From 6acf0cd01911d64ef38e9459bce94ca882924d8d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=A1=D0=BE=D1=84=D0=B8=D1=8F=20=D0=A5=D1=80=D0=B8=D1=81?=
 =?UTF-8?q?=D0=B0=D0=BD=D0=BA=D0=BE=D0=B2=D0=B0?=
 <132402521+sssoneta@users.noreply.github.com>
Date: Tue, 8 Apr 2025 20:34:42 +0300
Subject: [PATCH 1/3] dz5

---
 project/UML.png             | Bin 0 -> 314378 bytes
 project/example/example.py  |  14 ++
 project/example/example.txt | 170 +++++++++++++++++
 project/scr/desk.py         |  77 ++++++++
 project/scr/game.py         | 368 ++++++++++++++++++++++++++++++++++++
 project/scr/objects.py      | 325 +++++++++++++++++++++++++++++++
 project/scr/persons.py      |  70 +++++++
 project/scr/strategies.py   | 237 +++++++++++++++++++++++
 scripts/test_blackjak.py    | 122 ++++++++++++
 9 files changed, 1383 insertions(+)
 create mode 100644 project/UML.png
 create mode 100644 project/example/example.py
 create mode 100644 project/example/example.txt
 create mode 100644 project/scr/desk.py
 create mode 100644 project/scr/game.py
 create mode 100644 project/scr/objects.py
 create mode 100644 project/scr/persons.py
 create mode 100644 project/scr/strategies.py
 create mode 100644 scripts/test_blackjak.py

diff --git a/project/UML.png b/project/UML.png
new file mode 100644
index 0000000000000000000000000000000000000000..b7af98de2a4cf3288222499913cf51e201819020
GIT binary patch
literal 314378
zcmeEP2RzjO|BtRZsT@jMMnWOy%*r}M6f#079nQ$O!&xmEg*2$NNJ}Y6N_`V)pp=G|
zN>NeKFrpF}{a>HE`{;1ej^F<t5B0eF+-JT&&-ZIQU*jEVYOFtW@R-5<`t=(+%Rtwx
zU%!DN{rdGkh#d%93LV1x_v2ckM$@yVc?3AQx|91!5Of%yBoyRbC_Xd^g06&u0*OkM
zb95y+dy_nT<UGkV*aW|moXMWd9p+G$y1KiQBot;U$;rb{Qr3991c3;jEbV<gX}*ll
zvt2!X17KT#s<NH3B4eAkp9Dc0Pr%E`OT#bvWCu42oJBz%FAsls*fi7Co9sk^!)DU=
zVqDsf?Cs-9@nqbcf})(N9D(tJj|0h_%-9SEBX?;}@pdG8Gd97kDM%3XB;>W>2jq{S
z!+b3G;z{yg9%%|sCd)kB)sb<inu3~|vI@yjR*m4KCaX+#Qj~Qf*(=K`ITBRW6cm-5
zl+_$MpB=r1M}WRJiRv<k;z)Lf3poZbZxFAh#JEStK*knj1?FQod!yH7-UhP4+;u7X
zX!HkBkazZVbtL;R&&D_vjY4s!xl%iBbf9>8k{y`uig`wow>QPV<8DqAcjnVF_o2c-
zbT~V*!JK7?<(LnEY`1cCq`9C^rmV)e9pncCva7QT^Wv%s%8VcENe*t#-V|R?<`q3D
zo@B-^9whWM%%}8mAvsd~8E5YD1{q_Fj<h$00vBR=!K^={J9z}?klm3NfsQeH9a<oY
z9l)jw&BGn8$$&8eBtYPgpPD(4KHokymE_$%m6$j4Q&%%5yLj4pSkAPZV@kz4_!0aJ
zY<y54`;pvzQGC0T0%1zfrw7ZO`S`nfxRc;D5Qt6`PntRNo{<qCxw|?ev&{i+6PZjz
zn1D1_0D?C22O5P6TOC|n-5qC>0x7=8>-M3+J4U||T`1nJOX0L6bX4JIZyFQRfC?D#
z_C3szBjF4%L5bdEAHcmSIzq_Zux^?|LT1y~9cPp1LJQg8Kmn0N^>MYQzg6Tw50baD
zt0$2{qftDVNBO(B(#Ymik^?d}{xD5pH<nkA41^BFoq`AuN3s*i*Bzb=<Qf79*0;ud
z&&aTl(dmus^=&MEMWjBN;z6c)2f}I5!xdGSP+$rNWM|~E{$J;rf;@AZ3(FM46I2;H
zl9;pGx&7SV3u6EubCP`f{t;!-X)dvaAY2!@Gz$bJ6cFX$L-Qul$j<P9+UDp-`kpMP
zgz-d%9CoF+k<nK{mlgn1P&%D9p-6QnJ0XX$$EgpzZ&y#}+4OyM6_L|3wwN&`6|#u}
z$2z&w1&@oXqazvcPxPkHNHh`yPzc1R6jx6geVCPq5=umP8#?kJ6O}+|>cDqE1^OK}
zA-j76nmm!mhT{NIWI(Gw89^-ZHvy6D`2R+9qF`i-@t(kliu>M!MW)7L!J;f65hNF)
zGZDK0c{3ak{*pBOOCVHY%9UOXLL)VTwV%F`tEG+^*~G-pz}m=9M;6Z%(JUzJf;o3r
zx+-P}KGe8EwAhbJJr9syh!RA_oCTt-b-^-d-@+7ygs)prQHX5nEcV<<_GEWcijOOe
zVT5}##AYXP*ptNTDAHX$UC7?9h$Q@$&h4ULJF4OCf%k1i##RwCMV$)sUG;>rf2noZ
zf~>2gV`yDRS0^Vsy1<hHhlECI7kl7=A!?eVd^=l#O!K9)arfu3r`i|PEL-s5nGRqF
z35VEx4ldy1+u4(8Qt*!jdUV}u58f*WgzR0Be+EK4zAxxm_M%^bDFj($8rcyz45n`j
z_P7hhnc_)u*ZaB^)E&?gWQ3SNZ2YhL%%;%wCZaZ%kZH6)rt?bjrBPrLi`PgFaHUxz
zhl4LbAR^yvm?uL%>!Kbj{R^UHkjsE)?H!05gq)uKg?<d<49IU^52JtkdYq#+Lgm1b
zz-4(*JRL1uToGS|?CCfY{G;|WqWlpaxhDpg*m$r$6kl(;FARII9a5%50_WWsnZF1<
z$aX(96~;A?;b!bc_d<R`4||i{Ni<hK*0TWlNcXxF6j+_x?>RTB>0!)Bh_{WNhv~_I
z0{s57py%=kFh%@8I2-D_E2(w%-I*>}&$JHXq^$oVUUp9xz^gJZ_`5DZKu?I^4Bq6Q
zzkre=128}K1Oy$}pYbxk=f%-?2R;_1M816tb8kAu(bJya$AmCvPY122Oy`swUHv2!
zl${X+7-#`^gr8&l0MEzr8)J-E#2JUR>s_p!5>w+a=M6+M7_W@QeZVWTqwP?Ufl4G+
z$%cr7p4n&E=eN>!ObN~cR)i|?^!2cF1RJj1cIyI34rNDI;@|RPSv4<P^@$3K&OXPt
z#n4xc)Gpx^<WVFtPuUX?dmm*7u|&FK+6jmV2$`InyDM<0N+37^mI(REabtfanDLhY
z$%ejTD;awsAejyk3m_TPiVF#eXCf@J0rH81LognHUOoRM9I_$!`Unm=EOO_-;P<h}
z|3LfV<(ckHPwMwy(#UKlTz5pCGq~h-4kT|!DQSkt(}B+%k;k6A>t(heJ4&yQ$UHnV
zMXHPL^>Ly2+aU@V@W@fTeL?ZIXR7oh6MLZr$du+?Pzbq6NL~{MgTI<l#a0*ney<o0
ziKujG(FZZuHNb1$T^1FGCH#3e1Z5w70~N!J4=BiYV%L?`&`equ7!vU8XhJj?*L!F}
zJUT1CW|%U(VX_|?(orF?7Z@nSC+0+Ve?8HZtyqMR9$kiY$&UH0Y$01b_CnR%ox|ef
z>g_{AG7mV+^&d?({rzBM!xZ)vFm{chba75NiGJX$d@H&BZ^=A1L?Bz3^*V}%Z8|BE
zY(<4i4=Ln7vNzo|X62?Ka4zpik2KR+oUa@Xlg`n0pl5e-T<PEMO8)_U%_dWjEp~gK
zzE)&fxgA{TE;$hhx#+`5i2y7j`C)ykkVzHwW0-+x6?vwG#^UQJpeX22$<+IN9Yxd`
z{Oao<IT4Of6$Ap*(G`j;m57YkA6>pOHbAC3QV^sduk9I##HTqS>OaB`p&0tJZV+3j
z_Cm4BE=JucFM?xv{*2}M2Vlm=!C?!tUT1kKb{CDYv-2U-?Chjmq1ut;iDVQb3=ks!
z=z@@`?3vp*0h?beKW6J8{Q;b!iHkjH?p`Pky9?OZ*+CwA_duoidm;tz?Lb98b@Adk
zNsqst^!Nu5%f>HaD_MIVVxiqh7m)pr)vh>_d|c>tHi%Dyd@&`_puYstX#z3*<>9|y
zDOFKqGLb#ksxSunFP5tOn}lw>@;`=@%1_iowHHU(04$glRo|$G>MfW;Il2EOP!R&{
zy{zXNII9w!9bKHAd|U{6M6|yQOG!$vl~O%c$}zCkp;GQ=>+%>Pg*DR<oqIi1*nPLy
zQtsU)c3rF1)X;u4Xh{Of457hnhNNZ|yCE8&=du?@$pc#W@Mp{V)Cf!~l6n6Gv}WhW
zZWFy(HUre@0(dnhRrX^S=wn53DBxKbV79rWoFtasl*EEpB`}%$&a;8VJ7k+w+Ma!i
zPWyjf>ZaIT4hy3zAmaYfN!(5Z8prAT4Nl+h&lf<Qxt@TC?$>*8sMH;w=HrX>#^K=b
zw{+`cPnZ3E9I~Nx*$TwoheJh9-pU^;EowJ*enUDbQ}MFNTVV^cUKc$;YvoxCKSt&#
zL=R}*K*};xQ8=pimm8G+Es@8DZ)J<c-Y4?#Xl(x<F&RL0ws#7WgT6(KuV1_VAf1Dd
z3E$3#{Y0~%KTM7(gL#Zt$UPFS|BK`p+Zl$ANCyeWNN%FieG0VYw6d2k(z=?N*#nlI
zJQa!sm|tb-?Ul6^>6;LrLzda*R=d3+Gkq&`M4&)NU3ybSZKQ{lH#5QyiBrt;qdjKO
zR4RLx4I~dLT$0)0SefOU9`yVmGf%9*{DI!71`cNIhJIrnJd^db=r(v@Mt>Xh+P!Q9
z(1geKwmaYT5H8+q-y@^#yew0eX!}syk#6IVllk>U$RmxdVM2f11`OfXpF56KhZaE0
zLHD%xL0*`twz)an13Ieqa6=uB1@frl;hh@}!2yid$ULKkwvpcNJ)^nl>{%AS_msLO
zmc-e=`8F-gX2Fdb>;K+6HMcY~(=*o9Gy9>_B7&4f6(LDYT|2IKYIn`jvl{6V3*cbs
zO2;ng|9k6_ncVg7$gDzZpp-iGH&+4#BhSq0w4=syhpa#)=FRjTuf5&EVR>JFGYA6j
z`H!0p&`$90_8DYS?LFEODol#}e<{$$-j-mO!osm7dgTZpDZsFb1r1hb`P#;>i#@?s
zRd+Fy{-%Y&qH$O(k#1~9sy8gz!sZA>sle(u9<J`-uK;BU+&Y1v20{wf@*%+yI<OE9
z%T~w;gd$N2vIGN|L-h9FvJBHm8|nO|L!cstF&xm%Z6KF}UzsMTysq9%ZOhqUY%qYq
zI>z!=vy9>TY=4?<Vh+Xxn`4NxwRMaP;UC*yb<MP`jP3Nb=deH6#FFieFfIdjS;u>C
z-%KA_^oiQHb}-`p*UbY(<UJt|$t1ayQFDRSK0s}T-fkbDCd<FE59l3*(I&*-O)a$Z
z3^w1Dck|cngYQl5!lUIT|N9048o~HloPx?MP%3N$HKUA(LpJ<_5zvR#=KkjmgboUe
zp~iZr&0q>TUeAuGX8}YC<32l>4Z3>Tv-QmE%q_Id*s61R3lkF~I~{E^-ESYSr(<HQ
zOW*HXmw}tHVf(w8o;esc>@NZQ0(#6ZnHB#pjSWah>1b>yp{XrB8XIU1UN0ge(0l(k
z#s=MN_<m#KXGn#=pOUG}Ob_ajj6hoz|3zEldwRSe@P-$|W+4HUPRLh4O==X-y*wqe
z^ZnyhnNjW^%b5Iu0Yb6xV;AUSDVcaB<}m(cA@WlsW4k<%my=ghMaWR3K7ir2sHwHD
zr}8zF+Gzn5*x3};9)krqky0;4fE6Sd5?}2o&AuI!{rQY<RVIbmvv`pQj~H=^`;2^P
zZ$iOz<MF6=>KpQ<OQ$wfv;^|UrhoVHMGY-_V-C=tzrgQKT9;QLbYN;!f9?W5C?UTw
zqUiO}5k<eFkYlWI^b=JE%=KW}*H-wp>SgZz8&{7}LBnug9elQZ<~fP-y(v+?iwtQm
zKxOqVP<oS<%2erGAM@`M<yDvph$W!~X@G+CU8ZyW^2|m(h>~Kl9XN^dzn&=HKKp-P
z<|vwl*Ao!Y{ZI+o3vk$#>qKzK>Fx;TFS|q!mOPN}&G}#(g#H5{WS1q+P8#+?5Hjud
zPQnm6p3snnC#*qWjy(MBE5ZB$D6-3>?;}v`u7J(e(++6>>FVjs(9fJE%zvl}^B;gC
z+eKsg2p-uL5J3`$yd%Ze-ktol4KoLieF%>zdD92&MJAugqN*9CMl^3%syo?{14vjw
zvCo-~cqOLV>w|z~l4czM*~v=e^k)PL{)an*{|QB`%A`Gd!db5?u~boHaI+m?hUj55
zTL987Jv(qUENPaV5-yz~XT=UFF+h(64xDi~BUO>J=+Pf(Yx^hgj8ft~;rXBX+(v2y
zYd?J>S4$l;vWbbGfwhsJj%>H<zfpWSt8V>NnQ)h)9#}Vk<P9-Y5WXNhzCi<hJO1%C
zLem~FY0u_WKo<?_)c|Ds=&D_VFbqqQ>Df3!%TSIa>?@K`LGe2^`#+V0?19L<^WUM`
z>0S_UM07XkYsCsjyZ1$}NI{uthW2XV%C?zgcaZj>y1OFko!$Y7<_jKshjkX{QE<+x
z3VlZCsxiHiz6VftUOVDWk?5->*-=S8J{;xWS8zx`OjHm#9cs>d9ly?2P<H3me_eBv
z1H(Q8L$v#3uNH^wZP4!IAxaVb0KG(VbY!eV*Ny$@084;<%`xJv8S*FA4EY0G69G*b
z>x1ZKCuJEGQa^xebSiXWOVQ0ga3&<f$${kV;Oh<vCU!p1><Ou(NBb*roVC8_tP!AM
zq4(P$?5Wspy){@-r+u|$srF9{;qB^4<H+uxklp`+rd3qv3yvCl%cN_9D4nMLhvK^%
zS&eS~0Y!l50T;4^o1MKo$-!+2{6kODNy(s;2`9_O{HJ0g-x&x|knb`(e?B&%h?ZdX
zL4&EAEX4njNypaILTBEOO<!cuIl4pBjs51z^o8hzCtYRIKe1gGgHv45D~l@>Rr+jl
zl4>{Eo1A$}Z_*1>9_==U%;O#$F?0$u%zRE!6p0FnhP}BS1ue4fgZj6dELYCFWjAjT
z{p*9O?`{ldcUK@4l!$b_%Ly>{6-Hf2q3@C^3^mausRL0oP~C_+bYK#4^a;{E{l_Z3
zySQn8A=gbwsV|TyY#F|8)QLRZ>Ora;Q8;o+9e<_j_Mf0qwJ*fJ*;9L6%WnUdsJ-9F
zGWfVq{Ow%8grRr`c5mp=!w($4q__Je%JO}dPe98JI$JdyzofHY!kDp0H<fOPK~C)K
zCt_#+g2q=SFvFvLk4Au=d32q?nXW90Gr@%HR1XJHIQn!&n$C1HTY<($x-TGo1(CH|
zIkE4)6NXbpTVVFV`P*$`24WA=+tYBeL;DJsrL5W)=mWNnnp)TFP+hVQ<SHOJ*DMYP
zr(a=L7K<@RtcDA!lz&6}H|82_czHC{fY~Zjnfbz4M4kfL^Mkc9VSD*z?`a5!mXvh1
zS>ze|*N35inRLD>T_^iUa2WmWq+nP0eS3!g22e6Fh!$G*h(V?|+>0RC9bJla$7BM~
z(bWNoCoupBi(7#pWMUGnVdG%3_hFJvi$eBtvG-xJn@kRQM>5GBbxU*^pF68%PF@i`
zujt>2LN@IP`wHyXW#4wP`u^W`H~M#gV%JcREuxs3tQYB4rey76-ZA8uowKVSnVwK7
z#aKka4$-*`n-5W}9M-fO*7P4@5#N;`Ws61BAnZk8>?Qz-_!<u6uca@XQt)n*?*Ad~
z2wii7`Uvju^30fh7b(c-kHK(t8B&kc!vGfgt2pFv(A{&;{}7eRU0J?9gUascW>0be
zrzcx)6oF_wzVC4?c=@iJGP0H`W9D)=Wmg*cN5tD7;FJky0~IEprzqc5vJu#k%I$S;
zy~ohQ%Xg(ukRp6XB#J?g=(3GxaEOy1Ax^$d=|4&$v&CHR^DXf5U0GxVa-O~(cHR_U
z)T!m<fB&jp{S&Zb!xQ%z*md=35!lfo$4D7q2t3w>%R3Qz9I^MW#U2~NxX<8^!x$5Y
zJ~VHN8(D|qPC*u;b@g<CWoeOR8NSmm{SzXM9c9cGQ@xHd#-sVtENlrP(&#yRoFu3p
z^c2{J|NjW!*zm$^0oMy{H*~$u&WMAU1kDavK7o^V_lv>(KY}<mjBuYp9O}V;T@o0P
zag0C&Bqno05WUe)z_Vd{`wZf`vb_l6m~mLptKJ;Ww^w404e8rwFxQpzMKDJrxiNzN
z94Xfu5!aq2%o1kkGl*l7)}1UlRLYSZD4vWCT^x(97g}`eR(0t!$m>dku;^DL#Lt0T
zZ-gA1MOXR^<hmx;A&_HmzusgYUw4il@gq^yzs8j)pj9t@QNywit&%~n(t*d3a=lQ>
zv0E{v&qz7;fmKAK+WEWET$q*GQeA7d8Fe~L+mGJ0wY?cDb4e;rf8|~&9|>$$R_QZv
z?HZ6}tTWlA`4%T6+f(zce;7YwxBN+8fgrj(7K;<ifFNCF`q0Q!A1Nd+h5m_=9>B>i
z?wx{=%_<|k4~1-d5Gr(yaIk=o;iJ&i_t&^ChYsw44*Z#hgUra=FRW|8W-*c8he7tf
z8+z+33C3hcXR<jh5LO~3=u*6CE)-{qC&^v!>sBHin`Ed$)0KxDj<5U7rcj^-Ha%Bp
z37JL<G<RK!oP^{{qjc<yO%8CSSu+nuKHDIl<rI`)FXXE((#8QfHT?^cZcG2-N%Iaw
zj>03qAYT}V(T_sDem#u-?dx%l+TPw2e>l?7mE-~ZriF{EC;c3bGhN-?+fVPvyk9(W
z#;%%`IbD4yzTWhjGuVUum@*D!l<UFCyW0<Sagp(6>_)%BPvFp-4C`h=ogm9uVBt<E
z%tx;6Os05{VRRUG1>K+}zI_D(l<v>;2+$!TBcmrFkF<AU|3}ba?gssz5HNK2pm+k4
z`RffJ`%a9>%c-cSb;OyP5&_-NEz)3|O(C@j1{^6tXTr7}e)jGZSkMLm?>B%)cDJWv
z6%l2wuqfIuVG-3aJ%SO>^dbKu7{3iJR?s0pGo{cogXPf<FDjj~@fFY#G8FfWM`j*|
zBy{!+{f^fJp+SOK(>{@?$Q}3~)9Cx}oN-1r_pd-eUkxIr;G_xwH+r9fAG^&TxB#;2
z&x`}y5MwC5=dqd6Lmw47-#+FK$QpFn`#vOVdb>VjlIT6k5N7^FFBKtwb~?+WU8kAT
z`8UsIyy{;*ztT@hBl>*qOTwaS`UOKqvPchSuy(18t>4qM3>L7XczTi@Xe4{O1r0~i
z<?goy;a4%<E;$uYRi~hcsy0+rC^98EYj6aNR=RX&?Pf+$>ve5=P$k5|ybwt~t`1#>
zozpt6YwJ8GiYE=tp?4%Zksy4@);eVGmMEfN{|(h>9gxRFBP;S$dmr$+V_Uv1EYBeN
zI1!KTiN0R~VEfen4fsPPArodj!Lj#c)GMHq@oRc>7ZLdXwvSYAgBiQ(N3<fMgVbZZ
zLevBO_sk2*?y+dwuN1Qs66_#*vvg3QcWR`!Zv0kASlfZ-O7ZN1R!)&5otW%x*i~j{
zE3(npy--AU%@;(w(L1{O!AJU1mD<kEkjUkOu%BOB)wBHG1*aUtvNs!+OnQ&`>X~Yu
zfC}55^n5QgJG&->F(j~)tG5q*;k)+mau=j>B<gRFsHhO(7@a<{=;Dzb%mIWFW}!Pc
zMkny`KkQsX=#tL9bcsLz1nt8VdVSaEWKxG6PzLc-dc%5-(Mj*kuLG^pfsoLVms3K%
z!ykc2g+RIv#S>a3kdROpL6=POq4|?xnO;IqX<)x*bpC$q_FbbB?cc=$UK1+K)q~`Y
zhX0wF5is95mSk_XBvFb8Z9CPI?TBKw_jvCF4oB2|=a^p-dF`|O?^}hvuA2;@Ye6W7
zBLejd90{tPffDOI>iQ307llAiBC_{!L}+GNS9Q%yMqu%5IO@6&sB1hLCF%)_y-;0u
zEyL<U>2SR3pYg8SXE{e*%Mv)=H6yQ<nT`p`lS;(itge5*cinf@HJ-qv!&%fak}hce
zFUk#i$^>su+xDb*LTY`NtZm5kMye6oOA~C6FU%r_Z<Hf2bOLKC*|*bmdaO4<6N!7w
z&qT#&FHO+wlH)ClS7J^cG!CRDC$FZYKu~4=uflX)P?!CuQ@;Os)-u|tuP@9C_P8e~
ztI+2N{Q4u+5%jidkTFUxd0<X7`nHahIi2?CSU~X)XB0D#{ugHyVjus55*{?=wC@6x
zQC;>8!3?14r7%OIOMek&-xIe&pa|NpYjKV&s%cOT7UeAZyr5QQCLFTx4Z38g>7ob!
zSmp+YYxsGt0Zr8X4bEPBu^0+KCLr-Bd%(gnv~Oe0=)<Eun^ENc`<y<|vy4#l*UV9*
zrxnt@rQ=%QI<SSa5O}u*<G&O@XqN-O0i>vq?3&BW7LvV>ghd;~u?WR30+B;}{S5K-
z2Vlk~t%NPidR?4KxhwI7$UPqyUneJbPXGBnBj>(X_4YnFhgO_-kaP6JCSP}V=<3(r
zJFScK<79IFdM4)|kcDWva8FiX?}H*972_;E$6q8;=@skkeOr1WQrQzm^rODd9q+Df
z3!`xUH??g+Yh`<+QQOx}{EP1RKVp59m;~#;K^fWs$c9d=e<#X*u$h7*$;XA>$p|?P
z`C>|<LGKo552TB@e)90&uk->!+cfqJs$SlJg;CW1hnk__RZx17=?f_;{8amaZV9O_
z7f?}V=GOI0rZP^7p1N-}LqX#w9W0hk3IBgXZG2@`yA$P%C|U<m-rgikPFaB-TVN_2
zyqvte8WL;hzEc?d(zy*2!zN=5yP*laJ@xZK2eHRFjDq+t>V;pTY4K{EXfY)iXF9ip
zvR#bWV{h%D)b;++I<X&{)#%*r5`g?&7wBtkQIwce)IU=Daw@2<r)&OiPj$qCM8fd0
z@^TDi&C(fA9t?M8tH5q!lI<~{?j#c%Ba?oK-zQ*DxzVG5QDuh1d#Ql=vo?Yny37;P
zMo>n-|E9^VAdfCG`)e2IbCdm_rsU9P*B|yIdy9>)%CtHE;`BvS+ds$_K)Z+iD+TiX
z`r{8{LxpK%{>4}ROD3TLUX|(cGld`??K_8>gbXG16DHyB&#|JP(VIen0?96YYSF8C
zlc+9pD30Wgy=>{fg2H$9CV4OqW**?=igay5PPf`d{$Rg;BK>CRYFh*>s><d$YkBv}
zm-Bg&L_;aP!$an*i8kDfU5$$gBkD@8o<ux1baJedMC{-lJNGNZ7DS6WMjVRJU1K_7
z{hcq(%fFm&Zo8RX>Qi&(Q$l*}lghLApWlA|q^5Y;(85#qpS(?Zu=M=X=IYj`FSh7v
zVUWK){digmzqD*z_Wp*-!rV1OdDDa@&&Pz!x)3)ud)cR9P6I5b@Z-3;`~_<XJ~Sbk
z?rGMK6@*`dPl~L<>E4;?Kc9Ab%nl>&ofFPr4qnq^T%zOu3jEQyL@rToF%fPt(`T5I
zw--KJxMwhC`>G24Rnzq?k+XIB3&!E1al=E5%|;+66e;3y*+0E#nRWFwKCT-ZTW;OW
zMo!h`Pas5bAmbSa$FEY(<UPx45!_GkRtuI|d1y)3-NkU++yWuQTeFh)VIx*y6Q^-`
z(2|z&9a+&Nx~C~-!qQAD`fzo87F{i3^yO8^{t;Tig12V+2m1$4#w~`&=c*ZJIfu;=
zL@h0%DR$Z{%i+kVSxD|#Ut>{AIyF@jqo`T0WAbS&<7^%OH^AN*#TXPRD)*yT#_l66
z4x3$C@v+Ei^p34kb90nj@8qo?O}IQ_>Y|bnvh(j19kD8uwN4W`K48k}MBJ?wgTeF2
zli3WBs}9`EBigAk^dB!uz|G_}3?<&m^nF{h;!|m*U-hGxi<VU@eY!YkLWc3KX=h@@
z^$oFxGBt^n2b*GOd6PSj2J*mWJ6DD6*;2De$r$;TqpdZzG_qPUY|qfK${9x&oSuLA
zeB0+EQ*2Ho9<e;Kb-}sifs;gCF0CDr>X5&|^JPJ7!>6sX3o=Q$YtGDEKjw^t(bkOJ
zD?eY2oordKe*E<NzOQawl(o&cWqE8-=_$(7C$jTTUKzi#v8-lqxUjd!qm#$qj#YMl
z_4!k6t<cm(RdQKX3WB@0)az@#7Y}Noou<F2jzgO`or_2NwFdXSl?d#x{ot)j{wFB5
zdQWMVe^%x0peLo&+@Yy9=}w;aQ*3sG?b&RiQQTU8ZEC93u|<<!ylZM{dSCkJl-r!V
zz|XIfN_`(Jtek(+rEF2)%MpUHJC!}&Xtcc3d64B#NmnkfHQFYp^j17<Pwuek*JG#J
zJ^5Ju_(7h?ffoK-EmtOJmddhuL$8hD4P|cZcghYKOl~duV1}jOL$0)5=+CEk`{2sf
z&pX0IbjPJEZFm|K^y#6)#xt`vPddXRILUhU-V@|&nfH$_$dcT>{7y>9&FQr>RF*eV
ztukq@g5d*)j&a<of6DEF-{}(XsI0cnZ|7i%ip1q+hC^Uh<xwr<*vygWRLn%o0k^^<
zrO1ys5jq&1buVMZoMmWonXkmG&UlljU`J9vzA5tI6_TMo&)?|JH{;!^$0x{9(`z!G
zm%J%DG6%cl@qb6BH&>;XetH^2d9cgxk!ww{^KRb}Q|2j-^s%{nrZT}zh=|n|pSh0e
z|0K}aer)XJGp;KWy(*8qW_MHlVB!k&n`?ti!&TT98)3G;&!76X)W!irZY>cEak?4*
zdc(9gQt2N8YtB@be0Y6V_Wnjm%uwMO4}EK&l$I`id(^Z3#nlR-nvI%m)De>{s4G5y
zz;i{ObiF$%T~+KRj(F?U?fs?W{YzpV&OG4dxpRVRNz;-{HfWl-3b2pSvJhh2W|KI~
zsFu2GM{r>oJ|AxDXC+%6+4(TkErW8d=`sT6(v{8Co~4<-HI2_V`VSgMT@|U95%W09
z|CkM?6*g6j@r0RCA?JC0J3e*(sU`53k7KvSRp<-snQYWWOmguXh{H(SIh0;xK?n0>
zI+&fyPhoRjj@_!R6F$s0f1J-coBkiGitm<Oxv^XG>1507rM~GOv|qixwKufYqxJL0
z)D_JQHAhcBInhWQKi!W8H|JxtRVD_v<zec{5=G5T{VZ+R$SAEbAl0|b+;#LI@}rh1
zBBBqeKeyc`x3J_&jJWK)TgUT-$Cj);NWGz`X_nzrq{!y>-vzG<6PX}#1WW%d_#x<q
z@z?nk{A+CeA1w4gvq{1z)BouTmF=;!L+1k=v?Y&3vV(&z48kU?l}!{K4te@8XX?D_
zo0IJ?tvz$+fKirYLO@Q@F}tU=<(5yrz}&lK+F9Yy_0JVPkPJ<Gla?{Gm;e#Z!*lV_
zY_lz(sn+_X?37?LUo~JDUI@C35U2l!+XYZmnx?DuUdMr*sMHlvgZU&8e#CnO<B|j5
zl4n*OFuJ<|uJOqC%orIq*Wi&55tZ7j8oPs`cNSq`tf$_inPei8ep|x4>1@<)*9&>j
zD!I4H&6y6m%Jsp^C28!?yamu4R&L?M80A1wDome89#JEw-n>?wsozP`GjwhH|C<oW
zb%*~*KgP?9$EMwGt%##M(v$$bw}MB}{h=xG@W3@}E*qhhfHlAt2r8FOq(6PM0r!aj
zgNbuq#x4ORX?9e<NSGaXwgGs80*^3&73>b9vLKg;*#{c`)~nz2g4J*VO)s~VhEG^2
zWh@pwm0M}aJ*q%R{c2Mc2G%Bq!21@|o*S4r8DywMwaqD`&L)7p;5+-tf-3W7Vd{yi
z0!r3R;L#2Qj4_5yz!1UMrNh_F)*%@4J+za7BNp4nOW@Y0i>lhMPaMLW#ZMG92-T%u
zVb28~7n1om4XNOX$e|N+=4?)pz#N#Re`E*pT5$up=RXYFyUij$L=0Eh@-V||zvZ#n
zI}`?3&WWAU1$@zqx$Blsm`#l-kh=u$$nf|uOCPxPO**T>U+VCwZ;8Ji>o$p-S_8_{
z>`=+F+U!)zBlBvum>XZUY};n*9VssFaN_QT;QpmyJc1<^H{uq8UN7-DU0N~!w5P`<
z4NwU+PlLXI7WH44njG^$xiT5Iu!=W0UZ^c}5Z}%1(`_FgU7%w3<^ZuBX6+v~8Hps-
zhF+CjcqWM(X3^nWQA<KW65Zi>wK|TLH&5$?njQ7xfSR`ETK~#;ALD}qs~?@ZHbTx;
zb&2?w_q)Gjz%5IT$jOToEIl-Le+j7h*U47LYUUkVRJ~KltpXR`SbQGzYoX?sD)CL#
zC1>uA4|o<l_pQ*;8?!vtDVPinc6sk`GhR(uZi8l9<B*GEw0+_H%1LtPTj~?<>F?+&
zG8=V7ikl?@T?ahXD?WBb?s(AgFKx}05|6pFDpRv+ykQu!<M0k^iuStP-j}*4d#UHe
zkO2ecCFu&uh)TsM#%_aWD-ghz^Qw#>Nv?gL;q`pBYH_CT5x0kFd*hTnc8;A!J2Y0&
zB^oibmc2QgR1z(!Q*vpfUDcYQW1ZqEmE7(t^2YONG+r1_y}(toVR}v4jgQylvaa8V
zQz_iFy#6Ypcs%bOoKtgu0p-+Flj+rpDvK}l^S^K7Y~#Q9l+V)@pUx0h58m<eb{ys0
z>x96H(f(lnWZA83x+J9irL{I_;p;mGN<V*mdsKL{q>0~zpqAq=ZO#UGg<FPiTNF?>
zAgeY^(3pxCY?=;b=}%WSpFym=6>UwGsmq({?{A%6d-m>)UDF>_KRlkhE2yn$w8uL4
z_dA>uURyuBo}m7C{E82^YihD;{ZvY*I`=ZCF8J@dWs3Ff8}Vv)^P~l$_6Vyl%`lpp
ztvDil@t((9d{-`qX{$W5>8i8UqCoP&&vz-yH~5a;{oZ_5=jo{{vK%9D%5Kw@t;>kF
z0xNe_j)5_hemiKw@>>mZIa}U5I97Y^^P4H3-X1$Y|5S0Uv{kBYs(L!+)D3}<kVk}M
z<gM(%V(M0pmD1u#o_#j{c!KNW|2+OXAQo?N$n;{^PKOO@ml12dG~oIANhfvZ=i7Q+
z8sT@x;q!;reywv<N?)Y4jK<rCMJhVSd#zb;+H)7rq!=M>-#7R0yu2lzqB!5Ghqp1K
zOU~34gElOgt3Uqb7^NjIhN->4g>UzU*E}sxTBFjD=luDGM{YIw6M|@(Us}?2_Usit
zdDuM3xMrtYD!xqG`}S~Ge%<jT0D;T`_c^<!6%C!BQBtq#PQ>n8DGOtH&q)_7m3ccB
z>lK$62}d~GezV~Mrhm=q>PX217yF0WgT`;!v&c%hPT*a|pm`qH84+t*jye}6;L|T8
zuX{UM(00Oq<~DWpw|zTn=jdcidFj-;^wtF2y<>Ko4~|$JTd}qwM~15P5zb6K-`WsY
zz4rrH#jPJ7*!hiW*t)>;c%gyyvwNuvA2%r9)M$49RO<h<c+sUP$IjXha_8UH*wzet
z#!1}SB5htJA~$Ghu@Q-K(CmQWf0a4G3y&>NeZCC8Ow@Yv-0;{bR`Ua^;;35Nb1#(W
zm&@0vJYBx|s+dc2L-qZc4W4an&6Mf18Qw1p9{aB?GS+MsPdYIX^HSGfHzL_Dd|}MA
z>5~@S%xi3_N-sR2cY$}u$U29O>P|ueF(G>MM6n~i7G~0pFSm{!xAfKCNuQU)d}uHW
z`qKKzgTIVN8>=IufupP)E^Ys*PVN58_%Wmyg{2={ny2%Q#*7IQDKw0@#4n^O<M`+_
z<-AENOb8DZr|Y9myWAE#?O(fS0gl9_n|)%iHt1gEYr+CsP4f6A&r8`JX`RQvZ=6iL
z*yHQ7wpJ!AyICMvr^Y)WEWyP$2)x6EyERsnETCk+#64^L^1e`W_uAa4?>y{Rrdg9`
zlVUD?xwchytW|bUW7#0VP~23tmtjhAo3M#ff&o*i_UEM|=>`<Ts*cdI#^z<&ZHj1E
z-dGks>4d3Mq=1A1d34k0r*rrnRo4{=uiZA;Dz$2-3MD}_`kCvCH%E>sQ)HK`kiUej
zh=>o{^YYrZ$NqK=cJH=_d=hHFH%<=^;*Y?sQ!R<Aj2$+9+V*QJ8dkqMn^POGO0DHx
zZuP2~#95oKzvT_L<@Z&+a;@xP)BDOSFoDlpUOzT2%C{lzq19U2(EKO-;og+<t$zMh
z$MdH(_`i#<2dQw`y%Ky#nF__a-DS8PxU_vMwnnVHzB4>*kE5w&0)SFl|3cwN&w`8B
z^g_#Djx)mUQ?<4CdK9`%Wvch0Huvq$BSNT8FWZOZ@oqO4-QI7rW==lHqs_Ch_A%7k
zdLwOQwq6M{2$c^d56u}Gxf<h?C$sKK_*wp~hOv{$Nfy?Ld4?sS1M+7{?sD`Bg?ARe
zN?TFvc!cYN)LpV+s%Oe~oroMBlGaALofooBkg&-9_L{6r7hlPN{Jt|Wx$30=Xo`@L
zVVmIMmCKu}Pj6VQ@_cmgO&_D}<{qP-#wXa{zA;Iamn^|=pwFFA^}=X!&Y?{mjHs~H
zT4zDAc<?RKNm%*WRdCfq%7tq@!gEx&kEu>qc@iN0c$Bu)Lf_bBb(cmGFamkQY~O3v
zUlFfZlF%wJ>`=u-jF-X4+9@El2-<^3(zZkf<X*J1trjFSJ#ssCO6N=!W%2P)7o7#*
z4m%KWgH~tfW<=;?bpi^W)kf$DPMR&xCu_4XS8uTPxU-j<c>_1${HUKc9`PG?&CJ-Z
zI&YU|vlP}m<59lD&}P#YH?nVQTyxXBoC!>l!lB6~U>R-^kFJ7O!*$Ie)ZVLMQHtYw
zsj^*(7ZSJYt=ZPr^4>(yhe}!_LYy6sIMKSBQ;4{!w#J8})2g)dw5u*ijJ4;B+H4Rd
zVtkakG)ymMTfh$Sb7`*-t0vNTVv4i%77+~TXz2Ekm$w@0p3iYC*!ggKWth>6%No4n
zFv7-(jw3>>mj{qV8>8ioocQy&FGmFqFLp96Sq{!yDp*B+>f?Av%DG2vbMdX5CS)!1
zDATWc36YyuNa72R?oGbSV{?W@8DK<(DvKp_HBud9yvK(m!i*wtWf$H(^Qp-1tKEv7
z8tj<rP;9P+yC^$$-(cc^eS@U$JJ)5dc`omMYDtm%ZcL%%)a(zNBE{5rw1Lw|Q^(3I
zZ@hU;cv$mDtlXS9<x{n1GE((z3wAzRoiqY7W`^5heqDQ$ir_)QRO>h6{7bw%!3S3x
ztX-uqia7;hBv)r}Gd|;vzID#z2=5baNtKcPtb77oE~<}DuvI;~Zu)eyltKYDdkKuh
zri1kY;7i(YUtL~yQ0C+eMVUo6kIRIlZPiM#$)N6yR0SvO^e*9x$phzN@cpNV+GF5t
z>k9`a^5Em;%kJ@(3nZCH@jjrsA9^piZNB@#^nkrX>KBu8G>xx|)JKmbi=Gg{#Hh-K
z>|T(v{E#3|-2gY?>ydRnQJ466#Z&xn>F?%79T=8mfeSg*f8NaP;`)QN@5SnkXtB<_
zcXYu6<E->U5$*)1J87e)`=!L*a2+Wmx`qEpjSLnaH#2ePbbn9brSnct4AHvZRM%X4
zKJH5BfN^4cUTI>2H9Sq@L^l?0R4tFtwa^KS&^pAE*POuPD{dM1^|jCec->c~nX4*@
zDlfz*5;MrcVa3)7q7kjwyb*;<vD-c^cfPdx{77*rVNvYy@a=^_>YP&F0M=vN2>wCV
zkw?Z#-@;W63XWPaL3eaYxQkDs9&X;Wag!(M&Bu6M*xvtKg!051@o^VM+ASyDH-UG%
ztf9&)KyoqPKsO1QEfI5c*M77S8|1wP_u+hOY21}{bVIL==SujBw$YO&hdZ953R>n{
zT#;OKQ|-(e@kxAf&oEqhg;R(F=gG@(3$OSvciAk5U4qgWujIsKpKAWYX&aQ&u=fcW
zK01oT#W@Ot9@xf5kYd6Lt#wBW@lPx$bL5$HO~y$TaIagA0X20zr>?}?YpSGm+GP8>
zkR2Zgg)<x;L=So?)-rDSyNmxVz7Sig{;9-4?upoHks3)kT#}=8nkZ)IXob9jB->**
zK-T4z=MCN`RQ(cTWb7+BNub4Lf{>Kq03DG>En3TU--*K968lm(YYO59e%Y;cwsv$K
zpzW)J7A(SVP%&+5_T~Y+LfaRpmKU6O5G<0%W!^Ah--^pmh<^BxXP2LFaU015hppJS
zFw3u4bY6K|TCMiky(L)faUU*;52=e+n|)3A8klIFsqei+N7`uI*9kB@JpWYk_{0~t
zWOIgvlOtPor1}+Ho_>Ax$VJ;hu1_6cLbx|V<HfZn!#`d&pL5a=EZQCa9m~DwFs#Df
z<B~Q>cgh>VYQl`7iPOkf?JsYqETcG@Ch3k$YytnnTVG3~$|rwHv6bZe)p1ps;nH8^
zeI^O>Cu!q+0!aQfCB-rxX^Em`6+_)b@(%1d5D(T=p8e~r+H;#NXL0#0GJc<%-f!R9
zVjJv;#YL7Uv6wYl#_y%%M_B5$J2%J&k&sn(Zy$npk()xstxjvaIQX)WjiwGp8(Sb(
zH+qnR1CRDN+e;3E=gp|vE3!6zMMLo!fsnN8qE3Wxr@CljJb6a)vnRq%F)x=rja3`9
zZhLNafU|Z#V$0s_aa%7fiO9VGhGvASZPcLAGEr%^IqD_A*kqZg#mK#*J}oS>&D(u#
z>iLop(q<F;j|{OqS-1(`NU*_3yDU+_O)=rWGM4MS@Vmex3qO}0XnU7bn0tM+`6Auj
zdQn2fVgrp5%W}iBLWfPeBE%yjPvr_fWp>LfCG)P3asIR1PeoRCL+2OWP1dNp&Q~&M
z)4oeX45ut!l7e;A5lIM~NbO)l;uN{(=MOxppJN4nbJ<%$k?I`mE3OGC8|DQy-kor`
zVD#Kq(K^G9Zx#m^JwsDo&|)w?78gw2HS)Ejs&?^|I9r@SQBw>rCes2_KS%4m)KhG1
zFpKik;=<jU_Fum}N!Bp}A!aE$Me+AU_Y7K|_Svkp%p@oxT{GU&P?g7JWmx)Ci?@sU
zZ_OAyFX)gm>mAl1;8h-7=Thz#o4R)9D%CrY#=zRmv1p3a3c*f%&$WBc<b#&VZ}@^T
z7sR)grI_W+0&Y6K_2B$LQL8HErhV2q#m_PuA~vqNf4t7?j^%A0hylF91+Nmg)nCJU
z{C*f?&Aj3XHz!NP11FMGOiA`l!@39$IHGAWmUS{UVqM0MogleTLeu~lD5Aa=U*lrm
z9iMY|x8a<FRF7<-3P3=_`TXJg7sd7j4o_(_^$5{h)l_#&hi||Uv*h*2*u2=>ukh59
z>}8dNk)?9n15C8uJ`AZ1O?#jvq)n>TF483PAa5Ym5_tpVRea8|0>;28l~t(}O|!-*
zZVV}!kUR(OZ6$_rZ^12sw|r#342jjDVqInoN}8H8Uyw(etL6+6U%0(r-}zn|DB9F{
zN9TJszAH<$J>wmi8hF~Xl<vTS=BWf<HDgBQ{;`{l;9*|W-|(n;v#~_r+T)Mq@hYB~
zQ`F&7iTavHVy{Z<3EX0Pu0Xv68KKgk<ZLkH&tX2;-aTX>!9Aw%@V&SJcZGPg2jr@!
znyHJv0%yYup+}or-WQgx8%?l<U_>d1>9WBiCJ(pLu$j6Ew?)$Z+|$HCXKSxG2p#&y
zD~JPZv|yRoXp-DhIlpc6<nU*>TLHt2?ZQq?5(wcg@kzj)T;AlJju8tC(!pic7fdMy
zFLQB_&ctMk!<)vcc*akEo04AjNMpt43~=O15SJ)*>Bl!tvn=+{96C}OQ&$X}c{T9q
zl@*1%Yk~d$(ob;KvE<kAGTP|V;Rjzv;<W&$ji0MeKlKC7J!vf7UU$RzceUrgWP-(=
z1)h8s;shVHKAzKXe$cRSua<sz74y9HH7z)7Prpxoe#BegklI50u(b8l)7nh`+M3B0
zhn^X3ldGOad-V)LIWG|x>r-9+ggC8xiL(SBoFE%L*rVP50r-Jdi3a?qoo?(LA{y~F
zHS^8nPrJXg<clgpI6&rt;+<nlUe~YUGb&8hY<Vs1)exR;$Ri2t8nq#A$#@y=o%$Ew
z@D)u0q-i2Z1H@{z@w_^-HE2zNs4C_TX~vg#xqL#G9c!Ot|MxT-!b;=R?W>E^^AXzP
zzx>wnsHvF<^RRw`<`aZxNCh_j;_D0j?&%Uu`%M#_KjMU72te^AxWg5mnXf1Lm#rR|
z>2@spemNGr=j(HJYn1xFzH>>v^~2qoJ)y%+@Z35ySKJ@W_PE6-9rA}+B^i$wjE>-^
zRqSxyX=s>WnQXx$ANe6={<Im-a_^;s7+7l5jTqn&f`?rEF?<P7fYCwROH*I&-s_DW
z7EBve2wd2IE8NzN6_T@kwN-B6lC2tv;I(ID)nr~n=o@&2E1g>8X|GKE3#5}Wj-C5l
zkScn1aPu<|@Q+VZ{Ii#SJh8%Oh?IQVC^hfI)uiCpK;6B7c+iW4fO4Ksk16NRY#2X1
z^W2K&fl1E=tzmZZgzb2h0wb}#A5do-Y%maGR9qmaGHt{s%IQ+SIO)AuE$dbh0d;UA
zMhj`r9y9zt-r@3gy%@7JY+^3Rt2~DXGeLq<BD;~GAbgtm5#a-RuHHne@Kg)IV%SB|
z=wZ)C?_a$;sI52~!c(bBijpT4JmZQ2HekAnSGn$E!F)59JBhPAAEuFnrVW^~FG?=+
ziu%WU=HLRJyEYMXe8Pq(HOxNn(eIyi;wQcY>fi=01l;CVspmc~QSo@B7;Y($BOq#R
zCOZo|n7YwP60>)?z}rD1eBRza>NNVr;r&_uwSI9b6y@NE-FaVt>xp0XGS+JL)s_!;
zO>QWS^9oDD?o=SJmEKp9|CCSt!>xXI4bCSYfCrwPoea|9bT<OJJLzEXv99H#cFVid
zXf6OS8;#|c22O)VDm`|-?aj)TI(;9${FN%gN1p_JSrI;6b)5(<>fzXpw4vLSX5#jm
zB0;Xl&67@fmOjGlpL)LKj{29D_fGsDAYPdjHTB%XAyN@j6ocB93vOjT@=_vhtGd+I
z4C=CmVrLJt<=_%3k2aoBr%Pw^K+xvSNtjVQ+<ah5$r+#0{(LhQ7reG{-bH>RO}qtS
zLDpJ`h#M^3f2^Cxz!fmV`p*_`P2T#3uQBc8A<!|xf}M5Dg&9jB&ESSa7`UujPTbdr
zmLxt~Vmk{X^6rk&)Z)En4PwJI8W&p~nJ0^|jXqYpl~nybBaa8wT_+T7pOR4j@>*qs
z-@>WfUi`N{tI5sIl$wY!TQTF)>$~|42YEtsmaXx};j6dF*pwRsVQB??qo9|(d&F!A
z1cHlF{rI#$RvRM5^0&kDgw^Z?*9ck<I4%jC<L(7JtjCot+Hhj(nHQU)*R5+jA}uPl
za^5++*F4&bF6vKMaZ>i|JBNq^e6DrnmueE0)p!AO<tMp|w(0HD^XH|vH)>XuoJr>)
zf&fX%&0Ei-S0Xap+ypE3PRL$BNZ0K_sxQQ`KV<KkEWeEVaN8=r1?P`|FvE8dX7LTp
zAgw|zrHf`_xQVOQ2P(g4+HiOxxBFVJSHr#D0eg2`usC2+nZ-w7uN-PB#Z4AhZXGqq
z%B%3X$GMjcD`8~na4<5$hMALOkdbjH4UILvA6D1D{Dx@wxN>8SrWZq^M4pHP8|DWj
z`>yPbz}I-z9!?R`4j=EgeTrrBmh3D8pk=*)EmRWcB_<nS2VWavs;(!pnkVsde+~DA
zNr3G9O&51eJ<{l}ULGN?pj4jy=?US;RUnf^_O6doc?z`u18`vrxW`V8)S7|Wgx4&W
zwtB#zkrD?5`xL4_7&BQ^O8xWnq<I<*4;?B3UtE>kO#^CT|H2JQxbQi}T3J;mt|<(t
zJO|VM#-mNShu4W}I3Hd1_UP$MuqbVomxCVMAt~5q5>1)P|CXy(V%!?NQ8~PW<*MsE
zi8<pnj*q8qG+Bs?o-}*=V!YVH3(G)i%ly}WY=ry71_*t=NuJ0(U(;<!qCl{PaN!iI
z)RP(NXZ>ex6y9~)W7O1lRh8o}zMo)p2hn%KBp1&u$XffUf0?oB^^l^%o4ckHE@ADT
zIzvRZVM7_Uf9=o)Qkrtk8%v_;<QoE~^e{w2k=P0ENL2zK!!@(F4!L<dY!3+Mhua=N
z7-7;$ueyq57YB^Aj?VSDsV2Gi<&^Y$64C3LVlq$OQ=r>z!6gC^NL^rmctvZ-0N(45
zv0NEI#kjdu04L`-F7-3SF9-uFXI{|8mcLMn^Kwtk{`}_1e@&MUR|EL+^L9Y!CGPD6
zd-=5Da5v$4jj}Moal%H_g@so^leaxy(NdQ$G?JTi$RI{+(qw)@6Q~M;D0ZW?dqvsh
z{Kq&)!I&HB8s-9AM|Ud?G>g=lywQ1Bfk44TH+SK)3(H1F^&glK{``w==m1>Ktz14$
z_gbF3ajDNK*4VmpVwz8s;7-%~a}%7d%k#~zy7(?ZbO`Tkh&A1jnY%z&BoSlQ#5Liv
z+*~je-I~g-^9a}{^DmB`x_S9%nFar)oH-_9XS}uw_<ls8$^^@gtayPH(q4WzueRy>
z4uVIFmZ_GpsCRPM?s)eX!w1`WCe4X+daz*>KF3(Get?pKUd6Smy4>TuH*Go*r{w0u
zJ4|Rw=)j>0{_z;;lm(|#HCI_09^!f7yfQ4aEcq<M=AR0|*QO9Pi`63KGuj%TI~@qs
z<BRjK{+zZ_Z+j$dsQ!>a_X}dYMp-+juaPr9xGs|D{*0uS*BX;r29YX46i={x-qO(z
zG*XHqt@L(S3n4bd{VGt-%6J}8Z1CWbO<8``Cq`Y#$O!_2p!!841p7x$zMt&wjdf{_
zAD3EiYB5;ABZqvlm~4p;F*~eMv~`OZ;%;P`BksnFSb|+!qd?=U{Tp{i=*XP>`1V0o
z;QNXlxO-q?5JWE@#cWlLmw&<Qk%&`tzB!JU7!eRQ;lpIh<cHg?gUPzxC5*>w^Q1Xd
zVLWz3ydEOuN+EzU+8q1&#by(kexh8y{HqTQ`WT}Zt?4j1*6`}v76XZaOQPsQqg9VY
z_k<s(Rj#`<y{;(jRUnw39yjLfsjdnW8D>;B+O_xu*{Lk_n)3boDd*D#Ln5960#hPS
z@sU5gDY{m+a!2kW$Z<I6kg}8*?Z0-MQIKsRzj#Qft)W;y1Y5(*B3E7Znf%e|;z>QC
z=|!s5VX<g&F7;<Vx-Z@i0IP^v8B5^42@2aIWT@-=iW?0Qbp=z7DT-pJ2#1X{1Lj%A
zKqP6P_-c{14Y9*s@876#Vi}KcM87a4{x6!Z_Jn$4Vv5ADyZ^K97o#c~c_nY81&_Ac
zzIy{~urGX9ViTDjUmlYEG-J!1%AH^q?ig_siI7Qbv$Y?ZzkckX{b>~!vllp0JqyLK
zM}$k#V^mwhPHAk_9#@ic!xq?nHxb=~ah{o1hj}2W1l3QnAK!luvgwrXjPSIyJjn|x
zM8W&S1f1>@tgdaS{;Cwu)pwHe@7<|_SD)e)V~8;eoH6M3T0x_S7DnJ|LQFehi)eHK
zU!%O=CeWOVqAmrN=S5aLj}1!;d8{;*L=HV~Wt&BwgVjDV@2G-y#EVNq3MONAt^EAj
z&pze@<SFfZtb8tQljO^KiBn$BFIPModStJQG~}JUdOjfc(#rvIQvm9O1hb>V)x+B(
z=vW~LlwN$ZF(h>;x6!U?zE!vpZdUeRo}E~)q*(I+XD1fw12M_kiZOC#*kqtj#JA!+
zw;sP2^nATamFYc@<IaQ9B8o3$CMYQ!UunHjTuk)y;=sJj&sW4Z(!hA9fdh3df<Nr7
ziU~GEkXtY)cOl)388+kHg^<fh_5F!jZ!1^P!jfZf;EwTJ+<t!tfbMOL)$`EVh@bj#
z1LCJ%nc{p-3e25Wp;24&M$Rp-r&T{v(rHu!)l-%3Ld5M}Mii`D1F2R^%K7Xj9e*P#
zRlU%^lx!DLw@p}<JR$_QvN6YK6YfQ!s$r2?{WMLp*ziY;%#CPkByZ>%VTAVrpf%q<
zK7GvQ>Sn~yN9@NTynC-aE7=ACO%38L2qik=XQ)}O|ELjI*9^f-l4m^sc7y@H|2bo0
z76e|9yrvs33FWsT#OnU?=1!$+_djO%U*6~P>egP!&yopC8(zNWA@9_)(Su(Vt-&DX
zi?&GY91+9?-Zz%GkcUTmZ;ghfMl9W#enNMq17Du?c&zN7!z1}{B+=HCnn{I_vS&@n
z882S#c(c5XYN^lCo=MxCJL^`Od1^r3OC#z5!`Lcb8M3H?*PC~6>t0A6AVyM!h_iD!
zu3#n*9Dv|IZ}aI?lw;u09@AfY;=9*LyEBhF<YyxB74XA$Y}Z{UW_w%7<<^Dd8Os{n
z{V$EMvX;3Ce$as9qm#|K;O@_?x}ZP2`L2m(@m@pwip!e|wn#r$O|&^=x;H@1AEB#j
zU%r^|x?swqTI-SJtqu?c+c+gi*RB3(y0eLGlb}NeBrtkap7$)B_IQEFmw18un+_Ol
zyGd2wefQ&MTg(Z8E4Yxh2VwrT<(H!L41oI*!JJ&oW1!z+r*v;Z+XwxXs)AZV4?JZO
zsZ)ZjL*uZ8+)|QJBFp19=ksZ{EVdWWxP82>?4Eer<u6T@6PmfFx!#$WU%tgWTqaKg
z1pRDk`G}B#e8TQ__IAtb^myHmOypMMzf#z8MVhigj+yj;q>qLetWwGtDd<vpb5})j
zDuiSG-bLj-%7WBCA-jkh_u%FRbr_Iq{)<Wsz4s_2<dqqvG*ds0GAUoC=2?E4^0Y?m
z`E}jqY@f=TlP2fKX{MGPH(aOk1>CG@G83QQn6Njlf69y9=EN;IW(r%wH^tZAs(d|1
zWHU~8u?NDFKEqweKY@9)_(Ql*blV3VZLdig?tq1bpl@4U4;k_Y&%j9?YKjR@ej~&%
zB5+_t49_t(+CSOnQ4n}Dsn*Bscg$Zu-FcXk=n#k|ORtYObeP3dR7}XdV}9{yl5OsQ
z(-ezzMM*>r4P1rHKPjo30wFE0?P51+at~0_thaJwCMx2DVEW|TF;7kcTmCHO<jM`(
zwxsg*=UQJW|G_Nnt(2(L!`A(e4&XtohZZ$~Q2)Gtk804ni-Rh{GawbKP0`{&gqARN
z_`Q}ZI+ngny@LgwYcMJ4Z~>5Ncpi|IT$>CriV5b2Rd^4|3WN+^lVvy}cpOZU-Qr?)
zQ%!>!Lp#xENVpb4m2eg#lfy`?uTB8w+9I<|xgnUdb+bqD9H`={D$TNTvBz1TaEx4$
z`4pVrlMrKl5&}{IhvS}~hDj7}J2nQnVV#b?`M1x#{H9uE*xt-!hYQ%{5T^EoTsq)+
z?oUnLfE~ppYaOD5L!cY3+MH{1AZ#<__oz0{IVA*SUvL_d-n=!zOdS%p$|d%E^f{YY
z?_Ub(F{PlAY#*d9TnHMke%p?k4TeCvsqqc7!RR?p6qTy1+doBj&jqZ@MYF}P(q*l?
zl9X-;9I(7xVIs;ro@eVWr&-v1q$eI{V6XHyv)x|5{5GUKzDhk?Z>Zu^G28z-KL3T<
zgwj!Qt7JkHiS<TWsb%#>rk|p;HevJ9%Ra$;w|NZ{)!0mS%Taxigf`sK7C~Gk;;kPx
z0v^-1d7dmi8(ai=qOe7#<H4a9nDy+2%F~lokdPaH#XZ&vflbS3fzKMR_$n>rE{)Zh
zwVwZ^_e;a+mLGr}8nl+|n2m4|U@WPq1iX88$oK50C(9Os+AEPxiZ7STc*gw+Y?&-b
ziJ{XJ`!`H0XoUkkA$uUbd82xRb^b?~8$OUcnzq>z2&wnkt^4&i`n5KH@&~u)epI3V
z=cXz0NrPrM6iqM6IRy!h*X)8br%wuw|E$v}praKpm^=4O;v`^S?p0ZxGDI>&LWnRB
zA$J6m!vS3T5bI59)A9E)xEFlR>%qlM@|Kns$G}DD`9xMLMo44$Wg=&QADwq7P^7H{
zl-GtmwlU{@`V(*EtmZ|`u-ASyC9j>XZ!h!AC>!_*h`cio7A=!KOvJrp<Q1(iegL?H
zcWkwP|FfwZ8dmKlIB($50wVD2$0?D!!KFNvK?D&Wc+pB@fZLvtshYbc-)y7KlP;SG
zRLaej=0}kvKoJ?Ra91BlvOprM0Wh7$O+DPQP*PMXsHHr6!E^ew7!w@Eqdj|RUd&zN
z={F#0`~s3&;|Xf3W|NVeS4_w?5K<+({GYt8xVh_XV=IuO{wC@lw`4uvFumk(Qo@@|
z+G~4XJ!<i}6<IIwOYTXfAH4XeBKb(FcmF}dPCh-BQ9jroScS*V?qS=c%`YSQ+bh5t
zcH$!VzOk@6U^pWE%8nU<^%_F6Vm|JRN4OrD5MUOZ=P81?^@W^q8YCg5FI}u;al&!d
zk*O~O_u)RSA09H}A!1;Fuupq@($#zoE{uE&Nh8x}eAeIp0@onxtm1o>^Ow6q`c|pu
zF{@(<Z*T7p$7jDy%_^xXPGA2Tu`Ia-eZwu`?ax{D8JzC)qezl_6%5yVuIP_kUtVsr
zdt_ZExw|Y#NddpIF&<x7*<xWhjw_ogn6EI;xFfeZ9|1`Y5zI;JrH04^i`cg6eqr6F
zf~UTa9+*G%NtRN=bBP0Q_wiqe#VG=H_255`$L{a#?>rwi^fvcJ%$K+bWw-k&UU~RB
z+uS)j6fPn8I4O=uMz>%H)pfNJANTbm$IdNjdcJb~7tp0oktE$oPQ$0nyRPK%=$?!)
zP8Sz)?n~P;l@*_9R(pv9{OhkuW`Qf11$huVBkCa(mYKAB<)gVOy22^GV&;BCoUiCH
z7(we*>67N{v@WG&f4o0?o)$!T{c4|h<lIntVEbI-^DFTqS(RgIz|Qsl(o`5UJ3eRd
z>ve>iR8S~{i`c!D=bCGLAipijW|FALN8n(N&OcS44mo`HH^2nWfSma=NX$o9M7fPW
zjA}IU=o8QvP5s_Yk_-`VU1xCh>C%R((dpXQ1Ga_M6-z62m;rISIxc8U-3yVMb*6We
zUK@f)S-5ObH6kRrf<UI%1NjT4%R7jQ6t<;pWto2v>cTlweYY;OzcT@1-!+h@M++BT
zdT60{F?rjLq8zYr5=1d1@_TTDcODAGc|)rHj@7)!{c($M>v0}w5uzr#qf(aP3=FVh
z)|HF7LUCbn%7tsOZ+C&)zH=1qMT~yMLrO7^w)JPzjrqdMuD{vvto%T{ZLUZ0iA#_%
zex()CYic6IH>5RcgC$)sTs%Tm_nr2T&j?jm{t8~1ico|G4D(hUst#@$cw-CCc)u(G
z8@lq%y~7Sg7j_<kJip89R!?i1J1NBG<WeJSftM7fo_ms{0}Xtw8sNv2<|erppYSUB
z2*E9RyvvibfvJ%F?{qk6izYW_hvT`WUOVcy#no(>sl_{X{ZRf0V@NyRuS3sp6WM{}
z8E;OlhoO|h0{PLbvyd3A8LyPkS{7e$V4?+<`0nD)OGa45jM=$s^V6~A@1gc%$E6!Q
zlJSd)GxIl$pYtr^T@G*cCnR%SCi3!z@ox5mQlH>7&xSn<o~{@sf=L8`KGGm2W`eXX
z-KQ6Iec!7XePR4wsrhRw#ta*|z}hm}?)l~QH<ZSYeFtIhMeFvYyd<?gx51PNY*4m8
zsym*4<&(h5@m5d}VwG$mojz>I-Gl3qvV?_ELbBIaj3bV+-?$B4hSU9dCtHEcywbP~
z8KuFM!=1bp8u-;m2Vqw`@A92zyPg!U-uNtZnD!xvIylK5+!Dl{mREYftR5?0!(ZB%
z1WCndQ@Jp%DXFnAYmq;Z+sNqbJX8<5OUi}&k?N<ULEtXs+B}B{+hJS74Mf~L=|x&g
zbqCuQM}KapcC!!LHX~{e)UjL}?|ntI!8vRUfZRk~6nimT5mLzfp9K%vc|<KGpmBOo
z?$9yi1CwgJ#)ikK$VB1P4S<HpvKa!|bvNqu<z1hgJ@Yu-d83WVvnA^v>VPiJ7^}H*
z`J}xQ-SmNuCS3|O2z67|oorh=-tQp^DFfi)4*?h7(SC$IpVtP+H4RV!J~#jAaV@cm
zxnL=H^q)}2>+hAZO}vdiQscsU;c+fm&U;r&%wvw#hUFJF3adW~8(+a667B4}?GzbF
z)~udpZ|^mJ-~LV5u*g+H%A_~4MMV?{;;j_43Phrt(TiY!4p;>pIVBc73@p8dq-_!~
zK5AKY>%1^)xJKc?qIxjH;<(+$x!jirOw2j>%w}zNHDq-;T5QdBY}ix0tzywxp5sqz
z#}l1{5(?GaZ$1eCVKJ_G^On8CjkzI9CFVxKRi$zGkWnJmcV!l4-m`DQJrvfyBp5Pk
zU4-iJv%vSQQ&q_@U6AERFC6iBS0=WBt2u{vs&z{|k|+?T942c2Jgs=VG^Wh62ITqa
zq0|x=(Z&1=21Ko0KUQVu0W(O4A$&9g(dV@SmylMY9WN?$8^kb@>g!6xRp%N?%>zv8
zh~5#yZu33)`t19gKEPz9U5$)VeQ3N>@oLFcd>QCKw_<s1=?QP89tFouEqM+VS2H%_
z!io&&<NN8w2iq9YHhfLs1@U^^>B$P}b$NmfY9V5~-(DPdbEV0r`EGj?YQ(jwH|oLM
zeBNPh)|?A`zakZ8-Q4H$mH#QnG!HwIZS^2ZD;ez9;@t_cAnYo^-L2iUtmaI9!(Cum
z)x#yF*G&Vv$M49fnPWzIE{?viGJ6{?+yE?3f{E_2v-NAUH@~yhJ0!49yXNeBWBs$u
zp?n$-kz9@P`-0^Y@A$-O9<`E=6p1z!#a`QuM76>O%LrjT3!g;|w*^PZA&OUb_E-#`
z?)ZUsz$JDIO@R!zgYsgpqDSRnwXGZ7pPnntlPLeRcm$GgQVo+5i3*Z(1h*<%j}nbf
z6A{xa*f9IHZ7%=Dh&jIYt)?R}V`RKt3W2X#^0MEsy;ptI!+FmJ*ybWZ8=>5YQM0fr
zc_VVl!)JvZ5PWGWoHIJI@c5g>3IbLU%XWm_Q@<k?;kLhM0nt?K;My;Nx(m{t8RIU7
zhdt9-+oD-_YKyK&3g)CKpIhPKCo7>8;$7&80wk>@_yg2STzPtK8}7waO6=AII)0zh
z@hd5M<;W#4PTiX;Ks5P4sOLZG#DFbgAFyTC@-IZnhPpurdIwP??R-QX_ioW)q9TE1
z^W}3ctm1YUFl79e9DNZhqG?J6{`FiDFW0TR!>D&5Jza2CNc@TU7>RwU5e^w4;t8f=
zN7X>Q?DKCa6i*ru<;<mf<3u{H{`}?1b0OFzlH|4fi@=spY}ht%2?Cmpl|~}>ERQqT
zg3C+jCpO3{*o#1bV95Jb7t3ayB?Z(O#VNYlKN*5#%*z!+)~(0W@VbJsU1k-+!)ADk
z2tKH{_0J#gpO+UIH%|EwI1!a#YPt?mRdIi~c#+P-A<v_wlH$KKKIeOIZpE@sm&a1J
z`|}%{ER%cf5PR*@Ti~~|Ai3-eDDa1C9wNL)!dRF7uOaq)S|?flS-&YShmBR1G0!IH
zp2@qMH{Edm%=Oo^)t_kE7kNMmbH%K%8|np>w`8hg^-M=O?RfJ_1=zk;(VG`rk@U}V
z)EF_n7HP$yMv?m1^6b#0lmzu<J4Lycg`WiqrABSpgt{ls78>Tf>&ItnE$GFUW8zp|
zdSlsIf8nwP@=Htys{~&F*gf52yl+~$&@dIMwsE8oVzBHwzG^vSy9z#{L8$W5>jh4A
zGS(41x@(09S56lsLp~80zn52ptTj^>!%YLRw)d@qr8Oe?iFzW7L|uSr!<X(TI&7Xl
zBanJQ5Wi;qK`<XmO1-biR?H3kLMhEGij`aV=&0TKCqu_iyJtV(xlLIAgYIT!O9$4D
z!zzG{da{se8ET#5FQU8G1*l*A;NSs;V*;c{L+&`w?0}+Yx{CYwQauggw_pB0*4{gw
z>hS*?kB+=$y${*z9D7zo_Bh8885zk65k--VvWes1h>XxSl1hb)hS9>wE?P!WHc3{B
zLj7Lv`h0$m`+MJy`@j3YdU&KWuJ?64U(a=2*N=cX<-?DNd%+5;V1cg)9q!3Lv(xos
z6YVMQ!}ELlYMz+BC07uenO8-^RZ=VwQVnf#Rp(~4_MtN#6pm!ylT8*#{ytv;Ns)1z
zn?CswyT~>rhc7FB5x_oZ$+F-z0&0bfpv=SjD*A@kTca|=MCLet^hZb>IPuCPlt4}3
ztI3cWPE1{Hfppsd1Ox|ETSUF)G`5xN&z-luwyVE>^S1q1I^eF`B4y0<xZKS&m=8*x
z-hKSTC0`{%@E)V7p(>u`U^g$j8GlO24Q20dm+ATp1xeSI2vY6hWw~WuMrxwQZM{^V
z>+@(gRYou7`X$-)xaOD<C&im@r65Zi8FQN-vlbg=B4EHv;nNMr5^w;NG#^fWycKwG
zZZU7m!%0klIzP>Lkp~KE6xjD)nCQvn;xo-K!MXNoL!us)<FdVgd-&BD2bc_aj~$HY
z_Snc_o7{&0>#l}R3cqvZXfvnoWjDqK*?A4-oJq;#b!3k*rcT**<iRxc?w#(Vlt7bp
zwi+vC&mTLqG+ul{{Q_eoREW4-$kb94dA5!&q*1@y-K!(2z4{G_%07Jt`G)cjufQ`I
zVOgc;Oi99Cw^NZ7E5gUgOP{{mMM!1}FfTaaOeRvVvdn2ITcvzXz;BvU`uHyYWO<nW
zrp+@i!a&+!h%*XXX{lNt^%0Yr^P@vlyA?#*64`v<%|0(YvJ;~U1`Th!fk-eQ2gxui
zPFS(GUS%9j#As_=E;!~cI+4*Hl`A?gkFLLlP)hZtgT|Y)O27-p)f*^UF}7uggH~4j
z&B~F_Kxwk6g>yC3CDHisJthFVFypUtAIv~4Knc{C(XE{}2YK*RCR(XdeX>z+)KhC+
zw9IvOw7^l~?lyc9dY<lRY3cnw(Suish$CJi8fEoeF`jBvalKdfil3skZI|*gvBq`|
zh6@+6jg8#->3tr!k(hEmCzgE`$QacqAd%pjG*Y$g_4Y#0E)K^9e!bsYx7S+#9hdpM
z_>b41C=cxn81U_>6@5SSY?rrWG5cI#SEAq6+jlKCVoB)@I;4GQJh0{sML}8VIQ7{#
zmA&sKV3dtzSeW<zGpLUx8kH_{%z6jj+%+Ptsv2rW*Pw582U+ZH=k0MAeO|7Jzz;7c
z#l^ql5I3Ulf2Og<Y0KhwLG4hx_gKa_dl8<>(P(|k?)pqMg^fhtnPU|blxW=eLii_d
zw!BO2wkIn^?{b|Cq-n(vSN&9ak$&oJkqLW9TS9#v;P2-%n*jZm#cyPNe-flbb<I%B
zy#KatcrjUr_gg$;llVXdLVkAE#KwKsliA&e0DRvJp1X12L$DHVxG`s0r6}V(Jv-Tm
zQGc<^+Tf>m)cJ4u;~O?su2uy6u8TDNuNT1NF9SbHU}Y7>B=YK{=XO(YnrE8Wm?am`
z3Nu5m+|K(2B<b#T0|)kHnZlsEwnXMv@HzRO_@}%|Xa8L}{3M1PA(V6W4L&+>WAaH3
z)hN23PUwsi77R8Oe{<9N;e-di8hGc`w7soYc1|p*@l)EiQ2RP1W0}W!CX<~gPVAKm
z{AI4w&R|1IPOaJTQmbiaSD?^S@EH5C2fVK^7{O^2WcN8Wot3*IB2FV5<b>gJzwE?4
z|Nhq)?Bx{P{%q&_syiZNG~g54nw5V4kTD0j#LEjaf&0)GxuoL53;aC&FHc{I#RW^@
z77VmL7iV`S%9js6qA2Tfd7w|z6HrFiXx>Pz@Oa)#qS5fPP30pFrB0sZ4iTm>H3^iM
z4@*Nv60_CaWP%6kbz$~9MhrYqel~QY6Yx9<i~Te0wMnCMQL^Vy$~wA_{w*Pq<FX~P
z1$Rf5xa9E}?ESMx%otn;-=tj7p-X_nS2I2#$CBs^dKAAYvTzeP)pFnsWnISYw^qj7
zlByIy6X_QN2ZCDqV=%7)<e#^bnk^h%gA((uycDiyB_d(r2+6<G4SW`};I6FR+gmxE
zbVuBEd5D}IH;AGCATXJA_FsL}WDc8(i_?djkv9c^QC*sz^S<ZsACtV<2B1`VA@5AX
zw$-UROTHgjaTCcI75j3qEuxsn^w?+d(`Q5-c&CR^;9UP!a4{H=P^%`Be(&NL8zCGW
z#J4ydsp5TE=$6;%d++Zph!dZ`^{M6P@DtowjafE^#fHH@zn7nej%+D-uo5syla<>F
zN#3KfFF{aLJ5XE6jQaE*zp!ugWr9buGjDt{o2VX8b$VgiTkVmtsk|HC)qcT)*Ewl$
z)6DBS2*m??ZUn#o4un#_+>lp*;;s$8f@iHu$8ww64n%Wgh-hxrd_MKwACdNpMIQV|
zoiidR&j}qI^H@LYrZbjljM@L9=WKd%ou;mUH+L2|w8{%4bNCurtO?3p_o;H%g=z8d
zz62u``cCGcTV{USetmrb-WO!Gj|?n%pNX<Y6hUWZ=__-tzOhb08jtI;J*~wVZnZ6<
zlt&THq4#3fBfs5@8cC6x&^Zub^r$Ew4!!pV%abOMpWgiB%ePBlpWqGo91LVYbEjn0
z6_MkQUVWc>)wJ)Hoy^_m(@)T2-M6a$&6JJ13C>?C0$;bfm4VU7TQv&vwWVqo!|;i6
zfSHTsnuL8S0}O$CI9g7;1PxSY`N?~6U*^bXtaVZLwbFRAw>1YIg;#!gdn?<yeLgX|
z?W_zxB@71f3HI2Zi{s_N<Lv=JZq5RQa1yg_{Jeb^Y=iqB_&Zys7Gg=DwJHp_U?w$Y
z@w;yIJ&*xtbPMf2#6M7T;`8hBQ-6LRZrA}Hv1K4G_Gdn4-&*sGS$<SEf5VyRq;bgD
zMGZjSeHbW8Pw$0o8GM{6(XDfk-+l0BulO=%p6lV?^Ei^91OQO&&o^N73hqY@J&XTQ
z?}I<l#Hs0Ib8x33vO>3xq#E1(fk2k?6JRTAb9rABtY1IxestXK_}Z&aVCS6pO&fY1
zHVBjGMy{-JRBA)2{4XPgIUEm24elEU75*iF+i^?zyT?E7`#>S+WA48E_59mqJ>h%a
z<$u5H26VDl*;xv~5w{fA#kbY%iX?7CY?)VX&xvCn9=v$)4s+$)ajGn0nKj8YvcV2T
z%;LF&MsRKNd~R&y;o6XOnqTE8m2;~}kxXeanvN)@jiW_uR0~+`maZf^3Ari;{;N7R
zl`QZ}snca@QjJSTKEh{v!LJU;n7+pg%_3Y%5)8@d@YlQLv`a6d1?VmLWh^lBr9D^4
zhjh*m$TVK6d(8KJyqW0ZK&D<`E$>K>t&9C?%q;RiuCKw|CPdVB*VEYbPP@~%0*pEn
zJGHiOF%Uv2!aMzaM|vTIG>X|k_$}$=tnoF7HykEWYz`UhZ5N3TpEUg#-0SvlTZ7h!
zxr-+xn2tV0ipAlfx4Ucorq;x{Wyh-MM<zS2^TEhHo2%p`Ur_DJVN0HnESOvMYEBCh
z(>c-ltmO$VC85}T8XQeg@Vy>;I*d7Eclf;QZ0)K~dVB#2C2odj((1lKTgqU}?Px__
zKzX@Qj>l4W5k3Z;0?VPdNJesW<!w?XNu7vAWtF2p0_uupC%rumi8I;ROw3V<<^%QS
zPuDy#bv0Q>*#k1KI(NK^(G%GA`Q6o+T!L9*-C(H?VzI~@d-A5Id&(|X_BCD??d3Tk
z=*gla_nxdL#gnRDdKRTRrFo9i61mudlBx_LK2}2R#&ivMe_pOb@PajY{SID-tWfOy
zwny{i`s?0pXD5zk=o~&tGA+OyZqbuU<7bQ>eUmy~`(^7t3GIYj;j-nw<_)`yxO~6g
zOA4P77VKX^V}@R>%ks^0!`ozf!Veq9uQY(6vi+?M7d^OdOBQ`&i90CsL7%YMiPb@S
zDH0}=df+d#8N};viEt{N`~~JaRnVD=osw^B2jS^q11&sMt<Thz1u$kD&Ph4+qSU#1
zQ3Z$fQlwYS0TE&OwIOm-Z{V7=Ni)akEmSl$765TtRFJ@Atz(lU=7Hq*6||7ye~pn#
zciie9NWDZ;o(r<q?mYIs?~f)1u#91HQvA!j_m5r2QmD|Pvi&xSlNx9tzV`^C?Xw1`
z#@ISAkQL~eJiZTTk|F_q3jwziwys_zfv*;Xk3Wf_|5g_Ub0fRP5--gH7mKLD`da%e
z#w8g14A0K<BtcOo8AE+I9j?5^ygcJ;pQ68=dA=$G{=1EvIJS2Xx!zP6lHioPVxT<b
zDud%Y!CexKr<0A?`8m&=kVgFmcn>sJ)0`ptT6;>$lj}^X#<t28h9o^(Sz0+T(Bc7X
ze6W%Li9KBf+k>SZ5%0sg0K&Io`Js+;LJsDyKy9aLof)kykmBNQ<l@($t0Iauhnv=+
z0fR2<)bD|3u!-a3Dd)h_zW?WyzTq)$P+g=Rc>J~^QySm7fpe`2Tm#X8tGh%>{(!<T
zbhM}Rq8||%PF^7w1M;TwSJHs9%}zJgB_8XEA`%62Q~sS<Pfq!AU<WW8Hj91sWh4C#
zd|8GrF?g*nT-F?TR=wO^NJ`*fYUQQ&ITAOit)d^9tX-sttpx_(>$k9x{<3YT9N?7^
z-1QE-(?*X*4bGXMWcJGAfyPO#ji34XcV3}3u)GI@Z@@y>B99b3T(4$VQ*mAR!pOwA
zGc5an#2AI86q8V^N9Kt@n^+x>)&xm4bD!L5S4?L!hCUDVjsmO{1~%T_J+tT^)+~Cq
z4g)zDl+>YHnuZ0lQ+M3+XkbqLny{UAS^MCILuc?5QHebpF_TLfoFc9t27$Al1>r}H
zDTVU5mGxYf!X3XFCvv91Hu@@l)X8;muflQMNS;JQhg;Y`d=3>mk9}Up;7ZmdGb6!&
z|LvyrU-sqU--NZ#>?duH_68TO!&Rq@$VenM>Rm%vEf&nE=XtkP_GY@JVcF4+!Ft2N
zyc|1VQdsV&@nI<fstlGQSV0)NvygqccWLi7r!K-#!h<0fPJv@T`KYZT%UGdF2nb0U
zw!Q;3L9(|QUU<ws&l`-!(${x{&wKD@x3Y%?LV|f4IH$v;Ow&(bEi53f<)v8VTWIhf
z$DJ6f#?s`ZgiROsy@oYog+zjhZt%y~4`qX(HeOJ2tqGJR(jNupC2wF9WeOV7E$17{
z#J5g3;ey2uk7YGT9Xa)gY~!u1tQ^EQq@vTLsg__n3fuMeqjy~XL37%fxQ{zLzo@>0
ziYbB|X7#4Xs}@)w<{T|og4@oZG*<M<WJuh7*s~}4amN+H9fvFA06mYLSJ{8qo!pl}
z*BetvExZybb)`Vg9%;-V+>8m@pPfeWXNl$?nUH;&!eot>VQ$_%2mbu{a9pT7vZ`Yh
z(3tA=9NMb5>>FV2e3$bf_5Xlq`W{eYSuCcW7P|P;u$=k>;$+xqWnIH#S72(c6=*Ep
zt~Si<3r0#Tq``LNQm@=t%f?9{2&>On{Yp8%nY#q@HWCjd5_wge8oMoSdlj)2FV;+I
zCq=2dR*cI6{%RgzcrNhv(3I*SU-rheS7AKQNU<y9(cV{i|Mr*^N!Wdh%oHVn3oAss
zLtYf*L$M0?BxJm1EW92!5$(4H4K0;czYNxo`q2q$OIJ#A+D-hAxZRjpVeeCmEC|p6
z)hj)pO{ep!5P#2rL(3q2O{|yfKNEE6GUIc|p3`2zlln-u0`9+c4*d?gc1XLmNYJgZ
zn<!1oR8o8s8BO7xW^4kfcUg)&o)Y&Iw>k3aZxE5*GsXL+OnP2aJG6+Cxl2Y9F=f;B
zA^we=KbumiUwN&p7fI;BFV<U6So9644D!i;mK!rYcoulN?6si~Vv91ibq;SMwo1IL
z#Z$3(`?p{5raaq*Pm9M-jb<2rG)3M+Z<3G>njj<qW`e(OY<CSxNvbLG9K8w2L;le?
zTB^uE2O-D4%;g=X974jNZJ-+qpYKG11`|peKULI_9!<f1^;1zA)vCm!P7u@f7deS>
z+m9d)FbBMy*HhDwL*GE8s)sg*{`Lk9YcL#i9!!p&Po{U1<^!00D_<p7B-H0aj_Yy;
z119-?+B4VmFM77}cTkF$d}`d#g<)H;d9|lAozrpBxmqVl!CNy~h&Z95q9*6mu=<G9
zkoe$^VwvNXKLS7O0Gimp#i|$`yN^F_++R7G6MAoFvlI?%^BRQWv5BMf!r>Z^#?6Wj
zf10Oxg{6s8r%DezE}V}w6t&NX2`%2<y=1f;xCHl(?0p<_dp>eM+TVB%tUcU2NL|8y
z^8Q~Da?IX{SX}(wY0xhS%>O$gf{zm0p3@zX#wLJldEQYkYLdS3tbW1F-p?(Xuj?ti
z=F<yG)n@FDKJ;kkFiZQlxbg9z4wZ*BK$87@<B@wdkQqcr**#aL-aj}da-XLr$?t7R
z)^ia8@`FXd)#KnMN%j%WhFWVd)AQ0RmI&p{WD@v@1_8AlXU41I0wtQ-CQtJz)nUZa
z4Iwi3ZRrtKrqYxIBMyX0l{%ZLnQ)~ctuO2Q+Y^BEtu~ApP}p4`6FVuJNeHh~K6;Jm
zDni`Ibh?h=ho{6~jc4!x@AlJNmcl_fn!aKJ#<DoQ1y+-x66;f)`DZWdK`p{-!tGbF
zTU>RpHnYQRG>@}x?RyYOdboP%tM`H#C}WcZQ`>Q{B=EkeINfOFaqsu_ZC>BmzGk6d
zMHsf$J0uv+>{7z|B-Orw8H@(Dar2N@#Ev{BY>S5}n}Rpn0UOxQH^Z=8hf5b`Fpq}}
zu;hChV2R%V%Pup@rkc3nz0<Wo;TnKp0WeKZjIi_Z7$zbth$*0*^uYuTJegaPX|q%j
z2b+K|LtL_3efi^wH&y{~>|y~O{v}0+5orznT{&l1xKeZuE`a=i7Zus7RqAX8K9Q<O
zsa6I=AGeUBCGpYhm8qfKo2=7_tzqh$7_o(%bU8#Jr?)1^t9lFBa&bD1h3{n3^d>)r
zoPH+-U6AmUh3nyzA8~N#jkw2mDx0x1d^Mw9TDbwOM(<X*obGv=7Vi{8u9wE`M$E^P
z@4i2$&!pn7u0|o~FumYD!}Sk6dh>Kg$zD8>3zc(5By9hZnEqcskYV@_*r;eqfm0$T
z;xpUgr-XU$f#c)tKym~bmGyCI$lPw#G|j?Z1PG*C+3ykUIk&@hCoa!-YT#u{?zjl!
z^+)2!7U-|}@TAea<j9*Fvk=^QvGNFLz{9(@DBwJA(fSg(pO-4QrxunjM;zqfH+v;W
zpn85y*tf;i_fPMEQ(w`HDwmza)^08XQ}2{q$oBxVG*mLP=!K^ka*Aqo<96Vb-jA`7
z`oGTs<Pfj6_B~G{K()@={JsxWa)pU!b|oPcxx$}UU;S%y%(MhtDE<Gd%&-jphsr!A
zkHcrPe&Ku#^@5#lB5b|xHTtQR`SS_mTy3j{2Al9lSK53qZ^>oDQ?X#_m{$c}s=fsr
z5U-L<&~w|QOOK@MEt)+9uTWT@XY$3NxDr}7Wk{*?DB_*#*2vRZZLpvm7RZ}?$g&uv
zaEO<>qUvUh3DSwHd4L8$>f_I4YG`|0ql1;wVN(2k_VeM0fxG0BSD4Jr+z0A%@KW1_
zJQSDeJF}>W1G~^moKiT9_s`csQY;5+q!8IAbiLzQ>N~l6&g4v5p?&Pi?PLAC-Hp4X
zjQ>a1l$k6%5odQhMu2H-3l7`puWrRQdf~3$fqTa7g11m5MI<u(v~X!KmYg$mT2zKs
zLn7THFX6J5E8X+m**u%L%jzR9QSRg~_#^av2`AZt0?^zQj)zAu(%bnh(D%&)?ZhA6
zoaSdLXj<_ycmRrNyV3-+b|Ws2Bk$KQy|z$!f^WDNC<fIqU`V?hPA;4U!gYJLJng<M
zxRnNBFsc@l%NI=0;yJ^l`2bQwBh15gXBN@q3a27B+z3_dob{M-=?ya?@<jx90eBp}
zy<)IT(1D`L)Sl)m2XUDEjhPT8s<^&vJE%zfF?@EL%OA<b=hKiHdpTm16}dc{*WN|A
zZ-rCCch~gBq2M?c-uUl~hfKKbmkP|6G6<L>bUp;Etv?3qOf$rTG^b%=JP8u9+HtX0
zJeSj>xhGeH0(ef2)c=5TD1mP~ABD`jiFS!s@)UpprK9#1wHzAM=Ch27^8E}Pv~x_3
zfiy`rop=4wxSv)<WV(B=IpmqHJnr2$6GUeyZe!=|P59&$ZD8p%E910uAZ^*{fRN<@
z>}YmciBKEC{WIHh_rP)0;0hzs7Gf+~WDWb&AT-&DjT2&Xpt})NA7$Ry+2_ntV2aa-
z9|vDd#TQ8xDym-bfJ7trk+w7*65Bf2D&TmMF6R0$YB`|8(49yRC%qEP)I8@1kr4Ge
zO4bbWg}Sc~K9s)1f~RbblX+qJ>uFo4;jMe1xuwA5D2!pLADw2Z{Ji#%QB|r=*sOnL
ze#PR|E;J5}^tA{jORS6BvG!0(g~yT@o^<;(n`~jC6*XS59+Ct<v+M?MHDeIdXb6#>
z*53=qLbAm-0B0>v9tIE1*Nmoj_-=F8qGySy^lV#*#-FV*bZLhJ{jt2^&8E<T=*5(n
zhtHLEmu~sIPbyIC_GW<dQ*mX@M&|g|S^YDtW#w-5mnO@gCH8&3ru(CpI$xCibU#)@
z+v1TqSA&-Dg!Lmo&O!YB$ngHwgoO31&%mKnC5YxHZ#5YG4X~~6w=Ub9jUY`vG1;h`
zRyeP&RI6dte31JUyI*03!7usV+yW?*QF5!h*EHx>WT*E2!iT!u2#k&&RIhz$vL@Jy
zpu=)k`LR&1x}mz_KjQ7|ZUTNz2zRa_f%U`kUarkAFYb_Ql0>-q!ykgDEul9050F?h
zVFQ)CQsHo%ggy&`p=^SL+s1qHOV5i3ZO|cL_UPxtWvroWrb2e>M{Q|y7ch#7ISkx8
zlI_=B7++f?C%es^V&)`<m(qGqvg1nR>E%zAaQ!%X`u8hS2@P%{MrVo1KhM?u^c-XK
zb(bJ-B#G)Vr-YZY+}(HEwvMMYK<^XuiY9HmmV}LC&`S0E{;>}|2OoNEh(~GH`8Nq6
z+H;SW^CO-~a&RmIGK!GIv;Jap9uYDv_A{i_4uY+$J1T{L_x7vaBzW3S?)RA3d37Wd
zoW0X2w+AL#zNZ9uxla>_W@H=p<}<TwnoKHhn;-)n6AY4o^!dBNe^uraazKA?QcbqH
z;rkg%jY6A}xiznDYIq*PnT*oHv3?@Qd%m{3HW23x$X5*%!)mW|2HKzd;c3sg0^Z@{
z=qhO?367k4hbwFo&5Yf^&~@Zgi<O&0DnT!L=n!r13z!a3S~B-Qdu3(P<)1SH%{Nz9
zY*$SPbW9~;shqu?jd;ss4u&^><DVNabG!i|GDvNY41$F+;%5+@dCoC>5WaEuQA@Io
z_^-<*GB*N2k2y##4Rj)eP*X1Kb{<_DyBf(>EA#4Dr`7d9>z0JmjL+NeZtc=vz7liM
z?0hkcMcvZ9_d>gkM%_nA|JxQFAO2~F_H(Xu862@MaKl-~CcdC(e1fD~#dFYR0=Uj=
z_#ezdFRmo7iagQf5jju$j?RwP@}FN^s7dNj#CZ-<{15W<m@{OXAMZiAY-B?$<&WbL
z-M{k1@te~H;<zf2E=KkK3Xgc_wfWgbMR_#{Bw#{CZ;%<)SPZ&4y=KD8a^U@~m-Qsk
z<DlDZT6v#_C+J+fNp?cl31C9qGGnjtx2BAlLio6B8A~gK$LPIlSytf9r03mWv=WUl
zfZQ#=fV?|#3PjW}*bpr?kz5~VX_C1eOaafHkgY?E8Mv3JD8=pZT40o^s4E2Sg&*BM
zjZlc_T!_^M69NbJQfs0{D;obJ3oAG4Npk1koWDmy)gc0q`o(x4-1W-hLkO4D0>ps8
ztZ|E6MLGm1kiE_+Mv`p44QI_RwJRyZYX>j+Z682AYx2mn;$BQ>f1L~xT_-Tj#}{yB
zU>^(t@8P{qY-U#7pIf(dNcz6Gq#;{4<e8vF!g|j>RC*;xVM8`_^oW(e2eW<!B6&gM
zqlQ^clGH3yd2h;Ni&$xz#K=Yv?R_MA17AgI-Z4S{X?Vd<=etAh?dy47@O`hvl<=<^
zGHajlIk-&t6}Ww5I9Xz+s=RJ*pnE!|j?fC#MLghZC~bN-FpsuEc0T`H$qh-=km=0o
ziu338B}}i5jVb5Htlx13uxPu+S|owM(aKHRztom3cR5v<b^{>&C)Wfi98~_j(+C*Q
zI{GbWc~=?%7JdKy6QAvRA4P;DKgW}8&IoG^T+t4(^d*-@0<-#JkuFGLEDLS-h#QC8
z<U!oN5*y-PXTZsi!b!0|a@(O7g`Q#bsO$bp@SA;-HWj$4^hqoQh9UcP2<9AK7`@Ia
z9{%juK+P-s8<5W$emHlVpGUXc6Ss|_4E@1W)2o^PchwY>WC}uhn{lCDKXU1B`<gC{
zP%5$3z?uvWH6#pTMx6p^Y1{VU1_6ghR<U^-Bf?Y6fgfn4kA4GJ;zcu9bcPq@)jb<=
z$t-{C03k+lsB+6nGM_C*-&S}wspazCMH%ainsxa67pbnxa41P%#NL^-qj7&K&_3Rp
z`IU|OKgY7ac5gg)M>MboBY)uxbOrMtP(HNiX>ZB@lRE9L$zadkYQ7f~y(8}zj{cR7
zRwzbuLIp;51ObSDLZ)gbM2_-}ZH%93{swSo5RAWHKZcKTre22TKJDJC#;m=SUZ!_!
z^*|Tpl+4dOk2xDzWljrho5k9u97~;4R}uDN-aT3k!_++rSHK6Ch?Re+6AD=@gilmm
zs^O0J&WK`0u)4L0%LY$B1Cd?DTb!pGmacn;*IMrt@T}c?9yec}&ou<cv!-u%VcPyA
zZixWdp)0{MPjBW&Dt?@q(P7>ZB7BbbaitwNWM*>8C!-AO{iMENcHW1e#r6VX7X1=}
z<zJy|x*nkHMEEg1fxyixz`A^xq#V~<P8z@D-IY-h2vOa?rpB|z1mC%fwIP#rV2|_u
z&<EeqAW+v^wFx<Q01&jJ$omkLL(gs#S;$7CwMG>;?XN-(?Y8c_3-(wBQStkY_dHNh
zm9e^gGil{si_dQeyKqXyPHE?T16iXMoF}rd;Pglr6jIfoXL1PZpD1w?bT(u*1QYkq
z*<45WphR|o^nQ>DM)LUn%+<lm8|XC)`$u^d^8h@?B9B;L1-_*lL7D&8oQGQ`7382<
z_`*I1)PLQ{F6df)*lHPA)fzZEb!de4a43Hp0Gm42HmqiO1M=Sod-S0MQ*B{m$P|x^
zxfX+yCRiH0{bDmu3D`=0ys1Vafgo`MspW4w9!wRjCHe^_=uO94UEr7&J%~JsQf+!!
zz6RLl(p;3~BgoPR^Tr~<u27vF1_|~Le<~YEC8T@Wm@>^83!HH&7p8=<3oSmKzI|r^
zDo)Gtodj}~onI@bV7djq^rD*N-d9B?gJ3Q4Keuz9@z5Z4MaiMmH~wO!dArL~qH)p%
z7F%c|RJr5(Wci5h@3n<@PeZ@D^g`HEGj=VU?OK%Af4u-Zpw#hOSJ@+wZfb$}c0Lfe
zc%Bj!^?iDL=kbsJ>r2oaEfV64yFRp$Wc9CGX+GDesK@h(gG=IH;|-=j{@kIr641Q-
z3_#rbFyHHAm=?*^d!ikJ%sSs6yoXBo$Y;s7>+Sn-m_lKFMN9RzMJ4R~7YXFdVXSEZ
z!%NZqFQIS({12?S9LR;}D0^WoAJtV2y(+z^e<irnuog0|^U??>qK^~%SmBuvlRnk>
z38mh4D4Pzb9p~o_UO81tLuqtlZA`pi(Uv8s8D7gRfcz$D42r;a#M44uPpy9sXigJK
zFKh-4ArK@SD!9zMIu9bMNs+^E9>VY|D?4nc1!uq^`*PO^iSrR4)G^5i?G6J(ed)py
zSV8JQ9jGn+9tDGtl~$9fDqC+5tJsNdgV}kMI%Kh)4!Myqu)PZ!;_|>>>lEi>JrfC8
zr9lYVk7CYx-uwzJov-)aSY}?E8v+%Flx@@hpQWr4AKvSEgx!o2Fcl*9R}QIyjNmJ`
zi1FNjiO!3&7rfFJZg#6Hn~Z96>gqRGokMdx3fuSKcuXMXNXfD{@RUU7YUCbp9yASQ
z^JJ>@g6`EHsyDMMbUZyzqv}vp!Amx~;z0Je00eA`5?w@^zY6Cpy?x}+dAGwqiT5)3
zRb0HOEIm8N^r6vn^)VRuw2O8j!qD!)W-raS4Pd&JR_Hr^3KqRk`7m0`rN3mud-pfg
z-d@;1j>UlH_%f`^3)j0acIw<Nx-NW71u;M1L$14OoLmPSM4$vw-sQPeVj<@DU?{b>
z*s%^L8MK1Z#Tkvb2!T7O;Ztf1j@9Xnz}RCLl+!-oKcIgwNB1%eQ<vWHwy`JG#;k^p
z77CKIxkzq*Svi;kZP^(0T)bqAHG!NGCM79g#9UAKkK_==^RVY*$|Ac4I3A2Ku~D4u
zAKOota%v3}R`4SuWX6zOTdI&cfYp|#aA4`YQ9+DrB%}-n3Qc5Pn}~Z{0glgtRi@%p
zjYn!ON=0?hoo3ZOU7?ub39B>J<u`1vYP3SE2j(3#jVfO;yO+CG!MuoBhE=RfgP*Mb
z*q?l1RA!G4CYr&vhs}bH2VxF6d#WVfsf9?if#FO=Q|EJc*u9HJjF85<TTr$h%2^~L
z$=@hU<)dCckG`y^b9oY7d!Ln^xXpIcITt72-N<R}wWX{7@jEcUilSpF_@CGgrjp$J
zUp(lifohm_yRY&VG;Z#b%eNLIFsTgN2e6wDs;=No>3fZ*LE28?ahFWulthY{*w}Ni
z-v2eesKze5si<c`#Gg};#Cf5XET|!^>{oFc8e!8@)W>?Ejc1j^mhPlY-wrxj@bW-N
zM$<9j-T@d;RQIQ&wC|bc1!g-j?*kNbl$DH|x(p0$;;h7_5-4)hMzQYuQ+#L_65m;a
zvAnbK25d?~qV#3swZhW!;i7#yV5e?&8qGWi(TPcPHMyXYrb;9Himx$|9?`+v2?saa
z2V;=({YSL<<Wz%!m&9FoueL8Az?|je90)10kd@EIe>pV(L%WSW8LArrp0qpjSCK7&
z#&0bj&HR+M)Q;~^N9-&UNW`-CJ`D1YqtFspc{Zh`gUR-Xbe{$B$+i@I=7E={kvhb4
z+uV17ZS*UI3$~?BT0|dwGPzfL>)=mu_ouNjAFe%%LRj^JtRyZ53UuYSF|mqfex94T
zLy2TwNIn>eEy>5L2g<m&gHFdhpncor^<M$X8Hbq-M!pUiq=g$XRy~=TM&vJ{41K5w
z-~L6|wXl*vj%!dngjLl@(7T+5K)hSgg%AW7Za?^7Mf(R%t)H}15~LAEhEXZxVK8_&
z%2k>x(9;eE-;Ee#95GjAe^0EO#I{}NvHG#I7M(AIPm%;1&8GlTg@V<nLQtQs1becE
zpD=&KSU>Qa9ob(}?&N4x_+awWdIe9T`R4PX(hRBgyC5Xp2ueV4$qzq9b`U3Q3@M6z
z)qOMX?wqd&C&P<F;zj2Kq>xnk5$?)NX?HwXLh|ZCoXjy7MX$f#M7%nT4LZtJfhAal
zxPp;Um&Pd_TP#m<jg17N>=P^;W?9j&cQ-(NvDGb%w=zM6{EiE7!-od96zKB_Qrjp*
zt}N&;(h4B9@<z(1M&Go&cu-s?7}QITRY<w5Y&5@40pGS+f-#QSoRH7b0im$6AO1y9
zDWZtdeFomV1d_IZcL(cvFDI_ySZFobbD6?Rt*|7`eE#^jzG+>!h=a_tT=DEPoW5*I
zSE7fPx4PA7fft=UnT4OtHkn0`c^3>flv2q>$pbWwc4-r(g6DQHh(JQ6#|%plUisDF
zCQL@zbw(D#Z~8+{%Dcx7Iu$jDh=s87;jI`X$a5OQZjl(_^<;Za<zUZ;iQt1qmV!7@
z-fz8x7liWF<hn*Sl8PD;7jzmSP66wB5-P<i-^~pra6eepeoh<57m1%T{MNbSYiOVT
z{IiV@=*6@5GGcSloXyr7?vwuJQwj;#CaKh_bK4*L{98F;pA0run(H-3+I8y(1}<i!
zW8;bEw=f95Si%-+GFo_3CigwBPfsYSiIbBJe#B48;y0Xm7+AOkkK9#+kQX!?-?*6_
zcFZo3u=A6a>I=3{kED0r2>f>TU9s}Eh#>BtmCnt(#7PhSTl?J$82-`Wi~J(i5lPoX
zzL^kivapNP0yui0R&(yf5gBagz0w@h_u)MKmzPe=r<mnKr`dDp(Y(2=%zTaed%`VJ
z+70u*y;?Jy{OI`M?-utHbMzQ^<-$2Iv<=($hgC3obYU|IDhKXHjC)wG1B9%6yiqUF
z+ba6_WZ)Fsv=XB7SF6LaW>}L+=u>4#l5YH|B6FQq2+*W|_axY_>DevFUWW6t7_J*3
zP?!9GcQj&mt$utO`O-VNgYNC`^lw=c-cu8UQa1or(B|D;x#^iCeP{;qp`Ruxr>3**
z*!QLw^$ASZ_;v)un@B9=H(Zj#v-!y#<i+ebDXm*QBT%1|MCoMZ93E@J%r~h8O3t;v
zn>b9tQ~KF31w-pU0r{(R3t)S=HV}XBbHGCWD80gzRO+F3#M`Q-(dT%))}sX%T!boh
z-iX}z3)z?oHOVsval?Ex{XH^&5G<8t)9{C|)g4iM?A^xm?L)?V<6KQw|9^<6pEA<?
zOw(hKdpvBw0VemudP>P=sfE7Fqrpk>2^~Vwyd$q!mv_pab>0g~Tg<M&<9i%=6Fe{v
z*|ZPHx1nc+-e_%`*UV1%!|OvG8iFJ1KIgI-QNCwAM;}6pmpv?vWbmyiMm?|{?z|v^
zv-dF;GR#ji-KKijc0K5bhIcE<{{DtEv!_&HVq~=bE>}@5^`W}0W+}@@z^-O>!NFV)
z2n@!Ked+%Q-#-}}<}L!D+HpX92jed<K&8a_2&b%@UmwG+57B#&rQM>0gQiq$Xmahu
zsNGfjV2q~A%m$OyW>UVN7OfC~rc3Dv=EeOXJ0|?HASkoIE*K~r?|U#ReOMGkR)nQ*
zG-B9j!V?!QJHJKb?aGRe2j2Vgv%!^h)7(d&Z(PP$-gYRpQ!&#t*i;J|Lq$<as3Ex<
ze(T%~>w<vzl86<;z3@t$q|)U)6s)Uie!NUig6>xzeWzGmL6E@R!QzcD=sJJI_3aWQ
zt3xw<fx?X}h=Z&uh-}3I#|&2+p7<+Bcs6e(4-?`Tc-}qo7F(H~|23Ir#rRKwd@Z7s
zGDjW=f5Wv=SZq`ofiaea<aX%q4JyANL7ig|Q+DpOQ62no%-Y%l;*TMZ$Y?VM>>`i9
z9t&~G)?e%b(YPR?n4x{R0N<1hIIu5Ye-n>8dB}6y<(MB>LJhkU%A^??(J760dEkF!
zbsTgkA=aM?m?&2|G*FTXb4wL3MLL>=C;D>S;F_^s1dOs@cWjajC==9kZ80KoNNTBX
zq{lmeLmC?T9$g!>p1*^U$jo7rIhYzyTv12}MNbx8(K$Ko<lv+Tej+zgUeMFTcii7S
zv&Ft#`?sWCu;G|bC}53H7p^${Avj28@)(Kv-4Ew&RCd^iG2hRd?Ai^P^7*cW+L()D
zSJz~e1#=m~3YuTO>hc#al4QXZkQrrq5`ucI7+PE;t1K_WT_O6_j|Q@`(R)5!;>q=)
zltLH=FZFJ<yX*eg-!KTAADDC#0PdKHeeBneAEFE0-=R>f?<63=${31Xq^Fkc{iFLn
z%ysQSBH@(`K)!qqV+q2bQ6KhLi<`fqts7~CO`FSaBkJSVv-jw<DIcxqY2cCd8}g-H
z1udX&h|$h;HAT>Xqu7k-3#lR7xt4b>SZE#UP4lGc03){71FigmoDzQk?nrWkri`>l
zSe(&;@17qRCuN;%HKzp?aQeTduBpz!anlFo{<&WIC*u*LxVE35zx_4sl#ZiTA(z3!
zVF(VwH~uPP!IvHD4$RfI&AU3@(9Q6^e}MhFch<fqw~Qb=%NJ>T>~8{U2vZ|#Vy<0+
z>2C~!P7$N$KGZ*eT8eYq4$L0yE&mF|^k+YOI2m&VSHwnQFdwuQ2|LEX2Yh<+`0IVs
z8wz`GTtI1Z%H;bVY&1c}oV#Qi2$NBnHPJ1vll3dWswi)a=)mbE7ImhkVANfi4vcX=
zUaECfPpzx^rA`N4iXL4hOKwJ~CT$W}-jA{3>5&=OpOnkQ7DUrBa=)Y<a)Xn<YfiSu
z0Ggz|lT0?A9s~C8Yq0BM&+iH2oJH+WGWO;Dy(3;5Q<!v5{a&*qqvh|520=l~d5MLb
z%#=a94LgpgJ24aOZMn`Fx<_G6-R1X7dO>7Yp4}Yq(8i|P1!q$K2kywy_#)f6P;vHe
zB?`r0D54}?RQt8~G$cpb7eXiOfd5iC$LFAI4&9wB-oJ&KvlFyhjCc1IE5T|!@ipT9
ze(YRcBNi^FK_%>I*!)jnyM4xeb8`c5Yme>N^PLA%_?Q^MoXIJImAhrF^Zoe|Y<R^k
z!}>{(_Eo?9DrBNlRlt#M2L1I9ntX@{UqL;L$6=ouS7X;k6VXF(E<Et>=lT8{1j319
zuowQRTo(7Rv+r77)?g|+xM8EmCcfu!OuT~Vj)^qwc=~2}dD(7(YI*r0v+a@%r59`(
zl&MT1n>v|HOr#YG=jQf)I(4^6OaDW5Xn=J=q_}u!Sl#hn{e2<z^)JhA7pR@{u#lm8
z-0`&34W03g^sgO1mp)Sj{E?2JO*1qxvav)obpd&DQmMSA=nEh$5rgQO+(B~{PJvK^
z>t86ieZL%Ut$edMFtng)X5HH3-%pC>_M9O)%7DdIb6~{t;xXxA*Bx446c;E)pEAPV
zTe+|^5eXnN#?9^ss?1s1!b~P8GQ0E^NnRfviHlp)=5kov-%^ApS!Q2~gPRppsbg^C
z**$wTH6E<9u9)4K)BBatW_b2GFWHky=ED$vVH?t~%v|GU8zbC;bKlR;6qLc?{T5{L
zb%`+{?xN(^$JegK5@~!fDQLOX^Z-y|$7F`OQ-2ZwJKT><@m`+SG#`Ft?^kxeC%Cxs
z``jnPFFz%zYriE|O5FOG|GeF6ebI2y?UH^}XzujV`=U;wQ1r^>;u>TsX}_yp)2@w^
zg&YT7xY|16nHNY#GK!;KN=FoNv^bP5#T@zZ%(ZvL)zB}}k9OC|{wa~hjdkUEzgX;)
zk&`UZQ=Gm{CR{}9A3ig#I(yPIbU)CZr4qe$U&=3ARvh){dDnk!lf4;31}o_NqA7=m
z&uRy={|)R)fg$*d5rQxgZBF2p9_>G2rcnt0%b{IHUtBJ77Ip~mG5rj&9kv|vkfib)
z3ANJULMYBdZOA2qn7Z#3XLI=D<$uVRZ4!3FenqA-j=~|=@zLO=1LIDHL)M<_6r-;C
z>l8;X_^xaq`CFd5@uXX|J<?iWk?0@X%!khr-S2lp)!2yr(Sc3b`fF9m_u%CeF5+!&
zwYER3?Al?ku&ba<YS26}E<em7v9y^Fe(H+&x+w`P)&(e_X3LY<r85|y+lrOcKV<M&
zI4Nxz!${Q4WUwO#TBzK&&(*o7SBs_3=UTyvd_+aocOe)qp{R#|e{1s!3>>Z)VeOeL
zHsRYuz8maf*9rH)yss~A$M|cn?i-0Oru|AQlkVGph8D|ro}xPfhm4ki+j##8o9x4|
zD;+Lh1paamn@95?UyB9#<ylS$fBQouNSc)saC*ShD8~xz`Z3=j{(2XJ#J8IoeF+%<
z&^Ia6{wMlD`UxdI=EYSz;+>$xI8<H$mT`OHqw@WmaaBLRfxBo7tEU<>Ct%sNC<up(
zK5otew(CReaoAul>zurO|Gb$T^}(musNTm>VNz6>HO9DfrU+6ISlMwa#RiV9L35?D
zF|76zz_@XJs3Kk+xE}b0AB2A5obL?;cL)(rKm$z;)V5xVJ^PwKkStNJIciU|+0Gp4
zp&-~;#6^hO!ncQ07F&@e{7hx_@5sd)&FOJ#cWFaMeUIZaKSlWoE5MK8`Ym6#4qmOi
z2^r=@9+Kr|YI2%*>s2uO!QYrR0I5)gUEO%iI^t~_0l5WdZQb0H)t6@=SDs`pLxn35
zrZFcL)`Gh&X-7X9ocQ-*r~Y5Qq@1vNXc;I|AAoCLT%m5Eiid>xIX-}-Gl-qtd$h-M
zuFwl5VpkfDRkg`tZ6*O&fH4h`WKZcV01;r&;);^90-luX@Ag8a2bqe0zU^C^cx=D%
zd+bU2iTDndMT@NJ8UHNk>d6ZDDLD^xv(wWJ)9XHG?N--;b^o#lj9p)dnRhXoBmgMk
zu==kL_D`WpYAevT>h|aN`nSiHq{&wT!;miz(i}Xomo!St>nxwf;@g<t!MWwrk(+t0
zH}~XW3Cz7G2fzq14R=R+#@cXPy}MEODX{31SCXWVR#EIul{XO$4RGsCRln9_YZLc`
z+@WIEKn$p<$agGX15ec7`kM_$u7+UuD!$kWX@ZLHXe|V<LM|yxzJx22LYHp-5NK0x
zg}lPl?32|1jP2}efv2w&yJjjkO7Wrn!*W|$_LvX%{Tu`Kos1#cs4)LTN!}BguM(7@
z7pA@GF<45<fnxaaaOrhmhTbB5f8^)uUu!^;*#C*plL2T`NlucUCNb-n6SWEh?f{rO
z_r8;&R|R1ix(^3aRV<Ui))-qE<x)9o`;Ph2vyBv-O!w5KU_G>CHeN=&x<pjvP)2*@
z^}~{|4E12A6yet0cl+GpQ}g@&Rg)&qvc;KkpQ4tg79_|eZ(|S0<0&?m3PZbnbviFy
zm!$%q;Ctp?LejwJNIG)+ca*~$3d+<;KwTb()`~T7MNzNc;>ks@2HHWemmDMYKu3NH
zqCvfZ^PGdPwDq+2r2|H|yr5>GUC*$d&|}b;l+{ex_QY?Bl#%7nP})d7{5QJ=YwY2|
z&r<*u%g-5|F1CzkO)T>(O@>{lT3i;2e9Cm851Rea**yZx*G&l$lJfUW(Jy?<^3fpL
zJ$<<9<djjk)DNYWaz2uD)hSg*NXvr{0sBpG$L_y#=;@e(_I0-zHRuYv?`K5bZJrQE
zFMK_8+cwdt0m=DgQtH9NyEASkYkPuU9qjx$V(=&*(j*n}W&V+Qzs9E@!K|uIhwDl6
z_rnFT0#dZo!^_^>ppb|^=s5p-BeoHK=ks_N0~-i5NAzB|5Wp@PYU#>J7J?I9%Gavd
z8rs%7xp3$Eg*pYX@R~;6jRT|yUy6B1-e<00cXgi8{8&-AbM!!Pn@j-oLWV*2aps{G
z!c3a1P5LaZyB~jM7B_w*^Gs$U+2%@fPbbTo8XIY+uz$74F9?!{!G%QkSw8l-_^EUp
zY%As-Ev=ireNKoRD14AfzgzR!G!R-3ei{o}Uf%g4)cP80$OY|?iBoE3w>yx{)lB{P
znGG#mQ1O!NR$wG9w8uLsGdEHHS$5hrDEBFUpGKg^V}LVVO6pABP>!6WR%_Q%>IFFL
zvRI)GH26+wmAONy-41Hw%jDKRu)(YBNZU1ODjGzY3KJ2n?C>dwIox1wF64O^%TPtf
zL3aA^M|I7~@aUZ1!oXjhMCJMeNuRLm$zEFVt#O^508~)+>l+9}Bj5x5;vW0+(8Er(
z4)3*mk84XmUk&BWZ4wIm;9q^lvvp+!{grhHH-UV@FR^vJyk@J%5PD3BUH|T=36r$$
zaZrbT<Vml}&A)<k>pjMb!YZ=f`4lg;tA@|PsR6qY^xToAWllpfCRePAkDB&JmtIH>
z{8h(~8sRRzngc)Vjsv5sTiYa<psg~_YST)7u{yHF9Cwv(**>4CCZqhp`=c^))F(eD
zM51J$Y1Rl-TufN~isg#ns-Vx$<0KR4tDJrr@Q>(-B4@eqK4eRpk*YgmvjcgfJ&_RA
zDg=iI%B+zZ*E4wO`F_X!qD(&v^os281jO9q@-vR>&MN{aIVX&a3>@ZdMI%|fd6+H0
z41FU@FegQF%6O>29k;}em^WcJ<??IE5hyl-l2edjyFav}AD@#++WEnM$Ev=suhpB+
zXLi1CWd^H+kj7E_eMhv^3XvPUE5AX?5v|W8+G3Su>}%vl=(CR=37l#4LRt@49mWI`
z{ERG(p83PUxC{5U<Nex2qp%A`zl?Z{3|>jZ+a1Vc$AV~wybrx*bt6zi2TwZI{{XK~
z8V7+Ll)G{@QMOFH{~)jjqNG!FLZ~nAm>j(7CwFut<vTZ-ik-}1`ONkjFD>W6r9g=*
z@Vf_>5M#x!We^p7a5)ODk#$ZXm?_d9LZ_%utVcA|4>{i=Ojxm1VXZ-5E1I}^lBtU)
zQCfe+W;&s&8rGrwPY6qj89e<M`6cmvs{bWU&4F%2%WUTwdVnPN^2=cT!>srf@twsp
z5){;+EsSM<Ha?qEz~DH<Qtljh8k;LV(|PlR@R;DYHtBx<90S8>R%YZ=(lctb7&r^?
zq`nHsF8mOqJ!^PKJW5b_lej_o!MHB6bA>oL{1B^>KO2iBPgh~6d~tY1*4y*wE&$N+
z`V`Ry_h{B=g}v`U0<w`9jeMRn6wCFQex`xiDUaxF-yk<3rNF@yJq<T*s8Sz;F(i@m
zMzt|seiNQCgp$w(t4rz%+IYtImD$R3RfhvVO+Kjr=0!MyLxB;}S`#$tFE;E<yKibI
zjHBRkH#weB#BJVX#6+hOm8UmUnc5D&^}v8_{84;&-Rd^VOp)xopc#-A%JRF>To0=!
z<cie6%QG}LDE(O!yCK<SQS_(s&TqKTrPa4<njXKD&Ae8mlib{uZeO{;rsjv7mBQU@
zbN6i7N_sV2&Z=#!ech70J@Y$_z&-AUQvcu(1w&qx<eCwu9Aksjy=h7E(Cufz;o0<f
zixY=DESZ0&u>!2u`EXLcgKr5bvSynMdI_;M9}2s(jw>&D1fwgJC8!_0Eh=vC%zp}M
z5wi-bq98}B9e7IRro_DQiO!sozGJOfySNCrQIKvzI!f5yum-bBB2|aLu5|BP-z}`j
z?()kk9e<D?3B@U|r4X{heVQ04w!*_S`~v5N9Tap-u$Y;=^958Z9r45cCMR(=iuOOw
zD>Ll|h0FIB>lbz$;+3iK+;E<YZa8zAb6z67!&?_yYRz1_1^K+0he@H%7alvU5=@Vr
zPB(gRRmlKEmk^>(4ga!LG_Pa{XorWb=f@55l(w$mKFKt5W&7`>BAX{n`SA?-y6kak
z=3vq>0u?Pc;kS`pl>7c`iOVy;Z&SQFo0-}Khl6^CDd9>FT;JIw&5Os~qJPR{quI=+
z&xtT>E@WIke|j^E+z9JHMui+h*-!L~u~DR#ZjT!BVShmF^BExxUMAPdpF9@I_}$s&
z5~skTzH6vz*GMWrM;m2H`pFH8mz48dEt*KOH-G6nUny-H5FniIvu7f8{o+IVY3Muj
zH7UYzr)Nd}{Q6i_%X&UEk>ZS~7XRY}l~&zgyWLkt)aZviu}0YC@7TixN9Z3a4@$~O
zCmOYHJ<4o1vsLy2zUO9<P74oyOS%ouddB%*+nEW-FNev!4U;+sx_0A?WPbtkJy~p&
z!y9?jOOokB2d+|3k_@w6Jm?dyYF^Be^sY7z!pfd!^bIo*S3bgEryz#B@^aa90&o1`
zDi1;+%Dm*+8?T4Z3Dl=)EVd+q=m={tZAe2mvyDp!s=JkKIQvPpJ9>KFp3L>o5mLt0
z$%{3+R=v@+i2JV>fQ^D`eKS1!euuneK^!6}FP2kM#3A}c|5xUBUi3$%rMH3!kt#p9
zsf{1$--$Y-q<(*+%=|dL_-I-j@uFMi);@eE8fl5TcJ0Eco?7B=PY4nh(&~&)!|&87
zK0i(slpK}L{%V&(peA`v?rr2gm{se|+p0Nx%Ui9OUFQHmj9vLbZqafCs-P}mOU<tG
z8jp?9e4nhl5J@EtH6A;;nJ8xbmQHT{n%^LN*lMu6Y}(0$#}O?gM5}_U*lJYg;#y^%
zWvmmk2&@gKIV$aU&*8Ut<4Epydont8aKR})LTh@RE#@R#&@nR~M8#~!uS`N|40@;3
zcI8MpeBfV8C^kIJK3`?~4&07)br#bkO2{t!R43jjt2}ZVxvg4ql1R)HVRjh`X}a^)
zv_t&*E~nAI%mY7+aI;Utag=Vj^i_oyVf($yD9Np3=84p^rr7}xM|ZR&3TpHQu{4K1
zSKoK@6lwDv0D@*R$B)l6kFG5781MepCH+gHp|nKzYW4(ouKEFL$IQeb)~(*GBv12D
zJiTjT*}DUW;~!(Y{scvM5ULT?Qmki7yE_qA%4Ww);Gh!_37Wsq($%-3<8PoXEWwOe
z!>M4>uVr4GMKs=pKDqQ&M^pqQ;pUwGIB{!dsA-i3t>xX%7FV00->?^N@Cz(_E96W-
zLS#zXxWalI=(pZ3x?hZz%3T@$_V!ct=Zy`x1>Plb?4;S=T0&giNIHO2l2Z!(cM{Ap
z+}b9LV3E!UO-N)W=DaPU?cXVhP+8X7wivbkAja)+;4GF7XVv#@U6b@;H++$_FB6aR
z<Owy1Wo>X2t3-38M#NK%wiwZj3Wv94=6XLLl3MXK!l{O43Yk8=;~F&V5-nG;@%n4#
z;a@8{%l_@mNio)TlcusZnRt7+7DYF?veL>3M=B=MaQy)nqXpI$VrH0sBOzn`8%8p#
zj(Pu{0m|=(P!|Es@jjk-2uG6L<SpH@*PW|qTW8DdYDd|})$1xTZeEYCQTV#7yk%aq
zE4HS0p7a;nhC3=8FKtlJqu!hw`&dzO>d%E=ry9+f>~)Y!1?W5<Q)oAiyYtEPg)NUy
zG+CX#u3-&4R5S@&4d+@ZIDFcucKkE;c&AX$s?keBqA~*V_2dWv<`i5=A7AH7&!gY>
zCM6ZJPTI}V)wpnLeG8U^G`H&A=cRuY&<op_BdfL{9NS=CYR!$?u=%j~ySY0`oTf&?
zRaDE(J;cZjHX~_+KbY{0OU7MX<-5<BcOOsup1whgq0W#dg;kwEnI9~bdaf0k+T8fE
zy(<o(1{R9G1KZPKl3%$+iq=FROx5+6{YH3j?`wo8LiJ~=%4`k2+QsF)QQA_KUg-ae
zh9`MHs+hnobxfb(I3d|80mqf4DCxIr6z)(p$thEOotQ`$vu!*x+PWAgUC4p<9jt$w
zrf5cr{F-UYOhjs5f#pw1aB6RZ>(nXAAK6(S@ADP6h$0T~;EH=rn#xcsG@Xw`OAvJA
zEoj_!{M@BKFRmV3o*f*VA8tA$Ph|iLP+={&b%k%)p{mfq`B3h2e&Gr8iyE!?b~QV$
zEGx0hj3xQ@!vK&;f@J%vnir3ECZ3Ui-=6Qyv}pL`%!}6@qKh*dZaoiE+t*s?rA)VN
zHJo`KwtZ^3Aq4JAeo|nTiD!gy^JGr)L^Cs|XvArsONBFv4<&nWEeQDM-&)g+KP>2j
zEBbhIRL}IYy9+J{Z|@SVQHR1kWh(O##*Sxs4`rNvcAErr4a$5K#{G4HOO_P0ew?j{
z=dH}t#^I*){g<lu?ny2rj62FJPy#35g4&9_Id#(spU_t0%_N+y+!e8|w6=1el#WY6
zwzpPb;<WGht-I~XYUVRs<{OKkAp5r`AL>ZbpXrf;kS*;N-{D@{$Xry98y<S!9%Z#?
z!a)MnDFcx<-d@3<#l#kYBG=2i^OR+&-JZQf4>5S^XS;Og^w`Jt3bT*Iaqv{_K{jNs
zZy?g#k%GPHX>nR(T9Z72rl(1`LvQ5LkfLZl9TG>T0(lb~PK1J9;OUg>kj-NxGg=*l
zw_fHX=&K#VWtEW}*p|5wolDUnpRP&^(a>dawJNHA#j{Wd#b}BWZ3&y&e2csVmsRO3
zfk{hEH*iyJYHdmT;JKcx_;!tlG)14yj6=n8L}`LX$>1Gx`6SzFx+GW6-k2EcaEpR^
z(S~_exSn(lypd@&8xiMF-9*Htw2M~vBZGE3@}K4SFt=*$JY`f$8Um6whPZL-)M`^0
zE=JIBpO%E-Be=C#U{yCn?#mbGV1M9$5D}jhTC;S%50yGE%;e3RNi~}&==HCJb!x1H
zzuE9bmQM<r_$`*`scWZpXA&KMhB3d`{t}@)wr+_?fBhJuX^5iA6W}&M^x$84bXBP>
zrOI@-$GgJ6z|sbHZ}vC4m8>M4K+t{LHtneyWTUo~&xX1WmoRnJC@@v5{}xY95<9Yi
z*%j9){c7V#E-jZ{XkK+VHbkMh&f=MnTAZ3oA|k!XZ_Ah!A?Hv%aFi;DTiO>RGL%LL
z?{l0j%dD0DJOEdCIi1`0x+C@1;gG7t{-vy6F^g6$cAxjAoh-AjJK{>rRAOG7bj80}
z_zbW3tTn=9jqhfjJMZbu{d9(S4CCvfvDfN1YDwvauHa)hZkbGE)@Tup^O{s4e~tPT
zU=xs}rb*g`>SpwRei?8))T?@9L0%nhR$joQP{S8>`{~Ya&;(~y2-mMz>HZ(P^P^PP
zp4d>6#AR|guH}dbNs^vsKiiu5uJpj#W3(~ld-~#W@9gfo(gIiSn%M*>y@@DoB4u24
zZ9cPF*_UdynRI|yFwyilo#*&PT#Ukfms{liofBr-52ejjd+tk*+|XW4iCgQpu4n94
z!jpok&YiH@0w?gg+QXUQ^ZLbS;F-&X4%fj`$3y{hsa)0L9QTs1rOZiOy0tf~AdSFW
zku!C*Q$!jsWU%!AvG(TiQ1<QLc*bBXWrk!+##Xk7?1W)L(n2U9L{y|iNQALXWnV%m
zvXf+qEM+OllD#aY$gYr3mMqWlQTOwFpY8X0{`mcMy{`M}zOFgX^Rpbs`*<%jj~2FN
zI3uS^p_{m8MYIp+uB^7U$sOBMqrcxXil*@$E_c=B;QqXA{%7+lftXc%lelcZtDPaF
z1b6oS+}%m73o`l<hJsHbzaN)nXQR&9#%9Lg{Php|6<vzmX3)lz|9&~M?J?K)(?$iI
zYHPa07>)V0v4{1=YA=6f(`#0j41Otv&g~R;<9qc}_~F<H`x96#R`rHeqH0ofy4{$O
zO4M8z!gas5KYD$(@8OG>$c*LzLX&>cmwh_91&yICymo_`C;9zSGFf*m&ZoQ=%MeOr
z$j}huZ61#eJt!)+3~0~Z?vU{6H2xpTvC})1uiAYWx67K_B?qQ|yocw0sJ60gEOstN
zoLL!R)2iXeF4PP~>}@CiPf+wswZb-P%AoWMJ;;wTVt>Ka!Rt5HEJw|Jx@ApR^l>2m
zR7T*=13NvHM}lsDp&?yRq7u){DfC*?B1Ol?{0o%4LpzqOqMs!9mpA{2alFxDfip(K
z81WB9jY%hW`v$LSBEE}|Tf?fxXzYo8XqfYkoiY}riqn#EF5vjQ?l=R^s#7!@(U$D>
zO{{W9V&Uph4b3mSW!uR3I0nxiJx*~BIp_Y~C(k<d@BV7O!4Q2ETj(El)2`u|#@fh)
zgU%=gdG2+%vIu4a!q{ECH^%P`7!O}u`yL6cQr8!c>`?q&j(c*)jC787O6D)u)6*O%
zSUM-(_geaf9xq(r;`~s=$6dBfb`af|?^Ee~0#k|~|A&!7!$_OcNGh8q%=&Vr`YJsx
zOBCw&HG5Nu^r?Xhy@j@W&G$S2`&*(T!&NGHP*GDrv|0fe_i4HSNGH6X-$zZcmc?t>
z$srqkgx_Vy`rQPqZtd7^Louh3KM8^q@8^*Vha~}a|Jl4y6k_!|QUT5zV|C1F4~c<#
z^|s^d<Hgs~&2#XP1My5|VG;m!mIKsjDm8NLvk@_}F*ygbg<E^$1?aH{iYP`R{P3DF
zv8Pob@nUBjW9KcR?4_BPi)SRZa>m}gqlGoR87`TISek%9NU#yeiIvEs7hFDt;n(Ri
z7;6ky*zG%H%yU$oh<&j+$4I2V@dA!2j?a4UrRy?|WC#|tD14GEHP0~`&$-SK?l%(<
zWSdQx)74y$*Kk+aUj;96*irR@6#P(om;p8J7iaW0SU0e5r4oel6k8z|)JdebRb#{Z
zJ0rRJVK$u5y%VZUp7;Yvi!f&A@&*uj{(Y`V^UbX1_|dOtye35?J_Or+BcQ2;S%j?q
z>nOqkaK5;ffmJYG1`?o}`eW0C*wZ{x1)o5M@FZ&hGTe526hcmo&Om}M-9@tF?Qp=^
z?3(xm$x){!oo-Qy%YOLT9h+-{=i+SuxbEK+6S)+q`3{e3F#aVRn)tOTJ%1+P9Og{C
zJa)wQYM`(nv3l{-H*5tATeg4Xt-U;*@c=K%ibgj9mHDT6u(|UNI3BM6cc3{VCa(_t
zK@p144J=^B4l}T4OV-nX<lmY=a05KK5}Bv^fHSua@n9p4g406N8>IVq4(dMB!`O~L
z6R^=*C4|lcjX58L`_5lq1px{_BN1kJRv}ma^_zFrMvBU40Jw*NvBel*4}B;|PiLGe
zvL6%h&EZbE7WWR<urb1Yh`gn`8gTd+rE4u_@W+^R{H`70?GW#+VZ3*GWquN9siO)5
z-mZ)ARgBNoZqWq}L}-dYOvs$G>Qq4hnhrdU^a9lOGY}gGQKH-*-q8{0@7CRc9IC4S
z7};KO-@u|EK@=b2wVnr<p)!FK>xBS(3m|#+BOQ+q^^4D<WxSZsc|gJ4k;>@HflcFf
zyLR}SSmH6p<XkGT+#f%?oraF6CLj8q4ThGNVfl=1wG=!STP@r<^~K=y`goXFIu-7N
zjgT4yrqIZ`Tf*4pD>5BtlhPX0z$LkL9vYJf*?E;(IgxrNFcSVON3dq*_zAs-U<~xW
z<FErvK|FwOUHF8?XJtW#4aN<540f|q0I8d1PRE`&T6pF_?Mr|o-aoa{SOk=-5?l-%
zgeiVI1gW<7<Ss<B6Y9!C2jC<S%8@0IB9EgnJxK0dLB~*T4vtx_GlaTpFu|XK;2O>E
z`-R0^E|l$k-rtFwQ0~seMI5jG0WaGP2IFVdvvk!%7B1d%M2=8VZDKwC`^w&%s11ki
zHwfZ&F01Xes(Od0g|Sa_8OsML%%M@Aor+tE&PZjyIR$Ej=xmNRZ>?Y1lwZ)fD~^0c
zV;f=p<cn6&l`4a!^`%<mq{qy25<s%Su0}XiJgI)&@~mM)h5B=x5n(Y~K8*M>?kma^
z#hwkU0gsl8KD@{t7y;5sO`q|8aPe)Sp*(;W{d}{vg$3wXx$u+Y7P^oF&`WK4(&6f`
zy7V(#T?3YMX`HK2D_;Y+)`Tty1W>0Qps<Ca5g=sQhS&*GCK|P^h)s2dreUFvK*(}Y
zX5VCU7r4qcPYJB6!gTUeL8Lf9?9YWB@0lQM_b!D2ukHp=hk1+%D26Y)lW{0yU3&q4
z_9%{5fI3Wnz>iW`rYo!Ag(cno1s;|9bZ!(YPjGq2+iuyJk<Lvrq%8EYL`o1AfB|J9
zpWpPRK#49a&D)h~Fn>sdV&C>~n#gB;l;Aw~J|I+Ft|b@d`tM(Js)qB#q%+PGHA|Vb
z9TOyA4|#ya+s9YM@QKEQr1ywziBfb^F$5q8s;-K$92+&0!|OviHf*VPSEU7lyXAs3
zS@2*Md0R-SJJ&?QF|P&0IWI3z_YyEP7^n9l^d2+i;}s}n5^|V}$!IuC(rn9$vu(cG
zk`9OAwKb%SMK)`dD6{_M($Jk57(bPS5^v6YEP0%fQj2}1lV8*%e)p&A#|v3z3fn?=
zkrq<i9mEOS-POssN<J`mxcn>h&<*AoM0&$4Oob}Jfb}y9Wq_^1u-K~l<J1#Uz0z2M
zZK%LOXe(s1ADnvbY>DJei{0jz`?mf<fefHIes@u^_ecX<?=c*w-p<)X4d1E{dv@-(
z4_!03t(fCp@L+Pj4{|p?!=dVw<GwG$nqG+MC>j9?Jq~KinZ}tj&)*7vPk9$8A<ub%
zr(Sym?#s_$88V6TU8TTT%l6WB&y+-T_Bgo)7}CXy+&%Tm2{~Y?@?5iYZIQ1ROjEQC
zVqE%a_deDx--6dvg_nNc3p?e{fR?)NJW|(go0D<3=wp<PdT_P%38Qr*5q%x}WbnI~
zihJq0Utnh7oED}~^!||1<68denq8}xV2Jt5{+vxI4Y(QqxwkPcae8q6;L&kL8i7Fx
zILf_CLX*8pKq<d{_=_|FBOm71{rH}aMGrfiMJzOD8D89B)4&CKcntBNxtJe2rnGAf
z(yN|v(P?>y^>#u!o`f4Jzlqod@6)7u$u8rof|T>J2l;9ZWFar*IWWl0r`7cs^=%E4
zXOYGrgx#oyD4xCM0mIu$C~e;Fvdgw<3cri20(UvXNh*PIM^ynXgJC6?m%5^&vwM2>
z6X0P#+P98^9e`G-V>bol0G;&X;-Q7Kf>bsa&<$Rdi>4gE4*Cr~k#@a8^t!^Vt##?<
z{oYwS_ce7R60-jA0az98wDvRD{Ynwh&USN}&d|0=<y?oa3a>6~%Vx+h99j;e1H`@U
z$)9&kVJHQfXc%@Sh#FAl?-USAk{+Y)puf;nddd7b;uH|-oYRnWOx^Y&01wGFnoTxR
z+@#P$N1A}n*w}g>1Sykz!8ByN!dDymN)nTcx>$&fI(^>~%xUh0bv3t}=wiP*CcS;#
zNzdzKT^8wn?1G{mKhB^*6DR0?<`F~HgBFE}BVIye+bXk@rw5y#iGM4Cb1jyI=82<7
zK&c=@y;X10D~(%tQ3j0KcadAv5!EdFkE644mM3t5LN)Zq6a?m^R$yZIk^V^FqRE&0
zVm(3xOr2Pn1Br+J)$R)lgsk-=daxYum$3`#e3$<$r}!Kt<JYVkavOz_*!bKftn~L-
zJ|QA&`6~^~2A&zGdNTV5!}U5BSi~=)xa`hcnHQYcR!+c^Liya)6A#4h5%Nqv($G%i
z#j(*p>UbXv=Q<YwOVC`?ZlG@vzbLLH#pgBgNT;IRAvli@yOv+Q?j@xAD)2#G<Zk>i
z9Jv)pJ&u9QWSSAC5l%C`_cFo(41%HwtbJl?3mrs>GAS|>x2)YBf8KQc<BRffxK#Tq
zyDb_ENYd&KoE`8P9n8$$Tabxw8>}ffj5&_2C<reblCb~Om=-n+NNnM{4d*d`fS4L|
zwuW|ZAj=5nD_WLH^T>Vt&D|kPfcA;$Jva8pCMmh3_U*6|e$ANIC@J(fY*`CS(mIl`
zulA@&@vVBto$U7aQ+(M6IAvpr;d4>ME%hF+PWq<@CdC3hf{0BerZDt~ikhs;EbJdK
zOI7*0tEu=trt5=1M%!V3O|~8h%sXtn2z<w-fGs~xJ9#FldgQ-hq4Lk(JQT#SqGj+v
z<--tX^oXD)OZXDr<*sq<<ymfp(}&ScW5Gr*d7tLKJMXaLpb!O@FmH5B`Kc!H^sbvr
zHqG7s=k<=62XhGu1x@i_eqdXzu8(psS7gCtIy~?JtC_a){)PajIY>-AjZc>34`dLN
zwSwhMq@2%VprIjP%!1VMB2WQEICiEto{jgjApQ|EAnqp?Hk?1ianFOK1elnCm>%~&
z92EKLQ%nBmS0Erd_vpBqeplgefb2OEx9T=M5lX`0-4+5AuH-^O(+84Y<Z|FX-=;4z
zzBr-$m#>&4j4i8S7-niD#VH8CL|t}{nbC{hP(hl7)`@XhCwRNouuM}YsV->w)HO1G
zT<dBE)45;gW$Odv>i8>jW({%AfHL~>Hjc6?tcnw8W|oaIqS%x&Qi<Mhsd?|={|W7h
zKYO%3O@(*fW7j}UFxCigl%0O4{ylq9kwBV@zbY76_e-CAT=~G!o*kG(!URkZhxwAi
zq9mR?i+;&If3#({ZtjEGhXw|uXbUWnIj3LCTg0-LjB=LDSG5rJVwIpbG=E|B$yg6d
zt_WqqMe8^1tD_X_=ZqPJo)fqcBsp>lOYz2V?})8W-Lo|vGx{Nzh5KxXc2(pr?L)FS
zemyFZ%yuRTm=lGdxRl-hs&!95u#P95p)XL@wx25?bhmr!Y)$M@Y}6wxca8jH5G`Q=
zt{twg^8j#|SW+I!Bqi%dr&}e<S$JVxh9Q5p8-j<$wJ2<tW4EUDfB8$8pqm#^6=_gG
zN~SOpJ^i_X8fHvLXbSOwS{#n)HHRSeC+$w6kDlUNciiX(?uGS!D%aRnm%zwJbpd<Q
z4@;5pOLU_jF@mBEvQ2?-h*fde(a@3c^DOg)T2$idy?3J2LksCY2<lugkytGlW`eI!
zrL_4ms1;y)l9)U$R3$xI^*x?-Ohp%N{!YP^YiZAx>d!4?-^BkaD|=_|RnP3di41!#
zXFmh>n<lQHqa0KuCUKWL2?FYG$BfU)vLPO1geF_^vEj?|)%M5t5@K_mOFOT?El?R}
z7(gbTz)<|7E%4h=gasxbZ>QH5l3-M(evVv&psbAhr%Q@L`ot}wgtsM>&MU?CEldYA
z#5Zds?s9CDW-$FVqei^`9!i2V7JJ|#Dn^=fABFOoDXn?~zxlKdi(M0n6YdkTL2lyT
zS?gYih3G~QS1|rt>*xl%B*Z5AUjOPG*EZcIOfb`rG+OxdD4EUf^=Zc^=X~6V!gy**
zU$Fyk`SxH5SE#~7UF#D<Ta@u?v6ljHucRy%p$@%@H^ggCH02A2qa3DBP+1mvxoNJZ
zvy<#Jf^6_Q*p9<u@7{9Vk-cp9$nd6=N$z(7`S<oj$KE&tnN`l{t-iutd)Wzr{O5|d
z_AngVRkozT|I^2R(X!fmUym}nE`ZL)oAE{wc=x{ovfsWr_oZ|H8Juv(1{wRNSvTVe
zl($-bT#Tawy`npJ*Hu#slfdf^V*np4K`63ryer}UJJnxzU;tM9bNDT9t=1SWCnt7q
zS{4h*3l}xts@jI^e{{aF0N1q0wxMe9$3nBfA22nT;+G9ZW4>3d5x}wFx4k-_c2}x$
zcbOEoZ0va<lO0-H9`O_G7)Q2>yu#yn8<_RPjfw?qiBF3fA=|ef)UA$zOwy*S2GT-B
zw<eg$3dg`6HOxXlh|VYrEC}zMQ}P$!*0kW1WfBZi*M=@yNoFHuflK3rN|@6g*>c&h
z`q!Ts3*y#*;OZ_kO*~E<YNs^{<oODJvi2SHZ%$0t=?&jMziid)UJk07Cp0z*FdINZ
zGg129-6wSLW>$UPEWZ9!a+`5ZI4+fytta4FdJ?~?eY<~o^j;1f7#D1<(q2|yu^pB8
zyUwL&RF0u_E&%J?t+)Qd$mTj%YO$=SXZX9$mzJ{RHrIJ5_x^eXPWHB@-F90Eys4)n
zP-*PB`My2P-)T&{0=4#pMukqScfgeY6#>;CBlp-pg4%oXLHf5{_m(_0)0=K?7;s>B
z#Hflg!5iAWwtx_W<!w3h>#Q^@W9>IY%~^B4`OR%Eg>SPT#$hSESHqlEytvDi|0&l8
z*3@0&!#guhz-gAcaSM05df*~G{1IUW{|D|L8x?R2FXM_^lEn8p?Xy2ofPUQ)oSfrb
zOQ)NUwMf7LJR-omJLdSzH0tgU<LDe8sQDUX)f@DiPKy3HXn%aS8AzBz-NZ}WX8QYz
zPEI_DjgSNjJ3aoUg{azRz|Aa2O`m@4!KiQh2H@^aV>w~?Hu79>wIN#p{_hIbD(ERk
zE0`Xw;97a-G@Z>Ad}+N?aTkvKQ$w7O2#sLq+w|NHBl7Ws35GGSmWedD<B370SoYlp
zR5|makn<gQ<Jwzp(c7TMu2IFNswI16J=;FV<bPX`@<OXLIQ9_>zmGqYz_W{bAL=Z|
zpsaNTKi_9Jb%Llrm|y%@;h?;1Ma*0|7=(y!&3ECI&*di{$lpD7O!^&yzn~LVe%eeo
z8s0}Hw>3KXCx8c@nXi5muIoh1%0{fnZuXsDFWUv47|H-9V)v)W=$ElZQe<Rkcwgu~
zWkRHeUc3pr$0T=NW8WUyonKGsYreIFt@)YKX$5aRov6t_iTtNe(~&u3spm&)4xHKK
z{}HlhmgOE6M|aEL0t(zcvUv|1MwvMH2lJ!yrn2S}Z5ZBU3t*o)zsI*fwA#J-lY6@|
zl2A-4CvWK~GTz_4frwGExeEEEWnf0Y)3{~Q6;O^!pbUT6mc=;&ZP>K)%U`D735+t=
z5cUMt)GAp<wV*eT|K)8E=Z8yu#}e&kM0~f=uvD*LH*5uj$6$_)6LEg7oi|K&e9}0q
ze5Ui#8GR@DQgp_ooWh|<uNk<U9(yTJRgm45wHaB>Tn|bc>K@Ffm&WZqr#n^HF@ls4
zPOX{~SZ}LApPsf8#rk^%BG1F42!C8PiHZ2eU>MOHDBdTV^}zielDdbwajUpxn*%dl
zvN@<aCW`htW|DL~dARbL`QEQy|KS3BAA`DY6ggM~GG`#84lh-yiT#W}wUi6cZ47*z
z#wXBcy1#=g__Mapz!1EM(pBtA2_+VJaozb|4W1j}Y}F8n8U$f<iNBmyp@?^K3Vcun
zoufr4?jpp@szRXAXE@PM%U>#bowK$9T-VBTcg^bE{}^F{jY{m;QF7g0h;J`-^aL4T
z2@zlS1|XLokUVd<iM?DZ?W-SqwIoPcXSh_cfmD2B602<vy=o>C8cpNSCKdX$Zn&_+
z)Sx(F0oEk(7Ve7gS8f}yTHWM=cl+jj&(Z3ZHhbgp<yj>8zGJHvQ8OiSQRtB<)@Z=x
z?ou`9D__bSJq59}=Y;+)meJ=aa%kHCRg!n^mI2N0ea3UYIEyS5`mbpB04mgc?~xrR
zwi$YO`><$%!xKLn2L98kAqx%&B-jy*Kl|JX>=}?J&1Z1Hm_{0U#p`o+%{7mn@p7w%
z)+X3>dIJ>b+?u4ULnLM56As0}xCU^nZ_uBsInMP%^eyjw%PJdglAU-*K=d$|bWA@S
z3Od#(GcS`C>W*<9JUX6BY<2gII!+GbXxv8QPM&_+0I5*rG_>?o+=_1v{kpEsj+-Pg
zrFD1cF&)+f5eLIvt<2TCLVD>16BRcH;P(+kYZ>eZ{}G#v&mc|vF|n95$^SB0Y`Vz-
z-k%*I!$BZ`Vqj|n`UK4v_=d$b(KE%dr9k)RCT-IF{W192&0`gl(vySECmg&w5sZTU
z>&^2i<JblS9Y4Ez0nx5~7699<09U$NStS%{c^6_Sg#uu29T3t!1D8$nLojFv^Qi*S
zas}dMQY1-}($q)D;9QSuT>+GaLPv;6sd_MobBuX{<$e!%Q?AV`Y6_YNz<ROIbWDJC
z!ryI04Hc$Jt+0SGy~|xXI>IwxFLQMq+|hB*G5<nu<lex7;sj-Jp$XrKAz!=eQ5Q<!
z();woF76<KN@^fdwY+4V3g7H7JSwroMLmFf=^dne(bl?ZVp0w2uRzUZf;DZk$w|vm
z6hGNCYY=%$lIhcNqjhdQTKMNh0+B!dL>=b<%m?-^^Z(c0?F&y+P*3E<aqrCui_i__
z`nt<|mZY1L@AgQTm-_%-B~m=r;r>1wN|Xd3rO*CAX)(RM9;sAvo1P&IdB?kPwFdSg
zt+IMV`3I}iuSYf37V_#_$k><gM5Z5uoOZgE!>BYYn^A}Y;wJ~;9d;&OTO1%5-Wj5w
z<czX_sj5Rk(U9Tis(@>-Gm&@R?q+NTbKrbJ8Z~7@fU@DwYIX*%34m##k&hu(1F;Ox
z-6pk0`kP$zp9-|t9(Cekx_lL0R2qX{whQTo3j4+FiEn2FHFfrJ=e4QLhsLiQ5`*``
zq)q&G!`S9zM_*hh9Mbxw`C~gA{~l}7-SrXWbFq@dE4zV^it&mQU2rf1GlspV%=E?F
zPI`-7>+0Q@e#sVmsA`ehz1FDpP99%iN*{b(o4^(w-b;2P%s!r?@!)RGz6j3fJOnpj
zF{=OtRbAB%OGo+zBZK~?CK49%N_CwdT<ezU44M)uI**JBkYM@vd?q_-LZ_55W3ucw
zax;PiiO>{yV_Z82g5OWJKhpkCk}z=b@slwym%mu)M<|LzN;uFCY;RTp#>|*rqH04P
zQAiZGTUNn!J7GcN{E4;8Al1{EYENba;|Zx4h)MVqUI_)_w31%gy=d=hY59!gd?8p&
zE_my;J+I?Bv)BY8!?*Zs6SMpSL}A9A@W#aPG!GXkEgBq32ae>><8iCH2PhY}I9X~z
zUXI@n=>_N}n7dU<L00rN{fe4^fw+n3DH}TGf_VLB5>>_xJ1+NUFGgg)&2Uukn6YRM
z=2mb@aQsyI<lGO<XjF3P7l6FM#~DVF#i0)o;HY0`s3H=FqYOUM#jleih(+g-yaedJ
zdz#Z9RUd=-faGw1BmtKWYyZPbco=6udY+c*;%!OWNL<I=r(h?`&)sI(4!i|AJwE-;
zpNlDXHL6|J>Ifn=Xn@eH1EJ}6;{<HgVQ8SpHVk;tlW#Z3IjZ`vp_xiBWMYk`hKkFk
z)!ZJ8g2i{;xMG7;Mw*MfK;IanU+=OCUt-!7^UV89(>1BC=Q0C74*#s`<2eN9_dU%)
zK;z`JHb9S)eXDtrM@HLk>vhEAcCwB_LXGRV^Iexl`=0IGf0j0dnsSx+)RD<w61TLj
z1tr4O{5b*QfGGd(imMAw>^3Iq*wlOiT&!~J)VnC>3{ItTgyYLn8io+}{TKALt!OF&
zV$o5g*dwjNgj43vukYi?Uia=9uM0hJhHu)$7|ugl?5QypGPB(_ZXF~+VMzw{ZCT=e
zhmT7+(K&Mc0OnmDHmVAHCtTz?v4?h<%=jDvS$T6}^|3Pckw3}yMi3hiicdAz)#C{-
z{E2isSc8S8T>Ow5S2<Jhy&sySF_6+!a1qF?0%J4;okL1RF%7-!unwDpmig1U16eU&
z1Jc*OfO$!gcRAB?RqR0<OEfupfge0m0*aiEKG4f|60EzJM_u`S77YDiVIsPV77-F^
zNmsWC402<ts@wnMCp(N8#H!4NkrO{vHa2a%*x%^8{r1JQ?6=Dr!>jTJZMY`i^!~PV
zw=Xb>jrntn<EqZJbxyl2GM^2&wdA*IUt8&Zre5(7{>>RAKejkKH=`kA^dI&Fjn<{E
z!<p*0v<{>DhzR?_&krVb6#Og;&4ASeR6hG%ckA;l>!iM${%kku`z3KigXv(2;rrp$
zWS4cG+qfqFO0BlgA>Y_p?N?s(9J$(3<GLM+@ju=2;J<cL@Cx*=6_AWs>s50oP*Vki
z#FcUPMZTYc^6~GJvcQwhZT)|IQgnSk(daNbymvIyATfGFQ#py-BIbl>P`vT2`x|eL
z45Y8bjqSxU9rUZZ5-V^3+<=()^v6#UF$<)020^<>XdyPA5<Mf-dyIeIu9d+%i>QlD
zisGGlg^97NZN4Y|Jz=3-s1U|wzPfCZS2sDbRjY;pi<<KuNPoz^>8w5>%(qIihBF;S
z1-XJX{erI8g?|tBMIa@V_`^UjC_4P#k>R!>HoiK_J4&|dxx2CagVylU_FqaH4w74k
zsha(((#|XO9eY53I7{PS+@}8N3Eaad&t5%Sh)+#>5W@Fd0IYuYi!u7&6X0>$n&e1t
zZJcND-~YgPqB46%xVMnu*l*{eIgK9%bNoT6A4R{pthb%wsVDfCEnD?3gryzRa}XG$
z#UMGiIjC9CxX}B!u8L=Hjitxu(TBe4*O4WYS7ze-f3n47yr>vq0lK^jWR~_4vhm~<
z#+T>ED}ns?38fBno;`W`f{5bfFE1K;HsG$@v~bHWAG?<~?gp0Ymmp40S<%r6l#nR7
zC-(!xyIt2u2#;y{vySn5z_8f#M))>B6}^2Q0bbBFOYv+KY^)-cheKeODR(|JGC-~?
zbye)Y5@3E~x@2Bq0wgMzrB{(??WgcpoN(X9&=4njoBu&`{yu5EqMo_HAS(u$@jeEz
zjVf62tQqRx^9-l~t@aE`dhz&l>G}1vnC<_bbOWEQBzPXg=rgw^@(VuWk^kTg=teBT
zsceV#Iutu@V9?fjeyj6CWD)Wp`N*6I<*u1M_jBu7%Ebd;Qv$Zh&N+n1ckh#Zf{eAm
zt2Y0)8Xw;G-?J^<(DZhQv>O2I5*Y-75n-<nI9PQT#GRgT+II@XI`gu5)Rc(=k-m-8
zG~+KqY3TEfJ_yS{lY^^k*|_~k+SUU8RNO_QmczK%tu!m8FJRor{nQu48l&kwQk7Ff
z+muJ}J@6}&TDxi+HLbzT#u=_>q_1mCD6kj%yX&rl=8&)ew@~?k-@UIdLCLk57coSs
z;YgIuf$H}g1Q5LmgS+bIJbZZPUx(u@-OW_P-^GKQ!MjWFIo#&~Tq>DTy7)Dk`*qoP
zk}Hxy4r}dEfnxlV%o_CLWiV{%t!I>_K&rjYA~+MDy5bB!^BM$}M5zLEddj}MN&Im_
ztD41sbTl)d9(DuULfp$8<n|_sr&B2HLJ=(aAdA}^32-XUBz!^P5Aw60z-xTkKhlyn
z(i9v&LOwx}zBeETiR@klGCI!=)`l;f8G+bPH%Q^W2B7KXGdkUeerly~NiSq175-R>
zyE?<8s){-EJIoKK-_p;xPA^b*=MVHYUzd0nA0b^!!_66GSTGS$YyIr74m_SW?!JG@
zonV#)n&rhH(D`+uLeLquN-2w}taa@=7%i6ksRv+q8mD%}>S#1tmS@Ma6>fW3Sd8sG
zFdPJ%{3+LpRAC*PyENLw$1SitV9N)row?8e<O`d1=QBK3DakE}!?a;ijW3A>OXW8-
z(TTH*wz=G5gpRIAX-pKA*m?LE$R|U4{BWmXVi#8MD2XRg4n<7ehZ!hA8puPG6R{1~
zfd=szPReuSc7$Wq79!OOm{Yb*!};?2`O~VECLDWbGRi$-KJUUuIfFdJy62SS8irLO
zjqKKeNc}?8MSU0_YF>8bQ2=r1^uz|d(o-szKvb%?S2jFh18E$vUGP69nfxFM;fgN6
z^n1S+_49CSF|7n{x0?;8@(7ofJv(dEAotzI*r&7;Y8!abdS)LnhtbNL8A+A(t>K!{
zK0+|xU!#dmzQJ_uv)Meag3|=pC{KANCd2INWx)ZkyV}eP{?h=9YyyIM&Mj_H5Qm=!
z66~wsKwT8Z6w3Uwxa+8D-vLP?r8lJjxFw%-0^pd?FqFD)w{hprqs^V<lK6ZfloK83
zXD1{CMbD^<Um6bBGO$;{$37Y?(A2YTp>lE5>*!I=_S!<7L69;Rieu~2grU9KJau2;
zsbnnbcV?h=Xj7ucwy1p18wNXNvtQO)W6an#_<xuk#QxlZeN;GfTY{&<^_|SyQ|_#_
zQnEgj*hy#{KNLABC08+l`$%aG7e?mQwgjgO@B65rM=H95$DE()--Q%GhW;a3*r-F>
z7ufEpT`#izg7OMYU&5qu3lWP~bQ(JinpAN<$d{dlEnoCT|Dz2p&&DNq`Ez4Tx|@qU
zu)$+ZU)dw<0iVC{A06o1!dRg6^VsviE&3DSAC}0*uq&i98QsTV0d>C;#B`GLAua6q
zgUD6-vtqotv+}1TuzW<5%)5K_?~0lgLBQT~1H&2B*nq*R`U9gNeLI%^csE+-v;W&V
zzm2;cAEEzgiJ0=k)Z2-}<4B*~ZxCggdmsKEH@|->h>+Cn%CK5j96y+xyaq*<6$hbi
z9HrpA%7Wq#SqjboK}l|oTBHmb#`d!+Mh~qgGvXCeMN6JzXyo*w_ciKZ4ZWm#1!%sK
z(+=hz>BhH|F+LpC!pdBBcNfCjJbqGo@0`H7E>#eCx@HEl3V%9tWBQzGo!Bki#*{+a
zx2KOoknipVAxC|qKHT=<psHBha=w+=cz$8A5FFLOHb<IT)2p$eL<O1S>w7J0LPO1G
zBEU5|{gSZtFd1I0ZKL}UPT7dwi7f<jj)^QKTJ+HG>at$d+_2PLoblHWw|NTS6*nY;
ztC&pR0@i)W8{h6Bp<a5sGSm2t&FgC*L>^Y>n3Vo|G>edlvADDGG9(c@@X|05u_xMy
zuI&IeHQ9<5a}u|9o#+qi@4mR8D8TsJ=$h^mmQwkTLkyavo{sxAEnj~31KRHjh7%3$
z!^GcY((tkAUWqq5cEb{Lx6m#nPQBk9b8FW@YzHx+_g0HV+TGwZX<G6&t=TY{Dxf{&
zc`<+`LkHY+Q;2&g^hT87BzTew<<^94OnPj`sn&}nbK2>~weOL0OC)UW<gCH}WhYm(
z9_H@Th*Y(sCv7uqy;Mt~3*X9!C)=$p%zXaz5)6aFLyyng0RXSZSC#@)6T1u}NZoK0
zF>RF0Fu*MeuC%zcWp3cj^bW-e|FN=|;`^Xu7;&fhMTWx*JoWwthqe{n<KfY?P6c(`
zL?s8m`5Vg<XAdsmTTBn^nQ@I>|GQU@D`61LE5X0af4e}#!3_!s=z4MCDZ2O(Lao>t
zQ}wll6W?90G>XJ?36O*F=PrRX)X_`@*=H;a2z(0m<E)i-zKCtZQ3_wH7^Je1@Fw@2
zD{_9FO<CQxwX*S@fA3i-q*pg}n;g})3P$$e)Lg{98S{XZeC`7;u*&&*1e-(N@yq&&
zM!dBUs1;4r4Q_`*(L96p0EL*lWc9x;xP#ar0X&}M<Tdz^YvH-APu3X^&DEGgUiI7#
zr*hh0U}<(&@`z=5Ta-*)Ceuv>Ds2W*p1Pnklha?>uq(BPua4C$JJx@2T>>N(`{!q!
z1b(vnXD_mbry1<q5Y|fBiE9rDGiX-Qd;i0vv|RKQAH(6pZ<Pc!uRL7zh5^1j2CmDJ
z#W2JxdLQ4p^!HWbZLzoEG^*A5L9553|I&qBP&HUG5r9_#1t0(3T9!!+P!Q;F@kq-L
zpoj}DniL%QWIwQrB={N1Y5lc|r@`0FE=X0JP?vwf-9cNw!{QHxS?UV(WLKd!ynjyI
z@N`I#iw%ZAZd9QXoy-2rR6hM>pAFTo>C48usnxW0oTN&`p9F;X^l#RdmjZD*;X5z%
z(yfIMvnGVd!&eTC3LOvSTro-=`LBzSNWla)oIhu|hxzW+%Q(`(m6a<4j}-aL)cRG+
z_zTP^p`F^0=h37R3hK~q@1nroB+YKg;iwP#cL}4WjrdWIddv+13D}}mX`6lNyiZl9
z^uxcE*i&kX=SuFg-~2QRer)_vIDaFdn#_F)v4t{b|K)(wBOg~-B8rKc>zIb6E8#R*
z5Yxr|&Iz4}&n-w~eGY#}oZtWKi}rXnk%3<lqy6&k?${CDxyY-6#0`l(D;@cNSHHUn
zp9uD<Hqm+gB#X~M?C6+~%BC)EkQd{$4?@u&R>HpWIdNp=Uvr>~tAlBg+DFiVClIu}
zwsSn*eesCpo_Cx&5xb_x+LAAejH95AMVNkjjVKsp#G<25y>YWHGKa3B#tHx;)wP59
zV{&&;oB1JWtimYz-}WPr4$WO@F@Zi-pPX_mS=||A(WWAr|K+hMw}|e1YJizGnTi!f
ztO2oV2B=UjNdJ8wY=q#+f8F+`+d3|RdE@y=dbHoW=V`2KPcOiBwCjQ?C5={}u=oC#
z7Hspz4u2^AU{epWA$UCWhW{)$(jfh5lWvN&VD_9qCrglKi{RiN?W7+oAM#4vN{rFF
z4EcY5mv72AmlXC){_Dcu=xUOW|CISa)k*>NkuP=LvYJxU(@2LP3!M4`3nFH&8bg!k
z-?dp>$fp!ZMQLJ(#(dwj$IDC{693y&_<N$CN>X!>db0V;wTPS`rX8i+4d=kf`}zG~
zb*cJ(l6H5eh!#Hba)W#mu=1ZKP3Ts-8}lAKAobA%f>Il{{_WjbT6q3`^-X%m4bt{l
zw@?HYXd+1hD~_fOKMO(b9&2j*9a7&%T^4TY|8m51ef+_p^|a&Y-`2XpefYJKAlyZF
z?pL`iFiuLMD`%FE<})+mLj&Uq=pg>k8f#1u_XcWmV5&6gmh+P>a=dX3icKLe^QV%a
zH<qUU#T{%faOt9_b?H(JR~PvES*nCmZutM$5q)#><j}z52_qqV=|S*UVSefX%%zXd
z3YFEMsxC~vvm390UBFCLxBOpPRDdG<e&8m~3|0oEC!?pVVTJd$Y{PIt0RGy@a5iF#
zG!gj>uK{K9DzO08)@`aJ!z|$`x4a(VR~a?_gN+t(&%a-|$9BRRp&QWi2l~y>MOk-}
zHdjua5K&O{rxbCEw|329h|<8X`Id)=I(4g}gdP7Vo?K4$RXEfmith3F=1>UcHdzD$
zL>ZXU>8ejd!k9aFMO5Dh<_sZJ@r!5LcQBaa)wuZrbwX%tFb=aGlWEVLs$gd00Yys%
zN>Y=*(3$?x)lG95FB-HFcU8fq>v`;{^G*V=lfmM>;6@M`7s~@*1R`VVInfs}rUy7%
z{B`)fgLDFam$pfyM4hwvTv%^nUg~y$5-zuHR$o&4TSNYuqX82C1A+UM#$B05K10|3
zRpH}#q2~ZKk_9)NYY-XG88W06p(3jU?MoT(ZO2n9!k^e+^}qUNBx&_8>=oI;V~$%2
zu3?hM8$@O?1Mj2NIjHdRIL;d%0q74J7z})gxGlz8#js4IzpqaMd*gvQ7iKQ()ofQ^
zkN})vEY<gvHY=g{L*uaTKUliF;n?AsW;_T<EtZff&qu}&e*r?X^ZM#C!aMFhn1`8G
z1soXzs8&$eS+`ZSEcyBv_j9Ov%8`rBG~A)i$N&o55U&Az@Gy!5Ei3Xb`3_{w`(PyT
z>H7GC&oH9s7k`|0c6b`PpKTAit$cm{tohMyyMhAN9lyM|end0hal~I!U_JirD3iK~
z^U4l2o2^8|%LRS&SD4%+b6t=$8NLmV>{61numgRJ_mc22F7CK&c?4&C=ilbtTceU<
z2RlTc{t10J*kVaz^v<aZZ!Q}32gtFQdJmbnqHcd0Ad?yJsc{3}q<w<f;799RM<vX_
zOjGu%15Bl6pp|;3hb6Jd97V1((2v^0pLOy|L12&xHwmmyiwzzjmx6WroQ27(<O&(v
z`Z6?*x^2MZ7KA+wecE@RgeN|E)EolFHyd%x9c7c|Qb}%>$Quei7VR&)Gba`)b{{Q^
zH=k$ZHcSMNtVt`S4lr5#c;!tIg^1ubil4XkjNwtB;g6CvK9+1AaFZv4di}Quejf)n
z0T+-~<fveQtVYBU14H@rOF9%qPv{&6u0yB8!bHbiQeY(M84N%w0d76*-T`I}V+~wP
zBLK;|d3?E^MbKr_AOKp_B!B<^uOIly1!;ULAyj^v0_9kS>gw!k&dMdY;q8)vrt=5c
z3ta`5pP0C70XVSmi<t5ip5O+0(~%^l4rD21DlVn3(+;i&$Q%AMqMIh{!fV^HhViL(
z18`w!vYA8s0AP6?wnM9AVs7$Es!#vIFFM6Z0qjK#z?ky>Mfo(C3eE=Bf7m7ytd`W2
zblX79t^c`}QgYmK<ltJmdh36_mJU~on<)W$Tl@*kGWuam&<`xD>2Dt%k2`}@LVPF-
z$V~31#R+a;*lqTd80f^;kO|b~K=zSVn3_y^H&wj^sw)~UxgoCz%Revz&NbpVn+^UG
zCfwwX$Sy<qTbfntCu3AH&phT@k*ybrw=l{Vo2bxAIZMD3j0-b=IiH0K&{I)NOc<S%
zc+5fF6iISB`0hB$e?l$jrXf+Y*FcpIl3WjltFN4YoV5$r;@Nkh9NOLBqwB~s_a~6>
zYf@>qpVM?tE26yyCH6D>NG$g@)v0|RfQTWh@`8)`hV|mVWstYg*Ggcq?p0WT^VC^>
znlZsHhIpI}d-V2`0cfTtfw=uJ??}vIp98<+L_eq*^Ww){d6WQ=S<HvhqdQ4Cd}#B&
zf73A*m+C6Aq!TMI_U;V!K@T__IP&nDyy~8KKd%et9kRXmfOeeBrx5klgtB2$@pbd+
zne^=VF%IWKfn*ZGeI9_q#TjC*W&mrziBLT4djWmSd&=6TW=Htxb4MR*Nbh|AQmG3#
zwr78W(Nx~{o#~y>QIDe$)iC5cX6T#9#)=+Nw65{fSM+cikB4pO`07pj$=YvM&4S}h
zo&LiGIM(w7{NO(4x-tLRUwyWpc{MRi?qAJdByikaaN_aPs;h|SF#RRn`40?pr$Fhz
zd0+b6ew^QDu(UE8x4I}e@eh>T_JKz5JoL{V(4#8~;$E_Xx&_@ir-ujq7R2KSwNJqu
zj5`E5R(*zH_tf3f7jxdIN5HHC{;o{E$D&LAd^=GhuvxM=6s6(t(T+j>lN+2sE;-iH
zePx@afK?+)n*Za2DQw<pnYU63<W)GPKvMClh~=5NY9+HNywg7-ca~{3p;&_}7M@>&
z5bpT<f&XARpvIQq45D?5=tcMQMa}%~HXS&}^5Wv)jY2j<ldNxpI3fv$ZigW41aoi<
zF?e}Ip|9>C^GO)3{doyzeRnnWh!h+fqIDZhGHGWg9$Q~imdtY8O+(}JJlgu4#@Hz+
z3HkR&?bs|n1di)(wk_95Dc@j2T!D$rWBK7VKz>7e`#>g>Crn?`g$NkU-}G8=Lzgwy
zm(t>9h^pto;K>zImONgZ`T=Y2?uSCyR-f*;!d10*KKS#47n<{ZK&kivlu38%BHCEX
z1o%u9mN7NEHqgLMh>SE2@C*~gN9QHq^}hGl?RlJE(S$J0qU)ZIHeiQ1g($v7)Q)=_
znGSn0*hv0`h<hRAkWI^vC(Vzyd01AsxghKN)fdnSeX?j%8fJM8kX9w&n|+g!HXxzg
z|G7{3BTxa~Y73j`yni|FF!lnClN|6zkx`P4hQREWDj-B=%ZvdmISsC_$gi(LkDH`s
z-0sQ!pd)H(XW-{Niw!W^Db!?;pY8K!-t{houPljHtX{3=Vahl#InVW+Q<pnl1?cHs
zA*!;!ylrL^m}6aD8B(3<7HQBn<7iNJ9}M7E^}Fx_`Ssr0I{+m|v#jk@*{#j0mQ(S@
z$>^$}C7oW6`wxEEg!PM+lUcWQPfm7anVU`^!Mu+)aWvq@QH9XrCx`Nm*)8r<2wZ_{
zJFj*XLX(^xfcB;GakoHs3E+*gd??^!5m4b%^GgMPbPIHRA%=M7S=Z6xL74-(Wj|zC
z6$F#6ykb<KE3yyxLl#*+QaENr-QYOP5EjGdzgZydoE__<I_P#s1B1U$<xR18+f`Uj
z|A7?N77kwE0F%(qx2v}f%Lc%P9Fjdb`42E(7Q)}crJAmYWhF)b6pxfZWe42=gwjp@
z25?zOoha5YMBGPTt6dOQqcJK&JcYR`Or+pGX4zxcz&glOFnkLShM?WI72_(mb^v7m
zdq2_23dP~H;vuhCRlf;2^I=@%Fu@6aN~!jLkW-9?2J}zARi#Je=!5nAMu^#ZVV=&J
z=<uZ5B9JVqj4~QG?0SHqX-#vTG#fpYX`PRyCEAurrQ^u>o}hhN{5;G;@9qx><Pf71
z_K$RdY{a2L%S~n+R{ck0WFG9BfF;#K<@&nhpGA^k{Rq;{-TQ5*{~D#8g>lKeLhzTp
z&;rE*g>2he6qVzSvUwi7JWISY4rW%xbK=%*%czy5Q|uFCcF|r|(9iZ$B;(I5K!q?2
z;;o`yHxNWN=(x`u1@ihM-nw1s$1m#Odqe_oR?T-?;x<lZxjC$CmL3_!p*s27{I$Gk
z1x33&?q5^nCDb{{`GpK-_KP@TzAL0}{Vqf{Ou#2?HIM0iQRCUq*)TPt5*tow;8*i3
zh<hf?JP0I>lD6`<KNTCCs6Ww=rQ7+YLkml#FTln4zJ8(JeKjxd+`yPIHsNwx=hqjn
z3#@ZaGpXbr$8KH*LElcWkI$>UD&}n~@UFi8<&i~Foz>r-?quyn-!;Q~Z+PLj8M;)h
zbioSRx}-4)%YSX123C+j!n4OVHV9+Oh<*)RkKWVydt)&n4_dGv8$Ob7>#i()${z@%
zDQd7VF)~;aTgc99#OPmVE$MCu{CJCaHlkgs=91<!P5Q`#TCDsO$4D|>^941HIi22z
z2QPb?nAIRESg;85cH=vebqsF_)R7?mA^&R9ZF~yLn122CWbJQ}VdQECw@1=!T&a61
zG!<l6TLYX(9@shB&p1(82ffU1?jZ~$9ZJH&vcL_TP5gmG6+<df_65-o@1%L@M*W*o
zyDOxRGE%mWgj|7vrl_h_%2vmxWq%x>a4t(##ZWe=MlS)DT%^9;9RZ6CunH`7fs;`E
z^<{wtV#<*4-)kbt$?`(Qjv;VGNv!&WUk2=v%BJTVc<sTQLEa^3N%k8}K%O-&DDeil
z_g-B^T^aZT-DA1cTJiQJi`K&uMjH=}KvvtrU0URyu91x~Ev?{Muy(MaVB}%r%YbV!
zHvoG%KM<?6D*che&G-?HjK<MSAF?uAH-LyY)fc<5{P6Iq&R?*EBk%X8J&szcD|vI_
zVq#L57eWj_2Hx-~m!}z0N-Dn89wKLPEmDMs>XS;=MLx{kdyda`!oHe`r(-A8=#(n}
z6jVU55ZFRL$rfwNoD%em*sE)Q7*qj)n3kK3sU$Gfc?#0v-#0Y1+WGf@7Gx)Pg(j2c
zw@7(R?W+yDKQG|+FqI9L+<vA(OeX%JzgBzLpg{5ouWXe@V;(7`JtZu=`c44e{cK9i
z{a#yw%xkyQosjXXp<M(PQO>^=E`3bcMY6nJAGi<0ImY5&S5kZkZ->2ng%uMcr<=L`
z%yvPlpRl|3QgV%?-HzHmI5FFr)(K@;m<<UIPh_Ws7r8c&1$kMb?{Km>ZEpZ6%z09m
z1u*AB9R*{TUf)dwW1|u~<2O&XRsV$|J};Ui$8Ym@sv_BoIqt_CN~&@%EVWy4&SEFQ
zz*UaIkD>lb{Gst(qzKJh<VE)~^&Jg1R1l=jzwQ<;x<$Wj=c$rzY&MA{N`&baxzsi{
zlBQWrV7c+`h}E^xWLPWRN5<|%`qWj335r%!U%YfX+@w4_6hR7cj=L(H&k6m7%Aw``
zl$G{wo5tDVU2R9BhXgc(SY+Rdyde>14S%()N<V~3b^BZWpS%aZTdg|n4uehD0A^W#
z&Vk|N<v8m_Ce_Dc$pMiW+E>K!IPM0ltR_(b7ufM5L~@5L<@}4ozC74Dj4Z}97wkWx
zRk4#3(t(;-!W1j!7G5&+7qnp@fc)fK`6NmOzc)5)c>g**fxP=@`~LF{kpkV%{r!0D
zXabKBfe}_3B`3!j9O?f)_b5V%)p0McHSnbN?^^lku}=S0>$==7CsAI8D9TOvN2+fG
zPEc;D$jR-#l5m7V6nZIABkg$}GXQSoVr&HL(WE<AGQReB)7(#u<c{wXfHmX^Qf8xw
z%%Vh!XCwBQ>=V3q$lRq~Sx!KgrAx_0@Eb2zzx>4}c$j$fUm4uk9hd1SP*wXeda|-*
zMf)zRSt!ozX5`*CFc_=Dm+qEPPt59<#`CllNBR8<%sb+RHDu!O-#&CxSBKs41USSz
zvEqIk`HTBp*zU@>YcVXkWb*NufpFMti;pKmz44pq&uCgeD*^N2-&jMCk~@DJNGV)5
zjh977)wT5#7(N(Y)i8HR7b0qGU1YS0*;qW-I~wtz74W*6W7wi6j&u&#!uw#jBgr9q
zMKoSpgHOMpLns?^Wp%!^TLtq>V#|r4uc*ZEJ&&oKPOGfk0h-v3$Nqym&152lV~6}T
zVXRE_?<KUB+vVXy15*4nNyvWUd)sU0s|$fU1to$FcCIMzV`$*O31h<G^bnbyxItU4
z>6>hPOCL)Z37D-RVwZbMw#jJ6xVYIr$NRVw^F{aTmTpxY7kDjN{x(TDWS8DMWg=z5
zuy$wS;R^UJ?vzM>j`SA<l@lve$qXkpyar5y6E*DS{J4Jy$EF$Z(iqmalCdP`jy9^M
z)LW}-A56gF^GxY5pT+FfvBWR)qA%qv6a+;lIO~$YNLp~-H>qxsfiUpvPRtXDBRyK)
zLg0OS!v$vR#qu$>Wzq%ioB_DAX<BeX2Gc9-ZSM&%I_gC63r%IQfQiu)4ScepY<S82
z8;9G4sF}0g^)G|M=qGsHH=vqr(!my%7TW7CV0Jq2Q!8}H2c%k0>3R76t91yZ^%dJA
z7fe|+a>XpKJs1%rYJ70LBKE@SrU{d=d?qx0qvVuyGM-)Nj|0^2rMCC{`1<7VJTNrk
zCvNR!>;k>Q_s@~1rNSO6+Lh08Z!`>+L4$qit^C&K0us1b5|$J`t4iMblD{|SNP$#|
z+)f%L_jB4>9arw>3F_xXz0|tPZpXxZl%`MfN>peP6qs$eOz1Q^KuPWV+gF?r*b3U?
z0?8eEndoyxT_QIU^)ev!jVCAIOnRtxYvdd3O}q$>OAFhP;0;KQPI||k9O!TE5J(G0
zQbP&nsGTA>+2}aBQtZB36miylS<JjoxSk1^g+ij{WCbpfUgDSbh=*Em{1wI5NAO>E
z_XlZs?B!Ps6v;ge2iy$eptruc<ttr|&Rw1Z>9g^urWLIE{@-g_;Cxxhc1z5_afKg@
zd<(m)M*Y)0Y@fCQ+lbm?F<48QjkSS<#7UdR?m+GSo0$t@+=Y2I{UP|71NcGwfX!lr
zC>f7cPkfYDxEf=plY-x1&y)|)L^y4}{j+Y9=-|J6f!jF!d+{3{Rc~eye_B7xZ&hxm
zjZIATB4g!hVOJvbI!m`SYstseyUxbT_3e*TSY}%DC!WFOlCUznM&HkIU~XckL>GtC
zB3lA)+MOB|w_^k^?>#+T;=es*pOk{5<%<GIMbRn^>QcT{`E>lq_h!Xtr?t5@%k`5t
zd5yD3o4=^y`=qNtB<p`4o%0{d$#cE^RW~k(YCkN5nd`dp7<Yg5fa!nP<!29L4UdN8
z5%i}bOHSYjWStfi;ayqOXgznRA^mKKYNFnhT9(>rDXjsf`i3mdZQO_K+U?l9R_h9F
zP?F3O4Z=-!-JffuI*+xX^Yi*0eYyi&`|S-(o|d~Mf%5e*#6rH+_t*G2KijbZk~6t2
zn-$!vlekz6!pmhAI0(i}$8Pr|yqQh&Y&rF@o<OslLrpPSda|0kJ^P{o;K5C|G|iwm
z;q=-)67xBlX~!A|kNicuHQR|3#c*$r2Z@OEh(_qJ-Us7I<)<?G*E)qF)P_*RGxvc7
z@#yWrHE;pgC)!QE3})gj8EgH$`C~7(8dVsnVCJ?ila&dP8*%Jw=X=7@1$VGAH=GSq
z{_oTbB`LfVF2`+u9T9!!q5YW?xQ1h|qjo4P-*{Be%&+<YH6F>h*dW{-JJSUb?5b|f
z=3y~E@G9={Bk#1Umv}=G8#h*;8~q=U{aeBlk{bGyxM2<5%_sIO{4Y-ofZx^Br;g12
zI|;L!zxQgOc?c)G8y!OJ`{T+&#EoX|A|4F^1Yn*VW^bos_y0>z+x_a+S*1hEhGiYd
zS3*Mi*l0M+<V^YW;-cbAPdEAiQR-mv63}=p5q61m=P+qEdFCQ<j`;Vz7olHaI+K7v
z2;Yy_k*V+uw8QSm%>nVM9OpJI!tL*0LXjNP5LKMm!=-Wvheh)ZX`DY7;NJn%A1iMf
z-g9K-dZ!H;@H}ROcc6Ik|Mm{R8InOGKKV9IJSe&8slNZk(Qs}&*tC^rUc#Sxc0TIH
z*C5V*;3_#ys{^ehu4iPDQv-NL&!LQ}g!5!nKKLjbB9qf<5rY99B@R{Il##psCR=hK
zo9jgVRlzxpjg}o~JU#DQ-?=XwO$LWDFYojbWK?O6fYavY@+jw~#2+*#<?z^VOCZ7h
zGoaJqmP0TMaRJP$YLk5d{j<|^rOJ8)+o=AAp8Xm?#*a~!HQh#o3K;6x-)J~?5F2;)
zz+J02%!ekv{lrZ(`bE$=^pUIo%&0QRxV<;jEeEz-xzl1GScvypf)eZQkqRIza=Ewx
z@Kv@Dnu6$C$UPfz!rZ05CV}&L`Bnc9?{44rlcn5d%A#k-WE}jv(j=Z-lk-wQ1GI?$
z8L7PUKZcQ<iaxHz!QuI0*b&_i>?G6%#;r4s06yY@)cSPut!ncID2>xx^D?qiK^_N)
zcvv}h&oLBt_PLU98yxxH5eYzKw0F%1G7T${4rUrKN9Q|xORu1L^!!2WEnL&D%BMfB
z<WtvfynyXdDf}?;9)T=&OqPNOi;EB#UV)PIfbV(q(IU1X>4T|xi~Qv;{Rpi^NZ!6w
zho)FDon3SpUlCg$c^E6&*0TutA>uzm6?)}CPV!jzRlf2#6?Vu@ny#-}DM|mn`RFst
z&Zm0^^ePx)J=W){=Z5%4zionCqCM|rw1_AF4Vg~fm{__;(!6PWfDk)L1;n_Anu5A(
zWEqQSj5A;{>H>xr*AViB!Z`>ekcVCA$~vZq!F7%!6($6ZxB!ZmOt*+kr=P<Bq*8mE
za$TXh1vwIc`s(GCZR58Ss41(2!@m;~y|DhMqMtw)SR%=0F%U4~K6*6aDMRT$2y<#l
z))=5Z0&RdP_``$*Y*zs?)o1A29g`7+I0I(KuD~EVT(mW%Pge`Zox%xjk@#6!ngRYH
z@18bRiQOldmvv;PXPWayeXC9hT0|fuC23>--C!3oG+3|zh9}q$wVR=bCQIX*O6jz6
zb~W%)iC)@Bafj4Hhz9A-?>$?4mW;24;a{8kH=q=a1G2kI5pt_P{j>xl4Aor%F%z$t
zrMDBdpWU{6;cMHeL#NuVK1f>?KUo*;<DJ=gC1%gD(!66$6m@__%ziL}i%v-Zo@s^=
zq-4cMXrZfoEJ#6VJ)_!iD7$-f9@-<qd!<EP8pa-@boXH&kHU-!)mst~AKXmPu$A?H
z8@9Tteyiub>}GXyIph04b+tK|3Qdo5oV4hmx7qUr7MZi^bD*kxhgx$<FQB+9;wlNj
zz<&T1`;62OEq2#`N|3oCtcQReX4SAyMRBYD)fFrCdg}eE<#&eTvAbxN4VCHvsmpil
zNDY#wV-=zxOY({F8mU?E=5Jn6Pq)VgfjJ~IV)@S?6=3t;=SdItpRL0YNCIP#$-#ee
z3L1SMY<FL_jj!9kpFkRHe|F)7A~;=0jPGzimZGuq2gYt|;qUoBF!&e&kWE2?l_xeB
z5hVEzP(eqguoJ=UI@?G}z{kD@Cu{CD0Us%>n%2Mjr6Km#iGx*9N-;O&ZI+Wyjx}T(
zY*ql<_iq!T^p+pFjB{T$WKN3SirB~yGGSVBai`~?zx&RYf`EFSr>Qz|RQb)l+w2yh
zf-l@Ut<_A(*n=dgMcbxH-*U&tN4S=)GhNs3z>QM1`w~|W?xS7Ik$&)sna(d8Vp@dU
zAG7NzaLINeKurHNyb3hR86E)6zqJp41Mck0lNZX~?YNs9e4`59g9-lcd+54>-6R<R
zx~rfS;z760TDhnFSokh280%!Ig6WFW6Mp%Nv7Z$^b-~HEsXoe51?VX~U?{W*oYPa>
zhWXmNbI3U35ZL<P@SWpYH9|1($N#j{h%iW0G8y5$wZHr%``wM}=faEFNuZj0@ClS?
zP9A(Oxw7B-0F%jlM=QX<-;l$W#3#S@^tjBt)m=JRLjN$`Gzeo#T>*Yo`SkKg+Q~pp
z4X=ZdhhoJcFr?UWWYfXOKJi?xdTOI55Mrh(@LDfs4L5FpBa7kzfr$1X`yniz`@W+)
z5OP;^Q<`D<+ny5!vrLPDh)<{46n*YE?hP}Qgai1io{j)zm+z{J2YA#dV<>s!K-x`Q
zGstA+Wq@9)C<LiJnK26I+iTpty3{Plo0rP#HRIlqY91!2CJzsxBgOEb-<Ja*;3)D4
zcUr_g`~g;F_KWahKUTDj?GvX%gkux(@wSHtKBJrpV)z&D`6IkBwjZBgP9`_VkE_A3
zKbGOV%0XN#Gn^u1pmtcecU>!yFjGH%2K!7Qj&nowWCSXQ40ZhDvkZXr(?^GKvKvCY
zfNtw+=$LLA(Z^nbc%#zd`$f(TNM<2bwRHB=udvx7`a<pb9^<cEJk<G4&fN%xo&KG6
z8Q>1#6?o56I}7}l`rv+@b{He=#*a>Veoc1@q^5{|;ap3|Ugz|w>)gey=yUi^uudB%
z?8T~v$rty?nm2jEOA8tY*n=$BdnZv{xJm|UeX@I$sTNTPSP)PeC->+6hXo-iD7#G^
ze=UsawKIbNaW8A3Cqi(ky0j$~7skSldu@(Kd7TPKN6GDAXyj-MDH2)YkH-<zF3G3&
z&`GP0VIk0yiQVne`|PN+_)S~zUp0+b3XZaLeI!bE%DBE9>VcitUK5I9v!$g3v&+8l
zf7rNv<HM>hkO)IvV4^niG_W=F00t`C-hdQF&j{!W`pK_$`z~_8hRFJFoV|H8)PMg!
zT$*a^??K2qV~JFj78GK}*ds(rQi_ylu{Dx?Fm^&JlC6@Itz=L3B~g^EvW6sUDC_U}
z`h2eI{(bM?eV=ombN_Lj>wL};Gw=6nc|IT8b044~(=dRI?A@bf$?O;YpKHwd*?-+O
z`HU3hKN=ke#Kx}!AZa_Z0#Z)%Hdn||dk#gJb_Se6h!t%}$o+T?4Y_!i2x?&mg}Xp5
zkHYM|Nn&W#Fj8<OJxnH0kAH-;s!?RN+_lLSmaSkTweADN4??)u{itJynaDSqG&Qb~
zz#j3^kwhTZ&aY*zS$ugmqHd}1`}DaG$?Wj>|Ia!YJd^FRANBFJ)PIku*i-SGP_h2a
z?Tk++kYO-TIazBVFk^%O-L9<)3U6{l!~Aw0^+^I1aG&>M(M^x~m;g2K1}XQ??*VP~
z_Mkj-0Ycpc)g$vPl*zI=Bb|H2@?sYu-^lqN)hLc2uY(4cWo~`H{<d!~Nk8J+xToeA
z<m5dY$8>k*$6sVKI(Ts0zny0R5|*w5T;#M3iV~H4-)I1kzIZ>D5Tf{y8eoP^?fmyl
zZ_EFDXBf$(z83F%L187+G`CH4MGMy76+vj}`p_o)Z%daAf=we4FwPxc6T=)Ha|$W9
zS+989Gs<~0DK!C4VmKJ&2hfowh>jn6#*gP|SL$l?fsFbh^og?t)X37^9modIYN+m~
zF$Ih7((0k2br_c-n0h`^;6T$VTv3sx#<;teIKc35{-+*g!_9Yh6|Nw<cCLHA@98lq
z!F^{oiZgYca?I2EgxRtRL6X&Dmyhl@EJi^}vG82Rt0vFZSJ#2pZ`_gRBKLgJ_+I}p
z0`4TGu*E>Gd>BQsEkfmU%I5*}_3ynP8R?E)0R#nf-%tw2TY?Pa>p%Gve*E0262g6M
z`8Tn;M>^Q5_s77QoqNE9*jWr+z}K5ANjJxz9$$El#Qk8lc199St0$1paRp}hMSgSv
zrz{t_e-=_;ET0x3X2%1~@?fNhm0mYn-!*GDC-Q=S34BU|YoQ180HRVjdr|BgKpyN$
z5wPc-{>Ppt9;dd6cuv@~+z9v@35r^0TqhFAT%PO&qlzc_4$36`clkXtd&8(M3;iKj
zK_KHAi2+?tMR)xEc>?4YG3*%mOEdnr7$}=H?USwtAu^Sq4@Stc7;|7Xk6Vv`Np$lj
zNrg<l5g**wyhf<to&4OBcQ)YDviTGPgrYmxm`pkune~v%M81uNS2%JIHf^W1k+*Kw
z>j8J$vdVT%R6!C{Am|ceFMAegFbG^BS38{FMxqU<++*{?!M>AyZ`OS(GomoT2k@N~
z#8Zj>_s&D2=ERB58|wmW$3H#_i}$|_uHTrWfRA<SMAwIF27QEiM;CAj&<gx~W3rl4
zWYOaD^4wTrji>e+!(!wf95+o=?Sfh6jejo{9iSVoCCg0A)*IQgY$WwdF~QW5E<+FK
zs8H@oF^n8iaX-&JLFp=PV3j@40jdvsC7_s}bymPe={C3lqj-L~G}&Qs1A+GwPBkP$
zzf}xU|Msspe?B*Vq<-<Y!S;w-?jg&UM4<iU{ZIQFs~*pZ_@OEsWf&p?3TKVJs7P<D
zwS4&FFxcjw6={X>Ztr%6Hp1eoC%^asaYl9;w0B?1!8fm6iRi&d_AN<BdVFf=4|p&;
zVWL@8(|$5fhCT?a<?pbG;DzeOzZ4fN!*a^5;pu#$%6%S6fm4N!+x2mqhoS_P-Zv2S
zPY~$o!;p;Py=7BA|DSu63gnCOV)94sY)F(uWQfi>#E_o)Y{C_A^T(P_&=wrFuz6Sh
zqlOZ^EqH@+cVi7qdk;uA+gs|m{Z|VhxpU9IFWs*F&eS$9FX>mu*=*SRPc8xnS};JQ
zkNpY|)stcMJNM^|p_nO~S1heHQ%|sVz!F}G^1EY)>IBg%S%p_pvFCrh5+E@;3{e~`
z#h!2fH#>Vt_A9+mZvsW_w2lUzsqo$Cve|n1oxygFrXj3Mynm}r4TpidxoNc;JiC6r
zQZ<izoH#Cqa3?q+XSto=O|bmu1m1;tKd|1w^ml0sZj0C9Aa=nK;&S6kMqfS5wK_}w
zoV(lrNWk-3LTburf?H{;#+_*;_mQiRL3`YrSuCWlTd#F>lUU#~7v!CsTk_W6dI_!Z
z2UZDl2zdNQY0Y<VJTrawGhff`o#FQhJmH+o<efX4iWE9g*qLi!cv=+mJsD&?vfIDk
zv8#<i@^7<c%S~m<5BYd)f0z&I?BOS!==`^9Qmp%ZRL^K^h*`}8oHEg?;Xa;Q-(z5F
zTxj*wN3Rs$9J-Mqn4|?jHt4v4FA+aziB-m35f5YR7E0EWj1SgOSjmCt*q{o%GW(8<
zh{0>m&;2nG8rdm4gy6U@-f;2u@&50N*Uul~`yVOY-3z$$o`!GahA|r{+3zR19;~2|
zrhOhepX_HPzX8!#kFLmGzXm3;69K?ZPTbict$B8LG(5jHrZc!1kZW`O2cePqAT_x6
z$t|w{w+D{=4Q#`p`FQDQviHUxK6g>S{r=L-lcI3)ClL;Z*J?B2xx6{{x{d8q0@pr>
zP&T|C2oeORIXB@YNF*&olp9h|ykkV{E<#NRriz`6K4!}obKN&L&p%x9_oLtjkRI*6
z%)9-db4L7YvCDUS0BQ>Z|J(f*wP=Yg^V912KGIX5kvO&e%5T#Undq3`cBJzMUrm{I
z%(+?3<EMp3mp7Rt7|xzHFs;uB?*XgP<zeSM-1^yA?(RU<pIiDG&qd^1U(rb62Px9N
z|M5)sG?|Ez^?H@Zb_>VC6aQSQuVvUZeD&mWpZ}|(@e_bS8FRdG^~BuT$q}#z_PqI>
zza7&Q(kvFyug&9Dx|s3+;C;NHihFh`eA}$(2VkH1b$^GlG=V9e@gKcj$YDalLAyEj
zy#|dJ$!JK?4RFC)7>0TC+YTFF*oiR&7)e`2XvE)#Da9tb8E}=~QeZUv9=N&R`AMAM
zx<A)t+sDrD!T4`$I9&YDHWotZ7gd_xkX7?i2jDP&W$IMSg%pWn`cI2tC{(Kc1Bff=
zRV~_5bG*cOGMr7SsdjaJ;sJA!`T6DbtJ`ny)xNMqy7wf**!GTY<K+e`(lM>LV%cM5
z5_`4x#_nvFYd<C7GNN0%EAA<qNzjpbAN9rMGov?}x|b))TP;0v29~WXEZp8(woZFY
zmVYjIIOWo(WYdC0`rocKRGZ)o$|;)-4IbAt-d$M9el;5$&^(^cUdTmnNd4pgMY;IO
z2hYbEcT0A2G3gb&kzY!zy`e2p7u&C&CtkDEZ*1froc7{L(uS@25+~NYZdcRzGQPMv
za~y4bll{J|ZV%Is9nn9%io1M_bmcOC*zPFa4u<)G3<W8>owTv&(v+KT#qk67;D?wO
z-Aci1_F-49l>FGSeo*i@;2yci6|D41Ij>opPw!?xOVKz5dy>&jgxn{oXZJPv#rRD6
z?cydcwU6cN5+}ooiWcR<6uA==5*!3UNaWo5px;2)I-S;o`zBocO`Ps5&ejEG-!D8y
zL&I$rjlX+Q@lsVq*INiWLN)+D_^nsKl%>f_p%yJLlW^rh{IlIMxPes7H#8rVq>DdV
zuYyo9EmksKuWWt#j^v79GAp1p^Dp{6rlE*D1+iVKjxwp4)7MgofBL`IEZ?r?$))h1
zam0>rqby#|FM4Y6b#3}{)!bz;y)1w@xO0i%X8|YfgFTc$eLLp3rQJ^*b3Wa)#7nfS
zRgGQc?c07SuQRuf5F{M<iSLs*Q!&25$fY-^D{ifrQ2Md^hY<nuqj5=x=@=VQ+Ry+q
zK^Z#Zd^;NflwH=d$2x|E&Q-IRgdF>tl&fug?tIv>9x%uzNV$z%<@&uPu%!yni3TN_
z8I>4;8?3-WJl8*7<ks7M>}jE2138(u%6l+^{EdCKa>knY+w{dEP@9^n*#cAiH9XEZ
z0*g!op#wVPb>U*a!z<I8ixdif+7(rk#ysvaPs~Jj_nA|X#4-~?@YpWW59R%SZFDIX
z5i&g@ZN2R09}nV{7}l$B4=<L{kc3V2wgxWk>XRhr(9yNG$3g=*huOu}F`GWrFt=`d
z3GL333eSh|0B&%##0Nh~7o{t(&I%8ZB}rp%Z0fQ0v+sn4=Qyeslre5PcBE-^Ps{4=
z`IEOV><~Eh_uq$^Lr<CQwy-+IQXM&XNO69b8qW3!mw#IBg>G{mGxH~ShK3}w*%7>0
z{ia0Mq<41hp)IPn{XjwZm77+uQ74k<eRM4V#<<a(?Nz;}cMQH%YOLs{u~vN-@|?(X
zuiUux1UTk)HJUkvY1sbfS&Aj{n_KPjE9t5Otou1oeH9*2)8vbCW&1D<hnrFT-I~df
z``#;8Yw;-N{kie*u$L7N=?n)23oL47$ai=GBHPvR^w{Zlq_R_}o36Hprek)+4eFH@
z%24*s{X$bu)>fC7J;OG!3mvIUq+gSv{q7H;XTHqDYLpwIf)iL4Nn=Eu$)D{MOhkKF
zF*Rp+Z9q809-FFwa=%;c0lrvYM6k@4iF6T0B^JKjB3LwCTe>eS@n-P^=XLf|XNCQZ
z`QktdeiEh$ep$ZfaXf_6uSC^ga39OO#8ceu3qCf#mBOaNGkWaJRynF8)5a6WxB2<{
zPV9fZhhg~RA$Q)c2M;D8sDL5mcZii7FU0%TuPf<1Hxe>e91PMSoH_A-uDn>sq4f(6
zCQ&KQtv0+$^9}{-bgi<tSP!<=Kn^^c$)(Qps>N>QX12(q5RH!$a^Y*HI)<rL?fY{C
z47{0ax?=dlG&=tCUKRewdzG-ni``Qk<b0BpQ!*~2E^Jh!yp5o{%dbd|I{59)%N{MR
zI?3K>%uG*ZPtDpi9wu2|FqeI#zhL1s+V`%qIue&4BW8Ss$?un>7XQxSZi_2H4ssqi
zTb;(lg53)z++|48ETsvjwaDgfF!<f<wnkbYu`;ID28y}qH;o4}#F#Ga08g;)m*s>D
zy^Q59TfucD&8uZIL8B64qsH^9H%wY>+GKx4zm_)8T1ZIl7E!zT(xw^y<W44f!6amB
zJi0HJby{sXwCYiq3l=Thxc|0>hWmZbJ2j78S$Y5WIiOpH@f^(_kH=FriIc1`a3J14
zY4SV}i+19n=yg)HG#2=_o&2<mNer`Ry17v6;eNYasoTVPM5b9S{((L|v}(=H!o=A8
zff$h~UV=!<y#4!AA)ZI?-`|y^`=vyc%9J00*<@v<F*mG*YCjf)5!$928m$#PN6xVY
z8S|-F+QpkY0z&f{RMNWI`2Yp=qR;@+;9R`N+=z#)2Bitf@;*bXdMRXQk57En@p`!q
z5F2&xVRK7m*ejO?o=eQ>=k4_RR`bHu{m~Z>2#m1=3gkI7bdSEQz4m+oK*KV?5l(O#
zrFJ~grxMw561Yb;ya$5bVT@&p#N=vocxuhHl>7*OBmJNfiRI=t+sm2ooy>8+Ct?-o
z<P=$V9zGJItO^Db$DIbvY$0gEdqE5KT`&Upk8Cu;bF<A7Kw8<*sIZ>iMam?)+7)NY
zx?A4iIG#($+bLYp761N4oQfnao?^Jj(6|ntU|YR`MVN*bbBz~?YjMrob$0K!B1iF|
zEye=_&1*8M8y&21)D$uJ2+!~f9S1D=R4DktHAr3hHr)h*$EXKzt-ZepY`VApGLRbu
zNZX2nzZW}-Axgv%V)GXugsT^ZTaG-b{@aZ@V^q>TaBkYU02N4=-&T=}Q1+Ff<d{i9
zN3o0j<(`*6`i#7zz@!h9h7H7r#Ox^^zHL6|0@ISafkJYEakV9^6ckW~VE96qo!A34
zIr|KweHb0HD5=PL5zuWLDDLwSW3>6v!mr+5m|Fiud<=z0p}dDV`719!XuE^1ybGZ5
zq(h#JpT6Vgo?I6`c4Cz)@uOZ)Ei&l_HOv4!-UEv5O9(nd@CQwMwru&~`d`s06yoND
zeEY%xZ@EJtNDm;q=McoA{xDK`5dS8~6G4-u(4?IPpBYlaOc5K8qW|s%h1nHG515l*
zcmZp;wldreVVbYIp+~eYux~9yWD!bJLnF`G0V07o7R$?}s$sC2oKSuqN)bdo{Bqy=
z>KQ?RfTR4X3W|Kz*WMF5=LEE0s$BV^>?1M-B$Ru*p;9+^U7}xKMWMq;2EG6giRUup
z%+Jj<iD$a@c0TwaNhC>^yF80|6s>WBH72vIrrgIQ;?(?SBaK2674g#q;f>vATpi5|
z;JUfRJl4(ESFC5+I(|;a@kD;i4Y&?_A)Cwzg3@vU_wBA&Te{3=fbzFcm>VmVm3Mb8
zl0j&RD)LAjx!cb~NK8XQ2*}M|(*6#Eo1Q~xlt|Anph;X~wvaADwIVx_4ZH#CfL*l<
zXx1x8cc0}ld%}ZS*8U0p8ul1QwELIpKLqbCtKmL|35_Xlpa_VCQsY4ECD>_BLZJNw
zxH)E8jA~@6>Ym00Yn+~Vsj8N3wd>rmL(7zIG-<%2`W~8zDJ%OBcthDbB4ciXk}-0y
zYTd(#BronOuI&R67VcB!i#?EuG0-ZStO=HUXHq7VPHUy;B6^qy*;ws;s=Uvl1W0%V
z7QgS{Tq!hqaHu#~f~g%e-;=H9sSnRb9;~}MbW#=a+H#grb<;QMkWi=uCS=i0*cn&t
zlRX|ALMrjus#Xu)!I%(6RA%7W*q<H|*l%DBiW{Q-pwCfiT<b})=)?X%$Npo8Vw*0!
zRO6{bG5%4Qqtb3b%)x#?3~pu;k(Yt<Vun~5me@M%yK&Rthg7YbCS#PHcOF<g5tAhL
zGI(-iasquPj_wd+5{uS?)70+z=W0=x(-Vw{YBU~tGqDLJe<dG84h9rG`WYgy_{_R|
zZ406!3>8<pD$fVR`YRCxZ{ONl0%Yec@z|=T?#`Sr0OYcV3l2(O8}HfVmPV_<h|dQ;
zpQtXsBXV<$)R$e$%`l|WANEsK=G%hLMhycDQ3%LMeIU`h5QSCp*XX&%&vIpkJ-GAw
z$@i=5id{P?)E?Gk;`-X+8%;r!8~L;KNzcL=nj*>yK2R9eN&Nc`s}zKwiO&92Mi*=f
zp+yl1tM9P3r;=k(4uRX}ll5Aca1u?(Hr8rb#^v#1HXMZ{uHI19@z5S=t_{NIne7O9
zf`4>+f4w00R3em0U956tXAw;e2~Ezj8l)=YX=f{_KYwKxyl?83ty1>#8o6h`B9i>%
z_e1*?&;oo<TZJ%@Np;Qa#?hT2SdAxBOz13x-xp6uEt<ZlAnjG~=3q8P(N0cCG(G8&
zB}7j35;8N<$9IeqcovlZ$`5+eAWAIw^s8}hzav-|oCQCVy7tQ$Tl(r~DgK>rQoOAm
ztS90(A=g;&q$120AEytumucgVigDa*H$kdGuKc&@o0Z!JvI>fV&-fH~cbg4g)sNnH
z5iYG8HbD_bjTlOhAF7$ZnSu#sCFwrN&1-J-Fv@rXuxZ7~?QtkU<7nd$A?{hO-(OUy
zwTR=%q0vV`us?HX`&Oyu?i4LL3q1u?+HJK@Q`Udm0E{`mF7Eu{Fa`5^yr=42-cGPk
zuC*UsdKMS#n|!1A;Q2n<uw5!97T&tNw|O^r)3ur1PoH7*8>}`GeDlYtx+B~D#{6|%
zzg(~PxTvh^dgsJBW}gx8c;x`?Hq*bk(Isw_z14`tnyM;(ncE-#BqX>63}N3vjq0;9
zuqb|*lzRr=iEQBt_}L1(&V~J*!fY`ctkUIGv)2mq61OYA4Zc_Nh-S3C6&|!h6fA8!
z6OW{_yhcNbl^4u)v!%Ih+59%bdnkS<NjfgdUq+AMS4L^``BK0~S3CZt%q0Az1dLSv
zZex+N1m%K^t_JHgOI4Iddw#^-qsC38Qp*N^ahGYZ-wtrK6&wvBY=J7kgt&qejC;G@
zO&qpW@tG?{!<v(6gCQ~`hM}SJ5o0J>fxDV%(N%TzXQ!xM1;NSJG79D)gD<70R5#9~
zV3CoA+RkpdWP@)kMp@j4nOl=J@KvS0?mGdk4qR8kXbo~yekr5s_9zr*SspsJ&tnhX
zC&N^P$t|ya_P6o#xB!jl%fVKsZQ^dZ*KWK)6=Shhr@zP`ekPXS0_9_Aucp~pSPr(~
z6N7yHI@w5r4eE6VR@h87U*%Wb7O>>uO%j+9`o2AcX2fiOr@TycT0)wJn_uZC)4iR!
zzB}-dk}783r4uCxJ=|A!)GVMV#YLZP>m3spD*Uk;6_WDVN|$XL!~eefq%<q>VH602
z?q_xF%vY32(`9!so;oSATL!3;0*NA9fbfoz%Vq3q3_hrA7`r1=APjFC@5|eN^1<Jp
z`aQjw4`N<0B7G<14i#pd#8HVAP=tg{eUW%-@0OQtMZuEQX&*6;koILzwd4dViR~8r
zrgVUr>`2USG%$5}p;LKqs%&n>R#k+Yw13o=mP@dDpdk31CQLe<=h5`mOwpPUKP_|-
z^6R8_)E^76qT-J^?%)!5QG(%EjTX6OBBPDI=)BybNN3KY_Q6T5zOG&zQq>b~bi3Rv
zD+ivo{`|>bK}S=PD8}>OMp@aFV5WKN=>TGjOYo@3HwDIf)dZ{@)$NNCHR&P#I&7`l
z-iwgNvQ!W>x<kx0j?!kLBm6x%=A}WhD8+hk50&Gk)vu37lLJ&O2-SqDuBlp;+mySC
zWl?yK`l+48$(?xeu)s}}+VqYzD$sf(d9ipY2&29z1i9EvbwUTtdrs(hWXiPq`7DLM
zxF)~=I6^|h&V`2!fllP^cD_%;iJ{s=rdQl;Ol9ucC(9{?F`w>5)fd_M%-r2NPk}10
z3|B>4Fwt=;CN!zYygGgbmK1u<Hc`q7QH=KNllxG-k(Dn=6iZBo2c0x=AziL(SiDa!
z+U!9@73A7^n$D0kgI3rHdR@%Dbz8&_hTzxhiVnGs^>=c;q9+L^6+ZE<kyedvh%h;g
zAEX~lEba)nruB`3awYH0+lSB;dh$!ChBp|#9eJ+rf$yj_YHrtjWy-;HoyVVFDUGF-
zRn>O7ket$mNPp&5)nqkQO}x1t`Qpe)O(S9YcIT)W$296r+{`VBC~T0kN_onM`mCp?
zcJB7!LA?-ZYDIE?n|kXa64a=<_}@l%Y>l-7Y`>vv68+`AXCLNVrEmX!b^~b`5=w@w
z?_Il%nMEFb(4#Bhm(NVDxgfD!fHMy`{jUdSZ0fvnASYXI=<yn~P8}K=tk>>o$ui}$
zcWX;Rk0GOX#tBYovrrBG*2A+PvfWc=s@rje8CR8?&UOFO9x-E{jop__icCGPKcc5}
zFtOX5#qUQZNi^<>)bUsbPMmT5l-(5Uj0nT0JKxUWM(Z<c<BIDcW`vCd-NPIARK}_Y
zn?z_75}-3Wg@@{l6z3WN)maaoJ~I)E#VJ0M_Exp&GMRZk&OwldUG!4f`cnC!XB6z9
z^R)NVrwCfEK6j@ymoq%r2=a!$4Bbv5qv|1E%9xil25OeqphWp}y6pP4Y->hHce&?{
zsF0_qYH(z-^p)~lOPeTn6xT1wDph34f765iu?YY6k_1*)aFk*!uqVApA<#yr_Ejdh
z>S#>)?SuCo%|vFikgq2-gK_redyy*N+b+6Vu&YIIVtoFLz{cQ*^hgQw?5CLQm8IW5
zkZ7qkAoM1QNz9lFzO6ykosdLZ!FzW{x;RW1_<En3)WahZ`_JE_bH_zzRp+MD+04Hl
zk%uSiFC|y=!T`@gc$~!r&r8y;Aut8$jZkHZ%19R`t6MX*&!Lo4gxFX!%I6d+aTo68
zhSsJdeCqFW9nXKrsIFa<SQkdhubB48jOt>RkWjs}zaMl&_qaB~5CR9tn2VQU;Ydd;
zp2+d}j94R<^pYc;ggdKt0%=?1;6W)f*{Ci{;3Ti~Xii<@cjvkJ1UwzPDH`rFvA(`^
zVz(cj;fo)Z!iq6?8S<rINBRZ8eoJpGVvCZ!moBv=zzg+fzPK^DzcH-nr%iVdJ%u49
z^XOBsozKqNbej-gd`28vVZb09h!hLj79_mwLz3(^zuN$}!4oS<ga-49{sAJOZPCnB
zS=@&{bZI~#Vh2gfS`lB*09pA94G#xyHCR>ETne7H5xm}nSoi!MmS#oFKl3LPEU%#l
zJ_$I_1Sk>Q`N6UH3U+xHG;H>Xj`<;9!GE=8{eO0Q7oMMbz3>p+?3N2qa&?2h>|Eci
zxrlnXI?s9Feq5qb;Ji2m>ZbDd<BzbY6lf4X8>#%-zx-z?$_bFwS8oHh_99yz@Rj+{
zEPeY2{PaH!O3Dc+bMm17t<}sx6VjH8frjltur(+TV|0x1k45gR2DWW8hqXgw0^h|y
zOS1d%u0nQLl6k^Ohwcc#itIRI_h5?{DbwhWE9z>t;N!IL=QP9F3t^%_C*yaL%lkZ-
zbt}E8=m3?Vs{~81UG0tA_S30_*Fm{-0{jFniqLEz$`4vsCm3P+8b!`|ul=>>B$s^9
z7VL%|`3mCU{VoD%>W&P;W9<MtqWE1_D(V^$N4K=_>nG*ie_9~-x10I8V2b=jB%fp@
zGZcYte)$~wAi!>KF{9MBUyw$c8zZi>q;dt&3Tr8kxfgK3fO;WeeFa2#c_;~4Gec|x
ziEZZFL|bY=*eMov3%=&%sq(;{Gha`cvcnS*!$kjGn6>tYcYTVLpU5=zvMB8)5mj2=
zTa5}n{ogAS4g!X(5R~)_kX!&qVmt3RLGT9BvVx~p!$lI-_%(DG++q@+@J=nvL+NB7
znMf#*!jnOc7saZ<Q+A=@Q^7=ja}QLGXCVbV55C$r@Y0M~ABHkM2QX<Js5bNa0+c=s
zH^!c(LAc^&N8s((=D?TTBA|4|5rq*wKj`n?=XK*0h$?u;N1+U~gJyhIh)k<|kyj@$
z`RX^A`?|N=C)uF(pOs8&9DD`gmEy}F!kvKsEgbGorS=6ZlFm<bJdLHNU=#Vq%dDw8
z|6L(@q<>e4<Ku#J!pzl?GM_yTByEQk^6rS+H$Q#+ZDH7^3=MB4J8u8O>nkqm9xcqj
z;H+-<G=~H!)O!avoBZES<rL#yX)2zgO2*P~2Ln(hwaVPPeZOEezef5^EnW($!w0#(
zk$Idu2j;8Ebfl()H2BY1Hdl)Vf)=3f>XgC39J2P!nd9G)bMbj$Pg5XqTtfXDEdCz@
zKd@9LQYH-Zn-r$rJLHz-`EKyQOJtOVyUo=6JIE02VXi$~Em-*f->*7Whq4U>uf5f_
z0EGA#%ph(PLI(~V3bdW~8t_3et%?ne0b4mY=a_J?8{^x9bWO`~Rh-ExasBIbi&F5-
ztG*L>gCdGoSGn+HygCZH?Bj+i=KEW5hANpyHT1Z(hn**&2R|in2tpAtLOBsHA={@5
zPGuXwLr%wIHFu|V({L25ID{#RSoOgK&yCFPx_FkiBJ++c8`H8dIN*i+<deO){JE5V
zrQT3xQ(-AT2F6ETSkm!&8XA=72@M?F7PH*a&oHV*7mv7yBFR;^r=>0-6khBhJRGo*
z9^vPB0(-PcB4k=S@F%1{{f%=Fo7|1aiEjt!A>z9QQwJQl{2N-U69Qq^x8z7=qd1Py
z)E^B5`iTH(?pvw&qHu^*nDxNL^ge+IjI|fl|CW{X0P+1+h7`<6><EcJ<{t>^x0Nmu
zdcq50`+vNks6$)W__0KVr{dqGupr_1H=Pa&mjN9<nK*O5eiupC9riGhNrBtHD^q+o
z2kEN6yxM&UIO1KgHYQc!IsRH{mRAv`fyCK)VlFXr|K-o`jFbm(d}kZB{)Y1Nvovmi
z9ok-)v(0x2f2?U^Ae^+N*ng0>r!c(JmL&0tnM@Bt!Q7*ggSngL#kEPl`v+nT@M%uo
z>OjA8GV92adenbp#HQW7edN>0%vC#<&@BMaM&ATT;gNS}^InZ^laun#V$xXFzyDVY
zaEL4V!Dqr*{VNGr-0hGKd31q`8@oplwJDQZjPzsQkWoFL2G{V>R;wU2u5Rrvs`(iO
zoGk<oeSC$=3sQ>x8Osa#`9smd=6Bv$%7sH&BX4x+jSLG3rJL4?ZX=-8!0HF7_2!Rs
zGY>zm<z0C~RcJs`*E!tt+$t6mvz$X((9Tg&JLh*)qaZ&5cf>TbRC?K9X4v5({FQ@%
zZ%GZ|1AHf>*+R3#9pEI$WR^-_lcp23Utmug@A}M}Lb2@5v~30t1>$ne7z;sH3{_;x
zm%slga7<TC`lR0ipE{onUdnE04&}lMMAq)w=?v^Mj~L*Neh}2UKjeTuUVkwAX)FA}
z?+CL_dim)NJ|hWju7~}H*p3#OY^9aFdJn(GO*@kX9u=1^bo@9YOSaP8Dxm`znFc=M
zu)IzF=kkumBv|1f!o$Sr{?ybXr?v7UIt(wRZIM_u=x}{n0Dp=^fKMY4y*=l#nF^XO
zl??-L6~U38h6gCKToF1LnABN4`wd3gd8+&!j(rZ7y+_W_BETuKS)3)x9ytA1?ZXjf
zYVUQ>w$en~N&?ro1^%xIJ<+{>MrZfI6E(67E!n+n@WIwW8vg2L?zV!Qvt}jooPMUi
zGR;V12>n6T?&l;SXwf23l~4$v=;(ttklpbSh4QpqZ(@#fc(SwpcJY87zUfQc<x2QP
zT3pdO#xa$y#jtI!z|GTq$e9rB)SxJ%r?Ft6x8!gN-is-Ms(JppzB-j9O!_B+0sQ0_
zAo$=lXgPWSfO9}81R4l)o`>8%$hf?u^6O<RQjIIxRN+)WoUyV9f~Gs*CaOo47{f$G
zRPXG@xr4H<AX&|`M5hhq??A;;h|2ZjZ@j%!<~xr4f)X$v$(9hb3rQOHAQQ*)$9?{Y
z3nZpTJx}EyhxyV|Tw9)Ki^COscq20zEeEmalkh$4-N!Er7dfUMr<M>YP)_3qvK(OT
zbfL9Vcq}$EGTuQ4Po_;Ix%XU>-2mmGi|ja>WCk&-XF-wa7q%bSB_DW<+*_Li1=Ty=
zc|4jfx(6)og~;!-%^y@t_Vjp9fP<V+JUdG7(IR=^B%8kIPr|%KOXq+Rg*)&nYn?4S
zcEX?LCq!dB>5aw~RN@b`A(z;+JAJyf0RndU1#9xvzK`4Bef$uI4Y@S(l1p6XQzYh(
zO0;FbJ+r|R@Xbxt_0KVML4x2y4X(d6mlvnuv99!hw}zKT;0lmsz!&sFb$B!<M=y4V
zA^@b9zZB)sQ}p=_H}~xRWVEZz{J?e+JSaky`6u{mPl*6hZ)?vaGc;7;%od6AV4Rn;
z6}+AlKk-{{A>l#sXI<FYYf-+*Uim@t_~C+dxC3j;y`&SYoI_TJMAtJNxUSoSw6+k`
zbmpt!##iar#kaO0(*R)hJ(SZ_egTDZR6Poo+ULJ?{EU~IC#11R;sR|pFhA97Eu|kZ
ztN9v-@hF@%N98?@2`(J<KmlKTO)zY@nR{VH^VCAZ>+OxleQbTY?_Z5Lj-&R!`KPC@
z2QZetzZ!N=dZqXnh~|E;Xz);~A#A7-`5`>vLruWAj@gIeY%!u2A@TOlsf$HcV|?>?
z%Ab>9xLkE+CU@`aGX1sRGvlM$IcD;Y<TDXQu-Yf#oqc+c>Hx>ypLKXq2|K~{*c<I+
zcH4I%=N}%7q45Z{ga9AaIzk~DFW}!3u@Uy6G_c@kDlM-7Bzw7m2pe(XeExvOp7I1J
z(g{{@9sr?jXlAk}B<`NZi@j-u5Q|%&@fFuG&V4IL+^LOcncZBw;YkTbfZw=3Z8Ecm
z1s0AOY1#q4J5d2XoaBY94if@H9nME^ZN-nLI;NWrf09MV<EDR(NBmH?6iGVlPQmnD
zgID786yV)HSr$58f>S9E9oxX0jc2JIUSEE*ewjf<1zs2hcJpEq1qsa|IL8uVdsaEE
zssG)l9eI>M;-n80+>Sut+<(WzE&a*pShz&2ronv-#eMfc6X%4y6peH6Xf3*VPdN^>
z_F4tnPXV#C^m_DSV+60q5MT`U#~vTaL#C<*71q)u&CQ_QOhEY(9#;`+gvv9>{>jby
zi4t;9J1A<5Pw(Gc3WRhQXlss{xzSDS*g*4j$z*Xe-&~|!B(Mg5Z%;-oJw8`-O@0_W
z{fdW0Q7{d%1-ZlC?DTMiVYXSV>_0(-TvQ1G({*V<E#*SHZM+H~#B{X$2C_FH$_mwH
zolu2Wf(WfwhVw_70LNcg7Nkw)Zer1P=t%Kcn2Qqb?GID)F{7q%;m`p9#G94m{o{$S
zm&A+-Og7og5;$|)@pGRpLU;OX<I{alC5S49-(T{oWo848reE-MHOFUlm)x_=Kw_2W
zXBf%6-eCso?Ml;k-hTwgryQO>a+o^7Y_S6P7@UKvfs?z<@PqwzNU$Pi2zBFWV>n#f
z9YZAV<f2Tu{%;-oD>U$jpmMPC{RK`GC-{n+Kk2>sll_5nR(yop`YERH6B@?nJw9he
z&jH1kWv(HUxzDXkmtriuaoio2%({5W=Avv8-^T7B$MoB<km56P3elCWMGUup2**c@
z^{pOuP)qC+n3s4Cb<?E>-Gg=8bV&FqKh&ZXg2fVzAVzd)0n|RHf;oi043U67hqy@?
zbJvVyr9*x2N=lKVt?uyw5p@_=a-H8%cBq^pO#dh$1p&ohzZj4#R6Yd~%e0Y0+Y^K`
zUdIxb8aEe#^RzqDB>js@(8tGyf_>6Yj1=2MAs2p1C2<x)Z0KhK_*fDLrIKlBQIZYq
zg=5(y9AZz<NR8I5ooQOEL-r`A)HAQ6Lv;KE5Yu1w4TF)()yB`et<U!p1ddM(>B!#&
z<(en%17{ud3hFB@0U3%>DON7NO7-e%Gn@~DQ6gQhRG+Yj5iO`czV=oncT_s!5|2j_
z>3+z`lA{6(`4?d4$P?JA=Ktr$)VFe{k$3lGMZUmQSol$Y*LOSfuj103tN85*sPzm&
zJR}SkPe~G$SKcc}clin~oC#n@7ygRTcPUN-%d&iH9}1A}D?XnNS%K+la4SxRBu25W
zmP3oa@@;%`>CirqkVa*R`asnW<YZ|#;#v%3i)|wwa*tg6FoS%A>VmW{10HOe<Mlyf
zqSo5-Po}r0su!2dO&e<~i(AdYAmTIeL(>rUGTSQgP2DeI@Kd<bZ<t(g>lQ!~uh9gk
z1&}qB+G7uuCK%43fm<HsS81|8U#rGEcV-Vs`pEq(1KeICC|AM-HIfIK6>SAUHn99m
z!t2!@*cdzi?T*bI76#j|z{Ys^Ayev-By5bI?|7A7=rfZUB*>!5$txgN?5DC-QArze
zXV#~D)_XP2Z=tjS`#vc+N?Xj`8IHL^!0n%recuRZXieuEXpfiIyRX%U*$y|vASnT#
z|7Xj~d-x1^okn59JZE7?oz0&MhXngT_k3^kk|vduAV}e?VioK{Kmy4hvqX9?ldVp~
zBgD#=kS-PYk4_qHu%Cioi&@e*#WoJM??`kUOs&B9WJ|`xnm>sy`D0YGtv$eGB%Ef$
zJ2$+IiN5C81ZCIzy2t92HmHLp5?37m$O%+DQzS`u3JEzkkSg@nAw8=L!r;%L*d`MO
zyLXQ=G}%rQU3rDzyQImM0YA1Y2czo-PHXF${ds%W$^(HGts6-V$f?r{n9whjZ(Y?%
z!bZ+-gW|>hdx>bOEkJt<(2=k0nX;ZQu1>sA&93CSkt2p(rRMk;_rpAtj##VV7<Eu$
zLX?6jhBUmj8Y09-vA*fMm>!W0Nx|lZLwX&#Hk$VJGzXc#XvNs5;5E7wnRo|(g5cyd
z{u~DwtL)a<#QM+n;tw$5!gy;$NA9!|9dk7&hg=ZQ#`#01s29HWmr=G}>hA1G0uP>?
ztHbaF9sgJNeor8zx*>=$vAyo1+&fbRlJxsL)@odK36*Go0K$)sd`ic=wXc`Cx)s6M
zGcarB1jlJ3@m~yK@5rGh>mVB)lZB&hVH@D=$=`%G)5lYiJb*`bNWv-JZS<)d@d2IR
zih$xer*i)WK4&Hp{_1wcCyg^zk5xPzyBzCF&%$Ww-x2nTSp?<GSazRr%|B-~51mn=
ze`j&V#XO-e7ukcXLiT0<Bh5rS7s`k9{G0nD;x$gN-|^2BP}<*alu^4n>V5{c+*B;z
zB$sA&oPoI_3x%?V&Av653+o(p-)fco1qKN@b|XHt0-TjyfWMTterfLGQI)|iu{09s
zDQ0SP;l!2P%4V5PA^8h*VvPcIGt0OAAmq&ruFwI2QBW2C3~y6{_!t19vuj&z<_H>@
zn{sxpRjOcXcP&OXEDx02AK^Iehf%*4PpU&Y)am`KWiXqZPehBhS_b>UoB5oeSM7x#
zt&mDMK-ua<;<8D^lVJykwMyNZahJKns`%DEq{JoumJ1yNskEFJ-DD|}k7M(c;>Z>f
zgK~<t@sq@w*;OBy8-LhnB#5G1tRZp!U9Q+w(&o1zWkK>!_3#w4nk&<|G;A`mRj|rf
zR#4N5tpMPI*vI2se&Ilds19w3w*T}0irh1k<L{UMc_|nqTx-97T+foZ!KnREL2bmY
z7Zl8&(^`>iBQ6rNsr9Y~esGbtCHHTn&Q`&3fMD`j<?y7H#0Y2A5d5|}?S0nrcx4{N
zF_%{XGz_-FcVj-vX2AixIz!yintPmeDg^~>D`gQ>@Hc`+wcTduEv~93Fj2g4-HF+4
zbYfZPpF~qNd*c^tfP*jzTJPnlsvJ0==`+1VKATS5JMhSifl<d4sf8*1P65$@Slm^P
zt_*w4<cM1=-(+d-q!Rfk`Se?G0Eldne~8um`Qd|!gfCB5+OW~W`)#&`9~_ga!bGGF
zw48P$;dAn>7P+f9ZDR6<cQLLqROu{MFWW`|iR`G|*Hi1xL}HXk!4Rj{ZQ6NOcQ-k1
z>pjyUj?a;=(WO4HddN*rIeY7Oz=AsTj#;E@qAmdU;Pa)DuK9a@AKrEC{1?Ay6(H&#
z`cNfm$R`F!$IEx$7nh#KQ_3WgM3UWjY9y=rZAOb7k5G)i{rDh<Zgo94r}aNZ-RRAa
zptT<D@dVy=neFl|*mMU7|J07Ug@Aj9q~WQC8twuIV7&VoBiK>*Dp_$ebmKETI4+!j
z3NHB&?K`#*S`~C4v4j4cg-*nYvV7Z1&l2kp8fgD>P+e2k#wD@s6g@(mDj>3#hMRfu
zzLTSK3k6I3rx#@9`+^3p_oB$z#3kCTXw4Cja=qTZ??TZir#~+B{WMdm{PC(!15R%*
zs8yNGPg@^2sqZ!r+-I@wQ?W*rC#~R$gJGk~v8g&tucfq|!R3{(kpGVO8r~e)c9lb)
zh#OF={&SCyg;a$fxN{UwIh(ppJ7zMyKsvJB`oQp8zePi(ufuPu=UAimK#FxXKw-F8
ztHtv-@V8zuv!Y17t9KAxv+R3=oq}f)Snxj)V!@{99|7OMZEN!N;ZoR2PBZt;is0jU
zd3x$UHyZ4}*p`y?Fm`;Xnj2n#B}|uG6&b&91wJHcTWfM<@ADnKf$D{=Wt9(MlfTnc
zmT(lzMwxKy!f6<j<hN7Bs{R=FCWYHSndO4p5}NBA5_9e^J(#iwRK(IIaNCMzXmdJ(
z*KQ(sExDM@QzAbiHzcd#93CWisgEwhyZuP{w_<yFLK7wqR!8bzJ$)lo!v>Dc!0Y<U
z=HGezvkJ2J>=zRJ$k;|R#V&;6X6ee+b0o|k+phT4^%$oMBSAm<Y9MyX(KptUE`PzX
zWayFRNstAo*|X1YJ?QV?uvIQr15dd|6#4{4)$zp7FAW&Vb@JdkRsn~F>Ib!hm!xT$
z;6|6545Yti^>D`oF`FvPe|BCf0i)NmGQC5JE}DItl;Yn2Au>JWeC(SpA$W1b&K+v2
zIy_>o0;DfR9CDmgrmDMd?-W0qGzpYuJRdaiCo+nzL!xPMWiHr~|DI^@p|0%!O{f9>
z;GHOQBJQnt=V~Ae`JIyWrD1u>81X1UQTK%EiMgv_a9;Rz8AZvmYB5egeK{h<$2Y+e
z!z`BxVHB5A>$%QLz7rV4AN0ZpGdej2#SezgwtPq%(v+uWruES(6nLG_1Guc(c(tal
zOVOq9;$2NjA<1FpUbgjgO1doFq(*%ttd;0um@HS-J9Bb)PLEmD|IY-8$(dcz&$niu
zuL1y5%Yynt+<rV<a5x6W3Ve*V6(sqY9grkMug232l>h~nNcxv{Vpj6UN<=~B-Np`J
zx(A$(c0`Qv46CVAO&r8a(-TVI*{WGECyx4IzoADcP1qoE7_R^r@PLi@cE63B;m93D
z!+0pC^ZpuM+@+Z6@~S$>jfl&;-UrQTUxU<s-l~lv60`@oKOz@5`D3eT)1Fl^c_brc
z`ANI;NnAl#T#kA%Jp~N{U8c+XKEaDf;2rT~dAoh0a*0|GI5CCUz@TQOtqhR^9=L($
zein@&2DeH<E_fuN6uHet#*<m1vMU95-z^0u{g4iohfYqC#BUGcXXI|Mr>c+ekhjRi
z!k!~B6h=$l7?T)>Ki;5f&^ag^g9NJR&Be)I9*LOf5A|=S@v_4OS+Y129a&kwUuT=c
zy*_Z!BnCDI%LftZj3)lM7J}Ea$r;=+RbOlf&m5c^<Qst1jBe&EB2IBWBrIvJeqvob
zAH2RYs?N7`@O=HxpTOKtuaNPo+4ue`rHVq}&o40^(hz2~iNt7gXLy4us<cx_T7@p-
zs6&bVb{-DJ5a^C*gO9Tvh3`1UvA+_?@&mOOAW%r{^$#6s_raN0#D(8oB2)}J090uQ
zJ=Vkrn1WlbUDvePh~dXivxsd;e8*5ClXmx_sNTdjo0kE2Eoo(HdQVmPC%fGzOBRtw
zR_>Wrd2D_SG4i}*+!<hyd6!gc^B$V~)=M2oOUXK<MNCKa(pU|jykoca#%NbPTd0>}
zQq61OQ!4C$dMWCVf6=0XNLC3i$xnVlx@z*N1K}D|{;};$xR27fm~?uy4c)m~Q=;Jp
z*E?lvLE5wFxe~k)8jBR{Q=Le^C0QE>)4D7Id$WEVRweoiY1Vcm{=R%qPBk7T4__DH
z{C0s(d-k-g55VBsr=_V2fw@lMG1sn~+|A{mul!S>08d4bC8P3jie<Mp%MOLNn$fG0
zwAl_fXfkgD2yC(U(AN_Yz7x75(KrBsCEl6-_6kqO(8$r7hoi@nRcoV(oE+yklO-YG
z{7>(5I<NR|e$vPBj~suGV19UY?Td$yT6<$vA>oib%{s#^skly(Bz<|`i<wXjIy{^x
z>`K+MJ5Ca}n7F@rb)Tt{GXBc9ksuuc0G6apyI~lTOiTPDnWmo%t%w2@M4IR;OMrSX
zzba;3yi8L33s9+N1_JuDH^vH=R>*#PB}RT0nxCju3B*eyZM<1=Yix9MthGe(a7HIk
zD<|6cuU)EZ5lI*UYU3mHypxNuNAhI9BT#BBW<`FFq{FfSt5gJFyTsFgr5beJI_|JP
zKV3h@qO=x^e!#1_tGpA=h^a3Qw(uqgFaQcUbUuMHU|JMGvTQPvxR!hS>S2uCImF;(
z?-+oN?e!xF=1o1kM+S#5uUb(q7i0nc3`wuOw4sR0!u3FoO}U-<AEIVEG?WLi!bl2W
zIW_8!2tk-9`=;&HNp61>Z-Qt~;hSTrinQr||Dqpa|6u=28=p7dmN%H0N}rHkbIA}H
z_snzz5Dq|UE&!>iM0LOu^D%gWZTPXUa~GwLS(uzCB&QvzxebMM6`CCZL1n$FmqtLk
zbsmy}Um{BN_({8MiOSc{ed6;&v*9?PZhs>}*fJ$ZO@Oe#7SQ@K%Cgs&e#)r;H2VuS
z?X|b`Wf*0}Q!Ti4rXRYAIUgYBCPj`GgHz`-Xq=HA+7X1{ijbgxw5R*|DW&Q;dw5!}
zP~Z>}pNNnVEehke-yt#eRQ*4R>1Vu)kxY7R9U>We>Kn>GHQ!Y@ud4kUX1Us;9{N)b
z>iLfH=!e_mNOihg4QQ;U7Dr7w{G(3jyw0PhJWS`M2cTYmrxHMMH>x|^@X`}U+JJ!U
z!#>k?vWtKucX%!AN1A~NF8t50iKqPq4uWSOKR^QB{tlZvX;bO+l(Fs4Hp5TYaz|rv
z6Mv6QX%Ua31^vmjCTm|{PaP;H2d(!Uh=={zivmQDXZ>oIc*Yl)bGQKh*5im@S_Z7b
z1|D-ka44EJ;Msf&U`AP22vqq;c3Tn+p*Jes0B<;g@V2RuC1wQf@Y$MsC|Ux|-up*b
z0I1T`AT#qp(CA+0^M4PC!YaejnpXJx5SLHdKW&!*8V}hGr(w99?0c-l354q3eCU#O
z)Dy}q=dIvNpZ-Fjr9DJZ^FYU+OFb-Rq@YMovkEMP4)GRWDH_MZpwbLUI1&>(zkKE9
zRQM{5<u*v0{WH5D5&@?G_&o1mA^UB>T~^Hzq4jS(1-TcFpR-TqExpU!2YW=!*n%mr
z!t-ZO<m6GMjol_Cqe^?yV;?-W+woJ8gPH6$^rRG3Lq|0z#!I6o#CiPk382w(%;fGg
z{+Cr3v{pZ`P1Rz03x%HA&t3q=$2CKI35dtP1Z=&U0FR)r+zAap5!>qD0FY$=qIkt_
zXrd-pZ$AU>U3Su(n-)qO-VZ~0%Of!^L`d`9L$T$2R+-#T@3F*u2ehjISeWLD^)=5$
z-ZU^Th-fP-(}}79vSrhh^am-G8{|^7Y9Ee!WvIPCY_`jSFyRsyMXp691dH5k2$aF?
zpc8wR;f{Z9U4C<NpaZ=3i`HpGwhwP!dceaQJQy?<@bw7H0be)9x9~Bphb&)vT8$n~
zbbv<cl=Vg)$drXu7A1j6*BCWE)wA)+zHP*0f+qvaI~U-Eq)B4YTbSPNiwJldcIUsm
z-tiM}mEL!097Fs&7^r#se{6rQZOZ0HaT0%s{f0379ykrJqDU7dG`sHvh~C$C;9`ch
z%xpe-AB1u5@$ASY>Djff>?)@2@89o-#XdunsaD(`P?e?{OpByW0+`#5rbh9ZPiue(
z7BJ@$doD^f?C8rdQ%vvE>X+Bw@0=>ZIuNFl?Ju{-sw3~b4id{{L!Q9FWe_6U|NVo&
zd?E4OBGzh1TkLt1a46CpugQle|CYnZV^UtKz&w?yRY<hI+0Q~|gy3z5wX>|J7tkcU
zXWrC{YGrRB^XZBIJE5!lMqUBKviWq*(%gA2(V3@4#LEwJ{6)kzTbqrQzOEd1J*mx1
zwb98we<Cb*ve|;A{KZXS;G^f|Uklx5ZUS$Xn$IA>X97LDk>+6XKn2*;ZqiOF1A^Yt
znL_g!Vt#)=cAj@@T*HsMIsJL>N$KOO9M223XJ#s@Nd9)t{Zac$DB4s{)Gs%X$iY3k
zig;UCzf@nwfoyZ$JzV}D_cw!<GtN)L2`E8VvSCe}#^GL~s8N4J72g<`Yg%>uG+g@q
zwjPfd1NG5N%R=RO#up`qd{|J>P!NEj%`Rin^`o%!U0X$GnFzDHl{f32LarAdn^yDb
zE)tvIIj+qn7m)@z+Leb=MyQIohU^m{7obnm`2(bk`w7%Z5f-_xNPQnjT=Rk--NTa{
znM4_W!K|9#FfN!njwSO8p2xFT>#DCz2e1q5Z$OhY__FXmK!~zUogv~ryzvzepT@IP
z2pv;eHAuv8;&r;Z>VF2G2j0~t<2}K8a(?ND$!m12r0i$1Oo*>Dys_s9kwv9S<^KFO
zk&eImsh#FI>0c-q$)CA6Od5%WJjT93z|QB+u;H+p_WOBUmda;}0onI5Jk`#8y4&^_
z&bfQ%*2c5?wi`GkxDWbHF`<~Gw@EAaWPXsaLM%>XuRsWrjdd8oVeu>6H4r+i1575j
zOnCZTG3gFP5Ff0<RXW-23>Z?;Yc{4WA{3BnBI(7|KgUE5+DIed`RdA?N||++bpLO=
z3PXHFC45auuA^LlF7e@01G!Ve!;U>BzZb5`CJ4x4{udME9U1aTY$yoND|W+C5jwG|
zkz{$@%ea9+l@(ok{E#MkJ=E<(l@~1q>$w7#%lu(6@4OEhfA-Y{p9orX0)plj;EC#M
zJMI6g1#q1SXuI|)Fa_>{Z!Wk2v3Ci!s&tUjzcTy&?rGy%5U1$m!8qV%e@I>9n`hCl
zmpYmbbx*@yKT2Qu$qN~Iokrk>qK=s>_7ZgP&uez5T6@##_h0&0)478-JZNH=tW);-
zq-fstR=<?-&OB}M*@0-*<mT*JV3vMtI{Qm&%=)tv5;?rHV-MluT;I_uzDlIaT_%=Q
z&SZ_;phXr;QYUP@Y7a#P>=iOQw_0Pfqw0PBNX9yuB(x_ukYB~ic298sH2|@fTYpGh
z&PT2%P5z;SBD4!bn`_^S^n5R=<vsPTx4Jper{IpQVY3>oWXP!|^<j2`)-TuD;l?XE
z*`71ss-6Y7T{l)H)-6S8p3Ht!D=*9|L6R1p%QS&kY!(E3anm)6XJXMOnB4xR-R&Yj
z8`?}jFPWLVe?M^5C}HoaH1W!Bn0@&svtXwNe{{crG80R(GMawqIbsZO{*d*4`W<mA
zfyI16bPgJ2FLKD=O7qs=k-l38eISud&9xpuz-Oij{(rbaI!%EE;N$Yg2Zjc-lX|;~
zx*)XhR)K#6ha&ZlzW|+;?0Bf(cas$(mB=3>$5`OqBY(DF!%<$<D6FwxsOZl}PDP>q
zO63_<N*v`D>V)J(_Btx&Zfu7_Z|dm$HTTVi&f#)R_C9#ZyPc%JcRBwn*nro1X?so`
zfgN~6{7iu1$<G%VYBY=3p9F;`B~LgnOMJG=_BgRG&#3BhV#LyT=fKX0yH%FquQ)Fs
zn$>ph51hVCvmRnUoe2Bi|62815gaVian*k+0ZNyRTClFKbd6<bSp+S+;KJGRYap~M
zGT!#`sI<+b{VgGZtjXu6zlt+vNZ)d;zl(epr4Di83V^5uN!^V6TZUmcXRBSE(YK}+
z#ol+Fxi9PQv)iy-&B#uuG@7ZPY!KrSI(T?spZCh2BwDVC>S*4^5uQY2Jz1zU8y~35
zTfy-#3k9gTH%G@Nf-aAr=^N=C4$<R|dY}eopDiIS5R0%TM>K8uYc=2B4Bm=q@%Lz)
zy5_r?+W3|&irdYq#Ic~&z)rzW5=i;8F6o;RrSq1&Re0DYv`QAbx=hW~tg7UDwx2Ar
zgs-32vJpS!#8HeRIdAuJ^9xoDqbZATxqfj=D~V(6TBT&sm0zzq-E%l*)d!0;2Nr33
zq}yh4(2;9pCv$V<1uiwhph}LIt$v$rf&$cpZol2dOv3wZk-Nh3umw%NU5^On_}0$z
zIU`%^U{OG@@_ZZL=L`l*_E2U(fI8yCdYK2plz4(^_=s?0zu=qmZP#=}JTh3dkzV3;
z{nS&@9Ns_umm>M;i$zU7%x9kny|@XP7FI`URMueZ>P~)6im0_Enrb2X#kA3oXmKS(
zSw0ry2|j5o3P<g2eK`0`?TuD>{a6Un^I_)#OP*b5)+O%ltyBrRCQMtz`Vw0<pUX|K
z#J{a$?zpcHH~wkY(2{VWgCEQ9D|a_u*hKnfRwapnMznX=ryWpKxH0+hNpBSdW}p;^
z@@m05hOY4wTn+X*C?O(vcRKMFF~dx*OYx1A9Es#aIEJ}pfyS`D|7!rz-(ESj6?2CF
z?$A4NR@FO;uO?QDv$bP{K!3t@P-@0ZtW$!*^9w=(l83V!rRnb_cMCG{h_OKcrsIEv
zex;aMdA~`7J#!i%*$|!|#|EV1n7kBO&&|X?;KjGiUs-~xHeAHU(#imGxas5|VGWq$
zLpeTca}QsQST_03pL-%-oycm#k@upjYL3GuM)Rc10q)8d1o5GpNFKD?0$tDgQlK(&
zbuT%F+p9+mXAeH&-&~nuCs-_Xk6p1U`kF)Bv9z~*ciHZ0bq7f=?-F!40Bg+{2WwcX
z^5dgz;>P1DaR=B3IYbm0dk{zMSwYAuEWzmg0lDNzA;m$0C-S)j0!hGydkBnVZs;Z(
z7lLzEnB+>nA_@OCnDRoTEU)y&)TD@_AM8?Y+D3q>Rw*@NN3p=Xfrx;uEWtqy^O_g)
zQiv4dED>i27IwTvT{-@+)zL8})zAjRniKFaur&Uwoi9$8>#Le$>C;?khN{-e)PMBT
zP{gS2xBhJ4rc!$<^sik+1mAI|z`wOurc0`NwIr4NOdoFjeolszX_fIQ5^hZa=%Dq7
zvK@|yVI(kAvxy6pWW5OVb=2Hpss|Dr{f`#Cu-A(5y7=2p5`G#_r;JrEV*94gktaxR
zT97Kx`T(AcoT#46q|APjrtS+=2diDf6M|nGTod-;e!<%M036Y@2g527Y=ZvHz^|zN
z7D}vyYo{ZMk8|ig=r%_^YDx#<ju~B#dVHTwoW+|cCI6s>vq3Qe_oeFM`?t&V!!e#0
zfd}7P=8cgWdIK~9bg=Wa$G$@#hnt+(5-01p*b$+jx>B$nde4>&_yvE0=u>bA@~1t-
z>m$Z@F<?cBerMSJQq#{9<M!uFRJ2s9wXyzLkF5$h@6;(7`oD1cULqaVF3J|biUg-S
zKq=j@1KxXg(v1j9oIY}Z+ca)!k8s;T?9JRqF*n`{kLOTj`*V0aF-Rt~-O^l0%&(+O
zv+=qMM52}|f6Ox9C8%(Y9=w1io&5sx`X%I!RBExQDmQcoT%-Q!g9^BL4rl)6O1$~;
z>p8+yv8oJ058n*qBlO0L*&=&wb)UnQp$=~r3pQ!Kv*+~Lw+35NA7AaOIKV=thoKl0
zMAVnNEI>k9R+$DvANl=2uHCSCXIVJvpGm#bSMIBM7J3SyA>fC-={s-|s*jKk@@lMc
zJzkZzVaA8L5BeMLhRak>tAY+wgM$TNKq*bwHZOjN<ZV|zoPVt5B@8j?1zi0*<&-go
zmAd^L@wk6B$CMw^1UOkPr(Utp!)FPqe+AY!+6mM^`D*I|<#--LFrHVZZOss*h!cD9
z?2lw*0E5fHT`Ykzndl5ZC#I9Vg+ffZaS@N1YYpQZ3XWA(4AchZ7J@ps*q(PVRsTB9
zVaUqLX#mmHw;pGiz&})hE5y$J#Q{?S9wu({1*Ni?#>Y{?p39~9m4^!qAi6|zWB|#8
zp9>5<7OzFsm}+BGX7v~|^PGD^!?h$GY*%|~rY~-cAN}aSF)9lJv}<O2;3am0{!lt`
z?uAL1Mk7Q($nW+zTapLLLqViG9M=G89Y_();UnxtpI!4=sHOybc80vUIUssZVp30M
z4i>rG!OTQdjCaAW`+<JlgSjk|kbe>-<ij2Nvx<&`0snt7_Z~n|uG`w6Vk1rNCQGtu
zL{Je$1tmyqqGUmkpprzA5(FemlbQ?)iV8?DASjX*P*9Q>h@uiDN|GobNRU}??{n|m
z`_K6&SItzNU3F^LF8KJqH>~xnCz#_W$ygA_5X18Gpx@Qr3kQ<j)ykq+%o58%VHYWr
zBLGLfpNOuMoJ|wKa7v|DnxTV>i%1aC+=+xB9B7}$@Wx88q+E9qHcrcXstHSx7&7Sr
zP((2RQN~=KoF2r(k9#Jp)PIf(K)d1#<^Ml^wp$tozK8RvHBc@VwjAf%0(CV)1t2N<
zvwdp)p55x5VJ5)}G{uVDk??8x=|82*eFFxQg4{%As0$cLGQN&M*T3zX>)W$dbUF7%
zD)D$Hr?uxj)&j!9!PZX==_T^y54LnpXz8WH%6RB`PHI1%5{p()60;@eH*Hu!2Z&ZU
zAy`3SZYE{$Yg~}PyZ4Ef@jE+>sJjK9yw7->XFN%ebD&rLQM98BX@K-#C3MI<=LO3K
zb+p=>b{0fCgT6SIGf9Gy2$DUOmwD#!E6b5&Z>Doko^5dsN!RqE+yQv1&Ufi0U~1A8
zVvf3YQTV$j(@f*Efeeg}8oM2)4P|VJuAR}E+$2q~eh{)WfenVU0A_K6w-ehJ$!^2!
zM+ZvMQ_^oaOpUahRWb0LR(b{n^Mjj+VA+zdE&}HgWxe)raY+6!%xtBd)E+Prf#DH@
zE6C#lTaSa=Pp;d@NkvViPKz0SPU&v>p!f0UvirEWP_Ddp<{4OX9a>Oj$c~%ju!S^X
z>jSNMy5E?%KLdm{&g9J51_W)!@f|NHJ7jI?2A!p^|2IodU7jzS&*A}!>jd8e34iEr
zH+(?4kuLyI>Czsfnm@sqPL%sRNEnEYa&al@uECCWk0jA7<v!rO@9)>0h*k2YRyj^U
z?0ptzaH59#%7;Fc{O=sexH8uVR@h2Fd7gqu=PQ^SRsj^@0-2NEfZ=@gJy&d`-1+-9
zXFM-*iYdlUbl-mZC))Xny@q@GuH}4L*m1toLTJCGykqI4(&5p)IR47Xn26g&y%+gj
z?k`ADGAnlnMqLlMC%7QvOu*E;frn~~*lR+V_C|!VKzvsTWNvmryvOvWAE0<MNJN7K
zA3$3b<%9S$6uAU8tpsEOYh`>E#)MD7vLL7*I0z01YXv*!F@A1AWzc-wU9qhVqyPG!
zbSPxA=nef#FQD(U=j2e{JHq3BX5arGdAOS^OBG;RgG3a)pj){-)BEBDM6Oq8v;hoc
zhw#R4DEhmm9JtSaw}5b5eCBuMn?7l=xAK6xz&f%Efe&&v^~c0JF}diBBJ*51cuW1e
zxQtZ$WKLoC+i`ipISVbg4#7z164I){gfHqfyxwTER8onZQGD82<smOlfk?=ie*l<C
zu??G`gLOnyLX?#04gAU~2PT@2C@Tzcmf|3%`FEEs>pJkUW`G0p0x}_bkjWboaUiP<
ziKhe}pr|_i2K={Tuo!?VbFeORfj~-cWQqa2!pU!=`zZ<(U5X^qWFbN!($)WY9p<K*
zS%I=6!LY0ZslR$NPp(B>_d>n~0EkToK2oF@VGY8<R0~yBm8X@l!wz9#BN>^~Kq~SE
zD{FIbd17~9Oqp=Y`fVCj{X`d`?38GDLxxGsXI>ZQxHS71X`pS54x9g@Z4J$<^gw_o
zaLreop0ZlgZO4X;p@>4DWmitgVz^#~;;xGwcvgA?h65T@Lky^zT)_|XuV?)Or04gX
zC6wYkW(KzYz6eA(G2%i3JZB5JqlKv0;;R6q_sq~)(k|Jnxi2(YGJ)s$6|xUTZQAOi
z*sWIII#b#%FR>Rn0Uc&(9Z1r{n<KolKdvyG(??8n&?6~8g44AWQ^p#NHm9D_C(E#W
zV0|bFDi@=<iwm3BQvW?&SzfO!Kr6#<ba(-EOzt4-=6}CsaCdN}Q2O*j8#jti)~jmI
zJ)(ooNhW>WhRAcduA}T^o_Zh&P9sPBBJ6XXdiL5Pp+dsg{Be<{UG-(ieIXru^-Z7@
zyfm1h0y3N5g&qJlMhO5s{uM@}+>Y~&e_)AQ>3lE&h$f+C-^NM%4;*u>=TyQTsv6tK
zQ+YvKKo|*pCr44?c?m<90hE^OkBG4dIlC`>1izO>h;rDIa^@N@ZFrjpcqm*II`z<-
z2E<}BQj-kSfeH&8mN$-?y3abAdDpMS)E$AL1e4cGm}eCDQvikURr_`-_54?tr~S1O
z&|L?8{4sgX#sAt}_MmGp;Ar@MH|l3y@n@Ykd36NpVJ)!N^aV&}?K0r7{&es9HsnHs
zijK?2DrI$O{<w@FXGpNX^YaWd02br&#sExUvIM}2+oT_m+TE6xj$kTsWl!SyNL&u(
zaLD{;K$>e5Ruu)6vBzL3lx(FLxFM5%e+)i)208W{Vv8a+ln{KlRaMR$GF8jEd}d3L
z+=`+AH3Yn<Krf=nBX)<O2u5fF1vq@auY~OZmaQd8B`iA)kXOMo@UDW73VQI+{s3m%
zQqj#Tn((Upq`$OinxfkW@y;jjeb$Z@6PL5;S1V9|l~yP2g(o8r<|nCkJ;DkKGu>ms
zf!1*5`lrA8I*jhFUoP4w(VfH!*Wkh(#$^(QO&P5el9G<2C}UY%00@#kU7hY~&+wbt
z$8AEDNP>!I8tf&S_hFK*M1D>M^OQ1#OhD4Z4;$R@=U2XqE+|IW@FK8<GeXB-j>MOM
z_ssj(@xM|6RN9~%cY(!FGgJAq;5TT?DpJ*e6A_Vl`X=rwAOk&c{#;3r|3xX<r#yQf
z)B;~YNwgOkjhYP2Oqkx5umXh$%SXOyiHDDjI|>=fYHsZRrVDi+(SR6I;W@sx4z&qO
z^d2J>5T;J?9KN6wCEV%iNB||%+wH6~D5LMS`Mh@{BXZ>p+BShaD_9yv_k$ueJaS89
zU<CCq8DFk>v$wpc#MSr2mm$P->?d*v>PKeA=X+5}pD~c6(q+X$>owE!1p+lD>8_jg
z7M-grq12sty8F*AXSG)q3$od}5%dr#_!sU0%TDBf8u`<z^E92%r3OyZQlJxd!_;%y
zSLh13_gTPg6mSr`<JHHrpT8ppEa24s18B~um{!lrHRRG&Xx-RjB&hsHQ5U`-uR@7o
zJoVyKlUdIL;R#^|l~;igQVMwN9aLTkv6_D2R0b_zE>Ktm4_{kBP8DDO)f)M)j{d^d
zs$vbtfQVTFl2Z#f&o>yoa4PYKevC4y=n(DpZ3ThNNS+7t4_+GuAqq2H?I-+B&LIZt
z@n<i-|B_d(2qUO;;_r?ErnmA+*@Yz*E|Ks|@MP*0isT<2`az}<4QkyP3jG~?qaJ6f
zV1tMg+%BE+UPGhnG=OGSAom~YuDtM;Tv+vJjg6FPAU8ayU48fxf+5B&fgCT<V+mm!
z)xQx!<6$&ZLMDVBKVj{@ATgZD>??@3Be%IxISA{(6aT2j8<vTD;Z9bAN0|DR$XeOx
zt(#YhVJ-r)*YnR^!6Nl<w+rM0j9=Olb|^9oW<`qJDdaLSSGQD(-~6`c=YZJr;KYRL
z0KIDLg|N&LFeh0zFM{lB!CR@g#?iZW&EG%?kBFL9OGMF8xXwqvX<|QYW$o+KV_#~I
zu{WijFyhVX=}7gBDr)*WVqT{)!zyS?y>$CvqX=EiJ+EIo+@n);w@WMHVw>0>81X>u
zYn^sA1+H*kG*>@#rR)qZAkA520%K1kFs>!d(z99pAShjlR^IdW=U!8I5p;NKaz$jV
zHD5iiydz?ERl%%t-J1WE-+YF`Fh#g;k*<id9s7I_%T7bl?+6&f+#VckMs*SdKKwSQ
zK^Oq-TSS0eQq}rX+b0B@`AE}vQx;)u1Ljt1*YHJT@_bmLdT*&rz^dEU7pNmP64}0=
z0!fxhe5U+f%iQ!^zxTCSZ4W_r&mId-bU$^V=SS{!Av$cR1ipEb?WVG5vve0}u->77
z9Es1+{ITEVB2vE#8inaH>xHxJB|SWhOfqZLALb)~4NVq091ins9$FGGkHwKF79NWR
zAaR)beiEPytO8b8dFVP*##i8tUjcdM9R1=tvdG%W=Q1cGvhcF;vmd%!gM~*4wYd1W
zpMZ@mZPyoK5FDi_UgH#E7<#BGB<>EV93vZfVi9De6DvYCL(9l($lt6A6?0DFsAPlN
zybDDS2rbRy7_>p}D}Vn7XhVQN(W+5kAlKaZSh~`%5tSShW9Ov6Y~jIuxKWl`i`2zv
zvZ#N1=i9vEY;2QsbgjuqL)xAn7}t59e&<hm%*8d+pUTwt(`Z3wDW+AD(qBF1A9-WM
zY4A-5ivyqs8FP^yu;43So_=k-f*tm`R&Bt*NuI~9!B(>h%SyaQho_`PhiCu?ly>gU
z+VW5T+)88x4rWFXEBLIwCjam^CAk1{g({Kgfxa1qyG@f79iT|j3BQKHXP;=2(~`&m
zpD14zg34c_CM_*K?spS?l#=>{48yt8{PMNJ6L*~pl5ol6wz*~Jd@~IHPqZd>d}YKA
zT0C3grW)4N1UUrhz;}E<an*JgJ;xf$!A9X+4k3!X9MwDRz^GOq`1@Pw)czC&jlU0A
zge|{P!0J%>0Sco%%5S=Xz<qL@Q)n1`d7{YhH~1i?hze7N+^AHosu+$uu|tMTW}dj<
zgk{zc8-VG1djo&{?zIR_^_DD@(XxIL^*jSL!LU}pKC|crE6F%{?b2|bu)#+_po^<E
zkUKb{A)2DwknY)~(l*haGXvxl%^#Ld1_+W6c_Va}ML>3g8NTUsPnfy{jR<P#3Lhv>
zJ~DuBs`55hTN_%s1NgMd5C}j?hW}1;A@5fr7XH{cbF9thVI-S|Li5`q#tO*qv(}-H
zbqScH#YkKwk{+TAnea@M@_f>@sSF)87WrqMO@k)EQx?9G&^BENbw$d-QNmR3i|5NQ
zVnGJ<UW0c!A(w>jh+UIPJ)S%TxdAF|r~pM02}VuS4xxYn)efOELss>z7NwUE`(<lW
z9Xkw%yuXC7d^k1)^iDO(+)j^$0>zJ5==RA(9>hp~hui2Z*q9%Y8|Qrq2T>&!(|U~M
zgSa_dD$WH`ys+Y^J^K!W9Wv0;+VTGv$wDG13kluoY}ooCfe#L}`geer+DU&FD)*V#
zqb-G?N#wF@g+h2No0-g(^&Olse+-TMU49<SX&EUIo1w%%eQeF}Om+B6&v+Fq*-w}6
zQObShWvXU6S~PJ^MnWj}T1KTR<_Hzp($Uuor#sY}JCCt@<=cI}ky)vZJ0z72fWXgD
zhA+5?1_eG7aARN8hwSILrS86lqmPZD%Dg<f+6|hjlj8hYebM`_;gB7=G;o1g7bECc
zWnzqnRWmKUdGF&*ABj?z`w{P~Gk_A^P=#geF{XWzGo*_hooK(eo$gcaR*x1vzFqa^
zrpSdGooe_d0vz?b(v~nRGAwg#K<4;z(k*&n^hx|0OcaWc+n>bCL7JZ+J)v<>wkn8&
zzqqtq;nC0v`b}cYXC6sH0Y^eTfAUq67g#$gJhJOvycx4;IX-`~$<o0t_|82KW-}<x
z;6EZh+P9eADR*pfZz6VE@#kMUr>Tu$f<P`nIU%F)Dw6vuPPeKNyisKvD^SSfrCFYo
z^*(;cz&=<qjl)X|LwW;@BrI^d=Gyjry0IoEcQApu{=fE5|LgR~Mgv`5UE4nzMB4G~
zx;xFoV)C22PRsB%9M&#ijt9;NE8Q>*vV&2v<pc>?_(a~Yi&T(`$~(*?r`>t|OE(FW
z*S8#cs0HW0z>+<;VaJTNr29$G6j5=P>Ff5ns>N|ky53BY``2TT!WcGU5=eIsdDP#z
zq~x6(<*2touz&pHPVIkfcHSfFzzWaD88J%JRH#Jm!mg=A7h%<#0(Ds*{@R?_H+2Kn
z^C&`s_}%yNCJNZw++|nPgd+IS;eP=S#XTR;9s=k55;PD-Htxvo3JH#Y5q8@==@X<q
zfkx_(GwAM6U-TQ@IIvfp5wU1_ZlLDtI%CBJ=tfE;Z5z9w#+<s1+`)hub^)%bxu+24
z;|&AxF5n^=YnoK<Z9ui!H4mv{c(LOM<oMXEI<vVO6lG7d{+pr<B-h$W`leOIIaiy(
z)?OI3xzs!7SC=xEh4N~*OG)!I+)~AXzq{WdmKYycpa#NN)f<$fz2I$`%KZ_Rqx~uI
zqOkKQlIPmV<Na4}&Onb-4o!RnxwH}on8=~CQ2kkMQ@PH2#E$X|EG?w%KO`IU7>WG9
z@^ZVhpPzf(moo<MtQ>CD6J$jEYsdn(^e!lWU69t<3Ti#{rvH>7%aL^WYrK(-FBoH5
ziqyIVgN<*#JQ<*lk#A6exBLcXMi<zDOrzEvnbow&jPu_+>JaabN0MqTakD`|5~46~
zf|*PC`@Q#Gj_V^U)Joh{pb+#z`n<Ebg4gH&-~u2=S>%O+PFqBcX@<055Jo){*UM_$
z*n)!Q#y;4dVN{^8ZD(F|Gz=odc<)@sCIQe$O3l-T;#(3i6+am_ROS@tnCU0Tn!ahg
zMF@wT)4Bt2?&dNl^VByeQH<COsWqs7$X^o~{sf94S3sG(kv0a0&ULr|N2Z}JeE~>R
z4*&+PAewjqr`9(}0h%MWAbsd@A#6exGNb{vf)uwKKnOOdiR=f@jalC?!cslEeizc<
z$*m5Cx`L9o5DYL{>t4I-dx1wJeLLM1;VDS=%0Ia_kt5h~88TTqmM*5kUS!g*j*y_S
zrk-)r3whPGaLO|{JUmb*N?2#S|HKLT5i3G3KCGyrHIX$fMqlzk0dPy(9sep063foa
z*DA|*gR`Yo=96>S3IN?6cLUn=(}oFDBW-XJPS&1+1jA`W-fhjC${NZW$1=#10cJVg
z3SpCVtW_Gg6`>`J4f}9AaO@nB^&+!{=%-$!mxZqXYP&-_zp}C-{>D637T~HGc%e-_
zOOCIAsEXTj6L2AH9J-^jn2k9(bqNyXl8zmF6b(bGyo(+}(JDxHLTrz9eX~DOzdby~
z4G`6Ofcs<cMs_c%SxqubK)9<8?6_mCm;W^GmD|7Dxy%>=l#|oZ6-TKffur{-%Xw&!
z7A_$THo2cr0)a%Rr~VU`iQ-mO8<Cr8IUE(e5Xw22HSEMrW>MU5Tj8>4wT5%DY^W~M
z0$@4ny@)RV*Oc5HSA^w5fuSV;B*e>l;IB&xOX8EiR`@LxTRM8<6xUG~t9vSdcN?8}
z!g~W*dcmKVGMw$*TwBhpSKEnHu^gv0?`l5nLvF5vNcwg>|9nmLp}SyYkdU{IyNODt
z#{risesJAW46eRkY#0U9shLAT!p7)tCJoNP;=O4SrVRM+PPH%ie^}XD7QuZo!Weut
zBWujz58zAu6uDJhcys-5-f02)y>6gjMd4=-!s@Z}b(tI_A<XXI^a@CK!>>${p~xmE
z{(b2MxPe5)DBI6(&+o^a;3pu+?AhLgOqDPKOWBP>6W}#e2ua*s;PG^&23&Io<jM0k
z*#}?UKxV}`Tj1o}>lskA#nVC-JB;VUzbFH2St5Fh>N*~d4tp~paVP2eL(FK<7$bqG
zMX%w!t)5U0uoeo>{|$2Sr}iIj{f4|7UwJFNv8SaS+CdBvqcIzofgV{7zp)p(`k93Q
znYWAZe|)pm`d;FF%)|}fAzG(o&|`>XVwB<XD^;uNT!DwI;(_c#dHuGgEw2xIgZ4gm
zIK44OItjfVB?G{h_M<H-jKw+PpFDm6a(l3qzcGCgX}bm4!ag$Oz;H9vMs{iy*io)f
z?>_4}Wd`)m8AQ(A8lfk(WPp+Kmy=|`RZgzZs2qdsnINx^Bie&jFW^duCXrtn@Ko0s
z1lSFEfK9#kLauZIfhA|SJM?iYyzEQh;gf?>rX;jYSeaK)A_lSs_2&c4FX_vk!i3t4
zXl-x$CSkEE&*w=oN)xCyU<!PbS9k&Nww*-Q%O5KkVz~6aAgj@G0QCLHB$7biL%h?8
zN%A!)smHHvSYiZ%TC!tzVBtX_FYT!QQ_8i_{Ns#=TNDdZhZWXz0=^?-;QpF?e~f!^
zM53Gc8o5@5@k&m)YcJO)HSSxHN`T^Kd`EQ(5Y1WPIQdW~GxMw|zC)pIlIK2n<Sz2~
zv>^RmsMy{$RRs_C-B)}sT0O&HkWU8c9S~Hl#uKLTs-bB{l|C#J(9*7z1Gmmz+_gTg
zxHb7FNNfik%Tj1AHU-W;Gw@RI=ZD#Eraxh5p)La%%5QF)hGjP)6M?k`Ka)+f(!}%F
zLqs54K-JV=@vTXXaFxbfEIj_06epJxAHe1n?>d_S;xy~J0)lN=t;c$ya@<@x1ufwQ
zm)@(vO;xscNc+TX&Ug8xwTTzvp!i`0+Q7JWB#f1@1ajUQ8w7xyiZaN6=_7$DDl~`w
zH@{ZctsQ*HhD8Iwh3np3-%}rz04XVrH64MMgWC?X5>dJT!3JjQ#ozy|eWcj2VFZsI
z&%m;(b`MPvntI;)oUIq7bOgM#ty6XKCMUeI$_**FB|t5EkYflCtzMN~8Tfle)cnLB
zl~;X7Tjf6^dt3xPZH%(acEh9VeRgXG3H24^B3<P)az+5FT&l`Hm}$&SMmlp=TJCzf
zvAdwm?KmM4R#mS33d`XXyf9Vf2d*NC-ZWv)h2)!{8064ung&ax&a6JH<BR%@5yrZB
zC#R4)d1PS_*|V=4{(kOimbza5c9n?>dKL)BZ8-Du8@uc(q;>w9mv2ZeX_5U1uG3CW
z?LmFHDe>sp)He^!Xv}cfK&wa82fdO{rH_ixAdhg?MFj5XJWx+ti9s2TFmWgx%TNnm
z_;vdPxe1&&n5Ey&H)aj~P}Q+wC}@6p^3a++x6c@g4}r)Ec^!VTuEA<Y4X71X8{Ysd
z`7=WWs}7-ozkWX1eMRt9AT9u<C<+HjEH8v_0c4QNM!qx-E%KARDxjbghViJme9t88
zPg<V^W=wo+&VGrw>lwNa-9KBwleDv4IGySy1p7F8bo3H{k}5gs$<os1FY`_w1Mhu9
z-E^&cxKgl9S>?USf1@HgEVJ#?uPdBt_K(|_Pu!8B4lC_?Ow9e7l`+3@KvBP+M-g${
zb+0i=5KTdk-UdddF||44q)Q%R;c()E{*hLt4Wgc1p(I~=r3CVj`Oo+En2|8Dz(|9V
zQMv>b1zQ7vX0gh=<8~vZfoQp5<oAsoly&G;xqD$KbW(@^94Ra!UkeL(*L~#Zt}Y=w
z#iI~#?aNcPoZDtK%1nby`aaVU=W;h23Pz?9p~%jI523xh^ys3S^=nm`FjXF_+If$*
z$WU4Soq#F4rQ5x<5g#@sa(txFx-OQ~cg?pI0zz2n4CgB^9{dVE$j)E#QeZ09{LKcg
z`=zknrw!mFGi=a`@-qg6XFLS9T1@7#*@|J?9<_$?Jc@hc`Q*_W(j3h3{nTXP8nV~p
zu~osDu5=C^y`Mj@B+4BLO>dQhoG)R1+weBd?;0FHPxaXg^MK9(H(_2))HX$58a8<b
z&+Y_F8|`LeCj!yHm&W}(&zTGZZ-0YaR}Nu}vgcIi6ZO>cD0a~`M<+}gvIv2E!a&yE
z#<@RaotG@q%&WxLa6J3kyaWH{)rf60h7fA9!6^MFumH-0doYz82t$U0{<B$dRCg`1
z2}vVripfpw0WE$l|NRR@19*%1-=?S*n&>UAGd;Eq2(MO^+4K3e{|YZ%9D)4qIGN#l
zAGf7jNQ~UmZ@13*IVu(|f(&$h`|f&Ty&&cefa}QL9vRa!%3-CSel}Y{$-gF9L-T7F
z10yHG2z6Qakw3!RN0UppL)G}1dY~(JY0Fd=R8L(Vu7Q?3ZP{c6y9PjUV6$ef4d!9y
z{GHY>b0B>=C>lxNSOAFR5`quwN5ka4gCu?rW;6I@c#os3LP`ps8hV|1X|B2U(1qGT
zO_m(%YjjABfJZlt4yHcY`$$~*+uOh3ZLB_`<-?&0VVvFwijiYI=QqUiHDI$ez%gtr
zE@^e19uy0zivnZH#&2(41aB<`42iY&S2a{tvv=ZN;;)@;y({$*RZZ&vY0dzM|9%or
z!d}4aeRV|)tFxhO3|e*M=_YXtW9?7RC=Z<&x1$GVSSHy2_zj4SCf9#<fYMvy!jHV?
zZEbE9YnxD0kBwfZEW<K=Ng>HB)U&(7IO2Dx-Y@LhoDP@M<CImVhfkW(`J24g`Lh|6
zPT9y2h#@ZzIBnXfNsiS!B_5Hk-n$CNz{Z0qq?r{OnpY>rMrm(gAGH4+);No*1+iOD
zZ`(vou4&`MmIUtVV=G}~Zy8Vlh<|>BQj?U*(s#J((H-0aU*i0-oQFL4I*v!s=>VDW
z+7^hcZy%S5z{QbkhxmUTA6^ELvkOBo6lCXu4dw|N0cauz@+QL-8U_aI?>swXu6ukj
z&BZ0>4^Ygd+~0hh4E{SFY?;WQ(u0p+QJ^@pk^jn_g0Co|pz7#Et#lv0?>WW=Ur`J;
zMKn2YiX$YJ3TftiW{a_Y{6!ihCkK0hnk&94$5r?xss^u_zv+lbH8*qtpC}$bbl`RC
z3X8}));~^S;IX+le}$vA8Xt6A3{F9hb{bYEhHSb>ho<pRv7<0FtO6zercao+g>9Tv
zpD?A+Wz>CikeN1wPM-%DQqJ?)LW;QXHaB!2_sN5ppXdCmFmixwr|z`@!hpW6C#cWQ
zgE5cE)-vjK(aptpc5{8D_3Zg96@!8xL^GC=8N_3RN#YDci^y1M<?0RG(!kkH^XGe=
zS+*RgFwBOGX&kJ;Nd(GTEk0?dP(vxK%?wZMx?)+L<h~<!e%?NqGh>?+xGxz9!&P%6
z7Q`a(0b7N!Z+AZDdKEn1r96mCpXKLTj7}Z22MbbUj@$z;>LNrzct~QC=#`O(s2E`8
zuT-fE4RM?m9F98x#2p_RX=?9+#$+8@p}1BM=?;CVn?nMRR<*FhrO>WRj0ToY@MC1i
z$RP&oCPIe-LnCpu2GwggvYz7R)n4#z>2sb;_xZly{v}yB_fgQzcSpD*k+}bsrH57(
z=yv2;jR546|0MWo4@L{Sf86sy+qkmKy7B90h2fcnKl3@b%fLpDK(ihGykexVYNKK*
zJxV5q?ZC)V6aLIB1Q>5P_OuC5N5a(MYhMyGF$}{n1V)arlP3gK^a;PFp_W&tSp8Za
zOb>lSJ{-n_jA#)5BGOlNLk7AMs|lfFytPb%z+gl~+wmQ|M=!?42!%}`CPx29fOk<3
zuYy}1vL;o5;qmJjeKc3E2peljcSZq{MfUX+{OnBf^J?G_^m(c&fSHWoFmvl3`TO%@
z^bGg^SZ<Ep4dCZFI?TF^Xd0*odwvGo)hA&e<AaQk7<wVtv+wXZ1s~<05*?l5o%^7E
z@uT4vqkmPJ&+1*YC4QBQB<aU>sJ`@>VE*xFZ(~GY@kOPT**RXPXV4f%O#fG~2Atq<
zdwMl#w!c=rdHB>+4%D*JIG+{}5GASwm;loV7jg`jo2ro*mU%2Sp^-qth+1ufMH3mI
zrhO<f{U|H4pAT^x5Fu$HjeB{2|Ic-#U(*szPxidKphMJ1(gp%emAdcPf&WUF@t`**
zVa=htllOiuPrl)NVyXh$NrK}@wb}d!K-K%-ojZ66dxM#xWQ;)~E&5IqZQ;X!e4?cE
z$i}?$H9?LkqZL1a?@{(h_nx0ZlF`REAv@v}-4B9b<XI%+>8SYYU)|b^Oqz%AU>^AH
z$a&p=_54(>(v4HF2t3C1PI8BIQE5-!Z;o_k5vo0`q;rmhol<4Kv(5c=(aCTx?5?MI
zALX~7?EQI>W>o$TB*;mD2{b_1rY}Bt^fkY&f_m(>r4M0miluVc@u_Y>p*|-Q=fLxV
zElv+F1JloI#B`$HeVjiW_kdqH+&r=XfOk!Xkm7Od4GjY$9o0>akYQ?5*mvowS1RVJ
zlRP-><a5x-N}Xen>KCT@LqGBQ5aW|QFm3ali1GA2k)Kitf6&*s^I1Oc(mgp;a4uI<
zpBgZ>qx#WZl#r15vNdr{l!l&&cwuCNynOig=zlnW8o#-BLD5G2%J8EenWlZLJNv8?
zl)9ytHyB>aQK16ao0Z^?M#u-C&)#cH>N1{k9X0NFrn4r%J^(z4qI40S=YIE-goZcv
z!9VWobeCXtC^I?^q`}IC152~iI)6fEN){H*Q`DS%x%2I%8i5AYChCXnOkllIpXQE8
z=1S1T#|m6KZf~YI@YYu#`^SDTVE)<oyse5lkjy&z<R{!##fZ9o#^KxuR|uB6tKTM?
zJfuDf&>?PJ|Gs`Fea(!?5*jo|-1slIGY7m75sa@!WiB3Z@4{Oipf}#MAJ*sDbl*4&
z43U6d3`X@#%D-L=m<jsLftjGKq#JrM%Kx9gn7;#$gP?fWc^hSYAgByN$0CvL{wq|V
zvc2ySKMVP`LHYZz#p_`f4LtTm!~X}5T{GXA014}|-qV-hWO}u=3AJ+%%BF!zyc9Be
zn?Z*BM7A4>{P2bMf2Zv9Xa~ewXmdKkIjJ<BjsNWK;Enzp@D)|r)YY>m6}}gv{&ys9
zg$uR>flmnLshR%%x+;!GvKHO{Af??z1UGL;((6GG0itGEL3{tT(#v^;xE4H}PFKp&
z@U{H-ej6pGRLKK7KM{B81WYw-i_Wy+;@Cln-o;+aRzi%<&Ezq_8@_qUBdG!Z)+QcP
zlAHQx?)1vsh+AsLXU|8EA6Je)E3>3|Tva*sBrwnCn}xqAxUsnaaf-?hgUFn8x2wRO
zLH5iJ$iD}{mF3{Iyq`F+WqW&5<ft#j^GMoqatCq_AK_lUJb+4b*&ApRWtyY@?rV~1
zjanuXCcua3W1IbdE`O6#N^_gRkth+$`^g}3nFH;SQZp+u@w_MHeCrhRyP7$~MuQsm
zECPWcnl9oRsnmdgL8(sV$SE5t$hoPzlC}|u>ZV?b|0cw{PpWTg{=?rJcl_#QcD49v
zJHq+%a3RL;#T-x-)daMxkX89J*xV*RoAL*U20Ll0<s$4Sit_*_McsQy9&F3MHL4)W
zer^E_aumbm4dvumSrmv9+7wm?cUS~2f0+T)sSNa-vLO>ddwGs07gUV#!kj4b)V>#W
zEOK4QQyLZgAd|F^Xh;jo3~vAT5A}I3<~3ANE!?Rf6YmsC9j`>_qAQ^r-m~h@m%yH}
zs_X$))7N#Ghke(8{wP;7o|j*+|KZ&>|ES7pm>y3<U9#V73kuKijZ#R}$Q_f-E;!z8
z$A0gmzqh!n@UTB{dX7kAIZq?&lW&pm(rQwBoeJ!Q@wPgCHoH$rT4n_Qr8-T=4J4>>
zovjDV`U=cP9N!1ZK{4Img;+!_U;zljOHISvmn$e|7>xqP0PH2N=w--vBZ$&RPs|>M
z-{k<C?7ekVHOA>`Mhvh+wGCEasN9L0@0W~~HJ+|aKWwei`OzyK?FVC#5EP81CZ212
zDCFs@l@Xq|VDjGsNc6aZ#JhL_i_<?~2s+o%@a)yE4@myq1IZWmtS6TepFy~>RFX=C
z3U2pn@rn5bRDQk4ErK+&E*R2|C<vZ?YCvj3D`B{>ukt5uymHrM^arre-FtLrwP%5}
z5Jk;#BOZlDXKqinz{1C_ajE_@=DGFb02_Jg@1_m}=-IsPEICl;w+xV?1(d&IYwj?P
z^m>V!$f#o=_P@v8sV6(R$KpZ=?KG$^p7M=u)ez5b(Fo;&--h0G5vxVOo4B9o^)$@!
z&VXo#kAdO2F(uUtkN}i=Wwp713f$+Xm7+US*$#mtFj1*r0^AS|Z66S*%e7TgM9eJ+
zq}J`UxcblQ;taVEKi&J{ipjD?dip)3GvC+U<D7v#I|Gv>4~I2`k`|aN9FtjbfV1;A
z@U^;R#?x%65pVFkv#)k57<-0if@f+)e<no@`)ryb58m)rd+^H#h+_Uh4seAO_zka}
z6s??rky9yz`?zI1D(><GMle^OrNKb@q9NC})&60h<bn6O4}V`CC|pOz2P^&`eXZS-
zJq}GYwl`{b;%}#)$)0iado-68$U&wEu0p=gNVebZpjB%N04ZF7bAGw{WBNCQcgw+c
zM?<Zne_=c%S9O&3Z0W_&CIrFjgg4k{o96D=x)9EJ#%-LQ%TI6im2zhjnkhW5UdyxS
zd=#|t2Sr7>+F94p(<+9Cd-Sx>tif1mq^Bi&196!Ogc!or7Lb&8Ih<MY4^*O)u39NE
zhe#Nyt;W4~3V$24Ld4L>MkXpo=^0ByfA4%cTfKhyTkv-wmrCnJrWq7mG+!r=-{Yl*
z36I;}&i~-IJwM89UD-f#W3?0bT`I<0Bir?f@e+W3$_kTlJHf``gpUAU5K3Y|F7J!m
z|9HY6&C>5e{SIRDZMa9sPd$XmwX+&cUC`!R+3}1UdC&IATwMzh_CDe@W!!5MbI|gG
zP2*1>L|>IwN|qbZt&xK)l$W+SD9PRbJ~54BSj8VU+C`r$m*39Lrze1s#NFV(Fo1Fe
z5W(ZGC(vXT|7ILkFI#_Cb$fuBINS9Gi+m9;?KVWdh;l0awb_e$FlZM|L!KM(1>~Th
z|JfG^l!F)R>A<DGkh3Za_6bL%quOgwwiP_n#m|rReUg+ns;xCRTDJXI;gQ>KXX{=q
z3huvJr@f+-QrM}RT>k8&M(1Bt|4=*TvG+Dn_KSYqZ~I0*5Vc^A)R$e4^6wCBws(6|
z?OT`}j&dDdO?9viPfwcm9u>aJS~nP*sf4xJzC$vK^<06&0_CbZ^t$#E>!&WBi;6iK
z6D58t@z>!Ogos~N<kt{k)j`YsaO$SY6HCxG_g7mpgHgDIobIEt$v|z(Mdb>rl^zp4
zyFdO?>cISVP4@8fZ)z3!<e_RlbCWk-S6Doay}vH|uu2<%m{VN$7r4<a9LX%1UQ;sI
zvOy&a%l>>i5|&vw4tl`iy;6PFkpg1@j9k@_<aQc9N^=@Th1WbxFpQh45Z{C+YGyU?
z>k7#4u%_955h{qQDs375vyQ;V^=9n1rfsK7+P3fnqS+<Ba1YmlSEz&aOx8gF16qD`
zA9^)UX5T>}SI*Zm3TPPB7Kyd`({5pfCV;E$9<w37`b}@Z<Tj2&dMBJqH5Lz$U#+FF
zv|-PxxA^>G><*R_Z$MB-&pi{jrBOMk@?>a!0rwpotjAX0qL{x3R?QkMW$Kg}CKl90
z#NG>GN@f&jTk+|f-ij*kp~t-lvM6uI=sJeEA3*2srFWyabP1KK+<Tdzt4j&#613Iq
zQ4AxvuqA~SjR~BkFe;9xeS(+y^;oDTF?+yTBluT}imh=~a2E>=t~~B~_^K?B_eC+1
z5v%+gEXZMTb%c^!#yHGs`*H3chs6~X1u5}x_xSZ7K$tIoD%7p7?W4dVAfHs}123&%
z7rJUY#y6(e63Gm1ha?<_sO}dL8WX)YKJN}s&D%ZPZMdF~YGqQU8O>eM7?E4eKkexj
z;}7hP3i#yth}E~pr`w);B`J2WZdQvcSmE!g?F)l98d8jO(>K&6tbHy*Z0u3SgByI`
zim-3>tcYC6ytBc5_QCHz9dLIW^C8o=0j2lrjGM!YtFr}mf8(q=r-$P!Jr!Ic{j#jS
zTGyM++X+Pow8Cn*X_07cT>y415;h4nH2`~E)=*?j6d(NZeTJ|6UaJ4uHsXz2GS_JZ
zyx9&F)w8KjX|`Z&1qXS9AOK#+TH?ZSX~shFUqoWbL_Q^RBZnt4eeWV)%IxFSmI@=t
ze)t5nVY-jo*$d}j@@^HT4JH@;QsRG0+`|SYl?L~kM>IGF(0yM|v#qm{^Hc#av#<6j
z;}<M{=X<i^vQR)F!D>RrMS;ErJDkQgJ0cjs$@MX+W9ZH&e;8$_#r%4EU&x$H>&CSK
z<jI+ymRI%J(u*e+xjZl9)Hp8oW0etUlbudXyb(?PB(nEDJ^9HWt4uivE)iZU3oLhz
z*hf1bUD>(Il9H?RAj(4>M>e|5AjkE`S~=OIi>J+jYtq-0u-}04s2fV$zP|X>w;Q@y
z2_!~NHZpiFkMy*@=D^^l-s~P#yhc~Md*lPp1x3r-SEet35oo7?_ZHYiPLWComEtb1
zC}Kp_M2&s_!3CI#ToI%#iYvD%DRlsj?o}1KR+T-q(jZRXw3Qz^ReHL}vXDOLcCOr0
z4qh;e&I~5ug<?Crv)#m2xnr3jgV+I2o2<X_K2U+LXhPIhRVdw(aHHM949)pw9k6-`
zoU*;4kFCA`?lJ|Foj4mma6ByQFyF{Mp%$W$m>#l*%}cvJ7jNA;^j=#r`?u*gz*#6t
zMVMpyT&KkEef!8#EK}e(nKFIoPEz!TinjP`=6Ct<#{S<V7{x+x9QNb#a$gr`3>+lI
zU2c12?Ra^%PxwYce~Y-F`-JR1TeD>Ag`1rc;xr4MMqNT3^XF%Y`dyc8!U*0TC57q(
zX4`C%DB*_QKBv3-TZi`&@iCO?v5Q%gV1(o8=xT<wr<0DcLs}y>)<FHnspw#BW4VLX
z7yFY=z42+OW&m;~sT&g>evQOJv<17e`@)(ft1bL*$g+#fM2reH_#zA82G%+?{9^V6
zoSWDuW$RXDc^G14d*m`^k;C1>cGu&_M5LeL1nPH%iyI4jC(gz;#aS+dHC)96)zcY8
zw%=9>{}V5BZ|k&2D-(mWJ^>?Ch!;_{&bT=H@FZ!IP-uv|(<RTYh8?yFZQ|m{cEuIr
z$P97Y=XP+7o^5)Jo2eZWByl(5FLD@RN*ps2ailHB;6(7Eq`b*uCB@>bIhSNEffprS
z^iQLNDVCTgJ{@GVDkR<kzt@L_s6Tt>_S@ysKQL<c7~a+Tj~S)bJ?)`g4Ib7^g(faF
z-Xxr%^{WKDS8b5~{6X^Hc9x9RvHVQ8-L_OCN|#2${>KI(e48v`meR3@|57wpSqq6O
z;x9Vmd0B{L+##*U8g>G-?or3Cb@`^o0YG;`ek}_=zX<*6ERVYV&6FV0%yZO*<3eoc
zFJ*||GLwFOTfolK@^i<5?(;{4Ia55d1Qtq5!KtI_fQ%_Nx)_};vir`~dY}C!f+<ox
zX~typ_|TKUE`iosr^Ijfg9_&xh%7P26)FZ@mk1bliS2kpjw$<vDB9QH$=dm#VZ=dP
z+I{Dam};!L`0CE^tJ)OT={{Djt2c|TUzHchOMjGe{BL=OPm30VGU+46kU$ZowEfHT
zGD(*7BN03=$x<<$5B}wO>4|@mrzxeecI<C@u&SoD`@K$!2XVq`y!ay|4F0v*gp4KM
zC%#YCY26WSZv#oZo)SV^di8YecTvK5OoZJ6uQJ&;MhwRgC}Jg0E4@fqG`{TjJmBlS
z0~_;;eKp)ptTgPk`fk6WyOV7Bw5*(G*b_rIlZP|D#ANos!Sc?%eW3kr(%%a8JgtR~
zXgaTnoGv#>rwu8;g>-irN~MMFm>Xo`CDx>_;FTGQ7~q@e)Xu(}#jQ@UaUo;jl_23~
zlHts3SRw4CE=C)Xdz^)Mmt(+Tm7~Otyvnz!m=_Nc<)QnYt~~UoOV+TA^dn+3WAt}|
zW+s7nDJz`IW5wV&buB!3H5Yp6ML;=Yu*-e-5XEo>wmXq-O*>i4kSt=>fugc1NxBlQ
zh7sDk(`L`No99@v4t;8z;(Z=4Be8TP8oU2iY5{n)VMPY3Y<avUiaQc=H@_g<ifACd
zz4>MX+sCox;!u}(XLCOhsCFJ?dq|wYe&|n0JAnC8=9xiTP$)1ZZWb|93@K&9PwDHq
z7{&vO4Biu2hH;GNE5eVF#NE_5$rJ2E)^J)yu2N0LNL)RUJ*J4Go``{44R=pdlAWXH
zZGm-=gwUmPy$Mc_-*0@iTiI9bsb_@!An>EosIG_*XQsS|*6|VL$R38orbzI?w7ZZ&
zmNeY|*7v}JE}QNAS@(`z6t6r=XM~H_;y$(cc9-&>4Ao?<!&e9=662$rG`O;SCH4(Z
z)S;;pVu_s!)Y>B)UhxwUVJ|~XQk7c^i_H#@pQ{*T{SKp7CcPrOIc&<Fu6)mIl(hv*
z)a8%aXvo0Bx3A@zbX3g7J^9@A;Efl_m%x_T$W$I{b(zGOO>mgOQQb1zAM5WHJv@Id
z^U$lCCu-=elP1`{(}kNMFU#85g#EyfB|UVHz^l-Wb6AHyXb6=G-WxuIq=p@@iJb(S
z9}LQ|`aH-fGfGTMT6>ghEn5HjiP}2~F7*T_x`m|NK58ZNi4JR0davpDnu^~SC`v@f
z`4)m*?n&mSk0mC%Qp`8>#<++Rxs28v3keIuK8ZzRX@@#`7piz3c*6Ox+OLWk<#i#i
zob=olm<)6UpT~ZCY3@+h0B69NHGEkO!b_Ae(IT0?#b!3$ug-ZVHoNPddTtl|KGs;M
z#n7kzb4L$exuWWZvj$oS=y0fdR7X7=+|L*0+-?xyVgsmRCfi3dkRureo|UvmxK^^k
z5K3Kq+i5d#;b&}<PG&qd_~=DNG4a*nk@S*=E=e8U+{{AFkF`9G$$K!(&g*ws_bcw+
z^O;<$zhHv3vCdF)$8dC<O7gqmd;JdqM^3e%un}tMgup@B^O~8a`UL`wEvjyZl?^n#
z8itRnNXE2Tx4u7V5gYE`p;w&Px)I@a{Y}fp`)s*(RtN=+scqHLXe6JQeDm@)IJN%A
z_m2@TrZ}_LZmX5W|I33@a(8>W!Wb7R7V1~H2C_A(DTHh(%Klv^+%nlGu3aHa9epQ^
z71G2fagsu5XNFu(oFIZ_7+ZV&&TZdKu#9f%OSU?AJIaxL_4|Qx?%B0z7*6QX>;*Jm
zw(FA;3FkTi9)PDzqgOT8yN|t{6Bs`W>!1_gC{ifka;N&6gffZE&<$$~{=%x0wpo(d
zt`*g^I#Jt(19i1egh^re!w5m=Dc2cWHtFTY6V{9Bkc(cy#SWkBY6OMcmgNeG)D_sc
z)0yD%TPcia&rfy+3V9Dp`xQXe3KXOp2*%cOY~+AzF}cjJ_koBUy(1$fh(qeyKhpJR
z0;~h9*{*71@5G_Ey2{^R9WWUgw-^%1T>>#phW?UyzjrBn&7`sRX0;5T?~?dg8AjkL
zGc(?k&J<&w))0EFVHnRCPa28kJKl2eND@nALt`H_MpaQ-S5#k5t6vP^42di++>zF)
zBmT7bb4Gt-n!uX^kCvK2Menf^o{zgY#3hJK?in!i@O4$pgA&x@X8%7Ws6iIq;q5NT
z>%KY3WXfCO(P3Q!&5w<D9K}`ME%1}X!o0^)D?47Nbli5Tyv>pG=|@@4&Ah0Pgg}v$
z^8lxfuLb@pO8;@`rcqr=C>l@h|K}hy``1DE9#81wb4c$`dqDH{&l~!E$+x8|zD{Zp
zap~8`-cLL&4tO|C_?YuEcmQn-L*o`NdgbZs4nBwF%Db2Ox$sa%1!M_Kg?pck#E%p@
zc<(GpT?5#t(yC>;@e!e|us2Phfizp_oSLj%=Cd9#vsWEU++<Y2RYl4K??{Rgn4#!R
z>uGFQb01d_vJ{g~xW5oUOngaKBkAK1e1t18{xPZS^VOxsfir6@GBGN+f!F}mBXc+*
zwlp6$Gy2*S?fW<U_mSB>cV{1K-;-u)qytmnV5bh(A5{rh%3y4k&6MtXSuA&w_S*^-
zh2_XgM~z<nI8b^!j{fYy2d+5@4?Oc5i9)%&8diZ^AxH42neLf+x+Lrr>}7QXbK*i`
z=t=jE?)MGv6k|9!JSNzto)b~DmG6;*!Y1FnKd^lGXj;a-m#}`SQRT)c|GW6#cDWP#
zs_~i~dVxRvZ_F&Dmn5jPO?Q{{`w}XW<QrmljSmuj<Ibws+03XoNg5?25uYWG<Ax|$
z(wd~v=O+}UGxUW?p`9TloT+UR&NwcoKp{ry4i;53Gv5E4S`xcxP>xWqmukx&B(pu4
z+@J9PE2MF(!rFbbTjqmxBL7Y#ajBGV8*Nlm8aB{N<MLQ8_AG;jL83D)y$IZt<3Dzv
z5*im@PI_bqc$iOxGw1jFpH7&5Jbg_6%tuY`*~imL-Ui58#co3MeFfyoj?d$?Za#Or
zyhAR>q(hgWC8vA*Nr(BIoWt($%nr)W%LsU&Ae#C$-#lr2t`mZI4e3M=Y*Kp8TbTyY
z@V|OLRKvel#&2n&jqXNh#tv@!`=ZR-{)}kp*0LN1iX(o{N%}}$JrHutY!GYYRa${$
zK7tVn+snfJV^B?{a((%y!)Wqe#n&L*R#RHEv)uF9O23a$V)9YeS5aN1MS~;tH%Bd}
zTK?EkCiW??p${+}Y~=bY-Lo$!YHm~4sc)GdD#2+)wMZ{A)+c2SV~U5x>>uvtJdTyh
zjM(NtgQ&(5Pj_cU)^4vye2ev?Ns15RM=BwKsDl5)=*7LYk(LX9@_4GWfwXgUwgTvr
zRwNt^J{$iHdD00?M_oI1b;3ZD!@WyXCPyQV7e#l5e{_X4<Bn^S-$mE3^esfdt(<lX
zKC}q7aL5|0@Kdf`{UU#QG4KnMSDxH{Io*q^0Z8buORnuS+)f>6VCeTw?;!W~IqE8)
zz4cFBdq8n}%MPNH1z~ia^y|>gVP<QJu{&0IBhu^`umq48*98=%o{)f{(zgOB4(4E0
zovzdg?xiUI%E}zv-;rty*x~Z}k55EWj=AqA;^?3payQTk7AOu~O0d4`59|8k(*Ffe
z->@p}pQk$&@#dDr<w;`DiLFS1Pv~wmN7gX=kCO;t*}&JX{E21`7SK<}aFK!0=Paxd
zUy8IKfNuw_gJ!mGdp6ea!L8HykQ3NT8L^=@Yd;eO9t1Oe?%M&idyZ(!d$+HrwPEIV
zFz$S3RUmp}Q<~KIpA%>ta?Lf)ZNh%0BH%qg{&PR0e58>PyOuPWuVDJ<+*Ozr2O)sK
zJC(F>epZ%->jVn}5C^qRL!_>c!<sxm@n{y(4Vj--PA|pq$|OAg;MGn;jS4KG1-l0H
zpc(k*1&DM+KA$})vIvdm#UP5Xmw`8q{-(r!c9@>tdGj^zOxn$%r;n4>mAuV)YjS<o
zTPpYg7QGUx8(=6iNyY5uQ<M%rE(&?+?KnTskSFuw(lwaePXk1HHRe2W)9e;PwQI`N
zf1Lm_G$`0J9hA<$K{VZq_!B@lE&~otC(MP%TULF+(qbA|vEDZAFcP-|f`?CV2Dq#q
ztenVBf+muJqDWEfqYEgWsS~ah0G;G2;-|Fkh{EZ%sLKB&v^Z2V^{U|=@PPk-DL+#-
zoW|sE^5HR<xpu6oz3%mCac5M1Ff!I>LM5m+u~Fv=FjP9Oe;ekwj>%uR@tW6BcW#XN
z_voau^gF48uW!IW%jn<zY{QBP>bq&8K|@p?wPj@t+aB~9IMx1-%D3bbqIGBW_n|5C
zu^_{_-$<;JD@+~ik}jMBCZHWBwFICZ_jmqT9#puW;}je%MfJtLucrz3M(*A^_x|sV
zUHjbiB1$rhc!A#TxRL#R1`T)(HH`iN8){YBfY53W79(6faK126uaIcje-1tvd?A`v
zoBUbvBA0XL<Ghs=Tw9n7p2&wdmDXq*aF1&nd^;M0>|bt~Z5FJc$6Slcp9R_zP+F6v
zh?**iu{xMy4TQMuU-);=87S4G3`f!o&{fl%m5{A5nvE38GzqTc@y<MFs}pKT$t-Z-
zIt6P_ylBk|PHcMRWj@n>2!w^eT~0Du{2*w#OC;Ml5ECV-=eE1Uzr*%MibIiedyD*{
zP8IO+o`J2Y_i&V`IS5V!N&$T91|q}^KxJiI2hF=+$GE0@eJz41Ne`N-+=GZaq!odD
z@e4?~Xa%-@NKwXj_`-GkYt@RWynIUmI7ADPI0vy5SH=-<_A=n@VCFn*xnl{YQdQ##
zV70gD4^!i5$ek)fa+cYet)Siuq0GnwqXa1LEtg*o?UWl!wRHgrOgFz>f<Nc&P2WDX
zLw9#N1bti8SHlGCZ9^0rNK$Ln)d}nkxbGu^dHB7)69EMbY+@E9sK6NF^8^pl*i)BV
z+0TEseAjZlubXuMYsmnMRTdwN1IxN|*M?|&9UrGpLj~eJ`~iAXXEZed8hjrzvl9Kq
zwChMcJ=KToHB4S4cpZ@Pf2AJ=5@_7n1G>uo+GU7*nF>n-`gRW&*d#j-RKI4ALo=^+
z*y;U8!4p%)wD%>q@+44e3wH-^v`c@#X8SDyLB=rPK-48s7G^>DuGMNoty@aQ>!-?1
zy8Z8-aJZsV(k0yOcqC^Et$_DkAsNUUNEh}3U}RG%*yK1WkQV=MKYdQmThNbO0{qZh
zS?ChbiN2P*TKF`BU9mI7uDK6?fRWAM-@h=RJqx4kX*6FZ<5k+g#>CmD&pop0F%VU$
z5*J|E+wTrsN@5R3aIhT26s4z<>ha<JQCxRnNO{R0mx#QMslGTU)DFh9&&PUtpjvE&
z@#;+%mcsGZ(N4h`K6UG3Q6(#?4|e{&eSP}auA}p4@7PT%>HnuA8o%kXHs*sm+B#0}
zekiGnJVaP^UW(G)@pTlc6~Q7fMbyjYAcX?wYrlu~h*N&>Z7Xm0_T#XZSsHe!VO7M;
zVRmLEaNubn`P3hE46C6c5=yP_Y9$C*0RF?=u9%FZ#18{hU;Y7ZsZ|(W1vhu~?|r_a
z&ri=D<vV`a@{)g)ERi??+N@IKt;3?QdI3gOA6>xHGEJ0t2~lN9H?@CZrfwE9Nfv=;
zS`y#Kqc26B_Lcw5T|V=x@>uIi%IG5pQP{qBnXU}T{FGDpS7A_$6p+&IOkT5r12y#=
zTUZkpCq1+H=U6&}72^&*qh9=XQ*7z`ZYd|@W*S+%EY)hmVC(N@zs$)BS|M6Px}->m
z`+eZ0dVOdMaXU~H8L-^5vcobF_x0fwlssnDpC(0;eF;Ns#nKUE=*=F0yZ=3K#Yzs@
zi;~*WBr5^8gt(yPPxj~&4g@*;8jbePKEeI&l&NV1`aZMOhzS}iFtHC^W>gQek-T$J
zO5Oidw%7jMfv%7zK#IbC5XIpC)ZI}e&27NMcs5{nM|~U|0uLi%*zlxd`1X^?zwlsb
zn@YrCQfC{Qy`tDbFi`W%=)4@VdUdh+cyBG^p=qP90>EcMS|<2`x?jN{U?i3RnL#Bp
zxxY;-xGs*oA5M#N|KxYb?(zHBLd0AUv8D4vCQH)5sQ57UGS8G~AWc0U<)Cf+-$IKC
zPISv~oSI$O&V7>9^AZHe7U-OU81{1T5^9V(VHlQ4Xy%*T29s)6u)uL!xjBaH_KwwO
zKzY<6r38ZmkIBIk2e1p5z;dS?PSjm9lU+qQa_1+IyE0yfjlAEX*Kp>!$H7lqc0E=)
zewErU6rZIXXx44jzlGlN_x~Gj`9DyP4yRx^eh*_llqQ#Y=rNuzJ1Ph_{k0QcO=8f?
z_##DKgCtj=F@lTyo}XyU7)?DeTK*?<pl<$erAzyt{RaK?L9R#MkD`>!$3u@vK6*xd
zM(G$Me_+5uGLTTy<8}iJ5`h+xRdR@*{u_=Ta9>nwM#`yE>?hcPNI7;q=$f5eqhJDF
zgi`w3?DvTzo>{+%OEwBUUGjQw;@3ZKoO6A7$I<!bSRz&iPMU8-;d~3=WiC_07%5RN
z(c1rZiN0C!g;TYr6!iVwAa{8Nm41V_{x=Oc_XSAP5fZ2Ng_QO{Okpx_+T9tckkS^q
zbE+x0<nkdy^z@|6J@DhTYaicJ%Ur`gc)NYv&GAF@fdU>`CC3Sk<+Hc_#A%3w?Ar&%
zwcx}xi<$FIWTHL!&<F$X8os5yq~GOR^bfogG<#E5Th8MDXAeCOGN!WXz;)RhrBJ!+
zH=mMf3392Nd<5*17u~QY&M#p-=77`T0=Wb3qy7+}swFJK7Q=*8fGcn(g@1)HVUySB
zIZ(JMLO#Q@iy(3jd_7Awg6ZCa8<#)JqQXZ0pzQTsHt<ELo!G>_6e9J_8t;qI`dbmn
z??FK~3eavH5PJiOXx<;s*3L-%`iJ0JH}XGy-=^>8Vd(5dLrvgCs@i^_A4m`>p298Q
z*AJZBI?Jmg(;6aSnIOh%q1Oi%NlE((g!Up5SX?=W#U2djDV=XIN?tgMXNEYPBzW3o
z?uXVW&rjQ!rn*(tdJ}}~USXeo`{8iC9LFnF3<KM46a!#t=^p+X#%0JAs|PGt?zZSX
z%>3e*&T4eM+-2bXkBO?VOb-K-bU2N^sK7y57F)Wuk46$d-@N@k%+uJfe-BvH6(#FH
zI|@;&b$7o@)NK6v4MkQL2y-vPHE{=isNbx^!=)W>e<05uWR(LXlik#0x{x2(qZ`QA
z47ro{!XG^Y4xnX_s__ALIbLRrXC1zTE7&@fq0HMdk>h}3=EN5RS>r88T3P&|BX_;2
zEA1W(-DATz?@1z=Mo4V+Er?lMQKVqiZ4Z=$3e}cXC(iZNM0QNy{Ya)=!(o!dXbM5)
z@H()goQY&|up*fM^cAQho%@uC!3X&}7%6&XEdz;}z&kpO%gN&G^MQg4w&%gU^RF}5
zw8s7U12zm^hI2rF@vI&L|BNdjh`0}Gqbuc5uE@Q$LsTOuR;Y4{DtNohA++~@gQ(1{
zS_=QO6Qt12FW2?t7f4m`J(;%tLdNouY<C9E>g}s~FTwG(&mOVa7DJ;Ryu0$~@(17`
z*a5}u1yX_jhNf$5`N#8DzqG&IMr0XpJZ&e`7~>Y76+XZie{!k-jyUlx!FOL?z%F!O
zR(#?|ZIkz%YV(9^y;u`IBA13qAX~^0w)qCh4H(q-M+!U{L{4p@ALJnfA?qYu3}5=+
z2|O8ITba88G{1~0X-#2n&Rw-kyZupHzuSL$BS%4j;g6UVbc217$hV0)tp{PQ|7?y=
zD6@1SiVy)K(_oah?tEx+ltyR*H0$Na>{k$SLtoU*BhEnfSEP4`@33Xr{81FM(B1=m
z_dW2CMM~nYIU1~@+CoH7mwi_wEi5obBCeyr)9RlERh0?LpnR!f?Ey}fIgroOE_p+#
zKvK&_waXFhH@E*NFg=GthpE*^PBVg>VYH9Nv8?7P1JyKs@%SPqiCbE(=nr-v?b0$-
zVc)?i>(KZjo77^LZGub<mk{<oe+`cicK6Bo&=rvASAm^<dRyq6cKQP=6cjN7ba;3C
ziBDTDFKdIp11C(i*%Q#y%eEYUrCR&JUn(}v6iQxcuA89tybM?C{dD)nU3y_a)F@F2
z1V#09lEW_-qy1NadR1^xSs9aE18J;JI-l!2x_=Ly7NNucv(sYRyF}KqwG0h*4eqGF
z%Bx+#?{G;{<8qhVk;6qwgUa)(cb~h4Pq|)^N*g4pSe7y~i3J=3iDBo%h(Sf72hK(;
zfagXY%L0XU7}6kWqm-xpm>5*rq<dX#VG0|EOzQL!pX7s^8!ip!Mwzgt+q=6t;PC0y
z3xd#tut(soUpoU#85cBoQWTZqpCNS-J1E|T*R!BdHt5F$oW~-SPT<;Qy7vZcr}wK7
zp8dXUB7_|Z&AlHG&aC5d*v6A1+}IK2!LqBPq-gG})}0T*_^1<tSP9(Cp6~>6LdYMu
zc(%W)7i#yRjne^M8~t!@xBuB7Ee+-mI|vUOyz~Ja$`ik3O}pg}T0c)R+Yg|+9jg})
z%Yve!D)#}H3w|&!y<|;MUpn~d_Tz(gwAgAnG;n*di)l+%EI<@zU;pTE$||~BDECRe
z&P%8`{uN*21XHjkY3QcA1<x<*VQfU_0T`;(Ows1*$bSt>VOEt*=uubgI~l_x2QA7!
z9l$JvG+kC_NRkZ5-1!6OzuudyMmqCz0i(roZeVA4_xW1<e{caLbTLWF6|~k9AOdx-
zVn#}g7yk1gH7XMD?s}^RKMClB2dV&~lJ){)GaO9|2rXxcWVd+h`giUIxa)vhIDk2t
zSKx@M4=Tg6xyHrZ3p&7-VIn&Z7rp9s1}gj9%QV%8FrfHqRQRtqRld3q<>-M0A^ce}
ziXu1*=Pr8Uy(#rV`_m_UH*Ab>VCn4`i+7={YG8>e{Iu>7&8C#YjN*mYdnZU3eFKW1
zN-X^WAYOUEIVR5`?JD42k8feU==l}Y_m@xtz2UD1w;I3K2KbWpY`9d@in)~5zIYU;
z><dA{R>lgghEexXb+f9K{6ZaZare)y=A-mu7nHa&vtTkuB7DZIUgtY@DG@QhW(ZI7
z=MAet16RSSf;p&NgjdGyqlivpAK+$G!}M_v>*)qTEj}LfztHw3&{Vc>*mr4{9mU?r
zEcTXAqKM2>W@QSIp-h=&o<f<jB_bqcjzW@oP8pL5i85v?G800?dtQ2;|My?-H@xq+
z*7vRTJhh(n)OPRtzV2%{&*S(V&0+I!2p-kZUvMKnXt^d0wMDNF09j1NHHyi1)uj%3
zj~z6E$Pr3Uu2h^mxQ;9CUhq71?`rUm!y#-G>_eHgvImG<Y!V#nySUrMi8%8wYR8{j
zg0((*Bm4XO%=oupBVf&@`R-q<#etEBsegHp>Rx)<Q}E4Sn!W4EFm0iK`&9dh#>8S@
zl@Ce;J4sP$^4<G(bMrWK%Fpa4-ookQ{1?K+xMPEcG$<@P-{E=Lp@p@Fp5^&MF|5E%
z56d{d4L966BqB1xKpUt)FejHuI)AAo!E;1_mQAzIrfuWx#5>wh1v}BUL!%Nme}Vu~
zRQm@EjC>!-`VOc9n#QuF-LrVuWc?KFk_=#%B(K%Sr#1`ttc7sk=%gy0b%bHT^KciD
zeDW~vDup7siwG6_x0&CMyS~(YKO0k}vLUOBuYCP>Pn)tp2E}A=4?g@y1Nl$H&C86E
ztr<Bit+Jjr&S9M486*^>av|*G*(i5ngeWIgo8*2dv<-{6)El_(4UcR%r!3wO&8gU*
z%;bLC^I+Kxbkfzdq%{>2=(ZZqd3f62D^8Ac|0b@i`<NQ+)DQY|a?r%_5;G;Cc~lWX
zG6ggLGiIT#ddp9@EfgBRyp-EC=^@45$S}-62*D|ow8Nfme)C!<(^$f20y~_a`G3~t
zlQ=>+6j(O(MYLcMyXfS`!191pKz)2b`MT&Ml1j#~-?<FW1)ts{-2hl*IMwcxvNp2r
z=i@g6M`wA4k1M3JawCSBjb|5hV5sU!L~g(B)_aWh^?C_Xx#h46S_Ibwoq#4{>}wIo
zNQc3KRRkc`UbKXLqL7|bNo>|oYJ89uH##g0RnfUIdVDT$BKQ^Aw3YmZiLJOC1&Ot2
zR`4w$TfOHO6E8zZr!YA}g_rT;___PhaSWlcz>S6wUgCs=^%0`PR9~lbc0cTr+`Ahr
zjwLa)nL%!k)(RA{=5ILV@s8ilCYi)0vCy#&sAtmTh(H?=D<>I7$LbeHE0y{8zN#g1
z$EF=6@a%7084`^-H@zH}hjuir6x-QJl>6lhIA{*PdL(olp^YYJX%EV{j4Ck1Hz;v;
zmJ%>7Wz^bY#te!(e%*niTsrN@Ueg^ZW18*Ek7p?hT6R>}9_A$1o=;L&@clk)%sEA|
z^xd9P&iGxgd}bhJ=P5`DkC|iD2to5Kbg0%+jZK7qqw(p{``8z3VK@PpXBYn_4j(#t
zlcRy0oeV2aVZF-X+QnZhQE{j_`wfV+NwP0Fxnt$u$aCWFCrfyF;uDX3C#0=f`Bdb7
zwt)=3kD~FudaOy|@0AVYhHZgy8c{^>4YNtgqsJT-aV%syn11`iMFr}Zs7qV{WG+3n
zo6lQKBWvBFjIcjP4t$$2jPXU$0JdZf=YYgNDER>#qmwZom*Dhb&Mm9Z0nAg1l+v;4
zU=|5FJswmNR7BOvBL#O%`6XI@ynd2t1U;aA$N1Ya<&LtkWMHk=aWNW}ew7q>8GryE
zc%JgXtS(XhyUm_<<xRrFRNZ%$<QyhRYChOTfDhT4c-pN%0|v4KxHy%fO*kbub5f87
z{dnLNjdxzAkYs3AO0i<JqvE1+il@F?1$M#V-Oz<Nd?1MYd^DT)Nvz%g1}gr+0&Oc3
zk|5OO7FoQ}hSlQ*0Fdo^&-mvVi3wMTWs(^RvK4Gxq5Ip1l1=vkS!zw5-XpldYEWYs
zeu8=Y5V_XOK&dDsS)u6uT*vcuB})?dPgvLbU{0NdenlbdWh+Scx3BY(w-&pl3aROK
zDyHfDO5j(^D%Vd^x6Wp6)r2}~K(l0>R~t>xy}~_GFn@Bq1ZzW<*Gv~8o1+$D-H8k3
z$R-V#s-sX#7tB3(*??DqqqGaA412^%k^%*|=RcD4>89V%Y$yl%w0+88ZIW##kY;_m
za~|CzXz^~SKk?d<R80EAL|J+y37zn#|GUnmf`-^6n?xx}Ejb70XXkfaj(R(m#264f
z-MMq@6GO8V`Po|sPGJ&J6H9UvX$emhwGhG5a~6&nLk_U__F@HStJzrID}Z7&ut~}|
z4TvIdIbzT!H=Tm`)oCa~AfPb>;JvQw(S+csD)&iq1=<s(T{-|c*|mCLE7BF>(-|pM
zP<k*zFy_zgr#))vyV?1*vSLPHmbG;-z=i*eSvNa5JT_Lk>HG$C1E&t3H9@TSLF5ft
zU3<0)<Ie{@ExQib?kc2pLO(^{7qjUdpeupI{CS{|^kIo46$2v~`8TfuU8Wd$0}%TF
z=%JR$^0=H4J7rAFF(=r@6$!(}FMlFNEvh9I6_Zh^7`ZDDwb_eI7l&Vl!<;ytRJ;op
zg3Hj|<id4tZO+PxUpm@0$@tXnS{B@J%Kx`FT&Xk|H2fW3;nY{dhxh>8yG&q-MznA4
zQA}1oDa}t_i*W%E0lnw|1YnFJsNGhN7r{&&p)%yR6bVOpzr@4UeD3_Yf2`79#Lona
zUc6VE!WIt$;8;#){u0{Fbv9r0g6YE&aTI6n7W%@wt91lS3<JdK$L~;Wg2NL=x`pz?
zR*J`cas&b?>MnG;P+^^$eirJsp6Cb()h9_!1soAyuN}>t&q(+I1GE^djf0>IwLk}U
z<ne*y%3U8~5EVFuICE8+Zjfouwr{7b4a&Rxy6)FiwGFPoqZa%ByYe)NC}S@zp58}f
z!|<E)R8EOi&+T?DN)TS{7;UTH_41x4p{&D65%v?z1-&Ryd}{y&Oo9;Ft^!Wn)W4wI
zXaqhBSVDOFksA%VETyzTXkWd$4Rd;9Ry1d%k$*@Ua;xc^c<6bxbs!2iIJOcH*Txex
z+HLX7bFjmuiZpFqzVf8N%{DpA=FBAdC!Nk#u@ipXZQfv@8XGaVcmiF$?icLTL>BJ#
zWvSVcPf6_SQ2#`6h~NAMU(Zo=|AB?D;)4d1T#}Q4@50aZ6UNS*h&(W~s^hM94^%^C
zqzCFNPQ_5(dnT=O+AUxln#6SP+;ml|5TjsqEoTLWw-v{5b+FMb{%2}CQvKT?vB+??
z`Zb#Esl`LI0h4_hoSplCAmo6$Cx`?bE`um!!f?<*73&aA>3ag;#j26VeUw7j=rKuX
z_a>N9edH-GfdeIqzMKB^^sWR;HgME57MLK~;}1+Qo^W<&wgwxs+TBW}Qkfy&t~9wl
z2m^NQ1BIjJC9$Xb3YeKG;pOCGoX%_9c{y3-O|&%0urg~g$TSDF{Xs;Ixei#O!J|(A
z&F+?eUSs|6BJ^eY$td}T^Q+<fYAgjSfATCCOXmDqMb#V$*c%CDBiyuy?!I_NgkqWH
z9=HcYtq2$t`TNcx`PDJ#-CJfUzrH)6TpA2YI25Sk3Hxe^7;7YR(iTU!qCq%{YHY4U
zt?oHgsdrp9Fi=D;D&W}1(S+I`ui94*=)@|MyFj!_qJ++OEFvH8Sp85>L}KiYP#$Qt
zenA0^X>u63Fji0T6I!VIZ+G6#{>6}$f8+%)JdZd^J-dY6Daoz<v)Mc*H7f<ZYLhAR
z&2G*p(R$%cWc(S$eH@2+P5RP_7XUj!$LPUQhjaCBnzoIhGi!WAJal7GE|`=?t07uv
zQVs4j#+aW9oo-H75$7?lxUC4_WN#JmC>X`@h?!d#RFmvvw$>gawAw!E!wyD{rSYJS
za&YRij6H9+OC28D^Z4p4sM4J`Sb^6nGYY*{Q#W(#;C?1@4sS?`*?;Ea6gHe_OE!6U
zD*h*QzNuP2*>_J-?o=>2F7v6(LW>q^C$=nm&>2PTkz&@<mpg}5+pF&%nAN-q4iXgt
z%Rsr|BvgF7<Z&$bqQs|okny?y<}#E*dPgG!=5*-hp|qjVNcisJNFb5Yp<C5w8nAA~
zX&wMbx>A0Vs)|T$g~J4_6!dn=fEv~Z6QMQK3_N4zV0`3Pb6>>%hWEr6BT06McM=xP
zH=kPvb)d3$H19V|5jxQ8BtPRJ+kFKV)A#rV|CA#^A<6Ctn+>zQRn#j1wx5Gkej}Nl
zaba*s(L{j~a;5rb{(dvzf4|w|mkJ|%6~jSgzzUTJJX($m)VMSj!>KHOY99f+`^Uey
z(PUL0C=gb)qz!aAS6XBC;^mS?8kl4d=6@q<L^>oz)<Is9=(`13TF$)D;U}j628UYc
z8q06JlyLHN@-Yx%(Q}FBIS33fR#GU2R}Zbj7xrF=gfmJD%*Z>n-v`P6&$VB4s)2##
z__d}oq9qZSAI5k&k}PTGU#wh`oN8+YO1#xK^|9J9JRZ83fK%`_&j=O0PAWbTc97-j
zV3kKPf?|OqN{~TOflYx0&s5TS>=~mW4&CD@KO5U75YYLY)ZgjWFF?1CD*swd*I`r~
zMB|4S&VP^o`}_yDHj7WG{e$4&TTA=zt$q3u6@g9Q7&6DI-nN-ukRLhx32i5!sA=B@
zS#fe*l~5aW1am_PC4a?hL_y~soq-yfi0`t^I|e+RB9fa<?3DtgMH%$Z`T!1OE5|IV
z2-lf%the(vRt`FFo<4`*bmIDB#R0DHB=lV>n8nbPnSJ`-dOGQU>z@9B@|*+AC*`Q3
z7DmP9064HRSRdN|B&%?=%^-A%i9!cjfHH34A`z*^O9PiPU_d#s$<cP|53B+cVw^m;
z|I!d)rM3scOd0CC-a5c!4l`5|Z5F!0aN#hB<1=4a!`tbt3WN9yxy>V|svHC&Yh$7~
z&KlCvmIz54ha3cDxHk>azEKJT{SDM-Qm^fo6Qo;?Q$uJFRqSo2Q^1Y!46`g=I7CO^
zi-G_wwC9kJcj9C=2~`?OB9qYJE$c%@XSXRCvj(IWUkCnp61Jf_8=8pCz0z^ASaH~8
zIxY;?m9-EfdLTMuv~43Dr4Is2@>|=A7?9tmxG*kC9RMdABSw?>e?tp%7P`vLIg?Nz
zoo{1=v#;ktgJgUhKZ|-sDeQOC@~mM~n+c5{Dks7N(S4`@xbY&1>o#r~I-EUFT=?es
zj_z29&NhhfzB}YkAy|Im&H1IwkhBNnl9}Q|vm8tmzG_+6%<iot7{&4j&?*1gCcWW+
zy7ASM@Ly#xi}xWSEi%QSb>vqI=iQ34HF~*^{(kX?|9tVH?ys`gQHL}%D?D?XI)4@D
zE3NOfayel_biwm9)tNxB?+lRLN|4bIrAl~)F3<FST8ozYv;4_~|00EiNYDBDp|`>t
zuqO1vf??cuPCgQj+sX;*o}dsy_&)K+cK^aaW*%5X6~fN!Q!VV&C7_IpYNdrpe-}#4
z`IXKN`T3?jO9$LPormf3RWIVnQ3)pj|8$Ui4UEMTQ&!}yutAxXe;AG(4Nk&YQ>nf`
z5pvXP{_9|=)E!DOx3|gaP9{`(x=_$iArI6Ue|yB22c2%O{`ct6dxi^1HM(;Rak~uw
zf^65ha0eXhJytL4DUSD`n%euQhGNw-niPlQr%+Ff!}0O>-<oT-L+M3%t+HmJXD?x?
z>yFWT1<UW$`7}CmhPubFtnzElPzuxx*N61J6g8_436h76Va7pnY(8WS&(*@#uz$ZO
z>rs-HZZ=p+a&bX5;Fd#pw+X5v-_9mdTT{^;k(q^kHccNDD2%*K{T3BB;E5*jG`@st
zqPve1%v0kjE@;9nu#o<N4^rtb?2xCd45on7`g#S&eO_Yr*C)9wifQH;bhfuC2}wJ`
z11r_7q7)h>Xw~uL4H_N_>WKysTY1gkQS+D5wYIY)4iRBwkavou-J2RuB6N;s>;6xx
zfz`8@4lRFRBj^DA3@>}ZW5i)Zq>+xg-G>gx+GJUx6=101MusYX0{|X4w#M=Pf_Z};
zth5J48}4@k0)TrI7qbL=RWWT6Ye6X3TD2umAp;z#_HzaDNzrK~uyiXPAS{F^9wlIq
z=0lOFE$%fpGnI~Gq9bXuIsk?B0z<d%k2`6gDqpYAx*L*q`(gEAezFxJG-nK7kkk9b
zhNKY#yO&&tX46szI~{om(1v?Fn8X`>pC*0@6c~)mgC>`UPXAp(2ju#pDHXBA#tRa)
z=MieD!uI-?oO6?{h`4Tzcgt(JIiJx!F1MMkTN~z3Uqp-2bgnuNGfy>!;`|*FFN2AD
z%(P_C{!vvYIV`cM7xq{4TZzku6xzDzgOFAZNd@h78vdhnkg@<#hD+EsUKM;pC=*Y;
z?iVT{ySt()%lS_**PW37puTanYiQblX9oinD`J-q`jfLd-&Ego#{aiF!$*bIinKhq
zZ~W#tj07Hb1}I1Wk<w`|I{$h#bQhAQr2c1O2gPyp-PTID@SSh{5Ohk802XWmf%W-^
zr(JU5OE<(}N4mnRJ=^u=*V8PMS~n%^Z)myaK|`yM!;16B(5NlVyv1b2F{BIOgQuRz
zcF6-ao@uohH5X^RInD#l{bH+Y#sm@%yR;22yy3+W9pKfweHKe3!<vPfB#^{1gD5H3
zC!pNrVNDfUx(90h+$1uJ5DuLOI6NI|*9JoDLX&p3jd)B6+xkXPUd#Wv?rONK)&4j^
z&7&V(QP)l1uELUMn%4~G{IDWH*HBS6Q%lgw7+(xG=Z-%aN>{%YCztc$+QZ|R7`sH3
z>%^di^Sp4D5Xl%_zp-%5a3nu~PFZ$I&ZTDRdg*);HFOj<pmLGqO{)yXOmf9;I7dUP
z8Af&Ftv;xa4et%qVI4S0z=pZQB2|Kr7mBl>JmKd*ZzKlkg#|Cbd+Z5Ju*yNL%p}y!
zqP<08rm$CjfGdG!9|~idYkc-=w!a?$kT%f7af#6cp-3%=Ci35&D80}C?!mQ+i5d>n
zU4%4atCVvYVVV2Q1Mkr#%+O!trQQu=Sn{R*^IBXq2>fWY#Tag5b6;G8g|wCTX+<$4
z2fe5%+E_ws##WR-YyCV(PvQ?8kZLl517=r0Rc59cl(DM)UA`pTGt(D>t)U=-<liE{
zXabh5Y<Bp1wI>NSz~pr`e-F%zS->Ngpv+VMn)_$58DIQxXlC8737x$AI|p#!_rc!d
zbJR%!IGb7PQ6zM=@eC4T*ovvhgyB#OctWZ_<O;a$1`CUeOwNQftf0RSK0tarl_Xi-
zCCkys?mh36c1c_*cW}OC+<Q3lwjxK!uIk!gl9k*s>cH$Je*z)9wssQEMAy){#IfX~
zlE~Hfj1mwRo;0M|YC$ofIl2ThW7GG^8oToc1G7CJKBV&L(;5<cGgw<Oz2t7t%?CFW
zB%g%r+xv`6U=vAFlBK$QTB8cc$xx}cNy+<K*gjkNf9SI}8WT^qSf}_r8T6S*&R@dH
zCz&yU!TbPTs!5ql8jv}Dqjs_LNn=jz)>PUhBOLscI#RGk#-l{PPR_Imv|mx?a3U9s
zqWTIOUlheP@!Srms~!?Z^iUawgO^OYEmn2Kq<tN<R<fV}(S>g`ucIXq5@322dxoH2
z!EaS~8@qP(sUaI4eI02<Y3uHL-2$j-V}@Tjxb;ZhMnkOQw9`NjI&VOm#zrjnBI>{s
zwP*q>*jnx}KYoOY1h3CR<wA-zj^ebcr`GNtzLp0;Ml0An%wpY1=T`O&(T-8%Qd^*U
z^MRThx376lNfr2tbbubUTww0^RJcj`jtkTQLx-nhQPYDdKKc-u>spcEy(tvTR|$Gz
zK89ZR?&A_FU%aiJmqLM4N%wzN=!(t#YBIU3+xFKZ)T#(Wud*fA6rN2NO>{#-U;2dx
zxK<h>)b|J-DKbi)!|S`OYJ5<!)ZqW;&(a=(jsEwo3oxSr?FiSwBxlH^x`NIg^kgm)
z(S@hg;$fFYx~UA+GvCvUha7Itqy;Uk*Z_dv0`q?s+M=75;rjWnAC8|4>~ai--B3h~
zz63W-N5b6>B^=1ZXC1}#Exl=>l4MW2b4K9#FXd`bls|g-QT6yl*Dn3D76cB7$h{WH
z@oQ~=?_Kguk3dudjZ1_QmEazIRQ;Ib<Omb+w|GJigYF2D+7{LL`#+p{{Us(R$}_8b
z3j(yRxQ)M8{lJkG?`F5_S$ZKZI=MNhuuaWxg4JtdH2;Oh>na!`vY1vh86@t0<_q^=
z84pjS_SA(4cp~?}%_V5~wO0DP=kRoku5$H`)+V<>y8E5wj@IVB&ZOoN)TC!4s%txy
zWrfkLlC^~bWPG#cPDL&HCTr~hs@y6VH@M?))_4n*(_Sdb=0TL+XV@khY~;6YfrEz4
zCa>r`^ngL&)bE+|RqqzspSE`7ZeQ^K=UTVEc>o9PCbkj9B1p38MZizGg>4ym*Ks8m
zg>PCB;M@h!^2?V{-+88|5ID=lMJKhgu1fdS{Z~m^^fTe1s7nUoy`eO^9VR;zb$N5P
zP_iG)c?LRc-2ugSe=%5{gI#rcroBR%C>e-dZckf%fAQ;|FWY)I?Gk#|Q1p>r_1^4l
zxPm2IbOSdEYsUrF|9cpzgCCCCV^Clz1AuS<KNfuF)gZ#k{(1#AI|AHWe6a2KTxx$2
ztr|!Mvy%V9tl<`7#k6^BBdQ>@R%KsK=asSem%QS!K-fnv6Hc-wyvnqnma)L`8?b<l
zIb9xD(F?$IlflonLvhD_CuILXe;U+fi0p<7B<^3^GJ}-Imfg=MzQ>mYLpRD01zN%V
zDnnA5eFqq?NX!%jm)do=BH%Gfl|$+bB(+7jMF-%qh`>J9cV!u&vjJt<2cqP`KV5Ec
z0<2mNjzcA3q%Y>Q<0;>Sa~^`KvH)9eV*-p36*d|FOLg&zorUmMUDb`_q2(cIIzJ{&
z@hzGh&zI!-nm_$B8Fh+$wR-_i{TDi%e0`9MsGVFee_Z^For(4V6;mu6tWMQ|nz-d7
z>}w5JJb?Vdj6Y2}NjioY-AEmCpXHxYH9FjB;D{7Yr9-Qr&}NcAAN3Q7l3?@UNYYkd
zGP{Nqg}rvtv)>S((Z83$_==|FV#p5J9||G;P`R>06(4(b93J7)#a(RB`4BiG-A}{y
zd^B}8mJUXRa11Fa%)TYtIrdx3OH5SsGkE#ivRp%gI*#iwdaSOU>*j<fM6<tT<TyUf
z38@NrKK_XOR|_EdZ`I?9u0#){bIn4dA*xgxB}M?^=q);bhQz1A7ISNUd#fD~K!_Z@
zKJ{l41(+diPS+v2-wJeEj*Hx@1Wl=xIFJyS&AlgLat1`;Z>d<7Lrg(eb!4}<1zd4t
z<p2H^lVNIqPaR+Vbm*e-spVs0eu9LZD(pEpT<Ac9mY%hns+E8NiIbNnfPAY<?Efu-
z^RHZTB>X^tp0}~w2^dQ#&bs7$2Ri1;LtoPHh!3ECjBEyB>T<!$A-1?bL`rx<rL-Rq
z7FVGPGAxOgyYN(_6a@57GClSCKR^-v3_oZSS@5OYl$cI&_RPqa|GX7hjy2tAp_zeq
zA@1cVJ;a@t$40lOE}x`3`U>WL;rV!5*EK|zf*;$DiW>sH1IkgXlb!vn?j3S_=OcYh
z($!Ef2Soi8BEVTzhoNm&{7m#&szM0;;w%g<6o5_uqD&Tgx9lFuToZ$8ok_7Rc|<6m
zKK(K67sq{1x(8!3@BBTxTOP=bW^)dp(w2z3EB(9Wvw_9IRcoR>bg)wIv8;hOr3dg>
zc5rwsKxgYRkTdndadA2YVV79&BAK_&`tb@g+d{dZ9QgrI2IoUhL<skU+0O_-V`n$y
zbH#}_?q3e@`BH{(D#&Djz{(T&AcX(2hoGmo#<@9ULlfx*h0(ZW8NsZzg7=G?R6iy3
zs~4^hmD+#tX`9KNpWkQ8_T`fOi4EwXPa7#_ZY;!ybJPG1)Bz;5p4QPP3SN`N%ylbn
zZ<Ia84EdVt947l`mw7R2eG0Uj1(70sEub`d(;>$lImeyqXAT8rzPn~!+Q+5o0y^|A
zn&{o17y{Xv)A?`zfq+>LR(qw|{~3cCmm9wKeVt3(?^+=>AMQ@4&I3ZtD5H{fkn}Rj
zyrLk7@<!t7Ix&LJ9AdM)tljSSozan6lAcF0-(ok&U{qX*y$&@4>+%#~*nBe7B%HUg
z#9TRer+$@!iGtq5{bNRoVH9F~AtxuOAa3u9+ReWASzgiYeqlf8=v9-_fJM*)^_O|7
zxV956qJBw6vJ?+=bUrEs8r*J>9kR7HVLt~CxXYCO75Im^SBaL&D{zTFOlwbED&hIP
zmo-#<)?PG~?9I}XM^3I$hpE^jZjsJzZp&=1-!&5bmbNI*HRZ8l`*;o?cp6W#Na3@-
z0hbdy_sRnb_^t|=MePb;gBzqu`wy5qLxmz~xS@xZt?~L%egY|y65;A;<Ads%<oN6s
zZe&DSL#0L2?o7rFME^;UkD`7J_<Lq+7LUo!ru}k$=Ukg{?DI51x66A$rtSCP`^&TP
z9ouX7M;oqcW9yZV{>HtcdlmoT5^DbWM4mcZx+4_9{ksR5UF6rRXuf+zdjpUn$8FTU
z(+5pm+JNKa>NQ{9c?CNG{lCTN5MQxYkdV%8_7BkLVZn(IgvbI;k)-VFA`mv$sp@3W
z2<0B8%F6ZM3|Kkne7mzK`GWOZ1gp$j5-}v)S0_;wy`bh_$waXx!P13HW8!-=c^r9H
znD@(N(RBTRiT^9j10*dKb1v{M6!f_Pgg@a~t1y0c!<l8P!lT~%<3<-2$;O+UA<U&>
z;^!#wXGropzX60G%s)}XsA9VW8p*0(HDJAQ8oB+d_`(AdDH1&h#2#&MC^TOAvRfb;
z#(7)!yC^$Pxnhd~V6nl5VWMvdgq+n4`Mrq?fLE*v<)P5#pSgp;vWNlZ);<8IhFPfY
zd#rq^jbiwLP+p#*OFCBr?*s+EHU9#+DicNFsQ4olytir~xN4m=F*MTj%B^irbzJSA
zUq;-xgY41+ry5+oC53`;vlkQvNzQK~65J&+2bn-v2XAJZ6s<;A9g)%x=es7v-buPS
zb7Vh76o%3(b9!X#I<yTu{Ksp;{P+rD`o4U(Kn*39w!M?Lc`~Wwmv!)ge0X%a3<^fo
zAmyPz6NvWoBCDRj|MFSgH!nE8jMRQFmD@r3Zva+p*?I}7o^FO7hWR=9`B$jxJ+VY(
z1UQKS{u^IC{1kqkVp`)ub=#0M-v@c~OIijAHeM!=oTmaGJCrvzRYp@wK$%(s1dYaB
zB9vU{&`&)6C}u%D{7B}4L`yw)z;V297<)iP#ILJiC#2b5S(Gy9H~2NkzwR-+d*ki=
zvG#iM#?|raz`_RQrwbeBSr$hOmopA-nsS~SyWu9n_yw4XgCA6pe{U&vu_4^1{tcH}
zigY2<Bm{4GJZkwNBr`EzS?kujPi)VjH&#!XWVJu@{~gcT?9zK4?T6*X;yMhT!87g)
ze;!I)Uc8<<&k{GUHhvd1n<7s<VhA@)ry5T^q;F97Av824_BaMFT>l%hMCr!5BBY70
zl=7VEd3gRr(%lw$F-Pi+EeLQ9l!<+D^2><O2m{IvoxA?%h7sg#n9!-${?el6bqha0
zAbTWs-?QWE%|p`%mf7oWlAMV_S6Z9f5nwI_d#{QZRNs8_gJkGuoBFRzp#e;kFKG>f
zC&BM;v@x9A!rTcCMZIXfd>oypOzi|z*5*E|lltZdD&1^oM_MYPs~BJXh8f#yYz1mJ
zRl*#fyBV*|M6WRAzftm;kI_i<G9&PpB$PhxdsI{?q3{s;a2Zb=^QfUwtKszgj+c4u
z5N&g|=eGCVpVf8O6NF_Lb!0>oHVjzIPdtwnU_U(ypXyo9G_ARI47DdW@b)sB8b!_Y
zJv@n57xoy~XWX!CGIS_xN)nKjSX#&DtsE*U6y^5ZFXz&!YXWbsDej8kRaYzXAt+1_
zg!Mxd1Z|%PQMc#(xm;1{)9n)Tl3tGf(w#o(E>5q9>w*W5+^ukjK%YtmoWL&nEYwq&
z*ah{HuOQbef0RhA!74M?WL7UX7Mx(s!QWv@-#`JD`Ug)ABD$E{_^{j$cc)D13`zkz
zPufKqNO>b){bm}*c`IA7N;K&_D=q6u>E?9_f$~0b>#>5xz{AcT&fxrToH`@XB2ab|
z!`E2$?hlH^9#4rVR#V}R+ST5~Yrc3>QN+`Pvubz^C-UCO#k5BBnDHkNC39!M<}AR8
zL5xgW%azlgqOj#9|DC;MAu217jl?K%+4=Y6#W}Na)6I-*JW-1*P*nPky0>3)xf(W?
zR-2Du&569w{pYLrf=?^ku$!BKxyFm#XN|mO+MD5-Zj@|k&*ksf?fJLbV#S5zmycgI
zljDCMrg+4v4&pryyGYa4GS_2kuMwapx0juIF?6pm{Y~60!;=&`4TpXnY^WH$K=U?=
zU0GyzV-~bO2G`ryNPKA@^*62P+3!McvK*%>fxEG`Pqxe4411Y5^`U)5YN%B0?w*A%
zkXxL&JNz#8<;^(fwJ&l6iWqT&3f-JXAbxvZebhvobaQ97)%Ba=!wtDh?zQ3g;=~s{
z1u91tAS=sm{8_;@E&9~;qgSOL7gXLtM7n(!#;HU!>3yl()T04kcbe}BC)MJ?+TN>=
z#PFf>7Zr}xx){D5f5b`%5)Lbv-ySHXrmt$C7+UfhS~~xJ^NpAIDB1L$zsNbqz7iL=
zF;r{+yvtk07#tc=oNn9^Ke3a1zd12~pbG00le%}iZ?bxdCWzwso&uHBYmv9CLtYBi
zd(-)HcV7D}vEzD#+WVk5c3wnehTa@tU2Az}t4k@Huu2T{smxU-l=iEhK4{N?b*`E4
zPB1%O624*Z$z14U*&9Y0!d)DlL%@~1-Zf(Y$yxv5J_EoCg*=wU+Dw^swe(ZDgE9_~
z_ua4b+I;CG3vi(ftSuV$zY1RaWoB<!66nFZxjTT|ESSxe{gtwVv6<s6$aD)(hcc3>
zEIgYrGxtdp09*W}Wr8B#nTa$njq=e1jb`RlDWu)TN<!Aw+}>%Uk-d!?yIGlkhSOBi
zlr99ezTu3Jnjz-Az%@3u|3RVhv~12lgM!9rGq3X;!Hjq>)@Kx42=?WfvHP|szCPj}
z19!DOyf-zwTuN}k-o1OdE)JV{Q3;&I(3$>BVrhJrY{F6bWs&S#D0@=)>vo@<!V7|V
zxR00%(gD0!L%(n2p$y05X{hkpVxn#{T6Mc$Pd2z%p7Y@kwf@8~)nxm<Nu`8#;?MB8
z;ToNa1h!*=$)gTh3>v!x+&mKtf@>fE(k<)O@kZiBEQLj&MMP|KJ}xQU^(pxsyCEnU
zjUK_RG>EoY(_ct{@B*9e?LM8^o2yjp7V8xA7<w7+gfIMlN9cX$zdgHqSC?Mu=OH4g
zLFeqk)_18^3DRA05tW5&eA3yv_hoZ_UF`k*bYc>fPd7Q$#NUTmO`L@15JpGOu~^u%
z7$pD%jz%W4_q1y+bRmXYXLTt;(yII#{2t%Dd;11klFO}aQhWI2)_p<&+rg(}6cWb1
z6c+?DDs0`@RNuO@-Ttb0Ph#??8F8fn^HZ^|kh)h9&=A^0q^I5Uc{9l~4@6(h{Fd=m
zNa0uosl=h!os~%O`HPOtFwKY8YUK|owd~({TQVpg3fqs<r}Vmqzm`kpTNB#;mwcyj
zAcSL_7r=KJbc-&yY{NaW%W@r8O~+W0eV%o!%0#Tx^(hmoM_7`6*o|MHh*)%pvf84w
zryix3$1NSr@!-unbn;r~)w%E6bf1soY%`MZjjTx<{%uve_`E0RE|Fh}kse|`XfI?o
zf9K@U(68Znv%_xtb~ttmwHAN4hkEJ&N0Tiv6Yw>hbXCsX&|ss-;GGiX=VG3`_6|*l
z`@fvulg(dE2~`PkETVezka}$Yko<gS!Gt;OCcn0q0q<!gteo7o!551LtfO_XNN6eR
z*NM7v@^<2}oL@@geeG8-i2XSrxO?FPj%2yaq`yW^_xYi>SHqE19G#zX*DubVD>PRv
z98|)KAsr(2$rKF~cVB<2d&l9u{=3ryvzut^Z0W5{50oD>LTZAXGy(gR#CC&DS|c&f
zjZ)xBy|v3F+&di6n+X%jcF2#T=o9BftS?kB2HtV09DHNht{zmcLE(9DWQ|~lndDhx
zk`N}{;XQ(hxp%NG*mI$%8jkq`l!6zBM;<>Kyj?TXD_2jM<aWR{l*2Os7ZcobZ(4Fu
z@Su+8x3Lcqi%?2<e_iwl8E#hYWFR?vS3zAJp-U7OMDiv64y)HV+<fyoqqRe{%LLc^
zZ8tB@k<-PmwW^1A)w2XFyg%ekQHagwq&aqHFV*QX7f@eSmAKV>Gc4e{NcGj+(HLx8
zteA`d#y5+3zq{&`$=F|DCzHsZq61jJT++*{={FVkNzbh@3Y5eOwoT;bJCk)=-hi?X
zXn9YB6k!?18;Y>O^S4r&=;$4L4BzFi8WXVFcNH}|OABw_Zl|ZCFI#xEI2Ih*ZSL2Q
zcG&Ow6jq1RZ69Nd8g%x~t^HaHq@xdAm|iUH%?{C2*QNUyY^=_l&2i%3feL=nq=zPr
zYvp`bN3Rt>N{&>JGm8lyT$594RqK;wPGRfT_|(CW+W%vx5>i@Q6_UoYq?dlJWB867
zeJeh3o&RcjqEGO=83pCCS^G%8INYz+5s6f>NKekq1;;x#8dlG+oJibWyVZ1;fB8>p
z^)Kxfr^Id2vC-|g0iodfKA_#bf0Re9h3ic`V|aHsIq0>LoTd5sBe!vl8^Pya$PxN(
zov*>lg*M;xI$JButdLOPrNkI3Kd&Hy!x=I!i)HUIBdwa(7QQ<a)bf+YQ|8TH4!+N(
z4Y`9Fjb{`N>+Jo^pO?JAuOvBE*`oo-%~hx@4xR}8nq_9pxW^&BG#k2+d6bJOO)^-c
za(OJpZ)FKahR<O^_5L;OdkCZtoR)EBk@-6yK9LmXU&8k8YXYR;0%C-wQ;xrq77pLo
zr}uyzf7Jr283D(09f?|sm<(~nczO9oti*YP3W8s8e$cLv*RP?Pa5m?ff|m2JPzHrL
z6_t^ebJ^O$W0R6oJ%<|9F)_T5C>{{R1N_m9NI$-!>09;kba?5RS}bjlO6;5V@aWOA
z53OU{;}|URagF3Wd#{Ex|HP})X+sN`m8R38fHEZLG3j-kQ$#_%^2ut=o`Z?i91#VI
zbo4S4oD!nCkJyBhx|cxt1G<vBhY4Z=Hz=xQz4tj76r8mjeJsh7x%rwY%>>wf1p3M^
zp@mq_LyK;nX+i-CyxrfOyR9CB{dG;@^{UrR&Ag%k`d+#F%3ro?5gy{Pd=h>jxP{42
zep8%+C1k-WxZYgn0rk6EY@1wd?AXmSo9F05RJNDEk0qWOAN0C`X)oWm!d)%=UPKHp
z->yP2;0MZLk9}jg^>J)wx8KKMiJR^M*wb<fIFEf1B)+X-qhi@F2)L$Gs7-a5X*;&b
zh+bRu;)nXnbInhnZz*&OSEw?=N++T+a)vx+?(H2<-_(qCj>N*hWp0ZtN}`8d`$ayT
z*s@~}UQqZ4|BQ@^u0JE`M$5~s8lx0)V2NrP6PnUc8i;Egm3pLrzg}?-Rs#2-!A%J~
zdlI1`NukP({spD~cCi6_o<CjDYvYlyY1eJjY^nDV?;q9mo<*F%c8Smr&*}a{5L~<8
zUaoX@`CN|#=N;8_FXFUZ&DkI!8`37xoD!djd&WV;)*XR4ITq{Zv7gf6D#ydn53P0A
znFt|X?7!;88MIIJVC|J4zh)5Y6$kUNd|CZGW=uA>B&{Gn_<6BY@MYPz<xcVaGQ_|j
z5{FKQPWGc!AJ8Ra7EKacJY!#&EWF`1Gal9i(CIjV^<MHmeH5Ou!4$B2YyYFp1~{*5
zl1{Qbv#+_G{O1rJr6?igIks`uGJel-fdi&WSnkk_VBCJ^?4aZuS<5HBFpXx_IUV{L
zGB#Xj6hbW-Lddv9x@AUAK}F^BS~cc^3buaa@pF=rXjBbm{l_;&x}z9vYiAt$NG>=Y
z<;|@z?A>xvCP~awY4at#E%RzB*XBHCZVd+~nW&J?kgP>nM9nsn6A0|XrIfSZ4Ku$J
zqz-HFwe4oLq4mGQKpnBWt9_+r#wX_UmNQiZR{jTphTJSXK}x{jcOBIQ=_f9oxPC@j
z>2a4`-urUIaNf=wOkwhTt}iGp>hh1$V;H$T^Dq(>WE;oAo<GyTt2{)#%udJdrB-FY
zJV}@3RyqOdsfww|MG;IXo{h!3m|?n2Cp9sO{BXX-xkZqc+#*o=fBVXvo2<y@SzUac
zfk4`OSVV7RBc?1glx9<n{w&PA^0j7+ESriViyS|qqryU&h&L8o46%>TC*`QROH#^6
zMu@mb)r#cAFP<=LF;5Vuj5WRpw@rrYv0U`wyJa8Q=?4uS=>1=8(Wh=>&1*-EVpS-{
zkGB<aH8I6d*ZJxFV2Ru7+;-Tkfe<l=eNPwSGEOxGB*WHK){Jw&Q;p1gABjLC8=uE3
zGf9uBElaM<27QQ4Qa#JN{D(aZr<*NhCXd4lyzge+BkR6zdJo@Ea%&8}?!)!09wFeM
z%JmSXW~r}RN2l*X^h2e1G~w>*V9>!8CniK5ayUFLwF7Xj<DA*bmd8z>vJ4+d4(X`7
zL?P0$LH~I(k#nz#N$Wn-ue}b^ZHIy=UaA)!S2$W!94#$e-f{73mc#wwbL=9&envH2
z9KMsW!#l*@`iFN|@2ks!sf&%|*_3fO5fSIO#+4WI9kC80&YyH(G^<%-v&>t>_k?_y
zC%#IJ;~<iTUY?YZmyiDX3LREyNmF#|9s5Iew>_=1mA`|fNKekRzrt<d^uw0d|Jm$;
z#nB0icm{HT3(^NvCBkUKctS`)oVc3Ppu+LP{fuGt;~0}IS_<WP0w6$Vk~e+SQ3=r=
zsXEFeYW)mr@4zwa&%(D+L{=FL9nsy}Wzm%~-@<y|NJ|ZFKl%8cq+eRzcw)f0BvF?X
zGjLHFyNLKCE)m)(6RGVx?F)kA62yaWBc(MnwhT4iyvnKv$uCSb+FoJc^{Zfi8kicB
zAAHjKcW^`C<C_nf*J)?EEYD!`dJLJHgA~Ju#p`b@6}}z;wr~VTQ$?MmbC3U8vKEU%
znl4$^``9dLqt%VGvYx~32D%pNh*jqcgPC65)|SA2KNK%=CPpX0q-@QVZx?ZkBfG%4
zb>DIN&>q`}YmxJ}+gsbLHm_}Ji3}Am9^%WR|9Oz|W5buRlh%G`{A@al3&U?8)Gvu`
z$mLyovzB7~=7~^W-Bn(Z?E+Dy5Gnuqa=s%N7w<DH4}zA0vsqG>C1zgFSSra#X@=W9
zot%tGJ%Kzhvk{jro5<rRmq>g4Hj4Mvm2>0oR4yGF>M$EhBXufVDTs-ipG}J%47zqn
z$EXm?_KS7=4dB?FMp;rq{fcZkSaEXlUHgY76=XQDr|#;k`Sh&b_!s8r_v+KpkliAl
z`{wu8em84v)-8J4@n)c&StY&88MY0j;eL2Izv-}-LI%^a(dGw_kxUQvUHFkqDtOIU
z7|`^9*s?Hp{C|cv8cLYW-ipM*VNdw8E{@%he01>EaQ#sdEI8uz6$`~ziUsXOO4V^O
z3UsXXjPn5sx^u8qE;(^#G`v)hc+()3lZ&;UnT$$a%Q?ltc}}zE68)-0SRU7R`rmEi
z=53+lExR`vi8E_LId(p)obrWN8<`C_FooKp@9)A;YK(g57xwD+dewzz(EYI68PW<=
zbs?$@F}o&iV(CmVhCd!@CUv9SwHaEKF$s>1$M^iGt;pXBeWBgvLOecZ#4Y0!DfDhM
zKh%4`VAv9Wo(mzIPHSYDe&5;4Df{z@a_);(diGybMWx>=^yy>XRFC9U_lDUhR0+j*
z?spD2sjUx8MYR`(!g2C9@5nlIwEgivmQ{Ot&?u(MnJ1mc8nPuVkJ&xLzCX`F^)#F2
zTyJDu-7|&ekoc07NN0V|li!ZNFIV`Lz0+(?!gPvgHurjUuL$h!4g4Eo{<fo?**e2F
zsO022w#b%Glv0SPe)~FwM1ty@_mwz>y~F*n`AS^OfqA{QC8pdr;&+M{tSj-yI&cS7
zKOe~82*>Sjli076ntd=sZ*5fv!!78I`>1kyzinFtZ*AMNrsZ_jFM_5Dp8Kq262_QT
zp2rFkcR+~cApBc--nX%#(yzp>I-ws4;w9mzs^5HMr*NbO-rMO+Ryo9X{>Ld#>K%jT
zu?KSG3`S4Pp7fMue$RJ<&o1*17p~`UwYVOvB}@M6J)iRvm(OM9@83!ti2byE1c*Zx
ze|OHx{+660@1uV!9d@RoGN+E=f~?&}$vq#|`5ZjC6fg0%k4VxgaCF{{@w}i2@k=*L
z95boD&D7gL=^)potJ@k+3Y2Bl&_3{iNBxlDoRQ(p936Q0xtu`loVq1>Eey~Ia*v>L
zf5lt=rY=7pV~lC;C5p<Ww`?zTq{$x6zk8!E{05dgX7+dT!t4KN6a*h<KHYgHqM)2A
zqJq!!#kUk)i2GjKZC~IKi<<XcAo0*SN+8?9bI9KsI^V{ijSsBA+SJ~S$eiNkhVdje
zxn%@NH#-e&E+I99jJXNzUdRxG&i^@%cN*IFH|UF*vVmm=<W0C*HFDFzUk|~(`XcP<
z6PNd?wrbW-*QMD!^=6;Pft6<Wzp|(IJd=o)15zLr5%>)!6v!u`2;cnu)wC&iDRSe$
zJ93Q8+oRtej1@J9Qyw-8+jP7Jo?C2G^HzEM!}$wBx_X0J@B|wE?c%<@a}QykIR15F
zoTB~vZJQkS<)ZK)HKZ@<47^zW64XI0(<7i0jT+HDenCw(Kvi(h#6P{@|MjP=No%%2
z<RozMtiaS*1|2EgNuHekZWMtht_0%tNk;BcK@*JMs(PnlaR4Prdij)-M4I&1=TIA4
zOuudyjBaAwIgSUPerEAHEeu7k6gB2S&oTqqg8HF<rsGPk3-S_KE3{?Pd&01u`tW=0
zdZQt-1A{LPWE&_gN8Dq|@*nF!7DLG;elkVCKk8!gC`^wLvOyE`<2}v7puk%S?;ZUW
z&?!2Oo_i1-KXO3^Zm_~@e2?GKhvz`nTj~H=!XEf&EEldk_B-tq-|7eMHajHf0Q0o8
zrW#i5g=5b56{KU>nz>hVL0$`BEqQi8?Yn$0-UU2l^Rw@6vt_fxt^Y6j5g(>##?q?t
zTH%3Oq)*KD{VNB^{i^Vc21GON2Y^!Iekb-)B0pK}2G?W;)7MKwVjEv;^Lhe*gWm7I
z=v|HSFwpd2puHP@MuT1p?j1>g_SE^3W4pn9^rirGv~NUn;wOpdlK3A2_Dj$d>7igU
zMe>wWCGO9$x=b*{`s>o8`LeU&f51QAxL?;*j=j0NN^>`aLm*5s@|Y03DOUSfFRai6
znUm$%DR6|z-hXx8pV<aonRIHijw8M&P<Z#hS^%Wv=mX8b+oPz9z2CC;ZL8~$WhWZf
zK%3lgN4VT6no?nbkDX)$!pnb;456CRTDM;>{S6EE?C6^wA@PBmarbdNDOC1;avtC6
ziTiaIx!}X}H2=2nyC2!;kwM!$$_(O2qFi*2e^Pwy_3DVpX)cVFRZSQ5PP_jW+*@;-
z$xHZbK{7+nvf_dAV?q}^K;3{$lt_^uZ#%5_{0sarA1O)UNFWUTfAmQfTPR6LoOxqN
zzgc)F70?-;z-jCQVXvs>kQdU<0<Hv&w{&B~pP2@?$@wJFj~T|7*<aU4Kr!z423WZw
zs{FsT#O%Om<jC1{-G(4`#~fl#%D_`tS&QgAirqO85uCV$Wct;vsp>w68z_JAn@>p!
z<oGH`BWlZhqErt<b2QqD`}jM5^`3LfqVSmR(16<pj4av>7s@F>EPP}SFL_|a_{R3;
zgv^dx6_MF43T*?p^E%K|9J1o|i?R|-#UULO)c;*R{{9AC7k69-vp~bi#jhb>&{@_h
zpiItyyrvwwkzbr3oa^^#)m4QfaCJ(4#@(jl64pcg7fWwFjzYGkAb2#o2V$9d$>Irr
zukP8M(jPhSgbn{Oso2v$-BKX@)f8Re3ltsS*!r{4-^6g)0f_=>6_5d@9~2ypAmrpX
zzjb38GTt1E-X6Vmr36V}K&tBihJYFrbg4-3T?w-jHsQPFpJ;Wz5M_J==*QK*D?29H
zdo&y=g1($@WDF1iUuLDvkpSfsz!Pd=;8_f~s!M=P(Xpq%tPBV|eTobwJ20E-3Fxwx
znZEQzjZg<@$d`lQ=Ci5K_e0a2SrHpz+M38B3n<>t$UlO3Y#XacXvAN$Aa8Wz&q_qo
zi)l_8QiBGkbZ)KySUu-wpV^IRPk^+o24<I*4T8=d#WdG{CXV=GMkzy3<*1`^YYk2a
z$jmANfQ%8)LyJO8{qccXXsT!Z&Vlo&ef5unHvN1{Bz6iE2k>32ktYX*tDV4kLfz^B
zxOYUE!HY2q=)PY7bOX)$Cp(>nZu%flV2mlGf>XE-Kz`gCATU4)SVf<o8%ZqgD<s`e
z?#Tf<{yKNE`#sv5-qMc#Ah>Wqz#woBy!*FZF$)$Guk8RJP#8KXbRd04=Mdr8-o1Em
zl7Z_f&Vr;naRZ9Y7wV3IK`F`q+GX2ikMf<h0I`LC{?wZ#M%%$lTU0bquFI0lj1KJw
z!BhJ6KqxbjOc+fxjIDAUQ+{{X7Qu$03~C{~^6~+$i`YxRnw^EdZ2^Ers#EdjKS776
zAIutOL(?E?se*@<LR%SFhK1nSc`LpCjn+$p`a85F%K_1pr9t2sTC&}vt%zWT2;q|t
z)-XMN!=_RX$b%Nn8A0$BPBMz!$9OE7?-YWXsW)5&eEdj<zqCA+)4lTs`tm7Cfu2_d
zc@Jz^g-Z=VKKjoa*dn>ih~7Zj4#~vD-TJFK2<~?*?8AnVa`Jq{p&*rRWTmUxnn1B3
zLe_10BS8iV3Ys9mJR8b^;puGA);BQQix``rQt;8BBbu;$hujv|$zC`+Q(TwEOOe#-
z+=BKI4-9&bMGDK~VpJi-rg)?w?91{9tf=Lncw@9C&jC*zgp{;@diGR*XQ7!Yq+8L2
z@f8U-Hp``}>ndJ_^v}dgSn9BVDP2$lRGiJ^27~Hc@U?PSMdn#dY-+jpa$9d|NyD9b
zDC9yS5weV&a*}tvxcOq&pL!~E0ABe=XN9)Phg;YZU_6Gw_mV+P`D$rg5Xfr86AsUT
zq{ww!V@f>36}n&~Z0?_(^e?1p!ismP%Q24nadftSe{$<OLfs-bjWz4uy=Ek#J3%(Y
zd2*@HgK?Mdy-!<j>s|fzpKV#!T^L2bQPjNFp?_W>J+q{#OjyTzVe7*uVJ-!M07w~a
zV_8KYLxS!&QW~N~f-8i9dk%sVl0G(gBz*){Qo8}T$&7Q$LAv&Yhd|0Ew(a!=ZwvC5
ziG*Z>_O3vKK$=K}w*?`bDkk84_$q<`1{i|UAP9)cpmkWed<|;uHB#%}Vl}X%#zbNi
z(8kVwQEn5apfK-(cJRDT%m4O6ab1p#qlaOV4_@KZ>G;;|2cT7}-u%J(+9+G$Wt8~Z
zNonxJ8h>?{zTeAM^_q-Qw<OgUiF<wa$AOIJT7+KpzkYHTE?-8E+865Bs^v~~6W#aV
zvA5c@?*R5t5YC87ba)qM6Y~Vdg&i*jU;e7LJWl!yZ$IExE!0nx#zkoPLYkCGUs*wY
zr3|XTAc3anwDnuAgc79rAKxTo_~XK#oPRW~kPf<0CcMJtT+GxQ>d|i_|7%ttNavdd
z9;28$ueOg))wBBHguL6<N~NE2Z)!Jd{6>#7-o-|@bZ25fLg2Spy+EvG0vejG%O9XP
zl^pSpe6=>sMd@Xf8ZI8ipRdMf^hJWY>W%+GE^Kb#FNlr5o$1tRSKwfRdGg+fv7@G{
zYppV=7btl%Ac7TO5}MdoxIT$EMj%@>fJnK4L^%My=)d*&qPKfu&mU0B&n2P=st7xx
zE6Fn(-k^J)mr@RAKK}a#x!)<e1l)=Lioe7KqRZ6zEn?Y%OE_!}nqT1au~Y+Q%@Ogg
z!UlBafm>jKggq_`Xmy%a0ghy!e_Q~_63gI^^>?J7HObxIsa^vQN5jC-Je(^Jybg2X
z3Q3471w)HLTxUji2G*PY$QFJTPt!TDLOX?V<_NBFuz??ZgGXvIGuD`y()F3!Ah>xY
z+C4H$7$N3i<EE7|*QEHGknPFrRgj~U!mHW75=rg7^%WvuvG+68;f?Q9NsIFljQpeb
zNRi_pnitICI=?g@nb;5wO?$P?e%+evTKC$Y?J&|gZ8`!M4MB~G*(U8?<u!t!;*&VS
zJzjxhr$K3%B^WArRpZ4y-kfuxxW&k$@$cidS6>Ef`zPnS{TiGYfM2c;wfnyG)mxo=
znY!Zlxv%}}b^nyo&wZu8zP_)X0~f#~LK2*D=sD^2hRv(z`ZaOn<(&QVvZTIRV!kgf
z#^OB?3yms8RY!2xPnwixB7R&Jn-WzvdG*7{fSF=9K`RB1!|SFze^E&pNP|=Psji>z
z7JMbgq`C}l;pzZH3jRP5sa$+5QGwVD4Y6;OE6q6<wqRIPBOIAZ0ur;LBNwRh6qWL2
z86Dx7IW^rr^x3Z*#L7w?=OCqXWqxSr;gIXOqk)zg;U~WH!3M(63tokuxz^D{=^xjD
z0$2%|rk8Bb<e%SP-8E+8a2*7{)$sQ#TjP%#HJC~}5u(>#f0u0a@~#i#H&p)c=*$=Y
zBbX3bd3=jU@;M&Xhxc(L3>h(qXm+cS7eDT+Svdj;y-jRx?@txJiK&$b<#xfA{4(7=
zI{Ltqe@Y63!hdx?j&kc-i74~PO2*;HzO(Jd%Y7f*bNU8hI9T@H<s#Q)mPRTJ4s+1s
zHJ}GR5ix|I7OJ3K@-3a|wljOEC9fQG&N&q&d$c>x@YC<PXv4F;!Vf>RJv!6gYQzWF
zFpnP{b;+=|5K%f>3H-Sek4R5Sfd6|!JWmwg*rqP6^Y&XJ$k}%o(sMpjE&xP(`0V0~
zlxko=&xl7$TT%hm;~h4Y;?OaJvRLkDLzqB{m?von9GH9IviZ}Nf*(8bLS#?lz<hO3
zA}(|9v^5y1)WrD&Uw!dqXS$#g<NDi?nX5tBUy4)}pk0*Zjwh3s#e#iGL5n%{${Fc8
z&2U`H8M4!?T(8*~RDY`99~$@(c-HW=C2Se<L8UvLh|u}Ju20>IWjd=`3QKCQU=WAf
z8q1>ZJ{P`7%eSqInL1f&_jxhB@`jb6lnml(dL2pk?-11r3#)O0ys=tW=MdP{HLS=L
zxrOsmMimgV?%d_pijNZ$!1vf>z;}Tk=~JD(_qzu;)6@O11$2un5Qk+YLN^`1aF0v<
zPUAi7ZbI}q%)QM6Do>hDKl^#}CMnI^AhELw9Ccojaeq?pUz)eD$aQ_5y?L5-NttaT
zdzv9C{`75lr1BhRFbbHgx=3<LcEj~-wxmaf?)o(N1wXawJ&(->jW2W9Qm8k#AtBa%
zfy++cd(hd!3pVxicp1P(l&bnF;F#p%%u$r)pClb;%OBBr1%b4>U~(4T)4;TR=4M)G
zuDEeV%6k*jX^U`e(Q0w-Fxiheu(rOBX=*5ay9t#4sQ99O*!4emH-`P_mLet@l$81z
zkJS_S6~zNUA+0~p8WdjH@v4Ou%QHS}g;Q+E?Ucu210eupT0Bvc&HV&C^UvtHNlMDY
zFEd9@jD_C(s$O4svCAc@DqzGw^)cKe4vgzr^lS%~ue|+`=zd>@%w_qR=oD)gwOcYv
zZxHUNWoxv_S?c7=0qadM9%Fc!rf?2Hua<0-oQI}M6CjvJ9zd$kczeX}HU=!iAlS(q
zxO%I+=I3B*UZ0VddCo24F7=mcus}o(r&Hq%)#8GJYka1kmk%hj`&;_^**s96XIHBP
z%f#1aZnqYRIheYOXP%-RbL*hofse%`zN3Pf756XpQTapU^$%p)9_sXvb4(u#xrrkk
zC9BMjHT!^&Hf2!|P1;Mc4af0NV@cTCVeyKXrUs2pK~t>4pO;5WZLWS6*_p^P*8;~0
zFck>GM0SM8lbFI*XV|nD$`vI}F6?hRu0(&JR|?J)2?*jm2z2oi4cxSN%qh%AGP7dW
z?`mHFQtbs(9esSX{a{t;%7LJBK^-GQP&jebevy98E$CaiaX)~_r(mnX@}>ORW!PIz
z^fFrhnE^ehzDPa*PW7Fr>i5&!p5aofi}C-)wu(e!_z+XeV>_^A^6;5VNZe#PrJyRN
z35N}KWB_HkNZh$(o)6+o`-stBm$e@ob7%2!$)`zja1eP8>qDdrwS?+BZvt**^`3V+
zFOi4$SEnT0BOF;T{0S8WEs?DZ3w5XlUN#=fcGIHe@^O+o)iN%MeF{3}9yBAXwIR4b
zl(gK=q!A(8^+gpg_jX0<1D_Dgl%peT@QWF@Et3EyW6P9A;Itx!tI70X*v^~70&fmr
zr<*UkJMiL2V-fbO9#nQ2qVF96v7k@nkBv4{II6?=6L!tjyZ7v?0I0+z)e6{2SA%;&
z<W9QqE%+3xkiS3D>Kh|-**Ub#JXs(=Y^<PlCEGm>hrJ`)HKb0ad1RDaY3HqVSyGdN
zQQRs97iX@5nFYg*)7(Jic4yM~0+M^?8T*1vdJg32&QiY11v<K*N-a&{f>o$2en9PB
z<KIWVl6a*31kb+S!)LDla`*y7hKWDPv+Lrf@Is+`>7k>bRkNwuH%<=I{d!$t$3;BE
zeT8`Z`AI^TbI&|BdyLiN&%IIdog491AuUaz<LCExCRu9n=3&NN?0h&mgr605(Tt}*
zamirluISr1-V4SM9x%Y>HDH!VeLC~$nO5)wBv<%uYp*_Y51>^}4*q@P-UP&>vvFhK
z2`-%Fn3~QcajakSQ9y5X9zQ&A&wKY5<vFJ2xfbPxip#X$h)w<GYN9?({dc~vBc**1
z>3{#+pb%z&-JAP}q@#;SAm8_FX~YFogjI-!iGW)98p+~wNL7ep>vv=?uxA+naihIL
zL_(kt%v!5pzpn(#l;MRnw7!Bexe9rJ5bCm3PA}iMHxl+)Y(PI3`2{+EtAjt#MC%1Y
zPQzEY=5b93$DaKi9Y`&`mWBvB2w7nC(zg_WsP3$xZ380xMvT990le}bUnaR99^h{1
z_p^0xf7LEN%_s5ZTFTy=FS`=%j8UqiF_pdZxWqNEXbK~$NONFDL(vr9qyC?%u6F4E
zZmMe?t@{ZWl`5E~2C(<Rv3pJG`R(ntci1X8<$spQ2i6U2oXdoQFzI*Szy-}72Bj{i
z7e{aqPOdvw&kbJdDge|c85LQEBcsJ2paYx~+M%HI4V9kGI1kHu0cfxqxYG~Yz-;CM
z1tt-iuth>T*o7$H{md~c&!6tfiQP#nN4d{G>~&t4O1;~Xdx|h5f$Ly=8HeLg3XzB%
zCI3m`-*J7kF<eOtVMg=8XkWW@#E2pZ(jbP9>E~4()(Cj@ZdQcCQ{Qk+@8kzT_Cqjo
z+Qi<vQQi)WqD8AW344zkjbk)J{G$1HhDe()cb+-qk@PtKvZ#J15Bxc7-~j9kbZ^1>
zbq0kmut<33U7yN%Y7AfcIg>=MF#Mn{PEuYX><wAuI)RpW#%=subsJz}$9@gCX|95s
z1w#z_A*Aps9jZ8$fN~f-4~lJM=tnb)oZ1QFiVhMhmLq_?w+kHooW}Hg23N*nC(pjU
zkHBC4aQK;nJ!1KRG(@RFd|U*|^+IH?<EG5~rOLRFPf#|qOH_zfgcT{v>Wmx@swvbi
zg=7+^Z9q8X3CPh?=FcEs<ErDU|H0Xthf~?MeZ!?y7Q$LoGA|YxOPOU{EOW*tBqC!W
zvq;J^t;#%=%1ouqQijl=Ol6K-X)qI!DMN;DKU~+nz4voJ&-c9F_Wjcz*QPG#aUSP!
z?E7!pzm52E?o<?umJs*oR=6W`KQ2ZGsvSyN7P>W{c%u(~RFojfc)GCPBw>AX>$(s_
zcSdTC{R<p*^@{F`_V@X&Ti2!T?2Zo_utIkH1pY^n)Qn26F^d)1#zX}0q4rC*G%RLU
z-M37)*iLuLgxy3tC<tV2M<Mx%k&J;quNFf7+NtoUW4t<C*!_)ydNm}B$?6O!bI&r@
zAm0p%ijJ;>-u|+jbEm)`<zSA|B;n(y%Nz`z!@UM^gQ0Y+{cuc<LXNE?c^{3rRx(Zi
zZ)Sg2s3EHb^swi0I*?_-_lwWzC4dyop2dHgq4#;x@b~#vZvDTVB(1CL4?k#`SkDaE
z_&A$8VTK=uX)SLS^8WYDk+|#mVIJ{B`HOv}xhUw)zM2bg9XGhiWnI^T8fNr0FmX0J
zDhKVEJ9dmiI)F!>G6lXGs6r(@sgXQ(Bh5)#7fjw2$dfd}@gtwCuC4o^n^bWI9^6cV
zh{j_SYRB+sD&K+-I9H;pFq`%ap}C)=MjC~}5UK*PIIMaRH0rI<xz9V+R_4D9zP*L|
zu!~3ktRH&*xoI>1@S5nifCiJXjQs-WZfo~IJA1n-Cac!)CN_RESsU)CB<&|`L9Ot>
z3^CWWs5@xpy4>^k{Etst%p(r2%<?4VY&Pc22Q!Q{?gZ*u(Cs{JoB&;lW>AV`VnFur
zXPB<-z^(#wn7pAFZ@r7<KjQ?^&(p{RgmP>a7ZqTWcCkpaS-J;Tqsdp`@Np4e-FC9b
z>I<^Fohpp3)kwS&l982;@1<b>f`2a`^98ynr%)WbArkanffC@0i8l9$qETZy)Lp%^
zCH-(si9Od&hp3Yk^z6c)%xzXq9(JX0qZ8nR_^M(mrB$PW4i$OyVQSs!+xaQ<_Woz(
zn$b=#^%;C}#=Kw&tUI+yjOmbO=4zO;in;1Ip`q_T11-oyY&UAICT36;wk)o+vRFXZ
zFEzeZcm3+)prA36==&Sb0*h3@wDvny5ge|Ih6A6IJ>japd`?!>PAR$K6&#`t%KlPx
z_hctjkFESZ!RJs6-k!cCf0a;$GXI2?Y}Pi%)m1byW2GXnL#DaeX#VJp{g&ce_1XSA
zW}>LkjD`vz@!hih?77Tt_E6k<AmOxW?(&S}BKAUWVQP~4Jdq;Y(o^Z>*wWeu>+)qg
zE!X#H$iB(Hk%4Mf>^(F;i<e*T6=@idk$syS={%%FcVhNK;I_0pXHDtp>X#e1ydv?k
z@6*pK@l$Wx1+_B#KozV+FHRkedwVq`XH*(~nJ#SGTywMkoz?Zi*o!1MYn{i?9sOTU
z+GSKL#QSkHH_lmU^qu~v2o+^M#dIy&zWnP$mV<#rQyE(TBm9sJW-)(}imBy<I8;39
z#fdU#{?2D(^Y}e=hPn-Gn^$GYxQ|C-k?quym2~dqyHi+2JgG0hQaW1V$s%EV3eN6!
z|8aSE!J%sAr~+jC5m1~MCU)T3d->Iqa8dry)lEBNyxaReD$iarZ!bugW9Gvt!zZBG
zc0jjX*m_bqi5Iznm2L9WNaeJ8RE<VyII3k2c2@hsg!4j_Q=8;8;Aq#WHkH6lZt`eM
z1N5m1rMW%UGv2<#hRAnMXtKBH(x#)-2=uq#qm_Su;`cI6Q(8QVV@=e&1jR9TSMAj+
zpReoeyh&K?IG-%eRT^+-hhT4$sd^)}IUJkSymPF~CSH&IJ@=Qsj1@r6&BFK8?@y9i
zX6NK*J7_W73KhT~?t3CnPUP9z_v+unC+f1v+Cs8@C7LGSfvzeWRk(DC{x7p2L2w#|
zr9KtqIfvZg`xb?vB%V3RXFW;EG=`Qo?C$eIF;q@9(n7)5nem8LU&T_tS2$XYYh4s-
z|HT*oR#|+HxZb0?5`m-gz|UqmCI~p~@veaa^UvAmZeKbL{U<te3!x^O26VEl^XrKx
z8B?F3h;k<H5}>bt4>pPdi+V72yD6Wk>2&A8lbtKNkENkW1$y-+Ztq!jG9b9LrSuXm
zphbQBM1h5m@)y&hH+52Y5@`YT^ZhUU=f$Py&Z4+-GWMi+sUYR$4W25TF8D|vO@6DZ
z`vdn*<U(fTpZ6(JUr=TevTygobQapYoh7@rW7v22?}*dtTk2#XrFa7jL&2Jx#j1=i
zgGu6yJzhs<N=H|IN@J8cXK(HR4o{l3+Dj><wU=pmG<o_|g_GshuEL>Sm?RQO%V7UO
zphM$392bY~nA$9#tWAo^ZY3Sjnd_(6X8e7h24S626R%-|$5EvbAgfE!BK2OurofPy
z!#@*I-ATMYT{U@#czB$6OYLs#$<hlSMa78Gu^n%YBq~YV!18nM<hKK^M03bA?fz~;
zK*Or8F~2Z1NakXXbmu5C;PN#k_d%TDmsmaxXo=(Yp3GDaTRag`k;Qzd9)G9%k;xm$
zi+32-{rKJzC~&UuX**xWh>x|UU#e%Y+nb{g)%cX(`=2m#0<Mt@T@Ru@J7q`pbcJ<J
z$2upTYs+CE<7#|Q=bPF1hG4RbBz`wAYenw28Q$|c5TXIa_=V|iz1kebc`FM)nb100
zSNd9W2u>&hhO+hL=>-&HJh-_N4*siyKJ>qTdgB2z+L+)skc!X`sD_Ij3E~Z-#F6Ja
z>KEVn#st)l!F}&L4f8ET<FNcY#(=0pVSWdXUi&@OH;s~S)5?MdA+fatvt8qWiSFtV
z9y|I5NnRs@Eg`S;1C|2}`2+R-sHVrO^58MPiLW$ZuUaiZ6jLRXy=}!=>4iezzkTj`
zCHDii89o2{)ut2baHt<|8CF+ykwd|V%Q(Gl@!YwQ83Osc_$l!=njZjX0C<H5dLd_b
zx>bJhhb+)t;<W2-!j5o_AFkZ_R_*U=XAReWRAduJ(}6IC1($eC)LPFUNVhf9qF3kL
zUu49L!PZ|FvRgyY7qV2kt;~&_qK^(zO*|2D*bFrYAR_qr3ySrxR7Snjq(uAz%q}y3
zuzNjBDR_U?V$Yd^6DjF?geZlOB-xTfy72Mwh_DZBB`aBVm4<|kYZ|gK0kEZ|yD1#Y
zU-k&PoKxzTApOf5Rg@P2sKVnhvtDo<f8Ni7554*#k|5DVKLrNr_ojFfU~3jyX9<*I
z33go_QT$V=IyjoNb>%~?^cSRA@QI-x;h`k+ADM5#D4j%f{2rnY9QPH${A&yN{$IDi
zZ9F*~EBKAz+~`>ekn|S-5NvDg2}cMFjTEP*TQ2|70zj}~6=Jxf&Rsv;OvZR9nQNl?
znCk{cd`1L5R7NX%;`9nX!VIgEd8YQtUTUkCd-<w&D#Il#+ZD&7n3x)ZrO*0?at-E#
zbIe~{F1ZMaiHi)B-LN*4-9wh6Co4Ve{lHOLIXl#FhE5h+<W0uS=gu&N;*}|oIx}GL
z-0f#LGjuvQcFkD5&oE>nU>*&8v1biF`S^a|T$3AMxKg_%CtynsNF@G1twTT5bxyt4
z@BFh|xKsC){7da?bhqknUG?jZ<<t!6eT=L8_XQl=E$L(aIfcvZ_(ppGH0gzrQ%}HF
z@C#q*_T{J_q#xONSv+b0WVGnnWb9w#fjVW1PcPTZ8t?5qhhR#sVD<cjou@5!V1q-^
z`xw}DD0^b(*%^INM(y8i5JbYY?miwS1-z{`^C=qUSmqb=otGRS$D!xjDA4Ri`k2`(
zl0k%h$c}^ZI>dHp8}SU^_i8QS(E!u=^G%KG?NV5Z$Vt<pGRRRd6Oq@u2bkZ7Rrc9w
zCj1_<Di-L5fC>a|Q+Z2o1^k|Jeq*nJ3DyVlBcBb2+0%P{8)Y2&U0*GJY9!~^x2j;!
z7o*eO<SD&Npp0`aAPlG^K~xY=T0r09dHZJUVD&cf@eWM#f<EOtEB=I*oO@aXV-?S^
z|EHI?88lSiefOVSd_|vspJ@;3^~ig+Uov2Yib>5z$&CL8o9Po!Qgsv!BCKbhty|uR
z5M}(8wxrKH=bq*|m<!7`{8Un56y<k+Nez5@Tx-YR%^3F^P%SiRX5c36JUcqyhpNd#
z!AMU#HYcmc*ZXI4W2tUBBZR4x#i^2hJy=+o)_FVMj)8d?(^JVP`-#x2zc2MR^SbGc
z3{$bhH#-S-5I{joe40gwwMb-L&}$gcf2h9dT<d-8wNF;G*Z$S8BU&thnqCNaRl#1K
zybBvbwkwO5S35f4!9Q4}ke-wrM+Fne4q(dEJ72mE0^sDCOr@b`Jre!S-R7$vLKD?m
z?%O<1(_SdPh?fjwVNHCPVRPtpA$jK8#ON#9!y{6{w6CTcf%@n>3-;Vo9F!OvJ$GY=
ztboNI514tIrVa}@kyCC6>L-6{vnan{Z!h>Mv&rM@G^yDzgPHf^)V7>J+RY8nCroKX
zSE^Z}ZtXXzZFA*U5;_WxVf4xj53u4t=wTC;p;l=Ax{D-zZas2tSNfor@Nhsnb5<Cm
z&WkswT6P23Lm|jbP8~YL8_&Gnd>5NV?ne@cnF$lPg-wdpr;qOORBk-`<XY=)jk?3{
z8fo`cAz)0M072E1z}TBzSw<HWMV2vkO~Ou~wz%re<=Mzbx|-SD5a)Rf^Z}KC8=B+p
zXGoGfDJWN?<f*@D!>xxu*m&=RXIY*HEFrf?<R%+6!P<ZMEi_8c+I-vqS1vDQl)fA`
z<qTzYoRnQdjswQTFgQkC<-PET+R84DsZOWlD<K>|5Rac!@jZwx9uHxHa!&+RAox~H
z=v76xYy?*QAq3U7`uzOsWAk&9m~{xaI>L`Fb^7|kUrPt_Hr#yI;6FLbw2BZ(#y1?Q
zp*pJl_{U*4$FgUe`k~p54>BTS19ZIK(9oHWnA?9Zt>;;xgd5=wf|#sefOwofWeObp
zr1K{HUWE={@0DgMa$WzPKEtsB=1ohOW5soC-YQk1DI(~Fb<x@C9=&W$K`|lgWxFff
z%nY^M!XdYfL~_zckGEA=i~W#~$h6*nzW$3*P8}@pthyRGHl}8<bQ}t^$wE+fx<Qmz
zIol+v*-U8n^WC{O!&c-Six}vh>+w_DC@nZsOk&f4@0$Csw#QwUW5)-ntEIeLEVXYL
z^pV@yEqf>^>1ZmT>Fr#HdMo3?c5L?s54E|g=d`Mk=?%i&Z>BrAC8-u)fwh6a_~s`h
zj6v$rk9VK)lWq`nvVk0_La8pT*@g1Ck`+szJ~TEQ={p*m*R8e*>_a5hw&45u-N6Fk
z_jBtx>-UU#en1gE7jiVji9iG~bO^A%eyAQsuX$(oiZ8e6?fbi)Zy~z>6Actb&4|Z!
z&V)hO&UlT$mG_<s#DCUi*p@SuxK%HM{DP#?_}0DyC3qC<?gj(;!HP(ny9)53;*I(f
z5BG|lGJ8N{pZzIGOF@Lg*$pyE_Pu=2&pPq|i)HOrfoQ{i;6+`_Z#NyvV3JZVPMJJt
z_2Ls^ts%0u^<;s?M<0OhvAi@*m@s_MGXUAQW-tE^5n4;1y5YTgc;Cn~oG<>MS@BC9
zLCS~3r?x?BwO}UoeGk~tcS7c=A}iNd-VpV6b?Kz#n&@H=70MAd&ALjHXHlh2*r0E~
zsw^H-+(K8_{q5cvo4Nu>V<kbvZVIw8*(SWJO7yw{aMLX+!B@%+DlgwJ1TwC+b@tlY
ztajWX2u&>6P>)prmCBJ+LRy9O{uUyoBBF7FM6gsS1W+_N4?}vpu1`n1@%!#CPw~)R
zsNkFgrwLb@Jm*4p)h6D}=1L=PK}fho$894DLdWi$o8f-Zs}ri`ueOEgivLfD?(X&1
z`-vhOS^n@f;FzHtU-=}$bZY~}<T3Io`c66Jrb^NUJ4w<M*8H{*Q`T(1<Fbvqhf2`X
z#1CIZTC*|L+%JZz>)ynGMyKmIP<S{UOA)5LlMWKNbL@CCNQcZ)L`!$0*<)e`4ZMX0
z2OUf44V4#9v<MKqt`BuhylJC*q~U_M6w1)Z6C2gx-AnV+&er%s^ftv%mQc2wJ%Yue
zCm#=^yg!|9S3Q0`8Qk^`4i&@L$$o+%pP%oytA5qm!p_vX!QMP}QtXk1wSgf1{l*Us
zxtE}XWy6z}#q2~`La~&_FHlk-(D{tF6)-tb+u%m2nOaSJvc-e3O9+@iJG{i25{4kY
zR6?A&<D*w!tNkt!Gb@agGyA#be@jv+gN0j+5cA)RQ0y@s*%Gx@4NLw1KWnUFhYhyU
zVWDZqlAKn<2zDFBOgD!<9no(R)_WE=u;Yk^5pN@hB%ORj5Fh3^fl7#ypN4C}56#ew
z)3*Djwg-MF^9_?FO(*0tmUA<+YyM*AGy8DF^2ufAiesyhz;-nX7D;p2=c7z@F%fNF
z*4{e6cn`K<YebYIrW12R4rg)gv)jXa)+9-O`J~4by2k&E9hIBjfn}AnCtMlNoCiVP
ze!e%#SZA6%-IZq$FZ~{AM)b-D%Iv1%xS{6+{uJhD*)82oSL=-RulMLD_P2CfLP1jX
zMHQE~ur#nSlty`Pa}^tp4=uD)t$}1(-x$(v(zb*G?eK2+&Fv?{g<{(u-uJv&!1;ep
z5HlyJ2^gHCSOpQ7(8Q7AgZ~$@{5SWMV*VLOk?<g!$hbY%IuHj%MJ;#W)Sd{<^dL1m
z*2`q3E<VTxk)x@@JmRA)?=Y9Xwh`E}hd{Wmhnc<9dJjrPnFyhFeT{(CTWI_*mB7)+
z%5~__0v|uIfwh!b_?l+VHo*r-)^rtSVq)w&AIjs&$tD+m2g~hxMv^PNBdklioh%}*
z!{2(NwEk7m%^^0#s;D7)?>$t5;w{lumN240w$uk|!032qt4ea|48egA-B>u3mSJS<
z!B5R#5@5zeTeqd=>fg4I;FTN_KCQhX7qHjl8ROh<(214SqDY&kKG!`j;c`h-f@Cy-
zLb&RDSAQ3wx6rI4>&#Q9IJ`#W7+q-3!bF$rp?_A;xu@wc4pwYeGY9bk!sG60c1{xi
zm+wFRsef_aMwHyw;ycZd#diLPa)4qP-wGs9Fy)>O6jzIj-`u*sj@R3F2yHV6XI#G~
z+!u}`Jdoy8T;V%QxH+!*+GAlK)c0pg4^RNXcNF~E7?5G3B=}dO)paULXHW7ZK53k!
zB`b&r#Ktm`-P!r6oo<PFEjcmT7?PQZ+btEX1tMSju?KHZj${tYy8iOLAd$qkTb640
z&74y9*_j#hh&^UutJ8Zc0ccA3HLkJnRy*#vbuF#00PIWwc>UbUIz3~)?YI22Hntap
zK^1T;TR7|JNc>%OZjoFW!>efy$?S0B%>H76KZ+BcMzYbET$8xL2E(@3rUb3^Z^}8!
zF+b+QO9@8n-lMlY#OYRH<I5%zlAZTeedi;PV4fK%ZP~V2u^Twh?PQ#bgyM#I4jL5I
z5}9eJV%LPgNoe;qK7BA^W}$f=E447k4tz(_HTrlwS$2D$I_ir7gutwC{F!lkMcLVN
zJ&i{sQln=FhS8i31(YWF6Lk!p(VYJZ$-P>b+^z`8)2K&~JiTDj2Y`P8Z43O(86-|6
zh7<0M2hSnho5E3QE~qFun=tsNmuCmFzKWQ<zz;1;;$dU{rE=42D6Q$~q#a88N;}uz
z@*=jK?bdFdp6iE)0fl{50zqZYf&=XdbADUf=$6`^-lh{zfr0H^k_Oz!V+mHw<9WtH
z4FA?*@jFzG1FZd~T_W;`XGv76h&auO%bAZEl$)G)es?|Qc*6FuOLT5CLy1nQJM~Rz
zUaG+oiZwnN&ZmMn?pwFj#Rv53jc>dvwMjGh?UMCyj^*BJUh(UhiGxw?&xSxa_)Jtk
z*0f&bMjhelw+sq{M=un{Z7@9bJ-7SI?GJ9<#@FwM!{=%C-QBYs0gt^HZC@>1Yn67h
ziTn658#EaNUa@-Tj)-lU&VOJi#?a#vd4sL;L%WGaW>3dw=TWQAF4M>#kP@r>rp}qh
z=$rx;Uq85`?=}A$TXk8Q%_1PuGIiAMd_T;v_rp+=DdF61g{ROc5~-hR<Y5$#m{gJ>
z?>;?m!S(nD3kx=Ulh-B9VR%Gd)LWSt5Xgz0(PS6LW<mp3i3Z{9BL@_d%?F21^F#w(
z^oV(jK4|<cZxz5H>*6G#x^@t>FoZrV`D*GOe4@){*Azs+MrK^T9FB-bU5j@N!H?>0
z*E4*0{})ixhZh*mN9HW+$B&YH1Z{I&)P>UM1~jHvGnu7-9&xfEO~v1#vKiTj92#b1
zSEv`B@GxxJIKhv79l<#BbZ<tl!V#m#5+&?udfKsuI2j<|#Q0stH4O4^|In`2<a;FC
z*S6~_mPeL$z8d}#&CO;5oFP|>!y3?1RnWtjDam}PsCO4^Li!JNYQ}K&1y6_l4YzL~
zWykq=rRN#@|3;0k^U|1D3u$7SuC);1a2~Yr`EOs_;2P8YJ=V86m7MZOMLtDxxSS7A
z-*SE9j1^u8eNZH|H`b}915hk}e(Jna`l}L1k1jV@b%{3;q>R@clVu{a+K)BPRl<0A
z@h+;UUaSsI4~o+{O4Qm3wJeFF7(BbOOHjn8V4W;B>ALgTDuRD%A~T>XV^67HoT(h+
zc@8KKCtrQJRFwYaMcui-<r*tiJSFV!X5Y#nxyF@$<F=^t+Qt79N3Qnm(!i&G75t&+
z4Wd}~Y=68vF4JqSNr-b+y@&ayd?U?Y00-tXE7mjS4nYGTak2bWNn&{R9KNQ**(<pH
z%{mlxW--_RAZ!j2oxm&<9W?VcEZ#HmX7*E1eF^O8%+#Re44HjYNM2FE5-dX6-}r;Z
zdD0%^;VOCU5zitB9;-E&mAfB{AmYcbKSO?^Vi^cU;ZYdI!WMsxsMc5gNRP|<z}7u4
z^#4j<y?yf|KOK`e$d4#HO7?=dI51eR2Pe-e|5LU9^MHD<KBTK|T1Rx1alH=X8GSmK
zVab`pq#W>~(>*ab<WA<<U9c7XN<d6oppEIc!cCb#vUC?7s!+fZ!Hx?_|9jqrd>yll
z@pOCoCxaO!ea=M74`L@7PXI9~CaC?HxsgZjjWR@?YqFDYBrc<H6M#yi?o&?ivx;<8
z*fHwH$Dq%g^J0h52BYylsmN^Wt(Mc^-*97o%J2?#f)%<2Z`TZF6@0xlWSciv@DQX~
ze?XeG^(DFI=b2Cl-|>pED$syxcZkHQUhK&)iNCa<6YibKhU-&@RIpQcJXt{@fDVZY
zqc_GtZScJ=0OL=_*)iKO_6ociM?ihh6dFWtzZ<dAI=p(-9e+}-RNGYk<l{AP+6xD>
z?6`(V7!sE<=zb>gq?X$f&sn@;O;eBhc(NWib~qlM3RpZ6?vD4d_kZuj(L-FSi~k(q
z0b2jI7eQFW`*&Aq{9<Y^o9m(#7n^?4CFPNa1D{m?{Y_zlK`LOOD_iRL7l2;gdQhB+
zi#ehJ_xZ8-1)wv`o_zog`6t5n#XkQAIP|Z!#_r5euFXwA!nzzV9v(s3bs6kZ4i&D?
z`rb9BqOlF{a>coFa5W*%zWAQ$g+{%FXBBie+^fc;u5g^VK_gn8-X-S*KngsjWrUS_
z;@uFPENx@mp<S<Z8|pbxyZ0M_m`h{PkAMFI#BBT*JQK0!Q|;#?IPIC88b$FmBirHc
zjJ#?dXgl&_^6E0xsa}|$pvu@F3c8f7E{5ykg~ULDno*kmq00K7AgtAThbEgQJBOrO
z`hD?n0}WwvN*Ba65QE2CG<=!Kkz_1+C;X$pz6{<P(Tb6;Jys9B1yj<$Va$WaFiim<
zfI`H)l10rguR|?Mwj4G_ARs-^Wa#3nHD6XiU;V5NQZbjtvtz2zkc{x198vmuF>2<k
z&J*P^ZF~O(L<K6T-=(~SMD#C6M!fC|`U)q`4uqLc?j$?3fh1!RDwr|Zes>MOM!tv&
zNQymU&36MwrAL*mc&4VRsM}p}<-zgO=mzQiuLSf{!)Db`wFM--RK6!b$}Y8>aV}m5
z2!tbYK`ld9cEo*9bRiIi4J$*0CbuJF^+bm|%q6Y&IU3hlExs;os<y{0<)4z;<eb!x
zxt;3gKyBv++JEWS4=dK7n~HYiAjiR)F`}2ZuOo|B#?K&J<)4>#=D(i%7D6Mu>VyFF
z)ukE4XPDIXTxtGp3ANlb9CPj;pPYVPyAw^TA(6v(M-_@p(&?&ux%i&w!a)1K^env3
zuKhlINXdAFS(+W6Mi>d|<$bL*a2<-lyZR9Dq{2YU?H#+mn?@u-2~tuKGv|%kqcg?l
zf|I6ZeJ>uLi_rSEaQ6E*@Gh35F(-+hEIs>Gy2Gl%Jrl`Vk;(>je^MK3Ai61yS3?s0
zophBvtFzZAA@QAju@b5WQ};EgD*p3134BYC)&NQ-D-=#00Mq6gJTn>OxOrKTl<)Jo
z5XPIMmYsF_tIBM8e#cZ=|A42apu`eHAuNu>nts97oV&3A1o>;JP1>bx|2Pt=#sjY_
zkrwB3;~F2%yGH0u*t?G@Il%e#NMyoAr?s{K8fA`)ZRx7t%3b<82kK5fK5@j8x~mt6
z$rQYRl~y}&Ev{r-jy`xs3TAy_WF5l#h6&~bZ^G5_j>90Jc@CMc=4I1h_^gC!#z(TP
z?)w39&Qa-&!Yq-chMJC|7)#DKaENHOMS!$-Kc3v>RvS!sglLaHay7idbPF&_!eMO%
zR9dd{4-}rGc^u`Q>L7T&J(k20oDpQvY}33xVbZ|9UI^e8<@1dr@%w2-<lQ-t>_-Q_
zoyG=n9(kC(Y9<iHCYZYcrK@lrtMgf8H8@pZVb80QS7ARt;}X4d_snwVeQoC*zO{k|
zSsRpl(l2CqVZ{f5D{WftJH{cz3!AU$xrh1t!{Y&UaIjV&0x<jL=K9RFvw+WuvpOI(
z)1yG3U3!e~?fT0O%eP?iy_%{*KS3qLC{@8bo?3=(s|rx~qpLIh5Rb2gtpPdxZ^aGf
z4r$wZYGi=N8Z<uBAgSb543F|hqAZyA;EUw9bFN8Zr=RW4M#dAW_^L9F*{L(<MjR<`
z<cH)s*K1nIrZ)*Cgc4wi>~n7Ccw;e(U7b-`WnpDvRS}w)i6DftieN+4F>&u|VD9=m
zCMjmF&MLtTY>q=J_Q`v!arkvrs?~w40g-@LQQ_P<VX$91G-1DZyAnS%gQtqpT=bJc
zVLoH>s_vF@d5hf}X1dozxCjt`+?&e8b7F_!N!RZ4?6za`SI>Kf6!DU7glv#}@%-5<
zIE1`EEH?<HK0H9(Wlf@x!Q``KsTpu6oN=y#p-3MNTG#e3j(5DF65dUGeqNM4%)QFY
zA>F7~XTPd2W=uZ~orXBa7)mD9PZsxBV2V#<LVKXf#QT~1K739!dg;T!(l$0cXvWe{
z|AIfl_X^+?-va>s>af^?Nx-Xv(yyy(;n_U2;o15R3a=+n0euy)D4!Y|e*V?y&f&~Q
zySrds`vg)P9lvAhlIS;&c@1IgN3|Xq8+->JSBVQpkl};f=JK&*Y(u+W#JrR}P`1)A
zx==9)l>W)iWUY3%)m-bDuMq+8lnh6bA4SKXf0er-@871zG{J5+szFYzGS$F7a#?~+
zSsl*EX%Wsk6v?H~xyqXRX9biRQz%Uqc`?L@V@*k?ttPe<*)XIrsV4J$VK8W(k7U4O
z?}>isya#sbYjVbSGEUL=RH0>FWX9nFi(ThMh{E#YffYo43g!H%M&csnjE=XDEBtKy
zb=cJcW+q-b9Pno29Xg!e3juL^8;?=Y;JuGh`<nAj=v~*AXSD{6DN!4$aC`Y7v<K<A
z`ytVB=1|RoDUmY1xr1(K5&IY#7W<xKDUxqjXJd8ieF(XYgH2)BENz@QNt@dz(S9t}
zT-tBivKI_?r|W{QitW_A)(xNFUrT>M4*NLL5-&lJ_8qH8#>}^Co$Fk-w|Mln;!ySe
zviSlxn?<EU0K2YUgqpGH!7su`&&VrII)@)iJ$jM}w9eOYI>_u0`2G>CHGiOzXF{yC
zW{W^rF>UpZHt4$KAWyjcfPNmMO5!@l(u3m<K>Oh~H{2xnqscNY;D?A<jK>>Bxjjh(
z!n0&<0w_+V@WS;lLdIAcWIFHQ9uYmi1H+ZJC}nuBj#9WJm=rz<A*PEneIN^ud=dPV
zFXJfKcI1G3>xF5RplahFo)Z3f3iw%KD4IltVaREMK;5y+M!Aun(X_C8`uAimI<PC)
zeIC}skn~7LN!@DFnMN>Pd8&pxYZPuyUqFs!`LS0OqSMsvmW&TYy9kwr6RN>dIQvx9
z8ju%70H<%RvJD(Mw`=<@e*qD08q)ex@9c4xfwgPwB&AC=A70GS!+Q=@W5H!K4D8wz
z`?=`HcOW4&GDE)R1>5WJDu*C^JHyGKS@EAA>1|@<@uWO^`L$xZh6B7m65Fm151zl{
z$-C8z$Efl3G!17zV5bj$jcSqN<<ZF>Ck%w!9erz0Sk4^PwVvU7hz2{g7>hn_=N*TM
zfWC|z(#0+muv_(X2<eyxVB)7A=4uP8p2oF`GT!Dj>O6^1GZva=h>Z#72{iHiI6Tf@
zcQg5*S9!y=%|F(D5M}$h;R{m#VVc0%<T_?6iYFiR@Ksj<H^iyaFMz`MJxizC>S6aO
z5CmkWLhi4QL8D#9jchb#yO?@&Ewy?J&t45C5~<wbnD9@!eK4-+iM3&0)UjU0(L3j-
zvJ*`?yPX`hhp%tn;SzB`5red_&!J=JbJOy*h8@@AeeU@v1h*DQha+%%n278Lfp%~U
z8~dU?p*U5>ZVs|@r;#zU(#x0G4294s9aLb0BSpTsA~Q2&$ALnST|scWl47S&cN0EW
zk8J}0g>xBoAysPVjjgNPX7%2?RbHBa%$v!a!xxyTgyyqqpNy^Vrv^z^p&^|G?1iT}
znC9$&U7Ln8z+<oIiNpNkAa!}Z`aLoGoZ~q#eExI{Jgok{o$L#I)Rx>ivMWoIVF_e3
zRUlnDOuURE#TvlCPN^D%hM#_N>Y&)Y0Y7EXCH|xI(ZV9}2*~1mWoZjrxI|L6JBPtw
z-BuO56d|8}LHKP2ID;*Fn~IHtUxeQQ8G4z_U@|uNANvU`!_%qyYT22h#N$jF<2SlN
zz&9CH|1ExvfFRSg_oY)s{P*`(i(HXN+y(#E@Ky~@{qgGdCBMzCB-MXu0lF5|)PnXN
zRBimogOf&~;AhG&Bz?BMb<oAs?vvWDDNrbBl5yMhsH}HK=4P9s)k?1rPBktBUnT`D
z574%Z4y?h=YBBVe29rX45bgFdXw5BB=Z?|X9M9F;OU%Wo$ajo^c6YHRO&epv%uO>6
zu2M;E$jtA`%6!xi1S;EoGA+Yw-3kw#-M}=RWpC^Z6p`9jWU6{!&Ch<QG2nfb4Dc*x
z8U+5|oCzuBX9n^=8JGr|&%=%Tofq*MmqU00t4`7fUtfESdo&tkpn3%jz?z0(vCnQX
zbIG^+Ku6?6pozh$*mYi3=XSr_%j45+0*_h;<I=0~DzJs_$)x=3$c~89W)(7g`qKWf
zv35{)l&oZy6P#S)t9z9L2<Ii~$hcKY_g6c8v6z#9mG`6Y@+&pnJf!j0fvU(t`por=
zoNYZ;?BsK(f6bJAB*T@0?T!M!aqyll@?VtVsjFTfG%sB@D$|Gd_J4$c=pFGS-C6rB
z(eO1WB8P_<1Aa}M0&U3L6ZMR{e`LJMBW~lL1}t894yDaK8o0+^#HqooFEu=VIO#a@
zG!{gH<l;*{uQ4>A{m_sMZC#LlaCf@BzjLGze2YD7)W|fL&-ZDjc5P1oG#sU3E6KV9
z%A+gO_|pr#6eSBNq-PeDKu7M>S8DGDC6`?3O;7hkv#h6F^8+*8o!*Jv8TqNV%M+`2
zdOv{c-b9<w>m!-(f8+lK?w>)Qs7P%-qNRQ@qif{_iCN`~Xc+9xlh_kpbq<BUe+6Jj
z{c5$xKm(3EEp8iYD_S|1l|krm0$px&%P3VH$q9iDXXhh_w?p>87?PP3zL?Xy=%Sy_
zTN;4IK>NY(UH$eJTQxLaXX-K#gmN33Rfi_9N2(o#Bi^MS)yYc%I1;jx+Ha2=0a+Wn
zT4I3)ZKH@@UmTw|RMh*TjAD?koNRE(%TGB~1Y;Y59C2o>FzaYvCx?(8YK7uu$lw>^
z=#&&eej&2F5Nbc0N^%_F#b1xuI*F<{)wR?l3i)<ey>_qsE950HMG|Hml*UOX25>4}
zF18<tjT*v}Q2SG>w(#A#zoG=x9IMFfly818hnZb9(I><^a|ZdiokoVxs5n{^ueW<o
zSEMOlS#1rCB<MAfFmZieV9>CrOGgHdr2xAl$k(Uy2x)PlhHi$r71qh?NXSA=LMQUQ
zgx+h73{|zD>gteu@DzZ`3<7qz%5Wb!MVf8z|N4BvYj}8Z%NZwk@VUKw*9Jpr{#u;q
z5;|;r_0Mk~pxR1QYEnX%Y##f96iY6+s3mxuY;@jsUy!=)W!pV4IOdmf^fzd>6~wr`
zi(vx|pt6$oV$HeGG3to3z{<&|`KeEoJ+F4$?3oI2-f;wZ_m%)Tw(wV!ybD{`j-C9^
z^@-j^<A0J$Ce9WAfvwVy!7mJ};RUAZVtQVnA}U>f+wFKH6nLpDPpVwo{joih>Y6(-
zeoZuVm?K?H;@ueMH>1(k)Qh=4@^%jj<tbAWJSCiX)Cs!q!skjJ@C535LYj?c&|dB{
z8M=kP8y7Xjdi#!tyMlz<+32A*-drmz5qxUhNc3^aa~k?xZveHu`69Z?!{C0#ZE=kT
zB(kxY&c*w}Nc^bR)0ltjzoEkPWBuoM<~>p`Hde5MPRpHHc_H?y_7!##XsM#AK~`Fz
zhutfWv|O7H+Acwv15<d0`dN*XY?2fnpL{a<E{6BgUad~LnKvgE<soE0e@*EKjov4}
zsZSkD=`h|8m0QS@a<L;TEGaWx!Rvt{=uLhu*>RtRscAh+70}2{L!Er)(j7E<7{whV
z<DWu5QTe2>sySd+nMvZtYw+A<y6(xfT>pAy#x-{g-DzZI1SiOLgh&K1-NQr;pYABN
zE&RO<Tks2bfK1&=ZJ}Mf)NqJkMSr@$Vgz*A+y^!EIH9bf4@Y0vt39H=Hh7W^Ux5ee
z%QJ+{iDifW!$l@_QS5#QD=AiCVOy&JEpK2k8nA+w!5zvY<A$5SBQ=0xLx{%03~i(O
zdJQIXsimGIDN5s~%w7phJ7{gAx-*SDK;W6LdPIP+sy+Qq`tb*am$lkae}KlloCd<#
zntvQXr?OH{lsmAf^kC+2POKy)zLds^M^_WHAJmMTRbRWYT(-wfDO$g&$;vrVsJr{y
zc$G1}5-BaJ1ScJRjE9QWxpncpenXXaO}#Q%wO_|Brd1ifz{jSCpI05=x{uBBi?QAn
z$&1I>l!4?u#f0xP8pCd9e1Yy+1{pUp`su0erY%@<RTJ-Lzit4y&P8?XOC?mnzEjZd
z@jQ~YoQ))u-a}YxHBX=3{8*bGluhs<xBkpC2~}%FJ2%*nx%U3Lo=|g<?a+>@w)5vj
zMMU9tV*kgl#;y<^e(*Tlt4ShjH@KAmk+Ti%tuIwq3%a}4Qh`_e{u@3DjIWgvD-TyY
z5O-t&*mi}+TvEs`b8F;YL)=yJp|citUV1eit^wQPe4fJoFB)UUX#iu|dIPI|dL|&T
z<_|z>WXwq5^9eE^4#}kV6f?JdmX7DkfJIcGOyLh+(Lp;QE8=fgU&lPV^NGvwTkYi;
znv+-W?)@|8#QI_78@AEt%`E-E6mNA3cuA`Pe*dr^Ieb1t_y6-78VmHN?b=3JTHKl>
z1*8UEMB|>qvlp*Y5H(Zd4~UeNoaeBPb{<+@b`oGm&o<PsxdULKkk>Z<(^uyBc@anD
zTiwA8QmIWSuA8cFI$}s`Cjf_Mg3*Sp%4k_t&^;Yu8%xXkq0=k_&ryRg;yb^QiTT5w
zBX7_+jtlk<@hFuBdDDMfm~;OX>g7}+>oG_^lY^R-MMXK7IDC;qTe<B-rmT<;c|G)K
zhIERC=;=Zi=Z9cajwx9NM_J9muWv`d2<qhsDdSfl^<raDk>}93?w3v&?e|Fd!@~6)
zOzzA|zpf)AlS-hpM=y5p^o@Jop+W*z$e-j*4G?GpMWc@=9E8y^vk+TT4PHJ`mOHw1
ze8x^_c{v8={MR29Esb=E-u3YSOV7Y^s5?{bjpx$214{f(V94BeTp)SZ#OvyxsI=I3
z$kK^y40Y65kReNPq(_-7V6*c3Uk2~^0<`s6@b7W6YvV}u6KO%K&cwTfCukr;ixPCM
z9yRFhEi||x7l7G2LwBDMKfK65-*8+8nW>TKw2`)ez^p<g+<o*7+B>ZW_hImFq3zq7
z$l=C7#>7=s+P<Ynkk?<fKbu4z-nZ=yDR~&j#T@L_s?hMmRULB{GIo%+T(d#yfRE}r
zhMyXNEm!IjY_GZEcjo&_oi+x3;IwAGO7Gwj$jl%9G*oL3TQK>N+_{$M;6CO1UH<Mj
zK8Y8<D7i(ZUMJ=*VJOQ<?hMS^`-n>UGV$<R&XNJ+Q2KeGAR!8lC%ezj^Bo>8*5=3d
zR>1GC`e9c=f6U(20Tz<k4>Y3z4fF(Bzz@pNk_}}SSzfsGtAV8Yx#YrPB!nXsSd@E0
zU+Rl|32}OFBA6w93~5BN#8W?fja7$ACY=~UUAlp$y+i8K`;A!56VPZtg4Oc^xnssa
zqCplB#IcRu>rXXuL=Kq4s5VyP9DYTM4~;bsQTtSU8O)N;1zAYq-QvZ7o8RU{!4v=h
zPI1J_Kw3a~KCeWL^hpEA_BGaT-X^LdLnTmo?$-06fStFg9?GzQ>a$gudA-5E21p+5
zB-*{}ch1tr>c*<Z$KzqA30iAv4>Q2}SnK>aikh0eU>wmc&mZ0E2*1nQzJQL8XD6>B
zz-xn~7Q_)PhaLM)HkRMl@wbZ*Nue8Q-w>E<Pw<a}PRS3Y6eQ^p-<IY8@st=6=?^gt
z1;Ly?nl{7ffpfZI<-1Z64d+Oh0?W!yy;?ZvK0uB0_yidzquq3v-!+LN8-QWa8pi|6
z^rSV>S6wmv-F7jJDP(%^HsOw7o*HVsCzxtu*U&xX8KO?eSggf8+W00j_C93@As2qI
z)v7=skm*Ep9?&{AorD;eQ}Ws|*sQady_Y-bK88P9C2l1hEN_)Bsc6GyfVRxsAfPc8
zSfkHT(CPjJPy$vx=hQSHa;MT0x&dZW>N*QED1MvjB*)(7J7K9w&44=f4Bb&9Lh5p5
zdd_d`U<Cr~3;^=ctMF$h0X__*6_#=G<`Tc&ZTtdoITq&CFs$x%=P|xFI%oFsC4S`~
zt?9L#x{Ph)8L%mcLlA%vjfK#Vvz&9P3#`B3=URIi#e$_UjkFI5R@ba#?MyeELDVkZ
zf1R$j6iBln`mu35X~4`18T@1I(DpHtYYbuLr@p%g0JJ9pNUap|hL2VI{>-1X@IWGx
zNGCo{YW#&*X_(aL1GtuLMq~!mqgCgl6&{S2CVC;KMBr7Ka%rxkCi{>P&kGsH&Qy8l
zA@f`RRA1RaMY=rWOv<|W$xZ-C>Z}3cU)-JLa`s#8)r7#AVWsDUprrY?KDIsZT;>`N
zSmC*Z9l<do?zpSh`#h3%n%xoc)f>H7^H2`6?g}bH<=za^j*IGsQa-g(eN*Eyd}C);
zLRk%qqe5?6dfu>q|LZI|I;)@^=Lov*1$bYdmd0wAC)Yip^-!y-&u-NP$Ti1(Qt^=`
zshqL7;`rTEhpEJK5B1(b0@tN`#EFNb6`uh6MCoZr>OzXBe#nlnytVIOKY}HdDMAK!
z?Yz{+JGS@j*o7*FYz{1^7Jk*SeSXJPfj%N|4<CYt=X}BeTRc&#WL378W@HDdlzf@y
zzKeY&3NLyF<qjM$N}Gy?c`H{^G$HS6i#obU-@q^e*zN#azh#u)Sc?+duo}XA7Sj#P
zM1p=y#vqQuS=A+{^Z)InV*5XqB+19RmA`;L>NyKtlh(ThK!~rx(93C1#y&HMUAqRC
z#qaeM<i3ZAj#d8iqm?lGtG+{<%`?n%0*reZcoNj5ch;HIbKw|y*3tPV+VCNUjQdv6
zFC<a35KrVB1Eu&8$^=Y(FW2LURXH)N*4Uk*dad^CAEZqB=E!loBrpHf3nrE2bD!IF
z_c9(H7)!kI(33XzD_?nG?dk<$QN;oKfftvq)xKHz{rjG_`3kAKf$_&flXUIwMb=i}
z`KGfwjo@~h0&aCU#5|VP8pn_I6gqjiulhTMp&zIO<EISckdfyJ3>E4N(-mPH7q>*w
zTH1#4lp(*@SbkZnC-5jGHRL(<rXy*;yd*ELh2HK4H7y4TspUSIy~nQ;!Y<=Cp$j?2
zY}EcyY;Qwbr#)tt<qPolVz(M@DY27YoUn%2s=h0B3i3p%b<&Rg{8E~IM9BxS{HP$_
z1Lq-LCbC<!7YZ@a-cgB9!y$!Bc1wW($T=@&%p9x{6;_^3qIXOn+A#~h6?tN~cX#cj
zrdXR0f!t<ZnASEacT4S#3y31zU~ZjO<FECAdc5|hnQ;#WXAPgIW3T-rbC|yU02njk
z>YKTm7C!*{v%f&|=T-x=*O<1KgVMJzcUcQvs0F2(4?IBC;@q`}#(R1Z3fr#%A%cwJ
zJ(~{^0w|>;+*}7Hs`t&Wp1bsw>ewF^kjjI?VrQA7sRUQIQNq#9;!(l-nwYQ=s9a$2
ze{I!4KCt+@WaIhK51TSH5+86vQ+ipUcZHDq-F~$A9yLf~ipy4Lj{sgGX6oqR5{~rt
z%2KQaQe;}U>Hfr{Y_e*UB6K$hCfVJD;-Ky&lsp&LZz<_&V`fcT^pz%lzlik%&9B}F
z$xwGAN)1=X;s*WWmC%TCEf(Nm(%^WAeE22DrxbQWqRXG;k5d3F2a$31T|ku2031~I
zRT{Y$`_y6&#ge6E9!hhuAN?wKXs@Ia->rz5V_5du^rk}6rx!ZDCc0$YJ?N!o$<KX%
zS`aB4Yx72m=`*LM(*ddnBBi#xCMqT{ecnJ35llm#JlB-2DqZCZC*1yI=MTn{PcI1h
zIO^d>i6Q|W)$I3*_7gAl;t`xIUH#<8_#hSwcu$oW9BMkK3J!F6Olv*Ir9v+iZ>{-?
zesi2~h1|l^sRfHW&X?gM86X>{1#&%OrNHZpW!7~;v&O%#S*#W;EtF-lU_gzc?2BvU
z&|w3#VwjbvAiQ_F>Ar$jz6*!Biroie{^+#lTyNS4C&y2oLHOI`aVy47EMPnhPb}gj
z__3YgB^@Z&>8Xdv*t^9E-9+bp=z{vaxfMKfQg_5j1LxS{khO`w$KRy03PEXSH#gRQ
z?w36UbXw}EAFT-?Oh+46dHl_dOFZJS!jG5Y-6IKTcMLgmXu<;=-ug73@dVmb*{-nb
z0}^?rg{Px50-+ATC&MdCGsua9EwqQ3m(Yb#Cmdq&u32iqW-YnG?UT=p3uR6@Y}e3U
z=6LJiGj~wRJx9IX5C<B!O487k)w?~9z#hfN7us2=yR+0;NGFCfZtfg1^$4*d_n%2r
zYLh+`pgv{HUkC|kTA>}M$KhTst2Abbo+9dfoxPHSh}rAGAwmmSO6cGvUBLMkyQT^s
zXWt^C$CxNY6rcZA@9*5t6WG1~L4(oJOv581BLem@YW-`Er|@F=yH=jkF1DjwWU=Zy
zw~U|Pzf$2j(p-6;XPN6qnjZ{URC!s3S7}_Sc-S8%6!^)MCxkmI@E4oJj`$CYKKZC?
zBoopn?W7%r3qsY_wie@Fk~TlKMeT9>Sj2AjQdeZ98fnB+c_gnk=1)MR<a*>+(hKd*
z<FvM;XqaXsUrsC;r|d7X*t%Z-N3QP-kNJ??G?3wrM^T!Y7JbJRKVX@UToqaR{hC1{
zf9dQ7(icI!^-b>$abfS#fl$_d0Q*^)wWIGO2T0I05MBcUEuZ$l1a>JF2Emh5oF-qf
zJrGMyFF`%MkIGNHES@}nrrE(x4B3;bNAF&AxowZj9NsDOc|zn}T@8y+;0tx8k|$nB
z56R^Rv=`}6-g#7^;AvJYg&2<fgkwftZ=XqYL=ZCzpD|+3_b`hIC1G#UuG(jhz>As;
z0wbu{pHaG*HV6;#Mn&>xy99%&vsG{+ol-Z7S4PssrXZ~jB@Ovo*qaX=^~9im)C&RZ
zjdx+;yI9o4iT-2Q#-gkd&fJm)<g!g(20>cB?9mS%bXxKF`noHNKSq+p%?FWjR!|5;
zg|^i@`uJABGhuPQAIOqos+lg>9lbXYKev^X2gSGfKBnNdW1dh)$#cxJ2;_F}tj0q~
z;e~4alao&zau}<n*B2@gMq^1kgy0m}J?xrmf2hQ0_sD^d)UosXN`-qPA36DlqtD=_
zMuzmH-G1Q!#V1fioai3oi}ZkS8)1E@#gYV9Uo`+5$@Rn~Iwyz2Tx$Z!J^dgjq-oUC
zUm=<0y4dMLY04OAi&sz2r2h+en$Afu^h86AladpL!%^yt=e?5)tt{xy982odfe#as
zfuZhx1-0uBuCk#B?nlxA0@x~Kxj*UOrwBQTy__~N%D;;(UZxeFryht$HH{)n6uT8<
zczqdaz-K4s2T>4-{&S<fz9aLdy4eOaZNQ@xFDxLzPUlnzw)dR2YVZ%#5VsNq>wpJI
zeqe2RxoOK7)zGb1Xi@%n=ZoVpnnE-~*eZOzgCDYq^xnJlqnwtR2l2Gr<PkF-*7_n_
zg_1j>01P5+0S22(AgERNJ;EDGoe4}0uRv<7l@(xdoR_Ac#2!KuZN>awHB3TWhHLp7
z=ZY2u9(;Y_t7Btc%rJPh@xo&uu@s3vkcsJA-O|@9`}U8hs0>9)^}}GxAyX-eUS~w5
z$!e5CRji(s7A{v>_@MT-9#)C-R38Hb=zHRTx!BDBU&MdMs2&D7HOa}>gni<Ax2t%N
z#-v`(9U45YZ}^~@*_C#ore2g2Vp_}w?;(D$Wh?$?6E(dJoD`*H0_D?(GRS+VMna(*
zIV<yH)i96w6_wCgn@2lEH<DzkuVA8g0TBIDw=RSFNwXn|{BP~9mqDSyVRmJNh@+$o
zKd9x-gtW%%shQNVvy34z4U*SD5!u!c->XwV$CM|q=dwLO{QpD&!o4}VtPK&2c|WoH
z)wtr=O9EFsFJf-0VMsc7HMsxUqOgOGTSFZ|*Y|V3uL}xmp$ub5p49vtJzSmMkN#}=
zBFmpg0Ny}^$P<z1?`^S}iTXp#OR7us>ap?fN&*)=xRz*xTCZw9XayTOw?K(D#^dL)
zzq314uk<OT91hC}-v$$5tKm$AKnx3SCsF+Hm!gyfzR4!y^W!%NdS}!E<0^;g0=k~;
zraAV1dmsOnJcTgB%^2*U|K!zqp|{Vr)_#O@`(P~R+NFr*Z(-LnQPLcLDo3bzy?j`O
z1K>K_&)(If;a5L}Qyg#FMu^p)c+kB&<AgGuWigSec5u5r<#k#jxNFa1lLl$<WJ2<n
zO&t*K99(>nvzxP&L&rvFd{XFBTm-?+<Z-OMT8IJ1c^0k%zale;UN4~B`MC*#h)TTu
zxly{hp}V+KBP7c5N&lC8qFxJSwDuiJ-@+al2wKm)m^VbmYB?~~X61p0uD9_-sZV3p
zK+v(h)JzmiMU@uJc6b-bo{YZ(#J8~H(R4{yK;q4fZXYu<JwUh4=K~<k#mU`$L4b}5
z{!n6(OI3+|E#0CCrZP!vr6Zs<XQbDlCo3Qoa(vTzTb&z$Yh;FV)eXYAVp4sQ{xb{c
zUycpn<RUY2mP=7G+DlceF!Aqge3O;5z>q1xI?}GkPeE!p27Xnb!}dAlD(&f$(s8~)
z2=IgDaA=3#V-)2QO)g>FA=~PLxh^nhE+%a;gF?AP%6A+DLJ4WNbMcQuJt9sY{g8!}
zSjMkG_eFSf^aho1P}ly3TI?UC)Sy^#oEVS4-m^B8?81KCZpgzZ3Ird9x%WAih~y)o
zP#+;jQ_JHg#~(%)Y=>14(0?%B^q6?O2n_PI#hJ|P0d`{XiyA39dS>e9a#=`%6B-A2
zQw+h!jY(-KuNYGf-k-IQeVguNvhT-FP|^=GBi?q+`TI5S-v0Mf;6HX9$~?O*gHpBt
z00GaS9LE3GULgwhS8Iw?gKx72249?e|E`c7gGm)*@Se<J7Ix_cnyk~bQA2U@TL?kX
zY1`<0ngCjVWlF_6hkLFLecNJ>lZN5QD+&J<Xx8~Bl;GEre6Uwhh8s@?e?q@qPz@N8
zQHT&+W3^?bJ`k)^VZ1c`<)HDgYkWpz%+8YNXpucMr0IF@^U%uvb>T`vuwS={w)wTg
z0YkA@cMQB%HfSOTZ1aezgDh{~Yij#Qt<S(IWTF>!bkJ@`eAG}<c*wC2-a!CCxd`@f
z_V72R=}Xjf2l$gzAk;@tC)rqc6`c0t4DT$#q{N_@{_V=^yCvsd(f9!v#nx<DRR~p&
z{q-$M?J*=L|L@+Q|A9W_YYZvMiOjfISm{s`kQFNhP>Baha{XB<#Cko5H!(ijKj$=0
zfdKy_2>IAIyojNpdV!J|lR~E_0l89?);_HAF{GgqyGT1g_E$9m9K!0$2}vTBMM^bb
z!&n6pzWBSSwS0-;L^XqqepZN5G1G1s<qRe)`9C=F@L0-V1>3~D=7BG8&HM~!;{Ndi
za!p2<i$G*Y)a3IKuG4w<ZFzR+=UAfYFi-db-=By`J~)2-Qd*hyFq!j^dp3fmHn8iW
zRywX7g@;BeVf#?+-Gf!V3LhuMFfkVh`<AITi0iPo|Bg6~5GqStVwelSB()!MCIQhh
z4#y@sr0&*mg^ieze<Uj$ppi%B0yQfCqD)5Tp1t{(7T}){K6DiR`=%u9;is}_-g7C{
zopNUeG{O*LVih&DO)_Ln?~MT{W25KY&7Zra+P7bh`m7JCr-iLq-uY8|f*p$@OpJ*c
zMsOz;q%-H{qGqil0?YCQtfapChghypf2-RBRdq*^6Vyu5@lCLdq=Kv=`e68UU*Lug
z4BTCywY@q6Tom3S*2Yd5zYvRHC>UUf0+{roi<}R<f(`=epHd}=)icj3DoMDxm7+(f
z1|%hMk97|+i!IXzQ&>qj+C*bLNj!N)sF5XLcsB?|8WXl|&<voA&D(HB%lo%6a6I0G
z%C|=!Yts+}g4lx>uXAtWNOm8MPc<h1Eb*yvpWaJ^Ay3NOS_&Go#~>Wv@cfvnvJ$p+
zl}_0JC2FzL+f71F=RW4X-8l6$*ypvI8&_HmFE0<pM13bUN)y!Hzc_vcGpoqa)kUPf
zr;`xVA;%`?l#&Up5*1Lpm9V%@U^->a(8!-rRNJqyQM`)@TriTE!fZmQEi(VdH6nhk
z%Y}j%!u^K!SNBW`eGBo31CZY1l}W(9ecM-@ZDtn>5B;}Z%NK4gb`l)9Wl36DMoc-o
zu3p1YGr&phUxOcy(pZt!BY%Nd6s^R|>hqVe1gzkO6+n9dW1ZK4s^QVXTwR@bIJwUB
zIwFl1;yZmOXJGbo^oA<XZ0_&+)M-kdq?fuP;CxYR;4WZ9XJ+mXl5x2Yz??M<4QN8v
zkg$wq3?C@{4FOnaai0yn$m2g8mn;(sZ0&q<ui0BX)-#=<{5=+ljI?&m`-yrSe2-dT
zroY`Kf(^aW3E!dPqK{6B-W?50(4X&89dWu^2t4))E2Cz(D;XfL5_bUg&h0%R630%9
ztDbKv%Cf)MN3{4ID~nJUW+XUA<*rocqhAPkQF=Gx5sEmDjsth+>k#`kwD&)I9GF(O
zAQ!c$idj(Kdr&S`Im@1TORcg10dGfvapMpx{|v5~tkSX$4HFK!;58_sh3hW=xUh*B
zt1ErFh?$z-UO;QX`et4>3Z(~|q5roKST=60=?Wpyi6ZzP-po~cszzHq${L|5)qXm~
z=Zd-_9>8t09a)_Ppzd#?1!YUmAN7pK7e_haRoCJTt||YA&MCuF9TPTjf}@3ZdyTPX
zi-ui{Q=i6PXknBQ%H4JV>U@;uw})ms;J<1od*91}^COTb$}h~CHCOH)`1dLz1cHnS
z8*}Ao!nL=MpK_7@jm!k9W5I<#3JIiF)cA1i0oK?PJ|6;!Ky=7ufqM1kmciu<yW{-<
zG!LiDB!ZE1^!FD(>t5iXBYXF&1Xlb3<P2reCC|&}D$GLapEtnxH-Lp8J0D7z?FWSD
zFJ&~T82T&7r3L`$3)1yLXlr8D5XzPT`20^L#Hz_iR29HgW#Z!u;uQ0<m%v!_T%o(j
zTJ_2Nix3c*ah7>NV4T;{x^^M^kQM;-1`4Zwh&HSv$4X^(^wDNU9;J$IFBDk$UKWno
z7u68p*bjNY2_K&-EWxkgx$v+6FzJ~}-fL?mNK9T0TBoTBy(H1fk(S%lkh`Pw<OqWY
zIEHvn=m)&2fEo_r)m>HtfB^e|80R{tv{Njnq#gASU~Bol%F#%!s5k~0gr3MLhs>Rd
z;~63EbuaYfJ2$8T!KazqUy%Y-;OH(Uu5m-fpHErVzXRMXS7X-h3&xdd8!&N%X3d+e
zRb;xW8?h}QUD3}wNOb|x^WFO<AU3-i$-U921&GsliP#Ws_}XNPP!>S6WAJ$%$cQOj
zPxn^^he{z_ID$Vcc$kHoikgWB>=hjQvc)@kQAJa~9Y9Kt;&^ZIOK87d+Q;lzYcccY
zm3ej|6KubUUkm}y+z+iC_zLk*m|VIGb>PIPbvP)EUx4ec62&oY<iOc3_|3<Lm4qzt
zCQ21{KT8z{5u*b)OH+W*PC*J)^*NlTwqwZoczGVy<%cW~piAIl*m+9e%bF7ZIA9af
zG|!CERovjd*zIkwFleO_1E|xt+m}ZuvF0f1;oec7$7j>)U^PwngZJp9nDPifBl|L*
zClH_<A9OOE9k_Eo`bhTg=`Wu*V!F!UfK`iCyQ!7|>5H2lTP+M^aQ4Iel0mcB7~p)p
zT(FrSt9MoAN76m5W(0-9)$oMug{1mA_n?OQj3h}YZBp*p9FW-0yx378Bd6{NKRXS5
zeQ~;10_iIVt=>f2D7nes|45_&?IyPb8{zNhQ}fdL8lc2|4|)9svoN?7@v$hc7-gVp
zT|j82Zjj*n*P0Niz;-F>02~`A42fO!p6<{vil$kSJ#>pbyyoklo#B+}U#)V6ZyW4y
zFQ+)BP4EC_598Q+893Jdfa2t!!^@?kN4W!ky>bJuPH>1>N}>^Cqz{Nb4DK9$MRV39
zxp-Z=*=&!g+`xTae(L3ZScz#4X{U1~l7k-o)u7NRX8a4f-q~k}&v8uie>XcL>-tv(
zr2HsSdhFIL0nBi4qqO92w><yy`gS*@Oo5dC<hSdRQ3-mOl8!a-VfwgOjaiMYuCFe7
zp;@44T84cC!9`2RxK+kn2ytv(sXmY3ZTapt7skAmsQ6BEPahV%t4e^}j;x&!)j#CW
zdVc~GKf}v?aAdfci%%>Jr~~qnY9HPY<Jk9|*JNH;EDiuep6do~SG{NxR-JjY7=-%Q
z>|+xX@rg~l?7X~xbVF2&!~6Z5>GFr}jWzA&+jmp>WkMuTcIkE^o}8b=xX7h_%m+fm
zo^2o~=*A|CpJ3~;z~}JE*P7)zVC;|b@`qGNr`W(IuX+1Kica44Hoy(<L9SSjgjG|T
z?>Tt?AJX1Cp6dVq9}Wk{J_nJA<CvA~y^l?Zs3^%EQ6h?rIQBkfl$}|UQA(lgkx^Pm
z_NeTYSzV8p&-Zuze%JRJx9hrHf4%?d9lc)9*Yo*&+$VVO#d=129t=u#Og(IU3PDTS
zv3z?I#KzV<1-Wx8N=wn;FMj=_<K+4B8&591xCL~?t8Y*a@^MsXuylK0@8$R?ZYm*+
zQha)M8?&1vO*V_(Lhm|s_aS$`IQczT{imE%L9d|1WF73E+{)Om``Y`bou}XAPR}yZ
zJ%SXc?12K~lvLCngPI1C#_&3El~0Z5-P}bn_%wRmxdX)05w0eQKWF@<_+(CKrMg#2
z59G+7{i~-vRjlzcT2HD)!eXHdJt>gtrZIWL9Wx<txT7`XBMoDx(Ou~Mxc~4wtmj)9
zx+uTtKBU(;S}4cG)dPSPA1s>(n}e!)T@_MSbJM+(N52d0Fa@-*k!~s*+0esh=wAUa
z`y?lTIKJ?b#d_?4chu#1_18@GcD_VqyRl$YPR!}S`9Mn+!p~c$*oBC~^VNu$^8Hik
zWY>-W3_fbvgBXgMfsF9$P3;H*PhRPc5~yU=G8pFV-9(r&us&ts?wzc$Z~pyJ@_`En
zlL#UZ8c_n}!f7`N^iew}(vfl@%%So(?!$j!D2j>71B=YX@hpa!)?YXx(nUn4%JOvm
z#0uaes=x1&FO~C<K9!pWO;s-W7fnQQFdGW3AUZn4QgAd{gAyOb9mP&Q2wTHzEVdON
zZR^F*9-I*z%v-D{o@=yPu8VplMN`PcPd5D#xxE)P;@Ee$mOZrm6$9UgzvPoJZ4Uw<
zFP<K!;|H-Z90}!LQ8Urn%n!oQyKn%UiqL=i#&@Rz=08JOc_cGMPYsm*TY%LJDbgI*
z;!2f3QXu3UyIH@{Gv%v74)qm0I+=D7$wiaef4dE#uHHZ$B@G1+uE0OYElf?q>5&Qn
zvuitpB`CW|W;2;;kT)Z0vCp}ZH2pEJeCCgmQFle~OrHGF>c=mqPBrO2NUFYzA#C&W
zQ`ZXewKK5?T>-f70*GMoGQNUliVR+HJB2K2oQHFH8Y#%aJTz?xjcb1?uwe{kUTrGq
zkWden#K=Ms(bndwQ(L&f@yM*mF_4Khu#E{t^hYqbM|fzk6+0n@%R!cu5{)B`6phMX
z{g#~8B--DVfMSUt^?>?{B0fr+ig^x!xJ_*wC2*EnBl4i%$$1#ZNo1MS3i#BNI>jZ=
zNAD*4(_=w0_?)rh8MBhkcs)VpSXq&!=5V^juQ2b6O)qHKR5|C18bcNu6OYB+?yl9d
zJqq$Wkf+SNtu39Kz*XYrICa7E{sk+aG?mG#3O`RBT-{s%=yQ$dLlKcpSpK}?8V{;o
zo%i*fG7ExCthojxVg?o;YjH_-jjSAF^ibnuj(P=y_q7`QSdmrk)x#)%bZuoy##_E7
zK4K9{jb<Ek1MuPW#gW->a^L&B$P-XUb_AV5g~)WtWl?@_Np7qDZ;mat-R@bB&vs?^
zEj~67;4DSeRBUcrA~Ck{gXG#cv_NX7LYZZXRYjK?H1113T91vfZdQOKi+}}a+%j}i
z{Bqgu2I}7n$c|Qu7*|{5#Cbtb-52pm;)XBp4`Uuq;-Ull`?V0CSalf`^;iD!{!N_f
z%PW7{d9CNLglAFX%6&T^4grS$bfg3?f2U&DApVC@EsE^M7fZW9beJ^Z+85(5B40Gc
zVue$dHBc(h&85SLE5AU;CQ=!9-*C6mS`$U6^-YT_bw};Q_hr2sLvtJ^NfSWbD_oRb
z$=Aum=3wbqO?FdIk4b^5ESO}}GV3?mfs^X2BxLaQ<KM*PK?v*pzge;B!8afRV4ut*
zal55r@X)`Bm)^A$Ct-#j&g?7sjP9osdo7aE5OsOoo!2RTWkK^o09&B%-EqGr3zF8h
zpq2?hBE31|?{11-vRa{85~#@5-*<0a?|Z=0AB48>-jY2{`W|r&N5r(_qw&XfQWXCp
zksCRKRQXw1y2%pGm1~b~tp06zO_@>1N5mao<&+G;O#T`1z-VR$(0fVb;{R)F1$S0f
zw01V<AzM!X5wDQ8Bs=1#XpAbi)r^X6NcLTU@I0*K#v%>AvIFEdU+<`d)UWGV+VeVk
zh8><Mo_2D)ZF)5kmoPWm#DU~|r=y88(`_ubcHxTptPQh9>+I|~QH*{ib-c4a+1pSy
zD6P;>*GIHk*ZdS;12Q(yB4dLc%}?I{z)SVK7f_wFll~+d+OZ6G<qdRx-h9K@((R4z
zXZA<l^gh$fz+EgpHkvtWpiaWke=VA(8!N$F+J_YuMirt&v_|c1&xY6A=3vwo+~>wJ
z+Z|shkjdP7$oIpT1Am8-r9MR6ZcbUrPioQS%y)_YE#KnH;{hop-*|gpqJpY82^HQN
zhvYs6)C}?VK1XoJDq7j+I_HSHuI8JGv9MC_Y5}9?7A{n--#XhFw;F{DV)|gXk{!oL
z{_B~0a9A*UVhq*Y#b024q)IO8($d|qLxIJfI3~#2_4(jNNx*SUQhSAs%k)!Jha#XJ
zow?>MQb^}+vOx~UP$)EfGya5bPum$BG0JUCE_ql=I7Jn*ESq$)SZx1<x9al9mTOi*
z0$ce{2wyRHL8$iAsAbS%(<0?Epa)n;LZoFCCf_eBac>iBA}}qEp^ZG)*8LA-Cz;y)
z9LB||Cm%Uj_}%=y5q1PW#h<+h7>%r^gJN#)J|-6Vp<_`-W+y6N=1KLd=lI3NF8gX>
zz6$2Vi|f?Bw4n$%JSLWdZM`{oTDj9Zccp%PHj+DTSwJn1WzLo}5ltis)P<CDsgcRe
zE@JCi-}><cYfT=bDDT9aPMcIA!Z+M``b7BZ-w<`tq|T6B=QRl|p>eirS**ryWk!iF
z>aPk(T3(P&{TKO!-03%QIE%|0R&`!Qi^-GuP2`N3Dfh8j7kQ4~to(=35pYBm{b~t~
zZX>l-d5ksg$mnTQq{m+oaOh6TiCDn8uInkld5uTRZ0I9|6X*~-7ztt`Pge$=6%;U-
zbfolCOd2AZ6oj2sap+GitbxT<k>=U~JD_bv<|3oViU3a2gp&7tXN7B{?aqVmQwvlP
z4tQZ*91XC}kwNTHV=poI@n-pjM&~I(vPVd*K%c;N=aw|5%AZ@V{z|idZiQ%~9GR=h
z<nJwSd25v2H(c+%6)7!?$I-8{p8MdhFQ1O>`>Tanl@y~xt)Y_HLvZ@N&o(4|I=k~1
z4mSttlkPw7hHo|iQ|(yFs8y!$1!9`}EymE-YgEl-6XLYL$V(Ds2W>(N;_|$wZopZM
ztrt2^?b+n#{<runR!wLslv~oBaT~lOBI}X9{6&48<b;fqGru}(aTZJxX%|ONX5TJm
zR@+zI{1>#VE>76@ZHR;w%5X?oto}Fa4?5oS7xA6_RH&a-Cx^JuTESnxMeg|b8ySR|
zHuu7-wjtcjVL;Er=^aJ(k(#=`fsIeuU++XPYPF^N9ePsX-p_!2M&eU`y9uNkih;~}
z9#icWrLUPcAx27bXdCC-E(PA?nwV5VJoC^bIok-!An$Xn<5N@f&&=~hY>M~zeS(xB
z7y~ysHu}6O9`qB20?|$HZk?WcJ)XF1&%UCY!N)0$V=UXHJl{Hu&H0Q;b?|`jNWAq@
zQWDo0KZU49L)G)i4Ff+Hx4?$Elb6yPDs~Lp+RWgeV2bTLHXvmA_9unbDeXaL4~dxw
zgGFpBHFxYL9PZcpT9>kt!`!(tcD+@Ios!L64bmQ5M?0LU)mOO*3*zHV5saC4P*pBT
z9OhPU<1dp{c0!N^DgFx0n`S<oKP$I=JPr=tdMpe-9(($GNPs@{R%}+EBf6Ycui_^c
z+*B;@wZ=$3nD+-)W*^PCU$Y$kb)V}Et7LA#;xlj2?=4~k$M3;1B-j3-@%H$+?LNmE
zK1(H(1g|TaAKG@t9*y>SzX9@hwZU*4J#r*={H*8dw+}h5>_<J=U&8k;CpR-2>kOkz
zO0bCRJLxg^+j=Zwd1B}W!-5kCF)j(47zY7W$a}K0Ft3MoDAqw*Lj-=CLh|`TFIj{(
zcAu#CF&}b1hO0@+Q}Wb7;d66jti~>V`xvj89u3(@@|Cg(ls*|3cRE`Bi=18hg)4#2
zpB&#PBy>guF9_=B)zS?S%Zo_jJ2>M0Mi*#aSDSaK^ZT6+c@x#aBx5R=@LcH|Hz7#2
zleJLyt{(5`JK>3TPX|ABW|ALW?Vza4L)z+u9?QvE43Wf{Dj#lR^N{Q&FJIbUBTTHr
zJrl1UCOAaN6AkC61&Eb$%jgYp;*aqiBGXwK+VUMa0s0|vb$8AJD&~`E=w{W-8%<Af
znVm%Z1G#e<CmkUsFC|YsF01@lj?aehT8-)I&MG67Y4~VsLnhq=0(gg%^~1(J0yrNW
z7Q3m}B|9onKiE)ml2h;{TdVLi&+kd@)-M}71@*_a@LNKYkae<5AorqICA;_b=vyvO
zjjX$`9}_sZ_x*qXW9g2_p-@6=^pHd=of4gDLJN7jW-uq3+HN&iq&cbnPSQS)ZV1hm
z+uL>r3kQo8o5VWNuVF=wo*`9jA)ex>^bAUFXXhpd?AnnlXV-j7-<sE~M^D{wsOZ4I
zeaFk#%Sb-kGJ-3&nW&TSvrJeMEvWW>>c0O-WK%@U{vG#B+ra%2U@7YGAi=pX?VW=W
z<^rgUHuBoQ(;m5_TIjI{=&?HiCpG^1zApO+xc2QgEQLMK><2v}#U0O8f1l{Reo2pq
z_!#{<^*Dv*8SBRU9Y$we$&eGb>S48@j@a`CO)<CfVum2X!VS}VJ&4@bCV~{*T5*TG
zHuKhC36%ltgL_!Q53-6jxr@w|1+A<@6q&#qGP*Wy(r68ct3E0KwT%d0);erp8A7t1
zM^O?gzAHAd49v<~QF#hCXD!kXaj3SASzLJeH2O6`9Mk{sv(~R-4};5|iDttwIPYRl
zv<3Vw^SrH+Aps{Kkp;uaKOdH-RCO9$_8QwNGo9$D8r$HTDr+h{a7L<TM&Z8LcoUD{
z%uoZ|#CYc;8P&4MjxAhfl*}zprL%Gwd~X#4ZE}f%JUho5<E<=)8}MTzps~3EeDB^-
zt#2SbP!s5AjfHfJ6aB0?l*+kHnLa!ZG&)I+laXn<D2cg)Mfo24otG?{%*>0#!2RR>
z;|pT`Yy{N;HBK&^)eq-11C(dH;meiRhAOMu+4qvPm?``DCVQ3V6gv8Kex%=DMHROe
zN2i#n(AmYyD?<h#9mfspF`gyt(y5Bv6IqEoi~x5u<VxMHZUofK`N?IRZ&c#CN6tI*
zNe}=Xqi6=`#wDNpr|y_W*x6f{bK403IOkkzeCV(~ej2u2&y34iJa*yQ6z5aeWy0>6
zW>FVJw2|qq96evmO!&my9<ohAcP07lNXz6p@yt;LCMGtGBbTol+&P|Y6}f7Y;Wt>w
zOxRCgcjKm#7hu9?-RBH8H|4gfEJn{gRQfur{Xnr`gx~bKxFvH$_<o>l&^bL7@5O(b
z>c2@sfb8N>`(P(j?7FT*o?3s`cjk_R8868jk@<da4$sVeVV^asj@m+VGuy1)!J;lN
z|B)mP)@S_ZY;Ny+)}q966eTvKV0eh6fVB{K-_~b)=TxOcN_H>iGK!9-9kZY>K-_OH
zVh8oQxCZ(Qws2~X{cI#dI_j73g;dwbz^i$mJlN&G!Y%TCF!LK{We+nUvKC+l-pj@^
zbM^dZul&S+#3)$g5%Z@r4<FsN+F<V6i9TGp#JtS6bm6G&Wr0q5zbP6wj>1#(XH?sH
zMLX9I4U`soc@$-y_EX$~g67I?_tk~MwJ-wfTzAAhKrEl!R?{}^*Z2W)aMjXZO&8Gj
zKFZ8o_^=i-Fc_MBSA08w-kDi-8)vl>9Q&xUC6r13O?K3eK2fIXBVFVU)QqoH4xF|;
zAp_+pws}*FKk&jGR7d`2*+Snxt_Dd_cX303;QFNhIMLrdv(I3N$-ITG@s#`sV;%os
zopG0sETuZF>DtJ*F#XJ9M-?=4#J2$4dIyRU7ZrS`hgCvRx>|1~_SQSIgoJ-)R};4E
zx=*G(JFWCQGGa{h#+zdUsgrd8J;!B$KqK3cN}Vc6;O!RG1LKdq_r7-%ZtF?uy3<vi
zuQ$E@w?Sg-B-Q4}5#9HhkZ!mhN^qq)TFqVe!qN?w(-0(-b1(UM)9&mhS%2CMd`fcs
zoV$V014&L>qqXj9MM{bEQJ)xo4p}Mnb=Zl0F?;&dBz3lVJ!8&5kucW6s3c<WYxW%b
zqstPy-F0cYU>>#iQQ7XEI%T@8@a`#Cm1Ox++qn@rmt&uw5$(r#P5~&n(ECLy{!*en
z2LY$YYYMBGfU81sRjX>k6Ri(&_;e2hOh16Kp>T}H%D{Tm?!|Y#7XllRWuBqsk?wkv
z^MXG$m~0GA5x!_!g1y8-SAH%MO>{5j(Yq>mMwYFRiP3>+Yz)lpzuy=aJv&m=(RoLt
zS3Wf_3^mD_8>7z5OEO0d78?Cj-<AH)@4YFky1jptxMq@P{E&Ybt@Yt)LA-_S^LL-Q
zQ^WFppA@sI7fCvAP^N1jRo#+@2wQLdiU#-Gg1Lh;B~R*tZ{a?rlHkv}&*16)7GhR!
zX}vo1F)#h%+kny!9kFX{7}G)zGtPTX&p)v{>Ly$m%RUyET02gee829~#iF15GDbrx
zQQWEL?A(84Y3{Cg+~T<7$m_!0!}tyfhclnlP9L}@;D|l^<<6^DaE938!065qaXpR~
zFDz0hW1Nae-2~kDb?=ov*)kn;G)@eGiZctQ(Ly?biS61IgSWaqji&1#ODD}QHiX>_
zipm=pCd|<x8^9XkaOaWU9Di5x_~%7#FbKQsXt*vU!3958q}p*XT=UO`DBRWUv;M~p
z;OsBc2~Kx6zT3?)2EHEjxxLavt#q^|2sl0+{v8@4!7@$b8s)2GnyQ}Hy3OC;i%pbM
zwQvp7wO6~kuK|8slSX2WJxL*zV#0Yrg!rkTQC^GHQqzi}L0FA&b?2Y`ve9_O{n4#|
zMZ#&kWhQ4&@Ma!PE@VEAPt}OfkYvTCO`nNiJ<MDnS(sQBCXF6*U=xszj!EU!EGta@
zw-tCdXm2NH&a=<&_s#Z({<@{F@96r6{29Jpbk1)0`n+O3BA{82*n3_K7xI!V^952^
zIHH5g^pRxgj6!##x*zemz+^%@95t=uPi!1@rgQYduDWN>W7C7K<~TVCBChJpOxH(<
z4%HBDl2B+TIQQV!?iJLA`r&d{Qjf0|Godt4{`pG~9392BL@Rpz8b8?x2=9@fYk)c*
zQK$hshQfZ&zxCXItlP`gNnJjA=3s=JE5#A&Q^l@~Lc(}4j&~PiXZF#a`eCm@Y0bfb
z1e2N}xV-Ll;Hc#AXvz=ZdT`SB;_)3P>!4y@1vJ?buV=1~G!hGxH6Rrs;NPSjM)H05
zVoezR2nwJhl4{mNDhIFgk$My}X&kgrj^vJ?vC{ByQC`#XdZ<FUvV4=maT9Nel0K87
z{eJ9Ev!%b>U#FM~L=0_+jBHXDcPHpRnlqlcB=eLgbAGiwO&~t^e`o<Hlw?xnP$zQ0
zH&tRX#TSh;SForJ1U$WXBc#0PF9W_5Tvruf@JJUX@jvIUF0y7-6uyNIZH*HSDxiFj
zEqv4D2P=cfL@Cp2xrz{Pd=jL>odT8v3I*B_`Asi~TfhfXn}^oR)lU($Y!FVt`MF4`
zHt~NIkb4gv3a&>&V)7E5&KNx;Q^SihB<~-hI9l8(8P(PWos5%?A_l#CCpSU$anA~G
zp5OgU2}JWRDfQ7j7Cxy=q}Pt?*`NItPx8Ah5e&B!-+%=tT`JUU3sv%`$}@&u))D$(
z%vXfm(}0_H;H(|g^dlrN8PD4Dos&kom1{`wXRZ$2a5Bpb;IlA8N><CW2f2Q;q=p!=
z*6DPrGzShEV^@DouLC*MySe!7>+rdTJ3ev_oh0H29<k?fDteb5X9EqBjh{|;>T&xM
zRH;-TUzgk;d?|RP3+x9rU+o`LGH!&{3ji5X6SNcUy9f!oP*CAjRB3tU5_-6;UA9SZ
z8%gl$reTh%@-%K%PUV^oQvi<m*dK#dwW0p{wU4@&Q;wpNITH2oQTxiMtM@^`iuhPw
z!QnWxl{|8pBp1+uONA1?`wWt8>zulap~OfZ{_`Y{QjlR>2a#VfJ-&0QWh->2`!?yH
zQ_hz%vyR4l+}LN{KhK)|HO!L#Fe^jLOHRqWp0P}^wHgSL(yX$(+X>aygzE$xq>Mk$
zbH!-#Cx^d03wIFGH#C2Zu@gB5d|c|5J}4wUjKG;R;&SHw*S!&S%LY9*vfhsl*p1t7
zK%p`1QT*csyx(TT%PaqIW-gKm?*ch72lWq+g}lN<K#gPk1w=#T*$n5!fO$EmuDtRB
zu*?y!@GM~SY6%P4o?0fLI;SgR!AS{o;-hXogXpl{9F=dW)b4}HhN{*!0@~Gg%*l{u
zCQX$Uz|SweMuS7hXW)t+^IGgP;2f(adWz|NYV_%MTHYWMT@<ZD|EU)vb`N?WgXcxB
z13IHX3zKdJz4V^2<YkH|zg&bKKGENMho7p9^h7Aj6Mh5(x%#BsFbY>*;X?h@_ZO{f
z4-keB0OGV5ewDnLp}L}Q!r40Qnc?MJdGf9}YAwv8Oi*16&2B>1pMwDp+BAxz@gL%m
z=OQ3Vr9FtGi5o#``W9a|->nH6S4h|#20mdPY5{v+W>2X8nz*BPtWgQEr^-ZT5g?vR
zPMc|Q!arUS?IsT}f);Vaq2k50Z(lmy(5{Wki0#$weO|~PwYrdP>-F1fv>$Z4SI8xi
zm`2IT)Bhj$lASk@l<{J)0x>DT3=|yvqWId@8_v{!g%34>fq9s1Jr@=**xb*=qxBws
zz{Qo8x2{2Gx+wCfJS5SGL@vxsAuBqFYd;ZVbWZkKgKz@6z$%qWn}Qh1Fp9)&soL?q
z0Qp_5<uNf6AxO7wR2MMWxQM*9>ayOA{Lz9B5JSjUs{EPa{3YQdh|gUMQ)hxCSD5Lr
zLb=iBclP8--cUBqLC|DO^~J(_(p5v$2MMaXE@nskIET2g(@9Ds6Pp*H{vpSA?%gLb
zQ9U>{Ozn9-c}41}!P%S&BgPPic8VEJEx|e;lV2oubU1EIDGHZ*jg&3?9!Esa(zD$p
z3HR(k?D>%-isP+Y7;WQvb&1<xB)cDT1Ju#t{tBJBB{_GxyYaeMxA(xJEcl0a3n!xs
z7T|TXd<r7H$DE#)p7ih9pWUNKYEu!8LrARNvMX;Ihu-?_#JXrg(#+-gFHcf94}C{C
zhjFZ)Q+AKm-2aR1erTx4fsyjRUQpPfy+lyE#FYXYQ=s)8fTP9E!)rihv7JV#tab9G
zmr(2p>QSZ&jtbtW*20`%mkl<h%4bH_?});?2no)wrOAUwkg%Hkc?hJ4<i1n-aEzp?
z9m;ScmEdl|GHCRn7$o>F5fdXqe-1}3vg*(CJ(<7*2wGnCl&G!<3WKTN0@nU7v9DEc
zM4(?mebWe{wXfI1qt}w-y?*g+#gw39rcaw*1lIiFTjRdvf^lc-ol~d33M2-%x9~(v
zQ#b2WfL>2WvdTEmy4l$HKZ_Q#fmEtq?^oLtRfGv#T52Ff9rhaU4NJ!eCfI9w;xcda
z(f1+eJZw)4$n}%(y`SldDL#s2!y?nRBLa_hlUeKaZPE{1=tQHL*Jx~4r-!tWZjaxV
zo>~|`pZXD5tHkO<)$Q-Ftz(_M8U_A(N7Ys#FZ03K2=>!aICBF;P5<3sX!V!YXK-n|
zaDmEVsUTMLgbT$qD~f)Hh;zUZ-%=Nm)fw4g2_&u3ccTzTV|Q5I2J=|O!iw-JfBeu*
zuVS0;k)O{yB;a;Kvbkvmy`E_C5j%gjI?4ZfnjDtvcP@F&`<fRTbxj!r*)tk-zIgx0
zG=ACWp(yZY4Fz;`GtwD0N)5IhhmW!|5JjvtX48hQ+ogWO=E@kH_ygs7)Z}n4`1rv&
zjYAdg1yWK@%?eWTkJ(@c4pWy!Zk?1qYH9&($BEa$HB9a=^(Z*2%)Bg_?<+k&>M9OR
z-5CZ>KaLPBR$Wy7`4ICp^0iQZWjFcqg3k4jVahfmeewayv#68uojlAPny9u%QkL2n
z1!E9PIM-SXp0y1OFFLQtN)S%2>}@Hgv!l5rC-Up9+F2~@*myJ3`~{+TGQN~wO4#b_
z?oLkRW8I%_r|McdH{0HHxfLQ#8#Orc6{9boeDighe+1IU*X&I`y{m`ofXV2YkY|T5
z8=I=9vd`z|YpSnHOKA#bNrl#21<9N`+5s_Bnwo5_w_Hd?H(SS}7KB45XVA@Vsq^(C
z`dPI?_xd2mh|7^p7q!!cedf3BWL85`DlF_;bYY4)XwLw3MnAIs+ycMOF!w246R-(9
z5gQ!GmgeL9>bVHQ#Lg>P+{|8&^9*8iP^-6Db%VT6ZEpP_ez8%wT(7JNa$6ZmwD!3V
zTm&#$RmTfhKZ|81SSRGY7`^i@pZ<jYcIE|v<7d7KMh3U@DeQZ{KsMGyK-oul>^H>&
zkgM!iMtFW3=x)Rfu0EV~b(#(SZ|hosfU-?PSrX$1qp*B&%0oyaF8ZkajXcXlP~I|h
z@|cU0j$#;4VW{Xa(>9V4_1ZL8HAKCCajzA4{~Q2m3nURR_xVo*&K@V4G+_8^azRZq
zvlhh;iHNdZRB6}}S6=N-b}eRqbiS`oAIM)i57JD^A9ng|fXs>GLwGX{-Af3M%}73!
z#z4!ZC-8R>OBnnqbA4ox`MbZ?dkFck>)P3-R}vYP^<5dad&9FjtQo|cYBYX^#90O=
ztn{xmujM<-Aw}k`>K-U+keJ7j)@T-^t~MXxx*t5W1)#tBQ2xu{KA$#|c^jjxj@Wx&
z2iEK75LpZwL57<NW+^w3LNNukR?DAxU-ru1Rv4U)3Z8YPAv?X|HIaq8@d`o@KnO>U
z>;)YBu;NQ^n^Z4A)1!_9DJ8OA4ScHndJg8LCQVPSyctEVetPlXmU=O6?=`J{h#<V`
zu{NYdrQnZ9$oL%D#H_7;NTGRBWwfJl$*+Nc9xZ+rU1Al2!qX^s-$VArP$tzuJ^rYp
zc<bx{V{4Yq<020qU3hx^5s#Sjdl(5FPjE#Y_Bxc$NTiSDCkj&(P?C4?5lxnZTfo+9
zzY{9VgYP+`<;FC1_QmzfFCb2xuc*EhsNxw{J1)4Jt9A@0*6Wb$o-qwXa%sYW^Ju~h
z3b@pIk9)_%wlS&UquGphQD%-XmV*!gfQnqQds0Hq%V~zgt;>Yrg3Hmzo2t^9X<r#W
zEBE%S(9QLXmtQ<M*@uua5!O$eE%)NfGWL9~T2s2k$#6FvWqOX-562Pvp|5=b8x1_|
z`xHOb)#n2hP0nSO>A?5*IZ8+DjJz|I^JgUP2-8V*AS86OADwZ`zLQ7$rP58AD8qdf
zNkwVW1R>!{yVfaYWdEifK(vkCd5<K`;^Cz|!XCz^a%i1HZuJ^Cq&}(}sub>N|BYrF
zEXsx}U{hv%+q1BJ4d_RBaos7LefoW<TcwWf6t;ZT#0`Ej8c|M$$_ZXYe1wGxBu3P;
z(GrStX_;4H3p9&&*;os^>2%}#DcB~^A?`^39__E)iFfVhJ%1FD$*BEkqD_3{MI@GN
z1(99VOPIzYipSm%Zyc#>^DhV}ZXyD{2THfEYg|@<w7kW?km}l2{|_9(-e9{doS9{~
zM7pvCN}$^M7DNw)c*#%Qt!S<@?E4tdeo20A{m0{F`1#Yw0R|B$yIf3$|6mHu$ghlW
z`M-Zm>VjYQ3^4>G$o4hJ4Zp+HDvj|av*<eFX?A><9`CO<{TT^gL4xqNKz{rh4vIQZ
z;!95`-u&4PKisPMNns$iPe9x7>j8Yw9IAdXIu8unb&`(VVbt;ezMh4z1C)m;-zsfG
zzrmJk1+(XOSh6#WN(<sALp~}&pxB8|9^A9gMQ*<G0;yt$+?P5qPW2<AILf<o&-wuZ
z90%We>D`H2Z^@`%f+3TAcP|V&h>;NFgSKZFdR`3_oqi3Q@GB$|<+9J`b8UA2;GX{y
zybA9s9xg$VRvnU9h1k)5zeqoI=@oKK{L6Mq_WH;Vq!{Kalt3fB_OGX5fc63TU9LY=
zKwqwL>Rs?a<j#n_0}%%zX__(DE{Iyg<zmL}P3W*tysn9^&LT1w3&WQ|->p}AOk?~X
zvRH;-u_RkA^WO5kZH0s(K|>a&%AIGBq!MwdF`(|xANaj%w|bEFas~*M(`F{Xsr~{h
zTY4L(=Ns9u`|I&{ByI>Ka?>!;+?p4i?gSU};y=E_1W-P{LA<Jn7}N^Jzwe;7nSf}Q
z1xTU!0RW7U8ACjjx**Zvw*Y3iHrEHKrHD!W+ue>>fZzs)K@od*P-zh{wRPVU^H@y$
zGKPeR{%r&w1)mp8e=|sS)$7I!PpyA5DqkCxMnX9=7V{H@vThJ<&p$Fh-ZBnK$f|#1
zfIkdKY~-Ji%Je(>C;{Sun-$(s<o$v`Dh#=+77j9pnY@&xXC$%GerpbV%HSrN0>|kR
zlJinXt3}!(&Bq^O@=CH;G3lf@sc;9W%q8-QCZK;ov8WQ%-qKc-7$W7Y#{dq!frRTK
zAs#9&Yd8)uh>rnTZUvaDmcY;{W%m>8V{(#@<nd^FTo&#QBK5;{qE`-4>z$gs?^faY
zX%iHLMV^Y(uU%A3DBS|2|L->=+=op>FI)lj;Y*-<x{0Xk<VJ3UUV?}O|CzK4J)`t%
z;LpbXet|U527=z|mhXp}2!F^*8-eWOMeD%La~*ra)cmHaE8Lg3u&um?j0PIXE5?jN
zYk<wjqqFq1k=VGA!xyY3AZ}>}Nv}l$|M;RH<nUF7i3WZV2DT-b@;s3U?9ilVJ0M_<
z8y*9|qYgqM-$OQ9$7QcB0UcFDa=(O3Vs>ces6S-GdjIU6N8WZD%~x3X*#_o3cZVT(
z-MI<*Oy@GA`#zfZNFItm+(&i<B||v4)e0gf8QB+@x5B8I%5x?u&%K8{yrt`}ehT{g
zB7rh=$Aj^YcVI6t@>_qe$u#7e2L?j+04px6Rvku0n*#3w0b##M1~+IQ!Pq5{rabxw
z5CBLN<V!(w+d+%{B1|vmMX+Ip+0<84RkPk-9cyezt8#qDb{jHZhFnkTJw>AQj5Sx)
zv!BACJ9AQY=*Nq8KF-p|#{Ze`p`<20efYi@vVq@A>T_3PxxCDK0cQ?iR2FZ>!+MrG
zktp6!-?5p79Y_?DBzEu-k>t$RxtejUkO1tu+=bz7!xAuOAd>R+V=#w)e&;V4$x<hm
zjQ%xH6pPhCFcih@ec+j89F5E0?og0NZTak0PbDY~7gg=X@=-63f~oVAFmpKbm+Pfb
zKZJa51WQ@_;{tAMue66Yo9KnmEYIz5!{ZI;cvAR>{eRsR;hYq<2Xx}8Ft(NAs8FQd
ztOYEHt`blQ-rp?-SmKssOr$Q}5)Y^H`QO3ulITqUm9I_qwF7WK%WbZO$U27_+RRX%
zlULz_P)ltENWr0>_t=pvjCsgiww(Q-rk5YUsJ4D0ceTrVOolsC$7&M^rIAGu8;uK;
z=jt9qh_2J;#~AXRVEBCkj?DCINsQj<f1R_%9hstCs6^S#zVKGaIBWZ<&cjcuS$s%x
z#ufmT8K>^>W2c9rc_6PUd`avogYF-aRMM9l^>be6l-t&f(rZDDAgJ7r1&_OBt$XG-
zP}tM`3Z5qTu!{X)*#wjI9OF4gYI#uFFP8qv29_@AG{(|e-Q2UX`ug`*2nx6aN8Pi;
z$>-E=3gzP_W_}^Vw1*G>_TAwo+!gIgTgCR(Nhbe_L{9K=WD<#yJh{KWa#(<fQ%1t{
zm{KtWi^g;17FBuPpEN99V6%LQToVMgJsIwe6+^)oJ|b9q&vn`*P+v8c_fsGWhnnds
zKhrQi%ns@u-f&$h1Md1v75YgmA!}cQW)t#8hcrFletxVQZ>;Fo6e{ZF>#(}hs6XXw
zeOS(VSJ?+HLuVt443I5f4vUNw&G%D=-M>_omHZzbE^HuRTX4EGI}~Cc*w>`K_AXf{
z^0vdmr_K(|Mhiv_9cBX3<hG}Bflg0=5tHnnZuJbKfuhB)F~U(0pz8g@Byy8e!7Udw
z%_rZK;?Z&pqiwwQ=;2N!@ukC6<VDzD_^1=9%Jn?!qNjrjT>i{t-@D^Gw@JPhOOc0|
zr5ni50(fixxA9wRNDN6HSEuN&->gQNxyPEyBtG~Z&EgJwDA26HHC1^2pgouw9E5OP
zL!NC3dXg-OT}BM&+}YF;D#r9kxv-#qqUk!s)lv#2!z1l{-VzEuqUs}4mMus&83R;e
z8msJdb-;Yzxrv})*J9&9qa^=9Hy}aZUH_jvgpUY{Az-N()!>gAf0$Qx5=;g6p00NC
z4?j8$mnU)je`fI`&S}G0B5;*S);8JF)r5_hTav2n9XL9j5?JfM1%Xm_368y6NO;he
z>F60=U*vGBi2_B~r4SkDqHkGHNg9za|2d6r&Lp3~)P>5sM=f%mpPK$0%}<2ATuJvj
zH>2GEqt`zh7TwDhErds;t#sKuhvp{oSh=VpUcX5#Lg#HbJCx)cHC=44=pN(KO^6%5
z!pMI^J|&=$be@YoyeH%caC4@>xLEs}T>0R%vN4IIy&x@qA(Z~A{o`75uB!IwdSl@?
z@ufex4<=-JWI}e#`p?q!&6u7@F*~zb4jG*{FN5jii4%92>3d)9ZrG(@_Dgz}!g3)i
zX3gtJ+x>(OUMOvA&~f3Ju>5SQ?Wj$!vovcRbXdZ_$l_eT!wHUjau+kZSI1{k?|t?a
zqMOPGv+8TaS8}QGiB+7#sy-`=u}8JS#BtF`ICv|$Z8byg`6&+{hC5@c4lZrpK@qXL
zF`A!qdXM<wHn6nLdvI|%h@gF_%T%K@m&v4!dBi=cy|?kxzW)X3#9-82V0o?s)IzC+
zxz36lj7c)35!=gfTn^P7m38Hk9C_vQcqrS<i67hQFFMU^z?rIq(VqUm`;;jc{D_@7
zd)B@alrW~u?E|KYU_-Ycn#ysKk!|_;v$S1k7I(S1H9qc8{#V1xXL<BH($JWrRyNhi
zm-tM%E-41L@Y()35)Ld=K#T98OVu8KyQk5pE4eN`R&`SUU-(V^_XE!lF6B26ms_{*
z&dx+AdUBeXDLZR~17gZ9K_Kn7|9!?)*?=$o)HD-Qsq)~Y>zNgs=z9_+L8SYdsMWmN
zkch)Na++w=$+nzoS}0btm05ybbJ#@8c4lXiRNFoj7xUoJl2O!<=1G@)hdXRlQ59}p
zi+uaBxxSOp8h_ZZ8+S|hGA~SARBiq~Y@VQ7t);6E&6-~)t*t|)6}u*<O2j^h!|F6S
zRo(8@&B|%`MaEsXIqV@1mv=lyPk_A4O2}+T^r;Az@KgPqBh@hk7U#*oQ7iUiK2YA&
zsQmL_`}$JgkNn8{>+Q&pygWpE!RAW8bu2y*?V_T`Q+vYl;H1BHFtd`utsFz(FX#Ws
z@8{Lor8&w&jxU8+yKsYc&Ww0HW67i+Nhzw<;}7V`Q2DKIh6>~8naz@2SOD_UNg|sF
zU1$)-hCN+HG+7UF^cL*HdGJ5L-YD#?w6<Y(M&<K&n05tyw#b!UAs!-V<9r$Td#K~D
z2>!>vymhIk)eX35vp;}hi#bk@H!EeOM8P#%(u-Xi^Y43&^O@7Xz%*ed2kHDZMp~;a
zT{_HjYbv@$lMSuFs8uaRwvPKphEsK`CqL|nL9VjOpY;p-qB{{kiC3g;z9#^vifjI>
zUVh-lQAiCOeHH$Me=Ezgor7_c{x?TfLF+6z`GdbV?lV7TDwmMo(Yr3)xNf1bCif-V
zcf0kf)I4|kePrxPnkC8G#AOSnXIR9ojA7)n#7_ZrfmKS%cprg;Pz-DRmATR3&t2X`
zQIOM5qda4Ik9Q9Fq_lJoXvB<|{|g$igu*e0vZ^YU?U>RnkuR2f$F3C^<~;Vhju3ji
z%n1}F%Kj$-oxRF9J#~k4-2GhQ!Pxrk;Lsz7vGVt`p59$#tgMJmpyodrCUip8T@)$S
zt|>#TB5|ZPT4IVnrgFPbb5z6b#w#XD9<iJC<N3QS8UVH7(cw1bd%P-@vB4QgF`PXS
zsp%M+BdPG8(G`fgpn$>@%G^3Kc>TQ&+G)7q>gN->$(LmB;m*9IBGPG~3(zz4t=Lxj
zu;1W4j^7Abfp&;QWcuiR!L>&xqF^_{(yz!wr14h?n7mM7<+@D}{-CONT<_YaC=B5l
zk7R?eAjuk|K6ouJqE%-aZj(S?o@3@$QC&v;oyVNfj$X3qJqyr)1AB<p*Dk~77qv`^
zN#|7vNIVfqhwt_<Gaz)gNx#3ch-~;?SoyoSTru*-0I)iHk^hF(;fd7}%WKuf$R&S}
z<row5`+@;`zKCrY0Y|`~aXn~apJGVdzMXzA&dA`CCrQ^TG8rR@7GNl(<fm;7gRt>i
z<$GPuH=AjMSgT#*B7_aJ(YSyvUT2d{Opb@r3H49XC?Cj$YtT75@vZMJ3!+b#)l9eX
z`zcT!?ogn%{n$)3j5KH4_wVMZKQZBF;K}1-wCa(;<vAI?1nwghaO+Dy&2ZNbEp4o0
z4H@9Y6-4x!cvA(l40E@@)4M|1-pNUpB%7QVmVKIvk^oi{gvc!5BmGd!Jg%&ba-?<r
z%z~IXDP5(#N=_ksEw$?=KL^6sLK}2L^N`y4S7#4!D%pc?zRGHMV#@Kuw@T6BbE(w)
z73L09Ay)S<WD-Ogp0{!`xgV|a=Qp{hH9Mn0Bjw!3bvkTb8_E5JIyJ*5>mb^{-z_AG
zi!iCnd)s83Q1}$0BNCE?le941_bj$j!d}2FE_|4oE+6Fe7x(mY{E;#<2@32d2=Rx!
z+iZ;&wkjctQL?s8Y#hQKm2c|7{Yd+lzDwf@u3VUhQ;}So>DMt45&H=iIOWEUY8}xJ
z<fB;oS^Z8_syar-4XnTs55XRkL3&yaAOvHqf!IqX0hX{MwVoLLq$)_S%m~lb<tn<}
zUgph<QAH8|z81g>2i!C`WsE?1UG}BSV1s-A)jvKHa|i+TdnhKdII{i=72-rbFXF49
z?P`g`H{TfdKDLSXESMteBCjG|;|TGh8e2kqSqLX6!nLv?Sb0ZtGgN>x><subk0~_4
zS*yh706v`O8d~oGmarR5+@+jM=eoQ~{H-oaC|AEva-(jhxFlD;08v0^Zz$Dy<1Tbw
z(z&FvMmIpIK{*+wYy;-#T7Q!d5Wm^WN|;t9ZcmKCBFtZiMwU*Lm8!*aTSr}W+9b2y
z$DFZpUerjfH5{U)!LVcqG}Fh4%7<BHJuTKkkOG=hPdNu!Hpm3n{{8;TR?hM03uM?G
ztedj*Sadh*z#}2Vx=TCYGHrYblnrx?1Hbeo(JncHp*jEj!)xxAdlwz-I_?L%jvdha
z-=rCJ{gGYAI69#Nw)9if{g4N37~%yxY#m@W8H!ZRlxw!Yyw1)WE#=)F`zBI9@jtWx
zfnVMGaIrc5OA24>{ciFpbT)-*a~W{;A7%8fwb<GdAev06ZyP?4?bQ=p_=3POCJOlp
z1f!3@(Cu+CBiXc4Ug*=c;MDyFcl-|vpGp(o<QMWL8bgGap#+L!19+&533)DBP;eJ&
zHwg;}Fp!KHYH}sn+g(^KYuw6=YI|(JX^0`q;si7sdr4a&B#g+f(H1`W2>Qv>Q=+*D
z@G!+6D|O<DtIbXc5+(?|d+cBnnt;+yGrh+-IOGzc_b^H68V$$>l=L18b>ToZK!4}&
z^5MR}RRd`USB*;eQy>HxoOO5k_L%yS4Z~&0DKd%4Bs}osQZw{BJ~$Da)b7U5DWoB~
zka5Syq*fW&_}{m^zN>#y-_7IJkeJ320@&t*fFTv$n*Z9LDqR|Ykd6@1a>Q&+z0jtN
zYKOqhtH&I5oal!0?92pr)XXKgIK|Zyf9wJt&-Lh7s@n8ov^FZADkkzwxn(6x*^!5j
zZRWq9ILM<iIYk)En{^$m%*PU7B$PQ@0;E{D`Pn!MTHG%6Pf8A5Zd?@3eex~FUzxbC
zV-<PS0Ab3!0m>4R3Mm8ASYEjz5s8Axi`TpU4031!ujiFp^Tz_(T8-X)eMNq+MgIbf
zp1{i^MLog~XXhdlxp33z!k1q?yS8Tcrd8jcIKqJ-f98@x>s1!`%Db<iHD_dP=G|hd
z;#O5zPlUvSr&&LQDt+u)(pdJg>^z35i-rU*Lv>bocY7bryE2zm05rZi38$Y%vf73$
zK0m!OCac!KFfML9B3=u^23q?!&BIRdx)!>kRky@0zxo8%GeL5VDCINS>k0zrUoA0{
z*G0pQpdaPN8UxN$aqVIa^fU!b9c#9!Qez!?Pq#94wPdKU2NYn;f1^qcC_tVEky&{i
zccSWX@Sm$cArDj>kDkYSMfjQmI;M2FpJoFZ1uok8iQKDnXf8m<7HdW40sQdI^L~!H
ztNISNrIs;+@zO&^Ul@^%4ug1#=SWC+d@}wf8iu1xxA?2H>%MtWJ3tgZWVC}M9R!Ue
zmwStu5GEC9htnQ5oJm;%t}}A}`|B4iqlufCZwg2UR23Gzu2|-In7l3q9>3MGp@rY4
z=L2u8UReB#d9S<~X;yuKX3hco>CeHE9Y<8~-Gkf6?HI2cb#IXC9bVRk*9J$t-mv+F
zoYKVMFiFLN*)MuFHYhy`FI-JO+#<MQB=_BX#TlG%9h%0+t0RMR{n8q~%v|X`nz+w-
zbBz<>J)+jZrEfqW-8EBJ5#<FC{WLm5l%1Rd;RLAE`&v#s8=afTi>!Qiza*1G`oC?*
z2FYl!+osKIt8;|Sm>^gPGhrIV+d<jY({<`BL`o3Z_#dH9B%qH%2ml{;51jV)i~Oz)
z_;vJ;kQ3_}9gj`_e<1iY_$V%d*3R0@F~2)VR_NYcRzm3wsNl0OQB1#P(S=3u7_3x%
zC$uA29eYsgjU~YFS%X@hZpo1I$oaep_o9o}`Lt&-?<vmBR{l0iX>VaNc!iK20Z5^n
zYGUIqw>ffn`#3az7k#od!Tlz{wE;*uos0jC^C-w$nv3kOiEtaOuj3TA3BFY8Zb@MF
zipm2}Ybc6a3>OQP=jNrgP^I?HmNGJa1RnYb0*(WoHVyED{a28YwIjNzK+ub&e=7lC
z-${FJ=3lg|ZGmNrw)Ga=iC#g{O*>Lua~<}kp^Te;mrCG;z8H7NpE{Z-5?pZtuw3+E
zc!ioWR#)9~5R&5zm14{HPj_-{-=4dyfgB#FOVJ+8eF71Na<F;gP#TbumnW<VpqH4W
z?t92Jxr|hYz;NKW;|J8~iHpas0u<E!62edw-KR|0db5?jifVdlsUY|q618rofihhx
zx2&;3T0CveIxvy}wsrG~snJoUUgQUasRuw1#nlLeM}ptS*FKr<)WDrwx`!Eif$xEb
zIzl%$eIrNG(V%H!Y1&}myGEWkIZ|v@#Ak{QaX28WRbUK)8;my6=nX=o&q%YI4hn(d
zVO(*hEb@5q{Z<9UlwSY6Fog6jGd_NK`PNVyoX~U^8}2;EN+Ci9#DIQ)?88Wj<T@Zh
z#A%`UsJl=cd(PxKc>ccw*I}~q?*l8rVqg*SiYoxXWjuVgKS$-1?Jf*!$vq<<8U(c7
z_Sc`VO?stFgOcXf4=%X`&%Bck{)w|~;*V2!HQ}r2q!Qup{d|WfIi!2t2@xTZqv793
zr55y9fghqI%QW6TFk6s08Ky3IaG}XLP-|cwWh1H4YySWrYesr&Ou7IY5?`ZaM&qgW
zUi?pbQSay^c>X0GD@5_?@s>{_=>Hx)^TAwazGUf~Jp=Q4IPr}(c%RY%#+e4j#=+qe
zjZl`2U$o29|G8-m__{D2EP+YBJfE{tT4)p!hTX)OV&#(bWzXtJ+sJY#tn_vZd7;Rf
zmD7xB-;W(5>pj^UXO6r_+T>kg(bYR;S$ZgGxNz|NKOyuF=2c|nERG*$e`$y4%)HjU
zqP(CfV+p=K(WP2q|DR7%LHoXPz+Kb?i0N;D#;*LU!n8huH!Q)hBwIoPl+`k445;Vt
zjDhfVe^A}xM56wY+v|5|lA0RzLSmgEdAc3Bad~{WmCa+N3dKCPA>z3XT2th&4<*Up
zu-P1P&h-H(A~1TGWqH!Q?_;VD9z{trH%+C#EfATzl#mcSo93kR=wQ|92R5CRX)rhD
zC%=E8=|0o{6Fu0w$^GDFmgFR*f?w6S4qFO4G7UQ<ndFUM2&Li@dsXupm1t%HGAbYh
zUawzh@U#3tB*^~(G;X{G=z0V=y_rrX!^TnIhpJ4Zrk?+Sf9&(>IyGy41Jq8FBUspH
zMTvod^---8aH78WJ5XV+Ej#D>XcrUV6?W6>A^(1I%bDA{y6Wo&QhetQ)>UDnP1$|x
z6GF^na8vH3j@PYh*L;FR3;81a*JwtmHd6mStFDOA`+Voj-%EP%ptf7q?DK+;Uump|
z48r9&%r-P#_59rjro#(5x}Vk8O+UE?^0L75#fF)s-`&2ydj3E)@D&nhGE(oiPRmL_
zWMMP?D!{%Oc7;Oz>B*<wKvxx{9n{qT+SPko1w$CDNQq>hEJ7$7_|9tXnEtkcYxxqA
zkuunN1q`xFL^{zMaFcwERRg45MgaRK6hkaWOz}p~Hvoh_P$0!~tGrUjYN^^Rj3e=4
zNck306L)(eoc$sX?6%WL?%YZ0EQDr5(jIm9>?pU9Hk0%RNH_W|gr)-cs`NAa<3;UN
z9+|7DGr~E*?&e=T@&GAL%!NDwu7lE6BqgAZba}8ZWlNqp65<L5SPe#4GC#^^?mT$p
zci)h6ZNhpYYebmt&GgF0?+4^|EKtV)0%hBtXgEZsf<d`S{;v!#?#DboXZ&EFH{^4?
zFIySsEn}kz`GkB|J{FG(|7pHmzLVI^m%e$#p$%|Qw=iP}#^_~S1gViEc_}DdbsR=+
zv@q!G+elH7RU^7(eUmIay3Me#Xc!)-+m{<qOQz1>AAz~A>{pFtp8gE5-IF9q3>Kpp
z^ee<g0TN{5^%NJMeT=KT1NmnophCQ^#jq13WSB9I#8@FLtFEYZRF0ki{k6Bu3G-;Q
z$grZ%FLU-G=ux{-Lf`!gVLc&q;n-bjK0P7yvt(f+k{@Ztl|=Uvs3je7ME<)u|5eQC
z8=q(X6@Vy1LtCTcfG2SnfiMe?7#=;|8=<}@9>pm_UX)R<_x@@}$@Ki}?VRGmYGqNV
zQIgvxbR=?32SmnFjh%~H6sIio$yE(pkP9C>g5E$l;y}2G2~;yOXy%5hK5Y)pAKt!#
zOwgG$yTqUKNsO>B4TM~sbiAs}++lnhQAF^dQGwYGEsvn4C|!zZq#KUuq=x@vrbXDf
zF)`??zt^~Wj8-hH^TCVz2zxe6udj(z7$wB(q9b4}4~<nC*|#7u*^TJ?fSJp>L7_C#
zAR{S5U+33ZMnPyDt6w>d`VRDPaW^l{$z(SG@S+M3Q|YXzU&SI(j|l7X9g6qpusHYP
z0^v1raAxK!YS?NafhM(n`hPGNtW(3#8v4^S%)8JvYee%Lgdi_qux)jJ&0`hAOh7nq
z>~&P61TMRi#yk~G93jU2`u_U1f#CBozx&DEYcDad7RRZNsePG3;zN;S5EEVA!r0;S
zf{|Lg%MlyME!`g;o)H5rK?T4S=z4~<Vmb6~y}>`1#}WUvzXvti=sn~Cg`39hb@3>!
z(RFfbD2>ZD2?X$b1UAxZOmCEhy_jh|rhv}BzyE+mQCZ}K7bkN3zrdo;FaNJ6!hZ;Z
zmD#w9sX_3>i+H92%A1z&@PGwTE@}rx$8VkZPdJMRP&O@|M_`EecD&T46OmC-x+our
z`wrZhxp)3I@cZ_twhRgT5s18W`o%r^rYdXQjH#QARi!kzUVfpn#XSZ4pq7GBJ`YpZ
z>aQmBgO3LD8&#<lO2Pwc1jVKjZH%kPJqqa``J`8xEB`s+`DOhmoUE_^#t++>KY(@V
z*o7SPXQ~=rqP_=<wGe4=cH2ZkWPU3aU<pWk{2pw;be&}mZ!QJRxmIsZ@BL|3sN=wR
z?^_6&IJBR&yAIcpB_!{@p$E~7f4R<@fY}y4Y$~rIuDS0|%k^8iYa)U<^*Nd2Bow<`
zb34kGIcQ#R$nl!fsiNz@36?i4AHMSbIcXtQ`rxj`8DXB}e?aoyLX^Fh9t9iapc;MB
zC2UglbAvcPcr)G{^+ELam7ih;`hWM5Pm5Mvn%tDC;~?d{hL2+4qg&d2M&zR)eC}tb
z$47<7M_v;1eLKm+jrJS2=bf5(0s!$Re~_)uHuw)Y-W#H*yzM1)EVNuQGz4aa!*S`e
zx+kT%cO?3LAy`8+-+x}@r2S*17TDqPRuV3gE^&J_bP!pd(~^~$y}osA&jXD?9}>fH
z53XpQYMwpD8R5=fKsyo0KF~rp8VVv;x0s`9T3Ud#Rd<*jw=L4~b7vHVL|#2wS<WuE
zsS<8k0lzo8nVKt&XDp!+P2vHsxh#MsC*P72qkG#x{ch9=d;4I!05%bQ@s+o0QxJLW
zO)ge#nKr=_{<IrpJ*FJ_+%fDHD@Y28PAeA7M44$-?n9FEn&q{7O+`TMzI;fItOs{;
zDe5Dk=34J_PoMn&+Iilr6wYw>aVXGJ+=4qcXH&DaqXX-o9y3&u+9}Dw2M&uw94`>9
z%6wnDHmT|;IHtk86g>M@soOBhW`WO=Z-^(H|1QwJag`m{Yrs_h#R<K~s~K@5TJuji
zC!($?3oy~Ej;NW<>*RtF0sF1=t_#Z>GMj`oQs@q(=nT{QY5Tj#@hiEV+?kdy#XQX|
zEqYr9cb|XVm)VzVrk#xUU)M-H9T?(C%T__&YSU;xd(R~+OP3fbCM)GnUXsz&6`=bC
zg~8QoAMzo*yPQz)*)%N)|Bw1I9hz`)WhQ27s!t^;l%Gq1*EyjD=TIRy8~aW^S5$z%
z@W1XrW1Ffu@On_qkw&tzV{rB>zVMvV(Y5CF+9b7*z2;VRo?;w`h*upAQP8)pbsx%T
znYxFzlS<l}hogxje*oUr<9kN>YMZ_a2>(chi<B`%6X^4w<*R`v(o6m>X)D4=&-g`6
zSJy1xgV>FG4BbFX_w2&o(k%ECbVapYG_tS_OA}+q6uXOie95q_A!fh1agPUIs=!r4
zq~El@R(De4P3LA9?FmT(4V0h!4YfTkcR~BZ88ItLXUt45sNO?f=O4ofYEu<()NDZo
zTSgndmbk&`Pco~jv1xqh&-89oD4Kbu^o-ORgoZ<;8X{7EzyQk}T)#9_Q(}3!5#*q?
z)@w?Kj#DZyBvFvHI+Khn{4~n%LV3`6wP_hQp5HkTO7}ceT9*u^V7ycZP1}QtmY<G_
zO6p?5Sl(y~M{oCrnCpH=<%jh;*9U2w%~!Mw)bi=}h`OhAYSg)_H*5R0^HuAj8m1FU
zM-E^-M%Z?NSp>paLSNym2e;LMx;hVTyKUzxtcH^1s$ZV)Vn;UF+x|TDIYnN|z8M-Z
zJ)vg!rk2kD%ovGg_l-N?n2fWU4W&uMn~$j1?%%WJmPQDVX+eaa`WQlr%0qN_p$YH>
z=OvoRf1A7AAl7%3ai#-Mic>EbX^*-G;{-tO=&s<?c{=2KKj=(F<`_L^Go9~GM)a!6
zl0OWGVl=oeRx+qH3QY?Wqipz?zcQ+ALQwRQ31g%puhS2T6T-6MylJ>wgIxTC9Vm>l
z@15aSD*3LEbVik1?}#if4M1lPDhInl4lh$>r8@VM#lA+oEbDSY!}dT{J}AZzglj5q
z;jfs%fIn5M!pyEGs`sp5nKmdoPmgPm$+ofb!|A(cv9JlB_|-ipiQIy7Y4(w5&N0tU
z*55}>X9>0H*IW1Ea(1dB9vXg;DuBOVrzZez8jVpny%pdx?=x^tEAum==%<;lnu4#n
z2$7Is-j{y_B;$m|)2gEazN5NN!i<j1pPa7&>QH7LSZi89kkdU)LN+<d?KV^HxV}TW
z<fQGG?v0U^E1I5c+Y_l9V%b|V?0M|{{PGMH5)y9O-qOz=7b%%>n+*qkr&eMlK8l*B
zKG3$;Hqjokm_DtMx@mYEl4C}+;VbR_K1%)6xr{5(kcIF-&Ld%`fQ#v;Lp09VZLNA6
zP^ND`am-R{UYzqqD!vaZK$gjC!i+jwXPjPW{pM>7dM!4!;++6|&NBlpR3&0SIfRC$
zzDZaAvGMEkJL9)=ZZ?jQ8Z<<abNPPvPw-FO<I75EQDE7&^}ID1Ep#-Wai^D?e=kw1
zzJFVp1DGg=>6jAk=D$gS?>o5&U_u0m9*06$L1ges#HZ%=C&Gl#X0%c*(wC-sa;u7m
zxeVPrz*3-k7K?HXz05^@%T!Q9oiJ-7)sg>aG72~R9Mndy(<#X4*hQsfgsFF8;c;=W
z{yBJD&n`JqAw#HXdn<BWxqzcf@cD0IiYP{t8><zB+ZVA!`yJu;<cNF8N@&`{I}~UK
z&7Kn+&R6^!t1da!6w44amsJ}&J!2coA|y=Q_HcN=jmYvoE%3pa;Sj@Ptl^a$@lka9
zJ0pMx!|<!d_^6@iOM-k?noAl0WHpUUjyGL6?z=jTqAR)Wi)$XmC!=FNb0-UVO_J_i
zAh(n&@e!sd+W{8g9LXkWGfpP)`GG%SiTA*uC;fkNYz$d1gFDBmzrBk5wX~Sr8ObQ;
zM>)W>4&Ica88AC+fzL0DVN*_Ax8g3enM(V8=Rt9q%cWXa(lFQ^Ea}wa`xefXDtzor
z|Keo?zFfm-{b&11Mmd@)6H(y;{>OlIkqT#W8v(@sv>_^=%`VVJEiH7_!XO9I*i31T
z{%@4Mc{J4FA1@vTgD_*?jX@+!$y&lNwiY`HDJ7*4vWFPk*eNMOltPv)AxnuAimcgF
zgoG%S%6i{VeZTj2f4}oP=id8YoleYrmgjjtueDfi*g|Ycz$Yaev>7X98&+)NmvThv
zRacDK&Q48ZPravQ*=JdR(~-^u*U=I2g7V4uL6f%IsOhKwo`H~OL~Tya4~b~=hOVo(
z)W0pg>2ElAR@BmW_g|fa(0WkQy3O<QZ2=K@soA@D(z<Y${WO;OB`-0n1S~ea#~NSB
z$7|pDNFv&cTqXE_BT2Or^DWi5=|1aB+KI>~9$2wyW#V5D{>mS{{iQyIQADJ}Kj&F~
z8lpi36KsCzVuE4hU4%CC-B-qJnqErB-~+Hz-fVf?PTW^rjUV|Bc$ANXAI^y0wf>e#
zR`~aopvwX0$N~wO8QSewv#G499wAOR^w!`Ey|X&Fn87?4`Dl&_X^<&O0=0c<fFL`P
zGwho*mOfp`ySoJ_XZn$5b@j~3Fwv#2IGu%EWD)CpDLpONGiFmQjLXCGzF|3Bht-l}
z_c6|QpiI$T$5cyv#0!nJdqc-3m6{zG$!q~1V+WGlq}TYR1IyN&6e2em4GD8+O`^_<
zuXZVm;3E#~yb`V5Ja_E?2Ug5gDaL<;tlaW+JI1t!OXQp8A@`Y<#{GidWwJKRhKCWN
z#I>*y)I{*NDN7~<n!(UBI{TB0B&+><9(b4`uPbmCxzz%;weqg7bxsqfo?EAkB51lc
zLAxyQ#;pI>vgTje(af}$4H}#Sa1)|bjwG_<H4ag`Hu2K$9FvntZ*~fB?ACbzKjutp
z-R)@H)t<nESvKd)p;u11#CT5J<}@KQVD>weupV%?D!MN%Gb2ptRHshJk@RFs`y`I5
zK8b`@{t9;y?B88Rgf^!1TDp$T|C<#aa%*ai`g%7HXc<x0ytj3Sk(-l0FY=O?+P`CS
z55Bvhx5JQ|)<u%iCN<&4cmF|Cx^ydA2(X1GolZW9_+kedl~0B{zCHXwB{(J9=$4ZX
zZ{}=ntnD|H&~q0@N?#S)rn%z{-~3^Xe+LMP8!PI!?KBu*F>Xli_FWhPrSfxHuiiYP
z`vU>!1T@XbkJCl%8SiZurc8%D&PVmBr;xk9PCP$7o+MHCk1b_2Q_&X-LUw6Nl2}sm
z9`UdhDnWMcR8>Bb0S-fS%bA13ydj)!Td8^&C%?p@7KVqAyVN9b@~?hA542}XY0wlJ
zZ9^Y{gXK+3=#i{a0Mt0q^RyICH(Yrc2hZTKP*W=g9AYuH$9fpEot?NL6jHZ;AAnC8
z{DnG5o!kCg+$1p{T@-^8WruSzulSq;p?(Laa1zQ&gQ6H^+G*DD!KwEoVgf7W6Jug5
z!&~+T+SuDqgVzN9#BEQPh<^BF#a4`Xt~r8f&ys=s5u)K#_qS#X%*5@u6KYLQlC=%<
z4<Mz~*UjV5#b#8{ej0V}GM1Yx>ks&hm?ZDVDs%oNE6uEt<lc}0QEyIjvh7cZl!KJC
z(oSx)>%20&pD%h%%B1?NDx1)xVN<1KEP-t?x$-`M5N5mBfR+<^WO;rFIF8N^Du8bu
zZ!jT&zl`1EdwqVv{(G(T&b3#bS=IW5A58yp9%W}vO|VV7n%%voqy1fG=Wp|nOThXz
z=_F>OakG}fsKDe3|L@Y}_9)j0Ng)#tFUBW;UZthtDyS_K<d{^j4{|T3>IzRCdk#lk
z`I!w#G0NA*7a1H@eSikKDQv)I<UQ0BKsIey`SAQuqPU2YDQ)ga31Ds=1Hz<l=X6O>
z?ujB|-;4b(+b9+$H;ob%9j1#LnZ=UXVRU;HZ6+Te8t^83+vG`(S04TI8eIWlw!y$R
ztYoSK&U0m21B}ckyvW`L@<e#_)6KZwv`@(X?_j%Vr<dVV5h(I@x-HD9N&X9nd3;cZ
z$FS}S=!&si4%MQU7xug!{thS3;rHrUMsGw6Mf-K7qkxd^I`!@Z2ay`M-)GJ#U%P!i
z`~WuXx+|MM`kuSZCG;ekh!Hn<r<Cpr!)2+~LP~T)!n7L8z!=<L?FTq;As)68v`mDv
zLL>;z^7!#F+te?Jh58S`X{N7(a#L5kobvpzP1<Sh)-cJgZ2S2qGgf?mZ>%)Bu{<H|
z6Z!Rb#d8AgrKiZiv!O>aKT&3RMBzGTOz4bN*^F*!BBQm@)bfO+R=OIrvbkN`*87TO
znI3?nuGAx#2wd&e*ctM{`ws7R0g>Q=YkTXXsD>?))XABGu6w)^L|<CeNN*;Do;?)L
z{3PhlGQtp-SuKFZ{i4n3`wWb>Dbw*6r@S3i@ZFb;oV$7dkFNRdB*rw66kpMlwrVgL
z9|vrbiIX&?+`P=nvfuIuK%A#@!{+}>=n<CymQ=l<fhawoDsK+9j|t&_9>l8MxCp5_
zxM25z;`FFSEG;Av?tSf$`WeX4qcPvaT3f*OuwWW=;fcbYpgJ)Y;AWf|yeBe+-ZU#9
zUl?+p{P>qw5P0_HK?QAXc1%;o_S$_SDBbunh$~rk1{|bl<Qs`yM#3Tl;#_(6>T?fV
zyL!YH0Kp`qU|{s@ZIuqe2|G2nkH5mv^irg@7>1xH4|R=<Ov&uIJ_WzcUIy}=VXqZF
z#G!orzghs*i{thStT=7y#&YGC#y4?qUNqllp$!G**qMR!KluPTqTwaN6L|JH@Tuec
z_j~m)^xkZ<G;%p~cb$=>nGaYI250GhoS13oGbHycV>p<YD7=40L(w*RopleR&l|0+
zAe3kN!iq6PoSLZvambFjl*mf0Odl&>JS-5~H_=ucdBW@WXZOG6qkpB#Ki=%VE>D!s
zxung0jz*YI8Oc(g5x}OjTQ@ffOQZV%C3%Ct#Rqa4@-IVl?bzgtxJx4ZCAyM=lZ6)f
zbr5Wv@kD{yT><6Q`n5~mcnk>qTOhaHw`45UZfrk$JWCi^C&TMuE&jB7!l1Mkf>Sy&
zwe^!kj(2;8NCC-s)BNW*z=9{i%)1B#@(c=NMLp$pu%^62*(l3n!k`y(-hTzO^R>vl
zsa$aMA~g6trc;C**J#C+2Suf*e&>+;SK1)In=@9Sj+T9!yl4QvANKU*s(T(CK=<A`
z8UE=4NiF4lS?CPM2=w0f)0uBye<gT81-zQd59kJ)M1T>rXuAon)e6~0a|j-HeGe^n
zAIi2nU%LpqT`4G;_ONiiiJ2&_S408;=dV2QCJ60Vuh7F-L8Gy7$nS~7D|g+YmV4<P
z6kdIR4y?(it8=?*;A=kyxx#oBaBXQf)EpY13#1gt$wi>Sy^lHsobd|h{ZRj0=^^d*
zyBRx((yKz<yocW>c!TQVI-4b@g2l%aHQzc5xIyb+9CHeZuMFg8!$5q7@E~+2)<F8e
ze)W2&8M0tRARh|LU^kHBICpO#7o)v)r6vcv3UC`DC1d5tYP>8d^4M*(h>6CaSH%&$
z=lF=9vnWLz&91NYP=S%E`<R-zTbnv+2(}&nfIzsAi#rbAJlQ2_|Jsqe*cCSX1=RCw
zA)Jd{g#%T>BmVuq#r3my;<kJAYooAq#4O3Z|8_?&Zegk5I^=km`F%`Kt;+|OcggYk
z0VHFfiY^#K{zDgq<}yqMUygyDJH#FO&77~lYD$Owr)%WZUd}(s&?tL4HlIcQ@w2(<
z${Yi@%iZN2zgCPuV$ATBUL1b`!~)B~Aq87I$v_MK_S#jE6dGb!sl{+U3>K6oL_?Rg
z8kD?Nz<`i>;?03y_M`FND4lTtD%V0%j>_4l?tQ80x!iBkn~qgUM8m!H`u*pLo!Xk7
z%&HGuA92dxzA`ww^LU>F>5YDwtX*}7?4W+4kc$k6cWMMYDcqw#qOo98VOHv*-bMn=
zY%4-I!Mn|X(aal(RWs7p#3*GUAD5u4%^S9HAFykBA(nPD_}9A<R<I0|1P_3OO#DjQ
z6+fU&ZU!QjVjvBm*k>r(#F|%q`Pmb{rEzy~;*?(5+AIZl%pF!l6i{yc3nG=5&^bW-
z=D9ThgD<${1ue1%Mz}c83%?G!cpC`ny(a;qe2D|j^a1`uYYAXth4R+ZlA&;^IJX_C
z0Uk5?DDpcNO<13$i+=_fv1=toS5NO_vM!q^;IX&%L$??0Sa{2=`##d13kSXOJ>;2e
z>s0LiF1rE$d6vW}cYgBiH>2hNv@x^+&mFXrop(pIO3`eS7+QMCBXA;p2mNYOsJ{L+
z$q!bRI!O5FM#}nJ=S}z?odeZCFF0GwO=Btc#Ead$CxRR$%nQx$9cCief(L4s6KgaD
ztEa?FpbA-%f#LOdAN(t8*LhY(5{Bjngtyz(N~fS0k+e&w(;m5)P(^_(V-Moy;j+hj
zGyktwCg?WJTqCZ{0`o&RBcJY}JNWxP2-1{Xdz#xD2~#8X<OK@7kgpV&!I~TA0)Qoz
zO<7+5^Sy5%%9W?sM|$sUzTjEeh#@GLx-8XKgiLwM!sYy*RpFCpjRwh{zt*}#S};~Z
z&_trmGfa;o|DAaFwfW3gt@+~0gZ2v$0$y@h<?!7XFfa>acHb%jW?gCY?j+HAE3RH)
z;FC1tGr7K);l2YVoG-)&7dhXwC!D+aHSP>+YX)lo9bO3{t1B&#z&k|)r<y`GbrKOZ
z<W40g8%*tKd6hQLluEX#4j?B-FQZTeg68e6O!Yq3vC`U^3>`8~Jp$7E6)SI#!fOzJ
zGgc2{CiUkVF+4~Yz?+IXGlqcbOFtO(%zBhH{^H@VMD>P;9z4PgCqMJZyjO?`wZzUw
zD_K6j_Y%=V^vHp;%t4jparoeW4IVXlq6frs4@W6%{w}92N<|xhtErb9<tf`V^Z4hN
z>hGyJ;1C=8s}%qhjAS+uqHLEnNzDyVM(?|tjrojnMfw)M!BHqCTI}IP!(V~P^B<y^
zw6w^$fk%js@M5HAo#ThU`d%Cw+=Pp+KjOh2aG7u#WnFYB4^;;wsu!Qh&-98Newt!T
zv&^8l0SDo~)TnS^SSkcg<Y~IaSYnWY(D`&LiAK|4C)resS<H(zmzj8?2dJ5jmx};e
zKhhu-l!l8qW*jB8D!w0;B1*qM8@j51S>~eINS^uptY{QAm*4t`n9QduPrs?=d8f$X
zN!?P&XC3HRfzfT_ii-WK^7Qdzw;ue=KJo%c*fAB>VT{BcHK`UcO2J16p|7wQ1}SO<
z>;6+<=)1r82QAzXE%C0v?5;hqWbw18EyFDwNj2CcQ96a&)(XS~Ot+m0+rZtrTuw`j
z=$m>J-S=r~q}d!VnUxP^+nG92$JDo2+b554kzRUf1<1^@t4o{C>d8=kucKOttv_8?
zWRoKEvBaiTP@|k}?-7eeyst%X%iIYGJo(h6g*XRD<3~++^)R_TN0r=*h$tzoh<U{@
z53WAPEp{V}3cT67FnYzCr{`N-f?vib9OGyR*V-Tsg-Q-GY@Bs1^Q1kylhV5u+~NDV
z(3ZX8U6Sit(H-mSI1<cTHJJyBwH`Js$Kqv+J&S#c#mc!z#lkqbp(nzgE%^JtNz>c$
zEdnN`qMNM4&%@*gZXwIZqDY9AsYH3U=WN`2wt@TL{OLhk3(C^2&PK(Y&bodbds=O7
z*{qpzmFru)haKx2a}K0Sbq-vbdp?=y<)8EzJb!WzaTi>=#Sed%nYEd@yaorbwe6-`
zk=ohB9MN74VSgVxP#3>P)F&5y>aw(tBNd;loC)&4!zUW&k+s!5=E+_9{K|QVZ@A?l
z@p}g&T`k{`S_F2|Qa!(^ebl4ZNU9dlcr`A08&NM-qXsceUVw(3w&&52yMSt#vjz$I
zZQYiysw#}bnF|9Yx9~>mR~kaGVcNOb2)Fh+nyitz5=ibw-q*6@x^5PLV2D#@=^~7Q
z>RZ@E^dIQLF6r}a0O6S?_`K3nJepT(M685ZGSTMJWaMj{Rq4s>87XI0yxDW~)s#14
z;Fd(7--5#V9jBB^<EWb~EnLyD{|}pI%geK7EDbgr(cMh~F)d5UhvM1Yru#Jn8-FBe
zI^M!CwfcJ|z;_SYRe3b}{ZGPWx$VB4f2W8Rf$hf*KNSWMy!yK8K|#y?<y3-Z^y9Pd
zBDHrM?HVdS%~Cu;h>(u+iBm)ME&JYBU`f4W!1Pw@pMVGvYj$fNoWXi^*&>NHC0ei|
zM)_tqT|q423ZYwU?ASw4YNpwA5emv`PnS&9P^<hwk;}T0xB7k{5_UO}4vmXB)Etf5
z+vc9)+K!^N{Gs^65VV%_hUK6JqxHnRQkMO+r(edGce1Le$1>FFn5rPJgyWKP{|aNU
z@GI`_jmg7z++4fv&_`^)6@lt?g5zT&)Z@H30Y?*2d51MVqpOk=5;jDjQE#d<-egQw
zc3m(Nk*Z}%RmN%JBvOyC32&}}bye1vkDxPT8j(XJ6rt`N@jyW0>U+=t8%4Mk5A<(#
ziYo5JROYjTZIxp86oNht4NZv}7hM6WoIb)5Ub@?AVTrG(2}X1FAivuGt%dXM3VYD-
zqdZM2pNcUPRYgTntlM|Uk#S8hkLPChia)++%eB`lHd|uBLA#9f8+1^YtB<WhBh0V?
z?nFFmtty`dNYQ*pB3Eb|=Sk=G-<*kJX;h-i%zptV@Xyckb4Q7k-fa(JxnN2Ejh6J^
zr}KEwl71X5>EQ2JTsQrauD4B@{uM^Hq~X~LW-Hj$!b|z@O1>E4nFt&_SJ>>c(#mt%
z7{>!I8<`Z&;?DDKiq-l%X>WJb(+^0_N}&m$F{3fleZ#!jlr8O`9ihFC<b^}NPC+P1
z9;Sf`12W@^JMLIPD&bVw!waL;C6JwCqDL3`24oVQ@@!{Vt1prz@R*eFtJq}*Np=hi
z8Rr%|pf$qO9&sFR?OPCSD(=a?VvuZ^LZcp8;!x`~eM$#cv}FTF2tySG!fR7oq<C<L
z>#q`-%-bJ!DyB;h(3YC5_Zwg9MW*Cw+kYPZ|H_zBWhAW7@jX{ZTPrVDKwjAz260)!
zKN^>3N92PJ0op0;&)vD<Sz`uSp-Gyz-r79yS1~(w!CnBMntiD+OV2)<^;gqXI@P0_
z6Igh>zIsD7P1EM5{~ln~DtCr!6HJS=DJOeA;<eyCt`DBs*n&8Zi?4*PMsX&~p!SLB
zM1q*wwBvVO5ZLDlHOGwMaFVP7N@W*J7h71UxPD!SV^c|?<VY-Rp2bPnRNbNaTny3V
z1w{Y42KcufYVu18)gZ-0G{-b>4~dYN&306~0vn+9`%0T6mB?|+Vn?2F@#eQt{q&(7
zj)M&C^xq=o?0_*of}g^lD7XZsdR2?hE#l8l{S>{!LgDvG<4tz3k#yY8$VAUSognpU
z$6RxU5!rNXC+{44)#47-Ajjs1Y8(gDC7Lg(TKkcbc6S!#oK9c*!ii5^+N-_6OB)ai
zQ|EW@4{zN75DNN7i6<}R_D8BywlPhVM-gj`Zpa^Z)g2$r=}M8`ED|K0i1woQYgB8s
zz_^V)?rAeBZZR!OS^5M@gz6wISZ5Z+8tG*CuEyxfr$`G$GOwJQy8mJCz-4e3HnzUT
z_k-7JZM)*>_N$UFWKOM&U%vR-Dy)LhPGw=tln9Sq9*?@rGm3_u%~K?a^!kXR&NS}^
zOK1O<*O}3tOYVvm+9!RT5NPPX2%$uervA_{%6=j|G_+we;wie~kVq(Ag5%*dO2boy
zfvkvb8joQO7As59<yVFj!?i04OY{LTL^`6h?H0e(E2*;#YA5>)ID8ZgaLf|R9@>vn
zcw>BZx62rqmIi&l`|J5qVDhN6#Q6S+8*uR3tAY?r;X>pMdXQ|X4pT-1bJ*^UWUuDr
z8x9WImH5nzsi{2*#hg11@80ru*cZoQeD>6@rXAc0pY=EgdHfnRdEu?D|F?gozWb9q
zvNvhlfR*bb)MS6({bc37LGjsn)6x%1yq^HTwn=_m4-Izrg(m-S>)~XwZNQi<)|QRJ
zyUJ;IVp3WTfHJo{Xdg67WfmU)eUVE8CqMNf3`->V*jBr`IO!LMV#OY^o-vcpWAM2u
z{Z5>Mtjk?B((Y}YP;25N)N%+!{2gg{JJo(ejrS@r_^;05NfP>>)|k{^RPF`)<laY|
z+??d?KTjDM+$#5tAJAINhrl>WISG$tit-Vbh`%vU^%4tXU>9*6T;?D8+9ASU4y?e~
z`zM$E>`cySVTMx-L|kgZ2SaJ-WOr&X??}~&zXt;GKQjhPXwRxKGi>l{R`6W#KI;l1
zC#C)7DB#6Xtk4U^pm5U`{uX=j^p0hr`HIQDyLo^cI|Vo%u>wz?-<W6gu&;-XqC**e
zn~PG=Z;$;}hQtfT+A`=rveOpYNc9cdfohF>^b8|6jf(%DBf-BXqUG8zml|{r=Gr%%
zP|Np~5Wj@ESK(AFw)S)WEad))VTP@hTFqtVx0@eB=W<pLePNzXU!{!5w9OfN35KE5
zcc`+9!RmYO7TS8z)0A8RkNEIXG&N@-Da;nhgL!a<GtpcM?hSS7-`b3tZ(rFQp4er~
zVJ~v?+s~+mrV&cC_JR<$4Ik9|=ZXW)DCG6)Q9xT{cgp5p2BZI%@xXbx%G?}#R~ZOr
zIHLmP0Hn+gRIm31LtxGX+uS?j!&kT8xbYlha&OlA;4pg84`LxQy7q-t(F-2@HZq4N
zN!<>r_>;>C2e&F#>CM|Apww<(?^Sm*dITBmDuo)N0VB`bTU`q?Dt5oHyobXcplvU{
zmOk4+&6$-pNmqOUF_%)Dx;gtQBA#uncWpgJpQLc5!#YlpqFt4u5GDg3ScU(KJLHv3
zK6@784(eVrg^%e%l1J`SJ_Rm(Kr&r_Ut^!In_(}6)Z^O9TfX}|B@caav3bc9N8&ow
zv)np+vDC&s+kcYDVkQR5{5qc9V1LzS<mt@j?t@VJ$tPK51`B&C#Duc%B;#5bs&R1E
zteko{bSSx-J1z<|U@t6pOVoqdEX9<ko~d|%5c!yQ7egDT>z)F-s|;GpNTg{)oJBs}
z6J9}0>T=jUkuuSpz3|C1USP(jZC-u&)yX0e@)7mor5Klw2pqykyc3**RH94ozU#MM
z7M{n={iNKmEPdgigM27z2tDo~v+H5>4AMPcOWh%GUOM6f8LVO-g8lYB3F8BgpvCX}
zJK2yeFf68E*a0AuCbu4sOt{8@`N4;(arFRT)*bg9mYnv%RB3ni<$$-F8(`zY8Qp!j
z?uLVm5B?mxPt92u^80Cvoq}7ko~(n(9Z?4v*cjZ<Q?K}n1sV!KEwwX>;16=2-#)sp
zaNr=3F7qexx9D#}39RbteP-{t^0E7%`MzU+))1T|5BI)*?0FiT1c#u6tP*YG_2Afi
z2k{;*<DarivPAW8=kFR`P)jv1e81GV6#}^{Pbiz{VA{4T^{_*Z_GBdAztGnI^$MuZ
zeoTw0&D{r8kodNNU#fOLqkJ;^7Z;E>NqEfa0<<n_$niVWjflc>x#`hs^#aO<3jE`}
z5vr_C)ou_qH_SwXav-Z&c!Hgi_TQM*tM^Vdg-ecr#o(9+Q^>nahL)>xSFsc&v7N<d
zSq*59akO&)aHc5uSTy8!lWcHV{CC=`^vWiXqzPjEk`jNeOSIoY>DZ)G8?kHZT>3v;
zpt~;Z_fHT%)sP<-P)=1TrxCH!?7y^^EkShcx>S~-8>s{`?P%HYI1{ane$DW16b6c8
z10!EE%;Z;Yy6qqbYGzU8-B&QdW3d;|;{7LHyZUM4@B`J`t+v4m>HywJwjTC#5<yhg
z>1=l8^A1RWc^`rc760Y95l&(yzUwmMBWSVr*kTE7Ld72%p7DHvMRAV~!)vvE6-+Fj
z;SR{Y{QZsN03_ExeJxEf6~g}w46(1Rm?nbVgwh{oPbB0LPQXLtytM_jjgP^_U~}v5
z)EU%sM^9hdQM}a$9^*J{b6_dFu+B-*tBiMI6q}r;+EOALM#Q2(0IjJC6L)AQM%e(~
zE*lhi%KUagEXVV;$jXCbSc$m*e6wQ>-d92ppLyb=@W6QBccl|ve<b%BFC39x7yD3K
z;xw{v;KaPQ)lEd0-|sO;mH|V4+V%!RSd^!pkOKNZwtUDbUU(GD+2>kt4{{+IW#p>c
z9TB3Vtgq)cY7PV;*(Qn_io1@rmFPNTq0C@185iRONarky0D}GdjL`+Nxt#T|_}n0D
zCj4F9YeNVQpL<4aAqwwCrf<(vSY>S1+79%WIqF{cgp`ND36EZfkI{0vPH~}anJ&y}
zpxxugZdJQN&)1!vRu7ly!d!!|R9amOiGL87E`xHk05>s`q8-<GQGFaQO2gJ96Kil1
z<T;5~uz1pokN0-P{u$p;pu5GxoObOK<Q|n>`-E7t2<>iI<n=&Hap%PMPhEbXR#k1z
zhZlblRo8*eAg}NuA(C)%p!E0!6<3@jOoChoE;f9Ns-MZYAk=<xwCchqko`NZT#_`y
z0A%?Fn+?#)=)U-oXvK2#JHk_h5lGKHLc*xS(em?6w*(2wV|JFh@h6uBhjpKdt+MWb
zrCnJFyDZ^IXW&4b>^-F51ND=BLo+Bu5zB~{^NCqU_iFQxX}3N;Z~#-{@am>>(fB%_
z)lWq$zzFX%8(qI<jLE|;C%I=--`!9pNF0S4Tq!abG~09NAE%pR=>0wVmEg<vMu$2`
z^hV1`UK7RI)dSJ6&u}MJZdv+sze%9y(a$=B>i|?9mdr@sb?9T-UnQQQ@_&Av<;Q%G
z^}vfUCi33?a~2nA)4=>P-Ddfub}5yx<^TEB5Rfgs3n(E?0goMx!_@kj&XD6kb@n({
zH=5zRQ2Dd)^>mPY3xeCKFx?dQ_v^9KI@(D+4|ff;d*FFU;i=4VY#!%-ZtT8t%i&st
z880Td$gdcFSK>8LUJLGRc(utlb&%yRm-|2zo{dy2phRid(g^t-2If%yCVP}ao%;8o
zVF9dhLO|hRl|YS0yr4=2I-$OA15ZsY0+SJSW~3@oZ|%i94vm*-T_$*WOvTTQPU|NV
z0>V`)qX>SzjInGKg@<b+C-1_+Sl|VX@O?ka;a$nAMH`6&!Z8|ze0^-;9pnI#rvh`5
ze%3s}E|fuB-=a;_A4a*yD7O){p(3yuS%6Am26TrP*~kaxPV=v4HfH`k$pbV;>2xCD
z3l~J~x+qu_kWr(1dm!<G!#(ct1{p}vbYQL72UKHdG>rJlyzp?~XuqMf!8iPOJXuyA
zBPB{HJhv-du-Gk>`GGB?2*vFdhC|xP(O&>a)uRS<ulW?iT5u1!L)y|`1Nns|lsW?O
zP<}`;{;@U5k5kH6iM|_p1jm3qyeM%kADaONrFn?bg84MP2qMLii>xq-tBFjod;i|`
ztzd_lX%Pi3j|+-1izRD+xBM<9j6LUYkM8ZdtOeOLB(XkF4znzR@VpH!+%~vvT;UKb
z>0yjduq$i*0>8%(wm%O@!|;TyFPL;%%o9bH0h2WwxU%5#ieM;cNR%B%wpTQplDknk
z>R)~g=A}B|?J~H9=7@$lj3WNeHUOW}$DlZDs}kF{%Ri2LzTkETdBq}-n%*#$`M^WM
ztYR84Qupt=XR}q-Wf5TQXQIs?RYc!Hf8x!pS4t0;oeTQL&G00bXd7;{m5>4a5NI(o
zyM<m%4&b`Nl>|0N?|6PsvQjB+fDAnQrST{4u55l675BVDe=zs2Wo4lgI#<CCm5ulr
zY7T%&eaJ;u3NtYBdt9PpWb+0n*0032$_pj39cRaTLkrt*75J>mx7@*En{ouFi!+Y>
zAeLORbg_DlbsSruM?(3AZFlch2f$N$UjDyi-0d^>uVY6Zd)^&|prr4d$6)#Zs7o9k
zQf>Am0V#N5x1s#NP?BK|lRynd`}GGoblAXai|faG0Fi%$)M@kA#8(QD#O`l0)zcKt
zO*BU?KBHszM!lh8#MzF$T}u1aCIO|~bC3NO;A_LntdOnw_?f^WY)Zb#CPBuKo&FMg
z4<h5_!k<`Mh5TozSbd>4$V3CP#9Iw49b9ckzI$J*%)ZM6o#yH1$xpzJKnC0xOxR$S
zE)aj{%M_+z0JuPaCQW}ZtX#2r2>y+YfhlOfwYuz;`8uFaNp??@`aoFP`SJ^RB9wch
z{C0TJeu2%_9s1rE!J~5t7|hBbl5{wng<NZciQFXk|JBb=B|cMRyQQA0i)>F25!fo*
zh>Im?+JT2)6apFEfnDgZ9471s<P{2r<w1JmzZLgGLZ~g56RZZ|8vD-Qf~Bc&M(6)`
z+*=jvT-!<529Jp}1H#B`PFG!gKdw>@N!g{})~~>MaZJhd4O)QQk=G2KS3elSebBB_
z7=kEU*f-BSAaLoRQxOV^TexIN9%^@F<u$@Q0sP*X3DqRj%O9_^NhB;rVs$nyfv9No
z6N0~hV92<&+mUj8xZ2H*Rd3sWuD&9sqaC4SLY>}Ug@b?;?37RWa5<n&Ec!88qKiuF
zUbPL~*#qf%^{wKk_r7)3`gxU3*1BGN)M&HpP1gR=x~9OE12%@MF_8H&h%^w$-&hK=
zhUzqJ{iVA3(h&Eg0M;)Bb~UBbnsMC2nfXRO#;TvV;Z6rAkMYkJ&se?8dGjY07VdRi
zQ8=ssWL>zw8NBc(Y}oIAXK1eAb&96@>bUwZ7$a^f!xHsz3`v+`vw-*QM&9kx!+h5|
z1pliANLO40sNM&eSB@LF-$vy2reGDWdK4^~{#T{2*SkOhedbm@0?^B%p7Wq(*BwqD
z&ccIL7mn6D;1B<2#gC_r_ykax+@Mt*Wx2iZg;omQ2!kBRj`i5uI#Au$KtAvwkOfzi
z+u6mgxoZ8EzX3u9(>)<`34@Dm&8F-?@pdkPf4fYXs!a2`ZIX8M6^e&Mka;Pv{m$y`
z0x#+!zTA7S{khGSmmgr$JNfgJL~WJ9zMRu{S0usSXEizW`Asw0M&ti&quuN-4s7-N
zQ0z7n${mJRWA!9M@YuoK^ge~Sw*BbCAF+A@FHJ2Xh=u0&GAp;efu+12C0U~0ZB&ws
z(rQyZ3f^n3&WekZm@H+02#Hmn0k>}vYViH*??Y-LCmAw>ZyYWkq9Y|tQ~mQeYRd>l
z?X{7VGq7nbf_OlMmQLroisPPGB39=-$h8zlTOj17g)1<%)}?R9>rW87EwLs<I|VcU
zPHTJw<suX?vkCU?eWUigUmuz8jw$I3Y%af6xW9QB<h&gMS2shs=boWlO&Aeg77vPF
zh1uT@`>@SdcN=Q9_GGQThBEV0ny0t~HT=s^=3a^x3Srm}a6HNYwaSU7Rei~J>s8yi
z-n^KEi(rso-aM>@=xFDzY&Yv0Cx6246Lg1r^=Kb3oM$CQ3-hzj2R8yaQF7qqku?Yf
zl|EW%E@|+`95el{;>IOm<F|nsm!1{q8o|(fohztLofbq78Eo*FPD#?joBi7HXg&<d
z(liicEv1Kv!OApw58TrsaABP;y!&aVkjMO>;#}C1Q5r^}50Luk?l<C+KOD6Q^7SKM
zzJtB&))(ID2=EjX?;Jof_K2_(dZxfN#8Y#F`lWDeS{pyEO}9`&LxsY=s!dYG;|qo%
z2}wADL828Re9zTBMpBA^N52JS`1*Zu3OG_hIN>7U43UZ3kflwEOFh{v*(8|{GQl7l
za~8!Hf^O}*mcEB}fwsmsc0SVXlwI2=S*k>t1xLEnwADzd-`<zcP5)Yg6eJIrcAe~@
z1Ch0YwV7#`RcU?~WMnaiVoqfeO0f&?&wYXw-oaE;MpE`Wq34;ht@e46f9?aydN{TY
zRn;Rq5*b&0U7_JCWfYbPlr<2)tqz8*cPKv5IxX3WNYN>`EKKpleXY-CCL?XQ(nF-q
zONa(fVkbK+^vfJ*NSH1w>Vqquy6qpuaEINwyR`&kVS0l(tU(usjuVYPuUas?HF@+*
zWqTF2g*j~62A=Bplc9uAr}RCiqk><qJ^^1EAE{;Lisyhwkg+DPjRSwJ*RT9RZ*bV9
z5^N(if9p^pXc;Ip%XB)7&9sWsWzX3zA-rL_o02I;P$BGVE27(}%a6_rUXUce+7X<3
zNZTHSdkXS+O!m;Dcd%dhvLBRKW&YOGX!cQChNMU;PL=}qfA!NJy@z~Fh(CF(Fi5?I
zm;>u^WT__<%pNi$VqHzQk@{aSzr3cB`y+<C;5Gr%&WqV;F#cDN!DR<F$#cmk3aDi*
z7`lEPjN2(l?0FG!R5SuZ(>U*9Sa;W%0Rs}8(CuV4C0(*9+x9zk=rq%ghgG9}PoFS)
zV0r&Nqk6ZKNTN0Ct2~7@Iyp{Qr}?${nsERmWBJ>n;ILwsFLur}S$lH3fKr>KdVx2Q
z?$kLrZNK0k760mI^)yv79khouT>S!%PQV>bPeuPfMJld6grYM2@3eQA>De9#TWTir
zk|lDRpNt)?bUw1Soi_IJB*b{{?BTIIltRN(kZY<GMnBaja(Tf_8`EPwVkBXH5iUe8
zTNVBrWU}!Y@g`uAvG5&YKJ#pfe*;`!@+8?)kDG)$mLRCAV$QgxITA)2e}Nw**+maS
zsrE<IxPUTGYk<0?%spO$Nq`$~Ss|%=lo5xn)V5otJLvh~N^N5|ZbK>{C%XGHh=375
zT$X^4RV)nBwQ(rN15fJt<j~zJ=hwJrE*)nmf=(hI-yUKUPThq`yD(IQw~2T3nR$Uc
zq7tF`e1GLR+e6o%5!osxdJIIXUjGq%Alyc$HQ)i(2o-Y|m_DG{#B?<I1XW0Nig&WH
z26BEN%1HRv$c2a8I1&t_%F|sCEuMfO`RL|!6e5U#jWR{L2j+i8BGYwUHuaW8-A)~Q
z*!lGNUWlVmeCK&_6<^FWDG<Kz3$HEh(4HO3pSrD0@MOEfIK#Yga0a%p*WRT1lTnGy
z7>Hnw<_yIuUo%r8&1riFgw-)F@LiOh!CE?iQc78{31$uWDB!Yvx*voS@!j7co8Smh
zdJWv|Ja{gapp64cu(AoWvud9|2Pq8>l9*fWu@rb+kFfmTysj@TFr<N;=CHPNV9YLb
zG{usr#kN(iR94McQ=T(&#Aql@MTKE)8$*I=VsIC_+bebDMHeIcNlg`C%6Kgp(R?3N
zMb29c>XCXvZhEx<nk5@f-EX30Rc6kkSN9~8O4q<QVN+zG5U-lj+=9(7{fwaRMK}vJ
z{FH={&d++;dH>D-k?kte-(_(>zA%R1V0yTR&5&y(LD^mc&-KvZtArLunqP_;2kde4
zU@)&9<=<QUp5Q@6aqzAirU&&HPU|-kuD5;G%_hDK3vt}VxN_2Hw4H|DNY&$mwja2!
z?6wZT-D}Uq7?BeRmiA!f-o4$4^@iVThc`Y$U4e7Q%ue|*iM<J&3cg+5BOzt$R91~l
zhMO${FZOykCEtk4w8zW~$Q({hnQ{`hu`7Gl@-(F`1`v{)Iw?z%gWsPZix<oF*Y6t?
zW7+zX#dPEOjyf?Q+tpq<GdV}pMKEMw`UO!FVwj2z-B)p60csZhC8?O3DCx<m(3UyY
zVjB*g#cKf?@m_&RoVhSJ1#>9RTEqQpj?k_iY@aOSz;g-b!hr4h@j+Llwx&3oJs!Rf
zgka8iL7Ceqx=uw9Me@Rd%p%z$idBD8beVxT4F>PMl5Fai%eH?E+>GH)W|KA>8f!6T
zEmDW%f92Fe4jBmaZWEyLjc^!2HU|g`95LK7JXFcl1JZE>1=GDGYwc5d95Nd0r!O+}
z(71#X87NY~s2#T%FRx|hNtUvCWX9vfG{t$VB3b$efAa!-bU8BSMG<c^M(4ufSZ(Bo
z<wKbAusB+(rV_M(c(_4G@XxGms;?OD-a)ir^khtff$J$<Y;Mq`$cU~3A=L`G`Je-f
z3u6ay8W4zivg<NKo^YB6E&>jUARp~wIhpw5T~{D8|MQS-^V=>UL6KZEZ;Q&(IIf_8
z`-!yGgH(JehTIJ@Qddaq>Utcd22-C~LimTUbCMjA6o}>#eVwoW^|<qw%b+9tvelk%
z4Ooy6f7J)S=MXa#qR`(=K!GUOyE!UIF6N1Do2@zcuy)Jx^z!S)<TQmdj<y>c@6iMy
za@x9bFEcUA&1@K!cqh>?*N4|$Abb8k9a!WiUs~N}frJ=zFt_Y~U3vc3;ybs>?#ffo
z)8enqIbu}~B5q{vP7pH{`U3io4M8x3`5u$-F|8^&RovITCSnvq>n-IA&RP20n3Mz^
zMk02<u?dYO7dMqa^%(DoW9WK`dyxEEdV^pa16f3E%VI%di)KnPib*KI=Z|&a%iTyB
zAAk403TOn9l@RtF0@h@Gs>fU+r>kMD`cM5y3u7j)x|BXI|AI@*jPv1e_!(v*FA&r|
zKabfTH1IKN#}t<-VAl&u!tC}E4}u{z!5;dRI_>IR`tCb-hBp2&lFD*oPaKlLr6yV4
z+L9WURjAHf4?m2`?OBXRJy)sMlBI(iZumi7iuZ>|V_FAe-ky-Fv&&nVn`Y<=ul=it
z5{4DuY(_Qu>+(sbQ?iyQ9=MQ1u$y;(FsG-d=(}KJF^%%nRKx@1S2!~jp^M^t{~`5>
z#8Xo5dLY!)VmA4#w1bD5Om-yyz~LWS+nd&CVx_xw$hWx}ztv#%5`A0!6|5iGJkVon
z(z1wWOmw0WRv2u;+Yh`H*|x@7+yqfkryU|2Z_JT)0H~D8lxl@OLm2pB>1>PjFP)0M
zb_wO84wFyvk`xCZtu|v&eu_Dd-4`bjo^{}Ib=I7t1)C&Jf=z;@<)9}y`BMq2Si~7j
zL?2YeCxlLh;hUX7_6a+ArV0@+=PQt`I~IL}Zu9w&6C|E#I&Vc?XCULgOo^plZumft
z--T`H`D>HW{7WKfGu8_`+}scCVJk|pTf}FNJx^V`@)ucSr^y~xJTk=){G_X2`c!_)
z-ii~;G8SJBo;{$kcKS6>RJR7}bLql_kQo;<Vgbl0olDv#OvanaeZ(7Y@)ksvC5xM4
zNY$6EJ(J&LFEiGwd9kJ(Vr4O2;1w%@j@>H`s}OU#bT2TO3B+BfRSscoA8>|0qEjuD
zGY_`pH-Lp@Kw+WJ^lfrz8t+aAV!-3)`|BawVTLX-f8VmJ$>xz$W3XRvAHsN6Tn4Am
z{a3kc4dLG`rPP!$Vd2sK(3CYPLm|XHj(YY!(~FQ_w)o*ahUsL=O<fG>_lH086D^-`
zCWf6K|I{$L=e&jBHVlV8p~pvYg4yTvL0tFadl-FgBBp}!ACP?)p{TbToD%{gxvz_&
z*3$E#zs8?8Nu%51X;1(<UOdMWsrxjzMc;^AUaZYm_y6jiBtX^8e;ir%ep_ve2*W`G
zHZwETCK_vd-L{^WGy`b2S(rN&Sv*m(KVhSzXrbD)Dw-Y+B98n!tB_zhm55u4P7PAi
zn8n%DXF*3Ey-!z$@@v<@SX+JVg|DiX%GtM<Kv>Sg(M;@oInue`@i!nhH;>qR#u7w2
z$}+`G311*2e=*I@edjrLC$58es9gky_kOiAf9PknvXB!OI~6nFEq$|$@|S4(+RXB0
z33bQa!;^75e$r`bdQ<cRc)+;siu)!1tv)o+RuGyHI-Ovr)_SCbhnDaUuX<a+5_T21
zBwLP0-fq=siGk9Y$Dv;x5*4M*E<E08ao>jD!NE~hR{ij+++LJ$;qVN|t3{M$A3g)f
z)1zO3{rZr`scg4CI1hD93P+C4>_s7K0YBdQi6ysUC02Er+n+!0y()Q#xm)+;acC&1
zbl<<WP<g~n7g&sFAukBJQ_Ip#ZafRP5WEa+&0?NdE>gc%;E!`_SAZ>RGQ)Th3rucn
zI^zv;#=^RnVX?b?U}fmJCK$_hf9UUi7Do4FDRUd;cNq3gaPm<IJUC6#Qn!lO;E;+&
zZ^V=)GM3dQ9WJ|E0ATFz0v{d1e_$hrPKLkd9Fs%Hg~US}G6O8A^#Y2@cv6|fx2Jgp
z=Wh~?o!rO_YI1M|Jup3nJvIk*)@EuDKgRAV-OUo{dMyU1g^@KacOodD?*opMJ*_^o
zyWkV1IfS0k3iQv(x79pP_3!(0my0x<VYXE=2v%Kp7OTrPh}TI=G2f4Pot?BXo5@}k
z3~#r?80a(aY<{6F17OQH)GUx9{LI}E2pZguz8f0*f3NUB5f%hpgUa6Gb8;W!PSpS^
zA>4mRH>O2*0`|G$BW@#)JR#&W{t$-5mLiTFR_D1SeH-N(Rc3w+OBK?|W)=Qm(F!_<
zPa`!KZ}*m)I>Keba3YJ+$(0>fZ0~u#zFlr@a*6#vaQ|XLPGG7rfNOm*rLT;+|6L{-
z;W5oNEr|GZhjd}8-;cHe(_G55g7_&&;om16<iY%n_7i*YNB<frQ9dX9p9s`wGnM2g
zw{_s;vrru$uajKLDZC)5Ro3O$)!Nl?u-NI{^^3?5Rf9iz1+K_$%+)_)(^K0AT(-0>
zpQZJE`*?FJy2GjMrntp@kE>S=ofRX(yHBR&TIi&oJ?hN=N;W3-uo&JfN-<7cH{4YE
zyXj2%$E~fgPl2l?)lXI&mO=){L8kolmrw1Fr%Se7UxODv9lP-E{pGI;X8vCn9`zZg
zHOs|giRT%YXMr*-7dSAc<=7mt52mU$NQ9`o@d!GxA479XEh5E<emjga&lP^4irfiy
zSj5UHaN%2C3x6IbB>9shR-<pUrtdv#>&>YU<<Ah1`v!m7!7-9M4|0>RPTRs(Gq3T7
z=AX|m@F^O(*Yhkw+YEDcq|xZ79T)C60Yi&5G`Yv-qOBd65oLAoJ>R{fpY1Bo3zt)K
z&L8JbK!%kHVh*E=r@s-WA1Ex3Vfs;ls02?-3K{;*m5_5uF>Aiq=g_A*mwugX|9*0j
z6)&w1NjuP%7hZF7_yyN=o>s42cX+$9YVGAqq{zfnuR_{=&Usux6yr{p8G!ZqW0_L4
z`nMfCXz1eyMJe@Rbd0tK)rhyPZC&o=Mcoa78&t5H_N|Apwf`&So~(hA9cgCQ+N5cX
zzP!&{?VMG-Fd|7L9FW7Sy|XsM<mGcm?%$hb^iZ*~|IF<IYEFf2F&iMT1^SpDgoJ9D
zDGqmtmYL?7+2VlJydpgGO}lrgPv(3z=zi;=^{MuV%CH1iJmRq@hC-if7wR-Qt>(Iq
z!(r7C3dnqTcjRfpd9FzO%x6fQm@5eI1N_r&@v?c=8whnZ&kGbwkWpS0xw8JvJo@po
z;HGc-ZeQVtU79bc$zB8SOqO+5z%Q7+c$to7DwZ}ZAAF9<Y>sG55;?GME}7?XVqJ7U
zZrf{}`QruOkSS!26<jaTix3i6ArKoMJ$s7z#GzPm+^0>s@zJg0DVB{s{C7*aX?ra`
zYYX6#v+p}z?^V_RY_&R2Y-=D(EU-PQ{}Em7V(cu**4qE=W0)$GL(zAZKb1Mp6N#nd
zp~Ln3nOSy@UDtLTsnBVQB$|p{L9tRbKv)Ulr?w7U9B;71k;r4nc;gPHE_SO5LaEsv
z2k#!ecSvfXeXF<=G?}hjkxZwLxmCR#dntI)Rhb<M1ML*1Pn^q*jK}Is_O}MI%1Ek8
zMIJTZxbJjKsY3=mB$6z-j$CBzKpFa-jI_N)UO5?^DiBC~>7DWgvE%@_W1S7m*llp5
zV*fsDGUm5^?uyvMNGN-L8dL!uh^7KO<)I$+Z#oz1WqBX;Jps>v7$jJhyaCR7Orw`-
z4CC)R$d~(om}ta<)j-ZKA|&f1Z`k*uu)b1=Z*_-*gF8HdHS;2Yvtomwv52G102E0N
z^94|w0k$5wu}fo~t+jxa)I%v}?>optS_H{+88ySo`q+z+rynRRb7Ty@MCTUb&U&d_
zC7QNp0Jrx=RZDN5pN@ZW`B!!0-Wz4#czUmbDYzct?W>N)<e#B(jccH3kOR%Tkd#Sw
z!?scY0Q-U}SS#Z8AmRLgvY9DP751{Ex67kIQ@#UX-lFw!AFH^ppTXt#v#HCmIrxUX
zSIopSji%8<nul$~4V%Fw)T?~1``HE%7}be5d*p}#7p&I)xymbtmaHO<2bc9dxvonn
z+%T)<e~@phopUpL5z!_^CDmRKFg-%s_o93{C!Eb|S8*9dZ;)XcKpDRq$9>a3KB4$s
zLrj*CDsFrab>`i<y9wuMG&{vGVW$EmKSXGwL`prqgTVBNnOdNS&*F7qJ51cB3>a_Y
zG@5>9rDJn-r0!y<r*TJ)9~o_Vd&mTIwzq-vddIVE0XkePg-@x!ksQAyEfbU!Ld}MI
zU=kO{5-7$8ep(LJUOLN|PMRx&W%5{kvN4$!5uZH6;bszs7)RXj$#B;A)G+Bg!c+#s
zxK%Pn7*e7V+xX|kdqiAJ#B!<BupdLfGzooPMAG{j04D|8QQUh}qN=MA1o9jkymyFq
z??5s5I7(rlOmkK?#DpCm5_(Rg=*ROp=COXc>h50IsZ!~an+1)ZQ4#~Fh0zb@7TJP;
zyq}|>=@kAvLGaEFS~e07Ox}GM>eSul-vdA;e($&Hd?FPq6MXkZ9o&a+SNwLoy%^kD
zvQ`-x`a{WXK5Jl@r|&OZjwsAU=$f}V87t`iI9L4o7x=Mf&7R}59zI=tfby{t7ACaD
z%RL$<{v_UIBNC-cGR$i{dHag$s_qw6#)IZg_TkeRd5mIws7M~aL(q}quy7k<E!WiB
z4U|Ni{7ryAtw@<kjf^{ZTs8g=D8948pJ@YWs9G}_yQ>bob1&LNsl+=T1b1q4Sa;}d
zCJvrrrk@lT7uCg&5%|~+{4kAs#>89|JPQHTxvb{R^f83Ce2_BQc(dp<#<QA?oPq{B
zE~g0Im;FzJFBjp-ZXzR5N&&DOx^CyQ-Th2Ea11;VzH-e$0Dhkzj7U&DoKG#L)hg3L
zoeniHmdI;kkRc-X(spsmvu%p=;1uy5b*=2A-TB@NewoUhGUj%miCoEV0fRm3&_?Df
zzfG;vY2GdNAjbO=>O(8p6M_3hL{GlU!FP@Ke*JFTe{HVj>BoT$I64Prl>9WFca8hJ
zv!||KuBH4QoGp2nSeq*Q%?}M%4h^bJ{aFR>pdKt$-kMzYt@RzLJ1shDBhEge<~%xo
z@ZHPfhab)jWR$*9gjuwrVKt*3*S4EBFxlDvo3anATtqR`FQ2B6+AD8c_SMb1>b))J
z*YD`eM}4DVw}>dXdU6i0VG%P_3#;uXadIlt=TU;u*OO1+i_3a35F;&5*OGCC&3~<D
z7r};lK>T<j4BL%(5}*9+g_5d)il@+k<-UQ5AyB2-b`Gc>jm49o*~&p%>TB7HS<1JF
zS^k%p6nZfvnX>zlBt;nyS#!H3uHzkVQp2G#zT1b^jw*6{-x44)RkP?(mN)`WIh8;x
zx2XrXp&pGQ2!AwxBUe3i>Pj4z_(ITzmtz1VyrufIVvXy|ac|EOBSo4w{_GBb13s&P
zp^`UE<}(&N-f~s!T7T^=%f@@+_x4v(;o*)e%6_WO+t<g$xx+>Pv}%{%^wySEzS);}
z?mgV%(L2dc#1HY|mB1W%sOR<f^m8tt^z2J9*SA0X@SuyuFDFho`V8Cqb=qP<oOQr(
z??YNzL>m&(6Ai4`7HwLcJ5HYb1gA&QZqde?o``DWZ(}fz!cXhCWdqr=aFLvxw4R%d
zQ}#uJZ&k3DI3+{%qI+-(2b=nIs)Z~?+7RQp1$gr5@iJ^x459FGwI&%C!R}&3IJdj{
zL&9r&4Ll}yV*ax~@f;9P+l4O<soWW0v-z{C>;HqJol}AWZ)i=odKBCQD57X)tT+!1
z6lo!eb^17yT099(G*1(9lNSJjbVosLjrwGh9u=Q#!@=m>ouWH%1|P^y=#-IQ`khED
z_|bZ(aINNt$d=z(^Nj(&u{td~n5ZhN#$B@9_8hpUe-eI&?14q(TmEB|s<3pp;Znd(
z*ns%Sa(bDH5MpO#F{iS4D$uHCz}qJc?D477ZwJRBwWILe;Y!cr9u@*LgJTLiuLT6w
z^of_|g9(9q{qK!~P>H8q=XfR@1qbht2+`Cb=+TT9zs`3>%1xk07RU26@eQK2REC{6
z_sX2kB>3=?vDpv%vBV9*DcY9_-#)h5rM>|b!^NL2@XW-~QweRncVY{UkxN8~S2cfs
zpc9uSPzVtOb4p$}kn9}<b7>;hD^8j(R(5a5%k4dS-llw?Nm*HC-;A?0hUK38=$&a7
z<SLEM6T2>0I7m2eSZUDDHN_M~@L;}m8aGJTImM(t-~txuM@qir8Esb*rkp4F98NhK
zOUq7GB3`95XXvha9N+z5kt&{8w)PMfb<@Lyo2WRjh$X^6(Pg~>V*9;YW}X!EZ`cJU
zZPo8B4EcPkO$TdeZe(4QyqSC{)G}#76+3lG$8B%v3md^5nWB85!7?vTxjAnv8O^y2
zln<`o702nB7&Qd~t3F!K)$<CDM&;Bq#P9|#BE>^~;y~0drSb5fA6`Gq=3YG#!|6Tj
zRgp?i<%gY8GQ2*J8*o}jn0Su()AT=qG0iGTyXrRBYL<&kg9p9<Tj@ov&=y#qKe1II
zEo>O4JYe%2%V{V!M{mY50|52k@I5?39?!?K@f>`Y#DdX&0~Eju5c@c}bQnhJ=iyyC
zb|+sI6&1(gb@L7~>0=x<BGu6Ja0_OfQQ)oRh*WPc=-{{nTzl2=uAmqpVx2TIo~+d8
zlAq=56E9u89j})iY@Qj3Cry1}7gTuZU|zrbzP)37^3G%nzM$PJAQic!iElaJcqu}A
z`7uZo9xlSttYn_B?{HrD$E8EE<q`8k<#)wt<20#@Vv)iT{5|`!L2(y<h%cR@=+uVW
zW!*UzuH(%&CER16`NhsXm5Yp1uG{Cg=sHgqGpk27{oHyvc;({fCp!*d=wsfjcI2jB
z9zHr3Xl40dEkJ+WWaDnfBpvICFq4f$$Q7}*blMa4duAabh>i1nv}ed4!;XJ<?MqVe
z$I?H8tYOlDBak6-*`3pj1s`#Z<tNs&`I~t>whq=O*#}fVF+9n~?yi!9Fx)XUg@oE=
zI5|Ghn3+RzW*Nvl=#{a-utNJ?*aQ8XkuG45{=Cf}8I*Is2lAsA?|f5N2+jp0a)f!U
z1e@+3I0>I+SWu&%Og=}TKFWbHKzKTD<)ZzD4iBf<%Rh%(_);#QJU}Gb(tDc)l|PGd
z3Qs*?QOT<ReE(K_#1EfGhQt1F9E+qFtV&QF+xin!M%sujh7W&$JlWPwu`PASk&MSs
zrNFmm`=9MD3E4U}oSk&Fv0wKp_nk|!Jd8xB`)6*RE`G|Na*6M>SS9|>BI1*3pa*-T
z3)i-uHEZym=X-<(J2U-<dT0R$ilrmZBSL7$sfS>X*1yxsXhs>k470-ZQZ@eCz48Y-
zvGy6xI#_a##fsLO-Q=0cp(b@*%nvgI;WPNFnMVEIQ}He8biLFqLS7*rM4^Ko@@C;4
z;2`#uKX{%OF27ZM<zUAo85oPqa}VTT(=-o<d8P1}467wv)(Bj|$|We)4tY_q;uB|=
zb!`XbYYUpaJHI#Do1Y+JbvaVlLDZ7qMn$_3|JRu1=4ZlOc+$P+nf;pyBMMVYKC;V9
zFAr@`;ZqPJ!HM%T<k4IA9U#&eg;OW4ZRk8O=rx$67qx1PX%Np~r<1|^_K)6q%CM8K
z@RMF+Ua-Uiychi`#;}%jXzjdrQ-U^nj}C@3o4i;82gYhu!fApEzc@bBe~9yp;fP_j
zsskbHCg;v{+<RyD1EoNuU%W#6Yw#3gaS@9706MrI8yr?9cWVlEMQX33%l3xd@cbLP
zH?43w;TOXg8DkVaJB2r=xAOB!P=4<}%abyg=}Dwao_Kx!{GM0`t3H3_v^hwbF#TDY
zppV*y`C&bO6#wCY3tYPMwEtPpk5-;PQS(@p(WQ5H%he3Fk8X0zFVCC*NIB2`+)o+%
zb9+GoIZHBr4y*5Wv4MF0`9oGS5i-RvRy_IdHOs@AY5j@#DJ6iVr58&WVGwjZnaAXU
zH9GwR7MqdxYK?_iku<6OQJOy;#b6<?9`iaR9v)PuyeRJT5u|%x3Tnf)i)xOc-bw2%
zW`zKgUXBgj7F>$KtSkVB>6$+!eCiA8BV8X)L{)z>Y=qgL*H-BouXi?<fW518^$-UC
zEqqW*=iuS(HQ>+X#9Id0bu<zmWDn#T9hbqJGSChb*q?X)>y|j-$yI?C<Su!fY`Gs$
zk341=3@obb1&U!2Wg@`v1@V#V2hG0Br-jNC9YTCBKp<voKo^-6QR*B~>BGbZ9D)Up
zVUOvSuwak0rRQjGXBE*<<G^ER=wrUap$E1z#6_FwuiyINLCQ7eZ@~p~=0=(Bx&V3Z
z@ZOZ}C7uHgEUkj|YP9VM;4zNpuoADII}7x=zEgl0H*x2u0%1!-m{(seeKNstMx;}7
z>fAaD0&Q!oeX4L&UYgG?4gL?Rw#4A;?D)U`I+3eLR#5VkP3K4a!|S?v9k4y`79G9e
z5ZHeH=Wl8*!jY*0ZG7e}q#RQ|;R(MySqVwI)P9e=b9d9&=?75YOVMjRJ0v|zInqB&
zP{fK|jeZmfOmHGzZtHie*1|93o96P$Q;fg{f^bpz^Vs7kB~P5L!?_#fx*YEy>)E>F
z9ZL(YDkR9;;-=R$2MMPI$J+<T<%tE4<kI^$rQIIY?J7d^OWpExR?UwG4ZocWeek4X
zwr-tP@S6XDW1`F%P&|Ps^uG(~;KPx@BWuR+uO9!oYg~BpgqQgPyr*siYgHf>NBNO^
z^AC@m>jYJR(h)``-V^VE^zQ!GxhuvuO|M{=qf%XQvdI?x17h@IRl#Utxp3{y9C<zn
zU>lU~?i(jxSAbU5ViA1dbU_oTB$t3EkKS^tPQRBnB|;VH6@Wta*TDb_pwQgCy=^St
z#lHye{E`f6vtjcOW*^0+&0=zp#*1{v2%s4&len5ZaOVF3I3|sKRJKO;$*bkL*8P5X
z(znUN0f3?mnP{i|lP3!e-Mt&IyX2;nq_p-qq#)fZ>h2LsUZyo^9iy$jmzc!<2_-iG
z)Zleb1^?$Mh(`K*sispmYC(9nbn(~9;=54|m7Rw6v1p#GcILxhP5w|9`P0PFF`f&M
zV~?yTbbhvzV~Q8QXDB&Xm-=mD<MoJzY~9V1uFZ5e55%^9g_yuItClAYtgJ?hAg!!-
z)lF0JZQ^~9{~UK*;PcY7hTkhsWBm-vmN<~?F+PBeZE|@|Q7qZ$f4)ZLC&_<=eEQ86
z0c<I&JKq0YjKUZIUiL3%4FwEyQ3rF?fbHwHbO^H?`MJ)!1Ib(O{twFDJRHh5eB%y-
zvCmjCw#<xOWMr2x3<i~@v`Ut;CVP>6Y=gmAl1f5TQPQfiwAfV=5|S26_CzR4^j=Tj
z-|yZ2d5`z64#$z@ndf=#`?{~|JU^$a;g3XCPy+n}a!{^{&NLXqZLQhUB=92>q0nAS
zpZ@%`w(9M|2Ao>-Xxu!)<Ujllp-A%u38^R2fgI!Nk4K^?uf_X;ICg#4F_2?C&p!H0
z9es1~%u8iTC(@D%%R;LArAP8fSo+Ny4Gw7eR5*oLFZw+Wm~{GH>3_{jkyzGEzhwdo
z=S}wbU?b{lAEr8&Bxe93(da8Cb1cOC0sr|-{zEMJ`TDO*N22DNzb8nsvts{l%z_0X
zIQ&8-DClAnN(NH#CRgq*@l_t_*Ku&XK3e;@n}YFs2<5NX-uF2hMMAA*6io9i&_kV(
z^C1nGN+X%O>i64-NF;E&QBl+a-;``3k?STHJ;Y8v*iSFU%2^gXJ_9~YH32(eS_$^f
zT!NsXcTD!2e2asBbpFW7e*ci3@I=$wL}bandaUR@Mgval*9`F3%jVi0;0NFP)VYWI
z;*K+wh{5?2QjjMKW$$|S%l@X)`TO;)B{x(q-A}$`hAN#<342FZOvJxc>M<QryVfiD
zqdKZ}`G?xG>>$%dg9o4!-~6U+hXG69M=6B!=g!B*M|j=d{9#_5a2g{EZ_36fOJNh^
zlg^VFIY?JE(X@(SKb0UVv<T9?_l{JiM&Wr?Mlodw4ykpr5d<mV*hiSnLL%2@Hq%jF
zmsmu9OEx>44pw}H9;P8=hE0!vR4nMV+5?8g_P|F!9d_4->*s7v*e6~0$1N`!f|1p}
zH$w~TluJhYxgKd}yXSRkn}yNhF8VK@3YIq&fadFDtpZMgrZk3NT|F9)<OrQWOB?^O
z0;Eh|mXuxxV}y}pG#y3B_2lU09RaK1T+Jxwxxq>3+dqm3egryQm++V7LeJVW(3uEo
z#b|eGO~a+UHSl>HfNY+(yGu=R;u`OmY!3yB>LCMnf#A+<_6-T29hWG#4X;lwByz{(
zt#rnwZ&q)oGo|8bXH-HOb44}wjahs=;$L;{8;e`y=b)S@_1D?lfOSxk#0qthJ7VTq
zsCy{(xL;AGU)gp94O8SS5BeE-hV&WE#LRt!1KDn&GTFG#i9Blw&)Vv<es5i3Uk<M?
zVAnKPXETa86u?k(ejD}pgQrw_aeQYyZuAG^z0{>B3e!#^cRD*g#<ZI`+stQJ3+=92
z<n$eC^u^`G_mJ_{n#4)j5p%10Y4V*zeS;v%!Ba&SQ+4Dn^Sg9ah^$@C)3w{Vl=oeS
z)NCtB7Jm@{#oMDVA511Z4R|J^#^pV3=(3OVgp4p6rw=*qMCYwW-71psmPbsn@>Q`T
zQ|HG&Lvea4@g;P?PyM(;S*Ku&l<E%~)klVZc8FExA*fzD!`D49zolW}VrWp#D>=;h
zqWv+WQ<}C=9=!vQ%2Mm{_2IvTSEKyq{Q;OY{^2+AOPA2jMP{_d&!f$HDqWdH=;5`F
zJ~@ux3i$_#wsU<m&hT`;+2bOTE(T45dky6+$kNNle$ALlp!FW~5U`Se0nx9glAT@J
z+hhbU4DD?sr_J3%yCu|%6DNRFr(?H;iazF=FDmtiac{>iyb*$O@k_|^F|M>uA4>n}
zj{B9!UOogJ1X8$1R1s*cvkxMwO^Z*7JQU2RNTRTh4mE?#fP?27#B*jKRXQ3~+3NZi
z%A-Rur>|6|SDWqX+-w;cV|NM2UR4PGM@@iRb{*G#NXW%I;$AaOaGPreyfZ&*6I!89
z?T%tcNu&0Z^aNrwqw35(O^p!dd#Uc+{hwg$-~DXmt}f=xpDVN$*7vVp09Ii)x?*Zf
z7B;1h8d||mp(;4uG`*P}%mN~0vR59UggLtT&EjdT;i*9yqioA5Zn5xQU3_+z3u8a}
z{RT$+_i~lu<JkW7uiOUQ2Br>UB*I0m1`c*MV&+rR*a?o`^KRy<rh(rY>7mIO=@~w{
z%3}KBDa>W2JVXFpM~-=7MZYMRSTBJD9}Hpl2NCB+=!N#Xd>^cSFliMSle8#g-et4(
z_VMveYkce95*T6pyph>iI-xx5Nf*%+#d$3E%fw<2$P(4LpB52sVrXk4Q&}QFx@yU`
z_S{)TC1ISU6jz|+L9h@)ZIv%))nJsDR`wr#(CM9Ms*9!i{)MjU;r$XGTe%KxysgcF
zJVVxvmtQMgrjt!YcS7|yRA)IrE!>`sBz`<gi5MS0lJ#TLNlf){D&At#d;h21!C|6L
zZC*QTcN|ALCu$p0Nb@uiehWD*NPIh2;|{C>I{6I$@9?c>6Vz1FNd>}ewE9b{+i*4W
zvr0B8;@U2~cy909^G`{H(&Pygqcz;z3^i}I44m&Lz#^8yI@14TPGL>aSZ!Pu-+O*q
zf64AEYtkb-KU;-IoDGhfkE>U`RpEXdo6U#&$m5hkil@gtX>fh3Pq<C`8OhEtSkN25
zfgt*6JJ(e@UK4)`V}~MdAvbi%uFJLi0kuip2BjH5-+}rWx72Xtl?b_A=cI8@zFTy-
zo0q@^+2oC~1r^pXnMgl>_UA{r@J=(7JyV|-iz<InHCk_lH$CV<e7LM}vcfrKi#gZ5
z7K{wjfl|0!b^ZI@H6iGoAQK{S#y)qW3IzufN;08>;Bi8xlasNPQqU<bk6nW-cj%F6
zcZ>WXh+Pn=Nj7EP{gvd4Dw$@cJu@aht3rRgMfp-J8xuppioG*gb-yy2V^SY;$m^H*
zG|lwky{s29Bf_!~iwO`7+}LJI9L19onr{;L4Y=_hudGp;MX)U$GGRs_9M8WP^wz+f
zed6$Xf4S+mSuz9DruaVt!ZB69-fPH!P|V=d0c+Und<aDPfR}#y(5B=$nWPY0`Ipp|
zRtLcwRH9fAY7yFQIk%iwo|R5GeY3i@<sFhcr(JomMGdyKr&#4heDf0<lwgBxZ#?%F
z{pTi(USp?%zY1NvJ;Ma|7OCU&P&U`kW$2l-oOHbJrrlij`njf~N54YzUP8PP!H%TT
zfaj)@>h0}n)Ji$RdDJK<x3&{e)Vzyi7_oJ_dRyYXse3I$bCtO1_u~o(Iq?(NHk4T>
zf%by2VirSo(e#{Cys4~bq8V~kz({S9qeNiLlEnqj>|IU|$Ds%|kM2dWbhP^$(oMOd
zIf6~I2$`}u=?!;q@=}BSTj|vM_2yfrqIhrnGtopMs?jj`wff_OCJylJa#QqBH5bx=
z$O#L1tNw!o;R{m7dF5N+U=(nCAN=q8>N7E<q(T1K?5KsLKXIdkoyRIkp1-6ZexUog
z63zQ#uHa(*>e{}DD>19(x1-@RewaR?xLN=sL~C1W^WCRuG0Q8OneMCHD!Y93>`If}
z>Nj|Mm59U)w2tE&Q~s424(9m^tWIV>H>EzJM97HC;Ml9(sFEXv(U=tSkdfP$xGPF6
ze3Lt3cR-_d*K_L!IE#WaM+dUHJ#QboD%5=VnA@8>`Tz6YtKYdcw%LTe``xmpQ~Es8
zmjgkce%}14ED`AjJ#R3_v~#^U!}n#B^{u5`kVyYwQ8;ddP2^zx&3}=asV1GX2;Xz3
z+r;)wu`tr@LA{zXhnN4Rd2tVSc^10Ve&=yC?H-OpQ{{CXxi|9V$co(}6-);e+jyUk
zpJ8g@@~tB+Mt2d0u4SRw96MJe$+#TrYztoX(o9|~*?B}Ru2h&@XYU?mNtH?_6i8YX
zq0fT<s69|1+x+97j4y7^O7|1%T5z-0;;rf1P~6%2#!WIFdKy)fzE0yrTr^SiO=ACC
z#PCimB|};^dniOE!C>+Ybp9D#xsjBMaU+m+HYP%LmDZId)<%7oY!N!-cz#9n@JBor
zH9ulqUjwHzwXfsvP^PD|uQRG0xs~`M>EeM&&O-?$F#mXz32TCmK&|Yjlo`3*M+hbR
zMVIa>r=?+za<HKeQLkXGv!pzB_bWl`&L6!!(a^Z!>K(VlB6zz26k=}Ge};u#H2X^#
zvOiDnIZ1u?RI*UkVOR5Fmh&e{XGxzMyGqO92sMO)k5Y+b47ki3DCNQ$Zz{LuBvWo0
zws~k^&P?!(+Z%w+o+AWJ9^*PaBc2#dIcs7a26g4a?HbRLF3=w!W$*5rUrtuu_dKc!
z_u4W0UGzB$KCLKU5<AMXWh^0K^B2}}+ZSZPHPjb@sK*gX_T4!PJFHi+^wI;9SFU;5
zBf2st_7h8puIxQhvi9_$8*a(C$JgwjjAE9|dgArk%m(at<;To5%vbFp&A3Nh-y<l`
z!xg~$MO?)3G|c!3xJJz?dwTzT%P7+6iA0w<4=br+DL1$AfA5*DZv=OLOn%F25jYJ~
zE<t|011^D-99C+K>3)WNW(>k3+cQb=vI$10%_h0489go({ZV1^*My;uH|lwN(e7#(
zPKpe<4WsyWy_t?@yvGo6{hDQ9x*70X{tdVLPG=M~KKZwp&uePX{_`vLxv4#enJ8Ff
z#fY?nc4)rt0y0Ee)Q8gQmiPLlJUm{(*$45d)t04dF!(F!H{XoCU?!v7EBN$UG?nXl
zFN?^3q!!L8hGwI@5w{O%l~NCS^=2<}|B*6VfWD8eBlbR<@K!1+dvm-bIsAFaF-!*Q
zz=Do%KOcoW#3?Si$XMiMDpF#G)UA31bt*d3%_4Qq#56Ao84-e)O=OQVGQ6)8FH4ep
zTHTE(>vuJ9CW+G74j5XY)J){q>zMK9pho=Ifpc8S-k!4%x1mZ#tr*-*?onC=6}sBG
z0@k+ZiR$Uh$sgy)apQt#NA_CmzkXne2zSpJ9r$bf)?dtlwUq&trKe8{Jsdxp;i0Wk
z9!eDWpBhq-)z7_rnoPZ#*+|S}RX5b?f6?wI-sLu3Ewi+je8`&hV3ZyR3vfDf919iW
zA?L6+NYJ^QH%9g-Mobt!{b3v{H4iT%EYDMALvoHxP+N&iJspQal<Zh?-3y1U+;X~I
zLW$uXPqK&@r8?B=LvM<Mskgle7o1;lcT%^#c#;|a2r92JD}O?Zoi?ZbQ2gjiw$-LM
z!7fu(1O92;e7V2*-#@!3LSf-`rfS*K1?TsYnu>Qlz2;1$PZO$1Keg^acjBo+sVROd
zm5-|&Rlb*j9zJpL7d{YAq|e0T=P4M=^JWXx&<zs!0aF{?5RGzK=q#IrX`bM5EBjK|
zZrT-gN-R6><u&7RZ?j^p{Gwyze7S@<gM}+z2%2p_S;5>^!81a<Ex{5>l<PVib9?1T
z|2k|CE$9ta4u`qp5*FLEibT=xU(EBCf~&wK@+}q602GDEHd96QiV#u4%|CcrUdOo4
zRu9YNEXTQjf(X#y>KmOdCr;MWZ<wvNe%N$q%I|M+A0y>gLW8rx4*U|GSDu&tbhj^e
zX(kvSPjN*BdVT3Kp>-_ip@~$TUy|i=wDCQ84_Mp!L=BZbJZCji0`#}azfUkNfBSgq
zLcAr2Fw;#P*YwI`9@KVUj8K6#lP!7o?|qWcU4{vU;f(9R$!AqTqfK2o0YTdXz81bb
z!}o}b7#nzUG&1=&xRU7ThI^Sm{GQ3-Y0Ddmmy*N>_$`H<B!!m5h1eYyRf~#xY;GgG
zi{1kV7Z6sz26B+l{N+whu`kQQaFRoLG=knMVs_NUndzQWrCc>7x;@dbC=)^j(Fo6;
znc0wf`G4GW<jhH0qRCqyUj6mmFPl)^WnrDQb$Z==z_fkeNYS=1f1903w%_f3H-jl!
zeT($_Gz^tJ9;O41>x%LBR(EN@BDYw1G~aTI1?aPvT?Bbm?!F|^n4jW*cu!p(uAqiu
z5(VWkUt0B}bD|T}1of{Ct4;W!zPay&gJeiU9!m;7jdQaSzKiD&*3mWQeiLZ6?Gr;T
zW!e_yAeEvl=@>LrZGKydc$&VYO^FhXu@=1{I-RBMbWNDds+GGe%@stH<$Z6XqOH_0
zm^$OCRxj$kGOMC1+kO2#h!;5v^Nv!M3d4F21#Ppq{L##dYSj-cW5X{;W~Dm!mUjN+
z8Bv(Jx&Ip+fvR-K$Na3}g8g_Bv^y-O^IM$W6(qg*i}_c%-|0Py?%{;4=+VyZDr*9M
z=WgDWf!PCRojy(<dHEp~eN%p53K6K?B*TuZ!rD~jZnLy13zV~Kt;Cxn(ATS!G@@~o
zRK5jXWq{9*Db*eU!h5kI+a81RtX)@(KQ#>5N?6@JR#!ZCm$<ZF#f;YkZQwh>wAn>O
z1!Lp>$kX235U3DKME<-J8$Op)dev`ly%B`uq;!Lb1<khe3rqI@Q#f~M$Y}&V;GRwF
zhDz_M>4zVOR$j7HN11})28QB?VKWnlGf5cPwoegJeyEK4)gGI4^1l<H;H3mrsP`=m
zq%Fk-{p?$wD%u$23sJVqxlT1Ci{xwnBfEom#89MRYWh~shx-3h0#@3qetG)3!~a|r
z#7!GAV^E7*@;7uUS|q4<ZU#AZ8z{mA-dQgLF_51{#DF{5dcGsqWc2Tvn*R5iC@5?0
zL@)zQnyz9yBM+|j-IeshKfT%FWIbL0bLfyy+P?7TPo=_GX9;Eh80OCQ5U*RfYh2jx
zQ+M?#$tuvN%FAFtLc!vgw8FoN?B9RH|C>w1H?d1IzPE0V!31yyQOhAMIVAb2775x!
zbWJ6$g|MH^LhN8L;@A5KW1;b5tvf+zby~=1E^ZC#SAYS39|X@X5<u?^>0X}@Gw?r>
z=_$w>2?Xd;rIfZebA6VTyC;}~i~nvc<b3|aH1I7x2i|s#-j}F%(I9*~b+37I`n9)<
zcTRuvgz`{TPjV^G6H16{5lh;q;qq)OVHlWG@-72DpyMdGQ<Ki#w~rY1?Y@erppjzb
z*zFoj86UjHHO1Hba?PMtF+<4!@MRW2f?+_wDaqarx~q%Gc#eoALkp~WZR}FOAR$G1
zcw6;o&dT1M%U=^dfk|tYOHg_u|5!}K83^+_d<mmpgoaLM(GHM`eL@<au_;kz_{`-W
z2)qQdoBSU&sloT0)Bd0YZlTja7RkEymes4Av2NLQOS{9?qtXaId)2J!vj8Y8=EcA~
ziFY&E9J=wvH?f@thu*&yG1UKmUCUS$C>U!0Zv1{O$l-a7(_;#B2!o23PCDM5Mzn2E
z-|b+OAZbMsf$1+nQ#78bKaBo;CwBd{I$X<`#d9!5rhM)N#I_zHz+QuUIE6H3&cJw~
z7Q`kEZ$MFrbV>P}khz}4Cd7SM368GjWQVyQ&>6q`=1L4AiTV9f>B<vLj}`<1{><fj
z{`8pq(nIGE$J(kCIO->%hodH-o6HsnO3K=A`A7aa?k!UBzb3(c_x)7eyd}hW_Wf%t
z4-`P!=||9x4Z_^YFWZ<L0n#HVqyeH4f=C@N>(#eyiCu@VsX+;^!h4&rp)dxmMk0@^
z0Cqr^YmxA}QgwrK{;hJFOY@gohbt`L@pwOc({~R%5~3FHc$8u$+2K*yo{5MsRZRza
zzk?qCAFulFpK+6E&L?_3?%q4W-t!6kfVRFGmGgN$5%HIh-1ajt1g=JC@nv0xv(FZ)
zQqE{vRv?*sCqKCr8!vX6e-h9e{CakA!XelMS5+$c!**8wx>PQr_vJeYkmq|5C14R>
z(JWd1?ddK1<ID!1jVO`6&z5Nj0=Ka6?jO*QolR9LYV$J{cqAiFD~cGCfRV??H^1xN
z>N&JsK{ys0qJMtFC+`}$@nj_P!9={p=0i=0AOG~0U66WAy{>GgS_V|asCe51X#Mnq
zlH?PoxHA}mc#9#kIuVcgW+Wg?*79r*M9@{&&L&_q@+A@XK@dzZY7qv*Amp*mJc&h=
zP1GWYUzG?d+**V-1X`NRTT0v26ZwY}fAIgrk|8(Yo-wFMt`ROXpRDF>7qh~rxrhZ=
zu-V2c{I3?^0Ge$aRwh2^{I$s=nylatIzGrcgh*fw<D4wcU-$on7z_{~c+cxOoOVwP
zJbb4>vU3U!#%exJ!#F!@8Sy8R&^v#er-sZMK7tm)`OCBDk=fFY?@lx{JVQ?hp{qZ>
z`LH)Z50P<ab$ziHABlc>xhd2GN|MSLV2}IDq2~`O`Hq2FG!Pl@WATV|RjDYv6fsyb
zM5|!}HHd`b70sdL)pS%F61}DA80t9~<sVe%BOajMLRqt~@L6|*v}=Xc&AYY26}pT>
z!yOPWp_LZ{PQY2v!`{`*mh`!rp?iO=>{k4rwJ(2*p1;N4l+-&lgZSImq{58wh1Fc~
zbM-r916GPrsl@<yIb3rc>6iwXAmY+U=l~90zjSKKC3Zc`?@O*(VOBfVU0azV>Bi*b
zs9Tu4vd^JV1QFLFdPFzW2BwXXeeX(O1CM9ji9M{V4fI_U49F0#XH<A@&oIqf&<4Tg
zT{>DCcIL}&&GcdzQ+}*M0xMwPyIC_Fe%(NIFhE(4<T0}_yxV9>G=15BfPzN}RY|nI
zF=4WiIiWBr5!8s4-s38$Fz0B`!*0Yq#%V4VCvDE3kcJ&u;=rce$I!>cV4sB1;>E`>
z_jQ0ZOG2!Cs%G2Eu*R=+hx6=hAb!2yV}T5b*eI*Q{O52>nlD^Ve>gF=eiqDlM><vz
zHL(Aq57*^M3CV{UWvs*X%DV)kx($X5){s0-Fwpk3#5+iiaQ+_he~v^B3^k^F3{MzY
zX*Jov`nt9#2lpQGs)l<wP&cE}!60yf4?a@uxv~E{r7jy>+=4_yJx>C!<>l&-@x#6a
z&h@6d1U3;BNyB>}GIm43Z3M60jyU_j6Ga(-w0$ST#d5BnNEF}RLO>0tp4RO4=xpu(
z1BnJtoDLj)Pioi%Zz%pPRW89wix#wkq&{7NETj`lJO|Ie(f!zd6igiXMO^h%AqUgQ
zYFd|UqfQgM<tm&D-a4KjxJ}!bR6JG^Ar<FZrfZJFQsMZJcQEh2$v>}8(oderPY1XD
z(mmS}^N-&=!2L;a30K3I$3_#Pu@>!DEm1?+f)V63M8ozy2@x7ErlTdPLZMH<O>B9}
zw5ZgBsRsS5_Qj;G6`4fU<b70OqF=uUGDXPFno#vZ`{dIl-R;aP@CctayU#g&7f8K+
z={3Jk$jL&V1&)dN8V@wGet>}XrKSEikum;R#u?az_116Tjl84_?mohpFa!Qb5i_Xx
zUpRSTF3<{!^$$)Fwa^p;y979p?U?`Epp}X4hw;IW?HEl8$GunsTczC>kE_UBrHs$*
zdJa>N47IUmgUaO1JVdm6(fGE8+SkA~m_@?wy<|MbKP5jKnbOjF<&c#M+vHj8J4dvj
z$SLMme{@|H1t3L2F?abxQ2szQgG09a9%k9}{<h*HQ|;C-le@%6<hYKtVd-%o{_L(B
z*HknO#Y_%8{V+m*!6WG#l8Wb-!JX1{G#6%$7iotgqxNhO=1!bG-Tg(ew6u`RBR4Kj
zx-eRP>QWux{VdO7E+n!^-dRP^H*KUbo_UKkx8ynKw!Iv>onGH3?k`x$CjSOlLeo^C
z6oRzT1ubD+!S3g-=#+|vgPHLKRDOnjHM=f6pfyOiyoEgT7q46=f+=d81Pj;$QGZ+`
z;RpRmnhmPT|6?!7)o21`NE?#biD)z0<1!q!9>8Nr58o+q1v2_4MA)%;0Y^2zdi>#%
zO$;0JLbo`NM3)=7%&wKretFW0tvW_ZDtxUpK|0&9O2lCaj`|`b!~6L${v=C;jc<v;
zzHwC3k&Zp)TYjygqXV`czwhRs-5K)VWmPi!Z-qBJ&>(0MPQe0ijHjgjibs0}5Lq_n
zzwwo+829HMc#C6EyarT<0ne;%W3wIsc?C)U;iVOL%SWeEPB+l;9m*}4^Ey{ZmVt@(
zDT9P6sfD4F!)l%z6v^eCY+SC1rVaPbgJJmT)gG68EIqhr44N%jBNg-DaRAXx*>vNM
zJf*<!m=KpB-Jgu{!Nx-@`wV=?JD}E8BkZO^tzb@Wfwqv2(8O`QJsKPQqcJT)Zb`XR
z>1fYG$4WbP!usTI+jl|=)~63Hv2R?^$2TK_)!N02hzA3UtWVf7)mpWw8nNvY`GyMo
zOeE7o;<V<bNP>kZS1gqW#i8IJiIbSJMSc7Cw4gCY>)9{?ZUZ9JOS&xM7R#RCu0qX3
zwI;YJw7=kI<0wZY)_ziQY7U$@JE~plAB>}e8!O3(M=LQ`S9KRD*n`wsmgyj6CSb0g
zZA<Ofe-TgT=GS4y;J#YNiz%`O8v+wkG<+W7<47B>cT?mW_$185o;OAszzJFkv!B)U
zGi(<L7cY=pVP?i_v`Va@2YDnmxMrE6z8yKxOA9*@wa9a?Wmz-FLg(}On`=Rz#KZo!
zyuT8l<oi6SL!fk5|6WWgy2dt5i;;|>&j_@=EG|9|)#x@G)V<6_fv)VW%&iy(Q`R}5
z!@T1x5q(v84P_VEAx%U*4cs%-wWmhDgQtbow+7X)yTkDb0WLdP_3<AbLhhmJ`>rU-
zVo?{)-I3*el!7Hw?7_U?16pO;!Es~aO#fqwb-aex=B~&hwEIT;rz_&FrniMq+cQ?u
z(P@!Z<xkb*pf365JoJg~r7_N*9aXm#qcb5#3+LwH9Tj@()j|GiG#+*0xe$Ocfa{m_
zN5+fyvxL4l)$gU&r3W-`GU~oGw@8Og(R|^V!mIjsmnUpL%P?V%e+YHmE+sV=?LTy!
z{ep|l6^uhp4I=!U>MgD8P=$i~{=faIO&_UEoY}Ph5w3{EA@`LjP7&muu&vzK#Cf&^
z`NVoqpa)FWlcX^ObGIS<P<(ySKE)*k8fleVpsJc;-<m0N6qq^ww@EO7Yy3gIWSOQ?
zDh1LCy>!vyS!$qB5`Q}`zq(KAQECrCn6{FjZkbzrcbi*s;H-cMLmy5^IthYiHy@na
zfB2<uGTv=uuEmv@2cl#*DPgr;RIP;gRoKn6hI!0ITih9AUlX$>8x1Y5g0`O`B`;Vd
zhz}0l>xoS=^{ph8L_3Zan=|!|t8gLl&J7cyk1CmD+yi{aT*Y<4jPuGZYb?F6o^`3;
z`g}|2_1GM7H-Wkv20rim?Of<J4P}#Z$#bGgyqQal%Bk~#Uth$ZHhA8Mv2HpDVkOKo
zrVS;Ud>xNTo6yA?QSQzMINFs4OgvDcb#gGy%|!e%z-~ud9{459<rvB#<7Hw#5WL^i
z`Ra3K9R=0(=u5(k)vwaON1rXVS)qa~$ELY23(C>-tv-lg-jWHgct$i^+!#-ozU{f%
zm)gI;u<S~N9L3^ifTIoJp}<Q8qVlZFwO{ucrg<SoP}TfQdcb^Y{gCkX81<s_(8+80
zm`wb}(_}yWweZhogMpU9JNfEg8>E|~x}H!rz!foj)l&-8kf{{PLO{pi7A}>J9at~p
z@#UEh4>CwU8}uW%FzU|mh;$n_D8H_8JYQ|qb*tv#4ZjCSi1ke6Q%`VlWWe$Hf=kS9
z7dVZh;rPth<S6DMoI-xw<AYsikBB>O^m&Tq-%yt3z}{$*$VL0T6)PnwS*ZaHut&uu
zx{zIL|D6YBgxM__6Kth6b5Bl0;b`+0g)+I#gA3v%*tv$e?VOuL$UWqSW%dXfX$zIK
z3X6w$gOtbirdLqRd!5hb;_}d`aw7F6W^g2sy7sVQ>icceo|$D^v0j!QWwcTPT<HzV
z42R1GU$MKSz`uCHg-EA!-Zdv-+?cc(k536Y8K1*P1hiDeB>5&Q=xQEnF8G+JQ4Y(6
zsOeWal&d8vJ^m%vyJ6VwzT5R-mV2Fs)9;ZQjq(sZSVhps`ROwfj0+fLW(fs-PZnYS
zZ}r{NgVBj1;d3PCAcb6)NRKYmuyDW}aO4m64Afu=%4-?E`kX*gNaGil{DilV_Bioo
zCpPh(Ffsd~6@}F$TUAG@98MC-ZU9xIQ3*X>%tKlJ{2hnpC6?bJ=Z933{7vQ~q$r^k
z@K1AJRn|A1L0J=sc?Eh4OIuc$t^gH#poJdhzN*n==y0$-Lkp|cZwvF2u(6YhPSV-w
zxJ!q`vE-odu2piV!uP{w;at{8znXey*v#=e9i1bHfCxZM6~%P#f5B|DI<6wT6vH@x
z`?QSNrik-gR&MH~s7eG+Sz-CNhARwRqYelf=gjk$1{me45^x{UDXp)C&Ba3SJzG?`
z_n#}sUB$4!k2B2~Kd#ema@&JmbLt!PHaA&kNHA_Oxp1?Z7l&8u9`dy8=f56)f4JH1
zyF?oYowqdD<{UgBF#A2kNsb>9Z485|v^~yd(KgVC!j~vf(1^y>O+)n<07o2Dx_3PP
z!sOuw{vnFg*V}yeL@Ka}Lz2N<=G$0l;!}5Bzb1#O!hpRi5=3?%4NavT-l0Zx%CAQW
z<l)CKo{XyiBwp}aaaC5lpK#Gww4!x_KU;j405@CX!j<_rl`XG(q=>@c@X>wns4SrA
z3w@WF9{nI~hM*(<xs&V`#YAJyxR5$Mdd8x!VX#dHtFJ8TF;N>w<@>^q-K;#f_N;78
zZ#IbX{c(SFy^E3nwkG+RDJEw>=08nZZ_?8jwNjB-jnK~rs*C<Mpnl@+gq3#VT~qHS
z{2?p(h|8QfW*%r-vppX=?X^9Z%^r8U(G`AeW$`)$PAGZ|F@Ca$tLAwkk+@*Kq08r^
zN8f4v)N8X_G*Mom<3ImxTpc=FH`?V^dIiq#Su1bmccA@_{ipy*sP3uHkp@cm=bb@q
z{%yp-#KblD58`-Qcz~CQFk#c7rX5;d=Rn4KCU4;Nc6Nui@6<e4)qOH-lTM!i<Fo=A
z1o<}z4=xuizk<)mVZYj0N_qNxn@cb5CO(#=@8(*1P(k*5GfGZCb})*6gy$u<Dn+=u
z_iB!H^Q!XV8Kca3qvmj{GzJ3hW1M1||9LKb@V!@E%>guvCtm*TOYNb3{vH`Mr<(a|
zuC??12<0sa+3XS5tWpes7tg#DbPcpl)V6bQs9D-f9a#kO-_&zgx_pwgim-N%C4olz
zne!_()QPgybzhP;;kz|FH$UOINym~X?_K#*M292VoE!Ovn5ssNRHr1(kFSAPqh`aC
zaN3@p99I!<Uym=+*q%zq^N+WuR?nZj(IR+U#DN=kF(gUS5?3BHGnc)Gm03S0(3QY;
zdERvH>mvCQMvK)cCnlZj&3Mh+U$Ha$zyy1XC;#}LE!(+Ab^=pxu2{&08o_Nq`OZey
z)}RE=XPU8uC>~&P?_1btxY+&ZJk+czXZNQ@{;OGqt)5fP-TPv_cWm!HESnZYu|7#T
z()A1PZlHlr!lDVEcZ5vKG#iw;f=vDUX20~OyCqGp7*_T#o!f|%K6`jl^Vahj=v2S?
zeDQ#LOZP3C;=&AL;8u8C*0F`2LokLuO-}3LFh^U8l-iRFzfRJvG|I(E#rC^S*M5KW
zz-S2<Le+|Ql_xwtQ^3qmqA_u$%J-vz<#=)2eJ;lMiDvuP$@EqQ75h2HsGM{#R|3Du
zUMAMgtTbjhuz5{FPLSrM&|<*92mP6jYly<yFm&7bJnZbL=JnDq@C75{YRfz21)eMI
z=5xsjKW2vM657&_%blxhisQ7z(MW=BYE9ns8*(nq=;K2E%ygL%A&Su77A;Y8?8s6>
ziXoT0BM_?IgImWL@Hp!4xoE8ltn(Cfk1Bi6<=m<?yQ+?@>C^dsWw(2)$`_x|wTCYV
zRny$I3{_j?*^_>jb&sf?iKS?gcX3W*oEpMqTkAlD9kld%jVRBv$BfCuJVQibpHHMp
zwksihPm3{mg}N+}6XuHotgw69r<Y`IfQ%d6a_z>_QQ1!#C}1}4JQtJ40k4OF{Tt6q
zP)k+MUaZ2j8L>OavX~Okv3s2^J}HCu#w*M%C&|x!ZVq2EqcG3Liz<j_wQSALf|jr<
ztFK<<f~X;7;;VqI@!Z#Srt13IZ@mCph9QQGGkKzf!zL=p87x?z<8t88<jhWJRYPGH
z(7m#F8NtNO5wEJZ+;MMX6$i?0X%63+!qX(?Kcc>?hL^5E-BNcIBpXRV*=ITG=C*h`
zGd%&VN-Cv0PcgS){K9Q=&!W50+td9H${Jm@_TliXZ+IHyo;0o_FXm%GLNRBPBZjKg
zu<TWI)1Hv~o5zdB4Bnsl6#i)5=(Up3NBYV6=7EI1IinXmCHsb4x@Etm+0BcoIZD?h
zPhd921gd%-X6xH-ZGMoaEMIgL{Wh%IbmgLTRkBFeQT-cV_cb_M&nGbzD}-Ct^yeym
zY^aCv0})^<d~399$1Y0uy*q*TORpa^NIK&#&FnnVJ>g^)b%nz9Gul3bxbt?4Q&&MS
zmRV$@^eR4X@uqZ{=Y!q<mnSd2OsLzeJcM3IkfhT$k3zRc%W;!A3MC-x_<5QAg4sq8
z!<*6;xceS^&W@S831TOb`Dq37!t#359SL26G*ov|jX7o2bPD8ZtDZ*S>8SEt-jQ@s
zvo1R=rJ<IUFxOBwKh>@fOG`7K(wQYp@eulUBr?RuI#T3A#;#tFG-h3j>!dMh=(Drc
z<0qZb9v+nscX+h$%O%m1iQT;0dA9Qnxs)cokS*ya`8x#S5(r^vI<<7ewD2I=H2WvL
zP0jbn=&d`<l?;VVzP#~e<HvXJuHjP5eIK@vJW$rft#h}B^M>gs1uDLjR*uc%Buq(|
zG0T)bbJk%r_w$TIOXHNPVsoeT1lxNoPlEv5)9dy-qj$<@U!75PsAay2;sA$8+Cfyx
zo+}p;%3P*Wvd#K-=miSwA?bVMeBijT_t@Vo-GqiSKgrxOqY&fyonA3&e5H$>+i|-;
z(~VW|H8jmbRj`niFn-B+VqeaVEdwKxcfU%>T#A3p^AIGfpY0~jSICp=Ege!9e>RUD
z$%xG=J@8)tR7UwK%vbhB`;}r5LpB?#)R+o_7^H=Mtqw-HN++8{FSf`@=A?*grS*@E
z38hFA4#bKQ(RcQv1zd`7t%&)-pf+KYOQn-~j=URXwDN3Y)o&}ilst{TK^W%Ru0VA$
zePX&P`+5d@M_o?@;WDp-{xj04X9@%&_giZBqi?(@%t#W6FqIPO?^+LRb8hfarY5&;
zH&Ycaz4e6Kk&nnvq>Ec^2nBshGUQ+w90-?CB=VYN_Q(ghH`{7q$rWF~TUL?DPLcdP
zFHEpcM^j)n7c}crl-sNR=GKjSUNZkpN<E{W>in&N)#0|3OO2rfqHX@t)GyptQ2$s#
z!ua~0%Tv$~JxRgrQIBdJZrg-y`a#>nVjAkdnA`d4x5|?EJ#*%%%aX}1%VWtWOQH+S
zmXj4%2fTz+%}_zn3uAA-U%E1Ft!G=aR@`VY8$KaRgzGO192osr7!W-+lZq$GCOkr&
z-26vrWe3cSZJzMND}#V2jh0ghVSdG97(_tEks>ah*p>&r=e`XApN7|2=Mkd@6^E8e
zJTKpdqi%6*%1%2DXP;ksF|B+eTTC1r8|NJ<^AiYT|GTWqSMPM<%lfrwL%mW4b;R<b
zb}1cHsCYMAmfh{fIy^~vW7jJlmkyc_URg0?JUa{j>c*zgAf>}85!`!^Gc5(%m)9VC
z*7rCM3b({Fw<`v^?tm?rALi4159($qF6hYJd#QK{7s2^bbn&!enACK`li{W`Z@J({
z3Eu!F=!|I6Z6<-qQ&GKM!h_J9xvdPBOsDfZ!rql7LG4-DAuxIDf2NVzEyxHSB|#gJ
zYtYmCQmb$O^Ls4-x}<z)Uq{i_O-twtmdiT?%bCg)4#Mt>+V{@BpnRXp*rO{r!_Bm2
z{ZZgUv-l;pQ6Fs4A)?3a3-QsLvj)WkrG$QoZX^pN9&S9|ho4HFKMSZ$Q8rQkx0J41
z-s+P#D_~l$JS|zg!WYjVVwT8<m5CEb7vEdb<psUa0MayOlK8v1*-<o3JJ~jAs=V2Q
z)Y<q#{M+ynvj?omBS2A6+bOk_g1>>lFwz-3pM^9z2@O$T<<l1UY{v4I(~&NBLyp+C
z=ggXfyYv_t6H-Q@bQ1BLbl+Ep(WHH$OqoAdrc;f64o#8I4JBn(^W#V_{2`pya$kHS
zfVuqJna6wn5YDD-R2MC(A=AoiI|m{Se|9dEc91Avblzhu0+Bku-d>eZ_Kngc;xiuJ
zbxo+3xTm6w8&%SLQ4PjunRic@-VrqUGsl=E?yl{{($DVcH5Bx@bfs6_f9v&m-uS-=
z-vJpnNB-Z(%`g{xqd;3eJsx@K$2cCl2+0_i)Z>110Pp)lULZ!HhHKoWG&XP!-tX1$
zhYK)qy>wN+d}?KEEMQOw-kB9HB`4>7M6{Q~3}@8uvD~}!rXZ+I)jE+PC^HjTq)cp6
z1=nO>2YCB_?+>%uj3{WEo%#xa<wBpCbfKf!EsdO!p@h}#BFHWI;5&$^Pne@Mz7<tV
z7A;K{)Nlt0e}%>4z@0!9;dFo5Y+Iw3c1&c6BVMHLin#F<d$v$P84fK>45aTLw#X^l
zGC(N2bY8E!AN#)H3H~G#dh<sXyAXZ1$w>7BAnw;9EQ6Tj(fB_va+MBH1zK%+G%nOl
zU^jQzZuy;)li7xEQ$-URUd$`IcQCavyBz3vmn}ZI=XW)Ho_8INAzoaTQkej!*S1KW
zQ$1zbNpH>XJG2|^sW|A-wtXOxIR$d(+o=tGVZ~4ot$wABzw-T`x%Q_2mL=c!Nv20(
zN9g5LKYB?NmIm$fXrheKmjQh{E<cne_x7W6yX~g*Q0{2%-O{`7X<~*F<m7eE0It?E
zvy37+ug++cf5dwq!ru^Ve13i6myl!75~0Y>`|sA}zh7i8L=SLtY!Z_MhySvtCx1U%
zU@yNgndubc@-S&9jbAC}IU;FHyo8|xw6j=Qc~TOJQeVM%{VgoS*zz=xT*v%11M`K4
zbg49z>(+GFQo*+X-d1}mjkN$O{r$PnROnoOu)t@AY_FYA&<nYJB)I>U7s3A6f8?Jd
z#~@<l5whv|J$+~RQTeQ-0ugQ11~B0!$3_sW`Q@j{r0ks$q;O<^NSMI^ZDu?BB*aT^
zx3b-S@T<JFufhGWxqxTC3ADCR@~0!+ow>K=q47w;NRXnEvE6S(b~hd5&1i-0#BN0*
z&y79kGCVd>%a3HsE8#$EvOgSFEIpE0df?l{0sr1D&|C8j*sUW6*GR-;`V#V}5@rwP
zD@!&6vsIzc%yzhHd8p<=l-Y5C&nD-DgExK>Q@9<0K+&E{MdW0Uu=MG|cHOjdSDyCX
z{#kI#ihvRSPfnb{r&pCL0J4n_2ge`2-JVtiBdX&|_$0$@a<vX^Sy!o>+dV1olyl00
zqM6|e1flkCr!#3AFx%dK3H8CSOlnt82ef?Dlj@XcZ5!lR3Qw1A=Ix}!fkq6H@A=?0
zRo0Fo{3yX}r2bGogHSJNdYtOu`=ndjD9z<u@BP_Vl1SU|(eTAPa5IvjAH=81uF^ZF
zNL-8rdd)9)Wh689<3DM|>i<eBs6W^DjVCW^2!JZ;-CY|=4`e9bFrU*i*;At+w$@X=
z_vlDV#$QX6!;O6U#Vq61w(gc#psD1uZy|Jhy>~iLCA!G8=$TPCG5EpjioeeQgsEyZ
zoq482dy>^Idt4Lx`F?5etWkT66idB8c3!1MWIAEUtVlj7q4M!`Xviu$1bnbf;1W>F
zcslw6^mto`6j7xJ>yp<7=ECUsi{>yJzj(xPHx<4pK)T~v#bUmDruP$y-)CTyIsW_b
z)j1M<?<Yg`**iDf+t~wKI|bvQ3(S>Jq*T;CMfrdS#=;*#VK0a?(B7VGXy`nW^w7Vj
zLcwBZSUgpVJE*7jOu#LUT`c=;*ipHNCpX|MxXx@&)DC5u^=wi+Dx;I!mME-S1Y=iG
z1zLffy&+H!!}1kkk>N6r9s$D{e;-RGRf}Skb~(U<0$eFw@a*4yB-R!F-xr~K_J2&H
zNM-e^OYW($LGP3M?=QdIqSIM$6R~+Bp50%ua3`(_d;{{9li<DwA0Q*@WM!?VItu8S
z(BV74#)gN75DTcB>w7I8p88LK6ZfCKt!m1wcpH5CG+^X*^TH2wZ0kGyy!+OG%NLst
zunsA7?LP)$gM9+fQ{Ml^(HoC^p#o~1TcK{0uh&A{K#7B4i)VFwVy$LuO5KKKUqH%v
ziS9VlfyG6HW^~wW<v2io?mZ068O2gBxhP~6SYnb9aZfTla~CLZr*>_tRYY*~G8oW?
zaNoT-;duLqn0j(D5j92s@6Vh@XKj0{4@wutR#Thn8asEtG`n`Pp!-&y`av<F0~C8P
zjm%`ky1ar&O``g5o<LS3j(?gNtN)kI<o~Ie0bq;h&#F&%7H>Z4dz|wCoWa%)-=Bv+
z%n9*~J3SDUq$6LX!GvFRSf{)2f3*Nd3y6)~HUBA*m`=nAB&Fi-`j6O8u;e-0^YlX8
zh@NLVt`F*)wpZIB0O1TAy=%bG6}7Pw4uxlc^?e2j5nqFll21Sm+W&Cp>|X#J9DN?M
z&g=5=KNZVF>Gw%mH>Pvpw)SA?ffMk-?%({QVo~wwt5-JXV~dEiS6TPYBBB~z#IemQ
zZefnlxo#q#qSI?^1j*;f<EZ3;wp;<{W%$ic<g$#qKY{7%pH2xu7&j^(IgsjS6b8Mm
zC18I90{|v}$JaQHe|YDapBLBH5dBlkpLwf)nizlu=NY|)Sd<z-<MbhB!|adrTR+TX
z6p>SkxZ1X7%kIBHO;iIaspnr0=b53N0^f1AFr*{st<eIbDr5E^s02b&PJOP6$Xpdi
z%+g0r-fQOlwGN=`YDj4Mq?v4XA3_IeKpi4?f6rt?4S~rj>h)*U7`{eyx1hCpF9W)c
zS+Gk4B5$f!VNmjpNmefZBZMk*`_X{!EGRx6Ua<nIQY>WE{{AFvt2P93p~uhbU$1q5
z$MWKde!rMmhsB#?<W7_a8I+4^q?=fHUb>O>&*z9{#sOF~Y~)~W(oJuDcWj_nz0dxL
zDsT8q0JzIf!Fm)1n@zA^#l7SsKG)7o4LUoIl!vrhoO*%R4j!fc23*+_(2g~zKWN_`
zbSB$7689lpV&qulcO9)^^Us4&;++CfmTE-%pq=O|%~olV@(lg8Z9Q)Pr?%rv6V?jn
zV8s%!Xag06mfZK2EZH9jbN}5JkdJkI0$$g@d<q0cMh*t-e1~4*SR-1Pt8w!7<Ihvi
z{%%AbsRdF+P15GWZREV43kcjx_v1;eGXSKjL5>Mkr#=ltgG^!e*x%LjwV+sIg!&FX
zxwm7g>}mzyulEoZ^?d`L<&SSC`VdVMGGXOR4uqAv8t@x~M4F({oN2mp4fu6rTAVuy
zk(|L<#6|f}>+ymtY{DKi>GMZ!Z@SsnzZiPy4TM*g-B~DsNYX%c^hxa^^T9m~7R9p&
z%FPcSKERw&@64?8bb;1p;$g>HxY4%;E<g3$tOT3WFlDbloHD^@4=|HAjy}b(0Ldr9
z>zEjK$Lq0mrBrelLkbs5a0fID-{YUJw$?(Z2kCyvqrAzHZ%~SStW6bBszG*6gt}G<
z-N(&X%3sDLSO-r73ggN2r`pHUA0FJW;Fyc83&Qf(<H|z>S|RK0;q<#3S!-Vtga)Ci
zm?EJ)_B})52*6u+qKUtY9Wh<5XGY`(^Csvxpy8kqglPTd-L$XAqGjr$eSQNmzG|bI
zYtUQc3KFV#H29^$W}To09;GLUbNPhZi0dHu=!=(<7~mhh9J4y<sVpQ?mx*{yrA%7N
z5LP3Z;kGXp?o)>Tp_r9#w`D3lV!r-?l_q(3*Rd6J9Pk}33`HS()73SQMY$%D3H*DE
zSB}f2lW3YPz&#w;M;|tveKMlvo)%%YDNanKN%u=sh#Wg9r$mXlsiM!(R{exua)+x?
zBxq)iBG%F}ATPXL7bw+V^PF|d`6u8&0s7_BPeIxBexR}qo;ZKhGm^Q^>0|y-=!vjH
zQWt_E)ctXK(X*$_AuJ8;zV3~9Z8eFVB3j5U8XYlJeq-QEDmn_*znSL%gjzN2!Lu}f
z!bA4q%ayZJ<tsIbt<k4etHJMZ|3c&iEfz-p9YDEG)&OY1anHQ>Z_4|3-nS=J3HDo%
z)Au-m@+dZcVVmjkPRNcs$VC5?8@o^k0-FBFBzY<1(K(4PIn*l-lkz1C``-vH+6H-a
zzTNlfMD{KWYzkti9W9lml}88(r^*{83`5JZxxtkCB;pBzV6&_=MOo<d<ahsuWT%rY
zM}0H!z|3k9-uMO5s*CqFn{D#YJkG}T6+xVqqVb$SGrh&h---w>K!moyxLcA15P&vl
z(Z50C#*z=r!h$gK>q~4b_n}7T+R#b&L=LW??kD6yQ_Ic{fPbA-qwF$$WCVh{O8A~%
zT3GIf*;_$|StJn!Ty76Uq_Ts$WsH%SkzlN;tts1<i^$kkQ1Q8T_!@luoIMv`CASj8
zaWG{FQrsmTT?d||*4q<zmjN{KT@w8`O8=cDjtx2!h4|tZ)fg_O31X;>{P|Kz-9mgC
z9h5jEL!>M7`#vWHv^0mo(^M!i82|v6cP3PmjopVyD4~<oICyD$K}BSjStdJ{e<`?9
zIyz-C0^mlkj8#JIK;;IR^vRaBak5>eObzte+IlW2;BEnc=c7plU&G-CMHmX;C;dlz
z0-bHAtWd#_9<q`pv6m3uZvxbJ7Cdm=KRuJg_7^<A(j`TlJm~ksAj?IBZ4vH(Xd-g7
zF-%?HFn|<tXDF(rC%GWA;`Rl;N8(O)uXpfPZA0<O+3&J55Q*4kK6bMDTYfL@_pSm`
z{by@l3)D&x5FqXN(kGpMVY(Q$7O>uzw@n(udhdwLKg(T=1(5kCFAyQ`v*XW~r2miR
z<}MsDG_+8ATe(V>cXUbg%%-}RsaG9itl)mE4}#jJZppfYR&oE8Dw1vL+3x8qnv*h!
z&q2T3Es#=w78G=PcaG3eL_t8XyLyWU^ThL)94$RU*DbmG2T`ZQB7t?+vWSP-J@i>9
zz!PqH@l4RP|Bq&<N1fujOti@DO>E|&d;0aMP8KGEwlboOS*8&nr(b^*NIunK;@yc~
z+tDQU+deY2O=9)BF&kxb0hxpi3IkAr6b~0RPsq!z`<vV*s*EKo?@qeC_qrxCetqkm
zI`{LMLi9~kwtoqWl82H?u4uX!cx9w=dH54j@Cz@RZm=cH&`|h+8{N?<OQ5*OuQ@RF
zF`y(Qp~_RX*sBde<N5TF&eBZe6<O}~o#_B|vr>d;`(ZJ{fWW%f->cl!4o0l_BM^@^
zE<&D>3B5qSTYT5f`oOp~D+&i352o?u9cLcd5@HM%KrwUsTx@^_5F<A(l&2gv17f6^
zQOGRDsr^ElGyH^Kne|#wgsS_PM}??dKQO<<%J-FlH@oUt!^JC5mqkF`vo8(&R*;;-
zKqRof)PFJu)Q1E@L62P<0-Boa#}i9&zP1+9W}F-vO@aV}A)~`Nw6hH<YRn?OtQbaT
z$XL5(BVQuF1G#>mV-FkQKDS+LDL+2O2u6040zAd6Yn#fQ7x_IW$b_4T1G-FdQRwgw
z3<Z3q!u(%ALaD2UMGLhEOePbIzUbp=1+s~viP8%R6Fef+%~X}r{5n_Mjbn_j%p<@M
z)uttdB`pH{$2hvijR(0=jK6LuQy{jgsSD~HNaN|dzxkQ2u#P+FMXrq?5iMtweHg9M
zN@~~I(wnaBMmd!|kfmeBjiJk<H&H9i95bIauBO+-rVlywJr|#Xk?N1Co@lg#8?zRD
zcG~(_jG~jI3N|`gy0o3nm4GH%NO$cpzAXFPV>f;be{%W4!<3{%W$DGUpuBthntFqp
zrt2s+3%052=`PfJ)GxTh>oJOk0z{nSxf(z+DLI(``SN$$7aX8x#@@J?f*RTBqs?<;
zxPhzmFt5#6<?A<^0Z*3mwc_U<BIV6+55{Oj6q~p6jq3FSAIvFQ+YtBsHR6pmxML6k
zd6s$o?22|cyo{=dhL$(ZIeFVK2?R`Pj{a`w7Ue8VJh_RJLJ>9hFJCVTex+hbam84|
zFK7H^ZlWvE==<Xa<MA}p7X|4hNfWXG`oFsLdfpS*lW2>qATTd|cbwY-|8RMf;;DMv
z5wy8lSWyPM&%5#85JFuwoOCPODyrC1SwFWM#njv9!LOwAu|U&<fqtbYSXqzWGoVV}
zOjj=5-jJwC<KT)!-A@g2$KTkDsXB;HDvI5Ru+?aNvZB`{!M?E)2xZrrInO4?MzVcG
zG>?>&rB;jCO}%3uIq%2ANpc&uJkjm5$Wa*NOKyAiNJJjx@r&Ij<$&(A%Ou=D+o{2f
zfdB&gg&w~Op&IUa+l$6@w_c(3YXD){NeLhmvC&o+&4HpI>Fa=a$A0np$Pf-gj+>QV
zEJ((yz%n00DZ8i9x%Jo9wUh<69NrR7@xQ+!1$Pcdx!=K0V036yCtO|)hTPtq`-oGC
zWzs7V54fQ5<gf$`9Sc4l8<gDJ<DYk8+GM3=^xx4Bf0HIQ;NlsrZt=glHHb8K=ev2@
zVitECw~GmAxq%8@p>ER@vTh#7DF05#6S6Z3YfLk4-RW27X8qo={Z>^^%mr>0eWO#?
z9_`IsqwY>1SLD#ngM}Tp){rQP!4;E8_R`WE7N-VsY%&i;^TgK>?Po*oSB`Wobz%NT
zWYD>Xg@(WTdE#ReFIkT?-?9wd*PM4sKVw^tUg=eXUQQ^FPdQzAXqQr$w1-043#m+k
zZkeq4@kVeVd#N;)C7`Sw>2W3A>tG^qZ<$5d=L|z8p#EraG@}O6o{vUb=@mm>)QrvQ
zht){c0Hc1#_>9@@QuaL2p-@*716Vp|yR&*}eG7$6n}jYjE+5J&wtt&craC2JdeE(D
z+;BiieM@o(TI{nPPAl;kGRHj3%OgiC2*9y1ZBa>XrZ+WPIO;`2sg|W?2Op5YbEkRN
zL7ZNO08QHi*xA#(w9CKKg$Xq%ewq&L)%!CAMOFY5oyj%({ZDQ{&U=eM&vef8Yf!RQ
z+{d7mQnt?cTq-ithahlvg2Z~WM9WfA_hQLHg;?0*PM=WPFHC4cn<@Ho?4J?Y>*5If
z#$t{9*>P*yL)!`Uv*XkJK`udAv9V(T6X*0^oNWi+@Sxpod8)ECki*`;TaMhHZiY96
zexj%qs^~Fur03O$%yFpf_WNapq&}=PdTh=1`hra&Gq%-Do=7XbV|CkE!s3qaiOP#s
z5zv&~=Mwe4=HGeQv|5&RPLUSuC)p&hquh@gn|tZw&e<3nAC1(8x^Sz#qdOiKhX($s
z-=~D@4=-trgGQ4Nxp;8=_~Ah6NBygnM>G>osVo$M3FUocy?y$S*pJAxW{)|#Z<+eF
zV+`e<>A9t|rQmUYh|!6|O1?cpW#jGgcp)g6NeCdlGEH<1Pu@fnj;F_y*po|_khHf9
z?w^T0-neVlUR)ng)h{0K$?5tNag~H?!7|i|6k`BfyyYxrJ4VNqs)MJRX^#nETA{fD
zy4#njMXpyXJ|Zz>Eg_};_$&Bu=H)Y=KTs9W+KfR&G3?DR&p?-2+Wzlte`PXoBiYRU
zMdo1(N+39A@eewMh<9n(TV~jNg|a0XzjdikK(;M8IeyLg|6WH)uWx|A3^}6YchJL9
zP)Uo4iMKWr&N9%XP?>m)*^OKC>yHkTZe0&*D!K7qTQ&nQPH?)Sd=##yUscF9trH3-
z7NT8<g%L_|RHcE74??e<+ka80fL=V%b2-@L#)FiLo`piN0Cyu9OWRXT^TZ#zzpytQ
zxN-GGTZ5JSmIO)}j7nX;lJ<Z7*7&^q+$KUC8@G9;Mh=6JaNcYHEmEg!tzk~E4*nUU
zUepBH0lIeXbxh5J+4&p+mfdZx()AwaV@GydU&Pft(lpcLa;(E+b~43r^>db`Pjr0z
zNypvzCAS}^A5GCr&hieP&RwN>iw_LBjE(!rxSk~L<TA<4Pd<O3JHl1jwK816qp*G<
zNBroO5aW}xD=jH*DZ2A6ieW<48=BAN1L`hYYq9IP{)A)J1p>b#*I@abO<P#0guf<L
zLS>&!kBh0qH89K=S%$p0Rh(WC3pYC#)0*2Knfd@E4TBY$KG`@SmtkQ7lM6Mhuk?=e
z_OO&k&UA9nIWNR{KU1MJoaEI7r{=w<3pD#fddDnl@}7>U{@CQ6;w@hE!KI8NF~n>?
z>W*J&Jk2L8aN_Gafo^Ls*VcEYB<4RULL|0{m!&lMhZ;iZu@fEIdExhDl-aswX~Zz*
z^9t!n&a>>Eq-MwN=B^t9bnf^ki`qq9Wnu)TESQY7uS$cU>s7q3?nl@>--Pv5B$<w3
z>8p5!PyAfBqu8;DDI^M-#cjz+6|WN@oQ(3-N9Tb_>XMZ_Rvx4V@^hBSgD<XbuGczu
zg-@a#2v2L`cMkF67XOd--aMSj?tL3C?Va6L*~U=VsE{-eGTX*&9ycPS$(%Bjp)!;Z
z6+%hoZQACsh@?>}^N=Z;44Fep<aaHe=kwI_`TmaMegFI($NP_GVDJ0B*S*%Yu617L
zb)MJkPC*#@HnoOF@nV&C*#xNMsuLnNV?V5WTt8&?LWNWRL{-vsRFD#1`uaH>hawe!
zK`A1_ZCwB^P>5e*<8~(N#{>m0e|G*r4#C7uFJ%^6%*pT#H=MiYgF<F(H@#$&d<Z8z
zhmhMM-^Dbh9vgGH9$%<s=w=b)*hcPO8+v&QJC*sJnb#J^t&*=d+AVSy4ENS00q%b9
zV77tKpl*E>5TZGM#8@tJsH}4eHq_(C0FKz^F1E@2WedmXgHzPf%e{?)s@hXyU&~wr
zJ0o`8EZMSd6b?~i`Vr#DfD+@Ea*=OZGc2mW#+7&lnAUU8h6Ow=o@{w<sB@gLar<39
zF9m+N0x$L-NlqMe@m!lF51u59v*cn`wGO^Z59H}MbBw72Ug4Jqe%c8tMoY0W?-qJB
z>Q&xZYVLhep`0M3DMP4>O3XN>cA)+|LS47~PpGR>8@)qHn>2ysC4H1b0QzK)`)_=-
z4|IE~()OPw)gQ@v{9S3S%hwc@N+m8PE4uee@qk&J9$rw2CE)cIxLU@&pu_vq<6!gY
z^^CZ!{J}&CV&MJGA@vt5u_5fK<{S)(@!?4aq^&u&JYAoCngG4zTA#$!zlUVp-?QD6
zk019qB$|(vCF6p^Q_UC3$2Vh>r-_?b$Z>_!-uxladj~ehw)rXvS-kF@UO4_v)rgOy
zmP6`YZ7z!{$8iIbO9l}*I)<d{xWz|-A-T{f-aG46uWSG$LoNbT12b&*9O60_+j&hq
zLFOQ{9aHf^nSo?wt+-q*P{GJe<ZV3iPP)oO+3fMco>KNzO9h&><GXaIpo?yj>Z2Tj
zT1U(?S&?eZg5u(q&q+YT`Fbpu2cg{{9Hu}9bf3Ven&ZVMx=D3|A@m`Bn532vyRQU3
z2k5U%jYhEj7-oDkKb)L;^f?z^-PmdErS;DQgza@_^&@bBg?w)~j|XibM??D_2a~Fy
zR;n4{E3*n6Q=aOcb4GR500+f}afn@7*c>6;J38}4Trq$8M;r7nI2%gAA>V~G@V)Ic
zN1BZ1mrq%IYk{q^Q~4=tq%!DG<TGqdf_&3IZ~zpTN9eeQTKo39Cb1+kOl>|n;T0J5
za2&{@<ypHO2@$6XA|Q$u9+|)i&pzVkWxCuPTK4t03EL@#O`5pfXg(Vif~|$cy)PDw
z*VLged9|e1f!%5)0;AIsaDWI6%1?HsyN0*q%;p^`yKb&(P?jJ5d<OvBhmdAW`l)OB
z#_G|b1y<`)*2cYmrh#E{3A`t){Qej@8OdrZFnuznF-0otavQy23V1yEgIm~Rr+(sC
zgW~de?X70y`>{gX`K^fx<RY-cJ|N<+)5qP?8u=l-Zae>V0>5iWREp##Q0~TN_Y<JQ
zQ26O2IrMoyAB{{;Pogt?eE7R!VjkI?WM3Bn4S-$zwuF#<A=HB*#MJ!9&xb;Zr;oe?
z>**v$zA|58KY87YZJ`;;#57_tv5CwWNH5*2Ly)Gj9hJvxM&R%wEeTtJX|Iln)yJBf
zV$D7(6W3#AYnj%CY&ekOslCR-Qy6npd9~!czc%BfSRiyZ3os;nb<ol`e&|n;IX^;)
z*`tFG20nFASOy>J1om5*M`{>Dfsm|isWv-xZXcO*Aa7b9_W@W9H;A^wGtli@#-$XY
z(54IhuZ+FW^f_MlS^w&NXg{P5d?Ln?=adsT9VbcwCQWrGrgOYNs>g<Vks$(G?~bqQ
zkA^4+Z{F0bGHvZRXdvr9oC0<33Q&e?C9~Gy`Teu{)@7?iZ*~|9A*4Rv%&ZQ4C52wc
zQNkgWPt46ms*j$MFeKBUYmEFjvEXIn?RV{S0G93^e0;k$)Sme3&2nwTf-2-AH`1>-
zL2WqQ8M#MuZzo>nVxp*u1Jd{sTro_sPDqr4<JMkfP*MK0<x>)D2x}3=h-)w^eZOO0
zacTD*r#!w@Q4<SG#W$H%5U*Z^6<FM$hcb`cL0Sx!DZ*M)-~(#jKTM`^BS<6&g|7Yl
zA@*@#1AXwzlz|}5C`c#)T9!eGS=9vZ89-zwo+6$=&C>!d#3QJn+$<Tv^bFGy@S%sG
z^{G-YozwFi(KAHM@m*EL*8N8>L%HsrK^BMG)ouya?^ke;Fi3_6XKg2EBY4^okCb;!
z6@#Y@9?-XhIK?r30y1=nq&=f@xFo8UHEW+CkY=|^Dx?+?uM*pdr#aSn-XI5SJOz{0
z@#7NLu59LZJkE6A*e3Lcq4o&0+nfU~N@e6ZYRsjqF2LGd8mKn}xI!27NTJM3s+F>-
z#X@%*{7^#JZu506?rL7yuRWM3b>bK4xY>j4H43ch>y(pC3FI%mpao-bNa{_R7z~@p
zr4pZPUMN2;yw7|vgG6Fa{;8^p>fWmF38KnRH7{UV-nSX+mxbJNV^AeTidA@e;O7{!
zRX#PL@^_gP7nVQ7TV?$YJ}7roGr6g1lWwmD*N=B~p2#`5gj?LRurz!lsExGlfc&NH
zlvB|GS%JgSeiMezpj2ydF}&pMrP*C!Io65NVYpq4`Ho2Nbe9J4I=#b%P$G`KrIM*g
z+2UZ!eZ!$ELb2zSx%v4+zX+tmKx%MFfG)vA{^7^HAzxk!CdhcY0dr2}rInyA2sVVw
z`cvQD#08WR2`{AMVk<gY4jz)?XC8Gw*}=?nCyYR5p}RQAoM)xk>}>;OGP7(L8f`Vb
zc?$E|>R<}<_}FYr+fUM2LPLWNfNpkq62>s6=x|S``AFSmMaN#mZhI*XC6Z1sx$C^=
zYLnw&yn$2Rrxe-Bi(a#`y3C~xN~;t4T%Tb*h!tPM<!`@ip$aC;I;l=`!q5*7T!G?G
z4W7qd;!Zc*++&PN>oQEl@qHtnBEmA0BH#tIiwy<}@V$px{K@l$TB!+n52L&k>)0Q6
z-OhumXl}_#VffHyx`l`cEg;Gh0Xa-|H{x2Uz1FR#lbI|jYH&zOpFJ=xk~Iy%gb07#
z0*osyA8cC<9Be{~$&8_@N6az-N~YofN*1=*y$MDi!VV+G<6Y-Exju{i5X9LoS#U$u
z0)6}%7#C)#Z!v8}wYh!h%2!sd?4hRx8VB&5%8#V0w`eo8_(N#hltbMQF0$xz5=bB9
zj+w?W&<9C&#to~aIycd5;a84G3qYbc;tr~xDktOMuG9JdQjcii07?w{zjx4S>`hKu
z5n|{;QJK$B(!RYAekBnIKnvF)vMwl4!G%Dj!C)P3xSG12-Ow05i2M&!5xque@ylWO
zcPgPNSk<U5#|AFkf<9WB8~q%{5cX?cxbSFj(nS77SQzyKxz?*slYACk`hinU9J=(H
zwHL@_`IX-UK^<Or6A%hO&R`u-#D79le9zK+&9WyFm(@I?QN8?o^Lp5~8B|J(6ws*r
zcwM#usmb<%s=V4Hswf;r4m=PR?>#Dm&h0bchI$&-y-%}b>ftGHIa#q@g67=*JvGRS
zJehQ<7aGDxQM5XyFjn|v%++vc+gzp4N0A{3klU9YN-tj>Hz*L`;eZsQY_WviDFqEX
zEmq|cV%tHHRr*j+SSsJji>*gZ5lhgVJ_e$)`scun^VWfqtocb$Z(gZ*BKOngi{JXr
z*$DW7_=LntRWEeiOCxd=G4Rxbamf{x0QjrEx(vjj-+#7_?-#FUgJFRP{&3u0vXTBP
zISGlqXr$5m<VU(;xYu7);hi|DDDg74m-BPofM2y=ftTWF$#hL)Wft;aKFCb2bcjPG
z=o3qrRs{@RFEn`$aviWA_`8xg5BGilzlw>vSF@sT>d6Ru1Om!iHHxJCk$wRx`@n&B
z1RAN+uoHs(L*Pe$I!*nW8}fSZfr3yksQdQCm|2hPsabTpSHCOgZ6h#z+>kp_7H?f5
zpdI&-*6_kbP(AJg`QZ;&HgX<StO0;MALOhi5M-G|4+^|9{Ek9Gh9Sv7ByHk93pQoL
z?1zpsb(Yxa3p|4K#ks}?3e>$2MSEe8M?t}Y`5ru6x7_Vk<bRF&(!aeu#wY~@-7uUC
zXkPT9Hn)#6ZcwtVKt088;>3qs-SZhGfp}Rmt$=)x+H01VT*G2%)Tim0G22Rzml&)7
zc=NhwyffOH%+XBefK{8t(e?$gNur^lPAr{4DkrP>Z!SQl?MkEou0q>PA};z=U*%_)
z1kkQLpqe%YHAi)>UfAQ<$Hk$<339`u6^O&+Qa#r(5iZ_PILwBN&tj5~KUJ9xXie*3
z=(`hD9)h;oVpfORpM0(h^OKX$uX8(nVo_~ECiC-{H0uVL(cMzes;^-oO%@3e4vB>{
z4ou1Ah$jhyWi47FoGE3{;@dL^=~+djCJD%<$gk*Bv)UF3u?@^QsHgf1EG4PyKVa6~
zLDeexAD?al$2rk`%i+@jzED%%Cj6eftghM$Q}={w6DU+T%lpiI`3#xyahKhtP0BOD
zY<Jx+6vat6#YP&xo`I$89uFedOcq<63rXB%d9$z5zBr7?59^^=hNf`YHQ<SLTid)r
z^4kOmNPO)p;Rdv3hNuT%T0^036DBorzO9q*i(nE#TIE0!)YFdnWteW>z8134r_d;S
z?9+O>xMJW3agjVgN<&?1(>>HW4|A%eju^tZ7nDh$D!%W-Nhkt|r~b|U)@1q*``Z^t
zQKD{H(4ja1x+bOe+xEVW7fL?-RWM{7X2Vk!km%rx8idlM#MduSH9w0KgCj|<)pN1O
z^i<UXn5cxy1ixTv;M=uM3p<y#cJjYI*Fx2taatuhfVI+TR5(Tr{=GnGaA~HLvY=7w
z0#S!;s_c>GU^H~i5*naI9!jTvad<|bvH9<?L&ZSY5k4fe3E6OZF+tD;0e1-$?HM83
zm)*;_6N%ygkr!HdenJa6lL(UP@CCAUZWzkKk*kTi8{xehlnX7n%Kwp^7pALS`@Z&I
z{{0X5u<LNFq^iooDnW){r<lluy+8w5q*Em{D@I7DE@577bo`>U#s%>lKx_U~f@epZ
zV%vppT`!DMB4b5$K<|r*^u#OV<k8Kb-o(bu;W_rU85KJ32wHpr!H9kQ_Fzv|KI=if
zrh;sQnzcdX8}Guj7Q0rXFPX?>V&hC+r=dz1RqL)mbd8XbXY7`_N8@+V5>1_z(DGV~
zri)TNQh<J{&Gv3@ZPTY{+qCzvB^o?IvG~dsGh?Ke#SUbq=X7aYE4zt_JY7(Ouj9vv
z#EpE{IOm)oPpQN2T@eD<r*L!_WDxd=t&h6IdIps#dy(m;+eMA-8=0hm2yJWT1q(sc
z@yZ?Pwy6|R$5G7W{75g@q+i?W&>F&De+?HHaa0bL<HDjh$v(H}IoGK$(egsv)|RIr
z#NX3yW+Sf{DAXSaElb2WVo6rm<pe579!?<7YPm;lP#f&<h`^y-SPet+;mf@1c2!Ll
z<^P5(S)QXEGr>;}n$u4)GLz<>U9lJ7k9)NFF%1mdue18n^LQP3HfuS#<Nq#~sK}ap
z7Q`P2!XxLxG9Z(4yJ8`wiX*w52Ok;rLV^S)Ze6IVmUyZ!(G3xMw3FC({ue!23JPM_
ziC~?s<2QAk6(WSG`(&3FXY#IWrsB5O!`?LrsxY=p+ERTUH?O$>p`*-)6d_8D`pCs^
zT@`V4^Vdtu?()qNuqm4#xev_OiQxNPP>JhZ$0&E?6Ql5l)2W*PC4cpO`eR>Rq`-=h
z_%W<@2+>h=TFOz1>AfnIsVvU)&<VCD3I~T4j;b7BVLF^!8>PKx1WZ3&n{^{BVxpjU
zkv*Noi>3>TE)FVii0L-f`G|aP*fI4>d;y*O-YxxJAFr7|k2euuef=!{=8gIV-1@Z&
z?8tjKF#=*hSu}?ilhLDF9vpLQRZ>65FV;ksVusA^cf%&!wmv9mnmyfrpZYZjF}p`b
za+bMP0P1Uxs17Uh)aVAp`-ebd!L16`U-vlx(hSAzGk*)#4*{3sbx1f!)&n^d=`6fs
zSZNXb@T9YgFX~DbFFA5xiLkp33$G?=x7K?8kl7QeA|xKBb`{wwscCC1F4@$>fbp;?
zf020i5q2=QpK(6wNOQy&mf6@4GG*b6`gxr<2*(8^hB>uO0xfSyV}v+Wu6z6Vhrv;x
zs7T>!oq+ljp-bHMF(#hgP=;EE_&&QWa)7dW3g|K7MlRXuvB;(;UeT^)ww-=!2dmr0
zs$1qqH~5~j+vMiNALA|#l>xWwB$w5E7z%gDC%2`_hwA~1{0;$w3C2(@!it`KdbLpI
zQ#g6I#d>V@QeNL`(zc6<_3p8hxlkV~^Q~S@UuzY?bWi(*R^-ueu9(-I)_P0EJugw|
zN35$W``qf=oWeal^KQnvyW01hfVJu2Givj2NVfnje<25E{@Z7v=p_Y?UcQYxQ?o#O
zTD_MXrAFK=uX}bqQdC|IAjK$UXZM@i1HnWYoj(GCiB}epkO|U4iWUC}l%C?OTYx+Y
zZnwSyxr|r=Ieg9w!+heB#s}mAE)?;Bu0RnX-h^o?qOSA;5y~Z7LdZI73%{^<-TUii
zI|73RmXIIf#husIJzZ-k?#1#U=%vP<ig&`l>NO@IusSE+^^*RO7iwn<l3{ptjLSnH
zynEmOIq{(9#WFbe41rX?>}V3}Ru1e`{-<Rx`46_C8Uh@IHz4l*9o70<E)bfH>mEe>
zxOs)HQRg|mwU;9^u;r)Bp9;j>ID4^^s>vea7|)m~bWD)m=QcPw%<@xr^6hwmvNf<3
zEPAw!Q{&+reGZZRF#WXp7e<e4Fe8pl#01h?@d0I5PUvk|gkJiYkI&0b;#@vVzm$MZ
z7#jqKkIJrxrnr@r^;v_MKa)u21nz<d@SR6qdi96%R|(E2+}8K9Nt@73{{#x4Md$2O
zJ7gg@5FX$D5~;24RAjlfl1QO&E{5L5`g3z2a-#GYr@9nD9Fhz=IkN%SwtF}x_YW)1
zycUn`c)v8?FID_J^U7AJ5HRt0sVdhiVG@8K#9np=N%}l=!6;u&(mckrlR#Q7sP?^a
zujuu%;;-gIm!geXLiM(vh-!`AX2ho~bj`6|^}tb&Hcg+)!?yNR7Pd`>n>8gegmz>L
zd7Q4}OON9JWT>ahv#|6ossH1<nuKo_zT+JhzIncV1*1uC-pyA|j`~=4j!kwhO}JZq
zL6UE-2y|gsGb_!#uP0TFq2JvqMYW2U1zwTw1hK53Iq^-QYOcewBlnOvqd)6LhyJrc
z7Tw?Y-gTVrcD1rp9ai9~d7kaB>6>{=)Ux9q4atYmlPddvv5~sBT3Md8_YFf$3-=}>
z40AKa_s`lm)hqWLue0HcQsxbE?0!-OsCWlhJt`8-m%eo5#rK~A=QM32^L`NujkRR2
zTN^vCk74q-R;koau-#kA+5h~G--VquL7RK6D%{d14GYTBf3(XvIp1Lnx`2PnD(4tp
z+!DrAo!gYS=;&sWoaN+z+b$9@Jo)l?(vu-3r8R3YVj_<wf7<*UT^6KBJouHC0|IZB
zb6?uUWl3^<So6%?7z_dZSriKqr`2#nU%+39jAdSeoTa*HuGo4xChb>%In6})R#Q0>
zO|O|@fe0wl)y);{7YA(4PX|pl=4mlfCQR}Vcd4Yg)+}|1xO(Vwb8O8KUNjh>(wNG`
zPSksOB`ay^whPnzo4drP1Bu*i4?IUrC+{f~EO#6FUK~}BaZ+zram4Qekcc3KYw@ej
zCh8=0CXR2h_DV?-nH^GG?pjC0%F|{;*)YOQ1}DPqjD2F6d`+atiypq;v~Jf0Nqc;t
zi8qy~qpN81;B1~5$1^u0bBcNYN6|A_JA6FrdWW6|$)6d$j=rg(6@RrcD|4hUdZNs`
z=GsN;N)HFdQr6?y-DE!7LIn?@l0p5S15Wwv2QqyeQtVm?WK&{u|IL>Q!Hoj`Yv^#t
zP5t}l<K~7&LN^rZvABQOwK{%BHxMvIk_07U6j*4h0K!EMXat?RnT?c4qZDPVpEoat
zv`|rAVqSbalzzKqx%0G&&0OkoAj6(1MUg^-9g*LRZgjF@@(z_e&#_@$BQ#*(sT0b)
zWAPYXLelx|!21`!ND;eiW;tCpWhSfHw3`-KkH^U2`Py7v+}RJzS+WUD?+WzLcUewx
z>1RDH%xXx{hERAurvg$B5P$aoy6Bw-ypi^&*F-D@q}$?MV=}G^P_{dW9EcNYYcZ3!
z(9RLOX)AlHrlLsh<n-XNuHq9smDxU8PHObu9A&E(*XmdOV9Yn3yHWncc!$|9q5<FC
z@&IEs1caRrljUy95glM%s55_m^jC&N5oAqO`}RgHud{0sW+KndAJ^h}uDdeL#CF1L
z3~!zp;Kvo}h50{A#}Uz>vJ#aDPm7h-1d#Dhe+FMJ-)Y<)dZ%2Xl~cx1=7vUqW>er*
zqDyDG`=K^OOlCALt+pUwgr^LRVJarHo4V8Zo9E}`gSLp6=1x7a|L78$;VXnQIzmm>
z&}9?A%bFabc4X@FDvGQ3gbU-k%bafc0Xe(c)hVTk-08HfTT4$A7x)g2QB_xoLF$fN
z!8F4USW+cA{1+|EjsCP_4bS>O<_p?b%6%CA#3hp5WX%p{%uDSUbDD)w5?idjj!d+<
z&ANCZuBHE@G+W1f)rS5_Mwtg1b|i<~ZjFnOflI<}ne=LrWKac}YeECo=yt{tEnO%5
z9(qS#<~J!9^QT8%^S^Ng70RXD9oRD@LY8=jhiUY~_5ES(eh+Ub+0s>=7S;(#LX)t9
z4@R-~_KU;3*wF)>cDJYb&>&JR{fR<-k9&F7Vv3E3&3<H7QzmL&n@MD9ebkScAxp4b
zmOo<Ht`8&UAq}c|7i^q@_H@6~PPw5R5Bcj4c1NGC0?`j9Y|I7YycjExRODgKiXzW<
zn4aiP)wS!47BOphvO(Tvs58rAhc$uvB^D;QFF73_jb&i}9;f1S(v}nvSc1%H8cd9W
zZZzaJdrQ+y4RIxYgzY7<qL#lh=!7DPWK#TdsUwfpC@N~Kjb2c2>d3uKGLXxByujn6
zP}-NIyoEbFcUoAf?S~vK%jgkH`2Kf`Lp6Fm3&QtX51;D2-~2>^Vmcy6On6Tn5?aL5
z>JIF5S(oJ=5SX|2b8_BXDL3z?fa4Is<m1NNzsu3V{8BoJ{eB_okY8fr?KPtZ_6z1K
zL%f5P8oP!>a01zKe&L8=#qD^8K$HDx_)XP?o!+8Pf*I%WY?P}QQWF4L1AB)k6)8>1
zq=@owEez)q7~JcHGtJ@nxuoW2>#|HF_fSX1xPugBE|J&XEZ=P08YhPLGpV5Z4+CT{
zlh=Kef)Q3VIGu(kVmEX%>$UHZanC3u4g<qm%*8dMUVt*1^+mIBL&NAxUd&7TPj!)~
zl*1Tz)q<(?{13Ouw7|XC{WPU0(l0#mn&R^61oLuF-7j?9cq(%y(&T;m8o^}_iYU9&
zFE$=5J3`G}Sq}Psb7q_`eP#`cgiBStZ?Xr?V=QQiE$*ybj-v{clx!KY3mkYA&UN!g
zTu~~9sFkSmP5L~~%FvY1Ek10_Rn*tl@%OxQo5gHpeh~@U{=|^7fHicHtmJ2k+gS_k
ze~MGmx3{ux1E#TP2XLW}pL${^Ae8JvSEkzw%|jPv@s|-!)l=7u!YEsf;LY)MNg8PQ
z$u(<gCR<%!vqWt<lCaaYj(a6Mq>3sLSjT_Z&Eizy&zB>}lDHO07usWKsG(f2JV^1)
zx~fhQ!%9$Mv!b%@$CoRGnK4mCy93=_jGFI#xM*FaPy=YmHvo&ac)bK2&i8E6ai_YC
zCP27QEP;}aPX{T#la<<XiW(TBX<so7A&&JhLn2zy<Bp8HiK{Krb3eI@6Sp#Lu3&J1
zVwlEbOiIKOy6o@4Pbc~wjKe8$sV_zjvHz-{)&NH@4>%ri*!LqTY;P+)bH?8mJ-PVn
zhh2_&QEH5^@y^m?DIdFbL|pB^*(Jg6a|u={(I5hz{LmI#!jXh4wz0zkE7y`_AYe|+
zj5SHY$i%OK2j40xrM@O$8`Hr2@#{L<pP(%o5C0*ry?rBHZtK3+%okMAFxH)18<^~z
z$h~l74V?qa057vc+UMu+L;`e+iLWC=k8_~-3e%iOkNLi=?RJEjlWygD>R5QT`o<^l
zY{qnpD*VP@<+iNFJm!Bi`Lgge>k|6zD3*cw3vL(BS9)R0GoO8YZM^b^+{_@zoEwUb
z<nveBw)Pia?N@Yb9~h>5qu2uB@l;h{du+ZvRCpuGlj|ENb$KPg$FUP}^|KqN&chvt
zz~j~7WEs`g1Q0e%`HHFDR5OE2C15)SRw;&uS5$IsheMI!3=wUt0o&N%lg*(g%yC45
zjnZ<&ZJ-fJICX4X*>(e<N4Gz69sTB~^efXg80%G=_~d51ipsHs!KUM4tSes;j)Sqi
zoDj_KuatoW?X44GTlO}1NO>v1NJO;LK{8YV3+}il@5r{5v2mjtxmarro3>?{V@!;Q
z*`A8)JHL+r81*&@<Y<Tc&!<APJPY8=i=`SDBu?r%-rWu*tWr#p`S%mN=cfYR+?8$x
zGf4-S9tH9M(FFO0h+NzeG2w}qLRWwQzDJ1=Ju3eC`^uR9_9KKZVq(i!7c1rhNYa|7
zYq5#gJUst;?lfT1Jz$x20uUNq`!|a~-#$B>68yZnTqSrj&#!G4W}D9fPuX($*U~r$
zK57pCIy}3KX{pvbqmE{Y60sBBx7&p)oV=^oU%82u9}b%XSv@v#iakXLLjOhYub*C?
zg-gc=604sg*B@u#!gUnbC>GQk#skf@56(K}E<KV?E!5C!oZ`=sW%O1!u%bll+jx&1
z;E{6!s-4x-&$o8unC-Q!_VIlE!$hqqRV}QF7ct=zya(elCQ;wdS->!V3{l1{4E#`s
z4|HJHP;$+i0V}rG*mmZ!*>vvViWbMVEL+5tOE~kK^BdrBKMpoPQw2IR=eqVklmvXZ
zV8PHA@WZM`?bzK#pn?11#oVOda*tnz)dqxH8E5F&>avskiCH-F!&lMz^H*9+hav4|
zKyw3)&EG%xcSQ^{d_#(is7Zz{M$+}2b9;{2{6L!9=lTvBMWo<7vc#n)0T@yU1~XlY
zKW22<q-4)cltTBCIUKuP!1B>FQ;!>Uo1j3jPJq(j+*jGI0IffamPlNnyW4weptk(X
zNA;SWrpCi*5rc42x@S@5c&Mk`B@fVrUtN$wxF4I7%7TAT%1}o}cmJ<%Qj^P2=ym~P
z?YSdl#ZGA-pAH!9aqtT&MZF7~r9TmP!=5KkeF5CE5K1FH$<&O~+H9Kr=Vx?NVpN%|
zqOhA~n?0{`{`LA<cSF8a|DQ+yYY>R3bfFKAbjC@)iWL<x(5$^qN5mfBmOWdFP?Jd+
zEQz++=uzs~HbV{X+h3tB+J(H!QRA1Njjd5Tjp728oQ^C*fr8gRV1mkf#DViw@NU|t
z5jZ3%3YZ!bs6*Ms<Z)=Ln;jZ?oeU?5XGXcd){_jfjnZ=gDSH>3ENpH>%+Sh&#G(fh
zhA{vlr+AY<H2&hRMs&*lXz(bVsL9s>RiMR%<d;St&B~ZX_XntUIvhRs@vlrgRS@=&
zYH7Qt|4edN=TGQpcbmQ$AHXm(84wNgEF}8RiUI(D8FBCCa(P>5%fnvg+|_bvi_41)
zBH8x(LmJj@{YZC9y0&kOZj)B*?vWT}NP=u%A%$(*aGIM*WSYA#oDx6U8s)+#8e1WS
zwkmUMF9;-JwTc$DVn{SSBSjF)ovYG7JzAH9D9auYCroqABj-y1H)y<+0*!pV2+U=y
zXe&*>TYkeBYt#hEsU6UO6GkqUK85{<`M(Yre@|C<X93%on;2TXX9j;riypw>S(+@Q
zp;RJg-&HfREgrPbU)1Eebb}|n*K*0O!=RIX_gqD}y799HKt(>;hLo#_7-xhB?;}ka
z=O31?QTYCVP6&GdHP86jSk`9q(r`vX>Fyq^cv17hNB7KIW1SXC%Fl;&ex+Be%uj}-
zr}Ehs*I!qYU+sDDIwv$RW1r5@(&BqYzo|k&zo0gRnSgdhHtLo71`qn()WZ=X?To?!
z=ePSN6=+^uApC2_)Xa>|ov%Z4R;OUd4w#qSf6hxcvj3J0=>u`k_C>39YD4->NzSfU
zi`h%_uYf4y(wT21d2#XkZBi4&rSlAkjcEaGGkFjmc-9M_?=G>sLCH3Hwu?ghM3F+h
zi&hXWI-uFNK;~*E^M*i=e)A;?!2@84pB3sdJsNz`4YEF<PL>BgjyxNMA9X9@jFL)s
z^g<nNY>2Zx&$9D@H-sObpy+(YI}NxN`PrD*+e)dU2Z|n?VOw2Z&G1ZD-`y2I`df4%
z)Y-1JzCAegDZ|!2H2>D)@+%LZ;8j&=H~FsGdPxjWu!5;j3KJEd0<H+%{%hBb0XR5c
z;$h;#;>?#xRIk1Im<q^s{j!{0!`-TqD@P5tGh-Z_?!(RRh@yFB=*ACHbdeD&gsh)u
zPs^;+99lRJ9VFQGr0$7vkUJ;`XV4)}{UNpyp9F8}x4BN6pPU-dC#Lnpd2m9M#VWHx
z<Wb$~TOjMf9xtt^aBU@A{{7>4%$mJAC5|%155;qDwdqDG=Rxy_PXy}$>@nzoy$4M}
zZ#K)%8f-myIyqRAMo!(a=aGzV?^Tf%6oMc`hTPE;a>w6R<);YvMt6I$Fk2y8%e`<s
znGyYPR3d0UO?6j8hIoKUJJnyqPITPw+l}&=*0bki+_NA^e1l>StKGcYheNiOnbH9b
z#Fcw?(}G*h%(z**a-96@hf*8X%a&`7&7gF;#QN$WrKuf=ZC~B5Bhzr$REAym7#hsJ
z59WpV+wKgPH-+*0#LHDATc{@>`F<lLM6h#yhRs4)@bi`5?i;p{L5!E}9qFI@6yoNX
zpC1adPCAs?9Ph3kj-D^V0&=o8A2-P5ew)d}G`gyNZ-o4Yc^Z5+^hK)`Kgn$+>&aeP
z*`UcOYe)w8{S|*rpuesa;Q&+Sq|f@tbLTs>?+=R5s!t@Sdk%80BoRt92oFX(US9b9
zB5MM669$4D*maacDj)f5Xg;oXKPR^pr>~&0nskT4WwbadHvIk;h!NcA&aQjowD+3t
zwT;WRpTFtJwUUIRaeT=@6~*l`Ouf7WS@p~L2-)997#?@~?nex*m$UCrE#nKV&Da0`
zrnSEfMREvSP?X>N>0Y~P)$?mFAOD`)8ll75x0=zS(aTNXl7Wo&w=rk6f+(OS74}uo
ze6L~R(o}eYCs$%cYUawHbO}Q`l`x}R@aI_c?|@tDf5zU$SY*vFJD0+BaP)vltD4=)
zj4U>Q8N(nLv}g5fgAUN>$!g5tIyGi{?j#+nR&{^(MAEUt;qA%rU;O9~j>p6JmA}C)
zk$#K9kSU`vXZB)R6dK(Tc$4QbUx}W|K?e`cmHCqcuc_EkW_aW8PeY8j3se8*Ojvj3
zp}mIj)2FZB^5kmpPU~EK907P7d3TcjpN~U@*?i}KE%nqRpU^gs;IoCftxbB|+$(Rk
zju}R6cO0(r&lN^hhNsCk2yc)#-`gBFy6;A`C)Z$C+T`kG0Cb7oRHDTD=Ve$oOg%oJ
z7Qs}ZTiK}{glBVxo!vD5aK%w3l#?!bgB7vfs~Kyr_gG&jcDR5d{>A0K2j?si)fz+~
z%JdRsx)6;w`O;>sS0!oZTRjR|g?hx!qSX*!54pJ7u|&=tzmJBhD%>Sg+%W4V@xIX=
zotKgk-2-KQ;Cx(&5;Bxo%KOZco5xPaD-rufSshlFmYWs4!~ftPf3JEftWB=$5j~&6
zY>U0%<8gb_U+FDC4RN}lB@UuEry&`!lY@dvxm_dAXlFqZvMP8tIfAj!KKC~lpf$4x
z(02#O%t}D@;VpC%je(6sIUFIo9|tkWn+RK$=gv*`_ZRqnye5?k%9Z6%R=@qx^nc8D
z%P%hZpx)$a=q<7?YlZy>1<HQyr7GA4oZ-xm^7(FL)1YXU1hL2o@ivyGZ}^pV<XQG0
z(gzz!_Uu?81p0-g%?Ah8)RaTn=hr+`L=TQsykEa;L!^wd8n2E^>Mlru+T-kcSKjkq
zKO%0TW&YAZw<np|YA@|9(&oUAy?|6J5AM7C_)F%T!xhC43hVP9E;ts18$N0QfIR+y
zL)4oj#eT#$?Wu6jgj}iiOJ{*Aw91tOgTNfbK;_I0k9fdV(*t)Ze_9$^S@NI`A?ehy
zdn-6lx&UNT5ZcbVAmnVsVVq+s>eN%7hiWZ~IikI+P2pS;^$)*)V92q&m#?VOXspO~
zL>_7g-=J;SE2*6~$OxZYy5laL2v!mA%ZeHC1^gswg9Rk0<M|IJirpKe1pxqkwpFlF
zFU|hR?bW@8tP0bDX-EE_t5lgEqCtd?Nvwx>^PF3^Lnc6+ukd4Kf!tUnyuHlXG5cWd
zv*zzS@#ZGn)HUSsthDRJWlo*l#`^LnlFok(l?SKfUl)Qjp-?p^evY-&{#6+u^lh*-
z99VYh6rwyF*1|DZ3iEbP9r1y)auoJ#<jV3(E`hW3#4C1y_@jeK$Sj0jk;c{1aq3Iv
zV;aMr`;QdOijJyHCgnHV%~5nI`lz*@o`gS-TJsCM@OS9)9NTGKx>9}7JH+p9?IU|6
z3_iE##ICNO>@AQ#RSTb6iJkw6v0^*nS;L!yzt%#o;YEsAuK*{dt&+~SgVktP4ZTvw
z-?A18y>2!u8+c8$vIGSD0c4~zy{maH=n+o2d{$UqbRTU=4@0C*o|Y^wun>^o+4*ec
z!7fl*J7E~S{Y-XHT}D`UImj`v@1FXS1Q~rlg`1k;G7!7h<`f*$OP_^p=>c`%p?<SE
zs>=sfj6=M1kSXl|{!V#pPpQLA`a`8{8@5;K8PBUuTwaO5N~Zuw|J-q2jO+K^Gvq#m
zOM!=Lt{z<U8a)9P!)Kwn`8#)6P&dd^P5SYGNss45uk@s5&pp@q)1Sev=PPY%LIE89
zI2mmN2Qm*=xLv$3bq;X2WN$DKSo+zGpSXSO`ZQp<&m_-(Kg}4#+A(}S84($lbN2~a
zzv5jSu3Ypp8*m4;i5{?3II%-3x{yWhVC^sP&KSg!@E1`+d5<wL8DXFyRD#wx*ztUG
z`3!Tkk?TgoK72RySq~oT^j3i<obna*jP2d9vLD=me4_d8IrTr1E9qVEzP2U2J+Min
zd5*b&pUfr>Icy11rc#LkZ;@uGPn`f?kQ<zskt%LV>+1yBPRGuCmkU3?&Fv$<Z%vf<
z&DDz8#Z#YaUi7$epaS?<dFLkIS$nb;JYxu~zqQvD%yP>>F9dn~Ni&v|s@;8_p_G|S
zgrLJyj9iRPHr#apt*}c;OU4D(@{EBVr&@pr6o0QeVWB<SSn$E(*H<4;+1$#=em&B&
zvr^BUE1-6D<BYfrdm^*fdDh?0(u6uml)ChUA3x^@!6pc*7I+TI{8-<EPA*Ajd(ae`
z!6%usk!S=XA#uQhe*)WgC1{*{eY;BZlSQfhd%N|*I?#?^u-FI;hL^HVLa~ljMEeg<
zLm$5BWzyL8zBVQ>Azk~{f;6cpkmzEVIsVDoXJm(>6uGAs#j8unh$e=NnftBx*t@kf
z-hgO7miRqlZcno(SFvrg)$07Zs!Plx85k&AJEy%T{T?p;m7ZeZ-44<BFm5}z?*sEj
zMW(gZg@UQ9Ke-YZ+W-;CG0wd1>X7+}JcMaB5`kPdPw<=y$_qjgmAV6y2bReiH=>zV
zYDWJ_nc~=*Iw-JZzH}D!@)5#3{uX!v;Ms?2hjTOV0u+8WvsP~sc&HQs!FC}Q5%a`c
zNE+ICN^d(H95ovIRyS(0HPMA+@YU+$=0ENiEuRQIbdLo&<Da3pINI}iL_~BB;KH_e
zHx2Z-0rs%C)SMo2ivGU+qPz2^t2$!0|1!znlk3W%)Eg^b+yx*(^qUfu{y*{qctCR6
zn!RsZLtf<>?}W$Z+#$6BsE1_1L{Jq>wEgpual}UqXFHTQ07SICy<;I{<u>2IBIuHn
zo!#`uq=6eefS(GKzjnV@<^<e!w4s~8%58_>;kG?Ka7zCBw&A8X{lA-D37qK1|H_d@
zfG687lS0@~4pq2)zKVUIaf1iJNDq*B*lZPGvv0w)_NzjP@BAq!kHw>A*^Ww2rw6|l
zMs+*K`Wp^@+=x=qae$;mTV?{oV5yTWp}xm`<|$Ln=*-LW_RXnk3i*IADo(fnQ|9bw
zcUcbdf#`t97B6<<Dz`@y>7`s{U6Ss+jHp}i_rC^|56<!UuFHRiihq`Y5n#Dn#%wnP
zZdUky1j#<lm`~O$Uj!n~70=<OT7|_al^mn=t1)|Reo2jW@nArJXSoD67wV(WU!iN=
ziwtIUm+rqB0rA9p4<;&K-H~*0)K~aHh1z^0>wPvmZMcnwqN&}hAxex7*5L-v#L)k|
z=hefC5<8Ci1^e^Z4f&HOW<X-mj9Agg^cuCs!%oBrBM~y*Rq_Igm_rmHN9g6b2Sm(P
zKuS&m526TKtKI@_*#$u4vLJ>nJ_82j&WA?(Yiewe^pSaib+t2KHv2!b8f4X<1f8ey
zQ++Z{&8b1xxMe#g(}wy_KiMSdKByxLY~peFKq-h)v>*Tr@%7sCt$Jd2A3BQHXZ6W4
z8blsch@G00bCo!k0^Aj#Q}-VNhV1woss34foEIBOFu@}VnG?NZ#OeXz_}g?AmW>q5
z<CBH>;@0P<)h34k1GHli)B`21jIMA_@v25gFuc9vo0SBIl5!|@Khla8dVhfv5}oBA
zAn{TTy?9N2C|7t3d)$h}(1od5`Wm-Z`2%~d%>WIp2v`<<s6^FhwkG3eKd`qm404Y?
zyZSB(^^l9?&vIT%zi&b7a5yt}YiKo$N4TKiB`GkwQgit2AQ*6znLLYqu#0rm<zBh>
z%*h<<>dIgiC?J#r>7xq@N8=O$d?4#{9bljgHYT~!_H_MsLhW_G_}GihjNz;;e<R1T
ze<jopU3vy;FS)?pc~Lhv!)}+3p;FqeMCk1?hn#&3V2pCviTh7bI*L#(<Vwh+AP-!>
zjzU`%Mv1_y$v~&5y?43Q(~(4=KHTQFIQ<RqV=pfcfaSYkK05#S`Z;JliR<R$ok7-M
zP?awaIyS&n>8XN}ZDY-^xxKznVi*T)*ID8)@@0F#qA%BcNs#ibPIEpAeDbV@KDL#+
zRhpqXbFUF$7C<MTrVQ_9bKs^~GM@Q*Z|M?|qEHQ)CeI^b)|R^?KYoY=&i(+L*C(h+
zdg!jPPEMPgjtQKLjugq?r2i|u(1y{jI1nF>{r6VEkK4WwXf^a?Hypzr$%?!+TtoHG
z0KYmgO$`2Ql}RMGlkhBUNJ-QZaYC}dElQAwmL;r1vnZpDIP*~MX5l}>>z^Aq`S|h%
zp37PMnNZ666>hFeibzap%n`M!Y*X2a)vv6fODt)SdF9su>~#^V%F?Boq~%2lff}J=
zt8;_}fZk}sm~h&)Hqd<%RQyS{8;834Eb!bG4yiZ_Xk0G^NcHU|ATUsPg#8V(W`M8%
z7KUb`UF%EQLQa$B`v?H;btuM=F3+CU)=g<g4DIO;&v-D|shaI()7^;qam!>aH3Pph
z$X<rU9tzI3(T~dz%^W8pTA>Zt-Anl(VD6y!5b*$wm+ZapjsG66`v}~>0f_h7?uR;-
zH-&c_Jqw|`3DKiNB#?D*ILG4j$%uRk&K=}KhUN;6zl`E}E7a6f?go_PieM&SyXTl?
z3iX<ooV-RaUvwvsn9wr=4Ih1bLGtT?t5ZhPARPF=PN}A7H<e=V(0sm{VT8{casXf|
zRkIrx2sNZhQdqe@LpNA5j#1Qtpqd~?qvXW;>9i}-42<T0mXPfSAYz^wC7aG34tNiP
z5*JcWtNWQ9^mxW*=H=f}<?TKnY1_p5yc(pzcrd8ycF#Ag^BZFz>%w91Yx@;hv-o-M
z!@k2Lpdlb%h0j4Q&!!cNpgM47Hpp&u^%B$pDB0J*bz!wI_RsPqV(F%>3rEMcChSkk
zfD}#i&YeJ(APmX)ZR%BSO0-a6le8S1Jf;f;a2HLa)7TfmYAwe!uNZ*t-kO5Ef$P$P
z90j-*X*t9lFtD!;#9mCiUar=f<SD6Jg(?I_P1KbNmbVmMw>16EuFJD`kLXkult;Yc
z#h`AOclrs>(%3O1yc6r`Y(Gb?bFdzlIANR??v}qc-YVf3O|K>6E{F?cJ)52FMjpaC
zY<&naeqKPt;G+x+y#;YGoAY6ESV@|P(E!VLho4e&Dx?THfuMscQ)tknHr}F&I1Dym
zGe0)bB0nf>S5w@M(_-3wZ{?gEk_+kE@yx`4f2D7#R>bBlW56zAbrgy=kkA6Ryv4hB
zfN|^nStplBw)>p*<s|5fnm#z|GTL*mAee+tD#gY>j)UV!*Ht!=^F88}SF;m+vU>+Y
zC0y}=9_%-i1+WTu9oFmiA`+Ah**2)Wmn1<IKjq;H`Zl;0PBpx(2TxQ({f6>92AF0P
zDx9{Dj_C08xj*t>YmKFtL-+zm%;h53bKm87vwAp$J%E$vRGP270^&v435>{Xb9s`H
zA^P6qN@Rl;4WI>G@Sqe4vcCA^ME_O&k8O4)wq<?<YLVk;wc^r<e$7!4EduqlPK>o9
z{<cX1t83UpljX@>&7OC@47n+;?!s<r04kWBj5_n5Wd5)0+dwry)uwPuxx`(Xib-F|
zxk+oMw}mO!Knd>BZF5ot?JVm_A+q7Y)QA(n0`j)3Lnen$7=tuzF3iO&dpF#b_O{#f
zj4J~UF-tH_C|WD5H!Eu1;KlFn>HAOV+KmZ)H)Oj`CnOOhh(~CU9WB?e@GAf$-x~wr
z1+WU=$1CRmME28Lp}RgGm}hK~G9sEo!rAp2eEPs4`x;2392P#@+z2?=WXko1Jx0Xz
zA{*#g;#siwm64#V7sf@!(X!gMTr+0V`$<otDH(aqL6L6ps%SHjoDx%h31IU(jW;_U
zL&dXt@>x5xRpzsH-ozC)gTK;S05~5@9-Cc(%l}zVYXSgB46Hsv{?BPiucf71OuyYo
z@b2lylMipvi3h3TkBfDciYD&LzxseTw^gS>rg3Fi)?s0h9=@&id^OSeC+~SQNx=0m
ze1l`}5B4)fcj#^oY)tO2jhttqET9@TG}l-TyP#B|phI}2X8dqd@8qUHlOJhUv(bhQ
zINotkFf>hB(j3A9bt@>cL48*Pd%35@T}e5Hm53Rjipp+=AO64}5U}EA;J;<8NYHbn
zCH*3gezES*!tl0KBPh}TFhykr@o3~kY`679WqLg|>*J9Gc1rl@DjxN{3@8{PbpH;>
zA?U1=%EDW03wW`lt;cG;VHDxTr?iF%|32$H6L`e%?ZcTt;g>)>ygBo+b#?v$?Qs1+
znd$1k!BK4QTLGDAzO9m@2adC-pIb$|QCVj+<MQS|5pQ6m$ieAvFOOYf)LkG*{Zse>
z-pFt7b9Hq7-%bDV8vmD$v{I!_p;?weEhMHVKG(;*%9?^u?F?xU2;a`X7}rSc9NGEc
z+$7}tm0h49<^;{f#lprJZ^3;cy7|oDmnW^&dhYGl&aBQP0)YBO?EgIp(N*lU3A3Pt
zvqD~R1w-ZDIK5e!KGhT>*hg~V-0|(xP5o|AIL(DaACr~l2>02_P6r>~#=h01Q&$DM
z(%_Ik&%dK?1XF-X^$|ZXC~&Te;QRLRhN1<?-a7w7Uy=V9slihu@B7UhWO&Cwj(5HY
z{8alUs%G>6(T|sKXn6_}i^uRsQQJAgmOZma(w)1CJ8Jy=YV`Q@{yzHT0ndNygD<ok
zXE;J4=0g``XSs0vFs-?Gp{L5bqzue8P*f?X`Bm8mEh^}Ulw*<|Wzz}F*#5)FzuI>o
zfIe3>l)0nA-40=}%idthcd;Liqx}fT=d}c!XK@lW&Z-ZJ-QvAN^Stv)J$w}!3Gjk+
zE|+n23;Jh-SGX!?DOHEv-$-D02_MOZqlW0A;-|aJ@|}?)JMx;%ImU`v*=ueA`LhQO
zsjh!fRlJ79fE$S5D`cVlgbk~%!F!?9<u9TNI&R1(qh`QOJaTU(PxTIW0-7E7ZKBT!
z?z0&AQJlK}e*I5?=H!ml+b~goy#oW>vrrOW=r~n*ue|V;Umy5!j6q*#2?7?8wKszN
z={4~YsA#m@3xW7%XAGqM3*um)a5p4BV|EjeQ|>(EIc>N4ppOv^aN<YW{J#ON#Zzdn
zO@gC)IlL^oq_iU%Ya{6HfR1rD2rtK;OMtD8UU-d>aZCf8L&u@XvKY?QPZPa9Y*>Ey
zm<svkLWL|Xx4GO2+)=ahfMC@4?FXo^qHM7PnpFJMbJ1zhOQIPW?n3Fw?cE935s_XA
zxBKh;)-Pv3TdE6s6pzw&*8BB==B`=Jp^}U~fVeO61}V_CSyXuy(JR17G}%lRt7~hD
zj%GkUT<DvrrDtFV=>9|SU%MF=7=J}#|Gd(_BezoO-nSpIq}2HR$F8`>XB3iS_IDeN
zgA4o|aFeAMXzX`6sZLM_AE!u@L=F{G&G5p`&8t~DY)#KjRnM6g2(_3xL$PampRj4G
zq&uQ}2g`PwP9TJumbi2&dHUlEU%#?z@eM!VKz;5a3r52AUGLB2i|cg*I+3s>teV+J
zkT#w2R@@3S4Fp)K?6z>k!p5I{Vb?0ksYE;nJC&Q=9?n0j$*^UsF^9|tUI3In1HV<m
zrS&Wz?mv_0H*tL$`iyab)1h^ddyA%E7tDb3vgObq@D|{TyIU3!zj!Yryg`hENcrDs
zwMk0aRbm46zzRsuzT13YXdSm3v{&0D)Up%^dmu?%os_6@kI|--ONtsxjOAC1KR0d=
zygOjol>j1x4?Pcqwmdwc6&w(Np(+c|kCQboI+4l(1zP?{i@C=|!zNZGhh7raFmh*L
z*hjz`i<DO1#{W!&fbzdRmia<058(8c>B9RA)5B>_9#<NG|EfcgB259^nOe_HY5NyQ
zf3w^PfkG6T$x9#sy|*m>4-w@<Xs5^Z8xx2W&FJN^Ig%D#%69j0JZdF}I?BY*_gu?+
z4UNB=O-}*#F*}swCUhSum7axW=q{kvjP>*hbF{>%(T=vnX;zvQmL)6z_x3Ny-fByC
zP}KYDP#+WT)#bUdGWnD`;2BShw>9nmcU)<K-VugxKu$4c3#a*_H4oIbQmE#62kAqy
zUmFp~j+I1W7ZRs>-$MAjc`xWNa7Y$k6N5DI9<xKz?t`BaBv`f8_->Rt0r{^CH7lQm
z8j2+Z)J0C(f;1@4Ug)`xB<&vd-K7%@GN4(vn4YF7;PVl^ke>AA)~*<64Fv0vY?%>w
zxY;9Q%X+M?`pi({{7{P#+@Fl@xN*hS)$FJORmx@EUy}d5j)bBh0}6XOmlMWz5Qs00
zFkBydx}dREJHtKDJ{vg48SkQHSBm<oz-kt<n0aHry5sz(hDNAk`90U%6>bu0vTYo6
zrVII0uDpF)bnJrmePk8^$F#7I&5^$90MdWj8M1ce{B*n;_N@+mChkA$a=<5%hg?g)
z0cDY<_fA<cJluIsN`YJ3-?RQN+7nS4a^Fvj-w*sd|4U&T!1~%}?lI!FOHg;L<lK$^
zK#2<$VT*sg=)Z3scL@gI$v*AbNY8q%)o1!&@Xh|#g^lr2jR0e^Uzuom5rW4!d4Ssy
z6Wfuut6RZ#AooSz<xTpp(|ai$Z2!ejZVWD8_K=hLo7Jmo_b7a|G8MoIlRlkr)AHZh
z?;mH3uI=#sx+NqVG@<;b@2^?2hODVdGWjFv`)7(k#p3_)Yp49y9HS2AiZENAhVNXX
NsivozuY4r%{{z(udW--7

literal 0
HcmV?d00001

diff --git a/project/example/example.py b/project/example/example.py
new file mode 100644
index 00000000..31d8eb2f
--- /dev/null
+++ b/project/example/example.py
@@ -0,0 +1,14 @@
+from project.scr.persons import Player
+from project.scr.strategies import Aggressive, Optimal1, Optimal2
+from project.scr.game import Game
+
+
+players = [
+    Player(strategy=Optimal1()),
+    Player(strategy=Optimal2()),
+    Player(strategy=Aggressive()),
+    Player(chips=15),
+]
+blackjack = Game(players)
+blackjack.play_round_with_show_states()
+blackjack.play_round_with_show_states()
diff --git a/project/example/example.txt b/project/example/example.txt
new file mode 100644
index 00000000..440924f2
--- /dev/null
+++ b/project/example/example.txt
@@ -0,0 +1,170 @@
+The round has begun
+Round: 1
+
+
+Bets have been placed
+Player's 1 bet: 10
+Player's 2 bet: 10
+Player's 3 bet: 20
+Player's 4 bet: 10
+
+
+The dealer took a closed and an open card
+The dealer's first card:
+4 of Diamonds
+
+
+The players took the cards
+Player 1's hand:
+Hand: 5 of Clubs; 9 of Hearts;
+Score: 14
+History of actions:
+['add card', 'add card', 'pass']
+
+Player 2's hand:
+Hand: 4 of Spades; 6 of Diamonds; 3 of Hearts; 8 of Clubs;
+Score: 21
+History of actions:
+['add card', 'add card', 'double down', 'add card', 'add card']
+
+Player 3's hand:
+Hand: 10 of Spades; 9 of Clubs; 3 of Spades;
+Score: bust
+History of actions:
+['add card', 'add card', 'add card']
+
+Player 4's hand:
+Hand: 8 of Spades; Q of Clubs;
+Score: 18
+History of actions:
+['add card', 'add card', 'pass']
+
+
+
+The dealer opens the second card
+Dealer's Hand:
+Hand: 4 of Diamonds; 3 of Clubs;
+Score: 7
+
+
+The dealer takes the cards
+Dealer's Hand:
+Hand: 4 of Diamonds; 3 of Clubs; 6 of Hearts; 2 of Spades; 5 of Hearts;
+Score: 20
+
+
+The results of the game are summarized
+The result of player 1
+Player 1's hand:
+Hand: 5 of Clubs; 9 of Hearts;
+Score: 14
+History of actions:
+['add card', 'add card', 'pass']
+Hand condition: Losing hand
+
+The result of player 2
+Player 2's hand:
+Hand: 4 of Spades; 6 of Diamonds; 3 of Hearts; 8 of Clubs;
+Score: 21
+History of actions:
+['add card', 'add card', 'double down', 'add card', 'add card']
+Hand condition: Winning hand
+
+The result of player 3
+Player 3's hand:
+Hand: 10 of Spades; 9 of Clubs; 3 of Spades;
+Score: bust
+History of actions:
+['add card', 'add card', 'add card']
+Hand condition: Losing hand
+
+The result of player 4
+Player 4's hand:
+Hand: 8 of Spades; Q of Clubs;
+Score: 18
+History of actions:
+['add card', 'add card', 'pass']
+Hand condition: Losing hand
+
+
+
+The round is over
+
+
+The round has begun
+Round: 2
+
+
+Bets have been placed
+Player's 1 bet: 10
+Player's 2 bet: 10
+Player's 3 bet: 20
+Player's 4 bet: not enough chips
+
+
+The dealer took a closed and an open card
+The dealer's first card:
+6 of Spades
+
+
+The players took the cards
+Player 1's hand:
+Hand: Q of Diamonds; 10 of Hearts;
+Score: 20
+History of actions:
+['add card', 'add card', 'pass']
+
+Player 2's hand:
+Hand: A of Spades; 5 of Spades; 7 of Hearts; 4 of Clubs;
+Score: 17
+History of actions:
+['add card', 'add card', 'add card', 'add card', 'pass']
+
+Player 3's hand:
+Hand: K of Spades; 7 of Diamonds; 3 of Spades;
+Score: 20
+History of actions:
+['add card', 'add card', 'add card', 'pass']
+
+
+
+The dealer opens the second card
+Dealer's Hand:
+Hand: 6 of Spades; 5 of Hearts;
+Score: 11
+
+
+The dealer takes the cards
+Dealer's Hand:
+Hand: 6 of Spades; 5 of Hearts; 6 of Clubs;
+Score: 17
+
+
+The results of the game are summarized
+The result of player 1
+Player 1's hand:
+Hand: Q of Diamonds; 10 of Hearts;
+Score: 20
+History of actions:
+['add card', 'add card', 'pass']
+Hand condition: Winning hand
+
+The result of player 2
+Player 2's hand:
+Hand: A of Spades; 5 of Spades; 7 of Hearts; 4 of Clubs;
+Score: 17
+History of actions:
+['add card', 'add card', 'add card', 'add card', 'pass']
+Hand condition: Equal score
+
+The result of player 3
+Player 3's hand:
+Hand: K of Spades; 7 of Diamonds; 3 of Spades;
+Score: 20
+History of actions:
+['add card', 'add card', 'add card', 'pass']
+Hand condition: Winning hand
+
+
+
+The round is over
diff --git a/project/scr/desk.py b/project/scr/desk.py
new file mode 100644
index 00000000..3b2a8cbc
--- /dev/null
+++ b/project/scr/desk.py
@@ -0,0 +1,77 @@
+from project.scr.persons import Player, Dealer
+from project.scr.objects import Hand
+
+
+class Desk:
+    """
+    The game table.
+    The class contains information about the players, their cards and
+    the dealer, as well as methods responsible for events in the game.
+
+    Methods:
+    -------
+    `dealer_give_card(target_hand: Hand) -> None`:
+        Issues a card from the dealer's deck to the target hand.
+
+    `place_first_bets() -> None`:
+        Players place their first bets in the amount specified in the strategy.
+
+    `split(player: Player, id_hand: int) -> None`:
+        The method is called if the player has chosen split.
+
+    `check_bet(id_player: int, bet: int) -> bool`:
+        Checks if the player has enough chips to bet.
+
+    `next() -> None`:
+        Sets the starting parameters for the table.
+
+    """
+
+    def __init__(self, players: list[Player], dealer_num_deck: int) -> None:
+        """Initializing a Desk object."""
+        self.players = players
+        self.dealer = Dealer(dealer_num_deck)
+        self.hands: dict[Player, list[Hand]] = {}
+
+    def dealer_give_card(self, target_hand: Hand) -> None:
+        """
+        Issues a card from the dealer's deck to the target hand.
+
+        Args:
+            target_hand (Hand): the hand to add the card to.
+        """
+        card = self.dealer.give_card()
+        target_hand.add_card(card)
+
+    def place_first_bets(self) -> None:
+        """Players place their first bets in the amount specified in the strategy."""
+        for id_player, player in enumerate(self.players):
+            bet = player.strategy.first_bet
+            if not player.check_bet(bet):
+                self.hands[player][0].out()
+                continue
+            self.hands[player][0].set_first_bet(bet)
+            player.diff_chips(-bet)
+
+    def split(self, player: Player, id_hand: int) -> None:
+        """
+        The method is called if the player has chosen split.
+
+        Args:
+            player (Player): the player who chose the split.
+            id_hand (int): the number of the hand for which the split is selected.
+        """
+        hand = self.hands[player][id_hand]
+
+        split_hand_1, split_hand_2 = hand.split()
+        self.dealer_give_card(split_hand_1)
+        self.dealer_give_card(split_hand_2)
+
+        self.hands[player][id_hand] = split_hand_1
+        self.hands[player].append(split_hand_2)
+
+    def next(self) -> None:
+        """Sets the starting parameters for the table."""
+        self.dealer.restart()
+        for player in self.players:
+            self.hands[player] = [Hand()]
diff --git a/project/scr/game.py b/project/scr/game.py
new file mode 100644
index 00000000..5e77101c
--- /dev/null
+++ b/project/scr/game.py
@@ -0,0 +1,368 @@
+from project.scr.objects import HandStates, Card, Hand
+from project.scr.persons import Player
+from project.scr.strategies import Action
+from project.scr.desk import Desk
+from enum import Enum
+
+
+def show_hand_player(hand: Hand, id_player: int) -> None:
+    print(f"Player {id_player + 1}'s hand:")
+    hand.show_hand()
+
+
+class GameStates(Enum):
+    """Enum to indicate the state of the game."""
+
+    START = "The round has begun"
+    PLACE_BETS = "Bets have been placed"
+    DEALER_START = "The dealer took a closed and an open card"
+    TOOK_CARDS = "The players took the cards"
+    DEALER_SECOND_CARD = "The dealer opens the second card"
+    DEALER_PLAY = "The dealer takes the cards"
+    RESULTS = "The results of the game are summarized"
+    END = "The round is over"
+
+
+class Game:
+    """
+    The controller class for the game table.
+    It contains information about the players,
+    the current state of the game and the game table.
+
+    Methods:
+    -------
+    `_show_state() -> None`:
+        The method of displaying information depending on the current state of the game.
+
+    `_start_game() -> None`:
+        Sets the initial parameters before starting the game.
+
+    `_place_bets() -> None`:
+        The players place their first bets.
+
+    `_dealer_start() -> None`:
+        The dealer starts the game.
+
+    `_take_cards() -> None`:
+        The players take the cards according to the strategy.
+
+    `_dealer_second_card() -> None`:
+        The dealer opens the second card.
+
+    `_dealer_play() -> None`:
+        The dealer takes the missing cards.
+
+    `_round_results() -> None`:
+        The results of the game are summarized.
+
+    `_end_round() -> None`:
+        Completes the round.
+
+    `_play_with_player(id_player: int, dealer_card: Card) -> bool`:
+        Player takes the cards depending on the strategy.
+
+    `get_state() -> GameStates`:
+        Returns the game state.
+
+    `play_steps(num_steps: int = 8) -> None`:
+        Performs the first n steps of the game.
+
+    'play_round_with_show_states(self) -> None'
+        Starts a full round and outputs information to the console at each stage.
+    """
+
+    def __init__(self, players: list[Player], dealer_num_deck: int = 1) -> None:
+        """Initializing a Game object"""
+        self._desk = Desk(players, dealer_num_deck)
+        self._num_round = 0
+        self._round_state = GameStates.START
+
+    def _show_state(self) -> None:
+        """The method of displaying information depending on the current state of the game."""
+        print(self._round_state.value)
+        match self._round_state:
+            case GameStates.START:
+                print("Round:", self._num_round)
+            case GameStates.PLACE_BETS:
+                for id_player, player in enumerate(self._desk.players):
+                    self._desk.hands[player][0].show_bet(id_player)
+            case GameStates.TOOK_CARDS:
+                for id_player, player in enumerate(self._desk.players):
+                    if self._desk.hands[player][0].get_state() is HandStates.OUT:
+                        continue
+                    for hand in self._desk.hands[player]:
+                        show_hand_player(hand, id_player)
+                        hand.show_history()
+                    print()
+            case GameStates.RESULTS:
+                for id_player, player in enumerate(self._desk.players):
+                    if self._desk.hands[player][0].get_state() is HandStates.OUT:
+                        continue
+                    print(f"The result of player {id_player + 1}")
+                    for hand in self._desk.hands[player]:
+                        show_hand_player(hand, id_player)
+                        hand.show_history()
+                        hand.show_state()
+                    print()
+            case GameStates.DEALER_START:
+                print("The dealer's first card:")
+                print(self._desk.dealer.hand.get_card(0))
+            case GameStates.DEALER_PLAY:
+                self._desk.dealer.show_hand()
+            case GameStates.DEALER_SECOND_CARD:
+                self._desk.dealer.show_hand()
+        print("\n")
+
+    def _start_game(self) -> None:
+        """Sets the initial parameters before starting the game."""
+        self._round_state = GameStates.START
+        self._num_round += 1
+        self._desk.next()
+
+    def _place_bets(self) -> None:
+        """The players place their first bets."""
+        self._round_state = GameStates.PLACE_BETS
+        self._desk.place_first_bets()
+
+    def _dealer_start(self) -> None:
+        """The dealer took a closed and an open card."""
+        self._round_state = GameStates.DEALER_START
+        self._desk.dealer_give_card(self._desk.dealer.hand)
+        self._desk.dealer_give_card(self._desk.dealer.hand)
+
+    def _take_cards(self) -> None:
+        """The players take the cards according to the strategy."""
+        self._round_state = GameStates.TOOK_CARDS
+        dealer_card = self._desk.dealer.hand.get_card(0)
+        for id_player, player in enumerate(self._desk.players):
+            player_hand = self._desk.hands[player][0]
+            if not player_hand.in_playing:
+                continue
+            self._desk.dealer_give_card(self._desk.hands[player][0])
+            self._desk.dealer_give_card(self._desk.hands[player][0])
+
+            if player_hand.check_blackjack():
+                player_hand._state = HandStates.BLACKJACK
+            if (
+                player_hand.get_state() is HandStates.BLACKJACK
+                and not self._desk.dealer.hand.get_card(0).name
+                in {
+                    "10",
+                    "J",
+                    "Q",
+                    "K",
+                    "A",
+                }
+            ):
+                player.diff_chips(int(player_hand.get_bet() * 2.5))
+                player_hand.game_over()
+                continue
+            elif (
+                player_hand.get_state() is HandStates.BLACKJACK
+                and self._desk.dealer.hand.get_card(0).name == "A"
+            ):
+                if player.strategy.even_money:
+                    player_hand.even_money()
+                    player.diff_chips(int(player_hand.get_bet() * 2))
+                    player_hand.game_over()
+                    continue
+
+            while self._play_with_player(id_player, dealer_card):
+                pass
+
+    def _dealer_second_card(self) -> None:
+        """The dealer opens the second card."""
+        self._round_state = GameStates.DEALER_SECOND_CARD
+        for (id_player, player) in enumerate(self._desk.players):
+            for id_hand, hand in enumerate(self._desk.hands[player]):
+                if not hand.in_playing:
+                    continue
+
+                if (
+                    hand.get_state() is HandStates.BLACKJACK
+                    and not self._desk.dealer.hand.get_state() is HandStates.BLACKJACK
+                ):
+                    player.diff_chips(int(hand.get_bet() * 2.5))
+                    hand.game_over()
+                elif (
+                    not hand.get_state() is HandStates.BLACKJACK
+                    and self._desk.dealer.hand.get_state() is HandStates.BLACKJACK
+                ):
+                    hand._state = HandStates.LOSE
+                    hand.game_over()
+
+    def _dealer_play(self) -> None:
+        """The dealer takes the missing cards."""
+        self._round_state = GameStates.DEALER_PLAY
+        while True:
+            dealer_score = self._desk.dealer.hand.get_score()
+            if dealer_score == -1 or dealer_score >= 17:
+                break
+            self._desk.dealer_give_card(self._desk.dealer.hand)
+
+    def _round_results(self) -> None:
+        """The results of the game are summarized."""
+        self._round_state = GameStates.RESULTS
+        dealer_score = self._desk.dealer.hand.get_score()
+        for (id_player, player) in enumerate(self._desk.players):
+            for id_hand, hand in enumerate(self._desk.hands[player]):
+                if not hand.in_playing:
+                    continue
+
+                hand_score = hand.get_score()
+
+                if hand_score > dealer_score:
+                    hand._state = HandStates.WIN
+                    player.diff_chips(hand.get_bet() * 2)
+                    hand.game_over()
+
+                elif hand_score == dealer_score:
+                    hand._state = HandStates.DRAWN_GAME
+                    player.diff_chips(hand.get_bet())
+                    hand.game_over()
+                else:
+                    hand._state = HandStates.LOSE
+                    hand.game_over()
+
+    def _end_round(self) -> None:
+        """Completes the round."""
+        self._round_state = GameStates.END
+
+    def _play_with_player(self, id_player: int, dealer_card: Card) -> bool:
+        """
+        Player takes the cards depending on the strategy.
+
+        Args:
+            id_player (int): the index of the player in the list of players.
+            dealer_card (Card): dealer's open card.
+
+        Returns:
+            result (bool): a flag indicating that the player has not finished the game.
+        """
+        target_player = self._desk.players[id_player]
+        for id_hand, hand in enumerate(self._desk.hands[target_player]):
+            if not hand.in_playing or hand.get_history()[-1] == "pass":
+                continue
+            while True:
+                score = hand.get_score()
+                if score == -1:
+                    hand._state = HandStates.LOSE
+                    hand.game_over()
+                    break
+                elif score == 21:
+                    break
+
+                action = target_player.strategy.play(hand, dealer_card)
+                match action:
+                    case Action.PASS:
+                        hand.action_pass()
+                        break
+
+                    case Action.TAKE:
+                        self._desk.dealer_give_card(hand)
+
+                    case Action.SPLIT:
+                        if not target_player.check_bet(hand.get_bet()):
+                            hand.action_pass()
+                            break
+                        target_player.diff_chips(-hand.get_bet())
+                        self._desk.split(target_player, id_hand)
+                        return True
+
+                    case Action.DOUBLE:
+                        if not target_player.check_bet(hand.get_bet()):
+                            self._desk.dealer_give_card(hand)
+                            continue
+                        target_player.diff_chips(-hand.get_bet())
+                        hand.double_down()
+                        self._desk.dealer_give_card(hand)
+
+                    case Action.TRIPLING:
+                        if not hand.double_bet:
+                            raise ValueError(
+                                "Before tripling the bets, you need to double them."
+                            )
+                        if not target_player.check_bet(hand.get_bet() // 2):
+                            self._desk.dealer_give_card(hand)
+                            continue
+                        target_player.diff_chips(-hand.get_bet() // 2)
+                        hand.tripling_bet()
+
+                    case Action.SURRENDER:
+                        target_player.diff_chips(hand.get_bet() // 2)
+                        hand._state = HandStates.LOSE
+                        hand.game_over()
+                        break
+        return False
+
+    def get_state(self) -> GameStates:
+        """Returns the game state."""
+        return self._round_state
+
+    def play_steps(self, num_steps: int = 8) -> None:
+        """Performs the first number steps of the game."""
+        if not num_steps:
+            return
+
+        self._start_game()
+        num_steps -= 1
+        if not num_steps:
+            return
+
+        self._place_bets()
+        num_steps -= 1
+        if not num_steps:
+            return
+
+        self._dealer_start()
+        num_steps -= 1
+        if not num_steps:
+            return
+
+        self._take_cards()
+        num_steps -= 1
+        if not num_steps:
+            return
+
+        self._dealer_second_card()
+        num_steps -= 1
+        if not num_steps:
+            return
+
+        self._dealer_play()
+        num_steps -= 1
+        if not num_steps:
+            return
+
+        self._round_results()
+        num_steps -= 1
+        if not num_steps:
+            return
+
+        self._end_round()
+
+    def play_round_with_show_states(self) -> None:
+        """Performs a full round and outputs information to the console at each stage."""
+        self._start_game()
+        self._show_state()
+
+        self._place_bets()
+        self._show_state()
+
+        self._dealer_start()
+        self._show_state()
+
+        self._take_cards()
+        self._show_state()
+
+        self._dealer_second_card()
+        self._show_state()
+
+        self._dealer_play()
+        self._show_state()
+
+        self._round_results()
+        self._show_state()
+
+        self._end_round()
+        self._show_state()
diff --git a/project/scr/objects.py b/project/scr/objects.py
new file mode 100644
index 00000000..411adcac
--- /dev/null
+++ b/project/scr/objects.py
@@ -0,0 +1,325 @@
+import random
+from itertools import product
+from enum import Enum
+from typing import Any
+
+
+class Card:
+    """
+    A class containing information about the value of the card.
+
+    Methods:
+    -------
+    `__str__() -> str`:
+        Returns a string representation of the card.
+    """
+
+    def __init__(self, suit: str, name: str | int) -> None:
+        """Initializing a Card object"""
+        self.suit = suit
+        self.name = str(name)
+
+    def __str__(self) -> str:
+        """Returns a string representation of the card."""
+        return f"{self.name} of {self.suit}"
+
+
+class Deck:
+    """
+    A deck of cards.
+
+    Methods:
+    -------
+    `shuffle() -> None`:
+        Shuffles the deck of cards.
+
+    `pull() -> Card`:
+        Removes and returns the card at the beginning of the deck.
+
+    """
+
+    def __init__(self) -> None:
+        """Initializing a Deck object"""
+        self._cards = [
+            Card(card[0], card[1])
+            for card in product(
+                ["Spades", "Hearts", "Diamonds", "Clubs"],
+                list(range(2, 11)) + ["J", "Q", "K", "A"],
+            )
+        ]
+        self.shuffle()
+
+    def shuffle(self) -> None:
+        """Shuffles the deck of cards."""
+        random.shuffle(self._cards)
+
+    def pull(self) -> Card:
+        """Removes and returns the card at the beginning of the deck."""
+        return self._cards.pop(0)
+
+
+class Cards:
+    """
+    The data-descriptor class for storing cards, scores, and game history.
+    """
+
+    def __init__(self) -> None:
+        self._data: dict[Hand, dict[str, Any]] = {}
+
+    def __get__(self, instance: Any, owner: Any) -> Any:
+        if instance is None:
+            return self
+        if instance not in self._data:
+            self._data[instance] = {"cards": [], "score": 0, "history": []}
+        return self._data[instance]
+
+    def __set__(self, instance: Any, value: Any) -> None:
+        self._data[instance] = value
+
+
+class HandStates(Enum):
+    """Enum to indicate the state of the hand."""
+
+    DEFAULT = "The game is on"
+    WIN = "Winning hand"
+    LOSE = "Losing hand"
+    BLACKJACK = "Blackjack"
+    DRAWN_GAME = "Equal score"
+    OUT = "Out of the game"
+
+
+class Hand:
+    """
+    The controller class above the Cards data-descriptor.
+    The class responsible for the current state of the hand, the bet and the cards.
+
+    Methods:
+    -------
+    `game_over() -> None`:
+        Resets the bet and withdraws the hand from the game.
+
+    'out() -> None':
+        Switches the hand to an out-of-play state.
+
+    `double_down() -> None`:
+        Doubles the bet.
+
+    `tripling_bet() -> None`:
+        Triples the bet.
+
+    'split() -> tuple["Hand", "Hand"]':
+        The method for calling the split hand.
+
+    'action_pass() -> None':
+        Performs the pass action
+
+    'even_money() -> None':
+        Performs the even money action
+
+    `add_card(card: Card) -> None`:
+        Adds a card to his hand and recalculates the scores.
+
+    `calculate_score() -> None`:
+        Recalculate the scores.
+
+    'set_first_bet(first_bet: int) -> None':
+        If there is no first bid, then sets it.
+
+    `check_blackjack() -> bool`:
+        Check if there is a blackjack on this hand.
+
+    `get_card(id_card: int) -> Any`:
+        Returns the card with the number starting from the first received one.
+
+    `get_scores() -> Any`:
+        Returns the scores.
+
+    'get_cards() -> Any':
+        Returns the cards.
+
+    'get_history() -> Any':
+        Returns the history.
+
+    'get_bet() -> int':
+        Returns the bet.
+
+    'get_state() -> HandStates':
+        Returns the hand state.
+
+    'show_history() -> None':
+        Displays the console history of the player's actions.
+
+    'show_hand(self) -> None':
+        Displays the cards on your hand in the console.
+
+    'show_bet(id_player: int) -> None':
+        Displays the current bet in the console.
+
+    'show_state(self) -> None':
+        Displays the current hand status in the console.
+    """
+
+    _hand = Cards()
+
+    def __init__(self) -> None:
+        """Initializing a Deck object"""
+        self._bet = 0
+        self.in_playing = True
+        self.double_bet = False
+        self.tripled_bet = False
+        self._state = HandStates.DEFAULT
+
+    def game_over(self) -> None:
+        """Resets the bet and withdraws the hand from the game."""
+        self._bet = 0
+        self.in_playing = False
+
+    def out(self) -> None:
+        """Switches the hand to an out-of-play state."""
+        self.game_over()
+        self._state = HandStates.OUT
+
+    def double_down(self) -> None:
+        """Doubles the bet."""
+        self._bet *= 2
+        self.double_bet = True
+        self._hand["history"].append("double down")
+
+    def tripling_bet(self) -> None:
+        """Triples the bet."""
+        self._bet += int(self._bet // 2)
+        self.tripled_bet = True
+        self._hand["history"].append("tripling bet")
+
+    def split(self) -> tuple["Hand", "Hand"]:
+        """
+        The method for calling the split hand.
+
+        Returns:
+            result ("Hand", "Hand"): two hands after the split.
+        """
+        bet = self._bet
+
+        split_hand_1 = Hand()
+        split_hand_1._hand["history"] = ["split"]
+        split_hand_1._bet = bet
+        split_hand_1.add_card(self.get_card(0))
+
+        split_hand_2 = Hand()
+        split_hand_2._hand["history"] = ["split"]
+        split_hand_2._bet = bet
+        split_hand_2.add_card(self.get_card(1))
+        return split_hand_1, split_hand_2
+
+    def action_pass(self) -> None:
+        """Performs the pass action"""
+        self._hand["history"].append("pass")
+
+    def even_money(self) -> None:
+        """Performs the even money action"""
+        self._hand["history"].append("even money")
+
+    def add_card(self, card: Card) -> None:
+        """
+        Adds a card to his hand and recalculate the scores.
+
+        Args:
+            card (Card): the card that was added to the hand.
+        """
+        self._hand["cards"].append(card)
+        self.calculate_score()
+        self._hand["history"].append("add card")
+
+    def calculate_score(self) -> None:
+        """Recalculate the scores."""
+        scores = {0}
+        for card in self._hand["cards"]:
+            match card.name:
+                case "A":
+                    scores = set(map(lambda x: x + 1, scores))
+                    scores.update(set(map(lambda x: x + 10, scores)))
+                case "K" | "Q" | "J" | "10":
+                    scores = set(map(lambda x: x + 10, scores))
+                case _:
+                    scores = set(map(lambda x: x + int(card.name), scores))
+        filter_scores = list(filter(lambda x: x <= 21, scores))
+        if len(filter_scores) == 0:
+            self._hand["score"] = -1
+        else:
+            self._hand["score"] = max(filter_scores)
+
+    def set_first_bet(self, first_bet: int) -> None:
+        """If there is no first bid, then sets it."""
+        if self._bet == 0:
+            self._bet = first_bet
+
+    def check_blackjack(self) -> bool:
+        """
+        Check if there is a blackjack on this hand.
+
+        Returns:
+            result (bool): is blackjack
+        """
+        return 21 == self._hand["score"] and len(self._hand["cards"]) == 2
+
+    def get_card(self, id_card: int) -> Any:
+        """
+        Returns the card with the number starting from the first received one.
+
+        Args:
+            id_card (int): index of the card in hand.
+
+        Returns:
+            result (Any): card is under the desired index.
+        """
+        return self._hand["cards"][id_card]
+
+    def get_score(self) -> Any:
+        """Returns the scores"""
+        return self._hand["score"]
+
+    def get_cards(self) -> Any:
+        """Returns the cards"""
+        return self._hand["cards"]
+
+    def get_history(self) -> Any:
+        """Returns the history."""
+        return self._hand["history"]
+
+    def get_bet(self) -> int:
+        """Returns the bet."""
+        return self._bet
+
+    def get_state(self) -> HandStates:
+        """Returns the hand state."""
+        return self._state
+
+    def show_history(self) -> None:
+        """Displays the console history of the player's actions."""
+        print("History of actions:")
+        print(self._hand["history"], sep=", ")
+
+    def show_hand(self) -> None:
+        """Displays the cards on your hand in the console."""
+        print("Hand:", end=" ")
+        for card in self._hand["cards"]:
+            print(card, end="; ")
+        print()
+
+        print("Score:", end=" ")
+        if self._hand["score"] == -1:
+            print("bust")
+        else:
+            print(self._hand["score"])
+
+    def show_bet(self, id_player: int) -> None:
+        """Displays the current bet in the console."""
+        print(f"Player's {id_player + 1} bet:", end=" ")
+        if self._state is HandStates.OUT:
+            print("not enough chips")
+        else:
+            print(self._bet)
+
+    def show_state(self) -> None:
+        """Displays the current hand status in the console."""
+        print("Hand condition:", self._state.value)
diff --git a/project/scr/persons.py b/project/scr/persons.py
new file mode 100644
index 00000000..235fa096
--- /dev/null
+++ b/project/scr/persons.py
@@ -0,0 +1,70 @@
+from project.scr.objects import Deck, Hand, Card
+import random
+from project.scr.strategies import Basic, Strategy
+
+
+class Player:
+    """
+    The player's class stores the number of chips and the strategy of the game.
+
+    Methods:
+    -------
+    'diff_chips(delta: int) -> None':
+        Calculates the difference of chips.
+
+    'check_bet(self, bet: int) -> bool':
+        Checks if the player has enough chips to bet.
+    """
+
+    def __init__(self, strategy: Strategy = Basic(), chips: int = 100) -> None:
+        """Initializing a Player object."""
+        self.strategy = strategy
+        self._chips = chips
+
+    def diff_chips(self, delta: int) -> None:
+        """Calculates the difference of chips."""
+        self._chips += delta
+
+    def check_bet(self, bet: int) -> bool:
+        """Checks if the player has enough chips to bet."""
+        if bet > self._chips:
+            return False
+        return True
+
+
+class Dealer:
+    """
+    The dealer's class that stores the dealer's decks and hand.
+
+    Methods:
+    -------
+    'give_card() -> Card':
+        A method for extracting a card from the deck.
+
+    'show_hand() -> None':
+        A method for displaying information about the dealer's hand in the console.
+
+    'restart() -> None':
+        A method that returns the dealer's state to the beginning of the game.
+    """
+
+    def __init__(self, num_decks: int) -> None:
+        """Initializing a Dealer object."""
+        self._num_decks = num_decks
+        self.hand = Hand()
+        self._decks = [Deck() for _ in range(num_decks)]
+
+    def give_card(self) -> Card:
+        """A method for extracting a card from the deck."""
+        id_deck = random.randint(0, self._num_decks - 1)
+        return self._decks[id_deck].pull()
+
+    def show_hand(self) -> None:
+        """A method for displaying information about the dealer's hand in the console."""
+        print("Dealer's Hand:")
+        self.hand.show_hand()
+
+    def restart(self) -> None:
+        """A method that returns the dealer's state to the beginning of the game."""
+        self.hand = Hand()
+        self._decks = [Deck() for _ in range(self._num_decks)]
diff --git a/project/scr/strategies.py b/project/scr/strategies.py
new file mode 100644
index 00000000..2efd860a
--- /dev/null
+++ b/project/scr/strategies.py
@@ -0,0 +1,237 @@
+from enum import Enum
+from project.scr.objects import Hand
+from project.scr.objects import Card
+
+
+class Action(Enum):
+    """Enumeration for actions taken by the player."""
+
+    PASS = 1
+    TAKE = 2
+    SPLIT = 3
+    DOUBLE = 4
+    TRIPLING = 5
+    SURRENDER = 6
+
+
+class Strategy:
+    """
+    Parent class for strategies
+
+    Methods:
+    ______
+    'play(player_hand: Hand, dealer_card: Card) -> Action':
+        The method responsible for taking action in a specific situation.
+    """
+
+    first_bet = 10
+    even_money = False
+
+    def play(self, player_hand: Hand, dealer_card: Card) -> Action:
+        """
+        The method responsible for taking action in a specific situation.
+
+        Args:
+            player_hand (Hand): the hand of the player making the decision.
+            dealer_card (Card): the dealer's card that the players see.
+
+        Results:
+            return (Action): the action taken by the player.
+        """
+        raise NotImplementedError("Subclass must implement play method")
+
+
+class Basic(Strategy):
+    def __init__(self) -> None:
+        self.even_money = True
+
+    def play(self, player_hand: Hand, dealer_card: Card) -> Action:
+        score = player_hand.get_score()
+        if score < 17:
+            return Action.TAKE
+        return Action.PASS
+
+
+class Optimal1(Strategy):
+    def play(self, player_hand: Hand, dealer_card: Card) -> Action:
+        score = player_hand.get_score()
+        cards = player_hand.get_cards()
+        dealer_card_name = dealer_card.name
+
+        split_res = check_split(cards, dealer_card.name)
+        if not split_res is None:
+            return split_res
+
+        double_res = check_double(score, dealer_card_name)
+        if not double_res is None and not player_hand.double_bet:
+            return double_res
+
+        if "A" in set(card.name for card in cards) and len(cards) == 2:
+            soft_hands = check_soft_hands(score - 10, dealer_card_name)
+            if not soft_hands is None:
+                return soft_hands
+
+        steady_hand = check_steady_hands(score, dealer_card_name)
+        if not steady_hand is None:
+            return steady_hand
+
+        if score >= 17:
+            return Action.PASS
+        return Action.TAKE
+
+
+class Aggressive(Strategy):
+    def __init__(self) -> None:
+        self.first_bet = 20
+
+    def play(self, player_hand: Hand, dealer_card: Card) -> Action:
+        score = player_hand.get_score()
+        dealer_card_name = dealer_card.name
+
+        if score <= 6:
+            return Action.DOUBLE
+
+        double_res = check_double(score, dealer_card_name)
+        if not double_res is None and player_hand.double_bet:
+            return Action.TRIPLING
+
+        if score > 19:
+            return Action.PASS
+        return Action.TAKE
+
+
+class Optimal2(Strategy):
+    def play(self, player_hand: Hand, dealer_card: Card) -> Action:
+        score = player_hand.get_score()
+        cards = player_hand.get_cards()
+        dealer_card_name = dealer_card.name
+
+        split_res = check_split(cards, dealer_card_name)
+        if not split_res is None:
+            return split_res
+
+        if not dealer_card_name in {"10", "J", "Q", "K", "A"}:
+            if score in {11, 10}:
+                return Action.DOUBLE
+            elif score <= 16:
+                return Action.TAKE
+        elif score <= 16:
+            return Action.TAKE
+
+        if score >= 17:
+            return Action.PASS
+        return Action.TAKE
+
+
+def check_split(cards: list[Card], dealer_card_name: str) -> Action | None:
+    """
+    The function checks whether it is worth doing a split.
+
+    Args:
+        cards (list[Card]): the cards are in the player's hand.
+        dealer_card_name (str): the dealer's open card.
+
+    Returns:
+        result (Action | None): the action taken.
+    """
+    if len(cards) != 2:
+        return None
+    if cards[1].name == cards[0].name:
+        match cards[0].name:
+            case "A" | "8":
+                if dealer_card_name != "A":
+                    return Action.SPLIT
+                return Action.TAKE
+            case "5":
+                return Action.DOUBLE
+
+            case "4":
+                if dealer_card_name in {"2", "3", "4", "5", "6"}:
+                    return Action.SPLIT
+            case "9":
+                if dealer_card_name in {"7", "10", "11"}:
+                    return Action.PASS
+                return Action.SPLIT
+            case "6":
+                if dealer_card_name in {"2", "3", "4", "5", "6"}:
+                    return Action.SPLIT
+                return Action.TAKE
+            case "2" | "3" | "7":
+                if dealer_card_name in {"2", "3", "4", "5", "6", "7"}:
+                    return Action.SPLIT
+                return Action.TAKE
+    return None
+
+
+def check_double(score: int, dealer_card_name: str) -> Action | None:
+    """
+    A function that checks whether it is worth doubling the bet.
+
+    Args:
+        score (int): the player's current score.
+        dealer_card_name (str): the dealer's open card.
+
+    Returns:
+        result (Action | None): the action taken.
+    """
+    match score:
+        case 9:
+            if dealer_card_name in {"2", "3", "4", "5", "6"}:
+                return Action.DOUBLE
+            return Action.TAKE
+        case 10 | 11:
+            if dealer_card_name != "A":
+                if score == 11 or dealer_card_name in {"10", "J", "Q", "K"}:
+                    return Action.DOUBLE
+            return Action.TAKE
+    return None
+
+
+def check_soft_hands(score: int, dealer_card_name: str) -> Action | None:
+    """
+    The decision-making function for a soft hand.
+
+    Args:
+        score (int): the player's current score.
+        dealer_card_name (str): the dealer's open card.
+
+    Returns:
+        result (Action | None): the action taken.
+    """
+    match score:
+        case 3 | 4 | 5 | 6 | 7:
+            if dealer_card_name in {"2", "3", "4", "5", "6"}:
+                return Action.DOUBLE
+            return Action.TAKE
+        case 8:
+            if dealer_card_name in {"9", "10", "J", "Q", "K", "A"}:
+                return Action.TAKE
+            return Action.PASS
+        case 9 | 10 | 11:
+            return Action.PASS
+    return None
+
+
+def check_steady_hands(score: int, dealer_card_name: str) -> Action | None:
+    """
+    The decision-making function for a steady hand.
+
+    Args:
+        score (int): the player's current score.
+        dealer_card_name (str): the dealer's open card.
+
+    Returns:
+        result (Action | None): the action taken.
+    """
+    match score:
+        case 4 | 5 | 6 | 7 | 8:
+            return Action.TAKE
+
+        case 12 | 13 | 14 | 15 | 16:
+            if dealer_card_name in {"2", "3", "4", "5", "6"}:
+                return Action.PASS
+            return Action.TAKE
+
+        case 17 | 18 | 19 | 20 | 21:
+            return Action.PASS
+    return None
diff --git a/scripts/test_blackjak.py b/scripts/test_blackjak.py
new file mode 100644
index 00000000..7948a176
--- /dev/null
+++ b/scripts/test_blackjak.py
@@ -0,0 +1,122 @@
+from project.scr.persons import Player
+from project.scr.objects import Card, Hand, HandStates
+from project.scr.game import Game, GameStates
+
+import pytest
+
+from project.scr.strategies import Optimal1, Aggressive, Optimal2
+
+A = Card("suit", "A")
+K = Card("suit", "K")
+Q = Card("suit", "Q")
+J = Card("suit", "J")
+ten = Card("suit", "10")
+nine = Card("suit", "9")
+eight = Card("suit", "8")
+seven = Card("suit", "7")
+six = Card("suit", "6")
+five = Card("suit", "5")
+four = Card("suit", "4")
+
+
+@pytest.mark.parametrize(
+    "players, nums_steps",
+    [
+        (
+            [
+                Player(strategy=Optimal1()),
+                Player(strategy=Aggressive()),
+                Player(strategy=Optimal2()),
+                Player(),
+            ],
+            list(range(1, 9)),
+        )
+    ],
+)
+def test_game_states(players: list[Player], nums_steps: list[int]) -> None:
+    """Test that the state of the game changes depending on the steps played."""
+    states = []
+    for num_step in nums_steps:
+        game = Game(players)
+        game.play_steps(num_step)
+        states.append(game._round_state)
+
+    assert states == [
+        GameStates.START,
+        GameStates.PLACE_BETS,
+        GameStates.DEALER_START,
+        GameStates.TOOK_CARDS,
+        GameStates.DEALER_SECOND_CARD,
+        GameStates.DEALER_PLAY,
+        GameStates.RESULTS,
+        GameStates.END,
+    ]
+
+
+@pytest.mark.parametrize(
+    "cards1, cards2, expect_score1, expect_score2",
+    [
+        ([A, K], [J, six], 21, 16),
+        ([nine, ten], [A, A], 19, 12),
+    ],
+)
+def test_players_hands(
+    cards1: list[Card],
+    cards2: list[Card],
+    expect_score1: int,
+    expect_score2: int,
+) -> None:
+    """Test of whether the score in the hand are correctly calculated."""
+    player_hand1 = Hand()
+    player_hand2 = Hand()
+    for card in cards1:
+        player_hand1.add_card(card)
+
+    assert player_hand1.get_score() == expect_score1
+
+    for card in cards2:
+        player_hand2.add_card(card)
+
+    assert player_hand2.get_score() == expect_score2
+
+    # Test hand data-descriptor is working correctly
+    assert player_hand1.get_cards() != player_hand2.get_cards()
+
+
+def test_players_chips() -> None:
+    """Test that the score increases and decreases correctly."""
+    players = [
+        Player(strategy=Optimal1()),
+        Player(strategy=Aggressive()),
+        Player(strategy=Optimal2()),
+        Player(),
+    ]
+    game = Game(players)
+    game.play_steps()
+
+    for player in players:
+        delta_chips = 0.0
+        for hand in game._desk.hands[player]:
+            first_bet = player.strategy.first_bet
+            if hand._state is HandStates.LOSE:
+                if hand.tripled_bet:
+                    delta_chips -= first_bet * 3
+                elif hand.double_bet:
+                    delta_chips -= first_bet * 2
+                else:
+                    delta_chips -= first_bet
+            elif hand._state is HandStates.WIN:
+                if hand.tripled_bet:
+                    delta_chips += first_bet * 3
+                elif hand.double_bet:
+                    delta_chips += first_bet * 2
+                else:
+                    delta_chips += first_bet
+            elif hand._state is HandStates.BLACKJACK:
+                dealer_hand = game._desk.dealer.hand
+                first_card = dealer_hand.get_card(0)
+                if not (player.strategy.even_money and first_card.name == "A") and (
+                    not game._desk.dealer.hand._state is HandStates.BLACKJACK
+                ):
+                    delta_chips += 1.5 * first_bet
+        assert player._chips == 100 + int(delta_chips)

From c3af871ff8830e3c8822dc91f900031f56d8fa58 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=A1=D0=BE=D1=84=D0=B8=D1=8F=20=D0=A5=D1=80=D0=B8=D1=81?=
 =?UTF-8?q?=D0=B0=D0=BD=D0=BA=D0=BE=D0=B2=D0=B0?=
 <132402521+sssoneta@users.noreply.github.com>
Date: Tue, 8 Apr 2025 20:54:01 +0300
Subject: [PATCH 2/3] dz7

---
 project/jupyter/data/test.csv  |    1 +
 project/jupyter/data/train.csv |    1 +
 project/jupyter/jupyter.ipynb  | 3600 ++++++++++++++++++++++++++++++++
 3 files changed, 3602 insertions(+)
 create mode 100644 project/jupyter/data/test.csv
 create mode 100644 project/jupyter/data/train.csv
 create mode 100644 project/jupyter/jupyter.ipynb

diff --git a/project/jupyter/data/test.csv b/project/jupyter/data/test.csv
new file mode 100644
index 00000000..896f49b7
--- /dev/null
+++ b/project/jupyter/data/test.csv
@@ -0,0 +1 @@
+PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q
893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S
894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q
895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S
896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S
897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S
898,3,"Connolly, Miss. Kate",female,30,0,0,330972,7.6292,,Q
899,2,"Caldwell, Mr. Albert Francis",male,26,1,1,248738,29,,S
900,3,"Abrahim, Mrs. Joseph (Sophie Halaut Easu)",female,18,0,0,2657,7.2292,,C
901,3,"Davies, Mr. John Samuel",male,21,2,0,A/4 48871,24.15,,S
902,3,"Ilieff, Mr. Ylio",male,,0,0,349220,7.8958,,S
903,1,"Jones, Mr. Charles Cresson",male,46,0,0,694,26,,S
904,1,"Snyder, Mrs. John Pillsbury (Nelle Stevenson)",female,23,1,0,21228,82.2667,B45,S
905,2,"Howard, Mr. Benjamin",male,63,1,0,24065,26,,S
906,1,"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)",female,47,1,0,W.E.P. 5734,61.175,E31,S
907,2,"del Carlo, Mrs. Sebastiano (Argenia Genovesi)",female,24,1,0,SC/PARIS 2167,27.7208,,C
908,2,"Keane, Mr. Daniel",male,35,0,0,233734,12.35,,Q
909,3,"Assaf, Mr. Gerios",male,21,0,0,2692,7.225,,C
910,3,"Ilmakangas, Miss. Ida Livija",female,27,1,0,STON/O2. 3101270,7.925,,S
911,3,"Assaf Khalil, Mrs. Mariana (Miriam"")""",female,45,0,0,2696,7.225,,C
912,1,"Rothschild, Mr. Martin",male,55,1,0,PC 17603,59.4,,C
913,3,"Olsen, Master. Artur Karl",male,9,0,1,C 17368,3.1708,,S
914,1,"Flegenheim, Mrs. Alfred (Antoinette)",female,,0,0,PC 17598,31.6833,,S
915,1,"Williams, Mr. Richard Norris II",male,21,0,1,PC 17597,61.3792,,C
916,1,"Ryerson, Mrs. Arthur Larned (Emily Maria Borie)",female,48,1,3,PC 17608,262.375,B57 B59 B63 B66,C
917,3,"Robins, Mr. Alexander A",male,50,1,0,A/5. 3337,14.5,,S
918,1,"Ostby, Miss. Helene Ragnhild",female,22,0,1,113509,61.9792,B36,C
919,3,"Daher, Mr. Shedid",male,22.5,0,0,2698,7.225,,C
920,1,"Brady, Mr. John Bertram",male,41,0,0,113054,30.5,A21,S
921,3,"Samaan, Mr. Elias",male,,2,0,2662,21.6792,,C
922,2,"Louch, Mr. Charles Alexander",male,50,1,0,SC/AH 3085,26,,S
923,2,"Jefferys, Mr. Clifford Thomas",male,24,2,0,C.A. 31029,31.5,,S
924,3,"Dean, Mrs. Bertram (Eva Georgetta Light)",female,33,1,2,C.A. 2315,20.575,,S
925,3,"Johnston, Mrs. Andrew G (Elizabeth Lily"" Watson)""",female,,1,2,W./C. 6607,23.45,,S
926,1,"Mock, Mr. Philipp Edmund",male,30,1,0,13236,57.75,C78,C
927,3,"Katavelas, Mr. Vassilios (Catavelas Vassilios"")""",male,18.5,0,0,2682,7.2292,,C
928,3,"Roth, Miss. Sarah A",female,,0,0,342712,8.05,,S
929,3,"Cacic, Miss. Manda",female,21,0,0,315087,8.6625,,S
930,3,"Sap, Mr. Julius",male,25,0,0,345768,9.5,,S
931,3,"Hee, Mr. Ling",male,,0,0,1601,56.4958,,S
932,3,"Karun, Mr. Franz",male,39,0,1,349256,13.4167,,C
933,1,"Franklin, Mr. Thomas Parham",male,,0,0,113778,26.55,D34,S
934,3,"Goldsmith, Mr. Nathan",male,41,0,0,SOTON/O.Q. 3101263,7.85,,S
935,2,"Corbett, Mrs. Walter H (Irene Colvin)",female,30,0,0,237249,13,,S
936,1,"Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)",female,45,1,0,11753,52.5542,D19,S
937,3,"Peltomaki, Mr. Nikolai Johannes",male,25,0,0,STON/O 2. 3101291,7.925,,S
938,1,"Chevre, Mr. Paul Romaine",male,45,0,0,PC 17594,29.7,A9,C
939,3,"Shaughnessy, Mr. Patrick",male,,0,0,370374,7.75,,Q
940,1,"Bucknell, Mrs. William Robert (Emma Eliza Ward)",female,60,0,0,11813,76.2917,D15,C
941,3,"Coutts, Mrs. William (Winnie Minnie"" Treanor)""",female,36,0,2,C.A. 37671,15.9,,S
942,1,"Smith, Mr. Lucien Philip",male,24,1,0,13695,60,C31,S
943,2,"Pulbaum, Mr. Franz",male,27,0,0,SC/PARIS 2168,15.0333,,C
944,2,"Hocking, Miss. Ellen Nellie""""",female,20,2,1,29105,23,,S
945,1,"Fortune, Miss. Ethel Flora",female,28,3,2,19950,263,C23 C25 C27,S
946,2,"Mangiavacchi, Mr. Serafino Emilio",male,,0,0,SC/A.3 2861,15.5792,,C
947,3,"Rice, Master. Albert",male,10,4,1,382652,29.125,,Q
948,3,"Cor, Mr. Bartol",male,35,0,0,349230,7.8958,,S
949,3,"Abelseth, Mr. Olaus Jorgensen",male,25,0,0,348122,7.65,F G63,S
950,3,"Davison, Mr. Thomas Henry",male,,1,0,386525,16.1,,S
951,1,"Chaudanson, Miss. Victorine",female,36,0,0,PC 17608,262.375,B61,C
952,3,"Dika, Mr. Mirko",male,17,0,0,349232,7.8958,,S
953,2,"McCrae, Mr. Arthur Gordon",male,32,0,0,237216,13.5,,S
954,3,"Bjorklund, Mr. Ernst Herbert",male,18,0,0,347090,7.75,,S
955,3,"Bradley, Miss. Bridget Delia",female,22,0,0,334914,7.725,,Q
956,1,"Ryerson, Master. John Borie",male,13,2,2,PC 17608,262.375,B57 B59 B63 B66,C
957,2,"Corey, Mrs. Percy C (Mary Phyllis Elizabeth Miller)",female,,0,0,F.C.C. 13534,21,,S
958,3,"Burns, Miss. Mary Delia",female,18,0,0,330963,7.8792,,Q
959,1,"Moore, Mr. Clarence Bloomfield",male,47,0,0,113796,42.4,,S
960,1,"Tucker, Mr. Gilbert Milligan Jr",male,31,0,0,2543,28.5375,C53,C
961,1,"Fortune, Mrs. Mark (Mary McDougald)",female,60,1,4,19950,263,C23 C25 C27,S
962,3,"Mulvihill, Miss. Bertha E",female,24,0,0,382653,7.75,,Q
963,3,"Minkoff, Mr. Lazar",male,21,0,0,349211,7.8958,,S
964,3,"Nieminen, Miss. Manta Josefina",female,29,0,0,3101297,7.925,,S
965,1,"Ovies y Rodriguez, Mr. Servando",male,28.5,0,0,PC 17562,27.7208,D43,C
966,1,"Geiger, Miss. Amalie",female,35,0,0,113503,211.5,C130,C
967,1,"Keeping, Mr. Edwin",male,32.5,0,0,113503,211.5,C132,C
968,3,"Miles, Mr. Frank",male,,0,0,359306,8.05,,S
969,1,"Cornell, Mrs. Robert Clifford (Malvina Helen Lamson)",female,55,2,0,11770,25.7,C101,S
970,2,"Aldworth, Mr. Charles Augustus",male,30,0,0,248744,13,,S
971,3,"Doyle, Miss. Elizabeth",female,24,0,0,368702,7.75,,Q
972,3,"Boulos, Master. Akar",male,6,1,1,2678,15.2458,,C
973,1,"Straus, Mr. Isidor",male,67,1,0,PC 17483,221.7792,C55 C57,S
974,1,"Case, Mr. Howard Brown",male,49,0,0,19924,26,,S
975,3,"Demetri, Mr. Marinko",male,,0,0,349238,7.8958,,S
976,2,"Lamb, Mr. John Joseph",male,,0,0,240261,10.7083,,Q
977,3,"Khalil, Mr. Betros",male,,1,0,2660,14.4542,,C
978,3,"Barry, Miss. Julia",female,27,0,0,330844,7.8792,,Q
979,3,"Badman, Miss. Emily Louisa",female,18,0,0,A/4 31416,8.05,,S
980,3,"O'Donoghue, Ms. Bridget",female,,0,0,364856,7.75,,Q
981,2,"Wells, Master. Ralph Lester",male,2,1,1,29103,23,,S
982,3,"Dyker, Mrs. Adolf Fredrik (Anna Elisabeth Judith Andersson)",female,22,1,0,347072,13.9,,S
983,3,"Pedersen, Mr. Olaf",male,,0,0,345498,7.775,,S
984,1,"Davidson, Mrs. Thornton (Orian Hays)",female,27,1,2,F.C. 12750,52,B71,S
985,3,"Guest, Mr. Robert",male,,0,0,376563,8.05,,S
986,1,"Birnbaum, Mr. Jakob",male,25,0,0,13905,26,,C
987,3,"Tenglin, Mr. Gunnar Isidor",male,25,0,0,350033,7.7958,,S
988,1,"Cavendish, Mrs. Tyrell William (Julia Florence Siegel)",female,76,1,0,19877,78.85,C46,S
989,3,"Makinen, Mr. Kalle Edvard",male,29,0,0,STON/O 2. 3101268,7.925,,S
990,3,"Braf, Miss. Elin Ester Maria",female,20,0,0,347471,7.8542,,S
991,3,"Nancarrow, Mr. William Henry",male,33,0,0,A./5. 3338,8.05,,S
992,1,"Stengel, Mrs. Charles Emil Henry (Annie May Morris)",female,43,1,0,11778,55.4417,C116,C
993,2,"Weisz, Mr. Leopold",male,27,1,0,228414,26,,S
994,3,"Foley, Mr. William",male,,0,0,365235,7.75,,Q
995,3,"Johansson Palmquist, Mr. Oskar Leander",male,26,0,0,347070,7.775,,S
996,3,"Thomas, Mrs. Alexander (Thamine Thelma"")""",female,16,1,1,2625,8.5167,,C
997,3,"Holthen, Mr. Johan Martin",male,28,0,0,C 4001,22.525,,S
998,3,"Buckley, Mr. Daniel",male,21,0,0,330920,7.8208,,Q
999,3,"Ryan, Mr. Edward",male,,0,0,383162,7.75,,Q
1000,3,"Willer, Mr. Aaron (Abi Weller"")""",male,,0,0,3410,8.7125,,S
1001,2,"Swane, Mr. George",male,18.5,0,0,248734,13,F,S
1002,2,"Stanton, Mr. Samuel Ward",male,41,0,0,237734,15.0458,,C
1003,3,"Shine, Miss. Ellen Natalia",female,,0,0,330968,7.7792,,Q
1004,1,"Evans, Miss. Edith Corse",female,36,0,0,PC 17531,31.6792,A29,C
1005,3,"Buckley, Miss. Katherine",female,18.5,0,0,329944,7.2833,,Q
1006,1,"Straus, Mrs. Isidor (Rosalie Ida Blun)",female,63,1,0,PC 17483,221.7792,C55 C57,S
1007,3,"Chronopoulos, Mr. Demetrios",male,18,1,0,2680,14.4542,,C
1008,3,"Thomas, Mr. John",male,,0,0,2681,6.4375,,C
1009,3,"Sandstrom, Miss. Beatrice Irene",female,1,1,1,PP 9549,16.7,G6,S
1010,1,"Beattie, Mr. Thomson",male,36,0,0,13050,75.2417,C6,C
1011,2,"Chapman, Mrs. John Henry (Sara Elizabeth Lawry)",female,29,1,0,SC/AH 29037,26,,S
1012,2,"Watt, Miss. Bertha J",female,12,0,0,C.A. 33595,15.75,,S
1013,3,"Kiernan, Mr. John",male,,1,0,367227,7.75,,Q
1014,1,"Schabert, Mrs. Paul (Emma Mock)",female,35,1,0,13236,57.75,C28,C
1015,3,"Carver, Mr. Alfred John",male,28,0,0,392095,7.25,,S
1016,3,"Kennedy, Mr. John",male,,0,0,368783,7.75,,Q
1017,3,"Cribb, Miss. Laura Alice",female,17,0,1,371362,16.1,,S
1018,3,"Brobeck, Mr. Karl Rudolf",male,22,0,0,350045,7.7958,,S
1019,3,"McCoy, Miss. Alicia",female,,2,0,367226,23.25,,Q
1020,2,"Bowenur, Mr. Solomon",male,42,0,0,211535,13,,S
1021,3,"Petersen, Mr. Marius",male,24,0,0,342441,8.05,,S
1022,3,"Spinner, Mr. Henry John",male,32,0,0,STON/OQ. 369943,8.05,,S
1023,1,"Gracie, Col. Archibald IV",male,53,0,0,113780,28.5,C51,C
1024,3,"Lefebre, Mrs. Frank (Frances)",female,,0,4,4133,25.4667,,S
1025,3,"Thomas, Mr. Charles P",male,,1,0,2621,6.4375,,C
1026,3,"Dintcheff, Mr. Valtcho",male,43,0,0,349226,7.8958,,S
1027,3,"Carlsson, Mr. Carl Robert",male,24,0,0,350409,7.8542,,S
1028,3,"Zakarian, Mr. Mapriededer",male,26.5,0,0,2656,7.225,,C
1029,2,"Schmidt, Mr. August",male,26,0,0,248659,13,,S
1030,3,"Drapkin, Miss. Jennie",female,23,0,0,SOTON/OQ 392083,8.05,,S
1031,3,"Goodwin, Mr. Charles Frederick",male,40,1,6,CA 2144,46.9,,S
1032,3,"Goodwin, Miss. Jessie Allis",female,10,5,2,CA 2144,46.9,,S
1033,1,"Daniels, Miss. Sarah",female,33,0,0,113781,151.55,,S
1034,1,"Ryerson, Mr. Arthur Larned",male,61,1,3,PC 17608,262.375,B57 B59 B63 B66,C
1035,2,"Beauchamp, Mr. Henry James",male,28,0,0,244358,26,,S
1036,1,"Lindeberg-Lind, Mr. Erik Gustaf (Mr Edward Lingrey"")""",male,42,0,0,17475,26.55,,S
1037,3,"Vander Planke, Mr. Julius",male,31,3,0,345763,18,,S
1038,1,"Hilliard, Mr. Herbert Henry",male,,0,0,17463,51.8625,E46,S
1039,3,"Davies, Mr. Evan",male,22,0,0,SC/A4 23568,8.05,,S
1040,1,"Crafton, Mr. John Bertram",male,,0,0,113791,26.55,,S
1041,2,"Lahtinen, Rev. William",male,30,1,1,250651,26,,S
1042,1,"Earnshaw, Mrs. Boulton (Olive Potter)",female,23,0,1,11767,83.1583,C54,C
1043,3,"Matinoff, Mr. Nicola",male,,0,0,349255,7.8958,,C
1044,3,"Storey, Mr. Thomas",male,60.5,0,0,3701,,,S
1045,3,"Klasen, Mrs. (Hulda Kristina Eugenia Lofqvist)",female,36,0,2,350405,12.1833,,S
1046,3,"Asplund, Master. Filip Oscar",male,13,4,2,347077,31.3875,,S
1047,3,"Duquemin, Mr. Joseph",male,24,0,0,S.O./P.P. 752,7.55,,S
1048,1,"Bird, Miss. Ellen",female,29,0,0,PC 17483,221.7792,C97,S
1049,3,"Lundin, Miss. Olga Elida",female,23,0,0,347469,7.8542,,S
1050,1,"Borebank, Mr. John James",male,42,0,0,110489,26.55,D22,S
1051,3,"Peacock, Mrs. Benjamin (Edith Nile)",female,26,0,2,SOTON/O.Q. 3101315,13.775,,S
1052,3,"Smyth, Miss. Julia",female,,0,0,335432,7.7333,,Q
1053,3,"Touma, Master. Georges Youssef",male,7,1,1,2650,15.2458,,C
1054,2,"Wright, Miss. Marion",female,26,0,0,220844,13.5,,S
1055,3,"Pearce, Mr. Ernest",male,,0,0,343271,7,,S
1056,2,"Peruschitz, Rev. Joseph Maria",male,41,0,0,237393,13,,S
1057,3,"Kink-Heilmann, Mrs. Anton (Luise Heilmann)",female,26,1,1,315153,22.025,,S
1058,1,"Brandeis, Mr. Emil",male,48,0,0,PC 17591,50.4958,B10,C
1059,3,"Ford, Mr. Edward Watson",male,18,2,2,W./C. 6608,34.375,,S
1060,1,"Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick)",female,,0,0,17770,27.7208,,C
1061,3,"Hellstrom, Miss. Hilda Maria",female,22,0,0,7548,8.9625,,S
1062,3,"Lithman, Mr. Simon",male,,0,0,S.O./P.P. 251,7.55,,S
1063,3,"Zakarian, Mr. Ortin",male,27,0,0,2670,7.225,,C
1064,3,"Dyker, Mr. Adolf Fredrik",male,23,1,0,347072,13.9,,S
1065,3,"Torfa, Mr. Assad",male,,0,0,2673,7.2292,,C
1066,3,"Asplund, Mr. Carl Oscar Vilhelm Gustafsson",male,40,1,5,347077,31.3875,,S
1067,2,"Brown, Miss. Edith Eileen",female,15,0,2,29750,39,,S
1068,2,"Sincock, Miss. Maude",female,20,0,0,C.A. 33112,36.75,,S
1069,1,"Stengel, Mr. Charles Emil Henry",male,54,1,0,11778,55.4417,C116,C
1070,2,"Becker, Mrs. Allen Oliver (Nellie E Baumgardner)",female,36,0,3,230136,39,F4,S
1071,1,"Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll)",female,64,0,2,PC 17756,83.1583,E45,C
1072,2,"McCrie, Mr. James Matthew",male,30,0,0,233478,13,,S
1073,1,"Compton, Mr. Alexander Taylor Jr",male,37,1,1,PC 17756,83.1583,E52,C
1074,1,"Marvin, Mrs. Daniel Warner (Mary Graham Carmichael Farquarson)",female,18,1,0,113773,53.1,D30,S
1075,3,"Lane, Mr. Patrick",male,,0,0,7935,7.75,,Q
1076,1,"Douglas, Mrs. Frederick Charles (Mary Helene Baxter)",female,27,1,1,PC 17558,247.5208,B58 B60,C
1077,2,"Maybery, Mr. Frank Hubert",male,40,0,0,239059,16,,S
1078,2,"Phillips, Miss. Alice Frances Louisa",female,21,0,1,S.O./P.P. 2,21,,S
1079,3,"Davies, Mr. Joseph",male,17,2,0,A/4 48873,8.05,,S
1080,3,"Sage, Miss. Ada",female,,8,2,CA. 2343,69.55,,S
1081,2,"Veal, Mr. James",male,40,0,0,28221,13,,S
1082,2,"Angle, Mr. William A",male,34,1,0,226875,26,,S
1083,1,"Salomon, Mr. Abraham L",male,,0,0,111163,26,,S
1084,3,"van Billiard, Master. Walter John",male,11.5,1,1,A/5. 851,14.5,,S
1085,2,"Lingane, Mr. John",male,61,0,0,235509,12.35,,Q
1086,2,"Drew, Master. Marshall Brines",male,8,0,2,28220,32.5,,S
1087,3,"Karlsson, Mr. Julius Konrad Eugen",male,33,0,0,347465,7.8542,,S
1088,1,"Spedden, Master. Robert Douglas",male,6,0,2,16966,134.5,E34,C
1089,3,"Nilsson, Miss. Berta Olivia",female,18,0,0,347066,7.775,,S
1090,2,"Baimbrigge, Mr. Charles Robert",male,23,0,0,C.A. 31030,10.5,,S
1091,3,"Rasmussen, Mrs. (Lena Jacobsen Solvang)",female,,0,0,65305,8.1125,,S
1092,3,"Murphy, Miss. Nora",female,,0,0,36568,15.5,,Q
1093,3,"Danbom, Master. Gilbert Sigvard Emanuel",male,0.33,0,2,347080,14.4,,S
1094,1,"Astor, Col. John Jacob",male,47,1,0,PC 17757,227.525,C62 C64,C
1095,2,"Quick, Miss. Winifred Vera",female,8,1,1,26360,26,,S
1096,2,"Andrew, Mr. Frank Thomas",male,25,0,0,C.A. 34050,10.5,,S
1097,1,"Omont, Mr. Alfred Fernand",male,,0,0,F.C. 12998,25.7417,,C
1098,3,"McGowan, Miss. Katherine",female,35,0,0,9232,7.75,,Q
1099,2,"Collett, Mr. Sidney C Stuart",male,24,0,0,28034,10.5,,S
1100,1,"Rosenbaum, Miss. Edith Louise",female,33,0,0,PC 17613,27.7208,A11,C
1101,3,"Delalic, Mr. Redjo",male,25,0,0,349250,7.8958,,S
1102,3,"Andersen, Mr. Albert Karvin",male,32,0,0,C 4001,22.525,,S
1103,3,"Finoli, Mr. Luigi",male,,0,0,SOTON/O.Q. 3101308,7.05,,S
1104,2,"Deacon, Mr. Percy William",male,17,0,0,S.O.C. 14879,73.5,,S
1105,2,"Howard, Mrs. Benjamin (Ellen Truelove Arman)",female,60,1,0,24065,26,,S
1106,3,"Andersson, Miss. Ida Augusta Margareta",female,38,4,2,347091,7.775,,S
1107,1,"Head, Mr. Christopher",male,42,0,0,113038,42.5,B11,S
1108,3,"Mahon, Miss. Bridget Delia",female,,0,0,330924,7.8792,,Q
1109,1,"Wick, Mr. George Dennick",male,57,1,1,36928,164.8667,,S
1110,1,"Widener, Mrs. George Dunton (Eleanor Elkins)",female,50,1,1,113503,211.5,C80,C
1111,3,"Thomson, Mr. Alexander Morrison",male,,0,0,32302,8.05,,S
1112,2,"Duran y More, Miss. Florentina",female,30,1,0,SC/PARIS 2148,13.8583,,C
1113,3,"Reynolds, Mr. Harold J",male,21,0,0,342684,8.05,,S
1114,2,"Cook, Mrs. (Selena Rogers)",female,22,0,0,W./C. 14266,10.5,F33,S
1115,3,"Karlsson, Mr. Einar Gervasius",male,21,0,0,350053,7.7958,,S
1116,1,"Candee, Mrs. Edward (Helen Churchill Hungerford)",female,53,0,0,PC 17606,27.4458,,C
1117,3,"Moubarek, Mrs. George (Omine Amenia"" Alexander)""",female,,0,2,2661,15.2458,,C
1118,3,"Asplund, Mr. Johan Charles",male,23,0,0,350054,7.7958,,S
1119,3,"McNeill, Miss. Bridget",female,,0,0,370368,7.75,,Q
1120,3,"Everett, Mr. Thomas James",male,40.5,0,0,C.A. 6212,15.1,,S
1121,2,"Hocking, Mr. Samuel James Metcalfe",male,36,0,0,242963,13,,S
1122,2,"Sweet, Mr. George Frederick",male,14,0,0,220845,65,,S
1123,1,"Willard, Miss. Constance",female,21,0,0,113795,26.55,,S
1124,3,"Wiklund, Mr. Karl Johan",male,21,1,0,3101266,6.4958,,S
1125,3,"Linehan, Mr. Michael",male,,0,0,330971,7.8792,,Q
1126,1,"Cumings, Mr. John Bradley",male,39,1,0,PC 17599,71.2833,C85,C
1127,3,"Vendel, Mr. Olof Edvin",male,20,0,0,350416,7.8542,,S
1128,1,"Warren, Mr. Frank Manley",male,64,1,0,110813,75.25,D37,C
1129,3,"Baccos, Mr. Raffull",male,20,0,0,2679,7.225,,C
1130,2,"Hiltunen, Miss. Marta",female,18,1,1,250650,13,,S
1131,1,"Douglas, Mrs. Walter Donald (Mahala Dutton)",female,48,1,0,PC 17761,106.425,C86,C
1132,1,"Lindstrom, Mrs. Carl Johan (Sigrid Posse)",female,55,0,0,112377,27.7208,,C
1133,2,"Christy, Mrs. (Alice Frances)",female,45,0,2,237789,30,,S
1134,1,"Spedden, Mr. Frederic Oakley",male,45,1,1,16966,134.5,E34,C
1135,3,"Hyman, Mr. Abraham",male,,0,0,3470,7.8875,,S
1136,3,"Johnston, Master. William Arthur Willie""""",male,,1,2,W./C. 6607,23.45,,S
1137,1,"Kenyon, Mr. Frederick R",male,41,1,0,17464,51.8625,D21,S
1138,2,"Karnes, Mrs. J Frank (Claire Bennett)",female,22,0,0,F.C.C. 13534,21,,S
1139,2,"Drew, Mr. James Vivian",male,42,1,1,28220,32.5,,S
1140,2,"Hold, Mrs. Stephen (Annie Margaret Hill)",female,29,1,0,26707,26,,S
1141,3,"Khalil, Mrs. Betros (Zahie Maria"" Elias)""",female,,1,0,2660,14.4542,,C
1142,2,"West, Miss. Barbara J",female,0.92,1,2,C.A. 34651,27.75,,S
1143,3,"Abrahamsson, Mr. Abraham August Johannes",male,20,0,0,SOTON/O2 3101284,7.925,,S
1144,1,"Clark, Mr. Walter Miller",male,27,1,0,13508,136.7792,C89,C
1145,3,"Salander, Mr. Karl Johan",male,24,0,0,7266,9.325,,S
1146,3,"Wenzel, Mr. Linhart",male,32.5,0,0,345775,9.5,,S
1147,3,"MacKay, Mr. George William",male,,0,0,C.A. 42795,7.55,,S
1148,3,"Mahon, Mr. John",male,,0,0,AQ/4 3130,7.75,,Q
1149,3,"Niklasson, Mr. Samuel",male,28,0,0,363611,8.05,,S
1150,2,"Bentham, Miss. Lilian W",female,19,0,0,28404,13,,S
1151,3,"Midtsjo, Mr. Karl Albert",male,21,0,0,345501,7.775,,S
1152,3,"de Messemaeker, Mr. Guillaume Joseph",male,36.5,1,0,345572,17.4,,S
1153,3,"Nilsson, Mr. August Ferdinand",male,21,0,0,350410,7.8542,,S
1154,2,"Wells, Mrs. Arthur Henry (Addie"" Dart Trevaskis)""",female,29,0,2,29103,23,,S
1155,3,"Klasen, Miss. Gertrud Emilia",female,1,1,1,350405,12.1833,,S
1156,2,"Portaluppi, Mr. Emilio Ilario Giuseppe",male,30,0,0,C.A. 34644,12.7375,,C
1157,3,"Lyntakoff, Mr. Stanko",male,,0,0,349235,7.8958,,S
1158,1,"Chisholm, Mr. Roderick Robert Crispin",male,,0,0,112051,0,,S
1159,3,"Warren, Mr. Charles William",male,,0,0,C.A. 49867,7.55,,S
1160,3,"Howard, Miss. May Elizabeth",female,,0,0,A. 2. 39186,8.05,,S
1161,3,"Pokrnic, Mr. Mate",male,17,0,0,315095,8.6625,,S
1162,1,"McCaffry, Mr. Thomas Francis",male,46,0,0,13050,75.2417,C6,C
1163,3,"Fox, Mr. Patrick",male,,0,0,368573,7.75,,Q
1164,1,"Clark, Mrs. Walter Miller (Virginia McDowell)",female,26,1,0,13508,136.7792,C89,C
1165,3,"Lennon, Miss. Mary",female,,1,0,370371,15.5,,Q
1166,3,"Saade, Mr. Jean Nassr",male,,0,0,2676,7.225,,C
1167,2,"Bryhl, Miss. Dagmar Jenny Ingeborg ",female,20,1,0,236853,26,,S
1168,2,"Parker, Mr. Clifford Richard",male,28,0,0,SC 14888,10.5,,S
1169,2,"Faunthorpe, Mr. Harry",male,40,1,0,2926,26,,S
1170,2,"Ware, Mr. John James",male,30,1,0,CA 31352,21,,S
1171,2,"Oxenham, Mr. Percy Thomas",male,22,0,0,W./C. 14260,10.5,,S
1172,3,"Oreskovic, Miss. Jelka",female,23,0,0,315085,8.6625,,S
1173,3,"Peacock, Master. Alfred Edward",male,0.75,1,1,SOTON/O.Q. 3101315,13.775,,S
1174,3,"Fleming, Miss. Honora",female,,0,0,364859,7.75,,Q
1175,3,"Touma, Miss. Maria Youssef",female,9,1,1,2650,15.2458,,C
1176,3,"Rosblom, Miss. Salli Helena",female,2,1,1,370129,20.2125,,S
1177,3,"Dennis, Mr. William",male,36,0,0,A/5 21175,7.25,,S
1178,3,"Franklin, Mr. Charles (Charles Fardon)",male,,0,0,SOTON/O.Q. 3101314,7.25,,S
1179,1,"Snyder, Mr. John Pillsbury",male,24,1,0,21228,82.2667,B45,S
1180,3,"Mardirosian, Mr. Sarkis",male,,0,0,2655,7.2292,F E46,C
1181,3,"Ford, Mr. Arthur",male,,0,0,A/5 1478,8.05,,S
1182,1,"Rheims, Mr. George Alexander Lucien",male,,0,0,PC 17607,39.6,,S
1183,3,"Daly, Miss. Margaret Marcella Maggie""""",female,30,0,0,382650,6.95,,Q
1184,3,"Nasr, Mr. Mustafa",male,,0,0,2652,7.2292,,C
1185,1,"Dodge, Dr. Washington",male,53,1,1,33638,81.8583,A34,S
1186,3,"Wittevrongel, Mr. Camille",male,36,0,0,345771,9.5,,S
1187,3,"Angheloff, Mr. Minko",male,26,0,0,349202,7.8958,,S
1188,2,"Laroche, Miss. Louise",female,1,1,2,SC/Paris 2123,41.5792,,C
1189,3,"Samaan, Mr. Hanna",male,,2,0,2662,21.6792,,C
1190,1,"Loring, Mr. Joseph Holland",male,30,0,0,113801,45.5,,S
1191,3,"Johansson, Mr. Nils",male,29,0,0,347467,7.8542,,S
1192,3,"Olsson, Mr. Oscar Wilhelm",male,32,0,0,347079,7.775,,S
1193,2,"Malachard, Mr. Noel",male,,0,0,237735,15.0458,D,C
1194,2,"Phillips, Mr. Escott Robert",male,43,0,1,S.O./P.P. 2,21,,S
1195,3,"Pokrnic, Mr. Tome",male,24,0,0,315092,8.6625,,S
1196,3,"McCarthy, Miss. Catherine Katie""""",female,,0,0,383123,7.75,,Q
1197,1,"Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead)",female,64,1,1,112901,26.55,B26,S
1198,1,"Allison, Mr. Hudson Joshua Creighton",male,30,1,2,113781,151.55,C22 C26,S
1199,3,"Aks, Master. Philip Frank",male,0.83,0,1,392091,9.35,,S
1200,1,"Hays, Mr. Charles Melville",male,55,1,1,12749,93.5,B69,S
1201,3,"Hansen, Mrs. Claus Peter (Jennie L Howard)",female,45,1,0,350026,14.1083,,S
1202,3,"Cacic, Mr. Jego Grga",male,18,0,0,315091,8.6625,,S
1203,3,"Vartanian, Mr. David",male,22,0,0,2658,7.225,,C
1204,3,"Sadowitz, Mr. Harry",male,,0,0,LP 1588,7.575,,S
1205,3,"Carr, Miss. Jeannie",female,37,0,0,368364,7.75,,Q
1206,1,"White, Mrs. John Stuart (Ella Holmes)",female,55,0,0,PC 17760,135.6333,C32,C
1207,3,"Hagardon, Miss. Kate",female,17,0,0,AQ/3. 30631,7.7333,,Q
1208,1,"Spencer, Mr. William Augustus",male,57,1,0,PC 17569,146.5208,B78,C
1209,2,"Rogers, Mr. Reginald Harry",male,19,0,0,28004,10.5,,S
1210,3,"Jonsson, Mr. Nils Hilding",male,27,0,0,350408,7.8542,,S
1211,2,"Jefferys, Mr. Ernest Wilfred",male,22,2,0,C.A. 31029,31.5,,S
1212,3,"Andersson, Mr. Johan Samuel",male,26,0,0,347075,7.775,,S
1213,3,"Krekorian, Mr. Neshan",male,25,0,0,2654,7.2292,F E57,C
1214,2,"Nesson, Mr. Israel",male,26,0,0,244368,13,F2,S
1215,1,"Rowe, Mr. Alfred G",male,33,0,0,113790,26.55,,S
1216,1,"Kreuchen, Miss. Emilie",female,39,0,0,24160,211.3375,,S
1217,3,"Assam, Mr. Ali",male,23,0,0,SOTON/O.Q. 3101309,7.05,,S
1218,2,"Becker, Miss. Ruth Elizabeth",female,12,2,1,230136,39,F4,S
1219,1,"Rosenshine, Mr. George (Mr George Thorne"")""",male,46,0,0,PC 17585,79.2,,C
1220,2,"Clarke, Mr. Charles Valentine",male,29,1,0,2003,26,,S
1221,2,"Enander, Mr. Ingvar",male,21,0,0,236854,13,,S
1222,2,"Davies, Mrs. John Morgan (Elizabeth Agnes Mary White) ",female,48,0,2,C.A. 33112,36.75,,S
1223,1,"Dulles, Mr. William Crothers",male,39,0,0,PC 17580,29.7,A18,C
1224,3,"Thomas, Mr. Tannous",male,,0,0,2684,7.225,,C
1225,3,"Nakid, Mrs. Said (Waika Mary"" Mowad)""",female,19,1,1,2653,15.7417,,C
1226,3,"Cor, Mr. Ivan",male,27,0,0,349229,7.8958,,S
1227,1,"Maguire, Mr. John Edward",male,30,0,0,110469,26,C106,S
1228,2,"de Brito, Mr. Jose Joaquim",male,32,0,0,244360,13,,S
1229,3,"Elias, Mr. Joseph",male,39,0,2,2675,7.2292,,C
1230,2,"Denbury, Mr. Herbert",male,25,0,0,C.A. 31029,31.5,,S
1231,3,"Betros, Master. Seman",male,,0,0,2622,7.2292,,C
1232,2,"Fillbrook, Mr. Joseph Charles",male,18,0,0,C.A. 15185,10.5,,S
1233,3,"Lundstrom, Mr. Thure Edvin",male,32,0,0,350403,7.5792,,S
1234,3,"Sage, Mr. John George",male,,1,9,CA. 2343,69.55,,S
1235,1,"Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake)",female,58,0,1,PC 17755,512.3292,B51 B53 B55,C
1236,3,"van Billiard, Master. James William",male,,1,1,A/5. 851,14.5,,S
1237,3,"Abelseth, Miss. Karen Marie",female,16,0,0,348125,7.65,,S
1238,2,"Botsford, Mr. William Hull",male,26,0,0,237670,13,,S
1239,3,"Whabee, Mrs. George Joseph (Shawneene Abi-Saab)",female,38,0,0,2688,7.2292,,C
1240,2,"Giles, Mr. Ralph",male,24,0,0,248726,13.5,,S
1241,2,"Walcroft, Miss. Nellie",female,31,0,0,F.C.C. 13528,21,,S
1242,1,"Greenfield, Mrs. Leo David (Blanche Strouse)",female,45,0,1,PC 17759,63.3583,D10 D12,C
1243,2,"Stokes, Mr. Philip Joseph",male,25,0,0,F.C.C. 13540,10.5,,S
1244,2,"Dibden, Mr. William",male,18,0,0,S.O.C. 14879,73.5,,S
1245,2,"Herman, Mr. Samuel",male,49,1,2,220845,65,,S
1246,3,"Dean, Miss. Elizabeth Gladys Millvina""""",female,0.17,1,2,C.A. 2315,20.575,,S
1247,1,"Julian, Mr. Henry Forbes",male,50,0,0,113044,26,E60,S
1248,1,"Brown, Mrs. John Murray (Caroline Lane Lamson)",female,59,2,0,11769,51.4792,C101,S
1249,3,"Lockyer, Mr. Edward",male,,0,0,1222,7.8792,,S
1250,3,"O'Keefe, Mr. Patrick",male,,0,0,368402,7.75,,Q
1251,3,"Lindell, Mrs. Edvard Bengtsson (Elin Gerda Persson)",female,30,1,0,349910,15.55,,S
1252,3,"Sage, Master. William Henry",male,14.5,8,2,CA. 2343,69.55,,S
1253,2,"Mallet, Mrs. Albert (Antoinette Magnin)",female,24,1,1,S.C./PARIS 2079,37.0042,,C
1254,2,"Ware, Mrs. John James (Florence Louise Long)",female,31,0,0,CA 31352,21,,S
1255,3,"Strilic, Mr. Ivan",male,27,0,0,315083,8.6625,,S
1256,1,"Harder, Mrs. George Achilles (Dorothy Annan)",female,25,1,0,11765,55.4417,E50,C
1257,3,"Sage, Mrs. John (Annie Bullen)",female,,1,9,CA. 2343,69.55,,S
1258,3,"Caram, Mr. Joseph",male,,1,0,2689,14.4583,,C
1259,3,"Riihivouri, Miss. Susanna Juhantytar Sanni""""",female,22,0,0,3101295,39.6875,,S
1260,1,"Gibson, Mrs. Leonard (Pauline C Boeson)",female,45,0,1,112378,59.4,,C
1261,2,"Pallas y Castello, Mr. Emilio",male,29,0,0,SC/PARIS 2147,13.8583,,C
1262,2,"Giles, Mr. Edgar",male,21,1,0,28133,11.5,,S
1263,1,"Wilson, Miss. Helen Alice",female,31,0,0,16966,134.5,E39 E41,C
1264,1,"Ismay, Mr. Joseph Bruce",male,49,0,0,112058,0,B52 B54 B56,S
1265,2,"Harbeck, Mr. William H",male,44,0,0,248746,13,,S
1266,1,"Dodge, Mrs. Washington (Ruth Vidaver)",female,54,1,1,33638,81.8583,A34,S
1267,1,"Bowen, Miss. Grace Scott",female,45,0,0,PC 17608,262.375,,C
1268,3,"Kink, Miss. Maria",female,22,2,0,315152,8.6625,,S
1269,2,"Cotterill, Mr. Henry Harry""""",male,21,0,0,29107,11.5,,S
1270,1,"Hipkins, Mr. William Edward",male,55,0,0,680,50,C39,S
1271,3,"Asplund, Master. Carl Edgar",male,5,4,2,347077,31.3875,,S
1272,3,"O'Connor, Mr. Patrick",male,,0,0,366713,7.75,,Q
1273,3,"Foley, Mr. Joseph",male,26,0,0,330910,7.8792,,Q
1274,3,"Risien, Mrs. Samuel (Emma)",female,,0,0,364498,14.5,,S
1275,3,"McNamee, Mrs. Neal (Eileen O'Leary)",female,19,1,0,376566,16.1,,S
1276,2,"Wheeler, Mr. Edwin Frederick""""",male,,0,0,SC/PARIS 2159,12.875,,S
1277,2,"Herman, Miss. Kate",female,24,1,2,220845,65,,S
1278,3,"Aronsson, Mr. Ernst Axel Algot",male,24,0,0,349911,7.775,,S
1279,2,"Ashby, Mr. John",male,57,0,0,244346,13,,S
1280,3,"Canavan, Mr. Patrick",male,21,0,0,364858,7.75,,Q
1281,3,"Palsson, Master. Paul Folke",male,6,3,1,349909,21.075,,S
1282,1,"Payne, Mr. Vivian Ponsonby",male,23,0,0,12749,93.5,B24,S
1283,1,"Lines, Mrs. Ernest H (Elizabeth Lindsey James)",female,51,0,1,PC 17592,39.4,D28,S
1284,3,"Abbott, Master. Eugene Joseph",male,13,0,2,C.A. 2673,20.25,,S
1285,2,"Gilbert, Mr. William",male,47,0,0,C.A. 30769,10.5,,S
1286,3,"Kink-Heilmann, Mr. Anton",male,29,3,1,315153,22.025,,S
1287,1,"Smith, Mrs. Lucien Philip (Mary Eloise Hughes)",female,18,1,0,13695,60,C31,S
1288,3,"Colbert, Mr. Patrick",male,24,0,0,371109,7.25,,Q
1289,1,"Frolicher-Stehli, Mrs. Maxmillian (Margaretha Emerentia Stehli)",female,48,1,1,13567,79.2,B41,C
1290,3,"Larsson-Rondberg, Mr. Edvard A",male,22,0,0,347065,7.775,,S
1291,3,"Conlon, Mr. Thomas Henry",male,31,0,0,21332,7.7333,,Q
1292,1,"Bonnell, Miss. Caroline",female,30,0,0,36928,164.8667,C7,S
1293,2,"Gale, Mr. Harry",male,38,1,0,28664,21,,S
1294,1,"Gibson, Miss. Dorothy Winifred",female,22,0,1,112378,59.4,,C
1295,1,"Carrau, Mr. Jose Pedro",male,17,0,0,113059,47.1,,S
1296,1,"Frauenthal, Mr. Isaac Gerald",male,43,1,0,17765,27.7208,D40,C
1297,2,"Nourney, Mr. Alfred (Baron von Drachstedt"")""",male,20,0,0,SC/PARIS 2166,13.8625,D38,C
1298,2,"Ware, Mr. William Jeffery",male,23,1,0,28666,10.5,,S
1299,1,"Widener, Mr. George Dunton",male,50,1,1,113503,211.5,C80,C
1300,3,"Riordan, Miss. Johanna Hannah""""",female,,0,0,334915,7.7208,,Q
1301,3,"Peacock, Miss. Treasteall",female,3,1,1,SOTON/O.Q. 3101315,13.775,,S
1302,3,"Naughton, Miss. Hannah",female,,0,0,365237,7.75,,Q
1303,1,"Minahan, Mrs. William Edward (Lillian E Thorpe)",female,37,1,0,19928,90,C78,Q
1304,3,"Henriksson, Miss. Jenny Lovisa",female,28,0,0,347086,7.775,,S
1305,3,"Spector, Mr. Woolf",male,,0,0,A.5. 3236,8.05,,S
1306,1,"Oliva y Ocana, Dona. Fermina",female,39,0,0,PC 17758,108.9,C105,C
1307,3,"Saether, Mr. Simon Sivertsen",male,38.5,0,0,SOTON/O.Q. 3101262,7.25,,S
1308,3,"Ware, Mr. Frederick",male,,0,0,359309,8.05,,S
1309,3,"Peter, Master. Michael J",male,,1,1,2668,22.3583,,C
diff --git a/project/jupyter/data/train.csv b/project/jupyter/data/train.csv
new file mode 100644
index 00000000..bda035ab
--- /dev/null
+++ b/project/jupyter/data/train.csv
@@ -0,0 +1 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S
159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S
162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S
163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S
164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S
166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S
167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S
168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S
169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S
170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S
171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S
172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q
173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S
174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S
175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C
176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S
177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S
178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C
179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S
180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S
181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S
182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C
183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S
184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S
185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S
186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S
187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q
188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S
189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q
190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S
191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S
192,0,2,"Carbines, Mr. William",male,19,0,0,28424,13,,S
193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S
194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S
195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44,0,0,PC 17610,27.7208,B4,C
196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C
197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q
198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S
199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q
200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S
201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S
202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S
203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S
204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C
205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S
206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S
207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S
208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C
209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q
210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C
211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S
212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S
213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S
214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S
215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q
216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C
217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S
218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S
219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C
220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S
221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S
222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S
223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S
224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S
225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S
226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S
227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S
228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S
229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S
230,0,3,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S
231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S
232,0,3,"Larsson, Mr. Bengt Edvin",male,29,0,0,347067,7.775,,S
233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S
234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S
235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S
236,0,3,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S
237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S
238,1,2,"Collyer, Miss. Marjorie ""Lottie""",female,8,0,2,C.A. 31921,26.25,,S
239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S
240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S
241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C
242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q
243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S
244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S
245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C
246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q
247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S
248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S
249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S
250,0,2,"Carter, Rev. Ernest Courtenay",male,54,1,0,244252,26,,S
251,0,3,"Reed, Mr. James George",male,,0,0,362316,7.25,,S
252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S
253,0,1,"Stead, Mr. William Thomas",male,62,0,0,113514,26.55,C87,S
254,0,3,"Lobb, Mr. William Arthur",male,30,1,0,A/5. 3336,16.1,,S
255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S
256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C
257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C
258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S
259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C
260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S
261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q
262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S
263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S
264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S
265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q
266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S
267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S
268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S
269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S
270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S
271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S
272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S
273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S
274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C
275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q
276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S
277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S
278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S
279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q
280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S
281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q
282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S
283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S
284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S
285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S
286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C
287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S
288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S
289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S
290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q
291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S
292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C
293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C
294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S
295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S
296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C
297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C
298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S
299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S
300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C
301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q
302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q
303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S
304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q
305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S
306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S
307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C
308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C
309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C
310,1,1,"Francatelli, Miss. Laura Mabel",female,30,0,0,PC 17485,56.9292,E36,C
311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C
312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C
313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S
314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S
315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S
316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S
317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S
318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S
319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S
320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C
321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S
322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S
323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q
324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S
325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S
326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C
327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S
328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S
329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S
330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C
331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q
332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
333,0,1,"Graham, Mr. George Edward",male,38,0,1,PC 17582,153.4625,C91,S
334,0,3,"Vander Planke, Mr. Leo Edmondus",male,16,2,0,345764,18,,S
335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S
336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S
337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S
338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C
339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S
340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S
341,1,2,"Navratil, Master. Edmond Roger",male,2,1,1,230080,26,F2,S
342,1,1,"Fortune, Miss. Alice Elizabeth",female,24,3,2,19950,263,C23 C25 C27,S
343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S
344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S
345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S
346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S
347,1,2,"Smith, Miss. Marion Elsie",female,40,0,0,31418,13,,S
348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S
350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S
351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S
352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S
353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C
354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S
355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C
356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S
357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S
358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S
359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q
360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S
362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C
363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C
364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S
365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q
366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S
367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C
368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C
369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q
370,1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3,B35,C
371,1,1,"Harder, Mr. George Achilles",male,25,1,0,11765,55.4417,E50,C
372,0,3,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S
373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S
374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C
375,0,3,"Palsson, Miss. Stina Viola",female,3,3,1,349909,21.075,,S
376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S
378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C
379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C
380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S
381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C
382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C
383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S
384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S
385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S
386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S
387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S
388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S
389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q
390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C
391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S
392,1,3,"Jansson, Mr. Carl Olof",male,21,0,0,350034,7.7958,,S
393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S
394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C
395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24,0,2,PP 9549,16.7,G6,S
396,0,3,"Johansson, Mr. Erik",male,22,0,0,350052,7.7958,,S
397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S
398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S
399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S
400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S
401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S
402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S
403,0,3,"Jussila, Miss. Mari Aina",female,21,1,0,4137,9.825,,S
404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S
405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S
406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S
407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S
408,1,2,"Richards, Master. William Rowe",male,3,1,1,29106,18.75,,S
409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S
410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S
411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q
413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q
414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S
415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S
416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S
417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S
418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S
419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S
420,0,3,"Van Impe, Miss. Catharina",female,10,0,2,345773,24.15,,S
421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C
422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q
423,0,3,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.875,,S
424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S
425,0,3,"Rosblom, Mr. Viktor Richard",male,18,1,1,370129,20.2125,,S
426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S
427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28,1,0,2003,26,,S
428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S
429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q
430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32,0,0,SOTON/O.Q. 392078,8.05,E10,S
431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28,0,0,110564,26.55,C52,S
432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S
433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42,1,0,SC/AH 3085,26,,S
434,0,3,"Kallio, Mr. Nikolai Erland",male,17,0,0,STON/O 2. 3101274,7.125,,S
435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S
436,1,1,"Carter, Miss. Lucile Polk",female,14,1,2,113760,120,B96 B98,S
437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21,2,2,W./C. 6608,34.375,,S
438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S
439,0,1,"Fortune, Mr. Mark",male,64,1,4,19950,263,C23 C25 C27,S
440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31,0,0,C.A. 18723,10.5,,S
441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S
442,0,3,"Hampe, Mr. Leon",male,20,0,0,345769,9.5,,S
443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S
444,1,2,"Reynaldo, Ms. Encarnacion",female,28,0,0,230434,13,,S
445,1,3,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S
446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S
447,1,2,"Mellinger, Miss. Madeleine Violet",female,13,0,1,250644,19.5,,S
448,1,1,"Seward, Mr. Frederic Kimber",male,34,0,0,113794,26.55,,S
449,1,3,"Baclini, Miss. Marie Catherine",female,5,2,1,2666,19.2583,,C
450,1,1,"Peuchen, Major. Arthur Godfrey",male,52,0,0,113786,30.5,C104,S
451,0,2,"West, Mr. Edwy Arthur",male,36,1,2,C.A. 34651,27.75,,S
452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S
453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C
454,1,1,"Goldenberg, Mr. Samuel L",male,49,1,0,17453,89.1042,C92,C
455,0,3,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S
456,1,3,"Jalsevac, Mr. Ivan",male,29,0,0,349240,7.8958,,C
457,0,1,"Millet, Mr. Francis Davis",male,65,0,0,13509,26.55,E38,S
458,1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S
459,1,2,"Toomey, Miss. Ellen",female,50,0,0,F.C.C. 13531,10.5,,S
460,0,3,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q
461,1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.55,E12,S
462,0,3,"Morley, Mr. William",male,34,0,0,364506,8.05,,S
463,0,1,"Gee, Mr. Arthur H",male,47,0,0,111320,38.5,E63,S
464,0,2,"Milling, Mr. Jacob Christian",male,48,0,0,234360,13,,S
465,0,3,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S
466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38,0,0,SOTON/O.Q. 3101306,7.05,,S
467,0,2,"Campbell, Mr. William",male,,0,0,239853,0,,S
468,0,1,"Smart, Mr. John Montgomery",male,56,0,0,113792,26.55,,S
469,0,3,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q
470,1,3,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C
471,0,3,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S
472,0,3,"Cacic, Mr. Luka",male,38,0,0,315089,8.6625,,S
473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33,1,2,C.A. 34651,27.75,,S
474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23,0,0,SC/AH Basle 541,13.7917,D,C
475,0,3,"Strandberg, Miss. Ida Sofia",female,22,0,0,7553,9.8375,,S
476,0,1,"Clifford, Mr. George Quincy",male,,0,0,110465,52,A14,S
477,0,2,"Renouf, Mr. Peter Henry",male,34,1,0,31027,21,,S
478,0,3,"Braund, Mr. Lewis Richard",male,29,1,0,3460,7.0458,,S
479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S
480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S
481,0,3,"Goodwin, Master. Harold Victor",male,9,5,2,CA 2144,46.9,,S
482,0,2,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0,,S
483,0,3,"Rouse, Mr. Richard Henry",male,50,0,0,A/5 3594,8.05,,S
484,1,3,"Turkula, Mrs. (Hedwig)",female,63,0,0,4134,9.5875,,S
485,1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C
486,0,3,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S
487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35,1,0,19943,90,C93,S
488,0,1,"Kent, Mr. Edward Austin",male,58,0,0,11771,29.7,B37,C
489,0,3,"Somerton, Mr. Francis William",male,30,0,0,A.5. 18509,8.05,,S
490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9,1,1,C.A. 37671,15.9,,S
491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S
492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S
493,0,1,"Molson, Mr. Harry Markland",male,55,0,0,113787,30.5,C30,S
494,0,1,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C
495,0,3,"Stanley, Mr. Edward Roland",male,21,0,0,A/4 45380,8.05,,S
496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C
497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54,1,0,36947,78.2667,D20,C
498,0,3,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S
499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.55,C22 C26,S
500,0,3,"Svensson, Mr. Olof",male,24,0,0,350035,7.7958,,S
501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S
502,0,3,"Canavan, Miss. Mary",female,21,0,0,364846,7.75,,Q
503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q
504,0,3,"Laitinen, Miss. Kristina Sofia",female,37,0,0,4135,9.5875,,S
505,1,1,"Maioni, Miss. Roberta",female,16,0,0,110152,86.5,B79,S
506,0,1,"Penasco y Castellana, Mr. Victor de Satode",male,18,1,0,PC 17758,108.9,C65,C
507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33,0,2,26360,26,,S
508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S
509,0,3,"Olsen, Mr. Henry Margido",male,28,0,0,C 4001,22.525,,S
510,1,3,"Lang, Mr. Fang",male,26,0,0,1601,56.4958,,S
511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q
512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S
513,1,1,"McGough, Mr. James Robert",male,36,0,0,PC 17473,26.2875,E25,S
514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54,1,0,PC 17603,59.4,,C
515,0,3,"Coleff, Mr. Satio",male,24,0,0,349209,7.4958,,S
516,0,1,"Walker, Mr. William Anderson",male,47,0,0,36967,34.0208,D46,S
517,1,2,"Lemore, Mrs. (Amelia Milley)",female,34,0,0,C.A. 34260,10.5,F33,S
518,0,3,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q
519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36,1,0,226875,26,,S
520,0,3,"Pavlovic, Mr. Stefo",male,32,0,0,349242,7.8958,,S
521,1,1,"Perreault, Miss. Anne",female,30,0,0,12749,93.5,B73,S
522,0,3,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S
523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C
524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44,0,1,111361,57.9792,B18,C
525,0,3,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C
526,0,3,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q
527,1,2,"Ridsdale, Miss. Lucy",female,50,0,0,W./C. 14258,10.5,,S
528,0,1,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S
529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S
530,0,2,"Hocking, Mr. Richard George",male,23,2,1,29104,11.5,,S
531,1,2,"Quick, Miss. Phyllis May",female,2,1,1,26360,26,,S
532,0,3,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C
533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C
534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C
535,0,3,"Cacic, Miss. Marija",female,30,0,0,315084,8.6625,,S
536,1,2,"Hart, Miss. Eva Miriam",female,7,0,2,F.C.C. 13529,26.25,,S
537,0,1,"Butt, Major. Archibald Willingham",male,45,0,0,113050,26.55,B38,S
538,1,1,"LeRoy, Miss. Bertha",female,30,0,0,PC 17761,106.425,,C
539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S
540,1,1,"Frolicher, Miss. Hedwig Margaritha",female,22,0,2,13568,49.5,B39,C
541,1,1,"Crosby, Miss. Harriet R",female,36,0,2,WE/P 5735,71,B22,S
542,0,3,"Andersson, Miss. Ingeborg Constanzia",female,9,4,2,347082,31.275,,S
543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S
544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S
545,0,1,"Douglas, Mr. Walter Donald",male,50,1,0,PC 17761,106.425,C86,C
546,0,1,"Nicholson, Mr. Arthur Ernest",male,64,0,0,693,26,,S
547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",female,19,1,0,2908,26,,S
548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C
549,0,3,"Goldsmith, Mr. Frank John",male,33,1,1,363291,20.525,,S
550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S
551,1,1,"Thayer, Mr. John Borland Jr",male,17,0,2,17421,110.8833,C70,C
552,0,2,"Sharp, Mr. Percival James R",male,27,0,0,244358,26,,S
553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q
554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22,0,0,2620,7.225,,C
555,1,3,"Ohman, Miss. Velin",female,22,0,0,347085,7.775,,S
556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S
557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48,1,0,11755,39.6,A16,C
558,0,1,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C
559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39,1,1,110413,79.65,E67,S
560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36,1,0,345572,17.4,,S
561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q
562,0,3,"Sivic, Mr. Husein",male,40,0,0,349251,7.8958,,S
563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S
564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S
565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S
566,0,3,"Davies, Mr. Alfred J",male,24,2,0,A/4 48871,24.15,,S
567,0,3,"Stoytcheff, Mr. Ilia",male,19,0,0,349205,7.8958,,S
568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29,0,4,349909,21.075,,S
569,0,3,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C
570,1,3,"Jonsson, Mr. Carl",male,32,0,0,350417,7.8542,,S
571,1,2,"Harris, Mr. George",male,62,0,0,S.W./PP 752,10.5,,S
572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S
573,1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36,0,0,PC 17474,26.3875,E25,S
574,1,3,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q
575,0,3,"Rush, Mr. Alfred George John",male,16,0,0,A/4. 20589,8.05,,S
576,0,3,"Patchett, Mr. George",male,19,0,0,358585,14.5,,S
577,1,2,"Garside, Miss. Ethel",female,34,0,0,243880,13,,S
578,1,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39,1,0,13507,55.9,E44,S
579,0,3,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C
580,1,3,"Jussila, Mr. Eiriik",male,32,0,0,STON/O 2. 3101286,7.925,,S
581,1,2,"Christy, Miss. Julie Rachel",female,25,1,1,237789,30,,S
582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39,1,1,17421,110.8833,C68,C
583,0,2,"Downton, Mr. William James",male,54,0,0,28403,26,,S
584,0,1,"Ross, Mr. John Hugo",male,36,0,0,13049,40.125,A10,C
585,0,3,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C
586,1,1,"Taussig, Miss. Ruth",female,18,0,2,110413,79.65,E68,S
587,0,2,"Jarvis, Mr. John Denzil",male,47,0,0,237565,15,,S
588,1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60,1,1,13567,79.2,B41,C
589,0,3,"Gilinski, Mr. Eliezer",male,22,0,0,14973,8.05,,S
590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S
591,0,3,"Rintamaki, Mr. Matti",male,35,0,0,STON/O 2. 3101273,7.125,,S
592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52,1,0,36947,78.2667,D20,C
593,0,3,"Elsbury, Mr. William James",male,47,0,0,A/5 3902,7.25,,S
594,0,3,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q
595,0,2,"Chapman, Mr. John Henry",male,37,1,0,SC/AH 29037,26,,S
596,0,3,"Van Impe, Mr. Jean Baptiste",male,36,1,1,345773,24.15,,S
597,1,2,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33,,S
598,0,3,"Johnson, Mr. Alfred",male,49,0,0,LINE,0,,S
599,0,3,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C
600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49,1,0,PC 17485,56.9292,A20,C
601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24,2,1,243847,27,,S
602,0,3,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S
603,0,1,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S
604,0,3,"Torber, Mr. Ernst William",male,44,0,0,364511,8.05,,S
605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35,0,0,111426,26.55,,C
606,0,3,"Lindell, Mr. Edvard Bengtsson",male,36,1,0,349910,15.55,,S
607,0,3,"Karaic, Mr. Milan",male,30,0,0,349246,7.8958,,S
608,1,1,"Daniel, Mr. Robert Williams",male,27,0,0,113804,30.5,,S
609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22,1,2,SC/Paris 2123,41.5792,,C
610,1,1,"Shutes, Miss. Elizabeth W",female,40,0,0,PC 17582,153.4625,C125,S
611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39,1,5,347082,31.275,,S
612,0,3,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S
613,1,3,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q
614,0,3,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q
615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S
616,1,2,"Herman, Miss. Alice",female,24,1,2,220845,65,,S
617,0,3,"Danbom, Mr. Ernst Gilbert",male,34,1,1,347080,14.4,,S
618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26,1,0,A/5. 3336,16.1,,S
619,1,2,"Becker, Miss. Marion Louise",female,4,2,1,230136,39,F4,S
620,0,2,"Gavey, Mr. Lawrence",male,26,0,0,31028,10.5,,S
621,0,3,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C
622,1,1,"Kimball, Mr. Edwin Nelson Jr",male,42,1,0,11753,52.5542,D19,S
623,1,3,"Nakid, Mr. Sahid",male,20,1,1,2653,15.7417,,C
624,0,3,"Hansen, Mr. Henry Damsgaard",male,21,0,0,350029,7.8542,,S
625,0,3,"Bowen, Mr. David John ""Dai""",male,21,0,0,54636,16.1,,S
626,0,1,"Sutton, Mr. Frederick",male,61,0,0,36963,32.3208,D50,S
627,0,2,"Kirkland, Rev. Charles Leonard",male,57,0,0,219533,12.35,,Q
628,1,1,"Longley, Miss. Gretchen Fiske",female,21,0,0,13502,77.9583,D9,S
629,0,3,"Bostandyeff, Mr. Guentcho",male,26,0,0,349224,7.8958,,S
630,0,3,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q
631,1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30,A23,S
632,0,3,"Lundahl, Mr. Johan Svensson",male,51,0,0,347743,7.0542,,S
633,1,1,"Stahelin-Maeglin, Dr. Max",male,32,0,0,13214,30.5,B50,C
634,0,1,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0,,S
635,0,3,"Skoog, Miss. Mabel",female,9,3,2,347088,27.9,,S
636,1,2,"Davis, Miss. Mary",female,28,0,0,237668,13,,S
637,0,3,"Leinonen, Mr. Antti Gustaf",male,32,0,0,STON/O 2. 3101292,7.925,,S
638,0,2,"Collyer, Mr. Harvey",male,31,1,1,C.A. 31921,26.25,,S
639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41,0,5,3101295,39.6875,,S
640,0,3,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S
641,0,3,"Jensen, Mr. Hans Peder",male,20,0,0,350050,7.8542,,S
642,1,1,"Sagesser, Mlle. Emma",female,24,0,0,PC 17477,69.3,B35,C
643,0,3,"Skoog, Miss. Margit Elizabeth",female,2,3,2,347088,27.9,,S
644,1,3,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S
645,1,3,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C
646,1,1,"Harper, Mr. Henry Sleeper",male,48,1,0,PC 17572,76.7292,D33,C
647,0,3,"Cor, Mr. Liudevit",male,19,0,0,349231,7.8958,,S
648,1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56,0,0,13213,35.5,A26,C
649,0,3,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S
650,1,3,"Stanley, Miss. Amy Zillah Elsie",female,23,0,0,CA. 2314,7.55,,S
651,0,3,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S
652,1,2,"Doling, Miss. Elsie",female,18,0,1,231919,23,,S
653,0,3,"Kalvik, Mr. Johannes Halvorsen",male,21,0,0,8475,8.4333,,S
654,1,3,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q
655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18,0,0,365226,6.75,,Q
656,0,2,"Hickman, Mr. Leonard Mark",male,24,2,0,S.O.C. 14879,73.5,,S
657,0,3,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S
658,0,3,"Bourke, Mrs. John (Catherine)",female,32,1,1,364849,15.5,,Q
659,0,2,"Eitemiller, Mr. George Floyd",male,23,0,0,29751,13,,S
660,0,1,"Newell, Mr. Arthur Webster",male,58,0,2,35273,113.275,D48,C
661,1,1,"Frauenthal, Dr. Henry William",male,50,2,0,PC 17611,133.65,,S
662,0,3,"Badt, Mr. Mohamed",male,40,0,0,2623,7.225,,C
663,0,1,"Colley, Mr. Edward Pomeroy",male,47,0,0,5727,25.5875,E58,S
664,0,3,"Coleff, Mr. Peju",male,36,0,0,349210,7.4958,,S
665,1,3,"Lindqvist, Mr. Eino William",male,20,1,0,STON/O 2. 3101285,7.925,,S
666,0,2,"Hickman, Mr. Lewis",male,32,2,0,S.O.C. 14879,73.5,,S
667,0,2,"Butler, Mr. Reginald Fenton",male,25,0,0,234686,13,,S
668,0,3,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S
669,0,3,"Cook, Mr. Jacob",male,43,0,0,A/5 3536,8.05,,S
670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52,C126,S
671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40,1,1,29750,39,,S
672,0,1,"Davidson, Mr. Thornton",male,31,1,0,F.C. 12750,52,B71,S
673,0,2,"Mitchell, Mr. Henry Michael",male,70,0,0,C.A. 24580,10.5,,S
674,1,2,"Wilhelms, Mr. Charles",male,31,0,0,244270,13,,S
675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0,,S
676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18,0,0,349912,7.775,,S
677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S
678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S
679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S
680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C
681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q
682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C
683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S
684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S
685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S
686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C
687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S
688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S
689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S
690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S
691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S
692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C
693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S
694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C
695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S
696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S
697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S
698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q
699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C
700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S
701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.525,C62 C64,C
702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S
703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C
704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q
705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S
706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S
707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S
708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S
709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S
710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C
712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S
713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S
714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S
715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S
716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S
717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C
718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S
719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q
720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S
721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S
722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S
723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S
724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S
725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S
726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S
727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S
731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S
732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C
733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S
734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S
735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S
736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S
737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S
738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C
739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S
740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S
741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S
742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S
743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S
745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S
746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S
747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S
748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S
749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S
750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q
751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S
752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S
753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S
754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S
755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S
758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S
759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S
760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S
761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S
762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S
763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C
764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S
765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S
766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S
767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S
771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S
772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C
791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S
796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S
797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S
798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S
799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C
800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S
803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S
804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S
806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S
807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S
808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S
809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S
810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S
811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S
812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
diff --git a/project/jupyter/jupyter.ipynb b/project/jupyter/jupyter.ipynb
new file mode 100644
index 00000000..ff6b4ba0
--- /dev/null
+++ b/project/jupyter/jupyter.ipynb
@@ -0,0 +1,3600 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Задача 7. Jupyter"
+      ],
+      "metadata": {
+        "id": "raUC42HwFHTr"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Подготовка данных"
+      ],
+      "metadata": {
+        "id": "yHtLqkoeFfvp"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "импорт библиотек"
+      ],
+      "metadata": {
+        "id": "obyYmXCGKHux"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "import plotly.express as px"
+      ],
+      "metadata": {
+        "id": "-3ZWb5oJFwAS"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "загрузка данных"
+      ],
+      "metadata": {
+        "id": "rL2lqfqZKDDf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trains = pd.read_csv('data/train.csv')\n",
+        "test = pd.read_csv('data/test.csv')\n",
+        "len(trains), len(test)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Xc7dExDNJr1H",
+        "outputId": "c22e3561-3cb3-41c9-ffbf-ca2df5107c2d"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(891, 418)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Отобразим данные с начала и с конца."
+      ],
+      "metadata": {
+        "id": "SVpzMHPmhVI1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "display(trains.head(2))\n",
+        "display(test.tail(2))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 207
+        },
+        "id": "DxG5Lk9JKb1v",
+        "outputId": "24804399-0857-4192-d871-9ece7c7bd19b"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   PassengerId  Survived  Pclass  \\\n",
+              "0            1         0       3   \n",
+              "1            2         1       1   \n",
+              "\n",
+              "                                                Name     Sex   Age  SibSp  \\\n",
+              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+              "\n",
+              "   Parch     Ticket     Fare Cabin Embarked  \n",
+              "0      0  A/5 21171   7.2500   NaN        S  \n",
+              "1      0   PC 17599  71.2833   C85        C  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-74787946-d38e-4086-a36c-523bd5b6514b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>PassengerId</th>\n",
+              "      <th>Survived</th>\n",
+              "      <th>Pclass</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>SibSp</th>\n",
+              "      <th>Parch</th>\n",
+              "      <th>Ticket</th>\n",
+              "      <th>Fare</th>\n",
+              "      <th>Cabin</th>\n",
+              "      <th>Embarked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Braund, Mr. Owen Harris</td>\n",
+              "      <td>male</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>A/5 21171</td>\n",
+              "      <td>7.2500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+              "      <td>female</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>PC 17599</td>\n",
+              "      <td>71.2833</td>\n",
+              "      <td>C85</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-74787946-d38e-4086-a36c-523bd5b6514b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-74787946-d38e-4086-a36c-523bd5b6514b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-74787946-d38e-4086-a36c-523bd5b6514b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-deb1f27c-fb04-4fdb-9c3e-87346f5b9d80\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-deb1f27c-fb04-4fdb-9c3e-87346f5b9d80')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-deb1f27c-fb04-4fdb-9c3e-87346f5b9d80 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(test\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cumings, Mrs. John Bradley (Florence Briggs Thayer)\",\n          \"Braund, Mr. Owen Harris\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.313708498984761,\n        \"min\": 22.0,\n        \"max\": 38.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          38.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"PC 17599\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45.27838065175255,\n        \"min\": 7.25,\n        \"max\": 71.2833,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          71.2833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"C85\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "     PassengerId  Pclass                      Name   Sex  Age  SibSp  Parch  \\\n",
+              "416         1308       3       Ware, Mr. Frederick  male  NaN      0      0   \n",
+              "417         1309       3  Peter, Master. Michael J  male  NaN      1      1   \n",
+              "\n",
+              "     Ticket     Fare Cabin Embarked  \n",
+              "416  359309   8.0500   NaN        S  \n",
+              "417    2668  22.3583   NaN        C  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b180682f-5af2-4716-8577-8d78abbc7a58\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>PassengerId</th>\n",
+              "      <th>Pclass</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>SibSp</th>\n",
+              "      <th>Parch</th>\n",
+              "      <th>Ticket</th>\n",
+              "      <th>Fare</th>\n",
+              "      <th>Cabin</th>\n",
+              "      <th>Embarked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>416</th>\n",
+              "      <td>1308</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Ware, Mr. Frederick</td>\n",
+              "      <td>male</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>359309</td>\n",
+              "      <td>8.0500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>417</th>\n",
+              "      <td>1309</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Peter, Master. Michael J</td>\n",
+              "      <td>male</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2668</td>\n",
+              "      <td>22.3583</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b180682f-5af2-4716-8577-8d78abbc7a58')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b180682f-5af2-4716-8577-8d78abbc7a58 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b180682f-5af2-4716-8577-8d78abbc7a58');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-526ca2de-5893-46e3-b250-852ccecc9aca\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-526ca2de-5893-46e3-b250-852ccecc9aca')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-526ca2de-5893-46e3-b250-852ccecc9aca button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "repr_error": "0"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "конкатенация данных"
+      ],
+      "metadata": {
+        "id": "zh8Un_IsLs1d"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Конкатенируем таблицы и преобразуем столбы `Survived`, `Pclass`, `Sex`, `Embarked` в категориальный тип."
+      ],
+      "metadata": {
+        "id": "ljD5sJO13Oio"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trains_test = pd.concat([trains, test], ignore_index=True)\n",
+        "trains_test['Survived'] = trains_test['Survived'].astype('category')\n",
+        "trains_test['Pclass'] = trains_test['Pclass'].astype('category')\n",
+        "trains_test['Sex'] = trains_test['Sex'].astype('category')\n",
+        "trains_test['Embarked'] = trains_test['Embarked'].astype('category')"
+      ],
+      "metadata": {
+        "id": "DAHkFFI1LWLm"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trains_test"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "iL2bJYn5OwrK",
+        "outputId": "494f9d32-ff61-4bde-f6ab-54c370581a64"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      PassengerId Survived Pclass  \\\n",
+              "0               1      0.0      3   \n",
+              "1               2      1.0      1   \n",
+              "2               3      1.0      3   \n",
+              "3               4      1.0      1   \n",
+              "4               5      0.0      3   \n",
+              "...           ...      ...    ...   \n",
+              "1304         1305      NaN      3   \n",
+              "1305         1306      NaN      1   \n",
+              "1306         1307      NaN      3   \n",
+              "1307         1308      NaN      3   \n",
+              "1308         1309      NaN      3   \n",
+              "\n",
+              "                                                   Name     Sex   Age  SibSp  \\\n",
+              "0                               Braund, Mr. Owen Harris    male  22.0      1   \n",
+              "1     Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+              "2                                Heikkinen, Miss. Laina  female  26.0      0   \n",
+              "3          Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+              "4                              Allen, Mr. William Henry    male  35.0      0   \n",
+              "...                                                 ...     ...   ...    ...   \n",
+              "1304                                 Spector, Mr. Woolf    male   NaN      0   \n",
+              "1305                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
+              "1306                       Saether, Mr. Simon Sivertsen    male  38.5      0   \n",
+              "1307                                Ware, Mr. Frederick    male   NaN      0   \n",
+              "1308                           Peter, Master. Michael J    male   NaN      1   \n",
+              "\n",
+              "      Parch              Ticket      Fare Cabin Embarked  \n",
+              "0         0           A/5 21171    7.2500   NaN        S  \n",
+              "1         0            PC 17599   71.2833   C85        C  \n",
+              "2         0    STON/O2. 3101282    7.9250   NaN        S  \n",
+              "3         0              113803   53.1000  C123        S  \n",
+              "4         0              373450    8.0500   NaN        S  \n",
+              "...     ...                 ...       ...   ...      ...  \n",
+              "1304      0           A.5. 3236    8.0500   NaN        S  \n",
+              "1305      0            PC 17758  108.9000  C105        C  \n",
+              "1306      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
+              "1307      0              359309    8.0500   NaN        S  \n",
+              "1308      1                2668   22.3583   NaN        C  \n",
+              "\n",
+              "[1309 rows x 12 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-883111c5-e635-4538-90d7-5f97369532f5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>PassengerId</th>\n",
+              "      <th>Survived</th>\n",
+              "      <th>Pclass</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>SibSp</th>\n",
+              "      <th>Parch</th>\n",
+              "      <th>Ticket</th>\n",
+              "      <th>Fare</th>\n",
+              "      <th>Cabin</th>\n",
+              "      <th>Embarked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Braund, Mr. Owen Harris</td>\n",
+              "      <td>male</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>A/5 21171</td>\n",
+              "      <td>7.2500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+              "      <td>female</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>PC 17599</td>\n",
+              "      <td>71.2833</td>\n",
+              "      <td>C85</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Heikkinen, Miss. Laina</td>\n",
+              "      <td>female</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>STON/O2. 3101282</td>\n",
+              "      <td>7.9250</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>113803</td>\n",
+              "      <td>53.1000</td>\n",
+              "      <td>C123</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Allen, Mr. William Henry</td>\n",
+              "      <td>male</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>373450</td>\n",
+              "      <td>8.0500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1304</th>\n",
+              "      <td>1305</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Spector, Mr. Woolf</td>\n",
+              "      <td>male</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>A.5. 3236</td>\n",
+              "      <td>8.0500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1305</th>\n",
+              "      <td>1306</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
+              "      <td>female</td>\n",
+              "      <td>39.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>PC 17758</td>\n",
+              "      <td>108.9000</td>\n",
+              "      <td>C105</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1306</th>\n",
+              "      <td>1307</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Saether, Mr. Simon Sivertsen</td>\n",
+              "      <td>male</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>SOTON/O.Q. 3101262</td>\n",
+              "      <td>7.2500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1307</th>\n",
+              "      <td>1308</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Ware, Mr. Frederick</td>\n",
+              "      <td>male</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>359309</td>\n",
+              "      <td>8.0500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1308</th>\n",
+              "      <td>1309</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Peter, Master. Michael J</td>\n",
+              "      <td>male</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2668</td>\n",
+              "      <td>22.3583</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1309 rows × 12 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-883111c5-e635-4538-90d7-5f97369532f5')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-883111c5-e635-4538-90d7-5f97369532f5 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-883111c5-e635-4538-90d7-5f97369532f5');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-37142ea1-c2f3-4e3d-95c3-e67aefb5d945\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-37142ea1-c2f3-4e3d-95c3-e67aefb5d945')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-37142ea1-c2f3-4e3d-95c3-e67aefb5d945 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "trains_test",
+              "summary": "{\n  \"name\": \"trains_test\",\n  \"rows\": 1309,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 378,\n        \"min\": 1,\n        \"max\": 1309,\n        \"num_unique_values\": 1309,\n        \"samples\": [\n          1149,\n          1050,\n          983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1307,\n        \"samples\": [\n          \"Bryhl, Miss. Dagmar Jenny Ingeborg \",\n          \"Borebank, Mr. John James\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.413493211271334,\n        \"min\": 0.17,\n        \"max\": 80.0,\n        \"num_unique_values\": 98,\n        \"samples\": [\n          45.5,\n          23.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 929,\n        \"samples\": [\n          \"PC 17531\",\n          \"345765\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.75866823917414,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 281,\n        \"samples\": [\n          11.2417,\n          35.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 186,\n        \"samples\": [\n          \"B71\",\n          \"C51\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Анализ таблицы"
+      ],
+      "metadata": {
+        "id": "4VJTkXaQT91l"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Вывод базой информации о таблице."
+      ],
+      "metadata": {
+        "id": "rI_B5qKR3b0j"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trains_test.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dCvCjepeTCGB",
+        "outputId": "3716cbda-f09e-41be-8f21-73c3f82411ae"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 1309 entries, 0 to 1308\n",
+            "Data columns (total 12 columns):\n",
+            " #   Column       Non-Null Count  Dtype   \n",
+            "---  ------       --------------  -----   \n",
+            " 0   PassengerId  1309 non-null   int64   \n",
+            " 1   Survived     891 non-null    category\n",
+            " 2   Pclass       1309 non-null   category\n",
+            " 3   Name         1309 non-null   object  \n",
+            " 4   Sex          1309 non-null   category\n",
+            " 5   Age          1046 non-null   float64 \n",
+            " 6   SibSp        1309 non-null   int64   \n",
+            " 7   Parch        1309 non-null   int64   \n",
+            " 8   Ticket       1309 non-null   object  \n",
+            " 9   Fare         1308 non-null   float64 \n",
+            " 10  Cabin        295 non-null    object  \n",
+            " 11  Embarked     1307 non-null   category\n",
+            "dtypes: category(4), float64(2), int64(3), object(3)\n",
+            "memory usage: 87.6+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Вывод базовой статистики."
+      ],
+      "metadata": {
+        "id": "x26fS0eCWQoC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trains_test.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "9KlRpKhWUFTM",
+        "outputId": "ad5ac1b7-4c76-4c1e-f59c-7216fb56ca95"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       PassengerId          Age        SibSp        Parch         Fare\n",
+              "count  1309.000000  1046.000000  1309.000000  1309.000000  1308.000000\n",
+              "mean    655.000000    29.881138     0.498854     0.385027    33.295479\n",
+              "std     378.020061    14.413493     1.041658     0.865560    51.758668\n",
+              "min       1.000000     0.170000     0.000000     0.000000     0.000000\n",
+              "25%     328.000000    21.000000     0.000000     0.000000     7.895800\n",
+              "50%     655.000000    28.000000     0.000000     0.000000    14.454200\n",
+              "75%     982.000000    39.000000     1.000000     0.000000    31.275000\n",
+              "max    1309.000000    80.000000     8.000000     9.000000   512.329200"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5c366252-1b41-4ddf-a99a-68c8361e77cf\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>PassengerId</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>SibSp</th>\n",
+              "      <th>Parch</th>\n",
+              "      <th>Fare</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>1309.000000</td>\n",
+              "      <td>1046.000000</td>\n",
+              "      <td>1309.000000</td>\n",
+              "      <td>1309.000000</td>\n",
+              "      <td>1308.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>655.000000</td>\n",
+              "      <td>29.881138</td>\n",
+              "      <td>0.498854</td>\n",
+              "      <td>0.385027</td>\n",
+              "      <td>33.295479</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>378.020061</td>\n",
+              "      <td>14.413493</td>\n",
+              "      <td>1.041658</td>\n",
+              "      <td>0.865560</td>\n",
+              "      <td>51.758668</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.170000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>328.000000</td>\n",
+              "      <td>21.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>7.895800</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>655.000000</td>\n",
+              "      <td>28.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>14.454200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>982.000000</td>\n",
+              "      <td>39.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>31.275000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1309.000000</td>\n",
+              "      <td>80.000000</td>\n",
+              "      <td>8.000000</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>512.329200</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c366252-1b41-4ddf-a99a-68c8361e77cf')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-5c366252-1b41-4ddf-a99a-68c8361e77cf button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-5c366252-1b41-4ddf-a99a-68c8361e77cf');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-ecef6b69-86d0-4380-a4f8-7b92e8b67b21\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ecef6b69-86d0-4380-a4f8-7b92e8b67b21')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-ecef6b69-86d0-4380-a4f8-7b92e8b67b21 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"trains_test\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 471.4651737906,\n        \"min\": 1.0,\n        \"max\": 1309.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1309.0,\n          655.0,\n          982.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 359.84044215622254,\n        \"min\": 0.17,\n        \"max\": 1046.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.881137667304014,\n          28.0,\n          1046.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 462.27680922007715,\n        \"min\": 0.0,\n        \"max\": 1309.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1309.0,\n          0.4988540870893812,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 462.29399018424664,\n        \"min\": 0.0,\n        \"max\": 1309.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3850267379679144,\n          9.0,\n          0.8655602753495125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 462.70446257758823,\n        \"min\": 0.0,\n        \"max\": 1308.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          33.29547928134557,\n          14.4542,\n          1308.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# List of categorical columns\n",
+        "columns = ['Survived', 'Sex', 'Embarked']\n",
+        "\n",
+        "# Creating a shape for graphs\n",
+        "fig, axes = plt.subplots(1, len(columns), figsize=(15, 5), sharey=True)\n",
+        "\n",
+        "# Creating graphs for each category\n",
+        "for i, col in enumerate(columns):\n",
+        "    sns.countplot(data=trains_test, x=col, ax=axes[i])\n",
+        "    axes[i].set_title(f'Count of {col}', fontsize=14)\n",
+        "    axes[i].set_xlabel(col, fontsize=12)\n",
+        "    axes[i].set_ylabel('Count', fontsize=12)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 507
+        },
+        "id": "fAeP7lxZ2XFW",
+        "outputId": "3fc59954-543d-4c0e-ee10-f8888a26fafe"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x500 with 3 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAABdEAAAHqCAYAAADrpwd3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABWUklEQVR4nO3debyWc/4/8NdpO+1FVCIVsg1jyTLFWBJZso2dIfsg2QYz+SF7w9j3YUYxmFEMYxmRhjIj+zLWLBOyVEhFqHSu3x8ena8znZvKqXPK8/l4XI+H87k+13W9r7tze5/7dV/3dZcVRVEEAAAAAACYS73aLgAAAAAAAOoqIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEITpQq2bNmpUzzjgjXbt2TXl5ecrKynLXXXfVdlk16sADD0xZWVnefvvt2i4lSd2rB4Alw4+hpwPAHItb3+vcuXM6d+5ca8c/44wzUlZWlkceeWSRHre2z5slhxAdFpJnnnkmhxxySLp27ZpmzZqlSZMmWXnllbP//vtnxIgRtV3ed3r77bdTVlaWAw88cKEf66KLLsqZZ56ZDh065MQTT8zAgQOz+uqrf+c2RVHk5ptvTs+ePdOmTZs0atQo7dq1y3rrrZejjjoqo0aNWuh1A/DjoafPGz0dYMmg782bBel7nTt3TllZ2XcuLnaCuqlBbRcAS5qKioqceOKJueSSS9KgQYP07NkzO+20Uxo2bJj//ve/ue+++3LzzTfnrLPOymmnnVbb5da6e++9N82bN8+IESPSqFGjedrm4IMPzpAhQ7LUUkulT58+WX755fPll1/mhRdeyJ/+9KdMmzYtm2+++UKufN4NGjQov/3tb7P88svXdikAzAc9ff78GHo6wJJM35s/C9L3kqR+/fo59dRTS65v3bp1DVQH1DQhOtSwU089NZdccknWXXfd3H777Vl55ZWrrP/yyy9z5ZVX5pNPPqmlCuuWDz74oPLKs3nx6KOPZsiQIVl33XUzatSotGzZssr6KVOm5JVXXlkYpS6w5ZZbLsstt1xtlwHAfNLT58+PoacDLMn0vfkzv31vjgYNGuSMM85YOEUBC43buUANevPNN3PBBRekTZs2GT58+Fx/dCRJkyZNctJJJ+XMM8+sMv7xxx/nuOOOS5cuXVJeXp62bdtmzz33zEsvvTTXPrbYYouUlZVVW0N197seMmRIysrKMmTIkDz44IPp0aNHmjZtmjZt2qRv375V/ggaMmRIunTpkiS58cYbq3ysbF7vXTZ48OBsvPHGad68eZo3b56NN944Q4YMqTJnzv3Qxo0bl3feeafyGN93r7IxY8YkSfr27TvXi+3km3fte/ToUWXshzxe99xzTzbZZJO0aNEinTt3zqOPPpqysrIcfPDB1e5v0qRJadiwYTbZZJOSx1iQfSTJZ599loEDB+YnP/lJmjRpktatW6d3797517/+Ve1+Xn755fTp0yctWrRIq1atsv3221f7+wTA3PT0b9S1np4kM2fOzMUXX5z1118/zZo1S4sWLfLzn/88d999d5V5v/vd71JWVpYjjjhirn3MWXfkkUd+zyMA8OOg731jYfa9BTHn8ZoxY0ZOOeWUrLjiimnSpEm6deuWhx56KEkyderU9OvXLx06dEjjxo3TvXv3PPnkkyX3OWXKlPzqV79K+/bt07hx46y33nr5y1/+Mte8Dz74IAMHDszPfvaztG3bNuXl5encuXOOOuqoTJo0aa75c/79/vvf/+aiiy7KmmuumfLy8u+9tc5LL72UFVZYIUsttVSV17bjxo3LoYcemhVXXDHl5eVZbrnlcuCBB+add96pdj9///vfs+GGG6ZJkyZp165dDjvssHz66affeWyYH65Ehxo0ZMiQzJ49O7/61a/Srl2775xbXl5e+d8fffRRunfvnrfeeitbbLFF9t5774wbNy6333577rvvvjzwwAPZdNNNf3B9d999d+67777suOOO6dGjR0aPHp2bbropb731VmWzWnfddXPsscfmsssuyzrrrJNddtmlcvt5+aPgmGOOyRVXXJHll18+hxxySJLkjjvuyEEHHZTnnnsul112WZJv/hhIkksvvTRJctxxxyX5/o+utWnTJkny+uuvz9tJ/wDDhg3Lgw8+mD59+uSoo47KtGnTsummm6Zz58654447cvXVV6dx48ZVtvnLX/6Sr7/+Ovvvv3/J/S7IPiZPnpzNNtssL7/8cjbZZJMcccQRmTZtWv7+979nyy23zLBhw6r8W7300kvZZJNN8vnnn+cXv/hFunbtmieffDKbbLJJ1llnnZp5gACWYHp63ezpM2bMyLbbbptHHnkk6667bg455JDMmjUr9913X3beeedcccUVOfroo5MkJ598ckaMGJE//OEP2XbbbSvP/8knn8zpp5+eNddcMxdffPE8HxtgSabvLfy+90PstddeefHFF7PTTjvlyy+/zC233JI+ffrk3//+dw4//PDMnDkze+yxRz766KPcdttt2XbbbTNu3Li0atWqyn5mzpyZXr165fPPP8/++++f6dOnZ+jQodl3333z8ccfp3///pVzR48enYsuuihbbbVVNt544zRs2DDPPfdcrrnmmjzwwAN59tln59p/kvTv3z+PP/54dthhh+y4445p27ZtyfP617/+lR133DHNmjXLo48+mrXWWitJ8sQTT6R3796ZPn16+vTpk65du+btt9/OLbfckvvvvz9jxozJSiutVLmfm266qfJN+f333z+tW7fOvffem169emXmzJnz/WkBqFYB1JgtttiiSFI89NBD87XdQQcdVCQpBgwYUGX8vvvuK5IUq6yySjF79uzK8c0337wo9fTt27dvkaQYN25c5djgwYOLJEWDBg2Kf/3rX5XjX3/9dWXNY8aMqRwfN25ckaTo27fvfJ3HqFGjiiTFGmusUUyZMqVyfPLkycWqq65aJClGjx5dZZtOnToVnTp1mudjjB8/vmjZsmVRVlZW7LvvvsWwYcOKt99++zu3WdDHq169esWIESPm2ubUU08tkhS33XbbXOu6detWNGrUqPjkk0++8xjzu4999923SFJcf/31VeZOnDix6NixY7HssssWX3755VznfPPNN1eZP2DAgCLJXPUAUJWeXjd7+imnnFIkKU477bSioqKicnzatGnFBhtsUDRq1Kh4//33K8ffe++9ok2bNsXSSy9dvPfee8W0adOKlVdeuSgvLy9eeOGFea4VYEmn7y38vjdnm/r16xcDBw6sdrnmmmuqzJ/zeG266abF559/Xjl+2223FUmK1q1bF3vssUcxa9asynXnn39+kaS46KKL5jp2kmKzzTYrZsyYUTk+fvz4YplllinKy8uL9957r3J84sSJxWeffTbXOdx4441FkuKcc86pMj7n32+FFVYo3nnnnbm2GzhwYJGkePjhh4uiKIq///3vRZMmTYrVVlutyvyZM2cWnTt3Llq0aFE8++yzVfbx6KOPFvXr1y/69OlTOTZ16tSiZcuWRbNmzYqxY8dW2c9mm21WJJnvfyeojhAdatDqq69eJClee+21ed5mxowZRePGjYs2bdoU06dPn2v91ltvPVfDXtA/PA444IC55s9Zd/nll1eOLegfHgcffHDJYPiWW24pkhQHH3xwlfEF+cNjxIgRxYorrlgZBicpll122WLPPfcsRo4cOdf8BX28dt1112q3GTt2bJGk2HHHHauMv/LKK0WSYpdddvneY8zPPj766KOifv36Rc+ePaut5/LLLy+SFPfcc09RFEXxzjvvFEmKn/70p3PN/eyzz4rWrVsL0QG+h55e93r67Nmzi6WWWqpYeeWVqwToc9x9991FkuKKK66oMn7XXXcVSYotttii+OUvf1kkKS677LL5qhNgSafvLZq+NyfILrWss846VebPebxGjRpVZXz27NlFw4YNiyRzBdbvvvtutY/ZnGN/+82IOc4+++wiSXHhhRd+7zlUVFQULVu2LLbYYosq43P+/Ur12G+H6H/84x+L+vXrFxtttFHx0UcfVZn3t7/9rUhSnHXWWdXu5xe/+EVRr169YurUqUVR/F+o379//7nmPvroo0J0aozbuUAte+211/LVV19lyy23TNOmTedav+WWW2bEiBF5/vnn8/Of//wHHatbt25zja2wwgpJvrkv2g/13HPPJfm/j7d925Zbbpkkef7553/wcXr16pW33norjzzySEaPHp1nnnkm//rXvzJ06NAMHTo0AwYMyHnnnfeDj7PRRhtVO77qqqtmo402yvDhw/Pxxx9nmWWWSZLcfPPNSfKdt3JZkH089dRTmT17dmbMmFHtF9C88cYbSb75XerTp09eeOGFJKn2Y5PNmzfPuuuuO8/3BARg3unp829+evrYsWPz6aefpkOHDnPdjzf55pYCyTf/Dt+2884754gjjsi1116bJNl+++1zzDHH/ODaAX7s9L0FU15enq+++mq+tll33XWr/FyvXr20bds2X3zxRVZcccUq65Zbbrkk39zT/H81aNAg3bt3n2t8zr/PnMdhjr/97W/5wx/+kGeffTaffvppZs+eXbmuuv0npV9Hz3HJJZfk7rvvTu/evXPHHXekWbNmVdY//vjjSb7p+9W9/p0wYUIqKiry+uuvZ4MNNqh8/Vvd71j37t3ToIHok5rhNwlqUPv27fPaa6/l/fffz2qrrTZP20ybNi1JSt53bk4DnDPvh6juS7vmNJRvN8MFNW3atNSrVy/LLrvsXOvatWuXsrKyGjmP5Ju6e/XqlV69eiVJvv766wwZMiRHHnlkBg0alN133z3rr7/+DzrGd90LcP/998+TTz6Z2267Lf369UtRFLnllluy1FJLZYcddpin/c/rPiZPnpwk+fe//51///vfJfc3ffr0JN98sUySkvee+757HAKgp9fFnj6nH7788st5+eWXS+5vTj/8tl133bUyRJ9zz3QA/o++t+j63oIodf7f9bjMmjVrrnXLLLNM6tWrN9f4nH/DOa8lk+Siiy7KiSeemGWXXTbbbLNNVlhhhTRp0iTJN/eDnzFjRrW1ft/rzUcffTRJ0rt377kC9OT/Xv/ecsst37mfeXn9W79+/crvYIEfau5nDrDANtlkkyTJyJEj53mbOU1v4sSJ1a6fMGFClXlJKpve119/Pdf8bze9Ra1ly5apqKiovBLs2yZNmpSiKKpt8jWhQYMGOfTQQ7PvvvsmSR5++OHKdQv6eJX61vgk2XvvvdOwYcPKK8dHjx6dd955J3vuuWeVL9r5LvO6jzmP2a9//esU39yGq9pl4MCBSVL55S7VfWN6Uvp3DYD/o6fXvZ4+53i77bbbd/bDwYMHV9nflClTcthhh6VZs2Zp3Lhx+vfvn88++2yh1A6wuNL3aq/vLUoff/xxKioq5hqf828457Xk119/nbPPPjvLLbdcXnrppdxyyy05//zzc8YZZ2TgwIGZOXNmyWN81+voJPnTn/6Ubt265YQTTsjll18+1/o5j/M999zznf1+8803r1Jzda9/Z8+enU8++eQ764F5JUSHGnTggQemfv36ue6666ptvt82513b1VdfPY0bN85TTz2VL774Yq55c2678e2Pby211FJJkvfff7/K3IqKisqPMv0Q9evXTzL/7+ivt956SVLtrUKqO4+FoXnz5nONLYzHa5lllsm2226bxx9/PG+++WZlEP7LX/6yxvex4YYbpqysLGPGjJmn/a6zzjpJUvkt9d/2+eef19jHEAGWZHp63evpa6yxRlq2bJmnn3662qvrSjn88MPz7rvv5rLLLsvvf//7vPXWW+nXr19NlwuwWNP3ar/vLQpff/11ta8r51wdPudx+PjjjzN16tR07959riu8n3766Xz55ZcLXMNSSy2Vhx56KBtssEGOPfbYXHbZZVXWb7zxxkky369/55zDt40ZM6baN2xgQQjRoQatssoqOfnkk/Pxxx9nu+22y7hx4+aa89VXX+Xiiy+uvLdXo0aNss8+++Tjjz/OoEGDqswdPnx4HnjggayyyiqVVwYk34SqSTJkyJAq8y+++OJqjzm/llpqqZSVlWX8+PHztV3fvn2TJGeeeWaVj7pNnTq18t6lc+YsqOHDh+fvf/97tY3wzTffzLBhw5JUvR/4wnq85ty3/I9//GOGDRuWLl26VPl3qql9tG/fPnvuuWcee+yx/P73v09RFHPt54knnqj8w3XFFVfMZpttlv/85z9zfQTuvPPOq5F7BgIs6fT0utfTGzRokCOPPDLvvPNOTjzxxGqD9JdeeqnKlWh/+tOfMmzYsOyxxx455JBDcvTRR6dPnz7585//nFtvvfUH1Q+wJNH3Fn7fqytOOeWUKleSv/fee7nssstSXl6evffeO8k3t0Zp0qRJnn322SpvkHz66afp37//D66hdevWGTFiRDbccMMcd9xxufTSSyvX7bzzzllxxRVz8cUXZ/To0XNtO2vWrCoXjO28885p2bJlbrjhhrz++utV5p166qk/uFaYwz3RoYadc845+eqrr3LJJZdktdVWS8+ePbPWWmulYcOGGTduXB566KF88sknOeeccyq3Of/88zNq1Kicc845eeyxx7Lxxhvn7bffzrBhw9K0adMMHjy4yn3LDjrooFxwwQU544wz8vzzz2fllVfO008/nZdeeimbb755Ro0a9YPOoXnz5tlwww0zevTo7L///unatWvq1auX/fffP506dSq53WabbZb+/fvniiuuyFprrVX5ces77rgj7733Xo455phsttlmP6i21157Lccff3yWWWaZbLbZZll55ZVTFEXefPPN/OMf/8jMmTNz5JFHVr57nSy8x2vHHXdMq1atcvHFF2fWrFk55phjvvejawu6j6uvvjpjx47NySefnD//+c/p3r17WrdunfHjx+fpp5/OG2+8kQ8//LDyC32uuuqqbLLJJjnggANy1113pWvXrnnyySfz1FNP5ec//3m179IDUJWeXvd6+plnnplnn302l19+ee67775sttlmadu2bd5///28+OKLeeGFFzJmzJi0bds2r7/+eo499th07Ngx1113XeU+brjhhvz0pz/NkUceme7du6dLly4/6DwAlhT63sLte3N8/fXX1X5h5hx77713Vl999Ro51v9abrnlMn369Pz0pz/NjjvumOnTp2fo0KH55JNPcvnll2f55ZdP8s1td4466qhcdNFFWWeddbLjjjtm2rRpuf/++9OpU6d06NDhB9cyJ0jv3bt3jj/++BRFkeOPPz7l5eW5/fbbs91222XzzTdPz549s/baa6esrCzvvPNOHn300bRp06byi8RbtWqVyy+/PAceeGA23HDD7L333mnVqlXuvffeNGnSpPLe/PCDFcBC8dRTTxUHH3xwscoqqxRNmjQpysvLi86dOxf77rtvMWLEiLnmf/TRR8UxxxxTdOrUqWjYsGGxzDLLFLvvvnvx4osvVrv/559/vthqq62Kpk2bFi1btix23nnn4o033ij69u1bJCnGjRtXOXfw4MFFkmLw4MFz7efhhx8ukhQDBw6sMj527Nhi++23L1q3bl2UlZUVSYqHH354ns79hhtuKDbccMOiadOmRdOmTYsNN9ywuOGGG6qd26lTp6JTp07ztN+iKIpJkyYV119/fbH77rsXq622WtGiRYuiYcOGxXLLLVf06dOnuP3226vdrqYer/916KGHFkmKJMXYsWOrnVPdMeZ3H0VRFF988UVxwQUXFN26dSuaNWtWNGnSpOjSpUuxyy67FDfddFMxa9asKvNffPHFYvvtty+aN29etGjRothuu+2KF1988XvrAaAqPb1u9fSvv/66+MMf/lBssskmRcuWLYvy8vJixRVXLLbddtvimmuuKT7//PNixowZxfrrr1/Uq1evGDVq1Fz7ePDBB4uysrLiZz/72Vz9E+DHTt9bOH1vzjZzXvuVWu68887K+ZtvvnlRKrr7ruMnKTbffPNq50+ePLk4/PDDi3bt2hXl5eXFOuusU9x6661z7WPmzJnFueeeW3Tt2rWy1/76178uPvvss2qP/X2vMwcOHFjtv8XUqVOL7t27F0mKCy+8sHL8vffeK4499tjK47ds2bJYY401ikMPPbQYOXLkXPu/8847i27duhXl5eVF27Zti0MPPbSYPHnyAv07QXXKiqKa+wIAAAAAAADuiQ4AAAAAAKUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKaFDbBdRFFRUV+eCDD9KiRYuUlZXVdjkAUGcURZHPPvssHTp0SL16P+y9eP0WAKpXk/020XMBoJR57blC9Gp88MEH6dixY22XAQB11vjx47PCCiv8oH3otwDw3Wqi3yZ6LgB8n+/ruUL0arRo0SLJNw9ey5Yta7kaAKg7pk2blo4dO1b2yh9CvwWA6tVkv030XAAoZV57rhC9GnM+3tayZUt/YABANWrio+D6LQB8t5q69YqeCwDf7ft6ri8WBQAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlNCgtgsAAAAAAFgYup10U22XwCLwzO8PWKj7dyU6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAkNarsAAABgydftpJtquwSocc/8/oDaLgEAWARciQ4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEupUiD579uycdtpp6dKlS5o0aZKVV145Z599doqiqJxTFEVOP/30LLfccmnSpEl69eqVN954o8p+Jk+enP322y8tW7ZM69atc8ghh+Tzzz9f1KcDAAAAAMBirk6F6Oeff36uueaaXHnllXn11Vdz/vnn54ILLsgVV1xROeeCCy7I5ZdfnmuvvTZPPPFEmjVrlt69e+err76qnLPffvvl5ZdfzogRI3Lvvfdm9OjROfzww2vjlAAAAAAAWIw1qO0Cvu2xxx7LzjvvnB122CFJ0rlz5/zlL3/Jk08+meSbq9AvvfTSnHrqqdl5552TJDfddFPatWuXu+66K3vvvXdeffXVDB8+PE899VQ22GCDJMkVV1yR7bffPhdeeGE6dOhQOycHAAAAAMBip05did6jR4+MHDkyr7/+epLkhRdeyL/+9a9st912SZJx48ZlwoQJ6dWrV+U2rVq1ysYbb5wxY8YkScaMGZPWrVtXBuhJ0qtXr9SrVy9PPPHEIjwbAAAAAAAWd3XqSvTf/va3mTZtWlZfffXUr18/s2fPzrnnnpv99tsvSTJhwoQkSbt27aps165du8p1EyZMSNu2bausb9CgQZZeeunKOf9rxowZmTFjRuXP06ZNq7FzAgC+od8CwKKh5wJAzapTV6IPHTo0t9xyS2699dY8++yzufHGG3PhhRfmxhtvXKjHHTRoUFq1alW5dOzYcaEeDwB+jPRbAFg09FwAqFl1KkQ/6aST8tvf/jZ777131l577ey///45/vjjM2jQoCRJ+/btkyQTJ06sst3EiRMr17Vv3z6TJk2qsv7rr7/O5MmTK+f8rwEDBmTq1KmVy/jx42v61ADgR0+/BYBFQ88FgJpVp0L0L774IvXqVS2pfv36qaioSJJ06dIl7du3z8iRIyvXT5s2LU888US6d++eJOnevXumTJmSZ555pnLOP//5z1RUVGTjjTeu9rjl5eVp2bJllQUAqFn6LQAsGnouANSsOnVP9B133DHnnntuVlxxxfzkJz/Jc889l4svvjgHH3xwkqSsrCzHHXdczjnnnHTt2jVdunTJaaedlg4dOmSXXXZJkqyxxhrZdtttc9hhh+Xaa6/NrFmzcvTRR2fvvfdOhw4davHsAAAAAABY3NSpEP2KK67IaaedlqOOOiqTJk1Khw4d8qtf/Sqnn3565ZyTTz4506dPz+GHH54pU6Zk0003zfDhw9O4cePKObfcckuOPvrobLXVVqlXr1522223XH755bVxSgAAAAAALMbqVIjeokWLXHrppbn00ktLzikrK8tZZ52Vs846q+ScpZdeOrfeeutCqBAAAAAAgB+TOnVPdAAAAAAAqEuE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBLqXIj+/vvv55e//GXatGmTJk2aZO21187TTz9dub4oipx++ulZbrnl0qRJk/Tq1StvvPFGlX1Mnjw5++23X1q2bJnWrVvnkEMOyeeff76oTwUAAAAAgMVcnQrRP/3002yyySZp2LBh7r///rzyyiu56KKLstRSS1XOueCCC3L55Zfn2muvzRNPPJFmzZqld+/e+eqrryrn7Lfffnn55ZczYsSI3HvvvRk9enQOP/zw2jglAAAAAAAWYw1qu4BvO//889OxY8cMHjy4cqxLly6V/10URS699NKceuqp2XnnnZMkN910U9q1a5e77rore++9d1599dUMHz48Tz31VDbYYIMkyRVXXJHtt98+F154YTp06LBoTwoAAAAAgMVWnboS/e67784GG2yQPfbYI23bts16662X66+/vnL9uHHjMmHChPTq1atyrFWrVtl4440zZsyYJMmYMWPSunXrygA9SXr16pV69erliSeeqPa4M2bMyLRp06osAEDN0m8BYNHQcwGgZtWpEP2///1vrrnmmnTt2jUPPPBAjjzyyBxzzDG58cYbkyQTJkxIkrRr167Kdu3atatcN2HChLRt27bK+gYNGmTppZeunPO/Bg0alFatWlUuHTt2rOlTA4AfPf0WABYNPRcAaladCtErKiqy/vrr57zzzst6662Xww8/PIcddliuvfbahXrcAQMGZOrUqZXL+PHjF+rxAODHSL8FgEVDzwWAmlWn7om+3HLLZc0116wytsYaa+SOO+5IkrRv3z5JMnHixCy33HKVcyZOnJh11123cs6kSZOq7OPrr7/O5MmTK7f/X+Xl5SkvL6+p0wAAqqHfAsCioecCQM2qU1eib7LJJhk7dmyVsddffz2dOnVK8s2XjLZv3z4jR46sXD9t2rQ88cQT6d69e5Kke/fumTJlSp555pnKOf/85z9TUVGRjTfeeBGcBQAAAAAAS4o6dSX68ccfnx49euS8887LnnvumSeffDLXXXddrrvuuiRJWVlZjjvuuJxzzjnp2rVrunTpktNOOy0dOnTILrvskuSbK9e33XbbytvAzJo1K0cffXT23nvvdOjQoRbPDgAAAACAxU2dCtE33HDD3HnnnRkwYEDOOuusdOnSJZdeemn222+/yjknn3xypk+fnsMPPzxTpkzJpptumuHDh6dx48aVc2655ZYcffTR2WqrrVKvXr3stttuufzyy2vjlAAAAAAAWIzVqRA9Sfr06ZM+ffqUXF9WVpazzjorZ511Vsk5Sy+9dG699daFUR4AAAAAAD8ideqe6AAAAAAAUJcI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKaFDbBfzYdDvpptouARaKZ35/QG2XAAAAAAA1zpXoAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoIQFDtF79uyZkSNHllz/8MMPp2fPngu6ewAAAAAAqHULHKI/8sgjmThxYsn1kyZNyqhRoxZ09wAAAAAAUOt+0O1cysrKSq57880306JFix+yewAAAAAAqFUN5mfyjTfemBtvvLHy53POOSfXX3/9XPOmTJmS//znP9l+++1/eIUAAAAAAFBL5itE/+KLL/LRRx9V/vzZZ5+lXr2qF7OXlZWlWbNmOeKII3L66afXTJUAAAAAAFAL5itEP/LII3PkkUcmSbp06ZLLLrssO+2000IpDAAAAAAAatt8hejfNm7cuJqsAwAAAAAA6pwFDtHn+Oyzz/LOO+/k008/TVEUc63fbLPNfughAAAAAACgVixwiP7xxx+nf//+ueOOOzJ79uy51hdFkbKysmrXAQAAAADA4mCBQ/TDDz8899xzT4455pj8/Oc/z1JLLVWTdQEAAAAAQK1b4BD9wQcfzPHHH58LLrigJusBAAAAAIA6o96Cbti0adN07ty5BksBAAAAAIC6ZYFD9F/+8pe58847a7IWAAAAAACoUxb4di677757Ro0alW233TaHH354OnbsmPr16881b/311/9BBQIAAAAAQG1Z4BB90003rfzvESNGzLW+KIqUlZVl9uzZC3oIAAAAAACoVQscog8ePLgm6wAAAAAAgDpngUP0vn371mQdAAAAAABQ5yzwF4sCAAAAAMCSboGvRD/44IO/d05ZWVn+9Kc/LeghAAAAAACgVi1wiP7Pf/4zZWVlVcZmz56dDz/8MLNnz86yyy6bZs2a/eACAQAAAACgtixwiP72229XOz5r1qz84Q9/yKWXXpoRI0Ys6O4BAAAAAKDW1fg90Rs2bJijjz4622yzTY4++uia3j0AAAAAACwyC+2LRddZZ52MHj16Ye0eAAAAAAAWuoUWoo8YMSJNmzZdWLsHAAAAAICFboHviX7WWWdVOz5lypSMHj06zz77bH77298ucGEAAAAAAFDbFjhEP+OMM6odX2qppbLyyivn2muvzWGHHbaguwcAAAAAgFq3wCF6RUVFTdYBAAAAAAB1zkK7JzoAAAAAACzuFvhK9DlGjRqV++67L++8806SpFOnTtlhhx2y+eab/+DiAAAWVLeTbqrtEmCheOb3B9R2CQAA8KOywCH6zJkzs88+++Suu+5KURRp3bp1km++WPSiiy7Krrvumr/85S9p2LBhTdUKAAAAAACL1ALfzuXMM8/MnXfemV//+tf58MMPM3ny5EyePDkTJkzIiSeemL/97W8566yzarJWAAAAAABYpBY4RL/11lvTt2/fXHDBBWnXrl3leNu2bXP++efngAMOyJ///OcaKRIAAAAAAGrDAofoH374YTbeeOOS6zfeeONMmDBhQXcPAAAAAAC1boFD9BVWWCGPPPJIyfWjRo3KCiussKC7BwAAAACAWrfAIXrfvn0zdOjQHHHEERk7dmxmz56dioqKjB07NkceeWSGDRuWAw88sAZLBQAAAACARavBgm54yimn5K233sp1112X66+/PvXqfZPHV1RUpCiK9O3bN6ecckqNFQoAAAAAAIvaAofo9evXz5AhQ3LCCSfkH//4R955550kSadOnbL99tvnpz/9aY0VCQAAAAAAtWG+QvSvvvoqxx13XH7yk5+kf//+SZKf/vSncwXml19+ea699tpcdtlladiwYc1VCwAAAAAAi9B83RP9uuuuy5AhQ7LDDjt857wddtghN9xwQ/74xz/+oOIAAAAAAKA2zVeIPnTo0Oy2225ZaaWVvnPeyiuvnD322CN/+ctfflBxAAAAAABQm+YrRH/xxRez6aabztPcHj165D//+c8CFQUAAAAAAHXBfIXoM2fOTKNGjeZpbqNGjTJjxowFKgoAAAAAAOqC+QrRO3TokJdeemme5r700kvp0KHDAhUFAAAAAAB1wXyF6L169cpNN92USZMmfee8SZMm5aabbsrWW2/9g4oDAAAAAIDaNF8h+m9+85t89dVX6dmzZ5544olq5zzxxBPZaqut8tVXX+Wkk06qkSIBAAAAAKA2NJifySuttFKGDh2affbZJz169MhKK62UtddeOy1atMhnn32Wl156KW+99VaaNm2av/71r1l55ZUXVt0AAAAAALDQzVeIniQ77LBD/vOf/+T888/Pvffem7vuuqtyXYcOHXLYYYfl5JNPzkorrVSTdQIAAAAAwCI33yF6knTu3DnXXHNNrrnmmnz22WeZNm1aWrZsmRYtWtR0fQAAAAAAUGsWKET/thYtWgjPAQAAAABYIs3XF4sCAAAAAMCPiRAdAAAAAABKqLMh+u9+97uUlZXluOOOqxz76quv0q9fv7Rp0ybNmzfPbrvtlokTJ1bZ7t13380OO+yQpk2bpm3btjnppJPy9ddfL+LqAQAAAABYEtTJEP2pp57KH/7wh/z0pz+tMn788cfnnnvuybBhwzJq1Kh88MEH+cUvflG5fvbs2dlhhx0yc+bMPPbYY7nxxhszZMiQnH766Yv6FAAAAAAAWALUuRD9888/z3777Zfrr78+Sy21VOX41KlT86c//SkXX3xxevbsmW7dumXw4MF57LHH8vjjjydJHnzwwbzyyiu5+eabs+6662a77bbL2WefnauuuiozZ86srVMCAAAAAGAxVedC9H79+mWHHXZIr169qow/88wzmTVrVpXx1VdfPSuuuGLGjBmTJBkzZkzWXnvttGvXrnJO7969M23atLz88ssljzljxoxMmzatygIA1Cz9FgAWDT0XAGpWnQrR//rXv+bZZ5/NoEGD5lo3YcKENGrUKK1bt64y3q5du0yYMKFyzrcD9Dnr56wrZdCgQWnVqlXl0rFjxx94JgDA/9JvAWDR0HMBoGbVmRB9/PjxOfbYY3PLLbekcePGi/TYAwYMyNSpUyuX8ePHL9LjA8CPgX4LAIuGngsANatBbRcwxzPPPJNJkyZl/fXXrxybPXt2Ro8enSuvvDIPPPBAZs6cmSlTplS5Gn3ixIlp3759kqR9+/Z58sknq+x34sSJletKKS8vT3l5eQ2eDQDwv/RbAFg09FwAqFl15kr0rbbaKi+++GKef/75ymWDDTbIfvvtV/nfDRs2zMiRIyu3GTt2bN5999107949SdK9e/e8+OKLmTRpUuWcESNGpGXLlllzzTUX+TkBAAAAALB4qzNXordo0SJrrbVWlbFmzZqlTZs2leOHHHJITjjhhCy99NJp2bJl+vfvn+7du+dnP/tZkmSbbbbJmmuumf333z8XXHBBJkyYkFNPPTX9+vXzLjwAAAAAAPOtzoTo8+KSSy5JvXr1sttuu2XGjBnp3bt3rr766sr19evXz7333psjjzwy3bt3T7NmzdK3b9+cddZZtVg1AAAAAACLqzodoj/yyCNVfm7cuHGuuuqqXHXVVSW36dSpU/7xj38s5MoAAAAAAPgxqDP3RAcAAAAAgLpGiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAACihQW0XAFCbup10U22XAAvFM78/oLZLAAAAgCWCK9EBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAASmhQ2wUAAAAAdVe3k26q7RJYBJ75/QG1XQJAneVKdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAl1KkQfNGhQNtxww7Ro0SJt27bNLrvskrFjx1aZ89VXX6Vfv35p06ZNmjdvnt122y0TJ06sMufdd9/NDjvskKZNm6Zt27Y56aST8vXXXy/KUwEAAAAAYAlQp0L0UaNGpV+/fnn88cczYsSIzJo1K9tss02mT59eOef444/PPffck2HDhmXUqFH54IMP8otf/KJy/ezZs7PDDjtk5syZeeyxx3LjjTdmyJAhOf3002vjlAAAAAAAWIw1qO0Cvm348OFVfh4yZEjatm2bZ555JptttlmmTp2aP/3pT7n11lvTs2fPJMngwYOzxhpr5PHHH8/PfvazPPjgg3nllVfy0EMPpV27dll33XVz9tln5ze/+U3OOOOMNGrUqDZODQAAAACAxVCduhL9f02dOjVJsvTSSydJnnnmmcyaNSu9evWqnLP66qtnxRVXzJgxY5IkY8aMydprr5127dpVzundu3emTZuWl19+udrjzJgxI9OmTauyAAA1S78FgEVDzwWAmlVnQ/SKioocd9xx2WSTTbLWWmslSSZMmJBGjRqldevWVea2a9cuEyZMqJzz7QB9zvo566ozaNCgtGrVqnLp2LFjDZ8NAKDfAsCioecCQM2qsyF6v3798tJLL+Wvf/3rQj/WgAEDMnXq1Mpl/PjxC/2YAPBjo98CwKKh5wJAzapT90Sf4+ijj869996b0aNHZ4UVVqgcb9++fWbOnJkpU6ZUuRp94sSJad++feWcJ598ssr+Jk6cWLmuOuXl5SkvL6/hswAAvk2/BYBFQ88FgJpVp65EL4oiRx99dO68887885//TJcuXaqs79atWxo2bJiRI0dWjo0dOzbvvvtuunfvniTp3r17XnzxxUyaNKlyzogRI9KyZcusueaai+ZEAAAAAABYItSpK9H79euXW2+9NX//+9/TokWLynuYt2rVKk2aNEmrVq1yyCGH5IQTTsjSSy+dli1bpn///unevXt+9rOfJUm22WabrLnmmtl///1zwQUXZMKECTn11FPTr18/78QDAAAAADBf6lSIfs011yRJtthiiyrjgwcPzoEHHpgkueSSS1KvXr3stttumTFjRnr37p2rr766cm79+vVz77335sgjj0z37t3TrFmz9O3bN2edddaiOg0AAAAAAJYQdSpEL4rie+c0btw4V111Va666qqSczp16pR//OMfNVkaAAAAAAA/QnXqnugAAAAAAFCXCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKCEBrVdAAAAAAA/Xt1Ouqm2S2Ahe+b3B9R2CfCDuBIdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoIQlNkS/6qqr0rlz5zRu3Dgbb7xxnnzyydouCQAAAACAxcwSGaLfdtttOeGEEzJw4MA8++yzWWedddK7d+9MmjSptksDAAAAAGAxskSG6BdffHEOO+ywHHTQQVlzzTVz7bXXpmnTprnhhhtquzQAAAAAABYjS1yIPnPmzDzzzDPp1atX5Vi9evXSq1evjBkzphYrAwAAAABgcdOgtguoaR9//HFmz56ddu3aVRlv165dXnvttWq3mTFjRmbMmFH589SpU5Mk06ZNq/H6Zs/4ssb3CXXBwni+LAqekyypFtZzcs5+i6KY720XZb9NPL9Zcum5UHfUxX6b1HzP9fz9cajN/uJ3bMnn94uFbUF/x+a55xZLmPfff79IUjz22GNVxk866aRio402qnabgQMHFkksFovFYrHM4zJ+/Pj57tH6rcVisVgs87csSL/Vcy0Wi8Vimf/l+3puWVEs4FvbddTMmTPTtGnT3H777dlll10qx/v27ZspU6bk73//+1zb/O+79BUVFZk8eXLatGmTsrKyRVE2C8G0adPSsWPHjB8/Pi1btqztcuBHzfNxyVEURT777LN06NAh9erN313h9Nsll+c41B2ej0uGH9JvEz33h/I8YmHzO8bC5Pdr/sxrz13ibufSqFGjdOvWLSNHjqwM0SsqKjJy5MgcffTR1W5TXl6e8vLyKmOtW7deyJWyqLRs2dL/NKCO8HxcMrRq1WqBttNvl3ye41B3eD4u/ha03yZ6bk3xPGJh8zvGwuT3a97NS89d4kL0JDnhhBPSt2/fbLDBBtloo41y6aWXZvr06TnooINquzQAAAAAABYjS2SIvtdee+Wjjz7K6aefngkTJmTdddfN8OHD5/qyUQAAAAAA+C5LZIieJEcffXTJ27fw41BeXp6BAwfO9TFGYNHzfIQlm+c41B2ej/DDeR6xsPkdY2Hy+7VwLHFfLAoAAAAAADVl/r/mGwAAAAAAfiSE6AAAAAAAUIIQHQDgR6Ioihx++OFZeumlU1ZWlueff75W6nj77bdr9fjwY3TggQdml112qe0yAAAWS0J0FmtXXXVVOnfunMaNG2fjjTfOk08++Z3zhw0bltVXXz2NGzfO2muvnX/84x+LqFJYso0ePTo77rhjOnTokLKystx1113fu80jjzyS9ddfP+Xl5VlllVUyZMiQhV4n/NgNHz48Q4YMyb333psPP/wwa621Vm2XBAB1yoQJE9K/f/+stNJKKS8vT8eOHbPjjjtm5MiRtV0awHf66KOPcuSRR2bFFVdMeXl52rdvn969e+ff//53bZe2RBCis9i67bbbcsIJJ2TgwIF59tlns84666R3796ZNGlStfMfe+yx7LPPPjnkkEPy3HPPZZdddskuu+ySl156aRFXDkue6dOnZ5111slVV101T/PHjRuXHXbYIVtuuWWef/75HHfccTn00EPzwAMPLORK4cftrbfeynLLLZcePXqkffv2adCgQW2XBAB1xttvv51u3brln//8Z37/+9/nxRdfzPDhw7PlllumX79+tV0eS4Dx48fn4IMPTocOHdKoUaN06tQpxx57bD755JPaLo0lwG677ZbnnnsuN954Y15//fXcfffd2WKLLfx+1RAhOoutiy++OIcddlgOOuigrLnmmrn22mvTtGnT3HDDDdXOv+yyy7LtttvmpJNOyhprrJGzzz4766+/fq688spFXDksebbbbrucc8452XXXXedp/rXXXpsuXbrkoosuyhprrJGjjz46u+++ey655JKFXCn8eB144IHp379/3n333ZSVlaVz586pqKjIoEGD0qVLlzRp0iTrrLNObr/99sptHnnkkZSVleWBBx7IeuutlyZNmqRnz56ZNGlS7r///qyxxhpp2bJl9t1333zxxReV2w0fPjybbrppWrdunTZt2qRPnz556623vrO+l156Kdttt12aN2+edu3aZf/998/HH3+80B4PqMu22GKL9O/fP8cdd1yWWmqptGvXLtdff32mT5+egw46KC1atMgqq6yS+++/P0kye/bsHHLIIZXP5dVWWy2XXXbZdx7j+57/8GN01FFHpaysLE8++WR22223rLrqqvnJT36SE044IY8//nhtl8di7r///W822GCDvPHGG/nLX/6SN998M9dee21GjhyZ7t27Z/LkybVdIouxKVOm5NFHH83555+fLbfcMp06dcpGG22UAQMGZKeddqrt8pYIQnQWSzNnzswzzzyTXr16VY7Vq1cvvXr1ypgxY6rdZsyYMVXmJ0nv3r1LzgcWHs9HWPQuu+yynHXWWVlhhRXy4Ycf5qmnnsqgQYNy00035dprr83LL7+c448/Pr/85S8zatSoKtueccYZufLKK/PYY49l/Pjx2XPPPXPppZfm1ltvzX333ZcHH3wwV1xxReX86dOn54QTTsjTTz+dkSNHpl69etl1111TUVFRbW1TpkxJz549s9566+Xpp5/O8OHDM3HixOy5554L9TGBuuzGG2/MMssskyeffDL9+/fPkUcemT322CM9evTIs88+m2222Sb7779/vvjii1RUVGSFFVbIsGHD8sorr+T000/PKaeckqFDh5bc/7w+/+HHYvLkyRk+fHj69euXZs2azbW+devWi74olij9+vVLo0aN8uCDD2bzzTfPiiuumO222y4PPfRQ3n///fy///f/artEFmPNmzdP8+bNc9ddd2XGjBm1Xc4SyWd4WSx9/PHHmT17dtq1a1dlvF27dnnttdeq3WbChAnVzp8wYcJCqxOoXqnn47Rp0/Lll1+mSZMmtVQZLLlatWqVFi1apH79+mnfvn1mzJiR8847Lw899FC6d++eJFlppZXyr3/9K3/4wx+y+eabV257zjnnZJNNNkmSHHLIIRkwYEDeeuutrLTSSkmS3XffPQ8//HB+85vfJPnmo6TfdsMNN2TZZZfNK6+8Uu192K+88sqst956Oe+886ps07Fjx7z++utZddVVa/bBgMXAOuusk1NPPTVJMmDAgPzud7/LMsssk8MOOyxJcvrpp+eaa67Jf/7zn/zsZz/LmWeeWbltly5dMmbMmAwdOrTaN6Pm5/kPPxZvvvlmiqLI6quvXtulsASaPHlyHnjggZx77rlzvdZp37599ttvv9x22225+uqrU1ZWVktVsjhr0KBBhgwZksMOOyzXXntt1l9//Wy++ebZe++989Of/rS2y1siuBIdAOBH6M0338wXX3yRrbfeuvLKlebNm+emm26a69Yr3/7Du127dmnatGllgD5n7NvfSfLGG29kn332yUorrZSWLVumc+fOSZJ333232lpeeOGFPPzww1XqmBNifN9tYGBJ9e3nXf369dOmTZusvfbalWNz3oye89y76qqr0q1btyy77LJp3rx5rrvuupLPufl5/sOPRVEUtV0CS7A33ngjRVFkjTXWqHb9GmuskU8//TQfffTRIq6MJcluu+2WDz74IHfffXe23XbbPPLII1l//fUzZMiQ2i5tieBKdBZLyyyzTOrXr5+JEydWGZ84cWLat29f7Tbt27efr/nAwlPq+diyZUtXocMi8vnnnydJ7rvvviy//PJV1pWXl1f5uWHDhpX/XVZWVuXnOWPfvlXLjjvumE6dOuX6669Phw4dUlFRkbXWWiszZ84sWcuOO+6Y888/f651yy233PydGCwhqnue/e9zMfnm3uZ//etfc+KJJ+aiiy5K9+7d06JFi/z+97/PE088Ue2+5+f5Dz8WXbt2TVlZWclPNkNN+L43axo1arSIKmFJ1bhx42y99dbZeuutc9ppp+XQQw/NwIEDc+CBB9Z2aYs9V6KzWGrUqFG6deuWkSNHVo5VVFRUfiFHdbp3715lfpKMGDGi5Hxg4fF8hNq35pprpry8PO+++25WWWWVKkvHjh0XeL+ffPJJxo4dm1NPPTVbbbVV5ZVV32X99dfPyy+/nM6dO89VS3X3pQWq+ve//50ePXrkqKOOynrrrZdVVlnlO68oX1jPf1icLb300undu3euuuqqTJ8+fa71U6ZMWfRFscRYZZVVUlZWlldffbXa9a+++mqWXXZZ996nxq255prV/j+N+SdEZ7F1wgkn5Prrr8+NN96YV199NUceeWSmT5+egw46KElywAEHZMCAAZXzjz322AwfPjwXXXRRXnvttZxxxhl5+umnc/TRR9fWKcAS4/PPP8/zzz+f559/Pkkybty4PP/885UfIx8wYEAOOOCAyvlHHHFE/vvf/+bkk0/Oa6+9lquvvjpDhw7N8ccfXxvlw49SixYtcuKJJ+b444/PjTfemLfeeivPPvtsrrjiitx4440LvN+llloqbdq0yXXXXZc333wz//znP3PCCSd85zb9+vXL5MmTs88+++Spp57KW2+9lQceeCAHHXRQZs+evcC1wI9F165d8/TTT+eBBx7I66+/ntNOOy1PPfVUyfkL6/kPi7urrroqs2fPzkYbbZQ77rgjb7zxRl599dVcfvnlLvbgB2nTpk223nrrXH311fnyyy+rrJswYUJuueUWVwrzg3zyySfp2bNnbr755vznP//JuHHjMmzYsFxwwQXZeeeda7u8JYLbubDY2muvvfLRRx/l9NNPz4QJE7Luuutm+PDhlfeHfPfdd1Ov3v+9T9SjR4/ceuutOfXUU3PKKaeka9euueuuu6r9gjNg/jz99NPZcsstK3+eE5j17ds3Q4YMyYcffljlvqxdunTJfffdl+OPPz6XXXZZVlhhhfzxj39M7969F3nt8GN29tlnZ9lll82gQYPy3//+N61bt87666+fU045ZYH3Wa9evfz1r3/NMccck7XWWiurrbZaLr/88myxxRYlt+nQoUP+/e9/5ze/+U222WabzJgxI506dcq2225bpZcD1fvVr36V5557LnvttVfKysqyzz775Kijjsr9999fcpuF8fyHxd1KK62UZ599Nueee25+/etf58MPP8yyyy6bbt265Zprrqnt8ljMXXnllenRo0d69+6dc845J126dMnLL7+ck046KauuumpOP/302i6RxVjz5s2z8cYb55JLLslbb72VWbNmpWPHjjnssMP09hpSVvj2DAAAAABYqN5+++2cccYZGT58eCZNmpSiKPKLX/wif/7zn9O0adPaLg/4DkJ0AAAAAFjEBg4cmIsvvjgjRozIz372s9ouB/gOQnQAAAAAqAWDBw/O1KlTc8wxx7iNHdRhQnQAAAAAACjBW1wAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRgcXGgQcemM6dO9fKscvKynLGGWfUyrEBAACoezp37pw+ffoskmMNGTIkZWVlefrppxfqcbz2heoJ0YGSXnzxxey+++7p1KlTGjdunOWXXz5bb711rrjiitouDQD4Hvo4AD9WcwLnUsvjjz9e2yUCi5kGtV0AUDc99thj2XLLLbPiiivmsMMOS/v27TN+/Pg8/vjjueyyy9K/f/9FXtP111+fioqKRX5cAFjc1MU+DgCL2llnnZUuXbrMNb7KKqvUQjXA4kyIDlTr3HPPTatWrfLUU0+ldevWVdZNmjSpRo4xffr0NGvWbJ7nN2zYsEaOCwBLukXRxwGgrttuu+2ywQYb1HYZP8hXX32VRo0a1XYZ8KPndi5Atd5666385Cc/meuFd5K0bds2SfL222+nrKwsQ4YMmWvO/95H7YwzzkhZWVleeeWV7LvvvllqqaWy6aab5sILL0xZWVneeeedufYxYMCANGrUKJ9++mmSqvdEnzVrVpZeeukcdNBBc203bdq0NG7cOCeeeGLl2IwZMzJw4MCsssoqKS8vT8eOHXPyySdnxowZVbadMWNGjj/++Cy77LJp0aJFdtppp7z33nvf93ABQJ0yL318jptvvjndunVLkyZNsvTSS2fvvffO+PHjK9cPHjw4ZWVlueGGG6psd95556WsrCz/+Mc/Fso5AMDCNOf17IUXXpirrroqK620Upo2bZptttkm48ePT1EUOfvss7PCCiukSZMm2XnnnTN58uRq9/Xggw9m3XXXTePGjbPmmmvmb3/7W5X1kydPzoknnpi11147zZs3T8uWLbPddtvlhRdeqDLvkUceSVlZWf7617/m1FNPzfLLL5+mTZtm2rRp1R73008/zUYbbZQVVlghY8eOTeK1LywsQnSgWp06dcozzzyTl156qUb3u8cee+SLL77Ieeedl8MOOyx77rlnysrKMnTo0LnmDh06NNtss02WWmqpudY1bNgwu+66a+66667MnDmzyrq77rorM2bMyN57750kqaioyE477ZQLL7wwO+64Y6644orssssuueSSS7LXXntV2fbQQw/NpZdemm222Sa/+93v0rBhw+ywww41+AgAwMI3r3383HPPzQEHHJCuXbvm4osvznHHHZeRI0dms802y5QpU5IkBx10UPr06ZMTTjihMlx/8cUXc+aZZ+aQQw7J9ttvv7BPBwAWyNSpU/Pxxx9XWT755JMqc2655ZZcffXV6d+/f379619n1KhR2XPPPXPqqadm+PDh+c1vfpPDDz8899xzT5ULteZ44403stdee2W77bbLoEGD0qBBg+yxxx4ZMWJE5Zz//ve/ueuuu9KnT59cfPHFOemkk/Liiy9m8803zwcffDDXPs8+++zcd999OfHEE3PeeedVeyX6xx9/nJ49e2bixIkZNWpUVlttNa99YWEqAKrx4IMPFvXr1y/q169fdO/evTj55JOLBx54oJg5c2blnHHjxhVJisGDB8+1fZJi4MCBlT8PHDiwSFLss88+c83t3r170a1btypjTz75ZJGkuOmmmyrH+vbtW3Tq1Kny5wceeKBIUtxzzz1Vtt1+++2LlVZaqfLnP//5z0W9evWKRx99tMq8a6+9tkhS/Pvf/y6Koiief/75Iklx1FFHVZm37777znU+AFCXzUsff/vtt4v69esX5557bpVtX3zxxaJBgwZVxj/88MNi6aWXLrbeeutixowZxXrrrVesuOKKxdSpUxfZOQHAvBo8eHCRpNqlvLy8KIr/ez277LLLFlOmTKncdsCAAUWSYp111ilmzZpVOb7PPvsUjRo1Kr766qvKsU6dOhVJijvuuKNybOrUqcVyyy1XrLfeepVjX331VTF79uwqNY4bN64oLy8vzjrrrMqxhx9+uEhSrLTSSsUXX3xR7Tk99dRTxYcfflj85Cc/KVZaaaXi7bffrpzjtS8sPK5EB6q19dZbZ8yYMdlpp53ywgsv5IILLkjv3r2z/PLL5+67717g/R5xxBFzje2111555pln8tZbb1WO3XbbbSkvL8/OO+9ccl89e/bMMsssk9tuu61y7NNPP82IESOqvMs+bNiwrLHGGll99dWrXIHQs2fPJMnDDz+cJJUfRz/mmGOqHOe4446b/xMFgFo0L338b3/7WyoqKrLnnntW6Y/t27dP165dK/tjkrRv3z5XXXVVRowYkZ///Od5/vnnc8MNN6Rly5a1dYoA8L3m9K5vL/fff3+VOXvssUdatWpV+fPGG2+cJPnlL3+ZBg0aVBmfOXNm3n///Srbd+jQIbvuumvlzy1btswBBxyQ5557LhMmTEiSlJeXp169byK42bNn55NPPknz5s2z2mqr5dlnn52r7r59+6ZJkybVntN7772XzTffPLNmzcro0aPTqVOnynVe+8LC44tFgZI23HDD/O1vf8vMmTPzwgsv5M4778wll1yS3XffPc8//3yaNm063/us7pvR99hjj5xwwgm57bbbcsopp6QoigwbNizbbbfdd744b9CgQXbbbbfceuutmTFjRsrLy/O3v/0ts2bNqhKiv/HGG3n11Vez7LLLVrufOV+w9s4776RevXpZeeWVq6xfbbXV5vs8AaC2fV8ff+ONN1IURbp27Vrt9v/7hd577713br755tx33305/PDDs9VWWy2K0wCABbbRRht97xeLrrjiilV+nhOod+zYsdrxOd/ZNccqq6ySsrKyKmOrrrpqkm/uu96+fftUVFTksssuy9VXX51x48Zl9uzZlXPbtGkzV03VvW6eY//990+DBg3y6quvpn379lXWee0LC48QHfhejRo1yoYbbpgNN9wwq666ag466KAMGzYsBx54YLXzv/0Hwf+q7t30Dh065Oc//3mGDh2aU045JY8//njefffdnH/++d9b2957750//OEPuf/++7PLLrtk6NChWX311bPOOutUzqmoqMjaa6+diy++uNp9/O8fRwCwJCnVxysqKlJWVpb7778/9evXn2u75s2bV/n5k08+ydNPP50keeWVV1JRUVF5VR0ALK6q64HfNV4UxXwf47zzzstpp52Wgw8+OGeffXaWXnrp1KtXL8cdd1wqKirmml/qKvQk+cUvfpGbbropl112WQYNGlRlnde+sPAI0YH5Mudd/A8//LDyCz/nfPHYHO+8885873evvfbKUUcdlbFjx+a2225L06ZNs+OOO37vdptttlmWW2653Hbbbdl0003zz3/+M//v//2/KnNWXnnlvPDCC9lqq63mukLg2zp16pSKioq89dZbVd6Bn/Mt5wCwuPt2H1955ZVTFEW6dOlSecXcd+nXr18+++yzDBo0KAMGDMill16aE044YWGXDAB12ptvvpmiKKq81nz99deTJJ07d06S3H777dlyyy3zpz/9qcq2U6ZMyTLLLDNfx+vfv39WWWWVnH766WnVqlV++9vfVq7z2hcWHpeOANV6+OGHq32Hfc6901ZbbbW0bNkyyyyzTEaPHl1lztVXXz3fx9ttt91Sv379/OUvf8mwYcPSp0+fNGvW7Hu3q1evXnbffffcc889+fOf/5yvv/56rm8d33PPPfP+++/n+uuvn2v7L7/8MtOnT0+SbLfddkmSyy+/vMqcSy+9dL7PBwBq07z08V/84hepX79+zjzzzLnmFkWRTz75pPLn22+/Pbfddlt+97vf5be//W323nvvnHrqqZUhAQD8WH3wwQe58847K3+eNm1abrrppqy77rqVt1upX7/+XL122LBhc91ffV6ddtppOfHEEzNgwIBcc801leNe+8LC40p0oFr9+/fPF198kV133TWrr756Zs6cmcceeyy33XZbOnfunIMOOihJcuihh+Z3v/tdDj300GywwQYZPXr0Ar2gbtu2bbbccstcfPHF+eyzz+YKwr/LXnvtlSuuuCIDBw7M2muvnTXWWKPK+v333z9Dhw7NEUcckYcffjibbLJJZs+enddeey1Dhw7NAw88kA022CDrrrtu9tlnn1x99dWZOnVqevTokZEjR+bNN9+c7/MBgNo0L328devWOeecczJgwIC8/fbb2WWXXdKiRYuMGzcud955Zw4//PCceOKJmTRpUo488shsueWWOfroo5MkV155ZR5++OEceOCB+de//uW2LgDUSffff39ee+21ucZ79OhRY71r1VVXzSGHHJKnnnoq7dq1yw033JCJEydm8ODBlXP69OmTs846KwcddFB69OiRF198MbfccktWWmmlBT7u73//+0ydOjX9+vVLixYt8stf/tJrX1iIhOhAtS688MIMGzYs//jHP3Lddddl5syZWXHFFXPUUUfl1FNPTevWrZMkp59+ej766KPcfvvtGTp0aLbbbrvcf//9adu27Xwfc6+99spDDz2UFi1aZPvtt5/n7Xr06JGOHTtm/Pjx1Ybv9erVy1133ZVLLrkkN910U+688840bdo0K620Uo499tgqH2G/4YYbsuyyy+aWW27JXXfdlZ49e+a+++5z7zgAFivz2sd/+9vfZtVVV80ll1ySM888M8k390vdZpttstNOOyVJjjzyyMyYMSODBw+u/Gh4mzZtct1112XnnXfOhRdemJNPPrlWzhMAvsvpp59e7fjgwYOzxRZb1MgxunbtmiuuuCInnXRSxo4dmy5duuS2225L7969K+eccsopmT59em699dbcdtttWX/99XPfffdVuRXLgrj22mvz+eef56CDDkqLFi2y8847e+0LC0lZsSDfiAAAAAAAAD8CPncJAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUML/B3n6likr/VkxAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "*В каком классе больше всего пассажиров?*"
+      ],
+      "metadata": {
+        "id": "UVHSw71VWYmk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.countplot(x='Pclass', data=trains_test)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        },
+        "id": "Nj9145YpVzRF",
+        "outputId": "e85f9ac8-d96b-49d7-ca57-2c437cc51642"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='Pclass', ylabel='count'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Больше всего пассажиров в `3` классе."
+      ],
+      "metadata": {
+        "id": "YqZ1xk2MfB34"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "*Группировать таблицу в два уровня: класс и пол, по среднему значению возраста. Кто из возможных комбинаций самый юный, кто самый взрослый? Насколько отличаются эти значения?*"
+      ],
+      "metadata": {
+        "id": "SFxz9J8FgRF_"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Групируем данные по `Pclass` и `Sex`, затем выбираем таблицу `Age` и считаем среднее."
+      ],
+      "metadata": {
+        "id": "KyuXGLTthgpS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class_sex_age = trains_test.groupby(['Pclass', 'Sex'])['Age'].mean()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Lw3YPEbZV1Jm",
+        "outputId": "b556a2d0-1c4e-4a3a-a35c-79660cd7d574"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-10-bb0369ba0dde>:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+            "  class_sex_age = trains_test.groupby(['Pclass', 'Sex'])['Age'].mean()\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Для корректного вывода используем `reset_index()`."
+      ],
+      "metadata": {
+        "id": "CdHaGAGW3toH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class_sex_age.reset_index()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 238
+        },
+        "id": "1qkIhDZXhoDz",
+        "outputId": "6c368a87-0472-4382-caab-3a62a7e4db5a"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  Pclass     Sex        Age\n",
+              "0      1  female  37.037594\n",
+              "1      1    male  41.029272\n",
+              "2      2  female  27.499223\n",
+              "3      2    male  30.815380\n",
+              "4      3  female  22.185329\n",
+              "5      3    male  25.962264"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0cccee14-c8d8-4a29-8a3b-e39ec3d253f9\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Pclass</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Age</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>female</td>\n",
+              "      <td>37.037594</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>male</td>\n",
+              "      <td>41.029272</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>female</td>\n",
+              "      <td>27.499223</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2</td>\n",
+              "      <td>male</td>\n",
+              "      <td>30.815380</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>3</td>\n",
+              "      <td>female</td>\n",
+              "      <td>22.185329</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>3</td>\n",
+              "      <td>male</td>\n",
+              "      <td>25.962264</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0cccee14-c8d8-4a29-8a3b-e39ec3d253f9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-0cccee14-c8d8-4a29-8a3b-e39ec3d253f9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0cccee14-c8d8-4a29-8a3b-e39ec3d253f9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-f57bde88-0695-4c84-afae-73173d3fd235\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f57bde88-0695-4c84-afae-73173d3fd235')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-f57bde88-0695-4c84-afae-73173d3fd235 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"class_sex_age\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.099928929842331,\n        \"min\": 22.185328947368422,\n        \"max\": 41.02927152317881,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          37.037593984962406,\n          41.02927152317881\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Берем минимальное и максимальное значение по группированным данным и выводим соответсвующие строки таблицы."
+      ],
+      "metadata": {
+        "id": "bui-amzZ3-7n"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "min_age = class_sex_age.min()\n",
+        "max_age = class_sex_age.max()\n",
+        "class_sex_age[class_sex_age.isin([min_age, max_age])]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 178
+        },
+        "id": "IBrorLZAhokk",
+        "outputId": "9050a838-fdaa-4c91-8e79-9cb84f71c52e"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Pclass  Sex   \n",
+              "1       male      41.029272\n",
+              "3       female    22.185329\n",
+              "Name: Age, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>Age</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Pclass</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <th>male</th>\n",
+              "      <td>41.029272</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <th>female</th>\n",
+              "      <td>22.185329</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Самые **юные** - девушки из 3 класса\n",
+        "\n",
+        "Самые **взрослые** - мужчиный из 1 класса"
+      ],
+      "metadata": {
+        "id": "Pv9kf5l9lTB9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "max_age-min_age"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "O_N4pL2BiBNV",
+        "outputId": "c6dfc96c-f080-402e-cbbc-6a102674ab85"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "18.843942575810384"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Разница возраста: `18.843942575810384`"
+      ],
+      "metadata": {
+        "id": "v_3ManBPl0Md"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "*Отобрать только выживших пассажиров с фамилией, начинающейся на “K”. Отсортировать их по убыванию стоимости билета. Кто заплатил больше всех? Кто меньше всех?*"
+      ],
+      "metadata": {
+        "id": "ZazXsl7Rmpzz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "survived_k_name = trains_test[(trains_test['Survived'] == 1) & (trains_test['Name'].str.startswith('K'))] # data selection\n",
+        "survived_k_name = survived_k_name.sort_values(by='Fare') # data sort\n",
+        "survived_k_name"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 332
+        },
+        "id": "TmxrmAzXlLla",
+        "outputId": "0aeb907b-34a3-438a-d13f-bf9ee665ead4"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     PassengerId Survived Pclass                                      Name  \\\n",
+              "300          301      1.0      3  Kelly, Miss. Anna Katherine \"Annie Kate\"   \n",
+              "573          574      1.0      3                         Kelly, Miss. Mary   \n",
+              "303          304      1.0      2                       Keane, Miss. Nora A   \n",
+              "691          692      1.0      3                        Karun, Miss. Manca   \n",
+              "706          707      1.0      2             Kelly, Mrs. Florence \"Fannie\"   \n",
+              "184          185      1.0      3       Kink-Heilmann, Miss. Luise Gretchen   \n",
+              "316          317      1.0      2       Kantor, Mrs. Sinai (Miriam Sternin)   \n",
+              "457          458      1.0      1         Kenyon, Mrs. Frederick R (Marion)   \n",
+              "621          622      1.0      1              Kimball, Mr. Edwin Nelson Jr   \n",
+              "\n",
+              "        Sex   Age  SibSp  Parch  Ticket     Fare Cabin Embarked  \n",
+              "300  female   NaN      0      0    9234   7.7500   NaN        Q  \n",
+              "573  female   NaN      0      0   14312   7.7500   NaN        Q  \n",
+              "303  female   NaN      0      0  226593  12.3500  E101        Q  \n",
+              "691  female   4.0      0      1  349256  13.4167   NaN        C  \n",
+              "706  female  45.0      0      0  223596  13.5000   NaN        S  \n",
+              "184  female   4.0      0      2  315153  22.0250   NaN        S  \n",
+              "316  female  24.0      1      0  244367  26.0000   NaN        S  \n",
+              "457  female   NaN      1      0   17464  51.8625   D21        S  \n",
+              "621    male  42.0      1      0   11753  52.5542   D19        S  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4bb5efa4-88c0-45bf-9aa5-d00b1207d8de\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>PassengerId</th>\n",
+              "      <th>Survived</th>\n",
+              "      <th>Pclass</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>SibSp</th>\n",
+              "      <th>Parch</th>\n",
+              "      <th>Ticket</th>\n",
+              "      <th>Fare</th>\n",
+              "      <th>Cabin</th>\n",
+              "      <th>Embarked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>300</th>\n",
+              "      <td>301</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Kelly, Miss. Anna Katherine \"Annie Kate\"</td>\n",
+              "      <td>female</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>9234</td>\n",
+              "      <td>7.7500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Q</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>573</th>\n",
+              "      <td>574</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Kelly, Miss. Mary</td>\n",
+              "      <td>female</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>14312</td>\n",
+              "      <td>7.7500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Q</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>303</th>\n",
+              "      <td>304</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Keane, Miss. Nora A</td>\n",
+              "      <td>female</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>226593</td>\n",
+              "      <td>12.3500</td>\n",
+              "      <td>E101</td>\n",
+              "      <td>Q</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>691</th>\n",
+              "      <td>692</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Karun, Miss. Manca</td>\n",
+              "      <td>female</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>349256</td>\n",
+              "      <td>13.4167</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>706</th>\n",
+              "      <td>707</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Kelly, Mrs. Florence \"Fannie\"</td>\n",
+              "      <td>female</td>\n",
+              "      <td>45.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>223596</td>\n",
+              "      <td>13.5000</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>184</th>\n",
+              "      <td>185</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Kink-Heilmann, Miss. Luise Gretchen</td>\n",
+              "      <td>female</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>315153</td>\n",
+              "      <td>22.0250</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>316</th>\n",
+              "      <td>317</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Kantor, Mrs. Sinai (Miriam Sternin)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>244367</td>\n",
+              "      <td>26.0000</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>457</th>\n",
+              "      <td>458</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Kenyon, Mrs. Frederick R (Marion)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>17464</td>\n",
+              "      <td>51.8625</td>\n",
+              "      <td>D21</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>621</th>\n",
+              "      <td>622</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Kimball, Mr. Edwin Nelson Jr</td>\n",
+              "      <td>male</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>11753</td>\n",
+              "      <td>52.5542</td>\n",
+              "      <td>D19</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4bb5efa4-88c0-45bf-9aa5-d00b1207d8de')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4bb5efa4-88c0-45bf-9aa5-d00b1207d8de button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4bb5efa4-88c0-45bf-9aa5-d00b1207d8de');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-f5b67e76-19a6-4cf5-9672-4a448e484765\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f5b67e76-19a6-4cf5-9672-4a448e484765')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-f5b67e76-19a6-4cf5-9672-4a448e484765 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "survived_k_name",
+              "summary": "{\n  \"name\": \"survived_k_name\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 193,\n        \"min\": 185,\n        \"max\": 707,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          458,\n          574,\n          185\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Kenyon, Mrs. Frederick R (Marion)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.778776504121787,\n        \"min\": 4.0,\n        \"max\": 45.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          45.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"17464\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.59643991750257,\n        \"min\": 7.75,\n        \"max\": 52.5542,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"E101\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Q\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Меньше всех заплатили `Kelly, Miss. Anna Katherine` и `Kelly, Miss. Mary`\n",
+        "\n",
+        "Больше всех заплатил `Kimball, Mr. Edwin Nelson Jr`"
+      ],
+      "metadata": {
+        "id": "sFDGWkWnqFN5"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "*Какое максимальное количество родных было с выжившим пассажиром?*"
+      ],
+      "metadata": {
+        "id": "wnaVSA-dBkvV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Добавим в таблицу столбец с сумарным количеством родных. И выберем из него максимальное значение."
+      ],
+      "metadata": {
+        "id": "dfPuQjsu4c_R"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trains_test['Relatives'] = trains_test['SibSp'] + trains_test['Parch'] # adding the \"Relatives\" column\n",
+        "trains_test[trains_test['Survived'] == 1]['Relatives'].max()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ox_EIv-2ph0O",
+        "outputId": "141be790-ce38-4d40-98fa-56ae7e03a7ce"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "6"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Ответ: `6`"
+      ],
+      "metadata": {
+        "id": "y4v5oaDiClwe"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "*Посчитайте среднюю стоимость билета пассажиров, для которых указана каюта (Cabin) и для тех, у кого она не указана, во сколько раз они отличаются?*"
+      ],
+      "metadata": {
+        "id": "-4b7mxl4CpDa"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Берем среднюю стоимость билета пассажирова с указанием и без указания кабины, после делим первое на второе."
+      ],
+      "metadata": {
+        "id": "jE8FcU-w434o"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "mean_fare_with_cabine = trains_test[trains_test['Cabin'].notna()]['Fare'].mean() # mean value without NaN Cabin\n",
+        "mean_fare_without_cabine = trains_test[trains_test['Cabin'].isna()]['Fare'].mean() # mean value with NaN Cabin\n",
+        "mean_fare_with_cabine / mean_fare_without_cabine"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WU_s-mJwClhb",
+        "outputId": "51798e49-a5f2-470e-f526-78a62f380b8a"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "4.282143526350037"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Ответ: `4.282143526350037`"
+      ],
+      "metadata": {
+        "id": "Tj0kKMgSDjlS"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Визуализация"
+      ],
+      "metadata": {
+        "id": "hb3gRVFZDt2R"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Стоимость билета в зависимости от возраста и пола."
+      ],
+      "metadata": {
+        "id": "9b5vCambFRnj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# creating and displaying an interactive point plot\n",
+        "fig = px.scatter(trains_test, x='Age', y='Fare', color='Sex', title=\"Scatter plot: Fare by Age and Sex\")\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "AMYzj038CLtE",
+        "outputId": "0db08d2b-0886-4bb3-c461-f601c06965e3"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js\"></script>                <div id=\"c6f860d1-9a39-43a3-ba48-3534f7e0c980\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"c6f860d1-9a39-43a3-ba48-3534f7e0c980\")) {                    Plotly.newPlot(                        \"c6f860d1-9a39-43a3-ba48-3534f7e0c980\",                        [{\"hovertemplate\":\"Sex=male\\u003cbr\\u003eAge=%{x}\\u003cbr\\u003eFare=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"male\",\"marker\":{\"color\":\"#636efa\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"male\",\"showlegend\":true,\"x\":[22.0,35.0,null,54.0,2.0,20.0,39.0,2.0,null,35.0,34.0,28.0,null,19.0,null,40.0,66.0,28.0,42.0,null,21.0,null,null,null,null,7.0,21.0,65.0,null,28.5,11.0,22.0,45.0,4.0,null,null,19.0,26.0,32.0,21.0,26.0,32.0,25.0,null,null,0.83,22.0,29.0,28.0,16.0,null,24.0,29.0,20.0,46.0,26.0,59.0,null,71.0,23.0,34.0,null,21.0,33.0,37.0,28.0,null,38.0,47.0,22.0,21.0,70.5,29.0,24.0,21.0,null,32.5,54.0,12.0,null,24.0,45.0,33.0,20.0,25.0,23.0,37.0,16.0,24.0,19.0,18.0,19.0,27.0,36.5,42.0,51.0,55.5,40.5,null,51.0,30.0,null,null,44.0,26.0,17.0,1.0,9.0,null,28.0,61.0,4.0,21.0,56.0,18.0,null,30.0,36.0,null,9.0,1.0,null,45.0,40.0,36.0,19.0,3.0,null,42.0,28.0,null,34.0,45.5,18.0,32.0,26.0,40.0,24.0,22.0,30.0,null,42.0,30.0,16.0,27.0,51.0,null,38.0,22.0,19.0,20.5,18.0,29.0,59.0,24.0,44.0,19.0,33.0,29.0,22.0,30.0,44.0,37.0,54.0,null,62.0,30.0,null,3.0,52.0,40.0,36.0,16.0,25.0,null,25.0,37.0,null,7.0,65.0,28.0,16.0,19.0,null,33.0,30.0,22.0,42.0,36.0,24.0,null,23.5,null,null,19.0,null,0.92,30.0,28.0,43.0,54.0,22.0,27.0,null,61.0,45.5,38.0,16.0,null,29.0,45.0,45.0,2.0,28.0,25.0,36.0,3.0,42.0,23.0,null,15.0,25.0,null,28.0,40.0,29.0,35.0,null,30.0,25.0,18.0,19.0,22.0,27.0,20.0,19.0,32.0,null,18.0,1.0,null,36.0,21.0,28.0,22.0,46.0,23.0,39.0,26.0,28.0,34.0,51.0,3.0,21.0,null,null,null,44.0,30.0,null,21.0,29.0,18.0,null,null,32.0,28.0,17.0,50.0,64.0,31.0,20.0,25.0,null,4.0,34.0,52.0,36.0,null,30.0,49.0,null,29.0,65.0,null,48.0,34.0,47.0,48.0,null,38.0,null,56.0,null,null,38.0,null,34.0,29.0,22.0,9.0,null,50.0,25.0,58.0,30.0,9.0,null,21.0,55.0,71.0,21.0,null,null,24.0,17.0,18.0,null,28.0,26.0,29.0,null,36.0,24.0,47.0,null,32.0,22.0,null,null,40.5,null,39.0,23.0,null,17.0,45.0,null,32.0,50.0,64.0,null,33.0,8.0,17.0,27.0,null,22.0,62.0,null,null,40.0,28.0,null,24.0,19.0,null,32.0,62.0,36.0,16.0,19.0,32.0,54.0,36.0,null,47.0,60.0,22.0,null,35.0,47.0,37.0,36.0,49.0,null,49.0,null,null,44.0,35.0,36.0,30.0,27.0,null,null,35.0,34.0,26.0,27.0,42.0,20.0,21.0,21.0,61.0,57.0,26.0,null,80.0,51.0,32.0,null,32.0,31.0,null,20.0,null,48.0,19.0,56.0,null,null,21.0,24.0,null,23.0,58.0,50.0,40.0,47.0,36.0,20.0,32.0,25.0,null,43.0,31.0,70.0,31.0,null,18.0,24.5,36.0,27.0,20.0,14.0,60.0,25.0,14.0,19.0,18.0,31.0,null,25.0,60.0,52.0,44.0,49.0,42.0,35.0,25.0,26.0,39.0,42.0,null,null,48.0,29.0,52.0,19.0,null,33.0,17.0,34.0,50.0,27.0,20.0,25.0,11.0,null,23.0,23.0,28.5,35.0,null,null,null,36.0,24.0,31.0,70.0,16.0,19.0,31.0,6.0,33.0,23.0,0.67,28.0,18.0,34.0,null,41.0,20.0,16.0,null,null,32.0,24.0,48.0,null,18.0,null,null,29.0,null,25.0,25.0,8.0,1.0,46.0,null,16.0,null,25.0,39.0,30.0,34.0,11.0,0.42,27.0,31.0,39.0,39.0,26.0,39.0,35.0,30.5,null,31.0,43.0,10.0,27.0,38.0,2.0,null,null,1.0,null,0.83,null,23.0,18.0,21.0,null,32.0,null,20.0,16.0,34.5,17.0,42.0,null,35.0,28.0,4.0,74.0,51.0,null,41.0,21.0,24.0,31.0,null,4.0,26.0,33.0,47.0,20.0,19.0,null,33.0,28.0,25.0,27.0,26.0,32.0,34.5,62.0,27.0,14.0,26.0,21.0,null,46.0,63.0,35.0,21.0,55.0,9.0,21.0,50.0,22.5,41.0,null,50.0,24.0,30.0,18.5,25.0,null,39.0,null,41.0,25.0,45.0,null,24.0,27.0,null,10.0,35.0,25.0,null,17.0,32.0,18.0,13.0,47.0,31.0,21.0,28.5,32.5,null,30.0,6.0,67.0,49.0,null,null,null,2.0,null,null,25.0,25.0,29.0,33.0,27.0,null,26.0,28.0,21.0,null,null,18.5,41.0,18.0,null,36.0,null,28.0,null,22.0,42.0,24.0,32.0,53.0,null,43.0,24.0,26.5,26.0,40.0,61.0,28.0,42.0,31.0,null,22.0,null,30.0,null,60.5,13.0,24.0,42.0,7.0,null,41.0,48.0,18.0,null,27.0,23.0,null,40.0,54.0,30.0,37.0,null,40.0,17.0,40.0,34.0,null,11.5,61.0,8.0,33.0,6.0,23.0,0.33,47.0,25.0,null,24.0,25.0,32.0,null,17.0,42.0,57.0,null,21.0,21.0,23.0,40.5,36.0,14.0,21.0,null,39.0,20.0,64.0,20.0,45.0,null,null,41.0,42.0,20.0,27.0,24.0,32.5,null,null,28.0,21.0,36.5,21.0,30.0,null,null,null,17.0,46.0,null,null,28.0,40.0,30.0,22.0,0.75,36.0,null,24.0,null,null,null,null,53.0,36.0,26.0,null,30.0,29.0,32.0,null,43.0,24.0,30.0,0.83,55.0,18.0,22.0,null,57.0,19.0,27.0,22.0,26.0,25.0,26.0,33.0,23.0,46.0,29.0,21.0,39.0,null,27.0,30.0,32.0,39.0,25.0,null,18.0,32.0,null,null,26.0,24.0,25.0,18.0,49.0,50.0,null,null,14.5,27.0,null,29.0,21.0,49.0,44.0,21.0,55.0,5.0,null,26.0,null,24.0,57.0,21.0,6.0,23.0,13.0,47.0,29.0,24.0,22.0,31.0,38.0,17.0,43.0,20.0,23.0,50.0,null,38.5,null,null],\"xaxis\":\"x\",\"y\":[7.25,8.05,8.4583,51.8625,21.075,8.05,31.275,29.125,13.0,26.0,13.0,35.5,7.225,263.0,7.8958,27.7208,10.5,82.1708,52.0,7.2292,8.05,7.8958,8.05,15.5,21.6792,39.6875,7.8,61.9792,35.5,7.2292,46.9,7.2292,83.475,27.9,27.7208,15.2458,8.1583,8.6625,10.5,73.5,14.4542,56.4958,7.65,7.8958,8.05,29.0,9.0,9.5,47.1,34.375,8.05,8.05,8.05,7.8542,61.175,20.575,7.25,8.05,34.6542,63.3583,26.0,7.8958,77.2875,8.6542,7.925,7.8958,7.775,7.8958,52.0,8.05,7.925,7.75,21.0,247.5208,73.5,8.05,30.0708,77.2875,11.2417,7.75,7.1417,6.975,7.8958,7.05,13.0,15.0458,53.1,9.2167,79.2,6.75,11.5,36.75,7.7958,26.0,13.0,12.525,8.05,14.5,7.3125,61.3792,8.05,8.6625,69.55,16.1,7.775,8.6625,39.6875,20.525,25.925,56.4958,33.5,29.125,7.925,30.6958,7.8542,25.4667,13.0,0.0,15.05,31.3875,39.0,50.0,26.55,15.5,7.8958,13.0,26.0,7.75,8.4042,9.5,69.55,6.4958,7.225,8.05,15.85,18.7875,31.0,7.05,7.25,13.0,7.75,27.0,10.5,8.05,13.0,8.05,7.8958,90.0,9.35,10.5,7.25,13.0,7.775,13.5,10.5,26.0,10.5,12.275,10.5,7.125,7.225,90.0,52.5542,26.0,7.25,26.55,16.1,7.75,31.3875,79.65,0.0,10.5,39.6875,7.775,31.0,0.0,29.7,0.0,29.125,7.75,7.8542,9.5,8.05,26.0,8.6625,9.5,7.8958,13.0,12.875,7.8958,27.7208,7.2292,30.5,23.25,0.0,8.05,151.55,24.0,7.8958,26.25,14.0,7.25,7.8958,69.55,6.2375,28.5,153.4625,18.0,7.8958,66.6,8.05,35.5,26.0,13.0,13.0,13.0,15.9,8.6625,9.225,35.0,7.2292,17.8,7.225,9.5,27.9,27.7208,7.05,15.5,7.25,55.4417,6.4958,8.05,135.6333,211.5,4.0125,7.775,7.925,7.8958,73.5,46.9,7.7292,120.0,7.7958,7.925,7.7958,26.0,10.5,7.925,8.05,15.85,21.0,7.75,18.75,7.775,7.8958,6.8583,0.0,7.925,13.0,7.8958,7.7333,7.875,20.2125,7.25,7.75,8.05,26.55,7.125,55.9,263.0,10.5,9.5,7.775,8.1125,81.8583,26.55,30.5,27.75,19.9667,27.75,89.1042,8.05,7.8958,26.55,7.75,26.55,8.05,38.5,13.0,8.05,7.05,0.0,26.55,7.725,7.25,8.6625,52.0,21.0,7.0458,7.5208,46.9,0.0,8.05,91.0792,29.7,8.05,15.9,19.9667,7.25,30.5,49.5042,8.05,14.4583,15.1,7.7958,8.6625,108.9,26.55,22.525,56.4958,7.75,8.05,26.2875,7.4958,34.0208,24.15,7.8958,7.8958,7.225,7.2292,7.75,221.7792,7.925,11.5,7.2292,7.2292,26.55,14.5,26.0,106.425,26.0,13.8625,20.525,36.75,110.8833,26.0,7.8292,7.225,26.55,227.525,7.75,7.8958,13.5,8.05,24.15,7.8958,7.2292,7.8542,10.5,26.3875,8.05,14.5,7.925,26.0,40.125,8.7125,15.0,79.2,8.05,8.05,7.125,7.25,26.0,24.15,0.0,7.225,56.9292,7.8958,42.4,8.05,26.55,15.55,7.8958,30.5,7.05,7.75,8.05,14.4,10.5,14.4542,52.5542,15.7417,7.8542,16.1,32.3208,12.35,7.8958,7.7333,30.0,7.0542,30.5,0.0,7.925,26.25,16.1,7.8542,56.4958,76.7292,7.8958,35.5,7.55,7.8958,8.4333,73.5,7.8958,13.0,113.275,133.65,7.225,25.5875,7.4958,7.925,73.5,13.0,7.775,8.05,52.0,10.5,13.0,0.0,7.775,8.05,512.3292,76.7292,9.225,46.9,39.0,41.5792,39.6875,10.1708,7.7958,57.0,56.4958,7.225,26.55,13.5,8.05,110.8833,7.65,26.2875,7.7417,7.8542,26.0,26.2875,15.2458,26.55,52.0,9.4833,13.0,7.65,15.5,7.775,7.0542,13.0,13.0,53.1,8.6625,26.0,18.7875,0.0,13.0,13.0,16.1,512.3292,7.8958,7.8958,30.0,78.85,16.1,7.925,71.0,20.25,53.1,7.75,12.475,9.5,7.8958,14.5,7.7958,11.5,8.05,14.5,7.125,7.2292,7.775,39.6,24.15,8.3625,9.5,7.8542,7.225,7.75,7.75,7.7375,30.0,23.45,7.05,7.25,29.125,20.575,79.2,7.75,26.0,30.6958,7.8958,13.0,7.2292,13.0,120.0,8.5167,6.975,7.775,0.0,13.0,7.8875,24.15,10.5,8.05,0.0,37.0042,6.45,27.9,8.6625,0.0,39.6875,6.95,56.4958,37.0042,7.75,18.75,7.2292,7.8542,8.3,8.6625,8.05,56.4958,29.7,7.925,10.5,6.4375,8.6625,7.55,69.55,7.8958,33.0,31.275,7.775,26.55,7.2292,14.1083,11.5,13.0,50.4958,9.5,11.1333,7.8958,5.0,9.0,9.8458,7.8958,7.8958,7.8958,10.5,7.05,13.0,30.0,7.75,7.8292,9.6875,8.6625,9.225,29.0,24.15,7.8958,26.0,26.0,12.35,7.225,59.4,3.1708,61.3792,14.5,7.225,30.5,21.6792,26.0,31.5,57.75,7.2292,9.5,56.4958,13.4167,26.55,7.85,7.925,29.7,7.75,60.0,15.0333,15.5792,29.125,7.8958,7.65,16.1,7.8958,13.5,7.75,262.375,42.4,28.5375,7.8958,27.7208,211.5,8.05,13.0,15.2458,221.7792,26.0,7.8958,10.7083,14.4542,23.0,7.775,8.05,26.0,7.7958,7.925,8.05,26.0,7.75,7.775,22.525,7.8208,7.75,8.7125,13.0,15.0458,14.4542,6.4375,75.2417,7.75,7.25,7.75,7.7958,13.0,8.05,8.05,28.5,6.4375,7.8958,7.8542,7.225,13.0,46.9,262.375,26.0,26.55,18.0,51.8625,8.05,26.55,26.0,7.8958,null,31.3875,7.55,26.55,15.2458,7.0,13.0,50.4958,34.375,7.55,7.225,13.9,7.2292,31.3875,55.4417,13.0,83.1583,7.75,16.0,8.05,13.0,26.0,26.0,14.5,12.35,32.5,7.8542,134.5,10.5,14.4,227.525,10.5,25.7417,10.5,7.8958,22.525,7.05,73.5,42.5,164.8667,8.05,8.05,7.7958,7.7958,15.1,13.0,65.0,6.4958,7.8792,71.2833,7.8542,75.25,7.225,134.5,7.8875,23.45,51.8625,32.5,7.925,136.7792,9.325,9.5,7.55,7.75,8.05,7.775,17.4,7.8542,12.7375,7.8958,0.0,7.55,8.6625,75.2417,7.75,7.225,10.5,26.0,21.0,10.5,13.775,7.25,7.25,82.2667,7.2292,8.05,39.6,7.2292,81.8583,9.5,7.8958,21.6792,45.5,7.8542,7.775,15.0458,21.0,8.6625,151.55,9.35,93.5,8.6625,7.225,7.575,146.5208,10.5,7.8542,31.5,7.775,7.2292,13.0,26.55,7.05,79.2,26.0,13.0,29.7,7.225,7.8958,26.0,13.0,7.2292,31.5,7.2292,10.5,7.5792,69.55,14.5,13.0,13.5,10.5,73.5,65.0,26.0,7.8792,7.75,69.55,8.6625,14.4583,13.8583,11.5,0.0,13.0,11.5,50.0,31.3875,7.75,7.8792,12.875,7.775,13.0,7.75,21.075,93.5,20.25,10.5,22.025,7.25,7.775,7.7333,21.0,47.1,27.7208,13.8625,10.5,211.5,8.05,7.25,8.05,22.3583],\"yaxis\":\"y\",\"type\":\"scattergl\"},{\"hovertemplate\":\"Sex=female\\u003cbr\\u003eAge=%{x}\\u003cbr\\u003eFare=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"female\",\"marker\":{\"color\":\"#EF553B\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"female\",\"showlegend\":true,\"x\":[38.0,26.0,35.0,27.0,14.0,4.0,58.0,14.0,55.0,31.0,null,15.0,8.0,38.0,null,null,null,18.0,14.0,40.0,27.0,3.0,19.0,null,18.0,49.0,29.0,21.0,5.0,38.0,29.0,17.0,16.0,30.0,null,17.0,33.0,23.0,34.0,28.0,21.0,null,14.5,20.0,17.0,2.0,32.5,null,47.0,29.0,19.0,null,22.0,24.0,9.0,22.0,16.0,40.0,null,45.0,1.0,50.0,null,4.0,null,32.0,19.0,44.0,58.0,null,24.0,2.0,16.0,35.0,31.0,27.0,32.0,null,35.0,5.0,null,8.0,null,null,25.0,24.0,29.0,41.0,29.0,null,30.0,35.0,50.0,null,58.0,35.0,41.0,null,63.0,45.0,35.0,22.0,26.0,19.0,24.0,2.0,50.0,null,null,null,17.0,30.0,24.0,18.0,26.0,26.0,24.0,31.0,40.0,30.0,22.0,36.0,36.0,31.0,16.0,null,null,41.0,24.0,24.0,40.0,null,22.0,38.0,null,null,45.0,60.0,null,null,24.0,3.0,null,22.0,42.0,1.0,35.0,36.0,17.0,23.0,24.0,31.0,28.0,21.0,20.0,null,33.0,null,34.0,18.0,10.0,28.0,28.0,19.0,null,42.0,14.0,21.0,24.0,45.0,28.0,13.0,5.0,null,50.0,0.75,33.0,23.0,22.0,2.0,63.0,null,35.0,54.0,25.0,21.0,null,37.0,16.0,33.0,54.0,34.0,36.0,30.0,44.0,50.0,2.0,null,30.0,7.0,30.0,22.0,36.0,9.0,11.0,19.0,22.0,48.0,39.0,36.0,null,29.0,53.0,null,34.0,39.0,null,25.0,39.0,18.0,52.0,null,null,24.0,22.0,40.0,39.0,null,24.0,26.0,4.0,21.0,9.0,28.0,41.0,24.0,2.0,0.75,23.0,18.0,null,18.0,32.0,null,40.0,18.0,43.0,null,15.0,4.0,null,18.0,18.0,45.0,22.0,24.0,38.0,27.0,6.0,30.0,null,25.0,29.0,48.0,21.0,30.0,4.0,48.0,33.0,36.0,51.0,30.5,57.0,54.0,5.0,43.0,13.0,17.0,18.0,null,49.0,31.0,30.0,31.0,18.0,33.0,6.0,23.0,52.0,27.0,62.0,15.0,39.0,30.0,null,9.0,16.0,44.0,18.0,45.0,24.0,48.0,null,42.0,27.0,47.0,28.0,15.0,56.0,25.0,22.0,39.0,19.0,null,47.0,22.0,30.0,18.0,23.0,47.0,24.0,27.0,45.0,null,48.0,22.0,33.0,null,null,21.0,30.0,45.0,60.0,36.0,20.0,28.0,36.0,22.0,null,18.0,60.0,24.0,29.0,35.0,55.0,24.0,27.0,18.0,null,22.0,27.0,76.0,20.0,43.0,16.0,null,36.0,18.5,63.0,1.0,29.0,12.0,35.0,17.0,null,null,23.0,10.0,33.0,23.0,36.0,29.0,23.0,26.0,null,26.0,26.0,null,22.0,15.0,20.0,36.0,64.0,18.0,27.0,21.0,null,18.0,null,null,8.0,35.0,33.0,60.0,38.0,null,50.0,30.0,22.0,53.0,null,null,21.0,18.0,48.0,55.0,45.0,22.0,29.0,null,0.92,19.0,29.0,1.0,null,26.0,null,20.0,23.0,null,9.0,2.0,30.0,1.0,null,64.0,45.0,37.0,55.0,17.0,39.0,12.0,48.0,19.0,58.0,16.0,38.0,31.0,45.0,0.17,59.0,30.0,24.0,31.0,25.0,null,22.0,45.0,31.0,54.0,45.0,22.0,null,19.0,24.0,51.0,18.0,48.0,30.0,22.0,null,3.0,null,37.0,28.0,39.0],\"xaxis\":\"x\",\"y\":[71.2833,7.925,53.1,11.1333,30.0708,16.7,26.55,7.8542,16.0,18.0,7.225,8.0292,21.075,31.3875,7.8792,146.5208,7.75,18.0,11.2417,9.475,21.0,41.5792,7.8792,7.75,17.8,76.7292,26.0,10.5,27.75,80.0,10.5,7.925,46.9,12.475,7.7875,10.5,15.85,263.0,23.0,7.8958,7.65,24.15,14.4542,9.825,14.4583,31.275,13.0,22.3583,14.5,26.0,26.2833,15.2458,7.75,15.85,34.375,66.6,7.7333,15.75,55.0,27.9,11.1333,28.7125,69.55,22.025,15.5,13.0,7.8542,27.7208,146.5208,7.75,13.0,10.4625,7.75,21.0,113.275,7.925,76.2917,25.4667,83.475,31.3875,7.55,26.25,14.4542,15.5,7.775,14.5,10.4625,20.2125,15.2458,79.2,86.5,512.3292,26.0,7.75,153.4625,135.6333,19.5,7.75,77.9583,7.75,20.25,7.75,78.85,91.0792,8.85,151.55,247.5208,7.75,12.35,110.8833,108.9,56.9292,83.1583,262.375,26.0,7.8542,26.0,164.8667,134.5,12.35,29.0,135.6333,13.0,20.525,57.9792,23.25,133.65,134.5,263.0,13.0,13.0,16.1,55.0,13.0,7.8792,7.8792,14.4542,75.25,7.2292,7.75,69.3,21.075,82.1708,7.25,227.525,15.7417,52.0,13.0,12.0,113.275,16.7,7.8542,12.65,9.825,8.6625,25.4667,90.0,8.05,32.5,13.0,24.15,14.4,26.0,26.0,16.1,26.0,120.0,34.375,18.75,26.25,13.0,19.5,19.2583,51.8625,10.5,19.2583,27.75,13.7917,9.8375,12.2875,9.5875,25.4667,90.0,78.2667,151.55,7.75,7.6292,9.5875,86.5,26.0,59.4,10.5,26.0,93.5,57.9792,10.5,26.0,22.3583,8.6625,26.25,106.425,49.5,71.0,31.275,31.275,26.0,7.775,39.6,79.65,17.4,8.05,21.075,51.4792,7.75,13.0,55.9,14.4583,30.0,110.8833,79.65,78.2667,7.75,33.0,27.0,41.5792,153.4625,31.275,15.5,65.0,16.1,39.0,77.9583,27.9,13.0,39.6875,69.3,27.9,19.2583,7.55,23.0,7.8292,6.75,15.5,52.0,39.0,9.8417,46.9,8.1375,211.3375,13.4167,7.7333,227.525,14.4542,13.5,151.55,49.5042,227.525,10.5,33.0,21.0,7.7375,7.925,211.3375,34.375,262.375,13.0,23.0,65.0,86.5,120.0,77.9583,7.75,10.5,23.0,12.475,211.3375,7.2292,57.0,7.4958,69.55,25.9292,8.6833,24.15,26.25,7.775,53.1,31.275,7.925,93.5,12.475,80.0,14.4542,83.1583,31.0,89.1042,15.2458,39.4,26.0,9.35,164.8667,19.2583,25.9292,69.55,13.0,13.8583,52.5542,24.0,7.225,83.1583,26.0,10.5167,29.125,30.0,23.45,7.0,12.2875,7.6292,7.2292,82.2667,61.175,27.7208,7.925,7.225,31.6833,262.375,61.9792,20.575,23.45,8.05,8.6625,13.0,52.5542,76.2917,15.9,23.0,263.0,262.375,7.725,21.0,7.8792,263.0,7.75,7.925,211.5,25.7,7.75,7.8792,8.05,7.75,13.9,52.0,78.85,7.8542,55.4417,8.5167,7.7792,31.6792,7.2833,221.7792,16.7,26.0,15.75,57.75,16.1,23.25,25.4667,8.05,46.9,151.55,83.1583,12.1833,221.7792,7.8542,13.775,7.7333,13.5,22.025,27.7208,8.9625,39.0,36.75,39.0,83.1583,53.1,247.5208,21.0,69.55,7.775,8.1125,15.5,26.0,7.75,27.7208,26.0,7.775,7.8792,211.5,13.8583,10.5,27.4458,15.2458,7.75,26.55,13.0,106.425,27.7208,30.0,21.0,26.0,14.4542,27.75,13.0,23.0,12.1833,8.05,136.7792,15.5,26.0,8.6625,7.75,15.2458,20.2125,6.95,41.5792,7.75,26.55,14.1083,7.75,135.6333,7.7333,211.3375,39.0,36.75,15.7417,512.3292,7.65,7.2292,21.0,63.3583,20.575,51.4792,15.55,37.0042,21.0,55.4417,69.55,39.6875,59.4,134.5,81.8583,262.375,8.6625,14.5,16.1,65.0,39.4,60.0,79.2,164.8667,59.4,7.7208,13.775,7.75,90.0,7.775,108.9],\"yaxis\":\"y\",\"type\":\"scattergl\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Age\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Fare\"}},\"legend\":{\"title\":{\"text\":\"Sex\"},\"tracegroupgap\":0},\"title\":{\"text\":\"Scatter plot: Fare by Age and Sex\"}},                        {\"responsive\": true}                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('c6f860d1-9a39-43a3-ba48-3534f7e0c980');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Средний возраст в зависимости от порта и пола."
+      ],
+      "metadata": {
+        "id": "aCgde5yzI8Mg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# set the size of the plot\n",
+        "plt.figure(figsize=(8, 5))\n",
+        "\n",
+        "# creating and displaying linear plot\n",
+        "sns.lineplot(data=trains_test, x='Embarked', y='Age', hue='Sex', marker='o')\n",
+        "plt.title('Linear Plot: Average Age by Embarked and Sex')\n",
+        "plt.ylabel('Average Age')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 487
+        },
+        "id": "3TQgevkjGddf",
+        "outputId": "594ba823-7e5e-42e8-d6c8-d1dd28ed07a5"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Суммарная стоимость билетов в зависимости от возраста."
+      ],
+      "metadata": {
+        "id": "IzcGHWtmOak6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sum_fare_by_age = trains_test.groupby('Age')['Fare'].sum().reset_index() # data select and calculation\n",
+        "\n",
+        "# creating and displaying an interactive linear plot\n",
+        "fig = px.line(sum_fare_by_age, x='Age', y='Fare', markers=True, title=\"Linear Plot: sum Fare by Age\")\n",
+        "fig.update_traces(mode='markers+lines')\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "JwoTjR98Jgvd",
+        "outputId": "f82da4f6-cfbb-4248-bba4-0cafdde976ac"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js\"></script>                <div id=\"73b9e65c-5507-49ed-b048-546e5859b2b0\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"73b9e65c-5507-49ed-b048-546e5859b2b0\")) {                    Plotly.newPlot(                        \"73b9e65c-5507-49ed-b048-546e5859b2b0\",                        [{\"hovertemplate\":\"Age=%{x}\\u003cbr\\u003eFare=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[0.17,0.33,0.42,0.67,0.75,0.83,0.92,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,11.5,12.0,13.0,14.0,14.5,15.0,16.0,17.0,18.0,18.5,19.0,20.0,20.5,21.0,22.0,22.5,23.0,23.5,24.0,24.5,25.0,26.0,26.5,27.0,28.0,28.5,29.0,30.0,30.5,31.0,32.0,32.5,33.0,34.0,34.5,35.0,36.0,36.5,37.0,38.0,38.5,39.0,40.0,40.5,41.0,42.0,43.0,44.0,45.0,45.5,46.0,47.0,48.0,49.0,50.0,51.0,52.0,53.0,54.0,55.0,55.5,56.0,57.0,58.0,59.0,60.0,60.5,61.0,62.0,63.0,64.0,65.0,66.0,67.0,70.0,70.5,71.0,74.0,76.0,80.0],\"xaxis\":\"x\",\"y\":[20.575,14.4,8.5167,14.5,52.291599999999995,57.1,179.3,280.50419999999997,418.575,168.4667,295.4333,122.25829999999999,247.5708,110.3083,171.7,241.92489999999998,128.075,216.9625,14.5,65.99170000000001,340.7417,329.9792,84.0042,287.2751,453.8334,538.1041,1295.9251,27.5125,752.0791,259.8335,7.25,1003.979,1005.5791,7.225,903.1583,7.2292,1690.5291,8.05,743.5001,628.9667000000001,7.225,1079.9416,870.604,51.05,924.1831999999999,1226.9334,15.8,859.9250000000001,510.25,264.07079999999996,629.6833,275.5458,14.2667,1904.8708,1785.3582999999999,43.4,359.775,726.2708,7.25,955.1333,615.7166,37.35,353.3916,623.7334,411.0458,280.825,1095.0417,35.725,346.81669999999997,597.0125,876.2834,450.5751,1129.7583,240.6667,308.4167,189.2833,493.1167,438.4541,8.05,175.9041,347.2375,981.8375000000001,72.22919999999999,585.2917,0.0,346.78330000000005,153.2875,335.325,473.9583,96.2792,10.5,221.7792,81.5,7.75,84.1584,7.775,78.85,30.0],\"yaxis\":\"y\",\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Age\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Fare\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Linear Plot: sum Fare by Age\"}},                        {\"responsive\": true}                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('73b9e65c-5507-49ed-b048-546e5859b2b0');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Графики суммарной стоимости билетов и количества пассажиров от пола."
+      ],
+      "metadata": {
+        "id": "AXl-SFvlSSQ0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# creating and displaying bar plot\n",
+        "sns.barplot(data=trains_test, x='Survived', y='Fare', estimator='sum', hue='Sex', palette='Set1')\n",
+        "plt.title(\"Bar plot: sum Fare by Sex\")\n",
+        "plt.ylabel(\"sum Fare\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "exI01huzMpLP",
+        "outputId": "f4e0fd3c-f701-479e-b07c-392170076ee9"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAHHCAYAAACiOWx7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABOIElEQVR4nO3dfXxP9f/H8ednZhfsykXbrGZWcj1XE00hjJFEUS5WpKELKxdd6pul9EuUq1BSMX2/U6KoyFgkYblYRoSkidK2wrY22djO7w/fna9PG51l8/ngcb/dzu22c87rnPM6n/WxZ+e8P+djMwzDEAAAAM7LxdENAAAAXAoITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AymzdunWy2Wxat26do1vBP1T8O1yyZImjWwEuGYQmwAnEx8fLZrPZTf7+/urYsaNWrlzp6PbK1Weffabx48c7uo0KVxxKSpv69+/v6PYuqg0bNqh79+66+uqr5eHhodq1a6tnz55auHCho1sDysTV0Q0A+J8XXnhBoaGhMgxDGRkZio+P16233qpPP/1Ut912m6PbKxefffaZZs+efUUEJ0l69NFHdcMNN9gtq1OnjmOacYDFixerX79+at68uUaOHKlq1aopLS1N69ev11tvvaWBAwc6ukXAMkIT4ES6d++uVq1amfMxMTEKCAjQe++9Vy6hqaioSAUFBfLw8LjgfcGadu3aqW/fvuW6z5MnT8rNzU0uLs5/s2D8+PFq1KiRvv76a7m5udmty8zMdFBXwD/j/O844Arm5+cnT09Pubra///Nq6++qrZt26pGjRry9PRUeHh4qWNTbDabYmNjlZCQoMaNG8vd3V2JiYnnPF6dOnV02223afXq1WrevLk8PDzUqFEjffTRR5b6Xbx4scLDw+Xp6amaNWvqnnvu0S+//GKuv++++zR79myzt+Kp2K+//qq9e/fq1KlTf3us999/X+Hh4fL29paPj4/CwsI0Y8YMc/348ePt9l2s+FbowYMHS5z3unXr1KpVK3l6eiosLMwcs/XRRx8pLCxMHh4eCg8P1/bt2y29Hudz7NgxPf744woLC5OXl5d8fHzUvXt37dixw66u+Dbf+++/r2effVZXX321qlSpopycHEnS5s2b1a1bN/n6+qpKlSrq0KGDNm7caLmPwsJCPfPMMwoMDFTVqlV1++236/Dhw+b65557TpUrV9Zvv/1WYtvhw4fLz89PJ0+ePOf+Dxw4oBtuuKFEYJIkf39/u/mioiJNnz5djRs3loeHhwICAvTAAw/o+PHjdv24uLhozZo1JXpxc3Mr8foB5coA4HDz5883JBmff/658dtvvxmZmZnGrl27jAceeMBwcXExVq9ebVd/zTXXGA8//LAxa9YsY+rUqUbr1q0NScby5cvt6iQZDRs2NK666irj+eefN2bPnm1s3779nH2EhIQY9erVM/z8/Iynn37amDp1qhEWFlaihy+++MKQZHzxxRclzuGGG24wpk2bZjz99NOGp6enUadOHeP48eOGYRjGpk2bjC5duhiSjH//+9/mVGzw4MGGJCMtLe28r9fq1asNSUbnzp2N2bNnG7NnzzZiY2ONu+66y6x57rnnjNL+iSvu8+xjhISEGPXr1zdq1apljB8/3pg2bZpx9dVXG15eXsZ//vMfo3bt2sbLL79svPzyy4avr69Rt25do7Cw8Lw9Fr9G8+bNM3777Te7qbCw0Ni6datx3XXXGU8//bTx5ptvGi+88IJx9dVXG76+vsYvv/xSYj+NGjUymjdvbkydOtWYOHGikZeXZ6xZs8Zwc3MzIiIijClTphjTpk0zmjZtari5uRmbN2+21F9YWJjRtGlTY+rUqcbTTz9teHh4GPXq1TNOnDhhGIZh7N+/35BkzJw50277/Px8o1q1asb9999/3uPUq1fPCA4ONg4fPnzeOsMwjKFDhxqurq7GsGHDjDlz5hhPPfWUUbVqVeOGG24wCgoKDMMwjIKCAqNFixZGSEiIkZOTYxiGYSQmJhqSjAkTJvztMYALQWgCnEDxH/K/Tu7u7kZ8fHyJ+uI/aMUKCgqMJk2aGJ06dbJbLslwcXExdu/ebamPkJAQQ5Lx4Ycfmsuys7ONWrVqGS1atDCX/TU0FRQUGP7+/kaTJk2MP//806xbvny5IcmIi4szl40YMaLUMGMY1kPTyJEjDR8fH+P06dPnrClraJJkbNq0yVy2atUqQ5Lh6elp/PTTT+byN998s0RgLE3xa1TalJaWZpw8ebJE8EpLSzPc3d2NF154ocR+rr32Wrvfe1FRkXH99dcbUVFRRlFRkbn8xIkTRmhoqNGlSxdL/V199dVm+DAMw/jggw8MScaMGTPMZREREUabNm3stv/oo48svQ7vvPOOIclwc3MzOnbsaIwbN8746quvSpz7V199ZUgyEhIS7JYXB6Kzl3/77beGm5ubMXToUOP48ePG1VdfbbRq1co4derUeXsBLhS35wAnMnv2bCUlJSkpKUn/+c9/1LFjRw0dOrTE7TFPT0/z5+PHjys7O1vt2rXTN998U2KfHTp0UKNGjSz3EBQUpDvuuMOc9/Hx0aBBg7R9+3alp6eXus22bduUmZmphx9+2G68VI8ePdSgQQOtWLHC0rHj4+NlGMbfDpT28/NTXl6ekpKSLO3XikaNGikiIsKcb9OmjSSpU6dOql27donlP/74o6X9xsXFmb/T4ikwMFDu7u7mmKTCwkIdPXpUXl5eql+/fqm/x8GDB9v93lNTU7V//34NHDhQR48e1e+//67ff/9deXl56ty5s9avX6+ioqK/7W/QoEHy9vY25/v27atatWrps88+s6vZvHmzDhw4YC5LSEhQcHCwOnTocN7933///UpMTNQtt9yiDRs2aMKECWrXrp2uv/56bdq0yaxbvHixfH191aVLF/Ncfv/9d4WHh8vLy0tffPGFWdukSRM9//zzevvttxUVFaXff/9dCxYsKHEbGyhv/BcGOJHWrVvbDQQfMGCAWrRoodjYWN12223muJDly5frxRdfVGpqqvLz88360sbwhIaGlqmHunXrlthPvXr1JEkHDx5UYGBgiW1++uknSVL9+vVLrGvQoIE2bNhQph7+zsMPP6wPPvjA/Bh7165ddffdd6tbt27/eJ9nByNJ8vX1lSQFBweXuvzscTbnExYWpsjIyBLLi4qKNGPGDL3++utKS0tTYWGhua5GjRol6v/6e9y/f7+kM2HqXLKzs1WtWrXz9nf99dfbzdtsNtWtW9duzFe/fv00atQoJSQkKC4uTtnZ2Vq+fLlGjx5d6n9zfxUVFaWoqCidOHFCKSkpWrRokebMmaPbbrtNe/fulb+/v/bv36/s7OwS45yK/XXQ+BNPPKH3339fW7Zs0UsvvVSm/zEA/ilCE+DEXFxc1LFjR82YMUP79+9X48aN9dVXX+n2229X+/bt9frrr6tWrVqqXLmy5s+fX+pzb86+OnG58Pf3V2pqqlatWqWVK1dq5cqVmj9/vgYNGqQFCxZIKj1ASrILJ2erVKlSmZYbhvEPOv+fl156SePGjdP999+vCRMmqHr16nJxcdGoUaNKvUL0199jcc0rr7yi5s2bl3oMLy+vC+qxWLVq1XTbbbeZoWnJkiXKz8/XPffcU6b9VKlSRe3atVO7du1Us2ZNPf/881q5cqUGDx6soqIi+fv7KyEhodRtr7rqKrv5H3/80QyO33777T87MaCMCE2Akzt9+rQkKTc3V5L04YcfysPDQ6tWrZK7u7tZN3/+/HI53g8//CDDMOxCx/fffy/p3M8XCgkJkSTt27dPnTp1slu3b98+c7107jBTVm5uburZs6d69uypoqIiPfzww3rzzTc1btw41a1b17zCkpWVJT8/P3O74qtijrZkyRJ17NhR77zzjt3yrKws1axZ82+3v+666ySduX1a2pUsq4qDRzHDMPTDDz+oadOmdssHDRqkXr16aevWrUpISFCLFi3UuHHjf3zc4iuqv/76q6Qz5/P555/rpptu+tugX1RUpPvuu08+Pj4aNWqUXnrpJfXt21d33nnnP+4HsIIxTYATO3XqlFavXi03Nzc1bNhQ0pkrHzabze6KycGDB7Vs2bJyOeaRI0e0dOlScz4nJ0fvvvuumjdvXuqtOenMH0B/f3/NmTPH7nbhypUrtWfPHvXo0cNcVrVqVUlnwsFfWX3kwNGjR+3mXVxczD/yxccvDhXr16836/Ly8swrUY5WqVKlElerFi9ebPeIhvMJDw/Xddddp1dffdUM1Gcr7REBpXn33Xf1xx9/mPNLlizRr7/+qu7du9vVde/eXTVr1tSkSZP05ZdfWr7K9NdHAxQrHjNVfEv37rvvVmFhoSZMmFCi9vTp03b/vUydOlWbNm3S3LlzNWHCBLVt21YPPfSQfv/9d0s9Af8UV5oAJ7Jy5Urt3btX0pkxHAsXLtT+/fv19NNPy8fHR9KZwdVTp05Vt27dNHDgQGVmZmr27NmqW7eudu7cecE91KtXTzExMdq6dasCAgI0b948ZWRknPdKVuXKlTVp0iQNGTJEHTp00IABA5SRkaEZM2aoTp06Gj16tFkbHh4u6cyTsqOiolSpUiXza0XGjh2rBQsWKC0t7byDwYcOHapjx46pU6dOuuaaa/TTTz9p5syZat68uRkuu3btqtq1aysmJkZPPPGEKlWqpHnz5umqq67SoUOHLvh1ulC33XabXnjhBQ0ZMkRt27bVt99+q4SEBF177bWWtndxcdHbb7+t7t27q3HjxhoyZIiuvvpq/fLLL/riiy/k4+OjTz/99G/3U716dd18880aMmSIMjIyNH36dNWtW1fDhg2zq6tcubL69++vWbNmqVKlShowYIClPnv16qXQ0FD17NlT1113nfLy8vT555/r008/1Q033KCePXtKOvOBhQceeEATJ05UamqqunbtqsqVK2v//v1avHixZsyYob59+2rPnj0aN26c7rvvPnPb+Ph4NW/e3BzrBlQYh352D4BhGKU/csDDw8No3ry58cYbb9h9pNwwznyM+/rrrzfc3d2NBg0aGPPnzy/1I/aSjBEjRljuIyQkxOjRo4exatUqo2nTpub+Fy9ebFdX2nOaDMMwFi1aZLRo0cJwd3c3qlevbkRHRxs///yzXc3p06eNRx55xLjqqqsMm81m17PVRw4sWbLE6Nq1q+Hv72+4ubkZtWvXNh544AHj119/tatLSUkx2rRpY9ZMnTr1nI8c6NGjR4njlPb6paWlGZKMV1555bw9Fr9Gf33tip08edJ47LHHjFq1ahmenp7GTTfdZCQnJxsdOnQwOnToYHk/27dvN+68806jRo0ahru7uxESEmLcfffdxpo1ayz199577xljx441/P39DU9PT6NHjx52j1g425YtWwxJRteuXc+777O99957Rv/+/Y3rrrvO8PT0NDw8PIxGjRoZ//rXv+wedVBs7ty5Rnh4uOHp6Wl4e3sbYWFhxpNPPmkcOXLEOH36tHHDDTcY11xzjZGVlWW33YwZMwxJxqJFiyz3BpSVzTAucDQjgMtGnTp11KRJEy1fvtzRrcAJ7dixQ82bN9e7776re++919HtABcdY5oAAJa89dZb8vLyYsA1rliMaQIAnNenn36q7777TnPnzlVsbKw5mB+40hCaAADn9cgjjygjI0O33nqrnn/+eUe3AzgMY5oAAAAsYEwTAACABYQmAAAACxjTVE6Kiop05MgReXt7l9vXRAAAgIplGIb++OMPBQUFycXl/NeSCE3l5MiRIyW+DR0AAFwaDh8+rGuuuea8NYSmcuLt7S3pzIte/HUXAADAueXk5Cg4ONj8O34+hKZyUnxLzsfHh9AEAMAlxsrQGgaCAwAAWEBoAgAAsIDQBAAAYAFjmgAAuAgKCwt16tQpR7dxxalcubIqVapULvsiNAEAUIEMw1B6erqysrIc3coVy8/PT4GBgRf8HEVCEwAAFag4MPn7+6tKlSo8APkiMgxDJ06cUGZmpiSpVq1aF7Q/QhMAABWksLDQDEw1atRwdDtXJE9PT0lSZmam/P39L+hWHQPBAQCoIMVjmKpUqeLgTq5sxa//hY4pIzQBAFDBuCXnWOX1+js0NK1fv149e/ZUUFCQbDabli1bds7aBx98UDabTdOnT7dbfuzYMUVHR8vHx0d+fn6KiYlRbm6uXc3OnTvVrl07eXh4KDg4WJMnTy6x/8WLF6tBgwby8PBQWFiYPvvss/I4RQAAcJlwaGjKy8tTs2bNNHv27PPWLV26VF9//bWCgoJKrIuOjtbu3buVlJSk5cuXa/369Ro+fLi5PicnR127dlVISIhSUlL0yiuvaPz48Zo7d65Zs2nTJg0YMEAxMTHavn27evfurd69e2vXrl3ld7IAAODSZjgJScbSpUtLLP/555+Nq6++2ti1a5cREhJiTJs2zVz33XffGZKMrVu3mstWrlxp2Gw245dffjEMwzBef/11o1q1akZ+fr5Z89RTTxn169c35++++26jR48edsdt06aN8cADD1juPzs725BkZGdnW94GAHB5+/PPP43vvvvO+PPPPx3dSgmZmZnGgw8+aAQHBxtubm5GQECA0bVrV2PDhg2Obq3cne/3UJa/3049pqmoqEj33nuvnnjiCTVu3LjE+uTkZPn5+alVq1bmssjISLm4uGjz5s1mTfv27eXm5mbWREVFad++fTp+/LhZExkZabfvqKgoJScnn7O3/Px85eTk2E0AAFwq+vTpo+3bt2vBggX6/vvv9cknn+iWW27R0aNHHd2a03Lq0DRp0iS5urrq0UcfLXV9enq6/P397Za5urqqevXqSk9PN2sCAgLsaorn/66meH1pJk6cKF9fX3MKDg4u28kBwGXGMAzl5uaak2EYjm4J55CVlaWvvvpKkyZNUseOHRUSEqLWrVtr7Nixuv32282aoUOH6qqrrpKPj486deqkHTt2SJJ+++03BQYG6qWXXjL3uWnTJrm5uWnNmjUOOaeLwWlDU0pKimbMmKH4+Hin/NTB2LFjlZ2dbU6HDx92dEsA4FB5eXnq1auXOeXl5Tm6JZyDl5eXvLy8tGzZMuXn55dac9dddykzM1MrV65USkqKWrZsqc6dO+vYsWO66qqrNG/ePI0fP17btm3TH3/8oXvvvVexsbHq3LnzRT6bi8dpQ9NXX32lzMxM1a5dW66urnJ1ddVPP/2kxx57THXq1JEkBQYGmk/5LHb69GkdO3ZMgYGBZk1GRoZdTfH839UUry+Nu7u7fHx87CYAAC4Frq6uio+P14IFC+Tn56ebbrpJzzzzjHbu3ClJ2rBhg7Zs2aLFixerVatWuv766/Xqq6/Kz89PS5YskSTdeuutGjZsmKKjo/Xggw+qatWqmjhxoiNPq8I5bWi69957tXPnTqWmpppTUFCQnnjiCa1atUqSFBERoaysLKWkpJjbrV27VkVFRWrTpo1Zs379ersHWiUlJal+/fqqVq2aWfPXy4lJSUmKiIio6NMEAMAh+vTpoyNHjuiTTz5Rt27dtG7dOrVs2VLx8fHasWOHcnNzVaNGDfOqlJeXl9LS0nTgwAFzH6+++qpOnz6txYsXKyEhQe7u7g48o4rn0K9Ryc3N1Q8//GDOp6WlKTU1VdWrV1ft2rVLPHK+cuXKCgwMVP369SVJDRs2VLdu3TRs2DDNmTNHp06dUmxsrPr3728+nmDgwIF6/vnnFRMTo6eeekq7du3SjBkzNG3aNHO/I0eOVIcOHTRlyhT16NFD77//vrZt22b3WAIAAC43Hh4e6tKli7p06aJx48Zp6NCheu655/Twww+rVq1aWrduXYlt/Pz8zJ8PHDigI0eOqKioSAcPHlRYWNjFa94BHBqatm3bpo4dO5rzY8aMkSQNHjxY8fHxlvaRkJBg3kN1cXFRnz599Nprr5nrfX19tXr1ao0YMULh4eGqWbOm4uLi7J7l1LZtWy1cuFDPPvusnnnmGV1//fVatmyZmjRpUj4nCgDAJaBRo0ZatmyZWrZsqfT0dLm6uppDYv6qoKBA99xzj/r166f69etr6NCh+vbbb0t8QOtyYjP4eEO5yMnJka+vr7KzsxnfBOCKlJubq169epnzH3/8sby8vBzYkeOdPHlSaWlpCg0NlYeHh6PbMR09elR33XWX7r//fjVt2lTe3t7atm2bHnnkEfXo0UNvv/222rdvrz/++EOTJ09WvXr1dOTIEa1YsUJ33HGHWrVqpSeeeEJLlizRjh075OXlpQ4dOsjX11fLly939OmVcL7fQ1n+fjv0ShMAALj4vLy81KZNG02bNk0HDhzQqVOnFBwcrGHDhumZZ56RzWbTZ599pn/9618aMmSI+YiB9u3bKyAgQOvWrdP06dP1xRdfmEHj3//+t5o1a6Y33nhDDz30kIPPsGJwpamccKUJwJWOK00lOeuVpitNeV1pctpPzwEAADgTQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAAAowTAMDR8+XNWrV5fNZlNqaqpD+jh48KBDj382vnsOAAAH2Naq9UU9XqttW8pUn5iYqPj4eK1bt07XXnutatasWUGdXToITQAAoIQDBw6oVq1aatu2raNbcRrcngMAAHbuu+8+PfLIIzp06JBsNpvq1KmjoqIiTZw4UaGhofL09FSzZs20ZMkSc5t169bJZrNp1apVatGihTw9PdWpUydlZmZq5cqVatiwoXx8fDRw4ECdOHHC3C4xMVE333yz/Pz8VKNGDd122206cODAefvbtWuXunfvLi8vLwUEBOjee+/V77//XmGvRzFCEwAAsDNjxgy98MILuuaaa/Trr79q69atmjhxot59913NmTNHu3fv1ujRo3XPPffoyy+/tNt2/PjxmjVrljZt2qTDhw/r7rvv1vTp07Vw4UKtWLFCq1ev1syZM836vLw8jRkzRtu2bdOaNWvk4uKiO+64Q0VFRaX2lpWVpU6dOqlFixbatm2bEhMTlZGRobvvvrtCXxOJ23MAAOAvfH195e3trUqVKikwMFD5+fl66aWX9PnnnysiIkKSdO2112rDhg1688031aFDB3PbF198UTfddJMkKSYmRmPHjtWBAwd07bXXSpL69u2rL774Qk899ZQkqU+fPnbHnjdvnq666ip99913atKkSYneZs2apRYtWuill16y2yY4OFjff/+96tWrV74vxlkITQAA4Lx++OEHnThxQl26dLFbXlBQoBYtWtgta9q0qflzQECAqlSpYgam4mVbtvxvUPr+/fsVFxenzZs36/fffzevMB06dKjU0LRjxw598cUX8vLyKrHuwIEDhCYAAOA4ubm5kqQVK1bo6quvtlvn7u5uN1+5cmXzZ5vNZjdfvOzsW289e/ZUSEiI3nrrLQUFBamoqEhNmjRRQUHBOXvp2bOnJk2aVGJdrVq1ynZiZURoAgAA59WoUSO5u7vr0KFDdrfiLtTRo0e1b98+vfXWW2rXrp0kacOGDefdpmXLlvrwww9Vp04dubpe3BjDQHAAAHBe3t7eevzxxzV69GgtWLBABw4c0DfffKOZM2dqwYIF/3i/1apVU40aNTR37lz98MMPWrt2rcaMGXPebUaMGKFjx45pwIAB2rp1qw4cOKBVq1ZpyJAhKiws/Me9WMGVJgAA8LcmTJigq666ShMnTtSPP/4oPz8/tWzZUs8888w/3qeLi4vef/99Pfroo2rSpInq16+v1157Tbfccss5twkKCtLGjRv11FNPqWvXrsrPz1dISIi6desmF5eKvRZkMwzDqNAjXCFycnLk6+ur7Oxs+fj4OLodALjocnNz1atXL3P+448/LnWw7pXk5MmTSktLU2hoqDw8PBzdzhXrfL+Hsvz95vYcAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAcFHcd9996t27t6Pb+Mf47jkAAByg27hFF/V4iRP6XdTjXY640gQAAGABoQkAAJRwyy236JFHHtGoUaNUrVo1BQQE6K233lJeXp6GDBkib29v1a1bVytXrpQkFRYWKiYmRqGhofL09FT9+vU1Y8aM8x6jqKhIEydONLdp1qyZlixZcjFO7x8hNAEAgFItWLBANWvW1JYtW/TII4/ooYce0l133aW2bdvqm2++UdeuXXXvvffqxIkTKioq0jXXXKPFixfru+++U1xcnJ555hl98MEH59z/xIkT9e6772rOnDnavXu3Ro8erXvuuUdffvnlRTxL6xjTBAAAStWsWTM9++yzkqSxY8fq5ZdfVs2aNTVs2DBJUlxcnN544w3t3LlTN954o55//nlz29DQUCUnJ+uDDz7Q3XffXWLf+fn5eumll/T5558rIiJCknTttddqw4YNevPNN9WhQ4eLcIZlQ2gCAAClatq0qflzpUqVVKNGDYWFhZnLAgICJEmZmZmSpNmzZ2vevHk6dOiQ/vzzTxUUFKh58+al7vuHH37QiRMn1KVLF7vlBQUFatGiRTmfSflw6O259evXq2fPngoKCpLNZtOyZcvMdadOndJTTz2lsLAwVa1aVUFBQRo0aJCOHDlit49jx44pOjpaPj4+8vPzU0xMjHJzc+1qdu7cqXbt2snDw0PBwcGaPHlyiV4WL16sBg0ayMPDQ2FhYfrss88q5JwBALhUVK5c2W7eZrPZLbPZbJLOjE16//339fjjjysmJkarV69WamqqhgwZooKCglL3Xfy3esWKFUpNTTWn7777zmnHNTk0NOXl5alZs2aaPXt2iXUnTpzQN998o3Hjxumbb77RRx99pH379un222+3q4uOjtbu3buVlJSk5cuXa/369Ro+fLi5PicnR127dlVISIhSUlL0yiuvaPz48Zo7d65Zs2nTJg0YMEAxMTHavn27evfurd69e2vXrl0Vd/IAAFxGNm7cqLZt2+rhhx9WixYtVLduXR04cOCc9Y0aNZK7u7sOHTqkunXr2k3BwcEXsXPrHHp7rnv37urevXup63x9fZWUlGS3bNasWWrdurUOHTqk2rVra8+ePUpMTNTWrVvVqlUrSdLMmTN166236tVXX1VQUJASEhJUUFCgefPmyc3NTY0bN1ZqaqqmTp1qhqsZM2aoW7dueuKJJyRJEyZMUFJSkmbNmqU5c+ZU4CsAAP+zrVVrR7dwQf6UJF9vc377LZ3k6bBuLkyrbVsc3cIl5/rrr9e7776rVatWKTQ0VP/+97+1detWhYaGllrv7e2txx9/XKNHj1ZRUZFuvvlmZWdna+PGjfLx8dHgwYMv8hn8vUvq03PZ2dmy2Wzy8/OTJCUnJ8vPz88MTJIUGRkpFxcXbd682axp37693NzczJqoqCjt27dPx48fN2siIyPtjhUVFaXk5ORz9pKfn6+cnBy7CQCAK9UDDzygO++8U/369VObNm109OhRPfzww+fdZsKECRo3bpwmTpyohg0bqlu3blqxYsU5g5ajXTIDwU+ePKmnnnpKAwYMkI+PjyQpPT1d/v7+dnWurq6qXr260tPTzZq/vvjFA9fS09NVrVo1paenm8vOrineR2kmTpxo9ykBAADKwtmf0L1u3boSyw4ePFhimWEY5s/z58/X/Pnz7dZPnDjR/Dk+Pt5unc1m08iRIzVy5MgL6vViuSSuNJ06dUp33323DMPQG2+84eh2JJ356GV2drY5HT582NEtAQCACuT0V5qKA9NPP/2ktWvXmleZJCkwMND8mGOx06dP69ixYwoMDDRrMjIy7GqK5/+upnh9adzd3eXu7v7PTwwAAFxSnPpKU3Fg2r9/vz7//HPVqFHDbn1ERISysrKUkpJiLlu7dq2KiorUpk0bs2b9+vU6deqUWZOUlKT69eurWrVqZs2aNWvs9p2UlGQ+bAsAAMChoSk3N9d8LoMkpaWlKTU1VYcOHdKpU6fUt29fbdu2TQkJCSosLFR6errS09PNZz4UDxobNmyYtmzZoo0bNyo2Nlb9+/dXUFCQJGngwIFyc3NTTEyMdu/erUWLFmnGjBkaM2aM2cfIkSOVmJioKVOmaO/evRo/fry2bdum2NjYi/6aAAAA5+TQ0LRt2za1aNHCfPLnmDFj1KJFC8XFxemXX37RJ598op9//lnNmzdXrVq1zGnTpk3mPhISEtSgQQN17txZt956q26++Wa7ZzD5+vpq9erVSktLU3h4uB577DHFxcXZPcupbdu2WrhwoebOnWt+WeCyZcvUpEmTi/diAAAuW2cPlsbFV16vv0PHNN1yyy3nPRErJ1m9enUtXLjwvDVNmzbVV199dd6au+66S3fdddffHg+4XBiGoby8PHO+atWq5tN9AZSP4qdnnzhxQp6el+pTqy59J06ckFTyCedl5fQDwQFUjLy8PPXq1cuc//jjj+Xl5eXAjoDLT6VKleTn52d+aKlKlSr8z8lFZBiGTpw4oczMTPn5+alSpUoXtD9CEwAAFaj4k9h//bQ3Lh4/P7/zfiLeKkITAAAVyGazqVatWvL397f7JDcujsqVK1/wFaZihCYAAC6CSpUqldsfbziGUz+nCQAAwFkQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWuDq6AeBS1W3cIke3cEGM0/l2833+7yPZXN0d1M2FSZzQz9EtALgCcKUJAADAAq40AQDKhYek8dl/2M0DlxOHXmlav369evbsqaCgINlsNi1btsxuvWEYiouLU61ateTp6anIyEjt37/frubYsWOKjo6Wj4+P/Pz8FBMTo9zcXLuanTt3ql27dvLw8FBwcLAmT55copfFixerQYMG8vDwUFhYmD777LNyP18AuJzZJHmeNdkc2w5Q7hwamvLy8tSsWTPNnj271PWTJ0/Wa6+9pjlz5mjz5s2qWrWqoqKidPLkSbMmOjpau3fvVlJSkpYvX67169dr+PDh5vqcnBx17dpVISEhSklJ0SuvvKLx48dr7ty5Zs2mTZs0YMAAxcTEaPv27erdu7d69+6tXbt2VdzJAwCAS4rNMAzD0U1Iks1m09KlS9W7d29JZ64yBQUF6bHHHtPjjz8uScrOzlZAQIDi4+PVv39/7dmzR40aNdLWrVvVqlUrSVJiYqJuvfVW/fzzzwoKCtIbb7yhf/3rX0pPT5ebm5sk6emnn9ayZcu0d+9eSVK/fv2Ul5en5cuXm/3ceOONat68uebMmWOp/5ycHPn6+io7O1s+Pj7l9bLAiV0OA8FPb1lgzru2HsxAcAfb1qq1o1vAf7XatsXRLeAiKcvfb6cdCJ6Wlqb09HRFRkaay3x9fdWmTRslJydLkpKTk+Xn52cGJkmKjIyUi4uLNm/ebNa0b9/eDEySFBUVpX379un48eNmzdnHKa4pPk5p8vPzlZOTYzcBAIDLl9OGpvT0dElSQECA3fKAgABzXXp6uvz9/e3Wu7q6qnr16nY1pe3j7GOcq6Z4fWkmTpwoX19fcwoODi7rKQIAgEuI04YmZzd27FhlZ2eb0+HDhx3dEgAAqEBOG5oCAwMlSRkZGXbLMzIyzHWBgYHKzMy0W3/69GkdO3bMrqa0fZx9jHPVFK8vjbu7u3x8fOwmAABw+XLa0BQaGqrAwECtWbPGXJaTk6PNmzcrIiJCkhQREaGsrCylpKSYNWvXrlVRUZHatGlj1qxfv16nTp0ya5KSklS/fn1Vq1bNrDn7OMU1xccBAABwaGjKzc1VamqqUlNTJZ0Z/J2amqpDhw7JZrNp1KhRevHFF/XJJ5/o22+/1aBBgxQUFGR+wq5hw4bq1q2bhg0bpi1btmjjxo2KjY1V//79FRQUJEkaOHCg3NzcFBMTo927d2vRokWaMWOGxowZY/YxcuRIJSYmasqUKdq7d6/Gjx+vbdu2KTY29mK/JAAAwEk59Ing27ZtU8eOHc354iAzePBgxcfH68knn1ReXp6GDx+urKws3XzzzUpMTJSHx/+eM5uQkKDY2Fh17txZLi4u6tOnj1577TVzva+vr1avXq0RI0YoPDxcNWvWVFxcnN2znNq2bauFCxfq2Wef1TPPPKPrr79ey5YtU5MmTS7CqwAAAC4FTvOcpksdz2m68vCcJufBc5pQ3nhO05XjsnhOEwAAgDMhNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJXRzcAwEEqucm19WC7eQDAuRGagCuUzWaTXN0d3QYAXDK4PQcAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACw4B+FptOnT+vzzz/Xm2++qT/++EOSdOTIEeXm5pZrcwAAAM6izE8E/+mnn9StWzcdOnRI+fn56tKli7y9vTVp0iTl5+drzpw5FdEnAACAQ5X5StPIkSPVqlUrHT9+XJ6enubyO+64Q2vWrCnX5gAAAJxFma80ffXVV9q0aZPc3Oy/3LNOnTr65Zdfyq0xAAAAZ1LmK01FRUUqLCwssfznn3+Wt7d3uTQFAADgbMocmrp27arp06eb8zabTbm5uXruued06623lmdvAAAATqPMt+deffVVdevWTY0aNdLJkyc1cOBA7d+/XzVr1tR7771XET0CAAA4XJlDU3BwsHbs2KFFixZpx44dys3NVUxMjKKjo+0GhgMAAFxOynR77tSpU7ruuuu0f/9+RUdHa/LkyXr99dc1dOjQCglMhYWFGjdunEJDQ+Xp6anrrrtOEyZMkGEYZo1hGIqLi1OtWrXk6empyMhI7d+/324/x44dU3R0tHx8fOTn56eYmJgSz5TauXOn2rVrJw8PDwUHB2vy5Mnlfj4AAODSVabQVLlyZZ08ebKieilh0qRJeuONNzRr1izt2bNHkyZN0uTJkzVz5kyzZvLkyXrttdc0Z84cbd68WVWrVlVUVJRdn9HR0dq9e7eSkpK0fPlyrV+/XsOHDzfX5+TkqGvXrgoJCVFKSopeeeUVjR8/XnPnzr1o5woAAJxbmQeCjxgxQpMmTdLp06croh87mzZtUq9evdSjRw/VqVNHffv2VdeuXbVlyxZJZ64yTZ8+Xc8++6x69eqlpk2b6t1339WRI0e0bNkySdKePXuUmJiot99+W23atNHNN9+smTNn6v3339eRI0ckSQkJCSooKNC8efPUuHFj9e/fX48++qimTp1a4ecIAAAuDWUOTVu3btVHH32k2rVrKyoqSnfeeafdVJ7atm2rNWvW6Pvvv5ck7dixQxs2bFD37t0lSWlpaUpPT1dkZKS5ja+vr9q0aaPk5GRJUnJysvz8/NSqVSuzJjIyUi4uLtq8ebNZ0759e7tnT0VFRWnfvn06fvx4uZ4TAAC4NJV5ILifn5/69OlTEb2U8PTTTysnJ0cNGjRQpUqVVFhYqP/7v/9TdHS0JCk9PV2SFBAQYLddQECAuS49PV3+/v52611dXVW9enW7mtDQ0BL7KF5XrVq1Er3l5+crPz/fnM/JybmQUwUAAE6uzKFp/vz5FdFHqT744AMlJCRo4cKFaty4sVJTUzVq1CgFBQVp8ODBF62P0kycOFHPP/+8Q3sAAAAXT5lvz11MTzzxhJ5++mn1799fYWFhuvfeezV69GhNnDhRkhQYGChJysjIsNsuIyPDXBcYGKjMzEy79adPn9axY8fsakrbx9nH+KuxY8cqOzvbnA4fPnyBZwsAAJxZma80SdKSJUv0wQcf6NChQyooKLBb980335RLY5J04sQJubjY57pKlSqpqKhIkhQaGqrAwECtWbNGzZs3l3TmNtnmzZv10EMPSZIiIiKUlZWllJQUhYeHS5LWrl2roqIitWnTxqz517/+pVOnTqly5cqSpKSkJNWvX7/UW3OS5O7uLnd393I7VwAA4NzKfKXptdde05AhQxQQEKDt27erdevWqlGjhn788UdzgHZ56dmzp/7v//5PK1as0MGDB7V06VJNnTpVd9xxh6QzX+EyatQovfjii/rkk0/07bffatCgQQoKClLv3r0lSQ0bNlS3bt00bNgwbdmyRRs3blRsbKz69++voKAgSdLAgQPl5uammJgY7d69W4sWLdKMGTM0ZsyYcj0fAABw6SrzlabXX39dc+fO1YABAxQfH68nn3xS1157reLi4nTs2LFybW7mzJkaN26cHn74YWVmZiooKEgPPPCA4uLizJonn3xSeXl5Gj58uLKysnTzzTcrMTFRHh4eZk1CQoJiY2PVuXNnubi4qE+fPnrttdfM9b6+vlq9erVGjBih8PBw1axZU3FxcXbPcgIAAFc2m3H247UtqFKlivbs2aOQkBD5+/srKSlJzZo10/79+3XjjTfq6NGjFdWrU8vJyZGvr6+ys7Pl4+Pj6HZwEXQbt8jRLeC/Eif0c3QL5WJbq9aObgH/1WrbFke3gIukLH+/y3x7LjAw0LyiVLt2bX399deSzjwzqYz5CwAA4JJR5tDUqVMnffLJJ5KkIUOGaPTo0erSpYv69etnjjUCAAC43JR5TNPcuXPNT6+NGDFCNWrU0KZNm3T77bfrgQceKPcGAQAAnIHl0FS7dm1t375dNWrUkIuLi2bNmqVBgwapf//+6t+/f0X2CAAA4HCWb8/9/PPPKiwsNOefeeYZ/f777xXSFAAAgLP5x08EZ9A3AAC4kjj116gAAAA4izINBH/77bfl5eUl6cz3t8XHx6tmzZp2NY8++mj5dQcAAOAkyjQQ/K233jLnAwMD9e9//9uuxmazEZoAAMBlyXJoOnjwYAW2AQAA4NwY0wQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWlPkLe4tlZmYqMzPT/PLeYk2bNr3gpgAAAJxNmUNTSkqKBg8erD179phfpWKz2WQYhmw2m9330wEAAFwuyhya7r//ftWrV0/vvPOOAgICZLPZKqIvAAAAp1Lm0PTjjz/qww8/VN26dSuiHwAAAKdU5oHgnTt31o4dOyqiFwAAAKdV5itNb7/9tgYPHqxdu3apSZMmqly5st3622+/vdyaAwAAcBZlDk3JycnauHGjVq5cWWIdA8EBAMDlqsy35x555BHdc889+vXXX1VUVGQ3EZgAAMDlqsyh6ejRoxo9erQCAgIqoh8AAACnVObQdOedd+qLL76oiF4AAACcVpnHNNWrV09jx47Vhg0bFBYWVmIg+KOPPlpuzQEAADiLf/TpOS8vL3355Zf68ssv7dbZbDZCEwAAuCyVOTSlpaVVRB8AAABOrcxjmgAAAK5E/+i7585n3rx5/7gZAAAAZ1Xm0HT8+HG7+VOnTmnXrl3KyspSp06dyq0xAAAAZ1Lm0LR06dISy4qKivTQQw/puuuuK5emAAAAnE25jGlycXHRmDFjNG3atPLYHQAAgNMpt4HgBw4c0OnTp8trdwAAAE6lzLfnxowZYzdvGIZ+/fVXrVixQoMHDy63xgAAAJxJmUPT9u3b7eZdXFx01VVXacqUKX/7yToAAIBLVZlDE987BwAArkRlHtP0559/6sSJE+b8Tz/9pOnTp2v16tXl2hgAAIAzKXNo6tWrl959911JUlZWllq3bq0pU6aoV69eeuONN8q9QQAAAGdQ5tD0zTffqF27dpKkJUuWKDAwUD/99JPeffddvfbaa+XeIAAAKBvDMJSbm2tOhmE4uqXLQplD04kTJ+Tt7S1JWr16te688065uLjoxhtv1E8//VTuDf7yyy+65557VKNGDXl6eiosLEzbtm0z1xuGobi4ONWqVUuenp6KjIzU/v377fZx7NgxRUdHy8fHR35+foqJiVFubq5dzc6dO9WuXTt5eHgoODhYkydPLvdzAQDgYsjLy1OvXr3MKS8vz9EtXRbKHJrq1q2rZcuW6fDhw1q1apW6du0qScrMzJSPj0+5Nnf8+HHddNNNqly5slauXKnvvvtOU6ZMUbVq1cyayZMn67XXXtOcOXO0efNmVa1aVVFRUTp58qRZEx0drd27dyspKUnLly/X+vXrNXz4cHN9Tk6OunbtqpCQEKWkpOiVV17R+PHjNXfu3HI9HwAAcOkq86fn4uLiNHDgQI0ePVqdO3dWRESEpDNXnVq0aFGuzU2aNEnBwcGaP3++uSw0NNT82TAMTZ8+Xc8++6x69eolSXr33XcVEBCgZcuWqX///tqzZ48SExO1detWtWrVSpI0c+ZM3XrrrXr11VcVFBSkhIQEFRQUaN68eXJzc1Pjxo2VmpqqqVOn2oUrAABw5Srzlaa+ffvq0KFD2rZtmxITE83lnTt3LvevUfnkk0/UqlUr3XXXXfL391eLFi301ltvmevT0tKUnp6uyMhIc5mvr6/atGmj5ORkSVJycrL8/PzMwCRJkZGRcnFx0ebNm82a9u3by83NzayJiorSvn37SnxBcbH8/Hzl5OTYTQAA4PL1j75GJTAwUC1atJCLy/82b926tRo0aFBujUnSjz/+qDfeeEPXX3+9Vq1apYceekiPPvqoFixYIElKT0+XJAUEBNhtFxAQYK5LT0+Xv7+/3XpXV1dVr17drqa0fZx9jL+aOHGifH19zSk4OPgCzxYAADizcvvuuYpQVFSkli1b6qWXXlKLFi00fPhwDRs2THPmzHF0axo7dqyys7PN6fDhw45uCQAAVCCnDk21atVSo0aN7JY1bNhQhw4dknTmipckZWRk2NVkZGSY6wIDA5WZmWm3/vTp0zp27JhdTWn7OPsYf+Xu7i4fHx+7CQAAXL6cOjTddNNN2rdvn92y77//XiEhIZLODAoPDAzUmjVrzPU5OTnavHmzOUA9IiJCWVlZSklJMWvWrl2roqIitWnTxqxZv369Tp06ZdYkJSWpfv36dp/UAwAAVy6nDk2jR4/W119/rZdeekk//PCDFi5cqLlz52rEiBGSJJvNplGjRunFF1/UJ598om+//VaDBg1SUFCQevfuLenMlalu3bpp2LBh2rJlizZu3KjY2Fj1799fQUFBkqSBAwfKzc1NMTEx2r17txYtWqQZM2ZozJgxjjp1AADgZMr8yIGL6YYbbtDSpUs1duxYvfDCCwoNDdX06dMVHR1t1jz55JPKy8vT8OHDlZWVpZtvvlmJiYny8PAwaxISEhQbG6vOnTvLxcVFffr0sXt6ua+vr1avXq0RI0YoPDxcNWvWVFxcHI8bAAAAJpvBs9XLRU5Ojnx9fZWdnc34pitEt3GLHN0C/itxQj9Ht1AutrVq7egW8F+ttm1xdAsXJDc313x+oSR9/PHH8vLycmBHzqssf7+d+vYcAACAsyA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABa4OroBAACcTbdxixzdwgUxTufbzff5v49kc3V3UDcXJnFCP0e3YOJKEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALDgkgpNL7/8smw2m0aNGmUuO3nypEaMGKEaNWrIy8tLffr0UUZGht12hw4dUo8ePVSlShX5+/vriSee0OnTp+1q1q1bp5YtW8rd3V1169ZVfHz8RTgjAABwqbhkQtPWrVv15ptvqmnTpnbLR48erU8//VSLFy/Wl19+qSNHjujOO+801xcWFqpHjx4qKCjQpk2btGDBAsXHxysuLs6sSUtLU48ePdSxY0elpqZq1KhRGjp0qFatWnXRzg8AADi3SyI05ebmKjo6Wm+99ZaqVatmLs/OztY777yjqVOnqlOnTgoPD9f8+fO1adMmff3115Kk1atX67vvvtN//vMfNW/eXN27d9eECRM0e/ZsFRQUSJLmzJmj0NBQTZkyRQ0bNlRsbKz69u2radOmOeR8AQCA87kkQtOIESPUo0cPRUZG2i1PSUnRqVOn7JY3aNBAtWvXVnJysiQpOTlZYWFhCggIMGuioqKUk5Oj3bt3mzV/3XdUVJS5DwAAAKf/GpX3339f33zzjbZu3VpiXXp6utzc3OTn52e3PCAgQOnp6WbN2YGpeH3xuvPV5OTk6M8//5Snp2eJY+fn5ys//3+Pqc/JySn7yQEAgEuGU19pOnz4sEaOHKmEhAR5eHg4uh07EydOlK+vrzkFBwc7uiUAAFCBnDo0paSkKDMzUy1btpSrq6tcXV315Zdf6rXXXpOrq6sCAgJUUFCgrKwsu+0yMjIUGBgoSQoMDCzxabri+b+r8fHxKfUqkySNHTtW2dnZ5nT48OHyOGUAAOCknDo0de7cWd9++61SU1PNqVWrVoqOjjZ/rly5stasWWNus2/fPh06dEgRERGSpIiICH377bfKzMw0a5KSkuTj46NGjRqZNWfvo7imeB+lcXd3l4+Pj90EAAAuX049psnb21tNmjSxW1a1alXVqFHDXB4TE6MxY8aoevXq8vHx0SOPPKKIiAjdeOONkqSuXbuqUaNGuvfeezV58mSlp6fr2Wef1YgRI+Tu7i5JevDBBzVr1iw9+eSTuv/++7V27Vp98MEHWrFixcU9YQAA4LScOjRZMW3aNLm4uKhPnz7Kz89XVFSUXn/9dXN9pUqVtHz5cj300EOKiIhQ1apVNXjwYL3wwgtmTWhoqFasWKHRo0drxowZuuaaa/T2228rKirKEacEAACc0CUXmtatW2c37+HhodmzZ2v27Nnn3CYkJESfffbZefd7yy23aPv27eXRIgAAuAw59ZgmAAAAZ0FoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWuDq6AQAAUM4qucm19WC7eVw4QhMAAJcZm80mubo7uo3LDrfnAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWuDq6AVxZDMNQXl6eOV+1alXZbDYHdgQAgDWEJlxUeXl56tWrlzn/8ccfy8vLy4EdAQBgDbfnAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAKnDk0TJ07UDTfcIG9vb/n7+6t3797at2+fXc3Jkyc1YsQI1ahRQ15eXurTp48yMjLsag4dOqQePXqoSpUq8vf31xNPPKHTp0/b1axbt04tW7aUu7u76tatq/j4+Io+PQAAcAlx6tD05ZdfasSIEfr666+VlJSkU6dOqWvXrnbP+Rk9erQ+/fRTLV68WF9++aWOHDmiO++801xfWFioHj16qKCgQJs2bdKCBQsUHx+vuLg4syYtLU09evRQx44dlZqaqlGjRmno0KFatWrVRT1fAADgvJz6OU2JiYl28/Hx8fL391dKSorat2+v7OxsvfPOO1q4cKE6deokSZo/f74aNmyor7/+WjfeeKNWr16t7777Tp9//rkCAgLUvHlzTZgwQU899ZTGjx8vNzc3zZkzR6GhoZoyZYokqWHDhtqwYYOmTZumqKioi37eAADA+Tj1laa/ys7OliRVr15dkpSSkqJTp04pMjLSrGnQoIFq166t5ORkSVJycrLCwsIUEBBg1kRFRSknJ0e7d+82a87eR3FN8T5Kk5+fr5ycHLsJAABcvi6Z0FRUVKRRo0bppptuUpMmTSRJ6enpcnNzk5+fn11tQECA0tPTzZqzA1Px+uJ156vJycnRn3/+WWo/EydOlK+vrzkFBwdf8DkCAADndcmEphEjRmjXrl16//33Hd2KJGns2LHKzs42p8OHDzu6JQAAUIGcekxTsdjYWC1fvlzr16/XNddcYy4PDAxUQUGBsrKy7K42ZWRkKDAw0KzZsmWL3f6KP113ds1fP3GXkZEhHx8feXp6ltqTu7u73N3dL/jcAADApcGprzQZhqHY2FgtXbpUa9euVWhoqN368PBwVa5cWWvWrDGX7du3T4cOHVJERIQkKSIiQt9++60yMzPNmqSkJPn4+KhRo0Zmzdn7KK4p3gcAAIBTX2kaMWKEFi5cqI8//lje3t7mGCRfX195enrK19dXMTExGjNmjKpXry4fHx898sgjioiI0I033ihJ6tq1qxo1aqR7771XkydPVnp6up599lmNGDHCvFL04IMPatasWXryySd1//33a+3atfrggw+0YsUKh537uWxr1drRLVyQPyXJ19uc335LJ5V+Le8S0P0xR3cAALiInPpK0xtvvKHs7GzdcsstqlWrljktWrTIrJk2bZpuu+029enTR+3bt1dgYKA++ugjc32lSpW0fPlyVapUSREREbrnnns0aNAgvfDCC2ZNaGioVqxYoaSkJDVr1kxTpkzR22+/zeMGAACAyamvNBmG8bc1Hh4emj17tmbPnn3OmpCQEH322Wfn3c8tt9yi7du3l7lHAABwZXDqK00AAADOgtAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWODUD7fE5cdD0vjsP+zmAQC4FBCacFHZpEv3u+YAAFc0bs8BAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYSmv5g9e7bq1KkjDw8PtWnTRlu2bHF0SwAAwAkQms6yaNEijRkzRs8995y++eYbNWvWTFFRUcrMzHR0awAAwMEITWeZOnWqhg0bpiFDhqhRo0aaM2eOqlSponnz5jm6NQAA4GCEpv8qKChQSkqKIiMjzWUuLi6KjIxUcnKyAzsDAADOwNXRDTiL33//XYWFhQoICLBbHhAQoL1795aoz8/PV35+vjmfnZ0tScrJyanQPnMLCyt0/7DudP4JR7eA/6ro993FwvvbefD+dh4V/f4u3r9hGH9bS2j6hyZOnKjnn3++xPLg4GAHdAOHSL3f0R3gv3xf4XeBcsb722lcrPf3H3/8IV9f3/PWEJr+q2bNmqpUqZIyMjLslmdkZCgwMLBE/dixYzVmzBhzvqioSMeOHVONGjVks9kqvF84Vk5OjoKDg3X48GH5+Pg4uh0A5Yj395XFMAz98ccfCgoK+ttaQtN/ubm5KTw8XGvWrFHv3r0lnQlCa9asUWxsbIl6d3d3ubu72y3z8/O7CJ3Cmfj4+PCPKnCZ4v195fi7K0zFCE1nGTNmjAYPHqxWrVqpdevWmj59uvLy8jRkyBBHtwYAAByM0HSWfv366bffflNcXJzS09PVvHlzJSYmlhgcDgAArjyEpr+IjY0t9XYccDZ3d3c999xzJW7RArj08f7GudgMK5+xAwAAuMLxcEsAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCzmH27NmqU6eOPDw81KZNG23ZsuW89YsXL1aDBg3k4eGhsLAwffbZZxepUwBlsX79evXs2VNBQUGy2WxatmzZ326zbt06tWzZUu7u7qpbt67i4+MrvE84H0ITUIpFixZpzJgxeu655/TNN9+oWbNmioqKUmZmZqn1mzZt0oABAxQTE6Pt27erd+/e6t27t3bt2nWROwfwd/Ly8tSsWTPNnj3bUn1aWpp69Oihjh07KjU1VaNGjdLQoUO1atWqCu4UzoZHDgClaNOmjW644QbNmjVL0pmv1AkODtYjjzyip59+ukR9v379lJeXp+XLl5vLbrzxRjVv3lxz5sy5aH0DKBubzaalS5eaX59VmqeeekorVqyw+5+g/v37KysrS4mJiRehSzgLrjQBf1FQUKCUlBRFRkaay1xcXBQZGank5ORSt0lOTrarl6SoqKhz1gO4dPD+RjFCE/AXv//+uwoLC0t8fU5AQIDS09NL3SY9Pb1M9QAuHed6f+fk5OjPP/90UFdwBEITAACABYQm4C9q1qypSpUqKSMjw255RkaGAgMDS90mMDCwTPUALh3nen/7+PjI09PTQV3BEQhNwF+4ubkpPDxca9asMZcVFRVpzZo1ioiIKHWbiIgIu3pJSkpKOmc9gEsH728UIzQBpRgzZozeeustLViwQHv27NFDDz2kvLw8DRkyRJI0aNAgjR071qwfOXKkEhMTNWXKFO3du1fjx4/Xtm3bFBsb66hTAHAOubm5Sk1NVWpqqqQzjxRITU3VoUOHJEljx47VoEGDzPoHH3xQP/74o5588knt3btXr7/+uj744AONHj3aEe3DkQwApZo5c6ZRu3Ztw83NzWjdurXx9ddfm+s6dOhgDB482K7+gw8+MOrVq2e4ubkZjRs3NlasWHGROwZgxRdffGFIKjEVv6cHDx5sdOjQocQ2zZs3N9zc3Ixrr73WmD9//kXvG47Hc5oAAAAs4PYcAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQDKYN26dbLZbMrKyqrQ49x3333q3bt3hR4DQNkQmgBckn777Tc99NBDql27ttzd3RUYGKioqCht3LixQo/btm1b/frrr/L19a3Q4wBwPq6ObgAA/ok+ffqooKBACxYs0LXXXquMjAytWbNGR48e/Uf7MwxDhYWFcnU9/z+Lbm5uCgwM/EfHAHBp40oTgEtOVlaWvvrqK02aNEkdO3ZUSEiIWrdurbFjx+r222/XwYMHZbPZzC9kLd7GZrNp3bp1kv53m23lypUKDw+Xu7u75s2bJ5vNpr1799odb9q0abruuuvstsvKylJOTo48PT21cuVKu/qlS5fK29tbJ06ckCQdPnxYd999t/z8/FS9enX16tVLBw8eNOsLCws1ZswY+fn5qUaNGnryySfFN1wBzofQBOCS4+XlJS8vLy1btkz5+fkXtK+nn35aL7/8svbs2aO+ffuqVatWSkhIsKtJSEjQwIEDS2zr4+Oj2267TQsXLixR37t3b1WpUkWnTp1SVFSUvL299dVXX2njxo3y8vJSt27dVFBQIEmaMmWK4uPjNW/ePG3YsEHHjh3T0qVLL+i8AJQ/QhOAS46rq6vi4+O1YMEC+fn56aabbtIzzzyjnTt3lnlfL7zwgrp06aLrrrtO1atXV3R0tN577z1z/ffff6+UlBRFR0eXun10dLSWLVtmXlXKycnRihUrzPpFixapqKhIb7/9tsLCwtSwYUPNnz9fhw4dMq96TZ8+XWPHjtWdd96phg0bas6cOYyZApwQoQnAJalPnz46cuSIPvnkE3Xr1k3r1q1Ty5YtFR8fX6b9tGrVym6+f//+OnjwoL7++mtJZ64atWzZUg0aNCh1+1tvvVWVK1fWJ598Ikn68MMP5ePjo8jISEnSjh079MMPP8jb29u8Qla9enWdPHlSBw4cUHZ2tn799Ve1adPG3Kerq2uJvgA4HqEJwCXLw8NDXbp00bhx47Rp0ybdd999eu655+TicuaftrPHBZ06darUfVStWtVuPjAwUJ06dTJvuS1cuPCcV5mkMwPD+/bta1ffr18/c0B5bm6uwsPDlZqaajd9//33pd7yA+C8CE0ALhuNGjVSXl6errrqKknSr7/+aq47e1D434mOjtaiRYuUnJysH3/8Uf379//b+sTERO3evVtr1661C1ktW7bU/v375e/vr7p169pNvr6+8vX1Va1atbR582Zzm9OnTyslJcVyvwAuDkITgEvO0aNH1alTJ/3nP//Rzp07lZaWpsWLF2vy5Mnq1auXPD09deONN5oDvL/88ks9++yzlvd/55136o8//tBDDz2kjh07Kigo6Lz17du3V2BgoKKjoxUaGmp3qy06Olo1a9ZUr1699NVXXyktLU3r1q3To48+qp9//lmSNHLkSL388statmyZ9u7dq4cffrjCH54JoOwITQAuOV5eXmrTpo2mTZum9u3bq0mTJho3bpyGDRumWbNmSZLmzZun06dPKzw8XKNGjdKLL75oef/e3t7q2bOnduzYcd5bc8VsNpsGDBhQan2VKlW0fv161a5d2xzoHRMTo5MnT8rHx0eS9Nhjj+nee+/V4MGDFRERIW9vb91xxx1leEUAXAw2g4eBAAAA/C2uNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAgv8HlAfjmzk0knYAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Гистограмма распределения количества людей по возрасту и полу."
+      ],
+      "metadata": {
+        "id": "Es1ANc9mSBBh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# creating and displaying an interactive histogram\n",
+        "fig = px.histogram(trains_test, x='Age', color='Sex', title=\"Histogram: Age Distribution\")\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "Dybxv3c-OzBQ",
+        "outputId": "e78963d0-e986-4bce-8721-13a4aabf11bf"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js\"></script>                <div id=\"62961e6b-d485-4cd8-99b3-563feb2905c3\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"62961e6b-d485-4cd8-99b3-563feb2905c3\")) {                    Plotly.newPlot(                        \"62961e6b-d485-4cd8-99b3-563feb2905c3\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"Sex=male\\u003cbr\\u003eAge=%{x}\\u003cbr\\u003ecount=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"male\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"male\",\"offsetgroup\":\"male\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[22.0,35.0,null,54.0,2.0,20.0,39.0,2.0,null,35.0,34.0,28.0,null,19.0,null,40.0,66.0,28.0,42.0,null,21.0,null,null,null,null,7.0,21.0,65.0,null,28.5,11.0,22.0,45.0,4.0,null,null,19.0,26.0,32.0,21.0,26.0,32.0,25.0,null,null,0.83,22.0,29.0,28.0,16.0,null,24.0,29.0,20.0,46.0,26.0,59.0,null,71.0,23.0,34.0,null,21.0,33.0,37.0,28.0,null,38.0,47.0,22.0,21.0,70.5,29.0,24.0,21.0,null,32.5,54.0,12.0,null,24.0,45.0,33.0,20.0,25.0,23.0,37.0,16.0,24.0,19.0,18.0,19.0,27.0,36.5,42.0,51.0,55.5,40.5,null,51.0,30.0,null,null,44.0,26.0,17.0,1.0,9.0,null,28.0,61.0,4.0,21.0,56.0,18.0,null,30.0,36.0,null,9.0,1.0,null,45.0,40.0,36.0,19.0,3.0,null,42.0,28.0,null,34.0,45.5,18.0,32.0,26.0,40.0,24.0,22.0,30.0,null,42.0,30.0,16.0,27.0,51.0,null,38.0,22.0,19.0,20.5,18.0,29.0,59.0,24.0,44.0,19.0,33.0,29.0,22.0,30.0,44.0,37.0,54.0,null,62.0,30.0,null,3.0,52.0,40.0,36.0,16.0,25.0,null,25.0,37.0,null,7.0,65.0,28.0,16.0,19.0,null,33.0,30.0,22.0,42.0,36.0,24.0,null,23.5,null,null,19.0,null,0.92,30.0,28.0,43.0,54.0,22.0,27.0,null,61.0,45.5,38.0,16.0,null,29.0,45.0,45.0,2.0,28.0,25.0,36.0,3.0,42.0,23.0,null,15.0,25.0,null,28.0,40.0,29.0,35.0,null,30.0,25.0,18.0,19.0,22.0,27.0,20.0,19.0,32.0,null,18.0,1.0,null,36.0,21.0,28.0,22.0,46.0,23.0,39.0,26.0,28.0,34.0,51.0,3.0,21.0,null,null,null,44.0,30.0,null,21.0,29.0,18.0,null,null,32.0,28.0,17.0,50.0,64.0,31.0,20.0,25.0,null,4.0,34.0,52.0,36.0,null,30.0,49.0,null,29.0,65.0,null,48.0,34.0,47.0,48.0,null,38.0,null,56.0,null,null,38.0,null,34.0,29.0,22.0,9.0,null,50.0,25.0,58.0,30.0,9.0,null,21.0,55.0,71.0,21.0,null,null,24.0,17.0,18.0,null,28.0,26.0,29.0,null,36.0,24.0,47.0,null,32.0,22.0,null,null,40.5,null,39.0,23.0,null,17.0,45.0,null,32.0,50.0,64.0,null,33.0,8.0,17.0,27.0,null,22.0,62.0,null,null,40.0,28.0,null,24.0,19.0,null,32.0,62.0,36.0,16.0,19.0,32.0,54.0,36.0,null,47.0,60.0,22.0,null,35.0,47.0,37.0,36.0,49.0,null,49.0,null,null,44.0,35.0,36.0,30.0,27.0,null,null,35.0,34.0,26.0,27.0,42.0,20.0,21.0,21.0,61.0,57.0,26.0,null,80.0,51.0,32.0,null,32.0,31.0,null,20.0,null,48.0,19.0,56.0,null,null,21.0,24.0,null,23.0,58.0,50.0,40.0,47.0,36.0,20.0,32.0,25.0,null,43.0,31.0,70.0,31.0,null,18.0,24.5,36.0,27.0,20.0,14.0,60.0,25.0,14.0,19.0,18.0,31.0,null,25.0,60.0,52.0,44.0,49.0,42.0,35.0,25.0,26.0,39.0,42.0,null,null,48.0,29.0,52.0,19.0,null,33.0,17.0,34.0,50.0,27.0,20.0,25.0,11.0,null,23.0,23.0,28.5,35.0,null,null,null,36.0,24.0,31.0,70.0,16.0,19.0,31.0,6.0,33.0,23.0,0.67,28.0,18.0,34.0,null,41.0,20.0,16.0,null,null,32.0,24.0,48.0,null,18.0,null,null,29.0,null,25.0,25.0,8.0,1.0,46.0,null,16.0,null,25.0,39.0,30.0,34.0,11.0,0.42,27.0,31.0,39.0,39.0,26.0,39.0,35.0,30.5,null,31.0,43.0,10.0,27.0,38.0,2.0,null,null,1.0,null,0.83,null,23.0,18.0,21.0,null,32.0,null,20.0,16.0,34.5,17.0,42.0,null,35.0,28.0,4.0,74.0,51.0,null,41.0,21.0,24.0,31.0,null,4.0,26.0,33.0,47.0,20.0,19.0,null,33.0,28.0,25.0,27.0,26.0,32.0,34.5,62.0,27.0,14.0,26.0,21.0,null,46.0,63.0,35.0,21.0,55.0,9.0,21.0,50.0,22.5,41.0,null,50.0,24.0,30.0,18.5,25.0,null,39.0,null,41.0,25.0,45.0,null,24.0,27.0,null,10.0,35.0,25.0,null,17.0,32.0,18.0,13.0,47.0,31.0,21.0,28.5,32.5,null,30.0,6.0,67.0,49.0,null,null,null,2.0,null,null,25.0,25.0,29.0,33.0,27.0,null,26.0,28.0,21.0,null,null,18.5,41.0,18.0,null,36.0,null,28.0,null,22.0,42.0,24.0,32.0,53.0,null,43.0,24.0,26.5,26.0,40.0,61.0,28.0,42.0,31.0,null,22.0,null,30.0,null,60.5,13.0,24.0,42.0,7.0,null,41.0,48.0,18.0,null,27.0,23.0,null,40.0,54.0,30.0,37.0,null,40.0,17.0,40.0,34.0,null,11.5,61.0,8.0,33.0,6.0,23.0,0.33,47.0,25.0,null,24.0,25.0,32.0,null,17.0,42.0,57.0,null,21.0,21.0,23.0,40.5,36.0,14.0,21.0,null,39.0,20.0,64.0,20.0,45.0,null,null,41.0,42.0,20.0,27.0,24.0,32.5,null,null,28.0,21.0,36.5,21.0,30.0,null,null,null,17.0,46.0,null,null,28.0,40.0,30.0,22.0,0.75,36.0,null,24.0,null,null,null,null,53.0,36.0,26.0,null,30.0,29.0,32.0,null,43.0,24.0,30.0,0.83,55.0,18.0,22.0,null,57.0,19.0,27.0,22.0,26.0,25.0,26.0,33.0,23.0,46.0,29.0,21.0,39.0,null,27.0,30.0,32.0,39.0,25.0,null,18.0,32.0,null,null,26.0,24.0,25.0,18.0,49.0,50.0,null,null,14.5,27.0,null,29.0,21.0,49.0,44.0,21.0,55.0,5.0,null,26.0,null,24.0,57.0,21.0,6.0,23.0,13.0,47.0,29.0,24.0,22.0,31.0,38.0,17.0,43.0,20.0,23.0,50.0,null,38.5,null,null],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"Sex=female\\u003cbr\\u003eAge=%{x}\\u003cbr\\u003ecount=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"female\",\"marker\":{\"color\":\"#EF553B\",\"pattern\":{\"shape\":\"\"}},\"name\":\"female\",\"offsetgroup\":\"female\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[38.0,26.0,35.0,27.0,14.0,4.0,58.0,14.0,55.0,31.0,null,15.0,8.0,38.0,null,null,null,18.0,14.0,40.0,27.0,3.0,19.0,null,18.0,49.0,29.0,21.0,5.0,38.0,29.0,17.0,16.0,30.0,null,17.0,33.0,23.0,34.0,28.0,21.0,null,14.5,20.0,17.0,2.0,32.5,null,47.0,29.0,19.0,null,22.0,24.0,9.0,22.0,16.0,40.0,null,45.0,1.0,50.0,null,4.0,null,32.0,19.0,44.0,58.0,null,24.0,2.0,16.0,35.0,31.0,27.0,32.0,null,35.0,5.0,null,8.0,null,null,25.0,24.0,29.0,41.0,29.0,null,30.0,35.0,50.0,null,58.0,35.0,41.0,null,63.0,45.0,35.0,22.0,26.0,19.0,24.0,2.0,50.0,null,null,null,17.0,30.0,24.0,18.0,26.0,26.0,24.0,31.0,40.0,30.0,22.0,36.0,36.0,31.0,16.0,null,null,41.0,24.0,24.0,40.0,null,22.0,38.0,null,null,45.0,60.0,null,null,24.0,3.0,null,22.0,42.0,1.0,35.0,36.0,17.0,23.0,24.0,31.0,28.0,21.0,20.0,null,33.0,null,34.0,18.0,10.0,28.0,28.0,19.0,null,42.0,14.0,21.0,24.0,45.0,28.0,13.0,5.0,null,50.0,0.75,33.0,23.0,22.0,2.0,63.0,null,35.0,54.0,25.0,21.0,null,37.0,16.0,33.0,54.0,34.0,36.0,30.0,44.0,50.0,2.0,null,30.0,7.0,30.0,22.0,36.0,9.0,11.0,19.0,22.0,48.0,39.0,36.0,null,29.0,53.0,null,34.0,39.0,null,25.0,39.0,18.0,52.0,null,null,24.0,22.0,40.0,39.0,null,24.0,26.0,4.0,21.0,9.0,28.0,41.0,24.0,2.0,0.75,23.0,18.0,null,18.0,32.0,null,40.0,18.0,43.0,null,15.0,4.0,null,18.0,18.0,45.0,22.0,24.0,38.0,27.0,6.0,30.0,null,25.0,29.0,48.0,21.0,30.0,4.0,48.0,33.0,36.0,51.0,30.5,57.0,54.0,5.0,43.0,13.0,17.0,18.0,null,49.0,31.0,30.0,31.0,18.0,33.0,6.0,23.0,52.0,27.0,62.0,15.0,39.0,30.0,null,9.0,16.0,44.0,18.0,45.0,24.0,48.0,null,42.0,27.0,47.0,28.0,15.0,56.0,25.0,22.0,39.0,19.0,null,47.0,22.0,30.0,18.0,23.0,47.0,24.0,27.0,45.0,null,48.0,22.0,33.0,null,null,21.0,30.0,45.0,60.0,36.0,20.0,28.0,36.0,22.0,null,18.0,60.0,24.0,29.0,35.0,55.0,24.0,27.0,18.0,null,22.0,27.0,76.0,20.0,43.0,16.0,null,36.0,18.5,63.0,1.0,29.0,12.0,35.0,17.0,null,null,23.0,10.0,33.0,23.0,36.0,29.0,23.0,26.0,null,26.0,26.0,null,22.0,15.0,20.0,36.0,64.0,18.0,27.0,21.0,null,18.0,null,null,8.0,35.0,33.0,60.0,38.0,null,50.0,30.0,22.0,53.0,null,null,21.0,18.0,48.0,55.0,45.0,22.0,29.0,null,0.92,19.0,29.0,1.0,null,26.0,null,20.0,23.0,null,9.0,2.0,30.0,1.0,null,64.0,45.0,37.0,55.0,17.0,39.0,12.0,48.0,19.0,58.0,16.0,38.0,31.0,45.0,0.17,59.0,30.0,24.0,31.0,25.0,null,22.0,45.0,31.0,54.0,45.0,22.0,null,19.0,24.0,51.0,18.0,48.0,30.0,22.0,null,3.0,null,37.0,28.0,39.0],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Age\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"count\"}},\"legend\":{\"title\":{\"text\":\"Sex\"},\"tracegroupgap\":0},\"title\":{\"text\":\"Histogram: Age Distribution\"},\"barmode\":\"relative\"},                        {\"responsive\": true}                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('62961e6b-d485-4cd8-99b3-563feb2905c3');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Горизонтальная диаграмма количества пассажиров по классу и полу."
+      ],
+      "metadata": {
+        "id": "o0chtYnmgsPB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# creating and displaying horizontal bar chart\n",
+        "plt.figure(figsize=(8, 4))\n",
+        "fig = sns.countplot(data=trains_test, y='Sex', hue='Pclass', palette='Set2')\n",
+        "plt.title(\"Horizontal bar chart: count persons by Sex and Pclass\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "id": "k5dTiTUtRMAd",
+        "outputId": "c818406d-5cfc-4046-bf0e-3fc96f585575"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Круговая диаграмма процента выживших."
+      ],
+      "metadata": {
+        "id": "3zbWOhWigkcS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# set diagram size\n",
+        "plt.figure(figsize=(7, 7))\n",
+        "\n",
+        "# creating and displaying pie chart\n",
+        "plt.pie(trains_test['Survived'].value_counts(), labels=['Not Survived', 'Survived'], autopct='%1.1f%%')\n",
+        "plt.title('Pie Chart: Survival Distribution')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 598
+        },
+        "id": "bpFvbGhZUWx6",
+        "outputId": "4f5c11a9-7a41-431f-f38e-0e754901431c"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 700x700 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Коробчатая диаграмма по возрасту и классам."
+      ],
+      "metadata": {
+        "id": "_zbNMc2bghdM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# creating and displaying an interactive box chart\n",
+        "fig = px.box(trains_test, x='Pclass', y='Age', title=\"Box chart: Age Distribution by Pclass\")\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "ir6nVJreXguF",
+        "outputId": "8665cce6-7607-41e7-b099-40b384310252"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js\"></script>                <div id=\"65d53898-ad37-4acb-a2da-faa0f02419d8\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"65d53898-ad37-4acb-a2da-faa0f02419d8\")) {                    Plotly.newPlot(                        \"65d53898-ad37-4acb-a2da-faa0f02419d8\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Pclass=%{x}\\u003cbr\\u003eAge=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\"},\"name\":\"\",\"notched\":false,\"offsetgroup\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[3,1,3,1,3,3,1,3,3,2,3,1,3,3,3,2,3,2,3,3,2,2,3,1,3,3,3,1,3,3,1,1,3,2,1,1,3,3,3,3,3,2,3,2,3,3,3,3,3,3,3,3,1,2,1,1,2,3,2,3,3,1,1,3,1,3,2,3,3,3,2,3,2,3,3,3,3,3,2,3,3,3,3,1,2,3,3,3,1,3,3,3,1,3,3,3,1,1,2,2,3,3,1,3,3,3,3,3,3,3,1,3,3,3,3,3,3,2,1,3,2,3,2,2,1,3,3,3,3,3,3,3,3,2,2,2,1,1,3,1,3,3,3,3,2,2,3,3,2,2,2,1,3,3,3,1,3,3,3,3,3,2,3,3,3,3,1,3,1,3,1,3,3,3,1,3,3,1,2,3,3,2,3,2,3,1,3,1,3,3,2,2,3,2,1,1,3,3,3,2,3,3,3,3,3,3,3,3,3,1,3,2,3,2,3,1,3,2,1,2,3,2,3,3,1,3,2,3,2,3,1,3,2,3,2,3,2,2,2,2,3,3,2,3,3,1,3,2,1,2,3,3,1,3,3,3,1,1,1,2,3,3,1,1,3,2,3,3,1,1,1,3,2,1,3,1,3,2,3,3,3,3,3,3,1,3,3,3,2,3,1,1,2,3,3,1,3,1,1,1,3,3,3,2,3,1,1,1,2,1,1,1,2,3,2,3,2,2,1,1,3,3,2,2,3,1,3,2,3,1,3,1,1,3,1,3,1,1,3,1,2,1,2,2,2,2,2,3,3,3,3,1,3,3,3,3,1,2,3,3,3,2,3,3,3,3,1,3,3,1,1,3,3,1,3,1,3,1,3,3,1,3,3,1,3,2,3,2,3,2,1,3,3,1,3,3,3,2,2,2,3,3,3,3,3,2,3,2,3,3,3,3,1,2,3,3,2,2,2,3,3,3,3,3,3,3,2,2,3,3,1,3,2,3,1,1,3,2,1,2,2,3,3,2,3,1,2,1,3,1,2,3,1,1,3,3,1,1,2,3,1,3,1,2,3,3,2,1,3,3,3,3,2,2,3,1,2,3,3,3,3,2,3,3,1,3,1,1,3,3,3,3,1,1,3,3,1,3,1,3,3,3,3,3,1,1,2,1,3,3,3,3,1,1,3,1,2,3,2,3,1,3,3,1,3,3,2,1,3,2,2,3,3,3,3,2,1,1,3,1,1,3,3,2,1,1,2,2,3,2,1,2,3,3,3,1,1,1,1,3,3,3,2,3,3,3,3,3,3,3,2,1,1,3,3,3,2,1,3,3,2,1,2,1,3,1,2,1,3,3,3,1,3,3,2,3,2,3,3,1,2,3,1,3,1,3,3,1,2,1,3,3,3,3,3,2,3,3,2,2,3,1,3,3,3,1,2,1,3,3,1,3,1,1,3,2,3,2,3,3,3,1,3,3,3,1,3,1,3,3,3,2,3,3,3,2,3,3,2,1,1,3,1,3,3,2,2,3,3,1,2,1,2,2,2,3,3,3,3,1,3,1,3,3,2,2,3,3,3,1,1,3,3,3,1,2,3,3,1,3,1,1,3,3,3,2,2,1,1,3,1,1,1,3,2,3,1,2,3,3,2,3,2,2,1,3,2,3,2,3,1,3,2,2,2,3,3,1,3,3,1,1,1,3,3,1,3,2,1,3,2,3,3,3,2,2,3,2,3,1,3,3,3,1,3,1,1,3,3,3,3,3,2,3,2,3,3,3,3,1,3,1,1,3,3,3,3,3,3,1,3,2,3,1,3,2,1,3,3,3,2,2,1,3,3,3,1,3,2,1,3,3,2,3,3,1,3,2,3,3,1,3,1,3,3,3,3,2,3,1,3,2,3,3,3,1,3,3,3,1,3,2,1,3,3,3,3,3,2,1,3,3,3,1,2,3,1,1,3,3,3,2,1,3,2,2,2,1,3,3,3,1,1,3,2,3,3,3,3,1,2,3,3,2,3,3,2,1,3,1,3,3,3,2,3,3,3,3,2,3,3,3,1,1,2,1,2,2,3,3,3,1,3,1,1,1,3,1,3,1,3,2,2,3,3,1,3,3,3,3,3,3,1,3,2,1,3,1,3,1,3,1,2,2,1,2,3,3,3,3,1,3,2,3,3,1,2,3,1,1,1,3,3,3,1,1,1,3,1,2,3,3,1,1,3,2,3,3,3,3,2,3,3,1,3,1,3,1,3,3,3,1,2,3,3,3,3,3,3,3,2,2,3,1,3,1,3,3,3,1,2,2,3,1,3,3,3,3,3,2,3,3,1,3,3,3,3,3,2,3,3,3,1,1,2,1,3,1,3,1,2,1,3,3,3,3,3,1,3,1,3,3,3,2,3,2,3,1,3,1,3,3,3,3,3,3,2,2,1,2,1,2,1,1,3,1,2,2,3,3,2,2,1,3,2,2,3,1,3,2,3,3,3,1,2,2,1,3,2,1,3,3,3,2,2,3,1,3,1,1,3,2,3,2,3,1,3,3,3,3,2,2,1,3,3,1,3,1,3,2,1,1,2,1,3,3,1,2,2,2,3,2,3,1,3,3,3,3,3,2,3,3,3,2,3,2,3,1,3,3,3,1,3,1,3,3,2,2,2,2,2,3,3,3,3,3,3,3,1,3,3,1,3,3,1,3,3,2,3,1,3,3,2,2,3,3,1,1,3,1,3,3,3,3,3,1,3,1,2,3,2,3,3,2,1,1,3,2,1,2,2,2,1,3,3,3,1,2,3,2,3,2,3,3,1,3,3,2,3,2,2,1,2,2,2,3,1,1,3,3,3,3,2,2,3,1,3,3,3,1,2,2,1,1,2,1,1,3,2,1,3,3,3,3,3,2,2,3,2,3,3,1,1,3,2,3,1,3,1,3,3,1,2,1,1,1,2,2,1,3,3,3,1,3,3,1,3,3,3],\"x0\":\" \",\"xaxis\":\"x\",\"y\":[22.0,38.0,26.0,35.0,35.0,null,54.0,2.0,27.0,14.0,4.0,58.0,20.0,39.0,14.0,55.0,2.0,null,31.0,null,35.0,34.0,15.0,28.0,8.0,38.0,null,19.0,null,null,40.0,null,null,66.0,28.0,42.0,null,21.0,18.0,14.0,40.0,27.0,null,3.0,19.0,null,null,null,null,18.0,7.0,21.0,49.0,29.0,65.0,null,21.0,28.5,5.0,11.0,22.0,38.0,45.0,4.0,null,null,29.0,19.0,17.0,26.0,32.0,16.0,21.0,26.0,32.0,25.0,null,null,0.83,30.0,22.0,29.0,null,28.0,17.0,33.0,16.0,null,23.0,24.0,29.0,20.0,46.0,26.0,59.0,null,71.0,23.0,34.0,34.0,28.0,null,21.0,33.0,37.0,28.0,21.0,null,38.0,null,47.0,14.5,22.0,20.0,17.0,21.0,70.5,29.0,24.0,2.0,21.0,null,32.5,32.5,54.0,12.0,null,24.0,null,45.0,33.0,20.0,47.0,29.0,25.0,23.0,19.0,37.0,16.0,24.0,null,22.0,24.0,19.0,18.0,19.0,27.0,9.0,36.5,42.0,51.0,22.0,55.5,40.5,null,51.0,16.0,30.0,null,null,44.0,40.0,26.0,17.0,1.0,9.0,null,45.0,null,28.0,61.0,4.0,1.0,21.0,56.0,18.0,null,50.0,30.0,36.0,null,null,9.0,1.0,4.0,null,null,45.0,40.0,36.0,32.0,19.0,19.0,3.0,44.0,58.0,null,42.0,null,24.0,28.0,null,34.0,45.5,18.0,2.0,32.0,26.0,16.0,40.0,24.0,35.0,22.0,30.0,null,31.0,27.0,42.0,32.0,30.0,16.0,27.0,51.0,null,38.0,22.0,19.0,20.5,18.0,null,35.0,29.0,59.0,5.0,24.0,null,44.0,8.0,19.0,33.0,null,null,29.0,22.0,30.0,44.0,25.0,24.0,37.0,54.0,null,29.0,62.0,30.0,41.0,29.0,null,30.0,35.0,50.0,null,3.0,52.0,40.0,null,36.0,16.0,25.0,58.0,35.0,null,25.0,41.0,37.0,null,63.0,45.0,null,7.0,35.0,65.0,28.0,16.0,19.0,null,33.0,30.0,22.0,42.0,22.0,26.0,19.0,36.0,24.0,24.0,null,23.5,2.0,null,50.0,null,null,19.0,null,null,0.92,null,17.0,30.0,30.0,24.0,18.0,26.0,28.0,43.0,26.0,24.0,54.0,31.0,40.0,22.0,27.0,30.0,22.0,null,36.0,61.0,36.0,31.0,16.0,null,45.5,38.0,16.0,null,null,29.0,41.0,45.0,45.0,2.0,24.0,28.0,25.0,36.0,24.0,40.0,null,3.0,42.0,23.0,null,15.0,25.0,null,28.0,22.0,38.0,null,null,40.0,29.0,45.0,35.0,null,30.0,60.0,null,null,24.0,25.0,18.0,19.0,22.0,3.0,null,22.0,27.0,20.0,19.0,42.0,1.0,32.0,35.0,null,18.0,1.0,36.0,null,17.0,36.0,21.0,28.0,23.0,24.0,22.0,31.0,46.0,23.0,28.0,39.0,26.0,21.0,28.0,20.0,34.0,51.0,3.0,21.0,null,null,null,33.0,null,44.0,null,34.0,18.0,30.0,10.0,null,21.0,29.0,28.0,18.0,null,28.0,19.0,null,32.0,28.0,null,42.0,17.0,50.0,14.0,21.0,24.0,64.0,31.0,45.0,20.0,25.0,28.0,null,4.0,13.0,34.0,5.0,52.0,36.0,null,30.0,49.0,null,29.0,65.0,null,50.0,null,48.0,34.0,47.0,48.0,null,38.0,null,56.0,null,0.75,null,38.0,33.0,23.0,22.0,null,34.0,29.0,22.0,2.0,9.0,null,50.0,63.0,25.0,null,35.0,58.0,30.0,9.0,null,21.0,55.0,71.0,21.0,null,54.0,null,25.0,24.0,17.0,21.0,null,37.0,16.0,18.0,33.0,null,28.0,26.0,29.0,null,36.0,54.0,24.0,47.0,34.0,null,36.0,32.0,30.0,22.0,null,44.0,null,40.5,50.0,null,39.0,23.0,2.0,null,17.0,null,30.0,7.0,45.0,30.0,null,22.0,36.0,9.0,11.0,32.0,50.0,64.0,19.0,null,33.0,8.0,17.0,27.0,null,22.0,22.0,62.0,48.0,null,39.0,36.0,null,40.0,28.0,null,null,24.0,19.0,29.0,null,32.0,62.0,53.0,36.0,null,16.0,19.0,34.0,39.0,null,32.0,25.0,39.0,54.0,36.0,null,18.0,47.0,60.0,22.0,null,35.0,52.0,47.0,null,37.0,36.0,null,49.0,null,49.0,24.0,null,null,44.0,35.0,36.0,30.0,27.0,22.0,40.0,39.0,null,null,null,35.0,24.0,34.0,26.0,4.0,26.0,27.0,42.0,20.0,21.0,21.0,61.0,57.0,21.0,26.0,null,80.0,51.0,32.0,null,9.0,28.0,32.0,31.0,41.0,null,20.0,24.0,2.0,null,0.75,48.0,19.0,56.0,null,23.0,null,18.0,21.0,null,18.0,24.0,null,32.0,23.0,58.0,50.0,40.0,47.0,36.0,20.0,32.0,25.0,null,43.0,null,40.0,31.0,70.0,31.0,null,18.0,24.5,18.0,43.0,36.0,null,27.0,20.0,14.0,60.0,25.0,14.0,19.0,18.0,15.0,31.0,4.0,null,25.0,60.0,52.0,44.0,null,49.0,42.0,18.0,35.0,18.0,25.0,26.0,39.0,45.0,42.0,22.0,null,24.0,null,48.0,29.0,52.0,19.0,38.0,27.0,null,33.0,6.0,17.0,34.0,50.0,27.0,20.0,30.0,null,25.0,25.0,29.0,11.0,null,23.0,23.0,28.5,48.0,35.0,null,null,null,36.0,21.0,24.0,31.0,70.0,16.0,30.0,19.0,31.0,4.0,6.0,33.0,23.0,48.0,0.67,28.0,18.0,34.0,33.0,null,41.0,20.0,36.0,16.0,51.0,null,30.5,null,32.0,24.0,48.0,57.0,null,54.0,18.0,null,5.0,null,43.0,13.0,17.0,29.0,null,25.0,25.0,18.0,8.0,1.0,46.0,null,16.0,null,null,25.0,39.0,49.0,31.0,30.0,30.0,34.0,31.0,11.0,0.42,27.0,31.0,39.0,18.0,39.0,33.0,26.0,39.0,35.0,6.0,30.5,null,23.0,31.0,43.0,10.0,52.0,27.0,38.0,27.0,2.0,null,null,1.0,null,62.0,15.0,0.83,null,23.0,18.0,39.0,21.0,null,32.0,null,20.0,16.0,30.0,34.5,17.0,42.0,null,35.0,28.0,null,4.0,74.0,9.0,16.0,44.0,18.0,45.0,51.0,24.0,null,41.0,21.0,48.0,null,24.0,42.0,27.0,31.0,null,4.0,26.0,47.0,33.0,47.0,28.0,15.0,20.0,19.0,null,56.0,25.0,33.0,22.0,28.0,25.0,39.0,27.0,19.0,null,26.0,32.0,34.5,47.0,62.0,27.0,22.0,14.0,30.0,26.0,18.0,21.0,null,46.0,23.0,63.0,47.0,24.0,35.0,21.0,27.0,45.0,55.0,9.0,null,21.0,48.0,50.0,22.0,22.5,41.0,null,50.0,24.0,33.0,null,30.0,18.5,null,21.0,25.0,null,39.0,null,41.0,30.0,45.0,25.0,45.0,null,60.0,36.0,24.0,27.0,20.0,28.0,null,10.0,35.0,25.0,null,36.0,17.0,32.0,18.0,22.0,13.0,null,18.0,47.0,31.0,60.0,24.0,21.0,29.0,28.5,35.0,32.5,null,55.0,30.0,24.0,6.0,67.0,49.0,null,null,null,27.0,18.0,null,2.0,22.0,null,27.0,null,25.0,25.0,76.0,29.0,20.0,33.0,43.0,27.0,null,26.0,16.0,28.0,21.0,null,null,18.5,41.0,null,36.0,18.5,63.0,18.0,null,1.0,36.0,29.0,12.0,null,35.0,28.0,null,17.0,22.0,null,42.0,24.0,32.0,53.0,null,null,43.0,24.0,26.5,26.0,23.0,40.0,10.0,33.0,61.0,28.0,42.0,31.0,null,22.0,null,30.0,23.0,null,60.5,36.0,13.0,24.0,29.0,23.0,42.0,26.0,null,7.0,26.0,null,41.0,26.0,48.0,18.0,null,22.0,null,27.0,23.0,null,40.0,15.0,20.0,54.0,36.0,64.0,30.0,37.0,18.0,null,27.0,40.0,21.0,17.0,null,40.0,34.0,null,11.5,61.0,8.0,33.0,6.0,18.0,23.0,null,null,0.33,47.0,8.0,25.0,null,35.0,24.0,33.0,25.0,32.0,null,17.0,60.0,38.0,42.0,null,57.0,50.0,null,30.0,21.0,22.0,21.0,53.0,null,23.0,null,40.5,36.0,14.0,21.0,21.0,null,39.0,20.0,64.0,20.0,18.0,48.0,55.0,45.0,45.0,null,null,41.0,22.0,42.0,29.0,null,0.92,20.0,27.0,24.0,32.5,null,null,28.0,19.0,21.0,36.5,21.0,29.0,1.0,30.0,null,null,null,null,17.0,46.0,null,26.0,null,null,20.0,28.0,40.0,30.0,22.0,23.0,0.75,null,9.0,2.0,36.0,null,24.0,null,null,null,30.0,null,53.0,36.0,26.0,1.0,null,30.0,29.0,32.0,null,43.0,24.0,null,64.0,30.0,0.83,55.0,45.0,18.0,22.0,null,37.0,55.0,17.0,57.0,19.0,27.0,22.0,26.0,25.0,26.0,33.0,39.0,23.0,12.0,46.0,29.0,21.0,48.0,39.0,null,19.0,27.0,30.0,32.0,39.0,25.0,null,18.0,32.0,null,58.0,null,16.0,26.0,38.0,24.0,31.0,45.0,25.0,18.0,49.0,0.17,50.0,59.0,null,null,30.0,14.5,24.0,31.0,27.0,25.0,null,null,22.0,45.0,29.0,21.0,31.0,49.0,44.0,54.0,45.0,22.0,21.0,55.0,5.0,null,26.0,null,19.0,null,24.0,24.0,57.0,21.0,6.0,23.0,51.0,13.0,47.0,29.0,18.0,24.0,48.0,22.0,31.0,30.0,38.0,22.0,17.0,43.0,20.0,23.0,50.0,null,3.0,null,37.0,28.0,null,39.0,38.5,null,null],\"y0\":\" \",\"yaxis\":\"y\",\"type\":\"box\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Pclass\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Age\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Box chart: Age Distribution by Pclass\"},\"boxmode\":\"group\"},                        {\"responsive\": true}                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('65d53898-ad37-4acb-a2da-faa0f02419d8');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "3d график по возрасту, классу и цене билета."
+      ],
+      "metadata": {
+        "id": "QUzRCXBNjKyt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# creating and displaying an interactive 3D point plot\n",
+        "fig = px.scatter_3d(trains_test, x='Age', y='Pclass', z='Fare', color='Fare', title=\"3D point plot: Age, Pclass and Fare\")\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "aZoA_J7RX9b2",
+        "outputId": "22bbfb51-9b1e-4a26-f478-1fc5b571d744"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js\"></script>                <div id=\"6286ea82-29ed-4ba2-850a-41105b5a45fe\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"6286ea82-29ed-4ba2-850a-41105b5a45fe\")) {                    Plotly.newPlot(                        \"6286ea82-29ed-4ba2-850a-41105b5a45fe\",                        [{\"hovertemplate\":\"Age=%{x}\\u003cbr\\u003ePclass=%{y}\\u003cbr\\u003eFare=%{marker.color}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"\",\"marker\":{\"color\":[7.25,71.2833,7.925,53.1,8.05,8.4583,51.8625,21.075,11.1333,30.0708,16.7,26.55,8.05,31.275,7.8542,16.0,29.125,13.0,18.0,7.225,26.0,13.0,8.0292,35.5,21.075,31.3875,7.225,263.0,7.8792,7.8958,27.7208,146.5208,7.75,10.5,82.1708,52.0,7.2292,8.05,18.0,11.2417,9.475,21.0,7.8958,41.5792,7.8792,8.05,15.5,7.75,21.6792,17.8,39.6875,7.8,76.7292,26.0,61.9792,35.5,10.5,7.2292,27.75,46.9,7.2292,80.0,83.475,27.9,27.7208,15.2458,10.5,8.1583,7.925,8.6625,10.5,46.9,73.5,14.4542,56.4958,7.65,7.8958,8.05,29.0,12.475,9.0,9.5,7.7875,47.1,10.5,15.85,34.375,8.05,263.0,8.05,8.05,7.8542,61.175,20.575,7.25,8.05,34.6542,63.3583,23.0,26.0,7.8958,7.8958,77.2875,8.6542,7.925,7.8958,7.65,7.775,7.8958,24.15,52.0,14.4542,8.05,9.825,14.4583,7.925,7.75,21.0,247.5208,31.275,73.5,8.05,30.0708,13.0,77.2875,11.2417,7.75,7.1417,22.3583,6.975,7.8958,7.05,14.5,26.0,13.0,15.0458,26.2833,53.1,9.2167,79.2,15.2458,7.75,15.85,6.75,11.5,36.75,7.7958,34.375,26.0,13.0,12.525,66.6,8.05,14.5,7.3125,61.3792,7.7333,8.05,8.6625,69.55,16.1,15.75,7.775,8.6625,39.6875,20.525,55.0,27.9,25.925,56.4958,33.5,29.125,11.1333,7.925,30.6958,7.8542,25.4667,28.7125,13.0,0.0,69.55,15.05,31.3875,39.0,22.025,50.0,15.5,26.55,15.5,7.8958,13.0,13.0,7.8542,26.0,27.7208,146.5208,7.75,8.4042,7.75,13.0,9.5,69.55,6.4958,7.225,8.05,10.4625,15.85,18.7875,7.75,31.0,7.05,21.0,7.25,13.0,7.75,113.275,7.925,27.0,76.2917,10.5,8.05,13.0,8.05,7.8958,90.0,9.35,10.5,7.25,13.0,25.4667,83.475,7.775,13.5,31.3875,10.5,7.55,26.0,26.25,10.5,12.275,14.4542,15.5,10.5,7.125,7.225,90.0,7.775,14.5,52.5542,26.0,7.25,10.4625,26.55,16.1,20.2125,15.2458,79.2,86.5,512.3292,26.0,7.75,31.3875,79.65,0.0,7.75,10.5,39.6875,7.775,153.4625,135.6333,31.0,0.0,19.5,29.7,7.75,77.9583,7.75,0.0,29.125,20.25,7.75,7.8542,9.5,8.05,26.0,8.6625,9.5,7.8958,13.0,7.75,78.85,91.0792,12.875,8.85,7.8958,27.7208,7.2292,151.55,30.5,247.5208,7.75,23.25,0.0,12.35,8.05,151.55,110.8833,108.9,24.0,56.9292,83.1583,262.375,26.0,7.8958,26.25,7.8542,26.0,14.0,164.8667,134.5,7.25,7.8958,12.35,29.0,69.55,135.6333,6.2375,13.0,20.525,57.9792,23.25,28.5,153.4625,18.0,133.65,7.8958,66.6,134.5,8.05,35.5,26.0,263.0,13.0,13.0,13.0,13.0,13.0,16.1,15.9,8.6625,9.225,35.0,7.2292,17.8,7.225,9.5,55.0,13.0,7.8792,7.8792,27.9,27.7208,14.4542,7.05,15.5,7.25,75.25,7.2292,7.75,69.3,55.4417,6.4958,8.05,135.6333,21.075,82.1708,7.25,211.5,4.0125,7.775,227.525,15.7417,7.925,52.0,7.8958,73.5,46.9,13.0,7.7292,12.0,120.0,7.7958,7.925,113.275,16.7,7.7958,7.8542,26.0,10.5,12.65,7.925,8.05,9.825,15.85,8.6625,21.0,7.75,18.75,7.775,25.4667,7.8958,6.8583,90.0,0.0,7.925,8.05,32.5,13.0,13.0,24.15,7.8958,7.7333,7.875,14.4,20.2125,7.25,26.0,26.0,7.75,8.05,26.55,16.1,26.0,7.125,55.9,120.0,34.375,18.75,263.0,10.5,26.25,9.5,7.775,13.0,8.1125,81.8583,19.5,26.55,19.2583,30.5,27.75,19.9667,27.75,89.1042,8.05,7.8958,26.55,51.8625,10.5,7.75,26.55,8.05,38.5,13.0,8.05,7.05,0.0,26.55,7.725,19.2583,7.25,8.6625,27.75,13.7917,9.8375,52.0,21.0,7.0458,7.5208,12.2875,46.9,0.0,8.05,9.5875,91.0792,25.4667,90.0,29.7,8.05,15.9,19.9667,7.25,30.5,49.5042,8.05,14.4583,78.2667,15.1,151.55,7.7958,8.6625,7.75,7.6292,9.5875,86.5,108.9,26.0,26.55,22.525,56.4958,7.75,8.05,26.2875,59.4,7.4958,34.0208,10.5,24.15,26.0,7.8958,93.5,7.8958,7.225,57.9792,7.2292,7.75,10.5,221.7792,7.925,11.5,26.0,7.2292,7.2292,22.3583,8.6625,26.25,26.55,106.425,14.5,49.5,71.0,31.275,31.275,26.0,106.425,26.0,26.0,13.8625,20.525,36.75,110.8833,26.0,7.8292,7.225,7.775,26.55,39.6,227.525,79.65,17.4,7.75,7.8958,13.5,8.05,8.05,24.15,7.8958,21.075,7.2292,7.8542,10.5,51.4792,26.3875,7.75,8.05,14.5,13.0,55.9,14.4583,7.925,30.0,110.8833,26.0,40.125,8.7125,79.65,15.0,79.2,8.05,8.05,7.125,78.2667,7.25,7.75,26.0,24.15,33.0,0.0,7.225,56.9292,27.0,7.8958,42.4,8.05,26.55,15.55,7.8958,30.5,41.5792,153.4625,31.275,7.05,15.5,7.75,8.05,65.0,14.4,16.1,39.0,10.5,14.4542,52.5542,15.7417,7.8542,16.1,32.3208,12.35,77.9583,7.8958,7.7333,30.0,7.0542,30.5,0.0,27.9,13.0,7.925,26.25,39.6875,16.1,7.8542,69.3,27.9,56.4958,19.2583,76.7292,7.8958,35.5,7.55,7.55,7.8958,23.0,8.4333,7.8292,6.75,73.5,7.8958,15.5,13.0,113.275,133.65,7.225,25.5875,7.4958,7.925,73.5,13.0,7.775,8.05,52.0,39.0,52.0,10.5,13.0,0.0,7.775,8.05,9.8417,46.9,512.3292,8.1375,76.7292,9.225,46.9,39.0,41.5792,39.6875,10.1708,7.7958,211.3375,57.0,13.4167,56.4958,7.225,26.55,13.5,8.05,7.7333,110.8833,7.65,227.525,26.2875,14.4542,7.7417,7.8542,26.0,13.5,26.2875,151.55,15.2458,49.5042,26.55,52.0,9.4833,13.0,7.65,227.525,10.5,15.5,7.775,33.0,7.0542,13.0,13.0,53.1,8.6625,21.0,7.7375,26.0,7.925,211.3375,18.7875,0.0,13.0,13.0,16.1,34.375,512.3292,7.8958,7.8958,30.0,78.85,262.375,16.1,7.925,71.0,20.25,13.0,53.1,7.75,23.0,12.475,9.5,7.8958,65.0,14.5,7.7958,11.5,8.05,86.5,14.5,7.125,7.2292,120.0,7.775,77.9583,39.6,7.75,24.15,8.3625,9.5,7.8542,10.5,7.225,23.0,7.75,7.75,12.475,7.7375,211.3375,7.2292,57.0,30.0,23.45,7.05,7.25,7.4958,29.125,20.575,79.2,7.75,26.0,69.55,30.6958,7.8958,13.0,25.9292,8.6833,7.2292,24.15,13.0,26.25,120.0,8.5167,6.975,7.775,0.0,7.775,13.0,53.1,7.8875,24.15,10.5,31.275,8.05,0.0,7.925,37.0042,6.45,27.9,93.5,8.6625,0.0,12.475,39.6875,6.95,56.4958,37.0042,7.75,80.0,14.4542,18.75,7.2292,7.8542,8.3,83.1583,8.6625,8.05,56.4958,29.7,7.925,10.5,31.0,6.4375,8.6625,7.55,69.55,7.8958,33.0,89.1042,31.275,7.775,15.2458,39.4,26.0,9.35,164.8667,26.55,19.2583,7.2292,14.1083,11.5,25.9292,69.55,13.0,13.0,13.8583,50.4958,9.5,11.1333,7.8958,52.5542,5.0,9.0,24.0,7.225,9.8458,7.8958,7.8958,83.1583,26.0,7.8958,10.5167,10.5,7.05,29.125,13.0,30.0,23.45,30.0,7.75,7.8292,7.0,9.6875,8.6625,12.2875,9.225,7.6292,29.0,7.2292,24.15,7.8958,26.0,82.2667,26.0,61.175,27.7208,12.35,7.225,7.925,7.225,59.4,3.1708,31.6833,61.3792,262.375,14.5,61.9792,7.225,30.5,21.6792,26.0,31.5,20.575,23.45,57.75,7.2292,8.05,8.6625,9.5,56.4958,13.4167,26.55,7.85,13.0,52.5542,7.925,29.7,7.75,76.2917,15.9,60.0,15.0333,23.0,263.0,15.5792,29.125,7.8958,7.65,16.1,262.375,7.8958,13.5,7.75,7.725,262.375,21.0,7.8792,42.4,28.5375,263.0,7.75,7.8958,7.925,27.7208,211.5,211.5,8.05,25.7,13.0,7.75,15.2458,221.7792,26.0,7.8958,10.7083,14.4542,7.8792,8.05,7.75,23.0,13.9,7.775,52.0,8.05,26.0,7.7958,78.85,7.925,7.8542,8.05,55.4417,26.0,7.75,7.775,8.5167,22.525,7.8208,7.75,8.7125,13.0,15.0458,7.7792,31.6792,7.2833,221.7792,14.4542,6.4375,16.7,75.2417,26.0,15.75,7.75,57.75,7.25,7.75,16.1,7.7958,23.25,13.0,8.05,8.05,28.5,25.4667,6.4375,7.8958,7.8542,7.225,13.0,8.05,46.9,46.9,151.55,262.375,26.0,26.55,18.0,51.8625,8.05,26.55,26.0,83.1583,7.8958,null,12.1833,31.3875,7.55,221.7792,7.8542,26.55,13.775,7.7333,15.2458,13.5,7.0,13.0,22.025,50.4958,34.375,27.7208,8.9625,7.55,7.225,13.9,7.2292,31.3875,39.0,36.75,55.4417,39.0,83.1583,13.0,83.1583,53.1,7.75,247.5208,16.0,21.0,8.05,69.55,13.0,26.0,26.0,14.5,12.35,32.5,7.8542,134.5,7.775,10.5,8.1125,15.5,14.4,227.525,26.0,10.5,25.7417,7.75,10.5,27.7208,7.8958,22.525,7.05,73.5,26.0,7.775,42.5,7.8792,164.8667,211.5,8.05,13.8583,8.05,10.5,7.7958,27.4458,15.2458,7.7958,7.75,15.1,13.0,65.0,26.55,6.4958,7.8792,71.2833,7.8542,75.25,7.225,13.0,106.425,27.7208,30.0,134.5,7.8875,23.45,51.8625,21.0,32.5,26.0,14.4542,27.75,7.925,136.7792,9.325,9.5,7.55,7.75,8.05,13.0,7.775,17.4,7.8542,23.0,12.1833,12.7375,7.8958,0.0,7.55,8.05,8.6625,75.2417,7.75,136.7792,15.5,7.225,26.0,10.5,26.0,21.0,10.5,8.6625,13.775,7.75,15.2458,20.2125,7.25,7.25,82.2667,7.2292,8.05,39.6,6.95,7.2292,81.8583,9.5,7.8958,41.5792,21.6792,45.5,7.8542,7.775,15.0458,21.0,8.6625,7.75,26.55,151.55,9.35,93.5,14.1083,8.6625,7.225,7.575,7.75,135.6333,7.7333,146.5208,10.5,7.8542,31.5,7.775,7.2292,13.0,26.55,211.3375,7.05,39.0,79.2,26.0,13.0,36.75,29.7,7.225,15.7417,7.8958,26.0,13.0,7.2292,31.5,7.2292,10.5,7.5792,69.55,512.3292,14.5,7.65,13.0,7.2292,13.5,21.0,63.3583,10.5,73.5,65.0,20.575,26.0,51.4792,7.8792,7.75,15.55,69.55,37.0042,21.0,8.6625,55.4417,69.55,14.4583,39.6875,59.4,13.8583,11.5,134.5,0.0,13.0,81.8583,262.375,8.6625,11.5,50.0,31.3875,7.75,7.8792,14.5,16.1,12.875,65.0,7.775,13.0,7.75,21.075,93.5,39.4,20.25,10.5,22.025,60.0,7.25,79.2,7.775,7.7333,164.8667,21.0,59.4,47.1,27.7208,13.8625,10.5,211.5,7.7208,13.775,7.75,90.0,7.775,8.05,108.9,7.25,8.05,22.3583],\"coloraxis\":\"coloraxis\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"\",\"scene\":\"scene\",\"showlegend\":false,\"x\":[22.0,38.0,26.0,35.0,35.0,null,54.0,2.0,27.0,14.0,4.0,58.0,20.0,39.0,14.0,55.0,2.0,null,31.0,null,35.0,34.0,15.0,28.0,8.0,38.0,null,19.0,null,null,40.0,null,null,66.0,28.0,42.0,null,21.0,18.0,14.0,40.0,27.0,null,3.0,19.0,null,null,null,null,18.0,7.0,21.0,49.0,29.0,65.0,null,21.0,28.5,5.0,11.0,22.0,38.0,45.0,4.0,null,null,29.0,19.0,17.0,26.0,32.0,16.0,21.0,26.0,32.0,25.0,null,null,0.83,30.0,22.0,29.0,null,28.0,17.0,33.0,16.0,null,23.0,24.0,29.0,20.0,46.0,26.0,59.0,null,71.0,23.0,34.0,34.0,28.0,null,21.0,33.0,37.0,28.0,21.0,null,38.0,null,47.0,14.5,22.0,20.0,17.0,21.0,70.5,29.0,24.0,2.0,21.0,null,32.5,32.5,54.0,12.0,null,24.0,null,45.0,33.0,20.0,47.0,29.0,25.0,23.0,19.0,37.0,16.0,24.0,null,22.0,24.0,19.0,18.0,19.0,27.0,9.0,36.5,42.0,51.0,22.0,55.5,40.5,null,51.0,16.0,30.0,null,null,44.0,40.0,26.0,17.0,1.0,9.0,null,45.0,null,28.0,61.0,4.0,1.0,21.0,56.0,18.0,null,50.0,30.0,36.0,null,null,9.0,1.0,4.0,null,null,45.0,40.0,36.0,32.0,19.0,19.0,3.0,44.0,58.0,null,42.0,null,24.0,28.0,null,34.0,45.5,18.0,2.0,32.0,26.0,16.0,40.0,24.0,35.0,22.0,30.0,null,31.0,27.0,42.0,32.0,30.0,16.0,27.0,51.0,null,38.0,22.0,19.0,20.5,18.0,null,35.0,29.0,59.0,5.0,24.0,null,44.0,8.0,19.0,33.0,null,null,29.0,22.0,30.0,44.0,25.0,24.0,37.0,54.0,null,29.0,62.0,30.0,41.0,29.0,null,30.0,35.0,50.0,null,3.0,52.0,40.0,null,36.0,16.0,25.0,58.0,35.0,null,25.0,41.0,37.0,null,63.0,45.0,null,7.0,35.0,65.0,28.0,16.0,19.0,null,33.0,30.0,22.0,42.0,22.0,26.0,19.0,36.0,24.0,24.0,null,23.5,2.0,null,50.0,null,null,19.0,null,null,0.92,null,17.0,30.0,30.0,24.0,18.0,26.0,28.0,43.0,26.0,24.0,54.0,31.0,40.0,22.0,27.0,30.0,22.0,null,36.0,61.0,36.0,31.0,16.0,null,45.5,38.0,16.0,null,null,29.0,41.0,45.0,45.0,2.0,24.0,28.0,25.0,36.0,24.0,40.0,null,3.0,42.0,23.0,null,15.0,25.0,null,28.0,22.0,38.0,null,null,40.0,29.0,45.0,35.0,null,30.0,60.0,null,null,24.0,25.0,18.0,19.0,22.0,3.0,null,22.0,27.0,20.0,19.0,42.0,1.0,32.0,35.0,null,18.0,1.0,36.0,null,17.0,36.0,21.0,28.0,23.0,24.0,22.0,31.0,46.0,23.0,28.0,39.0,26.0,21.0,28.0,20.0,34.0,51.0,3.0,21.0,null,null,null,33.0,null,44.0,null,34.0,18.0,30.0,10.0,null,21.0,29.0,28.0,18.0,null,28.0,19.0,null,32.0,28.0,null,42.0,17.0,50.0,14.0,21.0,24.0,64.0,31.0,45.0,20.0,25.0,28.0,null,4.0,13.0,34.0,5.0,52.0,36.0,null,30.0,49.0,null,29.0,65.0,null,50.0,null,48.0,34.0,47.0,48.0,null,38.0,null,56.0,null,0.75,null,38.0,33.0,23.0,22.0,null,34.0,29.0,22.0,2.0,9.0,null,50.0,63.0,25.0,null,35.0,58.0,30.0,9.0,null,21.0,55.0,71.0,21.0,null,54.0,null,25.0,24.0,17.0,21.0,null,37.0,16.0,18.0,33.0,null,28.0,26.0,29.0,null,36.0,54.0,24.0,47.0,34.0,null,36.0,32.0,30.0,22.0,null,44.0,null,40.5,50.0,null,39.0,23.0,2.0,null,17.0,null,30.0,7.0,45.0,30.0,null,22.0,36.0,9.0,11.0,32.0,50.0,64.0,19.0,null,33.0,8.0,17.0,27.0,null,22.0,22.0,62.0,48.0,null,39.0,36.0,null,40.0,28.0,null,null,24.0,19.0,29.0,null,32.0,62.0,53.0,36.0,null,16.0,19.0,34.0,39.0,null,32.0,25.0,39.0,54.0,36.0,null,18.0,47.0,60.0,22.0,null,35.0,52.0,47.0,null,37.0,36.0,null,49.0,null,49.0,24.0,null,null,44.0,35.0,36.0,30.0,27.0,22.0,40.0,39.0,null,null,null,35.0,24.0,34.0,26.0,4.0,26.0,27.0,42.0,20.0,21.0,21.0,61.0,57.0,21.0,26.0,null,80.0,51.0,32.0,null,9.0,28.0,32.0,31.0,41.0,null,20.0,24.0,2.0,null,0.75,48.0,19.0,56.0,null,23.0,null,18.0,21.0,null,18.0,24.0,null,32.0,23.0,58.0,50.0,40.0,47.0,36.0,20.0,32.0,25.0,null,43.0,null,40.0,31.0,70.0,31.0,null,18.0,24.5,18.0,43.0,36.0,null,27.0,20.0,14.0,60.0,25.0,14.0,19.0,18.0,15.0,31.0,4.0,null,25.0,60.0,52.0,44.0,null,49.0,42.0,18.0,35.0,18.0,25.0,26.0,39.0,45.0,42.0,22.0,null,24.0,null,48.0,29.0,52.0,19.0,38.0,27.0,null,33.0,6.0,17.0,34.0,50.0,27.0,20.0,30.0,null,25.0,25.0,29.0,11.0,null,23.0,23.0,28.5,48.0,35.0,null,null,null,36.0,21.0,24.0,31.0,70.0,16.0,30.0,19.0,31.0,4.0,6.0,33.0,23.0,48.0,0.67,28.0,18.0,34.0,33.0,null,41.0,20.0,36.0,16.0,51.0,null,30.5,null,32.0,24.0,48.0,57.0,null,54.0,18.0,null,5.0,null,43.0,13.0,17.0,29.0,null,25.0,25.0,18.0,8.0,1.0,46.0,null,16.0,null,null,25.0,39.0,49.0,31.0,30.0,30.0,34.0,31.0,11.0,0.42,27.0,31.0,39.0,18.0,39.0,33.0,26.0,39.0,35.0,6.0,30.5,null,23.0,31.0,43.0,10.0,52.0,27.0,38.0,27.0,2.0,null,null,1.0,null,62.0,15.0,0.83,null,23.0,18.0,39.0,21.0,null,32.0,null,20.0,16.0,30.0,34.5,17.0,42.0,null,35.0,28.0,null,4.0,74.0,9.0,16.0,44.0,18.0,45.0,51.0,24.0,null,41.0,21.0,48.0,null,24.0,42.0,27.0,31.0,null,4.0,26.0,47.0,33.0,47.0,28.0,15.0,20.0,19.0,null,56.0,25.0,33.0,22.0,28.0,25.0,39.0,27.0,19.0,null,26.0,32.0,34.5,47.0,62.0,27.0,22.0,14.0,30.0,26.0,18.0,21.0,null,46.0,23.0,63.0,47.0,24.0,35.0,21.0,27.0,45.0,55.0,9.0,null,21.0,48.0,50.0,22.0,22.5,41.0,null,50.0,24.0,33.0,null,30.0,18.5,null,21.0,25.0,null,39.0,null,41.0,30.0,45.0,25.0,45.0,null,60.0,36.0,24.0,27.0,20.0,28.0,null,10.0,35.0,25.0,null,36.0,17.0,32.0,18.0,22.0,13.0,null,18.0,47.0,31.0,60.0,24.0,21.0,29.0,28.5,35.0,32.5,null,55.0,30.0,24.0,6.0,67.0,49.0,null,null,null,27.0,18.0,null,2.0,22.0,null,27.0,null,25.0,25.0,76.0,29.0,20.0,33.0,43.0,27.0,null,26.0,16.0,28.0,21.0,null,null,18.5,41.0,null,36.0,18.5,63.0,18.0,null,1.0,36.0,29.0,12.0,null,35.0,28.0,null,17.0,22.0,null,42.0,24.0,32.0,53.0,null,null,43.0,24.0,26.5,26.0,23.0,40.0,10.0,33.0,61.0,28.0,42.0,31.0,null,22.0,null,30.0,23.0,null,60.5,36.0,13.0,24.0,29.0,23.0,42.0,26.0,null,7.0,26.0,null,41.0,26.0,48.0,18.0,null,22.0,null,27.0,23.0,null,40.0,15.0,20.0,54.0,36.0,64.0,30.0,37.0,18.0,null,27.0,40.0,21.0,17.0,null,40.0,34.0,null,11.5,61.0,8.0,33.0,6.0,18.0,23.0,null,null,0.33,47.0,8.0,25.0,null,35.0,24.0,33.0,25.0,32.0,null,17.0,60.0,38.0,42.0,null,57.0,50.0,null,30.0,21.0,22.0,21.0,53.0,null,23.0,null,40.5,36.0,14.0,21.0,21.0,null,39.0,20.0,64.0,20.0,18.0,48.0,55.0,45.0,45.0,null,null,41.0,22.0,42.0,29.0,null,0.92,20.0,27.0,24.0,32.5,null,null,28.0,19.0,21.0,36.5,21.0,29.0,1.0,30.0,null,null,null,null,17.0,46.0,null,26.0,null,null,20.0,28.0,40.0,30.0,22.0,23.0,0.75,null,9.0,2.0,36.0,null,24.0,null,null,null,30.0,null,53.0,36.0,26.0,1.0,null,30.0,29.0,32.0,null,43.0,24.0,null,64.0,30.0,0.83,55.0,45.0,18.0,22.0,null,37.0,55.0,17.0,57.0,19.0,27.0,22.0,26.0,25.0,26.0,33.0,39.0,23.0,12.0,46.0,29.0,21.0,48.0,39.0,null,19.0,27.0,30.0,32.0,39.0,25.0,null,18.0,32.0,null,58.0,null,16.0,26.0,38.0,24.0,31.0,45.0,25.0,18.0,49.0,0.17,50.0,59.0,null,null,30.0,14.5,24.0,31.0,27.0,25.0,null,null,22.0,45.0,29.0,21.0,31.0,49.0,44.0,54.0,45.0,22.0,21.0,55.0,5.0,null,26.0,null,19.0,null,24.0,24.0,57.0,21.0,6.0,23.0,51.0,13.0,47.0,29.0,18.0,24.0,48.0,22.0,31.0,30.0,38.0,22.0,17.0,43.0,20.0,23.0,50.0,null,3.0,null,37.0,28.0,null,39.0,38.5,null,null],\"y\":[3,1,3,1,3,3,1,3,3,2,3,1,3,3,3,2,3,2,3,3,2,2,3,1,3,3,3,1,3,3,1,1,3,2,1,1,3,3,3,3,3,2,3,2,3,3,3,3,3,3,3,3,1,2,1,1,2,3,2,3,3,1,1,3,1,3,2,3,3,3,2,3,2,3,3,3,3,3,2,3,3,3,3,1,2,3,3,3,1,3,3,3,1,3,3,3,1,1,2,2,3,3,1,3,3,3,3,3,3,3,1,3,3,3,3,3,3,2,1,3,2,3,2,2,1,3,3,3,3,3,3,3,3,2,2,2,1,1,3,1,3,3,3,3,2,2,3,3,2,2,2,1,3,3,3,1,3,3,3,3,3,2,3,3,3,3,1,3,1,3,1,3,3,3,1,3,3,1,2,3,3,2,3,2,3,1,3,1,3,3,2,2,3,2,1,1,3,3,3,2,3,3,3,3,3,3,3,3,3,1,3,2,3,2,3,1,3,2,1,2,3,2,3,3,1,3,2,3,2,3,1,3,2,3,2,3,2,2,2,2,3,3,2,3,3,1,3,2,1,2,3,3,1,3,3,3,1,1,1,2,3,3,1,1,3,2,3,3,1,1,1,3,2,1,3,1,3,2,3,3,3,3,3,3,1,3,3,3,2,3,1,1,2,3,3,1,3,1,1,1,3,3,3,2,3,1,1,1,2,1,1,1,2,3,2,3,2,2,1,1,3,3,2,2,3,1,3,2,3,1,3,1,1,3,1,3,1,1,3,1,2,1,2,2,2,2,2,3,3,3,3,1,3,3,3,3,1,2,3,3,3,2,3,3,3,3,1,3,3,1,1,3,3,1,3,1,3,1,3,3,1,3,3,1,3,2,3,2,3,2,1,3,3,1,3,3,3,2,2,2,3,3,3,3,3,2,3,2,3,3,3,3,1,2,3,3,2,2,2,3,3,3,3,3,3,3,2,2,3,3,1,3,2,3,1,1,3,2,1,2,2,3,3,2,3,1,2,1,3,1,2,3,1,1,3,3,1,1,2,3,1,3,1,2,3,3,2,1,3,3,3,3,2,2,3,1,2,3,3,3,3,2,3,3,1,3,1,1,3,3,3,3,1,1,3,3,1,3,1,3,3,3,3,3,1,1,2,1,3,3,3,3,1,1,3,1,2,3,2,3,1,3,3,1,3,3,2,1,3,2,2,3,3,3,3,2,1,1,3,1,1,3,3,2,1,1,2,2,3,2,1,2,3,3,3,1,1,1,1,3,3,3,2,3,3,3,3,3,3,3,2,1,1,3,3,3,2,1,3,3,2,1,2,1,3,1,2,1,3,3,3,1,3,3,2,3,2,3,3,1,2,3,1,3,1,3,3,1,2,1,3,3,3,3,3,2,3,3,2,2,3,1,3,3,3,1,2,1,3,3,1,3,1,1,3,2,3,2,3,3,3,1,3,3,3,1,3,1,3,3,3,2,3,3,3,2,3,3,2,1,1,3,1,3,3,2,2,3,3,1,2,1,2,2,2,3,3,3,3,1,3,1,3,3,2,2,3,3,3,1,1,3,3,3,1,2,3,3,1,3,1,1,3,3,3,2,2,1,1,3,1,1,1,3,2,3,1,2,3,3,2,3,2,2,1,3,2,3,2,3,1,3,2,2,2,3,3,1,3,3,1,1,1,3,3,1,3,2,1,3,2,3,3,3,2,2,3,2,3,1,3,3,3,1,3,1,1,3,3,3,3,3,2,3,2,3,3,3,3,1,3,1,1,3,3,3,3,3,3,1,3,2,3,1,3,2,1,3,3,3,2,2,1,3,3,3,1,3,2,1,3,3,2,3,3,1,3,2,3,3,1,3,1,3,3,3,3,2,3,1,3,2,3,3,3,1,3,3,3,1,3,2,1,3,3,3,3,3,2,1,3,3,3,1,2,3,1,1,3,3,3,2,1,3,2,2,2,1,3,3,3,1,1,3,2,3,3,3,3,1,2,3,3,2,3,3,2,1,3,1,3,3,3,2,3,3,3,3,2,3,3,3,1,1,2,1,2,2,3,3,3,1,3,1,1,1,3,1,3,1,3,2,2,3,3,1,3,3,3,3,3,3,1,3,2,1,3,1,3,1,3,1,2,2,1,2,3,3,3,3,1,3,2,3,3,1,2,3,1,1,1,3,3,3,1,1,1,3,1,2,3,3,1,1,3,2,3,3,3,3,2,3,3,1,3,1,3,1,3,3,3,1,2,3,3,3,3,3,3,3,2,2,3,1,3,1,3,3,3,1,2,2,3,1,3,3,3,3,3,2,3,3,1,3,3,3,3,3,2,3,3,3,1,1,2,1,3,1,3,1,2,1,3,3,3,3,3,1,3,1,3,3,3,2,3,2,3,1,3,1,3,3,3,3,3,3,2,2,1,2,1,2,1,1,3,1,2,2,3,3,2,2,1,3,2,2,3,1,3,2,3,3,3,1,2,2,1,3,2,1,3,3,3,2,2,3,1,3,1,1,3,2,3,2,3,1,3,3,3,3,2,2,1,3,3,1,3,1,3,2,1,1,2,1,3,3,1,2,2,2,3,2,3,1,3,3,3,3,3,2,3,3,3,2,3,2,3,1,3,3,3,1,3,1,3,3,2,2,2,2,2,3,3,3,3,3,3,3,1,3,3,1,3,3,1,3,3,2,3,1,3,3,2,2,3,3,1,1,3,1,3,3,3,3,3,1,3,1,2,3,2,3,3,2,1,1,3,2,1,2,2,2,1,3,3,3,1,2,3,2,3,2,3,3,1,3,3,2,3,2,2,1,2,2,2,3,1,1,3,3,3,3,2,2,3,1,3,3,3,1,2,2,1,1,2,1,1,3,2,1,3,3,3,3,3,2,2,3,2,3,3,1,1,3,2,3,1,3,1,3,3,1,2,1,1,1,2,2,1,3,3,3,1,3,3,1,3,3,3],\"z\":[7.25,71.2833,7.925,53.1,8.05,8.4583,51.8625,21.075,11.1333,30.0708,16.7,26.55,8.05,31.275,7.8542,16.0,29.125,13.0,18.0,7.225,26.0,13.0,8.0292,35.5,21.075,31.3875,7.225,263.0,7.8792,7.8958,27.7208,146.5208,7.75,10.5,82.1708,52.0,7.2292,8.05,18.0,11.2417,9.475,21.0,7.8958,41.5792,7.8792,8.05,15.5,7.75,21.6792,17.8,39.6875,7.8,76.7292,26.0,61.9792,35.5,10.5,7.2292,27.75,46.9,7.2292,80.0,83.475,27.9,27.7208,15.2458,10.5,8.1583,7.925,8.6625,10.5,46.9,73.5,14.4542,56.4958,7.65,7.8958,8.05,29.0,12.475,9.0,9.5,7.7875,47.1,10.5,15.85,34.375,8.05,263.0,8.05,8.05,7.8542,61.175,20.575,7.25,8.05,34.6542,63.3583,23.0,26.0,7.8958,7.8958,77.2875,8.6542,7.925,7.8958,7.65,7.775,7.8958,24.15,52.0,14.4542,8.05,9.825,14.4583,7.925,7.75,21.0,247.5208,31.275,73.5,8.05,30.0708,13.0,77.2875,11.2417,7.75,7.1417,22.3583,6.975,7.8958,7.05,14.5,26.0,13.0,15.0458,26.2833,53.1,9.2167,79.2,15.2458,7.75,15.85,6.75,11.5,36.75,7.7958,34.375,26.0,13.0,12.525,66.6,8.05,14.5,7.3125,61.3792,7.7333,8.05,8.6625,69.55,16.1,15.75,7.775,8.6625,39.6875,20.525,55.0,27.9,25.925,56.4958,33.5,29.125,11.1333,7.925,30.6958,7.8542,25.4667,28.7125,13.0,0.0,69.55,15.05,31.3875,39.0,22.025,50.0,15.5,26.55,15.5,7.8958,13.0,13.0,7.8542,26.0,27.7208,146.5208,7.75,8.4042,7.75,13.0,9.5,69.55,6.4958,7.225,8.05,10.4625,15.85,18.7875,7.75,31.0,7.05,21.0,7.25,13.0,7.75,113.275,7.925,27.0,76.2917,10.5,8.05,13.0,8.05,7.8958,90.0,9.35,10.5,7.25,13.0,25.4667,83.475,7.775,13.5,31.3875,10.5,7.55,26.0,26.25,10.5,12.275,14.4542,15.5,10.5,7.125,7.225,90.0,7.775,14.5,52.5542,26.0,7.25,10.4625,26.55,16.1,20.2125,15.2458,79.2,86.5,512.3292,26.0,7.75,31.3875,79.65,0.0,7.75,10.5,39.6875,7.775,153.4625,135.6333,31.0,0.0,19.5,29.7,7.75,77.9583,7.75,0.0,29.125,20.25,7.75,7.8542,9.5,8.05,26.0,8.6625,9.5,7.8958,13.0,7.75,78.85,91.0792,12.875,8.85,7.8958,27.7208,7.2292,151.55,30.5,247.5208,7.75,23.25,0.0,12.35,8.05,151.55,110.8833,108.9,24.0,56.9292,83.1583,262.375,26.0,7.8958,26.25,7.8542,26.0,14.0,164.8667,134.5,7.25,7.8958,12.35,29.0,69.55,135.6333,6.2375,13.0,20.525,57.9792,23.25,28.5,153.4625,18.0,133.65,7.8958,66.6,134.5,8.05,35.5,26.0,263.0,13.0,13.0,13.0,13.0,13.0,16.1,15.9,8.6625,9.225,35.0,7.2292,17.8,7.225,9.5,55.0,13.0,7.8792,7.8792,27.9,27.7208,14.4542,7.05,15.5,7.25,75.25,7.2292,7.75,69.3,55.4417,6.4958,8.05,135.6333,21.075,82.1708,7.25,211.5,4.0125,7.775,227.525,15.7417,7.925,52.0,7.8958,73.5,46.9,13.0,7.7292,12.0,120.0,7.7958,7.925,113.275,16.7,7.7958,7.8542,26.0,10.5,12.65,7.925,8.05,9.825,15.85,8.6625,21.0,7.75,18.75,7.775,25.4667,7.8958,6.8583,90.0,0.0,7.925,8.05,32.5,13.0,13.0,24.15,7.8958,7.7333,7.875,14.4,20.2125,7.25,26.0,26.0,7.75,8.05,26.55,16.1,26.0,7.125,55.9,120.0,34.375,18.75,263.0,10.5,26.25,9.5,7.775,13.0,8.1125,81.8583,19.5,26.55,19.2583,30.5,27.75,19.9667,27.75,89.1042,8.05,7.8958,26.55,51.8625,10.5,7.75,26.55,8.05,38.5,13.0,8.05,7.05,0.0,26.55,7.725,19.2583,7.25,8.6625,27.75,13.7917,9.8375,52.0,21.0,7.0458,7.5208,12.2875,46.9,0.0,8.05,9.5875,91.0792,25.4667,90.0,29.7,8.05,15.9,19.9667,7.25,30.5,49.5042,8.05,14.4583,78.2667,15.1,151.55,7.7958,8.6625,7.75,7.6292,9.5875,86.5,108.9,26.0,26.55,22.525,56.4958,7.75,8.05,26.2875,59.4,7.4958,34.0208,10.5,24.15,26.0,7.8958,93.5,7.8958,7.225,57.9792,7.2292,7.75,10.5,221.7792,7.925,11.5,26.0,7.2292,7.2292,22.3583,8.6625,26.25,26.55,106.425,14.5,49.5,71.0,31.275,31.275,26.0,106.425,26.0,26.0,13.8625,20.525,36.75,110.8833,26.0,7.8292,7.225,7.775,26.55,39.6,227.525,79.65,17.4,7.75,7.8958,13.5,8.05,8.05,24.15,7.8958,21.075,7.2292,7.8542,10.5,51.4792,26.3875,7.75,8.05,14.5,13.0,55.9,14.4583,7.925,30.0,110.8833,26.0,40.125,8.7125,79.65,15.0,79.2,8.05,8.05,7.125,78.2667,7.25,7.75,26.0,24.15,33.0,0.0,7.225,56.9292,27.0,7.8958,42.4,8.05,26.55,15.55,7.8958,30.5,41.5792,153.4625,31.275,7.05,15.5,7.75,8.05,65.0,14.4,16.1,39.0,10.5,14.4542,52.5542,15.7417,7.8542,16.1,32.3208,12.35,77.9583,7.8958,7.7333,30.0,7.0542,30.5,0.0,27.9,13.0,7.925,26.25,39.6875,16.1,7.8542,69.3,27.9,56.4958,19.2583,76.7292,7.8958,35.5,7.55,7.55,7.8958,23.0,8.4333,7.8292,6.75,73.5,7.8958,15.5,13.0,113.275,133.65,7.225,25.5875,7.4958,7.925,73.5,13.0,7.775,8.05,52.0,39.0,52.0,10.5,13.0,0.0,7.775,8.05,9.8417,46.9,512.3292,8.1375,76.7292,9.225,46.9,39.0,41.5792,39.6875,10.1708,7.7958,211.3375,57.0,13.4167,56.4958,7.225,26.55,13.5,8.05,7.7333,110.8833,7.65,227.525,26.2875,14.4542,7.7417,7.8542,26.0,13.5,26.2875,151.55,15.2458,49.5042,26.55,52.0,9.4833,13.0,7.65,227.525,10.5,15.5,7.775,33.0,7.0542,13.0,13.0,53.1,8.6625,21.0,7.7375,26.0,7.925,211.3375,18.7875,0.0,13.0,13.0,16.1,34.375,512.3292,7.8958,7.8958,30.0,78.85,262.375,16.1,7.925,71.0,20.25,13.0,53.1,7.75,23.0,12.475,9.5,7.8958,65.0,14.5,7.7958,11.5,8.05,86.5,14.5,7.125,7.2292,120.0,7.775,77.9583,39.6,7.75,24.15,8.3625,9.5,7.8542,10.5,7.225,23.0,7.75,7.75,12.475,7.7375,211.3375,7.2292,57.0,30.0,23.45,7.05,7.25,7.4958,29.125,20.575,79.2,7.75,26.0,69.55,30.6958,7.8958,13.0,25.9292,8.6833,7.2292,24.15,13.0,26.25,120.0,8.5167,6.975,7.775,0.0,7.775,13.0,53.1,7.8875,24.15,10.5,31.275,8.05,0.0,7.925,37.0042,6.45,27.9,93.5,8.6625,0.0,12.475,39.6875,6.95,56.4958,37.0042,7.75,80.0,14.4542,18.75,7.2292,7.8542,8.3,83.1583,8.6625,8.05,56.4958,29.7,7.925,10.5,31.0,6.4375,8.6625,7.55,69.55,7.8958,33.0,89.1042,31.275,7.775,15.2458,39.4,26.0,9.35,164.8667,26.55,19.2583,7.2292,14.1083,11.5,25.9292,69.55,13.0,13.0,13.8583,50.4958,9.5,11.1333,7.8958,52.5542,5.0,9.0,24.0,7.225,9.8458,7.8958,7.8958,83.1583,26.0,7.8958,10.5167,10.5,7.05,29.125,13.0,30.0,23.45,30.0,7.75,7.8292,7.0,9.6875,8.6625,12.2875,9.225,7.6292,29.0,7.2292,24.15,7.8958,26.0,82.2667,26.0,61.175,27.7208,12.35,7.225,7.925,7.225,59.4,3.1708,31.6833,61.3792,262.375,14.5,61.9792,7.225,30.5,21.6792,26.0,31.5,20.575,23.45,57.75,7.2292,8.05,8.6625,9.5,56.4958,13.4167,26.55,7.85,13.0,52.5542,7.925,29.7,7.75,76.2917,15.9,60.0,15.0333,23.0,263.0,15.5792,29.125,7.8958,7.65,16.1,262.375,7.8958,13.5,7.75,7.725,262.375,21.0,7.8792,42.4,28.5375,263.0,7.75,7.8958,7.925,27.7208,211.5,211.5,8.05,25.7,13.0,7.75,15.2458,221.7792,26.0,7.8958,10.7083,14.4542,7.8792,8.05,7.75,23.0,13.9,7.775,52.0,8.05,26.0,7.7958,78.85,7.925,7.8542,8.05,55.4417,26.0,7.75,7.775,8.5167,22.525,7.8208,7.75,8.7125,13.0,15.0458,7.7792,31.6792,7.2833,221.7792,14.4542,6.4375,16.7,75.2417,26.0,15.75,7.75,57.75,7.25,7.75,16.1,7.7958,23.25,13.0,8.05,8.05,28.5,25.4667,6.4375,7.8958,7.8542,7.225,13.0,8.05,46.9,46.9,151.55,262.375,26.0,26.55,18.0,51.8625,8.05,26.55,26.0,83.1583,7.8958,null,12.1833,31.3875,7.55,221.7792,7.8542,26.55,13.775,7.7333,15.2458,13.5,7.0,13.0,22.025,50.4958,34.375,27.7208,8.9625,7.55,7.225,13.9,7.2292,31.3875,39.0,36.75,55.4417,39.0,83.1583,13.0,83.1583,53.1,7.75,247.5208,16.0,21.0,8.05,69.55,13.0,26.0,26.0,14.5,12.35,32.5,7.8542,134.5,7.775,10.5,8.1125,15.5,14.4,227.525,26.0,10.5,25.7417,7.75,10.5,27.7208,7.8958,22.525,7.05,73.5,26.0,7.775,42.5,7.8792,164.8667,211.5,8.05,13.8583,8.05,10.5,7.7958,27.4458,15.2458,7.7958,7.75,15.1,13.0,65.0,26.55,6.4958,7.8792,71.2833,7.8542,75.25,7.225,13.0,106.425,27.7208,30.0,134.5,7.8875,23.45,51.8625,21.0,32.5,26.0,14.4542,27.75,7.925,136.7792,9.325,9.5,7.55,7.75,8.05,13.0,7.775,17.4,7.8542,23.0,12.1833,12.7375,7.8958,0.0,7.55,8.05,8.6625,75.2417,7.75,136.7792,15.5,7.225,26.0,10.5,26.0,21.0,10.5,8.6625,13.775,7.75,15.2458,20.2125,7.25,7.25,82.2667,7.2292,8.05,39.6,6.95,7.2292,81.8583,9.5,7.8958,41.5792,21.6792,45.5,7.8542,7.775,15.0458,21.0,8.6625,7.75,26.55,151.55,9.35,93.5,14.1083,8.6625,7.225,7.575,7.75,135.6333,7.7333,146.5208,10.5,7.8542,31.5,7.775,7.2292,13.0,26.55,211.3375,7.05,39.0,79.2,26.0,13.0,36.75,29.7,7.225,15.7417,7.8958,26.0,13.0,7.2292,31.5,7.2292,10.5,7.5792,69.55,512.3292,14.5,7.65,13.0,7.2292,13.5,21.0,63.3583,10.5,73.5,65.0,20.575,26.0,51.4792,7.8792,7.75,15.55,69.55,37.0042,21.0,8.6625,55.4417,69.55,14.4583,39.6875,59.4,13.8583,11.5,134.5,0.0,13.0,81.8583,262.375,8.6625,11.5,50.0,31.3875,7.75,7.8792,14.5,16.1,12.875,65.0,7.775,13.0,7.75,21.075,93.5,39.4,20.25,10.5,22.025,60.0,7.25,79.2,7.775,7.7333,164.8667,21.0,59.4,47.1,27.7208,13.8625,10.5,211.5,7.7208,13.775,7.75,90.0,7.775,8.05,108.9,7.25,8.05,22.3583],\"type\":\"scatter3d\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"scene\":{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"xaxis\":{\"title\":{\"text\":\"Age\"}},\"yaxis\":{\"title\":{\"text\":\"Pclass\"}},\"zaxis\":{\"title\":{\"text\":\"Fare\"}}},\"coloraxis\":{\"colorbar\":{\"title\":{\"text\":\"Fare\"}},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"3D point plot: Age, Embarked and Pclass\"}},                        {\"responsive\": true}                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('6286ea82-29ed-4ba2-850a-41105b5a45fe');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Гистограмма плотности распределения стоимости билета."
+      ],
+      "metadata": {
+        "id": "XU4WBhoii9tN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "# creating and displaying histogram\n",
+        "sns.histplot(trains_test['Fare'], kde=True, bins=20, color='purple', stat='density', orientation='horizontal')\n",
+        "plt.title('Histogram: Fare Distribution')\n",
+        "plt.ylabel('Fare')\n",
+        "plt.xlabel('Density')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "GLCdz9D_ZQOD",
+        "outputId": "b79d0124-dcf0-4a8d-99b2-a9dca3f5647d"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Sunburst Chart (Диаграмма солнечных лучей) выживших по классу и полу."
+      ],
+      "metadata": {
+        "id": "3-Jj8MRHjXo2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# creating and displaying an interactive sunburst chart\n",
+        "fig = px.sunburst(trains_test.dropna(subset=['Pclass', 'Sex', 'Survived']), path=['Pclass', 'Sex', 'Survived'], title='Sunburst Chart: Survival by SibSp and Sex')\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 770
+        },
+        "id": "D6WlG-CieBcs",
+        "outputId": "d30be019-385c-4dbd-87f0-b5ac9e194ffc"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/plotly/express/_core.py:1727: FutureWarning:\n",
+            "\n",
+            "The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+            "\n",
+            "/usr/local/lib/python3.10/dist-packages/plotly/express/_core.py:1727: FutureWarning:\n",
+            "\n",
+            "The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+            "\n",
+            "/usr/local/lib/python3.10/dist-packages/plotly/express/_core.py:1727: FutureWarning:\n",
+            "\n",
+            "The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js\"></script>                <div id=\"a5016647-442d-43c2-9160-c11d927ba180\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"a5016647-442d-43c2-9160-c11d927ba180\")) {                    Plotly.newPlot(                        \"a5016647-442d-43c2-9160-c11d927ba180\",                        [{\"branchvalues\":\"total\",\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hovertemplate\":\"labels=%{label}\\u003cbr\\u003ecount=%{value}\\u003cbr\\u003eparent=%{parent}\\u003cbr\\u003eid=%{id}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"ids\":[\"1\\u002ffemale\\u002f0.0\",\"2\\u002ffemale\\u002f0.0\",\"3\\u002ffemale\\u002f0.0\",\"1\\u002fmale\\u002f0.0\",\"2\\u002fmale\\u002f0.0\",\"3\\u002fmale\\u002f0.0\",\"1\\u002ffemale\\u002f1.0\",\"2\\u002ffemale\\u002f1.0\",\"3\\u002ffemale\\u002f1.0\",\"1\\u002fmale\\u002f1.0\",\"2\\u002fmale\\u002f1.0\",\"3\\u002fmale\\u002f1.0\",\"1\\u002ffemale\",\"2\\u002ffemale\",\"3\\u002ffemale\",\"1\\u002fmale\",\"2\\u002fmale\",\"3\\u002fmale\",\"1\",\"2\",\"3\"],\"labels\":[\"0.0\",\"0.0\",\"0.0\",\"0.0\",\"0.0\",\"0.0\",\"1.0\",\"1.0\",\"1.0\",\"1.0\",\"1.0\",\"1.0\",\"female\",\"female\",\"female\",\"male\",\"male\",\"male\",\"1\",\"2\",\"3\"],\"name\":\"\",\"parents\":[\"1\\u002ffemale\",\"2\\u002ffemale\",\"3\\u002ffemale\",\"1\\u002fmale\",\"2\\u002fmale\",\"3\\u002fmale\",\"1\\u002ffemale\",\"2\\u002ffemale\",\"3\\u002ffemale\",\"1\\u002fmale\",\"2\\u002fmale\",\"3\\u002fmale\",\"1\",\"2\",\"3\",\"1\",\"2\",\"3\",\"\",\"\",\"\"],\"values\":[3,6,72,77,91,300,91,70,72,45,17,47,94,76,144,122,108,347,216,184,491],\"type\":\"sunburst\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Sunburst Chart: Survival by SibSp and Sex\"}},                        {\"responsive\": true}                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('a5016647-442d-43c2-9160-c11d927ba180');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Тепловая карта корреляции по возрасту, стоимости билета и количеству родственников\n"
+      ],
+      "metadata": {
+        "id": "_wi0gXzPmN_u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# select the desired numeric data\n",
+        "numeric_data = trains_test.drop(columns=['PassengerId', 'SibSp', 'Parch']).select_dtypes(include=['number'])\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "# creating and displaying heatmap\n",
+        "sns.heatmap(numeric_data.corr(), annot=True, cmap='flare', fmt='.2f')\n",
+        "plt.title('Correlation Heatmap')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "zyOMOT_7gg2i",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 545
+        },
+        "outputId": "363eef2d-9bda-4c71-db4f-93a4637d918b"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Sankey Diagram в Plotly на данных датасета"
+      ],
+      "metadata": {
+        "id": "PveeiF9roPZP"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Диаграмма sankey: потоки по возрастным категориям, полу и выживаемости.\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "dHci8SejyDjC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import plotly.graph_objects as go\n",
+        "\n",
+        "# Defining labels for nodes\n",
+        "labels = ['children', 'young',  'middle-aged', 'elderly', 'Male', 'Female', 'Survived', 'Not Survived']\n",
+        "\n",
+        "# Indexes and values for sources and purposes\n",
+        "sources = [0, 0, 1, 1, 2, 2, 3, 3, 4, 5, 4, 5]\n",
+        "targets = [4, 5, 4, 5, 4, 5, 4, 5, 6, 6, 7, 7]\n",
+        "values = [\n",
+        "    trains_test[(trains_test['Age'] <= 15) & (trains_test['Sex'] == 'male')].shape[0], # number of children-boys\n",
+        "    trains_test[(trains_test['Age'] <= 15) & (trains_test['Sex'] == 'female')].shape[0], # number of children-girls\n",
+        "    trains_test[(trains_test['Age'] > 15) & (trains_test['Age'] <= 30) & (trains_test['Sex'] == 'male')].shape[0], # number of young guys\n",
+        "    trains_test[(trains_test['Age'] > 15) & (trains_test['Age'] <= 30) & (trains_test['Sex'] == 'female')].shape[0], # number of young girls\n",
+        "    trains_test[(trains_test['Age'] > 30) & (trains_test['Age'] <= 50) & (trains_test['Sex'] == 'male')].shape[0], # number of middle-aged man\n",
+        "    trains_test[(trains_test['Age'] > 30) & (trains_test['Age'] <= 50) & (trains_test['Sex'] == 'female')].shape[0], # number of middle-aged woman\n",
+        "    trains_test[(50 < trains_test['Age']) & (trains_test['Sex'] == 'male')].shape[0], # number of elderly man\n",
+        "    trains_test[(50 < trains_test['Age']) & (trains_test['Sex'] == 'female')].shape[0], # number of elderly woman\n",
+        "    trains_test[(trains_test['Survived'] == 1) & (trains_test['Sex'] == 'male')].shape[0], # number of survived man\n",
+        "    trains_test[(trains_test['Survived'] == 1) & (trains_test['Sex'] == 'female')].shape[0], # number of survived woman\n",
+        "    trains_test[(trains_test['Survived'] == 0) & (trains_test['Sex'] == 'male')].shape[0], # number of not survived man\n",
+        "    trains_test[(trains_test['Survived'] == 0) & (trains_test['Sex'] == 'female')].shape[0], # number of not survived woman\n",
+        "]\n",
+        "\n",
+        "# create diagramm\n",
+        "fig = go.Figure(go.Sankey(\n",
+        "    node=dict(\n",
+        "        pad=15,\n",
+        "        thickness=20,\n",
+        "        line=dict(color=\"black\", width=1),\n",
+        "        label=labels\n",
+        "    ),\n",
+        "    link=dict(\n",
+        "        source=sources,\n",
+        "        target=targets,\n",
+        "        value=values\n",
+        "    )\n",
+        "))\n",
+        "fig.update_layout(title_text=\"Sankey Diagram: streams by age category, gender, and survival rate.\")\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "gNi_2iC3jlrU",
+        "outputId": "098e615c-0b17-462b-b2f5-4955e4d45a81"
+      },
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js\"></script>                <div id=\"c89bd8b3-588b-4bf7-9ba4-c78a24ae07dc\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"c89bd8b3-588b-4bf7-9ba4-c78a24ae07dc\")) {                    Plotly.newPlot(                        \"c89bd8b3-588b-4bf7-9ba4-c78a24ae07dc\",                        [{\"link\":{\"source\":[0,0,1,1,2,2,3,3,4,5,4,5],\"target\":[4,5,4,5,4,5,4,5,6,6,7,7],\"value\":[59,56,315,179,221,121,63,32,109,233,468,81]},\"node\":{\"label\":[\"children\",\"young\",\"middle-aged\",\"elderly\",\"Male\",\"Female\",\"Survived\",\"Not Survived\"],\"line\":{\"color\":\"black\",\"width\":1},\"pad\":15,\"thickness\":20},\"type\":\"sankey\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"title\":{\"text\":\"Sankey Diagram: streams by age category, gender, and survival rate.\"}},                        {\"responsive\": true}                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('c89bd8b3-588b-4bf7-9ba4-c78a24ae07dc');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}

From 887f81abfcf25671da3c34508f24737aeeb8c5db Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=A1=D0=BE=D1=84=D0=B8=D1=8F=20=D0=A5=D1=80=D0=B8=D1=81?=
 =?UTF-8?q?=D0=B0=D0=BD=D0=BA=D0=BE=D0=B2=D0=B0?=
 <132402521+sssoneta@users.noreply.github.com>
Date: Tue, 8 Apr 2025 21:03:24 +0300
Subject: [PATCH 3/3] dz8

---
 project/numpy.ipynb | 655 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 655 insertions(+)
 create mode 100644 project/numpy.ipynb

diff --git a/project/numpy.ipynb b/project/numpy.ipynb
new file mode 100644
index 00000000..b236d31e
--- /dev/null
+++ b/project/numpy.ipynb
@@ -0,0 +1,655 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Задача 8. Numpy"
+      ],
+      "metadata": {
+        "id": "fc102A6TPEWn"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Импорт библиотек"
+      ],
+      "metadata": {
+        "id": "OMKhts0VPTi0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np"
+      ],
+      "metadata": {
+        "id": "YtJB_Qr8PPqO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Получить датасет Iris"
+      ],
+      "metadata": {
+        "id": "pGr7FzsmPeX5"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Данные датасета получены по ссылке"
+      ],
+      "metadata": {
+        "id": "qw7soQKjS4Az"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
+        "iris = np.genfromtxt(url, delimiter=\",\", dtype=str) # read the data by columns as str\n",
+        "print(iris[:5])\n",
+        "\n",
+        "features = iris[:, :4].astype(float) # select the first 4 columns as float\n",
+        "iris_names = iris[:, 4]\n",
+        "\n",
+        "# print matrix sizes and memory space\n",
+        "print(f\"features size: {features.shape}\")\n",
+        "print(f\"iris_names size: {iris_names.shape}\")\n",
+        "print(f\"size in memory for features: {features.nbytes / 1024:.2f} Kb\")\n",
+        "print(f\"size in memory for iris_names: {iris_names.nbytes / 1024:.2f} Kb\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KvVw5yvqPhau",
+        "outputId": "0654842a-ed4e-4da7-bccf-bbe67afbcac2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[['5.1' '3.5' '1.4' '0.2' 'Iris-setosa']\n",
+            " ['4.9' '3.0' '1.4' '0.2' 'Iris-setosa']\n",
+            " ['4.7' '3.2' '1.3' '0.2' 'Iris-setosa']\n",
+            " ['4.6' '3.1' '1.5' '0.2' 'Iris-setosa']\n",
+            " ['5.0' '3.6' '1.4' '0.2' 'Iris-setosa']]\n",
+            "features size: (150, 4)\n",
+            "iris_names size: (150,)\n",
+            "size in memory for features: 4.69 Kb\n",
+            "size in memory for iris_names: 8.79 Kb\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Нормализация данных"
+      ],
+      "metadata": {
+        "id": "jk_5LYGPSqMw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "features_min = features.min(axis=0)  # min value\n",
+        "features_max = features.max(axis=0)  # max value\n",
+        "features_normalized = (features - features_min) / (features_max - features_min)\n",
+        "print('features_normalized: ', features_normalized[:5], sep='\\n')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XrqLBGlKQO9v",
+        "outputId": "8270d3f3-d770-4eab-befe-55aec093fe4d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "features_normalized: \n",
+            "[[0.22222222 0.625      0.06779661 0.04166667]\n",
+            " [0.16666667 0.41666667 0.06779661 0.04166667]\n",
+            " [0.11111111 0.5        0.05084746 0.04166667]\n",
+            " [0.08333333 0.45833333 0.08474576 0.04166667]\n",
+            " [0.19444444 0.66666667 0.06779661 0.04166667]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Заменим столбец на категориальный признак"
+      ],
+      "metadata": {
+        "id": "mBcs1bV-Vm3q"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Возьмем первый столбец для признака"
+      ],
+      "metadata": {
+        "id": "ZvBhS4CrXBom"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "first_features = features_normalized[:, 0]\n",
+        "\n",
+        "categories = np.where(first_features < 0.25, 1,\n",
+        "                      np.where(first_features > 0.75, 3, 2))\n",
+        "\n",
+        "features_normalized[:, 0] = categories\n",
+        "features_normalized[:10]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mVH2e3SGUP0-",
+        "outputId": "286d9c0e-99c3-427b-df18-15d23471a909"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[1.        , 0.625     , 0.06779661, 0.04166667],\n",
+              "       [1.        , 0.41666667, 0.06779661, 0.04166667],\n",
+              "       [1.        , 0.5       , 0.05084746, 0.04166667],\n",
+              "       [1.        , 0.45833333, 0.08474576, 0.04166667],\n",
+              "       [1.        , 0.66666667, 0.06779661, 0.04166667],\n",
+              "       [2.        , 0.79166667, 0.11864407, 0.125     ],\n",
+              "       [1.        , 0.58333333, 0.06779661, 0.08333333],\n",
+              "       [1.        , 0.58333333, 0.08474576, 0.04166667],\n",
+              "       [1.        , 0.375     , 0.06779661, 0.04166667],\n",
+              "       [1.        , 0.45833333, 0.08474576, 0.        ]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Разделение данных на тренировочную и тестовую выборки"
+      ],
+      "metadata": {
+        "id": "fOKUVMwiYEFY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "np.random.seed(42)\n",
+        "indices = np.arange(features_normalized.shape[0])\n",
+        "np.random.shuffle(indices)\n",
+        "\n",
+        "# split into training and test data\n",
+        "split_index = int(0.8 * len(indices))\n",
+        "train_indices = indices[:split_index]\n",
+        "test_indices = indices[split_index:]\n",
+        "\n",
+        "# split normalized data\n",
+        "train_features_norm = features_normalized[train_indices]\n",
+        "test_features_norm = features_normalized[test_indices]\n",
+        "train_names = iris_names[train_indices]\n",
+        "test_names = iris_names[test_indices]\n",
+        "\n",
+        "print(f'trainig data length: {train_names.shape}')\n",
+        "print(f'test data length: {test_names.shape}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SC9gEIZaWgtg",
+        "outputId": "72935399-f90b-4df6-d37a-af340f741cb7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "trainig data length: (120,)\n",
+            "test data length: (30,)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Классификация"
+      ],
+      "metadata": {
+        "id": "WsTRSGZDdFQT"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Был выбран метод классификации случайный лес"
+      ],
+      "metadata": {
+        "id": "7hhBurADeJX5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "\n",
+        "# create and traning model\n",
+        "clf = RandomForestClassifier()\n",
+        "clf_norm = clf.fit(train_features_norm, train_names)"
+      ],
+      "metadata": {
+        "id": "VGMGHW4wYemS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Оценка результата"
+      ],
+      "metadata": {
+        "id": "TOWLnvEdiT_N"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import accuracy_score, f1_score\n",
+        "\n",
+        "# predict results\n",
+        "names_predict = clf_norm.predict(test_features_norm)\n",
+        "\n",
+        "# check metrics\n",
+        "print(f\"Accuracy: {accuracy_score(test_names, names_predict)}\")\n",
+        "print(f\"F1-Score: {f1_score(test_names, names_predict, average='weighted')}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2Kmn5G9NeCvO",
+        "outputId": "462b0146-f067-4d85-d177-4d00a9a57717"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy: 0.9666666666666667\n",
+            "F1-Score: 0.9666666666666667\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Можно сделать вывод, что модель случайного леса хорошо справляется с задачей классификации. Высокие значения метрик свидетельствуют о точности модели."
+      ],
+      "metadata": {
+        "id": "KT2fIkOvCFhq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Эксперименты"
+      ],
+      "metadata": {
+        "id": "i-QpSfw7DMJm"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "1. Использование ненормализованных данных"
+      ],
+      "metadata": {
+        "id": "iusIBOq2DRQC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# split features data\n",
+        "train_features = features[train_indices]\n",
+        "test_features = features[test_indices]\n",
+        "\n",
+        "# model training\n",
+        "clf = clf.fit(train_features, train_names)\n",
+        "\n",
+        "test_names_predict = clf.predict(test_features)\n",
+        "\n",
+        "print(f\"Accuracy: {accuracy_score(test_names, test_names_predict)}\")\n",
+        "print(f\"F1-Score: {f1_score(test_names, test_names_predict, average='weighted')}\")"
+      ],
+      "metadata": {
+        "id": "0M9n10b5ppNn",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "16776b0f-ee92-4911-bad9-bc7783c5f7d2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy: 0.9666666666666667\n",
+            "F1-Score: 0.9666666666666667\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Можно сделать вывод о том что при работе с ненормализованными данными модель сохраняет высокую точность."
+      ],
+      "metadata": {
+        "id": "Nyx6AzJRTn4B"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "2. Эксперименты с параметрами модели"
+      ],
+      "metadata": {
+        "id": "dXBZ1vnLLYo6"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Для экспериментов рассмтрел 7 pipeline'ов. Для сравнения будем брать последовательно по 2 pipelin'а и изменять один параметр. Для всех моделей зададим одинаковые случайные значения (42).\n",
+        "\n",
+        "\n",
+        "*   1, 2 проверяют влияние модели `StandardScaler` на модель (в 1 используется `StandardScaler`, в 2 не используется)\n",
+        "*   2, 3, 4 проверяют влияние `n_estimators` на модель (в 2 значение по умолчанию 100)\n",
+        "*   4, 5 проверяют влияние `regressor` и `classifiter` на модель\n",
+        "*   5, 6 проверяют влияние `max_depth` на модель (по умолчанию глубина не ограничена)\n",
+        "*   6, 7 проверяют влияние нормализованных данных при наличии других параметров\n"
+      ],
+      "metadata": {
+        "id": "itFCujJ0SSzD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "\n",
+        "# create pipelines\n",
+        "pipeline1 = Pipeline([\n",
+        "    ('scaler', StandardScaler()),\n",
+        "    ('classifiter', RandomForestClassifier(random_state=42))])\n",
+        "\n",
+        "pipeline2 = Pipeline([\n",
+        "    ('classifiter', RandomForestClassifier(random_state=42))])\n",
+        "\n",
+        "\n",
+        "pipeline3 = Pipeline([\n",
+        "    ('classifiter', RandomForestClassifier(n_estimators=10, random_state=42))])\n",
+        "\n",
+        "pipeline4 = Pipeline([\n",
+        "    ('classifiter', RandomForestClassifier(n_estimators=1, random_state=42))])\n",
+        "\n",
+        "pipeline5 = Pipeline([\n",
+        "    ('regressor', RandomForestClassifier(n_estimators=1, random_state=42))])\n",
+        "\n",
+        "pipeline6 = Pipeline([\n",
+        "    ('regressor', RandomForestClassifier(n_estimators=1, max_depth=1, random_state=42))])\n",
+        "\n",
+        "pipeline7 = Pipeline([\n",
+        "    ('regressor', RandomForestClassifier(n_estimators=1, max_depth=1, random_state=42))])\n",
+        "\n",
+        "\n",
+        "# models training\n",
+        "pipeline1.fit(train_features, train_names)\n",
+        "pipeline2.fit(train_features, train_names)\n",
+        "pipeline3.fit(train_features, train_names)\n",
+        "pipeline4.fit(train_features, train_names)\n",
+        "pipeline5.fit(train_features, train_names)\n",
+        "pipeline6.fit(train_features, train_names)\n",
+        "pipeline7.fit(train_features_norm, train_names)\n",
+        "\n",
+        "\n",
+        "# predict results\n",
+        "names_predict1 = pipeline1.predict(test_features)\n",
+        "names_predict2 = pipeline2.predict(test_features)\n",
+        "names_predict3 = pipeline3.predict(test_features)\n",
+        "names_predict4 = pipeline4.predict(test_features)\n",
+        "names_predict5 = pipeline5.predict(test_features)\n",
+        "names_predict6 = pipeline6.predict(test_features)\n",
+        "names_predict7 = pipeline7.predict(test_features_norm)\n",
+        "\n",
+        "\n",
+        "list_names_predict = [\n",
+        "    names_predict1,\n",
+        "    names_predict2,\n",
+        "    names_predict3,\n",
+        "    names_predict4,\n",
+        "    names_predict5,\n",
+        "    names_predict6,\n",
+        "    names_predict7,\n",
+        "]\n",
+        "\n",
+        "# output of metrics\n",
+        "for id, pipeline_predict in enumerate(list_names_predict):\n",
+        "  print(f'pipeline {id+1}')\n",
+        "  print(f\"Accuracy: {accuracy_score(test_names, pipeline_predict)}\")\n",
+        "  print(f\"F1-Score: {f1_score(test_names, pipeline_predict, average='weighted')}\")\n",
+        "  print()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XDsAdX1uDQrT",
+        "outputId": "db5761f6-786d-4980-9bf3-28ece6a344b9"
+      },
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "pipeline 1\n",
+            "Accuracy: 0.9666666666666667\n",
+            "F1-Score: 0.9666666666666667\n",
+            "\n",
+            "pipeline 2\n",
+            "Accuracy: 0.9666666666666667\n",
+            "F1-Score: 0.9666666666666667\n",
+            "\n",
+            "pipeline 3\n",
+            "Accuracy: 0.9666666666666667\n",
+            "F1-Score: 0.9666666666666667\n",
+            "\n",
+            "pipeline 4\n",
+            "Accuracy: 0.9333333333333333\n",
+            "F1-Score: 0.9330808080808081\n",
+            "\n",
+            "pipeline 5\n",
+            "Accuracy: 0.9333333333333333\n",
+            "F1-Score: 0.9330808080808081\n",
+            "\n",
+            "pipeline 6\n",
+            "Accuracy: 0.6\n",
+            "F1-Score: 0.47058823529411764\n",
+            "\n",
+            "pipeline 7\n",
+            "Accuracy: 0.6\n",
+            "F1-Score: 0.47058823529411764\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "***В предыдущем варианте разница метрик в пунктах 1, 2, 3 была обусловлена разными случайными состояниями для моделей.***"
+      ],
+      "metadata": {
+        "id": "N0e4OKAm-J6X"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Выводы сделанные на основе нескольких запусков:\n",
+        "1. Наличие `StandardScaler` оказывает незначительно влияние. Результаты метрик не отличаются. Метрики равные 96.7% свидетельствуют о высокой точности моделей.\n",
+        "2. `n_estimators` оказывает влияние на результаты при малом значении, в частности, 1. При значении 1 результаты метрик снижаются на 3.4% и равны 93.3%, что говорит о сохранении достаточно высокой точности модели. В случае с значением по умолчанию (100) и 10 результаты метрик не отличаются. Метрики равные 96.7% свидетельствуют о высокой точности моделей.\n",
+        "3. `regressor` и `classifiter` оказывает незначительно влияние. Результаты метрик на этом этапе не изменились.\n",
+        "4. `max_depth` может значительно влиять на результат обучения. При `max_depth=1` значение метрик падает на 30-40%. Значение метрик 60% и 47% свидетельствуют о низкой точности такой модели.\n",
+        "5. Результат обуения на нормализованных не отличается от обучения на ненормализованных.\n",
+        "\n",
+        "Итог:\n",
+        "\n",
+        "Для датасета Iris классифицированного методом случайного леса наиболее значимым является параметр `max_depth`."
+      ],
+      "metadata": {
+        "id": "6zD6oaEiWdAD"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Визуализация"
+      ],
+      "metadata": {
+        "id": "eFISLjXyc_KQ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Сделал две функции для вывода `PCA` и `t-SNE`"
+      ],
+      "metadata": {
+        "id": "sqaEQwZ9wfrc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.decomposition import PCA\n",
+        "from sklearn.manifold import TSNE\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "\n",
+        "def plot_pca(features_pca, names, ax, title, pca):\n",
+        "    features_pca = pca.fit_transform(features)\n",
+        "    scatter = ax.scatter(features_pca[:, 0], features_pca[:, 1], c=names, cmap='viridis')\n",
+        "    ax.legend(*scatter.legend_elements(), title=\"iris_name\")\n",
+        "    ax.set_title(f'PCA - {title}')\n",
+        "\n",
+        "def plot_tsne(features, names, ax, title, tsne):\n",
+        "    features_tsne = tsne.fit_transform(features)\n",
+        "    scatter = ax.scatter(features_tsne[:, 0], features_tsne[:, 1], c=names, cmap='viridis')\n",
+        "    ax.legend(*scatter.legend_elements(), title=\"iris_name\")\n",
+        "    ax.set_title(f't-SNE - {title}')"
+      ],
+      "metadata": {
+        "id": "5aVXyn6_LzAn"
+      },
+      "execution_count": 31,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# set plot size\n",
+        "fig, axes = plt.subplots(2, 2, figsize=(10, 10))\n",
+        "\n",
+        "# create methods linear dimensionality reduction\n",
+        "pca = PCA(n_components=2)\n",
+        "tsne = TSNE(n_components=2, random_state=42)\n",
+        "\n",
+        "# get value of all predictions iris_names\n",
+        "predict_names = pipeline2.predict(features)\n",
+        "\n",
+        "# encoding iris_names\n",
+        "label_encoder = LabelEncoder()\n",
+        "iris_names_encoded = label_encoder.fit_transform(iris_names)\n",
+        "predict_names_encoded = label_encoder.fit_transform(predict_names)\n",
+        "\n",
+        "# drawing plots\n",
+        "plot_pca(features, iris_names_encoded, axes[0, 0], 'Original Data', pca)\n",
+        "plot_tsne(features, iris_names_encoded, axes[1, 0], 'Original Data', tsne)\n",
+        "plot_pca(features, predict_names_encoded, axes[0, 1], 'Predicted Data', pca)\n",
+        "plot_tsne(features, predict_names_encoded, axes[1, 1], 'Predicted Data', tsne)\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "2YrTGvwOhtLS",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 853
+        },
+        "outputId": "3c02c87f-2d3a-401e-e06c-aa5c9fd463f5"
+      },
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 4 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}