;LueC~;5s!3<}uwW;{Y&g$k~s|>3JAe+l=iJE+;ne z&d+{!gQX;BrIlxC+JCki-MNi~o9`|xWLqFU zrOW~1Hb%`tq{e|-J0(Ha7D!c1GCG87?pc}Ny=mu2U)#oPCm1k4r9&~%?5m97V`Ji0 zvzq9%lylQnrFh0W$u_~}UF4mI$QX?hE%`BQzsNo==f!xsx(pv`_a@`G<8jg~MH#|; z3ncVn`fG8HLSmDZ@!>%I%=VIn>hUS>rqMD$s#NkGODA+cYV7IGN`n=v8DGO=)}C^K zKsp14mh_#9r(&=G+&cZNM0oenpDOK3_;Bf$2SR_g)j=z~^_XXWKFm>gd%ARk+;gH> zU6~-R`pPe1I~3AZFHC-i!G#tWzTz(<;3kc9>2RO^L9PJZ@t{K{}730y|xb zU?W8LQOY@nLRgy<)96LF^=1^1+7d=bM54DH?$lqVK#5YU$3A^3QG=N;56U(owPsEV>lu zkc3+LK1Rx7O=HGjWm`mz;TJh{vwje=u8}x8xREkAY+sxwm3==+tnYiv5YbLsAo3qz z_HE?>-cS{wy3@mr+H-0|DVC3Ca)SU>B-9hcVCqeruT914?7=!tWagt3XsEHqtfb%j z^NBhoRgYY8N&<2)_cJBt%HhTeZRBO;{)eS8Yf1#tFb44nk$IhskE`MhE<5WSA>=Hw{@^xHSraXCQf|JErDP>l(jo*msd|V?^uA_(D z2wDHG2VLbfuC38^Q2Al^p3FqqWu|DSBW%5@cP2r1g$O20JhE%M(&OgdJ!Q783Kb6D z$-j_)*Mr^*K_i8GmC!&s&pQ1}NP><1eIu2B0R`GA0V2LCl=om<(5IPZjvw-#1lG~B*+nNPl%Jxaw68+7gzgmE zpZ>Dde4CYYHrl%qlRBY}KeTeI``N;-HoL6W=XM3;u_hCHOicO!OGjjbsj#ON5q!;z z9UO;1;j<-uwjaAw9*yN1KPz*4by@`l^5&YrS!>nV!pK*YCTa4PZ+jL$Cgi%;?vWRw zhg>UH9~JUXRK6@?ZtC9Deukft8%M;&xM-7iJkIE;;;>mI`qtqvg0&QHeqwVqA!fa~ z0Bm&8QOJwd!;$}M1vX%@B(GUJTu#2Hs}(YKxOm%T=ks7goI zKL6D2EuW*#-*z*?zpWH*d1zg>J@}R*v!Pv3e)++7Sa7dg&YT~I;{*LR;|R01IrBm_ zKo^w-`T!xvoO0Wlzmg60&uLo}3Ai2WKXp|Z6ejfi9g_>G=?l4npMbP}ri7%_&@R)L zPFm;}ds6Ij{_aoK1ocfiOY6#b{2J9|dQ51;omN$|-SOQW1v8hXpldZt*~^bfjHz}X zUTk%l6$MgjXEot+nv4ynZcR_vrVXt(fR)YUZwI%1fEiIMlI-6i)pEF@SEpAi7Dp)u z3YF2D^zCMX9sDQR%-*Y`7Dev+IFC4w(HwU@l*$b`^?2cDc6$BZ_~;9-&HWrU^7i^K z%zJ+}c%o_`%DJq6__V*9T3U>~DHd+%x0#icCU%{=IXHA`7Qr)=DRmGZ+9jsj0U(;F z?vyA$sHAAerRo&hm1If;=(}WBR2TMDL!S@uN4~zo8SjL*v>!{oJ3_hS%rfvXfgysU zMABd(ENMwKeC04D#adI!l9g!Y%Z0=$#YtnSQrQh7N&V9G){~ztJU)&%t4aQqnnu5+ z!PIb+^_Lt*x!)n_h@JlF{k9b(A7FjDnxByROoe1Ng!STtZPjLpNo|=`V8GI{1huLl zwu!~bqG1fBJarWvkuQBv7_eA$GbCBZw2SGWU%5fTr;F$$ajqbP+HBvZ0l(2oO2JC2 zhprD`Z(xG`rrsdpysL@;F`lp-i*8tW@kZR(zp>9a^>%Y_i`FmvbnM%*2=LXu5A{qp zRBB}>YwgQ$cw=$I+Zzvzy{l$+TaNJpQ| zv)QVj5^v_mfF4*IE#^lRJ=4l&=eTZA7mp;}nAa%vP&Y;oR_fcYHnu9=G)P`}#?L?G z<}cDs)F5ff5NS!VT-PE`t_XHGLy+Sh7+y?8(ID$k7^J+!XBI+cQv|H~HJ-x&vd54Z74_-89@Zk{_6%p2X!vtd8TULDKJzidzB!7-4=YP4Yr0|K6Y!gBD=Yz`{m*l#zV1 zBVI%A!&*ZO|Hpi-%S*;U;OVHYlx~HN!^5<Oaj)jG3M7Xw(|Z(`9H@hREMVq%OisayPtMkjxsR+6%kodSXs2lj9^Z?3!&XS{ctEA)gcm|!PG*0tj{ zu80MpU88HCK?jvp$>C#Qy$4$z9GdcLs)f9L8F?JmR!_VtI&i@Etp}ezct`X+2V+Zh zRhYlde4~@(<#@mBKsr$>>vX}`k#wTH&)G-ngFR7z94G1tADfHtOW6%~`OT<8GZRE1 zxk=1Wu)$KlmjT*&?BNX~Xmw20QZk(bOPrxq&UbzvN9K1WA6NBeE4?r`W(2ma1V$p- z`w-JyV;#XZGj!xY+Sj1u;d-^|Ed}N--VS#i?`hAWpIc-}vO|i6_jXCk3N(-_pxLqU zc^0pTyAmCh>_pxN=)R0dpF7w97P?x`&WM`eG&Ou(&ehK-`4(7m&$hJ9(^_G30qZZs z>^YvbsUF__Mrb`WbsN4iE!yws*m5nGy45lCsX;pw#TO04`n8}R+t0Jnf^jbCKB_?% z)aqLu@;ynRsC$AR@6&qCzes+-FZ4S1nV4oc$i|@j4JXCAS`4q4E@dSMIrRJ{`kK99 z&sYns7qX|bV%&`ZN|8sNdXQ7%^DC!vE521%!l0#3${}cBH2&d-EGK!?D&pHtwr_V;-DJ$JK}8gJz4r7~&1F&PLml9j5EQ*~aavEa*49#rrPwEw+Tm2}}M=i&aF3TiTzRn^ViPOGgmqa}$kd)0E z!DM3&48Od}X=QngesPCW*j#SKZs85uj`$L_n`-i}{JB4`r00C?*I&=m%x%u0t>WYn zK{MQJ6wG%v{x`aHeJ3|72<9tT^%FQq)D$vq3YAbAErOWQ7a~N z>|n6yJAZngYwEos3FR%OEJReT>M6!^GeLOM7wA}iR|S}xo7A1GZCPV~2V3FLgVAK1 z{BLM!(*sS|a)Ql3!|xK5Iql64<0E=OO@$iPAM@PlKhDX`b&U%06*>Cw zJmyz+?B)Zis8)!`8>|@!=Jp-tsiiMR#?@Hs!ae08H|$s7YPQ<>O@!;`(QO+Sx1HOZ zf*haf_@P$lm@wUp_e}+SSD?o>4b~0*rA<~j7!IIm; zc2TRh&a#t|s_(M3=tlY{lirhYdZ8|ov0cS?Z2t_ZLTDWoKb?s*zl`gps7Li}egEda z{xVwmsOruMmd7~n){m?R1*v~^={}>OkgJAehMbPm7i!Z;Z7b#wa#0QuhTcNpwgu-w zWxtGfkbv5kCA?xjVdecbF9$bb1Cs8s>~Tsgutt`k{KRfnvGCs@;XmBM^U^X0ghs=W zB3x#7mbLyF%rEuS5+EZ4h=Gz53HnLdF7KjCHQ}D%hRa?itSka+qz2Iic39Ia0;bNb zlMB96 z{6<4uEeFvLsp)XiA9NPBD}(~v2seXia$H>Y_$uG?FE2J;A8=j%p+=srw%eL}p=QA# z%E5SHMa#SO5BJ3-LyDN)6>Ze6JSl+dS3TOgAhlJi1%30>u@uGiRLEAfp1G_g=Mtu3 zL>bd&uho_^&-b0FLOCR*CRCako7l=|z!;B`aTv`}!jz0CQ?7+IO2#siB(;8&f^79m z2~JT8mtHgO&;R!p#69)WDUeZwlrJ_4@q9I!<0C{pg;tPLx85)VAaGT-GzFFDw>q4b2jjgRhPB7k9-pBTSzKHI( zj98DP2*v!lS}R8<{$b-}|KVcF`{dl@jkxW44yTgckf9*eMwQX$8!=jo>)_1Z$h^sJ z8qfV-J~hibf0vU16ySSh}!?=-<-phBY6pAoL69^;$w)ZJiHWa6`z{1+x@l^Gna^l zZ_R0W_~>tNVS6W4klDhXIA^VOIUZcR%l*~3zL0(mJkXL*toXjBtZNJ1|L|v*gl{%{ zV7*&{2OuIAZDmMMZ8!-y3Ql|9!S%SqA1{oG>?6US^m|A<^{n$4kbjm1qFDHPL*}$I z=N@$VVE5m1`9Bzq1uQ@4)`eqCuKQx~o=fR5ChfNOLGXU+MZsM?B?;qt5Qb0gXIaf9aFi1pEz+V88-u z9%E8>r)pHnMFfXoiy6-^T;;~b&&YBa#2Llj;qw|Gix;CGT2Tuxcg)}x(p7n1?xTKT zAkoZ2aS`^hNxP=bk&a(|6KWAZBmRC|b^C+xXYJg|+ES;nkoLBkMo)~ZC+n4(kR(XK z6vp{UOGc%^o3YUfyRf3~p%~lK7+`#f3p*g=gA}Ncc0dYr{ymGt!}7M`x{9;JG2Ax! zE4jTe`{{R6dDo@0UphFY#wsl?IR^0k?1f&}>&r&2X|MX_RT$jNyWEZHGnDkuF*#_r zsd?{-QTD{Fds1>eDw{DX82T}U&ezg5n_2Lmj2l-inIW95KB#uph&f^mWTh z5oYg4o9Hu=U(7P6lSU8E3(#`vnomGlV&=>^d=pMkY6bI>b`t4-u4(Y-)*(Co%+AFm zm}yu)o7axfi)-v^vgXuulW?u6c$J1gpvF*~OkRr;+L|O`S9b?o-Us?tULQ>Qj=o zAzr!Hc<|zCuQST(z+;D$iR*@(fz;lDWD;+5{4sX+jpz814K={y4!TE)Jz=QRx0)_sS}DS5VssZE*2s^@joF zChD%z!M(bLKSx%XRO`3)V<3jouE*p*kmfJ9U!J~wKPZpju~~b8e2bpom^NYr{!d2k&2z0(yl=*@le?5 zbK=Br5um{_f6`UDl5$;K>w6{P&iuSv#3+qs|Iz483D{wy>yPfAi^`-7Q@UIdGZVvH zBC1Gc&Sq<=-G)Z0cWFOK@Y25R?D`K%|Ag=12jD+MZds(US%0qwG)!tDsE>q94`^g> zRRY+C069OueVOpIH}F_#CKe*N7^2LK>FFa9FcbuW!eWR0)t-P6P{b|JU%y)}x5)o~ zfFK|Uv2!s|2<%)83I&~y!NKQa2uN&10D=fCE*{$&zy*L|@H2ZvL1LoNb1|6s+1f-w z2r<#K`@mqtxqM(T=-E0%!En&u9I*dx7LI_QC5FJ^uyX>yMPsc3=m8Mq>~0VY0unz{ z5(ox^pNql4u(KtB;LvkYgT%ow$k}}2aIwGT2mY&c5JD7kRv!>TT>NZ-U=S2~E(QjP zoox{ehC$E8Advsjk-!fEfki>0XAc1^3O!FO28Nt92v`gTKieG`dfpIVDD<2mz)%DL zKYP+(nAkZNf?){AITE<&Ip=`k2(fed#Nm*$M+Zhg{v|&M_|I!XFdg`;3J?$ke(p5^ ziHn|<6#^DJryzu2&vRHp@P9n>m;MllsQ5Wz2m*ArW5{2IKVu|_DD1qoMG@e0`^3PA zbH#{>!GW`K5rjE+o)9s_c`y`$I4dgzDt`W>0!4t&5yQk_{}}!Df4)*M1n7UHAt)mX X0f7;*nt@8>V7NFr7nh=@68ZlDiRO0< delta 7220 zcmZuzcOaGT+wUvMF3Pd8Qz4u^%w#8IkIYE+-s>qcvS*YKvUj0l7um_)WRoK^C&zwI z-}g7ZFYo==bFTZjug~ZDT-W`a&r&c+dI(8b!e4-4>Gh9Je(A=LXk^@CoK{QeMM(v< zx~I=bb-kX6je3E#`{kMpS>s}VY%FW`q^{3Rntt%v_||@}WDQ|~dtcBYyg7!KKf|fxVKFZ2E^)&8tP9DxUK=88ZXHMhku>;O9aoe9v(Pu5`J$zuyI__=%6#^ z_^AVMIrpvRUQDQ*K3;VstZ(j9G3|*ae7{D^hrMH}{%AR9Yj$3$z=OA{|DwR6jL=}b zXSqrCK%l>v5ky$iaET`bcUSCqXKhO|3J`BN4p7nBwwzJr*aReJrhB3TN~f=V12Bi= zGAhPl9(-~f8WcfMU+3=fFPtB7_C47m>`w!STZN6?*&VgXBrgaLn$_OwI}6gK%Re^u zl224~%dE(NH!R)J^iy`y6}g`D0=uAYJ+6KQUZUM25pCCaPso#7cM@xEJxi6b9xYaTy0YcR5=mORU+wO)lbQEPZcP*BF| zE6Xs&lbcNw%@gzM`BC!N=NZgOT-s!!-TTtXp6hcC#*Nk54P6%?wZD>n<&hs*>=`S+ zc&c_Kd;xc*t8AyxujNFIW$)zUmz&)@Of3M8-Flljz9Cg!^*GqeHTy8lOQtzz5=(k( zQe&1oA&^?Baa_muknp1*Hv%t*V~W+Oy=PVf+In&QK_{zzRUzSqNwrCx;??oq3cnUf zrJlfV_wpR2l|s#YzC98C?1M$HQZgF5goZ8U99 z^7$NNaa9%cfgRd0?akOeF1)v9Iz&>%?G@=ZvkhLxOpy}-S^$R>A>ArpYsDeiz0*<8wz5I*vRuVti*p0Heqfgw?OiAQijdnmv?D_>8 zTSuDP+w7CU&JEj0e^rk!VP9l#i{)pS@k~Vy_0I5X>t0M&Q?Y($7bQ)^q&K`ABO~1= zSHoJA@mZ^kRLEbpnq=B_qMhG^R6sU~4)_ur73qr7l@2U4nYALhD4||#gnXv8L9VSx zYx;WdznhOMtoeZuDQve3xrgh_*iD^0b1TBA!fqx4i46uBc(iF?-d1$G5jSU<8Rpb& zteD0WJhF(jgn=>_p^3lZx{wv?Kbk*qUJKt3$g!H_o{AhMvelpQeAT&E;bb}07qa}^ z@pjBYaNK|S`13C-QBL&MWL=3KoAq4+uCb^f`=>04!e?r=x8xgaI?cB>v(bsT1N-( zM%xBlH^fi+dCClAC4`x#*1Gjn=0>S$y9~`!Lp%-yJz~3ljw-!~y-+`(RvLZezRFyu zn`S&#)rJLaRWbWcaZ2PWt>7c6+?F5QOHstAl)f1LPJ`LlU@{VVDSfBzJ|yEVje%SP zV`=LGiLOm~xNdLK8!kp_w$#ein5jNJ*jg8md9ddFvM|Utb|d)fYj_L-bP)4coIzgp z=^Bno;r@iNc?=!wa5K5SB9wX0=nG3c)uZATp-1FMwwG7IH*>)OKN*?LH7RJXjP5>) zVcxTEFH$_OXI6emuR|#7mG13M*WY#*p!>3N%*~9&j?mSXTd?i=7{m?6cKvX}_A((6 zfI`=FwndTs4(Q>D@>-pG_+P8{ho+2uxpl5@2k|fsfgW^Iqex5cWRI;&axi9ziq-ex znP#MJh)G4$B$X5ndwAS+(t>}AASpHfSS#JfE4-QH`Y^xHs3t9~pn0H>Gql8eZU0ql zj;R~2VyrQ+4@qv7XXITSr;JSi>dwaz0dM|l1aRm))2jN(b|h6U=yoFHLu3*|wNBkyw&Mz!Xf zW#>jr19b5qj$`hX@B$C#k$%R2$fQ@Y?eh_a_?fZ-xpM>>|KG zueuPYJ7=W0`fs;v13DC&c5bHs{Nw6Z!~J5a1H|rzKVN2gWJ6o>G9kc*Z^mf_H*#ELO28lzGUdY$et3uqlh1@)N#j-c*_6JI50RFO zQ<{5YZQK}b9LBn%vw5C}tdkeoEO1PEL~@wR&}l~MPjbva$U2cVespOI@BuuJWW@wu zM~|$O1*ZxPurVn?nB2D^JeeEL`ut&OH$ACkdgd;Mw(KdDQ!azi)dbB@hWQG-C-_n3 z;Vpm5mX=Zc6VueSZwwEgZzv0ZR?ffc$fn|JBR5{ZE-Km4xot2aTT?NU5t}o%w1=VM zA93_6Wjx;~W>CA1I`DeJ=M9iaoS3bh_moKWCNqk^BEwM50x?6=w>BvTn`*ggus4y= zc3ie-Oa4(*eAf=$oKF*vE$9I>XZO@vc+nR9l&kZBD*I3F*JoY7w%W(S+bgp8doPA7 zrfDfimnGV_EV*S!9EIdo;%`T6Q{ukX&y;YRva$8_I)3k+ z{s2ebBD=->B&3fPJe~$$U*K-{k>4rs;eZH8TlBf2`083zFK=6ex4UdTx2mp1&E#aU z=u5fV#i3V~RzyL9l_&r=#PFpfE^p9(u~v0C;MG&vAky#M$hJ=fRCjs5v*a7CpwRm@ zC{Wf$qt>mAT!|L8Pmy@oaeexa_4j)9okSk}&+JnQ?(OfLeLrKTWVCb?}D zzk|~92gXzNs*0PN+H;zyyi%%|Gg>}}(pa<>J@d$y9$-}RQ*P{5wy7a>dXC=vU9gQ+ z_3ZfVJ(G-W?aT$hvwF7Eyaq)GT`(0|6ra_*UYBCT1^ZEhqswDAbstjv$R_9eu;xv( zsrvHF*sj@((6n>fAYJrAZ?v>}Qi+%P?xstAc5E;hc(<;A32JcoXBN$k@Q0wO%&Ctm&__fzG(ukREa4uuB*|-myiYLYLb`j z_!VV$K7Am~{YXkWtgMi6()EF!sfc*n_B%={ZP{sb(`JLg*NhD7vY;H3UTTVq4+k>a zUVK4CIrawHXDkMP$TL;Y*!+r$Y=NOFFQt2Z9dxk}o5+;&^sk{~Nr>pa(6hJ&+Rx6( zNxC0Tu$tHd4tFApLKVtyzrjYYs*ltUm@zWpWqpAz+r(Aytaa;*(HJzV#9K{=giW9t zSa^5h>RE-5?*?%ntu-|~RB=;ZhG*L_6LcJUUbx#*asc1mLLK4d`UXFr=tL6y1pLa> zYwhgriz(J;J^VewUM_j??Q`&gQqI#*FZ~^F{5%6-;(6r-B9fekTg)(QDN%Irr!$^+ zr7o|z-#gOiZrF=vvH{(2HH>Fj(0m!(0wPP>X9jnG7`|{Bo>~{X9T}QJ^@FS^wl&AC zkVDORj;S^K!>_lAnzw?S#7$U^;uJ#eG9~?f`_{@mN7=5Z`tq^H9Fs(}L=QFDn-4<8 zBj@WQfjcsR!A>g?vOF(~^n!zf5DEdM!IvGH9h1A( z`R8vjrlE5vA^$jaUP<&rS3(Y?v+FS*LeHVp?} zO}6;igYYMUv`!?GuNQi%o99?o3T92T#}k`oA_1%ZN7aetD>|<^yPpKO==5VNT8A&} zWXo5EWa|xFvF@zLcun0i{o*zw=U9`*IsU+I$3UU%wv&?8AgdFH7V&pc+S|Gx9IB9F z#@{6*5&Z0Zn^<-d9bLHXH!-r5oS_dO&#Ctr>_xG;n*6Mrmy=xO>u?7ZG&yGG)HhXF zKLO32otpucBDd-v7iC`DlH7tR(HH;iG`%G$q)uF*gh~41*M{wT-1AYf>vDfwdU0ooy1xo# zI%`1U?25S%BcBJ6fUa3!Vf9HF6Zx%G42GBu#OUS^2s^Jct=Lx(-FsitqjSUF+VtrU zGa14gpbZK&|E4A&my6m=k8fFiY5DkhwhW^lF&j!>I&f7FO;I7+qcrcLgteA^-oj9$bIkKmDv&j>7~=Xce`wAl9LmyQ&7VL-YN{RWW$ zmPlask&_kw?#in_h4xF|*yH~7?{Wk$`z3dZ^Cb2N&hm$Lf1u;o`R7}Bz%^8kC-i(C=No}O~HF_#%t`) z4nVEF))Aac^yh!mc5;U%`pAi-KWs|8{PuB!LL*UvS)#=b60p$8b?)g|K2Q7n$yeb$ zU!(0kgMw+v^d7_!zx9WQo`F%Kz|gp=RaDcO!d&rYT!iHdg>QegWzQwPP()P2PCT{bvxk%qv_h^BUwC55bN*n$I@W|Z~(Zh#Z&sbJ;$ z<{NfCGG&s>#@jqcEWdcxnD&o+B|{vdatBmY;|@oeX>Vqq+jH(<3HN0`PA}9RQK19K zICfzHNQF4_+!`ZbFkGCGHXDfD=!kbc!pScqFMc(uy}DiQ!AH8dYanP9K4GBfcMxcx zsJQd>&=1CYA!nsklp^g~`c+@W#2o>}#K9n;5J8;b54cu8j2@9#$EBvxxQ(z^%U#d= zRma<>@p0?5rhCyehldogeWB94%?ez_hx%H4uTG*HjbO59J85F@1iwf135@+@(v?T04@NSqweyN(KC$`*-I0 zb~)RkmE8KfI`3)|&Y1#z0#~(@@5y4pF9=SiNN%Z{sy292I$V4DT6YtALR6AFXY6}5 z|A!xb8f$uo=gOBFYcIDgJaI7hf55X)ou)0ObCSXE%htndl)7C@Yh2E`fnU?j2(M?#Y=e7!k4h=kJ3(LBitEr|O6fW@20THDMP7)OHwGiQ}~yQW`D*LW{KA-Q$-~(YX~`FXX+}c z_;bw_UnX-2L#!-#mF7rY7IIC#-do-C5?uVXg7Vd4=?uA=kjq+G#a`4w+0W;cP>g7@ zE_KM#klv5^CTOs!`%Z9eqMs^Kzl&<8l6us;q@`4^lr1T|FJjoavFb`w z&DHa^kl8mmm^Uy-yWgXuIAPLEyH8Fe^~%VkYNTGbcA^lunB}8F=;H)yT%lrF0AW~< zc5Cweg+Sm#(c;(ag(mUgr6x_~3m|=1Vq)Q1oDGxiN!HSL0nm)Xhg2wqiX)vtFR2)MfQAUPdr@4X0sK?p9=C*Qofjjm6HN>{8XK^Kn? zQvk{1^-z(6c}cdcUEp|a@%?SsU_VFlWRbGAY>x=HE1%+A=PMapfxrb*b2%xVkHrz% zspjF{>-noI;ThhnNeG(Hd4_I3AE;{C(6A$*wUIV*yeVi%KwSAncC^00i&fxlvZA`G{T;Nc zXdDe{iIGYb$ZDMMV}dgsg(fJ;P#;osj~qQK_ix-Usy(N1d}n%^LI0U5)PR~WQ4aJD zTfXm$elOMN8p7fGI5+>s5zB6lkXNkt_78UD{UP9St@;r?^2csRJlN;`w|f5mbU%T8 zh6MpkHuq8e97C+*&-xEN7#IaVO&JFH z^Tw(7AW+1ad=LcqG(8vuh5Q=>_y-6S4ThZthN95WGrFK)$kQND3>ba-EEoy>yWW2m z0!E@>e>C`ajEGNMC@ADKLofСвязный список\n", " Случайный\n", " Вставка\n", - " 0.193393\n", + " 0.199516\n", " \n", " \n", " 1\n", " Связный список\n", " Случайный\n", " Поиск\n", - " 0.023572\n", + " 0.024629\n", " \n", " \n", " 2\n", " Связный список\n", " Случайный\n", " Удаление\n", - " 0.013566\n", + " 0.014065\n", " \n", " \n", " 3\n", " Связный список\n", " Случайный\n", " Вставка\n", - " 0.193837\n", + " 0.196946\n", " \n", " \n", " 4\n", " Связный список\n", " Случайный\n", " Поиск\n", - " 0.023969\n", + " 0.023807\n", " \n", " \n", " ...\n", @@ -111,35 +111,35 @@ " Бинарное дерево поиска\n", " Отсортированный\n", " Поиск\n", - " 0.055234\n", + " 0.062731\n", " \n", " \n", " 374\n", " Бинарное дерево поиска\n", " Отсортированный\n", " Удаление\n", - " 0.068269\n", + " 0.062908\n", " \n", " \n", " 375\n", " Бинарное дерево поиска\n", " Отсортированный\n", " Вставка (среднее)\n", - " 0.882379\n", + " 0.952690\n", " \n", " \n", " 376\n", " Бинарное дерево поиска\n", " Отсортированный\n", " Поиск (среднее)\n", - " 0.056078\n", + " 0.060593\n", " \n", " \n", " 377\n", " Бинарное дерево поиска\n", " Отсортированный\n", " Удаление (среднее)\n", - " 0.068386\n", + " 0.064886\n", " \n", " \n", "\n", @@ -148,22 +148,22 @@ ], "text/plain": [ " Structure Mode Operation Time\n", - "0 Связный список Случайный Вставка 0.193393\n", - "1 Связный список Случайный Поиск 0.023572\n", - "2 Связный список Случайный Удаление 0.013566\n", - "3 Связный список Случайный Вставка 0.193837\n", - "4 Связный список Случайный Поиск 0.023969\n", + "0 Связный список Случайный Вставка 0.199516\n", + "1 Связный список Случайный Поиск 0.024629\n", + "2 Связный список Случайный Удаление 0.014065\n", + "3 Связный список Случайный Вставка 0.196946\n", + "4 Связный список Случайный Поиск 0.023807\n", ".. ... ... ... ...\n", - "373 Бинарное дерево поиска Отсортированный Поиск 0.055234\n", - "374 Бинарное дерево поиска Отсортированный Удаление 0.068269\n", - "375 Бинарное дерево поиска Отсортированный Вставка (среднее) 0.882379\n", - "376 Бинарное дерево поиска Отсортированный Поиск (среднее) 0.056078\n", - "377 Бинарное дерево поиска Отсортированный Удаление (среднее) 0.068386\n", + "373 Бинарное дерево поиска Отсортированный Поиск 0.062731\n", + "374 Бинарное дерево поиска Отсортированный Удаление 0.062908\n", + "375 Бинарное дерево поиска Отсортированный Вставка (среднее) 0.952690\n", + "376 Бинарное дерево поиска Отсортированный Поиск (среднее) 0.060593\n", + "377 Бинарное дерево поиска Отсортированный Удаление (среднее) 0.064886\n", "\n", "[378 rows x 4 columns]" ] }, - "execution_count": 4, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -177,17 +177,17 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 17, "id": "a3737f45", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(np.float64(0.023409), np.float64(0.192404), np.float64(0.014352))" + "(np.float64(0.023733), np.float64(0.193345), np.float64(0.014249))" ] }, - "execution_count": 47, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -226,17 +226,17 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 18, "id": "5434d260", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(np.float64(0.033107), np.float64(0.193206), np.float64(0.022902))" + "(np.float64(0.034479), np.float64(0.193979), np.float64(0.024509))" ] }, - "execution_count": 48, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -275,17 +275,17 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 19, "id": "3deed9a5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(np.float64(0.0), np.float64(0.003041), np.float64(5e-05))" + "(np.float64(0.0), np.float64(0.003635), np.float64(5e-05))" ] }, - "execution_count": 49, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -324,17 +324,17 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 20, "id": "490e5c46", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(np.float64(0.000147), np.float64(0.002862), np.float64(0.0))" + "(np.float64(0.000163), np.float64(0.003181), np.float64(0.000109))" ] }, - "execution_count": 50, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -373,17 +373,17 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 21, "id": "9d7274ab", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(np.float64(0.000454), np.float64(0.005367), np.float64(0.000334))" + "(np.float64(0.000481), np.float64(0.006081), np.float64(0.000336))" ] }, - "execution_count": 51, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -422,17 +422,17 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 22, "id": "92a545c9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(np.float64(0.068386), np.float64(0.882379), np.float64(0.056078))" + "(np.float64(0.064886), np.float64(0.95269), np.float64(0.060593))" ] }, - "execution_count": 52, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -471,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 23, "id": "b2e93d6e", "metadata": {}, "outputs": [], @@ -497,13 +497,13 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 24, "id": "208784a5", "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABWIAAAJKCAYAAACmkjw+AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3QeYFEX6x/GXIDmICEoWJIkBcwYEc0QROSOgnhEVDKgYQP+KnGcATIiIWTEgeoenGDCA4pkzOYgkSZIlM//nV3s19szO7s4uk3bm++GZZ5menqnu6p6e6rffrioTCoVCBgAAAAAAAABImrLJ+2gAAAAAAAAAgBCIBQAAAAAAAIAkIxALAAAAAAAAAElGIBYAAAAAAAAAkoxALAAAAAAAAAAkGYFYAAAAAAAAAEgyArEAAAAAAAAAkGQEYgEAAAAAAAAgyQjEAgAAAAAAAECSEYgFAAAAAJTY3Llz7a677kr3YuSkTz/91J5//vl0LwYAIE5lQqFQKN6ZAQDpsXz5chs8eLBNmjTJdtllF6tWrZpVqFDBevbsaQcddJCdc845NmrUqHQvJgAAyDFr1qyxc88911588UWrUaOGffzxx/bkk0/azz//bD/88IN1797dnn322Yj3PProo26e77//3ho1amQnnXSSPf7442lZ/m3bttmgQYNs1qxZtnr1avvtt9/s6quvtgsuuCBivi1bttj//d//2ebNm22nnXZy63fddddZ27ZtI+b7/fff7c4773TrtXXrVluwYIHdfffdtvPOO0fM9+WXX9rw4cNtjz32sMWLF1v16tXt1ltvtXLlykXM98ILL9jnn39uzZo1s+nTp9vRRx9t3bp1i5inf//+dsQRR9jxxx+f8PoBACQWgVgAyHDvvfeenXfeedanTx+7/vrrrVKlSm66TgTUsF+0aJGNGDHCOJwDAIBUUzCya9eudvjhh0dMf+aZZ1ym5ocffmhvvfWWnXzyyfnee9xxx9no0aNdADdd+vXrZx07dnTLIp999pl16NDBBV1vueWW8HyXXHKJtWzZ0vr27eueL1261Nq1a2djx461Fi1auGmbNm2yAw44wEaOHGkHH3ywmzZhwgTr3bu3C7zusMMObtq0adPstNNOc9Nq1qzppqk8BWQVpPYU3H755ZddGT5ofMIJJ9jf//73iGCsAr5nnHGGvfTSS+5iPQAgc9E1AQBkMGWVnHLKKa5xriwJH4QVNeaVcbFs2bK0LiMAAMhNkydPth9//DFfENZ74IEHrF69enbFFVe4zNloel86g7Dz5s1zy+WDsKLMUgU11cZSYFSU/aqs3ssvvzw8X506dezEE0+0AQMGhKcpy7dKlSrhIKy0b9/e/X366afD026//XY7/fTTw0FY6dWrlz3xxBMuSOszcBX0vfLKK8PzlC1b1s2nC/MKynrKoj377LPtjjvuSGj9AAASj0AsAGSoP//8093q17p1a3cCU5ChQ4damTJlUrpsAAAA9913X0RwMtqOO+5ojzzyiAt43nTTTZZpdMu/ugeI7hbh0EMPddmtel1ee+01a9Kkies+IEjdErz55pvuLiV55ZVXbJ999slXjubTZ8iGDRvs3//+d775ateubQ0aNHAZwjJx4kR311P0fPqs+fPnh5fNO+uss+yNN96IGfAGAGQOArEAkKGUOaEGuLolKIz6IIvV6AcAoCSCmXa5VDbyqKujeLaDApWvv/6667O0MF26dLEzzzzTBTsVXMwku+22m1vX6OCl+uEP+vbbb61WrVr53q/g6fr16+2XX34pcr6vvvrK/V/zbty4scj59FkSPZ/mET9f8E6p/fbbz/71r3/FufYAgHQgEAsAGertt98OZ2UU5Z///Kf7O378eHc7nBrpmjZw4EDXrcE111zjujj47rvvIt6nPsV0YvTggw+6/mY7d+4cLtcbNmyYGxBMWbfKZtFthvqrvt50q57oBEblKZNDt+SpTAWRdYJ27LHHuvdq0It33303/LnvvPOOXXjhhfaPf/zD3Uqnz1SWiCgbRJ+v96kPtA8++MDdqnfzzTe72/LatGnjllkni/qr5+XLl3eDVUyZMsV16dCjRw/3fn2Oz0KRL774wg1yprq555573C1+K1euLLR+NXiH1mnXXXd1g23ce++9dv/997uHbl/0y6k+8Iq7DUTLp1sPNb/WQf3VrV27Nvz6U0895W7fVDnqZ07roBO0a6+91k3Ta7odUoO66RZI1edjjz3mtpX6tNNgbspG8n3fXXbZZe59nTp1cuugetCy6ZbIaPoc9U+srCf1laf10e2SQao/3cKp7a911WeqXJWh2z3Vx50GK1E5yiZq2LChW1ftN+o/8Mgjj3Tzqo7++9//un4FlXmkWzdVngZF0cmy9hX1Raj+73799ddwIEB1prrQ+qrfPG2HoKlTp9oNN9zgBjpRuVo+BQaC203zqDzd3qm+/rS++n5Ee//9992+rPdq39Y+rn1d6619X3WgMlatWuXmV5aU1kH7tz5Ty6E6jbZixQq3D2i/VV1qXdSvosRbH8HvjQbH0bLOmDHD7Q9ar+D3piDF3T90wUjHDX03tTyax2eGqcxLL73UfV/U/6I+U+uvkdWV0RW8hVZZXEOGDHHzan/V+geXU8clbSt9hvqi1HLpc9RXot4b67uiwXb0XQneOaBjmbaZFPfYEYv6Y9Q+rvm1z2u5dZuyBsxRv5Dx0kCMuk1Z3zUdi7W8GvBHjjrqKHf7swYTuvjii11ZOu6ovk899VT3um6fVjBMxyhlxaketBw6lmj5guuh75jqWJ9z2GGHuRHXRcccbaeKFSu6/Ur7v68jbUvtf6oj8ceeY445JryfxrO9i1O21lnfp0MOOcRlHKp8PTQ92Idm8NiiwSxVT9HHdG2L888/35WrQaFeffXVuLaLMjkPPPBAu+iii9w+p22jYJf6CVWZfh/V90z7r+pLdaNjxMKFCwtdxnXr1rntpM8PHv+0z+r/mqbXNI/m1fbWe/UZOg4oIzL6t1K/q0E6Pup4VL9+fXec0PLrfToG+XLVx6nmU53rlnx9H7S+0UG+WLSsuhgcK6AYqy6VHatjtP+tzwT6vf7jjz/C/b56+q1W/SiwKfoNDXYP5VWuXDn8uvY77csFzaffBf3u+d/jgubzrxc0X7DMaPpeqR0CAMhgGqwLAJB52rRpozO80JQpU4r1vk2bNoV23HHH0L777hv6/fffw9PffPPNUJUqVUJffPFFeNoDDzwQOuKII0Jbtmxxz3/77bdQ1apVQxMnToz4zJEjR7pl2bZtW3janXfeGapXr15o7dq14WkXXHCB+7yg999/37131qxZ4WmvvvpqqF27dm5Zvd69e4cuuuii8PPx48e7982YMSPi8xo0aBC6/fbbI6bpeePGjSOmvfPOO+79+hzv008/De25556h1atXh6cNHjw4dPTRR4fiofXr0KFDxLQPP/ww3/oVZxvcf//9oS5duoS2bt0anvbGG2+EDj744ND69esL3QbaNzRNr4nK2m+//SLqVc477zy33p7K0vueeuqp8LTZs2eHypYtGxozZkx42lVXXRXq06dPxGepvk4//fSI5fBuvvnmiM+Lrn/Rdu/evXvEtJtuusnN6+vg6aefDn300Ufh11XnqnvvjjvuCM2ZM8ctw6mnnhp69NFHw6+tW7fO7SOffPJJvuW77bbb8u1fwe0mjRo1Ct16662hwug9eq/27SDt+8Hl1PKddtppoSFDhkTMp339mmuuCT/X/rjXXnuFBg4cGJ722muvuX1l7ty5cddHcL1U/96SJUtC1apVy/e9KUi8+0f//v1DBx54YGjlypXu+Zo1a9z30K9Hjx49wvNqHYLbWMur5Zavv/461KRJk9C0adPc882bN7vvTrA+gvWu75yn7VyuXLnQl19+me+74svSdrj00ktDDz30UL79Nt5jR2Fi1fmzzz7r6lzbryjjxo0L1alTJ/TLL7+Ep+mYcOyxx7r/X3jhhRH1Flw2rY9e90466aRQ7dq1Q5MmTQpP+/XXX93x6OGHHw5P0zF/t912C1177bURy6K6HTRoUL460vfC0/FFy/fKK69EzBfv9i5O2VdeeaX7TQrudytWrAg1bdo0dP311+c7tuhYV9zvbVGeeOKJiOOE9pfo79IBBxwQGjp0aMRxXb/h0cfiWMs4YsSIiLoKfgf1WpDeq88IKui3MijWb/OqVatCu+++u6vj4DF89OjRoXjpO9W1a9cCX9d+4I9NomOKlrVfv37haQMGDIi7vFGjRrnfoHgf/rexuLSP1axZM3TWWWeFp6muon//g22AF198MTRv3jz3/1jrpOOlXluwYEHo+eefd/8PHte99u3bh1q0aOH+f/HFF7v5oul7r+mXXHJJvtf+85//uP0RAJC5yIgFgCyjbB0N/qDReJU94ylrbe+993YZS0G//fZbOMNRmS177rmny/IKUhaqBPui3XfffV0m4KxZsyLmUzZNkH/uP0OZMMpCVTasHz1YlCWl7Dpl/gTnj/V5/rVgucFlU3aLsjCDnyPqx063Rwb7eFO5yh7RgCNFiS43WCfB1+LdBjNnznSZksoAC75fmXHKmlEGVHTZvjzd1ugzFP1rKlOZdMF6FWUB6lZIn6UZa3s2bdrUvd/fNqrMQGUwaUCQoKuuusplwGnQkmjBTNlY9eKfB8tVJuDXX3+db97ogV+Cr/lBUMaMGeOyrJUl6CkrVVmCWvZowX3Jf16s5Yve56LFu2+qjlRX0f0nKrP4oYcecnUsym5TNrOySj1lNmo9/e2x8dRHcLqvY2XsaT9SNlys/bew9Sts/9D3RZmOygT2g80oS0vZk5o31jIHP1sZvXXr1g0fE5RFpsxzX4/K4nzhhRfiOg4pky/YV2Jw22qfVIaiskSVIRvdn3a8x47i1peWS+v0/fffF/peZQ8rQ1DHQ9Wdp6xsZZH6uyJi7ae+TH2/g4MH6RiurDhP2dTaB5Xx57Nslb2qrEetq89g9t/H4MBAvixfntZJfZYrMzU4Yntxtndxytb66O4CZQB7yqr0d2cE7zAo6rtb0Pe2KNo/g+9RnUdvD+2Dc+fODT9X1q2+I9F3QMTzG1mS37+i1itWuRogS9+xESNGuLtO9BuhutVvZLyUia8M/XhpP1cWte4OKOq7EYsGo1LWeLwP7WclobsSdOdG9F0Ase4m8NOCr23vfNHTC3oe6/3KTvZ3jAAAMhOBWADIUDpxlSVLlhQ579KlS/NNizWAl26X/eGHH2z27NnuuQKAuiVbt63qlkWddChYp4HCCqPX1WWBbhHea6+9irFW5m5F1fLqJEzdIviHTsJ1i6UPFm4PrcuNN94YMU1BT3WloMBxsFzd1qzbrnVrZqIVtQ1efvlldwIfDMB4mhYdEA/yt6UH6fZF3WYapACfbm0PBsxi0e2ver9uSxedoOtEXSejQTqZV5Ao1rLFG7jydBun6l+3DAepq4Lo/vmCdKuouojQexX8UwA/uE0VjPXBwHRSHao7BN1uHaSAlIJLvg41uIuCacH6Ux189NFHLiAbb33EoluQVb/Ry1Bc0fuHjhkKAgS7TlGATbdG6+KGqGuDwqhrB9Ht0DrOLVu2zAV3tcw6LhV1HPJdk2hAw7/97W/5XldgV8cUXZhQ8Kekx47iUvBXF0S0XkWVq+OebhWP7oJG328Fq4pTj4UddxTYVkAx2PWMgmI6Pvj+JLXc6uuyoBHsdeFNXZ9of1OgOVpxlrM4ZRe0PhLsliJZdFFMAcrCKOCqALW6Lxg8eLAbiEmK2ofTTfudgurqjkPdPagrieLWTTzdEgSpnaHjmS6gxer+Jd3GjRvnuqt57733Ira7LmZFd8sj/mJC1apV3TyyvfNpHl9mrPmCnxVN24PBugAgsxXvkjAAIGXU96FOmtVnnE5+C6OgqPo4LEq9evXcX/UnqACRPltZTcrEU1+CCqQUFvxTH3jKxPzkk09cME4ZSdHBNwUVNF8wABrkA8vKGFJQMkiZa7ECQMGMG581V1hdKGsmemRjX64ClT6Y5BU24nOiBbeBspElVoBUAcZghlWQTvKVLRcdJA1SH4gKPP/nP/9xmYCxMoN0oqm+SdW/obKhFPhr1aqVe03LVlDgtqBlixUwKYyCw9pv1f9jUPPmzQt9n4KTfptqWRK9/fS90D6swJD64NPFBgUNooOZ6p8xmNWlfd9fQPF16Je1sDpU/5EKthYk3vqIpkzjYB+HxVXY/qFlloKCdvEst39d21FZ4AqoKoCp766m6YJFLKp39ZH8448/uos6Wq5g5rmnbG5dZNEFCx3ngpmjxTl2xEvHKtWH9h/tKwpK66JAYRJZj/EedzwdP3QMVmBMF9UUfFLgOhZdIFOQXK/feuutrl6VaVvS5SxO2fGujygLVd9dXVjT90/LqDsQCrsIVRT95hS2fXwGpTJ8tW9q8ExlJAYz3GMto/fll18W+h0M9ner9xa0T/nfSgW4ldmtfb5Dhw5Frp8y9HUxS98ltQGKq7jHfV0k0wUPfS8VuM4kOtapb2XdyaB+dYN0x1CsPln9XTzapxW4VfDUT4ueT4FTzaPPCr43er7GjRuHy/TTgkHhYJnRCusDHACQGQjEAkCGUsaQTlYUGC0qQyv6VvSC+MFDdIKhIJOCH7qNO/p2UE9ZmwrYerrVT3Rb6NixY91t9gqiKYDi6cTAzye6/To4sIo/uQkOZOJp5OLowK4CiMHgVvAENlZWkk7Adat29K15hZUryswpyUlocQW3gb9VVydV0cEfBT7860E6EdQgKgqKFEYn4XrcdtttLtChIIECuMEMGmWVaeAyUcBNJ+0KUqvrCJX9zTffxPzsWMumk//iBDt8AKaoTLPCqA51wqzsoOjvQPT2VDZgYVml0Vlifh9WlqYGQdIATxr8Krh/avk1UJIXHVBWHcU60Y6uQ/0tasC44vIDoRX2fSlKUfuHaLn9CN4lpUGuVB/Dhw+PeSyLPg4F613BNmWeKntUA7AF6UKRAmjKOj3vvPPc8SFWBllRx454BY9VChQrIOcHoytIsB5TcdzR4FdB6hZBt6LPmTPHDbykQaRi0XZRdxra/xX4Vpa1ju3bc8yMt+zirI/uJPDfXX331KWBughRcLykYmUtBikrVwOR6W4PbfNYmcTKUvQDLgWXUXTHwqhRo4r8Doou/BS0fwb3P2V16rilQRyD749FxzYtj34rFJBV2yNe+t3SsaG4NOik7gRQvRWnKwTdZaALXvHSb5IyleOh76CCwzqOBwP92td0MeuAAw6IOdimLhrpt8XfHaT59LsRa77999/f/V/dh+hiTUHzqW3mP0s0X/C30l9Y9p8XpCB8SS8mAQBSg64JACBDKatCJx3KatTJVEHUN6JvrAcpczWaMrQUZFKWkDLcdJKhzKZYGVqiIEJBFMBVBo1uaywO9SOoYK2yaqPptnKdRJSEAm06idRo07HoBFUZcbHK1e2xqo9EK2ob+MCRMvui6YRb/VpG04jkykiLRSdruqU7SCeICngoYFnYSMoKFOjWYdWfgng6Oda2CO4P/lbb6dOn51s2ncxrn4iHsroUJInVp2RxKNCsgOtnn32W7zWNUC4+g0mjvceTORhNt/wrwKH6K+4+ojqcNm1avm4v1D2G6tjXoQKLyooL9pfpl93f4lxcWn99N4ubrVac/UP8qPdBwQsv8dB+qYBTMAgb73FImWO6TT46y118FqMyXbUNlBlekmNHSShAouzEWFn+QQrSKps3Vj3quB/rGFLS445+U3QnQpC+swoGKzu9sAx7ZWH6/kn1u/TTTz+5vo23R7xlF7Q+WhZ9dwqioLsuGvhs7pLQ97eobk60/ypIpgsCsfZf9WMdnbmbijtqdKH04YcfLnQ+HYvUxtC+r++t/kbfxVIY/Y6VJBCrbaf9WxdfY+37BdEFAGUax/uINwir3xFd8FYd+CCs6Ljsj986Xiso6/tZ9hSc1b7sA+36XYp1EVPz+X6VNa/6kI+eT5+vIKvfr7VPaXmi59Nn6U6IWHdLaXsEL14DADIPgVgAyGAKfCg7QxmoyhaKPiHVbee6/dj3lxekAE7wNn71R6rMspEjR4ZPoHSiqqCap5NVDY6iIJne6/udjNWPm07E1eAPZmTopCr6tjhNC/5VYFC3UGqgJT9Ikz8B0UAwvhsCP3+sz/OvBacpOKQApQ88RZcrOvHT7c6+X0LR+7QcsTKZosUq2wfPoqfHsw1Ud+oSQpnPwTpWBpcyeXSLZHQ9KuDkT/ii11G3uyvoFL0syuRU9o3P2Im1Pf0JsbqcUMBGmWTnnnuuu+U2SJmHOjkMnuAqYKhgR7C/YL/dosvy+4gyoqLXLVYd+jqOlZWmoJKyt3RrrYJpngJFJ554ogu+KlCn74lufQ5mr/rlii4zVt3oe6HsWx/IjXffVBDowAMPDPf16d+jANYFF1zgTsRF21mBzugsZw04FiubsqD6CC6btl2w39hY+25B4tk/lCGmwIW6lggGYhSUipUh678nsZZbGYLB45Au8ChwpaC/6ssHaGMtl+bR9zf6OCQ+gKIgmb4XCrY+99xzJTp2FCbWcilYo9ucY2WsBem7rMGS9J0PdsWgLEp9r6K7wyisHoPH5mCXGVoOlaGM4+i+hBVcVf+gysj0fftGUz0ELyYoyKPfIwVQCwpwxrOc8ZTtLzAp8BrcP9SHri42BPvXjrUddAFF2YEK1kfvF0XR760ufEV366J9MliWlkG/XdpmnrKF9T3Qb6n2L/+diOc3sri/f7HWW/uOtrsP1EVvQ1HAT8chf2FP/9cxUtnj8daRgr06zhZEQV1lPMei3wv9/mUCdWOi/UpZ3+onVw9lCev41rJly3AWqy5qqg/r4Pdc2cf6DffUjY32be17ngZD0zRl/3v6HVD3U8FxAPTZ+k3z7REd+/T7Efxd119dtP7nP/8Zc4A2XeiMpz0DAEgfuiYAgAynjFWd6GhQGgVcFRBSppduCVXWS0FZXgpSKbtD8+kEQycMClj47B4FXBWgUqBNJ5E6WVXmhQIV6qtPwSFl1CmjRrdOik7YNJ+CJMrE1YmEgsQKNqpvR/VnpxNPvVf98mmEdQU/RNkp3bt3d+ujALMCNjqR1km91kcZVz57TCfm/n0KsulkXQE1LYcyjRTQVABDJ3EKCOi5Tvx1sqKsFc3js4h1wq4TZJ1c6uREt8Bq+XRipKCEgi9Fdf0QvX46OVNAVOvnR1UOrl+820BUB6pznbxpHRXU0vwKevlb/X0QSRSwUSat5vEBXZ2UqasJBf4U0NDtlQrg6IRNGU+qG50U6vZufa7/LNWblknrpO4OtFw6ufMBKX9ru04etW/ohFEZbDp51Ami1kkD06iutc+orj0FAfz6KcCk7aJgkG7ZVlBCfQOqb1ctg6aLgrsKBvjgo/Y9Ba61bMr01f6nE3/14+mpDjSfAo86sdc66/0KgKpOFJBVEFZ96mqfj96//HbThQlN0wUBfS+0f+n9vk/GCRMmuEC31t1/HxSkUZ2oPAW5lKWkgKICqvpeKgCok3R9T6666ip3kUEBEi1LMBCtIKyyerWvq57U1YcCMFqnYP/IRdVHcL10AUfbSN8v1VH096agTNni7B9afwWnNVCW1kkXdtSnofZlTxnoCkrps0TfZS2zukPxGavqV1P1oTrSPqrAktZF3yXNr/1a29Avl+pat5qrOwP9VV+j/rb24HdFZSiooQsHPtCrQIr2QQVOFHSJ99hREAX9fXmqG32H1U2Hjo/av6MvZMSibDptLx2HFOjWfqoASzBI5Qf3U12KjmE6lukWZgWIgrTva3vpeKVAtTIedfGpoP5CVce63Tu4r3n6/qpc1YOCU7rNXd8z1ae2U+fOnd2+7rNj49ne8ZbtqfsB/UZpe6hMbT8dj/0dBeqeQsdATdf+qvkUSNSxT8dTLY8yW7Xf+HXS+qjsgij4pe5cdAxWWZ4u+Oi9Wj9tI62XtrO2uY6TCmTquKF9Seur7a/vg5ZD+4eWUcFJHSP0e6cydGEyePxTtqiO86Jl1rpoH9SxVhfVFNjVb5D2ZR2X/P6n9db3XbfYqzsOHU/0vdLt/9oX9H3R8U6/XdrfdXzXb7nqR98h/V/7mY55amvouBirb/Ggww47zL0nuh9d7Qf6Tup3VheU1E2Cyozu0kbLmIy7UYpDyxjcxkFqIwQHI1OgVPuPvpuaroseGrgw2D2Tfnv1e6vvqLaDjuXqTkJtnuCFFX3X1a2F6kB9b6v+9brfTz1te/3e63umY7D2Ze1r0YNceiqzsExxAED6lQnRozcAZB2djOmEWSdgSI9c2AYKAigYqECCAmjR/UUqgKdAmepBwZJE3SYPIDZ91xT08QHbeKi/b11gC2aMp0pRZev4qQsfJe23tyR0MUcXFDTgobqzie5bWoF7XRzVBQIF5TQAZGmTyD7RFSjULfcKyiO9FLTXxTEFt+knFgAyF10TAACAElEGjwKxyuyJdVKvDEllQimzShljANJPWb/KTgxmQacqCJvOsuOlY5UyU5VJHWuAP2Wa6+4KZaWmuu/XREnkwJS6rT94uz7SRxnWukOBICwAZDYCsQCQhZSxEz3wD1IrF7aBbstXX6FFUf+MwX7wAKTvuKOB9XTLum4nV8Z6PN/hRClu2ek4jnJcKx71g6yLceoOA+mj/phfeeWViL7lAQCZia4JACCLqJ9J9SOnk12NxK0+Sn3/c0iNXNoG6m5AfWIWRX0Wqt/DWH1EAth+6q+5b9++rs9J9VGqPiJ1W78faChIWZx6zffPHe/I8okQb9nqHkD9YKq/Zr1Hgwdq/dQnabIpSKzy1cdzoo6B2U79z6qLgpdeeonjfJqor1n1A63+fgEAmY1ALAAAAACgxObMmeMGAFOXDUgtDRyqfpTVpQYAIPMRiAUAAAAAAACAJKOPWAAAAAAAAABIMgKxAAAAAAAAAJBkBGIBAAAAAAAAIMkIxAIAAAAAAABAkhGIBQAAAAAAAIAkIxALAAAAAAAAAElGIBYAAAAAAAAAkoxALAAAAAAAAAAkGYFYAAAAAAAAAEgyArEAAAAAAAAAkGQEYgEAAAAAAAAgyQjEAgAAAAAAAECSEYgFAAAAAAAAgCQjEAsAAAAAAAAASUYgFgAAAAAAAACSjEAsAAAAAAAAACQZgVgAAAAAAAAASDICsQAAAAAAAACQZARiAQAAAAAAACDJCMQCAAAAAAAAQJIRiAUAAAAAAACAJCMQCwAAAAAAAABJRiAWAAAAAAAAAJKMQCwAAAAAAAAAJBmBWAAAAAAAAABIMgKxAAAAAAAAAJBkBGIBAAAAAAAAIMkIxAIAAAAAAABAkhGIBQAAAAAAAIAkK5/sAgCk1m+//WZXXXWVzZs3z77//ns37bDDDrO6deuG51mzZo2tXbvWzj//fLvyyiutXLlyaVxiAAAAJAttQwAAMkeZUCgUSvdCAEg8fbV32WUXW7FihW3atMnKlCkT8frHH39sxx9/vB1++OH29ttvW+XKldO2rMh8EyZMcCdw5513nns+a9Yst99ccsklVqlSpXQvHgAAKAJtQwAA0o+uCYAspcZ1lSpVXEZDdENbjjrqKPv73//uGt133XVXWpYRpcfpp5/usmR+/vln9/yGG26wa665xp588sl0LxoAAIgDbUMAANKPQCyQw4499lj3d+zYseleFGS4fffd11q0aGG77babe96hQwcrW7as7bXXXuleNAAAkCC0DQEASC76iAVy2KpVq9zf6tWrp3tRUILbC2NlsyTL6NGj7Z///Kd1797d1q9f7zJqdNuismdKg1TXVzajLgEge9E2BHIT7bvEoS5RFDJigRz27LPPur99+vTJ99r8+fPt4osvtm7dutl1111nAwYMsCeeeMI2bNjgXlcArmXLlnbjjTda//79rUGDBu4H5/LLL3fzHn300RE/QFu2bHHl3X///XbHHXfY3/72N7vsssts6dKlEeX+5z//ceXqvToJ0GcNHDjQLWPHjh3tjTfeCM/7+eefu2WrUKGCe+j/n332mXtt6tSptsMOO7iszZNPPtm++OKL8Ptmz55tjz/+uN1zzz120003uf7Qhg8fXqI6LKqePE0744wzXFapbve7++67Xd1p+XbccUd3q79u+//3v/9tp5xyilv/WrVqubr1fvjhB9tjjz3ce1R/CxYssBdeeMHOPPNMN3/Dhg3t1ltvtV9//dU++eQT6927t6uDihUr2vXXXx+uG/n000/t4YcfdnWrLgZOPPFE++ijjyKWecqUKXbzzTe75ahdu7bNnTvXzjnnHLcdlSF76aWXunJPOukke+yxx9x7Bg0a5AYA0XQtq+pj3bp1NmrUKDv77LPd9MaNG9vtt9/u6u6dd95x+4GmazlVh9qu0V577TVXtuZr1KhR+P3PP/+86zZB0/XQ9pStW7eGl2Offfax2267Le5t+tNPP7n60nbp27ev21b/93//F97Hta1VbnH2J9XBnXfeaW3atHGfceihh7r5ZdiwYa7+NX3PPfd082nQlGCdaVn02domfr9XfQW3bXB76T16rz4j2ldffeXe49/vvzcTJ050A7Romt7frl0727x5s3uP9lVNU9+CvXr1skWLFsVdnwCA0oO24fa3DdUO0zpfe+21bln1/yFDhri2iaf11W+7BlHT777aBVo/lavnuvBdvnx5102Epwvhek3LrnW/5ZZbbOjQoW67BKn+VLbadyr/6quvduX9+eef4XnGjx9v++23n9tGhxxyiH399ddufn1u165dXdvqxx9/jPhcLZ/uiFIbTO0jLZ+2h9pYan+o3ecv0Afbg75ts3HjRre9u3Tpkq89+OKLL7r2Wr169eyss86y9957zy23lufUU0+1Cy64wNVrrIDXyJEjXX1pn9O2U/t08uTJEfOp7X3wwQe7ctUHsm/bqSx1vaUB7aLbbSNGjHBJCFpG1YfWWdOD1IY74YQTwu1w1cXChQsj2rc77bSTq59ffvklYv/Ua1pWzav3qBy1cTVdbV7VSXT7UXXq249BS5YsiXi/2oB6v9ZLZateNb1JkybueyA659A21FgPqoNY7e+CaL/T91l16s9rzj333HA9aN/U2BLBdvzgwYNdnffo0cPNq+9cULDOVD/ah7VM0e3bK664wiWDRNeZvjvaHtH7n7a3jhfR20vv89sr2n333Rd+v/5q/UTbYv/993fT1Ye2P/bMnDnT6tevH16O4HkCEJMG6wKQnZo0aRKqWLFivukbNmwI9e3bN1S9evXQs88+m+/1KVOmhHbZZZfQ0KFDw9Pmz58fql+/fqhXr17u+dFHH+2meRdccIEG/gtt3rzZPd+2bVuoTZs24ddvvvnmUO3atUO///57eNqFF14Yat68eWjNmjX5lkFltWvXLmLa448/HipTpkzoP//5T8T0ww8/3D2Cvvnmm1CrVq1Cn376acT0LVu2hBo2bBg677zzwtMWL17s1vemm24KFUc89RSkurn99tsjpjVo0CB07rnn5lvG1q1bh/bff/98n/Hcc8+FbrzxxnzbU3Xfr1+/fPMfdthhoSOOOCJi2s8//+z2C9Wn99FHH4XKly8feuutt/J9hpYvuG2D20/TZ8yYETH9vffec9OfeOKJiOmbNm1y02+99dZ8ZajOordhNP/+6PWXSy+91L2mdfMeeeSR0N/+9jf3vpK44YYbIp77fTz4ecXdn1Qn+gzVUdD06dPd9BEjRsRc5+h9RFRf0ds2uL2KWm+9/+CDD843/aWXXnLvV/15qtc99tgj4vsLACh9aBsmt2349ddfh3bbbbfQ999/HzFdbZeTTz45XBcDBgwIvfzyy+HXR44c6erq/fffD0+7/PLLXftM1q1b59p03bp1i/h979ChQ2jPPfcMP58zZ46rv3fffTei/Mceeyx04IEHhlavXh2eps9RfdaqVSt01113hbZu3Rp+7aGHHgpVrlw5Ynm07YLzNGrUKKIdsmDBArcPRLcHo9s2X3zxRcz2oNaxadOmoV133TU0fPjwiNeuv/56t6/88MMPEdN79Ojh9hltw2AdNGvWLPTBBx9EzOuXR3XtaX20n2ib/fnnn+HpZ599tluWjRs3uufabscee6ybN1hWsA13yy23hGLts8H9ytPn6LVo+oxYbe6C2o8FvT+4LjJ16lT33e7atWt4mr6Pu+++e+izzz4LlcS0adMitu2sWbNi1oO+o1WrVo34Ttxxxx1uewaPF0WdE6h9q/OmgtZZ2yHW9g7uw0Vtr1jvf/vtt/Ptp/vuu69bFr9/yFlnnRVxfAQKQ0YskOWUbaCr4P6hK5S6sqzsx1deecVdYY6+sqyrqM2bN3dXxj1dxV+7dm34+d577+2uonvKLhBdWRVdETzuuOPCr+sqvJZFo/R6/fr1c1cQldUZzWcsBOmKvZYvOnNT8+rhKXtAVzL/+9//2hFHHBExr7IJ/PJ4devWdZkWysgIrmNh4q2nINWNBsiInhZcdtE8ygL49ttv7bvvvot4TRkLPuvTUwaj6OpxNE3z28TTaMkqM5i1qwwGf1U7ml++4OdMmDDBZVBGTw/OH71esT4n+Fr0/AUtR6xRnHVlXv3VXnTRRW4/W7lypbuKrivSRX1uQaJvKfL7Y/Dzirs/FbduCprfTyuoLgt6T/R8sepSWRgarEVZAj5DRJk848aNcxmxAIDSjbZhctqGyvhUNqkye9u2bRvxmtpXapf4zDxRZl5BdRX9uupFd0YpAzm4XmrvBCnT8IADDoioZ1EWodYzuP30Oc2aNXNtRd1RFaxbtUNbtWrltrvvrkKfGZxH/w8urzICtQ8EPz96nVRH2g7R00XdXilLVg9ligYpI1Gfp7r1mcXKWNadP8q0DLavNZ6B1leZ23/88Ue+5Yleh4MOOshl2wYzNFVX2i+1f/plVUbspEmT7P33309IW62g+WPVTXHadhLdvtO2VDaouhpTJqzozji175QxWhKqn+C5R6x2suj7o+9o8Huucxntu1qGWOuQiHZvsupS++nLL7/sll+Zuv5uM931GPx+AYWhj1ggy+nHR43saLq1WLeUq2Hz9NNPu1vJfENVDb1gQ1HUKNIPjg9O6fahogTnUaBMP/bB4JYaf/L7778X+VlqHN97773u9ibdxlWQJ5980gWMdCIRHfSUatWqueBSdJBNy6KG3bJly9w8RYm3noLUmIvVgIhFJ0H6cX/00UfdOok+V40c3eIUi7+NvChHHnmkrV69OmYd+OBqYXTLmxq+5513nrtFJxPotiptczWmFUDUdvzHP/5R4iBsvBK1P2UafV91sqETOt1CqFvytG8DAEo/2obJ+S1XkEsBPd3KHE0BqwMPPNCtr4KeChbWqFGj0M9T/WqZFBRUAFZdBtWsWTNinu+//z78f3XBoGCvgpWxKACtulCdKdAcbEPFarfq4rYCS6o3BUaLu31j0a3pCpKqrgoS6yKxllFtkgcffNB1q6CgsILkumU/uk78uioIq/1Y3TEVRF0YvPrqq27/0Wd5b775Zr5+Pouzb2YitdsVRNZ2VbcL6s5gewKH6jYhnu+F6l/dMQTrUttT3SWU1rpUYPuRRx5xdalzj7feeivcrQsQDwKxQI7Sj58aJ+rnplOnTq5/UvVVpSxMUf8+0YI/oBdeeGGRZUTPo8as+nxSn1y6mljUj7caxQqmqQGqDEw1WvXeqlWr5ptXn62rq/r8OXPmuPc2bdo05udqPdT4+Ne//uX6PVX/p9GZp0WJt56ClAXgs1eLoj63FIzVNlLmgPpFeuaZZ6xnz54x51fWhIKo27ZtC1+RVsBU2a96b6xlVJ9L6mNJQVnVgTJQiqJGqU4g1PdSrKvY0X26FaeB5be31kHvU+Ba/d+qD6p4qAGtk7pLLrnEnWTE2jbFEW8n+yXZn1566SWXleNpOxXVb63qJrq+CguO+vl1IrJ8+XJr3769a4THczHAX+1XlrROFNWABgBkN9qG29c29H3O7rzzzjFfr1Onjgtcz5gxo8D2XJD6cJVvvvnG3cVUVLsmnvLVxtLnqW/6oigDWnxfsSXZvkEKvipT2Ac0iyu4PApqq916zDHHFLiu8uWXXxbYPtX2VmBS/cCqj9RoqnO1kxXsVlsoevyHaBp/Ibqt5rOJY9Fr0fPrM+JpPyoIqrEi1C+vMrcLStKIpgQPBev1nu3t7199qyoLOl66oKPvrNqhqs9gBnpB3/PotnBhlPEbPOeJ7oO2oO2lTF31U6zvvL6XCrLGQ/N+8MEH7gKBvnvxJtsAwt4C5DANuKRGjRqE6kRdASx/u48aaomkRoM60tdVwwceeMB23XVXN72wDAYFmfwtH6IfO93ypJMEDRYQpEwNPwCW1kvZAMoMiJURqYwOXVHXlXQNYqB5FOQsTsfqJaknBcRiBUULooGTNAiW1lcd1qvhGWvwDN+41ZVZXXVWA1zLpb/Rt/D5W4S0rdU4feqpp8J1qROtohpluv1LneyrsVIUDSYRfaKhW+vi3d66sqwGtgYlKOx90cFYdb6v7avlLOmt9MoujpU1E0tJ9ic12vyAFqIMGp0IFET7fbBuRNk9hQnOr5MInVQrq0VX7WPtF9HUuFZmy7vvvmsffvihez8AILvRNix529Dfxl5QPemCfHC+eMVb/4kuXxffJRGjvysopu61FPiKNehWcZdne9Y12D7V+xWUVDv6ueeeC2cpq62kgaPUXZPutNIFCS13cBDdWHecRbfVNABcQZTJGz2/gr3K+I2n/aiECw3wq/a/2vTxtHl1sUMX2fXdVlJFrMz4eCmhI9iWLcj06dPdcitYrPrwgXh91+L9nosGGQsOXhdNmda6MONpXmX+xrO91O7XcUcXCtS+jme9RO1ktZF1nqKgfjzta0DYU4Ac5xujyooM3lJUUCOpsCu7BVFDQSNI6kq++vzyDW3foPKKyshUUE4/3mpARS+HsjfUAFeAUKOEKqgYK3inW7vUoFKDX1ft/foHl0UB02CfUrGUpJ6UYRHsO60outVODQFd4VU2x7HHHlvgvMoEUdaHAqXKWNUoomqQxLpdS7eXvf76666hETxpCdZBrG2hK+hqsEb3rZYsCqTqNjxtr6K2h9/PFLhW0FG3tSmjOHofi5eubKuhWpRE7U/JphNQNTA1Iq3vG6wwWn4F/zUabOfOnV2mSPQo1gCA7ETbsGS/5b5/1ILuBlIGptonPrMzXq1bt3bLVFD9K+ioRzzlB5czngCar8ftoYxDBbq3J+gXvTzKeFXgcXvXVYEz9d+rTOhevXqFuys444wzXLtbwUIFYUuybyab6kDBVGWP6m88dMeYLn7o4r8uUihDtaR0t1VRCSbaL/U9VxLI2LFjI7KhizrvSCV9v9RtiJapoKSXaKo7ZearXa27EpU4AsSLQCyQw3Qlc9q0aS7zz9+ipAat+gRT30ixFJalUNgPlRrzGsAgeFVdgckgNZKLogasbsfRj39BdAu2GnvKbNAtRUFqBIg68A8KLosyT4u6/SXeelJ5ugXMd+QeT/9aQWoUqnGiwKpu008ELZMCc+pUvqA6iLUtlBlSWD9byaDMBAV/1T9bYTSPAodqWOqKu26r15VwNThLYsyYMRGDZBQkUftTKvgsD39yUhg16NWNgbJiR44c6fq2U2C8pIFtAEDpQNuw5L/lyp5U0DdW1p4CUcpa1GCYsfpALer3WxeXlUG8ePHimP2uaroChwraxirfD2imQdmCWYO+e6TozFI9VzahEgii66W41JWVkgXUL2g8Ym1HbV/tCxqITMF17TPa75QlGSsYq6xSZX8q27Uofnv47a2L1sqkLWx/iHffzKS23SeffOICh9pP1DesLl4oU7Ww701BdIzQHWhFUfcmuoBw2mmnRXTPpgzU4F14mVCX2j+1jPHUpeZRoowu4KibDGV66/iijHsgHgRigSylBpf63inolh3dcqZbtPS6GqW+g3r1b6NO69VXjm7Nis4YKKgx5vv58SPPRmd26kpjsE9MLZ8CPApMqgGg266Ct4rFGnjql19+cZmhCpAFr6hqXjXQgpTxoECjGq5a1+j+toLLogacHx1ey6KgX1G398RbT2rgqKGoZdTyR/elpOmFDbKlQZL0Hl1NDo5MWpwshOjPVx0ouBtseKlxrqxanRRp/uCtNf79Ws/gyZKfHv35/nlwdNTg81jrq2nR+6oyW3Wbj2670gmgf1/0ttbn6tZJZZj4+tUoyqp3BbCL6m8rmrpEUJ9b0ScMfvmC61Xc/am4dVPQ/H5aQXUZXF5RlpD2S93qpZNeP190XYpOvHSrp/Y5UbbDQw895E5MCus+AQCQ2WgbJrdtqMxJ3fKtboCCbQ/VZ+/evV0XDAVdIC6srkR3PKnOdEt9sG9NXexXHen3XdtJt9gr81fbK0iDq6r/XdVvNNVV9O+7AksK7ir4Hqv/Xb/MBS2v/1xRffuB34LTC2r/zps3L2LgI+0XSgTQ8mv9fBtVt5UrsKw7wILtJAXNhw8f7rqVCAYLCypPdeu7BBMlK0TvD3qv+pZVP8rxtu2KaqsVNH+sZY3VttP/lcWt4KHP5i2oraxkEH23ddecp31Bmem60F7crtbURcPll19eZDtZfTLre6E7zYIX89WuVFvdn4tEn3eUpN1b3LZ19DorsLpmzZrwuAgF1aW+F7pIpa4rfDdmSgbRfqOLMdxBhnjQRyyQZdRoVABKjRj/Q6AsgGAn7urcXR2sq0GoTtt1+3eQ+sdRw06NbV01VeNJjTvdJq8GT/AHTrfz6AfJ3+6sEdZ1tVrBHt+Q0dV5NZI1rxpjaijrvbo1Rp/pG5W6kqhbg5SNqOXTD58aDAr+qfGvwNwNN9zgrj6KGrlq7KpxoR93NaDUyNCVco34riv8aiyoDyBlIOi2KDUcdOX7lltucbfYa1AsNWDUZ5EamloW/Yj6YFVh4qknvabl1BV5Lbun9VSdaT2VmaEGphpC0Rmz+jwFxPRjXxzKBFBjVXWj+rv66qvtrLPOcvuC6lf1qnU8/PDDXSNCQUzVpRoWvnGhq9jqc00NT1G9a5RarZ8afhowQFTvGmVZjRBtQwXsRPNocAyVpTJ1m7uoca1MAzUaNRCG397KFtFJkhpj2nfVkNaJgObTbYvqTkHUh5fer/KUpTtq1CiXEaEMD53oKHCo/UXLrfm0bNrXihrxV1fjdQKlbRPcz4KDiYgykw899FC3XvHuTzpB1Dy+DvReXTXXiZEGGFOZohNf1ZlOLnTy479XWhetr/YjDfShdY7etlpvjYasfs1E9abMHM2vLB7Vg+pXdaWTE71fjVDViy4Y6Oq+unZQxoSySPQd0v4hOqERvV997ul7pi4LAACZj7Zh6tqGHTt2dOXozhK1ofR7quVWd0cK9ERfVNfvq7KQ1WYT9Wer32G1M3QxPpj5qCxitYvUptCAQlpOXYDWRWdPwVptJ912rval3qdtofpVYNFnUAbVrVvXbW/Vo5ZPGYxab7XRorMe1T2A2l66fV/7kgJpqkcthwJY/jb+YHtQQWFtN7Uh1Ab0QeJgezA4+Kj2EbVp+vbt6/Yxlak2ntYruM/qNe0X2k5qx2gZtM+oLO1bPsAu2j983/oKQCohQbfNK5CvJATtq34/1iCx2h4K0CprU1nB2jd9sE37ifq81TZWQNG3u/z6KNtUdad2q94fbOerLL9/KtCnrjD0/dCyqj2o9q5of1MigvY5ZTz7dqK6SlCfpFpHXUzQfqsgp4LCKkPtQ9E+r+4V9L3TcqqetGxq5/lb77V/ar3UxtR3TvtbYYOzaj/S/qe2sroU0T4WpP3Yf662g7aJ9ntlaKv+tS7aP7UcWjedU/jur7TvKTNdbV99X7Rvab9QPWif0jZTPStQqrawMmy1bsq29tm0Cshrus4FdJwKnr+o6wDVo9rZfntp+6gdrDpQmTo+qp2uelD73GfK63ij44r2X9Wx5tF+r64MfF+yOu/S9tD21XdX5woqN9b3DZAyIe4zBICMpcaBGnSFdfafyxQgVeA2EQNJqDGlBqMa2mrQRne4r59LNULVMFNDVY2tRJSbKXxmAAMNAACQGxRkVqCspANoJZoPbBU2KBPip7ar2nfxDkBbGG0T7S+6mK/b8WN1saEMaQVkdUFBFyWCGbjZct6RiLoEONsCgAyiW8J1xdzfdqa+TnVFGbGpMZSoYKg+S1f6ddtZrGCkytFgIuoGQVkTyqrJJlpngrAAAADZQW3XRAUOlYygO7aUPV1QP8earsHUlHkbqz/j0o4gLBKFMy4AyCC6jUu3D+l2ct328vXXX7vbB5F8um0v3kCkbtnPxgYmAADIHbrwv2HDBssURfU7i/RRu1ddd8RDXZcoexRAbARiASCD6JZ39Yf0yCOPuAEdlKGJ1Ny6pVGh46WGaHAAEQAAgNJCfWaqr1n1jaoAmy4wK4sxXdRfp9phSkBQ/6mdOnUK95uLzKB+enffffe451e/zABio49YAAAAAAAAAEgyMmIBAAAAAAAAIMkIxAIAAAAAAABAkpVPdgG5aNu2bbZw4UKrXr16wkbzBgAAQF6fzmvWrLH69evHPcAe8tBGBQAASG/7lEBsEqiB26hRo3QvBgAAQNaaN2+eNWzYMN2LUarQRgUAAEhv+5RAbBIoy8BvgBo1aqQku2Hp0qVWp06dlGSGpLo8ysye8igzu8rMhXXMlTJzYR0pM3vKW716tQsm+vYWMrONyr5PmZSZWeVRZvaUR5nZVWYurGMulLm6GO1TArFJ4G/1UgM3VYHYDRs2uLJSdaBIZXmUmT3lUWZ2lZkL65grZebCOlJm9pTncWt9ZrdR2fcpkzIzqzzKzJ7yKDO7ysyFdcylMsvE0T6lYy0AAAAAAAAASDICsQAAAAAAAACQZARiAQAAAAAAACDJCMQCAAAAAAAAQJIRiAUAAAAAAACAJCMQCwAAAAAAAABJRiAWAAAAAAAAAJKMQCwAAAAAAAAAJBmBWAAAAAAAAABIMgKxAAAAAAAAAJBkBGIBAAAAAAAAIMkIxAIAAAAAAABAkpVPdgEAAADptnWr2YQJZkuXmtWpY9a+vVm5culeKgCl1dZtW23C3Am2dPFSq7O+jrVv0t7KleWgAgAACkcgFgAAZLUxY8x69zZbuNDsgAPMvvnGrH59s6FDzbp0SffSAShtxkwZY73H9baFqxfaATUOsG9Wf2P1a9S3oScMtS57cFABAAAFo2sCAACQ1UHYrl3N5s+PnL5gQd50vQ4AxQnCdn21q81fHXlQWbB6gZuu1wEAAApCIBYAAGRtdwTKhA2F8r/mp/XpkzcfAMTTHYEyYUOW/6Dip/UZ18fNBwAAEAuBWAAAkJUmTsyfCRsdjJ03L28+ACjKxN8m5suEjQ7Gzls9z80HAAAQC4FYAACQlRYtSux8AHLbojWLEjofAADIPQRiAQBAVqpXL7HzAcht9arXS+h8AAAg9xCIBQAAWaldO7OGDc3KlIn9uqY3apQ3HwAUpV3jdtawRkMrY7EPKpreqEYjNx8AAEAs5S1DjR071iZOnGjNmze3WbNmWdu2be3cc88tcP5QKGRPPfWUzZs3z5YsWWJTp061Sy65xM4555xif+6kSZPstddes9atW9vChQutVq1a1kejeQAAgFKjXDmzoUPNunbNH4z1z4cMyZsPAIpSrmw5G3rCUOv6atd8wVj/fMgJQ9x8AAAApSYQ+9lnn9k999zjAqJl/nem1LlzZytbtqydffbZMd8zbNgwa9++vV188cXu+S+//GL777+/zZ07126++ea4P3f27Nl24YUX2g8//GCVKlVy03r37m333nuv3XTTTZZpNNLzhAlmS5ea1alj1r49J5RAuvG9BDJHly5mo0frt9xs4cK/pitTVkFYvQ4A8eqyRxcb3W209R7X2xau/uugokxZBWH1OgAAQKnqmqB///7WrVu3cLBUevToYQMGDCjwPUOHDrUnnngi/HzPPfe0008/3QVeN2/eHPfnDhw40E444YRwENbPM2jQIFu/fr1lkjFjzHbbzeyYY8zuvz/vr55rOoD04HsJZB4FW3/91eyDD8xuuCHv75w5BGEBlIyCrb/2/tU+6P6B3XDYDe7vnN5zCMICAIDSF4hVsHPChAnWrFmziOlNmza16dOnu4zVWKpXr+66JIh+z5o1a+yPP/6I+3PHjRsXc55Vq1bZ559/bplCQR3dajl/fuT0BQvyphP0AVKP7yWQuZSV3qFDXoa6/pKlDmB7qPuBDk06WPvd2ru/dEcAAABKZdcECohu2bLFqlatGjG9WrVq7u+0adPyBUrl66+/jvlZtWvXtrp169rkyZOL/NxddtnF9Qlb2DydOnXKV87GjRvdw1u9erX7u23bNvdIxm3P116b17+dHmXLbrMyZULur4rTtOuuMzv11OScaGqd1CdvMtaNMlNbZi6sY6rKTPf3Uth/KLO0lEeZ2VVmOsoDAAAASqOMC8SuWLHC/S1fPnLR/HP/elGWLl1qb7/9tvXr1891RRDP55a0bHVbcOedd8Zchg0bNlii/fST2S675D2kTJlt1rz5Kg1ZZqHQX0nOH39stvfeyTkBUoawTrrUv24qUGZ2lJfNZab7eynsP5RZWsqjzOwqM9Xl6W4nAAAAoDTKuECs779Vjfkg/zx6ekFuuOEGO+WUU1wgNt7PLWnZKuM6pboFMmIbNWpkderUsRo1aliiLVtm9s03fz1Xxp3Gav322zq2bVvZiPnq1k181t/Eidts2bIytvPOdaxdu7Ipub1TJ3naPqrTVJ7IZnuZubCOqSoznd9Lj/2HMktLeZSZXWWmurxgP/4AAABAaZJxgdiaNWu6v5s2bYqY7m/9968X5rHHHnPvf+GFF8InBPF8bknLrlixontEU9nJOCGpV08nPZHTQqEyLtgTDPhovkQWr/4t/ajTBxxQxr75pqzVr1/Whg5NzYAnOslLVp3mcpm5sI6pKDNd38to7D+UWVrKo8zsKjOV5aWyHgEAAIBEyriWrPp/LVeuXLifVU+3vEmLFi0Kff/YsWNtzpw59tJLL7kuBdSdwObNm+P6XPUFW69evRKXnSrt2pk1bJjX52Qsmt6oUd58icIgREDmfS8BAAAAAEDpkXGB2CpVqtiRRx5pM2fOjJg+Y8YMa9y4sbVs2bLA93755Zf2888/23333RfuZuD55593XQrE+7nHHXdczHn0/iOOOMIygboCUBaqRAd9/PMhQxI3IJC6I1AmbKyeGfy0Pn3y5gNyVaq/lwAAAAAAoHTJuECsDBgwwEaPHm1btmwJTxs1apTdddddLsA6efJka9u2rY0fPz78+qxZs6x///4uo/WZZ55xjxEjRtikSZOsQoUKcX2u3Hzzze5zgwNBaB5NV8ZsplBXAKNHmzVoEDldGXmansiuAiZOzJ8JGx2MnTcvbz4gl6XyewkAAAAAAEqXjOsjVjp27OiCqn379rVWrVrZ7Nmz7cwzz7Tu3bu719etW2dz5861tWvXht9z0kkn2fTp0+3dd9+N+CwN2BXv50rr1q1dEFeB17333tsWLVpkTZo0sRtvvNEyjYI6nTubTZhgtnSpWZ06Zu3bJz7jbtGixM4HZLNUfS+RGsr0Z1sCAAAAALI2ECudO3d2j1gOOuggW7lyZcS0adOmbffneurCQI/SQAGBDh3MlizJG4k9GeNXaHChRM4HZLtUfC+RfJEDFJp9841Z/fp5XVCQ3bx9CHADAAAAyEWEB1AkBiECSkdg65NP8oJb+kufzduHAQqTR3W3225mxxxjdv/9eX/1nDoFAAAAkO0IxKJIDEIEZDYCW4nFAIXJQ4AbAAAAQC4jEIu4MAhR8uVCRmMurGOqEdhKPAYoTI5cDHBzzAMAAAAQRCAWcVOw9ddfzT74wOyGG/L+zplDELa0ZjSmOkBA1mbi5WJgKxUYoDA5ci3AnSvHPILNAAAAQBYM1pUNZs82q179r+fVqpntsovZpk15J5vRdt/9r0y2DRsiX9OAP/qsVavMli2LfK1iRbPy5c22bcsLjEZr0iTvdQUN/vwz8rXatc123NFs7VqzxYsjX6tQIa/vV78uPrCjLNjKlcvZvvvmdUegAYnWrIl8rz5Tn71+fd5AN0F6j05GRYHd6JM2DYZTubLZ8uVmGpNNr3/5pdkff5RzZWusNU2LrkN1k9CsWd7/9ZrqOUh1r22gz9RnB1WpkjfY2JYtZnPn5k1TfS5fXs6tm7aNBlzSumidgnbe2axmzbw6UF0EVar0VxbxrFmWj+r3rbfMzjzzr3XYsKGcq2sFLJTR+OKLZgcfHPm+HXYwa9y44DpUmSpb+4r2maAaNfICHddck7c+e+1Vzn7+Oa9+br/d7MorC67DXXc1q1rVbMUKbY/I1zRdrwfr0Hv3XbNevWKvo9b9+efNzj/fbPXqvMF7YtWh5td+WND+/fvvZuvWRb62005mtWrlTf/117xt6QfOKmj/9rSv6bul5dFyBWl7a7vre6rva0H79/z5ZSPKFO1n2t9Uf6rHoOIeI/773/yBrS1byoTXxQe23ngjb19K5DFC+9x33+VtTw22pPqKHpRM9at6TuQxQuugZRV/jAjScVLHy1h1GO8xInjc9vW4aVP+65ZaFv+9bto0cceI4DGvdWuzo4/O+y5rGwRp39Y+rm0WHRQu7jEieLzTttE23bgx//5VVB0WdoyYMiV/vfpjQdAvv/z13fS0PDp2be8xQuvn11PbK3iM0OtB23OM0DH2oovy3hPrmPf663kXMX/7zWzz5sj3JuoYsWBB5DGvsHaEfnP121vcY4SO7QMH5tXdvvuWcccE/1ty/PFF12FJjxHRxykAAACgtCAQm0Q335x3MuwddZTZ9dfnBQ+UpRZt7Ni8v4MHm02bFvnaddeZdexo9umnZo8/HvmaTn4U5FJgJtbnvvBCXhDgySfzTu6DLr7Y7PTTzb7/3uzeeyNf08m27xtWy60Am4RCZWzTpur2xBN5wZKXXzZ7//3I9yro06OH2cyZZrfcEvmaTqyeeSbv/3fckT8oes89ZnvvnRegfPjhvJPyDRvKWM2a1d3Jrk64b7st7/UgnSQq4CTKPoo+Ib/pJrMjjzT7+GOzkSMjX1OgUyeOOhn3dejXs0KFMvbqq3knxqp7nWgGXX652cknm339tdmDD0a+1qpV3rJIrG0zbFheRqOnMn/7rbr76117rdmBB0b2z6sTddW/3Hpr/iDAffeZC+C8+abZv/4V+Zreq30h74T4r/J0Iq39SIEUBQgGDcp/oq96P+SQvGzo556LfO2II/L2eQXGguuqcsaPL3wdtX+fc05eYFHbPGivvfKWRftfrDp8+um8QJf2qc8+i3yte3ezs84yF2geMCBvW/p6VADgscfy/q/ljg6eqd9jBTXU9cbbb0e+posBf/97XoCrb9/I1xQsUvA87zOq2cqVf5Upd95ptv/+ZuPGmY0aFfne4h4jooPAsnZthYi6lZdeyvtOJuoYoWCM/14edJDZN9/kBVP23DNv//IefTQvGJjIY4TW7frry7vAkI4B2j5Bxx6bd5FB+3P0usZ7jFCQS8E9f0FMZS5bVjliXr2u7ad1k1deScwxIli3wWOegrHRx0p9Z84912zqVO3fka8V9xgRPN5pWa+4Ii9oGF2HCtjpeCjFPUYE942CjgUyaVLe9yPo6qvNjjtu+48Rn37613rqexk8Rtx9d+T7SnqM0DFP6/DXBZH866nl1HFEZUYH0bf3GKGg+/XXl7FZs/LWU98nrWth7Yj99jP7v/8r3jFCy63vvijY++ef5SN+Sw44IO93IVY7YnuPEdEXSwAAAIDSokwoFOumVmyP1atXW82aNe2771ZZ9eo1UpARu83Kl19iO+9c1+bOLZv0jNht27bZ8uXLbd99a1ulSmWTlhH77LNmPXvmTStTZpvttddy+/nn2q5HDS2LTuCUcZO8jNi89axdu7btvnvZpGTEql4VXPhrHf5az1CobMRJ8KGHbn9GrObTibw/8Y9Vnra5MqK0ronIiFXgRNmuRa3jRx/lBR+SkRG7Zs02++WXvG1Z9n/pYcnOiNX+8+23y6xmzZ3DZSYjIzZYt2XLbrO2bZfa99/Xiajb115LXEZsZHbzNjvwwCX2zTd1bdu2vPKC38vkZMTmHe+aNKlrK1aUTUpGrD5Twevgeu6zzzL76aedY65nojJiFfQLllnUMS+xGbF/He923LFsUjJiVZ6OY/4zCzr+KFAYnSWauIzYv9ZT38tkZMQW55inukxkRuwPP+Rd3Fuw4K8yd9mlrLvQeMEFicuI1f7UocNfdaZjz777LrXvvvvr2KP1UEDaf5cTmxG72vbZp6atWrXKamjHQLHbqKmoOx1XlixZYnXr1o34HcymMnNhHSkze8qjzOwpjzKzq8xcWMdcKHN1MdpYZMQmkU6wYtW/Tjp8QCWW6AGxgnQyr0eQTpx0EqP9qrDPjc5Gij6506Mg/sTbl1e9+la3HqKghx6x6OSusGXyJ2jRdJKnzKrgyX+lSlvdX5Wvv//4h9lll+UFbaJF39YafXKnRyw60dTyqnz1d7d06VZ3EujXXyeqBdEJbmFZOrHqITr7MLiewRPWwrZtQXXoA0B6eMoGDgZsYpXn+2hUwLYgClzoUVgdFncdtVz6vhR0zNL8he1LCgDFom2pTES/Ldu3z7/PBPfvaAoA6RGLAkCFLVPDhtvcdyPWMV8BID1iifcYoW2vTD0FZn1dli8fCtet/ipYdMYZea9t7zFC32dlHnrBTF//PNb3MlHHiL++l3nBRW1LBWpKUodFHSPURYf2KQW1FASqUGFb+H3KhCyob+ySHiO0btF1G+8xT0G7khxnRccHfZdjHe8UZCxpHRZ0jFB2pC4KRK+jp7r1AeRYtvcYkXdRc2u+76WCx4W9tzjHiOIc8wo7zhb3GOEH7lMZWjdfpi6gXHXVX3c7xGpHePEeI/RbEh24Llfur2OPXz99T/3+V1gdFvcYEX1xDAAAACgtGKwLGSmdg7qkcoCVwgJfJZkvEwchSvU65tJgOQrI+dt+YwVFfWAr1sWKksiV72U6BihMV92mul5Vd7qVP/qCoy4YaHo2DP6YjmNeqgfuY0C7xFi8eLH17NnTXlIKfhwmTZpk1157rQ0fPtwGDBhgQ3SABwAAQKlCIBYZKV0neT6jKDogooxDTU90cKJdu7wARHQQzdN0ZZ1pvtIaIEj1OqZrW6ZLKgNbufK9DFIQW7dgK/tWfxMV1M6Euk1XvaYywJ0O6TjmpTqQn84LbNng+++/t379+tmzzz7rHpui+/iIYfbs2XbhhRfaoEGD7LLLLrM777zT5syZY/dGd/APAACAjEYgFhkpFzKK0pHRmI4AQarXMV3bMp1SFdjKle9lOqS6btNdr6kKcKdDOo55qQ7kp+sCW7bYd999XUD1xhtvjPs9AwcOtBNOOMEqqU+c/+nRo4f7nPXRHVNngK3bttoncz+xCb9OcH/1HAAAAARikaFyIaMoHRmN6QgQpON25HTeQp8uqQhs5dL3MtVSXbe5Uq/pkupjXqoD+en6Lcll48aNs2ZRHe02bdrUDQjx+eefWyYZM2WM7TZ0NzvmuWPs/s/vd3/1XNMBAAByHYN1ISP5kzzdHputGUVBOinv3PmvgYg08EusQaUSVZYCAX4QomCAoLBBiErTOtJ/YXLk2vcym+s2V+o1nVJ5zPOB/ODAfUF+4L5EXiRJ129JLlq3bp0tXLjQqmpUuYBq/xtlddq0adapU6eY7924caN7BEf09aMI65Fob05907q91s1CFrKyVtbKWBn3d9HqRdbt1W726lmv2umtT7dk0TqFQqGkrFsmlEeZ2VVmLqxjrpSZC+tImdlTHmUmR3HKIBCLjJXqk7x093nnMxqXLLF8I3qX5gBBOtYx3dsym+Xa9zJb6zaX6jWdUnXMS8dFknT+luSaFStWuL/ly0c22/1z/3os6rpA/clGW7p0qW3YsCHhJyAjJo6w/Wvs754rCNu8SnP3fwVm5cmJT9qhOx5qZZP0ZdAyKEtYJ13JKiPd5f289GdbvXK11VhSw/aqs1dWrmeulJkL65grZebCOlJm9pRHmcmxZs2auOclEIuMlu0ZRemUysBvquXatkw1vpelv25zrV5zQboyVLP5tyRTlPlfNF0nEUH+efT0IA0Kdt1110VkxDZq1Mjq1KljNWrUSOhyqi/Y935/L/xcmbDy7epvbZv9L0tktdm0jdOsQ5MOlmjqh3bibxNt2cpltnPFna1d43ZWrmy5pJ/gafuoPpN9gqds42vfvdYWrl7ogt2q1/o16tvg4wcnNcs41euZS2XmwjrmSpm5sI6UmT3lUWZyBPvxLwqBWGS8bM8oQuKxLZOP72XprttcrNdcQIZqdqpZs6b7u2nTpojpvssB/3osFStWdI9oOhlJ9AnJ72t//yvg+j/KhNW04HTNl+iy1f9s73G9XZDygBoH2Derv3FByqEnDLUueyS3nwyd4CWjPqPXr+trXcNdPvh6nbd6nps+utvorFjPXCwzF9YxV8rMhXWkzOwpjzITrzifT94CkMYBVpA8bMvswbZMDuo1O6Vi4D6klvqCrVevXrh/V0+32kmLFi0sE9SrXi+h8xUrSPlqV5u/OnIEwgWrF7jppX2QMGX6Ksjsu3cI8tP6jOvj5gMAAJmPjFggChlF2YNtmT3YlslBvQKlw3HHHWczZ86MmDZjxgyrUqWKHXHEEZYJ1BVAwxoNXQA0VtBQfcbqdc2XqiClylSQsnOrzknvpiBZ1N1CdJA5ej2VGav5jtrtqISXrzqeMHeCLV281Oqsr2Ptm7QvtXUJAEAmICMWiIGMouzBtswebMvkoF6B9PCj60aPsjt58mRr27atjR8/Pjzt5ptvds+DA0GMGjXKTVfGbCZQcE5dAYgCoEH++ZAThiQ0iFecIGVptWjNooTOVxzKJt5t6G52zHPH2P2f3+/+6nlpzzIGACCdyIgFAAAAUuTXX3+1p556Kpzh+tBDD9msWbPsoIMOstNPP93WrVtnc+fOtbVr14bf07p1a3vmmWdc4HXvvfe2RYsWWZMmTezGG2+0TKJ+StVfqe+v1VMmrIKwie7HNJ1Bylzp8sH3Sxvd5UMq+qUFACAbEYgFAAAAUqRx48Z2++23W/ny5e2ll16yUCjksmJ9ZqwCsitXrsz3viOPPNI9Mp2Cc+oKIHw7+y7Ju509XUHKVKLLBwAAsgtdEwAAAAApolF1d9hhBzeKr+hvuXLl3LRsoeBchyYdrP1u7d3fZAXrfJAyuisET9Mb1WiU0CBlqtHlAwAA2YVALAAAAIBSJx1BynR2+dCgRoOI6QpCJ6OLgFzo8gEAgHShawIAAAAApVKq+6VNF7p8AAAgOxCIBQAAAFBqpTJImQldPiypvMTq1q3rurnIln5pAQDIFXRNAAAAAKBUS1W/tLkg3V0+aLCwT+Z+YhN+neD+6jkAANmCQCwAAAAAIG390npjpoyx3YbuZsc8d4zd//n97q+eazoAANmArgkAAAAAAGnt8kHB1q6vdnXdIZQN5AupiwRNT2YAWFm34fVcn51dWwAAMgOBWAAAAABA2vqlVSBUA67F6pNW09QlQp9xfVxgONEBUgWA/WBvB9Q4wL5Z/Y3Vr1Hfdc+QLYO9AQAyB10TAAAAAADSZuJvE23+6vkFvq5g7LzV89x8ycjCjS7bZ+FmU5cI9L0LAJmBjFgAAAAgh/y+5ndbV2Zd+Hml8pWsVuVatmXbFlu6bmm++etVr+f+LvtzmW3eujnitR0r7WiVd6hs6zats9UbV4enb9u2zVZtWGV1ra5tC22zxWsX5/vculXruuzGP9b/YRu3bIx4rXrF6latQjVbv3m9rdywMuK18mXLW52qddz/F61ZFFHmsnXLrNbWWlaxbEX3Pr0/qGqFqlajYg1XnsoNKlumrO1SbRf3fy2vljtop8o7WcXyFd16an19eVvXbLWqFau6ulD9qJ4KqkPVr+o5Vh2u3bTW1mxcE/GaylO5CpotWbckokxlp2p5tdzL/1xum7Zuiniv1lPrG6sOdyi3g+1cZed8deipflXPK9avsD83/RlRpraLtk+sOtT21HYtqA5rV6ltFcpVCNehN3nJ5HyB103bNuXLkNWyxqpD7b/aj2PVod+/fR16en7V21eFywiW6add8841Lgt349aNbn8O0npofUKhkP2+9vcC92/V4YYtGyJeq7JDFft20be2YOECq7S8kh3a8NBwpm9w/9bn6vODtN20/bQ8f27+M+b+rX1B+4T39oy37faPbnfbRFm/X6/+2nattqvd1fEuO6nFSRH7t+pP9RirDktyjKhRoYb7q+29dvPamHWY6GNEsGuLWPu3r8NEHSP897LmlppWuULlfPu36DueyGOEL7P8+vK2c9Wd8+3fXqKOEfr8/87/ry1fttxqL69tp7Y61e0vsfbvRB4jgse8ahWrWc1KNWPWYZkyZdw+XVAdFucYEX2c1efq82PVoZZH32d9F7fnGLF43eJwecH9W3Wr14MSdYyILjO4f6seoi/WJOIYsXz98ogyC2tHJOIYUaV8FVu/Zb3bh4NlFtSO2J5jxJo1kftVYQjEAgAAADnk6e+ftopVK4af77PLPu4WbJ0ADf9meL757zjqDvf3zalv5ssc1Pv0/l+W/uKCPZ5ODHcut7O1btzanTTH+ty+h/d1JzTvznzXpi2fFvHa8bsfb4c1Osxmr5htr01+LeK1etXq2WUHXub+/+S3T9rW0NZwmevWrbMb6t5gu+6wq+vzU8GuoCMbH2nHNDvGFq1dZM98/0zEazqxuu6w69z/X/zpxXwnhD337Wm77bibfbngS/v0t0/D5VWtWtUOqH+AndbqNFuxYUW+dS1Xppzd3uF2939lWKrsoLPanGV71t3Tflr8k707692I11rVbmXn7H2OOxnX5wbLVGCg35H93Imx6n7WilkR71WA7eAGB9uMP2bky+zUoFt/3//v7v+xts01h1zjTro/+vUj++H3HyLKPGq3o9xDGaov/PhCxPv0Hr1Xnv3h2XxBgIv3u9ga1Wxkn8/73D6f/3l4+q8rf42YT4HQpZuW5gvE6mT+1V9etaV/Rp7on7PXOdZq51b23aLvbPyc8RGvtanTxrrt2c3WbV4Xsa4qM3pbRJe5YM0Cl4WrfePf0/4dMa/2Be0T2v9i1aH2Jb3v/dnv2+SlfwWapyyd4upVQZ02VdvYz+t+tuoVqtuJzU+0PersYXWq1LFeB/dy8z793dMuCBx02QGXuXrQPvjVwq8iXjus4WF2fPPjXcBi5Hcjw+W9OvnV/AHKtYvs72P/bt3adHPlnr/P+dZ8p+b2zaJv7ONfP4743O05Rpze6nTbtcyu7hgxbta4iNd2r7W7XdD2goQfI3apuoud0eiMfMcI78qDrnQBnEQdI/z38spaV1qznZqFjxFB+9fbP6HHCF/mfuv2s/P2OS98jIiWiGOEugV5Z+Y7tmbTGqtfob4t3LTQ6levbw+f+LB7/cfFP0a8N5HHiOAxT8t6csuTXRA2el0rlqto/dr1c//f3mNE9HH2tva3Wfky5W3s9LH5jlXaptq2U5dN3a5jxNdzvw6XJ0c3PdraNWlnc1fOtVE/j4p4X6KOEc9PeT6iTAWUbzziRvf/l39+OV8QPRHHiHG/jrPVv64Ol1lYOyIRx4hDGhxiv63+zT6c8WFEmQW1I7bnGLFxXeR2KEyZUHT4HNtt9erVVrNmTVu1apXVqJF3BTCZdMVmyZLk9tuUzvIoM3vKo8zsKjMX1jFXysyFdaTM7Ckv1e2sbOLrbtr8aVa9RvXkZ8SuWOUCsVYmL/MpJRmxy5a5MivukKKM2GXLbOedd05tRuz/ykxpRmygzGRkxGr9Dhl5iFsW3yfsPtX2sR/X/hgOjDaq0cjm9J7jykxERuwbU96wXu/kBTMkVpnyUpeXrHPrzgnJiFWQQcFPHxTdr/p+9u2ab8PlPXnqky6wk6iMWK3zwU8eHA7sqUyfEevLrF+tvn3x9y9cmcnKiF2zYo1V3bFqSjNiQ+tC7ndJWX8pyYhdtsxaNmqZ2ozYZcuswa4NkpoRqwDtma+eGfM7oufPnP6MHdvs2Ij3JuoYsXrD6r+ycHeubZ2adrKdquyUmozYwDEvmRmx8p/p/7FFixe5dfTZ8cnOiN2weYNN+21aeB1TkRGrep0yd4rtuNOOKc2InbtwrlWoXiH5GbGr11irhq3iap+SEQsAAADkkF2r72o1quc/SdCJiQ8GxOIDd7HoxEQPTydc5daXC5/cFfa5OrkriE7O9ChI8HN9mTp58id3esSik8nClsmfjMaiEzA9fHl1q/91EUJlF/a5/sQvFp106xGLTjT9iWx0maIT1UTUYTSdWNesWDNmmYmowyBl16lf1jL/+1ehbAX31xtywhBXD9tbh16bum0iXg+WGQzE6j0KtOgRiwI0RdWhKKhxxyd5WWGevhu+PP2985M7XQad5wNLsSgApEcsCl5omZS1Fp1d6dfVr+PCtQtt5oqZ1rBmw3DwQo9YSnKM0D67xta440P1SrE/N9HHCHeB8H9BycI+N1HHCP+91HsK2r+9RBwjtC8pU2/p0qX2R9k/rH2T9vn272glOUb4QfQK+47c9uFtdt7e58UcRG97jhEfzP6g0EH0Mu04W5JjRDwDBSrAWdi6bs8xQpnj0esYHSSOZXuOEbUr1y6wzOh2xPYeI3y/2EsXL7U6u9QJf0+iJeoYUTUUe9ljYbAuAAAAAEBaKfAwuttoa1CjQb5bpDXdByYSpV3jdu6zg8HeIE1XFq7mK60DksXK9Nqe+ZB+Ct7tNnQ3O+a5Y+z+z+93f/U8GQPLMYhe8qRzHdMxcF+qyxyTwu9JSRCIBQAAAACknYKtv/b+1T7o/oHdcNgN7q+6I0h0EFaUGaXMM4kOxvrnPgu3tAZFC8v0Ksl8pSHgk81SHbxLxz7rs3Cj+4cWP0191pbmfSmd65iOAGWqyxxTCgL5OReI1UhmCxcutAULFtj8+fPDj99//6vPjs2bN9sff/xhc+fOtUWLFtnatWtdejoAAAAAIHkU+OzQpIO13629+5uoQGi6s3DTERRNddZvJmSkpTr4m6ry0hG8S8c+m64s3FRuz1zKNE51mVtLSSA/Y/uIHTt2rE2cONGaN29us2bNsrZt29q5554b1yAEAwcOdAMR3HLLLRGvvfPOO3bSSSfFfN/JJ59sb731lvt/lSpVbMuWvzp37tSpk40YMcKaNWu23esFAAAAAMgMCrZ2btU5r8/NIvoSTERQVAGIWEECBUX1eiKDoj7r1/e9G11eorN+o4MvWk8NnBUdfElGVxPx9rlZWssrTvDuqN2OKrX7bDq700jV9szETGNtSwUodSxM1PEgHWVOTMP3JGsCsZ999pndc889NmnSJNexsXTu3Nl16Hv22WfHfI+yV4cPH26VK1e2p59+2q688sp88/z000/2wgsvuBHM/OfKsGHD7NFHHw0/13u7dOli69evtzZt2ljjxo2Tsp4AAAAAgMzIwl1SeYnVrRt7IJnSGhT1Wb8+wOQpgKbyEh0wTEfwJR3B31SXl47gXTr22XR1p5HK7ZnpmcaJClCmo8xFpaRf7IzsmqB///7WrVu3iGBpjx49bMCAAQW+p0mTJi54e/vtt7uM1lj0g3reeefZqaeeaqeccop7lC9f3k3T+71atWpZhw4d7IQTTiAICwAAAAAodQOSpaPv3XTcdp3q25FzpZuAXBhELx3bMx3rmI4AZS72i11qA7HKQp0wYUK+bgCaNm1q06dPt9mzZ5f4s6+99tqI58uXL7f3338/ri4PAAAAAAAoLUHRdPS9m47gS6qDv+kINqezv99sHkQvHdszHeuYjgBlrvWLXaq7JlCgVf2zVq1aNWJ6tWrV3N9p06aVuK/WcuUid2Rl2CqDNpoG8ho8eLDttNNONnXqVJche+ONNxb4uRs3bnSPYD+1ogG+UjHIl8oIhUIpG1As1eVRZvaUR5nZVWYurGOulJkL60iZ2VUeAJRmqegKIV3SEXxJdfA3V7oJSNc+m+ruNNKxPVO9juno7zeX+sUu9YHYFStWuL/qMiDIP/evb69vv/3WBU932WWXfK9t2LDBevfuHT64tGvXzipWrOimxTJo0CC78847801funSp+6xUnJCsWrXKnQSl4kc81eVRZvaUR5nZVWYurGOulJkL60iZ2VPemjVrkl4GAKD0BF9SHfxNdzcBqQre5cIgeunenqlax3QEKHOlX+ysCMT6fmHVmA/yz6Onl9R9993n+oqN5fnnn494rr5ilT17xRVXWIUKFfLN369fP7vuuusiMmIbNWpkderUcQODpeIESPWm8lJ1wpXK8igze8qjzOwqMxfWMVfKzIV1pMzsKa9SpUpJLwMAUHqCL6kO/qYj2JyO4F26pSoLN53bM5szjdNVZmn4nmRcILZmzZru76ZNmyKm+1v//evbY+XKlTZmzJhCB/8K0omFMj3UR+1ee+2V73Vly+oRTV+iVJ106QQom8ujzOwpjzKzq8xcWMdcKTMX1pEys6O8bLqFFwCyUaqDL6kO/qb79uds7toiHdK9PVMpHQHKdAVFy2Xw9yRzluR/1P+r+nL1/ax6CoRKixYttrsMDQamQG+9evlTy9u2bZuvCwIfBN68efN2lw0AAAAAQDZL9aBkPvjboEaDiOkK/mp6ostNdXlIrlzanqkauC/dZWayjMuIrVKlih155JE2c+bMiOkzZsywxo0bW8uWLRPSP6xEDwgmlStXtr333jti2pw5c1xWbKxsWAAAAAAAkN6MtFRn3mX67c8oHrYncjYjVtRlwOjRo23Lli3haaNGjbK77rrL3fo2efJkl7k6fvz4AvsqK2xE3SVLlsQcEEx69eplnTp1Cj9fvny5vfrqq/bQQw/ZDjvssJ1rBgAAAAAAsiHzjky/7ML2RE5mxErHjh2tf//+1rdvX2vVqpXNnj3bzjzzTOvevbt7fd26dTZ37lxbu3ZtRL+vgwcPtsWLF9v8+fPtxRdfdN0PtG7d2nr27Bnx+XvssYcddNBBMcs+//zz7amnnrJXXnnFlTN16lQbMWKEnXjiiUleawAAAAAAAADZKiMDsdK5c2f3iEVBVAVegzSI16233uqyVh9//HELhULusXXr1nzvv/rqq90jFmXcXnzxxQlaCwAAAAAAAADI4EBscSmAWqFChYjnfgRfAAAAAAAAAEgnopQAAAAAAAAAkGQEYgEAAAAAAAAgyQjEAgAAAAAAAECSEYgFAAAAAAAAgCQjEAsAAAAAAAAASUYgFgAAAAAAAACSjEAsAAAAAAAAACQZgVgAAAAAAAAASDICsQAAAAAAAACQZARiAQAAAAAAACDJCMQCAAAAAAAAQJIRiAUAAAAAAACAJCMQCwAAAAAAAABJRiAWAAAAAAAAAJKMQCwAAAAAAAAAJBmBWAAAAAAAAABIMgKxAAAAAAAAAJBkBGIBAAAAAAAAIMkIxAIAAAAAAABAkhGIBQAAAAAAAIAkIxALAAAAAAAAAElGIBYAAAAAAAAAkoxALAAAAAAAAAAkGYFYAAAAAAAAAEgyArEAAAAAAAAAkGQEYgEAAAAAAAAgyQjEAgAAAAAAAECSEYgFAAAAAAAAgCQjEAsAAAAAAAAASUYgFgAAAAAAAACSjEAsAAAAAAAAACQZgVgAAAAAAAAASLLyyS4AAAAAwF/Gjh1rEydOtObNm9usWbOsbdu2du655xb6nk8//dS++uorK1eunK1evdoqVapk1113nZUtS14FAABAaUEgFgAAAEiRzz77zO655x6bNGmSlSlTxk3r3LmzC6ieffbZMd/z/fff24wZM+zaa68NT3vnnXesf//+dvfdd6ds2QEAALB9uIQOAAAApIiCp926dQsHYaVHjx42YMCAAt8zatQo22OPPSKmnXjiiS6oCwAAgNIj5wKxW7dutRUrVthvv/1mCxYscLd2aRoAAACQTOvXr7cJEyZYs2bNIqY3bdrUpk+fbrNnz475vooVK9qVV15pc+fODU+bM2eONWnSJOnLDAAAgBzomqAkfWeJAqsDBw60mjVr2i233JLv9datW9vMmTPDzw844AB74oknbP/99w9PmzJlij3++ONuXgVtN23aZLfddpuVL5+x1QUAAIAMp0Drli1brGrVqhHTq1Wr5v5OmzYtX5BWLrvsMnvkkUesTZs2LnP2tNNOs/vvv9/uu+++QsvbuHGjewTbybJt2zb3SCZ9figUSno56SwzF9aRMrOnPMrMnvIoM7vKzIV1zIUytxWjjPLZ0neWMgSGDx9ulStXtqefftplDcRy+umn2xlnnGErV660Fi1auEeQpp900kluMISdd97ZTRs8eLBdffXVNmzYsISvKwAAAHKDLvBL9MV9/9y/Hq1Bgwb25Zdf2gknnGA33XST3XXXXTZu3DirU6dOoeUNGjTI7rzzznzTly5dahs2bLBkn5CsWrXKnQClakCxVJeZC+tImdlTHmVmT3mUmV1l5sI65kKZa9asKd2B2IL6zurXr1+BgVjdmqXgrYwcObLAz1YGwuGHH17g6w8//LDts88+4SCsdO/e3XbZZRe79dZbrWHDhiVcKwAAAOQy37bVCUGQfx493VPQVBmxQ4cOtXnz5rm7vjp06OCmXX755QWWp7bzddddF5ER26hRIxfArVGjhiX75Efrq7JSecKVyjJzYR0pM3vKo8zsKY8ys6vMXFjHXCizUqVKpTcQ6/vOuuaaawrsOyvWLVuJouyCgw8+OGJa7dq1XQD3vffes4suuihpZQMAACB7qessUbdXQb77AP96tL///e/ubi+fTHDmmWdar169rE+fPnbqqae6jNmC+pbVI5pORlJxEqSTn1SVla4yc2EdKTN7yqPM7CmPMrOrzFxYx2wvs2wxPr98tvSdFS91PTBkyBDbaaed3IBdSh9Wn7L+ljAFezt27JjvfSpfZWda/1u+HPowoczSUB5lZleZubCOuVJmLqwjZWZXeaWV2rDlypULtxU93TYn0V1mydq1a+2nn36KuKNLmR2vvvqqderUyb744gvr0qVLCpYeAAAA26t8tvSdFS8FXnULl08bvuCCC6xv376uH1j/+bEG5dK0gspOZ/9bQh8mlFlayqPM7CozF9YxV8rMhXWkzOwprzh9cGWaKlWq2JFHHhkxcKzMmDHDGjdubC1btsz3nq1bt7o7xmLZe++9i+wnFgAAAJmjfLb0nRWv6P5jNehBz5497YYbbnC3dan8WGVoWkFlp7P/LaEPE8osLeVRZnaVmQvrmCtl5sI6Umb2lFecPrgy0YABA1y7U4kA/uL/qFGj3ABcqsfJkyfbOeecYw8++KAdffTRrruC9u3b2xNPPGGXXnpp+HPmzJnjkgQU2AUAAEDpUD5b+s4qKZ00qCuE7777zgVi9fnRZfvyCyo73f1vCX2YUGZpKY8ys6vMXFjHXCkzF9aRMrOjvFTWYzKoCywNTKtAbKtWrVy3XOrzVYPDyrp162zu3LmuSwLv8ccfd4/evXtbrVq1XB0ou1bB2eDgtgAAAMhs5bOh76x4de7c2f3917/+lS/Au3nzZvdXt4RFl+3L356yAQAAAN8m9e3SaAcddJAb0yBImbNXXXVVipYOAAAAyVI2G/rOipcyBtS4DdJtXRUqVLAjjjjCPT/uuOPylT1v3jwXsD3mmGNKXDYAAAAAAACA3JVxgVjfd9bo0aNdlwFedN9Zbdu2tfHjxxfYV1msEXUvueQS1yesp4G01GesPrdu3bpuWq9evWzatGk2f/78iLIvuugi23333RO8pgAAAAAAAAByQcZ1TVDSvrN0C9fgwYNt8eLFLoj64osvur5eW7du7QbjkpNPPtkFeN9991332pQpU9xgCRdccEFEn7Fvv/22DRw40I1Eq89VOcOGDUtDTQAAAAAAAADIBhkZiC1J31kaSOvWW2+1HXbYwQ1mEAqF3GPr1q0R83Xt2rXIstu0aUPgFQAAAAAAAED2B2KLS10WqK/X4HM/gi8AAAAAAAAApBNRSgAAAAAAAABIMgKxAAAAAAAAAJBkBGIBAAAAAAAAIMkIxAIAAAAAAABAkhGIBQAAAAAAAIAkIxALAAAAAAAAAElGIBYAAAAAAAAAkoxALAAAAAAAAAAkGYFYAAAAAAAAAEgyArEAAAAAAAAAkGQEYgEAAAAAAAAgyQjEAgAAAAAAAECSEYgFAAAAAAAAgCQjEAsAAAAAAAAASUYgFgAAAAAAAACSjEAsAAAAAAAAACQZgVgAAAAAAAAASDICsQAAAAAAAACQZARiAQAAAAAAACDJCMQCAAAAAAAAQJIRiAUAAAAAAACAJCMQCwAAAAAAAABJRiAWAAAAAAAAAJKMQCwAAAAAAAAAJBmBWAAAAAAAAABIMgKxAAAAAAAAAJBkBGIBAAAAAAAAIMkIxAIAAAAAAABAkhGIBQAAAAAAAIAkIxALAAAAAAAAAElGIBYAAAAAAAAAkoxALAAAAAAAAAAkGYFYAAAAAAAAAEgyArEAAAAAAAAAkGQEYgEAAAAAAAAgyconu4DSaPPmzbZmzRr3qFChglWvXt2qVKliZcsStwYAAAAAAACQRYHYsWPH2sSJE6158+Y2a9Ysa9u2rZ177rlFvm/16tU2cOBAq1mzpt1yyy35Xn/99dfthx9+sJUrV9qUKVOsc+fOduWVV0YEWRV03bJlS/h5p06dbMSIEdasWbMEriEAAAAAAACAXJGRgdjPPvvM7rnnHps0aZKVKVPGTVPAVMHSs88+O+Z75s6da8OHD7fKlSvb008/7YKrsYKwCtD+3//9n3u+cOFC23fffe3nn3+2xx9/PDyf3tulSxdbv369tWnTxho3bpy0dQUAAAAAAACQ/TLyXvv+/ftbt27dwkFY6dGjhw0YMKDA9zRp0sQFb2+//XaX0RrLY4895h5e/fr17cILL7QnnnjCFi1aFJ5eq1Yt69Chg51wwgkEYQEAAAAAAABkXyBWWagTJkzI1w1A06ZNbfr06TZ79uwSf7YCrMGAq//cUChkv/32W4k/FwAAAAAAAABKVdcECrSqf9aqVatGTK9WrZr7O23atBL31Tp69OiY5ZUvX95atGgRnrZgwQIbPHiw7bTTTjZ16lQXwL3xxhsL/NyNGze6R7CfWtm2bZt7JJvKUDA5FWWlozzKzJ7yKDO7ysyFdcyVMnNhHSkzu8oDAAAASqOMC8SuWLHC/VVwNMg/968ngoKnL7/8svXs2dMFXb0NGzZY7969wwN4tWvXzipWrOimxTJo0CC78847801funSp+6xUnJCsWrXKnQQFBx3LlvIoM3vKo8zsKjMX1jFXysyFdaTM7ClvzZo1SS8DAAAAyIlArO8XVo35IP88evr2GDhwoOua4KGHHoqY/vzzz0c8V1+x6p/2iiuusAoVKuT7nH79+tl1110XkRHbqFEjq1OnjtWoUcNScQKkelN5qTrhSmV5lJk95VFmdpWZC+uYK2XmwjpSZvaUV6lSpaSXAQAAAOREILZmzZru76ZNmyKm+1v//evb66233rJJkybZf/7zH6tcuXKh8+rEQpke6qN2r732yve6smX1iKaTkVSddOkEKJvLo8zsKY8ys6vMXFjHXCkzF9aRMrOjvFTWIwAAAJBIGdeSVf+v5cqVC/ez6ikQKsG+XEvq66+/tjFjxrggrPqeXbduna1du9a91rZt23xdEPgg8ObNm7e7bAAAAAAAAAC5Z7sCsT/++KN9/vnn+frtuv/++8OB0+KqUqWKHXnkkTZz5syI6TNmzLDGjRtby5Ytt2eR3eBcr776qj355JPhLNZ3333XFi9e7P6v7Ni999474j1z5sxxWbGxsmEBAACQnZLR1gUAAEDuKnHXBOPHj7fTTz/dtmzZYuvXrw9Pr169unXp0sVuu+02l1navHnzYn+2+mO94YYbrG/fvuFBukaNGmV33XWXu/Vt8uTJds4559iDDz5oRx99dMy+ymKNqLt8+XK7/PLL7eyzz7bnnnvOTdPy/+tf/3IP6dWrlx1xxBER71HgVv3I7rDDDsVeFwAAUkl9qW/dutX9vsVLv5m660MDTKayH1PKzI4yE1We2lm6KypTJLOtCwBArlH7tLh3GdOOosx0l6mYpNqnfjyrtAZiv/jiCxcc9bftR3cvMGTIEBdIVbC0uDp27Gj9+/d372/VqpXLYj3zzDOte/fu7nV1JTB37txwdwKycuVKGzx4sMtsnT9/vr344ouun9nWrVtbz5493Tznnnuuvf/+++4RpExXv1HOP/98e+qpp+yVV15x5UydOtVGjBhhJ554YrHXAwCAVAZg9Vu4dOlS19At7nvVUFGmXyIbGZSZG2Umsrwdd9zRdt1115TVVbraugAA5Aq1E37//XfXTi3Je2lHUWa6y1Qgtm7dum7MqkQse4kDsfoSnXLKKYUuaKys1Hh17tzZPWI56KCD8n2JVSG33nqry6Z4/PHHXYX7rCBPXRAURZV68cUXl3i5AQBIB9/ArVGjhnvo6m28DQX9Xirrrzjv2V6UmT1lJqI8fcaff/5pS5Yscc/r1atn6Zbsti4AALnURlUgS11RFqetQDuKMtNZpn+/xrBatGiRu0MqEW3UEgdifZ+qhZk1a5aliiq1QoUKEc/9CL4AAGQzXXRUf5Xqz3znnXfOiYYRZWZOmYkqT/30i4KxOllLdzcFmdbWBQCgNLZRfRC2du3axX4/7SjKzIQy1S2VxphatmxZQtqoJY5SaiWef/75Al/X7VrKyAEAAMmlvo/U0KhatWq6FwXYLsqUkeL2IZcMtHUBANg+/vfc/74DpZXOs3S+lYg2aokzYgcOHGiHHHKI60/12GOPtfr167uFUt+tb731luun9auvvtruBQQAAPHJhH41gWzZh2nrAgCQfb/vQElkxGBdDRo0sE8//dQuueQSN2qsFkqNUznssMNs4sSJ1qhRo4QtKAAAAJAqtHUBAACQaCUOxMpuu+1m77//vusf6/vvv3f9f+y1117Wpk2bxC0hAAAAkAa0dQEAAJAxgVhv9913dw8AAAAg29DWBQAAQCKUeLAuAAAAlNzixYvdyKs//vije37ooYfaQw89lO7FAgAAQI6ifVpKMmIBAEB22rrVbOJEs0WLzOrVM2vXzqxcuXQvVXZYv369rV692jZt2uSe6/96AAAAoGC0T5OH9mnyEYgFAAAxjRlj1ru32fz5f01r2NBs6FCzLl3SuWTZ0//ov//9b3vjjTfshRdesAsvvNB6q8KR9caOHesG+2revLnrf7Zt27Z27rnnFvm+V155xSZNmuT2HQ0epiwVPQAAyBW0T5OL9mny0TUBAACI2cjt2jWykSsLFuRN1+upsm3bNrvjjjusV69eNnjwYHvwwQftt99+s1WrVtk///lP22GHHezSSy+1kSNH2v/93//ZOeecY0uWLAm/X7dWXXTRRfbwww/bM888YzfccIMLYt166602f/58u+CCC2zo0KH2/PPP27Bhw6xnz5527733/m99F9iAAQPc/FdddZVNmTLFxo8fb8cdd5zVrl3bHn30Udu4caP7u9NOO7npej3oww8/tOOPPz7m/A888IAdeeSRds0117hlrlixoltXLVdhRo8e7Zb7vvvusyFDhtinn37qpj/99NPWrFkzO/roo+3JJ590D62Llv/KK6+0X375xT766CO3nDvvvLM99thj9ueff7r1rlWrllvO4PLr9YsvvtjVucr56aefwq999dVXri6feOIJu//++10der4MreMjjzzisipmz57tlqNDhw7h5c1Fn332md1zzz1uu2i/1V8FWF9++eVC33f33Xe7fUn1fO2117rvgPYbAAByBe1T2qfFaZ9qO6h+M659GkLCrVq1KqSq1d9U2Lp1a2jRokXubzaWR5nZUx5lZleZubCOpaXM9evXhyZPnuz+lsS2bdtCmzZtcn9ly5ZQqGHDUEithFiPMmVCoUaN8uYrqegyC9OnT59Qr169ws979uwZ+tvf/hZ+3rhx49BHH30Ufn788ceHLrjgAvf/jRs3hurWrRsaMWJEuMxZs2a532lf9jXXXBPaElgZ1WP16tVD77//fnhZNf+cOXPC8zz99NOhDh06RCxn+/bt3fRY6zly5MhQu3btCp1/zJgxrpyivPHGG6EDDzwwtGHDBvdcn12/fv3w6927dw/ddtttEXUbvfxPPfVUxPIvXbo0tNNOO0Usz5AhQ0KnnnpqeD8cMGBA6NBDD3X//+mnn9wy+G2ov3r99ttvjyhD6+g99thjoeHDh2/3vpzqdlaiderUKfTggw9GTHv99ddDLVu2LPA9EydODO24446hdevWRewH2q+LI5V1VxqOnaWtPMrMrjJzYR1zpcxcWMeSlpnINmoq2qfRZRaG9mlmt09FZaltqnK3p30az75cnDYWXRMAAIAI6nOrsAveajbNm5c331FHJXdZdMVfV7KDV7pPPfVUl2Xg6Sq2t27dOpeNcNJJJ7nnykrQlfz69evnm9//DWZyim4Vr1GjhrttPPrzC1PYfHqtbNmyBc6/aNEidwU/Hv369XMZCspOkIMOOsiuv/76uJajoHmUTdCyZcvwc2VFKNNi1KhR4eXu2LGju11NlK1x4oknuu2Q1442O++882zPPfd0mQ277rqrK8OXo8/fa6+97JhjjrFc73dtwoQJ+TJZmzZtatOnT3dZGcoYiaaMjqOOOsqqVKkSnnb66aenZJkBAMgEtE9pn24sRvvUUyayuoBS5nKmtE8JxAIAgAga+CCR822Pzz//3LZu3Wq77757eFqXGB2AjRs3zgWy3nvvPddA+9vf/uam16lTxzXA3nnnHdcwK4zmUXm6LemTTz4JN+qCt1vpdikp6NYlP3358uXu72WXXRZukBZ2a5tuBdMtVoMGDSp03mXLltnUqVMj6mPvvfd2j5J66qmnrFu3bhHrpBMLnSQEy9EtW3r4W7ui67Nhw4a2efNmV4dnnHGGm6Ztd+edd7pb1FSnuU6B1i1btljVqlUjplerVs39nTZtWr5ArPYP1bf2peHDh7vnv//+uzuJuP32261cIaOT6IRFD88PtqHP0COZ9PkK0ie7nHSWmQvrSJnZUx5lZk95palM/x7/KAn/voUL9bfoYJ7mK2FR+cosaJnVX7vaOPrN9vP4tk/wPWpb6rf9/ffft/79+7v2qV5Xe1Lt07ffftu1p4LviS7Tt0/VtdHHH39sTZo0iZjntddey9c+jf4M9Uuvab59evnll1uFChUKLFPPtX66fV/dJ6h9Wtj28+3TYH0owKlHrPeFotY3ur71V+3Ts846y62Tn0fdOah9Giynffv27qHnai+dcMIJEZ/foEED1z7VNtM28uumrhbUtZbqNJ590y9DQW2o4nwvkhaIVd8R//3vf61SpUp2yCGHuBMhAACQ+TT6bCLn2x6+UVNU40aNLmUMqi8uNS4nT57sAoDy4osv2uuvv+4yFZR50KJFi5ifoYawHj/88IMdfvjhLri73377hV/v2rVrRHB25syZ+T5D/WmpDy9RFoCu0isDsjDKqNBACIUF1IpbH/FSPSkwuM8++xSrHL0n+jX/XK8F60j1r/Zg9+7d7ZtvvnH/zwYlaeuuWLHC/S1fPrIJ7p/716NPbtauXesC2dqn6v3vi6d95uqrr3b9pBVEJ07+exC0dOlS27BhgyWT9gedLOmkJTrbJlvKzIV1pMzsKY8ys6e80lSmAmB6n9oGwfZBvHzQTOrWLRtXCKtu3a22ZUvJI7HBMgvK4tR6ifoYLawujj32WHcRW8FMZWT+/PPPLmFA1C/smDFj7OSTT3btU5/pGl1P+gw91D494ogj3ICfwfap7pDx7VPVtdpewc/Q+qhde/7557vnN954o2szK2ip9dTr0fPrc5SRq/5efZCysO2nevD1UtB8PoC5efPmiHoN7hs+cK+Aqz6zTZs24eXRPMF6j1WO/yz/mt7r36ML0779qjpS/SsYrfbpF198UWT71L9Xwexgxq23Zs0aS2sgVpWmBukuu+xi//nPf9yOMnfuXHdi5BuPAAAgM7Vrlzf6rAY+iHWBWG0nva75ku3ggw92DVxdZd93330jMjYLygJVwPWUU06x6667zmrWrOkae2pcqfGrzFM1otQI9b777ruIBq1uX2rUqJFroAWnF5cCZRpEQAMQFERX59u1a+cyT3/99dciP7Nu3bouC0D1oeCzp9vdtK56xEsN0ueeey5mFq4yGJSlqXJat24dnq512WOPPeywww5zZQZp+RVMPvTQQ8PTWrVqZY0bN7a+ffu6NuFtt93msg9Ku5K2df2JR6zMk1jTxZ9MKOM4+Nm6aKBsmptuusllxxR0m6C+B8GMWO3bChrr9sZk0vdM66uyUhkkSGWZubCOlJk95VFm9pRXmsrURT8FqHTBMfoiZHEo8KXuBho2DP2vfZo/QFqmTMi1T486qpzFcW07rjILonaQ6kABvcLap2oX+fU+7bTTXBtVA0n59prukNFFcV1UVRtAv9t+/uj26QEHHOB+w1944QV3278XrFstk7ZRsK599wN+moLCuiVfmbpavljz60Kz2qdqx/n2aWHbT4FktU9nzJjhAsux2qdaBj12iKrX6OVXkPWll15y7VPfjYBffrXR1T5VvautGqt9qgvlwWXVc62ngtiars/Seml51YZS4oXPji2Mf68GN4sVtC1OokFSvrE+0q2RyNRXmDa0+mp49dVXk1EcAABIIDVefbdU0YkA/vmQIXnzJZuu8OuWbI1GG+xnU10QeNHBq2+//dYF/3ygSbc09ejRw2UdBPvi8t59993w1XL5448/XAPvwAMPjPj8wm4bK2g5NNKr+v8MZlcE59ct5so2KI577rnHjQSr0WQ9ZZ3629ujb/+LldWq1xVkvfbaa2Nme6gvUt32rmxdv9z6nH/961+uEaplePPNNyOWQY363r17u5MEX4Z/r96jwLYC4bo9r7QraVvXn3j5zBHPdx8QK5C+4447ur/RXWXoREDL8OWXXxZYnrrF0Pcg+BB/MpTshz95SuUj1WXmwjpSZvaUR5nZU15pKtMH00ryEP+3fPkyNnSo/q/XIn/v8p6XsSFDFFQsk7AyC5pHbTu1T9X28dMUdFYbxz9XOyj4HgVW1T71SQLqakB3Ual9qtvn/1qXvPnV1tXFWP9cd82ofaogbHTbLbjs0csdazmi26fR8y9evNhlikbXSWEPtQ1HjBjh2ul+mgKq1atXD3+ub5+W+d/z6M8VtU91EdnvO8F5FLhW+1RdJvgLA/qcf//73y7YqmVQW9Uvg6gtq75rVfd+ml9nvUftUwXCP/jgg7j2j8L29bRmxOqKgG55Cg4ooJRfNc4BAEDmUzeso0eb6ac7ODCCMg0UhI3RTWvSqAGl26sVsFRwVI0gNX5Xrlxpjz/+uC1cuNB12q+Gmxqouv39rbfecvPdfPPN7v3q6F9BM2UdPPnkk+5zdWvYJZdc4hqaatSpcacAmd6vjASVp6vo6pdT7r33XncruAZoUCapsh4eeughtywKMCpL8o033nANcQ3AoGwC36+Xshc0/wMPPGBXXXWV+0w9V5tJ86i7BL0m6kNMy+UDmtF8/7caHEtX/hVsO/PMM8ONSfUDpuCdPltZm6ojUT+tui1OAy+oYaz51fhXkFoNZy2/putkQLfAKWu4cuXKLutSWa5qYCrgKPrckSNHukCuMgrUYFdWrzILZPz48e6zlIWsW9uuuOIKF2zU56k/WtV9nz59rLQqaVtXdaV69321errVU2J1m6EyFIQNXiwQfwJTnIY/AAClGe1T2qc3xtk+VbtU7VO1vVTfmdQ+LRMqaY/JKJAa17rKoUZ1sm/7El0J0BdKtyumojGe6vIoM3vKo8zsKjMX1rG0lKmG1Zw5c9yV7ZL0v+n7htItN9FX2JXQqNFnNTCX7opWdwSJyIQtrMxE0hVv3/dqqsoMyoUyE1lePPtyqttZiaZ+2Y4//nh3MuU9//zzrtsG3f4Xqw6vvPJKd3ufTuA8ndzpZExdIgQzaQqTyrorDcfO0lYeZWZXmbmwjrlSZi6sY0nLTFYbNVnt08LKTCTap2VKXZlF7cvFaWOV+BurNOqiFDSiMAAAKB3URlSfXOeck/c3Fd0RJFI8A2ABqWzrKtti9OjREYNMKKh61113uZMEZcWoDzRlbXjK/lBXF8qA8dQNgrI24g3CAgCQLWifojQrcdcESmFWZ7eFUX9lGj0YAAAAKE2S1dbt2LGju71PA5hpsIjZs2e7W/d0C6KsW7fOZbmuXbs2/B51TaBBwfQeZWIsW7bM3fan5wAAAMiBQKz6jlCDsKCR03SVX31UDBs2bHuWDwAAAEi5ZLZ1O3fu7B6xaBAO9S8XTSMnK3MWAAAAORiIVafC6sw2mFL98ccfu36vfONUWQIAAABAaUNbFwAAABkTiD3//PPdoALRnTer36tgB8QAAABAaUNbFwAAAIlWNpGdC7/77rv29NNPh58HR4MFAAAASgvaugAAAMiYQOyaNWsinm/evNmN9HrllVfadddd5zIGFi9enIhlBAAAAFKKti4AAAAypmuC6dOn2wcffOD6yfrjjz9s0KBBdsUVV1izZs2sS5cu9vnnn1uVKlVs/PjxiV1iAAAAIMlo6wIAACBjArE9e/a04447zmUGSMOGDW3gwIGuQTpp0iT32ty5cxO5rAAAAEBK0NYFAABAxnRNcMopp9hLL71kJ554ol100UWuQaqGqTRv3tyNKlujRo1ELisAAACQErR1AQAAkDEZsXL22We7RyyNGze2q666ykKhUDiTAAAAACgtaOsCAAAgIwfriqVv3740TAEAAFDq0NYFAABAxgRi//nPfxY5zz/+8Y+SfjwAAEBWW7Fihb3++ut28cUXuwGhkFlo6wIAgFxD+zSDuyZ47rnnXAZA+fKxP2Lz5s324osv2j333LM9ywcAANJp21azpRPN1i8yq1zPrE47s7Ll0r1Updqvv/5q11xzjetz9KCDDrJDDz2U9lIGoq0LAECGon2acLRPS0Egdu3atTZx4sQCX1fjdMmSJSX9eAAAkG7zxph909vsz/l/TavS0OyAoWaNuqRzyUqtlStX2plnnmk33HCDjRkzpsAgH9KPti4AABmI9mnC0T5NrRLXrqLk7777rpUrV86NJtusWbN88/Tp02d7lw8AAKSrkTuxq5mFIqf/uSBvervRNHZL4JlnnrH777/fOnbsmO5FQRFo6wIAkGFonyYF7dNS0kdsq1atXNry5ZdfblOmTLGHHnrIRo0aZX/++Wd4HvUpAQAASuHtXso0iG7kOv+b9k2fvPmS7J133rFOnTq5W8SHDx/upt13331WoUIFFwSbPz8vG2LGjBl22WWX2aOPPmoDBgywF154wU2fNm2aG9le77/tttvcbVeaZ6eddrJjjjnG3n//fRs/frwdd9xxVrt2bffali1bYi7LHXfcYaeccooNGzbMTjvtNNtll13s8ccft+uvv94to8+SVNkjR450y9m7d2+XWen98ccf1qhRI7v55pvd+qiP0bvuuitfmVoO3RKmeUaMGGF77LGH7bfffq7PrgULFrh11DqpLfbLL7/Yhx9+aMcee2x4HTZu3GiPPfaYe67pWk+1166++mr3Pr3f193XX39tzz77rCvn0ksvtQ8++CBJW7N0oa0LAEAGoX0ac1lon5ZCoQRavnx56Kmnngo9/PDDoQkTJoRy1apVq3QUcH9TYevWraFFixa5v9lYHmVmT3mUmV1l5sI6lpYy169fH5o8ebL7WxLbtm0Lbdq0yf11fv8oFHrRin5ovhLKV2Yh1q5dG2revHnohRdecM9ffvnl0OjRo8Ovr1mzJtSyZcvQggULwtMOO+yw0Jdffun+P3v2bPe7vGXLlnCZ7du3D40cOTI8v9ovHTp0KHQ5Bg0a5MqSp59+2n2Gd9ttt7m/w4cPD+2xxx7h6f369Qv16NEjvJ4333yze/2PP/4Iz/Pcc8+Funfvnq88leHp9f79+0fUn9Zpzpw5EfNoHYJ1G72evi6CGjZsGHrppZfCbbmddtopNHPmzFAytmUi9uVUt7Oyqa2byrorDcfO0lYeZWZXmbmwjrlSZi6sY0nLTGgbNQXt03xlFoL2aWa3T1PdRi1OG6vEGbGxKHq/9957u4j2CSecYMcff3wiPx4AAKSCBj5I5HzbqWrVqu6KuK7sT5482ebMmeP6sfJ0hV1X8evXrx+epgwCDaQkusIuZcv+1ezRtOjnRalXr55Vq1Yt5nuaN2/u/ipLoGfPnuHp7dq1i+hnVP1uHXDAAVarVq3wtL/97W/28ssv27fffhtRXvDz9f/o50H/+te/YvbnFc96Xnnllda2bdtwW65FixbutnzkR1sXAIA0oX0aE+3T0ichPfAuXrzYnn/+edevhFKvTzrpJHvppZfs5JNPLvFnjh071u0Y2nFmzZrlNsC5555b5PtWr15tAwcOtJo1a9ott9yS73U1nJWm3bp1a1uxYoVt2rTJpYIHd45Fixa5VGxt6A0bNrj1U7q3vmgAAGQ9jT6byPkS4PDDD7cLL7zQBb7U1gj66quv3O1Vaod4oVDImjRpktBl6NGjR5Gv7bPPPu6WsIcfftjdnjZv3jzbuvWvW+TU7mjQoEHEezXfzjvvbJ988ontv//+xV4u3cKlOlGjWm2m4urXr5+999579vbbb7tlV1squMxITlsXAAAUA+3TmGif5lAgVn1M/Pvf/7ann37aDWSgviEuuugiO//8861u3bpunu+//9723XffYn/2Z599Zvfcc4+LdvvIeOfOnV3E/Oyzz475nrlz57p+KipXruyWSdHzWCPBqeGsL4R2KBk8eLDrj0L9afj10lUKRf733HNPN+2NN96wrl27un5AAADIenXa5Y0+q4EPYvbDVSbvdc2XQrqIWrFiRddPpxq9ni6a7rrrrhFX+tNFjUW1K9R2UKP3o48+CvcHJspYiJUZoD64ovvhUmO9KNu2bXON6rvvvjucYVEc6qurS5cubiCqBx980HbYYQfXfxiS29YFAADFRPu0xGifZpYSd02gTFV1Oty0aVP74osv7Mcff7Trrrsu3DCVWBmp8ejfv79169YtIj1ZkXx13FsQXVVQ8Pb222+3KlWqxJxHO4J2Oh+Ele7du7vOf32HwArAKuDrg7CiDo//+9//2ueff16i9QEAoFQpW87sgKH/exJ9q9D/nh8wJG++FJk+fbqtX7/eXnvtNevbt6+7AOvpSrsGPYgWfStVKuhCsJZP7Q3fkPQ0YMHBBx9ss2fPjnjPunXrbNmyZXbYYYdFTC9oUIYgDXhwySWXuAZqSeikQcHEIUOGhD/DL7OWN5cls60LAACKifZpidE+zZJA7MKFC61Dhw4u1fqRRx5xGQL+oeCmRppV9kBxaSeeMGGCi3wHqRGsnTx65yiOcePG5ftcjdamLgeU8lzQPOXKlbPGjRuTEQsAyB2Nupi1G21WJfI2JZdpoOl6PUXU1hg0aJAbMVUjs15xxRUuu0BX20XP1SjU6LKegmb+Nqh4rtwXZz5R2bHmV/bDjjvuGLEcvsGq0WQ1Gu3HH3/sRsf1dMvaqaeeakceeaT985//dI1l9TMWDPhFl+Wfqxsl3/9XcdbJT9fy1qhRw7V1/C34S5Ysccus5c1lyWrrAgCAEqJ9Wijap1neNYEaoeoKoLCdUrf4F5cCrarc6P5YfefDuqIQHSiNlwK5HTt2zDddn+2vVGgeNawLmyeaIvPBKwrqu8J/CfyXMJn8ly0VZaWjPMrMnvIoM7vKzIV1LC1l+vn9oyT8+yLe3/AMs/qnmS2dmDfwgfrc0u1eyjQoYTlFlhnlueeec42/NWvW2B9//OEuoOr/aixqEIHevXvbEUcc4Z7rrhj1Y6WGmzr11+1hv/zyiw0dmpc9of7fq1ev7u640W/666+/7i4A+wuxP/30kz3wwAN2zTXXxLw9S5YuXWqvvvqqu81Kn61la9++vR1yyCHhK/j6jN9//92t17HHHmuffvqpux1MDzVKdSX/vvvus4YNG7psA/V3pTtyNH/Lli3tyy+/tKeeesrdCaRp6ptUn6Hy9tprL1eWb4NpcIiff/7ZBQ11i5nW4aGHHnLttCeeeMI1tDUAg19P3/i/6aabXN/7OmFQxoHWWe0f1c2TTz7pGuTKBi3uSUJJ97/g5/h9v6D9P1XfxWS1dQEAwHZQsLVB59jt0xRR+/Tee+91bVKNPaT2qdoFvn3ap08f1z5Vu1TtUyUbBtunar8F26d6TXz7VIFIUTKg2na6PT+e9qnagmovqp0ZbJ+qnXn//fe7gGZ0+1Sfq8CpuivQPMH26SuvvOLerzai2qfqrsnfqR5sn2og02D7VGMzaXp0+/Tiiy92f3371K+nb5+q/an2qerohx9+cO183z5V1wRqv15++eVW2pUJlbDFrB1JG7YwivwfffTRxfpcbUilcGsnOOqoo8LTFXlXAFYnPkUN2rXbbru5EwsNsBWknVYDc0VPV7cG6lxZJyw6QVLEP9ihsigjQv1++MzZIH3enXfemW+6gro64Us2nZCsWrXKDVAWHHkuW8qjzOwpjzKzq8xcWMfSUqb6stT8+j2rVKlSsctTU0CNLV11jmd01kSIt0zVRaLqPZPXM5Vl6oRBgV5dOI7Vfkl0ecWhBrlu69O+X9DtbDrpUcBa+7w/cUmGZLV100nbXHWb7Lrz311lryhzJpXHzlSWmQvrSJnZUx5lZk95palM/a4rlqM7nEvaRlWinmI5qWxHFVVmotunmbiOqS5zxYoVLhlAbZW77rorJWUWR1H7cnHaWCXOiPUNUy3M1KlT3Uq1bdvWNcTV58VBBx1Uooapr5yCUpy3J9NCnx3r/cEMonjmiTWam/oMC26ARo0aWZ06dZLeyPUHAS23ykvVD00qy6PM7CmPMrOrzFxYx9JSpn6LFZxSI6OgK+XxKGk/TtuDMlNfpvYrZRQoCLs9+0u85RWHlkf7vDJLCjphK8mJXCa1dQEAQOmWqnOCXFKrVi3Xj20ikgQy3Xa1vpVCrXRsBR6POeYY10+WsiEUJVbqs0ZIq1y5crE+UxFk2bRpU8R0f+u/f70k9N7oz/Wf7T+3sHmCfWEEKVNWj1hfzlR9QXVykM3lUWb2lEeZ2VVmLqxjaShT82h+/yguXWj070vlVXHKTG+ZuvUsEcuUyHX0+3Bh+34qv4fJaOsCAAAgtgYNovr/zUIlbsmq4an+L5Q6PGPGDNedgNetWzcXyVbDtbjU/YAauL6fVU/pvaK+K0pKt7FFf67/bP+58cwDAABQ2v39739P9yJktGS1dQEAAJC77dMSZ8TOnDnTDWzhVahQIeL1XXfdNWZAsyhVqlRxfbTq84PUAG7cuLELlJaUBlT4/PPPI6bNmzfPZbsqy8HPo5HvgpQhq/7K1KExAAAAsl+y2roAAADIXSXOiNWAWEXxI6AVl0ZhGz16tOtU19MoxOqwV7eraYQ59dGlARJiKWik3V69erlR6ObPnx/xuRoVd/fdd3fPzz77bJeR+/XXX4fnefPNN+3www+3Tp06lWh9AAAAULoks60LAACA3FTijFgFQ/3oYxI9kJUyTfUoiY4dO1r//v3dLV+tWrWy2bNn25lnnmndu3d3r69bt85lqK5duzb8npUrV9rgwYNt8eLFLtD64osvukzW1q1bW8+ePcODU7z99ts2cOBA23vvvd179BnDhg2LGADivffec1mxkyZNcp+h9XjjjTdKtC4AAAAofZLZ1gUAAEBuKnEg9sQTT3QZorfccosdeOCBrnGqhxqkCmRqpLOnn366xAvWuXNn94hFo9QqiBqkQbZuvfVWN2Lv448/Hl4ejWwb1KZNm4jAa0GDVzz66KMlXnYAAACUbslu6wIAACD3lDgQe+GFF9pvv/1mp5xySjhDQIFQUTD0kUceCfe7mgrqsiDYd1dw1F0AAACgNLd1AQAAkMOBWN+X6xlnnGHPPfecTZkyxQU999lnn4g+VwEAAIDSiLYuAAAAMiYQK2qM3n///YlZGgAAACCD0NYFAABAomz3ffsfffSRnXfeebbffvvZ/vvv7zIEvvrqq8QsHQAAAJBGtHUBAACQERmx119/vQ0ePDg8WJZ8//337vatQYMGWd++fROzlAAAIC22bttqE3+baIvWLLJ61etZu8btrFzZculeLCAlaOsCAJB5aJ8iJwOxw4cPt1deecUeeughlyVQq1YtN3358uX2zDPP2H333Wdt2rSxk08+OZHLCwAAUmTMlDHWe1xvm796fnhawxoNbegJQ63LHl3SumxAstHWBQAg89A+Rc52TTBq1Ch3W9ZVV10VbphK7dq1XfbAF198YY8//niilhMAAKS4kdv11a4RjVxZsHqBm67XgWxGWxcAgMxC+xQ5nRG71157Wb169Qp8vUmTJtaqVauSfjwAAEjj7V7KNAhZKN9rmlbGylifcX2sc6vOKbkNbMuWLfbYY4/Zyy+/bBdeeKGFQiHr37+/7b333nbjjTdax44d7c4777SKFSvazjvvbNOmTbO7777bdthhB+vZs6e9++67dtNNN7n3adT7b7/91s444wzr1q1buIwZM2a4AZk0MNOSJUusRYsWdv7559uUKVNs6NChLjtSr+v2dAXnFIxTGfo80TRlT7Zs2dJWrlzplqV3795JrxskD21dAAAyR6a1TxPVRtV827ZtswoVKtjXX3+dkjbqNddck5L6QYIDsdpxiqIdKWj69Olu4wMAgMylPreiMw2iG7vzVs9z8x2121FJX57y5cu7BmOVKlXs73//u5v20ksvudvFjz32WLv22mtdUKxPnz7uNWUpKvD6yCOPuPkOPfRQ14h94oknXIN5w4YNtueee7oGateuXW3t2rV2yimnuEGZ6tev7z7j8MMPd0G2gw46yH2WGrnKgpTu3btbnTp1rHnz5m7gpp9//tmuvPJKmzRpUrh9pEa3GuL6i9KJti4AAJkj09qniWyjDhs2zH3WunXrXLdHqWij6oFS1jWBTmA+/vjjAl///PPPrWnTphHTtBMCAIDMpoEPEjlfomzevDnftAULFrjGrBqr3gknnGAvvvhi+HmlSpXsiCOOCD+vVq2aayz369fPPX/00UetUaNG4QauHHfcceHPKFOmTESZs2fPdn/VUJZbb73VTjzxxIjAnRrg//jHP+z3339PyLoj9WjrAgCQOTK1fVoa26j33nsvbdTSmBGrlOp77rnHDjvsMJfaHPTHH3+4frO0wdVIFWWffPjhh9u/xAAAIKk0+mwi50sUf4tV0HfffeeyXMeNG+cyCWTr1q3WqVMn1yguKKuxbdu2NmDAAFu6dKm7ZUsZBxqAydOtZcpgCNLra9assXfeecc+/fRTd9uZKEtBbZ6ghg0buvLVDjr11FMTsv5ILdq6AABkjkxtn5bWNup///vfiCAxSkEg9vnnn7c///zTpTjHosi+Nrq3fv1627RpU0mLAwAAKdKucTs3+qwGPojVD5f64NLrmi9V1AitXLlyvukKfokakjvuuGN4+sUXX1zo56kR6xvO+oxdd93V9dVVGP/6pZde6hrRylbQ7WJqZKtvryD/XK+hdKKtCwBA5sjE9qnQRkXKuibYZZddXKr1nDlz4noo7fnII48saXEAACBFNMDB0BOGhhu1Qf75kBOGpGwgBBk/frwddVT+/r6UragsA2UvBn3//fcRDU/fqPU0YNcee+zhBjRo165dvvf7eWJRdqT65/J9a2kZfvvtt4h5fv31VytXrpzr+wulE21dAAAyRya2T0tzG/WQQw4p5poi7YFYjexWvXr1Yr3nqquuKmlxAAAghbrs0cVGdxttDWo0iJiuTANN1+upoizDX375xRo3bhwxXQ3XBg0a2HXXXef64PJ029ebb74ZcZtYMHNRI8Y++eST9sADD7jnV1xxhcsKUEPa+/HHH23WrFnhcmI1gNWHqOj2dZWn7ElvyJAh1rt3b9evF0on2roAAGSWTGqfJqqNGuyPPlVtVA0wRhu1FHZNoA5+i+uss84qaXEAACDF1Jjt3KqzG31WAx+ozy3d7pXKTINXX33VBg8ebPvvv78baVaURTB16lR77rnnrGrVqm5QrPvvv98FwTR4kvq96tWrV8TnqLGpRq0GNVAmgvrS0mAHUqNGDfvkk0/s9ttvtwkTJrjnO+20k1144YU2efJkGzo0L/tC71emwZdffml169a1hx56yE1XRsHIkSPdQE277767LVmyxP1VIA+lF21dAAAyTya0TxPZRlWfrfoc9RmrIGoq2qh9+/Z1QWGUskBsNEXkn3rqKddB8EknneRGgwMAAKWbGrVH7Zb/dqtUeeyxx8KN3AoVKoSnX3LJJS6gqmBnt27dXIOyMC1atLAePXq4rALdJhY9yqxGo1VDNZpGnR0+fLh7FEYj3gZHvfViZSqgdKKtCwBAZkh3+zTRbdTzzz8/Zvs0WW1U2qelpGsC9Xt19tlnW82aNa158+bhVGlRZF4juynar7Trk08+2S677LJkLTMAAMgR6nNL/awGG7iirIGDDjrIDUhQFGUn0OBEUWjrAgCAeNFGRVIzYtVPhQYfmD17tnuuTABF95cuXWoDBgxwGSZ16tRxmQGK4r/77ruuXwt1LKzIPgAAQHHplqlmzZoVmUWgEWU1gn2sfrvUXvnuu+9s3bp1rmH8t7/9LYlLjNKKti4AAEhXG1WDZ3Xv3j2JS4xSF4i9++673cnL66+/7qL6apy++OKLNnDgQNcB8emnn27//Oc/3Tyifi+UJaBUbRqnAACgJOJplCqDsSDKUNCABHqIMg7UNQEQjbYuAABIRxuV9mnuiSsQ++GHH9qnn35qtWvXds91y9ZNN91k++23nxsF7qefforoy0KNVDVMW7VqlbwlBwAAABKAti4AAAAypo9YZQL4hmmQRnJr3759zA6FlX7dsmXLxCwlAAAoEn1MobRL1z5MWxcAgOShjYrSLpTAfTiuQKy/DSuWxo0bF/ha9erVS7ZUAAAgbvqdVqBIfUwBpdmff/5ZZNszGWjrAgCQvN9X//sOlFY6z9L5ViLaqOW3N/IbK0MAAACktp8q3UqtgYU2btxoNWrUcAMKxfsb7fumKs57thdlZk+ZiShPn6GTtCVLltiOO+7o9ulUoq0LAEDi6fdcv+v6fZcqVaoU63eVdhRlprNM//7Vq1e7R6LaqOXjHRGuIIWtTGHvAwAAibPrrrta5cqVXUNXDYXiNjK2bdtmZcuWTWnDiDKzo8xElqcGrvblVKOtCwBAcvjfdR+MLQ7aUZSZCWUq+FqvXj2X+JIIcQViP/74Y7v44otjRn5//PFHmzlzZsyG6YQJExKykAAAoHBqXCiIpQaCfoOLM/qqGijLly93fWSqoZIKlJk9ZSaqPN3qlepMWI+2LgAAyWujKohVt25d27x5c7HeSzuKMtNdprJp1T5MZPA4rkDs2rVr7emnny7w9S+//DLmdG7lAgAgtfTbqwaDHsVppCgIpsGHUtkwoszsKDMd65hotHUBAEguBbOKe8GVdhRlZmMbNa6ztN12283eeustq1q1arEatKeddtr2LBsAAACQdLR1AQAAkDGB2D333NPatGlT7A8vyXsAAACAVKKtCwAAgFSIKzf3rrvuKtGHl/R9AAAAQKrQ1gUAAEDGBGL33XffEn14Sd8HAAAApAptXQAAAKRC5vRWCwAAAAAAAABZikAsAAAAAAAAACQZgVgAAAAAAAAASLLyyS4AAAAAwF/Gjh1rEydOtObNm9usWbOsbdu2du6558b9/mXLltnZZ59tH3zwQVKXEwAAAIlFIBYAAABIkc8++8zuuecemzRpkpUpU8ZN69y5s5UtW9YFV+Nx5ZVX2syZM5O8pAAAAEg0uiYAAAAAUqR///7WrVu3cBBWevToYQMGDIjr/a+88orVr18/iUsIAACAZMm5QOyaNWts4cKFtmDBAps/f3748fvvv4fn2bx5s/3xxx82d+5cW7Roka1du9a2bduW1uUGAABA6bZ+/XqbMGGCNWvWLGJ606ZNbfr06TZ79uxC3z9v3jzXRt13332TvKQAAADIqa4JStJ3lm7xeu2116x169Yu2FqrVi3r06dP+PV33nnHTjrppJjvPfnkk+2tt95y/69SpYpt2bIl/FqnTp1sxIgR+RrNAAAAQLwUaFUbs2rVqhHTq1Wr5v5OmzatwPZmKBSyJ5980mXOPvfcc3GVt3HjRvfwVq9e7f4qwSDZSQb6fC1zKpMZUl1mLqwjZWZPeZSZPeVRZnaVmQvrmAtlbitGGeWzpe8sNWwvvPBC++GHH6xSpUpuWu/eve3ee++1m266yT3/6aef7IUXXrAaNWpE3A42bNgwe/TRRyP63erSpYvLWmjTpo01btw4yWsMAACAbLdixQr3t3z5yCa4f+5fj+WZZ55xXRioPRyvQYMG2Z133plv+tKlS23Dhg2W7BOSVatWuROg4ixzaSozF9aRMrOnPMrMnvIoM7vKzIV1zIUy16xZU7oDsQX1ndWvX78CA7EDBw60E044IRyE9e9RNus111xjlStXdhV/3nnnRbxv3LhxblqTJk3C05RJ26FDh6SsGwAAAHKTb9vqhCDIP4+e7k2dOtUqVqxY7Luz1Ha+7rrrIjJiGzVqZHXq1HGJCck++dH6qqxUnnClssxcWEfKzJ7yKDN7yqPM7CozF9YxF8qsFIhFlrpArO87S8HTgvrOitUIVUD1xhtvzPceRb8///xzF5C99tprI15fvny5vf/++/bAAw8kaW0AAACAPDVr1nR/N23aFDHddx/gXw9SVwajR4+22267rdjlKXirRzSdjKTiJEgnP6kqK11l5sI6Umb2lEeZ2VMeZWZXmbmwjtleZtlifH75bOg7a926da5P2MLeo0BsuXLlIl5XH1u33357vmXQQF6DBw+2nXbayWUgKEM2OsibKf1v+XLow4QyS0N5lJldZebCOuZKmbmwjpSZXeWVVmrDqj3q24qeEgekRYsW+d7z9ddfu4Flb7755ohp6sZA0/bee+98d3wBAAAgM5XPhr6zSvKeb7/91gVPd9lll3yvqc8s9S/rI9rt2rVz2QSalmn9bwl9mFBmaSmPMrOrzFxYx1wpMxfWkTKzp7zi9MGVaTQg7JFHHmkzZ86MmD5jxgw3JkHLli3zvefQQw91j6A77rjDfcY//vGPpC8zAAAAsjgQW5K+s0rynvvuu89OPfXUmMvw/PPPRzxX37PKnr3iiiusQoUKGdX/ltCHCWWWlvIoM7vKzIV1zJUyc2EdKTN7yitOH1yZSG3KG264wfr27RtOGhg1apTdddddrh4nT55s55xzjj344IN29NFHx/yMrVu3lurMYAAAgFxVPhv6zirue1auXGljxoxxDeF46MRCmR7qo3avvfbKuP63hD5MKLO0lEeZ2VVmLqxjrpSZC+tImdlRXirrMRk6duzoBqZVILZVq1auW64zzzzTunfvHu5ya+7cubZ27dp87/3111/tiSeesJdfftl1V3DllVfacccdZ6effnoa1gQAAAClPhBbkr6z1BdsvXr14n6PBgNT0Fbvida2bVs76qijbOjQofkCups3b96udQMAAAA6d+7sHrEcdNBBLmkgFnVfoO6w7r77bheQJjMWAACgdCmfDX1nibIBYr1Hn3fEEUfk6x9Wogf3ksqVK7tBD4LmzJnjsmJjZcMCAAAAqRCddazkhejBaAEAAJC5MvLeLnUZMHr0aNuyZUt4WnTfWcpcHT9+fPh1jRqr58EBHPQeTVfGbNCSJUtiDu4lvXr1sk6dOoWfL1++3F599VV76KGHbIcddkj4ugIAAAAAAADIfhmXEVvSvrNat25tzzzzjAu8KqN10aJF1qRJE7vxxhvzff4ee+zhbvuK5fzzz7ennnrKXnnlFVfO1KlTbcSIEXbiiScmcY0BAAAAAAAAZLOMDMSWtO8sdWmgR1Guvvpq94hFGbcXX3xxCZYYAAAAAAAAAEpR1wQAAAAAAAAAkE0IxAIAAAAAAABAkhGIBQAAAAAAAIAkIxALAAAAAAAAAElGIBYAAAAAAAAAkoxALAAAAAAAAAAkGYFYAAAAAAAAAEgyArEAAAAAAAAAkGQEYgEAAAAAAAAgyQjEAgAAAAAAAECSEYgFAAAAAAAAgCQjEAsAAAAAAAAASUYgFgAAAAAAAACSjEAsAAAAAAAAACQZgVgAAAAAAAAASDICsQAAAAAAAACQZARiAQAAAAAAACDJCMQCAAAAAAAAQJIRiAUAAAAAAACAJCMQCwAAAAAAAABJRiAWAAAAAAAAAJKMQCwAAAAAAAAAJBmBWAAAAAAAAABIMgKxAAAAAAAAAJBkBGIBAAAAAAAAIMkIxAIAAAAAAABAkhGIBQAAAAAAAIAkIxALAAAAAAAAAElGIBYAAAAAAAAAkoxALAAAAAAAAAAkGYFYAAAAAAAAAEgyArEAAAAAAAAAkGQEYgEAAAAAAAAgyQjEAgAAAAAAAECSEYgFAAAAAAAAgCQjEAsAAAAAAAAASVbecszWrVtt9erVtmbNGitXrpxVr17dqlat6v4PAAAAAAAAADkViB07dqxNnDjRmjdvbrNmzbK2bdvaueeeW+h7Jk2aZK+99pq1bt3aFi5caLVq1bI+ffpEzKPXZs6cGX5+wAEH2BNPPGH7779/eNqUKVPs8ccfd/OuWLHCNm3aZLfddpuVL5+x1QUAAAAAAAAgg2VkZPGzzz6ze+65xwVWy5Qp46Z17tzZypYta2effXbM98yePdsuvPBC++GHH6xSpUpuWu/eve3ee++1m266KTzf6aefbmeccYatXLnSWrRo4R5Bmn7SSSfZV199ZTvvvLObNnjwYLv66qtt2LBhSVxrAAAAAAAAANkqI/uI7d+/v3Xr1i0chJUePXrYgAEDCnzPwIED7YQTTggHYf17Bg0aZOvXrw9PUzcEhx9+uAu2Rgdh5eGHH7Z99tknHISV7t2724gRI2z+/PkJWkMAAAAAAAAAuSTjArEKmk6YMMGaNWsWMb1p06Y2ffp0l/kay7hx42K+Z9WqVfb555/HXX6sz6ldu7YL4L733nvFWhcAAAAAAAAAyMiuCRRo3bJliwt8BlWrVs39nTZtWr5A6bp161yfsIW9p1OnTuGuB4YMGWI77bST/fbbb27QLmXT+v5fFezt2LFjvuXSZ+lzYtm4caN7eBoMTLZt2+YeyaYyQqFQSspKR3mUmT3lUWZ2lZkL65grZebCOlJmdpUHAAAAlEYZF4jV4FgSPTCWf+5fL+l7FHi9/PLLw10YXHDBBda3b1/XD6yfN9agXJoWq2xR9wd33nlnvulLly61DRs2WCpOSJT5q5Mg9aObbeVRZvaUR5nZVWYurGOulJkL60iZ2VOe2nIAAABAaZRxgVjfL6wa80H+efT04r5n5MiREfOoX9mePXvaDTfcYA0aNHCfFasMTYs1Xfr162fXXXddREZso0aNrE6dOlajRg1LxQmQllvlpeqEK5XlUWb2lEeZ2VVmLqxjrpSZC+tImdlTXnA8AAAAAKA0ybhAbM2aNd3fTZs2RUz3t/7717f3PZ5OGtQVwnfffecCsZo3+nP8ZxX0ORUrVnSPaDoZSdVJl06Asrk8ysye8igzu8rMhXXMlTJzYR0pMzvKS2U9AgAAAImUcS1Z9f9arly5cD+rnm55kxYtWsTsv7VevXpFvqdz587uEStYu3nzZve3ZcuW+T7Hf1assgEAAAAAAACg1AViq1SpYkceeaTNnDkzYvqMGTOscePGLlAay3HHHRfzPfq8I444IpytcdBBB0XMM2fOHKtQoUJ4nlifM2/ePBewPeaYYxKyjgAAAAAAAAByS8YFYmXAgAE2evRo12WAN2rUKLvrrrtcMHXy5MnWtm1bGz9+fPj1m2++2T0PDuCg92i6MmblkksucX3CehpIS33G6nPr1q3rpvXq1cumTZtm8+fPj/iciy66yHbfffekrzsAAAAAAACA7JNxfcRKx44drX///ta3b19r1aqVzZ49284880zr3r27e33dunU2d+5cW7t2bfg9rVu3tmeeecYFXvfee29btGiRNWnSxG688cbwPCeffLIL8L777ruuH9gpU6a4QbouuOCCiD5j3377bRs4cKD7nJUrV7pyhg0bluJaAAAAAAAAAJAtMjIQW1B/rp66F1CANJq6NNCjMF27di2y7DZt2hB4BQAAAAAAAJDdXRMAAAAAAAAAQDYhEAsAAAAAAAAASUYgFgAAAAAAAACSjEAsAAAAAAAAACQZgVgAAAAAAAAASDICsQAAAAAAAACQZOWTXQAAAAAAAACA9Nq61WzCBLOlS83q1DFr396sXDnLOlszeD3JiAUAAAAAAACy2JgxZrvtZnbMMWb335/3V881PZuMyfD1JCMWAAAASLGxY8faxIkTrXnz5jZr1ixr27atnXvuuQXOHwqF7KmnnrJ58+bZkiVLbOrUqXbJJZfYOeeck9LlBgAApY+CkF27qj1hVjaQkrlgQd700aPNunSxUm9MKVhPArEAAABACn322Wd2zz332KRJk6xMmTJuWufOna1s2bJ29tlnx3zPsGHDrH379nbxxRe757/88ovtv//+NnfuXLv55ptTuvzIHZl8aycQC/ssEPt70bt3XnAymqapKdKnj9oipfv7srWUrCddEwAAAAAp1L9/f+vWrVs4CCs9evSwAQMGFPieoUOH2hNPPBF+vueee9rpp5/uArqbN2+2TDsR+uSTvGCI/up5tsmFdUzXrZ3pqNtUl5kL+0861jMd+2wu7K8o/dty4kSz+fMLfl1Bynnz8uZLhlxZz3gRiAUAAABSZP369TZhwgRr1qxZxPSmTZva9OnTbfbs2THfV716ddclQfR71qxZY3/88YdlilwI3uXKOuoWzugTWn9rZ7LWNR11m+oyc2H/SVe9pnqfzYX9NdcCztn6W7JoUWLnK45cWc/ioGsCAAAAIEUUaN2yZYtVrVo1Ynq1atXc32nTpuUL0srXX38d87Nq1/7/9s4DPopq++NnU0gIpFACBAJIRxRQEUWKYAOsKPJ8ir089dmwYgd9FuzY9QmK7YkFRf8o2AsoogKCIIp0kNAhJAQSSHb/n9+dnc3M7GyyG3Z2k53f9/PZT7Izs3Pm3rlz5txzzz23iTRr1sxWVllZmfroFBUVqb9er1d9os2HH4qcdZael80rHo9P/UWHB9vffVfk9NPFEbk33CBSUOCVww7zyfz5XmnZUmT8+OjLc0MZ4XiALARs42MsJ5oNtt14o8ipp0Z3amc86jbWMt3QfuJRzni0WTe013i1HzfIjPW9bNHCnC/VKNN6XDTNA7eUE0RiV9ERSwghhBBCSIzYsWOH+puSYjbD9e/6/urYsmWLTJ8+XW6//XZTigMj48aNk3vvvdf2t6WlpRJN0P+YMEHksMO07x6PVzp23ImJgOLzab2iiRNF+vQxd5L2l9mzRR56SKR5c3SszDKxHfTtGx1ZbigjWLRIk4VPqHKCb78V6d697tZtrGW6pf3Eo5yxbrNuaK/xaj9ukBmPe9mli8jgwSLbtoWW2bSpdpxl8k2NcUs5dTBDKVzoiCWEEEIIISRG6E5Tn2UlCf27dXsobr75ZjnllFOUIzYU2HcjwsAMEbGtW7eWiopcqajICmxPTxdp1EikvFxb4MZKXp72d+tWEWs62pwckfr1RT79VOSzzyq3I/pk374kWbSoqVRUJJk6u0cdpf2PQF5EpyGzgiFwV5GZiShhpHIQKSw074PPGovwIAruuusQvVQpc+/eZFm0qEmgw4X9kKlHwSEQOStLk2fN6ICOoO7I2bQpOFpm8WKRzz+vXATEKM/rTVJRd9hnLKO1DlG/qGe7Oty1Cx254LIiQgs57ULJxC2eMyd4Ci3KifLa1WFqqtYZtZuiuWwZIrC1KEI9iskoE2Afftetm/m3qGc9QNuuDps0EalXD21RpKSkcvuPP2rtpzqZS5dqMq11iPaLdmxXh3r7Rv0YO966TP3c6LBbZQLI7N1bZCf68gZQDpQH17txowSht2+MrWDcwyhPqytEa3rk119zTfJ++klbSAbgvFaVgPuG+4fr2b3bvE9v33v3ao4I6zMCmSIemTdPk4ly688Inqm0NK3+UI92dRiOjvj++2BdYJSpA5kDB2p1iHaC9hKqDqvTEXqbtcqcPx+6rlImjkP70esQzwV+b1eHVekITFm3ltGoe/R7ZtQFeMbxrEOHop5C1WEoHfHzz5HLBLheXDfaA9pFJDoC97wq/YPzYmGkH34wRxrjvuD+2NVhdToCZa1OJvYjx6dRJtoyohxD1WFVOgJlramezc4WycjQnsVwdUQkuufIIzUdYvcOjFRHIEXH5ZdrMkWC35dwmup1CF1pLWvjxpHpCGs5dbvgt9+amsr522+a8xTvBP/EnaA6jERH6OUEWlS8pmf1ciLdPn6j16FdmoKa6IiKinQJFzpiCSGEEEIIiRHZ6LUJOsTmHrGeQkDfXxXPP/+8+v2bb74pSVWEkaSlpamPlddeS5K0tMrf9eghMny41rFCR8zKPfdof//v/4LzL+J3+P28eebOYEWFRwoL01VnyxiRNnmy5tAEt9yidWi++ELrcBoZMkRzJqxeLfLee8EOiyuu0HL4If+jUebmzQ1MMrF/zBgtHx3o31/LT4cO3auvms+LjpXut8Z1WjuE6IQanQa6vIoKeAM8gfIbywjQ4bv77sppmtZO3z/+gcXXRH7/3exo0WSIrF1btcw1a0SeflrrsBo56SSRI44QWbEiOBdffr7IZZdp/1vvOeoc59XLY5UJsA/lsv4WdQTHHnjjjWBH4aWXirRurTkb0Uk3RjSGIxNOhyVLgp2B55yjRTgtXCjy1VfmfXC8YQqstX3rMvVzw1FglQkg86+/tPZvBG3qoos0x4Pdc4O2hDaF68E1G+UBtKXS0pQgeZBzxhna/6+9FuyARNvHMwCH2y+/mPfhmcGzg/p5+WXtXhqfEcj0+TyBZwTXoz8jd90l0rGjyK+/apGjRiLRETNmBJfTKFMHzwkcHOefrzkn7c4bro6AkzIcmTgODqarrtIcOHAaz59vPm84OsLq/LLTPVZdgKjA007TnGPWsoajI2oiE2CsDo47DJZBF0SiI9AeqtI/0E94Jxh1LBg0SPugbb35ZmQ6As9rdTKx3yoTrzp9XHLKlMh0BOo1HD375JOaw9EI7inubSQ6IhLdA+cw7qkROBCvvrpmOuKOO7R2jXujy8zK8sjQodoAgW5SIGWA1Yl+3nmR6QhrOcGOHfWDyol7Avl//CEyfbr5vB061ExHwBn7zTfaAERpaaqpnKhTPGeoJ/DKK8FO55roiLKy8MN66YglhBBCCCEkRiD/a3JyciBfq85OfyhNp06dqvz9tGnTZNWqVfLWW2+p6FqkMkB+2VSEboTJxRdr0Uo6escSTga9Y2IH8rjZRcSCww83b0dOtpycMtV5NTJyZOX0Tl0uHCrotBvRrw/pcq3XpGd1sDorIDM3d7ds2JBh2g5Hg+7Y0lPz6s5cI0af9rnnBkdqoVMZSh6cPnZltIKOql20G8CUaaNjAXz8cXgy8TvkvzSC+wnQpKxlNTYX6z50SL/8Uoui1JwEwTLhTEWdWiPAjBFqF15oHxEL4EBDx10HDgOjIyhUOXHf0AG2i3YDhx6qOQqM6O0M995YVqtMEEpm166VEYs6uuMbZbZ7bvS2dsIJIgMG2MtLS6tQZTXK06Nh9WfVLtpNdwboU36tMhEFiWuaOlVz1FQHnhHcU9Crl+awMhKJjsC9CGcRHjwn+nOP9mh33nB1xP33a9HqVS3Ag1yfOA73S28vRx+tRTsbCUdHWNtCKN1j1AWIiAWQXVUdhtIRVsdiODKNzzqcrnYRsVXpCDi3Qsk0tlmjjgX+lOeqTVnPW52OgO6piUxjhh4MvESiI8LVs2hrVj2rj51GoiMi0T1t24Z+B9ZUR2DgBM7oVat2S5MmGdKnj0ddo/EdePbZ9hGxkegIu3Lm5JSKx5NpKuchh2h/MSCp6yFrHdZERyBSGU7U9evLJT3dFyintQ71QUkjNdERiBTW01hUBx2xhBBCCCGExIiMjAzp37+/LF++3LR92bJl0qZNG+ncuXPI3/7888+yePFiefTRRwPb3njjDbnyyisjugZMPdQ74EbQMbF2JO06d3ZgWiEiqBBlo0f4pKRU9g7xHfvhqLEulqN37uyA80J3YFgJdoag04aFOcwdU0RYWY9F9FRVZdVTFBhBdIy1jEZ5VZVRR58KaQecF7oDQ8fqlw9VRjSbUOWpqg6B3e8QYYuIIn3xI12mDiLD0MnFJ5I61EH7M7ZB1Fk4dQuHZlWLLdnVoQ5+ZyxrpDJDlRXHVdWW9A69VR6A48Mqz+jo0acJ2wEHUKgAejgvcE3W1BHGa7Y+I3rwPBycxoGaSHWEXb0aZdo9J6iHqs5bnY7A+Z59VmuzuixjWcEzz2jHWR2c+kCIlap0BNpETXUBHEpVlTWUjtgfmcZBkEh0xP7o2JrqWasjLlKZ8dKzVelDq46IVPdUVYc11REY0Nq82avea3YTa0KsARqRjrDTeampvqBy4lp0B6dlHdMANdURSH+CKPhQ5QRVnTcSHRHq2u2IYhpnQgghhBBCSHWMHTtWpkyZIuWGkJ3JkyfLfffdp6JclyxZIj179pSvDPMnV6xYIWPGjJG8vDx59dVX1WfChAkye/ZsqWedkx4H0Fl86intf+vaYfp3OO+itWK50TERYq0ytR2dehwXDdxQRmNUHqb3tmpl3o5rwXbsjybxqNtYy3RL+4lHOWPdZt3QXuPVftwgM17PSKxxSzlrAh2xhBBCCCGExJBjjjlGOVVvueUWefHFF2X06NFy5plnygUXXKD2l5SUyJo1a2SXYTWMk046ST777DO5+OKLA5/LL79cHVtbcIPzzg1l1EFZkH8TU4Vvvln7u2pV9MsYr7qNh0y3tJ943MtYt1k3tFe3OJzd8C6JF24pZ6QwNQEhhBBCCCExZtiwYepjR+/evaXQsoT1UutKNbUUdKpQLCykhZyGmCKKHGtORbzonTys3K2vDK938tBxdsoZkuhl1EGZwpnaWVfrNh4y3dJ+4nEvY91m3dJe46Fj3SIzHs9IrHFLOSOBjlhCCCGEEEJI1HCD884NZYwXsa7beMh0S/uJx72MNW5or25wOMdLphueETeVM1zoiCWEEEIIIYTUadzQyXNDGYlzsP2Q/cENDud4ySTug82KEEIIIYQQQgghhBBCHIaOWEIIIYQQQgghhBBCCHEYOmIJIYQQQgghhBBCCCHEYeiIJYQQQgghhBBCCCGEEIehI5YQQgghhBBCCCGEEEIcho5YQgghhBBCCCGEEEIIcRg6YgkhhBBCCCGEEEIIIcRh6IglhBBCCCGEEEIIIYQQh6EjlhBCCCGEEEIIIYQQQhyGjlhCCCGEEEIIIYQQQghxGDpiCSGEEEIIIYQQQgghxGHoiCWEEEIIIYQQQgghhBCHoSOWEEIIIYQQQgghhBBCHCbFaQF1kX379klxcbH61KtXTzIzMyUjI0OSkui3JoQQQgghhBBCCCGEJJAjdtq0aTJr1izp2LGjrFixQnr27CkjR46s8jezZ8+W9957T7p27SoFBQXSqFEjuf76603HvP/++7Jw4UIpLCyUP/74Q4YNGyZXXXWVyckKp2t5eXng+7HHHisTJkyQ9u3bO1BSQgghhBBCCCGEEEJIolMrHbE//PCDPPjgg8qx6vF41DY4TOEsPfvss21/s3LlSrn44ouVkzU9PV1tGzVqlDz88MNy6623Bpyw2dnZ8p///Ed9h7P2kEMOkcWLF8uLL74YOBccs8OHD5c9e/ZIt27dpE2bNjEoNSGEEEIIIYQQQgghJFGplXPtx4wZI2eddVbACQsuvPBCGTt2bMjfPPDAAzJ06NCAE1b/zbhx45RDFTz//PPqo9OyZUvlvH3ppZdkw4YNge2IpB04cKA6H52whBBCCCGEEEIIIYSQhHPEwmk6c+bMoDQA7dq1k7/++ktFvtrx6aef2v5m586d8uOPPwYcrEaHq36Mz+eTtWvXRr0shBBCCCGEEEIIIYQQUitTE8DRivysDRo0MG1v2LCh+rt06dIgh2tJSYlKM1DVb5DndcqUKbbyUlJSpFOnToFt69evl/Hjx0vjxo3lzz//VA7c0aNHh7zmsrIy9dEpKipSf71er/o4DWTAmRwLWfGQR5mJI48yE0umG8roFpluKCNlJpY8QgghhBBC6iK1zhG7Y8cO9RfOUSP6d33//v4GwHn69ttvy0UXXaScrjqlpaUqv6y+gNeAAQMkLS1NbbMD6Q/uvffeoO1btmxR54pFhwSRv+gEGRcdSxR5lJk48igzsWS6oYxukemGMlJm4sgrLi52XAYhhBBCCCGucMTqeWFhzBvRv1u31/Q3el5ZpCZ4+umnTdvfeOMN03fkikV+2n//+99Sr169oPPcfvvtcuONN5oiYlu3bi25ubmSlZUlsegAoQ4gL1YdrljKo8zEkUeZiSXTDWV0i0w3lJEyE0eecT0AQgghhBBC6hK1zhGbnZ2t/u7du9e0XZ/6r+/f3998/PHHMnv2bPnkk0+kfv36VV4TOhaI9ECO2oMPPjhoP6Jl8bGCzkisOl3oACWyPMpMHHmUmVgy3VBGt8h0QxkpMzHkxbIeCSGEEEIIiSa1zpJF/tfk5ORAnlUdOEKBMZerMRdsXl5e2L+ZO3eufPDBB8oJi98ix+yuXbvUvp49ewalINAduvv27YtKGQkhhBBCCCGEEEIIIe6i1jliMzIypH///rJ8+XLT9mXLlkmbNm2kc+fOtr8bPHiw7W9wvn79+pkW53r33Xdl4sSJgSjWzz77TDZt2qT+R3Rs9+7dTedZtWqVioq1i4YlhBBCCCGEEEIIIYSQOueIBcjHOmXKFCkvLw9smzx5stx3331q6tuSJUtU5OpXX30V2H/bbbep78YFHPAbbEfUK9i2bZtceeWV0rVrV3n99dfl1VdfVQ7ZSZMmqVyx4Oqrr5Zjjz02cA78Bo5b5JFNTU2NUQ0QQgghhBBCCCGEEEISiVqXIxYcc8wxMmbMGLnlllukS5cuKor1zDPPlAsuuEDtRyqBNWvWBNIJADhX4ViF4xURrRs2bJC2bdvK6NGjA8eMHDlSvvjiC/UxgkhXPd/YeeedJ6+88oq88847Ss6ff/4pEyZMkBNPPDFm5SeEEEIIIYQQQgghhCQWtdIRC4YNG6Y+dvTu3VsKCwuDtiOlAT6hQAqC6kDE7aWXXhrh1RJCCCGEEEIIIYQQQkgdS01ACCGEEEIIIYQQQgghiQQdsYQQQgghhBBCCCGEEOIwdMQSQgghhBBCCCGEEEKIw9ARSwghhBBCCCGEEEIIIQ5DRywhhBBCCCGEEEIIIYQ4DB2xhBBCCCGEEEIIIYQQ4jB0xBJCCCGEEEIIIYQQQojD0BFLCCGEEEIIIYQQQgghDkNHLCGEEEIIIYQQQgghhDgMHbGEEEIIIYQQQgghhBDiMHTEEkIIIYQQQgghhBBCiMPQEUsIIYQQQgghhBBCCCEOQ0csIYQQQgghhBBCCCGEOAwdsYQQQgghhBBCCCGEEOIwdMQSQgghhBBCCCGEEEKIw9ARSwghhBBCCCGEEEIIIQ5DRywhhBBCCCGEEEIIIYQ4DB2xhBBCCCGEEEIIIYQQ4jB0xBJCCCGEEEIIIYQQQojD0BFLCCGEEEIIIYQQQgghDkNHLCGEEEIIIYQQQgghhDhMitMCCKmLVHgrZOaambJl0xbJ3ZMrR7c9WpKTkuN9WYQQQgghhBBCCCGkjkJHLCEWPvjjAxn16SgpKCqQXlm9ZF7RPGmZ1VKeGvqUDD9wuGNy6fx1BrfUq1vKSUhN4TNCCCGEEEIIiTd0xBJiccKOeHeE+MQnSYbMHeuL1qvtU86a4ogzNl7O30THLfXqlnISUlP4jDgHHdyEEEIIIYSED3PEEmLoTKKjDiesFX3b9Z9er45zwvn7d9Hfpu268xf7nQDl+G7NdzJz9Uz1N9rlijfxqtdY1208y+kGEv05cQN8RpwDdXfAUwfI8a8fL4/9+Jj6i++sU0IIIYQQQuyhI5ZERCI7JWatnRXUUbc6Y9cVrVPHJYLzN5E7z/Gq11jXbTzLGQ9dEGuZif6cxJNY3ct4PiOJ/s6kg5sQQgghhJDIoSOW1HqnRKw6shuKN0T1uNrq/HVD5zke9RqPuo1nOWOtC2It0y0R1fEglvcyXs9Ior8z4+3gJoQQQgghpK5CRyyp1U6JWHZk8zLzonpcbXT+xr3zjPNu/k5k00ztbwI51eNRt/EoZzx0QaxluiWiOh7E+l7G4xlxwzszng5uQgghhBBC6jJcrMtBVm5fKZnlmYHvDes1lOYNm8veir2ybue6oOM7NO4Q6KiVlpea9jVr0Ewy0zJlZ+lO2bp7a2A7HAELNy0UX7FPmuxuIq2zWgctktE2p62kJKWojubufbtN+5pkNJGc9BzZtXeXbNq1ybSvXnI9aZ3dWsm4evrVAQcE/pZ6S9Vffds106+Rg3MPDsjGOXHuPfv2SEFxgem8OOaAnAPU/6sLVwc5M1pmtpT6qfXltQWvyUUfXaS2ecQTkIn6OfPdM+W5k56TIR2GBH7n8XikfaP26n/UL+rZCOoe96CwtFC27d5m2peRmiED2gyQVpmtZH3x+qBy6teQ1zBPWjZsKSu2rwj8tmlGU8lOz5bismLZXLLZdN70lHRpldVK/W/8jfG3Rqwydbw+r+n3qcmp0ia7Tcg6hEzIRltBm9GZ8/ccU+fZKs/Yee7QqENQHbZo2EIa1GsgO/bskO17tpv2YTv2l3vLZU3hmqCytt+1QLzzRsn7mwtkY9LB0rhisRyR00ySu4+R3I7nSVZalhSVFcmWki22dejz+WTljpVB59Xb98ZdG6Vkb0mgvsKpV92pjvPi/Ebys/IlLSVNXQ+uywjuN+4dnlO0R7u6BWXesqC6/WLlFzK041BVf6hHI5HqCGs5QbmvPKicaC96vazasSpkHVanI7DQkVUX6NjpAugP6BE8F3g+jISrI9DurTL1ctvJhJ6EvrSrw3B1xIzlM4Kek73eymP1e/n24relT34fta1do3aS5ElSZUGZjISrI57/5XlV1nB1XqP6jaRx/cbqnlkdiZHoiO27t8vPBT/L9q3bpfHmxnJ8u+OlRWYLKSsvC2rT1dVhVToC8owObrt3CRzch+cdLvu8+0y/zW2QWyMdEa4uQHu06mj9HRipjkBdo01WV85hXYap982+in1BegnvpEh1xGcrPgvINbYf3EO0nzfPeFPO7XFukB0B8M7FuzdcHbFgw4KgYyp8FUH1unjTYhl0wKCQdVhTHbF7l1lPEUIIIYQQUlegI9ZBbvvqNknN0BwgYFDbQXJT35uUE/D6z64POn7aOdPU3/FzxsvSbUtN+27sc6Mc0+4Y+X7t9/LivBfVNnSIft/yu3LI9M7qLXOL5qpO4UG5B5miNtH5ghNg4vyJqrNt5NJDL5XTu54uCzYukId/eNi0r31Oe3nqxKeUUw5OLh10tNaWrjUdu2HXBuU0RacJjDhwhFx4yIWyfPtyuePrO0zHNqnfRF49/VX1/z3f3iPb9pidog8e+6B0y+0mN3x2g61MvaN30+c3yfR205VjAKCTOPWfU9X/j81+TFYWmjvkt/a7Vfq36S/frv5WXv71ZdO+I1oeIXcPvFvGHTdOLvjwApNMY8eyb5u+ctMXN5l+e2WvK+XkzifL3IK58sScJ0z7ujTpIo8Nfkz9b3fPXzjpBdWZ150dVpkoWbMGzWXy4sny9u9vB34Hh/BLp76k/r/z6zuDHIWPnvCodG3aVT7880P5aOlHge2609Bar9bOM9rWu7+/q5xNRu4acJccmX+kfLnyS3n9t9dN+/q17ie39b9NObqDyrpng5y/b57ctFXk73KR7JS1srPcJ+mbN8lBq6+WBwdtlMF9/qOcmc/8/Izpp3CwjTt+nHLw2tXhpGGTlKPr1QWvyg/rftDK5fMp54xxQMNazgapDZTzHdz25W2yp9zsPHtyyJPK8TllyRSZvny6aR8cKJcddplycN3yxS22davqsWxDUN3OL5ivHLGfLv9U3VcjkeoIu3LuKt9lkon9eL5HdBuhjrM7b7g6YvQXo4N0QVCZDboAjkM4A+GwhAPaSLg6Ao5Jq0w4mELJPKH9CXLdkdep31jLGq6O+HrV16btkLl1n9lxBR7/8fGAE/WdEe8o59mLc1+UXzf+GrGOgPMOOi0SnXfOwefIyO4j5c+tf8rYb8eazhuujhjzzRiZtGCSahvZKdmys3yncoC9fNrL0rN5z6A6rJ9SX979x7vq/3Hfj4tIR+CarA5u63OJ893+1e1StNd8vdceca0M7jA4Yh2BZySvfiPZuGeHkmIns3WDJqr9W3+Lgc3nT34+Yh2B5xdtsrpy4t361qK3TMeCewfdK4flHRaRjkA5v1r1lcn5a5WJd+rZB59tsiN0Dm1xqPznmP+ErSOsg5lgd8XuIJ3wzZpv5Jojrwm0X9wjIzXVEZm+ykFuQgghhBBC6hIenzU8gew3RUVFkp2dLb+u+lUys5yJiEXkizFy6vCsw5UjVu8EGSOn9jcidvKiyTLyg5GB7ZB3cMODZfGuxaZO1/jB4+XULqdGJSL2p/U/yTGvHVOtTHQO9Yi0/Y2IhfMancSX5r0k9313n2wq2RSQ2SqzpTx14tNKVk2j3ewiYlG/H8++S878+lHbcsLd8r+B18sRPbSObDQiYs+bel619frNhd9ELyLWWyGfTT1Sri4oDCnzjdaN5LyLtkjRvpKoRMQC/RnRXFbBMv97yn/l8l6XRzUi1li3SZKkZC7atchUtzPOnRG1iFhVzl8ekqvnTAzI7JnZUxYUL6jUBX0uk9P73htRtFsoHTFx3kS54fPKARJd98wrmide8QbpgmhExD4952kZ9dkok8zDMg+TX4t/tZUZjYjYj//6WE6dfKpJZo+GPdS9NMo06p/9jYjFAFGkOm9/I2KnL5uuIiXt5OH75DMny+EtD4+oDqvSEZ+v+Fyumn5VtWV89sRn1TMSjYhY6J/5046Sf67driTYyXy/bRMZcvZK2bh7S1QiYqctnRb0nNiV863hb0m/Nv2iEhEbiW6H03V/I2LRnga+NjAwSALdc0jmIeq51OXB8T77ktlyQKMDHImI7dG2h+zcuVOysrKCrpdUb6PGou68Xq9s3rxZmjVrJklJscmGFmuZbigjZSaOPMpMHHmUmVgy3VBGN8gsisDGYkSsg7Rv3N72BqDToTtU7NAdd3agM4+OGCKRjB0u/a/egX7o+4fkil5XmNIUVJXbFOds2Lih7T7r73D+9KT0gDydQ/IOCSoXOndVlVV3tlixOhVCyYTjw+78eufZDnTu8LEDHc2rWrSQKzoky8xtHtmSki652R45uolIMqons2XI88IBhE8obOvBWyHDt02W9/NERm0RKSivLGd+ik+ezBUZXvi+SM5jIpaUE9XVoe4AMqY/wLGI1F5f9HfAKWGsV7Sk/KzWKlLUmuLCCBxA+ISqQ2NZKzZ8JeM2a05YCSHzjg075JxN30pW3nHK2WIHHEBVtSU4gIxcVdJCWlRVr80q60V3LNkBBxA+dsABpF9TZd2uD7TRtKQ003MJxw0iNgEcaPjYEbaO8FbIVXs+NZRTJMWTYi7nns9EGrxY5fMSro7AM25E1z1WrLoAjlF87KhOR/RodlCQTJQjSGbz7qbzVFeHVemIEzueqO6Vfi8hs15SPdM1YD+iC63PCZxZNdERGywR1SF1no0ugdMuUj2r58ENLc+nor1XjVoVUhdUVYd2OuLAogPDKuNBzQ4KWR7oh4h0xKZvpUPadkmuShfU2yayc750aK5NobcjEh1h95zYlRPPm+4styMSHfHz+p/Dkol3K1IFwJawIxId8dwR58sI/yAiSPYk+3WCJu/ZI84LOGGrq8NIdURRinlwjJDKPPQzRTZhUCVXpNnRIW2nOikvXrCciSEvXsSjnG64l5SZODLdUMZ44a295aQjtg4SySIZem62/QFOufyMJrJ+9zabicja9Pn8Bk0C07yjQV6IDllNjwubdR+IzBohyeKTgRlJsjlZpFmFSNKeArVdBkwRaT08evK2zBLZ/bcMbygyrIHIzD0iW1JEcjNFjq6Pji1Cf9Zpx1XhJAgXOFSeOvwc1Xm2utD0708eHuxc2h9mrflWpSMIBdrUunLtuEF5x0VP6c4bVUW9ekTmXS/SaljUlLGq26FPyYh3zwxRtz55cuiTUa3bWLefeOiCAfVF8lNE1pdLaJkp2nHRovJejghyNuvfo30v88q3RPW4/X+PSFTfI4H2oxzc2kCQFeNAUNTYow3qVfmMGI6LBgPy+0p+arKs34ecqSHKmZqsjquzi036BxGnGAaBdPA8Kgf3trdFvONqjcFLErzzA/tt3iiR3QUiyb1Efp8nktFSpNdT0bXb4iXPLfXqlnKyXp0rpxvuJWUmjkw3lNFN+icC6Ih1kuKVIh5D9FNKQ5H6zUUwlRPOESuZ/oiP3etFKsypCSS9mUhqpsjenVK8dYG0r0w9K2VePZ+oz7Qdx0mT1iIN2ookpWgdzXLLAhdpTUTq5Yjs2yVSak5NIIgAa9Ba8HhMyK2Qa/wzAyGnVXKp/IYFbPAYJYtMaFohycUrKh8mnBPnRk49ODGNeJJFGvqjZHatFvFZVh6v31I5VQ6uJ7LHpzkEkvwy1yb7ZFOFCGLTjkgXGSAFIpCrzusRaeiPuClZJ2JYXEerw+YiqQ1F9haKlFny26VkiKQ1E5l7XSCaB3+TfbgPWnSY6j7/cq1I1sFmpZHWVKRetsi+YpFS87RjSU4XyfBHL+rXaQTXqR/qERmY4ZNtSaXSxOtTkW8BdiwQyTBEnyWlijRoE7oOIROyS7eK7Ntp7jxvfVN1nm/ZgugnrV53pPqkRbLInY1FhuqdZ9w3ax3WbyGS0kBk7w6RMvO0Y7Ud+5EDsKQyNUHxnu2qXa70z75tmVwpU5/kvaVCZAM68vuKREq32NchprTuCk5NUNm+N4qU+1MTbJ2jHJSh69VndlDivNYsLRn5Islp2vXguoykZoukN9WeUzyvfoYnb5TpLUX+tVlzTDRPKpP2/rq9q7HIkGRc426tvaH+UI+mOoxQR6Bd6NWkyimyJalccq3tZ+cSrZxYuGhX8LTjcHVE8u4CmZhbIVf7H+l9hjprn6I9gs8ZdQHabHI97bnA82EkTB2RvPN3mdhM5Ootlfonzd9ymiSJ5CSLPJeL4xZpv4GehL60q8MIdMTw/J4y4+hL5b5fXpFN5T5pkqQd2zbVIy8edYkMye9pfqYbthNBpC5e9hXm1ATh6IgBObnSL11kY4VZ50HPliFtQrJIp1SRAeneSrn1GomkNdbumdWRWI2O2FS4KlCH2cmV8vBcFnpFtkLPekSKt8zX3iPh1mEVOiI5pYEaCPrn149K29TKdwlkoszQEWogqHSjzTswVyQ1K3IdYVisq0odi/Zo1dH+d6BWh+HriOStc+SFphVy2gaRdA8ck+ZyVvhEHsdzsm22VpeWhcmkfl7EOmJAo5bSLzNHZhcXKhlWPQvnb2paY83JvXenSJkl53Fyfc04DVdH+HWs0cG9I6VC8rJ86t0cGARa975I27OqqMMa6ogEWKxr2rRpMmvWLOnYsaOsWLFCevbsKSNHVqaBsmP27Nny3nvvSdeuXaWgoEAaNWok118fnNM37sSjI4vBcr/2DIB3pROD6LGW55Z6dUs5Wa/OldMN95IyE0emG8roJv0TIXTEOsmC27AiUOV3OEIOvElk7zYtGs/KIG0hHvlzvEiRebEuOfBGkebHiGz5Xo7a/I48WTmrWhaU+WRaudbhM27HcbLrK5G+b2pOgOUTRbaZpy9Kh0tFWp+uOXSWmBfrUh3Ew59Szqqh9QplVr7I4r2a4zc7da38WiyqgzetpUjPeoUiP10kUk9brEvajBBpf6HIruUiC8yLbKiO1VHaQjyy6J5gp+ghD0py2WaZ1NzvnPM7XyBzilfkmZ0iLVJE3s0TSV46XmSttnCM6iQerS3EI388Ftwh73arSLP+aqqqrDAv1iVNjhBpNkBkT6VTDTIzvWvVXw2fSGmBuZyg05UirU4W2T5X5A/zQjyS1UXkMG2xLtt73v4i09dgmX5Qxo1fmTvqR2oL8cjCO4MdhYc+KpLdVeTvD0X+rlysS7W9PRsCnecdXp/s9ayVeo3gaNJ8LAEHJWQaHMWKg+8SaXqkyMYvRVaaF+KR3H4iB92mOboNZT1qz1bVLoej/47Fl3J80q/BWtmZXjlhFvc0r+UgrXO/1LwQj+QcLHLIOBFfuX0d9pmkOUVXviqyRVusy3gfdWzrVXde4Vm1LMQjvZ7UHJ/rpoisNy/WJfnDRDpepjm4fr2lsn1s/EqGNhBZfYDmmGiTtkGyGxvqdv5NIo17izTtLbLhU5HV5oV4ItYROM5CPdkVXE7Ua+erNOeW3XnD1RELRssQgy74o8wnb/r9cM81E+mZJpKXYtAFvZ/TnIFr3hbZYF6IJ2wdsWG6DGkgMiutUv/8r0xz0l2QJXJLI8g0PCN5J4h0uU4EzjxrWSPREb/fL0O2z5PBrUS2eUU2+rbK6AaIovRK8oaJIj/8KpJuiC7s/47mPFv+osh282Jd4eiI5Aat5L08kbmlZp33c5HIhgqR8zJFbmokkvz3FJHN32gHHXCOyAEjRYr+FPnNvFhXdTqiXdrB6u/pfl2gy8Nz+clukRd3apGNRxW8LFLilwdS6ov09+vcJeMi0xFNj1JRlFPzkLaj8l0CmelJIsVekTMwELTUow0eGOlyrUje4BroCJ9WFxioUWkmbHQsnIEYXLH+Fk7Y3s9HriP2rJdTGooa8Hq6UOTmHHM5O6eKtEn165/VbwU70XvcK9L4sIh0BGZxvNesRFoVa07X6xv55IgMs57NTitXA6uwI2SZebEuaXyoSI//hK8jDDpWHwTakbRbGnu1dDMBVr5S6YiF/rMs1lVjHVFetxfr+uGHH+TBBx9UjlV98b1hw4ap3GVnn3227W9WrlwpF198sSxcuFDS09PVtlGjRsnDDz8st956q9QaYt358c+AsZ8z4R9Ej+YMmFjLc0u9uqWcrFfnyumGe0mZiSPTDWV0k/6pAXTEOskhD4kYFutSkSwADgp04ELR9Qb7iFiQ218adb1KHl40JxA5Veb1SMsGIqU+keu3ap2wvGSRb4+/SiS3rxadBOA4Qqfd6vAAjQ4JviY9J6K/kwhnR7NkkZ9LPbLd00Zebb5YRa4Gpna2OUuk1amVkSygYcfg8yLaTaf7PbYRsXDOHp4u8lmJyH3bRTZVYOGRNvLjrsWBaY/K+dL1JpGm2sI1mqfLz4E320e76R3ZnO7mfXCgwPliAN3J4qQ2Us+72NxpN5ZT1aHf+9348OCyIlJLx+6ep7fUoqr80ZvBMj1afRz5qllRINpNp+cD9hGxIP90zYGvs36ayNYftUvziDRO9si2pDbS2FpG3PNut9tHu4EWx4s0Osy8T29nuPeGsjbyVsjDLx8pFaLliX2u0CM/eduoRWS8/hyx9dIayQBMf64oEWnYwb4OPSn2dai3NTi12/xD+x/Omh8rF64BtvcSDhr9WbWLdgOtR4i0GGzeB6cNQASmfk2QWfCJyTGxNSlPGnu3Vsr0llY6T/OGijQ50lKHEeoIvGi+Gqg5Hf3slYb+rKZ+mXAWoo2oC0u3P2+4OgJtf+uPAV3QwOORFg1spnnrz0i6v720PVuk5cnm84arI3o9J/Jlf8kr3RjQPyMy0+Xy+gaZKKP+jCAiVpW7hc15w9QRuQMCkc4e/3PiTWoq3VI2VJoPGNE96q3K51Jvpx2vtI+IrU5H5A6QvIYtpF7ZRpPO21ah6bxjMrTFj0y6ABGxIKurjf6uWkf02jRTnfejXSLf7IFZpMnDc1nod6Z5fCKNOsERP8y+DiPVETsWKl0HJyWiQvV3SZPkxXJEuq8yihL11PFyy73x52CFvo9UR+yYL/L9P0PrdfymUc/Q78BIdYRf/+gDXrblVHWVpzmu7SJiI9URW+dIXsEngVQBT+7wyOEV2v1skexTEflHpxZpg2y5/bU2Y6rD+pHpCBsdWy4ZZt0D2l9S+f9hjwfXYU11RDEiYi1O6jrEmDFj5Kyzzgo4YcGFF14ot99+e0hH7AMPPCBDhw4NOGH13xx77LFy3XXXSf36EeZnweBEauUCl+reQ6fAWV5mkwJFb5eYaeOztNnUHG2QZm+RyFwsLlo5gO3BAIn/f8Xca0UaH1GpxzAbCf8jAtyL+H8DKZnaLAUMguyrzDUfeN6hF/wpeirxSZIPeskwiA69goFp2MTqvA20CPuKMpG9ltk90PKIOld1hJlilVH1isJFkcsLqsMt2sCRXR1ihlp5cXB3zdSpNMr0b0Onsml/xNxbzpullde2DlO1gWxVVsuA0JbZ4ZVz01fBNjXe4Xq/xa4Oob8QCY8BQn0mE4BNg/ZRXTnRecZ9s9Yh2i/asV0dBtp3hUjZ5sjLiXaGoA3jLDP9PQE7CbrNYIsF0Ns3ZjfAbguSp8/csMiDvY5BcFWH2kCi+bxNtfc8ZjhUWGYIBNr3Xs3mVPVqfC4ryxr0XOKZwkwPzFAo3xWiDsPQEZu/C6rXoL/6c4J+mapDb/DszEh0RMH0EDLF/tkM1GFhsN0Wjo6oie7BuxbvNrz3rTNTwtER236qmf6BfYtZWwh+stpt1emInYtrJhP2CmxyuzqsTkcU1lCm6je3CF2HVemIbXPDk1nwsaYLTHWYrfkSMGMoXB0Rie6B7WudIaW/AyPREZHIxHOJ2UpWH0O9xpHpCBv941E2hM+sf5oPFqmHZ7kkOMAsUIdh6oga6VmbFGU10RF7rO/v0NAR6ySZ7UUybRYVgfGhTzG2Q3ei2VEvW5KTUuTGRiIj/O0F3R4sD4MO0Kp9WgN7pCnyHKaY5ejK3Q68xPCxw/A7dCCPrO+Rzcnp0qzCo6azBoCjxlouGJVVlVVPUWAFjpCMfBki6+X4DJ/M3KMtnHVdhkeOro+OrEfrBKPzZjeSoU8ntQMvQL2DF6KcGh6p8KDDU7n4SMhyArxsdCeQHaHqASH5apTIKtPP4U+LZHcOfd5QdQhgYOtGtn7t4ZQRdVFlHTaqdAJZsbQ73J0bT3xZZvtzpxZUeGR9Rbqs3Fe5iMyUMyZq+TaTsjSlZgfueVVtSX/xggYHiCy8ze9M89mU099+0M6APtXaDrzk9BedFbxw9GuyRpLi3eNJC65b/UWKKeX42J43Ah2BiLJA+xHx4cVsbD+9n628lzDCqqzDanSEof1AF/RRusCfR9l4rPUZgbGlG1xWqtMRWR0CZazUP0l+mZ7KMlqfkerqsKr2jTQHpg6VR7weg1MOlG7QIuStuXcxzSYUVekItP/ez8mQWSNC6zy7cgIYfxHq2eTd6+SpXO09gohfvEcaWZ7LR9Afa3JY6HNHqiMMz0iV7xIYzqFkptZAR2DbgOTAlKiALoAOgINPH4Wvsg4j0BEG/ZPs8dmU06B/qhqJj0RH+Ou2MlWAf7HJQPuRSkMTbRbRrXaEqyOCdCx0T3Kwjm19Zph1GKGO8NXdxbr27NkjM2fOVM5TI+3atZO//vpLRb62bx9cV59++qmMHj066DdYmffHH39UDlk7ysrK1Me4oi/wLX9FfA3xjtLwwZmGZ6GsUDx/WSKmsb+7P+p+3QfiMaTkUftanyGS00Nk9ZvisXRm0nw7xaveEFpEukozgSh9tCFsxaAYOjQFM8RT9Jf5vIiCb3qUSPFy8aydYt6Hd37HK0R2Q555AKC+b0tAZkAuos93/Kb9FjN4MFhUsl48q14znxf6BYOdYNWb4rF0CH1qRpShPH55wKsNYWn7DPICzwcGXsDaKeJRHWfDeREBnn2QGrDybPjcvA8dYQz+Gcqpy9RLqJ7FP8eLxziAhH0tT9ScBkVLxbNuqnkfbIkOl6n/g+75zsX+ofJKGyZIJj47fhfP5h/M54Xe6nytP5T7VfFYUh75MECD98fmH8SDQR2dktXiU+1Hv3chyokcgzt/F48lTY0PfQLMMtk2Tzybvjbvyz5QGyTeV2wuK5xMhvL4LDID+9DOkpeIBx1343mRtgWBAN5y++cGbQltquAz8ez8IyBPPy/aaZJK9GZ2o/gwm63VadqXFa+IB519434MVkIfb54pHjiQjPvgtMkboq7Zg1kJJasDTgZdptZ6DYs5+p9L30F3imR2FNn6i3jgTDWeNxIdseEz0z5dJv56jIMFq98SH5xi7c5XDg3b84arI/DMGff5y2jSP36ZeDZ9nf6tvXc2fSsey0ymsHRETXQPZp/gvpZuE49lZkpYOiKETK2cHluZ6rfdbtOcZ39/LB7LbLBqdQSc3eq+VTpLw9J5zQb607+tEc/q/0WmI9Jya6ZnUUaUFax5JzIdAWdbCJkmnbd+hni2zTf/FsEnmMlUGIGOiET3FK8SD2YOGc8Lu7PTVZHpCItM1GVImV6vmpXlsaQZ8x1wbmQ6wqR/ktTzkebTHP6BVrynQHxr39YG73csEk/BDPN5YT9GoiNs9GyySvRm0bPrP67Us8smiMfidK6JjvDtsgwW1UVHrFO5s/744w958cUX1TE7duyQvXv3yl133SUpKZVVsWHDBnnooYekU6dOUlpaKps2bZJ77rlHGjTwR4TEm/p5qrNV5SIZDatxqkSC3ylq7HCZsTi1ogE6p34HJRwQiC6sdPj4e5ToQEcznDwe5QRQWAjJ1/Om6FidBHW4jMMPHC5TznpfRs0YJQXFlWXEwj1PDn1K7Y8qhvZjckoqHGo/4T5v0XouXdR+YlrGSBZuiuICT8ZyJs8bJQM9BZU6D1PnHbiXw5vlyxT5O/R7pFnr6N7LeD0jAHWHKCp9kYDmDi4SEGf9o0fk2w6SRKtu41HGBAGO1vLy8iCbsmFDbTB86dKlQY7YkpISZddW9ZtQjthx48bJvffeG7R9a/apUpZZOQDvS0oT3+bNqrOYlBOsa7zYh7ub1k889cxRRt49WSJ7N4unpKF4knpWbhePFCV1kN2+ppLkQ1fIH0lbr69IjjabybutSMRTIp6Uw8SDNCMGfHsbatdU0TDomuC0UPvKmkpSUg+TzOKkdtLAu06SPB4VkascCI3+IZJ1oPZbb4a/rMnBZfUkBcqa1HCwKc+0On/J3yq/HTpscCjp8jK9qzT3CAZDUVaDvKA6TB8gnjRzh8+7u6GK1PSUtxCPtaxb54kPOfV86MbuM8mEe8srSLrtEY+3o3gwo8BUh5n+Osy2qcMUbR/Kaq2HpAPFW7BWO6+vXDnsjDJ9SIiiBl86S1JOvqUOkyvrMPOk4Dos9oiUbBaPr6N4cgyDl/vmiC9pUaAOEd1rlKkNiqYqHe5pNEg86ZY6LKmvIrg85fnBdai3b1+FuaxJB4oUrAjUoc9XLruSDjDIRJtCpBVyvTcOrsOkVP95ffbPzfYSEU+peJIPEU/OgQF56reSLBWeFCnytFMyjAOSvoYnVd6bzFOCbC7vTuSL3ywe6SKeHH8+eP23Pr19e7Rr2jdHJGmhfnOk3FNPdiZ1VFFpcIsEqNdXvCVpIns2i6eiTeg6DEdHNILzuTLvermkKpmo/2SVqMxPo3+Ir14Pfx167c8bro7AM2eQCY1TmNRR1VyKz+Ac8T+b3sJ9IkmowwPFk9POfN5wdITSPdB3vhC6R9MRJt0j6f7z7qu6DkPqiKbiSTo04MzWZTb0rhEPnke/jjDKVMdt3aGu21Ovt3hyDolMR/hzemqRk5Uy8YwoV5KuZy0yfRUN/GVNs6nDanREyfoa6llPZR3Wj1BHFK8QX/KP1evZBieIB45I43nLMkVUHUagI4J0T4XsSmpr0j1Kz0L3lNQP/Q6MREf4Zer62+vzSUlSa5NMVYeQuXmzJGUcJ1LfWocR6giD/kFQC+p1lydfSr05Jp3nLU5XMj0VzWzOmxqZjrDVswdIhaSY9WyDIZV1mD0s+Lw10BHFyYgStqT7rEuOWKdyZxUWFspJJ50kv/zyizRtqkUJjh8/Xq699lp54YUX1Pd9+/bJ4MGD5e2335aDDjpIbZs6daqMGDFCZswwe+fjht8ZMlzWy7AGPvsV4aPpDIlXhyvWzpd4dixj5SSIYxnhbB3WZZjMXDNTtmzaIrnNc+XotkdHdeX5uLafeDkpXdJ+YupIc4PD0H8vh88aEfo9kiiDXTooCyI0ZLNIM0xjMrkoo4sb9E+sy5ggIAgAGAMAjN/1/fv7Gx2kO7jxxhtNEbGtW7eWJvkHSVZWiOhyNc8qFCEilxWtRf5YZI6C89ST3Ir5ZodPfmeRZt0jOG8VNB0k8ueOQNsPyPQu8Mv0t/0Dzwyhz6qI7Le7Jm83kb+uCpLX1Ls4THk1KGvadpGl8yovIUimn1btbeo1XJoFl3PFrdWXs/Pgat4TzcLfh3L+tbD6cqr3YjepOXlVlhPTsYPK2XFQGO9Df0qLqspqIw/+s+D2c7pBXgR1GES+v17NzyUstqbeRTbPZeswz1uNjmjZObicniTJ9S6s5jlpUfV5q6J5vk2bTTXon1Ayqytr6yp0z/b90D1VzIANdU0o459bbWT+FqbMGuhZb2eRPzftp86riZ79d2z1LGQuv6F6mV1P2w/buHk1uielhronTB1hI3OLJ6kKmfuje/w6wk7/mHSen7xWmm1eLS1qoZ6tbN/1/LOO6qwj1qncWc8884z06NEj4IQFF1xwgTRv3lzuvPNOyc/PVw5YOHx1Jyw47bTT5JJLLlFTv4466iiJO/GIFo1XhyuWzpd4dyxj5SSIYxnhdB3YdqBsrr9ZmjVrpp41R0n0KDiXtZ+YldEtDsNYRuDWhmck1rhB/8T6HZ0A6Latz5IvV/9u3V7T3+ikpaWpjxW8f6P+Dsa9R3oWg+70BCYjGjo/qo1ESTbO02u8IUWP1yDTn4Ou1xMiKal1U55b6tUt5WS9OldON9xLykwcmW4oo5v0j59I7CqHvSA1z51lnZZlzJ1lB3Jn2f1Gz50V6pgmTZqoqV6ff/55yGOSk5OlTZs2tSci1ugMseaTRUOO9spzRpmnrRY59kuRrjdrf09b5XzUi+6UaH609tfpDl68yhlL3FDGeLSfeDyX8SDR24/u1FIkuMMw1vfSLc+Im/RPrN/RdZzsbC0/L1JjGdHzuOr79/c3rtKdsW77sZbnlnp1SzlZr872VRP5XlJmYsl0QxndpH8iJMVNubPgyD3mGMMK8objcAzAMV26dKnymHAXQvB6verjGK1OF8k7VbxbZolv01bxNm9aufiHY3I94m06QHxIRt4UibQ9DsqqBPWIiA9H69N15XRDGeMgMy7PpQbbT5TvY/8pIvNvEO/ugsoFH/BCP+wJbb+D15DQ99I1z0gcZMapbmNdr7G8f9EGNiwG+HVbUQeBAwDrE9jZoHl5eRH9Jm64aQaVG2ZssZyJI88N9RoPmW4oI2Wy/eyvPLfon7rsiHUydxb+Wo/Rj4vkmHAXQtiyZYta7MtpvL6usjNtp/h82ZK0dZvz8rxeZfyj0+X41HLKTCh5rpIZ4+dSyWT7iS5YUOaIH8W7c7HsLCwSX06WJGUjCXySlpTfQVxxL13wjMRNZoLbBcXFxVJXycjIkP79+8vy5ctN25ctW6ZmX3Xu3Nn2d1i/wO43OF+/fv2kVhGvzk8s80DHQ55b6tUt5WS9Jo5MN5SRMhNHXjxkukn/1FVHrJO5s3Cc3e+xLZJjwl0IITc3t4qFEKLbAcJ1Q16sOuyxlEeZiSOPMhNLphvKqGQ2ayaeLVsSupyuuZeUmRDyjOsB1EXGjh0rN998s9xyyy2Bwf/JkyfLfffdp+pxyZIlcs4558gTTzwhxx13nNp/2223ySmnnKKc0JmZmYHfYLs+A6xWUYs7P3Uat9SrW8oZa1ivhJB4Qf1Tux2xTubOwl/rMfpx4RyDxYXivhBCCGC4J7I8ykwceZSZWDLdUEa3yHRDGSkzMeTFsh6dAGmysDAtHLFIh4W0XGeeeaZaQFZPubVmzRrZtWtX4Dddu3aVV199VTleu3fvLhs2bJC2bdvK6NGj41gSQgghhBBS5x2xTubOwnQv6zH6ccZj9N9Zj0GeWUIIIYQQQvaHYcOGqY8dvXv3lsLCwqDtSGmADyGEEEIIqbskuSl3lt0x69atU9Guxx9/fMhjECGLyIQTTjghKmUkhBBCCCGEEEIIIYS4i1rniNVzZ02ZMkXKy8sD26y5s3r27ClfffVVYD+mauG7cQEHa+6sq6++WpYuXSp///236ZhLLrlEOnTooL6fffbZKiJ37ty5gWM+/PBD6du3LyNiCSGEEEIIIYQQQgghiZGawMncWVhEYvr06fLAAw+oYzDtC+d44YUXTAtAfP755zJu3DiZPXu2ioZF1OzUqVNjXAuEEEIIIYQQQgghhJBEoVY6Yp3MndWtWzeT49WO/Px8ee655yK8YkIIIYQQQgghhBBCCKlDqQkIIYQQQgghhBBCCCEkkaAjlhBCCCGEEEIIIYQQQhyGjlhCCCGEEEIIIYQQQghxGDpiCSGEEEIIIYQQQgghxGHoiCWEEEIIIYQQQgghhBCHoSOWEEIIIYQQQgghhBBCHCbFaQFuxOfzqb9FRUUxkef1eqW4uFjS09MlKSkp4eRRZuLIo8zEkumGMrpFphvKSJmJI0+3r3R7i9ROG5VtnzIps3bJo8zEkUeZiSXTDWV0g8yiCOxTOmIdADcatG7dOt6XQgghhBCSsPZWdnZ2vC+jTkEblRBCCCEkvvapx8dwAke87gUFBZKZmSkej8dxefC8w6Bet26dZGVlJZw8ykwceZSZWDLdUEa3yHRDGSkzceTBdIWR27Jly5hFVCQKsbRR2fYpkzJrlzzKTBx5lJlYMt1QRjfI9EVgnzIi1gFQ6fn5+TGXi4YVqwYdD3mUmTjyKDOxZLqhjG6R6YYyUmZiyGMkbN2xUdn2KZMya5c8ykwceZSZWDLdUMZEl5kdpn3KMAJCCCGEEEIIIYQQQghxGDpiCSGEEEIIIYQQQgghxGHoiE0A0tLSZOzYsepvIsqjzMSRR5mJJdMNZXSLTDeUkTITRx6pG7DtUyZl1i55lJk48igzsWS6oYxukhkOXKyLEEIIIYQQQgghhBBCHIYRsYQQQgghhBBCCCGEEOIwdMQSQgghhBBCCCGEEEKIw9ARSwghhBBCCCGEEEIIIQ6T4rQAQkjtpaysTIqLi2XXrl2Snp4umZmZkpGRIR6PRxKJbdu2qbIiJbYxLXaDBg2kUaNGcb02QojIvn37lC7Cp169egFdlJSUOOPFhYWFsnv37iA9hMUDcnNz43pthBBSm6B9SvuUkNqAG+xTQBs19nCxrjrOtGnTZNasWdKxY0dZsWKF9OzZU0aOHOmozE2bNsmtt94qgwcPdlwW2Lt3rzz33HNKAf7999+qnLp8pxTuBx98IFu2bFGyf/rpJxk4cKBcddVVEiv++usvueuuu+Tdd991TAbqsnXr1oHveKGcccYZ8sILLzimcKFucP5Vq1ZJq1atxOv1yoknnigHHnigOAXayiOPPGK779FHH5Wbb7456jI/+eQTWbZsmeowbN++XdXzZZddJk7y+uuvy+zZs6Vz587qGTn11FNl6NChMXv2Ifu9996Trl27SkFBgepAXH/99Y7KBEVFRfLAAw9Idna23HHHHY7JQ9t95ZVXZN26dbJ582b5888/5V//+pecc845jskEH374oapPdNQWLFggnTp1kttuu01SUlJiose3bt0qZ599tnz55Zf7Ja86mampqVJeXh74fuyxx8qECROkffv2jskE77zzjmq7BxxwgHpe+/Tpoz7Rlge9F+odcvXVV8uzzz4bdZng+++/l19++UWSk5PVswKHxo033rjfHYiqZE6fPl3ef/996datm6xfv14OOeQQueCCC/ZLHql7xMM+jbWNGmv7tDbYqIlqn8bDRqV9Gj3cYJ+6xUalfeqMfRoPG5X2adUwIrYO88MPP8iDDz6oHlJ9hHjYsGGqAUMxRRsoWCgFvMBee+01GTRokMQCGCMXXnih5Ofnq+9ffPGFerjeeuutqLxgrNx9992yePFiZehi5AvGbl5enjJ4o/Hiro6Kigq56KKLlGwnwUvl4Ycfll69eiljs0ePHtK8eXNHZcIo6NChg7qn4Mwzz1Ttd8qUKY7J3LNnj1K6xvpER+all16SUaNGRV3ejBkzlAFibCt4wU2cONExY/fpp5+W//3vf6ou8UJD+fByycrKkr59+zr+7K9cuVIuvvhiWbhwoXqRAtQt2hdehk7IXLNmjfz3v/+V+vXry6RJk/a7E1qdPNzDo48+Wi699FL1/ffff5fDDjtMXQeMTidkvvjii/LGG28oQxedz9LSUmnbtq0yPp988klHZFpBvS5fvrxGsiKRCTnDhw9XzysMpDZt2jgu8/7771edFrQjAAMQ75Wff/456vLgWIDjAu3VyFNPPaU6ajWhOpnYjw73DTfcYNJPY8aMUWV3QuZHH32kdB/eoYjo0jst0AtnnXVWjWSSukes7dN42aixtk/jbaMmsn0aDxuV9int02jJTAQblfapM/ZpPGxU2qdhgohYUjc59thjfU888YRp2/vvv+/r3Lmz47LRdCZNmuS4nNLSUl/jxo19Dz30kGn7EUcc4evSpYsjMkeNGuVr06aNb9euXYFtzZs395166qm+WPDMM8/4Lr30Ut/AgQMdlbNq1aqY3EOdN998U7VNr9cb2DZx4kTfBx984KjcRx99NGjbfffd51u6dKkj8s466yzfhg0bTNuKiop8p512miPyiouLfRkZGb5x48aZtt90002+IUOGxOTZv+SSS3zXXXedadu8efN82dnZvt27dzsi00jbtm19Y8eO3W85VclD27322muD7nVmZqZv7969jsh8/PHHfU2bNvWtXr06sO3II4/0de/efb/lhZJp5O2331b6EPUbLULJjOb9C0fmrFmzfDk5Ob6SkpLAtqlTp/omTJjgiDw7PQRZ33333X7LCyVz9OjRvh9//DHo2EGDBjkiE7odbeWKK64Ieqc59b4mtZN42qexslHjYZ/G20ZNVPs0XjYq7dPo4gb71C02Ku1TZ+zTeNiotE9Dk1jJLVwERmVmzpwZFBbfrl07NW0II4CJAEbFMWqK6TPWcmKUzwkwiodz66MlCJfH6N5RRx0lTjN//nwV2aBHVyQSGH0+6aSTTPm9MHKL6WZOYhxt06dCtGjRQk2RcgLk0kGEDPJ+6fz6668qosMJMOqNnD7NmjUzbce0uq+//lpFyTjNp59+aquLdu7cKT/++KMkAsgJhele1jJiSqpVP0ULjIAj2gkRBgCRQatXr46JLsJIPMqFyJVEBNNBMVqOPF86p59+umNRQVY9hCgOTJ9CBItTQBchksP4rsSUW709RRs8H5Blp4uWLl2qZJPEh/apc/ZpPG3URLZP42Wj0j6lfRot3GSj0j6t+zYq7VMNpiaoo8CQhRGoG2I6DRs2VH/RqPY3d0ltAOWzezhQfkwPiAUIyR8wYIDjU74wpQO5S5B7a9GiRRIL/vjjDzXtAJ0JhPRjGguMNCcUIMqE6UGQh2lYuIdQuNdcc404CaZC6WBK1PPPP6+mdzj5MoMR0qVLF2XYw1CAvCeeeMIRefpUKxhARjAgiPLiZerks1JSUqLyQ1WlizD1o64zd+7coG1ow02aNAl6sTsFpihh+ldNp+2EC9oOpiqOHTtW5XaLBcjVNH78eGncuLHKbYapRaNHj3ZEFp6Vb775Rq644gpVp/i+ceNG1QHHtF+jzogW1nPec889qo6dBOVDXi88/7iXp512mjz22GOBabdOGNahdJH+vkHHkCQ2tE9jZ5/GykZNZPs0njYq7VPap9HCLTYq7dPo26fxsFFpn2rQEVtH2bFjh/prTYatf9f3JyIYYUVy5zfffNNROcgvggTgGEFBfiNr3pRoAwMMCbFjBQxNKCQ9DxU6ThiFz8nJUbncoglGSPWRaeQ1042iY445Ro2WO/VCs4JFNbDwgpMceuihKqoBCxFg9LJly5by1VdfmUY2o8nBBx+sIlSQ38fIb7/9FlgF00ncqosQBYCO6e233+74Ks5Tp06Vzz//XC3K8vbbbzu+eumrr76qOryxXBEWHX3oIl0mHAswnJzIk4foMazE/d1338lNN92korwAOuHXXnut0sVOgnxq6AjrnVSnwEg/8olBFyEX3n333ad0sFPtB+8OdPLjpYtI7cCt74RY2qextlET2T6tLTYq7dPo42ZdlKg2Ku1TZ+3TWNmotE81mJqgjqIrVd2Tr6N/t25PFGCYYXT6lltukXPPPddRWVD0SEKOUaHu3burRRicAgoXKwpjlC1WwAAzjjzBMDnuuONqnNS9KvTVJjHyZRyZhtEJ5YupjE6DRSYef/xxVUYnwXQZjCLCMMGoMAw9GL//93//59go5ssvv6wWk8BUK/3Fgs4DcHpRDbfqIqxmfMoppygj12n01aLRue/Xr5/SS06B0X4YmLGOWEOZjIY1jDOMkjsxdVHXR+gg6kauro8QgeDktGKASCSn9ZDeeUDEASK80H6w8i9WV8cCG04BOZiWrhu7mEKIaJJY6CJSO3DrOyGW9mksbdREt09rg41K+9QZ3KqLEtVGpX3qvH0aKxuV9qkGHbF1lOzsbPXXqgTKyspM+xMNGGGHH364yp8SKzBtpWvXrsqwdsIYQ36vefPmxaRjXh0YicKLDjmFoj0SBQ444ADTdkyZwcgfVjB0GozWYioUDHyngFGHlRcRPYER0zvvvFOWLFmiRuGQawwvHifAKs2TJ0+WZ555Rn0wHQnyQevWrcVJ3KiLMCKN8sLojOWoPDqJMAAvv/zyoFHdaBmA6DCNHDlSaoMuQscNOSWjTVX6CM6Umq5KGw6Y/jRnzhzV+XUaRDxBH5188sly5ZVXqmmYWPkXU5h14zPawGGD3H+IWkGE17fffiunnnpqTHQRqR248Z0QL/vUaRvVDfZpbbBRaZ86g1t1USLaqLRPnbdPY2mj0j7VYGqCOgpGgzDSCCPJiD7i2KlTJ0k0MEqCJPZIDg6QRLp58+ZRlYH6Q/g/RvfOP//8wHbkDcGUCxgtvXr1iqpMKIW1a9eaRvoxVQjlwzYkysYCAtEEhuxBBx2kplVg6oPVONFH5KJFx44d1WgTDE0j+mh0LAwFRIsYRxedAO0D0wONo7V4mWK6BSJWsB95zpwA58dHBy8zGEXRfkasINcW6tUtumjatGkqLyDyqiHaAhElqAOM5kYTPIPQRVhEAxFWRl2EDhP0UbQXTUGOMRjPRl2EbSgjtqF9ORHpBeMICxNgZNyqi6w6IxpgGiaey3joI+ghGNpOp7qB8wA5D/v27WvqPLz77rvKcYP2A6PXCVC3yCVpXFwIZY6F85nEH9qnztin8bBR3WCf1gYblfapM/QM7kgAAA6DSURBVLjNPk1kG5X2aex0kdM2Ku3TSuiIraPgQe3fv79KdG5k2bJl0qZNG8dW3IzniwVGEkZNdJCk26j8owFGtzBlB3lRjEYuVhjFCw2GdrTBSoj4GLnooovU9KiHHnpInAB1ifNb2wle3kjeH+0paJCHiAo9D5cxhxFGpJFHymmw4q81YX+0wYvSLiIF5T/wwAOladOmjsh97733VH6bf/3rX6YIC+sqmE6BiAc7XQQ9hWlKiQJGohEZY5wyiSlLRr0ULWBcIpoB0SpGPaevduxE5EyfPn3UxwimveLeOqWLAAw+YydN10UwzJzSDZjmZaeP4ECy1kFd00P6VNdQ0XGoa6fycMFxg+fE2FmCLsLquExN4A5onzpjn8bDRnWDfVobbFTap87hFvs00W1U2qfO26ex0kW0TythaoI6DPKTIEzfODqM6R/IZ+RkYm59xTnrynNOgZER5BjCKBDCyfFBnhS8SKMNRkSGDBlieokhhwiS2yNJNpJLxwIoKSfrFzl2kMsML1AdvMwQpv/00087IhMvTCSO16eVoYxYFAF5qvTVDJ0Eq+JaE/ZHG7yU8aJEhIERjNpi9V10Qp0Ao4gfffSRaVoSRvox9SMWzz5eaoiSMU4ZhC7Cdn112mjLtB4TrecllLwVK1bImDFjVHSFrocmTJggs2fP3u8XuJ1MGCKYtmPMl4T6/fjjj9XqosZnN1oyndZFoWRiERjjysUw5NGmoYv2N4ojlExMz4TBaZw+B5mI1NkfPV9dvTqhh+xkwnmAaLWXXnopqAOBDhQcZdGWCWbMmGFaqOiTTz5ReQmNEQgk8YmXfRprGzWW9mltsVET0T6Nt41K+3T/cYN96hYblfapM/ZpVTKd0kW0T6vG40vkTNUuAC83GCdY3Q55d/AXuX6cAKMzr7zyijKI8BJDuD5ye/Tu3TtoxDxaYDoJpgxhJMgKDDXkG4o2eCiR0BkKHlMDkB/rzDPPlEsuucTxDsSCBQtU3aKeS0pK1CgmXmiYFhFtkD8I5cSoFKbp4L4iufuRRx4pToEVftFp6dChg3rBIDE3prbEgn/84x/K2HzssccclYNFCMaPHx+YDgQVC8MI0QAwgp0AuXXeeecd9dLZsGGDknfHHXdEbYQvnGcfHUHsw2gmrgHRBjAmavrMVCcTERaoZ0yRxMsc06Fwj5ErDxE70ZYH3WqXDwqLISAiyoky4tnEM6pPq4N+gHGL6a81NQDD1eM4DvWKFXDxP3QRIktqouurk4lnBPthAELvIQ8g3mP7s4J0OOX89ddfVT5HtB2sVIt3DSI7atJmw61X6FgstoAoof2lOplwgqGTBKcQosjgLMJziXdnTVfDrU4mnkc4onBP8d6GDrr33nslMzNzv8tL6haxtE/jYaPGwz6Np42a6PZpPG1U2qc1xw32qVtsVNqnztin4cqMpo1K+zQ86IglYYOXKAw/jJRAEaDp6CN90c4941ZQv6hXKCR89NE+1i9x27NfnUx8RycU/+v78cFvanJNtbGM8ZSpH4fO2f7qotpczroqz00yCQkHtk1noX1KaiNusE/DkekmO4r2ae2X6YYyRgM6YgkhhBBCCCGEEEIIIcRhmCOWEEIIIYQQQgghhBBCHIaOWEIIIYQQQgghhBBCCHEYOmIJIYQQQgghhBBCCCHEYeiIJYQQQgghhBBCCCGEEIehI5YQQgghhBBCCCGEEEIcho5YQgghhBBCCCGEEEIIcRg6YgkhhBBCCCGEEEIIIcRhUpwWQAghicDixYvl1ltvlUWLFsm6deskJSVFjjvuOElPTzcd5/V65fvvv5cdO3ZIdna2HHHEEXL++eerDyGEEEIIIdGC9ikhhNQ9PD6fzxfviyCEkLrCkiVL5KCDDpJ+/fopg9aOu+++W+6//355/vnn5d///nfMr5EQQgghhLgH2qeEEFJ3YGoCQgiJgIyMDPUXEQehSE5OVn/r168fs+sihBBCCCHuhPYpIYTUHeiIJYQQQgghhBBCCCGEEIehI5YQQgghhBBCCCGEEEIchot1EUJIjNi7d6889thjUlBQIM2bN5dt27apvzfffLOkpqaqY15//XX53//+J59//rnK8zV06FApLy+X+fPnS5s2bWTcuHGSmZkpq1evlnbt2smIESNUTrCff/5ZZsyYISeeeKJagOGXX36R6dOnizEN+Ndffy2vvfaa+t2+ffuU/NGjR0v79u3Vfiz0cOmll6rra9WqlbrWd999V5KSkuSPP/6Qnj17yj333CMNGjQwlWv27Nny6KOPSteuXaWkpER2796tvjdq1Eh+//13mTRpkjzzzDPq2GuvvVYuu+wyWbNmjSrrW2+9pcp10UUXyY033igff/yx2oZrh7x//vOfcvvtt8vjjz+utqMeTj75ZLVdX2Biz5498sgjj8jSpUulY8eOaoGKwsJCdf35+fnquNtuu03VGyGEEEIIqYT2Ke1TQkiMwWJdhBBCwmPVqlWwHH0DBw4MeczYsWPVMZMmTQpsKy8v95144om+Rx55xHTsQw895DvppJPUfp2//vpL/f6VV14JbCstLfW1b9/ed8YZZwSuY9iwYYH9X3/9tfrNF198EdjWs2fPwP9vvPGGr0+fPr7i4uLAtqVLl6pzLlq0yHSdgwYN8uXk5Pgef/zxwPa9e/f6TjjhBHWOPXv2BLZ//vnnvhYtWvjWrFkT2Hb//ff7Bg8ebCpnv379fH379jVtwzlxzXfeeadp+7Jly9T2iRMnmrY//PDDajv2GxkyZIivbdu2qo6M5OfnB52bEEIIISTRoH1K+5QQUndgagJCCIkB48ePl4ULF8pNN91k2o5og3nz5smTTz4Z2KZHH3g8nsC2tLQ06d69u3z33XeBbccff3zgf/1Y4yINxxxzjPq7bt06ufzyy2Xs2LHSsGHDwP7OnTvL8OHD5dxzzw1EJmAhh7Zt26pRe0QAGK8Jo/5z5syRBx54QG0rKyuTiy++WM477zwVNaADWYiY+OGHHwLbcF16uazltC4soX/XF5UAa9euVREH1uO3bNkin332mfTt21fVkRH8vqpFKwghhBBC3AztU9qnhJDYQ0csIYTEgGeffVZ69eqlplFZjbHevXsHpkaFAkbjzJkz5cEHH1TfYYh26NChyt/06NFD/Z04caKaHoUpYVb69Okjv/32m8mABlajEcDQxufll19W37/44gtZv369un4jubm50rp1a/npp58kGni9XlXuK664ImgfDHd8tm/fHhVZhBBCCCFugfZpzaF9SgipKRyKIYQQh0GuK+Sc0iMArDRp0kTth7HWuHHjwPZPPvlENm7cqIzJb7/9VqZOnSoDBw5U+1q0aKHybVUFogEA8lYhIsF4bqNs/ZhBgwZVWxbk60Kurh07dsiSJUsCBu/KlStNxx122GFB8hA18NBDD0lNojX+9a9/KblW6tevL0899ZTK7QVjXa8fQgghhBASGtqnGrRPCSGxho5YQghxGCxmAIwLE1gXSTAep4Ok/1gkABQXF8uQIUPklFNOkTvuuCNi+ZCNj3E6WVWyqwPn0aMnzj77bDnuuOOq/Q2mh2FRAiNY6KAqYIDjuhGtYWfogksuuUQtHDF58mS10AIWUTj44IPVggiEEEIIISQY2qcatE8JIbGGqQkIIcRhmjVrpqZDbd682XY/8khhPz6hwIqqV199tdx5551qxdZIwKq1uhw72cZjqmP58uXKYM3JyQlMLUOOLzuw8u3+gOlqL730kikXWCi6dOmi6nfXrl0qQgGr5+IaCSGEEEJIMLRPawbtU0LI/kJHLCGEOAxG5zES/ssvvwQZf1hQALmqsICANRrAbppTVYZlKDAij1xfxsUJdDBdql27djJ48GDTdozWWyMksGjD77//LldddZX6fuyxx0qnTp3UwgdW/v7772rzilXH888/ryISrHnL7MBiEq+88op88MEHKv8XIYQQQggJDe3TmkH7lBCyv9ARSwghEY6CG//asXv37qBjxowZI127dlUrwxqBIYd8VXfffXeVI/UVFRXy3//+V+XMGjZsWMjrKi0tDdrXrVs3ZQjiGpA7S+fnn3+Wjz76SN5+++2gFWMxJcxoqOK8WFEX09Gwki7Aiq+YboVVYWfMmGH67bhx41TeLGOZrOXSv4fajtVysUJudce//vrrKirh0UcflaOOOspUZ5FOaSOEEEIIqWvQPqV9SgipOzBHLCGEhAFG2jHtauHChQEj8eijj1bGK6YngRdeeEEZjrNmzVLfcfzHH38sI0eOVEYbFg2AAYj/YbBu2rRJGaEwFPVVYLHiK4xHgBF0TLWC4Tx37lw1len7779XCyHoYJEETAWDHHDrrbfKN998o4zhvn37Bo675ppr1Cq2MD7xe0Q6wDjGSreYNmU3XQ15rG655RYVrYCFD5D/6/rrr1ffdZAba86cOcqIhsGMBRAQqQBjGNPVsOItyoTrx/brrrtOrrzySlm1apVMmjQpYKjCKEV+rg8//DBQ/ilTpihD9a677pL//Oc/8tZbbwXK8s9//lPl/cJ0OCwaAbBwhB5FgXNgEQmcG+dA3rKsrKyotglCCCGEkHhC+5T2KSGk7uHxhcrOTQghxJVgAQYY0KtXr5baDgxko+FNCCGEEEISD9qnhJBEgakJCCGE1Flo5BJCCCGEkNoE7VNCSFXQEUsIIcQEpoTZ5fIihBBCCCEkHtA+JYQkCnTEEkIICeQZO+mkk1QeMeQH69evn8qDRQghhBBCSDygfUoISTSYI5YQQgghhBBCCCGEEEIchhGxhBBCCCGEEEIIIYQQ4jB0xBJCCCGEEEIIIYQQQojD0BFLCCGEEEIIIYQQQgghDkNHLCGEEEIIIYQQQgghhDgMHbGEEEIIIYQQQgghhBDiMHTEEkIIIYQQQgghhBBCiMPQEUsIIYQQQgghhBBCCCEOQ0csIYQQQgghhBBCCCGEiLP8P4MkCDvX8yviAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -581,13 +581,13 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 25, "id": "7de42c9d", "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -652,13 +652,13 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 26, "id": "0fd42f30", "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] diff --git a/stepushovgs/data-structures/docs/Отчёт.md b/stepushovgs/data-structures/docs/Отчёт.md index 3227081..b5c5c67 100644 --- a/stepushovgs/data-structures/docs/Отчёт.md +++ b/stepushovgs/data-structures/docs/Отчёт.md @@ -38,7 +38,7 @@ ### Как удаление работает в каждой структуре. #### Связный список -Находим элемент перед удаляем элементом, и заменяем его поле `next` на `next.next`, то есть теперь он указывает на элемент, который идёт после удаляемого элемента +Находим элемент перед удаляем элементом, и заменяем его поле `next` на `next.next`, то есть теперь он указывает на элемент, который идёт после удаляемого элемента: ``` Go current := ll.head diff --git a/stepushovgs/data-structures/source/results/benchmarks.csv b/stepushovgs/data-structures/source/results/benchmarks.csv index ae7cd89..c1e916c 100644 --- a/stepushovgs/data-structures/source/results/benchmarks.csv +++ b/stepushovgs/data-structures/source/results/benchmarks.csv @@ -1,379 +1,379 @@ Structure,Mode,Operation,Time -Связный список,Случайный,Вставка,0.194844 -Связный список,Случайный,Поиск,0.023535 -Связный список,Случайный,Удаление,0.015070 -Связный список,Случайный,Вставка,0.195476 -Связный список,Случайный,Поиск,0.024576 -Связный список,Случайный,Удаление,0.013662 -Связный список,Случайный,Вставка,0.196501 -Связный список,Случайный,Поиск,0.027028 -Связный список,Случайный,Удаление,0.018409 -Связный список,Случайный,Вставка,0.194328 -Связный список,Случайный,Поиск,0.024307 -Связный список,Случайный,Удаление,0.014566 -Связный список,Случайный,Вставка,0.192402 -Связный список,Случайный,Поиск,0.024709 -Связный список,Случайный,Удаление,0.012597 -Связный список,Случайный,Вставка,0.195884 -Связный список,Случайный,Поиск,0.024688 -Связный список,Случайный,Удаление,0.014169 -Связный список,Случайный,Вставка,0.193099 -Связный список,Случайный,Поиск,0.024969 -Связный список,Случайный,Удаление,0.014594 -Связный список,Случайный,Вставка,0.190199 -Связный список,Случайный,Поиск,0.024101 -Связный список,Случайный,Удаление,0.014087 -Связный список,Случайный,Вставка,0.194119 -Связный список,Случайный,Поиск,0.024697 -Связный список,Случайный,Удаление,0.015095 -Связный список,Случайный,Вставка,0.192847 -Связный список,Случайный,Поиск,0.024285 -Связный список,Случайный,Удаление,0.014059 -Связный список,Случайный,Вставка,0.191560 -Связный список,Случайный,Поиск,0.024087 -Связный список,Случайный,Удаление,0.013595 -Связный список,Случайный,Вставка,0.192786 -Связный список,Случайный,Поиск,0.023233 -Связный список,Случайный,Удаление,0.014077 -Связный список,Случайный,Вставка,0.197807 -Связный список,Случайный,Поиск,0.024156 -Связный список,Случайный,Удаление,0.015051 -Связный список,Случайный,Вставка,0.191335 -Связный список,Случайный,Поиск,0.024676 -Связный список,Случайный,Удаление,0.013941 -Связный список,Случайный,Вставка,0.195091 -Связный список,Случайный,Поиск,0.024660 -Связный список,Случайный,Удаление,0.013566 -Связный список,Случайный,Вставка,0.196118 -Связный список,Случайный,Поиск,0.024149 -Связный список,Случайный,Удаление,0.014061 -Связный список,Случайный,Вставка,0.192031 -Связный список,Случайный,Поиск,0.024358 -Связный список,Случайный,Удаление,0.013602 -Связный список,Случайный,Вставка,0.192075 -Связный список,Случайный,Поиск,0.024104 -Связный список,Случайный,Удаление,0.013106 -Связный список,Случайный,Вставка,0.192208 -Связный список,Случайный,Поиск,0.023780 -Связный список,Случайный,Удаление,0.014568 -Связный список,Случайный,Вставка,0.193080 -Связный список,Случайный,Поиск,0.024706 -Связный список,Случайный,Удаление,0.013573 -Связный список,Случайный,Вставка (среднее),0.193690 -Связный список,Случайный,Поиск (среднее),0.024440 -Связный список,Случайный,Удаление (среднее),0.014272 -Связный список,Отсортированный,Вставка,0.193077 -Связный список,Отсортированный,Поиск,0.033712 -Связный список,Отсортированный,Удаление,0.023180 -Связный список,Отсортированный,Вставка,0.191460 -Связный список,Отсортированный,Поиск,0.033173 -Связный список,Отсортированный,Удаление,0.024734 -Связный список,Отсортированный,Вставка,0.193809 -Связный список,Отсортированный,Поиск,0.034748 -Связный список,Отсортированный,Удаление,0.022083 -Связный список,Отсортированный,Вставка,0.198505 -Связный список,Отсортированный,Поиск,0.034239 -Связный список,Отсортированный,Удаление,0.022935 -Связный список,Отсортированный,Вставка,0.192402 -Связный список,Отсортированный,Поиск,0.036666 -Связный список,Отсортированный,Удаление,0.022155 -Связный список,Отсортированный,Вставка,0.198979 -Связный список,Отсортированный,Поиск,0.033534 -Связный список,Отсортированный,Удаление,0.024246 -Связный список,Отсортированный,Вставка,0.196372 -Связный список,Отсортированный,Поиск,0.033731 -Связный список,Отсортированный,Удаление,0.023694 -Связный список,Отсортированный,Вставка,0.209801 -Связный список,Отсортированный,Поиск,0.057158 -Связный список,Отсортированный,Удаление,0.027851 -Связный список,Отсортированный,Вставка,0.194425 -Связный список,Отсортированный,Поиск,0.034020 -Связный список,Отсортированный,Удаление,0.021955 -Связный список,Отсортированный,Вставка,0.195977 +Связный список,Случайный,Вставка,0.199516 +Связный список,Случайный,Поиск,0.024629 +Связный список,Случайный,Удаление,0.014065 +Связный список,Случайный,Вставка,0.196946 +Связный список,Случайный,Поиск,0.023807 +Связный список,Случайный,Удаление,0.013115 +Связный список,Случайный,Вставка,0.191475 +Связный список,Случайный,Поиск,0.023083 +Связный список,Случайный,Удаление,0.014584 +Связный список,Случайный,Вставка,0.189964 +Связный список,Случайный,Поиск,0.024014 +Связный список,Случайный,Удаление,0.014049 +Связный список,Случайный,Вставка,0.192273 +Связный список,Случайный,Поиск,0.023643 +Связный список,Случайный,Удаление,0.013426 +Связный список,Случайный,Вставка,0.191623 +Связный список,Случайный,Поиск,0.022900 +Связный список,Случайный,Удаление,0.014242 +Связный список,Случайный,Вставка,0.192131 +Связный список,Случайный,Поиск,0.024910 +Связный список,Случайный,Удаление,0.013999 +Связный список,Случайный,Вставка,0.190054 +Связный список,Случайный,Поиск,0.023244 +Связный список,Случайный,Удаление,0.014556 +Связный список,Случайный,Вставка,0.199543 +Связный список,Случайный,Поиск,0.023660 +Связный список,Случайный,Удаление,0.015066 +Связный список,Случайный,Вставка,0.193103 +Связный список,Случайный,Поиск,0.023620 +Связный список,Случайный,Удаление,0.014555 +Связный список,Случайный,Вставка,0.191255 +Связный список,Случайный,Поиск,0.023310 +Связный список,Случайный,Удаление,0.014155 +Связный список,Случайный,Вставка,0.190051 +Связный список,Случайный,Поиск,0.023622 +Связный список,Случайный,Удаление,0.014049 +Связный список,Случайный,Вставка,0.194320 +Связный список,Случайный,Поиск,0.024634 +Связный список,Случайный,Удаление,0.014369 +Связный список,Случайный,Вставка,0.191525 +Связный список,Случайный,Поиск,0.023547 +Связный список,Случайный,Удаление,0.014032 +Связный список,Случайный,Вставка,0.189879 +Связный список,Случайный,Поиск,0.022658 +Связный список,Случайный,Удаление,0.014757 +Связный список,Случайный,Вставка,0.193771 +Связный список,Случайный,Поиск,0.023675 +Связный список,Случайный,Удаление,0.014797 +Связный список,Случайный,Вставка,0.203894 +Связный список,Случайный,Поиск,0.025087 +Связный список,Случайный,Удаление,0.014177 +Связный список,Случайный,Вставка,0.192419 +Связный список,Случайный,Поиск,0.023327 +Связный список,Случайный,Удаление,0.014068 +Связный список,Случайный,Вставка,0.191059 +Связный список,Случайный,Поиск,0.023409 +Связный список,Случайный,Удаление,0.013834 +Связный список,Случайный,Вставка,0.192096 +Связный список,Случайный,Поиск,0.023889 +Связный список,Случайный,Удаление,0.015085 +Связный список,Случайный,Вставка (среднее),0.193345 +Связный список,Случайный,Поиск (среднее),0.023733 +Связный список,Случайный,Удаление (среднее),0.014249 +Связный список,Отсортированный,Вставка,0.193317 +Связный список,Отсортированный,Поиск,0.033389 +Связный список,Отсортированный,Удаление,0.023146 +Связный список,Отсортированный,Вставка,0.190249 +Связный список,Отсортированный,Поиск,0.032418 +Связный список,Отсортированный,Удаление,0.023950 +Связный список,Отсортированный,Вставка,0.193341 +Связный список,Отсортированный,Поиск,0.033679 +Связный список,Отсортированный,Удаление,0.024198 +Связный список,Отсортированный,Вставка,0.192107 +Связный список,Отсортированный,Поиск,0.035083 +Связный список,Отсортированный,Удаление,0.031384 +Связный список,Отсортированный,Вставка,0.196266 +Связный список,Отсортированный,Поиск,0.033967 +Связный список,Отсортированный,Удаление,0.023633 +Связный список,Отсортированный,Вставка,0.193438 +Связный список,Отсортированный,Поиск,0.033898 +Связный список,Отсортированный,Удаление,0.022652 +Связный список,Отсортированный,Вставка,0.192293 +Связный список,Отсортированный,Поиск,0.033186 +Связный список,Отсортированный,Удаление,0.024209 +Связный список,Отсортированный,Вставка,0.193963 +Связный список,Отсортированный,Поиск,0.041383 +Связный список,Отсортированный,Удаление,0.027010 +Связный список,Отсортированный,Вставка,0.191974 +Связный список,Отсортированный,Поиск,0.033188 +Связный список,Отсортированный,Удаление,0.024141 +Связный список,Отсортированный,Вставка,0.193575 +Связный список,Отсортированный,Поиск,0.034097 +Связный список,Отсортированный,Удаление,0.023053 +Связный список,Отсортированный,Вставка,0.195551 +Связный список,Отсортированный,Поиск,0.033767 +Связный список,Отсортированный,Удаление,0.023550 +Связный список,Отсортированный,Вставка,0.193096 +Связный список,Отсортированный,Поиск,0.034052 +Связный список,Отсортированный,Удаление,0.023542 +Связный список,Отсортированный,Вставка,0.192483 +Связный список,Отсортированный,Поиск,0.033566 +Связный список,Отсортированный,Удаление,0.023538 +Связный список,Отсортированный,Вставка,0.191346 +Связный список,Отсортированный,Поиск,0.033764 +Связный список,Отсортированный,Удаление,0.023127 +Связный список,Отсортированный,Вставка,0.191555 +Связный список,Отсортированный,Поиск,0.033191 +Связный список,Отсортированный,Удаление,0.024127 +Связный список,Отсортированный,Вставка,0.190323 Связный список,Отсортированный,Поиск,0.033676 -Связный список,Отсортированный,Удаление,0.022606 -Связный список,Отсортированный,Вставка,0.191872 -Связный список,Отсортированный,Поиск,0.032348 -Связный список,Отсортированный,Удаление,0.023074 -Связный список,Отсортированный,Вставка,0.195098 -Связный список,Отсортированный,Поиск,0.032630 -Связный список,Отсортированный,Удаление,0.023753 -Связный список,Отсортированный,Вставка,0.194327 -Связный список,Отсортированный,Поиск,0.032106 -Связный список,Отсортированный,Удаление,0.023585 -Связный список,Отсортированный,Вставка,0.190991 -Связный список,Отсортированный,Поиск,0.031614 -Связный список,Отсортированный,Удаление,0.022668 -Связный список,Отсортированный,Вставка,0.193774 -Связный список,Отсортированный,Поиск,0.033153 -Связный список,Отсортированный,Удаление,0.022583 -Связный список,Отсортированный,Вставка,0.206674 -Связный список,Отсортированный,Поиск,0.033185 -Связный список,Отсортированный,Удаление,0.023165 -Связный список,Отсортированный,Вставка,0.191183 -Связный список,Отсортированный,Поиск,0.032613 -Связный список,Отсортированный,Удаление,0.022630 -Связный список,Отсортированный,Вставка,0.190937 -Связный список,Отсортированный,Поиск,0.033334 -Связный список,Отсортированный,Удаление,0.022092 -Связный список,Отсортированный,Вставка,0.191349 -Связный список,Отсортированный,Поиск,0.036153 -Связный список,Отсортированный,Удаление,0.022576 -Связный список,Отсортированный,Вставка,0.195836 -Связный список,Отсортированный,Поиск,0.032643 -Связный список,Отсортированный,Удаление,0.022599 -Связный список,Отсортированный,Вставка (среднее),0.195342 -Связный список,Отсортированный,Поиск (среднее),0.034722 -Связный список,Отсортированный,Удаление (среднее),0.023208 -Хеш таблица,Случайный,Вставка,0.002008 +Связный список,Отсортированный,Удаление,0.023664 +Связный список,Отсортированный,Вставка,0.192296 +Связный список,Отсортированный,Поиск,0.032708 +Связный список,Отсортированный,Удаление,0.024118 +Связный список,Отсортированный,Вставка,0.204537 +Связный список,Отсортированный,Поиск,0.041774 +Связный список,Отсортированный,Удаление,0.026976 +Связный список,Отсортированный,Вставка,0.193468 +Связный список,Отсортированный,Поиск,0.033044 +Связный список,Отсортированный,Удаление,0.023545 +Связный список,Отсортированный,Вставка,0.204401 +Связный список,Отсортированный,Поиск,0.035750 +Связный список,Отсортированный,Удаление,0.026609 +Связный список,Отсортированный,Вставка (среднее),0.193979 +Связный список,Отсортированный,Поиск (среднее),0.034479 +Связный список,Отсортированный,Удаление (среднее),0.024509 +Хеш таблица,Случайный,Вставка,0.003026 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.004030 +Хеш таблица,Случайный,Вставка,0.003299 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002006 -Хеш таблица,Случайный,Поиск,0.001004 +Хеш таблица,Случайный,Вставка,0.003808 +Хеш таблица,Случайный,Поиск,0.001001 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002157 +Хеш таблица,Случайный,Вставка,0.003292 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003016 +Хеш таблица,Случайный,Вставка,0.004268 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002017 +Хеш таблица,Случайный,Вставка,0.003100 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003028 +Хеш таблица,Случайный,Вставка,0.004619 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002514 +Хеш таблица,Случайный,Вставка,0.004010 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002955 +Хеш таблица,Случайный,Вставка,0.002825 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002034 +Хеш таблица,Случайный,Вставка,0.004394 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002006 +Хеш таблица,Случайный,Вставка,0.003335 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.004015 +Хеш таблица,Случайный,Вставка,0.004183 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002255 +Хеш таблица,Случайный,Вставка,0.002352 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003012 +Хеш таблица,Случайный,Вставка,0.004124 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002006 +Хеш таблица,Случайный,Вставка,0.003422 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002301 -Хеш таблица,Случайный,Поиск,0.000503 -Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002568 +Хеш таблица,Случайный,Вставка,0.002977 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002006 +Хеш таблица,Случайный,Вставка,0.005030 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.003140 +Хеш таблица,Случайный,Вставка,0.003815 Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка,0.002015 -Хеш таблица,Случайный,Поиск,0.001009 +Хеш таблица,Случайный,Вставка,0.003015 +Хеш таблица,Случайный,Поиск,0.000000 Хеш таблица,Случайный,Удаление,0.000000 -Хеш таблица,Случайный,Вставка (среднее),0.002554 -Хеш таблица,Случайный,Поиск (среднее),0.000126 +Хеш таблица,Случайный,Вставка,0.003805 +Хеш таблица,Случайный,Поиск,0.000000 +Хеш таблица,Случайный,Удаление,0.000000 +Хеш таблица,Случайный,Вставка (среднее),0.003635 +Хеш таблица,Случайный,Поиск (среднее),0.000050 Хеш таблица,Случайный,Удаление (среднее),0.000000 -Хеш таблица,Отсортированный,Вставка,0.001504 +Хеш таблица,Отсортированный,Вставка,0.002509 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.003014 +Хеш таблица,Отсортированный,Вставка,0.003017 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002020 +Хеш таблица,Отсортированный,Вставка,0.003126 +Хеш таблица,Отсортированный,Поиск,0.001002 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.002257 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.001503 +Хеш таблица,Отсортированный,Вставка,0.003013 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.001668 +Хеш таблица,Отсортированный,Вставка,0.002519 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002023 -Хеш таблица,Отсортированный,Поиск,0.000000 -Хеш таблица,Отсортированный,Удаление,0.000503 -Хеш таблица,Отсортированный,Вставка,0.001509 +Хеш таблица,Отсортированный,Вставка,0.003346 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002282 +Хеш таблица,Отсортированный,Вставка,0.004243 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002008 +Хеш таблица,Отсортированный,Вставка,0.001588 +Хеш таблица,Отсортированный,Поиск,0.000000 +Хеш таблица,Отсортированный,Удаление,0.001585 +Хеш таблица,Отсортированный,Вставка,0.003053 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.003019 +Хеш таблица,Отсортированный,Вставка,0.003009 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.001504 +Хеш таблица,Отсортированный,Вставка,0.003074 +Хеш таблица,Отсортированный,Поиск,0.001185 +Хеш таблица,Отсортированный,Удаление,0.000000 +Хеш таблица,Отсортированный,Вставка,0.003145 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002523 +Хеш таблица,Отсортированный,Вставка,0.004152 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002006 -Хеш таблица,Отсортированный,Поиск,0.001000 -Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.001503 -Хеш таблица,Отсортированный,Поиск,0.000000 -Хеш таблица,Отсортированный,Удаление,0.001148 -Хеш таблица,Отсортированный,Вставка,0.001504 +Хеш таблица,Отсортированный,Вставка,0.004280 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002013 +Хеш таблица,Отсортированный,Вставка,0.003098 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.001002 +Хеш таблица,Отсортированный,Вставка,0.004386 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.003522 +Хеш таблица,Отсортированный,Вставка,0.003416 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.001008 +Хеш таблица,Отсортированный,Вставка,0.002529 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.002546 +Хеш таблица,Отсортированный,Вставка,0.003863 Хеш таблица,Отсортированный,Поиск,0.000000 Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка,0.001001 -Хеш таблица,Отсортированный,Поиск,0.000000 -Хеш таблица,Отсортированный,Удаление,0.000000 -Хеш таблица,Отсортированный,Вставка (среднее),0.001951 -Хеш таблица,Отсортированный,Поиск (среднее),0.000050 -Хеш таблица,Отсортированный,Удаление (среднее),0.000083 -Бинарное дерево поиска,Случайный,Вставка,0.003507 -Бинарное дерево поиска,Случайный,Поиск,0.001002 -Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.004530 +Хеш таблица,Отсортированный,Вставка (среднее),0.003181 +Хеш таблица,Отсортированный,Поиск (среднее),0.000109 +Хеш таблица,Отсортированный,Удаление (среднее),0.000163 +Бинарное дерево поиска,Случайный,Вставка,0.004532 Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.004086 +Бинарное дерево поиска,Случайный,Удаление,0.001019 +Бинарное дерево поиска,Случайный,Вставка,0.005690 Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.004518 -Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.005591 -Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.004526 -Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.001503 -Бинарное дерево поиска,Случайный,Вставка,0.004028 -Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.004618 -Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.001503 -Бинарное дерево поиска,Случайный,Вставка,0.006937 -Бинарное дерево поиска,Случайный,Поиск,0.001400 -Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.006121 -Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.001001 -Бинарное дерево поиска,Случайный,Вставка,0.007520 -Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.001006 -Бинарное дерево поиска,Случайный,Вставка,0.005534 +Бинарное дерево поиска,Случайный,Удаление,0.001004 +Бинарное дерево поиска,Случайный,Вставка,0.005536 Бинарное дерево поиска,Случайный,Поиск,0.001001 Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.003024 +Бинарное дерево поиска,Случайный,Вставка,0.008002 Бинарное дерево поиска,Случайный,Поиск,0.000000 Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.007068 +Бинарное дерево поиска,Случайный,Вставка,0.007454 +Бинарное дерево поиска,Случайный,Поиск,0.001012 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.006524 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.001000 +Бинарное дерево поиска,Случайный,Вставка,0.004504 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.007206 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.006102 +Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.007414 Бинарное дерево поиска,Случайный,Поиск,0.000000 Бинарное дерево поиска,Случайный,Удаление,0.001003 -Бинарное дерево поиска,Случайный,Вставка,0.004552 +Бинарное дерево поиска,Случайный,Вставка,0.005723 Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.001503 +Бинарное дерево поиска,Случайный,Вставка,0.005705 +Бинарное дерево поиска,Случайный,Поиск,0.001007 Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.004518 +Бинарное дерево поиска,Случайный,Вставка,0.006501 Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.003011 +Бинарное дерево поиска,Случайный,Удаление,0.001005 +Бинарное дерево поиска,Случайный,Вставка,0.005375 Бинарное дерево поиска,Случайный,Поиск,0.000000 +Бинарное дерево поиска,Случайный,Удаление,0.001000 +Бинарное дерево поиска,Случайный,Вставка,0.004520 +Бинарное дерево поиска,Случайный,Поиск,0.001006 Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.004291 +Бинарное дерево поиска,Случайный,Вставка,0.005931 +Бинарное дерево поиска,Случайный,Поиск,0.001034 +Бинарное дерево поиска,Случайный,Удаление,0.000000 +Бинарное дерево поиска,Случайный,Вставка,0.007446 +Бинарное дерево поиска,Случайный,Поиск,0.000634 +Бинарное дерево поиска,Случайный,Удаление,0.000521 +Бинарное дерево поиска,Случайный,Вставка,0.005628 +Бинарное дерево поиска,Случайный,Поиск,0.000513 +Бинарное дерево поиска,Случайный,Удаление,0.000510 +Бинарное дерево поиска,Случайный,Вставка,0.005162 +Бинарное дерево поиска,Случайный,Поиск,0.000511 +Бинарное дерево поиска,Случайный,Удаление,0.000512 +Бинарное дерево поиска,Случайный,Вставка,0.006672 Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка,0.004562 -Бинарное дерево поиска,Случайный,Поиск,0.000000 -Бинарное дерево поиска,Случайный,Удаление,0.001002 -Бинарное дерево поиска,Случайный,Вставка,0.003511 -Бинарное дерево поиска,Случайный,Поиск,0.001003 -Бинарное дерево поиска,Случайный,Удаление,0.000000 -Бинарное дерево поиска,Случайный,Вставка (среднее),0.004803 -Бинарное дерево поиска,Случайный,Поиск (среднее),0.000220 -Бинарное дерево поиска,Случайный,Удаление (среднее),0.000351 -Бинарное дерево поиска,Отсортированный,Вставка,0.900417 -Бинарное дерево поиска,Отсортированный,Поиск,0.052424 -Бинарное дерево поиска,Отсортированный,Удаление,0.061880 -Бинарное дерево поиска,Отсортированный,Вставка,0.889120 -Бинарное дерево поиска,Отсортированный,Поиск,0.053632 -Бинарное дерево поиска,Отсортированный,Удаление,0.062189 -Бинарное дерево поиска,Отсортированный,Вставка,0.881248 -Бинарное дерево поиска,Отсортированный,Поиск,0.055152 -Бинарное дерево поиска,Отсортированный,Удаление,0.061669 -Бинарное дерево поиска,Отсортированный,Вставка,0.885030 -Бинарное дерево поиска,Отсортированный,Поиск,0.055543 -Бинарное дерево поиска,Отсортированный,Удаление,0.062420 -Бинарное дерево поиска,Отсортированный,Вставка,0.891156 -Бинарное дерево поиска,Отсортированный,Поиск,0.056153 -Бинарное дерево поиска,Отсортированный,Удаление,0.062845 -Бинарное дерево поиска,Отсортированный,Вставка,0.891750 -Бинарное дерево поиска,Отсортированный,Поиск,0.058122 -Бинарное дерево поиска,Отсортированный,Удаление,0.062453 -Бинарное дерево поиска,Отсортированный,Вставка,0.888773 -Бинарное дерево поиска,Отсортированный,Поиск,0.055758 -Бинарное дерево поиска,Отсортированный,Удаление,0.061916 -Бинарное дерево поиска,Отсортированный,Вставка,0.888226 -Бинарное дерево поиска,Отсортированный,Поиск,0.051592 -Бинарное дерево поиска,Отсортированный,Удаление,0.063321 -Бинарное дерево поиска,Отсортированный,Вставка,0.880372 -Бинарное дерево поиска,Отсортированный,Поиск,0.053502 -Бинарное дерево поиска,Отсортированный,Удаление,0.063089 -Бинарное дерево поиска,Отсортированный,Вставка,0.889972 -Бинарное дерево поиска,Отсортированный,Поиск,0.055111 -Бинарное дерево поиска,Отсортированный,Удаление,0.062310 -Бинарное дерево поиска,Отсортированный,Вставка,0.882075 -Бинарное дерево поиска,Отсортированный,Поиск,0.055492 -Бинарное дерево поиска,Отсортированный,Удаление,0.062256 -Бинарное дерево поиска,Отсортированный,Вставка,0.884940 -Бинарное дерево поиска,Отсортированный,Поиск,0.056025 -Бинарное дерево поиска,Отсортированный,Удаление,0.062441 -Бинарное дерево поиска,Отсортированный,Вставка,0.881076 -Бинарное дерево поиска,Отсортированный,Поиск,0.051258 -Бинарное дерево поиска,Отсортированный,Удаление,0.061709 -Бинарное дерево поиска,Отсортированный,Вставка,0.882319 -Бинарное дерево поиска,Отсортированный,Поиск,0.053783 -Бинарное дерево поиска,Отсортированный,Удаление,0.061596 -Бинарное дерево поиска,Отсортированный,Вставка,0.890215 -Бинарное дерево поиска,Отсортированный,Поиск,0.051424 -Бинарное дерево поиска,Отсортированный,Удаление,0.063194 -Бинарное дерево поиска,Отсортированный,Вставка,0.886962 -Бинарное дерево поиска,Отсортированный,Поиск,0.054314 -Бинарное дерево поиска,Отсортированный,Удаление,0.062138 -Бинарное дерево поиска,Отсортированный,Вставка,0.887173 -Бинарное дерево поиска,Отсортированный,Поиск,0.055819 -Бинарное дерево поиска,Отсортированный,Удаление,0.064069 -Бинарное дерево поиска,Отсортированный,Вставка,0.884293 -Бинарное дерево поиска,Отсортированный,Поиск,0.052575 -Бинарное дерево поиска,Отсортированный,Удаление,0.063521 -Бинарное дерево поиска,Отсортированный,Вставка,0.888483 -Бинарное дерево поиска,Отсортированный,Поиск,0.053261 -Бинарное дерево поиска,Отсортированный,Удаление,0.062347 -Бинарное дерево поиска,Отсортированный,Вставка,0.889441 -Бинарное дерево поиска,Отсортированный,Поиск,0.050554 -Бинарное дерево поиска,Отсортированный,Удаление,0.062955 -Бинарное дерево поиска,Отсортированный,Вставка (среднее),0.887152 -Бинарное дерево поиска,Отсортированный,Поиск (среднее),0.054075 -Бинарное дерево поиска,Отсортированный,Удаление (среднее),0.062516 +Бинарное дерево поиска,Случайный,Удаление,0.000549 +Бинарное дерево поиска,Случайный,Вставка (среднее),0.006081 +Бинарное дерево поиска,Случайный,Поиск (среднее),0.000336 +Бинарное дерево поиска,Случайный,Удаление (среднее),0.000481 +Бинарное дерево поиска,Отсортированный,Вставка,0.993672 +Бинарное дерево поиска,Отсортированный,Поиск,0.060430 +Бинарное дерево поиска,Отсортированный,Удаление,0.065743 +Бинарное дерево поиска,Отсортированный,Вставка,0.984657 +Бинарное дерево поиска,Отсортированный,Поиск,0.060576 +Бинарное дерево поиска,Отсортированный,Удаление,0.067630 +Бинарное дерево поиска,Отсортированный,Вставка,1.077915 +Бинарное дерево поиска,Отсортированный,Поиск,0.064100 +Бинарное дерево поиска,Отсортированный,Удаление,0.066554 +Бинарное дерево поиска,Отсортированный,Вставка,0.986610 +Бинарное дерево поиска,Отсортированный,Поиск,0.060386 +Бинарное дерево поиска,Отсортированный,Удаление,0.065383 +Бинарное дерево поиска,Отсортированный,Вставка,0.976014 +Бинарное дерево поиска,Отсортированный,Поиск,0.060724 +Бинарное дерево поиска,Отсортированный,Удаление,0.066072 +Бинарное дерево поиска,Отсортированный,Вставка,0.954288 +Бинарное дерево поиска,Отсортированный,Поиск,0.062234 +Бинарное дерево поиска,Отсортированный,Удаление,0.067913 +Бинарное дерево поиска,Отсортированный,Вставка,0.948662 +Бинарное дерево поиска,Отсортированный,Поиск,0.061164 +Бинарное дерево поиска,Отсортированный,Удаление,0.064309 +Бинарное дерево поиска,Отсортированный,Вставка,0.940560 +Бинарное дерево поиска,Отсортированный,Поиск,0.058861 +Бинарное дерево поиска,Отсортированный,Удаление,0.065901 +Бинарное дерево поиска,Отсортированный,Вставка,0.944873 +Бинарное дерево поиска,Отсортированный,Поиск,0.060448 +Бинарное дерево поиска,Отсортированный,Удаление,0.065882 +Бинарное дерево поиска,Отсортированный,Вставка,0.928810 +Бинарное дерево поиска,Отсортированный,Поиск,0.061107 +Бинарное дерево поиска,Отсортированный,Удаление,0.064740 +Бинарное дерево поиска,Отсортированный,Вставка,0.925909 +Бинарное дерево поиска,Отсортированный,Поиск,0.060174 +Бинарное дерево поиска,Отсортированный,Удаление,0.064934 +Бинарное дерево поиска,Отсортированный,Вставка,0.926721 +Бинарное дерево поиска,Отсортированный,Поиск,0.062980 +Бинарное дерево поиска,Отсортированный,Удаление,0.062940 +Бинарное дерево поиска,Отсортированный,Вставка,0.932508 +Бинарное дерево поиска,Отсортированный,Поиск,0.059849 +Бинарное дерево поиска,Отсортированный,Удаление,0.064563 +Бинарное дерево поиска,Отсортированный,Вставка,0.941225 +Бинарное дерево поиска,Отсортированный,Поиск,0.058925 +Бинарное дерево поиска,Отсортированный,Удаление,0.062112 +Бинарное дерево поиска,Отсортированный,Вставка,0.935714 +Бинарное дерево поиска,Отсортированный,Поиск,0.059868 +Бинарное дерево поиска,Отсортированный,Удаление,0.064928 +Бинарное дерево поиска,Отсортированный,Вставка,0.925400 +Бинарное дерево поиска,Отсортированный,Поиск,0.060723 +Бинарное дерево поиска,Отсортированный,Удаление,0.063271 +Бинарное дерево поиска,Отсортированный,Вставка,0.935481 +Бинарное дерево поиска,Отсортированный,Поиск,0.059515 +Бинарное дерево поиска,Отсортированный,Удаление,0.063816 +Бинарное дерево поиска,Отсортированный,Вставка,0.930136 +Бинарное дерево поиска,Отсортированный,Поиск,0.057873 +Бинарное дерево поиска,Отсортированный,Удаление,0.063642 +Бинарное дерево поиска,Отсортированный,Вставка,0.931535 +Бинарное дерево поиска,Отсортированный,Поиск,0.059197 +Бинарное дерево поиска,Отсортированный,Удаление,0.064474 +Бинарное дерево поиска,Отсортированный,Вставка,0.933106 +Бинарное дерево поиска,Отсортированный,Поиск,0.062731 +Бинарное дерево поиска,Отсортированный,Удаление,0.062908 +Бинарное дерево поиска,Отсортированный,Вставка (среднее),0.952690 +Бинарное дерево поиска,Отсортированный,Поиск (среднее),0.060593 +Бинарное дерево поиска,Отсортированный,Удаление (среднее),0.064886