From a5e9b870fd63064edb545a509174aea12eebf08d Mon Sep 17 00:00:00 2001
From: Sam Shaner <shaner@mit.edu>
Date: Sun, 31 Jul 2016 21:43:34 -0400
Subject: [PATCH] added mdgxs-part-i.ipynb notebook for mdgxs

---
 .../pythonapi/examples/images/mdgxs.png       |  Bin 0 -> 23788 bytes
 .../pythonapi/examples/mdgxs-part-i.ipynb     | 1394 +++++++++++++++++
 openmc/mgxs/mdgxs.py                          |  107 +-
 openmc/tallies.py                             |   10 +-
 4 files changed, 1503 insertions(+), 8 deletions(-)
 create mode 100644 docs/source/pythonapi/examples/images/mdgxs.png
 create mode 100644 docs/source/pythonapi/examples/mdgxs-part-i.ipynb

diff --git a/docs/source/pythonapi/examples/images/mdgxs.png b/docs/source/pythonapi/examples/images/mdgxs.png
new file mode 100644
index 0000000000000000000000000000000000000000..b93d0f0423065531db8547394919d68976f370a0
GIT binary patch
literal 23788
zcmbTecRber|37+(%xofiB`Yf=LM}oidsMdUghI$(*`vtH-Xo)s6fV1vy&^lCkiF02
z_4)jM=X*Qn{BdsQyl-`TSGca%>-l;<ACLQbT%qa@6p0Dx2vH~$@jWGZO%w{N9DcLl
z<HElTadFzf|8QQQ?`h-1f1dbHLg4QNj!F+-pio2<$ZxFv3GWjWiUoB~URK-f{n~`P
z0oCp~-sT8@{%=0F^7l*`ZoDyJT-JB<Smr}7x@EY_hb2#mL^J;DC^_*sspQidM3qq}
zYc&*R#0P(#zuv)G;1}Y?+V1V`L&i?@m7(*3WasfOb7tY~q@>fXHL;CThV8=;TJuYR
zWatn~2)!gW@&^enUve})IR~K${0%4c0xfGGnQRtH0iRVq=KuGXG^5WB7nyf9rkLNo
zdzW~_>Oo!o$Y7yXh4Zpz)xL4P=dH<C8&bpzAu&gHvF@7~7#NtCnOz_vB1%k2X>Mw|
zFp#h2u=Z4E_1}D_+g#*LmRKeDH9bEcgpEfsxcZ8ORuDDiwQpEgujnH7?03lJ>yM`U
zvK95eJhw=VreS0x_VV(|O+ODNrWf=%^Q?-Na+tjR^mk1~w|fbfgan;g1+B5?R#20N
zqM{;$q-#lcdvsVBT3=s3HCirEv7zF|9a}{X3e%~2FIqv9J9YJH_wEtw?CglRe#2ny
zd^<lo9{udVjGBh~v0f`&67h=a$jQykG;fcxT`%VGnVg)Y`?7VDBUIj>npfB4-=80X
zH*VN%?psC3a!`mK9KSibdsPri(`7C?DoUZj`&7`Xn?Z?#qSAg`H#6>SdU|vy<ux=<
zd{bW^IS~#2pJ-~hA7*Ceu4GXnF)^`_urPlE{`R$BpZdPJzknaQe7^5<j#r_7>5D<s
z#MhV20|Qz!rws?QM3?BrN;K=4ZoByo4r<?jFHU}RbksXCqL?o2HNC!0j8alo_Rr2{
ze<xrZweT|~TQx;2Bs6qpUx1IV<u$W6zLk~L=&xU<W0f{;TeBC_nV6ZQnWQ{0{rwcB
zrKRG|OJtY%^nK^rquJTm(3x>;m%}T(Pd#eBd};joli0?_=Hym9xAAjcAH$LlANooS
zCEtrXUFhrUbA0|BU99_|++|faH@*E}YmS_eF%p&u0}ov>ib2-Yl$DL0-PFN>6b}!t
z>-{atyLayrj?8@Okq$^rW#qh{m@!MA@f&`w!n%*`EEx|Mw`pvQ4%TIJcX$5t;|jBj
z3=9k=rlwdgU%q6J@}Rn7bB|L*ByNhIl@%*h@@23*TO-B~+i7=2NBV=y>hQsAxIpiR
z$2avKKQ`}56yoCJ(@wexKf1F#fM@J|nA`8%Mk{@~qg81)Lh>`^rlO;x;J<(Ws7h7b
zKRo{27R#>axH&D$_qh6!r0bgRmoK+Gc9yU*c5WDyn_zi*dYahUhK+xAnEN~N6`zpM
zKQfZ|_wV1E>z}9owTFj=H7)gJ3%Rbrw$sm0N#GZMwfD4YBtk@30jIUKHHcB#Q{QIn
z-Sx+bnVGEPl{S=bJLjJ^2NU1tiT}4OD=YhSWw3DVaC?zYQZi+CWhmVKcTMb+p0RQI
z>D2itGcPx{>BWl|ksm10%74Je%NssFKVLCo^eQs6)l64Yv&rb2TjSHGPe+G}^$zFu
zm-+%u59Xqkw-*)`+AdtUFgjWH>euP<K6O6@C1p~5z4Y;BGwDNkI4a7@W_fveYe&1Q
z5khU2U5SnU{{H$#Moh2%{UM;2aOOIOm$_mdaeBCz-qqj#rh)U}!-s2QRd(>*CnqPP
zd8#Q?lPVk(LOwn|?-CN)2?+`HZEbmc9$0j2e75MkY-wVG$;!@7GT^r&y3A+t;K2hv
zHa36i%a=dOy(STQ@#4ka&azhSqSUkB;?<5bXm9tKrl1nFbZNe8*Iv*6`6KAy;7|=a
zBWP6+j=ZkE{&#qTIPbR8qut`YI@spzc@I)Ml$DkBot%V@;YrY%M;Lx}jl3lx@$uFE
zU!gWy6%`fp&(9{z;Ch`Hh=_^jKi?1)wRla+Yza?g4fdh=`dF1Y?8=CW{teSIqq@<$
zSNo&QuSt`>ew92vS}luTDCX;-;WseR)zw{_X?_ha1Y1&HQ<K6xqC%%ww?f+MsJx_v
z54pI!`$<i05m%axX`m7XnZG+f&+x48JUBcQg45?Z-$9K$sj#rH#IiDh^Dn6~K53#3
zlkXXCJr9&o_+<HW)DIgk5mst@;ioxVMd@C0Y#UNrJXSw^hI(mYZ%+(G2oCm^O?P*9
zqM+#oc{cL4D8}%wFE?h>ea;$X&QAiMqM8g9=?IF61%LQ(9S$EkJ$>hfor6QP%|M<k
z-{t1--|@e?t~W(oxnW{ug$L`?l`5GqHQd`vmaQ1wwzocxg-YNzL_c~&7kueTmPn6c
zG}GPH;SXCgubGiD*3(0J(_!+@z+E=-%K}E1u(7d`t{@kDsj)j%5(_oueSBT_!=sDv
zx{1ljW`BQw@vLt+{wo>y`nCBq#t(&(l$3<3qT_xleRg23|L9TE_wNM?BVXb0;u8@C
zC~+Xi$Z4^g@%s~DT3Xua-4Wy0nVFeQU$n1sa^eanU4q(BUQyw9pR>8ze*C~$N=i!p
z?%hTx<Q8WS-iv2$9$ve4O>e9XgF(IAm~34i|D2hh|GM7m_+L9x38#F{BWGvL#Bbfo
zgm;YJoBHzN8k`S7$LTvu%gZy(q%tkri#=QI%s!jrP(ZM+KdugpkEgzXjqP-Ha>&zD
zXE##1xxe2YM$OBU3ulxZPI?ejvP+jPmBT(=o2+}B`NZJyTw5gUZ$_5O;Z7TqQYUA}
z8~RYn;G2-*1hrdZX6*RjU^?>Vq^}YO+S;0n-{>2D#sZX!+k_?;sGv6g{%vsE^MLHj
zmoNCFq>BTOs%%@&CrTdPKw;iqQ;T``E;uWT4eI&U%?56*JQg@DQ}t$|s=B)Gnrg$A
zO-)Tbb*A7^3fT^F!76h7{G`p(nk;JPIM+rDy{)mO1qa&O{J@6~A55P<?Gk{8!p_S}
z^y}9zx4pG1w{PD*AYpN+KjegNgF=1({{7B)wY{C|%-Q_(w0iOS<LakB&JA5v?d|QU
zho#*A<tVgwbaeQ?eM{M3;F@NUeAA)yAKkm<f&A#E#q$o{GVL+T?)SH>$E&ZOoSq`h
zL)!X3jqY^Ar_qVwQT5Z%my_;z`+q02lGWg#ct7NzXoPN=l$3N*`){PRr6osUWcsa|
zbRz%&$MZ9<pj~K4ZgAWWdure|1O){PDh=)9&g(5LEM(bYF)b~D#*1rfC63bb=TkyL
zLQr0@{yhKEZ|A!I*RX2)uE^^Pi6|ja2^nz-iKfv}T2iLl(+=M5?(QdL_fhe5U)I;w
z9AO7~-;$CtWUbRHGZGXLY1x`<C#R&$?)BEBe)Z}Vf*KBvHyRFINSURPEkix20*7OG
z_%bIaXN~hR#q$?0VpCGWzkR!X+hZq0rYtUwDt9BhsIUISLrY5wh52OFbKuhPY$@3c
zPG)7Li1)4Q*NHU?wQVL>n!36!!D+swsj0a+pe8fZDbVm|+}=MR!0F_`Nt&72YeysZ
zG*`0_E$!FOOjJdDMqTAF#RRRP`J+WARrk=39|R32|A@}7$iN1IT8iw?`>Lwc(&=yB
zP&~1++U^uRe4ney4VSnD)z{n5!$V?iecdi@BW$F^(;mm>HWem01P*cI)D-g#tL_#+
z1&y@L&>@S~eu}}4Dlw{~gEsKJv$NSM-AmUdrMFjIUS1x9!T4^(X=p(A5RZ~mRwkr1
z|Ni4g<I5?p*H9xj#|7J1-u_V6)((W%Ge^Lfs4W!~FizFv!0+Fc&hp}ul0xCsV4<3t
zn=9P6Es=g7A@sz;Vg~vRN?8RT>Uhz=+R?GGrpZYrXvD7pIKivu3$wiIDXxVDs(A8)
z;?JKya2)W$X#`50rpqF|j`y|}O)V{P0YV}t92%Xt#|{@E5s|62b%^E9WbXB4ak!_o
z@z114rGq{qeSY$SmY$y1({kler8T*j!(`XXew6Qzx7X(8qGc9*I4rEJ*a3f_rWY21
z{rxYT{Oc6p;^%LBb5(^(BBJu?FN(s#LLF~@5)zWly}h<??%SKQtwaXj+{jbJ?9oa}
zAst>FQF;S*3Uo{t1+fS(T?z~ex_C|}?OD@d-+4|rQR~7Bm)BQet_UzPg5lQ3j{dfs
zdvB*VHlj-nzrx~RL$&pTCf+ieV2rH&$?5)-j`VADxFLXs-_DL!%PTA8M@kLhY)1gh
z*&c^V(FnLovycjE=>c@LnUxj7j7mIx=mfoAUpnYNe26^kkuqaEl$*P|DRiKah={=4
z+^f*!4^pO~S(Mw2@Q;p;vIq-*?BC_u{&{n18bNW;V@eIaG<~KS`jcEFkyliCk?1mJ
zV1N?3@LhOb)62^b^Q}H>e{q__HF~u!3Va~jVXEHk{B*ys!N=#D+t!5_i`{qO{aE9b
zV^dQjEjr)nj7<UNgy*X$FOLJoBnqD4On$nze|>#@|8@~E4Gm3Ta4=6?Fu)bRj0~28
z<Kt$4v4GP($J1abhs%tkH#Z%{#l<mDMKd|XpZ`%3Y%?)8Z{Cm4dDk#Hs{gy*iyqbj
zx_tX=YxrTcmPJRD2J%2;&3Bm&=M#+ekN4J-ii>%VcLvmWbD1SwBX-w*y<J{i{Ur5j
zPg}XCr^jk<Z*MJtRN6XZjY9V*=}S!wSIMihlUtw4%T1s^Y9H5hcqEadm0aj@aX1q?
z$;rw60`N#@`*T%7A|rL&+Hkn+O(LVB+P-eo7w^ylF$hdgH^%-`z06Zw&CSg%YiY@u
zm6gRKwO9o(93II~E&D~*z(Jkth?dy*?=R`;>D4D_0eqdEo9oMz!}Cu0nD(fn=mON)
z0H3o%yZ~I{CMdx4oCB(4$Da*(#KmcUCX345xr2gNJ=pnJ+5+&%zJ!kgom{R!U;?F<
z(9nR9fgw)1GwYK1C1&pf)@p~TC}W?~s``&g>SI#hihAauq0qTA8yin4OED0z=14?p
z27Qm)xm;wEy$Q!k=*f4o`s3n{=D<GtydpI(yl`|>KWO!WN*ZKkTJ(Ijh2Ugw+(Ey=
zTZ&1>I|BL_YzDm*YI*FJ6zC8S94$fXh%|OUWhM6G)qJ9&@pW!n{?Mc&4eLE0E>}ZK
zH3yudn_LaBGQN&%b$jrM@WZ;ldPA;Ns>L;>)t$*Xks}}SR+npa8jL8XWK@fKR&^)D
zcHJjdwv&Z)8}=Wl+Qsq617Ik5{F&tJ>}+YUkP?c<%-_ExN%w=H;$A^H9_`pbQ!s;G
zlqhULSnIspPy0|ud15kJbfT<i-8Mzn$<U>9_a1%OW7UxXmtyZaiK&Jo?evikwf2FY
z6M^zAiRv9;RqPa@fm)Y_*%;dW=4#rygQt64v`7s<j}>p&apt_b7TF?8)hi&i`@jA*
z+is5{9)r(%n?F`gTT?rCSJ?Q9xJ>EH4SBlvF@IB4OK}!vXD`Gm$qRTCdcsB%*&C@e
z^4w~t5j1{Aq7jFm#YJ8tUs~}=UMW>X`Q}ku+`D(2iN-WJP9gHOhxoV&Ei?B_e=b~S
zm&Z>wK(Xf(7Tw#NJrOVB;qaHmiiqJ1`B8A$AD{W6AUYX8sjTF|ae~8*Y)!g8T-8#V
ztfJkVy%sZ}XOrJT#q7tlzZe4UMWfL%v9W!3Zt;qGpSZzt%|dT39P-{_)ObL(`_Vo|
z*|AndY3iJFgh(R==*jO|7Zp=OZSA-Y?nRIUgq&vOYieo`=E=mwB<?tkO7{k640{~u
z-ED2S@MvnD&xbWt5EByvlEDI&0Gx_NRJ5w?@Pn!qp5Z#JEI!htDnp7=z7_4lmH`^t
z1Pyv?sgK?B-;ZlZXOU6=SXU>txArRliU86S1O>?&8ygAR@kvM&yuGE7JqX3^kqawL
zN0|sZ<XILf@2)L9+5P+X5hUmRtcq1GI0N>d_=|;0DB!dH>9mzOIXECT!1=UYbeQjm
zk5SHbbacc3523zb2W(3EWK&LDQnI<bn`9tQwHbO)9FtXR7`63CDI*^rU+<R}R^I5Y
zrY1BL(rG9KC)I$s1cihep){aSfZlH)gwxK>?f}4HS;N`vXQ`Ke832x%*w}<lPn%Ft
zQ5ATV29m8SVwGHo!j{kK5YUl}<{q!F2zLGIb1wbtca7hJR0-~q&(+nzp`ipwfdo#;
zn{x_vth}n~E&%(@AwA>5+j~GYHlfhXZq78<IL#^K{*XW4PCuW9Td8r|Vnd#HSJ(Y`
zdo^H3%^MBpQ70#!fXD*_1E<eUj}bhSC}dvlb?oXr4)yduEvKTQ@z*CSaweuIK#sTo
zToZ4*3kQ=hhQUdvqNbh!cGfmpVUD08Alha<X;M{nfq`ULnW#MWu<&ql3JQOxxi*j+
zR9V-7XSwYxkq>`({QB8M?ba%E=15+>QWi-`ditBsTCSyg`ZG$pa!H**LllHfSo8I3
z_QOw>)8Y>@TMpoj%zyzw4-70U<X-4b4Tnq2ZHERc4p@@_MSJTxhk>EtVNyCsQ=8B`
zx5uB2S0h{P<kkHo;frZ$X=I`D_{79Pv9aV2(r$+W;?An6p#>4^by^xzFex*MfPjE#
zY0lqSid$#7BuQlvKIB=%1@v{7`vwN&G&EwHYT=#%ccW0aq)ZV2cjP!IW>(7@+F;R8
zak^5$fq@vHVZbGHJk|h%n1ZNeJ^AfcsZkx_<HwJkmwNrwHU{!Zk>>{*konG1U;JJV
zK<xbdYYl5_YhCB5&?>EZ(i)qa1B|aFbn57a!a7ld5;H3qaog*N>ej7WC|^Az_luU6
zmLLov8cocbH~v7AP`TL!PbX^QWfa?Jfr`e&$6rL9@J)35Q_2u*%aoXyE?m9cCq{+g
znXVbr!5VfwE&{T|Dk@41RBCZ#qTVaTc(TE#q5l+sNbK9U0iY_#si~>WZ-JDsx%TVE
z6H7~g!PS}`Iulj^q^W8f^K)~z>`khxtC5|atC9e;Q*ZuOUVi>u-Tq`V=zM+oYRsrR
zAV*P6zFZrv7_GLa2fh<T&aQv~R*ITlS_*+(Y(DwTJwEEy+Ncn`g|0`L>-v~Z5;Ho)
zQG+_`@)ao$k#~F#{bADzwr#>v&n~9>w1tqfYyZ7r(LrTU=Rr+CKyVUS-v!4i4Dh-f
ze*(LV3^TB9e|a`P;QA;QfONnCP`-cuJSlnj3D==+_g(+~6(}XuPk;Ht?wf|~O}PG;
zEf)VRrz-l%6ONCcK4GDtb%J`Po;!yuI}``F&zR2n(QocI&d!b<g6yGMZUSta1+uVM
zOp0aFpL6eErMT>1r7d4A-R%7A)a_`8`)Fqw8vtsF;n&MouU<U>s(rAI&srOLA@TA<
zfS}*%>SpHVuz~!4d9la_JQy~=cC8oSc0?_OyU7OFbn?l-!d?yTfcA#vrIM18>6MkR
z$Dg0gfIjA#5gr~s_X<0T{w4`PEjhrT{rhwx)?^?L2m$p4LOug!K}Sho_2KCM1rC-u
zGk|>7l_o_Gf`kI>BFE+at5BV|o|*%1Nt1eo=Ahutad&f@PI0K01-!;VK>(r{8EBj+
z;AI$6nG;_?pmJ=nCV)q2r9It<%WkvEhgfrvWW~b{0%g#6RYOEXR<D6w<PB~i0jt+}
zA#sFtDgfCNke<X}{xvYSx|`ptA0j`ZP7q-oe>eQ{OvCx903siNa`9Q?-<>J)`2w5~
z5&Y$2HE3vrOOja(7t8Zxm8N`FT4eu6_RayIgGa(}2Z%X>+(D((R+jNT5l855X`cnZ
zK@AGzCTRH=;moyznn5o-4ib8$Qouesu5&&lBm@I>32^ER2-ko+gG?jer=}u;XOGOe
zfvGX@x~EV_0KK%r3(U;S_{j#~Ha0b7WvT^`HEtQVu?i7v{>A3txgs4r=POXpb^3B|
zt>burHbj10#0{nL;6X4zPMHUW|Alqb7xaHou~08I^2qST0EGiC;<vW;6lDED%z0QA
z%7KLdh0#$~Q@xmWTZ0i(fE-(5qoBm3q#r%T7g4}I;@irLLt!^V_05E<O73Fn^Too(
zoklQcUv-CNW~l>6xJ!Wg1M2h1P9zHm(r(-Q_9WBPB4)y9yV#dS0^nlf<GTcezfZli
zG9Wyh2-+he$AVyRKT!}1in!LQ{)>e!zmybuzU$W!(gJG`srG7f`ag=PogESET9996
zqh-#f2axvvV&VSi_;@okdH}^-+}wrRhum7@Jw{6#D)@cxam&!oj*ip$-|&1TU;e$r
z6F=;B-X6`&&ccG~e0ROrwf<VEfmB0!Iy3Zc+ms31s}!N-4pY*Xz(D}LC#a!81}Ot@
z9EOhcq4pulzRbvQhHL<G96$wa26CNp(*ap&6pOSplb@g8_wMdseZDsECY<)x9vakp
zUI9b_4i5$h9m`vrdSZF5o)ogIVvtT_6B9>Af2ot&nf$-u)P)4N*I#h<eyt1@7ke@5
z<PYQL8<_L+^RGe6J^A<k)(d?TlZ<|ms5yNlC8fw{DJUX)(2;c?2_$qrM58ZIhPNVO
zuuxlKetv$FOLS<cDafZh(Oyfvtiu5LLF{N{V`D?KL_|+cueTZCT=@I98%`iS9BmNb
z<_3UXtK7S18X*K=UV1oNk<`-EwDJA>_q6R!C$7LEO+nbxa+`uK`tjhvt&P@PuhhU4
zDx}0)C5{r{;!hX5Q%_t$A2ES-0^s70NYmF1YGQU*N7UvIt+!w1@MHXZxK-ObX>4i(
zcTOEP|34$_3#d^L;k=L6Z$AC?@uHN+&K=lQPFu5gBNw7o{;-5m=y((+x9a}dTj_Rt
zY^0!oySFigRGy;Wd3i%7J?S#-g<AQ&g<98wc7ZwvR8$B}daPc*pDYrZ&K@C@r!M@a
zbH3)qA_)LKgXK}U7Yww8@~6Lq0TgDyu{Zz(aJU2IZ5G6qR@fp44fQ@f+M;yQg-#O;
z-!cPD3fW2ZM=QJ>92_XOTZ;Ngz}Z+pGeeY55OWZA9uq@`08KHm%Lw|5=hZ`@;12-+
z#Qsh-M1yWSRO9tv<Ush!xuyc=t3%Da(i;+#Sx%9Rva8`mTE&0kEEFvW8?-x(K4yQU
zb11VNUjdvf2=Ld^-u@?eYWh%LdAYcz2YB9L&~kEi&cDmc1;yMpO>%7njJ}yTIXOLr
z=Xp{K6kHG-rgy2SKmShFb$aZsv;u~Qvuu=m3Y@$Hx=nU@cVnZUfM4C=LdqJ{gSi0^
z(G%fHRn*k1BDmjSc)}tgo<Mh;-L?VZ40!Mdt@#?rqfY<~N`vwZ9jF|TAn%%_`@bi9
zKdz;hgSdpK{iVhY-T7W8TkWbJadh>D;-^ENk@fGVE6nx^Z*6UT|M?TqWGh_PAJJa9
zqVu%}Ot-@?7{rqas=W1LVH!>$fW+7RUI6}uJa(-4jO!VEPIqZgCZNB9G>ljKa)ShJ
z?%V0!I5e0$;g_%Ttf$Av=k^v;`8GWV)fzw(R;c_1_(e}oA0p3|E2VaRvaNQBL7eRR
zqe@>;bL-EJ?BGFd9x@=!-C>HPjn>TE{O||=K!3mEzd!fRq`>3?u`l3we}f$$Cqgiw
zCQaq9k&~c9%KMyos2#lxKBT=YT;Cfq<&h8trJH9*>D$W<rt^K!D9cTogO!f~E;HPA
zBWsUh3`k0P7{BotlrQMVnV=YhINt_pY`Nu6IuxcYk{)_RW@SfzclU!#EfV!drw`i@
zv+y#X`a^sDNq9QpK;`GA8v~Gz+Ld%;Yq}9zS_<kih&fB+p9MhN+X9(&b7N+4@ikBh
zU5Pvc{?~qfSi-`>(CKhd@&8nu)gGQ-*Ey${xN}v|Z)vhla&~rB-M}CMK&?5jU7cqQ
zo`=s{+S+D)&X1V^Jy7+Z!J;9`H5U&L8U$3`5`8~JS*Y{ay`cW6sP@%9KWh5VkGJ|C
zpM-XVUVVM(ihpN<fceH`9n|zjD0<ZXt8v^~$U#F&DrytBS-iUs*h;XtrY0r`%xY<A
z;mUz`rw7~wXGj5r@CIq9^w5#oL2S@~>jNPDUfPQez(WAkXjoZ{6GNJePZYe@_9t=5
ziFf#|d$nPm@&_Um7$#}Yf~{^URSlg~Velp*pw6S&Vp({22si4FpT)N)%lJqmOR}=^
zsL*4pHJk>Kp-iDgfj7w@?udfNtq2eYg#v;H%3W|$(v`Mo=I9>j<4a}rM@9Yn6~J+z
zR&MqwGB<+Mi+tPG*37^Z)Ej^zG(a!QKYm2RwK`2>um;KZBmZ|6Ab0T_$T)lkHDq9)
z!-^3oL^ByKpEd#i*q`!Y=imr5wt`Xv6_=Htp9G{S_yxKcT%rI91vH@*IK-jVD)^=9
z^HuD%yh0U~R0>yxCNlOsO}xFmp@G~1;SzvMZ`NI+o%L}V#O%!5F1VabdjBRAFyblC
zZQ>|KNnMAH%;G8XhUYKsb<A0Lj#G^D%@i#EqdQQcFkQWu-V40`HL2W|Ws92csaUJ%
z`S|U5C<6s0R)f#$zATo!_c8V7ws_@W98drL^5OvGoY>e{zk&iTX8_}N%4Sp;=N@>F
zn*ea3HD!%aSQ{>_yv(U3;O^8z4KD0-;t6Jmgh-|79ma+StJ}xuk_Y}!K_8tJPlRRz
z!$KSzz+FhHKy@KBMwx_BA`s3JNRtf;jA*4fcg<0^0B#<lNAfQV88MI&2)sGzhAk~E
zz4`B76X??inW6Z7B49<{&eX81(tN1)``^{uA9U&|UmKfS_*;jKVLkoxJr?1A4ZrhL
z>A`eYWKUuX#E%y1qR};^X6w5SHwPHY6XZ=LRaHa`Zm2IL8IV)bx)vgKHR$$p9x-so
zT~S1YglMq(H^=PCWP$v=b!Q74#MUuqc~|)Q2kwf_L!07~937{mCFv^cQCN?1vsOaY
zg&;D`=%TL`H$#ul8thF%r<KonM_QT4l(^q)4w9H)VPTCn_{acZEctN)F%D&KRkn^d
z$(qNvkPd*5i{Rj3C;(hiiAsUM#v?7&4R;u5HE6sPD--f0Y_$2DxarM`3g5Ls8`gWp
zRS2jFNKSS}1}d6arVTbw6{j0PJRBleD5_y_Jh>x|^^8EZ;C%2NA2e0Oi)(I{Gfm6`
zu7m093}?PLIW?7@&R)DW3U6IrUOxRgMOjt#L&A$<hmKN48+~FSV|}*vEdduN2H%$F
zL$9gdg*pz}h#Un5;&>}D%gD&!;NVzuXsN~*Rgmd?5a8zz28$?)S%w7MQABUgR)}l@
zLyj06J3xfy(Dp&cxnO<?nwT75U;yM8W_D06p=u5teEB~AJfxNQrmnN<wf(0&JW){^
z3Jbi&pXpr=_bHzWW-PYg++COKf^Avhv1{wRHcAdHss-wh5Ksu!g%3Xp?JFk0_kxBo
zyEsKB`~?5e=Vy^%Z=g^h%}oFKgM)&?<)_c5psXDB$Q{`1|1C{HnB58s$%M4_7*;ux
zXV0QqTIByMF0w-*hQ@LSu9{lmX=qebaC9^&kot?D;~+VMYY*Pvads8~)YV(0!yAN=
z0KMXVssuI2T(G7XK;#EDk3GG-8foPa$==k|547__mnc=)UnbQZam3GyfBwAh&CuR}
zukjV9+UMN+J^04w@lp^q09_!vKQIifHE|nVb^4igqQQsx=Cj{-@*kv94TF#{2Ymy`
zsQx#%3iOzo%qb518(0A|fOy)WHfuAT1Kfd@d{<ZZGMJ}+phGny58m%8U>L+h^4R|y
zNh4r{kXE;o10hW<EdWQvaKMyuH6vg#P%-b{N9<3&A_cieABZZ*sX~Bbb8>Q6goMZu
zeI2kC5(Pp*7NrsJ0@9~UK%{nCOnvDHb{5CgtKdn`0pvu(`iZ*Vb9tai5ffK%a(qRn
z6qDDvl{x!`n{VKSnZIT}Ps_J&x4{xY<!;}OZPSA00Dvok{$^BqO+^>H*Vf75i+X1*
zOIQ^|dP00fP;Sf)HfPGKt92zJ3Dh5{es<e317Rk^=lqNowt(y3-=QEaU~_qil|%7C
zp}@{w`~8Iy{C-~mLqP9@AjW|(8d4KDHValdQ2In5#eti29~ZtC5xx*H3@C12m7N|c
z_i7h#oeQXU{Dy=8z!5D3tg1WfD&@8Jf5{ius&^{)xW)d1<Brg9P&@=pFet$`M-Vjt
z=oq|pw|fr3B=E>`%1a9hLN?}a0lH(5@n!(g0kn8gnS$%)msnX@1%-v1z!ZyT`U<KS
za<hm{Qmj{o7?6w-&eH?OJIeuAZdjT?bLGKKtf~@?h>Sc0-^vQ?w5?7Hog`EYXgAPY
zgM63Q)*?VQ@%{p&iSQ5`1$!jOWxid8&&LxOEvz^4BfzUAvc&{tHF$l6+NS5`0$^ol
z0lS$*n1a4}5*UKkwb=Ei>#Umj^Cu9vd!@rvn(@oyJ$r9=kT{h0iAYHmA3Pud=x#h;
zzy%ry;xh~1xIum4ZqiC^&%5|P9sa7IXMp0KRqk^It^uAB%k}G#q;cT&x)6$4*c;th
zUi%6AjOts5%~~Ov&Wul<ZZ#$@U-ueGtWqGdbwPH84tiHzJ>~&US(_Mw9v^<h0fELO
zVrInHrxj>=1c*unzpXmrxNAIic_=6-OsuU7V)hWh=;Ux4&dT?mo_0`wka!yS0vVMw
zHehG^_U{#l+6_;C_&lk3XcQp?&<;^FA-3dMrf31!S|O4y2=>t&c!WwSD$PYY#oE#o
z>aHh6Yf}x(J||mvVCYpqzGVln4zzX@RDxp8sYq!a?l2$G;E%Q{jeB=+l*ch0M)IqL
zhC$FX)ZR<D5E>a7C3mU4MG`RZmLR*Z2ndiuJHLDX{v`?5wF2kkogHTg!jZSNwRyT2
z9Imv&3Gv))#QWaWB@f{Je<kcT{eX6oP;%bC1JxeFD@c|Gl-z=@bdVJQje(?L3ehGw
zgvlG2%o6%zz^3pb%o#vgtcOdkAlbh9Q<bE!_JW#|ZpjVbIdLTZ1?v3);KM_puw<7n
zBTgui$?M3N($={-oJrP(&e5ddR$Gr1mvCP41uWL|>(>>?8kr!uxgj@&1xTuZn@I8e
z3j^TagQe_fs(vW2QQ!q5UMVE7FaY-sDwme5`3!5R-~@oswJ3Rk>pg8`OQy!Xd-p)1
zz3buOk^Su)p2Q5Oglf`<Ig?W=1yivrmIj<dyv)ogwt!^!jn^qgVS@~LYeA?kBm|#9
z+LIO@11vA(xHOlvr{M^|+jv68k?>d=T=R6#e+1oWQbcXEfT_r+sBqvgS8dDTljGWA
zv(FcvvTXZbB!ZtR*?d%PA{)7<`d{q^>IH&qL8)(PZ|8kF2WJ7`j9!s~gxe+yWFJgz
zYzTOHdBL#cy_7EROc_}zONUeCM-Vo4wH#;xH(x?r+$Q@S?Q1t~Xl1HY1_WTgdHZ%~
zY-DoMDAP`?kQofnNL?@dUim`NzR??OH{}JCcwA!t*)n2~)E}JR8Q6qKqh3`8`m_bl
zZD@(MXe@8#l`-1+q51w<b_LlzxndU-JPoFE&HY9TK)yl7+b{qAp7|;_@?;7+T96ms
zCE5(<$ECl^M4^R4c&YHMA=(-3!bla^>*qIy5fKm)(wIr(;jrCD^U(*@XF*xl`|1Fh
z%CXN5(NKqxb6EE6A0fi27T<Qz%Xr^LA~v1}j;P#J@!L{Th|B`r+|9#d7T63xX3f!P
z4^|47Wq^vFcY{6-6!X`O&DoXH<P=rhrR<DU(`UvisbIz(!Tk?Z#I1fluY9%v`;Ft;
zwPvuxA!u0WYWzROp65*u5G~=vfTzR?bOMCdwsD7sNH`oPC)+*K;v@*8gpH3JNw^Bo
zFl9O4>JS<Ql1<3x@@uAzVXcARtf}7($(JWjo<#05gP;PyKd+p>=(po27zBiJc=6C_
z#i9L_E+kzIj7iRb>R32ny)%DHT%4<GPX*sbf7kG|)d@pm9Cs%9%5{Ecg!qZl{R?4)
zSQ=kGC;YIT%xiNJC?@Dser&*ocW^4HO|7#P`+U%w1hJJdkXRJ*I&y|sMTT*M_tLLV
z`JCIse-PgcFwEu4m&-qWY7QizL=^X`C{c)<f`;1&mKp+jkYH3L9w`&1y&Vscqm?)i
zmUwnBM=D)cUQPy>ClIVLNDGpIEB)rp8;Nvdb0iu2cG=%}q=M{cm-0DK1$Pfyp3Og@
zD<;lnw_az-y*J}luwJ?aE#NE8!t(OrKbE5-L}`}Mqtu9o*cS9Xq?!G$dqo4t2H`l+
zCUsZMq5LArTSpg{yU(8s;}X;1fOn15K}4Mcm0Tvu)YJ1e1gsEbQK26(-<dE6z`Vxu
zP#AHA$jE%Js=T|C=hyfT6pA7Sp>*qDCG9VNv?kj1#OObThyUAITI%=d{OOo&vbWaH
zqF0?NAx<()QLL1YL3fzDAPeLQs#PsR)?|T1l|<U}y5~+#rVs#JNLcJjYKAwC&vRmE
z2btd1b1hTu^+$n*laKu#UktjTJ0$A!S%O6enLpUJzaaN{Xm4g_miYGV+Ya+^dA4dG
z@WVhPh9SBu2G2m+9yH1l$TvJ)=t{cJ7F!-fL?aBZ1Z1=eYQsbL>Mu|?enHR<f{M9$
zdBU+uEl9qEfsrvup3T(QYnNKmWrb>Wtcv$jb+si3r14SbP>BVh{vg_(aXuXI8?e)d
zAqr;s>{++;!E^wfA+P;el=ejiqk2r@{XB3A=2i55JP|NfjYsI39_tyne+cDA79S<<
zb9NhCxuV#NvBa7d!re6&16{rUs%pj-RZmr!r@bM@04`$Cf1JdP;ScnH9kzxaJD36r
zJYfJaG3Z$(6m2LPjle2&>e~TCB1ihY)T_%N@emHVf=+M^;K^&CQeEjX%usFGz{-n_
zi$esE88B)=OIH^+g8hIPjj&~rJ^+i;48|Zrh|NKE6bG1`J166|iDGCs9W6IS(ncW2
z_LdpTApZT~;i3K~OV!9ynnGratKW*onoq=|e8@E!tFS@bRC@4W;VXjZ7$J(2+@be&
z7p@=d<^SM5K@$|%oO+Aq55VjLz0tR!p`qyr<h*I*%0fdm3eLQM03tL3RHA9PyA!@1
zovQYqK=FaTgA@csfdi>-B9H{TpCU#K@`Zwe0s?UXIzU^vL`g}B2v?xU0i-}FV{d2O
z4F{wV0)bs(Xo1MNf#b&#uiOW>^Ir00l#DmrK62n8qhvZ*pkX~#F9iyS-r_3flL(us
z=I<IB0>+Pu(e(OWr+2O<XS&iSLI5ZdWlRbI5S=K#tPUiN4}L5NUY;PnB}%^JPe-<-
z0cbDap6b+=LGBn-he%KXvLO4h)SpYV(32hwU(1BQB8n7bj2zES+$*j7&=yPp6Al`{
zWsHrFZxu*j{z`%R`0*ps@yfk`6@#AHQ^tF41eub#MM>Vr-kPPXd9x*5)Kd+Gn-;s0
zm(8l|t?8Cnm3rJ`$J8Z#UH<;Q0zo`P8H%L8iRfN#uU^>+J49y9p1ZjimR`@BC2#|@
z8AQ<wLhM1q&8MTj#Pnj?V~U(_XF&K8cU`0H>ZnaD1Zez!6VVQMKEq1_bItDH>5PcD
z<joNepeQJ_k>~F<h!nkh*Cqf4Bm_iJkmb}qTr<|;Az65zmKFsLPVUBs&XwZ@<B1s?
zKKcIM!BVurSJ&(OhP62C?Cg+v!i7S6u;FuF{Nr2fa}XY*YHGx(XlUl3I73YMW&F4R
zJY*1s4yMk}^lWn8fP+ujaDFBUzN)&qx-U3P(-13>g;X&r?p+LMu|z0w_kSGVP-w2p
zzX6&p58x6MsdCtAICyx)zg;17s$XSG4Mc0}&ksuE@PMd97Ov6|Rpej813D(@aH(N9
zBv>JXO$5orR#+UYI9-9sT9;zoc6QNbr`$TmPNU=udwY}j%=u&yy!>z5a$O0!plbla
zXob*$Xi_@ohob%_NInT!cF_XBKR7y?op?EQ_t~@3j`hW!bW*tcn%dfbpDYq|1nME8
z1tMWPsI~~UfyFTaL~lJ*M2%v(di5gkRm4Am!2{{zwHx1#R*H2Nq`0{70URPw8i1LQ
zX%kNT_ycf@;5^=ipWT8q+$A~@rObf4t*h!2&tZ9xTq@-HvLH_l*q0PqGZyNGWfumD
zWrb-oF2ue1qxm#*S7te-ls*^^GIi}k;OGE)d2DiW7_5f3muegODZWUeLo<1JYmNIh
zC(JnvOm8rPIR_xNkip;);|^otm8jxg)_`IUL1WekSJDC|rJ9{9qJQUICFn4SXz~?D
z45DI41c36|gB(090vmrG*H9Xrfv@T*0lJYI^hvsfS^_O#H`odb$Y&&LDiYWakG`<5
zF;iG-lg4A?gIfEX;WaY70vUv0NDnj^vBg^Ry>OA$36VEMpGH|fmo9C~TE~hgBC84q
z(6{7+cbiv6AW?}Ui^({Utjfl`mu*aC9pwtlNp&Fr%tz)bKO5@EpLj67qf>ZKK;FI#
z%`uky<DwvNxfSO-McUqpyji#Azy5|{67I@ZP9RVbBVVR-OvE3e@$tms4{d(i)bGS%
zQ7;@6g|jPl+04Zn+3Wxs0pUKjkoehgU+I*K`CFXLT1vx%h#-ANTkHXzDiRYAu5eKh
zIhls-z65Q6)*Ok3AovZUT#;69$ozQ%5_;o&@M}*R25}W^vxfr?fgV#qNkJl~@ao~P
zapnNxAuevN&2@a%-9YmYI?NXKa(`XDpLU`^gb07tI0leaFzl<%PMGLu1KY&*+d0Hj
z$gW%=0JsiGKF^&#MZ)Evpdd(yHz(b&GO@D@ho*snpeso8iQ-Od$a{hRfW!j3eC~#n
zzB%lONyO*4D(d=RP)jUb-z;mzzF_dKt=u|l7<f7|Jp(KnQXZrAUXrz7Mr3=<KfeW%
zrQ^Y-DQFuAaD+{aQ0dRVz+3}Q4awHPn@-D|ACiK0j>I9UE?=I7Q{)Ey64+P>5@H62
zZY7P7)gN={G)>``)6&*`Z{pyPz{+Gwxq$e7aRDO++I<9+HWR=kC|`iiBW_PyKnh3B
z+eCaoKma*2a~T;1syg|uL&G@G)oFl(D0PXkc34aeO-+9!_5tA$C`~JAxUBw~PAEhB
ztk4RwlqPjm`4=E902xHWYe@72TF8j)6tMdsn*~Vsfm&1YoF3o<XnkxqZ_=<UMBD-J
z1&NYP&`wGoRTcFIMh3<N>ltGDhB1$McNh|NH6(qna&RD;4NTLtKox)>ygT2cN_@yu
z4E-&(jhQH_=1z$dx^?Rc3?$Wn2LhpSU+~Xnsz!{!F%GZIhddyK_6l1S2&XV7gNC*b
z<}E7jfxGd*>p<kP($cyGAc>C$`Dasl5C;KsgY%mOX)9>+Al@hd-$lR+z(eTVNPtw_
z>!`9H)fRvJV{vh@eKDS_e-mf?uTC0CwI9q0uvHYLi9RZMu<iHo(W9cOvV;U01ZY6a
z8q_aDVTYPcIF5sh%Ldb9U}!YK4n#%(P`-#j$#BabQpGfooNfg^kNDNO8_fiJ=M-oj
z%A*^a7W?0|X-JejACB?Z?enbXG;NJ**_4=SpExm7>-_A>c_a^;43b`8Ze+k60879C
zpN0`F!)&kJot^S$6B0lGw<ne+CMH<l{y;douP+MbBiKbms9+WgrW<4p3>Z+;h)M_b
zvkzVib;r$3{QM?sE#E!U4)fBAkC|fi#|<&B*>c4MKV-S09z)wgG$$zB-?qCYkr}&k
z$C)O&4H@fR7U*fJriO>#9iBW1gcGJa)&l~~vIpqt{qoD9%7!^&gK4quNIc`#^CwlS
zbm1|U)K_yjE4|w0SN>h2!_?iE>CjQ8xsYnzUK|X8#40A`qAu|1>?)(k(D;R<Ri)??
zFs{o%ngqei4BW+&BM_*UR&Rd!^$?#`RHq&<U0s7dQMb^gC003ib7w~$>}@1<u?yo#
z(C*?DZYbir7gj7D7x5;Qe^RSd|A4IzMkO*V@f##f$oT6hJ+AW*1#2&}qKh1DL}4~^
z_>Y2GneTnrhsyUP(s-OtY(M+OyVwah0FH9`LBuMLc~UQYc2`NRz(t@1@?Y>Yraw=4
z%YkZtunRInFeJIWPj~eqtuz_YEBAof2j^{OR3W!lirxY92P)X}=g$v*CHeJ>l_x)d
zO$_nd_^}>88%fsFFqRx{vLkhUloVc`y{+Me?X9gwhlW#JQ0jxo|8y#ZLqd4O8kc}I
zH;@8VBTu4nFTQ=?(|txF-Wn0l;E?Vj7Zi*yOrhec-O15(QN8VG&$tDDMGJeS$8<vM
zF0$}0@e)$*Cz~^Ja27IZY7&h{pd3qBg&`{*HD+%DaJ2;pRxhxRkk;I~Ef~jvjhoeX
zsYpd>w<d5fusRwlxgi~q5IMuiNt_x1yED4>Ws%{9aWLCqvH;P&5T5~_k7oWM2BzQ;
z>qvFsH3S`;P54?qQqU$8u+<UVzLux@KXN6KU<BWGYn76MLgbAQI)p7VGU)r%+WG0o
zubbqk>DdJQ%$L3ewC{Ozuf7Qel&TL>v>-Udkg5rCz{W-y8yiPjl=25^N2`SJ7aT-r
zx1u=d7WIx4S2<*>XRx5)R0F@XG&9Q*OeKWD)&7`4em_8&M1ZepC7cV;)LGTe*);uS
z_~_&dnfqBK1cuhwl>K#DpzuI83Wd6+miA}FM(d^r+*xg88=51~THa+{u2%g5$d~I`
z;Fs-hOew-?n)KXOMPYz#RzPkd(jtP)aJ!Yp_scC73hsLC3H)27P1V@q&e4CU+T;fn
zLZ_*U`zcfsQj-ME`!CK*7I8i1+*MoWX@SI|>B+&CeyuY%3InNiV0H>_ZZ&PfiXr?n
z;?qCgN-$sq2x*L%K9v6It652lRSxG#zzjJUt*E$ME$^Q`b*Q~FjtP`sr_cv^6lBEK
zmzg6YBakyRirklhY%FMJsGB!$mgrYxX?lQshXj@($pQlvEK*XbYG0uvLpltp5zec_
z#Ly#=ra)}D_ta!<w5>bHwV>$FJtft`3ETznh9SI;3{<s4kj)8vZlte5IIzgo_=^Dv
zyvhH_Vj<z-prkS$*oy%WZ#X+5S{*3^BU=(lvavz^1OXQ$iEH)eXRn+A+QSSCOi9Hz
z4f6YzuQ(AD64*P)6+Nq^dGVr^qTs&ntq_8`$a_nNc!4e(+Fx;yl#AI?Z>G|{d$OPm
zkb`zuWj8WxJ@wx<h{%MvxVT76*VFS`lNeA++T)F$A!vo0b@ZfQ2}0B{6Wmd7M(tLW
z8^g)6yu?WaZxnl^6ci)~po_9$+kuya&`q#T{e*&{3pQgg84AZBIlvfhtg$OlxaZG5
zcG!d1jznuAA#Bj#&4?0&Y6+Cj1aS~<SVh(5%i{ONtZ@E%FZFdNcfh*799IX<%Fga?
zA7H>o8~G(}k1D8ioKiksNry(a1ra}l8X#P%xU8NKj6sMcWegv($SXamZ=xfrU^8_4
zo?x9IaZ%6(QHh*8kX%ZRW?_?9R21l*-+i@t?t+f?Z+qJmX8k~$17wcO!=ePi1^{}k
zYlE8WIAz<}-VT6ifx7pkti-b84o5>(es{0dmCy|Cz0i1k*ywpm$4goDHZd^-iXNoT
zAJJX)g;?MWv`!GGk-1yIFJ$!e#pS9dCM+<W*;lM5fba<rWN}d6w%X`n@cJqYT4t1$
zMd##jz@)Ou^06b@{_a(Ydo*+hp??bx=A@PH5XZO>B9jB~qH>5+S_-Dh&|e5Ey@u2@
zGEVYeW!LfL%h&fg1E2?k3P=X=V3=3|w9^CuFyO~16bPcoEHsk34R1wEnR7u=*0GWk
zFIgIFeHVKZQC3RV>)cg-qJmXl`+Q)_0x7@)y!Bn?9Kv6fmePW(E*Gz=${kux<{b<T
z?zN*tWNF3@1D%n*Rt=$-|MWi&4|Is7W9WNyipG_svio)p5wL2a1Q(uSQwmpM>%(LY
zP)m8N2r^A}^v2zLm1?P+l`zdGY2>x56U2fDJ&{i$$k_OX;%h6b{uF2o;tpp(U4&E8
z+M6jSnnZ(keq=Af1-X!cni)z>JeOqBlJfEx<}2A^>`o$*17VaX6oV`Bxd1q<qVg13
zL?V5-oI8rdfoVQ>^!FcBshEU@hSEy9@<GlALn{Y@3_KXS!N@c+%_ROElVV;&pFv;i
zcVkxtNl8f&XaPepC|@|1$ec4o)FTvb6!0UB35QJg<_qt?0@UY4DofFHOHI*atP_$$
z{C?Eb)LV^2T&cYu3ksAF+k1)0u;vTpI>3kRot;05ohV<(_5g2VqU)q(mH(=(S6lQ<
z%#4mG);bxCbC@qXXgEKX0fhrMLI|1p1SfRh$s~RscWp1mSmBp^oJC`JOt5CYQOMYR
z=s`I)1*Kk!>h>n*??gJ3HK`Ng3x6CENPS;LixxtgjTEMnVny$?G8_A<%x>mv5}<~f
zB?{?q3B83|dGZLYa|lgwulKvbCl+YR%?IciCMFe%*S|YA6F7fQqxvEL0z5q~BnzqT
z(pbE9mWGdFn;%b1T2|oBHY@*dZ!?#Ivrh!!7RC)xKq+*SN!Px|veR`M7~&T)CE&}s
z*o&imf<p%x;dS_6_1P7pTEG$&wCU&YmKH=iUx{bI@8fr&3zd7UFrluX)W8<<taq0}
zU#@Ha>VZ+n`+p5V@=E`CZjg*aW?Ck_kL`n&wK*t46@(Q+N()Op?$H#buY^Gh0^mzf
zf{dGza#GBI5a!Yy$BDnJhto&w;?=F9)Z;fqlbK-VGNTv*>tdULc{Wx5;fE&~n_E@V
zMlpAPP>LjSC0#G!5g<Yc3Ij8^;J*zQ-D<?QQK~hX*BKEL{NK+B=ExzjGutln8)DQj
z9UTU^MAtltT|Pyfz@H3x+K9r4c<TbB5Q&1w+<;OZexHJB7LG>H(Tcl%+H|ogbv@1d
zu*i@+;44rfk>ogN$`?>jcy)>w8%Y?%eZi#x2_I1FTz0gyrV`B7%IoXX0!zRK?+!xT
z*uaE5!JLtha9Ih2fCy4VCI(n?8nyQsu1Hzs9S@bue}V%9B1j8Z;0TTpY(wT+!Ac#m
zhKBV2@M+NhZAkWGIFKlXDF72_NC!PWX8|C}NPB@GN_*R_y!O|%oJJiz+krhzL%G{{
zoNnj3H?H~fo|awC-hDf!CFxrS3Iz;2-~!^uZ39HHl(BhRUk7X!WFihYHR{g)nZ5;P
zly&CC^`s?)9MLP4>|lTuqE<scrCI6niAXDkukn~tsDhg!`TP$)E(t>jOijs}dcoi$
z$^-<<Atv@4Hz=X7pM3nUk)!i0J3k)}%H5xZ1!U|r-D~g4*?z;hDNK@7!He!Hj4(HS
zuq!O)j+5@-k1hHkRmdJ=IV9l=-PROlrw^9%(|K}3<+JE63R;l<fIskjHbu-dFfU9&
zaE5rjhC3g6MNW%XcJrn=^*P0mcI2?Jv~O%JTqJMt@Z(BrjodzQ+at$Ma7FzC1Bdhf
z&Diohodfp_#5172adB}h5R4xl9zNLak*ToiVLDlqId24C2N|S>T*KDHVdosxg22B3
zW>qu!m5d_eu3AT}vQk2kttlfH1GCe3ra&X&$A)KB6@&5r(}V_}Vbj9|1~B#F@@KH5
zqIA<Eot;E5D<==3J)oduG&HXPs0Z1DB#(rujp{s15mF-V$b#A=eSn?YI0${(X7d6S
zHZ7}*&Ol0W2LZ1QUyJRshiHjd5ws*8<sVOH9~BK_twHPq8NvlMBBz(+9ef4{e5wH*
zBg`p~LdqZjLc&v#lM@q+FiwtmfcW?by@Es~vIihE7{nA3C=#6f+n_kP{A2Cnce>+S
zpTZ7HYwk;t7VAx1&0^+G)>&=zoSU1gfc64`P!m8iCmsO)LBwkwAAdZTr2RSuH;1E1
zeZ<c)<;bL7^ou)@m!NURO0^s-jv&-yJHrb|hDcqV49Q&pnj2J1!)J~3oinO_5g%XO
zA=98elHy>VAXs%FGQL2=%;n_?WU6A|6BY!Jsb27Mdy!MC?ESlwhBc?Ja3XXLlnAiW
zW)onh+v)G`Taiz~$Ugkht9MGVD=`>M>E|is=@+G~`6B;ddCSv+;lVV~fj_q3WAlxX
zn4+gDITaM5%IheYRNMm20Uw`%je_xD2;h}eM386M1qxoyDq2hsX@rQF8ezKjmq?X;
zZ4N^>f68EnKst<4GpGbVVMt(da*|fa3<rgT{S#$;>f4Y~*tb_fw)(0>t~&ucp+)CP
zOO}8<_MIxf2j!rMWJ3u632}3C69XArm@-BN1^{T<3OfXnF$#~r60>}zRTyR+HHLKw
zg_IHiG21;px%v6L_ub)BZb&@3i?P({6BQs227Zkoq%|O}kO@Nq2-JbG4$zfyew>hJ
ziB@JthLQby1D$N9DF{(vk#SUz-bp~NY6XNSnsgIgscimPW9X)R;znn}@<B8X2w_}R
z*oly&MAZ6(PS%7@A@I256%>%`vyQJN5m7N`)fM0ld-VlVI2IP8`5$g<S5_AcGPW5E
ztiS-YEF_K`R|dI3m1j=w&VCdq!azulOQ&3Pw%nwysYBirO@%aQrcic>qr>z#qW^-}
z5eOBRCzU;dEGt#K^m*Z%BPTLejbg$IkQ5*)hRiTS?f^dgp?KMo94&I6e&N{m-+vaA
z&;-gZKq+K^3L^QUsn^hzlrDbHXuTPxX)bC6*%SgZCM=Qn8%q`M>gwvvRiXJ$j73y*
zex&T-Ck6H^?DDIFPCx-O<Jw%cjLe0q$MxlnLxPhs!>$OeDrMrl`;O%urZsVVDC+$~
zg%g_si$m0we}()MzaLf$DqpkU(@{Yr@0O}77WXDRKD^(VhL<MsO0wVfJe`Q~SkBWs
zGaRXrCV$SyzwRe;b?=f+PhHQ-M{95DI&v4UKf<sPvMmd=89MahD{FA55G#k<RM`&d
zw~AFr=7`$brR-3QSy%4%f8-j~91AY^uD+YWVMJF(t=afd?B~5(40hBsrrth$l7Hjt
zHoDfN)1PQs?WLwBeEzz<_KMN?`75d{ZQb#hekD3KVom0&*3vzlJ2>v!bcu)c`hpSj
zZ_EAK)#@g>8g&Z4rG}B?Yu3K3HN<|koSOiG%0(0gj+us`;rjM{a`Z`OdU5yhc-^ah
zVw0B7ze!9MVh=%M*tW66XFa2&vSTc|l9PjKQzRF@VAX~_h_|5i3{NoW*L}HdA4-Zf
z>+3C;PiZNfyu8f<(Z<Bit|8xR7%|?5^EB{DKbIKVXCSdaEB2HYW&ZO%xFBFYO80QE
znz7_sioI$nA2$7-?QSAH?}}PoU*A=f#wVBJcE(UrDBm_B(pbw8c@wN8S6()7?V=_#
zfwCRz(Ytm#R*&M+QWB8u5XBUdPd3}NjV8Vd*?yXZEkpv}d|~1m8-<i3_@EG0koK3C
zm#wX>@tubWSOY_R_0*p1X&W0eL!%8iI`V+1Fq@PVBjj-)d^$a_Hu04PW=X7VY-BAg
zu7bvSDfPi?IMH+P(L)$5HK`y7C$T|V9t3&x$(6v6s6v~8NuPjB()<5<h_a;x(<wN7
zMQ`4eE7WF#Q-I3ovsTyAGAGB_+S;lQ%LE8PN3t6(!9qc@Y|{q$!~igGv!I(oas=yy
zNJ;6<cQWhC5v~5;@=i@mHr!1wb!L=oZ9TObu;rvbkLC=!C@Fg((Wp!jgtZ2kuju>z
zdreRk0E<Edtslg$<rNjTZHkJD1YK6N35_@ezZno|SZjW8`Ldce!<48Hiz`^nEIZ7b
z5#n-}s)8biw95!DbN9XQsz{#c5hr59Ja(uHf}x+|Yi|RU$eM2(adu347t~nQTywsx
zgu>UvhEMxB$+(1CT3WjE^l8c6WWX#)wi;%^?>aaLfHM>h7)kmRY(nG{h`6W-Uyqpb
z4SQ?JF@{Y&p%7-+b-H+5=C1H_bkIazCD6-+uhZY+Vdke9a@pX%{k^@}FN<k%US3jQ
zP78vGsI9FH`MX#(EhD2S09YB{zA;FolE5cyF*-{y*4EZ$=oV)V*~TU$EN;?i(h(}>
z$gauN1enGI-_T6&U<vvX2%DZzjI;!{3zEfzrw-nGIm}&KS3Szf&u53vR{+W{uc1K>
zpIZ_FrY$;uKsHm|1U@AMA!l*QSEowcAk!8>C))9k^lk4s4jZu<sY&J?LM#luS>iR$
z+*j9BpK!K>)hW86e3d&#ZwCkB_Z*!kj52=~?$yR4H2F)OLmENGtr>uoeOZDgDhN%P
z#mjM8LruOH8=7Ro>jVMWhTqw;A*_DMWCXD+$udvU2kkNkfzuTEk+W&=+1-k1^pK$v
z?dUj&#F1ISW|ua7)ZISSd7v*q|0GMt#_K3tRDHzhwfnk%o`}RX-nRrt=bh|uL@&e7
zCkXL1J47YE+lsvqEY!^_JQWt6s}XKGcxlkG^Wnpd*G1nOW6@TAIAlYZkj^eU4(!vS
z_<zFE$NTiJO&zp}QYjw~dr1uY=@R;lOwm$f=fyf5T%nR<9`NP(Xi}-)%R(|MY(?qb
zD%>~Hcbosiuq#`Mi$p~aE88S_r_~_25RN|ggk&ME0{U{ZQt>d`F1f28%VE4k0cqED
zDojX^wN8`t)83ucc+tP^p3*E&x8G#P(}lcP5Vhs`JgtZoaS+k#Md-<#dTCmGe|y%J
z*_dVY6<6+~>z?%?=^D(5kAYDogysfc?2UX+hQ^AhkzLAQ&7*rGBc8lyVem&3lSH72
zKF5C%c7*v2UnV2}NYdL#TOM?0p4I?<lnte2-zk#Z>yhrJu$tj-A#H&k)Vlh=uMK}v
zu{C+3?zS~Ih1WKPQ%C%PKz#d$UjLun8YrUY!*)l9FFv5RR};_Ht*=Wc+7SnzKMqeb
zj~;leCe74~Qh^VWn7WB8I#enzR~h}dVhP)vA(!ox%QuL2;89Q-j-Zuj2`s&l0Q-=+
z<H;kzp_q~s!oua=hWl@aErhMOL*vv@M6yx-U9Xwx8vZOaJossM=dvj0y9&e7<%_rh
zH?Gh=>7Fvqy$Dmz^2(~$2HUSb;JI#I-7c<ll_C7cDxbM%QYzNqB~kF#QJ3Y{hGJ+%
z%?E=2ZhE$L=Edhly79hXPsEbwSKTPX#U7ru>_5M%bLHl%eRr!)oLeiHYb`DRZka}?
z|6-9wWATNEr(By~qe*EvzWBbb;ZsMbdt}d!!#1563(w7{ckX7iD|2jEceY#j)KToW
zx9K82y{h<3G~@C7*^@sQ+1_!Dn9?){hooz(p>2chcfXmQI%T>Dt!JYY+|16OC;I1Q
zvn;GDGft+d$%g$OO`Ll;)M+2ae`BHprP$^|N~FU|LOE2D9K&iUd6`rbIgPNCsVSxr
zB}PJ<idI=$Yn6s|hz*TID><KXh-$)Wwo<4eoA-Wduj_sP^2eB&-}Rf{^E}`0{r%jx
zbaB0M!BThX=FOW`n2I(-J;W#sApp#>V0{(hzW^}21gy0scIFJ;WB?^Xxsy*!b872n
zflf34hj~h>EYr}pR9;k{Ik<O(^Ubvaa~(gUYxi1J4&Dl%`a`%u?abCXGufGv1#wZ6
zOBVDm(B_YikMD;vn=KUD9XzOuZh|<wfqt9UgP0%%d}=Dg)y>Tdq-)dg;S$bkwRsxm
z2~aGuva;wT^uy6n{(z>wd52^xLyzUbV2^GpoER@xP}}~g<VqVqBA@YspG6P(&GVSz
z@jA17U5N|N@~OpDlM_ujg)EP2`39_uV{1~k?p`D?mX(()rWaZU9eAN^=~-A>s_JlE
z39=!QlVWYNwmLbTwMk1$QzJwMm4s#hy|WBOv`hc>YUCcwIVFQP)YsS3rWeKJg)VE_
zr_Bl2#pD&l?pNg<dXT=zpo*%luGv>SxLb6hy>EwyUP7HdPlL6U_IO>Co{4R}#oQW+
z{aZ_Rpf`=u4V-xTd!M50$r<|Pd(UB?=k3$GXeI32^qn$<&gWIJSp@~HgS`-ryRJw=
zGI+zDn;Ts=dh=P8p?Ocf(_k94F{%c0`)}ULSMb?)!K3X!1eZQBV#S)~Jrt(ztJ|2J
zoy)cu6c*@idM25gaQb$h@M(L;;FMHq%k%bN=vIvw86E!=rPVeRbglF6#YYA%sXRKn
zpEbJJaC%8;ik)A;)UIF!?}PU_t^r2#R;fwt*!l~8nVl>)P516_;wQG0gz24#V9-IR
zCMe4+ln9#p+I`FHY3D-Dyk?8n8WstLJ*d~WB*RW`H5w^#dtdpMIvMV3#&A(}jcB~(
znB263v$)Yg%ynKTTEpn}>W~hUU6;`-qKSJnSD)W6%^i?x^!S#!otQi`oyV$F8LGSV
zzV+%o>9k&~SIqtAFMADS72e#TMXs#Btte{i@_elCoZTL*$oXOXhDn*cyRhZO&FNwf
z^$-c$>eF(@?}n;dxJ)-Uxm*Xq#c$^5|1)7AEM=MmZRq*9@_XUHD=z)|p|P}b@ojbG
zouwO?4Z0OuPkpfM7%Xw9Gt02+pjd9xq1y#DhBcF~oC7>=a{X#XUD=W`U8?B($C<h{
z^%o7!{3m}EJ=(DKijtfBDtFIGhp6eI)y}?u*q+cAbmlbc1<p{L+%i<~XmYwwuFxds
zdg<YXEBQBWtk6S3Uq8g{O?SK>&J(O(TOrkg+1DdcZ2AUOQ7^dt*Ei}PJ$li4nQpNt
zsIb{KeP+5V>IB&D>;eN<clY$be?~_Q6(2uGupU-05k|$tYXhAMoLd*g2U_Me+;Sb|
zHht{aX6EkQgdR!{{DH$}!?b)db}0?E1_h!qV0lmMAA`Ku#4g%~&&Qwv$2sKA8P|8j
zeLcKs6fi)7tU&ICrjrFyQrmW^lI5&?ZE<!EMk&@OXQ-aqh7s7&)xXF*Aq)?@U5Bs%
zg0&U%H*QLC*|{^Cz)cuH0Ng5R>Pk)k>P6V_RjXFb-?%y@6uDglogw1~lm3WhhdJ8I
zT(<;jcE$V|s!r%th|jZrT4+D~>eYRO3g$s~$SlzM7NLix5z@S-uA~@9t(vfM0mp0d
zUmwwL66%y4*`e)0M>b+5styB8nFQkS@Nm?lq6cxx`G<zK-yMRCp@nJ%<glW(n@x3k
z7@be-3aCau95J?e*b^r2^CoqVon36Tj9g~C`5`@}Fok=VS#7M|Q+6~Nmh8cE;~zeR
zHJXhobar&qs4B?mEnj}>F7Ka>4@1?Dz~vPTQLYzLRp}TmjMjLk$!v{D`QFA0si|Su
zAT53NIQQ}TAn421-cc#p&4GsXf;aWf4iCcZygcV5GeOkd0beEdRU`Wj5s7<187T0k
z=L+2cI-N70oCSU4!5`t0JRq-#?vhUq-^yhMx3{;GZFkqMg`Bs2YpgqD2Lejxsv6FK
zdF?&|7A!w>{(jfz;>C-xIXODF5Y~m~XTDb5&(DHO!#SMOFa6f|XUy~siL{Z}@OJ4e
zbk*5)tzZTF_K!Va_TbFyw(xUm^ZfSCzeIFYL)u?+T{%SQ2ICp?C@p)gYjEbI$N2+u
zA1`S*Yot``?HZhs88)hZ;U~ZB-h;AE6sp~l$WO-i`kR}hAnDK!Y2z*b_b>Lw{7`kj
zcY+5Ikv7ql6drRt&2uhUh5j5is&FBFDEn_StARlonq1=1T!`ob<d}kHcnsJh6v0el
z(?l_Ea(HF%iwAMN$e^kV>aW=I%rjo`LOQ<R%+6;>RmY?==B2eS9^96dT`R2dH}mY5
zAYA*OzXai?d<nu${+f}i{WT-^e=j8`k*yj-a$?f~KuBzYry7((YEzc`2L{q9l;`Qy
zzyA8&Wv|hMRe^-7cXSj~=M$;}x(Bjr=IrGTEWA2+d4A@MdiGdMiH??*3@jxh!^e>t
zb?hIi26L%Qp->SA4?i@s1IXB-W93Vba8!kr<;g570;P+B9V<wd<(ZP5lQYN3$q6K3
zlVltn1m9Edqt3mUS!dZS?U$y^2ww~!St@eyNnoN@3H_8tkr^ijNkGepq}tuhq`vGQ
zIH2LNWg(Oi40K4@J2|ZSB&4o^(<EVQn4LaA2O^aax*yiV;?mEsO`L`5Ch*2W#3@ek
zu^*Ko@N6m&aTNsL#aHMfU<1VN%r9M=i%aFNpAZ&m0DRM&g$U1)xAtd#bfK1M4xyiA
zIGq8k{e0gd0FVt=x157A?}b}N@nLG%5AhhFkUB|R!49Der#3|qCjbN*8!Ib%;*!l@
z(pT}2ngXZCiB3_NiNii?GJ?ybQ593~9SMMDJh2gxJuNLQ(n#bwfkcSFtexWHE(l`H
z&_)yNMTB8-Q*UnqdbX_c^2N2awS+CgG?+%=#$y+o;u515#(WVwQe$StrOmhhQx)^)
zsuje9L|H(x)G&okMeZ#zTu@YWrD#hBk$hC3a#DnuA`CQzF>L-Nqd>-V1sr!IW*1O1
zfn{M0ZW`<+)(}9ML7}<;QCwVHvTxn8M#54?#C!YCMZg}O1G9*rD&VzcGMV!=G!)O;
zl0yNzbX!~7GcbDtjpSD12Nm1KEzsKjjcAs^I^=LsHBn38E|Os#3_}xlddF{t+m7Q)
zL6eVsqUQ?sz&iz#IP1?OO>&VLNADNE^M;1`*PA!R8}{NDK#Hsw*#(S;8xpHP!pCz*
z2fME9bay`kl!&DG!ud)vmZq6Uj@+?Xs%@Q<Jv%u@{fLGLaWbdi!ied=cX&AE>0vN@
z7UAJWrioSHAZMrgOmw0{#pqFh-#fFU<jI=}983N|K}EHGAR!$b9g{C#4mdA-|31vT
zskpFk#3>!CON7BqKAKO#pcO^XzKj!}xt*VnT3RQ_sLb8~iiQ!%!jQw5dU(#SAK`hB
zzX);wbAb4#BYpYl!2k}icrL9EPy#V=8Wd0kcLA|z#iKfddi!Ma33$sPbv*m%G!d7O
z@BrtJzJHQWbS+XolQ2cbPJj>dhD;x!PfsYygA?mQ2*W7A-`|46Db9MKX>IrymlY#z
z0{<Bed&yxA2f-?IzlJN7B=CnAcx~A1oGPw6F80#q<HmT>#VtQ@zgJO^%Wh|1F^}vH
zUbjMX2Whu7zzQ+YqBpg+Mz^%=fX#@epExDJRRK;$xYC=EDL~iD3=En>4i)0mCsC>R
yMOxRs{eIh0EVG#TTJ~+HEpTH0yT|=9J#!Dw%e*)KmOSFhDSMmk*0(KLC;tm|z$YvK

literal 0
HcmV?d00001

diff --git a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb
new file mode 100644
index 000000000..2eeeffa3c
--- /dev/null
+++ b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb
@@ -0,0 +1,1394 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This IPython Notebook introduces the use of the `openmc.mgxs` module to calculate multi-energy-group and multi-delayed-group cross sections for an infinite homogeneous medium. In particular, this Notebook introduces the the following features:\n",
+    "\n",
+    "* Creation of multi-delayed-group cross sections for an **infinite homogeneous medium**\n",
+    "* Calculation of delayed neutron precursor concentrations\n",
+    "\n",
+    "**Note:** This Notebook illustrates the use of [Pandas](http://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data. We recommend using [Pandas](http://pandas.pydata.org/) >v0.15.0 or later since OpenMC's Python API leverages the multi-indexing feature included in the most recent releases of [Pandas](http://pandas.pydata.org/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction to Multi-Delayed-Group Cross Sections (MDGXS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Many Monte Carlo particle transport codes, including OpenMC, use continuous-energy nuclear cross section data. However, most deterministic neutron transport codes use *multi-group cross sections* defined over discretized energy bins or *energy groups*. Furthermore, kinetics calculations typically separate out parameters that involve delayed neutrons by delayed groups. An example is the energy spectrum for prompt and delayed neutrons for U-235 and Pu-239 computed for a light water reactor spectrum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbAAAAEgCAYAAADVKCZpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfmI+3IqsCwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {
+      "image/png": {
+       "width": 350
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename='images/mdgxs.png', width=350)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A variety of tools employing different methodologies have been developed over the years to compute multi-group cross sections for certain applications, including NJOY (LANL), MC$^2$-3 (ANL), and Serpent (VTT). The `openmc.mgxs` Python module is designed to leverage OpenMC's tally system to calculate multi-group cross sections with arbitrary energy discretizations and different delayed group models (e.g. 6, 7, or 8 delayed group models) for fine-mesh heterogeneous deterministic neutron transport applications.\n",
+    "\n",
+    "Before proceeding to illustrate how one may use the `openmc.mgxs` module, it is worthwhile to define the general equations used to calculate multi-energy-group and multi-delayed-group cross sections. This is only intended as a brief overview of the methodology used by `openmc.mgxs` - we refer the interested reader to the large body of literature on the subject for a more comprehensive understanding of this complex topic."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Introductory Notation\n",
+    "The continuous real-valued microscopic cross section may be denoted $\\sigma_{n,x}(\\mathbf{r}, E)$ for position vector $\\mathbf{r}$, energy $E$, nuclide $n$ and interaction type $x$. Similarly, the scalar neutron flux may be denoted by $\\Phi(\\mathbf{r},E)$ for position $\\mathbf{r}$ and energy $E$. **Note**: Although nuclear cross sections are dependent on the temperature $T$ of the interacting medium, the temperature variable is neglected here for brevity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Spatial and Energy Discretization\n",
+    "The energy domain for critical systems such as thermal reactors spans more than 10 orders of magnitude of neutron energies from 10$^{-5}$ - 10$^7$ eV. The multi-group approximation discretization divides this energy range into one or more energy groups. In particular, for $G$ total groups, we denote an energy group index $g$ such that $g \\in \\{1, 2, ..., G\\}$. The energy group indices are defined such that the smaller group the higher the energy, and vice versa. The integration over neutron energies across a discrete energy group is commonly referred to as **energy condensation**.\n",
+    "\n",
+    "The delayed neutrons created from fissions are created from > 30 delayed neutron precursors. Modeling each of the delayed neutron precursors is possible, but this approach has not recieved much attention due to large uncertainties in certain precursors. Therefore, the delayed neutrons are often combined into \"delayed groups\" that have a set time constant, $\\lambda_d$. Some cross section libraries use the same group time constants for all nuclides (e.g. JEFF 3.2) while other libraries use different time constants for all nuclides (e.g. ENDF/B-VII.1). Multi-delayed-group cross sections can either be created with the entire delayed group set, a subset of delayed groups, or integrated over all delayed groups.\n",
+    "\n",
+    "Multi-group cross sections are computed for discretized spatial zones in the geometry of interest. The spatial zones may be defined on a structured and regular fuel assembly or pin cell mesh, an arbitrary unstructured mesh or the constructive solid geometry used by OpenMC. For a geometry with $K$ distinct spatial zones, we designate each spatial zone an index $k$ such that $k \\in \\{1, 2, ..., K\\}$. The volume of each spatial zone is denoted by $V_{k}$. The integration over discrete spatial zones is commonly referred to as **spatial homogenization**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### General Scalar-Flux Weighted MDGXS\n",
+    "The multi-group cross sections computed by `openmc.mgxs` are defined as a *scalar flux-weighted average* of the microscopic cross sections across each discrete energy group. This formulation is employed in order to preserve the reaction rates within each energy group and spatial zone. In particular, spatial homogenization and energy condensation are used to compute the general multi-group cross section. For instance, the delayed-nu-fission multi-energy-group and multi-delayed-group cross section, $\\nu_d \\sigma_{f,x,k,g}$, can be computed as follows:\n",
+    "\n",
+    "$$\\nu_d \\sigma_{n,x,k,g} = \\frac{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\nu_d \\sigma_{f,x}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n",
+    "\n",
+    "This scalar flux-weighted average microscopic cross section is computed by `openmc.mgxs` for only the delayed-nu-fission and delayed neutron fraction reaction type at the oment. These double integrals are stochastically computed with OpenMC's tally system - in particular, [filters](https://mit-crpg.github.io/openmc/pythonapi/filter.html) on the energy range and spatial zone (material, cell, universe, or mesh) define the bounds of integration for both numerator and denominator."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multi-Group Prompt and Delayed Fission Spectrum\n",
+    "The energy spectrum of neutrons emitted from fission is denoted by $\\chi_{n}(\\mathbf{r},E' \\rightarrow E'')$ for incoming and outgoing energies $E'$ and $E''$, respectively. Unlike the multi-group cross sections $\\sigma_{n,x,k,g}$ considered up to this point, the fission spectrum is a probability distribution and must sum to unity. The outgoing energy is typically much less dependent on the incoming energy for fission than for scattering interactions. As a result, it is common practice to integrate over the incoming neutron energy when computing the multi-group fission spectrum. The fission spectrum may be simplified as $\\chi_{n}(\\mathbf{r},E)$ with outgoing energy $E$.\n",
+    "\n",
+    "Computing the cumulative energy spectrum of emitted neutrons, $\\chi_{n}(\\mathbf{r},E)$, has been presented in the `mgxs-part-i.ipynb` notebook. Here, we will present the energy spectrum of prompt and delayed emission neutrons, $\\chi_{n,p}(\\mathbf{r},E)$ and $\\chi_{n,d}(\\mathbf{r},E)$, respectively. Unlike the multi-group cross sections defined up to this point, the multi-group fission spectrum is weighted by the fission production rate rather than the scalar flux. This formulation is intended to preserve the total fission production rate in the multi-group deterministic calculation. In order to mathematically define the multi-group fission spectrum, we denote the microscopic fission cross section as $\\sigma_{n,f}(\\mathbf{r},E)$ and the average number of neutrons emitted from fission interactions with nuclide $n$ as $\\nu_{n,p}(\\mathbf{r},E)$ and $\\nu_{n,d}(\\mathbf{r},E)$ for prompt and delayed neutrons, respectively. The multi-group fission spectrum $\\chi_{n,k,g,d}$ is then the probability of fission neutrons emitted into energy group $g$. \n",
+    "\n",
+    "Similar to before, spatial homogenization and energy condensation are used to find the multi-energy-group and multi-delayed-group fission spectrum $\\chi_{n,k,g,d}$ as follows:\n",
+    "\n",
+    "$$\\chi_{n,k,g',d} = \\frac{\\int_{E_{g'}}^{E_{g'-1}}\\mathrm{d}E''\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\chi_{n,d}(\\mathbf{r},E'\\rightarrow E'')\\nu_{n,d}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\nu_{n,d}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}$$\n",
+    "\n",
+    "The fission production-weighted multi-energy-group and multi-delayed-group fission spectrum for delayed neutrons is computed using OpenMC tallies with energy in, energy out, and delayed group filters. Alternatively, the delayed group filter can be omitted to compute the fission spectrum integrated over all delayed groups.\n",
+    "\n",
+    "This concludes our brief overview on the methodology to compute multi-energy-group and multi-delayed-group cross sections. The following sections detail more concretely how users may employ the `openmc.mgxs` module to power simulation workflows requiring multi-group cross sections for downstream deterministic calculations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate Input Files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import openmc\n",
+    "import openmc.mgxs as mgxs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we need to define materials that will be used in the problem. Before defining a material, we must create nuclides that are used in the material."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Instantiate some Nuclides\n",
+    "h1 = openmc.Nuclide('H1')\n",
+    "o16 = openmc.Nuclide('O16')\n",
+    "u235 = openmc.Nuclide('U235')\n",
+    "u238 = openmc.Nuclide('U238')\n",
+    "pu239 = openmc.Nuclide('Pu239')\n",
+    "zr90 = openmc.Nuclide('Zr90')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the nuclides we defined, we will now create a material for the homogeneous medium."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Instantiate a Material and register the Nuclides\n",
+    "inf_medium = openmc.Material(name='moderator')\n",
+    "inf_medium.set_density('g/cc', 5.)\n",
+    "inf_medium.add_nuclide(h1,  0.03)\n",
+    "inf_medium.add_nuclide(o16, 0.015)\n",
+    "inf_medium.add_nuclide(u235 , 0.0001)\n",
+    "inf_medium.add_nuclide(u238 , 0.007)\n",
+    "inf_medium.add_nuclide(pu239, 0.00003)\n",
+    "inf_medium.add_nuclide(zr90, 0.002)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With our material, we can now create a `Materials` object that can be exported to an actual XML file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Instantiate a Materials collection and export to XML\n",
+    "materials_file = openmc.Materials([inf_medium])\n",
+    "materials_file.default_xs = '71c'\n",
+    "materials_file.export_to_xml()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's move on to the geometry. This problem will be a simple square cell with reflective boundary conditions to simulate an infinite homogeneous medium. The first step is to create the outer bounding surfaces of the problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Instantiate boundary Planes\n",
+    "min_x = openmc.XPlane(boundary_type='reflective', x0=-0.63)\n",
+    "max_x = openmc.XPlane(boundary_type='reflective', x0=0.63)\n",
+    "min_y = openmc.YPlane(boundary_type='reflective', y0=-0.63)\n",
+    "max_y = openmc.YPlane(boundary_type='reflective', y0=0.63)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the surfaces defined, we can now create a cell that is defined by intersections of half-spaces created by the surfaces."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Instantiate a Cell\n",
+    "cell = openmc.Cell(cell_id=1, name='cell')\n",
+    "\n",
+    "# Register bounding Surfaces with the Cell\n",
+    "cell.region = +min_x & -max_x & +min_y & -max_y\n",
+    "\n",
+    "# Fill the Cell with the Material\n",
+    "cell.fill = inf_medium"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "OpenMC requires that there is a \"root\" universe. Let us create a root universe and add our square cell to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Instantiate Universe\n",
+    "root_universe = openmc.Universe(universe_id=0, name='root universe')\n",
+    "root_universe.add_cell(cell)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now must create a geometry that is assigned a root universe and export it to XML."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Create Geometry and set root Universe\n",
+    "openmc_geometry = openmc.Geometry()\n",
+    "openmc_geometry.root_universe = root_universe\n",
+    "\n",
+    "# Export to \"geometry.xml\"\n",
+    "openmc_geometry.export_to_xml()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we must define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# OpenMC simulation parameters\n",
+    "batches = 50\n",
+    "inactive = 10\n",
+    "particles = 5000\n",
+    "\n",
+    "# Instantiate a Settings object\n",
+    "settings_file = openmc.Settings()\n",
+    "settings_file.batches = batches\n",
+    "settings_file.inactive = inactive\n",
+    "settings_file.particles = particles\n",
+    "settings_file.output = {'tallies': True}\n",
+    "\n",
+    "# Create an initial uniform spatial source distribution over fissionable zones\n",
+    "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n",
+    "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
+    "settings_file.source = openmc.source.Source(space=uniform_dist)\n",
+    "\n",
+    "# Export to \"settings.xml\"\n",
+    "settings_file.export_to_xml()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we are ready to generate multi-group cross sections! First, let's define a 2-energy-group structure using the built-in `EnergyGroups` class. We will also create a 6-delayed-group structure using the built-in `DelayedGroups` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Instantiate a 100-group EnergyGroups object\n",
+    "energy_groups = mgxs.EnergyGroups()\n",
+    "energy_groups.group_edges = np.logspace(-9,1.3,101)\n",
+    "\n",
+    "# Instantiate a 1-group EnergyGroups object\n",
+    "one_group = mgxs.EnergyGroups()\n",
+    "one_group.group_edges = np.array([0., 20.])\n",
+    "\n",
+    "delayed_groups = mgxs.DelayedGroups()\n",
+    "delayed_groups.groups = range(1,7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now use the `EnergyGroups` and `DelayedGroups` objects, along with our previously created materials and geometry, to instantiate some `MGXS` objects from the `openmc.mgxs` module. In particular, the following are subclasses of the generic and abstract `MGXS` class:\n",
+    "\n",
+    "* `TotalXS`\n",
+    "* `TransportXS`\n",
+    "* `NuTransportXS`\n",
+    "* `AbsorptionXS`\n",
+    "* `CaptureXS`\n",
+    "* `FissionXS`\n",
+    "* `NuFissionXS`\n",
+    "* `KappaFissionXS`\n",
+    "* `ScatterXS`\n",
+    "* `NuScatterXS`\n",
+    "* `ScatterMatrixXS`\n",
+    "* `NuScatterMatrixXS`\n",
+    "* `Chi`\n",
+    "* `ChiPrompt`\n",
+    "* `InverseVelocity`\n",
+    "* `PromptNuFissionXS`\n",
+    "\n",
+    "A separate abstract `MDGXS` class is used for cross-sections and parameters that involve delayed neutrons. The subclasses of `MDGXS` include:\n",
+    "\n",
+    "* `DelayedNuFissionXS`\n",
+    "* `ChiDelayed`\n",
+    "* `Beta`\n",
+    "\n",
+    "These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. In this case, let's create the multi-group chi-prompt and prompt-nu-fission cross sections with our 2-energy-group structure and multi-group chi-delayed, delayed-nu-fission, and beta cross sections with our 2-energy-group and 6-delayed-group structures. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Instantiate a few different sections\n",
+    "chi_prompt = mgxs.ChiPrompt(domain=cell, groups=energy_groups, by_nuclide=True)\n",
+    "prompt_nu_fission = mgxs.PromptNuFissionXS(domain=cell, groups=one_group, by_nuclide=True)\n",
+    "chi_delayed = mgxs.ChiDelayed(domain=cell, energy_groups=energy_groups, by_nuclide=True)\n",
+    "delayed_nu_fission = mgxs.DelayedNuFissionXS(domain=cell, energy_groups=one_group, delayed_groups=delayed_groups, by_nuclide=True)\n",
+    "beta = mgxs.Beta(domain=cell, energy_groups=one_group, delayed_groups=delayed_groups, by_nuclide=True)\n",
+    "\n",
+    "chi_prompt.nuclides = ['U235', 'Pu239']\n",
+    "prompt_nu_fission.nuclides = ['U235', 'Pu239']\n",
+    "chi_delayed.nuclides = ['U235', 'Pu239']\n",
+    "delayed_nu_fission.nuclides = ['U235', 'Pu239']\n",
+    "beta.nuclides = ['U235', 'Pu239']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each multi-group cross section object stores its tallies in a Python dictionary called `tallies`. We can inspect the tallies in the dictionary for our `Beta` object as follows. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OrderedDict([('nu-fission', Tally\n",
+       "              \tID             =\t10000\n",
+       "              \tName           =\t\n",
+       "              \tFilters        =\t\n",
+       "                              \t\tcell\t[1]\n",
+       "                              \t\tenergy\t[  0.  20.]\n",
+       "              \tNuclides       =\tU235 Pu239 \n",
+       "              \tScores         =\t['nu-fission']\n",
+       "              \tEstimator      =\ttracklength), ('delayed-nu-fission', Tally\n",
+       "              \tID             =\t10001\n",
+       "              \tName           =\t\n",
+       "              \tFilters        =\t\n",
+       "                              \t\tcell\t[1]\n",
+       "                              \t\tdelayedgroup\t[1 2 3 4 5 6]\n",
+       "                              \t\tenergy\t[  0.  20.]\n",
+       "              \tNuclides       =\tU235 Pu239 \n",
+       "              \tScores         =\t['delayed-nu-fission']\n",
+       "              \tEstimator      =\ttracklength)])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "beta.tallies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `Beta` object includes tracklength tallies for the 'nu-fission' and 'delayed-nu-fission' scores in the 2-energy-group and 6-delayed-group structure in cell 1. Now that each `MGXS` and `MDGXS` object contains the tallies that it needs, we must add these tallies to a `Tallies` object to generate the \"tallies.xml\" input file for OpenMC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Instantiate an empty Tallies object\n",
+    "tallies_file = openmc.Tallies()\n",
+    "\n",
+    "# Add chi-prompt tallies to the tallies file\n",
+    "tallies_file += chi_prompt.tallies.values()\n",
+    "\n",
+    "# Add prompt-nu-fission tallies to the tallies file\n",
+    "tallies_file += prompt_nu_fission.tallies.values()\n",
+    "\n",
+    "# Add chi-delayed tallies to the tallies file\n",
+    "tallies_file += chi_delayed.tallies.values()\n",
+    "\n",
+    "# Add delayed-nu-fission tallies to the tallies file\n",
+    "tallies_file += delayed_nu_fission.tallies.values()\n",
+    "\n",
+    "# Add beta tallies to the tallies file\n",
+    "tallies_file += beta.tallies.values()\n",
+    "\n",
+    "# Export to \"tallies.xml\"\n",
+    "tallies_file.export_to_xml()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we a have a complete set of inputs, so we can go ahead and run our simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "       .d88888b.                             888b     d888  .d8888b.\n",
+      "      d88P\" \"Y88b                            8888b   d8888 d88P  Y88b\n",
+      "      888     888                            88888b.d88888 888    888\n",
+      "      888     888 88888b.   .d88b.  88888b.  888Y88888P888 888       \n",
+      "      888     888 888 \"88b d8P  Y8b 888 \"88b 888 Y888P 888 888       \n",
+      "      888     888 888  888 88888888 888  888 888  Y8P  888 888    888\n",
+      "      Y88b. .d88P 888 d88P Y8b.     888  888 888   \"   888 Y88b  d88P\n",
+      "       \"Y88888P\"  88888P\"   \"Y8888  888  888 888       888  \"Y8888P\"\n",
+      "__________________888______________________________________________________\n",
+      "                  888\n",
+      "                  888\n",
+      "\n",
+      "      Copyright:      2011-2016 Massachusetts Institute of Technology\n",
+      "      License:        http://openmc.readthedocs.io/en/latest/license.html\n",
+      "      Version:        0.8.0\n",
+      "      Git SHA1:       bc8a346a978644f5b2b21cccb6c5ae7f8397ebee\n",
+      "      Date/Time:      2016-07-31 19:49:00\n",
+      "      MPI Processes:  4\n",
+      "\n",
+      " ===========================================================================\n",
+      " ========================>     INITIALIZATION     <=========================\n",
+      " ===========================================================================\n",
+      "\n",
+      " Reading settings XML file...\n",
+      " Reading geometry XML file...\n",
+      " Reading cross sections XML file...\n",
+      " Reading materials XML file...\n",
+      " Reading H1.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/H1_71c.h5\n",
+      " Reading O16.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/O16_71c.h5\n",
+      " Reading U235.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/U235_71c.h5\n",
+      " Reading U238.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/U238_71c.h5\n",
+      " Reading Pu239.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/Pu239_71c.h5\n",
+      " Reading Zr90.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/Zr90_71c.h5\n",
+      " Maximum neutron transport energy: 20.0000 MeV for H1.71c\n",
+      " Reading tallies XML file...\n",
+      " Building neighboring cells lists for each surface...\n",
+      " Initializing source particles...\n",
+      "\n",
+      " ===========================================================================\n",
+      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
+      " ===========================================================================\n",
+      "\n",
+      "  Bat./Gen.      k            Average k         \n",
+      "  =========   ========   ====================   \n",
+      "        1/1    1.21670                       \n",
+      "        2/1    1.24155                       \n",
+      "        3/1    1.21924                       \n",
+      "        4/1    1.22486                       \n",
+      "        5/1    1.21719                       \n",
+      "        6/1    1.24330                       \n",
+      "        7/1    1.22322                       \n",
+      "        8/1    1.24133                       \n",
+      "        9/1    1.21840                       \n",
+      "       10/1    1.25141                       \n",
+      "       11/1    1.21217                       \n",
+      "       12/1    1.25625    1.23421 +/- 0.02204\n",
+      "       13/1    1.22056    1.22966 +/- 0.01351\n",
+      "       14/1    1.21757    1.22664 +/- 0.01002\n",
+      "       15/1    1.24571    1.23045 +/- 0.00865\n",
+      "       16/1    1.26489    1.23619 +/- 0.00910\n",
+      "       17/1    1.22323    1.23434 +/- 0.00791\n",
+      "       18/1    1.26108    1.23768 +/- 0.00762\n",
+      "       19/1    1.23145    1.23699 +/- 0.00676\n",
+      "       20/1    1.23548    1.23684 +/- 0.00605\n",
+      "       21/1    1.20446    1.23390 +/- 0.00621\n",
+      "       22/1    1.20533    1.23152 +/- 0.00615\n",
+      "       23/1    1.22520    1.23103 +/- 0.00568\n",
+      "       24/1    1.18367    1.22765 +/- 0.00625\n",
+      "       25/1    1.23614    1.22821 +/- 0.00585\n",
+      "       26/1    1.23746    1.22879 +/- 0.00550\n",
+      "       27/1    1.23626    1.22923 +/- 0.00518\n",
+      "       28/1    1.21334    1.22835 +/- 0.00497\n",
+      "       29/1    1.25169    1.22958 +/- 0.00486\n",
+      "       30/1    1.25579    1.23089 +/- 0.00479\n",
+      "       31/1    1.23828    1.23124 +/- 0.00457\n",
+      "       32/1    1.26911    1.23296 +/- 0.00468\n",
+      "       33/1    1.20090    1.23157 +/- 0.00469\n",
+      "       34/1    1.28606    1.23384 +/- 0.00503\n",
+      "       35/1    1.23129    1.23374 +/- 0.00483\n",
+      "       36/1    1.22535    1.23341 +/- 0.00465\n",
+      "       37/1    1.20367    1.23231 +/- 0.00461\n",
+      "       38/1    1.22886    1.23219 +/- 0.00444\n",
+      "       39/1    1.24056    1.23248 +/- 0.00429\n",
+      "       40/1    1.25038    1.23307 +/- 0.00419\n",
+      "       41/1    1.21504    1.23249 +/- 0.00410\n",
+      "       42/1    1.20762    1.23171 +/- 0.00404\n",
+      "       43/1    1.20597    1.23093 +/- 0.00399\n",
+      "       44/1    1.24424    1.23133 +/- 0.00389\n",
+      "       45/1    1.24767    1.23179 +/- 0.00381\n",
+      "       46/1    1.22998    1.23174 +/- 0.00370\n",
+      "       47/1    1.26352    1.23260 +/- 0.00370\n",
+      "       48/1    1.23155    1.23257 +/- 0.00360\n",
+      "       49/1    1.22059    1.23227 +/- 0.00352\n",
+      "       50/1    1.24724    1.23264 +/- 0.00345\n",
+      " Creating state point statepoint.50.h5...\n",
+      "\n",
+      " ===========================================================================\n",
+      " ======================>     SIMULATION FINISHED     <======================\n",
+      " ===========================================================================\n",
+      "\n",
+      "\n",
+      " =======================>     TIMING STATISTICS     <=======================\n",
+      "\n",
+      " Total time for initialization     =  7.4000E-01 seconds\n",
+      "   Reading cross sections          =  3.9400E-01 seconds\n",
+      " Total time in simulation          =  2.5930E+01 seconds\n",
+      "   Time in transport only          =  2.5267E+01 seconds\n",
+      "   Time in inactive batches        =  1.5300E+00 seconds\n",
+      "   Time in active batches          =  2.4400E+01 seconds\n",
+      "   Time synchronizing fission bank =  6.4500E-01 seconds\n",
+      "     Sampling source sites         =  6.0000E-03 seconds\n",
+      "     SEND/RECV source sites        =  0.0000E+00 seconds\n",
+      "   Time accumulating tallies       =  1.0000E-03 seconds\n",
+      " Total time for finalization       =  1.1000E-02 seconds\n",
+      " Total time elapsed                =  2.6689E+01 seconds\n",
+      " Calculation Rate (inactive)       =  32679.7 neutrons/second\n",
+      " Calculation Rate (active)         =  8196.72 neutrons/second\n",
+      "\n",
+      " ============================>     RESULTS     <============================\n",
+      "\n",
+      " k-effective (Collision)     =  1.23260 +/-  0.00309\n",
+      " k-effective (Track-length)  =  1.23264 +/-  0.00345\n",
+      " k-effective (Absorption)    =  1.23111 +/-  0.00186\n",
+      " Combined k-effective        =  1.23135 +/-  0.00185\n",
+      " Leakage Fraction            =  0.00000 +/-  0.00000\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Run OpenMC\n",
+    "openmc.run(mpi_procs=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tally Data Processing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Load the last statepoint file\n",
+    "sp = openmc.StatePoint('statepoint.50.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry. By default, a `Summary` object is automatically linked when a `StatePoint` is loaded. This is necessary for the `openmc.mgxs` module to properly process the tally data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The statepoint is now ready to be analyzed by our multi-group cross sections. We simply have to load the tallies from the `StatePoint` into each object as follows and our `MGXS` objects will compute the cross sections for us under-the-hood."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Load the tallies from the statepoint into each MGXS object\n",
+    "chi_prompt.load_from_statepoint(sp)\n",
+    "prompt_nu_fission.load_from_statepoint(sp)\n",
+    "chi_delayed.load_from_statepoint(sp)\n",
+    "delayed_nu_fission.load_from_statepoint(sp)\n",
+    "beta.load_from_statepoint(sp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voila! Our multi-group cross sections are now ready to rock 'n roll!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extracting and Storing MGXS Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's first inspect our delayed-nu-fission section by printing it to the screen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Multi-Delayed-Group XS\n",
+      "\tReaction Type  =\tdelayed-nu-fission\n",
+      "\tDomain Type    =\tcell\n",
+      "\tDomain ID      =\t1\n",
+      "\tNuclide        =\tU235\n",
+      "\tCross Sections [cm^-1]:\n",
+      "            Delayed Group 1:\t\n",
+      "            Group 1 [0.0        - 20.0      MeV]:\t5.16e-06 +/- 3.38e-01%\n",
+      "\n",
+      "            Delayed Group 2:\t\n",
+      "            Group 1 [0.0        - 20.0      MeV]:\t2.67e-05 +/- 3.38e-01%\n",
+      "\n",
+      "            Delayed Group 3:\t\n",
+      "            Group 1 [0.0        - 20.0      MeV]:\t2.54e-05 +/- 3.38e-01%\n",
+      "\n",
+      "            Delayed Group 4:\t\n",
+      "            Group 1 [0.0        - 20.0      MeV]:\t5.71e-05 +/- 3.38e-01%\n",
+      "\n",
+      "            Delayed Group 5:\t\n",
+      "            Group 1 [0.0        - 20.0      MeV]:\t2.34e-05 +/- 3.38e-01%\n",
+      "\n",
+      "            Delayed Group 6:\t\n",
+      "            Group 1 [0.0        - 20.0      MeV]:\t9.80e-06 +/- 3.38e-01%\n",
+      "\n",
+      "\n",
+      "\tNuclide        =\tPu239\n",
+      "\tCross Sections [cm^-1]:\n",
+      "            Delayed Group 1:\t\n",
+      "            Group 1 [0.0        - 20.0      MeV]:\t1.17e-06 +/- 3.00e-01%\n",
+      "\n",
+      "            Delayed Group 2:\t\n",
+      "            Group 1 [0.0        - 20.0      MeV]:\t7.60e-06 +/- 3.00e-01%\n",
+      "\n",
+      "            Delayed Group 3:\t\n",
+      "            Group 1 [0.0        - 20.0      MeV]:\t5.75e-06 +/- 3.00e-01%\n",
+      "\n",
+      "            Delayed Group 4:\t\n",
+      "            Group 1 [0.0        - 20.0      MeV]:\t1.05e-05 +/- 3.00e-01%\n",
+      "\n",
+      "            Delayed Group 5:\t\n",
+      "            Group 1 [0.0        - 20.0      MeV]:\t5.47e-06 +/- 3.00e-01%\n",
+      "\n",
+      "            Delayed Group 6:\t\n",
+      "            Group 1 [0.0        - 20.0      MeV]:\t1.66e-06 +/- 3.00e-01%\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "delayed_nu_fission.print_xs()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the `openmc.mgxs` module uses [tally arithmetic](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) under-the-hood, the cross section is stored as a \"derived\" `Tally` object. This means that it can be queried and manipulated using all of the same methods supported for the `Tally` class in the OpenMC Python API. For example, we can construct a [Pandas](http://pandas.pydata.org/) `DataFrame` of the multi-group cross section data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell</th>\n",
+       "      <th>delayedgroup</th>\n",
+       "      <th>group in</th>\n",
+       "      <th>nuclide</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std. dev.</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>U235</td>\n",
+       "      <td>0.000228</td>\n",
+       "      <td>1.038855e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Pu239</td>\n",
+       "      <td>0.000081</td>\n",
+       "      <td>3.258333e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>U235</td>\n",
+       "      <td>0.001175</td>\n",
+       "      <td>5.362249e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Pu239</td>\n",
+       "      <td>0.000531</td>\n",
+       "      <td>2.121977e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>U235</td>\n",
+       "      <td>0.001122</td>\n",
+       "      <td>5.119269e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Pu239</td>\n",
+       "      <td>0.000402</td>\n",
+       "      <td>1.605842e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>U235</td>\n",
+       "      <td>0.002516</td>\n",
+       "      <td>1.147782e-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Pu239</td>\n",
+       "      <td>0.000733</td>\n",
+       "      <td>2.931785e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>U235</td>\n",
+       "      <td>0.001031</td>\n",
+       "      <td>4.705745e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Pu239</td>\n",
+       "      <td>0.000382</td>\n",
+       "      <td>1.527096e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>U235</td>\n",
+       "      <td>0.000432</td>\n",
+       "      <td>1.971220e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Pu239</td>\n",
+       "      <td>0.000116</td>\n",
+       "      <td>4.621961e-07</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    cell  delayedgroup  group in nuclide      mean     std. dev.\n",
+       "0      1             1         1    U235  0.000228  1.038855e-06\n",
+       "1      1             1         1   Pu239  0.000081  3.258333e-07\n",
+       "2      1             2         1    U235  0.001175  5.362249e-06\n",
+       "3      1             2         1   Pu239  0.000531  2.121977e-06\n",
+       "4      1             3         1    U235  0.001122  5.119269e-06\n",
+       "5      1             3         1   Pu239  0.000402  1.605842e-06\n",
+       "6      1             4         1    U235  0.002516  1.147782e-05\n",
+       "7      1             4         1   Pu239  0.000733  2.931785e-06\n",
+       "8      1             5         1    U235  0.001031  4.705745e-06\n",
+       "9      1             5         1   Pu239  0.000382  1.527096e-06\n",
+       "10     1             6         1    U235  0.000432  1.971220e-06\n",
+       "11     1             6         1   Pu239  0.000116  4.621961e-07"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = beta.get_pandas_dataframe()\n",
+    "df.head(12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each multi-group cross section object can be easily exported to a variety of file formats, including CSV, Excel, and LaTeX for storage or data processing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "beta.export_xs_data(filename='beta', format='excel')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following code snippet shows how to export the chi `MGXS` to the same HDF5 binary data store."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "chi_prompt.build_hdf5_store(filename='mgxs', append=True)\n",
+    "chi_delayed.build_hdf5_store(filename='mgxs', append=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using Tally Arithmetic to Compute the Delayed Neutron Precursor Concentrations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta` and `DelayedNuFissionXS` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n",
+    "\n",
+    "$$\\frac{\\partial}{\\partial t} C_{k,d} (t) = \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t)\\Phi(\\mathbf{r},E',t) - \\lambda_{d} C_{k,d} (t) $$\n",
+    "\n",
+    "$$C_{k,d} (t=0) = \\frac{1}{\\lambda_{d}} \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t=0) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t=0)\\Phi(\\mathbf{r},E',t=0) $$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell</th>\n",
+       "      <th>delayedgroup</th>\n",
+       "      <th>nuclide</th>\n",
+       "      <th>score</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std. dev.</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>(U235 / total)</td>\n",
+       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
+       "      <td>9.430766e-08</td>\n",
+       "      <td>5.356653e-10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>(Pu239 / total)</td>\n",
+       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
+       "      <td>7.631830e-09</td>\n",
+       "      <td>3.816602e-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>(U235 / total)</td>\n",
+       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
+       "      <td>1.107191e-06</td>\n",
+       "      <td>6.288818e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>(Pu239 / total)</td>\n",
+       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
+       "      <td>1.426298e-07</td>\n",
+       "      <td>7.132776e-10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>(U235 / total)</td>\n",
+       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
+       "      <td>6.713761e-07</td>\n",
+       "      <td>3.813401e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>(Pu239 / total)</td>\n",
+       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
+       "      <td>5.434458e-08</td>\n",
+       "      <td>2.717718e-10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>(U235 / total)</td>\n",
+       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
+       "      <td>4.907155e-07</td>\n",
+       "      <td>2.787253e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>(Pu239 / total)</td>\n",
+       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
+       "      <td>2.633756e-08</td>\n",
+       "      <td>1.317115e-10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>(U235 / total)</td>\n",
+       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
+       "      <td>1.475656e-08</td>\n",
+       "      <td>8.381692e-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>(Pu239 / total)</td>\n",
+       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
+       "      <td>1.278385e-09</td>\n",
+       "      <td>6.393074e-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>(U235 / total)</td>\n",
+       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
+       "      <td>1.190878e-09</td>\n",
+       "      <td>6.764161e-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>(Pu239 / total)</td>\n",
+       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
+       "      <td>5.385798e-11</td>\n",
+       "      <td>2.693384e-13</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    cell  delayedgroup          nuclide  \\\n",
+       "0      1             1   (U235 / total)   \n",
+       "1      1             1  (Pu239 / total)   \n",
+       "2      1             2   (U235 / total)   \n",
+       "3      1             2  (Pu239 / total)   \n",
+       "4      1             3   (U235 / total)   \n",
+       "5      1             3  (Pu239 / total)   \n",
+       "6      1             4   (U235 / total)   \n",
+       "7      1             4  (Pu239 / total)   \n",
+       "8      1             5   (U235 / total)   \n",
+       "9      1             5  (Pu239 / total)   \n",
+       "10     1             6   (U235 / total)   \n",
+       "11     1             6  (Pu239 / total)   \n",
+       "\n",
+       "                                                score     mean  std. dev.  \n",
+       "0   (((delayed-nu-fission / nu-fission) * (delayed... 9.43e-08   5.36e-10  \n",
+       "1   (((delayed-nu-fission / nu-fission) * (delayed... 7.63e-09   3.82e-11  \n",
+       "2   (((delayed-nu-fission / nu-fission) * (delayed... 1.11e-06   6.29e-09  \n",
+       "3   (((delayed-nu-fission / nu-fission) * (delayed... 1.43e-07   7.13e-10  \n",
+       "4   (((delayed-nu-fission / nu-fission) * (delayed... 6.71e-07   3.81e-09  \n",
+       "5   (((delayed-nu-fission / nu-fission) * (delayed... 5.43e-08   2.72e-10  \n",
+       "6   (((delayed-nu-fission / nu-fission) * (delayed... 4.91e-07   2.79e-09  \n",
+       "7   (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08   1.32e-10  \n",
+       "8   (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-08   8.38e-11  \n",
+       "9   (((delayed-nu-fission / nu-fission) * (delayed... 1.28e-09   6.39e-12  \n",
+       "10  (((delayed-nu-fission / nu-fission) * (delayed... 1.19e-09   6.76e-12  \n",
+       "11  (((delayed-nu-fission / nu-fission) * (delayed... 5.39e-11   2.69e-13  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Set the time constants for the delayed precursors (in seconds^-1)\n",
+    "precursor_halflife = np.array([55.6, 24.5, 16.3, 2.37, 0.424, 0.195])\n",
+    "precursor_lambda = -np.log(0.5) / precursor_halflife\n",
+    "\n",
+    "# Create a tally object with only the delayed group filter for the time constants\n",
+    "beta_filters = [f for f in beta.xs_tally.filters if f.type != 'delayedgroup']\n",
+    "lambda_tally = beta.xs_tally.summation(nuclides=beta.xs_tally.nuclides)\n",
+    "for f in beta_filters:\n",
+    "    lambda_tally = lambda_tally.summation(filter_type=f.type, remove_filter=True) * 0. + 1.\n",
+    "\n",
+    "# Set the mean of the lambda tally and reshape to account for nuclides and scores\n",
+    "lambda_tally._mean = precursor_lambda\n",
+    "lambda_tally._mean.shape = lambda_tally.std_dev.shape\n",
+    "\n",
+    "# Set a total nuclide and lambda score\n",
+    "lambda_tally.nuclides = [openmc.Nuclide(name='total')]\n",
+    "lambda_tally.scores = ['lambda']\n",
+    "\n",
+    "# Use tally arithmetic to compute the precursor concentrations\n",
+    "precursor_conc = beta.xs_tally.summation(filter_type='energy', remove_filter=True) * \\\n",
+    "    delayed_nu_fission.xs_tally.summation(filter_type='energy', remove_filter=True) / lambda_tally\n",
+    "    \n",
+    "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n",
+    "precursor_conc.get_pandas_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the delayed neutron fractions for each nuclide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Beta (U-235) : 0.006504 +/- 0.000015\n",
+      "Beta (Pu-239): 0.002245 +/- 0.000004\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0, 7)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEZCAYAAAC0HgObAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x110b20c50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy_filter = [f for f in beta.xs_tally.filters if f.type == 'energy']\n",
+    "beta_integrated = beta.xs_tally.summation(filter_type='energy', remove_filter=True)\n",
+    "beta_u235  = beta_integrated.get_values(nuclides=['U235'])\n",
+    "beta_pu239 = beta_integrated.get_values(nuclides=['Pu239'])\n",
+    "\n",
+    "# Reshape the betas\n",
+    "beta_u235.shape = (beta_u235.shape[0])\n",
+    "beta_pu239.shape = (beta_pu239.shape[0])\n",
+    "\n",
+    "df = beta_integrated.summation(filter_type='delayedgroup', remove_filter=True).get_pandas_dataframe()\n",
+    "print('Beta (U-235) : {:.6f} +/- {:.6f}'.format(df[df['nuclide'] == 'U235']['mean'][0], df[df['nuclide'] == 'U235']['std. dev.'][0]))\n",
+    "print('Beta (Pu-239): {:.6f} +/- {:.6f}'.format(df[df['nuclide'] == 'Pu239']['mean'][1], df[df['nuclide'] == 'Pu239']['std. dev.'][1]))\n",
+    "\n",
+    "beta_u235 = np.append(beta_u235[0], beta_u235)\n",
+    "beta_pu239 = np.append(beta_pu239[0], beta_pu239)\n",
+    "\n",
+    "# Create a step plot for the MGXS\n",
+    "plt.plot(np.arange(0.5, 7.5, 1), beta_u235, drawstyle='steps', color='b', linewidth=3)\n",
+    "plt.plot(np.arange(0.5, 7.5, 1), beta_pu239, drawstyle='steps', color='g', linewidth=3)\n",
+    "\n",
+    "plt.title('Delayed Neutron Fraction (beta)')\n",
+    "plt.xlabel('Delayed Group')\n",
+    "plt.ylabel('Beta(fraction total neutrons)')\n",
+    "plt.legend(['U-235', 'Pu-239'])\n",
+    "plt.xlim([0,7])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also plot the fission spectrum for the prompt and delayed neutrons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.001, 20)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEdCAYAAAAIIcBlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x110b42a50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "chi_d_u235  = chi_delayed.xs_tally.get_values(nuclides=['U235'])\n",
+    "chi_d_pu239 = chi_delayed.xs_tally.get_values(nuclides=['Pu239'])\n",
+    "chi_p_u235  = chi_prompt.xs_tally.get_values(nuclides=['U235'])\n",
+    "chi_p_pu239 = chi_prompt.xs_tally.get_values(nuclides=['Pu239'])\n",
+    "\n",
+    "# Reshape the betas\n",
+    "chi_d_u235.shape  = (chi_d_u235.shape[0])\n",
+    "chi_d_pu239.shape = (chi_d_pu239.shape[0])\n",
+    "chi_p_u235.shape  = (chi_p_u235.shape[0])\n",
+    "chi_p_pu239.shape = (chi_p_pu239.shape[0])\n",
+    "\n",
+    "chi_d_u235  = np.append(chi_d_u235[0] , chi_d_u235)\n",
+    "chi_d_pu239 = np.append(chi_d_pu239[0], chi_d_pu239)\n",
+    "chi_p_u235  = np.append(chi_p_u235[0] , chi_p_u235)\n",
+    "chi_p_pu239 = np.append(chi_p_pu239[0], chi_p_pu239)\n",
+    "\n",
+    "# Create a step plot for the MGXS\n",
+    "plt.semilogx(energy_groups.group_edges, chi_d_u235 , drawstyle='steps', color='b', linestyle='--', linewidth=3)\n",
+    "plt.semilogx(energy_groups.group_edges, chi_d_pu239, drawstyle='steps', color='g', linestyle='--', linewidth=3)\n",
+    "plt.semilogx(energy_groups.group_edges, chi_p_u235 , drawstyle='steps', color='b', linestyle=':', linewidth=3)\n",
+    "plt.semilogx(energy_groups.group_edges, chi_p_pu239, drawstyle='steps', color='g', linestyle=':', linewidth=3)\n",
+    "\n",
+    "plt.title('Energy Spectrum for Fission Neutrons')\n",
+    "plt.xlabel('Energy (MeV)')\n",
+    "plt.ylabel('Fraction on emitted neutrons')\n",
+    "plt.legend(['U-235 delayed', 'Pu-239 delayed', 'U-235 prompt', 'Pu-239 prompt'],loc=2)\n",
+    "plt.xlim(0.001,20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/openmc/mgxs/mdgxs.py b/openmc/mgxs/mdgxs.py
index d36b29f7f..4d65aaacc 100644
--- a/openmc/mgxs/mdgxs.py
+++ b/openmc/mgxs/mdgxs.py
@@ -709,12 +709,113 @@ class MDGXS(MGXS):
 
         """
 
-        df = super(MDGXS, self).get_pandas_dataframe(groups, nuclides, xs_type,
-                                                     distribcell_paths)
-
+        if not isinstance(groups, basestring):
+            cv.check_iterable_type('groups', groups, Integral)
+        if nuclides != 'all' and nuclides != 'sum':
+            cv.check_iterable_type('nuclides', nuclides, basestring)
         if not isinstance(delayed_groups, basestring):
             cv.check_iterable_type('delayed groups', delayed_groups, Integral)
 
+        cv.check_value('xs_type', xs_type, ['macro', 'micro'])
+
+        num_delayed_groups = 1
+        if self.delayed_groups != None:
+            num_delayed_groups = self.delayed_groups.num_groups
+
+        # Get a Pandas DataFrame from the derived xs tally
+        if self.by_nuclide and nuclides == 'sum':
+
+            # Use tally summation to sum across all nuclides
+            query_nuclides = self.get_all_nuclides()
+            xs_tally = self.xs_tally.summation(nuclides=query_nuclides)
+            df = xs_tally.get_pandas_dataframe(
+                distribcell_paths=distribcell_paths)
+
+            # Remove nuclide column since it is homogeneous and redundant
+            if self.domain_type == 'mesh':
+                df.drop('nuclide', axis=1, level=0, inplace=True)
+            else:
+                df.drop('nuclide', axis=1, inplace=True)
+
+        # If the user requested a specific set of nuclides
+        elif self.by_nuclide and nuclides != 'all':
+            xs_tally = self.xs_tally.get_slice(nuclides=nuclides)
+            df = xs_tally.get_pandas_dataframe(
+                distribcell_paths=distribcell_paths)
+
+        # If the user requested all nuclides, keep nuclide column in dataframe
+        else:
+            df = self.xs_tally.get_pandas_dataframe(
+                distribcell_paths=distribcell_paths)
+
+        # Remove the score column since it is homogeneous and redundant
+        if self.domain_type == 'mesh':
+            df = df.drop('score', axis=1, level=0)
+        else:
+            df = df.drop('score', axis=1)
+
+        # Override energy groups bounds with indices
+        all_groups = np.arange(self.num_groups, 0, -1, dtype=np.int)
+        all_groups = np.repeat(all_groups, self.num_nuclides)
+        if 'energy low [MeV]' in df and 'energyout low [MeV]' in df:
+            df.rename(columns={'energy low [MeV]': 'group in'},
+                      inplace=True)
+            in_groups = np.tile(all_groups, self.num_subdomains * num_delayed_groups)
+            in_groups = np.repeat(in_groups, df.shape[0] / in_groups.size)
+            df['group in'] = in_groups
+            del df['energy high [MeV]']
+
+            df.rename(columns={'energyout low [MeV]': 'group out'},
+                      inplace=True)
+            out_groups = np.repeat(all_groups, self.xs_tally.num_scores)
+            out_groups = np.tile(out_groups, df.shape[0] / out_groups.size * num_delayed_groups)
+            df['group out'] = out_groups
+            del df['energyout high [MeV]']
+            columns = ['group in', 'group out']
+
+        elif 'energyout low [MeV]' in df:
+            df.rename(columns={'energyout low [MeV]': 'group out'},
+                      inplace=True)
+            in_groups = np.tile(all_groups, self.num_subdomains * num_delayed_groups)
+            df['group out'] = in_groups
+            del df['energyout high [MeV]']
+            columns = ['group out']
+
+        elif 'energy low [MeV]' in df:
+            df.rename(columns={'energy low [MeV]': 'group in'}, inplace=True)
+            in_groups = np.tile(all_groups, self.num_subdomains * num_delayed_groups)
+            df['group in'] = in_groups
+            del df['energy high [MeV]']
+            columns = ['group in']
+
+        # Select out those groups the user requested
+        if not isinstance(groups, basestring):
+            if 'group in' in df:
+                df = df[df['group in'].isin(groups)]
+            if 'group out' in df:
+                df = df[df['group out'].isin(groups)]
+
+        # If user requested micro cross sections, divide out the atom densities
+        if xs_type == 'micro':
+            if self.by_nuclide:
+                densities = self.get_nuclide_densities(nuclides)
+            else:
+                densities = self.get_nuclide_densities('sum')
+            densities = np.repeat(densities, len(self.rxn_rate_tally.scores))
+            tile_factor = df.shape[0] / len(densities)
+            df['mean'] /= np.tile(densities, tile_factor)
+            df['std. dev.'] /= np.tile(densities, tile_factor)
+
+        # Sort the dataframe by domain type id (e.g., distribcell id) and
+        # energy groups such that data is from fast to thermal
+        if self.domain_type == 'mesh':
+            mesh_str = 'mesh {0}'.format(self.domain.id)
+            df.sort_values(by=[(mesh_str, 'x'), (mesh_str, 'y'), \
+                               (mesh_str, 'z')] + columns, inplace=True)
+        else:
+            df.sort_values(by=[self.domain_type] + columns, inplace=True)
+        return df
+
         # Select out those delayed groups the user requested
         if not isinstance(delayed_groups, basestring):
             if 'delayedgroup' in df:
diff --git a/openmc/tallies.py b/openmc/tallies.py
index c68b0faac..2073bbd28 100644
--- a/openmc/tallies.py
+++ b/openmc/tallies.py
@@ -2197,8 +2197,8 @@ class Tally(object):
 
         """
 
-        cv.check_type('filter1', filter1, (Filter, CrossFilter, AggregateFilter))
-        cv.check_type('filter2', filter2, (Filter, CrossFilter, AggregateFilter))
+        cv.check_type('filter1', filter1, _FILTER_CLASSES)
+        cv.check_type('filter2', filter2, _FILTER_CLASSES)
 
         # Check that the filters exist in the tally and are not the same
         if filter1 == filter2:
@@ -2280,8 +2280,8 @@ class Tally(object):
                   'since it does not contain any results.'.format(self.id)
             raise ValueError(msg)
 
-        cv.check_type('nuclide1', nuclide1, Nuclide)
-        cv.check_type('nuclide2', nuclide2, Nuclide)
+        cv.check_type('nuclide1', nuclide1, _NUCLIDE_CLASSES)
+        cv.check_type('nuclide2', nuclide2, _NUCLIDE_CLASSES)
 
         # Check that the nuclides exist in the tally and are not the same
         if nuclide1 == nuclide2:
@@ -3318,7 +3318,7 @@ class Tally(object):
 
         """
 
-        cv.check_type('new_filter', new_filter, Filter)
+        cv.check_type('new_filter', new_filter, _FILTER_CLASSES)
 
         if new_filter in self.filters:
             msg = 'Unable to diagonalize Tally ID="{0}" which already ' \