From b8650bfcf95044d8ff78db12ff3a651ce371499c Mon Sep 17 00:00:00 2001 From: revengel66 Date: Fri, 13 Sep 2024 21:04:41 +0400 Subject: [PATCH 1/9] begin --- README.md | 3 ++- lab_1/.ipynb | 0 lab_1/requirements.txt | 0 3 files changed, 2 insertions(+), 1 deletion(-) create mode 100644 lab_1/.ipynb create mode 100644 lab_1/requirements.txt diff --git a/README.md b/README.md index 4281401..fbb4cf1 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,4 @@ # AIM-PIbd-32-Katysheva-N-E -ПИбд-32, Катышева Наталья Евгеньевна, вариант и ссылка на dataset согласно варианту \ No newline at end of file +ПИбд-32, Катышева Наталья Евгеньевна, вариант 10 +Cсылка на dataset: https://www.kaggle.com/datasets/spscientist/students-performance-in-exams \ No newline at end of file diff --git a/lab_1/.ipynb b/lab_1/.ipynb new file mode 100644 index 0000000..e69de29 diff --git a/lab_1/requirements.txt b/lab_1/requirements.txt new file mode 100644 index 0000000..e69de29 From 93a18dbb4dc42e8609c7c56639ef3a41824b7640 Mon Sep 17 00:00:00 2001 From: revengel66 Date: Fri, 13 Sep 2024 21:23:43 +0400 Subject: [PATCH 2/9] create lab1 --- lab_1/.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 lab_1/.ipynb diff --git a/lab_1/.ipynb b/lab_1/.ipynb deleted file mode 100644 index e69de29..0000000 From e3084d33b8a5cb8f6b061454363731294e90606d Mon Sep 17 00:00:00 2001 From: revengel66 Date: Fri, 13 Sep 2024 21:24:33 +0400 Subject: [PATCH 3/9] create lab1 --- lab_1/lab1.ipynb | 57 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) create mode 100644 lab_1/lab1.ipynb diff --git a/lab_1/lab1.ipynb b/lab_1/lab1.ipynb new file mode 100644 index 0000000..4a70425 --- /dev/null +++ b/lab_1/lab1.ipynb @@ -0,0 +1,57 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Начало лабораборной\n", + "\n", + "Выгрузка данных из csv файла в датафрейм" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['gender', 'race/ethnicity', 'parental level of education', 'lunch',\n", + " 'test preparation course', 'math score', 'reading score',\n", + " 'writing score'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_csv(\".//static//csv//StudentsPerformance.csv\")\n", + "print (df.columns)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "aimenv", + "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": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5528f802bc971a2cff2dfefd4c340a040c3112a9 Mon Sep 17 00:00:00 2001 From: revengel66 Date: Fri, 13 Sep 2024 21:27:08 +0400 Subject: [PATCH 4/9] edit path --- lab_1/lab1.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lab_1/lab1.ipynb b/lab_1/lab1.ipynb index 4a70425..bcb08cb 100644 --- a/lab_1/lab1.ipynb +++ b/lab_1/lab1.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -28,7 +28,7 @@ "source": [ "import pandas as pd\n", "\n", - "df = pd.read_csv(\".//static//csv//StudentsPerformance.csv\")\n", + "df = pd.read_csv(\"..//..//static//csv//StudentsPerformance.csv\")\n", "print (df.columns)" ] } From c433335be3a5aed63cde7934773b0b2c5395cc3d Mon Sep 17 00:00:00 2001 From: revengel66 Date: Fri, 13 Sep 2024 22:36:58 +0300 Subject: [PATCH 5/9] =?UTF-8?q?=D0=B1=D0=B0=D0=BB=D1=83=D1=8E=D1=81=D1=8C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- lab_1/lab1.ipynb | 51 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) diff --git a/lab_1/lab1.ipynb b/lab_1/lab1.ipynb index bcb08cb..155007f 100644 --- a/lab_1/lab1.ipynb +++ b/lab_1/lab1.ipynb @@ -31,6 +31,57 @@ "df = pd.read_csv(\"..//..//static//csv//StudentsPerformance.csv\")\n", "print (df.columns)" ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1000 entries, 0 to 999\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 gender 1000 non-null object\n", + " 1 race/ethnicity 1000 non-null object\n", + " 2 parental level of education 1000 non-null object\n", + " 3 lunch 1000 non-null object\n", + " 4 test preparation course 1000 non-null object\n", + " 5 math score 1000 non-null int64 \n", + " 6 reading score 1000 non-null int64 \n", + " 7 writing score 1000 non-null int64 \n", + "dtypes: int64(3), object(5)\n", + "memory usage: 62.6+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " count mean std min 25% 50% 75% max\n", + "math score 1000.0 66.089 15.163080 0.0 57.00 66.0 77.0 100.0\n", + "reading score 1000.0 69.169 14.600192 17.0 59.00 70.0 79.0 100.0\n", + "writing score 1000.0 68.054 15.195657 10.0 57.75 69.0 79.0 100.0\n" + ] + } + ], + "source": [ + "print(df.describe().transpose())" + ] } ], "metadata": { From da8f00a7a701c70e4ad413a5cd7fcb6c5035ac65 Mon Sep 17 00:00:00 2001 From: revengel66 Date: Fri, 13 Sep 2024 22:39:13 +0400 Subject: [PATCH 6/9] run flask --- lab_1/Запущенный сваггер.png | Bin 0 -> 76166 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 lab_1/Запущенный сваггер.png diff --git a/lab_1/Запущенный сваггер.png b/lab_1/Запущенный сваггер.png new file mode 100644 index 0000000000000000000000000000000000000000..0f107479bc029b529d068221bfe069fa6bca0010 GIT binary patch literal 76166 zcmcG#hdZ0?`v`!Xk}r<%Pq{!!NI|6 z{NSDq2M6af4vu4_QzzL+q}0Z@*?&jEZH(@6)D4NQuy>C8-!Z?#!BL;av;Xh}d!Osc z1LtrK4!*B{wj;eE6^}SL4$F-1-LZS>u}b9h%Am7)e_Mj?o;~{|OV{w;8=0dwk6Yh9 z9{L`42LOa&aYPp*O4T`JZDf1%JDWjQ#`SeKNPoEH(sC zs+MnTaA#&_Ry5U^adLCp+1Simgzg-FHb#KUB7v?^r~Y^9@Vj4&zvQA0AyE&iOlZ{c zN|n!^|LYAfB*2^P%Tr;~mo+%1n}MzV`|&SFZ}Yd3e+X#G?abO-xOZO46=cTFg@iNB?`}Gbw8q8}HsLW@cZ$Nu9YY zS8W3!;@hja4;&Oq=}C!+)=g9Vs@3!JSNNL#eb(&z`g3Cp|LTg??_w`-m7|GMuS)g& z!9soyYjw_;aa&lm{e{*rMA8l{eWb0R4QZillbz1e2yWQD?cUl(4e}M&~ zV*b5xfItDxY;b|PezN<9f!gpT}<< zF*G!kZ!;HR-UjR$RMylCKmT%9O!LX7wGd6|36X<$SN^@PXzb8tu&r+Jc}yooMnS=y z>o?ch8JC6^e_;Y^m9HNC6mY@!*RQ^r7W`!R{#QjGAhAWhCR&1US#!ft&A(Ev~*mz%>4Bd2TroLc79Ad znrUJcVHQt6xJHSxi1a(%eXcLakja0*+DnQ5%J8f`U^#q!eEfVQ4U(f{N2MU}?Qw5=uT_6T0rA+7cxI-608SfW6%q07 zT{>7sN@^t9=L~0Q`^u9m9B*C>9fz26OnBbC8$U{bqYP3S^`p>9$rPOW$IfGCIR2Qh zKkeQP9gfFGo#;=`i;IbsGS@oo6U1;>Fx`PTv55ETeg*{}Ooq@Fw6Xy0(MeDbgyv5dur)A~koV;K1 zMlW_Ux9muIovU*p=&27WB%p)yKQvcs>q=IemYjk@Xzy_&hSGn8=a{v!A}!5{>41i> z@{_21yD_YkLoyOi3QfrC706~yK;p<4%p_v&5#fkB^+>|lgz|!_?}nYu<+{2>rJbUg ze4SpgppGFJY677Xwf507-zNO{vwjnX3z1L96aR?nkmXnrh3F!5`k=JS;FPaQT{a-d z^Y(})h%4X@JleW(xHab@5`Z@XPH9u5{~_q1^hRre6oTip7u}xxw%1QyBMPl@*~~kJ zp}F4@44p93j&9(S(0o9_A;NoA5qh0qXZeQOFve2551*c`$>QJErIB+d1Xtb&`3H`F zA0d;V&j@gMP=D5-_OGAD?HfteYBXp#F-o;FRZM5HNXzTfZ84CO2CuH1EdXDOeSYl3 zn&_Org<@N@8Z;pz<9v5F?z*v$#l0n1)T`k3l_1!YFHZn*Wfex-UsZvGYp_V1s-&b; z5SFaAHjN!%nmM|;wJ@G>ar`^Su6ShZqSr8A*~g{v)5yU8WIBcOMaUo5jxHMI`YG>y-bt=1d z(Dh5cxdh|%>^aK%$a|Cw6vC+D)`G>z86Cef66_@elWwk`c=bWkppVhbwVf zliJIv;wpESU|@KbqI(_s)@Fz9Og@)7jfip;fIql@s!OsEQjAu*_yHm1QOP;2Jh~FI zHE5c-B6_ZDUx~i+#Jp^LIBOTRZ#3WR0$7anv%z`$=v>W6vS>V&eBRKa9beW#* zgw5iaBB23i^w^aCyMqhfU2io8aF|!kp$W~)of0eyrFMhS`u3H?qM(2V6}$Crm3D82 z|Io8CW!fJn=ucBc@A=&Tt&AG;>pm&hv4g^c7IkQl?>`z&H>zMX%~dS)v1`zUK`oGA zFnqFa?-2#p^XS_UlLU_Bdu^fZ+aPw2*MGG_&7UKo&#T4fF1Ty_A#7eiK&-SR{)&L8 zj9n4Ls3PRBRCNiBpyU)A0hp=od$S9_$djd|a!17;tHy*xW)&=?H{^lwcp{n010v@g7>5Ol*57x66GKB-a+$#qwF=gCY z(@F(_{2tC~T|s+(vicMv$7j!T5A>XcoS~tMtg-CAoP8zTm@J*isXv3eU4QRe4NEb0nC$XdG9zFqstL))u-wdU+)nNKf0 zK~UK$Z(lkZw0ekXH#wrVRKYV(ZX)XnBStlSed8)I`ku9k=Pa9gU*N@zVnMpS58 zR{oY0o=`BB7R@Y{^#KPg{yfmGa(qcZRt{^aVDKwT1EX9EmhUF*OiWBvD;yj6XqgpV z3EkBI)J5NGM)HcG*YV$f{1^#=fb-Ydmoy&o9|%9>;Q+^@i>`YTO5)Mt zQm6J=sbZYEr)~xPl+pRb+>lqoR-B#6m|h+!O;8V+{FxdUgFl#$;gx`N%p7_>KUm-K z0lBm#tY|C@sH&vpYD}B0a6uD~@PW9vezz}|gl}zBhX6L=wQEi*UwAMhCjQG1bpj^qmbo2!$V(Z6f+$<1KSomVgG-{%kJ>PN)U6DJl zTBiT<7!s~Lko6ezU?MMsSXdv9lWTlFdVKg;(QkFI`17w>V4? zs%nU3$!OamuUYghW(R{x zEO&**!PYt{4(9pVlj6|n&|F5f@$}8fFEdHpJeftF?a{LQ_Pd;^CJ0k9s-l zI`(_psW=K1uD^snEWXfj2nC^q{NcIL109RHi=8Rf4i1Zqmzczb9>B&m>2R&HxrC4x zTxzxTCMFwAV9&P~Vms_Z23*TE4qGe^;|_H?(T`v=F)U9x1$l?Ce7nfCVQ1jGvl;oW z6(uESGqbZ~FB78O zaLWH691-lIO+h|+9&GUIE;te!b9)ECT%GjewdC~9H8U+Y0q<#JB2pVRyu^rf{ zvrNq%OM7?J@6g0QZLKt85D*L3U8g&gZAnx%5+C*|skZue;go)JUqLt9Q8rP4^})4| z*_@DD?&l!;;HdBTmr=TvOSG_~^`PNqybMwGi}?6=<+zMhF0#iBt4{8&u@Ya#wS1Ms z+wM+S(#P$8BL3$dn7bfzh0W#5cThexV*1L;KMFC&pM&YA z$3cLnkxigk_R`&I&}9Z&Iv{?wvPqERA>Xt#u&eUp&>XHuyS997Qqt+IBTr6E`kS}t zg!mN}#4)kdk9kKZ_X{15YPzSTuGQ_+O#vs94SWxqkN1n?1uSFBA&zV<9v*yvWHIXf zh;u&(wTAyQl&@u`nuGC&aUbuEjEs=S%F7w6gSe$4gbhZVs2VW*jO@SZVChpd24xbZ zh`;lhTdZvzY7n?6c2A2Ku^F=U-N&*u{6V8c^cV3z6U?mfJ$iSLwHNHO5ad;RBjwwx zkveysEdphzk$!OOBmogQQt2ZNQ~RosStBWTU9PDmkq1*$cJ-vaG?iC8_Ji)w`BO4V zA!ZJd>#I}m-W>(()@xh2)Y$-Jc%F9N4p}nsxOOJh0Rvx-Ckai*U zej5MDb2aL>fM|7DO5*?&sYU)?%3cQVMJ>-R<6DZRo;{89?@=qOh&i9~I;=$@gouhl z>Oh|_s(r~%3?Zy{+T~7es_EYT2mfg2Wd8wBQE4XBH}X9r-jlK`VGP}D`ux%Fec>S! zLCGqc%K6^}Dep63yJO_#(zNk@L`NY0O3c>q6Q)?V{)~-^76um?;#-!J=Wq7lTM7vk6M+j43Ra&B)BI?5J_$3jLjw zmSXMZ)>Kkkq$H_kE^Vnd7+ggkG4iM6`XAJEOvj>5PHr@q0P;aTuNEaVD(pPOX9j4u zI}9?ct=XYX`^XWmO@mx=d`g@abL*!qlzUPQS%YyrL3Qw}ZMe#kT1Q3qs{^OcYpQ4z zFjgmPgWUlb)k1Y@gqW7*{$B@Hdvr>O;~gq7H`H8K`}X_hTet1Ub-GBE3?b^cx&l`0 zoSTb_t8bGjKC5#l4rkzlQ$lm25^cXllX8ASG^xSRkFl4dy|9ON>F2Nb(^6UW{iy?@6><{eT z!U7aGdzB9_q*727n{N9~jqMx(3X@mWp&B7GL1`LatWPul!=F5fcwPvDKC#nc10@?)jRL0aN$OB)f&x7Vp)%H@YYtF6r-)4i{Hm z@E7F3cOg;_$EBUeiEii4*-roU{BM8o!T#p;&VwF6%r9g%{Z|!+SU-0zO9B5lVzKuY zXd+a5yI^y=ULl!_z%M**>5htE8z&M3H3A!L(sTV?8MD0x6g&L_Kd2YMTeUz)%fQREOe;n!86!ue5+5DTAkdHY_5ZY8rTKP zf;tJ^<e!m6*bY+^R^^p^e0Qv!!X%)jUc$ zfsUnsYfcF8wjbr&IU~SIgC#Xo9F$TM?BJ`Jk?km>rsYhEU3g?_UKsI?DCCK7uKt;E zO~*-C@wvA!d&yw&%d2c&9Q3?_spD&}3qHW|f9VPI4HR67wbrd<)SA&0|OJM9BV5+J~-UluFlhoYhcfrh15)s zIJy{Wb2-F3Uk}(&2%VD@5X8HsR#MRQ5G_`POEpLxt6zS+?`P+(v4Y$B2T0(aM69E; zwuvA{k68G#NvMG4$D2_H|Jdii*d}M?vefqsWi& z!v9ef9V!8NzrEN+;X^|B^>-?WyDFguwqi{yM}3tz!Jab3d2T5wfYOGIi;*kiQ^Em= zrVkNx@`N+3bJNq8Mnbgi1fZTglg+Slaj5!Ia#kQStLht}$JDg1cH5z%Wcrb-DUfiS z^ol&X3NSr`E8&^BGpmAmtd(NTzf5Xzjz`}Qbk054TP@1W&o1wW#Jp5QZ^^;*;$A_X zj&!$gYD7`SWo_;MDMCPR9lAYjuy@t8OuQ8GIlgIg>9DBlAf^@)*B=RD9__&ar>H|0 z)gM!-soOCQrp6JGYtijP%}WgM`zAkS$9PzSsZ^3+p=$CcJ+>2O!_H zGAm_6_+M>xBLQAjFVx*z|CTcu*6XYu$_Hf*Y0;(0Q)SuSKHkW1j7OPdvo%K1eVP!^ zPvv;xYnUOn8p%kI0n7w+ci)%4eA(s44_OmSN`Tby^L6v5R{~e|sA5sBME9U6^+7Qa zJ!_x)=KSD_n7*Htpvu%J5)+{VkEX8>O|3_)Idyj^3hL_jdnNR37A$WW3sG;bhVJg} z?#(A(N6l>R#~%f|*{YPus>h-{MAU0qrkGTG3)G&DDTcpHo~#Gah`vb=b{;gUZ~eV< zJgUOQ&8=oASIfZG0#*^n49n=m2R3#3^fH3R;3)_DOK7&y!K{WJB-OkK2wsCk$!Koc zf5~n5ml?QidVXzbu2G_EYUn_1t!wF_j#EKS#)pzGcfr#ywhtW|CF1U~!McV75sH!i z{&Ne_sQ3e&C1?z#c6-&#;;N#$g^FK6)tH5@Ar1QR0S3J;0gm;#=X@BsIy^$=A_;?M zyAqJ0V^JVcxT0AN+!5okhedSW92Kq%Us7aHhG3D1ca8f=hb<89^Nb}nW*+EoqMak@KH z-Z^GrmH*+A{q2NMPGJqkJ(wQrH0m%#!*gt2GI{mjN#Hw<8!mOXSGH!3ctPj*I7GCV z+t}}WDT#^o#}=%upQn$S2NsW%8dbQr+j4tDg9|Sx>}duVtoij4(>w8mTmXWCzJHbU zUxUwa@BI^fe)Md7u6E?cJ>bMTOgxVzEwCer(%qVc#VpV12jS~<*K&H=X7?MBvn<0q z$NJpcBSv@5^)8c?PE^G-Ld-_1NpdQm&c zAEF{JoH=reqttnoR8%_8LEV&UF>aP@TA`r^h0S65)e@n<8~2P`r>Lqa~;=>HDo3Sy&MouS3~oN+F=CiB977O7m%HTJ0N2e)CrJ^owQ{QS{hf~&S>c9rwqCP*a>PwRT% zxO!RV*|RnR64}c`LT-R*C|kzKb{C&^A_g~pK0Zt zDE#}pQ~Kv#1Qd;U0Bxt_wDYW-AH8m9Y@_!1iJU#&JhNbD2Vjl$y+EG?a+G$n`7gUr zhWj)V~#J~{U7(A&%T-i!nd6@&2tCb=^f2vGBNP;%rLOVdO)5IX(+qSOlH&q?Cwpg>EbhNQ))|U*e`kERu0X8LwM28Rh zsY@~;pU4AkYI=IcKwBNTPM|+n+sgA8Cj+~pB1OsZNvJZ-PGz*x(4?|V`7Sn?-BC`b zbT#&h_1)Sg_@QrEwR%dHan{5&+6iR-!ZmS&3oB&8i^P41p53x~bt!~vWv)#BE88mp z0;kw?&ojUDyu6y+rJ~|sB1%qO5hw2G-~tD_qVe*FF$U4mCm5{;*4B5WINSP);MO7&p3QbSA;0k*Xs1;h=-Ygh2yk_OsS`5QEF>KB zGeCu_BKxo3%m^Ry$;d=moKZJbRR@6p{dN5Q#c$5NaFU>doRqpnNOK~7y1_dW#ed+- z-FDBgGGC2aun}N>y>i@Ez;G|*E;~>U6u7l-B(^$*HeiM|Pae5?ZcFVmiBC*hMxhg? z`?!vkoW%XdCH1Bhe&UidblyH`-S>b7-)d4Eihrj4;K6G=67LBQ9FuPIISSsSN6`~V zhxU?^37{jxU%t5g`U#<(jDt~JHpy-L8q9L(VStDE?N)ae4M|BjVc}U;&F3StBhsRX zF}Q`@UGI3E9_5f3&OAWmZJ+v-6Fo6p`nbx4Uf|{jT5bOHHA+cOC%!gQwjziqlO2^d zqR)8yi&CdRcTY5-aiLGX?&Oe@tZuT$^U+w!U48S5KVbZIejRNa?{s1($c{qPnM@8B_%b* zF+q}2Qsj?c`D`QXIwC88)yvB{RrwZ|GM*c0mN7+wsCX1n?^3IzDM>luhUsiCmojcn z4o5br{UHEp%<-9AemgdO`F-&H9)C*DcnDStJ|T8B&TZO)|J)yaxo|dB_t}nDy2+*1 z4PWJwl&ko7^13xndB6@7Y2%gz7wnNiH{7M&6AW}V3&g6vSI=*bEpFI&SM`SUS8tkC ztap4v56k|lV14GGVdS5%zQMrT!DLVxOVym8q5?OSD3S}9TTNOA5g?Q7b;LvHVB zYhUBUq#1~zAl0Rhl+ zzX&asAV+76HfvZLO=EBZ9u>YgU>y`x)p;1h?U#eM5ZjYe#BDp@93EBGM;Wb`Mdw@+?d8|f8^{Fcom3@*Xm@MWGi8TpviDQIz=Bt3F66ljs1I=2tFA9Wh6tMk&OKitlmwnvDluGTW?>42c zCa>^cxIUUdgFd1=Rz(#R?*0BH*dCq>o-r9@(U_5^zO+plFl>nH%iy)vAO;BjY0RZ6 zv-cJ}e!OeVigoQgb$s1YF*0HQgkD{K9Q}j)cFXI9Fk7BOhGUez z6IE7Wj9w;&r7ruZ#q~WB5D)|m=BbgV+Zx#rOX*S}*kSXq#R;av0A-(v*D7}#_bmMC z%}T92H2{VN<^f8c05&*2l0dk#L$aDX7ohviQ_`%+j!5csk2byw;4XgO_^|NjY0n+~ zK!d`F2ykFn)XG~-+0AWT_A(R2+~1}77h`CD(Js+KX0`(qRv!((O z9qr|DF37*O+{zY1mi9X<`#XCkryP>9T(>P779|QDr$`}%MO(O6Ju@M@r0l1U0W)#8 z+OL@S%NkS)DEB4T4eKaR%GEqk=^C)LrT7NKx{A4GT(|Wfc-JQyCtzDq(Z_9nUkkg9 zJ0Aes6<-*jZ<}>4)G_m>%1R#qJX$^b(>76vy85AkrIZXX0QeiF-;9rI@2pv$K6ZRY z%Fk_EKGNmlEVF$z4v5crM(Xwp$tNOzs0_x^AR?|TEw8UVmeuiS>7uYC;?{k)*bYe8 zk2C=ZiE^c`ox7fK^h{i>YdK6ODsU`|X6IpDvXgTSbtwQn)7xgsedT-HGnzW?#+~ek zdMe|a*Na%h$<)CR;jT^7*FJ5B^*uz-0n*tB)Z=g3LB^2iek^D)q{-2K}h=4gS^07bx7^jP_bSYy5m) zE4fOZ>VMQyZ?}oig88_l{Pk6?belPI*uDwxK%f5gz}PrZ?)vqx zggj3(jCjH!j*7^(Eid?BsXy3o?5b3NcY93@HYj_>VWB5w^r!K{3y?C7uK^MV#GVC> z)=Tqdba~VW=uBJVEL12H>Ns^1vVc*`Q zZqOy-7?rGLb8LG6pT0QZD~3^|pbapj4NjJiV-!rCb-ZkdX%{(leV}>L5$w@!w9Wc@ zeftp%;+?dp;7+qh7<7ALk%sTp(5unUA$@YeEZhwaH^Xau@R4Dv5zHHSxI`Ab-%%A(BboK?=EA{Wr#kYE95Er+_%>V9KD88MWH*ve64YJM4|B(;WPu6FIp!CeKsAI zCIY8b+_d2@BDT3#=HBA8Rstv;YL)~SFIY>>oWHcG5U!YBiI~=KV(0NpQ#Y5usd=G9 zd*Kd>0E?WMHocN?hFM(96|aeJXCNcUTaQ-R10(jYgyIo15UfdV;~ESn}ikO-2RY2|;>)!mYI2 z(0}>1T|C3EH_$b6#!ZUMj#0eRvm8uo$l2 zxB8E-RTa`FNU}z~79wx~d7Q>-^!w-wdRR)UU&v;9#(F(oJp>5Im2SrN`iVCZ?WhJ4 zGJ)fUd1{Z+4t56s_M5l~#jDQ(H@D%Q2LnURYw;8}NH7{5M zKy*@?i>jy{o48B4B2A3xRJ>FF!smRxXYs^ETd}O@b#L=VooZ71P<^?t%ch%1`NU6| z?hAP2(6X#{pjB!XN>W}$r>y#kZI-Qzs=R4F_ZU`otk5pRxhUBF;eAfEb?Vz{|D<=# zz{l2-r_J-L7fZfB@>nBM3$Xl@8GGqBBE{p)YSm{oB*_C{@)aE&_peaxZ(?DO#PRNv z`OWPH?6lVnc*29nD?noSEjGgWF{2lEn&b0G#*`<}B5exsbga7AgsUSR+K_i7g6W+bM6(##zv)AEp^jnCYfrFpZ zqt5qJ1IQ@(u7kCy^0X;O1=-5`N!BodMdX@~+?Rg0lflh=*8W@XAQ68@bp6mI%dkzl zFy67s8}j;y`u;OcFgA^7V;AE0@!$T4sie#JK; zEcJv+ok-;yHN=kJk6cPNf4FMEm-A77N3LOpMiVAkk)tbXul+JI*dy|ekLp)3keN05 zO)cU4bACvFUmsSn6Be^gF69;J!m{%dW!lV3VAOneMu|HLw9+Ubk6g@03g3A0OtBy` zRZ0RZ<(&)M{GOBLWvyh9V-1~SF{^>=CUqvJzPgw22;wv!| z6JGIqY3Iu^Igim3_UUmU29%YRRaR6?6fXYQ zQKiW$@LB^wew@DNgRx=3->(vSP4#WjU7ZoXyO$<0xn`r|My6SmZ=t2CK$m6pR{pG$ z{v7!O8t3+Jz|e#FE%xS{w3|0asiQJdPtWFsRJINjd6Xmy6p@F}u7car$dM@fvSc&R zw0--!s7h)$F&uI_KAFcj0 zzKZQvu=ZD?#ivtuJA1(@BubOTT0^YKja=Wt8Viu|84{*2_ECw7<>CXF`ulsnx&AA6 zw?j5r9-q6y|M}${+hv(DH%!~C@jMKi^U7M84Bmg~FS6-Dy3h!m4rtIBAgNST*` zt!6N`*&9NsPAID&PWlfB@jE18SN>uin=Y&I76 zsZ*|gkD)7c+fOl^-Om!ix(TM~2wNI`PJh+bd-SmNU8xV$uc|T8KB0ImW+kE_q_S7I z?2^dZyULdq20%FPtdGwJ1&6~dZrRVm>Ke_9i}-2MqU%CxT4r5Rb`5i;B8&nQ7>Rj! zM@;-$5v0LF-fi{Kp$jNNqGar(zb3j;Hdjy6ar4j~Pr@r1{M>KMXd?5+5DD2=sB4{J_oZNg9)X;n$>5v~kfLn84tS0q`7*-8I)uWtI zl{2IoI`s32M8dj##~wIalz+pOQztDCI2*rK`0n@6Eq{7ALZKsD;O8{yOg9T?=!bd-~(Mh#2TPY!Ri)bT8crp(OUMG*r%dJWdH2sW(U+A1yPDoKKV{ zuYculF%?HR)!$7t<}!J$!pq6&CFbhi?tJUciK)dGKr{qhwyjD6e%j8HozR+EdMfm0 zcc9&)oRf&#ucIC1!-IHfEDe^)Leec}(&miyn*Z*hdE}l5fBbEsPm7mJ16#-=Mo@%* z;chbQ$m0&W!=SV}(w761qit^NdbqF;n^`&!cGcFJ)f#n18?4f`D?U?Hcj(@&IT)46 z%l6wT8ZkjqT=dCXI76KiWTOgEpJ}guly21Ab|Q#ETsZ3f7;&r#kXi z4+U?DGj`o$>ke62_~C`=RBpP?ie_Xu;H_}z{KZU#0tD-Y?!v2p>A*PqhVk6BPNF8u zqqn|_HVdP!KrpW$`mPnE+1cu*wJ6htb~$}H>oy#4J#R}fsrI_v-g9Er>y$A9!{B_8 zV~IMhX}VQiP6lKBvSza^@VMqD(K`h*q$8JgWC}VG#crIpY_qYZK~2 zyWiXwB`#dp9q+)}34OwnBtOM6q9!Y9I_{8{!UysyeYT<1EO(S$h)Cpj4u%#Z*T*fc zuu=RP3KU|4V1p#%QqHjU2;A-xta`rWn4IR5+xZ$mn^5EWF6Nq!E*%e*yV_ZL^7Pw97>j(UF%fnsw zq0LXXdvhqT88dD$MuXHev^WO9veMxA;}fj4eqC6?UfTQ%=+se zCXS90s=_wal0-h5guZ~wJ)Dco<>!PTFOT4-1sjm=s(vP(+DmOGaUQECt4>AONgAdaU8`zJuiVRk(WyU4&k zw!=2uX=?n^AZ-Vh3@9`*AU2*nbRGPy=+)6ro0+?YY2v_tm|@Bb`;)p@pw-!obW6LI zVE3(VK~LAOuC+cfiEt;%^zpyZEKs^m)aEJh$tPn^OjS-??;dlnG=?8+k;q?xYck1` zLS|yWK|in}o+EINv4~2jOAgXtU{z9}{e{{IkLE$e?s^Z5YrlsrlEbo6eo3XeSL2%; z>lcGeLrYy9+dYsD4EJ@+=#Q`&$}A60tW05=7`q*MtI&bgPw3&~8D#s|BW@`vzThVCusRKlnxAnuWFdKr)y;|NRuarvZUg&sjX zksc!r>R7b%+^E<3ICY3l4$-voG^GgjX2L^ePub1Q_cO2-QcKfG@+v0u^H z$73h$J5_kksx4okbmExi%yk+HQ{Pw^T~2OY$v9mb43HYv1NvXfCw7sg`Hq3{*_3H zg^*6uWh-MSxM;|RY}kc<8QrQWuhyTpn62zp$i>yUR8jB0u&z8VA+=y26(+ zg`qH7lhI6=ip*fw;$*@;V(u=G^xTe`Y>w}7@yT$`P7ngtxY}3ePn(my+^?=-(*1Zd z4m8~N<~c{vmZAQ2=P06T70JyC{E_awrMM-HrZoVu9*M^MIt1tWWNb6{ z>p}+g<_dfOYv=4&eeAoA>M3sYubnP)3@Qo792DbAyR_X}RJ(Uu>di#Z{7dw)a!HHG z%;z~~v-vh?dsM}J)u}Vu5LhuZ<`eACK#iIK?tcjp~B{2z~x_q3PYW#66 zq`$VYJUuc{QOHNGKjM;HPhMe#7;fvB^$_x*@9=EM*aKnJu};u-usm@ZokN-^>~_S7 z`%1Ur$>PpR%K`ut4<^oEFlDp7=mxQseE&6ozW9aScMWNK?SRI z#n8x5R$I#9#7jx-=y_RCqe^%8P<@c0LssF=CduB#p>nIqjnl17;Mbs0r08DY^~m?LV^Xkf)i>Gl$*TFR~P{Ydlbeum$oN^d8bu2}T+!Pi_4*blPq=735K zlSZL`zse7o>Db;{knmhpeeNHrzxzWnPY1t8l*p~6AkZHE1);B4g-J;Nu@x+}Kw z81s7smBJvXs?ON6rn~FzlxG2K{_Z72lE`gnmg|c&O*cWeCs}6H%ZJ;ajbdn>FF}pz zrIwmAu*T@7B#w`^nTe#Yu4&IS?k|4+TPyb|=tlaTC68r?dOp!b_yP8ey-7m*%FX8! ztGec>u^f@1+a!|qoW1TPq~df|2&Dv4$VohcZj2lrj_I7YkIZWOzEO8j9^)SES%Y1Q z!zH5BB<@+zYJ;a5_QfkS`CB`9#CJHgdGH+zUA?G1SZ$wtlVUuL;r6ZhCa$@*3f;ef zn z%PFcEbY?o~;UKO%hwD`Sc4s%#eB%X@AYJodK6ugUnxK-VGdb z-9+xZD08Xb4`(fP<0Ic&SVmRnl40$16%7ckdBJn}lXtMkIPeixTje>Xiv0dAjLFNq zOmpaLm_`9b8&lfRcRnq+nf^r5T47vl^qJ%sE1}oc8*sRp5E;xaZa{kGCZrv~G$K#6 z)_pCNZ9!Yzy0g?b9hW4oX5BYvy{j~2J&;&l8Qb6Dvh$sGz9_3MK|JQcHZiK`6`xp@ zE|@WKl$}SZSnQD)O3$bmja}Y&7uMK`&@d>X z1hrZ4BWou+B<}AK1M(MwKLK`Tq3MI{Z?u%|xX;J;98v6=HRRszR1R8d#yqx6@#f^c zD|(g6=`D3C_T^sgXNaTbXVUFE@iDby1cYEpPmxM3Eri?0dE2v7J-WHxid{J*uTz!R zT#EmL8= z%ac;C_`1Clu}BLPe8s!5f5oTO^-@XmC+GL?Q#23@PU{Q*XcY;{hBWFpm1R+~q;4HH zbAKK>_qh=BG$e|pzGvs`5!d9M#KmqNqNV>L_{eTMum4Qz86q;Y;U=-9fO6u5C$B2SvBk2HtyAF|w$h0RowH&Qf zfqUJ{%;l%RWDC14EJ^g%ODX*(NH&=wzIj^io~_A|HDCvi8K*sR(A9l-$<^b;rD;W{ z%Yn>`eASdG0e?>#@ zd)-M>iQG-u$GpYuP6pR?A37Uhb}drB?M9D4)B|fS{a=jI;k$$ zc{zA6E*C7V@ejrJ`|ySNLgby_b!&OMPv9FK6^b{6$$94=`q2QHpobD^CgCq0C8}d4 z^Q|`_G47&KFJahUasRA4WCkwtIacp_jWKVBWCsv`vSusbv;EER-OSj~r{Wi%g6>8| z804IrS&wb`(1JgaAxot&PTKXqjJ<))?VzKasv$*O(|?!$G!H+u4_41`^&aHA?>u#P zqH#Ab%USU9q~uyvi=gF7aaF$YVKU>==2eas)q_`%#fxY6pF57#Ti7h#^0jb*7kkym{MInehm5d1nNNkb3E;I9=5*nU_c5&8v$aSG1Q}Y$+r+jTKU&ad7R69=7t0P`=l$X)5p!Z0 zz-J2Ozbt1K_5C0E)-x<}^_U(&Q8{j#L3J6$^ zpdfZm;kIQdjf6SaI(Yq4uVyg{h6<=LZDrwm1A zx@$2B+HK|eERuac3KC56lFqL%vq_fHIJ(jMp?Q^tt#=y(6^{xfWlA>HnW=pDTwF-X z6Wi&}Y+6xl@HFS{s^j(76L>`Mk_?0PTOK-wEzV9hF1lv;dVGq%Dc1fYO>n(pL|v+) zhL~6Y*i}q)CD7VOcvUV#(SQQ+Vg1!xfe>W*)jknp*uMUsvSYNdrh&H!va-vPkZ`fL zJnHkw*%Jk}pn*}&c}8IPDW*+!@N%u<_%qsN%9QQI@|uqD7}Kkwr7taxdlO`4PR`bm zD~f(;=I@v8f2Yj?=e|O<7n$zyEW6m^oH>8o688uoP(C@h=C4@coH^}wphxj0I7T2c zpuJYiSD$Y)+B<<+Iib>?3Aw)-ER=Bhrkrr^_%*X4= z2Xe6VP`mI`uX>O%!#F$tih=UiwizQ^N(Z5_8#9#B^V14I&v5Is6jsc>Atq(c#_IMu zof%xHGRtuQk^n!}goMPXE`ONDb(&6Yo=Zf97IX$0sgG&U3p5fD8;=_pDplO{aEi( zUgk91b9CYB=VK*BtqZSwuCC-AJ?3cR?SgPI@yEj_*-_NF88@&UqK47%zAPBv7mDM2-h|)vOy|zrEhUZ7 ztlhAMzD>tAuM1mGgGEI3&&baR?s-J6)TfTPRv}6|8I;ZL0D!w%kwg@ zZarWFtW&XG-nqQt&_kz*MKAaDT@l};BW&7yWm$i{ZGjCX3z3IHGWTzX6r8!4>6@;5 zfga|qdhh|4(+YA3(@iJ)R7nRbzAH zQ%BusNq~e-=ZnhiQoxLMTib(pJq)+!QIj{P;4iyef?rC1^Yrd@jxQMalFf#5pg12i zu%M&PQT%R& zJ=mnd_g!sGE)_DaC6!oe?sG59pFE!XAy;dG3S>;6KsqCR8Q6Ox<28wwY)hAyJsL{J zvfUc^_lu8y_|jBU>}J&2%!JSTV6KZ*C(cYheGQ2*?S@*@>=EGD5CuQIGkTqzn397B z$NH_(r<_zH>&?nGhoClDS9DG|Wo6UR76l0n=*o^j`W*eBG_(EUw~!EZi6f0)t7MjO zp{1mrG8MH2wLg|Do2c%K8eK(bN2~~m1#4qiJMqIELYveiLPH{0 zB_39$tl|Ov43)9r-bPKoFXN|s6s=97f=yr_SL)qf1PEB-@@mscKETU+Z`#D4z0OJx zdY{Go#_kT-GK1}Qz?N_BNJOjXJNG2#a2-1C%G}|fR8BKE#Lp%ndGYRCtZ%Nv&@3W8 zJtN_}Nr1!9bgw3JF1G*?y0j$X9IQVb3AW?2$x#@o)H_g|B6Msa*n(#RH5F}|PfRn) zFf}z|u_?e*I;fpy2EKBn|6U&iZByP5_~cP@6-BjayrtABT%y3w4otaK@Y#=)pXCMv zf{`3&N)|!9x)s>V4_CH=(@AYGK&s>9v^tR3%3T#>Ql7M}I^nkOx&*kNFd&0gnKrdo z4K&YiX1~0+GzS?JeGH8oudVW267jPnPCL{DVOx7f(nBporZC4-J$nO_zj0Ug1ov{~ zBK1kHf&-eNZp#s2Za)E%Am!j!X^$qC+AYhmdLYMYid*}9Lk8I1+STTiX`)5BkCUzM zIx(7G+K@YZ*NgDlEwHg9v{*$nHR?X5!_`-Ax&F$2lmWiOiz*C}H$YGAeOfb!ti!0hER-(eeA&;s$`xlYt#(bhmSh#qk3^E-h$$Phs-e*3!J zWcp5*$ivQYTh+2juZ+M?`8Df6a6+Ul?zTdWvoD|DA+M3-`o$`(8G9G%TGIxwocPstzOC3|ENxOo)JV`~xK4$ZQ zwLU!-vc=39gR~`oB|S?Q8*(ZSANGngz)+fvNe{>QjLeymEHFM5iR*?%zW$iCtE03l zZcX~NPsg$02YqlP9~lN zJXs28mcyqsc?O9NDL7;(Za@pv($_ElL^qXd^Cv`Wj4xp2nEniE@HUVXw~qG3jA-}8ww8Q*Vx<0B1O+d z>|`>@>mdP2ohGzNiSUZV*=oS@<&LH0b)TY|2pcNoPD_iy0!w{k1vpGfB{yajt@v)T z3Mm86ZmtWq66;P%j?VqDkvG zl31f)n&9~;f9NL3b@-MtiSF)x6P6%au@aEmm0xrJ!Iv(sEd3(m0Cl)d5wtlrEKW9L}%#;KxI26L?vFVGA zXm$8wZhqcv)?9hKQc;Vgnhu&v`7aC$WVw*1JQ~`wT2ZN|J@Qrjm+WHjaQ+k0M%LCI zh<>Z^DW1Avj)Os$Dycr?`93VF3+^m1(C72%Vl??ut3_X=WoBXIPRv9&yD9Q}isuyh zlxtRD+SOMR-!FPUgw2gz?Bc_5gvvfy=ba~uU0qel&*w9-xKm%ooCYyw*5@f>Wf;qP zy1Q!CwxbPtS}?&Z(N!bKB}R0C^9lj#o5>K;rBHMzDoVKXeR4_LHZpWw_%PMXS8zs! zr>(sme`EUPOKtiTK_2}sJ-swN-CEkB^0pXs^ZjFAU((Gnr&JbLf%}yG3MRtm;2=*O z?eN77-{RM6m(yHhJ5*03JAXl1$LFkRX31I$D|(Dn^)v8Lkp02sXcnN%A@$9>zEd{S zeVE1^78sZ<-k2ULTDR-(IbPky(&Z@+#o-NfK(3HrFw+kX}t> zYO3YAb~)yjrNV^WQ}~dSN7@N#+$(91(e_rpm$o4_X3Bkat`af*{iJD~Py8ePZ<3N%?k8Yl930D;EdRPUeNHd+gQ_M^X`1$M z52`-w`3rqB?X=jHKP}55eEKv=+@gbZo{s-YWd@2bEWo1uyqf`ar4&fU#$DB`c4?o! z#u5DKdFa?ARLzIjePk8$*xk%a;HpR#XjJtX1C?>q?ew8rsf_w>#fGq+qD7FHVZ;5k zG1M7~6*%^aKs5cJ!Rm=96+jENrm5Lg=|S)#M1)xi5|Lm@`<(E>=tfv^gSqxSBR}hC zZgUUQhGj1jQ7O9&3!@byGZI`M6p9rajJu9oZq}l&6SQ4O0N>`b8TV z8NEaPkZaocA>}_kxpWZ4m^qx~?eZ zfn3VyQ+JHAw03#Z51{Jym*JInbKnZ{McVv|Ni(uc*ARDoUcR5B2kLzAp|x)>h7|_G z3>)l@fp&46o9XO=PMH_ZOE_Lo?d{FvKlI255ff-@U-QhU+^Kc^uk%Y94Ar!*BVXs%gclv2q;olucyJ z{jBh|?a^R|CWY^n_sx6JF-YC0@gPBYx5lef$Uq)l1fR+RJZzIxkU)IZCVqC*x5cV@ z6_tHP@zE^ zq1$Fbo0eY@Ki=ZQt^U#K_o?oWRC&k7Z7SIlXx*PywR)Np7LypTB6D{I9{!G%#*E}q zdp%`EP88}VSv2}!hjVuV+B4H%S_@hfj>8z^LJAP1nUvW6-_kM>&s1ln_yU!m_5dF(|`D&qt(^EOOKQm4qZZ z_ATZTD^sZS%6UslhX(DG-Z^n`eJ&oV6+PAJ2CggZqBtelJ{j?Ty42K zZ=s!L^>}{3Pjtg){!fm?yLt8~+GmOcN_jry`Ye>b?=Tw=$JlKi1EWW?`aI9>Ozh+^ zjf~?J)_3Ga5W(hc?aC+m9P=Ih)8GZ;lZ}l_Zm;O|~)^LEg%;Qa;xVUhfz2M1l{s*F61<>dXo zxH$i1$barDZIR_$S^CqsH0~wdRq-S6>5KXIusgby)Dr=NeJ_tWN=RIgRK9n`5vyR~ z`fNP$M0aY=w=Po2B^lPz2X%H%7B54rNNh%T?i52HKwGniS%2weIkv-WYy&q1&if6`+0MgH z*T)?+a(ChgPNs(2cmGI8tkRD14A!~GMW{C(_?j%NO`G#^G07~rL&6Ls-vmcrG|mS{ z)GMjnm+FqE(06JiqxCE2<&?6nJKVNG-itM!FDrie3J&}jHuUx}Y5Yxjc=(?IhmTl4 zeAtYI(CdHvpqi>)fX67urc8V7Hs4Bk_nAXZzUQCiS)8yzPTl&3vtUf~OFr#;6NMZ; zi;|ZUP)VMZ`iX%n)*6iD?3HE)-qicF*7*W$ALC11P0cj*xYRW#>yQu@P~%X-V3{zD z-Q_itW7}nWF<#zd)DW!NbMbZsgw6*b;;Of-2nDNW-^|-KG&YWmtJ8|}stxlU+o%=J z1o>gT0&i=Na3rwie%fch1_rP{I4m)mP4@3nyxY$sF}!=|j;xPee14W^?OhsAPyd|X zz3Y$g|LxZO>@9oWuwY}){HNS_*6l~0U;;?}?-ykYykfD;nSavNTUt>uKytrU68iT@ z{&PwNUAU%#h|;w(*Zw*5flq>*;-7^!Dvr?b8~yo+DI|hJ++wc2`%s&CT0)?p#zjZxIDvEDGZYA}*YX zYA_Z8QOf2iq$%a`l--mcI?d+!U6U83&EGwG_b0(L!#LKIKs5$C6k4nt`?cw!Qa^pV zNmr1P>V_iXdU|x}2|vZa{!UJ)`h#lg`oOJ~>vN_SuJOt|Gd}5h};V-{Vruze~YJM_2Dh~@87Ph7?O@dgPSUwVO=6G zPo>zD4?Jy-tGo4!20pHt{cOWO_s9Ms<#2T5mhSo(_>ba`z}6j`Ir1-w6}tT|apfk9 zQB%@d#kGdiZ1KHMcFQ`aut|K#R9g@DRa32DbDv9|jjd>gX3%Wdp$L;$O10vF+Yg%c zW-w5Rb@uL$=Y6AIDoSI?whtEhxSYm0nQaLt>TfBiR)yHbD8izgSFlev`~tpw2+&Gr zqS{4P*PIy+J8_t)jYR2@Pi^tl6Qbm9SW26+5|L&Rhq|@TFRKu^k(JFEAiFu=zzr!* zYdHLv=W(?wM62l=#W_VW45ku-0Q{;qKmGP!d_&w5t*yyzEL|23-z^wOrg1T*w+{2?ezmpp~WT4u8y8h)Zz7jw_Lx3eRkOwHtw@+ zef6mCQT=YkfCsIABrRY%#HCtA4m(lAWQH*0cc@zm8Nmb{)Z=7;VVY?_aYuM+kS|~W zr!lz^*Pwu8MRwt)L63h-ZpQ(>C6{*0+vc2`eBa(hsJOjOu+6)Z<%MZiZAh^Bs{h;o z8_FkwSzq`=NFgw~oo_jB^JKvInZiX_6!tW1YBE&~aF_CB4ZHoM)lv*RrOA#M9x%G9 zQsVxovPmy9q}?HAIb&J>!0CYx?-OS~c9-{KQo)4!TVXNH3s?145>5*Uz%N|6@#6i} zP`&I;7veA#nh(gdH1xgp^ajWFLb{wp!!67db2|jl4;>b%G~qI0Ty=?v!GTr0dLott z(sG-4@~ECe98T2K>>mS)wib<<-?_R^Qgzi=(?xf_RhBHz*jcnPyUW)C%b0?4@{H%O z`aP~aDIs6aOIiTT`5CKc_0UWF z+l&S&!A4m@fGTJiB9-cA z>JggwbK!@mHvV(4IjV9&HMr7+wz7&)vv7|c`jmbOE@9Kex8B9R0xdy(wk&s)qO?&H zBx{0>8tG%agC8NA&d)W8zryu)ArooSJU&6Pcfo_E!|L@tA`id0bYyCnY61(ZpRA)a z@e_9B;Cg)n?HsSj54(gtFoBP(x_&-EDdg8zmf=Ri=C1bP!?%ejBIw10wjVkQU zbGEgQZ_qmyHbK2rsZ~`gVdS?AcgnZkBw}oUf4#l;-LcBM<>lUV4oOUUn}yr>k&5zO zF;#eXS&$jL^%!SgcX?B2>)R7dYj@#hQ4!-tsoT`IqXpQd+z3e0&Rg=NTI6$xb|}W_kB&ZQ2| z_vd9BrAZ|#hHkq7X^DMna8x7Bp||9Z1J*fPVhRPR-os^;yFoQ5<$fXwc0HTkw}dzr zpzoDN2r=`mspE)wo%&~v-VFC>2005xlvJ5E;S#x7L4Nxu0Yj1##Xy6B`sUi90<)b| z#Mffxn8_i}@Tyt8g0chqbq*{hT{Bgs*XhC(Fe+)FC@XW{P>+YL)idsiw4;TZZmvYl zv3s4Knp_W7a9v6%9s7RJf*l!ri#gv*QlQEVFWCv!K7%7aHQeZyCpLk+nn=L1 zEwMUA+2)6H;K9zLZkK#J8XYaoSNKlpKC`f_ZYdw1-yB4dpYh1Mhj=;uRX-b@r@y4Y z{8N>=aYtw|wxn_m%;;CrkGM;v(1TnX@MM)A-#S$SKCwj z*-xrP6_O@co0xc=+_Ic40}pQq_Ra7QEIg(7QR(!u{cBWUcr8ewPbrROIhX2;ym)s2 zTeK}IGI4n3M()T|V}u_}R&iqb3!jxxt+2}GvRf^lg-B>tGf-(~^-q=v&lFEyiS7oEDbR>xO6IOw zo)qdzI^60y*|))Js#>kKQ3JjUL6@n~a!MShygPI(KR{ostfj#|#Q}%K1AJ~k7>#|j zV8S;Z^|h`!+k6Q|fkE<)2g39${JK_}+7mrAddrQm5WPgxSEISv_^*p?wDSlj0JFl` zVr;T_t5;f|LGgM@8P_Y*jADhQl&UQ}+y0d9_NS+5bZu6F^Jyz2OH}>ZzUeRy_DglP z;w){Ue9)%Wwn(mL8URSwB~T*}KG@A%xt`blUKCX$&OetMHayiaBwvKGHt>|JD#oJ8 zAJ-OBD*8kphP1DOnbj)JVaCOA4HiwHTGM?TSP@I_f-g zpT5!)GM~b2=Df^eWR(oAjnq((sW+Ir3=E)x*oESW(vxe7Zth8}^bmFJIt6Sv7i zUI4=L5A8Gq$p?hST7&euM_wqDgxTir(nk(lP>cZy$$lnLYJAd!gag!qzRb9#36;p- zhDZ;Hkr`bq0O}mQSCo!`iLlc>0kVYiug?K`jocc&M0&^&BazRP8kQy}y7Re{iRk;n zs`RjFcFte!DrqWlx6QITJ%5c_abufm)*ksc&8}In$rO9a)CN)A%UKXN z6uk2KAya>S4@XqPhIPc9wL!N+==WIF5PFFnm>^O zQkJr!>P#j1Pl{vqscc`7~Vdj@Xoe&16)!sGvGP8+<1IO&5hXsc_+&7bF8SJ%Osp}7m z?C6%#8S^Kmb<;|V&k2b#C)fHWRd(la)j92oCp2a>RRh;4L67geKJ9(9ynp=NrCPrqr*4#~dGUl4StYmZu&V4RWTW_I2)nQ$pc_Vi!Uiz;B^m=nf?|EBlli z5ol3cPj0J>fK^=8rL543dBB(rOF2G~%(wAdZI|T?4ZE8pZ+I)@YkZbVKJ55HpQ+{C zWSQ%ZtU@*SsEE#qZBw4*q#Hs-LJMzc3NVYhPk!Kc{&||bzVs#!=7NQd|H_^1H^qrh zrYuQcyf$f;vXL`CA9}X(;9vZbZvi+Gfy{@B5U!1W$<`MosHk3e_{TD z!8a6R4flCc8M9Rd#n3EPvz|%gN=3WnC!!cNMhWeXWT-8AV`CoZ<}CLO#0oIU zoX3+#SJhYx#fY{0NM*yQ)6|R$Pt8zqI7xS_RepW{CG34cPec;yL@t&4^Z}A@M)sp4&7_V8IxG5*JkJGKlrOr$lI04@u#knU8$yM0 znmk`b51a5-C%Q1TW{OH`p{SlZD;Rp_32%|Y!R7lU%ch4Y1JW$hL_x+C+(HeYWwgdt?oH` ztvSHZUMrRH+59I

z2eaGP?BwT`U%37gv$~)77x{!+_^P z`H@&4)H{Xj+FZUhHAhcFN4RDEQmW-mOzz#gmyhSqR=_Kp*Y2!Z5K7V&EaLi`Zfm(E z%~^wEM8~INYjhj8EGC^wc&0W}FE{L*NBDL~O$GG}V}l=+(yZN^I%K@TD-fB*E;i5k zk`P3u`mVn!QRH^1Wg=kNaZ)a6p)!dP9@Gjet0fDH2`ys1Fr+L zYkdW~!3wMnln@BWzHQ;l!M$|a$s9Zr+#rtKFOK~l@MSPu_%cpaO>E3cNtG5_fUPnN zLtMlzb_~ zlWvtw!6j4nu-#m)K6|GB`s_cvN{8*HUk}{lh}?40xxDFsw1Id~=cV8g==Q-VxH-q$ zIqk%z!V^1J@6Jkyh{{7;Xzv$?}5?P&B$Dh=m^LV$UUd!3lx8VoH+-W%8 zYf&(#&CYj0-1efs#^yYV2$WeM(F+#82vPXFhI76*OfVBK*tY7QBAMI#kn9D13z-)p zQQG6MBS+bh<-z;M>vVk;Q4CY;(t9!P@q)>f33Zn+fHRW4siH!SbR^9U2^{*QF)3_7 z?C)1C8w;>`FDK4xlB7}#h1P5~QV4PVMJk(6D9df|WuahSD+DfUTkDhNh3svO z8)_+Wa~MvzU#Mv1TkXNru2nx(eO{GTzE@9!l^JZ#^SzQ4I1zJc?ZS<_fiFl2RM&5`b5Kk&sO*eN`Yz zPLj3;X81keudVGeBTddzxhqyu;FMf30)n8vmU{e>6$|mCsir3asm4gDdBX^wjWPQ1 z;y7XhaY1B{&%If3xZ9Ob5zG`xf&0{V9A8D&KCKOE*M%5!H=7}yD)WV1vpUAZ${^-T z6~|e{2lHw|r?MF{u*NyO=wn%c;z(&%tyHG-Lw$~pw2=Zz zN93yJx1H(=^aj41n_zP5nD?LCfxn(YY;{L)QEOc(KPJZ;Aqi7Y*3VD+c92wdCoHxZ zXO$j|TNC9p@Lyt#OO#(#?TmNrMTbr|vRol=E~!mP#LGdaXM`51xMPKHZn<@O@Y;2R z2ULF{(H0(KC-;?1^A7c9&YgHy?U4@8R~Wb9Eo2!>ADo11(}0->i6q?HwR%VybeDfu z!zsO;cfAY{oPD)p+sR5NYoT-AjO#GdGiWVIg&u_-QH^WLQx+>7D#+lHyci%6IsAvj zV&fDD?Y^FQsqO27G48Hm*d=braD|k4g0b9Q^cy< zQgc0s`$rxw^BV9>#d7$05e%zJR^M{z6mtvxT8n|apMIXVp(*8}9q!(&Oy0?Y{B=ge z`_5Dql;|OqM^6i|oB9oup&b-!Ax1*w`VJjA+&>5dHCQWMS2Ty$ZgTVL_=84XCOH!; zyR$GxyG8a&#gp!mw7@{ zQ1v5BqBf(F7Bab-!^Tz@3Ve)jF%&1|!PbmJ`#9wtDxJ2R|76J@tFlytGI0JHv8M6% z7!ZvBfC9DBGL%a`vml|*mDId)dXV)~b1WJWSv{F^G)^ag?w<5`!X*Ywhf5Fc#C>{z zVEOR>?SlRs^1CpV$4~J@uP-w~$=|3rOuJ+1Y*xJmLGr0RyYuWya;+{g_3R;R?z#ix zYf0F?ag;RXWk41Nquz9z1xi(TAU*^1gCABboVj(I=$4mWK%5ZnD@joZ{%o<`_SY^N zZbF<>MZD?{NCGe(+fYACD1>J%$?8>!V^s$phzZ(DaPHSe1zDkOVrqhgOX3S`)exEk zH4gI-Q$$zk9@0E)TZnscYX|aCCsr&sUux^%q zczw6TgJ|(@X27A1PoGRdFynjJH|DBkut_<+bHhs~9z>Hv8bRB$!zg!4i2z-;iyR8X zIR1s258y*r3lFCkjP*|7Y=3Ya6=tXTZhr+g}jh4HxGFkmt{eX(LtAzCzs@tDLO8@k@ z{(HDfmrC;T@`gw}120L|)jDw(Ecm1K{5yVspRI85(xqx(Rb}NeZu?75Pc@Jg$N%H& ztJ;8tS*VTQmYw@FTs3v=MvB}S1zT8V!uvk6@|5|ujVBaF)n6B>J zfaKpiVT-wOD;TOqlc*XZwZ44UpLI9ht~raKcrT*F&ZFD#XX8c6J<{Avt#8t zoc#oaG@duh)osCY8mM2W;;8eoO(^L$&FP++BKJ61tUIOz^~evul?Us*&t!H9?zZ7s z1gdrldFcs@!Ij09hth5Op*quAO?oJ)4y*uc3hGvzwK4mEjJh9%8tTTr>qSn zabda-b*6fOUTlvO2q8%eCH-<1eYIAw`AO66YBd6V{c#S(nMQZl@extq!_{Xy{rIQ* z>!W#o>hQU~&Z0@<_X@>pDy^Im38lpysdg@|U_46o&k2ul`3xVE>NyYDkETa85(}ul za!x!FV_{R$!Ra?Tc`sXa18M>&5A7E=F56nvd{}z3L`89nBzy}g)E7imXgy<+)7XPU znhviRQUn3Mk~@hPX>adntMmM3sjKO! zFpMp;B30s*Nkr~h6HIBY(d3pMWnkqmb5k~Dja9{Jd7Bj$CTBkWCqtiy2crOE-|39s zdsl<)qzlPkfVg`MZclQ{kQSesL@7*fZ{b9oIX=2JlvLX4VYEp_ocwGD#ak3eN zXqGH}v#-&~{)2z2=(@;y4m^D1Jqb5#@C2#uk=HRQ_(gm+pf~ARAFZ}|_SF=|Y)>?@ zv2@abrIf=!B@H*?yZ1cp9Vm?Q{Gw|cvEP)mp&b!)S7zK)>ckR0*9crcSyS%Xk6Y@$ z=vQI`ZR=d7=pO2E1wQFb3hWD3{JaQqAH!InhekFI+RR-pCvWP5@U0mC%j5ZiUgotk z#Jj`aXLBhVQgZ;yNuHs}6&m?^O*}==5!KX3_+xK+rw9HtWu>AnQaPS`0~?!e$rKb~ zjgY?A{MH~X0GO|J?RcohU7&)kO2exAgMJZ8PumBGQVhL?;?v9eUhPFbLf%19#P%NhYe_;{SeNXu;3npVV4hq5+4E3mYPW%+0S;^Q-OQiYXs! zp|fTkJHmh>`dWdFlZT#7otgK?>)QyMD7r!S&(ycotXpcik@ zRj6=9x@#O@y%8LPyxz^4L5K6d_jmTipNiS1q@W3h@3xGx%E>D=%*llJx$;vETxjIT zfo3Q*$+F5P!z~*<*Z~YIfhjK4rZ3~XJ`@mg0{kCz16X*X!cV*dMCNq=Ch_Rcp!3sQ zKZ-i56K**(XIK>@$KPz?RXXrh$8I;f>PBK}H6LbH^^V!J11GEPzFMc_SI@ zE$7tK^jQsjFwah;=bq!{HPO|L2JAJd4owVG4Ba>Sk$?dr24j>6WsX@PnyV-Fe_9rH z61zfwIp*Bcyf^0>d1p9Iy0rDISQ#1R;9=_k$hJkIzH(sW<@(K=`T0ojJlpgK7_2Ol zfq%74*x~n^_>h94_W`BU)H0W{_o$WT7Hnxb(|44O`L!EEmHh}jeXYtX8S+_{9z7Ot zfn$%b=khG0)~tuKysN9Lqy!?qdFbifm{yOPJyqm))^g}wR%(aKcS%Xf2{ks#$KQlL zzy0&x=;b@vKBG~4)A=UyKa}=;DxBIx5ze?yVV~T_UJlSb-LSDjK@TjZ-t&_=YKtLMojV_P!#SN)YYd***I zqE*Tc(d2HHdv%z@eq6#YUWcV=ed({o<>Kh>rGS786bty}=xV?o&1{GRtfn&ilBdbY z)TVZk>Xhl3rl2+vw`fRrqD#tlk@NQ{@wuIs-qdeZeKpV^eOI z+%ueZuRSruaRU1T-7)|UJ;VJRTt!Wz`)tr;;b;qH|1OcQu5q_!Aj^xPh7ABZ(HgKj zdh%|_G z=`mtONS#lxSQ3t=Tp|DlHW`WFA=g%gYb_N`x<_S2qUl*GS7sRiDq= zZgq8X$v7^9&-X7RxW=vf$pEjkq$=KnZH~QhDiC6qanDrS=thB;hlcodSHYp6Cw)D; z$)J-Cyk!J^;z;yKb{Kyo?avq&*Gy>aCsMoV9J;955Ad`mPt$&q4 zp%K38YXpCPJkM$_YZg4VPR?+M-00HEWb^4I=1WoRsTVC@oBDJ)>Vp#h6(@uQSmM~{&PuY9S%z}oCncfZsx&G4Dy^Yw&#h=0WZ z+C|@&sisf6#ffGt<0Jv|i!+5-i|$mRzp^cSEg}rO!*i@^I7>Z`{b;4T|0BV+Crs(p1z z3Sv(#jKd0K2I^`eD49|d*~JJqT)8Jc^J>e&J*1m&?M)L8CL8k z7^(J8XDJgm6Ra{yT>;l%KoU{%w7>W^}9W$YOT>U^=%0>{#N%13nsU7h)))9;} zs~uR9(^b&KwMl=xCyLd~A|$+}fWQgu-Kh6vA;9kmlTdYbmN4=7yr~Pgb^&Z61MtSn z$~i0F&;%5@QiQ`IauP{+Qj3Ra3l>(_cinS5^)yoA^zyi}flR6gHhT;$ZIXT3%QO;1 za^ntk@8qLmrwM0vKur|0tkV^Xy6PW+dppsM#S9v`T)b3nyV?e#na(MfI#6Q-TA|8= zCT*4Dh;?Ui$Mg+yDCTs)%JFP%H?1d?P63OPJQJlh4Kcu4-dP3MaI06!VS=R^>^q_4 zY9vV!IvGte2Z2jwsU`_F5e+NCRDiq)`7}!+?!9!0m53RtX+^z}n3j*yA*-qPuN*pc z3_L!G=(|o8D|_^nmh}^^we>(H_erI10WLwAtfBvHQ5q_- zjIE~?`c<%yFnwmcuzYpzaDSm~ww0b)NmSwFSTWA6M&xrTUo(^I@a}^B~3L=_3R2ax%2qNjnobIxKjw=*vaM(^6O8} zAz-&)(1t&96+ZRs5Q0gxOb?z;cp)))MqjuubFo(CBglD-vMa)OoTZTb-W5f{tV4vo z2-ICW{zj$W4=Tevb!(Y{~dHx6*wJ5X|tYWqzEfM^r)1Ldo#s_)ll3yw8+LF<7 zp95;E0Sx^@%Q?yTjVP__Br&C_=`5DUQW4RDahjqV*vxR}-GV~r@1bzpD=Z*6`+>Hg z`X#zZvDAdydsR*AwdAVhpk2PbHO|g|SyElcV6#lAth`Dhpn>x{(|-m(>BGy6mGivn ze63(nBL4abc~M#>_}G4jrH%DvT+%M1bZBVkn6R)r|DJ3fnD|)({U4}}(TGSY2UqoaJKqj}l(R#$x}7 z;2vl6Gf8*CTQrV15oGDlA5sOY$a_CL;+i8V4N?y^E6B;Y$;HLBy-}5b+Wkm?S6BCB zu%A8|&tPFV!`{!vs%mz2c5;UAZFy-;P+9y!QbK|w8mr~xRKVym_R1EJ!{FLGI*4CB zM)WK*E}G?^AMVjweQf3n8>Bb|dy&)L*?8eSY4J`$>ss%~`1C1T{Xkm`|Zmj-R(cjhyrdRLs{ zmm{;sdg)-B*`B_hzP>4daQ$2rf&SYVVhcTXGl7X8WHP{+g@*$dnGThiSw&9L^RFA`j{)kOI;4FJFf8 z(Sf=r#K>yPCP+)_D>w@rCqt{XJj5^{rrTfi_k#HOQYaK^V@XL@ro>q#C0wTQzQcab zyu!#wu1_}IH<2E_#{+&lFW5StvYlLLXD6Dw`m{if%+2P@EK1Mwi2r`t-Lsr68nprg z?MSZ&yWmq0{jhdt6hFJy0>4-?w!?nr6w@G2#{tOe{42TQ#OmRYmD%j$GITf<6_-Q0)L`m-%-X>~DFLIM)tK-6 zvTS&3UWM4l7!(QG>=$$Kil9F|BF9}(Ww22Hi?Y`ax|;xZ?4@AP5q6FR?fs!)AU84* zUOj4qopE;ATuV`L)eVdFH(xA=S3?UMCL&wtI^bc)0OAYLC$pH+@~#UsDtcw*;XaP+X7w<-9v-w*DZFPj>oE7*z?tz zTPQ9)OTbTl$2L*sA0yoJYZsclR11Vpc=(9@I?Oa4zv6=#s5AV6RnN$Vmi?UTSpVjH zj8}bk0ezm#|M<8Zx9cW`@HWtXJ6mmzs`>@Kky1G#5KnpbIdvJ`yON6uv&8*)Q9Dd1a41? zULJA*uetAFNttz~GjUKVRq_x1Z_pPsT}3lw_;h;5vz&i+D3D(XuxDK#o$ym8=RiPyHaM zd-s@%XfLQk>^#lCMfm&0KL@UF!Kc|X^p=6EN{b}g+b89{i-5-1DZ*Axtf4Tj*G7&Z z`q8aYyEktYk+bj4y*{;`Ztcx#NWo~W(KBe?J*WNwcMBP_7b3K~Sc`1FM%;UQk_Vd& zcKpQiK#Be^oa?{~yp%ObNF-Fn;bVi)6h!p8@=}JrcWXxRjR)Ia!}04Iey|GrO)y0J zRk=;3Cfj!(f>uu44^)6JJh9otV1y_O_v6;ST_IX4-X!@*2J+0yn)2JgD>fDLHFPSD zUH&t&6qC%|oR814Bgt54*_?SItBY@{_D9TYCF!r6NZP(X|EaL4tu44SKQC|2@ATFQ zR_WO1Ki4?^J6p$FzJ0smhVB3L$Di>TH8mqi7G`FHwCmp|Cx;*s|L^WY+jp;*_aHD6 zv+MULraKmmIv_C|f2Xgl!TfE^%$L#`E%{tYnZ5MWo7k6NMkYWfD?z$H?l!^czEizxin^FQxUN}y}g>PyJ|HSx13T* zdd|(ssVl}d$houq;gfq{mESjYa@qG3f-6Y&^5wZ)ok$US!r|k(Hf-NhaC~CJyJAJM zb0bI<7AToo`#7BI`bOEF5MAfk6Fn(v+yb#>78^VjB90D2r;52kcKj{M4g!L*{nckV zw+Hi@76_C$o2l)torMZzcRoe{bfn)eX;P8`jd zvYhvVHh26jgWw!6-Hcmcyceq5K8;NqwyEBSfCq-fz=q@>cqm2$$L5xK+5h&`gC2fTL@zpb}y7khT8#`=Gmv?-}{yt#6~3;geg z9QKaPp}uoR)SJ~~7MG5{CsFYR#|GnM^JnNf7{{3QxvuU4ZIW+d#_4{yr- zj*7Y$6$+ZVfU8J0&>7V^Wdc+74%R5mMIb7s%*@Q_SB_LxR?-u|YH#Xpt}tQTqu|pw ztz+e7-L`QmjyJCC-R&<233#_m`JYrnSNm@vjs5A172{6Z6UXu9_5bK3*7b68amYp3 z?_+1+`{T6XA9mV<5RU1eC%zg(Ga4Xh?)m-R05?ws?^i>f#QVaGh)g79e@yizU*VMC{#XManxS9mK0I&$&P_ee@#Y-oJ&r_&hCyeTYruE8uf$2+ z-o#sk`fR^!W&dLd0{$2UqF+>*E&f?kA1kP)Hv_8?Xj0Zc?$t7cM=NH-m+1huL~%HD&4<@|>Vj98tO z5PtB!x#!rkE5It(=NLYYqdFW1@3Dkqvi!z-y#ijt8xtG~lRXlc8r&|PF%Mk4Dqu8o zIRN?q(9EE6b1FI|!11qhtbI@--T`BC5tx5(GFM<@V}ydHo3Tc%fPswxQfr{XbqX4_ zyZnClHMiU9;nK&y7m;sz4^_G5;#_Igh^1v3mKN17PnZBdvhcd@Rw;5}FwNs@nVbXx z=r|MD0fs-c5=<^F06DlTDF{$ zD{MGKCY7j^QyLWJ&4A|Kt2Cd(%JyT;vjgWu=gAxOr=x2u^D0e3Q5yo(qP{V=o;P(g z@3C46TJd0;wDHJ`FaVPQSt{E>X+h0d@F=M$GbeV3QCr9QdVNFXzpU54|PB?JTFYT{K*SJN{~Uv(UCja2h_<6Qy< z7oo#vTZU#l(dLp4f91DXNl3aBaYoFUE3Xf?Z{8<}L|h@gnDQeO;HxtphLsP-`MdFY zT>N1!@heOT9iM;x`5Bf&S#Zat;0;JZA88$PX5o2~)rZuizzC1*gY9=&&(|F`dZZHq zHG86u3?clK&-Jdpd;eijyu2f3^jV|xiBJH7d>=6z=Tq1}HW755aW_#!HUNjD0 zjST9@>%`}O@tO&!-8r;SS=Qo!iyt=#y4|0ZxBN^Gy&N;;qU#fI#AkWzd7klyix*z( zuNIswPhGlrY~xFnD0#)girPs0{!+xwXY6~v7h}D`)X@U3OxznOssg0R$2XpxTcUFx zpbxp(>T4Ftw@Kr?eFzPTA@@7*Lyl|DJLoUpA#UE(1em&Pe)g;f zb>o@YQM^>325k?FXwccaG&^SO#C~-7^zc@$!%1Um4gvgv0_ zT3n07(-sj0J+Z2y93Hhh>Dtzge3tpgjd?^WJ$% z<392Zd!v-i+R!(&jIf2=nBjeZ!>;hQG1cU}G*^_!Fye=|H441^P9ZPHH9c=075){` z!YrU6>A4>DKfWc+TbX;7IZ&r8u^ulEl@MLbRlX$p6on}IpE>;9-Sq1sr@@lM%d*h( z@JE#rhng&_GyxM|Cl8~G9P-N!>r+gnP*1JW%>n)NYh@D$h{hwi&rq6nAgqD~xck#J zsvk}sR9V{Z5nhkZj__6e88dMErbb9Kz4OTBU~S zQ)p9l>&Bymdy&E-djkYyC;OVZ{0S0LdF7k82xL>@%Ct#`AK_U3+mO4XsFy0yo9hq# z11CZ!4dU;07bIuTJZ-l!nV+2(G+!3bVeWa-F+x;ore_)+Q*JOU8Q5kv#QAXw6S$ht8<4u_NjL$2n;&sra_ z7FC4xLTF}6L*z-n4`;2Y(s!qI4&fd`dEB6+ghIQ$Dnwm>LV~kP&Sm8&M*R-ZWvZVa z#G>cdK4`1J%NhSIW`Y0n<7-HV;e)h`~PJ8RAsxfKNPtkRn0KaPwGap@f0uR*doO`?2n4D{uj)_2t5D>s> zGh~*a18>hon-fZV9=TOc+OS@<2z2x$WG8ozIqENcd!I^au=Huh5`YJJEtn30Ch0+b z$s%x^t8|YX8B&TF*qpbT(m5EqlJG5PVOM)#U7;mC%N%`p1PMvT^zC3TMcB~E=wn*p zny!(ny#yxY@1yj{djslWbgy@_r0RS3c@HCU@;q_TTUU|gV$I!5WJR)Ba!utSwpjW8fMn zu5wLo3g+Yyu2W<9z4Dpc0c0wssHG~Iy>Zkh|JqbFqGTA)s4WOHwstQ-ExD&RcjvAs zgTTOQRYcy6D(m{;yHjUnc3gUsfi~oDbuPg3dtcpfSsY#Sxo(8b%AYb?nm$D`?$m4x z@!|ZY%8m9i`>w7VCC1@X-v_nkx)v@BGFv3C!u;ZGYoGc{!oU2@JxF`OR}gmST;QVp z9{Z5|cI5{-+8Zm4=mqtf880qjM~*q$I|X%21yCD;&uM4@hq0kwBd3&_D*8V@8xn3U zWWljcc^4-S$zA($uo?)+pJP-PoIPiYxQbpF(o=qLzhttRJ9xsvd!{7at0!UONw8`il1I(VLwQG+P&SYJVcW+$I1~ zqbTc#ag?N*A7ce<^_W&zWwFbVhqjcPpQxlml{`lsVLMvg;+i~KU-z&7{B;A5bI*CDaMQB0pUb12WqW=8e0TF%sl)r;cyj3F zl$6}6y=Tm%&-b?BBl{9=aZ~tye@X-DRsZT>yV``w^4y?_TC&m1Oy3U9=B)ofrP%iT z^V$Nu9{>H}!v_b}6G>UkwI}RT=daf^s!jC)1TioyE6<47;bqHqhN#P|oeCz}+97+E zosB0B)pY#+XcurBrkEqD@99!~dwhpp+j>;%dS}4H4x26b1cA>m;o4Ra5?@q`yHdF0 ze?uE>16U2lhu>hDUKV?J+}zxyHH||kYpXku9MoK!aq!`7^)Y()PYeYx3TD+8Hpa$2 zV}mC7|AAa^9M^qdU|>Lhc9ALFCXphlb4oysoy}ov zp~bmf^6$8Us{aK~(duk?clozI!~O)P4v#&rVJBI?UR~9`kELs+->m;TxZ$PE(YEi~ z@6WLR`R`zf|6|_E`LRYyGjPwFFL7}Rh*E`Zh{lV@+1dFBgrhnCKjxnE-pS_J&vc&HeRc@l1m_TM(h?O5sRP{X-FqjCR0>qcSEjIDl} zhX;1mb42m%xYQLMJ8Q$s5x>AtWr<5{u5`wE^+COFAjZ8Og>(|P@vt{7YT$jd%c`ud zI-?QO?r!xyti7;(=x3XlE{-UD$6cS-*LUhZu-RRlyABwo#_AkAwY~0x-MXR zw|rVL;G+fyk#{TL*EO;LM78*qZSYQm@sM&P-v8@lQVEoGT!X=e8rUG8pp)#Rbo8J3 z58QV5{|c1@t_mJ(p)x=>+OJ6|jvj^TXQZG`930Ir{zv3$;`A~A1(8H1++QE922*w!JW|jT zpD7wHc9Y6Fj@Ur%pHB48o>?!OnnnUCd4}c|YC7s_{&5cL6zYC7*^0No*YsUSQkuWb z$b8XD&3hFtBbK@9BSB?ZN&JpYj8Xly=w)S#bxI&q>!6R5v#@khuGZ?9#6W!>;v|OzplL6x#4`(DGpw_)RoBw;^25*+!X5zfl|NoB!%M_D*BCs z>6;7*Jw398I~pu7*Y)*@NRBs(>=^sf+0~OjZTZ$!-^0xIFyw_POZ!S5%|n|$uRr^| z7YQFx7`FuS@o_33>7^)ng`j1%yP4`m`&*!sy4>n@Kn8MESCUn1Slh;a_Eld!(R)5W zXv|`5b>3)ZP(dx41jM6)NBk!J+;B1iP+Y~ZccOEJJS+Du!j0fq-GlGM-p}lp7i}he zE1GVACHt2JsZIu}F3pj)J#pcSASdr8=k!f>)~5WB+7Sa-%zOZa-2oHqbM7e5o|6^L z)L~^t-gz-Zb=&B*A?T1qy`rigG97G%L6e&mVvN=%9G1|S-xrS1!s4c2{_iT>Ybdh$ z=ohmw9|o>YpW&Zuo+=`{Rv&QhdIy*2R#jK6na8gX1ft#E?J-ibASrzT@`paoe%+^+ zZJS}MPPbi~(SX`p`xN%-v%$BkV)Hz_iFNZ4ewn#0NQ||6J`9&z6kbirw(<+4p{6e; z)nj~u#=N2SoDpky@E!h5op!Sdx}Sn5t>aAA?V*Hvp=u|oYF~ZYs+NzWu#KLOcLz57 z;775fDYr5k=&Tr3KfXZ8?DNlMnG)tCz3c4y?yf<$S9;xSs8ybBswd9e%gl_8Hn^P* zIB>wLldx1#ykXOAACGcDAZNPjYlwGuS20*~-Yo*`4mLz$=OLe|*$`m|&A7`jE$jy* zD!YP`QbcQX#Vc2Ta!r;i(8zwiH`NB;nCcu?wE8vOYBdRoXy?Wh!S$$B>p4NoV!h9o zxVflNmW9xlr@LHW;Kz zRLT^Y>#{-LMGUCFBM@M^1-h|26ehxEpOmFtqlOS+Q@P9~+js5Ek_h-}FYG7mX5rHl z=`fu{+JNVk<%6n?X?s-5o38}f@&MWR10NrXQ1cCO;*zEXF>*D4=cT z^iV>pb>w?cSwvrxKe)>h6j@TnH!WUYu$wm2{|-P3n%{jS`ioRYCCu=goIL62Pt=Vr z>QIPTx9&L%3Zm+sk+1s|d_F(!wfY4Rv6<_xQ=uY3Rs+YYvd>q9A}_DJ99g*@_YI;9 z84(;7BVBd30<{`PvItp>c{!;9!sJjPMR{fbF4KrHgsXwj?uw<^m!swort^7cU{YNs z>TBIEJ`inn_PH?q$xB__ogj#>LS1|k4!*&D2q0C4`=}|A)Us@=C7p{KjQhwVx%9x^ zcyL^NS`HyOtGOuWdDG~8v7w}7T>UN9P&BMLjtA>izQ;k4JZ5g28?Eo7Uw^S@8jK;k zVvrR2i8X|}dzT7km`Cw9Am>aRBTZK@0X6Qe^unMq?2>I5tH=f8W;e)oKI9fuiV=+1 z%LvGb5sA%IvL#z$Nj=g0LevX~B)g(DNAPHcw2rBF+*TDc)&iT=Vu_8nn6d70$(R<^ z5H*u<;Vcz-Y|U+fciFY{bTNSEqOO1Lcm&>P)2mE-sGoTo*e>sQ>cjna%XiP^Y?%Ip z?Cd)+r%+sx#IAuM#9}`KfKxV_^6OB-F3;|f2>Q8KX~ggeZ^a!&o4^>FYT^=+mr7#KJ-8;^0+JR_4$BGil=5qM%O|O?h5m5AM0-T3pQ-0 z6W-VE9-XdQ{irPH!7I<51XEt~QOQ-Wp6~I?-@|Zf@b%{n^ip%T{5}=;Gugu5W2ZV@ z)H7)4v9pWqV z(6P$3uhmeOROj&pyMRyHokb}6jBSdo`h?I_Gr`aV^#lgWB2C{2iS8*fS1(~0vIW7aWB;wTkNPBN~pKut7D&t z%6H)&i;h+uAL2T}Vm`V=%hWx_DG@Vt3BR`9dMtg6z*TIZ&@%!`fVZ|N5{~{1cHhXy zME*4cy59$CaOHHR=;g)1K?=lHv1BYYwnOprk%+ad8vDHGVv4{kB-RJ$6>!0~r(Smv z*VA(lk??-n+PFw^$6Du+79afckCf1Q%r!Ntq3Vyf7^!P;ZgrY<%Vo1q)e*NM2oOk} zPhmGCjL{5*y_WHdR5q~dT+#!_kdZuvjiT;dac;NiAK!9?^SXk3ZCgc|7-3lCrcD;6 z2MMW|d(vT|hvQ8Iwk4w${3FHN>$KQbJbB;NYYWYcNl1A#kDHZ-2oHr&`6(ua z0WS8ImZ}EzL@Aq;xo7hiRc;zSGkGhh>_|q#oZjIxmpF-6DzViT7vhrxZ-B#pp#5s@ z@LzvUNEN+e`HrZ%BqUozRd_G^Wk!35FnRMGQNrP;rGtywpMCy~Cb84Y>5ngDf(x6u z;o;@8^M=9xO%+HdW8Ywv8yQZ{j*s*i98>i-EF`L{Odko=fy6F^tv{~cGlZWNI!8MN7f3<4=md>69a!T zq1j#{fV+$eJDVvFn2)^;&n%r{mnj4_RidZPS3DSuw>n{ja(7(OXyFq0kX>SxYx_KM z+!fha)|ye0{jB1uWlX2fCjnf4*;EcM;7S!}`Z)9XH5>o4^)K2K}N+Km`nyXct)_gp=3V$oCE$Iz@Zi!i)sZXpukJ#B|bY@Cxd` zQupcEn+y6|9D&|}U7UKn2o5LQz(CHuOXsj8#CgC0AM{`V{-`N1NBYJM( zDH6&6p%*rAv%3VBCc4#e$;{{8l7geMQt){_)q!5UgYld8ReXVESl)v=D|gLAucX;- zfPIs@#b@xW&euw=Zp^9lwf-GKaRBpp)LF|PITw?VhC8LJO|WX}%6%c=Oz?(@t2ZQBC*j$VR88dVmUP)wRkbY$cDI4PoAv zxz@2X!pz2jhFyOGe+J90zWklutp)T|w};H@RNiDP`6c^AuEluUJzT@r>rIowp=BhO z(*z?zQ1O&^(!lIdI#bsAbY%acqWfVGl8gZ8U~fa1A@C9LaGA%&URPhgvf?e@l(>-L z{S4@jT0o(O;J8PHn9S8H)pi5(%hlXCf8Ul|MO4vCTUMj;#%%v+2eNw%78#J(1mPk$ zRD>xxEcyM)gfi41M!r`&{ z=eafoH{HXtP}(*0(f}QizTw?h?Tl;dJMagNFoTLJ6o}ceEWF8yBP{KiS38OYvo;H( zPxh@iB?c*wd8{hklAnYM6^)9Wby`T;4NE(%oU+s$u> zc!Gmzo0KlfnhZ?zZoRT0hg~j;sqRwFNm}bpAk980_6Qr_AIw4-TMQc)i9HAzDdNFT zL!oM!F=_`}@MLTBw-{cpFRZq1P0yY8=1rDOXiM>5;dnYM8OBhz84vQ!7+E9UsOCMw zVO>Y)vKd7J`%v-(d{A0Do_w5Jop}won9j-`u~{xBXNe=h52g#iNPi|2A^=qEiNkZ%{)lyg# zlXPa+2k)Fio|15}@&SCd2Itx0wW&0^7fQyZ`b%IlZLfGgHB_kshIg>RieKS$*FVt0 zqftJ_M*aySKJ~U@L~+{{#af3~$2J9|=x$QO1}54F6bjzY37ay?FRy-TH|-Bj0EqD|$QNO?1@`NdUh3gOA!{K#`od*@$US3Wl>n{x_quH3DG|SOyC3|<=kEiVP zeQ#5V;|v~0_Np2?;c-HK5RJ;&Zbr)SC6b+Iqo0WZ7Dsl&C`Qcg4cVVJ%>FS=|91e1 zJ%a5^92&3K=h52(wI|e|Trbn+w|}p3nXQz%J#fA{0uuA)+|cg?nuFtSF*dOC95@VR z*Bm|A*I&SO$<2PK?O);?jnb{yWq|Ze0uyS^b-yL(`Mhd)iOhy9l zMv5}U{zm{}Ys|<`a0B`Kc7rJmKSfaOs&)`68%$wO*5Bu8>W^gYA3mJ^lC-h3mIVJ~ zy*)N!c%_ajrln{;IxtF4i-hT;oa%rc)Jx_)L`1nwm^nN(b<;QAY` zk+EZO60NOH9(MAK+3|O5LxVOzPOfQ9ges((mzh6FRhO#|YUHdG8qsc0$ zj>K+ncbi?#|NldY|Bpe5{{VnfYKhzP_NM2uY{u0<0lCYLh&54n)sD7u&3n8Dcj~IK zW08VGp1%QyYaOW&Be1*>QV*CU#l6Q@+}eAGRlkjmX4FUvWV8s+SkZ0biWEI6_6WUo z|Gz@~FHs6`G<}LW z(=%q^oHzWoNcsU-`!AZLQmH0_1WjBX^1(B13>KVn!H08+G8Ou1A=8c}!ye zh3=bV@2qyxSka5x0~$vUnR2czM2iC$vzYR%0}p*`-9YoGSj?5AcQ2=43)c!1hgO3@ zel<2H`HX_&nvjEWh9FWW9#m^UL%POe>!%i@_pczG+jmOXtUSh~ak~YOTx_d=TK0lw zv`S1`8FJrc%(W&V({`0kasw0Z0>6BZ*T&}dWsNf10PQfMGyU@_`tJ^g6Wn5Gohh%| z3nvB4eU;{O9$p;#w6k}oXpyqIwx#ie+Le1XdHAhG7>vMYv!;gMzEIdjue0S{zi5N1 z10LXb$!5ZeSrPiZRX?1S)f%vNhGn=;#g4#~J%CDm835Y4+D|f0fcU&9FY7LgcEh+v zcK>_$GYkb(29%B|GP^`G4c`m`LxIdZ)}z9r8oL_44WxX>r86nqVSFy{)|>-sPeOr? zdA*bN`bdX(5-byu%u0+YKn-4mUx|(b&ca@3@+*?qwlNSywxREp>ewK9__qXk4>5x1 zxYW1@b)Q2on%iV8f5?-spzVB#ssSU17S?<~YP!850XM>I6Y@@Vg!~oFX8A1L7;Iq= zGi0^4)gf@&UC**Vp#82p=TMWppr_W`hTOZy_WoT^uI5i(Ui=_E$Em(KEu>jgtQIL5 zf{y&T5ZHL|mL!kOFa&SN+r8-Py)ZYDtrzJA%?`YL{@d?EdSF)<}TJtW#M-Dk;-Mn%~Sa2deuo`Ze(ZjC~fX;TIY{!Yw zR#gT9w|#32uMBKqrK`JJ1U*U|SV0j%@}Gf&AAlg_@S)MOGkcXN$^$j8$f>gP)YXPS zH=iiq4h7cIyXfdqwdod5L-QcVK+>M%ER!{M6^-&8pU3XYc@{)7Ta1nPc@~K{acIc& z*2VCoj9XGc;@@^HnRpUI`7lTE`N4Dv&-_VpRFqVY11L#Xdhg)I`*f+b1u=$4jt&Rd zGcVG>`$yXZ?waq{M_vFQU+-dXwf4prv%r&Mek1ZU^jC?p4=t~hZ4w1se?!VJBx& zZcZ89wsJ`BQnn4@70I?2!c`T8vY$aPgftI}VP+0PykWkVded6_{vl@suXI|baZ8GJ zwKZqy6RrU-)AZa{*w|{%1J@RL3}>Oq*ECOZlO|wNeN8E!Er2`Pn@QWv16=9YKS=wpU8+^;C)VI%*jnY2yLe*2laCpm{ZUTaywVqOJ37>V>=mn>wAyDwhf zbsi_@v1o?&-?V0$gwwb>kytktD1hfM!`QiZQ2ekF@v#A(xT$mAiTVYArd-MBJ|uZrq6;89J@@mC#k zV}^wyrz!abco|>_qCyQ^<{EfND?b5*roVo&g5|# z`Fy$}%!$16v=!N;97KgUIMba#q1`xnOdSp#L5)QuJSa;BXqg_hPQKcWo1|og?PY5* zM@XC?g8ltYSwcZ${G5x6c%P`F+X0>`v5y=>n*s6SDj(fbCszCaI<+5)cSRME& za@)7%vPOurm^D0B5-7>Tg^+K35B7hWrP_NKIiibyQ7jW!om-hd{oRZ?3$fyDx;>g9 z8$D4JCY2M%z`2YV96+AGQZTT-_ZSgt9#8)WKfK3`LAia5SaV$_4MFeURx|YMLx6^# z8sT!fak0B`E?QcUlyR1R<%c`8_49UsOWik`+55xS$4Xkww^v9hAu-MwH? z{Y8Z$u3dzE?n1S{C7Z87>v&wbD`{37T;8Zvh$6`IVI`<#9SW{L73uxh;uXI71O<|3LyGy2nt8~)o=&7Sytn$ zntD=074xlLtkm3bv@A1+@E3uX1;V}E+$}3jC0z;~cd41p5E(9S@1P=#e+*k?OFm%1 zqVK9%XHw{a9LT)d28>e#Svf5;C-d$g_S5WMYL-2CR^I{joP2_8 zCpDj|g-((i$n}i|KAns;5VC3Wh_-lMkX9yqwGZ|@C9jpzFRb5Q+W*k3b7tAC zOFtMhI=d-*6%65TL9P||`#{myL%VZC`*V7LUaoAJ`>^PP?}q&kq~8}=AxdjayptGJ z!;twmN|cL51{`joz3uOWZ&sug^l>N6xBM`rMhR`X-q1e)^Er0!j*)^S)xTz^SN~S% zOYAbqeCO}-7`N@Qf%gHId=}%*gV{$1=a~BL|K!rT6X6-Cx zjAB5GWP*L9g-fLKV5=3Mf`@S$m!a`{@ z0$St~oqPs7bY*I}AouG^X`u~|xxaJ(LD}U)!(Ek5RUYbw)&ZAxbm3ZU_!+D9Cm5r9 zWiDzSC=}6Wu;qkPyU$#y`qb=hlZO)FF|pqY)N-tZbJJ_?OZg9>gH%b1ckrqYt8oV) z!}LP4c@akwCf>og7ADJZZfeSv*fe;_9EsnV+Tmn zV{a#Qj^kFbM-n+4XRni)eq*)Prk3$wac z^NyrwR-yJiNPNDTd2k&@TlMV!h}AS9QkxE(i3?1cxte0wS>T@E_&B1e=M#Z%y7n6H zuoc2NaO4c#k(H}9Xixptw4Sw}diP%96em%6xL}~js1ByRAmrZ=SwMwo*h4>pRP)eS zU9jkhN3Waa{Ln!>Z>t8})gX5O84>;E{^GG$xRF5Za+}7o;wc5F?Re=ra zt>_VqPwr3~7M``pM5*!RvZ~Fj9?{IuU%U+5kOre0WTF(;i}b+IzeaLr#MYdtlT!d_ z12WFn#+09s+^>9}fXa+pIUKttM8qM>^?iMx%WYRrW!k(rsHkSnUDJ6Jm&79!xipgE0!tAgteNl zsYS4`Kvf|x7ucq`8P;x695k%7kpPlSuLwB^$-DJbc-E>?DqBKbT0hi2_Cv)c zJy631vq zSV`l@y-2HDRwDHAP|$lY^)F4gN8Zy~Erh)V*?B%4nc#WCej;gVAe%tY}ZP zT5n1-@r75knPzyfD~4(5gSU5<0co}Syzu*7vzyO1~9i8u7%iytSf#++1Cre*BX+=KK%>*AoHNTdENGw=*`6L^~> zWFK0TkLs-G4H*C1psMV8ru3D}MmQw=?4fcs41ng{DBJN7+pRoQ;{zzoEf;nRY=}0R zoq!U#U+S(5y_TZgn_6GI7-w(mKZ-YN{oIaFX#L>QJM~nq-PB6w`75^|QdKcEH7pwc z37B~RkIE^R!#}P3ATQ--7!4?SFexMqvd~9Z4ED?$egidpEqe5XI`@>9J5eET$ZMi% zYNu2@oj-%U>QfsW-moouRBu6%MBHsC#~pS5?4PhR@5y)aRv?G4CW730|IJ z_wFf?_64dDm^MzK^@**yH^6E|yh))FiCd{|O1x~XG`r$#=W~wZpEvc4@CneGN1WyL z-@P^hu1ZT)l_vw1-_IzTVg#2y#)f_o*|{Pktzmer*1OZWR^GX3Rm|>ns^A+E813^T ze=efg=gjCANvheMw*B{b)jsuy$nxb~J~@#9DXIZkSmRv29(0T+PUIY!9g)3Iyb@5& z7lZ1JrgGTjZ%MDAf7nGzgs)%}%dlLgx?|(v0q&ZQ>07-Oi zkcz*5$qk&6M>ry;GgnzC;KSMEZ)F_}KOlp5N^!(X$Lnhx^Mr-Ezh+zDP>o241k1J8 z#zq0lKO3deg3RqN?KJ<=>(luCoRO&0*E+KyzlKNLs~vg*CG%&btd?_;Qr3aP#3413 z?-z(08u(ZIOjR@bJ3o)pEhGf6G$VX$jAFq=UoQ1}P%~4il%t^|VKH7KPmo`j(`zZa zSlp5g<}y2~Vp4GN)y5(f>G0I3_k4FWb3TxCMM+$e^nJ5rgvRCQ{3=(30VdWlZae1%v;$TPQ^*ELqyVTCcq$rkBl7QveDffjE z=8L&egu)rsTs>oUgtWf%^g`vTdUtLMixI(kQA?p?EK?bFS^77=n+~GK7vn^aw}}i4 z7qU~_-Es4qx)}_Hp-B?4+Ow5{5pIX;C+1oLvW67oCK0k(kEWR>9;L*Ma{hfPW-T~* zUPVWKr!-_cIRbqet2q%WRY-+~{Bn)mfo2pjWCN?B$XJ+}{FmM>GMHDTo)N zFSSHc!}^GTSb3M}Dyl&_R3^Kn!Vx1s-`w+jcZtMv!Vk&~Dfv@(M^{TQ5go@fUVe=M z-Hd=dWUJF9stV6}77#Xf#|3p8PYiT2a-ppIahH)A;zNS<%eWWGp~(Bst`AkjA}rLy zKRIOuLBkm%Wq>i9iZ_v`n&u3oirSMWU1Kkj}z59I0jEK15E7fcak%p5)}HnjK6 z<#L(dYwY#$=}#_qwk}+x6^t_k9NN{*mJfk?u?{K-(Z*Ggb0dHt@<0=wNTSdGHl@J!7x-%BjS|po zzUXhG2>4gyDP&df0AX@Z$LN&I8GJ4$l+#?lkNLtkF$cm$niQ5bkLfxxb@XgN;1{)S zs?uDgTlH@uhi=3sFkT47@oL<5a&R_tr0S4U+7NUF$&w+ttac-pwbz;ZU&m)gUDvES zY;x5(&#m1zLUYXGT8 zx8sDW3DiiX`Ghq_G9Q>(X`z+|*qJr^{c-kTlh;8K%m2ikk=pcD%B%qA1KsP37_>UoMHJu zr)-I2&zlzg3LzGTztMFfFI%6kmq+&Akq8^3aBe@U2F-yE-V-ItzV_3SKYv{NcNSp_ z$)7t33#MH97ry_CB%D}w^ZGwY13;Me%H2BDXI^iTD`5s1;iu%P@l`%(gbOp#*ORmfGg zazx!^4^Kcq;9+1S(ygMrbkf*uFgBIr)E~a_z&2mF8EPFVE6erx@!#hEL9VbBeX%YJ^82ko>!e+X>V#O&i-k@Yxhq2vF$)i z;DQ-^1O3~qX*|VjFO8F#_71vF*W{eol_<;V!?1){=g`k*kMDUCuvN9tX3B`;7xRX6Ua1Rt^r`KwCN8Xq6u1T(um~W6Ygf@ig(!M`~FOo3cKX2z4|^Ra$s2Ukm%XNziJ6`7kFZ(!0>d?e&xVHX^Hjqfq76a(6R8F_tf(!w(<|NRfmmEPa2a86bZ7oXOIPT(aZCH4P{S|N@7 zvZGWo;2HMi|E4KmS5n>nn7gjk*!2%;@a-dx^RHV-5$W50&+${V@ZWbp4e_;BP`>x~ z`)Z6^585D&ktggscch4I#km!M|1Wf50lT5XFEX$LM)lSQzf>$zkjn9dPA?{)URJ2a zk*Mq*B0niDHm5~m7^x7G#05D0M-ijKJ2{TB#R@6CZHg)ek>QaES)T|(&S+T|{}nl^ zsUzZvi5-}crDEhsC!;xs9@(ZgL`vb}ewH0DrXC!Nkm~cMDM(BgfeaoIclvztWwa3=!-f3>=M%VfqaP9y#1U7r=uO$biGAV-*7&)C+^~# zw|goIkpu_IqOW0AIC+$|Z1t!&K#DL4W*55yJD7(B@ux5 znpyR``T5k32rYQwubKt<3syM>v)Mm(KV_FJpagZ{QY1xtKJ9|o2TUi z2~&0#41~81DEQ%)h8WprO1+0(Ktkd65yRm?GsYCIyRxQ{-N~NONYfo-y2qQLsQyBs z35B%RC_@XXb6oQos@)pVDkm2 zM&+|?R(&sBEuK>ClJ?fSE}uxXpt9!7Sjc~OHmr?31j_KjwAhd17_mfgkBfZD8+wO2ZFsjXGD+e_(em`&J$2#YpAjadw+_|=nTyU*t1F4JZ+lad zVKFpw{*{|#MsfTFjxBLAnh8FUl%$oY(Xmlu=$KKv#iZp-Co4L^x9W4gI|1KzD{Ie8 zh+E<>twXHe`+VOYKkf8B%|5wBYwRsCx&6Zx0=7YkxF`ez59KXhQTZyh3Yq%t=&5d z)yF2tw=^Ylt&ml-ea&qd9>HbNPPjY@xmm!$mxUr(iO8%MYm>LjAKBWC5Qy;5%xd}x zcxSD?=21wlHM;n|)g=LDqUk=D>u-S47kE9Aoo`WJgt^q7Wb?w<>4V+X&X}^^Z#{ah z({SH8g&fugnGf4y>s|2GT)Iu(b}|!b&$d9aZ z(ZbvznMgh6NF~SDY93&AL7z`*VTIVUAN56q-QY29k-JixKYfmpQYc8oURK%a%5<;&C?BknTXh4c!HYtf`bnssi1e!3|)p?N1c3uIZ4 zZZe?69nC`vUX}bHkFN)th#pSx(+0f-kShn9u3+VkLRiqn0!n9Q7q1Btytv6jGBS$~ zXflbiie^OHwU{X2scPUVJ7MpEt$@KyEk8mo%ZmNkc`xO^58-2r`jjr zrQaM;j$n0#x|`(&QVXldUh-ZVy^ly*x07T{XX?G=CJ6{!h}=!qaL%#3s(}D)^O^+S zLoSY>RMp516*Zoo%v~T&L55J;HNOY zRdAsJ>%NOEnMO?u*zI6wv#;@kZuuo1zzYSDhb>xBh+{m3B(#B=Zxud<-yPf@(OVKX z^&qU&W+nljpT;#nD|A-sjO-z4>a^Aec#$;QM*3XjzPyxLa*2$AV4#ngjU%22dR}ty zb|BTQS5Q75&#@pce!`Y~QC`uWs}sxoqmFYIDt`9X)b*L;TgXd&FSG)r+>+fG2_BPN zk#}vj5*C7XxajeU!aghh6-1%}igM?chh%RjyMRybW!b$fFv2y{2P zsmInH+Q(vm&f3%NfSd#GR?!x(KxBqz?-O)_(#Q0U2=2t%4e|52@vpDbVka-GYSkXT z19GcQ6*OO>#OS+I&yLlpS@rZR^)FLf@zzbHA;56ing6ca3aeC+D6SD!Ome9e8MQXS z56$f>y2R@rJLdyYI_&qf^s=M2j{_7wJ@d)cFv_#g%@-&=e23LwBNAIta+q7$p8;s7 zv(gpf=D{}St=PfHcgUE&rf!is9;O+;YOn(43uNhOHQz4u84=E(kdo5$q}xRlC_$J% zK9t@?|MI|Bd&og-r`w>9-Mw!|zEV4@v9dE&0iTX>23v_(nf+wno^S>$22=dv+4OZ% zaBVSkRXw#`_BXhA;o2Kz37cD%C^6oV4yN)F{2n)2T~JhVHSf0Kh;y$O#b1L|30W6Y zo)?WRdETi*3VV5lFl(5av0MrfK$#45vqBm1c=%_EgRHs{D|0?FSIoKQCqL(!XhVqW zz-hvH(n0@)ikt%i@H$T83FAVUzo|WduYn@Q@rYiyar>V9|DgDA6EuLLy!J0b^PECZ zn-9Q`RMZ%mWs0p_^Yz3R(yAZMIh ziTEiSS(uUo$?&-?UQTwre)Y2hH)yC5yD$irZg2!N-Na;gmu==|ePH}(zV?qtV=qO5 zr5z!&ods$2g@zWCm-4`QiYb`>;)9miy@R{Rx4W$oS9@m@GO-Toot>2( z3Z7#(DIX!DiBd-noMW$J=M(At5!#n%R_*$@(M}e+FqUMO0a!GyzFHzkZy-n zG?m}dea{8uSN1NN%cQ$G&I=ARDo0<6i1oZaCK(_&v-RhR19xc&T=s9CC#amO;X!R# zU{Eu%gwf`oz?lkD_1=11`CC0ghQLGm5UgQFeZX4?wqy}8va5SOlfhmjM@Wv#PfPsmm8@wF5aZ?Ttq0e!4RUdY+;`IOmSenMPuVK=Vqy`+CW zn{=!0F*I1t;AcD~J$~Ew7XzfEtdS=e^2(OX7FTo1_Rp;(>vHUFxd&KeZ*xIT+=wIk z!_^Lj;zyoTi9vL&{j###;}>{W!ffyt|4paeBPBpO+^IcM-`VlbCSts!&v=%N{0jl+ zzfp;Ld|vM?Z`~e5OknGnV|4x&wa7 z*Pz<`yeYv&Zto>M*)$`a?6M&|5V#Kz4YVhn1sm?mx?Ri_@6X0S;!lHb?o!6xoZELTZe*gn!Kkb6$f;ZR|>tJ)b<;3Q?5 z>&NTds==V}PGGF;zT`khQG#Bic2uuQshV8)aC)z#ccJ5i*YFAFBCz5oHt88egA7Fx zbG-JUaz%2jc(|Sge~GpsG7mrQ5=wenQx_g%?2b9?H+H5(>oota7stb40Fm)2zYsoD zj_6bR6%x88i0SL_X>753E>9>OwWH79JBsjaY@vsrcT)diS7W`tj4?4~iUW|?)xAxu zN6tvg$_n$W+8B`2g6*3Q;m%#a+*7qn4P?3epP|rKbN(|FIx`5GGA3~lp4QzFs@{Dr zJ9u^fSR4@*-jk40qPn7PY)*Kwh<$rxDh#3c{!Bi9iUNP=WYQA^w2K^nqKld z0`2@+b?w;?5UcIsEE6f$H!?DsUhui8oxXS9b=h?za^We;HV7oqmgC>zEED#!f3C$p zU^>UBX}U}Jg1DMNJGe$oRD!t6l=`A$`-#2^zduOis=#!u@T>b4gpabF3IV6=>X2LF z74@1rM3kRqs)Z9LxyuVcpH(-GH;w^4GhZNkQuca7FA5m1o8hqv+q zdZk>p;cuK!?v{sv5pJxV`$ z^ID0Ke=8~p;&{0sD!Dc^hWkWooAuH>A6c<6GA&`har^-xN+<(x-QBcUc>r7sM7$PKZ|O}{QRH{4p4)DGN2z*%L>)5~U3vHf_^CWQMzS$s_9(#Ime zd*p;T5W*id83x3Q8y7$f;hx{Ww6$(kboA?W`zqj!63VoR&;rL-hxXT>|4XRvD*Cqh zT0`(RfB8F@`Io_Tc4pmzus3AmA1~5gMn!Q+vb-$asP$~#dm$#~*9ojXn9RI9$%4(> zcis2YtjrxRFE8uZBO9jA06Y%(11)Y0-@f06PyDA$r4wLJq_j4GoLe^#WsMfQt)_o_ z*LZt=`4*oMmAbN?!(t+JB$yLwSHUsjzH`itbnnfUs-Qnykeufw9xzttt0NcppDX{di}}ekoq@_c27;)`6oZb5 z_P$L6gfNT8sFqhH%U?@@Q$qzm!_+n|tZon}>7f341^v~^VWx@Yb_}b(Y3|V_&8L4Z z9PL?ky3!aFKmYu9-@y`1`^}RS z{TfgI;YD+L-lXIvA}Kk@T>Klqx*ZV-Zc-Edh;>@paYuTl91kz71QT`kxOGdE8@7uV zge$n0$(Cf@xs#*cC^3Y!?qmlrSeS_Q++;3Ax#kgWfRdkcL0Poe6Ik|SgT{1 z-N{wZcW$gOh~|?ugr1n<${B{6uDOn0Y+L-F6P9pK0K$^12Ywfp{MdcnHp)6}?tJ@@ zle58>{vGP9Ec$=rGaa|#GcC|uSKzGVQ)a^E7?*27SQ_^W2Vbtp)wC11HT9Xj=b(f2 zfLDg^l>5O*2i+zTB^b-aZ{{BKQXwM_nuHId<>HnfRGcyce24=q%jZM6(CuT7X54Nh zOym&*zCBHjZZgnv{x+qk_+YskXRB&cGM(0x-#c(NtloXy)U72*nV~`o*DgK!k$X9= zA3F8vAjO#J`r%a$1N3g(P?&c6l;ko$Bo66U<4oBmlTil_&TJ))Hi25d4U4(z$qX*S z3?5G>>bzSz-s1)o_Sc9nvFWz6G;cJy+kg>h=QhVbgB;F9IzQLj;$T>CEB>4I02^}& z`TU(kS#c0thOpEj;>%c?D1Crza$gw8eChyzk_9>q*>Z1{roAG|CPN*S(zDWTU6HAN z`iLEzU8+GA)pKwZ+HFtv%D_Fx7lCFj#Zmk3006u09%tDP8TMhQT3d9wrEQN~Bl|g7 z@)fB1XZVoa(DbFaW%?h25j{%qUc0@j=|53fXQ@+i&3HDt?-D{tDDdZ6A?Tr5^&gHq zR6^*{+uHdby(HBfdBU}8M+OtnpPT69 zH--+xQ{iG+s3lLu`M7ga58UkEMo{OL@4dQddKTA{d4=E?1~ssqlC`=1ec}9+85De& zd5HV8)0gTv23=vXi)qbW2j`;rGvR~a&tkG!2+8ZK3gdx=MKL?-Q^K3bJ|lcpf4l3= z6%E=Yp$4&*9QQdms~Y;n-^xS|q!{CX%Ec9c6=0dWwVV99#I{WAOxH07$JFE^s)L@r zz=x#?)$)r8wGsWUzWlv8o*uLotV;<&sW4V!M?L}PF`V~Zfb@Ogo*vOz-<1Hq(2Rfl z5#oleJq!WzB|tHfJ=eQ%O*gze|#_w_wJIuwe ztacwnBAH{oEzT7M@yaPRKQs@fWhx*TxOF?^M=q&k9K-OW}>>99~Ly#`WEK$K|`# z2f#V^-11hJ=)4EC>%58*2#|8g-!#x@W z`{#gy-c@NNb(XzRL%;mD#CU&9^Mp~{U=!ssv%ieLQBOBNA zj0|$9ig7St1m9el&YVm5C2(-dhLdI&W%ECpoQ!VPA5nC3g!Xl~6*U!rv!|lG$uCl} zjpn>CMg!S4?{VZxy?m2{ry5nHMJ(mV{0GGtvqLJ@UEaM5S9g_3naY_6Dm#AiF2kE< zsZt}hpVtA{X8mLyv5b})0I|ZOo>-W z%|Wj%PK9bl4Ut)Wj(eAAA&{lg_P~-_LHJDCp3%;!E4ap02EPDo%-tao!14%eM?KrI zdmQpkjH*Zp2>X}r6iE8WN9sKhbx@x?q_z!G{+u4o5q+s$FE==vI;WV7Oy$v-fDmQ%iR5(j1RRBQjHfl`-qGVkYbG29ygrJQG{+|=M{b$J z#(!upA9Q?i)M|cESd!#5u|2j&WL^g%(d|y~{3J~&+~M`H|o^$-2RqQX?asBlT!({NFd6CjL0DV!j(@~-{~J@aAD$Ef$k zh1Ok5MLAp(Ig;hG%Ezr-P=Zu)6RAPZyth^tN?%hiWIoJom&zK**%f9$aNvHupn0&j zq~d(@4wPH5Q63ofRFC2vt~7A1V~*=q7yq5?!VtK!Q#7ZKnonWLoh#!~-UVnaJXM2(AILl>*RAgV#T%HNzzvfl`J9^HaRaRKwsr1F+WsRw&Qr}(n zK*6KAFA1EcghcpK=~@0LizElIXe0tuDxm5v0Q~W^IU};|7xtj)DiJgIda17ZHc{d%p0)xxT=)Bdq3&~b-l*J|uI6LHGdtnJ<3 z7@)u56jcq8VSXrRabnK&L{V^|!6UUjPDc3R%%8>wl`yK?(&B|HtqO+UlVuQz5Ao%m zIl_f;seh3CpSS@ef5U%<dk zt286n8)7xp34dPYzWf)`cH|QwfQRW={YkExR~+|gljQ#dT<0L{a1BJdweZ2j4^Mpg z;weF_u<=RaOat0FiU{;FLaXZKXI6-U;F>V@wt~P$+O0FZO`mL z?uVAb{CKVRcQ~jQcr;OY9yx}GBePQRS@xM|VkqJ6_w9XMEJD2Jopg~S^`3w_RCeNq z-gy!Bn9*o1+omLqFf{IN`aK+Jsy-3kmC_{N`6?1mjVWy)vUKPz5!+cZv%ei`==@kK znVKM>{|1sedXkp)^I-XEHyo90msU!0r!vL0DZf_FK{|vbP(sP-k$?z5B%tyUzgHI8JPm9lmALMzR)3>CX+)xzBIrSo!%i;3f>p&Z=ec~jFFol% zwI%XZD(8%5bkn6B=WmzrLJ3HR{;oG~tz<3^wii7W4oWIKc3%*#mcc_mv%u%2E1@xtRnGtLO~I!}Hi%DW8BSSQIx#XgZ^E?DTP5zMTYL@^@ND3GnD5`f-cQ`Zc=V z2ueV-=05aeGV?Q&0LTX5AC9n+(u?^-LCM+WUYmM?eIatJj9`h1TxRIvp1K|Nts8Bh z^)T!d^z_LJyT)BTpBGTFoQ}%1pn`JEiHLS|+WWZu)Urg`uoT-3M05D@D&ZjA)xzGc zrJ&ZG>?FGQ%nj{j(!tXw7@rpY!3=yUQ8-GW67L^gczoh?Y`sXSn)#xY!ofTin*K7o zIR1clc8j(0LeH_9?BmaEdzwkZl*}vUx>JQ>(#&@6Gp8NG-1eGlMLIRRJ9NKEWn;Uq zx2tGXb8mkc`+3G3%AY8CoijunA9I-Wwany+t_SFqVpMLH|D!ZhX zv?YjI^AIERgLKb<=jbb{!w?y#eOxRqvkyFgq1lHO&uM{aS0;Rn@$^9aa196Jo1$f< zjW_;^P!M{&`xW?IEEJ4z?ik#k5X}lYUK_ZV?c9|C ziy{t3wTMjql?<{c;PJIR<@j@nqbPTDk)$dv~>Iqh7iMjX(47bvcn4CBKh5Hu{_ zF@2auLFzROs3keE2p6n_fdOL2{=cah@aH^~S}hp3zf4>4Q+XWtT-12B9i+=Ee+>eg zcP8V~>44O^$TSr#!a_y~1xqLAIBTQhuOb5B3+^&d>~Ji~yZZ*Vvl(nOF5HBf=EylD zp%-!O>Eocg>=a}f4%Ojr4kn8V2i^IW6lUxS$-7x}(ApjJU1W7dVyf_y_=imdcJZZ) zYL%2=V=Vt!%6i(m^VIH+<_~~qo84^QF4I%fBJz?&MEuoxzN3}o_bW_;p!~SOd_517 zP(&I!G|boxHH8`D$YH%+_K0yzrpseko3gOcA}#Qz*yh?SB!Ng`6Iyd zVpjfO^oyN3a=sd&vxCg%7siE|4|P0zhfp!*$D^6PXD74%g8j zG|=>K{WZLGwMgLy{B7d^@M9-H9sTv&8YTI|$0x&EP*uMh&j1A715x;*R`BKIKOdI$ zyZ%P2&gMg$%$BqqjriC<@Wt!00A3&PcH8yKK)XQz@a!&ff?1aNeHaS0eOR=4N>mD; z{sU|385rEEIqA>Uq2IC1%{Q!D`=7`wD3GRe-v#;#av#`qz`0mtvFR9I0^R+NzG@2e zc0sT>_T0I1(iz%MgrSpe8`l~@ssCFn_sh=f_lKhI4*3rO=(BB?w~ZI(RN_sujk6A` z{nkhj@X)GR-hnDAxOk)m=;wL;9B_HI*48iQlsXEDZS`?ikQP?Yw?3`dcyEhnj3Z2Q zFqhSzzUGm1VmtF|!@p-_@3*tH{UpS>)qi0yn%bjriii4ZByUIHtMl=9KE!&>4&Q4Y z0d67CbCK!t(T}kjlVs6Ndi8A>H#P1^CkP;&tg+emiO&KJQr5pj0HvDEht==VPoVGI z#@7OVoJj^+I;b&ix-kKMGXJmZ^B<%0PlMm<`iWm}{2wCu|MTUZ|8@rhU{d{h{a0Eh z{HiZCPV}X-7fsv(6CGt_rjWm$wIiaUniy|EggJc<==-7#6%BZx_xE~@tw4`UY@h5% zJR<`4s&6<2(3n6st7%6f0S~$1_Gb6dCk}$6ihTl?ftp?kK>A;O8n&cOy-q6GHus)T_o9=p8Wb-$ZRd>R zid5b4`FZByvd@c7*@zGEbNt?DN0szkWWg1GVU(tYd#*!t6o6S0pS8x227rlarRYXLr@0)yZzEEkjNmUr(&;1 zgXc0KBym#C9=2c3*^mA#<0d$SHM~i=C_-imF=%cV!9`v^Bc$yIB9Bet$=LJug1K>? zwJIP_LB-jc_}1LbGH?(pyFZ|^8N4;Aeo`VJcN@@yLvs(^DClNzZP&qqqmk@^Zi%H= z1RaXFrN=I$w{=j2QJs5?#_M}6o9NfC(L#EtR6fU#OU_0Rxki&6E~mfF&EjZfV4e|0?jm$MACxMsfwOofZblB@dKwN;2F+BB@28VmeYWN zn;k#%|ZhB#Qkl?^W>x3nJ3*=8<8gtRm>KCbW8|jG?KZxAO*`vTnq9^V@#=I zf6s++5>KfKt&K}6>7#;W2HUWNZ?iM%WnucqEfMB!mo{US@u|QFefgx=8k#;>6-Ys3L$;!_mU3Hgq*yXH#po0%myBmG%Qv)fbRt7dx>ohE?xLY7iZH@%n`T_9Gs{e8 z)W_UHEWQg)zC6qPxFIPpEB4ugleo)-#%-^<-%9KWYq7}PwR?MYv^V7S&D%Oz(l?;t zF!t@un358?w90A*RRJixeto(bnZ9J*vcZTs!nt*WjBxSPcC+;%3((kEu`zhD`!*Rc z=Zb#^2-CLX2c_0UhB3QoW{+-g1U!1xxE^xH99}=LLDBi`3trw#xBj&&cK(2~#b}wE zsW~Y79B3e6({iRdiA2rgYu9(`o}Zs`SdIC9TJWP0TiJM<+2zK6&uRa|fzCstyg6`@`w`jJ!hlM`GuTNGHC$H?nN161{9q zYXfv~?{+h@n}x|H*bWB~%ajh=5hQQqG~-3hxEKBO`Cy~g>WLWvsT3B!J}UX<;xkys z0G6@5eLZ#hsm8>dcAvBi)j??az= ze?%{`om_LtoH9an^7G6)?C8HD?_PfrPKyfZE%SEh_bw>EvEa9YJf)yWuYAJdyfi;m zu%77o?A*c=6Ko1|`!w_;+UkB^yQQ`Pt=U!dn2q>z_k%jF5?}pd#xrsqTtB;JVi+oN zKWge*%BZwXZs=+gGNGb!Ld*V!<4QTTNCJa>)VFGZWj8Ycaju6NNGUR$A8>igIzM80 z!H+y)@MVJ0;40@RcuZdKHb(&4vGoVlM;Ohl z`yg~mqGSzC$spGx42eX#oZY$>237AHT9f^PCyO@=cB+N|6TkwAqyJPcw9y=>44xIf`r}E+Zy@bWH%(we1&UQE(hBhl} zRwe1s{REX&6pB5lQ*T(XSdL~`wYJT?V^w*j_cBfK1jqU+sGPvGTSHY&AD9FlE7MJ@ zkJ7h%U)b{D^0a#+w>YF!RFwFZ22ss=$}dg*!l#GtG&rXzhRlZ*4vpo@QM}-Y4Vp3o6<+Vb`I#faiy7%sv zT$#z{j`N1a-(YhdYBbKj8m#++ReHqOVb+k&?kj3H!1q0{>o#>ZXeFUSDy;4Eka`!DPPGV%+ zX8hIj;%;uZp5#|_F0M#VKc(9@ z?IFHmf&d-MwDs}5_eDH9JXdAJ8|%W+hlWkm3@lsii-_GI`V9b!S33m7i|lU7NV-W- ziZsl@F<&0~{EVyvnyC4T_)$!Dig-D0*E3c#&~{%pcQ?$cYRls z#uwI@L!okEX47IOrKguygRaTeSQtz@Wh&+i=Dry!uE~-YqaqcsWxgq>d~J!65csce ze*dPu<&Q=FU;MyFuZD((+SXL=0nQM1u9yQ`cz8xS^UT+cvMY*9SlBdznRbV|&JMTE z|9qppWV#Mjn(OvJL@5|9n%S09-~Vn$UmG8v&>oTJU3h!h_F+csmJL+vUyB|7#jE`n z_WJMw;~c!r?#7I!z=-<`JzWJd%DE<(U}`zX)bwCO!5Tsrz%D5#$6XuFACht{B28&P zD$~2VVpcl7N!rWbay1s2qE&}*ug|~=o`bZzA>O&wN`?V9_5oo@aP0c5(s=IuvEl=; zDqXi+n}tjo;p+$1#*~TaFJr3z|HkD%j{5#V43sj}S>k^jK_qH{=%OZNhk*Fui@xtk zS>AE}NL+?q^syvh-!oP!G?#>v@hL9JkR^hHs;|ZI`u4k-s&lSZPPK$yZ**_pcU>7r zzdJ$C3jAGqSI}#TQn~-k%#4k+z?)(qU^K|@(nz6MG5;2$nxTTb@{z{ho|{yU^?klA zGcIDevYuf*JiS$75`FvZ7R;wMKU`5;lHBR2=VIaHvR=0(&;fPDXwrNk2Z-pr0Iz+7 zUHs5Xi^Y1nw}F(Z`idBEl^YXod-(Gw7?R$T{g)1__+4;VpKJ=iz3sZQ9Y0-C;mgaH zQboRZA*Z&c0us!ZHx<6~Zp{Fp`i>X9pc#_00>O}Y*ll~tIKoIa=3gYk2#KT>t($*r z-2lm&IV~$R<%!fic5X*#@>GNtSI*rLv7^3H@bqrQZqhQI1sgY>2*L1%3?`{9O{PJV zoxUU<2;~;;OdO<7yHFVi2e>d8@Hd`&#$4b#}IrZ>txQ+i4L(z!L>ZXQ7Cy6tE z+K?{_0GmMJ1^>hG+KN>*q4fw(rr53;Jl`ZS_7Ps#osauTIZc- z5BHlCIQUb@v%3dgJj+-@(klh)jVjo<5z&{5Jh0AILFDhT7{#f1y5sd_VU)ItN%6xo z&wkW#tm9%w#J5UFfYag5QcVxXF=d%oTmws-s>fh@trp!3o zB%rmJ#8K*;A(I$UYc**9qi+AkhX?{6lERgOQaQpz1nJ0p`qU?@pEg#*(vc#*B9^2IJvb?k)y*@iEUoq`& zjN-JqUlNKY=&V>lZH96((hn8RO?zUzFCm?Q%63%`a<17cI3HG9$2n-Gy(0A#CiesV z%|XP}mD@s~V6t`)tI81eUQgR6DI2TVdaS8{aIJ?UwMD6&VVm5i&84LYCHxa7Tt|R-|Sq^lO3m20ifXA%o=~(%w^-i`i}aqW%>WMNNi)q z{wFv@(D597vK#LHZM`yV6J?bCskLF5 zu=^{3LfIv((=fAEzk7XAjIpp}y29*|u;oJk4JCa??sk_xWja+8?=gV%#*12V;*-5l zPRtK2WNh_jd{!?opiXid5053YL`9&y^QkCpx92KKgpu8Q?L|gKMDl=b7w8hv?6UNM z^Hsw*b(sQmKf@^0U}!+D&8W01C(jH__E$cU_gnJV_yR7#@C)XWBFv?fROV;iY=gXJ zPi-AmmiCZTrc3W(8)smkT3mT*?Y+OWC%?;CO3M`0R0L+k> z5nbu&Ok7zxlU*=I||C1HvM%K^H&5fR-^JskJAY$+GI(O(E;IfOV&FPsPc0wOId&_A3m{gY4|*Rf?m X-%4v(`Az<{RR4ym)>Xu1v&a7j0nn-v literal 0 HcmV?d00001 From 3b82c638b15fb43213a3ad5c865edc0e0767945d Mon Sep 17 00:00:00 2001 From: revengel66 Date: Fri, 13 Sep 2024 23:22:35 +0400 Subject: [PATCH 7/9] =?UTF-8?q?=D0=B2=D1=81=D1=91=20=D0=B5=D1=89=D1=91=20?= =?UTF-8?q?=D0=B1=D0=B0=D0=BB=D1=83=D1=8E=D1=81=D1=8C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- lab_1/lab1.ipynb | 58 +++++++++++++++++++++++------------------------- 1 file changed, 28 insertions(+), 30 deletions(-) diff --git a/lab_1/lab1.ipynb b/lab_1/lab1.ipynb index 155007f..93e6167 100644 --- a/lab_1/lab1.ipynb +++ b/lab_1/lab1.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -27,6 +27,8 @@ ], "source": [ "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.dates as md\n", "\n", "df = pd.read_csv(\"..//..//static//csv//StudentsPerformance.csv\")\n", "print (df.columns)" @@ -34,53 +36,49 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 1000 entries, 0 to 999\n", - "Data columns (total 8 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 gender 1000 non-null object\n", - " 1 race/ethnicity 1000 non-null object\n", - " 2 parental level of education 1000 non-null object\n", - " 3 lunch 1000 non-null object\n", - " 4 test preparation course 1000 non-null object\n", - " 5 math score 1000 non-null int64 \n", - " 6 reading score 1000 non-null int64 \n", - " 7 writing score 1000 non-null int64 \n", - "dtypes: int64(3), object(5)\n", - "memory usage: 62.6+ KB\n" + "ename": "TypeError", + "evalue": "'RangeIndex' object is not callable", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[1;31mTypeError\u001b[0m: 'RangeIndex' object is not callable" ] } ], "source": [ - "df.info()" + "df.index(2)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " count mean std min 25% 50% 75% max\n", - "math score 1000.0 66.089 15.163080 0.0 57.00 66.0 77.0 100.0\n", - "reading score 1000.0 69.169 14.600192 17.0 59.00 70.0 79.0 100.0\n", - "writing score 1000.0 68.054 15.195657 10.0 57.75 69.0 79.0 100.0\n" + "ename": "ImportError", + "evalue": "matplotlib is required for plotting when the default backend \"matplotlib\" is selected.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhist\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmath score\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m80\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\Наталья\\Desktop\\5semestr\\AIM\\aimenv\\Lib\\site-packages\\pandas\\plotting\\_core.py:1409\u001b[0m, in \u001b[0;36mPlotAccessor.hist\u001b[1;34m(self, by, bins, **kwargs)\u001b[0m\n\u001b[0;32m 1349\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhist\u001b[39m(\n\u001b[0;32m 1350\u001b[0m \u001b[38;5;28mself\u001b[39m, by: IndexLabel \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, bins: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m 1351\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m PlotAccessor:\n\u001b[0;32m 1352\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1353\u001b[0m \u001b[38;5;124;03m Draw one histogram of the DataFrame's columns.\u001b[39;00m\n\u001b[0;32m 1354\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1407\u001b[0m \u001b[38;5;124;03m >>> ax = df.plot.hist(column=[\"age\"], by=\"gender\", figsize=(10, 8))\u001b[39;00m\n\u001b[0;32m 1408\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1409\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhist\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mby\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\Наталья\\Desktop\\5semestr\\AIM\\aimenv\\Lib\\site-packages\\pandas\\plotting\\_core.py:947\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 946\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 947\u001b[0m plot_backend \u001b[38;5;241m=\u001b[39m \u001b[43m_get_plot_backend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbackend\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 949\u001b[0m x, y, kind, kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_call_args(\n\u001b[0;32m 950\u001b[0m plot_backend\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parent, args, kwargs\n\u001b[0;32m 951\u001b[0m )\n\u001b[0;32m 953\u001b[0m kind \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_kind_aliases\u001b[38;5;241m.\u001b[39mget(kind, kind)\n", + "File \u001b[1;32mc:\\Users\\Наталья\\Desktop\\5semestr\\AIM\\aimenv\\Lib\\site-packages\\pandas\\plotting\\_core.py:1944\u001b[0m, in \u001b[0;36m_get_plot_backend\u001b[1;34m(backend)\u001b[0m\n\u001b[0;32m 1941\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m backend_str \u001b[38;5;129;01min\u001b[39;00m _backends:\n\u001b[0;32m 1942\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _backends[backend_str]\n\u001b[1;32m-> 1944\u001b[0m module \u001b[38;5;241m=\u001b[39m \u001b[43m_load_backend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbackend_str\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1945\u001b[0m _backends[backend_str] \u001b[38;5;241m=\u001b[39m module\n\u001b[0;32m 1946\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\n", + "File \u001b[1;32mc:\\Users\\Наталья\\Desktop\\5semestr\\AIM\\aimenv\\Lib\\site-packages\\pandas\\plotting\\_core.py:1874\u001b[0m, in \u001b[0;36m_load_backend\u001b[1;34m(backend)\u001b[0m\n\u001b[0;32m 1872\u001b[0m module \u001b[38;5;241m=\u001b[39m importlib\u001b[38;5;241m.\u001b[39mimport_module(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpandas.plotting._matplotlib\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 1873\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m-> 1874\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[0;32m 1875\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmatplotlib is required for plotting when the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1876\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdefault backend \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmatplotlib\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m is selected.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 1877\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1878\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\n\u001b[0;32m 1880\u001b[0m found_backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "\u001b[1;31mImportError\u001b[0m: matplotlib is required for plotting when the default backend \"matplotlib\" is selected." ] } ], "source": [ - "print(df.describe().transpose())" + "\n", + "df.plot.hist(column=[\"math score\"], bins=80)" ] } ], From 701bb1667f04ec2b69aeeb85ef2d45336c082931 Mon Sep 17 00:00:00 2001 From: revengel66 Date: Fri, 13 Sep 2024 23:24:00 +0400 Subject: [PATCH 8/9] edit lab1 --- lab_1/lab1.ipynb | 55 +++++++++++++++++------------------------------- 1 file changed, 19 insertions(+), 36 deletions(-) diff --git a/lab_1/lab1.ipynb b/lab_1/lab1.ipynb index 93e6167..128028a 100644 --- a/lab_1/lab1.ipynb +++ b/lab_1/lab1.ipynb @@ -28,7 +28,6 @@ "source": [ "import pandas as pd\n", "import numpy as np\n", - "import matplotlib.dates as md\n", "\n", "df = pd.read_csv(\"..//..//static//csv//StudentsPerformance.csv\")\n", "print (df.columns)" @@ -36,49 +35,33 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 30, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "'RangeIndex' object is not callable", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[1;31mTypeError\u001b[0m: 'RangeIndex' object is not callable" - ] - } - ], - "source": [ - "df.index(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, { - "ename": "ImportError", - "evalue": "matplotlib is required for plotting when the default backend \"matplotlib\" is selected.", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhist\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmath score\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m80\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\Наталья\\Desktop\\5semestr\\AIM\\aimenv\\Lib\\site-packages\\pandas\\plotting\\_core.py:1409\u001b[0m, in \u001b[0;36mPlotAccessor.hist\u001b[1;34m(self, by, bins, **kwargs)\u001b[0m\n\u001b[0;32m 1349\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhist\u001b[39m(\n\u001b[0;32m 1350\u001b[0m \u001b[38;5;28mself\u001b[39m, by: IndexLabel \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, bins: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m 1351\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m PlotAccessor:\n\u001b[0;32m 1352\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1353\u001b[0m \u001b[38;5;124;03m Draw one histogram of the DataFrame's columns.\u001b[39;00m\n\u001b[0;32m 1354\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1407\u001b[0m \u001b[38;5;124;03m >>> ax = df.plot.hist(column=[\"age\"], by=\"gender\", figsize=(10, 8))\u001b[39;00m\n\u001b[0;32m 1408\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1409\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhist\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mby\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\Наталья\\Desktop\\5semestr\\AIM\\aimenv\\Lib\\site-packages\\pandas\\plotting\\_core.py:947\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 946\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 947\u001b[0m plot_backend \u001b[38;5;241m=\u001b[39m \u001b[43m_get_plot_backend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbackend\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 949\u001b[0m x, y, kind, kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_call_args(\n\u001b[0;32m 950\u001b[0m plot_backend\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parent, args, kwargs\n\u001b[0;32m 951\u001b[0m )\n\u001b[0;32m 953\u001b[0m kind \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_kind_aliases\u001b[38;5;241m.\u001b[39mget(kind, kind)\n", - "File \u001b[1;32mc:\\Users\\Наталья\\Desktop\\5semestr\\AIM\\aimenv\\Lib\\site-packages\\pandas\\plotting\\_core.py:1944\u001b[0m, in \u001b[0;36m_get_plot_backend\u001b[1;34m(backend)\u001b[0m\n\u001b[0;32m 1941\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m backend_str \u001b[38;5;129;01min\u001b[39;00m _backends:\n\u001b[0;32m 1942\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _backends[backend_str]\n\u001b[1;32m-> 1944\u001b[0m module \u001b[38;5;241m=\u001b[39m \u001b[43m_load_backend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbackend_str\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1945\u001b[0m _backends[backend_str] \u001b[38;5;241m=\u001b[39m module\n\u001b[0;32m 1946\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\n", - "File \u001b[1;32mc:\\Users\\Наталья\\Desktop\\5semestr\\AIM\\aimenv\\Lib\\site-packages\\pandas\\plotting\\_core.py:1874\u001b[0m, in \u001b[0;36m_load_backend\u001b[1;34m(backend)\u001b[0m\n\u001b[0;32m 1872\u001b[0m module \u001b[38;5;241m=\u001b[39m importlib\u001b[38;5;241m.\u001b[39mimport_module(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpandas.plotting._matplotlib\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 1873\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m-> 1874\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[0;32m 1875\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmatplotlib is required for plotting when the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1876\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdefault backend \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmatplotlib\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m is selected.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 1877\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1878\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\n\u001b[0;32m 1880\u001b[0m found_backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "\u001b[1;31mImportError\u001b[0m: matplotlib is required for plotting when the default backend \"matplotlib\" is selected." - ] + "data": { + "image/png": "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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "\n", - "df.plot.hist(column=[\"math score\"], bins=80)" + "df.plot.hist(column=[\"math score\"], bins=100)" ] } ], From 745576461b3f5d3718de657e164495645d840699 Mon Sep 17 00:00:00 2001 From: revengel66 Date: Sat, 14 Sep 2024 11:05:45 +0300 Subject: [PATCH 9/9] end lab 1 --- lab_1/lab1.ipynb | 95 ++++++++++++++++++++++++++++++++++++++--- lab_1/requirements.txt | Bin 0 -> 1392 bytes 2 files changed, 90 insertions(+), 5 deletions(-) diff --git a/lab_1/lab1.ipynb b/lab_1/lab1.ipynb index 128028a..eaa2ec7 100644 --- a/lab_1/lab1.ipynb +++ b/lab_1/lab1.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -33,9 +33,20 @@ "print (df.columns)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Данная гистограмма в диапазоне с 10 по 51 строки отображает:\n", + "На оси X значения оценок по математике, разбитые на 100 интервалов.\n", + "На оси Y будет указано количество записей (частота) в каждом из этих интервалов. \n", + "Анализируя гистограмму \"math score\", можно сделать выводы о том, как распределяются оценки.\n", + "Например, оценку 70 имеет 4 человека, а оценку 18 всего 1 человек из этого диапазона." + ] + }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -44,13 +55,13 @@ "" ] }, - "execution_count": 30, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -61,7 +72,81 @@ ], "source": [ "\n", - "df.plot.hist(column=[\"math score\"], bins=100)" + "df.iloc[10:51].plot.hist(column=[\"math score\"], bins=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Данная гистограмма отображает прцоентное соотношение мужчин и женщин.\n", + "Что позволяет сделать вывод о том, что женщин среди студентов больше, чем мужчин. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "\n", + "labels = 'Женщины', 'Мужчины'\n", + "sizes = [len(df[df['gender']== 'female']),\n", + " len(df[df['gender']== 'male'])]\n", + "\n", + "plt.pie(sizes, labels=labels, autopct='%1.1f%%')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Данная диаграмма отображает соотношение студентов, которые прошли курс подготовки к тестированию по группам.\n", + "Что позволяет сделать вывод о том, что, например, больше всего неподготовленных студентов в группе С." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot = df.groupby([\"race/ethnicity\", \"test preparation course\"]).size().unstack().plot.bar(color=[\"pink\", \"green\"])\n", + "plot.legend([\"Прошёл\", \"Не прошёл\"])" ] } ], diff --git a/lab_1/requirements.txt b/lab_1/requirements.txt index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..9d0e49a1a533b1ae79f0f9ae2dcceab5faf01c4f 100644 GIT binary patch literal 1392 zcmZvcQEu8`5QO)+QjbCvn~<~*y+dyh(cls%;JC(wki)l~Z^u7Q)e0Gb|Jj|{*|C5B zTE`(qeV*efn&|ZJt3Gvnifdg}e2foquhX5bA=a_i1>$N#+*}ZQos|>n>&r=({g0~e z^`BM0%6iVxh(0E9mzB=pagQcm6Ll1# z)tuX8LMIsO_@P>~(u0ZZ1wO1&tvj`;0=EXC8{wGxMyNWzBo@z%8si~*fDCcxY4~jx|bDgvI>bT3vPm`*WbnC(u+#xH2 z6tY2j(FuM$vOm4h)TxOVDKLg}W?QEA zZ*p`iM(1RnyvppAK4-b=`DgFY;tY@{C(p@Uq>JS6y*4;W0{p!uW2bZfG2YUTTKDR7 zr8siV^mWo(Ct@jt-ym?lN&C;K(&;YKALQC=p7@ELa{{x|P?XN^Cs20AZ-q|CjnuL$ v&gQ&~@^+n@;Vm>y`}7;cTg4aVB2zFrRb@Ws@}yMqbZvxqNJQCBHR<>R_Rh;? literal 0 HcmV?d00001