From c368d7c319efdc22e68e2b2516f592377cddfd8f Mon Sep 17 00:00:00 2001 From: Brenda Praggastis Date: Mon, 27 Sep 2021 12:04:53 -0700 Subject: [PATCH 01/41] typo --- hypernetx/classes/hypergraph.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index 0af2c2ce..f320fc98 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -1142,7 +1142,7 @@ def _incidence_to_adjacency(M, s=1, weights=False): """ Helper method to obtain adjacency matrix from boolean incidence matrix for s-metrics. - Self loops are note supported. + Self loops are not supported. The adjacency matrix will define an s-linegraph. Parameters From 5e510c127f001304071c9b0a2e34a0870ac4fd7c Mon Sep 17 00:00:00 2001 From: Brenda Praggastis Date: Tue, 12 Oct 2021 14:09:15 -0700 Subject: [PATCH 02/41] work on FT library --- .../algorithms/modularity_and_clustering.py | 276 +++++++++++++++++ .../modularity_and_clustering_original.py | 293 ++++++++++++++++++ hypernetx/utils/toys/GoT.pkl | Bin 0 -> 116969 bytes 3 files changed, 569 insertions(+) create mode 100644 hypernetx/algorithms/modularity_and_clustering.py create mode 100644 hypernetx/algorithms/modularity_and_clustering_original.py create mode 100644 hypernetx/utils/toys/GoT.pkl diff --git a/hypernetx/algorithms/modularity_and_clustering.py b/hypernetx/algorithms/modularity_and_clustering.py new file mode 100644 index 00000000..09450c8d --- /dev/null +++ b/hypernetx/algorithms/modularity_and_clustering.py @@ -0,0 +1,276 @@ +from collections import Counter +import numpy as np +from functools import reduce +import igraph as ig +import itertools + +################################################################################ + +# we use 2 representations for partitions (0-based part ids): +# (1) dictionary or (2) list of sets + + +def dict2part(D): + P = [] + k = list(D.keys()) + v = list(D.values()) + for x in range(max(D.values()) + 1): + P.append(set([k[i] for i in range(len(k)) if v[i] == x])) + return P + + +def part2dict(A): + x = [] + for i in range(len(A)): + x.extend([(a, i) for a in A[i]]) + return {k: v for k, v in x} + +################################################################################ + + +def factorial(n): + if n < 2: + return 1 + return reduce(lambda x, y: x * y, range(2, int(n) + 1)) + +# Precompute soe values on HNX hypergraph for computing qH faster + + +def HNX_precompute(HG): + # 1. compute node strenghts (weighted degrees) + for v in HG.nodes: + HG.nodes[v].strength = 0 + for e in HG.edges: + try: + w = HG.edges[e].weight + except: + w = 1 + # add unit weight if none to simplify other functions + HG.edges[e].weight = 1 + for v in list(HG.edges[e]): + HG.nodes[v].strength += w + # 2. compute d-weights + ctr = Counter([len(HG.edges[e]) for e in HG.edges]) + for k in ctr.keys(): + ctr[k] = 0 + for e in HG.edges: + ctr[len(HG.edges[e])] += HG.edges[e].weight + HG.d_weights = ctr + HG.total_weight = sum(ctr.values()) + # 3. compute binomial coeffcients (modularity speed-up) + bin_coef = {} + for n in HG.d_weights.keys(): + for k in np.arange(n // 2 + 1, n + 1): + bin_coef[(n, k)] = factorial(n) / (factorial(k) * factorial(n - k)) + HG.bin_coef = bin_coef + +################################################################################ + +# some weight function 'wdc' for d-edges with c-majority + +# default: linear w.r.t. c + + +def linear(d, c): + return c / d if c > d / 2 else 0 + +# majority + + +def majority(d, c): + return 1 if c > d / 2 else 0 + +# strict + + +def strict(d, c): + return 1 if c == d else 0 + +######################################### + +# compute vol(A_i)/vol(V) for each part A_i in A (list of sets) + + +def compute_partition_probas(HG, A): + p = [] + for part in A: + vol = 0 + for v in part: + vol += HG.nodes[v].strength + p.append(vol) + s = sum(p) + return [i / s for i in p] + +# degree tax + + +def DegreeTax(HG, Pr, wdc): + DT = 0 + for d in HG.d_weights.keys(): + tax = 0 + for c in np.arange(d // 2 + 1, d + 1): + for p in Pr: + tax += p**c * (1 - p)**(d - c) * HG.bin_coef[(d, c)] * wdc(d, c) + tax *= HG.d_weights[d] + DT += tax + DT /= HG.total_weight + return DT + + +# edge contribution, A is list of sets +def EdgeContribution(HG, A, wdc): + EC = 0 + for e in HG.edges: + d = HG.size(e) + for part in A: + if HG.size(e, part) > d / 2: + EC += wdc(d, HG.size(e, part)) * HG.edges[e].weight + EC /= HG.total_weight + return EC + +# HG: HNX hypergraph +# A: partition (list of sets) +# wcd: weight function (ex: strict, majority, linear) + + +def HNX_modularity(HG, A, wdc=linear): + Pr = compute_partition_probas(HG, A) + return EdgeContribution(HG, A, wdc) - DegreeTax(HG, Pr, wdc) + +################################################################################ + +# 2-section igraph from HG + + +def HNX_2section(HG): + s = [] + for e in HG.edges: + E = HG.edges[e] + # random-walk 2-section (preserve nodes' weighted degrees) + try: + w = HG.edges[e].weight / (len(E) - 1) + except: + w = 1 / (len(E) - 1) + s.extend([(k[0], k[1], w) for k in itertools.combinations(E, 2)]) + G = ig.Graph.TupleList(s, weights=True).simplify(combine_edges='sum') + return G + +################################################################################ + +def HNX_Kumar(HG, delta=.01): + + # weights will be modified -- store initial weights + W = [e.weight for e in HG.edges()] + # build graph + G = HNX_2section(HG) + # apply clustering + CG = G.community_multilevel(weights='weight') + CH = [] + for comm in CG.as_cover(): + CH.append(set([G.vs[x]['name'] for x in comm])) + + # LOOP + diff = 1 + ctr = 0 + while diff > delta: + # re-weight + diff = 0 + for i in HG.edges: + e = HG.edges[i] + reweight = sum([1 / (1 + HG.size(e, c)) for c in CH]) * (HG.size(e) + len(CH)) / HG.number_of_edges() + diff = max(diff, 0.5 * abs(e.weight - reweight)) + e.weight = 0.5 * e.weight + 0.5 * reweight + # re-run louvain + # build graph + G = HNX_2section(HG) + # apply clustering + CG = G.community_multilevel(weights='weight') + CH = [] + for comm in CG.as_cover(): + CH.append(set([G.vs[x]['name'] for x in comm])) + ctr += 1 + if ctr > 50: # this process sometimes gets stuck -- set limit + break + G.vs['part'] = CG.membership + for e in HG.edges: + HG.edges[e].weight = W[e] + return {v['name']: v['part'] for v in G.vs} + +################################################################################ + +# compute change in edge contribution -- +# partition P, node v going from P[a] to P[b] + + +def DeltaEC(HG, P, v, a, b, wdc): + Pm = P[a] - {v} + Pn = P[b].union({v}) + ec = 0 + for e in list(HG.nodes[v].memberships): + d = HG.size(e) + w = HG.edges[e].weight + ec += w * (wdc(d, HG.size(e, Pm)) + wdc(d, HG.size(e, Pn)) + - wdc(d, HG.size(e, P[a])) - wdc(d, HG.size(e, P[b]))) + return ec / HG.total_weight + +# exp. part of binomial pmf + + +def bin_ppmf(d, c, p): + return p**c * (1 - p)**(d - c) + +# compute change in degree tax -- +# partition P (list), node v going from P[a] to P[b] +def DeltaDT(HG, P, v, a, b, wdc): + + s = HG.nodes[v].strength + vol = sum([HG.nodes[v].strength for v in HG.nodes]) + vola = sum([HG.nodes[v].strength for v in P[a]]) + volb = sum([HG.nodes[v].strength for v in P[b]]) + volm = (vola - s) / vol + voln = (volb + s) / vol + vola /= vol + volb /= vol + DT = 0 + + for d in HG.d_weights.keys(): + x = 0 + for c in np.arange(int(np.floor(d / 2)) + 1, d + 1): + x += HG.bin_coef[(d, c)] * wdc(d, c) * (bin_ppmf(d, c, voln) + bin_ppmf(d, c, volm) + - bin_ppmf(d, c, vola) - bin_ppmf(d, c, volb)) + DT += x * HG.d_weights[d] + return DT / HG.total_weight + +# simple H-based algorithm -- +# try moving nodes between communities to optimize qH +# requires L: initial non-trivial partition + + +def HNX_LastStep(HG, L, wdc=linear, delta=.01): + A = L[:] # we will modify this, copy + D = part2dict(A) + qH = 0 + while True: + for v in list(np.random.permutation(list(HG.nodes))): + c = D[v] + s = list(set([c] + [D[i] for i in HG.neighbors(v)])) + M = [] + if len(s) > 0: + for i in s: + if c == i: + M.append(0) + else: + M.append(DeltaEC(HG, A, v, c, i, wdc) - DeltaDT(HG, A, v, c, i, wdc)) + i = s[np.argmax(M)] + if c != i: + A[c] = A[c] - {v} + A[i] = A[i].union({v}) + D[v] = i + Pr = compute_partition_probas(HG, A) + q2 = EdgeContribution(HG, A, wdc) - DegreeTax(HG, Pr, wdc) + if (q2 - qH) < delta: + break + qH = q2 + return [a for a in A if len(a) > 0] + +################################################################################ diff --git a/hypernetx/algorithms/modularity_and_clustering_original.py b/hypernetx/algorithms/modularity_and_clustering_original.py new file mode 100644 index 00000000..b7e716e9 --- /dev/null +++ b/hypernetx/algorithms/modularity_and_clustering_original.py @@ -0,0 +1,293 @@ +from collections import Counter +import numpy as np +from functools import reduce +import igraph as ig +import itertools + +################################################################################ + +# we use 2 representations for partitions (0-based part ids): +# (1) dictionary or (2) list of sets + + +def dict2part(D): + P = [] + k = list(D.keys()) + v = list(D.values()) + for x in range(max(D.values()) + 1): + P.append(set([k[i] for i in range(len(k)) if v[i] == x])) + return P + + +def part2dict(A): + x = [] + for i in range(len(A)): + x.extend([(a, i) for a in A[i]]) + return {k: v for k, v in x} + +################################################################################ + + +def factorial(n): + if n < 2: + return 1 + return reduce(lambda x, y: x * y, range(2, int(n) + 1)) + +# Precompute soe values on HNX hypergraph for computing qH faster + + +def precompute_modularity_parameters(HG): + # 1. compute node strenghts (weighted degrees) + for v in HG.nodes: + HG.nodes[v].strength = 0 + for e in HG.edges: + try: + w = HG.edges[e].weight + except: + w = 1 + # add unit weight if none to simplify other functions + HG.edges[e].weight = 1 + for v in list(HG.edges[e]): + HG.nodes[v].strength += w + # 2. compute d-weights + ctr = Counter([len(HG.edges[e]) for e in HG.edges]) + for k in ctr.keys(): + ctr[k] = 0 + for e in HG.edges: + ctr[len(HG.edges[e])] += HG.edges[e].weight + HG.d_weights = ctr + HG.total_weight = sum(ctr.values()) + # 3. compute binomial coeffcients (modularity speed-up) + bin_coef = {} + for n in HG.d_weights.keys(): + for k in np.arange(n // 2 + 1, n + 1): + bin_coef[(n, k)] = factorial(n) / (factorial(k) * factorial(n - k)) + HG.bin_coef = bin_coef + +################################################################################ + +# some weight function 'wdc' for d-edges with c-majority + +# default: linear w.r.t. c + + +def linear(d, c): + return c / d if c > d / 2 else 0 + +# majority + + +def majority(d, c): + return 1 if c > d / 2 else 0 + +# strict + + +def strict(d, c): + return 1 if c == d else 0 + +######################################### + +# compute vol(A_i)/vol(V) for each part A_i in A (list of sets) + + +def compute_partition_probas(HG, A): + p = [] + for part in A: + vol = 0 + for v in part: + vol += HG.nodes[v].strength + p.append(vol) + s = sum(p) + return [i / s for i in p] + +# degree tax + + +def DegreeTax(HG, Pr, wdc): + DT = 0 + for d in HG.d_weights.keys(): + tax = 0 + for c in np.arange(d // 2 + 1, d + 1): + for p in Pr: + tax += p**c * (1 - p)**(d - c) * HG.bin_coef[(d, c)] * wdc(d, c) + tax *= HG.d_weights[d] + DT += tax + DT /= HG.total_weight + return DT + + +# edge contribution, A is list of sets +def EdgeContribution(HG, A, wdc): + EC = 0 + for e in HG.edges: + d = HG.size(e) + for part in A: + if HG.size(e, part) > d / 2: + EC += wdc(d, HG.size(e, part)) * HG.edges[e].weight + EC /= HG.total_weight + return EC + +# HG: HNX hypergraph +# A: partition (list of sets) +# wcd: weight function (ex: strict, majority, linear) + + +def _wdc(wdc): + if wdc == 'linear': + return linear + elif wdc == 'strict': + return strict + elif wdc == 'majority': + return majority + else: + return wdc + + +def HNX_modularity(HG, A, wdc='linear'): + wdc = _wdc(wdc) + Pr = compute_partition_probas(HG, A) + return EdgeContribution(HG, A, wdc) - DegreeTax(HG, Pr, wdc) + +################################################################################ + +# 2-section igraph from HG + + +def HNX_2section(HG): + s = [] + for e in HG.edges: + E = HG.edges[e] + # random-walk 2-section (preserve nodes' weighted degrees) + try: + w = E.weight / (len(E) - 1) + except: + w = 1 / (len(E) - 1) + s.extend([(k[0], k[1], w) for k in itertools.combinations(E.elements, 2)]) # BP + G = ig.Graph.TupleList(s, weights=True).simplify(combine_edges='sum') + return G + +################################################################################ + +def HNX_Kumar(HG, delta=.01): + + # weights will be modified -- store initial weights + W = {e: HG.edges[e].weight for e in HG.edges} + # build graph + G = HNX_2section(HG) + # apply clustering + CG = G.community_multilevel(weights='weight') + CH = [] + for comm in CG.as_cover(): + CH.append(set([G.vs[x]['name'] for x in comm])) + + # LOOP + diff = 1 + ctr = 0 + while diff > delta: + # re-weight + diff = 0 + for i in HG.edges: + e = HG.edges[i] + reweight = sum([1 / (1 + HG.size(i, c)) for c in CH]) * (HG.size(i) + len(CH)) / HG.number_of_edges() + diff = max(diff, 0.5 * abs(e.weight - reweight)) + e.weight = 0.5 * e.weight + 0.5 * reweight + # re-run louvain + # build graph + G = HNX_2section(HG) + # apply clustering + CG = G.community_multilevel(weights='weight') + CH = [] + for comm in CG.as_cover(): + CH.append(set([G.vs[x]['name'] for x in comm])) + ctr += 1 + if ctr > 50: # this process sometimes gets stuck -- set limit + break + G.vs['part'] = CG.membership + for e in HG.edges: + HG.edges[e].weight = W[e] + return {v['name']: v['part'] for v in G.vs} + +################################################################################ + +# compute change in edge contribution -- +# partition P, node v going from P[a] to P[b] + + +def DeltaEC(HG, P, v, a, b, wdc): + Pm = P[a] - {v} + Pn = P[b].union({v}) + ec = 0 + + if HG.isstatic: + memberships = HG.nodes.memberships[v] + else: + memberships = HG.nodes[v].memberships + for e in memberships: + d = HG.size(e) + w = HG.edges[e].weight + ec += w * (wdc(d, HG.size(e, Pm)) + wdc(d, HG.size(e, Pn)) + - wdc(d, HG.size(e, P[a])) - wdc(d, HG.size(e, P[b]))) + return ec / HG.total_weight + +# exp. part of binomial pmf + + +def bin_ppmf(d, c, p): + return p**c * (1 - p)**(d - c) + +# compute change in degree tax -- +# partition P (list), node v going from P[a] to P[b] +def DeltaDT(HG, P, v, a, b, wdc): + + s = HG.nodes[v].strength + vol = sum([HG.nodes[v].strength for v in HG.nodes]) + vola = sum([HG.nodes[v].strength for v in P[a]]) + volb = sum([HG.nodes[v].strength for v in P[b]]) + volm = (vola - s) / vol + voln = (volb + s) / vol + vola /= vol + volb /= vol + DT = 0 + + for d in HG.d_weights.keys(): + x = 0 + for c in np.arange(int(np.floor(d / 2)) + 1, d + 1): + x += HG.bin_coef[(d, c)] * wdc(d, c) * (bin_ppmf(d, c, voln) + bin_ppmf(d, c, volm) + - bin_ppmf(d, c, vola) - bin_ppmf(d, c, volb)) + DT += x * HG.d_weights[d] + return DT / HG.total_weight + +# simple H-based algorithm -- +# try moving nodes between communities to optimize qH +# requires L: initial non-trivial partition + + +def HNX_LastStep(HG, L, wdc=linear, delta=.01): + A = L[:] # we will modify this, copy + D = part2dict(A) + qH = 0 + while True: + for v in list(np.random.permutation(list(HG.nodes))): + c = D[v] + s = list(set([c] + [D[i] for i in HG.neighbors(v)])) + M = [] + if len(s) > 0: + for i in s: + if c == i: + M.append(0) + else: + M.append(DeltaEC(HG, A, v, c, i, wdc) - DeltaDT(HG, A, v, c, i, wdc)) + i = s[np.argmax(M)] + if c != i: + A[c] = A[c] - {v} + A[i] = A[i].union({v}) + D[v] = i + Pr = compute_partition_probas(HG, A) + q2 = EdgeContribution(HG, A, wdc) - DegreeTax(HG, Pr, wdc) + if (q2 - qH) < delta: + break + qH = q2 + return [a for a in A if len(a) > 0] + +################################################################################ diff --git a/hypernetx/utils/toys/GoT.pkl b/hypernetx/utils/toys/GoT.pkl new file mode 100644 index 0000000000000000000000000000000000000000..1690cec16399ddf7f5b444c7f1fb8244d011c4cb GIT binary patch literal 116969 zcmYJ6b-)x=7se?I6|hhgTM@;0w|B+BLdAl?Ld8HuEOZZ8*b3NU2cqJKfPvTwVk>sH z*xlXz{m$I`-2H>|{Laof&%Jkcc6Z*{;G~X2o9%kqthu^x?WL`2Xlw5r zO6BI7l&zID`*_*fDqAPtS}ir#RoQx!wPnqMmlZ>Ewc0G@GbuF7vaRge(RWv|tyxuP zEo>_^>sU0h=K5~YS0inp&JA;@)|wlsabxQHWz9{zepA(NruxlQzXfIev*wmwwpA!A zHn)yDaR3{)$(q~x#_f8C$yMsj?c?Sh!Yzebb6^;?-W-%~DmJtHhFY;XIE+|n?uge; zS#xLi+9i6?xod0~5%vcAe9}Qn-Q9bkr%-lk)*S0)<5Jmpm7SIY zd*$>T%H`%6vGL6O1_nGUK(%=`R_A2RbKPn}vN|u;pU>6{vgU=p^&%Yx7pHYz!sbh} z=4HP5a_x?Zd2gwC1sf-2%`1K5RoZxUK5n7hoE)pJ$+g=2U)WGdosJ>xQg(qmMO1W8IW*trnXzW8ckj%v-3wHEZ7H)wiqqjyRD! zbFDS+ip6(RJS%I?_TqcO4r2Oq@~!3Oz4;i$M)SV>hDP)LFr{Mifv~0Ad@w+%`A~pL z^WkXyNUqi9qhUj}IXAzd-h3>-frr84VN0#~M1W%R$*`%=d@9^hZ$2GvX=^@{ch{QF zhAoBWb4>mDtoedZ{Y9W&Tpz#n!n`0s(#I(P-y;^L#f#OJ>1gP{3FyI>nJYQt&>VhHlJ>;mTZ3K{MDdPvjw!F zQLqKmhEmlQ(uP{mI*UtnTUcBv+9Ii;t<4rqNxfiQR8=fmS8<_W-NdD$Eha9s+2Zj2 z<7^2evn7e_3|k6K`@_1E<*RH|ZE0uzwJX~)j`?aT4eO!X8@8-t+$qb6*(p6$QnKZB zGdpDkXYG^~wV}|kmBj3zl~WNrXqA+(_*GR^t=MW}*1o!!_4X38-ZjL5-i z=#2x>@YGzX*dUTvoH_bTI7kMIIWBe-SE{xXjN#5mX1fsG(EGKvA+St#6JxTwxL&h8 z#25~RG29c$Y%ii4?v2WDA2>I}WSAI}eVxPIRZ#X8s$lJA1%iH2r=$Q!nhxW zWOg*s-H$2;+VdlG({bcRvM9?x&JO_py%A zeViD>@y=ma7wt3>98X6wJA>$sXQIK8XM(dxqT|^h#pfWIolErM31}$hS^PYbSbRQ6 z@dZd`7ZSbrA~Y7)>|&Bwd;k=K%cb%BkUN0_}?FKQEx)H{3 z29nuLL^qs?#+lUZW|BDiEg;3WBAMMr^y1r5jeZ9l?yq9O?)1dh&e3pJ+Q19f-P*vs zW{G*(m@Vc->>e>MhjYX{k>3k5h5L}q?kD;b9zZpP2jO&ZKIBQh`|I|wm>1he#5g_* z<2V<|>@lJ{K90)q2{<`EnUZ4Fp7JExKP|@o88P3@W}x}S477llffh{6*(A(BosrBICi*~&plP5*NwlIa&P;a;feX4Y62a~C%fS8aV5^S(%C z8xY-mLo}Gvxe-Y)=XB6HnD+x|+yu#NQ=&I+hU)RSIjlXrg_x6s{$ggbW#X!B1!K51 zlGy;F8*YQfWtMGQk~sQyAjR7woD34Zcp#dlJ%~j6By$ea9t_gBBa+!pL~q;~jg1A{ zg(MbxKgC0k%yuJs@$P6EWe<|D$bt=Z4x{V|(zq9r+1^BN+y_mK!$@NBz97Z>A(`z@ z^y1-YDn5WDtfgWjoWm#wf;1k4WOgvo8xKKK0^7&J_UC(~m|!WznUoVZr8 z^F(wxjNwEivnz;hI0+4g4Nm_^qTy8_#aAPlO(uHrHK^{~|H1i)y!1|S&R5Kz!`G&S zKQX6z;`d9#rm2COr;GW^^SV}j!>&&oigrWVz+d+_k}#hc2_t)B4DPxo$18<^;z5=_5BGW(k7rr)44t=YF^ z(eyiz>hF=vejs}Fk7%ptBuSJ1 z%ahRgH%R9{NM`>Mz4Je`HGVtRoR80#?VUqs2awK=NM@ag-Z>wd9&+=O#Nq`U`PZ(% zg~bIuVF-U5EF{LfGmPWHNM?%=-EmR0HJ2`A>3;9(NtjGGkk-YJ%oZnl>k??IbxE?= zTDPS<39a2hT9-yLTZZVZJx$w1JCZtB^2gOEB+yumBQzWy^i0-mE8pq^OxdmCQ?hjJE zC6d`zM6cc&)eLJkfGiGLwrx_vsM~rHM%@m?XM2P*LZbT&M57OvatD#c>I|fMFp}Ah zM6ccnjn$Q!?MxO%rF9oi!a#l-twRt_3W?sjI~sRV$@UNebyc+4M=xXng5hY25;>Y2kqDdEw6 zuo`$Y93tkCf2f#=jubPu!(iriIKp`&(dTAp+-v2UjUtPatJ&yQ!i7Rl!i-$?Qy`SD%H(yQF4klf?Pf?Ho_SLeB;1oPcC@9??6` zN7J!#0ZG0%UO_Gt^YFjOIhb6mo4J=RapqsULbYa>Y6ExKWn%8K%Tp1@)|r@*jRJBMK>JMu3d>Y9{b`9BgYry!YKOLWVrXt2a)8cDR7?&$X( zv%k)nf4SxL+Q5u&fN{JL$!rGE9dAO@BxjOnk~cetN#5efzufWGlyGd{=1F|8-JUiu z`#VS&>rN!IyNEv4-DnzX7Kz51?HtCsM>n&QInMmc$GtZtjC-G|Sl0b%1LHnG!nhA2 znLR}GaUVw0xQ~!%+(*ScxzBYDb9l^=fBCqNr-X5z@FYG;o)j~Or&1Afc$$PcJcDHR zEYaui9GdRA=SgxK4vZI^!?-Ux@-H9vrIgfa_Od5FE?3505pzJk+A6BsYb1>OI+EEN zL?8D}G>totMB~2Y9L9ayk$?HP@1%rr-}NMp`<|F_-%mxX_k)zw>h>WCllTbXl#=L^ z_ymm)dXC#q$>PYLIr1+*JNVp_crbmDHt=`hmn3+8g>XhmbkA?lmgl!*!KhNV?>q@h z`##;yz4t>(F#VAP)1Q#cekQuZ`Uc>W7A&Deci#C!gd9AZATI;J0Y3PM|8{iQCThkC(8vr@fl;e zkeC^FZWZy4H3^oBAek*nbjvPiIAnNObR~)VvYTU=OwAVaB-$_D+EBM8NU&TI$!sa2 zTXsivXEkhTvgo-CNOcb+vt@~1y&S4F^@KIf^3M4LI4@cuCHw)oqN;e2S_#H-WhApz zh;F$m8Z67U8k`SbvDHCpdm)*vLG;?*sA|`Q!~I^fwVeI1s#zZ)??9`f$Xn1bcIzU% z1x<9jHdJ;6IJc{|Sy9ZKOJd%gD2odft0d+vXlEb3Vl~~&n01&j8whVe6Mf9SsK(p? z&d1~s+E9%5Mu|DebPnGAK>RjAxa3H5zs*qjZ4SrXumwnMenCl&7GXX zF;lUfNpRc+$!u4mI}SnRxEmbTw!5Rxmt$=Yn7*M%W_uF7Z!c7Rd&8l)X#2nv4?{S& zBzp0FsEYT8LvhK5!xSHYWHy56#RsA)J_yc>IZ6(ODLw?r>`~`7@SYPYKJ@e z^m*QEiiypc?*II3JVy;5eA# zzzcT?sPKRS0iB6Mdk`s0O+Q*6RKz zX3!~Oyss7GJ=HmQPXqCuj%0Qn(Y>!n<$VJjym{|AaouK!@xDpSm@}P&_st;Qw;-9_ zN_6krP-{qL!*V<6A z`#lMc4}ds6h;Y(KbjOELIX(i1)fenhXWfN!wE_3X#7zBh7{@1&%$_8=<5Q>{pN6$R zpK%UPQAK-J8*qHCwV`6qlVJG*lG%$yw|ohe<;$>E`ihvl`Bj+4*AU)!CVJx=s2bmd z^G41T<~fJewb@(Rz-KhxP8;}q*E`z4+26Zq1JC8}kucW#NM;`peXI{rjr9?%b${%v zb$_A_?7dH08!Gl036`HDnSDWY%P&z`eg(_&YiC)0(@HA#t*Y3s--#LPd*^T%`~Wi2 zk4R=e5q+eeQH}HqoZNqHB^CS4lX#ToZu0Rsqaz0d+^TVln0TQYgL^4~5=+&K3RWA&u>P0;932@4| zC`@M;B(ttW@9c)Eb1^t{mP)p`CwXVXmJsvT+>&Bmk(Npg99rF5NyU~{6$@VmX51bK zCx=8IcR5t!_Js4HIC7U4rM3PHBnWs1&3;0(E5;2y*85BIz+Et7Y)@lTMy2UjEc2^)E1D;ibSt1 zp{gyz`7#Sdt0d-((AkeJ&Iolg5BNHaUjyNckm!DW(Qs0a$p$33p`Q|pc@^5oIT&s% ztXV%0zfBO%2#M~u8JheyC&~TT_gjeZ?k~oBOXuLdm9S=8gLn@>GTVme-rJ(ddpi=% zb$c;>J2(fwfx4L;KS(#%ECcZ#jBxpp=-xY_!MomOJClUFtgURjc#4m*m=6)w zY&Q_Y-I2`pAiCjDG#Tzmk_`9qBpUAB+EB86JPGc@gnT{`#C<=6cc_W(J{*mII5EWo z$fAA8Mx><3CloyimIpcJ!t0$dhLm5yz|R; z5=od6=ZMZerK+8xo4FQxs%~!BSl!HD660XJ$0M1YMs)Af(coRK*cl|jyKHAV2k*0V zGbhVu>*j`?lWs2CxnjndAm-16^I*n2AIarO#<_|lj&rr6k5g{gWHDo2qmqXGPd78>6fu9M zT`OkHsfo)rP0YNei&_75F!Q<|$?OKA&+A4s-k&9#K@#V6ldxek9es+7bF-LBG`EO( zGvihm@7s{fZYR3;9jK10J7L|Ocfl0jjbt{9=*6?qFcFURdr0C$=71F6i)3~m(Tnd# z<@^8~_F&B(geiUq$?RdG7e9gq=SrJBN)nytI>uf9n3(x?@67wlT7>|AkUpgVW$EK(}HiP4w zH+PI4TZnnC=?~+vCBoavMEBSlmB#=$Z{!4h8!`KCTbRb}kj%CxdgBhL8VAB*#DWci zDbA401{1w_M^wc-!QnXJf9X;#k`>mQ#=&mA|KI< z_d->?H=GyqQnwFG@i2sod_*ta4-Jn^D)uMIcVEMXJNlG(;W5!$vy$1GQm?Ife&G$qr97WoyDX8j{&4qC1X8qhrI4Ac-zVI{Flu z)=|!KJX#xA(lIcOW01^_CA#BrsP@qDaF}AnPJk&s5#haJq8Fcxs`wOG>p3-X-Nq&^ z*fA1fq! z@kOYLFNX7CUcD|6v)3+#X}k={>~f+vPDIss1*}mfIp@2WzeBGiq4O$)3wuQGoQ$gT z8aNDDvHyu#*c6z?YZ1OxLiEOIXt;+t$DK}+cNY0tiE}t+uGh`%sT;%?-Uwqj1L0GI zL^qs?Cc~RaWO$2nFuc_!=#vfYtb>vkt3y+JMVjFfMN+nY}}Fmv>RQya&tWeQ~{FAHXzzh-CH=(HlQT)%Xdl z#!sE|;rX!VXC!ofj&R+M=$&7p>ih~;=hx1f_%|eUev9y(5u$f~kE-(rSe-vQtMjLn z@b=NqB-s3dWcDl3ZGJ;#^E(`DiuMOg@t;U$e-XX-Z&bzqzQFq*k&;Nb+hP(Th5V zwYOOpZQyySt8;i{cGJx~MJ*=g^=9$J1zQ4U)Flz#vL*Vc-O)7a(j*#n88HL(aMlc# z^(2hCoN5@gr}sDyAjNA!Uz zsP5`2oR5QHO-y55jA28}K4{ouTjsqQakdc(zxet?+0KLW<^KqRw+ zh;Dc=D#Jry&E!xqjU!-@jfQDF0?F)1qBkCes_|%8jmL;- z9OE4Bo{}9)g3ED8X2%oVM`TEjVF9vkP1xi2Tcn4E`Xc0SQfERMD zaw{DA_>zco=(`=H?+zrhJBi+R7plIy;WYRxPx8SFHrqLv+yi1V2g&SSqMO`@%H)1H z^fl}OnBoT!-l`>f@x!Q!AAwc;sF?e4E==QNNM?@{z3~ZDjZea1s+_z$MMCw{NM_Fv zz4}>H)z88CF#JjPymNl9)A<4koi8Gpy+riRmr-@T0;gT_sweS@?=_gt*AYIxNA%7& zQFYFPLnm+YyhTFw+X!cLM6Z4qRrPyts(#-SABTtb2QZxB)ZF2s9e5=LnnWfe?vm`w+N?nM6doHRrL>WsOHS=M-r-k zLilJN(W`$!RsAa*s@dberG(bsNof58$?Q*}xBi8O*B~xE{7n*%ynjH7|3xzUkLbni z7SuRpYY*ppg1!zg#T^meY$bZ}d}x?d)#is)yZ}t`f(Y-m61})Hnx?TZNvJN{A~4m9 zBAIm|dUaPcRd*u^)fHO|rh0KCvn7aLy(Ai{dG=h2L^J6QQ@u38w>yYl-2+Y4%aW*i zIp_TDW?%Lsp>=sAvlWQmx+0pUwGv6*%KHf`!&I+=@NO#6t5-v{e^!SzPOrpeTSJUV zZy1v`5l+>JZqf%0Lvk9hHi=x;fvH{>;p28huWmzAb%8|HMdy5NT%|9O&{{?^s}Q}l ziiXyD*=i(et;19|5U#foy}B=&sy84}^@h&*K)i>&5ecmuBboIhdg~@=Xyw4#lq8nd zY%@>d6ZGb-4OQEM1fTv0AGIU8&sJ#g;riOvB$~ninCfki%(f+Z^>%2g-kwC&JBWEz z7zooj2+1rXdgov?c2;ah5_RqbQ@u07i5StVcSZG{Wx^4$?m#fV-$6jgcA z_9RpJUNGf*Bbn_(^zvb7+IRbsq+Puq3FZ4EnGGj;`2lDuA3>6K^?|LVY6p?fdoYsO zAw=&z6pb_EQ8kiGGds*V97}xEj)dMOl9>^`cNCh&9ZeF-t5rLKgz_Vi%#I>@`O#?F z!^ez<&9F3_#AvbNbv;- z*V~9*d=Z+8FDA*0D|U&HZ}EckU50SEjp%(7(eQLrwkzPUw1Q27DZUcP>?)!cUyZ7G zGOXfjoc+{_L#mhY;QhC3@o{XdIev^gh)$gFG`dyN|n!Wa(n7i_Qn9dK7%swP~=SOJh zhTv+t^seG1a}8IsxOL~r~84UJfTNfPdOK2iD=3FTiSnSDd_@^4WcGT*@( z=6mP-kfHMj5;}iG`0N|eJAX#QK&=I-wV<1!z@yPZULSpbbs-JE_dNT$|> zV9Glq{M-uB%NIf8E-l)kB>DcYSr=i&x`Om|Lo!>8=zWW$sc#9A*taA|@lr@;-HBej zG@6Q+A<2uY)se6}IfGsZ>a?sUm!-mgqKhREJdq4lXrYAEvl3!i6%T7jKBFcq2G3 z=I^zQ#k_mpPt2dvo4}ZCitv?MqMK}v%47>zBlZ{bB(Wt-<5md2wny~F0cc!t-L@f7 z=e97_+abL7N%ZO+&{RE;MAd_w^L@gFrOcD?%rF?lWk-acSRuO0&S-S0*)Al(rD40m zR1ZP;IxW$wcSp6fJzxzvRLtq?o-mDjA^gS)(Hr+c)i?}R@j%q}5%^`&Uqv6qqL)%X`GE@b`Q}T=b&M-JXhUIqG9fXslFe{>;a-zKZwTahCM`*SM##? zuo#y|64&j~#08rR*!h^BdeM4}ac3{(9HlG&$3ul@`T)jTnLPNM2BV5+}F zGW&|?)nB7J48DQmJic}G7kbXDzJuxe9^ofYh~D=j8v1JX6RhH&VTyl2cvq9?#lNAU zm@8m#C@$L{FvWi&ysJs{;=fT9{{!d6Jjwhk=7r`zn8tRUy|F#f8#|zC>v4Ws%I5Bf3dXR3^*Axe1Lch3C3h) zgwrvio2-h;WHmT9;T?|E#f;tyrg05~uNe@%aZOZ>Yr%PAqhWo-JW|&dW3mp6$+`#^ z#)xjxhQ^0P!3rc{NAR{KOmzw2ZA+q8SJ3nTs*;52g4Gi9z9md&1L3+D(L4L1v9n?u zkZ7C@VX8MmGTWHw)&0;=%^Ef#3Dvw+nYe75!E|npWVQv-JNu()oGnT6PTu#~O3a?v z8m4mq!uJb^-nlKB#@voXV{Y%9zXY^3Y=^X=Yy)9z1|eJ(Bf8CCG)-VflF-Vh=XdfX ze{`2^XPDMq5WaIt^wuG0n!#=)n!)bQ`3$hxBW)<#P#Bv%5q|cV=r((!(T0ztk!a9i zFxC4ane9jP>iy9)=x~y}n$Oo9AZCY+fayFC;XO*CcOHy}PR`N~AyMa{Vme2{bRLHA zCMD53n`j!-Nb*kZq*2aV=xA-g<_H*@BM~l-5#8o!G}!PFh+{~!&@nL8$0C^>NA&9B z(Nui`NvP(>jm3O);UqC*o($u13c~x8M0XjB1{WUN<4AHB#vd={75ucsWjh_lWml`#^{{2Z9-bCJv@5WV_5G>%!c^GU*(Rl7jUY%hfAya?eNr$q0(1Pz@$ zjxHrp=Vf9#FNf)zh;V8~^v+3W=;YVquO!JkakoDFoiowYc{7PRZxQqE)U9G> zdmD_)?FeUQM0dFp)md`U?jp-=cuu`rOzSL|*4YT>XGCwEgSJ}lB?~hs+I@)&c7Nis zJpg0#Ai|rLM7Mbu4eMZ6KSB~84P|@O6My#LVKP_DY#xKLc^u)o8PRQ?M8iu1ziSMK zxm4_FnBr#;PSA*6{2Us_>wdQ~e^s2^!I>Uq(~)Dp_+T{ClXcv3{(9J!buy^tA9gN_3tF{0I1j>p2X$-3Df!) z!q-NL-ue$3TDjc*FNv!EgQ;$}uvfPydUXdhR@bZ}iK;unRL_TS<&5an3!tHz^Xvsl z@@jqvX(2H?t20dJ!U(@qNA%7`(a_2JNL@%YPFI-fZU~poh+e%ossp3VmLOC4k}%~< zAzVBodim0*9(6TahD_x>V9J+8IA0@rc~3N!b1`ChGL^3YQ@$dS*-At&Ul|Qk=TF~N zNaCSZwN+EX)uPoriFeHEFh0EyzBWK~pWbNj;doh-B>0r8ww5QcwGT||+6Z3`C3@?+ zXm}u(Z9O;?SFH`ExPW91(S zu{Dg#0ECk^qPuL1%4It^xoq!AblIV`p=bk1uo;AK(nfTf!DzgP_)Hl|Xf4@JFx5LF zyp>7x>Rr)L&4rC2BpPTpnCjgT-pM3-^-xq(+!GE{O%b-N}AC3n9svQ7_$<%BFO!0vT=WIkTJ{VQ;A+U-M74zN6kuZ&iA)K-i zy|IagHS@_lBT?rlXMft~H|NsL{Nx;r$&m;rY(zIX8jZWTX2*~OlY)(bsXi9rgpKIc z$D^9-39v>yF)_bS4AXcr!kd{yZ#)%^!xU{SN$9NGIGF132wxl^diCjOsy>4xR5$F* z#C1Chrt@qhvvY{vc`h2CPWWGJCXj{JqMetxZs)_aUV!jkCed3jLgSzfyO<<&7VHw3 z>Pr#+Lkgl-Uyi0JP9)J3uMl&wWD-p0l?ZQV620?kG>tQvB=6)8&`N7#@9%J{wbk0F|Qv#LVnN)fep}-OOx07Bibq#LVVXF|+v$W;UNAe7}I`v-uJYPb4M#3eLx5oUg^D zhJE84_Q1F4=AwNE1R)TN!b4)#{O65VE^XqK~s2s&RV4ah&BH-JA=wD~LI) zRuuEs&q^?UDBf8(JsQgxgwfk02+-AL;!>(Kd#G^ODyQM_;SPPX$A2`;o?HEq5 z3bsy4`26C!DdFw$^*r&Z^R7mlvyarUf^Oz&O;ODATuIEXD<|exLY(u__yLq^+EBNe zm>)x_ry{m!-Q^H#MrG&L?;z?Y~reY?#nV7|F zE@m-Xh`Fo!J8J=3rVU)&6SIJ=QxOXoK*9pHLHNiU(HF2Css(Hh#|7-*s09p62@4qH zNt|=0hK3E+%}jYmF-zGganW{m)|7YA1`f4d(*~wFgoJ7ChGe!o(Wkiws%Z{|<23hl zjFZ{RId^B4d#8jKuYJ_Oe|M$^rn#?}Y3`S}X!|>Bn!~k$y?H>|z%)mYFwFxIPAQ2# z&4bY}O@7H34mSKDgq?lzK zmAGg}JLk*bXEKjT3C|Q`NEqo@gfmQ{k90hm&Kgc2$^DtviO%8Sd=g0G$p~kdL~lG5 z)fC6VY0cw2$tT%n%;C>%@$7 zy_k`15c83P8^w$|!&yiCO=&~jX2MM2W`r|SqEF#gG@ZxYMv@PU&F#+NNVx-~@lJ#j zQ=&KCjcV?*VBH(D#oQbBIEM$yoOCnq@``!3xDUqreuQ^Wg;k1-2XxNA2NvU{vRs$c8j*$+b8B56JidBj$+(9Im>-M65QuUc*B_J?hB%F zUkJ|U&U`zIIoK8!mM3Zf6y2h~t(!)d5>QsO76p2Tt2OB)!sO&b`u zAZFa6m~l&Do|wyGUTZ31UTdl_bE_d-tt9%~8fg04XMK_|InHVOdJ>+DHvs9}5aDVi z(K|Or)9Zadl6>;aWfNx|e4Ba_Ts8x7*&N|5W}>_FN7HM;mLzi7%2_U3dlFm*fVgaf za1u&%m+esP;qBpI(q=n|*m<6%9%y_9=8hanCYwE-L+4%~oqHpE2ZQLH!_ZdezGR`ZZu=$XOw&2I z3#d}$gRN8~Dzwm_G<6q$2(xIFE!`pO5g{c0`}`g=m;HCz`O9dofJ$B?w=dC3^8? zsERL#RXou-+y@Q2f`raV2w$2ddgoQBIRR znwV##>CSSwjs%zM5iWBQ-Q`9!JluHso#DZ z+%D$MxWhTP+zH}x7s59;i0(2AO~cP73Bz+XnYd_kU^?$b_!T>%cixYt=ZObMH1P+; zJi;D=>3kUBhf#>$`6wDXIdbNbXw1hF7wvJF&LxV?F~@{Vc+T zO`=ynkEZGuNHoriiHr6UOy|o;X0H&v^Hnr<^1r8%Xq?w!s^36(8=C0V^UzpbvA0M< zb;;gNT(ox*SM6OGm-mp&-Y2@t2WWEnkVLcnC~?U?7IR_nlf(u4RLoueS>mdFp15vb zz>NAO!bb>+KI+$K8uc3z&HY<(nah`A+`ms;wI9UglKm*=wf85Ok$y%vXC(SazoKcR z-$*pl?}@p3nV74WFfM-~ygN;Fmw(XY@-K;8{u49bc3pIP(b|idV+S#F>u`lE++P0 zI8I!&C16~ZMELC#qPuiQ(;irwB+RjB%fM9kK)8xY^y=l%RNa##RCAh`nA60>{Gu_8 z%Ss5pmO^xwRnW9wRwdDXSxwA;W3sxK)%6l{#H^9HYQ4p*dQCC=Yb}^j`yiREP4rRM zLDSsVB? z_Z8#5ftWRID8_vw82614zMV^S_f62WrcFt-rp?5xX>&1a+Ct1o{b3xpMEFTOqC0Ml zruhya(R{Z_T(oTyS8Y2Om+cY$pEaVp3`CR5AQHJ`iR(5vG5-$+jLS|)W;+wzWfwHA zj#EUE;8L_9Fx9&uoFfvwdJi;J46Z{wGa z(}ubk%&?;ne(IR$!ybXAg&awug&ZYjsH4T$A0y^a9wX*pI~KSX79#=Kykq796Ds+e)drXv188mA5XuC1Tm91Pt3UIiy8L zVJ{Qoez}-kJ5h}L6)^6T5KbG3?tT>-cP)SEUri?a$%*TBO=AAfH8B@Or-=FE^jaAE zsYqtih;Ba}4U^{%x{f5>LDzfYXXecK1~L2NMlmy<0b_X+lG#k6Ti%Q&%Uej2<*g}U zkKE=-?$3#2VooFz^BrIq|GN;*B#G`n3l0A4zu6?Y3y$}QneiMk&z1Lznelxvj`t&6 zm?XO6gJ^Pmh$MI9+4o^F?vIFZe^iY7TruvC!MHz;@Zm{lRatkc9cx?P)Qc z&m=C|voJ2tA(=f-be9*<;KKWXFOr0PSGSiE7wu)3&R3AkUL|_xYiK-z_@CNdCrfw8 z8=m-m!2SBBnDxy|T(`GiJl{t6jTWMNzKcdr9!Bqx$>x1AHXk_W4?G^rAEpg;`v}JK zV}y@865aDtR8JzDT$0K2b1|M@ILq_Pw1KY;!+3s;a7B{np5LM^&+o|6+Q0WC9^XHt z4NUY$ZD6PTB<3#q8D@ZA5I+A%^Z|ZDTRZo6vM>M#%pWOX7yao;9PO{Pfzkff21ffw z%xM3@jP@VG&+~N+qqRp{%jrOtmebLb_;Bi!m^aN6*KK|n&jk?PH7EKC7D8Jq=uDPY zuy9IP!6Kf-6)Y-d(p|(%x+}~8-4H$_N%R30M>RmnmLStSmrTs{$;5T*?wsGpd{BL9 z5P=+(VYEu>&;kcIMw^>)rj;`6v`rVT|~OUz%(ePBG-M)<%a(LL8i!wh)gT#rPS zZO;DpH1C_IoB99N5_7f*V^~J``4^%aR?%cwBavZU%xhgk%$1h)6Bn&7jN=AKW*ZXS zaU(Q2ZcHM_eqtOqNnEr|#XMX$6Z1^DIgI-jNM`+s?!G0O+_xf;`_^LI2Z(XpMvUXO zFpk?Hd<&WAjys^qaUh8t2Z?da#5fLi&hNc8K21r2kxvWAKocTeZMndgDMNEml-gws!=k2?%a>RE~ZkBv;z z9PO;#b41$EW=AG2+EJ;90gg_Ie@BLd(Z(SBR5Q^>I}S}NIG!X7z(f25PvQzr6m#Y0 zBr!YXWEjg+5WXZsbjz`5+RfugH2?95i*}lGxWIKfh{+iU-;p7@$ysQeSKZDg$xV1v zpCe}Yb749sAp9@CMDIKwO~YS6qTw%0T(pZ|Ixj}}`ZCcwFGcl?RIF2lZ4suzPFge`+qQ%^E`4`Xrz!jH-j-DC!uhQEnKJ7Z?zqTMXU>UwCifE^WZ(ZWx&4)(QWQQ<77CcB$3U%FxB@V{Ae@Ls~>whQ~ff+#Yv)9zlz57wApJUVODi}UCgZBaMqL9nUd0qE~-_#zFaicuDeVIzJLK=*KXfpCEjtndqILp{et8k}xfv zn7{DEpBS+DQjE=4Fg9N!oH7#K=36vArfB_+Os(IGY5f7F^+$xCnjw1Y&uE&#FC^;x zRZQn^&e~zWlVI})!Y}<1-R3W}HG{v&xpF{vM>}+CY?QrJ9A+%K8wKkEQ)X?lIT8N(RklCtQ$$* zN%dkd)r%wi{vXk+mqhghvJ{*b^XE?Y#QfK4VqRF6NnErZFow$_e1V4OhCNXkE)VC1 zyg9dmn9sql=$!9IUP4yV2HaOpT(ngZ*KJjpkyb-EHzfKxh~1y3XNza6R433x1oJ*SUh2$rr`^;a?K7-^6HN4J7m%+^R|1BkwuZP0j3bLnkc zvT%HHq0%|rN!vSWWjmyVl?_w_D;tzHu(C`WSlM7PE89`b%63Z3|KBTSWxI%3*{;r7 z*${1DWxI)4+3qka+XKmLDA8B8C)!%sUSwL?-p*lV`#5T4!&1V^_EiHb+b?ZkW&3Lb zD;q9mWe12^*$6SKIZ(`M4szCN4%P-%bBLJL9163Vkw|8T5q&j>qpj66$+Q}C4yzfZ zn>mdg?VJzAVvf)T&b*FH8yM@Tl(4d+RmGBz5wo%}VpevnnAIPbxM;_VS>*}NTIGq_ zz$#A?GxEtWt2_naoRjFQ9E+xh);N-Uk*sFCvn)?b8~6!nF_ve*Se}XSqd7#kJR41x z=a9(qTxVHMNE^7$Ddt(?d>G3M5We?Abjyp-)_rj?neK~A#7y~8=ipzk%hCq^zc(?v zcA}UA<%-n6DgUID@O8s0RmCE&f|=3PNM@6XKBH^U){Oo~rWs8UGox#rHKVC%Lz_(# zGo$HZW^`R@U`E$#1CzJ`W)e3dnav>jByK`mlbA`SN!;wLfp5_Uj-Xq`41AlIfp1R@ z417mQ*cErGidozRGmEne27FY4~ubmBrz91VO-`SnLS2ym&eg~?A7cElH7&PClmAAJ20J3Bm5vW(L0|- zO`-ehsFtTwUZ>Trt@tvc$Tw*!+la+Dder zpV45$v*9l!xecwqI)~@m-#{9FNBDh2qBs7DrpCWWLSw`JPF%EqoP)`~ASVAIT+>`E zn6yXZV~IC7I*{cqOr@iksdRD6!=tE+n8!lb#Jq_v=1gfZnDG}!ICUlZ_)DT1e*Ji8vXr&chN7*LHt;OBZc13!dZ~(&SuyJ> zh*?(=W?dzO-}WK;x+Wk#Y~?vBQ1C$52#gq>frp<-P26mv4Q7mUl^2$w*K?lKIG6XRo# z`;y6KKWE+3`)dO;9xi5w93W<_5vhSY@jz|xbJetgA2K_bgjpPd@N11kpT$U2yQgA@ zk%isE+wd^uO@yxm5xsmA8kWvQ>d_>5H7^WDIOlWbP5UFYfw>(8V{l{`3S=GP@;RzMO%}8j4XIE zz~d?59(}@-d;osrF)=5aiFubDW`Ji9enW`p13ZVe26&z<4e&xr7~n-u;tF08vx1k! ztl$-x0bWJ;&+Ujl!0TvhfH%m}UU}1#=s8b}=UZYt--hvg2g&SRqIQ672yd(teT0S3)*;ZDERC>mN*G}gPvQuRrVWhHMH?8QYuZq$ST_=eTMXg9Dkl1H zOQ5a2x+IxQmvYYcDx-BLp?7J7|4N1Ey*<#@RF@@7^~O^nd2u-aUljN;DBKtY#ty}}xL>qWSZkjeQoy|z_+#KPQlIWiO zQ7x7q^d<}QX|t`w46wBr&jHSw=QbpGZj10=-x1w&do(`e`LYj5oLj*LdJ+$YL1O$f zG5&*719RSy1pl27-dZQR|1M~2&byN3o*eQ+#Q5yytaYbRgb|KH_&so< zk8ljy8et5XOpk>rKMvt@okTA`0d19^NEXWZ;mO3jrw-G43c~eGqPLDkV=JFJ8%Gw_ z!h?K#N_a{+&6BvdP8YMc&VVsJ6X6Oc(M``rWm>Xx$Ta zBw-3X0bS@x+?^M}v|fzx&N|UsFGX7`zl<#PUhYZkoe0x=1(Mk$qW4~jwq|)1naZzr z&JSabtH~tvUW4!hltk~Hf~F~7OOl42>PZ}STH1imbP{~7Lo&Ob=sq`~t@+v{{}+vY@H*E^`z^)4LO^`2u~*Za;|*9R$KEgzRM zqWAucwt9adQ}3_Np_e!BTS>uwC&B0sgg5VrZuA!#A3R0-nL+O_Cz=8g{G&vHAq5h z$$E=9e^@hd!PXMJnL+toB6@S zO~uS)v&3cFT+BtYEyVnvn*E*Krea&_W*)Iy>E@bkt($qS7yvWFZ4ka+PV^aWhsL$F z+4dx1ztwFAnCgK@W`l@couR3EFo~*nOx&=YU^;h3_%TePckYVD&bkdD2}cfh)^47} zBWHJ*);$p3W+!^7@vI+e!`sSKKr9#3cMT+C&_L2 zi~0aDyM2V11MNUD*Od=~aXc8||M(-iy9R^c= zy5&h|n$5{1X-A%t5N7(VFk%eXsveP}u?@hi;o;F}~X4=5d z7QvXFjc}=w=%(kQX+jf7(uB?&A@`Q0QETCYU-lqS(zuSRt&w%KH|G}JYo}`a zhULj^8cBSmpAJ%d9m0tu(Ti_DbyjdAoIBGu!`UD5{Kv~6eKQeGB8lF23z`PGl_X4w z-^p|ijkkj|-hprmN%Y3M&@doBV-9P;Sz_K)o}HLCzF|z}AY7Cry2*X0Ozwwe@_-nV z2Voi?Lin%$h~D@Jnx_3IiKabQ?5Bt@osT1Y&4=inPok;wDH3%)4O9IL!Uag8S3ie_ z36|`6SQC7~Ilt%meCms7L&08xad{cxjc%g5yo!b~In#QLL^iJ{F4!9|oo^zU%_Dl} zTWFf?+a#LpJ7PW)`z}o9dk7!UBzoruXqY+I8sRYH|Cze$07=TLYv8cB1r6>NoSmK7 z-5Fei6KFhGg1bCu2<{eKf(EysL0=q#1cJK?{~ZEKK=c3&pA`~p66C|b#+Z? zUwIRk{1$MYTa4s)U?#td*I9cnM69HSc^_B#0dS~MjLMH-E})O`O#TFy{6EkspNf(E z8En4|7Tf1krhkD;{}Q-ET#WRuVKejgJ-;Ek**3J_CU@HAJ6z@Wz-wp3sQeK&l{Vc! z5#36!!umP6=OO=++&`LQOx5PNF=R}f<1o-EV~OE7Hmr{05KZR;Wn5hLc)&45F|sFs z?Z-A)CM4Q#(nP4_iGdf-h>`p&*d$v!CM6o{!7^F!<|{Zks&ES6WVRTEQ^7jQuZeE* zs4_Jwc^Y6pPK@N~U`?K$Xp?6M8lQ0AK$#I&I1{iFCr05cu=#|C%B*;HH(PRD&yFjc z19ZxqVie8=vv6)a3xAW`+i2!V&SYMk$$X$w<`=_c0a)8!kZ5}KkRV+4!oZvV#K`W0 zby1EWa?JkZ_J{_8cfY&-%NR+p83pXXiD5GctIZIRHjCi07X@DZCr0+-u(r4a(arXd z;3aX{OMy;VT8!*vVD(;>Nblv6TfvvdRsI%u0kasDE5df1p|TRut@O%)m6QA5ZI$E} z=&H&6&aQ@YTphT{TMWlFVB_d5)oT*jOD8$^wQ!Yd126v*qjFu?R`!+kh^$;6m%RaS zidl^84PnjRh{)`XgU1@~<$tKcO+lypL5#vb!dkc)k%gNlw->xc@Gu!&wnUk11snqu z!(p1%mInJMxtK1h?xgYTUKQSr~fVJ{KA}bF{uJYjI{v$sG z=W-~pev$wC`zYWJ8ZokufpyGdi5&AU$?YQ_hpRjuIM63X<%zJV zlyMT#d<|XEadL=uH}I6?7Tu{e$xpy(1kclfoii~!&w#a?Gl}fxtl-_Rxz+#dD(qUG zL-08lIFl@f&v~#egY${Dw!d6JNWT!+ITIuOVp!8JA)0jVmlD!119s2ENWTKsF8@ws zmsbYwcB%9#Lh04O9-0`X*TOo|bwrcy2kak&^y`6_&4`hHBW$+1qsmQqGmw2WT=Ffz zjWc2--wIoA2lj|=w&is@F8dDP#u+iP?}AOXmlWMir1w3^Ro;uM{3mdtSd7a1VV(U4 zh^%}Nm;Eo`$ekG355qRw%m0YXegv2ODDYGtF|r?rO}35I6GXc~c`|5s*JY?Yg)4j- z*eesG@LAXv4wmPL%zi$(*?9q1`694aCPwAUu&MM9*egW0(zW-mCil*#*OGJj56p=&?)bVQTZOs{ixyPeX6dkAB1S<`orX&SoIOk z=VQ<*pNQe}KiK;850p=-Ed30Z{yA{)O^oy}VY|RQhMZ`Z^w+55Z-6tvVkCbDYx4I* z*V;$)L-6hz??2fe38g;)&wCW3^cPr5$M|(y>Pi1&;Dh zmpvY^XC_AW1h8gLNVLBk69vt#CMHx)0^B1bM&+cit?Vn45#7pxGC3-F3SievjO3|c zP5w2}CQlu-*(kY3hEO>zaLcq9mD9soIRnuy<_6(Peg2 z-5kKKnHY6*!CE&r(boMYXuC5w53X=t-~o3@Jty8op9NHmLRuxysx!LH45m0JKWkP)MD zD_ASHCc2ffx4~s^3q0;yjO^`U+t#SE1JV7&`^%1~@G zKy*zM9++Gw2L*3T4n~A-%N7%peP#zp0s zM7DBPa+PP}D$fCqk%>|HH(19%kI2gNldHS{S9u|@6DCIG#jvd$D3=i3O4*kt*ZF0^ zo7uk{WpV{@C$ktPSHi|b@>N8dT#d`V26)P%7}?jsc9Gi&`UjQi*9ULLxdByqBk;&> zF)D9{y~M{G zt3)&AV0o=_Z)Z-<{dJtn8=zDED~8LPu5qJ}f7}-C=>irAR&GwJ-7*qEu$HY|*1NO(ns2m%%;|!K@ zh^EqGZE)G+0sCWOWKRHd<|o9PWKZ12B~J_-DH9|4SFkoUDbdY#Vr#PG*5Ao-l~Vxw zV`5ZJ1)EAgW4|Vvah&tSWlsYfD-$DoI@mNdP^QPbU&v8qhNL6Pj6vg9*&F0$!dc7= zI%O6yEM|qdX&x-IQJFqFE`1JQhfIw0xnPbsH{O05JuI;bYr#Ar+EM4l*~|wVEfd3L z0a#~qL86=ODx-yP*$V?l%f!g;gEe~u(ap9I>c?dd08gwDBYPBVvR#lenrJsugF(AZ z^_QXKuAx{YIm1PRcQ-J8{};;%qXx?2$!*P+z>T^ju!|yY=8qR!mV0TRn^EF^y7Hbl% zVJB!T3x;bYXSjB9hU?%A*9A^@ieb1uYz&9`%LYVt`}^Qcw;Sf4t->26H_*nxbK!21 z6O6hkZlpf|H)@D6(q^zOm(7XReT$&okI@aq$+>S8Jl(g>3Ea0yZl1QqjkF!;lm4DUpX*%c5qOgFi;N0jdTdG zpC`shhrxEFfpR#}9f|u9!5jA@^UvIms(hdvjWaw3bjq<}82$xT!{dlFJU)11ctZZ! zLOro^hu@N0s3!-{**GO9Sa_%6Mm-I9wze3f{uS0y&meNtGlMsyo|S(#>e-d|m2-mU zNaxlG1LbeHk$Gxb0Lu*%|*#Ayo-Z3qh6AKHtMCx&EI9o&C=z`tq)h= z2K_s5)36wWUIp7Ncc0g`P~A~|FRw{%19)w6>+^NNn-2bwf7ZeE!MpS73c4F|g7x;s zONRM*!zjr@qwW27XHEDT}S%@|H=twfd$Kb4%}(>TLtfTt&lVfY+O!{_ldd?7i*7n3u5DYAsrWpsyv5qj|UnUI&imiP6A+VY~1g%_F)CZ&Z0JXe_)qn3r5L?&H`=Jbo? z8u&8#=uzdXz~X9h)pU`^hUGC{y4prv#lcl^B-4hV6p!qCuirFr&*fl@F9@ zah1~nPv{n-at7E`8goXXsq8B=CAZO=Ik{Py1?Ms=a67RWF0;crOLGv7OMjWO@_{lJ zu5xbRHV!c==Yh3yUZPnTHa_!(=xzrF%KW&}1%Qj{#3)?|*47s$+6~C?pt%|9tHM9f zBSN&6{mE^a2XOWyL8puo!+tbu?MIeDqFZSx4Fzwuev9OvZITuZp2IFyg<%)ZEQ@c6 zgwH0tqSB#;yfjQK+cpYlH5bYdppWGboklaW+;v9DZjwy=axC?9?N0nXi z?6=^OcLN^NEk^PluvrsbX0#`f*?Zx#_Xh3{79)EfShN33WcI$v4(4mOp81Lb%k$2=jqE>Em{csU8@ax(B7ZZTX=h1KOWB3({T&gHMT$}>QxoGC`- zS+G`~O=Rci1n+*)EtzwB!h^Uo%aS=Sxg~Qx&i?}71ukOvUj(cF#YESCXk@u0IhRY5 z>+&+3%jLieT*PqsJFG5O5;=cYC0BWM@a*^+g3Yy{Q?3)k<{z-yTuQHcF|wbBt#^NUf#_!Y33)NOF<-(}z6{)rAx7n^u&MN& zc#UYr8CCvM`N;BmatrSboXdYfr@SeK%UiIzyiKIbJC%D0HLmhK&?)bWQTYL^l^+sW z`B8G0sC=B#urZbon|&e3G`^ z-z4YxZE_#ecR2s=feZD-@c$7u{{D~piD=p%S$+(wtk;Q-{cy|KnjZmeNA|FJ-) zj4g)$I52;^-TzB<2k^;`m)tTLKe_fNNUr4xah4MSJBwmiP6FFS(qDc>G;R7#pEN`) z1@@CAXFqvz_ERKhKPAq7D&Tc4V%Sd&>k^-a=sH?D)8ewH19le0$esbV%g=4UM3dcL zW=d}SnUiyw1?Ms=u(K$J%j~dqanmo+bx}EIa+P!8D(41v7R9KX2iEcDC9-lpT=x9H z3tYs=UJ%yog^0{vIC!%IHyl;i2i$)lMqxi}3jKT!5Y245d@s3XM+I+8Mx#syL8lCf zVX_EpO@^06i8NU(Ig`b4l}mt5SyGJ3rC?L(OTILbeJ@kFH%$l6FV=E7!HT&&&hodw zj-eQqE5dfg9PZ`PRC=ylxyQ!@PtR3z0?*ZOo~r{#CB^Vu1J*XzB$^osZ-^`12-q7GqjVG4mU^Pfrc_fps{A2% z^FjSFX!AAiE1Ol}%8<>oz^>{Rb%HB4x6BDP>{})GPxRKwo12UcT()I%02Bcc+8}CPUoNf-+p9rE5T989Zxtq`3N^}R6bCSO>TYrOL7e#himwF z;A%lJ8a@#=4ZE`HB%<4}?2~cXr+`j5RgCP@V3Tc$olfNJ{WZDDGjNq>0xy{sqw;Ln z`)JRhvh>{KEdLff7yNm3LSH!_=Xn9JD=CKOMX>gMG12rsqFfR@Z7!|Cafi!lmPc;m zJg)#wREyzxC2TycD_0TO=GDo~;WfC*Yk_@9F)IH7dvka_m8CZX&o%bOo)}(k$}FZg z<4kV>b|%Fzy%qLMZ==%m_TXuHM^4c6oymFLh4Z`{bjm$qc-{+J&(Q=$5fA-zat`b)ucMR++UcsKGZ$<67jIM3IB#~F&@`8sSm z@I`)uNX!3L?$s^HeW~9H9$(&3lEgLf1Mtp3F`D=Z)+T-?Y7@VNXqy;gx|UlcV+QYjSb7*%h3>}6 zEDepFTtnj|*U-3?_m%N*4UG>vWdbo8nh@58CL(G>6NhLUnk2b~e$`7FD3ey9p~*5! zLz5@h#1xhHl__yeOa<(CiqXW>ur@Ib(TwXMh{=sRUGVOLFx2!pf&C11f@ibMSSL7Y zi5qKX;Ow{xDV z51#EWkQ0oxU~={g;p`U%&XtQ{-v_Jx2qNwKld~TPp7tYi0{c;Qf``S7&I!gE#Emrs zI%N?t##$7%8x0F{F{(S5Z_MJ!l`erRT@pB5E=K9nu(r7j(X`|HwQLnGBwVfv{VX3M z)-k5P#hI=EyirgL)0JRzXl$UYj5po%msOJ6saZ96ch>Fgtd`in{6#fa9y$|qO7csK;g>}gNh;FtG!2Zcq9)PPn5Om5xVpJXs>o|uH zInJTMyQ_{9D~II-%jIyK%@LqejugY@C|Dafnn;^taM{NKhb+a&J`Oh7+B%--X1lBO z1YGusz&*lZWS5b1qta?ANNT;=J&)sJFSo&lRm7oMLAcKYK}i*;z%flRMtBvrBaC);G1ZLV>-nFa7$~n-Vea0j!WHrVt-^ZtW{7rizm;4g zZzuOvf2S5WD)BC%srP^bonkcg0qiyPAyu3Ds0yQfT!lV93DFMyzvPDgG`XQa>lHXD zP8j+N&?#SvG4xlkH}uz39r~Lp4E=2thW;)@JM{O-4gEuML;r{y?I+;w7coZr1-7GE z;A2c*M;j<(hG;#9CFeO-a-L)N3PzT32m_1@JcC<|0mg^D*_nW9X2%>%ScN&5C`62A zgo%3-MwUqkuD=4EGN~A@lfiVg=SgMl6v3PA`%sy(3ai#s1fySrPMKN^qiJ9o^_6L< z+TL_kXm9!u?ZTKLxmIVyna%{_j1AHZn9c5xh?l%!E?*K zc!*~EzXWQeC4qfUF-BS%HoJ+V%QASg(1yyg$*o|^;R=@rF4YvHa0S?Y;%<0Zk!lv& z@UjvvePz%otB8@lDs1M+DzqBW&9*F7$7TNxbjliHWUmR^>=C6ybZ4Qztc6Nm8@QK7 zjO2A;`%(6n^@ygC5oP`44()FcJeEU$`8~>HL(nN3iD9xatRrqhbWI%b+7y@l2jF^4 zF|s#NJ3OETaM(H-NmTpUA>2|@JQg=4@gga{pK0AU=*+~qa zoniIah3NX&Vciv%U4UDg#mL?r*6clq9Cgp+D)+)w?hTv=7o&0?SS$ZbWaYleoo3iC zIhXx$E(ZX|F~x8>2v(PaiLQ&4^^oKi^`XhR9ENi_95@RuhRcz#x*SDxUF;kjom}NH zxXNRJT}m-3kAqF6+o_Kyx|L4toRD1QiMYy>fVZ@WQF#iiW1dPhpOG_1r-kTl%SM*d zaixC+opOd4rDwugdKOVj&khl#dOs(*4$j5d{0(%19L?dO0rp3gB3!7};0CCflC$RYW)2Lb^J5e0fHdYx2*wg4g0qt^?i< zD2B=Punu_xQMQcX#kp55LE@v^)?}s)00ivcq7$W+$`S=&E^daDu8Zk=$4ckqiOXnY< za)CV>yxXPq+?CY6h*f)|J>A$#<-UJ?m zEyhT1!)7F3=68r3>0Mm*d%(V-7}+1dCfnNfA<@lN`BCyX3687$1a!*(#Hjofw#(h= z@Xx3${XDtSFL0$_0#BzAqx5T77sfY4w)ibB`#a!ygJNX=0NZTO8zj2fKBS+L8|Pl}=iMrzcCPaK= z9m}03xt*_hEBBB=oaOw$Ic_m57lhSvA);~d29@MK#^K4i_a*mT7*Tm&>BqSb05{=? z;XVph_t8YU4<`2w7)s84k>o~QG`ZDoF`WD2pi`C*!+lBExI53i6w!6}Jz6@s?d>wj zEwW{CF3SP;NsHm~TiCd$T!HAiIC{Baa+NFLDpv;f?8K;C71qkth;F6(=~hq9<#)-s ztbub`6S(?O441WF<6@rICYtd_mvxe>To+fl9_W*2>=#S-D|yy=+u@f7ux4 zvI%g@v=}abfQ^fB{z!CP^s-rU>(S$p4BwKQ9M;L`U5o;oN- z`rfd%_9vp7?I&TM;LUHC_x05Yo@rD0K-sTyC$Mq$2LL;MV%Q%98+-j8Ok^{MBsbKd z!E>m?>IC=O;4F^-4kn6Wc@%7C(EkrdQ|WmOF8x?w*H4V}<6xWax9xbU8A|O5A!2!3 zI49z2PXc!S#Hc+5)`fE_Q9rlSs_?m;9-{r+{+it9c1GpimV)y?3wWBf82;zL>VGa# z{r^@4|MNn${^uvRowy*mu`aBtbMEP>iuIg>|gUh_0jExXXigm%gE{sKUbf zd(9dwS0?v4UWN0&8hGT582;D7b~*bm`Z}sRfb@R^&jGH_36}W{IG-DVTdu|MxfwQn zx;x<(qHE*K^8ewoZv~xln;6-*!zSA{^bR7k@5E)_1v=$!F|zN0HTzzon{AwbCO6J~ zxXSy1gNR~OJ_y^TH&FgXbSoXXcnFvMFmMb}jO<5XlkI5GqeM5`BkLYZ?skC3gE#xY zPoPYm1WsCuVe&L=ha6R&A-X1(=d-x%=YW&eVr0Jnd#m}2R5#t{`%>`k;&FKTd@iHQ_o%`jfHToz6#fKj;m<@C{t~>qp#57n#*EcvOq9zo z;EroCT*iiV*2W>4%HjQGTtfPIz%AEeq)z~A`h-NAK2RnK5$%pF6Qe380Um)PM&+ci zT`?R}pNz_WCl8+ePEm!652hp-O$D557Q<+2SX-EeXlotsBcx9UT;(W6`V6o)?u=B9 zJ5%r+cVel*L=Sc2joilj)%ti2-8#uo#hR-~(=QA&rJ8Zwt%&6OQKun+0v-wt%3bKF_O21O|otMc0|+C zVA(!+v#s9&Rk$N?G*67eoncexH+UDKTgYVB+n`ZY%nH&J@(}`hn5Nu5Rx*bg9kcR|szK|ozp@h=I zfLGp$QF;Wd;~z z+={cg4S1%s7&dpnYI7%%Hh1B&?*^_o6eIgyn49&H<)2ih-?fjI=u7f* za&7;Ds~lsds2o#_%3-ipjz#2N0S?`Xkv$%48|*9N6PY~$E_*`Y(483B6T_N4 z3DIP`J*9H538~ygf;gASfkSs54sOmIs$Y`zZj(hur2K`BZ>*gQ7a=lx(aHzPVz|o1frE8oR4xg7o1CSnrnJ8-UHR~`Oy!=LgR@x{VcI&{e7IcQsu4>cAZ-Vx+GD>tbA!Xun$>RPtJ& zQ`QzEc^%j!+pArdXskV7I(YM?Tpv}q0q{seF$yWZlO~o8wYO+H$fF{3Ors~ zjKV*{TDTd}jM!f`58f1Rfhyb*bjns@6mAXM#o!qs+fdo-ww2p!3*NYFk8;@oc)YY2 zE<3?=8C`a!(q)(6P35kr$^x8&6{B)@*sI)w%E~>HoBh3lH!gdlT>b<+`9=(vKf}7f z_a)L~KV0_yz`e*~WFH7?_CZ89+lKexuOw>Hol`Uk^N1 zT8#7?VbhCG{3arMxfz#z3vkX=jO<%s+qq}f5KZ<#xgD2%2XLvL7}oTro9S$HP7s|KFM_4yod)J=>&Ux3-?i+Izg ze+6C&(YE$-ouKDes_+E$S3|@~>kMob`15~~JEr-1&3Bp28+C&BgCy76o4DTI0^ScI zMsM%H?Co8=_V!+gwzv1|1igJwg(f}>(e(CF7WnhW$@TV0&DY!is<3DEY0Vl@K1;6A z&vA`@0bHslMx$TBZ1iiqHu_B!n)^0HbZsN~UGS#2@AJ>xf55r_2t2<=4ELX5yIR}J zBigOR7&HHWBaK;wk%on6+{X&qT~wpW*f{rbfPFqO+{c4W?*nCgyy>0E1R)xe34?}- z1D6ve*Y3nP!%2X{bz&G!3L8TQ>+otgd5Eq{Uzs8~mnm^BQvt{7#BiA!rpq*Vx6qei z+T;qS!xc^sI%Nhi3TK2_I1}D2^ij_oJdfqfk`uVhigTF_bjs{vxXb~YW$q~doJ7|~ z_FT!G^_e@lW5~Y=-nDnyVV)4pqMJ8!^gds5=UnHnyss>P>tI3PkewJEEDUo#hU47^ z95U-m&TvF>jrHS927p6$VwjAAX)+pbI`r)xtimTf6rx>67Qxvp3cPDX44cJavox## zOAvKIE*YXNT?$vaH0YFN#3)@BHl_Mqj_79F4lbWu@4v-Wt^nNsAx7m&uvV^2G?gRD zD#=x@imO}=bjs>tRQ?V&l`_^Kx|MFrSTng^I>~KG*Q(q@6LF5~0Pi6d!*M;>ILcn1 z=sH>$8wAg_>i0Rpup8oRHUbXeiD9z|th2N!(Y2BN2VC|aL8ojcM)u~gW^X}sv;7Ec znOx;oxXP`8qj+LeZVPMWc0^M-RJKp9atB=Hj=*6&F)DY4O{K5QE=0G|`K4VeA6`mw zF1z7eb_boZhZrtTk@=v(ReSjN1#HidCHY;p@*$;0D`^)~pyUW?i zdq5Rd-UDlvXVoNk828|svI|FAY*XcR9fzJhY8pRm+JXi-lpQr<0P=$dn4ABmJQE~%c zoSgq9IR8t5gMDK7Uko3 z4(36KMAuUGjkxTaK&RX+M)obRS@E2w#hdZ_%dN@%n{iw4{IcJk6KqTFNN!K;&g4eA z3pdi;z_C9uM!FX^eX0B>(F`@Z+?QPC{kX~pfSZlQsQedfDxLd$h{(!^lgIvLaxRbH zTpk6E{E6Z6IBZ-j(kF;?c`|s;-cvb&&C@uWXMjf^ied8{Y;F3=^F-H1_6y0)-iygK z{8DoFn7y3bFX1aV_g8`AfMU4+2iAr3I?;9a^Y=z_BmEaw`6h4}P>jmAVN>Z+uy=@N z_B`$|xnK16l3QT!S3XoeNbVQ?!{FV;;f%&dRrq=TI7IXF{z)zH_`~F8{L`B6Dvi%_ zg1P-Xx&FVv_5USskWh^NzlQDq8NLeNP`O~g#if4-T;V51`VX*|{v*|P_EXUK64{Xd zoZK(gFUd79#w=aOF+ryc6T@*V*e*c3j6_{0#tG5P_qajB-Otf@$*oW0C$|hHNN%<# zth}#Ggqx3vfro61G3u{iJ0G_2lTvlq$wD;4P9C(KsVS11ktuPOQvth>VpvWM+ck7V znTDv2(}oBerPJX`rw4W-#VDN-*41w&qML30W)7a;ky&yAn^|!-vjKaNV%W?9YX@@@ zP46ShT*1?3?wr8pH#nPlK&Q+rhRuAiv2lUi{6sBXAVfPO3*t%_0$zq9M(J?aTdaLl ztsN1ft?kFv4gfokV$_a;z1q=KtsM*zwLa#d+D<$`%yK?YmsG+h-o!|-F zILp<5y-6`Fe+S!jp|7k#kNL-q) zbaz-w_aK^5o76o+w55CDO7{lN6N^#04{TR79sikXYHd^Z4bj%_hpXKmIOZru?SZfk zdl1o-x+v`6o){>HglK&Z#rYft>{N>3a|CP`=IC-H(am;*<|thD(V$a~5hMFpShN2^ zWcG2vo7Hu&98V}c0eF{+7^Nq{rqsK4PbQkSMwe5PyKv^zt9=vOBuj;P^ z`!j$ek7C%L1>0rmnM!9Gh>70GFjLPd^ zt^5bk%#^vko{)Y6aL!na^qXK!znQ4%w}gmw!btyzE4>xC+)s?s+hHxegQ%rD|DNqZp<4!nSm<{FBIm?!#r@4_xmjM)rfS%^oWMBD&eONDoy$R2~lA{Ivc% z|Lp7XNbv5*>Z|r>POuO6SaM(X$8n=R0o(~9#;8xhI_lFzGwM)zCb>PUXM;DRK3D%d zSe{R=gBOBl2QTIXqrQ|}H!mkQSFhl@c@@};6r-E}z}n60M0WE=@TP?tOI>OG`;&$d>A4=RmY1y>P;9ZABSjK_#|@-`@iId z`V=?RXTY-&#Te=fScm$O$j|jFT=v(XQ@#-+`&(GEzaujH`^tyP55aR`|5zssl%H^x zKZ8#BMGVU^X8r#x$0V9k`^CdTbmz6NjD;&58+6JzVw8>xYd7N&xn#!2WlsP)WkNBs zCxT73UD%0^YPBp3D`zSw-fq ze;zEqNp7*slicbuZ{==)%eUYcxE$2Ks_G4UDLN9xVOI zH87A|10yT%E2D4?j0R3Si_yRktPLzeWCM#P_f1|5SGhQF+F6XsC1I^xipa{Plly)x z6TG_~TQQcc6Z*<>ILqaMT}3f0SAf-WMItR%N^WVa9K2ZqtK^@3OIHov9mxiAwVYrP ztX_Fv`5kW5HGt>Nh%ssh)=}3Ya@4hx8+D!JW@_EwIaBL}Xxdr7=8PyCB-h&SYrfVt z%n4fCD7h`?#>p-3O>k{)3Y?=Bqs>3U+U90Nwz+w7ZElfVn_C9YHn*z6zs_4{mgcre zZckv_$~_?w*TeRpQ+5!ehaF+*S{+XqR1Cs0Ez{>l|LAV|c2Ay(< z7(E;cYY&GJ*~8(<^>9S+ocANE(8N)hW!$5a8~2#xj-(u$+&b_VTo1>APB~tT9!`L@ zhZBkH;iTjoPp;gX`;uG3PEF4JG@SeCz_V+_a6bc9_cMufKP$Pl>g?o}@;SkCZqKd4 zqhtP-S;jgKH`e)}Q!Wr=tP5cs>mnk@x;S{((@*s!Il*9;_9hIJ%c}5|x|}e;6~K+h zVhnI4tOHy{)JCrkF>DM6fd|Vq$=%O&EzagT;E<&lHrK;ya|4l;Hzv1CZVKM*SvNOV z!T*+;W#<#;e=D%_DTe>;u=?LYr2n1Cb#_G`jm;J?;~>ID7|6a4=TJb+sa|3_ig%`;-T+?zCq@Tv!rH-GM0W6Ya?; z@hW#vv+}+&0nTwk;7Uj_94Cg=aS|dOf0dl$q{(ewCJUZRZ1Or`uuPHM`Y|PLsHuR1 znqmw!HLOETL*!7?CO6b{$qhAq@EmG}Dr`1qtXYF)rsP)YnJe!rv*3D|71((dqlejH z?O_fgdzdr19_9+33Z;4{wF!W`D)xI$f#q zzOpi|>s5ezV8rNpHCVe|ov2;^E=1e)8p(CNW^!G3dIgSuR-x;)tI+j2S)$W*lk0T7 z6Il%iC6NR~Fa9_Q0;J7(MI=n;wj`6VV;Xzp^_g_v^Gv z@UFdC*)_zNv8t4Ua@-BLYeo#mJz$-yJ&F8;_6nXZd*=kp`%je*lzl4qY;T_E^a!Gw9vLD`?GhZ7 zT+c_>BwxH^sxaEIRT%9rgwc)zc3;I9?F3jyJCUfPofM*-g_G+9Bb-u&5l*eb2&WN7 zI33u96=Q@mU>)I1qA?vRX9e$m1{T=ab%IMZ>ja~nTZPg7R)x{dBaC)FuoElBXcxjd z+C@Y&g4&A->6ZWxT@)kzGT5BZ9$hZSn`~>=6@=`+1Fs(xBl{}Ym|N;s6E*!BLi)A9 zqrJsQ{|Bt;*AwlkdPC4$`EDds-UOUy7o+kP*i`yL{U4DZ#I3=z^fp52?Z8W@#VEZK z)Q%)ZPp1rHWB|A8cyfsB%A1Yabw_KL}jCDMtE3usz~!i9JlUGx+bM zBg-Q}n}!`ECAd5Wyo6c|mnUF;1W)2!kCEl6pjr4dq3{{t+`1Tr&%rEw94g1!(R3qR6A?`O**o?i7I>xxEfQ8!gpY=@Lj4M z=DnafKkpMtKLDNbp%|qf!CvXdR6FJ;sOe=LH_*uZg5FKxCzxYjZ^2i^gjSkNE9P|GuTVtoa(0AXWJsV!(3Y? zH|kb6o2`LMF~zXi7WQnmqnh>|n%JI@zXNc#T#WpkU@w1Xs^;$!Vpy}2v@5Q*0H@2v zsNEfAt%v1M-E^((iA&!Lbjsdhr2h%FAELLt5bc6>a5Ka(BPrb%SGphQl>NmhJpk6y z1Bq_7tKSX^-h4i8(IJ!`0=$k|jMBqkQ|cJx;Y3Y8f{=bB=#-HE=KxUu$dMq-x(2pJzE;;# z-E^zMb-46@fKItyjPx5|T@`L5nqigR6k?c>l-`Uhy#=^mM~u>2VLOt&*xRUDdwYms zP3;}H+B<=VB#KdcH>|_nL)6lH3F-d?9+D_V`u(t`KS0#<2dnVVh<_1k9|E3|C`RqS zVN>ghhewEP?9t%emEGJvRwsBc4$kHY;MBMnHc!E}g`x5^QA?i*F|3){XK|&^0XON0 zQThUGN^MDBBx>nPg!GqzCuxh3{wl2LuMu5ymw@~ycsBexq4W*l@S_-|Z^E|8zVa4P z)87s;Op~_4@8C+`1c zqq;uUq0fV-&lfp?&zCr#uYjlHh~e`MY})aueM_Xxcfr%<`<%e$2b|51z|A*e*!&E8 z--chPrq+G+W6aT-svQ$oI}A9`C`Rqru(mJ`QHLFukUk#pgd8!_CxA75LZYTm6k=Gj zf=rAnodme&MvT%)VY}={l*x#0wi%y1_*g3YtfEuo1PgOYoXu3g!9_7_riQhHX^5_k zU&v{LkJZ{tmlN1bkF%KpI2A62%}lVh8C_;3YUwP5^jU$!iDIPB4%cjsWhq7Nd3m*4mLoJFIuE^n{1x5NZd37Xpe=y9lhcixPF*#j3C;wRlgs=Z0Xk zB=DRZF^ram?Q(OW(lS)rg_GYQ+L>PtSGzp0yC+8N3b1LyO0pu+l=4}r3NycQh}LHn zoX@JDQ&tnhXLZ>7di;)RjfTn^g#0yurzDDzzZPu9^#xj+Xj|?p>x77*Y~t2UZfAVG z;PJ~oRMtm1ZUEedEr#QUuyItl5m6mCCZulyJSI_$^gqBR-7oSViJHDyh-MdJb5!LP zz@?F5RBi=p>su2|r8kssQ-yEDwjr8+w+kAxWHz=>uFD;8_B#T1-iTqpGi?4}_52*X z?Q2BYm5^P4T|F_fcZZq12VS%H3=w0F8d3JbRqhR37%4{OJ}@i)jJK5|%f3A^qU=W~ z-5}T}!CF z4mcYvM(y>m*4{ugwRY8RtU~QggxZ^dSEz_l`+qP$@PTqG)fD^p^EN{M?ZB};G4k(( zxmMnVH`y*fg!Bi2BYR?`KLnd}`~D9T zZMwJg5z-$4-qI&V`eQKXW3W6-We=bDK zk}c5lnKM{kKsmk$9NQDa@nzU^e1&QqUky4|>-buT*6}}ef?vefL&U~tu)Kk?|1aHf<61Usn-6Tpksx-lh*H6q5Jnjv}3&=yxG=$P;)$?F{JXQvL1RXI z_s6d}KDY@89Zv|HW){PKV%W2vglg@76*TQ9&A&U%T>s8;a)RX)pi`z4!*VLvv-~yH z`1sVP4iO91)3WQ|E&ORiv92DJ||e>GvEfB5p>E-VhlDjYzN~r3)KwfkkG6l zn!#p6mCg>l8%2!LIbpAKE~+UtUvpQ%=Qkl5pLtL|^MX#9PYj>=VQ+pHpxW#OgXSV! zC`3Dp3)cz88cy)+1D!HL49|Yp^BkaB&yhjXb5w}db99}+bFd1_aELI#BA`Z5RJ)iQ6?(@_hE}+ zvJ%W+{gv@rxC$YARbYoujO^86X8#Va^S?$FaRe|#^D%W$E^7e?0L5@w2d2xqc(-xC z9qR?p#@8oQZUF2Kicz^C%*u`MZl!P6#=*046GG*tzHEW)egIL^ z53E9)2N7xy1}V*bwoJ z@+mw`^XT#WoHV6G#csz_D;E2~g^Rfw3wp>lQ5m~m^yHOZ~a*VcS%+jWG&{s9~e z6l1U(U~jM+sXEwART%8%5Y1q>1nqu`E?TTP2Dp_lz-_=|7R4Cg4%i#uPO1)YR}}`h zJ47?UJwe+6?!|fj6F4>~hUfh-m*`M=fXeg-gXgF5uPXRIL@;_7cxbj5MvuUr(W6u? zeyj?`j}wZY01gj|QT!C_&DqmbTkPJMo)|38R>A2xg46S$Q(h3m=|z}MedQ&pw(&9{ z{}s?FuZofX8q9ph2&tO?IwAiJ;25D8`ESBD-_O@uRLy^zkpB+wz-%${--GGzc5ABU ze^5mnCnOYq1UltoF^WHdZL#_NA61J#t-_Y!GeYs_pi{mOqxeghoeq_+sHT|H*Hzf4 ze^UjgZwXG{0XvIgIQ;-yCtHResoKU*RVebco)?l0q`vL^s;lMy3(BG_)32g<}mYwkXo5b-7ONJdoUq@YtK6Qgo+ z*i4+i{bej*vz2g z2=frNw|NQa^8u%d#YkTOW(y1AIqE{ebFLO9R1OE7(kDjc2-pnd;`@H0rVkL(M*?rh z6C-^ztbGj`N1jmI0lztQbbi z!FDb41o7pm)~UbzHbi{44f7g!*W&FTFPFH>bi_BtD~}i z2fQOsjO;aGFS|oE3w%Ub3zxn&@RmF=($|HV?nE%v^y~ll^+R-55% zZ9u1ND@N&duvfZ0)h?+Wl8!7p22Gcp2rfH=PT55amtA4&;v+0n+rVx?vvl{K7+&_s zq|s$hl;vK)ZlM^Ke}X;BeW=#*&q332-=6U9yb!VUU5c}R7I;bz%Kkv$bJ* zR$<`Fsxa{7A)0}&sDC%`-+RJydopQsxe7J#)xg6a#TfWn*e)0T*HP`r9@AX~)9ZU; zc)1}&j6AyBh%&tixS&uB(_3I~VgDahXXn-s?ee{?PB8TCRhYFqLNv2>XZ^b_?#jQ9 zE_b5_z6W&5y7E|Ea{D2rr%HQ{R1CV?un2%`;UM~fj#^GQEC5aa`vAkXa9L} zuNwFwxuL$Sd{p@gH`Ld_HHl&j^)2iT^&OQ%eV?5D56Rj8n4JAjILn`byRpTv9AoaV zY(K0qiDrrWu!dF9Kcb99s2v;F&lIC}T-a-8JgRGCZpW{DuuPC#3lmm8s!W8loEUhF zj~JG}f<4Pgshq>flCz(@a(BEWXFp}-qsmk``(Fe5onqKe1JmA~CzbZo;nJrE9^)fM z`i!ubJ`>d}9Tx)59HRRb9V)Z*CO8sVg=3Vn^+aEpoiM~4z`Oj!7-BBi8)9xMU4Ik2 z`N4FZVotEu&YRq!g87nLD)ZL@2T&Kt2`|H%7QS| zo4Peo)3+g{ZwoqQJ2BF?hi!Ua*@39(I}*}&0-dt680ovfn!YPh(@Ti>{pv5fp(=L= zo(L&M<({yqv^MQUbk_o3fW3n^Ym-MphG<;&LAm@HbjrSBxak!OWd)#>QTX5^FhRQz)>Gy$7xnGR*2VhNqkf`bZ>Io0;A=Ew$9Cs9>_7T{2X;b`WP<# zanLDGh>`vztn1`cL^G7qrwQrL05_wEk^UTP-CdCPJk@n~V&sM3xdnf*P8cjNCD+2s zIL}vrLyls2z6N_8|A*>&T8UoArN03jaTFu{O;|gAi>RZ%-4i3rJA~SIfy)xbsC^&S z+7F2CNPYo744&P8)Dt7i#{{2GfYa1s_oPL5mr=O{|jZx*7o){=&{H9JBS;izd4FfJt z6vJt3*vyZcH^w2l%f(Utaf3I@Wn>wTP&z*7lnKNroe(yqR_%$1nm#cheG=f}L^0AQ zg>BFFLMEfSLpkm`dGH)+3PSCapi`z2qxRRZ9cr*lO*H9zrs;`+GHn%hGNvOKO%Ln} zieWS(%qB*bnW)xjpv+u_;#mmAvjSHricvf}Y>Pe4Y!0e+JtrZ5F5tMM82P_}z5IEo zI@r8b7;HX5@%+GD&SDfV2z#9_L^Z`mUbqU+QXXCfr#^zy2;eG3F`Ncqw$WEcQq^fx z6{CjA=qfl35}byBm-&g|v?y#phT&x~qMd&`eT4KSfSo=u(wBnmz;?BlrkZ?v)XVfl zUs;w=yd3aq7%_@}3wy;YP_=l)D*U{yRE3pxWrEWxzyRs>&@(;j`C}LD@2HSDW=;l-{-6BL&x+SV~E8tYL7^U05I_S1Ut=x`~ zzCCa%T8#7^VO?H35jA~hLi#SCQ+5?2y};_e8_}fOwC)}v_7}V)c8@y2QreT?vlnp4 zQ4F6y!N$imq5BZc7TRqn!MmFWrTY>}_XF-j5u@|~*p%AcJ&>sB2Zd-B&B3V3Lx9^* z#Hc(B*2=?)?x5}@IU;xtdSr;kHH_P>lR*VK4tWs?8rL{~+XF54zcLeV)P3LIt%n6?Bephn;%G{mYminIHvBZbUz4>Rm=l=}e^|AkZ zUr)GlF|$0P`GGpYTS*=y^!zW-DG!O!^TV*|xxf4y&z>I%-t`|fygb?y_5?G_y74&9 z@(JMlvly07!FI8Y@(vlQU1OdJ8ee9W&jwHb=Lj~>11F!wuz3;Y(tHVTvpp&&MA-Nd zcqO@ulwYk$jw!wtA{N_-@}JCcjPP~bNN)f;g<_2KCd`rE!kdwtgLu0N_U}~THimaY zL=W~`-U}W>jV$k@>^}hRSr)_oBbfFd<8?+p3DM5T|B`F+)0$*PJ|kFu4xEk_!}3d* zmS5r3^6L<-Vu>T3B{m*!{|0P7&vm9fdZsnN3b&FzD zjs>%FY`j*E6QXTw+&aP18?Prumhm%7n-kzVn-I8gQ4IfyVfs&kH~!}1S5;_j(hxBi zo5^rClLI#~i(xY*%vE$mnTo1Lzb52Q4LW5SG4iK{z5MB@nm>INR*M-3#WMmID~eG( zGwc=5LN&$mXC>s%20ZIqjQlxZ=6huf)y&IanG2UbH*n*M80quCUi!RLV{gZ9z7XBz zFrv&~`CwU~mo&00NHARpI3_5D>2TOH?W3ych$`&k^@nIj7^r-(jI2pEZKDVyj0TPh ziZQ|v%n?SGMW~$1MT0k+hW@fxPYjgB2}Vl*yNF^KEd_fSyg(}S2idEPOuT+J-+?9J`WLbqU#;U-zi(-tiI&6QFN0#3a?GRofLr7l} zbV^5z^tE8l!eCjOYLduH4qcQGriE9+MxJBb`qS3e{Mx#;V7Bz8;iP6L* zZqXN)=aR(tt$Xk3=97QkhYzRfH&tETU0q$>=iIX-J*bs+pptpM{j8uc9{fxVPuUF+ zJ{-eQ%N_y-`8{MK?nDk(#3x?S#6b5g;kL+3`)%rF>$h8-STK zge!#{)yx)X$xM@E08YIJfO;0fl|qiHz7<+>Ws__J6l-k~Q9h|D=LFon2#=XQNV^=# zQO#_J7R@m2jsm!%c?Bhx?SQA~(Gac`a@3;7K)t(<1r^k>^Bv`rCLEUtxK#07c4!)p`g_IGvO)veF!hF z;iyH=hPqnM0l1>)0#H8>!WUyWs`?M0PW=Kv(Rh8`A1C;%d0=2_EUe~~5=UNEQv~W~A z*Fl|~>j6bOJZN%*1K7C{K-rrh?Q%0mEqe>JG|W7Uax2KS|0e+Se+uED7LKZaJ2VLk zeq08XDyxz^93WfV383)LAw1Q>Q48MBWp@vsA!eFT8nMNxN1i0MMQY;VL0V)t&@Rv?s$C zn#I<1it>eK@dcJW0F+EYxJ1ZNOHP9(CHI6+N={e4Sh7a;@}O2`05CHX(k`<&s+ql^ zr4c6JXM+lcSe)j-qdphHGt3-SeIB&v4E6>40!mf!qzVA_DhN-ia8&ggsJC7VDp=mlvZz){s3p(S-1&;)Yk_fw#O!sEjW5+0@f;jwc7gjd#ZR6AdX7F)^0{RY69 zX?B2=*5Uy9bP)hUtq?8^a#TYHLVf#}fQpvLC2auo4}$RBXpX9HpoxB?ECZElZV>B1 zl`IEPcm;&p6FF+(RnTIqIVEog6w5Yodm;e!4hXj=a#Zy-&{AFe7QPl#(yx(2JYeIe zKs|@m^(jL@Gr-D-Dkr&j- zW)G^R%Y!QE22gn#!fueGR=x%5E6;#jmxT>NpwFE~jwRI!=Wqb=1rEK&3ir zJoN&ginAcR_J*TYaSqg1aW1G-1uyOfpnpDuUzs_o{squPzd|Ms8B1D`b`2tn5(O_AuL-&ty^^ zp=9!MOQKONw<(j!tIb~&_C}d}Qo}uo zMx)%Dm}pOL+M*3CG~pMC0vl9i2V-J&n9YSH`F;AwlGlHwymp~k@`?O@C$EwRl-D+N zbVpRMM56o=abAAyRI25{M5R6Ml=PO=qA-_>GV+j9;jN5`%IauqZ)W2r${(Fdt-P%KNVOAh-mFe)2_@{+ag#qewL1B;^21OgCOTtDEy_e)IvTGy z9X|4{y!N*(MnztAq6T?QdF>NX9r48lU&&vcR-^n)`B7-uyljdxnQ#}q*PULIypdSz zh#8aYE?RFUT5KKPa$0#w6NcW8e|K6uex$tSvGzD8?>IrVysNx6rZtS{Xg;TjpevMo zl$H0KUX8r3ygG1AI`>~{|8Q!x@`3U?aa)VCu9#ImbQ)YDR$gb7=?*igR*Ab}`KOcf zbx7say{qEh?pQu{q6Yayc{Sb=Wskri#VP%%)8L6F zjmbjQS@|N7@eQ#rl^=kty*p&!Gx9Gdtdf5#uj#Jf0bDKranfq}O8J7>Sfa37zBU`I ziG*981LZXdA0(2NGAF5(@ytu)XG4o)gXhEFV6ft-d^1sZ6}>drBi8?!OB-+Xc?I&wT6+;$q*;!X)5K%B40ux z?@T3AY-L_UHdgy59c9wFZW-lNs%5nD zIt!QONeYFWSQy4JPKWn*DzCAyG-94613k`Z)XMn8N^^!9$O;{V6JFfS>2S}4@)~R_ z^IP%bL?^13-4hWxHJg=5PShZimDheUzlEHo3tW`x$;%X{#P^Ss*8yTe;;LQdWy)za z$yDVF5f^Uh4keapiI88i_EcV5w~m;~%5*2HlD(8yFDB#B9N{9F;Z%6vyz<(Bj&6yf zeHQ1V&QyDNB!*@=E$+%q46P1(wlkuoeZXv|!lyTtFDyS{HqND16Og?!*C}vOUim_r zkBGm#%yY7O*;jcTOWepzcNulQ(`k?@<+YzJ665M>C*qq+%Ih!_oguj?FSJo=oko+? zDPO4?`COQ4W-?@@K9O^J)u6n#o}8S{mJ}MD0w;LN>+D>SV+8hdl4@C?yn3)DVs@ok z<1CvIO|r7T)8f?x%4;-qSf`}T=`_hf%4=^-bR==@SCgfQ5>L+<<+U|z3Okuu2eP8dGN-`1QI%KEEsgV0 zUoJ{5CV)4SU}U+|;zg;-tLNERM6%LJYGhSXUN>*L0W_g7&)7q@J1t(gue?s4wzxO7 zG95{W6V}PW%BxoywqC-vEoP;q0c)HNr>ltp6CcUQ#&U=gHpn{V)f4PN(>a{gfd#ft zvfe2*%AtvcMTDnrUN$&klYCQojq4?BzVmWeBI2fv!hN`v$yMhNuKZJV=B zqfz3-NK2U6WoNzBsqo{9@;W+Jw<}DFBsYi(3TJHGvyf9zn8A`Ji$|$=vnDtk+3VH# z9+m=)=jI;WVUue%b<2@ngLjH4(8YfxL6Fwn^HE-0BY6etm-aYT=f#;`vcwLrQY%L* zP{(wHI^xXGs4Fk>ZLd=&$0*R|9>P+PI;1;h2DXMrv1L2ft8v@6feA6|8!C@nwqa=s9z zppwE%W8E*eM^UF-;T3A+N(I{66&a>Sn9QN8yiToLtw6Kbio)9aBQL9yYZO!>v&3Ln ztl(O&P%l4LP>6bBgcODAIqQ+@ybhmfP@okwXS@3Ha)X!gH4lndjpkuVSv!_p@lFDsfrCM%R zpp9S^(hU-cMC4~)qekvfph4OaZlsZ6kKF0SweoWX+C!2RV|167)ydro3i-J^%}n0) zH18g-S18Xq%&C^VONBAxfqm@C%jUf zJgJ~CzPkRWLq>-b^^{lP#xMoyI!+@v-Iu4mutAP{MXymMFDX!0F3v=3z(xM(rQBbp zps;(@$o`WT)ySU}=xWlIW}vzywIkNGAlxRec(q#ji-JN9*pUslsM5-?J0q`pojQ3< zfyTFuNi=%dK_yD-;%*XJ%vq?s;}ke&dRKw^lWkv?)2=qX?)jeA;Dc`p zbhH=6@m3o64==2i4-}|7mqp=b`Ou4MXJ3BZAYUtCf!ws5@J6wU5b< zy-uBcqM#C;W{F~UroD{Lr(U66{#SweTYa%MOmCLYytqL=SD>A2iMNw6hBMMNbpbQt4$pud6_(&)PnwXkS)_cxi(SRWJeRvRE&W6vB?^ zT^sgp>h7UUdsfLXug|xZ|37`+L+$mOWTXNO>f}6qZJgOciW%jUIGq};K!dJ%3#;T9 zFRGHU3bbeJv6%bgysTQrE6^38a3zi070Yg3ULz9}=pJuf+?VZ2=>~&y0M4N%daYX7 zU4c%AwfqV|4xZ$NJTQ`&Xd^^2yPSqi@mlq=hXM`x#eJN>yB&SX>+ndi0(C?oAXl;- z=X5ETlWAV5QT9|&Si`tb!yr%hLcXk*SXjn3GQ%c2&3WOTH8R5~ab7huQCi)@merr) z&hkopFhzmRu7h?&Jp_L&v%Q#`4;ARdSQd72xt&=Y$y~2cBl{@OkXjV>5R9?R^HRR4 zqCh**5^vNoo9~4@u;PSl88?Me?S=JHqd?oWh`vYJ9kJAUG0!e5(0%NGo${n?@tS=- z@;=_O1DRzS&9BVQ&1N&zj5mjx=b^`%8Z+69GLy_iv)v3eBg}qgm3hPrL2sma*{%vq zxtV2J&Alcv``Gb;T@aY#5g%=~m=$K2?H^!ezS(SFC^ui4Zu2|ysA)IT?3IBH0<)Xh z*G$L4MDs0kw4EB5qs&DnV+Px+18iPy>P(y68rYG6nQNz1nDb1Pskci5GsRqKV)G62 zzPZrMpn_@UTKi#OD+ikA><59_-7GXOm~rMdb0aCz_NKErv!^-S9AtLV{dW89z-*ua(@olZ9c#ZecbfZ67tLR6I|BZ6u8HiBz^pOL z%}e&8z}#nEq`=CS|}7=-=JYIBRdtIYJ+J86Kq-mzczFBS$3eY=`0U2P* zwSVi)Id*$sD$Fo5+C~9goMoq!gLlW%tqkIccJ%;rg1OjCFiTCR{cB+N4DeN@$=jNM z!Jci#n!#v~rm}0ydV67D4rXwMFtDZ{L*I?Z#+W69$yogLGjo>KGRpjsfzsG0qx~s$ ze?Qw2m=ShDnHi2(mJWSSo{M*LhTxm|B)K+sGrS~#-(EHYb<|K2xy|ltiu=kdkpWA``@$en=H)XCiH#5w! znMnhN6UMg(<{nIbpHB9p;_-~kH}Ka8hOWZAN9S|&qrbg7pzKbImG4)dtAUDrqY5M z8muuelA#s|+s>VC)~HNvG1Zfi!Zi8LHtq9KBU`V!-fSK!>}S`IGoRjy!Rr zom*z_SCuntc|c5TAdu4xAZ5S8Ba`XoFyd~Txz#jdB}M2CqK8xP z*%3m**o6UY znnLvpX~gdK>42^*psLYCTx~gVGMn+7Xbz)Slkn&a?2g69mk`PowCpv)bu2aaH>cRW zbSfWeer+o%DDf6kc9^}SjPPqEg+6A_4;akn%xQ(bRN|?@^k4*)?Ie-U#n$1(>&fN@ zw0bD{>k#0aI$6cD&6TVP3Zn@<;QSdBz;WINpHvU(7y*=s5tdtdp5H*=^qstC(-WVIDQYhJ<6`Cuve9l2Ns&8 z(JUH19xrWXmh6r@K0w~B>*zytY%M6!0?<%EB7HDFScP|8+CfkrY_t`N;;F01JRgf9}VdA z3AC~d=h%PtW72#b*Y%<@n6^weQ|%Q21JOig7{w@z#mb##xIL&J#=mJUwHpF^4mpKB z6Is_XU^DQ_AkAMaK=#BkRx*p3tIe|*o=v^_Z!43cizR{BoJwt@=;;i0R6CfNnlHBE zySapl=99sN>^+^@K7-p0A0J2tFm_O2TL&>SE6Ev$5=pBVhLIJF)qozn%*Bq{KM-v4rL{vxyi&5W`5vuiL#f-uMf~5N zjz34ubEsqiNJs5<^BtB)*3dodKDtS1vp=oCfp6oXDfD%BX30EubkFNPC&zA$?NlYr zxRsPQhcMp2%sGmdFQC-}Y2re1`%JT&ol-`-24H9>e;Q}bqNUFeek?;r6U3J>`Inm} z-B?}>7{#m^Y5qor+Dgj?Aq;WFL_)5z5c!jE#Yp^oJzbhD{_83_7-Mtz+kWr|Fp!n}n~7`@^Y>xu9zj!wo5{8-U^DXPLi%5b S+FE=%+Vqc3kw(rq=KUY5HJnEP literal 0 HcmV?d00001 From 80ae32a462f024ccf1719e20f1cb3ddffb9b9f21 Mon Sep 17 00:00:00 2001 From: Brenda Praggastis Date: Thu, 14 Oct 2021 06:10:25 -0700 Subject: [PATCH 03/41] first pass integrating FT code into hnx --- docs/build/.buildinfo | 2 +- .../.doctrees/algorithms/algorithms.doctree | Bin 258203 -> 410625 bytes docs/build/.doctrees/environment.pickle | Bin 409510 -> 456237 bytes .../algorithms/contagion/animation.html | 2 +- .../algorithms/contagion/epidemics.html | 2 +- .../algorithms/generative_models.html | 2 +- .../_modules/algorithms/homology_mod2.html | 2 +- .../algorithms/hypergraph_modularity.html | 763 ++++++++++++++++++ .../algorithms/laplacians_clustering.html | 2 +- .../algorithms/s_centrality_measures.html | 2 +- docs/build/_modules/classes/entity.html | 2 +- docs/build/_modules/classes/hypergraph.html | 4 +- docs/build/_modules/classes/staticentity.html | 2 +- docs/build/_modules/drawing/rubber_band.html | 2 +- docs/build/_modules/drawing/two_column.html | 2 +- docs/build/_modules/drawing/util.html | 2 +- docs/build/_modules/index.html | 5 +- .../_modules/reports/descriptive_stats.html | 2 +- .../_sources/algorithms/algorithms.rst.txt | 24 + docs/build/_static/documentation_options.js | 2 +- .../algorithms/algorithms.contagion.html | 5 +- docs/build/algorithms/algorithms.html | 546 ++++++++++++- docs/build/algorithms/modules.html | 8 +- docs/build/classes/classes.html | 2 +- docs/build/classes/modules.html | 2 +- docs/build/core.html | 8 +- docs/build/drawing/drawing.html | 2 +- docs/build/drawing/modules.html | 2 +- docs/build/genindex.html | 207 ++++- docs/build/glossary.html | 2 +- docs/build/home.html | 2 +- docs/build/index.html | 8 +- docs/build/install.html | 2 +- docs/build/license.html | 2 +- docs/build/nwhy.html | 2 +- docs/build/objects.inv | Bin 2511 -> 2883 bytes docs/build/overview/index.html | 2 +- docs/build/publications.html | 2 +- docs/build/py-modindex.html | 17 +- docs/build/reports/modules.html | 2 +- docs/build/reports/reports.html | 2 +- docs/build/search.html | 2 +- docs/build/searchindex.js | 2 +- docs/build/widget.html | 2 +- docs/source/algorithms/algorithms.rst | 24 + docs/source/conf.py | 2 +- hypernetx/algorithms/__init__.py | 1 + ...clustering.py => hypergraph_modularity.py} | 336 +++++++- .../modularity_and_clustering_original.py | 293 ------- setup.py | 5 +- ...Hypergraph Modularity and Clustering.ipynb | 730 +++++++++++++++++ 51 files changed, 2655 insertions(+), 391 deletions(-) create mode 100644 docs/build/_modules/algorithms/hypergraph_modularity.html rename hypernetx/algorithms/{modularity_and_clustering.py => hypergraph_modularity.py} (50%) delete mode 100644 hypernetx/algorithms/modularity_and_clustering_original.py create mode 100644 tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb diff --git a/docs/build/.buildinfo b/docs/build/.buildinfo index 891c7f71..bcbc2873 100644 --- a/docs/build/.buildinfo +++ b/docs/build/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 580a8748e0d0b3613c35d39d96ee687c +config: bc26a298b47969fef8902e20a4ac0867 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/.doctrees/algorithms/algorithms.doctree b/docs/build/.doctrees/algorithms/algorithms.doctree index 542dcfee917bcabf35ee8f2fbe7e9b416e399339..b732b4074f10f9a2f0dbe688957dfbf1192b15e5 100644 GIT binary patch literal 410625 zcmeEv37i~9b+_(g7AWf&&*0% zgE9)=app+!&pj z@ayfFduP@>Z|1IGf-I`E^b%bHD4^`q-SPObHVv-MG%wVy-H7Bu0koeQQ$Dl3HK zBhjst55g6Dr$$&%Eg`8@=CepL8-c;~g2CODvrr6`v!U%355#*e{QrFT|Ap}X0cclc zEwyJ*kXJ4OMiv7j%b~X$6O)apdRgl=(V>-jAdJRnyXosa%4KOEs`Q7aS(^`C0e#r4 zQU~k~A8#cZ8g0};{-|CroGFFJRqLa*sj`2lTBFx0HndTknL`@jSu_E1_^ zS5}jhmV%J4iix*W@yZ>`^{3jdf2~h7{2J&TAui@Y^|MmUW{1DA+*ycD(|nnsF<+s(S)@U+BYv; zT&jt}g*KISzJWim80TA8Yk4cDfIsq!(foC-R``5>74 zr2lic7gHQdTJTvIZ^A1rRS&H32C8*dj7-MX%!C`VWMpUj7cDpND4xW+dQdgGhckO|@Y$I<&3IN)d~gN4fC@T^Q(NfUHXDcge% z@2`9^-K++{D^|zWO+o~Ke1yj%TQsQC-@fj5!{tXOYFk?{d;;Mzcm>An!#)jue+FN9 zP<&-Bm0NVuauQ`+j;C09K}yeBX3Fd4(bT%d zToDzepAirOmG`gf(7m;3#3NqvyixhLDF4Fy1MdVVJ5*-@K0eW%fr4y zRlimqjtvIFmC)p$$~9?3Mf_^q@Aus-N0?}yqCGO({Y-34_7pdm7A-v-wE|v&&f@Qn zG|JNn{-Aip2zUy=o!+$N*yv#}!HLA{!EX4N8aPVh8@+*YmE1z9IlabPx6a#FskXe5 z7d`ckR6ujQcE$I`{b?wJKM-B<0J9+b$CAUP+LW)AmHjc8h_{BKLAM6{G)uK%h`t;3 zF^w@AM0wtq7*B)WamX=T0h-;K^hc|Qs-rV60{1t)m3?n;4ZI71)$~^3WOt*=ya9#X zAKXXPf>JN=1|n<5=uQ?vNIB`(%Mh{;AB9h74D)a_FI*|x35xL= zX0_TQQ(HdbXeDy~VZr$yIY`OW%Z-UrwLTLrn4F#=3He7SYt_+e8^D95(3&>Pmu7-% zp(-Nh!87X-S`s)7`zAmL!$db_z}rxRXBf*U2?0!#onXOz1K@C-Q8ss zQ=$yJT{E{<_oZ4akOZxSL+TG+1(I?Z|Gh$*uOQN32L6#|DfG*<)H}yF&CXK&@>r^+ zQW;w+49u;DH(qZXspEi8MXl4v4d1MzTmHcqXNd)2X&{5hK)~&CSiQ4#Ag1KTl3!Ce=;iW?6n(YLB3wh`dUYeuU7YkC109>(7TaeyCq`?&YWBQAM z;7;vXxO9&XI7(ebD(Y|5FErTgb|^FsJRGL%1X2#RfZ5m8x_>VSIOCK~*1RoSHnH=ndl?XFd`073g$Xipe zgb5RRi12Za4&g(q(wM5j^xgNGjj6FpZJI**7GP#W-VKNFk77#3B8im)Z}EN2?&IkRDkaP?{8?NFrOBE|H(oMqs@;`&{Q|BFw3twT0($l z>sG0+%jAP}S#W%g0FuA7DGj%SqHotuZ>To6xtfyz)v!o}kl(YTyFTP!^ts zk?Yt*pY~o8l)oXK)V*!q#tj>&6s%&K_spud)e{6m_)oRo9)MK^pjm@RmRCif3JS*j zf%=-e#Cx0Mdo5DMf$@z)V(L4vX3c@>!L;|Thu6j?Y%4QRL$r_qI{mBFXruG zDk~0SOgZ>}sC)wUImJ+?YzKZlIdB1{!s}xBzhio=P%VYQSxlsV z1`I)}3gDpH-%INav3mMq@ZFUM$j#%DB{7cU#;jHmEBA*_ERVKECbVVALTiVa>#IZY zssWj{Ex4A+Riiy-!tBKsT$A6%ERo7bVsUQe63L9a34kCo&q z;lt`h$8sN!+72pHdCy#!HIxrjK3Dl$V~Id6kib%3uu8of&ZT6(tmLsWE=V~LBn!+| zuTh-aa(V_T->7`k+G<$G(^?%2U^Ud+-&Q=GCOl)TFNv`MV?B(Q?DX|8eq>po3$B7s z#hpz0de{>!)G6y>!4{%5SSz+v_jJq0(plSUq0Z0RSW;^85l9Vwn*H3cd@P-{aSL?; z){;^i<44F^e1Y>fEgwr~?fDk!+^j7ocOq;fQaAl6V4s4<6yKXHUrp!n^%m-E9?enA zMhok(R)Y(M67%@fNxO~}7hPx#E`ml`*IG5U;>)eUN_a`ExCU1NpTU#hKZZXEo(xYM zi>!omSC$0VVGYOzj>oXP%3cRoSc6mGF^

8s7k9(g_I^W5AqWeI(~<>Ww9sR82Hs4JLarb>oK3>u;`1xAypluNk_0!_bC}n=adM346~~;VX`{HbnmB z7~~BU5{${SmR44TCVg_5_75v7U)s>xg4|C18@DP$#~byc?_(?7--0|$XJ zE9@ZN>d=tcIEWtPCuMXZC8*?g5SvmQLbEJBaT)l)tcp z*na}dB?%ez1~xs}gRQNTm~GH3X-UVFB>IY&GzG$XxWsV4G!Kiz15dmhw*)=!|IW8K*>*b7;n1qv0r);Rbcf?qR8o&PB42=t`)@} zE32Tz-Df!X*8I~g4$@Bi(*dYET}14kl!y}kNtIarlPdiY1u(yA3avUk{MBqfcw5wf z!rpe9L*EnLHXF3z{L=+Fd^l&yA(ZL@hB^QAyF}Wx{L?q(XgcG)Z$Qzy@ZQ}TWGN8p z)YUAMD&t%4&cOwX3c2XAsEl+|cM9sKb1=&yd$_2`vhz5*l7;$+gZ&Pidp7q@um2?n zXPUx%AqOcd4E8}Gg~>K<&O()oGRhzzQzrNjq#?R#yF5qHq=t1^nVz9?Z?0twdFv z5}Yoq;Iu+kU@noK0ahdpB4!0)c^~%X9A!h#&Br3yT_9w4UI(&UoguqfBqeX`fR(dN zR=%dW(*2kbf;q*%$P8it;LVI>I3uAt>JJ>)02#{%QxlfQRKhYRI_G;$c%W@weR7B< z=>rE2Kw32BMjssX;3s8CA3V5*lA<}eIi5n8m5T}f?AJo=rcjkx(p>vVUTn^MVC5Qdm94v1yLl#J?N9wsYX4l|WtKK^<-Fdi&WzZSx;c=j}N z+erfTcE4F}w1#1G%5-(MY1Esu_8|<}KVVO$<}(^H(wukLs~e~fdJ}`*&@TPSoTNZcX8Pak6zH0IHw=1_Ty7U39O#9L6eU8hN~6O=AXkNH#isc^ zN42GRQgkGT;MrJBEGhb;0)3uHm4=guXnGl?uF5B{nZF)o;E@k`6?{GJL()@LUumJX zrJ=%kM#0O7R?Cx~Kat>zp8#JIF}~hy`BFMx@3c_o=W8;-7e4{MCS!a(Z23|;U!Sm0 z=jLlA;?cTwb5BS8xuRZO9NHAl z-zL8dzs&D_$MU^!ej{;CR`4w#1dZV@!bZz#oeojeHd-!1hbO-z{xCoG-?opzxon3@ zQ?>Rm?cOp!37`By z@HLh>|NH4#=ht9Jwr&rGWZh1g@9gZcdyWa7BL^BDBMgC690CK?!9z1AKId^^40z|H zHa%Yh7>LIxx*v`*;qO37aNzqvMVa&XOhtcDLA|TzI2t9B8fCeHN%cPC{-q8Xq9}Md z;YG;TYK}{Hg5z4nuL6t! zZ6Z_tcc}vF^0+8+E{`vSrpRHNew#}B5eJ7TaL$eYkb_iTD=C^A|50qbcGj0E3|kRWAN9&c!6y3KBzlg z7_5#;7zuS$C02D*rJBx(-n6PCcQl+P1#Kv-j^B24d2w~zHt7u|NW36#HKIjaNBc<+O+ z;YgUZJb`cW4un_bV2(#C90&@HR_X0P1~{Qyt~to_jj~_j^e?&gr^Y2q`qy8BL&M9B zYzJzJ^e?0k>0c-H!7obxqF=W3ulPdeK;`Y>c~LIby6Dosb#M&`Bt+pqa32($yFKmH2&@csXM1KFa~^LQDPnzDi@FQEQA%)9s9#Vi!N!2w zH7ShaMm6TbohmNTPaHK~&N^+>Ae7fQW_01%*K@y7PXFEs+>W}t(VQsNyldef>HJ+} zs{4W+DChoQyAro8H7vFSNgZ^hdZX(yfV+K-Kq>qH0gY45$wmwE)(?5GuZQOp*~HPsmC18M!^;my-2JJN5|exLNge z4($*_$LioFeCe#63qiZ2uEhFv|Fa=V#IE(lC2n*uJY-F=IH9lOU5j@-sb0nV+qS}P zl*-oI$8U!_jNl$PyeT9tcrA`C1Ra|E{|&W zjo=;5(Cu(%AKWg5U#L{a@K>W*hW{GX!SVEEzf`MD4|+#@IC#EJ=hOGWiTiM;85{^B z?w_NZm7^93)}E-|D0#5Ori%94f`bzqhx&|9z$JK<>PVGTT~kDAARJ5&InAZR)kbs3 zqkX+vO?^l3=zBO*9~NLraB(*oMbcP=n&MSCGRHxS&Y43e~vcoH6{A_#zB3tM+dJmHf3X!I=Y*R_4|v;Ud=_$nJ}bPc!>))} z=)5Y6bb=5VRanJo){d8=IVqtO5pyJ;H_t*or`gp?xvMaRz|xi?mWWrIM3GO{#O!z# z=j_7i2*drcr;6R+W1{~*ivQ%@BTX^;1L_SKot+X7$zDkz8N|q9MkGxZLt}_<<>VtC zX~$hby{|L^22mP?OL*{(Ubx~B!!iilYp^Cxhvl!#zYftKZZ+ErfQ{TM29|2ehq7;`XJCG z*Ci_|S4(3UN5VCOWnx9I8JH#=?bD`R=F(H1J{Oe-s)2Jf2M-(^k&p3Hoq3N*XYayszR2A{`jal$? zQufidXb-Ph8STi0*8l{`MW%CZMH#9!;fk!{Ut;|VPfuXf+shv(kIHSR}_92qLt z;D+&`qtkb;2l%MHe$$4Hm#*8mVcn)J>skn(t*aA!whlfuv7U?+9qo?Ua`5PLUn_ag zOltdVt5Xd^XLIrjbHF$EHEijm&Cujozpqr6oLN6GndM-ky$@$0B_RF0_W@hyT0bzS zI{k(U&HCP|iy^ugZa0R-QF#MvFvnVkKM{gIBnN1MjMvPB7p9dB8TFZ)tCh;j^^B}* zq*fUf4eT29s)JsT+%o;RR8HielR;tj-Ze25GlBZ-tFL>8QU3sydfmn z;Mh~Mk^2;*HaIi;a;T`0!CtH;whwwKcM?02AF42A{w~2%Bfeg7_k*gpZ3ZUk= z4W;jUJ!GNQ5qo)~7w&N$AzFQpb2Y&iKLNg~F~07$d?}r;XIrTA^A#lc;wQjY5aa80 zmM^9A^=b=sHec!Env^YW#&!td7DY@+gD^@>o?xh~c z+NN97l?R0{MycL9K@#t$Bk}frL*7^6V{xYxZ#n0LJ%W5GMS!=uwIOfSE^k1d{X<#y z0&*1I*4T`PPL*+#gnRujM@#es(N6(wVlUp0sRD?ZlDF1~B4@trGT92rWb{BO9}NX7 zk8?|%cjX=LAl36ai|)!h8R||K3+K%$u_RKyRf#pPQJJMwM^@^oRaDk@Q=&|o#pCZe!cJ6KNd zG}hqXrV6YYEQ&hS;CDGV)HL`V4$@9E_}x%2#_rn}b;sYYm0R4$*%>KI4 z!B_|WNc*PKD{XbKfhNrTz9k1K_dANRP`_t822LH8vm-fa%YKPdhvke_^{gdz_ySPV z?9^eT5UIm=K+{C(F#U?B4huYx?{1wng1M0~Ft{xwH+-ojC!5fw#M_{<4zSBpG?wN9 zxO@r7Ss##0f0hkgzEHgz-qHi$k^~t;E&C$zNEfX?GHw60iofD`Wz@FdKCGrEc;$Pc z=D62P$KT#%q1J%}iK!u8`Flh=TfFj9mM^9A^^k=+KVKXdYxr(@r2J2oFQxPK6$^DX zU+JDs$`DeQs(wdccQ^df**-H;fa zV~56VR(K^CIDnOu)nY>_MX>=<&gxy|6oQja05e0TBu69knCv*9O`wri zPzBa$vnX;-o1>tUnT#GNrK4%gJ_oNTaE5@M>LAqv)}m?%z-aR?QVf zooepCI5^Za_v;SQPBi!5pzd^Gu;waZBs5o*ST$Fbo@dkCco#=1oQoWE8S5{s%b#^f zGoj1b7Q$%a<{Y&$YJnj|PSoP@MB26EIfhQm9rs)Wq|UgfV>EFGekFG#%4p(Mg53dp=l;Q_ce#HGqKSucFw61jC@3YusQ(T+IS zKojPEZ|5N8en(Lj>i2BNz-XeJ*c3t&<$O~0tOZS^5GC^<0;CXV;(MTJ0!^e}y+IQP zA-$$u%?%`;ot7aqMD=ulA)X`{87U8XAnQF5IFoA(Q3fq5}`uOa>O6(8kWmJdh^5D(q41$^``bKs!MR_TWFcqelt|R`W~ejEH@@#V-BTZ3a@G=U}if0yB*Eer?R_&HUWA*n<}u*>qU_zAPyeTF#Yhd*|u1Y z7qhQGSpWqduW`r%Re(X&83(Bzw-*IfUyEYaKvh%hj36knClCZxVnq-{Y5JI;Mf%Tl zv=K=eR6RN!QxGpZ!#pDCzH~BB~qnq-7biISL6RBQ2Td8Jh=~EmW z7nkTV=}x6kqX(v359I&!eg|nMay{4+xwae}7niGf>NmR`^Pwr<7vyK*c@EM} z@`a%$5l^T=$Qn?N?nm2}dx&Qqk)C*pC-dJ!m9yo&7ZXE6VZIV8*7kCN?j z_ZM8mS$eWNxMDR}1f<2(U=dD2soO0vHFSq4S6x8!#nldmI&j+jJ!cSihJyt(4esmH za*%RgqxcH-b+)qc!i|J;3N7475Ki@`W#NWq8<`6?NFf$(Zi1$Xg&X?Sn}wT;$x#e5 z_?LSDC;cD`qrMIna?UU+8mP?4dQJsIp6(H|*K^A1-R|z zg~x&>;(>zNF^lq`B6;fN_Psm$O08A{&R~RVH*tTjGS;5dtvpol@7T=#;4yBk9D&Wf zIl6VLxM6i~{VsLF_oGheeX9DhQ<7dV-g1s27XvB89jq?kJG*sy#?=*Xpi)0Nbp7@#03_DeiO*kg~4{l zEjAW{oC_Ckba1UTFsDSFi$U+n|H*ebNIQ|~`(`WC%w>yDIyf#a&uEo`%_w@HTpf)G z|B(OFpLdXUBG)hVM6Ta+a9muj<_blQWs7;Ix+5eWbARsOwiEd-05)>^0&t9pF;`eI zlvu}HReFrA4k%4O2A3^RW`(1Mz@Z_i&qmac9gFa?#i|^&GM0%!d`>KL0g-lX+2Yz9 zcEmG{9YE@wX&B2Emy0O@tOIO^J3dD7On$f!1ZSG&TtQ@vK{lbvbP*uZtkyOTAe%+k zHl7}P2~6@5TH7ehcw`(G;{r(G_aX9)Gn1`tjGXVw(UGie*pXeuhD?RT2q?NQ@@? zB|a=i!dR*|EvqEEz`JE1mV*>xmEr$W3~1Ww!!k)BxQrG@Vg+!>m!dS;Uw&FL1WsR!SZtxz zAw?appHH+JvrB}I#aT5TuL|X#CY$G2KAg_xSr+PSHq(8NY!WUj*!pWumKFXTw8Orv z5V4e4R`?3M)V*Z|WC2DGT2>IsN{IF&sJA(n70Ns45>>j4&C^o;C%r>}^U6Dtb__pr zeBH*oZeK?p3GrdR(grSSx*ab#f}5i7;F~_YF%>Rc(kSqHCAN`O;n~?=i(YMHD9B`;74$^!{Bz~7J1nP^#5!DvzPn}%KtAS9ClEa1Y zM^qniSDyuUr>FEwRCSn2lACYUyAW4ft~7dHQNGh{UPVc(dp6pLZ9l*=8&B_-gWixs zJ{&H;7WrDuV^TM_{ahnQA>18N_K%iEYQ9&6v#aq3tqk3Sm)^pXq1ZQpdxJJc6FR)m z4fZPt6E`;}MB|&p)a0ik9VGSskV6bQ5VIR-6YE2NNELt%CQo7)MNWkJrP_#>*7ROE-TO0pM^*;B{A3<`2mQgc;b+6pcF=kQVe!TW&U zc)E)o?qH8F^7L*Zb*@)kEkd>YR+?z3IGs4QY%60sHc|Pf5(-KGj8)mKN!L1FLOPWy zuo|c+>aBsUbMUVj=voJWcUvi#XS|+V=6RB%mlNihT`L2_zn{bC8NR{cJSTkf zYT+Ncbq4thb2Ob}j^|^)x?&Ckq8l0+M{rb|NWi}Gqp&T~gGkY9wHp{mz%9`ujYiEc z)j4V~ZU$ZgneO9Jn)`y0crg6>LfXbT?yrfx1lo26;lLJFqoD^PWP5Ay9=>>$=KMpB zYmNx{Ju$v=nNIx6WI9RV>ZX${wX`5x$r+=Z0ghcG8!I{LPnVUvARhgN9L%z^{~h*3 zA!YC6=%sHt80%n|NjZAS44iqe`KE&n^n={*U&}$t{f?q6)bH7fCo}lL59-5I_DhUS zODsh7tOc8s90@Gn;Bi5VMyWy=}MXX~&jp5cOn4*&N%J^gD zt|{ccR`FUK!;ab+T!YobFzgjtUkPTSqnnat5rCa6TNN75%of}RpNG<$|3$;FB zl9(@^>a;H>E#kvAS7+C-hPWZ4;1cJCWz+*I_JZa~5 z@x?YCoFCL`4vU-F#3AQ!yenSP*hI{?Q7_}k6#xul7P?>aC;WPwPPKr)`^b5D-WZ=! zRQ4bvfOL={%En>8DG}w{;1iXRk!Rj|mku0P=Zp<4_?yFzf}*~Rdqpn2n&ITB@_ze- zcp&&YP8V9#0P-WRMjaS2y`*keIKnerf*iPpZ3+ff_t<@smcAs?(%u8H@6RD6juYR5 ze6423ng+xglcIf^zlDn{rzR$+hj0*XmLO%O>9t0y@SrtWYPS5L$jAtK-Be!3bdXv@ z%Pi8ic%rHGAmN1J7d^*;q z2dmjr7M^H*JV*cYL<@b9%VYCRcJCt6uB8`V<>+8kQg)bE0BIqI3F8*-c|H5CnSqK2 zKhA~!pAY}P5dJ@Ka0aVpQSHjX1&a!~=(4B^ViA=b%(BS*q9O}ZO&CRKal>F4sjn#Y zrU2qD2iqN(ayko6`<`}ir76kb9HguyDEC52l5NloT*yd9Mt0dRF>oP+bY(df;DQ`y zCUAiiBB}0u&@_Qp(68R$6&KSPqrPvLQ1 z4Conp*53F!@&+wB1l2etc(Ua+{X3?rxWuRJAeZM5^c{k^E}nx*XQ<*)sh&D>y@y{= z-YU2Sv=o$KAO4ej8wukb&*&yxY`W9RS=1pY!3D52fZ>e9xiT+sG)0djZwA@~i1A#izzSlBB1`n#g|h|j zbMTE_z?11$IY{*gyeN$EIuwZpW0)dh7(Ja03E& zC7BV~Cu;>_*bY~sQ=IX*XuRzc4~kaX-&*JyNBrneXU`uS^5CrKac`SF0EGcPRjM1j9_bXK}TF>v^4s=#Vflhbu16fCx^)Hb2I2xx)uTY3 zE)2FVZgDY?@FxzgwFc&t=t`O1E0tNIgUJFlrV>D~{LEwk8y`&AlPz(Ob|UK)Q1`54 zoq5v0IS!7C%Q-q&fQPi`fhpHb`9HnhLE4F2FYbw4pW@)SxLgaLG;kn43;P|UoyhlK ze)-zqY~@O=Bd{v{{;%Aari>~4)oVLMN!auHG$&US_Hd3-#fZYPYvr`qBROhi02XH( zIRV&FBJJ9O##-UX5L z2XQvDm6Df^-kYPpSt+r3+c z{hosbG+6HIZ|5N8zDDsC>g#M};}swY?i5-9k`SHhP0I=p%||j7v|L^~iQVX&Jtpe*x)s3$;EU)>koZBihauke+4vUi$LZQKHVhfbihd`tyu(cWJy;8 z7@sOXhCuz@#X98Uw#u_QgzGmtWHGrp*$)28Y{X}6-tnbfWp-zXvrH!ZGj+MMK^tZ&{7&9|>_ zMl2=PH!p>ky0^ZGETAcU48o*c@SB&@F2kQWbKKu5*EdU?V~gNA=CR{Vy0#P5DW82% zW~^TiXLr!ukyXInF}1zaM=N}EhBln)B?^7H1&7zG9=YgUs2N^CTy-n&&x>Hkgwl425h)1nr$^BetBKwHm?=WcRkS9;! z??AqkHv{$W*2({WB5!!6y#ExJfkEMC%Ow~0!}ajo%yc;&!`gM&jugZ#(Fg9rQ6#kp$f6{yh%M@X4)f#wl2 znPL0O9U|9Pi_QVsL~6iGsRHXVr6_VPQ%2Yd)MC5E>unAm(IR;9>@5yb9djv~2Jm)l zzLo}Hih~y_l{gY<0II~g2%$>fKj$hpuT!0iyxH6{e_N-_!zh8W*D;11zPCSkkweo^ zDR~*-9wK!mV}y?-Gch2q0?xRUx5Zj6-_};FyvatZ+JWuRjP z5-q}mkb1g(a#qEcr~<3I5=BmT<;be|frCTMS$)?*synOT0U$1rRq+^*rwfCf z6}PxpR>ktO+<_^34{tqI5;jY*TUHpwfrnp9i*Mex1L|V;3yM;jLNVp$WVs$^N^qylwr&JGw#|T5<2vA0a9l;wN8j)}W*|R84d%8P^d*Q4D zM0|2)9n^`mYiUpalcVVjOZ^N*>w=|>%#yzrj&1;_{RZU3C&8eG{NzPxq%fN92G4*JhXSJ~+V;&+1sa)*D!hZC`tgG+yv;bkVWR>p=3N zAR$;e6f7n|$HuG2pY5K-afVwzk;X?RI?n{0i6{+zZ~44=>UQoWqDLhIHcW3*j_QrBV@4sjbCDuuR>=$Psku1aCJZ98Zc15<|6?^r93Gk4x3V0HT_w*b+!9dojL}~z8 z;r6v_s}m_!T*PDO78mS=4~hLYMjj^|dy3zZdbOZB5%mN`IQw(7g|+%=M4G16qO`D9 zkLUP4Yqk3R#imy0lvxy4Y&JA7=j+h#f`KC1aw@#_)Eq4|v?cum!FR#FgYUtAFm|AT zV70E8E8$aO>B&44AifI935>_*;EY2F9Hk3|66uXV);WPuE|nEZV3cd{Drm4IFg^gr zntd-6Qiue`_k$Ef0wewEO#IAwnOb)TX^q#WRvI01F{q}B zGQkmX^yVbQP=0yazUGwQ(?rM5u(nI>F?BvW(eab&-O%smChSzHC2}4gmq9AhA2TSF zFC!oRCDoYXnUIknf-hna^1ey58edC9iaA(MU`0BS43&LUq?0L3HiS`x)DhkD)>5)8o&}r6ebRV z;pyI*%#e}?aagjXyloyvkpz!uYtcPEt%g*kz&SBn9GZ~cle}=anJTao?v5cls2iIByb4oOM91JfXCksp$($3Gy z9S+h?WPNzHvd+wG3LP95mvipSrZ?vQ^y?g?oyhe~J(24lIXEsZ*AvSAq0&^X%`Q|8 zf3e4=?tU^q8xJ{1JCX6H^UD~VW@7kL?$kQ`snTE$M-(OIa4x?JF*deVkFj$N`x_j6 zi`rOh*ngWuO+3mNvJCYCf1!~+O8cla8sjvNQLi_BKyzDg>k1@4L!LAy7f}+?Xrtw~ zak2x`pgwc*1Rukt^RWg@p^(1m*XU(<)|$XXXt)d*4|100ju>}ML~xJH9+IrYFth)5 z$Si%_;~?NasRC;dAc~wpfHQmOlyh=Nw<{f_otXD&P5BZHzT83DiCnMjiCp(NI4&;N<9Q6yAXiZP*i+>(X*| z$u!tHj4zY5>S(pi39&dx`OOJ=ZJ64FXqk4xy>rn5rVQbXXR61@Oy20w{dBL(gV1ZK z0_z|oiaH;JKH%U{^C$0hkal9q_e0(3;^0Au+nj$8`iz5Pt#LW!nR^iWO8!rO(LvgY zTp#I)Tpx3ATwJc&{77GXi9a^&Ytgy61E~29(oST&1nSPIyS8CS8M$>BQl(dR=+2br z!cJ>~ApyB6JS4n2ht?SLnC#gRku!A#Ew znf3?Q0jYB>!2ogZ;Dl$~^hYx`x%r>7)iWg;cHFncuA8o+f>Mk&$x;YL0qoJ+|`&~kXT=IT~+ zbO&cO9wO5C5G-?bMQJ>%k(w$QScHGYW~a}}@qNx}RNvnsvKosEFEac~XQv%_?Yv+k z+aNg;_OoC*kO{jI-Wti#PPX`AA~hB)!VPN^YPmLtUh2jXyb4q>ZxT&6DBaF|NaneziKVKp4iZTpuPPbXF+PY#4i9>8z=2_NMwL&Pn7$hiRVg3!@g1Z zrmx$g%*p!_xQVD?!HHN+EaUT3TQ8C4)Kfbb?BHC4}>b)9Y*|GAHSE zCxc+@>2(oHiS)XkfT!wSdL6O=BPXTT3I9NuS^vrI5Hhoz>2=%o(3L?VyRKYnmvB3# zT1kQgI+$kc7>9?+j5=kztHKUzQAQKI9a;pP`ZoNhX!={vTt5l*EzHuKq`N(U9XnzU zfx6q$rl=o8zE<<_(9LwWOQG8bFh-_{in{3vFYr!2>)%qBy$|1k$*GKu>OLpgG zh7gT0yhfNDZa(j5tB&Tp8E6yvaDPJ;#Ak&x_KG4mFiX=y=Fm1r{8tB$*dHGjkij<` zq&m1&H23XW*nI7bHd7o7%qnpt7XMX=72Hy#Yx4KCGTgAhOJLvlE#1D;T|G*cXC|L@ zNES(X&h!}~H72m24~dMs3&DkX1NF%v__s7I^c;U7%z|6e8IciVzj^GSBT6~<{A>eG z??qm1IGZZ4DzzwbD)lniE*YXk4^+32k!*4Bswwxy4pLpYga3w3D3AenIn zCWKRG)ZmRr&|ofny-^lN)ZriG&21_!Lt6HW@=qlzQl5sRh3P%Yiat&iSQT9qIThV` z!sZJcJZcL5JO^nf3jRWn_sS= zW(mik6wvB8ROu5tu`XpZVMo<~8$x~xyP=9h`Uy9bJs$I7&!=*X$BYzWkeU-I{6jHx zcWbfdu^dh3#h&k@XkC%Cj%SAy9@Cl($HZndOw%cvKgl6D{|k}Et1wE_YJ7ZVNvtc;8fb2S|NK;dU#qPHi2o$58af@Kr=b4xLC*SUWxAtmQ2#mV z-SFw%1obrmBw+m&Mxg-qjc3X&Q}FxSipAoGIYiA1UWwJjp#4R7n1hJQHN0G&$q>QN z@`>e?hIc@ZxyHR)b{_>I~7bcxayxkmjEd){+s9CYV1$FE4l2sLN zz}&e^u!jY^2rf7Uco6r?O)ATEIWJ_pFtg~9rygptrERbtgAReDl%fUN9LWnH1A5S%`~s|T;Sv~t8D zsf1Q$*UC`KsX1!pSsQxaoU^uhMB253gdfVmBo7e}0;zL|FrbzzOJIr3GORV0DHWFD z4bFtsm#bm*nhz3&Zsy|?;3QxfjCNX0q%)n?1PbHb#Q2w_|J;os>A#ZeV!tx5O=qX3F>_(6R)~ z7RaZS<>As=6?TG^YT9R_;#J}4Dc_2vvR!2n;6S+ODB!d+BJNo-IlT^;pD|MKunV>! zhyB4;OAUJ5tIgz0-GTSQrSP3L9J4bMF2rxx21U4Iygu{daQWo)RyfCMs^-rGs5Rj- zcraRnmAjeURBs#ZBKO-f=?zDoNyAnL|AP~QvvwW_>~C?fuawPrYjcjbHUbTNAkq;2 zEFg2ihZaQGX}EZI18%604w(N^el(a6^^D^`xqaaW;RCjh?>rH{?%WE|-sQI|u;cwm zqdAUwEO31W?B}WAErv8b^}PdE!&zbeLAV-h$RC2`QqM2LEr+8G*jrz%!-@6v226XK zbkaQj@F7(eS5>|86mDmPgU9HFmNy`6V2zl@nnv(!Y_pi8{9;&&dVyH!51tDg#UUeI z+mq^oI}4tb<1LQFr&Tv=>N2B2?cs&dxm2`H?M+q7Ep;Vi3$8oC8`o^&d{rdQyp==( zpqKlD*W_plA?eCqFq{A09B*X^Al(&HZhts`-3$tzVnOv?6udM(m*Zy?UE(`xn6;qj z6uf7m=tv<@^p8NZ1&U6;dW53a8e?Emwc&E3-31g~tE~eR{WPtbfl_lL=E`sCDU$wo zsNAXkF&jz$YxQn-F)2+5NhIAUWF%dGrc95pjYk!W#gX)=dBHzpH8CW8nd~1igV#`N z$l>CNL4>DA&nWwRV=SQf349>#Ak(qyA6ck%cPjB{%z6Gk(VE)$_@EEN(53+tx&&bA0uY8#L`gcrK50_v*OF%nf?%)pbPMh)+fa>A|RKTj*M;hM7 zb!FVSQ<}!TZG8mKmnM9k3oA6e$6ry_9qa%t1EtuG|D^gaUG1V6qnF-_y5&k{3c98D z2>LA!bHjG?TzD_?wVJ(IH*>l{F*ZWN>(s>L^iaJVWxXn$5k4}J>Zcg@b)B1XBHNV|r{ zpSdO(3&w|89&SztQs;1EK(Fg%Tmmr(9O|SJ%y|Vr5t~StT=>068w$S)PVFLWSh1j^ zKN!d%IPP5(T$g*dAi#b@4raNRuSH?Ff(oOTqeBxd4i4O*?H1PGa4^(?FXHbxz0s`> z7PJBE={ZQbuTgwm?&~FcO4U{bjmkfj&Qo}GPYxEjhwlc`Vm*8lfo;3}_Wo;{O*sD( z&Y8L4keodTOjAubTG1>5fKSh-7t~dp*pjlCT?|;X#f&IVFh483L;SHRh&SdC1P|D+ zbCBj&!f>H~baciT5#I!rJ9Y5Y!~xidb=*^>+d7lB5>VJ-Mc^xuC*4cu662t9Hm+>Nf`h&uM&s;T>5xL;pkIZi2^^Gu^#%vM zM1p4%ut~dI&JBe=AvK}ctgr(#^h~p+0pJi2QTT6fA<>`4G}cVY%Z5b%UA-ILN=ue? zzkPIlB&zlDoaAA^RyGa6VqCc~3Mq`ZvBbE{v z>G#1}x`&Y>3otMcMkN#?;wN8K1H=s~jz$;IVz6*t8i7-`=~m3GN8Oxl8D z_aLnZQ-@%&-s8EAj7sL@TS`ZRDo6(uWDNf)Hg#FuV^>DKRA#>B1TuXVYD&IiFs6~O z)$HWD37Kv{Cr5__=z!@a39{p8U`&RsN98-+=4%`+)dS8^piO|N_fiE`j7k*Qg7OFS zh4(%(Wp6#B;wQeB~gPXKWNxb+SYKg<{=W|CB@{Z_Oc;`VNsd~1B8nqL&WY$@BJCO^`=pC<4`K>3^9-fRIAd90jMr6 zW2F}1?B0i<89C6$j$9t{Qx2)3IkAnP0v587Yz7#Xmb2tSu(Sj~ls#Br=}$mJk_}5E zg@C0`8H8U87Q%n%SMOoziK$w5VQHwaORzMu27$?3z|w;tc3orRaP8+Re`{q(h*AuSo%D8se7Z>Ij^k!!Ii958i+x=?`AVl97X)>F9KI zRN-syAZp>(sK;It^;qw-dfy8li#wKh(>d1t1LSKpJG@^$i25^*mg=GAtAREFQGb#u zup)Y*sJn>XKRNhCrDTZuA04E66kZgf{$~`022q=$VMI@fCV}Xw5-XyoO1p&UVSRe+ zl0IrElkN{5a!3zJ8KQoWNRJVS`iynirkmbtJb62nDzGZFDC(|4uXFILDfC(gsjkq$ zkDyZuK-BA@?sUcNuja6!U}zoLwX5?&NdTeg|otsG6~;cOB`>l)4sxULE_u;Qp{EL7?xQ2nBqwq!>%3{<}$_7eDmu~`7s zvudV_$-+Z(CD{HT-r#KQXVCh2ITVYv9~G<1+FuZ~eqRn|*$Uo`yg4We~9TwJ# z!>ZVh9wk{X8p@7|uunPqneQMipoa%^7MLp| zH-+Pn|IDG1Y&EEog{&sqX)}y1XVP+ppZyYNS4+S|S%?M3rjRr9lnJB|F!r^m3j|}+ zuin7en;|rT5g5_{r$+tUAoc~RdBA#g9YEN)1|P8n>57AJ;S#LIxBP9_!XZq4Pmi1! z2Epq(Yeqxa`PB#1yW#$B=2z?TNMu(ZXO;>O+kB`Dnjq}D;<7k|9kno+z-nR;_Q}c4 z64?_PQXJhO6eU^4EJ-$l|1ul#VcU0RaR;LKe7YA+ z_J^AmVSAe{&IxQk34*c1_7O`7*!~uHse7@={@~xZg>rB<-Lr1yDiB}YQ)f(a|7Ik2WA6WwYhimK$jIB~9e{rhY&bZG z|7;ZhY&tkJgqN@#s2@BCBV2o`38$>d(@g}qpjKpk5Y@28za#K|8d5==~zd@#~YL zQd|Lf?$igQ5E-(i!dTX=!@;kGz7qFUVoJWe`;Ky(@peagcyuC4YK$EkD_;=foVi`q zF(wZ~C00yXl@2N?; ze)6IYoK~w&(%?wn8$2f}mTSae37>HW;W zLX#@6nu92EnnQGO0n}nUrQoFw9yPUpk%LrM`(O_c7g$Pp8IY%ogVmnf{D8h!XThK+ zEgXNE9q?8M$6DiZ$}{(kfcNMB^!pv8oyhfr`Q-`N8%N${v5S3{)EGNPW)*pk#=oU#ee2tl4Fk_1F19i zFrwD;Z^mVnoB5bMh%2Ojl=s@Gi=zk3T@j1duDvWQc z1wEe@nq2%v`gdNg=JW^gPi3nMFYzn`{^Hm~JPKrW!Jg={x)faE*^+}h}4WBnU80)|RAg+y$94ZJKGv51Wf%LXqtej=~r)H>L&|;x;)%$92xdUD}Jp}A3oG* zPS7zGxpC`DQ*rCe`a1!#GbYat;0fN%tH+#zh&`Br#qUV2UPzG?MQ%pr%(kDvoG)lV*XAP zIZ=%$dSd`4dZ09sLdc68yrS|ks5jss)uYX#px!#DJ6$Y{ZYZ%NX5p&DIty2&7v}5o zNaSJOH^89rD|+M*RQj}=ZB}drA6D6VJE)Vm;|O|pA?lXd9Z!u_lI<17;0u;7XUDW0 ziLZ3@CU!hW;%I9*Bk`XRY1by~?Ho+9L%0JaSI8mkym8RmJLugA;|)%AZ`=ioLwmg= zbVtu#yx<21BRQrd#*@BMy$qwuHhO~;P*ujW1auvLY>bu{d;0B zfnHrrGyTrmqtr6o-UZ?G2XQuY>FC`#x}S9vyT8jiS`a08e-36@SKotuR!CQu%QYu) zWR%7KL%(Z8s{6>vf6~F4?zw{}0dam@TK}lif&Zz44;AV32OrNt%3Y7rEY$Vcy2n6) z418pKmi-a~1v2PWoofLKNWPi*Y)B!1f}i3q%4efry#WfgwI(3w8LnfPU7H0$pdE!7 zJK@q^aR<=AolR2`RgK{<|aDsX_9O)(|U>8IJ3|N&$Cs2U2V$*$`quNk> z`X9AF@UWULPya84I`dEeH(99l`Gr3HU!Ri}r~kG_&dK!uJYdp3{f}5mO#hd|OWm9P zBMacSY5FgEnC6Wu;BnsRzu)onpU$J&i*C%@O>WGC6rz-Q{~UcynNUzgQNSskED<@N zm|jFzLp2Ppo+F<@Jywo-toJkhXTitfb{iJ~2TRk)*J}12X*2zoYb)Lm@_=_6aHc$5 zmLCu(!#Uc~fv=W?hfF-+HIByWanmT!Ch&lJsRAn=Ac`#UVDNw@5^Xidfd{IJxI#MC#P^8sMm-;~u5<5cE}r6a4VeHcW;D%&S&|49Y5A7&q0XX>Sa8 zEw$)OKM9h-pVf9>x!M}V4Mum2Lh2YX`*G}V)~j)L*c)aOlgktHAkz{ zwSiNCzDgBXjYJeVjl`J>^iv0qnsNMxgH$(;;6WhHKjjMk3&_*O!N#G)kuVNbVl@s` zdRtGGM0I}H+wXu8Vv7nJ;UkXzOBi9cX>*p$OLEl8vsxVXa?WZG6KU5#x2rbi9-FTK zQs>xgpzv1%`n4y9yX_(1jR7}n6o*J0rPCbsc8Rq4SPsoegtbK#M{wKm#s_1F+dkyK z=xL)j$6f-Lb15TkMOzYC!6gD^D_Q}lcbuJUMdUGNSB^erMZ`YtvLY5l)}EDvSs5&1 z-?)NBLm@TL3_~N!Ad!Wvz0$#22W~zMS+j;hFLUsL?uxtqB{@i?4RoY$)>cwSho!LGll?&h=tVq=1K?(MNm&O`(IIdL4WT+3Ox}IMe zt?(y!R}!s`V|DZ;svqQnJC>#)cte@dmb^Z}mt@Q21_XfxV?OLXhsH>8-1r;MT(|MA z+u_S_HAW5UgNmWD>Y?eakr<_XsgdeUT5JJfX?jq%sr4l431(d`4^R8O%Ocr-rS zi($O`1UV&hb#=LN404WB;^&QIZOF2R~|I_2+iZZKZ{l@lTJ9QD~z6{@OGl&P52YZ*;01lQC-B>!i(Ke zZM^@Nh*2P_DU?rAcskw~FeFEdY5a(4A%0fPf}j43At-#aKq(h2`Jw=zX5F&jU>^LS@1T0D5@lcKNLsV(K}5aRtHl6Ho(5& z6XC5t$>Hk%7-)briut*M#KL$S{pS^RxsIeJb%_@T1bmjcqW&N-EmqWD0s<2&>h!BO zE9#qDM@o~!qmA0sM7@^u%WEQX?lYaes)udfwu2HHe^!6;P#fJbt_fhe+|;%0uqLf~DhEd5q`MuX2Y z9UP*BIji8PgH%VE z&K3R@LALtbr>ReT1b(lBn{1rQflcYhej z(}lt6qlA%AA5~&iA5of1ecs_{V{!F46-T6KdsHjU4RQFwj>s0v{D*_LbosF|{g5iK zDw8O3DpPDl5316hn0{%+rOC)E?)lj-Epw1|qFBd6-RXj1#ZrPvD3&U*DwZhCrC8r{ zbY4QSvTOY<7{DOHxHU(uyx@q#YtFUiYl*aLNe?$Vx)+-+V*ns^#sK=VR=8OnCNHtVwr79WF@Ts1ni2n$JPWMOIDB>5@yW!Kix(5g0RP2_-wwvWj zK+=tCPfV--mSV0rco;P-_&2PkXW-%dEy*}39i;m?@Dhg_^{7fmsD4VcvjqW&US<2lB8a?7degvhSAGn!n5aM9RVKT}O^e{6O%vw? zJp2ymj~zUWSW1A0e+Msh4?IK`P`)37K}DD$L0EqekMn|uJNG8QLktBwLJqrHr}9}r z6Z8!_d=mdD79Xzeo%bbyPcTb!f*IapF}s+-$@#+F$d^(L@QmF$#<&yA@XZeX^-!?} zvn}U{QVA6J#sFJ8GaBOuVIF!FnDgRgprt=s}k$n zT$HA}SS{Xut)q=d%9!D+iL|Gf;lDVzNtYPIJYS;r{f=MWrDzPe-D9xo<1CBN(6f3({ z#tiSxp{|TB;P9FgU3du z2f&!&pXMOtzDDsCvg&NLV9ZcX5ei|3aw4aC(}EdNRFH`oB89*Ve+Eqxm?8bj#0;ZW zg~wMK6Aic!XnGx-WVvaE=C3&8hzjtCODwn$_FxYjoB_dC`e3SdC7u#TW*OXsKhN5E ziZ0pkY*jHNuB@X2W7dhofY#yZnc9~WC8f9!r6(xqoXzB?&q{oHf&S@%;E6yGK0X2~ z*C}4GE!fhjnm^-L&V?pnTQySitRZo{r=MV^Um4CZ)DL}jIX z09*TOV~r-P$4s<_2qzqCmL@B=;d#k;L?rF{5)84QwZAaW@AS*TK9!lk^QZU)8jws1g5|B0SeELtOH z!Y2sQbxz94lHKi@5^Cma3}(Xvc3^gT(r;2PXrCLJoZeb)jJ9C!Uww?$(rn$Py#Vm+ z&iUD0f7B7SQshTKJS?wA*j*AL`p)E=|JjM(8;-Fk$Alw(PyrxwA+)dt?Sp1fzeHk48dW(>kq-6MXG6i^v-bf z&XuPWNHoQ4*Z#{41bP~3Y~ z>qXn)UN&p%qm0rp=*4&KGRM6Bol3ogLI%&!a>~M8UF{Z&v1om*$w0FvodDR~2kd4o z9Kb%|(u6+~rqn<<@Km!p6K*Q5SU6k;`$fhAiVgh5@^A@kBOdeH+Ro(g5^pwID}*CG zZ+Kc{Te$%gyLjC|@qTG^I;S)Qt8s+FZ ztHz>`QG630jj&C;<>V>QG;rZ~Rh{&g;LAR=Ks#O+vT3>DD$`njq#6*s0PE@x?#6#| zPgOn$pVB@KfgrQAjI?Cgp+=(}-@*O?P^Fzy6L7NhzM(k4NzF_3-#+*tT9NwMwwvx-w0KdrF6^^)hS&_pToD1~zTjv|)|6-P;YR z!n^!ZYpO}x#x<`3&Mgo&s#|YBU?(HSl9kW3*E8 zZUj#KGGvkA!EQDGD6rc;(rAv04+BTlrnepM&Xk|O<%Sz>81i=9wCm;@ui5YIzGmOk zZ`!kSueWK##>yL8=Ggj;8-_M+*sx{&dAeM)V zYo!strW^kJjnc!xFQrff3EcI)zETUL+)&3X$T5XOIwTb%p z*PU|A*fIt+Zx<$HgffGLjU0l&VlSB{{~lr@{S3!wfPNoRMzMw9zR0+@9NH}0M)X#= zuv*@z#S7q))Vv?dE}1EXOJR~k@ls_Me*YEdWz@)pmCC_1T-NkkjoM)f8en97B~Z#a zWB4{0c6OE~s`b`5QH@-QX$MW@w>d9|_L7QW?|LuTec^i60iLmR&Y z{Hs0V8MJZR+hRJXXlCaERJpIB`1=E)a;B~8cps>2TGu5OG?O;jJgo1DSJFCcGlOR4tIJK+Q-@8-r- zty;(M^@tB7;36Kr8+-&jXx`~hOt;{#Bi`OSrw;kGLGM}!W*_>!2Aub%P=i4V^#-k> z+U6}A*9Q%_<-1fHnrzhlp$(fhUA%e2mcBj_ti`N;vx?F-$E^O;t+}m!C6OA&5q{{G zR8Abyt_ojz3^gF?nq!MCFqd;h-@C($G{uw(Xbmq?UsB$TW)v4WbTtYu)*)pSUI^|Z z=i#1KuYfiSeUIM45Mr|)Uf>@LJ*4^?tVsHrS;Nlvhg(Lf_2J3Mi9_iJCq!87gbE_o z%!F%P@bi$YI+pt^xt7a9m5<2qM-1%)m5<8@;YqTNf%2d?I_OO%)1J?9@*!0u2c@YO z2cfsqaf|FU=x3;R!!qGdb-#U7yS!n21WIY(d116&6LRceg=JE7viZ>ivz0Z)sSQUT zsC=XHO8H{5!v7G3o+-vL=wOa>y$4!)aJ74X=fnO$rcSFtb05Uxv z`q>t08~Z;1_U|NGJs)~hNK503pTJ=XqcOf-XZcb(U$3@M=jUrO!52RPz9wUQeZcai zbiUqWq0Z(j@?3F$CshNJlx?ap>m=nh!bbd-(}HHg#TfmSXM#F3j665Y%w|NmwoV|< z5o;}z% z=yCqh$sunN588x8Ww7nW1TM&c=a%-JL80s^_&R>FKlp$AW$A;nXJ;aepEEy3viWKx zo8F&E@Duo0+{5TLfI~iKn*2NRwVJ(MSI;CUXKpGFGaTe8Pt0at&s_T_;6IUGd?Hm~ zP3aRw&XhjKkpu${4pG3IUVMRrR8O)gnqIsX>P{C1r|~IaB+~d)i8YN+lt#WJ?i{g= zWJ&$OagH`3DbIqJ6R81rFalzvGp!hPXixeucAJBlbb)a~=zglesy(8pQ|-CK!J(!- zbqA@gJ;630E|6W^0`hcWu=XfnB(z7BShYu#=F*<0IoeoUdyb#<1`gF4rSua~u3iRi zk55F2#~W{R@RKexR-iXi1y%(TMNS1;Ox(+nkpEJn9!>H-?BG&UqCawwcA`Wdfx6QL z!b+qBl29U5VpSqhnoEh^=xAd?iLz_uOyVUuYUP|m97y%_G@HBR{L&v1Y1cA|f0~0y z9_{}FNS&j-?udnNPR;s4sqz@G^os038%bGI$VHb$RWKFz+#JlZ$Q}=*g+vx=#%nQj za0^y@-r``X10$6mblT}=2M=0zZ^%K)-Hj3~)ZN*p$=SFvpq3$j_Dj5jU(Qfehg!06 zX%x-O#zhK|jT=DIL^dw{N@U{_HndvI%4V5Ld4G7_sJNzn7-vgWTp1gN6|Rv|#)keB zjO%hIwv_g%4lu9Br`0)d{dJJaGN%aG^P#_Hi-0{xy&FP$4J^;}2mwnISK^$2GtwzV zWRp%{h7!N6T2>q}i=+~~6039Z+?ALYoc& zz0E?cPk$w>gOJ&qiB^Zqw#PY}&rblF+8*QULCcrY`TD4ZI-9R_rzYEg(3GtY<`kNm zfritf4$N zI*gNGD=5K)gYDjcJfNxNxA`<=800&wMd^R=7_#0U{D4~^$Gcn**DuOZk<7mv$-MWF z*OF~X524!@hAte@Ed){s09ZS~uh_QK>p^gM-u2gc15sim9dNhZ+Y0&NErKtxC^fh9P82AcR-X0l~*c)l#dCe@pk`7((%bH0oeV!r$+{#vjQ{zJcdGhe>E?2k2lf4E&bS~v^j zBIgV_s=R|a@##@5Gz-2N_8S~2kIv~__`jep(p^?|Hpq|FyWyc;&V?f}C1$^UdYPE- z>W`FXr`V%~crYdtf5kC$ZW}k>htR*JmKLlg${zM#Ef$@(f;=*odQurhG z9C>m?euF3CaPKKz!+LZQ_65+kVZL@0Pq-_O!7j2xaFHnPF|NY>mq4j-QVbkR9%Kxc zVWY9wH~_cL19ayd*mQ81t#ws+Ywx}j8BtHa0=oA+khkv6Vb{UQa%=#v(!Kx zdyi4PBPLSY5+R4s{|EV6%>zz1({fI+KpDai3P2YRIU1tR&+h`-1a$Fns=x|eh@$R7 z7hiPni~7gV#os$f^;tzx=;F&L3hgvNQ#3rMP@+jd7plYxU5L^gHr5|})X_#1$Gsx8 z@Bt!qrt4gAb5p>e1dZK{t5YM@8t&%OC-qi6%ZfRUyDAwD>Em>|FgX)r8C77_Gg0K! zvuI`twb%|`KgYqNrdww_NOj!`z5|laKVuEfgSykj!Mdfyk84$A-CfCdIdz7kRmiyA0>r7;?q6q_&0%11OCyi2tQRv+ks44Qe zgR~Pxo`ky7#lecK#F0>BRbo|SRXS9#%ay3YqHluI<5vqS{T_#m6H1?LN}SO2!5pri zVJHkia>7u*`~TT{6EM4~B5^zm1Za>&Oa@F~o(xMTc6S!`NPv)pBoaa-5l{l*b@xlU zZ@OPM{az;oj3O>5K4p}48I*Bj+;?zyU|et+P;tX$#tjj1bX?}=IwJn7&Z)E9d+XkF z&b#luwEoBM!@Rz??m2a;>QvRKQ+1BC5|d2Q_qEou#sPF~of1R+w4lr1t4pZS=FpAE8$^#1`6=Kn1QUyk5zw3c4k?cF6Bva`_NH|8#fKp-; zn*PUuQUN8QI&|uZUpR2UNkr`anFlDb8z~sX?)<4q0a~>b2@24v<+PpyOn_E+61f3d zgpdK+6V8QSB0wvCwI@J(`e0?GUfwoX4-U_|i}X~}1%k83s5WG177mB$DV2Ll{hbfd zo(KR-mfTz}i%LKj7>RDkwC^G1ee&4akQzffZuo_=IT8EwF362Vzm?Lu%i z&Ox~fu_K{@$w&jxk$;eO8v|{xVO0DOgS3|mSPQk8o202U;bZj#A~u^$a7mcW>zJOh zW1zbkXn(wRrSPIBfY+`BUbiqkW#e@-1MQDjIfWNJ0ldlyyxz(5l#SP08EAjJ`crt( z6TquKf!C**p0e?}n}PPm%Pd=rgqde-r2Rjb{<5+ACIg+1UAC=MGX*2<+`R8-r2Qfo z7$0djsHGz9r@}|AkF+By;K+zbyHtWAw0Sx_M&scVX}?&dyukr%aQYd>&E@u52{utn zy}ex~q`rv)Jw0%E?_j00tF&VbL+|1&&rO$?ufXGYHeKEiKDN~Qn{%6f@cLVtXp0=u-Ri*E60BD{KyAT_wgYfLnE7=8o=qMSEKQ!2 zVCgrkV9B>$g7s|27{dkYNxMYFaS6^cg~+c63kw4`JP65#aDHh;rb{HHEZgniK{UWhnSAAxGDj8rgANQWnfks1w1`YS1Z`&%gJg;>a02g|ZaPBZ!((EvN6<*TAI`Z5QGmcT#V z0a`@AB4TT10x@1Z`&%gQQ0jBPBih4J$qJt(WvX-!aB;>FM;g^Vb|B$fh!h z+?PcItjNh%PLY#4%@hmZww)hw;Ajcmj~$>zgzmqf?QC+8&}nj{gigO-8?oH zHS|j`xY%p3!M)Ex;vZ0h-?`oue^dfVQmAsc)6rd844olL6Ej zG-+h_p)hjli6I9LIEiT6 zx#9sz>_!R(u{(bf(YEtJFp2!N1l0wp*s`7j%(kGT2`;vLzoQPDY|t(< zzKLx+!vk$iMMUj$8q@G?XEVy^v)D{0ww=EV9VKJ>*&A=)W}xjI0JWKh+s@w*u+45e z|CZ?~8?Rq8(EfOZY&$=0bqKu3eP^ubDh-TP%~1gH?PuS8=PkOZE3fUu{glPO>T(XW_llG%*TWU$x;SNw+vZ5crPy%+I=R(`r{l(6=*w~7&N6dF;W7k->?EG-+GD9osKa!AwKzg&(TXQ@k!>C&pJQf zh-Xub#OEH4A58Euz892S2hyhI2RsHt6vx4|nBswZ z*kAKNmMDKe5+jK6`)zr~rZs}(jIsjNCf{rFi%`xZ4ulG58tTufYaVi-fD@5;{X-8> z;x&>lh}Zekn>Mgjd^4zST1BZfH<=A=5unK3z(xqUfjxgM{1SE3;#Ye%u+JET14))) zDOT_f_9FMWma6RnTiAyf1Xp)-$NCf?zin;eS}MO#dWrtFQEBU0mWIHIE$f3c*_3j& zp2T&-DvfOTrnMPpbRIU7*tDLxTJ~<;YD!PcCY^$TmHX<%cKS1*N&oHXr!&yDF;Sat zxIO(ePgvZb+L=flAR`m5ExVq|Z*G*URt=CC){g(Q_bbAoBC0n`jJ5=f~%} zElAo@E$H0r7G%EBkh%RQ3%(vYOHRHd708Eq9pbfsG(gt2Tg-c$SY7HyZiPr`k!APvLSdSyptqHDN1Sh)tv_Too$ z;$PSRyz8%wj8%8QX`f?4aN?y_(Pl)PqGL(rbEjkcw!`y6fKBbzy-zg2t~KQ=#(j&I ziSB0$y!=Cpt#Io;@4yiym3HXvae&$$VCW9rzvJ+&9lAIl&H|$zdzun#; zIng9;bgDqTaU~D&@er6%m(Ya z6g*-erU;a+a6lP`I{<-CPe2sIax>;fM|hBc_=F^AF`ok0uFE`-CB45Gi4mmt&C!>P z^24<&)-Wf$z_kv9Y|RyY7lhY;2bj5(fz@5B94KgyV19J22Pp9x$rr@ye5F8ZSLGxq zKPLYpO1@SOoaQF8b`_+}UArQLT)SQiLz8P)@vA*+*K;wiYnz?d6}*OB=3K+3`Y5oL zJtozdysxnHr-0o8{K-~h`HS0|^|y`Db}nvH#7gC5AHje~Z;pARwY>10-lPW+zUVas zh~9|JBo@70;(kKGQ|WVBSvobxB(|79gQ2@4g=%k z1_rfP&X@r?DDb~j0O2P1y7d7B#0jjW2q4JGBUZKzc#QhUCxCF)np8TkI4Mf#B6%Z& zSTs&lYIwUtmkKV7!NrPw$|Wq;h}I?1{PlqN{OD->C(JEe(0T1CNIj2G?MUu!A9R+~ zU-sNW$BCTDCnH`9NTX&25pK2;ra+H_vhQ|gS+?YQg7uDp*vsF!0Go;itPu^c(E$01 zafsv506S5af)@58EtA5&UFpCV2~UxM%N?M$e;pbbxC%*OMF!%eQM=HJG^xmdCK?+V zkZ-eXfP{Brj>rJTz*h(m5$B`U(eD*_5MnhLfxux+GTdhjGDrs{?0Cux@vN+JY6;0N8)w7QG$7v&ln(rOA^LEd7QREcw<; zuwLRAW4K^-I;ZA4juB*2nS}5G(EzL2A=wvx}Q5hiwNDXq3vvPkkDyz zq=ZhtVTDe=4b6QlWPnq>g$bZG2d5$VwQ+#|5c0}CP*V$ot!1qKwa^gxz;z{^8PrjAcQ!^Nl>P)h7Q z0yPIp1(by9(5WZJ95~=4qB#GE2Pm-{DHz1={HaMX#d$D^{Ivwd6xEte&jBW;D6D>N zOc5buOz|x+G#OJAzuFU1JYzdt1PFKYlt+SNiSB$D-FAT(;vu?`?rKN#VeSU<+cFT} zAI}dNeq4Xs-W_vIn^dUqAnTP35n4~Q1rpoEWYCB#tn zq!o74`IIxO38(%?&}MQuogLWvJ_BtpzE!Xa6Jq|;6BYwo+#ov|*t#E(9xPj)m?-b*9U0zPDUJ0<5S$eGFLIY4cGR@d6cBt*YJsyI=-RHw;Q*V8 zMJ*Q%uq!tCigBglu_#9==EV*Sk)RZPy2t@)yJDfyr!6>mEBX{C2Cdg<#2>XHi;evS zx71{#zw|T`R$`|EZ)=F2G(93E9SQ;FO&pnL`6IK(0cz7b+5x};L78U*xF@|q_)_a* zEtc&1Sia47SIJ*K-7!Yw4f)GBpFREMe-NN8TS>m(14&Xk?*Y&trQ^SnyxRi{A{AoM zVp0XJB){WWnZuF2KE_|=}(|7x>`ylr%B zaB`%KxAX=_0#0*g6|wykhzgv{HfUuRtvE|()5|M|&+cgOUm;X0q98jCss#fCHoX_NXt)jKMr7i^d%aHvWf9%Xfk*p)+Gz} zjrtN~zt@nx{gp#Mg3j{v20AR{rvCu(T0p8SE1c3y($BH$)|R^0?HR93h>S21*S>CWa5S4El+v+I zG{6e0eB~6@$EnNc#0ZsjPCV7D5+Zw*18bx-iR={)P+Mf9ZU7DlV)X-fHi1ZFHGxth ztKYC9tKTlj<+mnGBOeY75w;jC)N35{3m5A1!7D3u;~E1(r{YkM@`#Y@-Id`TLv>Tj z+g-V4Io^f`HJ?4D(Q*WPt@S-UCAlb}HAyTK-Y^N^?B&6)af~IKmn7^{q5)ResjLfI<7#8qW(KBi3>Km{Vno&j|Nbu&uecXQoQ-ty;I#o zCAb9pIQZ{)`0qscultHAq$)Wpi#)I(QXv*CCRJcGe~kySM6&ZjNv5)}0`tP2=|HJ~ zl28~q_3tGP94vNk_5dYzBL#!loj)}xGOU)BY89OS5#@!c5P+ToOk}tlG)W^jGK>&1 zGW=2)nv4vKU+swupR#juv^)_U2|m#obrkIui2Tl#h+)m)U}dCUUa_Qa$sqC2sfM6CAE zIM0NE={6Bm6OS;dVYwlSd!dhH*d;qT>~jpXZB906Zf5h~ijp_FVboBE%)>+vzSEvr)SG@6GbB+H2J}%qCLoNC4YWu7>dCQsr8f0q?EF!?^K8ADW`i3RL(!b< zbu<+HVIUA6iZ-aFLeaOuN39PFJY!0eYff{{O zQMp!vcw^}Tv=k@R%!X6x@}*a7qj@r~aZYGc4_^OIc4J1|8!JMaieWVcVzTt2xbBW# za<(Bmwd12~u({KDojdBVG&0ptLe#-jXueamPSx@3$9VQ(^KE z(EuBak*}P=7&|8J43odWfiDUx#T;*NfZA(=&@lOpND3=aF-{r^%xKc20tosI8$gh6 zv&{yMk=BpHjxi!A1-rKkP-n3FOyLbcWN>7x3;{nC*b$*YIJpIM=Z;EspaK!WvGH-- zsfRO#>y-(0sBk>!-SF@&KI#}kHkm0L_(9PCD}nNrQv#{vh=h+VK>CscOH1zVb%5G( z7u^cL0kQ7;0X&-^BzH7w|A64b4vekAc~aEG|A?sOLEJ6-y?@ugaex+~>mU8;$^`_q z2(t58zdhD5fi!6vE=aJ&*s8sX^U@a|aL_QNFY;TZfZu>es}$BiYv^(v=w|PW1Zd0B zcUNqRdp|iQdpUqQO?GnuzY9FDAW|U~Ehbf9z;C+;vgG9qgp#ab&dOx;GfXn>_{{4Z z2o+EP)Spwi?slMnGl-0H-2;?(jpPgBb^g4hXrFS4gQ9(Em8B;D6YUdLD>vGQ5Hi~L z9vGU8_K9EZiT0f_Qm(<8yRUQw~Ad7`bVSOhS$DAG#uvX}A<^~lOgI{r4{H{p58Ht=xEgLTfjm-YG+VOMeXi_k6ItKLsY=}iKv~N9wILDZ}8YRYPTvNKDGfg ze(z?`_$F@Gclk1k+kr5&q%AE)(K#SN!0;9L&$Oz8?Ck@4cG-*=tNBxG4SLnBgr`W4!Q6v2h<^+CeYsYLOYA=%i0OvM+(Qvk5{PgGTMIF-9C1TZ8kYsJF)0>)-XY4$vZWeU3j}IgO!3kkuISt(PNk zhGUG#iP((IZ4enbVVh6wgcVHCmsmGBs+uTP5ZWtS!@O}E1tUSi(j zEm3`?wZpq0wv6`!!nvAwkLW}h%B@R~w2L={cEP{HaQ0@cv}2-Dh72@-ELTfQ`cBnX z#SX6_m_`Wm% zdw$$jOLV0ryGa{*@|OHrnawT=H7ynmu(B&(6_wq!4h${XUF`t1Wj8tufCB>4>j6BQ z7$m!z7%AD+Z&=yYZ%<%kmtnFIW6uvjqOp%)iSBVwG$qmb^Mryg-}aa%w3{WBYm)Ck=L8L+~T1=|I;LC?RkR_7c5lS*uc%2<}VKema zb)Zy0B`A!XTH$jJ94rg+84plmH&QT&-T70K;w;J~R4!}&M^p-~{ChnIm^jN}fNp-A z1tDadl-x&jJa_1?`X`W8wSS5TnuWdn9HH?QR`zahzeK` z5p$8kDAx9e!(-o=%LYfxl!HaS48YZO)^|Q5B8PlaGg^B*w}~+EY54RwoyHI zqZ@Hx^P?Bg_>`fFD=AyZw0T|7F_k1ZaD(AM`=cwjLT5>Rkt71SUav;H7Leka{?U~W zI!0!@U(W^DRCMM2q5*b=Dqj^{p?<-EAqpTxS3c_iwcXLs=*qn~cq_URCkCxhH8D~v zRQ-lsq3XAJaT7gBUC|Y6EqE>Z9tZ7^%oJUDrvTme(Un=3BDbmeUxts{^3|3W54_s)ScLP(U%F6qZ{& zK#A8#z93%b&r1reD1T6Stoa{NXhpg4dIB(^mG8mS%nz*~gbb~G5r!s1E80a5;C-E%7gmb_7>D?+N5GD2U)LV zaK(BO*A2hb$c9H(%s`{xU^DUPia9df)XBotBw};cN*kdkkT$Y1kv7tGY1%2v4q?n; zpzVc~3QJ&UWws|QhA_B6b~J?Xdmt?z!Z4_%LKxqLk6ItXKvY0eix7t7xL81cADQo& zHgbUqL7>)wV~5I6xp*BO>4xW&qu9zr`G>26m239)Kw%=ht5x)Q=3P+4h+i&4P!+*w zX`6a_L@Pi_KmeYC|4gfzQEPU~+gr~d6gzTw+9t>3u{^>H5w8WL9I_%9wnx}l#9r25 zij=0`y=lbq>2M?+lM(FU{mplvS@%^~aG{dO+TXibqu1^~=bY{QOokJvF130kD5ahB^6&ot(dLpn$SY)8c0ypu}q=Ul6bJ=OwMc zlsyYtfhjLUPXJ~GCPhXyRoY62oS+8s>*Wu`ee)v@j zUX8hLz-0R=urfQEZ4i#o>F6@;Y~Yct^+T3v%k{VIy(o?)RBFjKHv>#A-!k56!zf&Z z3-y4)mvV*}(FSZLzLYa(qno9iJ{Q8Q<@6&s&H--Gieonb%_cX#vR8B480f6k+~uCI zxSHd}+0oV9MSv{7nlq@SR&%GqN3CDYAu6Et#cED6U99F#hsVCFx&D=dgSC>pbE*fg znW|KG)Q3vt>R_pVSgh>^#wy#l4-X7i;I?n0;NUEpE=-pcnJ41Lz!H`ZNKJ^R*j&A? zv>i?es!Zr|fKGI!B{q5$5@CKcMYK?cD6U^@fz;-+&DTz5s0q0ae7Rv=dMDfMkale< zPn1V1Ky{g(3`-sRk*1E&HgAW%lJhx95c09!f@m!u#p1OLY{}u+cR4V$*E9bJu&D*# z$3z3{f=|A3F8G2D$NriFUlcJ~>fP@Ewf)J^rQSD?6xLENP8wS3Y0{*Ydio8!)RS-X zoj~$JKkOJIl7qZZ6fjRO^vxcvk{7CGlbZbbSkpd$(w%CY+MR~DO7=;&m5kMd^gUP&3~>~?0FwWsj(KF7#xck49(oAN|IFB)JyQTfX0i8=~T zf8fB-@;$%n04?HsK8S<2e9t&B$oJI5Nco=n4eNXAw=GGH#MW&8AX{@I?Z~IS$3Z&; zr9`q%2~elNw!QH5=x657%4~K~YUEte04uxlRZ-dPbzo@8?ji@MExXZgfdl?=_-HY- zolOjqT}_OX?CLkH?CQ6knU31hG(0~5V}yMKOZ4L%6irEV{yd@7$h$q}39bFm6nL)v z-YKV4s|rtR9z!R4HIAfp*(-Y^+<#;4xgJ;$sSt}6lPYjy?sXo>63PB4lw?h%M!w&H zPyvO&q(-vl?A;C&Ec5XW4^ZMYk}rtY`SX%OBFZ0B{%Zb56cSOcxSjw^NJQw`+>JSe zkRg$WU}!QmQv7O9NMw0^_t>^tMdT<2$3=Rag{M~Q1%e}oS+R3L1>0K0moE7k zl7H0SHg>eL_|zg#YQyc|*h?8Hi9ORMRD`<^xjcWk4-fsMVlP(Ia{b$2Sj}*wgRz-J zxI}N8HNUxb95kC;I%S6?j%A=Z+%Z-1iJq_+mf*(O(Xhm!fGi)DFsP-%62AdqY<*Y) zQ31^@HrFK6MOfl@B>6rkYz?gvM`OWdv~YG-b#QF7w7WdAv$U+2)KY0!?wsu&hAp=` zfYfB}q}-0H!Tsdh;TS;JiQ9vvpk3-Qq|oWDH#|kDrsEl%>rl_F=;=Tw5S`8VPiT^5 z2X%*F-X=n_BWadHC_w#jt!htFbPLE;svuqqNVjA~Lu~J|(USeOy$%fR#n43nn~G}e z5e={_L;1?NG7Q>ZdxZmEq$usL-RuCh-NMlQwJ9Wp72}ALhSrIiG^ur>e#5R4<=cF> zjA9YH9AiXskTZz_=IKoK3s84t@BqpKSFu$6w^j|p?0((@Nty-s0BG1On7LN;9yPkG zOSt!6v}o<3MG7qdi6it~?8RGAy#!hY?e^`UkBC1HcnqERgF|mIe*zbFGq!qbt3M+# zg0$5k($Mv-ohXkFNtWH2itU0LiNnSDHrIimJ)@(y0*d~TpG3dTxqXBK6`Xh^1BZHm zk_;eOgJd9INzj5%nY^F{pK@|Emzf2hAaU-34|!(G>Vri8DDmedB=76UjDa-zGhm;y!&b;0yK>P$7~qo?A}V=kCwQ@T4k4W2fv z&G25fXNg2*s-i9cv_IjGf`&`I0PhOm97k#B5UGQPo(#y8Ci{mDUijRpy;I#o`_o3p z(BbMqquW}Hj+Dno$^*mTDsJ62Ffxe=R+w!?y-+;@|G^CA13kq;I+-7B1Acnz(4oVC z16%bMV#M7r;@7AFIznZQ10eNm0`5PXx&r8Pd!t(p_9^B9wOXGci#5op#z_*imnEPL zIW%WueX5M&^6D6}jmz29E!$n+_@3p1V*|MV1{?t=-Q6qf(dD!L{B?WA;l_7Vo25+? zWBuj+;Srbur7dQ$aA6rb9H`WKN*lCn^oRt$Ra!ojF>Fot41T!e(z zD+FS%{-W(PhPfWQiSM$xvu4b=v;xVBAXk$EbvudCTBp@YlXal>wSt>)?H-i6jg*eV z1y}E2WxN9QZIBTu3*RV#vUVI?D?2ni0Qr*>X1NAug^t0Dt;0f2Ec7a60Fd8FHG@q; z<=pTDoIu(K2QLp)Mn+VNrLn45>wSbf`D1-GO zIHB1Z6du?-iMJ0z86J!ugcS);hLd?GM$0f*c%#Lri<-g2PQBn1ve;MJGE}a!A0cEF z?xLH(!=vGhX#8-v3hD78KeSw`jYCLo0w`&;SC^hB*e)Q*z?jJdvW*F=Kq6|LLtV)K z81y{>i|BfJc%%lzfP__O9MU6)#s#Iw`q$-( z$b;A4_J2b1*DWMQ`@LLhznir#^U!|MGh7h83_p@rSt?za&{K^^F6b+*EAJ`ShK74e zoBK*Dch<)C^psZhl_1Y|XHRLJ`f;3PCalTM*aS1{j_c_Hi!xBD41$8g=^>`-vNItR zwQ|*krS(;CEvk6pJF2}^W7X{y$PEYm0}5$<6;uK!2Y6RHv2;OoZ1;%JVHcLGFXM**T-wi7cU+h8}1vM*s*x&lD?%&PdRn*$tRz(K#0gs+xt=<|Zg>V^|>N@c|zteK^*v(ao&H%m-mhNqhRgpW6RkdEulb!!-V3{m{2si ze7Uo6>W)f%+xT$imgI=`;;jYBLRiaaFDYZW(_#dvR_p(vkHwc=-9vY)2aSUW zwN3dYfGmc358_EqJdGfVdHu_T=)@!D`TLMR)8A&~Y4f;-uGBPXlX=>TCtSRbKPvC%-O$pK;c|}^KX74&4 z#z5OkJGEnqyN-u=!s4zYH^|oRIz{A z&POvfCt3^t2vjf(aTkQ%B*(?Z)gvTB9=nd0Er52^87*jp;Fg~X9KteC7xj4)aAExT zSQVWTkEom_O%YAiY*CJ0Q_)%cJ1 z)`^IKXWg>IPKDqIn$gQ*maHWFI}u=U2aB zcYgKTW9&$8n$?;z4F@31SZvdtv1W56a}hK@+Toxe5}Wp{P_kWKbRC2eP}wX%Teh$K zuO3L!^!pfqhE2aS$A@vsZRRZq#!9Rxi=Cz;ov-frhV-08i^hk|j`XbY;lzyjDkA8u zS7}T7ONozQE}l_(@cLVt!rI8x=}ixOew0MXOWN-BU?fQ!GP1?e7Pw3PzaGev)cqd$ z6QuMXSDhRk-_uteEKf|7_XwrPXi?yE0#GwgsLI2x%(qLn{)=7>NCxbaCpvbj!h;>S zpeCV>@&i3UNeqy(L1K_EhqO_yoCmc~&i{xqqm^H-8O>~z3mWHclp}=PC_fLVAUDdz zuhd4lSj{hxYmufS5T~OzzYRRF;24B`9}n-rsiq4=Ax>0nbgzS*5^+aqZN>TJky-_p z0dT71iY*h9m5zoeE(f+`E9m?X#ijb&j7u?iLI#H@6xC9B(g&J1GE89}#5G5xF%1t? zm{CT<*i1Z7(O8hjO6k1XF^qN6BeY@|^aNblbqV8nJ#?4!!?R-;&tjmnVi?y5SPQSm z#iWtYHb@>ouO;&H8v5@znP0=8kd5$^fR27X$1E5cGgqiC;8(oyw{VUB0X{Cc!ao zqjvY%y4H>(VT||>uJJrZQ2Q}P z?1!38jY0kqAHq+$UYcNo>^y`GZdeTMacZHXp}i3x5FgqzsHH-C7sE%b5A7i;Ao(J+ zHw?dF^LHkYXbb)X=iei=*ZA~CCQXBa1ZoFYBrH*u#^ybvknS)6X&o{VgOA9mJP#3( zGoQ!SIFqoSBKNd1!fHK`R-!-#Wx8kA2DO!;mq^3am_qEaTt0(Nj7f_9IKjY1Tm!51a*u0oPSSmC6oCsDYA{ ztKm9$Ohd!WAh{0;VOJq@wZ7xJR#qWpKx7oG8XLt^)?+Qb)Je<=3%Q9NKi3SE|V6`mU&;-`YJ{}?G%+&>o2%aq?YucBrRJ4l61WyrRKG^df_>P|FSn8E?I zq+L1Z(&Ms&lB-h-kVyw^>?q0K0cFz3oh7 z``pMY3ZzvXyh6}hWJrFe0Ck>Fvt6!hOWk;LRZnS*a-n1uF#=e-LXBa#vIv*5@_|Oro0g3Pdi4O zEnRfL&E28_b^^**&IxEP^@3Ko8@#^dz|op)-*AB1lP!8Z0Q(1UqwfHCHaTdr(U?DK zZ6-Em^^_D7@MoL>zj9z~4bGFE2Pz>`otg*nu4i85HcX_%9&=z>gs!szjn>okX#c@^ z()F;!lvU(SJ&#j)kv}3QIY5h0xyPT%Fbz|tLedD{u%<%4U0y(iX|gqnv;wLhu?bfF zpKwTcxa!{|;$}(BFBJWyLr_Qo%Rxx~fwY^+2}~Hm%9LLD2|T76myvRRWdvT1kAt%% z1TLZEJwkQ;4ynqXd!*v86b-N{UcM@>;&(f6w6tK|0a|2|?18qk$w4Zf#_X@+Z+2j8 z4bGFEMx&EsZ64@ZB+UC8{JXx*0a}EvZ}O)rr+l;kvdTxlJ*~-UrAgLkwgpf+h()l{ zx!ghXl+wxfV(I+2ArDnX+rhZt^xO{KB0yV~gYvM)&}mitBP6YBRUC`QeFRd#Y6HWN z5Kx0lwQ7|kO~OfsVY=WXx7VJau21{jd${iHc3&Jwokx$H4|gI zMf`7IY-Dn@ircSPYKl64V9d@Wr7}pN>V=ArTr2#>&P9thEm~CSUX(M)MJ*boM9C&^ zG@}d*f&I`#t_~a?0az)wD3vh1KYZnigHV3XECHIdI3iyTrkZ>kz4ah#Tkn`$QYf6} z=g=$RmmlSU?OOl?jSpbEZlOpbDlSbr~3!#z_hjE#MGo{oZl)<2A7A55rxRx-+{cH?m>r`yk&O~ z{Q_oa^h@|Zw4}m3F0e)W_h?zf{$aduCK;Zk@X&8OW&#=39|_QG%n%<+?v#c#AC&T} z)E-Gto|W1p)h3k5vwA%k_xwva5JKizy&Cu<^Q^?LX7a3FIdjI$8Tdaazc~*6gY!sK zL1FyFiaQ%;;=b!Pk;xmJa`j|qIhAR=K+@IOrj^{ZtDe%pP-S4}HXUB?d@|PifOoc+ z8W;QgWUP1TZyVc%6I89#uUTwJyx43VE^H=|lC>-^C5vaX(EQz2LX_GSF*#7`36rSx4L~C4J7woReT9Lx=a`)s^hE({ z;nkW%t^a0F$VT|50@^!K>rY(cdHF%qek?!8BT?(1eunBzWC+Rbvo(A!KME#l9mkC! zGg0eU0qxC*C0yfqjG*>ojBqAuoz3;q1RG@MO|sz$obIMcZKm*H2`J_7rJl!C)ur4B zGnuqGj7b%)@jNC``(aF~alJIbB-wcgliaYFO~+~Uj%L$+5|kF7O=nO`Wz)SCK5Bh7 z9ijpbsK}<1=1pWby&WD)Z#Y^%)2yw*n2NQ&z0_*F1owo|8mm*aQngkXLP-^=LeyJd zVJ`tpIHj*^CDhU40Z(u;+c?BaM5+&-`r9LZRWaj-f+diMFfcS$gVU1w_voCTX@!%X z;L%%QW`F?x3;q+D-PSorOGv<*2uH|^`5~O@7q7C6-yhW2yty(0#l|8DL?)4lb8O_% zVC7+zHKI5sj_9*c)eF0@HY62D6vfCiJIB2PlgBW1a? zbw_!0wA{U`r?Rk1*q|6u8yi~LfwFPM8=YaWHnEXbBRfC(st2#)9LD^_FL`{G!{KZz z70p_XmH}>`?H?YPiuQxQ;6#J{;h(5}icr+i5Afj4`H`BQu##7zm&`=HfBp7YhDdd+ zE{=p7-VW)LLXW~p0dW2sC78%863Q%5$*SU@b#b!y_@#K0^8@3SQQZ(?k5 zrp=Fjq17C>{La^?2Ux=1ksQt018${HyHPU$6eb|~O<*dx0|{qtc|&7dzk}owB#QF zrhu?5c7WQO-=RsGr$F1;B)UT)ph=Xi1>2+){wC08@?nfD@PoqV%2Wf6jvs0ruh#1E=Pb7qz{XeOx&H#m(`S3;7^ z>%TR3gs|^)P$gReDa&VvXn>uh@|AOvIukjsci?DE$h{6wdqPHQ0XQI$^9BIVCI?MO z8nb_v&ubhQTZ8kYr%CD|)|&@$-oM?y>$f;Si_rC*ZK3NY9T+vIlQibE4l~4vCy?({h@wjkb0Jb{)qPY+wc*+zU4_O{9Wi zzEI^F%oeiqgVavH3_Qp4{XJ4ay9V>!+-$8#Mq-X=fHe~GRdFNn_YNGb`M$sbT4b6% z3EIvk2N?+(v%irz-GQ++I8S;So8T}K>-@W3;{Yu}*Qff^l~Z3@09o~=-)1IqI;NG; zOwG0cN(ZqBRywmCG*2m=d|z4Qy3CJ`@K9xxv4ZPb&s5Mqi%?k0QbAwlF?3pdzXVC^ zT71VgD*i7OboiP!rh>i=84{cd`fd-7Q>xuN1ZdLYs8mq-HlAwN@>I}&^T3u8^E9^8 zJrz_Uny*9RF>p);eTxSrXzBfC0UBF+cQO_9htNGEm!7Gh4||N0Ewl z+N>bMnmq_~0>)&((Ch^zlLnF*mG}`Ixu>Q5Hc0?oC^G!H5>j< z{Awl@)SUr(25u^ejKbgq&_yOYk-z2)>O-jQ0{Ndybt9Pm3E2vjYv9am*ciui0#|Gt ztHR-X6k(KS@pbh4y~}_{wl^A*^0`HS+o-fN+dmGOCy^-5*|SWmRKE+U{iG_u@= zQ~q56W}wk_Y$lQKd7>X*<*MAurBoT6CP7R+VRAh8LdQw%lbtfThk>>?)$E+YNdar) zwNq}|TX@kEz-y=3nG|$=CDT(jUN2{$z41CCHFJoh2GK)o5IG9C`!x12E*9@*5XwgX z?E>060rKNq<9WGt)P6j-4poF2%6{UH_z=Fp_0mLco$Nf@yyVJEYx060G-`c zZv2XCJdYdHevBI)dBKly{WQT3*?kB<+_0GI$Z4~V<~j}of%sfUgIdyUi7tYVTA%BP zsDK9~avh~=7nyEPgU8a{bLTp~ahk!GLJ+#lIjTBEaXFbItMiN{^MdR6acKmd-38`%Np!qtaHOG}|M zodNkT(*#pEhfd)tPL)ewtF>FSY&)=FezXMt3C)e{m`5T6rk7AmCR|O^Bhx4tJWa7P z0USww*MIQoFnQ4ZA&}-~Lr53nBKlk;wLikXKZ) zyTidN1f>Ylpa6|UEJ5mE(HTz`I%X%{@FmZC*f=@b56AdGr2`$kDYZxZjGpL=hXdkr z@tRs(ecXXxwk*(&?1x1I>~xW@oYQ4Cj7BLv@jE^-zU;uzni%&vK<$YUy&8Z6l6JlV z;Mv5WiJ^&+ni%>GJ2CXz$1@z&j)Q z;5KtOqD055jQjbNlDGhT_b^@@;c!*Eb`L{BIUF0RP9qe;6rTf$6*|p^yk+|VJMxWe zwk*)Zm@OJ$Cx(3GoEY|%T7so)hg2<)Rj}}H&{ph#l_Rmc%wT%vR2Fek~7OWg+I5-)u z9O)d^8hacco9hNfJ)Xo1w{%h#FMk$mE#n+kov=$fME|W>Ar#17$C$HcH7UG3q5)Rn z$yZK==S<*zg#$-R58UhkEixIWpzUmOkdmV@`zyJ3I54&b=Sfd*CHHavu0P@cEkf6S z^`|SR0ChY73{EoF)YQ}Jb=T`HD z0@NKDIe@N1Rx1Skw$S26?uh8$gYj3qZlhLO($m-1*R!*CDeS;PxD<}9ufhGxW83w4^?2bi z)HqI7YO>NWairZLix#QA&B=BLsz-^9vUxxQTV~=rt;q-x<&Qi*@exd{E5(^!f#ZDe z`nULwQ**9yMVkpaKS~ZSU*c)eewc?ak;J1gwOHZ}TBFzh7XflyT{K`$FF{5g%Hd{x%)`0-M<}i+4apEbQJKAeb=fb;jdP#@j zvC?AG4b@ZQeA8a;;U3Ul-t9Bc;)#&g(|yGhFnS0tT-mx+smADK`16pHJsYf~0I7x!` zIw6PVY^+a}QDR;lL$-0bkjms~b8L5cr_He+K*G@lGxNDWcXRJ5&GFKO#Bk*gWxUl~ zjs6X(K0mrk6Q9dYN;n6CT_4w9w4DMl8Dl~2%Wi^Av`Z_c9hGVYCH-3XIZ+-Tg6n|b zn0mO540fbzz3LCV$*gQuyP^jsH@b(x*{b#G^A5WP#zw~>*QHvo^_8{^!4VU9q0j(6 z1;=bi`fm6aL=x4Fc_lnmrnfv8m0{Wq?4g75@nB_Wty-&3Ov*E8aIGFcS6O0A%XoS4 zb6*Jt1&~$1uVTWUv9WcDbDjaQiOM)kY(Tb*4yM}WHyRS+0T4Q^0mJ|vU4geINpvuz zx~R6O7w$+Rx&f;8iO;m3Z<;DW`@4sR;piiH1&95Q>?w^+P9U|1#s-yVIyCRH75~SU zVr(%U`~T6hsEAL2R{NsXQBteLGu>yyEKquKX3fxEtQa4FPhGp{l;e)QfR3Kmcxnsh zl-AHKZIz*k@z<2sVtGgED5?99kjGpw8@^Y1KcSNwKik}UA*o-~xJG+`4u!kfAlYbO z6134EtbXBksc~5CLS|6cqLn*qW3V>f+=o{TK6R`%vZvZpS_Qu^g2T72EnQd!%``EF zk4liaR33rr2Ql3V%dy-SuXJ9%mE5-By^!0}U?8nh9$W*Y!L{4!UzKlE2jSfJ~je~n^c*eW#0 zAN(~4^3q;#xM$))v6fj&I zbMU^lRd)~V@i+jRC5Jv0>`6|dBxL4E!=Ps~GY#_(z&=KQ+o%iu*PbOw4j)BX!gn-x zj>_VWGP>CS+WLu^v_y}LTb$&VP8h00i?NwR>BLi8Cn#c6O&_6sJ|LI$H?r#h&SRkM zMVg%|f3|?N$}48W%phb<;Q}~76dXvoLRT`KWux{?0qtF)Fv2ySmlRL!$CBb_%JG2V zfM0Rhxt43ciDY=$ZML(NOYttFhEjyzU%XSqr@!K3xP==-W>vz?VGMX9*LWTSsQnlN zoQGt;m+PeoHptGKWW!-5RY<7z6fe3AfMSUKD9)A7b3@AH%4fp3@&MO(9#^RSFs?kr z_0j}aWamwCWgc&aQ_MLc1*dq#yos~w&)hgOS@p*-RvjEMnmdnG)P5MNj^=u4f>pBf zCRz2km}ivY(1Gf;VvKnZ=f^T`Fq!=56VTok5O3RtB|Hk=0S~QDBi3k4AJp)}had*1|sm6%5?p z1^Z!(p$81A1Bsr9KSfQwyU6NwE8*sAwXcHL&f~U9NraZG5JN^A-d!CVgwum-ll?lh zyROt<#*hcZPisSPns@~+u*du9F&=UCBwVa5a=5Hzk=+S|hOuT#+{#C%&D-6GSd-{U zFE^$)l3E2sH^AHh`d^R#WG;4WcUEu6F4$*F8zC`!3FAcU>$0Ok${^ZarfrDsWxeVg zO%sS(=-_o(ousxLHUoy1U$yJ1E(n3*zQF`W_*HG)UvgDxg-}UYMaABHt3rxvI?lM91Lh}pp%DPwkq4r+9vAG@u8WNcvP)GbqbS77D9w)47o zp#s@jn;hMCU9GgWTDiJZFKq>{@shozt=lKc1J`N(mg>OZz2@)w!o5leTF{a<-$HQC zcaX7hz~IGR%j@C=|8- zB!DX&<3Znna(^$En9}MPEJLfV4VkyD)IC(arcdnJ3*SIgc?urJi!gKR_V+l3V(0um z3p8e`ZhulV0IQH$rEW881>5B-CWATpD7Z^naFYf4v|W;X92cpVI`BpoP~HAb4p2KW zI<#*8W@tN`L{yTbNtCJ&)Nk0*8Tr;jSImzl9AiXs+yX?0nKC9oo%QY~Ld~42cZXZ| z#$Y3Rpk5io?QF=!f>*fv9N?g5xca!!Y*J%=T>seE zNDpLGZZA)c)a3!LJ$RuLq@K$wQta&oSxYDqGObo1A%5B7?(S;u(uMGU%N8!|>DoP1 zf&GmtX24_Y*DRQ&?gCeR9jAxv-E#7e>_?jDVnRIML7MC-NE+#S(Ex}FD-ina`K=+5 zna+(?wFp9mSlU7b3#Y^C!}XEMHrPi49zgcKy?Q$QsyL_gn()y0bAzlP^=*k>@@qd| zi_i>e^BO1kNrvXL?*~k8UdDN^kbwNrg!z+OQ&|SAp$6+LZcKx^T?m>E?||7N(26S90<#oBzzD$6&?5u^g9+F8> zr_}&zaxyZaPO-BV&RS_5sern*)@%DW*1nc-br1sm7vZ(&Jc(kB7@;cG4v5xeIDTux zC*P1!N&3@0=u1L?^lh;a1Rg)I%L7>wh8hw>C+PSHLo>$@yu^V}0cFS>KM)T^l9{>D zfdc9V;`Q@AK#A8#z93%bYaBX$Kt=RbHAenNbdQHhtkT?Mjvo+lrUSQ6R>k=!P%;k% z{;KdSnt^wAAcQ=A;4T=NJbpm@N*zBSN=6pQWU{79Vb(w**$8YH7cGQ2-C6U&cT}JZ z=1RUrwg@KW-crp=4JvvEDs&x)%H!;O0n9gn)!7;`zX0ZI`rF1tdkbJVK}p>WGLPqq z3J_(z*5*aH_z&wbg_ph2~09JX4x`TkGdiykObOdTtDvS>kKL7;q`qcpd|&{TKtBWiJ)3mnPUCJ8zN=9VoDPHaE^p zR$Uv$ss`719;>MRFjn2l_0j~ZWamw?>R`(ZlVUY$bdG$f42b$OZZPiPhLOpM_l9xe zlU(C@oS^pOoN(PI^kuG>CO9EGZ;}(ODs_2;8%!oY9tz{f@3_YE_(AQ*`LP9d3Y1Vu zjTRTF{l*wW*aSah=OO%X!(yom=jU{^)a4OyHTY5&gIcQ8q}h_6<9xtQWxp; ziNb~Z;W4_^kk_-`%4q=?6VC4IjvcPlxLc&@>r4t0%NCK8iORq*oaHtEr2rILfh;cd z6At;Pm+`zIQ-C0R04j~aEZ#DN%f;dCGA1M5)E45QAQ-37ar07rcoa6lnY}0aqAT?U zY@1KkN=tQ==dXSodKN}!zzhUIKMns03l}ZeM@u0gbte9#!5umWD3CM=lJPr8>SdV?# ztdEcDFe0cTkJ9D`(1XOHYiko@oM~t9!XHkYRNCsG-lCj2n80JK2|SL@X1Jkk4iJNP zww2M5xCPN_WPrTQ$r9@|5Tnuwg3DHQPl~)&k-=Im^_G@m;wmP&?vhEalPbw|uzOce zY0{*!HuI?-t_T#o$U)0yb608HnB8lQyZyPVZ*YtUSwn&2+mJO2D8|vsTdZ1?yZUj* zQ0)D)WdNJXUH!0VfZe8*ubkVo$s|>;+|@5T@J1F;?&^IGP&=v~n!EZHB#CwBRGdT< zQ`aO)Wew>!>=vVZ>!AVWM|V2Lh~%JLgZB$iXYT4;nY)^}0EZ0MZI+)FeH!!(@ho*b z%k1&Adv05-Y}!#8&0hqzta!;+PVsW&s{Wk=LrajJ-~hD+DS8MH56D$L8rse#1_=_4 z*gpqgu>)gkaGuok&H*^rzv~qa&?0m_&!4Us0#26eXz^nwsC?@+K_6}T1cftS;rK8% z0cd>{j`lA@ML#C$%9Bvh5uF?v6pNV2y4-X&xj&_D%F$a`N~QEt?O|lk`G!YMZ_UBg zY-y*dKPDPrr@nmUochk3-WNG=wC2YP9H2#n^d@LKn;bOxY0UnU{|yd|t-*QH(|hv2 z-@ohkI6#Zg^$vf!a+6;RAUpZ>+l?)rZrXQlw7O;RlM!ost>&e?MjSjsNec5WhEjWZ zp^8j4Ko^jR!%nyiM^Em;*;^-v7w~T4eIg zxY|Rh(U|>}+B|4AxhHEol-A%p>FKT1PV(>icn4?^x-RsmE2q@70J2I=zdbKUsrgPl zqsUs(8YnNsE?9Xz=#cSn<@F4?*H!9dG9}ZxREU=DRQX>NZlm@n8D~*^$H=ej0O6!(i6`P-D;d$?8%dM)&PLGI|!(eGj9%6rD5E0Yz zEQa3Q(fX4hLJ5|7aH??t{C_WW98Yz|negZw=wW_z2GfG=c3T++G3K2Q-!%?^mg{ga z##CcA_7RVYzDT^@U7n~;y(qc@QQ8lOi`Sn;VixG_t)ndBiPNDDO=vEyiyFx{)|YFWIDG*!yH$0HUl8A6Z^#6Xo}``3rKnG}D0xWlx)8D%Rhtz>@G@Q_`aU?{sSo?wAh zNbmMQmL}RekQlDb2DtyE31QM?zDFAz&=2h6<9*ZShunjM! z9*NyMy-T2g_~9rLc1^yF8Bjf*{pE<$W4R9%ull`EC8%wrE3yyxERO?q*a*|R57(45KBhqvn!_7Fwpj* z!mi(0DPS$ULMx`qOlR4sT_K>oE2hS{#`ALBseOAfht~s7ZMN#q zrFg-LsaJ7h$SgK^MHmBa=Nivr0JR@ufU{!i16(gnut9d-BpZsC;oQp&Ba;)K3**GM zxyJK2LG8ym;VQ%V3D-*#oRFP2$%zisuFa^2DAY&8Sam4Zcpj^${Wz;!6*>#JUYcN) z?7T@FcxW@DNLG6d}<7r$kP4Gi@9>NbdELKc$eojX#rcQ-f zWW_Uak<}{$T?VyO#ngQGsPz?7hzj&CDyEjfZ@L`l82ss3G4X^I)>r3zc|jiz*|TG-V`{re6_pXfU(Q_Hj|VsALEGqFWDqX>K}$ zk{$1lyvLPuo5Smy7}xPw9^<&2!>;7pmtC^w47)zw4F^NN(#^< zN>wZ9H|*xMeCwfO=0|^Uun{Hl7U>iHR)BV>oM^3s_Sv+f{E}6o0am=^E2nrl%84#_ zV2G3>LAuNVY70`-4Z!h@^B0?ab#bAj0I!!Pc2w$9(G>umO$-tw8nJ(NNzH+=H8@Xd zdS{nB-@ohUIzWri^@aX)1tCjKP%VD!1eI^SCg{12F@{gj>0VCsVaJHGrJbh!9ijnt z>dRNosqaiJyU&56H9x-K04*YwOL$HKDv@PLH+4$*00**G2l)G~fXq)Fqi+JJXL`kxHEqovE7rbX?1-heat!Iq`F0RxkC#p4-yQu%x!YlUdKIJj=J#t6kZUA-eamA_} z-X>S?s+`Eq+of6_%6t$|(uDd262rA=9Gg(Z%7*4#lkc|0RC?IP7E?A9-$te-*DQeoSY)njNWP32O3+c# z?y+S<3)(m!rfeu@MEM4R%7*^pAsQsdC>lX>oR0vN4XNyjpt2#A)TSksDH{^Pl3O-} z5VCCOB4C;<8xp_TQ#Q1^wr!|9F*;Tq9@u7%aS5&-I?bbci0P+55z!crxU-hX+{{bY z`A);?q9NdXw(`%fE*jL|Hm+`ObrH+YM3K>4mP^Xj%6O|yws5?5>j8zA92sInyRex= z$A}5z@afon;pxy-`4Wr;p#kHJ+C~PVL9i z$8(C1KFPJ;MB=#YHrtToQoLXh(s#HqWajTb5XOK=Hd z$%f)ZNQdnSnWYB{Xz!_T4A*!bC#d~6CtO8HPv&}Qf)ldyU{1Vp)(luM!~gb9br0>| zI6P^ZNsF30n(W)|$=$Ud#qGvwZg811<=im-T+B6|#~*4x#-IJgCFjZ*(qHjGT*dX% z1czkz!5or|=|Gv-9&VhO%$f{i){R`_dCa2rW6W}@wO4YzG{Gv_d6TSaRhig(xWQ!d z;~ioA_ypH@9zUr4Fn;_y*Gm)pke!F{!wrjNVw`W<(K4|;V27IEmze6O0~)U9TVkoQAf6} zue6e4tCg!K;leGbK3WQA&6le>3JZsx!bMnc;%TiiP_M;`$gC5YpdGk%i;irXC_`nM zzMyMK2`}YZ+Sj#4oOBN{+}iN9Sj<+#J`gWje3q8jYiqkhu~bp{t6r^Ua2kb zI{x@DQfwU<9IHzLE!%rtceQuvLioRB3-=y>eAjNM`L2}I6@FKt5k<9EogD3_VsN1s zX{|Z0vD1y!3}VoXYUS$EWN8N0hVCuRNHS;!VkMtuU}b3lk8b(P?ox8$CI((FSV4KT z%qFi2tEMizYLFFzQ*{WAr3$exQiWO6*FxDiDw{)TtV5>WkDrWqs1JvI04Xuj3 z3);>m5$*VD5~Ygn^cyw@OTP7(3G<^jI>w0PxJBl${IdYmyZbb#7|6nzB{52%Wr18rv$g9M32?4KmLz=5$fOFXIRog~@o z-}NE~Xc4+D_NObCB&o%ZouKlq*985kVzcSGCRl?FLhvS4bGFE-jn~I{kwjx1GETT-{4PIZt`mZ zWGBCV`&U&Ji&)#M125(E3TY-b(EW{#_s804+k-dC;~eT{)$u1&~#0`fV%f zSk2PE6|KP{AF&HoUSD>|c)0SKhE=f}90HduFr=5(i3V7`Bwso8lA|iN?7+~{N>@5S zi_D$@Xgix2q?KsI{#t2|17mA&p49Z#N;mm;eWL@k2wfZgbmg>?7C%-i>9>VTS&d36 z+!W{|#4A`It#NQ6TpvxJs@P9C#+E(vNDbX38er9seC1R_&NbgR9XMKZ_3IAMA`|A@ z&~`RCNDdQ`=0vF^_LKfU(Juos^8{Sp1uFzY7+0yVy$FAc0TOFQJ zZ-1*J1QqivlxA!HygYO)CQFy#D!Sv~zvJP*6XCz^E5tpZRM)u2W46$g#@W*1DIHj! zd#eYsG__xi#Bgn2$EJ3%`rP+AP>UItuq`&GJ~zIhO+ME<1q$FBaP_(JWz1yf*XQ2T z#sM+)xj7@sH%%nRZ*!oFTA$T$NH3RG+J|iM6CM^|?Y=a_e&uLe}U0 z0fr{)bH%Ur)aP!r3n2Dj?b*c)NgG24dYH)IDZ6mJzCaP~_x7kJ^m*NNEaXCVdkdT;C!u{}-l6Lt= zT%@1J4Ir}u>N#Q5e>vB99`&jH81?sam3O|L>!k@E$j+POL8~g#KEe$qlOK16@#C{x z<9Ym`_T&6;T_pE)u9qhGAv+J@hZ`0v(l|}r(TcR=fIwD^2;E4kNHeIVD$)*wk6K@m zhNwXQq9X0@;Wt&Jb>YvfinQD%Al)sk8UQC$Hk)=rWp-(<6MEQlloNV5{t|afi(k1; zsF8xk+*+?W?WLEh9 z7^MCwvdT6!!)j^Y*!<`ukPh!C#n55E&*SwMf_`TJ{a&NUMQhls zKu6<%O0_;Q3{r9hL_%(FoFfVJ6exb$0Ys}0jn)j2Ey5sOEV5{-Of}AyE%Y|qz|fqH z^{FyW<5$Oq*0_cB$+ZM{AlEm(XZhgRKn(_5-63o7ni^!^>dKCZN@Yv=nkiH^~! z%_6Cszvdgz(^V`NpQ&dKzXtls&{r$mTH!MwnwRKrr>kg!nHu_zX3jyHIf{zblQ>z1 z_0V{-JUsLhmMq_nktG_!W)e%ogDt@z2j8p$XRTW+aWg)@egv1Z!P_{V&w&p77lPL@ z&>VAr4WrmCV6BDVtW6Us)YKzD4SvQ^do|Np_CoL$2HG37*)YeFxS4138sc{xyZ18v zWn=eF209-*(OTG<6BsH_SI#m~sJ(|FS2$E+&4MCzKu$hDbJ(3ZP!iKaPI`TK9$1~7b_MjwSEkinUQ_@zcCvbJ) zri2wem+2|nPMyU-`{T7Mg%>>mymlq58?P-4v_D?u6khZM@G2+p8fSXS#%qLu z_Q$I~g%>>my!sP(y@=^48?Wax(B60%&&N2C<{9e^y@Ba38@tyr(D~TKEhq2st7m&`?BiKh*$u zLs}EZdFkT~eFw0U-q3&GFX0V|U+wgU)~uQy-q4y=g}osd3S;u7aZuqmz~<}#UcNW< zEB)=X^M;U>tT%)&Y;Oquj(bD1pPNsRJa5PlB$|cIbix}t8and#hUPNRoQ}t^!VvSdt)@e1*V)-ye2* zFXQ=mdqXd0`pd@dr3`dFc5w^J!J;?B&HL%#4ebx(<-H+;ampL|8EEy6c|(Yy@`j|! z6yDH(lVxez8&VB`H>5RjoR>b{(7k}2^oH)kU&0#_zuMysU4o~0u7U$PCx-hc>%(K! zR=Gv%nhg>q5h~&zCEF5?(#q)`E&4GqzFGh1hx*&bt(qr_wGXY(?^oR0CjX^~80?!G+NUh)A@R4;n#Lc-E$MA~jNz8l&?kBwHj}WV+B7zw$#4fW>mPD>F9Xe~cQl~y z^n^uIz>RY{n1a{C7Xf8IBFan3-a>~d?;U!fM^xc;zPKK>!pb*57~KKLFco<4U2}ElfCI+n2!Rw@rK!8oHERhfeq@IVMYvbDAw{d3tx=8;?#{-V$uph;H`b%(6aed5@ z8*M8m64Sx9eip{d+g5{d%C^1>KI)ilMGTc~m9t6M*7v~ULbg>k0Jc?6(l{@DZ0pI- zSGM&O{3UFw_|+cUx&_UUDMDilWM3GU5?EpOsdFNPsocvs>puh z1ay=%r`b!+Dg(_~0}Pi&0@fH`+*1^`*DQ(E6K22hCZ?xsyk5vad*gKo+HN%giJ%5Y zvm41~Jim$QIUCP^7SP^H_V;m(=PlW({n(QIz}V2hF)$C}qI(zDTN6uniA3DYEl2s;-_4P1I-(`+B6U1I`=p|u5fq3&?I^iHj{7{4j_q0xd<}{ zQ+Al1fE^x8tcuTomi_&MQyAzh)xSi*+WtYR_If5g0lWqhcx_^O%C@^37-)aI##8j7 zCxF*@0f7Or@TzabEhE$~OTT(p0_~e+g45eznI`UI{l6k5ASs+s4aqfjXumZ5y8$ z>o3<@WjZ%CyC%;ZzQI(-@&)=!XdHWTLli2kLo42dB)i&UG?tP5p=qeE$%d_P4~}WuQ50fh+ZI3s~C{ zhpg29$n=zL@qWud=i?REk{m2rJdOv`!Qy=p#>-nggK^5@-2oqU%;F)2%Hm1!6&CM< z@VJo0Qw@N{)3YMZOCO7O@(Y#4I|YBq*faiWkHxzP?>|_E=B`yHuIYp;jY$EUc8G4I zd-XInU>5>In+`4OZ>Lj^m8N^jVjX0?k|xS}61PcP_0Ym?(t7=EbUFTv+oWjoT6l)t zn_btLt5eGydIHOw)rn=!)zGHDF&br{IYlnQ#-6YuB8Vds*HD;2P6wrbCE&#?eZ!WN z(q9c9bxi3ahDzy6`H&GoB52d4jptJX7!kyuD0bmW|DEuuT;{wBe+i{8ezi&IZ!Ztj z;f&7m$Uay48j~VQUpLZSoyJQ49xW=ocFRl7`K11KIw^fk_q5WtUXju_PvT1d>w0KB zS@P->4OybEVKWJ(kLQIam3}p)^yvvG{c1w#{}kHvSNcC@ptF?zgPyRc^tnM!2c`c2 zjDc7B2IG{{{{(#0F{O_fDy1*wLn!^b;Bg^OUo`+qUyEJ1(*K7WmC`>Re+i{8eziyG zZ;%-mt4XcSTRG!P8;Ph)kz0+`yYkAJ5Aw(*bCCrG6@C$y|N9e9p3l15QJ2;DQMHDADZ=7^j9*_S&IId0@hx2glzg;%k-2T zYTCs>=i?PuS{y7IAdddi!2oT9@$v@9V4N~Qr@%)YGeC%;GC)#vg#kJZ9v3n|ssS)S zdPc-~=@V+Y6VQ;Wjt}B5VSvQ1_86dzqvZ%M2H4Xo6V!AbfCP5{+UraeEX^ zt%qmWy|wQVvf2GSXw%;qJ%@qj6ge8BXL-V+(&q*_9hCk!jDc7B2IG{{zXU$&n9@fK zmC~2;A(Z}Q@VJoDR}FyD*J2m0^#2vmkV^k9{3VpW_|+bzzkPh7qVi`<9SkOWl}9UZ z;akhnye?}tIOamIfQdNX)i$JkO=HvXO_JDV)-hkx-!|SceZpxjexxi)$^C^iG;YAS z{dri^C*1xR+D1RdX5#io+`N^rKRW5lGY{-hXx85Z{egkbGC{xbghdm?jdMDfphsW~ zya_TGr%ce7;G>S2AjD9aASoik1lxdEqALv==KAbUOYLCP@5hj|tj< zS4g&Q(b4oSj6|b=IXQ#|GR=2kHUU$cU2$yC-%h7p7{xiEXsuls^<0{OIrL{-(Qnfu z3Rm=o8qt;5OhVDOU>9Z*n)O%oS2NIAihfkU+H0VYU6_|LJ!S90yqJN`$1AS1I9N15 z9Q~(*0V>0Ic>`oHP8p!J@KMJM5MrndkQ7~EfY!s~LIy}R00v0Uh&V5O0$-m4G~^oS z^Y}{`An_~L0GWQeqx0alqK0J&dUa8zyk}cnZQTpkF87*?t$WqI*6FLbt(JoBnN&Aq z=ibnbH%#Z;(4D&D(se<%>Qwlc^BUauxwmqw@3o#}o5;N*Tr~l6^qVA7{36Ak*(i^A z0r%JS7vci$bDO>q`u4`D^7`!b1>8VG>v)mG{o7NGQ{;E4Yq#@1i(j^FhS7LV_6jSv zMduaU`CnUC6c@W#dyQ@>KQf$)4qT_riDEVzGB43Kl$A=f6_;rDCBqZBW9gF_bMqRZ zpXoUzF3j$_sd-Oqj{bH!h*#5>V;`-D5$=8&5=2K~GYR*rrI%wbh7SB4u_rUowyIP2 zHe=BDBmrwVVxgB~uVp&R_Qh5)(B7z-gHTMM!aQRSN7=^omyO-!40Jwr#_H!?j?IZm z)1KmKnU`bdDh+cb_Iwy6?;jdGQ~u#h_{hUQ476FXi?9(dlIs=`hzwZ~Ymf=%ZR~y@+o&@%dAUGPucWZz(D)s6>@#!8B9;vc%8yPd*fw16(eEh8SA}lV*1O*ZUY0I zk6qjfa@vEKQi|gX*c6cwTORB@Y7vD>&L*0;;crUjD``8k?AQLuirAz{&FH$`U@8vdlT*!M-4S@He zwQihmKHkf9ARE$qc^3W>-i!Fv9`9vC@`m4H`wpq?yIjAgFWGsKXiTy#>%jMPX5l{Z zXI=>mZPuT8x&F5C!hI8sNzyl!A(*}sn!LdDi+Ggkc0IOmSI5vLdNVeYaCKUFS@g%C zeSZ(cey7ls%~za`%ZOV1eo)x+u)f}-KWAwZC2fgmr~tQ9E9pV9Ud1_-Kqgl-CD}R zRrhUxhFsMAGyW2)Tl{K|>RyAh)Z-#&m);J{qSCz zL@J9z3Ds>~y-a%BdJa8zz7HN3GNh^jFr<3s z$NA>72D$)bOd8S+_)8d4@vA+CRIGv2b+*MKgH5b~RL2F(X{#<--Y2%U0xWKJrBv46 z?#q>u;b6kt249|B*W(E{x`q}}4Vy_A-L_qx{6gp~X??R4`3(Zr*wft0lcx$a+=|PS z-&myoq@$2~dGdRMdy4B+j@)PqIg!|Rws04WlDCBh&y+3P3LmxE79w8C7Rs3;Y~hvg zxR5PW4S+4w6Ee;>A6xh%KttNXhwzuMh2mFxY~fRJ@n8<_C}s`M3Xceyt_#@1p2Q%; zWjQ@1_CED#vTOEdlD1}6oPX5cHlDxl;)OQn5_T|oy)i8>l8@sH&_iy{_Y3k4EjJX2 z4#s8@w(wYM*L1~M+b637cn?`g9GvaAxvoPB4N4uw(jR>|O*vP!>!8P{g3gm@{dBxP7wrQgw9Z~8V3 z)c{x}JvHKd^RY^!fSt5TRs1EalK9m|t7MMm>9|!gT^F!Qk4LLyT4K+F+9xZp8-az* zuE3tBzulL%$51tuuOW<#{azm4k$fDtJ+IQE3SWmAibSu(W)ikX??o}@#l#Xd6lZbX z1tYK0w6JA`>a>9B_P7##pJ?tG z|Jc0de)Ruq?%HGJsLD9E<(A&|KH3Wfw3KYY;^om6N@?0#KrAh^Uf2qh@+dHwyE}Jh zrn5WiJo+H=)Z#6fScN4hN>mat2+@dyhgDL75Ur*}8VQM*Kody}{}6%!Oi+KnGq0V! zbEms^ds&bLRWbch2`a=gi{^x}bWRcV}l-4Y6Sw)(dfD^I)YACnWv!z)|$i zS|h~z>;0^z!A~EXQ0GffbMvlxaZ2vVT=gQ6H(p9GoVjSNfJ7d8;qv3eKeanLg+)eV z%=1f9E;%`utVzk;ynwC5Jik8WN|VcVO-k-;xstvzEiXAPt&en$^0?i$-YDZp|!J_CY$IcoNonxrqK+AN7ishWZP(MbYX=W(O6*83HRRu#mhUB(ZVPOSesAvQv z?bZxK<-Qv-)M6e9h7w29!%!QdXwmI0!&-c9FuH0R9F!enmM5#Q&L zaMI#vnw)eL%lR{P($Q7haneyGX|cjP;-sGr`p{JFuKyw`+bJgemW7d^XXTxmOiMhBzT3(tk$MB6)1yjs#66z_V@-7qeO6S~ z`+8?KZ91b{-o41aX;DhK(&Sq&g_PXcawQ#4T3!-HTKjj7FxEoLbi#<`oIn_hQD~Y8gK~w0 z;dfI(7-u56Ey4&Z0AWPEKWVpSwqAY$G5lHmV>}XsA&#bpFxH3RliQorw|E0Qyl5M9 z`lH3-?g(w{5BktlX7$fSWjl5EJGM##amZacD@{%wAExBamMiIm((;l#(%Qdsg+> ziaMJo4u@{>B~`Qnw>P|3Z1my7lf1EfaJEE zbXWnLbkxOKchWzH82$|ZNgfF&EsmzgNv{iHc7B#yLSb0Ssc#7dLjNCa3H?6kJyV(6 zABf6!YD*}zNP}+vRp~Wlcoa+Bdz^AM;L#Fmg$5M%Q=BC_M^}=69(l93B&KEf-ruE zdX5fLNlKb4FhCm1_r$>J22?L)n?H(lw85AI7-E_!DK#-t*}(N@ zbMe`;Zmp?9x7OI~bJT^fkg$?y^BIk9yxvaUEZcOgf>Kk@W!WyTzu;OHdW96E=e^!A zh4(*dd2hC@IF42+*=YTuS#TZAu$PTlqB9k1-mI;%;VO3R!P?pP)b7BCvj*~BPf4jL zmh6}o%J&pyN3k8bT*Z&M{$(W8y&XU21V3HNz^^{XEJ&kh0iMO>x*?af8%;1~noh-Z zl)956U0$E96r74%mUfMqR=I|heiCn0!qc{8(b4nXLO(?@6p}han%CnxV@p>rwKW`{mOLpbmStL!irB-3VxgTW zYQd>_1E%XFYv~rX)VxIvXBw)|f51-M@7Ej4(0%=uE0(Ut^^+$W3VZXYgN=q#(L@Gx zs(F2eTq(Keu4wThe$P-UHSa`ADVSDKDk@c_Qp9C6(bBUpXvUx>T0|@IZP_tx7ly9a zFT0K@#Yx@_4V|j5=$5Ne)bY@;B0+-#vnsMd(+!9ev~Iiz95@4rWsHU zgS5Y@R3+%7U?>%YzP(Htm2qkIZ7-)fPSqY>zI^A-o$x%4YF3t+R%v-r*|8is?d3IZ z0ZXt(mXI8!j8=Bgsv z?usMtl2omv8u*WHdc0w>hxgiLye}br&BgGwQSwCb+y5yUWpBzAxf<`+xG+*dgw)3rKq7_JCY+?B?S};o6pCE z&?_7`M6^v&8MER3yun0jMJ_{YB%`*m@W+tXwE`9B-ldq4d0iLqcz838T`{TCilW2` z%m;aJ^0j4n$*UMc0wvHB-UCS(?K5HR~5f99+{ zs~*tj%|x4?OeoC)7zAPXMeGM_2^q?W3{B}XlB$ntmQBMf?WQ&>vUEvg4D`?`Vz3Ah zJ@isaD|+Y)Ji2%;;LC)2`T+M4UK{|tOppcvMZ(`s0=!LFb_(D^!hur(uMk!(1gs^z zbUNTw!pCO-J|*0~1aKGO;1>a}5#B!=@Grud=K+QYTbBX05%!%As1bTr0Qw1!tOWds zaCjBqO@bI-r*asrB4inX8JaS}wsa$47+EDtuBvq(bILSET7l3g6ge=8a13iGy>JW~ zkA!1*fJeeH9O9914D;w6wh(S2+_A7w>gxjtH6bO&z0gn;>#Ea8Q>Mwp3@G{{`*8$cO`mYC^Ncj6!z!Abl zHv)zUOKt+3L-_D!z$XOZ)^4FUyq%C`GG=JQWQNS?l7@K-CZ4VhhQsjBGFfVY&}0-j zFq!bjcToCw3GWfEy$x^!;b$`7DZ*w2u!SJ}_9JxT&l9qgW`;JDhOV5v%ZBE_VJNcg zf?>0iPAw2BO_2ko1>x+c^rvcoMTA3Tz+u8d8;~PNy8uPP>^lK{1VK?>AY-i~WU0># zO{u>(b(p#pz9O2rvX@LtD`8G)WVK2w^@NQ@1%t-AhH7pl{G0G!!h3fE-Y2|u58xfb zo_hiJ6aGV(`3=BRgkKTXe-p5YFlP^7fbeU=^Mv*H0X7kSOL&Q}@>_s4gjWa$30ubj z+X#mUhY45i1zbfCa|S-`C^lI#O~YN7rcC2bZkkNX9MvG1r9(4>k8a{EX+~t%8L?ew zbW;(fRcE9y5%ElCWE2tMMdye1MZf@^UkVmqJl6T0MDe{xo!_R?`Ryv5pEnVoY0>!- zxOkae=gW5Dr9qupkbxX={cs~VpiS9 zP>r@qD^T~MZAOA@8x^@L)GS_Tsr#8!gk zaPMM4W97k_Tcd1QTC9-xbe=z2ObmT;Vp3Qp6|pY$qoq%I)BOPY&BL zIV|((z?NPxoI7LP8M&e0cW#CMr9FHa_0krqICA$7*B!b0(L^=TjwB}Lh`aI7#Crkt zn_HZcUkweA^~R%Q^}2%{o-XfrE={mr-ITv#Gq+j%{V`qMhRu@W#U9v54{P3vDp51- hiXAp@Fxq+un^RbKZj>xmj`&E%)_%>`ag5yTy9hPsrcL%5AgtrdO?db@?3Gs%O4q#ppw?S!4Az^)gAH&2UfA zjk~Pf`h2~trD{?8`@5}L6h~h1{i*|2j1adOoa(o%T-of$QF_l?Re_SRD_1w3uMSAzIq20x|t!3g_ z(lkMT5+eJ{t?7FEpR6av4%2q{UaSA0tz?jBuo~h$uh7?o(x$l$|4eRKziR!E2G<%< zzP=%3=6b#QupBE7W4LR@FnKWD+c+de9!S@15$=?TAPC)Z$XY62P4}MJ*_Q@vIVt+d zC)0KAwW_k;tH$_-UB1z#2zc|i=~|o;$?dPdV^zv9YaDw+o2yqNCo_&7=q9_LM}@5jZ-tM%b*HC*HvsPPN>h)n*f7jIURdgTJv^Bkt@KF{;` z^bkNC0qDj(vM-fMaU!0|RqtC<39&7Dr1+TuTYRXGj2h?No}Vmxj`ODVD-pN*6}~M< zVfQg>kz7kwkCeX}r=L4!wW4IO`}|m&C2v0rkqb?&WLT|dImzmJUgZQu&=rhU`^x_qsoWUy`2Js5-0RURz$*=(1n^*WCvL=C7Y$R|`iInyyKD8bbVIR>Cah~IV za8~%@Rgzb@@k*$X`XGZtfxhsWbusJ8HCx|Y?bSrD78{AZ&mT~ zTKqmMMZTZGTWdrnZ>(jDH6n%f&_0V zl)R--9{JLG745VOv$`)VEim9?s{y<97z<{$mfj=s;wiiUUr%4&LD_A{~h9(1K~|VXgPh>8YBN^nm!AH z`QoxL>;$19>gSaf?hhigxD*Iuksn!NT%)TrH8DJE+I!>5xvLQzA{kN5_U;ejMAmg&w zn7&!glx{eJnY}vG+xq)t84YhCU`A$Az?7R|92Vau491JEz5cv)pHQ!%?ydN1)FT(H zMEQD_PP$+vi``^;Z$0ON6)JaUY0m}g2Khmj&b^3mhWdpI)>bMh9H?Ab1~upYwqumh{(~ zR*K$%W51N8cMnm+<@PMLeG?qbwwoCI0d@vra3pPOlcfh`a15@?G%>i277@qb%4{bF zS7vhz-cY4(;}E>zyn0qH%;pGubGAMs)C+PY`cTNd8B#X#ifnycs=MUF*(SI?l+7{s zfov0VpRt>_0D)re%h?=rQKgtG-^@0l6Lk)q5aWn@+7>%ui+yFpQ00jGA6pD{N9-N9 zS|Foyj1oO_%xG8T@Mx&ASZaBw=9QOeZ&%58q0cdPWz|`wN%K~pY*5T{$N-Au?l*7 z$H#Wpml|dFba(x&^>c5&Vf`BS;#=q6c=IpTH@Mfhmo(i-Up-4l8P3^gRC1Iy!`{+E zZIW`EZ6YNBThkDXJR6+IW?FK84)4R)2_)XL0_(?40;}YxOg;Na6)QhM7vzCiIh+SR z&fz@pagNCY=W;j-b>#T-z`ubNmod|0a0=UM9FyofD{4$Y}_G_TX0h&=A$C3c53gw9^#MPcJPDddmuQW3~fQV_L1S&1eJ zh~nqICehFdZsC82yUQmFIA4+w>wV3lwG-qcmxXNmO!X7vji;d9{y6mx!4e4_#4!47 zf#~CS3;ANCE=f>TV27PtcEGsofbFs~euYkTQs^&TD+F0~+5h=uC7L*gbj#%h5R%Jj z$o72q+%7#28D0V?Tb{BKLNFN4C7eNGN&Q4hBLnH%02Swf-em56W07qxXO#DHII6ym zwTGy>JBKq$c`mQy5)92N+0G~))O{IcQLf1-3v}czC!;LLH5uj3Tod$Br;H*N=XcQ)Q=JE(%+h8C5cBEh zM3Y(U%}e9g=bFqiE0=sAN*Kgj9vcEbnCvne`wiLU#=soF*(Gee$u40u$(&t89= z3DlN5O^np1-*8R@XyJ?jd^$>q-}(VI*#IvF0HBT1o7iL8_)vj18v;pZX!jtKIOhz_ zqnv}YX~hBA&+A>PC&IgX>`I1WtOuE~t` zNTCRkgC^*`100QqHp+zOxK=;5pwzj4&vUfwEzNZfs(aA z`D&)hl*^44%L29Fgt9tIy(8Z0q{)8c2`Zc$gHwKWPIOY|lTPaBGj%drOwf@v7CV~# zDEGs)bkqpR=kTcVIXv1nh2{U$vknN`1X;ETWk#<02w3dd*E`wtc%FJv=HzpFwr4gY zpJz5B-yD`I&F~dL!&i42o+lPEd{|mwWKd-bR+;MUK@h7i3&9pZmMuUzpin(0p%vx$ zvH2W3k-?Gp3)&(68zFbtqT371VaJvTGZIN}Te&)VhVn{?w+$63`n?(Ii2O?d z<%jUoK;N+F>=pRaZNEaWPaW-=tczx<#qt|iPC0_Q*g2x3KwGoaV=}bRoCZ`OA4#q# z)E%uVM-H%?;|t9pVMHMx5>VaqFpgB2h31feI>&2>EtmsmRoP+{w%83u3{^f`G}>aQ zJ7V9~tC{ivTkKw2Y@-oF)!ZG~VyHV}hlJ}^x!V@oX^R~)VyLp%30n+xN37zIs*&ex zv9q?AoXEDJ%3?hy8rx8}#rOae?7JfwuBGxERx0jdPZyX2&_@Nj^-(0FbMUg0j5^Ge z(_QSxSTuA1%4$$0A}oI*ucW+*{$%v#&1#j*nMezxcYCczy)9$~Zmr%^Kht}bsyJCY z5n=2qy(ioi(yapGMBCOZ)7w32neMqz-5cC{VOPB;>C~UA$-3$mxS?XAy)4T~6?1-a zMzCAWM z6~GH51|*E&2`LW#C@J`hF}mUsLc!uKB3z6z;!NVxCh6_V)e3P}=kZsY@u>hW*ytaA zgZWB_yLpqC7d+av602<`P?8d%tI>_m7-v?f{`&X|l_G&oL}g`=O3V@NUy0>B?MIMG zK62VlIcStRILRD2|LWK3(@t9bt!u3y%jc(yK3Rz-K5|~ETcl;US2%r5^b+~8jqEwO zOHV_VJ&lsnEXp&h)f`~5Wp8!1>_D@6OB5P>;(@kld6SKo@-Pm2spw8@(cC+68IPYf z-l^uv*^?rW|;g{IH-~JNY z7fRD2xr5QkK#^vab7~Qm^9VDKcn%~IO!L`yMYxQe#MC9Mh*^ibi1%kl0aKU#_u~W~ zTf`xEPK*i_Ma(hWMa)+qNql9jksR$v8op9e#C&BQ7NEmd>@7DLc;G9A25U59iCEUi z+k7b;5uJ~M9|z`M<|@GAhduLXy)sv6#+c?P*j(go`$IJ4`N$>K1fBShTI);tA3dc0 zB!7)@HjnGRfwbi7%Qd%p+v<9oq(5aHg7g0tnWXRU;@MiHQ{PmBwR^3)R?nHGvgNKK zlm2%Uar*y5kxBo@?B=6ICjEa_#3>S0PXFH&nG}gSr$~r#`VXIC&_bpdv3^rn3{}po zgQplV)EzO-}4Z{Dv$Q2Erz-+M(IDm(VnfkzL!<4%WqPN@)=A%&Z8T2M=vW@t}8MO;2F${ z!vMC@44LET!*ClhfFX}5uL!#^g?ID$DZCudKCbSR-%T-(r5^Yn^|%Z#_N99nQfv}# zNHHhe1=q+>{g)@yVA~d|PNT)G>goj6H5X$FYbC!$nZ&XF3TXYt{Y*M$t4D1aS%e&Wf53yAhFsg zW8+*eNhn1J%@HL*6L9u9^|E|6Nc*iu`>nS2d(7Cc1&#fB*JDG5wLfmO-)gk~!XSJe zgz)8M5mn5O z^#9n>E+;*q)L2kzWKiXk{%ljN3xcS>ECibX*^1wY1W~F7{9ZjGp%GaSTN>p5tUss+ zqzW=w2@!Vi>5Je4k+mtVu@Gg;*ZC~x zo9e~IoaJX@TjN%uu9)}%UF+w7^6hysBs8ncAE#2T?o9&J?VBN zOP|TNQV4=x5n1GiSP%%I*Pc_^;%?oTZ4=2Y#Xiy?Ow@8WL!fD{Zs{-jh-TfEXZ6*W zx?6q4qn)Jm(C1~8Erza8U?8it%BPlA<0<&y_PMUu+&+JYn2OuyhkfJ#nV%^KG-1lI zg`yc#jzdtyP>w@oL6oEA4Yk@wIfm_1Z^+Nf_#XPxGCh31+9AIyGyLLHvfVsEJy%Bj zLW^*Hrob{6&mr{HzIeu)4RpDH`zGgSnOJUameaV&+$^JwSO%`IshfG*1Z|v+0YOYd zO!Grj8VLPhp%rZa&_bzPEDZoan*%`PK|E-FoAVzPCu|)aGfjNEeLo;TBE-`H5NLBC zh_;B=`8T>=m%D6;!vPRzV=YbvK%mWmATA=l_d|SZL%4LuR9MYgpp7A-OdDU3qs@lk zYl&d*^R>kHSi88Ez&!DD*^_04lzdN1&VGUB9L<>_C8x^_DXBe-MJo#`%!R|QBk)V9 zZo3|jg6K>hT2Nugh0Z;yhUx!=ySnRqeDev=9f7F#3(yazj^aFI5Oegmm6kc)Qw#Hs zf(q=`H1892eGO}U;C&Sxk%w{o#N*lb)t%xYvOZD&xJjis=j{inm$`?}tMCyIv4zK{ zO&zwGdc=FMB7@G`dkJf(%&j2OAzss>8no@T3Uk!{fW%Y8Q6A4*j_daiffC3$NDilL z2yo~^$)SttNZ5G&j~vEFvG$5CXom>XdutIC==5i+;L>TeP{B2oPumIR_f7TTI6jIOF>YtTo#IL zfjrxSl8H&vR7wxxR)z~QiO2~qmVY`qUmBCXv`tbv*Ms#Ts4!Oq|G?sF5+fq7|LN@L zwt5kbxnPsxSxy3m%^7ZBQi5PwV_g3QhRuOIn}d>>%9qBRzjro=*b1IqvYP%GlJ(a! zW1+%kEO=*9J83vO6F~-GK=YFsxg&J>LrvsS;^A+qb<}3f8ILv${vI^h$X?bLo!&h44-1+*I@6{R~UuitAK9(s} z%vT0g=@-1nA9x&cy}tN=c-)X)Wtht#VA;%NY?a=8K|L+XbkZ`Lx!{^$8B)c>DnnXs z^8F%js4}FbiJMHSMjMlsyG$FWYP4~x1_YCq$Ndl+ZHQ+AAkfCy{^bA&v^fw=TaNf4 z4%rZ=10c}GwB@S+2(%GI_(WKX`^9hY2Kt69SCw>Eo68zmZ7yq~tNF49RetO2s%mpt zgSw*uU)E&UVq(&}b;lBx{D$sh}DhBKDt)-arbRfgfTxLqA0Y)>_Qi1X~K=4%&z z3I2sp*X?p=wRs{pKFl>=$V1hB9wd)dBRtp-UJgN=AH4L{tzoXddStk3h_5$4DEY$Q zn||O2B?c}>{t)~d)1X7uM1yesa*#ALq-H05^CX6>m>A2@*Ol>vRqMv;Ay{<+zgmN7Da+!QIO!!iIPJn)#=*fh8lBl zom0~Vg_17@4gP`|oC@6BylKXMgT@{zy*0YN0m-@R(O;-BvHujxXH*Kk4MXn=d~G29 z>4QiLv;GSN8vrOa03{pnY_I`*Z*$eOK%e!W#y;ym&78c)Okw0X+u=@Lr460DS41ly~-g6Mx0mm=s+SKOgeeUcIlG#vsrc@7w! zZ_wZYV5OG@W5a;KhM^=uorDbn5?tUSl6mRmS#z3ZMLs9(ZF`S?g!i532fhG!PqWfz^(-R2NyFWRGA@tF`XHb_>%CV z^oPl=5#n!*t@rG}Rqc1v4O6m1RUASUM2RWUb%^5`T!$FX@RiTZ8m2nM*v$FK0uQ~` zT7&hv9b&L<+6s&4fS6Ki$2NX4)O&>M&+Ws9yWS8oyVhY$csiT(tWCI5YW+~vqKGx@{|tZHPU-=R_vlxfi0d!`dE|rWsrnnO?HG_SZfYp9={&bSr0k6 z#xSRy2&G_70|S{1F(={GZLL;PjF*BR6Q>;*-Vmo9wGMIW_>C)HPalbEW#@&Gt4FzB zkndp({7cchwK{UN>nr&ww4k>~-X&|zMbDX9J@GnMqc~5xn~R=v^wqxTIa_NUBlVnN zC`R`ghGO)e!4v~kevCA1hM^d!bJB;{y<=TdWPvS~Yl~fP#8Bl2JN330>W&ywjN5Io zWwzLTMhsP^7?0RusM}&pF@mjPit!m%04`8qKVP7{U2EvYXEcE}y*NX@F!bVJtv*xk z%857_vL zy+FrhyH@HQrFb>BafEwh*q6YIzT`W@DRjPZLl<9TtE3b9mW9hi--2FnL>Y!TBI-PNyd(;GIFe zkH3)WpU}q%`Z!J>;Up7^kES(Yx7073zjXQH`Ag^@8$D5VT8?oKoCwKM1}SBLQWh>{ z!P2IN0o1i8W&v>tj$>p6+@kB2Vwz-aM7 zuNCN4BGe!zf5bDufmz$FPe&-sQMZ@F3Hd}Zd_mA~~B8*dHo|JYY*ym|Ib z@{Kj$4*b$rXuMtUpKE;MjJFFfp5iMv-Yz=tF`p7LUN26%)i=s~`$fHPyzzEP!!y1T z^X;AMeEG)P<(E9{8)3X%;c)m0%(vZt@QpFw-h9~ywuc6ZHD?$CM9&k|(PN%S+XoiW+tbLZ?wV-d+o;^S669dRyJiSp9D5R%n#^ zuyYbo9ITqfs??u5$EY)$=}_Ux1F>^4$61x*Sh0F`WnP*GspaI4LxB40%0cP{l_jaW zseh42V+ejjWhSs-usX9UN%d7{sAW~-)FsYY>Z+=d>JO{VQU_NRFqhzFe*A8qqHeDq z!A8;F67^@*g=~cSOm*2n>>4DYs-{v+sTm77N2~QUbCR*+v9^*uD~r`X)wtM5HE+Tk zrm1I4C{;7&3{$V4kixE0@0f7r*lQqbM?E#ycP;!bJ->TLcZu%?_}O%h??!m6>-OEO zuADeB`J!%-25+OX?tmoUL-41g-tD^+o`9xqUYQ;8-N%t0pht)AL3sK^H8?zasRmNJ zQryiOp(}z-ErD*M#_$9m)&5HWOSSv%g75#RisNIcPt(Vdv)zG~?uv%zlBbC$fsYyp{tVV?zg^+=wsxVgTP+oD8BZ9=4 zte({yt5J>jPJ?#kWh_svo0{an;YV+R@j2Fdk@3BDtI~U2>dt9JYG_)HD1CGvOGlg2 zOH0B^ujymyXv2Cdy-*#g7N`Y+_Br;_dma6#8ha@nQo9B&^)`4~17V}yfO1J#X011T z(6#g?322F<(BN2h+Tu>DC~^?5{VZ$kQ3oPg`zSRgEf>C(wN?|%YxY@dHruUkUzysg zOrnp`3k~YB8TlnB_g!f#I_jIf4ep@V-x}!V)o!y^8+8Dn3)5R=2WDiCqRIver%=>$ zaUbhJ3*1{hO*4zis2<(<4AW%YeXMVE@AX|hYq=xZm}2M+t}#nN@@1DZX}N@ zKwbcPcE0QI4|=&|?a9OSq2(p-)`wP)JX;@{A@XW{XeP+x@u8w7Z`OyJ6Fk`v{6%Hv zBMpU$hdfvxDhcvleK?@WbM@g6Bd^tm;!Yl`55$W8E2;SV*v z%MtW9NG;Lg2dL|2=T%~dn9dDA2XrMjdjr8bZ_v}yt#*g`Kbo8Pkk|5CAX4&LP?VDmZK1AQQ=o<(8LS@tm18)0PQS)B5#r-^ z#K_(GsDbh`Lh=0w9$}3l0!w@k_n7in4=KLiNC_EGjLFHH)wOenjU#bSUENVHoF{z< zzXEvC+C4;lb?%_>cE4mt++;9}+98}wv)wNeN{{XKJp$=n13@~1?VcWcPjYI9S?lXc zY;!}$2fKYE)nA^xC@v#GopXV2AobUP1ohl`XLI-X3p?dbYe|Cghae;GWKOv!x<~y} z1La>-OOIXYi>W8A9SLt;DbR&9DN{dxB6TeFssVz@GbgtyA(l?v_J_>yPOY#bMrMuK zspIV^k>&q{vu3nDM^U#eOjlPe%wspGn--SwPG4gu(>A_`s2?mW31`VlJ7P4JEVH8= zBTGv9D=v!QiQ4&knmT-OP6A1cy7~1iwRUkPXU#S{xe-|Nv&EysS<_)hjNDFBTG|Pv zXBBXanlC=xs`+kOQm)>(WQ?%9*V^fg(9q?lj0$h)K09KxhN}H+XsjKbX3_9fu7XnP z+4RN!7JsvUW2aK$mO;9)!Y5!(HzYe;yz_uCPp$^JvJqL}L7fml&YeayLQ(Z`Vq zGi%x1_gc8HT55^h?Pe`ew0^(Gafukmt*`*&G}q?_NK0@EUnjLr6m&c*+F23MxaIiJ z&ZP*Va{02X$tDp}Vco)Vn(oni_5vgG(9{t}38n8kBG#d^ULHDVQ?qIDn=g36u<2-~ z1y3s@d&tANmYW!Zy`7pf%H&xEbr!G3#gcD!f)s|JLc zyevaKP+JJL{)^g5ZqOFlTNY7>%ft2Ed^=(^VVz4TCr((cdj1c~mr4u1e^_428{A~4 zJ3@oEtXLA>V2>R!nxxgoiegGyU92dkq;aaGri@CpSWK&s&opE@MBoBSw?42V+Gs zW$gY~QA`=*+&rE#7OMpcH$W%rGnWupcVkyfHtqv(gKdDc!#2R!8H;H5jS}SoWwU=4 z7I>Ca;8|GUNZM=DOXSAw4LfJ;9q*n!in|{bdQ;?zB@ZENqtIxDeSExXuQ!_9{M(Ka zc}SUZ6UAZcZ;wvV*-)tS0k%199g2pFFGL#orR?_ZQV-S+6~i%oU<@wDX_I>Dm0mYp z?R}AAuPs_%478(sO?{C`KAlHtP|tocN%fzXq}DvCZ?aU|sk95XdhMw>;abgUM~udi zvRF~VrPHFKBG+PSHNLnQ1BzM8kL?R$k-G|O(YK|0TD|OX#j8(qpCoyYd#t?U?E4wG z>|8VX>TMX^o1zHdj5#-%&$DE|dq8ZONemai-44(ng+q()>; zS@@9ovmG%STmEQA`I^|$oA&>}PGtm+oL5&I&XITRh|xH5BvzF0;S_@^H?==kRWYEL zwYcqsMeZu0{V7`L#A^Rpy7s3X4}J89g&z5yFbsh+sds0nJkrKpnH{Mv;x>?}5=#iB zM?>IauyPe?M^D~2>T%PmOuegqX58u|+_Xv+n^s`y3e@Nx0rlysgC%orG)bh>-=$Z1r9{N@PZv`sQlRsN?MS~7=at_c}IkEdZ|^8M4~wD$t6@vMhinbD0^K+`C~ZP%rWiR?n+TQGc*0 zH=I)rJ90EmF+0lF#HrrgrZPK~5oBj}OIA2XO6-WyI5H+ylyC(TgRwL1BUj!rpqRA; zVijuPHc?#hiM5X>$@b9*N0tTL_ljW`8|{n@>%{FB+w7v-j#L-LXF8iudbEqj<};P5 zXSPj=+m(zy(;&%b0&l5n&YqQH)dLp{RHtvww)juy*c%a93QhUojp&nnthLt?c_<6{ zpqTFyD<9^f3Ivez0J!%IdHA87-Y|J^wnPkoyU&goO(yOkloKQqW7TH@cMN_8s_;FB ze_p^p2b0xzu52m(2mbyi{&^n%9DqL_u?jE%WCon=qK+9{SO#)u`}dF~EIuxpu<+LP zNm%Z(*V3!76k@lXSYfd`+u;%a7Nx?BptqD18@IxQ=*3^zl*&LmQsl;)Zl{A#dgQ=Y z=xmqTiMO>lqO(2Lju=_6%vO)GqeNzuX{8j-sN?Ny7YKQ9s+&4->;2Nsk=$XbwUZcu z9ZBaX;jY0ccEo7xSY$`}n%I$Vu%l>u0kC7*_F~SC&2|zau;VA&ZC-Y(9Wfd^HrY|W zCUy)p*zxwcY0^~e;JNvnEqm>xMqtYsJBEcz$<=nmXl%L4j`B6Jr8k%Qr*TWGB`hTAgdX7c6!# z)3Af@+Ub|Nv-qD87bK6^k?Nv2*M|tDM>}|I&h!3HRL*MPjBMDcYEx z9xIAD+NRi1dgUmc66!NY`?>!Mhax#E%M4a-vePTE63^;5uD5xaO?ITnjWqo&525rt zx{lSWvc~o;yF@>wGv5(1F;mTui4KV)`pPy#M~o)_57|*7^Tw2!2RLtzkps3cv$1RZ3f(WIp1W^^q!#@9D!5vB!A@!f zwtTwJcCO?(J7P4pJYz@sn%FXI3BPkoQ46lgSJSW2caY;igu-Tf5xDWtHMX^_FYQf> z#*NQoMF|&@7<7@TzWA7n0mZE4YCB<(yGf`oy6B|O>Z?z-ZMr!bjeKc}?^}j8J1J@( z{_xv2Z8p`86s`Xz#foBbw#JUqD`)BYmOgW~PcGk7+2^z01>Myw&Uw?5AoNMqN}9H4O_QfpaW@3jvu_!$ zuDE5iaHZb1Q)uVL)wkM4#ani~Xv}!sj`B4zqug*Aa&N0plW&_Km@yV?{PrQRJD7P#9``3F+ex&`hI;WGw!=pg?1<6WQDsN@ zn%I%Evcuc3NeOz?AK#g&-h5{gu;i{gi#SWpw38WuC1bv43(`Ezju?$4YhpzSR~|8V zh^AA?b+H8{8skrttx867q|9BR{_L*QF?3ys4q2kFP+ex{Nd*2+{=RL6>JmF*f3~J;vAPTwi#Y3US_PHZWUX1D!W@hQ+c<&4YtN-sk`sCHU4{c#Aw2IyB#Gmzf9q~ zh4brpTdpA}6?oyVs@^lyNn&Au@D5;wVrIog#8Y;%Z7dq1j=0wr4ErZLVl);#VMqCz zSX5-NsO$&B)uJEh#{E4zg%P;%bzkjH?PIC4ABIqSI zGZrhq^M2dHK$aab8dnC}QNAXw3|ignZS}w=Z`e#;+f=g^ZnLM~udeGwdi|6F2e%(uO-T8v$4wbdwc(d$x6W!HXZUC zJ5uB!U^;5QB$N~9s6ju-*OmVH;SuWepQfl!KCI+c3BThtJJq(P4pz7QtY~DFJu>cP zJ91>9HJg0UjuM$mrZ~O8xpchUvw~%wt;#}=JE%VQa3SzSSDUGWqFLF3Cy*nyv&%_# z#Aw_|u%moU+{m1_eCgbil{Fpel|LV(?)(y0=per6xY|x?1h(vcBrV*Utgs_SV@s_aEX-?+7aubL}P3wlpa;+u?1?5Q2+MWCE>@PX6whFu6aC* zM``ZlGI5oi`p8{t8dpwzpNuQlhB2U+wQw^N1B$70AB|N_3lmPa-S^pq_i0UdpdMQS zH|O)ape~cBy z*5KO!oSjz~?EKtLZx}mU>LRl96FXwG0sJAMoS*?bM(z0Rl(=FzC)CR^#&7-3dW><% zV4DLFStiU8#v9S65%!*)RlUkY5r}`!TZeS=cO1A(TyMz4DR!=e$wbZhHYZ?_ z9Vv1n&EYklP?`z&3l{U`LBAK4zO6>?o1hWU7?)oJ~FT zd!IC`wA)rJz7OJ5&;8>_*gyErA4~ZD!4-BAZOq92)8ufAewiID8Z$1jqkK)w=tAJf+L{RsaKPg6aqVGiW@`bahCRM_J?dH;YN# zN;}Cm_GGE&{>{doWp>2KZ8e9Ei%?G7(3xi&I*Ci&o&JuXby}+f`LJoXx5LJOA?lNV zPYu`iemi0``DnJIMCO?}Y`mOj$J-%DUhQf2G{BHipZwnxvA3;*^g~qp?9@i!%%XqT zws%!KVl>X|wxfJaoJm>jZS*KB+^y;<&ln#J5+IL0Y96+e8-X>yea7~|p#65lXsmh2 zj`B6JCS!T9$h9=_b|s|o>S<}02UGvDy& znE8fh$jmo9OlH2}sWS5okC&Nm*mm=+7{mwPkO{jxy3*A@yim{XP%BP-iV;akp+9e5=!I8?p!;8uJnFI|}423fj9xY0@Q&8=bT6EBUCHb;$!{4 z8&ZRqr@Y=140_e-mrK=;{ymP+U#1+Fzg&lJhhE-YP`nsLJd4+GUGb zW^&Ux%Yx8m$Wi_3`IzycSKau?ea(Z9^Z4VH*Qk(HuhZkv*EjOA=4)5w#}%nd-e}_Q zGqq|L%Tb?vgNmqnb0ZdU?VIjaYE22h<^Lr8yJFVw3Q70;cXy4me-Oa@{*9c|ARjm} zrQX|G*Vfju0hp1$A<}oqOS`-DFLLx)xl=EtTP}N1gTJM%Bj~Av&jiBfG~sl0U0b`q z-mO->T;T1Ys%hrsv(jgmB5Uj!ez{1b&q44q1*`gO?DSeKo zaigcM0nX;Od+R%(iuViaBZ?r%R87=6tcBs(d|9}rZh+Wt_=1MJ8Cz1{@0J+FSW2I7 zCtJPm?VejRIcjU{1$u*>mO3YH>S%GdV{5+ELubsr4yUNxbM*B46FH5pFXUp z%MRCYo4H7R=Ww2S)8PPrU7+rKI|p8?)wAE8gZMYUeRAsFzpi2f@LyLYAdk|vFJtMF zgW7Olvb-HHRjd4r`3~%?+CxI*-v3x#R zvo&PQhJ|7(NR$HuWv$y?Ece2by_Kz?@0jYf_Yy( zB@5aOW9mi^UP@sYzP|R-;{I}G0oYulW^;%Q0{0YTf7Rk;r`Of!gSf9m&F?Q! zMy)16NKe{;uH~5I5|*yd^3!iwHfpu>+p08#F9i#^nj6SI6Ld@*(kqr|${5hK25BPo z0frSUU|&0J;jwg@s8_a{=_p6bbPvBjV{J}xiD|^0p+&HSIG``rSQ|J=qOKlH+M=1)R&fTRcJ4`Es8X)9L-~wH_!b z;-q-mtz&Mf;STqihy9i-tM^Pk>W*>8!ym3 zH-@Vpf(yN(gPR!Xi-@`z0&@`N>h|Cq&J(_2NvTreZ52y#>a3U3_|S?*!=!Kt=@kKJ zusemEf(7#|6ewe>eCYS!C2*?A2gWtSZJ89yLF;7)Tu8IiH#5PYDsie z8hKTEO+?Qvj+Ui```GJf1667BuzN*_ZN+*4`D_j}#wSc?M|KMr82*_)IQ)$r>Wr5& zV(XJWj!~mBi%6Wz9W#l5W{;xIemS)zdN)IdOzhkEh~NdzHx?XYYjqzbF`~n_!0^1} zcB?aAPLGX-K62iu&B}T1y2uo8&NrS2&WHO+>Y}4HnZsc(5Vl`SeB?KcRWJXbQ2o=< zRQ0~2mAII9;OO~@W{S3cB}-ASc&7@J9(|_+ACupu$Gz`fh;KFT(Hi2N{89Km0l)h` zWy$-XB@WgeFZp2M5Zsc*ZCkT)^~DdSV*v#p&c}!Q!=?DR@58h3k^WH=KDK^zDn6e2 zXazosKPKSiAJg0OAD@~+i!K!#I+~kR=f`Dg`KP&R=#wnWyZ4iNe0=f=Eqk8vDLsDr zDHS*PvlSVHS=#K`;%N?me|_*jqxOCZ`-0#5Y%rqz>@%Wb*yprO`R&i!5cuKeX9~1- z&qgoswo~2wS)O|I%TzV(lU!}n=`2}V@%;W5s}SiwUl1Mj|7qpxpXJ)HI5t{c{&9-x z_>y)@tG--@8Fzk3?f>(aF0+umW$ct>qI8_Qu@Q(Iw)ZegSO4)vjy8xf$KFR+DxmKD zBRiEBK3kjJ$qKX)PL`%EW9&2zow#odD^w5vCmoTshPABBtU=3;WA)HST4x+P8OqXr z5y#f>DyyMN?LYCXN;{CsMriBTF^AR?&(bj0_ITEg&m-|{3qChGSTnC|ns!baJ4tgo zSh053I(he2t4&~Ac%~Zdrc73*t)8X@ zY|QnYf$Us-79}ws&$Uo1-t8Ew-JHZaIbf#tc()^4D^FtMwX>7iNtpfmWVQ{T*(nS~ zNDHN~dY-#l`#gIh}n=dn$|N z2-*N9(KZD!mt?abJ|Bgr$mMTu^t5a41n2~B4lBo0Er;`AST1YE_e*kly&s8pPac2& zdmeAp@?pFYj}PPTC*`wkocpshx9Z5zp2}w%l1R=c1?sfoBsNLAIS*v+toI!0+D!#) zAkRHp`|h_vAS#krsdlJ<&BVIP3fTreDklWoTRgD!=GLB0U=`Y*3RyK~__C09xwnX| z=fyy8=RpmSLDBAh*O8(*hqHm4PZa_0Ioe+mSh?mK&dR8*!#OX%91g|6IPs2P3laXn z2reU;3JY-DDtCLk_LuQ&w6=a6OVu7$m=iO;sjw}az)1~F-sZ-3Ppg#c#$q;sYq9D^ zH;}I5JGIQsEI}Hl(?@b1-ZPTRm@o9D*Z&#C`()>6b`Dni`Dk_?KF=M)MC8Ln zZG9=5tSMtzjiARHsPnY61v|BO#;{85sRWZASp%u~EO7v={s*iq_tvxrM<#WE|0TGyEMJf9>KL59rU4YLs z$Lk$AIoR%Q4K%xhntME}QXl&~S^I1Y8!a(oVi^~tAC$2h5O--gYvkfOp~Kr4@MwER z11)cra|sw;!N=4M6*Q*yIoWkw5aw%txjP|Cd&tRx954sQ)Fka$C#cD`N;V6NzP}Rq z3H@GP#ma|aBoP=f3?YIChFl3ls%@@f^Eso&;IonR|E^-iyx^(Yi0v##Q>xi% zSZHT8!_XD&KsCeo6KzQif4{$mp~2A#Ch+IQ695jCAD+OKbM-{#!u0bdvIczqdm_g> zWfIT#z$7-ASLoFCyqPd`-()VVizlsg zWHwjH-TgHbXl&GBI2G z!O3hrW*9V&b>j2#c~B5Y|NHY;c?i$Qtn2RR=!)|Nx_3bQ*|%V0W=D6|Ku#0Fno3*) zI=UP!?qHLgBfZHDigj~`8)L9IE`}#wN@1M4C^&H!Il{XrG;tT{lU<}vc2St&E*NvQ z7>kGTo4bgKyNHLoh=sd|gO?HmcTwc#E@Iy21PlncFpV?D8ujg`R{ad74$RxE=r<=_>ISg8!YoP)1j z#74{DD>=ArF$l{?i`XDJZI4k>zIyK~8J3oG8+hYo6pe!)Ud$%R;OjUza|s(GgKy;E z>z6>~txH(CoOZL3HckfL%E3sl`t)l_Ss+N@b9c*3zQdYDz3=sb7SiwH>0TGqy89HC zAs2rSPkZDPHcbZK$HBQSRwILdXp~eUgMY-q(As?Mk1m!fr~QP|LeYaI?oJ=2=0<5ZRlui2x@DWvy{ch5XmT)`#5i|Yb8tTAs|u;QIrvX%@KDjWohLR zV|ScJ^r4LQyBN_Ruuw+(1EEce9yevKLCNS(@>)T%($)ahC8Ci%?1v2{6vDR*` zKFR3+!)ph*OrILD_K5tmQ1HLJ2+#}ZB@v6@k$G~h&&Eo0vmU``sqtUDMi{H(Lr37M5h3w>Zws zRV=MCVh1|qz%&{2ZI1b&i;Ws05%CUJ6k6F*tD<_JX9up9%gX2@4#ttIesU;BdkaRQ zoc<|K&#h$@GWZJ)ZmhLdJ|K&%n09R~o1ZQb6`!TeUBbXYu4U6@c%p!xwv5%t@MHmp z-bhoQgD5)XXo^~KBstTEuWR3R5TVQC*<}6>;cWq@fHZDD zcdqlBrAMUYM2k7&SQ!x6lFsJE zHXZLs8Z(ea7VMLcmE94H@&aCFsE^StGFgkQ)}xQRU_3Nt2*grbhMSAiwV?X79_ZERI&JYFGI?uwnWyw4t|6i=IE|Nw4oKQb(f15{Q2l52pQ?xoe;qGx zX+NSCdQkK0`(L8z-&$m@L2wRTvFR~zE~1^-zzeZkw%$b~Vh}{V+iD%Wg1}h2HnK5j z?qeyDb-$mNGQWRY{qg{47xcu+_J|6;qmPY>tk1XguTc^7>ULhz*c3Ao!Xj(uS(K($ zof)62K6x}{s9+3k!TT-{Q;bKKvzfx_C-_C8t*{oBrp?NX&$3`$DzKKXU{hqQ%LNw9 zzZ7-EyJ>?~>jY7uSBm7GQG_DYr`|z^GUx|c z+MdDc3_Rsa?1V~FFn@@H+ZzbM^ue5PpQA+b+mqqjgiawZI<@<>8wQi z{kM!&Ov?U*kv&(-Jk?n4qqIM1>#=paPGy60>%BMvyg`pH1eD_`j=l3#W094>e>K{f zn?(T;7Noy(`tzK6tb`@?8R9Y!I*p}SnD?Bh>ole>c~V_3Xa}|ck$*nT+TMd%`%Y&Q zWiG#L&`OaNDRSCtJgwkNmaD$~#lQ?Ps;GiDiE~rs?1#0gAk?Xx!A8qGKANSiJcCV= zLGNX0>KVoupz=P<(*Asgv1CA?PmJnE$klw#!DpVy3Uc_00p?+W;G>6#DU8JWI8doeyQ7#ZHzHin6t; zb;iO7wL+m>H5dyrNX|Nn;MLmUqmIEcd~CM%Rx8w5n8;Er)s+fjzD#7vSv-tF?)P#t zIn#nynXP?vHakVGqXwE<#}>$-N!i-m7O3{=I*?Fl$s@6ZFgJ zu-CiS{cuw|4Pq5Bw?6y#C zL9-D#+Ls=dDU-cbaM!hgrO5DeI2A?S)&{tDsI|dUhmM(r%{xRZmbF2^(}8s$BoMM= zC2eQ6RviS={+J7?RU27`T*ZZ=>g^k?RbMR19eXwgXk~@5bC+Nbg#1{CL>KY7DvJVu zEQDSq3f$Sms$>GL&ep!u0o3hkVoC(Gz1do-*V=K{WNXX4#@Z=y_j=M^y01f^o6J^^ zvh>t+%-XP@l2*^x9&NVTnV*X;zUOm?MY z1HCa(%MV&r!$5-1lC!3OVp#`8T!*n(_Xn9}WToeDqNZ+xMz-9NkR)Rc5{ba)Q9~it z%R1_Y!e&b=6bW?aYs>CT7$~FWN8YEO0|dTYgVM?l580F(l2m)RKI#iWTZ?qvYk#B;3ME*=`Ibg0CWYmiE}WFzJDC zwM(5NKo{v*&jp9$?vS;dOGV;aA(kSem3HHw1X{arN ze=3T5?|hhK-MiDUNt6hY>LhH_E?@=uB6Ok=V&`?EOPL1bVx}yu_kLgC~=5L{ObjVEh2cf zSy+Mg;yjk7_bH|3iPTLOvXl@XvIH-HfGA_g!Y6!+;T=P>#v{B8{&c0*!a1O34@5_{ zcXq3gB%-zOlGLViXzSKD6LW7WFaozQ*2+xn3QxsQ8#DQT!jf@+-AsKoEEPAv&D1Z# zQgKV!Or4)+B@4F7ij-n1$pQ>BD%Gr{+e1pko)-!H*`BEg@M;7?2N zhb8#WB>0>3J4Er1xu{GbGXNrL}EfiYId>8jF*AHrWw9hkj_^N zH2*F(=B$_CrNk$tbft0at}LNKFb+pf_Pq@Vmvmp;U4Kb;S7HFJcx!0t_PviKpW_QF zOgp-ZWtT{0`#zA6Q`)2xQvZ~$EeHMcvxLnb~&pA`+nZ#tO8Cp+;cgb zFz7|77ubPB!XQ0VpdGoKWtSt>f^>j>hHv->^K_*(`CI+%N}v-i$OBzctW|YERmIw> zE>;4M?Om)49(Q)JaqxJmi{*v>gZN0k?@Rav1;!P^Z#=ju858!iYj``xR{XKqBHg zn44X}a{0@xIoc!eGKNyPvg^u(C4V^l;M9){3e|ZJtP@_r@ zU1_4AMo$}rn{yU$eb<%Dli(bqfOCDi3G0jy3zw3{HDkT_@HZ>UK-bWU5VUH z6Bu0Mbqz9Bw-ks{Zui1KAmX^<>lzZMa5wtg4Y2$LAE1^q{3J*F@Jco!m2z{nr?Hb* zw$N)_;dLbj{cX)Sx+oi0c3lp>TuC6V=ekk@6}V_svDMwYNo3}l4pVSZSWe-(4#txV zqx=o<0vO9uf}MZCxpW z3UqDVummU)xN_qO`cf`oaP8KW1Q>MJoXF308&ZsIf#VaDnQJy|AqLdQJ%+kXb9b>} zoV{G7Y1Ok>RuZS|l^m^b4^#9zoYBF8vlf=KxcV*5mP1YYh0Rz$AmH$VM?X(<_(p3# z+ry?gcR(q4c`*%ExcE#1~U)0oHeL89p?WD!7C?_qta$4)#n*G7L z4bAS2+UqJC61p15zNmYZ@ZxuL6Fu-i&cg%!O?cpOGI${8;eoy&Jn+;NJdpG7KwiNE zxdjhAM*|O3Zty^r1`pI*@W694@W694J5svoCem2U1a|~_gewLMpzq&w~UBhzXyL}_~AKlOXx;}K0pI_>THZXNlLzCOviroe5 zJ_!7Go$T6xrlfvTtr+qI^|~%VGgj9g*y?T*IWKZ)1Z$9B*SWqAuw;bPwfnc~h2HCW z48Um!o((bR%^fXph=$kqE7$)*QVMLI)dl^CBjC@v-U4{KfO8y7(smAu%frTAH!Q9& zgv8a3V6_VwmJeF0rK7ps)7XK-mC}~5+6GL^X!dXPHn^MXAP;TSQR)U(+l8s|bphCs zA&|;yy9EfAkqM-++G{w-t>?O))!qQ0WWaP&Hf%ijJfBPpAzG!AgY<4}^aI7bQXQoCtPKh>A@YVv2<0ql*BnK0!}ZYuSBdAb zk=op%xS@HJE($~zLS_RvSY}NDrGq{!17(Li?aMsD)14`E8{KVSyp#>? z{uX7SQ(4pWtGl54H+seelttd);*NR+zdsi6!iCSB)0OomUCKPOo*7NSU|V2%W#vY1 zu&JXSuU)C!03V#7k}6G{Ivpp}x^dR}IwfVb#{(a@<(Ek-)e{uBxC+pepa2D+1T{!zAg-O~8tcdpS$EmuzVZ|rFC{^kN| z*lEA{HZ|>3ck{3AYWKo7YWyAGzoE!&^*|nI?{6-E1_Pk|98XJU0N%DLtIz4!;At*b z7J>l&>Q28mV2;jm>YQ?9ZuamAQ>rU{{*HF&6la^i)#I$HshK#TYD!89Y7*^%%=mom z_CbjSTGh#M2_ZTs2Op7w9&Ks!w+CT-quEOlZ(i~#6*nYrG+>}kf{2nrY9i3!ZR>Of z+B^+#1-xF05ju#dlmj6tDWk&vg~HzZ{0c=XTRor!JW37fCirv>OnIb{#G{??xUEuB zRRJyvs5gWua2r7B^eKQ|QeCRxzC6k?C=^hVB{ii=Gx+N$2QYOCq@<)YdNxody0X7y zzPA1umOEL(0&y~GDhH9;0!GuL5O+~3MKWD?uI;&jjb`(;CnqEpWWwDEKu~?B0tlcP zv##40-Bd?Sb94s)Qz5! z7WXzlHS#EBN_FXUDuOC<;_z#2ECB|WtlhNMyN(o}mluHe&7RhhO{JxZcZ0G8lV{FU zwyhIHfEon}S5}CgL7otAOXgA)n&%ldWRcW8+c}@6OWY%-?y&b2Q3(Bo*wow9U(UG- z=P7mrawTB2#Ox4A;@#-c4i1VNF>E>&LK$`bZX^C`PtAmbwXyWW5h`HJ4LJ##DGro`^qqT8f2b z(EqK<45h}D9l?fb$|hb6B~t>5lqj6JroF?%`8Env0|^Y*ZY9m`mik7wvMp4ufE%+- z8LxmvAiue!1|=d`(ppOLT8jE*h4Co?CE)icT%xhHaCk~q_&5L>$S9(>wRthH23;>* zMvYR;vz4Jh$>K#q2y_XV+t?TYm4@CyxdH*kp4N@QCNh5YUQ$pEe$R#t-Uct|ImJAs z2}7UQfhwZOa2H8PBG3d56AnVH>A{2%p+(S7rkS)df0P17oEP9xaDhdTgsw{orxNrR zU#bXjW+NL&C>JksQXG-laXMt}mUT{}X$3QtDxwAvJ-~ehvQZe{bTC&J#}CWd3WG+6 zt@W&P@_MzEvl5Czn8pPG8-)KxDKIlr&@T{dL=DmkmJqdY01R8RcLS(-U=(53seHK#%s!Zk+*`GrZDu3Q#Ul!FwST zq_zOOu}-uADgb;P9P|fay3CUxCD74Qg0vZCL9k+i$qEp2e+&8&B?-uUgxj;N_#qX;msK!H)n- zRSpGM4Hoi7uWBtSnN6j@%aE!6P-j#sC5Ao(;{}Svd~H=K0EdqeAWSa{$ku=MU5Af~ z=0FMM!_V85G?pt3Vd~~I(lo|EAkiZIfg{ifv%W3h?W0lwoewdhpe13vpjU?uM~YGi zTtV*;>_I?*FFmgE9+#V9()|eIVYTwDmFs1N7H07GS3m9Z}e@ z3NCH&mVcKlE6L>t@u4v!6DlRF!?bSWCn;%ILt>!Ob zZH~FU&|F|GxLu9T6s_UGgi$BFIart3htd2KJzX?bSgG)joS}(@S-w^5wGof8{Kr*fCe<8El;69h;>X`QQ-pueZj1+>i*634Pd+MZdCp_#H%2!1SyYPjzW z+gdH|X74#@CZQbqE(uNvKxA|iNYiQ4jnt2WP)F%9-v)X@IOeFhFgHBmLYirikX%9S`Fs3MyL$jh?IPlujm2ufC<*uMgfH25_{(!*%VYGA~ z0oGBrO~*cii9xxcU&>)NfM4Fy=I)Fefodv|cYxk+^VeXfP?^Xf&{t8+5^&4F(0j1~ zu}m`sy3R0DjW#o-z5N_Z47DN3igs(aV|iFUbbZ_S@eN>}N~*yswUv$^U(yIa8qi`% zwrMR_9qRxJ{NXPKd$6GKewI$j3GmVbVtHHRd!(T2(JuJP6GJNz+Vy=)$aavgBZbYPPK8 z$ugub<YPkfZHNNJv|POqZp{x>&G6Uv<%!a}394@uDyh(~1|y4Xx~x5-L^n z<(~)=1{x4NbDE#pz67cpNg_cE$X>@GD2vG29cRqZoKoFz$NCigDcuqTDj`f2%p>0u zR<*%4oJqz3BLS3}3jE|2Y6h{CNqtGzi1Hrf{0%^BHh>(+nKBo0yMJ1pR!J-9OIf_w`8d5@w zob9kT(CW{HRsV5fbjb5?nm3P*Ja5KH3Qq18ltL#~Yb$0Y4Ck|GYXUd8fR*V|F*RKT&Gc^MTyn1IfPKiNxVRXA zf;=JG3*iekpxpQry@VI#sUyzJ90#7qI8Kxh&_(^2FGUkMA!>9<7}*`Vn{B4Es#+WT z7|WgRmOK$q6DSvA$qz&?pr9W@drh!mW*h*=9jH23$^h4-IFhZQD$w~rt3acEIM_5O zmNrwszM<8Rfz`#{1dtDAwV;Cw@AtGBrs)`M~T0rrVNC@Lzj2-uzS z!jcHit3^%{w9`;TJz_#xAEnfHzzxOR;UdIEA;1h9k(2<-FR+``DQ!Fw zcpmouA`}n4Wm5;+sSp*9x2%hGq`=H^oHEhN^A;m*ynZxrh>A|lyhTp9MYAE~2M(18 zEr4P=Z?W#M(vtEb=a#_QZR^&KYlZllaeQ@|mZz~CTt^nvE?vHS1^one;Ti&8OoBBE z_|5&ct#CQB2b_0EhIuA_>IMzx6s(g*DTEdg@2M1oiIO-r0+4+2jlra9?xBvMArF)e zFMQB@b*~BYqF>F+gDmK6udT*`Sq*-kWRM%c8WU59Ba7FeZ3cPOJp;&$Lww$1$X1DX zd>_Bgzk!b??e8DPXNClwxKNKj%W=*Q<-jSP5pByad0LFU3m zj-&NW><|b4H3Vzh(b+N=FL5OU#BP={scJg1B-jLNDzq&bfK4<%9QgINz?2+V>2Q~z07aQ^fZuaUICjQ>4@U-$FY5%MqJmmu&L^Q1^ud7qjegX!G=o^N*1Zlry%lTg*OB6m zxJbT2OeyuX0j^TE095R2z+}-7eyRkI0S8enbGVyl<#_e-OZZHq3M58oh1i^6nvXix zoUvkowlg6idv8HpT4+TJbSO2gLfO7$2a&d-m0Y99u?+&!fHm2lTX3GD}c;=1sf1(9dLA` z+_$*U00P2$y$xQtvreu%tspETmgruj7<2kx-8oI;vDq_Ut1$<94Qak3d^3CaC!mH4dq%9 zL-OvIt~qI3nppKVtR9f+^}S)J3Ws+IRS7O18h-e#laIq3)9RJ7kS)=Tvv(8cdI9*Z?~_DYmO`x& zHb|jCw2d1Mjt5Sm%77!&7sqGNA5b3QxuEAzaoEB2k|V^|xNrj*$g?ejiIIy-Uzi?2 zt-$_95I#)e8Qw+%cPSY%u`B_+Kws0CdxT<`dn|?WDptcMl+D?P6W2w#u33#UE03~R zgu$2-6rl#-d?8F5AjoYajZ4_bfLJk)2X+(DYoCYH2iy$uEB-ccMPLmIHn~YG+HrdX zw+oh{X5+IA*m13ZwNeO*YBuLgWJ_VWWaltPma-I7pS-lri8L)AO@&;28sA)<6W?^b z3gHJpEyk#&kcO>~RH?3O=fWsix_0_HbiW|1h~`o>k?JIG47ShwZJpHQ`H&Nx2tDkC zfUxI}HdA}*H3;u8MmAYZkaj+ucfuU8Ma0d~VwYi1!5W!16jd8q;Q6pNPh}$j|Dm3u z5^0vx0Nb+4+WD678Prf7?}&{}_5Sui$rhM^bGC4NMX7}n)~?g${N0ho0$O#tBUiis z(s)M#%}})JOW>PHa~^^(>(JhhSKD(dTr&UwM@p^k=~m(Jfsd9XJMt-Ip^;*7&|sAf z9JB_AsNfD7EgFGH*9kinp02&~O5D&vKwV%hEp~6jnMtiS_^)w8lWCi;7?u!-p22h1 zLd$1DYz#cenb95?8ke1ou`&eqO8yKH8$w1xlGk8n%Bl(*o_%kn0)%Ch5Dh0(_(EUd+n3TmrvI z6-kp>Z7n7xh)d?N%-U6~_F@Dk>0?7=zMR$GLvS!LA#yH7!hNjvaS5T3g6g55U$ff7 z2v63nhB!h|dz95G5VPMw=Mi;-C1TZk;S|NCa~_3AY6XfQ6ziod65%|QU6K(3*!d|i zW%;0-Jux;!sXE(uEmA89V2BL8NwUJTOA<rOOn(r3NQ&_1B4oVorKTmuW!* zk-zuQ9qiVgo)tH0|M!dH_9}FS#5K{?2_2M(=P=s5ygOazy7pl2<34a*vvKUD+w~}R zCGJbrVT``W{7ct|q9ct$h(rmU-cwNA>u4RD6P#fwYvlev&5C=f0?&)w*ep(xxVF1~ z3{~c+JKxTY$733=HOH0F857r0thFm!Ek2T6EC~6eb{5K727Irk0S@ost|xw}3Gvpn z#Eplu4WX8}qCz6ZmWiNL9G)1`o^FX7OP`nluBuNTR^|fh?4P-g@$P6i7IA2Vw&x2l zJ;y1G`Pg-7_=X4Wbsa-gGGO;DC^>|}Uou~~5635lw=W^QYfnH7FS`BYr~Eg>%7%Ba z&AN(dxM2?=7>xUJV7Ft4UJZ*&wS}dIzLL09a|;xMX!x;I9-+!N8@_^IP}`CdIJ01_ z9iG59!Jyv+gMJeXYEwKiAexp!vxNHywl%YHFzFsgvyDbNUk(B27&g;Tp*!S=2|MI& z*t5gwGX^{o-T?8@&et%;ON8u_6 zPHAwVEOOW&nPDYz+@Kzm($mC+W{i52V$8vQ#$=Wj8$!6{fv$jc83E`O3uwl=y=8F| zXQurP_QO~|6b+nbDIpB@;_Kz}39=?^Cd}y)3yN56Z0m_oEVu&J?bJviacLrSo7NDu zgU_*{|3|~%M%g#6AZ7_etS#$hDDDejO=6xD0mtP?-w=cFct!c@Ld$HCWmG@XKvbOY z&@x+O87%eVc;KNv$1$|54mYUazv<-RvL&dNaa=i$6C)7YFVq)LpaJWF6DSl*hzML? z=?G?i4!7smRk9sJPZ2l)54cP}{|8IbDvJ{bhJZjZHu0NJrmH6j%uJnn(JX3{VziaY zjd|5fG+UR?kRJFf)rafrkrf3fmE-&`gnG&r4Mv;25P8Ry7BWWvvLxR8c*fL$bTpejbYiRMtB(DV{76@nNu$Z0xJQNyK- z2>@*LEtp4IH!L1S*wvV#k%onZBhi=#?`iQO&Hc~#q1v^d$7kf=h9`t@1*BNFz}hWZ z@t@Ex{CGTU+FiB+)~(Q53SrGzrJ8 z$9SViaw8^yI8h6im)--4LPnS!9od+V5fAg%^dL3V`Nua-CuD})*aLNX2)S`fAvebw zr(=)BkpgwTf;gQ}@6+lN9;Y(`!vlH>)cLQ7(}5*3Gi)Y=*T-1mW*|(r*QlAuK{M#_ z#27Lo%}tiNLxCVh_t5z0DOgCT*W(k)g65yczvFEh)3p~~i%b50DY9k05!phjNf@;7 zz!jRunwl#-`M*7`MW3VhZHkVfp(r{3C!<=@T|;sA9o-rV;1ZkOIj(#J8@qS{Ji;{+ zzz~XWM75a9C(=%t$UV+A7jk2?0??Fm3rqJA4fbVhr1;7KvNa^_6xe?U@O9qC654ojClzY(U7jP+o8+_t8XGVDB^++dVCYPfvc6r`vRkHA~(#d5c+Z{&{r0@VV&vHLK-aB zz=T+wy3;o&LVdg~r+;fGJ%>3&vj|rEVZI%&FbAJy2itV$j^(zR-ohNdxva+Q!`^?H zl5xT#93tKfBLW_JxfOq<(5Vq4veNU#Sagxo&-UK*Y3Kk*R>(hfXD0d%@T7?EJxPp8C7U6<*> z0Q#v6(V#u98z4pAswae-UB3aa8Gk^)C+W+e(%w8lpL6|-XAM!s`G@bH|_Z ze^NW7uh9pw1s{a?^~8EafD}?e8}(tK5CpSv%Y@2ZPf)4d^lpnQ3BkQ~;^q{}aNJ`O zjOZ3DzQYzSP#kRbG)l*j!ovmNl8x%j_+0hLqbd96w#HqTfp=Owe{S4JHvGps;tpP! zy!Y8%1L&W2Fg3maQ~#41ui#Tji_bordhcx4rO>bG`T?oBE&ibRLaOU>*DoMxXx&n{ z+HgMH>|f`stHUd->gt3{{LS?|WEf(}0A~Ssx~8VZkHWfsk``Y&hw6I8^(N#WEaitU z8N%(zqPRn@&%%;B+8W`jDVVGkr^gS+(&nbekHx1qJ$@WMuTGC2gU_F)$Cu;tweJTbqE2I zgH3RgSQC8d6Eg3w$c+E~4(&i!d=9qvsYdiVZtPF18Pc4a($zW3oPxp4kVz&{hu{Hr*8##Qn3 zG3NDG#ph#_&%KJ{UoYPGU&YJ$=T%%p;&yWpDcc=Cej3tW`w^>6LQzN+Ex?Zjh`vsV ztIfcqG?CN*AMxRmcjazg>;t>wl|m|}Dz0`u=GA#DK6k7g-pwn{-V?8sQqH!x+WScM zM5*l_xI|8`@`1S8UqYgGb9ej*Y)@!U{HP2<`+HpNTZope?LLj=V%igX;wQm#-_<;8 z@zwDPbxG~q_}cj-z`Talo}g1FXZ_WjKk#Ag3B25M<7_V`ut zxl1YUP{9G1lHm6r>Eih?M7r@>z;qcS7zrYk!WqJAFI1oYyeOB~txK)W-(qcNcYGlb zdt-O}NO(Nf9Y1U(m0<090wo))6P1h73|>+$K@kFlY!qwb_r{lVVmL?Re8pPlUaENC z-gsyHo;6G>njN=f|Db*Gon8A%99jEs8tFLjJM7V&Z#rlc+$5eqf73xy@cf&OG4oIg zT%XoDFywF0V&j{2&H#LjR~Mf|Ry!2ahe+vlaARV-7;X#Ra?r49ddopN|1$A@?^_Pi zn16fAF&YW^^etX-!6BZ1)*%OJ*bRpqG~6ybj`0Xz zbC~0udzj*_@7tWDpT5mWe(7!A^rR!46_rPLX=fba zEI9WF&v*L~M=93w^byDC6sjfDQCsfN1|4zaB-2SdD!%9_&$jreqYAU1Bi?U6>Tu%w z-}(E#cN`;&sgzF~wV4TF?QMilh*4?t-rQRHJ0|yJDlG|@ABu8cRA6` z@AAf|@ACfoiAaC>T}LJ2WxvO%S@0gu*ZiJ?Zo#_#J;!9O9=ZqC-Y^iz%GOqX*9s52fYq@xCd!JW({rkMzeGvGkT z+7BG1JY_+0?e~)*WsgX??*qrA>BOOul-ltr!t_et_!a|nsMN=x?dalmNYaLX=ulv^ z&;HOcvXBOCc*fSPP2kQKYdatZWV{(3eEeo*)KAEUm7AB_;_j4bxZy%NvMPVopd$jac>xwP`7T zwD8B3{BhqWj*a%4fOzK6eb1-it(CBi_?_xBNXv0Y`ls zMiG>C!{?5Cj*+b$C}cSTqwSC*4^oeQ?kJo7KzZ1a$?%>KerkbpTl zw0G9B?ES$n938jen0V{E2{blRZb=|3Pog0LC#m^Cw7Wwxr@KHKINV+wF{N|Edc|opQIlS%G1k&=?i1%OInn0@V z^;;9jDrDT2K-#}XJWsui<88l<6L#Be9RE)O{*HL&+|C;?MLbWxo#XAeJ%OymUAHHY zI{C-#31wXC{2;FOOVsWowCeFpA*W=*9SPHsc>f&7wr?)L`C~-%FT=&#v!r z+^4?B>-gk*oX~N1C6sdS@kvK*0&4B}x((!0gF~5|P@4iE3cXL0J3;oAA_OyQDs z@b-jake#RQN*D%@H||O(&;=|#$Z~m}!?#f4?C&RtaWnhQgdCntLG}#Oc7H!1nFBb# z(B*g>vU6CD_Ln;VL5%pP?noHU%bj^gLKRfH?Cyk7MO zWv04@K$|nXts9zQR$I4etGj(8AFKz{6KQlOWh9OmOL;3?i!m>%Uc4Qpj=tE&IpuP> zyf|yfNX#PHb~WP^9%<1AWhCb2Qp;_aaN8v$mQmtBK>}x&og&IKOuIkC3RvO(&oUBK z8il)OCgwL1b=7pmZ7TQS>hObsg0v+rH;|o-hdX3&qbmg9RQNPaSi%LaJ28Q)NCQL5 zn3YK5y9%D=RO2(QHz1<}2=|Ms4%KF(2qhy3FdO(>_1syBc_@?nW+jfCQiMVx6GH$i z*#t_#CV=bg4nltvX-8%yW>2OBQ6Vo#s)-k5X%B(2w1XoPv%pvtX)9+Z7G&{!mgW^{ KJ7*`3{r>B-$C1d`mPLJDb@%O&I+Nw4n1euf|C&ws3}8I3eE8jVJJ(t8IkCGGkn zUU!>redrP0Cq4#!aba;`&!gY^JQ^6DGInXbtzm-AJ<~{`+`*6>74YbnA*+MK!^ENm ztb&z^^Mb?mHFcG?B`qGmnmU);>TqzEvJwro&dNrIjazhUbhsz1!B+2VaJ#y*lsl1~ z%8kg5=o)EtEO0i|xT|Yj-C228izlqI!MeDnZb5fGg~BrQg-WJd!m@DoHos#B<5@rU$7gc+y~3;`C_hS)Es-dn6rNxY)=+*3pd|s z?;_?Laq0F{G3P4zn?2h%h?o9~yXyk(t-Jw#L?3Hfa=0#q-MNP1!JIWenX4&GgtRwz zM`13PSscNI7sR3NoUt&8d#NxM71RGFbr0lLcMlbF@n|r&wIGu-_6X&M7KH^6%|gAN zBBAvCMIodCk)VBYqI^Q0c7H*9u&vHr<6c@=4?UX!d9EI+;`!pX7Uc%5r9D83n|K~_ zQ3iLUIG)>H%%r-bFoK&`?9W{+9>M*oM4je>s>MwaopmPT=W39wIm&F;7*s! z&TIk38_TI-+1JDWxhq<3ZOO4e0RL7mvp)#QycVvuS7yLnEj)&E_R2`#1_=9M_-QP+ z+P6bWh<4J__T4n$if6|(HQEnB#?CiF?6u8C>>B~=36w@wi`b$!!cJR10pK=Ux9`7v zW3AF~$b_iDgKol%u}hT?R<OjZ-)oYF(|P&HQeAyjpdG-3ah)uStpDVcYDoVWt5OeqP@l@zZO$W8}sAC zI(~HAuGNm0IguEpopt~#29CyhYsEtA0xz=?!b%a#)V#JWJ^&7ru-YwTx6cUIWk?qo zHeH6q@?L#YTMY>HD+O)z1Uj5`3m}$p*jzL!AiO*@T8XcH;7Bz-cE=mI~nXWxU`euM*~Yc z845J0v=h^$0i~T-3k@diq(5mOX(t+mAQFD4=pLQhSs{~z1B@rAywTyXxh;+wm%CpJ z*K)N0$WBWJkRp~Ex2;xPjC*cquM%QhV$j6XG}x+a4Ys-po0&2kf^Q;ShV^Vt2=Km+dk6G|D{j^BZN=fWTAi0_ba_q<2#2jw} zV?WgvVk5^5j~FDE+7mRjv78?)!ubCf$Th`>!1jmXY2FGruSL3$_s)_6KA}U9 z(^lH-OZ)Y7W$@ea)bc%n(1pTkm~oh|?^q9>avHsRFjtFI(1+B6 zSz07DouLonQ!?>k2SnEOW8w{T!r}AV$3(Eso}|So8Q5UiK4bfN>&2`^s@BUmEs~0B z)l1H`8wxkMG?5!v8mp%Y;ZA)W%`GeqW9o2gG03aqrP5yB>R6;js`hksl%(x}enTCd zFZay2JeY)!kIUpI;q_V^^TN(Gm3Rw#zZR)l*frNF?6r3=S%7hq#wYZl9UL;&S?hE- z7c4cJos~WKKxJ5E?&Olrk*?X57I zZKst^8N%O9N#@?2k__hj^pr#{cxntAzTa!f!*{E5>LBlK{Z@-qZPAw~$+ZvP>$K=o z)5ghmgr1#dW-><^IySS-zInE{%;8$3g2hNPTS!MqBqIy#D2ZgsOtbA6na~+hD;n{{ zb<7iNA17DZ1}t>lWPzo*yTR(<2F*wT&ji35%1j*L$$~Sd8LjR zL`gc$_}-hD2J-$gGnvU-sl_laZ`$N2u5p%1UaJ|(lr}??I=!YMbb4} z=XHQYYUMGlrHL?3NGrm#gtiuvZLU48+gBu8xyD|}$~APWxQ70#mR3n0a?iY`T0_60 zMJPxud50G%Nt@oG?KSkZn=80UF68aFwiaHOw3z3mj^LaX;oi3Ur53SZRg&bNX_2T$ z@IRTd+KRTXT_v2oDj9zihXS}0mHx6h+fkJ$fCHEx4fpH7ZB9ba54zKU0e@Q4bGS zt9G4k)*@ExVM<3yBzMdPP6s@=Hvw<%4w|;Ex144>Cb7% z_A{dEU#F#8+LuE)mFr)vMQGvU5ov7SM@iar{r2BsYP{OMYTKK4+(#P{xpx;9`W*8k zJBH!>jv;LR#lZ^5Gh*H3EN;>=KkiXSd~O0jOggsbwp)vN!3ZVuV9Z~WdEBejikErN z$L^hH9$^*Sp59@~nBZ;EFfM2@PYuKPCzWBG(;^h4mJH(`l%!3==){=diWc*{)K`rO z{;NeSSd}FCFIpsO7ylDe)(wmaF4m{Wrn|4}`IS_aC0sTEuE)4Amm(3}p;)XULQ>%$?5SAiEZC zqAnh;4&+{S$9d~xfmQ`-eN<|Zbc#OI%iq;ne2Kc4drP#p9`4g3R_o#3j*>`G3Ue48 zn7O3MjydEGkVvh(r{&26x6j8y^z>KfO?G}21K1%`WgzsiIN!afrC8dLC!G=8{FOTI zjy$bJtPX}wQIa<2yz2;tf;@(LD?CPbx7gFT#Y_A6T=FMDQIISs;)cG_gPXA|MiC5s zsKvZsg_3s2iy zQj1t^8DW&hLN9<0bJ+_3-8_8#oHF%)FM`E;WaH1b>MizZGrpkt|VC?edVr1ri)*+coTK8_-@sc zs-Luo)w=jzi=;Djag`0lyPQY`J#zHh0vkEUo5 zsspBpl%&mx?*A4_r*N6~_3>F2c-2r^=TU~z3$>UREKsry#{4ze#(1q(yljKs$GB$O zxb}xj>{tR^>fq+CN@h`Xf@l!iwV0*`k+wr+5L>he1)WOs|05kGk;dCYS|naNrDw6P zS*O?i{5EVXx1)a?(`mM-)7P|^raIlWM5WVLwFm`iC7r%RN!lJ{H!^a9Lvpoi<9g9U zazP_)u8M}5dUEN3KO*9>XAAk+ zPJVUJ_a0Pj@*mS8R9o-Sj*>{$%kG6qyr>U zE6n+HfJAB~MN52P@bV6w-Uw~WmfJLq>mJb2rQDE>d|Wp|bm&*KnC3n^t2zwxUo9d* zQpuVBLP(P{?&91)= zuds4+p-c~+%>~RW4AbHbdOe$q+mi3?v&Zx8*}%fq;1I3(lMJ=jS4D6y?@^gV zfEKaZBn*_K(@dg`sG&fManTyC5;=6!B34^Nwib!{$xkLzR9n_?J-(N#+fQzF*6q*W z&rnU)VoOv*@}sKDjT5wp)oL(lk*EWQ8?FX!{(%x%0F`~92d|7qE#^dJ{BWSy+e0*H z5v!Hq)FSB|n+QIX3O12{D4xZU4{C8GYGKysD@!jlD&=LDJ^2P8d^I_B25=;WON`blp;yyeA^)r{I%4||6)>t0~nqFG6xxl zO)6FFMinU#Y=-(;OEp4Q4DbYIz#9mc7Fg?RTrH}HkzZ-mBM2|WFP~GAPMfUK9vAj2 zi5N!qRtkQLJv6kV^Yp&QRq;!Z7O~nO{k2F0ZAqr0rzAIUt;<#mk;GEqm6;0~to7AK z`u;hxfjLZk(2_#19d5ue)${!BTEuFt7KwVEzu{`&lAcVL&GRWw^2c44Y4IlNV&jvl_q#0CB3A3d ztwquqx;XVz{BXsKi^8WmYU*tAA;kvFCM|A7?eu**)_dN2Sc_P#od>i?Izu~U$1}LH zR&t4Y{&D_j*mGKZiF)|xxayhavs%PzJsj5}=?p!Do-oQrO!$dZ77~7@#ha*$RVP#j z#Qv#8tk%Ux9VPLedfEwWgjS?jh|TRCAdy;Ou}}v{q*mAtNC!xyR!(Z|90)Tnzq>@= znAmx{Oa2oFUhVaZ_F8d=DKD%ep^9sE*Gdcga& z*c8=L{DSIw(i$ydwOTw{B*J)-9MJzjEo8-KnBa87>uME;4*;0Upe+12rp2wOor5n1 zd50fIwTRW)Ijlu;&Dy!HU0R}^tG~-PUj#D+y|2ZvprF3~8SrLW`f^~^O89SDl~`2g zG~S^kZTi^jIHtk9J0kJ^SC)9+5FaAk5dX_7(d<6UZA|j-wU`!qSDJho)7LclGTZL} ziPQ?4emX!Rjj(hrSp>7=r+E58;%lDbuV;&xSR%aDm~#z^VrLtdi_>~Zct@u7U+%f; zv|g-5EcBiARP&S+CbW#2dI7uZ1zaHj8?-XfaKPbXmn!^*UFJ zSUr?yQIZ=ulx6oEyKwiv-pA+8@T(lo9;3M9*r>(0U=Y$kV~k(ZK%1`BikCsqmt=RI zL4-AoV4rFc->EHMq}#D*BL}qP;O>6$s)sc9X%PwvkjC9^O46o%+~`A^?Ys}_tQPYi zbWO{^$S`g>hSS-rl=biM??7Nd8a!>(Z}D(VN37tw78-g81Zgb?^x(- zEkd;hzS1I5uOx1`2Cnu#yXXk5d52g5+(Y6ny%*@MhX^fVwH`t{N+QiWOb;FKOp-HW zqpSlYQY$;OI!GAHeAGZ+A>VnkwN6%Qk*Hrrtf7@WOW)iwWiW$oMf%DIpoGuti&)2N{Q489mwIfi7O)p zdV(@sP;Ws!tyiV7M6HU{8jI5+=^Sf%|C8jt)S5ykf{$r1j<#9djScW+9HyaRT3iYy z#o7O1T^fdiu>G6QUn1G8yRCiXcbc$^!{`uL=OkrE|!U`^AuuE3qaY`e`C~ z&my^;0tq?l4LEWp*%VGcM+9_ag9(6SKLir83Wk!|+>f7*pghcLu?ND-8m*KC$Q*-L zOIaxgkhui!5wcP~=E2t#RWGuEi&K;>D7g&oM_MU6q-4jgWyOl~m<#G4EJ6Z1 z46l8b)I_*~FH+jcC!W?)wn&b=)pIT7i{!|At=3Y$NRH@hEq&9Dlq0X)k#ZvyIl?o+ z5zVfpJd+%`xw+QPH34T*mfS9ra^#wslq2`eq#U_;CgsQtG$}`}qDeU-yOg^VRsbYN zl(4qZ6T*G|c{$p~jr(FOO5nIJc6Z$i!^>U_So=QsPy8$tc>%x#`#}Irupf%#EMNA_ zJwh{&(O+_nZG!zM{NHSU9DYH+$7llg+4gczf0+g!b@}kiPlNsjK+k~63Rih8H~Fi+ zoc>~OpT9+NgD&0?@-{*7Czb}lcTy6#&n|Xj1SQtWN{bs?=b~6{&E+KS@HdIvYnS}E z+DoB?`qoPotXzJ%&F!w?R$R{IqQB0j6@Ohrl09ErND}bPLXzC_4F!J8audE?L4a?5 zYb8m^cQ%qd%o3l=l)%Z7bC(wwr~o7JgfTu{xQpNCaBIFFLdw7K{c@6+e^@}0V?S7# zI63S9)&P8DwNE;??ziG#O5Y2+D> zWfR=c;{aNXnirRFDxq2iwc*)us7Dz2vKb7j96RlZEW9ZhC3Ck`2I5n3sECmb z#h05=6b_6>c7v=GJ`s}4*L<#sqBPt-o!D*5nWL^v7CVqPeaMrl!5|q zSvE=|rB`I5DNMP!qH+`P`5aKs&U6%uzimN*G7T5xpxX)AksRb8X=*MiCx&oGE+VFm zU(7|*NSe|ORgv_rZm5Bz|LKNqA?cz#Mt32P`gwRlAdRB}%b)QweAj4zd-p14atl!^m-qHscy z)D!XJelR}uxh@u$PKS}Vr6-z0u)p_Yo$oARQvI`pxTo!L)SvXsklo%{A}EU0&!k{MEo%x&>z*2 zbbo(T#me`ikX`48^P54 z$OvR-9n-^QbK@g}P!29C0W$^xQt|weD1zB=p{v?jZ^PDp&~dv*qI@Q7PyE#oW+|z- zU=)fZ5)2!~M)%fHl1ko~jKD?)LyfH3(cn6%S54+;4I^GrilQ+$8fB3>o*vD{dcqjy zEmx00l?49d7-rOtu}r#iV_A2NEM?j_R?4*9Z5-3u1LIgZe-qQnN)yZiP>*3e`ZJTO z2mL-gUNi}H$0x?KKKpJwqb@Tu>eu*m@B~!FX0@IUYq`zg!jnsZ?7{?8%v4wmU|c^5 z72$^`gWqsWM8TxBWfR$8_}4__W_lY6j>Qm-8z!-aKAMDXXP&5-dLqgVRMDMKpPkHP z8$1OqVbtTXHO*ke=K#RkEh4Up^5U6_3JKBismM;!oN3I?cTE$Vp$Qk~plp2WByh+R zZWcV^NPMyk7&>vY#37h{FH#}|FPn}Alggf&&gRhGGf;j42^3*&A`v1aB%&6ksO2-z zNK*XN3}hy0_)N4&7#p5BC<_mri9+$yGf{zHM3?6PM--Ydi}62o7V{@h%|bOyPjEGR#cM7SU~Svyd*sx+Y73oyDIITa)) z%h8w;nQ)*Ef0v*qPY6|sm{+J}K!q1O)d1+BtjgdjuvNkbd3p8oIe9hkCG@hFQ6he? z!b@6I=_OqMN;F)-`aP8>Qvo>U4fqrQMJkRKTOA7}j%ZHK5$kK>WC;s7etk%muu?pnCivtA8&0ZX zW6Dy67V#dc!dY97hb;id7q9^3lkw7L0U~Pwyk!9!XnxhGmKDygsjG703stBa-cyap zG6la`jVgF2>aqxRjl*TfQLro;+FXN{vN1Xkuld0cgP*8Dqw!KZO5|yo?1YbT_)9yA zA_Mb^9py)n=Q#6P8XG-6cJ>+ICnE8ng~;dwsbwLGr%UvE=`KMtUHdlE__~>{e4D91 zYo_k3nXY1+=@4(Gs%@s4ZKjHCrh09rYQ2|gwVCb&3pae@Qo`O!}0gw z@QXZrb{)!96F|Gz);nPsxV1su`%Y?lNud7Fecu74936_C<33z}UV!&@MMrqtL--O^v zi%?Jmtf~|h1t{?0a?)TttQG>A+ZLf5IXH}gJ&REYsg~ROeU}h9YY;vWvJ1;lFqis6 zR|PIctTK_?@ z$JNa!mxt5Op^xCd^Hq4npFeCBp4s>=^Gux3rvxvj0ncxFb{-3Z>&VYVN(P{ zq9w*N^1~}pcX_Q78TgNtsJ9$Eh4sXt`%tP^JvWOOp^s?wGsSw0cta8rwMvn5#I}ui zfd_@jTPkC?4IY#y2U{8VV-JcO&6GxKsubiT7S-YjrMJ&)u0nW=R*ie9ATa$*OpUT= z3@%PbF=2`#jwoEd3gz=-n^HNW@WZQ6FFB|o3R@4sOn!Vd#FVhl5h-{UO3)aEUuuEa z^7Sq#P=Qzyg(tUwYkhSs+w1U?*STD(Q*e5;wm(PVuU4b(@}evF2FzT*P(1V zd~+0TdIUH-mw_@B3f~5MY4@WNdD)#3w+c1y;o+O_N2&7i`(?V@&=vXN!5s2e+RVWy z9L}M^a;hUycn+tuna88>BOEG}muii|f9Ftv9Q2gLMjRg4g8b#kXDCup#mOk_X;E6o z^Hi%nDLn%rvy&HvRZb1Fy4g5U{Z`y!{%0VAQ;T0J5z$qAo%i-stiyww1?qBmz zfI@BQHwnA5GNRUtU=derOd` z_R+&AN#1rSE!iW+<+g&$Q25G-XyOorV35`u9gV|x0jFzwBOM-eNFODyDh{f81ogo~ z_v;mAofu8bfG2vr#7jjflxm2)BBPw(k4I3j9GM|Uj^Ct2W<}$3q$7YogD}fe#&dB* z3xq(c_M?Ce+dyLkBMDs?@8Zy)vng@}(fKbY}-b_*(&cjho0#zAL>?f_BxZ&mv7k*uNE z8W;2_T=!7)=@?eup{=N!T$*uw^GQeaieYJH`i!b`R+8t{GLJBcRkU)OxQd|sPL0Md zZbQT5bx&t?L^~TQZ4HV#W<}#!+r`xhrJoy399Ex5y404pXAvb9mH@PTIV~?lO%!Ak zOQztHJCp;xI+~2M9(d?b6t3uVaVN7;eF)gn^|vt zbP)Dn2kt@XaydM7m={HnO+rQYtl=3tzXt`%E4yE8TrtD0kH&-cq8$0O@j$d}HPGTo zBm1#wN(cLJG(Nl+DTe>1X#Cq=rMz3kVF3J(h3G(T8ao(r?>;d~r~2H@z0H4lppd2_a;rRUKyFbNf-gy!259hK4h+(}es(6-^vt*v3bdF7(N0 z{KTW;8iiJWoRv&IfO^TRf0pU_>4_*Ynrra&qU+z4k)9Qmnf(N zI}f5jdC^yC2Pfh!2a!?U@N0}8uofXer)<8-z@X4vdFgi;o68T0i+Ni5Z_#+~Av8>0 z@_kl8;9=BP&c=sefQOZ1;UkH&M73f56R;#6Y%YbL)FaA?{xdpKC1e1un`bb{+xs#a z-*W`rEC*eR#$O&m{p8GkD=Gn=!##!!%Fg|sVOKq-j6Q#&vw}jpU!rl;Q8ZQF+JB?5 z>nJkIL06*jUyq_vIRgl{K8i{{xavQgKZa&q1NWEc2>5qb&(&xE>hTt zAC!YV8oc#!)P-Mg(y#x7@bEVu7j1{a!(%Y|ix{v`P*e=tF8b~-$QVt$vN#P;bS#hF z^B0uE%R}J_5}6RnQ+P5DzwH=2yBU8BMazj&C8V(bLy6L*%3|>OV@Q~YDY7e%3~xm- zav5`Y8T+(~?v9qvnlRrh4@JRC$ zC``^vAD)-GCs3YT;QoC1x+h_sxn`aedeGVi@z{SqfsAshp*+0LlPFFOA0gGI;A%7v z2b~AtzdWg|tdt>>{e&-w@Sc@&FrMc-;whBL`*nIf=LrUf!zy%5-VHBW03*@o5gEJ$7Af|M3H!51lb2BMkYpAwgY6nTnAc6(Y}4pR7O9zLQPbrU9W zig=Mn9DiDLMHK!D4+lnseMk!bD-Wlw!(Oql7^TQJc;va`C>G% z_oOO?@j&7KVDKit26o#>7^W0?PNB|5`zMem`??ni{1W_lg3Qj!Mu%-wox5Ra3kN8E zcb6O(s9r{U!G495o{=DK^pXu)32Tle*d_^0cIqVbe2SK^WIIj5F80EbjVuYfOv2I~ z8?v1xVOPkpO~3@%xRTI2T>?H&ocTf)*wh3xQ1*dB4B#1utdyb?=-ktPX! z)(cI1rG$N5!qSmIJf?*G-U~~7jfB;M8u^Z(v}E-2*vTv4(TNwD3^Iv0L&8$qAVW>U zPVvH?i^0h!Q4)C!(eEV6XnF$roID87k%wI^_SryuH+!CFpTqvYlhjZlgICJn^JMTc z8QdX**UI2EGPoW7Z-T~X164A_0vW-48Qdy^J7w^C8N5LTcgf&;WbnNbyvaUIhPYWm z*c)Z=TV(JhGWb#%e60+=P6l5tga1he-%tNGi{dqWmawY*+KMxNPUr4|ko=4q7UjeSF z2ijjH%$-fZZ$FQ^HoXLy9J>tT31%+VSXx&J4@1ekCP&-f0fKv4?rtf+r^RD%*{lr} z)h+h-NzKdb>dJzq(h_h+9-bJh>iQrdQK~GcerW+b8|4H1j}!2^(!PGUNi5YRAjrnyFQA^2&k$M3 zPxUewl5fF39z{D8Ot|MofI=UW9Qh$QPjI!f&e>peEp@qV;7GD?%L~wQ7C!a@%7Ns< z3n&kgpcheg6LGzsFt%o9oAPkx2Q+2>7pakcvh-8<4**{f0R8aNmke-;0O(hPzGi^$ z2!Or``!WOkNC5QZ%|9`~FCySS3{dzRfzUVb{LUaZfp+O*Q+TjT+tm>Oy^5z##1~#f zT}DtQm`C)4F{TUQvPLcZ3-K+*GQTKdE2?W8mGB{19$OZJhrWd3{AkhI7+m%eN@N*( z3~mBU6U8v!=LzPUsI=9)!E7jsxj#>Uq%sC)?#~lgExvG%Ct&{16XI$tSK*is^aw{p zpr%nEGIM~QaPf(NvDWHts9C~S$o!xu(p6xsv|Hgt6&3KpLwSLvF?jaND3!^zE(YHQ zryH1D%=LK;!eId_5p#R|5fmm2^LU;hR{_~UDqsf`Xkq5_2!<>wPdML1g|n^>?(*2+X)8rDC+7)-BOwr$ zvfWRLU|x62%K7b6(upX zPL9FzUquNlvkP?mDl!TO{z)_i=P48ic7-GViX&3O>3+gK5RefC$#tICuxI1cv#4L; zQmBQTbf@E!p0@XdRM#yjx4LR7T!r;@3&PY{A z)?b8fFby|V!F1+dzR=7MdkU>F-A*PsOM`2%wVp3|mkCo?g#g31P4$UMrsWTqP695N zVBqYHqp`M*){l=&^@;66OZ>xh5lV#6TL@0R3}5j#=9>`Uf*j5TH5FEerP2v2M3C~z zR39pB@-&}BB6Z(sKFPJDGIJF&dkBfI#RUVCf;ePu;X$z7O+gr$A7CJ>Q0if1-pGT5 zQk#)^3ju{$$c=(J+Y)lE;1-^RQ`3AhNz;^l?xu&>SO+pQc$3! zuCcb<*1&g``6D0mkF@b>XRQ;|wbUZ(O7p*c%>8|#z63Hi;F4caObTD=AYbz$f(ekF zTLC`1PWMTrW6l%E_e+CwF*72ODQaFJUd&LS%w}j_r4dHzB{?f9@gK}=k%AL&(XS|> ziRVDhEJZ7=mw^;E!|E15Fptr5H7aOrBOHmO&|oM7xdvNhBYE&eVJY~BX+Ehs@;`nR zEH9sxRGK!01yC5M47!H^8a?q`@uhhD#SEYDRyY>3-$;$XRAO2Rx;L;3E38LMe=@BD zm_GzqwHAjs#9Bbx|Ji z;nhAFt;vUd_7o6Pec?l9;Fmu1p&lmuyiXp14?6FYM$-A`eUeCe>v^AUf~#rzBQ}9o zRw$eWsDQ8mLW&A{6eACxJMWXsN~Y2qGQLn08!z~DLtVH10 zPv3MuGRe6-GW3C-zQX8;vt84nZtNH_#xDYRH>>T28;3t!+J(}z$XQc=KRt7CYa`Mhri zX{+>k-`x4M!cR?KLHP(-`Ikgmq1!9 z9jT)uz`?_tN9s~%(nj7gy-zeQvTIu;8r3;7BHbJ+;zWyrS<4rh^-8SE-M~mUjACM@ zj?$&EnIjyTGf81V77Ll4YG5TMv}Mjh=02o^pbD^Wh*L#$KV%-DM8n+b5YdB>d8o2} zSh88&z>uDf5PW)+E}hv}88X+AqQSgW^ez=|V)e+pO5O^^k;NaI;?^MZ9yuw$`T_M0 z8LdkTrY8I{GM|Ss*f>r{r~8$ob=jd5^EER6M5vSTg>|S4>68njS?ByZT1N*$>=@Qb z{l@5wG!iiv`ItwNDKd~}mQDdc**Hc=hu9-flGOC<7}nkA#^`cM-fygq4)}aNojg|8 zi@@(4t4k&6-m#4C)v=83i?OW7{7Q9nXbvpZ<&bh!rL3O&N_E9ibX~0ABn!V@s!Pxj z1a2CmOOByM1kKX>Tx6-8GESGOMDd!-!js485*fX5j4m!#LC^P0mM?}A`q8O0R`ta2 zny7`Bx7#c>GRSI3&<(7N(`6*k3It;od=lj^VyrH~K+#FKm_sRb^nbx1VK5V~@3y#| zEGYH_@eQ%3$h2?(15G6cK>>V204StALxH>n6QH00qMwLTT`t*ZFzLv)0~)VOhNR1Q zT_L)^)jVEz+JkJZ5&zKr_+kwmmdj0VL%)YhZ>cZjpH)%pyQT|XSpGl*#eQtM1lTAU zt#ovP7Wl^W3lxBznJuu@3f+9)Rz3Ag`?l(-Ii20Ar{3aQJ|D4-K z72EWwT}h4Ra%65K#w~A@MxgXu2sCkKo1R+i*W2`TYL3{hrxx0KyFQPNkH7KbBMMtE ziXm*>yT3uiK$d z3=p-4&+pLF(RXEso(1GPS#3jives&LGU3+jWWpZXsi$M%qn)fZ-(9R+{w{rPAZ_K6 zuQ^1A7wy!?`m+Ty@OtYmeOKlY3U%heFmK_*hZ&cz@6s0$Y0z#x9jp1f^@Suqm(Sm` zoAI-MHxM`0b zj?oW82450(ys<~0#`HbQVBTne+Tw8ct%yz%iF;X}4B5-{y>PF-JE?HvUdG|sy-XuN z?9~^rI-32=_xnK|Irxvg`Yz<^V=y6@y^mGYv`*EMD9Is!*oXD$(B>P5^<5zO_Au2&&=Jbw zgd_SyI+CbghaB;k^+)t^OpQag>(khO)?NBU#$X&?G#JJD5dzXaAfoXbLy*p&_tbHC z;t&+8qZMX9M)hxcjH+eLWBO#NG00`G8jX1Ea5#*1Vy`|JTL0iN%IB|->2p}q#XIzI z03Upm+T5I@`lMb|B{4FUxZo{J4x8Iawht&WOo1e)ld|ysqoTR)(8r^!)=!S=SMNxp ze!4%4v%{y-^IQCcWNo19f4pv{A(f3n2#!V*=bM1*_A!V4!At{n;8$iEsKf3$%Rs$u z=_~{Fl=fK$>V4PDGEiT5XqJKZ3XdT3Zcb@nB~!=ZoNdTt?(i}){}~Qas;~$qebXd4^a3)Xz5% zZ{V44NM`mIgHtDfMKi;Rz{3uqD8OHsZ(#A(29ON$X;wp4BDJjug^}=43$|JfDNwA* zY9M21tJTnxwHi9tV2o!rDW_wh4Gu3i^kC%WW-Q zZhfcRuy+8_OVcK1Pn$Ox(y1|+W|;1Vx$m3JhFH4eWNJ2@2CamX!x@%p@;nZF0yqdN#ZoNQQnV)3GdhS(&weR;JOGVzwphWG?p zSk3?+I>F7I&4vV&i7!IJmtwbREu@46cD)xu<*g}O3{!bwf=#{K6lU!)LrOFq#q)^@ z;3)zx0zP%j5Uu8dR|8`*kun*ss2++{*R~fb%-*e`KS(71GhPioct_gpxXPzmi#x>5@5yI4; - algorithms.contagion.animation — HyperNetX 1.1.3 documentation + algorithms.contagion.animation — HyperNetX 1.1.4dev documentation diff --git a/docs/build/_modules/algorithms/contagion/epidemics.html b/docs/build/_modules/algorithms/contagion/epidemics.html index 9fee137a..9def13fd 100644 --- a/docs/build/_modules/algorithms/contagion/epidemics.html +++ b/docs/build/_modules/algorithms/contagion/epidemics.html @@ -7,7 +7,7 @@ - algorithms.contagion.epidemics — HyperNetX 1.1.3 documentation + algorithms.contagion.epidemics — HyperNetX 1.1.4dev documentation diff --git a/docs/build/_modules/algorithms/generative_models.html b/docs/build/_modules/algorithms/generative_models.html index da24e346..6ec7f5dc 100644 --- a/docs/build/_modules/algorithms/generative_models.html +++ b/docs/build/_modules/algorithms/generative_models.html @@ -7,7 +7,7 @@ - algorithms.generative_models — HyperNetX 1.1.3 documentation + algorithms.generative_models — HyperNetX 1.1.4dev documentation diff --git a/docs/build/_modules/algorithms/homology_mod2.html b/docs/build/_modules/algorithms/homology_mod2.html index 65d495c1..891135b0 100644 --- a/docs/build/_modules/algorithms/homology_mod2.html +++ b/docs/build/_modules/algorithms/homology_mod2.html @@ -7,7 +7,7 @@ - algorithms.homology_mod2 — HyperNetX 1.1.3 documentation + algorithms.homology_mod2 — HyperNetX 1.1.4dev documentation diff --git a/docs/build/_modules/algorithms/hypergraph_modularity.html b/docs/build/_modules/algorithms/hypergraph_modularity.html new file mode 100644 index 00000000..647e8c59 --- /dev/null +++ b/docs/build/_modules/algorithms/hypergraph_modularity.html @@ -0,0 +1,763 @@ + + + + + + + + + + algorithms.hypergraph_modularity — HyperNetX 1.1.4dev documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + + + +
+ +
    + +
  • »
    • + +
  • Module code »
    • + +
  • algorithms.hypergraph_modularity
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for algorithms.hypergraph_modularity

+"""
+Hypergraph_Modularity
+---------------------
+Modularity and clustering for hypergraphs using HyperNetX.
+Adapted from F. Théberge's GitHub repository: `Hypergraph Clustering <https://github.com/ftheberge/Hypergraph_Clustering>`_ 
+See Tutorial 13 in the tutorials folder for library usage.
+
+References
+---------- 
+.. [1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S., Ravindran B. (2020) A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24
+.. [2] B. Kaminski, P. Pralat and F. Théberge, Community Detection Algorithm Using Hypergraph Modularity, to appear in the proceedings of Complex Networks 2020, Springer.
+.. [3] Clustering via hypergraph modularity, Bogumił Kamiński, Valérie Poulin, Paweł Prałat , Przemysław Szufel, François Théberge, 2019, https://doi.org/10.1371/journal.pone.0224307
+
+"""
+
+
+from collections import Counter
+import numpy as np
+from functools import reduce
+import igraph as ig
+import itertools
+from scipy.special import factorial as scipyfact
+
+################################################################################
+
+# we use 2 representations for partitions (0-based part ids):
+# (1) dictionary or (2) list of sets
+
+
+
[docs]def dict2part(D):
+    """
+    Returns dictionary to partition, inverse function to part2dict
+
+    Parameters
+    ----------
+    D : dict
+        Dictionary keyed by vertices with values equal to integer
+        index of the partition the vertex belongs to
+
+    Returns
+    -------
+    : list
+        List of sets in the partition
+    """
+    P = []
+    k = list(D.keys())
+    v = list(D.values())
+    for x in range(max(D.values()) + 1):
+        P.append(set([k[i] for i in range(len(k)) if v[i] == x]))
+    return P

+
+
+
[docs]def part2dict(A):
+    """
+    Returns dictionary {vertex: partition index}, inverse function
+    to dict2part
+
+    Parameters
+    ----------
+    A : list of lists
+        partition of vertices
+
+    Returns
+    -------
+    : dict
+    """
+    x = []
+    for i in range(len(A)):
+        x.extend([(a, i) for a in A[i]])
+    return {k: v for k, v in x}

+
+################################################################################
+
+
+
[docs]def factorial(n):
+    """
+    Computes exact integer factorial on integer
+
+    Parameters
+    ----------
+    n : int, or array-like object
+
+    Returns
+    -------
+    int or int64 or object
+
+    """
+    if n < 2:
+        return 1
+    return scipyfact(n, exact=True)

+    # return reduce(lambda x, y: x * y, range(2, int(n) + 1))
+
+# Precompute soe values on HNX hypergraph for computing qH faster
+
+
+
[docs]def precompute_attributes(HG):
+    """
+    Adds weight, strength and binary coefficient attributes to
+    the hypergraph for computing qH faster.
+
+    Parameters
+    ----------
+    HG : Hypergraph
+
+    """
+    # 1. compute node strenghts (weighted degrees)
+    for v in HG.nodes:
+        HG.nodes[v].strength = 0
+    for e in HG.edges:
+        try:
+            w = HG.edges[e].weight
+        except:
+            w = 1
+            # add unit weight if none to simplify other functions
+            HG.edges[e].weight = 1
+        for v in list(HG.edges[e]):
+            HG.nodes[v].strength += w
+    # 2. compute d-weights
+    ctr = Counter([len(HG.edges[e]) for e in HG.edges])
+    for k in ctr.keys():
+        ctr[k] = 0
+    for e in HG.edges:
+        ctr[len(HG.edges[e])] += HG.edges[e].weight
+    HG.d_weights = ctr
+    HG.total_weight = sum(ctr.values())
+    # 3. compute binomial coeffcients (modularity speed-up)
+    bin_coef = {}
+    for n in HG.d_weights.keys():
+        for k in np.arange(n // 2 + 1, n + 1):
+            bin_coef[(n, k)] = factorial(n) / (factorial(k) * factorial(n - k))
+    HG.bin_coef = bin_coef

+
+################################################################################
+
+
+
[docs]def linear(d, c):
+    """
+    Weight function for hyperedge. Gives the actual ratio as long
+    as it is greater than 0.5.
+
+    Parameters
+    ----------
+    d : int
+        Number of nodes in an edge
+    c : int
+        Number of nodes in the majority class
+
+    Returns
+    -------
+    float
+    """
+    return c / d if c > d / 2 else 0

+
+# majority
+
+
+
[docs]def majority(d, c):
+    """    
+    Weight function for hyperedge. Requires
+    c be the majority of d. Returns bool
+
+    Parameters
+    ----------
+    d : int
+        Number of nodes in an edge
+    c : int
+        Number of nodes in the majority class
+
+    Returns
+    -------
+    bool
+    """
+    return 1 if c > d / 2 else 0

+
+# strict
+
+
+
[docs]def strict(d, c):
+    """
+    Weight function for hyperedge. Requires c == d.
+
+    Parameters
+    ----------
+    d : int
+        Number of nodes in an edge
+    c : int
+        Number of nodes in the majority class
+
+    Returns
+    -------
+    bool
+    """
+    return 1 if c == d else 0

+
+#########################################
+
+
+
[docs]def compute_partition_probas(HG, A):
+    """
+    Compute vol(A_i)/vol(V) for each part A_i in A (list of sets)
+
+    Parameters
+    ----------
+    HG : Hypergraph
+     A : list of sets
+
+    Returns
+    -------
+    : list
+        normalized distribution of strengths in partition elements
+    """
+    p = []
+    for part in A:
+        vol = 0
+        for v in part:
+            vol += HG.nodes[v].strength
+        p.append(vol)
+    s = sum(p)
+    return [i / s for i in p]

+
+
+
[docs]def degree_tax(HG, Pr, wdc):
+    """
+    Computes the expected fraction of edges falling in 
+    the partition in a random graph as per [2]_
+
+    Parameters
+    ----------
+    HG : Hypergraph
+
+    Pr : list
+        Probability distribution
+    wdc : func
+        weight function (ex: strict, majority, linear)
+
+    Returns
+    -------
+    float
+
+    """
+    DT = 0
+    for d in HG.d_weights.keys():
+        tax = 0
+        for c in np.arange(d // 2 + 1, d + 1):
+            for p in Pr:
+                tax += p**c * (1 - p)**(d - c) * HG.bin_coef[(d, c)] * wdc(d, c)
+        tax *= HG.d_weights[d]
+        DT += tax
+    DT /= HG.total_weight
+    return DT

+
+
+
[docs]def edge_contribution(HG, A, wdc):
+    """
+    Edge contribution from hypergraph with respect
+    to partion A.
+
+    Parameters
+    ----------
+    HG : Hypergraph
+
+    A : list of sets
+
+    wdc : func
+        weight function (ex: strict, majority, linear)
+
+    Returns
+    -------
+    : float
+
+    """
+    EC = 0
+    for e in HG.edges:
+        d = HG.size(e)
+        for part in A:
+            if HG.size(e, part) > d / 2:
+                EC += wdc(d, HG.size(e, part)) * HG.edges[e].weight
+    EC /= HG.total_weight
+    return EC

+
+# HG: HNX hypergraph
+# A: partition (list of sets)
+# wcd: weight function (ex: strict, majority, linear)
+
+
+
[docs]def hypergraph_modularity(HG, A, wdc=linear):
+    """
+    Computes modularity of a hypergraph with respect to partition A.
+
+    Parameters
+    ----------
+    HG : Hypergraph
+        Description
+    A : list of lists
+        Partition of the nodes in HG
+    wdc : func, optional
+        weight function (ex: strict, majority, linear) 
+
+    Returns
+    -------
+    : float
+
+    """
+    Pr = compute_partition_probas(HG, A)
+    return edge_contribution(HG, A, wdc) - degree_tax(HG, Pr, wdc)

+
+################################################################################
+
+
+
[docs]def two_section(HG):
+    """
+    Creates a random walk 2-section igraph with transition weights defined by the
+    weights of the hyperedges.
+
+    Parameters
+    ----------
+    HG : Hypergraph
+
+    Returns
+    -------
+    G : igraph.Graph
+    """
+    s = []
+    for e in HG.edges:
+        E = HG.edges[e]
+        # random-walk 2-section (preserve nodes' weighted degrees)
+        try:
+            w = HG.edges[e].weight / (len(E) - 1)
+        except:
+            w = 1 / (len(E) - 1)
+        s.extend([(k[0], k[1], w) for k in itertools.combinations(E, 2)])
+    G = ig.Graph.TupleList(s, weights=True).simplify(combine_edges='sum')
+    return G

+
+################################################################################
+
+
+
[docs]def kumar(HG, delta=.01):
+    """
+    Compute a partition of the vertices as per Kumar's algorithm [1]_
+
+
+    Parameters
+    ----------
+    HG : Hypergraph
+
+    delta : float, optional
+        convergence stopping criterion
+
+    Returns
+    -------
+    dict
+
+    """
+    # weights will be modified -- store initial weights
+    W = [e.weight for e in HG.edges()]
+    # build graph
+    G = two_section(HG)
+    # apply clustering
+    CG = G.community_multilevel(weights='weight')
+    CH = []
+    for comm in CG.as_cover():
+        CH.append(set([G.vs[x]['name'] for x in comm]))
+
+    # LOOP
+    diff = 1
+    ctr = 0
+    while diff > delta:
+        # re-weight
+        diff = 0
+        for i in HG.edges:
+            e = HG.edges[i]
+            reweight = sum([1 / (1 + HG.size(e, c)) for c in CH]) * (HG.size(e) + len(CH)) / HG.number_of_edges()
+            diff = max(diff, 0.5 * abs(e.weight - reweight))
+            e.weight = 0.5 * e.weight + 0.5 * reweight
+        # re-run louvain
+        # build graph
+        G = two_section(HG)
+        # apply clustering
+        CG = G.community_multilevel(weights='weight')
+        CH = []
+        for comm in CG.as_cover():
+            CH.append(set([G.vs[x]['name'] for x in comm]))
+        ctr += 1
+        if ctr > 50:  # this process sometimes gets stuck -- set limit
+            break
+    G.vs['part'] = CG.membership
+    for e in HG.edges:
+        HG.edges[e].weight = W[e]
+    return {v['name']: v['part'] for v in G.vs}

+
+################################################################################
+
+
+
[docs]def delta_ec(HG, P, v, a, b, wdc):
+    """
+    Computes change in edge contribution --
+    partition P, node v going from P[a] to P[b]
+
+    Parameters
+    ----------
+    HG : Hypergraph
+
+    P : list of sets
+
+    v : int or str
+        node identifier
+    a : int
+
+    b : int
+
+    wdc : func
+        weight function (ex: strict, majority, linear)
+
+    Returns
+    -------
+    TYPE
+        Description
+    """
+    Pm = P[a] - {v}
+    Pn = P[b].union({v})
+    ec = 0
+    for e in list(HG.nodes[v].memberships):
+        d = HG.size(e)
+        w = HG.edges[e].weight
+        ec += w * (wdc(d, HG.size(e, Pm)) + wdc(d, HG.size(e, Pn))
+                   - wdc(d, HG.size(e, P[a])) - wdc(d, HG.size(e, P[b])))
+    return ec / HG.total_weight

+
+
+
[docs]def bin_ppmf(d, c, p):
+    """
+    exp. part of binomial pmf
+
+    Parameters
+    ----------
+    d : int
+
+    c : int
+
+    p : float
+
+
+    Returns
+    -------
+    float
+
+    """
+    return p**c * (1 - p)**(d - c)

+
+
+
[docs]def delta_dt(HG, P, v, a, b, wdc):
+    """
+    Compute change in degree tax --
+    partition P (list), node v going from P[a] to P[b]
+
+    Parameters
+    ----------
+    HG : Hypergraph
+
+    P : list of sets
+
+    v : int or str
+         node identifier
+    a : int
+
+    b : int
+
+    wdc : func
+        weight function (ex: strict, majority, linear)
+
+    Returns
+    -------
+    : float
+
+    """
+    s = HG.nodes[v].strength
+    vol = sum([HG.nodes[v].strength for v in HG.nodes])
+    vola = sum([HG.nodes[v].strength for v in P[a]])
+    volb = sum([HG.nodes[v].strength for v in P[b]])
+    volm = (vola - s) / vol
+    voln = (volb + s) / vol
+    vola /= vol
+    volb /= vol
+    DT = 0
+
+    for d in HG.d_weights.keys():
+        x = 0
+        for c in np.arange(int(np.floor(d / 2)) + 1, d + 1):
+            x += HG.bin_coef[(d, c)] * wdc(d, c) * (bin_ppmf(d, c, voln) + bin_ppmf(d, c, volm)
+                                                    - bin_ppmf(d, c, vola) - bin_ppmf(d, c, volb))
+        DT += x * HG.d_weights[d]
+    return DT / HG.total_weight

+
+
+
[docs]def last_step(HG, L, wdc=linear, delta=.01):
+    """
+    Compute a partition of the vertices as per Last-Step algorithm.[2]_
+
+    Simple H-based algorithm --
+    try moving nodes between communities to optimize qH
+    requires L: initial non-trivial partition
+
+    Parameters
+    ----------
+    HG : Hypergraph
+
+     L : list of sets
+
+    wdc : func, optional
+        weight function (ex: strict, majority, linear)
+    delta : float, optional
+
+
+    Returns
+    -------
+    : list of sets
+
+    """
+    A = L[:]  # we will modify this, copy
+    D = part2dict(A)
+    qH = 0
+    while True:
+        for v in list(np.random.permutation(list(HG.nodes))):
+            c = D[v]
+            s = list(set([c] + [D[i] for i in HG.neighbors(v)]))
+            M = []
+            if len(s) > 0:
+                for i in s:
+                    if c == i:
+                        M.append(0)
+                    else:
+                        M.append(delta_ec(HG, A, v, c, i, wdc) - delta_dt(HG, A, v, c, i, wdc))
+                i = s[np.argmax(M)]
+                if c != i:
+                    A[c] = A[c] - {v}
+                    A[i] = A[i].union({v})
+                    D[v] = i
+        Pr = compute_partition_probas(HG, A)
+        q2 = edge_contribution(HG, A, wdc) - degree_tax(HG, Pr, wdc)
+        if (q2 - qH) < delta:
+            break
+        qH = q2
+    return [a for a in A if len(a) > 0]

+
+################################################################################
+
+ +
+ +
+
+ +
+ +
+

+ © Copyright 2021 Battelle Memorial Institute. + +

+
+ + + + Built with Sphinx using a + + theme + + provided by Read the Docs. + +
+
+
+ +
+ +
+ + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/_modules/algorithms/laplacians_clustering.html b/docs/build/_modules/algorithms/laplacians_clustering.html index 10c116ae..55d2c3f9 100644 --- a/docs/build/_modules/algorithms/laplacians_clustering.html +++ b/docs/build/_modules/algorithms/laplacians_clustering.html @@ -7,7 +7,7 @@ - algorithms.laplacians_clustering — HyperNetX 1.1.3 documentation + algorithms.laplacians_clustering — HyperNetX 1.1.4dev documentation diff --git a/docs/build/_modules/algorithms/s_centrality_measures.html b/docs/build/_modules/algorithms/s_centrality_measures.html index d53fe5a9..d37a5971 100644 --- a/docs/build/_modules/algorithms/s_centrality_measures.html +++ b/docs/build/_modules/algorithms/s_centrality_measures.html @@ -7,7 +7,7 @@ - algorithms.s_centrality_measures — HyperNetX 1.1.3 documentation + algorithms.s_centrality_measures — HyperNetX 1.1.4dev documentation diff --git a/docs/build/_modules/classes/entity.html b/docs/build/_modules/classes/entity.html index 38a7b4e8..3f064e9a 100644 --- a/docs/build/_modules/classes/entity.html +++ b/docs/build/_modules/classes/entity.html @@ -7,7 +7,7 @@ - classes.entity — HyperNetX 1.1.3 documentation + classes.entity — HyperNetX 1.1.4dev documentation diff --git a/docs/build/_modules/classes/hypergraph.html b/docs/build/_modules/classes/hypergraph.html index a6bc1122..9d7a8fa5 100644 --- a/docs/build/_modules/classes/hypergraph.html +++ b/docs/build/_modules/classes/hypergraph.html @@ -7,7 +7,7 @@ - classes.hypergraph — HyperNetX 1.1.3 documentation + classes.hypergraph — HyperNetX 1.1.4dev documentation @@ -1313,7 +1313,7 @@

Source code for classes.hypergraph

         """
         Helper method to obtain adjacency matrix from 
         boolean incidence matrix for s-metrics.
-        Self loops are note supported.
+        Self loops are not supported.
         The adjacency matrix will define an s-linegraph.
 
         Parameters
diff --git a/docs/build/_modules/classes/staticentity.html b/docs/build/_modules/classes/staticentity.html
index 7eb3626f..b096c0fc 100644
--- a/docs/build/_modules/classes/staticentity.html
+++ b/docs/build/_modules/classes/staticentity.html
@@ -7,7 +7,7 @@
   
   
   
-  classes.staticentity — HyperNetX 1.1.3 documentation
+  classes.staticentity — HyperNetX 1.1.4dev documentation
   
 
   
diff --git a/docs/build/_modules/drawing/rubber_band.html b/docs/build/_modules/drawing/rubber_band.html
index 3d55ac06..d38b0b10 100644
--- a/docs/build/_modules/drawing/rubber_band.html
+++ b/docs/build/_modules/drawing/rubber_band.html
@@ -7,7 +7,7 @@
   
   
   
-  drawing.rubber_band — HyperNetX 1.1.3 documentation
+  drawing.rubber_band — HyperNetX 1.1.4dev documentation
   
 
   
diff --git a/docs/build/_modules/drawing/two_column.html b/docs/build/_modules/drawing/two_column.html
index 519f0802..e82201fc 100644
--- a/docs/build/_modules/drawing/two_column.html
+++ b/docs/build/_modules/drawing/two_column.html
@@ -7,7 +7,7 @@
   
   
   
-  drawing.two_column — HyperNetX 1.1.3 documentation
+  drawing.two_column — HyperNetX 1.1.4dev documentation
   
 
   
diff --git a/docs/build/_modules/drawing/util.html b/docs/build/_modules/drawing/util.html
index 7c9bcce2..bf1d0807 100644
--- a/docs/build/_modules/drawing/util.html
+++ b/docs/build/_modules/drawing/util.html
@@ -7,7 +7,7 @@
   
   
   
-  drawing.util — HyperNetX 1.1.3 documentation
+  drawing.util — HyperNetX 1.1.4dev documentation
   
 
   
diff --git a/docs/build/_modules/index.html b/docs/build/_modules/index.html
index 0196ccad..862f8f46 100644
--- a/docs/build/_modules/index.html
+++ b/docs/build/_modules/index.html
@@ -7,7 +7,7 @@
   
   
   
-  Overview: module code — HyperNetX 1.1.3 documentation
+  Overview: module code — HyperNetX 1.1.4dev documentation
   
 
   
@@ -171,8 +171,11 @@ 
All modules for which code is available

 
  • algorithms.contagion.epidemics
    • 
 
  • algorithms.generative_models
    • 
 
  • algorithms.homology_mod2
    • 
+
  • algorithms.hypergraph_modularity
    • 
 
  • algorithms.laplacians_clustering
    • 
 
  • algorithms.s_centrality_measures
    • 
+
  • algorithms.untitiled_modularity_and_clustering_original
    • 
+
  • algorithms.untitled_modularity_and_clustering
    • 
 
  • classes.entity
    • 
 
  • classes.hypergraph
    • 
 
  • classes.staticentity
    • 
diff --git a/docs/build/_modules/reports/descriptive_stats.html b/docs/build/_modules/reports/descriptive_stats.html
index c61d1897..07f8505b 100644
--- a/docs/build/_modules/reports/descriptive_stats.html
+++ b/docs/build/_modules/reports/descriptive_stats.html
@@ -7,7 +7,7 @@
   
   
   
-  reports.descriptive_stats — HyperNetX 1.1.3 documentation
+  reports.descriptive_stats — HyperNetX 1.1.4dev documentation
   
 
   
diff --git a/docs/build/_sources/algorithms/algorithms.rst.txt b/docs/build/_sources/algorithms/algorithms.rst.txt
index 2070dbee..ba6fcd09 100644
--- a/docs/build/_sources/algorithms/algorithms.rst.txt
+++ b/docs/build/_sources/algorithms/algorithms.rst.txt
@@ -28,6 +28,14 @@ algorithms.homology\_mod2 module
    :undoc-members:
    :show-inheritance:
 
+algorithms.hypergraph\_modularity module
+----------------------------------------
+
+.. automodule:: algorithms.hypergraph_modularity
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 algorithms.laplacians\_clustering module
 ----------------------------------------
 
@@ -44,6 +52,22 @@ algorithms.s\_centrality\_measures module
    :undoc-members:
    :show-inheritance:
 
+algorithms.untitiled\_modularity\_and\_clustering\_original module
+------------------------------------------------------------------
+
+.. automodule:: algorithms.untitiled_modularity_and_clustering_original
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+algorithms.untitled\_modularity\_and\_clustering module
+-------------------------------------------------------
+
+.. automodule:: algorithms.untitled_modularity_and_clustering
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 Module contents
 ---------------
 
diff --git a/docs/build/_static/documentation_options.js b/docs/build/_static/documentation_options.js
index 4c685415..dcf202f5 100644
--- a/docs/build/_static/documentation_options.js
+++ b/docs/build/_static/documentation_options.js
@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '1.1.3',
+    VERSION: '1.1.4dev',
     LANGUAGE: 'None',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/docs/build/algorithms/algorithms.contagion.html b/docs/build/algorithms/algorithms.contagion.html
index 44f41d3c..5f95825b 100644
--- a/docs/build/algorithms/algorithms.contagion.html
+++ b/docs/build/algorithms/algorithms.contagion.html
@@ -7,7 +7,7 @@
   
   
   
-  algorithms.contagion package — HyperNetX 1.1.3 documentation
+  algorithms.contagion package — HyperNetX 1.1.4dev documentation
   
 
   
@@ -108,8 +108,11 @@
 
  • Submodules
    • 
 
  • algorithms.generative_models module
    • 
 
  • algorithms.homology_mod2 module
    • 
+
  • algorithms.hypergraph_modularity module
    • 
 
  • algorithms.laplacians_clustering module
    • 
 
  • algorithms.s_centrality_measures module
    • 
+
  • algorithms.untitiled_modularity_and_clustering_original module
    • 
+
  • algorithms.untitled_modularity_and_clustering module
    • 
 
  • Module contents
    • 
 
 
diff --git a/docs/build/algorithms/algorithms.html b/docs/build/algorithms/algorithms.html
index 2a87e6b9..8720ad7e 100644
--- a/docs/build/algorithms/algorithms.html
+++ b/docs/build/algorithms/algorithms.html
@@ -7,7 +7,7 @@
   
   
   
-  algorithms package — HyperNetX 1.1.3 documentation
+  algorithms package — HyperNetX 1.1.4dev documentation
   
 
   
@@ -109,8 +109,11 @@
 
  • Submodules
    • 
 
  • algorithms.generative_models module
    • 
 
  • algorithms.homology_mod2 module
    • 
+
  • algorithms.hypergraph_modularity module
    • 
 
  • algorithms.laplacians_clustering module
    • 
 
  • algorithms.s_centrality_measures module
    • 
+
  • algorithms.untitiled_modularity_and_clustering_original module
    • 
+
  • algorithms.untitled_modularity_and_clustering module
    • 
 
  • Module contents
    • 
 
 
@@ -804,6 +807,371 @@ 
    Homology Mod2
+
    algorithms.hypergraph_modularity module
    
+
    
+
    Hypergraph_Modularity
    
+
    Modularity and clustering for hypergraphs using HyperNetX.
+Adapted from F. Théberge’s GitHub repository: Hypergraph Clustering
+See Tutorial 13 in the tutorials folder for library usage.
    
+
    References
    
+
    
+
    1
    
+
    Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S., Ravindran B. (2020) A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24
    
+
    
+
    2
    
+
      
+
    1. Kaminski, P. Pralat and F. Théberge, Community Detection Algorithm Using Hypergraph Modularity, to appear in the proceedings of Complex Networks 2020, Springer.
      2. 
+
    
+
    
+
    3
    
+
    Clustering via hypergraph modularity, Bogumił Kamiński, Valérie Poulin, Paweł Prałat , Przemysław Szufel, François Théberge, 2019, https://doi.org/10.1371/journal.pone.0224307
    
+
    
+
    
+
    
+
    
+algorithms.hypergraph_modularity.bin_ppmf(d, c, p)[source]
    
+
    exp. part of binomial pmf
    
+
    
+
    Parameters
    
+
      
+
    • d (int) – 
      • 
+
    • c (int) – 
      • 
+
    • p (float) – 
      • 
+
    
+
    
+
    Returns
    
+
    
+
    
+
    Return type
    
+
    float
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.compute_partition_probas(HG, A)[source]
    
+
    Compute vol(A_i)/vol(V) for each part A_i in A (list of sets)
    
+
    
+
    Parameters
    
+
    HG (Hypergraph) – A : list of sets
    
+
    
+
    Returns
    
+
    normalized distribution of strengths in partition elements
    
+
    
+
    Return type
    
+
    list
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.degree_tax(HG, Pr, wdc)[source]
    
+
    Computes the expected fraction of edges falling in
+the partition in a random graph as per 2
    
+
    
+
    Parameters
    
+
      
+
    • HG (Hypergraph) – 
      • 
+
    • Pr (list) – Probability distribution
      • 
+
    • wdc (func) – weight function (ex: strict, majority, linear)
      • 
+
    
+
    
+
    Returns
    
+
    
+
    
+
    Return type
    
+
    float
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.delta_dt(HG, P, v, a, b, wdc)[source]
    
+
    Compute change in degree tax –
+partition P (list), node v going from P[a] to P[b]
    
+
    
+
    Parameters
    
+
      
+
    • HG (Hypergraph) – 
      • 
+
    • P (list of sets) – 
      • 
+
    • v (int or str) – node identifier
      • 
+
    • a (int) – 
      • 
+
    • b (int) – 
      • 
+
    • wdc (func) – weight function (ex: strict, majority, linear)
      • 
+
    
+
    
+
    Returns
    
+
    
+
    
+
    Return type
    
+
    float
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.delta_ec(HG, P, v, a, b, wdc)[source]
    
+
    Computes change in edge contribution –
+partition P, node v going from P[a] to P[b]
    
+
    
+
    Parameters
    
+
      
+
    • HG (Hypergraph) – 
      • 
+
    • P (list of sets) – 
      • 
+
    • v (int or str) – node identifier
      • 
+
    • a (int) – 
      • 
+
    • b (int) – 
      • 
+
    • wdc (func) – weight function (ex: strict, majority, linear)
      • 
+
    
+
    
+
    Returns
    
+
    Description
    
+
    
+
    Return type
    
+
    TYPE
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.dict2part(D)[source]
    
+
    Returns dictionary to partition, inverse function to part2dict
    
+
    
+
    Parameters
    
+
    D (dict) – Dictionary keyed by vertices with values equal to integer
+index of the partition the vertex belongs to
    
+
    
+
    Returns
    
+
    List of sets in the partition
    
+
    
+
    Return type
    
+
    list
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.edge_contribution(HG, A, wdc)[source]
    
+
    Edge contribution from hypergraph with respect
+to partion A.
    
+
    
+
    Parameters
    
+
      
+
    • HG (Hypergraph) – 
      • 
+
    • A (list of sets) – 
      • 
+
    • wdc (func) – weight function (ex: strict, majority, linear)
      • 
+
    
+
    
+
    Returns
    
+
    
+
    
+
    Return type
    
+
    float
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.factorial(n)[source]
    
+
    Computes exact integer factorial on integer
    
+
    
+
    Parameters
    
+
    n (int, or array-like object) – 
    
+
    
+
    Returns
    
+
    
+
    
+
    Return type
    
+
    int or int64 or object
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.hypergraph_modularity(HG, A, wdc=<function linear>)[source]
    
+
    Computes modularity of a hypergraph with respect to partition A.
    
+
    
+
    Parameters
    
+
      
+
    • HG (Hypergraph) – Description
      • 
+
    • A (list of lists) – Partition of the nodes in HG
      • 
+
    • wdc (func, optional) – weight function (ex: strict, majority, linear)
      • 
+
    
+
    
+
    Returns
    
+
    
+
    
+
    Return type
    
+
    float
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.kumar(HG, delta=0.01)[source]
    
+
    Compute a partition of the vertices as per Kumar’s algorithm 1
    
+
    
+
    Parameters
    
+
      
+
    • HG (Hypergraph) – 
      • 
+
    • delta (float, optional) – convergence stopping criterion
      • 
+
    
+
    
+
    Returns
    
+
    
+
    
+
    Return type
    
+
    dict
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.last_step(HG, L, wdc=<function linear>, delta=0.01)[source]
    
+
    Compute a partition of the vertices as per Last-Step algorithm.[2]_
    
+
    Simple H-based algorithm –
+try moving nodes between communities to optimize qH
+requires L: initial non-trivial partition
    
+
    
+
    Parameters
    
+
      
+
    • HG (Hypergraph) – L : list of sets
      • 
+
    • wdc (func, optional) – weight function (ex: strict, majority, linear)
      • 
+
    • delta (float, optional) – 
      • 
+
    
+
    
+
    Returns
    
+
    
+
    
+
    Return type
    
+
    list of sets
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.linear(d, c)[source]
    
+
    Weight function for hyperedge. Gives the actual ratio as long
+as it is greater than 0.5.
    
+
    
+
    Parameters
    
+
      
+
    • d (int) – Number of nodes in an edge
      • 
+
    • c (int) – Number of nodes in the majority class
      • 
+
    
+
    
+
    Returns
    
+
    
+
    
+
    Return type
    
+
    float
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.majority(d, c)[source]
    
+
    Weight function for hyperedge. Requires
+c be the majority of d. Returns bool
    
+
    
+
    Parameters
    
+
      
+
    • d (int) – Number of nodes in an edge
      • 
+
    • c (int) – Number of nodes in the majority class
      • 
+
    
+
    
+
    Returns
    
+
    
+
    
+
    Return type
    
+
    bool
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.part2dict(A)[source]
    
+
    Returns dictionary {vertex: partition index}, inverse function
+to dict2part
    
+
    
+
    Parameters
    
+
    A (list of lists) – partition of vertices
    
+
    
+
    Returns
    
+
    
+
    
+
    Return type
    
+
    dict
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.precompute_attributes(HG)[source]
    
+
    Adds weight, strength and binary coefficient attributes to
+the hypergraph for computing qH faster.
    
+
    
+
    Parameters
    
+
    HG (Hypergraph) – 
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.strict(d, c)[source]
    
+
    Weight function for hyperedge. Requires c == d.
    
+
    
+
    Parameters
    
+
      
+
    • d (int) – Number of nodes in an edge
      • 
+
    • c (int) – Number of nodes in the majority class
      • 
+
    
+
    
+
    Returns
    
+
    
+
    
+
    Return type
    
+
    bool
    
+
    
+
    
+
    
+
+
    
+
    
+algorithms.hypergraph_modularity.two_section(HG)[source]
    
+
    Creates a random walk 2-section igraph with transition weights defined by the
+weights of the hyperedges.
    
+
    
+
    Parameters
    
+
    HG (Hypergraph) – 
    
+
    
+
    Returns
    
+
    G
    
+
    
+
    Return type
    
+
    igraph.Graph
    
+
    
+
    
+
    
+
    @@ -1089,6 +1457,182 @@

    S-Centrality Measures

    + +
    +

    algorithms.untitiled_modularity_and_clustering_original module

    +
    +
    +algorithms.untitiled_modularity_and_clustering_original.DegreeTax(HG, Pr, wdc)[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.DeltaDT(HG, P, v, a, b, wdc)[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.DeltaEC(HG, P, v, a, b, wdc)[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.EdgeContribution(HG, A, wdc)[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.HNX_2section(HG)[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.HNX_Kumar(HG, delta=0.01)[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.HNX_LastStep(HG, L, wdc=<function linear>, delta=0.01)[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.HNX_modularity(HG, A, wdc='linear')[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.bin_ppmf(d, c, p)[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.compute_partition_probas(HG, A)[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.dict2part(D)[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.factorial(n)[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.linear(d, c)[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.majority(d, c)[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.part2dict(A)[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.precompute_modularity_parameters(HG)[source]
    +
    + +
    +
    +algorithms.untitiled_modularity_and_clustering_original.strict(d, c)[source]
    +
    + +
    +
    +

    algorithms.untitled_modularity_and_clustering module

    +
    +
    +algorithms.untitled_modularity_and_clustering.DegreeTax(HG, Pr, wdc)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.DeltaDT(HG, P, v, a, b, wdc)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.DeltaEC(HG, P, v, a, b, wdc)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.EdgeContribution(HG, A, wdc)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.HNX_2section(HG)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.HNX_Kumar(HG, delta=0.01)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.HNX_LastStep(HG, L, wdc=<function linear>, delta=0.01)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.HNX_modularity(HG, A, wdc=<function linear>)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.HNX_precompute(HG)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.bin_ppmf(d, c, p)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.compute_partition_probas(HG, A)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.dict2part(D)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.factorial(n)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.linear(d, c)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.majority(d, c)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.part2dict(A)[source]
    +
    + +
    +
    +algorithms.untitled_modularity_and_clustering.strict(d, c)[source]
    +
    +

    Module contents

    diff --git a/docs/build/algorithms/modules.html b/docs/build/algorithms/modules.html index 03ed0491..baa77383 100644 --- a/docs/build/algorithms/modules.html +++ b/docs/build/algorithms/modules.html @@ -7,7 +7,7 @@ - algorithms — HyperNetX 1.1.3 documentation + algorithms — HyperNetX 1.1.4dev documentation @@ -207,6 +207,10 @@

    algorithmsalgorithms.hypergraph_modularity module +
  • algorithms.laplacians_clustering module @@ -215,6 +219,8 @@

    algorithmsS-Centrality Measures

    • +
  • algorithms.untitiled_modularity_and_clustering_original module
    • +
  • algorithms.untitled_modularity_and_clustering module
  • Module contents
    • diff --git a/docs/build/classes/classes.html b/docs/build/classes/classes.html index 8bf9fcc3..9b73f570 100644 --- a/docs/build/classes/classes.html +++ b/docs/build/classes/classes.html @@ -7,7 +7,7 @@ - classes package — HyperNetX 1.1.3 documentation + classes package — HyperNetX 1.1.4dev documentation diff --git a/docs/build/classes/modules.html b/docs/build/classes/modules.html index 799191af..ac7690f6 100644 --- a/docs/build/classes/modules.html +++ b/docs/build/classes/modules.html @@ -7,7 +7,7 @@ - classes — HyperNetX 1.1.3 documentation + classes — HyperNetX 1.1.4dev documentation diff --git a/docs/build/core.html b/docs/build/core.html index 71612045..8ee28377 100644 --- a/docs/build/core.html +++ b/docs/build/core.html @@ -7,7 +7,7 @@ - HyperNetX Packages — HyperNetX 1.1.3 documentation + HyperNetX Packages — HyperNetX 1.1.4dev documentation @@ -214,6 +214,10 @@ +
  • algorithms.hypergraph_modularity module +
  • algorithms.laplacians_clustering module @@ -222,6 +226,8 @@
  • S-Centrality Measures
    • +
  • algorithms.untitiled_modularity_and_clustering_original module
    • +
  • algorithms.untitled_modularity_and_clustering module
  • Module contents
    • diff --git a/docs/build/drawing/drawing.html b/docs/build/drawing/drawing.html index 588f3c19..cb791a33 100644 --- a/docs/build/drawing/drawing.html +++ b/docs/build/drawing/drawing.html @@ -7,7 +7,7 @@ - drawing package — HyperNetX 1.1.3 documentation + drawing package — HyperNetX 1.1.4dev documentation diff --git a/docs/build/drawing/modules.html b/docs/build/drawing/modules.html index e32035b5..744dd4f4 100644 --- a/docs/build/drawing/modules.html +++ b/docs/build/drawing/modules.html @@ -7,7 +7,7 @@ - drawing — HyperNetX 1.1.3 documentation + drawing — HyperNetX 1.1.4dev documentation diff --git a/docs/build/genindex.html b/docs/build/genindex.html index 5702d9da..f8cd8952 100644 --- a/docs/build/genindex.html +++ b/docs/build/genindex.html @@ -7,7 +7,7 @@ - Index — HyperNetX 1.1.3 documentation + Index — HyperNetX 1.1.4dev documentation @@ -234,8 +234,6 @@

    A

  • module
    • - -
    • algorithms.contagion.animation @@ -243,6 +241,8 @@

      A

    • module
    + + - - + -
    • draw_hyper_edge_labels() (in module drawing.rubber_band)
    • draw_hyper_edges() (in module drawing.rubber_band) @@ -491,6 +560,8 @@

      E

      - + \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -672,11 +666,11 @@ ], "text/plain": [ " character strength\n", - "18 Daenerys Targaryen 31103\n", - "0 Jorah Mormont 19344\n", - "27 Missandei 13683\n", - "9 Grey Worm 10497\n", - "13 Barristan Selmy 6514" + "15 Daenerys Targaryen 31103\n", + "24 Jorah Mormont 19344\n", + "7 Missandei 13683\n", + "4 Grey Worm 10497\n", + "11 Barristan Selmy 6514" ] }, "execution_count": 18, From bb51e7d04969a8d7e4777937535f9f25ab3bb805 Mon Sep 17 00:00:00 2001 From: Brenda Praggastis Date: Thu, 14 Oct 2021 15:55:22 -0700 Subject: [PATCH 07/41] updated hypergraph to support memberships in a consistent way for static as dynamic --- hypernetx/classes/hypergraph.py | 3 +++ hypernetx/classes/staticentity.py | 2 +- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index f320fc98..ffa9deb9 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -168,6 +168,9 @@ def __init__( self._edges = E self._nodes = E.restrict_to_levels([1], weights=False, aggregateby=None) self._nodes._memberships = E.memberships + for n in self._nodes: + self._nodes[n].memberships = self._nodes._memberships[n] ### a bit of a hack to get same functionality from static as dynamic + ### we will have to see if it slows things down too much else: self._static = False if setsystem is None: diff --git a/hypernetx/classes/staticentity.py b/hypernetx/classes/staticentity.py index a84fd71b..8e8f59ea 100644 --- a/hypernetx/classes/staticentity.py +++ b/hypernetx/classes/staticentity.py @@ -62,7 +62,7 @@ def __init__( arr=None, labels=None, uid=None, - weights=None, + weights=None, ### in this context weights is just a column of values corresponding to the rows in data. keep_weights=True, aggregateby="sum", **props, From 0f167994c53d76c91ec88375c74736664c61743b Mon Sep 17 00:00:00 2001 From: Brenda Praggastis Date: Thu, 14 Oct 2021 15:59:02 -0700 Subject: [PATCH 08/41] documentation for modularlity2 includes hypergraph_modularity module --- docs/build/.buildinfo | 2 +- .../.doctrees/algorithms/algorithms.doctree | Bin 410625 -> 406574 bytes docs/build/.doctrees/environment.pickle | Bin 456237 -> 456030 bytes .../algorithms/contagion/animation.html | 2 +- .../algorithms/contagion/epidemics.html | 2 +- .../algorithms/generative_models.html | 2 +- .../_modules/algorithms/homology_mod2.html | 2 +- .../algorithms/hypergraph_modularity.html | 40 +++-------- .../algorithms/laplacians_clustering.html | 2 +- .../algorithms/s_centrality_measures.html | 2 +- docs/build/_modules/classes/entity.html | 2 +- docs/build/_modules/classes/hypergraph.html | 5 +- docs/build/_modules/classes/staticentity.html | 4 +- docs/build/_modules/drawing/rubber_band.html | 2 +- docs/build/_modules/drawing/two_column.html | 2 +- docs/build/_modules/drawing/util.html | 2 +- docs/build/_modules/index.html | 2 +- .../_modules/reports/descriptive_stats.html | 2 +- docs/build/_static/documentation_options.js | 2 +- .../algorithms/algorithms.contagion.html | 2 +- docs/build/algorithms/algorithms.html | 64 +++++++----------- docs/build/algorithms/modules.html | 2 +- docs/build/classes/classes.html | 2 +- docs/build/classes/modules.html | 2 +- docs/build/core.html | 2 +- docs/build/drawing/drawing.html | 2 +- docs/build/drawing/modules.html | 2 +- docs/build/genindex.html | 18 +++-- docs/build/glossary.html | 2 +- docs/build/home.html | 2 +- docs/build/index.html | 2 +- docs/build/install.html | 2 +- docs/build/license.html | 2 +- docs/build/nwhy.html | 2 +- docs/build/objects.inv | Bin 2883 -> 2877 bytes docs/build/overview/index.html | 2 +- docs/build/publications.html | 2 +- docs/build/py-modindex.html | 2 +- docs/build/reports/modules.html | 2 +- docs/build/reports/reports.html | 2 +- docs/build/search.html | 2 +- docs/build/searchindex.js | 2 +- docs/build/widget.html | 2 +- 43 files changed, 83 insertions(+), 118 deletions(-) diff --git a/docs/build/.buildinfo b/docs/build/.buildinfo index bcbc2873..faa7924a 100644 --- a/docs/build/.buildinfo +++ b/docs/build/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: bc26a298b47969fef8902e20a4ac0867 +config: 68dc8fcdf1a2d3c9a53105fe3a9dc22c tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/.doctrees/algorithms/algorithms.doctree b/docs/build/.doctrees/algorithms/algorithms.doctree index b732b4074f10f9a2f0dbe688957dfbf1192b15e5..7ff0c5924672a406684ed405a9d3f8483e1d6320 100644 GIT binary patch delta 31711 zcma)lcYGFA_P29q=7B&WCA5&xr6qtMDAI`(5keq9LP!V@LJ1E95}I@fy@ZlHz(txw zP;?OmQL`B*qx|-Y7-2-&t zHZwNRHfyk&Iv|j8wxuyyU#n~0qTg2LT(t&WqvBQH0f8$YN2?VB0*$g`j1{_CoLMK( zaPrN%>RHuV?H&*~Jf)@DF(7avH(npAYqYJlXTVK2`5!fIx@oolAdddN`f!|C0|<1% zaaD)fMzKa5U#F>-YUu!dqBq*b0|G|Prn<1FS)ktWkUk}(PlZTVUap#`FNO3)BAuX) z3Ph8_04yv5S{4EA`@PIz z3JjLkXs_N3O0wk)1X?g4Q2bO&H5nv(w^X?U0_|c3sey=Z~+f&8z}m+gWBx!sM}RBS_k63s%|*C-INqK|3^!;UJxyC5$VL~ zW=l+S;L2C+^}PD#EzpuKjW-(BIznnPOdMV#QI(wOf`jIWvX1wdiH7L~ZZh+ZGT}sjgr&Z^uj{%cwxczNr$_7GJs zcnQ<)suJ8wGE^@zopMRdR97I*Gj)h|;W;BleUPNDY*52uu7K>U-1yw*nJ!qUH^9h^ z_5UkL8=n~Q`j2FDM@0HS^vC+s1_sXWZl$YMFe>OVS5y}9A6bEi4;`r6UNvf!Wezqg z8W9s^{CH>l0i#0HIO?i5zyunM4X%*618L?WX474gTNms#2kRX}%v9Z@wdo6_j%ucc z4CJ-4X&|i?Ma{XUvQ8cu$Y%D?z~DNmegK%l*9if~MgvNW3@sZO+Lgo1I|&;Z+4_O9 zT-FZc?! z#|u4A==(kNrhokC`YxD|p;+L?O}bGJvq|9i6BQzUcZUbs zO>3#Yc}7*&_l-7hGivIB?q-8P)xJ@fH(vC*`53bVF-V`7 zU|xziLtU}{(*Smz!Z#Dm$?7ejaRQ)mF*F_LX?UC;f?$b1Wc*3&C8#$C>1rRU8pa@w zgTLz1yUb?#c#hEkf%tqM%;exH6^yEM+8{7Ex@+~CgS`vn%|X1pe+5a8A&$N zVlc1uvE?(;Z?G_emKj0YbFn#A0V6TehcOZ(#*Z{7O#+t>S1`U~_Vk#`7U=Ybr~hOS z*}d@)k7i@u3v!}8vVG%8x)ZPV4rbfOaI*c=q2X`2!`b$$zNzlORIn071Tp)BgDRRO zhuME7z-0F41o>Nx9PWzwTr#TyW|pl!gJx0!6=t)vgIUKIELPuwl)_eDJH@s7+9_i7 z!)BOu4Wq3t9c0$ipH)!xRjU*|_zB`eXgOiQx`9e<6Y)i}Cne)2DU-p}PW(=+dE%Lpnq-aehd{E8rCIn4q?& zkmVU~Xd?|y=#3P%ybA;sZ+yyPu;E_>aClrJFvPVtfQ$?uE!!JfHhkbAdwY$kv*Ev- z5;FW)eIkPv6b#=XUt71!0QuTOnEb7Be@}L0qZOUDQdd*h>e~$|my6m%gbTFH1zNUx zpd&77Fgktc6;)ZioI-xyI3a2w*Lo<0jrd>++x#qtbXMhv=^!ThVw+zkSgJm2R94f6 zxG-e+yF)NLf-q;EHL{E&bceVXgO7UDtj-d{;2#%s`41VDjYq>!?{HCzr{4iR!_&8< zxf7f34o2bWfs;M`Rhk&K`7cO0ob-JGeIV&GI_WMA%r1KQeX6lOf3F>*U${@TGFs@K zSL{yuvu{+U`V-WU5dJzupEoQg3LN^*j;&ECZ`!z7Q?jQ|nmKoKZiTGPxwVQa=__;X z7(}J>IcArrUrAEoC~Bzwq>(w=Xs8qY?xKhrswX!#6O1S}OC2ttR^IyH&-Cgdolj!8jlMrI|U{*vec74&_7{L)lrE3}t6cjgt@> z2WU*Z@vsb~fN>C*!q?4Mz+&B$05h!{v~00xKW$;Q!8Kd#MrMWAP0>(ZH=A3UW8p(d z!C@?XCM-|`78(?p6l<7;A7v=H+v7ud?OZFLlg}JZ&=MyVKf|zDV{>ci;|Z9{vIMvY zpr>VY?mDJ8Pi;F4nO=gUlUBo6C#{A_n%ZX=C&+gV<0R8ztFyajliu@a*V66g{wThPWRB#0WRLfzU>lsC4a}9Lc{#bfzSf~ITfiXwAFP}#D zES-{XHrJb;Hkzv^At2Jf!=NcP!RgN}VJu+Zn& z!vBWxbKZXi;fF3l;xVw~&&nzXCIc7nbNGT|7jS&2xPap{v+M$zrn)YmX{v<1$Co4Q zsqU$~%sZ#bb6?&{JogR62zu`83>ar2C#C9NS0cQR)s1F5fo>F?CK#F2G}k>ZDeEr) zyjBOL(poh->oW_?7}I|{A|*w#K|t+BvLqrSi>_m;tkuSA%mWcys6W;p00uXW<7YeY z=seqPN)4^ihl!5{fjw}~4-#OUKPi=!oVd=+G@g^Y6jwH;@)|vs%JxW&yFku3C=gsu zIpeWT{|Rj2>y+TJPLDxJSf|D@-Dt5{ui`O`;>9s~y;(=6tv7GM9mw3lILfFrM~4Sf zb@AU+yaCQo9EeO~^NC1v*wt)4b<@~iHs-O@}H3 zEFHM(o5q&YOK{!h;^N3*Y$C>52QHUZi0VkH5?@W~Li7MKjvil-*laorK~$p0!Zb0R zb|q#HLv2lC(Qj|I%r>OtHjt0^HK1?9dpUHg!os-8)cnpasGmko}j>Cz}){9i`0 zx$FnuC0fUgkQ{MV;Y-F{k?;ykI$Kwmp^Of_=Yp4<&AGRJA2Bk z@iQ7uo7%8na=N}%n@tSFsI8{||9@zXenFd+BK}9zV*THMFV_e+G&QsI_7bypMd9LW zFuTq4D`PlM=N|+S=iisc)_3~vs*kFLJ9uFKDvd3#`f#>9YMkhx5{9!kY%v`3DP^_l zt%rNfj({<-)rU)XK+BGduIXvD%5b)tKQJeF=KK}Sl*R!{jlTvQe8fiL%AFOOXrret zEjAm~>Pd8Xp7ZF8vxGb#rP5H@PI?|(3FeRqEFB*9JUDH)*;Q@9Fq-kD0CaF~9?l7| zIso>l%DI0y&-h-z;wezQ;6WBoq>gF9n*`peQ_IYLT8SAf+mghr(Er1{bT6z2!~lmV+-(2EJXJe()2 zVLDG3HRpP(JJWftZch)*RcAnly9k2ATs2IWxkAfxg_cubAfljCFI@@@b<>0SuPO*5 zT*DB$c|^AlE~xUll*lHif$1E%SCvmV&moMK38Uq#7nq2!KS-D>8@1EfbbKHH(=pT8 zH@zu(`A1MM?3@0TuFp(G8WS8!zUc=UdL=lN6Wj>h=po8T&fRRJhvm4U8pEc$O#<{# zr{t6p?kg@{$#AbgjWFHC;pPR?tpkMYq6U)mY`R@XxTf26L>WwXo?*ZzKHxmhSt+<3 z=;o^@wTz9An*)q-a+@FM=(c0OIV0F(8S{uKsbZKN`5!?+6^k#O@zGtYeFJRaR=Z!PJcz>m znGw3+m=$A;5Zj?kI2ekEG-EVP&y8S9c^Yh>7SK>PI$*{})XNCx?XfbmgStHfOR&9a zM@=i9>ozo2kkrd?kQtcM)cD$WG%v}F-fqI5lsjo zPIVDWuzQ#$qeEmesZRV~j&5|&tV0;difaBzYG5nc1M1=c_vrkCW}~vD&zSoRRhFTR zv8t_lGDBxPXC9!oUAAhVFL&|b1;O*?3lSd@+p+$$&cOFV1NxV}`+}KaMSLwIzGPMC zs#JIWg1V&c3;?cCuYcKGqtuTXf#)*gbcff>iN+r!eZ!Kf5!G(WWFzvY;~fiV{g|N} zEihWTSSq2*#nSj6W-UWSXEK(w*Udx~m#MG(&uXHNzlH+d@z?Q^AR1{~82HTs-slbU zkOG?01k~4mf5V)u?#^W2-z$@R{{t#5Q#^hzFym-5HB;|=3q{Y#z(^i{VkUe1ahVe1 zy(0@xqBfkK$-W&p*|&Gk)QPuQ!(!4!KrZ*E5`I}FJu_*Qv@Cn^6yCfJdL_q^^q8_NMFTnuLn*9u6*~noy-LR*sC};<{XvOi~2erWQ_9VT@njzgG zIRqYFGnYseyk@q8uB@5uI{O*3CY#TP=Fy-TE&Ip}s3X9S6youbR99<2vvs%b`4Nh1 zFMn*_sa_l@R`oc^AoNa*)P2sGdB$1dPyFz^ERGb`-X5t>-)f~AmxH&}HzWC-DOLUd?(W0Y@0+pY|K0;r+X#O6tT!5B3K;%vesjJ=q8@MpE=NIORh!PpK>H1L> zWfVJ|A3In*jEFrlcK7vT$+ZAua8W&x#f$230*LctqPP&!BidS^=N4OW>Ll)CX-&)G zrS*CiFRjDHTDkW^pQY z+o*Ec04&uWjDA5_g)XW;AV3DurCo~%i9Y<9$(lOW2)*P&c~LzL`) zz!qx??Qd7iBEn0KqXy%V5~riBD&L!{ji&{Fn6{3JwvIa5LeZYoN!ohrI$q9{&kJB# zTVDoY6}nhkKL@+$1p{FYQ~tFZqGQp3gG8%%6vOx-`@~OXC;j)IP^$pbB;^rC=}O6I z?eQ#jypO};;CLU+;ov21${MX3T|T>flU(J$4`YJ6!cITJCu(C5Q=St86}eHN z7O6}8JLuB3hO%!WtOO(CPr4E7{~ajYJO_2^T3eMhtU7ONxaKH&)>!}fPJz<1_4T)v ztz1;({1!@w7VwJ;9 z1z^7h?YQY$W;QR*bYd*d5BqT=%R+TJOOJlQs;g#W2!hKmV>q}>&u0HVJ)43{CTyg% zi5+ibhhj?+P=&`9f@v}EaG9RHr2w%-1XfeBIkp_DWxcFk^3Z)j=)RE6 zaYdcY=AiZ-5l&F|XLCIH@H*1S!BI{DDcYHISzv&IYuUzFM)8LSg*o=G+O2n;cU ztQGO&P6tc#vEhugx*-F;4EZNs&8<6b~ccW`FLOJvr<@943ApD}kbupyoi)Gz@na z2RB*@6-G)JcJv`!Jmg@Hbg)yj=*Xk^W&W?a)CWCqzzCy%b*u!8AI6gO#=m70z2JZf zBx+p4T0zt>97n@?;HYsw)){RoyPqtaqlO0e5;b&Ac+}9te4*w8=6Tf5bv9F7~0 z=Fs~F=SkqL4pyY@+sf*sUdYkK%IXpce2!~+K1ctfm31%b98kDC2%Mo)L=m4A1=)AB zwz?YM@?G7~vSK|q+qbcDls!(eeAIk+S7r1#&hl}IU6t)sqjBCv!M3sm>1A z9Rj9zH?V3*OhOMoR-`$QxON~p6g=wTo&{FwzYL&l8k{f=6;B z6r8=y$}}R@j>C;u|LSp}P=MDU&I1BeS52)LC*=dlHr-8erqeZ9YZ6+_@}myeU@0S|b-eoUOe=Fg!Y#n z+TWB<8!+sl)dW^KF4;pz%VpIG&J5@)AF4LhCX_n^fZ<}I!#yq=_O%{WV2Zdu737{> z@v(92iXVnq!4*F^j$QGH32dZkz|Tg?HJlOsto>^01gr>DvnS||_gLrj=+4#%E>EU` zKY^#}^j3inxbT|aoE-=<@9wQ-8;7ax2UhH@-8X@G3Anh#C$4}w!zVrxzQUz2GERKr zC9;3^iJy={_-)E36I`E|HPCv#$&*y~lf26*0jgPJ$LH2xxVc4c!>r9M{O@Fxbx*e1 z8tO-EQ$XRd>UYFDL*yo48Mh4-JkUwMIR-x9&p}qQ`fh?wNwFsBzYn%XMbw#yv9bPI z6QyPuJ+Tb;9qOhmcVCy`1d-P|fVMdxx#Ei5kg{a- zMDZ)5hg_G-(H7OLnyAfD)(-5T0WtCW+nKHufVNK5SBg=$ zI2ydIo|wq)-FTV1#8ad1c!|~8ct`p;^+z9jYoM4i#!5E+8$$7=gW{Jk6zJophYGqN z!*ZS;(C4D~KHJJd37qcH-bWReD_pdZ;arA7AEW3RLV?{a*+~?{Xqy-~IceC_@ya5I zCdEMmpG;I44f+^OK^U48FPfB-)^KBO2+c|d&9*Q!=wmbwg`rvLLBmnZI~QlV*fZ7I zi5$f+e;mc~C+gzuc%WH}S#zVx%)P8l6 zwNSm6OEy-sEW>ZTVW=;2bc15%GVZSw7SIx6l zdPkc&Wh~~P=ai~{f#|2(PPXpRsZ*`tfN-q*3t~r}hg=TKX)taTH%U0Cyc--e_2Q(P zRSPUX+Q8s7ae7;U)zP@~`Z2#1So4C2c1{PP0oM%*SgRWc$F!P(yrt7w)?whW8CDmgz>B-03#muVv^we3nO2`59aigd@AwAp_#&fC zh>(9~2c9nw61U(ej8-@lHhz}%9F`_)eDKI9{pTL5ZH=8^87|rx$*&Z0gUJR~Dvv(& zMD2(pG7w9=-TT&j_u0-k!z1+Dos2r_-CRyMPEO*`e{vEh9LDD^>$QywGB|&))vx-O zz!#o$5HLJBpg44*<K0lnPEsI6X`qz})v=IiIjA-QuXYhyp2)BH@iCK@Ci zZTuVo6Z{#CstQmn+prFe|Qt!+!Kaq34urL_^?V+2eK zz3rD-Q*f6lyceXPjpf!ERCr70SW@A26jXaMPgCv5tRULNO9Q0Bi_-+t?@ZQhlgphf z0Fsta-gN;Lx=2$f@48(E_N#>B%Df>iRu>TP#Mj0!p7`HZVIh9M3JVeJLUX9e5^AYd zzBY++A4Dx)9{DG0t0R**e>sGvSF*zdb~Wt;IxMeM6FCEVWet+mLz78){ATr_pyXXf zJ14sc=q1sq5Q;Zl6r2RnQ=cPNX#bF`9qjLA4tWpi$6=`7bWuw_^d43k@}cSBahvm@ zYm+1&x<*Tr^PyT(aK`yittp%jAvd7PqHeIk+89g}KHOlvscxM@&xnx;fRJpUXnLS@ zWKBsJ25f=@?mk5lhR_gyw<)^qt5$RE+i2aU$BeSN8`x>M$VnZLvMh3=ZPFA;9i{|t ztK2CPwC8b`L|gQ6>abe+ICVfD$9Mq2NPt4==UdIDNLtVn)90oI-DoQ1;kLyTNef!+ zumVQJraWHv8}j(!_VP|^salsOFS^$5vUaM{JUHIQ9MRRTJc+)$@;LfVxfO3O|GnG7 zHq6kV`n!OT69pqYY~ljm1qhH`^iZ#b#uL)gNV`8;tjuzachqt;~{c8f!Rbv(i*kEOQnhT*Pg)?LEeu(jTti*F?du zO_gZznUK$asZKZ_x>|jsO||m*03?Ttp9KI@qtL}-Md-wRR+GTnFIS`!dAsGPsPeRO zXAF=WFVJ#$MO!!4ei9ftNW3ikhs$;OQB=|s1wXyGbL^(uG`5>=m?wJ8nI@djZl9so z@seIXFMwsirUqdZx>&Hq!7gLLb?6Fjh>k@A4i*jVr3Us+;AGJ{6P|eG805jj@C0Ro zI!vk~A{8~~n(2n0sQP#h$vP&2XC!_b*7iCm z%|ASIJZddGXw&mTG!K&v+^WD~O7fp|@V&@?S_Cr{xOvWhzy;UHyOJN_{3i!YNdA*k z;K_dqU$Pbl^Pg5PTQ8{vAcEa8a|(3pSFESiI`Bc;Z0C^NQXN@aK*^3aN{Kb8j_eXH zxu9@gfuuOcg16OE*l|GbR$t>TNpa9eDUN>aC?0+|!9foPdm!Kh=YJst|8Wrf7KQ+Q zoSjtCrBA!H5%jqzC_BN%CA!D8kp|O+hjucYiyG*oEQNkx7es+R7X{}?*zW0~80Mgu z5QYMM%*D(w6zFqNa7|>r+#B;N9>yIMJHk+`aZo%Gh5~&K3Ql~y+ULY)eE}yvm?utr zW)w)`v!2Y&`Q7?jnlnj!W)(={lkqkTR(&*G>I~1H0Y?{!Y=gjO4X`^8)sBiEO_wA_ zC%$8~(!W-=BXw__1%bFYan%b%H$Qm?PiKO;hfzr2-!c8{zh z3H91{tr}5hK{1r-9C+7SikySt|N3~}WClw_Z-&oK52iatR~b!@_nl}&oF6voNqDd` z9Q;Va^TBjZc!tj4z%_J+Bs`N{j%ypJQPE&dc=CWR{PCWEk?;VDJuzDLW^_%p9sWoKS?p=H^&F#;Vf8|6Bx%1n+ij#?|vWVKeFDOH18W^#~eG?RmjBSo{B zel{*brMG*WTli(CC*_1j7nmevT)8&R} zSQNmsC}=rZ0c#}64_;9yTR}CES)@FSt*M4Fi;XF7mL!)eg@t9;v9P*)768YICF=Mj z2`*U@JSLMu9=#6D;Txi1NdV81pykvBEvGK;&*Ibt`$d5_T7S^eZezU5$o283tvc$$ zEJ;gEN@^vQnuW(`g2W{5xiF22*!7zmBRs+wpN! zj4A7W#j37U!t7uQ(_%KKFtum9p!6o{9s%te4$A3^8a3NlnxWK1oSZKMgWc36V|Flg zQ8PUdlD0U0F=ICS3N>7K=2!!gC4ESeDF?jAaRVB+gj&gSKQW`)9iu zOL45Pw?1~&Dhg&S1wUIKtK%SojOE$c^iwc?volox{)?5RPJ?>|8;W}^l*FzAmu9RwtEF% zn|lSYrvT!T>lw-Sav22gz7BuymVyAv6_kk%XpRFqOF(hS6_jNTXif;U@;RQE-0xZz zZr;)jKXKdXpxEZ1_?w`>CAJFOVK^AJg)k@(r|d?ZaUf4Rke>=9E}8Fd9nh0rP|9lb z$sZ7F=&h5QBQn>R!^*-X!#1A7GV8`aSqz%$%Z0lw{`EY)NJcefAY2B5e+C#QKNL;VKS)h3Bz~_B@p`M_wTIjIhTWDrZh8 zxBU!mgv+SGHROM^mn&7KcXD4SoqD=z6FU?>)|Oa^_HugKExL z;!LVJDRdgF=G+-&FHr~PTvyF;0H~Vda^L=Dd!eD8bGU!MiXE$74Wo%0^{d)_Zr&2@ zYf04xiDmIX+h>k)u6puc9O{3p5m-oyFNH$=gl1C}{Q%Q+DE zqQHCDD*z`!ZRUkfdi*@rd;B~Rpw~Q}^q%v=72juGxzi39WmhlgU!r*kL?GG3w~^1Gws;F4V!zm=-l#{O4X>(=&5hI)6tn+@T`E)S=l4j!NH z;-R9>cYH&SdC;g>(SwJJIu&$E)1Zb!nUQ!mpSd8I8R-r6ZK_m9OIfGhb#Rg&Id0Kh zAkR`a(J=Wf;w6_W$&pgvKanFH3(r|NM`}4&a-^2D>N!Um3@#)`8oa>Gk*?ft_tqoY zVX9u_l+mHOR75P<4mblGux@^pocRhAXs^)J;3-Vgx~`Q!Pj93(8nd2 ztJ24XA@sQ@_~lX6g_3MVE|kQh{z6VXaLI{BtA&zmMe3AW@o`HoO08VLsfSlyPCe!= z;M4<};nZWy0!cmQ(F{AO$GxN_Nj=7b<-vB3sIeZ!HCk_Xsx@(;J1@C(#%~*Q7rOJZ zy_-F%NoX&XyVL0}yRWKFEQ{@K&o_+4`brzSRp9h;TP;|q-{@igi`t&O>^*w-vvxPV zG|}EmJ-oqb>vMJsef%DKjqwD5Ip2|bjK}{A-xT6L{;~06;LMrt;|ulT7wpmM14o(v zT*#U5>4kdqPSrzQa=QN;>`r;dz6)PCJlqj+tYPyQ!=%$_{n<{uT{$hP=AR~eY2c^H zJ@xT2dw_arAuAeev7#SYs8fpU$uTd1C0EhMi9J#D0n9Ba`hbUHR&?SbSJ8LT87um( zMXYFOgkS5v*3Z5_sGdDTY@c5LvZEuFxrmp-@I`u6fBT=h>YcXpUAf`F<|t>~+a%-e zXCdH^y|;NHeh%IK!E>AK+$&0G4yG*PIe_du2SXOg987`a?i@@aEyx@U!w2$c4u*LY z!*g)kVs{SO(;3e}`^BC)crV2}2W2X0vCeMd>!Mfox3}vy!|cu~b20m&$bLZLs6Iny z_l8;ZJdNj?g2l4X^A@vhPQt2y+SMATdquFD)U8UL?p?udQnNbdbUzjBCjF{2PWQ=R zH|bSA>*D_QT76HJ zouQJJh=&i|_77O1vp+(F{WA-07{7l=a@;(AHkV{4sEj50_*xhqk{|cZ?BE@?xsuSG z4elLQkC#YDJ;_Vh+&V4MrMvCASWUd*lfkj8o&m(YsOOF)Vr_Q>1tN}FKX0K+Lf%O_ zWBr`;>Sy3s`;nl2nxC`r+8z#3eZfjn7nbPCT4o`|ht2w+-|nNP7RvNz0++L}@(OjA$@W-dxd;l^<_fA7 z6o$S7ZS3UxYFnX1_rK94etYn}QO_u(*esudHvTUCm*2vV+qUU~bi28(I?}N4nil5? zQC`9!edQOjNq||_S6ZRiL_YX*^_53Fi@wsq1bLuzj~J}4@6jbT@dKT)zJBoP>&i5- z3FANZbWv5*Qc{%>p(~BE@6&nHvG*bl9}J~bC}L?)Q?X0wJHsf}R2yw4M91R>3ItSa z;cbxqY6JD(X26`g@_iM*RP*QK?g?Jfhlh>f?j^4f5B4wzQhzC{BVwtj!&)lps82MV z>?4*K6Lt9T6J1h=FPv9dBJ0p4>L{W!R!7lNkNFI|*FG3rBF*O5&#MPn9g(F$bVb|5 zg`B(BuBxB?&hD+Abkq|X<3F;Lf@5v%pJxxoe#XMlST?VUv@b3dA96u@KMD3eHs4M% zu5qvW2~yAp#w#x4v<;VQ7ua1?t!1)R2-lqLy||P$=Or96q@$v_Qb%(;9nBquEUxB` zkX)j<-HzsV2Sp_@*pO0|xtbe7XRNs)%UsQIK7DX8@@Y5QD*D~VzF=}Llb7+ro48DW zRf>gowGi=R-ZC+#iNNJ7yt&KtrnxFzZCWPLtY{fWv(?MwvqTR&-TQ*wrov;bVV+L%imi8jV96FVB`VUo3R4P9bKztS0N@rG0|5abl5?pwF&VZABY3ma{ghE-!m_we2%hljWj`s>_3#XuMoM?(@+HAPeU^ z!RK~5O;GKYi{Ye%?hJJAkl>NwSaiWcY({RnoJFp>Ttu#nt;Lj2HU&9XfCQE0zYXB_r>+8kyCbsY;==(O<52*)5+S29XbgONG(|yvBxHLrL z$Z~!ECVX4+^^jbzuw2HA)X1mnBEtbhxT7K*(DQumbc8z!%&u@ph>By~j&Qp@gjqOq zg)5vzXDpnx!Xw=CfiU4VY=&_4R`68DtPtVu*=$$V*Q%PGRjU;;r!m0gSa-q-y|9`& z1UrLDmpOCVjZPEr**!GQ$N9VfA-ip}f+rD_NKWic9b9BjiD?7$u9yi#O2n+Sg6A-@ zRv2~NaJ}8R)_nBH6fU4MmU4kt$|LvLyU1#J3KwpNlv_l~;uSi3hyAwNC$dErhp_L} zg-af#3PZk8_JIeB@CsxP@cHarzIpyBr#Tt%jMVQlnNQ4Gq7P?4;eh743q4yFG zBJ>7F=ndg)vCxq#U7@4sjD?O`=}s*i*TtaES|8^_4W4{fvz0uxjaQP-dSBg+FK1Kq zYYYs|gmzddpA{aU6SXs;chjlFchzg9NR3|+y0`J$^03=1x))z)zB{hu`38Bm>v}6? z(RKuRSN0CXipbtzr7YS89`4w#ccDvWd^er3@VkS;^P)ZRP?%kxY)oI^A`O(W2Jo7_ zV2iy)C+&xA&shoEeib}jw7Za29ZkHlQocL+QApGeMARjp+t?GNk>?!o3=awV4-xbT zB={1!f+OcYFxr*#6@nBw4>@uk@*rb5Yp!zTtVL%mXRTH4B(t1HAGOZ}XZpxN`vrCD zDxTzoRb_`Bv+LtKo2z7&6M({zt=lS2d)2U25|jq7;-HkYO1_-r9T^g<=PI7RgjF(s z%~r|$^#o#9u5Ls`=C6f^F3Yt6T{3?g>5S#t=#{I}k>HB$CsX$@%Vq4-V|HNb z=0AlnS>}kgFGS+#D)|iHNk_ETMYLB|Nxc2s=|1mvv%z}dLjoTYf%iEA-|q-~4CGva zj}mDSxYQB2)WZ!6++ejUa6>v{fg7%-iLASTY8-LNx%HC0zs1|Wux#Tod$pl%T`fPT zYqeSrc*cH3p)GjcE}~Mg!1QbhQ=)|mk& zT%F|;L!z@xec}Kfi!(hevdXTYOKjvjI%Ac6$0{SC+4kRl$zB)KfLrL%FsJlkgP+?6_4Ket_E-E~#?HW--t2L~K7Hg!$(9h}a9qi^3!x*P~WU!k{40D|B znZa%@F>G?Wi-O&(%ZHureQr1F(#sp`GHwm)5>jxSP&)DNb~DyxOdKR}bs0lai7u@* z5+SWHL3Oobh>#c1Bf9)Iov|+ey@vfrm@a*K^IP_caD}$fkNH|8!0Rm-LJ&BSlt(E#m#>al!aJi{VZ7L>SrOHv3?e=^XO;7$M%+> zeo(p5YU?^)y(R1PZ|Ce&>cMqlXC=VkII(@}BzisL$Wtcr99idl+t)La1=_!kXB{}% zFRfn}^h+`OG1`@CA3=`p_c8$HNasvprMv;GsEu~a{KrD}A+-WZe$wVFLf6@BId zyp99>!@K7>j=!Tj(kNy9o1`>)*} z+wNI0$3i6DEaHCy^$F`E`a|3(uRNo^5q*7F#N`?FOYoBaFI0u~tj!Na86NDKm2pax z0bW=cPlhSu6p?hQGj9@KqKu=WjHBV4$|~rx-c?3dI%8#YUGGuG?8`R(2?CWlq#v=Q%XOqvGsjI2o3eS<4m4?1JXdTel~k9VcL`x`0}u9HWFXq!skz;ifk zgC0}Qw?tp~1I3|<8+2hSd|?JyoOztEL25$_9l_>`V6!$zuDZqPE)I5cZRnuW{fOJm z^E=FgmbEZx1J5s*U@Z*V5S-sSlQ7!V!UTd8Eua8%2`f6qgNs-6m*|rD{ff?53tt7b zz}Z-t={rbT2vvqCc`yuZeD8R^$lrsAi#CMCyKa`RuD;plOUHjT!r(4Zp>h5y8@Xzv znr)QHZ;ZCkNH8`^r0ecKjAKsDg7x87NqiGOh)A zCSF(>b1+DhF_+F*8FM$fCc(-WQOU;KqmGzQY-H1ae507YcO=WXSL6h8p7b>vgOeV!7kJ%C-$OKH(${U2 zP`A!QmIb|xE)nz!ow1--yn_Bw#a9@!WlsB>_%GL&Jacn45z*M*0b^kKQC1T9Ri#b5 zE_|Cr^h(uzeN+rCD3kPU3W{B4la!U(=+X+l*7zbY8s}wmbLSkdWg^2N^d`f%RK;%M zSq6TV-q;k9J{IU*>FW?Fk>1=S(wiRYEPXDzMEXf|#?nvPO zbI~Tws3NeV45$gO8Qx5&mL!|{ml0TCWl zS@^kO!UsUq6@C*jAi~dggrD!>iG}|KT_XH7I%DCl1%-#(dpqdyP+!`jqKkQEs}z&R zdr>tmmPxHr9Guif#qwl+XR)m04#jNCZHvX*4R*Sd+-_dUUU;_fTZ(y(L5anzR2-V) zTR_bfvk_4iF(Zp*jw3zHu$XJnC1S3lGZu4Qv1g8}H4aSu$+N*c0VTi1H3($(|C)fR`oP5hmGFKloGX!D@;KBwYJcmnH`kj*};5=vwO>j2nsM)4(Lu8I0YeXPoE z)X1t-ol3-9N60N~DGR&B6Y0gn;IwSKm>@2iLh30tgxFPt-dY<*9O{_uMu%O5bvqqip~H(f%qohQnw>X(=G^@8 zGw3~}|73|)9es@}647K3)U7)D;%*~a$xJ7iX(TiLWX7G$c#|1t@~qs3gyoHrvUweS zBUB`_U8fVM-O&mEchA>A|Ix{}O+kijoqcO3yh(S44#44qBpm3Uvij2p;Xwb4(ce4; z2l`KP{-1{6K>vda|7-^b`X@gA^=UZJKW6bS#6MH;(?8t6e<#3!{)vGohCWM$Pfsyn zg|>8$s;Q56^i|iVyZUO;$L^EwaX(#!AH=f?-y@jrnK;mA-u-_Yi35Ga+yCci9IP=o z?9ai0zKAEPrtMe$suP6@S`U?RL*3n3Ai(nxJrTq6=|G<&@;94;1AT$OKcfH#`bnO@ z47>FFw4dES1{=csw7pDbkV<;~?bt&FO)9h@Qm4B4N)~g)Pqu?ULL+I@Z9chmcIzlFD%etNa+-$@60Rq7v0ukGkHp}#WT z2Kni|nSUo8=p~cC2Hqn0WA3735gq6ig}>fP9H?UNe~b=P()Kr7g#%TA{rA&>%A;bD zw3PfO%L~e zUl9)UVCsL84)j#!PeMBGr$-n6`*fgG-#>^FP)c|GKhuG7J^va?Pd3~|M}xu@Wd{Da zbf7TluT+c!g%bY`I*{x2N7LdWuf!|NzlYAAD-p@ih9tY()0cQV-6l6UYwWC|s0q`i z&73-I=J?Um$4`*;*d}n(O<9}$Mcr?@Dcj#xF5o35B>H|(MnI3e%lC;o65!9f9SZQy i|CPObjiQfX4HQ{ZCQTi`c|ZjGo&T|be!7?M?*9Y)TDtfE delta 33115 zcmb7tcYGJc7B4%y8wi9NdO{OWLXV(AKq=Cc5E6QT1VRXerS~Ewp_haZ5*$&HVnq}| zN+{g zjpeG?yv``Izp+`Vve98vtBC7#a%Ho&-a5;?PUkGQYwH&`tCpM2Mby`)eo!NHVimKu zezb|1rn{+#o;t3oIb5Go5#7|6cj`tr8ZFg-@6^*f+s$?BYG#C9@q&4M$?0gbzp{OQX2Yy`8JV*hPM9()Ykby>hU4%sb;g8@Ne!n?@hXe6%cd1)*UbC>>a(Ia zw|G%;vuxX`*|$uY>=>sy6-Kt19h;P0Pe0MkY^Fz_S8359jK$gY`Wk3hwXg2h-JEE| z>D(BzanYft%c|@9>ZuLPHudrvO=;K+q_%80bw89^2GINFMt`?ibmwY=K%Dc0QKrml z=Sf^0>J}5-Bmvd4$2k8+Rh-LCp3TTTnb<9L;TbF1n8+XnLDSAvucU33*S?fDAxULN zfcu3)+kBy|O0SB>cpYEQY+Y?}sU!lUo_{3vd2Pd6<5 zS-S8CqmG_ESk*Lsrd|z-LbSK4o@b(Uakzl>_6(!C%>vfB^^IEUdyLV^T6LjlHp4Cs z7mS@)lrg@ANTXG#&Hoe37{H7WGOqv{69if-)Tk7wkkxdO>sen`|M6s{4=0#$sw#Bm zY&-2-XEsse`|9n@jfOSH)1Vn9E=h^FD7h=z{;%q~pp)6t=%G)KRyFkVr|dd1(OWIW zM8B>;)zlqM+m(2>8||dgq-;A%=byG4^VEkRGMbp3qA~$jrd8f_J$;cHDZMXFu&Wp~ zbZ&;KRaRzi4T4fr$9-v6GUia<26}Xk5gP#D%+`fX&1hXS!|bP4C$iyC%M*2BhS@^h z?LU8)VGd9ahMtSM#Wk52OMdNrudJP{v7f@iOa2Cu-w!qq+Vv)7(_<7}du(?-H5y?%rk8)SXtF>C9QA+y1RYwi4>g z#G?3b;v!E`4+2;eKQ&gL$}*R$ONsjU3#z$#Gm%ZeUx{o2s5seFSNTY_Qqld2QYOZ! zD*bf)YGZh{8vQUt0V6;iW1`hx;FM+h09;iAA~h#;FU5MMjOL z`l)Plj7sbmhPh8a#!Lk;7f&$TsnLQttsm=jGkvdit3jxLbbZ$AbmwHl=KOTaix?>M7}#`N#i{cc!q_-M4h6u!Ij{xGp0QBx2beZyRK|{{At2mmJk6jxp;J39@vwkxFn~^|x{^tX| z%>VqP(EKlnuWZH`>XD?Py$>d+9Z7m^6?3cFpQNWBRS6Y$5XA!anIwJreS3`Y7SR%2 zv|&SC<251cP^5}dFDL1?)yyR0%g}4}-z0r{sL{$e&sF-+^Hx2o>o&}Y*1JEn8yc7S z#ogzu_QrpSs|K1t886HIsn3k4D+u68U6RC;8l>{1-jXDfx&)+pllmDVTPF3^B$?E0 zYnUIY`2M*?wktg-%=|qJ$kyrGmiL8rnGaD8q!=0}Jkl;+Fo|ws#`pZN@$$Ci7u<2?-f1W|( z`tuZ?P}iKU{vD2Uzu=?-IMd)+)w_&Sz15$lu(}r@ZfRBRSm}uO0~$@DucZ$jc@q6D zj~Bu~{CQ0fJcrPiX-9dmo;gT?B$`7{do1uA%7iR2hX(ZLIn*Cdb^57WlUcwC$-aPP z{;0lqgXRgzUUN(xHBOpmdd+o4Co4_emF%*s<0~7j^yo8IH3jLW?5}Pl7oENor)CMP zOb?b!l-3vkOq8ubjV_AEM7g)WOq6?xYn~{(flwyO?qr@Q4Q83WN*XpcyBg{>K*dCP zIaxo@#N4GmOqPlAGSMg#<%48>OQtbGK4|0mkpt&icg(#=AHaielXrVES`|CYNR0TCXg=Z7_ zBjb&$=r|&D91%LwA5)Et&wV;Rm0LuR>fscgSf{QcN1q<4sv9Xhv5epRzAngZ-HbS+ ztnM}fL2&Y`=Lk}cRv?DHyDA`$oVq)F({;Dukl4AsWsNDqDSK6{FNb3m4NqZjd{A&>SrB;um^|%sz46=hwz)bm zfUOC-kR$%a@A6fkOPrqC(d=Pd400frrE-Ek3L2O~#4D>8an}K9uN)U>t8miAgHZDB|>aOd}ak_P9 zvz$I#Hln>6I#A3^5A<`*%#eY)>TTv=W3n#Ha{ciHx)mp?vIgp(i|o$E3jcL(*lV^h z;qchPqz+{F3L4nGb{QxpCKZ@H6ElRU5VO$@p$WyzC%T$PBA=!D80TLD$;3RM_6-zE z@fP829{D=Gjdg|$3|Wb`x0^$ZubHYMG7wMe!f`MVUkqde@r6D-4hG^-rqRIoiyFx+ z`rU00191@$g9hU1fjm~|INq!SFrs28EA=!J2`tSXL>d4^nx;M*$o8SjP%}fd8^j*o zqoK_po;_5Y?4f!O;wZhxAoe_!2eCc4V^C?>RhvOUIaZZK2u^7PTJ4Z}h2LE5>`l3LOrF6}s7AR%jq* zg|0MM6uKGEc?#W>P>VuW87vCjdw}_5tW{JX2lR#u0bcJ_w<(nREdUEDHFZVcxEE5QB_K*RoG4kV z0gJSHHv>5>)vrTD z=f5C|1@?s@de0n$h2?c_f!WH4pdwmNJ7Ux}Ou276#I6$+)`9i=_aUs`K*IWca){{n z?`Y}i_XX-D`u$(5E6HI0XP&t$^46iKk8wH;4a9^Xva0GZRFoWXCa5Ud^K`Aqe$+PB zxiADu9;bg>V2&`dr^@e(Ou^vU)OIaYr>sFS%IDr@I>t1 z0I{qxTvw8!nkY@oS`pYM3$3_W}5y zc<@DyI)E#v(SL&@$7%$i92Tr6F|Zmn9pUN#u};KF!n0>InRtXkZPFzpjl6!Dy!mA#-Em?|47{ZnN@**BGapql8`j}O(83ycav(A~D1vsIT=8c9t0;g>QKz1gqGLSc-#sF6V>KaHVtZTDu*PysYR^Q z8BgxokD@yDcB+I^_vn;|5z@V$%68#(o%^sG(y3Fa98;Z4mCWZCfD;_4ds5jJpvNPy z1+Rlu+FC$KUe9=)SgCV&i4miYq;eSbQmXIt>gf2L;`9s`e~sa+EL6bhwW9ap^qNak za(cf45b`H&hL=W&sxe$t1|>Va8fbQf(*tyJdes;@2beFVaxUaW2nR9R?o_r`yYK{e zHaLKM3`njlkR)YUmJm@O)Xw`5yXX$0FMz+@kH zVYsMpr4hagSHQii!oMM_Nu?q|iFGRA z39gOojaoE95`O(hu(6pxg7t?AFX6Xw1nY0X2-e@K#efmiU*z@Pfa1>wYh-CqlG?x< zHhRu%X2m#jfGjo+E)>sD#kytH7}Ov~_PWq?B#fprk7C_$5<+;Oo%)zLi3f}i1;&vu z7^Em=^ywqet$)H`wu8jq4NSW)_n^c1C!KK!OxgZ7-F^rx+`j}$jfRVX>O z%gi^_sFB(jquQ#GBX!}E$f}`?&rl8Y*288m{oro%mB^)lgHgSO2td*}?q zRx41US)+wUTtyd7c~p6DQlB+z8tMSUNi$yYKt$@_|BX#J)KkZ3w__#l2$0bIqUk$2 z>CMlXQx!mMZvho;f54ohejdp)=KGQ44w&m?J@k3((Od!wO2dCoyvW@7XQV#yJi^3i zoqNozUexf0I!0x>bwkA{xjz-DYG2U{^JuWHc+hO1%B1mR0y>^dUyr286tAbgXEv@= zCe5Ew|A+)>L;Nh{{<#rX^5;S1dn0?H5h{D6k*M#WQ5vkeAwNS~=BZ2oMU3T!P_tO) zAD#O#Jl>%WMn!$_C37^gIz(>~g^gX)czQW$JiVyE^eWrNYGo{yMie$Kk=sz%xEyeU zQ;mSYsXJ*h)lkw@EAJ31Sb68w)5l*j>+4TmF4N$rmD0;FvqDg1Fg^{i!8d`(ZJ#KYzI`0apM?k>PHVTi)=dGL6GwIs+ z6+44?P8n`ASM$>K2S?2$^^V)^4Y%8;(hZNfDONl6t=kRHZZ}Q?gp=i~{v^l|8^U0U zRewNwK=mhq6)zr|Kx|1oGzb{rOiDg@&%DowtT&2PKW3D8lWB{vpAG$I(Ns~jN3o&N z@gJD~M7ETs&ABNIQXDuY1?cS9Fpk%qmlXh@a` z0BQEA;iK504W*W`&IG878;lsb3#oS#bMdRQ5=@_J!x)JFNb4(Q82$a zN*}Ii_fj7T)(=MUB-o1X2*y6S3b#IA&Z=$z5^ZLklRf~!328$&`c0Oa>QWdObM%!g z1i@g#Hq;NG&Yv7h&o~)ufH0h&xXwpZsfUsc_B5BrDB+b8yBpxL6B|#Ylp}q=L zEUayzDr3u4;C5US9CHX5Jmx6(e2sGsD7#g)+A|p4@UmG;O&rCMZ#MK1k?+t^Y+?uD z36byDf{xbdUjmP(sBtB4SF=VNW=5GZMJZ>Z^)27QSJ@c=aGjoZ92*13Wm#5@@_DLZ zvtsDH%Fx@tGc#4~3|+N`)f{Pq`0vd}6{^YoM!D0iCR$z!5cc3qla*Fuok^%0`TNZOkqh^Fcwo%Eo*vraf&UY^5o|#$1C13Bq+o zz?e6R++>t7%Fv(AIA?ASnf2?>BedF+!Dju53|;wu=5Af3oMq{mM^%T4PXL;0$q#1e zh+oWQdKsn~ZSS2&i%) zux|)w)b|aCGuSskGVB{3%?S90!w`<=8xE47#5e5B5Z_SuFYLQTJ~{?3Vw{IT!B`24 zdh5^sHmgg$+`X14nG7`*~zxqnN)14MCSU1gV1HfkSL)$3~k zTnSgxaZdVo6zvjH79e+w5g%h_vX3c~2@+$SgP1zLk6C2Rg?j4a@0hh!{Y-H*+sCk@ zshi2xj0(@u+?dIZrcI_en)ZMgbTrfvj;3y=I2x2}bWpOR0VFb`wK93yRnOFgt*qs7TH>djx8HzAi?*uWB> z;*LVZSRRFlvEox2NCS$pW963IoGX$>V@n+m^ktuNi*z{jN^N5-OGQ815cYy#^}Gt! zou$G;mV0}gRYw6N^Mw-rq#<$8K>z%;*_iIG!)sy_X>j63{7dyr_AgbTi|{XHGTDay zGlu=kdcjWqWgXCZO3MCalwm!Fx{!l0o)n;?k0YJ@_*i`!2fe5y&te=BKwT$Q(XCUk zUx<2kF(}!^xRZli%p(EX9vmw!=CmMU7t@6HIf;5k^redkB{>)4&npn29!}YI>74va9Km z<+++JS)QwDGzg0fFxE;?<;KpsDfRC+SKjdjLlUBgP#Vm!f;4Z@b=WHCODO@xEOHPo~i zCxCQ_1&eWRkL3Dtzz*7L>QC$Naqc>NoJ?vwv%LmD%wnCBU^U_7=-6?*G^p3YN-_>y zMMa5FK?O3lM|}d`mZp@$(&N~4A1|Gb5|<8?Y+O+O($eaz0FtZ;c8kE1O9}aoUvO4u zdVkkky0S3Gvn;6a5)-k~h|F9axh0e7l2U(ssUk7Qi^*&s>Z?%2VR+9_mC^kwaJ{by zj@JUrCm6gkp?#FrROSL!Ejw07VC&z1bn7k=lD z7x%EyC$8Ws;)+Wr4t?1@JRa(+P{q6+2vr%+UIp%jYl3550fTv+@9}!4K6j(lS3iEU zm5Quth}Ss`*yEMAoDt9^Sj|=PIJSQM#&Md}$mS^K62N)$BTjGVU_Gj8W^<0GTDG3j z(K@NBWwVd1jP~#}m&SWQkls9+Q<_ zpMDI}Fs-9Uej6vR#J`?ufOkVjfp%&48}MI z-PQ-LY*q4OH|thQy_2n5_Od#v53_l#RPG&g;|1_fy{vxvOfM@HO(4zq%CXMd1X9wo z2eM0YdRr?E^)vU6EopLxwaid|2o$O%Cm8Mahy)=As55yu%BS}2YZWPw#QD_niPmw| zXac*SdK2{abx2)#%(T}dhg)^@v8MP{!(;udJ5<{V;*9D|5O7e0HWPG0l69ZaOE($| zQ$VMjyVI?Es_M20I=;WvMK8{DS4P7p$RcJEx0glC@CkP(BZIkstN3*Da9ubd03zi_ zn7pD8236n!*&YCbI$p;-DJsHYTU>jcxBhx5utPy7*&3<>Kl#_ zlEhWKG(;=%uvqIcFh1@bJ`(I^F?tQujT6|-Vno@^)}A14w$d=Gah)46dY+qYLnA3} zw(bORv$KX-{fx+^4qjmYz!5k5PpY*@Epo`O)+i|)ZoO%!0!JS|Y&FrpkF{d3Z3ylg z=&oa}3grsWm9;n}&6=%)?+ALHD6D7Wg+LGOBiKsHPkCEy33=1 z?wtS#yjZ*5a@+>7`e=rAi*YKvLAx>5f)IogV*ufo@CGrN2%9|=nVD!1p${*_>hjP? zt1P|Pm1)I>P@KuMI>DKRHRw0iS{{PHGHDs!pi7n&g9hPz(&Pl6_h(rzU<$?0#X@Ak zIIC^-9upykSjUwHZ;3b(tE4kj`iWiS2x-)2LMT>w*RWgoD@vGoK$KWzp~JDt?1}83 zsqkWzd;v(Y%35mQLe82L0C+H1Api(gW>1u01tkXw^h_rKW-t_v4yI1zIHjUv4KWU1 zMZpU~0TqZ-KJ<~Fpr$R=j)@$jd{jF67hLox89mDHCt67g;3!6U?kXw{O|s@-bME{K zH%@WoP<|52q5LHA+l{3G#V8HsmSPl_l%|tPl?3{--|iIZt5C&$8$X*Od<5=}tH32) z6CC?(z+lc$)}CgK22AFxEW;5u?F#tvH0upxxxl9^sLNUo1E^OPXm}Rp&am##^JX9m zg*QiMSYy?eNpRRLBkw~Cn&OkRGt-)cw~T1f)rit&Sx?|cCRfVvMUml)t_(kt2E_CS zavPB0Cs#2o31wG?=R$oIz_APseR`EE!%J6zGbdj;Bpx`HAz-i!QGP!c(-M@Dc0A8; zJQ;GR1KKgm`6rhnCUS5wY$C6TdkcD66ZZfz_H>wLar)wX%4&q*y0z@O>Cm+)s1W+cL&xj$P2~H!Nn5^sOSnsK?CyUEJ zK`bDGfFRCH*5m(W-Jow>V%?|@{mZ%yXTdR6vbXw&&`Zbg)P>3H{Eg~*??#CYRFx^> z>5$e$m3Yf4Q`r3@t%)l3bZEtENdREie}@mCrvT7LwH2ZNwb801Fi_2Y8<|ZH2&z0F z?6Vj9Ko+=EA55n6ktrTOQ58){n!r=e})X_p3Yp)OusPZ7N&p+!! z_{xRwTL1#;mo-t&)c2lApIme=yz7yL|9F%dH8poQlqAaQ9oij%bkjo*XxF#Woqc6n&W&l*|jt zQ#-Bh1anOt`%aZGLr;4gyP%y3V6?7O*KTce5a5?9+xp_;REN{jlgrYU0+S`Pbk$GrtJzs( z;PC^Y=^w%M(y=30w!#!RiLmZ-IR5A|hXW3GD0WQ$s^l_%%914uDI&COGB^FqkKl z9KN9B@MZB_314y;poA|AUEm|DO8BxC_$hpG%~&+T7j)3f}7mp-F)5b=((mmsG9ys#kV=Wne1P!RLjS?e>EJ&%Hz zsIllF=@x|K+eg+2?8)qDYM0j+zp*0qt>0OL)Lc+XN8bG&&YXGrlkcpZ_%sCAleEkl z=9GUOsaDLB*yiETYqfozIQD&9rT6@TLmk`aacpy#s(Mok_UY)%am^=w=MUY^X9Jy4 z$5Fv=(i@LnZ_cDt*5@N_-YrrU<_qe2TqRKn>Nu`x!F3#!po$<=U%^9}{Xcx$ZIkj& z?M*yq4c5mjJ3{{!X;0Rgb6qazE(Xdf)9Ji<6!n$NEo!fY49ssIGkF|b>4`)G#@WwoQv}%#q-%8*c174z8sa9@~4$$s0s@tpsKFB zRkqz+59+%8ZS`^AxdGJ;;SG5IMLN4_o`moQ979!D0J}kHp7INrLOOQ$C(#s2^Njn) z$}#W}gvxe!n#b)xX&#T@j7!!cLuE0+O*UC}3=U@aL}xA7lxX)P1X+v}`=>69iUNaE zMfu!=Q$=fuq*%1CB4y zxwDPdRqra5k{=*JTKUIvAL9pozLMQvwGs%;JqR4swYeq)_BMdTyr5)Wz76u?M`LRA zT@u5%VjOuFi*ckrRMYOQW((pOVTk9Jj(8*h5$XBd2dT#@wL$s=0^z zCLJXQV4#Vdf9hSFvv^2z-<1f{rAR^pcNaKVP zsZ4lveo|Ezx}(N2ueq?)Q3C`f93>Nul0z@hM1&vp2>0_vi3`Js@3WAD7Am~ZD?{+7 zhvCmG9e*D{BM#!dx-xC^7q4#e_T3p^kMpcmOgl7`pYchiSthw2(_rxAwVCdA0^X|l7lRi z9Aw?Fh=VK@w@6>CV|P_e7hzl*Y^-8QeARA|#8<9aih)@|PmZs^7fIlNCjkzv?&I0S z>7lg?I4oMVfY0SjT*T*cvO%S1z~V~Q#oG5Py?mNoMUQT8SE9Wn{QPk~n&L=K_9Cx8 z9m%;@`s-kS4yn{rL~#oR`{5^r#C}EWLTfK6v};i)pi)OdT{xJM)@m2dO~JaU#{|Fj z@Jk%^Crskm_EDjLhmA18DB$H>&~Y-M97g>?Bf+_#KNf|BQ3Z|d?V&JgWD|Qgewmq@ zV0Tg#7wbMv?H{qJi%0o*X2pt&#pOU+4HoOg*W1(8&5QN%BlvCSH2nNZwO-8OkxE)D z$1Cq#%mIoTxtNbj-np2l>@F3{2PK5OTunV{r`@<_6Gfxv01^v3tP z=Zqj*_w>;f2dSRTE*a3;zROTIERlm$O;EPAKUOG{hug(^TD<)OJ@DbAcb>GH={+~v zYt-;1yem1xZxy9$wYNK}2}>mW9RiXhV0N;X=*{izQEJH&8Nj>HgwDs#MY+fBqO@CI zC)pI-+4mp0ky-rPB!mgjjd2{pe(jcE3FbjJeyP6P@G@c!(j4%lPy#BuN$yS0eJ%wcg{UZM{s*e6uA z91+50A4Sz1-EFTLubSnE5SpL~387(*-qP9bth&1Oo&EYlhfqJvtsmsqzjG_E_Uzw9p%pGmi3v-pmfYAxRsB{+oR74a+)6F+Pz%ifiYx;(|do3g71xrTo-XZ*n}Sg?C1Ide+w-|;BV6%3;gZeFoDg-_?| z9}?}K)CE_}rwO%)`4IY`SBS%wFn= zq}zaieM^7GzD_3e`lTcd9KfnD28#E|AZ#*i0qL}`UkfO%MDAZI-+|cYa`%+W^)8$@ zf5B{iMN=N}txI{t*P;cj8dog!+1m=h9((r_5@B!EQekgZ5U)StKcZSj{3p6&_I?Vn z$40AQnEhaA#M4vlXXv2#4EW#VPmou@@8n?VKgN%^QP-)Wxh%w9jb;4BT-9=!40f|+ zGT2R)@uzZC54XPSGV#a*ma#|1BYdlTB3F%GCVm-}>?xZpRfdrP!ULVcN%QSx?^+Sg%-5P%3ALMQc$p{^+3a>@vpYz%FPnDD zJ=wIUJC;rRPMu(%RU?;+5xxVR-MN4-k}QA`zTdG2 zs~O8hVWyxNIXP#!SckQ4{Yt<7%tTm|9d7*tI9-JgE~`DDWM~_O$HS8lH6C0pI)rD| zA!j+uDTsWz*wggoq(gKzW+{ZPmTD4uF&z*yP&35a@hU!TaQ9;1E?Rq;;HCe&4 z31pDQ;D>b6S0f~90s=fKG$LX|3Q;R~B1J_7=+oWSASBv>8WF*RbjKoia7CC1D$TVI zlL(H_wZBr&Gxt%?h5+U5(7Ek!xZtt5c3s{1H+z_R%N2&-#(8svKKYxSs!qGyopibT z81>{;{&eeq4b?N(K?uV2JHqv|F4y~n>vuqn$Mu^;nQ*<|<$8aJSm#aVx<k(gEM&as=UWHGHKQ z$zDP9Asw%=3)M2fPtYB+`a~G3`qyPXul#RQcm;4o_&x0Mi+`+AK_{w+1oghl?_nR| ziIw`OiWp#gEBu}b@cTu8-|!yH?}x%Ku(08KKEUsXfba2pf^Z4H2VH&-2GNK3t=V{$ z$8Qt5V}6^g3RCFs*4SG@b9!oLe0hZ!#z)RhKV)~p*NN*rY;V#n^AH3kQkA=m?Fex0 z@b+CL-#r_%O6GR@Dlvn@SIKwJ7Oc{Hx8W0sp=vf~;Q*NXq*XlH9g*@*w(AI7_`0p%sYkecbRYX<|;C1AL4U^ zKF@FZXg+g!{>A0_XP3F}g=eFJ-n$2?UX}_`b@lx!<~tk+^Zj4p8z_0yj|3S1FHm}n ze@5tq@z-3&Ukg&?Gv0Hx$9ONgW5#=}4jc79?)J4@zos#xN3G@=Jz}-Y=&qaXo@&Z! znam>q#vSd+tNFW^`u06YhtcnCmiRmx3+0}tb9f(SblPejV4z?|hphHTcp5Nxj7}zO z!sxKoGQz`x1o(`;fNB}xgLKD?9t>yn$NTN$A@%-li~XYdk1%>-b;&mm*!A(%PnXFP zK8`c17 zEp*2$-m)faET=sfn86(%!dND+;jv6yqw7CxA5){&$S@`Xf;)`qYh=>SUL%usI+{@I zGI@a{l*jaNA%#OcDAVUYgtMWYeNm(tWKS^BCm+GA$iqaD_Na3Yeily*6I{9 zq6L;roYd*(t76vj%m67VRzzK6Eeo;oal3P!7|`Ge zu{O~oLNwQk5X~Toz7RK~T7-Bn-LVkwUCUYn5pXP5_8F|mJ$WFv^Pt)%@_1^k-nt+2 zPH@6X`FKE{uYem*o`;Duk>?YxJf8?M z>&tWPI!~VK=#J&NZe5r>k31hPPfqGI(>d?hv(-cESktzxBgw9i%+5Y=K@03{0O(H2 zr`L&i-*6c{;xc-8orw3eTYu88=d9VEZvC%*J5 z8}{w0n_GQrs5!|uDoCJ?KV~mg``59yf*ls`ed~O6-4AX&@jgwQiFmiH6LsAZWY!n& zU3s2(7t$SzcVS+bc$Xc4k@M6Qd7KB;raTsEVV-XFn*E7-2vn0i3jx8E=Yx4V<5Min zZ%#sL{5hAqf4ST}ohQ-udv5)^em!g3cW(XHp?cP~aPX{ckL0m9KnRN?FV7dpBOt~T z$Ad(eh+}=8h+}<_R$m-V@;z}hr8^c!)BG@TY6cPs#Ia=M8&*DhP*J+>H%XQ2< zIH&Q|AZ-2g%NM~U0G2D5#C*x*Waf*4j6xHd$0PD3lM~*Qne3m>OaeSJc~id6WPbqm zm`o%j!sIRa%w*Ip0lYfn2Q0I;phm3I19ZpyJrK^{u=nf&pTAVQg-oOpVR9c*jVJ6Q z>Q$GueLjjK`I5!?%w_E(m$movC5v;xtv{D9i*L1_7vFgFvp7}O%L*KoyaInepNBpi z3rpp7kqYQxsq79&<#o{HN#zL9CsNttN@Y)gWRXhW^`2A`>5iq62t(<*0LC5gnd>!Ya8}|K20D5vUX_}`DV7Fm>3YfFZ15w^Px6*?|`%~geREx@d zMRzQmufm1%*f@fp*9CfGexRDnq7Paj!@0-f_E zwr%R;6-|Y@`1v;d7rU1JGpDpVhMt-Ca0sjul?r&K04wu;A;5d30-yH^xOaK~&E@^K zAUQtolhIOmpF(%c`;>w(#kl>O0Pp3_;$c+*>%_7G;{APfx6AP|!YT>wO$A~Eo+uC_ z@R-ZlLj__4!kaQ{_XukM%B(Fe@Q3vt0QQD;6Csgd%_)#!%?aZ5S^FB*!rC`<$E&G-u#pOlB&ExM)abOr_u(_IT=lLZe)af8=w*0wovsp zP>@?ca?`(WFT;O&dgX}vSn`G%b__M7Jd}a|Md5+KL307Y!yk^0WmI(o4?hTF8T}rR zQPmB;j4I*YmC>Ifqo_XvwCi{~BB4eB8i{x|&>f3sgWg*qq6SH%+jzT&{`wbW;qZWb z)CiHxPL@p6!-RlGI&VEr=S0lW6EAp4sb^hj`E8t%4V;uR-t}31Q&@c!Z;+P47m-3j z_`-X`LI{V#LU@jaU_7jI%SY5z+cx-m@*K$W^rVDH6g}CoL7c^o5Y_mS8p)t$!$MC6 zjp&YL(5Ns>`&a!Ha27~U)fYh|6tarkj4di!09^VDIhzMAVpHvoLROZjn*q?B41M(J z>JicUYz`b|dZA2+;b=ytU}zyvi16l(szfYReG7T4fq}<5zR>5hFJOCo_8~mNXS+g~ z5A6b|3;p?U52|IrH`5(6yE%;6O)VpG_1fijC2vk3M^&Vr7FKr`>cd9F5p~#QaJP@; zkT7_1B^dnBW$-bd>o@ArWh0(f9r2wTiuA4r1b6s4Y?S3} z|BW(qebI!5uJ=Y+&W?BMGeh;Px#194b31M1Aq6hxxY0&`NILCwGHqY17EwWus*Y6ah$6kchutX(@Z4b^Uc|{L zH4R^9p>L5*Dw1KJP$Vv_PgqlCG_8oo7too}+l&11O+#ys(c#on80}dkO^!_C%^&e_8}kHdqtw#-?~hm zahW__B)V-B3-kYk>UogEAu#VB2=741ydMbg{sHiLyuU}dh4&X+-d_lk)2CS1Y!IZs-shZR^k+B{{t)XrNH~SiJ8(#gFj*ue*1i3)pz@=sQwQ?FqQhhllW) z0bJ;l1WwFMT&L|w@bLb4aQx3<-96F*V!`*~NzdN?G2ro(xZ#DgQtB8$D=rtJ? zph}^ZbnMFci!O9#$oU@UPn>gf`3%P{96HG1te^u3bUebjL>JoQcb=dN?PxnaXb+Y4 zY@PDhaC2zO(%DBB+QxHwV7tbl4KL?;x*YCK*Y?QfJG3w29H9&C6gWdx<3g!)=Uuu` z#@Xpj>Px9uQC~`ZI+$e48A_kCyW2!8UgBj&!I^>NmP6S`=W)7F;>@`Z85xICJWd{x z5e}s{oSU#Vbtnhm?4}E?d!3P3n>e)6b1u+@);-Q*1gs7%6P(}aLcy@J4FQ}(5up>0 z2*A0yJ6(6ug`x|m5xj*%Zr6F5F64=v+lz1^Q|%m}3mGn+j!qw%m|4Zb8A_kCdhrq6 z$a0L1kEmDkLo`b(cJgj3QzpatNFHE2isBVT2r*&^pU|V6&Dp3-Rk?=usb{^vt)j~qhW%>Gg)S+x;L`mCeZh3 zWJ^R$w9Z<{1*}Lbix?DLQDb+z9qyjBuFA$5hr7*ds&dsj+Pvnu)s-_Ho;GhpePek| zb%oti?WzNEbc3Vb)!=dWY*1?!r}p>i0yIHw-Z*>BOjkp-r>a(v8eQ37pIcowv!|A- zv+~nMhg8?OJ@%TKHm_7&SLv7s(*0}!bt+s9pibz_8kgH`ZSuO1yXmFc#2202^-|rRK5}LTO8>5LX6q$ak7Ld(y)?(w&6#2Bto6QfZpCu- zz3zj;&>pMW3cN$rt%Z52+&fAg-YZhI6vwGCg~?>NDi@`z^9%FUhl;wAVpf{iGex!c z$WW_`(je_i?qKNEqFx5CpCMdr=@~_OGQ#UcNosb{9qP!!G%`+aWKr)>bz+ZLwV`-O z=T!e-Z+=-Zz)BSfP;k+avm#8iEz|;sy*|I>cJN*P*&@qM|s7u z*dujoudZROY{YQuEe75FHQ-y*{bWRJvN(~U0euOh*isqL)FiV zW1wN1vDKNbKHfWuAl=m;dv_;U>cn0tYIUCkwV+Q=;LA{#_8CsPs;~6% zfZ)EjeyxriU{OnMTctYsTGXqz%~C%Zkf%2H^{68TTU5E9Lp|KzqV8EJtFQOFpX91b z2U^tUn=;gj{*%>*`&raW{T=EHgDmQll@_&mz%*4EVo}dLmZ|<_V2V0y;BfV_UY@L8 zA8JvDY{*ro4k}Zx_03Z+4ysg>q3(jG;?y~V74;vtS=3*T!Z4kSRU?NCS7!o${ScRW z3^cjtbfTI(bfVe?cvcK;R4t&@(X(0V@L^6imz+<4<=Fr_4`L4Ikn>gigG1sxgMV=D zI4|KJoHx!d@DI)z=T-cJ^Tiozfb2#R3YB(pDtne4- zg>x!`I47J=%;9`+&chte1?MA}!+GFr!(0aDo`hVZ*W__kxZ4)D6*w(E&+uME9j@m2 zULVm7GqI|1QPs$I(lL&$<*ekSE2d^%YsZCm#SqK*4v* zLz{T!{XehbnYoWV!1>DNg!$r^JBT`Tp$le9qWW8NsVXnZQ{5}t)$bR@`1(BhJW;LN z>R<}ZQFFHEso$+Uspgyl>$dDve>;`ududexflRErsZI7Jt{zX+f4%L3Nmj2OIi2UL zRJ#($#QHY1)v}D5vGz3S;`@B<2L^`!{qbLjI_aznre33Z^*ry!`y0v#6ynsW&kpb{ z*|eM$d?{OY@XUK#=M(jrjtwjrD!= z)L;UcICXPdwy*4&`&q&F<_<7DpuVZk?&F!v-6weF`#l?ZX4}4%Jaha0>pWBH8^bfZ z4mR=3;6odD=Ch8iJhSnI6+AQG@LHa^^3r;qX?gi~q9!|C5JIZeZ=HF*<*$q*5C!*p z;?;G>(|p%oeON2{)*W94S)Y7z5hfd7n}dXeH!i3rX2l$MGsmD#d6##+en^_S^HizY z|2k`bw`9}V|g=JK9j4$2?$0%uXK`5K8{=AIWx#t55fkK>b#kqQ(iMRm6@$<3- z-{TjDapbd0RXkJqA&fdQ|Id{8pGnDRbs)=N?0Vcnpqfx62?GCFaQFG z(GN-(`XIr$XEm^(XEm{i2MLBdpkb&3(HQ5T9tJr8F~UKD;SD%5)bBqYS%6D=S(`IT z>eR{3RDeQhC>AgfL!mhg3;C+;Um3C3K0wK^F|f0C)emWL7;6RKZ1v>7y0bXn%0$4L z$t1Qi8CXJ*tfS;t#oIwimVgJXhbB=%odu_nL>vU14i}oJ2a|KiX>{8PE|!$f6|D zqhn<2EfY1Np4!q!)+!`YG$DGs=MPPzd!)lPaq(rhRFA$ir#_^$J0 za5vYRL8c@pkm z6?!_zK%hAtq&FlR0ld8>sZ*y3o;C;be&|0AKP0%`LxSE933@*y=>3qO_d|js1xZ$i zaR$kkNCxdbi;QRPx|~hHX6CHTYyvhjS8Zl4+RSEOGjqFUHusuYqU(bqj>f+*t*XM`b)r1Bh+p)+o6B&n3uUore#_eC#Hloao9VT3=rXuvGE{(`8Q{5 zUjajx@l%p|=Wh`~L-aYL!&pSnBQ1ucRB>p(@{dTfr|kjt+Hc*1YJQ8|3G%z|>AP+i zh_d-4S5)dJuEOT|B&Z8l^_JDQeou-M8@|RTsl0%U76o;pvloyFy6tKffbGC1gC??> zPFfB+3|Sc5SQu7Kpspt9=jRJZ3f;SqWb2Y z7+tU^c(~)}>P6&Fx+L!+a&PC+BXtS%t3@EXdNHx;EE5+4vUv#^lGNMnsKBcpof~~K zixw{>N^;4B$u?_a?F}xJ!@B{sgJwV;hs{l7oZifrOQ0F(Z%|0;#u?vhBBi2X3$e|~ zdet6F$z5?X>=hokTC&&Z+R@gfAhP{IGT@KAyRUBH0ZFxXC*Dd0d-4F*F&h40umK0t zoCk?Z)Mgl+^k>qAwl9GwFf5XU1bN-X{Jx#1zA#0W!$#-D&q-y6tjvIn+N1sQd^BhS?oKd&EGHni@~S1H>jI!eGbJ_9&7?7O3!Gw zr=hyS6L6mE%Ry;ZGl>*ee|*PHcN#^49J_|!Tu%S(NX35lAy z81Z3!@JD#bE%Xcoyy(09%`=YWygG!!o?4F>{3wU7uONLzxP^{*l-#b3Q4c!!Q8FsJ zG)P{XUQwoN(@CO#_?c9yHzw1WUVZWJL5sX#hqYePT^GF=kt<%3JcchLto{~%cQE$0 zd84!|-y(h?=huT>3_TUhnn;UtNn%tG%MNN^sRu+>X%~HBrQTtN?4?^9fkPyv!jMjm|28+mx^wSp7E0TAS@f^_v zE35Q*%J8FH?z~l`i^zT~fu249TdAXKNMvSFbzMburK7IGVXG9}jMYD>*H5OYtt3hm z@DVi(MQ{YJ>WVsp@02mq78KK(E5?+AuUx_lzuEg)La;w>2a&U2wUmM2If9nDbecNn^W{%wUVe(tFpEfui0C zw0I*arl;4E7!gjUODX9?GiflKMwhGy?zg*>u%PkGMD~%odJ8G(NBi%QVnvY_`t1`? z_s(^}6XF)Gz|tIK+Wsej<_O63o$33p^3YkrN(t95#W5kAf(>tDl zhMIbk&Ou7vM*s6TnW)SE@^O$h{fS^V97MN1K}LzvhN9Yo#Ss~yjmX~3B#B-u4y@r! zpHXO{K_dUyM7n+@%z_Ws>n9&rz}7?St?Nln0(`lGnI7cZ3Z1Y)U#pnZiFEY_QX=YA zN|$UT{ps*sQmEdg4>y3&eQQZJZS6};^qpI8u)q|otEW0*{%w*kSq zjr0+9SVxC$C;j6c@DVnst@VMv`cL^X{R$;OEb1XzWov*llOEk3ycTZ92~$j60Mnrd zqzJvAr?-PBHeyJF9aE6eXx39e{&+jdp^Fa$$B8{$;x|u`c(EVtI0K65@TW<#sGLtz zZpqX7Wra+q0TVs?bnp~J^w*ulzgcJcUDW3Q=w3qd14bG4EG%$4pCyI*V74OC zeIKzzR@yyw-H+%+#E0z)UM@f7f*~CEx1y}QFF1T*7wIAP`)dwEa~A5dlF+b#^yu9n zy>WN&g8c*S*sY(Onc$zfgp57Gq5M}gXgWQ7CrOLvuL8uW_B&3Cbb53TvFN?rvInd^ zelIApX}Z~eKO@u6_K<3OU_OS?=5e5R z_I_9!g#P3v(fX$V_Y=ctU76Xeo2Ga_)LyrrjM9+@5V^Xa6o^u~2YPRz?FWcCS|3e} zZ$%n;4cQa<|CSk2d)9`w^Me`k?tsKGI)oen=91emCi}_8=^N zyUWe~OGKGw9)u|r;(u8p`oT!JRE8_lEeA=qc1UAioX2ta%t4Z;DZ$|J^vVmc(j`2n zpEnqMCx^dzj>s&~>QS9h@8Z-Q&*_(txpdWY;M%~SFG`=x8J8Y{8~gExNP=eTT$*(V zZbdI0f)KmmkiPL|vZv`SCnAG?9nGcfhoFI&4w5MLs*?ArZwIl6?at&v=6C4#!?|>N z2UjqRMn6wF)0G|Y=TZj=73Ru>+_%WU=9~j_X3Hc ztI9~UKMdv4Ezg6LpPtt*6m#h%MEbozk^`1`0T8I0CF=Dc7YTL@qtCsdU!1b$9!6T0 zsMm6?SImo~ixy?sBY^K2tna4ba%m<^59f;{PHT#B{luHPc9^6mSGa2H;lSqB?*$mg zRnAd(1ftEg7xmF&v}>H!?J&vLdXz{PJ1c1%?M7r@DjQac>czesBX7 zgJpnvfm54soY>sLpcx=v4^(DU3_byB91O2E#*B*5AwVs>fr@i9K7YGN03I_%JIRQT<;BO0f zhk!pX;GYQizx_D0?^Fdso1Xyqc>%v5;C~nJ_XYftfL|8y7X|#VfWHU%CjZ#~RUjM@ zD!eS+74Ve;enr4Pq9w;j z7CB329wUY11G?rITu5C67q1%Zyo8>9nZ9?7+|qO&@&!(T;EmuxSsuq8&*Gh|NOpb# z6_&L<(pJ6BS`UEfdo1A}qS_I$BZ2IDY7!e1>jvz}__`a3C20_LMP& zUV4pW=P@04`13|<4`3ZNj#|jVu9Z=@(45yvikan~pu_b1PFnUlN$2>}bP3>MO!NAm z(^fj_J>Va#IuC2!uz+zm&cm8Fyb52P&up;Q`z!Fc=8beWmfu9*V^Ih%sfp49^bri?m~N|(+=;wE9M6?JV&RzL1f;mw`tQGB%Sw) z2Q;tQ7+M_h3EfD7rY6xmo>^`l{ZEcUE!*$gRiX)cnQJCJY;#p-G#Uv z<=2ahz+)CCz*U(CZ;u#(2Q6%|`t51pH|&_l8u~%&5knH8>xm z-<%>BSy!6>S04{G|Ch%@-lV$n`WlzVHlxNqla4=4GMEyJxji94dqRRi2@ft0udXY%yQ{&#=je!1Lt@u+V0de_^+8;7Yz^+Y_Ie;8_OO*QtU|+RQ>h^-ossrf zj{+$I3APGXO=B(m&;;bQml~4$GKLSVp94cAycvQAdm4)-_A8|m0b1u8*G%}4N{y}3 z1?RYP^jfI_yOw^pAsv!_cN;Pll%&iciWm8fHaE;62B}18(;&DVW)LOHIu7Am2L`Pt z$_5QWF1qz@Ll#IrdbgnqB>%eG&?B4inG8xF12hm-?rN;7gdeQfW;VDQ(FnBo9zy}} z-gl27t%>o@G$^ge8_Dbl{i@|LixE|WauA79xx?eB*0ks_C>L1`2HNTxYs(!CXe{j8 zhX&<))-?Qr#|8G7Z}SfgQhzci1C1ar6$3QA^(sls(CQ5_DzlLiR$T{?rM>~yLeMtv zPX?KJsyAG-dxL8(_X%Am<}Hyo=)%Cz@>z-&&sq5ncfY z(liitW#nLIMgR|#8XT34_??Ggne@B64cR8l)3@$6 zr1jvO!Jf;oFouE2U~~{@^rooAU!>4~-)kr^^y%FZH`(xu$pk}Q-jP#gXf)Bh3PXO> z#0Cd?L8HeZyIr;P%twYk9b+pD-x?EVRH=0wEl>}L<`?Eh0we?h6H*hM>5eP3k)rW%9-?zg$7F!$Sa!b zsGeEnk%!2=if)s;VH+Kb7aEddEa;BbENgFcs(O2ar#Jr|&fwduV*pN6?Y}0CWqxb5 zT3vvc{jbv)m&f{Nz(NC$b{N`heH=Oc&o~&i$+`y~7Z|hHl(HVMz6KoOd^NOr?63o1 zTyI+c1$Zo)0p=OpVpNwm@@Gzr{HbEZ$sH~}cte5`PLxcfboR`3+4QGTjFd~10Z5W& zu_sYYd=d^NN&}*ym5v&Z-G-0+824N)q=c4N*V*dpYiDRU#Fae)e@0-;8)YX3Q>V^53W zu*}`yR_lDQof&?>Xsg#^r*)BaHI54$5^8NV`m)q!{R>*zUGJ!{Rn#->l^SDd4nC7EJWghWHIe%dyB!+k zUozl=Y`bL0fMn4nLwEm5+;lxDRU3;acmq-aTVZ^}#2&I)=mn_2IkRc~K|{6?SUcp) zh8c1SdXY8K8jrmb=OEp5-H-&0Ja^qtn8=v&ts~G_`utm)YfR*niPlL-(dXNm93!rF z6LXCqVa+sSW;_!x-}(TsnQU__(c9Ow7}LixT(zzRJOZB$2A-)g%0}xBpo9qz3K_iH z`YymBqj2irE4XuOzA+{yG13>-FLlzWyMaX0i7{<3(r}VmV6Lz!5jKi#gzv_g&qjz# z!zw}%gaCO$LFjQm!$T zPs`QT4SzK3TdXJav1P-KuKTw2eEwf@bV;C47#{P>E#~p(X zuo$DVnT`I7L-0M%|FT0ctH91mTii8A5IV;+OF=S~B8&+$|3A4(U@l{Wjb6GslJx9& zI?s3{3g&fXAEPA%PkC-{mO6VzBKwJ9$DTgM4_koR@~SZhlJ-}PNoCBq)2z$E?2&%& zw9RpOV4uWDk6MoaDc&|7&atClNwF3Ae`l*P{LOk17~+B$Y8-Vo8_sOYF=HOIRC3JN zZ5R{xvGp_Hjup6J=i zE#?F<+{F)eH`PMBdJ|~O5Mx3wjU@t(`4zC;M!EiKA_i?~wPL*_DWBzUG&cjoJXH-2 zca^IKZW4NQynMoVbTRa#X`~5{D4Rx_lJ94BT5f$5x)VK`|9(1feZ+_>t&agQP9xSj z*vX=*y57GcwppJPDpyoh*HpqL5=|U4%7m*{;V4rYBqgIvxRlM};PO!>c>t6Dp7k8s zQj>49IcBrL#`$|eAuvn5Q94ccge#*=c#OL~iuELGG!s2yv?+)Ag>nl~`e99fm%z#3 z1ml+r+cRZQFd0sWHQdLxf)J2=Y_!QTnT_@|qSPUCgr-G}V~zuk0*qEql$D|&M#DQp zPF_Wn9b#4OD(xH{I)*81A7eu2YaU}Nh+^`-B+3t1A%k9C4OhSC=%q2tcE5}-(kuQCTW(D>7+YMX}rEX)|8wSR9~~aY^1HzNkTTe8Sy4+ zU9W|UM~BUUE?kZGI-trOCQB+4;CC+nvs0#DY>6q=%*g4qSS6WtEbsSom^SQ9INU4n zD;RHQts(vlgE5D3s1ygB0a`-<6tSUTpyq@K`YnL=iA96L{j(Kp4zEVXgUw}{HQrQY zc;}vu3FA%oX3)>Fj1i=c#{R{WLwh|82d^{7OlI1C+9c6_Crr_maKdssYGxGK*weEj zn=m==uPcKyp2Z^D~YDk`<2~rT`13JtYg3Fu-Cx0QJ&G@s+IY> zF4^vuE9b-OyXp!`t{GD4(hOrRsu0&7WzKcTb0PGBz&Q>S=yt%75sr!=wgwKivZo6E zM-PzTnp+2Cmt5OeQAMv*nq)fsf+Q__rOMRtN|lt;pZtaX&lV}Sn#F;IY+a9G{~HOr zgDk>BnLo;$wSEMCE>gAUcC79{tzQE*UZ@***2fsWWh+4{UA={*hce`v5<*vRm6F4- zq=XSAiSFMj@%@OBLX;ewhoVHA4gQCdKn$@Er3bRa360up{s4tCi-6*RD{$fQrW!&%>zaco^KMiF z8Ti{u#l2ziqQOBdDy6|rAMBUa=5|P8I@hYMOW_jd+Z*g za*l>SEy-NW1*7sMib81!XHt5?1_c_NJ(MIx`x`7a zDI)-;?d?)F7dg_TG$Lazop#xj+gYnV*93}%J|pFF#U3{)2eDd@eNrAr4w;n8h;-W{ zWpi_1F)3H!y`_}S$Lcqe5+{LNajf_U7^ZcCq!iG1pYeCBo21-}+^KZZ4k>{>Dd*}< zk(5@Qkm^_OF$sKe{0^xbZ~C01q|&Axl7$!3&B|?9gsbZj_~@B6*w3uoX9k7$`lMto zda7C33<#L0=T@RQ`Bt-XistN;GI%dfo0V^|Qnv$s5B|=qkPtww9gwM#_ezIDe0n%YoI;NGY66a6$n{HS^`4-nm1{=Qtmb5N(S>-+|vIaDp(RT==z} zk_B3;-OG~Id!=+J?b$1(WU$S{ACHktuk4i)`}_fi|MMW4%4erKojvd1VF(o`{97Uzh^eVH)AF}mDcwoX%g!r{bHAtoyp)oT8NN9 z-+5BXxVck-r3kv+F6Ba-XWFGKNWN}ow&=W{sW@T3loqOmYneg`cTM&CrDSf#;oGDf zp0htKrEvv$gbM-pVC@3|F06+UQ>eCE<003CbeeyF+24AAnPnBMLV?DhWZ@EpnGCI; zKfv_7dO#}RO&4#Mk^vv;lW;G0zfVfPjhQ4-7za0OjcObo{6EvUcZgw7+gs9IKHXhm zi33?5`J`J7>UR2eo-vIsR7@!ycN~-&Li@5UUN!8ps;b>G-d{WBd0=~n*Y9o_AZMf7 z13N$d@eFr?bL=(nd0^`#b9}6)$|2)-2)N>x@fmng$4f6s2iL@V%>e_E`0WXOO#&;edRuLJZ-UeBp!Jkgg;HRG-7imB!zNM4v~9t6n`Q_Vdg z=~8CKJ4|bt8P7S5WoA4((K6QhVP3jeX1*Ojc^YfIWE!hCZ<-m;RokYS`#|!KY36~D z#Mv05S4U=i_clLGr-{MtAJw`ufq8yF5Ap-cxAWLiAkoW~t%X_+|* zfcoiXj0)cA<_zwKi8OlxNz%egEFH0r!~^~EbTdDDtt|t)$TY`pz9o&%$x`-njo=du zwcE{^z}94kuF7<)-Q0(_8a34{r?7g_Vm*G$p)uv=-kfjmG#Ct-y2{O3)Jvza_mYfI z=GTp|6M`QyBy{|(+`JRbykrw|j!m1)7B<$_d##VaD)rT7b5b@M#oBB=4kn4g|LMg-pn-ZA``yMnZ_W6zmh)bG{;J;sl zHwk!6#w%+@+pM|vhM5qya^ck$G?&});0d!@#11P~*;)jhjAF|_MltQ-G#l7r_GPcL z_U(!-kG z0=41n&X6yZM06MUI6T7z_3)?Oh~qhLI1<89DfpOX$467=XvPm_OB23|2-1xK!M$Km zP-TYPWCn(63bDMX2?K(A3lQl<|MI;##e`J45prpqFR1efHNw{Tff*z<1Rm!#HkKFt EA3X|zW&i*H delta 21326 zcmcJ133OD&(my>jce01<`!bUR!j_PP9oaX*@v;~rB*FmG=!mfy@xN8u|`=0vbA;PEc=|h39s{78J9`rlk`JeOtp5t`)ud1u6tE;Q~ z-rM2A<^xPV|YpmVR@)*jURd(Pmt+dtF+H1S3 zWs@h~Ryo7zs&JQA)pqw+n>>=*(Kx%JY=+$}D5`Rn)m7TT7(=PkWmi_^#^nToiT3JR zQGv!4R8d{)wpCUtLvj;Cf@f4ZYin(;x!!;0p3v22>D#rtd*~MvFFhqCv^p}hR$^SP zqpQEQ;*`UzwMu_}+0j{R*_(SgI{I6ejyck_*5kLb2&Q z;4O}9f9s7BM;ER2ROuIvPX5;QwT=|6wd|?a9GO~cxxwH_^|u<|b#(BzE`RGt*ILiy zyBsdJd1*>nUW)Qz_nz>7#}Hd} zMHLGOrLiE7QRnBUD6Ve96+^*5ooA5RIZY|dzemZ3jxTlVOu8%Qx~D3s`3dA6R$H4_ z5Yot84hoeIy7#(6^zH7$be;lW?Vzme9zlFUQ^OkBT%eWN%JqUIWobbKF=H*_Et0tJ03q33(OXn0I})&F!ded2Fz6jcSi^vK48a<@ z&@t9vfnSVt$7BRC${h}@VT3#8U=5?&u>@;4FODXxAY99iD- zAq7Oa@>I04er2rp^`Tv`5u^M%td(SWKN()7W6g$-GrW(E`U|6ZgGL|Xm9t|W=M|e} zDzBWo_ch+(!b5S&*q;-;Z;z8XrQ?JFeoEBu3EmCG`#8n=09WF@@X&0I%$Vq8yxzZ0 z`kf>Hn({oa?6f{g6#1MJ0;Y>{$X2X$`zXiTwIr87C0befai;h2(j~lcd3q*&Ybgm; z$}UBFi|risUY_2>$ljIZ3y8Alh!a9CNm=bERvO=CK2Snt?pOYHI?8)`=24=AIh+uS z=}JO%vGR>0$Gf~5RDI*%s{ZDj&KllHkFMbrL+vI`URfUMeapRwqvK~ei86RgZzbye z{t(eKl=!VV%4>7>lg{3mbKfRPWrGuf+oT+wU#tv&I!EcdV84>MEXw=qf)P4p9?|289Xc6yMEoM`3yMw55Kwn?nvePcVg0#NU` zo%?wuYWFE#`EC#R4N1-WR`AD4=bl)LMckiVXJw8OclMO>z-lMtrgG)>Gkn56eSg1l zp*hO?%m*-|@$dCkj#Twm>IbDMU!E&guDzGz9sdyo&9(QUy-PoaF<_H05SDJfq4%&bCkb-GyFc>y{9xeBBgfiY|I2`6bEAiGc+_B zLa>2$Oo62M1+*Nk_;Ue&+Th2|%mU6tI2za-u+e8tXbr1vR1EvR-b@Y z|A5xOfYy+J*06w zzY3MEH?n9+2uUC#lmp}9=>Q{1Ru=u1r0l$rrL6uro}Mw12=80JyiUB={(VvkdFG@0 zNIU$GUY|>fl<#ier}Oky$So6na~_GHS6X$U%GbBTUwVqvB#?}jUlT~N6pY(1%!Rq7 zIE~Ef-(HNJfE@KNSXW6bNOkuCteJ&kmWSe4MO-B>rwmOB;(Q1}jsrLi`W4CFi; z>*(OcHXE4>pt*!hfTFg9bb?}C3F!vKX^A84PJks>I9?IdTIYL%TWC-49U4-|N^fC85j6u6wAP@@``6SQ!i zp-6A3wv$|mFI*kW#A${U#O2Bx}! zX>MSOmomK#Y!x>!tqp7qH#ly+n8|hda7R3fJdXxC`Jg7ZlV(zciwqLGzQTEK&mrN8{A+w{fX>5w@;15x zeex#n^RtT#5Jes3@c3GiFTy7{yrGtK)2ujGOGavqbT=6#b~wvBz)VEYMmO=>{+630 z)81~9m8j*q0CPX)9l=E`$3+GZKSQFKPN^mFku&S6Y%YyTH`e*&!utTXwbpH~(TM2! z^Ehq1D{ZVJ!J@Gjwb5nKuj)vsNc$_Np~Z@PBh(*z7iWP&FteY?{dd2XxLUWXqSPH= z#6P&ON0!0T7(1I}i(RjB8crfzKbs_`3JdT$n?qQ^4eqAJ_$+$#rkP%w9XN`gwNZpC z@7+vLe)~NnLJYoNI31=ooYu@CS)%eAoOW;y$rIt5noZeBf;sKtFdwMqD4T8#4A7&e z=N6K<=J^ne18?UAkc^B(A;!b$t+|0|BZSkyqv3S?yg)m`Ic@#Cz@^yUr>Vb~tzwvU z)H0vQT6mHLKrfnIyu) zG&TL`?~4OtbR?&xRKlPO9w#|*LR5|RMHM}|g!H0&BS>gK@K{v2PmrEr!c6`(AMzyHN5|W~g zWbYC(n%<7R!%LN{kAD!JI3gr1>r*iLu1iUGI{vA^nQ;lmEQp5#nV3520);j&4Ge@i zyf4V@BaUxAgEK;fm$0JHg?ycET1vX8%NJjlIv(e6>r!oxWAKw)2b?O*BKK0xJ+p!I zRaG2IBD{&iKQAMR;@GzEv88!PiVCwg zWZOA>w}+TjAq?I{-5xD#GGs5^;~~8x_!48tfjIi;Za4&eu$)BFuhL13ZB35qNEU^?-yOHFILN3q`8@0nFb5$EnTcw@B81gsGti%MD zeMSYG80Pw7k1>458Tu+D5x$C4!6y)nOw(7xeEhhIlYLc%8u$fL(Ab>%0rr7IuZqRInq>Auux7syOI$;Z^Ws6e6;Uf6q7eyq}{m*EdArp~8Z$3kYYff0v3{IZfOgd}G*Uh94y|PORFeMv% z6;aEJfpAA!NXY=L>rqMuN7~`rQ>}j%BiG=n+DI~+^GHbZ8WJR$*PX_$(T*a_ydvsY zLq_=|Ha`oUoiCD1`okJ+YhiqSsq`!v@8hHUkg@hz(n~{bBQj|}385c6OFD~6hoaGi zNo6*-ZMw?_pV-9ZK0=+4*ZPnI`q)}+3t>v{rmNPHyTtjsC!X$w>_vYoBq;$?AtP%K zDnCbpHIp_z2PUoDMlxf{>THz(YK!UC=g4S{bMQLgY>&q8UPsbt!MZ>@CgQ9X(#Bpy zqWM5;@Ao$*^vQJ9y1D3px62l$x%c*B4SY=%YF1os2 zdrD-|W^!r2@0Fso(QMv;qiKd&T)F`=vXHJH^?N%#zK9k=f;|GA3cD`~@KGD5Urbv%82FGSD$0i;AYHA{!z7|KUgPA^t48GuH4B86~Gd;VN45M#tB|(}a(_aXX z*M2C8@tw`h^id?f{2X|B(J%;w72Bm)(Sa|E<6|6eZkIwe(a|qL@2=>c^Dk(R`^=B8 z^GOapC~$8^Zb&x1lM7=!#*zhX-5j`RPT~ah6-{|3o?hBR(&)Ht+Jh$(aE98RgJ|8m zjTprw^gc5n!R@T6w9gUTi|h9s*Lh~7|K!U=eL}}=C!NKU(&ubHpbgst&qZI+W7`80 zz}GZ#hjyH2!Y<*8NT+Lu7?bIdyNNJG*LJ|Z`1%f#Cl2r1c-nO@=^cYvGa$sir-OEC zZ@rl0E0`X9Z><>eBW>MDhG^5`+{G1S)2X|(=K;p}PwL)9?h*a;D+YWj9q)xMw{`{Y zkH68>-DHf&eG5I8N>{+5isY9ee7T+8+zqqKmdf`sEV&cD@*Xl+bVO(ZJ&MOLkdhg&W+Uj(1F&;ejU_<=cogDsbkJVXS(Fo-fG5?SbjY|n z(nJD1yf@IH$y`GEKJ5w6Ob_hyh4zAdBwrMq={GPXwzgtkU`EJFpywLF`~?$8PJo#m z=$QRvgy_sp^x6I7K@sUnf7>5;zQ{+@e9wby3JbZhn+}kkV%MJZ(gE#F5@YN`I~*kU zi#+}5lLyIIZB`1rFvRNzg+VVyrJwrTnsTVeq;qY?ZW2~~f%bjIS{2fw|@4k#L z1aN7M0zU1M~z+v(NGO2J$C~{Iqf+38o(&A_nap9_urX zw!I1yo_k1JlnlO)!{=XuZwb&TjAd(W<7O-mAPejXhqSk|X|(Vqh7LrFdy-dRBzDfS|z_@EPl_9hhIr3UatY}j$XPDMP+Kb9(apj9zv^TVA z^m+?8=e-t^;nPbCUjZca6%sJYdM@#vS4g6$=y481|46#>72-Rvq|qNy2sqStY-DyX zW!xQteAl?tuwt}jfnkzNqYsnhSYITun-;%t^C!MadaK5!)AO%_o~Xl+u~ts^=X%C| z6SWOc*EK`hB~sfVOeC*quN4`tr!}u>DT*P+MEWRx<@oS5V$yUC1dC8tM^R=t zI#S_XtAAbAIn2<;TPR%+?+QPjFqz$98(EEykP49SOOwL|?~TjnmCs{K-Z2olXQs06 zKuE>UI)18$QS1F={BYnWe|$%0d?oFtetQQM-w^w$H}9b08(KegM3P`uJ=F0jiw5fbSOY z?E=0-z+d3?dZzeAfv{2Nut~u83iv(&KOo=-1^hb!|GR)I0^XDW0SlvNBR(V$UJ^RA z2>2@k{(*phDB!ON_+bIREa3m};kaO$1p@UE0KXvM7X|za0sm6K+XVct0{(`8zbW9K zLcQKM_n!%bBSMFx0{)SJe=Ojq1^itBKPBLA3;1zAUg9_*5Z?0>9A^c*Rlv^+_(}n9 z6!579=SxfAA?t2e}fP&AK>@~BmGD&s4}q zK4D=sbMxS~vUvpR3>NY0 z+Kv?JJ^{T`=-Lx-StFC4JVCm{j{MIPAS;JvpCmo{T|_6~Px(9uhAU9<1X#de9G5yc zVEPSPztNQ^Ng{ld-F6bLgP7^*lW>{CO#k~N=?X>0DUw@{snydSZd<$2FuaUg3pckx zz%3Mjw^SUrp$3314eBfx=%EIH5dn5BG>8MC2;hJnVH}7=fZYde&w&^Xh~+?D0wQ>^ z!;#356a?6P(Nqp(Ai%DOX431YNai#l5qjFI7l`eZ_A02tah6eeI@GA8E_<~~ zO1JFw@DJOzexAJP2$TL&a<8;#P2b#muEdsXgOZ%pW(9A zK)kUIJneZRYU@h=K;x;;V=S}JaM|H$iFIhA-QFR|T=PrR@{Z4Yd9!VmGgW4u z)R2Pjb~K6^riAA;SXUYfAWv$Z!dm+1X*dB6IZZMmmj)< z%QTzLJwq}f#XNfk5tTq zuT(Ys?^mk8>V`T`LUl<^rPFPlUTK>_t!H62@U;CHcOw+&Mkp|OL4j_B0+SUKcxHnF z-3SGG5eghX6zD}L@OBv#n5v+_1O){qAt*5QK!LZ*pulq&6ge$7&XS+9NE^L+jif^n zejP5fWzoXxB+-DT(F4~=axi-)sPm*LQKQpap1w}@O{>4l$!`ooWO%}6mX_Nps!MFO z72w@AaJOZcWjgEx!6h?gb=@+w!tyxoSXNi;yoN(~orrNg zN91)XxNKUA&f%g*q7wfuZ zGLZ(I+(QQpktNQ$>M}@$))_8m9eR(BxL?-^?5?_BmjcDo`*mpv+*X}@2)jrncDK6% zIbQ=BuzY?$8}C1Pzl;ZTNdWeFKxblM80bEneN6&uL@;sI>bfd;r2~#-;yJ}ppZ*yINamIK8XOuya zzDb5H1LDD_OhDm^9iY_qpf1+Px};OvC|x>aZ`Mjs%s(aO@;VDPf2=XO7?)Y}>j!nC zf}K6uwad!N3RU)xk8c_BknVyZLAyy~velYuODk&TB0|k}-Tn0P*Sh)i*QvVHmhdUM zWqNw;qArFmtbhoVhIOj$W@5?*-tVfjcV=AK)O1O= zoW3`Ugwoy9bTKV@o9+iaT@a+-NRQ0dNiFm2x_^YS8^-Tm)`inW5&DQ07)HL4z8a

      y7tV}xy=4z_`YzQm?4ac+J$h4@ zF@m*DSz3W3l<%!pw+#Z6;TJ470FTDa6+!@BkW`e^;U6EWB&pXAQGii)0M=GaqqIVI~<6?>}l-tCTm!h2GCa`rN{M38P?uHqWdj%rIvcqKE-JP_lSe z-^8<4;KXFLm^`{R6HEG*o|d`bKqF@98cdoPr3);paCWMlt}1J#wq-S0-a=Q`*4Rs} zrImHQJ?NO_U#g=uAxx*xowY-2TzG@uWy9SIk;nj9SuHE}=sw7U%sjPK*CCwQG2iks zXiT7+PwNu&U>n`us_O*5V4o?d9xCG*;W zR$Vqrd3B!Ex3XF;o!1REL4VUtT|yF5lwe80p$W6rM#tXN<;621i zoHX7t0ZBe*!dKh&~63wj=s3P=p`VV+?iW#XUzEi{q$17r@m= z^>~Oqd{o~NioYJ!W9ExGrtg`KlOsPs!&?my*2j5V^$l8V)vN9OQ5z86iXs>ekKcu%&^Du*nOZmw1l_14xwLfaL zqLeAanDTkU4ETt%W|#qIpkd)A zy!H8T*8RqCLvAFSol8Xi2hI*$qyAl=%yuTr2t(JtjQ9(YgYhvWOtqhXGiRhwog9gz zaFtYA>8yshZ=-uhFb(HM7|iU+O)k*MBaqYQT6UsuqY)zw7(Bg38qA%U#2THv5BYrV z^j&&n!~;6{7!c{v(QGC*jx=C!p5VpzBMrTwWg2C`_#HaRU}nyjhw9}KdcVYCyE|W1 zzGxJ4*v3(Yp3viyQHIV?+#1D%X5VcnN?qi$xPoI4bx+$ZKDmz6PVnQ<#`4ipVSP@Q}; zwTv-zGcda@qKD0N?HH!DWsD(Tzv_NM7rks8L(gtB2DS9P$6!pO{XfztJ=HYB(9$$R z>d=?i>1!LMEPke+Z}}MJE^4&)!ZII^@xG(lrX@| zbm%52^B&f^AsaB@MZvY@wi*mxOjkxC$0D8oTPE!7TfPC33_QIfL%pB&CF~ z4kjXZre{GJZ+0beANnIu3mCN@k%ytUL}46O_ACPLTWU3#BZxen_S-C_b0P3lF^yU` zOG&(0LgYHSXtR{U*X(Q}KaDaW1~(Gb3J;N6=$Xw@D(~?skx$YeH%n=}$J<2y6nmu8 zRnL=5&hj~t|4D~F?-L4Zy*)0!WIBF5QKdxdcomUkc*G`AR2W)d*?qupwX5$$DJu;=HQ21eq>LFGnFgQqWaaQKim$j9yNlH@5iP zmjs!EW^9#AT;C|YJOlf}{h=)+Ax!O4p_doYs;yE6_s-*bS)ol^B{Oe6qnEeSH@Ett zWT#&KoVI~P?yN8M@@0@nH|>%#xsEG(Il{nnOnX5}2v>EqH^|)#w0f(Q!0sn<)_j9J z2w79<;(jD8l*jE5gY2SvUXVI-^=<=LbMXZ!i#MAM@&Ow9qLjbzpH2DXi4rsx4$TvxXN5fzMr1cnh#3y za%?8!O=muim6H4^%FCv=5zi-K>R3LPU^X7yCS`MLlZPuYoCGB83axjOSvo{BPt*rncTBMRodb!SZl=WT%wH zm5mIMcZPtnANNY>53*jnL*%Sb=oRV@ktRol4$^HrCq!DSY%}fF9#-}~%jbtqp>h}K z#!lw}UCq?COG<%FV!3LH~+LuOYu1J=Uc z^}D4meCwIEgUR`Dw}czs54)xAP^9mXI&yO3^OC6_+rh+G7hJ?>f0nb@#&&6kl$gj^ z{*Xz`^!+_jN)jXfagCUp_e#0G!vjou3a|432m)_0@nd&NY1~fpRw>=bf+^Wo$0^f+ zYp(2NUW?erMq98?O7aQEUBS$(i1klZK6`o1E`>7^PaY^}+de6StGu~IO5(K@JABGn z@=fESg+!dpg#1aeHB;+;DF-}$bib4s#6N49=|yO;48{rRPymD{gj@TiY_9Y5=Y2z; zxQz{M`T-y5vF%b0XWFx!g}}K35`HhfaX?Z>8ZV5r);7xyJGKoI7jx?Su#*D!*n?79 zDzjUh9lqf(z60AN{3gBrAk*>cL8+&%-&Sh+T9?ui;+38brUQzM1@y;hhW0JpUYF{g zGvT_h^q13dZ;dKupQ_bU9V`LJn)K*HMs*K?gTN?cOQwr=5ImTNJ#562eE!2md~AB) zVIv;e=RR!2Gwj-jjd;F&>tQ24cU%Nco@&+;c^e9erxSL;=W}?ln`p#S^tg$P-#yWY z4{IAI8u7)=$%#zl_q>RBgs~JoV#J5FNsk!S#Mf0P_oe$EF~-r2j~Ii2w4F1aeS}$Z zl^5+NvEDr=8M^|@v`I#M##=GTh_A2?Pcr61ae0#QE+{%qHg<>N!O6^q1(S_+QTaB1c z-mn@mQ+x$Q3JZ_ufT^ssmEh{6QPYgcynEqfV-A;`NzLQoMqkY|V{rPPbQNODHra@| zZvQl+nRo35D}>~8frBR*O={i{Rr3=#9cnWcalVUFVD`*(lg+4Rl2kgyOVTA&T}P*F zCy6b_661E9hkff=?0m4^n9X)P%R~5pOEWhb6WC`dOM~SEMlD|2w3g!`+v+=C)4~l# zeB>Or!I+uHJe;TouK~H$)4&)a|2M=U%j+thMxTS8$@rb~&jhE@a~q6FJ^nyePwotl z!8BE7pKhzGbX#4vvI=}Tn?}<%8k1GImA1LgI`|F;_W`W%@c`}vq_vFQXe?HT6>jPM zzYXiiOU8^?wq&QVhkf5@==GP3@&AX_Da^WPRXKhf;GZv1`-GQ`T_V6=3ywoL!IS!N zwxZuU&KA%m4*q_exisYjtfMqK_=GWjDE9-7w-){f$#xg~V~G4Su?PO4LHNXs8H};P zoqg7{&YW#?%|Msn!{25Un%3eyVN~*`vw@i``7nC+mu6NBKK0HjqZP6*Ghynt0)Cm5 z5yn_-&?!W?BKq`G7YAKwLUsF+?iBKKnalFYK1lO_Z>Pqte2SU1j{{R30 diff --git a/docs/build/_modules/algorithms/contagion/animation.html b/docs/build/_modules/algorithms/contagion/animation.html index cdc7d4fe..18ad160d 100644 --- a/docs/build/_modules/algorithms/contagion/animation.html +++ b/docs/build/_modules/algorithms/contagion/animation.html @@ -7,7 +7,7 @@ - algorithms.contagion.animation — HyperNetX 1.1.4dev documentation + algorithms.contagion.animation — HyperNetX 1.1.4 documentation diff --git a/docs/build/_modules/algorithms/contagion/epidemics.html b/docs/build/_modules/algorithms/contagion/epidemics.html index 9def13fd..95ed44d4 100644 --- a/docs/build/_modules/algorithms/contagion/epidemics.html +++ b/docs/build/_modules/algorithms/contagion/epidemics.html @@ -7,7 +7,7 @@ - algorithms.contagion.epidemics — HyperNetX 1.1.4dev documentation + algorithms.contagion.epidemics — HyperNetX 1.1.4 documentation diff --git a/docs/build/_modules/algorithms/generative_models.html b/docs/build/_modules/algorithms/generative_models.html index 6ec7f5dc..9cdf00db 100644 --- a/docs/build/_modules/algorithms/generative_models.html +++ b/docs/build/_modules/algorithms/generative_models.html @@ -7,7 +7,7 @@ - algorithms.generative_models — HyperNetX 1.1.4dev documentation + algorithms.generative_models — HyperNetX 1.1.4 documentation diff --git a/docs/build/_modules/algorithms/homology_mod2.html b/docs/build/_modules/algorithms/homology_mod2.html index 891135b0..408a6d42 100644 --- a/docs/build/_modules/algorithms/homology_mod2.html +++ b/docs/build/_modules/algorithms/homology_mod2.html @@ -7,7 +7,7 @@ - algorithms.homology_mod2 — HyperNetX 1.1.4dev documentation + algorithms.homology_mod2 — HyperNetX 1.1.4 documentation diff --git a/docs/build/_modules/algorithms/hypergraph_modularity.html b/docs/build/_modules/algorithms/hypergraph_modularity.html index 647e8c59..d3eb9038 100644 --- a/docs/build/_modules/algorithms/hypergraph_modularity.html +++ b/docs/build/_modules/algorithms/hypergraph_modularity.html @@ -7,7 +7,7 @@ - algorithms.hypergraph_modularity — HyperNetX 1.1.4dev documentation + algorithms.hypergraph_modularity — HyperNetX 1.1.4 documentation @@ -190,7 +190,7 @@

      Source code for algorithms.hypergraph_modularity

      from functools import reduce import igraph as ig import itertools -from scipy.special import factorial as scipyfact +from scipy.special import comb ################################################################################ @@ -243,29 +243,9 @@

      Source code for algorithms.hypergraph_modularity

      ################################################################################ -
      [docs]def factorial(n): - """ - Computes exact integer factorial on integer - - Parameters - ---------- - n : int, or array-like object - - Returns - ------- - int or int64 or object - - """ - if n < 2: - return 1 - return scipyfact(n, exact=True)
      - # return reduce(lambda x, y: x * y, range(2, int(n) + 1)) - -# Precompute soe values on HNX hypergraph for computing qH faster - -
      [docs]def precompute_attributes(HG): """ + Precompute some values on HNX hypergraph for computing qH faster Adds weight, strength and binary coefficient attributes to the hypergraph for computing qH faster. @@ -298,7 +278,7 @@

      Source code for algorithms.hypergraph_modularity

      bin_coef = {} for n in HG.d_weights.keys(): for k in np.arange(n // 2 + 1, n + 1): - bin_coef[(n, k)] = factorial(n) / (factorial(k) * factorial(n - k)) + bin_coef[(n, k)] = comb(n, k, exact=True) HG.bin_coef = bin_coef
      ################################################################################ @@ -454,7 +434,7 @@

      Source code for algorithms.hypergraph_modularity

      # wcd: weight function (ex: strict, majority, linear) -
      [docs]def hypergraph_modularity(HG, A, wdc=linear): +
      [docs]def modularity(HG, A, wdc=linear): """ Computes modularity of a hypergraph with respect to partition A. @@ -524,7 +504,7 @@

      Source code for algorithms.hypergraph_modularity

      """ # weights will be modified -- store initial weights - W = [e.weight for e in HG.edges()] + W = {e: HG.edges[e].weight for e in HG.edges} # uses edge id for reference instead of int # build graph G = two_section(HG) # apply clustering @@ -539,11 +519,11 @@

      Source code for algorithms.hypergraph_modularity

      while diff > delta: # re-weight diff = 0 - for i in HG.edges: - e = HG.edges[i] + for e in HG.edges: + edge = HG.edges[e] reweight = sum([1 / (1 + HG.size(e, c)) for c in CH]) * (HG.size(e) + len(CH)) / HG.number_of_edges() - diff = max(diff, 0.5 * abs(e.weight - reweight)) - e.weight = 0.5 * e.weight + 0.5 * reweight + diff = max(diff, 0.5 * abs(edge.weight - reweight)) + edge.weight = 0.5 * edge.weight + 0.5 * reweight # re-run louvain # build graph G = two_section(HG) diff --git a/docs/build/_modules/algorithms/laplacians_clustering.html b/docs/build/_modules/algorithms/laplacians_clustering.html index 55d2c3f9..4b63eaf4 100644 --- a/docs/build/_modules/algorithms/laplacians_clustering.html +++ b/docs/build/_modules/algorithms/laplacians_clustering.html @@ -7,7 +7,7 @@ - algorithms.laplacians_clustering — HyperNetX 1.1.4dev documentation + algorithms.laplacians_clustering — HyperNetX 1.1.4 documentation diff --git a/docs/build/_modules/algorithms/s_centrality_measures.html b/docs/build/_modules/algorithms/s_centrality_measures.html index d37a5971..ec1c3f6c 100644 --- a/docs/build/_modules/algorithms/s_centrality_measures.html +++ b/docs/build/_modules/algorithms/s_centrality_measures.html @@ -7,7 +7,7 @@ - algorithms.s_centrality_measures — HyperNetX 1.1.4dev documentation + algorithms.s_centrality_measures — HyperNetX 1.1.4 documentation diff --git a/docs/build/_modules/classes/entity.html b/docs/build/_modules/classes/entity.html index 3f064e9a..c9e2e838 100644 --- a/docs/build/_modules/classes/entity.html +++ b/docs/build/_modules/classes/entity.html @@ -7,7 +7,7 @@ - classes.entity — HyperNetX 1.1.4dev documentation + classes.entity — HyperNetX 1.1.4 documentation diff --git a/docs/build/_modules/classes/hypergraph.html b/docs/build/_modules/classes/hypergraph.html index 9d7a8fa5..26c41639 100644 --- a/docs/build/_modules/classes/hypergraph.html +++ b/docs/build/_modules/classes/hypergraph.html @@ -7,7 +7,7 @@ - classes.hypergraph — HyperNetX 1.1.4dev documentation + classes.hypergraph — HyperNetX 1.1.4 documentation @@ -339,6 +339,9 @@

      Source code for classes.hypergraph

                       self._edges = E
                 self._nodes = E.restrict_to_levels([1], weights=False, aggregateby=None)
                 self._nodes._memberships = E.memberships
+                for n in self._nodes:
+                    self._nodes[n].memberships = self._nodes._memberships[n]  ### a bit of a hack to get same functionality from static as dynamic
+                                                                            ### we will have to see if it slows things down too much
         else:
             self._static = False
             if setsystem is None:
diff --git a/docs/build/_modules/classes/staticentity.html b/docs/build/_modules/classes/staticentity.html
index b096c0fc..eaed21dd 100644
--- a/docs/build/_modules/classes/staticentity.html
+++ b/docs/build/_modules/classes/staticentity.html
@@ -7,7 +7,7 @@
   
   
   
-  classes.staticentity — HyperNetX 1.1.4dev documentation
+  classes.staticentity — HyperNetX 1.1.4 documentation
   
 
   
@@ -233,7 +233,7 @@ 
      Source code for classes.staticentity
               arr=None,
         labels=None,
         uid=None,
-        weights=None,
+        weights=None, ### in this context weights is just a column of values corresponding to the rows in data.
         keep_weights=True,
         aggregateby="sum",
         **props,
diff --git a/docs/build/_modules/drawing/rubber_band.html b/docs/build/_modules/drawing/rubber_band.html
index d38b0b10..b1fbecce 100644
--- a/docs/build/_modules/drawing/rubber_band.html
+++ b/docs/build/_modules/drawing/rubber_band.html
@@ -7,7 +7,7 @@
   
   
   
-  drawing.rubber_band — HyperNetX 1.1.4dev documentation
+  drawing.rubber_band — HyperNetX 1.1.4 documentation
   
 
   
diff --git a/docs/build/_modules/drawing/two_column.html b/docs/build/_modules/drawing/two_column.html
index e82201fc..080db161 100644
--- a/docs/build/_modules/drawing/two_column.html
+++ b/docs/build/_modules/drawing/two_column.html
@@ -7,7 +7,7 @@
   
   
   
-  drawing.two_column — HyperNetX 1.1.4dev documentation
+  drawing.two_column — HyperNetX 1.1.4 documentation
   
 
   
diff --git a/docs/build/_modules/drawing/util.html b/docs/build/_modules/drawing/util.html
index bf1d0807..2c6b8a49 100644
--- a/docs/build/_modules/drawing/util.html
+++ b/docs/build/_modules/drawing/util.html
@@ -7,7 +7,7 @@
   
   
   
-  drawing.util — HyperNetX 1.1.4dev documentation
+  drawing.util — HyperNetX 1.1.4 documentation
   
 
   
diff --git a/docs/build/_modules/index.html b/docs/build/_modules/index.html
index 862f8f46..f675e43c 100644
--- a/docs/build/_modules/index.html
+++ b/docs/build/_modules/index.html
@@ -7,7 +7,7 @@
   
   
   
-  Overview: module code — HyperNetX 1.1.4dev documentation
+  Overview: module code — HyperNetX 1.1.4 documentation
   
 
   
diff --git a/docs/build/_modules/reports/descriptive_stats.html b/docs/build/_modules/reports/descriptive_stats.html
index 07f8505b..24b36eee 100644
--- a/docs/build/_modules/reports/descriptive_stats.html
+++ b/docs/build/_modules/reports/descriptive_stats.html
@@ -7,7 +7,7 @@
   
   
   
-  reports.descriptive_stats — HyperNetX 1.1.4dev documentation
+  reports.descriptive_stats — HyperNetX 1.1.4 documentation
   
 
   
diff --git a/docs/build/_static/documentation_options.js b/docs/build/_static/documentation_options.js
index dcf202f5..97abb98a 100644
--- a/docs/build/_static/documentation_options.js
+++ b/docs/build/_static/documentation_options.js
@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '1.1.4dev',
+    VERSION: '1.1.4',
     LANGUAGE: 'None',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/docs/build/algorithms/algorithms.contagion.html b/docs/build/algorithms/algorithms.contagion.html
index 5f95825b..273fe9c8 100644
--- a/docs/build/algorithms/algorithms.contagion.html
+++ b/docs/build/algorithms/algorithms.contagion.html
@@ -7,7 +7,7 @@
   
   
   
-  algorithms.contagion package — HyperNetX 1.1.4dev documentation
+  algorithms.contagion package — HyperNetX 1.1.4 documentation
   
 
   
diff --git a/docs/build/algorithms/algorithms.html b/docs/build/algorithms/algorithms.html
index 8720ad7e..c7ce1c84 100644
--- a/docs/build/algorithms/algorithms.html
+++ b/docs/build/algorithms/algorithms.html
@@ -7,7 +7,7 @@
   
   
   
-  algorithms package — HyperNetX 1.1.4dev documentation
+  algorithms package — HyperNetX 1.1.4 documentation
   
 
   
@@ -980,44 +980,6 @@ 
      Hypergraph_Modularity
 
 
-
      
-
      
-algorithms.hypergraph_modularity.factorial(n)[source]
      
-
      Computes exact integer factorial on integer
      
-
      
-
      Parameters
      
-
      n (int, or array-like object) – 
      
-
      
-
      Returns
      
-
      
-
      
-
      Return type
      
-
      int or int64 or object
      
-
      
-
      
-
      
-
-
      
-
      
-algorithms.hypergraph_modularity.hypergraph_modularity(HG, A, wdc=<function linear>)[source]
      
-
      Computes modularity of a hypergraph with respect to partition A.
      
-
      
-
      Parameters
      
-
        
-
      • HG (Hypergraph) – Description
        • 
-
      • A (list of lists) – Partition of the nodes in HG
        • 
-
      • wdc (func, optional) – weight function (ex: strict, majority, linear)
        • 
-
      
-
      
-
      Returns
      
-
      
-
      
-
      Return type
      
-
      float
      
-
      
-
      
-
      
-
 
      
 
      
 algorithms.hypergraph_modularity.kumar(HG, delta=0.01)[source]
      
@@ -1104,6 +1066,27 @@ 
      Hypergraph_Modularity
 
      
 
+
      
+
      
+algorithms.hypergraph_modularity.modularity(HG, A, wdc=<function linear>)[source]
      
+
      Computes modularity of a hypergraph with respect to partition A.
      
+
      
+
      Parameters
      
+
        
+
      • HG (Hypergraph) – Description
        • 
+
      • A (list of lists) – Partition of the nodes in HG
        • 
+
      • wdc (func, optional) – weight function (ex: strict, majority, linear)
        • 
+
      
+
      
+
      Returns
      
+
      
+
      
+
      Return type
      
+
      float
      
+
      
+
      
+
      
+
 
      
 
      
 algorithms.hypergraph_modularity.part2dict(A)[source]
      
@@ -1125,7 +1108,8 @@ 
      Hypergraph_Modularity
 
      
 algorithms.hypergraph_modularity.precompute_attributes(HG)[source]
      
-
      Adds weight, strength and binary coefficient attributes to
+
      Precompute some values on HNX hypergraph for computing qH faster
+Adds weight, strength and binary coefficient attributes to
 the hypergraph for computing qH faster.
      
 
      
 
      Parameters
      
diff --git a/docs/build/algorithms/modules.html b/docs/build/algorithms/modules.html
index baa77383..f822f856 100644
--- a/docs/build/algorithms/modules.html
+++ b/docs/build/algorithms/modules.html
@@ -7,7 +7,7 @@
   
   
   
-  algorithms — HyperNetX 1.1.4dev documentation
+  algorithms — HyperNetX 1.1.4 documentation
   
 
   
diff --git a/docs/build/classes/classes.html b/docs/build/classes/classes.html
index 9b73f570..74a3c2c1 100644
--- a/docs/build/classes/classes.html
+++ b/docs/build/classes/classes.html
@@ -7,7 +7,7 @@
   
   
   
-  classes package — HyperNetX 1.1.4dev documentation
+  classes package — HyperNetX 1.1.4 documentation
   
 
   
diff --git a/docs/build/classes/modules.html b/docs/build/classes/modules.html
index ac7690f6..5f9037ae 100644
--- a/docs/build/classes/modules.html
+++ b/docs/build/classes/modules.html
@@ -7,7 +7,7 @@
   
   
   
-  classes — HyperNetX 1.1.4dev documentation
+  classes — HyperNetX 1.1.4 documentation
   
 
   
diff --git a/docs/build/core.html b/docs/build/core.html
index 8ee28377..bd580beb 100644
--- a/docs/build/core.html
+++ b/docs/build/core.html
@@ -7,7 +7,7 @@
   
   
   
-  HyperNetX Packages — HyperNetX 1.1.4dev documentation
+  HyperNetX Packages — HyperNetX 1.1.4 documentation
   
 
   
diff --git a/docs/build/drawing/drawing.html b/docs/build/drawing/drawing.html
index cb791a33..67b2937f 100644
--- a/docs/build/drawing/drawing.html
+++ b/docs/build/drawing/drawing.html
@@ -7,7 +7,7 @@
   
   
   
-  drawing package — HyperNetX 1.1.4dev documentation
+  drawing package — HyperNetX 1.1.4 documentation
   
 
   
diff --git a/docs/build/drawing/modules.html b/docs/build/drawing/modules.html
index 744dd4f4..ed370ae7 100644
--- a/docs/build/drawing/modules.html
+++ b/docs/build/drawing/modules.html
@@ -7,7 +7,7 @@
   
   
   
-  drawing — HyperNetX 1.1.4dev documentation
+  drawing — HyperNetX 1.1.4 documentation
   
 
   
diff --git a/docs/build/genindex.html b/docs/build/genindex.html
index f8cd8952..88e9cd82 100644
--- a/docs/build/genindex.html
+++ b/docs/build/genindex.html
@@ -7,7 +7,7 @@
   
   
   
-  Index — HyperNetX 1.1.4dev documentation
+  Index — HyperNetX 1.1.4 documentation
   
 
   
@@ -625,18 +625,16 @@ 
      E
      
 
      F
      -
    • elements_by_level() (classes.staticentity.StaticEntity method) -
      		•
      • levelset() (classes.entity.Entity method)
        • +
      • linear() (in module algorithms.hypergraph_modularity) + +
      • logical_dot() (in module algorithms.homology_mod2)
      • logical_matadd() (in module algorithms.homology_mod2) @@ -710,6 +835,14 @@

        L

        M

        + + + + + + + + + diff --git a/docs/build/reports/modules.html b/docs/build/reports/modules.html index e2b83705..d4c39a5a 100644 --- a/docs/build/reports/modules.html +++ b/docs/build/reports/modules.html @@ -7,7 +7,7 @@ - reports — HyperNetX 1.1.3 documentation + reports — HyperNetX 1.1.4dev documentation diff --git a/docs/build/reports/reports.html b/docs/build/reports/reports.html index 169030af..bc73e2dd 100644 --- a/docs/build/reports/reports.html +++ b/docs/build/reports/reports.html @@ -7,7 +7,7 @@ - reports package — HyperNetX 1.1.3 documentation + reports package — HyperNetX 1.1.4dev documentation diff --git a/docs/build/search.html b/docs/build/search.html index c433626d..b82bb118 100644 --- a/docs/build/search.html +++ b/docs/build/search.html @@ -7,7 +7,7 @@ - Search — HyperNetX 1.1.3 documentation + Search — HyperNetX 1.1.4dev documentation diff --git a/docs/build/searchindex.js b/docs/build/searchindex.js index 3c5bc892..3c7a2216 100644 --- a/docs/build/searchindex.js +++ b/docs/build/searchindex.js @@ -1 +1 @@ -Search.setIndex({docnames:["algorithms/algorithms","algorithms/algorithms.contagion","algorithms/modules","classes/classes","classes/modules","core","drawing/drawing","drawing/modules","glossary","home","index","install","license","nwhy","overview/index","publications","reports/modules","reports/reports","widget"],envversion:{"sphinx.domains.c":2,"sphinx.domains.changeset":1,"sphinx.domains.citation":1,"sphinx.domains.cpp":4,"sphinx.domains.index":1,"sphinx.domains.javascript":2,"sphinx.domains.math":2,"sphinx.domains.python":3,"sphinx.domains.rst":2,"sphinx.domains.std":2,"sphinx.ext.intersphinx":1,"sphinx.ext.todo":2,"sphinx.ext.viewcode":1,sphinx:56},filenames:["algorithms/algorithms.rst","algorithms/algorithms.contagion.rst","algorithms/modules.rst","classes/classes.rst","classes/modules.rst","core.rst","drawing/drawing.rst","drawing/modules.rst","glossary.rst","home.rst","index.rst","install.rst","license.rst","nwhy.rst","overview/index.rst","publications.rst","reports/modules.rst","reports/reports.rst","widget.rst"],objects:{"":{algorithms:[0,0,0,"-"],classes:[3,0,0,"-"],drawing:[6,0,0,"-"],reports:[17,0,0,"-"]},"algorithms.contagion":{animation:[1,0,0,"-"],epidemics:[1,0,0,"-"]},"algorithms.contagion.animation":{contagion_animation:[1,1,1,""]},"algorithms.contagion.epidemics":{Gillespie_SIR:[1,1,1,""],Gillespie_SIS:[1,1,1,""],collective_contagion:[1,1,1,""],discrete_SIR:[1,1,1,""],discrete_SIS:[1,1,1,""],individual_contagion:[1,1,1,""],majority_vote:[1,1,1,""],threshold:[1,1,1,""]},"algorithms.generative_models":{chung_lu_hypergraph:[0,1,1,""],dcsbm_hypergraph:[0,1,1,""],erdos_renyi_hypergraph:[0,1,1,""]},"algorithms.homology_mod2":{add_to_column:[0,1,1,""],add_to_row:[0,1,1,""],betti:[0,1,1,""],betti_numbers:[0,1,1,""],bkMatrix:[0,1,1,""],boundary_group:[0,1,1,""],chain_complex:[0,1,1,""],homology_basis:[0,1,1,""],hypergraph_homology_basis:[0,1,1,""],interpret:[0,1,1,""],kchainbasis:[0,1,1,""],logical_dot:[0,1,1,""],logical_matadd:[0,1,1,""],logical_matmul:[0,1,1,""],matmulreduce:[0,1,1,""],reduced_row_echelon_form_mod2:[0,1,1,""],smith_normal_form_mod2:[0,1,1,""],swap_columns:[0,1,1,""],swap_rows:[0,1,1,""]},"algorithms.laplacians_clustering":{get_pi:[0,1,1,""],norm_lap:[0,1,1,""],prob_trans:[0,1,1,""],spec_clus:[0,1,1,""]},"algorithms.s_centrality_measures":{s_betweenness_centrality:[0,1,1,""],s_closeness_centrality:[0,1,1,""],s_eccentricity:[0,1,1,""],s_harmonic_centrality:[0,1,1,""],s_harmonic_closeness_centrality:[0,1,1,""]},"classes.entity":{Entity:[3,2,1,""],EntitySet:[3,2,1,""]},"classes.entity.Entity":{add:[3,3,1,""],add_element:[3,3,1,""],add_elements_from:[3,3,1,""],children:[3,4,1,""],clone:[3,3,1,""],complete_registry:[3,3,1,""],depth:[3,3,1,""],elements:[3,4,1,""],fullregistry:[3,3,1,""],incidence_dict:[3,4,1,""],intersection:[3,3,1,""],is_bipartite:[3,4,1,""],is_empty:[3,4,1,""],level:[3,3,1,""],levelset:[3,3,1,""],memberships:[3,4,1,""],merge_entities:[3,3,1,""],nested_incidence_dict:[3,3,1,""],properties:[3,4,1,""],registry:[3,4,1,""],remove:[3,3,1,""],remove_element:[3,3,1,""],remove_elements_from:[3,3,1,""],restrict_to:[3,3,1,""],size:[3,3,1,""],uid:[3,4,1,""],uidset:[3,4,1,""]},"classes.entity.EntitySet":{add:[3,3,1,""],clone:[3,3,1,""],collapse_identical_elements:[3,3,1,""],incidence_matrix:[3,3,1,""],restrict_to:[3,3,1,""]},"classes.hypergraph":{Hypergraph:[3,2,1,""]},"classes.hypergraph.Hypergraph":{add_edge:[3,3,1,""],add_edges_from:[3,3,1,""],add_node_to_edge:[3,3,1,""],add_nwhy:[3,3,1,""],adjacency_matrix:[3,3,1,""],auxiliary_matrix:[3,3,1,""],bipartite:[3,3,1,""],collapse_edges:[3,3,1,""],collapse_nodes:[3,3,1,""],collapse_nodes_and_edges:[3,3,1,""],component_subgraphs:[3,3,1,""],components:[3,3,1,""],connected_component_subgraphs:[3,3,1,""],connected_components:[3,3,1,""],convert_to_static:[3,3,1,""],dataframe:[3,3,1,""],degree:[3,3,1,""],diameter:[3,3,1,""],dim:[3,3,1,""],distance:[3,3,1,""],dual:[3,3,1,""],edge_adjacency_matrix:[3,3,1,""],edge_diameter:[3,3,1,""],edge_diameters:[3,3,1,""],edge_distance:[3,3,1,""],edge_neighbors:[3,3,1,""],edge_size_dist:[3,3,1,""],edges:[3,4,1,""],from_bipartite:[3,3,1,""],from_dataframe:[3,3,1,""],from_numpy_array:[3,3,1,""],get_id:[3,3,1,""],get_linegraph:[3,3,1,""],get_name:[3,3,1,""],incidence_dict:[3,4,1,""],incidence_matrix:[3,3,1,""],is_connected:[3,3,1,""],isstatic:[3,4,1,""],neighbors:[3,3,1,""],node_diameters:[3,3,1,""],nodes:[3,4,1,""],number_of_edges:[3,3,1,""],number_of_nodes:[3,3,1,""],order:[3,3,1,""],recover_from_state:[3,3,1,""],remove_edge:[3,3,1,""],remove_edges:[3,3,1,""],remove_node:[3,3,1,""],remove_nodes:[3,3,1,""],remove_singletons:[3,3,1,""],remove_static:[3,3,1,""],restrict_to_edges:[3,3,1,""],restrict_to_nodes:[3,3,1,""],s_component_subgraphs:[3,3,1,""],s_components:[3,3,1,""],s_connected_components:[3,3,1,""],s_degree:[3,3,1,""],save_state:[3,3,1,""],set_state:[3,3,1,""],shape:[3,4,1,""],singletons:[3,3,1,""],size:[3,3,1,""],toplexes:[3,3,1,""],translate:[3,3,1,""]},"classes.staticentity":{StaticEntity:[3,2,1,""],StaticEntitySet:[3,2,1,""]},"classes.staticentity.StaticEntity":{arr:[3,4,1,""],cell_weights:[3,4,1,""],children:[3,4,1,""],data:[3,4,1,""],dataframe:[3,4,1,""],dimensions:[3,4,1,""],dimsize:[3,4,1,""],elements:[3,4,1,""],elements_by_level:[3,3,1,""],incidence_dict:[3,4,1,""],incidence_matrix:[3,3,1,""],index:[3,3,1,""],indices:[3,3,1,""],is_empty:[3,3,1,""],keyindex:[3,3,1,""],keys:[3,4,1,""],labels:[3,4,1,""],labs:[3,3,1,""],level:[3,3,1,""],memberships:[3,4,1,""],properties:[3,5,1,""],restrict_to_indices:[3,3,1,""],restrict_to_levels:[3,3,1,""],size:[3,3,1,""],translate:[3,3,1,""],translate_arr:[3,3,1,""],turn_entity_data_into_dataframe:[3,3,1,""],uid:[3,4,1,""],uidset:[3,4,1,""],uidset_by_level:[3,3,1,""]},"classes.staticentity.StaticEntitySet":{collapse_identical_elements:[3,3,1,""],convert_to_entityset:[3,3,1,""],incidence_matrix:[3,3,1,""],restrict_to:[3,3,1,""]},"drawing.rubber_band":{draw:[6,1,1,""],draw_hyper_edge_labels:[6,1,1,""],draw_hyper_edges:[6,1,1,""],draw_hyper_labels:[6,1,1,""],draw_hyper_nodes:[6,1,1,""],get_default_radius:[6,1,1,""],layout_hyper_edges:[6,1,1,""],layout_node_link:[6,1,1,""]},"drawing.two_column":{draw:[6,1,1,""],draw_hyper_edges:[6,1,1,""],draw_hyper_labels:[6,1,1,""],layout_two_column:[6,1,1,""]},"drawing.util":{get_frozenset_label:[6,1,1,""],get_line_graph:[6,1,1,""],get_set_layering:[6,1,1,""],inflate:[6,1,1,""],inflate_kwargs:[6,1,1,""],transpose_inflated_kwargs:[6,1,1,""]},"reports.descriptive_stats":{centrality_stats:[17,1,1,""],comp_dist:[17,1,1,""],degree_dist:[17,1,1,""],dist_stats:[17,1,1,""],edge_size_dist:[17,1,1,""],info:[17,1,1,""],info_dict:[17,1,1,""],s_comp_dist:[17,1,1,""],s_edge_diameter_dist:[17,1,1,""],s_node_diameter_dist:[17,1,1,""],toplex_dist:[17,1,1,""]},algorithms:{contagion:[1,0,0,"-"],generative_models:[0,0,0,"-"],homology_mod2:[0,0,0,"-"],laplacians_clustering:[0,0,0,"-"],s_centrality_measures:[0,0,0,"-"]},classes:{entity:[3,0,0,"-"],hypergraph:[3,0,0,"-"],staticentity:[3,0,0,"-"]},drawing:{rubber_band:[6,0,0,"-"],two_column:[6,0,0,"-"],util:[6,0,0,"-"]},reports:{descriptive_stats:[17,0,0,"-"]}},objnames:{"0":["py","module","Python module"],"1":["py","function","Python function"],"2":["py","class","Python class"],"3":["py","method","Python method"],"4":["py","property","Python property"],"5":["py","attribute","Python attribute"]},objtypes:{"0":"py:module","1":"py:function","2":"py:class","3":"py:method","4":"py:property","5":"py:attribute"},terms:{"0":[0,1,3,6,8,10,13,15],"0020034":1,"00231":[0,15],"01":0,"012805":0,"019":1,"020":[0,15],"021":15,"030":15,"04197":15,"1":[0,1,3,6,8,10,13,15,17],"10":[0,1,3,15],"100":[0,1],"1000":[0,1,3],"10000":1,"1007":15,"1038":1,"10431":1,"1063":1,"1093":0,"1103":0,"1140":[0,15],"1145":0,"11782":15,"1186":15,"12901":15,"15":15,"16":[0,15],"17th":15,"19":0,"1_1":15,"2":[0,1,3,6,8,13,14,15],"2003":15,"2005":0,"2016":0,"2018":12,"2019":15,"2020":[0,15],"22":15,"27":0,"287":15,"29th":0,"2d":0,"2z":0,"3":[0,1,3,6,11,13,14,15],"3340531":0,"3412034":0,"35":6,"4":[1,3,14],"48478":15,"495":0,"5":[1,3,6,14],"504":0,"6":[1,3,14],"7":[11,14],"755":11,"76rl01830":14,"9":[0,11,13,15],"90":0,"978":15,"abstract":0,"boolean":[0,3,13],"case":[0,3,14],"class":[5,8,9,10],"default":[0,1,3,6,17],"do":[3,8,12,13,14],"export":13,"final":0,"float":[0,1,3,6],"function":[0,1,3,6],"import":[0,1,3,10],"int":[0,1,3,6,15],"long":17,"new":[0,3,6,10,13],"null":11,"public":[9,10],"return":[0,1,3,6,8,13,17],"static":[0,3,14],"super":18,"switch":8,"true":[0,1,3,6,13,17],"while":18,A:[0,1,3,6,8,12,13,15],AND:12,AS:12,As:[3,9,10],At:0,BE:12,BUT:12,BY:12,By:[3,6],FOR:12,For:[0,3,6,8,9,10,11,13,14,18],IF:12,IN:12,IS:12,If:[0,1,3,6,8,11,13,17],In:[0,3,6,13,14],It:[3,6,13],NO:12,NOT:12,Not:3,OF:[12,14],ON:12,OR:12,One:3,SUCH:12,Such:12,THE:12,TO:12,That:0,The:[0,1,3,6,8,9,10,13,14,18],Then:[6,10],These:[0,18],To:[0,3,9,10],Will:3,_0:3,_1:3,_2:[0,3],_:3,__dict__:3,_edg:3,_node:3,_version:13,ab:[3,15],abl:1,about:[9,10],abov:[3,6,12,17],ac05:14,accept:[6,13],access:[8,11],accomplish:0,accord:8,account:[1,14],accuraci:14,acm:0,aco5:14,across:6,action:0,activ:[10,11,18],ad:[0,3,6,14],adam:15,adaptor:13,add:3,add_edg:3,add_edges_from:3,add_el:3,add_elements_from:3,add_node_to_edg:3,add_nodes_from:3,add_nwhi:3,add_to_column:0,add_to_row:0,addit:[0,3,14],addon:[13,14,18],adjac:[0,3,8,9,10],adjacency_matrix:3,adjust:6,admit:[9,10],advanc:18,advis:12,after:[3,13],against:3,agenc:14,aggreg:[3,17],aggregatebi:3,ah:15,aksoi:[0,14,15],al:[0,1,15],algebra:[9,10],algorithm:[5,6,9,10,13,14,15,18],align:[3,6],all:[0,1,3,6,8,11,13,14,17,18],allow:[1,3,6,18],alpha:[1,6],alreadi:[1,3,18],also:[0,3,8,9,10,13,17,18],alter:0,altern:18,ami:15,among:[9,10],amount:6,an:[0,1,3,6,8,10,14,17,18],anaconda3:11,anaconda:10,analysi:13,analyt:[14,15],andrew:14,angl:6,ani:[0,3,8,12,13,14,18],anim:[0,2,5,10],annal:0,annot:6,anoth:[0,6,8],api:10,apparatu:14,appear:[3,18],appli:[0,3,6],applic:3,approach:6,appropri:6,ar1:0,ar2:0,ar:[0,1,3,6,8,9,10,11,12,13,14,18],arbitrari:[6,9,10],arendt:[14,15],arg:[0,1,3],arg_set:3,argument:[1,3,6],argumetn:6,aris:12,around:6,arr:[0,3],arrai:[0,1,3,13],articl:15,arxiv:15,asc:0,aspect:17,assign:[3,6],associ:[0,3,12,13],assum:[3,14],attribut:[3,8,10],author:14,automat:[1,3],auxiliari:[3,8],auxiliary_matrix:3,avail:[0,3,14,18],averag:13,ax:6,axi:6,azsecur:15,b:[3,6,8,15],back:13,backend:3,background:18,band:6,baric:15,base:[0,3,6,8,13,14,18],basi:0,basic:[3,8,9,10,14,17],bat:11,battel:[12,14],bd:0,bdict:3,becaus:[9,10],becom:[0,3],been:[0,13],befor:3,behavior:0,behind:6,being:0,belong:[0,3,8,13],below:11,berg:0,best:0,betti:0,betti_numb:0,between:[0,1,3,6,8,13,18],big:15,binari:[0,12],bioinformat:15,biolog:15,biomedcentr:15,bipartit:[0,3,6,8,18],bk:0,bkmatrix:0,block:10,blue:1,bmc:15,bmcbioinformat:15,book:14,bool:[0,1,3,6,17],both:[1,3,8,9,10,13,18],bound:0,boundari:[0,6],boundary_group:0,box:6,bramer:15,brenda:[14,15],brett:15,brian:14,briefest:0,browser:[11,14],bsd:14,build:[3,10,11],build_doc:11,built:18,bulk:18,busi:12,button:18,c:[0,1,3,6,10,11,13,14,15],c_:0,c_b:[0,13],c_k:0,ca:15,calcul:6,call:[6,8,13],callahan:15,can:[0,1,3,6,8,9,10,13,14,18],cannot:[1,3],capabl:18,cardin:3,care:3,carlo:15,categori:3,caus:[3,12,18],caution:3,cdotfrac:0,cell:[0,3,14,17],cell_weight:[0,3],center:6,central:[2,5,10,13,14,17],centrality_stat:17,certain:3,chain:0,chain_complex:0,chang:[1,3,6,18],check:[3,9,10,13],check_connect:0,cheeger:0,child:3,children:[3,8],chmod:11,choic:[0,1],choos:[1,3],chosen:[0,3,6],chung:0,chung_lu_hypergraph:0,chunglu:14,cikm:0,circl:[6,18],circular:1,ck:0,classmethod:3,claus:14,click:18,cliff:[14,15],cliqu:[9,10],clone:[3,11],close:[0,13],cluster:[2,5,10,14],cnx001:0,cockrel:15,code:12,col:13,colab:[3,10],coldict:3,collaps:[3,6,13,18],collapse_edg:[3,13],collapse_identical_el:3,collapse_nod:[3,13],collapse_nodes_and_edg:[3,13],collect:[1,3,6],collective_contagion:1,collumn:6,colon:3,color:[1,3,6,18],column:[0,3,6,8,13,14,17],column_index:3,com:[11,15],combin:13,combinator:0,come:11,command:[3,11,18],comment:[9,10,14],commerci:14,common:1,commun:[0,9,10,14],comnet:0,comp:17,comp_dist:17,compar:[3,13],complet:[8,14,18],complete_registri:3,complex:[0,3,9,10,13,15],compon:[0,3,6,8,13,17],component_subgraph:3,comput:[0,3,6,14,15,17],concentr:6,concern:0,conda:[11,13],condit:[3,8,12],conf:15,confer:0,conflict:3,connect:[0,3,6,8,9,10,13,17],connected:0,connected_compon:3,connected_component_subgraph:3,consecut:3,consent:12,consequenti:12,consid:3,constitut:14,construct:[0,1,3,8,13,14],constructor:[3,6,13,14],contact:[9,10,14],contagi:1,contagion:[0,2,5,10,14],contagion_anim:1,contain:[0,3,6,8,13,17,18],content:[2,4,5,7,16],context:[0,3],continu:[1,11],contract:[12,14],contributor:[9,10,12,14],control:[3,18],contruct:0,conveni:[3,6],convert:[3,6],convert_to_entityset:3,convert_to_stat:3,convex:6,cooper:14,coord:3,coordin:[3,6],copi:[0,3,12,13],copyright:12,core:3,correct:6,correspond:[0,3,8,14],coset:0,could:3,count:[3,6,17],counter:17,creat:[0,3,11,13,14,17],creation:3,criteria:13,critic:15,cross:6,csr:[0,3],csr_matrix:[0,3],ctrl:18,current:[0,1,13],current_st:3,curvi:6,custom:6,cybersecur:15,cycl:[0,3,6],cyclic:0,d:[0,3,13,15],damag:12,daniel:15,data:[0,3,6,9,10,12,13,14,15],data_subset:3,datafram:[3,14],dcsbm:[0,14],dcsbm_hypergraph:0,de:[14,18],dedup:3,deeper:3,defaultdict:3,defin:[0,1,3],degre:[0,3,8,13,17,18],degree_dist:17,delet:3,demo:18,denorm:0,denot:1,densiti:17,depart:14,depend:[0,1,3,13],deprec:3,depth:[0,3,8],deriv:3,descend:3,describ:[0,1],descript:[0,3],descriptive_stat:[5,10,16],design:14,desir:3,dest:13,detail:[0,18],determin:[0,3,6],develop:[9,10,13,14],deviat:17,df:3,diagon:0,diagram:[6,18],diamet:[3,8,13,17],diamond:15,dict:[0,3,6,17],dictionari:[0,1,3,6,8,13,17],differ:[3,13],digraph:[0,6],dim:[0,3,13],dimens:[0,3,13],dimension:[0,3,9,10],dimensionsl:3,dimsiz:3,direct:[0,3,6,12,13,18],directli:[3,9,10,14,18],dirti:6,disabl:6,discard:3,disclaim:12,disclos:14,disconnect:6,discov:0,discret:1,discrete_si:1,discrete_sir:1,discuss:0,disjoint:[0,3,8],disonnecct:6,displai:1,dist:17,dist_stat:17,distanc:[0,3,6,8,13],distant:6,distinct:3,distinguish:[3,8,9,10],distribut:[0,12,13,17],divid:[0,1],dlfer:0,doc:11,document:[3,11,12],doe:[3,6,14],doesn:1,doi:[0,1,15],domain:[0,15],done:[3,13],dot:0,down:18,dr:6,drag:18,draw:[1,5,10],draw_hyper_edg:6,draw_hyper_edge_label:6,draw_hyper_label:6,draw_hyper_nod:6,drawn:6,drop:3,dt:1,dual:[3,8],duplic:[0,3],dustin:[14,15],dynam:[0,3,8],e0:3,e1:3,e2:3,e3:3,e:[0,3,6,8,11,13,15,17,18],e_1:3,e_2:3,e_end:3,e_n:3,e_start:3,each:[0,1,3,6,8,13,17,18],easier:6,ecc:0,eccentr:[0,13],echelon:0,ed:15,edg:[0,1,3,6,8,9,10,13,14,17,18],edge_adjac:3,edge_adjacency_matrix:3,edge_column_nam:3,edge_diamet:3,edge_dist:3,edge_incid:13,edge_kwarg:6,edge_label:[0,3,6],edge_labels_kwarg:6,edge_nam:3,edge_neighbor:3,edge_set:3,edge_size_dist:[3,13,17],edge_state_color_dict:1,edge_uid:3,edges_kwarg:6,edgeset:3,edit:11,effect:[1,3],eg:0,eigenvalu:0,eigenvector:0,eisfeld:15,either:[3,8,13,17],element:[0,3,6,8,13],element_subset:3,elements_by_level:3,els:1,emili:[14,15],emploi:3,employe:14,empti:[3,8,13],en:[1,3],encapsul:13,end:3,endors:14,energi:14,ensur:3,ent1:3,ent2:3,entir:18,entiti:[4,5,6,8,9,10,12,14],entityset:[3,8],entri:[0,3,8,13],env:[11,13],environ:[10,14],eon:1,epidem:[0,2,5,10],epidemicsonnetwork:1,epj:[0,15],epjd:[0,15],eq_class:3,equal:[0,1,3,8,13],equat:0,equival:[0,3,13],equivalence_class:3,erdo:0,erdos_renyi_hypergraph:0,error:[0,3,13],essenc:0,et:[0,1,15],euler:18,evalu:3,even:12,event:[1,12],everi:[0,3,8,13,18],everyth:18,ex:[3,11],exactli:8,exampl:[0,1,3,6,11,14,18],exceed:3,except:8,execut:11,exemplari:12,exhibit:0,exist:[0,3,6,8],existing_lap:0,expand:[6,18],explicit:0,explor:[9,10],expos:3,express:[12,14],extend:18,extens:[0,11],extra:1,f:15,facecolor:6,fail:3,fals:[0,1,3,6,13,17],fan:[0,15],fast:3,faster:13,favor:14,featur:[0,10],feng:15,ferrario:0,fig:1,figur:[1,6],file:[3,11,12],filepath:3,fill:[3,17],fillna:3,filter:13,find:[6,9,10],firoz:15,first:[3,6],firstlevel:3,fit:12,fix:3,flexibl:3,fly:13,follow:[3,6,11,12,14],forc:18,fork:11,form:[2,3,5,10,12],format:[3,13,17],forth:13,forward:1,found:[3,9,10],four:14,fp:1,fpath:3,frac:[0,13],fraction:[1,6,13],frame:[1,3],from:[0,1,3,6,8,11,13,15,17,18],from_bipartit:[3,8],from_datafram:3,from_numpy_arrai:3,frozen:3,frozenset:3,fruchterman_reingold_layout:6,full:3,fullregistri:3,further:6,g1:0,g2:0,g:[0,6,13,15,17],gamma:[0,1],gene:15,gener:[0,3,6,8,9,10,11,14,17],generative_model:[2,5,10],get_default_radiu:6,get_frozenset_label:6,get_id:3,get_line_graph:6,get_linegraph:3,get_nam:3,get_pi:0,get_set_lay:6,get_singleton:13,gillespie_si:1,gillespie_sir:1,github:[11,14,18],give:[3,18],given:[0,3,6,8,13],glossari:10,gm:0,go:17,goal:13,good:[0,12],googl:14,gotten:3,gov:[0,9,10,14],govern:14,grant:12,graph:[0,3,6,8,9,10,13,15,18],greater:0,green:1,group:0,grow:[9,10,14],guarante:6,h:[0,1,3,6,17],h_k:0,ha:[1,3,8,13,14,18],halfmann:15,handl:6,happen:1,harmon:[0,13],hashabl:[1,3],hasn:1,have:[0,1,3,6,8,9,10,13,14,18],hayashi:0,header:[3,14],heal:1,heath:15,held:3,heller:15,help:18,helper:6,henc:3,henri:15,here:[13,18],herebi:12,herein:[12,14],hereinaft:12,heterogen:1,hicss:15,hidden:18,hide:18,high:[0,13,14,15],higher:0,highlight:14,hist:17,hit:18,hnx:[0,1,3,11,13,14,18],hnxwidget:18,hold:18,holder:12,home:10,homolog:[2,5,9,10,14],homology_basi:0,homology_mod2:[2,5,10],honor:3,how:3,howev:12,hpda:14,html:[1,11],http:[0,1,11,15],hugh:15,hull:6,hunter:15,hyper:[3,6,8,18],hyperedg:[0,3,8,9,10,13,14],hyperedgelist:1,hypergraph:[1,2,4,5,6,8,9,10,13,14,15,17,18],hypergraph_homology_basi:0,hypergraphedg:3,hypernet:14,hypernetwork:[0,15],hypernetx:[0,1,3,12,14],hypernetxerror:[0,3],hypernetxwidget:18,i:[0,1,3,8,13,18],i_m:0,i_n:0,iacopini:1,icc:15,id:[0,1,3,6,8,13],ideal:0,ident:[0,3,6,18],identifi:[0,3,15],idx:3,ignacio:15,ignor:[0,3],illustr:6,im:0,imag:0,image_basi:0,immut:3,implement:[0,1,13],impli:[6,12,14],impos:8,improv:18,incid:[0,3,8,9,10,13,14,17],incidence_dict:3,incidence_matrix:3,incident:12,includ:[3,9,10,12],inclus:[0,3],inde:3,independ:[6,18],index:[0,3,8,10,11],indic:[0,3,13],indirect:12,individu:1,individual_contagion:1,induc:[3,8],inequ:0,inf:[1,3],infect:1,infin:3,infinit:8,inflat:6,inflate_kwarg:6,info:17,info_dict:17,inform:[0,3,14,17],infring:14,initi:1,initial_infect:1,initial_recov:1,inner:0,input:[0,3],inquiri:0,inseper:3,insert:3,insid:3,inspect:14,instal:[3,10],instanc:[3,8],instanti:[3,8],instead:[3,6,13],institut:[12,14],instruct:11,integ:[0,3,6,8,13,17],intel:10,intend:[0,6],intens:3,inter:3,interact:[14,18],interest:[0,3],interfac:18,intern:[0,3],interpret:[0,13],interpreted_basi:0,interrupt:12,intersect:[0,3,6,8],intuit:8,invers:0,invert:0,investig:14,invis:6,io:1,ipython:1,is_bipartit:3,is_connect:3,is_empti:3,is_s_connect:13,isn:3,isomorph:[3,8],isstat:3,item:[3,6,17],iter:[0,1,3,6,17],ith:0,iti:8,its:[0,3,6,8,13,14,18],itself:[3,8],j:[0,8,15],jacob:15,jason:15,javascript:[14,18],jefferson:15,jenkin:15,ji:14,joel:1,joslyn:[0,14,15],jth:0,jupyt:[11,14],jurisdict:14,k1:0,k2:0,k:[0,1,3,8],kaminski:15,katrina:15,kawaoka:15,kbasi:0,kchain:0,kchainbasi:0,kdx:3,keep:[3,17,18],keep_weight:3,kei:[0,1,3,6,8,13],kelli:15,kernel:0,kevin:15,keyindex:3,keyword:[3,6],km1basi:0,knowledg:0,known:[0,3],kocher:15,krang:0,kritzstein:14,kth:0,kving:15,kwarg:[0,3,6],l:[0,13,15],lab:3,label:[0,3,6],label_alpha:6,laboratori:14,lambda:1,landri:[1,14],laplacian:[2,5,10],laplacians_clust:[2,5,10],larg:3,larger:18,largest:[0,3],larissa:15,larremor:0,last:3,lastlevel:3,latest:1,latter:3,lawfulli:12,layer:6,layout:[1,6,10],layout_hyper_edg:6,layout_kwarg:6,layout_node_link:6,layout_two_column:6,le:15,learn:[9,10],leas:8,least:[3,6,8],lectur:15,left:[0,6],legal:14,len:17,length:[0,3,6,8,9,10],lesmi:14,less:[0,3,13],let:3,level1:3,level2:3,level:[3,6,8],levelset:[3,8],liabil:[12,14],liabl:12,librari:[0,3,9,10,13,14],licens:10,like:[3,6],limit:[3,12],line:[0,3,6,13],linecollect:6,linegraph:[0,3,8],linewidth:6,link:[0,3,18],linux:[11,14],linv:0,lisa:15,list:[0,1,3,6,12,13,17],liu:[13,14],llinv:0,lm:0,lmr:0,local:13,locat:[6,11,18],logic:0,logical_dot:0,logical_matadd:0,logical_matmul:0,longer:3,longest:[0,3],look:0,loss:12,loui:15,lower:6,lu:0,lumsdain:14,m:[0,1,3,15],mac:[11,18],made:3,magnitud:0,mai:[3,8,9,10,11,12,14,18],main:18,major:1,majority_vot:1,make:[3,6,14],manag:[0,14],mani:[3,13,17],manipul:3,manual:6,manufactur:14,map:[0,6],marcin:15,mark:14,marrero:[0,15],mat1:0,mat2:0,mat:0,match:[0,3],materi:14,mathbb:0,mathemat:14,matmulreduc:0,matplotlib:[1,6,11],matric:[2,5,6,10,14],matrix:[0,3,8,13,17],max:[0,3,17],max_degre:13,max_depth:3,max_level:3,max_siz:[3,13],maxim:[3,8],maximum:[3,8],maxlevel:3,mcdermott:15,mean:[0,3,17],measur:[2,5,10,14],mechan:1,median:[3,6,17],member:3,membership:[3,6,8,18],memori:[12,13,14],menacheri:15,merchant:12,merg:[3,12],merge_ent:3,method:[0,3,8,9,10,14,17],methodolog:0,metric:[0,9,10,14],michael:15,might:18,miller:1,min:[0,3,17],min_degre:13,min_level:3,min_siz:13,minim:[0,6,11,18],minimum:[3,6],minlevel:3,minu:[0,3],mirah:0,miss:6,mitchel:15,mod2:[2,5,10,14],mod:0,model:[1,9,10,14,15],modestli:3,modif:12,modifi:12,modul:[2,4,5,7,10,14,16],modulo:0,more:[3,8,9,10,11,13],most:[1,3,6,9,10],much:13,multi:[3,9,10],multidimension:15,multipl:[0,3,8,13,18],multipli:0,multiwai:[9,10],must:[0,1,3,12,13],mxn:0,n:[0,1,3,6,8,11,13],nama:3,name:[3,11,12,13,14,15,18],nan:3,natali:15,nation:14,natur:[9,10],navig:3,ncell:17,ncol:17,ndarrai:[0,3],necessarili:14,need:[0,3,6,11],neglig:12,neighbor:[1,3,13],neither:[12,14],neq:[0,13],nest:3,nested_incidence_dict:3,network:[0,1,3,9,10,14,15],networkx:[3,6],netwrokx:6,newfpath:3,newuid:3,next:0,nichola:14,node:[0,1,3,6,8,13,14,17,18],node_column_nam:3,node_diamet:3,node_incid:13,node_label:[0,3,6],node_labels_kwarg:6,node_nam:3,node_radiu:[1,6],node_set:3,node_size_dist:13,node_state_color_dict:1,nodes_kwarg:6,nodeset:3,non:[0,8],none:[0,1,3,6,13,17],nonempti:[3,8],nonexist:3,nonzero:[3,8],nor:14,norm_lap:0,normal:[2,5,10,13],northwest:14,note:[0,1,3,8,11,13,15],notebook:[11,14],noth:3,notic:[10,12],np:[0,3],nrow:17,num:17,number:[0,1,3,6,8,13,17],number_of_edg:[3,13],number_of_nod:[3,13],numer:3,numpi:[0,1,3,6,13],nwgraph:13,nwhy:[0,3,10,11,14],nwhypergraph:[3,10],nx2:6,nx:[3,6,8],nxm:0,o:15,obj:17,object:[0,1,3,8,13,14,17],obtain:[0,8,12],occupi:8,occur:3,off:1,offer:3,offset:6,omega:0,onc:[11,14],one:[0,3,6,8,13],oneapi:13,onetbb:13,onli:[0,1,3,8,11,13],open:11,oper:14,opinion:[1,14],opt:13,optim:[6,10,13,14,18],option:[0,1,3,10,17],order:[0,3,6,15],ordereddict:3,org:[0,1,15],organ:14,orient:6,origin:[0,3,13],ortiz:0,osit:3,osx:11,other:[0,3,6,8,10,12,13],otherwis:[0,3,8,11,12,13,14],our:[0,9,10],out:[0,6,9,10,12],outlin:18,output:[0,1,3],outsid:3,over:[0,6,8,13],overlap:[6,13],overrid:6,overview:10,own:[8,14],p:[0,3,15],pacif:14,packag:[2,4,7,10,16],page:10,pair:[0,3,6,8,13],pairwis:3,panda:[3,14],panel:10,paper:6,parallel:[6,13],paramet:[0,1,3,6,17],park:0,part:[6,14],partial_k:0,particular:[3,9,10,12,14],partit:[3,8],pass:[0,3,6,13],path:[0,3,6,9,10,11,13],pathogen:15,pd:3,per:1,perfect:18,perform:[3,13,14,15,18],permiss:12,permit:12,person:12,peter:15,physrev:0,pi:0,pickl:3,pin:18,pip:[10,18],place:3,placehold:3,placement:18,planar:6,pleas:[0,3],plot:6,plt:1,pnnl:[0,9,10,11,14],po:6,point:6,poli:6,polycollect:6,polygon:6,poset:3,posit:[0,3,6,8,13,17,18],possibl:[1,6,12,18],post:0,potenti:1,power:[9,10],powershel:11,pp:15,practic:3,praggasti:[14,15],pre:6,precis:8,prefil:3,preliminari:13,prepar:14,prepend:3,present:[1,3],preserv:[3,18],press:15,princip:14,principl:14,print:[0,17],prior:3,privat:14,prob_tran:0,probabl:[2,5,10],proc:15,proceed:0,process:[3,13,14],procur:12,product:[0,14],profit:12,program:14,project:14,prompt:11,prop:3,properli:8,properti:[3,8,13,14,18],proport:0,provid:[0,3,6,9,10,12,13],ps1:11,publish:12,purpos:[0,12],purvin:[14,15],put:17,py:8,pybind11:13,pyplot:1,pytest:11,python:[11,13],qing:15,quantiti:[9,10],question:[9,10,14],quick:[6,10],quit:3,r0:6,r:[0,1,6],radiu:[1,6],rais:[0,3],ralph:15,randint:0,random:[0,1],randomli:1,rang:[0,1,6],rate:1,rather:17,ratio:[0,17],rauga:14,rdc:0,re:18,reachabl:13,read:[6,14],readthedoc:1,real:3,reason:[3,6],receiv:3,reciproc:[0,13],recommend:[3,6,14],recov:[1,3],recover_from_st:3,recoveri:1,rectangular:[0,8],recurs:0,red:1,redistribut:12,reduc:[0,6],reduced_row_echelon_form_mod2:0,refer:[3,14],referenc:[0,3],reflect:[3,14],regist:3,registri:[3,8],rel:[0,18],relat:[3,9,10],relationship:[0,3,9,10,15],releas:[14,18],remov:[3,18],remove_edg:3,remove_el:3,remove_elements_from:3,remove_nod:3,remove_singleton:3,remove_stat:3,render:6,renyi:0,rep:3,repeatedli:0,replac:[0,3],report:[5,10],repositori:[9,10],repres:[0,3,6,8,9,10,14],represent:[0,3,6,13],reproduc:[6,12],request:3,requir:[0,1,3,13],research:[9,10,14],reserv:6,respect:[0,3],respons:[14,15],restrepo:1,restrict:[3,8],restrict_to:3,restrict_to_edg:3,restrict_to_indic:3,restrict_to_level:3,restrict_to_nod:3,result:[6,18],retain:12,retriev:3,return_count:3,return_equal_class:13,return_equivalence_class:3,return_full_data:1,return_index:3,return_po:6,return_singleton:[0,3,17],revers:[0,3,18],rho:1,rich:13,right:[0,6,14],rigor:6,ring:6,role:[3,8],root:3,roughli:0,row:[0,3,8,13,17],rowdict:3,rubber:6,rubber_band:[5,7,10],run:[0,11,13,14],s12859:15,s13688:[0,15],s41467:1,s:[1,2,3,5,6,8,10,13,14,15,17],s_betweenness_centr:[0,13],s_centrality_measur:[2,5,10],s_closeness_centr:[0,13],s_comp_dist:17,s_compon:3,s_component_subgraph:3,s_components_subgraph:3,s_connect:3,s_connected_compon:[3,13],s_degre:[3,13],s_diamet:13,s_distanc:13,s_eccentr:[0,13],s_edge_connect:3,s_edge_diameter_dist:17,s_harmonic_centr:0,s_harmonic_closeness_centr:[0,13],s_linegraph:13,s_neighbor:13,s_node_diameter_dist:17,s_path:13,same:[0,3,6,8,13],sampl:[1,3],satifi:3,satisfi:[3,8],save:3,save_st:3,scalabl:13,sci:0,scienc:[0,15],scip:3,scipi:[0,3],score:13,script:11,search:10,second:[1,3],see:[3,6,8,11,14,17],select:[0,10],self:3,sell:12,sens:8,sensibl:6,sequenc:[3,8],serv:[0,9,10],servic:[12,14],set:[0,1,3,6,8,9,10,13,18],set_nam:3,set_stat:3,setsystem:3,setsytem:3,sh:11,shabang:11,shall:12,shallow:3,shape:3,share:[3,8,13],sheahan:15,shi:0,shift:18,shortest:[0,3,8,13],shortest_path_length:3,should:[0,1,3,6],show:18,shufang:15,si:[1,14],side:[0,3,10],sigma:[0,13],signatur:3,significantli:13,sim:15,sim_kwarg:1,similar:[1,3,18],simpl:[0,3,8,17],simplic:[9,10],simplici:[0,1,9,10],simul:1,sinan:[0,14,15],sinc:[3,8,9,10],singl:[0,3,8,17],singleton:[0,3,9,10,13],sir:[1,14],size:[0,1,3,6,8,13,17,18],slightli:18,slinegraph:10,slower:13,small:[0,3,6],smaller:6,smallest:3,smith:[2,5,10,15],smith_normal_form_mod2:0,snf:0,so:[0,3,6,12],social:1,softwar:[12,14],some:[8,9,10,11],sometim:[6,18],song:15,sort:[0,3],sort_column:3,sort_row:3,sortabl:[0,3],sourc:[0,1,3,6,11,12,17],space:[6,13],spars:[0,3,13],spec:0,spec_clu:0,special:12,specif:[3,8,14],specifi:[0,1,3,6,11,13,18],spectral:[0,6],sped:14,sponsor:14,spring_layout:6,springer:15,squar:8,src:13,stack:6,standard:17,start:[0,1,3,6,17,18],stat:17,state:[1,3,14,18],state_dict:3,staticent:[4,5,10],staticentityset:3,stationari:0,statist:17,statu:1,status:1,step:[0,1],still:[0,3],storag:3,store:[0,3,13],str:[0,3],stratton:15,strict:[9,10,12],string:[3,6,17],structur:[3,8,9,10,13],studi:[0,9,10,14],style:6,subgraph:[0,3],subhypergraph:8,subject:12,sublicens:12,submatrix:8,submit:3,submodul:[2,4,5,7,10,16],subpackag:[2,5,10],subset:[3,6,8],substitut:12,subtract:3,success:8,sum:[0,3,13],sum_:[0,13],summari:17,suppli:6,support:[0,1,3,14],sure:3,surround:6,suscept:1,swap:0,swap_column:0,swap_row:0,symmetr:0,symp:15,synthet:14,system:[3,6,9,10,11,15],t:[0,1,3,13],tabl:18,take:[1,3,6],tan:15,target:3,tau:1,tbb:[10,11],tbbroot:13,techniqu:6,tell:[9,10],tensor:3,term:[0,3],termin:1,test:[10,11],text:[0,6],textbook:6,thackrai:15,than:[0,3,8,12,17],thei:[0,3,6,8,9,10,18],them:[3,8,11,17,18],theori:12,therebi:[9,10],therefor:[3,13],thereof:14,thi:[0,1,3,6,8,9,10,11,12,13,14,17,18],think:3,those:[0,14],thread:10,three:[13,14],threshold:1,through:[0,6,13],tiffani:15,time:[0,1,18],timothi:15,tmax:1,tmin:1,to_jshtml:1,todo:3,togeth:[0,6],toggl:18,toni:[13,14],tool:[9,10],toolbar:18,toplex:[0,3,8,13,17],toplex_dist:17,topolog:[0,9,10,15],tort:12,total:0,tour:14,track:[0,3,17],trade:14,trademark:14,tradit:18,transform:[0,3],transit:[1,2,5,10],transition_ev:1,translat:3,translate_arr:3,transmiss:1,transmission_funct:1,transmit:1,transpar:6,transpos:3,transpose_inflated_kwarg:6,travers:18,treat:3,triloop:14,tripodi:15,trivial:0,truthi:3,tupl:[0,3],turn_entity_data_into_datafram:3,tutori:[3,10,11],two:[0,3,6,8,13,18],two_column:[5,7,10],type:[0,1,3,6,17],typic:6,u:[0,6,13],uid:[0,1,3,8,17],uidset:[3,8],uidset_by_level:3,un:18,under:[13,14],undesir:3,undirect:13,uniform:0,uniqu:[3,8],unit:14,unless:3,unpack:3,unreach:13,unweight:[3,8,13],up:[3,14,17],updat:3,upgrad:13,upon:18,us:[0,3,6,8,9,10,12,14],use_nwhi:[0,3],use_rep:3,user:[1,3,9,10,11,13,14,18],usual:6,util:[0,5,7,10],v0:3,v1:3,v2:3,v:[0,3,6,13,15],v_1:3,v_2:3,v_end:3,v_n:3,v_start:3,valu:[0,1,3,6,8,13],variou:[13,17],ve:14,vector:0,verifi:0,version:[10,11,13],vertex:[0,6,9,10,13],vertic:[0,3,6,13,14],via:[0,15],view:14,vineet:15,viral:15,virtual:11,virtualenv:10,visibl:18,visual:[10,14,18],vn:3,vote:1,w:[0,3],wa:[3,13,14],wai:[3,6,9,10,12],walk:[0,3,8,9,10,15],walter:15,want:[0,18],warn:0,warranti:[12,14],water:15,waw:15,we:[0,3,9,10,13,14],web:15,weight:[0,3,8,13,14],well:[0,6,18],westhoff:15,what:[9,10],whatsoev:12,when:[3,13],whera:18,where:[0,3,6,8,13],whether:[0,3,12,13],which:[0,1,3,6,8,17,18],whitespac:6,whole:11,whose:[6,8,13],widget:[10,14],width:[3,8,9,10],window:[11,18],wish:11,with_color:6,with_edge_count:6,with_edge_label:6,with_node_count:6,with_node_label:6,within:[0,3,6,18],without:[12,18],work:[0,3,6,11,14],would:[3,14],wrangl:3,wrap:6,written:12,wshop:15,www:[0,15],x:[3,6,13,17],xor:0,xu:13,xx:3,xy:6,xyz:0,y:[3,6,13],yet:3,yield:3,yoshihiro:15,you:[3,6,9,10,11,14,18],young:14,your:[3,11,14],yun:14,z:[0,3],z_2:0,zalewski:15,zero:3},titles:["algorithms package","algorithms.contagion package","algorithms","classes package","classes","HyperNetX Packages","drawing package","drawing","Glossary of HNX terms","HyperNetX (HNX)","HyperNetX (HNX)","Installing HyperNetX","License","NWHy","Overview","Publications","reports","reports package","Hypernetx-Widget"],titleterms:{"0":14,"1":14,"class":[3,4,13],"import":13,"new":14,"public":15,Then:13,To:[11,13],activ:13,algorithm:[0,1,2],an:[11,13],anaconda:[11,13],anim:1,api:13,attribut:13,block:13,build:13,central:0,cluster:0,colab:14,contagion:1,content:[0,1,3,6,10,17],descript:[9,10,13],descriptive_stat:17,draw:[6,7],entiti:3,environ:[11,13],epidem:1,featur:[14,18],form:0,generative_model:0,glossari:8,hnx:[8,9,10],homolog:0,homology_mod2:0,hypergraph:[0,3],hypernetx:[5,9,10,11,18],indic:10,instal:[11,13,18],intel:13,laplacian:0,laplacians_clust:0,layout:18,licens:[12,14],matric:0,measur:0,method:13,mod2:0,modul:[0,1,3,6,13,17],normal:0,notic:14,nwhy:13,nwhypergraph:13,option:11,other:18,overview:[14,18],packag:[0,1,3,5,6,17],panel:18,pip:[11,13],probabl:0,quick:13,report:[16,17],rubber_band:6,s:0,s_centrality_measur:0,select:18,side:18,slinegraph:13,smith:0,staticent:3,submodul:[0,1,3,6,17],subpackag:0,tabl:10,tbb:13,term:8,test:13,thread:13,tool:18,transit:0,tutori:14,two_column:6,us:[11,13,18],util:6,version:14,virtualenv:11,widget:18}}) \ No newline at end of file +Search.setIndex({docnames:["algorithms/algorithms","algorithms/algorithms.contagion","algorithms/modules","classes/classes","classes/modules","core","drawing/drawing","drawing/modules","glossary","home","index","install","license","nwhy","overview/index","publications","reports/modules","reports/reports","widget"],envversion:{"sphinx.domains.c":2,"sphinx.domains.changeset":1,"sphinx.domains.citation":1,"sphinx.domains.cpp":4,"sphinx.domains.index":1,"sphinx.domains.javascript":2,"sphinx.domains.math":2,"sphinx.domains.python":3,"sphinx.domains.rst":2,"sphinx.domains.std":2,"sphinx.ext.intersphinx":1,"sphinx.ext.todo":2,"sphinx.ext.viewcode":1,sphinx:56},filenames:["algorithms/algorithms.rst","algorithms/algorithms.contagion.rst","algorithms/modules.rst","classes/classes.rst","classes/modules.rst","core.rst","drawing/drawing.rst","drawing/modules.rst","glossary.rst","home.rst","index.rst","install.rst","license.rst","nwhy.rst","overview/index.rst","publications.rst","reports/modules.rst","reports/reports.rst","widget.rst"],objects:{"":{algorithms:[0,0,0,"-"],classes:[3,0,0,"-"],drawing:[6,0,0,"-"],reports:[17,0,0,"-"]},"algorithms.contagion":{animation:[1,0,0,"-"],epidemics:[1,0,0,"-"]},"algorithms.contagion.animation":{contagion_animation:[1,1,1,""]},"algorithms.contagion.epidemics":{Gillespie_SIR:[1,1,1,""],Gillespie_SIS:[1,1,1,""],collective_contagion:[1,1,1,""],discrete_SIR:[1,1,1,""],discrete_SIS:[1,1,1,""],individual_contagion:[1,1,1,""],majority_vote:[1,1,1,""],threshold:[1,1,1,""]},"algorithms.generative_models":{chung_lu_hypergraph:[0,1,1,""],dcsbm_hypergraph:[0,1,1,""],erdos_renyi_hypergraph:[0,1,1,""]},"algorithms.homology_mod2":{add_to_column:[0,1,1,""],add_to_row:[0,1,1,""],betti:[0,1,1,""],betti_numbers:[0,1,1,""],bkMatrix:[0,1,1,""],boundary_group:[0,1,1,""],chain_complex:[0,1,1,""],homology_basis:[0,1,1,""],hypergraph_homology_basis:[0,1,1,""],interpret:[0,1,1,""],kchainbasis:[0,1,1,""],logical_dot:[0,1,1,""],logical_matadd:[0,1,1,""],logical_matmul:[0,1,1,""],matmulreduce:[0,1,1,""],reduced_row_echelon_form_mod2:[0,1,1,""],smith_normal_form_mod2:[0,1,1,""],swap_columns:[0,1,1,""],swap_rows:[0,1,1,""]},"algorithms.hypergraph_modularity":{bin_ppmf:[0,1,1,""],compute_partition_probas:[0,1,1,""],degree_tax:[0,1,1,""],delta_dt:[0,1,1,""],delta_ec:[0,1,1,""],dict2part:[0,1,1,""],edge_contribution:[0,1,1,""],factorial:[0,1,1,""],hypergraph_modularity:[0,1,1,""],kumar:[0,1,1,""],last_step:[0,1,1,""],linear:[0,1,1,""],majority:[0,1,1,""],part2dict:[0,1,1,""],precompute_attributes:[0,1,1,""],strict:[0,1,1,""],two_section:[0,1,1,""]},"algorithms.laplacians_clustering":{get_pi:[0,1,1,""],norm_lap:[0,1,1,""],prob_trans:[0,1,1,""],spec_clus:[0,1,1,""]},"algorithms.s_centrality_measures":{s_betweenness_centrality:[0,1,1,""],s_closeness_centrality:[0,1,1,""],s_eccentricity:[0,1,1,""],s_harmonic_centrality:[0,1,1,""],s_harmonic_closeness_centrality:[0,1,1,""]},"algorithms.untitiled_modularity_and_clustering_original":{DegreeTax:[0,1,1,""],DeltaDT:[0,1,1,""],DeltaEC:[0,1,1,""],EdgeContribution:[0,1,1,""],HNX_2section:[0,1,1,""],HNX_Kumar:[0,1,1,""],HNX_LastStep:[0,1,1,""],HNX_modularity:[0,1,1,""],bin_ppmf:[0,1,1,""],compute_partition_probas:[0,1,1,""],dict2part:[0,1,1,""],factorial:[0,1,1,""],linear:[0,1,1,""],majority:[0,1,1,""],part2dict:[0,1,1,""],precompute_modularity_parameters:[0,1,1,""],strict:[0,1,1,""]},"algorithms.untitled_modularity_and_clustering":{DegreeTax:[0,1,1,""],DeltaDT:[0,1,1,""],DeltaEC:[0,1,1,""],EdgeContribution:[0,1,1,""],HNX_2section:[0,1,1,""],HNX_Kumar:[0,1,1,""],HNX_LastStep:[0,1,1,""],HNX_modularity:[0,1,1,""],HNX_precompute:[0,1,1,""],bin_ppmf:[0,1,1,""],compute_partition_probas:[0,1,1,""],dict2part:[0,1,1,""],factorial:[0,1,1,""],linear:[0,1,1,""],majority:[0,1,1,""],part2dict:[0,1,1,""],strict:[0,1,1,""]},"classes.entity":{Entity:[3,2,1,""],EntitySet:[3,2,1,""]},"classes.entity.Entity":{add:[3,3,1,""],add_element:[3,3,1,""],add_elements_from:[3,3,1,""],children:[3,4,1,""],clone:[3,3,1,""],complete_registry:[3,3,1,""],depth:[3,3,1,""],elements:[3,4,1,""],fullregistry:[3,3,1,""],incidence_dict:[3,4,1,""],intersection:[3,3,1,""],is_bipartite:[3,4,1,""],is_empty:[3,4,1,""],level:[3,3,1,""],levelset:[3,3,1,""],memberships:[3,4,1,""],merge_entities:[3,3,1,""],nested_incidence_dict:[3,3,1,""],properties:[3,4,1,""],registry:[3,4,1,""],remove:[3,3,1,""],remove_element:[3,3,1,""],remove_elements_from:[3,3,1,""],restrict_to:[3,3,1,""],size:[3,3,1,""],uid:[3,4,1,""],uidset:[3,4,1,""]},"classes.entity.EntitySet":{add:[3,3,1,""],clone:[3,3,1,""],collapse_identical_elements:[3,3,1,""],incidence_matrix:[3,3,1,""],restrict_to:[3,3,1,""]},"classes.hypergraph":{Hypergraph:[3,2,1,""]},"classes.hypergraph.Hypergraph":{add_edge:[3,3,1,""],add_edges_from:[3,3,1,""],add_node_to_edge:[3,3,1,""],add_nwhy:[3,3,1,""],adjacency_matrix:[3,3,1,""],auxiliary_matrix:[3,3,1,""],bipartite:[3,3,1,""],collapse_edges:[3,3,1,""],collapse_nodes:[3,3,1,""],collapse_nodes_and_edges:[3,3,1,""],component_subgraphs:[3,3,1,""],components:[3,3,1,""],connected_component_subgraphs:[3,3,1,""],connected_components:[3,3,1,""],convert_to_static:[3,3,1,""],dataframe:[3,3,1,""],degree:[3,3,1,""],diameter:[3,3,1,""],dim:[3,3,1,""],distance:[3,3,1,""],dual:[3,3,1,""],edge_adjacency_matrix:[3,3,1,""],edge_diameter:[3,3,1,""],edge_diameters:[3,3,1,""],edge_distance:[3,3,1,""],edge_neighbors:[3,3,1,""],edge_size_dist:[3,3,1,""],edges:[3,4,1,""],from_bipartite:[3,3,1,""],from_dataframe:[3,3,1,""],from_numpy_array:[3,3,1,""],get_id:[3,3,1,""],get_linegraph:[3,3,1,""],get_name:[3,3,1,""],incidence_dict:[3,4,1,""],incidence_matrix:[3,3,1,""],is_connected:[3,3,1,""],isstatic:[3,4,1,""],neighbors:[3,3,1,""],node_diameters:[3,3,1,""],nodes:[3,4,1,""],number_of_edges:[3,3,1,""],number_of_nodes:[3,3,1,""],order:[3,3,1,""],recover_from_state:[3,3,1,""],remove_edge:[3,3,1,""],remove_edges:[3,3,1,""],remove_node:[3,3,1,""],remove_nodes:[3,3,1,""],remove_singletons:[3,3,1,""],remove_static:[3,3,1,""],restrict_to_edges:[3,3,1,""],restrict_to_nodes:[3,3,1,""],s_component_subgraphs:[3,3,1,""],s_components:[3,3,1,""],s_connected_components:[3,3,1,""],s_degree:[3,3,1,""],save_state:[3,3,1,""],set_state:[3,3,1,""],shape:[3,4,1,""],singletons:[3,3,1,""],size:[3,3,1,""],toplexes:[3,3,1,""],translate:[3,3,1,""]},"classes.staticentity":{StaticEntity:[3,2,1,""],StaticEntitySet:[3,2,1,""]},"classes.staticentity.StaticEntity":{arr:[3,4,1,""],cell_weights:[3,4,1,""],children:[3,4,1,""],data:[3,4,1,""],dataframe:[3,4,1,""],dimensions:[3,4,1,""],dimsize:[3,4,1,""],elements:[3,4,1,""],elements_by_level:[3,3,1,""],incidence_dict:[3,4,1,""],incidence_matrix:[3,3,1,""],index:[3,3,1,""],indices:[3,3,1,""],is_empty:[3,3,1,""],keyindex:[3,3,1,""],keys:[3,4,1,""],labels:[3,4,1,""],labs:[3,3,1,""],level:[3,3,1,""],memberships:[3,4,1,""],properties:[3,5,1,""],restrict_to_indices:[3,3,1,""],restrict_to_levels:[3,3,1,""],size:[3,3,1,""],translate:[3,3,1,""],translate_arr:[3,3,1,""],turn_entity_data_into_dataframe:[3,3,1,""],uid:[3,4,1,""],uidset:[3,4,1,""],uidset_by_level:[3,3,1,""]},"classes.staticentity.StaticEntitySet":{collapse_identical_elements:[3,3,1,""],convert_to_entityset:[3,3,1,""],incidence_matrix:[3,3,1,""],restrict_to:[3,3,1,""]},"drawing.rubber_band":{draw:[6,1,1,""],draw_hyper_edge_labels:[6,1,1,""],draw_hyper_edges:[6,1,1,""],draw_hyper_labels:[6,1,1,""],draw_hyper_nodes:[6,1,1,""],get_default_radius:[6,1,1,""],layout_hyper_edges:[6,1,1,""],layout_node_link:[6,1,1,""]},"drawing.two_column":{draw:[6,1,1,""],draw_hyper_edges:[6,1,1,""],draw_hyper_labels:[6,1,1,""],layout_two_column:[6,1,1,""]},"drawing.util":{get_frozenset_label:[6,1,1,""],get_line_graph:[6,1,1,""],get_set_layering:[6,1,1,""],inflate:[6,1,1,""],inflate_kwargs:[6,1,1,""],transpose_inflated_kwargs:[6,1,1,""]},"reports.descriptive_stats":{centrality_stats:[17,1,1,""],comp_dist:[17,1,1,""],degree_dist:[17,1,1,""],dist_stats:[17,1,1,""],edge_size_dist:[17,1,1,""],info:[17,1,1,""],info_dict:[17,1,1,""],s_comp_dist:[17,1,1,""],s_edge_diameter_dist:[17,1,1,""],s_node_diameter_dist:[17,1,1,""],toplex_dist:[17,1,1,""]},algorithms:{contagion:[1,0,0,"-"],generative_models:[0,0,0,"-"],homology_mod2:[0,0,0,"-"],hypergraph_modularity:[0,0,0,"-"],laplacians_clustering:[0,0,0,"-"],s_centrality_measures:[0,0,0,"-"],untitiled_modularity_and_clustering_original:[0,0,0,"-"],untitled_modularity_and_clustering:[0,0,0,"-"]},classes:{entity:[3,0,0,"-"],hypergraph:[3,0,0,"-"],staticentity:[3,0,0,"-"]},drawing:{rubber_band:[6,0,0,"-"],two_column:[6,0,0,"-"],util:[6,0,0,"-"]},reports:{descriptive_stats:[17,0,0,"-"]}},objnames:{"0":["py","module","Python module"],"1":["py","function","Python function"],"2":["py","class","Python class"],"3":["py","method","Python method"],"4":["py","property","Python property"],"5":["py","attribute","Python attribute"]},objtypes:{"0":"py:module","1":"py:function","2":"py:class","3":"py:method","4":"py:property","5":"py:attribute"},terms:{"0":[0,1,3,6,8,10,13,15],"0020034":1,"00231":[0,15],"01":0,"012805":0,"019":1,"020":[0,15],"021":15,"0224307":0,"030":[0,15],"04197":15,"1":[0,1,3,6,8,10,13,15,17],"10":[0,1,3,15],"100":[0,1],"1000":[0,1,3],"10000":1,"1007":[0,15],"1038":1,"10431":1,"1063":1,"1093":0,"1103":0,"1140":[0,15],"1145":0,"11782":15,"1186":15,"12901":15,"13":0,"1371":0,"15":15,"16":[0,15],"17th":15,"19":0,"1_1":15,"2":[0,1,3,6,8,13,14,15],"2003":15,"2005":0,"2016":0,"2018":12,"2019":[0,15],"2020":[0,15],"22":15,"27":0,"287":15,"29th":0,"2_24":0,"2d":0,"2z":0,"3":[0,1,3,6,11,13,14,15],"3340531":0,"3412034":0,"35":6,"36687":0,"4":[1,3,14],"48478":15,"495":0,"5":[0,1,3,6,14],"504":0,"6":[1,3,14],"7":[11,14],"755":11,"76rl01830":14,"881":0,"9":[0,11,13,15],"90":0,"978":[0,15],"abstract":0,"bogumi\u0142":0,"boolean":[0,3,13],"case":[0,3,14],"class":[0,5,8,9,10],"default":[0,1,3,6,17],"do":[3,8,12,13,14],"export":13,"final":0,"float":[0,1,3,6],"fran\u00e7oi":0,"function":[0,1,3,6],"import":[0,1,3,10],"int":[0,1,3,6,15],"kami\u0144ski":0,"long":[0,17],"new":[0,3,6,10,13],"null":11,"pawe\u0142":0,"pra\u0142at":0,"przemys\u0142aw":0,"public":[9,10],"return":[0,1,3,6,8,13,17],"static":[0,3,14],"super":18,"switch":8,"th\u00e9berg":0,"true":[0,1,3,6,13,17],"try":0,"val\u00e9ri":0,"while":18,A:[0,1,3,6,8,12,13,15],AND:12,AS:12,As:[3,9,10],At:0,BE:12,BUT:12,BY:12,By:[3,6],FOR:12,For:[0,3,6,8,9,10,11,13,14,18],IF:12,IN:12,IS:12,If:[0,1,3,6,8,11,13,17],In:[0,3,6,13,14],It:[3,6,13],NO:12,NOT:12,Not:3,OF:[12,14],ON:12,OR:12,One:3,SUCH:12,Such:12,THE:12,TO:12,That:0,The:[0,1,3,6,8,9,10,13,14,18],Their:0,Then:[6,10],These:[0,18],To:[0,3,9,10],Will:3,_0:3,_1:3,_2:[0,3],_:[0,3],__dict__:3,_edg:3,_node:3,_version:13,a_i:0,ab:[3,15],abl:1,about:[9,10],abov:[3,6,12,17],ac05:14,accept:[6,13],access:[8,11],accomplish:0,accord:8,account:[1,14],accuraci:14,acm:0,aco5:14,across:6,action:0,activ:[10,11,18],actual:0,ad:[0,3,6,14],adam:15,adapt:0,adaptor:13,add:[0,3],add_edg:3,add_edges_from:3,add_el:3,add_elements_from:3,add_node_to_edg:3,add_nodes_from:3,add_nwhi:3,add_to_column:0,add_to_row:0,addit:[0,3,14],addon:[13,14,18],adjac:[0,3,8,9,10],adjacency_matrix:3,adjust:6,admit:[9,10],advanc:18,advis:12,after:[3,13],against:3,agenc:14,aggreg:[3,17],aggregatebi:3,ah:15,aksoi:[0,14,15],al:[0,1,15],algebra:[9,10],algorithm:[5,6,9,10,13,14,15,18],align:[3,6],all:[0,1,3,6,8,11,13,14,17,18],allow:[1,3,6,18],alpha:[1,6],alreadi:[1,3,18],also:[0,3,8,9,10,13,17,18],alter:0,altern:18,ami:15,among:[9,10],amount:6,an:[0,1,3,6,8,10,14,17,18],anaconda3:11,anaconda:10,analysi:13,analyt:[14,15],ananthapadmanabhan:0,andrew:14,angl:6,ani:[0,3,8,12,13,14,18],anim:[0,2,5,10],annal:0,annot:6,anoth:[0,6,8],api:10,apparatu:14,appear:[0,3,18],appli:[0,3,6],applic:[0,3],approach:6,appropri:6,ar1:0,ar2:0,ar:[0,1,3,6,8,9,10,11,12,13,14,18],arbitrari:[6,9,10],arendt:[14,15],arg:[0,1,3],arg_set:3,argument:[1,3,6],argumetn:6,aris:12,around:6,arr:[0,3],arrai:[0,1,3,13],articl:15,arxiv:15,asc:0,aspect:17,assign:[3,6],associ:[0,3,12,13],assum:[3,14],attribut:[0,3,8,10],author:14,automat:[1,3],auxiliari:[3,8],auxiliary_matrix:3,avail:[0,3,14,18],averag:13,ax:6,axi:6,azsecur:15,b:[0,3,6,8,15],back:13,backend:3,background:18,band:6,baric:15,base:[0,3,6,8,13,14,18],basi:0,basic:[3,8,9,10,14,17],bat:11,battel:[12,14],bd:0,bdict:3,becaus:[9,10],becom:[0,3],been:[0,13],befor:3,behavior:0,behind:6,being:0,belong:[0,3,8,13],below:11,berg:0,best:0,betti:0,betti_numb:0,between:[0,1,3,6,8,13,18],big:15,bin_ppmf:0,binari:[0,12],binomi:0,bioinformat:15,biolog:15,biomedcentr:15,bipartit:[0,3,6,8,18],bk:0,bkmatrix:0,block:10,blue:1,bmc:15,bmcbioinformat:15,book:14,bool:[0,1,3,6,17],both:[1,3,8,9,10,13,18],bound:0,boundari:[0,6],boundary_group:0,box:6,bramer:15,brenda:[14,15],brett:15,brian:14,briefest:0,browser:[11,14],bsd:14,build:[3,10,11],build_doc:11,built:18,bulk:18,busi:12,button:18,c:[0,1,3,6,10,11,13,14,15],c_:0,c_b:[0,13],c_k:0,ca:15,calcul:6,call:[6,8,13],callahan:15,can:[0,1,3,6,8,9,10,13,14,18],cannot:[1,3],capabl:18,cardin:3,care:3,carlo:15,categori:3,caus:[3,12,18],caution:3,cdotfrac:0,cell:[0,3,14,17],cell_weight:[0,3],center:6,central:[2,5,10,13,14,17],centrality_stat:17,certain:3,chain:0,chain_complex:0,cham:0,chang:[0,1,3,6,18],check:[3,9,10,13],check_connect:0,cheeger:0,cherifi:0,child:3,children:[3,8],chmod:11,choic:[0,1],choos:[1,3],chosen:[0,3,6],chung:0,chung_lu_hypergraph:0,chunglu:14,cikm:0,circl:[6,18],circular:1,ck:0,classmethod:3,claus:14,click:18,cliff:[14,15],cliqu:[9,10],clone:[3,11],close:[0,13],cluster:[2,5,10,14],cnx001:0,cockrel:15,code:12,coeffici:0,col:13,colab:[3,10],coldict:3,collaps:[3,6,13,18],collapse_edg:[3,13],collapse_identical_el:3,collapse_nod:[3,13],collapse_nodes_and_edg:[3,13],collect:[1,3,6],collective_contagion:1,collumn:6,colon:3,color:[1,3,6,18],column:[0,3,6,8,13,14,17],column_index:3,com:[11,15],combin:13,combinator:0,come:11,command:[3,11,18],comment:[9,10,14],commerci:14,common:1,commun:[0,9,10,14],comnet:0,comp:17,comp_dist:17,compar:[3,13],complet:[8,14,18],complete_registri:3,complex:[0,3,9,10,13,15],compon:[0,3,6,8,13,17],component_subgraph:3,comput:[0,3,6,14,15,17],compute_partition_proba:0,concentr:6,concern:0,conda:[11,13],condit:[3,8,12],conf:15,confer:0,conflict:3,connect:[0,3,6,8,9,10,13,17],connected:0,connected_compon:3,connected_component_subgraph:3,consecut:3,consent:12,consequenti:12,consid:3,constitut:14,construct:[0,1,3,8,13,14],constructor:[3,6,13,14],contact:[9,10,14],contagi:1,contagion:[0,2,5,10,14],contagion_anim:1,contain:[0,3,6,8,13,17,18],content:[2,4,5,7,16],context:[0,3],continu:[1,11],contract:[12,14],contribut:0,contributor:[9,10,12,14],control:[3,18],contruct:0,conveni:[3,6],converg:0,convert:[3,6],convert_to_entityset:3,convert_to_stat:3,convex:6,cooper:14,coord:3,coordin:[3,6],copi:[0,3,12,13],copyright:12,core:3,correct:6,correspond:[0,3,8,14],coset:0,could:3,count:[3,6,17],counter:17,creat:[0,3,11,13,14,17],creation:3,criteria:13,criterion:0,critic:15,cross:6,csr:[0,3],csr_matrix:[0,3],ctrl:18,current:[0,1,13],current_st:3,curvi:6,custom:6,cybersecur:15,cycl:[0,3,6],cyclic:0,d:[0,3,13,15],damag:12,daniel:15,data:[0,3,6,9,10,12,13,14,15],data_subset:3,datafram:[3,14],dcsbm:[0,14],dcsbm_hypergraph:0,de:[14,18],dedup:3,deeper:3,defaultdict:3,defin:[0,1,3],degre:[0,3,8,13,17,18],degree_dist:17,degree_tax:0,degreetax:0,delet:3,delta:0,delta_dt:0,delta_ec:0,deltadt:0,deltaec:0,demo:18,denorm:0,denot:1,densiti:17,depart:14,depend:[0,1,3,13],deprec:3,depth:[0,3,8],deriv:3,descend:3,describ:[0,1],descript:[0,3],descriptive_stat:[5,10,16],design:14,desir:3,dest:13,detail:[0,18],detect:0,determin:[0,3,6],develop:[9,10,13,14],deviat:17,df:3,diagon:0,diagram:[6,18],diamet:[3,8,13,17],diamond:15,dict2part:0,dict:[0,3,6,17],dictionari:[0,1,3,6,8,13,17],differ:[3,13],digraph:[0,6],dim:[0,3,13],dimens:[0,3,13],dimension:[0,3,9,10],dimensionsl:3,dimsiz:3,direct:[0,3,6,12,13,18],directli:[3,9,10,14,18],dirti:6,disabl:6,discard:3,disclaim:12,disclos:14,disconnect:6,discov:0,discret:1,discrete_si:1,discrete_sir:1,discuss:0,disjoint:[0,3,8],disonnecct:6,displai:1,dist:17,dist_stat:17,distanc:[0,3,6,8,13],distant:6,distinct:3,distinguish:[3,8,9,10],distribut:[0,12,13,17],divid:[0,1],dlfer:0,doc:11,document:[3,11,12],doe:[3,6,14],doesn:1,doi:[0,1,15],domain:[0,15],done:[3,13],dot:0,down:18,dr:6,drag:18,draw:[1,5,10],draw_hyper_edg:6,draw_hyper_edge_label:6,draw_hyper_label:6,draw_hyper_nod:6,drawn:6,drop:3,dt:1,dual:[3,8],duplic:[0,3],dustin:[14,15],dynam:[0,3,8],e0:3,e1:3,e2:3,e3:3,e:[0,3,6,8,11,13,15,17,18],e_1:3,e_2:3,e_end:3,e_n:3,e_start:3,each:[0,1,3,6,8,13,17,18],easier:6,ecc:0,eccentr:[0,13],echelon:0,ed:[0,15],edg:[0,1,3,6,8,9,10,13,14,17,18],edge_adjac:3,edge_adjacency_matrix:3,edge_column_nam:3,edge_contribut:0,edge_diamet:3,edge_dist:3,edge_incid:13,edge_kwarg:6,edge_label:[0,3,6],edge_labels_kwarg:6,edge_nam:3,edge_neighbor:3,edge_set:3,edge_size_dist:[3,13,17],edge_state_color_dict:1,edge_uid:3,edgecontribut:0,edges_kwarg:6,edgeset:3,edit:11,effect:[0,1,3],eg:0,eigenvalu:0,eigenvector:0,eisfeld:15,either:[3,8,13,17],element:[0,3,6,8,13],element_subset:3,elements_by_level:3,els:1,emili:[14,15],emploi:3,employe:14,empti:[3,8,13],en:[1,3],encapsul:13,end:3,endors:14,energi:14,ensur:3,ent1:3,ent2:3,entir:18,entiti:[4,5,6,8,9,10,12,14],entityset:[3,8],entri:[0,3,8,13],env:[11,13],environ:[10,14],eon:1,epidem:[0,2,5,10],epidemicsonnetwork:1,epj:[0,15],epjd:[0,15],eq_class:3,equal:[0,1,3,8,13],equat:0,equival:[0,3,13],equivalence_class:3,erdo:0,erdos_renyi_hypergraph:0,error:[0,3,13],essenc:0,et:[0,1,15],euler:18,evalu:3,even:12,event:[1,12],everi:[0,3,8,13,18],everyth:18,ex:[0,3,11],exact:0,exactli:8,exampl:[0,1,3,6,11,14,18],exceed:3,except:8,execut:11,exemplari:12,exhibit:0,exist:[0,3,6,8],existing_lap:0,exp:0,expand:[6,18],expect:0,explicit:0,explor:[9,10],expos:3,express:[12,14],extend:18,extens:[0,11],extra:1,f:[0,15],facecolor:6,factori:0,fail:3,fall:0,fals:[0,1,3,6,13,17],fan:[0,15],fast:3,faster:[0,13],favor:14,featur:[0,10],feng:15,ferrario:0,fig:1,figur:[1,6],file:[3,11,12],filepath:3,fill:[3,17],fillna:3,filter:13,find:[6,9,10],firoz:15,first:[3,6],firstlevel:3,fit:12,fix:3,flexibl:3,fly:13,folder:0,follow:[3,6,11,12,14],forc:18,fork:11,form:[2,3,5,10,12],format:[3,13,17],forth:13,forward:1,found:[3,9,10],four:14,fp:1,fpath:3,frac:[0,13],fraction:[0,1,6,13],frame:[1,3],from:[0,1,3,6,8,11,13,15,17,18],from_bipartit:[3,8],from_datafram:3,from_numpy_arrai:3,frozen:3,frozenset:3,fruchterman_reingold_layout:6,full:3,fullregistri:3,func:0,further:6,g1:0,g2:0,g:[0,6,13,15,17],gaito:0,gamma:[0,1],gene:15,gener:[0,3,6,8,9,10,11,14,17],generative_model:[2,5,10],get_default_radiu:6,get_frozenset_label:6,get_id:3,get_line_graph:6,get_linegraph:3,get_nam:3,get_pi:0,get_set_lay:6,get_singleton:13,gillespie_si:1,gillespie_sir:1,github:[0,11,14,18],give:[0,3,18],given:[0,3,6,8,13],glossari:10,gm:0,go:[0,17],goal:13,good:[0,12],googl:14,gotten:3,gov:[0,9,10,14],govern:14,grant:12,graph:[0,3,6,8,9,10,13,15,18],greater:0,green:1,group:0,grow:[9,10,14],guarante:6,h:[0,1,3,6,17],h_k:0,ha:[1,3,8,13,14,18],halfmann:15,handl:6,happen:1,harmon:[0,13],hashabl:[1,3],hasn:1,have:[0,1,3,6,8,9,10,13,14,18],hayashi:0,header:[3,14],heal:1,heath:15,held:3,heller:15,help:18,helper:6,henc:3,henri:15,here:[13,18],herebi:12,herein:[12,14],hereinaft:12,heterogen:1,hg:0,hicss:15,hidden:18,hide:18,high:[0,13,14,15],higher:0,highlight:14,hist:17,hit:18,hnx:[0,1,3,11,13,14,18],hnx_2section:0,hnx_kumar:0,hnx_laststep:0,hnx_modular:0,hnx_precomput:0,hnxwidget:18,hold:18,holder:12,home:10,homolog:[2,5,9,10,14],homology_basi:0,homology_mod2:[2,5,10],honor:3,how:3,howev:12,hpda:14,html:[1,11],http:[0,1,11,15],hugh:15,hull:6,hunter:15,hyper:[3,6,8,18],hyperedg:[0,3,8,9,10,13,14],hyperedgelist:1,hypergraph:[1,2,4,5,6,8,9,10,13,14,15,17,18],hypergraph_homology_basi:0,hypergraph_modular:[2,5,10],hypergraphedg:3,hypernet:14,hypernetwork:[0,15],hypernetx:[0,1,3,12,14],hypernetxerror:[0,3],hypernetxwidget:18,i:[0,1,3,8,13,18],i_m:0,i_n:0,iacopini:1,icc:15,id:[0,1,3,6,8,13],ideal:0,ident:[0,3,6,18],identifi:[0,3,15],idx:3,ignacio:15,ignor:[0,3],igraph:0,illustr:6,im:0,imag:0,image_basi:0,immut:3,implement:[0,1,13],impli:[6,12,14],implic:0,impos:8,improv:18,incid:[0,3,8,9,10,13,14,17],incidence_dict:3,incidence_matrix:3,incident:12,includ:[3,9,10,12],inclus:[0,3],inde:3,independ:[6,18],index:[0,3,8,10,11],indic:[0,3,13],indirect:12,individu:1,individual_contagion:1,induc:[3,8],inequ:0,inf:[1,3],infect:1,infin:3,infinit:8,inflat:6,inflate_kwarg:6,info:17,info_dict:17,inform:[0,3,14,17],infring:14,initi:[0,1],initial_infect:1,initial_recov:1,inner:0,input:[0,3],inquiri:0,inseper:3,insert:3,insid:3,insight:0,inspect:14,instal:[3,10],instanc:[3,8],instanti:[3,8],instead:[3,6,13],institut:[12,14],instruct:11,int64:0,integ:[0,3,6,8,13,17],intel:10,intellig:0,intend:[0,6],intens:3,inter:3,interact:[14,18],interest:[0,3],interfac:18,intern:[0,3],interpret:[0,13],interpreted_basi:0,interrupt:12,intersect:[0,3,6,8],intuit:8,invers:0,invert:0,investig:14,invis:6,io:1,ipython:1,is_bipartit:3,is_connect:3,is_empti:3,is_s_connect:13,isn:3,isomorph:[3,8],isstat:3,item:[3,6,17],iter:[0,1,3,6,17],ith:0,iti:8,its:[0,3,6,8,13,14,18],itself:[3,8],j:[0,8,15],jacob:15,jason:15,javascript:[14,18],jefferson:15,jenkin:15,ji:14,joel:1,joslyn:[0,14,15],journal:0,jth:0,jupyt:[11,14],jurisdict:14,k1:0,k2:0,k:[0,1,3,8],kaminski:[0,15],katrina:15,kawaoka:15,kbasi:0,kchain:0,kchainbasi:0,kdx:3,keep:[3,17,18],keep_weight:3,kei:[0,1,3,6,8,13],kelli:15,kernel:0,kevin:15,keyindex:3,keyword:[3,6],km1basi:0,knowledg:0,known:[0,3],kocher:15,krang:0,kritzstein:14,kth:0,kumar:0,kving:15,kwarg:[0,3,6],l:[0,13,15],lab:3,label:[0,3,6],label_alpha:6,laboratori:14,lambda:1,landri:[1,14],laplacian:[2,5,10],laplacians_clust:[2,5,10],larg:3,larger:18,largest:[0,3],larissa:15,larremor:0,last:[0,3],last_step:0,lastlevel:3,latest:1,latter:3,lawfulli:12,layer:6,layout:[1,6,10],layout_hyper_edg:6,layout_kwarg:6,layout_node_link:6,layout_two_column:6,le:15,learn:[9,10],leas:8,least:[3,6,8],lectur:15,left:[0,6],legal:14,len:17,length:[0,3,6,8,9,10],lesmi:14,less:[0,3,13],let:3,level1:3,level2:3,level:[3,6,8],levelset:[3,8],liabil:[12,14],liabl:12,librari:[0,3,9,10,13,14],licens:10,like:[0,3,6],limit:[3,12],line:[0,3,6,13],linear:0,linecollect:6,linegraph:[0,3,8],linewidth:6,link:[0,3,18],linux:[11,14],linv:0,lisa:15,list:[0,1,3,6,12,13,17],liu:[13,14],llinv:0,lm:0,lmr:0,local:13,locat:[6,11,18],logic:0,logical_dot:0,logical_matadd:0,logical_matmul:0,longer:3,longest:[0,3],look:0,loss:12,loui:15,lower:6,lu:0,lumsdain:14,m:[0,1,3,15],mac:[11,18],made:3,magnitud:0,mai:[3,8,9,10,11,12,14,18],main:18,major:[0,1],majority_vot:1,make:[3,6,14],manag:[0,14],mani:[3,13,17],manipul:3,manual:6,manufactur:14,map:[0,6],marcin:15,mark:14,marrero:[0,15],mat1:0,mat2:0,mat:0,match:[0,3],materi:14,mathbb:0,mathemat:14,matmulreduc:0,matplotlib:[1,6,11],matric:[2,5,6,10,14],matrix:[0,3,8,13,17],max:[0,3,17],max_degre:13,max_depth:3,max_level:3,max_siz:[3,13],maxim:[3,8],maximum:[3,8],maxlevel:3,mcdermott:15,mean:[0,3,17],measur:[2,5,10,14],mechan:1,median:[3,6,17],member:3,membership:[3,6,8,18],memori:[12,13,14],menacheri:15,mend:0,merchant:12,merg:[3,12],merge_ent:3,method:[0,3,8,9,10,14,17],methodolog:0,metric:[0,9,10,14],michael:15,might:18,miller:1,min:[0,3,17],min_degre:13,min_level:3,min_siz:13,minim:[0,6,11,18],minimum:[3,6],minlevel:3,minu:[0,3],mirah:0,miss:6,mitchel:15,mod2:[2,5,10,14],mod:0,model:[1,9,10,14,15],modestli:3,modif:12,modifi:12,modul:[2,4,5,7,10,14,16],modular:0,modulo:0,more:[3,8,9,10,11,13],moro:0,most:[1,3,6,9,10],move:0,much:13,multi:[3,9,10],multidimension:15,multipl:[0,3,8,13,18],multipli:0,multiwai:[9,10],must:[0,1,3,12,13],mxn:0,n:[0,1,3,6,8,11,13],nama:3,name:[3,11,12,13,14,15,18],nan:3,natali:15,nation:14,natur:[9,10],navig:3,ncell:17,ncol:17,ndarrai:[0,3],necessarili:14,need:[0,3,6,11],neglig:12,neighbor:[1,3,13],neither:[12,14],neq:[0,13],nest:3,nested_incidence_dict:3,network:[0,1,3,9,10,14,15],networkx:[3,6],netwrokx:6,newfpath:3,newuid:3,next:0,nichola:14,node:[0,1,3,6,8,13,14,17,18],node_column_nam:3,node_diamet:3,node_incid:13,node_label:[0,3,6],node_labels_kwarg:6,node_nam:3,node_radiu:[1,6],node_set:3,node_size_dist:13,node_state_color_dict:1,nodes_kwarg:6,nodeset:3,non:[0,8],none:[0,1,3,6,13,17],nonempti:[3,8],nonexist:3,nonzero:[3,8],nor:14,norm_lap:0,normal:[2,5,10,13],northwest:14,note:[0,1,3,8,11,13,15],notebook:[11,14],noth:3,notic:[10,12],np:[0,3],nrow:17,num:17,number:[0,1,3,6,8,13,17],number_of_edg:[3,13],number_of_nod:[3,13],numer:3,numpi:[0,1,3,6,13],nwgraph:13,nwhy:[0,3,10,11,14],nwhypergraph:[3,10],nx2:6,nx:[3,6,8],nxm:0,o:15,obj:17,object:[0,1,3,8,13,14,17],obtain:[0,8,12],occupi:8,occur:3,off:1,offer:3,offset:6,omega:0,onc:[11,14],one:[0,3,6,8,13],oneapi:13,onetbb:13,onli:[0,1,3,8,11,13],open:11,oper:14,opinion:[1,14],opt:13,optim:[0,6,10,13,14,18],option:[0,1,3,10,17],order:[0,3,6,15],ordereddict:3,org:[0,1,15],organ:14,orient:6,origin:[0,3,13],ortiz:0,osit:3,osx:11,other:[0,3,6,8,10,12,13],otherwis:[0,3,8,11,12,13,14],our:[0,9,10],out:[0,6,9,10,12],outlin:18,output:[0,1,3],outsid:3,over:[0,6,8,13],overlap:[6,13],overrid:6,overview:10,own:[8,14],p:[0,3,15],pacif:14,packag:[2,4,7,10,16],page:10,pair:[0,3,6,8,13],pairwis:3,panda:[3,14],panel:10,paper:6,parallel:[6,13],paramet:[0,1,3,6,17],park:0,part2dict:0,part:[0,6,14],parthasarathi:0,partial_k:0,particular:[3,9,10,12,14],partion:0,partit:[0,3,8],pass:[0,3,6,13],path:[0,3,6,9,10,11,13],pathogen:15,pd:3,per:[0,1],perfect:18,perform:[3,13,14,15,18],permiss:12,permit:12,person:12,peter:15,physrev:0,pi:0,pickl:3,pin:18,pip:[10,18],place:3,placehold:3,placement:18,planar:6,pleas:[0,3],plot:6,plt:1,pmf:0,pnnl:[0,9,10,11,14],po:6,point:6,poli:6,polycollect:6,polygon:6,pone:0,poset:3,posit:[0,3,6,8,13,17,18],possibl:[1,6,12,18],post:0,potenti:1,poulin:0,power:[9,10],powershel:11,pp:15,pr:0,practic:3,praggasti:[14,15],pralat:0,pre:6,precis:8,precompute_attribut:0,precompute_modularity_paramet:0,prefil:3,preliminari:13,prepar:14,prepend:3,present:[1,3],preserv:[3,18],press:15,princip:14,principl:14,print:[0,17],prior:3,privat:14,prob_tran:0,probabl:[2,5,10],proc:15,proceed:0,process:[3,13,14],procur:12,product:[0,14],profit:12,program:14,project:14,prompt:11,prop:3,properli:8,properti:[3,8,13,14,18],proport:0,provid:[0,3,6,9,10,12,13],ps1:11,publish:12,purpos:[0,12],purvin:[14,15],put:17,py:8,pybind11:13,pyplot:1,pytest:11,python:[11,13],qh:0,qing:15,quantiti:[9,10],question:[9,10,14],quick:[6,10],quit:3,r0:6,r:[0,1,6],radiu:[1,6],rais:[0,3],ralph:15,randint:0,random:[0,1],randomli:1,rang:[0,1,6],rate:1,rather:17,ratio:[0,17],rauga:14,ravindran:0,rdc:0,re:18,reachabl:13,read:[6,14],readthedoc:1,real:3,reason:[3,6],receiv:3,reciproc:[0,13],recommend:[3,6,14],recov:[1,3],recover_from_st:3,recoveri:1,rectangular:[0,8],recurs:0,red:1,redistribut:12,reduc:[0,6],reduced_row_echelon_form_mod2:0,refer:[0,3,14],referenc:[0,3],reflect:[3,14],regist:3,registri:[3,8],rel:[0,18],relat:[3,9,10],relationship:[0,3,9,10,15],releas:[14,18],remov:[3,18],remove_edg:3,remove_el:3,remove_elements_from:3,remove_nod:3,remove_singleton:3,remove_stat:3,render:6,renyi:0,rep:3,repeatedli:0,replac:[0,3],report:[5,10],repositori:[0,9,10],repres:[0,3,6,8,9,10,14],represent:[0,3,6,13],reproduc:[6,12],request:3,requir:[0,1,3,13],research:[9,10,14],reserv:6,respect:[0,3],respons:[14,15],restrepo:1,restrict:[3,8],restrict_to:3,restrict_to_edg:3,restrict_to_indic:3,restrict_to_level:3,restrict_to_nod:3,result:[6,18],retain:12,retriev:3,return_count:3,return_equal_class:13,return_equivalence_class:3,return_full_data:1,return_index:3,return_po:6,return_singleton:[0,3,17],revers:[0,3,18],rho:1,rich:13,right:[0,6,14],rigor:6,ring:6,rocha:0,role:[3,8],root:3,roughli:0,row:[0,3,8,13,17],rowdict:3,rubber:6,rubber_band:[5,7,10],run:[0,11,13,14],s12859:15,s13688:[0,15],s41467:1,s:[1,2,3,5,6,8,10,13,14,15,17],s_betweenness_centr:[0,13],s_centrality_measur:[2,5,10],s_closeness_centr:[0,13],s_comp_dist:17,s_compon:3,s_component_subgraph:3,s_components_subgraph:3,s_connect:3,s_connected_compon:[3,13],s_degre:[3,13],s_diamet:13,s_distanc:13,s_eccentr:[0,13],s_edge_connect:3,s_edge_diameter_dist:17,s_harmonic_centr:0,s_harmonic_closeness_centr:[0,13],s_linegraph:13,s_neighbor:13,s_node_diameter_dist:17,s_path:13,same:[0,3,6,8,13],sampl:[1,3],satifi:3,satisfi:[3,8],save:3,save_st:3,scalabl:13,sci:0,scienc:[0,15],scip:3,scipi:[0,3],score:13,script:11,search:10,second:[1,3],section:0,see:[0,3,6,8,11,14,17],select:[0,10],self:3,sell:12,sens:8,sensibl:6,sequenc:[3,8],serv:[0,9,10],servic:[12,14],set:[0,1,3,6,8,9,10,13,18],set_nam:3,set_stat:3,setsystem:3,setsytem:3,sh:11,shabang:11,shall:12,shallow:3,shape:3,share:[3,8,13],sheahan:15,shi:0,shift:18,shortest:[0,3,8,13],shortest_path_length:3,should:[0,1,3,6],show:18,shufang:15,si:[1,14],side:[0,3,10],sigma:[0,13],signatur:3,significantli:13,sim:15,sim_kwarg:1,similar:[1,3,18],simpl:[0,3,8,17],simplic:[9,10],simplici:[0,1,9,10],simul:1,sinan:[0,14,15],sinc:[3,8,9,10],singl:[0,3,8,17],singleton:[0,3,9,10,13],sir:[1,14],size:[0,1,3,6,8,13,17,18],slightli:18,slinegraph:10,slower:13,small:[0,3,6],smaller:6,smallest:3,smith:[2,5,10,15],smith_normal_form_mod2:0,snf:0,so:[0,3,6,12],social:1,softwar:[12,14],some:[8,9,10,11],sometim:[6,18],song:15,sort:[0,3],sort_column:3,sort_row:3,sortabl:[0,3],sourc:[0,1,3,6,11,12,17],space:[6,13],spars:[0,3,13],spec:0,spec_clu:0,special:12,specif:[3,8,14],specifi:[0,1,3,6,11,13,18],spectral:[0,6],sped:14,sponsor:14,spring_layout:6,springer:[0,15],squar:8,src:13,stack:6,standard:17,start:[0,1,3,6,17,18],stat:17,state:[1,3,14,18],state_dict:3,staticent:[4,5,10],staticentityset:3,stationari:0,statist:17,statu:1,status:1,step:[0,1],still:[0,3],stop:0,storag:3,store:[0,3,13],str:[0,3],stratton:15,strength:0,strict:[0,9,10,12],string:[3,6,17],structur:[3,8,9,10,13],studi:[0,9,10,14],style:6,subgraph:[0,3],subhypergraph:8,subject:12,sublicens:12,submatrix:8,submit:3,submodul:[2,4,5,7,10,16],subpackag:[2,5,10],subset:[3,6,8],substitut:12,subtract:3,success:8,sum:[0,3,13],sum_:[0,13],summari:17,suppli:6,support:[0,1,3,14],sure:3,surround:6,suscept:1,swap:0,swap_column:0,swap_row:0,symmetr:0,symp:15,synthet:14,system:[3,6,9,10,11,15],szufel:0,t:[0,1,3,13],tabl:18,take:[1,3,6],tan:15,target:3,tau:1,tax:0,tbb:[10,11],tbbroot:13,techniqu:6,tell:[9,10],tensor:3,term:[0,3],termin:1,test:[10,11],text:[0,6],textbook:6,thackrai:15,than:[0,3,8,12,17],thei:[0,3,6,8,9,10,18],them:[3,8,11,17,18],theoret:0,theori:12,therebi:[9,10],therefor:[3,13],thereof:14,thi:[0,1,3,6,8,9,10,11,12,13,14,17,18],think:3,those:[0,14],thread:10,three:[13,14],threshold:1,through:[0,6,13],tiffani:15,time:[0,1,18],timothi:15,tmax:1,tmin:1,to_jshtml:1,todo:3,togeth:[0,6],toggl:18,toni:[13,14],tool:[9,10],toolbar:18,toplex:[0,3,8,13,17],toplex_dist:17,topolog:[0,9,10,15],tort:12,total:0,tour:14,track:[0,3,17],trade:14,trademark:14,tradit:18,transform:[0,3],transit:[1,2,5,10],transition_ev:1,translat:3,translate_arr:3,transmiss:1,transmission_funct:1,transmit:1,transpar:6,transpos:3,transpose_inflated_kwarg:6,travers:18,treat:3,triloop:14,tripodi:15,trivial:0,truthi:3,tupl:[0,3],turn_entity_data_into_datafram:3,tutori:[0,3,10,11],two:[0,3,6,8,13,18],two_column:[5,7,10],two_sect:0,type:[0,1,3,6,17],typic:6,u:[0,6,13],uid:[0,1,3,8,17],uidset:[3,8],uidset_by_level:3,un:18,under:[13,14],undesir:3,undirect:13,uniform:0,uniqu:[3,8],unit:14,unless:3,unpack:3,unreach:13,untitiled_modularity_and_clustering_origin:[2,5,10],untitled_modularity_and_clust:[2,5,10],unweight:[3,8,13],up:[3,14,17],updat:3,upgrad:13,upon:18,us:[0,3,6,8,9,10,12,14],usag:0,use_nwhi:[0,3],use_rep:3,user:[1,3,9,10,11,13,14,18],usual:6,util:[0,5,7,10],v0:3,v1:3,v2:3,v:[0,3,6,13,15],v_1:3,v_2:3,v_end:3,v_n:3,v_start:3,vaidyanathan:0,valu:[0,1,3,6,8,13],variou:[13,17],ve:14,vector:0,verifi:0,version:[10,11,13],vertex:[0,6,9,10,13],vertic:[0,3,6,13,14],via:[0,15],view:14,viii:0,vineet:15,viral:15,virtual:11,virtualenv:10,visibl:18,visual:[10,14,18],vn:3,vol:0,vote:1,w:[0,3],wa:[3,13,14],wai:[3,6,9,10,12],walk:[0,3,8,9,10,15],walter:15,want:[0,18],warn:0,warranti:[12,14],water:15,waw:15,wdc:0,we:[0,3,9,10,13,14],web:15,weight:[0,3,8,13,14],well:[0,6,18],westhoff:15,what:[9,10],whatsoev:12,when:[3,13],whera:18,where:[0,3,6,8,13],whether:[0,3,12,13],which:[0,1,3,6,8,17,18],whitespac:6,whole:11,whose:[6,8,13],widget:[10,14],width:[3,8,9,10],window:[11,18],wish:11,with_color:6,with_edge_count:6,with_edge_label:6,with_node_count:6,with_node_label:6,within:[0,3,6,18],without:[12,18],work:[0,3,6,11,14],would:[3,14],wrangl:3,wrap:6,written:12,wshop:15,www:[0,15],x:[3,6,13,17],xor:0,xu:13,xx:3,xy:6,xyz:0,y:[3,6,13],yet:3,yield:3,yoshihiro:15,you:[3,6,9,10,11,14,18],young:14,your:[3,11,14],yun:14,z:[0,3],z_2:0,zalewski:15,zero:3},titles:["algorithms package","algorithms.contagion package","algorithms","classes package","classes","HyperNetX Packages","drawing package","drawing","Glossary of HNX terms","HyperNetX (HNX)","HyperNetX (HNX)","Installing HyperNetX","License","NWHy","Overview","Publications","reports","reports package","Hypernetx-Widget"],titleterms:{"0":14,"1":14,"class":[3,4,13],"import":13,"new":14,"public":15,Then:13,To:[11,13],activ:13,algorithm:[0,1,2],an:[11,13],anaconda:[11,13],anim:1,api:13,attribut:13,block:13,build:13,central:0,cluster:0,colab:14,contagion:1,content:[0,1,3,6,10,17],descript:[9,10,13],descriptive_stat:17,draw:[6,7],entiti:3,environ:[11,13],epidem:1,featur:[14,18],form:0,generative_model:0,glossari:8,hnx:[8,9,10],homolog:0,homology_mod2:0,hypergraph:[0,3],hypergraph_modular:0,hypernetx:[5,9,10,11,18],indic:10,instal:[11,13,18],intel:13,laplacian:0,laplacians_clust:0,layout:18,licens:[12,14],matric:0,measur:0,method:13,mod2:0,modul:[0,1,3,6,13,17],normal:0,notic:14,nwhy:13,nwhypergraph:13,option:11,other:18,overview:[14,18],packag:[0,1,3,5,6,17],panel:18,pip:[11,13],probabl:0,quick:13,report:[16,17],rubber_band:6,s:0,s_centrality_measur:0,select:18,side:18,slinegraph:13,smith:0,staticent:3,submodul:[0,1,3,6,17],subpackag:0,tabl:10,tbb:13,term:8,test:13,thread:13,tool:18,transit:0,tutori:14,two_column:6,untitiled_modularity_and_clustering_origin:0,untitled_modularity_and_clust:0,us:[11,13,18],util:6,version:14,virtualenv:11,widget:18}}) \ No newline at end of file diff --git a/docs/build/widget.html b/docs/build/widget.html index 53d5acef..032d4c20 100644 --- a/docs/build/widget.html +++ b/docs/build/widget.html @@ -7,7 +7,7 @@ - Hypernetx-Widget — HyperNetX 1.1.3 documentation + Hypernetx-Widget — HyperNetX 1.1.4dev documentation diff --git a/docs/source/algorithms/algorithms.rst b/docs/source/algorithms/algorithms.rst index 2070dbee..ba6fcd09 100644 --- a/docs/source/algorithms/algorithms.rst +++ b/docs/source/algorithms/algorithms.rst @@ -28,6 +28,14 @@ algorithms.homology\_mod2 module :undoc-members: :show-inheritance: +algorithms.hypergraph\_modularity module +---------------------------------------- + +.. automodule:: algorithms.hypergraph_modularity + :members: + :undoc-members: + :show-inheritance: + algorithms.laplacians\_clustering module ---------------------------------------- @@ -44,6 +52,22 @@ algorithms.s\_centrality\_measures module :undoc-members: :show-inheritance: +algorithms.untitiled\_modularity\_and\_clustering\_original module +------------------------------------------------------------------ + +.. automodule:: algorithms.untitiled_modularity_and_clustering_original + :members: + :undoc-members: + :show-inheritance: + +algorithms.untitled\_modularity\_and\_clustering module +------------------------------------------------------- + +.. automodule:: algorithms.untitled_modularity_and_clustering + :members: + :undoc-members: + :show-inheritance: + Module contents --------------- diff --git a/docs/source/conf.py b/docs/source/conf.py index 2b5003a0..89363870 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,7 +19,7 @@ import os import shlex -__version__ = "1.1.3" +__version__ = "1.1.4dev" # If extensions (or modules to document with autodoc) are in another directory, diff --git a/hypernetx/algorithms/__init__.py b/hypernetx/algorithms/__init__.py index 6e9889fc..a3b6fd6a 100644 --- a/hypernetx/algorithms/__init__.py +++ b/hypernetx/algorithms/__init__.py @@ -3,3 +3,4 @@ from .contagion import * from .laplacians_clustering import * from .generative_models import * +from .hypergraph_modularity import * diff --git a/hypernetx/algorithms/modularity_and_clustering.py b/hypernetx/algorithms/hypergraph_modularity.py similarity index 50% rename from hypernetx/algorithms/modularity_and_clustering.py rename to hypernetx/algorithms/hypergraph_modularity.py index 09450c8d..027e87b5 100644 --- a/hypernetx/algorithms/modularity_and_clustering.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -1,8 +1,25 @@ +""" +Hypergraph_Modularity +--------------------- +Modularity and clustering for hypergraphs using HyperNetX. +Adapted from F. Théberge's GitHub repository: `Hypergraph Clustering `_ +See Tutorial 13 in the tutorials folder for library usage. + +References +---------- +.. [1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S., Ravindran B. (2020) A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24 +.. [2] B. Kaminski, P. Pralat and F. Théberge, Community Detection Algorithm Using Hypergraph Modularity, to appear in the proceedings of Complex Networks 2020, Springer. +.. [3] Clustering via hypergraph modularity, Bogumił Kamiński, Valérie Poulin, Paweł Prałat , Przemysław Szufel, François Théberge, 2019, https://doi.org/10.1371/journal.pone.0224307 + +""" + + from collections import Counter import numpy as np from functools import reduce import igraph as ig import itertools +from scipy.special import factorial as scipyfact ################################################################################ @@ -11,6 +28,20 @@ def dict2part(D): + """ + Returns dictionary to partition, inverse function to part2dict + + Parameters + ---------- + D : dict + Dictionary keyed by vertices with values equal to integer + index of the partition the vertex belongs to + + Returns + ------- + : list + List of sets in the partition + """ P = [] k = list(D.keys()) v = list(D.values()) @@ -20,6 +51,19 @@ def dict2part(D): def part2dict(A): + """ + Returns dictionary {vertex: partition index}, inverse function + to dict2part + + Parameters + ---------- + A : list of lists + partition of vertices + + Returns + ------- + : dict + """ x = [] for i in range(len(A)): x.extend([(a, i) for a in A[i]]) @@ -29,14 +73,36 @@ def part2dict(A): def factorial(n): + """ + Computes exact integer factorial on integer + + Parameters + ---------- + n : int, or array-like object + + Returns + ------- + int or int64 or object + + """ if n < 2: return 1 - return reduce(lambda x, y: x * y, range(2, int(n) + 1)) + return scipyfact(n, exact=True) + # return reduce(lambda x, y: x * y, range(2, int(n) + 1)) # Precompute soe values on HNX hypergraph for computing qH faster -def HNX_precompute(HG): +def precompute_attributes(HG): + """ + Adds weight, strength and binary coefficient attributes to + the hypergraph for computing qH faster. + + Parameters + ---------- + HG : Hypergraph + + """ # 1. compute node strenghts (weighted degrees) for v in HG.nodes: HG.nodes[v].strength = 0 @@ -66,32 +132,83 @@ def HNX_precompute(HG): ################################################################################ -# some weight function 'wdc' for d-edges with c-majority - -# default: linear w.r.t. c - def linear(d, c): + """ + Weight function for hyperedge. Gives the actual ratio as long + as it is greater than 0.5. + + Parameters + ---------- + d : int + Number of nodes in an edge + c : int + Number of nodes in the majority class + + Returns + ------- + float + """ return c / d if c > d / 2 else 0 # majority def majority(d, c): + """ + Weight function for hyperedge. Requires + c be the majority of d. Returns bool + + Parameters + ---------- + d : int + Number of nodes in an edge + c : int + Number of nodes in the majority class + + Returns + ------- + bool + """ return 1 if c > d / 2 else 0 # strict def strict(d, c): + """ + Weight function for hyperedge. Requires c == d. + + Parameters + ---------- + d : int + Number of nodes in an edge + c : int + Number of nodes in the majority class + + Returns + ------- + bool + """ return 1 if c == d else 0 ######################################### -# compute vol(A_i)/vol(V) for each part A_i in A (list of sets) - def compute_partition_probas(HG, A): + """ + Compute vol(A_i)/vol(V) for each part A_i in A (list of sets) + + Parameters + ---------- + HG : Hypergraph + A : list of sets + + Returns + ------- + : list + normalized distribution of strengths in partition elements + """ p = [] for part in A: vol = 0 @@ -101,10 +218,26 @@ def compute_partition_probas(HG, A): s = sum(p) return [i / s for i in p] -# degree tax +def degree_tax(HG, Pr, wdc): + """ + Computes the expected fraction of edges falling in + the partition in a random graph as per [2]_ + + Parameters + ---------- + HG : Hypergraph + + Pr : list + Probability distribution + wdc : func + weight function (ex: strict, majority, linear) -def DegreeTax(HG, Pr, wdc): + Returns + ------- + float + + """ DT = 0 for d in HG.d_weights.keys(): tax = 0 @@ -117,8 +250,25 @@ def DegreeTax(HG, Pr, wdc): return DT -# edge contribution, A is list of sets -def EdgeContribution(HG, A, wdc): +def edge_contribution(HG, A, wdc): + """ + Edge contribution from hypergraph with respect + to partion A. + + Parameters + ---------- + HG : Hypergraph + + A : list of sets + + wdc : func + weight function (ex: strict, majority, linear) + + Returns + ------- + : float + + """ EC = 0 for e in HG.edges: d = HG.size(e) @@ -133,16 +283,43 @@ def EdgeContribution(HG, A, wdc): # wcd: weight function (ex: strict, majority, linear) -def HNX_modularity(HG, A, wdc=linear): +def hypergraph_modularity(HG, A, wdc=linear): + """ + Computes modularity of a hypergraph with respect to partition A. + + Parameters + ---------- + HG : Hypergraph + Description + A : list of lists + Partition of the nodes in HG + wdc : func, optional + weight function (ex: strict, majority, linear) + + Returns + ------- + : float + + """ Pr = compute_partition_probas(HG, A) - return EdgeContribution(HG, A, wdc) - DegreeTax(HG, Pr, wdc) + return edge_contribution(HG, A, wdc) - degree_tax(HG, Pr, wdc) ################################################################################ -# 2-section igraph from HG +def two_section(HG): + """ + Creates a random walk 2-section igraph with transition weights defined by the + weights of the hyperedges. + + Parameters + ---------- + HG : Hypergraph -def HNX_2section(HG): + Returns + ------- + G : igraph.Graph + """ s = [] for e in HG.edges: E = HG.edges[e] @@ -157,12 +334,28 @@ def HNX_2section(HG): ################################################################################ -def HNX_Kumar(HG, delta=.01): +def kumar(HG, delta=.01): + """ + Compute a partition of the vertices as per Kumar's algorithm [1]_ + + + Parameters + ---------- + HG : Hypergraph + + delta : float, optional + convergence stopping criterion + + Returns + ------- + dict + + """ # weights will be modified -- store initial weights W = [e.weight for e in HG.edges()] # build graph - G = HNX_2section(HG) + G = two_section(HG) # apply clustering CG = G.community_multilevel(weights='weight') CH = [] @@ -182,7 +375,7 @@ def HNX_Kumar(HG, delta=.01): e.weight = 0.5 * e.weight + 0.5 * reweight # re-run louvain # build graph - G = HNX_2section(HG) + G = two_section(HG) # apply clustering CG = G.community_multilevel(weights='weight') CH = [] @@ -198,11 +391,32 @@ def HNX_Kumar(HG, delta=.01): ################################################################################ -# compute change in edge contribution -- -# partition P, node v going from P[a] to P[b] +def delta_ec(HG, P, v, a, b, wdc): + """ + Computes change in edge contribution -- + partition P, node v going from P[a] to P[b] + + Parameters + ---------- + HG : Hypergraph + + P : list of sets + + v : int or str + node identifier + a : int + + b : int + + wdc : func + weight function (ex: strict, majority, linear) -def DeltaEC(HG, P, v, a, b, wdc): + Returns + ------- + TYPE + Description + """ Pm = P[a] - {v} Pn = P[b].union({v}) ec = 0 @@ -213,16 +427,53 @@ def DeltaEC(HG, P, v, a, b, wdc): - wdc(d, HG.size(e, P[a])) - wdc(d, HG.size(e, P[b]))) return ec / HG.total_weight -# exp. part of binomial pmf - def bin_ppmf(d, c, p): + """ + exp. part of binomial pmf + + Parameters + ---------- + d : int + + c : int + + p : float + + + Returns + ------- + float + + """ return p**c * (1 - p)**(d - c) -# compute change in degree tax -- -# partition P (list), node v going from P[a] to P[b] -def DeltaDT(HG, P, v, a, b, wdc): +def delta_dt(HG, P, v, a, b, wdc): + """ + Compute change in degree tax -- + partition P (list), node v going from P[a] to P[b] + + Parameters + ---------- + HG : Hypergraph + + P : list of sets + + v : int or str + node identifier + a : int + + b : int + + wdc : func + weight function (ex: strict, majority, linear) + + Returns + ------- + : float + + """ s = HG.nodes[v].strength vol = sum([HG.nodes[v].strength for v in HG.nodes]) vola = sum([HG.nodes[v].strength for v in P[a]]) @@ -241,12 +492,31 @@ def DeltaDT(HG, P, v, a, b, wdc): DT += x * HG.d_weights[d] return DT / HG.total_weight -# simple H-based algorithm -- -# try moving nodes between communities to optimize qH -# requires L: initial non-trivial partition +def last_step(HG, L, wdc=linear, delta=.01): + """ + Compute a partition of the vertices as per Last-Step algorithm.[2]_ + + Simple H-based algorithm -- + try moving nodes between communities to optimize qH + requires L: initial non-trivial partition + + Parameters + ---------- + HG : Hypergraph + + L : list of sets + + wdc : func, optional + weight function (ex: strict, majority, linear) + delta : float, optional + + + Returns + ------- + : list of sets -def HNX_LastStep(HG, L, wdc=linear, delta=.01): + """ A = L[:] # we will modify this, copy D = part2dict(A) qH = 0 @@ -260,14 +530,14 @@ def HNX_LastStep(HG, L, wdc=linear, delta=.01): if c == i: M.append(0) else: - M.append(DeltaEC(HG, A, v, c, i, wdc) - DeltaDT(HG, A, v, c, i, wdc)) + M.append(delta_ec(HG, A, v, c, i, wdc) - delta_dt(HG, A, v, c, i, wdc)) i = s[np.argmax(M)] if c != i: A[c] = A[c] - {v} A[i] = A[i].union({v}) D[v] = i Pr = compute_partition_probas(HG, A) - q2 = EdgeContribution(HG, A, wdc) - DegreeTax(HG, Pr, wdc) + q2 = edge_contribution(HG, A, wdc) - degree_tax(HG, Pr, wdc) if (q2 - qH) < delta: break qH = q2 diff --git a/hypernetx/algorithms/modularity_and_clustering_original.py b/hypernetx/algorithms/modularity_and_clustering_original.py deleted file mode 100644 index b7e716e9..00000000 --- a/hypernetx/algorithms/modularity_and_clustering_original.py +++ /dev/null @@ -1,293 +0,0 @@ -from collections import Counter -import numpy as np -from functools import reduce -import igraph as ig -import itertools - -################################################################################ - -# we use 2 representations for partitions (0-based part ids): -# (1) dictionary or (2) list of sets - - -def dict2part(D): - P = [] - k = list(D.keys()) - v = list(D.values()) - for x in range(max(D.values()) + 1): - P.append(set([k[i] for i in range(len(k)) if v[i] == x])) - return P - - -def part2dict(A): - x = [] - for i in range(len(A)): - x.extend([(a, i) for a in A[i]]) - return {k: v for k, v in x} - -################################################################################ - - -def factorial(n): - if n < 2: - return 1 - return reduce(lambda x, y: x * y, range(2, int(n) + 1)) - -# Precompute soe values on HNX hypergraph for computing qH faster - - -def precompute_modularity_parameters(HG): - # 1. compute node strenghts (weighted degrees) - for v in HG.nodes: - HG.nodes[v].strength = 0 - for e in HG.edges: - try: - w = HG.edges[e].weight - except: - w = 1 - # add unit weight if none to simplify other functions - HG.edges[e].weight = 1 - for v in list(HG.edges[e]): - HG.nodes[v].strength += w - # 2. compute d-weights - ctr = Counter([len(HG.edges[e]) for e in HG.edges]) - for k in ctr.keys(): - ctr[k] = 0 - for e in HG.edges: - ctr[len(HG.edges[e])] += HG.edges[e].weight - HG.d_weights = ctr - HG.total_weight = sum(ctr.values()) - # 3. compute binomial coeffcients (modularity speed-up) - bin_coef = {} - for n in HG.d_weights.keys(): - for k in np.arange(n // 2 + 1, n + 1): - bin_coef[(n, k)] = factorial(n) / (factorial(k) * factorial(n - k)) - HG.bin_coef = bin_coef - -################################################################################ - -# some weight function 'wdc' for d-edges with c-majority - -# default: linear w.r.t. c - - -def linear(d, c): - return c / d if c > d / 2 else 0 - -# majority - - -def majority(d, c): - return 1 if c > d / 2 else 0 - -# strict - - -def strict(d, c): - return 1 if c == d else 0 - -######################################### - -# compute vol(A_i)/vol(V) for each part A_i in A (list of sets) - - -def compute_partition_probas(HG, A): - p = [] - for part in A: - vol = 0 - for v in part: - vol += HG.nodes[v].strength - p.append(vol) - s = sum(p) - return [i / s for i in p] - -# degree tax - - -def DegreeTax(HG, Pr, wdc): - DT = 0 - for d in HG.d_weights.keys(): - tax = 0 - for c in np.arange(d // 2 + 1, d + 1): - for p in Pr: - tax += p**c * (1 - p)**(d - c) * HG.bin_coef[(d, c)] * wdc(d, c) - tax *= HG.d_weights[d] - DT += tax - DT /= HG.total_weight - return DT - - -# edge contribution, A is list of sets -def EdgeContribution(HG, A, wdc): - EC = 0 - for e in HG.edges: - d = HG.size(e) - for part in A: - if HG.size(e, part) > d / 2: - EC += wdc(d, HG.size(e, part)) * HG.edges[e].weight - EC /= HG.total_weight - return EC - -# HG: HNX hypergraph -# A: partition (list of sets) -# wcd: weight function (ex: strict, majority, linear) - - -def _wdc(wdc): - if wdc == 'linear': - return linear - elif wdc == 'strict': - return strict - elif wdc == 'majority': - return majority - else: - return wdc - - -def HNX_modularity(HG, A, wdc='linear'): - wdc = _wdc(wdc) - Pr = compute_partition_probas(HG, A) - return EdgeContribution(HG, A, wdc) - DegreeTax(HG, Pr, wdc) - -################################################################################ - -# 2-section igraph from HG - - -def HNX_2section(HG): - s = [] - for e in HG.edges: - E = HG.edges[e] - # random-walk 2-section (preserve nodes' weighted degrees) - try: - w = E.weight / (len(E) - 1) - except: - w = 1 / (len(E) - 1) - s.extend([(k[0], k[1], w) for k in itertools.combinations(E.elements, 2)]) # BP - G = ig.Graph.TupleList(s, weights=True).simplify(combine_edges='sum') - return G - -################################################################################ - -def HNX_Kumar(HG, delta=.01): - - # weights will be modified -- store initial weights - W = {e: HG.edges[e].weight for e in HG.edges} - # build graph - G = HNX_2section(HG) - # apply clustering - CG = G.community_multilevel(weights='weight') - CH = [] - for comm in CG.as_cover(): - CH.append(set([G.vs[x]['name'] for x in comm])) - - # LOOP - diff = 1 - ctr = 0 - while diff > delta: - # re-weight - diff = 0 - for i in HG.edges: - e = HG.edges[i] - reweight = sum([1 / (1 + HG.size(i, c)) for c in CH]) * (HG.size(i) + len(CH)) / HG.number_of_edges() - diff = max(diff, 0.5 * abs(e.weight - reweight)) - e.weight = 0.5 * e.weight + 0.5 * reweight - # re-run louvain - # build graph - G = HNX_2section(HG) - # apply clustering - CG = G.community_multilevel(weights='weight') - CH = [] - for comm in CG.as_cover(): - CH.append(set([G.vs[x]['name'] for x in comm])) - ctr += 1 - if ctr > 50: # this process sometimes gets stuck -- set limit - break - G.vs['part'] = CG.membership - for e in HG.edges: - HG.edges[e].weight = W[e] - return {v['name']: v['part'] for v in G.vs} - -################################################################################ - -# compute change in edge contribution -- -# partition P, node v going from P[a] to P[b] - - -def DeltaEC(HG, P, v, a, b, wdc): - Pm = P[a] - {v} - Pn = P[b].union({v}) - ec = 0 - - if HG.isstatic: - memberships = HG.nodes.memberships[v] - else: - memberships = HG.nodes[v].memberships - for e in memberships: - d = HG.size(e) - w = HG.edges[e].weight - ec += w * (wdc(d, HG.size(e, Pm)) + wdc(d, HG.size(e, Pn)) - - wdc(d, HG.size(e, P[a])) - wdc(d, HG.size(e, P[b]))) - return ec / HG.total_weight - -# exp. part of binomial pmf - - -def bin_ppmf(d, c, p): - return p**c * (1 - p)**(d - c) - -# compute change in degree tax -- -# partition P (list), node v going from P[a] to P[b] -def DeltaDT(HG, P, v, a, b, wdc): - - s = HG.nodes[v].strength - vol = sum([HG.nodes[v].strength for v in HG.nodes]) - vola = sum([HG.nodes[v].strength for v in P[a]]) - volb = sum([HG.nodes[v].strength for v in P[b]]) - volm = (vola - s) / vol - voln = (volb + s) / vol - vola /= vol - volb /= vol - DT = 0 - - for d in HG.d_weights.keys(): - x = 0 - for c in np.arange(int(np.floor(d / 2)) + 1, d + 1): - x += HG.bin_coef[(d, c)] * wdc(d, c) * (bin_ppmf(d, c, voln) + bin_ppmf(d, c, volm) - - bin_ppmf(d, c, vola) - bin_ppmf(d, c, volb)) - DT += x * HG.d_weights[d] - return DT / HG.total_weight - -# simple H-based algorithm -- -# try moving nodes between communities to optimize qH -# requires L: initial non-trivial partition - - -def HNX_LastStep(HG, L, wdc=linear, delta=.01): - A = L[:] # we will modify this, copy - D = part2dict(A) - qH = 0 - while True: - for v in list(np.random.permutation(list(HG.nodes))): - c = D[v] - s = list(set([c] + [D[i] for i in HG.neighbors(v)])) - M = [] - if len(s) > 0: - for i in s: - if c == i: - M.append(0) - else: - M.append(DeltaEC(HG, A, v, c, i, wdc) - DeltaDT(HG, A, v, c, i, wdc)) - i = s[np.argmax(M)] - if c != i: - A[c] = A[c] - {v} - A[i] = A[i].union({v}) - D[v] = i - Pr = compute_partition_probas(HG, A) - q2 = EdgeContribution(HG, A, wdc) - DegreeTax(HG, Pr, wdc) - if (q2 - qH) < delta: - break - qH = q2 - return [a for a in A if len(a) > 0] - -################################################################################ diff --git a/setup.py b/setup.py index ace3121d..1b0ddf37 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ from setuptools import setup import sys -__version__ = "1.1.3" +__version__ = "1.1.4" if sys.version_info < (3, 7): sys.exit("HyperNetX requires Python 3.7 or later.") @@ -70,7 +70,7 @@ """, extras_require={ "testing": ["pytest>=4.0"], - "tutorials": ["jupyter>=1.0"], + "tutorials": ["jupyter>=1.0", "python-igraph>=0.9.6"], "documentation": ["sphinx>=1.8.2", "nb2plots>=0.6", "sphinx-rtd-theme>=0.4.2"], "all": [ "sphinx>=1.8.2", @@ -78,6 +78,7 @@ "sphinx-rtd-theme>=0.4.2", "pytest>=4.0", "jupyter>=1.0", + "python-igraph>=0.9.6", ], }, ) diff --git a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb new file mode 100644 index 00000000..e45902a8 --- /dev/null +++ b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb @@ -0,0 +1,730 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using the following packages:\n", + "\n", + "* pip install python-igraph\n", + "* pip install partition-igraph\n", + "* pip install hypernetx\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import igraph as ig\n", + "import partition_igraph\n", + "import hypernetx as hnx\n", + "import pickle\n", + "import hypergraph_modularity as hmod" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summary of functions for Hypergraph Modularity using HNX\n", + "\n", + "### Build hypergraph and pre-compute key quantities\n", + "\n", + "We build the hypergraph HG using:\n", + "```python\n", + "HG = hnx.Hypergraph(Edges)\n", + "```\n", + "where 'Edges' is a list of sets; edges are then indexed as 0-based integers.\n", + "\n", + "Once the HNX hypergraph is built, the following function is called to \n", + "compute node strengths, d-degrees and binomial coefficients\n", + "and add these as attributes to HG:\n", + "\n", + "```python\n", + "hmod.precompute_attributes(HG)\n", + "```\n", + "\n", + "### Partitions\n", + "\n", + "We use two representations for partitions: list of sets (the parts) or dictionary.\n", + "Those functions are used to map from one to the other:\n", + "\n", + "```python\n", + "dict2part(D)\n", + "part2dict(A)\n", + "```\n", + "\n", + "### H-modularity\n", + "\n", + "The function to compute H-modularity for HG w.r.t. partition A (list of sets covering the vertices):\n", + "\n", + "```python\n", + "hmod.hypergraph_modularity(HG, A, wcd=linear)\n", + "```\n", + "\n", + "where 'wcd' is the weight function (default = 'linear'). Other choices are 'strict'\n", + "and 'majority', or any user-supplied function with the following format:\n", + "\n", + "```python\n", + "def linear(d,c):\n", + " return c/d if c>d/2 else 0\n", + "```\n", + "\n", + "where $d$ is the edge size, and $c$ is the number of nodes in the majority class, $d \\geq c > \\frac{d}{2}$\n", + "\n", + "### Two-section graph\n", + "\n", + "Build the random-walk based $2$-section graph given some hypergraph HG:\n", + "\n", + "```python\n", + "G = hmod.two_section(HG)\n", + "```\n", + "\n", + "where G is an igraph Graph.\n", + "\n", + "### Clustering: Kumar algorithm\n", + "\n", + "Given hypergraph HG, compute a partition of the vertices as per Kumar's algorithm described in [1].\n", + "\n", + "```python\n", + "K = hmod.kumar(HG, delta=.01)\n", + "```\n", + "\n", + "where delta is the convergence stopping criterion. Partition is returned as a dictionary.\n", + "\n", + "[1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S., Ravindran B. (2020) *A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering*. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24\n", + "\n", + "\n", + "### Clustering: Simple qH-based algorithm\n", + "\n", + "Given hypergraph HG and initial partition L, \n", + "compute a partition of the vertices as per Last-Step algorithm described in [2].\n", + "\n", + "```python\n", + "A = hmod.last_step(HG, L, wdc=linear, delta = .01)\n", + "```\n", + "\n", + "where 'wcd' is the the weight function (default = 'linear') and delta is the convergence stopping criterion.\n", + "Returned partition is a list of sets.\n", + "\n", + "[2] B. Kaminski, P. Pralat and F. Théberge, *Community Detection Algorithm Using Hypergraph Modularity*, to appear in the proceedings of Complex Networks 2020, Springer.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Toy example" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
        " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "## build an hypergraph from a list of sets (the hyperedges)\n", + "## using 'enumerate', edges will have integer IDs\n", + "E = [{'A','B'},{'A','C'},{'A','B','C'},{'A','D','E','F'},{'D','F'},{'E','F'}]\n", + "HG = hnx.Hypergraph(dict(enumerate(E)))\n", + "hnx.draw(HG)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "## compute node strength (add unit weight is none), d-degrees, binomial coefficients\n", + "hmod.precompute_attributes(HG)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: Entity(0,['A', 'B'],{'weight': 1.0}),\n", + " 1: Entity(1,['A', 'C'],{'weight': 1.0}),\n", + " 2: Entity(2,['A', 'C', 'B'],{'weight': 1.0}),\n", + " 3: Entity(3,['A', 'E', 'D', 'F'],{'weight': 1.0}),\n", + " 4: Entity(4,['D', 'F'],{'weight': 1.0}),\n", + " 5: Entity(5,['E', 'F'],{'weight': 1.0})}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## the edges (unit weights added by default)\n", + "HG.edges.elements\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'A': Entity(A,[],{'weight': 1.0, 'strength': 4.0}),\n", + " 'B': Entity(B,[],{'weight': 1.0, 'strength': 2.0}),\n", + " 'C': Entity(C,[],{'weight': 1.0, 'strength': 2.0}),\n", + " 'E': Entity(E,[],{'weight': 1.0, 'strength': 2.0}),\n", + " 'D': Entity(D,[],{'weight': 1.0, 'strength': 2.0}),\n", + " 'F': Entity(F,[],{'weight': 1.0, 'strength': 3.0})}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## the nodes (here strength = degree since all weights are 1)\n", + "HG.nodes.elements\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Counter({2: 4.0, 3: 1.0, 4: 1.0})" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "HG.d_weights\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "linear: 0.414445267489712 -0.03746831275720153 0.0 -0.19173004115226341\n", + "strict: 0.43490699588477366 -0.02385843621399164 0.0 -0.12887572016460908\n", + "majority: 0.39379753086419755 -0.0343506172839505 0.0 -0.22078024691358022\n" + ] + } + ], + "source": [ + "## compute modularity qH for the following partitions:\n", + "A1 = [{'A','B','C'},{'D','E','F'}]\n", + "A2 = [{'B','C'},{'A','D','E','F'}]\n", + "A3 = [{'A','B','C','D','E','F'}]\n", + "A4 = [{'A'},{'B'},{'C'},{'D'},{'E'},{'F'}]\n", + "\n", + "strict = hmod.strict\n", + "majority = hmod.majority\n", + "\n", + "print('linear:',hmod.hypergraph_modularity(HG,A1),hmod.hypergraph_modularity(HG,A2),hmod.hypergraph_modularity(HG,A3),hmod.hypergraph_modularity(HG,A4))\n", + "print('strict:',hmod.hypergraph_modularity(HG,A1,strict),hmod.hypergraph_modularity(HG,A2,strict),hmod.hypergraph_modularity(HG,A3,strict),hmod.hypergraph_modularity(HG,A4,strict))\n", + "print('majority:',hmod.hypergraph_modularity(HG,A1,majority),hmod.hypergraph_modularity(HG,A2,majority),hmod.hypergraph_modularity(HG,A3,majority),hmod.hypergraph_modularity(HG,A4,majority))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": { + "image/svg+xml": { + "isolated": true + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "## 2-section graph\n", + "G = hmod.two_section(HG)\n", + "G.vs['label'] = G.vs['name']\n", + "ig.plot(G,bbox=(0,0,250,250))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'A', 'B', 'C'}, {'D', 'E', 'F'}]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## 2-section clustering with ECG\n", + "G.vs['community'] = G.community_ecg().membership\n", + "hmod.dict2part({v['name']:v['community'] for v in G.vs})\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'A', 'B', 'C'}, {'D', 'E', 'F'}]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Clustering with Kumar's algorithm\n", + "hmod.dict2part(hmod.kumar(HG))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "start from: [{'A'}, {'B'}, {'C'}, {'D'}, {'E'}, {'F'}]\n", + "final partition: [{'A', 'C', 'B'}, {'E', 'D', 'F'}]\n" + ] + } + ], + "source": [ + "## hypergraph clustering -- start from partition A4 defined above\n", + "print('start from:',A4)\n", + "A = hmod.last_step(HG,A4)\n", + "print('final partition:',A)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Game of Thrones scenes hypergraph\n", + "\n", + "REF: https://github.com/jeffreylancaster/game-of-thrones\n", + "\n", + "We built an hypergraph from the game of thrones scenes with he following elements:\n", + "\n", + "* **Nodes** are characters in the series\n", + "* **Hyperedges** are groups of character appearing in the same scene(s)\n", + "* **Hyperedge weights** are total scene(s) duration in seconds involving those characters\n", + "\n", + "We kept hyperedges with at least 2 characters.\n", + "Moreover, we discarded characters with degree below 5.\n", + "\n", + "We saved the following:\n", + "\n", + "* *Edges*: list of sets where the nodes are 0-based integers represents as strings\n", + "* *Names*: dictionary; mapping of nodes to character names\n", + "* *Weights*: list; hyperedge weights (in same order as Edges)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "## load the GoT dataset\n", + "Edges, Names, Weights = pickle.load(open( \"../Data/GoT.pkl\", \"rb\" ))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build weighted hypergraph " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "## Nodes are represented as strings from '0' to 'n-1'\n", + "HG = hnx.Hypergraph(dict(enumerate(Edges)))\n", + "## add edge weights\n", + "for e in HG.edges:\n", + " HG.edges[e].weight = Weights[e]\n", + "## add full names\n", + "for v in HG.nodes:\n", + " HG.nodes[v].name = Names[v]\n", + "## pre-compute required quantities for modularity and clustering\n", + "hmod.precompute_attributes(HG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modularity (qH) on a random partition\n", + "\n", + "Should be close to 0 and can be negative." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.023085535915365468" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## generate a random partition into K parts to compare results\n", + "K = 5\n", + "V = list(HG.nodes)\n", + "p = np.random.choice(K, size=len(V))\n", + "RandPart = hmod.dict2part({V[i]:p[i] for i in range(len(V))})\n", + "## compute qH\n", + "hmod.hypergraph_modularity(HG, RandPart)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get the 2-section graph (with igraph) and cluster with Louvain\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.5372359319251633\n" + ] + } + ], + "source": [ + "## build 2-section\n", + "G = hmod.two_section(HG)\n", + "## Louvain algorithm\n", + "ML = G.community_multilevel(weights='weight')\n", + "G.vs['louvain'] = ML.membership\n", + "part = hmod.dict2part({v['name']:v['louvain'] for v in G.vs})\n", + "## Compute qH\n", + "print(hmod.hypergraph_modularity(HG, part))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cluster with Kumar's algorithm\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.5351500884869287\n" + ] + } + ], + "source": [ + "## run Kumar's algorithm, get partition\n", + "KU = hmod.kumar(HG)\n", + "G.vs['kumar'] = [KU[v['name']] for v in G.vs]\n", + "## Compute qH\n", + "print(hmod.hypergraph_modularity(HG, hmod.dict2part(KU)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cluster with simple H-based (Last Step) Algorithm\n", + "\n", + "We use Louvain or Kumar algorithm on the 2-section as the required initial partition" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5475162906819371" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Louvain parition already computed\n", + "part = hmod.dict2part({v['name']:v['louvain'] for v in G.vs})\n", + "## H-based last step\n", + "LS = hmod.last_step(HG, part)\n", + "## Compute qH\n", + "hmod.hypergraph_modularity(HG, LS)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example: top nodes in cluster with Daenerys Targaryen\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
        \n", + "\n", + "
        +
      • algorithms.untitiled_modularity_and_clustering_original module
        • +
      • algorithms.untitled_modularity_and_clustering module
      • Module contents
        • diff --git a/docs/build/install.html b/docs/build/install.html index 9397d33b..9f99b4fb 100644 --- a/docs/build/install.html +++ b/docs/build/install.html @@ -7,7 +7,7 @@ - Installing HyperNetX — HyperNetX 1.1.3 documentation + Installing HyperNetX — HyperNetX 1.1.4dev documentation diff --git a/docs/build/license.html b/docs/build/license.html index e1fab594..2fe93c82 100644 --- a/docs/build/license.html +++ b/docs/build/license.html @@ -7,7 +7,7 @@ - License — HyperNetX 1.1.3 documentation + License — HyperNetX 1.1.4dev documentation diff --git a/docs/build/nwhy.html b/docs/build/nwhy.html index 8aa8ae10..720fc66c 100644 --- a/docs/build/nwhy.html +++ b/docs/build/nwhy.html @@ -7,7 +7,7 @@ - NWHy — HyperNetX 1.1.3 documentation + NWHy — HyperNetX 1.1.4dev documentation diff --git a/docs/build/objects.inv b/docs/build/objects.inv index 231ed0342dd40f30802a40628d4a1e6be4c27b3f..a331baaceb2dfc0a9b50182a2ce22b7fcdde08b3 100644 GIT binary patch delta 2789 zcmVgn1&(_yQ^_L| za0|vTOcoJ!xdo-Mm@mIkqu6&WoUBDb)rstttScmFexOX)=Ui4CupI4vZK-BhmD%?e z@(?}~22^EZ1`_YZ*DR)o_Ai#(Xj4doOtiU|?VR|~vVZ1C&;V0V)Hxs_1FUf45rO;w z+~%P>smg9FMX;euTS|nb+6qC3A8FN$&>9nIDL~uvY-Y(C_GAZYU|tL$HuzV_9zAx~fLzLa`6i7{#{r zn*Noq~293>C&<;w} zga!@R!;!XJbjQt65)FA$_v5tUG-p(3lJnZOFvY|^Eu4%Z?NrT>isAaXae<%MOUPIk zk%&<=A?%NXbp^Tqy_jopU`Jib-D*V%RM!d;XMg{rNLy-WY)1egK*tiep1V>mH9Xn~ z zyUjFbbFZJi{*A^yy#ITQeXwcTdyFh?mer4+zmd18foZnIUz5_V+2%gkjO;ljA)9Rx zA%9_=WixpKILjLPyl|HF^m)=ObLrEYS>_V6{8_e=lmA)P(&q&8%=J@+NZL9L0DsR2LRg<*A~l%bHh~+$Z`;BP-1qGugzNi8aDw!GJ6NIl zzNLNydZZtdn@C~)wh^S@et+8tMo7PH0Vj~(wt*DBZyQ+>E^|(`K3iC6 z%AT5kFG65j4$h@hg6if&NAMS{yQ6@snww^`(IzDaSu9c~z@fCINEl6aN|q_G{J?mj zz{e#!P*k974b7);DIm!Wzmac;Ed_gKTh=KBRzPJADS)X;??Q#c1!CAORr)E`7OpBBZX4R*Q9|b5HN=2At`-iRuCdAF){_C*4U-i!#mFS-J1BiZphUjfs*%40|qU)_M!o?!dgnzKPtW9tp z=I(_7Ebgm~gs#VHLv1L)fELuyom(VMJ}fbKEzMjEC`7cg;neQ->_W7Ofm=j*6rV3!pl+^K&X zjL02cI@lx8vRA1JoOtB6?rb*Y=VLyd0vu{s&Ow@xML3Pfb58>qhv}YXV$Z=yfeJ;- zn4@(L%qP=B(dtt_YKEhcXEsp?KquqPNi=QR5e^LuBJ4mbz=;F6y?<~1Lh?ArNcYMv zPPgzc6Y$wJA4ZUb4docHdGPy6k8Ej^+*O!j0qWL^s{Q zrFD}RBqS?AXx8*6G-lheaDYqquJ04c_Cyx!vt9?M&1>_Y`-m6_0}zZZGWjKQuQ~k?Ci|!DCU-a z_yU(-u^@dUL?U9bP#x>mQI@jYa(n?hT604bEm)mIq_%`Yi)CSK;U-1iFl z51!3#oxS>^#(AcmQYqu8Weq7}HT65Jra7$IFutg2ouY4gJ0<_JQ%ASSXMfuReQY%;j^Mjy3iP4#Y~!lN-8sa2cao@YW}@= zpl!Jwg>uCz|7AoMpO7e<=;H((HoBKl@ExKot8S}04b^<}2?@ru!MexGJ^3`AZBP=2 zLPlP`S%387y8g;N0ibM@-#Is;OcE=yyOMg54^y1nS@2pd0w*Dd8bE!2| zr=a0V*PY6`S6Q!`h1@&3Z#v0-C3Lz zD;6?$de`3{i!Nmq&0pvqR(HPfdmlXf9B?-Njq*!lVNAc~cysj}x#|z-T z=Wm%aqKA6Dg@HS7uIL_QIcOs8_jwcfrxpKKFWFh>WC!h|l@?NE`lJn%6Kd6quN(38 zDte`1LJfCI9$B;GaYv_rp}<rCxWZ znJ0FJNTcr#wy=FyWICqRh%x=q#O_?mzcamd>Fe0F`&K%ob*HpG=k=F{K8#qNVedlM zv8TsCE889ipD5j&%$_9ePs~l0T+m2{YVjlXS0>l!n=zdk&wr?S_N4vABjqpCyk6+f z9#0bYkAFISvLsNIABJO0C<|TuIb8qZyEk)h>}S)%V^CN}3f5qJ5NBG#6RS*5lXShE r7s$g5bc*DW*+*^S;W3m%5nF)yzW&3P#^RmZkwTll#47&>(AqaO^fhkT delta 2414 zcmV-!36b{07S9uqdVjT?OON9=5`gdi6#{k+yTHo5*<;@}L68ZMOa|KvBsYSV*cQ_i zDUeiqlwZF^QlcnZZp%{n)D~6su~>W+A8IO&N^@J)CTqIqT9vI}*}pOxlb^<)`^whh zZ$T#il;ykRRiUJ%N3P@oOq5*&+*8SGY9EUp)5s*d*H#w(_kVZGGQ64-Yk0|OUKspB zMD+eAE(9|TXXNGQ|31e2Nggi`%A-(_2SDE$nY611WL0ugXl5Vju_TK}IgHDazw@%C z;^7Xf>7VX(N8XiX56Z(EWs;*)BsA*Fz zGJBD+`?4^H`hPKKrb}grX0qcL8C0rPLLEDY|Mxa0Pf&YWmc%M(*rKf^j(G!9s}mCN zz^vt%EF#%Hb~H-qFh2f*NtFUhQ_A@H5|*CV{ai3 z;d5!3ZeY$p;=Os#Vp<5lcmcCbsVp)PVlU{N_|Uo)NPo}()2wU@hJ*~T(v3%i6%`W@ zhrTLZ$FUS44^u-bkw689puqIuNU=ME2|6G_3H?tGR$<5=;{=ysQ~DErfyF>T4DWsW8Q!7?YBj zR07v>mwzghVUNy)6+WF8kAPQH*Glr@kp&p@*zB%CP-D&-O`rDE|Gj7~Lpo&H=eJUI z|6?Th7hvuz;Hu`npZRKLD^;#iCrq#!Qjsu*ylPc%ffW@OB`h)=1I8W*oWMi(Q(#79 zo6PW0B`m@^7Tx=xSII_v_Z$ z1Yn3!|^%TM0nb=Yw4=`*~$E=xO5>~v~*m7!EzA)m`ct2XMGzms) z3xVrp6Iu)G3)P#ozKChZ*g~n*`=)^>5HN=2DQWx243n>m8Z5DD3&!xjHaV!}<+eHk zK!4w_Rx@6L{<>Kkls8V?j<9ux^9&^*HaCp8zG(f*H=SuVkjMUA$8DqyxYbQ>x=(p` zoHnZOKTk-DYkrmxBBt`l))r=twWfn#CrVW^x8sRvr94#~4(m?}0e{i0YFt;C?KdvC z+o8l`P6Cpxu-I39U853RT|aBu8DMeW zVJ37v-dGA#0S2_BmcD9QqqP5$9W}!taWDy19XPBS2LoQ01y1o=Dlj;%=aNSZwH}OF z2@{|}B;r+y2TI0|)j=Ve=wfMaCl>MUMbtdXMIr=s`twB80mTz#TQ?n{T2qwB#D8t< z^Ad|BxMXAOiU5>IuG_W&bc=~M(r`oKgo(o&C&%q2MjZ}(ypB2s#;6dXr2c9GQLlL6 z;D|(vUX?C!<|EAt1<{mWkNI$l;ZVbN4$^`w!5KuJs|{ovrg?_TqhMA^R47`+oa}R8 z*31Y+^QU>#3dbO?Y?3Jeos738F@LmgM>sT?>+xU}Z5YlxfZO}--$PkGLhngux4ghJQ$ ztV1m>@DQ7%pojib0W=<3O`vv>4Oa@TL#Jo>)IuLQv@nNmf~H|Fa@$VP;hqK-`Tm&Z zItw8dxdJ48IDi6ewG1~)I={aMbp>8sxzl$&n-#YPK7_5fL2!$QJ)PZqN#P?9-c^Ac zxx{0rAaVBvs1NZEnuk~2P=6>jJ)K`K@6(?8zZcQ|kgnT<+x$9ES#H(eyR@@Av$L3w z^uy<1e#e50Ga(WYi-l@f+(%iuG*VS#bGwFUy|N zukY?|oum4y#(Ac`#VPZsbqy(D_5Pi@=^j>Vxme{|r|6sBt5$!((tpt%GP%;MF(4jM zk-6_cmm`7^UHsVQZU~pJLzJ^tm%N>*KAKes9~CvqO^pFo-bvG}QQA(wLFm(-`o9+y zI;6uWRA}D#&vf1VghbgVA6`UkQkGfp4Wg=>cvQU&6}DZmU_iktzBHXbx6g+qxO z8rx7|a~`R5hDnesxqllk=oFI{8bX5n%Hkzz zAye1{buzv#=mFX)a>O;<~YS+#odL zxU*Li{hhxixPQRDaUFiUX%1+wHAT5>7OlK^s7kJf+=e>BoOFn5> z;NiICk@Z_1H*}(W-pf10P(!^RlFB(R8kaC%3Ne-TNq<9S`Uls5qv}q$T@^ENb)OWJ zehCi~x#+S~5_~<#LYtpySxuJ@F9Q145Gsc@=D$x!jBw9E?91<;yYY3zMFGe-iabAO z&jvyPR$dc!zw%zhLqiAMO$V;jl9?eb`>jvgHSGbM;Qs~Lr2D7oLFH?ba;Y|Y>0A+t zH&q?ocz?scUpouUck1RLVwxGT&RzvZpSU}VwC42EAS{dPs(z$2dA!ZMurnkYV|ReW z;aidAm_Z}Oj8_xmy_A1uCU@cM+`HSkM5R0`<>$Qd)zpTWobSl*Lf5gE&w(I2TnAq$ z-QUb!B<*j^EtXu-NTzCO75g`fJ^E=*=Z^ZHYG - Overview — HyperNetX 1.1.3 documentation + Overview — HyperNetX 1.1.4dev documentation diff --git a/docs/build/publications.html b/docs/build/publications.html index 92858c14..7013cfb0 100644 --- a/docs/build/publications.html +++ b/docs/build/publications.html @@ -7,7 +7,7 @@ - Publications — HyperNetX 1.1.3 documentation + Publications — HyperNetX 1.1.4dev documentation diff --git a/docs/build/py-modindex.html b/docs/build/py-modindex.html index 7b5be15b..9e4bcdf5 100644 --- a/docs/build/py-modindex.html +++ b/docs/build/py-modindex.html @@ -7,7 +7,7 @@ - Python Module Index — HyperNetX 1.1.3 documentation + Python Module Index — HyperNetX 1.1.4dev documentation @@ -214,6 +214,11 @@

        Python Module Index

        		    algorithms.homology_mod2
            + algorithms.hypergraph_modularity +
            @@ -224,6 +229,16 @@

        Python Module Index

        		    algorithms.s_centrality_measures
            + algorithms.untitiled_modularity_and_clustering_original +
            + algorithms.untitled_modularity_and_clustering +
         
        c
        \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
        characterstrength
        18Daenerys Targaryen31103
        0Jorah Mormont19344
        27Missandei13683
        9Grey Worm10497
        13Barristan Selmy6514
        \n", + "" + ], + "text/plain": [ + " character strength\n", + "18 Daenerys Targaryen 31103\n", + "0 Jorah Mormont 19344\n", + "27 Missandei 13683\n", + "9 Grey Worm 10497\n", + "13 Barristan Selmy 6514" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Index for \n", + "inv_map = {v: k for k, v in Names.items()}\n", + "JS = inv_map['Daenerys Targaryen']\n", + "## JS's cluster\n", + "JS_part = hmod.part2dict(LS)[JS]\n", + "## Build dataframe: all nodes in JS_part\n", + "L = []\n", + "for n in LS[JS_part]:\n", + " L.append([Names[n],HG.nodes[n].strength])\n", + "D = pd.DataFrame(L, columns=['character','strength'])\n", + "D.sort_values(by='strength',ascending=False).head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 266bd3481d4677ee6e460c4118279627a72b62ea Mon Sep 17 00:00:00 2001 From: Brenda Praggastis Date: Thu, 14 Oct 2021 06:37:48 -0700 Subject: [PATCH 04/41] updated version and setup --- docs/source/conf.py | 2 +- setup.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 89363870..59a67ec0 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,7 +19,7 @@ import os import shlex -__version__ = "1.1.4dev" +__version__ = "1.1.4" # If extensions (or modules to document with autodoc) are in another directory, diff --git a/setup.py b/setup.py index 1b0ddf37..b2f9da02 100644 --- a/setup.py +++ b/setup.py @@ -30,7 +30,6 @@ "matplotlib>3.0", "scikit-learn>=0.20.0", "pandas>=0.23", - "celluloid>=0.2.0", ], license="3-Clause BSD license", long_description=""" @@ -70,7 +69,7 @@ """, extras_require={ "testing": ["pytest>=4.0"], - "tutorials": ["jupyter>=1.0", "python-igraph>=0.9.6"], + "tutorials": ["jupyter>=1.0", "python-igraph>=0.9.6", "celluloid>=0.2.0", ], "documentation": ["sphinx>=1.8.2", "nb2plots>=0.6", "sphinx-rtd-theme>=0.4.2"], "all": [ "sphinx>=1.8.2", @@ -79,6 +78,7 @@ "pytest>=4.0", "jupyter>=1.0", "python-igraph>=0.9.6", + "celluloid>=0.2.0", ], }, ) From b0934741aeb30e304b8640acd3368254c7763903 Mon Sep 17 00:00:00 2001 From: Brenda Praggastis Date: Thu, 14 Oct 2021 15:18:03 -0700 Subject: [PATCH 05/41] added memberships to nodes for static hypergraph --- hypernetx/classes/hypergraph.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index f320fc98..ffa9deb9 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -168,6 +168,9 @@ def __init__( self._edges = E self._nodes = E.restrict_to_levels([1], weights=False, aggregateby=None) self._nodes._memberships = E.memberships + for n in self._nodes: + self._nodes[n].memberships = self._nodes._memberships[n] ### a bit of a hack to get same functionality from static as dynamic + ### we will have to see if it slows things down too much else: self._static = False if setsystem is None: From 0f8a084cc2e3fa6d74ca57d47594a8b3e3cafbf0 Mon Sep 17 00:00:00 2001 From: Brenda Praggastis Date: Thu, 14 Oct 2021 15:34:16 -0700 Subject: [PATCH 06/41] updated hypergraph_modularity to work with static and take advantage of some optimized algorithms. --- hypernetx/algorithms/hypergraph_modularity.py | 38 ++------- hypernetx/classes/staticentity.py | 2 +- ...Hypergraph Modularity and Clustering.ipynb | 82 +++++++++---------- 3 files changed, 48 insertions(+), 74 deletions(-) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index 027e87b5..60c92e54 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -19,7 +19,7 @@ from functools import reduce import igraph as ig import itertools -from scipy.special import factorial as scipyfact +from scipy.special import comb ################################################################################ @@ -72,29 +72,9 @@ def part2dict(A): ################################################################################ -def factorial(n): - """ - Computes exact integer factorial on integer - - Parameters - ---------- - n : int, or array-like object - - Returns - ------- - int or int64 or object - - """ - if n < 2: - return 1 - return scipyfact(n, exact=True) - # return reduce(lambda x, y: x * y, range(2, int(n) + 1)) - -# Precompute soe values on HNX hypergraph for computing qH faster - - def precompute_attributes(HG): """ + Precompute some values on HNX hypergraph for computing qH faster Adds weight, strength and binary coefficient attributes to the hypergraph for computing qH faster. @@ -127,7 +107,7 @@ def precompute_attributes(HG): bin_coef = {} for n in HG.d_weights.keys(): for k in np.arange(n // 2 + 1, n + 1): - bin_coef[(n, k)] = factorial(n) / (factorial(k) * factorial(n - k)) + bin_coef[(n, k)] = comb(n, k, exact=True) HG.bin_coef = bin_coef ################################################################################ @@ -283,7 +263,7 @@ def edge_contribution(HG, A, wdc): # wcd: weight function (ex: strict, majority, linear) -def hypergraph_modularity(HG, A, wdc=linear): +def modularity(HG, A, wdc=linear): """ Computes modularity of a hypergraph with respect to partition A. @@ -353,7 +333,7 @@ def kumar(HG, delta=.01): """ # weights will be modified -- store initial weights - W = [e.weight for e in HG.edges()] + W = {e: HG.edges[e].weight for e in HG.edges} # uses edge id for reference instead of int # build graph G = two_section(HG) # apply clustering @@ -368,11 +348,11 @@ def kumar(HG, delta=.01): while diff > delta: # re-weight diff = 0 - for i in HG.edges: - e = HG.edges[i] + for e in HG.edges: + edge = HG.edges[e] reweight = sum([1 / (1 + HG.size(e, c)) for c in CH]) * (HG.size(e) + len(CH)) / HG.number_of_edges() - diff = max(diff, 0.5 * abs(e.weight - reweight)) - e.weight = 0.5 * e.weight + 0.5 * reweight + diff = max(diff, 0.5 * abs(edge.weight - reweight)) + edge.weight = 0.5 * edge.weight + 0.5 * reweight # re-run louvain # build graph G = two_section(HG) diff --git a/hypernetx/classes/staticentity.py b/hypernetx/classes/staticentity.py index a84fd71b..8e8f59ea 100644 --- a/hypernetx/classes/staticentity.py +++ b/hypernetx/classes/staticentity.py @@ -62,7 +62,7 @@ def __init__( arr=None, labels=None, uid=None, - weights=None, + weights=None, ### in this context weights is just a column of values corresponding to the rows in data. keep_weights=True, aggregateby="sum", **props, diff --git a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb index e45902a8..552f4de1 100644 --- a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb +++ b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb @@ -23,7 +23,7 @@ "import partition_igraph\n", "import hypernetx as hnx\n", "import pickle\n", - "import hypergraph_modularity as hmod" + "import hypernetx.algorithms.hypergraph_modularity as hmod" ] }, { @@ -128,7 +128,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
        " ] @@ -140,10 +140,9 @@ } ], "source": [ - "## build an hypergraph from a list of sets (the hyperedges)\n", - "## using 'enumerate', edges will have integer IDs\n", + "## build a hypergraph from a list of sets (the hyperedges)\n", "E = [{'A','B'},{'A','C'},{'A','B','C'},{'A','D','E','F'},{'D','F'},{'E','F'}]\n", - "HG = hnx.Hypergraph(dict(enumerate(E)))\n", + "HG = hnx.Hypergraph(E,static=True)\n", "hnx.draw(HG)\n" ] }, @@ -154,7 +153,7 @@ "outputs": [], "source": [ "## compute node strength (add unit weight is none), d-degrees, binomial coefficients\n", - "hmod.precompute_attributes(HG)\n" + "hmod.precompute_attributes(HG)" ] }, { @@ -165,12 +164,12 @@ { "data": { "text/plain": [ - "{0: Entity(0,['A', 'B'],{'weight': 1.0}),\n", - " 1: Entity(1,['A', 'C'],{'weight': 1.0}),\n", - " 2: Entity(2,['A', 'C', 'B'],{'weight': 1.0}),\n", - " 3: Entity(3,['A', 'E', 'D', 'F'],{'weight': 1.0}),\n", - " 4: Entity(4,['D', 'F'],{'weight': 1.0}),\n", - " 5: Entity(5,['E', 'F'],{'weight': 1.0})}" + "{'e0': ['A', 'B'],\n", + " 'e1': ['C', 'A'],\n", + " 'e2': ['C', 'A', 'B'],\n", + " 'e3': ['F', 'A', 'D', 'E'],\n", + " 'e4': ['D', 'F'],\n", + " 'e5': ['E', 'F']}" ] }, "execution_count": 4, @@ -191,12 +190,7 @@ { "data": { "text/plain": [ - "{'A': Entity(A,[],{'weight': 1.0, 'strength': 4.0}),\n", - " 'B': Entity(B,[],{'weight': 1.0, 'strength': 2.0}),\n", - " 'C': Entity(C,[],{'weight': 1.0, 'strength': 2.0}),\n", - " 'E': Entity(E,[],{'weight': 1.0, 'strength': 2.0}),\n", - " 'D': Entity(D,[],{'weight': 1.0, 'strength': 2.0}),\n", - " 'F': Entity(F,[],{'weight': 1.0, 'strength': 3.0})}" + "{'A': [], 'B': [], 'C': [], 'F': [], 'D': [], 'E': []}" ] }, "execution_count": 5, @@ -217,7 +211,7 @@ { "data": { "text/plain": [ - "Counter({2: 4.0, 3: 1.0, 4: 1.0})" + "Counter({2: 4, 3: 1, 4: 1})" ] }, "execution_count": 6, @@ -254,9 +248,9 @@ "strict = hmod.strict\n", "majority = hmod.majority\n", "\n", - "print('linear:',hmod.hypergraph_modularity(HG,A1),hmod.hypergraph_modularity(HG,A2),hmod.hypergraph_modularity(HG,A3),hmod.hypergraph_modularity(HG,A4))\n", - "print('strict:',hmod.hypergraph_modularity(HG,A1,strict),hmod.hypergraph_modularity(HG,A2,strict),hmod.hypergraph_modularity(HG,A3,strict),hmod.hypergraph_modularity(HG,A4,strict))\n", - "print('majority:',hmod.hypergraph_modularity(HG,A1,majority),hmod.hypergraph_modularity(HG,A2,majority),hmod.hypergraph_modularity(HG,A3,majority),hmod.hypergraph_modularity(HG,A4,majority))\n" + "print('linear:',hmod.modularity(HG,A1),hmod.modularity(HG,A2),hmod.modularity(HG,A3),hmod.modularity(HG,A4))\n", + "print('strict:',hmod.modularity(HG,A1,strict),hmod.modularity(HG,A2,strict),hmod.modularity(HG,A3,strict),hmod.modularity(HG,A4,strict))\n", + "print('majority:',hmod.modularity(HG,A1,majority),hmod.modularity(HG,A2,majority),hmod.modularity(HG,A3,majority),hmod.modularity(HG,A4,majority))\n" ] }, { @@ -284,13 +278,13 @@ "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "\n", @@ -321,19 +315,19 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -405,7 +399,7 @@ "output_type": "stream", "text": [ "start from: [{'A'}, {'B'}, {'C'}, {'D'}, {'E'}, {'F'}]\n", - "final partition: [{'A', 'C', 'B'}, {'E', 'D', 'F'}]\n" + "final partition: [{'C', 'A', 'B'}, {'D', 'E', 'F'}]\n" ] } ], @@ -447,7 +441,7 @@ "outputs": [], "source": [ "## load the GoT dataset\n", - "Edges, Names, Weights = pickle.load(open( \"../Data/GoT.pkl\", \"rb\" ))" + "Edges, Names, Weights = pickle.load(open( \"../hypernetx/utils/toys/GoT.pkl\", \"rb\" ))" ] }, { @@ -492,7 +486,7 @@ { "data": { "text/plain": [ - "-0.023085535915365468" + "-0.032074856299121574" ] }, "execution_count": 14, @@ -507,7 +501,7 @@ "p = np.random.choice(K, size=len(V))\n", "RandPart = hmod.dict2part({V[i]:p[i] for i in range(len(V))})\n", "## compute qH\n", - "hmod.hypergraph_modularity(HG, RandPart)" + "hmod.modularity(HG, RandPart)" ] }, { @@ -538,7 +532,7 @@ "G.vs['louvain'] = ML.membership\n", "part = hmod.dict2part({v['name']:v['louvain'] for v in G.vs})\n", "## Compute qH\n", - "print(hmod.hypergraph_modularity(HG, part))" + "print(hmod.modularity(HG, part))" ] }, { @@ -566,7 +560,7 @@ "KU = hmod.kumar(HG)\n", "G.vs['kumar'] = [KU[v['name']] for v in G.vs]\n", "## Compute qH\n", - "print(hmod.hypergraph_modularity(HG, hmod.dict2part(KU)))" + "print(hmod.modularity(HG, hmod.dict2part(KU)))" ] }, { @@ -600,7 +594,7 @@ "## H-based last step\n", "LS = hmod.last_step(HG, part)\n", "## Compute qH\n", - "hmod.hypergraph_modularity(HG, LS)\n" + "hmod.modularity(HG, LS)\n" ] }, { @@ -642,27 +636,27 @@ " \n", "
      1815Daenerys Targaryen31103
      024Jorah Mormont19344
      277Missandei13683
      94Grey Worm10497
      1311Barristan Selmy6514
      - + + -
      +
      @@ -854,6 +850,8 @@

      M

  • merge_entities() (classes.entity.Entity static method) +
    • +
  • modularity() (in module algorithms.hypergraph_modularity)
  • module diff --git a/docs/build/glossary.html b/docs/build/glossary.html index fc331899..97c7ff26 100644 --- a/docs/build/glossary.html +++ b/docs/build/glossary.html @@ -7,7 +7,7 @@ - Glossary of HNX terms — HyperNetX 1.1.4dev documentation + Glossary of HNX terms — HyperNetX 1.1.4 documentation diff --git a/docs/build/home.html b/docs/build/home.html index ec2c55ff..e4d868a3 100644 --- a/docs/build/home.html +++ b/docs/build/home.html @@ -7,7 +7,7 @@ - HyperNetX (HNX) — HyperNetX 1.1.4dev documentation + HyperNetX (HNX) — HyperNetX 1.1.4 documentation diff --git a/docs/build/index.html b/docs/build/index.html index 11e318fe..ef8a1331 100644 --- a/docs/build/index.html +++ b/docs/build/index.html @@ -7,7 +7,7 @@ - HyperNetX (HNX) — HyperNetX 1.1.4dev documentation + HyperNetX (HNX) — HyperNetX 1.1.4 documentation diff --git a/docs/build/install.html b/docs/build/install.html index 9f99b4fb..f77d4e60 100644 --- a/docs/build/install.html +++ b/docs/build/install.html @@ -7,7 +7,7 @@ - Installing HyperNetX — HyperNetX 1.1.4dev documentation + Installing HyperNetX — HyperNetX 1.1.4 documentation diff --git a/docs/build/license.html b/docs/build/license.html index 2fe93c82..a05739db 100644 --- a/docs/build/license.html +++ b/docs/build/license.html @@ -7,7 +7,7 @@ - License — HyperNetX 1.1.4dev documentation + License — HyperNetX 1.1.4 documentation diff --git a/docs/build/nwhy.html b/docs/build/nwhy.html index 720fc66c..4ce4ad23 100644 --- a/docs/build/nwhy.html +++ b/docs/build/nwhy.html @@ -7,7 +7,7 @@ - NWHy — HyperNetX 1.1.4dev documentation + NWHy — HyperNetX 1.1.4 documentation diff --git a/docs/build/objects.inv b/docs/build/objects.inv index a331baaceb2dfc0a9b50182a2ce22b7fcdde08b3..556d5558656f805a6b4885c42aef7a39b438ceee 100644 GIT binary patch delta 2182 zcmV;12zmFz7QGgbe1BW7+cpw_-~B5DY#z42+S%qYZ`(9zil%AM6QFq`XlZP-hav@% zI*$A6mls_qTec-}oTnU9GvACTk~6$Wp?ohDGlx=V)#+_1i<(3B-;8#}ulqkY2UGHY z1)24$Y`uyuwk#Yy6w& z(3@Wv=b$SFm?+o_D{9Ig zUSTEu+g(197ir)@Hq1eRK1g05@b@4r&vj*D{qnA_2Ex z48vp*QI}g#8jJb-J2i@Z!@|j06jYtaUdg&bg60RxgniCs#R1FF?$?%ThE_uy0oQ4SgNfMboh~0%?PbAk+wn-czrkZafZ6QB{4FMAvaq)Y*kgc!}WCI zM#9?WjTKdfIVP!;w7nSFOaXfZKn(qc4(5iE!aM{EIX0H%=8darWG)o@FpW`cTTh-> zPUk<@C4W`OOir~S+CYWPWdhKhZD%Cd*ujv22zG7kjcaY#Tm|ijK}~3FfIU`d%SCtG z93|0^C-n_ZD^7Dpg(f+#ZPQgu?9;~jQKX$m8B#G^KQ}Ha6MG36>%s*wiYCP5aj>o+ z_rDi&Ee`CENV!`|D1qu)LE_YW6lqKCbm|Bo1b^sQ0@rg_%B6-!8-d)Hv-}b8168FI zEPvz;^foZ7s}?Rn;x>cshZRCa5w%%jW%o=_p4Q0bSlayTGpjENiPcPVHZl77%Xc*P z{@wR6_TJ`D?=VuRSyn%M`byrU2Bz5-e@n{eW}EwHQ@y8@OmDVDgv4r=&E(nHENkeK zwkuiI(r%<`MnkGaI@!z^3Lvx-^P(&q&8%=J@+NZL9i%oNa~m+_#-lto&+X;m!yFw5HN=2 zAt`-iRuCdAF){_C**{4YRI?)O8CgKnuNE_&g8s6oZIu%PwF6AtTdn%l;8TGY4+t}yFwj5Bv)6O&8@Fae8`eFZ53=kk-N1!w|Qi<1%tWE4Iv zPPgzc6Y$wJA4ZUb4docHdGeEr23HQA;e>j-mWS9RIo&p&PLuBjaDUg$zZcnlTdwM@ zTm0HmQKaf`T-n)~*-^|b{qT7#zhXi9NQgwlVxcOE5yEFhm2{yo zz>1kP1(j50_zpr-_PY7^;(@m1b`;7LtNdpRU3@~KY@&}7blB)#M!|Q8vaGtT>NHgI zK_(;^(+2AvFZblrc(y@F910nE`DOtsEY2g{ohE~W8)($QR(~iGHLMZOg8kGTe|RVD z^wW!LQ@piKmD&2oY|o|ERGor`D_wUg>t1EOY8GGECfKV7AJ zp<%j8yx1^Zt2@a*L1pyyFt1q1-059^e=NF`RWyI5dsyB10PlV9@LRpv_y@!kGkoz^;!8tIv6?8z7<>t4KTZl)C)xs$U?1O1gvpy+n! zwcC4tVQ_ry*9+SY`R64WQXCt%TfVA3lA}oDn_L>n#l2d38niAj?4$alg--$iJ=l54~h(p_3i7 zk5*bpmFbfky z^Y14_PPpGe?9*Q#PW|N$*9lm|oygm_*;{Q>0Vu8syT8_rh=+y_x*85#u_q%#TJ}d% zwyNr_tpxvHG7P$ZTpmC zl-8Zn`kdEa8u~C|d4|0UUB{jt1FdX(9DJg5b2599v_CO7S#m)m8LGvP*k74kqp!wv zW@|kEq2}3>_7{(oKTq?bKYKh$+&}*5^vRMyRel(bF`+DU@uzV8v+v%_y|JH75061% z9Vu9Y@j;wv2~VstJx$W}c3vP4GteoLM`j@$&R-N9JvZy&^|H)`q{M7%sIhd0F zBgm}ZWb0LQDVM^~J(FSrrjwln+)%+vY95R3%gDgot`@of`+tXN2CrIziWN|@T;tzF zhu-|mI0s!ZAkV-2?=kKVY&<{6MlP)l3w;GLXjcy~RWO|^FpsRU$i$;6jERE1vZAK^ z;T2ZWKi%aMd6fnpWWyX3=!4`10)Gzz6!mS%b|m15}Tk_ZIRH zJ`)C1Wn%^s@5R?Frib<~mfL7kNP|qYxtHyn_|UTENPo}(Q&7}7ARz;+aN`ky`~ckM zp*yL{ZY)Kxp-Wpzgr(XFL5Ck{)r`;@6KN|Xf!B9aA7`k`TM{GF7;>|#&K+ItXfywoLC$V^VPA=*HN&1C}6oo#1R$XM5m-?|2k%~jA2O4Nh~ z4cNnxwp?__%~28!c~bY|wBj^pRA`d(+O{yo#6B&Yj3Vt+&5(-W`nhp|pV&*tSQn9q zQ8Xd!kArmux&OVGYjI#lUCP~RMF~{b3KD1kqkl+SYG-Um03krf61bkbQZ6+-+6d&n zoaK*zAE+v&VEH3&ptpfpUA1sI7`I7!KdcZcim1&JE4ybRMB?R*Y>uVPPd<73nvlEA zG-q?KpT7Q$#y-6NdyIXsY1(^?ENzz6kDtGhx2b_?w#8qQ(yrO&KG}@yIVB;RZ4n`1 zoiSxIc>*}g8v49&mi6>`(kyf7)0gGd7@E5GRqkyZLn`X1oCM5@1EK(=Hp|qt)7)^FcmMO6O zz<8m+$0a*ZRG@4P&8KiFAju8Ck#C1B1$$;&)+q&6KxGaofVOtiDrHx5j&VCo z195%P_`^nVq)8z6{k!Z=?j_(BH@)G}(AjxfufF*>AWeSqqlAz#*$MyBn;By*>EPFi zLKff-nPPe=j)xNt>ra~7#&ufMxCyQ>>+g&+cPJE-Mg=edtCM#HDFR3TlcEJ^0vn8z z3~9rr{>>_ zY`-m6_0}zZZK)_y^*65U?9A*a=9Yf=0+(O0Abli6B4V*n9qZOnma^P(d;vRJb3+s@ zSe-=U^dxJ8RcQ^)FDY{-Ugomg_X_$Cp3QEZz51fYd8VFHDdVVR4Jl$Z^*gMlIjq_+ zzNl)QqHlUTCI7NhN4Lr3?q-R91Kk!C8T<6!bVP9C)V+{48^Xk{&2pCNl9wY@d%XzZ zv!Y76&=_FFOqzm9Dl>csp(%T6{=ImhZMhwVa>XkDWkeUBkSLqz;{+Wxx|dP#9il9& zZmT*C)qL{_3C6U+y2r~s`81wwP!fkiMqa*IfC`KANOz~n;NS)tb+8qGibM@-#Is;O zcE=yyOFRAa;@TANtW#yS{vq3QsWnxnpy5i_oyxjbS+AOf+&j8&I>~+|bh=7-LuI;r zm-|mw>0W4JR9D@%BE9PFtB2O2ejYL`3&Y;_9ZwM3HMdg zid*!ELbEeCp#`3$1+hzG8AI018=H!2g+?mw?9xDg<&OzY@UNeLhu?1+m-2Vudk|q! zcff8-gTueLKUXavzP|x9`?-~nyX@oVZ<#ZqhkCt*fje)m=pJM_Xd>?Sc@z1k75`T+ z*;(jh2koPk7E)#Uqz#l4YSoLc8}aojdZl4P4R=c(S+nGEN2iltH}VeAKB1-_qK9*S zYfRX9$@x&)7qt(6)BkV{xKmwmx2mECRoxT?rBC6-IuG z{}xi^(8m1x36T@-cM$vh$H!BDxx;k=)^I2C?p^jy+f)FGYr^iYbtB@Tp@Xi516S-V zI=d7YP2uV+(;Cx@ld!CtS2ZK0UU#RNCw7KNqwfy3uzgo#I;Pc#G5yiR?p(^hGre}{ z>)5sXRyw71r?fuj^_PY|j98vw??TtHr^i4m+a3p>DBYaQo+Ry0%uSYD&`5@A@gw$E zCfDejF`XG}&wr?S_N4vABjqpCyy(v!PZIZ!e>#1#Bv6$fhGR@93tjvs*`H*;_7 zXVb%DP*_I_)?j=PXIjD&t4vRmbiJJy$iobDisX^mM{VNaF_c6RTY&k#{==8X;+@-( OLYu$DD*p%2+BY>A)JgLI diff --git a/docs/build/overview/index.html b/docs/build/overview/index.html index 8726a25b..531e077e 100644 --- a/docs/build/overview/index.html +++ b/docs/build/overview/index.html @@ -7,7 +7,7 @@ - Overview — HyperNetX 1.1.4dev documentation + Overview — HyperNetX 1.1.4 documentation diff --git a/docs/build/publications.html b/docs/build/publications.html index 7013cfb0..38c108e0 100644 --- a/docs/build/publications.html +++ b/docs/build/publications.html @@ -7,7 +7,7 @@ - Publications — HyperNetX 1.1.4dev documentation + Publications — HyperNetX 1.1.4 documentation diff --git a/docs/build/py-modindex.html b/docs/build/py-modindex.html index 9e4bcdf5..2d6df30c 100644 --- a/docs/build/py-modindex.html +++ b/docs/build/py-modindex.html @@ -7,7 +7,7 @@ - Python Module Index — HyperNetX 1.1.4dev documentation + Python Module Index — HyperNetX 1.1.4 documentation diff --git a/docs/build/reports/modules.html b/docs/build/reports/modules.html index d4c39a5a..5aeae66b 100644 --- a/docs/build/reports/modules.html +++ b/docs/build/reports/modules.html @@ -7,7 +7,7 @@ - reports — HyperNetX 1.1.4dev documentation + reports — HyperNetX 1.1.4 documentation diff --git a/docs/build/reports/reports.html b/docs/build/reports/reports.html index bc73e2dd..e3dcb111 100644 --- a/docs/build/reports/reports.html +++ b/docs/build/reports/reports.html @@ -7,7 +7,7 @@ - reports package — HyperNetX 1.1.4dev documentation + reports package — HyperNetX 1.1.4 documentation diff --git a/docs/build/search.html b/docs/build/search.html index b82bb118..47bda131 100644 --- a/docs/build/search.html +++ b/docs/build/search.html @@ -7,7 +7,7 @@ - Search — HyperNetX 1.1.4dev documentation + Search — HyperNetX 1.1.4 documentation diff --git a/docs/build/searchindex.js b/docs/build/searchindex.js index 3c7a2216..67e5d85f 100644 --- a/docs/build/searchindex.js +++ b/docs/build/searchindex.js @@ -1 +1 @@ -Search.setIndex({docnames:["algorithms/algorithms","algorithms/algorithms.contagion","algorithms/modules","classes/classes","classes/modules","core","drawing/drawing","drawing/modules","glossary","home","index","install","license","nwhy","overview/index","publications","reports/modules","reports/reports","widget"],envversion:{"sphinx.domains.c":2,"sphinx.domains.changeset":1,"sphinx.domains.citation":1,"sphinx.domains.cpp":4,"sphinx.domains.index":1,"sphinx.domains.javascript":2,"sphinx.domains.math":2,"sphinx.domains.python":3,"sphinx.domains.rst":2,"sphinx.domains.std":2,"sphinx.ext.intersphinx":1,"sphinx.ext.todo":2,"sphinx.ext.viewcode":1,sphinx:56},filenames:["algorithms/algorithms.rst","algorithms/algorithms.contagion.rst","algorithms/modules.rst","classes/classes.rst","classes/modules.rst","core.rst","drawing/drawing.rst","drawing/modules.rst","glossary.rst","home.rst","index.rst","install.rst","license.rst","nwhy.rst","overview/index.rst","publications.rst","reports/modules.rst","reports/reports.rst","widget.rst"],objects:{"":{algorithms:[0,0,0,"-"],classes:[3,0,0,"-"],drawing:[6,0,0,"-"],reports:[17,0,0,"-"]},"algorithms.contagion":{animation:[1,0,0,"-"],epidemics:[1,0,0,"-"]},"algorithms.contagion.animation":{contagion_animation:[1,1,1,""]},"algorithms.contagion.epidemics":{Gillespie_SIR:[1,1,1,""],Gillespie_SIS:[1,1,1,""],collective_contagion:[1,1,1,""],discrete_SIR:[1,1,1,""],discrete_SIS:[1,1,1,""],individual_contagion:[1,1,1,""],majority_vote:[1,1,1,""],threshold:[1,1,1,""]},"algorithms.generative_models":{chung_lu_hypergraph:[0,1,1,""],dcsbm_hypergraph:[0,1,1,""],erdos_renyi_hypergraph:[0,1,1,""]},"algorithms.homology_mod2":{add_to_column:[0,1,1,""],add_to_row:[0,1,1,""],betti:[0,1,1,""],betti_numbers:[0,1,1,""],bkMatrix:[0,1,1,""],boundary_group:[0,1,1,""],chain_complex:[0,1,1,""],homology_basis:[0,1,1,""],hypergraph_homology_basis:[0,1,1,""],interpret:[0,1,1,""],kchainbasis:[0,1,1,""],logical_dot:[0,1,1,""],logical_matadd:[0,1,1,""],logical_matmul:[0,1,1,""],matmulreduce:[0,1,1,""],reduced_row_echelon_form_mod2:[0,1,1,""],smith_normal_form_mod2:[0,1,1,""],swap_columns:[0,1,1,""],swap_rows:[0,1,1,""]},"algorithms.hypergraph_modularity":{bin_ppmf:[0,1,1,""],compute_partition_probas:[0,1,1,""],degree_tax:[0,1,1,""],delta_dt:[0,1,1,""],delta_ec:[0,1,1,""],dict2part:[0,1,1,""],edge_contribution:[0,1,1,""],factorial:[0,1,1,""],hypergraph_modularity:[0,1,1,""],kumar:[0,1,1,""],last_step:[0,1,1,""],linear:[0,1,1,""],majority:[0,1,1,""],part2dict:[0,1,1,""],precompute_attributes:[0,1,1,""],strict:[0,1,1,""],two_section:[0,1,1,""]},"algorithms.laplacians_clustering":{get_pi:[0,1,1,""],norm_lap:[0,1,1,""],prob_trans:[0,1,1,""],spec_clus:[0,1,1,""]},"algorithms.s_centrality_measures":{s_betweenness_centrality:[0,1,1,""],s_closeness_centrality:[0,1,1,""],s_eccentricity:[0,1,1,""],s_harmonic_centrality:[0,1,1,""],s_harmonic_closeness_centrality:[0,1,1,""]},"algorithms.untitiled_modularity_and_clustering_original":{DegreeTax:[0,1,1,""],DeltaDT:[0,1,1,""],DeltaEC:[0,1,1,""],EdgeContribution:[0,1,1,""],HNX_2section:[0,1,1,""],HNX_Kumar:[0,1,1,""],HNX_LastStep:[0,1,1,""],HNX_modularity:[0,1,1,""],bin_ppmf:[0,1,1,""],compute_partition_probas:[0,1,1,""],dict2part:[0,1,1,""],factorial:[0,1,1,""],linear:[0,1,1,""],majority:[0,1,1,""],part2dict:[0,1,1,""],precompute_modularity_parameters:[0,1,1,""],strict:[0,1,1,""]},"algorithms.untitled_modularity_and_clustering":{DegreeTax:[0,1,1,""],DeltaDT:[0,1,1,""],DeltaEC:[0,1,1,""],EdgeContribution:[0,1,1,""],HNX_2section:[0,1,1,""],HNX_Kumar:[0,1,1,""],HNX_LastStep:[0,1,1,""],HNX_modularity:[0,1,1,""],HNX_precompute:[0,1,1,""],bin_ppmf:[0,1,1,""],compute_partition_probas:[0,1,1,""],dict2part:[0,1,1,""],factorial:[0,1,1,""],linear:[0,1,1,""],majority:[0,1,1,""],part2dict:[0,1,1,""],strict:[0,1,1,""]},"classes.entity":{Entity:[3,2,1,""],EntitySet:[3,2,1,""]},"classes.entity.Entity":{add:[3,3,1,""],add_element:[3,3,1,""],add_elements_from:[3,3,1,""],children:[3,4,1,""],clone:[3,3,1,""],complete_registry:[3,3,1,""],depth:[3,3,1,""],elements:[3,4,1,""],fullregistry:[3,3,1,""],incidence_dict:[3,4,1,""],intersection:[3,3,1,""],is_bipartite:[3,4,1,""],is_empty:[3,4,1,""],level:[3,3,1,""],levelset:[3,3,1,""],memberships:[3,4,1,""],merge_entities:[3,3,1,""],nested_incidence_dict:[3,3,1,""],properties:[3,4,1,""],registry:[3,4,1,""],remove:[3,3,1,""],remove_element:[3,3,1,""],remove_elements_from:[3,3,1,""],restrict_to:[3,3,1,""],size:[3,3,1,""],uid:[3,4,1,""],uidset:[3,4,1,""]},"classes.entity.EntitySet":{add:[3,3,1,""],clone:[3,3,1,""],collapse_identical_elements:[3,3,1,""],incidence_matrix:[3,3,1,""],restrict_to:[3,3,1,""]},"classes.hypergraph":{Hypergraph:[3,2,1,""]},"classes.hypergraph.Hypergraph":{add_edge:[3,3,1,""],add_edges_from:[3,3,1,""],add_node_to_edge:[3,3,1,""],add_nwhy:[3,3,1,""],adjacency_matrix:[3,3,1,""],auxiliary_matrix:[3,3,1,""],bipartite:[3,3,1,""],collapse_edges:[3,3,1,""],collapse_nodes:[3,3,1,""],collapse_nodes_and_edges:[3,3,1,""],component_subgraphs:[3,3,1,""],components:[3,3,1,""],connected_component_subgraphs:[3,3,1,""],connected_components:[3,3,1,""],convert_to_static:[3,3,1,""],dataframe:[3,3,1,""],degree:[3,3,1,""],diameter:[3,3,1,""],dim:[3,3,1,""],distance:[3,3,1,""],dual:[3,3,1,""],edge_adjacency_matrix:[3,3,1,""],edge_diameter:[3,3,1,""],edge_diameters:[3,3,1,""],edge_distance:[3,3,1,""],edge_neighbors:[3,3,1,""],edge_size_dist:[3,3,1,""],edges:[3,4,1,""],from_bipartite:[3,3,1,""],from_dataframe:[3,3,1,""],from_numpy_array:[3,3,1,""],get_id:[3,3,1,""],get_linegraph:[3,3,1,""],get_name:[3,3,1,""],incidence_dict:[3,4,1,""],incidence_matrix:[3,3,1,""],is_connected:[3,3,1,""],isstatic:[3,4,1,""],neighbors:[3,3,1,""],node_diameters:[3,3,1,""],nodes:[3,4,1,""],number_of_edges:[3,3,1,""],number_of_nodes:[3,3,1,""],order:[3,3,1,""],recover_from_state:[3,3,1,""],remove_edge:[3,3,1,""],remove_edges:[3,3,1,""],remove_node:[3,3,1,""],remove_nodes:[3,3,1,""],remove_singletons:[3,3,1,""],remove_static:[3,3,1,""],restrict_to_edges:[3,3,1,""],restrict_to_nodes:[3,3,1,""],s_component_subgraphs:[3,3,1,""],s_components:[3,3,1,""],s_connected_components:[3,3,1,""],s_degree:[3,3,1,""],save_state:[3,3,1,""],set_state:[3,3,1,""],shape:[3,4,1,""],singletons:[3,3,1,""],size:[3,3,1,""],toplexes:[3,3,1,""],translate:[3,3,1,""]},"classes.staticentity":{StaticEntity:[3,2,1,""],StaticEntitySet:[3,2,1,""]},"classes.staticentity.StaticEntity":{arr:[3,4,1,""],cell_weights:[3,4,1,""],children:[3,4,1,""],data:[3,4,1,""],dataframe:[3,4,1,""],dimensions:[3,4,1,""],dimsize:[3,4,1,""],elements:[3,4,1,""],elements_by_level:[3,3,1,""],incidence_dict:[3,4,1,""],incidence_matrix:[3,3,1,""],index:[3,3,1,""],indices:[3,3,1,""],is_empty:[3,3,1,""],keyindex:[3,3,1,""],keys:[3,4,1,""],labels:[3,4,1,""],labs:[3,3,1,""],level:[3,3,1,""],memberships:[3,4,1,""],properties:[3,5,1,""],restrict_to_indices:[3,3,1,""],restrict_to_levels:[3,3,1,""],size:[3,3,1,""],translate:[3,3,1,""],translate_arr:[3,3,1,""],turn_entity_data_into_dataframe:[3,3,1,""],uid:[3,4,1,""],uidset:[3,4,1,""],uidset_by_level:[3,3,1,""]},"classes.staticentity.StaticEntitySet":{collapse_identical_elements:[3,3,1,""],convert_to_entityset:[3,3,1,""],incidence_matrix:[3,3,1,""],restrict_to:[3,3,1,""]},"drawing.rubber_band":{draw:[6,1,1,""],draw_hyper_edge_labels:[6,1,1,""],draw_hyper_edges:[6,1,1,""],draw_hyper_labels:[6,1,1,""],draw_hyper_nodes:[6,1,1,""],get_default_radius:[6,1,1,""],layout_hyper_edges:[6,1,1,""],layout_node_link:[6,1,1,""]},"drawing.two_column":{draw:[6,1,1,""],draw_hyper_edges:[6,1,1,""],draw_hyper_labels:[6,1,1,""],layout_two_column:[6,1,1,""]},"drawing.util":{get_frozenset_label:[6,1,1,""],get_line_graph:[6,1,1,""],get_set_layering:[6,1,1,""],inflate:[6,1,1,""],inflate_kwargs:[6,1,1,""],transpose_inflated_kwargs:[6,1,1,""]},"reports.descriptive_stats":{centrality_stats:[17,1,1,""],comp_dist:[17,1,1,""],degree_dist:[17,1,1,""],dist_stats:[17,1,1,""],edge_size_dist:[17,1,1,""],info:[17,1,1,""],info_dict:[17,1,1,""],s_comp_dist:[17,1,1,""],s_edge_diameter_dist:[17,1,1,""],s_node_diameter_dist:[17,1,1,""],toplex_dist:[17,1,1,""]},algorithms:{contagion:[1,0,0,"-"],generative_models:[0,0,0,"-"],homology_mod2:[0,0,0,"-"],hypergraph_modularity:[0,0,0,"-"],laplacians_clustering:[0,0,0,"-"],s_centrality_measures:[0,0,0,"-"],untitiled_modularity_and_clustering_original:[0,0,0,"-"],untitled_modularity_and_clustering:[0,0,0,"-"]},classes:{entity:[3,0,0,"-"],hypergraph:[3,0,0,"-"],staticentity:[3,0,0,"-"]},drawing:{rubber_band:[6,0,0,"-"],two_column:[6,0,0,"-"],util:[6,0,0,"-"]},reports:{descriptive_stats:[17,0,0,"-"]}},objnames:{"0":["py","module","Python module"],"1":["py","function","Python function"],"2":["py","class","Python class"],"3":["py","method","Python method"],"4":["py","property","Python property"],"5":["py","attribute","Python attribute"]},objtypes:{"0":"py:module","1":"py:function","2":"py:class","3":"py:method","4":"py:property","5":"py:attribute"},terms:{"0":[0,1,3,6,8,10,13,15],"0020034":1,"00231":[0,15],"01":0,"012805":0,"019":1,"020":[0,15],"021":15,"0224307":0,"030":[0,15],"04197":15,"1":[0,1,3,6,8,10,13,15,17],"10":[0,1,3,15],"100":[0,1],"1000":[0,1,3],"10000":1,"1007":[0,15],"1038":1,"10431":1,"1063":1,"1093":0,"1103":0,"1140":[0,15],"1145":0,"11782":15,"1186":15,"12901":15,"13":0,"1371":0,"15":15,"16":[0,15],"17th":15,"19":0,"1_1":15,"2":[0,1,3,6,8,13,14,15],"2003":15,"2005":0,"2016":0,"2018":12,"2019":[0,15],"2020":[0,15],"22":15,"27":0,"287":15,"29th":0,"2_24":0,"2d":0,"2z":0,"3":[0,1,3,6,11,13,14,15],"3340531":0,"3412034":0,"35":6,"36687":0,"4":[1,3,14],"48478":15,"495":0,"5":[0,1,3,6,14],"504":0,"6":[1,3,14],"7":[11,14],"755":11,"76rl01830":14,"881":0,"9":[0,11,13,15],"90":0,"978":[0,15],"abstract":0,"bogumi\u0142":0,"boolean":[0,3,13],"case":[0,3,14],"class":[0,5,8,9,10],"default":[0,1,3,6,17],"do":[3,8,12,13,14],"export":13,"final":0,"float":[0,1,3,6],"fran\u00e7oi":0,"function":[0,1,3,6],"import":[0,1,3,10],"int":[0,1,3,6,15],"kami\u0144ski":0,"long":[0,17],"new":[0,3,6,10,13],"null":11,"pawe\u0142":0,"pra\u0142at":0,"przemys\u0142aw":0,"public":[9,10],"return":[0,1,3,6,8,13,17],"static":[0,3,14],"super":18,"switch":8,"th\u00e9berg":0,"true":[0,1,3,6,13,17],"try":0,"val\u00e9ri":0,"while":18,A:[0,1,3,6,8,12,13,15],AND:12,AS:12,As:[3,9,10],At:0,BE:12,BUT:12,BY:12,By:[3,6],FOR:12,For:[0,3,6,8,9,10,11,13,14,18],IF:12,IN:12,IS:12,If:[0,1,3,6,8,11,13,17],In:[0,3,6,13,14],It:[3,6,13],NO:12,NOT:12,Not:3,OF:[12,14],ON:12,OR:12,One:3,SUCH:12,Such:12,THE:12,TO:12,That:0,The:[0,1,3,6,8,9,10,13,14,18],Their:0,Then:[6,10],These:[0,18],To:[0,3,9,10],Will:3,_0:3,_1:3,_2:[0,3],_:[0,3],__dict__:3,_edg:3,_node:3,_version:13,a_i:0,ab:[3,15],abl:1,about:[9,10],abov:[3,6,12,17],ac05:14,accept:[6,13],access:[8,11],accomplish:0,accord:8,account:[1,14],accuraci:14,acm:0,aco5:14,across:6,action:0,activ:[10,11,18],actual:0,ad:[0,3,6,14],adam:15,adapt:0,adaptor:13,add:[0,3],add_edg:3,add_edges_from:3,add_el:3,add_elements_from:3,add_node_to_edg:3,add_nodes_from:3,add_nwhi:3,add_to_column:0,add_to_row:0,addit:[0,3,14],addon:[13,14,18],adjac:[0,3,8,9,10],adjacency_matrix:3,adjust:6,admit:[9,10],advanc:18,advis:12,after:[3,13],against:3,agenc:14,aggreg:[3,17],aggregatebi:3,ah:15,aksoi:[0,14,15],al:[0,1,15],algebra:[9,10],algorithm:[5,6,9,10,13,14,15,18],align:[3,6],all:[0,1,3,6,8,11,13,14,17,18],allow:[1,3,6,18],alpha:[1,6],alreadi:[1,3,18],also:[0,3,8,9,10,13,17,18],alter:0,altern:18,ami:15,among:[9,10],amount:6,an:[0,1,3,6,8,10,14,17,18],anaconda3:11,anaconda:10,analysi:13,analyt:[14,15],ananthapadmanabhan:0,andrew:14,angl:6,ani:[0,3,8,12,13,14,18],anim:[0,2,5,10],annal:0,annot:6,anoth:[0,6,8],api:10,apparatu:14,appear:[0,3,18],appli:[0,3,6],applic:[0,3],approach:6,appropri:6,ar1:0,ar2:0,ar:[0,1,3,6,8,9,10,11,12,13,14,18],arbitrari:[6,9,10],arendt:[14,15],arg:[0,1,3],arg_set:3,argument:[1,3,6],argumetn:6,aris:12,around:6,arr:[0,3],arrai:[0,1,3,13],articl:15,arxiv:15,asc:0,aspect:17,assign:[3,6],associ:[0,3,12,13],assum:[3,14],attribut:[0,3,8,10],author:14,automat:[1,3],auxiliari:[3,8],auxiliary_matrix:3,avail:[0,3,14,18],averag:13,ax:6,axi:6,azsecur:15,b:[0,3,6,8,15],back:13,backend:3,background:18,band:6,baric:15,base:[0,3,6,8,13,14,18],basi:0,basic:[3,8,9,10,14,17],bat:11,battel:[12,14],bd:0,bdict:3,becaus:[9,10],becom:[0,3],been:[0,13],befor:3,behavior:0,behind:6,being:0,belong:[0,3,8,13],below:11,berg:0,best:0,betti:0,betti_numb:0,between:[0,1,3,6,8,13,18],big:15,bin_ppmf:0,binari:[0,12],binomi:0,bioinformat:15,biolog:15,biomedcentr:15,bipartit:[0,3,6,8,18],bk:0,bkmatrix:0,block:10,blue:1,bmc:15,bmcbioinformat:15,book:14,bool:[0,1,3,6,17],both:[1,3,8,9,10,13,18],bound:0,boundari:[0,6],boundary_group:0,box:6,bramer:15,brenda:[14,15],brett:15,brian:14,briefest:0,browser:[11,14],bsd:14,build:[3,10,11],build_doc:11,built:18,bulk:18,busi:12,button:18,c:[0,1,3,6,10,11,13,14,15],c_:0,c_b:[0,13],c_k:0,ca:15,calcul:6,call:[6,8,13],callahan:15,can:[0,1,3,6,8,9,10,13,14,18],cannot:[1,3],capabl:18,cardin:3,care:3,carlo:15,categori:3,caus:[3,12,18],caution:3,cdotfrac:0,cell:[0,3,14,17],cell_weight:[0,3],center:6,central:[2,5,10,13,14,17],centrality_stat:17,certain:3,chain:0,chain_complex:0,cham:0,chang:[0,1,3,6,18],check:[3,9,10,13],check_connect:0,cheeger:0,cherifi:0,child:3,children:[3,8],chmod:11,choic:[0,1],choos:[1,3],chosen:[0,3,6],chung:0,chung_lu_hypergraph:0,chunglu:14,cikm:0,circl:[6,18],circular:1,ck:0,classmethod:3,claus:14,click:18,cliff:[14,15],cliqu:[9,10],clone:[3,11],close:[0,13],cluster:[2,5,10,14],cnx001:0,cockrel:15,code:12,coeffici:0,col:13,colab:[3,10],coldict:3,collaps:[3,6,13,18],collapse_edg:[3,13],collapse_identical_el:3,collapse_nod:[3,13],collapse_nodes_and_edg:[3,13],collect:[1,3,6],collective_contagion:1,collumn:6,colon:3,color:[1,3,6,18],column:[0,3,6,8,13,14,17],column_index:3,com:[11,15],combin:13,combinator:0,come:11,command:[3,11,18],comment:[9,10,14],commerci:14,common:1,commun:[0,9,10,14],comnet:0,comp:17,comp_dist:17,compar:[3,13],complet:[8,14,18],complete_registri:3,complex:[0,3,9,10,13,15],compon:[0,3,6,8,13,17],component_subgraph:3,comput:[0,3,6,14,15,17],compute_partition_proba:0,concentr:6,concern:0,conda:[11,13],condit:[3,8,12],conf:15,confer:0,conflict:3,connect:[0,3,6,8,9,10,13,17],connected:0,connected_compon:3,connected_component_subgraph:3,consecut:3,consent:12,consequenti:12,consid:3,constitut:14,construct:[0,1,3,8,13,14],constructor:[3,6,13,14],contact:[9,10,14],contagi:1,contagion:[0,2,5,10,14],contagion_anim:1,contain:[0,3,6,8,13,17,18],content:[2,4,5,7,16],context:[0,3],continu:[1,11],contract:[12,14],contribut:0,contributor:[9,10,12,14],control:[3,18],contruct:0,conveni:[3,6],converg:0,convert:[3,6],convert_to_entityset:3,convert_to_stat:3,convex:6,cooper:14,coord:3,coordin:[3,6],copi:[0,3,12,13],copyright:12,core:3,correct:6,correspond:[0,3,8,14],coset:0,could:3,count:[3,6,17],counter:17,creat:[0,3,11,13,14,17],creation:3,criteria:13,criterion:0,critic:15,cross:6,csr:[0,3],csr_matrix:[0,3],ctrl:18,current:[0,1,13],current_st:3,curvi:6,custom:6,cybersecur:15,cycl:[0,3,6],cyclic:0,d:[0,3,13,15],damag:12,daniel:15,data:[0,3,6,9,10,12,13,14,15],data_subset:3,datafram:[3,14],dcsbm:[0,14],dcsbm_hypergraph:0,de:[14,18],dedup:3,deeper:3,defaultdict:3,defin:[0,1,3],degre:[0,3,8,13,17,18],degree_dist:17,degree_tax:0,degreetax:0,delet:3,delta:0,delta_dt:0,delta_ec:0,deltadt:0,deltaec:0,demo:18,denorm:0,denot:1,densiti:17,depart:14,depend:[0,1,3,13],deprec:3,depth:[0,3,8],deriv:3,descend:3,describ:[0,1],descript:[0,3],descriptive_stat:[5,10,16],design:14,desir:3,dest:13,detail:[0,18],detect:0,determin:[0,3,6],develop:[9,10,13,14],deviat:17,df:3,diagon:0,diagram:[6,18],diamet:[3,8,13,17],diamond:15,dict2part:0,dict:[0,3,6,17],dictionari:[0,1,3,6,8,13,17],differ:[3,13],digraph:[0,6],dim:[0,3,13],dimens:[0,3,13],dimension:[0,3,9,10],dimensionsl:3,dimsiz:3,direct:[0,3,6,12,13,18],directli:[3,9,10,14,18],dirti:6,disabl:6,discard:3,disclaim:12,disclos:14,disconnect:6,discov:0,discret:1,discrete_si:1,discrete_sir:1,discuss:0,disjoint:[0,3,8],disonnecct:6,displai:1,dist:17,dist_stat:17,distanc:[0,3,6,8,13],distant:6,distinct:3,distinguish:[3,8,9,10],distribut:[0,12,13,17],divid:[0,1],dlfer:0,doc:11,document:[3,11,12],doe:[3,6,14],doesn:1,doi:[0,1,15],domain:[0,15],done:[3,13],dot:0,down:18,dr:6,drag:18,draw:[1,5,10],draw_hyper_edg:6,draw_hyper_edge_label:6,draw_hyper_label:6,draw_hyper_nod:6,drawn:6,drop:3,dt:1,dual:[3,8],duplic:[0,3],dustin:[14,15],dynam:[0,3,8],e0:3,e1:3,e2:3,e3:3,e:[0,3,6,8,11,13,15,17,18],e_1:3,e_2:3,e_end:3,e_n:3,e_start:3,each:[0,1,3,6,8,13,17,18],easier:6,ecc:0,eccentr:[0,13],echelon:0,ed:[0,15],edg:[0,1,3,6,8,9,10,13,14,17,18],edge_adjac:3,edge_adjacency_matrix:3,edge_column_nam:3,edge_contribut:0,edge_diamet:3,edge_dist:3,edge_incid:13,edge_kwarg:6,edge_label:[0,3,6],edge_labels_kwarg:6,edge_nam:3,edge_neighbor:3,edge_set:3,edge_size_dist:[3,13,17],edge_state_color_dict:1,edge_uid:3,edgecontribut:0,edges_kwarg:6,edgeset:3,edit:11,effect:[0,1,3],eg:0,eigenvalu:0,eigenvector:0,eisfeld:15,either:[3,8,13,17],element:[0,3,6,8,13],element_subset:3,elements_by_level:3,els:1,emili:[14,15],emploi:3,employe:14,empti:[3,8,13],en:[1,3],encapsul:13,end:3,endors:14,energi:14,ensur:3,ent1:3,ent2:3,entir:18,entiti:[4,5,6,8,9,10,12,14],entityset:[3,8],entri:[0,3,8,13],env:[11,13],environ:[10,14],eon:1,epidem:[0,2,5,10],epidemicsonnetwork:1,epj:[0,15],epjd:[0,15],eq_class:3,equal:[0,1,3,8,13],equat:0,equival:[0,3,13],equivalence_class:3,erdo:0,erdos_renyi_hypergraph:0,error:[0,3,13],essenc:0,et:[0,1,15],euler:18,evalu:3,even:12,event:[1,12],everi:[0,3,8,13,18],everyth:18,ex:[0,3,11],exact:0,exactli:8,exampl:[0,1,3,6,11,14,18],exceed:3,except:8,execut:11,exemplari:12,exhibit:0,exist:[0,3,6,8],existing_lap:0,exp:0,expand:[6,18],expect:0,explicit:0,explor:[9,10],expos:3,express:[12,14],extend:18,extens:[0,11],extra:1,f:[0,15],facecolor:6,factori:0,fail:3,fall:0,fals:[0,1,3,6,13,17],fan:[0,15],fast:3,faster:[0,13],favor:14,featur:[0,10],feng:15,ferrario:0,fig:1,figur:[1,6],file:[3,11,12],filepath:3,fill:[3,17],fillna:3,filter:13,find:[6,9,10],firoz:15,first:[3,6],firstlevel:3,fit:12,fix:3,flexibl:3,fly:13,folder:0,follow:[3,6,11,12,14],forc:18,fork:11,form:[2,3,5,10,12],format:[3,13,17],forth:13,forward:1,found:[3,9,10],four:14,fp:1,fpath:3,frac:[0,13],fraction:[0,1,6,13],frame:[1,3],from:[0,1,3,6,8,11,13,15,17,18],from_bipartit:[3,8],from_datafram:3,from_numpy_arrai:3,frozen:3,frozenset:3,fruchterman_reingold_layout:6,full:3,fullregistri:3,func:0,further:6,g1:0,g2:0,g:[0,6,13,15,17],gaito:0,gamma:[0,1],gene:15,gener:[0,3,6,8,9,10,11,14,17],generative_model:[2,5,10],get_default_radiu:6,get_frozenset_label:6,get_id:3,get_line_graph:6,get_linegraph:3,get_nam:3,get_pi:0,get_set_lay:6,get_singleton:13,gillespie_si:1,gillespie_sir:1,github:[0,11,14,18],give:[0,3,18],given:[0,3,6,8,13],glossari:10,gm:0,go:[0,17],goal:13,good:[0,12],googl:14,gotten:3,gov:[0,9,10,14],govern:14,grant:12,graph:[0,3,6,8,9,10,13,15,18],greater:0,green:1,group:0,grow:[9,10,14],guarante:6,h:[0,1,3,6,17],h_k:0,ha:[1,3,8,13,14,18],halfmann:15,handl:6,happen:1,harmon:[0,13],hashabl:[1,3],hasn:1,have:[0,1,3,6,8,9,10,13,14,18],hayashi:0,header:[3,14],heal:1,heath:15,held:3,heller:15,help:18,helper:6,henc:3,henri:15,here:[13,18],herebi:12,herein:[12,14],hereinaft:12,heterogen:1,hg:0,hicss:15,hidden:18,hide:18,high:[0,13,14,15],higher:0,highlight:14,hist:17,hit:18,hnx:[0,1,3,11,13,14,18],hnx_2section:0,hnx_kumar:0,hnx_laststep:0,hnx_modular:0,hnx_precomput:0,hnxwidget:18,hold:18,holder:12,home:10,homolog:[2,5,9,10,14],homology_basi:0,homology_mod2:[2,5,10],honor:3,how:3,howev:12,hpda:14,html:[1,11],http:[0,1,11,15],hugh:15,hull:6,hunter:15,hyper:[3,6,8,18],hyperedg:[0,3,8,9,10,13,14],hyperedgelist:1,hypergraph:[1,2,4,5,6,8,9,10,13,14,15,17,18],hypergraph_homology_basi:0,hypergraph_modular:[2,5,10],hypergraphedg:3,hypernet:14,hypernetwork:[0,15],hypernetx:[0,1,3,12,14],hypernetxerror:[0,3],hypernetxwidget:18,i:[0,1,3,8,13,18],i_m:0,i_n:0,iacopini:1,icc:15,id:[0,1,3,6,8,13],ideal:0,ident:[0,3,6,18],identifi:[0,3,15],idx:3,ignacio:15,ignor:[0,3],igraph:0,illustr:6,im:0,imag:0,image_basi:0,immut:3,implement:[0,1,13],impli:[6,12,14],implic:0,impos:8,improv:18,incid:[0,3,8,9,10,13,14,17],incidence_dict:3,incidence_matrix:3,incident:12,includ:[3,9,10,12],inclus:[0,3],inde:3,independ:[6,18],index:[0,3,8,10,11],indic:[0,3,13],indirect:12,individu:1,individual_contagion:1,induc:[3,8],inequ:0,inf:[1,3],infect:1,infin:3,infinit:8,inflat:6,inflate_kwarg:6,info:17,info_dict:17,inform:[0,3,14,17],infring:14,initi:[0,1],initial_infect:1,initial_recov:1,inner:0,input:[0,3],inquiri:0,inseper:3,insert:3,insid:3,insight:0,inspect:14,instal:[3,10],instanc:[3,8],instanti:[3,8],instead:[3,6,13],institut:[12,14],instruct:11,int64:0,integ:[0,3,6,8,13,17],intel:10,intellig:0,intend:[0,6],intens:3,inter:3,interact:[14,18],interest:[0,3],interfac:18,intern:[0,3],interpret:[0,13],interpreted_basi:0,interrupt:12,intersect:[0,3,6,8],intuit:8,invers:0,invert:0,investig:14,invis:6,io:1,ipython:1,is_bipartit:3,is_connect:3,is_empti:3,is_s_connect:13,isn:3,isomorph:[3,8],isstat:3,item:[3,6,17],iter:[0,1,3,6,17],ith:0,iti:8,its:[0,3,6,8,13,14,18],itself:[3,8],j:[0,8,15],jacob:15,jason:15,javascript:[14,18],jefferson:15,jenkin:15,ji:14,joel:1,joslyn:[0,14,15],journal:0,jth:0,jupyt:[11,14],jurisdict:14,k1:0,k2:0,k:[0,1,3,8],kaminski:[0,15],katrina:15,kawaoka:15,kbasi:0,kchain:0,kchainbasi:0,kdx:3,keep:[3,17,18],keep_weight:3,kei:[0,1,3,6,8,13],kelli:15,kernel:0,kevin:15,keyindex:3,keyword:[3,6],km1basi:0,knowledg:0,known:[0,3],kocher:15,krang:0,kritzstein:14,kth:0,kumar:0,kving:15,kwarg:[0,3,6],l:[0,13,15],lab:3,label:[0,3,6],label_alpha:6,laboratori:14,lambda:1,landri:[1,14],laplacian:[2,5,10],laplacians_clust:[2,5,10],larg:3,larger:18,largest:[0,3],larissa:15,larremor:0,last:[0,3],last_step:0,lastlevel:3,latest:1,latter:3,lawfulli:12,layer:6,layout:[1,6,10],layout_hyper_edg:6,layout_kwarg:6,layout_node_link:6,layout_two_column:6,le:15,learn:[9,10],leas:8,least:[3,6,8],lectur:15,left:[0,6],legal:14,len:17,length:[0,3,6,8,9,10],lesmi:14,less:[0,3,13],let:3,level1:3,level2:3,level:[3,6,8],levelset:[3,8],liabil:[12,14],liabl:12,librari:[0,3,9,10,13,14],licens:10,like:[0,3,6],limit:[3,12],line:[0,3,6,13],linear:0,linecollect:6,linegraph:[0,3,8],linewidth:6,link:[0,3,18],linux:[11,14],linv:0,lisa:15,list:[0,1,3,6,12,13,17],liu:[13,14],llinv:0,lm:0,lmr:0,local:13,locat:[6,11,18],logic:0,logical_dot:0,logical_matadd:0,logical_matmul:0,longer:3,longest:[0,3],look:0,loss:12,loui:15,lower:6,lu:0,lumsdain:14,m:[0,1,3,15],mac:[11,18],made:3,magnitud:0,mai:[3,8,9,10,11,12,14,18],main:18,major:[0,1],majority_vot:1,make:[3,6,14],manag:[0,14],mani:[3,13,17],manipul:3,manual:6,manufactur:14,map:[0,6],marcin:15,mark:14,marrero:[0,15],mat1:0,mat2:0,mat:0,match:[0,3],materi:14,mathbb:0,mathemat:14,matmulreduc:0,matplotlib:[1,6,11],matric:[2,5,6,10,14],matrix:[0,3,8,13,17],max:[0,3,17],max_degre:13,max_depth:3,max_level:3,max_siz:[3,13],maxim:[3,8],maximum:[3,8],maxlevel:3,mcdermott:15,mean:[0,3,17],measur:[2,5,10,14],mechan:1,median:[3,6,17],member:3,membership:[3,6,8,18],memori:[12,13,14],menacheri:15,mend:0,merchant:12,merg:[3,12],merge_ent:3,method:[0,3,8,9,10,14,17],methodolog:0,metric:[0,9,10,14],michael:15,might:18,miller:1,min:[0,3,17],min_degre:13,min_level:3,min_siz:13,minim:[0,6,11,18],minimum:[3,6],minlevel:3,minu:[0,3],mirah:0,miss:6,mitchel:15,mod2:[2,5,10,14],mod:0,model:[1,9,10,14,15],modestli:3,modif:12,modifi:12,modul:[2,4,5,7,10,14,16],modular:0,modulo:0,more:[3,8,9,10,11,13],moro:0,most:[1,3,6,9,10],move:0,much:13,multi:[3,9,10],multidimension:15,multipl:[0,3,8,13,18],multipli:0,multiwai:[9,10],must:[0,1,3,12,13],mxn:0,n:[0,1,3,6,8,11,13],nama:3,name:[3,11,12,13,14,15,18],nan:3,natali:15,nation:14,natur:[9,10],navig:3,ncell:17,ncol:17,ndarrai:[0,3],necessarili:14,need:[0,3,6,11],neglig:12,neighbor:[1,3,13],neither:[12,14],neq:[0,13],nest:3,nested_incidence_dict:3,network:[0,1,3,9,10,14,15],networkx:[3,6],netwrokx:6,newfpath:3,newuid:3,next:0,nichola:14,node:[0,1,3,6,8,13,14,17,18],node_column_nam:3,node_diamet:3,node_incid:13,node_label:[0,3,6],node_labels_kwarg:6,node_nam:3,node_radiu:[1,6],node_set:3,node_size_dist:13,node_state_color_dict:1,nodes_kwarg:6,nodeset:3,non:[0,8],none:[0,1,3,6,13,17],nonempti:[3,8],nonexist:3,nonzero:[3,8],nor:14,norm_lap:0,normal:[2,5,10,13],northwest:14,note:[0,1,3,8,11,13,15],notebook:[11,14],noth:3,notic:[10,12],np:[0,3],nrow:17,num:17,number:[0,1,3,6,8,13,17],number_of_edg:[3,13],number_of_nod:[3,13],numer:3,numpi:[0,1,3,6,13],nwgraph:13,nwhy:[0,3,10,11,14],nwhypergraph:[3,10],nx2:6,nx:[3,6,8],nxm:0,o:15,obj:17,object:[0,1,3,8,13,14,17],obtain:[0,8,12],occupi:8,occur:3,off:1,offer:3,offset:6,omega:0,onc:[11,14],one:[0,3,6,8,13],oneapi:13,onetbb:13,onli:[0,1,3,8,11,13],open:11,oper:14,opinion:[1,14],opt:13,optim:[0,6,10,13,14,18],option:[0,1,3,10,17],order:[0,3,6,15],ordereddict:3,org:[0,1,15],organ:14,orient:6,origin:[0,3,13],ortiz:0,osit:3,osx:11,other:[0,3,6,8,10,12,13],otherwis:[0,3,8,11,12,13,14],our:[0,9,10],out:[0,6,9,10,12],outlin:18,output:[0,1,3],outsid:3,over:[0,6,8,13],overlap:[6,13],overrid:6,overview:10,own:[8,14],p:[0,3,15],pacif:14,packag:[2,4,7,10,16],page:10,pair:[0,3,6,8,13],pairwis:3,panda:[3,14],panel:10,paper:6,parallel:[6,13],paramet:[0,1,3,6,17],park:0,part2dict:0,part:[0,6,14],parthasarathi:0,partial_k:0,particular:[3,9,10,12,14],partion:0,partit:[0,3,8],pass:[0,3,6,13],path:[0,3,6,9,10,11,13],pathogen:15,pd:3,per:[0,1],perfect:18,perform:[3,13,14,15,18],permiss:12,permit:12,person:12,peter:15,physrev:0,pi:0,pickl:3,pin:18,pip:[10,18],place:3,placehold:3,placement:18,planar:6,pleas:[0,3],plot:6,plt:1,pmf:0,pnnl:[0,9,10,11,14],po:6,point:6,poli:6,polycollect:6,polygon:6,pone:0,poset:3,posit:[0,3,6,8,13,17,18],possibl:[1,6,12,18],post:0,potenti:1,poulin:0,power:[9,10],powershel:11,pp:15,pr:0,practic:3,praggasti:[14,15],pralat:0,pre:6,precis:8,precompute_attribut:0,precompute_modularity_paramet:0,prefil:3,preliminari:13,prepar:14,prepend:3,present:[1,3],preserv:[3,18],press:15,princip:14,principl:14,print:[0,17],prior:3,privat:14,prob_tran:0,probabl:[2,5,10],proc:15,proceed:0,process:[3,13,14],procur:12,product:[0,14],profit:12,program:14,project:14,prompt:11,prop:3,properli:8,properti:[3,8,13,14,18],proport:0,provid:[0,3,6,9,10,12,13],ps1:11,publish:12,purpos:[0,12],purvin:[14,15],put:17,py:8,pybind11:13,pyplot:1,pytest:11,python:[11,13],qh:0,qing:15,quantiti:[9,10],question:[9,10,14],quick:[6,10],quit:3,r0:6,r:[0,1,6],radiu:[1,6],rais:[0,3],ralph:15,randint:0,random:[0,1],randomli:1,rang:[0,1,6],rate:1,rather:17,ratio:[0,17],rauga:14,ravindran:0,rdc:0,re:18,reachabl:13,read:[6,14],readthedoc:1,real:3,reason:[3,6],receiv:3,reciproc:[0,13],recommend:[3,6,14],recov:[1,3],recover_from_st:3,recoveri:1,rectangular:[0,8],recurs:0,red:1,redistribut:12,reduc:[0,6],reduced_row_echelon_form_mod2:0,refer:[0,3,14],referenc:[0,3],reflect:[3,14],regist:3,registri:[3,8],rel:[0,18],relat:[3,9,10],relationship:[0,3,9,10,15],releas:[14,18],remov:[3,18],remove_edg:3,remove_el:3,remove_elements_from:3,remove_nod:3,remove_singleton:3,remove_stat:3,render:6,renyi:0,rep:3,repeatedli:0,replac:[0,3],report:[5,10],repositori:[0,9,10],repres:[0,3,6,8,9,10,14],represent:[0,3,6,13],reproduc:[6,12],request:3,requir:[0,1,3,13],research:[9,10,14],reserv:6,respect:[0,3],respons:[14,15],restrepo:1,restrict:[3,8],restrict_to:3,restrict_to_edg:3,restrict_to_indic:3,restrict_to_level:3,restrict_to_nod:3,result:[6,18],retain:12,retriev:3,return_count:3,return_equal_class:13,return_equivalence_class:3,return_full_data:1,return_index:3,return_po:6,return_singleton:[0,3,17],revers:[0,3,18],rho:1,rich:13,right:[0,6,14],rigor:6,ring:6,rocha:0,role:[3,8],root:3,roughli:0,row:[0,3,8,13,17],rowdict:3,rubber:6,rubber_band:[5,7,10],run:[0,11,13,14],s12859:15,s13688:[0,15],s41467:1,s:[1,2,3,5,6,8,10,13,14,15,17],s_betweenness_centr:[0,13],s_centrality_measur:[2,5,10],s_closeness_centr:[0,13],s_comp_dist:17,s_compon:3,s_component_subgraph:3,s_components_subgraph:3,s_connect:3,s_connected_compon:[3,13],s_degre:[3,13],s_diamet:13,s_distanc:13,s_eccentr:[0,13],s_edge_connect:3,s_edge_diameter_dist:17,s_harmonic_centr:0,s_harmonic_closeness_centr:[0,13],s_linegraph:13,s_neighbor:13,s_node_diameter_dist:17,s_path:13,same:[0,3,6,8,13],sampl:[1,3],satifi:3,satisfi:[3,8],save:3,save_st:3,scalabl:13,sci:0,scienc:[0,15],scip:3,scipi:[0,3],score:13,script:11,search:10,second:[1,3],section:0,see:[0,3,6,8,11,14,17],select:[0,10],self:3,sell:12,sens:8,sensibl:6,sequenc:[3,8],serv:[0,9,10],servic:[12,14],set:[0,1,3,6,8,9,10,13,18],set_nam:3,set_stat:3,setsystem:3,setsytem:3,sh:11,shabang:11,shall:12,shallow:3,shape:3,share:[3,8,13],sheahan:15,shi:0,shift:18,shortest:[0,3,8,13],shortest_path_length:3,should:[0,1,3,6],show:18,shufang:15,si:[1,14],side:[0,3,10],sigma:[0,13],signatur:3,significantli:13,sim:15,sim_kwarg:1,similar:[1,3,18],simpl:[0,3,8,17],simplic:[9,10],simplici:[0,1,9,10],simul:1,sinan:[0,14,15],sinc:[3,8,9,10],singl:[0,3,8,17],singleton:[0,3,9,10,13],sir:[1,14],size:[0,1,3,6,8,13,17,18],slightli:18,slinegraph:10,slower:13,small:[0,3,6],smaller:6,smallest:3,smith:[2,5,10,15],smith_normal_form_mod2:0,snf:0,so:[0,3,6,12],social:1,softwar:[12,14],some:[8,9,10,11],sometim:[6,18],song:15,sort:[0,3],sort_column:3,sort_row:3,sortabl:[0,3],sourc:[0,1,3,6,11,12,17],space:[6,13],spars:[0,3,13],spec:0,spec_clu:0,special:12,specif:[3,8,14],specifi:[0,1,3,6,11,13,18],spectral:[0,6],sped:14,sponsor:14,spring_layout:6,springer:[0,15],squar:8,src:13,stack:6,standard:17,start:[0,1,3,6,17,18],stat:17,state:[1,3,14,18],state_dict:3,staticent:[4,5,10],staticentityset:3,stationari:0,statist:17,statu:1,status:1,step:[0,1],still:[0,3],stop:0,storag:3,store:[0,3,13],str:[0,3],stratton:15,strength:0,strict:[0,9,10,12],string:[3,6,17],structur:[3,8,9,10,13],studi:[0,9,10,14],style:6,subgraph:[0,3],subhypergraph:8,subject:12,sublicens:12,submatrix:8,submit:3,submodul:[2,4,5,7,10,16],subpackag:[2,5,10],subset:[3,6,8],substitut:12,subtract:3,success:8,sum:[0,3,13],sum_:[0,13],summari:17,suppli:6,support:[0,1,3,14],sure:3,surround:6,suscept:1,swap:0,swap_column:0,swap_row:0,symmetr:0,symp:15,synthet:14,system:[3,6,9,10,11,15],szufel:0,t:[0,1,3,13],tabl:18,take:[1,3,6],tan:15,target:3,tau:1,tax:0,tbb:[10,11],tbbroot:13,techniqu:6,tell:[9,10],tensor:3,term:[0,3],termin:1,test:[10,11],text:[0,6],textbook:6,thackrai:15,than:[0,3,8,12,17],thei:[0,3,6,8,9,10,18],them:[3,8,11,17,18],theoret:0,theori:12,therebi:[9,10],therefor:[3,13],thereof:14,thi:[0,1,3,6,8,9,10,11,12,13,14,17,18],think:3,those:[0,14],thread:10,three:[13,14],threshold:1,through:[0,6,13],tiffani:15,time:[0,1,18],timothi:15,tmax:1,tmin:1,to_jshtml:1,todo:3,togeth:[0,6],toggl:18,toni:[13,14],tool:[9,10],toolbar:18,toplex:[0,3,8,13,17],toplex_dist:17,topolog:[0,9,10,15],tort:12,total:0,tour:14,track:[0,3,17],trade:14,trademark:14,tradit:18,transform:[0,3],transit:[1,2,5,10],transition_ev:1,translat:3,translate_arr:3,transmiss:1,transmission_funct:1,transmit:1,transpar:6,transpos:3,transpose_inflated_kwarg:6,travers:18,treat:3,triloop:14,tripodi:15,trivial:0,truthi:3,tupl:[0,3],turn_entity_data_into_datafram:3,tutori:[0,3,10,11],two:[0,3,6,8,13,18],two_column:[5,7,10],two_sect:0,type:[0,1,3,6,17],typic:6,u:[0,6,13],uid:[0,1,3,8,17],uidset:[3,8],uidset_by_level:3,un:18,under:[13,14],undesir:3,undirect:13,uniform:0,uniqu:[3,8],unit:14,unless:3,unpack:3,unreach:13,untitiled_modularity_and_clustering_origin:[2,5,10],untitled_modularity_and_clust:[2,5,10],unweight:[3,8,13],up:[3,14,17],updat:3,upgrad:13,upon:18,us:[0,3,6,8,9,10,12,14],usag:0,use_nwhi:[0,3],use_rep:3,user:[1,3,9,10,11,13,14,18],usual:6,util:[0,5,7,10],v0:3,v1:3,v2:3,v:[0,3,6,13,15],v_1:3,v_2:3,v_end:3,v_n:3,v_start:3,vaidyanathan:0,valu:[0,1,3,6,8,13],variou:[13,17],ve:14,vector:0,verifi:0,version:[10,11,13],vertex:[0,6,9,10,13],vertic:[0,3,6,13,14],via:[0,15],view:14,viii:0,vineet:15,viral:15,virtual:11,virtualenv:10,visibl:18,visual:[10,14,18],vn:3,vol:0,vote:1,w:[0,3],wa:[3,13,14],wai:[3,6,9,10,12],walk:[0,3,8,9,10,15],walter:15,want:[0,18],warn:0,warranti:[12,14],water:15,waw:15,wdc:0,we:[0,3,9,10,13,14],web:15,weight:[0,3,8,13,14],well:[0,6,18],westhoff:15,what:[9,10],whatsoev:12,when:[3,13],whera:18,where:[0,3,6,8,13],whether:[0,3,12,13],which:[0,1,3,6,8,17,18],whitespac:6,whole:11,whose:[6,8,13],widget:[10,14],width:[3,8,9,10],window:[11,18],wish:11,with_color:6,with_edge_count:6,with_edge_label:6,with_node_count:6,with_node_label:6,within:[0,3,6,18],without:[12,18],work:[0,3,6,11,14],would:[3,14],wrangl:3,wrap:6,written:12,wshop:15,www:[0,15],x:[3,6,13,17],xor:0,xu:13,xx:3,xy:6,xyz:0,y:[3,6,13],yet:3,yield:3,yoshihiro:15,you:[3,6,9,10,11,14,18],young:14,your:[3,11,14],yun:14,z:[0,3],z_2:0,zalewski:15,zero:3},titles:["algorithms package","algorithms.contagion package","algorithms","classes package","classes","HyperNetX Packages","drawing package","drawing","Glossary of HNX terms","HyperNetX (HNX)","HyperNetX (HNX)","Installing HyperNetX","License","NWHy","Overview","Publications","reports","reports package","Hypernetx-Widget"],titleterms:{"0":14,"1":14,"class":[3,4,13],"import":13,"new":14,"public":15,Then:13,To:[11,13],activ:13,algorithm:[0,1,2],an:[11,13],anaconda:[11,13],anim:1,api:13,attribut:13,block:13,build:13,central:0,cluster:0,colab:14,contagion:1,content:[0,1,3,6,10,17],descript:[9,10,13],descriptive_stat:17,draw:[6,7],entiti:3,environ:[11,13],epidem:1,featur:[14,18],form:0,generative_model:0,glossari:8,hnx:[8,9,10],homolog:0,homology_mod2:0,hypergraph:[0,3],hypergraph_modular:0,hypernetx:[5,9,10,11,18],indic:10,instal:[11,13,18],intel:13,laplacian:0,laplacians_clust:0,layout:18,licens:[12,14],matric:0,measur:0,method:13,mod2:0,modul:[0,1,3,6,13,17],normal:0,notic:14,nwhy:13,nwhypergraph:13,option:11,other:18,overview:[14,18],packag:[0,1,3,5,6,17],panel:18,pip:[11,13],probabl:0,quick:13,report:[16,17],rubber_band:6,s:0,s_centrality_measur:0,select:18,side:18,slinegraph:13,smith:0,staticent:3,submodul:[0,1,3,6,17],subpackag:0,tabl:10,tbb:13,term:8,test:13,thread:13,tool:18,transit:0,tutori:14,two_column:6,untitiled_modularity_and_clustering_origin:0,untitled_modularity_and_clust:0,us:[11,13,18],util:6,version:14,virtualenv:11,widget:18}}) \ No newline at end of file +Search.setIndex({docnames:["algorithms/algorithms","algorithms/algorithms.contagion","algorithms/modules","classes/classes","classes/modules","core","drawing/drawing","drawing/modules","glossary","home","index","install","license","nwhy","overview/index","publications","reports/modules","reports/reports","widget"],envversion:{"sphinx.domains.c":2,"sphinx.domains.changeset":1,"sphinx.domains.citation":1,"sphinx.domains.cpp":4,"sphinx.domains.index":1,"sphinx.domains.javascript":2,"sphinx.domains.math":2,"sphinx.domains.python":3,"sphinx.domains.rst":2,"sphinx.domains.std":2,"sphinx.ext.intersphinx":1,"sphinx.ext.todo":2,"sphinx.ext.viewcode":1,sphinx:56},filenames:["algorithms/algorithms.rst","algorithms/algorithms.contagion.rst","algorithms/modules.rst","classes/classes.rst","classes/modules.rst","core.rst","drawing/drawing.rst","drawing/modules.rst","glossary.rst","home.rst","index.rst","install.rst","license.rst","nwhy.rst","overview/index.rst","publications.rst","reports/modules.rst","reports/reports.rst","widget.rst"],objects:{"":{algorithms:[0,0,0,"-"],classes:[3,0,0,"-"],drawing:[6,0,0,"-"],reports:[17,0,0,"-"]},"algorithms.contagion":{animation:[1,0,0,"-"],epidemics:[1,0,0,"-"]},"algorithms.contagion.animation":{contagion_animation:[1,1,1,""]},"algorithms.contagion.epidemics":{Gillespie_SIR:[1,1,1,""],Gillespie_SIS:[1,1,1,""],collective_contagion:[1,1,1,""],discrete_SIR:[1,1,1,""],discrete_SIS:[1,1,1,""],individual_contagion:[1,1,1,""],majority_vote:[1,1,1,""],threshold:[1,1,1,""]},"algorithms.generative_models":{chung_lu_hypergraph:[0,1,1,""],dcsbm_hypergraph:[0,1,1,""],erdos_renyi_hypergraph:[0,1,1,""]},"algorithms.homology_mod2":{add_to_column:[0,1,1,""],add_to_row:[0,1,1,""],betti:[0,1,1,""],betti_numbers:[0,1,1,""],bkMatrix:[0,1,1,""],boundary_group:[0,1,1,""],chain_complex:[0,1,1,""],homology_basis:[0,1,1,""],hypergraph_homology_basis:[0,1,1,""],interpret:[0,1,1,""],kchainbasis:[0,1,1,""],logical_dot:[0,1,1,""],logical_matadd:[0,1,1,""],logical_matmul:[0,1,1,""],matmulreduce:[0,1,1,""],reduced_row_echelon_form_mod2:[0,1,1,""],smith_normal_form_mod2:[0,1,1,""],swap_columns:[0,1,1,""],swap_rows:[0,1,1,""]},"algorithms.hypergraph_modularity":{bin_ppmf:[0,1,1,""],compute_partition_probas:[0,1,1,""],degree_tax:[0,1,1,""],delta_dt:[0,1,1,""],delta_ec:[0,1,1,""],dict2part:[0,1,1,""],edge_contribution:[0,1,1,""],kumar:[0,1,1,""],last_step:[0,1,1,""],linear:[0,1,1,""],majority:[0,1,1,""],modularity:[0,1,1,""],part2dict:[0,1,1,""],precompute_attributes:[0,1,1,""],strict:[0,1,1,""],two_section:[0,1,1,""]},"algorithms.laplacians_clustering":{get_pi:[0,1,1,""],norm_lap:[0,1,1,""],prob_trans:[0,1,1,""],spec_clus:[0,1,1,""]},"algorithms.s_centrality_measures":{s_betweenness_centrality:[0,1,1,""],s_closeness_centrality:[0,1,1,""],s_eccentricity:[0,1,1,""],s_harmonic_centrality:[0,1,1,""],s_harmonic_closeness_centrality:[0,1,1,""]},"algorithms.untitiled_modularity_and_clustering_original":{DegreeTax:[0,1,1,""],DeltaDT:[0,1,1,""],DeltaEC:[0,1,1,""],EdgeContribution:[0,1,1,""],HNX_2section:[0,1,1,""],HNX_Kumar:[0,1,1,""],HNX_LastStep:[0,1,1,""],HNX_modularity:[0,1,1,""],bin_ppmf:[0,1,1,""],compute_partition_probas:[0,1,1,""],dict2part:[0,1,1,""],factorial:[0,1,1,""],linear:[0,1,1,""],majority:[0,1,1,""],part2dict:[0,1,1,""],precompute_modularity_parameters:[0,1,1,""],strict:[0,1,1,""]},"algorithms.untitled_modularity_and_clustering":{DegreeTax:[0,1,1,""],DeltaDT:[0,1,1,""],DeltaEC:[0,1,1,""],EdgeContribution:[0,1,1,""],HNX_2section:[0,1,1,""],HNX_Kumar:[0,1,1,""],HNX_LastStep:[0,1,1,""],HNX_modularity:[0,1,1,""],HNX_precompute:[0,1,1,""],bin_ppmf:[0,1,1,""],compute_partition_probas:[0,1,1,""],dict2part:[0,1,1,""],factorial:[0,1,1,""],linear:[0,1,1,""],majority:[0,1,1,""],part2dict:[0,1,1,""],strict:[0,1,1,""]},"classes.entity":{Entity:[3,2,1,""],EntitySet:[3,2,1,""]},"classes.entity.Entity":{add:[3,3,1,""],add_element:[3,3,1,""],add_elements_from:[3,3,1,""],children:[3,4,1,""],clone:[3,3,1,""],complete_registry:[3,3,1,""],depth:[3,3,1,""],elements:[3,4,1,""],fullregistry:[3,3,1,""],incidence_dict:[3,4,1,""],intersection:[3,3,1,""],is_bipartite:[3,4,1,""],is_empty:[3,4,1,""],level:[3,3,1,""],levelset:[3,3,1,""],memberships:[3,4,1,""],merge_entities:[3,3,1,""],nested_incidence_dict:[3,3,1,""],properties:[3,4,1,""],registry:[3,4,1,""],remove:[3,3,1,""],remove_element:[3,3,1,""],remove_elements_from:[3,3,1,""],restrict_to:[3,3,1,""],size:[3,3,1,""],uid:[3,4,1,""],uidset:[3,4,1,""]},"classes.entity.EntitySet":{add:[3,3,1,""],clone:[3,3,1,""],collapse_identical_elements:[3,3,1,""],incidence_matrix:[3,3,1,""],restrict_to:[3,3,1,""]},"classes.hypergraph":{Hypergraph:[3,2,1,""]},"classes.hypergraph.Hypergraph":{add_edge:[3,3,1,""],add_edges_from:[3,3,1,""],add_node_to_edge:[3,3,1,""],add_nwhy:[3,3,1,""],adjacency_matrix:[3,3,1,""],auxiliary_matrix:[3,3,1,""],bipartite:[3,3,1,""],collapse_edges:[3,3,1,""],collapse_nodes:[3,3,1,""],collapse_nodes_and_edges:[3,3,1,""],component_subgraphs:[3,3,1,""],components:[3,3,1,""],connected_component_subgraphs:[3,3,1,""],connected_components:[3,3,1,""],convert_to_static:[3,3,1,""],dataframe:[3,3,1,""],degree:[3,3,1,""],diameter:[3,3,1,""],dim:[3,3,1,""],distance:[3,3,1,""],dual:[3,3,1,""],edge_adjacency_matrix:[3,3,1,""],edge_diameter:[3,3,1,""],edge_diameters:[3,3,1,""],edge_distance:[3,3,1,""],edge_neighbors:[3,3,1,""],edge_size_dist:[3,3,1,""],edges:[3,4,1,""],from_bipartite:[3,3,1,""],from_dataframe:[3,3,1,""],from_numpy_array:[3,3,1,""],get_id:[3,3,1,""],get_linegraph:[3,3,1,""],get_name:[3,3,1,""],incidence_dict:[3,4,1,""],incidence_matrix:[3,3,1,""],is_connected:[3,3,1,""],isstatic:[3,4,1,""],neighbors:[3,3,1,""],node_diameters:[3,3,1,""],nodes:[3,4,1,""],number_of_edges:[3,3,1,""],number_of_nodes:[3,3,1,""],order:[3,3,1,""],recover_from_state:[3,3,1,""],remove_edge:[3,3,1,""],remove_edges:[3,3,1,""],remove_node:[3,3,1,""],remove_nodes:[3,3,1,""],remove_singletons:[3,3,1,""],remove_static:[3,3,1,""],restrict_to_edges:[3,3,1,""],restrict_to_nodes:[3,3,1,""],s_component_subgraphs:[3,3,1,""],s_components:[3,3,1,""],s_connected_components:[3,3,1,""],s_degree:[3,3,1,""],save_state:[3,3,1,""],set_state:[3,3,1,""],shape:[3,4,1,""],singletons:[3,3,1,""],size:[3,3,1,""],toplexes:[3,3,1,""],translate:[3,3,1,""]},"classes.staticentity":{StaticEntity:[3,2,1,""],StaticEntitySet:[3,2,1,""]},"classes.staticentity.StaticEntity":{arr:[3,4,1,""],cell_weights:[3,4,1,""],children:[3,4,1,""],data:[3,4,1,""],dataframe:[3,4,1,""],dimensions:[3,4,1,""],dimsize:[3,4,1,""],elements:[3,4,1,""],elements_by_level:[3,3,1,""],incidence_dict:[3,4,1,""],incidence_matrix:[3,3,1,""],index:[3,3,1,""],indices:[3,3,1,""],is_empty:[3,3,1,""],keyindex:[3,3,1,""],keys:[3,4,1,""],labels:[3,4,1,""],labs:[3,3,1,""],level:[3,3,1,""],memberships:[3,4,1,""],properties:[3,5,1,""],restrict_to_indices:[3,3,1,""],restrict_to_levels:[3,3,1,""],size:[3,3,1,""],translate:[3,3,1,""],translate_arr:[3,3,1,""],turn_entity_data_into_dataframe:[3,3,1,""],uid:[3,4,1,""],uidset:[3,4,1,""],uidset_by_level:[3,3,1,""]},"classes.staticentity.StaticEntitySet":{collapse_identical_elements:[3,3,1,""],convert_to_entityset:[3,3,1,""],incidence_matrix:[3,3,1,""],restrict_to:[3,3,1,""]},"drawing.rubber_band":{draw:[6,1,1,""],draw_hyper_edge_labels:[6,1,1,""],draw_hyper_edges:[6,1,1,""],draw_hyper_labels:[6,1,1,""],draw_hyper_nodes:[6,1,1,""],get_default_radius:[6,1,1,""],layout_hyper_edges:[6,1,1,""],layout_node_link:[6,1,1,""]},"drawing.two_column":{draw:[6,1,1,""],draw_hyper_edges:[6,1,1,""],draw_hyper_labels:[6,1,1,""],layout_two_column:[6,1,1,""]},"drawing.util":{get_frozenset_label:[6,1,1,""],get_line_graph:[6,1,1,""],get_set_layering:[6,1,1,""],inflate:[6,1,1,""],inflate_kwargs:[6,1,1,""],transpose_inflated_kwargs:[6,1,1,""]},"reports.descriptive_stats":{centrality_stats:[17,1,1,""],comp_dist:[17,1,1,""],degree_dist:[17,1,1,""],dist_stats:[17,1,1,""],edge_size_dist:[17,1,1,""],info:[17,1,1,""],info_dict:[17,1,1,""],s_comp_dist:[17,1,1,""],s_edge_diameter_dist:[17,1,1,""],s_node_diameter_dist:[17,1,1,""],toplex_dist:[17,1,1,""]},algorithms:{contagion:[1,0,0,"-"],generative_models:[0,0,0,"-"],homology_mod2:[0,0,0,"-"],hypergraph_modularity:[0,0,0,"-"],laplacians_clustering:[0,0,0,"-"],s_centrality_measures:[0,0,0,"-"],untitiled_modularity_and_clustering_original:[0,0,0,"-"],untitled_modularity_and_clustering:[0,0,0,"-"]},classes:{entity:[3,0,0,"-"],hypergraph:[3,0,0,"-"],staticentity:[3,0,0,"-"]},drawing:{rubber_band:[6,0,0,"-"],two_column:[6,0,0,"-"],util:[6,0,0,"-"]},reports:{descriptive_stats:[17,0,0,"-"]}},objnames:{"0":["py","module","Python module"],"1":["py","function","Python function"],"2":["py","class","Python class"],"3":["py","method","Python method"],"4":["py","property","Python property"],"5":["py","attribute","Python attribute"]},objtypes:{"0":"py:module","1":"py:function","2":"py:class","3":"py:method","4":"py:property","5":"py:attribute"},terms:{"0":[0,1,3,6,8,10,13,15],"0020034":1,"00231":[0,15],"01":0,"012805":0,"019":1,"020":[0,15],"021":15,"0224307":0,"030":[0,15],"04197":15,"1":[0,1,3,6,8,10,13,15,17],"10":[0,1,3,15],"100":[0,1],"1000":[0,1,3],"10000":1,"1007":[0,15],"1038":1,"10431":1,"1063":1,"1093":0,"1103":0,"1140":[0,15],"1145":0,"11782":15,"1186":15,"12901":15,"13":0,"1371":0,"15":15,"16":[0,15],"17th":15,"19":0,"1_1":15,"2":[0,1,3,6,8,13,14,15],"2003":15,"2005":0,"2016":0,"2018":12,"2019":[0,15],"2020":[0,15],"22":15,"27":0,"287":15,"29th":0,"2_24":0,"2d":0,"2z":0,"3":[0,1,3,6,11,13,14,15],"3340531":0,"3412034":0,"35":6,"36687":0,"4":[1,3,14],"48478":15,"495":0,"5":[0,1,3,6,14],"504":0,"6":[1,3,14],"7":[11,14],"755":11,"76rl01830":14,"881":0,"9":[0,11,13,15],"90":0,"978":[0,15],"abstract":0,"bogumi\u0142":0,"boolean":[0,3,13],"case":[0,3,14],"class":[0,5,8,9,10],"default":[0,1,3,6,17],"do":[3,8,12,13,14],"export":13,"final":0,"float":[0,1,3,6],"fran\u00e7oi":0,"function":[0,1,3,6],"import":[0,1,3,10],"int":[0,1,3,6,15],"kami\u0144ski":0,"long":[0,17],"new":[0,3,6,10,13],"null":11,"pawe\u0142":0,"pra\u0142at":0,"przemys\u0142aw":0,"public":[9,10],"return":[0,1,3,6,8,13,17],"static":[0,3,14],"super":18,"switch":8,"th\u00e9berg":0,"true":[0,1,3,6,13,17],"try":0,"val\u00e9ri":0,"while":18,A:[0,1,3,6,8,12,13,15],AND:12,AS:12,As:[3,9,10],At:0,BE:12,BUT:12,BY:12,By:[3,6],FOR:12,For:[0,3,6,8,9,10,11,13,14,18],IF:12,IN:12,IS:12,If:[0,1,3,6,8,11,13,17],In:[0,3,6,13,14],It:[3,6,13],NO:12,NOT:12,Not:3,OF:[12,14],ON:12,OR:12,One:3,SUCH:12,Such:12,THE:12,TO:12,That:0,The:[0,1,3,6,8,9,10,13,14,18],Their:0,Then:[6,10],These:[0,18],To:[0,3,9,10],Will:3,_0:3,_1:3,_2:[0,3],_:[0,3],__dict__:3,_edg:3,_node:3,_version:13,a_i:0,ab:[3,15],abl:1,about:[9,10],abov:[3,6,12,17],ac05:14,accept:[6,13],access:[8,11],accomplish:0,accord:8,account:[1,14],accuraci:14,acm:0,aco5:14,across:6,action:0,activ:[10,11,18],actual:0,ad:[0,3,6,14],adam:15,adapt:0,adaptor:13,add:[0,3],add_edg:3,add_edges_from:3,add_el:3,add_elements_from:3,add_node_to_edg:3,add_nodes_from:3,add_nwhi:3,add_to_column:0,add_to_row:0,addit:[0,3,14],addon:[13,14,18],adjac:[0,3,8,9,10],adjacency_matrix:3,adjust:6,admit:[9,10],advanc:18,advis:12,after:[3,13],against:3,agenc:14,aggreg:[3,17],aggregatebi:3,ah:15,aksoi:[0,14,15],al:[0,1,15],algebra:[9,10],algorithm:[5,6,9,10,13,14,15,18],align:[3,6],all:[0,1,3,6,8,11,13,14,17,18],allow:[1,3,6,18],alpha:[1,6],alreadi:[1,3,18],also:[0,3,8,9,10,13,17,18],alter:0,altern:18,ami:15,among:[9,10],amount:6,an:[0,1,3,6,8,10,14,17,18],anaconda3:11,anaconda:10,analysi:13,analyt:[14,15],ananthapadmanabhan:0,andrew:14,angl:6,ani:[0,3,8,12,13,14,18],anim:[0,2,5,10],annal:0,annot:6,anoth:[0,6,8],api:10,apparatu:14,appear:[0,3,18],appli:[0,3,6],applic:[0,3],approach:6,appropri:6,ar1:0,ar2:0,ar:[0,1,3,6,8,9,10,11,12,13,14,18],arbitrari:[6,9,10],arendt:[14,15],arg:[0,1,3],arg_set:3,argument:[1,3,6],argumetn:6,aris:12,around:6,arr:[0,3],arrai:[0,1,3,13],articl:15,arxiv:15,asc:0,aspect:17,assign:[3,6],associ:[0,3,12,13],assum:[3,14],attribut:[0,3,8,10],author:14,automat:[1,3],auxiliari:[3,8],auxiliary_matrix:3,avail:[0,3,14,18],averag:13,ax:6,axi:6,azsecur:15,b:[0,3,6,8,15],back:13,backend:3,background:18,band:6,baric:15,base:[0,3,6,8,13,14,18],basi:0,basic:[3,8,9,10,14,17],bat:11,battel:[12,14],bd:0,bdict:3,becaus:[9,10],becom:[0,3],been:[0,13],befor:3,behavior:0,behind:6,being:0,belong:[0,3,8,13],below:11,berg:0,best:0,betti:0,betti_numb:0,between:[0,1,3,6,8,13,18],big:15,bin_ppmf:0,binari:[0,12],binomi:0,bioinformat:15,biolog:15,biomedcentr:15,bipartit:[0,3,6,8,18],bk:0,bkmatrix:0,block:10,blue:1,bmc:15,bmcbioinformat:15,book:14,bool:[0,1,3,6,17],both:[1,3,8,9,10,13,18],bound:0,boundari:[0,6],boundary_group:0,box:6,bramer:15,brenda:[14,15],brett:15,brian:14,briefest:0,browser:[11,14],bsd:14,build:[3,10,11],build_doc:11,built:18,bulk:18,busi:12,button:18,c:[0,1,3,6,10,11,13,14,15],c_:0,c_b:[0,13],c_k:0,ca:15,calcul:6,call:[6,8,13],callahan:15,can:[0,1,3,6,8,9,10,13,14,18],cannot:[1,3],capabl:18,cardin:3,care:3,carlo:15,categori:3,caus:[3,12,18],caution:3,cdotfrac:0,cell:[0,3,14,17],cell_weight:[0,3],center:6,central:[2,5,10,13,14,17],centrality_stat:17,certain:3,chain:0,chain_complex:0,cham:0,chang:[0,1,3,6,18],check:[3,9,10,13],check_connect:0,cheeger:0,cherifi:0,child:3,children:[3,8],chmod:11,choic:[0,1],choos:[1,3],chosen:[0,3,6],chung:0,chung_lu_hypergraph:0,chunglu:14,cikm:0,circl:[6,18],circular:1,ck:0,classmethod:3,claus:14,click:18,cliff:[14,15],cliqu:[9,10],clone:[3,11],close:[0,13],cluster:[2,5,10,14],cnx001:0,cockrel:15,code:12,coeffici:0,col:13,colab:[3,10],coldict:3,collaps:[3,6,13,18],collapse_edg:[3,13],collapse_identical_el:3,collapse_nod:[3,13],collapse_nodes_and_edg:[3,13],collect:[1,3,6],collective_contagion:1,collumn:6,colon:3,color:[1,3,6,18],column:[0,3,6,8,13,14,17],column_index:3,com:[11,15],combin:13,combinator:0,come:11,command:[3,11,18],comment:[9,10,14],commerci:14,common:1,commun:[0,9,10,14],comnet:0,comp:17,comp_dist:17,compar:[3,13],complet:[8,14,18],complete_registri:3,complex:[0,3,9,10,13,15],compon:[0,3,6,8,13,17],component_subgraph:3,comput:[0,3,6,14,15,17],compute_partition_proba:0,concentr:6,concern:0,conda:[11,13],condit:[3,8,12],conf:15,confer:0,conflict:3,connect:[0,3,6,8,9,10,13,17],connected:0,connected_compon:3,connected_component_subgraph:3,consecut:3,consent:12,consequenti:12,consid:3,constitut:14,construct:[0,1,3,8,13,14],constructor:[3,6,13,14],contact:[9,10,14],contagi:1,contagion:[0,2,5,10,14],contagion_anim:1,contain:[0,3,6,8,13,17,18],content:[2,4,5,7,16],context:[0,3],continu:[1,11],contract:[12,14],contribut:0,contributor:[9,10,12,14],control:[3,18],contruct:0,conveni:[3,6],converg:0,convert:[3,6],convert_to_entityset:3,convert_to_stat:3,convex:6,cooper:14,coord:3,coordin:[3,6],copi:[0,3,12,13],copyright:12,core:3,correct:6,correspond:[0,3,8,14],coset:0,could:3,count:[3,6,17],counter:17,creat:[0,3,11,13,14,17],creation:3,criteria:13,criterion:0,critic:15,cross:6,csr:[0,3],csr_matrix:[0,3],ctrl:18,current:[0,1,13],current_st:3,curvi:6,custom:6,cybersecur:15,cycl:[0,3,6],cyclic:0,d:[0,3,13,15],damag:12,daniel:15,data:[0,3,6,9,10,12,13,14,15],data_subset:3,datafram:[3,14],dcsbm:[0,14],dcsbm_hypergraph:0,de:[14,18],dedup:3,deeper:3,defaultdict:3,defin:[0,1,3],degre:[0,3,8,13,17,18],degree_dist:17,degree_tax:0,degreetax:0,delet:3,delta:0,delta_dt:0,delta_ec:0,deltadt:0,deltaec:0,demo:18,denorm:0,denot:1,densiti:17,depart:14,depend:[0,1,3,13],deprec:3,depth:[0,3,8],deriv:3,descend:3,describ:[0,1],descript:[0,3],descriptive_stat:[5,10,16],design:14,desir:3,dest:13,detail:[0,18],detect:0,determin:[0,3,6],develop:[9,10,13,14],deviat:17,df:3,diagon:0,diagram:[6,18],diamet:[3,8,13,17],diamond:15,dict2part:0,dict:[0,3,6,17],dictionari:[0,1,3,6,8,13,17],differ:[3,13],digraph:[0,6],dim:[0,3,13],dimens:[0,3,13],dimension:[0,3,9,10],dimensionsl:3,dimsiz:3,direct:[0,3,6,12,13,18],directli:[3,9,10,14,18],dirti:6,disabl:6,discard:3,disclaim:12,disclos:14,disconnect:6,discov:0,discret:1,discrete_si:1,discrete_sir:1,discuss:0,disjoint:[0,3,8],disonnecct:6,displai:1,dist:17,dist_stat:17,distanc:[0,3,6,8,13],distant:6,distinct:3,distinguish:[3,8,9,10],distribut:[0,12,13,17],divid:[0,1],dlfer:0,doc:11,document:[3,11,12],doe:[3,6,14],doesn:1,doi:[0,1,15],domain:[0,15],done:[3,13],dot:0,down:18,dr:6,drag:18,draw:[1,5,10],draw_hyper_edg:6,draw_hyper_edge_label:6,draw_hyper_label:6,draw_hyper_nod:6,drawn:6,drop:3,dt:1,dual:[3,8],duplic:[0,3],dustin:[14,15],dynam:[0,3,8],e0:3,e1:3,e2:3,e3:3,e:[0,3,6,8,11,13,15,17,18],e_1:3,e_2:3,e_end:3,e_n:3,e_start:3,each:[0,1,3,6,8,13,17,18],easier:6,ecc:0,eccentr:[0,13],echelon:0,ed:[0,15],edg:[0,1,3,6,8,9,10,13,14,17,18],edge_adjac:3,edge_adjacency_matrix:3,edge_column_nam:3,edge_contribut:0,edge_diamet:3,edge_dist:3,edge_incid:13,edge_kwarg:6,edge_label:[0,3,6],edge_labels_kwarg:6,edge_nam:3,edge_neighbor:3,edge_set:3,edge_size_dist:[3,13,17],edge_state_color_dict:1,edge_uid:3,edgecontribut:0,edges_kwarg:6,edgeset:3,edit:11,effect:[0,1,3],eg:0,eigenvalu:0,eigenvector:0,eisfeld:15,either:[3,8,13,17],element:[0,3,6,8,13],element_subset:3,elements_by_level:3,els:1,emili:[14,15],emploi:3,employe:14,empti:[3,8,13],en:[1,3],encapsul:13,end:3,endors:14,energi:14,ensur:3,ent1:3,ent2:3,entir:18,entiti:[4,5,6,8,9,10,12,14],entityset:[3,8],entri:[0,3,8,13],env:[11,13],environ:[10,14],eon:1,epidem:[0,2,5,10],epidemicsonnetwork:1,epj:[0,15],epjd:[0,15],eq_class:3,equal:[0,1,3,8,13],equat:0,equival:[0,3,13],equivalence_class:3,erdo:0,erdos_renyi_hypergraph:0,error:[0,3,13],essenc:0,et:[0,1,15],euler:18,evalu:3,even:12,event:[1,12],everi:[0,3,8,13,18],everyth:18,ex:[0,3,11],exactli:8,exampl:[0,1,3,6,11,14,18],exceed:3,except:8,execut:11,exemplari:12,exhibit:0,exist:[0,3,6,8],existing_lap:0,exp:0,expand:[6,18],expect:0,explicit:0,explor:[9,10],expos:3,express:[12,14],extend:18,extens:[0,11],extra:1,f:[0,15],facecolor:6,factori:0,fail:3,fall:0,fals:[0,1,3,6,13,17],fan:[0,15],fast:3,faster:[0,13],favor:14,featur:[0,10],feng:15,ferrario:0,fig:1,figur:[1,6],file:[3,11,12],filepath:3,fill:[3,17],fillna:3,filter:13,find:[6,9,10],firoz:15,first:[3,6],firstlevel:3,fit:12,fix:3,flexibl:3,fly:13,folder:0,follow:[3,6,11,12,14],forc:18,fork:11,form:[2,3,5,10,12],format:[3,13,17],forth:13,forward:1,found:[3,9,10],four:14,fp:1,fpath:3,frac:[0,13],fraction:[0,1,6,13],frame:[1,3],from:[0,1,3,6,8,11,13,15,17,18],from_bipartit:[3,8],from_datafram:3,from_numpy_arrai:3,frozen:3,frozenset:3,fruchterman_reingold_layout:6,full:3,fullregistri:3,func:0,further:6,g1:0,g2:0,g:[0,6,13,15,17],gaito:0,gamma:[0,1],gene:15,gener:[0,3,6,8,9,10,11,14,17],generative_model:[2,5,10],get_default_radiu:6,get_frozenset_label:6,get_id:3,get_line_graph:6,get_linegraph:3,get_nam:3,get_pi:0,get_set_lay:6,get_singleton:13,gillespie_si:1,gillespie_sir:1,github:[0,11,14,18],give:[0,3,18],given:[0,3,6,8,13],glossari:10,gm:0,go:[0,17],goal:13,good:[0,12],googl:14,gotten:3,gov:[0,9,10,14],govern:14,grant:12,graph:[0,3,6,8,9,10,13,15,18],greater:0,green:1,group:0,grow:[9,10,14],guarante:6,h:[0,1,3,6,17],h_k:0,ha:[1,3,8,13,14,18],halfmann:15,handl:6,happen:1,harmon:[0,13],hashabl:[1,3],hasn:1,have:[0,1,3,6,8,9,10,13,14,18],hayashi:0,header:[3,14],heal:1,heath:15,held:3,heller:15,help:18,helper:6,henc:3,henri:15,here:[13,18],herebi:12,herein:[12,14],hereinaft:12,heterogen:1,hg:0,hicss:15,hidden:18,hide:18,high:[0,13,14,15],higher:0,highlight:14,hist:17,hit:18,hnx:[0,1,3,11,13,14,18],hnx_2section:0,hnx_kumar:0,hnx_laststep:0,hnx_modular:0,hnx_precomput:0,hnxwidget:18,hold:18,holder:12,home:10,homolog:[2,5,9,10,14],homology_basi:0,homology_mod2:[2,5,10],honor:3,how:3,howev:12,hpda:14,html:[1,11],http:[0,1,11,15],hugh:15,hull:6,hunter:15,hyper:[3,6,8,18],hyperedg:[0,3,8,9,10,13,14],hyperedgelist:1,hypergraph:[1,2,4,5,6,8,9,10,13,14,15,17,18],hypergraph_homology_basi:0,hypergraph_modular:[2,5,10],hypergraphedg:3,hypernet:14,hypernetwork:[0,15],hypernetx:[0,1,3,12,14],hypernetxerror:[0,3],hypernetxwidget:18,i:[0,1,3,8,13,18],i_m:0,i_n:0,iacopini:1,icc:15,id:[0,1,3,6,8,13],ideal:0,ident:[0,3,6,18],identifi:[0,3,15],idx:3,ignacio:15,ignor:[0,3],igraph:0,illustr:6,im:0,imag:0,image_basi:0,immut:3,implement:[0,1,13],impli:[6,12,14],implic:0,impos:8,improv:18,incid:[0,3,8,9,10,13,14,17],incidence_dict:3,incidence_matrix:3,incident:12,includ:[3,9,10,12],inclus:[0,3],inde:3,independ:[6,18],index:[0,3,8,10,11],indic:[0,3,13],indirect:12,individu:1,individual_contagion:1,induc:[3,8],inequ:0,inf:[1,3],infect:1,infin:3,infinit:8,inflat:6,inflate_kwarg:6,info:17,info_dict:17,inform:[0,3,14,17],infring:14,initi:[0,1],initial_infect:1,initial_recov:1,inner:0,input:[0,3],inquiri:0,inseper:3,insert:3,insid:3,insight:0,inspect:14,instal:[3,10],instanc:[3,8],instanti:[3,8],instead:[3,6,13],institut:[12,14],instruct:11,integ:[0,3,6,8,13,17],intel:10,intellig:0,intend:[0,6],intens:3,inter:3,interact:[14,18],interest:[0,3],interfac:18,intern:[0,3],interpret:[0,13],interpreted_basi:0,interrupt:12,intersect:[0,3,6,8],intuit:8,invers:0,invert:0,investig:14,invis:6,io:1,ipython:1,is_bipartit:3,is_connect:3,is_empti:3,is_s_connect:13,isn:3,isomorph:[3,8],isstat:3,item:[3,6,17],iter:[0,1,3,6,17],ith:0,iti:8,its:[0,3,6,8,13,14,18],itself:[3,8],j:[0,8,15],jacob:15,jason:15,javascript:[14,18],jefferson:15,jenkin:15,ji:14,joel:1,joslyn:[0,14,15],journal:0,jth:0,jupyt:[11,14],jurisdict:14,k1:0,k2:0,k:[0,1,3,8],kaminski:[0,15],katrina:15,kawaoka:15,kbasi:0,kchain:0,kchainbasi:0,kdx:3,keep:[3,17,18],keep_weight:3,kei:[0,1,3,6,8,13],kelli:15,kernel:0,kevin:15,keyindex:3,keyword:[3,6],km1basi:0,knowledg:0,known:[0,3],kocher:15,krang:0,kritzstein:14,kth:0,kumar:0,kving:15,kwarg:[0,3,6],l:[0,13,15],lab:3,label:[0,3,6],label_alpha:6,laboratori:14,lambda:1,landri:[1,14],laplacian:[2,5,10],laplacians_clust:[2,5,10],larg:3,larger:18,largest:[0,3],larissa:15,larremor:0,last:[0,3],last_step:0,lastlevel:3,latest:1,latter:3,lawfulli:12,layer:6,layout:[1,6,10],layout_hyper_edg:6,layout_kwarg:6,layout_node_link:6,layout_two_column:6,le:15,learn:[9,10],leas:8,least:[3,6,8],lectur:15,left:[0,6],legal:14,len:17,length:[0,3,6,8,9,10],lesmi:14,less:[0,3,13],let:3,level1:3,level2:3,level:[3,6,8],levelset:[3,8],liabil:[12,14],liabl:12,librari:[0,3,9,10,13,14],licens:10,like:[3,6],limit:[3,12],line:[0,3,6,13],linear:0,linecollect:6,linegraph:[0,3,8],linewidth:6,link:[0,3,18],linux:[11,14],linv:0,lisa:15,list:[0,1,3,6,12,13,17],liu:[13,14],llinv:0,lm:0,lmr:0,local:13,locat:[6,11,18],logic:0,logical_dot:0,logical_matadd:0,logical_matmul:0,longer:3,longest:[0,3],look:0,loss:12,loui:15,lower:6,lu:0,lumsdain:14,m:[0,1,3,15],mac:[11,18],made:3,magnitud:0,mai:[3,8,9,10,11,12,14,18],main:18,major:[0,1],majority_vot:1,make:[3,6,14],manag:[0,14],mani:[3,13,17],manipul:3,manual:6,manufactur:14,map:[0,6],marcin:15,mark:14,marrero:[0,15],mat1:0,mat2:0,mat:0,match:[0,3],materi:14,mathbb:0,mathemat:14,matmulreduc:0,matplotlib:[1,6,11],matric:[2,5,6,10,14],matrix:[0,3,8,13,17],max:[0,3,17],max_degre:13,max_depth:3,max_level:3,max_siz:[3,13],maxim:[3,8],maximum:[3,8],maxlevel:3,mcdermott:15,mean:[0,3,17],measur:[2,5,10,14],mechan:1,median:[3,6,17],member:3,membership:[3,6,8,18],memori:[12,13,14],menacheri:15,mend:0,merchant:12,merg:[3,12],merge_ent:3,method:[0,3,8,9,10,14,17],methodolog:0,metric:[0,9,10,14],michael:15,might:18,miller:1,min:[0,3,17],min_degre:13,min_level:3,min_siz:13,minim:[0,6,11,18],minimum:[3,6],minlevel:3,minu:[0,3],mirah:0,miss:6,mitchel:15,mod2:[2,5,10,14],mod:0,model:[1,9,10,14,15],modestli:3,modif:12,modifi:12,modul:[2,4,5,7,10,14,16],modular:0,modulo:0,more:[3,8,9,10,11,13],moro:0,most:[1,3,6,9,10],move:0,much:13,multi:[3,9,10],multidimension:15,multipl:[0,3,8,13,18],multipli:0,multiwai:[9,10],must:[0,1,3,12,13],mxn:0,n:[0,1,3,6,8,11,13],nama:3,name:[3,11,12,13,14,15,18],nan:3,natali:15,nation:14,natur:[9,10],navig:3,ncell:17,ncol:17,ndarrai:[0,3],necessarili:14,need:[0,3,6,11],neglig:12,neighbor:[1,3,13],neither:[12,14],neq:[0,13],nest:3,nested_incidence_dict:3,network:[0,1,3,9,10,14,15],networkx:[3,6],netwrokx:6,newfpath:3,newuid:3,next:0,nichola:14,node:[0,1,3,6,8,13,14,17,18],node_column_nam:3,node_diamet:3,node_incid:13,node_label:[0,3,6],node_labels_kwarg:6,node_nam:3,node_radiu:[1,6],node_set:3,node_size_dist:13,node_state_color_dict:1,nodes_kwarg:6,nodeset:3,non:[0,8],none:[0,1,3,6,13,17],nonempti:[3,8],nonexist:3,nonzero:[3,8],nor:14,norm_lap:0,normal:[2,5,10,13],northwest:14,note:[0,1,3,8,11,13,15],notebook:[11,14],noth:3,notic:[10,12],np:[0,3],nrow:17,num:17,number:[0,1,3,6,8,13,17],number_of_edg:[3,13],number_of_nod:[3,13],numer:3,numpi:[0,1,3,6,13],nwgraph:13,nwhy:[0,3,10,11,14],nwhypergraph:[3,10],nx2:6,nx:[3,6,8],nxm:0,o:15,obj:17,object:[0,1,3,8,13,14,17],obtain:[0,8,12],occupi:8,occur:3,off:1,offer:3,offset:6,omega:0,onc:[11,14],one:[0,3,6,8,13],oneapi:13,onetbb:13,onli:[0,1,3,8,11,13],open:11,oper:14,opinion:[1,14],opt:13,optim:[0,6,10,13,14,18],option:[0,1,3,10,17],order:[0,3,6,15],ordereddict:3,org:[0,1,15],organ:14,orient:6,origin:[0,3,13],ortiz:0,osit:3,osx:11,other:[0,3,6,8,10,12,13],otherwis:[0,3,8,11,12,13,14],our:[0,9,10],out:[0,6,9,10,12],outlin:18,output:[0,1,3],outsid:3,over:[0,6,8,13],overlap:[6,13],overrid:6,overview:10,own:[8,14],p:[0,3,15],pacif:14,packag:[2,4,7,10,16],page:10,pair:[0,3,6,8,13],pairwis:3,panda:[3,14],panel:10,paper:6,parallel:[6,13],paramet:[0,1,3,6,17],park:0,part2dict:0,part:[0,6,14],parthasarathi:0,partial_k:0,particular:[3,9,10,12,14],partion:0,partit:[0,3,8],pass:[0,3,6,13],path:[0,3,6,9,10,11,13],pathogen:15,pd:3,per:[0,1],perfect:18,perform:[3,13,14,15,18],permiss:12,permit:12,person:12,peter:15,physrev:0,pi:0,pickl:3,pin:18,pip:[10,18],place:3,placehold:3,placement:18,planar:6,pleas:[0,3],plot:6,plt:1,pmf:0,pnnl:[0,9,10,11,14],po:6,point:6,poli:6,polycollect:6,polygon:6,pone:0,poset:3,posit:[0,3,6,8,13,17,18],possibl:[1,6,12,18],post:0,potenti:1,poulin:0,power:[9,10],powershel:11,pp:15,pr:0,practic:3,praggasti:[14,15],pralat:0,pre:6,precis:8,precomput:0,precompute_attribut:0,precompute_modularity_paramet:0,prefil:3,preliminari:13,prepar:14,prepend:3,present:[1,3],preserv:[3,18],press:15,princip:14,principl:14,print:[0,17],prior:3,privat:14,prob_tran:0,probabl:[2,5,10],proc:15,proceed:0,process:[3,13,14],procur:12,product:[0,14],profit:12,program:14,project:14,prompt:11,prop:3,properli:8,properti:[3,8,13,14,18],proport:0,provid:[0,3,6,9,10,12,13],ps1:11,publish:12,purpos:[0,12],purvin:[14,15],put:17,py:8,pybind11:13,pyplot:1,pytest:11,python:[11,13],qh:0,qing:15,quantiti:[9,10],question:[9,10,14],quick:[6,10],quit:3,r0:6,r:[0,1,6],radiu:[1,6],rais:[0,3],ralph:15,randint:0,random:[0,1],randomli:1,rang:[0,1,6],rate:1,rather:17,ratio:[0,17],rauga:14,ravindran:0,rdc:0,re:18,reachabl:13,read:[6,14],readthedoc:1,real:3,reason:[3,6],receiv:3,reciproc:[0,13],recommend:[3,6,14],recov:[1,3],recover_from_st:3,recoveri:1,rectangular:[0,8],recurs:0,red:1,redistribut:12,reduc:[0,6],reduced_row_echelon_form_mod2:0,refer:[0,3,14],referenc:[0,3],reflect:[3,14],regist:3,registri:[3,8],rel:[0,18],relat:[3,9,10],relationship:[0,3,9,10,15],releas:[14,18],remov:[3,18],remove_edg:3,remove_el:3,remove_elements_from:3,remove_nod:3,remove_singleton:3,remove_stat:3,render:6,renyi:0,rep:3,repeatedli:0,replac:[0,3],report:[5,10],repositori:[0,9,10],repres:[0,3,6,8,9,10,14],represent:[0,3,6,13],reproduc:[6,12],request:3,requir:[0,1,3,13],research:[9,10,14],reserv:6,respect:[0,3],respons:[14,15],restrepo:1,restrict:[3,8],restrict_to:3,restrict_to_edg:3,restrict_to_indic:3,restrict_to_level:3,restrict_to_nod:3,result:[6,18],retain:12,retriev:3,return_count:3,return_equal_class:13,return_equivalence_class:3,return_full_data:1,return_index:3,return_po:6,return_singleton:[0,3,17],revers:[0,3,18],rho:1,rich:13,right:[0,6,14],rigor:6,ring:6,rocha:0,role:[3,8],root:3,roughli:0,row:[0,3,8,13,17],rowdict:3,rubber:6,rubber_band:[5,7,10],run:[0,11,13,14],s12859:15,s13688:[0,15],s41467:1,s:[1,2,3,5,6,8,10,13,14,15,17],s_betweenness_centr:[0,13],s_centrality_measur:[2,5,10],s_closeness_centr:[0,13],s_comp_dist:17,s_compon:3,s_component_subgraph:3,s_components_subgraph:3,s_connect:3,s_connected_compon:[3,13],s_degre:[3,13],s_diamet:13,s_distanc:13,s_eccentr:[0,13],s_edge_connect:3,s_edge_diameter_dist:17,s_harmonic_centr:0,s_harmonic_closeness_centr:[0,13],s_linegraph:13,s_neighbor:13,s_node_diameter_dist:17,s_path:13,same:[0,3,6,8,13],sampl:[1,3],satifi:3,satisfi:[3,8],save:3,save_st:3,scalabl:13,sci:0,scienc:[0,15],scip:3,scipi:[0,3],score:13,script:11,search:10,second:[1,3],section:0,see:[0,3,6,8,11,14,17],select:[0,10],self:3,sell:12,sens:8,sensibl:6,sequenc:[3,8],serv:[0,9,10],servic:[12,14],set:[0,1,3,6,8,9,10,13,18],set_nam:3,set_stat:3,setsystem:3,setsytem:3,sh:11,shabang:11,shall:12,shallow:3,shape:3,share:[3,8,13],sheahan:15,shi:0,shift:18,shortest:[0,3,8,13],shortest_path_length:3,should:[0,1,3,6],show:18,shufang:15,si:[1,14],side:[0,3,10],sigma:[0,13],signatur:3,significantli:13,sim:15,sim_kwarg:1,similar:[1,3,18],simpl:[0,3,8,17],simplic:[9,10],simplici:[0,1,9,10],simul:1,sinan:[0,14,15],sinc:[3,8,9,10],singl:[0,3,8,17],singleton:[0,3,9,10,13],sir:[1,14],size:[0,1,3,6,8,13,17,18],slightli:18,slinegraph:10,slower:13,small:[0,3,6],smaller:6,smallest:3,smith:[2,5,10,15],smith_normal_form_mod2:0,snf:0,so:[0,3,6,12],social:1,softwar:[12,14],some:[0,8,9,10,11],sometim:[6,18],song:15,sort:[0,3],sort_column:3,sort_row:3,sortabl:[0,3],sourc:[0,1,3,6,11,12,17],space:[6,13],spars:[0,3,13],spec:0,spec_clu:0,special:12,specif:[3,8,14],specifi:[0,1,3,6,11,13,18],spectral:[0,6],sped:14,sponsor:14,spring_layout:6,springer:[0,15],squar:8,src:13,stack:6,standard:17,start:[0,1,3,6,17,18],stat:17,state:[1,3,14,18],state_dict:3,staticent:[4,5,10],staticentityset:3,stationari:0,statist:17,statu:1,status:1,step:[0,1],still:[0,3],stop:0,storag:3,store:[0,3,13],str:[0,3],stratton:15,strength:0,strict:[0,9,10,12],string:[3,6,17],structur:[3,8,9,10,13],studi:[0,9,10,14],style:6,subgraph:[0,3],subhypergraph:8,subject:12,sublicens:12,submatrix:8,submit:3,submodul:[2,4,5,7,10,16],subpackag:[2,5,10],subset:[3,6,8],substitut:12,subtract:3,success:8,sum:[0,3,13],sum_:[0,13],summari:17,suppli:6,support:[0,1,3,14],sure:3,surround:6,suscept:1,swap:0,swap_column:0,swap_row:0,symmetr:0,symp:15,synthet:14,system:[3,6,9,10,11,15],szufel:0,t:[0,1,3,13],tabl:18,take:[1,3,6],tan:15,target:3,tau:1,tax:0,tbb:[10,11],tbbroot:13,techniqu:6,tell:[9,10],tensor:3,term:[0,3],termin:1,test:[10,11],text:[0,6],textbook:6,thackrai:15,than:[0,3,8,12,17],thei:[0,3,6,8,9,10,18],them:[3,8,11,17,18],theoret:0,theori:12,therebi:[9,10],therefor:[3,13],thereof:14,thi:[0,1,3,6,8,9,10,11,12,13,14,17,18],think:3,those:[0,14],thread:10,three:[13,14],threshold:1,through:[0,6,13],tiffani:15,time:[0,1,18],timothi:15,tmax:1,tmin:1,to_jshtml:1,todo:3,togeth:[0,6],toggl:18,toni:[13,14],tool:[9,10],toolbar:18,toplex:[0,3,8,13,17],toplex_dist:17,topolog:[0,9,10,15],tort:12,total:0,tour:14,track:[0,3,17],trade:14,trademark:14,tradit:18,transform:[0,3],transit:[1,2,5,10],transition_ev:1,translat:3,translate_arr:3,transmiss:1,transmission_funct:1,transmit:1,transpar:6,transpos:3,transpose_inflated_kwarg:6,travers:18,treat:3,triloop:14,tripodi:15,trivial:0,truthi:3,tupl:[0,3],turn_entity_data_into_datafram:3,tutori:[0,3,10,11],two:[0,3,6,8,13,18],two_column:[5,7,10],two_sect:0,type:[0,1,3,6,17],typic:6,u:[0,6,13],uid:[0,1,3,8,17],uidset:[3,8],uidset_by_level:3,un:18,under:[13,14],undesir:3,undirect:13,uniform:0,uniqu:[3,8],unit:14,unless:3,unpack:3,unreach:13,untitiled_modularity_and_clustering_origin:[2,5,10],untitled_modularity_and_clust:[2,5,10],unweight:[3,8,13],up:[3,14,17],updat:3,upgrad:13,upon:18,us:[0,3,6,8,9,10,12,14],usag:0,use_nwhi:[0,3],use_rep:3,user:[1,3,9,10,11,13,14,18],usual:6,util:[0,5,7,10],v0:3,v1:3,v2:3,v:[0,3,6,13,15],v_1:3,v_2:3,v_end:3,v_n:3,v_start:3,vaidyanathan:0,valu:[0,1,3,6,8,13],variou:[13,17],ve:14,vector:0,verifi:0,version:[10,11,13],vertex:[0,6,9,10,13],vertic:[0,3,6,13,14],via:[0,15],view:14,viii:0,vineet:15,viral:15,virtual:11,virtualenv:10,visibl:18,visual:[10,14,18],vn:3,vol:0,vote:1,w:[0,3],wa:[3,13,14],wai:[3,6,9,10,12],walk:[0,3,8,9,10,15],walter:15,want:[0,18],warn:0,warranti:[12,14],water:15,waw:15,wdc:0,we:[0,3,9,10,13,14],web:15,weight:[0,3,8,13,14],well:[0,6,18],westhoff:15,what:[9,10],whatsoev:12,when:[3,13],whera:18,where:[0,3,6,8,13],whether:[0,3,12,13],which:[0,1,3,6,8,17,18],whitespac:6,whole:11,whose:[6,8,13],widget:[10,14],width:[3,8,9,10],window:[11,18],wish:11,with_color:6,with_edge_count:6,with_edge_label:6,with_node_count:6,with_node_label:6,within:[0,3,6,18],without:[12,18],work:[0,3,6,11,14],would:[3,14],wrangl:3,wrap:6,written:12,wshop:15,www:[0,15],x:[3,6,13,17],xor:0,xu:13,xx:3,xy:6,xyz:0,y:[3,6,13],yet:3,yield:3,yoshihiro:15,you:[3,6,9,10,11,14,18],young:14,your:[3,11,14],yun:14,z:[0,3],z_2:0,zalewski:15,zero:3},titles:["algorithms package","algorithms.contagion package","algorithms","classes package","classes","HyperNetX Packages","drawing package","drawing","Glossary of HNX terms","HyperNetX (HNX)","HyperNetX (HNX)","Installing HyperNetX","License","NWHy","Overview","Publications","reports","reports package","Hypernetx-Widget"],titleterms:{"0":14,"1":14,"class":[3,4,13],"import":13,"new":14,"public":15,Then:13,To:[11,13],activ:13,algorithm:[0,1,2],an:[11,13],anaconda:[11,13],anim:1,api:13,attribut:13,block:13,build:13,central:0,cluster:0,colab:14,contagion:1,content:[0,1,3,6,10,17],descript:[9,10,13],descriptive_stat:17,draw:[6,7],entiti:3,environ:[11,13],epidem:1,featur:[14,18],form:0,generative_model:0,glossari:8,hnx:[8,9,10],homolog:0,homology_mod2:0,hypergraph:[0,3],hypergraph_modular:0,hypernetx:[5,9,10,11,18],indic:10,instal:[11,13,18],intel:13,laplacian:0,laplacians_clust:0,layout:18,licens:[12,14],matric:0,measur:0,method:13,mod2:0,modul:[0,1,3,6,13,17],normal:0,notic:14,nwhy:13,nwhypergraph:13,option:11,other:18,overview:[14,18],packag:[0,1,3,5,6,17],panel:18,pip:[11,13],probabl:0,quick:13,report:[16,17],rubber_band:6,s:0,s_centrality_measur:0,select:18,side:18,slinegraph:13,smith:0,staticent:3,submodul:[0,1,3,6,17],subpackag:0,tabl:10,tbb:13,term:8,test:13,thread:13,tool:18,transit:0,tutori:14,two_column:6,untitiled_modularity_and_clustering_origin:0,untitled_modularity_and_clust:0,us:[11,13,18],util:6,version:14,virtualenv:11,widget:18}}) \ No newline at end of file diff --git a/docs/build/widget.html b/docs/build/widget.html index 032d4c20..b010f785 100644 --- a/docs/build/widget.html +++ b/docs/build/widget.html @@ -7,7 +7,7 @@ - Hypernetx-Widget — HyperNetX 1.1.4dev documentation + Hypernetx-Widget — HyperNetX 1.1.4 documentation From 49f7ad323ca483c1708260604ad8e96a608d3a56 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Wed, 20 Oct 2021 12:13:16 -0400 Subject: [PATCH 09/41] update documentation --- hypernetx/algorithms/hypergraph_modularity.py | 25 +-- ...Hypergraph Modularity and Clustering.ipynb | 175 ++++++++++-------- 2 files changed, 110 insertions(+), 90 deletions(-) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index 60c92e54..ad366e4d 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -7,13 +7,12 @@ References ---------- -.. [1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S., Ravindran B. (2020) A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24 -.. [2] B. Kaminski, P. Pralat and F. Théberge, Community Detection Algorithm Using Hypergraph Modularity, to appear in the proceedings of Complex Networks 2020, Springer. -.. [3] Clustering via hypergraph modularity, Bogumił Kamiński, Valérie Poulin, Paweł Prałat , Przemysław Szufel, François Théberge, 2019, https://doi.org/10.1371/journal.pone.0224307 +.. [1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. "A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering". In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24 +.. [2] Kamiński B., Prałat P. and Théberge F. "Community Detection Algorithm Using Hypergraph Modularity". In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13 +.. [3] Kamiński B., Poulin V., Prałat P., Szufel P. and Théberge F. "Clustering via hypergraph modularity", Plos ONE 2019, https://doi.org/10.1371/journal.pone.0224307 """ - from collections import Counter import numpy as np from functools import reduce @@ -40,7 +39,7 @@ def dict2part(D): Returns ------- : list - List of sets in the partition + List of sets; one set for each part in the partition """ P = [] k = list(D.keys()) @@ -57,8 +56,8 @@ def part2dict(A): Parameters ---------- - A : list of lists - partition of vertices + A : list of sets + a partition of the vertices Returns ------- @@ -71,12 +70,16 @@ def part2dict(A): ################################################################################ - def precompute_attributes(HG): """ - Precompute some values on HNX hypergraph for computing qH faster - Adds weight, strength and binary coefficient attributes to - the hypergraph for computing qH faster. + Precompute some values on hypergraph HG for faster computing of hypergraph modularity. The following attributes will be set for HG: + + v.weight: if HG is unweighted, this is set to 1 for each v in HG.nodes + v.strength: the weighted degree for each v in HG.nodes + HG.d_weights: total edge weigths for each edge cardinality d + HG.bin_coef: to speed-up modularity computation + + This needs to be called before calling either hypernetx.algorithms.hypergraph_modularity.modularity() or hypernetx.algorithms.hypergraph_modularity.last_step() Parameters ---------- diff --git a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb index 552f4de1..096c184d 100644 --- a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb +++ b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb @@ -128,7 +128,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] @@ -152,7 +152,7 @@ "metadata": {}, "outputs": [], "source": [ - "## compute node strength (add unit weight is none), d-degrees, binomial coefficients\n", + "## compute node strength (add unit weight if unweighted), d-degrees, binomial coefficients\n", "hmod.precompute_attributes(HG)" ] }, @@ -162,24 +162,22 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{'e0': ['A', 'B'],\n", - " 'e1': ['C', 'A'],\n", - " 'e2': ['C', 'A', 'B'],\n", - " 'e3': ['F', 'A', 'D', 'E'],\n", - " 'e4': ['D', 'F'],\n", - " 'e5': ['E', 'F']}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "e0 has weight 1\n", + "e1 has weight 1\n", + "e2 has weight 1\n", + "e3 has weight 1\n", + "e4 has weight 1\n", + "e5 has weight 1\n" + ] } ], "source": [ "## the edges (unit weights added by default)\n", - "HG.edges.elements\n" + "for e in HG.edges:\n", + " print(e,'has weight',HG.edges[e].weight)\n" ] }, { @@ -188,19 +186,22 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{'A': [], 'B': [], 'C': [], 'F': [], 'D': [], 'E': []}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "A has strength 4\n", + "B has strength 2\n", + "C has strength 2\n", + "D has strength 2\n", + "F has strength 3\n", + "E has strength 2\n" + ] } ], "source": [ "## the nodes (here strength = degree since all weights are 1)\n", - "HG.nodes.elements\n" + "for v in HG.nodes:\n", + " print(v,'has strength',HG.nodes[v].strength) \n" ] }, { @@ -220,6 +221,7 @@ } ], "source": [ + "## total edge weight for each edge cardinality\n", "HG.d_weights\n" ] }, @@ -232,25 +234,41 @@ "name": "stdout", "output_type": "stream", "text": [ - "linear: 0.414445267489712 -0.03746831275720153 0.0 -0.19173004115226341\n", - "strict: 0.43490699588477366 -0.02385843621399164 0.0 -0.12887572016460908\n", - "majority: 0.39379753086419755 -0.0343506172839505 0.0 -0.22078024691358022\n" + "linear edge contribution:\n", + "qH(A1): 0.414445267489712 qH(A2): -0.03746831275720153 qH(A3): 0.0 qH(A4): -0.19173004115226341\n", + "strict edge contribution:\n", + "qH(A1): 0.43490699588477366 qH(A2): -0.02385843621399164 qH(A3): 0.0 qH(A4): -0.12887572016460908\n", + "majority edge contribution:\n", + "qH(A1): 0.39379753086419755 qH(A2): -0.0343506172839505 qH(A3): 0.0 qH(A4): -0.22078024691358022\n" ] } ], "source": [ - "## compute modularity qH for the following partitions:\n", + "## compute hypergraph modularity (qH) for the following partitions:\n", "A1 = [{'A','B','C'},{'D','E','F'}]\n", "A2 = [{'B','C'},{'A','D','E','F'}]\n", "A3 = [{'A','B','C','D','E','F'}]\n", "A4 = [{'A'},{'B'},{'C'},{'D'},{'E'},{'F'}]\n", "\n", + "## we compute for 3 different choices of functions for the edge contribution: linear (default), strict and majority\n", "strict = hmod.strict\n", "majority = hmod.majority\n", "\n", - "print('linear:',hmod.modularity(HG,A1),hmod.modularity(HG,A2),hmod.modularity(HG,A3),hmod.modularity(HG,A4))\n", - "print('strict:',hmod.modularity(HG,A1,strict),hmod.modularity(HG,A2,strict),hmod.modularity(HG,A3,strict),hmod.modularity(HG,A4,strict))\n", - "print('majority:',hmod.modularity(HG,A1,majority),hmod.modularity(HG,A2,majority),hmod.modularity(HG,A3,majority),hmod.modularity(HG,A4,majority))\n" + "print('linear edge contribution:')\n", + "print('qH(A1):',hmod.modularity(HG,A1),\n", + " 'qH(A2):',hmod.modularity(HG,A2),\n", + " 'qH(A3):',hmod.modularity(HG,A3),\n", + " 'qH(A4):',hmod.modularity(HG,A4))\n", + "print('strict edge contribution:')\n", + "print('qH(A1):',hmod.modularity(HG,A1,strict),\n", + " 'qH(A2):',hmod.modularity(HG,A2,strict),\n", + " 'qH(A3):',hmod.modularity(HG,A3,strict),\n", + " 'qH(A4):',hmod.modularity(HG,A4,strict))\n", + "print('majority edge contribution:')\n", + "print('qH(A1):',hmod.modularity(HG,A1,majority),\n", + " 'qH(A2):',hmod.modularity(HG,A2,majority),\n", + " 'qH(A3):',hmod.modularity(HG,A3,majority),\n", + " 'qH(A4):',hmod.modularity(HG,A4,majority))\n" ] }, { @@ -278,10 +296,10 @@ "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "\n", @@ -315,10 +333,10 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", " \n", @@ -327,7 +345,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -399,12 +417,12 @@ "output_type": "stream", "text": [ "start from: [{'A'}, {'B'}, {'C'}, {'D'}, {'E'}, {'F'}]\n", - "final partition: [{'C', 'A', 'B'}, {'D', 'E', 'F'}]\n" + "final partition: [{'A', 'C', 'B'}, {'E', 'D', 'F'}]\n" ] } ], "source": [ - "## hypergraph clustering -- start from partition A4 defined above\n", + "## hypergraph clustering -- start from trivial partition A4 defined above\n", "print('start from:',A4)\n", "A = hmod.last_step(HG,A4)\n", "print('final partition:',A)\n" @@ -438,10 +456,19 @@ "cell_type": "code", "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "198 nodes and 1492 edges\n" + ] + } + ], "source": [ "## load the GoT dataset\n", - "Edges, Names, Weights = pickle.load(open( \"../hypernetx/utils/toys/GoT.pkl\", \"rb\" ))" + "Edges, Names, Weights = pickle.load(open( \"../hypernetx/utils/toys/GoT.pkl\", \"rb\" ))\n", + "print(len(Names),'nodes and',len(Edges),'edges')" ] }, { @@ -486,7 +513,7 @@ { "data": { "text/plain": [ - "-0.032074856299121574" + "-0.016649297401793245" ] }, "execution_count": 14, @@ -520,7 +547,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.5372359319251633\n" + "qH = 0.5372359319251633\n" ] } ], @@ -532,14 +559,14 @@ "G.vs['louvain'] = ML.membership\n", "part = hmod.dict2part({v['name']:v['louvain'] for v in G.vs})\n", "## Compute qH\n", - "print(hmod.modularity(HG, part))" + "print('qH =',hmod.modularity(HG, part))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Cluster with Kumar's algorithm\n" + "### Cluster hypergraph with Kumar's algorithm\n" ] }, { @@ -551,7 +578,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.5351500884869287\n" + "qH = 0.5351500884869287\n" ] } ], @@ -560,7 +587,7 @@ "KU = hmod.kumar(HG)\n", "G.vs['kumar'] = [KU[v['name']] for v in G.vs]\n", "## Compute qH\n", - "print(hmod.modularity(HG, hmod.dict2part(KU)))" + "print('qH =',hmod.modularity(HG, hmod.dict2part(KU)))" ] }, { @@ -578,14 +605,11 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "0.5475162906819371" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "qH = 0.5478409583056635\n" + ] } ], "source": [ @@ -594,7 +618,7 @@ "## H-based last step\n", "LS = hmod.last_step(HG, part)\n", "## Compute qH\n", - "hmod.modularity(HG, LS)\n" + "print('qH =',hmod.modularity(HG, LS))\n" ] }, { @@ -636,27 +660,27 @@ " \n", " \n", " \n", - " 15\n", + " 24\n", " Daenerys Targaryen\n", " 31103\n", " \n", " \n", - " 24\n", + " 27\n", " Jorah Mormont\n", " 19344\n", " \n", " \n", - " 7\n", + " 26\n", " Missandei\n", " 13683\n", " \n", " \n", - " 4\n", + " 14\n", " Grey Worm\n", " 10497\n", " \n", " \n", - " 11\n", + " 8\n", " Barristan Selmy\n", " 6514\n", " \n", @@ -666,11 +690,11 @@ ], "text/plain": [ " character strength\n", - "15 Daenerys Targaryen 31103\n", - "24 Jorah Mormont 19344\n", - "7 Missandei 13683\n", - "4 Grey Worm 10497\n", - "11 Barristan Selmy 6514" + "24 Daenerys Targaryen 31103\n", + "27 Jorah Mormont 19344\n", + "26 Missandei 13683\n", + "14 Grey Worm 10497\n", + "8 Barristan Selmy 6514" ] }, "execution_count": 18, @@ -681,23 +705,16 @@ "source": [ "## Index for \n", "inv_map = {v: k for k, v in Names.items()}\n", - "JS = inv_map['Daenerys Targaryen']\n", - "## JS's cluster\n", - "JS_part = hmod.part2dict(LS)[JS]\n", - "## Build dataframe: all nodes in JS_part\n", + "DT = inv_map['Daenerys Targaryen']\n", + "## DT's cluster\n", + "DT_part = hmod.part2dict(LS)[DT]\n", + "## Build dataframe: all nodes in DT_part\n", "L = []\n", - "for n in LS[JS_part]:\n", + "for n in LS[DT_part]:\n", " L.append([Names[n],HG.nodes[n].strength])\n", "D = pd.DataFrame(L, columns=['character','strength'])\n", "D.sort_values(by='strength',ascending=False).head(5)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -716,7 +733,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.7.9" } }, "nbformat": 4, From 6c15aeaf371d19090d05cfa21e75d63993d6683a Mon Sep 17 00:00:00 2001 From: Brenda Praggastis Date: Wed, 20 Oct 2021 10:18:57 -0700 Subject: [PATCH 10/41] updated docs for modularity --- docs/build/.doctrees/environment.pickle | Bin 456030 -> 459845 bytes docs/build/.doctrees/index.doctree | Bin 10329 -> 10394 bytes docs/build/.doctrees/modularity.doctree | Bin 0 -> 15732 bytes .../algorithms/contagion/animation.html | 1 + .../algorithms/contagion/epidemics.html | 1 + .../algorithms/generative_models.html | 1 + .../_modules/algorithms/homology_mod2.html | 1 + .../algorithms/hypergraph_modularity.html | 1 + .../algorithms/laplacians_clustering.html | 1 + .../algorithms/s_centrality_measures.html | 1 + docs/build/_modules/classes/entity.html | 1 + docs/build/_modules/classes/hypergraph.html | 1 + docs/build/_modules/classes/staticentity.html | 1 + docs/build/_modules/drawing/rubber_band.html | 1 + docs/build/_modules/drawing/two_column.html | 1 + docs/build/_modules/drawing/util.html | 1 + docs/build/_modules/index.html | 1 + .../_modules/reports/descriptive_stats.html | 1 + docs/build/_sources/index.rst.txt | 1 + docs/build/_sources/modularity.rst.txt | 69 ++++ .../algorithms/algorithms.contagion.html | 1 + docs/build/algorithms/algorithms.html | 1 + docs/build/algorithms/modules.html | 1 + docs/build/classes/classes.html | 1 + docs/build/classes/modules.html | 1 + docs/build/core.html | 1 + docs/build/drawing/drawing.html | 1 + docs/build/drawing/modules.html | 1 + docs/build/genindex.html | 1 + docs/build/glossary.html | 1 + docs/build/home.html | 1 + docs/build/index.html | 13 + docs/build/install.html | 1 + docs/build/license.html | 1 + docs/build/modularity.html | 297 ++++++++++++++++++ docs/build/nwhy.html | 1 + docs/build/objects.inv | Bin 2877 -> 2904 bytes docs/build/overview/index.html | 1 + docs/build/publications.html | 5 +- docs/build/py-modindex.html | 1 + docs/build/reports/modules.html | 1 + docs/build/reports/reports.html | 1 + docs/build/search.html | 1 + docs/build/searchindex.js | 2 +- docs/build/widget.html | 5 +- docs/source/index.rst | 1 + docs/source/modularity.rst | 69 ++++ 47 files changed, 491 insertions(+), 5 deletions(-) create mode 100644 docs/build/.doctrees/modularity.doctree create mode 100644 docs/build/_sources/modularity.rst.txt create mode 100644 docs/build/modularity.html create mode 100644 docs/source/modularity.rst diff --git a/docs/build/.doctrees/environment.pickle b/docs/build/.doctrees/environment.pickle index f8d5dcb9030500863192e695aa5f1a10e34fd86d..a9a8d518fc0186d9cc4dbbafd8e4d37dee8e0da2 100644 GIT binary patch delta 60258 zcmd752Y8gl_CL-fyBiV$DWn&YP(pxonjpQyvY`vomW3=NOByL42nM_8s?j$vHku$! zs(QsP2w1S;SFhJ=L+<^1uMH9P+QHvBXWo6wW;e;3AkUxYVJGi7^M2;cnKNh3%scPA zpMI5i;fIsqm!q<{dS$)aTwGS@yr$mW zp?tZsa(S_HMUG4eTU6qxs&ZDT_k~qDE6OXYt8(PD-fdb`Ug@lNTUHboE^$`ZyMs!K z7dgwSfE4X0SyB!q6qRg6Js;J%Iw@Cs?|j;os$PA%-PJ?6dhO9DSAueNa_~x5mU8vR zs+q2?@>Oleqi?o*+?Av--+bXQSEh3H*8I<1ot3M%2i@XIRIW~Sx!KiSxq7c)lq*@e z`umaHt_1&aM>CRpil6SAF72^IRT1y*ycZxz|YPO!nXa_Yf&FzpIp; znIxUc=_6HUj+Cy;vPpT_+0y#l3@I`zNh-`r0A6~qqpY};^e-LE1a9_3;PzuV(mOec zQet)t<-ImLKDdq)eRq9^^jY>0>66U9Of920XK}kaQa142-KF!nank#_;Zl8W59x`# z2x)C@G)qHjM`u|zm|u3q%KAD|xd-ow%t)zYejG>_A$^?}OH#(=^_K3>OOzHvYK8QP zymYBJzYFUs-I<@llIXXy^k#k%OO&d6cMEVOOJC*p3PuH?7@1P1-f`00-qF(Id680f z@2J2!R~lr@kDU%VTr=V4=K87i)JT{=A^NjX~W~B zeSJdMM(L?OvwJ-Vx7I8qeYhTizk6I?zotIJwHf{_TIJdT$-H{kHb~q_JkJ>zEp2gx zTac?>x2<*U27>D_{M0OTxb{Gb7<(awoA-<4phzAQ$rF^+xSoX6CCh;3-h>Pxnr?V= zy+_nqkVeZ9;+z1u)`)}stqE~t|03PkFS3?&;&C{s6^=zq9ZQ_`LX>w~DGGM>CPa~< z`bP*Ef@G&Bmwl#{Wut3pqU^|VD=VCp1x{)AwNYST8?TLXih7@GW!dP+8Yx@aS(_~D zU3RiwdcV-h^3ioRQa*6qwLx;x^{PDrS)mF=v@^BN7SU__7Q{^bQ(843F<21*u96C= zOUAddTpS;Kmn(`ra&Iy;_`!iOqPXs@EDpyX1I6`iWpOybHC9~KL!F^^wks2BNtq*C zSsad_jT8r5chE#R_)yxCsI?xlu%;=k6a)wFCIpGMVWdUzRjQ+`wlH;MNljI?vl4hcL;?X46RV~g4*q%{@H9Jf+0mmmodN?R#Ha&6NJYdcAfJ@sO!WFgA6EfS>% zw3j?cVmQ@^HlziE6NF0)ovaNd-bz7I z8WSYIwM|+z#4|F;1$&JQkF-(%3{{)Zgw%Ivq{b)b4UH9UZZDKA?Bp(jp+ zFwW|NvTVd3oIX$(MpFkDMnDwFx-bB7bs{syc!1^$E*zaHaCPB0N%I634pcNpaN*EF z^8*)pb($Ku(Baawz=gh$qD>b%BbpAlu&2{hz=a)-rU5RrcbWpY!1nUmQ2g)0)~DFt zh0Q==zl*fuHdU7|(p2gJsdQLE4n|gLEcyUkxr0lJtE%nA)y~p-sopDOm7W~dx1XbG!TV1>GhrIqjK*VN;kjP?%SGhFZZxykjuKgKqxbVU4URE4|Uw3>x-I9nyh zlt003Ms$)T{W2oFmSTNXCqEbg@vW2Z2*R&UTyF#49RxO2UFmcV_Sfvqny*O5N6rjL zF-ueKa9OA#5z?Kb?4n0^FdI;Zwk9V)K4BoDCQhSsCwm*$Nd@Dq(1ynjyv?8`R&ejT zaihoxLcj<-i!Var;YKPA{US?R_imWC4Gb{?qSc(*FRnBKQLM%IOEo908Ji##j*Wu= za?RLY`Q!zhMnol*1&zz_R@p)$AlmAxDzh7bDAQszTV*ZsIZ%>YI{I~#lsi7gOlC({ z_VHmtf9s6MP5OItd{=M%-C_hpTX$7|Hxh(@x1;NU!!(0ei?kvgoREo=tO;Fvi=MZ~ zNDW904r>>0HS9D3;#&>dji>?EXw)&h1>+IgM*DH)rR62%OID`B>?B_wV0!U{re0So z*}3T@DO6Q$hc2+VONC^Czss+0FqFTlVm1xwTX84+L)1bT(#T4g+W%hikI^`UI_8M> z(~mh=e$xwfsu){aFNWw%vKX~S)(urLni%?;-|yO~M!HQ3o0K4ZUyuZyFKkkda6v&9 z14BI+)NrZb$C%m%Mr1MqSygVXYThO%@n zJwG)AZ2G{|gnOq&i*}r4r0!(a;eYrr>lsF1v?^4`)~N(>xy*Y0^y#39`O_!LL+eT- z6(D=I&lvA*&&!Q~_|`&|5ePr_Tww%5D~4*%qA~veY0o`Wd+s(9pKDE;nJbKWw~>lN zBVO&@fIEx;Xf>yr@iu~JmKkpj9NJp%{LL(_cm8IU?43^Ltr)5`i#GX7t+^$0Y!KExLNVqlY0{h!sqLKpV9~?oj29ODn~};>Lw+%5 zq_}Yxr9WrlsHl z->bwe2>#JB)Xg7aef$L=iaGYRMj8c-BT*W8d$hN4Tw?^pw{g@Egx?WYT_4AF(P4n0 z#>XwtNh1rR4;ZP7tdI!lws#vWkaHtIzLipM1j3KUxko5gx2V-h?MVw3C-4_v9jeM52#v%uFZV}SmMQPqT3^W4dTZe6oK=>K` zg!wf!bW!}7E>kSZ241+`U zt<$OT-H+7W{#+m7>sj$2JyPR9TIHyL<^Y25I~KYkVbzut)Yp5bXY-zgzP%wudTnWT zKw(>a9@uoX0)_6s>O-gG-YwB(ZU+Gyx^+FTm4!i2E<#m z8MIN0Qw?sY8d@}e;aQDFwn^9YOBPYqhl+XKX+(aquDVxzTG#DHfP7omtpwrMy3|N( z)L;AvdaseX$V!Ry^%#1$5g^}6*<%iMUzs9@qdyvvQ_-2Zs*`shaNY=zZ=HQ>1j5hb z`@g`{Kv;JoA3lafNd?ty(dfi`0tziy#HDe;Mq{eFS0bglqG0L8>R4}eMjHY1t}LeRPkbuOviU>3GSaKjf*75ef@nNG zy~#)q(`rMGPw9P)Mx%xCYPitaXd%3Rw1^hI07*p9Rb!+nq=^5^^)`n}BLG?js4-|c zLHLal)EE>)(Bawzg=WQ|RH)lh;eZ{T)%C7U($bav10D#*cr;uSk3uBs9(K=^bm_=_ zL6UVPEL{=Hq2`snVCLcZ7clWxBq>pL6*0y|j5vS=tx) z=hyi2E&iN`AE!Jki4ZfT?CS;zYYY;d_MecY*_&#$wO6fC)V_7r_)KfW0Bh`lc5%6^ z5nccjzR#rWf=vuDphmLB=df6)ZtR}<91ao9%!U4vO;{WNB1`Z zyTOQ zpKJbL?qe<_#vtDiP=2#E8b>x=72h?H=R))jwU2l}o`Lyu<1Jb_g~m|dyw)w$M=J3_ zU^@{XbZgc$gDhslH4OrUHZn@HKz!TC1yZcv06{-N$}>DclIE=JEcLvj8_SR;-?7y1 zoamQ3^mC&4mR3xSLPf=VTb(bX^ED^>t(8_sFUw`BJbzI#7P zXKbm8u}v|eO|&(ZC7pjG%GkkXaX@c$W~fowGsKL=l{0Z=R|d-5g@H1)ak);g7Dw_6~8#rd!pKc-71p3&hs+o zC}-88%Hj$zRdUt{gr8pWl~AkVX1u&ii+nCf>ux*Jvv*V;5=YWm zQX0Bg?TQUGvP?7O!<<@WQ)+{NJ<14{Zl%(fBk^Yp{*1#PEB+MV&lLQbjz3pP z?7p(xA^7KL{F#71HvE~4Khxkxzc7hQ=KaKyd8D1Ta=mIGPAG5Ir@7R63^_ou>? zIPd;Ac}nb}GMc$Bmb%!<`x`tITw(;ow>=aZf$-DCgua>@MW}p#o2`UdHU-i6KqPv4 zQy7hkA$+i32)X>mr?amcaa!6&k%LD%tjYIyI2qot^JkSUa%2*m{3L?M3CehQz=%aq zm~>r{&){*d5g@H1)G={4K{Vp6JleQ4W8yu#dqXF^XLm2TlfGr72C^z5rOA7Gde3WK zHv;5a6|WkB@Uw1pLa5WDv`evTNm{!(Q+jSU?z>sLIZG^&es4rnMQx#58f?ftX9UQ% z+P*OYaT(Q?vK9B;q-^!&zK-g&95h= zf_<(Fs*@AZ`I~}hd`#G3r1`blkex9dW#`)&%f8s>2Hci}2vEB3WAR+G6yH3lVap`= zKYCPp=T@JSm5B{e{DWwfqB`dB&4s{o7W_;c$21qi8$xayI^D0)wt+CVw!tBtnHj?|{ACWvN?p*S31Z&KEyT584WS%6x- zxK*+Uv+ws&w|Ho4-^X%*b~2?kA9nB_S~vJW@eAVHm?a|+e*9gX(DJ1mTG8f0$BT2c z>9A)y2xfxEpuKoPr_URy3aRwZk0QNQ`iv0}-zq(91fpdsZE-*DFrx}i_e`xWa;8o% zFE6RTSql3kXxwI0w>L{D^^B3qkWz2|q`}hmr$#`0EA=CSxST!NGSRWJyrx=z0+EL( zloQawci<9Db?|=lM%wx0(@<}d`?K}h`NIf=pGfalq2Wu}i=}r?$Brc93ZCXHaW2v< zNT>Tzoqx^E{H(#ra$Sso__nK5BM_HSa|LIVL-~peovD)@WzG_LEq{U$k;&-ZKht0x zf2MsB1mZHP?yY|%$sTPI__@l|F-}KyO(mS8DmrbM5wS^q^Z(tzqb)T8;#+-1 zMj-qQ6Qa*HHBAtGN*9T$GY#Qbn}QJK`-^5p0MHahqhg3i%`b#pe&ef8%ZzwIZKKFR zHJ(E4XN18}-k)b|@nGEWL~B>I5+;^D4KnKR|A9l!3dCV&3-4e;%|0)sSVD1;WIYO zcTh*dHi+qPH7aU#gfwYSgB83eBS5}Y5n%+v&k);zFm%0CYh*2%=t_4z+C^UYyz9}f zV&U^jBcdu66u!5?p`}+C0rIW3zD6J}quLVp4TOcy#C`el8oS+yoQlr=yD!h%pUpJ_ z=mHo23>@rw&>)2Z%g#Fj**cr;$o13hjF+$=d|C8v*jI(5*%w8da#@ zIFbr(V(ndwKTSc%5veY!R`>P(RU>;a4(F-ECy%d^-g+!po?9z4d4}^3BYITYbj;%o z=GX5T0rK6Z?+`>Yqes-K68*GfV1oPI?eI%|sc^0{d_+*cc%$QpxaSvL4VxKdM@XBV zXb@8VPlwc^Nc(#u5Pt0coY1$wkg}ypjYJ!R?|&_+bW{|j;>X9NeSZs?xEU4Rkms3d zCxy2pl*)NB&fB@f8v*idh%rVW{ETmZfpVps!@XdD%Q@UdjtPbtkyGispB!!wM-4Ut zC%jka&4~Xh&T0nVtGcud@Cipxe%q^ zdvDa9(@QA!QbyUJGkiV$^fAvsF{`<9qL>=JH5e#n7yO?~1mZHPF71`5VVgl@x22%G8a_xX_tq^&#Pz7I z)*aNPOrSJ)Z)2koK;J5Specx2+``xRAk&2PwfIpBeazQZR^_$SpnT(3gwMyDs)4ow z9<54oueJA{2p=^9Z|@(Q>!s`4qD*MBSvc&3J$HiK36>dL0@e-LuQ*X6iBiP<(-CwW5ZoBMHL4 zYLruqFUeV6J*8XT?jKMV+2{#!i%;oKOR%@Jma0<{wQPnrkV8&iPH9+c!~k!Q(p(dS zsG!v+$ib(%g*(`*m>f4EKe>ZLZ#8fS^+rH^TiiVa;n(6W@%Dm7z9I3Xk-DHlSc-Jb zyA2jAA2$NyTO|jLK=_GK_X)LXTp31}iYIHyg9mBtdpR&AI{03O2 zj-mcx1je_*-ZKJm85P#lK}r{m$SrGd*zyGFv-cYuCi}Y)DBoKA#R!C-fmxVc^F6DN zP)#_5rXWQ5{+812GEx<-Hstsq4QPH{5`}7)@F0l1p)vZiY@Sh^bpMV}s*EM_QI`KVhXFYe&>u+Lo)zFyv$<^u_a zf8TEa`PR~8BM^Q%kWFa%Qu=LFr_q;pRvD=ZMF*%>A20B%FaqRTt<^>#TBg<(2ln1? z=`{HQ&n6?4Q55UrC7uV30QpwzeFSkid$s3%9bKRH5JiuE(NDenyu{<_(LP?}dCm_K zDZ_i`sBaBDZ3MzkVDY5T@}=z2jl9V7ZzB>b_T}Scp3jT``PSYiMj$Su_8NJaCkQ6o z%{~xXQJ;?&dQ3)ue5;Qcfw+w7YvP5TUPi=LROsWSo(v;EzE#-G2!x-ZL-gOK;!!GJ zAC9W)4Z_hj1tH4UW2n!#L}1VqMx$bgD9$g$d~tK*3-{SZ`n0xDPDr-TyVRxw$!d1t z$Hc6RzVKwVxbV{raD`{;L(&5iiwn;%g)2N%5)wSd&>BDow82-d@LU>5@I(aQ;mh~d zwk~{K$Qtg#$Ioy(F17*>7y2Mc!NlZBlfL+TLTw71w&WlYAigVv&rUc<0=Rep&S7_u z9QZWW`;2)!PM*YqoEdPkj)TO(oQ4jEs=W#hf^d?Y0E7ZUQU((iM~yptadC;W%3fV= zuXHY~DK06j$K_xY_-;tMK;Al%7_U%=btE!gp$_XvWV}LE*3tR+>J=W8k5{#V`MrE8 zuCPAHM6K6Reeep;!>glHuhc6%{z|>VbFkDaJP=F0!jrMoD?B2LuC5?ZJS(Wxad^*@}X~-AjShjTQ7dyMvL!Wo$LpIlX z_)EDg0b6Qb(M-9+MO29GGN$=fvc0k^J->-MHv{ zVdVPYHmnW)D_T`h4N8Jcr_L`Cc%!&uOh6}T&iC^5U@7f~80pjR332!jOAv9-4~~Er zqNKNfkCkk{L`wsH%tvbJkL8$r`s3Z0l>e`)Jqg&q&{d$MjnD71^AK>mMv5Tqvg;lIQ|K_8B z5b2YPLE?@A{^BZ@0wC)@VB>`m43*A&*^%Gbj%D)2jEx5jeu%NnLT9#7sIyVi`56lv z#~%q`SGaaRsyNR_W-^$s59$nXhH6J5LX0gLs9RMTAjJ zrecUyQ!`Fn8elDK1{wCR@=^^USoHfTc#>wwh^ z4`CZIJph|hFrD0vy@_c`D7#woaf-*Vexq@6(Y@5{%Ju z)w}Ui9oaCU`_YRXi>k{jiyb9=czagL-BBz8VK+yyn?tB|MwL2T=Jt_>sUUJ~EzRuiyvc8TKRIErC^eGO$EO87A?rcd%%+lk${Afwm!$l?uHl3B4z= zoJbs3!299X7d`ULOJV~BXe!Sg!{YgobQa8COk$m|$kRz|BBoi%Y%ZpEB(ow+|Ch|J z!qnD@O~!O{CqWNR5!Z84*i}MjS&M5*O8DMRtQX&t%3}G26c#1Q%i%9v!QyyMDjS0k zD^eM{0{#yq%) z0hpA*N-*Q?8E_2^`)vjrf!Fyx*mO*9>%kUd`dtrJiD_X^wj9%sd$JM%IDntaXIJvl z0W5`o{ir#D+k3Mh%<@t%k#%6E(C_w40TGuaQg;@+ML-PVN5`3?cxn#o$*;=xv}td? zDTnptuV=G7gu9r{>M*U#Vb@_Ap3APm^!8j~AXL0#fG~@gfugmp8_0@+soVE2 zD=*|{db1h)!QJLCO_6i26hgjmC96irK7&NMXON(W4rXhSe%D}OAb$)NMU)H?*B=fM z*As?{W;i}nD5KvnaeWX{j}~7XXO0oATQ-~xLe#y(S()Gt<%e?3vHbbr(Dv^>!un}M z+%iJcrtL`P61B;%a#r*3kt~nz9my~r;h&Ba;tm_dTm(Ey^u*B7Y;h=QAiuiOQC3yL zQwFncd~Gjq5PL_%yC-S!rLiZUnm94~#N2jtvOuJ8GF3}Q$YKjZ70c$6)EBN21v4O~rn9ddo z0j$C^t)I?T5n~2(W4d_;yUC-7yE4EF54(!lvH7-M#WLe@egUHb&K4lSIhtp1_|H{r z6yBdXli9@mSMV*HEHQljOyO(4oynYn(VNem1M$q-S;9D9n8h%x<=w7k7$5RFIsN@= z=4?;#b%J`H!S4(rI*&SqT&P&b}ClO^!vIS`*seUim%jQNMzY!QOZnj^qp#1v|g zK9^yX&gai%^DupWE}MpFhg*b=QWOBB)H@O2`+g+f=eEd;F1R< z@`?g3dB|N9EHMo_60dcN*~v*K!wC-`yqL*T)!t%6zitW3`QUmfPEC;w&#qqNxEKi&Ll*m4$ zl%;FQ?+WsObu5(Em9l6p?|s30s0^(9bSVqd^8P7!9m-f&E%{?XE~^1&X)j~RTHa{| zu7{TVFa0g;`JZJhT$|x@ks+s?_0^KU5ajeqkZw~sYp3OXEqLJ-EJ0fJU1)okMnT^y zyzbgt&I__f^7fMTe3kugyu&Ih=YV${{^o$7SB3-d@ zGOu4X%hKMHDAS>J+w;0=eGNOwOt8TAd|nlcj#^q%>ZsHKr>Vf5ckaT2hv#D9QF)h)yZF(L4`UYxvwM79+j!Ta1p` zTa}=5bX9dFENbW^>?iYQ-vQmQUex!R7QY`hDvW-h>?E(BUmTjh-g0L8m6o4X!)MUuF*HxI8~Z1aT-CaT0x{O z7YFMKC=hpTxE2H${AZSqH$_GSyAS0TSF&`i=+jB96Sbt7a{i60*hnpXwo=AGtwWqA z8~ns;nGR-w43n^$4T%^vUDqCqRNe?}5sPK|>ebAur5DTe|E^|Zwe*r`{?1x95f{|- z)>5wCFc{a-bf#Acrgt;zDi-9*&CDE6p{b)1!#8b5tdUVb6QTUB>sWtn+1JR-&#z-Q zsg^Q;|8c$Emg}N<#0_j@l*aTVkLmHFx4`1U{`OwV-r&Kzk@fLz>HAgQ6cg3WRcn&o8=sJH^JQhT$opN9~N9Nc=`4%5zNGE!lHY3{Ih4p@WK8}c)< zK)r5*iS^`L^=)XL9@^5D`vcie}F^wrH(xAm1d4~kn!J`vmp4KzHG50bc-83-iz+@XwjRKnLV!~;pIm~Z78Cc9a@OvlAo*AFs%oSUV}&_w?!n%B6MQJ+Y^ zM)Un{B{U<_A3Tq@jtONfrnMHc)Nz6Z^`9~7W%g+LD=)3oxmjo}yWWS-hn)^;%hf5BaP{0;*Dj=NYlIq0J75&W}5AnV7S zK~{SzQ(``1Mk)8i^31zg2W`=@G5ornAX2BhSs1Ulo3+=X!n6=8v+-`0rX?pUb?l^t zN|i;h$R8GI|~Bu7&9l!{4b>`~@Lo#_;5OSRbtnIf`hpF_q3G z#So4tqYB-VFEU&qvDm0eXKDFzC4wb(UyZ`7_kt07OrN+{aN9mm=iqx;SFLDQ#_+Ox zSzj$_NDM!CFU!`FhR5))?`2t9X-6r-q-e=w1$l+U614ymV)$lBZw@xnZNB#A$=q=O zWL#GdzIpa|Q?eFeS`06*XM?rbuTm=9o__}SXtQ4(!!OpebU6?r4a|+909!<4e0~p> zrZazg3}3pQ4bXxtjNymYvte426L8lvPwYXsMKS#7PM9;V*`PRfqA#Va`TXpo=5Q@U z8PW5&b%$9e@-n$Ar98l5;uaR;v$f)CeG^xcK)D;TSeQ=s6$(9g!+k7BTiU7^9(5m^ zpe3z=n%>7oYe{Qk`1~DEhqL!7BNx@-#u#q7UzvOm>6RG2@P0N%+Ysyc_=D(H?q@x; zpl-E7y3TgDXt!tjvlzbS0cB(&^m}Dx?$1-lZDOpaE|^#wja!w)9V+7!{_K9R>1zkEbe&M237n|%%0j0y;iWtN9m7i= zW}~zw@E>wo3A|(*OOA9FF43#$OQqg1JY^UQ;zPFS`|daD?On9(cFya@j_}+G&(hWy z<@ZXLB0LRAgu_$>oe#hiHp?3a{1a3U^;j0 zGI!MK1fqz7eK0&jbH|7rFYGdVrdgy&Q!G!Yha4Y2q928VVuhm(c}$-_Bo>|5AZ;P- zV?|hkzLbBoRc|8UvDn2t@hcVCF_wR}k0tR*J7L9JqsEw6j3xSMN%17bKrJarTiWZR z)kvJMQ)2n#T`WTz#dU@!1h^J>`!0RR)-9GF+ocZ_GGckyZhhr?#qu{cgRl<_v%pjM zv1+(V#bw9x-MjUJR$eUcwV(CtNRQ66R?;Vy58b2880q%@vHZ?G`T=fWEdO|qUaG-V zNIyRRQN7b07RzfMWu9P|07u4Rw@BcVABQ~y7a!FR(_>^Ut96Bd`=*&z?m#pvyal6sNPS z`6ON+K5UX#BeuV=Q+(!8~1`NL%Er&px57kP>|xW$hiM^UTEAA>Z-h zQ!tD_^f&OW_P?zzu(fwU_ll%!zSmUQv9=GfHC+eG=UC4D&c9B26WnD1Pc0MMXrPp_cAh zttI*_O}PnG_C@8T74|}yVR~v!ndZou&OHlJ@Uzb=j(u1_T7T2wXNjM0k9 z&VV+M8-lp#kX3Fkl}p_W-LJ|m@ZzFdQn?NtcYYfeIu4b+TE}kV!cMAkYgI0p81_$< zyWfku1hx~t$P)3%^TZcfm)dP$O<;g-6K=t%cm00=eT&%P;rd+s{U1a)tD%3Zq5oGy z|3X9mNkjixL;pcT{}KMKg~F(S?=+0_nj5~-(7)Euf7Q@`)6oCW&@X7{`Jfd~#d?ds zvD{kMrOCwMZ z4Ny-_{w^ANR}DQ=L(kIC+iK{+wHijMhLNVBx6{x=HS}Z+y_1F>rJ+Y_=n)!vq)aE{ z?Vw?VX>N$s(Bm}pbPc_`hW?>Ob029mm!rv_OMln6EhUbH&XOu#{1OWf=$_24eTiiT zWF+(5FR`2snaR+F2D!2zab+j-|GdPy)%Jvo43~!E4zESXrJKWjv5`M zv2Sd`%_cn79CqOF>f@{%teih^ob`mE^TctM1B=7|I}U=R@h&G=?}6P>hxk*p3c6n( z_`|J}pU9{cSv8PpBwj(a>Q1m2*vY!{1WSYDwG%7@lJ8HjUXXNpnRTy4FYFGhs)lbe zLIrUv8SMK5jl0Gm0&doGjfKB}FaZg;ZOvsBgaRbsYgMjEf-n^c^pV+Vf^ZcQ=*y-v z1z|Q4=&MX~1fh1mLa__VLL|@$po;`yF%szHt0jWqLIRzyu#^b2565jr=Sw?-+!j$0 zoeOq{31YUqj}sO=ur5Pg!V@d(MMdz=RHd^_zFGKVg{!WtlOleGFz^+Y(2Xw872i^? zWG!(nhrR*26goQ}%MZT7qKYZ2aKi3jRpBC&aKi48BIRUu`EKEb-R-Mt7B=7sPwckP z1}vEQQ3nj z_*ZvGRTc)XjxyZCLk!_y1p`(i!H{Hz@UZT-a0l*J_v9D;73CJIa#?{XAh__ZSW)FP zZW*R~9G`NMC3dI6gj01#R27%P=DpMg&6yj=rIXMxF)o9>c)E8fJZ~B5_RxA>C`5F) z0Xrllt=shHO5^#jvjMZO12BAZj(R!0%ja%iRCdimM^*8ns_crgCFm(`k~2|9$9`Go zj+$8vja@x$5h$%}T2XnmC+{tC-ei9N>#TF<8U+6a`}6nsa}Iv$7P^B9OXw}ede?uG zx#@ZI7bNIaA;B%rkYIB`f*~y=xB(avY%WMpvyh-}A;CSGkf0BR1QiMi`b$XA zCqmL2T_L2nNfQzbS0O>Quc@zbx38=zt1K^Z+LzThYrtMf*cm+bEtX~xf*&687CX>6 zAh#1g_Z>^ZwEcNDD`NfDcH%Was!)wPNeUmAeE9bBZ2toM>a@Gv(nUp%;xcS`aGklG z;BD9u*2OT4wOzQ>CVLE`7F%z?!P;I~wZZ`#wupAKl@o1|hW3DU572^Fd%h@-HOUT-a5fpq@y)PVRTD&<>S8wMHLm6R_jUgRjT z7nXzbg&Yyn0;rxj(*hE(zE-%d#XD?^naz#21lp^h%M(drwtATaTiuAnne9G7!cA&K zdXU*3l1ZLio0)A3lEUot9*FarYI{ZTav=f~wT;=1Av>n36h7Z$m%9HD5s1S5HZ7o6 zDq)%eY`p^@M~8*wHD!h1iS0`&%WFu5eDw5y?kIZM^niFw*G&&dj1}?**q*?<%=D@# z)%RIoVx|wKi+cYg-<&cdAQtF{ub2_gyH6Lq+uF&R5BCP+)53h*IV7rQ?Q0zawO2b}tRebI>wkbAfj$-17|LK2T391KFiY$&ZGosjUCafwoO63XylBnN%r*dX znC0z+c$aMmvsEIot$6bk_b(ECh1{rZ7Z#Vs7fnrw$ajAf&)gnR}{*cx87#014;*c1yvh9Q>l`51MotT2QLuG zpBE<1M(Zx%1k<~%Vz(%f_gdcpauB^DJ9?z7?tfbU3&g=*O>6zedREDcI|B*eys#=N zlHQ1=ZKlvmPF)OerD9#0P#JjOgiEC|EG{KAFku{tcANEfkk^+o?y)`uoCeC+Vm+Z$ zP$+|6pBflNj>7tp^<#x8YR~tk2F5{=hhI(&>=%w@U(5~c%+e31e0#U39F7)1TXY-=B$u@MNMH(LY&;U!3Df6~1a`snUq=FaVA}p@ zU^1qea%w#)@-00Yn2vPmXdt<`=Z*$;!|Q(^4a~$e;-$deSE4>_Gnnl+?1G^XzCmD7 z?X*uDKTR~F?Ex;hh6xX~)^&YmWuqvL<-_zu|BQ{KwgF zQw&xoez++)ftvR^>n$K~$Y`2$Wy1?sB<QT0BW zWDx6V>kGh&=AYihV&@R$i1l@#gvpuJr(DE*(|QV+9aW}g-w0*+KkJ8@qU?6(GP|9m zoIgTHdF==j4XyW&Fj3<^HNr#=>7x-Q>N^)?deTV2zjCCY&l_o?5ajxiCK{4A%KYc# z>kmc>oZm;9(mS9iw$GUDYi#^@p87{%yl{gPN11vKAnwo1))t$;om?0342)PI0k#gv z3YA%lO3KS1FoLEyFv^rRgz}^X*s?KCq?`xrn|57D9qt$E(k&hqr+$u zS#Q75rqmvkvm(HD0CRd8$g|gqn1=#vF98!?qBT)x*)rNho#cd^el^+YU#2R8)$V0MUjA+D2gxxj@X}k?#62LhW0|n0h1Z-^Z9TF#U6k zD7NcZQ(g?M0O;z46h3UUDaJy)I395)ONgewy|mbiKRMQvqGuI4E2?3YrWgNetSMmv z-KCgV8O*H|zpewM>9Po%6?0Xm(3q9KJ=zp6Bue51U08A~h1s5ZXL}Ugi*FrgN`m!` z7si=7Lvm)EDSir-rMC_u1&bC3rO9P`Ymsuv{RdSmn@=xp9dF7skzA+8nDD*x2gYNm zw;mpEI(&G%x!YB!=ubDBsek+-rwLok)b3YqF?UPD_O)KH1)*7os~=;_t}1d=2n)S_ ziArRxiufU&z<(wwlRT=5ICA@H=a(fM2;)xldg8aQ|nm%7*w+k_CY+l7$Bwu{Ox-YzPC z=XO!r;q9U_A3{C~oNONl+Rg$h-}0zXOZFq?t|3%jq{)_J;$_=m11fHt!8%|2h?&Nz z9gmoE@%jV#I&go9NR14=I=0f7TWm3WQ#O=X+!KB1E8%tgxn_~4W`ns zHrwj>J3GwrxZMuRO4@18!*tS4q5o^;bmvY{rIR~FRnPA6LU+bt~Ms@-N9U2obggxe>l@9!2R{<>ROK-wO2 znh<(MTif~#d*J==wfoI&5vpX5XqUP@0^;dC<{m=0aly8I!64kZ{bm{!+COUUn?g13 z6=KT@xwXCqBhwJItE`tdsWQ9|_t;k)-W4Cqj<- zd(H7}amoPUGhN}`wR_DyMc-Von{ZC;HKRv3yVslpN%B5(x?tBmY)&0OT|7#aCL4FU zEUu(|X~c`r^5lge>iM7THpj+NmIj8~i@&wcoDfG$Z6^_C(QCtgb9aw-?eq{!5P!#W z_&Y8LtAY-%V2?RT$eFg?ob1Umc_T~o{O#z+5(1#!-|Q!?wLd_$%{^d_^MFTeW(jE| zMMq67m^_B2{Emm>x^g+PuJQkbnj@*{M7v3ZL6 zzyM|&BO;mWcf&(O=t-foEacvEW?9IcUp31@?t0ZM3yl^J&a#l(J~GQfG2v&BUqnS4 znQbRx!HZsxGMwD=jH@l=@+V&{?y0%jLT+u#)fO5eUcOpjeG_pRANu5)r4K1X2monxWl!!gG~&gJeo78+)rn`0p- z^z|IeK$NljTnjm=nRA6qYvx*Lyx2L{Lc`r_bA^m&wJNGXugo=^n6Q?XljEuO*S(+>_Gcs5yvIlEogv)?3QlA zyp#F$S6g8Jp54+`7~7$_7JQPj)owwz^Ss@H4&_5g63B}5%?%I6$RC9-u*8eo^PsK* zIfbW9hRtjh3oLDuecY-sLC0JRy2OJEENSA_Oz21~TL2E7V@Z|Wi`LRaB*TY0Ecqhe z>3J6H9a|h0+2JJed5^PXGZtOL7wlrOhb;>&yRJmV9o#4k?9@g}XBsH2GjWo_Q#M;- zC{nhrvmOVvh2yptdl5dRu*<%ey2dNk&%LXYzsWJu*2dOJhbR>xL~toS`4kpSwpS;yegzrmmmF`C7EJ$>jIj|X{rNi zf@KL^Wg4wT0!bSo3sb$K9M->Z=@X@y@}#9_du*IF$1F5gy8oE)5=V}SUU){PUpQu= zSTpgsr3>r9haR^?4;R`&EmXmOz;{-{b~mvU?G7p~gH>&GjFhc|d}&|0!cn;d6@_Ea z)?=1r*5mNw$1PI!5^CgBYc}Yl11iFf#wu?GB3>RX9JZ5}_QW0`VvM&s;HF^B)*&Jn zT5EyaPPeBBs=Dt73pulwe-IAg3rNK};UUQ9<&O-{IWuR@eCN!WGxN+m z`|7I1vtK1>mTT7M?9iO#blX}F@@H5xg?8d69 zW{1{XQ*OEy1UR$FHI`ZHpho|SDyz+AtXtIV@Pi6XH8vn>Y8F-kA=J9SRJXuvTA0hF zBuhs|#YhkL2;AwH{-#gE%a)Gwd`x{qL>ImQ>f%&tqS-q&T0-M^~@5 z#JirxoH=gk;d&aI@uj7U>#6*PA1vMFr`;zk>8_`_Kc2TF@TZ3C@`~jTTDrNIm01av zuJY4sYb+VAC-YNJTY9>ls-yQ?GF?wKQ$MvN$xmNYS(05()~uH;on220tUWD>&L>Uz z!m?G;h0GCBY1S}leReu3kPc-hNZ)7mK;7y867zaX$Ff2tO>Uy}LQXtQKg^Do;N|SO7XpmHt(^bmr)hS>t#db||x@626F8!1{S9&a~ugnix zOYsH1BUNh3>lAFNv77CSa%!RP(g?x7q{LifwwIJhYqPUa4?>a}^1?wbL7T{3lb0dA z)T_5NB0mAriPG%+?$DA5$(rvkt?m^j^~jX(4ro~p@^YNcvYX&CV@ z(oZ!}Ob7Z1?k1(yST@gCVd9FMXs04%G`FEhgCF;F%`2Pbj&ZoC43V?#RD?JZ&x&YS z@1Nt<(!#NAwsreMpA;Vxi5$?n~fpj%jDz#L2f(w-mmXU>v^IiE-C<)<iwsM!{@vq~}ENWe zO=->T)P=-$ZRjFt3gXej>k48T82XR3ugZEyV3oC|0^&BZ4j`dO8qr|``9lJ08mZ8r z+Cl;c8t_@j%x!6)^Dmh@X(VkSvl@--EM&f+5wwNO05p2G5PwV~XAAL^G-$REA4UUa z3vn$p@UoDxO#@{M8Dca@wh#-a0kVad8V!yucWENwzqIcN=rk&}JSG(mi|UD?dPh)c zeN~moUQ%VY*_)+ik6{^>?i<$0-x;b&TZi@S=X?MW5VuiD0d=OiraDtinaP`+%~IU( zlp9G=%M;`e^_H)JR#zNmN#7M^N#SLh1{&kNLK;Mc991KP>deE$!!8joQW2Kjkaz+i z-LhZcG=L0O*qUxRBCte`wvYG@nqd^j(!j zM|-@3v~pAln`QsF+Lr8sf(%mD5|i4+MM($CK9`J-sTvC7P*}iKn1)n%q0>le27zE8 z&W!-3a-LSOg0}jP8bR&tVeUqdtwxAvBj~9{1Q{(ZL;6dhgNhto<&|7cg{j6=XT;ww zLK$Ih(^;v+# z)B3Kx*>HSNO$#UtGbW_D!*IVEA)aB7)W9%QfZ=MUE@_}4VGJEfA!Du8)+%eoq7;L* zJfFvz`_(juVsgyT(H)b$YJ_;kWS1HdUR=};H8MmsSJs(quK-nCq^}xd3R6bYqqD!K7RmStlmNOR0s?%*Xtsrq$GtswPL_B^ngn z;EY=22=RM0!o;4Gea*L2#FhA(;cokyR}%y@6r}+m4Mhpu)x_%5&HQ8Bhg6gr0iF?v zP$R<2kP1~JLsUZ^QmiAq+A=L<+1C1y`U`ED?;}g!6bDO3ixZ(2&lcx0lOCg{@!X`d z+&gll8X+QrvMCRvBCf=g!IIlOUpzZyLV#Jl|FL^WiqW_|Mir&VXTVrnUAI(e}(W_lj$#0S(g+~uSlAs(IddNsmC6lKd^ zM@3wTWnT*?ePjl4(nn@Eo%B964FEK9CF9Ia?k2rQjS$Z$>{KJdi%D-+BSTa}Hfh!w zf2m1dO#lnZI)=L}+JIrK99w6Z9@Mf;^*ghZ+$zq72z3FK5Uq z_Nm$lA5`S%hEp->EG;mFvssNj4@zOX$$Y0~iyHkomQm7Y6^T;b++cU-^LI7EL@Z>V z{gfIJ*Bj3r1kW)QE*fe+yG-1ZFxAbhSo!gKlrOq)N|M4MxS%w^j4P*_%%gZ%KeZjuA~o-KA}3{ossmPy>HE zm{SLD?=4Bd)@vASBd@l_auCK^+^Y38G+SEVGVazMHNg7lAxiJoHvw$a1-gLwRc%E} z|EWo0*7t$S`j)8CU;1uIp8K@8NR0r`mbZY4@M?M2G5C5&O=AF!EXjI%iz5gRs1f2B zk@adscyZ6`7^>Gg@QUUKX>-^aZwR=gOY^nTiTUJ+;D_@QS-5amO#>*9#dQhprgcD# z5YIsFQzODlsPGp6DJ`=l3#;U1wiE{2ztpHpVf)>d><-&`H9|bY_K6x1*Acet`cwh7 z?D{SYHopKhQz#j>Tk2EYVbiD);u$uiM#Oc5%}ZdKsYYFDZDSWkxx<#OMu=zFx~UP- zs<&%(F84f@%i#c!y4bYYLjWzRM>LOdJRb5z6?8dkfPJw2ogOP>rs4Ru;R zB7e@3KNlpttfuP+8u8o=|rIVFW1GzFOEQ$QsV0{CMxK7Zv zb#_ujL$*wYMq8HIx(|G&J!*hu(pycfTIMy}mxe1+%rX)9Rt95Wpb8kN%bWN_3zz4o zMu^xKvdhy@5ne6xIxdSc)HH@80@pQLE{(dY5#$+_G&Lf;#8@c|*=xBplA7*_losC+ z1B;-hJ9t=XP@}2~wun1Z+~?2nY6N+PZHyWbUSh1j05(w85zj9R=GYfnJCu}JtLmlP zl^rM31DGOj7UfmfDEIj1t7(`EOkNKI2jH28w5JJ7sNq1XB)H{5?_!9%OJ;u|W$I~dn?gTK2J?&$9m8{}aAH_?2n|i9pExVWJ|u4}3)&+}^{`>(h&#NvD~djkY`ZtR3pNRk*-i9LsUa{TdZ%|a$Bv>k)`cX0~Xx$lM}6um20@*c8!$* zuCem68vUtRd@`?v-#Vg3h}akMI5|i~cpWF#c){J;K>1WnV>lua(mm@V+~e?bY6N*k zybP2N8L|pT(_XI&+B*D4SZnO?BGPrcd<4CiB+%KGjxJb5eBr{=ABVc*>8nPN zXFNMl5v^F67>5l98Lr9c%C@p;t@1&OZqf&MpY-|z1wC`1VoM(RlTZHiC4c&pKLg31 z!Q@W?`7=CNdSF>~_iXb2KIG2;^5;hKXDIwpZc0k;J~+69ZjX0iP zmJQ^yNvk)+u~q6+T7~h-;BfV(&qFOPA{46;lH2Z%Zv^RBef9 zeHvkdv@IE}s=+*{R}sAa)=$*zSL1KQPT`Rs{h*QO*{l#g<>AdRuP*ZAUq@om-x$}7 z_K+H@pcZ6aJUNwR|L$OPZ2rq+m2ZK`t4Jwm;^i>>=hw_f@ldUQIW0{bq7*iHV$2Z4F+c%SA zHTyPaGtYNcO#@tDj(RlH9n6o^2=WZ(X*D9e?9crLU`op$6OYy`e~cfk@d;5I0-R{- zC|!Q6vpaN`)wJ6)bib<+(W)JKb+xo&k7pO&UuK7ysp)j4xpX-q`3#qDFM)cv7PS}YXH zT9ML1hzN|&Jfo&H9M?bgbaS68o>U{oGp>&_OuUW3Yiw%} zxGBT8H`TO&17X}W$Q_8+)rj#7#4Bn?WH%Wg&e2JjE=f9M%b^$O8{2F-T{sX#|I~{&0)0*Gy9*#xtCg)re>n&bESk zTEDkVB{uc6C4$wjlp$aL{i~+*05XrK3@aRGlR6&_7(wT9ndW@oqy|Ucqjw!`aiQXF zHDWyX=$%x=6-JB%QCBE^HFFY_cLb52&Tt-;5>Iz4IV{|HR?))bHhzYHBXzPVvVl~R z&F@_E!>aNp$(~k7Z9}MM+@5G%1h>7`HxwUZ*k8*@p8(tuEKs`ia;FK@_Q=zsT2oy` zow2quh2D|kMtHpSCeU`f7U~>Y>nknpO~0*1jAt;9sS)92YWoYolp0>mgsH9J)ifRq ze6L1Nj#$!bEyAa7)rj$o*niZBxQ>V!Ur!NcFyrf~YzB)BQy)d4J0qoky`JPANkyp< zaAYxIL=Zd#xssH)QGr_qdWWU?tobK z+nqULcc{^mBX;!d9CyT)s}bWFv88H6v}$>6MU}LEWl$+^T(fAZEfG98@bbx77MQjr zqg6H7HlSA#y#CgMlUvj*(8W&S8?^NILp*N|!L6}t`?eFRcgR0FBF2=K`L0dcH(Vx? zb_Ks^B{kK0yx8@sOUe}p5n+)xX)sbWu($4g{(Ze%HOj)M%a-cjZ?O}UrACNn&@$ABxPG8X z^ZyZ5K*KxTM5`TsTZ)g*Vm0a#*c$xOYM6i)%iTgXvOJ?_Xj24n>aEWxZJ69uzeHg_ z$u+@dQvQ;Q>zBG%l)SLb76=WXc4+>f#RsnJYJ_-3ay}K|)%z>&i?{n)Z>H4u^xf{? zF&gUpj?pitqu6bS>8co*ngSaK$?()pRBa&ZYDJ@OWb2 zT{VI{8~s~qM0g3$-%uk%RD*X%GSIvaJ0Wnm78fth58)TC?sfIY?`rgyjOSZ?(CQa8 z0z?4h(DNrM!aEA$318oKJD+6fpP%&iNsaJ$(`wII2ejQ-wRTZ5jD`F4N3qH-v|-t&;l%EqgKk&gQN7PUBuGDeLMu`gsx8%afY zwX|z}I-r&B)|%Bch9lzf8v%3G2=a_dnHmvZg3vh(S;1kE@UdO5Wuz0I!f(K=`&T;K zt-V)`mM+BZ`d5oB#e38U@{HKsYD8Q|#JpU#-l0Za7j)e}Yq8?lsz#7!=$=(0!po$~ zjP1%@s)taFd4je?@I5Inq1Z4rO%c)HUO5qHUVRY_#jf2&vULTWSt$05%PD`QMtv!w zxy97~xf%gt`(=mxe^i855R?v?^hUcp3Y7PeSNZnb?B#YEEhJt$L z>lPk-q8cHdLDf?c*K^G3CKwl4>+Q;&6ep2^Q|F~!(XaI69()3YBZ*H_0+`{zacBB5#kxSwQ59MN8}#4 zFN>;7$v+xdId2=NTvE;S;qBXpg=OXMDH68O2gl+h-m zy}k~9Fr5wBvuf0)@NNCBg-82HjS$c9omL~l%QV5pY+C^(tzWsdC+`$6$J&;N)?-pO zTeT&lRW(>l=2gUOR=D+D^$S-U8t#%dexKHm1$F4P^tT4|h<@~4ud|Sky}}du5+@|& zvxh(=A1s0=@+lce$Y)ISKJ*I>@Zo!MwH1;K#v^xg^x+or1{lhbZBQtX%o4Ic0y#-Q zuJ^BZNTYuk(GUt>ku*{jAYvIujm2b}iMJJduxHthMAd!2LD(kz97T};B{5dAe&_j{7=pTl!+ESkS1CtL`nz$*E?e(eV9c5l6(0RErsy^MoSU=1=Nda z0vGhR?2P-_4+Yub*)gcR%vM@0ZTtBKsqB}2I!YUcn=YdmY5&jJ(x<=N=u;ahrTjY4 z#~vxw{%R)4N57Vlq~Nz1BzfpJO4R*cLY^vqpGT7Sf2YKuf6%Ar{#ZeXxqlkNX$AT7 z$|{ZKno^_9j8QO(m;U@cTzd1*Y?A4hKTRZ=bD5TW=W=;K<>HBUfXyDcegvuj-umZI zBU5xRzL4P)i*G_`4$$!~MEN+$2N^+Yyxa%PCiL?@$V$>8U$iESDlniNPJo$AC3d3} z@p}TEqeZc}r$6d~zg&TW@I(#rBl)LkSh+VKWfk6lAB}-rM|5Z)K7Kn2ly$?;X%T58 z&eb8(bi6@_=94tU50RGO#ePiK*M7)Gp6B?Z`Aq0YJTw4B<8S;?En&LI4p?OvOI{@@?#Ka!Jvr64Z&y( zt8xr3c*_ThhN3(i9fF24>L}c66^g-2LQyaLw-8i71pYe&-R4xY*BPh@xOqYAe=0&9 z$UjFvydngp;CDk&XOjJ5DB4KsyEhCu$a6wC+CzDzuq2d&QzB3g zJgFm^%&0fvkCRYOM*X!TDkNnlL^2uwi9}DZT$AydWR%5neI12vBDsb}L(Pz$jYbDa zx-o{88WPLWbFt__LSNhoZDdj=;pDDRIn`ioEV>at6^9}iYc5`X$tM+`i$hP7GL7-* zL0v5wE`4!R80v%z63{Hdcq9R>W+g`8BjKnUKG7Ax_#+YZ2_HGR*=4v;a0I?3Lf{V} zr3WW5l};z2CMIPRE{p_~sFZ9SX#kIwL>EAB(?>1b8TabTS%2q^wRx>q*)r zg{AvaoWo->UeU=n4%hgC#^=M4mX&)X6-^@fFQuZ@BwfKob}##)RhRlg>-jrHGttNZI-a^3%ggb1IVcU=GEo;s%ObRI z^HC=LrZ>2U^O-1;6#6j}Rg$zM3r%H8bjMr!qRzNB54vhoHe~%Z3x$v@0okaMdD-se zrdoR?s{yif4WYGWz~ZGEK4F7*m^!F)u%&4@%>Z)|<=m z$!r4YqbLDy>4W@ueZa7p?2PxXKCHgLzGyrv*Mq>G+7ETdcl2evzxQ<_JrtvXnnZlL z6r6N+KQxVL*ax4Rff5NJ9v|t4@)P-Mi#qkAf@b@f0vsgvF@X_InOGop!1lek?D-K$*>K1)l~tfgr-C9CFA8| zP~iPzS@Xijq8cJ+`B*fUq+g6hmCQi;*I3K3emI(fH*Z5>_~1C?$JGahOZ{y~gOkRi z08%!6JhGGY5lESg9BW-UzAy}BhtNYqY?jMZ$;syh>v9=IJv3x^u)2BLcErVg^ zc&iW%Cbb3@p}8y+>TNSFFqPQsMmsZ*XNyoTlH+U<^K)ZwLUWiLunPl7hYTtBligs4 zA;rj#H8UGL04qMO7-iCC7Be`{7K0cVe`6=3(S*KsGILNDCnFmxn`f-6!}DjrWcT0{ zltc0yo5F_Pu&E4+9aEXCxM>WE)zg?kU7p4utDesCeKMUvTQUPJCUQQSftHc<=9#FH zHK7k4Ive%FmuI4UHahy6Z6&7aTKghAtOPUw2iKcv)pwq05iXgD`rxxbV}7m|&}>ZA z@w3>Rb=xdt;mj&ysf|dvDU|$AFz89QFeLZg!mJ~AHoB9jwR<+YoumUwSo0n&VawT4j$~a030a3aC&@w+8p&GLoqRhQ&o@J8xTX??;vrtVZxlW+pMi!t$-PAikW*!8=ZY z%2^ZY7^>Y?_Jt58-) zvYG_6$U2>(1^MEund82>8pQ|Q7XxcAwq(J}t5J-C@Thx^*Q!yLBF7)qsJnuYQ{$Gy z1cWf57Kkr9;?zoLSt(WJO&Rgi0&QQ4k)rqI?D6ED%Bz_2Y{l zz)&fwMFSKW*Vm#k3c}~LsI!6)KHshO!R`dhd^bqf%ttpWWc+i!o5EprXt*NBtUAQoz0&tLM9w)gT;_Y7-vK23c?Z_il4x9nCPAk zSPt2MfQ0PnKtlF(AR&7?kdQqcNXVWJBz(h!?CCfM=9e}W@(r@H1?E;eDr56fZ@Q|* zm+B#=nNZJs>5_UjEdEuGs@ZVP0&BP8)P*n`+_?btAXdI*0V-otY;PNUX}`J_e_IdU z5M(6aFOH#L#c00~FaKN@jl*t5qw$%AD4uzm8}WtD7?m!?=idtB9LCF;TTxad*@}YK z*7Xh_i>-MnEaj&};+K-= z@*h1EqBn5n#^ta9)bMAZg1IpgKl%o&4OcBgad_r36e5b3QZ32!t++3 z8%3c0rbeBKjUE7ErCHDEJFbA4rg=MY94?h@$A% zJPY)AT1P>ri>$Np;JaW77U%S|$gPwA$GOl!!FXseN>S+WtE@vX z{v#MgM=5~)gRAoF-DqU6ph^e)=xs1`!&aezBH5RbVLarEWE~?@6dfUeB5WnbhKP0+ zz;gWJ03A~rQGc;?u&b_M{B1LAagp7;7=@Z)QBIe8cr_XtJCg4FPBz}E$kdU^$-M^+ z7sW@r$V0+MjZl>6#8}_ogY;sF1V)~{8jTUjof-M~y=Vabb2SPQd8w`9F_&fQ*KPDxu&GHwtB82LmA5e))av^C&9+$JE_^ z0FfPPOM}xs8tu(1njQH#6$1#_-BGktD$RB~lM2CBDz`35Ug<22-+B<1xXXH=;INyV z_|hD!SV8dZxxA&6yqb|AT1>&e_i(H1KE?zm?F5^aX>yS>!-^G^G`pnr6Dz@t40WgA zFEb!!;g&(AZD7ol4JcDAvyqYKZ9uVu1(-HF8_Hk`RsQy~QyKaj=1*{>4{tyzd`_bD zr=##U8(h;7B|RI33!j02YUo2KLCm@}3ODQl`uJ>!rC_T)2rr+iaYn_oU(p@tgW$cf__J>ifsL4TEk8^IN zxsGt@j*Z|?;x@WMLCSnN3gvo#|Y!q(ZgcQ15pt|(L%k$;HlIHzCuH?2y0hfZ!u2l?We$H_jx*3)@8>VRj#ah0K z!pn9zRe+K@{KjTis7{OjhqvnTW~HHg8-;s3igH9@-$&unM_mCXE&Y?MS+bb*7f!zj zuOvkJ?@>7BF{K?`rgqQ+U+OEzeN;BQ18sT?jTMW-E^OY5DE|J(QJP5h2lC_S29XpL zjq4vr-MOEl@iH2&@hSnE&!n-aU& zXng!Rpl1z05ej37kH+B#0QR$gQx4Q5USQA@sH3QD3a8)WMo)_-qcxAb+i-^Bv?<-G zLIt9T3`!b+8(!3E!xY+QMdQ^^qVZzp+-UsilW3Gk%8$nX*acOcyAcJ1!&xfmUOkurcW zvE^oCwF$O{6iSVWCJxTIs-dWi7YgCC1#W}L5Ka6+JPzA~Vj|1yjjmNFEn66k6aS9J zhz%-^Cc}5ANSewG0p6vADLQ-x&D$RrJgXd>v*ZF=yn2M2g_nrjIXjenTpEopJ&XE_ z)tjQ3_x0Nf?;Q$9K@|ScNH>v{TqJOn8qT98He3`}BZD7Hg=DPUJmGv-bzGyT*Ux<1EgAlJM_`wa%3^Mc^sk zYey!)WRP-1tC-pU&P#qd5v41vcZ*oBDQbHp1`@yMr(LWRO#(vH??LI}B=bI>WYAt^JoZ5} z9=KQOOwL5(yY{+vil_#kaE(9Rs~jfhIsKn6!b_V?Q{6QC7w0a2!L^%1rTv@JkG-Ir z$G_zC*nP^G_!~~2yHBb6B~GWczct;h_WyG3<$cOv>qk03dgFqbB*@&aH1D5zp(pn% zUHESlOnHlhw&hPcR-EfTNlT9}4x=feX{X0v+hNxdk`~N#$LHc< zq);@6Wr#h3`iOe;!nu!w9{WmBlEMS@fsMc;s93DEe+>TP2r`JIfiZa6Q8Z2@4UQqM z*ZBg0>Q)eg|2&Ek#F01xUJk+=0G#;}>dfi%SI@_B`pqx795kg*h+*#_9(lD7`mYr z)KSrdYPT$|UZsL-x!@08MPZ^MHm=CeuezKu&A))>|M@i(nncc>ke%fc`q@C|=%o1; z@q8;@bA{BDzJ$|{zUEqAQF?;2H{s4J(F zw_KJVIQ``7E)PcO|KoD*K86zRua~1U%KVix-vK7R`wiFI7RvmC;_6&MQc{OlW|P0Y z;R21)eK~#fF_g-CmD1t#34Mpq#==*}f?Ze;e` zZfvsEFLTeiamntx%>BuYOE$e_?%+;Jwa6AJa2vSRce=63&Y4{JZkbEBk;wj;%)QTz zOZLZP?hZFD*;JCb4ZCDEb(dr#NoEg?b7@VvWT7l`N5?C+Gj0Rv zCpyZA^Of0`<-(LpoR!S&2;Iy3m~x2+k-6D!Try5&Zl25qs~gIOtIRG@usc|YQM$P7 z&9%d~4lJ6#M1T1ml-4i>3;~`U8gfdZ*>XQrco#dIV!`bHn26mZ&^HV82L<{Df&R2W ze@37`A<&up73kXp`u_>^&t!UoWv#$yk{OmA z0{wY`zDuC*7U*va^mhdMUV;9CK>wWnzk!}fuzVpfB%wgFK>tXfe=N}dA<+LR(9a6= za{_(8K!348V4M;d?+NsS0{xIcKQ7Qu2=vzk`s)JyWr6+*r#BGeIwCNR3I&b{^fv|i zX@UNsKz9iAdj<@TpZkV62;7PJwnFMA;aCxcQu#Z-3;e|;FWS& zWwYf!MABl5TVXOa6w@4Vof3BF$Xf+jG|0M`fYB+`$>)azoNx+}D+dLqP&T|yt2u@8 zV8Y#W3T32`LqHC{D)>BIjm^|7|5z2&uOVMrhoA$ezlXZPshx@Mp`LI6cF}t%7pBK& z-UC^Q_=ERQ@1oPBjpR?|Vi=X5!G9b!)&t>8_8Oh1q(@`vyo!Ijn;Yh8+M zk>^(a_W85qk*_&N0<%HjS`*h@a7K=bpv9S{}GAg zK%wPl_#Y5{BLw>U=)W_dU&05%Iiu@cNrI)b?_^p*f;F<0pb zkg;V&n5%RIR=Pep&kHbL=?Jmamr@=+{!ipLh$_L{r9;PVJ@Nd^RXW1WHDz!WxTegM z0;gc=%rJszndf2g^#Po`qYunY;wCtX7tGkqLE`gP6rIN?%r!c6Eb`(~m|Jvc*v2N6 z)eQ0E2Pm>1ePC|T5oF6IyW8x%9TUeKq3aMHBc6-FUwr^)3~AXHDYQ7E4kh_B_Iok7 z_%!O~N7;v9(R&(oV%@;JnQA>hLSCnrp5ix24x5;(we$yHGK{ z=OdJTP0#){wsH2)5mQrITV=JEz;WM-k{al5TSKj}jA&I1jj*hPKP#JW;<5eGW((;Q zNJytZLR=mshTfxrFAPfnL!M*UyFN#lLi(fe(C2HS2FDlU_-k zZJ`m)p-|dNJ*KoWCk@Xx`b3*4Z;Sp2@PY{k&W={qSJyb@zpVc!a020;O^MxFQfFPr zrG2RX8t5UM&dPAsMMDP!yddYnymBkJN@&z2qYrIdNU2XeX=FyJPhtb9-Y^##9HfYE zi4ESwP?CfU&71`1(I^Qc!vl;&PKQy_L&)$jCpmL%LWa$R6jnl3{57Uq$tBALOa-Xw zab$Rzuw!i1@M}UP`11^(a1B-Obg55f7L}^;G4%F+>N? zfN)BOlKbjM138*pQH>cxfz#{tRv_t|N9|}fyZ%m~`OAlZD78`lAj#=GyF+P@=(pe< zIli6a>C^N2*C9tBbFpw8m$nwewa6r*X3B#;vExZTLm)CF5$J>L3#}zK=O;fXs|zv= zAS~^?`f69_4nc-GLiA@R7sQJdO$u(MJ*m6g`NOgMFh(E&YOhvtnqr9{ORBP3S(jF6A4=c!l~R!jn4; zKbzy*m6Si3!{nR`_DzhUau(@VKweGB!g2;~Scq@@Sjv&~_X8(*?8NCM`HH^MZ`3~x zoB+bn%e|$4PJa@}ej~{#Q*z7>>YE?p8x>2XeWU-6iwT!%3G?9)-#Ace@7@q!OUe~A zz9`2x{#rGjdL2D$d?;xM{Rt!^JNiZyUZuasOY@ub4|{6mZ5QHdfmwK{Wc)IwcZ*mN2p(A9Bxe5Y}S~Jg_ zg||VTEXot*z^SbkG>}bbIP|!0r%Wcz>C#GI9)KB`5=A@+p&oA|sLnj)yK-qK0%q7K zx+2OcDKXcW?Ik5d<@*aY6piFUO-u$il-1BSXI^xoLHlxF>7LUws& z0*EygYA7083pHe5;A4fFRFZzf=>bKW zOoJ~nXq8-W5{!$N7qOOYD$=B~NjV%Dx{}--U9!M|nqf`plGhy>`jZ^a)&o+of8}Bi zK!!m|HXPHhaENg5$|qDd#|zo#%cK~puoBI&AL zp-Bp!G*uI)Aq;%ESd$n{vpCV@XYEq(#;KZQC5xk#f=^D>#Iy37i#4(16yr9qyR=LCJT14A|T|@exgLKI3YUmAZRsB(PW?$Tryolwoq0~ z*Ce8pyC0dZ+555h^7B+mR z{|$1*;o3vw9NJc-BfUCqo0fW;%57R|J@;(WQjf5OKOg7M|J|mgzBg{Wmd^7-w`-~O z%-PQJ-?3dwt?=3HTDrj_zPWKI8fPpJ$b2JkOLJ`@A+ifC~S_$M7AV^gP^)Uo#%*sPKoM*V0+$!1G!< z(#|~3g#GzEtE|UP)&j#$)*|~(Ew%4Qb~2@o?qvDC+NsS5q?KIqHH2$$?oPNozxl9M z!{)w0yR_Zd(C(!%4Aa2St~tcCymyy2k3f5PmzEB|uXbr^XcxbmJ&)SW6f*B-ty#63 zRk(wvXCP(Jt3E40%$+O(*-i>&#bLwWP=a&RrVc=fUD&{LFT?gkX_*9^_XJ9EmT7?h zGjB6#x3&|b#u1#O@ZcAYDU-XSef z?7l-)SR2b4#k`7^R)u@*)JD-E>g=&toVrUJ z{o^M4QZ7TCj&!Y&b&c^N(nII13%#iDCkry$j#{ zDMVd}Ru_)YR{VZM3$7c-9o2HnjK(K-qu3-0T9^QuFmpuWqs1tyGlMEh$YZZF*4W5x zb3kkF=A+s#{zeARJ#%%`T|YBdN8Rt6b9LNTj75fRgA+W6G7!(@}4J-#i_i&$iEF%D&H^f10NoM#>GS zVoDmTSox+Z*2n`@x_rX_wn{gEq?y$$e@!)$`#?2Q?X_xM2HTB!M`O?uC+%A@mj+}I zF9v80fk3J!A%1W%ef7Zt1Y-GVHM(xh&X!wr#F%Spba5%vMxw;LIq)t9UYR&;7e4S3 z2bqu7=!lJ-tRx=g3rSO#aifV8bv*Ts+MSx>zA3>3BZn_As7g9u9aIc7?G zpJQl$#GikAPDkh9xGgO0zeU$MkIt#F$S|6itA>0uf`{4Tkm0{Tqp)#0*)_$7pVP&| z>9WmRbg>#rDA=Nl2Es`fVH12;qBCJRI#$uo=9W~F*B*4n003#B70>CSwB&#F_!2aA z@1(7|0WtnW8GW$6A4Xer{Aqyx7CZz*v&GjO{as2m^^If~LZM_!)skP;b>fv3ZPi8Z zEqYZK5kl9h`XGHr5E#n8B~dbOp|P$4bOaQapJD^{sZ+XSwnLJwA4~EQw<(@0qDt%a zX5fVr9xQ5LgK3ee4nD5TL&7TkDyAgfc1jmJgJRP{4yAgKK#o zu78R!6o+*Huq{4*bdVp1bTpN4TK^?v)R0dXa3H=hL=!s;AS4h;t`(A;hE9BG7)aEj z-?d?#(AU?mO9RG?&OB^kNJRtmoPDWy4yKwhOnVqS`Zb53b-NorAM zUiy?Cu3Wg>|u%3|Y~Woh90svLYrQlSpCIpX?weH`!CtlA{DDmm!^1G`U;M zP#_<~Va#C8kjc=_ko7j4d|EO_y%QwJ9y=vtj%MSO+9?_ttXXiOMF^qEeo{(|6DJo+ zSx+vI5}ABJN`-OuWJ5WF$)eI?tj0hqx`A@SKyjYQ8$?+q&z0i$$85kk(em-Q6+2y+I)&X1Gl$SN>?1KG*=8ETda69d!a4jKK)iE<*Wra)`o!z_V^ z07&#TOq2&X1SVgVvlM6mSpduk^5so0P9f+_55oRF507U2liP9d~MflTlN y>-Gcd^aJbi1MBE0%}@ZT0a=j212#Rw5n{9g$R;y>kg=e21U5TMeY3E-8zTVIMb0Py delta 598 zcmbOgcr$>dfn}=SMwV;hlLaL*7$YXvOSDbql1yaOo9rMaH@QpFlB1B3fgwXWD}Qpg zn4v%ph{Kq{oFS8;ogwROFquaxM!gLr$R0Z-V~%Fyl-em88LU}wp+yLx$^BAFjD3>} zrG+P_%VkbF#vACdQo2(#oru>)n9TSTZkxC>W*{ diff --git a/docs/build/.doctrees/modularity.doctree b/docs/build/.doctrees/modularity.doctree new file mode 100644 index 0000000000000000000000000000000000000000..2e6f5f93387233365edfd0c32f0c8de3cb1c7127 GIT binary patch literal 15732 zcmeHO&5s<%btgscE@wzCKlEYIGNlq7CCl~hjEoqdyp{-ClxR|lMuJJ%3}U&}GhH*) zvpwDI{@4!*MC=$2D5wlLQQLs?aZ64He8?e(BoH7V@E;%`Ctm_22Ooo+a?J0&>gw*9 zT`q?}Y#9*+XqHn`AFp1$_j@1p>U&3j;=gc+|I??^Soz^j$Mw80@?=`*V_r1KCo;^6 z4~mQ5FK!nrx|O)wX_O}eS?ELPF;ITsB{D4D!jH?;Kl6jMQ%o6Z5ZQULMz9`tD#e zkweo5|L{G`SFcdydMR^9)W1+|eKhbxS-g5!FUBtD&I)xz9acy5qTSV3x^Jd3NxO04 zjxK%iQun2j2WdC+CS4Wobg|2{Yr1#qbvj9!6?tE^^inDZnIDA}*VKYqq;0O?$A!$# z0@?UUpL?z1hHyhqTnX|tlZhXWm^T=6^rqY)CLYxFhXo%HmReInt?W6FYmkR zGlosPo8W`+C+gwM;!s_$53mCZSYUQE&*C~1i5oXI%-^*8E$04>K_X>%Lq%C94o7Vv#0HKfQybzB zpM5so;RZMGN8yG@{E^DqhKYBR5xu~pp?^Fae|a$e^5{Yz$rHcOPcSX}(*S)OK(c+$ zHTta&uNLZ0Qj)$`>@Ll&A+7574Aj(0bI~^FW3O+?WXqS^_T1}+n%^F#?9b0{l7FWm z|EEF1SL}+nE+0O0=q5z`+HNe9p3H6;9yoe^|7?|1yD%WH))+gUfgyO-Z~_^4J? z7bu2Vit3!o@8EviJS@On3m^^v31MTb-GSjaJ$nRJ22RcV&{$&6Pgw2Ug8)$A4s-~0a#+CG>=;_ouMdS6%ae=l9Hj0k; zPS(kdN-90tG^>t)7I?@SV%GH)bhSO&^fZk_zXI;k!TLk$0BQvYMPpD z4KM5Sn4sxpk(D{X(4Dv*rkNWAY%MI%O*d9Rs(y*N|Fwys_jdR`Y8brFG!32}5K1$m z5{@^m-InVIZa*N$OT7JcKf9Lq>!Yo~5s=TOg{((kwtce^Sh75YODkeOHa`D>CgJ1e zc2uoS#t1Yhv%E1#U;oh1i#(Etvbi=Gn^sK#|YxHDr~_emteJkM4V}Gs?MWj=Z6cbxSGu zl>&|r!Hr@aRR6$=N9%R9xTm00LOw^|Sw7FF*^grwYXc~1>63`<;Gug#bU=@48;AqN_z7ahOjM?q%9X*-Mu4VUJCk4|jp5EA*pA^= z!2gwWY~yp7#nNsgy?V>~L`kPXkQ$$6+Yi7f0)_)HYWYo8Xz~qj!V9%*m~F21KK?RO zJibJK8v1Zgv>Jb@8o`m#lLyna_(v{6P6YOdxFKZrYfZ;1hK_61taFCR7$%XuIZ;rSF?PfUQEs%Sec-EesF13wuA@Wd$>z>}4pIex;n zD1DK(DRPFhPk>7NXBWO}2cl({V{7yDQZD1?)eF_6@cd`&iqLkpHM7< zr~y4D976=mQJRZ35sV_FZq$TuFpM(pi8+|yG@3}vg9S#2SW<-iX&z)03n6+ZQn7d< zCKPuH3i)Xze-bCKq#}=_u+zSYPSfDzt~z#UkxMp8!;2{Eb24e{R6SPuN55B~HqQAvMCT*;C^tbEH4Jy3R0 zApW}&O(t|=E3dhOi((M?gH1XemnUu0a^OWE5!0b9_QMcU(t4B(!G@6GXi!rxS5a(J z9k(9H8L|0+oc+e+tOm+k1}MK-!QMPTXnapd6{f(A-SKl?eitcMy$P$f%Y6Qdo>J`Pq!%f6%48Xx zu7H7%sw3_-0stdpB$JRO1)d>MBwZw!A`DK}+I~lN)(AgUG-Jd}gq*AY@|bFboIC>JD6( zQrMD#aznHu^G9m%!c?^)U1@dPA{&BJhWm~Q``8xjjoAAR1ek?JEsW{b>KWar{h98b9m5U0uDwz}tXpGA97((qUL#g?-;Bq0ct1Ro^REWs8Fl zi;#Pb&@wy-I%{Aicr~#Wq=Ogch>NBvgPba;9v8%`^5%Ro&5{V0;`Ltj=jYgr%>z7j zk8Ri4HBQp}HCCQY(lj$Ol!E!0InbYSSEL%=`2-nPiP{9pjFG7n zTQ|TRYY2Z2zKF5-tQ>n>k{NGcuM=Y&!+m`oBZjqP{|oJfBzyc<%{@G}{~oLmpWVB` z8M*xkv&|{DA97d7?VmT~_LF2$EyKSn6hS@>m*$h_=g|M3<~V@)3 zOG7HD)!+ZbNn%ZyQJS-Rsj>>QJk7xwW%>0SXH|mye_CZhd<5(G+dG4rWc^!T8scpB zJ)ii08U6o#4wl|c{O`VsUNqiLp}s?g_kAomdaseQvo^7aj?ma($dY^tDa`~>a=K;t?3b6>X}k5wZ$-Sqray6M~Cz)KQ$00C|V z!r3HAt;sKS#R z1*gbpFj%mWL}2YB$zO3?z{F-4V6z?NS2CD`D2Niy#v{Z=-XGTtTm;q3K2W%jMDT9C zI+tH&NhsCD6-r$F#FJ54%otvpzckwLWjSNT{v~8*)6^s)C9*ApV9k)e<5Ci4&Co>X zhrE{AW1Ve(z-P$7Z;;QmqFgkJ@&}dJ-%oh-0#wW@%oGf?N$%HKW*Ruatwxg2dN2@> z;vE14%dbOA5^)vfsX1=DEEvo-*ZX|Y?DMPlwNJ`Fzfg@}K=^7C5cGfdIQh(Vwb*f( z`2#mFDZ`1J^x+gMTwa$sOPpum#$|aSl^v!z^pLg47BSlFrVQd?9;ARZ`up+i#UEdUjkpaxT&ymz=?ia@4%9Sf())@;CfkWm{Op8 zwvBRU?oty%mVo+9-L|I|)%E|=-PJ9D|neWNg zOOZ+&@Na&x!bZOwYytqawQs7Ixsr|R66rFcIj^a4Bgrd&nVxs51Q2B`^`tBMT>X?} z9pbw_XgmWsFKrfOsvnYbaH#5jQ_o)H%%03Um(puq)aP);gUeE0_zn z&=zlx&Y&L3yd{F?N6$p?s%P<=Zji&oRvEyRo^Fj~h^wwFN-(~~FAOrcb@+1JEQ17T zxQVar@@t+1_;xaj1`b^;(yv%<0dyt1g+4osq6}$C+4vEUg-+6wo-m&{J5&=x&xe+g zjr3y&_jq(Um}1H&Obe2$+~SZacI^8ltXK{xA2ea}cn%XKNZ=wN8WgWSq}vLKDEoCm zpOHI0cm+>7PwUnNPxkW>mRsI-6AIbU1DC23w6p-V14)}I?&xJdjPuMv6@5h4h=p$D z*>L^2_0-3|KR5lOBa=u@~io}OiaXDRggnO|n682=*#NH3dt zYZ?~bym@u~IjleZ!^~iPn)Z-_QL20~ROpidZaH(j2m;4F)WSW4CujO$lrBYy=h!>) z0bWyp4~vK&hS(-J;(HE^$k5*@)S_N?^DJ`eCjAgS6+7wWlh~;Q4~*poent`EJ`~fO z;udf>OzHURCb&?!7dfcWL$A}&l_-@?b0kX%w2WdgBci7q&rd1D!bVCem#jjl3;F~$ zlOnrg%Jv{NWjAMtyLEozwj9g2)>@+F1hvp>5r<@3SAe{|z1@kCIYL!cl#IHb-0Grd z+AZ{>G=gTN5e{w$aLb)>g`tespLzC6V9Rnc9Ly|3E%h>=v$#)4mv(%a4S5-rO#&>@ zLL3Pdz`LX3)nocR1Zv_`x>bRWV@ey}(~mJVs9B_NFbW~J#3K)ad>k(htFKVJ)9Eg1 z-*y~@OcrOe3cTr~djWkSoxl%qzMDgRQ;d9=zniZPxep}=&*RFRp47<1s1;6(@W;XE z%{(sN<9CewOs`b;_qZ<94~IBvtN&*S5rBWw{Z5ms?Lj4Xv6hl}`uLuHbPz=DrX%q> zCvfm!2tfk9XuPA3$Z)Gb7Mq@n5E4N3AVnzo;F3JYyq)ATKZAuJ6&U~#yz%I<<85Ho zR4YL@H)FP8^<|GKrB{gWs$a>Ji*xCjB|$kVB~Q4iUZQcPZN=Dt^W*oS$>Td@4?a}? ztbU<>KK>$N;&F#Q+VrtRA3va8-=hzoz7FZ*VfuQSJ~rs%5`DZ&A3OAs(npMsT(@Z1 z41NVa{^W)4@VY(qVNdg+2Ott#RkbIbEWRj`A)R0UCONM01DH)O}tTZH30LCPb$4vLhra&|APY!(GH60QT z!L$(Q*nZ|Pn=PgvUbjN5C;cd>bcDM;GSgV~ZcWYDva9l-vs3f)Wd|9oM`!0Me>tSv zfJ8jdq=#mOK2EVAL>qg6SzNkOwtoOI`C;)5-~xPoAPr4nlPAJ#`nG66Ezdskm4+h|@t1FP**V#-K9Zsya+D_*cRw>`2@C76>Zj(2kZ E3x-qtIRF3v literal 0 HcmV?d00001 diff --git a/docs/build/_modules/algorithms/contagion/animation.html b/docs/build/_modules/algorithms/contagion/animation.html index 18ad160d..546dedf2 100644 --- a/docs/build/_modules/algorithms/contagion/animation.html +++ b/docs/build/_modules/algorithms/contagion/animation.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/_modules/algorithms/contagion/epidemics.html b/docs/build/_modules/algorithms/contagion/epidemics.html index 95ed44d4..4971bcc4 100644 --- a/docs/build/_modules/algorithms/contagion/epidemics.html +++ b/docs/build/_modules/algorithms/contagion/epidemics.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/_modules/algorithms/generative_models.html b/docs/build/_modules/algorithms/generative_models.html index 9cdf00db..99ca0deb 100644 --- a/docs/build/_modules/algorithms/generative_models.html +++ b/docs/build/_modules/algorithms/generative_models.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/_modules/algorithms/homology_mod2.html b/docs/build/_modules/algorithms/homology_mod2.html index 408a6d42..8d12279f 100644 --- a/docs/build/_modules/algorithms/homology_mod2.html +++ b/docs/build/_modules/algorithms/homology_mod2.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/_modules/algorithms/hypergraph_modularity.html b/docs/build/_modules/algorithms/hypergraph_modularity.html index d3eb9038..e10db50b 100644 --- a/docs/build/_modules/algorithms/hypergraph_modularity.html +++ b/docs/build/_modules/algorithms/hypergraph_modularity.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/_modules/algorithms/laplacians_clustering.html b/docs/build/_modules/algorithms/laplacians_clustering.html index 4b63eaf4..3159750c 100644 --- a/docs/build/_modules/algorithms/laplacians_clustering.html +++ b/docs/build/_modules/algorithms/laplacians_clustering.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/_modules/algorithms/s_centrality_measures.html b/docs/build/_modules/algorithms/s_centrality_measures.html index ec1c3f6c..09e3bf33 100644 --- a/docs/build/_modules/algorithms/s_centrality_measures.html +++ b/docs/build/_modules/algorithms/s_centrality_measures.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/_modules/classes/entity.html b/docs/build/_modules/classes/entity.html index c9e2e838..6380bf6b 100644 --- a/docs/build/_modules/classes/entity.html +++ b/docs/build/_modules/classes/entity.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/_modules/classes/hypergraph.html b/docs/build/_modules/classes/hypergraph.html index 26c41639..99f91f7a 100644 --- a/docs/build/_modules/classes/hypergraph.html +++ b/docs/build/_modules/classes/hypergraph.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/_modules/classes/staticentity.html b/docs/build/_modules/classes/staticentity.html index eaed21dd..8a4decbc 100644 --- a/docs/build/_modules/classes/staticentity.html +++ b/docs/build/_modules/classes/staticentity.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/_modules/drawing/rubber_band.html b/docs/build/_modules/drawing/rubber_band.html index b1fbecce..040f5515 100644 --- a/docs/build/_modules/drawing/rubber_band.html +++ b/docs/build/_modules/drawing/rubber_band.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/_modules/drawing/two_column.html b/docs/build/_modules/drawing/two_column.html index 080db161..52d47bf7 100644 --- a/docs/build/_modules/drawing/two_column.html +++ b/docs/build/_modules/drawing/two_column.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/_modules/drawing/util.html b/docs/build/_modules/drawing/util.html index 2c6b8a49..f0d22823 100644 --- a/docs/build/_modules/drawing/util.html +++ b/docs/build/_modules/drawing/util.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/_modules/index.html b/docs/build/_modules/index.html index f675e43c..0b32af12 100644 --- a/docs/build/_modules/index.html +++ b/docs/build/_modules/index.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/_modules/reports/descriptive_stats.html b/docs/build/_modules/reports/descriptive_stats.html index 24b36eee..241dc8d9 100644 --- a/docs/build/_modules/reports/descriptive_stats.html +++ b/docs/build/_modules/reports/descriptive_stats.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/_sources/index.rst.txt b/docs/build/_sources/index.rst.txt index 72ba936a..d14f4f16 100644 --- a/docs/build/_sources/index.rst.txt +++ b/docs/build/_sources/index.rst.txt @@ -40,6 +40,7 @@ Contents core NWHypergraph C++ Optimization HyperNetX Visualization Widget + Algorithms: Modularity and Clustering Publications license diff --git a/docs/build/_sources/modularity.rst.txt b/docs/build/_sources/modularity.rst.txt new file mode 100644 index 00000000..d2eb603c --- /dev/null +++ b/docs/build/_sources/modularity.rst.txt @@ -0,0 +1,69 @@ +.. _modularity: + + +========================= +Modularity and Clustering +========================= + +Francois - I left the code from widget here so that you could replace it with content you want. +I think an image would be great if you have one. + +.. image:: images/WidgetScreenShot.png + :width: 300px + :align: right + +Overview +-------- +The HyperNetXWidget_ is an addon for HNX, which extends the built in visualization +capabilities of HNX to a JavaScript based interactive visualization. The tool has two main interfaces, +the hypergraph visualization and the nodes & edges panel. +You may `demo the widget here `_ + +Installation +------------ +The HypernetxWidget_ is available on `GitHub `_ and may be +installed using pip: + + >>> pip install hnxwidget + +Using the Tool +-------------- + +Layout +^^^^^^ +The hypergraph visualization is an Euler diagram that shows nodes as circles and hyper edges as outlines +containing the nodes/circles they contain. The visualization uses a force directed optimization to perform +the layout. This algorithm is not perfect and sometimes gives results that the user might want to improve upon. +The visualization allows the user to drag nodes and position them directly at any time. The algorithm will +re-position any nodes that are not specified by the user. Ctrl (Windows) or Command (Mac) clicking a node +will release a pinned node it to be re-positioned by the algorithm. + +Selection +^^^^^^^^^ +Nodes and edges can be selected by clicking them. Nodes and edges can be selected independently of each other, +i.e., it is possible to select an edge without selecting the nodes it contains. Multiple nodes and edges can +be selected, by holding down Shift while clicking. Shift clicking an already selected node will de-select it. +Clicking the background will de-select all nodes and edges. Dragging a selected node will drag all selected +nodes, keeping their relative placement. +Selected nodes can be hidden (having their appearance minimized) or removed completely from the visualization. +Hiding a node or edge will not cause a change in the layout, wheras removing a node or edge will. +The selection can also be expanded. Buttons in the toolbar allow for selecting all nodes contained within selected edges, +and selecting all edges containing any selected nodes. +The toolbar also contains buttons to select all nodes (or edges), un-select all nodes (or edges), +or reverse the selected nodes (or edges). An advanced user might: + +* **Select all nodes not in an edge** by: select an edge, select all nodes in that edge, then reverse the selected nodes to select every node not in that edge. +* **Traverse the graph** by: selecting a start node, then alternating select all edges containing selected nodes and selecting all nodes within selected edges +* **Pin Everything** by: hitting the button to select all nodes, then drag any node slightly to activate the pinning for all nodes. + +Side Panel +^^^^^^^^^^ +Details on nodes and edges are visible in the side panel. For both nodes and edges, a table shows the node name, degree (or size for edges), its selection state, removed state, and color. These properties can also be controlled directly from this panel. The color of nodes and edges can be set in bulk here as well, for example, coloring by degree. + +Other Features +^^^^^^^^^^^^^^ +Nodes with identical edge membership can be collapsed into a super node, which can be helpful for larger hypergraphs. Dragging any node in a super node will drag the entire super node. This feature is available as a toggle in the nodes panel. + +The hypergraph can also be visualized as a bipartite graph (similar to a traditional node-link diagram). Toggling this feature will preserve the locations of the nodes between the bipartite and the Euler diagrams. + +.. _HypernetxWidget: https://github.com/pnnl/hypernetx-widget diff --git a/docs/build/algorithms/algorithms.contagion.html b/docs/build/algorithms/algorithms.contagion.html index 273fe9c8..4e4acd56 100644 --- a/docs/build/algorithms/algorithms.contagion.html +++ b/docs/build/algorithms/algorithms.contagion.html @@ -124,6 +124,7 @@
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/algorithms/algorithms.html b/docs/build/algorithms/algorithms.html index c7ce1c84..4d271d62 100644 --- a/docs/build/algorithms/algorithms.html +++ b/docs/build/algorithms/algorithms.html @@ -125,6 +125,7 @@
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/algorithms/modules.html b/docs/build/algorithms/modules.html index f822f856..02a2ef53 100644 --- a/docs/build/algorithms/modules.html +++ b/docs/build/algorithms/modules.html @@ -112,6 +112,7 @@
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/classes/classes.html b/docs/build/classes/classes.html index 74a3c2c1..1506ffbb 100644 --- a/docs/build/classes/classes.html +++ b/docs/build/classes/classes.html @@ -119,6 +119,7 @@
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/classes/modules.html b/docs/build/classes/modules.html index 5f9037ae..b881007c 100644 --- a/docs/build/classes/modules.html +++ b/docs/build/classes/modules.html @@ -112,6 +112,7 @@
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/core.html b/docs/build/core.html index bd580beb..d96f9e26 100644 --- a/docs/build/core.html +++ b/docs/build/core.html @@ -109,6 +109,7 @@
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/drawing/drawing.html b/docs/build/drawing/drawing.html index 67b2937f..daa7acc0 100644 --- a/docs/build/drawing/drawing.html +++ b/docs/build/drawing/drawing.html @@ -119,6 +119,7 @@
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/drawing/modules.html b/docs/build/drawing/modules.html index ed370ae7..9e784477 100644 --- a/docs/build/drawing/modules.html +++ b/docs/build/drawing/modules.html @@ -112,6 +112,7 @@
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/genindex.html b/docs/build/genindex.html index 88e9cd82..b3a27000 100644 --- a/docs/build/genindex.html +++ b/docs/build/genindex.html @@ -101,6 +101,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/glossary.html b/docs/build/glossary.html index 97c7ff26..5dd7f7af 100644 --- a/docs/build/glossary.html +++ b/docs/build/glossary.html @@ -103,6 +103,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/home.html b/docs/build/home.html index e4d868a3..40174095 100644 --- a/docs/build/home.html +++ b/docs/build/home.html @@ -106,6 +106,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/index.html b/docs/build/index.html index ef8a1331..0250d305 100644 --- a/docs/build/index.html +++ b/docs/build/index.html @@ -102,6 +102,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • @@ -327,6 +328,18 @@

    ContentsAlgorithms: Modularity and Clustering +
  • Publications
  • License
    • diff --git a/docs/build/install.html b/docs/build/install.html index f77d4e60..335461a7 100644 --- a/docs/build/install.html +++ b/docs/build/install.html @@ -108,6 +108,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/license.html b/docs/build/license.html index a05739db..de70ed7a 100644 --- a/docs/build/license.html +++ b/docs/build/license.html @@ -102,6 +102,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/modularity.html b/docs/build/modularity.html new file mode 100644 index 00000000..dfb88654 --- /dev/null +++ b/docs/build/modularity.html @@ -0,0 +1,297 @@ + + + + + + + + + + Modularity and Clustering — HyperNetX 1.1.4 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
    + + + + + +
    + +
    + + + + + + + + + + + + + + + + + + + +
    + + + + +
    +
    +
    +
    + +
    +

    Modularity and Clustering

    +

    Francois - I left the code from widget here so that you could replace it with content you want. +I think an image would be great if you have one.

    +_images/WidgetScreenShot.png +
    +

    Overview

    +

    The HyperNetXWidget is an addon for HNX, which extends the built in visualization +capabilities of HNX to a JavaScript based interactive visualization. The tool has two main interfaces, +the hypergraph visualization and the nodes & edges panel. +You may demo the widget here

    +
    +
    +

    Installation

    +

    The HypernetxWidget is available on GitHub and may be +installed using pip:

    +
    >>> pip install hnxwidget
+
    +
    +
    +
    +

    Using the Tool

    +
    +

    Layout

    +

    The hypergraph visualization is an Euler diagram that shows nodes as circles and hyper edges as outlines +containing the nodes/circles they contain. The visualization uses a force directed optimization to perform +the layout. This algorithm is not perfect and sometimes gives results that the user might want to improve upon. +The visualization allows the user to drag nodes and position them directly at any time. The algorithm will +re-position any nodes that are not specified by the user. Ctrl (Windows) or Command (Mac) clicking a node +will release a pinned node it to be re-positioned by the algorithm.

    +
    +
    +

    Selection

    +

    Nodes and edges can be selected by clicking them. Nodes and edges can be selected independently of each other, +i.e., it is possible to select an edge without selecting the nodes it contains. Multiple nodes and edges can +be selected, by holding down Shift while clicking. Shift clicking an already selected node will de-select it. +Clicking the background will de-select all nodes and edges. Dragging a selected node will drag all selected +nodes, keeping their relative placement. +Selected nodes can be hidden (having their appearance minimized) or removed completely from the visualization. +Hiding a node or edge will not cause a change in the layout, wheras removing a node or edge will. +The selection can also be expanded. Buttons in the toolbar allow for selecting all nodes contained within selected edges, +and selecting all edges containing any selected nodes. +The toolbar also contains buttons to select all nodes (or edges), un-select all nodes (or edges), +or reverse the selected nodes (or edges). An advanced user might:

    +
      +

    • Select all nodes not in an edge by: select an edge, select all nodes in that edge, then reverse the selected nodes to select every node not in that edge.

      • +

    • Traverse the graph by: selecting a start node, then alternating select all edges containing selected nodes and selecting all nodes within selected edges

      • +

    • Pin Everything by: hitting the button to select all nodes, then drag any node slightly to activate the pinning for all nodes.

      • +
    +
    +
    +

    Side Panel

    +

    Details on nodes and edges are visible in the side panel. For both nodes and edges, a table shows the node name, degree (or size for edges), its selection state, removed state, and color. These properties can also be controlled directly from this panel. The color of nodes and edges can be set in bulk here as well, for example, coloring by degree.

    +
    +
    +

    Other Features

    +

    Nodes with identical edge membership can be collapsed into a super node, which can be helpful for larger hypergraphs. Dragging any node in a super node will drag the entire super node. This feature is available as a toggle in the nodes panel.

    +

    The hypergraph can also be visualized as a bipartite graph (similar to a traditional node-link diagram). Toggling this feature will preserve the locations of the nodes between the bipartite and the Euler diagrams.

    +
    +
    +
    + + +
    + +
    + +
    +
    + +
    + +
    + + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/nwhy.html b/docs/build/nwhy.html index 4ce4ad23..d3da8527 100644 --- a/docs/build/nwhy.html +++ b/docs/build/nwhy.html @@ -125,6 +125,7 @@
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/objects.inv b/docs/build/objects.inv index 556d5558656f805a6b4885c42aef7a39b438ceee..670aff205bc0a5adc20c4c291e613be5846585ce 100644 GIT binary patch delta 2808 zcmV?kDFr&48A^R^U4%}Dm&42R^`{hzy&F4@0) zp6#n_yNMpq}-J5tca5UEy~5spdz-YIJHUB99VRr%7f{ zGIm$wYG1B{CV#RJ3Q58n3ZdX6O2NdjvG_M>l{`T0a8V#FOvh$*$zjavm{Od9fP11f zg=8UKmwO@=6!ZCatR?*hg%dR|ux!v#in;=V<|jVyR zeHKWOJZWq&Trr$e%oWO6ZMv?c{FpY^k0Q-FN~4Ox+PSk{8JS1GSnDp3mN>yDkAqc3 za`$^N*Wiem5;3z|2_?a*mLzd$K8iG{W;t~xgnx6Q;vBG++f*hLSz8IoT{_LzfS<4| z1*iF%1?Y7^R#(k!fW%A$-A^kB^CIfA$mBgzj#*kKo1vx}_0Bm`D7)tSKP=P%z; z+530jN7;K5L%o9tp=N3Q@aZdhlj@jeYy2%Ko|~=iqY3q%QX;+C8UX^USvr$fXS1|{ zFMrx*X-{9o%u;v2xi&vA`#$Otmk+aaC9f)GX-i)d%v0Ad6{6h62wW1Kvgtg_ip0f) z({)!BfUCdDhjQ?blmNK(2`W;Z`F$0zF8sbOILG~14}iOVtOUkMKh^`~njdTGXP`AT z!MOhWI@WRc_jLlf`TI(Mocnzx5H9_`27eeweqRTW`@XMaiMz}hR_bD7rA>R?{(I#D zyL4hrY7#_u?*qX-Fy@W|Zc6UjlRy)coJ6rm91%uJlL~}U=paRz0?SX76_Pl=WG0G= zNK-@eDNJyZWQIFsH`A6RM{0UjlL9M9rB5kRk*;v`8URR(@D6r!ewDSW1B$K#`<@SrT_e=0vN+o>LNeB*>-FcD7t- z5+`Q5F3@!sS_*6n)ta@ui1ENkp_Jk!Y2xt&h-rC9N}rh}@g5dwk%Ce5pQH+^X_3}O z2GEYH#f&E}zszeJ;Y7!638@~=F@FSOxKae!v8dfmpxDzSko)mnhHG^RxW!#>cyDud zoYsr)J`6~cTYi+_BPO%pUs{+k){+5!o5)2$>|^3wyeyI$2C&*-X+0efoPnYSk?qMtACk$r2vZi zVmzT8@k(Rk3Lu~b*7#tz%t2soD;b1D{VqNXtAxRrTIV@T@oLN&TNqdOB#x%HR}rw9zyZ|49l$ReBqcWHL+eEwEfTe}S3lNf) zge%&PCopC=uwZ~o&#oU6(Y`?o&RK7R)7CXpxJ%YH_qYXtgqa5l3>O`vL-3wS16o|e z1MHFv@7qtOfbqau0)KRfBwQ)D4IQ50hnF+cpmEp>+_z)2zo!93o*&cPW&y+^H-M-O z2SA{;M_J0U+wlcC;F@V9aY5@O8pDmKHB_awG`FS9D1V;IvfnG{zeYB@clP3o zI_HIWN~MgWmUX0v)z1Hus%8v8EZbc8pe z2`{A0hERU!qJNyFvgrAY2j6+y}C+Le73Cbh?OtLuGpY5c^LT2`@BE7l}6;rb~sJ{1Ze*Uk`JFg~+Yk?f1u` zM_GmQXM7}^2Or>l3?6=~Hyi(e__Z=Wr{6NXeR_%9^cQsUK@asI!S=@DB}i@asFPk~ zjV&3YWPjnsyS6Z0rje3dJQ~PvOaO)VL#yrC`wJ!K*KWVC8<6k*`1rq{vM;rNWzz~u zGSFgvd$c>w`9$>B?6Ws6%G_5?m))R81lp6q2|e&4EQn1Six{%7Zfq;A%QRAP7mo(= z8+T1`f_?ou{BhT~&i?>@B;0T64%l6)H~3fk=YOUH#N``6vtPO#;*fp#^fj|e)KIS1 zFfixU4cU_@2VKPezU(6ZHrapZa|R6|@1TElxw(*;YLq7Bgj#jkw{77L9V_FBu!=c1c}hx*xU!2hk0)yDDl>)oqd&`sjZQ z@_&UYi*uT~?(&*GKhm?RX4^gR=-+*)?E6^sc7J3_^dY`p#OJd6FYm$&$d~d#>jlAB zd7I(hk%+Y0-(K&hzdkhm;{XpC3^rtW`!;*4OgKV{Tbb=&+ct}X`blCloIGPoMyA#5 zk2Y;n)qB$r?!Sc?OhIwF7x^5eT#NNBb$=;niZ>MPoAN;$e{QrHLc^x+qPCG)^Wu?b zw27OGXm3m|4g|BXwrgi=wGB6_Cl1I+q92kbv3p--I;N9|G5z((a5v^ZncCVAdhFR_ zEkS4-gtnKw{?X8e5zRC7UCCPZbU$qJc309T=5B9?Ps;6X?M=?Pppy*6;wE-aCUMv3 zt1+1w?LU;fXleKGjM?)vEBgD-ld`+!pH82glVF*jhI5Qh^F#c}KmY88H??04$fmn! zFR+aSjKcUb(6pc{7MUKZsd_&znDDFMDLK#7G_{VWN1qZ!4V>t2+rM0DoIRKw33M@3 KEb@N=Jwflql6z?Y delta 2781 zcmV<33L^E`7QGgbe1BW7+cpw_-~B5DY#z42+S%qYZ`(9zil%AM6QFq`XlZP-hav@% zI*$A6mls_qTec-}oTnU9GvACTk~6$Wp?ohDGlx=V)#+_1i<(3B-;8#}ulqkY2UGHY z1)24$Y`uyuwk#Yy6w& z(3@Wv=b$SFm?+o_D{9Ig zUSTEu+g(197ir)@Hq1eRK1g05@b@4r&vj*D{qnA_2Ex z48vp*QI}g#8jJb-J2i@Z!@|j06jYtaUdg&bg60RxgniCs#R1FF?$?%ThE_uy0oQ4SgNfMboh~0%?PbAk+wn-czrkZafZ6QB{4FMAvaq)Y*kgc!}WCI zM#9?WjTKdfIVP!;w7nSFOaXfZKn(qc4(5iE!aM{EIX0H%=8darWG)o@FpW`cTTh-> zPUk<@C4W`OOir~S+CYWPWdhKhZD%Cd*ujv22zG7kjcaY#Tm|ijK}~3FfIU`d%SCtG z93|0^C-n_ZD^7Dpg(f+#ZPQgu?9;~jQKX$m8B#G^KQ}Ha6MG36>%s*wiYCP5aj>o+ z_rDi&Ee`CENV!`|D1qu)LE_YW6lqKCbm|Bo1b^sQ0@rg_%B6-!8-d)Hv-}b8168FI zEPvz;^foZ7s}?Rn;x>cshZRCa5w%%jW%o=_p4Q0bSlayTGpjENiPcPVHZl77%Xc*P z{@wR6_TJ`D?=VuRSyn%M`byrU2Bz5-e@n{eW}EwHQ@y8@OmDVDgv4r=&E(nHENkeK zwtrdH(r%<`MnkGaI@!z^3Lvx-^P(&q&8%=J@+NZL9L0DsR2LRg<*A~l%bHh~+$Z`;BP-1qGugzNi8aDw!GJ6NIlzNLNydZZtdn@C~)wh^S@e%lB}NWX0XCx4LNwt*DBZyQ+>E^|(`K3iC6%3e4BUWCB5 z9Gpw11l7&^j^Hm?cSiwNH8;&ppiN2+vRI@}fJ13ZkuaLE%B8t$+L| z6X;??BQgh8CAORr)E`7OpBBZX4Rm!yFw5HN=2At`-iRuCdAF){_C**{4YRI?)O8CgKn zuNE_&g8s6oZIu%PwaedMFlR$B#Ng((AyX=nDCEyk}z2Ujd*?C&8 zzWFd9O@8vDgpe`W3IEcY8DlN!;Ma*l7T`{RVtOf#hZ7F#Z<^c2bz0Q8t}yFwj5Bv) z6OTDaNK#>4U-i!#mFS-J1BiZphUjfs*%40|qU)_M!o?!dgs{1+O>iFO?tg^=Ebfbq zgs#VHLv1L)fELuyom(e3c~?lJL38h@rq7cg;neQ->_W7Ofm=j*6rV3!pl+^K(6i^v^bI@lx8 zvRA1JoOtB6?rb*Y=VLyd0vu{s&Ow@xML3Pfb58>qhv}YXV$Z=yfeJ;-n4@(L%qP=B z(dtt_YKEhcXEsp?KquqPNi=QR5e^LuBJ4mbz=;F6y>I?P@;Jvx_kTVuPPgzc6Y$wJ zA4ZUb4docHdGbFNY%X7=Y;1-+w+UQ_<-j>Py6k8Ej^+*O!j0qWL^s{QrFD}RBqS?A zXx8*6G-lheaDYqquJ04c_Cyx!vt9?M&1db^f~ z*d#gKHlI#GaAP++EP)Z>Tg`x*?*bYQOqs<@Odo1VnO;y zh(yF4@OO zse2)9HiU^?n}6jj)g{kIs`h#j!e>R5bfGc8ikUP8l~iW<4nkA*y7~9wfwtv#6v`E= z{AUYYd_tmZqK^}F*yvtH!FPzVth%l0G*t6JCL|cs2J0R#_vF)fwn0f83K@C%W&tWJ z&LiEOCWC_;Xw<=0C=xZS5zm7C)E$3#C++mpi)&N7wSP{P+4{$9&!yH>oq~odU3V($ zUS+*%7IN?CzUn0VmC)%b;SH7P@?GvfU8Q@WVY*7Z*f3qIJIOymW%TthuUN?3>0N(+ zEV`6cG=HXhSl#&m?|ty_TfN!%2gEOpg)#k_F)?vIcE`ziZUhgUY8 zqJn`H`#YfhcFrd-U$f7_xk$LLnpWJRM--Z!!3iz!EG>v#8p{~6Zr<2bTq`tEac7qX z`YV4-aDspRJp6vsxRk#G--8H?x&wAw8XW%B{eQV?0rC9}pxH02gxqBxK7Gxc5k1uF zEezawbw&3e%Rv)yzt5Y6124PN-EczHY?VtLT-62{qg; zd1TF!#~qzce%;7BMEiuAdWas*`K>Ww<0a=qX{pAkV30T9O$lJHs zTWwPTD6R>+zt)Y2hlUQi8V+2sCnG~z_D55;s_Lz+1pi+$47z_@9#lR@C6{V_EuAac z+Z(F()p)0VKc9?kzQby6Gp3Od>+DisG=GJwvrKDDFHXX;ZeG=llzQErW}es?B8|Q~ z*uwT*k?ELLBgXVc6T5RM|H<^)rLSYx?px`U)}7M&oY!9(`Y>X7hP?}2$DSSot!#T7 ze4=!7GJBG=KQT91azP^*s>P4kUzuE^uf}v{JpZBQ*^~AckCZ=8^P)d{JW1R?{#NPq z$&x@-ei)81p)7Rqr*QqV@7~P4v7b#3k3nG_DOiK?L7Zs`PpmRMP15ytULX%M&?%Bf jW*@bQhsRJ7MQj1)+xjnG8jE*sM+$BJ607_lq#zM6PA_14 diff --git a/docs/build/overview/index.html b/docs/build/overview/index.html index 531e077e..bfe4665d 100644 --- a/docs/build/overview/index.html +++ b/docs/build/overview/index.html @@ -112,6 +112,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/publications.html b/docs/build/publications.html index 38c108e0..e9a6c9df 100644 --- a/docs/build/publications.html +++ b/docs/build/publications.html @@ -43,7 +43,7 @@ - + @@ -103,6 +103,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • @@ -188,7 +189,7 @@
    diff --git a/docs/build/py-modindex.html b/docs/build/py-modindex.html index 2d6df30c..5eac9248 100644 --- a/docs/build/py-modindex.html +++ b/docs/build/py-modindex.html @@ -104,6 +104,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/reports/modules.html b/docs/build/reports/modules.html index 5aeae66b..bf438184 100644 --- a/docs/build/reports/modules.html +++ b/docs/build/reports/modules.html @@ -112,6 +112,7 @@
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/reports/reports.html b/docs/build/reports/reports.html index e3dcb111..ee1b8054 100644 --- a/docs/build/reports/reports.html +++ b/docs/build/reports/reports.html @@ -117,6 +117,7 @@
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/search.html b/docs/build/search.html index 47bda131..72e73372 100644 --- a/docs/build/search.html +++ b/docs/build/search.html @@ -104,6 +104,7 @@
  • HyperNetX Packages
  • NWHypergraph C++ Optimization
  • HyperNetX Visualization Widget
    • +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • diff --git a/docs/build/searchindex.js b/docs/build/searchindex.js index 67e5d85f..57333ab5 100644 --- a/docs/build/searchindex.js +++ b/docs/build/searchindex.js @@ -1 +1 @@ -Search.setIndex({docnames:["algorithms/algorithms","algorithms/algorithms.contagion","algorithms/modules","classes/classes","classes/modules","core","drawing/drawing","drawing/modules","glossary","home","index","install","license","nwhy","overview/index","publications","reports/modules","reports/reports","widget"],envversion:{"sphinx.domains.c":2,"sphinx.domains.changeset":1,"sphinx.domains.citation":1,"sphinx.domains.cpp":4,"sphinx.domains.index":1,"sphinx.domains.javascript":2,"sphinx.domains.math":2,"sphinx.domains.python":3,"sphinx.domains.rst":2,"sphinx.domains.std":2,"sphinx.ext.intersphinx":1,"sphinx.ext.todo":2,"sphinx.ext.viewcode":1,sphinx:56},filenames:["algorithms/algorithms.rst","algorithms/algorithms.contagion.rst","algorithms/modules.rst","classes/classes.rst","classes/modules.rst","core.rst","drawing/drawing.rst","drawing/modules.rst","glossary.rst","home.rst","index.rst","install.rst","license.rst","nwhy.rst","overview/index.rst","publications.rst","reports/modules.rst","reports/reports.rst","widget.rst"],objects:{"":{algorithms:[0,0,0,"-"],classes:[3,0,0,"-"],drawing:[6,0,0,"-"],reports:[17,0,0,"-"]},"algorithms.contagion":{animation:[1,0,0,"-"],epidemics:[1,0,0,"-"]},"algorithms.contagion.animation":{contagion_animation:[1,1,1,""]},"algorithms.contagion.epidemics":{Gillespie_SIR:[1,1,1,""],Gillespie_SIS:[1,1,1,""],collective_contagion:[1,1,1,""],discrete_SIR:[1,1,1,""],discrete_SIS:[1,1,1,""],individual_contagion:[1,1,1,""],majority_vote:[1,1,1,""],threshold:[1,1,1,""]},"algorithms.generative_models":{chung_lu_hypergraph:[0,1,1,""],dcsbm_hypergraph:[0,1,1,""],erdos_renyi_hypergraph:[0,1,1,""]},"algorithms.homology_mod2":{add_to_column:[0,1,1,""],add_to_row:[0,1,1,""],betti:[0,1,1,""],betti_numbers:[0,1,1,""],bkMatrix:[0,1,1,""],boundary_group:[0,1,1,""],chain_complex:[0,1,1,""],homology_basis:[0,1,1,""],hypergraph_homology_basis:[0,1,1,""],interpret:[0,1,1,""],kchainbasis:[0,1,1,""],logical_dot:[0,1,1,""],logical_matadd:[0,1,1,""],logical_matmul:[0,1,1,""],matmulreduce:[0,1,1,""],reduced_row_echelon_form_mod2:[0,1,1,""],smith_normal_form_mod2:[0,1,1,""],swap_columns:[0,1,1,""],swap_rows:[0,1,1,""]},"algorithms.hypergraph_modularity":{bin_ppmf:[0,1,1,""],compute_partition_probas:[0,1,1,""],degree_tax:[0,1,1,""],delta_dt:[0,1,1,""],delta_ec:[0,1,1,""],dict2part:[0,1,1,""],edge_contribution:[0,1,1,""],kumar:[0,1,1,""],last_step:[0,1,1,""],linear:[0,1,1,""],majority:[0,1,1,""],modularity:[0,1,1,""],part2dict:[0,1,1,""],precompute_attributes:[0,1,1,""],strict:[0,1,1,""],two_section:[0,1,1,""]},"algorithms.laplacians_clustering":{get_pi:[0,1,1,""],norm_lap:[0,1,1,""],prob_trans:[0,1,1,""],spec_clus:[0,1,1,""]},"algorithms.s_centrality_measures":{s_betweenness_centrality:[0,1,1,""],s_closeness_centrality:[0,1,1,""],s_eccentricity:[0,1,1,""],s_harmonic_centrality:[0,1,1,""],s_harmonic_closeness_centrality:[0,1,1,""]},"algorithms.untitiled_modularity_and_clustering_original":{DegreeTax:[0,1,1,""],DeltaDT:[0,1,1,""],DeltaEC:[0,1,1,""],EdgeContribution:[0,1,1,""],HNX_2section:[0,1,1,""],HNX_Kumar:[0,1,1,""],HNX_LastStep:[0,1,1,""],HNX_modularity:[0,1,1,""],bin_ppmf:[0,1,1,""],compute_partition_probas:[0,1,1,""],dict2part:[0,1,1,""],factorial:[0,1,1,""],linear:[0,1,1,""],majority:[0,1,1,""],part2dict:[0,1,1,""],precompute_modularity_parameters:[0,1,1,""],strict:[0,1,1,""]},"algorithms.untitled_modularity_and_clustering":{DegreeTax:[0,1,1,""],DeltaDT:[0,1,1,""],DeltaEC:[0,1,1,""],EdgeContribution:[0,1,1,""],HNX_2section:[0,1,1,""],HNX_Kumar:[0,1,1,""],HNX_LastStep:[0,1,1,""],HNX_modularity:[0,1,1,""],HNX_precompute:[0,1,1,""],bin_ppmf:[0,1,1,""],compute_partition_probas:[0,1,1,""],dict2part:[0,1,1,""],factorial:[0,1,1,""],linear:[0,1,1,""],majority:[0,1,1,""],part2dict:[0,1,1,""],strict:[0,1,1,""]},"classes.entity":{Entity:[3,2,1,""],EntitySet:[3,2,1,""]},"classes.entity.Entity":{add:[3,3,1,""],add_element:[3,3,1,""],add_elements_from:[3,3,1,""],children:[3,4,1,""],clone:[3,3,1,""],complete_registry:[3,3,1,""],depth:[3,3,1,""],elements:[3,4,1,""],fullregistry:[3,3,1,""],incidence_dict:[3,4,1,""],intersection:[3,3,1,""],is_bipartite:[3,4,1,""],is_empty:[3,4,1,""],level:[3,3,1,""],levelset:[3,3,1,""],memberships:[3,4,1,""],merge_entities:[3,3,1,""],nested_incidence_dict:[3,3,1,""],properties:[3,4,1,""],registry:[3,4,1,""],remove:[3,3,1,""],remove_element:[3,3,1,""],remove_elements_from:[3,3,1,""],restrict_to:[3,3,1,""],size:[3,3,1,""],uid:[3,4,1,""],uidset:[3,4,1,""]},"classes.entity.EntitySet":{add:[3,3,1,""],clone:[3,3,1,""],collapse_identical_elements:[3,3,1,""],incidence_matrix:[3,3,1,""],restrict_to:[3,3,1,""]},"classes.hypergraph":{Hypergraph:[3,2,1,""]},"classes.hypergraph.Hypergraph":{add_edge:[3,3,1,""],add_edges_from:[3,3,1,""],add_node_to_edge:[3,3,1,""],add_nwhy:[3,3,1,""],adjacency_matrix:[3,3,1,""],auxiliary_matrix:[3,3,1,""],bipartite:[3,3,1,""],collapse_edges:[3,3,1,""],collapse_nodes:[3,3,1,""],collapse_nodes_and_edges:[3,3,1,""],component_subgraphs:[3,3,1,""],components:[3,3,1,""],connected_component_subgraphs:[3,3,1,""],connected_components:[3,3,1,""],convert_to_static:[3,3,1,""],dataframe:[3,3,1,""],degree:[3,3,1,""],diameter:[3,3,1,""],dim:[3,3,1,""],distance:[3,3,1,""],dual:[3,3,1,""],edge_adjacency_matrix:[3,3,1,""],edge_diameter:[3,3,1,""],edge_diameters:[3,3,1,""],edge_distance:[3,3,1,""],edge_neighbors:[3,3,1,""],edge_size_dist:[3,3,1,""],edges:[3,4,1,""],from_bipartite:[3,3,1,""],from_dataframe:[3,3,1,""],from_numpy_array:[3,3,1,""],get_id:[3,3,1,""],get_linegraph:[3,3,1,""],get_name:[3,3,1,""],incidence_dict:[3,4,1,""],incidence_matrix:[3,3,1,""],is_connected:[3,3,1,""],isstatic:[3,4,1,""],neighbors:[3,3,1,""],node_diameters:[3,3,1,""],nodes:[3,4,1,""],number_of_edges:[3,3,1,""],number_of_nodes:[3,3,1,""],order:[3,3,1,""],recover_from_state:[3,3,1,""],remove_edge:[3,3,1,""],remove_edges:[3,3,1,""],remove_node:[3,3,1,""],remove_nodes:[3,3,1,""],remove_singletons:[3,3,1,""],remove_static:[3,3,1,""],restrict_to_edges:[3,3,1,""],restrict_to_nodes:[3,3,1,""],s_component_subgraphs:[3,3,1,""],s_components:[3,3,1,""],s_connected_components:[3,3,1,""],s_degree:[3,3,1,""],save_state:[3,3,1,""],set_state:[3,3,1,""],shape:[3,4,1,""],singletons:[3,3,1,""],size:[3,3,1,""],toplexes:[3,3,1,""],translate:[3,3,1,""]},"classes.staticentity":{StaticEntity:[3,2,1,""],StaticEntitySet:[3,2,1,""]},"classes.staticentity.StaticEntity":{arr:[3,4,1,""],cell_weights:[3,4,1,""],children:[3,4,1,""],data:[3,4,1,""],dataframe:[3,4,1,""],dimensions:[3,4,1,""],dimsize:[3,4,1,""],elements:[3,4,1,""],elements_by_level:[3,3,1,""],incidence_dict:[3,4,1,""],incidence_matrix:[3,3,1,""],index:[3,3,1,""],indices:[3,3,1,""],is_empty:[3,3,1,""],keyindex:[3,3,1,""],keys:[3,4,1,""],labels:[3,4,1,""],labs:[3,3,1,""],level:[3,3,1,""],memberships:[3,4,1,""],properties:[3,5,1,""],restrict_to_indices:[3,3,1,""],restrict_to_levels:[3,3,1,""],size:[3,3,1,""],translate:[3,3,1,""],translate_arr:[3,3,1,""],turn_entity_data_into_dataframe:[3,3,1,""],uid:[3,4,1,""],uidset:[3,4,1,""],uidset_by_level:[3,3,1,""]},"classes.staticentity.StaticEntitySet":{collapse_identical_elements:[3,3,1,""],convert_to_entityset:[3,3,1,""],incidence_matrix:[3,3,1,""],restrict_to:[3,3,1,""]},"drawing.rubber_band":{draw:[6,1,1,""],draw_hyper_edge_labels:[6,1,1,""],draw_hyper_edges:[6,1,1,""],draw_hyper_labels:[6,1,1,""],draw_hyper_nodes:[6,1,1,""],get_default_radius:[6,1,1,""],layout_hyper_edges:[6,1,1,""],layout_node_link:[6,1,1,""]},"drawing.two_column":{draw:[6,1,1,""],draw_hyper_edges:[6,1,1,""],draw_hyper_labels:[6,1,1,""],layout_two_column:[6,1,1,""]},"drawing.util":{get_frozenset_label:[6,1,1,""],get_line_graph:[6,1,1,""],get_set_layering:[6,1,1,""],inflate:[6,1,1,""],inflate_kwargs:[6,1,1,""],transpose_inflated_kwargs:[6,1,1,""]},"reports.descriptive_stats":{centrality_stats:[17,1,1,""],comp_dist:[17,1,1,""],degree_dist:[17,1,1,""],dist_stats:[17,1,1,""],edge_size_dist:[17,1,1,""],info:[17,1,1,""],info_dict:[17,1,1,""],s_comp_dist:[17,1,1,""],s_edge_diameter_dist:[17,1,1,""],s_node_diameter_dist:[17,1,1,""],toplex_dist:[17,1,1,""]},algorithms:{contagion:[1,0,0,"-"],generative_models:[0,0,0,"-"],homology_mod2:[0,0,0,"-"],hypergraph_modularity:[0,0,0,"-"],laplacians_clustering:[0,0,0,"-"],s_centrality_measures:[0,0,0,"-"],untitiled_modularity_and_clustering_original:[0,0,0,"-"],untitled_modularity_and_clustering:[0,0,0,"-"]},classes:{entity:[3,0,0,"-"],hypergraph:[3,0,0,"-"],staticentity:[3,0,0,"-"]},drawing:{rubber_band:[6,0,0,"-"],two_column:[6,0,0,"-"],util:[6,0,0,"-"]},reports:{descriptive_stats:[17,0,0,"-"]}},objnames:{"0":["py","module","Python module"],"1":["py","function","Python function"],"2":["py","class","Python class"],"3":["py","method","Python method"],"4":["py","property","Python property"],"5":["py","attribute","Python attribute"]},objtypes:{"0":"py:module","1":"py:function","2":"py:class","3":"py:method","4":"py:property","5":"py:attribute"},terms:{"0":[0,1,3,6,8,10,13,15],"0020034":1,"00231":[0,15],"01":0,"012805":0,"019":1,"020":[0,15],"021":15,"0224307":0,"030":[0,15],"04197":15,"1":[0,1,3,6,8,10,13,15,17],"10":[0,1,3,15],"100":[0,1],"1000":[0,1,3],"10000":1,"1007":[0,15],"1038":1,"10431":1,"1063":1,"1093":0,"1103":0,"1140":[0,15],"1145":0,"11782":15,"1186":15,"12901":15,"13":0,"1371":0,"15":15,"16":[0,15],"17th":15,"19":0,"1_1":15,"2":[0,1,3,6,8,13,14,15],"2003":15,"2005":0,"2016":0,"2018":12,"2019":[0,15],"2020":[0,15],"22":15,"27":0,"287":15,"29th":0,"2_24":0,"2d":0,"2z":0,"3":[0,1,3,6,11,13,14,15],"3340531":0,"3412034":0,"35":6,"36687":0,"4":[1,3,14],"48478":15,"495":0,"5":[0,1,3,6,14],"504":0,"6":[1,3,14],"7":[11,14],"755":11,"76rl01830":14,"881":0,"9":[0,11,13,15],"90":0,"978":[0,15],"abstract":0,"bogumi\u0142":0,"boolean":[0,3,13],"case":[0,3,14],"class":[0,5,8,9,10],"default":[0,1,3,6,17],"do":[3,8,12,13,14],"export":13,"final":0,"float":[0,1,3,6],"fran\u00e7oi":0,"function":[0,1,3,6],"import":[0,1,3,10],"int":[0,1,3,6,15],"kami\u0144ski":0,"long":[0,17],"new":[0,3,6,10,13],"null":11,"pawe\u0142":0,"pra\u0142at":0,"przemys\u0142aw":0,"public":[9,10],"return":[0,1,3,6,8,13,17],"static":[0,3,14],"super":18,"switch":8,"th\u00e9berg":0,"true":[0,1,3,6,13,17],"try":0,"val\u00e9ri":0,"while":18,A:[0,1,3,6,8,12,13,15],AND:12,AS:12,As:[3,9,10],At:0,BE:12,BUT:12,BY:12,By:[3,6],FOR:12,For:[0,3,6,8,9,10,11,13,14,18],IF:12,IN:12,IS:12,If:[0,1,3,6,8,11,13,17],In:[0,3,6,13,14],It:[3,6,13],NO:12,NOT:12,Not:3,OF:[12,14],ON:12,OR:12,One:3,SUCH:12,Such:12,THE:12,TO:12,That:0,The:[0,1,3,6,8,9,10,13,14,18],Their:0,Then:[6,10],These:[0,18],To:[0,3,9,10],Will:3,_0:3,_1:3,_2:[0,3],_:[0,3],__dict__:3,_edg:3,_node:3,_version:13,a_i:0,ab:[3,15],abl:1,about:[9,10],abov:[3,6,12,17],ac05:14,accept:[6,13],access:[8,11],accomplish:0,accord:8,account:[1,14],accuraci:14,acm:0,aco5:14,across:6,action:0,activ:[10,11,18],actual:0,ad:[0,3,6,14],adam:15,adapt:0,adaptor:13,add:[0,3],add_edg:3,add_edges_from:3,add_el:3,add_elements_from:3,add_node_to_edg:3,add_nodes_from:3,add_nwhi:3,add_to_column:0,add_to_row:0,addit:[0,3,14],addon:[13,14,18],adjac:[0,3,8,9,10],adjacency_matrix:3,adjust:6,admit:[9,10],advanc:18,advis:12,after:[3,13],against:3,agenc:14,aggreg:[3,17],aggregatebi:3,ah:15,aksoi:[0,14,15],al:[0,1,15],algebra:[9,10],algorithm:[5,6,9,10,13,14,15,18],align:[3,6],all:[0,1,3,6,8,11,13,14,17,18],allow:[1,3,6,18],alpha:[1,6],alreadi:[1,3,18],also:[0,3,8,9,10,13,17,18],alter:0,altern:18,ami:15,among:[9,10],amount:6,an:[0,1,3,6,8,10,14,17,18],anaconda3:11,anaconda:10,analysi:13,analyt:[14,15],ananthapadmanabhan:0,andrew:14,angl:6,ani:[0,3,8,12,13,14,18],anim:[0,2,5,10],annal:0,annot:6,anoth:[0,6,8],api:10,apparatu:14,appear:[0,3,18],appli:[0,3,6],applic:[0,3],approach:6,appropri:6,ar1:0,ar2:0,ar:[0,1,3,6,8,9,10,11,12,13,14,18],arbitrari:[6,9,10],arendt:[14,15],arg:[0,1,3],arg_set:3,argument:[1,3,6],argumetn:6,aris:12,around:6,arr:[0,3],arrai:[0,1,3,13],articl:15,arxiv:15,asc:0,aspect:17,assign:[3,6],associ:[0,3,12,13],assum:[3,14],attribut:[0,3,8,10],author:14,automat:[1,3],auxiliari:[3,8],auxiliary_matrix:3,avail:[0,3,14,18],averag:13,ax:6,axi:6,azsecur:15,b:[0,3,6,8,15],back:13,backend:3,background:18,band:6,baric:15,base:[0,3,6,8,13,14,18],basi:0,basic:[3,8,9,10,14,17],bat:11,battel:[12,14],bd:0,bdict:3,becaus:[9,10],becom:[0,3],been:[0,13],befor:3,behavior:0,behind:6,being:0,belong:[0,3,8,13],below:11,berg:0,best:0,betti:0,betti_numb:0,between:[0,1,3,6,8,13,18],big:15,bin_ppmf:0,binari:[0,12],binomi:0,bioinformat:15,biolog:15,biomedcentr:15,bipartit:[0,3,6,8,18],bk:0,bkmatrix:0,block:10,blue:1,bmc:15,bmcbioinformat:15,book:14,bool:[0,1,3,6,17],both:[1,3,8,9,10,13,18],bound:0,boundari:[0,6],boundary_group:0,box:6,bramer:15,brenda:[14,15],brett:15,brian:14,briefest:0,browser:[11,14],bsd:14,build:[3,10,11],build_doc:11,built:18,bulk:18,busi:12,button:18,c:[0,1,3,6,10,11,13,14,15],c_:0,c_b:[0,13],c_k:0,ca:15,calcul:6,call:[6,8,13],callahan:15,can:[0,1,3,6,8,9,10,13,14,18],cannot:[1,3],capabl:18,cardin:3,care:3,carlo:15,categori:3,caus:[3,12,18],caution:3,cdotfrac:0,cell:[0,3,14,17],cell_weight:[0,3],center:6,central:[2,5,10,13,14,17],centrality_stat:17,certain:3,chain:0,chain_complex:0,cham:0,chang:[0,1,3,6,18],check:[3,9,10,13],check_connect:0,cheeger:0,cherifi:0,child:3,children:[3,8],chmod:11,choic:[0,1],choos:[1,3],chosen:[0,3,6],chung:0,chung_lu_hypergraph:0,chunglu:14,cikm:0,circl:[6,18],circular:1,ck:0,classmethod:3,claus:14,click:18,cliff:[14,15],cliqu:[9,10],clone:[3,11],close:[0,13],cluster:[2,5,10,14],cnx001:0,cockrel:15,code:12,coeffici:0,col:13,colab:[3,10],coldict:3,collaps:[3,6,13,18],collapse_edg:[3,13],collapse_identical_el:3,collapse_nod:[3,13],collapse_nodes_and_edg:[3,13],collect:[1,3,6],collective_contagion:1,collumn:6,colon:3,color:[1,3,6,18],column:[0,3,6,8,13,14,17],column_index:3,com:[11,15],combin:13,combinator:0,come:11,command:[3,11,18],comment:[9,10,14],commerci:14,common:1,commun:[0,9,10,14],comnet:0,comp:17,comp_dist:17,compar:[3,13],complet:[8,14,18],complete_registri:3,complex:[0,3,9,10,13,15],compon:[0,3,6,8,13,17],component_subgraph:3,comput:[0,3,6,14,15,17],compute_partition_proba:0,concentr:6,concern:0,conda:[11,13],condit:[3,8,12],conf:15,confer:0,conflict:3,connect:[0,3,6,8,9,10,13,17],connected:0,connected_compon:3,connected_component_subgraph:3,consecut:3,consent:12,consequenti:12,consid:3,constitut:14,construct:[0,1,3,8,13,14],constructor:[3,6,13,14],contact:[9,10,14],contagi:1,contagion:[0,2,5,10,14],contagion_anim:1,contain:[0,3,6,8,13,17,18],content:[2,4,5,7,16],context:[0,3],continu:[1,11],contract:[12,14],contribut:0,contributor:[9,10,12,14],control:[3,18],contruct:0,conveni:[3,6],converg:0,convert:[3,6],convert_to_entityset:3,convert_to_stat:3,convex:6,cooper:14,coord:3,coordin:[3,6],copi:[0,3,12,13],copyright:12,core:3,correct:6,correspond:[0,3,8,14],coset:0,could:3,count:[3,6,17],counter:17,creat:[0,3,11,13,14,17],creation:3,criteria:13,criterion:0,critic:15,cross:6,csr:[0,3],csr_matrix:[0,3],ctrl:18,current:[0,1,13],current_st:3,curvi:6,custom:6,cybersecur:15,cycl:[0,3,6],cyclic:0,d:[0,3,13,15],damag:12,daniel:15,data:[0,3,6,9,10,12,13,14,15],data_subset:3,datafram:[3,14],dcsbm:[0,14],dcsbm_hypergraph:0,de:[14,18],dedup:3,deeper:3,defaultdict:3,defin:[0,1,3],degre:[0,3,8,13,17,18],degree_dist:17,degree_tax:0,degreetax:0,delet:3,delta:0,delta_dt:0,delta_ec:0,deltadt:0,deltaec:0,demo:18,denorm:0,denot:1,densiti:17,depart:14,depend:[0,1,3,13],deprec:3,depth:[0,3,8],deriv:3,descend:3,describ:[0,1],descript:[0,3],descriptive_stat:[5,10,16],design:14,desir:3,dest:13,detail:[0,18],detect:0,determin:[0,3,6],develop:[9,10,13,14],deviat:17,df:3,diagon:0,diagram:[6,18],diamet:[3,8,13,17],diamond:15,dict2part:0,dict:[0,3,6,17],dictionari:[0,1,3,6,8,13,17],differ:[3,13],digraph:[0,6],dim:[0,3,13],dimens:[0,3,13],dimension:[0,3,9,10],dimensionsl:3,dimsiz:3,direct:[0,3,6,12,13,18],directli:[3,9,10,14,18],dirti:6,disabl:6,discard:3,disclaim:12,disclos:14,disconnect:6,discov:0,discret:1,discrete_si:1,discrete_sir:1,discuss:0,disjoint:[0,3,8],disonnecct:6,displai:1,dist:17,dist_stat:17,distanc:[0,3,6,8,13],distant:6,distinct:3,distinguish:[3,8,9,10],distribut:[0,12,13,17],divid:[0,1],dlfer:0,doc:11,document:[3,11,12],doe:[3,6,14],doesn:1,doi:[0,1,15],domain:[0,15],done:[3,13],dot:0,down:18,dr:6,drag:18,draw:[1,5,10],draw_hyper_edg:6,draw_hyper_edge_label:6,draw_hyper_label:6,draw_hyper_nod:6,drawn:6,drop:3,dt:1,dual:[3,8],duplic:[0,3],dustin:[14,15],dynam:[0,3,8],e0:3,e1:3,e2:3,e3:3,e:[0,3,6,8,11,13,15,17,18],e_1:3,e_2:3,e_end:3,e_n:3,e_start:3,each:[0,1,3,6,8,13,17,18],easier:6,ecc:0,eccentr:[0,13],echelon:0,ed:[0,15],edg:[0,1,3,6,8,9,10,13,14,17,18],edge_adjac:3,edge_adjacency_matrix:3,edge_column_nam:3,edge_contribut:0,edge_diamet:3,edge_dist:3,edge_incid:13,edge_kwarg:6,edge_label:[0,3,6],edge_labels_kwarg:6,edge_nam:3,edge_neighbor:3,edge_set:3,edge_size_dist:[3,13,17],edge_state_color_dict:1,edge_uid:3,edgecontribut:0,edges_kwarg:6,edgeset:3,edit:11,effect:[0,1,3],eg:0,eigenvalu:0,eigenvector:0,eisfeld:15,either:[3,8,13,17],element:[0,3,6,8,13],element_subset:3,elements_by_level:3,els:1,emili:[14,15],emploi:3,employe:14,empti:[3,8,13],en:[1,3],encapsul:13,end:3,endors:14,energi:14,ensur:3,ent1:3,ent2:3,entir:18,entiti:[4,5,6,8,9,10,12,14],entityset:[3,8],entri:[0,3,8,13],env:[11,13],environ:[10,14],eon:1,epidem:[0,2,5,10],epidemicsonnetwork:1,epj:[0,15],epjd:[0,15],eq_class:3,equal:[0,1,3,8,13],equat:0,equival:[0,3,13],equivalence_class:3,erdo:0,erdos_renyi_hypergraph:0,error:[0,3,13],essenc:0,et:[0,1,15],euler:18,evalu:3,even:12,event:[1,12],everi:[0,3,8,13,18],everyth:18,ex:[0,3,11],exactli:8,exampl:[0,1,3,6,11,14,18],exceed:3,except:8,execut:11,exemplari:12,exhibit:0,exist:[0,3,6,8],existing_lap:0,exp:0,expand:[6,18],expect:0,explicit:0,explor:[9,10],expos:3,express:[12,14],extend:18,extens:[0,11],extra:1,f:[0,15],facecolor:6,factori:0,fail:3,fall:0,fals:[0,1,3,6,13,17],fan:[0,15],fast:3,faster:[0,13],favor:14,featur:[0,10],feng:15,ferrario:0,fig:1,figur:[1,6],file:[3,11,12],filepath:3,fill:[3,17],fillna:3,filter:13,find:[6,9,10],firoz:15,first:[3,6],firstlevel:3,fit:12,fix:3,flexibl:3,fly:13,folder:0,follow:[3,6,11,12,14],forc:18,fork:11,form:[2,3,5,10,12],format:[3,13,17],forth:13,forward:1,found:[3,9,10],four:14,fp:1,fpath:3,frac:[0,13],fraction:[0,1,6,13],frame:[1,3],from:[0,1,3,6,8,11,13,15,17,18],from_bipartit:[3,8],from_datafram:3,from_numpy_arrai:3,frozen:3,frozenset:3,fruchterman_reingold_layout:6,full:3,fullregistri:3,func:0,further:6,g1:0,g2:0,g:[0,6,13,15,17],gaito:0,gamma:[0,1],gene:15,gener:[0,3,6,8,9,10,11,14,17],generative_model:[2,5,10],get_default_radiu:6,get_frozenset_label:6,get_id:3,get_line_graph:6,get_linegraph:3,get_nam:3,get_pi:0,get_set_lay:6,get_singleton:13,gillespie_si:1,gillespie_sir:1,github:[0,11,14,18],give:[0,3,18],given:[0,3,6,8,13],glossari:10,gm:0,go:[0,17],goal:13,good:[0,12],googl:14,gotten:3,gov:[0,9,10,14],govern:14,grant:12,graph:[0,3,6,8,9,10,13,15,18],greater:0,green:1,group:0,grow:[9,10,14],guarante:6,h:[0,1,3,6,17],h_k:0,ha:[1,3,8,13,14,18],halfmann:15,handl:6,happen:1,harmon:[0,13],hashabl:[1,3],hasn:1,have:[0,1,3,6,8,9,10,13,14,18],hayashi:0,header:[3,14],heal:1,heath:15,held:3,heller:15,help:18,helper:6,henc:3,henri:15,here:[13,18],herebi:12,herein:[12,14],hereinaft:12,heterogen:1,hg:0,hicss:15,hidden:18,hide:18,high:[0,13,14,15],higher:0,highlight:14,hist:17,hit:18,hnx:[0,1,3,11,13,14,18],hnx_2section:0,hnx_kumar:0,hnx_laststep:0,hnx_modular:0,hnx_precomput:0,hnxwidget:18,hold:18,holder:12,home:10,homolog:[2,5,9,10,14],homology_basi:0,homology_mod2:[2,5,10],honor:3,how:3,howev:12,hpda:14,html:[1,11],http:[0,1,11,15],hugh:15,hull:6,hunter:15,hyper:[3,6,8,18],hyperedg:[0,3,8,9,10,13,14],hyperedgelist:1,hypergraph:[1,2,4,5,6,8,9,10,13,14,15,17,18],hypergraph_homology_basi:0,hypergraph_modular:[2,5,10],hypergraphedg:3,hypernet:14,hypernetwork:[0,15],hypernetx:[0,1,3,12,14],hypernetxerror:[0,3],hypernetxwidget:18,i:[0,1,3,8,13,18],i_m:0,i_n:0,iacopini:1,icc:15,id:[0,1,3,6,8,13],ideal:0,ident:[0,3,6,18],identifi:[0,3,15],idx:3,ignacio:15,ignor:[0,3],igraph:0,illustr:6,im:0,imag:0,image_basi:0,immut:3,implement:[0,1,13],impli:[6,12,14],implic:0,impos:8,improv:18,incid:[0,3,8,9,10,13,14,17],incidence_dict:3,incidence_matrix:3,incident:12,includ:[3,9,10,12],inclus:[0,3],inde:3,independ:[6,18],index:[0,3,8,10,11],indic:[0,3,13],indirect:12,individu:1,individual_contagion:1,induc:[3,8],inequ:0,inf:[1,3],infect:1,infin:3,infinit:8,inflat:6,inflate_kwarg:6,info:17,info_dict:17,inform:[0,3,14,17],infring:14,initi:[0,1],initial_infect:1,initial_recov:1,inner:0,input:[0,3],inquiri:0,inseper:3,insert:3,insid:3,insight:0,inspect:14,instal:[3,10],instanc:[3,8],instanti:[3,8],instead:[3,6,13],institut:[12,14],instruct:11,integ:[0,3,6,8,13,17],intel:10,intellig:0,intend:[0,6],intens:3,inter:3,interact:[14,18],interest:[0,3],interfac:18,intern:[0,3],interpret:[0,13],interpreted_basi:0,interrupt:12,intersect:[0,3,6,8],intuit:8,invers:0,invert:0,investig:14,invis:6,io:1,ipython:1,is_bipartit:3,is_connect:3,is_empti:3,is_s_connect:13,isn:3,isomorph:[3,8],isstat:3,item:[3,6,17],iter:[0,1,3,6,17],ith:0,iti:8,its:[0,3,6,8,13,14,18],itself:[3,8],j:[0,8,15],jacob:15,jason:15,javascript:[14,18],jefferson:15,jenkin:15,ji:14,joel:1,joslyn:[0,14,15],journal:0,jth:0,jupyt:[11,14],jurisdict:14,k1:0,k2:0,k:[0,1,3,8],kaminski:[0,15],katrina:15,kawaoka:15,kbasi:0,kchain:0,kchainbasi:0,kdx:3,keep:[3,17,18],keep_weight:3,kei:[0,1,3,6,8,13],kelli:15,kernel:0,kevin:15,keyindex:3,keyword:[3,6],km1basi:0,knowledg:0,known:[0,3],kocher:15,krang:0,kritzstein:14,kth:0,kumar:0,kving:15,kwarg:[0,3,6],l:[0,13,15],lab:3,label:[0,3,6],label_alpha:6,laboratori:14,lambda:1,landri:[1,14],laplacian:[2,5,10],laplacians_clust:[2,5,10],larg:3,larger:18,largest:[0,3],larissa:15,larremor:0,last:[0,3],last_step:0,lastlevel:3,latest:1,latter:3,lawfulli:12,layer:6,layout:[1,6,10],layout_hyper_edg:6,layout_kwarg:6,layout_node_link:6,layout_two_column:6,le:15,learn:[9,10],leas:8,least:[3,6,8],lectur:15,left:[0,6],legal:14,len:17,length:[0,3,6,8,9,10],lesmi:14,less:[0,3,13],let:3,level1:3,level2:3,level:[3,6,8],levelset:[3,8],liabil:[12,14],liabl:12,librari:[0,3,9,10,13,14],licens:10,like:[3,6],limit:[3,12],line:[0,3,6,13],linear:0,linecollect:6,linegraph:[0,3,8],linewidth:6,link:[0,3,18],linux:[11,14],linv:0,lisa:15,list:[0,1,3,6,12,13,17],liu:[13,14],llinv:0,lm:0,lmr:0,local:13,locat:[6,11,18],logic:0,logical_dot:0,logical_matadd:0,logical_matmul:0,longer:3,longest:[0,3],look:0,loss:12,loui:15,lower:6,lu:0,lumsdain:14,m:[0,1,3,15],mac:[11,18],made:3,magnitud:0,mai:[3,8,9,10,11,12,14,18],main:18,major:[0,1],majority_vot:1,make:[3,6,14],manag:[0,14],mani:[3,13,17],manipul:3,manual:6,manufactur:14,map:[0,6],marcin:15,mark:14,marrero:[0,15],mat1:0,mat2:0,mat:0,match:[0,3],materi:14,mathbb:0,mathemat:14,matmulreduc:0,matplotlib:[1,6,11],matric:[2,5,6,10,14],matrix:[0,3,8,13,17],max:[0,3,17],max_degre:13,max_depth:3,max_level:3,max_siz:[3,13],maxim:[3,8],maximum:[3,8],maxlevel:3,mcdermott:15,mean:[0,3,17],measur:[2,5,10,14],mechan:1,median:[3,6,17],member:3,membership:[3,6,8,18],memori:[12,13,14],menacheri:15,mend:0,merchant:12,merg:[3,12],merge_ent:3,method:[0,3,8,9,10,14,17],methodolog:0,metric:[0,9,10,14],michael:15,might:18,miller:1,min:[0,3,17],min_degre:13,min_level:3,min_siz:13,minim:[0,6,11,18],minimum:[3,6],minlevel:3,minu:[0,3],mirah:0,miss:6,mitchel:15,mod2:[2,5,10,14],mod:0,model:[1,9,10,14,15],modestli:3,modif:12,modifi:12,modul:[2,4,5,7,10,14,16],modular:0,modulo:0,more:[3,8,9,10,11,13],moro:0,most:[1,3,6,9,10],move:0,much:13,multi:[3,9,10],multidimension:15,multipl:[0,3,8,13,18],multipli:0,multiwai:[9,10],must:[0,1,3,12,13],mxn:0,n:[0,1,3,6,8,11,13],nama:3,name:[3,11,12,13,14,15,18],nan:3,natali:15,nation:14,natur:[9,10],navig:3,ncell:17,ncol:17,ndarrai:[0,3],necessarili:14,need:[0,3,6,11],neglig:12,neighbor:[1,3,13],neither:[12,14],neq:[0,13],nest:3,nested_incidence_dict:3,network:[0,1,3,9,10,14,15],networkx:[3,6],netwrokx:6,newfpath:3,newuid:3,next:0,nichola:14,node:[0,1,3,6,8,13,14,17,18],node_column_nam:3,node_diamet:3,node_incid:13,node_label:[0,3,6],node_labels_kwarg:6,node_nam:3,node_radiu:[1,6],node_set:3,node_size_dist:13,node_state_color_dict:1,nodes_kwarg:6,nodeset:3,non:[0,8],none:[0,1,3,6,13,17],nonempti:[3,8],nonexist:3,nonzero:[3,8],nor:14,norm_lap:0,normal:[2,5,10,13],northwest:14,note:[0,1,3,8,11,13,15],notebook:[11,14],noth:3,notic:[10,12],np:[0,3],nrow:17,num:17,number:[0,1,3,6,8,13,17],number_of_edg:[3,13],number_of_nod:[3,13],numer:3,numpi:[0,1,3,6,13],nwgraph:13,nwhy:[0,3,10,11,14],nwhypergraph:[3,10],nx2:6,nx:[3,6,8],nxm:0,o:15,obj:17,object:[0,1,3,8,13,14,17],obtain:[0,8,12],occupi:8,occur:3,off:1,offer:3,offset:6,omega:0,onc:[11,14],one:[0,3,6,8,13],oneapi:13,onetbb:13,onli:[0,1,3,8,11,13],open:11,oper:14,opinion:[1,14],opt:13,optim:[0,6,10,13,14,18],option:[0,1,3,10,17],order:[0,3,6,15],ordereddict:3,org:[0,1,15],organ:14,orient:6,origin:[0,3,13],ortiz:0,osit:3,osx:11,other:[0,3,6,8,10,12,13],otherwis:[0,3,8,11,12,13,14],our:[0,9,10],out:[0,6,9,10,12],outlin:18,output:[0,1,3],outsid:3,over:[0,6,8,13],overlap:[6,13],overrid:6,overview:10,own:[8,14],p:[0,3,15],pacif:14,packag:[2,4,7,10,16],page:10,pair:[0,3,6,8,13],pairwis:3,panda:[3,14],panel:10,paper:6,parallel:[6,13],paramet:[0,1,3,6,17],park:0,part2dict:0,part:[0,6,14],parthasarathi:0,partial_k:0,particular:[3,9,10,12,14],partion:0,partit:[0,3,8],pass:[0,3,6,13],path:[0,3,6,9,10,11,13],pathogen:15,pd:3,per:[0,1],perfect:18,perform:[3,13,14,15,18],permiss:12,permit:12,person:12,peter:15,physrev:0,pi:0,pickl:3,pin:18,pip:[10,18],place:3,placehold:3,placement:18,planar:6,pleas:[0,3],plot:6,plt:1,pmf:0,pnnl:[0,9,10,11,14],po:6,point:6,poli:6,polycollect:6,polygon:6,pone:0,poset:3,posit:[0,3,6,8,13,17,18],possibl:[1,6,12,18],post:0,potenti:1,poulin:0,power:[9,10],powershel:11,pp:15,pr:0,practic:3,praggasti:[14,15],pralat:0,pre:6,precis:8,precomput:0,precompute_attribut:0,precompute_modularity_paramet:0,prefil:3,preliminari:13,prepar:14,prepend:3,present:[1,3],preserv:[3,18],press:15,princip:14,principl:14,print:[0,17],prior:3,privat:14,prob_tran:0,probabl:[2,5,10],proc:15,proceed:0,process:[3,13,14],procur:12,product:[0,14],profit:12,program:14,project:14,prompt:11,prop:3,properli:8,properti:[3,8,13,14,18],proport:0,provid:[0,3,6,9,10,12,13],ps1:11,publish:12,purpos:[0,12],purvin:[14,15],put:17,py:8,pybind11:13,pyplot:1,pytest:11,python:[11,13],qh:0,qing:15,quantiti:[9,10],question:[9,10,14],quick:[6,10],quit:3,r0:6,r:[0,1,6],radiu:[1,6],rais:[0,3],ralph:15,randint:0,random:[0,1],randomli:1,rang:[0,1,6],rate:1,rather:17,ratio:[0,17],rauga:14,ravindran:0,rdc:0,re:18,reachabl:13,read:[6,14],readthedoc:1,real:3,reason:[3,6],receiv:3,reciproc:[0,13],recommend:[3,6,14],recov:[1,3],recover_from_st:3,recoveri:1,rectangular:[0,8],recurs:0,red:1,redistribut:12,reduc:[0,6],reduced_row_echelon_form_mod2:0,refer:[0,3,14],referenc:[0,3],reflect:[3,14],regist:3,registri:[3,8],rel:[0,18],relat:[3,9,10],relationship:[0,3,9,10,15],releas:[14,18],remov:[3,18],remove_edg:3,remove_el:3,remove_elements_from:3,remove_nod:3,remove_singleton:3,remove_stat:3,render:6,renyi:0,rep:3,repeatedli:0,replac:[0,3],report:[5,10],repositori:[0,9,10],repres:[0,3,6,8,9,10,14],represent:[0,3,6,13],reproduc:[6,12],request:3,requir:[0,1,3,13],research:[9,10,14],reserv:6,respect:[0,3],respons:[14,15],restrepo:1,restrict:[3,8],restrict_to:3,restrict_to_edg:3,restrict_to_indic:3,restrict_to_level:3,restrict_to_nod:3,result:[6,18],retain:12,retriev:3,return_count:3,return_equal_class:13,return_equivalence_class:3,return_full_data:1,return_index:3,return_po:6,return_singleton:[0,3,17],revers:[0,3,18],rho:1,rich:13,right:[0,6,14],rigor:6,ring:6,rocha:0,role:[3,8],root:3,roughli:0,row:[0,3,8,13,17],rowdict:3,rubber:6,rubber_band:[5,7,10],run:[0,11,13,14],s12859:15,s13688:[0,15],s41467:1,s:[1,2,3,5,6,8,10,13,14,15,17],s_betweenness_centr:[0,13],s_centrality_measur:[2,5,10],s_closeness_centr:[0,13],s_comp_dist:17,s_compon:3,s_component_subgraph:3,s_components_subgraph:3,s_connect:3,s_connected_compon:[3,13],s_degre:[3,13],s_diamet:13,s_distanc:13,s_eccentr:[0,13],s_edge_connect:3,s_edge_diameter_dist:17,s_harmonic_centr:0,s_harmonic_closeness_centr:[0,13],s_linegraph:13,s_neighbor:13,s_node_diameter_dist:17,s_path:13,same:[0,3,6,8,13],sampl:[1,3],satifi:3,satisfi:[3,8],save:3,save_st:3,scalabl:13,sci:0,scienc:[0,15],scip:3,scipi:[0,3],score:13,script:11,search:10,second:[1,3],section:0,see:[0,3,6,8,11,14,17],select:[0,10],self:3,sell:12,sens:8,sensibl:6,sequenc:[3,8],serv:[0,9,10],servic:[12,14],set:[0,1,3,6,8,9,10,13,18],set_nam:3,set_stat:3,setsystem:3,setsytem:3,sh:11,shabang:11,shall:12,shallow:3,shape:3,share:[3,8,13],sheahan:15,shi:0,shift:18,shortest:[0,3,8,13],shortest_path_length:3,should:[0,1,3,6],show:18,shufang:15,si:[1,14],side:[0,3,10],sigma:[0,13],signatur:3,significantli:13,sim:15,sim_kwarg:1,similar:[1,3,18],simpl:[0,3,8,17],simplic:[9,10],simplici:[0,1,9,10],simul:1,sinan:[0,14,15],sinc:[3,8,9,10],singl:[0,3,8,17],singleton:[0,3,9,10,13],sir:[1,14],size:[0,1,3,6,8,13,17,18],slightli:18,slinegraph:10,slower:13,small:[0,3,6],smaller:6,smallest:3,smith:[2,5,10,15],smith_normal_form_mod2:0,snf:0,so:[0,3,6,12],social:1,softwar:[12,14],some:[0,8,9,10,11],sometim:[6,18],song:15,sort:[0,3],sort_column:3,sort_row:3,sortabl:[0,3],sourc:[0,1,3,6,11,12,17],space:[6,13],spars:[0,3,13],spec:0,spec_clu:0,special:12,specif:[3,8,14],specifi:[0,1,3,6,11,13,18],spectral:[0,6],sped:14,sponsor:14,spring_layout:6,springer:[0,15],squar:8,src:13,stack:6,standard:17,start:[0,1,3,6,17,18],stat:17,state:[1,3,14,18],state_dict:3,staticent:[4,5,10],staticentityset:3,stationari:0,statist:17,statu:1,status:1,step:[0,1],still:[0,3],stop:0,storag:3,store:[0,3,13],str:[0,3],stratton:15,strength:0,strict:[0,9,10,12],string:[3,6,17],structur:[3,8,9,10,13],studi:[0,9,10,14],style:6,subgraph:[0,3],subhypergraph:8,subject:12,sublicens:12,submatrix:8,submit:3,submodul:[2,4,5,7,10,16],subpackag:[2,5,10],subset:[3,6,8],substitut:12,subtract:3,success:8,sum:[0,3,13],sum_:[0,13],summari:17,suppli:6,support:[0,1,3,14],sure:3,surround:6,suscept:1,swap:0,swap_column:0,swap_row:0,symmetr:0,symp:15,synthet:14,system:[3,6,9,10,11,15],szufel:0,t:[0,1,3,13],tabl:18,take:[1,3,6],tan:15,target:3,tau:1,tax:0,tbb:[10,11],tbbroot:13,techniqu:6,tell:[9,10],tensor:3,term:[0,3],termin:1,test:[10,11],text:[0,6],textbook:6,thackrai:15,than:[0,3,8,12,17],thei:[0,3,6,8,9,10,18],them:[3,8,11,17,18],theoret:0,theori:12,therebi:[9,10],therefor:[3,13],thereof:14,thi:[0,1,3,6,8,9,10,11,12,13,14,17,18],think:3,those:[0,14],thread:10,three:[13,14],threshold:1,through:[0,6,13],tiffani:15,time:[0,1,18],timothi:15,tmax:1,tmin:1,to_jshtml:1,todo:3,togeth:[0,6],toggl:18,toni:[13,14],tool:[9,10],toolbar:18,toplex:[0,3,8,13,17],toplex_dist:17,topolog:[0,9,10,15],tort:12,total:0,tour:14,track:[0,3,17],trade:14,trademark:14,tradit:18,transform:[0,3],transit:[1,2,5,10],transition_ev:1,translat:3,translate_arr:3,transmiss:1,transmission_funct:1,transmit:1,transpar:6,transpos:3,transpose_inflated_kwarg:6,travers:18,treat:3,triloop:14,tripodi:15,trivial:0,truthi:3,tupl:[0,3],turn_entity_data_into_datafram:3,tutori:[0,3,10,11],two:[0,3,6,8,13,18],two_column:[5,7,10],two_sect:0,type:[0,1,3,6,17],typic:6,u:[0,6,13],uid:[0,1,3,8,17],uidset:[3,8],uidset_by_level:3,un:18,under:[13,14],undesir:3,undirect:13,uniform:0,uniqu:[3,8],unit:14,unless:3,unpack:3,unreach:13,untitiled_modularity_and_clustering_origin:[2,5,10],untitled_modularity_and_clust:[2,5,10],unweight:[3,8,13],up:[3,14,17],updat:3,upgrad:13,upon:18,us:[0,3,6,8,9,10,12,14],usag:0,use_nwhi:[0,3],use_rep:3,user:[1,3,9,10,11,13,14,18],usual:6,util:[0,5,7,10],v0:3,v1:3,v2:3,v:[0,3,6,13,15],v_1:3,v_2:3,v_end:3,v_n:3,v_start:3,vaidyanathan:0,valu:[0,1,3,6,8,13],variou:[13,17],ve:14,vector:0,verifi:0,version:[10,11,13],vertex:[0,6,9,10,13],vertic:[0,3,6,13,14],via:[0,15],view:14,viii:0,vineet:15,viral:15,virtual:11,virtualenv:10,visibl:18,visual:[10,14,18],vn:3,vol:0,vote:1,w:[0,3],wa:[3,13,14],wai:[3,6,9,10,12],walk:[0,3,8,9,10,15],walter:15,want:[0,18],warn:0,warranti:[12,14],water:15,waw:15,wdc:0,we:[0,3,9,10,13,14],web:15,weight:[0,3,8,13,14],well:[0,6,18],westhoff:15,what:[9,10],whatsoev:12,when:[3,13],whera:18,where:[0,3,6,8,13],whether:[0,3,12,13],which:[0,1,3,6,8,17,18],whitespac:6,whole:11,whose:[6,8,13],widget:[10,14],width:[3,8,9,10],window:[11,18],wish:11,with_color:6,with_edge_count:6,with_edge_label:6,with_node_count:6,with_node_label:6,within:[0,3,6,18],without:[12,18],work:[0,3,6,11,14],would:[3,14],wrangl:3,wrap:6,written:12,wshop:15,www:[0,15],x:[3,6,13,17],xor:0,xu:13,xx:3,xy:6,xyz:0,y:[3,6,13],yet:3,yield:3,yoshihiro:15,you:[3,6,9,10,11,14,18],young:14,your:[3,11,14],yun:14,z:[0,3],z_2:0,zalewski:15,zero:3},titles:["algorithms package","algorithms.contagion package","algorithms","classes package","classes","HyperNetX Packages","drawing package","drawing","Glossary of HNX terms","HyperNetX (HNX)","HyperNetX (HNX)","Installing HyperNetX","License","NWHy","Overview","Publications","reports","reports package","Hypernetx-Widget"],titleterms:{"0":14,"1":14,"class":[3,4,13],"import":13,"new":14,"public":15,Then:13,To:[11,13],activ:13,algorithm:[0,1,2],an:[11,13],anaconda:[11,13],anim:1,api:13,attribut:13,block:13,build:13,central:0,cluster:0,colab:14,contagion:1,content:[0,1,3,6,10,17],descript:[9,10,13],descriptive_stat:17,draw:[6,7],entiti:3,environ:[11,13],epidem:1,featur:[14,18],form:0,generative_model:0,glossari:8,hnx:[8,9,10],homolog:0,homology_mod2:0,hypergraph:[0,3],hypergraph_modular:0,hypernetx:[5,9,10,11,18],indic:10,instal:[11,13,18],intel:13,laplacian:0,laplacians_clust:0,layout:18,licens:[12,14],matric:0,measur:0,method:13,mod2:0,modul:[0,1,3,6,13,17],normal:0,notic:14,nwhy:13,nwhypergraph:13,option:11,other:18,overview:[14,18],packag:[0,1,3,5,6,17],panel:18,pip:[11,13],probabl:0,quick:13,report:[16,17],rubber_band:6,s:0,s_centrality_measur:0,select:18,side:18,slinegraph:13,smith:0,staticent:3,submodul:[0,1,3,6,17],subpackag:0,tabl:10,tbb:13,term:8,test:13,thread:13,tool:18,transit:0,tutori:14,two_column:6,untitiled_modularity_and_clustering_origin:0,untitled_modularity_and_clust:0,us:[11,13,18],util:6,version:14,virtualenv:11,widget:18}}) \ No newline at end of file +Search.setIndex({docnames:["algorithms/algorithms","algorithms/algorithms.contagion","algorithms/modules","classes/classes","classes/modules","core","drawing/drawing","drawing/modules","glossary","home","index","install","license","modularity","nwhy","overview/index","publications","reports/modules","reports/reports","widget"],envversion:{"sphinx.domains.c":2,"sphinx.domains.changeset":1,"sphinx.domains.citation":1,"sphinx.domains.cpp":4,"sphinx.domains.index":1,"sphinx.domains.javascript":2,"sphinx.domains.math":2,"sphinx.domains.python":3,"sphinx.domains.rst":2,"sphinx.domains.std":2,"sphinx.ext.intersphinx":1,"sphinx.ext.todo":2,"sphinx.ext.viewcode":1,sphinx:56},filenames:["algorithms/algorithms.rst","algorithms/algorithms.contagion.rst","algorithms/modules.rst","classes/classes.rst","classes/modules.rst","core.rst","drawing/drawing.rst","drawing/modules.rst","glossary.rst","home.rst","index.rst","install.rst","license.rst","modularity.rst","nwhy.rst","overview/index.rst","publications.rst","reports/modules.rst","reports/reports.rst","widget.rst"],objects:{"":{algorithms:[0,0,0,"-"],classes:[3,0,0,"-"],drawing:[6,0,0,"-"],reports:[18,0,0,"-"]},"algorithms.contagion":{animation:[1,0,0,"-"],epidemics:[1,0,0,"-"]},"algorithms.contagion.animation":{contagion_animation:[1,1,1,""]},"algorithms.contagion.epidemics":{Gillespie_SIR:[1,1,1,""],Gillespie_SIS:[1,1,1,""],collective_contagion:[1,1,1,""],discrete_SIR:[1,1,1,""],discrete_SIS:[1,1,1,""],individual_contagion:[1,1,1,""],majority_vote:[1,1,1,""],threshold:[1,1,1,""]},"algorithms.generative_models":{chung_lu_hypergraph:[0,1,1,""],dcsbm_hypergraph:[0,1,1,""],erdos_renyi_hypergraph:[0,1,1,""]},"algorithms.homology_mod2":{add_to_column:[0,1,1,""],add_to_row:[0,1,1,""],betti:[0,1,1,""],betti_numbers:[0,1,1,""],bkMatrix:[0,1,1,""],boundary_group:[0,1,1,""],chain_complex:[0,1,1,""],homology_basis:[0,1,1,""],hypergraph_homology_basis:[0,1,1,""],interpret:[0,1,1,""],kchainbasis:[0,1,1,""],logical_dot:[0,1,1,""],logical_matadd:[0,1,1,""],logical_matmul:[0,1,1,""],matmulreduce:[0,1,1,""],reduced_row_echelon_form_mod2:[0,1,1,""],smith_normal_form_mod2:[0,1,1,""],swap_columns:[0,1,1,""],swap_rows:[0,1,1,""]},"algorithms.hypergraph_modularity":{bin_ppmf:[0,1,1,""],compute_partition_probas:[0,1,1,""],degree_tax:[0,1,1,""],delta_dt:[0,1,1,""],delta_ec:[0,1,1,""],dict2part:[0,1,1,""],edge_contribution:[0,1,1,""],kumar:[0,1,1,""],last_step:[0,1,1,""],linear:[0,1,1,""],majority:[0,1,1,""],modularity:[0,1,1,""],part2dict:[0,1,1,""],precompute_attributes:[0,1,1,""],strict:[0,1,1,""],two_section:[0,1,1,""]},"algorithms.laplacians_clustering":{get_pi:[0,1,1,""],norm_lap:[0,1,1,""],prob_trans:[0,1,1,""],spec_clus:[0,1,1,""]},"algorithms.s_centrality_measures":{s_betweenness_centrality:[0,1,1,""],s_closeness_centrality:[0,1,1,""],s_eccentricity:[0,1,1,""],s_harmonic_centrality:[0,1,1,""],s_harmonic_closeness_centrality:[0,1,1,""]},"algorithms.untitiled_modularity_and_clustering_original":{DegreeTax:[0,1,1,""],DeltaDT:[0,1,1,""],DeltaEC:[0,1,1,""],EdgeContribution:[0,1,1,""],HNX_2section:[0,1,1,""],HNX_Kumar:[0,1,1,""],HNX_LastStep:[0,1,1,""],HNX_modularity:[0,1,1,""],bin_ppmf:[0,1,1,""],compute_partition_probas:[0,1,1,""],dict2part:[0,1,1,""],factorial:[0,1,1,""],linear:[0,1,1,""],majority:[0,1,1,""],part2dict:[0,1,1,""],precompute_modularity_parameters:[0,1,1,""],strict:[0,1,1,""]},"algorithms.untitled_modularity_and_clustering":{DegreeTax:[0,1,1,""],DeltaDT:[0,1,1,""],DeltaEC:[0,1,1,""],EdgeContribution:[0,1,1,""],HNX_2section:[0,1,1,""],HNX_Kumar:[0,1,1,""],HNX_LastStep:[0,1,1,""],HNX_modularity:[0,1,1,""],HNX_precompute:[0,1,1,""],bin_ppmf:[0,1,1,""],compute_partition_probas:[0,1,1,""],dict2part:[0,1,1,""],factorial:[0,1,1,""],linear:[0,1,1,""],majority:[0,1,1,""],part2dict:[0,1,1,""],strict:[0,1,1,""]},"classes.entity":{Entity:[3,2,1,""],EntitySet:[3,2,1,""]},"classes.entity.Entity":{add:[3,3,1,""],add_element:[3,3,1,""],add_elements_from:[3,3,1,""],children:[3,4,1,""],clone:[3,3,1,""],complete_registry:[3,3,1,""],depth:[3,3,1,""],elements:[3,4,1,""],fullregistry:[3,3,1,""],incidence_dict:[3,4,1,""],intersection:[3,3,1,""],is_bipartite:[3,4,1,""],is_empty:[3,4,1,""],level:[3,3,1,""],levelset:[3,3,1,""],memberships:[3,4,1,""],merge_entities:[3,3,1,""],nested_incidence_dict:[3,3,1,""],properties:[3,4,1,""],registry:[3,4,1,""],remove:[3,3,1,""],remove_element:[3,3,1,""],remove_elements_from:[3,3,1,""],restrict_to:[3,3,1,""],size:[3,3,1,""],uid:[3,4,1,""],uidset:[3,4,1,""]},"classes.entity.EntitySet":{add:[3,3,1,""],clone:[3,3,1,""],collapse_identical_elements:[3,3,1,""],incidence_matrix:[3,3,1,""],restrict_to:[3,3,1,""]},"classes.hypergraph":{Hypergraph:[3,2,1,""]},"classes.hypergraph.Hypergraph":{add_edge:[3,3,1,""],add_edges_from:[3,3,1,""],add_node_to_edge:[3,3,1,""],add_nwhy:[3,3,1,""],adjacency_matrix:[3,3,1,""],auxiliary_matrix:[3,3,1,""],bipartite:[3,3,1,""],collapse_edges:[3,3,1,""],collapse_nodes:[3,3,1,""],collapse_nodes_and_edges:[3,3,1,""],component_subgraphs:[3,3,1,""],components:[3,3,1,""],connected_component_subgraphs:[3,3,1,""],connected_components:[3,3,1,""],convert_to_static:[3,3,1,""],dataframe:[3,3,1,""],degree:[3,3,1,""],diameter:[3,3,1,""],dim:[3,3,1,""],distance:[3,3,1,""],dual:[3,3,1,""],edge_adjacency_matrix:[3,3,1,""],edge_diameter:[3,3,1,""],edge_diameters:[3,3,1,""],edge_distance:[3,3,1,""],edge_neighbors:[3,3,1,""],edge_size_dist:[3,3,1,""],edges:[3,4,1,""],from_bipartite:[3,3,1,""],from_dataframe:[3,3,1,""],from_numpy_array:[3,3,1,""],get_id:[3,3,1,""],get_linegraph:[3,3,1,""],get_name:[3,3,1,""],incidence_dict:[3,4,1,""],incidence_matrix:[3,3,1,""],is_connected:[3,3,1,""],isstatic:[3,4,1,""],neighbors:[3,3,1,""],node_diameters:[3,3,1,""],nodes:[3,4,1,""],number_of_edges:[3,3,1,""],number_of_nodes:[3,3,1,""],order:[3,3,1,""],recover_from_state:[3,3,1,""],remove_edge:[3,3,1,""],remove_edges:[3,3,1,""],remove_node:[3,3,1,""],remove_nodes:[3,3,1,""],remove_singletons:[3,3,1,""],remove_static:[3,3,1,""],restrict_to_edges:[3,3,1,""],restrict_to_nodes:[3,3,1,""],s_component_subgraphs:[3,3,1,""],s_components:[3,3,1,""],s_connected_components:[3,3,1,""],s_degree:[3,3,1,""],save_state:[3,3,1,""],set_state:[3,3,1,""],shape:[3,4,1,""],singletons:[3,3,1,""],size:[3,3,1,""],toplexes:[3,3,1,""],translate:[3,3,1,""]},"classes.staticentity":{StaticEntity:[3,2,1,""],StaticEntitySet:[3,2,1,""]},"classes.staticentity.StaticEntity":{arr:[3,4,1,""],cell_weights:[3,4,1,""],children:[3,4,1,""],data:[3,4,1,""],dataframe:[3,4,1,""],dimensions:[3,4,1,""],dimsize:[3,4,1,""],elements:[3,4,1,""],elements_by_level:[3,3,1,""],incidence_dict:[3,4,1,""],incidence_matrix:[3,3,1,""],index:[3,3,1,""],indices:[3,3,1,""],is_empty:[3,3,1,""],keyindex:[3,3,1,""],keys:[3,4,1,""],labels:[3,4,1,""],labs:[3,3,1,""],level:[3,3,1,""],memberships:[3,4,1,""],properties:[3,5,1,""],restrict_to_indices:[3,3,1,""],restrict_to_levels:[3,3,1,""],size:[3,3,1,""],translate:[3,3,1,""],translate_arr:[3,3,1,""],turn_entity_data_into_dataframe:[3,3,1,""],uid:[3,4,1,""],uidset:[3,4,1,""],uidset_by_level:[3,3,1,""]},"classes.staticentity.StaticEntitySet":{collapse_identical_elements:[3,3,1,""],convert_to_entityset:[3,3,1,""],incidence_matrix:[3,3,1,""],restrict_to:[3,3,1,""]},"drawing.rubber_band":{draw:[6,1,1,""],draw_hyper_edge_labels:[6,1,1,""],draw_hyper_edges:[6,1,1,""],draw_hyper_labels:[6,1,1,""],draw_hyper_nodes:[6,1,1,""],get_default_radius:[6,1,1,""],layout_hyper_edges:[6,1,1,""],layout_node_link:[6,1,1,""]},"drawing.two_column":{draw:[6,1,1,""],draw_hyper_edges:[6,1,1,""],draw_hyper_labels:[6,1,1,""],layout_two_column:[6,1,1,""]},"drawing.util":{get_frozenset_label:[6,1,1,""],get_line_graph:[6,1,1,""],get_set_layering:[6,1,1,""],inflate:[6,1,1,""],inflate_kwargs:[6,1,1,""],transpose_inflated_kwargs:[6,1,1,""]},"reports.descriptive_stats":{centrality_stats:[18,1,1,""],comp_dist:[18,1,1,""],degree_dist:[18,1,1,""],dist_stats:[18,1,1,""],edge_size_dist:[18,1,1,""],info:[18,1,1,""],info_dict:[18,1,1,""],s_comp_dist:[18,1,1,""],s_edge_diameter_dist:[18,1,1,""],s_node_diameter_dist:[18,1,1,""],toplex_dist:[18,1,1,""]},algorithms:{contagion:[1,0,0,"-"],generative_models:[0,0,0,"-"],homology_mod2:[0,0,0,"-"],hypergraph_modularity:[0,0,0,"-"],laplacians_clustering:[0,0,0,"-"],s_centrality_measures:[0,0,0,"-"],untitiled_modularity_and_clustering_original:[0,0,0,"-"],untitled_modularity_and_clustering:[0,0,0,"-"]},classes:{entity:[3,0,0,"-"],hypergraph:[3,0,0,"-"],staticentity:[3,0,0,"-"]},drawing:{rubber_band:[6,0,0,"-"],two_column:[6,0,0,"-"],util:[6,0,0,"-"]},reports:{descriptive_stats:[18,0,0,"-"]}},objnames:{"0":["py","module","Python module"],"1":["py","function","Python function"],"2":["py","class","Python class"],"3":["py","method","Python method"],"4":["py","property","Python property"],"5":["py","attribute","Python attribute"]},objtypes:{"0":"py:module","1":"py:function","2":"py:class","3":"py:method","4":"py:property","5":"py:attribute"},terms:{"0":[0,1,3,6,8,10,14,16],"0020034":1,"00231":[0,16],"01":0,"012805":0,"019":1,"020":[0,16],"021":16,"0224307":0,"030":[0,16],"04197":16,"1":[0,1,3,6,8,10,14,16,18],"10":[0,1,3,16],"100":[0,1],"1000":[0,1,3],"10000":1,"1007":[0,16],"1038":1,"10431":1,"1063":1,"1093":0,"1103":0,"1140":[0,16],"1145":0,"11782":16,"1186":16,"12901":16,"13":0,"1371":0,"15":16,"16":[0,16],"17th":16,"19":0,"1_1":16,"2":[0,1,3,6,8,14,15,16],"2003":16,"2005":0,"2016":0,"2018":12,"2019":[0,16],"2020":[0,16],"22":16,"27":0,"287":16,"29th":0,"2_24":0,"2d":0,"2z":0,"3":[0,1,3,6,11,14,15,16],"3340531":0,"3412034":0,"35":6,"36687":0,"4":[1,3,15],"48478":16,"495":0,"5":[0,1,3,6,15],"504":0,"6":[1,3,15],"7":[11,15],"755":11,"76rl01830":15,"881":0,"9":[0,11,14,16],"90":0,"978":[0,16],"abstract":0,"bogumi\u0142":0,"boolean":[0,3,14],"case":[0,3,15],"class":[0,5,8,9,10],"default":[0,1,3,6,18],"do":[3,8,12,14,15],"export":14,"final":0,"float":[0,1,3,6],"fran\u00e7oi":0,"function":[0,1,3,6],"import":[0,1,3,10],"int":[0,1,3,6,16],"kami\u0144ski":0,"long":[0,18],"new":[0,3,6,10,14],"null":11,"pawe\u0142":0,"pra\u0142at":0,"przemys\u0142aw":0,"public":[9,10],"return":[0,1,3,6,8,14,18],"static":[0,3,15],"super":[13,19],"switch":8,"th\u00e9berg":0,"true":[0,1,3,6,14,18],"try":0,"val\u00e9ri":0,"while":[13,19],A:[0,1,3,6,8,12,14,16],AND:12,AS:12,As:[3,9,10],At:0,BE:12,BUT:12,BY:12,By:[3,6],FOR:12,For:[0,3,6,8,9,10,11,13,14,15,19],IF:12,IN:12,IS:12,If:[0,1,3,6,8,11,14,18],In:[0,3,6,14,15],It:[3,6,14],NO:12,NOT:12,Not:3,OF:[12,15],ON:12,OR:12,One:3,SUCH:12,Such:12,THE:12,TO:12,That:0,The:[0,1,3,6,8,9,10,13,14,15,19],Their:0,Then:[6,10],These:[0,13,19],To:[0,3,9,10],Will:3,_0:3,_1:3,_2:[0,3],_:[0,3],__dict__:3,_edg:3,_node:3,_version:14,a_i:0,ab:[3,16],abl:1,about:[9,10],abov:[3,6,12,18],ac05:15,accept:[6,14],access:[8,11],accomplish:0,accord:8,account:[1,15],accuraci:15,acm:0,aco5:15,across:6,action:0,activ:[10,11,13,19],actual:0,ad:[0,3,6,15],adam:16,adapt:0,adaptor:14,add:[0,3],add_edg:3,add_edges_from:3,add_el:3,add_elements_from:3,add_node_to_edg:3,add_nodes_from:3,add_nwhi:3,add_to_column:0,add_to_row:0,addit:[0,3,15],addon:[13,14,15,19],adjac:[0,3,8,9,10],adjacency_matrix:3,adjust:6,admit:[9,10],advanc:[13,19],advis:12,after:[3,14],against:3,agenc:15,aggreg:[3,18],aggregatebi:3,ah:16,aksoi:[0,15,16],al:[0,1,16],algebra:[9,10],algorithm:[5,6,9,10,13,14,15,16,19],align:[3,6],all:[0,1,3,6,8,11,13,14,15,18,19],allow:[1,3,6,13,19],alpha:[1,6],alreadi:[1,3,13,19],also:[0,3,8,9,10,13,14,18,19],alter:0,altern:[13,19],ami:16,among:[9,10],amount:6,an:[0,1,3,6,8,10,13,15,18,19],anaconda3:11,anaconda:10,analysi:14,analyt:[15,16],ananthapadmanabhan:0,andrew:15,angl:6,ani:[0,3,8,12,13,14,15,19],anim:[0,2,5,10],annal:0,annot:6,anoth:[0,6,8],api:10,apparatu:15,appear:[0,3,13,19],appli:[0,3,6],applic:[0,3],approach:6,appropri:6,ar1:0,ar2:0,ar:[0,1,3,6,8,9,10,11,12,13,14,15,19],arbitrari:[6,9,10],arendt:[15,16],arg:[0,1,3],arg_set:3,argument:[1,3,6],argumetn:6,aris:12,around:6,arr:[0,3],arrai:[0,1,3,14],articl:16,arxiv:16,asc:0,aspect:18,assign:[3,6],associ:[0,3,12,14],assum:[3,15],attribut:[0,3,8,10],author:15,automat:[1,3],auxiliari:[3,8],auxiliary_matrix:3,avail:[0,3,13,15,19],averag:14,ax:6,axi:6,azsecur:16,b:[0,3,6,8,16],back:14,backend:3,background:[13,19],band:6,baric:16,base:[0,3,6,8,13,14,15,19],basi:0,basic:[3,8,9,10,15,18],bat:11,battel:[12,15],bd:0,bdict:3,becaus:[9,10],becom:[0,3],been:[0,14],befor:3,behavior:0,behind:6,being:0,belong:[0,3,8,14],below:11,berg:0,best:0,betti:0,betti_numb:0,between:[0,1,3,6,8,13,14,19],big:16,bin_ppmf:0,binari:[0,12],binomi:0,bioinformat:16,biolog:16,biomedcentr:16,bipartit:[0,3,6,8,13,19],bk:0,bkmatrix:0,block:10,blue:1,bmc:16,bmcbioinformat:16,book:15,bool:[0,1,3,6,18],both:[1,3,8,9,10,13,14,19],bound:0,boundari:[0,6],boundary_group:0,box:6,bramer:16,brenda:[15,16],brett:16,brian:15,briefest:0,browser:[11,15],bsd:15,build:[3,10,11],build_doc:11,built:[13,19],bulk:[13,19],busi:12,button:[13,19],c:[0,1,3,6,10,11,14,15,16],c_:0,c_b:[0,14],c_k:0,ca:16,calcul:6,call:[6,8,14],callahan:16,can:[0,1,3,6,8,9,10,13,14,15,19],cannot:[1,3],capabl:[13,19],cardin:3,care:3,carlo:16,categori:3,caus:[3,12,13,19],caution:3,cdotfrac:0,cell:[0,3,15,18],cell_weight:[0,3],center:6,central:[2,5,10,14,15,18],centrality_stat:18,certain:3,chain:0,chain_complex:0,cham:0,chang:[0,1,3,6,13,19],check:[3,9,10,14],check_connect:0,cheeger:0,cherifi:0,child:3,children:[3,8],chmod:11,choic:[0,1],choos:[1,3],chosen:[0,3,6],chung:0,chung_lu_hypergraph:0,chunglu:15,cikm:0,circl:[6,13,19],circular:1,ck:0,classmethod:3,claus:15,click:[13,19],cliff:[15,16],cliqu:[9,10],clone:[3,11],close:[0,14],cluster:[2,5,10,15],cnx001:0,cockrel:16,code:[12,13],coeffici:0,col:14,colab:[3,10],coldict:3,collaps:[3,6,13,14,19],collapse_edg:[3,14],collapse_identical_el:3,collapse_nod:[3,14],collapse_nodes_and_edg:[3,14],collect:[1,3,6],collective_contagion:1,collumn:6,colon:3,color:[1,3,6,13,19],column:[0,3,6,8,14,15,18],column_index:3,com:[11,16],combin:14,combinator:0,come:11,command:[3,11,13,19],comment:[9,10,15],commerci:15,common:1,commun:[0,9,10,15],comnet:0,comp:18,comp_dist:18,compar:[3,14],complet:[8,13,15,19],complete_registri:3,complex:[0,3,9,10,14,16],compon:[0,3,6,8,14,18],component_subgraph:3,comput:[0,3,6,15,16,18],compute_partition_proba:0,concentr:6,concern:0,conda:[11,14],condit:[3,8,12],conf:16,confer:0,conflict:3,connect:[0,3,6,8,9,10,14,18],connected:0,connected_compon:3,connected_component_subgraph:3,consecut:3,consent:12,consequenti:12,consid:3,constitut:15,construct:[0,1,3,8,14,15],constructor:[3,6,14,15],contact:[9,10,15],contagi:1,contagion:[0,2,5,10,15],contagion_anim:1,contain:[0,3,6,8,13,14,18,19],content:[2,4,5,7,13,17],context:[0,3],continu:[1,11],contract:[12,15],contribut:0,contributor:[9,10,12,15],control:[3,13,19],contruct:0,conveni:[3,6],converg:0,convert:[3,6],convert_to_entityset:3,convert_to_stat:3,convex:6,cooper:15,coord:3,coordin:[3,6],copi:[0,3,12,14],copyright:12,core:3,correct:6,correspond:[0,3,8,15],coset:0,could:[3,13],count:[3,6,18],counter:18,creat:[0,3,11,14,15,18],creation:3,criteria:14,criterion:0,critic:16,cross:6,csr:[0,3],csr_matrix:[0,3],ctrl:[13,19],current:[0,1,14],current_st:3,curvi:6,custom:6,cybersecur:16,cycl:[0,3,6],cyclic:0,d:[0,3,14,16],damag:12,daniel:16,data:[0,3,6,9,10,12,14,15,16],data_subset:3,datafram:[3,15],dcsbm:[0,15],dcsbm_hypergraph:0,de:[13,15,19],dedup:3,deeper:3,defaultdict:3,defin:[0,1,3],degre:[0,3,8,13,14,18,19],degree_dist:18,degree_tax:0,degreetax:0,delet:3,delta:0,delta_dt:0,delta_ec:0,deltadt:0,deltaec:0,demo:[13,19],denorm:0,denot:1,densiti:18,depart:15,depend:[0,1,3,14],deprec:3,depth:[0,3,8],deriv:3,descend:3,describ:[0,1],descript:[0,3],descriptive_stat:[5,10,17],design:15,desir:3,dest:14,detail:[0,13,19],detect:0,determin:[0,3,6],develop:[9,10,14,15],deviat:18,df:3,diagon:0,diagram:[6,13,19],diamet:[3,8,14,18],diamond:16,dict2part:0,dict:[0,3,6,18],dictionari:[0,1,3,6,8,14,18],differ:[3,14],digraph:[0,6],dim:[0,3,14],dimens:[0,3,14],dimension:[0,3,9,10],dimensionsl:3,dimsiz:3,direct:[0,3,6,12,13,14,19],directli:[3,9,10,13,15,19],dirti:6,disabl:6,discard:3,disclaim:12,disclos:15,disconnect:6,discov:0,discret:1,discrete_si:1,discrete_sir:1,discuss:0,disjoint:[0,3,8],disonnecct:6,displai:1,dist:18,dist_stat:18,distanc:[0,3,6,8,14],distant:6,distinct:3,distinguish:[3,8,9,10],distribut:[0,12,14,18],divid:[0,1],dlfer:0,doc:11,document:[3,11,12],doe:[3,6,15],doesn:1,doi:[0,1,16],domain:[0,16],done:[3,14],dot:0,down:[13,19],dr:6,drag:[13,19],draw:[1,5,10],draw_hyper_edg:6,draw_hyper_edge_label:6,draw_hyper_label:6,draw_hyper_nod:6,drawn:6,drop:3,dt:1,dual:[3,8],duplic:[0,3],dustin:[15,16],dynam:[0,3,8],e0:3,e1:3,e2:3,e3:3,e:[0,3,6,8,11,13,14,16,18,19],e_1:3,e_2:3,e_end:3,e_n:3,e_start:3,each:[0,1,3,6,8,13,14,18,19],easier:6,ecc:0,eccentr:[0,14],echelon:0,ed:[0,16],edg:[0,1,3,6,8,9,10,13,14,15,18,19],edge_adjac:3,edge_adjacency_matrix:3,edge_column_nam:3,edge_contribut:0,edge_diamet:3,edge_dist:3,edge_incid:14,edge_kwarg:6,edge_label:[0,3,6],edge_labels_kwarg:6,edge_nam:3,edge_neighbor:3,edge_set:3,edge_size_dist:[3,14,18],edge_state_color_dict:1,edge_uid:3,edgecontribut:0,edges_kwarg:6,edgeset:3,edit:11,effect:[0,1,3],eg:0,eigenvalu:0,eigenvector:0,eisfeld:16,either:[3,8,14,18],element:[0,3,6,8,14],element_subset:3,elements_by_level:3,els:1,emili:[15,16],emploi:3,employe:15,empti:[3,8,14],en:[1,3],encapsul:14,end:3,endors:15,energi:15,ensur:3,ent1:3,ent2:3,entir:[13,19],entiti:[4,5,6,8,9,10,12,15],entityset:[3,8],entri:[0,3,8,14],env:[11,14],environ:[10,15],eon:1,epidem:[0,2,5,10],epidemicsonnetwork:1,epj:[0,16],epjd:[0,16],eq_class:3,equal:[0,1,3,8,14],equat:0,equival:[0,3,14],equivalence_class:3,erdo:0,erdos_renyi_hypergraph:0,error:[0,3,14],essenc:0,et:[0,1,16],euler:[13,19],evalu:3,even:12,event:[1,12],everi:[0,3,8,13,14,19],everyth:[13,19],ex:[0,3,11],exactli:8,exampl:[0,1,3,6,11,13,15,19],exceed:3,except:8,execut:11,exemplari:12,exhibit:0,exist:[0,3,6,8],existing_lap:0,exp:0,expand:[6,13,19],expect:0,explicit:0,explor:[9,10],expos:3,express:[12,15],extend:[13,19],extens:[0,11],extra:1,f:[0,16],facecolor:6,factori:0,fail:3,fall:0,fals:[0,1,3,6,14,18],fan:[0,16],fast:3,faster:[0,14],favor:15,featur:[0,10],feng:16,ferrario:0,fig:1,figur:[1,6],file:[3,11,12],filepath:3,fill:[3,18],fillna:3,filter:14,find:[6,9,10],firoz:16,first:[3,6],firstlevel:3,fit:12,fix:3,flexibl:3,fly:14,folder:0,follow:[3,6,11,12,15],forc:[13,19],fork:11,form:[2,3,5,10,12],format:[3,14,18],forth:14,forward:1,found:[3,9,10],four:15,fp:1,fpath:3,frac:[0,14],fraction:[0,1,6,14],frame:[1,3],francoi:13,from:[0,1,3,6,8,11,13,14,16,18,19],from_bipartit:[3,8],from_datafram:3,from_numpy_arrai:3,frozen:3,frozenset:3,fruchterman_reingold_layout:6,full:3,fullregistri:3,func:0,further:6,g1:0,g2:0,g:[0,6,14,16,18],gaito:0,gamma:[0,1],gene:16,gener:[0,3,6,8,9,10,11,15,18],generative_model:[2,5,10],get_default_radiu:6,get_frozenset_label:6,get_id:3,get_line_graph:6,get_linegraph:3,get_nam:3,get_pi:0,get_set_lay:6,get_singleton:14,gillespie_si:1,gillespie_sir:1,github:[0,11,13,15,19],give:[0,3,13,19],given:[0,3,6,8,14],glossari:10,gm:0,go:[0,18],goal:14,good:[0,12],googl:15,gotten:3,gov:[0,9,10,15],govern:15,grant:12,graph:[0,3,6,8,9,10,13,14,16,19],great:13,greater:0,green:1,group:0,grow:[9,10,15],guarante:6,h:[0,1,3,6,18],h_k:0,ha:[1,3,8,13,14,15,19],halfmann:16,handl:6,happen:1,harmon:[0,14],hashabl:[1,3],hasn:1,have:[0,1,3,6,8,9,10,13,14,15,19],hayashi:0,header:[3,15],heal:1,heath:16,held:3,heller:16,help:[13,19],helper:6,henc:3,henri:16,here:[13,14,19],herebi:12,herein:[12,15],hereinaft:12,heterogen:1,hg:0,hicss:16,hidden:[13,19],hide:[13,19],high:[0,14,15,16],higher:0,highlight:15,hist:18,hit:[13,19],hnx:[0,1,3,11,13,14,15,19],hnx_2section:0,hnx_kumar:0,hnx_laststep:0,hnx_modular:0,hnx_precomput:0,hnxwidget:[13,19],hold:[13,19],holder:12,home:10,homolog:[2,5,9,10,15],homology_basi:0,homology_mod2:[2,5,10],honor:3,how:3,howev:12,hpda:15,html:[1,11],http:[0,1,11,16],hugh:16,hull:6,hunter:16,hyper:[3,6,8,13,19],hyperedg:[0,3,8,9,10,14,15],hyperedgelist:1,hypergraph:[1,2,4,5,6,8,9,10,13,14,15,16,18,19],hypergraph_homology_basi:0,hypergraph_modular:[2,5,10],hypergraphedg:3,hypernet:15,hypernetwork:[0,16],hypernetx:[0,1,3,12,15],hypernetxerror:[0,3],hypernetxwidget:[13,19],i:[0,1,3,8,13,14,19],i_m:0,i_n:0,iacopini:1,icc:16,id:[0,1,3,6,8,14],ideal:0,ident:[0,3,6,13,19],identifi:[0,3,16],idx:3,ignacio:16,ignor:[0,3],igraph:0,illustr:6,im:0,imag:[0,13],image_basi:0,immut:3,implement:[0,1,14],impli:[6,12,15],implic:0,impos:8,improv:[13,19],incid:[0,3,8,9,10,14,15,18],incidence_dict:3,incidence_matrix:3,incident:12,includ:[3,9,10,12],inclus:[0,3],inde:3,independ:[6,13,19],index:[0,3,8,10,11],indic:[0,3,14],indirect:12,individu:1,individual_contagion:1,induc:[3,8],inequ:0,inf:[1,3],infect:1,infin:3,infinit:8,inflat:6,inflate_kwarg:6,info:18,info_dict:18,inform:[0,3,15,18],infring:15,initi:[0,1],initial_infect:1,initial_recov:1,inner:0,input:[0,3],inquiri:0,inseper:3,insert:3,insid:3,insight:0,inspect:15,instal:[3,10],instanc:[3,8],instanti:[3,8],instead:[3,6,14],institut:[12,15],instruct:11,integ:[0,3,6,8,14,18],intel:10,intellig:0,intend:[0,6],intens:3,inter:3,interact:[13,15,19],interest:[0,3],interfac:[13,19],intern:[0,3],interpret:[0,14],interpreted_basi:0,interrupt:12,intersect:[0,3,6,8],intuit:8,invers:0,invert:0,investig:15,invis:6,io:1,ipython:1,is_bipartit:3,is_connect:3,is_empti:3,is_s_connect:14,isn:3,isomorph:[3,8],isstat:3,item:[3,6,18],iter:[0,1,3,6,18],ith:0,iti:8,its:[0,3,6,8,13,14,15,19],itself:[3,8],j:[0,8,16],jacob:16,jason:16,javascript:[13,15,19],jefferson:16,jenkin:16,ji:15,joel:1,joslyn:[0,15,16],journal:0,jth:0,jupyt:[11,15],jurisdict:15,k1:0,k2:0,k:[0,1,3,8],kaminski:[0,16],katrina:16,kawaoka:16,kbasi:0,kchain:0,kchainbasi:0,kdx:3,keep:[3,13,18,19],keep_weight:3,kei:[0,1,3,6,8,14],kelli:16,kernel:0,kevin:16,keyindex:3,keyword:[3,6],km1basi:0,knowledg:0,known:[0,3],kocher:16,krang:0,kritzstein:15,kth:0,kumar:0,kving:16,kwarg:[0,3,6],l:[0,14,16],lab:3,label:[0,3,6],label_alpha:6,laboratori:15,lambda:1,landri:[1,15],laplacian:[2,5,10],laplacians_clust:[2,5,10],larg:3,larger:[13,19],largest:[0,3],larissa:16,larremor:0,last:[0,3],last_step:0,lastlevel:3,latest:1,latter:3,lawfulli:12,layer:6,layout:[1,6,10],layout_hyper_edg:6,layout_kwarg:6,layout_node_link:6,layout_two_column:6,le:16,learn:[9,10],leas:8,least:[3,6,8],lectur:16,left:[0,6,13],legal:15,len:18,length:[0,3,6,8,9,10],lesmi:15,less:[0,3,14],let:3,level1:3,level2:3,level:[3,6,8],levelset:[3,8],liabil:[12,15],liabl:12,librari:[0,3,9,10,14,15],licens:10,like:[3,6],limit:[3,12],line:[0,3,6,14],linear:0,linecollect:6,linegraph:[0,3,8],linewidth:6,link:[0,3,13,19],linux:[11,15],linv:0,lisa:16,list:[0,1,3,6,12,14,18],liu:[14,15],llinv:0,lm:0,lmr:0,local:14,locat:[6,11,13,19],logic:0,logical_dot:0,logical_matadd:0,logical_matmul:0,longer:3,longest:[0,3],look:0,loss:12,loui:16,lower:6,lu:0,lumsdain:15,m:[0,1,3,16],mac:[11,13,19],made:3,magnitud:0,mai:[3,8,9,10,11,12,13,15,19],main:[13,19],major:[0,1],majority_vot:1,make:[3,6,15],manag:[0,15],mani:[3,14,18],manipul:3,manual:6,manufactur:15,map:[0,6],marcin:16,mark:15,marrero:[0,16],mat1:0,mat2:0,mat:0,match:[0,3],materi:15,mathbb:0,mathemat:15,matmulreduc:0,matplotlib:[1,6,11],matric:[2,5,6,10,15],matrix:[0,3,8,14,18],max:[0,3,18],max_degre:14,max_depth:3,max_level:3,max_siz:[3,14],maxim:[3,8],maximum:[3,8],maxlevel:3,mcdermott:16,mean:[0,3,18],measur:[2,5,10,15],mechan:1,median:[3,6,18],member:3,membership:[3,6,8,13,19],memori:[12,14,15],menacheri:16,mend:0,merchant:12,merg:[3,12],merge_ent:3,method:[0,3,8,9,10,15,18],methodolog:0,metric:[0,9,10,15],michael:16,might:[13,19],miller:1,min:[0,3,18],min_degre:14,min_level:3,min_siz:14,minim:[0,6,11,13,19],minimum:[3,6],minlevel:3,minu:[0,3],mirah:0,miss:6,mitchel:16,mod2:[2,5,10,15],mod:0,model:[1,9,10,15,16],modestli:3,modif:12,modifi:12,modul:[2,4,5,7,10,15,17],modular:[0,10],modulo:0,more:[3,8,9,10,11,14],moro:0,most:[1,3,6,9,10],move:0,much:14,multi:[3,9,10],multidimension:16,multipl:[0,3,8,13,14,19],multipli:0,multiwai:[9,10],must:[0,1,3,12,14],mxn:0,n:[0,1,3,6,8,11,14],nama:3,name:[3,11,12,13,14,15,16,19],nan:3,natali:16,nation:15,natur:[9,10],navig:3,ncell:18,ncol:18,ndarrai:[0,3],necessarili:15,need:[0,3,6,11],neglig:12,neighbor:[1,3,14],neither:[12,15],neq:[0,14],nest:3,nested_incidence_dict:3,network:[0,1,3,9,10,15,16],networkx:[3,6],netwrokx:6,newfpath:3,newuid:3,next:0,nichola:15,node:[0,1,3,6,8,13,14,15,18,19],node_column_nam:3,node_diamet:3,node_incid:14,node_label:[0,3,6],node_labels_kwarg:6,node_nam:3,node_radiu:[1,6],node_set:3,node_size_dist:14,node_state_color_dict:1,nodes_kwarg:6,nodeset:3,non:[0,8],none:[0,1,3,6,14,18],nonempti:[3,8],nonexist:3,nonzero:[3,8],nor:15,norm_lap:0,normal:[2,5,10,14],northwest:15,note:[0,1,3,8,11,14,16],notebook:[11,15],noth:3,notic:[10,12],np:[0,3],nrow:18,num:18,number:[0,1,3,6,8,14,18],number_of_edg:[3,14],number_of_nod:[3,14],numer:3,numpi:[0,1,3,6,14],nwgraph:14,nwhy:[0,3,10,11,15],nwhypergraph:[3,10],nx2:6,nx:[3,6,8],nxm:0,o:16,obj:18,object:[0,1,3,8,14,15,18],obtain:[0,8,12],occupi:8,occur:3,off:1,offer:3,offset:6,omega:0,onc:[11,15],one:[0,3,6,8,13,14],oneapi:14,onetbb:14,onli:[0,1,3,8,11,14],open:11,oper:15,opinion:[1,15],opt:14,optim:[0,6,10,13,14,15,19],option:[0,1,3,10,18],order:[0,3,6,16],ordereddict:3,org:[0,1,16],organ:15,orient:6,origin:[0,3,14],ortiz:0,osit:3,osx:11,other:[0,3,6,8,10,12,14],otherwis:[0,3,8,11,12,14,15],our:[0,9,10],out:[0,6,9,10,12],outlin:[13,19],output:[0,1,3],outsid:3,over:[0,6,8,14],overlap:[6,14],overrid:6,overview:10,own:[8,15],p:[0,3,16],pacif:15,packag:[2,4,7,10,17],page:10,pair:[0,3,6,8,14],pairwis:3,panda:[3,15],panel:10,paper:6,parallel:[6,14],paramet:[0,1,3,6,18],park:0,part2dict:0,part:[0,6,15],parthasarathi:0,partial_k:0,particular:[3,9,10,12,15],partion:0,partit:[0,3,8],pass:[0,3,6,14],path:[0,3,6,9,10,11,14],pathogen:16,pd:3,per:[0,1],perfect:[13,19],perform:[3,13,14,15,16,19],permiss:12,permit:12,person:12,peter:16,physrev:0,pi:0,pickl:3,pin:[13,19],pip:[10,13,19],place:3,placehold:3,placement:[13,19],planar:6,pleas:[0,3],plot:6,plt:1,pmf:0,pnnl:[0,9,10,11,15],po:6,point:6,poli:6,polycollect:6,polygon:6,pone:0,poset:3,posit:[0,3,6,8,13,14,18,19],possibl:[1,6,12,13,19],post:0,potenti:1,poulin:0,power:[9,10],powershel:11,pp:16,pr:0,practic:3,praggasti:[15,16],pralat:0,pre:6,precis:8,precomput:0,precompute_attribut:0,precompute_modularity_paramet:0,prefil:3,preliminari:14,prepar:15,prepend:3,present:[1,3],preserv:[3,13,19],press:16,princip:15,principl:15,print:[0,18],prior:3,privat:15,prob_tran:0,probabl:[2,5,10],proc:16,proceed:0,process:[3,14,15],procur:12,product:[0,15],profit:12,program:15,project:15,prompt:11,prop:3,properli:8,properti:[3,8,13,14,15,19],proport:0,provid:[0,3,6,9,10,12,14],ps1:11,publish:12,purpos:[0,12],purvin:[15,16],put:18,py:8,pybind11:14,pyplot:1,pytest:11,python:[11,14],qh:0,qing:16,quantiti:[9,10],question:[9,10,15],quick:[6,10],quit:3,r0:6,r:[0,1,6],radiu:[1,6],rais:[0,3],ralph:16,randint:0,random:[0,1],randomli:1,rang:[0,1,6],rate:1,rather:18,ratio:[0,18],rauga:15,ravindran:0,rdc:0,re:[13,19],reachabl:14,read:[6,15],readthedoc:1,real:3,reason:[3,6],receiv:3,reciproc:[0,14],recommend:[3,6,15],recov:[1,3],recover_from_st:3,recoveri:1,rectangular:[0,8],recurs:0,red:1,redistribut:12,reduc:[0,6],reduced_row_echelon_form_mod2:0,refer:[0,3,15],referenc:[0,3],reflect:[3,15],regist:3,registri:[3,8],rel:[0,13,19],relat:[3,9,10],relationship:[0,3,9,10,16],releas:[13,15,19],remov:[3,13,19],remove_edg:3,remove_el:3,remove_elements_from:3,remove_nod:3,remove_singleton:3,remove_stat:3,render:6,renyi:0,rep:3,repeatedli:0,replac:[0,3,13],report:[5,10],repositori:[0,9,10],repres:[0,3,6,8,9,10,15],represent:[0,3,6,14],reproduc:[6,12],request:3,requir:[0,1,3,14],research:[9,10,15],reserv:6,respect:[0,3],respons:[15,16],restrepo:1,restrict:[3,8],restrict_to:3,restrict_to_edg:3,restrict_to_indic:3,restrict_to_level:3,restrict_to_nod:3,result:[6,13,19],retain:12,retriev:3,return_count:3,return_equal_class:14,return_equivalence_class:3,return_full_data:1,return_index:3,return_po:6,return_singleton:[0,3,18],revers:[0,3,13,19],rho:1,rich:14,right:[0,6,15],rigor:6,ring:6,rocha:0,role:[3,8],root:3,roughli:0,row:[0,3,8,14,18],rowdict:3,rubber:6,rubber_band:[5,7,10],run:[0,11,14,15],s12859:16,s13688:[0,16],s41467:1,s:[1,2,3,5,6,8,10,14,15,16,18],s_betweenness_centr:[0,14],s_centrality_measur:[2,5,10],s_closeness_centr:[0,14],s_comp_dist:18,s_compon:3,s_component_subgraph:3,s_components_subgraph:3,s_connect:3,s_connected_compon:[3,14],s_degre:[3,14],s_diamet:14,s_distanc:14,s_eccentr:[0,14],s_edge_connect:3,s_edge_diameter_dist:18,s_harmonic_centr:0,s_harmonic_closeness_centr:[0,14],s_linegraph:14,s_neighbor:14,s_node_diameter_dist:18,s_path:14,same:[0,3,6,8,14],sampl:[1,3],satifi:3,satisfi:[3,8],save:3,save_st:3,scalabl:14,sci:0,scienc:[0,16],scip:3,scipi:[0,3],score:14,script:11,search:10,second:[1,3],section:0,see:[0,3,6,8,11,15,18],select:[0,10],self:3,sell:12,sens:8,sensibl:6,sequenc:[3,8],serv:[0,9,10],servic:[12,15],set:[0,1,3,6,8,9,10,13,14,19],set_nam:3,set_stat:3,setsystem:3,setsytem:3,sh:11,shabang:11,shall:12,shallow:3,shape:3,share:[3,8,14],sheahan:16,shi:0,shift:[13,19],shortest:[0,3,8,14],shortest_path_length:3,should:[0,1,3,6],show:[13,19],shufang:16,si:[1,15],side:[0,3,10],sigma:[0,14],signatur:3,significantli:14,sim:16,sim_kwarg:1,similar:[1,3,13,19],simpl:[0,3,8,18],simplic:[9,10],simplici:[0,1,9,10],simul:1,sinan:[0,15,16],sinc:[3,8,9,10],singl:[0,3,8,18],singleton:[0,3,9,10,14],sir:[1,15],size:[0,1,3,6,8,13,14,18,19],slightli:[13,19],slinegraph:10,slower:14,small:[0,3,6],smaller:6,smallest:3,smith:[2,5,10,16],smith_normal_form_mod2:0,snf:0,so:[0,3,6,12,13],social:1,softwar:[12,15],some:[0,8,9,10,11],sometim:[6,13,19],song:16,sort:[0,3],sort_column:3,sort_row:3,sortabl:[0,3],sourc:[0,1,3,6,11,12,18],space:[6,14],spars:[0,3,14],spec:0,spec_clu:0,special:12,specif:[3,8,15],specifi:[0,1,3,6,11,13,14,19],spectral:[0,6],sped:15,sponsor:15,spring_layout:6,springer:[0,16],squar:8,src:14,stack:6,standard:18,start:[0,1,3,6,13,18,19],stat:18,state:[1,3,13,15,19],state_dict:3,staticent:[4,5,10],staticentityset:3,stationari:0,statist:18,statu:1,status:1,step:[0,1],still:[0,3],stop:0,storag:3,store:[0,3,14],str:[0,3],stratton:16,strength:0,strict:[0,9,10,12],string:[3,6,18],structur:[3,8,9,10,14],studi:[0,9,10,15],style:6,subgraph:[0,3],subhypergraph:8,subject:12,sublicens:12,submatrix:8,submit:3,submodul:[2,4,5,7,10,17],subpackag:[2,5,10],subset:[3,6,8],substitut:12,subtract:3,success:8,sum:[0,3,14],sum_:[0,14],summari:18,suppli:6,support:[0,1,3,15],sure:3,surround:6,suscept:1,swap:0,swap_column:0,swap_row:0,symmetr:0,symp:16,synthet:15,system:[3,6,9,10,11,16],szufel:0,t:[0,1,3,14],tabl:[13,19],take:[1,3,6],tan:16,target:3,tau:1,tax:0,tbb:[10,11],tbbroot:14,techniqu:6,tell:[9,10],tensor:3,term:[0,3],termin:1,test:[10,11],text:[0,6],textbook:6,thackrai:16,than:[0,3,8,12,18],thei:[0,3,6,8,9,10,13,19],them:[3,8,11,13,18,19],theoret:0,theori:12,therebi:[9,10],therefor:[3,14],thereof:15,thi:[0,1,3,6,8,9,10,11,12,13,14,15,18,19],think:[3,13],those:[0,15],thread:10,three:[14,15],threshold:1,through:[0,6,14],tiffani:16,time:[0,1,13,19],timothi:16,tmax:1,tmin:1,to_jshtml:1,todo:3,togeth:[0,6],toggl:[13,19],toni:[14,15],tool:[9,10],toolbar:[13,19],toplex:[0,3,8,14,18],toplex_dist:18,topolog:[0,9,10,16],tort:12,total:0,tour:15,track:[0,3,18],trade:15,trademark:15,tradit:[13,19],transform:[0,3],transit:[1,2,5,10],transition_ev:1,translat:3,translate_arr:3,transmiss:1,transmission_funct:1,transmit:1,transpar:6,transpos:3,transpose_inflated_kwarg:6,travers:[13,19],treat:3,triloop:15,tripodi:16,trivial:0,truthi:3,tupl:[0,3],turn_entity_data_into_datafram:3,tutori:[0,3,10,11],two:[0,3,6,8,13,14,19],two_column:[5,7,10],two_sect:0,type:[0,1,3,6,18],typic:6,u:[0,6,14],uid:[0,1,3,8,18],uidset:[3,8],uidset_by_level:3,un:[13,19],under:[14,15],undesir:3,undirect:14,uniform:0,uniqu:[3,8],unit:15,unless:3,unpack:3,unreach:14,untitiled_modularity_and_clustering_origin:[2,5,10],untitled_modularity_and_clust:[2,5,10],unweight:[3,8,14],up:[3,15,18],updat:3,upgrad:14,upon:[13,19],us:[0,3,6,8,9,10,12,15],usag:0,use_nwhi:[0,3],use_rep:3,user:[1,3,9,10,11,13,14,15,19],usual:6,util:[0,5,7,10],v0:3,v1:3,v2:3,v:[0,3,6,14,16],v_1:3,v_2:3,v_end:3,v_n:3,v_start:3,vaidyanathan:0,valu:[0,1,3,6,8,14],variou:[14,18],ve:15,vector:0,verifi:0,version:[10,11,14],vertex:[0,6,9,10,14],vertic:[0,3,6,14,15],via:[0,16],view:15,viii:0,vineet:16,viral:16,virtual:11,virtualenv:10,visibl:[13,19],visual:[10,13,15,19],vn:3,vol:0,vote:1,w:[0,3],wa:[3,14,15],wai:[3,6,9,10,12],walk:[0,3,8,9,10,16],walter:16,want:[0,13,19],warn:0,warranti:[12,15],water:16,waw:16,wdc:0,we:[0,3,9,10,14,15],web:16,weight:[0,3,8,14,15],well:[0,6,13,19],westhoff:16,what:[9,10],whatsoev:12,when:[3,14],whera:[13,19],where:[0,3,6,8,14],whether:[0,3,12,14],which:[0,1,3,6,8,13,18,19],whitespac:6,whole:11,whose:[6,8,14],widget:[10,13,15],width:[3,8,9,10],window:[11,13,19],wish:11,with_color:6,with_edge_count:6,with_edge_label:6,with_node_count:6,with_node_label:6,within:[0,3,6,13,19],without:[12,13,19],work:[0,3,6,11,15],would:[3,13,15],wrangl:3,wrap:6,written:12,wshop:16,www:[0,16],x:[3,6,14,18],xor:0,xu:14,xx:3,xy:6,xyz:0,y:[3,6,14],yet:3,yield:3,yoshihiro:16,you:[3,6,9,10,11,13,15,19],young:15,your:[3,11,15],yun:15,z:[0,3],z_2:0,zalewski:16,zero:3},titles:["algorithms package","algorithms.contagion package","algorithms","classes package","classes","HyperNetX Packages","drawing package","drawing","Glossary of HNX terms","HyperNetX (HNX)","HyperNetX (HNX)","Installing HyperNetX","License","Modularity and Clustering","NWHy","Overview","Publications","reports","reports package","Hypernetx-Widget"],titleterms:{"0":15,"1":15,"class":[3,4,14],"import":14,"new":15,"public":16,Then:14,To:[11,14],activ:14,algorithm:[0,1,2],an:[11,14],anaconda:[11,14],anim:1,api:14,attribut:14,block:14,build:14,central:0,cluster:[0,13],colab:15,contagion:1,content:[0,1,3,6,10,18],descript:[9,10,14],descriptive_stat:18,draw:[6,7],entiti:3,environ:[11,14],epidem:1,featur:[13,15,19],form:0,generative_model:0,glossari:8,hnx:[8,9,10],homolog:0,homology_mod2:0,hypergraph:[0,3],hypergraph_modular:0,hypernetx:[5,9,10,11,19],indic:10,instal:[11,13,14,19],intel:14,laplacian:0,laplacians_clust:0,layout:[13,19],licens:[12,15],matric:0,measur:0,method:14,mod2:0,modul:[0,1,3,6,14,18],modular:13,normal:0,notic:15,nwhy:14,nwhypergraph:14,option:11,other:[13,19],overview:[13,15,19],packag:[0,1,3,5,6,18],panel:[13,19],pip:[11,14],probabl:0,quick:14,report:[17,18],rubber_band:6,s:0,s_centrality_measur:0,select:[13,19],side:[13,19],slinegraph:14,smith:0,staticent:3,submodul:[0,1,3,6,18],subpackag:0,tabl:10,tbb:14,term:8,test:14,thread:14,tool:[13,19],transit:0,tutori:15,two_column:6,untitiled_modularity_and_clustering_origin:0,untitled_modularity_and_clust:0,us:[11,13,14,19],util:6,version:15,virtualenv:11,widget:19}}) \ No newline at end of file diff --git a/docs/build/widget.html b/docs/build/widget.html index b010f785..86052ad0 100644 --- a/docs/build/widget.html +++ b/docs/build/widget.html @@ -42,7 +42,7 @@ - + @@ -114,6 +114,7 @@ +
  • Algorithms: Modularity and Clustering
  • Publications
  • License
    • @@ -249,7 +250,7 @@

    Other Features - +

    diff --git a/docs/source/index.rst b/docs/source/index.rst index 72ba936a..d14f4f16 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -40,6 +40,7 @@ Contents core NWHypergraph C++ Optimization HyperNetX Visualization Widget + Algorithms: Modularity and Clustering Publications license diff --git a/docs/source/modularity.rst b/docs/source/modularity.rst new file mode 100644 index 00000000..d2eb603c --- /dev/null +++ b/docs/source/modularity.rst @@ -0,0 +1,69 @@ +.. _modularity: + + +========================= +Modularity and Clustering +========================= + +Francois - I left the code from widget here so that you could replace it with content you want. +I think an image would be great if you have one. + +.. image:: images/WidgetScreenShot.png + :width: 300px + :align: right + +Overview +-------- +The HyperNetXWidget_ is an addon for HNX, which extends the built in visualization +capabilities of HNX to a JavaScript based interactive visualization. The tool has two main interfaces, +the hypergraph visualization and the nodes & edges panel. +You may `demo the widget here `_ + +Installation +------------ +The HypernetxWidget_ is available on `GitHub `_ and may be +installed using pip: + + >>> pip install hnxwidget + +Using the Tool +-------------- + +Layout +^^^^^^ +The hypergraph visualization is an Euler diagram that shows nodes as circles and hyper edges as outlines +containing the nodes/circles they contain. The visualization uses a force directed optimization to perform +the layout. This algorithm is not perfect and sometimes gives results that the user might want to improve upon. +The visualization allows the user to drag nodes and position them directly at any time. The algorithm will +re-position any nodes that are not specified by the user. Ctrl (Windows) or Command (Mac) clicking a node +will release a pinned node it to be re-positioned by the algorithm. + +Selection +^^^^^^^^^ +Nodes and edges can be selected by clicking them. Nodes and edges can be selected independently of each other, +i.e., it is possible to select an edge without selecting the nodes it contains. Multiple nodes and edges can +be selected, by holding down Shift while clicking. Shift clicking an already selected node will de-select it. +Clicking the background will de-select all nodes and edges. Dragging a selected node will drag all selected +nodes, keeping their relative placement. +Selected nodes can be hidden (having their appearance minimized) or removed completely from the visualization. +Hiding a node or edge will not cause a change in the layout, wheras removing a node or edge will. +The selection can also be expanded. Buttons in the toolbar allow for selecting all nodes contained within selected edges, +and selecting all edges containing any selected nodes. +The toolbar also contains buttons to select all nodes (or edges), un-select all nodes (or edges), +or reverse the selected nodes (or edges). An advanced user might: + +* **Select all nodes not in an edge** by: select an edge, select all nodes in that edge, then reverse the selected nodes to select every node not in that edge. +* **Traverse the graph** by: selecting a start node, then alternating select all edges containing selected nodes and selecting all nodes within selected edges +* **Pin Everything** by: hitting the button to select all nodes, then drag any node slightly to activate the pinning for all nodes. + +Side Panel +^^^^^^^^^^ +Details on nodes and edges are visible in the side panel. For both nodes and edges, a table shows the node name, degree (or size for edges), its selection state, removed state, and color. These properties can also be controlled directly from this panel. The color of nodes and edges can be set in bulk here as well, for example, coloring by degree. + +Other Features +^^^^^^^^^^^^^^ +Nodes with identical edge membership can be collapsed into a super node, which can be helpful for larger hypergraphs. Dragging any node in a super node will drag the entire super node. This feature is available as a toggle in the nodes panel. + +The hypergraph can also be visualized as a bipartite graph (similar to a traditional node-link diagram). Toggling this feature will preserve the locations of the nodes between the bipartite and the Euler diagrams. + +.. _HypernetxWidget: https://github.com/pnnl/hypernetx-widget From b725517abc31a1de9ec1683daeee6b8e35675ab0 Mon Sep 17 00:00:00 2001 From: Brenda Praggastis Date: Wed, 20 Oct 2021 11:09:38 -0700 Subject: [PATCH 11/41] added tests for modularity module --- hypernetx/algorithms/tests/conftest.py | 21 ++++++++++++ hypernetx/algorithms/tests/test_modularity.py | 33 +++++++++++++++++++ 2 files changed, 54 insertions(+) create mode 100644 hypernetx/algorithms/tests/test_modularity.py diff --git a/hypernetx/algorithms/tests/conftest.py b/hypernetx/algorithms/tests/conftest.py index f3da6c41..895bbb6a 100644 --- a/hypernetx/algorithms/tests/conftest.py +++ b/hypernetx/algorithms/tests/conftest.py @@ -127,6 +127,22 @@ def __init__(self): self.hypergraph = hnx.Hypergraph.from_numpy_array(mat) +class ModularityExample: + """ + ## build a hypergraph from a list of sets (the hyperedges) + """ + + def __init__(self): + E = [{'A', 'B'}, {'A', 'C'}, {'A', 'B', 'C'}, {'A', 'D', 'E', 'F'}, {'D', 'F'}, {'E', 'F'}] + self.E = E + self.HG = hnx.Hypergraph(E, static=True) + A1 = [{'A', 'B', 'C'}, {'D', 'E', 'F'}] + A2 = [{'B', 'C'}, {'A', 'D', 'E', 'F'}] + A3 = [{'A', 'B', 'C', 'D', 'E', 'F'}] + A4 = [{'A'}, {'B'}, {'C'}, {'D'}, {'E'}, {'F'}] + self.partitions = [A1, A2, A3, A4] + + @pytest.fixture def triloop(): return TriLoop() @@ -145,3 +161,8 @@ def bigfish(): @pytest.fixture def sixbyfive(): return SixByFive() + + +@pytest.fixture +def modularityexample(): + return ModularityExample() diff --git a/hypernetx/algorithms/tests/test_modularity.py b/hypernetx/algorithms/tests/test_modularity.py new file mode 100644 index 00000000..b0f94013 --- /dev/null +++ b/hypernetx/algorithms/tests/test_modularity.py @@ -0,0 +1,33 @@ +import numpy as np +import pytest +import warnings +from hypernetx.algorithms.hypergraph_modularity import * +import random +import hypernetx as hnx + +warnings.simplefilter("ignore") + + +def test_precompute(modularityexample): + HG = modularityexample.HG + precompute_attributes(HG) + assert HG.nodes['F'].strength == 3 + assert HG.total_weight == 6 + assert HG.edges['e2'].weight == 1 + + +def test_modularity(modularityexample): + HG = modularityexample.HG + A1, A2, A3, A4 = modularityexample.partitions + precompute_attributes(HG) + assert np.abs(modularity(HG, A1) - 0.41444526) < 10e-5 + assert np.abs(modularity(HG, A1, strict) - 0.434906995) < 10e-5 + assert np.abs(modularity(HG, A1, majority) - 0.39379753) < 10e-5 + + +def test_clustering(modularityexample): + HG = modularityexample.HG + A1, A2, A3, A4 = modularityexample.partitions + precompute_attributes(HG) + assert {'A', 'B', 'C'} in dict2part(kumar(HG)) + assert {'C', 'A', 'B'} in last_step(HG, A4) From 7cbea872d9c3353c9b3890d853b4701ecc01ed72 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Wed, 20 Oct 2021 15:50:47 -0400 Subject: [PATCH 12/41] bug with 2-section --- hypernetx/algorithms/hypergraph_modularity.py | 26 +++++++++++-------- 1 file changed, 15 insertions(+), 11 deletions(-) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index ad366e4d..53d7f82c 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -10,7 +10,6 @@ .. [1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. "A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering". In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24 .. [2] Kamiński B., Prałat P. and Théberge F. "Community Detection Algorithm Using Hypergraph Modularity". In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13 .. [3] Kamiński B., Poulin V., Prałat P., Szufel P. and Théberge F. "Clustering via hypergraph modularity", Plos ONE 2019, https://doi.org/10.1371/journal.pone.0224307 - """ from collections import Counter @@ -72,12 +71,16 @@ def part2dict(A): def precompute_attributes(HG): """ - Precompute some values on hypergraph HG for faster computing of hypergraph modularity. The following attributes will be set for HG: + Precompute some values on hypergraph HG for faster computing of hypergraph modularity. + The following attributes will be set for HG: + + if HG is unweighted, v.weight is set to 1 for each v in HG.nodes + + v.strength, the weighted degree for each v in HG.nodes + + HG.d_weights, the total edge weigths for each edge cardinality d - v.weight: if HG is unweighted, this is set to 1 for each v in HG.nodes - v.strength: the weighted degree for each v in HG.nodes - HG.d_weights: total edge weigths for each edge cardinality d - HG.bin_coef: to speed-up modularity computation + HG.bin_coef, binomial coefficients to speed-up modularity computation This needs to be called before calling either hypernetx.algorithms.hypergraph_modularity.modularity() or hypernetx.algorithms.hypergraph_modularity.last_step() @@ -307,11 +310,12 @@ def two_section(HG): for e in HG.edges: E = HG.edges[e] # random-walk 2-section (preserve nodes' weighted degrees) - try: - w = HG.edges[e].weight / (len(E) - 1) - except: - w = 1 / (len(E) - 1) - s.extend([(k[0], k[1], w) for k in itertools.combinations(E, 2)]) + if len(E)>1: + try: + w = HG.edges[e].weight / (len(E) - 1) + except: + w = 1 / (len(E) - 1) + s.extend([(k[0], k[1], w) for k in itertools.combinations(E, 2)]) G = ig.Graph.TupleList(s, weights=True).simplify(combine_edges='sum') return G From 3b2f66d40798032492eb3f51d32ce72ca38981c4 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Wed, 20 Oct 2021 16:11:50 -0400 Subject: [PATCH 13/41] bug with kumar --- hypernetx/algorithms/hypergraph_modularity.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index 53d7f82c..406e43dc 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -374,7 +374,7 @@ def kumar(HG, delta=.01): G.vs['part'] = CG.membership for e in HG.edges: HG.edges[e].weight = W[e] - return {v['name']: v['part'] for v in G.vs} + return dict2part({v['name']: v['part'] for v in G.vs}) ################################################################################ From 243a65759a263aae333ac0befba636d814167516 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Thu, 21 Oct 2021 08:46:23 -0400 Subject: [PATCH 14/41] reorg some functions --- hypernetx/algorithms/hypergraph_modularity.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index 406e43dc..3181ea75 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -379,7 +379,7 @@ def kumar(HG, delta=.01): ################################################################################ -def delta_ec(HG, P, v, a, b, wdc): +def _delta_ec(HG, P, v, a, b, wdc): """ Computes change in edge contribution -- partition P, node v going from P[a] to P[b] @@ -517,7 +517,7 @@ def last_step(HG, L, wdc=linear, delta=.01): if c == i: M.append(0) else: - M.append(delta_ec(HG, A, v, c, i, wdc) - delta_dt(HG, A, v, c, i, wdc)) + M.append(_delta_ec(HG, A, v, c, i, wdc) - delta_dt(HG, A, v, c, i, wdc)) i = s[np.argmax(M)] if c != i: A[c] = A[c] - {v} From b0c2ab9a1128e9e5b6b7a25c3b2dc8a7bf5d296d Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Thu, 21 Oct 2021 08:55:11 -0400 Subject: [PATCH 15/41] reorg some functions --- hypernetx/algorithms/hypergraph_modularity.py | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index 3181ea75..6fdfaaf5 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -181,7 +181,7 @@ def strict(d, c): ######################################### -def compute_partition_probas(HG, A): +def _compute_partition_probas(HG, A): """ Compute vol(A_i)/vol(V) for each part A_i in A (list of sets) @@ -205,7 +205,7 @@ def compute_partition_probas(HG, A): return [i / s for i in p] -def degree_tax(HG, Pr, wdc): +def _degree_tax(HG, Pr, wdc): """ Computes the expected fraction of edges falling in the partition in a random graph as per [2]_ @@ -236,7 +236,7 @@ def degree_tax(HG, Pr, wdc): return DT -def edge_contribution(HG, A, wdc): +def _edge_contribution(HG, A, wdc): """ Edge contribution from hypergraph with respect to partion A. @@ -287,8 +287,8 @@ def modularity(HG, A, wdc=linear): : float """ - Pr = compute_partition_probas(HG, A) - return edge_contribution(HG, A, wdc) - degree_tax(HG, Pr, wdc) + Pr = _compute_partition_probas(HG, A) + return _edge_contribution(HG, A, wdc) - _degree_tax(HG, Pr, wdc) ################################################################################ @@ -415,7 +415,7 @@ def _delta_ec(HG, P, v, a, b, wdc): return ec / HG.total_weight -def bin_ppmf(d, c, p): +def _bin_ppmf(d, c, p): """ exp. part of binomial pmf @@ -436,7 +436,7 @@ def bin_ppmf(d, c, p): return p**c * (1 - p)**(d - c) -def delta_dt(HG, P, v, a, b, wdc): +def _delta_dt(HG, P, v, a, b, wdc): """ Compute change in degree tax -- partition P (list), node v going from P[a] to P[b] @@ -474,8 +474,8 @@ def delta_dt(HG, P, v, a, b, wdc): for d in HG.d_weights.keys(): x = 0 for c in np.arange(int(np.floor(d / 2)) + 1, d + 1): - x += HG.bin_coef[(d, c)] * wdc(d, c) * (bin_ppmf(d, c, voln) + bin_ppmf(d, c, volm) - - bin_ppmf(d, c, vola) - bin_ppmf(d, c, volb)) + x += HG.bin_coef[(d, c)] * wdc(d, c) * (_bin_ppmf(d, c, voln) + _bin_ppmf(d, c, volm) + - _bin_ppmf(d, c, vola) - _bin_ppmf(d, c, volb)) DT += x * HG.d_weights[d] return DT / HG.total_weight @@ -517,14 +517,14 @@ def last_step(HG, L, wdc=linear, delta=.01): if c == i: M.append(0) else: - M.append(_delta_ec(HG, A, v, c, i, wdc) - delta_dt(HG, A, v, c, i, wdc)) + M.append(_delta_ec(HG, A, v, c, i, wdc) - _delta_dt(HG, A, v, c, i, wdc)) i = s[np.argmax(M)] if c != i: A[c] = A[c] - {v} A[i] = A[i].union({v}) D[v] = i - Pr = compute_partition_probas(HG, A) - q2 = edge_contribution(HG, A, wdc) - degree_tax(HG, Pr, wdc) + Pr = _compute_partition_probas(HG, A) + q2 = _edge_contribution(HG, A, wdc) - _degree_tax(HG, Pr, wdc) if (q2 - qH) < delta: break qH = q2 From d159085582e12ed246327cbcb170d401f50754b6 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Thu, 21 Oct 2021 09:16:21 -0400 Subject: [PATCH 16/41] reorg some functions --- hypernetx/algorithms/hypergraph_modularity.py | 20 ++++++++----------- 1 file changed, 8 insertions(+), 12 deletions(-) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index 6fdfaaf5..58a9d411 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -27,7 +27,7 @@ def dict2part(D): """ - Returns dictionary to partition, inverse function to part2dict + Given a dictionary mapping the part for each vertex, return a partition as a list of sets; inverse function to part2dict Parameters ---------- @@ -50,7 +50,7 @@ def dict2part(D): def part2dict(A): """ - Returns dictionary {vertex: partition index}, inverse function + Given a partition (list of sets), returns a dictionary mapping the part for each vertex; inverse function to dict2part Parameters @@ -61,6 +61,7 @@ def part2dict(A): Returns ------- : dict + a dictionary with {vertex: partition index} """ x = [] for i in range(len(A)): @@ -72,17 +73,12 @@ def part2dict(A): def precompute_attributes(HG): """ Precompute some values on hypergraph HG for faster computing of hypergraph modularity. - The following attributes will be set for HG: + If HG is unweighted, v.weight is set to 1 for each vertex v in HG. + The weighted degree for each vertex v is stored in v.strength. + The total edge weigths for each edge cardinality is stored in HG.d_weights. + Binomial coefficients to speed-up modularity computation are stored in HG.bin_coef. - if HG is unweighted, v.weight is set to 1 for each v in HG.nodes - - v.strength, the weighted degree for each v in HG.nodes - - HG.d_weights, the total edge weigths for each edge cardinality d - - HG.bin_coef, binomial coefficients to speed-up modularity computation - - This needs to be called before calling either hypernetx.algorithms.hypergraph_modularity.modularity() or hypernetx.algorithms.hypergraph_modularity.last_step() + This needs to be run before calling either modularity() or last_step(). Parameters ---------- From c858c19cfe49efb486ad2dad319b99af85ec7e31 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Thu, 21 Oct 2021 09:31:54 -0400 Subject: [PATCH 17/41] reorg some functions --- hypernetx/algorithms/hypergraph_modularity.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index 58a9d411..72ddea64 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -117,15 +117,16 @@ def precompute_attributes(HG): def linear(d, c): """ - Weight function for hyperedge. Gives the actual ratio as long - as it is greater than 0.5. + Edge contribution [3]_ for $d$-edge with $c$ vertices in the majority class. + If $c > d/2$, return $c/d$ else return 0. + This is the default choice for modularity() and last_step() functions. Parameters ---------- d : int - Number of nodes in an edge + Number of vertices in an edge c : int - Number of nodes in the majority class + Number of vertices in the majority class Returns ------- @@ -138,8 +139,8 @@ def linear(d, c): def majority(d, c): """ - Weight function for hyperedge. Requires - c be the majority of d. Returns bool + Edge contribution[3]_ for $d$-edge with $c$ vertices in the majority class. + If $c>d/2$, return 1 else return 0. Parameters ---------- @@ -159,7 +160,8 @@ def majority(d, c): def strict(d, c): """ - Weight function for hyperedge. Requires c == d. + Edge contribution [3]_ for $d$-edge with $c$ vertices in the majority class. + If $c==d$, return 1 else return 0. Parameters ---------- From eee38fd53d048086e49c814b4a8285c210434980 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Thu, 21 Oct 2021 09:57:37 -0400 Subject: [PATCH 18/41] reorg some functions --- hypernetx/algorithms/hypergraph_modularity.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index 72ddea64..3f31504a 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -117,8 +117,7 @@ def precompute_attributes(HG): def linear(d, c): """ - Edge contribution [3]_ for $d$-edge with $c$ vertices in the majority class. - If $c > d/2$, return $c/d$ else return 0. + Hyperparameter for hypergraph modularity [2]_ for $d$-edge with $c$ vertices in the majority class. This is the default choice for modularity() and last_step() functions. Parameters @@ -131,6 +130,7 @@ def linear(d, c): Returns ------- float + $c/d$ if $c>d/2$ else 0 """ return c / d if c > d / 2 else 0 @@ -139,8 +139,8 @@ def linear(d, c): def majority(d, c): """ - Edge contribution[3]_ for $d$-edge with $c$ vertices in the majority class. - If $c>d/2$, return 1 else return 0. + Hyperparameter for hypergraph modularity [2]_ for $d$-edge with $c$ vertices in the majority class. + This corresponds to the majority rule [3]_ Parameters ---------- @@ -152,6 +152,8 @@ def majority(d, c): Returns ------- bool + 1 if $c>d/2$ else 0 + """ return 1 if c > d / 2 else 0 @@ -160,8 +162,8 @@ def majority(d, c): def strict(d, c): """ - Edge contribution [3]_ for $d$-edge with $c$ vertices in the majority class. - If $c==d$, return 1 else return 0. + Hyperparameter for hypergraph modularity [2]_ for $d$-edge with $c$ vertices in the majority class. + This corresponds to the strict rule [3]_ Parameters ---------- @@ -173,6 +175,7 @@ def strict(d, c): Returns ------- bool + 1 if $c==d$ else 0 """ return 1 if c == d else 0 @@ -206,7 +209,7 @@ def _compute_partition_probas(HG, A): def _degree_tax(HG, Pr, wdc): """ Computes the expected fraction of edges falling in - the partition in a random graph as per [2]_ + the partition as per [2]_ Parameters ---------- @@ -215,7 +218,7 @@ def _degree_tax(HG, Pr, wdc): Pr : list Probability distribution wdc : func - weight function (ex: strict, majority, linear) + weight function for edge contribution (ex: strict, majority, linear) Returns ------- From fc37e34488a6d42263979283fcc28495792a79c6 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Thu, 21 Oct 2021 10:12:06 -0400 Subject: [PATCH 19/41] reorg some functions --- hypernetx/algorithms/hypergraph_modularity.py | 27 ++++++++++--------- 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index 3f31504a..3bf3581a 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -117,7 +117,7 @@ def precompute_attributes(HG): def linear(d, c): """ - Hyperparameter for hypergraph modularity [2]_ for $d$-edge with $c$ vertices in the majority class. + Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class. This is the default choice for modularity() and last_step() functions. Parameters @@ -130,7 +130,7 @@ def linear(d, c): Returns ------- float - $c/d$ if $c>d/2$ else 0 + c/d if c>d/2 else 0 """ return c / d if c > d / 2 else 0 @@ -139,7 +139,7 @@ def linear(d, c): def majority(d, c): """ - Hyperparameter for hypergraph modularity [2]_ for $d$-edge with $c$ vertices in the majority class. + Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class. This corresponds to the majority rule [3]_ Parameters @@ -152,7 +152,7 @@ def majority(d, c): Returns ------- bool - 1 if $c>d/2$ else 0 + 1 if c>d/2 else 0 """ return 1 if c > d / 2 else 0 @@ -162,7 +162,7 @@ def majority(d, c): def strict(d, c): """ - Hyperparameter for hypergraph modularity [2]_ for $d$-edge with $c$ vertices in the majority class. + Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class. This corresponds to the strict rule [3]_ Parameters @@ -175,7 +175,7 @@ def strict(d, c): Returns ------- bool - 1 if $c==d$ else 0 + 1 if c==d else 0 """ return 1 if c == d else 0 @@ -272,21 +272,24 @@ def _edge_contribution(HG, A, wdc): def modularity(HG, A, wdc=linear): """ - Computes modularity of a hypergraph with respect to partition A. + Computes modularity of hypergraph HG with respect to partition A. Parameters ---------- HG : Hypergraph - Description - A : list of lists - Partition of the nodes in HG + The hypergraph with some precomputed attributes via: precompute_attributes(HG) + A : list of sets + Partition of the vertices in HG wdc : func, optional - weight function (ex: strict, majority, linear) + Hyperparameter for hypergraph modularity [2]_ + + For 'wdc', any function of the format fn(d,c) that returns 0 when c <= d/2 and value in [0,1] otherwise can be used. + Default is 'linear'; other supplied choices are 'majority' and 'strict'. Returns ------- : float - + The modularity function qH for partition A on HG """ Pr = _compute_partition_probas(HG, A) return _edge_contribution(HG, A, wdc) - _degree_tax(HG, Pr, wdc) From 4c2eca7669181c01034e441f247c80939063ae65 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Thu, 21 Oct 2021 10:17:36 -0400 Subject: [PATCH 20/41] reorg some functions --- hypernetx/algorithms/hypergraph_modularity.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index 3bf3581a..2da53f1c 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -283,6 +283,8 @@ def modularity(HG, A, wdc=linear): wdc : func, optional Hyperparameter for hypergraph modularity [2]_ + Note + ---- For 'wdc', any function of the format fn(d,c) that returns 0 when c <= d/2 and value in [0,1] otherwise can be used. Default is 'linear'; other supplied choices are 'majority' and 'strict'. From 9c93f37c39537ada799160736b1a67e2a50a3d18 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Thu, 21 Oct 2021 10:26:51 -0400 Subject: [PATCH 21/41] reorg some functions --- hypernetx/algorithms/hypergraph_modularity.py | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index 2da53f1c..66b444af 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -129,8 +129,8 @@ def linear(d, c): Returns ------- - float - c/d if c>d/2 else 0 + : float + c/d if c>d/2 else 0 """ return c / d if c > d / 2 else 0 @@ -151,8 +151,8 @@ def majority(d, c): Returns ------- - bool - 1 if c>d/2 else 0 + : bool + 1 if c>d/2 else 0 """ return 1 if c > d / 2 else 0 @@ -174,8 +174,8 @@ def strict(d, c): Returns ------- - bool - 1 if c==d else 0 + : bool + 1 if c==d else 0 """ return 1 if c == d else 0 @@ -291,7 +291,7 @@ def modularity(HG, A, wdc=linear): Returns ------- : float - The modularity function qH for partition A on HG + The modularity function qH for partition A on HG """ Pr = _compute_partition_probas(HG, A) return _edge_contribution(HG, A, wdc) - _degree_tax(HG, Pr, wdc) @@ -301,7 +301,7 @@ def modularity(HG, A, wdc=linear): def two_section(HG): """ - Creates a random walk 2-section igraph with transition weights defined by the + Creates a random walk based [1]_ 2-section igraph Graph with transition weights defined by the weights of the hyperedges. Parameters @@ -310,7 +310,7 @@ def two_section(HG): Returns ------- - G : igraph.Graph + : igraph.Graph """ s = [] for e in HG.edges: @@ -330,8 +330,7 @@ def two_section(HG): def kumar(HG, delta=.01): """ - Compute a partition of the vertices as per Kumar's algorithm [1]_ - + Compute a partition of the vertices in hypergraph HG as per Kumar's algorithm [1]_ Parameters ---------- @@ -342,7 +341,8 @@ def kumar(HG, delta=.01): Returns ------- - dict + : list of sets + A partition of the vertices in HG """ # weights will be modified -- store initial weights From 8ca1f1fa28eb8f6ef45c59a4ca62ff28104e8c95 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Thu, 21 Oct 2021 10:44:48 -0400 Subject: [PATCH 22/41] reorg some functions --- hypernetx/algorithms/hypergraph_modularity.py | 31 ++++++++++--------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index 66b444af..cd0b18eb 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -285,7 +285,7 @@ def modularity(HG, A, wdc=linear): Note ---- - For 'wdc', any function of the format fn(d,c) that returns 0 when c <= d/2 and value in [0,1] otherwise can be used. + For 'wdc', any function of the format w(d,c) that returns 0 when c <= d/2 and value in [0,1] otherwise can be used. Default is 'linear'; other supplied choices are 'majority' and 'strict'. Returns @@ -311,6 +311,7 @@ def two_section(HG): Returns ------- : igraph.Graph + The 2-section graph built from HG """ s = [] for e in HG.edges: @@ -407,8 +408,7 @@ def _delta_ec(HG, P, v, a, b, wdc): Returns ------- - TYPE - Description + : float """ Pm = P[a] - {v} Pn = P[b].union({v}) @@ -423,7 +423,7 @@ def _delta_ec(HG, P, v, a, b, wdc): def _bin_ppmf(d, c, p): """ - exp. part of binomial pmf + exponential part of the binomial pmf Parameters ---------- @@ -436,7 +436,7 @@ def _bin_ppmf(d, c, p): Returns ------- - float + : float """ return p**c * (1 - p)**(d - c) @@ -488,27 +488,30 @@ def _delta_dt(HG, P, v, a, b, wdc): def last_step(HG, L, wdc=linear, delta=.01): """ - Compute a partition of the vertices as per Last-Step algorithm.[2]_ + Given some initial partition L, compute a new partition of the vertices in HG as per Last-Step algorithm [2]_ - Simple H-based algorithm -- - try moving nodes between communities to optimize qH - requires L: initial non-trivial partition + Note + ---- + This is a very simple algorithm that tries moving nodes between communities to optimize hypergraph modularity qH. + It requires an initial non-trivial partition which can be obtained for example via graph clustering on the 2-section of HG. Parameters ---------- HG : Hypergraph - - L : list of sets + + L : list of sets + some initial partition of the vertices in HG wdc : func, optional - weight function (ex: strict, majority, linear) - delta : float, optional + Hyperparameter for hypergraph modularity [2]_ + delta : float, optional + convergence stopping criterion Returns ------- : list of sets - + A new partition for the vertices in HG """ A = L[:] # we will modify this, copy D = part2dict(A) From 4e0978f4a284ab1c42762e45fd63fc121d1f0b14 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Thu, 21 Oct 2021 11:28:02 -0400 Subject: [PATCH 23/41] reorg some functions --- hypernetx/algorithms/hypergraph_modularity.py | 2 +- ...Hypergraph Modularity and Clustering.ipynb | 278 ++++++++++++------ 2 files changed, 181 insertions(+), 99 deletions(-) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index cd0b18eb..c83b008c 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -511,7 +511,7 @@ def last_step(HG, L, wdc=linear, delta=.01): Returns ------- : list of sets - A new partition for the vertices in HG + A new partition for the vertices in HG """ A = L[:] # we will modify this, copy D = part2dict(A) diff --git a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb index 096c184d..d86ed972 100644 --- a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb +++ b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using the following packages:\n", - "\n", - "* pip install python-igraph\n", - "* pip install partition-igraph\n", - "* pip install hypernetx\n" - ] - }, { "cell_type": "code", "execution_count": 1, @@ -19,10 +8,10 @@ "source": [ "import pandas as pd\n", "import numpy as np\n", - "import igraph as ig\n", - "import partition_igraph\n", - "import hypernetx as hnx\n", "import pickle\n", + "import igraph as ig ## pip install python-igraph\n", + "import partition_igraph ## pip install partition-igraph\n", + "import hypernetx as hnx\n", "import hypernetx.algorithms.hypergraph_modularity as hmod" ] }, @@ -30,35 +19,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Summary of functions for Hypergraph Modularity using HNX\n", - "\n", - "### Build hypergraph and pre-compute key quantities\n", + "# Main functions for Hypergraph Modularity using HyperNetX\n", "\n", - "We build the hypergraph HG using:\n", - "```python\n", - "HG = hnx.Hypergraph(Edges)\n", - "```\n", - "where 'Edges' is a list of sets; edges are then indexed as 0-based integers.\n", + "### Pre-computing key hypergraph quantities\n", "\n", - "Once the HNX hypergraph is built, the following function is called to \n", - "compute node strengths, d-degrees and binomial coefficients\n", + "Given some hnx hypergraph HG, the following function needs to be called first\n", + "to pre-compute node strengths (weighted degrees), d-degrees and binomial coefficients\n", "and add these as attributes to HG:\n", "\n", "```python\n", "hmod.precompute_attributes(HG)\n", "```\n", "\n", - "### Partitions\n", - "\n", - "We use two representations for partitions: list of sets (the parts) or dictionary.\n", - "Those functions are used to map from one to the other:\n", - "\n", - "```python\n", - "dict2part(D)\n", - "part2dict(A)\n", - "```\n", - "\n", - "### H-modularity\n", + "### H-modularity (qH)\n", "\n", "The function to compute H-modularity for HG w.r.t. partition A (list of sets covering the vertices):\n", "\n", @@ -94,7 +67,7 @@ "K = hmod.kumar(HG, delta=.01)\n", "```\n", "\n", - "where delta is the convergence stopping criterion. Partition is returned as a dictionary.\n", + "where delta is the convergence stopping criterion. Partition is returned as a list of sets.\n", "\n", "[1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S., Ravindran B. (2020) *A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering*. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24\n", "\n", @@ -111,7 +84,17 @@ "where 'wcd' is the the weight function (default = 'linear') and delta is the convergence stopping criterion.\n", "Returned partition is a list of sets.\n", "\n", - "[2] B. Kaminski, P. Pralat and F. Théberge, *Community Detection Algorithm Using Hypergraph Modularity*, to appear in the proceedings of Complex Networks 2020, Springer.\n" + "[2] Kamiński B., Prałat P. and Théberge F. “Community Detection Algorithm Using Hypergraph Modularity”. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13\n", + "\n", + "### Utility functions\n", + "\n", + "We use two representations for partitions: list of sets (the parts) or dictionary.\n", + "Those functions are used to map from one to the other:\n", + "\n", + "```python\n", + "dict2part(D)\n", + "part2dict(A)\n", + "```" ] }, { @@ -128,7 +111,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAHBCAYAAADkRYtYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABukklEQVR4nO3dd3hUVfrA8e+d9FCG0ItAQIW5yLVg7xpdXQV7wT5r241G3V2xRF31uhbQ37K6q9Go6+q4a8GGusRu7BX7Re+AApHeYYAU0u7vjzMDk8kkmbSZSfJ+niePeu+dmTeKeXPOec97NMdxEEIIIUTzXIkOQAghhOgKJGEKIYQQMZCEKYQQQsRAEqYQQggRA0mYQgghRAwkYQohhBAxkIQphBBCxEASphBCCBEDSZhCCCFEDCRhCiGEEDGQhCmEEELEQBKmEEIIEQNJmEIIIUQMJGEKIYQQMZCEKYQQQsRAEqYQQggRA0mYQgghRAwkYQohhBAxkIQphBBCxEASphBCCBEDSZhCCCFEDCRhCiGEEDGQhCmEEELEQBKmEEIIEQNJmEIIIUQMJGEKIYQQMZCEKYQQQsRAEqYQQggRA0mYQgghRAwkYQohhBAxkIQphBBCxEASphBCCBEDSZhCCCFEDCRhCiGEEDGQhCmEEELEQBKmEEIIEQNJmEIIIUQMJGEKIYQQMZCEKYQQQsRAEqYQQggRA0mYQgghRAwkYQohhBAxkIQphBBCxEASphBCCBGD1EQHIJKT4TM0oA+w1fJa9YmORwghEk1zHCfRMYgEM3zGQOBUYBwwNuwrBTULUQYsAhYDC4E5ltf6OSHBCiFEgkjC7KGCI8gDgMuBE4AS4HtUYlwELLa81kbDZ/QCclEJdAzgAU4HvgUeBEosr1Ub929ACCHiTBJmDxNMlOcC04DeQDHwhOW11rfiPTJRSfNyYCfgIeDvltfa1vERCyFEcpCE2YMYPqM38AiwG3Ad8HZ71ycNn7EXcBswBDjD8lpL2h2oEEIkIUmYPYThM3TgReBzoMDyWpXNvsB0pwCDgfWYgeoW3ltDjVivAX5nea03OiRoIYRIIpIwewDDZ0wFHgCut7zWv6M+ZLrHAZcAe6LWKkcBAcANrEYV/PiBJ4FPMQON/uAYPuMw4BngX8BtUl0rhOhOJGF2c4bPOBZ4HDje8lrfNbipRpEnotYidw8+9wEqOZZhBqow3amodcqxwN7ApUAlquDnv5iB8ojPGwr8D3jR8lozOu87E0KI+JKE2Y0ZPiMXNQV7puW1Pmxw03QPQY0GewP/AF7ADLRctGO6XUAecCUwATgdM/B9xOfuBMwFzra81vvt/DaEECIpSMLspoKVrB8DT1te6+8NbpruQ1HJ8t/AbZiBujZ9iOk+B5Vsr8MMPB7x+b8BfMDeltda2ab3F0KIJCKt8bqvf6KmVu9tcNV0nwW8AFyKGbilzckSwAw8DRwOXIfpvif8luW13kZtWZll+AzpKCWE6PIkYXZDhs+4CDgUuMjyWjumEEy3AdwPHI0ZeL1DPswM/AQcCJwaTMbh7gBqgIIO+SwhhEgg+c2/mzF8xiTgbuAwy2tt2X7DdPdFjSyvxgxYHfBR5wPD1HsHYPW8/zFv9r9Yu2BvBo1bC2B5LRZsWPDDm7++efvW6q19eqf3rgZWAv/pgM8XQoi4khFmN2L4jP6opHi55bXsiNsPAO9hBjoqWQ0Dlm7/GjLxS/oM/S+f3n8W1eUrQtfH9R/3ZVVt1bxn5z87MXhtWAd9vhBCxJUkzG7C8Bku4L/AbMtrPd/gpukeBUxGNRboPPtd+gFpmRv45skjwy8fm3vs84s2LTp+VfmqXp36+UII0YkkYXYfN6O2iBRGufd71J7JrZ0exbjfzmLl96dQuTE9dGn3QbuvGpQ96KvZv8ye3OmfL4QQnUQSZjdg+IzjUElxquW1ahrcNN3pqA4+D8X6fpqm7a1p2kxN017UNO0hTdMma5qmxfTiXY5aTFbOz3zz5G/CLx8/5viXlmxe8pt1Fet6xxqHEEIkE0mYXZzhM8YAT6CSZbT9jocDizAD/pbeS1PuA74CrkadkZkPzAFe1zTN3dzrP/nkE7fH47my/9SinT1Tbz175zG5Nzz33HNDAcb3H79+eO/hH7239L0jm3sPIYRIVpIwuzDDZ2ShinymW17r4yYe2wWYF+NbFgJ/bOLesajEHFVdXR2nnXba1bvvvvtPGwKbr/T/+8pPHv3jsasXLly4PcmeuPOJr6wsXznJ8BmjYoxHCCGShiTMru0B4GdUt52mjEUdCN0sTdN6EX39M9zJmqbtHe3G7bffvpvL5ap77rnn3gVg4qkv5o1i/xvyz1kRemZ039GBUX1GfQHc0lI8QgiRbCRhdlGGz7gEOAC4pEFzgsbGojr+tGQ/oG8Mzx0V7eL333+/08iRI3d8zvC91tB3+Od899QJ4c8dMeqID4CTDZ8xLobPEkKIpCEJswsyfMY+wHTgNMtrtVT52hfYHMPbxpIsQR33FZs9zprNxl+PYO38fqFL/TL6VQJ/Rx06LYQQXYYkzC7G8BkDUOuW+ZbXarGQBygDRsfwXCzv1eRzu++++7KlS5eOaXBx0PhN5Ix+n++fPSXi8X8CRxo+Y48YP1MIIRJOEmYXYviMFOAp4HnLa70Y48sWow6EbpbjOPOBT1p4bAPwUrQbt9xyy491dXVpZ5999vYq2H/+859jZ35c4WfzigNY8e3g0PXgqHg6qtesEEJ0CZIwu5ZbgUzghla8ZhGwc4zPXohKitHUAr9zHKc82s2UlBSee+65md98843Rt2/f+3Jycu659957Tx+t77mCgbu8ybyXTot4ycPAHobPODDG2IQQIqHkPMwuwvAZk1HHZe1jea3VMb/QdI9FHSI9CjNQ1dLjmqblAvcBU4CU4OXPgWsdxwnfunIdqjdsy7auzuLtW+5lkvcORh+kAfcAGD7jYuA8IK+FwiUhhEg4GWF2AYbPGIs67Hlqq5IlgBlYBHwDnBHL447jlDmOczKQA+wGDHIc58CIZNk6vYdUMnjC/7D/d3rEHR8wgiYqb4UQIplIwkxyweYELwJ3WF7r0za+zYPA5a15geM4WxzH+clxnHVt/MyG9v7dW1Ru3JUV3+8UumR5rVrUnsy7DJ8RW+s9IYRIEEmYSSyYRB4CbFSTgrYqAYZjuqM2HYiLTHcNw/aYzeL3j4248xyQAZyYgKiEECJmkjCT2++BfYBL27XGZwbqUAn3Tx0TVhtNuuA9qssHYroPD12yvFY98BfgjmAVsBBCJCVJmEnK8Bn7ArcDp1peK2plais9CkzGdI/ogPcCWAmMbNVXeq/huHd6HrgT0x0+BTsH2Aqc1UGxCSFEh5Mq2SRk+IyBwNfAnyyvNbvD3th03w9swQzc2GHv2foYUoAfgGsxA6+FLhs+40hUUtcbHVEmhBBJQEaYSSY4LfkM8EyHJkvlH8ClmO7sDn7f2Knp4ZuBOzDd2//8WV7rPVRXogsTFJkQQjRLEmbyuQ21//EvHf7OZuAXVDefCzr8vVtnNlAPRDYzuAm42fAZmfEPSQghmicJM4kYPuNEwAucFdxy0RnuBf4cPrqLOzPgADcCt2O6U0OXLa/1BWoq+rJEhSaEEE2RhJkkDJ+xC/Av4EzLa63pxI/6EFVgc1wnfkYs3gZWA+dHXL8ZKDR8Rp/4hySEEE2ThJkEDJ+RjWpOcJvltT7r1A9Tozs1ykwkFcdNwK2Y7ozQZctrWcA7JHoLjBBCRJCEmWDB5gTFqMrRB+P0sc8BOqY7scdrmYGPgR+BSyPu3Ar80fAZ/eMflBBCRCcJM/HygT2BP8StAbkZqCYZGhkofwFuwnT3Cl2wvNYvqBH3dQmLSgghIkjCTCDDZxyAqoo9zfJaFXH++IeBkzDdQ+P8uQ2ZgW+Bj4ArIu7cDvze8BnD4h+UEEI0JgkzQQyfMQg1NXqp5bV+jnsAZmADMItWNmXvJLcA12C6+4UuWF5rGfAEap1TCCESThJmAhg+IxV4Fviv5bVeSWAo9wF/wHRnJTAGMAN+VHu8aRF3pgNnGz4jN+4xCSFEBEmYiXE74KC2UCSOGZgPzAXOTWgcym3A5ZjuwaELltdaiyqEujVhUQkhRJAkzDgzfMbJwDnA2ZbXqktwOLCjkUFiz6M0A2XA00BhxJ2ZwBTDZ+hxj0kIIcJIwowjw2fsCjyCak6wNtHxBJUCtcAxiQ4EuBP4HaY7/JDpTcDfUCNQIYRIGEmYcWL4jF7AS8AtwRZwySFZGhkAmIFVqF8oIqeqHwAOMXzGpPgHJYQQiiTMOAg2J3gE+Aa1nSPZPAPsgeneLdGBAPcAp2G6dwldCJ4HehdwR8KiEkL0eJIw46MAmAhcFrfmBK1hBrahimv+lOBIQttd7qPxFOyjwATDZxwS95iEEAJJmJ3O8BkHofYZnpqA5gStUQycjukelOhAUOd2Ho3pNkIXLK+1DZVE7wqO2IUQIq4kYXYiw2cMQTUHuMjyWgsTHU+zzMBa4AWS4WgtM7AFmIHafhPuP8BgkqNASQjRw0jC7CRhzQmesLzWnETHE6P7gMvCTw9JoIeAvTHd+4cuBM8IvRm4U0aZQoh4k4TZee4EqgEzwXHEzgz8CHwPnJ3oUDADVagR5p0Rd15E/bk9Je4xCSF6NEmYncDwGacCZwHnJklzgta4F7g64Y0MlMeBXEx3XuiC5bXqUSec3GH4jJSERSaE6HEkYXYww2eMJ1hAY3mtdYmOpw3eAlKAvJYe7HRmoAZVMHVnRAJ/HdhAcrT0E0L0EJIwO5DhM3qjmhP8xfJacxMdT5vsaGRwdaJDCXoW6A1MCV0Ibs25ETANn5GeqMCEED2LJMwOEixCeRT4IvjXruwpYB9MtyfRgWAGtk/BYrq3/3m1vNaHwM/AxYkKTQjRs0jC7DhXAuOBgqRsTtAaZqASNa38x0SHEvQqUAWcGXH9JuAvhs/Ijn9IQoieRhJmBzB8xsGoH96nW16rMtHxdJAHgbMw3QMSHUhwmvgm4HZMd1rosuW1vgI+JzkOwRZCdHOSMNvJ8BlDUc0JLrS81qJEx9NhzMBq4GXgDwmOJORdYCngjbh+C3Cd4TP6xj8kIURPIgmzHQyfkYZKlo9ZXuu1RMfTCe4FCjDdiS+s2THKvAXTnRm6bHmtH4E3SJ4iJSFENyUJs32mA5XAXxMdSKcwAz8ANjA10aEAYAY+A76j8ajXBK4wfMbAeIckhOg5JGG2keEzTgdOo2s2J2iNvwN/TpJGBqBa492A6e4duhCcCn8OuD5hUQkhuj1JmG1g+Awd1ev0dMtrrU90PJ3sDSAbOCzRgQBgBr4H3gOuirhzB3Cx4TOGxz8oIURPIAmzlQyf0QfVz7TQ8lpfJzqeTqf2Qd5Hcq0R3ooa9eaELlheawXwGGrPphBCdDhJmK0QbE7wGPCp5bUeS3Q8cfQkcCCme5dEBwKAGVgAvAJcG3HnbuBMw2eMjX9QQojuThJm6/wJ2Bm4IsFxxJcZqEB1L0qWRgagCq3+gOkeEroQ7N37AF3phBghRJchCTNGhs84FCgETrO8VlWi40mAIuC88GnQhDIDS1AHSt8YcefvwG8Nn7Fb/IMSQnRnkjBjYPiMYagm4F7La5UlOJzEMAMrgP8BlyY6lDDTUUl8dOiC5bU2A/fQXbf6CCESRhJmC4LNCZ4DHra81huJjifB7gWuDG9Pl1CqG1ExqttPuCLgAMNn7Bv/oIQQ3ZUkzJbdDWxGbVvo2czAt8BC4PREhxLmb8CJmO7xoQvBfr53IP/NhBAdSBJmMwyfcSZwMnC+5bXqExxOskiuRgZmYCNq5HtbxJ3HgF0Nn3F4/IMSQnRHkjCbYPiMCaipvdMsr7Uh0fEkkTlADnBQogMJ80/gCEz3nqELlteqRlXL3hncDiSEEO0iCTOK4MkXLwHXWV7r20THk1RUI4N/kEyNDMzAVuAu4PaIO08B/YHj4h6TEKLbkYQZITga+TfwgeW1Hk90PEnqCeBwTPeYRAcS5mFgd0z39pFvsMfvX4A7DJ8hf9aFEO0iP0QauxoYTXJt0k8uakT3GI37uSaOGdiG2kpyZ8T66mygHtUoXwgh2kwSZphggci1qKbqPbE5QWs8AHgx3e5EBxLGB4wAjgpdsLxW6BzN2w2fkZqowIQQXZ8kzCDDZ4wAngEusLzWr4mOJ+mZgaWok0wuTnQo25mBWtTxX3dFjDLfAlYD5yUkLiFEtyAJEzB8RjqqOcGDltd6K9HxdCH3AldhupNp5PY8kA6cFLoQNso0DZ+RkajAhBBdmyRM5f+ADahKSxErMzAXWAackuhQtlNVvH8Bbsd0p4QuW17rY+Ankqu1nxCiC+nxCdPwGWcDk5HmBG2lGhkklxJgC3BWxPW/ADcZPqNX/EMSQnR1PTphGj5jImrT++mW19qU4HC6qleAoZjuAxIdyHZmIDQF+9fwvreW1/oG+IiedjybEKJD9NiEafgMN/AiMM3yWt8lOJyuywzUoX7pSK5Rphl4D1gEXBRx5xbgGsNn9It7TEKILq1HJsxgc4LHgVLLaz2Z6Hi6gX8DR2O6RyU6kAg3ATdjurNCFyyv5Ue195uWsKiEEF1Sj0yYqL2WI4A/JTiO7sEMbEZ1/7kywZE0ZAa+BL4CLou4cxtwueEzBsc/KCFEV9XjEqbhM/JQ3XzOsLzWtkTH043cD1yE6e6T6EAi3AxcHx5X8BDwp4HCRAUlhOh6elTCNHzGTqiG3OdZXmtJouPpVsxAGfAucGELT6YCY4H4JFYzYAFv03g24U7gd8E/E0II0aIekzCDzQmeB/5pea13Eh1PN3Uv8Mfw/Y9hsoBJwB9QHXcOi2NcZjCuAaELltdaBTyCGoEKIUSLekzCRO0XXAPcnehAui0z8BmwFjgx7Gp/4AjgclSP1wrgF2ACEJ8+tGbgF1RF9HURd+4BTjN8xi5xiUMI0aX1iIRp+IxzgWMBrzQn6HR/B+3PwE7AyajOOpNQvVyXAVWAgzpBZGIc47oduATTPSx0IXgw+D9QRUBCCNGsbp8wDZ+xO3AfcJo0J+h0qVz/q8U+F05g5Q/Xo5LmUmAlUBfx7FpgXyA+vV3NwDJUJe9NEXfuA442fIYRlziEEF1Wt06Ywc3pLwJ/trzWDwkOpzvLRiW/fLL6ncCQie/xw3N7AetRo8loaoA0YFycYgSYAZwdfvC15bW2BK/fHsc4hBBdULdNmIbPcKFGFG9aXuu/CQ6nuxqAWpe8HLVOuRVYyvjj3qB8zZ6s+al/C69fDxxMvP4cmoG1QBFwa8Sdh4C9DZ+xf1ziEEJ0Sd02YQLXA0NQey5FxxoOnAZcAuwOrEKtT6p9rX2HV9Bv5Ef89MoxLbxPJarwZ3TnhdrITOB4TLceuhA8LPx21FYTIYSIqlsmTMNnHAVchWpOUJ3oeLqh44BcYAkqWUauT8L4yW+woexIKja0tEa5GTiwowNskhkIoJLmXyPuPA7kBhtbCCFEI5rjNLXE1DUZPmMk8CVwruW1ShMdTzc1CTUVu6zZp94o/DOulDJqqpZTU5XOYM9qDrj8Z1yNtmmOQk2fr+6MYBsx3b2An4EpmIFvQpeD1dRXAAcFD50WQojtutUI0/AZGcALwH2SLDuVjRpVRmtQsENK+rcseOt45r10CmUfHcBPr+zHrPOOA6CmSgt7sgrYq7OCbcQMlKOmX++IuPMs0BuYErdYhBBdRrdKmKhOM8tRG9JF56kEvgaabl4eWJbBj7P3JaN3FUMmLqL34JVc9OZTLJurpl/TMsNHcGtRa6F9OzHmSI8CEzDdh4YuWF6rDnXI9B3BojEhhNiu2/xQMHzGBcDRwIUynRYXP6C2hWhR766a15fa6iwmeZ9h0LjBrF84kZcvP4ysnDWUfdwPgOry0J+/UCOD3To/7CAzUI1qmXcnpjv8e3gVNeI9M26xCCG6hG6RMA2fsQeqkONUy2sFEh1PD7ERmI/aWtLY+N+uxeWqY5VVR3XFMIYaP7Hw3aPY5ajPyD1kE58V7cxTZ54W9oo1wP7Eq5GB8l9gELC9mjf4y9ZNwF8Nn5Eax1iEEEmuyydMw2fkoJoTXGV5rXmJjqeH+Qro1eTdXY5+j0Xv70//3LcZPKEPu50yh4mn/wzAgQUL2bh4F54557fBp2uAdCB+fV3NQC2q+fpdEaPMd1EFTd64xSKESHpdOmEG15meBF6zvNYziY6nB1qO2lYS/aiuE+//lAMLXsE96nsqN+zFfpf+zE77bGbD4kyePPlkNi+fSGDJiLBXxLeRgfISalr51NCFsFHmrYbPyIxjLEKIJNalEyZwA2pK8JpEB9JDOcCnQL8mn9i01M28F3bHvdOnfHr/ady/91U8sE8RGxaO47i7byb/48fCnq4AclDbTOLDDNSjkuPt4ceSWV7rM+B71HFkQgjRdfdhGj7jGNTevX0tr7U8weH0ZKmopFJOqNNPuE1LMrh/0gOk99lI9dYRDBr/Hsf/7XlGHaDWmuvrwKnTSEkP/UHMQY00Z8UnfAhOx34IPIoZeDJ0Obg2/iawi+W1tsYtHiFEUuqSI0zDZ4xGTcWeI8ky4WqBz1HFM431G7WNwRM+BerY56Jv2eu8RYw6IEBNlUZdtYYrhbBkCaqYKJfmtqx0NDMQmoK9DdOdHrpsea3vgfdQXaOEED1cl0uYwTWlF4C/WV7r/cRGI4Js1PRs9EYGh13zGhm9NzL64NdYaR1PTYWLtEwnIlGGpAOZwNjOCzcKM/Ahqur3kog7twJXB4vLhBA9WJdLmKgDf39FbSMRyaGC5hoZ6Ceu5sArn2f0QQtwpdTgL4l29mQv1NqlG3gdtc8z3m4CbsJ0Z4cuWF5rAfAycG0C4hFCJJEulTANn3EhcDhwkTQnSDqhRgbR7f/7xfQaWMuwPV5j6RfHh93pj0qUDvAK6qitL1FJOL7MwNeo6eWCiDt/Bf5g+IwhcY9JCJE0ukzCNHzGXqiWd6dZXmtzouMRjawHFqASYNP2OOtTqitHsmreXsBI1LaUZ4B/A37UfsxEuhm4FtPtDl2wvNYS4D/AjQmLSgiRcF0iYYY1J7jC8lo/Jjoe0aSvUM3Lm5JBeq+hjDn0cz69fyIqSb6IOiYsOWYMzMBPqCnhP0fcmQ6cZ/iM+G15EUIklaRPmMHmBP8FXrG8Vvy2Goi2WIZqcReZNPugpl37AB+wsPQP/PDsgeH7HpPMbcCVmO6BoQuW11oNFAO3JCwqIURCJX3CRJ0e0Re4LtGBiBaFGhmEpmUHoBJlDTAblXDmcuojS4DngMsSEWSLzMAi1D7Qwog7fwNOMnzG+PgHJYRItKROmIbP+C1qU/yZltdK9NqWiM0iVBODkajWeU+hGkwsoOH65H1APqY7WVvP3QFciOne3rrP8lobUUfI3ZawqIQQCZO0CdPwGbmADzjb8lorExyOiF0N6iDmf6FGlcuItj5pBmzgG+CceAYXMzOwArXG+peIO/8EDg92ARJC9CBJmTDDmhPcbXmtDxMdj2i1dcCGGJ67F7g64qSQZHI3cCame+fQhWCLvOmoEagQogdJyoQJ3I+a2rs30YGITvUOavR5dKIDicoMrEONKM2IOw8Dexg+48C4xySESJikS5iGz7gYOAS4WJoTdHOqh+u9NN7CkUzuBY7BdO8WumB5rW2odcy7DJ+RrKNjIUQHS6qEafiMvYEZwKmW19qS6HhEXDwNTMJ064kOJCozsBn4P+D2iDs+YARwVNxjEkIkRNIkTMNnDECtW15ueS070fGIODEDVah2eH9KcCTNKQL2x3TvG7pgea1a1J5MGWUK0UMkRcI0fEYKqjnBS5bXej7R8Yi4ewhVXDOwxScTwQxUokaYkYU+zwEZwElxj0kIEXdJcYC04TNMIA84qqn9lrmFJWnAYcA4YAzq+Kf+qLZqi4DFwC+AXTZj8qbOj1p0KNP9GLAYM5Cc1afqnEw/cBFm4P3QZcNnTEFVze5pea26BEUnhIiDhCdMw2cch9qzt7fltVZF3s8tLBkOXAr8HrWn7ztUclyEOmx4JCp5jgV2BTyojfN2lK8VZTMmJ/43BNGY6Z4IvAWMwQxsS3Q4UZnu84F84JBgwRLB6dhPgCLLaz2VyPCEEJ0roQnT8BmjUUc5nW55rY/C7+UWlmSjKhTPRG2Ef7BsxmSrpffMLSzRUMUYepSvTNQoITyJ+oFFZTMm13bQtyXaynS/BfwXM/BkokOJSvW+/QG4FjPwWuiy4TOOBB4FdOlIJUT3lbCEafiMDOAj4DnLa/0t/F5uYck4VAGQBVxeNmNyoCM+M7ewpD8NE6gn+NdhwEIaj0jnl82YXNkRny1iYLqPQ01v7hUawSUd030q6giwvTED9aHLhs94G3je8lqPJCw2IUSnSmTCfAA1Ejw1fL9lbmHJCaiWZLcAxfGYQg2OZsfReES6M7CSxiNSu2zG5Fg62YjWMN0uYB5QgBl4L9HhRKW6Es0F7sEMPBe6bPiM/YCXgF0tryW/ZAnRDSUkYRo+43hUB5V9LK+1KXQ9t7BkT+BtYErZjMlfxD2wCLmFJamotdHw0Wjoq5Lo66TLZZ20HUz374ETMAMnJDqUJpnuY1DdqHbDDGyfyjd8xsvAh5bX+nuiQhNCdJ5EJcx3gMcsr/VM6FpuYUk/1AHEfymbMfnZuAfVCsF10uFEXyfNpuE6aejvF8o6aQxMdxbwK6qwZkGiw4lKjTLfB57ADDweumz4DAPV7m8XabwhRPcT94Rp+AwP6ofN6GCLsVACegk1OruiAz7mfNS6ZFusBP7T1g/OLSzJofFoVEcl2EVEXyetaOvndUum+3agP2agINGhNMl0H4I6umxceFWv4TP+C8y3vFZkZyAhRBeXiIR5H1Buea2bQtdyC0sOBx4Bdi+bMbkjthRcByxt42tHAvd0QAwN5BaWZBF9nXQXYBUNR6M2ap10fUfH0SWY7mHAT8DOmIHkXSs23SXAG5iB+0OXDJ+xC/A5MM7yWskbuxCi1eKaMA2f0QvVaGCS5bV+DV3PLSyZBXxcNmPy/U2+uHWSLmE2JbewJAXViCHa9O42oq+TLuv266Sm2wfYmIEZiQ6lSaZ7L+A1YBfMQHnosuEzHgY2Wl6rMGGxCSE6XLwT5unAJZbX+m3oWm5hSWg0kRvr9hFN07KA/VGdfn5xHOeHiEe6TMJsSnCaehjRt8H0AebTOJEuLJsxuXvsAzTdewJzgLGYgep4fKTt0XsBuexohDGWHV2lstjRMCP0tXiXE1fdlJpV/7l2247EbviMnYDvgYly+LkQ3UdqnD9vHKpTT7gLgOdbkSyvQp1PmBN27TvgQsdxIt97O5fL9VROTs4S9bhWf/311z9+7bXX/tyq6OMoOIJcEfx6N/xesEAqfJ30ouBfR+QWliwm+jppOV2JGfgO070AOAO1VtgpbI++C6p7z9moX8DKaJgU3wv+tQqVPEMJdB9g7C+vDtnFleqc6DzvOdKp154DXrX89jLDZ/iAm4COWJMXQiSBeI8wHwW+srzWw6FruYUlTwOvlc2Y/N+WXq9p2q00Psw3pAI4wHEciygjzLS0tMdramouBLjxxht3f+KJJ05asWJFtMKMpBhhtkVuYUkmO9ZJwxPqrsBaokzvls2YvC4x0cbAdJ8A3Ars25GNDGyPngJMBi4HJgGPA48Bv+h+u76510ZTc23OU5sW9cpc92OfWuBY4PuNvXjzLxekXLu2n7aX5bXKOip2IUTixHuEORZ1wkPktUUtvVDTtJ1RzQyako069eKQlt5r48aNWVlZWV1rxBWDshmTq1Ct2xpMUQfXSXPZkUAPAC4E9NzCkhqiNGYAlpbNmNzq5NHBSoCZqP+mH7XwbItsjz4EuBj4A2rk/iBwsu63q9rzvmm96m8aZGz5epCxRbefHb4ZODqnnFNn/qsufVMvvrKn6/cCL+l+W46tE6ILS0TCXBzDtWjOouXjyA7WNG1UtFFzXV1d+oABA6bX1dWlVVRU5Nx7773JeSpGJyibMbkO1fpvIWpdENi+TjqUhqPRKcG/unMLS6Ktk/4St3VSM1CP6b4PuJp2JEzbo7uAa4AbUC0XT9H99jcdEiOAGSjDdD8N3KD77T+j/h3POWX6xGuytzkLLyup9wzYwlu2Ry9HbZ+aDXyl++3uXbglRDcTtynZ4KkO1UAfy2tVAeQWlmSgThZJa6nqU9O0R1CnlrTkcMdxDqCZKdm//e1vu955552Xrlu37rqUlJTI13fZKdmOlFtY4ib6ftKRqF9wIrfB+MtmTN7a4YGY7l6odcUDMAMLW/ty26PnAE8Ag4Ezdb/d1mKw5pnuocCPwJ6Yge2fYfiM64F9npteeyZq3fMU4FSgFypxzgY+0v22NLUQIsnFew1zOXCA5bWWwvYRzkZgbEu9WTVNuwNVRNESj+M4J9FMwgTIysp66P33379+//333xzxekmYzQj+krMrjRPpOGAd0ddJ17brQ033dCAbM/DH1rzM9uiTUCPKV4HrdL/dudW2pnsGquHC70OXglupfgamWF5r+6jW9ug6KnGeAowG/ocafb7T3iliIUTniHfC/Bi4yfJaH4Su5RaWfAtcWjZj8lfNvVbTtP2AlvrLzgd0x3GupZmE+cwzzwy/8MILb924cWN+VlZW5L8ASZhtEFwnHU30bTD1RGnMACyJaZ3UdI9AnVwzFjOwqaXHbY+uAZcAdwEFut+OXDfvHKa7P7AAOBAzsL0C2/AZBcBky2sdH+1ltkcfDZyMSqB7AG+iRp6v6X478hc6IUSCxHsNcxGqLP+DKNeaTZiO43ypadrTwDlNPQJc7TTxG0BoDTP4j9qVV175UJRkKdoouE4a2opREroenEUYTMNEelzwrznNrJPuGA2ageWY7tdQSbDBUXCRbI8eKv7aGzhE99vzO+hbbJkZ2BBcc72Nhn9OHwWuNXzGIZbX+jjyZbrf/hX4B/AP26MPBk5EtXd8xPboH6GS56u6317T2d+CEKJp8R5hmoDL8lrbq11zC0vuBFLKZkxusStKsGHBg8DvIm5tAf7gOE6omXuXb1zQE+QWlvRlxyg0fL10NKoB+/YkemPqU84lKa/d6dKcseEnhIQLjixfQI1of6f77fhXQpvuPsAvwG8wA9urlQ2f8TvUftnDw4+za47t0fsCx6OmbY9FNUN4CZit++0lHRy5EKIF8U6YF6DWcs4MXcstLAk1Yx/VYFTRDE3TJgC/JdjpB3jJcZzwqStJmF1YcJ10FyLWSZ9PN3d/pvaojS/VH/oNjbfCrHn95Wv+hBrZHaL77Y7oSdw2pvvPwBGYgZNClwyfkYqaVv6T5bXebO1b2h49EzgaNW17AuoXitnIdhUh4ibeCXMwap1xrOW1Noau5xaWvAv8q2zG5GeafHHrSMLshmpvzTmllpRbPNt8NxKRTCeuW5h2yxdPZBYenP/Son4j5rIjmf4a9/2kpjsTVehzBmbg89Blw2ecAVwP7BvrKDMa26OnAoeiRp6noCrNZbuKEJ0sEaeVPIXq9nNv6FpuYclpqL12h3RQU3FJmN2R6U5BFdWchxn4LHT52912H5xeV/vtuyP3/uff9z4rQMNkGirEiWzM8HMHnYzTVKyXAlMxA0eHLhk+w4Vaq7/D8lovdcTHBKehZbuKEHGQiIR5MKoVmcfyWvUAuYUlaagK2CfKZkz+Zwd8jCTM7sp0XwUcihk4A7a3uXsT+EL32422HeUWlvQBxtN4G0wu6uScyIIjf9mMye2vTDXdacH3+wNmYHsvYMNnHIfqXmRYXquu3Z8TQbarCNF5EpEwNVQD9mssr/V26HpuYclY1DmCJ5XNmPxZEy+PlSTM7koV1SwG9sEMlNke/XbgIOAY3W/HnIByC0vSibJOikquG4m+DWZ1q2ZATPc5wFWobSYObP/z/yHwqOW1noz5vdpAtqsI0bHinjABDJ9xCaqX6eGW19o+ZZRbWHIi8ACwX9mMyava8RHno47GaouVwH/a8dmis5nu/wNc9rPDnwDeBvbQ/fbqjnjr3MISF+qXpmjnk6awI3mWAi80O61rul2oXw5vwgz8L3TZ8BmHAT5gvOW14nV02SDUdpVTUeufsl1FiFZKVMJ0oQ7e/cHyWteF38stLLkOuBI4u2zG5EZ71oTAdI8CvvU/P2y2U6ct0f32X+PxsbmFJYNQiXMCaspzT9TywsNlMyZH74dsuk8Cbke1zNtefGT4jDeAVyyv9VAnh91IM9tVXg7uCRVCRJGQhAlg+IyBwNfAHy2v9XL4vdzCkuNQ/T/vAf7eQYVAohupLcx54ZdXhxzv1Gu76H57RSJiyC0sGYc6S/MC4P+Aexr9WTXdGmqp4T7MwPYqcMNn7I1q2beL5bUq4xZ0hCjbVZagkqdsVxEiQsISJoDhMw5A/dA40PJaDRpr5xaWjEYdBZaBalbwdKc09xZdUtkhO/9fSnr9ZSMP2+DGDHR48Uxr5BaWjASeB1YD3rIZkzc1eMB0HwUUAxMwA9tPejF8xuvALMtrPRG3YJvRxHaV2agEKttVRI+X0IQJYPiMK1EH+Z5kea0F4feC60lHB+8fBswCtu+xa/SDSfQIwa0U83Y6dL3TZ8S2WzEDLyY6pmAR0f+hjkc7smzG5IadeEx3KfA0ZuBfoUuGz5gC3GJ5rf3iGWssZLuKEI0lQ8LUgN8DdwCXWV7rhWjP5RaWjEKdiTmRHa3UyolyOgawQqZxuy/box8OPOQ5c8Utmos/YQZaPDQ8XnILSwpRSebQhv1w3QcBTwM7h0bEhs9IQZ1ReoblteYmINyYyXYVIZIgYYYYPmMf1LTWbOB6y2s1e0hxsKn3TkSvZsyg8ZYAP7CobMbk7vSbcS9gL2ADsDn4tQXViL7bsj36LOAj/awVxajWiGdiBr5MUDgNKrLrHYd/f7z4guz0lE3n7D/61QZPfvi3Kxi53zuMOcwfvLLS8BkjgHGW17oofiG3T5TtKm+hkqdsVxHdWtIkTADDZ/QHngSGANOBV8O3ncQqt7BkANEPPx6K+o0+ckQ6v2zG5IQVXrTDeNSouyL4zw6q8fg6YBVqTW0TO5JpQtf6OoLt0fuh+qiO0v12ANN9NbAvZuDsBIXUaM/vqkBV9sy359+5X27/p8/YZ+SOkePcfx3G2gUHcPw9oX2+Iw2f8Tiqjd5Yy2s1eyZsMpLtKqInSaqECdu3nExFrVuOAR5BbfJe2d73zi0syaZx1xcPsDNq/2Vk+zS7pYOtE+wQYF9UcgzRgCwgG8hEJVEteG8jKomuouGoNHGNylvJ9uj7AQ/pfntvAEy3G3Wk2J6YgbY2q2iPqE0yXv1+xe6fLlx39l2nGDe4tOC//qpAGq9f9wD7/eFmRkxaQ7BJhuEz/gN8E94usiuS7Sqiu0u6hBnO8Bm7A5ehRlGfowp+vg1+/dqeBtbhcgtLUoGxNB6ReoBKoq+TLk+CddLTgYFAIMbnM1HJNAtwhV0vZ0ciLQOWd1yIHcv26GcBp+l++4ztF033vUA1ZuD6BIQUNWHW1tdrN7xk/f24icOK8jyDf9l+47271DmZR974NDsS5jnAiZbXOis+IXe+ZrarzAZsqbgVXVFSJ8wQw2e4gaNQ63V7AZNQP/y/jfia35H9OYPrpCNofF6jjhrB+Wm8VrowjuuklwNbgWbXe2OQhvp+3MAa1Gb8pGR79BuAfrrf3pEcTfcY1C9TuZiBDtt6pGlaCmrEdAjql4wfgFmO42wJe6zJNowPf7Bw8vry6lE3Hq/vaE6w6P3RWC9cwUkPXMuOhHkA8M9krJbtCMHtKoewo2hItquILqlLJMxoDJ8xhIYJdC/UGqXFjgT6DTDP8lodPuWYW1iSQ/SCo+E0vU5aEf3d2iQD+CNt75kbTX9Un9bXOvA9O5Tt0R9F/ZB9uMEN0/0C8D5m4IGO+BxN03KBF1F/tsKtB851HCd0pmXUhHnVVVftc//99199/t9eqC6+8qSLs9NT1S9y5esyeKPwYU595HdorlDCHAz8ZHmtgR0RezKT7SqiK+uyCTOa4Eh0D3Yk0L1QDbZ/pmES/d7yWp1SzZdbWJIFjKNxIt0FNe3ZaHq3bMbk9W34qEGAlyamT2tq6zWAtFRXa/4Dj0D1SP2mDfHEhe3R3wXu1v32Ww1umO6DUd2hPO1tZKBpWjbqGC69iUeqgAMdx/mOJhLm+PHjr9qyZUvOqAOOH/nwP/520x4j++3odTv7D8UcOq2QgeP6oBKmhqpuHmF5rVin17uFiO0quahGJrOBt2W7ikg2qYkOoCMFf9h8GPwCwPAZmYDBjgR6NmAYPmMFOxLot8C3ltdqd1VfsNr2++DXdsF10jHsSKAHA5cAntzCkm1EOWYKWNrMOunhqB8wtagprnLCtpNEJsqydeVZVz/33SmbKmoGHOkZ/Mm1x47/NjMtJdp7b2rVNxx/Y1FFPpE+RRU1TQFeaednXETTyRLUcsBfUdWhjZSVlWUsW7Zs/OOPP377H66aNmPpxr8ObpAwU7PWsH7RIAaOqwKwvJZj+IzFqD8f37Uz9i4l2H7vTuDOsO0q1wD/sT26bFcRSaVbjTBjZfiMVFS17F4RXxWEJVA6uLgomuA66TCiT+/2BuYTkUzvOHni0vMOGD0dtYF8KzsS5WZg/ds/rer16vcrdu2Vnrr8mN2G/pjnGbzu0LtLrxkzqNcCd1b6lrmLNxx0+8kTH/3NhCGRvyDshFq/XNdZ32972B49DfX99tH9duNTPkz3WUA+ZuCI9nyOpmklqLXL5lQDWY7jXEPECPOiiy465Kuvvprwww8/PDJszPjis64oLL132oXPbX/gzZuuYMDO37HPRb8SPErO8BmvAE9YXmt2e2LvLmS7ikhG3WqEGavg3s4fg1//he0dh3LZkTwvJlhcZPiMTisuCo4gVwS/3g2/l1tY0o+GFbsXAfp97yzYqV9WWmVWesqm1BTXOndW6sYBvTI2DOqTkbFyU+Uemytrxp93wOg1KzdW7vLruq2H/Lw668PBfTJHPnnR/v8Cyk+4/+PRb8xbOT5KwoTYK24ToS9QGTVZKi8C/4fpnoQZaM+08oAYnkkH+kS78d577x107rnnvg4w8cC8JZ+8PtvDtAt3PJCasZXq8t4RL1uPqngWgO631wKPAY9FbFf5m+3Rvye47inbVUQ89cgRZmskurgomsXrynfvm5k6c9XmKmdDebV7S2VtTnm1+lqxqbJuQ3lNym7D+y7NSHVtXbNlW86QvplbHIfMc/Yf9cm2mrqUzxetH/r1ko3uq38zfhZqxBaazs1CNQlPSsGCka3AsCan6Ez3dYCBGTi/rZ+jadpTwDktPLbOcZxBRKxhfvfdd7332WefoszMzICmaTipGX2pr6sObFj3+5SUFPXQ69dfw7A93mfPc1azY4T5DnCP5bXeavxRIkS2q4hE6pEjzNawvNZq4I3gF9CouOgQ1Pmduxg+Iy7FRWMG9hoCVAzondGgwfe6rdsyZ81devCAXhmu8urarOUba0bV1TvpazZvG+7OSqud/e2yIzNTUzatDFRmDeiVrgUqqw/onZFakeLavt75auNPSx6633Zsjx5a6/u+icceBRZiukdgBtq6n/Q/tJwwox4yPn369P0Nw/jw22+/fQzgxtnWX31/PDHtnnvuGX/DDTfMB6CmYhDunSJH92NRFcqiGcFCoDnAnIjtKm8C5bZHD1XczpXkKTqaJMw2SILiol1RlZoN9M9Or6qorss4cGz/eYfsOmh7Mi1+/5fDe2emVU0c7l6+bGPF4I0VNYPSUl2pJT+s+k1tXV16dkbqxp0H9q6e9dWSimfnLqtFrZX+UjZjcnv3d3aGRajkEj1hmoGNmO6ngALgxrZ8gOM4b2ia9gTwuyYemQ/cGu3Gxx9/fND555+/veioqqZu8N777vvG888/f/ANN9wwH6ceaqoGM2j8WoJTsME19RGoln8iRsEtKO8D79se/Y/s2K7yJNDL9ugvo0afsl1FdAhJmB3E8lpVqM3z23uHRikuuh7Yy/AZ7SkuCv1wbZTMXC4NY3ifnz9ftH635Zuq+vfJTK2cNCpnaWVNfeagPq6Nuw7ps6xsfXlmdnrKqsN2HfTjbiPc6yqqa9PWb9k2aFtd/ciFa8tBJQkdGJlbWFJGlOrdBJ9Lugg1wmzOP4DPMN13YAbauvf1YtR+2mlAv+C1WtQBAVdENC/Ybvny5beH/v7zRetHalD/8guzXt7eHm/DYjeaq5reQ8J7F48EVlleq6m1WdGC4Ggy9P/fjWHbVf4PyLU9umxXEe0mCbMTxVhcdEnwr5HFRd8AC6IUF/VDrTVqRJGnD1mYlppS/+v6iiFLN1QMX1T+afavrrmVP5aN3u2HVUZuBgNch+468Ft9eN91ANnpqTXZA1K3AN89n3/Qzai9ouQWlmSiRrKhgqPjUcljXG5hyTqib4NZG4d2gYtQ+1ybZgZ+wXR/AlxAG9dkHcepB+7QNG0m6ki5LMB2HGdtrO/x4YK1R+cO7PXu9mQJsOLb0aRnR/ZFbmqrjGgj2a4iOoMkzDgLjiIXB79eCl0PKy6ahPqf+zZgqOEzGhQXlZxSUjmq76g+qG0NDSzYuKDXO7++s9Pw3sPLT933iC/7ZfSr/bf1yS5UVvUqT/98y9zKd/tMHVvww6Hj9MipvzRUleb2Hx5lMyZXoQqbrPAHcwtLUlDbWULVu/uhGijoQH1uYUm0vrtLymZMrm/Lv68oFgO/jeG5e4FHMN2PYAba/NmO41QSNmsQq3Vbt2WuDFQddNkRO1/X4Mbyr/MYOP6jiMfHIwmz0wQraf8B/CNsu8r5wCO2R5ftKiJmkjCTRDPFRXuiEukhwJU3f3LzOO9u3opULXVNdlr26oFZA9cO6TVk3frK9SkPfPvApAFZA8q/WPnF6BcXvLjb/Ufd/+55E85bmJ6S/gvAxW9efPSq2i9TausPI9WVEv7xGur0khaPOCubMbkO9cN9EVASuh7cTzqYhvtIjwv+NSe3sCR8P2mo/+7PDQ5Zjs0i1OkyLfkQVf17XHic8fLi18uOyslO/3HckD4bt19c81N/KjZM4NBpD0U8fh7BalnRuWS7imgPSZhJLFhc9EHwC4Ct1Vt/V1lbee6yrctSNm3bNHDF1hXjyleUD/hi5Re1X63+Ku08/bz5Bww7YOkLC17Y6Y3Fb4w4y3PWr1W1Va7M1Mz67LTsbYDjUK9Bgy4/6bSz4CQ4Fbs6+PV++L3cwpK+NGxef37wn0fnFpb8SvR10qhrhKg2h/1sjz5O99sLmgzIDDiY7r8DfybOCfPDBWvHzF+95YTzDxhtNrhhz8nDvdMn9Bq4ffuR4TP2Qq1hzolnjAKC07HPAs9GbFe5yfbosl1FNCL7MLsWF2odcSI7ftmp21S1qf6WT285YFvttuxapzZrVfmqPjX1Nel7Dtqz+shRR65wp7vXfrri07TXy14f8/ixj78wqu+o8KKHVFTT9SLCqn7jIbewJAPVYzeyw9E41Ii3wdmkwa81r798zV1Ahu63r272A0x3OmoK93jMQFPbUNrrfFSnJgA2lldnPfHp4qv2Ht2/5LBxg+Ztf6qu2sUH99yAccajDBofmvpbafiMQ4Ellte6o5PiE63UzOkqsl2lh5OE2bX0Ay5FHcGVHfzqW+/Uu/Pfzp+8/7D911xsXLwY9d+1ftO2TekLNi4Y4PvRN2FV+ap+h4w4hBG9R7h6pfZa6850r+2X0W/tsF7Dyvtn9t/scrnuRiWkhMstLHGh1kkjj1TTAW2nLWsW/fP9+yZeeMyNZiCj9w+ouH+Nuk5quv8InAEciRno1G0yuYUlfVDdhuaVzZjcMJmb7tuBSZiByaFLhs/oh0rouuW1ViGSThOnq7yMbFfpkSRhdi1jgdOAZZE3bvv0tgO/WfPNvgcPP3jeLjm7lB8w7ICtP6z9Ycxri1/bc2DWwNrf7/77+UN7Da3ZUr0lY0Plhj5rK9f221C1ob+maUNWla/KfMp+ylq+dfkXJKBzUayC66SDAP3Rt2fc//GI3Vf7JhzvoBLpAGABEdO7k7QFv7yUYb4AzMcMTOvE2CaikuUHQEGDPaym+zjgEWBvzMD2whLDZ1wFHGh5rbM7Ky7RseR0lZ5NEmbXsj9qqmhF5I1NVZtSp385/VD/Br++sWrjiLP1s596Yt4Tv6+ur+5z1KijZh035riFBw8/eGtmamYmkIM6LLo34N5avdU+77XzShcGFnrYsd1lV1QCCm+60GnHorWW7dGPA+4A9tH9thMc3Y2n8Yg0tz+bl72Zcf2wx2t/++aDdSe9wo510nZ/L8Gq4QtQRTvTymZMfrLBA6Z7NPAFcDpm4OPQZcNnZKOaL1xsea24ToWLjhG2XeUUVHHeW6jkWSLbVbonSZhdy+mo7jAtNkivq6/jGf8zOy/YuGCovcH2rK9cP8IYaHwx/dDpb2enZYemLjVUxemTRBT9GD4jC7VWGn6ai4FK1g2aLnTEsWitZXt0F/ALcJbut79s6rncwpJ0YJeLUl6bck3q87ecXX1T6ffOLiNQyTVAlPNJgdUt7SfNLSwZjGqGn48qdLqkbMbkBltwMN0ZqFM2nsUM/D10ObgX99+oYqvzOvM0HBEfUU5X+Rg1bSvbVboRSZhdh4bqWbsB6JCTUlAFP/2A+2N5OErnokmo36zLadi16BtUIUun/uGyPfq1wETdb3tjeoHpvgQwgbNyq57+FFWdGu1YtRRU+7uFqDXGRahWhGOCXzujvu+XgIfKZkz+KspnDUNVYK4EzsYMbP93YfiMi4Grgf0tr5XIrkmiE0RsVzkWNZMg21W6AUmYXUcf1Gim0fplO7hRBUQvtfRgUyI6F4VOc9kLdchyLJ2L2sz26AOAecDZut9+P6YXme7jUWd+3g3cG57IQnILSwahfjEYg1o3HoMqsFrEjgT6VdmMyRsjXxv8jCOAp4CHgTvCGycYPmNP4G3gMMtrJUWRleg8TZyuMhv1/5xsV+liJGF2HX1RWxiyg/9cjxrZlaN6nLbFcNSUYZNTmm1l+IyhND6gO/xYtNC0bruKi2yP/hvgCdRaZmTLuehMdy6qJ+wS4CLMQMecAWq6XcC1wJ+ACzADb4ffDlbFfgX8xfJaz3bIZ4ouI8p2lQp27PWU7SpdgCTMrsWFSpw5qGrRkahG7BnB+w7qf8JyorTOi2IEqrIzLsdKRXQu6rDiItuj3wIcBRwVc5m/Wl+8F/WD6xHgUcxA20bvprsX6nSaAlS3pKmYgaXhjxg+ozfwHLDQ8lpXtulzRLch21W6JkmYXZ+G+p8tB1UQtFPwqw8qgYJafyunceu7nVDnR26KR6DRRCkumhT855iLi4IFQK8B3+t++/pWBWC6J6Kmus8B3gMeBEqjTdVGee144DLUyP9j4CHgrcjetYbP0IEXUCP5P8ipJCJSlO0q/0MlT9mukkQkYXZfWagkmsOOJNoflUQ11PFgGnAfOxJrUogoLgqti+5JM8VFtkcfCHwNXKX77VeivW+zTHcf4FzgctSaZWitMvTXSnasaYbWNR3gX8AjmIGoxRyGzzgLVVR1veW1/t3quESPE7FdZS/U4diyXSUJSMLsWdLZkUSHA1tow0kciRBLcdFxc+s3X/Bu/R8rMzhg3+/sn9v8YabbTePkGCr6CX0tBlY3NRI1fEY6MBPV/P10y2t91+Z4RI8l21WSiyRM0aVFFhed8Hn9EYdb9f1vOT/lq4pM7Ws6qLioFfGkAlOAv6Aqmn9nea1Nnf25ovuL2K7yW+A7ZLtKXEnCFN2K7dG1ban8uyKDXQsuT3mxNlXbk4bFReHrot9ZXqupU1FaJZi4LwH+gKq+LQKekaYEojPIdpXEkIQpuh3bo6ej1n2+DBUBNVNctJyG66LzgWWW12q2SjE4RTwY1f3oEtQG9eeAh2T6VcSTbFeJH0mYolsKNjX4Arhd99u+aM9EKS7aE3Xc2BDUdGp40U94p5/QumYF6nzOZ4Ang+eXCpEwsl2lc0nCFN1WsFT/A+AU3W9/EuvrDJ+RgTpeLFqnn1ASXZwsjeiFaIpsV+lYkjBFt2Z79N+iWuEdqPvtsgSHI0TCyHaV9pOEKbo926P/EbgYOFj32x1S5CNEVybbVdpGEqbo9oLrOg+j1iZP1f12hzWAF6Kri3K6yg8Ei4Zku0pDkjBFjxCtclYI0VDYdpVTUCNQ2a4SRhKm6DFiqZwVQiiyXaUxSZiiR2lr5awQPVkz21VmAx/2lO0qkjBFjyOVs0K0T0/driIJU/RIUjkrRMfoSdtVJGGKHik4xVQMDENNz0rlrBDt1MR2ldnAK91hu4okTNFj2R49DXgLqZwVosN1x+0qkjBFjyaVs0J0vu6yXUUSpujxpHJWiPiJsl1lM2p55Endbyf1AQaSMIVAKmeFSIRgLcHhwGXAMcAs4EHdb/+Q0MCaIAlTiCCpnBUicWyPPhx1tuzvUcskF+t+e1NCg4ogCVOIIKmcFSLxbI+eAcwEfgucrvvt7xIb0Q6SMIUII5WzQnSMmVOnaKgD2Xdmx9myGew4lH0RMH/arDnV0V5ve/SzgX8ChbrffiwuQbdAEqYQEcIqZ+/Q/fYTCQ5HiC5l5tQpbuAC1Lpkb2A+OxJkNaoz0FhUIu0P/At4ZNqsOUsi3ytYkPci8Jrut6+JR/zNkYQpRBRSOStE68ycOiUbuAvworr9PAh8NG3WnCaTzMypUzyoxHoe6v+3q6bNmrMs/Bnbo7uBr4Bbdb/9dCeFHxNJmEI0QSpnhYjNzKlTxgEvABZwzbRZc1a28vW9gD8DVwDnT5s15+3w+7ZH3wN4Bzhc99s/dUzUrScJU4hm2B79KuBS4CCpnBWisZlTp5yKKpa7BXi4uRFlDO91BPB08P1uD38v26P/Drge2Ff321vbE3NbuRLxoUJ0IfcDnwJP2R49JdHBCJFMZk6dciRq6vX4abPmFLcnWQJMmzXnfWBv4CSgwZplsJ7gE+CB9nxGe0jCFKIZwZZdVwB9UOszQghg5tQpw4GnUFOoX3XU+wanc08Bps2cOuWwiNt/Ak6wPfrIjvq81pApWSFiIJWzQuwwc+qUNKAUeHvarDl/7YC3PB+1/3m7pT/NG7fE+vb0PY6Z/M/eOf23T8FunPXciVp6WlW/U055C1gJ/KcDPj8mMsIUIga6314PnADcY3v0QxIdjxAJdj5QB9zRQe83DFga/jVywsR3NZfrw29ee+WQ8OsZu+4yu+KLL/aur6hYQUSS7WySMIWIke63bdT+sudtj56b4HCESIhgQ4IC4O5ps+bUd+Zn6Ycc+drWjRv227JhfXboWvakSStc2dnLN5e8tm9nfnY0kjCFaAXdb78BTAf+Z3v0PomOR4gE2A/oh9prGRNN01yapuVqmjZO07T0WF+XM2x4INvd7/v5n37YYC0zc8KEd6v8/iNifZ+OkhrvDxSiG7gf2A142vboJ0vPWdHD5AMPxTq61DTtcuAGYKfgpc2apj0K/MVxnKqmXudyuZ7KyclZkupyZWSlpe6r7/FI+uuvv/4qQOaE3X6p+Prr89r5fbSajDCFaKWwytneqNGmED3J/sQ4utQ07R9AETuSJUBfYBrwTnOjzZSUlOr169ffsGr16qsfvGFa3XNP/fed0L30ncducGpq+9Zv2xbXrV6SMIVoA91v1wCnA6cGN1QL0e0F1y9zUX1hm6Vp2lHAVc08cjBwXYvv43KRmp6+ZsPypYNC11wZGfVaevqGmpUrc1qOuuNIwhSijaRyVvRAQ4Et02bNKY/h2Yvb80xdXV36gAEDpg8YMGD6JeZdg2b+4x8N/h9zZWWurl2zpn8Mn9FhZA1TiHbQ/bZte/RQ5az0nBXd3VjU8VyxGB/DM7mapmVG6wcQmpIF+GTWf85Pz8reFH7flZ29tm7jprgmTBlhCtFOUjkrepCdUPshY7E5hmeqgG0tPeRKTausq63NCL+mpaVVOrU1MVfcdgRJmEJ0jFDP2ael56zoxkYAy2N89p2WH6HUiaHdnMul1TlOfUrExTrqnbjmMJmSFaID6H7bsT36FcBbqNFmi8UMQnRBw4EVMT77EGoLyk5N3K8BzKZeHFrDBMhMTXXvOcGztuS0s5/f/oDmqnecekmYQnRFut+usT366cDntkf/SXrOim5oBPBtLA86jrNB07QpwMuoytpwW4ELHceZ29Tr6+vrzw39/ZevvDClZluVO/y+5tLqqK+XbSVCdFXBytkTkcpZ0T21ZoSJ4zjfAxOBPwBPArOAG4FxjuO8EOv7aC5XneNETL+6XHXUx3eEKQlTiA4W7Dl7PtJzVnQ/rVnDBMBxnHLHcR5xHMfrOM5ZjuNMdxxnZWvew+Vy1eE4jdYwGyXRTiYJU4hOoPvtN5HKWdGNBJsWtGqE2VE0zVXn1DdMmJrLVU/Etc4mCVOIziOVs6I76QfUTJs1Z2tLD3Y0zaU1LvBxuepw6rV4xiEJU4hOIj1nRTeTkNElbF/DbFik6nLVOVL0I0T3EdZz9hTpOSu6uFavX7bCSmBkU199Bw7u12fAwN7h19KGD3fXrl7T5GknnUG2lQjRyXS/vd726CcCH9ge/Rfdb3+c6JiEaIPhdF7C/E9zN2fffdtU4NRJx514T+ja8iuvugg4ZPAfm+vv3rFkhClEHEjlrOgGRpCgKVmglsYDvGjXOpUkTCHiRCpnRRfXmSPMltQCaTFc61SSMIWIr/uBT5DKWdH1JHKEWUPj0WS0a51KEqYQcRSsnL0S6IVUzoquJdEjTJmSFaKnCVbOnoFUzoquRdYw4/lhQggl2HP2BKTnrOgCZk6dkgIMAlYlKARZwxSiJ9P9th84D6mcFclvCLBh2qw5NQn6fFnDFKKn0/126PxMqZwVySyR65cgU7JCiCCpnBXJLpHrlyAJUwgBjSpnZyQ4HCGiSYYRpqxhCiEaVM6eLJWzIgkleoQpa5hCiB2kclYksWQYYcqUrBBih4jK2TGJjkeIoESPMCVhCiEaC1bO3gW8KpWzIkkkwwhT1jCFEFE9gFTOiuQhI0wkYQqRlKRyViSLmVOnZAHZwPoEhiFFP0KIpkVUzl6Y6HhEjzUcWDlt1hwngTHICFMI0bywytm7bY9+aKLjET1SotcvQdYwhRCxCKucfU4qZ0UH6wPsA3iA0cDg4LXwdfNEr19Ckoww4/phQoi20f32W7ZHD1XOHqz77c2Jjkl0C6OBE4EtQPiUqwuoBAIHnnHOEZvXrAbYA6gIXq8M/n1VxOs6i6xhCiFaRSpnRUdzAxuAZahp19DXUmAjkJaelT1ypwlGBnAMcApwFnAhcAUwDcgHzgYGdlaQ02bNqQeYOXVKeM6SNUwhRHRhlbPZSOWs6Bg5QHUT9+qAiq3r12XX19YuQSXVpexIqqEkWwHkAv06OdbIBCkJUwjRNKmcFR3MTdMJE4Da6ur+WX37bmzmkRpU8qrsyMCihULDIp9aINX26Fonf+52kjCF6GKkclZ0oL60kDDraqpzeucMaC5hglrHrOiwqKJrsGYZnHGpo2GBUqeShClEF9SOytkUIG6/kYuk5kI1xqhp6gGnvp7a2tr+/YYOaylhasRnhJnQSllJmEJ0URE9Z/vG8JIRQAFwYKcGJrqKTJr55am+vo6tGzf00jStJqtP323NvI8G1APNPdMRJGEKIdol1srZ3VAj0mrgcGB8HGITyS2biC0htdXV2xOoy5XC0p/mDa+pqtqyftnS7GbeJ53G21I6Q8KbF0jCFKILC6uczSJ65awLOAy1124lEAj+9URgaJzCFMkpK/LC41dfdk1V+dYUgO/eKsmd+8rzJ61b+mvaW4/cf+K6pb/2auJ90lF/rjpbwvdiSsIUootrpnI2EzgJOAj4lR1rVdtQP+BORxV9iJ4pi7Ap2cDa1RnVFeUDMnv1rttWUZEy99UXz0zPzq4dNDp3aW31tqzXHph5VhPvkw5sikO8MiUrhGg/3W9voGHlbD/UZvKxqGQZOV22BfX//0moH3ii52kwzbr218VuzeWqA5j/2UcjarZt6zN2r33LBo3KLTv+imtmbVm/bucm3kcSphCiawlVzqYOHfpCddmvfwZ603wP0LXAEOBY5GdBT9SfsEKd8o0bs2qrt/V9/Or8gs9ffObczF691lZXVeakZ2VvXPTNlyPS0tO3NPE+KahfwDpbwtcwpZesEN2I7reXb37jzc82Pv30JQMuveTm1EGDWnrJclRB0Hrg004PUCSTBk0Ldjv8qCXZfd13VGwOZK4pWzhs89o1g8s3bJjQf/gIa8UC/4Csvu5VzbxXZ28pAVnDFEJ0kO3FPX2O+c1LWmaGve6RR65wampi2XO5DKmc7YkaJMzU9HRn1/0PWu0ePGRrdVVl5qZVK3fetHrl0Ky+/Tb2GzJs/QGnTH2tmffq7KYFIFOyQogO0KC4R3O5agZefvkT1NWnr3uo+OzQQ05NjVZpzeuzbfHiLID6qqpQMq1jR+XssDjHLhKnQZef0JaSz1545tiqLVv6ouHU1lT37jtw0Eb74/eP+PHDdyc18T4O8RlhJjxhypSsEF1bP9QJEv1RxT0AuDIz6wZccvF9ax8oun39f/+7vHbZ8i3ln366r5aaWl23ZUvO0Ftv8fU+5JD1dVu3pqT07l3HjsrZ04AnATk+rHtLQ/2iVRd5Y+vGDSOOPeuP//rshacznHpnH/eQoYG62pr03v0HrG/iveLR5QeSYA1TRphCdF0jAC9NFPekDR9e3u+M0/9W/v4H55V/9tnh1UuXGr0OOugb90knvrny5lsuderqSOndu66+oiL0cyC8cjYjbt+FSIQsVHee7VLT0x2AIWN3/m7uKy8curZs8YFA5YJPPxpWU1XVd8CIkWujvE8KapTaZHu9DiRrmEKINhmG6txTAaxr6qGs3XdfUf3rr0u11JSJOWed9dSml18+2ZWZWZPSu/emre9/MLhu8+aUsrPPuXjV7XfsG3yJVM72DI2aFoQcdu6FH6VlZlWmZWZtrQhsqvvwqcd/n7vn3u8beceWRXk8nfjNRsiUrBCiTSqJ4bT7uk2b0rSMjLVZ++77Y/2WLadkT5pUuu6h4sszxo37ss9ReWsA+hx7zMcbfE96M8aNW5Mz9cxfUZWzE1CJWCpnu6cmE2a/IcOqTrz6hre/eOX5qrVliw8/8oJL7u6V07+pEWQ6sKZzQmwk4QlTfoMUomvaBLwIDKCZNZzUAQNq0kYMX7rl9Tc8Wu/eC1KHDDbcp5zy395HHvl17caNqeVffJEz6PLL7T5HHP7ahscfP6lu69ZQP1qpnO3eslB/bprMAalp6ZnuQUNWNpMsIX5NC0DWMIUQ7bAMKEGtZTa5fWTUww/PScnJWV01b96W+q3lWSl9+44f+PtL569/+JHdlxVc8Zct7703qPrXJSNThwxZHiwAAqmc7e42A6uAQcBOqD9Doa+RwFBXSurw3v1ztqKSYlN/vuKZMBO+hilTskJ0bfNQo8wDgCVNPTT25dn/2vjss6O3vFtajaYduuHpp5cNKbz+gy3vvnvE6ukzznNlZW3tfcABPwA4dXVoKSnQsHL2caC8078bES/LUf9NQSW9rLCvbKDvmsULjxs8eswqVDXtIFTSDC0BhP6+F/H7c5HwKVlJmEJ0fR+jtpWMpZlWeL2PPHLl2vv+sV+vgw9+t+r7H85av21bhZaaWpNz7rlz+h7322WpAwbUAKFkGRLPH4giMaqDXw1OHJn33lsO8PJex53wBipBZhIlsaKSbzwkPGHKlKwQXV8d8DpqamxAUw+lDRlSPeTGGx7Y+tFHh29btOinTc+/cLkrK6s6a68914WSpVO3fVueBoxCTcs+hSTNnmgEO34BCzUn2IBKkL8AP6B+Wdsap3gSvoYpI0whuocq4CXgApoZFbpPPHF57bp1j1Z89fUu9X37OFpKqp42eHBt6H5wdJmK+mH5NfAe6oeS6HmGE7/RYywSvoYpI0whuo+NxFA5O+Cii34e+WDR66MeffSBTI/nh3UPP3JlWM/ZLFSyfAt4B0mWPdLMqVNCU64bEh1LGJmSFUJ0qJgqZwFwHAZefpmP+vq0dQ8Vn4Nqs9cPeBb4hhb2eIpubTiwYtqsOcn0Z0ASphCiw81DNRwY2dxDWkrK9p6zTl3tfpteeeUAVB/ZsjjEKJJb+PplspCEKYToFB8DC1AjheZoacOHD+h9+OHTV9321+Nsjz4hDrGJ5Jds65eg1isjlxqiXes0kjCF6J5iqZxNBUYD32RPmnS/U1FxHjDL9uhj4hOiSGIywoxCEqYQ3VcVqggoBVU5Gy5U3PMmweIe3W+/BdwJ/M/26H3jGahIOsk4wpSEKYToVJtoXDnbj6aLe4qAj4BnbI/eoIOB6FFkhBmFJEwhur/wytlhqOnaqMU9ut92gKtQXV3ujl+IIskk6whTmq8LITrdPNTIcSmqc0+TZ2jqfrsGOAM4yfboF8UnPJFkknGEKY0LhBBx8zHwPDG0udP99gbgBGCG7dEP6+zARPKYOXWKRnAfZqJjiSBTskKI5KT7bT9wHvCc7dHHJjoeETc5wLZps+YkW/9gSZhCiOQVrJy9A6mc7Uk6ZP2yKL9UK8ov7VWUX9p8x6nYJXwNU5qvCyFaUgTshqqcPVH323UtvUB0aW1avyzKLx0D/A4wgDGo4+YygJqi/NJFwCLgR8BXUJz3cxviSvgapiRMIUSzdL/t2B79KuANVOXsNQkOSXSumEeYRfmlKcBvgcuB/VHV108Di4FFBcV5G4vyS/uxI4HuD3xclF/6HfAQMKegOC/WBv8Jn5KVhCmEaJHut2tsj34G8IXt0W3dbz+W6JhEp4lphFmUX3oM8DfUKO8B4PSC4rzKyOcKivM2Ad8Gv14syi+9GVWFfQNwXVF+6ZkFxXnLYogr4QlT1jCFEDEJq5ydLpWz3VqzI8yi/NLdi/JL30RN1ZvAPgXFeY9HS5bRFBTnbSsozvsvcCDwKjC3KL/0NzG8NOFrmJIwhRAxC1bOnovqOSuVs91T1BFmUX7piKL80seAt4E5wG4FxXkvFRTntekIsILivPqC4rwZwNnAE0X5pZe38JKEr2FKwhRCtIrut99Ges52Zw1GmEX5pX2K8kv/CvwArAXGFxTn3V9QnFfdER9WUJz3PnAwcGtRfumhzTya8ClZWcMUQrSFVM52XyOAFUX5panARahp11JgUkFx3q8d9Bnno9o0AlBQnMfyBRvnLPNv+F95YNs/e7kztka+4Iyb7/Qs+mbu6IjLsoYphEhuET1n70lwOKKDzJw6JRUYmNHvir1QI8qzgRMLivPO68BkCSpZLg3/GjEu5x1Xiqv069fLTo68ByytCGxa3atfTnbE+8gaphAi+YX1nD3B9ugXJzoe0X6p2ccehZbpaFr6PcD1QF5Bcd5X8fr8SceOfqFmW/3QJT+ub7Q+7kpJqXMcJzJnyT5MIUTXoPvtDbZHPxH40PboP+t++8NExyRaryi/dCRwpysl53hwrQKMVuyP7DApqS4nZ2j2O8vmb/zNqN0GPBx+T3Ol1OM4kUfOyZSsEKLrCKuclZ6zXUxRfqm7KL90OvAd8Gv11v9dgVPxfXuTpaZpu2uadrumaY9rmnaXpml7x/ranScNfr88sG2fzesre4dfd7lcdY7TKGdJwhRCdC3BylnpOdtFFOWXphXllxYAC4ChwB4FxXk345T3px19ZDVNS9E07Z/A98BfUK3ybgC+0jTtX5qmNbne6HK5nhowYMD0sZ6dbrz4phO0N158/4gG752SUuc49dESpvSSFUJ0OVI5m+SCjdBPRBVqLQGOLSjO+y7skfaeg2kCVzZx72JgC/DnaDdTUlKq169ffwPA16+XnVxX5/SOuF8HiV3DlBGmEKJDSOVscivKL90P+AC1h/aPwDERyRLacVKJpmkDgWtbeOxKTdNGtPRemb3T1lRX1g4Ov+ZKSal36knoGqaMMIUQHSas5+zntkf/SXrOJl5RfmkucBdwOHAr8EQza5TtGWEegTqdpDkpwNHRbtTV1aUPGDBgOsCQgcMqHrzlhQbvpaWk1EHUKVlJmEKIrilYOXsCUjmbUEX5pTnAjajmA/8Efl9QnNeoIUCE3kBbD47Oac9z4VOyqxYFhv348Yrrw+/X19amgqs+4mXpQNym/iVhCiE6nO6359se/TxU5exBut9elOiYeoqi/NJ04DLgJuAVYGJBcd7KGF/+KzCqjR8d6xmXvwATmntg64aqgalprrXh17asXzcoLTNjQ8SjuUBZrAG2lyRMIUSn0P3227ZHD1XOHqj77c2Jjqk7Cxb0nAbMQFW/5hUU581r5dssRp1b2RYfBl8/pplnVgJv0ULCLN9cPTgtI2VN+LWKzYHBGVnZkQlzLOpg6riQhCmE6ExFqB+OUjnbiYrySw8EZgLZQH5Bcd47bXyrRahG6K3mOE69pmnnA+8SfS2zBvA6jtNi0/ZtFbWDMrJTGyTMbeVbB/cdOChawvykLfG2hVTJCiE6TbBy9o+oH6BSOdvBivJLdy7KL30eeA54GNi7HckSVMJsc/MJx3E+QSXczyNufQMc6TjO2029tqam5kIAp96hfNM2o+/ArAYjx+rKysGZffpGJswxxHGEKQlTCNGpgj1nz0R6znaYovzSAUX5pfcCXwDfoo7c8hUU57V3BD8fmDhz6pSWql2b5DjO147jHIhaCz0IyHUcZ+9gMm3Rkp827Fxf7/Taea/B26eTa2uqXdsqKnL7Dhi0JuLxsahp4LiQhCmE6HS6394AnADcZXv0wxIdT1dVlF+aWZRfeg3gR43adysozruroDivoiPef9qsOctRo8HT2/tejuMsdRznM8dxWnXKyfL5G4/JGZr9titF234w9fxPP5qUmp6+NmfY8PWha7ZH7wMMRp1mEheyhimEiAupnG27ovxSFzAVtZ/yB+DQguI8fyd93IPANOCpTnr/Jq1dumVg+eZte088YsR/wq+vWvjz0YNzx0ZO554DvKb77bg1iZcRphAibqTnbOsV5ZcehloTnAZcWFCcd1InJkuA/wGjZ06dskcnfkYj1VW1qT9+uPxPA0f0nt13QNb2/aIrf54/tLqyItdz8OFfhK7ZHl0DClDJPW4kYQoh4q0I1aLtGdujR7Y6E0FF+aXji/JLZwP/Ae4D9isoznu/sz932qw5tcAjwOWd+DErgZHhXwu+XJ3fb0h2xZ6/GfVD+PV1S8pO2WnCxG8ysrOHBl8HqrAoAyjtxBgbkSlZIURc6X7bsT36H4HXUZWz0xIcUlIpyi8dhGphdxbq38/ZBcV5VXEO4xHgh5lTpzwxbdaczzrh/bdPuQb3j16JWo/cZ8+jRwVC92ZOnbIXqj/tPsaRx4SvhV4OPKT77cjOP51Kcxyn5aeEEKKD2R69P2qq8W7pOQtF+aVZqC0416DWD28vKM5bl6h4Zk6dcgJqNmDvabPmrG3p+bYoyi/tBTwE7AWcUlCc90vY5/cDvgZunDZrzqzQddujD0FV847R/fbGzoirKZIwhRAJY3v08agOMWf01J6zwYKec1GniMwFCguK82JtM9epZk6dMh3YGzhu2qw5Hdp0oii/dDdgFiopXhZe6Ttz6hQNmA0smTZrzlXhr7M9+gxgoO63L+nIeGIhU7JCiITp6ZWzRfmlecDfgGrU1GvcutbE6GbgbeAW1DRxuxXllw5GnZt5Bupw6ccKivMiR27Xog62PjP8ou3RjwUuQCXxuJMRphAi4WyPXoBal+oRPWeL8ksnoNYnJwCFwPNRkkZSmDl1ylDgM+AF1PRoTVvepyi/NBt1ePSfUWuYdxQU560Pf2bm1CkpqGT6O+DgabPmLAndsz36SNQI/Czdb7/flhjaSxKmECIp2B79QWA00G17zhbllw5FJYRTgenAgwXFedsSGlQMZk6dMgCV5PoAU6fNmhPzmZlF+aUpwHmo7USfATcUFOctjPIZg4GnUbs3zp42a87q0D3bo6ejpu5n63777vZ8L+0hCVMIkRRsj56Gqpz9Xvfb3apyNljccjXwJ+AJ4M6C4rzIvqhJbebUKS7U+ZqXA+dNmzWnxS0dRfmlR6OmnCuAawqK8z5t4r0PBp4FngRuiVwvtT36P1B9Y0+Od2VsOEmYQoik0d0qZ4OjKy/wV+Bj4MaC4rwuvU47c+qUo1Gjza9RjQPemDZrToMkVpRfOhE15TwOuB54KXLKOZiAj0Il4IOBi6bNmjMn/Jlgg4LLUJXDe8e7KjaSJEwhRFIJq5w9U/fbHyQ6nrYqyi89BjW6CqBGV1+08JIuY+bUKdmoVn0FQH/U1pDHM3OuTkP9cnAiqo3fQwXFedURr81B/RJxGVCF2rry9LRZc7aGP2d79Ozgvf2BU3S/Pb9Tv6kYSMIUQiQd26P/BjWK6XKVs0X5pbujRlc7A9cBLydrQQ+qElVjRwedVps5dcq+kHIVOGeiZaRpWvpKp37rJ1A3H1gBDENNp44N/rUf8BJqdPrZtFlzGv27sT36rqgio3nAH3S/vTXymUSQhCmESEpdrXK2KL90OHA7MAVV4PJw5OgqibhR06C7o0Z5j6DWGVslOOV8IXCb49R/UFc1977aqk/S2ZEcR6CS8SLUMVyLgBWRU7jhbI9+Kupsz1uA4uCZqklBEqYQImkFK2dzgROStXK2KL+0D2rfYAHwKDC9oDgv0PyrEiYLtYfxINTezzWoEeBc1DR4TILt7H6LGklvQE05z21PYLZHHwvchFrXPEP32+16v84gjQuEEMks1HP2/1BVpkmjKL80FbgItU2kFJhUUJzXqrMf4ygV2A04Mvj3K4DQLyCrUeuEFtBiUU1RfumeqP8eI1EFPa+2Z8rZ9ugTUXtRjwOKgUnB81OTjowwhRBJLdkqZ4Ojq+NRo6s1wLUFxXlfJTaqJmmo6dGjUWuHq4Fo+z6HAAtRR3tFVZRfuhNqyvk4VGHPowXFeW1qYgBge/T9UZ1+DkCdxvKQ7reTdWQOSMIUQnQByVI5W5Rfuheq8nU4qqBnThIX9AwD8lAjwXVAeTPPasAowEdEAVBwyvl6VFXrw8DdbZ1yDm4TyUPt59wFNVL9t+63W71+mgiSMIUQXUIiK2eL8ktHogp5jgFuA/5VUJxXG88YWiEHOAQ1BbuZGKZZw14XQHXbcYJTzpeiim/eAv5SUJy3tC0B2R7dBZyASpRuYAbwtO63k7UoKipJmEKILiOscvageEzfBadf81HJ8kHgnoLivC2d/bltlA3sC+wH1KCmi5v8AV9f7+ByaZGXR9fXO88/dPl7oV63K1BTzt+0JSDbo6ei9mvegCoyugvV3i4pC7haIglTCNGlxKtyNtjO7mHU1ovTC4rzFnTWZ7VTGjAROALVh3U1Owp6GvjhvWVjNq2uGHjYWePm1lbXaanpKQ0SwIqfN034dd66M757e2ldfb1zLfB6W6acbY+eiWpOcD2wDJUo30ymLSJtIVWyQoiuptMrZ4NTsG+gtlscEH5WYxJxoZojHAX0RSXKJqc416/Y2uvL/y26rK62Pmu3w4b/NGB47/K62notJdXlrFu6ZeD8L1edWbmlZuLYPQeWXjTz0DsyslK/bW1AtkfvDfwB9d/lO+AC3W9/3JZvLhnJCFMI0eXYHj0H+AK4R/fb/+rI9y7KL81AFRj9D9UkPRl/SIamOmMp6AGgYkt12hsPW6dWbqkZkpaesuXMm/Z9fMuGqmz7kxUnbV5XdaR7SNZbux0yYk5233QHVVH7MFAZSzDBSuYrgStQW2xm6H671Qk32ckIUwjR5eh+e6Pt0U8APrI9+s8dXDn7N9TaXUcky/NR1aptsRJV5BSNhjpqq8lkGTnluvKXTf23btw2+ohzxj/21mM/XvvpS7+cUrG5+pjsvunf7HXMqOsGjOi9KezlqagGB82ODm2PPhw1mrwImA0ckgw9XzuLJEwhRJek++35tkc/F5hle/QOqZwtyi89E7XPcJ8OGlkOA9pUWYoaPTalBngXOI0oCbO+3iGULBfMXT18l70Hr+g7MKu8d7+MJRWbq8f0GZCZM//zVce5B2d9dvTvJjwe5f1XofZHzgM2Rd4MduW5DjXKfRLYU/fbSyKf624kYQohuizdb79te/Tbgf8Fk2abK2eL8ktdwJ3AxQXFeZs6KsZOtBBYjpo+3RR+w+XSWPDlquGfzV54ARr8/OWqH6qr6oZsq6jde9n8jXtVV9Ytr9xaPbZ3TkY9wLaKmpSM7LTwQqE6oBbVQu+10MUoXXnG6X57bSd+j0nFlegAhBCiPXS/XQR8ADxje/SUdrzV0ajRWsw9VROsHrVe6I68sXXTtnTr/eVHTDh4+KuHnz3+qZWLAqcE1lYesWVDVeovX61x93KnLxiz+6BntmzcNhYgIlmGrAEMYKjt0fe3PfrLwDuoUedY3W/f1JOSJcgIUwjRPXRE5ezlwIOxTsVqmpaKSrIeVIOA9xzHWdzGz26r5cBPqG02DZJX5ZbqYYt/WDfeen/Z2F7ujEV7Hj3q36sWBUZMOHjYvCFj3JurK2tT5hR9n7NpTUVW34FZlZF7Mp36eqf8iy9GbHn7nbdQo9h7gHO6SleeziAJUwjR5el+u8b26GcAX9ge/afWVs4W5ZcORO1jPC+W5zVNOxT4L6qdXEi9pmmPAVc5jlPV1GtdLtdTOTk5S+rr61NcLlf9wQcf/OELL7zwenp6elvXTD9GJW0XUF9dVZvq/3TFMY7j7L51Y1XF/ieNvW23Q0f8AlBXW8+QMe7N9fUO6Vmpdades/ezkW/m1NZqgTklkyq//fYkUlJ69Tni8M/7nXrqlVkTd1vexvi6DUmYQohuIaxy9sM2VM6OB/wFxXktHlSsadqeqFZxmRG3XKhWcv2AM5t6fUpKSvX69etvAPjiiy/6nn766Vf89re/zS4tLX2hFfGG2wDMdeqdSfZnK0et/CVwVnpmyrJBo/q8uGlN5ZDKrTWpyxds7Ff6H/9FKala1fBd+z3Rf1ivRqPE+m3bXIGXXz6wypp3EulptX2OPuqjXgce+JWWmvou7ThgujuRhCmE6DYiKmcP1v32whhfOhZ1uHEs7qVxsgx3hqZpeY7jlLb0Rvvvv//moqKif51xxhl31NXVvZCS0rYl2KfNz1N22WfIlZVbqsk1Bjy686TBP1ZX1qZ8NGvBQf7PVh77/TtLhw4Z0/eTKVfsMSfytXVbt6YFZs8+rMr2n6BlZmzoO3nya9n777dAc7k+RjUfiHa6SY8kCVMI0a3ofvudsMrZA2OsnB0DtLj+qGlaf9TUbUtORRXktOjEE09cA2hffvml+8ADD2xVlW9RfumuqEbm+1Zsrv7XYWftWuVyuZYCpGel1h31uwkfbdlQ9WVKmqs+u096g6O4ajdsyAjMnn30tp9/Od7Vu/ev7lNPeabXPvusBL5FHae2uTWx9ASSMIUQ3Y7ut4tsjz4BVTkbS8/ZoYAdw1sPjTGE4TE+B4DjOI26oDcnuOZ6C3AOqtHCeUecM74OuATIIqxDT5/+mQ1GiDUrVvQKvPLqsdVli49Nyen/Y7+pU4uy99qzHLVN5X0iiofEDpIwhRDd1Z+IvXJ2JbF15FmBOgGkpQS3LIb3AuDVV18drGla/X777dfi6LIovzQTuAq4FngW0AuK88IT3Duo0e2vka/dtmhRv81zSo6vXrbsyLTBg+bmnH/B9KyJu9WjEuS7wBKaOd1ESMIUQnRTraycXYQ6r7FZjuNs0jTtbdS5mM15LpYY586d2+eKK664+OCDD36rufXLYFOFs1GnfnwDHFJQnBetBd0vqKTuRp1tSeW8HwdveeutKTWrVh2YNnz4RwMuveQvmbvumgZUoKaN56P2dIoWSMIUQnRbraicXYxax4zFn1CnmPRq4r7PcZwme7DW1dWlDxgwYHpoW8mBBx740UsvvfRaU88X5ZcegZp2rQfOKyjO+6iZ2OqB94BzK+bO7bPlvfdOrF23fs/00aPeGXRFwTXpo0b1QlXzfgB8jxT0tIqcViKE6PZsj340at9k1MrZovzS/qhR5qiC4rwWi100Tds7+H6esMs1wAPA9Y7jhApsrqONvWQ3riyf9PRtX4xHddu5AXiuoDivxZGg7dH36zd16v2uXr30lH7uEvfJJ7+dNnhwb9Ta5leoU15a3D4jGpOEKYToEWyPXgAUAFErZ4vyS58DPigoziuK5f00TdNQvVZ11PTnh47jrI54rNUJM7Cmou9Pn648TdM46OvXf70VeKCgOK/ZkaDt0TXgSOBGYFz6zjs/OOb551Jc2dkbUNOzC1At/9a1JhbRkCRMIUSPYXv0ItSeyxN0v10bfi849VkETOzAMzBjTphV5TXpP364/PiNqyqO6zMg8+Pdj9zpy6w+6WZzr7E9uguYgkqU/VBbTJ7W/XY1cDhqmvndWGMQzZM1TCFET/IndlTO/jni3geo6tejgbfjFVBdbb1mf7LikNVlm6dmZKct2P3InW4enNt3Dc0c72V79FRUN6EbUFPBdwGzI7bPfIAaVcqoqIPICFMI0aPYHj0HtTH//yIrZ4vyS09ArUPuXVCc1xHTl82OMH/5es3EpfaGczWXti3XGPBUrjHw57DbI1ENz8NjzwC8wPWoxut3AW/qflt+kMeBJEwhRI9je/RxwEfAVN1vvx9+ryi/9G5gT+D4guK8lhoetOR8ouzv3LBi65Bff1w/uaaqbuDQnd2vjdT7z1NLog2sBP4TjLc38HtgGqq6dbrut5urlhWdQBKmEKJHaqpytii/NBW17vcZcEMHrmdSlF86DPgrcBLqsOqHCorzqpuJsT9wJXAFas/kDN1vf9tR8YjWkYQphOixbI9+OSoZNaicLcovHQr8D9Ux56JYtpo0pyi/tDdqdHgV8BhwV0Fx3qZm4hqG6k50MTAbuEf329EaFYg4koQphOjRmqqcLcovzQDuA44Czigozvu+te8dHK3+DrgNVYRzY0FxXlkzsYxFtb2bipqOnan77SWt/VzROSRhCiF6NNujp6EqZy3db0dWzlKUX3ou8A/UFO2DwJstNRAITr1eglp3/AW4rqA4b24zMewGFALHA8XAP3S/vaZt35HoLJIwhRA9XrBy9jPgNeB63W83OAqrKL80GzgL1fggB3gT1RloEVCGag4wFrXvcSJwGDALtUbZ5MjU9uj7obaGHIhKyg/GeByZSABJmEIIAdgefQBqGrQvqnp2eeQzRfmlGrAPsD87EuQYVKefUAL9BXitoDgvauILduU5gmBXHlSf2Md0v13Rwd+S6GCSMIUQIijYOecG1EjyPN1vx3QIdCvee0rw/fsD09nRlUd0AZIwhRAigu3Rj0KNNt8CinS/3eT6Ywzv1Qc4D5WEq4nelUd0AZIwhRAiiuAU7cVAPrAeVfAzK9apU9ujTwQuQ51jWQo8BJRKV56uSxKmEEI0w/boKcCxwOWo00l+RJ2fGVn0Mwa1rjkW2CV47VHg0WjroaLrkYQphBAxsj36UGA8OxJjtKKfxcGvHyOrbUXXJglTCCGEiIEr0QEIIYQQXYEkTCGEECIGkjCFEEKIGEjCFEIIIWIgCVMIIYSIgSRMIYQQIgaSMIUQQogYSMIUQgghYiAJUwghhIiBJEwhhBAiBpIwhRBCiBhIwhRCCCFiIAlTCCGEiIEkTCGEECIGkjCFEEKIGEjCFEIIIWIgCVMIIYSIgSRMIYQQIgaSMIUQQogYSMIUQgghYiAJUwghhIiBJEwhhBAiBpIwhRBCiBhIwhRCCCFiIAlTCCGEiIEkTCGEECIGkjCFEEKIGEjCFEIIIWIgCVMIIYSIgSRMIYQQIgaSMIUQQogYSMIUQgghYiAJUwghhIiBJEwhhBAiBpIwhRBCiBhIwhRCCCFiIAlTCCGEiIEkTCGEECIGkjCFEEKIGEjCFEIIIWIgCVMIIYSIgSRMIYQQIgaSMIUQQogYSMIUQgghYiAJUwghhIjB/wMNzefBD1fRygAAAABJRU5ErkJggg==\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAHBCAYAAADkRYtYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAByC0lEQVR4nO3dd3iUVfbA8e9MGgmEEHon9HmFsVBFLBgRSyI2NOqqUVfXKOquv+xq7GOPJfYorroae5RiSXRtcVVUQEBgwHeQNvQOCS098/vjzoRJMkkmyWRmkjmf5+FR3nnLiWBO7n3vPcfgcDgQQgghRMOMgQ5ACCGEaAskYQohhBBekIQphBBCeEESphBCCOEFSZhCCCGEFyRhCiGEEF6QhCmEEEJ4QRKmEEII4QVJmEIIIYQXJGEKIYQQXpCEKYQQQnhBEqYQQgjhBUmYQgghhBckYQohhBBekIQphBBCeEESphBCCOEFSZhCCCGEFyRhCiGEEF6QhCmEEEJ4QRKmEEII4QVJmEIIIYQXJGEKIYQQXpCEKYQQQnhBEqYQQgjhBUmYQgghhBfCAx2AEMI3zDlmA+qH4HC3XxG1ft+UYy29vrnPMQJbgfXABuc/l1tTrRt9+J9LiCYzOByOQMcghM95SB6t/U0+WJ5TBVTU+lXegmMtvb45xwD6AUPcfk0AlgIvA/nWVKvrPCH8RhKm8CtzjjkaSODoN8LBQG9qJgBfJRNfJw9/HGvR9dZUa5X3fxpthznH3AGYAdwM9AdmAc9YU60lAQ1MhBRJmKLVOUd7E1Hf7C4EtqOm2Vy/tgNl+DjxtNfkEerMOeYTgPtQP3jNsKZa1wc2IhEqJGGKVmPOMUcBV6ESZWfgFeAta6p1b0ADE0EpISO/E2rGoT+wC1hvz0za7+lc5w9htwL3AtdbU62f+S1QEbIkYYpWYc4xDwI+BoqAp4FvZMQn3CVk5BuAk4GbgKlAJ9Qin61AT9SUfRXwK+qHrXx7ZlKl+z3MOeZJQC7wpjXV+oD/ohehSBKm8Dlzjvlc4E3gSdR7pvr/klni4oBYYDuWosp6zxPtSkJG/uXA3UAkaiHPHGCbPTOpyu0cA9ANOAc1S9EPeB54zj1xmnPMPYD5wCPWVOs7fvsiRMiRhCl8xjlN9iBwHXC5NdX6U40TLHGud5nXA8ehRhBRwEEgHtgErAM+Ad7DUnTIX7EL/0jIyI8GXgImAbcB39kzk7z6JpSQkX8CkIUadV5hz0za5frMnGM2AwVAojXVavV54EIgCVP4kDnHfCvwV+Asa6p1Z/UHljgjcA1wCxCHWuH4E2rBz24sRQ4sca7VsybUe88pwPvA01iK7P76GkTrScjIT0D9MKQDN9gzk5r8A1FCRn4Y6oeyVOASe2bSAtdn5hzz1cA9wHhrqvWAL2IWwp0kTOET5hzzicBnwCRrqnVd9QeWuG7Au6hE+RDwNZaixt9lWuIGoN5tXQ9cj6VIFnW0YQkZ+R2BhcDbwFPejiobuN904N/ABHtm0ibXcXOO+VUgzJpqvb4l9xfCE0mYosXMOebuwBLgNmuq9dPqDyxxE1ALf3KBe7AUlTf55pY416KOD5z3kA3rbYzzXeQ7qO0+17YgWV4F9HH95rNlW0/dtL/YfMMpg1+JCg+rAigsLYx+e9Xbd16hXfF09+ju7iPY7c4YhGg2qSUrfOFN4MNayXIkkA/8A0vRHc1KlgCWol+BMcCJwBMtD1UEwF+BY4GbWziy7ANsdv1KPq7ve3sPle56qWDtqa5jXaK6/FnpqFz46dpPh7ufi1uiFaK5pJasaBFzjvlY4ATgouqDlriOqFWPd2MpmteC26sRhaUIjuwtYOGrf2ddQQ+GJq704loZUQQB51TsI8BZ9sykI768t9Fg4LrJg1975ps/n7XvPfxpQreOBwAm9pn4zRfrv7i9vLL884iwCJlCEz4jI0zRUjcB/7amWt1HkK+gpmhfb+G9j44oYrr9yYAJWazIPZ8ti8upOXrw9EtGFMFhJvCjPTNpeWvcfEDXmIM9O0f99qV1+xTXsZP6nrQhMiyy6KuNXx3fGs8UoUsSpmg2c465M3AZ7onREmcGzgBuwlLk25/uh01dT89Rn7H8w2txSA2EYOes3JOOWtXaKIPBcLrBYHjHYDD8YjAY5hgMhksbu8ZoNL6XlXr6iPuuPvuS7t27P/bUU08NB+jXqd9vG4s2HtOyr0CImiRhipa4GlXBZ5vbsZuBV7EU+XT6rdqEG76isjSe5R+Oa5X7C1+6BSiwZyatauxEg8HwKGof5ZWoPZoXAbnOxBlW33VhYWFle3btSL/umTm7L7/h1u+effbZywDiO8TvOlx+uIdvvgwhFEmYoiWSUKtXFUtc3RFnA5wjiqcMBsOrBoNhpsFg6NLYNT8vWtpp4r1fHRh8Vtrfu8bHP52QkHDHRx991LuZ8YtWkpCRHwv8H2orUYMMBsN0VNUfTy4C7mjsHjGR4dv3HTzSMzo6+jBAr5heO49UHOnVhJCFaJQs+hEtMQRY7fb7s4CfsRRtq+d8AAwGQxRqb+aMWh/dbzAYLnY4HPM9XVdZWcnFF1/8f6eeeuqPC58cepCYbpv/beu8ZN26dXHAjhZ8HcL3bgW+sWcm6V6ce3Mjn88EHvf0QWVlZWS3bt0erwqL6n74wP7Rz2Y9bQEYEjdkV2llac8qRxVGg4wLhG/I3yTRLOYcsxEYBNjdDg8DGp1+AzKpmyxBFdz+xGAweBwZPPzww6OMRmPlRx999B3HX/Eee9ac9bfpkw7fddddqz2dLwIjISM/DrgdL0aXTqZGPu9nMBhiPX0QFhZWtnfv3rse//inT6++9/nf7r333psqKyvp1bHXYQcOw97ivdFNiV2IhkjCFM3VF9hvTbW6v6scgip3Vy+DwRAHpDVwSjfUvr06li9f3n/AgAEbAOht3kP34V+x7P0rmxS18IfbgC/tmUne/iCzr5HPS4HDDZ5QXtnx+JMSt5aUlMQuXry485HyIxEOhyO8S1QXaTAtfEYSpmiuwahWTLWP2Ru57higQyPnjPUqggk3fE5JUQKrPhnl1fmi1SVk5HdBJcyHm3DZp418/pnD0fCy6IOlFT12/rm0zOFwGI899tiD6wrXdY8Mi9wr+zCFL8k7TNFcxdRNfJ6O1ebNT/zFng4ee+yxWxYuXDix+kCHuHISTn6Xtd+lMuKsu4iIlvZggfd3VN/KNU24JgtIATQPn+2jgUU/rneYxpgufapKDw+99dZbX4mOjnZs27GtR3R49K76rhOiOWSEKZprAzDU2dLLZT1qWrYhK2l8gc63ng7ef//9qyorKyMuv/zy013HXvhx974vl28r47fXz/QmaNF6EjLy41GLfZoyusThcBxCNZJ+D3AVwHAA3wATHQ6Hvb5rq6qq/rJ87eZHr3x6TsWqNfZ7nnrqqWUAe4r39IoJj5GEKXxKEqZoLtd7p3i3YxtQ07L1cjgc5UBGA6csQbX1qiMsLIyPPvooa+nSpebOnTs/Fx8f/+Szzz43I2zIqR+xc+WF7FvfuSlfgPC524HP7JlJ6xo9sxaHw7HP4XBcCXRFTdt3dzgc0xwOx9rGrs1fsf20HrFRS3rHdah+n77ryK5h8R3itzQ1DiEaIlOyolmsqVaHOcfsGlG6kucq4IrGrnU4HDkGgyECeAro4vZRHnCdw+Eoq+/a0047rXD16tUv1Pmg4NEfWZKTwpkPvub9VyF8JSEjvytqe8iEltzHOdr0ZisKABVVVYa1uw6dea65z4uuY9sObeu0p3jPmMtGXvZuS2IRojYZYYqW+BM4zu33BUAvLHFjGrvQ4XC8DvQDzgQuBkY4HI7zHA7H7mZFMuH6uRzaeQJrv21sSli0jv8D5tkzkxpcJe1rny7bNiY8zHj4tJE9qke1X9m/Oq17dPel/WL7HfRnLKL9k4QpWuI9VINnxVJUCbyKKsjeKIfDccThcHzrcDjmOhyOpiwSqatTr2L6j/sQPe9aqioMjV8gfCUhI78b6s/8UX8+t8rh4Df7vhnjBsXPMxrUH3lFVYVhfdH6Myf0nvC1P2MRoUESpmiJfKCvOcfsPqJ8A5iBJc7/dTzHXvMT4GDJW6f4/dmh7Z/AbHtmkt2fD527dOs4AzguHNNvsevYV/avjgs3hB+e3G9yk9+jCtEYSZii2ayp1rojSkvRTlR92X/6PSBjuAMt+S22LL6M4sLGtrcIH0jIyO8B/A3/jC63AwOAAZVVjoHbCosvTz627/+MBsMA1/Edh3fMOH3A6QuNBmP1Meev7X6IT7RzBodD9vWK5jPnmHsBNmCoNdWqFv9Y4voDKwATlqKWLO2/A9Xbsmm+eeAGug4JZ2zqqS14tvBCQkb+E0CsPTOpsXqwvn7uDOBOYII9M8kBYM4xTwRygWHWVGuFP+MRoUFGmKJFrKnWncBc1JYCxVK0BfV+s9EuE42oHlE06dekm+ezedHxWOI8bYQXPpKQkd8T9Q77MT8/1wg8AFhcydLpTiBLkqVoLbKtRPjCI8Bic475OWuqda/z2OPASixxWViKmjsd9k6zrurUC5a9uxd4AUvcNJ83shYudwAf2DOT/L3fcQZwBPjCdcCcYzYBk1H9NIVoFTLCFC1mTbVuAOYA6dUHVYuvt1E/9QfCy0Af4IIAPb9dS8jI7w1cRz1tt1rxuWGo0eUDtUaX/wKyazUDEMKnJGEKX3kUuNGcY3ZfHfsEcDWWuL5+j8ZSVI4qAv4Mljhp8eR7dwDv2DOTtvr5uZcCB4CvXAfMOeZ+wIVAtp9jESFGEqbwCWuqdSNqwcXR1bFqKvZN4K6ABGUpKgAWo0YfwkcSMvL7AKmovqb+fG4YcD91R5f/AN52ex0gRKuQhCl86THgBnOOuafbsSeBK5wrZwPhn8BtWOIGBej57dGdwNv2zCR/b9W4DFWG8RvXAXOOOR41NfyMn2MRIUgSpvAZa6q17upYtS/zDeDugARlKdoIvAA8HZDntzMJGfn9gKtR0+3+fG44nkeXNwF51lTrJn/GI0KTJEzha48D15lzzL3djj0FpGCJGxigmJ4CxmGJOyNAz29PMoA37ZlJjbVo87XLgZ3Ad64D5hxzNOo99ZN+jkWEKEmYwqesqda6q2MtRbuBfwP3BCQoS1Exqjj481jiIgISQzuQkJHfH9WNxq8JqoHRZSrwmzXVusqf8YjQJQlTtIYngFRzjtl9dezTqBqzCYEJiU+AbagWVKJ57gLesGcm7fTzc68Ettozk753HTDnmMNRi7n8uvBIhDZJmMLnrKlW1+rYo42iLUV7gVeAewMSlCpe8HfgXixxPRs7XdSUkJE/ELXo5ik/PzcCuA+199LdxcB2a6r1Z3/GI0KbJEzRWp4ErjTnmN1Xxz4DXIAlbmhAIrIU6ajpYr+Wcmsn7gZes2cmNa9fafNdBdjtmUk/uA6Yc8wG1JS/XxceCSEJU7QKZ43Z13Hfg2kp2ofaXB6YUabyEHAulrjxAYyhTUnIyB8EXIKfVxonZORH4nl0ORWIQrWXE8JvJGGK1vQUcJk5x+y+OvZZ4DwsccMDEpGlqAg1WnoRS5z8/ffOPcAse2bSHj8/NxVYa89Mml/r+J3AE9ZUa5Wf4xEhTr5hiFZjTbW6Vsce3YNpKSpE7Yu8LzBRAWpaFtR+QtGAhIz8waj3hVl+fm4kKlHXGF2ac8zjgBGonqtC+JUkTNHangYuMeeYE9yOPQ+cgyVuZEAishRVAbcCj2GJiwtIDG3HvcDL9sykfX5+7rXAantm0i+1jt8JPGNNtZb7OR4hJGGK1uWs71lzdayaFn0OtbcuMCxFv6HaQwUuhiCXkJE/FDgfP5edS8jIj8Lz6HI4MAX1blwIv5OEKfzhGeBCc47ZfXXsC8CZWOKOCVBMoKaKr5ZG0/W6F3jJnpm038/P/Suw0p6ZtKDW8X8CL1tTrYf8HI8QgCRM4QfWVOs+4CVqjjIPohJpIEeZu1DNr5/HEmcIWBxBKCEjfziQjJoJ8OdzO6BWVlvcj5tzzH1QK3Vf9Gc8QriThCn85VngPOe0mstLwOlY4kYHKCZQjab7oaYexVH3AS/aM5MK/fzc64Hl9sykRbWO/x14z5pq9fdKXSGqScIUfmFNtRZSe3WspegQautJ7X12/iONputIyMgfCZyDWpzlz+dGo0aXtd9dxgE34OeVukLUJglT+NPzwDnmHLP76thXgJOxxB0XoJjAUvQdsBT35teh7T7gOXtmUpGfn/s3YLE9M2lJreM3Al9aU612P8cjRA2SMIXfWFOtdVfHWooOo8roBW6UqaQDfw/1RtMJGfkaMA0/vyt0ji7vpO67yw7AP5AWXiIISMIU/vYCcKY5x+y+MnUWcCKWuBMCFJOr0fSLSKPp+4Fn7ZlJB/z83DRgoT0z6fdax68ClllTrSv8HI8QdUjCFH5lTbW6VsceHVGqfpWZ1BpdBMCTqEbTiQGOIyASMvJHAYmoxVj+fG4McAd1R5dhqBZeUmRdBAVJmCIQXgJON+eY3VfH/hsYiyVuXIBicm80/UKINpq+H3jGnpl00M/PvQn42Z6ZtLzW8QuAvcCPfo5HCI8kYQq/c248f5qao8wS4HECP8r8BNiO+iYeMhIy8kcDp6G6yfjzuR1Ro8gH3Y87W3hloIqsO/wZkxD1kYQpAuVl4GRzjvlYt2OvA8dhiZsYoJhcjaZvA+4LsUbTDwBP2zOT/F1FZybwoz0zyVrr+OlAJ+AzP8cjRL0kYYqAsKZaD6P2YFqqD1qKSoFHCfQoUzWafscZS7uXkJF/HHAyaouPP5/bCbU6+UEPH98JPCUtvEQwkYQpAmkWcKI5x+y+OvY/wDFY4k4KUEwuDwLJAX2n6j8PAE/ZM5MO+/m5twAF9sykVe4HnX8fRgHv+TkeIRokCVMEjDXVegS1AtJSfdBSVIaq7+pp1OE/RxtNv9SeG00nZOQfD5yI+uHFn8/tjFpg9ZCHj+8EnrWmWkv9GZMQjWm33whEm/FvYKw5xzzW7dhbwDAscacEJqRqOYABtRewvbIAT9gzk474+bm3At/YM5N094POjjZTUX8vhAgqkjBFQFlTra49mEdHlKq+68MEfpTpajT9eHtsNJ2QkT8WGI+fk1NCRn4cqnqPp9FlOvCqc7+uEEFFEqYIBq8Dx5lzzO6rY98BBmGJmxKYkJwsRYuAL3EvGt9+WIBMe2ZSsZ+fexvwpT0zabX7QXOOuRdwGaoalBBBRxKmCDhrqrWE2qtj1SjzIeDBIOhVeReQ2p4aTSdk5I8HTgBe8/Nzu6AS5sMePr4N+NCaat3pz5iE8JYkTBEs/gMcY84xT3I79h7QB7UnL3BUo+lHgeeCIHn7igV4zJ6ZVOLn5/4DyLdnJq1xP2jOMceiupKEei1fEcQkYYqgYE211l0daymqQI0yHwqCRJUN9KcdNJpOyMifCJiBN/z83HjUVhJPo8u/Ad9aU63r/RmTEE0hCVMEk7eA4eYc88luxz4AuqNWTgZO+2o0/SBqdOnvbRu3A5/ZM5PWuR8055ijnJ9JkXUR1CRhiqBhTbXWXR1rKap0/j7wo8x20Gg6ISN/EqChpsD9+dyuwM2oWYTa/gKssqZaa7f2EiKoSMIUweYdIMGcYz7N7dhHQGfgrMCEVMM/gX+04UbTDwKP2DOTyvz83HRgnj0zqcaUqznHbES19pLRpQh6kjBFUHGOMh8CHnJ2rAi2UaYdte3hqYDG0QwJGfknA8NRU9/+fG53VINoT7V5pwMHge/9GZMQzSEJUwQjT6tjZwPRwLkBiaimJ4EJbbDRtGt0We7n56YDs+2ZSXb3g24tvDKlhZdoCyRhiqBjTbW6Vsc+6DbKrEJthQj8vsyajabDAxqLlxIy8k8FEoC3/fzcHqgVsJ5Gl6cCXVE9SIUIepIwRbD6AOhBzdWx84Bw4LyARFTTPFSj6ZsDHYiXAjW6/CeQa89M2uThM1cLr0o/xyREs0jCFEHJ+U30QTyPMh8KeAcR1Wj676hG0z0CGksjEjLypwADUAuq/PncnsANwGO1P3M2Dj/e3zEJ0RKSMEUw+wjoQs3VsZ8CVcAFAYinJkvRH6hv+HUSQrBIyMg34FwwZc9MqvDRbTVgJBDWyHl3AO/bM5O21PPZc86yiEK0CZIwRdByjjIt1BxlOlANjy0BH2Uqwd5oOhG1gOp9H92vM5CE+oHlr8AIPCTOhIz83sB1wOO1PzPnmBOAc4BXfRSTEH4RDN9whGjIbCCGmqtj84BS4GIvru+DWljSOo42mn4xSBJ4tVYaXZ4AOIBNqJH+hajEOZya30/uAN6xZyZt9XCPdOA1a6q1yEcxCeEXQfU/uBC1WVOt1atj6xll1jct2AeYAVzj/Gdj04ctkYP6fynYGk1PRZUV/MBH9+sMTABc3UQOczRxXoQqnj78+Ie+7ov6755Z+wbmHHMPVGWf530UkxB+IwlTtAWeVsd+idrwfkmtc3ujvnmnOv/dDsQDQ1stupqNpju32nOawG10+aA9M8lXq1DHopJj7fsdBspQCfqu26cO/2DswC6f2DOTPLXpuhX42Jpq3e6jmITwG0mYIug1MMq8H3jAOcrsiXqvdg3QD9gI7HXeYi+qCELrjTJVo+n/OmMKBmehFkx95KP7xQHjODq6dGdALQTad6C4PLZPXPTEnOsmxKIKqg/F+X3GnGPuBNyEtPASbZQkTNFWfIp6d3aB27FviO1zgLMefxK1wGQAKlHuqXXtYVTyGNbKMboaTZta+TkNasXRZSV1R5cA3YBOQMki+74REWHGPzp1iDgEnIQqWPBPYGiYIewG4H/WVOsaD/cQIuhJwhRtgrN02gOoUaYR6I6lKIlL3lrB4d1XUlG6mbqJ0t0e1Ciz9SrzWIp2ohLE8wGuRnQO0BH42Ef3i0MlzBqjy8oqVc2uqsoxEijed7i0446ikpFjB8UvBQ4B25z/nFhZVfn0jcfeeP+1o679ADUiFaLNkYQp2pK8bh26Vfxr/L8eQa3MHMqACd9TUrSP39+d3Mi1R1Df+Ie3coyuRtPTW/k5HrmNLi32zKQqH912PGpkWeN+YUYDZRWVXY1GQ1fgyGL7/nH9ukSv6hwdUex2WjGwbfW+1XFjeo4p/b9x/5eKWlUc76PYhPAbSZiirehqTbWe/dSpTy0rLCm8tqKqYguwC4PRwdDTP2br0ouoKG3s7/NuYAqtO8osR1UAejZAjaaTgEhgro/u1wW1laTG6PL/cpedetUbCy/4YNHmy75etWPwxr2Hu+46WDp8nBpd1lDlqGL1/tXHdYvptsB5nxNRFYBORE3lCtEmSMIUwa4DagHL9cDIsb3HfltYWnjo03WfTqg+QztPJ6LDXpa+fQoAVfW+titGbY0Y2aoRW4q+BX5H7Tf0m1rvLlttdPlw3h9jflize2qPTlERQ3t0jFi/51DHnF/s5xWXVeyMjDDW6bO5Zv+aBKPBWDk0buhm1J7aJcAO4GTUVpTTUYlZiKAmCVMEu3hgIrAF2Gk0GB3je4+fvXzX8hnlleVH34UNPX02W5deyHcPm3n7/Bn8+/RUPkqdyi69IwCVZa5zdwOnARGtHHc6qtH0wFZ+jrvpqJXAn/jofvGoeq873A/OX7Nn/KQh3X54JuX41WMGxq9PHNlTj4+JjCqvdBz+bcP+/rVv8uf+P8cM6zJsicFgcH2/sQPlwFbUiPMEVEeTqbWvFSKYSMIUwW4HsAa3EchZCWetiDBGHPp03aeTqs8yJds4tLMYW94F7Fw1BhxgMDiYfa0qJnA0uRYDsbT+KNMOvISftlA4R5cW4AEfjy4rUKuTq00Z2WPhxr2HT9h9sNQUExW+z773yChTn9iVg3t03Przuj3HbSss7ug6115k71NWWdbR1NW0DvUOeQ2qSpNLJarry2HUalshgpYkTBHsHMBPqKlUAIwGIxP6TJi9YveKi0srne8ti7ZEsXdtMTHd+zH2mg8pKYrnkre+4/DePmxdGktkR/ck4q9R5hOoRtOnN3pmy12Amjb9zEf3c40u6+y7vCVxmPW0kT0Of7x48ynvLdw4YWth8dBJQ7svmTg0ar1+4Puur6188Zh3/nhnMIC+Tx8zOG7w0jBjmAH1Z7mxnufFAr/6KHYhWoUkTNEWbAE24FYT9sxBZ66MDIss+nTtpycBULwvgrJDUfQ6xk6nXj2oLOvAi+NuJT5hNVGxFRRtiWL+cyOclxejFptorRq1ajSdTis3mk7IyDdydGWso7HzvTQBVb2n+n6ubSSxHSLCUsYOKOwRG2XTtx8YvXbXoco3flp/alr+wzN2Vi40bjmyJvbbjd8On7Vs1nGHyg71PKbbMTY8jy5dYlEzCZt9FLsQrUISpmgr5qO+sQJqlDmxz8TZ1j3Wi0orS430PvYQnfutY8tvB9mx/AJOnDmbyrIoBk76g+7Di9m8sCvL3pvGp7e4tp/sAk5FrShtTXNRo7SbWvEZF6ESUZ6P7tcVNbrc5X6wqsphAHgkb9V1P6/fO+rPnQeHRIUbI6+dnPBpRGRJ8c6K5ZEfnP/v916b9lrBlAFT1n218asTOkd1/qNDeAds+2wxP2758Ug9z+uC+vP1VbIXolVIwhRtxVZgPTVHmX9EhUXtm7dm3skATLV8SnFhFBXluzAwmKkPvMe0h5YBMPri7eCAlXMud15eglqx2dqjTAdwG3B/azSado4uH8C3o8uJqARc434R4UbHyq2FXWKiwo+9bPzAr0vKq+KG9ui0dljP2H1FxsVlfTv229MlJrKssqqS84edv9OIMWbZrmWlQOd75t9j2lC0wdM2m2jgAGoGQYigJglTtCU1RpkAJ/U9afaqvasuKqkoCWPYGXs554m36dR9Ptut5zPsTFX5Z85fT+fRvi9weE9/Jqb92+1y/4wyVaPpd1FVgHxtBqoowxc+ul834FhqjS5d9h0qHzm8Z+zWb//YMcyBo8P5x/f74VBJefih3eOHX6PNXAYQZgzDutt6/KkDTl2/oWhD1y/Wf9Fzb/HeqNRRqWs93LIH6h21r0r4CdFqJGGKtmQbsBa31ZSJAxNtHcI67Jq3dp7ag9llYDF63gQ6dLYz72/X8VjfF1jzzTmMuept7rRnMPWBFW73K0Xt8zzGD7FbgPN82Wg6ISM/DDW6fKA13126MZw4tFtcYXF5+Rcrd5zePz56c8eo8LJPlm8+tnfnDjvG9B26e9XeVbHv6+8P/WPvH8dcdcxVP4UZwyIfWvDQiVMHTfW0GCkS9T5ZasuKNkESpmhrfqZWdZjJ/SbPXrVn1UXF5cVh9NQOExVbyMJZJ7Dx15MZPSOXjE13cM4TiwEoL6ldx3Q3apQZ1apRq0bT96AWAPnq/7tLUdOZX/nofqDetxpRHV9qd3fpFRlujJowKH5tj05RjqoqDt0zz/qXvQcrup53XN/f7v/l/kkv/f7S2Hf+eOeUHYd37OnaoWtJv079DpRVloXde+K9izw8qwdqZWydYgdCBCNJmKKt2Q78iWqMDMCUAVP+jImI2TZ37dwpACQ/+yHG8FKOv/x3ug/vTFXl0UQZ0aH2yMk1yhzlh9jfQpXlu7KlN3KOLu/Ht6NLgKXAq8ACoBfQl6OJ0wQcXr/nsHnysG6LLxrTb+F1Jw/Ku/6UIf/bWb4y3F5k7/XclOfmF1cUd5w6cOpvQNygzoNWPHf6cxkenhOO2gaz0oexC9GqJGGKtugXVDeOaif3O3m2vle/4HD54XAGnlhEwimfUVL0BztXnUfpgUgPidKlO6p7RuvXfT3aaDrTB42mLwP2Ad+0OK66DqHeF78KLEIlzlFA3I6i4uh9R8oGjk/otrx3XPSRoT06F3WMCq9YW7i28+R+k//8z8r/TO7Tsc/hUwacsm3pzqWdsn/PPnti74l7PTyjp/PexR4+EyIoScIUbdEOYDVuo8xT+5+6tmNEx81z18xVRQJS3v6cc574huj4P1n69pke7tEdGIja4/kWaqq39VmKFqIaTd/X3FskZOSHo0aX9/t4dFnbIdSCnH+jRuId1+w6dPKQ7h2XRUeGlbufeHK/k3eu2b+m55ur3hw9qe8kKxD3yvJXeo7sOvLXqPCo2jEaUaPWFQjRhkjCFG3VL6htIdVO6X/K7NX7Vp9/qOxQBFGdK4nqXIkpaQ679GSO7HO9o+wGDEJtU8kB5uGhmk0ruwu4pgWNpq9AxVzgu5AadBD4z/LNhYu2F5X0GDsofi+qElD1949BnQcVnz/0/N3HdD3myKq9q+LOmn3WOXuL91beM/EeT6t3ewDLUO9fhWgzDA6H7BUWbdb5QAJuWyAyF2X+s2/HvtarR119dCHMV3ffRnfTDsZevRC1yvZn1LvQwLHE3Q6cDZzt3KvpFefoUgf+Zs9M+r61wqvn2bmj+3Zek3fbKX+gSgv2RiXTQofD4fh07aeXDYgdsCgqPKpy95HddlM309I+HfvUruxjAAYArwOepmqFCFoywhRt2a+oBTvVK19P63/a7NX7V08/UHbAVSe2K2Ou+RX7j6djy58DzCbQyVJ5CZU4mtpo+kpgawCSpRk4beW2A48D76NW/L6FmrYdtPng5lEGg4ExvcZsHN19dOHpA09f7CFZghrh25BkKdogSZiiLdsF/IGa4gNgUt9J9s6Rndd9t/G781BTr3voMSIL68df8OEVMwIVaB2q0fRtqEbTHby5JCEjPwL17vOB1gytHg8AT9szkw47f78HNZ19B5C9au+qkWN7jd1sMBj6oBJieT33iUEt9hGizZGEKdq6BdQcZXa5cPiFvyzeuXjqkp1LZgO5qPeVDwH/54PVqb5ztNH0P7284irAbs9M+qH1gqorISP/OGAy8IqHjw+Yc8xFD/zyQHnXDl3/hSpC4MDzquMuwCaCY4QvRJNJwhRt3W5gFepd5iDgwIj4EU9/vu7zgmv+e8351WdZimyo1am3BSLIBnjVaDohIz+SwI4un3QbXdZ2Z3FF8VMxETHfAfcCn6Oq+AykZinDONRiLSHaJEmYoj34BbUB/n3nr80OHBbgX+Ycs/t+zYdRyamL3yOsz9FG0081cmYqsNaemTS/1WNyk5CRfwJwIjDL0+fmHLMGTALedB4qQ/1ZvIbq1FKO+kGmL2oKfVMrhyxEq5GEKdqDfajGyZtw1kC1plqtwI/AzOqzLEV/olpg/cPvETbsSWBifY2mnaPLewjM6NICPGHPTKqvwMC/gJesqdbarbsqUdOzbwEfoP5svkNaeIk2TBKmaM8eBNLNOWb3acGHgVuwxMUHKKa6LEVHaLjR9LWAzZ6Z5NfpzISM/LHAOFThgjrMOeb+wAVAdgO3cQAbgY+d/xSizZKEKdota6p1FWpUc0v1QUvROuBT4P8CFFZ95qKmLGs0mk7IyI8isKPLxxsYXd4OvGVNte7zX0hCBI4kTNHePQTcbs4xu6+OfQS4GUtct3qu8b/6G03/FVhpz0xa6M9wEjLyxwPHowoM1GHOMcejRr7P+jEsIQJKEqZo16ypVhuq/dXR1bGWog3AHNQ0aPCwFK3CrdF0QkZ+B1QZPUsAonkQNbosqefzm4HPrKnWzX6MSYiAkoQpQsHDwN/NOeY4t2OPAjdiietezzWB8iCq0fRY4HpgmT0zya8b/RMy8k8ERgNvePrcnGOORnVdedKfcQkRaJIwRbtnTbX+CeTjvjrWUrQR+Ai1yjN4WIoKgXsqHYZsA1WBGl1agEftmUmeStuBmopdaE21/uG/kIQIPEmYIlQ8DNzqfPfm8hhwPZa4ngGKqT5v7aZLnxvCvthpz0xa4s8HJ2TkT0I1in7T0+fmHHM4qjLRE/6MS4hgIAlThARrqtW1Ovb26oOWos2oPYJ3BCgsjxJK3o+6pey2mH+F5/YPQCm/B1Gjy7J6Pp8BbLWmWqVijwg5kjBFKHkEmGnOMXd1O/Y4cB2WuN4BismTtMWOkfMjDJX5tKDRdFMlZOSfDAxHFRuow5xjNgB3IqNLEaIkYYqQYU211l0daynaCryDSgQBl5CR3xE14rUAGbSs0XRTPQg8Ys9Mqq/TyDQgHPDUFFqIdk8Spgg1jwJp5hyz++rYTCAVS1zfAMXk7ibgZ3tm0nIsRTtR71mfwxJnaOS6FknIyD8VVcD+7QZOuxN40ppqrWrNWIQIVpIwRUixplpdq2OPttSyFG1HTUNmBCYqxTm6/CdqpOfyEqrrx3mt/PgHgYfrG12ac8zjgaHAh60chxBBy+BwSC1kEVrMOeYBwHLAZE217gLAEtcL0IFjsRRt8UccukkLA/oBg4EhC3of85fIyvLhY3avWYPq4bkBWN9NO9g1ftiRf1SUGrXoF/bUV6au2RIy8k9H1YvV7JlJFZ7OMeeYZwM/WVOtz/v6+UK0FZIwRUgy55hfAoqtqdaj+zAtcU8BMViKZtZ7YQvpJq03qiDBFcAQYC+wvswYvmnusFPPP3b3ukeP2b9xKaod1hDnr8HGiKoxVRWGMByGVahp07c0m97iGq4JGfkG4AfgdXtmksfpWHOOeSTwEzDYmmqtryemEO2ep84IQoSCxwGrOcecZU217nAeexJYjSXuCSxFPuvbqJs0A3AKqpzcWagp4WsAq2bTiwESMvLvAoz2zKTHPd7EEpdQVW5YvHl+14eP7Iy6ELhfN2lzgZc1m764BeElAr1QfUTr80/gZUmWItTJCFOELHOO+Xmg0ppqPdq5xBKXCXTBUpTW0vvrJi0WuAqVKMOAl4G3NZte5H5eQkZ+Z2AtcJo9M0mv94aWuAcBE5aiFN2k9QSuA9JQXU5eBj7UbHp9tV/rcI4ufwJesWcmvefpHHOOuQ+wChhhTbXu8fbeQrRHsuhHhLJM4BpnUnB5GrgES1xCS26sm7QxqPekU1GF34/RbPqLtZOl063ANw0mS+UJ4EQscVM0m75Ls+mZqIU4D6GmeBfpJm1EE8KcCnSj4YU8/wDelWQphCRMEcKsqda6q2MtRXuAWagelE2mmzSDbtL+huqQkqHZ9Is0m16g2XSPUzkJGflxqKT0UKM399BoWrPplZpNz0NN9b4M/KybtBmN3co5unwIeMiemVTp6RxzjrkL6n1rVqOxCRECJGGKUPcEcKU5x9zP7VgWcBGWuCFNuZFu0mKAHNSI8mTNpn/kxWW3AV/aM5NWe/mYOcBu1FRsNc2mOzSbPgs4B3hKN2nP6CYtooH7nAXEod6n1icN+MK5FUeIkCcJU4Q0a6p1J/AfVN9JxVK0D7X/8V5v7+OcCnU1eZ6o2fRGE2BCRn4X4O+owvDeqb/RNADOBUBjgRHA/3ST1q/2Oc7R5YOApYHRZQdnbNLCSwgnSZhCqKRwuXN/psuzwHQsccMau9j5vnI+8CKQqtl0b1eT/gPIs2cmrWlStKrR9Puo2rh1OLebTAfygN90k1Z7pHwOEAPMbuApVwNLralWa5NiE6Idk4QpQp411bobeA24u/qg6kv5Ao0UP9dNWjwq8dyi2fR/1/eusraEjPx44BaaMrqsyQKc72w0XYdm06s0m/44amHTbN2kdXA+1zW6fNCemeSxxJ05xxyG6hOa2czYhGiXJGEKoTwNXGrOMSe4HXseOBdL3EhPF+gmzYh6Z/m5l+8r3d0OfGrPTFrXnGBdjaaBFxupM/sisAaV/AGSgUhgbgPXXIR6Tzq/WbEJ0U5JwhQCcG6bqLk61lJUBDxH/aPMfwE9nP/0WkJGfldgJvVMqTbBm0AEcGV9JzhHvNcDp64yaamokamlgdFldQsva6pVNmkL4UYSphBHZQEXmnPM7u/8XgSmYYnT3E/UTdoU1CjxUs2m19dsuT7pwFx7ZtKGlgSLpagKNa2b2VCjac2mHwRmVBrDXhixf1MM8EkDd01Evd/8vEWxCdEOScIUwsmaat2H2st4dHWspegA8Axwv+uQbtL6oBbdXK3Z9M1NeUZCRn531HaNR30QMliKFgJf08iK3nMuePqPV0efX/j4z692/vKTf8Y2cGoG0sJLCI8kYQpR07PAdHOO2X117EtAIpa40c7f3w+8p9n0r5tx/3Rgtj0zyd6yMGu4C7iuvnetThfkDzlpT3RFaQFqdW4d5hzzWMBEw3VlhQhZkjCFcGNNte5HTcMefW9pKTqEWhT0gG7SOgOXoRJrkyRk5PcA/oavRpdH49uBajT9vKcFQAkZ+UbUu8sHDOrr+Jtu0jw1XrgDeNaaam3qFLMQIUESphB1PQeca84xu9dlfRk4pUN82Z3At5pN39aM+/4LyLVnJvmsE4qbF6m/0fRFQAmQr9n05YC99nnOEXUianuNEMIDSZhC1GJNtRahtpRUv7fEUnTY4eDJilLjrajk2SQJGfk9UatVH/NVnDVYispRlXmexRLXwe25rtGlxZ6Z5Fr1+jKqg4q7fwKzrKnWg60SnxDtgCRMITx7AZhmzjFXr45d+1mvlcYwok2Xbitsxv3uAN63ZyZt8VWAdViKvkF1SLnN7eglwCHgS7djc4BjdZM2EsCcY+4NXIoapQoh6iENpIXwwJpqPWDOMbtWx14OUFEc9te4hCOfGozOKjteSsjI743qXWluYVhXAX0aPOOmnzey6tO7qSw3VhDmuH3q8P+bOKTb5ycO6XYl8A6AZtNLdZP2Bmq17u2oBPuBNdW6q4XxCdGuyQhTiPq9BCSac8yjnA2bpzkczATG1VeSrh53AO/YM5O2tjCePsDmBn/1Gr2II3t2Yv24x8dLtvTfdbC0aMLgrt9RN9G+Clz9j3+M6olaiCQtvIRohCRMIephTbVWr44FpgEF/b7auBN4HFWPtVEJGfmxwDXAU60UZl29j/3GsXXpmb9vKrxo0tBus42GupXzNJu+EVjR5TBPAF9bU63r/RafEG2UJEwhGvYycGphRy5FFQgAeB04DkvcBC+uvxIoaNV3l7WZL1lYXnJ4WP+wfaXnmvvU222kNJxP++9xXIq08BLCK5IwhWiANdV62FDleDK8gmm4EqalqAS12rXBUaazM8jNeLmq1mAwnGEwGN4wGAxfGgyGVwwGw4neXHfbbbeNMxgM73/wwQd9AUrDoqusjsFMj11j8zS6dHnksrCwE9Y5Ij56vOIPb54jRKiThClEI559rXL+4Q6EX3pXeJzb4f8Ax2CJm9TApQlAN+D7xp5hMBheAb5FLQ46G7Ug5xeDwWBp7NqvvvrqpD59+qx+7bXXJgF8vHjL5IPGuIODwvZU1HeNOccctnqA4YYqI2uA0xt7hhBCEqYQjeq7j1P3xfIraj+jYikqRVXsaWiUOQRY7bb/0SODwfA3VIKs8xHwgMFgmF7ftXa7PWrLli0jn3vuuVeXLl06qbSi0rh8S+GF/bt2nG8oPdCzgceeDxR1LOFNVGEDIUQjJGEK0bhp/ffwAjDeWW/V5S1gOJa4k+u5bgjgTUeSvzf384ceemj80KFDl1966aU7OnTocPju59++oEN42L5h/Xr9TvkRjwnTvYWXQfXFvEA3aWFexClESJOEKUQDdJMWDUyKLeEr1OpYS/WHlqIyVE/L+kaZg4CNDd3fYDCEowqeN2R0fR98//33J02fPv1XgBMnnbTgf999e+4pw7vPptvQ3ZSX9KjnstOALsCnmk1fD2wD6kv6QggnSZhCNOwUYJlm0w+gVsceb84xu6+OfRtIwBJ3modrDwD19qkEcDgcFcDhRmIo8nRw2bJlnTZv3jzqueeeuyE2NvaFb78ruOjPBV9HnGHqYePIvhiMYUfqud+dwFPWVGul8/dzgIsbiUGIkCcJU4iGVa+OtaZaXatjLdWfqhquDwMPeegUsh4Y7MUz8hv5PM/Twccff3yi2Wz+8dChQ7ft3Lv/9pve+OFI506dtj355JMjKdzYk4iYOpV7zDnm44FjcVb9cZoDXKSbNPl+IEQD5H8QIRp2dDuJ8h9glDnH7L469l2gL3VXm65HvcdszF3A/no+20w9Bdvnz59/0llnnfUbwEeLt5waHRm2Y8LY4//38ccfT+bQrh5EdfJU6u4O4DlrqrXUdUCz6TpwEBjvRaxChCxJmELUQzdpfYD+wGLXMWeieZSao8wK4CHgwVqjzD+BBGenkno5HA47cBIw3/0w8F9gssPh2OPpuq1btz6cmZm54khZRdjKrUUXnjaix+x58+Z9tXTp0v9QvL8XUZ1rJExzjnkw6geAVz3cTqZlhWiEJEwh6ncm8J1m0ytrHX8LGGHOMU92O/YB0AOY6jpgz0w6hFqFel1jD3I4HDaHw3EKMAA4EejrcDjOcTgcmxu7Nve3zVM6RoZtO0Prtab64JG9Q4jtXbtnZzrwmjXVesDDbVzTsvVXOhAixEnCFKJ+tadjAbCmWuuujq1/lPkykJaQke/Vtg2Hw7HF4XAsdDgcO7w5/3BpRfiqbQcumDKy58fVBzct7EvZ4X5o05e5Du0p3tMJuALV59OTZajvB8d681whQpEkTCE8cC6AORP4pp5T3gYGm3PMp7ody0Vt1zjLdcCembQY2A78tTXifPPnDdM7RYVvOt3Uc131wbXfnEH84O+J6lRd6efnrT+fBHxkTbV6TMSaTXegRsMyLStEPSRhCuHZsUChZtPtnj60plpdq2PdR5mVzt/XHmVeBzyakJF/vC8DzF+xffSGPYenXjFx4OvVB4/si6Jw8ymYzi1wHdpXsi9q44GNJ6I6rzRE3mMK0QBJmEJ45nE6tpZ3gf7mHLP76tiPgRjcEo89M0kHbgVmJ2Tkd/FFcKt3HOxaYNt58xlar+wRvWKPrrBd9l4i0V3+pLe5eqHQZ2s/S4zvEL/Ommpd28htFwJddJPWWCEFIUKSJEwhPGs0YVpTrdXvLZ3l5sBSVIVqyPwylrjqLSX2zKQPgXnAzwkZ+VozY9oODLBuLTwp37rt4dNG9Pz5XHOfA6iFQgPYYT2R4sKLGH99getYWWXZoEPlh6aPiB8xu7Gbaza9yhmj1JYVwgODw9FgXWghQo5u0mKAnUBfzaYfbOhcc445HFgJ/N2aav2q+gNL3N+Bq4HJznZgrnZff0WV2LvVmUS95rz+JtS07432zKS5bs+LR21/uRtLUa5bfNcBl1tTrWd68wzdpJ0OPK3Z9LGNnixEiJGEKUQtukk7G7hLs+meyt3VYc4xn4va2zjGmmrdDeB8h5kL7MdSdKP7+QkZ+ScAHwG7gGxgjj0zqZR6JGTkdwQuR/XWNACX2jOTjm4hscQZgU8AO5ai29ziGgr8CiRbU62LvPladJMWjqotO6G+97dChCpJmELUopu0Z4B9mk1/xNtrzDnmx4FxwNnVNVotcZ1R7wW/BO50ltEDICEjPxw4D5gJmFGrcdejupvsAPqhyuoNRa3WnY/aovKNPTOpqvrBlrgY4BVgODDFWRAec445GvgFeNOaan2hiV//a4Cu2fRnmnKdEO2dvMMUoi5vFvzUdh8QBtxffcRSdADVBcQEfI8lrp/rI3tmUoU9M2mePTNpKqrA+9dAFaq83u2oyj8lwOfAGHtm0vn2zKSvaiXLEcAC1P/HZ7qSpdPzqEpDLzbx6wDZXiKERzLCFMKNbtL6ASuAnh4q/DTInGPujXqPeL011frf6g/UlOldwC3A1ViK6tvb6R013TsDNeK8D3gVS1H1/8jmHHOq83njranWBt/BeqKbtEjUKHe0ZtNrVwsSImRJwhTCjW7SrgHO1Wz6pc253pxjPgWYDVxqTbX+UONDS9wZwJvAJlSym4OlqN53l3VY4tzfZXYE/oKlaLH7KeYc83TgDWCKNdW6qjlfA4Bu0t4BftVs+svNvYcQ7Y0kTCHc6CbtfaBAs+mvN3pyPcw55rOAHOAZVN/Jo/+TWeLCgemopDca1f3ke9T7y03u7zmxxEUBCah3mWcBVwE/43yX6dzC4npmOKoo/GVAijXVuqC58QPoJu0C4FbNpp/RkvsI0Z5IwhTCyVkObycwTrPpG1tyL3OOeSBqJexOINWaai2sc5IlzgTcAJyASop9UXstXYt+eqLae21ALR56DUtRnbjMOeY+wIeod55/saZaPXY3aQrn1prtwFDNprf4fkK0B5IwhXDSTdoY4H3Npvuk0o05xxyJKkeXBFxhTbUubPACS1wEMBDoDWwFtjiLutd3fwOqO0oOalvLI9UrdH1AN2kfA19qNv0/vrqnEG2ZJEwhnHSTloEqVnBboyc3gTnHnAJkoUaKLwNznB1Pmnu/GFTnkZuBOODmGkUTfEQ3aZcBV2k2PcnX9xaiLZKEKYSTbtIKgGc0m57n63ubc8wRqH2XrneXbwCvWlOtm5pwjxGoSj9Xc/Rd5tfWVGtVgxc2k27SOgNbgAGaTS9qjWcI0ZZIwhQC0E1aR9T7xt6aTT/Ums8y55hNQBqQinrvuN7tl3vhgiGod5tDnL8cqET7b2uq1d6aMbroJu1z4EPNpr/nj+cJEcwkYQoB6CbtXOAOzaZP8dczzTlmI2qhjyshupKj6x2mexJdD+xsrdFkfZzbbM7TbLoUMhAhTxKmEIBu0p4Ddmk2/bFAxxJMdJPWFZWw+2o2/XCg4xEikKQ0nhBKc8rhtXuaTd8HLALODnQsQgSaJEwR8nSTNgC15/H3QMcSpOYgPTKFkIQpBKobyLdNrR0bQj4BztVNWlSgAxEikCRhCiHTsQ3SbPoOVJPsqYGORYhAkoQpQppu0sJQiaBlHUTavzlIyy8R4iRhilB3Amp17OZABxLk5gLTdZMWHuhAhAgUSZgi1Ml0rBc0m74JtRf0tEDHIkSgSMIUoU4SpvfmItOyIoRJwhQhSzdpscA44IfGzhWAeo95obMNmhAhR/7ii1B2GrBIKth4R7Ppa4DdwKRAxyJEIEjCFKFMpmObTqZlRciShClCmSTMppsDXKSbNEOgAxHC3yRhipCkm7RBQDdgWYBDaWtWAmXAmEAHIoS/ScIUoepM4BvNpvu1XVZbp9l0B1LEQIQoSZgiVMl0bPPNBS6WaVkRaiRhipDjLId3BlIOr7kWAx2AUYEORAh/koQpQtFYYLtm07cGOpC2yDktOxdp+SVCjCRMEYpkOrblZHuJCDmSMEUoOhNJmC31C9BLN2nDAh2IEP4iCVOEFGc5vDHAj4GOpS1zNtueh0zLihAiCVOEminAQs2mHwl0IO2AbC8RIUUSpgg18v7Sd34AhuombUCgAxHCHyRhilAjCdNHNJteDnyOTMuKECEJU4QM3aQlAF2AFYGNpF2ZgyRMESIkYYpQIuXwfO9b4DjdpPUKdCBCtDZJmCKUyHSsj2k2vQT4ErggwKEI0eokYYqQIOXwWpVMy4qQIAlThIpxwBbNpm8PdCDt0JfAibpJiw90IEK0JkmYIlTIdGwr0Wz6YaAAmB7oWIRoTZIwRaiQhNm6pIiBaPckYYp2TzdpnYHjgZ8CHEp7lgdMcZYeFKJdkoQpQsHpwALNphcHOpD2SrPphcB84NwAhyJEq5GEKUKBTMf6h7T8Eu2aJEwRCiRh+senwDTdpEUHOhAhWoMkTNGu6SZtCBALWAMdS3un2fTdwFLUDyhCtDuSMEV7J+Xw/EumZUW7JQlTtHcyHetf84Bk3aRFBjoQIXxNEqZot3STFg4kogqECz/QbPpWwIZamSxEuyIJU7Rn44FNUg7P76SIgWiXJGGK9kymYwNjLnCBs+C9EO2GJEzRnknCDADNpm8AtgCnBDoWIXxJEqZol3ST1gU4FlV9RviftPwS7Y4kTNFenQ78IuXwAmYucJFu0uR7jGg35C+zaK9kOjaANJuuAweACYGORQhfkYQp2itJmIEn07KiXZGEKdod3aQNBWKAlYGOJcTNAS7WTZoh0IEI4QuSMEV7NA34WrPpjkAHEuKWAwbguEAHIoQvSMIU7ZFMxwYB5w8sUsRAtBuSMEW7opu0CNQKWSmHFxzkPaZoNyRhivZmArBBs+k7Ax2IAGAR0EU3aaZAByJES0nCFO2NTMcGEWdbNWn5JdoFSZiivZGEGXxkWla0C5IwRbuhm7R4YDTwc6BjETX8BAzQTdrgQAciREtIwhRtRXegJw3/nU0E5ms2vcQ/IQlvaDa9EvgUGWWKNi480AEI4QUDcAnQGSgB1gJrgB2o8msuMh0bvOYA9wNZgQ5EiOaShCnagnggFtiE+js7GDgGlUj3A3pVaekmQ2TkWY6yshcCF6ZoQAHwvm7S+mo2fVuggxGiOWRKVrQFvd3+vQLYi+q3uBmoBMZXbN8+s+t118ab/lhlBo6n8elb4UeaTS8D8oELAx2LEM0lI0zRFowEDtbzWTFQfPCHH8yOisrfDUZjHDAVNfosRU3frgW2N3AP4R9zgNuA7EAHIkRzyE/gItiFA0Oo+a6yjvKtW4+NHDjQ6jxvK2oEut957fnATcD1QJ9WjVY05CtgjG7Sugc6ECGaQxKmCHauqdWq+k6oKikJqywsPKbjpBOttT6qPX3bHejYWoGKhjmbeX+N+gFGiDZHEqYIdgNoIFkCHPrpp2HGDh12RA4Y0NiUaxVqZa0IHKn6I9osSZgi2JmAIvcDB77+plfxihWdXb8vXbPm2PCevWqPLmuLAXYDh3wfomiCfOBk3aR1CXQgQjSVJEwRzGKAXsBhgMMLF8ZvuOTSq3c/80zKrmefnVqxf3/4/o8/Hnhk0aJJYfFd1jRyrzjA1toBi4ZpNv0g8D8gKcChCNFkkjBFMOsDVDeB3vvGfyY7Skuj4/9yRV7Fzl197Zem3Lb/7bfPK9+2vfuhH37s28i9wlDvMUXgybSsaJMkYYpgNgQoc/2mbP06U+zUqfO7XnXVekdpaXRkQsKauBkzlkeNHLm+bN06bVfWM8fWcx8D6v2ltPwKDp8BZ+gmTRZgiTZFEqYIVgbU/stCAEd5uQEHxk5nJG4CCO/Vc0u3G274qXLPHi1mzAkLHFVVRkOHqIp67tUZ2AiU+yNw0TDNpu8DFgLnBDoWIZpCEqYIVvFANM4RpiEiwjH0v18+FT1q1EGAhPff/yBm3NjCij17zeE9eq6tOnw4vttf/6rXc69YYLV/whZekpZfos2RSj8iWNUoMFB15IjRGBNTVThvXv8jCxcNLl3z57Do445fDw5jyR9/dIro3Xu9sUMHR303Q1X6EcHjUyBTN2lRmk0vDXQwQnhDRpgiWI3EbQuIISLCAbB31qsXV+zbF1e2afMxFfv3Dwnv1m0FlZXGnv9Mn1PPfSJQ5fP2tn7IwluaTd8BWIEzAx2LEN6ShCmCUQSqI0l1OTxDRISjYv/+8Ip9+/oP/PereYbIyGJjTEyvyEEJKwrnzbus8sDByHru1QX4E7fVtiJoyLSsaFMkYYpg5LEcXqnN1jkyIcG67e57TjYYjRWO8rLh4f36bjAYjZVxyUn1tYyKAta3dsCiWeYC03WTFhHoQITwhiRMEYz646EcXsdJk/bFjBmz8tD3359tiIwwlK7+s2TnI49eEjl06PJG7ifl8IKQZtM3o36YOS3QsQjhDVn0I4KRRq1yeC697spYGjls6J7COXOvAse2mPHjF3S/8W/1JcwYYA9SDi+YzUEVMfg20IEI0RhJmCLYRKEaRh9BbSmps3cy/pJLNpWsXNmh08mnvB975tT6tpKAKof3S+uEKXxkLvCjbtJu0Wx6ZaCDEaIhMiUrgk0p8BGq7mtnVLeS/qjkZwAo27q1U9WR4r4dJ5/0ZyP3knJ4QU6z6WtQRfEnBToWIRojI0wRjNY7f30DdEPtyRwJJACG0jVrRof36b3GGBPT0IjEVQ5vVyvHKlrONS07P9CBCNEQGWGKYOZAvYO0ArOB54EPCufM7RLRt+9K1OhzAKoxdO2VlnGAHSmH1xbMAS7STZoh0IEI0RAZYYq2pFw3aVuBEw598820rpddtgX1vnMYMALogEqyB1Hl8H4MWKSiKVYBJcBYYHGAYxGiXpIwRVsz0vnP1ajkuNb56yvU9G1f5zlhQH17M0UQ0Wy6QzdprpZfkjBF0JIpWdHWTAO+1mx67co9runbFcDHwMvAfj/HJppvDnCxTMuKYCYJU7Q104CvvThPSuG1LUtQW4pGBToQIeojCVO0GbpJiwJOBb4LdCzCt5wzBq5pWSGCkiRM0ZZMAmyaTZfOI+2Ta3uJEEFJEqZoS7ydjhVt0y9AD92kDQ90IEJ4IglTtCWSMNsxzaZXAfOQll8iSEnCFG2CbtJ6AMOBBYGORbQqeY8pgpYkTNFWnAH8oNn0skAHIlrVD8AQ3aQNDHQgQtQmCVO0FTIdGwI0m14OfAZcGOhYhKhNEqYIes7N7JIwQ4dMy4qgJAlTtAUaUAGsCXQgwi++Acy6Sesd6ECEcCcJU7QF9ZXDE+2QZtNLgS+B8wMdixDuJGGKtkCmY0OPFDEQQUcSpghqznJ4pwAFgY5F+NV/gYm6Sesa6ECEcJGEKYLdZGCVZtP3BToQ4T+aTT+Mqhk8PdCxCOEiCVMEO5mODV1zkKo/IohIwhTBThJm6MoHpugmLTbQgQgBkjBFENNNWk9gCLAw0LEI/9NseiEwH0gKcChCAJIwRXCbCvzPWf1FhCZZLSuChiRMEcxkOlZ8CkzTTVp0oAMRQhKmCEpSDk8AaDZ9D7AEOCvQsQghCVMEq1FACbAu0IGIgJNpWREUJGGKYCXl8ITLPCBJN2mRgQ5EhDZJmCJYyXSsAECz6dsAG5AY6FhEaJOEKYKObtI6oCr8SDk84SLTsiLgJGGKYHQyYHXuwxMCVI/M83WTFh7oQETokoQpgpFMx4oaNJu+AdiM+mFKiICQhCmCkSRM4clcZFpWBJAkTBFUdJPWG0gAFgU4FBF85gAX6SZNvm+JgJC/eCLYTAUKNJteEehARHDRbLoNKAQmBDgUEaIkYYpgI9OxoiGyWlYEjCRMETSkHJ7wwlzgYuffFSH8ShKmCCZm4JBm09cHOhARtJYDDuD4AMchQpAkTBFMZHQpGuQslTgHuCjQsYjQIwlTBBNJmMIbsr1EBIQkTBEUnP0OJwHfBzoWEfQWAZ11k6YFOhARWiRhimBxCrBcs+lFgQ5EBDfNplehRpkyLSv8ShKmCBYyHSuaQraXCL+ThCmCxZnAN4EOQrQZ84H+ukkbEuhAROiQhCkCTjdpfYCBwG+BjkW0DZpNrwQ+QaZlhR9JwhTBQMrhieaQ7SXCryRhimAg7y9Fc3wPmHST1i/QgYjQIAlTBJSz88SZSMIUTaTZ9DIgD7gw0LGI0CAJUwSaGTjobBAsRFPJtKzwG0mYItBkOla0xNfAGN2k9Qh0IKL9k4QpAk0Spmg2zaYXo/7+nB/oWET7JwlTBIxu0mKAE5FyeKJlpIiB8AtJmCKQTgGWaTb9QKADEW3aF8Bk3aR1CXQgon2ThCkCSaZjRYtpNv0g8D8gOcChiHZOEqYIJEmYwldkWla0OkmYIiB0k9YX6AcsDnQsol34DEjUTVqnQAci2i9JmCJQzgS+c9YEFaJFNJu+H1gAnB3oWET7JQlTBIpMxwpfm4tMy4pWJAlT+J1bOTxp5yV86RPgHN2kdQh0IKJ9koQpAuE4YL9m0+2BDkS0H5pN3wksR3W/EcLnJGGKQJDpWNFaZFpWtBpJmCIQJGGK1jIXOE83aRGBDkS0P5IwhV/pJq0jMBG10VwIn9Js+mZgHTAlwKGIdkgSpvC3U4ElzuosQrQGafklWoUkTOFvMh0rWttc4ELdpIUFOhDRvkjCFP4mCVO0Ks2mrwV2AicFOhbRvkjCFH6jm7T+QG9gaaBjEe2eTMsKn5OEKfzpTOBbKYcn/GAucJFu0gyBDkS0H5IwhT/JdKzwl1VACTAu0IGI9kMSpvALZzm8qUg5POEHmk13IC2/hI9JwhT+cgKwR7PpmwIdiAgZc4CLZVpW+IokTOEvMh0r/G0pEAGMDnQgon2QhCn8RRKm8CvntKzUlhU+IwlTtDrdpHUCxgM/BDoWEXJke4nwGUmYwh9OA37TbPqhQAciQs6vQA/dpA0PdCCi7QsPdAAiJMh0rGiyrJRkA9ALGOL81Q/YBax3/tqanptX1dA9NJtepZu0eahp2czWjVi0d5IwhT9MA64MdBCibchKSY4DrgJuQiXMdTgTJDAK+CswFCjPSkl+FXg9PTdvZwO3nINKlpIwRYsYHA5HoGMQ7Zhu0gYCS4Bemk1vcDQgQltWSnIv4B5UsvwaeBn4MT03z+M3qayU5BNQSfUS4BPgH+m5eUW1z9NNWjiwAxgj25pES0jCFK1KN2l/BaZqNv3yQMciglNWSnJn4F/AzUAO8FR6bt72JlzfBXgUNZMxIz03b3ntc3ST9h9ghWbTn/NFzCI0ScIUrUo3abnAfzWb/magYxHBJSsluQNqhJgBfAE8kJ6b1+wRYFZK8hXA88DM9Ny8j9w/001aEpCh2fRTWhCyCHGSMEWrcfYj3AUcp9n0LY2dn51W4L7IYzDQE/Xeaj2wAdg3c1ai/IVt47JSksNQ064PAsuAu9Nz81b56N7HAt8BZ6Xn5lV3xdFNWhRqWlbTbPoOXzxLhB5JmKLV6CZtPPCWZtNHNXRedlpBD+A6IA2IRS3y2IBKtv04ukpyA+q91vszZyXKFpU2xrnq9TzgMWA/kJGem/ezD259FdDH9ZsNy5Ycu22N7ZwxZ09/ITo2tth1fN8771wW3qv3hs7TzlzoPLQdeMcHzxchQlbJitbU4HaS7LSCzkAWatHGPODSmbMSf6vnXFfx9puBx7PTCt4Cnpg5K3GXr4MWvpeVknwKapVqLHAn8EV9i3maoQ+w2fWbwceP3bx19R9dF87LPWvK1de/6Dpu7NjxhyMLF5zRedqZs52HBvjo+SJESMIUrWka9Szlz04rMAOzUdV/hs6clbi3oRvNnJVYhUq+X2enFQxELRLRs9MKsoGsmbMS66yOFIHnnCJ9DLUd5D7gg/TcvFbvhzou6YLc79/694u7Nqzr3nPw0D0AnU4/ffnhBQtuLNu6tVNkv34yQyGaTKZkRavQTVosasqrl2bTD7t/lp1WMAN4BUifOSvx7eY+IzutIAF4AEgCngZemjkr8UizgxY+k5WSPBh4CNU0/DHg1fTcvNJWetwduI0wXX7OffdKg9FYcdIlV3zoOrbz8cf/EZmQsCT+8st/Qo0wn2ylmEQ7JCNM0VqmAAs9JMsxqGR55sxZicta8oCZsxLtwLXZaQUa6pvz2uy0gkeA12fOSixryb1F82SlJPdE7aW8EngRuDk9N+9gc+9nMBjCgFtRxQoGo34I+xB43OFw1PvDkdFofK97t67bO0ZG9j54023Hjx8//pcvv/zys/AePddU7No9CPipuTGJ0CUJU7SWadRqFp2dVhAPfAzc0sJkWWORx8xZiQC/7d58cNsWfd9t5aWVD29YseebQaO6/m4MM3qaQpHFHj6WlZIcC6Sjktu7gJaem9ei98sGg8GIKkiQ7HZ4GHAvkGwwGE5xOBwep1bDwsLKdu3e88+vX33hSdPk014eOPq4TQBhXeN3l+/aObIlcYnQJQlTtJZpQO1iBS8D+TNnJea28N41Fnm49BgQu7nHgNgF63/frW1cuffSDct3T+4ztMvHphN7/2Yw1ughLIs9fCQrJTkKuBG4G/WOeVx6bt4GH93+RmomS3fHo2YV/q+hG0REddh5cO/unsAmgIjefXYW//57Tx/FJ0KMJEzhc7pJSwC6ovbYAZCdVjAY9T5rYGs/f8gJPfTBx3V/cM3incdv/bMwZeeGovP7j4zPHTqmp7VW4hTN5NxLeQUqaa0CpqXn5q3w8WOu9uJzjwmzsrIyslu3bo9HR4R3dTgYknzBBeGvvvrqgshhQ3dVlZT2dFRVYTBKsybRNJIwRWuYCnxbq3bsjUCOvxblGIwGRkzovWzY2F7LbQu2T9z0x75rtq4p3D9oVLfchGO7l/gjhvbIuZfyXOBx4BBwdXpuXmu9D2zsh6tuBoMhxtPCxbCwsLK9e/fetejT2cnlpSVxky+9cgFARM+exeAwVO7dGx3eo0drxCzaMUmYojWMBha7fpOdVhCFKkwwubELDQZDNGrVYwpq6nU98AYwy+Fw1Fu83Wg0vhcfH7+pqqoqzGg0Vk2ePPnH2bNnfxkZGek4ZnLfBSMm9F6k/7ztlHW/77qtcOeRXT/PWfvVzFmJdWqOivplpSSfBDyBmj24C/jch3spPdkM9G3g830NLfwBKC8pjo+MjtlX/ftdu6LBQFi3bsUNXSeEJ5IwRWsYgtpf6XIMsHPmrMQ1DV1kMBg6Oq8b63Z4jPPXGQaDYYajnn1QrhEFwMKFCzvPmDHjlrPPPjumoKBgNkB4hLHKPKX/D2XFFb9s+mPvxcBX2WkF3wMPzJyV+Gdzv9BQkJWSPAq1NeR41Daed/yxlxJ4D5jYyOcNKisu7hHXq4+t+vfr1vc0dojaKdOxojkkYYrWMBg1MnQZAqz14rqHqJks3V2E2lrwemM3mThx4oHs7OzXL7nkkkcqKytnh4WFVX8WGR1ePmxsr5+/em3VX4C/A79kpxXMAx6aOSuxzkKiUJaVkjwIVe/1HFQBipT03Dx/Tme/jNpje5aHz1aitq945HqHGRsV2fdIeUW/ESNHDp0/f/6H5Tu29zBER0t1KNEskjCFT+kmzcDRuq8utX9fn2u8+LzRhAkwffr0XYBh0aJFcZMmTapTBchZi/bR7LSCV1BVg5ZlpxXkAI/PnJW425tntFdZKcndUcnoalTSGuGpz2RrczgclQaDIRm1XeU61A9iO1D7MB+qb0sJQFVV1V92rFvT8/f/fv5w4rVpGVExMeUAlfv29Qrr2DGk/3xF80nCFL7WGXBoNv2A27G+qK4j9TIYDJ1Q78YaMqgpgTgcjkaXxM6clbgPuCs7reB5VJKwhWq5vayU5E7A7aiR94fAqPTcvIB29nA4HBWo96ZPNPXadUsWTY3r2esHV7IEKN++Y1TU8GG/+DJGETpkIl/42gHAqJu0Tm7HdqDadtXLOVo40NA5NJJ03X322Wc9DQZD1YQJE7xKejNnJe6YOSvxVtSU8ABgTXZawR3ZaQUx3j6zrcpKSY7MSkmeCawBNGBiem7eLYFOli1RcvhQxIHdO08bMmbCt65jxdaVPasOHhja+ZxzFjZ0rRD1kRGm8CnNpjt0k2ZHTZ9ZnYfXAxO8uPw9VEPhhj5v1G+//RZ7yy23/HXy5Mlfu7+/9IaHcntrstMKHqUdltvLSkk2ApcBDwN/Auek5+YtC2hQPrIk/9NLOnSK1XsPHV79vvLQ/76fGtGv/w9hnTuXN3StEPWRhClaw3rUe0tXwtwADPXiuruBRMBT6bJvUe/TPHIt8nBtK5k0adJPc+fO/aKJcVebOStRBy7JTisYCzwC/DM7rcACvDdzVqI/Voi2GudeyrNReylLgb+m5+b9L6BB+dCqH74dd3DP7hMnXXL53a5jlQcORJRv2XJa/JVX3RfI2ETbJglTtIb1qBGmyyqgf3ZaQYJzBOeRw+EoNBgME1ErM1OAnsBG4D/AEw6Ho95EVVVV9RdfBF7bzFmJS4BzstMKTkVtrbgzO63gXuCTmbMS21yrn6yU5BNRK157o35AmdfKeyn9aottVb9NK1dcb5p86lNxPXpVLwran5t7tjG287po82hZISuaTdp7CZ/TTdo1wEWaTZ/uOpadVvAsUDxzVuLd9V5Yi8FgMNZTrMBjO6cmaFZbp+y0AgNqi8WjQAUq4XzbFhJnVkqyhop7PGABctJz8yoCGpTvXAX0sS9fYt6ir7qwz3Dt86Fjx//u+vDI8uUJh77//qoul1zyYmS/foVu10kRftEkkjCFz+kmrSOq2PUYzaZvBMhOKxgJ/AgMnDkrsaV9EQOSMF2y0wqMwAzUu79twD0zZyUG5crLrJTkAagEeR7qa85Oz81rV1VuslKSI1GraM8HLknPzVvi+kw3ab1RVadu0Gz6lwEKUbQTkjBFq9BN2nPAYc2mV28uz04r+BRYOXNWYr0bzr0U0ITpkp1WEI7aq/gAsAK4N1jK7WWlJHcDMlD7F18FnkzPzSsMaFCtwFmF6F3UD2jXpufmVZfB001aOKqDynzNpt8foBBFOyIJU7QK3aSNRJW5G6TZ9FKA7LSCnsASIG3mrMT8Ftw+KBKmi7NW7o2o+qqucnsNlgFsLVkpyR1R+yj/D5gNPJSem7ctELG0JucK39tQe2fvAt5wfxerm7Q+qL2khajXA216oZYIDpIwRavRTdrXwJeaTX/WdSw7rWAyMBc4uQVJJagSpkt2WkEn1Dfx24F5wMP+KreXlZIcAVwP3Af8BNybnpsXkKTd2pzTzG8B0cBV6bl569w/103aFNQWpFeBRyVZCl+RhClajW7ShgALgOmaTV/gOp6dVnAt6p3TX2fOSvy8Gbe+CtXJpLladbFHdlpBPKrc3o1Aq5bbc460LkFtfdkA3OX+Dq+9yUpJvgJ4zvnrSfeFS7pJC0P9MHUbcLVm078JRIyi/ZKEKVqVbtKmAy8CYzWbvsd1PDutYBKQixoJWHywECjoZKcV9EZNGV4B+LzcXlZK8pmoLSJVQEZ6bt53vrp3sMlKSe6K+m94HHBlem7eUvfPdZM2CPXDSRhwhWbTpZC+8DlJmKLV6SbtCVRrqHPdp8ey0wp6AK85P7MA786cldhetjpUy04rSEAtDEoCngZeakkj7ayU5PGoogMDUQl5dnvaS1mb8weD/6Cm8jPcV/k6i/1fBWSh/ts+LVOworVIwhStzrla8XNUoYwrNJteY3rS+V7zMVShgvuAuTNnJdbbLLqtciu3dxJqT2STyu1lpSSPRE29noQq7vBmem5euy3zlpWSHI0aQV8EXJeem1djilU3ad1Q7ylNwJWaTV/m9yBFSJGEKfzCmTQfQo0GUjSbXmPforMowJmoxGlAFQX4ui0UBWgqt3J7I1Ejz/cbKreXlZLcz3nehaiR1AvpuXnNHqG2BVkpyWNR20WWAze7bxcB0E3a2cAbqJWw92g23Z99OkWIkoQp/Eo3acmob3RPAs/Vnj5zJs6LUAllF6oowHy/B+oHbuX24oE65fayUpLjgTuBG1B9QJ+onTiCVB9UJaQmL3TKSkkOR33Nfwf+np6b94H757pJiwGeApKBazWbXtDycIXwjiRM4Xe6SRsMvI3aFnCzZtMX1T4nO60gDLgS9W7zD1RRgN9rn9cMHYAewBYg4H/5nT8gnI1KnBXA3SWFr/yMo/hW4J/AJ8CD6bl5WwIXpdcigBOBk4FDqB+MvB4JZ6UkD0WtXi4GrknPzauxcEc3aROcny8CbtVseqFvwhbCO5IwRUC4LdZ4AvV+8y7Npu+tfZ6zKMD1qMUtPwH3z5yVuLoFj54KTEZtwfgBVSEm4P8TZKcVGB2OsksqS1c+V1GyMB6Mv+A4fHN6bp4t0LF5qQdqUVNPVLnAnsBa1J9tg/99nd1TrkctZHoENeVc/Q7bOZ1/NzATlSg/ao0vQIjGSMIUAaWbtC6od5spqGnJNzSbXmfBT3ZaQUfgFtSo6zPgwZmzEjc18XFdUd+YtwCdgS6obig/0oTm1L7mTBgXoxYCbQuPOWN+eNRxV6PK7d0zc1biikDF5gUjapXzVNSocr/bZwmoEbJe38VZKcm9UCulB6C2i6xy/1w3aSNQo8pC4DrNpgfsz0kISZgiKOgm7XhUv8sw1DStx8332WkFXYB04GbUN9LHZ85K3OnlY84FRgDu53cFYlENlOej3pv6TVZK8gmolZ7hqNqv36Tn5jmCqdxeA+JQ08mDUaPK2it2o1D/ff+DSng1ZKUkT0d97W8ClvTcvOoVw84ZiBtRBe4twMuaTZdvViKgJGGKoKGbNCOQipqamwvcq9l0j4tcstMKeqGSyVXAK8DTM2clFjZw+57AtaiSep7+0ncHYlC9O38BWnVxjXNU+VfU1/ov4G33aUiXQJbba4ABtcL3HKCShhf39ED9EPIRqsACWSnJscCzqGbhV6fn5tVY1OWsA/sG6s/sSs2mt5VpadHOScIUQUc3afGod1kXo95dveVpmhYgO61gIHA/qrVTFvDizFmJhz2ceiFq2q+hb+4G1Df4DsBS1OISn1XmcclKSY5BVa2ZCFycnptX75Sliz/L7TUiBjgDGI0qMehNhaZBwLfA4qyU5MmoBV/fA7en5+YddD9RN2kXoWYa/g08rNn0drvPVLQ9kjBF0NJN2lhUYqkCZmo2vd5Vss5+mw8Crq0ar7mV2+uDGrlu9PLRBqAXapp0EarDyqHmfA21ZaUkdwH+hxrJ3piem9ek+zrL7d0N/IVWKLfXiEGovpoRwI7aH5YeORIWFRPjaT9peEV5Wb+3/3Vrr/3bt14GpKXn5n3qfoJu0uKA51ErbK/SbPqvvg9fiJaRhCmCmnOa9jrUgpiPgPsa2k6QnVZwvPPcUYClx8DYdy+9e/zFqCnXpk6zhqESJ6hp2mWoLQ/N4pyGnYdadHRrS8rZ1Sq3dzfwRisWeYhEJbKJqBF6jRH8ns0bO8574qGb+44wLU667V/f1754i21Vv/VLFt0G7NuwbElS6lMv1Vi4o5u0U1Gjzq+AdM2m++SHEyF8TRKmaBOcZdAeA6ajFse83dAikOy0gpOBxzp3j+4z5YoRC/uN7PqFMcxQ7/kVZZUGY7jRYTQaPH0cjkqcFaiFQVa8m4qsISsl+Q5UUYZT3Re4tER2WsEoVLWb31F9Rn1dAag3qkhAF9TCnhr/Df/39utjVv1YMKP7gIG/pzyQ+bHreFVVJTgwLP3ys7N229df2HvYiA+Pn5a01mA0LkCtSkY3aVGoRT1XAn/TbHqej2MXwqckYYo2xbl5PRsoQU3T1rvlYlHeBkO3fh2f2LH+QEpZcUVl/5HxucPG9lxhcCbFqioHG5bv7tVjYGxh527R3iTASFTiLEbt4dSpuzLUo6yU5NNQ3VkmpOfmNXU7TIOcW25eAU4Azpk5K9EXRQ7CgHHAFNR73DrTviWHD4XlpM+8K7Zbd/sVj2a9C/Dngp97jzhx8o49mzd2Xf7NF2mVFZVR5sRpL/cZNmInagvKAOA953aid4H1qGQZiPexQjSJJEzR5jj7Hl6PGp28Dzyg2XRP7/GGAJc6qhwbbQt2TNi+rvDSsDDjgYGjuuZ26RWz6aeP1kwrL6nsuG/74RN6De48/9w082fhkWGOqioHRqOBA3uLo+pJpB2AfsCvQKMttZzNndei3ln+t7lfd0OcFYPuQ22dObUpRd096IpaAdsftT+1xnvJqqpKyktKw6JiYip/zn139B8/fX/28AmTCtYs+vUMgLDwsOjo2LhBQ8aM/3xs8gWfhUdEVi/YclRUxBbO+2TazkcfPc1RUnIHakGXfBMSbYIkTNFm6SatO6qbxbmoxsHvuX3zNaK2kYQBBwEqyquMtl+2Td6xoeiSvVsPV+zfcaTjsaf3f/mki4Ytf//BBTcPH9fr+/FJg/X9Ow5Hb/5jX58l/914VVzPaNsF/zcm18NU7SDUiHFDY3FmpSRfjKqLeqpPvvCaqptpV1U5DCu+23xVRIew/aNO6ddQY+6GGmhHA2mo6ec9tT+srKgwhIWHOwDWLl7Qc9i4E3flPnjXjN0bN0zoO8L0c5defQbuWLdGKys+svb4s5LmHj8tye66tmT16m6Fubk3hffoER0zbvzDMePHvdm8L1mIwAgPdABCNJezIfX1ukk7EbUV4QbdpM3UbPpKYBhqoU/19Gd4hLFq9Gn9f4rv03HVjx/+ObNLr5gutgU7blq7ZNdmg4GSivKqiHW/7+r167x1l8X1iFlXXloZ73Bg9JAsu6AW7ti9DPVmZ3ytoQ9qbylGo4Fh43o+t/DzDY+GR4Z1Hzmxd32rigc0cL8S1GrigZ4+dCXLXEvGpfu2bRn122dz9ejYzoXDJ570o6Oy8uzI6OjfEq+98a7Pn828oUPHTmUAjqoqCj/++OTipb9fGTlsaH63a67JN0REDET9Ga1t5tcthN8ZAx2AEC2l2fQFwHjUiK/AduxxWZVFRefiYYQEUF5SGXZoX0mXC/5xwk3HJQ74olN81JCqSsegtYt3nmv939aT+w7rsviYyX0WG8MNJadeNuJzgMqKKvesGYd6h9no9ExWSrKGWrE7t4Vfplc6xXc43HdY3Ps71hUlN/MWDuAb5z+jPZ1Q8Oar48pKijve9O93Hyg5eKDHjnV/nlt66NC5g48f++rJl12ds3HF770ry8tiDEZjVdnWrZ12PfnkbSXWldPjLrjg8e433PC5ISKiClVtKQlVZUmINkESpmgXNJteqdn0l4HR0ccfP2RvTo5l/8cfH+eoqlvvoFv/Tgc6xndY+/17ttPGnj3os2nXj741rkf0lsOFZeaOXSJ7jzl70MrfvrCfF9+74/IeA2IPVVZUGcLCja7k2A21UMXbajt/A97w1apYbwwf12tJeVll761/7m9oJNmQg0A+aoFTneF18cEDseGRkSXv3HnbLaVHjoxJOPaETVNSb/hXTJf4zTn/uuVvS7749MrRp585r/f+gz33vPhSpjEmZm/PjDvv6TjpRPd9sMVAJ9TCIiHaBJmSFe2Kc4/md4fm//zHwa+++stOXU/snJT0VszYsdV7/2K7digdc9bAzxd/Yb/49f/78YxRp/R778QLhr46/+M1F21Yvmfytj8LJ5aVVBZf/sDE2wEMxhrbUToBs5sQ0nhUp5UmMxgMfVF7Lc9GFYtfATzrcDg+aei6yA4Rb/ft1f+AwWC890jpwX1vv/12VlJSksfRdgPWoqodmalVmD6uZ68965Ysujy2W/fIky654t/mxGm//PLx+6PDIyPLO8V33THj9rvePZz78aWHl+tjO50+5ZXOZ521qta9w1CLpjahGkQL0SbIoh/R3hyLqkazvqq01Lj/gw/OLNX1iyL6D/hf/F+umBvetWuNVa+r5m8b2CEmvGzomJ47ALavK4zLe3H5gxXlVV37j4wv6zO8y2ejT+n3VYdOEaWo2qZ24NPaD61PVkryNmBi7d6OjTEYDMNRez57evj4AYfD8ZDz3++g1mg3IiLizQ0rdz3w56IdtyRepd3h4foBqAbejekAXIOqtHQQYOf6tT1//+/nt+xYt6ZLWHjEqqk33PzBLx+9N2Xbn/rpky6+/GVTlx6VB/771c1hcZ3Xd01NfSuiV6/a+0LjODql/Ru1VuAKEcwkYYr2pgdqS0RfVGWfg6UbNsQVfjz7isr9+0bFjBv3TtyFFy40GGu+jXBtJVn42frRK77fkjr1Gu0pDERvXV14fsnhclP3/p0+GX1av7XhEWGvUs+70dqyUpKjUe2uOqbn5jUpMRgMhh+BU+r52AGMdzgcS6gnYRbtPXzTz7PXvDL1mmOuNdRdtORtwgS1teRKR1XVlmVff3HKjnV/Xt5jYMInvYYMX/DT+28llZUUx1aUl0Wfc+Pf/xP108+JZRvs02LGjXuryyUzFtT+klCjyiJUj8ztXj5fiKAhCVO0RwbUHszTUe8c9wBHDn77renQ/3641hAVWdg5+by3Yk44vs437WXfbhq2d8uhPmdcc8xPpUfKw6JiIio36/sG7bIfuGrHhqJu9hV7M4B3Z85KrGgsCOeCn0/Sc/NGNil4g2Ew6j1pQ553OBz/wEPCNBqN78XHx2+Ki+k+ILwDK/5cs7p2cmxKwmT7mtXnbV39R+a+bVs6HnPK6dn9tdHVhREO7tsbGb55a9fCuXNvNoSHFcdffvmsqGHD9te6RUfUiuXfUKPmJldJEiIYSMIU7VkYauvC6ahpwF1VJSXl+9//YFrpatsFEQMGFsT/5YpPwuPjG/sGbgT6zX5i8YqdGw7cjRrF3gfMnTkr0WMXFYCslOQzgPvSc/OmNCVog8FwGqpAe0M+cTgcF1LPCLO8vPza79+1ZQ4b23PWAK2rvda1XifMrJTk6cawsFcnX3rVkjHnTv8qPDKyenTtqKpi/3vvnVGycuWlUZo2t+uVV35tcG47cdMHVQ3pc7zfhiNEUJJFP6I9qwRWo0ZrJmCKsUOH6G7XXftd6fr1vxZ+PPsvu5586qmY8ePfibvg/N9qT9O66Q38NuPOcd9npxV8BUxD1bW9Kzut4B7gq3oKn+9wXttUWxs/hUbL31VWVMV1io8qbMbzXT0rnwHOqKqsvGTCBTNWowpBRADlpRs2xO1/7/0bHWWlcV1SUh6MGTNmW61bRKGS5SpUay9f17gVwu9kW4kIBeWogumvAgVAfNSQIR173XnHq50ST3+leOmSS3Y++lhG8fIVnpJbGGqKdzHAzFmJjpmzEr9CbYd4DNUI+QdnsffaNgCDslKSm/T/mcPhWAssbOS09xr6sPhgWZSjyhHdpWdMYVOeDZCVknwSqjOLETje2eB5Nyrx9S2cO2/83lmvZoZ1jd/QKyPjfg/JsgeqvN4nqJGlJEvRLsiUrAhF0cBxwGSAqpKSPfvffe/M0jV/To8YNOjbrn/5y6dhcXGufZP9gAWod291ZKcVhKN6Uz4I/AHcM3NWYnWFnayU5B3A2PTcPG9GjdUMBsMxzmfGe/j4SYfDcafz3z1OydpX7bp/9cIdtzZllWxWSnIkahvLX4Gb0nPz5rl/vvaMqXGxZ037hLCw0TFjx74YO2XKmlq3CEf997ID/wUKG/kyhWhTJGGKUNYJGAtMACpL164tK5w954rKoqLhMRMn5sRNP2+FwWjsDsyikVFSdlpBFHADas/lT8D9M2cl2rJSkn8B7kzPzfupqcE5F/88jFr1G4saJT/jcDjcR5d1EibAkv/azy8rrug56cJhr3m4dZ2EmZWSfAyqe8g24Pr03LwaDaJ1k3YK8HZY9+7/G/rlF6vDYmN3UHPxTjxqcc/3qP2b9b7bFaKtkoQphFoQNAEYA5Qe+Orr7od+/PHayCFDiiL79rmn8znnzGvk+mrOVlu3AunAZyWFr4ThKLan5+ZZWhKgwWAwODz/z1onYVaUVxl/eN/2/IgJvZ8eoHXd6OGa6oTpnC6+Bbgf1Yj6NffG1s6elQ8CV3O0Z6UJuBA1kjSiRpW7UdWBdrXk6xQimEnCFOKorsBJwKiq0tLKA/n5p+548KGTHKWlLwFPaDa92NsbZacVdAH+WVWx65ayQx+HR3a6dOStr6c0aVrWS3USpm3B9rE71hdNn3KF6YF6rhkAPJmVktwfeBM10r4qPTevRiF03aSNRo067cANbj0rDaim0qNRe0J/Rk1be9UbVIi2ShKmEHX1RCXO1bpJO4RaLToGuE2z6flNuVF2WkGv0qI3l4RHnxQXFjnyBeDpmbMSa+9TbIkaCfPIgbIOCz5Z90ifYV0+0k7qs6ieawZkpSRvAl5w/spMz82r3leqmzQj8A/gLuBO4E0PPStjgDNQi4OaVMVIiLZKEqYQXtBN2jTgJUAH/qHZ9Eb7YLpkpSRfBuG3doi/zQZMRyXgF2bOSjzsg9CqE6ajysHPc9beFhZuKK7n3SUH9+7puGbRLzd//9a/Y4Ar03PzFrt/rpu0gcBbQCRwtWbTGyugIETIkG0lQnhBs+lfowqRLwR+003afbpJ6+Dl5XOhIqFk/zPvAicDxwNrs9MKbnUuFvKJZd9uOre8tLLPmLMGveXp8z8XzB/1c+47meEREUeAMe7JUjdpBt2kXYnaPvM1cJokSyFqkhGmEE2km7RBqP2XZuBWzab/t7FrslKSp6FGbmPTc/O2Z6cVnAA8guqV+SDwjjfl9jy4qqK8sr/t1+3nHTlQPnzkxF5vxPWI2ed+QnlpSbg+/39nH9yz59hBxx7/cX9t9I/AO25fT1fUSuBRwJWaTa+v8bQQIU0SphDNpJu0c4AXUW23btdsuqcVqdWyUpIfABKBM1zvDJ0FDx5DbfZ/AFVuz+vEmZ1WMBJVxGAjcN3MWYlFtZ55AmrhziogLT03r0Yy1U3aWcAbwMfAXZpNL/H22UKEGkmYQrSAc1r2X8DfUe8mszSb7rE2bVZKchjwBbAiPTfvX67j2WkFBlS5vQdQ3UFeBV6fOStxp6f7ZKcVhAHnAjNR+0gfBZ53L8/nfNYdwO3OX+/X2i4SAzwBnA9cq9n075r1H0CIECIJUwgf0E3aYOA51B7FW53vPOvISknujqrg8wPw9/TcvBojuuy0gjHATcAlqCIC61El9g4CCcBgYASqwfPLwEczZyXW2O6SlZI8BHgbKAOuSc/N21Qr1nGoUecS4BbNpvty1a4Q7ZYkTCF8SDdpyaitGkuA/9Nsep0tF1kpyZ2B14GhwIz03Lw6K26z0wpinJ8PRrUqi0Xth9wArJ85K7F2/VayUpINwHVAJmqa9/n03Lzqiju6SQtHbRW5FbVF5sMWfbFChBhJmEL4mG7SolH7F28BngKe1Wx6mfs5zuR2G6qU3m3Ax01tMl3rfkOBLNQo9Mr03LyVtWIajlrocxA1BdtotxMhRE2SMIVoJbpJG4oabQ4BZmo2vaD2OVkpyZNQK277oN5dvpGem+fx3aWHa13vMm9GdU95GXgsPTev+h2qbtIMqBq3jwIPAdmaTZc6r0I0gyRMIVqRM2FNB54HfgX+qdn0OiXyslKSXe8uZwDfoKZ0Xe8v1wOHgEGo5DsY1Rh7BrAT57vM9Ny8Gu8ydZPWGzX12we4SrPpf7TClyhEyJCEKYQfOFel3g2kAY8DL2g2vU7t1ayU5C6oRGji6PvLIaiWZFtQydOVSL+pXanH7XkXoPZWvg48VHtKWAjRdJIwhfAj3aSNQO3d7I9KoHmaTW/w3aXzfafRm3eczmng+1AVha7WbPovLY9aCAGSMIXwO+c07QWohUHV7y41m+7Vu0sP9wtD9cy8GdWm7A3gYc2mH/JJwEIIQBKmEAGlmzT3d5dfAv9BVeXZ4aFDiPt1HVFTtsmoad6dwCtAblPakAkhvCcJU4ggoJu0eFST5hTUgp5OqPeUrkU/rsIFrkU/cah9mb8Ar2g23eO7TCGE70jCFCII6SYtlqMJ0r1wgWvRzw7ZHiKEf0nCFEIIIbwg/TCFEEIIL0jCFEIIIbwgCVMIIYTwgiRMIYQQwguSMIUQQggvSMIUQgghvCAJUwghhPCCJEwhhBDCC5IwhRBCCC9IwhRCCCG8IAlTCCGE8IIkTCGEEMILkjCFEEIIL0jCFEIIIbwgCVMIIYTwgiRMIYQQwguSMIUQQggvSMIUQgghvCAJUwghhPCCJEwhhBDCC5IwhRBCCC9IwhRCCCG8IAlTCCGE8IIkTCGEEMILkjCFEEIIL/w/sQd0RhndnyYAAAAASUVORK5CYII=\n", "text/plain": [ "
    " ] @@ -175,7 +158,7 @@ } ], "source": [ - "## the edges (unit weights added by default)\n", + "## list the edges (unit weights added by default)\n", "for e in HG.edges:\n", " print(e,'has weight',HG.edges[e].weight)\n" ] @@ -192,14 +175,14 @@ "A has strength 4\n", "B has strength 2\n", "C has strength 2\n", + "E has strength 2\n", "D has strength 2\n", - "F has strength 3\n", - "E has strength 2\n" + "F has strength 3\n" ] } ], "source": [ - "## the nodes (here strength = degree since all weights are 1)\n", + "## list the nodes (here strength = degree since all weights are 1)\n", "for v in HG.nodes:\n", " print(v,'has strength',HG.nodes[v].strength) \n" ] @@ -250,7 +233,7 @@ "A3 = [{'A','B','C','D','E','F'}]\n", "A4 = [{'A'},{'B'},{'C'},{'D'},{'E'},{'F'}]\n", "\n", - "## we compute for 3 different choices of functions for the edge contribution: linear (default), strict and majority\n", + "## we compute with 3 different choices of functions for the edge contribution: linear (default), strict and majority\n", "strict = hmod.strict\n", "majority = hmod.majority\n", "\n", @@ -296,13 +279,13 @@ "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "\n", @@ -333,19 +316,19 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -404,7 +387,7 @@ ], "source": [ "## Clustering with Kumar's algorithm\n", - "hmod.dict2part(hmod.kumar(HG))" + "hmod.kumar(HG)" ] }, { @@ -417,7 +400,7 @@ "output_type": "stream", "text": [ "start from: [{'A'}, {'B'}, {'C'}, {'D'}, {'E'}, {'F'}]\n", - "final partition: [{'A', 'C', 'B'}, {'E', 'D', 'F'}]\n" + "final partition: [{'C', 'A', 'B'}, {'E', 'D', 'F'}]\n" ] } ], @@ -428,6 +411,101 @@ "print('final partition:',A)\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random hypergraph example\n", + "\n", + "We build a random Chung-Lu hypergraph and compute modularity for partitions from 3 algorithms:\n", + "* Louvain, on the 2-section graph\n", + "* Kumar algorithm\n", + "* LastStep algorithm\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "## random Chung-Lu hypergraph\n", + "import hypernetx.algorithms.generative_models as gm\n", + "import random\n", + "n = 100\n", + "k1 = {i : random.randint(2, 25) for i in range(n)}\n", + "k2 = {i : sorted(k1.values())[i] for i in range(n)}\n", + "H = gm.chung_lu_hypergraph(k1, k2)\n", + "\n", + "## pre-compute required quantities\n", + "hmod.precompute_attributes(H)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "qH = 0.06072051073158535\n" + ] + } + ], + "source": [ + "## Louvain algorithm on the 2-section graph\n", + "G = hmod.two_section(H)\n", + "G.vs['louvain'] = G.community_multilevel().membership\n", + "ML = hmod.dict2part({v['name']:v['louvain'] for v in G.vs})\n", + "\n", + "## Compute qH\n", + "print('qH =',hmod.modularity(H, ML))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "qH = 0.1379748602509609\n" + ] + } + ], + "source": [ + "## Kumar algorithm\n", + "KU = hmod.kumar(H)\n", + "\n", + "## Compute qH\n", + "print('qH =',hmod.modularity(H, KU))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "qH = 0.18999957793453737\n" + ] + } + ], + "source": [ + "## Last-step algorithm using previous result as initial partition\n", + "LS = hmod.last_step(H, KU)\n", + "\n", + "## Compute qH\n", + "print('qH =',hmod.modularity(H, LS))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -454,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -475,25 +553,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Build weighted hypergraph " + "### Build weighted GoT hypergraph " ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "## Nodes are represented as strings from '0' to 'n-1'\n", - "HG = hnx.Hypergraph(dict(enumerate(Edges)))\n", + "GoT = hnx.Hypergraph(dict(enumerate(Edges)))\n", "## add edge weights\n", - "for e in HG.edges:\n", - " HG.edges[e].weight = Weights[e]\n", + "for e in GoT.edges:\n", + " GoT.edges[e].weight = Weights[e]\n", "## add full names\n", - "for v in HG.nodes:\n", - " HG.nodes[v].name = Names[v]\n", + "for v in GoT.nodes:\n", + " GoT.nodes[v].name = Names[v]\n", "## pre-compute required quantities for modularity and clustering\n", - "hmod.precompute_attributes(HG)" + "hmod.precompute_attributes(GoT)" ] }, { @@ -507,16 +585,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "-0.016649297401793245" + "-0.014324487155556065" ] }, - "execution_count": 14, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -524,23 +602,23 @@ "source": [ "## generate a random partition into K parts to compare results\n", "K = 5\n", - "V = list(HG.nodes)\n", + "V = list(GoT.nodes)\n", "p = np.random.choice(K, size=len(V))\n", "RandPart = hmod.dict2part({V[i]:p[i] for i in range(len(V))})\n", "## compute qH\n", - "hmod.modularity(HG, RandPart)" + "hmod.modularity(GoT, RandPart)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Get the 2-section graph (with igraph) and cluster with Louvain\n" + "### Generate the 2-section igraph Graph and cluster with Louvain Algorithm\n" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -553,13 +631,13 @@ ], "source": [ "## build 2-section\n", - "G = hmod.two_section(HG)\n", + "G = hmod.two_section(GoT)\n", "## Louvain algorithm\n", - "ML = G.community_multilevel(weights='weight')\n", - "G.vs['louvain'] = ML.membership\n", - "part = hmod.dict2part({v['name']:v['louvain'] for v in G.vs})\n", + "G.vs['louvain'] = G.community_multilevel(weights='weight').membership\n", + "ML = hmod.dict2part({v['name']:v['louvain'] for v in G.vs})\n", + "\n", "## Compute qH\n", - "print('qH =',hmod.modularity(HG, part))" + "print('qH =',hmod.modularity(GoT, ML))" ] }, { @@ -571,23 +649,22 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "qH = 0.5351500884869287\n" + "qH = 0.5346892761525917\n" ] } ], "source": [ "## run Kumar's algorithm, get partition\n", - "KU = hmod.kumar(HG)\n", - "G.vs['kumar'] = [KU[v['name']] for v in G.vs]\n", + "KU = hmod.kumar(GoT)\n", "## Compute qH\n", - "print('qH =',hmod.modularity(HG, hmod.dict2part(KU)))" + "print('qH =',hmod.modularity(GoT, KU))" ] }, { @@ -596,41 +673,39 @@ "source": [ "### Cluster with simple H-based (Last Step) Algorithm\n", "\n", - "We use Louvain or Kumar algorithm on the 2-section as the required initial partition" + "We use Louvain on the 2-section or Kumar algorithm for the initial partition" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "qH = 0.5478409583056635\n" + "qH = 0.5455873667030067\n" ] } ], "source": [ - "## Louvain parition already computed\n", - "part = hmod.dict2part({v['name']:v['louvain'] for v in G.vs})\n", - "## H-based last step\n", - "LS = hmod.last_step(HG, part)\n", + "## H-based last step with Louvain parition already computed\n", + "LS = hmod.last_step(GoT, ML)\n", "## Compute qH\n", - "print('qH =',hmod.modularity(HG, LS))\n" + "print('qH =',hmod.modularity(GoT, LS))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Example: top nodes in cluster with Daenerys Targaryen\n" + "### Example: show top nodes in same cluster as Daenerys Targaryen\n" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -660,22 +735,22 @@ " \n", " \n", " \n", - " 24\n", + " 14\n", " Daenerys Targaryen\n", " 31103\n", " \n", " \n", - " 27\n", + " 17\n", " Jorah Mormont\n", " 19344\n", " \n", " \n", - " 26\n", + " 7\n", " Missandei\n", " 13683\n", " \n", " \n", - " 14\n", + " 3\n", " Grey Worm\n", " 10497\n", " \n", @@ -690,14 +765,14 @@ ], "text/plain": [ " character strength\n", - "24 Daenerys Targaryen 31103\n", - "27 Jorah Mormont 19344\n", - "26 Missandei 13683\n", - "14 Grey Worm 10497\n", + "14 Daenerys Targaryen 31103\n", + "17 Jorah Mormont 19344\n", + "7 Missandei 13683\n", + "3 Grey Worm 10497\n", "8 Barristan Selmy 6514" ] }, - "execution_count": 18, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -711,10 +786,17 @@ "## Build dataframe: all nodes in DT_part\n", "L = []\n", "for n in LS[DT_part]:\n", - " L.append([Names[n],HG.nodes[n].strength])\n", + " L.append([Names[n],GoT.nodes[n].strength])\n", "D = pd.DataFrame(L, columns=['character','strength'])\n", "D.sort_values(by='strength',ascending=False).head(5)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 42fd5301b0028a04eeaf7ed55e4160ba961a10cb Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Thu, 21 Oct 2021 12:00:23 -0400 Subject: [PATCH 24/41] reorg some functions --- hypernetx/algorithms/hypergraph_modularity.py | 1 + 1 file changed, 1 insertion(+) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index c83b008c..e47670da 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -85,6 +85,7 @@ def precompute_attributes(HG): HG : Hypergraph """ + HG = HG.remove_singletons() # 1. compute node strenghts (weighted degrees) for v in HG.nodes: HG.nodes[v].strength = 0 From 0ecb1ef0b5cfaf334fb458d5dc09ffae63c0623e Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Thu, 21 Oct 2021 12:04:14 -0400 Subject: [PATCH 25/41] reorg some functions --- hypernetx/algorithms/hypergraph_modularity.py | 31 ++++++++++--------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index e47670da..c774f8ab 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -85,33 +85,34 @@ def precompute_attributes(HG): HG : Hypergraph """ - HG = HG.remove_singletons() + H = HG.remove_singletons() # 1. compute node strenghts (weighted degrees) - for v in HG.nodes: - HG.nodes[v].strength = 0 - for e in HG.edges: + for v in H.nodes: + H.nodes[v].strength = 0 + for e in H.edges: try: - w = HG.edges[e].weight + w = H.edges[e].weight except: w = 1 # add unit weight if none to simplify other functions - HG.edges[e].weight = 1 - for v in list(HG.edges[e]): - HG.nodes[v].strength += w + H.edges[e].weight = 1 + for v in list(H.edges[e]): + H.nodes[v].strength += w # 2. compute d-weights - ctr = Counter([len(HG.edges[e]) for e in HG.edges]) + ctr = Counter([len(H.edges[e]) for e in H.edges]) for k in ctr.keys(): ctr[k] = 0 - for e in HG.edges: - ctr[len(HG.edges[e])] += HG.edges[e].weight - HG.d_weights = ctr - HG.total_weight = sum(ctr.values()) + for e in H.edges: + ctr[len(H.edges[e])] += H.edges[e].weight + H.d_weights = ctr + H.total_weight = sum(ctr.values()) # 3. compute binomial coeffcients (modularity speed-up) bin_coef = {} - for n in HG.d_weights.keys(): + for n in H.d_weights.keys(): for k in np.arange(n // 2 + 1, n + 1): bin_coef[(n, k)] = comb(n, k, exact=True) - HG.bin_coef = bin_coef + H.bin_coef = bin_coef + return H ################################################################################ From 7897b314e108eba5e377d3e79f390bbd70c47db9 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Thu, 21 Oct 2021 12:12:43 -0400 Subject: [PATCH 26/41] reorg some functions --- hypernetx/algorithms/hypergraph_modularity.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index c774f8ab..15046c98 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -77,6 +77,7 @@ def precompute_attributes(HG): The weighted degree for each vertex v is stored in v.strength. The total edge weigths for each edge cardinality is stored in HG.d_weights. Binomial coefficients to speed-up modularity computation are stored in HG.bin_coef. + Isolated vertices found only in edge(s) of size 1 are dropped. This needs to be run before calling either modularity() or last_step(). @@ -84,6 +85,11 @@ def precompute_attributes(HG): ---------- HG : Hypergraph + Returns + ------- + : Hypergraph + Same hypergraph with added attributes + """ H = HG.remove_singletons() # 1. compute node strenghts (weighted degrees) From 4c154c4aa03369085f4f7334d2f88cdcfa574dba Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Thu, 21 Oct 2021 15:27:39 -0400 Subject: [PATCH 27/41] sync with hnx --- docs/source/algorithms/algorithms.rst | 16 --- docs/source/images/ModularityScreenShot.png | Bin 0 -> 48827 bytes docs/source/modularity.rst | 5 +- ...Hypergraph Modularity and Clustering.ipynb | 134 +++++++++--------- 4 files changed, 68 insertions(+), 87 deletions(-) create mode 100644 docs/source/images/ModularityScreenShot.png diff --git a/docs/source/algorithms/algorithms.rst b/docs/source/algorithms/algorithms.rst index ba6fcd09..5a819963 100644 --- a/docs/source/algorithms/algorithms.rst +++ b/docs/source/algorithms/algorithms.rst @@ -52,22 +52,6 @@ algorithms.s\_centrality\_measures module :undoc-members: :show-inheritance: -algorithms.untitiled\_modularity\_and\_clustering\_original module ------------------------------------------------------------------- - -.. automodule:: algorithms.untitiled_modularity_and_clustering_original - :members: - :undoc-members: - :show-inheritance: - -algorithms.untitled\_modularity\_and\_clustering module -------------------------------------------------------- - -.. automodule:: algorithms.untitled_modularity_and_clustering - :members: - :undoc-members: - :show-inheritance: - Module contents --------------- diff --git a/docs/source/images/ModularityScreenShot.png b/docs/source/images/ModularityScreenShot.png new file mode 100644 index 0000000000000000000000000000000000000000..5978e6047f996203caae12be0dddaf334c877759 GIT binary patch literal 48827 zcmeEtWm{aq((NAH-GT=Q5Ii`+Ay{yCNeC{1;67MLa0yP(KyY^*g1fsr!QJKVocF!= z%l!-I!#wlMOi%Bw>gv^1t5<(lQIf$#BSixM08>u(of-hZiT!(_AOQeimNLf+z6m%> z={T!@Hg|UW@Wl*J{NU_h``Ovn>LazQ*%v3P&-Pqw+-#gI)RxZ94o+{`+3o&c1Gdj! zEZCU?*LA>7P#t7-od5tG|KAG&q@h&H zz3l+_5hW`+5c033#7kuOf9--Ku#l+#{SZu!@jqP%ko>0uf-nE=;J-dVdHG)-{MQHn zfa3p;gAd4FK)&e9dVm@Z(3;S8g5{;OdIPIKCPn)TDvA82Eh0++00#-EMNDS$iVg`> zDMCrf3lv70WWW^yq5&ugKm(yhOFEf2Lk_jP+qvvX15{Qn%#Z-|uQWk-rHgQ^#K0d2 zH7>BeHiIo10jR(Mm*||DwbYo{FWWePEG+7Gu!k@5AvPV6_y9SuYQGaa3s76$(X7s> zXrKXGK=o^Apfrk0AlMT&Km$;}06s!*y$(QR=HO}t29Hr4C$61g4-{p_(FK(kH&EP0 z5DT605l{y)l-~lKaQL1Wqfg}%Mz$t-Lju4k0Nes9$YNe_4?-weO8u;(Xi!vPIUo~` zesDQ|L_!!59;Xqj9g+Ux6FgUh zT@Y;Ks+t%b*n|yY8A71w05@CVyZltp*QqmCxo}Xtq&W&QXV#1AugCzWiTPLC6B`mv z6B3CfRCpX~2^`pB4;A8|UB2|qrHJom9IV0LT~&YzlGuHK5M|I^{32WlfiI@;7ZZsn z#3X$K;b3$?%|zsaAF9ix0k1%^r`Q&yA_|3|K!BHs&@2E4c~C;i!l7q^iI+qD6bax* z6yrnk5JI`sZ9vq6ODf03RM?FH-8cvfiZZcTR#Jw8A}4WZU;=kwNIU>RjTrDMvb6j_ zGz5T$zz`M6chNgx50z{PP~;v zAi7|Wy=`9w_yC~2Kp;yB9)1C{eRKHvr9VKAUX5fP4h zx3Y2?X=${|3YbU45dr-`<_GaSG(SFS00t1ypu7aa5zU!B{EV6Lu?7dHAuQ?N?sD?X zbJl?9(TgrP3}L&MP~Ck->$~H(CayntOsb|wze4g?X#W5vcpOcP7wr`CQC8_lqJY>8 zk{1Vxi>7+j7>bFx$2zoOX#>Gr5qrAlZ_+l$JAl^)(~1Oo$0LCTa6xaKXDJ8`ytpVi zx)!g1(aUy93<-*O{md^ye7Bu?sAfJ(@ao+La{rf(Py1a^`4%z^Ag zC*Fx{P64wA^oS4Ishzo4QzG(!syxih$02FenH!sFJ#4_-wr@=I1F)9}Mx+9wIq|nQ z^`@wAKTOQ90rMoA71Yk)+Al!?Bii4ZH?%SDwgVqk;il8PMqrZXB55`> zGW5-Vj-m=3+Ff8r;9SB@Rs%PIL2$`7G^CgMS-kc@`RnIQ9}@6C!K*;ksxH>xy2u6d z3yytyS5p}e0u03==^PLXD)Tyomuv}KFnMhbWjKIKG6XW98&YKt5I^2AAe)ChP!{6{ zHR@AYf@Ap#uMlkpUp0o1T66UV^cZ3Q09Az@5VhN_*r~&vBUKYav4Z#N^O0|@c$whx zc%u+K-TrxuHMPbQS|DbKI9pt!-2qIp;}ifKNJ+|6yW817CJt1D?7%;>wXEPcfgCj3 zr)7?DFC}3}uLX8N-(go*`zcRv^e3p+pi>UupGo>XT5Rq{sDK>GJ{uj3B$pL(&oNVF=$ucJTR47r??jjb` zP6_Ox(BJ{%K!>s$2c+tVR$vxhT-Y4oxb(V!t|4Fgx|-MzMB1Sp=ZAq44=!6k8tz;= zqEUtxz<{R?YNtfmGXfEa5ZDC749j~0RFIRP1>7Rky&Jp4;2$UoU^-kN2=(vRojGhUV3GmnqY05UFM@N?pVaeZenR~mT8z0l2rV(&DP>VAAQNp2JfMaCar&8>a4-&-B0|33C_Mi5M4)hV zD_ULqwKW1Va0HlN_26!{Fa8*LUrBC(5L2pF+OI#NbvwvHcmwT-RpmKwC`(A)oJRZl zc5n8*v9TpwFPypdED=Du&wA+y`U6A;NgwF`t_pUJC6$IgI$_NfUi{orex;WJ6ZTf@ zsm>oHT!v!$aN?2(d<&}%L`RCpxla%=I^aMjx?tR;WAvT}L#&1L4GvJ5MSQH0B`mhv zrWNj-hCg_j+!upDc7YI8_zF=2^L17=0;*9)u!gFunpD#_l6lE@(GrEw#I5$OL~Bk4RfviZ`~2jC%WTpQd^Q2`RfK?SDar%XyGxjNB;Q3vnRxg~dWNKm5U7aM-M zvVmM#hkS`34Q6etbzhmcRh2Di58`LDo0x7gt9&gb@6oT2u9@$aY}T z23O6*+>bPqxb)F+`}|io)gODeM5k>WmU71l12O_PvMUFTWnSWhyJEV5lN#^h4NgB1 zGln>Uiu967!9mfm?l~`;-Y~>G{*QKGjJ##26H7$_@PYg7EXvi3c^#P=QKiXw`E3tI z?X)!F7LUv?{RyAuqIvl$RI0Iuc@73ycz*u0xg??g&R>}$H{MS6Rc6^YnNEk#YhD&$ zM26!3%UJT3l}MnTtRFEh#=BA?sF=HFBSX_I27DNTO>N0KjySiL9*%b+SeiOd6AWVo z(%)W{9H1OicKO_~Bf=ZBlnlq9Bms@^%ZX-uc6CO#G&_H%McCU1NbRr@y?|kzi+N6!wLg@#!9jn$<5;#~jW2cSI|SMR zj%@3v7h)DNyh_=-APDRb7%lQ_RZ$8kzSp0Gr)1sX?ZX%-Iy38*_pdMIiei}XE2-~M z?`aKA-8CY9O0pMb=wC?9`(g^1i1NxG>g!cEuEG=HNG<*P_gK{?tJY;*u8a{PV_9sn z22Y*M!2mIXh0Vg9DFetE}3WVP27dqHSnNwkx~XBK+ZJ{&q?J~jVd>y@n26A#Z( z`lB6&JklW1?GottWuSG@7Zr58BqMaLWZcHwZ*j_2{4gU&boHGFwsecewbX!$getI8 z`wFvwO=ZwYPoN`+bLOT0q#;dggY}>6L%mq3+wdBi_@Q2P0hUFCB7eU4j`Mh_+$3I% zwyFb^#N(!yDkRfi&GfIoQ|9;tiE&R~`JX{L{-*nM`4#}HIKUxabJp^!Ix#ZL!2@MX zlNt*GvD>dX6^W$R>{T8ZalaN%C?sj@br zTtC=GNRn9L=`+zFUd@w~E|Acc&qTmy)@#X~R~EiU zsj`r8jcz?ZQg54EC6DT*<>^auBCMUk>CNw#TjRIYQ8-vBs;BSgdmG8}T&$?Xz@Yga z$rTWSDpC?ZTIb)e;>QTUcdg zNoi&C9GuqE%1o*$D=QOnuSD~6s*$xF78A&uWNT{P`fiW7yosBCQjx!($5iEYB#k|TQ9gKzd}vaIXI^B)VrF!$?n!evu%osI<4l1CW&82$_bEi_QqT>K7X z1{u}+1DC7>hkYFP zhpy0LjJ61P`u4??3iagrI40kQm_D&Y)!L=Co}Hyk4?D#>zH3yD9}wnA(_Q2rk>2WP zJZ?BmPiMoLYbLT}K0?|52`=AcfP>s~ECglp`i2qPQK!jQAy=v@AiL#*>YvSHhEA5{ zmz%xzvvRVYKRr}>jN@B z%T`LM`_-OxJ|h6AXFj|xtE-SQf+&oX2mS-}`+jV2laxN5zm>WN3Mr%RzhxX6kexbj zq;GFXF=Vfg9Ljns3uoQkroGk-_IEGc3*hJ)qv8~KPda`QgZ*XiX$QI5~imCsX zt5W)m)4*6tXsSGz;A)rk^&;-HJWowyJ!aCMEcaAAm!yRq6N+LZE=3!5BP20*Q>hn4 z44rHOu$LZu2q=*Yox_9oH(CW1Ca)FCOFXw$r-Ju1L&}Uh7YTpO$VCd>9jpkG|BdG| zeR}$E?pW0nQVLDIx zr^mv3RII9RlZ(AO$9JDrk^A`vqT-JGRu*e4ZWdSp&aJ7PiFwn)^UH||nRi=fuhMw? z+f$tZ_Cs#FA9=}cvXnVX?}8laH)yvc8#{g$NKK*+EhW~iv8Wu!fJF-&zON+!n|_rq z$zm^j?J(51uuriiKSx)`rW$*`q%#!|_hhaeRBJ7+_e5Mpw>{n^lCb(-a&q_h{kV1- zJSRsRw6fz>orCQ9-m&z!-9}hw9gBk$me4wuwy~8+>6kHzrDH0Mw3Kr59b`&!tg%^o zV3;VeL+gbd%zmZ?4y?C7?Y^oK1%Ru#Ke^2qGxOUg@PLE6-8NI{s6M}=ly(O;fbWZ; zy@1E4p;A`#<@;GwLO8`2o5{UtDuyToAbN<0Sl%Mp@AtFBn|@b`oBWNwS*h5+)7XEG zI9;w8CLBPubXuGAWKa?0Id!nbJCiQyEbu3BYvl$H2SiAlfs#W4+3$klU_0YIXZw2_+t6`%u{7})!}-UldX8t-<@nu4h@ zR^Mn?;bcm!Z0u{n& z89s@6f)_jija9h;DRF@%eO!?c0A=oDNB~t#pXKZld~XY%Cx3!+Xypl`c3kb)auo;P2pwMZtEBvXUl)c z`#o$J^4*8|5Xx-16h5h4M{JP$g*^%0KGu{;`E^>R%VZRo-U%!^9_%(D5{a6IM znb_O-6ZX7#i=kNgQ!RP$W;G|u?i$s{yJ?0Ar?QK0fp7r;azOY;vW;&CDH#m}2rxG) zA4&-akT{vSIc7Ht>J6u*EvIqDF(DMvg)6gm&|w$F(e=>S0l5)I#7Ll*phiZ_h>oW zHl?g%&TFi;-11BJ`_ifY?0xK35=@*nHw-Q5&`sn1;!Yq_WXosc>0D!h9yaW-L z)vOpZaV6WVh@MTyhNCy}wJ?JhqxQwV0?D)hX2PVu_eSE+H6MAI2i?@ThmB^u4>t3D zpwfkLVl>pO=lxu1dnC%c^@e>uyywR3TyAlYv4QmN=gy)Nx88D~>wE-c(>-=@R^YJ} z&YNg+XbG-KhWippDec@vy;EGGZlbO>{(PJxKbMJpFhNlnc~$g1Dh=HhBPnz!^?`y0&R20p4f1IMUy zG&w`2sOQ)9T9fc%O7{M>B9r=NAX<_JU=JRZnbf${e;s*Oxujk1CTEdqWGf=A3e>#% zAfGc4Zf7h19B+hO!6(@B(dv<$H}#^__<6C%Y$v?qkUq*9w)u@MB*Pp5dAIjBnVXkO zabU!8$jFQzgWpKAWTmoW-5PT{wM7V#Yn=(aIi@xVM_s21pN+N48@ME2nb{q5|0&Z= z%2Hi7o0b;a7h$Wr_^$*qBBOMj2aPeQT3aF$)1T)k)YSX%sKT_>sf3SO*2BG&kqf^n z+Z4V>`+V0lw(lq-aL7vpt-f?ZK*Rs*V&oJ6rRCIH+CIU;2!S)77^+`{vy-js2Vp_z z4In1Lev43aZ}295RXzU0`JmKL58HtPx$G@UU+-M*p|#sfoJDU|{3y3`r)K;2u=VJr zv&7hvi-w$Mjk@XNk$b7<_=lrAKPm4%MdvbXKZUwT{H?=5+cdHy>M%&Y#V?2$xk*u2 z^lvw7M362RwsO{DU5^RKR=Az@3lB}-a8nQH)L>C!eQ<4^RTEk1h&L!zO1e#CE(qfm z=FuvU@R(TfqU)={NfM@G z0?|lf21uD+Lx+P;Aj@6^ow08%kRol0)>rcv^O{wows?v~;dC zgnq3G{WVTSglac0Xee|W0~UhM+CMp}dg}a@>rIWuvRKLXTPUwDgVD-jnSPW!**9-p z2@dc+l-tkGyNF^n`AMYV0;|34k8iM=0`Wx@#^yf!h2C<+AOR5e-C1HVd?OuzXfbwO zSi!T}8$3YFvF+*IdBvZNF;q5Wec>-!e~st8ewqzr+U{%3s_3GgKlq5UU<@~3;2qc< zo(|bSXA$(3##(<}C}_^wl$Fd*%-{M`r`s0cD%ZAH--zec9^r~=U1}WAj9K1?^vB=D z9X2C?td$W4aKrlRYh!7_RF}KSwBcoFEN;FTA}#;tI%pVA+{Zs2dE6lwn5=tNe`9XGl?4c6QoxvOJ+2*icVG zq)t#pok9)!t=4|!_W-|aQ^dHw+*0;5w3DUtyUsG>1;?v(W>u1z@wu5v)iWu2#aXxs z^~R`pd|R1d<@phmy=AJ8v%##G54|J}Vr zfXftlB57r0gt{!$iZ{-nAE4rAe!AGbDv2p*`Y6ABqT85`Av%OYBz9xkC@I41QGMFB zR;3`?w_JUMpMj$P@kW8We{7na0hvQzz5|$na|v+=WRY!MaMgw&+<{A&7qQ6Q#fR(1D01O) z0ltpxnUI25b?ffMFEUbIlmoR>eL6~}%>qvC$`Hx~!lbVSl3?b;AX%V^gp`>+llI`% zg`Mou=8nqO_ed)*ZO9txVrBf`U9s38$T_$87cL?>*<@fa$ce%yt#v+`Hf-JS=-?a) zPF~?Q`$?s3%y4+F;Qn1h@oC_R@=&T!>JRL{tvmFddiS||@a8h$DGdOg&AtarF#h43 z*q8`^-lLU!!`Av2?5)TM9J=Bb;qB8lIgnW_Ez9waj(MKbG3%JyqT5nU`ZGR5P@vs% zu+Hpt$$|t1zB!9Q`pQ@jY7Ik(2i+Uu*R`2~M#jP%RGik3Ohf--QIL*5kM;k{Na!bv(|RA<)b^k0oGiB5%A$3mi|?WgFYD$< zY#9(z!)>;(r=MQk=^Hif@1HP``I&z&*q_Nt0;nmVqpNo1uwK^DN+_!6>|S=pxhmzl z$yKU?8zzoj|m6cRi76LyUylylRYvNP$!Iyuz+gun5sq&uPI+D{n^hm zUn|n2U-lThnT+M#-?#TpK>|o7CYf@2ELHYE)8O1VYEI_@mN z{q6?;+wwO^fUE1q`2{IRS8J6uJZ>>*-hLqJ_S9399sB7z=Y z;cVEu4XN3;;CahLeE0L9{_H@lB;+ip6tV60Sm4jr;Tx}6EwHXO?ibhR+>lgLMKL3qhd$e+ zaV&L}|5&d<53b#tvp1Ukezu7?qWNlw>G1EHYi?|*{dyKoJSbA@l`~e44aG-w_Q9=k z0pB9wkkeOq0Vk&oES%$#WBxafv&=^-7YG<)!g<$=PiLe$$x_0I#E03%h_ePKS{YC2epOI9}FeWteDQ+Fc*Ye4q76p7>WP3s(4#2 zGcDmhed=(vFXqE=Aj@GWmU+bZP5n_>bs9cux=R5qIT;4hC`ws1YSB(dhdZ(vw3UoLU6L;J zqi{)&>1{dQ3qQ@>OnE~{>gmNKQIfGKz(`U8BPsV@!x8=UulJwa^h$}21e;i`7!~2& zI*Z3m#b2n`xO=)co5^sbH!<_88YS+HxU9ToJE_}ki(k=xR_a{%@pG{JP_wOgc*dlx zFj5}gd_mCO;~4quQf8g)?-fW_hvVlqpV-B1Xca5OVx2YRu-AIqd{LMxH4{qwP;E$1 z_3HB|*Y)3ykRUBbk$&XAh{loO`GQx!QpsGO9=?e_=G7fr?+<{?NScx3eb^PS#VL*?lY3K9OWTKpzJ+dH!g`Qbck=*-8U*!zth=Gn z7P#BF{<(QYfA2`S1FJ&DDj(OAUL=yaT;tw1v)ntZWd%Pww9VZV>?j>$g?xi93c}fWR z6k76*v+Js~DagV#jqvFO;9JzECR#K+bFlx%%mXM#Nzx*-$fQ~(wFc94tnw+T3@H%j zRj<#ZIVT#nDEpJi*<^LRY}O^8F*c-@ZqDbqC$en$TLQM6q*BeSY|y5Pp4Rc+1ufdj z*mkkYITE%NfAq5bOYbqQosz~-d%eT@*n$dfRbw{xegea&2`5!QZS?GG8AS+N=vd|x zn4{f=+DYQFw77-ePFrCc#Ya^%sQ!+BaM96}h~SZr#I$fYky%*DbuaTTA%nfI zYsLp8sI!Z2D-zTwr%&w}0Z;~DzKRKNiGe3Dj;pVEK?0E;sG{#iAG94+0MGY`UcWTNNAqijh@pR8w1gdcSA7-)dCZ>y@;)3jk}8_!g!A7$@qG z%_WC{#C=W&&NGn~O=~8d_xQF|BHp`+!Me;*Cq4TGSae| zWL>DT{g&lN!~FyU6(7*+kIHWD(%Ov(99vdS8pFY;Agi(T@{d%2yZ+CziW=V+fJp!r zX4~*pJ@mCEJKT1y&MV*m5lYE!@B#(g2)ruf0tjENcQN0jiPMRHLxG!BrAg2FbHI3T zz%z6ty}Xuk>Bi09pI9zuuUP54``lf)opGk!A!AuW@N379N8k3f;if72fVF8}vt~-| zfwv#K(QeeN0sZ@tXYcR5dOR~P!{ESvDlI(c@dVtp;&Pv;fCmZy zjYoG+8(2jL4PRk}I+ze4-Y>A(+T6v$!&+dAc!CH%miInkTEzA+En-NQY0M%cF1u>? z#%46ai8q0rtGWh-9L^#KC4-FoTX|uEr7Zn?%t*A;(|JrMm*JGA8{N+f{HtePUHe|yu^;C!lF*5E!xC%=}QUR^3;wQvgE(e`;EIRt8kghLNqX2 za~~8q(%Vu6pba(ZzK?v9w<(-}PF|l{6auskShWIIo(C|S2L>%he_TrGgqAxmw@)r0 zMg*h*(6~`_`-WOcUVste=7Sg>J%slzw7yS`k|7l^@p?VKo4Z@281XvAwMtHc^7VR` zwtkwKonet~u2c@cVVg@3XArt9A;$tV#9H(WX0wq_!C+M6H(gg`r7=3}df zOY8P8dU;_)z#Jad2CTZJuv|Lrc@OED_}X*Al%a3{$J2$n!R*7WCM(tyJb<1gHQoVP z1pou6(c7wVkmSMvvtuZZlTNlQF1z9%GYbgd&NweN@nqr7NSZcAv#jtt@lJflp>AHk z@PC7Y77dF#*Y-;y>vX_S7%YK4YJD(I;bz8)N%NHFehJ%)* z96$use3zXQ5>u=%u1OY7H_q<(o^FGVHk1oHGTdpjdH096FGWR6rr{icRV9;Y%%A9! z=x79B`NPjisZaEJ(+owRSc4R{lf@{kc%R{D9uxF!fW~wd0aqL+h)(HGMyXT%jFI1R zaJTSl@Ys2Pg7UnV!{{RK1uFJ38S^mrU6c7oD|>T|p{Ff_I*(}0v=jCAav80p4o->e zxEsu*lE<7s`^v>|K%t1|pQbL|>kO=`a+Q4$&DaGHJPxDk{JUJV9dm-M7NdsCF0425*&uI1Y3J-#KAA+qCMi})bH*`j3!W<6PXBxMM@{+Hz{iqZf z;8(prhpC+>Z?R?23a3%k`FcJ)9y8un@GZa3^^VrM87e=yi(}Riv1pb`sXSUA6>UDk zDUhf=^H4jTKiExq#E$zRQ;JP>rt5o^gDl~gP3pr_C;e+T z9lCA$o9}lx9N>BLGnXNdr}PYcZK4xxXW2v}{Yab$kOiQw>`%7yZ(RFaw)zM1F2Pj2 z`EgiK$YmPocD<_!g>pouT*pMzg-L<`W6+zUmFHz$ZmToP&!#!6jRWr>un(3c9x4vG zfRk65@{!)YqGjGj>C8rsJ<2Qsq;u|WV{gKxN4n_Un{+ux^?usmF>T`B3GPFBlmLoX z>yh^byJU93muG0~TdLBiaWmmjx(}gA_^ur#>VI}#a&l*cYwxlZQ(I>A5+v08E63Qd zwWrzlR9&QPZ>KlwL+zO7jPx5-OP@GJcTOpEI?Ip2BEb%nZ1J^a#ykH`L-@ ztz&2IF8EnLV?hiW<5KH+pCo76%XD8L6q6ZB~u= z+6wv#wO5+%oXxkjxTZREp72z9oGVSXoev)l4|w2v6cjM{)4gub^k<#X12wHoe$&re zfuN#@8QJ^_*$svu)-t5YvQ}Az0{3lujw%2EdI&f6Mm=Jg0gUb=pp$@Y+A>g)@TjN#}P=ZRwo ze>oVD{j`%VS3iUfVb3ij#>@wfea{sYhB>{EPBAMeLPs*e?Pvw{7T35CC;%w%305@i zifD&3%$g*y^MK^9tB0R$ernOn4kj} zg$uZ>dWJe##0Q}V*u|6?Lr16aX#Bkj$ji@*CWYb zY_z+%zq?T^og+=OqMWmfYu?D2a{aXUn?Kb4Kp815!zW!lp zwwNMY(X*iC%obElD}o8S_dDq(Lt>X4RKPold$>_q6C$M*hlL+k?wFy-RPIy02eT*x zSBJ{#SRwvbo;IcEqIf2?o!5mF42mUeNbu)b;L#_7O3RXF5*|Hi=SKMi3cxEB_!Frd zBb?;pMuq#2g&q1$@8DF$@`8_&;(EEc+*&X^QuRNaO3XKFZ)2$u01ktGGpaXDEkpqD zy+9{T?`FumBKM-f!Z{SED)^l&pc}0Xm z#P1!#;6NVi`g<@n+ZnevRf41!O2e1f1a^5fXVnU{;~lOyF}lO%v5sos(%5 z>ZRZ%dw8bP@yeirOEW`oV2Gn1tS56zp8bIk$LDb9ltY9>1vX*Fs3Mdp>?UW&m*?Jpf@lvGt7(Z8-%Y z+g5JFOpN#4p3hx#pko-_T4c$YrPpYlE&GvD?@PJ)*f)8+8fXCx05vHT2N<=Ekae6wW? zNhld1y^Rm;8o@_@0!4_*YMIuC6Z_SV(c)`D!E1CX*U?DleM1Yc!a%i;Vj5#v(d}%* zp`25&+Dr_8jfmxx?vfJGfcd7P){5@AdwDkwoso&Xa!u`JDyD+03Zx2X92y6P)_y0O z^qRcq_;hl-|Mb*|nlw%NJford))IXw^%MNW2!_6)bCu!N{T@*<=oP z-^V|^iu$^AxP;8xMe^ZSPU4<&;xPu*=3+mtV3KrOs1qUBk1V41F@agl6RbZ# z0SOA22P3gZz4=n7wN3jMG|STWNwuC5gX_W{gn~mAD=9BkVR`<4;pso1`&`Q?SC=bp z(fV%!DwJjV$sU6Y5jE9mmbOfVyQmvZ-A;1rm8sc*daxRXPQ$r0JpBeA!UY@UH~m#@ zgCicc3`B1*SV^F8k*mPOb3sp7i$}F>Yf~tfjAPW=_ia-Vc5!YE*#tUFd-uBEJrl7V zf7WIdn@=U}S)aL+Hc(=>-ALao3XM*}!#qt~BNgGHoq48D)7Xyw4K1#cZCQDtpu5tNG;v9zC1nHHILX5_4aX>)AF?!dNbw9@2V@ z9nUu(MNYEF!0R(Ya^>kd&%x`i36pL{oI{>2O{WDJ>fJa9hvaY1->WdgXMo|G(s7;8 zY5u~wom>V77xrLV{o54BBhYRtzz7~H z0p{O?z>Ner(~(3-1+XAKu%w`t*8aIFJ9MWTG2Gup9&4QNlIroQH(z&0Zhd}%n;Wnj zwTbF+@QLf|4q!~7<*uhiPCxrjQ&!tgy{H8lP)QK0FQ)n2eTc{s5PfHbk9d!2v=C;FxfN@5wj162I5mi=U^-cdCG-_8FaHB7M=$~d z9R4qB{{l|?ucSnP@=q=%)f10G0}zgo7oQ5MXQ9Xg6&JW=KI7M0;WdHSzD2 zC++l19ISWHW@DWK|NMS-lQokvLQyJaH+~a9oZGG$YFDDh_%^ctFU3u zhwX1RAg}nO5y|Mba4)T1-BpW(*79UXj#Z~B#-7Q(jboZDtu6YI7M#--o=atMj`(d@iiJXB5Pf<}jz(J{0;KMs!no>qUd#z9Hb zI?5kAfB!@==!lR^kz{DJBY3LA2c&^_wjylr)*&#`-PMm?prJoNe8L2d5a>}JmB0zs z0w;AmG7-Exh@9jpss&!}$Wm(j*oxy=`{ACZs>m_#8?7kEThF67-B3s0K&M|8l^cn^yUklTqP`w};+F6oU;TGpx@_hxMFnLDDt6{R8s5zJP>=#|CKJBZi za@(n+)Cb;q0@h3)>oW_!t*t#kqDrm9Nh2=vM>WBf%#P>j4&gq8cQD#!C&X~^o;q{C z4gQwfud|PB(8`{#K9_ja8MTGhoJbQll=VcniT)8!}5lix0_J??1wA%~%7J*xD*kl_m( z%&zs2C+_S0Jv5&Q?L_Vp$enClt`R0M0tb5mE=+a6-SB7Go7(HQprnZ3WSR*?CMEub za@rj5CP(mu4aKk3o(Jxi=6y?J*#uQ>PH|z=7~hJ8n*$Cj%yeIU-{*nP@SI^%tgR91 zQ5>VpnQ&s1=+zg8cV8mVJh~dYOngS;;2fKA>T5DPR!O6{jZO^zL&S{W|ZCOQ!MZ@8%lN7192ooABh5 z<++t%81PN&c|{|IBGJQk8VgH~aZ#lI_?e7I4n-H?<>^>;&DfEMnuVL@Wnb{f4{Xje zQ7Q@}zq6Mq)JrvQ@K>1nn%VPHMO0Rx5XN%PX1MBGVmUlBTCN|=mu1{kMeK3?wn$GG zr}Ah=$E_Chkkz^zfQ1)2F$GwJKfQ0=r1+NFN$jPBT=lHOz7A);qg9p`sl8)k4XnNd z&0GOakN}~AKD9Og*nedGVL!I_=U>ul==~#vU*o*vhN;M0K_2CqM8|mLulNRs zqm3An3vS&j4ct5gfFVw%N^Rm#qRn-}HP(v+hXve%k$FL=7WfD_+>iSqw7hcPF{LQf6*RPA z*Oi7_izKx0;S(_*dx_Q8^wQnI5J^dTnW3&PJa~XH*3>ebR1{~qoSHNqwZnb>ymZLEd*IU2Mom9lE(kJ`wq?R|j3ZBKap#>_p+m=U zFR>7@kuqU>V`!uQtJ)OVi|Ho(zpBkNK~S}6(Re)<(#5&fGpR8XG5}A)Nn)~#0~Fy> zm(^!Iq)i-tvcO6=I_){4kk42{0ua?%s;;%?HFs=WZ7L@_ARv2LJoc$T86@AqxXsSf zA`XO5PtK<}f16wC6*M^Spumg_eMKIt|C5RAYURz?xstJQ2Qnb`KODM%jO}avT{Cp~ znhNz5Mn!$kEM_j!OWrzqIeOXc%3j9biG{Y#_1temeveGAhZfFJ#p%?pK_UmZ7ir}0 zc|~_B5-YyF++zVjM$!R*VjnU)6XSFn%)u%Ba6=aDu(J|z^>pgD6Y}R{_t7xqTQ*dq?mf{4O&`WtE%vY%38c0tht zADH`r3_KbTX+Zk+0r_ZwirlTIdj`I?@uXg78@o;4-!m3GjmuoDW2oPkUR8TD8pL3|hi9`zj?a^R$U>t%;X zRV4Yj_{|>sv!ooI4Ozls)iQ(j`dtP+VZuNt*X06HvcaVria=!!E$y*4y}rQMaY)R! z`J5P3Gcsay@_*#`+I<+;u}?Fvt2NARd}`jn$JH(UpV(~V`D5~^7S{k<__C|xuG4kO z=A6&oG1guS2Y?uflf+j?ne{B~%K(ZW-wQ4Kx;%D)6mfSPOFy@zv=raW2?-IlSWN@Y zM7CR>kWvPxP-Hx|iRD{uI;14tgue$W+VBpo1go;bP_`d=<*!Zlwl3rlZR&aflfd21 z7|ah|ZX)F8M`SDf6PzM8I-V;^tIoC&@BP#)`6-=L>)Z;a?2>5KnA{Y?4ewM!ZSz-B zWZ%Ru;D2OxW!Y)C*f@}w(i(!T2xW$mEL(h*cR~Iq2=ut^_&-d2g`5CEeZK-6bW05|RSaF?4r#NOyNLbbg25z4v*(zhKUp_q;pSUTf`Usw9T!4Bs8{ zw{rn>%^m2UQCCpTwWO1pW9pUohOY{CO|amUbcpIW_+}dNeU|jkr2~IqfovG6g=3m9 zbm}xaM=hKNa6|{~&DsJ%KNvvv(J(2N@9w&j(3*a3LW8_2;3x6r^;RQbL^ABAdBAql z4H{i_&jKR8hw~;E;P#dQF3GYm9qT4jE^rX21mGV3HXzvTzAong_zu`~CMl*;AS?6{QX zeNr&E$w~ghPDo9cLwLkomhRXk?-t{~=~~?ImyI)&_`^PLVe*)BFNEuz+XA3MCB3S= zpGl0YHYk<)ZQu!!q?q;sYA#6>K_$en8z>&mC+ZyKn>Za3a06bOIKG8m|L)h7beeGL z8YJOf+TouzQHSqP+g&>7z)Stb40cFhrkT8y!-G%Pp%rPzXq)e$P+W`P)kwFY0SQ2` zA+(`zhq36^zsH-O>cbn~I1R?*&z3LiwtM1p!@6(gmqs*PntzjnAg(I~4X=_9G>b^sSm z%gZ!<{6+g z_@74e?Lr=7V2>$ic!utUUfxb!*Zs;06d3*Z;oWTV0h=$$=fTYic?z0Mr-*bX~OQ4IO-o_l+|I*wn)43LBt}ix@xGbn0rfhpXbn&RM4mmA1vk{nN z^>gq4{+~=pi(kTqK^sM6f;A`RjHZuy;S3y(+HYrUvF*nvWLI)8InY=m55z{p(!{mY5zQethjzWDx5Cd zvYEieY^7A1r7%nJUc?StM6B?X?9c%{3Dxsy^KFb2 zsvVh?nZ6v(FryF6@!VcLQU98oAMc^UX<$6TT6@2EnZ0xp^X=Eexjh8s(L~;x8DYt% z%}4TXNZ`6R7DRQya=p%NUP6ssAGuN3S+0IqbFfFfHz8$f851(STQl>iE0Fz8?9Fi+ z+rDf@`sti<fkL4j}Rr@$ajK(a2SjTRKZsRIG#uP7Z~;l zkCI6-O-$~Tt{aCr5d{UV%=#nI<` zE&L-=-NWYH1)q=4?xP0Kl5s*aikF;|C)Kzm2jVmFaGaK^VY;7LcGYLIJ}A;ZJH>bC z`!oZyYSq-Xr-Rfx9dEO^dP5bibafhBaAWeBWx8=9Re3uAW7nVL%nSL*VqKF0fkW&A zlY5^l{F1WK-?;$->=)JU=FxsRWaywGQ68W96Ej8EJs0*qakkE|8-BG>T*%7BLUF1} zS}meVctqJ0&H?rXFKd}?Bu*q$F#{+jyNRrZLfxf6ukc}~YhrE9B*~&yO9&=A6A0~}wm}nROWN{je-U{n zGwoVT$7gh8Eb6G%#&EsH3Q)jjK{8-#5(U4Ly^s4$U8vTV zUw@I`w+ofdbaskaAy=XBr_q=e_rEJ)r9LkGAkk}e;GMH0u*U4SYv8$R^r@hz z41i8c%KCkK0bRK>WK1jkWavL;QbIr}X(T1yiUK6w{aMg9{YI2vZTjzxxI=X=Qb35! zs8{Q$+=pk!efDHs*ABHl@FgF)ufj7Qk0K3m9rDTO+a+hxl8b5lvMlVv?21T2FKoe9 z_7mbH3`hnPj4Tqd&6q;Vwy6Ag=g^K}$^6XtYBu#ene}o@sNuWm5i5JTvj20np-kY1 z!i?Q0GgpT8>E%Lo;#11C%GT+lM+4buU#;~o)UVO%sF8)jZRtC;v^sz3j+l|}SA;9! z69b6qBQx9%LUt*_ab?&}Ut+oJo3fD65Ux=GNIixuNDIcaap8A{*`0z=8q+N?c5Iyi zGBPc9-W!wf0T_;Ou3WcpUrvA`+(vL;p;fS)G@(U4vq{7gB~Se#{z-k;bYk-@542+j z+7+rMEKHl|I3__lkm*2i`@Whz<|XNDX;6N(zA9SfnPN%=U{M}2#Yy#3h<5Q!_> zoenq;=V9v)5o6Oy>9Px%UzA{!Z-(oAdz^=0CZ48~3H5w325#`z)-sazKdH<*@ ze}{vcAdO4Ae?d2#pA7!FNc8v1Icb~3R(;C`iHJitWXRR-9eMOu@_;OyDnEw6lamCd zYSG9w8vp*Z19v|zjDnuQf33N^_F6N*K(G98 z7|AMok7JVeFAx9gk`r9_Cu?B$t?WDylQ`h5C^t@~Mvznhr+BQ|L#qM(s zv!J-1dl*wl5|Gfahp{4E7od*0KseD0Ujj-E9A52dp__OGZY9DOJTllv(jk^Ti{OIj zI8rZV&kNmntIL?UG7}nC%l`BglTD&xK#h{oYD*_wArBDM4FFk^Kp%wzqtt*4ha)>j z)+#wCHp_LT=VEvv>lv=uY5<1KT@5op=N4676hlR?(I}0^dyx)$$fms83ws~{USrPA z!MFwwfyWxp+&ZZQo4c^~x0x6J>%0i~>)a!bWTD17j0MG=Pb(CTz!VCH<%61XNH@CyBVj zF5=JTBN8ubCUEICZf_syY3$SPPw;YagIjF$nf{qtxPi(K6&r@6jM626we{&S%nE9Y zcVXk6Mp8X7#sPC`9GNOqiKRLpYpl*(FSh&3|1v-6xor|>YyYa&OO3)48gNPrPcb`K z!$I=AQf#34aP0Oob`FyL`hdk*Ct%@XABAxapEU_}X{M4nSf!7i!CO zs!~h1>&_z|ZwmuztN?(qbR^!<@*D2gg`^hE2}N!q!}Vio95996aA$W|F1)8^fPVJP zFq?#Uq9A%4e_P0VMZD2`z(ZX}Qi_=W5>r$eiw8-rI7zGaDnJ!xx}S@V(}*L}6nCB; zSJ^!EB2!M?^03odP8_9UooP;X(d&!nQa;Wf4hir9i}DUQw37DxAV8$Hf-_*oT&$@+ zrkKG2iirnG;OJ???GZ#~LvpB>vF5y!?&kV(lW%$fHXx6OIF)9T-LRje-g3sLB!=@{ ziGtLaP7+ztEjSv1TofLicERdT_s19(0@$p1;_k+SMkT2{Ml!65g+G7O>JbJeM7NDc zmI_t{pVXRZQcE8}-U(f^lyEdAuDT-pY5cY!=@H4a6P@vN{tVDYNv0*fTLPUC+TmNE zR)B@F4>1xX`+6@|x)I-Q<5_*5lCA!+hU3F29&f>Ryoiv72B*4A{-8;zPq5&3p#d0> z(Fk+nYGq1meGV`N1F!I0McsD+I9^N~8dZ758;)0XELGs%6pQ`xoa|!ST-Qo1t4Hj(d@o&G#p?BX}NXtvSHE7PV8-nhAa%idw@4ZF@yiwo*g73mN{pV3q}zgVg5DyMMd6y57_!?--u z9H>B85DI8Lt(sJ5NDPQV+-*at?ddswIhM+Yx0uHN!EhSFj!K{7!Tvj#!WShn?nPXF zFOLImSzxn%Z~5@fgDfoM0N02(CZy}&G&_n`1-7G0_^F)-Sya!3;Gw9$jqPB$%p;Er zp3iRre`azi5E@9N!yB3x;`LQg(C15!O6)B+smsCwV~Bc<65v|UDe8utNe38R1^JPDw z`R>n!Z~jUI%*#p#4K_$?5u;`dt?*%D#1FeBZ?t|B7r#Up8jaiH%gYCXi>q^32uml4 zBx%IVpETg#e4<<=J+pLFW!(MIQe0Dk{z^hDcpKABAVeYC_^E8Ygx7dDF-*U%P>PWM znluleA*Fcor(v_}+8D<{wx42u>-F4wWZ6@%(Zq!Bv9f7@vHbaYWPI+(gKypwQicb7 z1+o@aG+vHUC{&>hv^LygiHVb7;_|(bEYdg!Y3|3s2Zn!n7cj1*aF>lu#pSB>F8uQg zKy1HB^%nO-QxAC;=ga*PV0}vH`l)}&ZB0LkWbx6%zBdtkMCy4}Ai||}f|yfs+e7oqPJ8eo z1wIXk(A#?gFF6qmi2HrWp$`s`w^Pl91_!we)Tl&DLv^fX9+qVx>61)Po`d zvg>%YIabB4-A#sZxpBFF2obGr)O-6>Cie*3%PIh$nmoX#=BIITnM@M$g$}tuow1Iw z+|Ktm;(w{$-^En!^P(c<4up(MjhZ)IdgTzJi3%H0lT*(2*CpMaBfAGor;a)KOzi*}1;pSOTBoC<7 zUHewnYshQh631my{AxsbPepPJH_eF!>q)HTV0N3rvos{^y}ZjBLxACQR2@Q@zt%95 zdMT>isX^IAsK8`#ughw#b-0*g#PTvj7vp^2Xc}?9E#onIrtizJYXBA|zBsWM*ARXz z(0G_S`IKFz{UiQ%@N;FJZ?+0rn*o4_%K(YxKXxWE;BL!f_ z_~g$9W*Az?C4BYY{LN-T-G#=@5g!t8Y{qUqDZ%G`4Is^gBaWnLCeMHWkbM4iMfe9m zBC5Sa>1F;E%I~k-Gw(ga;Wv0Npb0n<5pd*g>Q?msL%n*`G3>YDMj7KK@}mZrn>ROq z`)BKx|Bc;fYH1}4H^^HvzH#zP^m(__K3^Ai1I9l`H+O&8;{0#)0#L@EUqUL%2qCc^ zBdzlp<8yl2SggJi!#KZ8(8WUe-oYr%x$=Uv+@rUA&1X9bg=gDmeM>99GS(f!*2n5A zwNwpLB2`pX$E)-jl|SpYp)wr16T?08fD%*2#CHh4wMG9WRHEYRTQJc1ia9?`43E{sL}cI}woMTwUW(@L{=66ppDM8o zwwviw@M+~R=?zO0MkAlbc=xA#Lj)J*hgX^@EB9D+JmyYo)7ZxHO%E@0(W7M-C}S;y zwbRrdO->PC|JJ>te^T-8Pu41QwU1o7$zqoN`@Mc}qtMgeod8w=8K3hwE~m}7y@=gQcNH0YWG`xiZUun0;*JX_mn6s$YB#VBl0g~cbZ+F6~q`vHLiFoF1- z@fMVuYTgYv#gp#ZnJPv;*>$&S#A)#mnUq-q1QWIK`fJU&=yAS!n++ZnzvEAFvVShU zJz0fVXrsvdaJZJr%pkhjZ3Dj4em$$itOvL-RcLIl-{^nmAZ&D4B__i-3BqE1DpP&P zu-hRO9HVKBt)~_Au2qHJfS`qpKb_de*E-wVjr?j)GF|mKedxz8wSOU(@8BHk7=xU2OZH*^frF_n6;e`i*Bfb_h!dB0e<|OOXIuKU|(M` z4e*>E#t_%ve$^B2Z#!;1_`hI;=a4*&Jt~4JKR?6$&wmg7!%1)6*S*0b%ka>* zFCKXR7pY-R;s4$~sMX!qe+)BjpCbH7TWHs~k@dy6(W!j!v{9nNOz&58BdzJ@u@?k7 zMZ#RPL+e-+uhvO?6L-&?YxUdnWs}4bD(IWGQl5oA^r9#Quury^zz~;f&)()w$t<`m z2Qh~w?EKnu9HBsUX#_dK zEe!#yOxRwOuZlVeDtzXv1+PfarIjA8Em&}dGw<`wV!Oz-Kr+ySWFaG4 zrmC72hayU3!}Wrr=<*EQlhwTe60R1*o<6C6i%`_OXO6X2>tL-E`L2B?>&L_FD?F>` z>$$|}Ms2rrxuvTto$~1=EZS%{k4*a6R{zRn&C>5b{W)nU1=`t7xR$vdbMCfTvwV-j z$=_PoqY+MMrnf8n^#b+wG9wnVEa9&kZp6zA@>_597!qvr1G`E z_8NkwjL^~TH<9UU0tZ$3!_F$ zDByr&ZumKYd!yNTV({O%Wu4jh3;k8xEI5(JX-pptv049MIMDN^%5z?yAEIeXArsTl zQ|KIV+_z%hJ7c@!Um7~$S=u3DPs3U#q<&aT8LT`H8?K3eS8P@jC?p>KN=ogTPY7%XF`+ie9L`X(I89o)mCuILg_cj7_bDk!vIwWq27H_=)l&`CFM^eN zfx8EO^!E~L+r(|p2Fa+a@hkED8hgV9(p?!o-a4}1{c}84JPxRSmSj2rT^-*i-=X|n z6Wh4N#LU+n1}^E%ha?;Sns*VVTZ4zFoH=P7#9nfjk%j#E9|^pVV_3? zy~{_=4{?2D420F^Q)c3i@J>^jf11@IVd}P_t^Rg$O`K3?!gIb%^m&)zw?&w6gTMh3 z09pS%3HFQGfX_Y+VHe5xf;KV8DUzRMs#Swa74>+fp(71U_2nVn11W%R{kB%%c72hS z1Qv9d1h4;!nRaw~$(+av5nA&)Ox~$WVE<_lDM#pYnf{NOEBbU3!EO8!Vq2aI)dp9epjs0&PZy( zDL#hBel``+jN|{}jupGdxzle*jYlyZ0+5jP3s!5=?*)o9 z`U$|gMUQ{(Q_8ie>hQf(`dAvB4Ir%5%rZ)PpSBxoHXaRPERfisUd4U{uT-_xv=d<;gon3H_6>I$e4*? zSxWix_4FH1#Kduu@(1+(n6PwbN(U|+x=`{=-j7c5_jyZ_D;0)KTGllUT&$lx&vnC` z21w+eUCER6gvk`V$&~4efm#2+(RjUFx;D*Hb2r&ayN+Fp-&kIGOqm+ct3iY^|JVvt zR{;zM8=!A`Fr~H*eE^ya!I2tizDVlZjCG6V^6*qdeoTj*=oMNMwiuva!@V8N7{i=r zMowv<2J`oI8be`c7%yn3P-Xo7!<9}W^(v^R=gu{3z9~PHsd{bc5d#Ay5=e0PPNngF zlj%d7HAD21TRyFfk7c<$(Q9=aQwxDBvqs7;UC(mXR1SarZb9xm<3DZ+EhlfO&-0Bg z*nQ-*ve$?#8o!VF|I$B&qiB;Au5-+mfXLRNKGv=uk;m6clF(R2E#dKA=601P_P=G1 zPCy?6;k(Y?VXxR+a9-m?{Ee(VNjxZav}W_-5Jl1XM?Mud*=;w35IoWJla`#37vFYc zW1^h-1pQ)`VMgL>PNhsbUc4d0t;g8C-uvzaA_KL5>wSygW8lJYlE+C%>ExRDrn&+6 z@O~`3$P1sL9Or&oM-Tev3j0#G_jhNSxr{YRXjWMx5Jezi{w zCUxPY0tUe`c~T0wLcRktTBBmA;Rn#nN4h@d0p;6!(g6=w?QwX71o=}Oy_5)DlxiPQ zSKBs&P^%2CfzP|DV5+ zJ^WyEg~7F|%;$`=hp!w$EWOStjav~b`ZrD%`V1eCNroi6trSgzsrIZv7+MUuevEB( zJ6<%+v^sPfB<%~=Lzm2hZGMlnr~)LnGr#dt^Lc6XwrWUUZo%0Q67S8*&Mlgm+MByA zNt8YDB0%Y9x5Q^S@gw{3IiuS={#U!63FL33B)qzYyVbjoEvKI1&V#78ko2Yt9O-{e z(PFRXsBfA6Y-$hJ;D)*eHP9WCzEi|P9>S_utJ%BzQ0|rMxX{Zg5Txd_gV#4|I++J@vIK+74H2eX!PnjyPkoZRC3I7A+(QqiWPh)JdU9 z!%91{;u~{j8*@<=Hur?oET|fzI?#GBejPr(8c_P?!L~-k1iv!9yA5E?l)Lg{rtBX zN?4!BrujFJl=j^2z2Y z%-;YNCf?olPj3J5|MTMl1+vMV`nnDdM`D@rEIP}!RIX;yYb^LT9UzL4mvt&<8~$A2CnzLr&QYdZvl6T|Z$`!ZJ&{-MLFRKO7J+o2G>A~Lw zy#SJ<2q`O;FbCQ6Fi@0FYamJGS=_@oyS}^^QeCvL^y@>8X);VxVVwwG`z>rxpfS^U(lsDx72~d2BOQ^czlYCdV^Qhl!Vr;usSY# z<7i+6+Myuek3eQ0yd>iD-fP%|C}`Y}`|_U-3FU%*NH+$Kd-ahM&mV}l=<+PD4D~)p zWO9}lRyt5%$SJ?_D@Q3WXa+}z{hp$Yi=s0)?@l=l);6+J)~8FGBv(^FaLJ_AJBI4L zVSH#?-#UU#$%%xocRqUHAJ9IR!^#(0s?|^3Uo}dwRW;OT%I+UZzHzSkB6CQf!-|6B zr#L{hbD+%}0$p!12A#c-6|rUKiXm1 zTjde4R)c^8>ddnt#)Z|u^&B#>)dr`zjRq@bPN;8H$=Wwz?&KvK#wsCbJPF}KUHz3l z|E{~*#=OOZE5>i9A2f9+uhbLyq)!N^n}s6sJ|Uft^hAxPwXQ_%Io($RdC8=G*CM5x z#zxM4Tt1-OLCg2{kt*8k=2kG6ECbVG^f9^pEJM)Gy5k$G3Aj<7({W(p3C71`ujVWf zbUrMr#*ysPZC5I~v$g}oHM?DtL zfEp1t=objoV}5v~;2@3S0SuX$*Y;pQKf>p*G z>?gdi(1q!I*e{Py5XYR_{?q0Ay#U<+Mz&7xdrKeTwDRO)GI{`t1+(T+v>d z5T_t1U#qSR$P4Gjn3vfL$?Ym_R)F7gWkdi(wIl%k$%vb)N2$f2{){$h*Sxsc9c&C> zXx=*=;{a8ljXS+J`^?FE?M!Y;EmY|Y=wc({DP)djp82-AaeZ1Dxzuz#2g=T?**Arm1aaoVic4kvG{(JF}8io2aOb=H`XdqD9wlyJfV(~8MZ#15EhGr5W6IbH5vMN;uP{%$JVB$nu zd{#2MU{)z(l=gh`MXoi^yJuR*U&HgXCMTzX>}%qWQo>Kxw)OSnhFXmDGGr3~vqtV4 z!&*ziR;6iLH(UGUwm@a?FuFmR_{z*F1R;C9TgNbCu85l{X(Yd-*9M$q{Y@ER)L7{8yz=wP$c;X?$$;ZCz}$uSP&|P^6~y_bVWsAd zGLwg|4R=FNQ@IB1j{utMV2$su-PHDdqLHn;#Ne3DkJurWeEJcH(5Wa61~}stM3;vP zn9GSoqL4}`75e(c*p|U2>&Y2nz2CVW_O)qPD)Pva(61VYB=*hf;uVzd>qC}u$D<$W z@SGlmTC2TjBwkZ72`InVqUj9nLR}wKUf~UfzpI9nYlq~Hu0#tm$pvU}J6%9M17sXS&Gt}cb`H(YKkVc z+&_LM*K%@LIV~WlXv*tz(2<^24)+18zPrErFvB`hV8p@9a@vk>WxKi_kC<~0Nq%a1 z>RJcP%)<cB?8|J;8ozwrR zv0LYCcdwG=Gesd=z4|oC-Szfpz*h?Kofw3656=D`TaK~1j6<44&a6{EsYIE&S5*x@ z{TnGWv}QX&Gj`(E_BT)Lz_zvHPq;V z_h8UK9p47{>lXy`S!ZU0D|SsJv`}p=?$aw_##;T{R^op2-zbov-S54tJ}L*Wtmu!$ zE;b#4SY=y4ONHVW?^^TsViWdaVI24Uajn>}%Gtxp;KkWft1^#Lzg21b3Y+`C{PI`n zrw<`$pIdQHp^s8`LE>)PqOgd8Y1^xo{B-FI2KFroAdv*n77q5a-z)cFTwJ+gGl#p; zMNm?7_Kb-~%hbJ5TTOM@8Nm^3@JiN>FV=6b>Gv}+Cc_7aL4Z5$% z;M?S#Wmd-Bta!GzGRpDfIZG?Z-UGoe)T&FN)Iv*v<|vAVKI@sk$wM@49!2*{QCR`$ za_>VWX5&rxTO#MP46f{o*-w|)yyo=N3WpK&0mNSjr>Q0rks%CAH@!wz7(BducH@nJ1+tqjJD4JYYIV7NtUtHIpNpSi7 zhXhjt>YwyARTJgOIEe;?-EErd8FNu6K^=3h{Iv#nctW*|pMNww08_zsO~Ehrkc$>k z#WVK{Y+CsAfJd|J+Dq#cTmWLi{+aNSk)6<{vtX%nd4KwE90ofS&`6IExvWRl=hj5{ z+x)V4b#&bfAY~rXcY_|V5w0G%u~noMAj$I|>oU>v8@qYCz~^rOA-AjZJK%HT>c0vB z=4xd1Q(hMAFf8%d5`dy}k1ZsJ`JUtl0XVs#6(q1`_9n@hQrhx%h0M?)$H{(;x zut5F7G#dNxp4pc1{;4Q5d-ObbM9$Y#5LiA^pB!){KD>n+-g@2^*Xw}id^i&akmRJT z?^}ytautMl1g|eR67NFF-({x^oxIOQ5qwT=h++z;mch6s$vQvvOBGc-$<|6I49Opd z2jJhc8}a@pIfy55Qz9_}9gTM)79$#~d18&RSUt_FZ=)Tgr?qG2iRn>( z9edP#f`lr+3%<=?Om@Jtkbd)F;2S+ZI8jUwh-0~_YkCQ#qCz~eZ?dV2(5o2fEYnnL58S*1p)s{3{V3M=y35d#ZPTbj+xuwp&fQG(4(Mltr@&? zE1u+Rr2Uc%0s1mttU*?Q9z+OeC01Pg9J44Qr}3% zG!1`hWXxh8V~$$U1%e`t-`x-QXT9x2oI#5Ahi%HoK+&@UoHf6OR&YVVLc0Fg8;EU# z{Jq(W2kQj7;o5>i8}dkiKJUrvD_$>C(r8so<5F|)mICp6_l zW_yJ|n-bGMs6ZpVm>*MZkH-cXV%7@no}9cmxjT*G9Sj$OW(^Qd#=7}{YnvxAF^YA2q&WW_k#sVk2(L)DNO)nw1pVN0cL_66i3GKY3V)dD(9FfRS*rA@#qkw)dHY3U>oylt4Pv zAqKDjq3<)iF#4m)tHNCwP%D*Pyq5Ua)w=lVb5-sH1Uw4)O=l(p$s5c4ns+HWM{54z zWlbnnCCfix;EHtoY8ny_f&*c}OyC9!{)CdoCuC+U7~4<4ZSbsn-7awoMwp7Z~CoXssp znPoJtjt2j*-J2~PU=e;u!HAT#@qg@pbumSqhI`Kbr-Cl29nQj z#)9QeaF@oH&DUvuUuHipp&O~%?73Q)?bp5iE+!kh$Q&UCYp$nLpDg%0z}V|LaiRD4 z6>vGK*z53;KjO-q!#)SW4gJvebOFE7>F2Nj3^%|xMSH?Ip*jAO9w*)_=%Iik-$!%@ ztStGyURgNy>R+3Yc%e!Gz5Oj9as+W zTM4bp${EC|*Ylh0hGtUO;B(@_mzU~m39xWhf$*=1-$(IOP#y`_c`RAac$frM zbB(HvVAs2k{jqAKr6Xl9Ow3+FWDxU+{*eLIq@Unwxb-w`A)NC2YX1X?tfo)Z&0YYQ z>FLN|Y}R4C*(wKz{xdbrS#|?Z~GYpB3T`uc?zz##4C^1kPW6zA@Gkw~L4-bjMK-M=AUW(wx(1>hy3U^tIq^!MVv!bz`@- zqaKzDHUnt!WtnKzcYuBF#5yF1&100uXf;gI5~Y4pzbx2PVc-mMkIA?r+XvDz<_ zDv$_``M1Q>`ZaD~c|VObzI0&6PA>hCUHPzgL4o2~#gPs0lzTwg#bzu38USSR110Lu zG&jih2|fnf)yVBN?qhXv2L5U}rGl(4<#F8bmyHhZ7{uUV;X$yVt9&5IwFYAcrrrZf zjVjDfLIInL4^cPo`=-OrGMqJqe_eU!)dh(OO^oZgLTjF6#q^SG3nLWuwA@LNJG3lG z&OG{*E^KVbz@-zKGwZy|W}ta|Uy%Ia{@P9ua3a|=qTVmBhJr{7{eh)CwgJ3v6(>pN zzc@aD*`s8P>BV3~;@*(~u>s8%!O`;c3~1UiF+6TXI~!Fbj{OL;wzP_q&hUDiRrjd; z*!k=i;k4kpP&>fQefda#6gl9t$32o)Nk51d)(-uH!8BbH_{2q5wzHU()F$9+_Bpo~ zf6~&eh?2w^zw?8U~t7=5=nzS=_;dj5l)=Ky|byjr`R zUK-X}5tty*rMx;KATJxcm=g<&^+@E>PGlN~eC4nbG-4`!|d6qa zr#CTz8|)a!O|SlBQ(5Tml4A2WOTfysgrCdX!EE1Cs3VFaFkl4Q)8AI{ds$qEjv@Ef zf3nnqz81)$hOOV0Qny`oR?+6#UaFq0VrtvaLJ>lPuVNwSdmB$kiC47Wik%R~R! z2c&*u{)d`uK1wXW+4FLqarIAVg$X3Q_l5iG5QBhLu|dO*?r7Cdvv5(y;PVx}PxPdq zKQa!P?VUs(o?@lDhrlEL%?_k9}D-G+PkbTr6{1N$NLPc19 z`yGFQnc03O5Mrv>TveU@r;II8pgu7Z8OqU%*ix{wnpcB*Xh456;XhX1PTxI%pdAr? zOSW~mk+3p^F7QAVrIdY7Q=e=)8*c=dCAeyYB)G2HwRJAIX^;H%9pxUUUdOnP`F_%o zHhVkvTyc3s=&}4V4=o2j0%Mvs^ueepF^sOUjFL|%yn6Nx)G|YJ8iYjjP!VQ@tbAS4 z34b=RyGm@r9B<&SIe`$({AcD5xS+wHUF|X9kmLv|UPfRr|Jp;L0#{6>Efkr`#+b2V zoe>m-*Kq{>iK}adBL4IHj&0aLjAQj@27-zYSI9m6D7}E?n0Kh_{8T!=Ru%_in+2*P z4>1Y>Kqj zcd^hTMBmS?WhvXPoM&p3H)uivSeBP6Hak}`-U~C)-O=}F3@VoDq}+5l!wK(-WN+L- z)^MM8T483^;;xl5QT!Ya!1aAkYJT?8^y>}4z-CBRjV4~TV?p1l!bKW7p-kay-#LQ` z2V@bTv=+)PqW9$ygh2Z#APMRSW$o*{UjB0UYiPj1$d=#Wxcf>)bB+bBSU&`crWXon zj6y1ihbx0|B59)N(z3g8mUPPr=36(2<_q+3>B;KFH9izm=7L70TWrg@+ZyAhbqTK5 zFLeGx63=6OKp(Kiy;+XtY?`TqNxHb1t=mpitj}N6<$T}yVK^^BHRoyu(0xQ`!X%JbMz3#O3bLk=PSMix5U`11H`3$a$;G>ND7xo--5AcYkE_g1Lwq%mPNLLsFMo*X^$4%3fT_}JYI_x6*;ss71ecVdf zzb6Ei+MX~gPb6D}V)>RQNMIO|kgiN*LUh7OK_I9ICh6~34q5E0(b^dPio*{uj-B*Pwhb00m;Kd#x3W8Gt>)S7|lGlXz~zOTU~H z9}ZxP%e69Rbsaz)1W~QDGjN`s1$e8tzKp2cj2yF+=zCrd8ir;a#m1z%d0F)hv!Fyb zoM=VNrPt&gx6TmCta=p=rLM_D!oNOj5|Fn}mDyA@qXJgJfMI+T6@tDb$y&=zLCMM< z`UgM<6f{Z6PSC|#$5~FXN--pT_xw657@a-71LL^#lvk*-G!OdL3t`%$p-)oZ#LPcCcMG22>K0i7HWe%kooIUK=u?LsjZoe8z(Sfxw z7NpOGES;PpP@)J>hFq>LKcyDom9E8?AA?1|&uqjUq1^LZtQ{7d-SC9P16(ZcK%BR0 zn3;uW*swc0cRj=s7W)#L~;vk*R#-GB#?DWsdFBtI-Ep33<@vl^zlyQcE zU!LE-dE!O|gH-g(uQgyn_Rl9}k0I=z3%LQ4Dl`tjzSxjJ*PQ{NR*No*4Ir;Y?A$&2 z5BE+4`OI$RfsWM;2t}bsv6Yx5h_q~HenxUqmwt@SR81iqw9E`-EBWeY(Q4mx;2-gJ z7rPn5QLLa=i{=pTeE;a)o`lW^v^~JpEAV%6oXqaoWcnSD-UNv~PRq?*cfmQ;X$>lt z>;HIos`{iq%^Lf1E?a`A?{y38r%=XPIbDG~(<8XmoT36f1K|d9MaLve;D@HhCYHeh zI{j3ijFUpgBH!N^)B$_2ZERPa?*jSDMzqOpk->3P?thCkP_aiD@mn1r0RS%WVlZ9^iSHH0lTB3JAB z3QDMS!+gv)DbtpP`jbL9Z3^^RvjW~UL1F+}00iyi! zAs~n#NJw`dx?4g@y5oR!hs2@byM4a(t?&IC-u0ZdSp0HWv-j+o*|V=1uJNEk%YxFH z0jAG@^ZqsC!=dp@O&lq*+X93`;0o@Y*$ydH=hHKN1FS<&q|_mZ({+9&{*Aup!Wdb! z9TThwDiu2~2jXDCIoo8yL_}lRt7IV64JKq9zRH^JWvU z!XNV)^tylw8RS)%7$+v+35@x(@PgD-3;-J{bk>jV@8%%MWBnwzX)Ic0cBu(!n0XZm z#U*m z?5W4cw?P$H{*@qOGwPq4k79ACG@3v^eL~gDBx)Z_L2SiD0Qqj4GAz>$Z>+Dr6C@mp zNBScOyoLam+i7A#L)@lgKBkV_7bS3|HrWEdw-;UK;;o`Djxx~S1fNrt8J`W@(UP+u^wll>8 zizCfoH(6&11PTz**i(;tFY4<)@#3~*bWHweR@GL^E7j7Z_{vY`Plk&L(V41MJ$mOp z(wsl(pK0vj8c_xX$#+`4qjmZ7NPf!!k{F`iQMEPT7!Yfaa!VQz1b|y48m06Kyb;rRdJBB1MbFB&Legy5mk625Ch zW6Fxa0Fu!}Yw?=(HqEOZS4)k=!Fj=rmp8{Aqh>qdHAq)W^)m?4{WY0eUGX-n->suZ z(TXF-n)^mz>;)*%vno)0X&yi`q64m;1Fx}vmyO!(`9-(G`ZkEIy8+Xe053AMAvJu? zNwAH7COf6>Rc-wmI8n&8C-PNCDrwCIUereIEeTIKeixq<0Cvx?f;>WmKhdEd$GS65 zDa5tDx}^~nSJ-%smVzVuSb#5RP_d>O_T>L?lGQl+CGZ8Jg6*#VgOltKC!RD^ zN(2MMRbIEE606k`Bom{jW%%iUJRa&P$U*Px{31GzYQBQd361TByJbXo1aSoze%F{}2z#hPAQk-{Rtl#QbX} z(O(D2#0lFFa8BLjj-ic`%a*29A8$J>r|9eva6qfK$5_bIY;aaB&RDDatp4qj)n|b# z)Vzm;SD0dU~J z=Mh6Y$%05UsAgwb-0?V{^F1n<@PY{TyycI;0DgI)P0Jbs0c+`j#g#|Hn`7Vo1It5w z^G}kXx$KdGX`=2^;P7UH_zgI_F8zD-Z>~o=h2{recn#^zmNWM`A4q-Tpy4PNpZ|5% zqPPPY3Z4OYB^8a#+G~NVqwXyhk_CfLfN&%Uq$}Tir7+nMws&!V3w7n>MgtZ$Yvi3; zPFKCe6Iw6kh|YRX5U;8J8R?YRRTRq!+fQ$e`ibo=hQ`3^gSe}7)IrKNw^DdsvaM~z z(YWf4P(1&!>wF&l-eC2H=yhuw9!WCK6UpoWjUmuoW`mmG+D`y7Pyy&5k7B1)9$EentCRhtizc^v6$hqytJ{N% z7xQP^4~DG)rUn_KG(9vj%I3hHd-YQ~l-=f=bnFnL=>?h5St7D;yUK7d8bIS{5x;kz zU7(K4Lgvu_umDN=Jg^1>9@xhRzZLhIfu~h=wP;9J6gvZN-$Pgn5rNJ>`R=J(2a2MX zI|?oC-J+_u+A3kDnAa&V>mcN9r0YWr+FHbeG~SJ5Ve;dxZOKLfg)go zW1g`u#fl7KN&sahLMskD2<5cPE))I%K~9YWbJ~?OlHY%@2X%Lw8d~ z_jGct>ek{smIFc8;CS8*n9X!~wN4=|Stx1f_ei9E@e&$@%#;2OrzsEZ`MBjI-5bZ> zd78)O-0l+n0E_UgEX;;D1eAvXeGAwE(x9vl>_29*cK9JB`+0b5m#umd8&kRq49F3+Uwhgey_>_|g60_UVI{UUgVH)=4&stMIB(gq9alK6JtKk=!e;c9ZWn4d{dpQPD(;ap4YfVi1hs?8K3C zC8sOxd4{FcbJIm-w|pl~R|6cBYp|*c^kyUo1;laxAS)GIgnw;!)U}>s=?1L>BO*wh z{^;ji-+v^s-YTq+238d|X@*nxltIAWNU!=)WV{v+<@orO-o*eVU~ljdd^ieD`eU zra5ue&+fGYVFai*Iur>q8j;0Y8M~BTYvr2XkP}v@Vnl+`W4(p`;=TUQoObPB#+BM~ zoxPv(W{ly-53;kkcl*lYH+k$c)x$BIzkN;%H}d`xw6A98h~lq1Hy+7@coG?Y&nj;LFl1Nw*g7Nd(l9 z#Bh*Jr{tPAj@>RxeuQMErIRiMPJb&gIWkV>yKHm%8pt)US=Z2*g!fOJ7vq3y1G&Pn zRh}B4Nh{og{`SFK;SxDEE|b+e_wS)`jNHxL#dQ=~^uw0IP;y00%bvHN7|~2S_hZ{s zZMxIfMvLo_a-S984RR$_tN|R-pJMe5c#Cv`EW-)wc*g@1zrMagrlTLSLUDf4)1S!N z87>`tWx*Nn+r;PR-@SVQUrVpIQq~>4pf%kM7Dd}$JjRbQuDAGq%Ecv``u%qRW0!!s z-t@GxCz`yjq%W|4O8E7{g)e4#ZNj3{xZSi+;#34K2PtLJJkjy}#kf~RiE9GZ53!x9 zK+hSwf>h-dj~P4ES6_ka1VeO}INTj3wT;>m%Om7Nq7h2C!qBN6)}ijR9%D5km?#*p zgh)Ek08Zp9R_JF@zB{0>ZZL}`CvGY^md9QKMP!9g7U9-QD_5ap7{GnRUk#h}xXSBd1+TK6rko z+|W@ZKTOl27n9A+EkuHupFyL1hfA0z$EXY!0b^p7+Zv~T?Tit(_eC~1H0$GzNyGLm zWrY3i$A4}NM}@>b5=kCRP^kU-%$M2xb#Fp+)7D{q5!{Wyd%V?4UN9(r2gSUG8UZ*+ zr1k@~?G6LAo(eTnG}Y)5>~k#iMc7fPX>RL(7&FMs6+33v-Q-{*h-G;)bd$zMwap{q zZq|RsQ3*!hjz|d*lan#lOrZaCgOkuk?WRBWp1XGCJKw{v6svK)Nw!B^vkiJ;vz~1Q zrGh$P8hH}aU90$~omLS5;Yk#V?fjpev$G27tJx$*#0{J>ARX3p!Kf6C9_ z<_1F(Dg{`H{s!TG@l1`oX2?qlRdx5#Y{bb=Br03Ph!9FCfdIE;PRkGYQFg+?a7zd} z*esOF=wu*hjsptH`4&1R#y5K&_-B0|?X{m~C`Z67xD7WFt6~3-ZV%Pp*bo16Clj)P zbp<(YDD?5Yl5HnfPu1LmTmSF>+$4n)8{)P+V5l8JVnaJ2?gCj zWzUFj)ubn_Q|x5C@0XQ)Rvs?c&RQ2WqNT+KO9xV#1ow+k_rJDz<-;}M_DVEp*^?n- z2uS|cOs4YlUIOJ=p$M8}G))SBVJ;iO6$(@{?(-jKgry#+sxL-Z{23AceMtU9 z76WDjwGZ8KHg)JXbInoiHN|*lmZV9)yv%JAblRCbW(rb&pFMCf?X@N;9W=!!QeEA4 z)yXd>8?Un2dxpkH?#SRRcoZGX=cmRDAMJ{JWpM)@=~>BW5NNW&kocue$tY}|)XMhX z5iY8FO0)O~9aZ0TybRo6)=V{bWY1jOZd*bJud+1>Y6F2fY6<2!mfS^61zSVY=A&&; zG!+%O!0u;T-Q9UsH)G?*y|E4__99Wj@q;tcP#q)T*Skk0!k0TS?IL%E0eVI?k2+J_ zpweG@7yNW@Tk>?M_EnzV#uwoIr}9J1W*VQ3zqcAIE>`;$*d2K{Kc;j~GoL&^5{%SL z?9f~UwQdX;AnojR6rl?;K2#r&g*-Yf@8lKZHnt){E55eh|BXxwd{})my=?V#C4n*& zjp0+$lHdCkbZp^^aX#_Q4jrvsjH?4l)gEJ0BgH*zQ!Hqg1hoSEC^mUbcb%9)Oy50< zc35xPxrP11o8JiS27l+j?3{2?GLt`jyQ$Yn$lt24SDK7ij1Y(jJ;*5RMvHpSuKQLe z5m~kDY~?Rsg{^~kM@b#N*Da++MJztTYwd{r-uPL|Tn|z|+JvA8VBHK=mHO*G)+XJ%5(^R;;)731vAC`ERt!@Tcy zgb+N+W!vGWvg)(^?QQ#!+4SZ?r#WGquyS7A{PL=;=O_8R?QPc;1gpiRQ-@fehi{{H zS5NcRIc?q^J!j_JTp~Cc$tw<%Ad+T0FQ3=eBXlhv zC84CP3fgbv??D7AG!S(VU$*DPv&LsI-5E`+L?)Kf`$rq-S_6Ve-_dute81o1|Lu@S z`x3Ht{7KkEg7c2cG1LR-8-#kBHJD64n2d?n_zDp)ZJwDPDV_B!i z^1a;Fy>ZKaLkBeiFAOtV(o}eA(A`Vxf}a4*`Q1V<-wN3yT}zeRgcOzmD2 zAB|d`mta$vx(a7!@IJX7AGfX>-_9Fw%Z!MLDoymCz^>jX5`4Uq^riCINpT>LZdTg9 zvgA~|G5($0oGEpjSnI0S!p=`OYW@IY#d_qNgX>0@-imd zg;obHlW8XVvf2I_!$&O;yVp;5ZT`E*nh_ECnf0Q?gPYpd2;!9Cwn##vlCoqrtR>Qm z-2bUjLKh2cJ!D)&8bG6YcF~rD%zRT2X<{otW~aCk^_;}ZX}2J3ad^AJpv8aMfm(~0 zv2A|YY)78D_dC3WyKhtKHPCwfvIHHg^EB|0g8EZv);#{RXt~Ynt8Vm_g|oKZprVIc zo{WsgpG0TdoRg!e0_(T09-k~0($P;Cwk>;C93;|7kCytfA}%cr@@M*}+<}8X=Sdxu zD6uFsRA!%FUpVV0EPJ`^9US{tU*59U6Xkx@IZg-D@w>%!=^H;`j*mAB7)EJlQ`_$0 zG_~)q>CWmsOzykrB+~NcS)?Vk;>g$I3LkmF z7h`5v&s*>2L2X`F*^#KI8Onf*EwkIImv-I39<~Riz0?_XF4L!vyrI;RI=YDoZw;?1 z0$W|~Om#}@9IM_pcPH>6yhCVv#udWzgXjG3`)=TKL?Ep`S0s%bRfj9g%(Pv`@y@+m=-BgTFel5_f)JBU|Osfw3^@lN& zG=XcAwxzIOcA>i>@KkR^$?JJV{V(2&E7PY^BK&?A5o|*X1xIczX#=dw{`ZKjjQtBA zKCj^nkJ*+pD$+We&AyQG%#V|}>F)171BjX9u5!WG-%vsiP)%iH4?IhG`o-=gW+xVg zvl;99tev>eQ}`O+B^o@W-&}uqYk%nBj^^~KMg>Ij9~KG+HOps+f2$@NpIr9DSTqn4 zKS~TZ6cj!cAItY%@FgNd{qyD?Q~WgdCjQ(wBA%alO0SB9s_p&Q`Xgy3&pJou!E~UT z-_PJp^vA!22d54L%A#VV!DGLs$smR*^2do(n#Njl2TkfQ9pB|^>s1jq6MwJW-WqB) z-{axgio;Mhb3*7d;8yrX3Qhzv$Ww-2UE)Rt?<5OZns}E^f6#T}D&Sw5Sx(QkvmM)` zBJ{ldTwJwfeX&g5pk%q!a`~>7LBaKS*+m)FEKFZb!W6*gX!g)ufSMt7*uO);N~L&R zc$h!tcJQJ3^e6C%2nTIWEF!*MJPD#Hz(sSq)CFQJciVBx4Ua1HyQ~d!Jj;fze~wiT znCH2&iu=%cFcOFs^hE`H+A^5rjU1xlkL23wUkZD~F7G;rZf$RdAwhAC>rjf@Y>M6& zUcfF|Dp;RE)H?e;fUmS~R5tYE`?8fhDoSp6?tcAhNF6n4s4wTSGP88iD6J#G$7ixV zF$&k$GwdZ#> zlPB!BtzPFi=uRZq39wjbHW+{BN z%Oe|ybW5o$NNoc-A7WlmkSE@GsS!dQfZGB0d@xBBrXQ*&vQLnV?jhIME8p{4StIw7 z{S|Yq#4*aYayJ{2Afo6?F?wTRe|qjvqN&5{zFAULrK=)(dKmdaNF}^&V4N99j0dORKOEHmy1knjVQ4p!ZPFcyr zD`Ieq*`F`{45}FRnq#w3AMF}Nt13J^j1dtcKrJcsrK;R`iMIDyl8Lj4=4D}l*@ZOj zdtJBDN5o>`Hl#DdDSj&%O~;>LrSxcy?12faaTrT$fiPWzPb43~#l2zF`WCTR?&phU z;nS4pBH*gC%?5c>ut%YlbL^&{o;h5dVzaq*)Tq?R_;vxrCpd&P+O|jN^?Z-s;!w0Y z`<+Iw>`&1$MpwIQ=c;S{StOHu&o(x5ZKzav~Xtf5p{^Oo%Bpf>cqf%d{>RfPtf@B52UuaI44)<`JQXZ zI=xwX?}wXLjZgV}7F&A^B8|U3N@)B{y>cFhDX0>29QB+)BJz*Xk*;g+-}FE3sV<94 zr~RI12~o+_4EJWdXue3*+bTTXp%6QO2REnLMAc+IoRsD|slr@z)A+#|4aHSag>dIT zr$1_rv7;jF=O&Qi@?3v0r0A?XTuolSKQXaSBOD- zVBaN42tnbfOV)NvU#Q(qAyzeGqRH?QFhS-K5XZ{;!e)R49io*BA~g7|;863=Po=d% z!k9LMq)XV8B}sC*0g>Gs8&^iFK+jChs!cL`c^r8x@&J|HVh{0d0y?Ya*7nJf=<7{H zC^hc)--_a#7ut0U*FaX|2=Q&M!#!*kr*YHr$uoCb?JiDP?I=}$$2JQ<9DMx14s9z) zDUmP*=aK$!1zQs`+1ta-wk6@%?ac)7ThhNO?z@bMMB5n}0%Z(Lw$t3yggosvR|{># zsPj&{V!u7{xAm;QOjcTnAxpTG7!(-^X)f*JEX_!|QG9qU9+s&V(Y1{!-X>P3?!S_g z{%6A5He_63_8?uz`^bE|#{}^PR_OXCMY@7R|Hs+s@tJLPj*5SoxP0i^TjsCX=D$el zbk3LF6~JsQT))59mA*oPmPNMF6G&?WBIE$i1Ifq_G~aD?zm^@F9(e@FVqsc^90 zI~|0EEw1@)YK6&scLI6wF9dG){m*#l!%O+Ze7_Wz5C6<}RhD^Nzszq{YY{sC3BIau zHB5$D#C_y?0|K9$x4nr-&lK__r^Jy;TQV@ReTxEguf2TU>@v2p6{DOH5F_s?UnY)t z2nXRAZ<<2aSsO`VQUpFrnQITBuE?k`E8l4YxnkjLHfu2}rPWumg$2}HGXtaAFeE0X zeoMUGTFEK~DnKesNX+Bl)o<%o>QkX4{~H|#*(F(VSldUx4;>~SvuUe#l3iR^vNuZ^ zwD-3ORr6o59gh~svn0y57b$rW#Li@Z!;3A}w+vD-` zz5o2zu*FNK6f>cd$=*?%%+`Y#|8U3S&Z?0P!iX=ZK!Z~L&^%EFRs6vnb9O^+FRgwi zJQsGGZyLHjYfe17>ZIGVtlq4)GHT?z)i>^_0MsL&e&g^{wG9Ei;u&-RZ}q!w^OR#R z!u*#5YNnCFUVq1O77kpT#&xPo{j+8Yr8+`4dMvi_M5&I2nHaF>pYy47rvnVRxDbnB z#b92d?eN>c>%OzU7FN9KA80Uggc!)bRVta~^J!uLZYPHeM4t;4WJDq{S-*J(RlvJ= zAz3A)y&GsQH~T<}aK3}|dWC;+zN^N=bT}k{S!2}YoG9PD6Xkh>{4|S$+cCkDES%1UT`Pi7U1hL$ zfJFmY#qcK@>f+-I_!hG^uJT9j$JJlhQeJ)mL!oz&?gzqDSL>g1`zw0I8g-AszS~K! zIQxfRUw$K<9RIZL=I}7tV4y;dXqycT> zbs(%JFXza zl8}^*qd|^EG7IT}o`KhaE!FS4A{LH(4S;dYsUv;XSw0++gO& zy10zWCpJ7Tc_2_j9t%ej!L-$uOJqmxB*H5_Z(MoekvzMd$UmR>br@E^d=TIEt7q&X ziZSo^m9Pv!z(W5Zvm_HQ_h6-&z5Y`36^3;D&>_XpV5#DQf8$-sDfz5+fps^!=BScU zwyhSy52JSWk5TOF7d7h~0e%);2{XjTEU29Y?lrqRWcQtzV7=Tu4+Th~d1D&{bb>8X zdUvi}EjK^E#t=Mcw3fJF6qiO$+@B6#kR)whq;6eUo6)Udc3?!Yh!NuJnkpSMh@0fP z`8;(UQ7mCoQ7DH+92#lI`4u0;pR0dvVjXR_o=zH9=u>^_!$;Bm7OaeV-IPp8Ul8{_ zt{rn;YgooYj3$4MXFC4Fn$SDF!8eQtUFJbAqHCHSNB80N(4pIT)T?uS9{!IvDNO!r zHmJYEGQTMQ=%vRYnR#34<|8i`YTX{qzjrA^QFmRiGZvi3B)$19XW=SGF}%2XB*9SQ zkbK+u<7g!2WB^~8>eOJ3k9_)hi*Kdbhl!csFWY9=Z8}Etml;Hb?I!ep@%&#lxHW7i zas#N(6@p2-1duUOvWNCAei?6O%89~oE-hcY)<5TT$l0!a9t$(zph(LU^{XDjPg!%> zjc=n$f5J0m3u}?c^;qEbkq_{zfTV^Qx4I_juL~WLjDCYX<-9fdZSNI?a5Zw8t#0&H zOtMwZfHq(R&E?Rbl-=+0^HkeEgN_3}kexk!ZVl)^cxsSig@5R%md)|=2`*AS%#o^xK$5HNdJn!UJwp#YmkZ}L}} zUg&mO(%EHcrDOM5n#_$apI_|ip2mw-=}-|!Rb0F_X96ERV{sh8bI*l~Foi&z=iKa0 zOTJ*$(eq zQs?e`Djf`tX0%djv~w&WcRL{d+6eD8fvat@wex6EH01|w2xX=d1pcV|G!7Puz)%B2 zC0hp*hz~^!3I0-p%4eF+=ys6T$kV&x%#A=(-Ecuc3`LE-Xwk$*q_>R19^F8~a zh*pUoYe0FJ^C#h(_N zE~np0+FX1U`3;R$*%}pdC$?XFLYVN;T5fqM!-Pd-v1EKRs2dmE7D1EdvtfWfV62g& z>1MplyNll$7zw$tsWymIC-ZA6!J9AOxv}44Qf(eafzqZvHXctaU@GOZ#B5Phg z(*8EdDuydGM5aY=SQrsH7r!e)^G9~hJq$c>7Jz_8-x6hVisi!8IMZN3qjIfvDdo4_ z^Nf^L@&pdm(uu|MJvD=fU)GbQ(yaThr~l%pDpwDE5NK-Sh}ZS(3)?pn!1_ww=4!pv z)9v8L3hAlGH36%7Anuz>Kne{`u>hQjdcvL6NNNX+vyKYKytk2zsteWZPIV&2=!eFN zy8QS(@&ruc|5WYXHrZ!y&k~{hEGeHUq9L~rKa?GG#`yMa4nnR0)Fa9GKAvFJTaq@g9jH-Q+QWfdTmM&E)#vJ7W^g$R{^y{F&(S&xt{#jT$Q)e&ThUEvA9@rh{)8-b155<&>p zP=px4^#pzUX8AN6LjnTGNFNYrVOP1(sq0QimT727eo>uara#YULGmb2TK9>k5ojC@Dagyt#br!-UakGk?4jlnI1oO58WqY;6`dHy@|F9 z?=p4ofg$3|0Ye7+ofwoJFS`%Fb3KRO4A@g=fPmTW?Her=#JgY>QW@&H3#cL4P6(|f zxP@X`PiNJjcg7VV7?KHi*%8g?L^3q~#lA#%KH_?2>;H2w4mpm>Yp0-@&N|G~F z;>$zXb|fce?H9IGOggL|G>NkmG+k;$J?>KtA?#}m1A1T1Fc)zyk|rPYBr+AcMWo^bmYGPHcCVdOc+mGAS8NL50qR7?yv!FXA2)-O29!ZQm zO^Rv~7`NC=kApzn@iG&4zQAKt%ZJH~Nt8w2BE`m_-}C3t@x;NpMr!@I>| zK6IMTbjN!fA&6-NX_VrG}`*Funj9F&Ggq)PwONSew|Pehii2Qd2I&ymqi4 z($#!~jWvHs7)HE3e~B8FL?n%@1lzaS+-=TpF(_H6C!%%%MT{j*GYeyxUxxJ;e*xSR z-B!8h6lgTPu~@)Jco8R)%;ia8d1-Arwg!%^h4>hf=de6HynO-qC5CmjL>W-D$GG*&i;E! zFOVk~hoY_}L~d5ob7ZVm{HgI2{`8No#1^F(vvIB!%%3)K?sCHlrp!md4%({Vi#P0M zfCd{gQm1#CppCoyIG%^H0dQ@dg&W31dNF1Ni%yLuMiRv0r^xpNzsSps0(SdDXjQ`C zn~?)RRfj4E6b>!>KJ6g$Tw|q-k66OVy}}CitY=Ec`u@_MU-9`bKnBS32DT*$u@Y&# z?LxHhbgLb~O>98!P+tlY6!O{~Lb%f@-wx}i+VcC5nh97rVk!Lls|yCc9>q9(Jl)`* z{OrU}TrL`LC0;&F|17NfYFNJw&m-7q{y1`2u6T$G7-t5}w41gZBOQ;iQ-WAoPKr>7 z7la6&Y|K2hV>~f;bt}JdpuYYQ&`MsYl@ZPJ=%INnjL$Sam*M$5cNzTLV(Do zuBalr>ngqd;$gWMiK+9dc_t4sG#cAadEt%EYQa;u7h;*>8rDYd#H1mKO<3qpU@GFZ zok>Bda?Z)tvopAjx7p7ewaO(VB!w15GdwYy{}>n%J)?yu8|fI)xh8STZ}3VY^tn;) z_GWOdsElNVdd&<<1lN>d&5UMh^wuAl74|SHOeQoMVMrW#4hJJ;jt))LLx)B;ghooa z`%?KnPFrf~;=4o|uO0A1dPN4VMgZWNf_|U;x3ob$fk45qeQUv%RlqcGO(9qG*?b%{U$jHd#3I6xje^u~b eEBN2#2#?hGOhS#T^L+dO_)(Bmktvrl4gNo*L0!-Q literal 0 HcmV?d00001 diff --git a/docs/source/modularity.rst b/docs/source/modularity.rst index d2eb603c..bcd6944f 100644 --- a/docs/source/modularity.rst +++ b/docs/source/modularity.rst @@ -5,10 +5,7 @@ Modularity and Clustering ========================= -Francois - I left the code from widget here so that you could replace it with content you want. -I think an image would be great if you have one. - -.. image:: images/WidgetScreenShot.png +.. image:: images/ModularityScreenShot.png :width: 300px :align: right diff --git a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb index d86ed972..cd8983bb 100644 --- a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb +++ b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb @@ -9,10 +9,12 @@ "import pandas as pd\n", "import numpy as np\n", "import pickle\n", + "import random\n", "import igraph as ig ## pip install python-igraph\n", "import partition_igraph ## pip install partition-igraph\n", "import hypernetx as hnx\n", - "import hypernetx.algorithms.hypergraph_modularity as hmod" + "import hypernetx.algorithms.hypergraph_modularity as hmod\n", + "import hypernetx.algorithms.generative_models as gm\n" ] }, { @@ -106,12 +108,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAHBCAYAAADkRYtYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAB4w0lEQVR4nO3dd3hUZfbA8e8kpFAndEIdijIXuEgTK6jYTewlFnTsoqjrml2N+1s17q5rdM2uDcHu2DD2ktjFghUU0SveEQSG3gkh1ITM/P54b8gkTJJJmMydJOfzPHlc79xyImxO3vee97yOYDCIEEIIIeqWYHcAQgghRHMgCVMIIYSIgCRMIYQQIgKSMIUQQogISMIUQgghIiAJUwghhIiAJEwhhBAiApIwhRBCiAhIwhRCCCEiIAlTCCGEiIAkTCGEECICkjCFEEKICEjCFEIIISIgCVMIIYSIgCRMIYQQIgKSMIUQQogISMIUQgghIiAJUwghhIiAJEwhhBAiApIwhRBCiAhIwhRCCCEiIAlTCCGEiIAkTCGEECICkjCFEEKICEjCFEIIISIgCVMIIYSIgCRMIYQQIgKSMIUQQogISMIUQgghIiAJUwghhIiAJEwhhBAiApIwhRBCiAhIwhRCCCEiIAlTCCGEiEAbuwMQ8Un36m2AfsBAYJD11QFYBiyxvpYaHmOrbUEKIUQMOYLBoN0xiDihe/Vk4EzgWuAwYB1WYrT+WQq4qEqgA4Fi4FngccNjLI950EIIESOSMAW6V+8HXA1cCfiAR4F3DY+xq57rHIAGXANMBmZb135ieIxAkwYthBAxJgmzFbOmXf+JSngvAtMNj/FbI+/VHrgQmGodOs/wGAujEqgQQsQBSZitlO7VewEvA2XARYbH2BCl+zpQo9V/AdcaHuO1aNxXCCHsJgmzFdK9+kRgJvAE8E/DY1SEO8+VU9QHGEzV+8qOgJ+qop/F/ryM3bU8YyzwKvA2cKvhMcqi/G0IIURMScJsZXSv7gHuBS41PMYHNT935RS1Bc4DrkMlyt+pveinG+AFpvvzMv4I86wu1udO4IT63okKIUQ8k4TZiuhe/XDgLWCC4TF+D/3MlVOUDPwdVSH7A6p45z1/XkbY0ad1zSDU+8/LgHlAtj8vY0GNZyagpn43Gx5jSvS+GyGEiC1JmK2E7tV7A3OAawyPURT6mSunqC/wCrAJuMmfl7G4Ifd25RSlopLmP4A/+/MyXqjx7E7AXOBfhsd4vvHfhRBC2EcSZiuge/UU4HOg0PAYd4d+5sopOh54DngQuM+fl9Ho5SCunKKRwGvALOBPoe83da8+AvgMmGR4DKOxzxBCCLtIwmzhrKrVx4EuwDmGx9j7B+7KKToMVZRznj8v4/NoPM+VU9QJtURlM3CpPy9j7/N0r34xatp3lOExdkbjeUIIESvSS7bluwbVtefSGsmyO1AAXBGtZAngz8vYCpwPjEE1QtjLmo79Hbg8Ws8TQohYkRFmC6Z79SOB14EjDI+xt4rVlVOUCLwHzPPnZdwWhUddDKSHHliyYVv3N+atmnKKnv70sN6dVlUeX7BxQf/PV3x+4dUjr74vKTEpAKwB5L2mECLuyQizhdK9emUhjyc0WVouAdoDt0fpcenAitCvQd07zOvTue2zz33rPyMQDO49Przb8K+LdxevfeuPt/pax9Jrv60QQsQPSZgtkO7VU1Ejy4drrrV05RQ5gBuAf/nzMvY0ZRxZB/f7JggJH/y6dnjo8bE9x77966ZfTwsEpd2sEKL5kITZwlhFPtOA5UBemFPGoxoJfNTQezscjqSGnJ/gcODu1fHDuf7NJ4YeP9F14i8OHIH3lr43uqExCCGEXWQ/zJbnOuBg4PDQIp8an0+PdPmIw+E4HDV1exSQ6nA4lgBPAvnBYLC8tusSEhJe7Ny583JwJCR27NqnYsG14/+Rc9McgARHAnp3/e2f1v10WuagzKcb+P0JIYQtZITZglg9Yu8AzjQ8xrZaTpsEvBHJ/RwOxyXAF8BJQFvAgeotew/wqcPhSKnt2sTExLJNmzbdtmnTxltPuODquU898dgloZ9nDsqcUxYoS/tlwy+uSGIRQgi7ScJsIaw9LV8GLjE8RthOPa6cohSgB2q6tk4Oh2MIav1mbbMQE1A7ktQrpWTFgpS0Hp02bd+9N8GmJKYE3F3chT+t/+noSO4hhBB2k4TZAuhevS1q1PiA4TE+rOPUAcDKCIt9rgFqHUFWnuNwOFLDfVBRUZHctWvXe9LS0u5//slHLzj63Ct8b/20emLoOacPPv3LrWVb++hefWQE8QghhK0kYTZzVpHPdGAx8J96Th+E2nkkEmMiOKcjMCTcB5VTslu2bPnLv//977y37v9LT9+arSfuCQQcled0SO5QPtg5+Cvg1ghjEkII20jCbP6uB0YDV9RS5BOqgsj/zCNd81HrbiaV/vKXvyzaub00eWfJxor3jLV66GcT+k74DjhR9+qDInyeEELYQhJmM6Z79aNRvVnPNDzG9gguWYIaZUbi+wjO2Ywa2dZp5syZvYPBYMKYof3f/3FZ8Umhn3VM7rgb9a70LxHGJYQQtpCE2UzpXr0/MBO4yPAYSyK8bDmQ7sopimQ95WOoDaPr8lAwGCwL90HlO8yuXbvec/311994ww03TD/3YNfXpbvKB85bVlyzu8+DwPm6V+8ZQVxCCGELWYfZDFlFPm8C+YbH+CTS6/x5GeWunKI1wEBgYV3nBoPBFQ6H4yJUg/a2YU55izqqZAOBwEXhjg/o2v6zT33rThwzoPOzlccMj7FO9+ozgT8Bf6vv+xBCCDvICLOZCdmu63cgvxG3+ADIiuTEYDD4LjAW8ALLUFOwX6N2GzkrGAzW+/6yppNH9Pp49ZZdR6zbuqtmEr4fuFr36s6G3lMIIWJBEmbz8ydgBHBlBEU+4UwHrnHlFEU0uxAMBs1gMHhpMBh0BYPBrsFg8MhgMPhMsJHb3BzYs2Nx1w7JP789f/XRoccNj7EUlcynNOa+QgjR1CRhNiO6Vz8GyEEV+exozD38eRk/A37g1CiG1iCHD+72waJ1pSeWV1QtMbHcC9xkNY8XQoi4IgmzmdC9uouqIh//ft7uUdRyFFscPbT7H0mJCaXv/ry6WvN1w2MYwI+Ax57IhBCidpIwmwHdq7dDFfnca3iMT6Nwy9eAYa6cohFRuFeDJTgcjOjT6YOfVmw5OczHecAtuleXgjQhRFyRhBnnrCKfJ4AFwAPRuKc/L6MM9S7zxmjcD1gD9GvI19lj+650pib1/8G/OTH0RobH+ApYDZwTpdiEECIqHI2s3RAxonv1m4HJwJGNfW8ZjiunqCfgA4b48zI2Reu+DYzhDqCvPy/j6tDjulfPAO4GRjeysEkIIaJORphxTPfqxwF/ZT+KfGrjz8tYB7wDXBnN+zbQY8C5rpyirjWOvwckAifue4kQQthDEmac0r36QOAF4ALDYyxrosc8BEyNdIlJtNWWtK1RZR5wmx1xCSFEOJIw41BIkc89hsf4vKme48/L+BHVLu+MpnpGBGpL2gVAf92rH25DTEIIsQ9JmHHGKvJ5CvgZlUya2oOoZgi2qC1pGx5jD2q7Mtn6SwgRFyRhxp+/AAcAU2JU8PImMMCVUxTJ/pdNpbak/QxwiO7VbVn+IoQQoSRhxhHdqx8P3Iwq8tkZi2f68zL2oBoZRGuJSWOETdrWf4MHgVtsiUoIIUJIwowT1gbKLwDnGx5jRYwf/wRwurXUJObqSdrTgQzdqw+IbVRCCFGdJMw4oHv19qjtsv5peIwvYv18ax3ma8DV9Z3bhCqTdo/Qg4bH2AI8CWTbEZQQQlSShGkzq8jnaVQP1Wk2hvIQcK0rpyjZjoeHJO1rwnz8ADBZ9+rdYxqUEEKEkIRpv1tQGzpfa2dXG39ehgGY2NuSLmzSNjzGGuAV7H3PKoRo5SRh2kj36iehqkPPMjzGLrvjQSUsO5eY1JW07weu1b16x9hGJYQQiiRMm+hefQjgBbIMj7HS7ngshUB3V07RITbGEDZpGx7jD+AT7H3PKoRoxSRh2kD36h1QRT53GR5jts3h7OXPy6gAHsHGUSZ1J+17gZt1r54S45iEEEISZqxZRT7PAt+hlkzEm6eBk105RX3seHhdSdvwGD8BvwAXxzouIYSQhBl7twF9ganxuHWVPy9jC/ASMMXGMOpK2pUbTCeG+UwIIZqMJMwY0r36KcBU4GzDY+y2O546PARc7copSrXj4fUk7S+BTcBZsYxJCCEkYcaI7tUPQE3Fnmd4jFU2h1Mnf17G78A84HwbwwibtK1R+T1AjjW9LYQQMSEJMwaspRBvAbcbHuNrm8OJ1EPAn1w5RbYkpXqSdiGQChwX06CEEK2aJMwmpnv1BNTyka8Nj/GY3fE0wIdAO2CCjTGETdqGxwigKmZzbIlKCNEqScJsen8DegE32B1IQ/jzMgLAw9jbXaeupD0TGKJ79fGxDUkI0VpJwmxCulfPRBWunBPnRT618QLHuHKKbNkppK6kbXiMclT3HxllCiFiQhJmE9G9+lDU8ohzDY+x2u54GsOfl1GKSprX2RhGXUn7KeAI3atrMY5JCNEKScJsArpX74Qq8vk/w2N8a3M4++sR4HJXTlF7Ox5eV9I2PMYOVHyywbQQoslJwowyq8jnOeBzw2M8YXc8+8ufl7EE+AaYbGMYdSXtR4DTda/eL8YxCSFaGUmY0Xc70A17+7FG20PAjTYuMalM2hfV/MzwGMWoqe+bYx2XEKJ1kYQZRbpXPw24ElXkU2Z3PFE0CwgAx9oYQ11J+3+AR/fqXWMckxCiFZGEGSW6V3cDT6KS5Vq744kmf15GECth2RjGLCBImKRtdU56g2a2dEcI0bxIwowC3as7gbeBHMNjfG93PE3kReAwV07RYDseHkHSvg+Yam2dJoQQUScJcz9ZRT4vAJ8YHuNpu+NpKv68jB2oZRx2juJqTdqGx1gIfI6aEhdCiKiThLn/7gTSgD/bHEcsPApc4sop6mjHw0OS9vW1nHIvkK179eTYRSWEaC0kYe4H3aufCVxGyyvyCcufl7Ec+BS41MYwak3ahsf4AfARpppWCCH2lyTMRtK9+jDgcdTeluvsjieGHgRucOUU2fJ3x0ras6g9ad8D3GpNlQshRNTID5VG0L16GqqTz18NjzHX3mhi7mtgG3CSjTE8RO1J+zNgK3B6bEMSQrR0kjAbSPfqiajikw8Nj/GszeHEnFWt+iD2Nmb4ilqStrXBdB5wm2wwLYSIJkmYDXcX0IHW3VmmABjlyimypel5SNKubYnJW0An4JhYxSSEaPkkYTaA7tXPAi5G7UBSbnc8dvHnZewCHsPeJSaVSdtd8wPZYFoI0RQkYUZI9+ojUEnibMNjrLc7njgwHbjAlVOUZsfDraT9OLWPMl8ENN2rj41dVEKIlkwSZgR0r94ZeBPItpYutHr+vIw1wHvAFTaGMYNakra1zOe/1D/K7AGcAoyJenRCiBZFEmY9rCKfl4Aiw2M8Z3c8ceZB4HpXTlGiHQ/352Wspu6k/QRwtO7VD6xx3AH0Ac5GraMdChwNtGuaSIUQLYEkzPr9E0gF/mp3IPHGn5cxB1gHnGpjGLUmbcNjbAOmUfVnlwAMBC5EvYtOB5YDa4FEYHgsAhZCNE+SMOuge/VzUT9cz2vNRT71sHWJSQRJ+5EER8LZ7y5+90jgcuA8oCOwDNgUct564HBA2uoJIcKShFkL3avrqDZsZxkeY4Pd8cSx14ADXTlFI22MobaknWx4jF7ZY7N/Kg+U342ail0OlIQ5twxIAfapuhVCCJCEGZbu1bug1vLdZHiMeTaHE9f8eRnlqIpZO/fKfJ3qSbstcDBwLXDKhL4T3pu7du7YNdvWBOu5z0bgSKBN04UqhGiuJGHWYBX5zATeNjzGi3bH00w8BpztyinqZsfD/XkZZcD0Lu2Ss1EJ71pUEc8WYMVA58DV3dt2/6FwSeHx9dxqJ2q6dlAThiuEaKYkYe7r36gRxi12B9Jc+PMyNqBG5FfZFELH9/40wTx/fL9zN5bumgRsAFaiplkBOKb/Me8u3br0xJLdJfW9o9wMTET+vyGEqEF+KITQvXoWqigky/AYe+yOp5l5ELjOlVOUZMOz3cPSO40IBoNzX/lxpQbs82c3usfo1c5k58K3/3i7vnZ524BuwICmCFQI0XxJwrToXv0g4BHgTMNjbLQ7nubGn5cxH1gCnGnD4xcCO448oPsHSzZsP2FXeUXYdaGHph/6zpy1c067+fObj5/66dSM/8z9z5itu7cmAlQEKkJP3QIcgSoSEkIIQBImALpX74rq5HOj4THm2xxOc/YQ9iwxKQF+PmJIt52pSQnr356/+uBwJ/2x5Y82y7cuD36/5vvTVpSucJmbzUE3zLrhHIA9gT2OGvfri2puIIQQgCRMdK/eBngZeN3wGDPtjqeZexvo68opGmfDs+cBKaP6pn3wy8ot+2z7tWb7mpRXF756av9O/b8bnDbYkZyQvPvpE59+benWpSPKK8odKW1SalbQlgKHxiRyIUSz0OoTJnCP9c/bbI2iBfDnZexBddaxY4nJRsB3+uje/t17Al2+XLhhYOiHK7auaLdzz85OD0166IXBzsEl63es16748Iqzu7ftvvyn9T+l7d6z2/Hekvd6h1yyGRiM6jUrhBCtO2HqXv1CVD/R86XIJ2qeBE515RT1suHZc5ISE9sO6d7ho6/+2FhtlDk+fXxx+6T2xdmfZ58wsvvIdwanDXYsLVk67PDeh88dnz6++OkFT2t5c/Ku+3Lll6FLY3ah1nMKIUTrTZi6Vx+Fquw80/AYm+o5XUTIn5exGbVX5TU2PH4N4D97bN+fNpTuHrNkwzZn6IenDz79g4XFC4cdmn7oT3079uWEASd8d6F2oQlw7UHX/nZglwPnPTjvwdCipQ3ACKBz7L4FIUS8apUJU/fq3VBFPlMNj/Gz3fG0QA8DU1w5RSk2PPub7h1T2vRypn73nrHm2NAPrjnoGnPysMlv//O7f57QNbXrrEAwMCa9ffruys+v1K/8vGR3SfcNOzZULo0JAuXI1l9CCFphCzCryKcAeMXwGK/YHU9L5M/LWODKKfoVOBd4IcaPXwGsPXlEr6+83/j/tH33nnfap7TZO92+fOvyLrNXzb5gacnSeeWB8mE/b/z5ulMHnfrhkpIl3eeunTu+X8d+i7q3615eEaggMSERVFP20cAcVCGQEKKVao0jzPtQC9v/ZncgLdxDwJ9cOUWxXssYBL4a2TdtV/uUNivfmr+qWqXrreNv/bFvh77zjuxz5Oyj+h71IUH6v+R76fS5a+eO79Ohj//qkVd/ClQmS4DKBZp67L4FIUQ8alUjTN2rTwZOA8YbHqOivvPFfikC/gccBnwT42cvAYoPG9T1s9mLNmYEgsGvEhxVeXtMzzFzvlr11YQXTnlhWv4P+UedfeDZD47tOXbNitIVqf069tsV5n7rUUtM5qEKgYQQrVCrGWHqXn0M6gf4mYbH2Gx3PC2dPy8jgHqXWdcSkyRgKGoz5yFRfHwAmH3C8J6rygOB9p/51h8Q+uHdR949u2Nyx2L/Vn9q/079P5m9cvYpFYEKakmW3VAbTa+27iuEaKVaRcLUvXp34A3gWsNjGHbH04o8C5zgyinqW+N4R+AQ1K4iZwBdULuLRHP6dlFiQsJ2vbdz1reLN+3TyOD2Q29/Teuibc0YlPHBuh3rDl1eujwt5GMH0BPoj3on+hyquUVZzfsIIVqPFp8wda+eBLwCvGR4jNfsjqc18edllKCKfq61DvUCTrb+fSKqBd1y1JRnV1Q7umgpB746fVRvc9P2spG/ry3tEvqh3l3f2i6pXaBfx36l6e3Tv3p/6fsnA4mo0WQfwASeQu3CsiaKcQkhmqkWnzCB/6D2Obzd7kBao+4dU6YP6t5+Sumu8isAD3AgauutattvAduBaLfU8znbJZcO6Nrumw9+XXNcbSed5Drpo7JA2bElu0tcwI/A48CHqO5BQggBtPCEqXv1S4AM4CIp8om5DsDBc//vuEnnH9x//UcL1k2kajRZs28rqFZ0BxLdJgG7gO9OHdl73rLNOyZt3Vlec+uxFKDfgV0ODCwuXvzFaW+dlgB8gRr5CiFENS02YepefRyQD5xheIxiu+NpRXoAJwBTgKOA7enO1Lfm+jcfHQiGy5N7VTYJiPbyDWNwjw4bOrdNXvLW/FWHW8fao95Ptgc+AqZ/vvLzWzbv2nyd7tXbRvn5QogWokUmTN2r90QV+VxjeIwFdsfTCiSiGpVfCFwGDEO991sF7M4Ymf5LIEibjxas0+q5zwZgLJAaxdi2Az8cO6zHnBWbd2QEgsH+qL/3b6OmXucDuw2P8SuqOcFlUXy2EKIFaXEJM6TI5znDY7xhdzytgAOYjOrq0wk17bqOkCUYCQ4H7l4dP5izdN9q1Rr2oNYGR3OJCcD8Qwd1/X1neUXCbW8Yy1EN4n2oEW2oe4C/Wt2ghBCimhaXMIH/olqY3Wl3IK1EENiB+m9e67u/00f3nr1lR7n7tR9XDPn3e+ao3HcWjHvxu2UDwpy6GTic6P7dLElwOGa89P3yuwvmrjiXWtZTGh7jW1TCPy+KzxZCtBAtKmHqXv1S4ERgshT5xNQcVJFPrbq2T9mdkOD46YnZSy979YcV537029rjXvlhxcTrX5p3LMCu8orKNZg7UIU//aIc445AkOeAo1w5Ra46zssDcnSvHuuWfkKIONdiEqbu1cejlpCcYXiMLTaH09qsAIqBdrWdsHrLzpRvF2/q1ibB0Xt4b+ePvZ1t/U94xr383ZJNEwFSkxJDK4K2AeOjHaQ/L2MbqpnC1DpO+wA1Aj0l2s8XQjRvLSJh6l69F/A6cJXhMX6zO55WKAB8i+rYE5axqsS5JxBwHDKwizHJ3WPrHxu2HTRt1h+jOqS0KV68YVvNytTNwEBUM4NoewS4zJVT1D5snB4jiDXKbIJnCyGasWafMHWvngy8CjxteIy3bA6nNVtEVdHOPk4c3mt9MIijdNeehb61pScN7dXxx8Jf1pw24YDu3/jWbHVe+8KPx89etCE04e4BRkY7SH9exlJgNqp/bW1eA3rrXv3IaD9fCNF8NfuEiWqoXgzcZXcgrdwuVJec7rWdcOigrrPnLS8ekOBgz4TB3da2SXSUfepbN+n/3vr1T0s2bh/0l1d/vu7vbxqHWKdX7kPZFOsiHwJurG3rMcNj7EFN78soUwixV7NOmLpXvwI4FrjY8Biyk4T9DNQOJGET0UMXjP7s0iMGFq0s3rns8dlLLu+YmrTpzNF9i+bfccJtH9408bGTR6R/8MXCDROs0ytQo9WhTRDn56gR7PF1nPMsMFb36lEf5QohmqdmmzB1r34oat3cGYbHkFZm8aEYWEgd7zIz9fSVW3aW7Xandwzcf87IN/564lADYP6KLZ1+WbnlwC7tk9eV7Cyv3L15I2ofysTa7tcY/ryMINYos7ZzDI+xC3gAuCWazxZCNF/NMmHqXj0d9Z7pSsNj+OyOR1TzA3VUy64o3tH2j/Xbxh0xuNvHn5jrT/x9bWn7d+av6v3U7CXjyiuCyX87RXvX2TapcknQTsAJhFuvub9eBMa7cooOqOOcGcDJulcf2ATPF0I0M80uYVpFPq8BTxge4x274xH7WImqcg1bhTqyb1ppv87tfn3zp1X9PlywdtI/C387Kf/jhVm/rysdesLwnt8fMqjrlopAtZ6zW2maJSY7UR1/rq/tHGvm4nHgL9F+vhCi+Wl2CRM1lbYB+KfdgYiwgtSzxOTuM/WXjhjS7asu7ZNX7CyvGHjB+P5FH/35qOk3TDrABEhMqPYKdAtqhFlrMdF+mA5c7Mop6lTHOQ8AF1j9iYUQrVizSpi6V78KtQPGJVLkE9cWofa6rLmdFgDjB3bZcveZ+ndXTxzkHdS9fZ8rjhy4CKDGyBLU9lt9UQVA0WzIDoA/L2MF8DF1NFw3PMY64GXqeN8phGgdmk3C1L364cDdqCKfrXbHI+pUhmqX16Ouk45x91jUJiFhxzs/rx4F1UaWTlRrvPbAp8CjqG5CTeEh4AZXTlFd/1+4H7hG9+p1jUSFEC1cs0iYulfvjWpOcLnhMX63Ox4RkQWov1+19mRNcDgY0bvT+z+v2HKSdV5P1D6VpajOTTOAeaj+sk3lG9S0b62t8AyPsQS1b+aUJoxDCBHn4j5h6l49BfXDc7rhMQrtjkdErAS1hVad7e3OHN3np46pbVyL1pWOBkzU+scXgcWoqdgmZS0xeZD6p1zzgJt0rx71qWEhRPMQ1wnT2jHiEdRmxP+2ORzRcD9S+xKTjkC/dilt2i7fvOPl8x77tjPwIWovzVh7BdBdOUXDajvB8Bi/AD8Bl8QsKiFEXInrhAlcg9ob0SNFPs3SalSLu47WvztQ7zX7o1rpvQXMePfnNXcU7yg/25VTVGtlbVPy52XsBh4Dbqjn1DzgFtlgWojWKW4Tpu7VjwD+gSryKbU7HtEoQdQ7wq5AH+vrD+A54HlUV6Byf17GWqAQuMKmOEG9Lz3flVPUuY5zvkKNgM+OTUhCiHgSlwlT9+p9UNNkHsNjLLI7HrFflqCWmXyFGsUVoabYa64heRC43pVTZMvoLSRpX1nbOdbWX/cgG0wL0SrFXcK0iireAKYZHuN9u+MR+60c1ZnpO1TXnrD8eRk/oLoEnRajuMJ5CJhaT9J+D9UU/sTYhCSEiBdxlTCt39qnActRv8mL1uUh4E92PdyflzEX9d611qRtvUu/F9n6S4hWJ64SJnAtqm/oZdb0l2hd3gAGu3KKRtkYQyRLTF4GBuhe/bAYxCOEiBNxkzB1rz4RuBNV5LPN7nhE7PnzMspRXX3sbEP3BjCkrqRtbTB9P3BrrIISQtgvLhKm7tX7oX5r9xgeY3G9F+Q625Lr1Mh1HkeuUyfX2aHJgxSx8jhwpiunqCmarderAUn7GeAw3asPb/qohBDxwBEM2jvzaRX5fAm8bniMe8OelOtMBs4CLgeGo5YpLEO9b+oJuFDt1JagWrL9an0ZwHpyS2R6txlx5RQ9CSz152XcbdPzu6OWvBzoz8vYUNt5ulf/P+BAw2N4YhacEMI28ZAwH0NtBXXePu8tc509UYvJr0QlwsdQW0etJrekIuS8BFTiHIJKqCOsLx0IUJVAK78WkFuypQm/LbEfXDlFI4H3AZc14rMjhqeAJXUlbd2rp6Fa+I0xPMayWMUmhLCHrQlT9+qXAP8HHLzPDiS5zqNRPUXfBh4mt8Rs8ANynZUNvXWqkugIVFItZt9EapJb0pSNvkWEXDlFnwGP+/MyZtr0/INQS0jqTNq6V78PSDE8hm3VvUKI2LAtYepeXQdmAZMMj2Hs/UCNFm8BbgIuIbfko6g/XD1jANWT6AjgQNRawNAkagCLyC2xZaQTBQcB41DNAlahduYoQa2JjNt2g66cojOBW/x5GbZVorpyij4HZvjzMl6u7RxrJ51fgaGGx6h1+lYI0fzZkjCtfQXnAncbHuO5vR+oEeETqBHgeeSWNNUeiOHlOpNQ07o1E2l/VLeamiNSP7klcZt0LOcD3VHJsa11zGH9+wZUIl1DVSLdxr5deGLOlVOUiGqjl+XPy5hjUwwRJW3dqz8OrDU8xh2xiUwIYQe7EubfgBGGx7iw2ge5zqtQC9cPIbdke8wDq02usy3gpuq9aGUi7QL8xr6JdE2cFBo5UNWem9h3qywHKoG2A1JRSdIB7EH1S11t/XMLKpHGfKralVOUDYz252VMjvWzrecnot5RnldX0ta9+hDUu/VB0vdYiJYr5gnT2ulhCWq95by9H+Q6x6C2dzqS3JJobBJ9MZDeiOvWoBqD1y/X6aR6kVFlQk1k3yT6K7klmxsRz/7ogGoGsbIB1yRSlUiTqUqku1FJdA3q+ymJaqRhWI3QlwDD/HkZa5r6ebXE8BdgVH1JW/fqBcAcw2PkxyYyIUSs2ZEwTwdyDI9RNc2llo38BvyN3JJXovSoW4DGTOn2A+7bryfnOnuw77TuCNR0p0H1RPpbE46m+wAX0rCEWZs2qCTaB7Wh989RuGe9XDlFjwIb/HkZd8bieWGeH1HS1r36aFTz9kGGx9gdq/iEELFjx84Q16EWhoc6A1gRxWRpr9yS9aiCpllVx5wOVDKuTJ7HoJbMuMl1Vo7aQr9+J7ekbD8jcaJGh/uqKFPHE5Mj/Y1pD6pQqBNqmjZWHgY+c+UU/dvatzKm/HkZxa6copeBKahOVGEZHuMn3asbwGTgqVjFJ4SInZgmTN2rHwCMBk6v8dF1qKbrLZd6p7nc+nqv6rizDTCYqkR6FnAH4CLXuYR9E+mSamtQ65aO2qh5XzUT5ef3DMcsOoyk1J0ce8d7DJxYXMs9g8QwYfrzMkxXTtHPqOIlb6yeW8PDwKwIknYe8Jju1Z81PEakf0ZCiGYi1iPMicB7hseo+iGe6xyOWs7xVqQ3cTgcJwDnoUZs61BrNd8MBoPxXrG6r9ySPcDv1tfrVcedqcBQqhLp5dY/e5Dr9FG15KUyka4KU2jUm3DFOj8X9GHBG6Nw9t2Afu7v9D9UvY/sM+Y35nmnsurHHxg4sZhABSQkhl7pQCXMWBe2PAj8y5VT9Jw/LyPmxVT+vIzfXDlFvwBZqM2va/MFan3vmagtzYQQLUisE+YgVNVhqHOAFyNZ5+hwOFJQBTnn1vjoYmCWw+E4OxgMbqnt+q+//tp5xRVXXLJ69erBiYmJ5U6nc8N999333Hnnnbe2Yd9GDOSW7EK9J6z+rjDX2REYRlUiPdH6Z1tynVUj0cSUBfz5VxcdelT/720W9uC7aSfQ7cClrJw7nGVfj+Gqzx/jiD//RlJqkF9evoQDT1LvPKsnS1DvMDcQ+/WbHwAPAEegNqK2w4PAP1w5Rc/XlrQNjxHUvXoe8Hfdq78uO+4I0bLYkTALaxwbAnwa4fX/Y99kWWkS6rf/sHsZVlRUcPbZZ988ceLEL30+38MAjz/++IDFixc7gfhLmLXJLSkFvre+Qo47uxNasZvU9iK+e/QgSteWkdxhBW3TVtC26yp+fe1ANi4awYAjfqbLoDkseOMYPvvXQZzwr/ks+aILwWACPbTaipDaonr4xpQ/LyPgyil6GLVExq6E+T4qaR8OfF3Hee8A/waOBT5p+rCEELFiR8JcGubYE/Vd6HA4+gFX13PaqQ6HY3wwGNxnzdw///nP4QkJCRWvvPLK3uR89dVXt5z+n7klG4DPrS+A/gQDWWzw7WDdb33Zuqofmxdr7N6q4+zXnsWfXceuLXtwOHaxeWkpS7/cjK+oF8nt1dKX8l0OklJrjpBSUUtL7OAF7nLlFPXz52XEtqEF1ZL2n6gjYRoeI6B79XuB25CEKUSLEuuEORBVol/fsXCORq0RrM+xwD4J8+eff+7br1+/msm6JUvDkQA9hm2mx7DNwC8EKmDa+BsZPOkbDjp/BRt+70fpmr5sW98f49XrWTGnNymddvPBbX8i1bkSZ98VdDtwBekHraNNSuU0bJOvvwzHn5ex1ZVT9BwwFcixIwbgWSA3gqQ9E/in7tXHGx7Dli5FQojoi1nC1L26A7XMoeYPXCdquUJ9OkX4KGdD4mrB0oGd1Y4kJMKAI35g8azD2bb+N5x9N9FnzPeMmlxEx57lPDTmJnqPXkr3oRvYuqYva40JLPumHxVlaSS1W0OPYZv4+n+plK6dh3pXujzGHY0eAb515RT9w5+XEfPOQ1bSfh5V1X1bbecZHqNM9+qVG0yfHav4hBBNK2YJ0yqIWIZqeu4L+ajymBH2wiqRdv8Je97IkSNXfv/994dEeI+WIHyF7PH/+J6Pb4e1xgGsnHswKR3ewXhtFOt+1dmy/CBSnZvpOuQLRl347d6lJ9s3prD2l37sLh1G6bo01LvEEUBHcp2h+49WdjRa1xTfkD8v4w9XTtF3wEVEMI3fWKZbS0X9nRyEmgEZBHQGlj2U1nf54yNO+/tLE057YvSGRUs1n1nbLwxPoYp/3IbH8NVyjhCiGYlppx/dq38APGR4jNB1iIXA4+SWvFPXtQ6Ho7Kv54A6TtsK9AsGg1up0emnoqKC3r17/3PSpEmzZs6c+RnAQw89NGjr1q0pf//730O3Dtv/Tj/2SwT+jHrfWP8f8JblKcx/cQhrf+3Lht8PxNl3Bee/+A7J7UOrYTuilpNUbbeV6+xC+NaAewi/B+l+T+e6coqOQxV/jYzGEhPTrTmAo1BJeCgqQXZH/d1ZinpdsAS19rQ/MGhZx54n99q+KTElsKdNyDnfA09rPnPvO17dq98BuAyPcfn+ximEsF+s32EuRf22Xt+xfQSDwQqHw3ExqpAiOcwpAeBKK1nuIzExkVdeeSX/6quvvqRTp06nJyYmlqWlpW38z3/+U9e6uuaqE1VrJuuX1n83R9+2ALVJ94e1nNWWmqN31Rt3tvVlHXM6UNPBlQn0UNQG4MPIdW4m/B6k1aeO6/YpkIDqlDSrnnNrZbo1J2o50nWo/05PovZfXQqs1HxmrY0HTs4pOh74b0HRHYd1Kt9ROQI9GVhgurVPgenAZ9zW5hHgD92r32l4jJgXKgkhoivWI8y/Ar0Mj5G992Cu8xIgi9ySjEju4XA4Dka9yxofcngBcHMwGAzdO9O+XrL2c6HWt66K4j37oZZMNG56Ue1B6mLf/roHoP6caibSWvcgdeUUXQOc4s/LqNkxql6mWxuJSpJZwEeoNo1f1jG1Gu75DtTfuan+vIzPQu7dCTVSvRZVUTzjuusSB290OnYbHuPmhsYqhIgvsU6YpwLZhsc4eu/BXGc7VLu4g8ktibiK1eFwDEBNka0JBoN/hDmlNSfMMcBxNO77r01f4AXUbiXRo/YgPYB9E2k/1B6kNZvVL3PteqlyPeh4f15GJBXWmG7tYNRUrgt4DHhS85mN/l5cOUVTgJP8eRlnhHmWAzgMuDYAp309zJGaXhw8dcgaPm5IYhZCxJdYJ8wU1A+6o6sVQuQ684E95JbcGsXHteaEeQBqeU3HkGN7UFWzO4B6uyqF0RfV7zc21anqF6lwe5CmAb99WzGs7R/B3psvbvPJ3ahEujZcxa6VvKYAdwHZwEzNZ+7Z3/BcOUXtiSBpm26t2/tjHUVH/hYc0nEny4EZwIuaz9y2vzEIIWLLju297gbaGx7jpr0Hc50HAN8AQ6O4Z2RrTpiVUlDLbJxAN9TWXL1QLe4CqPec5agkuIN9N5mulGTd4+Emjrd+uc40YPjiQPrEbwPD7jg/8bO5bRyBYajvpdq07uaF7Zeum+fMQyXcczSfuSiaobhyiv4DJPjzMrLrOk/36kMdgeDs6dMqruiyjctQa4pfBqZrPrO+6nAhRJywI2EOAOYB/Q2PUdWCLdf5P9TI6DRyS6LRq1QSZu3aohJgGtADlUh7UH3D6F1UjUg7ARuJs4birpyi14FZ/tQLH0XFv3dKd+fmpINXf5emp3Yu35U+ruSbhKTgL1RN75rR2IPUlVNU+Xd5gD8vo84Ro+7VXwNmGx7jQdOt9UEVQl0F+FGjztc0nxl+ZxkhRFyIecIE0L3628C7hsd4cu9BtYn058C75JbcE4XHXIyq1myoNagG762NA2hPVSJNt756oEaqnwHf2hVcOK6coomo9ZiaPy9j7y9Zpls7H3gYRzBHy1rzCftO6w5FFUTVLDRa2NA9SK2k/ak/L6PmHq/V6F79YOANYLDhMcqsONsAmagiodGo9n+PaT4z3Dt5IYTN7EqYR6EKSMYYHmPD3g9ynX2BucC15Ja8FfPARDgJqHeh26h9ytYWVrXqT0COPy/jAwDTreWgRm/naD5zftgL1R6kQ9i30GgAaq1vzUS6tLY9SF05RUehioiGhSbtcHSv/gnwguExnq35menWhqB6JV9qfU8zgHej8b5VCBEdtiRMAN2r3wOMBU6uttlurvMQ1NSfF7izAZsli1bIlVN0GXCePy/jZNOtnQA8A4xrVAWs2oO0stAo9Ks7YFKVQCundle7dr0ENZJ2bXSvfixqSdRww2OETa5Wl6GzUaNOF2oE/aTmM6O5REgI0Qh2Jsw2qHVwsw2PcWe1D3OdPajqKHMxuSV27ZAh4pwrpygVWHb2os/Pu3JBYQGQpfnML6L6kFxnJ6rvQVo5vZsAPHdlWfaGTwJjJ/rzMk6p6zZWP+U5wN2Gx3irvsdaa0anAOejXlfMAD7RfGbz2yhdiBbAtoQJoHv1XsAPwJWGx6j+23muMxG4A9W39D3UAvNvYtzsWzQD7r+88e9pn/33st7bNz2g+cx7Y/bgXOdg4OpgkMu+Dgx3JhC86fDE3x6va1ZE9+pnowrSDo10g2nTrXUELkSNOtujpoCf1Xzmxv3/JoQQkbI1YQLoXn0CqhjiGsNjvLHPCbnOzoAH1Z1lF2oD6tmo5GnLVlMivnyvj33S16X/JQ+POqfnDw9eVBzzAHKdqdP2nF5wSsJ34wcmrNuK6lw1P9ypuldPBH4Dphge47Nw59TGWlN6CCpxng68ixp1fiMNEYRoerYnTNhbQfgq6t3lbYbH2HdhvWqtNgG1IH8CcDCqE8zsvV+5JWtjFbOID6ZbOw/Iu/CkO+YVp3b62p+X8T874nDlFPUBjO9Trsvu6dhyH3AruSVPhztX9+qXA1mGxzixsc8z3VoXVIHQFNQvktNRDREi2SpPCNEIcZEwAXSv3hW1nKMT6odJ3UUOahnKWFTynAAcAWwiNIHCYpnCbblMtzYItUvIiSefcX8y8BJwgD8vw5ZCMVdO0Uzge3/qhR8CrwPfAdeTW1KtO5LV8WoxcLrhMX7cn2dao85JqFHnsUABqiHCz/tzXyHEvuImYQLoXj0BtTHvjaippicMj7EyoovVCHQ4VQl0Amqbq6+oSqC/SNVty2G6tQeB7ZrP/BuAK6foe+Buf15GnVvFNRVXTtFhqOVSB/pTL2yLtdwEOIrckmojP92r34x6j3letJ5vurXewBWo5SkrUaPOVzWf2ZDdYIQQtYirhFlJ9+rDUe8sL0AtmH8UmBVpkQRQuc2Ui+oJtBdq8X1lAp1Lbol0V2mGTLfWHtW0f7TmM5cDuHKKLgIu9+dlHGtHTNa60O+Bf/nzMt6x/g5OR7UlPDd0tkP36h1R+2gebniMqLbssxoinIIadY4DngNmRLs1oBCtTVwmzErWD5XJqOTZHrX/YWWyW9qgBAqVy1WOpCqBulFr6CrvKYVEzYTp1q4CMjSfeUblMVdOUTKq1dyJ/rwMW3q0unKKJgOX+vMyjgMq13Z+BbxAbskDoefqXv0fQHfDY1zbVPFY09ZXA5eh1o9OB97RfGZjGvAL0arFdcKsZK1fG0lVopuIah4e+r7y19oWg9cq19kRtQ1TZRKVQqJmwHpvNw+4VfOZoXug4sopugPo68/LuNqO2EKS9gn+vIxfAch1ulAjzzPJLfmm8lzdq/dBJTGX4TGatFjHdGspwFmoUecQ1IbZT2g+Uza2FiJCzSJh1mQl0EFUn27tDnxNVbL7obJnZ8SkkKhZMN3aYahpxqE1F/G7cop6oja5HuLPy9hkR3xW0u7jz8u4Zu/BXGcG6r38SHJL9i590b36q8BnhseosxdtNJlubTiquvYi1N/p6cBH0hBBiLo1y4QZjtUEIXS69UBUU4TZqCmxbwyPUdqgm9ZeSBSaQA0pJIot0609D/yk+cz/hvvclVP0LGD68zJi18Sg+vMrk/Zgf15G1XZ1uc4XgHnkluyNW/fqx6C2TdMb/IphP5lurQOqi9C1QGdUkdLTms/cUOeFQrRSLSZh1qR7dSdqurUy0Y0Bfick2RkeY32Dblp7IdE3VC8k2h2Vb0Lsw3Rr3YGFwGDNZ4bdO9WVUzQGeAsY5M/LaOrm5WF3xfF+s/S8tLbJ608f3efzvQdXzevPwg+zOOqW+0lIDAJrdK/+AqqRwTWGx/iyiWMNy5riPhg16jwT1VlrBvCVNEQQokqLTZg16V49FVUxWJnoDgfWUX20GK1ConlULySSxeRRYjUquEjzmafXdZ4rp2g28JA/L+PVJg4p7L6rsxduGPj2z6tvvucs/cakxAT1dyoYgHdvuodBR7/EiLMMrL1Xda9+A3CE4THOb+JY62W6tc7AJahR5x5U4nxe85lSDCdavVaTMGuyWpTpVB8tRrOQqPKe41Ajoqr1oFJI1GjW9l1dNZ/517rOc+UUnQvc6M/LmNDEIdW6Ufnf3jT+MW5A57fOGtN33t6D3z92DJuXjubkvP9SlTCdwGqgq+Ex4mKZkzXqPBqVOI9HdeKaofnMeXVdJ0RL1moTZk1WIdFgqifQbux/IVEK+xYSbUQKiRrFdGuPAfM1nzm9rvNcOUVtUOscz/DnZez3D3mHw5ESDAbDTbXXmjBf+G7ZhEXrSo+46/QReXsP7ticwge3PczE7FvodmBH4D4A3asvAk41PIZvf2ONNtOt9aKqIcJa1KizQPOZO+q8UIgWRhJmHXSvnk716dYDqCokmg18G6VCogSqdySSQqJamG7tYyBf85l17j0J4MopuhXQ/HkZlzbmWQ6HYwxqx5zjgXbAMtQ+rXnBYLCye07YhHnjjTeOe/jhh2++JP+1bTefe+ztB/VLW7f3w6K//B+uI95h+JlbqEqYHwIPGh7jvcbEGgumW0sETkaNOg9BtbJ8TPOZcZfkhWgKbewOIJ4ZHmMNairqVdhbSHQ4KsndDozRvbpJVaL7qt5CotySAGrtnQE8GqaQaCrQi1ynFBKFNwg1cozEk8Afrpyinv68jHX1nh3C4XCcAbwMpIQcHoBKoKc6HI6jg8Fgre+mP/zww8PT09N/X/TFW23nHzpWq5Ywk9uvZ9u67sCWkEuWAAMbEmOsaT6zArVbUKHp1lyoEefnplv7DbU05W3NZzZsBkaIZkQSZgMYHqMEeN/6qllIdCXwtO7V11J9utVfZyGRmopdan09p45VKyR6AHCT62z1hURWy7e+qJFevfx5GZtcOUWvAtcA/4j0OQ6HoxfqzyKlllNGo/5cLg/7XL8/ZeXKlUOfeeaZf14z9ca7tuy8o0e1E1Kd69lZ3APVJKPSUtQvA82C5jP9wN9Mt5aLqqydCjxkurWngMcr2xUK0ZLIlGwU1VJIVEHICJToFhKFdiRq0AiqObJGNbM1n9kv0mtcOUU68CHg8udlRDT6cTgcfwPurue0MqB3MBi8ghpTspdffvmRP/zww7Bffvnl8V79Bv731OvvWvfErZdUrQn98dnD2bBwHCf9+02qpmTPAS4yPMaZkX5v8cZ0axpqacpk1FKr6cCH1shUiGZPRphRZHiMCmC+9fVwmEKiPwNdda8eOt1afyFRbkkp8JH1VbOQ6FLgCXKdG6g+sl3SAguJBqFGYhHz52UYrpwiEzgXeDHCy0ZHcE4y6l30Pj777LPDL7roovcBxh565E/zPn/vMG69pOqETr3XsfqnnjUuW0IzGmGGo/lME/iT6db+BmQBdwHTTLf2OKohQov/pU60bJIwm5A1FfuH9fUM7FNINA04QPfqDSskUu8zv7G+7rUKiUZY9zwJNTpykOustkSmBRQSVaAKpBrqQeD/iDxhRvrfaZ/z5s+f32HFihXDH3jggb4PPvggQRzJjqTUdhUVFSQmJqqTAoEEcNScZUhswHPjmuYztwNPA0+bbm0catTpM93ah6hR55fSEEE0RzIla7MahUSVHYkaVkhUkyokGmjdrzI590Ql2Mpq3GZXSGS6tX7A95rP7N2Q61w5RYmo94UX+vMyvqvvfIfDcTOQX89pO4H0YDB4DSFTsllZWccuXLjQ9dNPPz0F8Py3yybmXHzKBddfMfmB22677XcAfnj6SDYtHsWJd79N1ZRsFnCu4THOacj31lyYbi0N1RWpcmeWGcBzms/cYldMQjSUJMw4YxUSHUz1jkRraEghUTgtoCORtaxhO9C5oZsiu3KK/gwc7M/LuLC+cx0ORxdUL9judZz2YDAYvIkay0r69Olz+8UXX/x2Xl7eLwAPfbronMIn8wdv/H3Ohnnz5j0NwJf3n0mgIomjb/2WqoR5G9DZ8Bi3NOT7am6shggTUInzJOANYLrmM3+wNTAhIiAJM85ZhUShW5tNQLUsC02gC1pLIZHp1hYCp1vvyyLmyilKQ70n1P15GavqO9/hcByN6kfrDPPxp0CG1cig1sYFAHcX/XZ9/y7tf7n4sAFVfWI/vuManH1/Z/zVi6lKmI8D8wyPMSPCb6nZM91aT9Q+ndegmnnMAF62pnSFiDvyDjPOWYVEP1lfD1mFREOoSnQ3A110rx7akejHFlxIVFkc06CE6c/L2OLKKXoJNbL5e33nB4PBzx0Ox2jgVlTjgs6oad1ngceDwWC97xs3bd+dsnFb2UHnjO33UrUPyrb3oEPPr2qcPgh4LZLvpaWwioDyTLf2H+BE1LvO+0y39iKqDd9vtgYoRA2SMJsZayp2kfX1NOxTSPQoqpBoLtULibbVeePmU0i0Pwv8Hwa+dOUU/cufl1Fvz9ZgMLgU9UO8Ud6Zv/rIzu2Sfhvaq2PVripbV7djd2l/0kctB9JCTm9wBXBLYS07eQ94z3Rr/VENET413drvqFHnG9IQQcQDmZJtgcIUEo1m30Kihu15WL2QqPKrB9W3NvuhqQuJTLf2VyBd85k3N+Z6V07R+8Ar/ryMZ6IUUtgp2UAwyN/eMPImHtj9+VP09AV7P/jm4ZMoXTuEE+9+hKrm691Rvwh0bXCv4hbKdGtJwBmoX1iGo6rMH7MaJghhCxlhtkC1dCSqLCS6GnhW9+oNKyRSU7FLrC+vOubsSdXI9iFgKLnOH0Pu+W0TFBItBo7Zj+v/C0x35RS97s/LaLIip1d+WHFoMEjiSSN6VSXLYAA2/H48Q09+vMbplwOvSrKsovnMcqy2lKZbG4pKnD+Ybu171KjzPWmIIGJNRpitUBMWEnUCDqUJC4lMt+YE/ICm+cxGbZPmyimaAXQFzvPnZezv/wH2GWHOW1ac/sJ3y3LPHN0nb8KB3aumWRe8NZzFn17CqQ/eiiMBoJ/u1fNR63TPNTyGVIrWwXRrbVENEa5Fbdr9OPBkY/8eCNFQkjAFYQqJJgBdqL61Wf2FRDWF39psvwuJrC2+Vmg+818NisfiyilKRa1HfcGfl/FAY+4R4mLUD28Atu/ek/zk7KVT3b06fnPiiF7fVzvzm0cuIa3/QoadVrkWdI3u1TcDdxoeY/x+xtGqmG5tDGrUeS7wMaohwufSEEE0JUmYIizdq/em+rrNIUDDColqql5IVPkF+25tVufI1nRro4B3gYGaz9zToBgsrpyigcB3wG3+vIynG3OPMPfsArwErAc81Uavuc6LUdW5Y8gt2btsQvfqRajp2GejEUNrY804TEaNOtugpmu9ms8stjUw0SJJwhQR0b16GvsWEv2GTYVEplv7GviP5jPfatQ3BLhyioahlnJ8C1zvz8toUDOEGvcah3rn9gaQ48/LKN/7Ya5zBPAZMIncEqPysO7VBwHfA/0Nj9HoZ4u9DRGOQCXODOBN1Khzrow6RbRIwhSNYhUSjacq0R0GrKb6dOuyRnQkCi0kmgAMBUILib4ht6TUdGsXAR7NZ56wP9+HK6eoA+pd2DDgJuCLhrzXdOUUOVEL7/8CXOvPy3i9xvfTEbXp+N3kljxXediaBn8OWNPSu/vEmunWulPVEKEElThnaj6zYTMiQtQgCVNEhVVIdBDVk1051RPob40sJArtSDQW+D1Q7vhm4Zu9Lu7QZ9fJfT/2f7s/sbtyihzAJagCHgdqLevz/ryMkjquGYUazZyH2j7sdn9exqJqJ+U6E4GZQDG5JdeEfqR79auAPwGHGB5DOts0AdOtJaCaTlwLTERNl8/QfOavtgYmmi1JmKJJ1FJI1JnqhUTzGllINA6YsHqO88qEBPr2GleyjOqJeWljOhJZiXMicB1wKrCOqqU0W4ABqCnkQcAO4DHgSX9exr5Vmqp3b2WHn0xyS/Y2StC9+hhUkj3S8Bi/NzRO0XBW4/4rra8lqFHn65rPbFYbEAh7ScIUMVNLIdEcqhLddw0pJDLdWm8Izu88ZMcNvcaVdKN6IVHNjkQNGtm6coqSgP6o5DgIleyXUZVAN9Y6dZvrPBwoQE253kluyd7CJN2rd0ZN0d5meIxXGhKT2H9WQ4RTUaPOg6hqiLDE1sBEsyAJU9imlkKiBVhFRERQSGS6tQuAO4Cxms/cYWtHIlUFfCNwG3A5uSVFoR/rXj0B1dB9qeEx/hT154sGMd3agaj3nB7ULzHTgaLGVl6Llk8SpogbuldvS/WtzSIqJDLd2kvARs1n3hj2xhEWEjU68FxnN1SRybVWvJPJLfHX+N6SgPtRhVJHSVef+GE1RDgXta6zH/AEqiHCalsDE3FHEqaIW7pXb8O+HYnKqFFI9Mo9e5zAz8CVms/8qN4b11JIROgIFFbW2Vg+19kBGAVcBZyGGjk+Sm7J3DDfR2/gZdRenpMNj7Gp3hiFLUy3dhDqF58sYBZq1DlL85kNK1YTLZIkTNFsWIVEB1A9gaYBX58yN7Dmos8CZ/6Rjn7WJ2bD2u+FFBJZX6NQrfNWUHvRTydU27/ngWfILQmbBHWvfgzwIqry9t8NrhIWtjDdWifgIlTyTEUVeD2r+Uz5ZacVk4QpmjVr9DYBmHDNexVZ7XeR9t8zE2bjcDSqkGivXGcq4EIlx4FUL/pZCqytq5DIel95K+qd5sWGx/ikwTEI21kNEQ5DJc5TgXdQo87vpCFC6yMJU7QYpltrW+Fg3nduR+GDZyTuYd9CosqORBubKgZrGvlU4M9AIpBleIyVTfU8ETumW+uG2lx9CrANlThf0nxm499/i2ZFEqZoUUy3Nhq1xnGc5jOX11JItIqQ96CGx1i2v8+1NvG+ErV92jLUFOyrhscor/NC0exYDRGORY06j0a9n56h+cxf7IxLND1JmKLFMd1aDnAicGzNYo1aCol2U9UA3kBNu66t632j1RrQBRyIetd1Amrt5XTDY/wc5W9JxCnTrfVB/aJ0FeoXpenAa5rP3FXnhaJZkoQpWhzTrSUCnwNvaT4zv65zaxQSHQm4Ue8sK/fdrCz6KUYV/VQ2MugKLEe9zywEnrM27hatkOnW2gCZqFHnaNQm649pPvMPWwMTUSUJU7RIplsbiOoiNEnzmUZ959eke/X2VFXE1iz6WQKsNjxG7ctORKtlurXBqIYIlwLzUaPOd6UhQvMnCVO0WKZbuwy1A8l46RkqYs10a6nA2ahRpwt4EtUQQYrAmilJmKLFspYEvAEs0nymbKElbGO6tZGoUecFwBeoUecn0hCheZGEKVo0a2/En4ELNJ/5hd3xiNbNdGsdUUnzWqAjqiHCM5rPbLKlTiJ6JGGKFs90axnANOAgzWdKYY6wnTX7cQhqTecZqMKx6cA30hAhfknCFK2C6dZmAG01n+mxOxYhQplurQtVDRF2ATOAFzSfudXOuMS+JGGKVsF0a+1RFYu3aT7zNZvDEWIf1qhzEmq69ljgFWC65jPn2xmXqCIJU7Qapls7BNULdJTmM9fYHY8QtVGbo3MFqnPUKtR07Suaz9xpa2CtnCRM0aqYbu0u1J6Up8i7IhHvrIYIp6BGnQcDz6Ha8C20NbBWShKmaFVMt5YEfI3aqulRu+MRIlKmWxuEGnFehmrhOAN4W/OZ0q84RiRhilbHdGtDUUnzCM1n/m53PEI0hOnWUoCzUKPOIaiGCE9oPnOFrYG1ApIwRatkurXrUL+pHy6/oYvmynRrw1HVtRehNg+YAXwoDRGahiRM0SpZFYnvAXM0n3mn3fEIsT9Mt9YBOB816uxMVUOE9bYG1sJIwhStlunW0oGfgDM0n/md3fEIsb+sXwTHoRLnWahfCmcAs6XIbf9JwhStmunWzgLuBUZrPnOb3fEIES2mW+sMXIJKnntQifN56XbVeJIwRatnurVngV2az5xidyxCRJs16jwa9a7zBOA1VEOEeXbG1RxJwhStnunWnKguQDdoPrPQ5nCEaDKmW+tFVUOEdaiGCAWaz9xha2DNhCRMIQDTrU0EXkY1aN9gdzxCNCXTrSUCJ6NGnYcCL6AaIvhsDSzOScIUwmK6tXuBA4GzpEBCtBamW3OhRpyXAyZq1PmW5jPL7IwrHknCFMJiLQj/HnhI85lP2x2PELFkurVk4ExUkZAbeAp4XPOZy2wNLI5IwhQihOnWRgCfAYdoPnOJ3fEIYQfTrWmo6drJwLeoUecHms+ssDUwm0nCFKIG063djFrDdlRr/wEhWjfTrbWjqiFCd+Bx4CnNZ66zNTCbSMIUogbTrSUAnwAfaz7zHrvjESIemG5tHGrUeQ7wIWrU+UVret8vCVOIMEy31h/4AThR85k/2R2PEPHCdGtpwMWoUacD1RDBq/nMLTaGFROSMIWohenWLgL+BoyTjXuFqM5qiDABlThPBl5HLU2Za2tgTUgSphC1sH4gvAys0XzmTTaHI0TcMt1aT9TuP9cAm1Cjzpmaz9xua2BRJglTiDqYbq0L8DNwmeYzP7E7HiHimdUQ4QTUqPMI4EXUqPM3WwOLEkmYQtTDdGvHA08DIzWfWWx3PEI0B1YdwFXAlcBCVJHQm5rP3G1rYPtBEqYQETDd2oNAD81nXmB3LEI0J6ZbSwJOR406R6B++Xxc85lLbQ2sESRhChEB0621BeYB/9B85ky74xGiOTLd2lDU0pSLgTmoUed7zWW9syRMISJkurUxwAfAWM1nrrA7HiGaK+sX0CxU8uyNaojwpOYz19oaWD0kYQrRAKZb+xtwLHC85jMDdscjRHNnurXRqOnac1ENER4FZsdjQwRJmEI0gOnW2gBfAK9qPvMBm8MRosWwGiJcAlwH7AHuRzVEiJskJQlTiAYy3dpg4DvgGM1n/mp3PEK0JNb652OA/6K2G7ta85ml9kalSMIUohFMt3YFcANqV5NmWyYvRCxNmzKrPdAJWDd1xqQ6X2lY7zkfRq3nPEfzmQtiEGKdJGEK0QjWb8FvAb9pPvM2m8MRIu5MmzKrDZCJatY+BBiISpalQEdgGbAEmAs8OXXGpLCFdKZbuwy4D7jR7gp1SZhCNJLp1noA84EszWfOtjkcIeLCtCmzeqIaFlwDLAe8wAJUclw3dcakgDXSdAGDUJ2BLgS+RBX8fDJ1xqRqicl0awcB7wFXaD7zgxh9K/uQhCnEfjDd2qnAQ8BBms/canc8Qthp2pRZZwGPAW8Cj06dMWl+hNd1QCXNPwGLgEunzpi0JfQc061NBF4Bxms+c3kUw46YJEwh9pPp1h4H2mg+83K7YxHCDtOmzEoC8oCzgXOnzpjUqB1Lpk2ZlQLkAycB59RMuKZb+ytqineC5jPL9ivoRkiI9QOFaIFuBiaabu0suwMRItasJPcR4AbGNjZZAkydMWn31BmTrgduBz6eNmVWRo1T7gdWW/+MOUmYQuwnzWduQ7X6etR0a73sjkeIGHsAtaXXqVNnTNoUjRtOnTFpJnAq8My0KbMGVx631mReBpxhurWDo/GshpApWSGixHRr/wTGAJnxtNhaiKYybcqsycAdwLipMybt7zv8i4H00AN//Lj+8OK12w8edVz/aUkpiXsqj5cUFh1VUbKlR5eLLno15PQ1wPP7GUOdZIQpRPT8A+iJqg4UokWbNmXWAcD/UO8ao1Hwlg6sCP0aPLp7we6de5b9+L7/6NDjbUcd9PbOn3/RylatKg45nl7LfaNGEqYQUaL5zHJgMvAv060daHc8QjSxPwPTp86Y9EtTPcCR4GDUcf2fKi3edXjxuh3OyuPJffuWtunWbV7pBx8c1VTPDkcSphBRpPlMH5ALPG/1nRWixZk2ZVYn4HzUEpIm1SEtZUeHtNTvl8xbf0zo8faHjP949+IlxwX37HE0dQyVJGEKEX3TgGLg/+wORIgmMhn4dOqMSasivcDhcBzlcDjucjgc/3M4HFMcDkfnSK/tp3X+eMv6HcftKQ/szVntJ0z4A3Ds/Pnn3g0LvfHkN2AhokzzmUHTrV0O/GS6tfc1nznH7piEiLKLgTsjOdHhcHQHZqK2xQv1H4fDcXUwGKy13V1CQsKLnTt3Xh4IBBI7tevS4ZA3D7v0hVeefiY5OTnoSEggoV3bNeWrVvdg7NiIE/f+kBGmEE1A85mrgetRU7Pt7Y5HiGiZNmWWAxgB1PuLoMPhaIPquVwzWQJ0AF50OBwn1XZ9YmJi2aZNm24rLi6+5cFc73fzjXnDTjrppLMrP09o3379nk2bejT4m2gkSZhCNBHNZ74KfA/8x+5YhIii3sCOmq3ranEucHgdnzuIsAlB//79Vt150/2+r7/++oSKigoAEjt2Wh/YWiIJU4gW4gYgw3RrJ9sdiBBRMhTwRXjuqRGcM9zhcAyu76TUDknrB/Q6oAPgmDNnjhMgsUuX9RXbt0vCFKIl0HxmCeABnjTdWje74xEiCoYCv0d4bs9onZfaLmnrnvKAMxgM7q2KTeyctiW4e3dahM/Yb5IwhWhims/8HHgJeNzaR1OI5qwhCXNZtM7bua2sy8p1i7c5HI7A+PHjSwACpds6OJKSSyN8xn6ThClEbPwdtYnuJXYHIsR+chN5wnw5gnO+CgaD9Va5Llni7/fvR3P6HXHEER8lJiYCULF1a5ojNaUkwlj2mywrESIGNJ+523Rrk4FPTbf2peYzl9odkxCNFPE7zGAw+JHD4SgAsmo5ZScwtbbrKyoqkrt27XpPIBBI7JDq7HzEoUcbzxU8/nrl54Ht25wJqW23NCD2/SIJU4gY0XzmL6Zbuxfwmm7tGM1nVtgdkxANMW3KrLZAL8DfgMsuAdYC1wFJIcd/ByYHg8FaW+sFAoGLKv/3Fy/9fkdfd+dPkpOT925sENixMy0xLW1NA2LZLzIlK0Rs/Q8IANl2ByJEIwwBlk6dMWlPvWdagsFgWTAYvAnoj1pmciUwAdCCweAPkd6nvKwivXN6+9XV7r1rlzOxQ4eYTclKwhQihqxRpQf4i+nWRtkcjhAN1ZD3l9UEg8G1wWDwtWAw+FQwGPwq2IC9JbcV72ofDASTO/dstyX0eGD37rTEtDRJmEK0VJrPXIYaYb5gurVUu+MRogEasgYzajau3JaelJK4xpFQvcg8WFbmTOzWbUus4pCEKYQ9XgBM4G67AxGiARqypCRqtm3elZ6Umri65vFgeXlaUp/eW2IVhyRMIWyg+cwgMAXIMt3aJLvjESJCTZkw1wD9wn0lJiW6u/bpuCP0WGD79sFJvXomtunevZt1rMmLfxwNmEYWQkSZ6dZOBJ4ARmo+c4vN4QhRK6vpegngmjpj0uYYP/sN4OWpMya9UnnMdGuDgFmaz3TFKg4ZYQphI81nfgi8AzxidyxC1KMXsCvWydIS7t1pL9RylZiRhCmE/W4BxplurbbF3ULEA1veX06bMisRGAQsqvGRJEwhWhvNZ+5A7WD/sOnW+tgdjxC1sCVhAi5g3dQZk3bWOC4JU4jWSPOZPwAPA8+Ybk3+fyniUaPXYDbRcyVhCtGK3QN0BK63OxAhwrBlDWYdz+2JJEwhWifNZ+4BLgbuMN3aMLvjEaIGu6Zka3tuL2BdLAORhClEHNF85h/A31BdgJLtjkcIgGlTZqUCfQA7dtmpK2HKCFOIVu4JYBVwZwOukY2pRVMaAvinzphUbsOz5R2mECI8qwvQlcDlpls7op7T2wCHo957dmnq2ESrZcv7y2lTZjmB9qhfIPcy3ZoD9Q5TpmSFaO00n7kO1TrvOdOtdazltK7AhaitkhKAU4DE2EQoWhk7318unDpjUs2WdE5gt7UkK2YkYQoRpzSf+TbwGWoPzVAJwEHA5aiq2uXAetQ7poNjGaNoNVp9wQ9IwhQi3v0ZOMZ0a6db/94JOAs1mlwHhLYpWwUcBaTHNELRGrT6NZggCVOIuKb5zFLgEmDG1g8+OBQ1quwL+IGaBRgVqObYpwIpMQxTtGBW0/V4HGFKwhRCVKf5zHmdL7nki/J1658JBgJbqXsqqgQ1Cj0yNtGJVqAHsGfqjEkbbXh23DQtAEmYQsS7fsBlPbJvnle2ZHFK8UsvHRrBNauB8cDApg1NtBJ2Nl0fwr5N10FGmEKIEEnARFRT9vKElJQVztNOe3SXYZy/8xejZz3XBlFFQJmoknwh9odd7y8HABumzpi0PcxnUvQjhADUFNhk4FBUBew2gHZjx65KcWtvbHn99esCu3fv8//dYHl5aPOCHaikeyzS1EDsn3h7fwk2jTDbxPqBQohaJQBjgElAKbCi5gldLp780bp77hmz4ZFHznEktjHLVyzvlpjWeVuv2/8+15GUFAxWVOBI3LsUcw0wDPgD+C1G34NoeYYCX9r03NqaJciUrBCt3OHASahEtyXcCY42bYJtx459aduns04ufv75a8rXrOm1c/5PI/wXTc4CCOzcWbNxwVrgZCCt6cIWLVw8jjCl6EeIVs4P7EG9g6zV5me9Ex1t2vjapKcnB3eXtR34+uvPlC9f7i797LPuiR06VOwpLg6dOdoNlKESsXQBEg0ybcqsFFTh2RIbHh/23anp1hKB7qj39DElCVOI+LES+Jo6Gg+Ur1uXHNixo0P6v/45vcOECT/v2bBh1OKMzOva9Oq1NLlv3x2BHTsSll18yVVr7/rH+JDLNqIKKMY0cfyi5RkMLJ86Y1KZDc+ubYTZFSjRfGbMG8FLwhQivnyH+s25c7gPk3r2LAvu3t12w4MPHdPlUs8zbUeNSghs29YnqWfPdSkHHLA9oV27QKeTT/pi6/vvn7lzwYLQHrSrUe9G66uwFSKUXU3XO6H6xa4M87Et7y9BEqYQ8aYceA+1HCQp3Al98u9/cuf8+UcVz3x5cKeMU6anjhzZuctVV87d9NRTB2z//vvO3adO/S154EBj3T15J4dctgfYilpqEva+QoTRVO8v6/s7eCCq6XogzGe2JUypkhUi/mwAPgVOAJbV/LDtyJFbu029bnpg586khA4d16YeMOTr0vfev3j30qWLNz35lKfrVVd6K7Zs6dJ2zJifaly6BfU+6nDgi6b+JkSL4Aa+ivI9hwDnoP4+rketp9yE+oVuK7Cdutd+2lLwAzLCFCJezUcVWnQP92HXyy9ftHP+/CHr77333C6XX/5KYPv2Ph0nHbMFhyNY8s67ExM7p23qcNRRf4S5dCUqYfZrutBFC9IUI8zewC5gJ2rN8XhU/+OLgGuBmw49Y/C5I4/puwcYhepY1RVItq6XEaYQopoA8CGq2Xoq6gdMNb3vu++9JRmZdxY///yQdocd+mTx8y/cmtSnz7cDX33lqbJly9omDxiwM8x9nda9pTm7qFNI0/Vov8PsiUqWZdZXSY3PE/aUVaT3PiBtM3AcqvFGEDXAK+1y5ZVHBkq3rkCNQhehNh2ICUmYQsSvEuB94AzCTM226dx5T+fzz3+5+KWZ5zpSU7aT2GZNcu/eBwTLyx1hkqUDtcvJJuBZbGgrJpqdbtY/o910vQeqMUdtAju2lvVMbtvmTdSWdaGSEtq169X2oJGlwLnAU6iCtpiQhClEfPsd+BVVBLGm5ofdb7xhQcqQwZuDFRWOiuLi1N1Ll07e7H3ulK5XXlEUclp71A+/b60vO5YIiObHDfw+dcakOtcFN1AK6u/j5tpOCFQEHXvKKnp169thn7/vQHnFxo3tGehajhql1pV4o04SphDxLQjMQq2jbI8qiKim0ymn7P3Bsus389FNzz5z9/bvvzfaH3LIctSazl3AS6i+tEJEqineX3ainsYcm1Zv65qQ4NjWtmPy7nCfB8vKnIndupVY9wnXmL3JSNGPEPFvB1CIGiXW2Ug9dZi2sd3o0S+Ufvzx1MCuXYNRP/CeRZKlaLimeH/ZkXoS5pa1O3onJifUOs0aLC9zJvXpswv1eiHcspMmIwlTiOZhGaqpQZ/6TkzLyvIl9epVvPzqa1yoNZ07mjg20TI1xQgzjVp+6avYE3CU765I2F6yOz21XdKaQGDfvBrYsSMxWFHRLqlnzwqkNZ4Qog7foN79pNXyeRIwwJGQsHbPho1n7Jwz52TTrR0Vs+hES9MU+2D2IuQXuC3rd7TduHJbe4DENgnBpJTEwLbi3f127SjfmZCwb14tX7XK6WiTVOpITGyLbO8lhKhDGWpq9lLUHpl7Qj7rArRDLUX5uedtOYHNXu+VwLOmWztI85k1S/eFqNW0KbOSgf7A4ijfugeqWAeAr15ZNGn5b5vPSW3XZqUj0VHevlPKiu1bd49PSHCs37C8tGOHLqk723ZI2vv3vHzdOqcjOWmL9a9biDEZYQrRvKwDPqNqajYRtVxkG+pd5U9Y73U0n/k+akr24ZhHKZq7QcCKqTMmhS28aaQE1Hv4vWuKO3RO2dQhLcUcPKZH4chj+hV0Tm+3pHx3RbvysorkN/87765v3/jjkNAbVBQXOx3JKVtQ70FjWiELkjCFaI5+RBXx9Ecly29RVbAbwpz7V+AQ062dG7vwRAvQFNOx7a1/BgECgSBHX+T+blyG62X/rxuPKVm/o9sR5xzwfVJyIiOP7ue9+oGjbj7k9EFzQm9QsXVrmiM1pQSVu7ZGOb56ScIUovmpQDU0WAy8gOr1GXarI81nbgcuBh4x3VrvmEUomrumWlKy98VkQoKDij0Bx7Ajei8/+Wp92pZ1O/rNes48Y/eOcgaM6LICoL0zpdrf68C27WkJHTpW9puN5ug3IpIwhWieioHXCL/9UTWaz5wDPAo8Y7q1OpelCGFpqoRZTWKbhCBAD1en0radkjdtWFE6JrVjckmX3h22VewJ7PN3NbBjh7NN1y47saFCFiRhCtFa3I2qrr3O5jhE89AUCbMbITMhlctG/L9s7P7KPXM9m1dvdye2SUgYPLr7t4vnre/9w/v+YTVvENy1K61N1647sam1oyRMIVoBzWfuQU3N5ppuzW13PCLuuWm6pusABANBB8CiH9YNT2yTUNbD1fEXR4IjqUt6+6UrzM2D//hh/aSaNwiU7XYmdu68k/Dv65ucJEwhWgnNZy4EbgdeMN2abCItwpo2ZVY3VPV1tKc9qy0pqVS6eVfP7v07LmqTlFiWnJqY1LFr6uqd28rT2nVK3medZbCsLC3R6SzFhgpZkIQpRGvzGGo66w67AxFxayjRb7qejKqS3TslW/n+cuBB3eetXVyiL5q77vxgINhhpVncZeuGnYO69e2wtOZNgmXlzjZdu5ViQ4UsSMIUolXRfGYQuAK4ynRrh9kdj4hLMSn4qTT6+P6L3Ielf+7s0dbcUx6o+GPe+mP6DO38/SGnDfop9Lw9xcUpQJuEjh12otYdx5x0+hGildF85lrTrV0LPG+6tVGaz7Tlh4+IW03x/rLOXUpGHtN3aVJKwnerFm7pcfSFQ/PbJCfuc275qtXOhA4dtjoSEmLedL2SjDCFaIU0n/km8CXwX7tjEXGnKUaYyai9MPuiulR1AdoSsi5z9449PdunpawMlywB9mzc4Ezs1KkUGzc/l4QpROt1E3C86dZOtTsQEVeaImEuBB4H3kC1dlyKGnH2RiXQvm2SE4Z07tW+FDUa3acoraK42Jng7LQNGxOmTMkK0UppPnOr6dYuAV6xGrTbshhcxI9pU2YlAS7gjyjfOoBaClJzOUgC0AHoZH6z5szBo3t8i9qRpydqM4G9o01HYmKfxA4ddmBD0/VKkjCFaMU0nznbdGvPAk+Ybu0MqyhItF6DgFVTZ0zaVe+Z0RFAVbxuXe8v7bfeX/ramBMHVFbHpqBGmx2BtJ2/maMSO3ZYjk1dfkCmZIUQcCeqkfvldgcibNcU07H1mjZlVntUJ6DlIYd3o0akS4B5xc8+u23jw4/MwqY1mCAJU4hWT/OZZcBkIM90a4PtjkfYypaECRwI/DF1xqSKOs7piQ2bRoeShCmEQPOZC1D9Zp8z3Zq8qmm97EqYkTy3F5IwhRBx4iFU67Jb7A5E2KYp1mBG67mSMIUQ8UHzmQHgUuAm062NsTkcYY8GjTDzszKjtV1cnc+1tqXrhY1LSkCqZIUQITSfudJ0a39CNWgfq/nMfZpli5Zp2pRZXVCVqfuM4vKzMrsAJwGDUZW0g4CBQHp+VuYaVGHOUuufi4EPsgsKNzfg8UOB/9XxuRMo03zmjgbcM+pkhCmEqEbzmTOBn4E8u2MRMbVP0/X8rMyD87Myn0ElwXNR3Xm+Bu4CJqKWfBwF/MM63tY6b3F+VubT+VmZ4+p76LQpsxJQRT91jWxtL/gBGWEKIcK7DvjZdGuFms/82O5gREy4AV9+VmYycCEwFbXUYzpwS3ZBYW17UC61vmZVHsjPyuyOWqb0Wn5W5nrgEeCF7ILCcD1g+wClU2dMKqkjNtvfX4KMMIUQYWg+sxj1A+9p0611sTse0fSCwd3Dyrd/nIbq8nMBan3ukOyCwvvqSJZhZRcUbsguKLwXNYV7Fyr5vp2fldk5zOnNokIWZIQphKiF5jM/Md3a68B0062dL12AWqb8rMz2wDU4Uq93OJJ/Bc7JLiicE417ZxcUVgBF+VmZHwH3Aj/mZ2Wel11Q+EPIaZIwhRAtwm3AD6gpuhdtjkVEUX5WZifU1PtNwOzkDmeuTmiTfsXUGZN+ifazsgsKy4Gb87MyvwHez8/K/DvweHZBYZBmlDBlSlYIUSurSnYy8D/TrfW3Ox6x//KzMjvnZ2XeiSrk0YFJqZ1vviChTXofYFFTPju7oPA14Ajgz8C11uFI1mD2xOYlJSAJUwhRD81n/oQq+X/WdGvyM6OZys/K7JaflXk3KikOAA7PLii8KLug8DfUEpE1U2dMavJlRNkFhQuBTCA3PytzPM1ohClTskKISNwHZKCm72TT6abjQFWNJqEqT/dbflZmLyAbVcT1KjAuu6DQX+O0mLbEyy4o/CM/K/Ma4NVgYGcPR0LbmvHUJAlTCNE8aD6zwnRrFwNzTLf2keYzf7U7phbGAfQDJlj/LAceA7Y19ob5WZl9UW0OJwMvAAdlFxSurOX0mPeQzS4ofPO/F1x0Rvn2d88K7FkZhEl1nR4XCVOmV4QQEdF85lLUD+AXTLeWYnc8LYQDNR06GbWUoxOwzPpsVGNumJ+V6crPypyBaj6xGxiWXVB4Yx3JEtR7xJg3XU/uNPnDYGDnburYWs56DdAdG/fBrCQJUwjREM+ipgr/YXMczV0Cao3ipUAW0B61F+QW6/P1wCFAu0hvmJ+VeUB+VubTwI/AJmBodkHhX7MLCiMZmQ3FhqbrDkfKkMTUUR8BN9TRl7YrUKL5zPIYhhaWTMkKISKm+cyg6dauRnUBKtJ85pd2x9TMJKL6sB6F6qKzmaoRZag9qKR6EPBtXTfMz8ocBvwfcAKqo86Q7ILC4gbG1RRTshcD6XWdcOR5B5zRqau+aM3CnukDR499BPgOeL7GaXExHQuSMIUQDaT5zA2mW7sK8Jpu7SDNZ261O6ZmIBEYgkqUaUAx4RNlqHXAYcB81LZr1eRnZR4E/B3V0/V/wLXZBYUN/rOYNmVWZ9RIdnVDr61HOrCirhM2r96e5uze9tfk1NTdC7/96qD0IUPD/TeRhCmEaL40n1lkurVTUXtoXmpzOPEsCdVYfALq/eRm6kkiIcpRiVYH9nbeyc/KPBiVKA8G7gcuzS4o3L4fMQ4FFoY2XY+FYCDInrKK9G59O6zp2nvimtkzvWdt31I8q33aPt3zJGEKIZq9vwA/mW7tbM1nvm53MHEmGZWIJgIdgA3Ukijnf1g4sFv/gcV9teFbwny8Djgc+CU/K3MscDswHNVm7vzsgsJorJts8PtLh8PRETgetQRmFfBRMBhsUEVv8dodnR0Jjt0dOqduh1TaO9Pmr17oG3bA+MNqnioJUwjRvGk+c5vp1iYDb5tu7RvNZ66xO6Y4kAJowJGoac6NqFHlPn4ofHPIL598cMr2kuL+h5x+7hPhEmYwEChftdB0f13w/GxU8cs9wOnZBYW7oxjzUGBhpCc7HI5rUQm7Y8jhUofD8ddgMPhYXdcmJCS82Llz5+UASYnJqfk5z26EYQCkdui4atf20h5hLouLLj8gCVMIsR80n/m96dYeA54y3VpGK2/Q3h84E5U016MqVcOa8/ZrB3772ks39Rs+8r3LH3jsoZqfBwMBfv929siV5q9nAs7RJ5766dDDJl4z6oRT9mfqtTaDgHcjOdHhcNyAmoavqSMww+FwJAaDwUdruz4xMbFs06ZNtwH8/OmK43Zs3T2w8rO2nTqtL9u50x3msl6oJTK2k4QphNhf/0JtHjwFtXdia5WC2kA5bDFPIFDBnrLyhOTU1MCYk09b9PNHRcsGjTn4V4Dv3nhZ69qn/+YhBx+6zvzq8zGrfv/tzEBFICX9gAPfGn7Ucd8mtmnTB1U01BSJYxCwpL6THA5HF9TIsi7/cTgcLwWDwS313W/ntrLeqe2T9hYadejSbd36pYvDbSUnU7JCiJZB85nlVhegr0y39qnmMyOe3mthlqCmX9sD+4wEExISSU5NDGxevbJtl959dw44aMyX37z60hXfvPJiQlJK6tZAoKLznLdf69Clb79t/YaNeGPYhElzExITK0fsG1CFQ7+hioGiaSARJEzgVNQvBHVpZ51Xc2kIABUVFcldu3a9B2BQH63zC0+8tncKt0vvPutX//6bJEwhRMum+czfTbd2J6oL0BHxsMjcBhXAF8DphEmYWzduSHkn/+7zt25cf0Baz3QzfchQX6du3f/oOXBIaSAQOLR004bE7VuK/Snt2n024ujjf6xx+W7Uu7yhQNTaEk6bMisF6ExkXXT6RXjbWs8LnZL91Gs+mNaj7d733s7uPUsDFYGU/KzMtjWKmdKJk4QpnX6EENEyHfXe7v/sDsRGfwBbCTMS+/a1l8a165S26bonXvr7nvLy1EVzvjm3fVrncY6EBN01ctTzZ992V/aest172js711ZtugG1jjMpWsFOnTFpN1AKhBvZ1RRp0qr3vN07ypMq9gTSuvbtuDdRbyve3N6R4CgHdlUeM91aJ9RU98YIn92kZIQphIgKqwvQ5ailJu9rPvN7u2OyQQUwG7V91fLQD7asXdNn1/ZtnZ+66ep/7N6+fUCvQUNWjzjm+EeHjDvUdCQk8OH0B47cU1bWvlt/V9iqWlTzgu7AAaip2WhZinqPWWuRkuU9VAeiuvJGuXVenTau2JbeJjlhfZukhEDlsU2rVvRISknZbG0qXWkgsDReislkhCmEiBpraclU1NRse7vjsclC1C4je0eZO0q3JgUCgb6lmzYe2albj8QTp/zpX2fddtdtKe07rP3M+8S4hy877/4V5q/jDz71rJcHjx2/oY57b0St7UyMYrxLUAmzTsFgcDWQV89p9wSDwXpHmCUbd6YnpSRWW4ZUumlDj6SU1Jq/LERUkBQrMsIUQkSV5jNft7oA3Q9ca3c8NihHjTJP2rG1ZP2Czz85buOKZRntOjnXlHfrPrdTt+4/Dh47ftFrd99+Rsm6tQMPPSvr1W79Bjw88riT6muVB7ADcKEat0eruMoHjAUKIjj3TlSyvpXqA64AKpneVdfF5eXllwGUbt41JLV9UrVGDju3bu3RtmPHmglzIFHaFzQaJGEKIZrCn1AN2jM0n1lkdzCx9tZ9/1g1aOwhR65dvHBicmpbc8TRx+X10YYv//a1l4ebsz876dGrLjy5bUfn6oNPP+f14UcfV9e2W6E6ogp0NgElUQz3eeCLaVNm3W6906xVMBgMAH9zOByPAWdQ1ennrWAwGEnCp2znnqTSjbsmjDiqT27o8V3bSnun9UwPN8KMm6prSZhCiKjTfGaJ6dYuAV62GrTXNc3YYuRnZXZG/bIwtbys7PtJl13zZNc+/fZWvB5x3kUL9Ekn/LFt86bU3ge6I016nVAN2zcCb6CmKCuiFfPUGZN+nzZl1q/AWcDMSK6xkuODjXnewrnrDklum7is1yDn3qnbnaVbU7YXbx7bc9CQh2ucPgj4oDHPaQryDlMI0SSsrb+eBx433Vptex22CPlZmd3zszLvQVXJ9gMOO/fv/zq7a59+K1B9Zffq1K377giTpRMYgFpS8hrwDLCIKCbLEI8C10+bMqtJ/5yCgSAbV5Qe32NAp49Dj/u+/uLw1A4df3f26Lml8phVIXsY8ENTxtQQMsIUQjSlO1A7bVyK+oHfouRnZaajmtBfhnoHODa7oNAfcsrXwCQi36EE1GiyE2q7rQ9QnYOaukr0HdSf1bWo5Nkkfp614oRgkOQh43rOqzwWDATYsNx/Qv8RB9Uc3V4MfKz5zLhYgwkywhRCNCHNZ+4GJgP3mW5tYH3nNxf5WZn98rMyHwYWYG3BlV1QeG2NZAmqyUAZka2d7IIaUZagpkZfAPw0fbJk6oxJe4BzgbumTZk1vime4Tc2Dtm4YttZw4/s/b/Q5SRL5/84JFhRkXrgIUcYlcesGYnraMLk3RiSMIUQTUrzmQZql43nTLcWzeUQMZeflTkwPyvzcao2ddayCwpvyi4oXFXLJbuAb4Bwu3BU6opKlBtRSXImag1nTNceTp0xaRFwDfDKtCmzukXhlmtQ09P9dmwtc69buvXmoYf2erOHq1NK5fFgMNhv06oVFw4YOXpuQmJiX+saUA0aHKjOSXHDEQzGxXpQIUQLZrq1BOAT4CPNZ9a3li/u5GdlHgj8DdWQYAbwQHZBYaTdZ9qipjo3oBb+V+qG6r36B/AtagrWdtOmzLoTNYV+7tQZk/b7/eG0KbMOBl4Fnpo6Y9I/Qz/Lz8r8K3A2MDG7oLCs8rjp1l4BvtR85iP7+/xokoQphIgJ0631RxVwnKj5zJ/sjicS+VmZw1Gt/o4DHgYezi4o3NKIWx0GHIFKit2BVNT6x++Ik70eQ02bMuscVKvDvwOPT50xqcGJwioguhbIBaZMnTHpjdDP87MyJwKvAOOzCwr3dkUy3Vpv1FT3AM1nbm30N9EEJGEKIWLGdGsXoUZqYzWfuau+8+2Sn5U5GpUsjgD+BzyaXVBYuh+3bI+a7kxCJYM5RNbw3DbTpsw6EFWduxF4BHjHetdZ33VJwGnADah1o+dY07175Wdl9gJ+BK7ILijcu2zEmokoAn7UfObfo/W9RIskTCFEzFjFHC8DqzWf+We746kpPytzPHA7MAbVqejx7ILCaG3a3Ae1RCQuGolHYtqUWcmoKdPrUF13ngLmotaC+qfOmLRj2pRZ7VDdhwYB44ErgMWogp03ps6YVBZ6z/yszB7AW8DH2QWFd4Z+Zrq121Gj+WM1n1lvco41SZhCiJgy3VoX1EbIl2o+81O74wHIz8o8EpUo3ahNkp/OLiiM2xGwHaZNmTUS9W5TQyXHAajCp7aoat6lqKbwz06dMckId4/8rMwjUL8wPQfckV1QuHdNqenWjge8qNmHNeGut5skTCFEzFk/HJ8CDtJ8ZrEdMeRnZTpQayRvR1Vt3gM8F1p8Imo3bcqsBNRSmM1TZ0wK1HWu9d/6JiAHuDy7oLBau0TTrfVFjVwv0Hzm500ScBRIwhRC2MJ0aw8B3TSfeWEsn2v98D4JlSi7AP8GXsouKIy7KcCWID8r8yBU4U9f4Nyaa1VNt5YMfA68E+8V1NLpRwhhl1uBeaZbu0DzmRH1MN0f+VmZCcCpqGKeVOBfwGuh04IiOvKzMlOoevc5ALUU5/zsgsJqzd2titgCVAP3+2IdZ0PJCFMIYRvTrY0F3ke9t2pI+7gGsZaHvIBqBvBP4O3sgsI6pxFFw1gjdx24CPWu82dU4U9huNG76daOAV60zvm35jPj/s9DEqYQwlamW/s/4BjghKb4oZmflTkZtTTkFuDZ7IJC+aEXRflZmUOA84ELgA6oop6nsgsKw27LZS0dyUEtO7lY85mfxCrW/SVTskIIu90LnIL6AdqoLaPCyc/KbAM8BBwPHJtdUPhLtO7d2uVnZfYBslCJsj+qk89VwHd1jdxNt9Yd1YS/MzBO85m1tRSMSzLCFELYznRrg1Fdb47WfOaCaNwzPyvz38DhwOnZBYXR3HC5VcrPyuwKnIMaSY5EraWcCXxWV8GUtfZ2POp95mnAE8D/aT6zvKljjjZJmEKIuGC6tSuBqcAhms/cr6Ud+VmZmajWbmOyCwpbxebVTSE/K7MjcDoqSR6J2m5sJvB+zQKemky31s667jrUiHI68IzmM5tN44aaZEpWCBEvnkJVseai2uc1Sn5WZmVHmjOjlCwvBtIbcd0a1AbazUp+VmYqcDIq2Z0IzAZeQlW51tke0Ho/ebh17fmonVr+DnzYHIp66iMJUwgRFzSfGTTd2lXAz6Zbe0/zmV818lb/QjVJ/yZKoaXTsA2gK/WL0vObnPW+dxIq0Z2OqnCdCVybXVC4qa5rrSnXUda1WcA269qxms/0N13UsScJUwgRNzSfud50a1ej9s4c1dDdKqw+pRnA9U0SYAtirUutHA2eg2pv9zLw9zr299zLdGsHWtdeAKRY154KGJrPbJHv+iRhCiHiiuYz3zXd2qmopSBXNPDyK4DXswsKI26353A4UoGhQAKwMBgMRqvZetyx1kqOomo0WIoaDR6eXVC4uL7rTbfWz7ruAlQz+VeAy4DvWmqSDCUJUwgRj24G5ptu7QzNZ74VyQVWMrgG1WGmXg6HIwn1vnQq4LQOb3c4HE8BOcFgcGdt1yYkJLzYuXPnvXs4jh8//tv333//nUieawdrA+zQ0eBMIDO7oDBsk/RQ1lKQc1HvJEcAb6K6NH0ejzuKNCVJmEKIuKP5zG2mW7sYeNN0a99pPnNtBJd1BzpmFxT+WN+JDocjAbUs4pQaH7UHbgTGORyOY4LBYNhq3cTExLJNmzbdFkFMtsnPygwdDfZGjQYvBb6vr3mD6dY6AWda1x4GvIfa7uxDzWfWWR3bkknCFELEJc1nfmu6tSeAJ023dmoEU36DUPs0RuIK9k2WoQ4HslE7mDQb+VmZ3alaKzkcNRq8Bfi8vp65pltri3r/ewFqT8ovUNttnaP5zG1NGXdzIQlTCBHP/gF8C1wNPFbPuQOJPGFeHsE5V1BLwqyoqEju2rXr3s/OOeectx977LHvInx2VOVnZXYCzqBqNPg+1mgwgrWSSajkeAGqYGcearr2Ks1nbm7CsJslSZhCiLil+cxy061NBmabbm2W5jMX1XG6C1gW4a3dEZwzyOFwJIVr7mL3lGx+VmbN0eDnqE2Zz8kuKKyzaMlaK3kkVdWxi1FJ8tZ43bg5XkjCFELENc1n+ky3dhfwvOnWjqyj0GQraiupSGwF0uo5Z0cwGIyb9m35WZnhRoMvAVfWVxVsrZUcQ1V1bDFqGcghms+MdFTe6knCFEI0B4+iksTfUNO04SxB9SqNxCxUAUxdPo/wXk3GWisZOhr8A2s0mF1QWO9o0HRrGlU7iSRa154UrX69rY0kTCFE3NN8ZsB0a5cBP5lu7X3NZ84Nc9pSVOFPJO4FzgPa1fJ5OWrfzLBqvsPUNO3nr7766uUIn10na3lMzdHgTGB8dkHh0vquN93aAKqSZA/UBs2TgbmtYa1kU5Lm60KIZsN0a+eiWt+N0XxmtXd1Vg/UYqBbfe/xABwOxymoRNSpxkc7gSuDweBL1r/fQuNb490X6cn5WZnhRoMzswsK6x0Nmm6tJ2qt5AWoJgxvWNd/qfnMOqtjReQkYQohmhXTrT0PbNV85tSan+VnZb4NFGYXFD4Ryb0cDkdv1D6O41FJah7wRDAYDB3JNVnCzM/KDDcanAnMjWCtZBpVayXHA0XWtR/t724vIjxJmEKIZsV0a05Uc/BrNZ/5fuhn+VmZJwJ5qG29ovXDLaoJMz8rs9bRYARrJdsBmda1k4DPrGvf1XzmjkbEKBpA3mEKIZoVzWeWmG7tUuBF060dVGN/xY+BR4BDUes340J+VmYa+44G7wE+yi4orHM0aLq1ZOB469pMYA6qwvUyzWduabqoRU0ywhRCNEumW7sf1azgnNBilvyszGsBDzCxvmQUoUaNMMt27Rr0sOecRVSNBmehRoOF2QWFdY4GTbeWCExETdeeDfxuXfuq5jPXNTQWER0ywhRCNFf/B8xFbfD8XMjxGcBJqG43N8YyoPJduxIXzf125Ab/ksMT2rQZA3yFSnSXZRcUbqnrWmut5DjgQlQF7wbr2nEtbV/J5kpGmEKIZst0ayOBT6mxAN+aAv0R+Ft2QWHBfj7mYtQm0mEFKioca/5YOHDDsqWjtm3eOCIppe3GtPT0+T0HDv44rWf6tAi+h+GoUej5QBCrOlbzmeZ+xi2iTBKmEKJZM93aDag9GQ/XfOauyuP5WZmjgY+AnOyCwqei+UxrreTBqEQXOhosyC4o9EcQ80CqqmO7UFUd+6OslYxfkjCFEM2aNZX5MlCi+cyrQz/Lz8ocBrwGfAdcX9+7w/rkZ2WGHQ1mFxTWOxo03VovVHK9ABhixTUT+ErzmYH9iUvEhiRMIUSzZ7q1jqj3mfdoPtMb+ll+VmYH4HFARzU9eLMhxUD5WZm1jgYjWCvZGTjLunYs8K517Seaz4ybPrUiMpIwhRAtgvUu8HPgNM1nVltSYk2hnglcD2jAk6jEtzi7oHBnjXNTUNW3J6IS3WBCRoPZBYV1jgZNt9Ye1ff2AuBo4BPr2iLNZ+6s41IR5yRhCiFaDNOtZQDPAHcBj4Z7H2hN016LSoj9gc2oxu0VqF60PYCVwNdYo8HsgsI6R4PWWskTURWup6DWgM4E3tJ8ZklUvjlhO0mYQogWxXRrB6BGhAuAqzWfua22c/OzMhOB3qhEmYhKnCuzCwpr20Is9DmJqBHkBajR6wLUu9RXNZ+5YT+/DRGHJGEKIVoc0621BaahOv5cqvnMOVG6rwM4hKrq2NVY1bGaz2xM+zzRjEjCFEK0WFYLvVxgHWpPzVca8x7RdGs6VdWxZagk+bLmM3+PWrAi7knCFEK0aNbU6cnAdai1k8+iCnGWAMtq7uxhurUkVOP0QVSNJjuhpltnAvNlrWTrJAlTCNFqmG5tMGo7r3GohNgHNfoMLfrpDaxBbUhtAK8A38haSSEJUwjRaplurQ1Vo8nKop/lsp+kCEcSphBCCBGBBLsDEEIIIZoDSZhCCCFEBCRhCiGEEBGQhCmEEEJEQBKmEEIIEQFJmEIIIUQEJGEKIYQQEZCEKYQQQkRAEqYQQggRAUmYQgghRAQkYQohhBARkIQphBBCREASphBCCBEBSZhCCCFEBCRhCiGEEBGQhCmEEEJEQBKmEEIIEQFJmEIIIUQEJGEKIYQQEZCEKYQQQkRAEqYQQggRAUmYQgghRAQkYQohhBARkIQphBBCREASphBCCBEBSZhCCCFEBCRhCiGEEBGQhCmEEEJEQBKmEEIIEQFJmEIIIUQEJGEKIYQQEZCEKYQQQkRAEqYQQggRAUmYQgghRAT+Hx6Dx+hYJyA7AAAAAElFTkSuQmCC\n", "text/plain": [ "
    " ] @@ -136,7 +138,7 @@ "outputs": [], "source": [ "## compute node strength (add unit weight if unweighted), d-degrees, binomial coefficients\n", - "hmod.precompute_attributes(HG)" + "HG = hmod.precompute_attributes(HG)" ] }, { @@ -172,12 +174,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "A has strength 4\n", "B has strength 2\n", + "A has strength 4\n", "C has strength 2\n", - "E has strength 2\n", "D has strength 2\n", - "F has strength 3\n" + "F has strength 3\n", + "E has strength 2\n" ] } ], @@ -270,65 +272,65 @@ "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -400,7 +402,7 @@ "output_type": "stream", "text": [ "start from: [{'A'}, {'B'}, {'C'}, {'D'}, {'E'}, {'F'}]\n", - "final partition: [{'C', 'A', 'B'}, {'E', 'D', 'F'}]\n" + "final partition: [{'B', 'A', 'C'}, {'D', 'F', 'E'}]\n" ] } ], @@ -430,15 +432,13 @@ "outputs": [], "source": [ "## random Chung-Lu hypergraph\n", - "import hypernetx.algorithms.generative_models as gm\n", - "import random\n", - "n = 100\n", - "k1 = {i : random.randint(2, 25) for i in range(n)}\n", + "n = 200\n", + "k1 = {i : random.randint(2, 5) for i in range(n)}\n", "k2 = {i : sorted(k1.values())[i] for i in range(n)}\n", "H = gm.chung_lu_hypergraph(k1, k2)\n", "\n", "## pre-compute required quantities\n", - "hmod.precompute_attributes(H)\n" + "HG = hmod.precompute_attributes(H)\n" ] }, { @@ -450,18 +450,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "qH = 0.06072051073158535\n" + "qH = 0.28051898403077047\n" ] } ], "source": [ "## Louvain algorithm on the 2-section graph\n", - "G = hmod.two_section(H)\n", + "G = hmod.two_section(HG)\n", "G.vs['louvain'] = G.community_multilevel().membership\n", "ML = hmod.dict2part({v['name']:v['louvain'] for v in G.vs})\n", "\n", "## Compute qH\n", - "print('qH =',hmod.modularity(H, ML))\n" + "print('qH =',hmod.modularity(HG, ML))\n" ] }, { @@ -473,16 +473,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "qH = 0.1379748602509609\n" + "qH = 0.4037729728695327\n" ] } ], "source": [ "## Kumar algorithm\n", - "KU = hmod.kumar(H)\n", + "KU = hmod.kumar(HG)\n", "\n", "## Compute qH\n", - "print('qH =',hmod.modularity(H, KU))" + "print('qH =',hmod.modularity(HG, KU))" ] }, { @@ -494,16 +494,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "qH = 0.18999957793453737\n" + "qH = 0.45336073927573545\n" ] } ], "source": [ "## Last-step algorithm using previous result as initial partition\n", - "LS = hmod.last_step(H, KU)\n", + "LS = hmod.last_step(HG, KU)\n", "\n", "## Compute qH\n", - "print('qH =',hmod.modularity(H, LS))" + "print('qH =',hmod.modularity(HG, LS))" ] }, { @@ -563,15 +563,15 @@ "outputs": [], "source": [ "## Nodes are represented as strings from '0' to 'n-1'\n", - "GoT = hnx.Hypergraph(dict(enumerate(Edges)))\n", + "H = hnx.Hypergraph(dict(enumerate(Edges)))\n", "## add edge weights\n", - "for e in GoT.edges:\n", - " GoT.edges[e].weight = Weights[e]\n", + "for e in H.edges:\n", + " H.edges[e].weight = Weights[e]\n", "## add full names\n", - "for v in GoT.nodes:\n", - " GoT.nodes[v].name = Names[v]\n", + "for v in H.nodes:\n", + " H.nodes[v].name = Names[v]\n", "## pre-compute required quantities for modularity and clustering\n", - "hmod.precompute_attributes(GoT)" + "GoT = hmod.precompute_attributes(H)" ] }, { @@ -591,7 +591,7 @@ { "data": { "text/plain": [ - "-0.014324487155556065" + "0.008670599366313703" ] }, "execution_count": 18, @@ -685,7 +685,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "qH = 0.5455873667030067\n" + "qH = 0.5475162906819371\n" ] } ], @@ -735,27 +735,27 @@ " \n", " \n", " \n", - " 14\n", + " 18\n", " Daenerys Targaryen\n", " 31103\n", " \n", " \n", - " 17\n", + " 22\n", " Jorah Mormont\n", " 19344\n", " \n", " \n", - " 7\n", + " 30\n", " Missandei\n", " 13683\n", " \n", " \n", - " 3\n", + " 13\n", " Grey Worm\n", " 10497\n", " \n", " \n", - " 8\n", + " 25\n", " Barristan Selmy\n", " 6514\n", " \n", @@ -765,11 +765,11 @@ ], "text/plain": [ " character strength\n", - "14 Daenerys Targaryen 31103\n", - "17 Jorah Mormont 19344\n", - "7 Missandei 13683\n", - "3 Grey Worm 10497\n", - "8 Barristan Selmy 6514" + "18 Daenerys Targaryen 31103\n", + "22 Jorah Mormont 19344\n", + "30 Missandei 13683\n", + "13 Grey Worm 10497\n", + "25 Barristan Selmy 6514" ] }, "execution_count": 22, From c20adfafd2eba2533f5d4cd58c12e2f65c4fc8a8 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Thu, 21 Oct 2021 15:45:50 -0400 Subject: [PATCH 28/41] sync with hnx --- docs/build/_images/ModularityScreenShot.png | Bin 0 -> 48827 bytes docs/source/modularity.rst | 60 ++++++++------------ 2 files changed, 23 insertions(+), 37 deletions(-) create mode 100644 docs/build/_images/ModularityScreenShot.png diff --git a/docs/build/_images/ModularityScreenShot.png b/docs/build/_images/ModularityScreenShot.png new file mode 100644 index 0000000000000000000000000000000000000000..5978e6047f996203caae12be0dddaf334c877759 GIT binary patch literal 48827 zcmeEtWm{aq((NAH-GT=Q5Ii`+Ay{yCNeC{1;67MLa0yP(KyY^*g1fsr!QJKVocF!= z%l!-I!#wlMOi%Bw>gv^1t5<(lQIf$#BSixM08>u(of-hZiT!(_AOQeimNLf+z6m%> z={T!@Hg|UW@Wl*J{NU_h``Ovn>LazQ*%v3P&-Pqw+-#gI)RxZ94o+{`+3o&c1Gdj! zEZCU?*LA>7P#t7-od5tG|KAG&q@h&H zz3l+_5hW`+5c033#7kuOf9--Ku#l+#{SZu!@jqP%ko>0uf-nE=;J-dVdHG)-{MQHn zfa3p;gAd4FK)&e9dVm@Z(3;S8g5{;OdIPIKCPn)TDvA82Eh0++00#-EMNDS$iVg`> zDMCrf3lv70WWW^yq5&ugKm(yhOFEf2Lk_jP+qvvX15{Qn%#Z-|uQWk-rHgQ^#K0d2 zH7>BeHiIo10jR(Mm*||DwbYo{FWWePEG+7Gu!k@5AvPV6_y9SuYQGaa3s76$(X7s> zXrKXGK=o^Apfrk0AlMT&Km$;}06s!*y$(QR=HO}t29Hr4C$61g4-{p_(FK(kH&EP0 z5DT605l{y)l-~lKaQL1Wqfg}%Mz$t-Lju4k0Nes9$YNe_4?-weO8u;(Xi!vPIUo~` zesDQ|L_!!59;Xqj9g+Ux6FgUh zT@Y;Ks+t%b*n|yY8A71w05@CVyZltp*QqmCxo}Xtq&W&QXV#1AugCzWiTPLC6B`mv z6B3CfRCpX~2^`pB4;A8|UB2|qrHJom9IV0LT~&YzlGuHK5M|I^{32WlfiI@;7ZZsn z#3X$K;b3$?%|zsaAF9ix0k1%^r`Q&yA_|3|K!BHs&@2E4c~C;i!l7q^iI+qD6bax* z6yrnk5JI`sZ9vq6ODf03RM?FH-8cvfiZZcTR#Jw8A}4WZU;=kwNIU>RjTrDMvb6j_ zGz5T$zz`M6chNgx50z{PP~;v zAi7|Wy=`9w_yC~2Kp;yB9)1C{eRKHvr9VKAUX5fP4h zx3Y2?X=${|3YbU45dr-`<_GaSG(SFS00t1ypu7aa5zU!B{EV6Lu?7dHAuQ?N?sD?X zbJl?9(TgrP3}L&MP~Ck->$~H(CayntOsb|wze4g?X#W5vcpOcP7wr`CQC8_lqJY>8 zk{1Vxi>7+j7>bFx$2zoOX#>Gr5qrAlZ_+l$JAl^)(~1Oo$0LCTa6xaKXDJ8`ytpVi zx)!g1(aUy93<-*O{md^ye7Bu?sAfJ(@ao+La{rf(Py1a^`4%z^Ag zC*Fx{P64wA^oS4Ishzo4QzG(!syxih$02FenH!sFJ#4_-wr@=I1F)9}Mx+9wIq|nQ z^`@wAKTOQ90rMoA71Yk)+Al!?Bii4ZH?%SDwgVqk;il8PMqrZXB55`> zGW5-Vj-m=3+Ff8r;9SB@Rs%PIL2$`7G^CgMS-kc@`RnIQ9}@6C!K*;ksxH>xy2u6d z3yytyS5p}e0u03==^PLXD)Tyomuv}KFnMhbWjKIKG6XW98&YKt5I^2AAe)ChP!{6{ zHR@AYf@Ap#uMlkpUp0o1T66UV^cZ3Q09Az@5VhN_*r~&vBUKYav4Z#N^O0|@c$whx zc%u+K-TrxuHMPbQS|DbKI9pt!-2qIp;}ifKNJ+|6yW817CJt1D?7%;>wXEPcfgCj3 zr)7?DFC}3}uLX8N-(go*`zcRv^e3p+pi>UupGo>XT5Rq{sDK>GJ{uj3B$pL(&oNVF=$ucJTR47r??jjb` zP6_Ox(BJ{%K!>s$2c+tVR$vxhT-Y4oxb(V!t|4Fgx|-MzMB1Sp=ZAq44=!6k8tz;= zqEUtxz<{R?YNtfmGXfEa5ZDC749j~0RFIRP1>7Rky&Jp4;2$UoU^-kN2=(vRojGhUV3GmnqY05UFM@N?pVaeZenR~mT8z0l2rV(&DP>VAAQNp2JfMaCar&8>a4-&-B0|33C_Mi5M4)hV zD_ULqwKW1Va0HlN_26!{Fa8*LUrBC(5L2pF+OI#NbvwvHcmwT-RpmKwC`(A)oJRZl zc5n8*v9TpwFPypdED=Du&wA+y`U6A;NgwF`t_pUJC6$IgI$_NfUi{orex;WJ6ZTf@ zsm>oHT!v!$aN?2(d<&}%L`RCpxla%=I^aMjx?tR;WAvT}L#&1L4GvJ5MSQH0B`mhv zrWNj-hCg_j+!upDc7YI8_zF=2^L17=0;*9)u!gFunpD#_l6lE@(GrEw#I5$OL~Bk4RfviZ`~2jC%WTpQd^Q2`RfK?SDar%XyGxjNB;Q3vnRxg~dWNKm5U7aM-M zvVmM#hkS`34Q6etbzhmcRh2Di58`LDo0x7gt9&gb@6oT2u9@$aY}T z23O6*+>bPqxb)F+`}|io)gODeM5k>WmU71l12O_PvMUFTWnSWhyJEV5lN#^h4NgB1 zGln>Uiu967!9mfm?l~`;-Y~>G{*QKGjJ##26H7$_@PYg7EXvi3c^#P=QKiXw`E3tI z?X)!F7LUv?{RyAuqIvl$RI0Iuc@73ycz*u0xg??g&R>}$H{MS6Rc6^YnNEk#YhD&$ zM26!3%UJT3l}MnTtRFEh#=BA?sF=HFBSX_I27DNTO>N0KjySiL9*%b+SeiOd6AWVo z(%)W{9H1OicKO_~Bf=ZBlnlq9Bms@^%ZX-uc6CO#G&_H%McCU1NbRr@y?|kzi+N6!wLg@#!9jn$<5;#~jW2cSI|SMR zj%@3v7h)DNyh_=-APDRb7%lQ_RZ$8kzSp0Gr)1sX?ZX%-Iy38*_pdMIiei}XE2-~M z?`aKA-8CY9O0pMb=wC?9`(g^1i1NxG>g!cEuEG=HNG<*P_gK{?tJY;*u8a{PV_9sn z22Y*M!2mIXh0Vg9DFetE}3WVP27dqHSnNwkx~XBK+ZJ{&q?J~jVd>y@n26A#Z( z`lB6&JklW1?GottWuSG@7Zr58BqMaLWZcHwZ*j_2{4gU&boHGFwsecewbX!$getI8 z`wFvwO=ZwYPoN`+bLOT0q#;dggY}>6L%mq3+wdBi_@Q2P0hUFCB7eU4j`Mh_+$3I% zwyFb^#N(!yDkRfi&GfIoQ|9;tiE&R~`JX{L{-*nM`4#}HIKUxabJp^!Ix#ZL!2@MX zlNt*GvD>dX6^W$R>{T8ZalaN%C?sj@br zTtC=GNRn9L=`+zFUd@w~E|Acc&qTmy)@#X~R~EiU zsj`r8jcz?ZQg54EC6DT*<>^auBCMUk>CNw#TjRIYQ8-vBs;BSgdmG8}T&$?Xz@Yga z$rTWSDpC?ZTIb)e;>QTUcdg zNoi&C9GuqE%1o*$D=QOnuSD~6s*$xF78A&uWNT{P`fiW7yosBCQjx!($5iEYB#k|TQ9gKzd}vaIXI^B)VrF!$?n!evu%osI<4l1CW&82$_bEi_QqT>K7X z1{u}+1DC7>hkYFP zhpy0LjJ61P`u4??3iagrI40kQm_D&Y)!L=Co}Hyk4?D#>zH3yD9}wnA(_Q2rk>2WP zJZ?BmPiMoLYbLT}K0?|52`=AcfP>s~ECglp`i2qPQK!jQAy=v@AiL#*>YvSHhEA5{ zmz%xzvvRVYKRr}>jN@B z%T`LM`_-OxJ|h6AXFj|xtE-SQf+&oX2mS-}`+jV2laxN5zm>WN3Mr%RzhxX6kexbj zq;GFXF=Vfg9Ljns3uoQkroGk-_IEGc3*hJ)qv8~KPda`QgZ*XiX$QI5~imCsX zt5W)m)4*6tXsSGz;A)rk^&;-HJWowyJ!aCMEcaAAm!yRq6N+LZE=3!5BP20*Q>hn4 z44rHOu$LZu2q=*Yox_9oH(CW1Ca)FCOFXw$r-Ju1L&}Uh7YTpO$VCd>9jpkG|BdG| zeR}$E?pW0nQVLDIx zr^mv3RII9RlZ(AO$9JDrk^A`vqT-JGRu*e4ZWdSp&aJ7PiFwn)^UH||nRi=fuhMw? z+f$tZ_Cs#FA9=}cvXnVX?}8laH)yvc8#{g$NKK*+EhW~iv8Wu!fJF-&zON+!n|_rq z$zm^j?J(51uuriiKSx)`rW$*`q%#!|_hhaeRBJ7+_e5Mpw>{n^lCb(-a&q_h{kV1- zJSRsRw6fz>orCQ9-m&z!-9}hw9gBk$me4wuwy~8+>6kHzrDH0Mw3Kr59b`&!tg%^o zV3;VeL+gbd%zmZ?4y?C7?Y^oK1%Ru#Ke^2qGxOUg@PLE6-8NI{s6M}=ly(O;fbWZ; zy@1E4p;A`#<@;GwLO8`2o5{UtDuyToAbN<0Sl%Mp@AtFBn|@b`oBWNwS*h5+)7XEG zI9;w8CLBPubXuGAWKa?0Id!nbJCiQyEbu3BYvl$H2SiAlfs#W4+3$klU_0YIXZw2_+t6`%u{7})!}-UldX8t-<@nu4h@ zR^Mn?;bcm!Z0u{n& z89s@6f)_jija9h;DRF@%eO!?c0A=oDNB~t#pXKZld~XY%Cx3!+Xypl`c3kb)auo;P2pwMZtEBvXUl)c z`#o$J^4*8|5Xx-16h5h4M{JP$g*^%0KGu{;`E^>R%VZRo-U%!^9_%(D5{a6IM znb_O-6ZX7#i=kNgQ!RP$W;G|u?i$s{yJ?0Ar?QK0fp7r;azOY;vW;&CDH#m}2rxG) zA4&-akT{vSIc7Ht>J6u*EvIqDF(DMvg)6gm&|w$F(e=>S0l5)I#7Ll*phiZ_h>oW zHl?g%&TFi;-11BJ`_ifY?0xK35=@*nHw-Q5&`sn1;!Yq_WXosc>0D!h9yaW-L z)vOpZaV6WVh@MTyhNCy}wJ?JhqxQwV0?D)hX2PVu_eSE+H6MAI2i?@ThmB^u4>t3D zpwfkLVl>pO=lxu1dnC%c^@e>uyywR3TyAlYv4QmN=gy)Nx88D~>wE-c(>-=@R^YJ} z&YNg+XbG-KhWippDec@vy;EGGZlbO>{(PJxKbMJpFhNlnc~$g1Dh=HhBPnz!^?`y0&R20p4f1IMUy zG&w`2sOQ)9T9fc%O7{M>B9r=NAX<_JU=JRZnbf${e;s*Oxujk1CTEdqWGf=A3e>#% zAfGc4Zf7h19B+hO!6(@B(dv<$H}#^__<6C%Y$v?qkUq*9w)u@MB*Pp5dAIjBnVXkO zabU!8$jFQzgWpKAWTmoW-5PT{wM7V#Yn=(aIi@xVM_s21pN+N48@ME2nb{q5|0&Z= z%2Hi7o0b;a7h$Wr_^$*qBBOMj2aPeQT3aF$)1T)k)YSX%sKT_>sf3SO*2BG&kqf^n z+Z4V>`+V0lw(lq-aL7vpt-f?ZK*Rs*V&oJ6rRCIH+CIU;2!S)77^+`{vy-js2Vp_z z4In1Lev43aZ}295RXzU0`JmKL58HtPx$G@UU+-M*p|#sfoJDU|{3y3`r)K;2u=VJr zv&7hvi-w$Mjk@XNk$b7<_=lrAKPm4%MdvbXKZUwT{H?=5+cdHy>M%&Y#V?2$xk*u2 z^lvw7M362RwsO{DU5^RKR=Az@3lB}-a8nQH)L>C!eQ<4^RTEk1h&L!zO1e#CE(qfm z=FuvU@R(TfqU)={NfM@G z0?|lf21uD+Lx+P;Aj@6^ow08%kRol0)>rcv^O{wows?v~;dC zgnq3G{WVTSglac0Xee|W0~UhM+CMp}dg}a@>rIWuvRKLXTPUwDgVD-jnSPW!**9-p z2@dc+l-tkGyNF^n`AMYV0;|34k8iM=0`Wx@#^yf!h2C<+AOR5e-C1HVd?OuzXfbwO zSi!T}8$3YFvF+*IdBvZNF;q5Wec>-!e~st8ewqzr+U{%3s_3GgKlq5UU<@~3;2qc< zo(|bSXA$(3##(<}C}_^wl$Fd*%-{M`r`s0cD%ZAH--zec9^r~=U1}WAj9K1?^vB=D z9X2C?td$W4aKrlRYh!7_RF}KSwBcoFEN;FTA}#;tI%pVA+{Zs2dE6lwn5=tNe`9XGl?4c6QoxvOJ+2*icVG zq)t#pok9)!t=4|!_W-|aQ^dHw+*0;5w3DUtyUsG>1;?v(W>u1z@wu5v)iWu2#aXxs z^~R`pd|R1d<@phmy=AJ8v%##G54|J}Vr zfXftlB57r0gt{!$iZ{-nAE4rAe!AGbDv2p*`Y6ABqT85`Av%OYBz9xkC@I41QGMFB zR;3`?w_JUMpMj$P@kW8We{7na0hvQzz5|$na|v+=WRY!MaMgw&+<{A&7qQ6Q#fR(1D01O) z0ltpxnUI25b?ffMFEUbIlmoR>eL6~}%>qvC$`Hx~!lbVSl3?b;AX%V^gp`>+llI`% zg`Mou=8nqO_ed)*ZO9txVrBf`U9s38$T_$87cL?>*<@fa$ce%yt#v+`Hf-JS=-?a) zPF~?Q`$?s3%y4+F;Qn1h@oC_R@=&T!>JRL{tvmFddiS||@a8h$DGdOg&AtarF#h43 z*q8`^-lLU!!`Av2?5)TM9J=Bb;qB8lIgnW_Ez9waj(MKbG3%JyqT5nU`ZGR5P@vs% zu+Hpt$$|t1zB!9Q`pQ@jY7Ik(2i+Uu*R`2~M#jP%RGik3Ohf--QIL*5kM;k{Na!bv(|RA<)b^k0oGiB5%A$3mi|?WgFYD$< zY#9(z!)>;(r=MQk=^Hif@1HP``I&z&*q_Nt0;nmVqpNo1uwK^DN+_!6>|S=pxhmzl z$yKU?8zzoj|m6cRi76LyUylylRYvNP$!Iyuz+gun5sq&uPI+D{n^hm zUn|n2U-lThnT+M#-?#TpK>|o7CYf@2ELHYE)8O1VYEI_@mN z{q6?;+wwO^fUE1q`2{IRS8J6uJZ>>*-hLqJ_S9399sB7z=Y z;cVEu4XN3;;CahLeE0L9{_H@lB;+ip6tV60Sm4jr;Tx}6EwHXO?ibhR+>lgLMKL3qhd$e+ zaV&L}|5&d<53b#tvp1Ukezu7?qWNlw>G1EHYi?|*{dyKoJSbA@l`~e44aG-w_Q9=k z0pB9wkkeOq0Vk&oES%$#WBxafv&=^-7YG<)!g<$=PiLe$$x_0I#E03%h_ePKS{YC2epOI9}FeWteDQ+Fc*Ye4q76p7>WP3s(4#2 zGcDmhed=(vFXqE=Aj@GWmU+bZP5n_>bs9cux=R5qIT;4hC`ws1YSB(dhdZ(vw3UoLU6L;J zqi{)&>1{dQ3qQ@>OnE~{>gmNKQIfGKz(`U8BPsV@!x8=UulJwa^h$}21e;i`7!~2& zI*Z3m#b2n`xO=)co5^sbH!<_88YS+HxU9ToJE_}ki(k=xR_a{%@pG{JP_wOgc*dlx zFj5}gd_mCO;~4quQf8g)?-fW_hvVlqpV-B1Xca5OVx2YRu-AIqd{LMxH4{qwP;E$1 z_3HB|*Y)3ykRUBbk$&XAh{loO`GQx!QpsGO9=?e_=G7fr?+<{?NScx3eb^PS#VL*?lY3K9OWTKpzJ+dH!g`Qbck=*-8U*!zth=Gn z7P#BF{<(QYfA2`S1FJ&DDj(OAUL=yaT;tw1v)ntZWd%Pww9VZV>?j>$g?xi93c}fWR z6k76*v+Js~DagV#jqvFO;9JzECR#K+bFlx%%mXM#Nzx*-$fQ~(wFc94tnw+T3@H%j zRj<#ZIVT#nDEpJi*<^LRY}O^8F*c-@ZqDbqC$en$TLQM6q*BeSY|y5Pp4Rc+1ufdj z*mkkYITE%NfAq5bOYbqQosz~-d%eT@*n$dfRbw{xegea&2`5!QZS?GG8AS+N=vd|x zn4{f=+DYQFw77-ePFrCc#Ya^%sQ!+BaM96}h~SZr#I$fYky%*DbuaTTA%nfI zYsLp8sI!Z2D-zTwr%&w}0Z;~DzKRKNiGe3Dj;pVEK?0E;sG{#iAG94+0MGY`UcWTNNAqijh@pR8w1gdcSA7-)dCZ>y@;)3jk}8_!g!A7$@qG z%_WC{#C=W&&NGn~O=~8d_xQF|BHp`+!Me;*Cq4TGSae| zWL>DT{g&lN!~FyU6(7*+kIHWD(%Ov(99vdS8pFY;Agi(T@{d%2yZ+CziW=V+fJp!r zX4~*pJ@mCEJKT1y&MV*m5lYE!@B#(g2)ruf0tjENcQN0jiPMRHLxG!BrAg2FbHI3T zz%z6ty}Xuk>Bi09pI9zuuUP54``lf)opGk!A!AuW@N379N8k3f;if72fVF8}vt~-| zfwv#K(QeeN0sZ@tXYcR5dOR~P!{ESvDlI(c@dVtp;&Pv;fCmZy zjYoG+8(2jL4PRk}I+ze4-Y>A(+T6v$!&+dAc!CH%miInkTEzA+En-NQY0M%cF1u>? z#%46ai8q0rtGWh-9L^#KC4-FoTX|uEr7Zn?%t*A;(|JrMm*JGA8{N+f{HtePUHe|yu^;C!lF*5E!xC%=}QUR^3;wQvgE(e`;EIRt8kghLNqX2 za~~8q(%Vu6pba(ZzK?v9w<(-}PF|l{6auskShWIIo(C|S2L>%he_TrGgqAxmw@)r0 zMg*h*(6~`_`-WOcUVste=7Sg>J%slzw7yS`k|7l^@p?VKo4Z@281XvAwMtHc^7VR` zwtkwKonet~u2c@cVVg@3XArt9A;$tV#9H(WX0wq_!C+M6H(gg`r7=3}df zOY8P8dU;_)z#Jad2CTZJuv|Lrc@OED_}X*Al%a3{$J2$n!R*7WCM(tyJb<1gHQoVP z1pou6(c7wVkmSMvvtuZZlTNlQF1z9%GYbgd&NweN@nqr7NSZcAv#jtt@lJflp>AHk z@PC7Y77dF#*Y-;y>vX_S7%YK4YJD(I;bz8)N%NHFehJ%)* z96$use3zXQ5>u=%u1OY7H_q<(o^FGVHk1oHGTdpjdH096FGWR6rr{icRV9;Y%%A9! z=x79B`NPjisZaEJ(+owRSc4R{lf@{kc%R{D9uxF!fW~wd0aqL+h)(HGMyXT%jFI1R zaJTSl@Ys2Pg7UnV!{{RK1uFJ38S^mrU6c7oD|>T|p{Ff_I*(}0v=jCAav80p4o->e zxEsu*lE<7s`^v>|K%t1|pQbL|>kO=`a+Q4$&DaGHJPxDk{JUJV9dm-M7NdsCF0425*&uI1Y3J-#KAA+qCMi})bH*`j3!W<6PXBxMM@{+Hz{iqZf z;8(prhpC+>Z?R?23a3%k`FcJ)9y8un@GZa3^^VrM87e=yi(}Riv1pb`sXSUA6>UDk zDUhf=^H4jTKiExq#E$zRQ;JP>rt5o^gDl~gP3pr_C;e+T z9lCA$o9}lx9N>BLGnXNdr}PYcZK4xxXW2v}{Yab$kOiQw>`%7yZ(RFaw)zM1F2Pj2 z`EgiK$YmPocD<_!g>pouT*pMzg-L<`W6+zUmFHz$ZmToP&!#!6jRWr>un(3c9x4vG zfRk65@{!)YqGjGj>C8rsJ<2Qsq;u|WV{gKxN4n_Un{+ux^?usmF>T`B3GPFBlmLoX z>yh^byJU93muG0~TdLBiaWmmjx(}gA_^ur#>VI}#a&l*cYwxlZQ(I>A5+v08E63Qd zwWrzlR9&QPZ>KlwL+zO7jPx5-OP@GJcTOpEI?Ip2BEb%nZ1J^a#ykH`L-@ ztz&2IF8EnLV?hiW<5KH+pCo76%XD8L6q6ZB~u= z+6wv#wO5+%oXxkjxTZREp72z9oGVSXoev)l4|w2v6cjM{)4gub^k<#X12wHoe$&re zfuN#@8QJ^_*$svu)-t5YvQ}Az0{3lujw%2EdI&f6Mm=Jg0gUb=pp$@Y+A>g)@TjN#}P=ZRwo ze>oVD{j`%VS3iUfVb3ij#>@wfea{sYhB>{EPBAMeLPs*e?Pvw{7T35CC;%w%305@i zifD&3%$g*y^MK^9tB0R$ernOn4kj} zg$uZ>dWJe##0Q}V*u|6?Lr16aX#Bkj$ji@*CWYb zY_z+%zq?T^og+=OqMWmfYu?D2a{aXUn?Kb4Kp815!zW!lp zwwNMY(X*iC%obElD}o8S_dDq(Lt>X4RKPold$>_q6C$M*hlL+k?wFy-RPIy02eT*x zSBJ{#SRwvbo;IcEqIf2?o!5mF42mUeNbu)b;L#_7O3RXF5*|Hi=SKMi3cxEB_!Frd zBb?;pMuq#2g&q1$@8DF$@`8_&;(EEc+*&X^QuRNaO3XKFZ)2$u01ktGGpaXDEkpqD zy+9{T?`FumBKM-f!Z{SED)^l&pc}0Xm z#P1!#;6NVi`g<@n+ZnevRf41!O2e1f1a^5fXVnU{;~lOyF}lO%v5sos(%5 z>ZRZ%dw8bP@yeirOEW`oV2Gn1tS56zp8bIk$LDb9ltY9>1vX*Fs3Mdp>?UW&m*?Jpf@lvGt7(Z8-%Y z+g5JFOpN#4p3hx#pko-_T4c$YrPpYlE&GvD?@PJ)*f)8+8fXCx05vHT2N<=Ekae6wW? zNhld1y^Rm;8o@_@0!4_*YMIuC6Z_SV(c)`D!E1CX*U?DleM1Yc!a%i;Vj5#v(d}%* zp`25&+Dr_8jfmxx?vfJGfcd7P){5@AdwDkwoso&Xa!u`JDyD+03Zx2X92y6P)_y0O z^qRcq_;hl-|Mb*|nlw%NJford))IXw^%MNW2!_6)bCu!N{T@*<=oP z-^V|^iu$^AxP;8xMe^ZSPU4<&;xPu*=3+mtV3KrOs1qUBk1V41F@agl6RbZ# z0SOA22P3gZz4=n7wN3jMG|STWNwuC5gX_W{gn~mAD=9BkVR`<4;pso1`&`Q?SC=bp z(fV%!DwJjV$sU6Y5jE9mmbOfVyQmvZ-A;1rm8sc*daxRXPQ$r0JpBeA!UY@UH~m#@ zgCicc3`B1*SV^F8k*mPOb3sp7i$}F>Yf~tfjAPW=_ia-Vc5!YE*#tUFd-uBEJrl7V zf7WIdn@=U}S)aL+Hc(=>-ALao3XM*}!#qt~BNgGHoq48D)7Xyw4K1#cZCQDtpu5tNG;v9zC1nHHILX5_4aX>)AF?!dNbw9@2V@ z9nUu(MNYEF!0R(Ya^>kd&%x`i36pL{oI{>2O{WDJ>fJa9hvaY1->WdgXMo|G(s7;8 zY5u~wom>V77xrLV{o54BBhYRtzz7~H z0p{O?z>Ner(~(3-1+XAKu%w`t*8aIFJ9MWTG2Gup9&4QNlIroQH(z&0Zhd}%n;Wnj zwTbF+@QLf|4q!~7<*uhiPCxrjQ&!tgy{H8lP)QK0FQ)n2eTc{s5PfHbk9d!2v=C;FxfN@5wj162I5mi=U^-cdCG-_8FaHB7M=$~d z9R4qB{{l|?ucSnP@=q=%)f10G0}zgo7oQ5MXQ9Xg6&JW=KI7M0;WdHSzD2 zC++l19ISWHW@DWK|NMS-lQokvLQyJaH+~a9oZGG$YFDDh_%^ctFU3u zhwX1RAg}nO5y|Mba4)T1-BpW(*79UXj#Z~B#-7Q(jboZDtu6YI7M#--o=atMj`(d@iiJXB5Pf<}jz(J{0;KMs!no>qUd#z9Hb zI?5kAfB!@==!lR^kz{DJBY3LA2c&^_wjylr)*&#`-PMm?prJoNe8L2d5a>}JmB0zs z0w;AmG7-Exh@9jpss&!}$Wm(j*oxy=`{ACZs>m_#8?7kEThF67-B3s0K&M|8l^cn^yUklTqP`w};+F6oU;TGpx@_hxMFnLDDt6{R8s5zJP>=#|CKJBZi za@(n+)Cb;q0@h3)>oW_!t*t#kqDrm9Nh2=vM>WBf%#P>j4&gq8cQD#!C&X~^o;q{C z4gQwfud|PB(8`{#K9_ja8MTGhoJbQll=VcniT)8!}5lix0_J??1wA%~%7J*xD*kl_m( z%&zs2C+_S0Jv5&Q?L_Vp$enClt`R0M0tb5mE=+a6-SB7Go7(HQprnZ3WSR*?CMEub za@rj5CP(mu4aKk3o(Jxi=6y?J*#uQ>PH|z=7~hJ8n*$Cj%yeIU-{*nP@SI^%tgR91 zQ5>VpnQ&s1=+zg8cV8mVJh~dYOngS;;2fKA>T5DPR!O6{jZO^zL&S{W|ZCOQ!MZ@8%lN7192ooABh5 z<++t%81PN&c|{|IBGJQk8VgH~aZ#lI_?e7I4n-H?<>^>;&DfEMnuVL@Wnb{f4{Xje zQ7Q@}zq6Mq)JrvQ@K>1nn%VPHMO0Rx5XN%PX1MBGVmUlBTCN|=mu1{kMeK3?wn$GG zr}Ah=$E_Chkkz^zfQ1)2F$GwJKfQ0=r1+NFN$jPBT=lHOz7A);qg9p`sl8)k4XnNd z&0GOakN}~AKD9Og*nedGVL!I_=U>ul==~#vU*o*vhN;M0K_2CqM8|mLulNRs zqm3An3vS&j4ct5gfFVw%N^Rm#qRn-}HP(v+hXve%k$FL=7WfD_+>iSqw7hcPF{LQf6*RPA z*Oi7_izKx0;S(_*dx_Q8^wQnI5J^dTnW3&PJa~XH*3>ebR1{~qoSHNqwZnb>ymZLEd*IU2Mom9lE(kJ`wq?R|j3ZBKap#>_p+m=U zFR>7@kuqU>V`!uQtJ)OVi|Ho(zpBkNK~S}6(Re)<(#5&fGpR8XG5}A)Nn)~#0~Fy> zm(^!Iq)i-tvcO6=I_){4kk42{0ua?%s;;%?HFs=WZ7L@_ARv2LJoc$T86@AqxXsSf zA`XO5PtK<}f16wC6*M^Spumg_eMKIt|C5RAYURz?xstJQ2Qnb`KODM%jO}avT{Cp~ znhNz5Mn!$kEM_j!OWrzqIeOXc%3j9biG{Y#_1temeveGAhZfFJ#p%?pK_UmZ7ir}0 zc|~_B5-YyF++zVjM$!R*VjnU)6XSFn%)u%Ba6=aDu(J|z^>pgD6Y}R{_t7xqTQ*dq?mf{4O&`WtE%vY%38c0tht zADH`r3_KbTX+Zk+0r_ZwirlTIdj`I?@uXg78@o;4-!m3GjmuoDW2oPkUR8TD8pL3|hi9`zj?a^R$U>t%;X zRV4Yj_{|>sv!ooI4Ozls)iQ(j`dtP+VZuNt*X06HvcaVria=!!E$y*4y}rQMaY)R! z`J5P3Gcsay@_*#`+I<+;u}?Fvt2NARd}`jn$JH(UpV(~V`D5~^7S{k<__C|xuG4kO z=A6&oG1guS2Y?uflf+j?ne{B~%K(ZW-wQ4Kx;%D)6mfSPOFy@zv=raW2?-IlSWN@Y zM7CR>kWvPxP-Hx|iRD{uI;14tgue$W+VBpo1go;bP_`d=<*!Zlwl3rlZR&aflfd21 z7|ah|ZX)F8M`SDf6PzM8I-V;^tIoC&@BP#)`6-=L>)Z;a?2>5KnA{Y?4ewM!ZSz-B zWZ%Ru;D2OxW!Y)C*f@}w(i(!T2xW$mEL(h*cR~Iq2=ut^_&-d2g`5CEeZK-6bW05|RSaF?4r#NOyNLbbg25z4v*(zhKUp_q;pSUTf`Usw9T!4Bs8{ zw{rn>%^m2UQCCpTwWO1pW9pUohOY{CO|amUbcpIW_+}dNeU|jkr2~IqfovG6g=3m9 zbm}xaM=hKNa6|{~&DsJ%KNvvv(J(2N@9w&j(3*a3LW8_2;3x6r^;RQbL^ABAdBAql z4H{i_&jKR8hw~;E;P#dQF3GYm9qT4jE^rX21mGV3HXzvTzAong_zu`~CMl*;AS?6{QX zeNr&E$w~ghPDo9cLwLkomhRXk?-t{~=~~?ImyI)&_`^PLVe*)BFNEuz+XA3MCB3S= zpGl0YHYk<)ZQu!!q?q;sYA#6>K_$en8z>&mC+ZyKn>Za3a06bOIKG8m|L)h7beeGL z8YJOf+TouzQHSqP+g&>7z)Stb40cFhrkT8y!-G%Pp%rPzXq)e$P+W`P)kwFY0SQ2` zA+(`zhq36^zsH-O>cbn~I1R?*&z3LiwtM1p!@6(gmqs*PntzjnAg(I~4X=_9G>b^sSm z%gZ!<{6+g z_@74e?Lr=7V2>$ic!utUUfxb!*Zs;06d3*Z;oWTV0h=$$=fTYic?z0Mr-*bX~OQ4IO-o_l+|I*wn)43LBt}ix@xGbn0rfhpXbn&RM4mmA1vk{nN z^>gq4{+~=pi(kTqK^sM6f;A`RjHZuy;S3y(+HYrUvF*nvWLI)8InY=m55z{p(!{mY5zQethjzWDx5Cd zvYEieY^7A1r7%nJUc?StM6B?X?9c%{3Dxsy^KFb2 zsvVh?nZ6v(FryF6@!VcLQU98oAMc^UX<$6TT6@2EnZ0xp^X=Eexjh8s(L~;x8DYt% z%}4TXNZ`6R7DRQya=p%NUP6ssAGuN3S+0IqbFfFfHz8$f851(STQl>iE0Fz8?9Fi+ z+rDf@`sti<fkL4j}Rr@$ajK(a2SjTRKZsRIG#uP7Z~;l zkCI6-O-$~Tt{aCr5d{UV%=#nI<` zE&L-=-NWYH1)q=4?xP0Kl5s*aikF;|C)Kzm2jVmFaGaK^VY;7LcGYLIJ}A;ZJH>bC z`!oZyYSq-Xr-Rfx9dEO^dP5bibafhBaAWeBWx8=9Re3uAW7nVL%nSL*VqKF0fkW&A zlY5^l{F1WK-?;$->=)JU=FxsRWaywGQ68W96Ej8EJs0*qakkE|8-BG>T*%7BLUF1} zS}meVctqJ0&H?rXFKd}?Bu*q$F#{+jyNRrZLfxf6ukc}~YhrE9B*~&yO9&=A6A0~}wm}nROWN{je-U{n zGwoVT$7gh8Eb6G%#&EsH3Q)jjK{8-#5(U4Ly^s4$U8vTV zUw@I`w+ofdbaskaAy=XBr_q=e_rEJ)r9LkGAkk}e;GMH0u*U4SYv8$R^r@hz z41i8c%KCkK0bRK>WK1jkWavL;QbIr}X(T1yiUK6w{aMg9{YI2vZTjzxxI=X=Qb35! zs8{Q$+=pk!efDHs*ABHl@FgF)ufj7Qk0K3m9rDTO+a+hxl8b5lvMlVv?21T2FKoe9 z_7mbH3`hnPj4Tqd&6q;Vwy6Ag=g^K}$^6XtYBu#ene}o@sNuWm5i5JTvj20np-kY1 z!i?Q0GgpT8>E%Lo;#11C%GT+lM+4buU#;~o)UVO%sF8)jZRtC;v^sz3j+l|}SA;9! z69b6qBQx9%LUt*_ab?&}Ut+oJo3fD65Ux=GNIixuNDIcaap8A{*`0z=8q+N?c5Iyi zGBPc9-W!wf0T_;Ou3WcpUrvA`+(vL;p;fS)G@(U4vq{7gB~Se#{z-k;bYk-@542+j z+7+rMEKHl|I3__lkm*2i`@Whz<|XNDX;6N(zA9SfnPN%=U{M}2#Yy#3h<5Q!_> zoenq;=V9v)5o6Oy>9Px%UzA{!Z-(oAdz^=0CZ48~3H5w325#`z)-sazKdH<*@ ze}{vcAdO4Ae?d2#pA7!FNc8v1Icb~3R(;C`iHJitWXRR-9eMOu@_;OyDnEw6lamCd zYSG9w8vp*Z19v|zjDnuQf33N^_F6N*K(G98 z7|AMok7JVeFAx9gk`r9_Cu?B$t?WDylQ`h5C^t@~Mvznhr+BQ|L#qM(s zv!J-1dl*wl5|Gfahp{4E7od*0KseD0Ujj-E9A52dp__OGZY9DOJTllv(jk^Ti{OIj zI8rZV&kNmntIL?UG7}nC%l`BglTD&xK#h{oYD*_wArBDM4FFk^Kp%wzqtt*4ha)>j z)+#wCHp_LT=VEvv>lv=uY5<1KT@5op=N4676hlR?(I}0^dyx)$$fms83ws~{USrPA z!MFwwfyWxp+&ZZQo4c^~x0x6J>%0i~>)a!bWTD17j0MG=Pb(CTz!VCH<%61XNH@CyBVj zF5=JTBN8ubCUEICZf_syY3$SPPw;YagIjF$nf{qtxPi(K6&r@6jM626we{&S%nE9Y zcVXk6Mp8X7#sPC`9GNOqiKRLpYpl*(FSh&3|1v-6xor|>YyYa&OO3)48gNPrPcb`K z!$I=AQf#34aP0Oob`FyL`hdk*Ct%@XABAxapEU_}X{M4nSf!7i!CO zs!~h1>&_z|ZwmuztN?(qbR^!<@*D2gg`^hE2}N!q!}Vio95996aA$W|F1)8^fPVJP zFq?#Uq9A%4e_P0VMZD2`z(ZX}Qi_=W5>r$eiw8-rI7zGaDnJ!xx}S@V(}*L}6nCB; zSJ^!EB2!M?^03odP8_9UooP;X(d&!nQa;Wf4hir9i}DUQw37DxAV8$Hf-_*oT&$@+ zrkKG2iirnG;OJ???GZ#~LvpB>vF5y!?&kV(lW%$fHXx6OIF)9T-LRje-g3sLB!=@{ ziGtLaP7+ztEjSv1TofLicERdT_s19(0@$p1;_k+SMkT2{Ml!65g+G7O>JbJeM7NDc zmI_t{pVXRZQcE8}-U(f^lyEdAuDT-pY5cY!=@H4a6P@vN{tVDYNv0*fTLPUC+TmNE zR)B@F4>1xX`+6@|x)I-Q<5_*5lCA!+hU3F29&f>Ryoiv72B*4A{-8;zPq5&3p#d0> z(Fk+nYGq1meGV`N1F!I0McsD+I9^N~8dZ758;)0XELGs%6pQ`xoa|!ST-Qo1t4Hj(d@o&G#p?BX}NXtvSHE7PV8-nhAa%idw@4ZF@yiwo*g73mN{pV3q}zgVg5DyMMd6y57_!?--u z9H>B85DI8Lt(sJ5NDPQV+-*at?ddswIhM+Yx0uHN!EhSFj!K{7!Tvj#!WShn?nPXF zFOLImSzxn%Z~5@fgDfoM0N02(CZy}&G&_n`1-7G0_^F)-Sya!3;Gw9$jqPB$%p;Er zp3iRre`azi5E@9N!yB3x;`LQg(C15!O6)B+smsCwV~Bc<65v|UDe8utNe38R1^JPDw z`R>n!Z~jUI%*#p#4K_$?5u;`dt?*%D#1FeBZ?t|B7r#Up8jaiH%gYCXi>q^32uml4 zBx%IVpETg#e4<<=J+pLFW!(MIQe0Dk{z^hDcpKABAVeYC_^E8Ygx7dDF-*U%P>PWM znluleA*Fcor(v_}+8D<{wx42u>-F4wWZ6@%(Zq!Bv9f7@vHbaYWPI+(gKypwQicb7 z1+o@aG+vHUC{&>hv^LygiHVb7;_|(bEYdg!Y3|3s2Zn!n7cj1*aF>lu#pSB>F8uQg zKy1HB^%nO-QxAC;=ga*PV0}vH`l)}&ZB0LkWbx6%zBdtkMCy4}Ai||}f|yfs+e7oqPJ8eo z1wIXk(A#?gFF6qmi2HrWp$`s`w^Pl91_!we)Tl&DLv^fX9+qVx>61)Po`d zvg>%YIabB4-A#sZxpBFF2obGr)O-6>Cie*3%PIh$nmoX#=BIITnM@M$g$}tuow1Iw z+|Ktm;(w{$-^En!^P(c<4up(MjhZ)IdgTzJi3%H0lT*(2*CpMaBfAGor;a)KOzi*}1;pSOTBoC<7 zUHewnYshQh631my{AxsbPepPJH_eF!>q)HTV0N3rvos{^y}ZjBLxACQR2@Q@zt%95 zdMT>isX^IAsK8`#ughw#b-0*g#PTvj7vp^2Xc}?9E#onIrtizJYXBA|zBsWM*ARXz z(0G_S`IKFz{UiQ%@N;FJZ?+0rn*o4_%K(YxKXxWE;BL!f_ z_~g$9W*Az?C4BYY{LN-T-G#=@5g!t8Y{qUqDZ%G`4Is^gBaWnLCeMHWkbM4iMfe9m zBC5Sa>1F;E%I~k-Gw(ga;Wv0Npb0n<5pd*g>Q?msL%n*`G3>YDMj7KK@}mZrn>ROq z`)BKx|Bc;fYH1}4H^^HvzH#zP^m(__K3^Ai1I9l`H+O&8;{0#)0#L@EUqUL%2qCc^ zBdzlp<8yl2SggJi!#KZ8(8WUe-oYr%x$=Uv+@rUA&1X9bg=gDmeM>99GS(f!*2n5A zwNwpLB2`pX$E)-jl|SpYp)wr16T?08fD%*2#CHh4wMG9WRHEYRTQJc1ia9?`43E{sL}cI}woMTwUW(@L{=66ppDM8o zwwviw@M+~R=?zO0MkAlbc=xA#Lj)J*hgX^@EB9D+JmyYo)7ZxHO%E@0(W7M-C}S;y zwbRrdO->PC|JJ>te^T-8Pu41QwU1o7$zqoN`@Mc}qtMgeod8w=8K3hwE~m}7y@=gQcNH0YWG`xiZUun0;*JX_mn6s$YB#VBl0g~cbZ+F6~q`vHLiFoF1- z@fMVuYTgYv#gp#ZnJPv;*>$&S#A)#mnUq-q1QWIK`fJU&=yAS!n++ZnzvEAFvVShU zJz0fVXrsvdaJZJr%pkhjZ3Dj4em$$itOvL-RcLIl-{^nmAZ&D4B__i-3BqE1DpP&P zu-hRO9HVKBt)~_Au2qHJfS`qpKb_de*E-wVjr?j)GF|mKedxz8wSOU(@8BHk7=xU2OZH*^frF_n6;e`i*Bfb_h!dB0e<|OOXIuKU|(M` z4e*>E#t_%ve$^B2Z#!;1_`hI;=a4*&Jt~4JKR?6$&wmg7!%1)6*S*0b%ka>* zFCKXR7pY-R;s4$~sMX!qe+)BjpCbH7TWHs~k@dy6(W!j!v{9nNOz&58BdzJ@u@?k7 zMZ#RPL+e-+uhvO?6L-&?YxUdnWs}4bD(IWGQl5oA^r9#Quury^zz~;f&)()w$t<`m z2Qh~w?EKnu9HBsUX#_dK zEe!#yOxRwOuZlVeDtzXv1+PfarIjA8Em&}dGw<`wV!Oz-Kr+ySWFaG4 zrmC72hayU3!}Wrr=<*EQlhwTe60R1*o<6C6i%`_OXO6X2>tL-E`L2B?>&L_FD?F>` z>$$|}Ms2rrxuvTto$~1=EZS%{k4*a6R{zRn&C>5b{W)nU1=`t7xR$vdbMCfTvwV-j z$=_PoqY+MMrnf8n^#b+wG9wnVEa9&kZp6zA@>_597!qvr1G`E z_8NkwjL^~TH<9UU0tZ$3!_F$ zDByr&ZumKYd!yNTV({O%Wu4jh3;k8xEI5(JX-pptv049MIMDN^%5z?yAEIeXArsTl zQ|KIV+_z%hJ7c@!Um7~$S=u3DPs3U#q<&aT8LT`H8?K3eS8P@jC?p>KN=ogTPY7%XF`+ie9L`X(I89o)mCuILg_cj7_bDk!vIwWq27H_=)l&`CFM^eN zfx8EO^!E~L+r(|p2Fa+a@hkED8hgV9(p?!o-a4}1{c}84JPxRSmSj2rT^-*i-=X|n z6Wh4N#LU+n1}^E%ha?;Sns*VVTZ4zFoH=P7#9nfjk%j#E9|^pVV_3? zy~{_=4{?2D420F^Q)c3i@J>^jf11@IVd}P_t^Rg$O`K3?!gIb%^m&)zw?&w6gTMh3 z09pS%3HFQGfX_Y+VHe5xf;KV8DUzRMs#Swa74>+fp(71U_2nVn11W%R{kB%%c72hS z1Qv9d1h4;!nRaw~$(+av5nA&)Ox~$WVE<_lDM#pYnf{NOEBbU3!EO8!Vq2aI)dp9epjs0&PZy( zDL#hBel``+jN|{}jupGdxzle*jYlyZ0+5jP3s!5=?*)o9 z`U$|gMUQ{(Q_8ie>hQf(`dAvB4Ir%5%rZ)PpSBxoHXaRPERfisUd4U{uT-_xv=d<;gon3H_6>I$e4*? zSxWix_4FH1#Kduu@(1+(n6PwbN(U|+x=`{=-j7c5_jyZ_D;0)KTGllUT&$lx&vnC` z21w+eUCER6gvk`V$&~4efm#2+(RjUFx;D*Hb2r&ayN+Fp-&kIGOqm+ct3iY^|JVvt zR{;zM8=!A`Fr~H*eE^ya!I2tizDVlZjCG6V^6*qdeoTj*=oMNMwiuva!@V8N7{i=r zMowv<2J`oI8be`c7%yn3P-Xo7!<9}W^(v^R=gu{3z9~PHsd{bc5d#Ay5=e0PPNngF zlj%d7HAD21TRyFfk7c<$(Q9=aQwxDBvqs7;UC(mXR1SarZb9xm<3DZ+EhlfO&-0Bg z*nQ-*ve$?#8o!VF|I$B&qiB;Au5-+mfXLRNKGv=uk;m6clF(R2E#dKA=601P_P=G1 zPCy?6;k(Y?VXxR+a9-m?{Ee(VNjxZav}W_-5Jl1XM?Mud*=;w35IoWJla`#37vFYc zW1^h-1pQ)`VMgL>PNhsbUc4d0t;g8C-uvzaA_KL5>wSygW8lJYlE+C%>ExRDrn&+6 z@O~`3$P1sL9Or&oM-Tev3j0#G_jhNSxr{YRXjWMx5Jezi{w zCUxPY0tUe`c~T0wLcRktTBBmA;Rn#nN4h@d0p;6!(g6=w?QwX71o=}Oy_5)DlxiPQ zSKBs&P^%2CfzP|DV5+ zJ^WyEg~7F|%;$`=hp!w$EWOStjav~b`ZrD%`V1eCNroi6trSgzsrIZv7+MUuevEB( zJ6<%+v^sPfB<%~=Lzm2hZGMlnr~)LnGr#dt^Lc6XwrWUUZo%0Q67S8*&Mlgm+MByA zNt8YDB0%Y9x5Q^S@gw{3IiuS={#U!63FL33B)qzYyVbjoEvKI1&V#78ko2Yt9O-{e z(PFRXsBfA6Y-$hJ;D)*eHP9WCzEi|P9>S_utJ%BzQ0|rMxX{Zg5Txd_gV#4|I++J@vIK+74H2eX!PnjyPkoZRC3I7A+(QqiWPh)JdU9 z!%91{;u~{j8*@<=Hur?oET|fzI?#GBejPr(8c_P?!L~-k1iv!9yA5E?l)Lg{rtBX zN?4!BrujFJl=j^2z2Y z%-;YNCf?olPj3J5|MTMl1+vMV`nnDdM`D@rEIP}!RIX;yYb^LT9UzL4mvt&<8~$A2CnzLr&QYdZvl6T|Z$`!ZJ&{-MLFRKO7J+o2G>A~Lw zy#SJ<2q`O;FbCQ6Fi@0FYamJGS=_@oyS}^^QeCvL^y@>8X);VxVVwwG`z>rxpfS^U(lsDx72~d2BOQ^czlYCdV^Qhl!Vr;usSY# z<7i+6+Myuek3eQ0yd>iD-fP%|C}`Y}`|_U-3FU%*NH+$Kd-ahM&mV}l=<+PD4D~)p zWO9}lRyt5%$SJ?_D@Q3WXa+}z{hp$Yi=s0)?@l=l);6+J)~8FGBv(^FaLJ_AJBI4L zVSH#?-#UU#$%%xocRqUHAJ9IR!^#(0s?|^3Uo}dwRW;OT%I+UZzHzSkB6CQf!-|6B zr#L{hbD+%}0$p!12A#c-6|rUKiXm1 zTjde4R)c^8>ddnt#)Z|u^&B#>)dr`zjRq@bPN;8H$=Wwz?&KvK#wsCbJPF}KUHz3l z|E{~*#=OOZE5>i9A2f9+uhbLyq)!N^n}s6sJ|Uft^hAxPwXQ_%Io($RdC8=G*CM5x z#zxM4Tt1-OLCg2{kt*8k=2kG6ECbVG^f9^pEJM)Gy5k$G3Aj<7({W(p3C71`ujVWf zbUrMr#*ysPZC5I~v$g}oHM?DtL zfEp1t=objoV}5v~;2@3S0SuX$*Y;pQKf>p*G z>?gdi(1q!I*e{Py5XYR_{?q0Ay#U<+Mz&7xdrKeTwDRO)GI{`t1+(T+v>d z5T_t1U#qSR$P4Gjn3vfL$?Ym_R)F7gWkdi(wIl%k$%vb)N2$f2{){$h*Sxsc9c&C> zXx=*=;{a8ljXS+J`^?FE?M!Y;EmY|Y=wc({DP)djp82-AaeZ1Dxzuz#2g=T?**Arm1aaoVic4kvG{(JF}8io2aOb=H`XdqD9wlyJfV(~8MZ#15EhGr5W6IbH5vMN;uP{%$JVB$nu zd{#2MU{)z(l=gh`MXoi^yJuR*U&HgXCMTzX>}%qWQo>Kxw)OSnhFXmDGGr3~vqtV4 z!&*ziR;6iLH(UGUwm@a?FuFmR_{z*F1R;C9TgNbCu85l{X(Yd-*9M$q{Y@ER)L7{8yz=wP$c;X?$$;ZCz}$uSP&|P^6~y_bVWsAd zGLwg|4R=FNQ@IB1j{utMV2$su-PHDdqLHn;#Ne3DkJurWeEJcH(5Wa61~}stM3;vP zn9GSoqL4}`75e(c*p|U2>&Y2nz2CVW_O)qPD)Pva(61VYB=*hf;uVzd>qC}u$D<$W z@SGlmTC2TjBwkZ72`InVqUj9nLR}wKUf~UfzpI9nYlq~Hu0#tm$pvU}J6%9M17sXS&Gt}cb`H(YKkVc z+&_LM*K%@LIV~WlXv*tz(2<^24)+18zPrErFvB`hV8p@9a@vk>WxKi_kC<~0Nq%a1 z>RJcP%)<cB?8|J;8ozwrR zv0LYCcdwG=Gesd=z4|oC-Szfpz*h?Kofw3656=D`TaK~1j6<44&a6{EsYIE&S5*x@ z{TnGWv}QX&Gj`(E_BT)Lz_zvHPq;V z_h8UK9p47{>lXy`S!ZU0D|SsJv`}p=?$aw_##;T{R^op2-zbov-S54tJ}L*Wtmu!$ zE;b#4SY=y4ONHVW?^^TsViWdaVI24Uajn>}%Gtxp;KkWft1^#Lzg21b3Y+`C{PI`n zrw<`$pIdQHp^s8`LE>)PqOgd8Y1^xo{B-FI2KFroAdv*n77q5a-z)cFTwJ+gGl#p; zMNm?7_Kb-~%hbJ5TTOM@8Nm^3@JiN>FV=6b>Gv}+Cc_7aL4Z5$% z;M?S#Wmd-Bta!GzGRpDfIZG?Z-UGoe)T&FN)Iv*v<|vAVKI@sk$wM@49!2*{QCR`$ za_>VWX5&rxTO#MP46f{o*-w|)yyo=N3WpK&0mNSjr>Q0rks%CAH@!wz7(BducH@nJ1+tqjJD4JYYIV7NtUtHIpNpSi7 zhXhjt>YwyARTJgOIEe;?-EErd8FNu6K^=3h{Iv#nctW*|pMNww08_zsO~Ehrkc$>k z#WVK{Y+CsAfJd|J+Dq#cTmWLi{+aNSk)6<{vtX%nd4KwE90ofS&`6IExvWRl=hj5{ z+x)V4b#&bfAY~rXcY_|V5w0G%u~noMAj$I|>oU>v8@qYCz~^rOA-AjZJK%HT>c0vB z=4xd1Q(hMAFf8%d5`dy}k1ZsJ`JUtl0XVs#6(q1`_9n@hQrhx%h0M?)$H{(;x zut5F7G#dNxp4pc1{;4Q5d-ObbM9$Y#5LiA^pB!){KD>n+-g@2^*Xw}id^i&akmRJT z?^}ytautMl1g|eR67NFF-({x^oxIOQ5qwT=h++z;mch6s$vQvvOBGc-$<|6I49Opd z2jJhc8}a@pIfy55Qz9_}9gTM)79$#~d18&RSUt_FZ=)Tgr?qG2iRn>( z9edP#f`lr+3%<=?Om@Jtkbd)F;2S+ZI8jUwh-0~_YkCQ#qCz~eZ?dV2(5o2fEYnnL58S*1p)s{3{V3M=y35d#ZPTbj+xuwp&fQG(4(Mltr@&? zE1u+Rr2Uc%0s1mttU*?Q9z+OeC01Pg9J44Qr}3% zG!1`hWXxh8V~$$U1%e`t-`x-QXT9x2oI#5Ahi%HoK+&@UoHf6OR&YVVLc0Fg8;EU# z{Jq(W2kQj7;o5>i8}dkiKJUrvD_$>C(r8so<5F|)mICp6_l zW_yJ|n-bGMs6ZpVm>*MZkH-cXV%7@no}9cmxjT*G9Sj$OW(^Qd#=7}{YnvxAF^YA2q&WW_k#sVk2(L)DNO)nw1pVN0cL_66i3GKY3V)dD(9FfRS*rA@#qkw)dHY3U>oylt4Pv zAqKDjq3<)iF#4m)tHNCwP%D*Pyq5Ua)w=lVb5-sH1Uw4)O=l(p$s5c4ns+HWM{54z zWlbnnCCfix;EHtoY8ny_f&*c}OyC9!{)CdoCuC+U7~4<4ZSbsn-7awoMwp7Z~CoXssp znPoJtjt2j*-J2~PU=e;u!HAT#@qg@pbumSqhI`Kbr-Cl29nQj z#)9QeaF@oH&DUvuUuHipp&O~%?73Q)?bp5iE+!kh$Q&UCYp$nLpDg%0z}V|LaiRD4 z6>vGK*z53;KjO-q!#)SW4gJvebOFE7>F2Nj3^%|xMSH?Ip*jAO9w*)_=%Iik-$!%@ ztStGyURgNy>R+3Yc%e!Gz5Oj9as+W zTM4bp${EC|*Ylh0hGtUO;B(@_mzU~m39xWhf$*=1-$(IOP#y`_c`RAac$frM zbB(HvVAs2k{jqAKr6Xl9Ow3+FWDxU+{*eLIq@Unwxb-w`A)NC2YX1X?tfo)Z&0YYQ z>FLN|Y}R4C*(wKz{xdbrS#|?Z~GYpB3T`uc?zz##4C^1kPW6zA@Gkw~L4-bjMK-M=AUW(wx(1>hy3U^tIq^!MVv!bz`@- zqaKzDHUnt!WtnKzcYuBF#5yF1&100uXf;gI5~Y4pzbx2PVc-mMkIA?r+XvDz<_ zDv$_``M1Q>`ZaD~c|VObzI0&6PA>hCUHPzgL4o2~#gPs0lzTwg#bzu38USSR110Lu zG&jih2|fnf)yVBN?qhXv2L5U}rGl(4<#F8bmyHhZ7{uUV;X$yVt9&5IwFYAcrrrZf zjVjDfLIInL4^cPo`=-OrGMqJqe_eU!)dh(OO^oZgLTjF6#q^SG3nLWuwA@LNJG3lG z&OG{*E^KVbz@-zKGwZy|W}ta|Uy%Ia{@P9ua3a|=qTVmBhJr{7{eh)CwgJ3v6(>pN zzc@aD*`s8P>BV3~;@*(~u>s8%!O`;c3~1UiF+6TXI~!Fbj{OL;wzP_q&hUDiRrjd; z*!k=i;k4kpP&>fQefda#6gl9t$32o)Nk51d)(-uH!8BbH_{2q5wzHU()F$9+_Bpo~ zf6~&eh?2w^zw?8U~t7=5=nzS=_;dj5l)=Ky|byjr`R zUK-X}5tty*rMx;KATJxcm=g<&^+@E>PGlN~eC4nbG-4`!|d6qa zr#CTz8|)a!O|SlBQ(5Tml4A2WOTfysgrCdX!EE1Cs3VFaFkl4Q)8AI{ds$qEjv@Ef zf3nnqz81)$hOOV0Qny`oR?+6#UaFq0VrtvaLJ>lPuVNwSdmB$kiC47Wik%R~R! z2c&*u{)d`uK1wXW+4FLqarIAVg$X3Q_l5iG5QBhLu|dO*?r7Cdvv5(y;PVx}PxPdq zKQa!P?VUs(o?@lDhrlEL%?_k9}D-G+PkbTr6{1N$NLPc19 z`yGFQnc03O5Mrv>TveU@r;II8pgu7Z8OqU%*ix{wnpcB*Xh456;XhX1PTxI%pdAr? zOSW~mk+3p^F7QAVrIdY7Q=e=)8*c=dCAeyYB)G2HwRJAIX^;H%9pxUUUdOnP`F_%o zHhVkvTyc3s=&}4V4=o2j0%Mvs^ueepF^sOUjFL|%yn6Nx)G|YJ8iYjjP!VQ@tbAS4 z34b=RyGm@r9B<&SIe`$({AcD5xS+wHUF|X9kmLv|UPfRr|Jp;L0#{6>Efkr`#+b2V zoe>m-*Kq{>iK}adBL4IHj&0aLjAQj@27-zYSI9m6D7}E?n0Kh_{8T!=Ru%_in+2*P z4>1Y>Kqj zcd^hTMBmS?WhvXPoM&p3H)uivSeBP6Hak}`-U~C)-O=}F3@VoDq}+5l!wK(-WN+L- z)^MM8T483^;;xl5QT!Ya!1aAkYJT?8^y>}4z-CBRjV4~TV?p1l!bKW7p-kay-#LQ` z2V@bTv=+)PqW9$ygh2Z#APMRSW$o*{UjB0UYiPj1$d=#Wxcf>)bB+bBSU&`crWXon zj6y1ihbx0|B59)N(z3g8mUPPr=36(2<_q+3>B;KFH9izm=7L70TWrg@+ZyAhbqTK5 zFLeGx63=6OKp(Kiy;+XtY?`TqNxHb1t=mpitj}N6<$T}yVK^^BHRoyu(0xQ`!X%JbMz3#O3bLk=PSMix5U`11H`3$a$;G>ND7xo--5AcYkE_g1Lwq%mPNLLsFMo*X^$4%3fT_}JYI_x6*;ss71ecVdf zzb6Ei+MX~gPb6D}V)>RQNMIO|kgiN*LUh7OK_I9ICh6~34q5E0(b^dPio*{uj-B*Pwhb00m;Kd#x3W8Gt>)S7|lGlXz~zOTU~H z9}ZxP%e69Rbsaz)1W~QDGjN`s1$e8tzKp2cj2yF+=zCrd8ir;a#m1z%d0F)hv!Fyb zoM=VNrPt&gx6TmCta=p=rLM_D!oNOj5|Fn}mDyA@qXJgJfMI+T6@tDb$y&=zLCMM< z`UgM<6f{Z6PSC|#$5~FXN--pT_xw657@a-71LL^#lvk*-G!OdL3t`%$p-)oZ#LPcCcMG22>K0i7HWe%kooIUK=u?LsjZoe8z(Sfxw z7NpOGES;PpP@)J>hFq>LKcyDom9E8?AA?1|&uqjUq1^LZtQ{7d-SC9P16(ZcK%BR0 zn3;uW*swc0cRj=s7W)#L~;vk*R#-GB#?DWsdFBtI-Ep33<@vl^zlyQcE zU!LE-dE!O|gH-g(uQgyn_Rl9}k0I=z3%LQ4Dl`tjzSxjJ*PQ{NR*No*4Ir;Y?A$&2 z5BE+4`OI$RfsWM;2t}bsv6Yx5h_q~HenxUqmwt@SR81iqw9E`-EBWeY(Q4mx;2-gJ z7rPn5QLLa=i{=pTeE;a)o`lW^v^~JpEAV%6oXqaoWcnSD-UNv~PRq?*cfmQ;X$>lt z>;HIos`{iq%^Lf1E?a`A?{y38r%=XPIbDG~(<8XmoT36f1K|d9MaLve;D@HhCYHeh zI{j3ijFUpgBH!N^)B$_2ZERPa?*jSDMzqOpk->3P?thCkP_aiD@mn1r0RS%WVlZ9^iSHH0lTB3JAB z3QDMS!+gv)DbtpP`jbL9Z3^^RvjW~UL1F+}00iyi! zAs~n#NJw`dx?4g@y5oR!hs2@byM4a(t?&IC-u0ZdSp0HWv-j+o*|V=1uJNEk%YxFH z0jAG@^ZqsC!=dp@O&lq*+X93`;0o@Y*$ydH=hHKN1FS<&q|_mZ({+9&{*Aup!Wdb! z9TThwDiu2~2jXDCIoo8yL_}lRt7IV64JKq9zRH^JWvU z!XNV)^tylw8RS)%7$+v+35@x(@PgD-3;-J{bk>jV@8%%MWBnwzX)Ic0cBu(!n0XZm z#U*m z?5W4cw?P$H{*@qOGwPq4k79ACG@3v^eL~gDBx)Z_L2SiD0Qqj4GAz>$Z>+Dr6C@mp zNBScOyoLam+i7A#L)@lgKBkV_7bS3|HrWEdw-;UK;;o`Djxx~S1fNrt8J`W@(UP+u^wll>8 zizCfoH(6&11PTz**i(;tFY4<)@#3~*bWHweR@GL^E7j7Z_{vY`Plk&L(V41MJ$mOp z(wsl(pK0vj8c_xX$#+`4qjmZ7NPf!!k{F`iQMEPT7!Yfaa!VQz1b|y48m06Kyb;rRdJBB1MbFB&Legy5mk625Ch zW6Fxa0Fu!}Yw?=(HqEOZS4)k=!Fj=rmp8{Aqh>qdHAq)W^)m?4{WY0eUGX-n->suZ z(TXF-n)^mz>;)*%vno)0X&yi`q64m;1Fx}vmyO!(`9-(G`ZkEIy8+Xe053AMAvJu? zNwAH7COf6>Rc-wmI8n&8C-PNCDrwCIUereIEeTIKeixq<0Cvx?f;>WmKhdEd$GS65 zDa5tDx}^~nSJ-%smVzVuSb#5RP_d>O_T>L?lGQl+CGZ8Jg6*#VgOltKC!RD^ zN(2MMRbIEE606k`Bom{jW%%iUJRa&P$U*Px{31GzYQBQd361TByJbXo1aSoze%F{}2z#hPAQk-{Rtl#QbX} z(O(D2#0lFFa8BLjj-ic`%a*29A8$J>r|9eva6qfK$5_bIY;aaB&RDDatp4qj)n|b# z)Vzm;SD0dU~J z=Mh6Y$%05UsAgwb-0?V{^F1n<@PY{TyycI;0DgI)P0Jbs0c+`j#g#|Hn`7Vo1It5w z^G}kXx$KdGX`=2^;P7UH_zgI_F8zD-Z>~o=h2{recn#^zmNWM`A4q-Tpy4PNpZ|5% zqPPPY3Z4OYB^8a#+G~NVqwXyhk_CfLfN&%Uq$}Tir7+nMws&!V3w7n>MgtZ$Yvi3; zPFKCe6Iw6kh|YRX5U;8J8R?YRRTRq!+fQ$e`ibo=hQ`3^gSe}7)IrKNw^DdsvaM~z z(YWf4P(1&!>wF&l-eC2H=yhuw9!WCK6UpoWjUmuoW`mmG+D`y7Pyy&5k7B1)9$EentCRhtizc^v6$hqytJ{N% z7xQP^4~DG)rUn_KG(9vj%I3hHd-YQ~l-=f=bnFnL=>?h5St7D;yUK7d8bIS{5x;kz zU7(K4Lgvu_umDN=Jg^1>9@xhRzZLhIfu~h=wP;9J6gvZN-$Pgn5rNJ>`R=J(2a2MX zI|?oC-J+_u+A3kDnAa&V>mcN9r0YWr+FHbeG~SJ5Ve;dxZOKLfg)go zW1g`u#fl7KN&sahLMskD2<5cPE))I%K~9YWbJ~?OlHY%@2X%Lw8d~ z_jGct>ek{smIFc8;CS8*n9X!~wN4=|Stx1f_ei9E@e&$@%#;2OrzsEZ`MBjI-5bZ> zd78)O-0l+n0E_UgEX;;D1eAvXeGAwE(x9vl>_29*cK9JB`+0b5m#umd8&kRq49F3+Uwhgey_>_|g60_UVI{UUgVH)=4&stMIB(gq9alK6JtKk=!e;c9ZWn4d{dpQPD(;ap4YfVi1hs?8K3C zC8sOxd4{FcbJIm-w|pl~R|6cBYp|*c^kyUo1;laxAS)GIgnw;!)U}>s=?1L>BO*wh z{^;ji-+v^s-YTq+238d|X@*nxltIAWNU!=)WV{v+<@orO-o*eVU~ljdd^ieD`eU zra5ue&+fGYVFai*Iur>q8j;0Y8M~BTYvr2XkP}v@Vnl+`W4(p`;=TUQoObPB#+BM~ zoxPv(W{ly-53;kkcl*lYH+k$c)x$BIzkN;%H}d`xw6A98h~lq1Hy+7@coG?Y&nj;LFl1Nw*g7Nd(l9 z#Bh*Jr{tPAj@>RxeuQMErIRiMPJb&gIWkV>yKHm%8pt)US=Z2*g!fOJ7vq3y1G&Pn zRh}B4Nh{og{`SFK;SxDEE|b+e_wS)`jNHxL#dQ=~^uw0IP;y00%bvHN7|~2S_hZ{s zZMxIfMvLo_a-S984RR$_tN|R-pJMe5c#Cv`EW-)wc*g@1zrMagrlTLSLUDf4)1S!N z87>`tWx*Nn+r;PR-@SVQUrVpIQq~>4pf%kM7Dd}$JjRbQuDAGq%Ecv``u%qRW0!!s z-t@GxCz`yjq%W|4O8E7{g)e4#ZNj3{xZSi+;#34K2PtLJJkjy}#kf~RiE9GZ53!x9 zK+hSwf>h-dj~P4ES6_ka1VeO}INTj3wT;>m%Om7Nq7h2C!qBN6)}ijR9%D5km?#*p zgh)Ek08Zp9R_JF@zB{0>ZZL}`CvGY^md9QKMP!9g7U9-QD_5ap7{GnRUk#h}xXSBd1+TK6rko z+|W@ZKTOl27n9A+EkuHupFyL1hfA0z$EXY!0b^p7+Zv~T?Tit(_eC~1H0$GzNyGLm zWrY3i$A4}NM}@>b5=kCRP^kU-%$M2xb#Fp+)7D{q5!{Wyd%V?4UN9(r2gSUG8UZ*+ zr1k@~?G6LAo(eTnG}Y)5>~k#iMc7fPX>RL(7&FMs6+33v-Q-{*h-G;)bd$zMwap{q zZq|RsQ3*!hjz|d*lan#lOrZaCgOkuk?WRBWp1XGCJKw{v6svK)Nw!B^vkiJ;vz~1Q zrGh$P8hH}aU90$~omLS5;Yk#V?fjpev$G27tJx$*#0{J>ARX3p!Kf6C9_ z<_1F(Dg{`H{s!TG@l1`oX2?qlRdx5#Y{bb=Br03Ph!9FCfdIE;PRkGYQFg+?a7zd} z*esOF=wu*hjsptH`4&1R#y5K&_-B0|?X{m~C`Z67xD7WFt6~3-ZV%Pp*bo16Clj)P zbp<(YDD?5Yl5HnfPu1LmTmSF>+$4n)8{)P+V5l8JVnaJ2?gCj zWzUFj)ubn_Q|x5C@0XQ)Rvs?c&RQ2WqNT+KO9xV#1ow+k_rJDz<-;}M_DVEp*^?n- z2uS|cOs4YlUIOJ=p$M8}G))SBVJ;iO6$(@{?(-jKgry#+sxL-Z{23AceMtU9 z76WDjwGZ8KHg)JXbInoiHN|*lmZV9)yv%JAblRCbW(rb&pFMCf?X@N;9W=!!QeEA4 z)yXd>8?Un2dxpkH?#SRRcoZGX=cmRDAMJ{JWpM)@=~>BW5NNW&kocue$tY}|)XMhX z5iY8FO0)O~9aZ0TybRo6)=V{bWY1jOZd*bJud+1>Y6F2fY6<2!mfS^61zSVY=A&&; zG!+%O!0u;T-Q9UsH)G?*y|E4__99Wj@q;tcP#q)T*Skk0!k0TS?IL%E0eVI?k2+J_ zpweG@7yNW@Tk>?M_EnzV#uwoIr}9J1W*VQ3zqcAIE>`;$*d2K{Kc;j~GoL&^5{%SL z?9f~UwQdX;AnojR6rl?;K2#r&g*-Yf@8lKZHnt){E55eh|BXxwd{})my=?V#C4n*& zjp0+$lHdCkbZp^^aX#_Q4jrvsjH?4l)gEJ0BgH*zQ!Hqg1hoSEC^mUbcb%9)Oy50< zc35xPxrP11o8JiS27l+j?3{2?GLt`jyQ$Yn$lt24SDK7ij1Y(jJ;*5RMvHpSuKQLe z5m~kDY~?Rsg{^~kM@b#N*Da++MJztTYwd{r-uPL|Tn|z|+JvA8VBHK=mHO*G)+XJ%5(^R;;)731vAC`ERt!@Tcy zgb+N+W!vGWvg)(^?QQ#!+4SZ?r#WGquyS7A{PL=;=O_8R?QPc;1gpiRQ-@fehi{{H zS5NcRIc?q^J!j_JTp~Cc$tw<%Ad+T0FQ3=eBXlhv zC84CP3fgbv??D7AG!S(VU$*DPv&LsI-5E`+L?)Kf`$rq-S_6Ve-_dute81o1|Lu@S z`x3Ht{7KkEg7c2cG1LR-8-#kBHJD64n2d?n_zDp)ZJwDPDV_B!i z^1a;Fy>ZKaLkBeiFAOtV(o}eA(A`Vxf}a4*`Q1V<-wN3yT}zeRgcOzmD2 zAB|d`mta$vx(a7!@IJX7AGfX>-_9Fw%Z!MLDoymCz^>jX5`4Uq^riCINpT>LZdTg9 zvgA~|G5($0oGEpjSnI0S!p=`OYW@IY#d_qNgX>0@-imd zg;obHlW8XVvf2I_!$&O;yVp;5ZT`E*nh_ECnf0Q?gPYpd2;!9Cwn##vlCoqrtR>Qm z-2bUjLKh2cJ!D)&8bG6YcF~rD%zRT2X<{otW~aCk^_;}ZX}2J3ad^AJpv8aMfm(~0 zv2A|YY)78D_dC3WyKhtKHPCwfvIHHg^EB|0g8EZv);#{RXt~Ynt8Vm_g|oKZprVIc zo{WsgpG0TdoRg!e0_(T09-k~0($P;Cwk>;C93;|7kCytfA}%cr@@M*}+<}8X=Sdxu zD6uFsRA!%FUpVV0EPJ`^9US{tU*59U6Xkx@IZg-D@w>%!=^H;`j*mAB7)EJlQ`_$0 zG_~)q>CWmsOzykrB+~NcS)?Vk;>g$I3LkmF z7h`5v&s*>2L2X`F*^#KI8Onf*EwkIImv-I39<~Riz0?_XF4L!vyrI;RI=YDoZw;?1 z0$W|~Om#}@9IM_pcPH>6yhCVv#udWzgXjG3`)=TKL?Ep`S0s%bRfj9g%(Pv`@y@+m=-BgTFel5_f)JBU|Osfw3^@lN& zG=XcAwxzIOcA>i>@KkR^$?JJV{V(2&E7PY^BK&?A5o|*X1xIczX#=dw{`ZKjjQtBA zKCj^nkJ*+pD$+We&AyQG%#V|}>F)171BjX9u5!WG-%vsiP)%iH4?IhG`o-=gW+xVg zvl;99tev>eQ}`O+B^o@W-&}uqYk%nBj^^~KMg>Ij9~KG+HOps+f2$@NpIr9DSTqn4 zKS~TZ6cj!cAItY%@FgNd{qyD?Q~WgdCjQ(wBA%alO0SB9s_p&Q`Xgy3&pJou!E~UT z-_PJp^vA!22d54L%A#VV!DGLs$smR*^2do(n#Njl2TkfQ9pB|^>s1jq6MwJW-WqB) z-{axgio;Mhb3*7d;8yrX3Qhzv$Ww-2UE)Rt?<5OZns}E^f6#T}D&Sw5Sx(QkvmM)` zBJ{ldTwJwfeX&g5pk%q!a`~>7LBaKS*+m)FEKFZb!W6*gX!g)ufSMt7*uO);N~L&R zc$h!tcJQJ3^e6C%2nTIWEF!*MJPD#Hz(sSq)CFQJciVBx4Ua1HyQ~d!Jj;fze~wiT znCH2&iu=%cFcOFs^hE`H+A^5rjU1xlkL23wUkZD~F7G;rZf$RdAwhAC>rjf@Y>M6& zUcfF|Dp;RE)H?e;fUmS~R5tYE`?8fhDoSp6?tcAhNF6n4s4wTSGP88iD6J#G$7ixV zF$&k$GwdZ#> zlPB!BtzPFi=uRZq39wjbHW+{BN z%Oe|ybW5o$NNoc-A7WlmkSE@GsS!dQfZGB0d@xBBrXQ*&vQLnV?jhIME8p{4StIw7 z{S|Yq#4*aYayJ{2Afo6?F?wTRe|qjvqN&5{zFAULrK=)(dKmdaNF}^&V4N99j0dORKOEHmy1knjVQ4p!ZPFcyr zD`Ieq*`F`{45}FRnq#w3AMF}Nt13J^j1dtcKrJcsrK;R`iMIDyl8Lj4=4D}l*@ZOj zdtJBDN5o>`Hl#DdDSj&%O~;>LrSxcy?12faaTrT$fiPWzPb43~#l2zF`WCTR?&phU z;nS4pBH*gC%?5c>ut%YlbL^&{o;h5dVzaq*)Tq?R_;vxrCpd&P+O|jN^?Z-s;!w0Y z`<+Iw>`&1$MpwIQ=c;S{StOHu&o(x5ZKzav~Xtf5p{^Oo%Bpf>cqf%d{>RfPtf@B52UuaI44)<`JQXZ zI=xwX?}wXLjZgV}7F&A^B8|U3N@)B{y>cFhDX0>29QB+)BJz*Xk*;g+-}FE3sV<94 zr~RI12~o+_4EJWdXue3*+bTTXp%6QO2REnLMAc+IoRsD|slr@z)A+#|4aHSag>dIT zr$1_rv7;jF=O&Qi@?3v0r0A?XTuolSKQXaSBOD- zVBaN42tnbfOV)NvU#Q(qAyzeGqRH?QFhS-K5XZ{;!e)R49io*BA~g7|;863=Po=d% z!k9LMq)XV8B}sC*0g>Gs8&^iFK+jChs!cL`c^r8x@&J|HVh{0d0y?Ya*7nJf=<7{H zC^hc)--_a#7ut0U*FaX|2=Q&M!#!*kr*YHr$uoCb?JiDP?I=}$$2JQ<9DMx14s9z) zDUmP*=aK$!1zQs`+1ta-wk6@%?ac)7ThhNO?z@bMMB5n}0%Z(Lw$t3yggosvR|{># zsPj&{V!u7{xAm;QOjcTnAxpTG7!(-^X)f*JEX_!|QG9qU9+s&V(Y1{!-X>P3?!S_g z{%6A5He_63_8?uz`^bE|#{}^PR_OXCMY@7R|Hs+s@tJLPj*5SoxP0i^TjsCX=D$el zbk3LF6~JsQT))59mA*oPmPNMF6G&?WBIE$i1Ifq_G~aD?zm^@F9(e@FVqsc^90 zI~|0EEw1@)YK6&scLI6wF9dG){m*#l!%O+Ze7_Wz5C6<}RhD^Nzszq{YY{sC3BIau zHB5$D#C_y?0|K9$x4nr-&lK__r^Jy;TQV@ReTxEguf2TU>@v2p6{DOH5F_s?UnY)t z2nXRAZ<<2aSsO`VQUpFrnQITBuE?k`E8l4YxnkjLHfu2}rPWumg$2}HGXtaAFeE0X zeoMUGTFEK~DnKesNX+Bl)o<%o>QkX4{~H|#*(F(VSldUx4;>~SvuUe#l3iR^vNuZ^ zwD-3ORr6o59gh~svn0y57b$rW#Li@Z!;3A}w+vD-` zz5o2zu*FNK6f>cd$=*?%%+`Y#|8U3S&Z?0P!iX=ZK!Z~L&^%EFRs6vnb9O^+FRgwi zJQsGGZyLHjYfe17>ZIGVtlq4)GHT?z)i>^_0MsL&e&g^{wG9Ei;u&-RZ}q!w^OR#R z!u*#5YNnCFUVq1O77kpT#&xPo{j+8Yr8+`4dMvi_M5&I2nHaF>pYy47rvnVRxDbnB z#b92d?eN>c>%OzU7FN9KA80Uggc!)bRVta~^J!uLZYPHeM4t;4WJDq{S-*J(RlvJ= zAz3A)y&GsQH~T<}aK3}|dWC;+zN^N=bT}k{S!2}YoG9PD6Xkh>{4|S$+cCkDES%1UT`Pi7U1hL$ zfJFmY#qcK@>f+-I_!hG^uJT9j$JJlhQeJ)mL!oz&?gzqDSL>g1`zw0I8g-AszS~K! zIQxfRUw$K<9RIZL=I}7tV4y;dXqycT> zbs(%JFXza zl8}^*qd|^EG7IT}o`KhaE!FS4A{LH(4S;dYsUv;XSw0++gO& zy10zWCpJ7Tc_2_j9t%ej!L-$uOJqmxB*H5_Z(MoekvzMd$UmR>br@E^d=TIEt7q&X ziZSo^m9Pv!z(W5Zvm_HQ_h6-&z5Y`36^3;D&>_XpV5#DQf8$-sDfz5+fps^!=BScU zwyhSy52JSWk5TOF7d7h~0e%);2{XjTEU29Y?lrqRWcQtzV7=Tu4+Th~d1D&{bb>8X zdUvi}EjK^E#t=Mcw3fJF6qiO$+@B6#kR)whq;6eUo6)Udc3?!Yh!NuJnkpSMh@0fP z`8;(UQ7mCoQ7DH+92#lI`4u0;pR0dvVjXR_o=zH9=u>^_!$;Bm7OaeV-IPp8Ul8{_ zt{rn;YgooYj3$4MXFC4Fn$SDF!8eQtUFJbAqHCHSNB80N(4pIT)T?uS9{!IvDNO!r zHmJYEGQTMQ=%vRYnR#34<|8i`YTX{qzjrA^QFmRiGZvi3B)$19XW=SGF}%2XB*9SQ zkbK+u<7g!2WB^~8>eOJ3k9_)hi*Kdbhl!csFWY9=Z8}Etml;Hb?I!ep@%&#lxHW7i zas#N(6@p2-1duUOvWNCAei?6O%89~oE-hcY)<5TT$l0!a9t$(zph(LU^{XDjPg!%> zjc=n$f5J0m3u}?c^;qEbkq_{zfTV^Qx4I_juL~WLjDCYX<-9fdZSNI?a5Zw8t#0&H zOtMwZfHq(R&E?Rbl-=+0^HkeEgN_3}kexk!ZVl)^cxsSig@5R%md)|=2`*AS%#o^xK$5HNdJn!UJwp#YmkZ}L}} zUg&mO(%EHcrDOM5n#_$apI_|ip2mw-=}-|!Rb0F_X96ERV{sh8bI*l~Foi&z=iKa0 zOTJ*$(eq zQs?e`Djf`tX0%djv~w&WcRL{d+6eD8fvat@wex6EH01|w2xX=d1pcV|G!7Puz)%B2 zC0hp*hz~^!3I0-p%4eF+=ys6T$kV&x%#A=(-Ecuc3`LE-Xwk$*q_>R19^F8~a zh*pUoYe0FJ^C#h(_N zE~np0+FX1U`3;R$*%}pdC$?XFLYVN;T5fqM!-Pd-v1EKRs2dmE7D1EdvtfWfV62g& z>1MplyNll$7zw$tsWymIC-ZA6!J9AOxv}44Qf(eafzqZvHXctaU@GOZ#B5Phg z(*8EdDuydGM5aY=SQrsH7r!e)^G9~hJq$c>7Jz_8-x6hVisi!8IMZN3qjIfvDdo4_ z^Nf^L@&pdm(uu|MJvD=fU)GbQ(yaThr~l%pDpwDE5NK-Sh}ZS(3)?pn!1_ww=4!pv z)9v8L3hAlGH36%7Anuz>Kne{`u>hQjdcvL6NNNX+vyKYKytk2zsteWZPIV&2=!eFN zy8QS(@&ruc|5WYXHrZ!y&k~{hEGeHUq9L~rKa?GG#`yMa4nnR0)Fa9GKAvFJTaq@g9jH-Q+QWfdTmM&E)#vJ7W^g$R{^y{F&(S&xt{#jT$Q)e&ThUEvA9@rh{)8-b155<&>p zP=px4^#pzUX8AN6LjnTGNFNYrVOP1(sq0QimT727eo>uara#YULGmb2TK9>k5ojC@Dagyt#br!-UakGk?4jlnI1oO58WqY;6`dHy@|F9 z?=p4ofg$3|0Ye7+ofwoJFS`%Fb3KRO4A@g=fPmTW?Her=#JgY>QW@&H3#cL4P6(|f zxP@X`PiNJjcg7VV7?KHi*%8g?L^3q~#lA#%KH_?2>;H2w4mpm>Yp0-@&N|G~F z;>$zXb|fce?H9IGOggL|G>NkmG+k;$J?>KtA?#}m1A1T1Fc)zyk|rPYBr+AcMWo^bmYGPHcCVdOc+mGAS8NL50qR7?yv!FXA2)-O29!ZQm zO^Rv~7`NC=kApzn@iG&4zQAKt%ZJH~Nt8w2BE`m_-}C3t@x;NpMr!@I>| zK6IMTbjN!fA&6-NX_VrG}`*Funj9F&Ggq)PwONSew|Pehii2Qd2I&ymqi4 z($#!~jWvHs7)HE3e~B8FL?n%@1lzaS+-=TpF(_H6C!%%%MT{j*GYeyxUxxJ;e*xSR z-B!8h6lgTPu~@)Jco8R)%;ia8d1-Arwg!%^h4>hf=de6HynO-qC5CmjL>W-D$GG*&i;E! zFOVk~hoY_}L~d5ob7ZVm{HgI2{`8No#1^F(vvIB!%%3)K?sCHlrp!md4%({Vi#P0M zfCd{gQm1#CppCoyIG%^H0dQ@dg&W31dNF1Ni%yLuMiRv0r^xpNzsSps0(SdDXjQ`C zn~?)RRfj4E6b>!>KJ6g$Tw|q-k66OVy}}CitY=Ec`u@_MU-9`bKnBS32DT*$u@Y&# z?LxHhbgLb~O>98!P+tlY6!O{~Lb%f@-wx}i+VcC5nh97rVk!Lls|yCc9>q9(Jl)`* z{OrU}TrL`LC0;&F|17NfYFNJw&m-7q{y1`2u6T$G7-t5}w41gZBOQ;iQ-WAoPKr>7 z7la6&Y|K2hV>~f;bt}JdpuYYQ&`MsYl@ZPJ=%INnjL$Sam*M$5cNzTLV(Do zuBalr>ngqd;$gWMiK+9dc_t4sG#cAadEt%EYQa;u7h;*>8rDYd#H1mKO<3qpU@GFZ zok>Bda?Z)tvopAjx7p7ewaO(VB!w15GdwYy{}>n%J)?yu8|fI)xh8STZ}3VY^tn;) z_GWOdsElNVdd&<<1lN>d&5UMh^wuAl74|SHOeQoMVMrW#4hJJ;jt))LLx)B;ghooa z`%?KnPFrf~;=4o|uO0A1dPN4VMgZWNf_|U;x3ob$fk45qeQUv%RlqcGO(9qG*?b%{U$jHd#3I6xje^u~b eEBN2#2#?hGOhS#T^L+dO_)(Bmktvrl4gNo*L0!-Q literal 0 HcmV?d00001 diff --git a/docs/source/modularity.rst b/docs/source/modularity.rst index bcd6944f..d76b0618 100644 --- a/docs/source/modularity.rst +++ b/docs/source/modularity.rst @@ -11,56 +11,42 @@ Modularity and Clustering Overview -------- -The HyperNetXWidget_ is an addon for HNX, which extends the built in visualization -capabilities of HNX to a JavaScript based interactive visualization. The tool has two main interfaces, -the hypergraph visualization and the nodes & edges panel. -You may `demo the widget here `_ +The hypergraph_modularity submodule in HNX provided functions to compute **hypergraph modularity** for a +given partition of the vertices in a HNX hypergraph. It also provides two functions to generate such +partitions running either **Kumar's algorithm**, or a simple **Last-Step algorithm**. Finally, a function +is supplied to generate the **two-section graph** for a given hypergraph which can then be used to find +vertex partition via graph-based algorithms. + Installation ------------ -The HypernetxWidget_ is available on `GitHub `_ and may be -installed using pip: +As it is part of HNX, installing + + >>> pip install hypernetx - >>> pip install hnxwidget +also loads this submodule. It can be imported as follows: + + >>> import hypernetx.algorithms.hypergraph_modularity as hmod Using the Tool -------------- -Layout -^^^^^^ -The hypergraph visualization is an Euler diagram that shows nodes as circles and hyper edges as outlines -containing the nodes/circles they contain. The visualization uses a force directed optimization to perform -the layout. This algorithm is not perfect and sometimes gives results that the user might want to improve upon. -The visualization allows the user to drag nodes and position them directly at any time. The algorithm will -re-position any nodes that are not specified by the user. Ctrl (Windows) or Command (Mac) clicking a node -will release a pinned node it to be re-positioned by the algorithm. +Precomputation +^^^^^^^^^^^^^^ +* bullet 1 + * sub +* bullet2 -Selection -^^^^^^^^^ -Nodes and edges can be selected by clicking them. Nodes and edges can be selected independently of each other, -i.e., it is possible to select an edge without selecting the nodes it contains. Multiple nodes and edges can -be selected, by holding down Shift while clicking. Shift clicking an already selected node will de-select it. -Clicking the background will de-select all nodes and edges. Dragging a selected node will drag all selected -nodes, keeping their relative placement. -Selected nodes can be hidden (having their appearance minimized) or removed completely from the visualization. -Hiding a node or edge will not cause a change in the layout, wheras removing a node or edge will. -The selection can also be expanded. Buttons in the toolbar allow for selecting all nodes contained within selected edges, -and selecting all edges containing any selected nodes. -The toolbar also contains buttons to select all nodes (or edges), un-select all nodes (or edges), -or reverse the selected nodes (or edges). An advanced user might: +Modularity +^^^^^^^^^^ -* **Select all nodes not in an edge** by: select an edge, select all nodes in that edge, then reverse the selected nodes to select every node not in that edge. -* **Traverse the graph** by: selecting a start node, then alternating select all edges containing selected nodes and selecting all nodes within selected edges -* **Pin Everything** by: hitting the button to select all nodes, then drag any node slightly to activate the pinning for all nodes. +Two-section graph +^^^^^^^^^^^^^^^^^ -Side Panel -^^^^^^^^^^ -Details on nodes and edges are visible in the side panel. For both nodes and edges, a table shows the node name, degree (or size for edges), its selection state, removed state, and color. These properties can also be controlled directly from this panel. The color of nodes and edges can be set in bulk here as well, for example, coloring by degree. +Clustering Algorithms +^^^^^^^^^^^^^^^^^^^^^ Other Features ^^^^^^^^^^^^^^ -Nodes with identical edge membership can be collapsed into a super node, which can be helpful for larger hypergraphs. Dragging any node in a super node will drag the entire super node. This feature is available as a toggle in the nodes panel. - -The hypergraph can also be visualized as a bipartite graph (similar to a traditional node-link diagram). Toggling this feature will preserve the locations of the nodes between the bipartite and the Euler diagrams. .. _HypernetxWidget: https://github.com/pnnl/hypernetx-widget From 0e6acaf106718225cf9f67dfa0f0689a39da95db Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Fri, 22 Oct 2021 12:33:21 -0400 Subject: [PATCH 29/41] sync with hnx --- docs/source/modularity.rst | 90 ++++++++++++++++--- ...Hypergraph Modularity and Clustering.ipynb | 50 +++++------ 2 files changed, 101 insertions(+), 39 deletions(-) diff --git a/docs/source/modularity.rst b/docs/source/modularity.rst index d76b0618..007d92de 100644 --- a/docs/source/modularity.rst +++ b/docs/source/modularity.rst @@ -12,41 +12,103 @@ Modularity and Clustering Overview -------- The hypergraph_modularity submodule in HNX provided functions to compute **hypergraph modularity** for a -given partition of the vertices in a HNX hypergraph. It also provides two functions to generate such -partitions running either **Kumar's algorithm**, or a simple **Last-Step algorithm**. Finally, a function -is supplied to generate the **two-section graph** for a given hypergraph which can then be used to find +given partition of the vertices in a hypergraph. In general, higher modularity indicates a better +partitioning of the vertices into dense communities. + +The submodule also provides a function to generate the **two-section graph** for a given hypergraph which can then be used to find vertex partition via graph-based algorithms. +Two functions to generate such +partitions running either **Kumar's** algorithm, or a simple **Last-Step** refinement algorithm. Finally, + Installation ------------ -As it is part of HNX, installing - - >>> pip install hypernetx - -also loads this submodule. It can be imported as follows: +As it is part of HNX, no extra installation is required. +The submodule can be imported as follows:: - >>> import hypernetx.algorithms.hypergraph_modularity as hmod + import hypernetx.algorithms.hypergraph_modularity as hmod Using the Tool -------------- + Precomputation ^^^^^^^^^^^^^^ -* bullet 1 - * sub -* bullet2 + +In order to make the computation of hypergraph modularity more efficient, some quantities need to be pre-computed. +Given hypergraph H, calling:: + + HG = hmod.precompute_attributes(H) + +will pre-compute quantities such as node strength (weighted degree), d-weights (total weight for each edge cardinality) and binomial coefficients. Modularity ^^^^^^^^^^ +Given hypergraph HG and a partition A of its vertices, hypergraph modularity is a measure of the quality of this partition. +Random partitions typically yield modularity near zero (it can be negative) while positive modularity is indicative of the presence +of dense communities, or modules. There are several variations for the definition of hypergraph modularity, and the main difference lies in the +weight given to different edges. Modularity is computed via:: + + q = hmod.modularity(HG, A, wdc=linear) + +In a graph, an edge only links 2 nodes, so given partition A, an edge is either within a community (which increase the modularity) +or between communities (so called noise edges). + +With hypergraphs, we consider edges of size *d=2* or more. For some *d*-edge *e*, let *c* be the number of nodes +that belong to the most represented part in edge *e*; if *c > d/2*, we consider this edge to be within the part. +Hyper-parameters *0 <= w(d,c) <= 1* control the weight +given to such edges. Three functions are supplied in this submodule, namely: + +**linear** + *w(d,c) = c/d* for *c > d/2*, else *0*. +**majority** + *w(d,c) = 1* for *c > d/2*, else *0*. +**strict** + *w(d,c) = 1* for *c == d*, else *0*. + +The 'linear' function is used by default. More details in [2]. + Two-section graph ^^^^^^^^^^^^^^^^^ - + +There are several good partitioning algorithms for graphs such as the Louvain algorithm and ECG, a consensus clustering algorithm. +One way to obtain a partition for hypergraph HG is to build its corresponding two-section graph G and run a graph clustering algorithm. +Code is provided to build such graph via:: + + G = hmod.two_section(HG) + +which returns an igraph.Graph object. + + Clustering Algorithms ^^^^^^^^^^^^^^^^^^^^^ +Two clustering (vertex partitioning) algorithms are supplied. The first one is a hybrid method proposed by Kumar et al. (see [1]) +that uses the Louvain algorithm on the two-section graph, but re-weights the edges according to the distibution of vertices +from each part inside each edge. Given hypergraph HG, this is called as:: + + K = hmod.kumar(HG) + +The other supplied algorithm is a simple method to improve hypergraph modularity directely. Given some +initial partition of the vertices (for example via Louvain on the two-section graph), move vertices between parts in order +to improve hypergraph modularity. Given hypergraph HG and initial partition A, this is called as:: + + L = hmod.last_step(HG, A, wdc=linear) + +where the 'wdc' parameter is the same as in the modularity function. + + Other Features ^^^^^^^^^^^^^^ -.. _HypernetxWidget: https://github.com/pnnl/hypernetx-widget +We represent a vertex partition as a list of sets, but another conveninent representation is via a dictionary. +We provide two utility functions to switch representation, namely *A = dict2part(D)* and *D = part2dict(A)*. + +References +^^^^^^^^^^ +[1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. “A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering”. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24 + +[2] Kamiński B., Prałat P. and Théberge F. “Community Detection Algorithm Using Hypergraph Modularity”. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13 + diff --git a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb index cd8983bb..97a45805 100644 --- a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb +++ b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb @@ -108,12 +108,12 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] @@ -133,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -167,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -191,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -200,7 +200,7 @@ "Counter({2: 4, 3: 1, 4: 1})" ] }, - "execution_count": 6, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -212,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -258,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -291,7 +291,7 @@ "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "\n", @@ -330,10 +330,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 36, "metadata": { "image/svg+xml": { "isolated": true @@ -351,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -360,7 +360,7 @@ "[{'A', 'B', 'C'}, {'D', 'E', 'F'}]" ] }, - "execution_count": 9, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -373,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -382,7 +382,7 @@ "[{'A', 'B', 'C'}, {'D', 'E', 'F'}]" ] }, - "execution_count": 10, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -394,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -402,7 +402,7 @@ "output_type": "stream", "text": [ "start from: [{'A'}, {'B'}, {'C'}, {'D'}, {'E'}, {'F'}]\n", - "final partition: [{'B', 'A', 'C'}, {'D', 'F', 'E'}]\n" + "final partition: [{'C', 'A', 'B'}, {'D', 'F', 'E'}]\n" ] } ], @@ -427,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ @@ -443,14 +443,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "qH = 0.28051898403077047\n" + "qH = 0.30826015238966775\n" ] } ], @@ -466,14 +466,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "qH = 0.4037729728695327\n" + "qH = 0.45084198402991216\n" ] } ], @@ -487,14 +487,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "qH = 0.45336073927573545\n" + "qH = 0.4988064689466768\n" ] } ], From 706ae752cc40d90d862e3772e748941fa38b496c Mon Sep 17 00:00:00 2001 From: Dustin Arendt Date: Fri, 22 Oct 2021 10:26:25 -0700 Subject: [PATCH 30/41] HYP-187. Fixing labeling and documentation for collapsed node labels. Updated Tutorial 2 notebook documentation for layout random seed. --- hypernetx/drawing/rubber_band.py | 8 +- hypernetx/drawing/util.py | 18 +++-- .../Tutorial 2 - Visualization Methods.ipynb | 75 +++++++++---------- 3 files changed, 52 insertions(+), 49 deletions(-) diff --git a/hypernetx/drawing/rubber_band.py b/hypernetx/drawing/rubber_band.py index 3473e953..55b32749 100644 --- a/hypernetx/drawing/rubber_band.py +++ b/hypernetx/drawing/rubber_band.py @@ -4,6 +4,7 @@ from hypernetx import Hypergraph from .util import ( get_frozenset_label, + get_collapsed_size, get_set_layering, inflate_kwargs, transpose_inflated_kwargs, @@ -325,7 +326,6 @@ def draw_hyper_labels(H, pos, node_radius={}, ax=None, labels={}, **kwargs): } ) - def draw( H, pos=None, @@ -397,7 +397,7 @@ def draw( with_color: bool set to False to disable color cycling of edges with_node_counts: bool - set to True to label collapsed nodes with number of elements + set to True to replace the label for collapsed nodes with the number of elements with_edge_counts: bool set to True to label collapsed edges with number of elements layout: function @@ -432,9 +432,11 @@ def draw( r0 = get_default_radius(H, pos) a0 = np.pi * r0 ** 2 + + def get_node_radius(v): if node_radius is None: - return np.sqrt(a0 * (len(v) if type(v) == frozenset else 1) / np.pi) + return np.sqrt(a0 * get_collapsed_size(v) / np.pi) elif hasattr(node_radius, "get"): return node_radius.get(v, 1) * r0 return node_radius * r0 diff --git a/hypernetx/drawing/util.py b/hypernetx/drawing/util.py index 7846ace8..67d16968 100644 --- a/hypernetx/drawing/util.py +++ b/hypernetx/drawing/util.py @@ -42,6 +42,15 @@ def transpose_inflated_kwargs(inflated): return [dict(zip(inflated, v)) for v in zip(*inflated.values())] +def get_collapsed_size(v): + try: + if type(v) == str and ':' in v: + return int(v.split(':')[-1]) + except: + pass + + return 1 + def get_frozenset_label(S, count=False, override={}): """ Helper function for rendering the labels of possibly collapsed nodes and edges @@ -60,13 +69,12 @@ def get_frozenset_label(S, count=False, override={}): """ def helper(v): - if type(v) == frozenset: - if count and len(v) > 1: - return f"x {len(v)}" + if type(v) == str: + n = get_collapsed_size(v) + if count and n > 1: + return f"x {n}" elif count: return "" - else: - return ", ".join([str(override.get(s, s)) for s in v]) return str(v) return {v: override.get(v, helper(v)) for v in S} diff --git a/tutorials/Tutorial 2 - Visualization Methods.ipynb b/tutorials/Tutorial 2 - Visualization Methods.ipynb index f1becc77..a09b4577 100644 --- a/tutorials/Tutorial 2 - Visualization Methods.ipynb +++ b/tutorials/Tutorial 2 - Visualization Methods.ipynb @@ -23,6 +23,15 @@ "execution_count": 3, "metadata": {}, "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], "source": [ "import os, json\n", "import numpy as np\n", @@ -48,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -76,12 +85,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] @@ -105,12 +114,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] @@ -140,19 +149,25 @@ "metadata": {}, "source": [ "## Collapsing Vertices\n", - "By passing in a hypergraph with its nodes collapsed (using `H.collapse_nodes()`), we show nodes with identical hyper edge membership to be collapsed into a single dot. The drawing tool automatically detects if nodes and edges have been collapsed, and the dot is labeled with the list of nodes it represents. In this case, `{CN, CC, BR}` and `{CH, JU}` were collapsed. The size of the dot increases to reflect the number of members.\n", - "\n", - "We will use a consistent random state across the next few diagrams to make the layout consistent." + "By passing in a hypergraph with its nodes collapsed (using `H.collapse_nodes()`), we show nodes with identical hyper edge membership to be collapsed into a single dot. The drawing tool automatically detects if nodes and edges have been collapsed, and the dot is labeled with the list of nodes it represents. In this case, `{CN, CC, BR}` and `{CH, JU}` were collapsed. The size of the dot increases to reflect the number of members. We pass `with_node_counts=True` to show the number of nodes in the collapsed node instead of its label." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A note on random seeds\n", + "We will use a consistent random seed across the next few diagrams to make the layout consistent. This is done by passing an arbitrary integer `{'seed': 39}` to the layout algorithm. The default layout algorithm is `nx.spring_layout` which takes a `seed` parameter which determines the inital random positioning of the vertices. Thus, `39` is passed into this function for that parameter to make the initial position (and final position) consistent each time the cell is executed." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] @@ -164,7 +179,7 @@ } ], "source": [ - "kwargs = {'layout_kwargs': {'seed': 39}}\n", + "kwargs = {'layout_kwargs': {'seed': 39}, 'with_node_counts': True}\n", "\n", "hnx.drawing.draw(H.collapse_nodes(), **kwargs)" ] @@ -179,28 +194,6 @@ "The collapsed nodes from above have been replaced with `x3` and `x2`, and the rest of the labels have disappeared." ] }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hnx.drawing.draw(H.collapse_nodes(), with_node_counts=True, **kwargs)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -215,7 +208,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] @@ -245,7 +238,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] @@ -289,7 +282,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] @@ -328,7 +321,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] @@ -367,7 +360,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] @@ -410,7 +403,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] @@ -444,7 +437,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAHBCAYAAADkRYtYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAACST0lEQVR4nOzdeVxU9f7H8deZgWFnEBdcUHEpGW3cKrdEjVbD9oWWW2TL/VHebovdG93bcrotl1vZTtFtpW6W7SW2WbSgZWWljgpWKiruCiL7Nuf3x3cwVGAGHBgGPs/HYx7KnO2LFO/57pphGAghhBCiZSZfF0AIIYTwBxKYQgghhAckMIUQQggPSGAKIYQQHpDAFEIIITwggSmEEEJ4QAJTCCGE8IAEphBCCOEBCUwhhBDCAxKYQgghhAckMIUQQggPSGAKIYQQHpDAFEIIITwggSmEEEJ4QAJTCCGE8IAEphBCCOEBCUwhhBDCAxKYQgghhAckMIUQQggPSGAKIYQQHpDAFEIIITwggSmEEEJ4QAJTCCGE8IAEphBCCOEBCUwhhBDCAwG+LoAQQnRmuq4HA6cCI4AhwFAgCigANgAbgaW6rq/1URFFB9EMw/B1GYQQotPRdX0IkArMBlYDK1ABuQEoAQajwnMYcBrwK/A08L6u6zU+KLJoZxKYQgjRiK7rocATwDnAy8Czuq7/5uaaQNf51wNHAbN1XV/crgUVHU4CUwghXHRdPxp4G1gFpOq6XtaGe5wIvAZkAvfpuu70bimFr0hgCiEEoOv66cArwJ3Af3Vdb+0vx8uBfgD79++PXLVq1aWaptUcf/zxr1gsljovFxdgO/BqO9xXNEMCUwjR7em6fhTwLXCOrutL23ibvwNbGr6oq6szff755zeYzeaKU0455TlvlPMQA4EH2+G+ohkyrUQI0a25+izfBu46grA8TEBAgHPq1KnPVlRUjPjxxx+neeu+wnckMIUQ3d3jqFGwmd6+cXh4eNXYsWMfKywsvGzTpk2x3r6/6FgSmEKIbkvX9TjgPNQAn3bpnxo8eHBh7969F65bt+7M9ri/6DgSmEKI7uzPwKu6rpe250OOOeaYr8rKysYXFxdHtOdzRPuSwBRCdEu6rgcBVwPPeHqNpmlTNU27R9O0xzVNu07TtGh310RERDzRs2fPZ2pqalauWbNmesP7PXv2/LemafMXLVrUq+G9xMTE8zVNm//4448Pa+33I9qfBKYQors6FcjTdX2duxM1TYvUNO0DIBe4C/gralWfAk3TLnB3fXh4+K733ntvX1FR0VSAl156aWBdXZ2l8Tn19fUsX748wWKxlL3yyisySKgTksAUQnRXxwJLPDz3FeCsJt6PAF7XNO2Eli6eMGFC7pdffmmrra2NMQyD5557btrEiRNzG5+Tnp4eX1FR0eOyyy57ZfXq1ZOLi4vNHpZNdBAJTCFEdzUWtT5sizRNmwyc3cIpAcB9Ld1j0qRJv1dXVwcXFhbWbtu2LWrlypWTb7755oPCesGCBdOGDRv202OPPfYdwF133TXeXdlEx5LAFEJ0V2PxIDCBUzw4Z1p1dXWLv08nTJiQ+8UXX9Q+9dRTk6Ojo7cmJCQUNRzbunWrJT8/f+Lpp5/+bWRkZP3IkSO//+ijj6RZtpORwBRCdDu6rvcAeqJ2HnEn0oNzTPv37w9u6YRbbrllyZIlS8I++uijiSeffPJBzbF33XXX8ZqmOXVd/wXgggsuWFpQUDD2xx9/lFG1nYjshymE6I7GAKs8XBg934NzdvXu3buipRNmzpy5p2fPnhX5+flDFi5c+GPjY4sXL55WV1cX3L9//6ca3nM6neYHHnhgynvvvfepB88XHUACUwjRHY3Fs+ZYgDeBh1CbRjfHo7Vi//73v/9UUlJSP2jQoOqysjITwOrVq6MLCwuPuf3229PPOOOMzQ3n3nbbbTOXLFkyDZDA7CQkMIUQ3dFYwKN1Yw3D2K9p2uXAO4CliVO+Be4FbnR3L5vNtru2tja88XtfffVVfM+ePQvuv/9+R+P377///k9OPPHEpJdffjn2yiuvLPSkrKJ9SWAKIbqjsUCGpycbhpGtadpU1GjYRNTvzh2omuX9hmFUN3dtaWnpXxv+HhAQUF5ZWRkDEB4e7jQM41LXoQ8PvW769On7nE7n5Z6WUbQ/CUwhRLei67oFGIFacN1jhmH8CJymaZoZCDMMY39rn22xWMrr6+vDWnud6BwkMIUQ3c1IYIOu65VtudgwjHqg1WEJEBQUJIHpxyQwhRAtKkzLDQBigaGuVz9gO7ARNS1jS2x6Qp3vSthqY/F8wI9XBQUFlTmdzlBfPFscOQlMIUSTCtNyjwJSgRSgAhWOG1FhOQX4EzAECC9My30FeCY2PeFXHxW3Ncbgo8AMCQmRGqYfk8AUQhykMC13NGoaxTjgReD42PSEjS2cH4faJiu3MC13FfC32PSEFR1Q1LYaC3zkiweHh4eXO51OCUw/JSv9CCEOKEzLvRL4AngPGBSbnpDWUlgCxKYnFMSmJ/wDGISas7i4MC336nYvbBvouq6hAnOlL54fHh5e4XQ6Q5xOp+aL54sjIzVMIQSFablBwFNAAjAjNj1hTWvvEZuecBHQo3ZXxcsVq3Y/UPbtttTQ42LeM1nM7dG/uR14tQ3XDQIqdF3f5eXyeMRsNhsmk6mqtLQ01Gq1lvuiDKLtJDCFEKD2duyDan4tbeM9+gFbAvuEbgmf3H9N6Vdbri3J3nBN2OT+j1j6he31XlEBGNjG68bSfv2X2/GgXD169Kiur68fDhS5O9eD54kOJIEpRDdXmJZ7FTAZmBCbnlDmjXuawwKrrWcMearsm8KkstzCe0NG9Xo8ZFRPtxs1d4CxtF9gelTj/frrry/5+uuv39J1/ad2KodoJ9KHKUQ3VpiWOwb4D3CBt8KygaZpREwfuCh4RHRm5Zo9N5Uu3XqSN+/fRmPx0QjZRoqBHj4ug2gDCUwhurdHgH/Epiesba8HhI7pvSp8cn+9dkf5aSWfbLzaWVNvbq9neWAsEpiijSQwheimCtNybcAoIKu9n2UZGLHTelrcXUat01qyaOMdtXsqre39zEPpuh4F9ALWd/SzDyGB6ackMIXovq4Dno9NT6jx9AJN047RNO00TdPsnpwfERHxhNlsfvXnn3+OMIdbqqxJQx81Wy2rx4wf85SmafMXLVrUy263p5rN5lcDAwNfCgoKem7gwIG3v/766/3b/F01rzV7YLYnCUw/JYEpRDfkmkbyJ+BZT87XNO0ETdNWAw7gE2CVpmlrNU2b5u7a8PDwXffdd98UAM2kGe9s+uKHPeXF+wGsuwOOB5g6derC2tra2Rs2bJgTERGx/6abbkpt47fWkrH4vjkWVGBG+boQovUkMIXonuKAvbHpCVvcnahpWgLwJar5tjEb8IWmaTNaun7ChAm5S5YsSWj4+rnnnps29thxiwFCyswzB4T2GaqhaQADBgyoSUpKWlpUVNTWaSMtGUvnCUypYfohCUwhuqehqLVhW6Rpmoba8zGwmVMCgOdd5zVp0qRJv1dXV4e89tpr/SsrK7WVK1dOvvnmm5cA7I6tfjgowBIWHxk3uW5fVVhBQUFQdnb21N69exe0/ltyaywSmOIIyDxMIbqnoaiF1N0Zh9o7siXDgONbOmHChAm5mZmZ0xwOx9ro6OitCQkJRQD1gVRs2F+4+uMfPj9hfv93/uvEWdanb8xvTz/99DMefRceausemO1EAtNPSWAK0T31B7Z5cJ6nTaODWjp4yy23LLnwwgvv2r17d++TTz4595DDxuQpUz5c9OAbW2sK9l8RNCzq67DjYry9dJ0N2NjWPTC9TALTT0lgCtE97cR9zRE8X35tG6rW2qSZM2fusVqtuzds2DD2s88++29T54RP7Le0qkfwtoqVu2+uL6keHHHiwHc0k2Z4+Hx3xuKjBdebsA8JTL8kgSlE97QBON2D85YDm4DBLZxTCHwPTG3pRhkZGc8WFhaGDRo0qLqsrKzJ8RPBR/fYaI4OvrMsd+tNJYs2DI6YMfBpc4TFG7XCsXSO/kuQGqbfkkE/QnRPG2ihRtjAMAwncD3QXE3PAOYYhlHv7l7nnHPOrr/85S9u+00De4WUWJOG3KcFmopLPi34V83m/X3dXeOBsXSewNwHWHVdl9+/fkZ+YEJ0TxuBvoVpuW7DyDCMj4CzOLzPcztwrmEYHzZ3bWlp6V/vvffewwbahIeHOw3DuDQpKWmPw+HI/Prrr99sfNxkMddbTx/yYmC/sE/Klm2/u2LlrtEefVd/CAUuA8Z99dVXPt0D81C6rtcClUCEr8siWkcCU4huKDY9oRK12bNHGz0bhpGNapadAVwOJAKDDcP4oL3KCBBxwoAvQo7p9VjVuuLU0q+2zDIMj7o0zcAZqIFNpw8ZMuRPJpOpUtf1ne1Z1laSZlk/JIEpRPf1NPB/hWm5Ho1lMAyjzjCMrw3D+J9hGF8ahlHbzuUDIGRkz3UR02LvqCuqmlzy0ca/OCtqLW4umYqa6lIIbKqrq0ucMGHCTiCy3QvrOVntxw9JYArRTcWmJ6xAhcqFPi6KW4F9w4qsM4fogLPkkwK9rrgqqplTbcAUoGEFI2Pr1q3hvXr12g2koGqdnYHUMP2QBKYQ3dutwGOFablDfF0Qd0whAbXWM4Y8HdArZEn5Dzv+UpiWe+g6tn2BWai+1gNttxUVFXEmkykPqEY1J3u0cHw7axyYJiAY1afZE+iHav4eTvMrLAkfkGklQnRjsekJ3xam5aYDbxWm5Z4Qm55Q7esytUTTNCKmxX5UvbGksvTLLW8VpuXeAzwTm54QBpwH7AcO2n2lqqpqcJ8+feYDpUAVKlR7A98Ade1QzEDX/S2NXqGoUAwDQhMSEqJ79ep1NmphiCBUwBuAxh9hHwrMB35vhzKKNpDAFEI8BpwAPF2YlntNbHqCtxYLaDdBQ6y/o5pePyBAG++srv/aFGS2oBZkOGD//v2hTqczol+/fg3v16LmlR4P9AGygTIvF88OJKFGwmquVz0qnOuA+qCgoKrKykoLsNd1rCkDUWErOglpkhWim3MF5GzgGOCRwrTcZhdS70xi0xPWm0ICJoeM6jWi7Ntt99buqjisdrxt27ZBFotli9lsbvwhwAA2o5pwLwdivFy0WlTz71ZUH/EWVDPxLqAIKDEMY19VVVUwzYclgBNVKxWdhASmEILY9IRS1Mo/04F/tzE0t6NqRR3x2g7Q/+7JQ6KTR3yMYfxY+vWW+6rWFQ1rXKCioqLBkXUhOyt+2dV/34frjzukvDtR4XkFni0T6Kkaml/oAYCAgIDyurq6MDf3qUM14YpOQppkhRAAxKYnFBem5Z6C2vuyGri7lbd41fulalEsMFMzaYWRiYM2lv+8c1PFyt1/qyuuei18Uv9cAGNLpX1QviWubPu2ffXF1UMq84pmxNw8/hGTxex03aMEFXDno6bZ7PNCuWpxE5iBgYEV9fX1ngRmqBfKI7xEaphCiANi0xP2AicDFxam5f7D1+VpgRU1yGcvKqAIGx/zU9jxfe+tKSw7t+SzgstLvy48uscGzabFBP3S57qx8/v9Y+L9pmDzvn3v/T7VcB6UZ72AZXgnLOGQQUdNsVgs5fX19eFNHauvr2+o3UtgdjISmEKIg8SmJ+wCTgKuLEzLnevr8jTBglqqzwDKGx8IGmLdGnny4Dvry2sH7P9qc5qplmBLhSl4z4urzwSIuXH88+EJA37WTAdanGNQ/ZnfeLF8bgMzODi4vKGG6XQ6D7wAXn755UsqKyvNSGB2OhKYQojDxKYnbEctf3d9YVruDb4uTyMaqgbcF9jT1AkBUUHl9bsrf3MGUrnBVu60nhr3Sc2O8rGlS7cOBbD0D28YFWtFNT1n493pJW4Dc//+/QFVVVURpaWlgSaTiYYXwK5du8bv3r073FWmEC+WSxwhCUwhRJNi0xMKUTXNuYVpuX/2dXlcjgXGoEafNk+DojGmT4OCg9ZXrtz9d5PFjGbWnI3OCEItFPAu3p9WUqNK0LycnJwrCgoKer733ntJCxcunPTNN9/YCgoKogBCQkJ2lJaWhqACU0bJdiISmEKIZsWmJxSgQvPOwrTcK31bGjRgMp4EnIEW6qhOjHSG/IZJy3bW1A+rKSw72XAaGur3Xj/gQw6Zt+klbmuYN9100x39+vWjtrY2eN26daevWLHi9E8++eT8V1555Zz9+/fH79+/Pww15SQYN+ErOo4EphCiRbHpCetRzaD3F6blXurDohjAG6hA6tfSib3/PPrtsrBarc8a0zH1xVURwcOiXtUCTP1KsjfMra+sHQ4sAda1UznrUGHXbNBZLJb6kJCQuosvvvi9W2+9VT/11FNfi46O3lRTUxMWFhZWYDKZGmrDGrI8Xqch00qEEG7FpiesK0zLPRX4vDAttyY2PeFtHxVlN2r6ShJqA+wtNDGFwzAMNg7YHxprsz3Y2zZgK4BRW/9V+fKdqfs+3HBN9bqiBf3vmtye5axC/X5tdkcXk8lUXlpaGhYSElIdHx+/Kz4+/nOA/fv3W4KCghoWNDBQg5zc1lpF+5MaphDCI7HpCWtQixtkFKblnu3DolSg+h6XoRYpP2z5uJ07d/bUNK2214j+Wxve0wLNEeGT+79S/Wvxv50Vdd8UpuWe0Y5lrMRNhcRsNpdXVlaGNwz2aRAZGVnTKDBBlsfrNCQwhRAei01PWImq3T1XmJY704dFqQe+Bt5HTQ05aK/LnTt3Dg4KCipoNH0kBDVF493+d056GjgX9T2ktdNSgBW4CUyTyVReVVXlbvECDQnMTkMCUwjRKrHpCctR8yCzCtNyT/ZxcfKAVwAzjdaELSkpiQsNDd3s+tKMWmj9fVxTUWLTE5YCE1GLH7xemJbr7SXoKl3PbeiDDEGNyo1CLZTQt0ePHvUWi2UQasWipl4DULuvVHm5bKKNJDCFEK0Wm56wDLWc3PzCtNzpPi7ODlS/5k7UOrNaRUXF4MjIyALX8YHAV8D6xhe5ps1MQ83FXFqYlhvnxTLtRtV6+6OC0kBtL7YNyAd+WL9+/YaffvrpV1Tz8huo4H8eeAZ4HJgHPIv3ViASR0gCUwjRJrHpCbnAxai9NKf4uDilwNvAL8Dgmpqawb17996EqqWtAX5o6qLY9IQq4ErgZeC7wrTcGV4qzzLgEeBhIAN4CXgd+ABYDCzdsGHDhrVr11ahgnwLKvCLUdNm3C7gLjqeBKYQos1i0xNyUFtkvV+Ylnu8j4tTC3xeWFj4dVhYWNSAAQMCUGvNforaKqtJsekJRmx6wmPAn4A3CtNy/+KFfk2DFkbIuhQDPY7wOaIDSWAKIY5IbHrCp8DVQHZhWu44HxfHeP755wNWr1692mQybUb1Wx62T2ZTYtMTvkBtSv1n4PnCtNyg9ismIIHpdyQwhRBHLDY9YSFwHfBxYVqu3cfFGVtcXPwDql+wuDUXxqYnbECFphX4qjAtt387lK9BMWoQkPATEphCCK+ITU94F7gJ+LQwLTfeh0UZC6xo68Wx6QllwIXAIuCHwrTcid4p1mGkhulnJDCFEF4Tm57wBpCGWhHoKB8VYyxHEJhwoF/zPlSteWFhWu5sL5TrUBKYfkYCUwjhVbHpCa8AOio0h3Tks3VdtwDxgMMb93M1NU8Hbi9My328MC3Xm+u6SmD6GQlMIYTXxaYnPA88CHxRmJY7qAMfHQ9s0nW9wls3jE1PyAMmAEehmpt7eenWEph+RgJTCNEuYtMTMoAnUaHZnoNnGhvLETbHNiU2PWEfcCbwPfBjYVruGC/cthjooeu6bN/lJyQwhRDtJjY94VHgBVRoxrg73wvG0A6BCRCbnlAfm55wO3A7qrn5oiO5n67rVaj5oSHeKJ9ofxKYQoh2FZuekI6a4vG5F5szmzOWdgrMBq6BTacCDxam5T5QmJZrPoLbSbOsH5HAFEJ0hH8BC4HFhWm50e3xAFfT5ljaOTABYtMTfgGOR83Z/KAwLdfaxlvtQwLTb0hgCiHaXWx6ggH8E8hBDZxpa8C0+BigRtf1ne1w78Mflp6wGzgF2Iiar9mWuadSw/QjEphCiA7hCs1bUQuTf1yYlhvh5UeMpQNql43FpifUxqYn3AD8B/imMC13VitvIYHpRyQwhRAdxhWaNwKrUWvPenMfyrF0cGA2iE1PeBE4G3i2MC33H61YvF2Wx/MjLe4ILoQQ3habnuAsTMtNBV5E9f+dGZueUOmFW48F3jzSm8xLnhUF2IEhwFBgEGrj6Y3ABiBv7oLszYdeF5ue8F1hWu4E1P6WYwvTcmfHpieUu3mc1DD9iNQwhRAdLjY9wYna4WQX8K6XdgYZyxHUMOclzzpuXvKsF1HB+CBqJKwZ1YRcBIwD/gYsn5c8K2de8qwL5yXPOmjln9j0hK2olYHKUU207mrQEph+RDMM2aNUCOEbhWm5AagpJxbggtj0hJq23EfXdSuwFbDqul7fmmvnJc8aB2QCfVx/vjh3QfbuFs63AOcA1wNHA3cAL81dkH3gl6mrSfZlVKXkCldTdFPlvgkYouv6ja0ps/ANqWEKIXwmNj2hDrjU9eV8V4C2xWjA0ZqwnJc8S5uXPOtq4DMgAxg+d0H2f1oKS4C5C7Jr5i7IfnPuguwZwBnALcCL85JnhTac4wrI61C13j+3cDupYfoRqWEKIXzO1ST7Pmpe4p9i0xNaVUvUdf0GYKSu69d5cr6rlvgsao3YC+YuyM5rVYGVy4F+NZWVltVfLT6vuryiny1helZUTL+ihhNqd1f0qvh51/Uh9l4vWPqHbz30Bhs2bBi5Y8eOCVOmTHm5hedsB15tQ/mEl0kNUwjhc7HpCdXAeUBv4IXCtNzW/m4aS+v6Lx8E+gET2xiWuK7fYgkJWT/utFkPBVgCv/j+vbcuqyzdvwvYAmwJ7B36S0DP4OfKlm672Kit39bwfsOrpqamoKioyHTo+4e8+rWxfMLLJDCFEJ2Ca6Ts2aiRqZmtDM2xwEpPTpyXPOsi4CzgkrkLsstaW86maCYTx806d1FwePiGH95/62rD6TxwLOy4vj9ogaa9FSt2H3fodcHBweX19fXenFoj2pEEphCi03BNw0gCjgGe8GQ+o67rgYAND/bAnJc862hUf+UFcxdkFx9hcQ+imUxMPOfCF6srygf98mn2SY2PWQaEL67ZWnbKodeEhoZWOJ3OcG+WQ7QfCUwhRKcSm55QCsxE9S8+7EFoxgObdV13N+cR1MbWD89dkP3zkZWyacHhETUjp5/05M4Nv19UVV52YMpJyJjePxo19f2rN5YMaHx+eHh4udPplBqmn5DAFEJ0OrHpCSXAaUAicL+b0BwJrHF3z3nJs2JQQfysVwrZjNj4UVuDQkM3rPv2m0kN75ks5vqAniE5Vb8Wn9z43JCQkGrAVFVVFXjYjUSnIyv9CCE6pdj0hOLCtNxTgC+BauCeZk4dAqz34JZXA2/PXZC9z5Pna5pmBmYAw1Er/eQYhuFRM+57S77f8vHi+64q2LbjqsDAwOrIyMjdp8045adHz7tjst1uD1m7du0JJpOpzmQy1R199NFGeXn5kCuuuOJXT+4tfEdqmEKITis2PWEPcDJwcWFablozpw1Frc7jztWohQnc0jRtGiqEP3dd8zawVdO02zRNa7GJ+Mwzz0x6fv4b0y44+cSaZZ9/el9ZWVnqPffc8/yPq34aUFNTHWTWTOapU6curK2tnb1hw4Y5kZGRtWlpaVd5Ui7hWxKYQohOLTY9YSdwEnB1YVruzU2cMhS1xmuz5iXP6gNEA277LjVNOxa1mMHgQw6FAOnAP5q7dt26dSEff/zxBVdfffWLp02flhfkrO9tNptJTU3dlJeXlxEUGrIryGwJaTh/wIABNdOmTSves2dPX3flEr4ngSmE6PRi0xO2ofozbyhMy51zyOEhuAlM1DqwvzRevq4FjwEtrW17t6ZpMU0deOmll45yOp2B6enpyy0hIbsrS/f3bnzcZDHvspgCgxu+LigoCPryyy9D+vbtu8uDcgkfk8AUQviF2PSELaia5t8L03KvBdB1PQC1cfQmN5ePA35x9wxN03oCU92cFoia+nKYXbt2RQYFBZWGh4c7g8MjdlWXl/Xp27fvPRaL5fmAgICsj376ojbIbAlesmRJksVied5msz1WWVlp/Otf//rKXdmE70lgCiH8Rmx6wkZUaN5dmJabglqHtVzXdXeLto/Hg8AEmqw5enpe7969S6urqyPKyspMweHhRbU11T127Nhxd01NzTVBQUGlNUZdhVkzW6ZOnbqopqbmmsrKyusefvjhVTabzRvbm4l2JoEphPArsekJv6MGAv17dtWJJwMhuq6HurnMoxomaik6T9axLWjqzZSUlN9MJlNtWlracc76+gBNM9U1Ph6IObLOWVvd+L2ampqeYWFhez14pvAxCUwhhN+JTU/IB041Y3o00DDvRfVjNmle8qxIoD+wzt19DcMoBRa6OW0/kN3UgZEjR1aceuqp77zwwguzX3p9QXxFVVVdTU2N9t///ndwXV1dUIAR0KPaWXtQbbK2tjYmKipK+jD9gASmEMIvxaYnrAZm9nFae9vqBiS3cOoYwDF3QXZdC+c0diPQ3BZfBpDqCtYmffTRR9mXXnrp/97K/mj8uX+5ZVJkZGTmnXfeec255577+jTbxNCa+tqqhnPr6upM9fX10b17997jYdmED8nCBUIIvxWbnvDL/ruWfjDAGX1zYVrut7HpCZ80cZqn/ZcAGIaxWdO0CcAzqNWGGuZd/grMNQyjydplYy+88MLS5QvfjawqL+sz9eIrsgAMp6EVvbkuZdm3391uCg2sAdi9e3e02WzeHxQU5GmYCx+SGqYQwq+VmqqW5Zu3fQS8UpiWe1ITp4zDg/mXjRmGUWAYxkxUU+4JqPVq4z0JywbO+vpAk9l8YDBS9caSgZrZVNIQlgDFxcUxgYGBO1tTNuE7EphCCH+3fr+pIgq4AHi9MC034ZDjng74OYxhGDsMw/jWMIx1hmF4MofzAGd9veWgwPx9X2JAdPA3jc8pKSnpb7FYpP/ST0hgCiH8XQ5w/PPBX2wALgXeKUzLnQwwL3lWMHA0sLqjC+V01ltM5oAagPqymuD6fdUnhIzqmdP4nD179kyLiYn5oaPLJtpGAlMI4dd0XS8F5gPXxqYnfA6kAB8UpuUeh9pX89e5C7KrWrpHe2jcJFuxcvcJptCANYF9w4oajv/+++9D6uvrraNGjVrR0WUTbSOBKYToCp4BrtV1PTA2PeFj4BpgUVz4qLNoY3PskXLW11vMAQE1htPQ6nZUnGKJi1zc+PjGjRtP6dWr1+dms7lVTb3CdyQwhRB+T9f1NahRrOcAxKYnfAjMGdfz5LmxoUdv90WZDKfTYjIH1JZ9U5iEieqQY3od2LOzpKQkrLS09PiRI0d+5YuyibaRwBRCdBVPA9c3fBGbnvD2qqKvt03qc+Y1hWm5Izq6ME6n0xJea+1du6siKWxS/yca7wq2Zs2aaeHh4b9ER0fv7+hyibaTwBRCdBXvA0fpun4iwLzkWQHrS1f0r3XW3Al8XpiWO9zLz9sODGzuFRXdt0e0qe8ZYcfFvG3pFxba8H5FRcXRVVVVZ44aNWplS9c3evmkhiwOp7VypLQQQnRauq6fAmQBx0bkLY8G3p+7IPuowrTcPwP/BE6MTU9wtxXYEStMyw0qqt6+A7QPRz96YUqj8plQwb5R1/Ub27scwrukhimE6DJ0XV8MZAILDM10LK4BP7HpCf8FHgC+K0zLbXJrLm8pTMuNA5ZU1Vc4v9z++rOHHP4b0Nv1p/AzEphCiK7mPqCiqv+Qv9BohZ/Y9IRngfOAzMK03PsK03LN3n5wYVruGcD3wPzcnW9vrTNqyhuO6bo+A7gZuMiD7chEJySBKYToUnRddwJ/qg8NH13ZLy648bHY9ISlwLHAJOCzwrTcgd54ZmFabkhhWu59wLPAebHpCY8CIUClq0z9gNeAK3Rd3+KNZ4qOJ32YQoguZ17yLK0+JKykYnB8DZp2pa7rB60B66pd3oHameRL1AjbnNj0hFb9QnQNJEpFLZawBPi/2PSEXa4yFAKTS23HmYH3gPd1Xb/nSL834TuyW4kQoiuKM1eW70fTLgIW6Lo+BbhL1/U6gNj0hHrgnsK03EeAy4BHAUthWu6zwA/ABmDHoQFamJZrRe29ORIVkuOAl4CJTQwmCikfOmoq8Bjwb+Dx9vlWRUeRGqYQosuZlzzrPGD23AXZZ+q63gfVHGoGLtF1/bDdQQrTcjXUriRXocJwKBAOFACbgZ6u94KAjcB64B3grdj0hMOW3dN1PcCyZ3tlTc++O9G0i3Rd/7Ydvk3RwSQwhRBdzrzkWfcBzrkLsu8C0HXdDNyNCsRLdF3PdXePwrTccFRtchCwBxWUu9012+q6HoNhvG6uKD2xPii0r37ffbJ9VxchgSmE6HLmJc9aBDw3d0H2+43f13V9JqoJ9SdUv+Unuq7Xe+OZuq4fA1wHXILTmRm+7ue5ty7IDvLGvUXnIIEphOhy5iXP2gZMnrsge9Ohx3RdDwGSUeEWg5q3+YKu67tb+xxd1y3Auagl+YYDzwHPReQtrwA2zl2QHdXmb0J0OhKYQoguZV7yrL7AWqDn3AXZ7ppPj0MF53mofTXXoAb8NLy26bru1HVdA3qg+jGHoppqhwOzgDxUbfUDXddrXWXoD/w0d0F2P+9/h8JXJDCFEF3KvORZM4Fb5y7IPsnTa3RdjwaSUCHYEIhDgWhgK2rQj8bBYboR+FLX9bwmyjAMWDx3QfbQI/tuRGci00qEEF3NOBqt8OMJXdeLgFebeD8UiEUN+inWdd3TGsaBRQtE1yGBKYToasahFgo4YrquV6D22WwtCcwuSJbGE0J0NeNxLbruQxKYXZAEphCiy5iXPMuKGvnallqhN0lgdkESmEKIrmQssGrugmyvzK08AhKYXZAEphCiK+kMzbEggdklSWAKIbqSVo+QbScSmF2QBKYQoisZh9QwRTuRwBRCdAnzkmeFoBYeWOPrsiCB2SVJYAohugo7sG7uguxqXxcECcwuSQJTCNFVdJb+S5DA7JIkMIUQXUVnGSELEphdkgSmEKKr6CwDfkACs0uSwBRC+L15ybMCgFHASl+XxUUCswuSwBRCdAXxQOHcBdmlvi6IiwRmFySBKYToCjpT/yVIYHZJEphCiK6gM42QBQnMLkkCUwjRFXSmAT8ggdklSWAKIfzavORZJiQwRQeQwBRC+LshQMncBdl7fF2QRiQwuyAJTCGEv+tstUuQwOySJDCFEP6us42QBQnMLkkCUwjh76SGKTqEBKYQwt91tiklIIHZJUlgCiH81rzkWf2AAKDQ12Vp4FqmzwTU+roswrskMIUQ/mw88MvcBdmGrwvSSAhQ2cnKJLxAAlMI4c+kOVZ0GAlMIYQ/kwE/osNIYAoh/JlMKREdJsDXBRBCiLaYlzyrB9AL+K0DH2sChgEaUA/Uuf488Bo4anTvXRvXV6OCs/Ex4eckMIUQ/mossHLugmxnBz6zJ3Ahf4yAPWxgT8KlKYM3rVoRCfzF9ZbmOq/OdV3Dn6uAH9u7wMJ7JDCFEP7KF82xZqAG2NrcCaV7dkeVFxeVcfhUF811vQmIBAYhgelXpA9TCOGvfDHgx+zuhLraWotmMjU1B7OhllnDH7VM4UckMIUQ/soXU0rcBmZ9Xa1FM5lq3JwmCxv4IQlMIYTfmZc8KxS1rdfaDn6028B01tVZTBKYXZIEphDCH40G8ucuyHYXTN5mRvVFNqu+rs5C002yjZmAaq+VSnQICUwhhD/y1Qo/h9UwVy7+eHBFyb5AgLqaGm1r/tq4wjWrYj968uHpJbt3BjVzHxOqL1P4EQlMIYQ/8tUKPwEcUsPMnf/yX7b9lt8D4KOn5p28Za1jjGYy1W1dt3biwkf+fcn+PbubCk0z0iTrdyQwhRD+yFcr/Jg5ZO5lgMVS1nPAoFKAwjzH9PipM34YOv74tdc+9eKDJbt2jigr2hvcxH2kD9MPyTxMIYRfmZc8KxAYCaz0weMtwEELJdRWV0X98P6bx/boH7vLWVcfrIHZZA6oAdA0zRkcHt5U06uBrP7jdyQwhRD+xgZsmrsgu9wHzw7kkMCM7NUnf+PKn0/c8Mtyc0BQUEldTXWIyWyuqSovM6NpzuCw8Lpm7iWB6WckMIUQ/saXC64fFpgpD2c82/jrb1576c/mgIAaw+nUzrw5bV6oNaq5plcJTD8jfZhCCH/jyz0wD2uSPZTT6bSYzAG1IRGRdQNH2vc1c1rDqj/Cj0hgCiH8jS/3wAx0/dnsXEzDWW8xBwZ4MmVEaph+RppkhRB+Y17yLBNqlxJfBeZu1PZeffljEYOGUbMGQFhUz4jIXjERQGyjY1qjPzVUTVUC089IYAoh/MkwoHjuguwiHz1/uesFKvhMqN+j5obXzx+9f26vgYMXDTpm9LJDjzV6mVDhK/yIBKYQwp/4sv/yUA1TQw6qKZbvKw4s31e8Hdjuk1KJdiN9mEIIf+LL/ktPhQCVvi6E8D4JTCGEP/HllBJPSWB2URKYQgi/MC95lkbnapJtjgRmFyWBKYTwF/1df27zaSnck8DsoiQwhRD+Yjzwy9wF2YbbM31LArOLksAUQviLTt8c62o2DkYCs0uSwBRC+At/GCEbDNTMXZDd4vJ5wj9JYAoh/IWMkBU+JYEphOj05iXP6gn0ANb7uixuSHNsFyaBKYTwB2OBFX7Q1Ck1zC5MAlMI4Q/8oTkWJDC7NAlMIYQ/8IcBPyCB2aVJYAoh/EGnn1LiIoHZhUlgCiE6tXnJs8KAwUCer8viAQnMLkwCUwjR2Y0B1s5dkF3r64J4QAKzC5PAFEJ0dv7SHAsSmF2aBKYQorPzlwE/IIHZpUlgCiE6O3+ZUgISmF2aBKYQotOalzzLAsQDq3xdFg9JYHZhEphCiM5sJLBx7oLsCl8XxEMSmF2YBKYQojPzp+ZYkMDs0iQwhRCdmT8N+AEJzC5NAlMI0Zn505QSkMDs0iQwhRCd0rzkWSbUogUrfFyU1pDA7MIkMIUQndVRwJ65C7KLfV2QVpDA7MIkMIUQnZW/NceCBGaXJoEphOis/G3AD0hgdmkSmEKIzsrfppSABGaXJoEphOh05iXP0pAmWdHJSGAKITqjWKBu7oLs7b4uSCtJYHZhEphCiM7IH5tjQQKzS5PAFEJ0Rv7YHAsSmF1agK8LIIQQTRgHvOqVO+lWM9Af1cy7C9iMXlLrlXsfTgKzC5PAFEJ0RuOBW9p0pW4dD1wO2IAhwGBgL7AV6A30R7duAzYAvwNvAjnoJcaRF1sCsyuTwBRCdCrzkmf1AiKBjR5fpFuDgQuB64EBwPPAE657FKCXVDY6NxAYBAwFjgEeBSzo1meALPSSfW0sdwCqm6u9aq/CxyQwhRCdzTjgl7kLsp1uz9StJuBvwFxUn+e/gUXoJfXNX1NSC6x3vRajWx8DTkCFrY5ufQW4Hb2ktXtwhgCVcxdke6OmKjohCUwhRGfj2Qo/urUX8D8gFJiKXvJrm56mmmKXAEvQrTHAI8B36NYL0Et+a8WdpDm2i5PAFEJ0NuOBj1o8Q7dORPU9LgD+gV5S19RpcWmLAoCBqL7MhkE/G4BNBelJ1Yfft2QnuvVPQCqwFN2ail7yrofllsDs4iQwhRCdzTjgviaP6FYNuA7QgT+jl7x/6ClxaYvCgUuBPwN2YCcqJBsG/QwFBsalLdoEvAi8UJCetPuPZ5QYwDPo1uXAW+jWY9FL/ulBuSUwuzgJTCFEpzEveVYEqiaYf9hB3RoEZAATgcnoJesbH45LW9QLuAu4DPgG+AfwdVM1SVfNcxyqJvlrXNqibOBfBelJfzTB6iU/oluPRdU0f0MvedlN8SUwuzhZuEAI0ZmMAdbMXZB9cBOrbu0HfAlE0XRYTkIN+tGAcQXpSecWpCd91mSzK1CQnlRXkJ70Y0F60tXAMGAtsDQubdH5Bz+3ZC9wPvAQunW0m7JLYHZxUsMUQnQmh6/wo1snAO8A/wXuRy85MHo2Lm2RBvwFuBO4tiA96YPWPrAgPSkJqF+zteT1z9bu+O8bP2y+8bzxsR9ZAkzqOXoJ/Prp52z95Qsq9z1JSFRVU/dJvuc/w7ety+sN/L2ZR23HW4sxCJ+QwBRCdCbjgO8PfKVbU4CHgGvQSz5s4vybgauAyQXpSeubOO6JfsCWUQOsW6LCLI5nv17/14LF605Lm2n774Ezjj5tC5uX9eSbB2dy2gNPNHWT/bt39Sor2lsKbGnmOQPbWD7RSUiTrBCiM1GLruvWAHTro8AdwIymwjIubdFU4DZg1hGE5UEGRIWU33jyUY8Uldce/cp3BdMPOjj15v9RURTPpu8GNHVtfV2tRTOZarxRDtE5SWAKITqFecmzgoARZw7I2wp8glrabgJ6ydpDz41LW9QHeB24qiA9qcCb5egZFlR94bGxj67YvO/Sb9fvGXTgQHBkLdFDc/j985Obus5ZV2cxmc0SmF2YBKYQorMYFaDVbzk6ck8uqh8zCb2kuJlzHwDeKEhPWtQeBTl+SPTWcYN6vJa9avs1Bx2wzcqhZMtUyvcEHXpNfV2dxSQ1zC5NAlMI0SnER+6aPSy8aBBwB3rJ35tb3i4ubVEPGkautqOLJwzMralzRuX+unvIgTf7jCwiNHotjrenHnq+s67OopmkhtmVyaAfIYRvqfVg9fCAuMs3lEW/gl4y380VVwKLCtKTdnn6CE3TwlCDbnYahtFcrRWAwMDAlxr+XldXH/RYQMC9GkYtwBVXXPF8+bZ11bUl7yS/823qF42vO/6s8y9/6T/3f3FYkoouQwJTCOE7ujUSNdWix8ri/otrDfNSD65KBWZ7cntN0wYCjwFnA2bXe18DtxiG0eQG1bW1tQfuHRER+eQpN/wn7Jm02XNiIoMrAU47acbAnmYjFMMJ2iGNdJomNcwuTJpkhRC+oVuPApahlqw7udYw90ctYdcsV3NsP+A7d7fXNC0O+Ak4D1dYukwHvtU0LcF9IQ0jQKvf6ygsOTAlpNYw1RrgpHhTxGHPNJlka68uTAJTCNHxdOvpqB1CHkMvuR69pAa1xqu7PTCHABsK0pM82ULrGdTasU0JAl7RNM3czPEDAqjft3N/VZ/G79U6qWLvb30OO1kzNbkIvOgapElWCNFx1OLpfwNuAs5HL1kCMC95VijQA9jm5g6ehCqapvUFTnNzWhyqttmiQJz7iitqDgrHD77fGBZqn3VnnZODAlIzaVLD7MIkMIUQHUO3hgLPA0cDE9FLGq+IEwts9WDT6IHAZg+eNgy1rqw7w92dYNHqikur6g4KzJPHD9v28SPXfcPUmxc2vKdp2nwZJdu1SZOsEKL96dZBqCbYeiDhkLAEKAKiPbhTCWD14Ly9HpbM7XkW6vdX1NT3bPye2UQggWFlh55rDgra5+FzhR+SwBRCtC/dOg21PuxrwBXoJU3t6LEXMM9LntXDzd02A4PcnAOwDvjdzTmVQI67GwVRW1JVV9+r8XsWkxFERMxh01rMoWGeBrXwQxKYQoj2oVs1dOt1wFtACnrJPNfmzIeZuyDbQI2QHdLU8Ua24MEi5oZhGMBcoKXBQf9yNycTIFirK62pc0bXOZ0HmngDTUYwPYbsbPi6qqzUAhAQErbf3f2E/5LAFEJ4n261AM+itt46Ab3kMw+u2oAa1NOSLcBA17ZeLTIM40PgcqD0kEO1qI2m/+PuHqWlpX994N57Vpg1rWpLUWUkQM7HH36w4OYZZnqPKGo4b0/h5t6fPPPY9vMvuGCHu3sK/yWBKYTwLt3aF7XZc29gEnqJu6bRBhtxU8MsSE+qAMpofrrIQQzDeA01oOhPqJ1PrgUGGYZxr6sW6pGgQNPeLUUVqo91+4p+mIP2EhB0YIBS6Z7dvQODgjxeeUj4JxklK4TwHt16HPAu8CLwr8abPXtgAzDag/Ma+jE9CijDMPaj+k/bLDjQvHfn/qpewEY2fj0N64AfGx8v31ccExgcIoHZxUkNUwjhHbr1cuBj4Eb0Er2VYQmqVjprXvKsQDfnedSP6U2hloA9+ypqe1JVEkjx5mkcffrnjY/v27l9QlTffodtQya6FglMIcSRUZs9zwPuBk5EL3mvLbeZuyB7LfAbcI6bUz0dKes1EUEBe0ur63rieHsSwdb1DBh/oDa5Ze3q2Nqq6n7xJ0xf3pFlEh1PAlMI0Xa6NRr4CLCjNntefYR3fBq43s05HV7DjAoN3FteXdeTHY5TiT12ceNjBSt/PqVHv35fBFqCmtyOTHQdEphCiLbRrccAPwAO4Az0kiI3V3jifSB+XvKskS2c0+GB2Ss8aK+tZlUczrpQRp27ouH98pJ9waV7d085asIUt/M5hf+TwBRCtJ5uPRfV53gPeslc9BKvLDo+d0F2DfAccF0Lp3V4k2y8scF8ujM3BvsFT2C2HBhdm7/066khEZFresYOcjufU/g/GSUrhPCc2uz5LuAqYCZ6SXv02/0XWDUveda9cxdkNzXy1Ns1zO0t3q+61DJg9zd//jh8pnHyoGlYXOfW1dSYayrKZx01ccoHHpZnu3eKK3xFa8VUJCFEd6ZbI4BXUHMgL0AvabdJ+vOSZ90HTAJOm7sg+6C+wbi0RQFABRBWkJ7UvruDqN1VXgVq46peOwm0aQXpSQWuMj6JmuN5nmulItHFSZOsEMI93ToctWnzLiCxPcPS5W7UbiN3H3qgID2pDtgJ9G/XEqjdVV4CbMAc0A7UbOclz7oYmAnMlrDsPiQwhRAt062nAkuBp9BL/s+12XO7ctUqLwWumpc8a2YTp7RvP6ZuPRpYhuq2moZeUtHwzHnJs2zAk8AFcxdk72u3MohORwJTCNE0tXj6XOBl4EL0ksyOfPzcBdk7gUuAl+clzxp8yOH2GymrW89HfUB4GrgcvaS84ZmhdRXDgHeAtLkLsle0y/NFpyWBKYQ4nG4NQfVXXopaD/YbXxRj7oLsXOBB4N15ybNiGh3yfg1Tt4ajW58BHkZNk8lsvLtKSH3F9qRdH18JLJu7IPsFrz5b+AUJTCHEwXTrQCAXMKM2e97s4xI9AiwCfpqXPCvB9Z53a5i6dQqwAggBxqKXHLRW7LzkWUdfXvj6jdWmIAswx2vPFX5FRskKIf6gW6cCbwKPAg83t3+lL7j6Ml8CHn4q7v9+MzTTNQXpSWce0U3VNmQ6aprMdU0t6zcvedYFwDPbg2KefbvfuWcW/GfWmCN6pvBbMg9TCKHo1j8D96I2e/7E18U51NwF2R/PS541EXjrqs1ZZa/FXuLRFl/N0q121JSRzcAY9JKdjQ/PS55lQe2ZeQ4w8+3+5xXgftk+0YVJDVOI7k7Vsh4HpgNno5f85uMStWhe8qygGi0wo9oUNDuivuwWIKtVo1V1qxm4Bfg7cBvwUuOa9LzkWcHAha5ztgJXzF2QXeTatLoc6FOQnlTmtW9I+A0JTCG6M90aA7wNFAN/Qi/Z7+MSeSQubZHWr2p75bnbP/zQjPMU1Pfw9NwF2b+0eKFuHQJkAQZwJXrJxoZD85JnDQX+D5gN/Aw8AyycuyD7wDZlcWmL1gHnFKQn5Xn7exKdnwSmEN2Vbj0WtdlzFtCW/St9Ki5t0a/A2TdsfKYIuBpIRdUIs4B81IbUW+cuyK53rdhzFZAOpG8ptz725ubRvYGhwFHARcBE1BSazLkLsn9v5pmfAw8WpCd91q7fnOiUpA9TiO5It16KaoZNRS95x9fFaaPNwMC5C7LzgAfmJc96EEgCzgMuQ4Vhz0eSk7bFBI8NjwysMm8qj3JUOwOvQvXVlgEbXa+3UQsRVHrwzA5d+F10HhKYQnQnqv8uHTgftcSdw8clOhIHTS2ZuyC7DvjA9QKg7J+9L65yBjy5qSzq89zdcd/UG+ZtuEJy7oLs0iN9puheJDCF6C50aw/gdSAQOB69ZK+PS3Skmq/t6VYr8GR4IJPDqTmr17+3f3es95451Tu3Ev5GFi4QojvQrSNRmz3nAad1gbCE5mp7uvUkYBWqyXUsesl37f5M0S1IDVOIrk63ng08D9yKXpLl6+J40Wbg4gNfqeX8/g1cAFyNXvJpOz1T+jC7KalhCtFV6VYTuvUu4CkgqYuFJTSu7enW41BTQfoCo9spLA880zUnU3QzEphCdEW6NRx4C7Vn4wT0kh98XKL2sCWAuoHG3da7UWvN3oNecjF6SVF7PbAgPakctXl1r/Z6hui8pElWiK5Gtw5FjRT9HrgUvaTaxyVqFwXBl8Y6nEMsNQRMC6JuPHrJ1g56dEPNdncHPU90ElLDFKIr0a0nA98BzwLXdsmwVE3NNwC52fWTto+qfvFvHRiWIP2Y3ZbUMIXoCtRKNjeh1kdNRi/5yqflaS9q67GXgDBgyrP1Zz6Cqu393IGlkJGy3ZTUMIXwd7o1GLWkWwowuUuGpW7V0K1/An4CvkTt0/kbvqntbfHBM0UnIDVMIfyZbh0AvIdaveYE9JJyH5fI+3RrL9RC6DbUHNLGC6z7ora3GRjbwc8UnYDUMIXwV7p1CmoxgveAi7toWCahFiHYBBx3SFiCb2p7UsPspqSGKYQ/0q3XAA8As9FLFvm6OF6nWyOAecApwCXoJV83c+ZmfFPDlD7MbkhqmEL4E90aiG59CrgV1Y/XFcNyKrACMANjWghL8E1tbxsQE5e2SCoc3YwEphD+Qrf2BhYDccBE9JJ1vi2Ql+nWIHTrf1ALLtyCXnK1Bxtab6WDw6sgPakW2AX076hnis5BAlMIf6BbxwI/AkuBs9FLSnxboMMc2e8S3ToG9f0djapVfuDmCuBAeO0G+qFqpBFADGovzGOABOAs1HZmIUdUxoPJ1JJuSJoUhOjsdGsyaj3YOeglb/q6OE0Ygtq4+XtU6HlO7c/5N2Auqpn5FfQSo4kzNVTghTZ6WYHoP00aVH7m6P43omp9zkOuqQWqXedGAe42iPZUw3SWpV66n/ADEpjCn5hQtYiW/mzq7xXADh+U98ioMLkPtSPHKeglK3xboMMEAdOA44BiIBE1mnWXR1fr1mFAFlCDGgG7qYmzRqL2n4xEBWBDmGpAPVAdHWYp2VJUETJxaM8tLTwtHLXYgbdIDbMbksAU/qIvcBkqABt+aTZVEzmUBtQBj7ZTudqHbo0C5qNqVRPQSzrbuqUDgFmoJtBNqJ+F5nrvVVTNrmlqVaJrgftdryfQS5zNnN0TFZbbaPbnre3cU1YT5aa8Bt4NzM2o5mPRjUhgCn8RgvqF3FItojkDUbXN5n4pe8vlqL60I7P7197MuD2F8JhfGXdZNmbL7CMvmteYUE2RcajmzSpgnOvYPlR4TgJym7xat/ZD7c3ZF5iOXrLWzfOKObhmeZiIoIC9+yprY9zcpxYVvt6yBTjJi/cTfkACU/iL+iO8viMCsx9tC/Q/rFwwng05/8eA4+Zz3OyWplP4QiQqHCNQKwsd+u8ZDXwLnOA6XnjQUd16Iaov9lngXvSS5muhf6jATUtCjzDLnm0llaPc3Kca6OHB8zwlC7B3QxKYwl8cSdgZqKbcOi+VxfsMJyx59Bz2/HYKo859mKNP/83XRWpEQw3sGYkKnpb2m3S6js9CrW9bhW7tATwJHA+chV7yfSueXeF6frP6RATt/am2vmdVbb22aW9FaFF5jSUiOKDumAHW0kanVaMG/XiL9GF2QxKYwl+4rWEWbS8PDQwy10dEBze1pVXnnUJVvieI3Hmp1Fb24oSb7qBPfLGvi9RIGDAGtWFyEZ59cClFzVGchm51Ai+i9ucch15S0crnu13ur39UcNGWooo+V2ctP6esqja8zmlYyqvrIvaW18SePab/W3fMGvlDcKC5GtUM3GLzbivsBsLi0haFFqQntfZ7En5KAlP4ixZ/Ua/J3Tr4mzd+vaNXbHjuhbcf/0p9nVMzB5ga/2LsnIG5fWVvfnxhLsHWAk75178IjvSkmbKjDARGo2rmew47Wl+rYQ40cNaB6ZBfJZXFe8lbqBM9PI6i31PQSxa3sQxua5jv/Lx18NrtpSGDokMjRsdG5fePCi6JDguqtJi1+vs/yvvz0N7he66aOmQ96vddEKrf9YgUpCcZcWmLClH/Rl1rAQnRLAlM4S+aDcy928rC8pftmGLtHbLCMJoMxoYm2c5l7QejWPfxX4gZ9QGT53yC1qkyvReqCXU3TdXuv8s4gerSUGakLcYUoJqUG8r/2+JhrP3geiL6b+LqTx8nrNe3R1COelQtM5BmRt6+9/PWM+P7Ruy79dQRi8cP7rG98bHHv/iteOOe8p7AetR/B6F4ITBdGpblk8DsJiQwhb9otkl244o9gypLa/oMG9fny40rd08F0Ezaoc1uXk+jjNQcDVVjCQKCrnhgirVif01NfZ0zoL7WGQBokb1C9kb2DC7VTI0qSYYTvnv6NHauPocRM59i5NlrvF02LyhG1SqDadwsajhh2TNT2L8tBsOAX/43lnF/WoFhgFFvInfeBRRtOInBU7IYf8Uy1ECok4CFtL0pdB9qlHSTgdnXGrx5T1l1zKJV24/ZsKfMXF3rNG8vqYpcnLdzSm29YZkwJLphIFbD1JKW+mBbQxZh72YkMIVfqKmsc5oCtICairqQ2hpnQF1tfWB9jTNgz7ay6Pzvtp895uSBH2//rWSYYRC17vsd4+tr6wMMgwBnvREYEmHpteyD9YEluyrraRRwbl4WD85pqPVUA9Vrl2wNKCuuqdZM1GmaVgtQX+vsaRhGQECgaVdAkHmXJUjb26v2p6H9zDtCQydddxf9xnS2+ZUN6lELoE9DTR9RNXzNBCPPXolhrKSyOISfs06h/7jNBEcZbF95KuV7NpNwy+30Onqf6z7bgVHAb0BeG8tShFrqrsn+zOtmDPv0oU/XjVmct2P6L1uKe9fUO4MABkWHbrpu+rDvxg/u0bCMoIaqYXqLbPPVzUhgCo+5alQ9UHPwwvFu+LR4bWCQ2TJq+oD6in3VtZqm1aJRp2laXcmuigBTgGbes7n0grLiqrDa6vqw7ev3nRIQYK7RTNRqmlZnYFgMp1GMqqlUo1aWKXX93dNXTVPvzclMbNxU/HeamFZStq86tHh7ee/KXTuGmrf/eG6Zs7fz2+pLQoK+CUyJGbpj8fDxMatM5sNqxJ3BfmAtak3WP/owrbEquKIGltN39Dp+fuUMeo8IYuBEBzPTH2uiaXk7cDpq8YG2rIFbhNo8ukmnjeq7a92O0u+LK2osIYHm1ZEhgVUDe4TsP35I9N4+EcGNa6VO1BJ53rIZNedUdBMSmKJFGak5xwH/h1r+bCiqWauA5gOnqWDxJJyauu7A66J/HB8QFRN6HY3m9jmdBu/8Z3lK6d7qY8uKd4UZ9UaQ02mY9haWbTn/78fNb/RtDBg9Y+CrwE4v//N4JDwqqCJ811eBFL1/PgOGf87Uy9+vqqi3/Pbjzslb1+27aMva4tk9+oZ+PvzYPl9H9gopdX/HDrURNbo0AvVz/EP5nnDMlkEYRgh11XnEjPqdHavC6TOqDNNBXcbVqBrracDbtH6K0H7c9EFHBJn3VtSYhv/99PhVLZzm7bmYW4ALvXg/0clJYIrDZKTmBKCWoZsD9AEyXa8NczITfTXl4bCdJkwmjQtvPz4LtR4pP2ZvHLnu+x2nnv/34+Y7nQYm00GDK3036OeH56azdfklDE38L2OSfwYIDjPV2GfEfg18vXnN3qGF64pP+X7hxkdiBke8bp8Rm6OZWhwY2pEMYCUwA/VvWI9hwNafRrBj1VSih/1C3Amf8+1TZ/Dji9H88OxAzs54jdjjDt2WazfqA9cY4JdWlsHttI2eEcF7Nu6tmHjI2xp/tG5YULXLyFY+uyWyeEE3I4EpDpKRmhMDvI7qn/sX8PGczMQjXWXHG1osg9OpWjTNgaZK4NCwhA6aVmLU1Wk1Gzf2qNm8uXd9cVF0UK1jQqC5aIh54ux/aYMnbWvqmkGjem4YNKrnszs2lHyQt3Tbzd++t37EsacNfjE4PLCp+aS+UI4KzfFUlZSx/osTqamwMvyU94kauJeSwjBqK8Koq3JyevqCJsKywTbgZFTQ7G3l85sSiCsMR8REmDfsLotBrXHb8MN3opqA96IGMe11lcFbtgAD49IWaQXpSZ2xSV14mWYY8nMWSkZqTgIqLF8E7ukkQdkgALiZQ5db88wA4E3UL2qvyou3BQEXABf3+stfJtYUFERpAeZyLThob2BgWe/6GgLqqwLqDKczWAsK2m0KDdseHD9iSWRS0nJTUNBhTZNV5bWWnz/ZdHV1RW2c7YT+j/Udat1++FN9ZNuKZIrWn0JQZD5DZyzDHOjEcMK3T04jpEcp46/YBGS3cIc+qA8ur+HpjiZKKPAX/mjKbQjEMlS/dPFvO0urr3vt50WL/jo1PijAXIEK2Sq8s0hBs+LSFhUDwwvSk1rzAUD4KQlMAUBGas7ZwH+BK+dkJn7cxtt4Z/Hx5k1H/YLcByxpxXUDgHdQ/XFekRdvi0P17V4FrAJeGLoo+7iAXr0c5qKVfVn5xlyiBn5HwtwFmC1GXVFRUPXvv/epWb8hrmrdukSjqqq3JW5wTsQpp+QEDRu2r/G9DaeB4+vCE3cVlF7c/+iol0ae0H+Zt8rdJmU7Q/ju6cupKRvJCTf+TNSgTTSe4vHHwgXRNB2Ygaifwe/AZ7Rt4M8Q158VjV4HfaCLS1u0DxhakJ7krWkjbsWlLVoFXFGQnrSio54pfEeaZEXDwJ7ngZlzMhOXH8Gtjnzx8ZYVu17RrbxOw0tNsnnxth7A08CpwCvANFt+XsPE9UH8/Oo4Ni25ikGTszj2ygMT9gOio6sDJkzYEjZhwhYgt/z77weVL/325D3P/vehwJg+P0RfddUrAT16VANoJo3RJw78ckte0cZff9gxF8BnoZmfHU/+R9cR3sfB9LQ0wntbgYk0HjV76Co/B+uJmsv5MeqDRVvXBPbkw05Dn2KHBWajZ67owGcKH5HA7OYyUnMGodb5vPYIw7IjOHGzTFoLjnjQT168bTzwFqoWNciWn/dH35puNXH206ex+bvRjLnkAYbOaGoz5APCJk7cHDZx4ot1u3e/vvfll1N2z3vkXuv55z8aOm7sgSbYgbboAmDeumU7bo/uF7apQ5tnq/YH8t2TF7FvywkMO/E5Ric3DNSpQm3hFYv68NIcM6pWuQ3VzN8RTZYNC6Kv6IBnHfpM0Q1IYHZjGak5kcAi4OE5mYnv+7g4nqhH1RRNqGY+U6OX1uhP7ZBj4RxBDTMv3qYB1wAPAHNs+XlvHnSCbrUC/6N81xCm3XoH0UObG/RymIDevSv7zJ2bWTx//on73nhdn33jX9e+mZt7YLSnYRiXFu8of2PZp3m3XHDD1N719fUWgKOOOuqHX3/99bG2fk8rVqwIP/bYY592Op0BACNHjsxds2bNMwBs+Howq9++Hkv4dmbcfhs9Bh861WUtqj8yCDVV41BW1+srYDlHvjWbp3wxalVGynYjEpjdVEZqTiCqtvQN8JhvS+OxItQapyGogSB1rldtoz/rG73f+L02jY50heXTQAKQYMvPyz/oBN06AlVD/4LJc5Zitngclg00k4noP/3py/Jvv90Yk5v7z0OP26fHfllWVB0/cew0y7c/fdkbYP369eNXrFgRPnbs2LK2fF/33HPPCQ1hCXDxxRd/RV21ie8yzmTPupkMnPg/jr1ySTPr29YCP6P2vWwcmBp/1DyzgB1tKdsR8EVtbwtg7+BnCh+RwOyGXCv2ZKAC5cY5mYn+MvKrocl4IGpeaEf4B3AsMMmWn3dwOOnWM1B7Pv4TveQ51Eo/bRY2ZUrBuqCgHODMxu9rJo3xpw9+4axlFz/47U9fAuB0OgN0XZ/6/vvvf9KWZ+Xm5s5o+HtERMTOf6ScXsQnaXejmauZ8td/EDPKXT/gXtRyd8NQH2SCUPtN/gB8h1qIoqNtBs7o4GfK8njdSKfaHkF0mFuBCcDFczITPdpUWdO0IE3T/qFpWr6madWaphVqmpahaVpfd9dGREQ8ERAQkBUYGPhSUFDQc0OGDPn74sWLowHsdnuq2Wx+teHYwIEDb3/99df7H9m35x158bbzgVTgnIPCUrdq6NY04DngXFdYekWtpjUZNMFhgTV/+r8L0vv2GnDgw01ubu70tjwjMzNz8N69ewc3fH1x4uhC80/P/4voYd8yM/3fHoRlg19R0zd6oz58rwC+xjdhCb6pYcoC7N2IBGY3k5GacwHwV2DWnMxEj5Zh0zQtHMgF7gdGoFZNGQBcD/yiaVq8u3vcfvvtD9XW1s7esmXL9RERESWpqalXNhybOnXqwtra2tkFBQXXR0VFFd9yyy1/bv135l2uAT6ZwNm2/Lw/mnN1ayhqEMv5wET0kqXtWY664uKghr8PGB69Y/qkUw8MJioqKhqcmZk5uOkrm/fCCy/MaPi72aTxzzOP6sWxV93DlL98iimgNa0N9cBPQD6qv3Jfa8viZb6o7W0F+sWlLep828cJr5PA7EYyUnMmovrjzpqTmdiaBQAeQu2N2JS+wBue3qhPnz61p59++vd79uyJPfRYv379amfOnLls9+7drQ4Bb8qLt/VH9Uum2vLzfj5wQLdqwAuo2tQ09JK2LKLQKkUvvHhF46//9KdL3zQ16ldsHH6eKC4uNjscjikNXx87InbH4CuevoNBE9u6As5+1H6QnWHj6w4Pr4L0pBrUFJv2nH8sOgkJzG4iIzVnCPAecNWczESP1/J01S6vdHPaGE3TEjy539atWy0fffTR5NjY2N8OPbZ58+agRYsWTbFarT5ZJB0gL94WigrLTFt+3juHHL4etWvG5egllR1RntpduyZU//77gQXDZyWfvGJ0/HEH1lZdtWrVCXv27PF4LMK//vm3E6qrqyMavj4+8aw3CAzpTCs6tVlBelI1qm+1o8NLppZ0ExKY3UBGak4P4CPggTmZiS0tXdaU4aiJ5+60OFIwPT19rsVieX7gwIEvbtiwwX7rrbceKMeSJUuSLBbL84MHD35x8+bNIx588MGnW1lGr3CNiH0J1Tf3wEEHdesE4G7ggo4KS4DAvn2/LV28OLHxeydOP+XAKkc1NTXhd9xxx3Ee3Wz1O6NXL/306oYvg4KCSu+9996fvFbYzsEXfYoytaSbkMDs4jJScyyoLZU+mZOZ+FQbbuFpOLS4o0RaWtq8mpqaa8rLyy+/+uqrX0pNTb1r6dKlVoCpU6cuqqmpueaTTz75a0BAQM3y5ct91bx1FzAYuNqWn3doX94TwE3oJb93ZIHCpp7wec2mzSc5q6oONDPedueNb0SGRx0o3yeffNLy4J+KoiC++Nfszcve/3OOY+uB2ujYsWNze/To0SVql434oh9TapjdhARmF+aaPpKJWqT61jbe5ndU31BLDNSgILdCQkKMJ5988kdN05zz588f0fjYaaedtnfOnDmvZGVlpWzfvj2wjeVtk7x420WodWHPseXnVR10ULcei2rmW9CRZQIImzBhixYSvGP/oo+ObXgvpn/vqinHTj+wkPzmzZtHf/nll03v8/jrp8NZfOe/qa8NnvPOzq+chnFgpaTU1NSv2rPsPiI1TNFuJDC7tttR+w9e2tadRwzDqAfudXPaK4ZhrPfkfvX19dx0003H1tTUhE2cePhAk/vuu88RFhZWfN11153UlvK2RV687XjgKeAsW35eU5Ptrwcy0Us8/jf85ptvYt96661jvvvuuyOuLQfbRn5elbf2xMbvXXBu8hcNfzcMQ0tPT5920EU15Wa+/s9FrHlvLoMmv8Gp9z7zzbffT2443KdPn/VXXnlluw9a8gFfLV4gNcxuQBYu6KIyUnMuQe2mMXlOZmJz+wl6xDCMZzVNGwDcweFruX6ACpQW/fvf//5benq6EzAiIiL2zJ49+5krrrii8KGHHjrs3PPOO2/h//73v8v37Nnzea9evTyaJ9pWefG2AajBUH+25eetPOwE3RqOmkJytCf3e+edd0Z+9NFHV1VUVByYS/rSSy9tOu+88547/fTTN7SljOHTp62o+PHHq+vLywPMYWF1AJfMPn/Jg4+Nmv1rwRoNYNmyZdNRPwvY9G0sKxdcT2BwEQlz0+h1VEl6evqI/fv3HwjvGTNmfNWWsviBzcA0t2d5/5lSw+wGJDC7oIzUnKnA48BJczITvbJhrmEYd2ma9hZwGWqrpV3Ah4ZhLHZ3bWlp6V+bO+ZwOA5bsefZZ59d9uyzz7b77hyuEbEfAk/a8vPeb+a0EcBG9BK3+zfm5uYOf+edd642DOOglpvS0tLBr7766t2GYfxr5syZHtXEGwuMiakwhYZsK//226MiTzklDyA00lJ18gmzdv1asCYGYP/+/X3//cD98bdPDx/GrrVnMeDY1zn+mq8alrabP3/+jIb7mc3m6vvuu+/bpp7VBUgNU7QbaZLtYjJSc4ajBvlcPicz0eHNexuG4TAMI80wjGTDMG7wJCw7q7x4mwm13ulq4MEWTh2KB1tLaZpmXrx48YWHhmUDp9MZ+O67717bpsICAX36OKp//e2gkchnn3HhssAAy4Fm4pWfvPJXijcdx8TUO5nw5wNhuXnz5qC8vLwDC7qPGDHih6OOOqrDRvp2MF/U9nYBkXFpi0I6+Lmig0lgdiEZqTk9UbuP3DUnM/FTX5enk9NRA3n+3MSI2MaGAJ40pU6pqqqKaumEsrKyQbm5uQM8LmEjQUcdtbpu165jGr931KhBK6aMP/FA8H3w/YbwgpHX/Yf+4w6qDd9xxx0T6+rqDkwNuvjii79qSxn8xC7A2pHhVZCe5EQNjDtsMQ7RtUhgdhEZqTlBwLvAB3MyE//r6/J0ZnnxtkuBy4HzbPl5TW1P1VjDno7ueFSrKSws7OnJeYcKmzLlN2dFRWztzp2hDe8N7Fe688xp5x/4uqqmLuDOu/818dBrP//88xkNf4+IiNj5j3/8I68tZfAHrvAqpOPDS/oxuwEJzC7ANX3keWA3kObj4nRqefG2iajtzM6y5ee57ZcEdgIxHpzn0ebOMTEx+zw571Dm8PBac2Tkr+XfLbMBsGL+cQFLH3zghNEjdkVH9SxpOO+LL744aE7m+++/32f79u0H1vqdNGnS12Zzl1/2VPoxRbuQQT9dw92oUZwnzslMdPq6MJ1VXrxtIKoWfpUtP8/T/t0NwHkenLfEYrGUoTarblJISMiOGTNmbG7uuDsBffuurtn4+1g+u/N4KopGMPLsx4I2xw07NeHsKW8sfNEKsH379vi333677wUXXLAD4NFHH53RcL2mac7bb7/967Y+3494XNuzZ9nNwCRgJKr5fajrz0Gobcs2oPqwNwDrgRxHiqOpUeeyzVc3IDVMP5eRmnMFcAVqQfUWV9vpzvLibeHAQuARW35ea5YH3ID6JdoiwzBqTjjhhA+aO65pmnHaaae9ZDK1/X+5sAGmcuferTMwmWs55Z40Rsz8tc/giNVnnXhZpKZpBz4oPfnkk9MAampqtOXLlx+YYjFo0KBVJ554YnGbC+A/Wqzt2bPsJnuWfZo9y/4Uqu/xKWAyarWqhcBNqM0GLkG13GxE7ft5I7DZnmV/3J5lP3SHHtnmqxuQGqYfy0jNmQ48DMyYk5noswXLG9lOx/zS8Kj5s4FrROyrqK2oHmnls34FjkK3RqGX7GvpxJkzZ67aunVr0TfffJNSW1sb2fB+UFBQ8amnnvpccnJy20YtV+0P5Nsnk4Ort0wuqo2sqR582XtBYb2qAfoPj9rSu0dfy9Ahw/LXb/h9JMDy5cun1dTUvKXrur2ioiK64TazZs36qk3P9z9bUJt+H2DPsptQNclk4AJU98WbQIIjxXHYRgAuhcCqQ+4zCPgz8JU9y74GeMyR4ljoeqYnLRHCj0lg+qmM1JyjUf/DXzInM3FtG25xOR23q8N2VGB5SwyqBvAzak9Gd+4DooFkNyNiD6eX7EO3foyqxT/h7vRrrrnmu+Tk5B8//vhj2969e6P69OmzNykpKT84OLhtTeXrv4xj9bvXExRRqJ34j9sC1s2/qmL58lFBRx2VC6CZNELCA1efe9qllQ8/86+RABUVFdF33XWX/b333pvRcJsuutB6czYD59iz7Bpqo/Rk4EKgBLW84UmOFEd+W27sSHFsBu6wZ9n/hQrIh+1Z9jNNllsynTV9pIbZxUlg+qGM1JwwVF/cXXMyE79wd34z+qE+FXcEb/4iiUTVEHqgFkpfRAsLxOfF2y5H/cKcaMvPq2njMzOA59CtT6KXuA3ciIiIuosuusg7c2BXvZHGoMmvMP6Kb9FMBA6IXV2zZYudRmv3RvYKWX1GwkVjnnrpwf1VVVWRAPPnz0/aunXrgWbDLrrQ+mHsWXYtqO/ECAztOFRTahUqJE93pDjWeOs5jhRHDfCGPcu+CHg+dOijL5avv3VQXNoirSA9qXUfyoTfkMD0M40WVP8J6G7TR4KAswEzagDGQOBPwDuoARoHyYu3TQHmATNs+Xl7juC5S1AbJJ+BCuiOc8KN/6DPyAPfW8jYsaur1qy5wHA60Vz9of2GR63euXH/JWPHjl2ybNmyMwC2qFA9oIsutA6okATGAhcBFwVG/UDN3ukRhmE6UdOcqx0pjnYLMEeKo9SeZb9Y04w5oXFPP4EzKBmSPN5QXfgXGfTjf65G/XK4bk5mYnf6JGsCTkM1x+52vbcDsAApHDJCMS/eNhi14lGKLT+vLU3Wf1C1yrnAf9Gt7deMbTiJC60cdtB7jcISIHjUyF1oWm3lL78cWAChV2z4XpNZK/+/lBubbGbswIXWQ1FNoEe194PsWXbNnmUfbc+y3wesQ/2sAS7QNGN4ze7Tq8vyHyhsz7Bs4EhxGI4Ux1NVWy/dqAXuy7Bn2T3aTF34HwlMP5KRmhOIWqHmim44InYyauj/oVuNFQGlwKXAaIC8eFsEarTjf2z5eR975el6yWfAc8Ab6Fbvt8zsWhvNx3+/PTqwZnDDWyaT6bAmVM1kIqBXz9WVqxwHrfoTEmlZPWbo5Jg+ffoctl9nOy+0Ho4aRXwiql/5ROAUDl+k3yvsWfaR9iz7PcBa1DrAFtTPfrgjxXG7I8XxiyskO3zUan3FsHW1xZOeAF63Z9k9mbsr/IwEpn85C9gwJzPxF18XpIPFo3agaK6WVI5ajSfJWV2diNk8H/gODwbptNK9qD6xp7wWmoYTfnp5Cksff4CIfnnf7gj8ruFQYGBgkx+KLIMGO2q3bTuoyTWqT+jq0qLqYxITE79s/L7ZbK5ph4XWw1BzFWe4XjbACexDBVU4ni324BF7ln2EPct+pz3Lvhr4FIgArgSGOFIcf3ekOJY3UZP0yeIF1TvP2gW8hApN6fLqYuQH6l+uB5729GRN0wahQrYv6hfZe4Zh7G75KuWaa66Z8sEHHyTt27evf2BgYGWfPn02paamvv/ZZ58d8+WXX56fmpr6+DPPPPM9QFlZmSkiIuJ/2dnZf01KSjqSvsKm9APORI20bWnQSi2wqSR70V3W887tGTZx4qXWWbO82xynl9SjWy8G3gC+QLdejF7SqikuB9m3OZzvM6+iav9ARif/h2GJG4uKn7uh4XBwcHBpU5eFTpq4pvzHH651VlWZTcHB9QADbT3WFuYXpWa99Oq8119//cumrjtCYUAvIA4VWAbqg8phfcdAHTAc1WTeJvYs+3BUn2Qy0Bt4CzWdY5kjxeHJiOP2Wqqu2dHlj140dnCt03nU+eNXfJq1Nis2Oij6M+CTNjzD26PKhZdIYPqJjNScAajNoN91d66maRpwD/AP1ACZBo9rmnaLYRjPtHT9WWeddcbixYvPuuaaa17429/+tioqKqpO1/UxH3744bHBwcHVFoul7PXXX7/wkUce+SEkJKQ9+4isqBGxJYC7NV8pmj9/WtXqNSP6zL0lI6Bnz4tQ/1benaivlxSjW88A7gSWo1uvBT72ZPTsAc46jVVvHsvG3Cuxxn7H9NueIdhaC7B9+/a4htNiYmKaXBXIMnBgqSkoeFf5d98NjTjxxN8AwnsElwcGmbcV5hUfNXRcb2+tFRsC9EGNRraiapEVNB2SjRWh/lv91nWNR+xZ9iG4Bu6g1vB9G7gBWOJhSDbWXjXMZkeXl9fUbdi0t8JuNpk3nzzo5HmZKzMfOjr66PdH9hzp0YfURmR6Siclgek/jgbWzMlM9GRqRBrqF/qhgoGnNU3bZxhN/35ft25dyMcff3zhNddck/nkk0/+2PD+I4888jPwc2Ji4vnDhg1buXPnzoGpqalTs7Kycpu80ZELAs5x/f2gmlb599/3CDr66NKAHj0ObC5d+sUXI6pWrro46uLkewN69tyGmnd5BSo0vTt9Ri+pB3R067fAo8DD6NZnuG1TMCFRzV9XvCmCNe9NZ8+vJ2MOLGfEzKcZedaBAUmPPPLI8MabPI8ePbrZwUoBvXuvrl736zENgQkQag1avXdb2SgvBuZ4oCfq339vK66rRtUKY3CzyIRrIYCGkIxDjXi+FfjGkeI4kmkwm4FTj+D6VusVHrQnf8f+XgCDIgft7x/eP/frLV+fNLLnSBk120VIYPqPoXiwzZSmaVHAHW5O+09dXV1GQMDhP/6XXnrpqPr6+sCHHnroxyaua2BcccUVbz7//PNXPPnkk9+aTCZv1zJNwOmoJsCDdgrZeMGFKfX79vVG05y9UlPfijr/vC2Vq9f0KV28+MbwGdOfDh03ruH8IlQz4qXAR4BX9wYF1EAg3XoMkABcz9InzqKyaBuWsF0ERezCEr6P6tJoqkt7U1seQ21lDOExyxl17pMMP2l9w36VAE899dSQu++++0BzrNlsrr7zzju/a+KpAFiGDXVULP/pbOC9hvei+4WtLlxXdBF/jBg9UntQNUu3tfsm1KFGyx4WmPYseyxqIYGLXOe8i2oN+cqR4qg79Pw26vA+zP5RwXura50HdqOZFjvt8zfy39DLasreCbeE13ZkWUT7kMD0Hx4FJjAdNby/JQPz8vL62u32w5r8du/eHREUFFQaHh7eYhPYo48++vP8+fPPvfrqq0986aWXcjwoV2ucgBpIsqnxm5uvvfasuj17+g/7aNF/Nv3p8pT9Hy06NmT8uOLi1+ffGmSzvRc5c+ahoVgO1ACzUDXOJXi2MpDnVFPsN8A3VOy9m8IfDfZv70NlUW8qi/pjCS8isv+PRPbbRd/R24joe2CRhWOOOea6ffv2RZeWlvbcv39/38a3nTlz5tujR49uapFvAMKmTFlX9tXXQ+uKioICoqOrAQaNjP5148rdA8v3VYeERQV5Y4PonTQzRcTpdJK7NXfY2r1rh/YL77drRuyM/KjgqManFKFGLS8BnPYse3/gfFSfpA34ADXiO8eR4miPMOnw7baG9Q4vqql39qitd2qBZpMxuvfoHdkbsjd9vPHjiReOuHBJR5ZFtA8JTP8RA/zgwXk9PLlZaWlpkxvs9u7du7S6ujqirKzM5C40r7322jcfffTR/9uzZ483m2UHoKYmrG/8ZsXPP1ur1/06pu89eqYpNNRpjooqrlr369jNKVfODIiJ2Rk5c+bPzdyvFhW8k1Ch+TFqpKv3hfas5OjTtwDNrU16kE2bNtnKysp6NX5P0zQjISHhg4ULF7a4QEJAjx7V5vDwDeXffhdvnZW0EsASElAbFBrwW2F+kW3EpH7N/Xu0RkPfcQCqxghATX2N6aXVL51YXlceNrb32Ly1e9cO+3D9h+GXxF+yLtAceOC0PRV7ev/1y7/e5tjjOB0Vnh8C/wYWu1bKaU+FQP+4tEXmgvSkDlnhKCwooM5s0so37S23Du8TsQ/gmF7HfLZy18ozUR8chJ+TaSX+oxo158ydXz04xxg0aFCTo1lnz579m9lsrr3tttuOc3eT++67zxEVFbXz+uuvP8WDZ3qqCMhHBeeBuXyh48eXDH59/oMRJ564u/SLnD6VK1bMCJ8+fXvImDE7cTo3FmW9MqWFexqoJjo7qpm309A0zbBYLOXR0dGbjj/++E9efvnl277++us3Pbk2oG+Mo2bD+oPmY4b3CF5dvLPC3tw1rWSgPmwctGWZWTM7p8VOW3nD2BuyEwcl/jZ94PSf91Xvs9Y6a01bSrdEvJb32kn3L7v/n1lrs27tH97/RNRqS/0cKY4UR4pjUQeEJQXpSdWoaS6tmt6iadr5mqZ9qGnaWk3Tlmia9g9N0yLdXRcREfFEQEBA1pNXTI60xfZ8LDAw8KWHHnroqLOHnz335ZtfPqq6vvrA79qRI0den5iYeH6rvynhc1LD9B+VqFGL7nwH5KGavZrzWWxs7P6mDowYMaJy5syZb7344ouzAwICnH//+99XRURE1N9///3HLFmyZFRQUNBB/Vl//etfF9x9991zPf4u3KtE9WlNR60aU4irdmMZMKAaIOKkxF09Zl/5ad2OHSfH/PMfd5V88GFsycKFibU7d1oCevWq0ZreIHkQ8DXNz+XscKWlpX89kuuDRoxYU/bFF1c3fq/XwIjVv/+08/ojK9lBdqIGnB1gNpkZET1ij0kzUe+sJyY0pqK4qjjmxx0/Tvuk4JMzo4OjVxzT65hPEwclro2wRIQ+PP3hRXi7KdwzDYsXbHN3oqZpJiALtdRiYycAszVNO8UwjIKW7nH77bc/FDjhkpPj+0Z8d9HxA79ftGhRL4Bta7dx/6P3H/evW//lSQuR6MSkhuk/PApMQw1/vRwoa+aUnaj5bM368MMPP7r00kv/N3/+/HMHDx78bExMzFNvvvnmaeecc85hA4Fuu+22X/v27bu+qfscgTogBzWHbQCHfN/7Fy+21W4pPKvHhRc9FNi3b/n+RYumm8JCSwNjYpoLy/6o5dO6VLNY2OTJ653V1b1qNm8+UAMacHRUgbPOsBZtL4/y0mNKUP/tHfThurKuMmjlrpXxnxZ8euZra1+7vEdwj9pBkYN+S5uQdt3fjv/bk2cPP3t5hCWiAjUyu29TN+4ArdnUeS6Hh2WD4cBbrulaLQoNMu8trqg5qBVj0gWTdr/49IvnelgO0YlJDdN/VKImjLtlGMZPmqYdj9orcybqg1ENriH7hmG4/cT9wgsvLAWWHvr+3/72t8P65woKCh70pFytZAC/oOZRnoeaZrKvcpUjpuzzL/4akZj4VNDRR+0quPiSy5zl5VHDP/yguYXoe6Ka5j7GN7WcdmMKCnKao6Lyyr//fpRl0KDvAMwBJiM4PHDttt/2HRPdL8xbHxA2A0dX1laW/b7v96GFZYXD91fvH2ANsm7pG9b3t1+LfyUhNmH5EOuQatR/Z401jJY9dEnDjuDR8niu2uUtbk47DrWqUYsigwP27q+q69n4vZOvOPnXH975Yfydd955zL333rva3T1E5yU1TP/haZMsAIZh5BuGMQvV/xQHRBiGcaknYdnJFACvAPV1xcVxxQsW/C141Ki3I08/bU3d3r0Wy9Chmwa9kvV4M9eGoRZueIf2GujjY4H9+6+u3bT5oH7MiJ7Bjv27K73Sj7m3cm/wxxs/HvTt1m+nf7j+w9lby7YOjw2P/e2s4We9dPqQ0z82aaZ94Zbw0nF9xm1fs2dNv4wVGaPqnQd9LtmLGvDTZNW/nXlaw4zFs1pwi/366enpc2849ZiL/nn2+FOHDRt2IICjI6J3TL94+u///e9/L/LgGaITkxqm/6hENW+1imEYlRwyPcMP7SlftuyN/Z8t/iZk9OiNPZIvygGwDBxY1f+B+5urRVlQtcvXUDXMLilk9GjHvrfeTmq83VffodbVjq8KzzOcBpqp9Wug76vaF/TFli/GrS9eP6m4uthutVjzzxl+zvqxMWMXhwaEHjTV5cstX04oqirq9eAPD/bsG9bXDHxrNh2UjbWo/2770fH9xwXAVC/ez6CFReXT0tLmjTrz2ojfdpVNuHPWyMcb+jANDO30a07fmDs/d8ott9wy3ovlER1MAtN/VNGKGmZXs/nK2fcRELBrxI8/PAKMo9FgoCaYUP2WC+lEg3zaQ8jYsdv2vflWQNXqNTEho+07AfoMitipadTv2FDSv9/wKI9aFEqqSyxfbvly7K/Fv04qrioeE2GJ+G2odeh3Vwy84rm+YX3LgWGoRfAPBGZtfa3WN7TvjiHWIZumxU7Ljw6ODgOa2qS5BhhBx/8sNqDmL7tT6HrFujnve9SuOc0qKq/pExEcsLPxe+U15b1je8TmnXvuue9kZWVdGBMT06X/m+zKJDD9R6uaZLuSvHjbdcAp1NVNNoWElKD2wzwVNYCpqQn6A1H9r12+v0gzmTBHRzsqV644piEwNZNGSITFsbNgv72lwCyrKQvM2ZwzZl3xuklFVUVjIywR6+Mi45ZdFn/ZSwMiBhy68PsuDhl5HWgONC6xXdJ45GdYM4/ai5rS8xUd24+8ERgSl7ZIK0hPanY1KsMwnJqmPUjLu9ssNQwjFzeBub+qtk9MZPBBC4xU1Ff06RXS65tnn302r1+/fmetX79+TN++fdu8ML3wHQlM/9EtAzMv3nYycDcw1Zaft8/19k+o+ZoHBgM1uqQ/alpNlxoR2xLLwIGrawoKxgNfNLxn7R2ypmh7+STUdlgHlNeWB+Rszhm9rnjdpL2Ve8eFB4YXDI4cvCx5RPIrgyIHNTnVyKUUtfB6IKqZtTXCUAPW+uBmbVlvKkhP2heXtqgO1RzcYk3bMIwnNU0bBfxfE4dXAxd78syyqrr+xwwIWtb4vcq6yj4DIwbuCgkJMS6++OK3n3322SOaTiR8RwLTf3S7wMyLtx2N6oNMtuXnHbox8kbUYKDzUZPTd6IWJShChURrd7fwW6HHH7e64pdfLjdqazUtMNAAGDCix5ptv++7qq7WaaqlWsvZkmNfV7Ru0u7K3ceGBYZtGRw5+LsLjrrgtThrXEkrHrUJ1SzryQ4wkah1aEENvvkGVUvtaB+ipou4HcltGEaqpmnvovbaHI76b2kR8IJhGC1u2F5aWvrXVYX7+ry8tKD/tKN65wMkJSXtKa0uTbl32b3PD7MO2wuQmZm5LDMzc1lL9xKdlwSm/2jToB9/lRdv6wFkA/+05ed91cxpu1H7Bp6F2tC4DLUYeZccEducoOHDizVLYEn5Dz/EhZ9wwkaA4GhTmTOgruzFpf+7pcCcf3RIQMi2QZGDlp09/OwFw6KGtXXLs8OaZQ8RzB/TOLaimsU3cchuMx3saeCNuLRF8zxZIs8wjM+Az9ryoC/ydp0cGx36TWRI4IEauGOPY0BwQPDOQHNge26DJzqIBKb/8Pagn+103G4OrWqGy4u3BaI2DM625ec97+b0ctTuHMejap372lJAfxfQq9fqirVr7N8OKA9du3ft5F0Vu463hU2q71060HTi1MlpR/U4yt0elp4oRf17N26WDeKPvssa1HzXTUBLzbsdpiA96Ye4tEV7gdNQu9a0i/2VtYFbiiqmp0yJu6vx+xtLNg6JtERubK/nio4lgek/vN0k2yl3dM+Lt2mowRfVwN88vKwWtVlxt1NbX6t9Xfh1fPmAipi+v2896bvt2zcPjBi47LS40/5p3tBjwPbfS87yUlg22ISaV9kwQrkEWIWq7feiPbZRO3IPAQ/HpS36piA9qbkVsI7I80s2XhgVaskbMzDqoBGyuyp2De0V0ksCs4uQwPQf3aUPcw4wDZhsy8/rUivzeEuds077pvCbo1fvWT1pZ8XOiYGmwJJhg/v9eOwP+2x32OfeYw5Xey9WWmpKNzn23lRVVhsUHB7Ylj0tm7IdFYw7USHZYt9eZ1CQnvRmXNqi04H/xqUtuqylEbNt8fZPhcdt21c56aaTj/rHocf21+yPG9N7TLcZgNbVSWD6jy4fmHnxtlOBfwIn2PLzOkWTXmdR56zTlm5dOnzVnlWTdpTvmBRoCiwdED5gWfKI5H+N7j16B8D2RfqY8qVLj4487bQ1ACERlmpLcMDGLXlFI446PmaVl4pSiWfbzHU2c1AbE1wPZHjrpiu37Iv5dv2ea84a0/+h2B6hB9Veq+qqzBW1FbFj+owp8NbzhG9JYPqPLh2YefE2G/A/4Hxbfp4nG2V3Rl7tFzYMg7yivIF5e/PG7CjfYTeZTDWDIwevTBqa9MJQ69DdrtMCG54ZMnbMZmd5+RQa9R/GDI3cXFtdPxnPRrYeqQ6bMtJaBelJlXFpiy4Avo5LW9QbuPdI98n8ePX2kZ+v3fmX0bFRC2aM6HPYBgQrd6+MDTIH7YkOjvZW7V74mASm/6iii46SzYu39UStyvN3W36eNzej7mhH3C9sz7JrwHggGbgI1Ze7ALjekeJocSGG7bf/43vgoajzzjswheKLl/OWABkjp/ZvjwXy/UpBetLvcWmLjgPeACa7mmeb3Be2BdvrncaghSu3Tf99V9nU5OMHvTFhSPTvNPFBqby2/Nijo4/e2dQxd89o5fmig2hqNyjR2WWk5mioVVIC52Qmdpm+vbx4mwU1jP8HW37e331dHl9wheQYVEA2LNC9AHgTWOVIcXj0P2levC0I2AMMsuXnFQNkpOYEut4bPiczcXdL13cXcWmLAoD7gBTgWeC5gvQkt7upxKUtCgYuAG5A/b94UUF6UrPL3Nmz7E8DvzpSHI95o9zC96SG6SfmZCYaGak5DbXMcnfn+wPXiNinUE2It/u4OB3OnmWPBa4GLkUtFv8mKjB/8TQkG7Pl51XnxduWAieiNuFmTmZibUZqztdAIiqEu72C9KQ6IC0ubdH/gOsAR1zaoob9Vze4XoVAD9T83qGonUquQG059wCwyHWflhwLvN4u34TwCQlM/9LQj9klAhO4EZiEGuTTZWrNLXHVJhNRg09ORDUP/glY3paQbMLnwEm4AtPlC9d7EpiNFKQnrQbmxKUtSgMuA05Abb4+BLXE4n7+CNB84ISC9KTD9oNtij3LHggcgwpY0UVIYPqXLjPwJy/edgbwd9T0EV+uBNNh7Fn2COA5YCzwOHClI8Xh7e/9cw4Pxs9RH05EEwrSk0qBTNcLgLi0ReYjHBQ0EtjsSHG0y7xP4RsSmP6lPbb4uhy1OHV7245rUExevG0U8DJwji0/z9/36vSIPcs+CrWR9TfAWEeKo72W71sFROfF2wbZ8vM2u95bC4RkpOYMnZOZ6K8jkDvUkY6gRTXH/uSNsojOQwLTv7THerL9UItjt7eBAHnxtt6oEbFzbfl53WJ1HnuWPQHVRHqrI8WR1cbbePTBxpafR1HWK9sCY2MfwvULe05mIqu+3LItLCr4IdSejp448AFHtIkEZhckgelf/LpJ1jWK813gDVt+Xrf4ZWzPsvdF9VNe7khxfHIEt/L8g43Z9H3F8uUjI05KfL/hLZNJ+3777/vGDRvX29MPRx21znBXdSzSZ9zlmHxdANEqfhuYrulLz6CmONzh29J0DHuWPQA1SvL5IwzLVgkdN351fVHRMYbzjx3O+h0VtaaqrHaUs97QOqoc3ZXr525HBvx0ORKY/sVvA3P/Rx9NA8YBl9vy87rLXpV/Qy1S/q+OfGjwqJG7MZurK3/6KbbhvZ79w4tMZq1066/FgzqyLN3USGBLOwzoEj4mgelf2mPQT7sr+XDhuOrf108FzrLl53WLUYP2LLsF+CtwoyPF0eFTZgJ69nRUOlbbG78XGmlZs2dL6TEdXZZuSPovuygJTP/SpkE/mqZZNE2L0zQttDXXXXPNNVN69+59f2Bg4EuhoaFPx8XF3Zaenj4C4M033+x79NFH3xgcHPysxWJ5oWfPnulnnXXWGZWVlQc1+ZX/8MPA8qVL/y/ipJNeteXndcTgos7iHCDfkeJY64uHW+IGr67dvu2gcAyJtGyuKq/t74vydDMSmF2UBKZ/aVWTrKZpfTVN+x9q49+NQJmmaZ9qmmZ3cylnnXXWGa+99toVF1988fvr169P3bFjxw3nnXfe4g8//PDY999/v8/ll19+b3R09N7FixffVlNTc/XTTz/9+G+//TZ048aNB8pXs3lzZMl7798acuyxr4QcM6o7hSWohQme9uRETdNMmqZdoWnax5qmrdE07QtN0+Zomhbk7tqIiIgnAgICsgIDA18KCgp6bsiQIX9fvHhxdHC8bcP18+eP1DRt/s033zweIDTSsqumqr73xIkTL9c0bf4VV1wx7Qi/R9G0Y4GffV0I4X0ySta/eByYmqYNQG3D1LhGoQGnAt9rmnaSYRjfNXXtunXrQj7++OMLr7nmmswnn3zyx4b3H3nkkZ+Bn0eOHHl9//79f1u2bNn/Go4lJydvT05Ofqrh6/ry8oC9L754S+DAgbk9LrrwW9o26rKj5oiCF6dRuBYoOB543925rlB8Hzi90dsjUasBXalp2mmGYbS4AfTtt9/+0L333rt6165dgSeffPJVqampV/62du1jZrBYIyN3ZGdnT3v00Ud/juwVsmvDql19Vq9ePSAiImJnS/cUbeMa8DMaGfDTJUlg+pfW1DCf4eCwbCwE+J+maUc1tfj+Sy+9dFR9fX3gQw899OPhl8LGjRvtF1xwwRstPXzvs/+9wmQJKun152vf8bC8TemoOaLg3WkUQ4ACR4qj1oNz/8PBYdnYccDzwHmePLRPnz61p59++vfPPvvsFaagIGcdWvV4my3/u5Urx61evTpsxFG2Pd+v+KZn796911RXV7utvYo2sQFbHSmOEl8XRHifNMn6F48G/Wia1gdIcnPaUGBGUwd2794dERQUVBoeHt7kaNbq6urwAQMGNLu/YvGCBVPr9u45pue112RqAQHdcTucoaj1R1ukaZoV+LOb087RNG24Jw/dunWr5aOPPpocGxv7G0AdVEUGBFhGjBjx0z333DM5MMhc/9nS9+pmnnLmCk/uJ9pE+i+7MAlM/1IBeDJwZxie/WyPaurN3r17l1ZXV0eUlZU1eY+goKCyrVu39miygMuXx1b+/PPl1rPPfjSgd+9KD8rQFQ1F9Rm7cwzuPwBpqJpms9LT0+daLJbnBw4c+OKGDRvst956azZALUZlkGGEXXzxxbm5ubkJ69atC3H8+lPANZfesMOj70K0hQRmFyaB6V+2ArFuz4Jma3+enDd79uzfzGZz7W233dbkL+ohQ4Y4lixZMqGpY/s/+fTSINvId8ImTOhug3wa83Seqae17xbPS0tLm1dTU3NNeXn55VdfffVLqampdy1dutSKpmmAkZaWtq6ioiJy9uzZ5x5nn1JqjYzypKlYtI0EZhcmgelfNqBqL+6sw32TYBVq26fDjBgxonLmzJlvvfjii7NvvPHG47Zu3WrZv3+/+bbbbhtzwgknXPrAAw+8vW3btqOnTJly6dKlS60A7777bsy4kSNvKdq9a1jUeed+2arvquvZiGc/p9WoVoOWGECTfcmHCgkJMZ588skfNU1zzp8/f0SgQXC1ppUDHHfccUu+++67pNMTztesvUN2eXI/0TqNBvzICNkuSgLTv3gUmIYayfM3N6elG4axt7mDH3744UeXXnrp/+bPn3/u4MGDn42JiXnqzTffPO2cc8758Zxzztn1yiuv3LVnz57eiYmJD1kslheuvfbam6cNiA3pETfkG3NkpEc1GE0Zp2na2ZqmTdQ0zezJddD8HNHExMTzR44ceX0Tz5r/7rvvxnh6/yPk6c9pP2pwVkveMgzDox1G6uvruemmm46tqakJmzhx4rYAjOAqzVQB8Nhjj31y1513/2f0URPCevQLa/bnLo7ICGC7DPjpumSUrH/ZjtqmacCczMStLZ1oGMa7mqZdBTzFwf2eTtTIzHvdPeyFF15YCixt6phrGsnjB55XV6dt/+c/M6NOOul5D74PNE07ATUCNL7R25s1TfuLYRgLW7r2rLPOOmPx4sVnXXPNNS/87W9/WxUVFVWn6/qYDz/88Njg4OBqT57fzjYCcfYse5AjxeGuPP9A9SWf1cSxb3E/KIh///vff0tPT3cCRkRExJ7Zs2c/86eLLtq+8D//CarSVA129OjR5X3CB+9as2RbUUCgqbssTdjRpDm2i5PA9CNzMhOdGak5rwHXAPe4O98wjJc0TfsAOBeIA3YCCw3D8PoelDUbN/ZAM9WHjLa7nd+nadokIAewHHJoEPCBpmnnG4bxXlPXupsjmpiYeP4RfBte4UhxlNmz7N+hpoO83tK5hmHUaJp2DnAhcAnq32AHaleXLMMw6lq6vrS09K9NvV/pWN3n8bPO3tv/3w8c2DGjdG9Vn0CLaSfA9u3b3f73I1pNArOLkyZZ//MM8OeM1JxAT042DKPIMIwXDMO40zCMp9ojLAFqNm3qowUHezoZ/lkOD8sGGvCMpmlNHnc3R7QTeRq4zpMTDeVNwzDONQzjWMMwklw/sxbDsiUVP/54rDkqat1B7+2v6RMYFLC7rfcUbklgdnESmH5mTmaiA1iPh5PZO0rdrt19TKEhbn8Za5o2AjUwoiUxQJPLtrmbIwqwbt26yRaL5fnGL3flagcfAsPsWXa3yxB6m1FXp1Wv//2U0OOPX9z4/bLiqviQyMB2+cDU3dmz7GZgDDLgp0uTwPRPdwGPZaTmDPB1QRrUl5VGmUJC9nlwqifTYpo9z90cUYARI0Z8V1NTc03jl4fP9BrXKj+PAk+6Rk92mNLPPx+lmUw14TOm/9rwXsmuisiK0poxw8f3+bYjy9KNjAB2OlIc+3xdENF+pA/TD83JTPwqIzXnKWBBRmrOiXMyE30+r84cHrGvZl+JJwHe4mClQ87rc+ibs2fP/m3evHm1t91223EZGRk/eHCfqUAUwKBBg04EmtteLMrDcjXwZO3ZR4FTUAOsbm/l/dus8udfTgk6+ujFmumPzxS//7xrRpg16IfwHsHlHVWObkaaY7sBqWH6r38DJaiaps9/jgG9e+92VlQcFnCHMgwjH3C4OW0X8HVTB9zNEW3ikiigCKC2trbE9femXvtQ69Z6+nK7KLxrH8w/AZfZs+xNjYL1uorly2Pr9u0bGZmUtKThvfo6p7ZvZ8XJA23Ri1u6VhwRCcxuwOe/aEXbzMlMdKJ28xgNZGek5vT0ZXkCBw/aaVRVuQ1Ml+uAlmrF1xuGUdPcwZbmiLaq0B3AkeLYDVwEPG/Psqfas+yau2vaqq6oKGjfe+/fFDJ2zP8CoqMPTGf5/add48wBpn0DbdEF7fVsIYHZHWhN7VYh/IdrtOwDqGkJF8/JTFzWylt4ZQsto65O2/3kU3dGnX/eU5ZBg5rajuqgJkxN06ah5mE2Xs92K3BDoyklf+fIdyuZhauG6UY0kN2K+w4EHvT0ZHuW/WjgbWAlkOpIcbS2abTFn5NhGBS/+uolmANqoi+79KAdYn75bNNVUX3DVgwZ3as1A1K8tt1ZV+ca8LMPGORIcXi6LKXwQxKYXURGas65qKkMK4BHgM/nZCZ26A83L972EIAtP8/dKkOAWukHtbB4w9zD7w+ZStFlAhPAnmUPRf2MEoCHgCxHisMrC9TnxdvmoObnTrHl5x24Z0ZqzmWADtjnZCZWeeNZ4mD2LLsNyHakOIb5uiyifUmTbBcxJzPxPdQ+jG+iAnNlRmrOlRmpOR2572EmcGVevM2jPTtd8w9/NAzjHcMwlh7JvEN/4EhxVDhSHFcCs4GZQIE9y67bs+yeNmU3KS/edj5wN3DBIWE5CngMOF/Csl1Jc2w3IYHZhczJTKyak5n4Eqpf81bgYmBjRmrOPzuij9OWn7ceWA5c2d7POpTT6WTnzp3BHf3ctnCkOL5xpDjOBqajmlnX2bPsmfYs+4jW3Ccv3haYF297BHgYOMP17w9ARmpOBPAOcOuczMRVXiy+OJwEZjchTbJdXEZqzjHAzajl8d4AHpuTmfhry1e1XV68zY5a9u5EW37e6iO8ndsm2aVLl/Z/++23k3ft2jXW6XQGBgYG7h8yZMg3N9xwwzu9evWqpoUm2VtvvfXcY489dp3ZbHZu2bJlzNy5c29sfDwiIuKJm2666b/33ntvU99Hq5tkm+OqYc5BDYb6FhWASx0pjmb/58yLtw1AtSYUA1fY8vMOfI8ZqTka6me9f05m4rXeKKNonj3L/g3wL0eK43Nfl0W0L6lhdnFzMhNXz8lMvBqwAXuAJRmpOR9mpOZMd/1i9Spbfp4DVbt9Oy/eFuHt+ze2cOHCo5555pn7duzYcbzT6QwEqK2tjfz1119n3X777ffs2LHDo6ZhX3OkOHY5Uhx3o9b7/RR4CfjOnmW/wDWg5IC8eFtUXrztRlRNPhs4q3FYutyIGkx1Q7sXvpuzZ9lNwFjgFx8XRXQACcxuYk5m4s45mYl3AYNRv2ifBZZnpOZc6um6tJ6y5edlAbnA63nxtnBv3rtBRUWF+d13372hvr6+yWbYsrKyQY8//nhT8zI7LVcf5zOoHVz+g2oZ+M2eZb/hs6m2KXnxtudQO6FMAs625ef925afd2CJwIzUHEtGas6jwF+BC6TfskMcDex1pDhky7RuQFb66WbmZCZWAv/NSM15HjXwZC7wn4zUnCeB/87JTNznpUfdgBoR+kNevO18W35enpfuC8C77747pqqqqldL52zevDmhtrb2vcBAr34eaHeOFEd9XrztfeDX9X2ZE1LDPUG1WL8ZpX2zswfT7np+7WELP2Sk5sSimmj3AsfNyUz0ZGSwOHLSf9mNSGB2U66FDxYBizJSc8ahajMbMlJzXgUen5OZ6NGmxc2x5edVAVflxduuAr7Ji7fdaMvPm9/K22xH9RUexmQyjRw4sMlDjVnKy8v7RkVF1Td1sH///oG9evUKM5vNxvLly30+Qjcv3haEGgg0y/UyD9tBNnDJ//3FXFAcod0AfP1Wlv09YJ4jxbEWICM15xTgFeAJ4D+un63oGBKY3YgM+hEHuBZz/wtwLfAV8MiczMQjXqw7L942FrUv5C/AX5roc2s1TdOuQ9VgW7Rly5b7Y2Nj1zR1bMCAAXdOnz59SXBwcO2iRYtO27lz552Nj4eFhWXcfvvtT91xxx1N1Y69MugnL97WBzgDFZAnA3nAQlSzucOWn3fQ/6D2LHtP1OCgv4RVRzlm5l+7s2fFgEQN7bI5mYlfHml5ROvYs+xfA/c7Uhyf+bosov1JDVMcMCczcStwe0Zqzv2oqSGvZKTm7EbN63xvTmZim2phtvy8FXnxtvGo9W9X5sXbrrbl5x3pL5jPAQO1f2Zz1sbGxu53d6Nhw4btKS0t7VlfX4/ZrMbYbN261VJZWRk5btw4r+4fmRdv0wA7KiDPBEaivpeFwPW2/LxdLV2f+t3jRcAXTupthmacszF6Ve0nI57fXBZc3Dczi0DXLimiA7gG/IxDapjdhtQwRbMyUnPMwFnALajtth4HXpyTmeg2hJqTF287CXgRVYP6uy0/r827Z2ia9jxwdQunnGMYxgiamZrSUMN84okncocMGTLvhBNO+OKVV175uKqqynT++edfvGXLlqFbt269uyFED+FxDTMv3hYMzOCPkHTyRy3ya1t+XnXzVysZqTnhwCXA9UA4aiPxlzMn37gPVUO9FbVwxWPA844UR6knZRNt55o3+6kjxRHn67KIjiGBKTySkZozARWcp6CmPTwxJzNxc1vulRdvi0KF72TUHMLWrn8LgKZpwaj1aC875FAN8FfDMJ6lhbmcAwYMuHPGjBm5r7322levvvrqgDvuuOPynTt3DtE0zTlw4MB1GRkZr5xyyinNNR+3GJh58bYYIAkVkInAalRILgTWHtrU2hTXQJ6G/sxpwJdABmrZw8P6Ke1Z9uNRg7hOAV4AnnCkOArdPUe0jT3LfglwgSPFcb6vyyI6hgSmaJWM1JzBqBGws4HPUP2cbdolxLWkWwYq9P5ly89rdoeSlmiadjwqVHoC64E3DMPY7jrcbGD26tXrgcsuu+zdxx9/fHkbHntQYLqaWsfwRy1yBOrfJxv4yJaft8fdDV3btB3ruv5M1Bq7n6BC9hNPRzDbs+xxwE3AFa7nz3OkOFZ69m0JT9mz7A8DRY4UxwO+LovoGBKYok0yUnMiUc2hNwKbUf2cC+dkJjY5IrU5efG2vsBzwADgclt+XpMDdI5Ak4H5yiuvxF511VX3L1y4cO7MmTPdhlkTBubF255A1R7PRAVlDX/UInM9+QCQkZoThhrscyaqRrqPP5prv21rvzGAPcveA/gzal7mGtQKQotbWkFIeM6eZf8SSHekOD71dVlEx5DAFEckIzUnADgP1RQYjepDe3lOZqLHfZOu2tlVQLrr9ZgtP69VwduCwwJz6tSpl/zyyy9TTz311A/fe++9Vv2yq96wIar82+/GGbU1U3b958FBqN1hslEhl+9hU+sg/mhqnQr84LpH9pzMxN9bUx5P2LPsFlT/562ogVIPA284UhxtqtGLAwN+ioFhjhRHWz5wCT8kgSm8wrXM3hRUP+c0VK3xqTmZids8vUdevG0o8DLql3qKLT+vwAtFO6Itwgynk4ply+IqV60aX7tt+3hnVVVMQHT0qtBJEzcVpl73F1t+ntsVXlxNrRP4o7l2APARKiQ/nZOZWNLW8rWGa/PqU1HBaUPN2/yvI8WxryOe35XYs+xHAZ87UhyDfV0W0XEkMIXXZaTmDEM11f4JFQqPzMlMXOHJtXnxNjNqEYXbXK+XPKm1taDVgVlfUmIp+/qbUdW//Ta+bvfu8ZhM1YExMT8H2eJ/Dp82bZ0pOLgeN4N+XKNaT0WFZBKwmz9qosta23TtbfYs+1hUq0AS6kPK444UxyZflsmf2LPsFwMXOVIc5/m6LKLjSGCKdpORmtPQh3YDsA6Yhxq84nYlGteuJ68Cm4A/2/LzdraxGB4FZvXvv/co/27ZuJrNm8bX7yuxmcPDCwJjB/wcMv7Yn0PHjd3exCWHBaZrQFRDf+YUYBl/NLUe0cpJ7cWeZR+I6uO8CrXw+zxHiqO7ziu8HLXdmlvv/vruGRazpWrWsFk5bk7djvrvWHQBEpii3WWk5liAC1E1mhDgUeBV17q2zcqLt1kAHTUi93pbft57bXh8k4Fp1NVp5cuWxVU5HONrt28f76yq7h3Qs+dKy5Ahv4RPS1gZ2Levuz7YgRmpOfOAifzR1BqDampdCCw+kvmqHc2eZbcC16BG1/6O6uf82JHi6E7L7HncGnH/svv/OT5m/MKZQ2a622vUa9vACd+TwBQdxtXPOR3VzzkRyASenpOZ2GLtMS/eNgW1VuoS4CZbft6+Vjz2QK3BWV5uqVi1anjNxgJb3e7d8ZrZXB3QN2Zt0PDheSHHHLNJCwhwGw7VFbVBOzfuP3rrb/sG//zJpnhgB380tf7g66bWI2XPsgcCF6H6OYNQrQKvOVIc3WHnE48C02k4+eeSfz5//djrbx4YMdDdAhESmF2IBKbwiYzUnBGo2kwy8C7w6JzMxGanlLi2CXsQuBh4DXjGlp+31t1z8uJtA/ljRGoCah/JhUC2LT/vNw/LOoQ/5kZOApa67rFoTmZigSf38DeuAUKJqFaBccBTQGYX38bKo8B07HHELMhfcMd9U+/zZL9RCcwuRAJT+FRGak4vIBWYg5qi8QhqJZsm/8PMi7fFohaHvxa1SMFyYIPrtRf1C2qo6zXB9fXHuEakelI7dS0JOJk/mlp7oXZ2WegqW7dads6eZT8G1SpwDurDymOOFMd6nxaqfXgUmO/89s6k34t/n3LbhNse8eCeEphdiASm6BQyUnOCUXMFb0FNK3kEeH1OZmKT66zmxdsCUaNQbahwHIIKts2o8NwIrAKW2fLz3E7+z0jNsQKnoQJyJlDIHwsI/ChbZoE9y94ftZvNn1HL9D3sSHF879tSeZVHgZmxIuNSi8lSce3oa9/34J4SmF2IBKboVFz9nKeggnMMaum8Z+ZkJnq9KTAjNWc4f9Qij0f1kS5EjWpt89zNrs6eZQ9Hjaq9GfXB4mFgoT8MENI0bTAwHvWh7EfDMLY2OtxkYDYs0j9//vwvAc689cyHsudlD/i///u/JzIzM92tgyyB2YVIYIpOKyM15xjUL+XzUP2cT8/JTGzzlAfXqkRT+GPqRxSuaR+optY275zSHdmz7A2rPN2K+rd8BMhypDhaHP3sC5qmRaEGmV3EH1vC1QP/A+YYhlGOB4HpNJzEjYt7dVf+rqp+/fr9tnHjRndhKIHZhch+mKLTmpOZuBq4OiM153ZUjeadjNScnaiNo990Ny0FDswFbWhqPR01r3MhavTsz9LU2naOFEcd8KY9y/4WakDVrcA99iz7M0CGI8Xh1b1E20rTNAtqEfuJhxwy/3979x9aVRnHcfxz54+lSToTIfNHZqRGj0mYY+1i6nLaBB3LSv+wUxSTHCmG2D+Kgn/UQlIkLmozOxoLCRS05R9FKrmpaIz2jC3NNJiRpeavJbuInv54rmk4t6Puenfv3i8QYfdwzvPfh+9zvuf7SPIkDY1EIlPCFA9bvt4ysqmuqVvZ/LJPY7HYgv379/fNy8u7L5OakHpUmEgbiWacIknvyJ0v+Zvcu8rWmn6GJ/5/WNIe3Rgg8LuQNMY3o+R2BV6VtFXSx9azR1O5pkgkMl9ua78tc4MgGKR2KsxJJZMWNNY1jjl17NTb/fv3L49Go3t37NjxTRv3pcLMIFSYSBuJbxx3StqZGD33mG50xA6XNE6u6eegpC/lQrSpbN3kKylZcBdkPfuzpHnGN8vkOp/3Gd/UyL3nrE7RSSmvhLhmlqSa9i6q3Vdrcqfn/iJJ48ePr66urp4gN6wCXQCBibRUtm5ys9yhzPWpXgtuZT37l6TlxjflctuemySdTZwhud169n4OeAgz7m5QexeUl5c/efHsxQeL5xTvk6SFCxfWFBUVvbZhw4ZhpaWlzOHtAtiSBZB0xjfdJM2Qe8/5iFyD0Cbr2aQ3WkUike8kFbRz2bYgCA6qjS3ZhoaGEXW2bnJ2dvZFBQokqaWl5aHc3NxdBw4c+OI292VLNoNQYQJIukRFuV3SduOb5+UmCC03vlkv6RPr2VNJfPxWtR+YW+W2+FsVj8d7NDQ25E19b+o/S4uXvn/976tXrx5fVVVV0tzcXNmnTx8ayDJcVqoXAKBrsZ6tsZ59WW6aUo6kRuObjcY3TyXpkZ9J2tvG71WSvmrrBpcuXerdvWf3IL84/0h+fv6F6/9isdieIAiyVqxY8UyHrhidEhUmgJSwnj0mqcz4Zrlc5/P3xjc/yjUI7emoBqEgCK5GIpEiSWskvSGpR+KnuKT1kpYEbbybisfjvUaPHn1y2gfTDuVk55y8+beBAwdeaWlpmdcR60TnR4UJIKWsZ89Yz66U2xLdLved7WHjmzmJ01PuWRAEl4MgKJVr7nlRbrD8oCAIFgZB0Or4RUnavHnz4PPnzz8aLYz+efry6XEFQwt+6Ij1ID3R9AOgUzG+yZL73nax3OdCayRVWM8me+j9/yb9RKPRObW1tdHCwsIdM1fOzDrTcmb44nGLY3d4T5p+MgiBCaDTMr55Tq5BaIqkCklrrWeTNXyi1dF4F+IXeq46vOqjgqEFsYlDJt7pEAYCM4OwJQug07KePWQ9O1tuKEW2JGt84xvfjLkfz78WXFOFrXirb3bfoxMGT0jpxCKkHhUmgLRhfJMjaZ6kBXJDK1ZJ+raDGoRuqTArGysnHTl35KVFzy5a1u+Bfrd919kGKswMQoUJIG1Yz56znv1Q7t1mpdwAhJ+Mb143vunZkc+qOl41tv5M/eySJ0pW32VYIsNQYQJIW8Y3EbnTaBZLGiVpraQN1rPn7+J2SyQ1Xbl6JbKxfuOspktNL0wZNmXtXby3vBkVZgYhMAFkBOObsXINQtMlfS43Qej4Hdxi7okLJ8bsbto9U4GyZoyYUTmg94Dme1zWH5K23OM90EkQmAAyivHNEEnvSnpT0iG57zp33W7ge+Ig7Blyp6s8LVellifO+wT+Q2ACyEjGN73kzuWcL2mkpF/ljnw7ISmiG0fDPS6pTi5Yt1nP8r4SrSIwAWQ845sBunGo+OOSArkAPSHpuPXs3ylcHtIEgQkAQAh8VgIAQAgEJgAAIRCYAACEQGACABACgQkAQAgEJgAAIRCYAACEQGACABACgQkAQAgEJgAAIRCYAACEQGACABACgQkAQAgEJgAAIRCYAACEQGACABACgQkAQAgEJgAAIRCYAACEQGACABACgQkAQAgEJgAAIRCYAACEQGACABACgQkAQAj/AvvimFS0LHFxAAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "
    " ] @@ -481,7 +474,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.8.5" } }, "nbformat": 4, From 50561cb9d4bbb19ec86b149c9b8e1d5478b82d23 Mon Sep 17 00:00:00 2001 From: Dustin Arendt Date: Fri, 22 Oct 2021 11:04:49 -0700 Subject: [PATCH 31/41] HYP-188. Added documentation requesting the user not "Run All" the notebook. --- tutorials/Tutorial 9 - HNXWidget.ipynb | 325 +++++++++++++++++++++++-- 1 file changed, 310 insertions(+), 15 deletions(-) diff --git a/tutorials/Tutorial 9 - HNXWidget.ipynb b/tutorials/Tutorial 9 - HNXWidget.ipynb index 3b11bbe0..23b201ca 100644 --- a/tutorials/Tutorial 9 - HNXWidget.ipynb +++ b/tutorials/Tutorial 9 - HNXWidget.ipynb @@ -16,9 +16,123 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    FullNameDescription
    Symbol
    AZAnzelmadaughter of TH and TM
    BABahorel`Friends of the ABC' cutup
    BBBabettooth-pulling bandit of Paris
    BJBrujonnotorious criminal
    BLBlachevilleParisian student from Montauban
    .........
    TSToussaintservant of JV at Rue Plumet
    VIMadame Victurniensnoop in M-- sur M--
    XAChild 1son of TH sold to MN
    XBChild 2son of TH sold to MN
    ZEZephinelover of FA
    \n", + "

    80 rows × 2 columns

    \n", + "
    " + ], + "text/plain": [ + " FullName Description\n", + "Symbol \n", + "AZ Anzelma daughter of TH and TM\n", + "BA Bahorel `Friends of the ABC' cutup\n", + "BB Babet tooth-pulling bandit of Paris\n", + "BJ Brujon notorious criminal\n", + "BL Blacheville Parisian student from Montauban\n", + "... ... ...\n", + "TS Toussaint servant of JV at Rue Plumet\n", + "VI Madame Victurnien snoop in M-- sur M--\n", + "XA Child 1 son of TH sold to MN\n", + "XB Child 2 son of TH sold to MN\n", + "ZE Zephine lover of FA\n", + "\n", + "[80 rows x 2 columns]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -52,9 +166,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e352155643ec495fa291518747e804e4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HypernetxWidget(component='HypernetxWidget', props={'nodes': [{'uid': 'JU'}, {'uid': 'CC'}, {'uid': 'BM'}, {'u…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "## Default behavior\n", "example1 = HypernetxWidget(H)\n", @@ -71,9 +200,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f9f773978a754c77ad79ed9cad283cca", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HypernetxWidget(component='HypernetxWidget', props={'nodes': [{'uid': 'JU'}, {'uid': 'CC'}, {'uid': 'BM'}, {'u…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "node_colors = {k:'r' if k in ['JV','TH','FN'] else 'b' for k in H.nodes}\n", "example2 = HypernetxWidget(\n", @@ -89,16 +233,137 @@ "metadata": {}, "source": [ "## III. Attributes of visualization:\n", - "The `get_state()` method returns the attributes available from a widget for reuse." + "The `get_state()` method returns the attributes available from a widget for reuse.\n", + "\n", + "**Note:** if you \"Run All\" this notebook, the following cells may produce an exception. Acquiring the widget state in python requires some time for the widget to initialize and render. Run the cells below individually for best results." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'_dom_classes': (),\n", + " '_model_module': 'hnx-widget',\n", + " '_model_module_version': '^0.1.0',\n", + " '_model_name': 'ReactModel',\n", + " '_view_count': None,\n", + " '_view_module': 'hnx-widget',\n", + " '_view_module_version': '^0.1.0',\n", + " '_view_name': 'ReactView',\n", + " 'component': 'HypernetxWidget',\n", + " 'edge_stroke': {'0': '#008000ff',\n", + " '1': '#008000ff',\n", + " '2': '#008000ff',\n", + " '3': '#008000ff',\n", + " '4': '#008000ff',\n", + " '5': '#008000ff',\n", + " '6': '#008000ff',\n", + " '7': '#008000ff'},\n", + " 'hidden_edges': {},\n", + " 'hidden_nodes': {},\n", + " 'layout': 'IPY_MODEL_a3df1d8dc25d4a86b1dfd39341c449d0',\n", + " 'node_fill': {'JU': '#0000ffff',\n", + " 'CC': '#0000ffff',\n", + " 'BM': '#0000ffff',\n", + " 'JV': '#ff0000ff',\n", + " 'CN': '#0000ffff',\n", + " 'FN': '#ff0000ff',\n", + " 'GP': '#0000ffff',\n", + " 'CH': '#0000ffff',\n", + " 'MA': '#0000ffff',\n", + " 'MP': '#0000ffff',\n", + " 'TH': '#ff0000ff',\n", + " 'BR': '#0000ffff',\n", + " 'JA': '#0000ffff'},\n", + " 'pos': {'JU': [167.25095746075195, 281.58263454123255],\n", + " 'CC': [135.75492160545446, 221.81829316643464],\n", + " 'BM': [278.61480696410683, 212.71528742142323],\n", + " 'JV': [249.92250491500195, 285.31995810389947],\n", + " 'CN': [162.53012168959805, 160.79651426243603],\n", + " 'FN': [454.1961016406691, 258.8662052248289],\n", + " 'GP': [424.8654480047309, 504.3322298621465],\n", + " 'CH': [229.60838922887152, 166.67980235536837],\n", + " 'MA': [489.50473371013675, 576.6717592487552],\n", + " 'MP': [344.83880096715603, 560.1598468493333],\n", + " 'TH': [421.5436288772694, 370.8729072562323],\n", + " 'BR': [201.4076830402482, 226.98341329000954],\n", + " 'JA': [427.60515300786, 194.81917087602267]},\n", + " 'props': {'nodes': [{'uid': 'JU'},\n", + " {'uid': 'CC'},\n", + " {'uid': 'BM'},\n", + " {'uid': 'JV'},\n", + " {'uid': 'CN'},\n", + " {'uid': 'FN'},\n", + " {'uid': 'GP'},\n", + " {'uid': 'CH'},\n", + " {'uid': 'MA'},\n", + " {'uid': 'MP'},\n", + " {'uid': 'TH'},\n", + " {'uid': 'BR'},\n", + " {'uid': 'JA'}],\n", + " 'edges': [{'uid': '0', 'elements': ['TH', 'FN']},\n", + " {'uid': '1', 'elements': ['TH', 'JV']},\n", + " {'uid': '2', 'elements': ['FN', 'JA', 'BM']},\n", + " {'uid': '3', 'elements': ['JU', 'BM', 'CH', 'JV']},\n", + " {'uid': '4', 'elements': ['JU', 'JV', 'BM', 'CN', 'CH', 'BR', 'CC']},\n", + " {'uid': '5', 'elements': ['TH', 'GP']},\n", + " {'uid': '6', 'elements': ['MP', 'GP']},\n", + " {'uid': '7', 'elements': ['MA', 'GP']}],\n", + " 'nodeFill': {'JU': '#0000ffff',\n", + " 'CC': '#0000ffff',\n", + " 'BM': '#0000ffff',\n", + " 'JV': '#ff0000ff',\n", + " 'CN': '#0000ffff',\n", + " 'FN': '#ff0000ff',\n", + " 'GP': '#0000ffff',\n", + " 'CH': '#0000ffff',\n", + " 'MA': '#0000ffff',\n", + " 'MP': '#0000ffff',\n", + " 'TH': '#ff0000ff',\n", + " 'BR': '#0000ffff',\n", + " 'JA': '#0000ffff'},\n", + " 'edgeStroke': {'0': '#008000ff',\n", + " '1': '#008000ff',\n", + " '2': '#008000ff',\n", + " '3': '#008000ff',\n", + " '4': '#008000ff',\n", + " '5': '#008000ff',\n", + " '6': '#008000ff',\n", + " '7': '#008000ff'},\n", + " 'edgeStrokeWidth': {'0': 2,\n", + " '1': 2,\n", + " '2': 2,\n", + " '3': 2,\n", + " '4': 2,\n", + " '5': 2,\n", + " '6': 2,\n", + " '7': 2},\n", + " 'edgeLabelColor': {'0': '#008000ff',\n", + " '1': '#008000ff',\n", + " '2': '#008000ff',\n", + " '3': '#008000ff',\n", + " '4': '#008000ff',\n", + " '5': '#008000ff',\n", + " '6': '#008000ff',\n", + " '7': '#008000ff'},\n", + " '_model': 'IPY_MODEL_f9f773978a754c77ad79ed9cad283cca'},\n", + " 'removed_edges': {},\n", + " 'removed_nodes': {},\n", + " 'selected_edges': {},\n", + " 'selected_nodes': {}}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "example2.get_state()" ] @@ -113,9 +378,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ed794c6bf56d42008bb673c9a8d123c9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HypernetxWidget(component='HypernetxWidget', props={'nodes': [{'uid': 'JU'}, {'uid': 'CC'}, {'uid': 'BM'}, {'u…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "example3 = HypernetxWidget(\n", " H,\n", @@ -135,11 +415,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d1348669567a441586ccf9c17bdb20fe", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HypernetxWidget(component='HypernetxWidget', props={'nodes': [{'uid': 'JU'}, {'uid': 'CC'}, {'uid': 'BM'}, {'u…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "example4 = HypernetxWidget(\n", " H,\n", @@ -156,7 +451,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -170,7 +465,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.8.5" } }, "nbformat": 4, From 0a1723993cf350084dd4ace79934b70a271f2688 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Wed, 27 Oct 2021 14:01:05 -0400 Subject: [PATCH 32/41] update documentation --- docs/source/modularity.rst | 28 +++++++++---------- hypernetx/algorithms/hypergraph_modularity.py | 22 +++++++++------ 2 files changed, 27 insertions(+), 23 deletions(-) diff --git a/docs/source/modularity.rst b/docs/source/modularity.rst index 007d92de..9a9caad3 100644 --- a/docs/source/modularity.rst +++ b/docs/source/modularity.rst @@ -11,20 +11,20 @@ Modularity and Clustering Overview -------- -The hypergraph_modularity submodule in HNX provided functions to compute **hypergraph modularity** for a +The hypergraph_modularity submodule in HNX provides functions to compute **hypergraph modularity** for a given partition of the vertices in a hypergraph. In general, higher modularity indicates a better partitioning of the vertices into dense communities. -The submodule also provides a function to generate the **two-section graph** for a given hypergraph which can then be used to find -vertex partition via graph-based algorithms. +Two functions to generate such hypergraph +partitions are provided: **Kumar's** algorithm, and the simple **Last-Step** refinement algorithm. -Two functions to generate such -partitions running either **Kumar's** algorithm, or a simple **Last-Step** refinement algorithm. Finally, +The submodule also provides a function to generate the **two-section graph** for a given hypergraph which can then be used to find +vertex partitions via graph-based algorithms. Installation ------------ -As it is part of HNX, no extra installation is required. +Since it is part of HNX, no extra installation is required. The submodule can be imported as follows:: import hypernetx.algorithms.hypergraph_modularity as hmod @@ -53,20 +53,20 @@ weight given to different edges. Modularity is computed via:: q = hmod.modularity(HG, A, wdc=linear) -In a graph, an edge only links 2 nodes, so given partition A, an edge is either within a community (which increase the modularity) -or between communities (so called noise edges). +In a graph, an edge only links 2 nodes, so given partition A, an edge is either within a community (which increases the modularity) +or between communities. -With hypergraphs, we consider edges of size *d=2* or more. For some *d*-edge *e*, let *c* be the number of nodes -that belong to the most represented part in edge *e*; if *c > d/2*, we consider this edge to be within the part. +With hypergraphs, we consider edges of size *d=2* or more. Given some vertex partition A and some *d*-edge *e*, let *c* be the number of nodes +that belong to the most represented part in *e*; if *c > d/2*, we consider this edge to be within the part. Hyper-parameters *0 <= w(d,c) <= 1* control the weight given to such edges. Three functions are supplied in this submodule, namely: **linear** - *w(d,c) = c/d* for *c > d/2*, else *0*. + *w(d,c) = c/d* if *c > d/2*, else *0*. **majority** - *w(d,c) = 1* for *c > d/2*, else *0*. + *w(d,c) = 1* if *c > d/2*, else *0*. **strict** - *w(d,c) = 1* for *c == d*, else *0*. + *w(d,c) = 1* if *c == d*, else *0*. The 'linear' function is used by default. More details in [2]. @@ -103,7 +103,7 @@ where the 'wdc' parameter is the same as in the modularity function. Other Features ^^^^^^^^^^^^^^ -We represent a vertex partition as a list of sets, but another conveninent representation is via a dictionary. +We represent a vertex partition A as a list of sets, but another conveninent representation is via a dictionary. We provide two utility functions to switch representation, namely *A = dict2part(D)* and *D = part2dict(A)*. References diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index 15046c98..5183e22c 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -73,14 +73,17 @@ def part2dict(A): def precompute_attributes(HG): """ Precompute some values on hypergraph HG for faster computing of hypergraph modularity. + This needs to be run before calling either modularity() or last_step(). + + Note + ---- + If HG is unweighted, v.weight is set to 1 for each vertex v in HG. The weighted degree for each vertex v is stored in v.strength. The total edge weigths for each edge cardinality is stored in HG.d_weights. Binomial coefficients to speed-up modularity computation are stored in HG.bin_coef. Isolated vertices found only in edge(s) of size 1 are dropped. - This needs to be run before calling either modularity() or last_step(). - Parameters ---------- HG : Hypergraph @@ -153,9 +156,9 @@ def majority(d, c): Parameters ---------- d : int - Number of nodes in an edge + Number of vertices in an edge c : int - Number of nodes in the majority class + Number of vertices in the majority class Returns ------- @@ -176,9 +179,9 @@ def strict(d, c): Parameters ---------- d : int - Number of nodes in an edge + Number of vertices in an edge c : int - Number of nodes in the majority class + Number of vertices in the majority class Returns ------- @@ -299,7 +302,7 @@ def modularity(HG, A, wdc=linear): Returns ------- : float - The modularity function qH for partition A on HG + The modularity function for partition A on HG """ Pr = _compute_partition_probas(HG, A) return _edge_contribution(HG, A, wdc) - _degree_tax(HG, Pr, wdc) @@ -500,8 +503,9 @@ def last_step(HG, L, wdc=linear, delta=.01): Note ---- - This is a very simple algorithm that tries moving nodes between communities to optimize hypergraph modularity qH. - It requires an initial non-trivial partition which can be obtained for example via graph clustering on the 2-section of HG. + This is a very simple algorithm that tries moving nodes between communities to improve hypergraph modularity. + It requires an initial non-trivial partition which can be obtained for example via graph clustering on the 2-section of HG, + or via Kumar's algorithm. Parameters ---------- From b2da60f4c7b5137e8f30d2f541d7772c566ad6b6 Mon Sep 17 00:00:00 2001 From: Francois Theberge Date: Wed, 27 Oct 2021 15:15:45 -0400 Subject: [PATCH 33/41] add CL example --- ...Hypergraph Modularity and Clustering.ipynb | 143 ++++++++++-------- 1 file changed, 78 insertions(+), 65 deletions(-) diff --git a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb index 97a45805..200eb688 100644 --- a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb +++ b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb @@ -108,12 +108,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] @@ -133,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -167,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -177,9 +177,9 @@ "B has strength 2\n", "A has strength 4\n", "C has strength 2\n", + "E has strength 2\n", "D has strength 2\n", - "F has strength 3\n", - "E has strength 2\n" + "F has strength 3\n" ] } ], @@ -191,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -200,7 +200,7 @@ "Counter({2: 4, 3: 1, 4: 1})" ] }, - "execution_count": 34, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -212,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -258,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -281,17 +281,17 @@ "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "\n", @@ -318,22 +318,22 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 36, + "execution_count": 8, "metadata": { "image/svg+xml": { "isolated": true @@ -351,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -360,7 +360,7 @@ "[{'A', 'B', 'C'}, {'D', 'E', 'F'}]" ] }, - "execution_count": 37, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -373,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -382,7 +382,7 @@ "[{'A', 'B', 'C'}, {'D', 'E', 'F'}]" ] }, - "execution_count": 38, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -394,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -402,7 +402,7 @@ "output_type": "stream", "text": [ "start from: [{'A'}, {'B'}, {'C'}, {'D'}, {'E'}, {'F'}]\n", - "final partition: [{'C', 'A', 'B'}, {'D', 'F', 'E'}]\n" + "final partition: [{'B', 'A', 'C'}, {'E', 'D', 'F'}]\n" ] } ], @@ -417,24 +417,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Random hypergraph example\n", + "# Chung-Lu hypergraph example\n", "\n", - "We build a random Chung-Lu hypergraph and compute modularity for partitions from 3 algorithms:\n", + "We build a Chung-Lu hypergraph and compute modularity for partitions from 3 algorithms:\n", "* Louvain, on the 2-section graph\n", "* Kumar algorithm\n", - "* LastStep algorithm\n" + "* LastStep algorithm\n", + "\n", + "We use the **strict** modularity, so only edges where all vertices are in the same part will add to the modularity.\n", + "For each algorithm, we compute the modularity qH and compare with the number of edges where all vertices are in the same part.\n" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "## random Chung-Lu hypergraph\n", + "## Chung-Lu hypergraph\n", "n = 200\n", - "k1 = {i : random.randint(2, 5) for i in range(n)}\n", - "k2 = {i : sorted(k1.values())[i] for i in range(n)}\n", + "k1 = {i : random.randint(2, 10) for i in range(n)} ## node degrees\n", + "k2 = {i : sorted(k1.values())[i] for i in range(n)} ## edge sizes\n", "H = gm.chung_lu_hypergraph(k1, k2)\n", "\n", "## pre-compute required quantities\n", @@ -443,14 +446,15 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "qH = 0.30826015238966775\n" + "qH = 0.0569711443530809\n", + "edges with all vertices in same part: 28\n" ] } ], @@ -458,22 +462,28 @@ "## Louvain algorithm on the 2-section graph\n", "G = hmod.two_section(HG)\n", "G.vs['louvain'] = G.community_multilevel().membership\n", - "ML = hmod.dict2part({v['name']:v['louvain'] for v in G.vs})\n", + "D = {v['name']:v['louvain'] for v in G.vs}\n", + "ML = hmod.dict2part(D)\n", "\n", "## Compute qH\n", - "print('qH =',hmod.modularity(HG, ML))\n" + "print('qH =',hmod.modularity(HG, ML, strict))\n", + "\n", + "## number of edges where all vertices belong to the same community\n", + "print('edges with all vertices in same part:',\n", + " sum([len(set([D[v] for v in HG.edges[e]]))==1 for e in HG.edges()]))\n" ] }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "qH = 0.45084198402991216\n" + "qH = 0.19310099840187803\n", + "edges with all vertices in same part: 54\n" ] } ], @@ -482,28 +492,37 @@ "KU = hmod.kumar(HG)\n", "\n", "## Compute qH\n", - "print('qH =',hmod.modularity(HG, KU))" + "print('qH =',hmod.modularity(HG, KU, strict))\n", + "\n", + "## number of edges where all vertices belong to the same community\n", + "print('edges with all vertices in same part:',\n", + " sum([len(set([hmod.part2dict(KU)[v] for v in HG.edges[e]]))==1 for e in HG.edges()]))\n" ] }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "qH = 0.4988064689466768\n" + "qH = 0.21300020106295142\n", + "edges with all vertices in same part: 57\n" ] } ], "source": [ "## Last-step algorithm using previous result as initial partition\n", - "LS = hmod.last_step(HG, KU)\n", + "LS = hmod.last_step(HG, KU, strict)\n", "\n", "## Compute qH\n", - "print('qH =',hmod.modularity(HG, LS))" + "print('qH =',hmod.modularity(HG, LS, strict))\n", + "\n", + "## number of edges where all vertices belong to the same community\n", + "print('edges with all vertices in same part:',\n", + " sum([len(set([hmod.part2dict(LS)[v] for v in HG.edges[e]]))==1 for e in HG.edges()]))\n" ] }, { @@ -580,7 +599,8 @@ "source": [ "### Modularity (qH) on a random partition\n", "\n", - "Should be close to 0 and can be negative." + "We use the default choice for the modularity (**linear** weights).\n", + "Result for the random partition should be close to 0 and can be negative." ] }, { @@ -591,7 +611,7 @@ { "data": { "text/plain": [ - "0.008670599366313703" + "-0.0054328760823038336" ] }, "execution_count": 18, @@ -656,7 +676,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "qH = 0.5346892761525917\n" + "qH = 0.5382594158646983\n" ] } ], @@ -685,7 +705,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "qH = 0.5475162906819371\n" + "qH = 0.5460841945299417\n" ] } ], @@ -735,27 +755,27 @@ " \n", " \n", " \n", - " 18\n", + " 16\n", " Daenerys Targaryen\n", " 31103\n", " \n", " \n", - " 22\n", + " 23\n", " Jorah Mormont\n", " 19344\n", " \n", " \n", - " 30\n", + " 7\n", " Missandei\n", " 13683\n", " \n", " \n", - " 13\n", + " 24\n", " Grey Worm\n", " 10497\n", " \n", " \n", - " 25\n", + " 8\n", " Barristan Selmy\n", " 6514\n", " \n", @@ -765,11 +785,11 @@ ], "text/plain": [ " character strength\n", - "18 Daenerys Targaryen 31103\n", - "22 Jorah Mormont 19344\n", - "30 Missandei 13683\n", - "13 Grey Worm 10497\n", - "25 Barristan Selmy 6514" + "16 Daenerys Targaryen 31103\n", + "23 Jorah Mormont 19344\n", + "7 Missandei 13683\n", + "24 Grey Worm 10497\n", + "8 Barristan Selmy 6514" ] }, "execution_count": 22, @@ -790,13 +810,6 @@ "D = pd.DataFrame(L, columns=['character','strength'])\n", "D.sort_values(by='strength',ascending=False).head(5)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 2c30ece813f71840fe5bea1422a1be9ddf7fd2a1 Mon Sep 17 00:00:00 2001 From: Brenda Praggastis Date: Thu, 28 Oct 2021 16:21:47 -0700 Subject: [PATCH 34/41] updated version number --- docs/source/conf.py | 2 +- setup.py | 11 +++++++---- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 59a67ec0..9f1dc007 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,7 +19,7 @@ import os import shlex -__version__ = "1.1.4" +__version__ = "1.2" # If extensions (or modules to document with autodoc) are in another directory, diff --git a/setup.py b/setup.py index b2f9da02..7c2b503f 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ from setuptools import setup import sys -__version__ = "1.1.4" +__version__ = "1.2" if sys.version_info < (3, 7): sys.exit("HyperNetX requires Python 3.7 or later.") @@ -19,7 +19,7 @@ "hypernetx.utils.toys", ], version=__version__, - author="Brenda Praggastis, Dustin Arendt, Sinan Aksoy, Emilie Purvine, Cliff Joslyn, Nicholas Landry", + author="Brenda Praggastis, Dustin Arendt, Sinan Aksoy, Emilie Purvine, Cliff Joslyn", author_email="hypernetx@pnnl.gov", url="https://github.com/pnnl/HyperNetX", description="HyperNetX is a Python library for the creation and study of hypergraphs.", @@ -45,8 +45,8 @@ * Visualization: Dustin Arendt, Ji Young Yun * High Performance Computing: Tony Liu, Andrew Lumsdaine * Principal Investigator: Cliff Joslyn - * Program Manager: Mark Raugas, Brian Kritzstein - * Contributors: Sinan Aksoy, Dustin Arendt, Cliff Joslyn, Nicholas Landry, Andrew Lumsdaine, Tony Liu, Brenda Praggastis, Emilie Purvine, Mirah Shi, Francois Theberge + * Program Manager: Brian Kritzstein + * Contributors: Sinan Aksoy, Dustin Arendt, Cliff Joslyn, Nicholas Landry, Andrew Lumsdaine, Tony Liu, Brenda Praggastis, Emilie Purvine, Mirah Shi, François Théberge The code in this repository is intended to support researchers modeling data as hypergraphs. We have a growing community of users and contributors. @@ -66,6 +66,9 @@ 1. Static Hypergraph refactored to improve performance across all methods. 2. Added modules and tutorials for Contagion Modeling, Community Detection, Clustering, and Hypergraph Generation. 3. Cell weights for incidence matrices may be added to static hypergraphs on construction. + + **New Features of Version 1.2** + 1. Added module and tutorial for Modularity and Clustering """, extras_require={ "testing": ["pytest>=4.0"], From eb6122d6d4f653f19c1a5521ddf35b85adc2490b Mon Sep 17 00:00:00 2001 From: Brenda Praggastis Date: Thu, 28 Oct 2021 16:48:30 -0700 Subject: [PATCH 35/41] updated documentation --- docs/build/.buildinfo | 2 +- .../.doctrees/algorithms/algorithms.doctree | Bin 406574 -> 380235 bytes docs/build/.doctrees/drawing/drawing.doctree | Bin 141915 -> 143227 bytes docs/build/.doctrees/environment.pickle | Bin 459845 -> 461786 bytes docs/build/.doctrees/modularity.doctree | Bin 15732 -> 25134 bytes .../algorithms/contagion/animation.html | 4 +- .../algorithms/contagion/epidemics.html | 4 +- .../algorithms/generative_models.html | 4 +- .../_modules/algorithms/homology_mod2.html | 4 +- .../algorithms/hypergraph_modularity.html | 218 ++++++++------- .../algorithms/laplacians_clustering.html | 4 +- .../algorithms/s_centrality_measures.html | 4 +- docs/build/_modules/classes/entity.html | 4 +- docs/build/_modules/classes/hypergraph.html | 4 +- docs/build/_modules/classes/staticentity.html | 4 +- docs/build/_modules/drawing/rubber_band.html | 12 +- docs/build/_modules/drawing/two_column.html | 4 +- docs/build/_modules/drawing/util.html | 22 +- docs/build/_modules/index.html | 4 +- .../_modules/reports/descriptive_stats.html | 4 +- docs/build/_sources/modularity.rst.txt | 131 ++++++--- docs/build/_static/documentation_options.js | 2 +- .../algorithms/algorithms.contagion.html | 4 +- docs/build/algorithms/algorithms.html | 261 +++++------------- docs/build/algorithms/modules.html | 4 +- docs/build/classes/classes.html | 4 +- docs/build/classes/modules.html | 4 +- docs/build/core.html | 4 +- docs/build/drawing/drawing.html | 11 +- docs/build/drawing/modules.html | 4 +- docs/build/genindex.html | 34 +-- docs/build/glossary.html | 4 +- docs/build/home.html | 4 +- docs/build/index.html | 12 +- docs/build/install.html | 4 +- docs/build/license.html | 4 +- docs/build/modularity.html | 129 ++++++--- docs/build/nwhy.html | 4 +- docs/build/objects.inv | Bin 2904 -> 2885 bytes docs/build/overview/index.html | 4 +- docs/build/publications.html | 4 +- docs/build/py-modindex.html | 4 +- docs/build/reports/modules.html | 4 +- docs/build/reports/reports.html | 4 +- docs/build/search.html | 4 +- docs/build/searchindex.js | 2 +- docs/build/widget.html | 4 +- docs/source/algorithms/algorithms.rst | 16 ++ docs/source/modularity.rst | 8 +- 49 files changed, 502 insertions(+), 478 deletions(-) diff --git a/docs/build/.buildinfo b/docs/build/.buildinfo index faa7924a..c934d15a 100644 --- a/docs/build/.buildinfo +++ b/docs/build/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 68dc8fcdf1a2d3c9a53105fe3a9dc22c +config: 38df5440d6c68b719b03372bcc1f1ddd tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/.doctrees/algorithms/algorithms.doctree b/docs/build/.doctrees/algorithms/algorithms.doctree index 7ff0c5924672a406684ed405a9d3f8483e1d6320..b49591f92c63bad464e5a94befac1c311eeb4576 100644 GIT binary patch delta 42847 zcmdsg33wGn*0x>UHxNkJ5|)qvmz{(~!lDuaBxGX^VJAQ!AqgaxkUe2135#n`Fi7dR zfsUgN%7CaD5fv2pa6l2)5kvv^1rZoI{^$SmKrhu*r_On+>YP)j zs{3yJtj)ncw60vOZ4e=v|5I(n*?HPF8IU6qg4`MTS?&oL<+=G;#TohT;eFhl5_$K^L-R#rZS}9w<-D~S3q%*KX^(0>H*3I# zY9lu*vj2u`S)DHu`%m*|S6wwaV?pk|)g|+D-QcO0JEb^d-^z?qw>zZ|b22ICrAOyv z7thOfyAz4!$U-qFyi*_d$owex@a+8D(gOE{K4VcZE+@MN3I8=EpFVVXNRVbaub6Gl&T_v_oQuk5fu z^wtLr=r330iUEzT8Q6cowY{&M9ob(lDiBwTHwM*2hIN{;anMy)<%D_;<5V`slMIQ% zt2Qj(&?Cp?IYN&bq~W^NwBf&3R6?DE?FrQX%i`{wFWU7R`gIpqhHTF(a!E1z z|Eu!;V$m|;k;^-KL0MjIzI%#qzei_XQZ_d`uf8F0U#aNQxD$p-UO|a_TylcUC>8x> zUWtg*n#rmqBB&-NuUWd;F9V{k=$F-h`j4>@owVZLK${f~gv>(Mx5dPmj2zUjbwLTDrZx^Zt$NV@?W5$p1){q=T_$E~-4(VeGg~zeCs6Hff^6Py;%ba?0rHu0 z(X%=Cl0m97^yAL(NxA6U!1F44*=Vh%=z+nt9lsK7H8CTqX8VItVrrDUuZuocesEeO z2v3wOIxM0Br=qSLEl1A@^W;X!1z(HtS|xGPTU*8q$-;eF8*P!gKH)FgTvlTI8Br?o zqH0b)+lDHrIgSd^4oo}KT`aA)Oa zmgW}ZXB01XFUTk?%*~(YF3rhy7iJWfy5|-YyR$PgbKK?G#iiMcdbx|UOUsI>6g|$R za_)>0+~(z$l)4M%x=XT4O9smyzY!hgn1l2`Tu-?F4#F_hvpq^~j@A3i^s^#gY>kq! z!}a;ve$}koo@nj=5@jQpYa$E6<(=om4PptJ_B@*Cn96nHu{PrIsG76y1&Mc}YC2|w z;3TUW>fK~al;+mXs9Z65t+lVzWy*;*a$8%iZ}YRD?RgAptJRVzg>6))N? z+R(XUs20}n9b_w;lhaBLxgc`ofD59GD%u>}Uwe}Y%9w>(Yw;5LOMJN44Ms!8iip8A zk)MY9cOUHFzRQ3yPPp9#`Pl@#-CEQ*^7KU!Qt?}Ix}l}47Uw2)jKcq2c{ImhVpx+8 z#p=C{P|tOcA}m~VA8d)X`TofwRf#sm5=|c2rA5f0GokN-SBl=k7%U^3YQ3fVd(lEO zA@vNdIr-0UF;CH(V$qX(qV#aIdd=C-`Ug&;mbL~}P2}gTiPGi>&E3jCRUS>pVKZF` zbyc(Z>0o)=U_GHWvZ)@biJhQ|0sHh|d2=)Ue(~~PHCCTyiFm9Y7%YE&(=|tYFj#i% zr}YxYkR{p`A>J7*`?k=N#kXeq`N8t!RIRsgMb~VedbPl9>@+P%ZhhP37D3TcyPzjh z;`r$rm92>lj|lXwg~#_n$c*s~et3LuAFRgrLGS}T#pC+`@utT24$ylV5Dsdohc!dX z<}5CRhgFx0U%V^*}8}Mz++uT$$tkmU?$Bxt@n8-d-d`%a~1~pX|{} z4-+ZTGO?B3rE9gTID2kx)&|c$=s(XZ2%aV3i;LzwHv zL!2a<4XKlNkYL?`GxLt~^X>FW0xUsch?gx9?l=O0mHv}3%a1Y>Tc||a1e@uN&CH>m>>;FMBQc=ofH-O#Kg6n$2J|Yb zF`}WfLRm0Kj~91Z5~~5-G)iyFjf*zgFoXy6dPT3&qNfJ*aI_-^^bY69;{m-rS`Fy! zgg+1Hg9IZF=z~LeKzAtB6Kma_^%zZjggU+$tPLRpyhWTJqQ>(H;!~-Kb3^2{nXdW5 zHB^mbZK&+oO^*;k_Wc(b`UKI@zQ;K4AP;1@+KH>Zi9CRP$?^ccfci0jPYh85_}CCN zfG>anYXF}kX4C*aK7hK7b>&d76E-7&q~nFR|9%Sy9hee<&ya(4#kr+93t$d0OD!$V%`TCjMCe_6 z<9TsGdA3uTE-1(<%gaFa#eEu$EQMoKRF+$u4U+lt{s_H&g?nL6E*z&!OvmSBy9?%& zX5{8)XDJ)KD1+-O&&_Zv&77H6R#J*7E_^R|RAh|%^)AU~zl^-95lOuo!4sqWqsta# z6n9587O#D{jeX{(Bx2A1|765O+vU@C|8F+lsmi;2n$N3mJu9fkhI*C_g-Yz+G|aP* z?%R!aA#Kr6ibsSzi;j#tt7}8At31iFsEw8_HX;|C#TqIX<~awQYMIpSLzPLLGL%i~ z<3rh`(#bNZ`-ie|dTFRKsRuyAX;P^m)b-;~c5rFoj&=N_Xveo?QU5%YE$YKV$)a|Q z8RPEZoDhV&d-rxb^_Wvx6@_TQqUBI|GSk&o{NT&xMaAaDp)&TUm?D}CQ}%Nh+s|XD z6DGLXusRt<8D{h%GurAR#39$ay4Mwaw7cBwU75wUfZ&MC?|G-ZaA^8mt+$C7L7o;| z?~Dc67|T4;9nP1nFWmiVHn$9x+ddKul3;ZVTKI1k%`tWO9fi=PsV-%Wy@o-U zmO(r^tmfo>ZG@)*8-&PTG6=q8tjT!BvF>8nFh>_vPT${r!QZdIr<3LMeW1X6&j-De zSD+`ypVERgvjM(UW%jEE&>G6vCpc=;T63w(loKJs?7WR)F7Lb+DQ*bRVCN5KcK*N` z^}d%ZQP}zWBdLaPQ8ScXv30l$Nmdonw0AUWuyjFqMYmY;6&!8^P4LOw(EL6(6XwhG z^;CfEw`TXMuBsO8w-+!sUsW-TU=shW5{hUbn)V`R*&2=?@e)*H&t8u$$mr(=}Yd6WcDUguXa zviVSbve09B7ODNLxTBDd_%TMFsc}V%Q1wi-j%CkO`;oZr8R$!eoy;>a>|~;5o+Z8& zqnyk$sLygTzoNR8bM#IOJDEv;`pqMo+)37HS-rEf5V2nfN6DROb{yGkgx;p2Pb15( zqArz{JvXB)uhgBHQ-GeuMQt6}Bb*li@-j+FXT$R;#Qof|e0F$Blt)8e>+et@=z^nE zWd8S*g5ntyYc+^h@mL<|WIXcmNE@;OO8g}SMC44h!>V)fFJrdg--ekz27jf>t*X%3|3oDJG!9JYU6-S5os@+5oMGjEvJ;`YGKb zn{UwF^7IN3rmd5&JtulZ&Pvn=V%*D{Tn>o%%aXUaDhR z{oWMImgM?aa(1--GG?mYQoePo(M+}-p*NH7ZPc5~o>h?JeIGfFDmjiii2qSd3Z@5g^Cg#eu6ZOW@HBP@){C&7m|NDq}rT*U; z&gx(Ln)nK;S+}mO_65r!cl(O~@yT$u0Ggkydf9v|=(29@kAO@@j(lj7XljONhSnLP zQ5aF=x0mcYQC}bi#HqEJeCo{+{`09x!aVN`uiq?mG;YV}lV?|8;&~H~ftr+I0iu5# z&t=!d@tlcH$K$no>3L_lt*_D0%o!Bj;(lr;hk@uHCpU-a!CIV(v95_z7Ge#{_^((9 zFhR>XE18LniCP0u5m&F3XdzC-%5OG8Z@hhlP_HYY=mep@V0twvc@4BrRaR1UPsFjk z`05Jbc&7fkDICsZ{+hjR33s_=sMlbiauVjSR~W5Ac02^5@XhuHv_1)(YoMvM`IH(V za^g7b+&7-BeeZa?S;NOQkF3vR)bYeN7GbAc%IP}GH~Pw#knvZEfMfnP+S zXDbF~S`5&#Qkp1@XB+!OoSGrr{G8T;I}lf^+a1nweHlqJmbDsWd0p(|rVlDmgop&RYH@$`aY zHHF35W~4jIol%OQ>>LEWODGr`<(8XP>K$qxZrN1c`hW-%(-KrPrAq>9hDiym8R%q1 zxfUd_=jcgLo?`)6a4H5Wh#oO1K`92DY3vM}glz21Cr^S6bHMT2xojI_ckBN>l~8ng zh(keu@d@mOCMS@7Y0^K|8FG`)=jd%@a=7kqt_V<&%{YrTmfAR18}TP!oE}g(J%Bc% zFxyUrQEdW`n3bppUg@s7kwO`cRNzcVHGaTCszZt#8m@<2v8H6E+YvQXPU76}6z5-w zsGakb?Q135*XV~7QEQOM?QbMHW%Ez0vlpNYN7Oo~GPvc4THi#u`F0VZ`H<*WFA0VR zWeJ{&uPu1~ea=q3z7Ew@Uqo$kqKc?B`t^vKFBxYPZPF3ECmcfF>ztA=nztlLtA`0qPtpgT*v3-!yY9VChHbN4& zQb;LM`qvn40#Y4!7 zrIrVXm}$epD|(Z8a7;P8O;2G~d&g8PE%MpR@4vH@ZAUII*yY#i3-vq=<)v2ecaW^% z;(WEV+o??s7Ep?}+*QTNTm_Q5{rxdLt7A+M`?FwTG$LSfTUqg;l z>`EYB-OyroL-rj_uF|feznm`E*VzsKdtmwZDjiyxvMS#yED2W}a1Bf1fGZaGXKP|$ z61!EANosdp=n_3Z_9@o~iTEUTtGY*`oat7@(Iu@q=X_<&dOTzl$(fNSlU>)z=f4)y z#e^hT^_(6N;4CRq&S1AzViG%2sEi${UP;Q4N(5cYky3T=PP;xy>`0}STRZLS9^kt? zW^Jp3uK7K?@dnG(#d?Qy(-r%V))FqeoE3LTpxa#Bki_#hIryCDC*qReyoCo}?c;uw zme7iyd=)#85?Z$QuUWJYLXQN%2Z#gBQ# z*=o_l%3OgBw3HjPO3?xcqUNIp*r@FMd8Ik3d}%=`cKN9F6_6^;DY5J0)J(kNkei>8 zMEr$_rTDH=T?%tBpXy%Yo zP*@0`YeNMoD&Ku7|0U!V@D)CI{Wp*?nei~ma< z-PS_?R9fgKoXX~~`o}_a>ws@5x!q`bJLNmM=aPD-!}2;UEd{SqemsIh9^FTB$b(L1$V0<|u+Xu#gWw@HAaZ_$M2C;`o%2$8 zL_WHYRP%Y9X~N3)*x;jeQAB8ACTJvkjLk;!I#D@2h!7=;t%|M_vHQhok2pe-k(C9t zVFzz4bNw50djYNfwE_VuI7HsV4I|mp#j5{moA(kg@3g4&kmCIT6Vl!fie@z%KW!z~ z+>Z6eo%B$xDNP>9LBqC}_yHjTCm;)2bd?YuaH~`G8fWa*96z&Y$&{%bMt2 zw8?UF6TM@r$p?Q5#uX=ej#4|*yCWM#5Ic`j`w8OA^kLp~4jMYCjFKEQY(0v7UDU_E?xm5+ z*KLjZEMGU6>Q+I+pGT@?q#GHc-?9nnx9=yYV{JptzyG6)NQ(=5S^MT~--s8HO1+R@ zv52&s&~ffzZT#ztNJP%Lh%}W=e!aUaM%TZHQ@$! z)?E%w0xuwSN|O(75`n+CgrpEsOGx+JuFn@mqtwoVEu(l6{s=V)`akaK+gadPLK6E& zsVH&bC{~#-j*=NKVG(8&EwzZFqc}kL4#r8iF{3%Pjbq`6ia<-E2#57>CN0_aMV9Sf zSvY!e6h}c{7{x)|U88=(qEV2n3N@9#DM{1Oto-SOMWgQYJc2YgWupb7GoX%8ZuscS zg_7C^BN#icSg59>S*T`Bk&T1izj?t3B_n$q1E|8zy!f?i6o&?%LBk-_@CY;*UTk^@ zci3RCfV1%i0}pDl+An-eGqqMUWJuO!y9=&-U>k-jpP{?17;Tw=rK&PAU!Ktx7i0qN zvAMX*;(`~9wC(i7!p*U0q`CmTj|3@eNZJ3{CVrPiTq*k^bQeTGeui2%LlD$Q@j}v> zQEKVv4D~No{U1@IuV;R~?4-cA2A~==tp+R!T1~-R#%IO;V@5Q+~G|`a^%GNF-j$k z8^bDT?HC@&bg~w%{xpX5(t~5v!qp?hrb97NK`5rRXm5Js5NCGZ=$`gx-;BC20vDs2 z9$31-9bOQ6PqFi^lbwo=G>x^}PBq<}vRrKBn_G-#w!@acA*Ve)e?w+~c6yAQ(?t)X z-I`eF`a&`Nu9sE;2iQ(Eu@sg2yEU)Q23xw#c!QOwyOGYLvVPiQ=8G^Dv zq2X8)1vN-h038PDMaM`&#TcZZ#Ou4y64pFOC!$t0NGFc925Epi*4q^z!{>^|wXbc_ z*J{EuR%&xZUomg2T>m&;IKnwH1N(OF-=inW2e;~b{nrxr2tk_}Z**j+ZLm$BW%%Et za{OT|X|R4xJa^H3yU{vIA$Q+zwN|xzpVV&>;*qh~Plh<#{91jU*iI1eyY4%XA8wlU zt~+wkOU!{LnMMvWsejd5X<|QMp&%@W(s`uRM3VNRlXx)i9Za@`=Y*YbiWA zkej1j*J&N3c8#vKMupO)+WVxnR@W!q(*gp0$dN{yECZj{JBY@~tk%E>tF^OZNv%c7 zg4gxVA&pV5<-;l-I9_`m&GEh#UiigSb$0gUi+z(^w;XBy#+%8`w&bUS(EMZLW|x>xUohvZFYB}`d1 z+od(u=E(KeVV*V{e8Tv?Y}N?@_WKAhWIr18ePDNAe{nQfez;#>k26zy$3bnsml};p zzi~V!^>I!p;{S>_UAMe}dFy)z^mZb29FNWi@n$KF&dD$9p~4iY^*A1#!JvZCsgEQ3 ztj(qxu)hQwFvdc14AAq=j>7{K#CU){NKo+rO$G~UfF_Tl0ZM&y{a`&#h91;Qyd8Dt zLA^%ILp?C6nd4}y9(U9v>Hik?>-}^H`}NAlsZAM~B$Mi`W#iNw{+A1lV1DQ z>-ycAe|QQWgnGKCP`B?d3zv&l)b|;iW}-_9>mC{TmVU|qI+gVr&I%nhknYv%N>;wL zZ|epAi7H?GW%(MS8Z%#Qudc>0&3}^0IN`F4q^*#VMxL0P!d)$sa)x=z&}EdzFUxgf znM^*4H7jKUV)~!hwlV2=SZW1*glL+rAc~- zSe~LxDbA0(j9s{APkl)Wd+G~Q$Wxc2kL#P{ZKHHIk9D)=gR16(DRNIMSEB4R8jm++ zG!`dQcqnWE1$g71)LFYgDc*eWlj0S(wD~{_l$%qaQ9wp(q%O&^H%T=eUzZ>jDXpuw zEG3;iq0iK=;Usw?3r-~yc2K*nPHNgg1q&#U{%Lp+?34bH6{qweGCja(Br8wp!%zdm zbijg1cKlF(T+3Ir(C$4GB+qzi9?c_TC*wm4uN^ZwYOB0_;$s+>bG4>Uu7qbi!=>GA zmo-Uliq*ok$L!=suOPX#UXrY7L7IIjoVR_VuSQj@4(Bl)xow|lEEcBlJf{?$6B;Zt zg=ZM)xPz)aXI2eWdj=I*y*9k|Pzz(WW&}*Q>Z+RY9|y^6ztB5q&1LKiy|XMlf}Mj+ zCdiXx5rV^c^A~z1CUX3&&v^AL@=N_;dPep+z#Mw5Fj5+EHlBVpm>>s#r7t%UR2{A4 z@m59`4RdF29Z6;#ILDsW2Wn}KI*_)twegU^*;z+x)PWtGAYs-4ebioVTBfxOa&V!e zO7;>u9`3GPMcE_p0gc${FhS`U5A?N$E*NG|>*-Dn%W8cK#(xC9*06;}2CMZ4K~1Uk z2PgQb^;O^K3%zQ++gUhUyC$$&|LX+WX17%woS^jhUrl}h|KBD^zw`Pkd{$`lRMWXQ zPM7A!>UZ%N>RkKY`&e86O8AU})v&&r> zdN6J477-Iw`DjOZq>+9{YY%@s~Sz>^d>U&Cp|~}&qTQf=Z62I%Dqe+cb3hzXbl2hprm%A4UpT% z$<(U?Uo;7!xn7u9JLi&Ks%a;#EJYKo_v$HyuGF$7rfq=yxkv;I^22MHb8<5`RQq8& z;oRIs8*==r{YuKPHnwPkr*r_z?Afi@v!vk4<0Fn#BLx~0QTg=yTUCSClI2|bUh}qaq2oruJ3JhlV|;mm&7vPnir{> z7foX0B-X21Z=J-ZX&TDIG}Y8yH_EVSdQ_FcE$@rlscvh1NbLG$60dtnkU0_`s@oGj zBtEW}1gU3y9ElMC7e6&NUKNmw5GeB^0J}3ywrg&<Ya-FS#vV;iUSYqO2vTwu_yFC&Fdw{mIpN0 z^56{1<0dIO$TOc{=-{doRk7!kK7gqzJJfX&+o4YAjIcw^C$SxBf>RvaZW5bjEcc*N ztB+b%=ozHhR_J*}vT`wA!F+bIvO+kM6_O2>>0Kn-Af&Jj!kO)ZIV#8o)jD{3a;A}HWHQQs$_*8SNUl+ zB<`7PLZeL4RtzebqNkijf=$u1Ny-#Wqdv%{C}oOeic+Rnrl@179xrceYm|CT(bb{G zeIj=Xo1*L~v^B|`VSL%vXe^h78Bt=<6lIyRLC~CGESw^L4l@>FMtEek-dnqwa4@aX z^;2ZayR>NSPwLSFzK=ZZjZE!%ek68IQO4#F-Qq3t^Kc=Mh7S+**N&>RnC~zjN80)x zuAbV(3&A2~o3$ z393K}7r=C69Nnww2r}}#Jxf;I29rDm0i`P zvg6HgRcR)n)u>D{_$Jsob!mMQti>Iuz^S;22xb*1eG6t4OQ))#jWa85(54wXzVrqL zrTb^SwJ4j)Q;T*zjb!c670et^%p6e6&|8r(kj!!6OKmJxLc}9eW%Kr0pXPNlgVMyz z0h1Y=&-XH-anH=`^I?We$$71fJi7sXGUh2QP*bP`Ph<5-Co7`Sn=(ZpB1Cz4<>8U3 zY*4PARu?8Bc$!k530#hDS#_ez5SfWrkeOC58CIJpKmKE_oj-DMWF$kr-`kiY--|Q~ zL7PQuPBfJRYr_0SKjQ%mJmT0X-OWn6o2Mz`@StL8(-kZ|R5wc~$A;lqE+=ryk{wcH zUcQ3NtM!s$$xxnyJ~vyUjgsH=Ge*llUt^?#IE!`z5w%1kbAXpSxl{4Sk(F$wSe-pp)k~^$Y7{ zP~z#qBh;r^Cri|*rUx?B(#c19yIRUehZv7`o@d96%rK@Xo0IMR%uo-{nz6NILyf+g zwv~kLRdaZ?OKeG%?+!Dz(3xYmX}v%$xzdP~U&a`78Xh4T@m*NEz@f`C)}D?vh8W`g zRM{)h7$iPTm7AAp;SJwMNuDo%ooI}eA14~Acukmat#Pjo4LnYCRhaisYHekbalIz4 znobnLYP*ayR%zHb8TqJX9PxyVVvt$N9~>Pb0%Y+hIy-u_Q6s<@M`s(2F^-6S)7d2T znJzcqf}ocr)c`qjx)CB@>*{JE9~oM!wc?&18IYBO-vw*<((@=GPv^;+RB17O1_uIh%SC6KMe%J9M^T=z`NGY{6iw7Y z#7$>oie_VD8a`ba(Hb>7`U- zxwv%(S!%xk)5NA3^2i>ei~Kgr2o;Y)g8m}B;U*O0&N@ELSS&}R8Ebs=?Na$D zaqukT5uBldBJt&-`>8%PtFx+!nr+;#iMPyZeojX<@0-Puc=N4>$9xzj4`&$HinI2^ z{&S2a-jZ+3F+w#%p4^8~uzRM_fF6Wq!X36qXJ#TpYu^l8GL0}V`HwS=!7xrf6vk#5 ztGyHm1MHRZ$uKM%48L`md^F-9-#yvJj^I05n48%7^U(ax>{KGrK`x$aLH?!YBdx)TRVdtlIv%(>7|o7d(`e@u6eW9 z-Dduv=;oV1+v$s}l|?UO_IdqGwU&T0&ok(rHhZmNA{^eSoXJi|pvRb^y?zA)uPO%U zgbfFu+I4@VveL|H$4oN)%|EZ3g;z}$aAp>8{&K#NEI^X{p@Ua&vOCWx$GT9}YtTXD zo0(9ArZE(uY06Gx4+3Q%zbsN+(nohpR<22_6AhGQTaBf1v#dZ8+v_xMl7=BjPaWiD z*GrCVHfXS9IJYS@(m|6YgO3I}%F{DJ{<+Y2S-V5gcZjxL5oov`_3&V6%9_$jjmPMlvm}Y5rq>jRubCizt}+ma zpQuZR)L+&uHOg{qK%2!;4BQfcqJ_F`I*WY)vmJtG)!7ax!@%Lpz~TJmLX1vuM_u9- zA92{hSuVG~jKE^vO!h`{XR>cIZ5I1BlTis8>5-S=+0T}#@wNZt+t3MH|AChRk$%doraX%m>n~f8?%E3 z2D>qb2@l=^a(EWIF)-b{cr|5(ahunUH?J@biqB997Oy^ zF3ytko;JG6Ar(eHx%+8js0f^`jGF&!wQl8_%@$j9HPbuJW@9J%&t`MgaW+|M5j$Hg zTj432NR%QoEvB2LrkJI2R4LrDkt;Wg;od67mi0EX*ezzUdsQ*qauUg>E%67l9G3Wdpl?~?i_{Xz5}%xZvV ziuiQ3sm4k@ZoH~8$r$p%KSIFQadn@*PbI-xSr z6qe+JnpI!eZgC3aofa%Eox`99(v_~jnRNx-lLgzS7~^^Li|K5_`WUVV<4p`USygMa z5y2TM5UY5>xAn&xMq8YkH$=dcKQh!KgH>494Axl}(s>3Okx?ff%|MMl_#4AzKqkZA z7$?Rn3duecCe}-VwH_$3BslZVoxltZ<-V9M#~%S_uQhPpFIJ;Ad>`4w5lzj!R}$|@ z;x1L`MxRRWuDjAo5F)m>a~Tj~wqN8sneK1+)nw1>E=}7=k7DIxvNGiBL z3I~`2;oX+5n`J=@;09OHlH-(;Kfl>Iti30l!FDMPcL6NQLZ2z~Uv*U1ECOZz*VAWD+ z5w+bPH^PP3lxcYm_aYNKhw4nLBzX?ctCG(;N`}kZw!!wkVWD7q4??Lhzr)ClmqV)B zVKB&mnGj}YeD7DjbTaH+c@d;1K8WR z0DD7gsuxI)vbPbIg)8+wZI+L>@z)(jh1c82d=g7X1GC86Xc&nSWGdwBy+)Bd_LLDP z;-;Q^ z6WmHkNtA<)GYWHz;9+*+mCgK3_v+^E283CaGe$k^xljVlQQIjb*q)(P|L)Py$hH3)8(ga^& zn5^!LIX_RMkuu~(Ov2TZE|O>H+rTvf0RbDb)!d`Hi+hMXvexJ*V_w2W8Y?e(9O5OF zmlE0A*Z>K!12%oHQ5Lu!8Uv*bLRiCmSnSz;4yE_)aGb@MW5uCfxVy+s9**ivA+ zXo+vNO?vzCb34$R%Ck{lRQ0S3W=K}mLCAQ$=3opX2^;5V0@7o9qEggBSY-p3i}NNneS zBZpPxYv9mQmEpBzC$ZcjKFYEDmXpW?zvZ^!G6;Ejy z4_eCMz9>EE7nJLDU$EbIe2&qE-52bgaFk*XMioG&^||o!o2feKjhvq?W>myvJrpn^ zCVfnA!;}U4@dwRSKFlD}DeS|fqjKfLr04qhFlRnB)_Q%IM?N#&6N_`nhY2V{2^Ar! zE~?o)v!!DRWLsBPBl**(hQA#0g)vFon5$eHyM*V4TzT{h<58J97mf{muk#Nqq~z~) zZpAMOpW=OcyP8~k*RNkbrsoepp? z=Nn@d-bMhEBD$Z5z(_bzcXEpz(^YI|m6F6T*ao+n53lxp!tKA6s-y7MQ2%FCi7B=1P zS!S3k6-bNy(HLPqQ!W-}tUeidAw(7KA~}*X+BXCHR$=qWw?ZT*lo(MiQzN?cMsns} zGAcFr+6%Gq$r;5l3s4ltVmRZU#&S)RGs9i(FkGQpg`>*y`FD(QCD2a0a;_AsJ0^`l zMS~+j_i+hUA$JoWVHhm+`D)J)S85@{e?I$aPt9lLO()A&dqokXx7Png`OUW?4uiNe z4nzdttvv;kOK(Ns%=(w^$y^9P6a#Oe zF3G5!n$PZ9>%2OdxdbH1xxqFFqK&cwNo*sBdXqH#iWfa~kc+989Lok8EE~=&+Ye6J z9R6C7BJ33@Sj%H{7vw3==6ailRafwEW8FNUEQ@rXx2!-Ci6j$NLm3i2k6l4*d%eV1 zAfoWT(GDIlJ8KK_*je*18D;PPGN0r+4kdSaD}AVB`p}f=Cof;vw|hCR|E!x0lx3NM z<;fe&W)@xfZhlf1DDiIgmIY-c9##fTiM2bgAa`%QX_+90DFz0v=wV`r~IdHy>ZY%@=iZgRZt<9;nyjR z>bDCetgzIsd~`(>mcplsnJu#zJ9efH-j%QP(ld%5-e>uU#gKCC8bMpbwJY{IVQeMf z+MSxOT)R`G-r2SDFTfRtr~C_iT)W#kyEc1WyO~{FTScb=UJPqrAV+j{ofeS=%5iHC z3d+^<^evDpy19xpeBA4P3?6db;^@-!yoO=Z3hiB)?R_j}V4TsGf3zS#9@|D>3cKR2vw*!FK+X*S~dOMv%`U54)-RVn+DR(EdKx(U8!vaH{ zAR?03O3r@N)wz|s&}yfSbj9t|vCwKK?7f=Rd5gId2_4tmpLE%_tCv%2C-iYG)!+e* z|BIovT@5UhT3^?@0_T+Jt_`v<()AVH@D8R|9(VPS+xod~!WXAeqMba^K(rV0?5qK@ z^#Io(ZHaoCvRZE+I1lWZonUdHylVhHs=TgH1@Nj1Wl#lNLzAG_g85pr}UQA3W zoU;hg2<)5iG+B&+f8BV94hKPsGo3NH@-^E#{uSr9${q^l1@B!Y{0P&6x&&qul5 z5S{UDI{LxkW<_2+Iu*&-?P8u7RHX12h%EFzV`P!MHyS@ZGu})eYp3ttjr3eIJ=;!y zWeC!%%=G1U`Zs%!ewUekhn>#IZa32(_oj0v_T`Xa?OMeB7qT#{jf-qpyFwld)=ngw zf;F&6!5Zik*M{{=Br90IqAP~=t0L~c4p{wUi{Y-joY?xw;*Wem;uICqvkJ~TBjRbnp&q*G2@JH9eW=S^?gimBaZHUJjM4hp$;Cr zk70nE#0h*%#JrEM_kB!lpgmwezR%||H-)b(+!U>fxhZH*DNUSyax_INNNzPn5TT%& z;-6+y{L^lVpkliz-a}5+6vyd`o8ow}PgArV4a}_`K4!nw!!N23IypiURteYQb~4U~av6R0!As?VNr3Sa}Z200Z_*V7dPb$y8iRO|$Y zqMY@LtCP|K_m^-_y}LvP%yHc*k50m3+Ex>&yQyjg)Z-;Gd6R3hw%3+&w=Jc%OI@?p z^DV`qZd0P75*Br1i7o0j)M53B$EiNmC+@_X>7@1Ubh2QJ`cEV)QGcc@7WHSZsI2vF zo924Z+b3qFV!^phDT^9hN|xhY(Y;i4hu~7Ll-)}CSDWc8Xot<7Fo-VA@i?%Qf4mvd z59_fQU#h%-OinVRAEji()sHyii=UL2eF5ZUTRgCoyM%AOj8k|icL{LFI5jP`aS8{Q z7EaxWV}(=mQiW4U<>_df;Z>@~dI|?Q6$0$ALMQPC0cYJc_D*p2%YQV_PlZ2D!9b(N4*t|(n zDC}M^v3tQOn2lY0nT1^fT`_hEWnS#&xxVqjmX_msUd$@v?lrwkrsldn6AQ{z_nMBv z<{-)|BX!?eZuYoRu!Ry3C{Gi?6fYV}%apdc+kPy4AAdaGHAdUveJuV`rn=xBPEuVE zX?yZqgE6n5O1SqSiF@DDGVXo8Y%#6{W!(E90OLBj%!5W+$*j|5T(PAyZtZ>m#9 z8^K297J`lGiVXzo-R_J<6bSY;T-Q}{w?eOpV6vM^pU#_rpgN%t~ zzjC>5v6v=vKQbNS1mq#-B(_|A$1bB>IW1{+`ZwRe*;s6*mwD3}4`0%Z$G~#N1Dr4( zq2)Fn1Hp)eM?Ydp;nA*K;nB{?r;W!BB&*T*BwaBcPx|7~WT9&h;c;Z4>m%_BLlN+@ zms<4$vZ_D!W<0Xc)mBDdbWIcQnaJ3AJjcrA@r$lhan2O3$fC0qPm zNLF3oIl5x;pYsu4URY(zf5$g|?UkK=TVe3Ji9zLRU@%7bMTw6U27#~J6@Ii(9u|HR zG{pa?r7t?6@A@8T4Z^`=+)uo#Iuqp3hXZ5zc0~T5G4x}rV zciU=(PX?LBjm`i-;_ zx#u6y1hko?fG*9RJ%15v1H4Y+eSCw@WA6CA^cacRi@4=cJtL8@$Zq-BsNF(hCUK&W zNLr*?KFP_Pjl@1AtCrtSSB%7dUnJ(QQ_67rSbdZN;R6N2u|=c|-xgn+@*lISIBm*r z$g}<~Wlr!rSpF{4W%bDR$ zO}r=s{%#`hcPD!`0#g=S2u!6bMquh@MuR<9A9_jwR4wLifaKIjjKOCbg-ApMe-Y2G{IkFR`4S zs3!(5=naxDZ*i> zRne`OrOGjiMiz3IuEQDOEjhc5OMQ%FlKp_gFokA;Ja0M!=}Y?Lc2||?wUmJbJ3Lxi zEM+8{9&ts7^a3LmlHtUZLNa)%LNeINr;X&Fk*tv1LRXCBmZjW{fCGDw4W2|;%>o6{ ztp~+k1^eu%bWMM>~bs+i{$a zudLHvcim>on(FGwBa1gtLg2kCNR{u?-f@Vz*RJdB6&#@wr%f?GS7Ls=f+JJHRiVZY z_Gi%IlnAQet;e|aMx;c?3bp?ji5w94cm?%@0B0o*gx*yMfh|Vp1qVX!f-MW7w~0lC z&>j<^Jx;D|gl1M)2&K^#Ba{X<@hL`2rEh-M(Hjtw8liMgc?Ij9;tJ{#uZlGlO6?Sb zjM)iRS4fIW35iG0&p|bKR98$&Jft#+nhK6bi6`uPZ!Aje_9pUB@vVo4$_*9VeZUcc zgQdXDBVFAj+q)a6YBT1vnmAP86aaj94gm!Yre)D6S5&KiAVu|@3v|V}T=2!^@e^2V zu=GapN!Je1q>>?NR4G6B45Os?NqlGaQm!jrw5wF$G^+H16IQ8yGW2RZ0yr&u;*k$H z_OCw!^oOO%kn`?e>ixIzRN;cnupvehkjMz~~G^kVqsezM2 zyPK>)vVx<6t{9GrN}p~L_o)MphM(c)P6fvXhT~1~unEEjyNW+mDmC$3r5Y&Dn36pO z#_@|@fxcN;v`3X_Aj+cM<`C^s5Vl176IG%_TW^ZC-dV3L+P6qnqJ2kKEZTRzqBT10 zTI+3TgvC0m5zxGfn>(;d-u1QXMd7aEMh*z9@-}h@Sv12HBo7w560!f_>|}E)(2p*A ziau4!Lr%7zjPgFA=ot2IddtkQo^YEx>!_04`#V)}?*}&wOv5U>%{zf33z!bXodTv& zm1^@w4u-32U^XFH0kfH|7?{mfR-4DpbH&R~&Lc#KA%OVU428)~#w6frFUjg#<>o-Y zaykDWR*>vv6DK>5=YYcL%tcp{_JNYQ@K2#i;&-Z4pzK>4i}ThK#==R*#NwdB;%O6$ z?G7vsf-?(?1H`4mVuy*v4lm=L0}d=AS6W!~qbtUu-%1M$_Iz*n!C^=cC~L0(8NHG_ zNzzI+C-~IpMfa!){&+!Kbcw0pu#)L==kYU5dQJYIgI8C1NJ7rQ4sie%Lj{!CXa{hxiLzoVC5 zmArYitBEyC5Gtz?FDcnyK%`Ie+b3RE5(mCu*K%Yf$Hv5`rgR^g(w#7+^Uccr{7oet zh_ZA$9n!rC!j^PLs1nt-yG-eJIqS8h>$u92t`l9cbe&fDwCz!s-%XZe@-{!e8teuv zDz;q2Ygfti27bHnX-K3E4!jmr%my8~io;(`SVd~DQnqg5H(WcWL_c8Hb<`C7tSRDYQ^e0r(fwB|NMI+qKYzZ8Mfat^ zq90eHqdpe>C5Pz8QJ*FHQL0;szRwhWpOYP1^tjcQ=<#&LqQ|fHY48y({A{lfkuP&F zr>*9}oVr>K<`-J{HI}o35%$f46chtX1wpgz=hNk7gV`qrCaO#bmYWbP!TUlqqw3JE^QzvVXFgy1sTq34h2T{3oc=68=M~UN!%#Ec~kuepcJUk6B|0 zKbEdo__1rO#y6uu`ciqmo!@8vvycV*0%xvKKM;_*Mz-qUS0Qkg9sDMX@-?+zckp{j zh{x8{?&#!qqdp>X1AZ*SlM)Te^lKK`)mlGJ$sCH5M~C4sITnYQIJ!#2fqsApx%zXi ziIhygyMvbsyZNYbO@|0Nbf?3qB{+OQhbdI`L^`aX!_qY^0d=*1K7K8% z6u)3yvH@Ss@m#+~4(jgLX$rBH%xonyOE)q*H+qPb8<~w8nT2GoKAEdd=4z9<%4DuC znX5`J%W)I!RcmUuclS#bB4ex!i16DZQpd`}5q@D6EZX5gV2VWxAo(WHVJaQA560nX zIvk`d+!Duz;OYb&{3r{zMFYCx26-z6S4Zj4kkYt)b`QtZK05eQ8aG!wT}_O`R!C26 zJPy0)(1?prL-tC-fqsG3bMpusZllAckvPy#b$J?7K5A*=DcUe1RUtY!k+XgIMCNCRjbo}KhMkfVhPoPvt5hzm9fYf8_W@9?F~e2+)wDM zWuEZqIM5fcJRi)$fxf`u>6L{8y#(tiB%tVZ4o?WSBzkD8qv}}$siRHAW;m)BXRF>8 z^2gqOw^z_((x9kn4&|g>d!EL$>x(v;d48e;?Q`;+rUPx{@oc0`C$!JS(-eCLJhYR; z^Bf&$w}NLfR>M8CzU~Rd8mNa>mOXpvK?a z&|-{d-eMeRNy7619Vlw=nYIK6ieRgeLLp-hR0m6jvn|=itNm6~SP^AN)?z7gQpDEt zEFCD)=y?nQEDwdmJoh4S8QIg?oOV%#ZFP8LSdZb^wF`puvum1CVZYKS*0`z=oW^-u3jgfvU{CJ!P$hQ6b W7WnN%p3#AFZ9l&@@_c{4!T$$&$D4jz>gkC~TDxla`MZp!zt_}N&<^MhB%)Ix8gkAmpe*ewqlesf@?y2|QbIv_;XWol9HQDz` z(-o_Y3KeBkd$^gpkY+rn;g`}? zo?)#0j}0{%KBG@8P_=$<3im-=BK50_Uc+Ws`uc|-ohNO zFE=;Um+#HU_7W6Y@RG2dy@|Q` zXpx%VRgcJ3SE^TfmmkP%U2A9WYSq%C{ijgq*RD6)U*)ebs#WV)TCuF6L%OGI&wrV< z(zRxT9v>4=HIgdQ8~-8tQ~vj=saR8COYUbTqWz!tEpk_Yj_QNn z<*n+s93GaMU6fV0BsaC9tY#591pStq?yD%Po|~UKKVxx4diApE1x54e)L+=ETD3Ay zL7^|duwslVN{{uQwH2?&6{=3*G(>t;`1~jInZ>G!J_2%0PNKyaqiCLgJ5B;UazwW( zQf=pU_72WTE6U1vb|sIVXIJy^nc~ZQ`kwrZRPThGqRfnJj3?isR5ZbudUhoS6)N(V zre-ZEz||se;?kn|shORMlj9>3@w0%+l7< z0n$B5B1-kOi8g{r!G&tla{$zW@v z)p@h8agBaI+|x>baa#2Wo0pM2CpR~1zV7yl>Z_kv=V_zoTw~5OlJv3Vs<}Q}L$$D` zgG%|nHqG?}pBk<^M0kc6nR0vXDXT>!-A*^s&GZ=mfqS$}hazK-KA6-pUun#hIZ-2fE)zzkHTO01M$ zh;E>!*{q;sR!}l4lP_f@EHc;IVSZ+gPY)WPnpuOxF!J_KYHIAQ#MEwKii$JsbeHQ@ z8{?56XAf86>@Sr#({Uq>fRc9GoS|gSZVlz^`qNNbb*hO~-}Na!Ftnrkx=(rcsBy-> zgyhi&YlMFf0^F_T-E%tVm?oY!<=YO{Fly)o@K(O!WDWIwUk7y0uE|Dgxj~>U`vpNe zk}S3Kx|dWZOjN+OEEoOib1$huJSw6a7(E4;2y_b)XxYypaA0Wb@)bojjI%T?qV(KY zBRXqU>j19T3FE0F&_V8EK!0cXkoq~7Nh+a`swb~&7S(p zZ_LI4y~fmF3-d)kwlJ!C|MKo1cMLcC^LX0c4@0qIn>HgycsqKOJF|VjFBPW|oI<8W)(+blI zsGG>nXYifW%+##Z>_X6!u=9U|Voacy%KkTKAbBxVTHWk!Mf;bC@aX~JVJy^5BD{8l z*KuoAl6s(j`LXm)Y8YVa7+v(qS*kvF9QxqC{)lBes=Ec5W@JV+DqxF>YN#*JRiE^X zEDoKGY!iTtZNf0>Teb-w&>7o=570eg6KeKONlnX7O`TKdD?j&XwEprmBH1(NRqL98 z`o$blgg0?2^vfeWUNI6EF^pg&e(10Nxyd#XW4b!+e2Au-$78&_9f*TNkUG5iO)ejr_mp$SLTD9;?js}Jn44NVN4c2Z3T=*-ccfyTALJG}yT&b?#}HYNn` zjO9B>8B|8HeXg^%ZzA z`@J)JB+X&{QCK~rYC0wV7lcBB=*y6~ES2tmKpwIfr33WSTU8HzdasHZQ%0(t=Sy!v z3tDedg})A5o-e&MmX~eu-yZ)Rk}AqPv}Q}G@Q35B{{+R#<0y#UNS#3#QF~JFjx{^$ zC;zFE8oCB1o7?3BB%r&S21Lv9-AT2=?;zk9KSmk_b$VYk{N~egn}WGz1K6BCIe^XS zlLOeC8gJR~zFNlXcH0L1>b*@2goJa;^!5X)p&}r%swl~-8c)(Nj5Qu(K)vlOQWZz^ z_J>saHtiMF*2v2g6mi$iKiPe{R+C1BT^CN%EIWcI5B+$5;!| z${&SZ~RPbaw0!jF>|*V(LC-)H3d%LDZ&vLbDp`PXpM^J~WU+hy#Rz z#CoB#KpEJBs57*06{37X#Tr{c|9>di6FVH2x&) zkb_!|83PCPZKxGz2le>?aZsPrG+_tTZV=AcLA4v?IH<5|Qc^PueRERuxz=V6UHui+ zI?S#q6vxy&aC!P&tGzg;0fWdfS$%*ou;3jA&%~GtEBU1WM#n1!E8IS5+#vBugxK~; zV+ZNE(>-ZMvfh5g_DOT-w50^UGY9FdZy@-+E_i#9`}QvhSS^Q5sRUFN)uoeUn;~%^8xJqDeq58$K9S)0QPjPJ=+u>{DB=Am%V;dX~0u*>B0y@-1 z5D$m@`I;fk38y&|UVSXGh{cow7r&60Il*|)K=4rW$r`D|&dOKufV;PlC_L_}3 zcWp)rV)cyq8L9c@zT`%F)SFf_b<)M;^TH$*XNIZ|gthlwtbJHDYtP5&K{bt@>L-C= z4DI#z{j3(M)?n6m*x*nJBL`Qhb)aFbqa+D?KM!i`^Xgn8L5NYWbPJ>Bd423z)dMSl z=Ul2$wS1DDd3d5HMhzJpI!Xo(=21cgWS=HXPj)dqqiUuH4i=_Sa<`!5Zs|8Tbf~CW z^Mv-Ix2zbo$whme(53>kcMI)XU9|sHHSKjaZInzKCDUH*qWyp6+h28Y{gQC~(qM{j zhO1A6?ei|S|4}vDfZ={dNo<$Agl62&f7C+<>_*uig~TW!s_a6_?{3&xod~Fma#ZTd zIQFJ3FreX{qhV^0qw1>j&ceqZeK0I+h!~wgp-w$aZc~D)(+~%a5>!(ixEkS;LI6Wt zJJP`2UzY@xF<+m3NyRC#L`J&6Qhq8mh<{Q5zmuN|r!n89{Hr}RBCiScjqK^?V;)d? zYCcV0_Vl~Jhj{v3LmW>ZZhQLFht&z@$`h)_VmvU5l?@HlqG4dztL$Y_4*KT^4|sTTnOAD|E?m3 z>Q+6B&MJIpd0vND0wBMx+S%2u0@dWtRe!s>cc7a5xSDKNCpgvYzug=L{rB@jIHCTK z_MK5trvHH<0skHKJcM%m_j4qm`0oQlWa;X7*#d9SgVS2oo1Bv!%*?nQ`S11MlLE`t z%iiT1A@8g$|H#f9^5U>Ck>sHE9$9?CKD4FnS7NB;)a zeCp6pw$CevvKN1FDBI@;hl&?}aVUG0zk&vN@sofL_2L8sFHWt=i=$-oh?33Y14G3$ z-#_%SUOY;zi_>Rkna$L1E

    _A5?@oay)M6{uPD)$mQ3iQ^_@-9m)>9(XcAnyJ)kA zlG#Ja?EUCs?{_$Kofl;^v|hsG)~zObni|7}16nTI>KinHJ3$6-D3>azOPmDK7-Nff8y=I22z~{)|g)*$0>bk zyZRQ^Fc^7#;jGovcvRR5QOaRa3KdYwOF;=vRFdFy)e;;Y#skba%n~SEQU@9jxmjV) z-@@8VtRy6&f0T~<4I?Ckm0yJwD!@u(x-WyNfi$HD7xrLkI=srk^sCLvufhs0nU#y7 zto$DZ0>-FH{0|$><7*g39)ba5c2Izsl?0euwE%$S$%>MLf?;U(-wz6iY9J`U$fSA#KQlEazh_xn7pD?U^`^Pa2>Esfh z`UycfrioMC4Y_?qW#egl$Q1PcWq3f!47?m*z>Yc8DwtVh77m1ZXpT3|!h%t}ahB{d z#~aZjaK@R1=n;0jG5qk5CD?MLdX3;5cDE6_&Mq~7=XGkB8ZttBe>XtczJKrtz2!Qr z@Ftq(3?o68n&uUHe24GBzQacF>KSM`aolM{(EAPpTF3hiCKBR(yNqD(+iJkX z)WV|t>;feDIQiwvUqZ{^z@oXc-nJasg|5q0jo;U{qwY}Myb+Q`=#$mWy%<&(YMQaS z%L=tFGLh&@BxDTLpVu&V=z)EVR(gM!`MLJ3RA&x<7;Z)wHcUzLu_2GhXkqWlSUY3v zKeP&ty8qA+ZML4|NDuB+2`|8pcw2uq-E{n4Y)bJ6~&Q5=Wa8jG$%NizBykCIe8 z7CEg!)5`Glj5UJwhUp10W+IF}15yYy({73_6?4Hw zsC6MwJaz|6b2CZ}8zrF+N_JORzk)a$+<~JcIJ@Mru%uY-`2<@$c2r_$3p1psa7AZ# z1zFoGK;%#hMsaj@PbCg@!fV*X#SCt-8AQnpZn~7g|0|#Rb|qon5Mkax|B+AqGAP4m zm1MY3Ne0&P8zKZyuuFXdt^WHim8b??>Z_p&E*s#2$+|0!B;h%pmHS z0o0DWYmA}$Nl)Frwb>S9M?72sDV{ytoueHOcjstlMSb}2dKGP`CxHr2X&)V}>uyxL z)Tz`6^;t4C?nD9CFao|S63?o*(JDOLmZt6k5`SNJ?n_!mx z=AqF+zxgs4cKqf^VqPrABcs`G>d*e6!t}ze*n#!g7S$dBdCpeVR*&3@^Zs&<`lu%K zuyOPlM|AQk*dDoQfWFw*?5GmQ=my(V+t&V@=%K{3c%)eQxgr5O&lT5iQ+_pKjF_%$ zl2)D~E+7Iie&{u@qBJ(hZ3?@Gk6{D0W()EK?TvYMe$VB^)t z0_*iy5p>pB9UQ|p>eEUr@B$YYPPa6!G=3A@yM473s!G38@mPiW24ez|HIAgymf9O; z)dnrvTvmVVr4l*lSR&wHiBN9QY7%hr3hPB-P6XqrTP!v708A|y!&5PR4A0*wLY_k6 zL=fYy2+ft%FjA;RtV$f~fYA&t34#?nU>w$#Z~nNaQ4uN#hp*)^pVa5hgiF~bur!S~ z3-00oZf6RQK))kmeI#_|am0EMeG!X@^&lyl=ksyW5l5`Y#|9$S?YF8yhB^zpn9uKy zrHJ)D^(D45U_(~KyTq~VllVekxE2$#?=RMLWsKu+;+L`dmfeWs>jW-+i#*t_7`Vg? z@25IBmpn&9F^<*2IeK9%&rvYIbM(!z!8v*XOgMA&3t~v-=-MBK4gHLKk< zu#Ew`7(#AD$&FwnF>M_1V87=$iX^t_C+<-7RN^?@D$i`}U6PnV8|S?vyUgt5gkOST!g)f_cwT%^#F5I4u4r zPYQ@?Ac9fvxV0Uv`&6iC{dQNYfnHmo+Nx>sp(A2aJdX$}5R%gFT9qXV@VgX{Q?&vn z#fuKt2n;S+hdbr6+=b$X%c3ymTTwc1w%Od6q!0Mu7GNY*LcB9-IJ2>C9A`EbqJxpy zD8SUfR>3#pd4{|O%sfL}CS0A5ClKQ`!Wl~-FyX4&cz!JViw&X&4S^r+e6t#&8jZJm zTsGOgc(ZDz>?u%hJnvzsJ)RN%5_x1T^~kllWQh9KMRk>hd3 zc6;RbKpfWm3HWQ}8_#w-X}r#PQvFHgj+Zzr2@tm3&Ka-oZ||9`){d8@;L7oAyO)pW z?e^+6yZR=lnys`OK4_)$Arv;wf*f1vY2$-dIv*q*E1g5+#Y)c@FZuszQ!m+mLmOs! z6-mptuHF$D3t>NL>5si^Mc8Yf@56eRcBf_LWT(|cnNjG)n<>~>=)-##Xo)wtx^=xO zu*m%P1jhewCNy*QpH85KyMHqfVw?a-$#onp<3 zg=nJ*ZVN-8FuoVZi-kc+7N*HX>KWQ%+bq5)3-6H8qkUqaRadiVD|a2bfOZIGd#O_> zss(*A$3KpjiS^xhGRLZ0a!`!86RMdW^nyb6VRm2L{XNyp7;CeLsMMBb|I zC{2+U zw(E~)Gx0S~QFRVe31;FwbQ}_(cM6Xb?cEMi+=nhxfs{D+3D#`J?K-cNx(7R@01lI( zk1sO4y!Y=!2yn>LQN3U>8i?4ka+A)(rhLXqA>WxGffFtQjtMz|{)P#eL1RpUv>Y&x zz55*|hzaRHx?~g5B*8HuO%eho>cUsxke345R*dp zZ7Ks2*rXVf^`52n(k_8c?d?h9668&utl;eo*KIa2ZhS!#6PG|H#^|i;m6~|Octt|c z#KeIG$HWXEcErSVOb`<@_CJ`IiFTA$Gub;Yl}(Ma>yKtcN>{IwB%PO&lj-EHem~M{ zeh=25DcMZ|MEh?`5L1F}L0RhN1X=YR)Nw1#*zo<-B*qVQ|0m;NoMHv@^Ou_v*nqr{ zz*CM2Ot}x}eys7n-3-R#Lm&^0+z1TD|2bYB*Nouw&Fq#qL4esrH$rCd!Wid-mJt|n;q zhwX;~1N307Av3@^y=M}5*?5d)+mMe)vmAze1U1NB|HFIEt7{GQ8*ty=$GE8f zvB~smeiiW7^D0tr_*nH*wG+jHTqGfcMr5L{{fU~RyonTyG>S>&&iLBd)bFoA4oV-` z?rExbZ8aO}+-HrZDkf3$^0F_(;F8UV8)?vt)J^0_<>Ew%RKA}WG$VD1n*EATB+hL! z^5aBVPY%+b|3fv2@JV#So8)!9f2wxIWp1=+5S-NMgf7}YKT#$%wutAbJAG7@=e??~j?up?2HWcMZVV&rZR#geSv zpFtpWV$ahFyUd1)TGNsYB~5@HJJ5_Lfa@_`K$J>P??99yfvt%W36v$$Os>;CB$tgB zcJ#KdR8x*__I;&dtUx!a4@3DsEBsS&rrlMM&Wk=&2Hw26q&C18Z>0HAB>pauqwk}M zH1X^GRJGh^ZMjjB+>_2aa>q;vycGfSPmw2W$>RWM$Z-g%Pb&WF)`v4Ml7DcL|<6(c59IRpU9xwWJKtU?v%*0KXt z#)3-daxaUHM-*_7W=lGw8IP#{>PnWVvQ?oyZ$?LAK30__a%8t5kr#lAFrsH4)Q%G*LFS%LFvO5MZ*( z{|pj%J3v6Jau{_tg1R3=jU^jopksr4l*Tz5j>0KzuGW9gLNrTQ$y?CLW;Jp%+uH_XoUkkSY+CzjnavOauJD-c|HJ*3x|2@@VnaDDbcz4C?*iX2Td)m3Lt(wzegwauyAokx4o$_ESJ(0&FGzoT17SV9->V0E37ePk<~sb!R z;|tCaJ(cGOc;Gp*Y-(_hXt3bSkqTl*=19p@nIl*J&b7QZ+1tNA*6z-7{SU(hN6z=e zMYR80Ds->vW;c!W-U4-Ds<@KyY3xd>Ps8mPKh*Z0*5s<~H1-)^P33{)oyG%+3cM0= z?KFTd$_*8m7yoBCEjE;t~v&<@uJUdJQrapYzDXHX~9~xf>pmox87tooQ&m}alWIk6+#e8mRKoUyyk0Y(|bnZ9Z zna=&D2d49lSDUR2HF-KsXDb0M%KKNhKev-~tvOcjf8Dh7!Hw$LNV;iLNS-dvZ7Fdk z(|VyWNz)ozo|$=mP2CL@oV@d=)8xg*#O~)P5jS>vaPrOvA!qU?6H%GG@%YdVzMg{@ zJa+dr8%Ol$Qh+UC^udLUG(DlOImSX5bD4W1LKe|~CXCVk`=*Nt&+c#Ls=KH2EX4Od zh;h!WK2FVI{E#$gzh9TzlsURWoucaS3~kI)-PA)fboL1IJ}SHWu=(`1QRZm9dZc+Y{B05; z+W(f_@O5c$7!CGX;h)OQk7vj;@Q>(Ho`ItsJ-*( zW;L!?Z6=Rh=d)O7MxVuswDHsgT?fzA6V1kkYA}=VBqYNTIF#HJq9hJw`= zu%ACPq<$u6W2ya>Wod+Z>@^o@9pUo>Q=iY06A?%2x~l zW42l+)9EE^vvqhEf|eF~&->Q3Sfm?2+VeV%P9^n@9VmRx^V)?^R%#C{0KI85*(rk? zcFF^0^1L3HiLYW!6W#E~Z-fV4sSTP*^Sa~V*Qc42RQPSgT(tj|nKC`$BpU}s3KL6k z<4l>Z2Z*0G+S^ctg%Mbce=;^w&N8!{cTtSzm}AQI8aH5gS`F1xGkL~7HIrs+d%Zl# z>}GN#u6Kblbk^Q5lV>d;Pj@k+uw|1cEiSjs#`kG}7ybjEMj)0rQ@aoS zf~kAoOrE-p^tqpLgYp9PXW6{c)YVaQ(V?-kcrLb|rJtN@ZddJRF|sxSL(hQf>sAe{ zSVBeYlBUmmEDugXmta|!a#fyk~q=cLD+o8 zJQ038%(L-3e&t4p3%@dm6f4sa%Dr$EUO$;B)A38v4^PLNz=KT3o51*?h8UmM=b1zF z4=H9rV65&;HOtk(S?p&X002J?!eE`K-U{;~4aP~iO&&8Ri9P1?Ni_eA=e4g6K7G)&i**&A9tjS3&G+fl z_L?68U+DZJ1n`>AL*L{zQSt;txoemC7_NEFV8(+X{wRrhI*FoCeZ?Ykw-K$4dSEn! zlP0s-5;U1jDbMzL{M}|NY7+||#!dODPZCF-J!V(Q3P3sIIg;TNE;-wYIEBz)Jx&w_ zjc#?X*|pxkN0aBEp}dd&dZ~F0zGld%Nh?WkCmJZokda{-B;>|DS>O>{3PFKwpfLTu;Gn~RD z>*+^9^Iau0KV23L3kP@>4&|Tk1?OPDrT*+**fgBA26~niS(c!z{1ok|3=NK}9!O&2 ze18%f=K(sdg(XR=zH{tROvd@}6ZccjbJ`r;av#hFOaPkcxu=aLYQ`LWZX8zKC~w%)6*0R~}){!p4|8Qv}Ud!+<7nj=pxkxy;%QvgHZq8|#<$ zn{ADqA%x0pLMVS52RnApC4^AF`+#|eLK#Y^2?%X<5yD!!h2C9kG-?pS%XktO=DqyD z-R z@Ca%X;19KBd`>4RMRRFk>l2CTWl7Ys_{x%z`La}}m#y-w&P`m$$7I<@V0ySd^9 zh6Qe`xVd7#6S+#<0P5Hc_@s_Ec%zOtcmoByfTckc3vCqZLr|bD=mK_z;K2=86-+Q$#z$dVHf9(Lf-6vel1`=I45s?|G|*4%=y_ zt9m}2k20*Dk5ORX(A8EOUH3ktMno;DYK}!%w7(8j$3{j_jYJN$e22$tv(P}uS_y*u zQu`_(boU$0R@eAdAAHidx-TE!kxH)cTewQE?)RYJj~zo;=M%>c%NQ~EY=yqj{3QH( z%YNJFaLG;PBZeC86OU-~S_71S!fqk@Kq`Vdh{EC7G~%0wo#V%fP5(=Hs?`}=&lgfV zaU=hl%MLBy$8LlQSZ#J_8wD9{C0}Q^$H%qGfH%~i{Y%I7vKrE2xw{pm@_k~|P_jGA z_g$Zd7sW`ED$~b)?1SCrGx%`Mr3^eN3_R%*KgJgNtsn=l3jn#HT|R!4`F53zC?H@f z{Z#;#s(AOLF^|$QSm!Y95LD*8s$_CdsVIiSWx%7USgE>V9%puJjyhL~2EjC|23)Yc zNBPAa<|#r)q4s0MO+YztWb4(ud7KloduH}L?wQ&1f{Ia#gnXfk{F184&(_EKTiw)F z0RbA;#csaT7y4VW!^#%nPgUYjfMTD8l4U>%pS2qGB!ikcPx57%KK4)XNsP@|8%{T*a@aE7?2YRTfBJi2rgrep4DxF3})?g=HkC%o4}_t-U|j$IQ< zUS-ewZrWS3+UAeVFvH{-o67v}{% zQV0t_kc75J!WU`2oyQrtzeDR-OhFxvH~I-YW1*ro7$hu-st{hdb-(C?|Z^b)H#RD-s;I@@k{pum2K3XNY)n) znTzoKyjDYOzjT^TZNGFfSvR`T=x2P+w{>ogC)#~?)+1)J@?fJRePsh{YkDzBHAvCN zJ~gkTYxtU;sukAUtBm1>p<7L|Iw>HfHIM3JgI#TdjkLjVOnGbrD zW6A7MAT@iGyOM(*FQXlVYzS zZj_rEoYHD!Ctb>FWK9a&UUgpz=f3ZzJ28H!wX)q+&!zCpUY)`=_o)<~*;LpzSG}FW z8RIv>13mwI7sx`5EdgTj@DxbW;vMRvM<%#rV+%wy8=p*(+4xur+uJ(Nn(?YZYAE44 zsZ5v(q}i`XWtweM*_M5o!jXB$)JrMLC$Uq-5Y$N((kRbAXZADdXrt8X!u!i3QeEWl zJ`P(q$3=doke>-$PVzz(3KMxQCKgs=g2e|QnRrzuFt{X>rfbcz+Va!mn_NhCT^0$O zG(eLQYtBS7er({=FCRDO=~FM7x$vg}v8K7jdiI3*C-u5ZtkWXa>C_NI{SRUBV;4uC zRm~9qSu8c5`vaG3sT;^;-T9&2(`f#s-NTvz3=4&lM+VAKFPrxpr-O2w5;@>^Fcn+s zZlm!I*O3%{1b(+v)s02f9T?4cNHBdq59%o($Ag-4Ju6?qiXeMF=X(6}_1ss@Gs-`o z$6nfewsTX!Gwd9Y&|`z0f~4F*5_lS}?sJ6V^bS7>w}<#$K#$Mw?iGCHj1M1MB%aGx z{8kGSlkMg`ZbL?S9gKc@VDKeP9k!eAk*@gp-+N$^^E!I%o8}Xd56ys7654$|dGoYd zlBP>nS$&aU!zaeyQtQ*iDHIV!QOCM8-T4!wYHlO!vRJu^PHm^KGfnqDW(~xLFrC}# zL1gRbmDFdrN){`qi+IZFf>$7^8Rx91UH6SvC(s_d&cYsi8@>A2hW}RxJn8}~6;&gh zT?jR_5yX6ECMj>ac%GParmwoDi|4_05VoHUi%JK!rIoKH+pq~XY_biD>mY2N4U0+} zHlejORc*9k*V?c**s!<`!rp1aqSA)#zSZg!;nK77EUA&+H60&P-I&HstO#91UtP~l zV<(26V~E)Kwb>?WBMlgPTjn|$A@l*- zm{l4KG*-Lsb^i^iW!wuDk!BD?-13`wP#3PTTHw(}q+T({>elcUNCbmutAd|wq;nXc zqt;rob>g5j!BpZv;ONZ=e4O>yX{KFH398r9ITkpX!7iB!XZ>|v7|gvMZy7*NJD>P5 z(1wNt1dov5WQJINlx+D?@>94M1POiJSRK#cfFR7WrWjGWbQ5Ow+54M195nJX2UMIv zP`LqO49aYRA4}ne^la>>3=R#t`K#ohkzY7K$sC~k$YaGTAf$Cpea53XU1T%zXmx8L zK6`g5BlCojdH$du#aCRp6*0895<|L~UxMm5IAd>GjH#rq#CYSJ4YN#I!RuW-M*j`c`kNG{L^nU+yX?Hdh60S7WtupbxQ4DK$eca z{r~3$9Le0G6COq$gx)m*EX5$F1o%YRvR+hIX43jC zq7wkbA(!Qs@4-`51RryC*~eyW{ikNuFgc=Ua=YZP?|Fy)CKW;9fAip96a%ptOTQ$&bwdaRW-6iAiVpE zD&Yrpz|+&XLh4u`)Hx`~r{mp)0E&J#iue!|s0*?(D+CX2*m!t->h6q7{QICx_V1lC z#lN@Dbo=)~=yJ!u_owccg=&XP@$WO*S>;Cfwk$RaTe8Hzcf7*7PHoEK7*RiVg>?&h z27)L3{S=_jH6`KOO{9 zgs7u6+PGC;c*yF}EXIEeaVE>+O>~NA61mq~GxYjcYdv1u13fG`I$7Nv2WkV5qlE;V z!~Bs6a1Qf3!VyD5y0Qyxpj#cXQ&=zy7K#a;bQNehDK}g%K+7B34e2hfHZo zna6(F>`U`1vBKVbYPPtpbpcp~Dt28r1gebdE1|pTvgp_r0|&bSRI=0_ z)^6iP!AsGJt=i)v(edmMmwZ+jS@CpOC>y$73!`hjs@VWIJLDe)k~VsyiWj-zIqaLN z=df?Gl~XI{(tcv)0EVSONz&9j4r(;Q%i^^!2Rm4g-eL991Fp7`fSn!YGfXd3%;k4l zkEpmD_LsJZ{d0Kq^v?;3sHO;4i7u|DRm~LudG($xFt}vFO60QGg`)JbC|EGSayOxT ztdDhz0&Cs_>^iNey|DY|xhXR937U4U& z{Mv=$hs&a15dg~~pxjen?NnfiMA+{ZA!m@aQFX};?S_uI+zlObL%LybE)R}@xuJt& zXl|8*1AyEOGXw^gEW-l1On0Hkx-1Ho0kA9s%6Ep~gK#KY4z)(B9l3hJP^+D)$kksQ zv<9hdV7Y$F9d#}J!%(Y%x-C~n46}wr+NP~3OdB9)4a1k_ARK9-eXi4y9Pm`PClb=- z-(_*U^K}*{U*5>&fag`P$yN{nPpsA>tp^XkHo{tj#Tt|(Vb6!ToOXE?QX=gFa0+=m zc?_=d93zUYHEF6@o~^}T%7tb?bc5=R(Xa@Q2U_5PZsRs9B1KI z5be~(?0D;GRSZ&iV_|Wgo-^M1i`tYYsf)!Vqogi2=284}g+8^?iqu=uF-{G(pSZU!W-6|ULE1CC-J^@R=h4;$`h z8xGfj7(vy{7hqI6z!a*eHu=I!%X}f)Jzt38nqm|+(1ydc14p5fnr6dIvf<|2aJUW< zEwq7A=>T)WVzUjr!3MtB2F7&|_#PV=l{PR(G;Z}?647kV*}fy!%VS^mIHN-L-zf;a0XVfE!e zQf#7X76c$EE}@%P&i5$aHLD55&ZG2Wv#o}aZ{^Eik;vrCY-^=zTcCY&tnp4%(x!m< zq-VNs9i8{RorJKpR;!S;R;w^*{*AWM ziq`(ELPMTPpIFhRS+orsbn5wHsfhM%M*JTCbt8o{gWm-gomA|DlYM55pPl^z1) zlXa0rp(C<>QI#F}g9xTN6!F}_C6CD7a(T52Mc>P!U||5u!l2}s1Wb`I-&PieFy|)jR-@!;;$s2A3?x19Ex43&nxUqF^xq%VMD9ummNC zC2tf-Sn^hpge9+m<&d!CgCYq_Y}?iVwhfRRmSh!iSmG~o3=D-OYIFfR_uiQMh$*gy z{loL1W((O2Hd$z!L4JOpd!1EZAyIgszNb0wl2;uTGHjED4lLpk9TDk~%;<0jnl}5U zso4wdaS)71#1vMafrd^*GGn1cB!z+29Ff>Y6L;ClOclbz1B9JC&08?8$kP;s1_~UF ztbkpjJk1I+XB>^(1*#H_+_f+?8u>@5wK))t+`iI!7xM`s_f?NAr1vLVii^3r+-fX2 zno+B*A=qSyCN1^c6-G?tVHjsPhXY6gan#!;&)#B`o>HuKw3Ti4bZn zk_Z9U9F{~alE?zr?56?8VM&igg0AZ#fg5DQ;X3H#<85G6I=~#3q}jm9HgJ&*jB5@{ zR@rd4cHojWTC>$HHrx$1+`Tp&uJIKAYQqM@r31#%%xN3!qz(4I4TkF=ubT|HOmy0Z*Romof`O+;u{=%bB1arB$F_9K|n z3pt=cuW~?hc%eI>IZcDo4roqMk4ivuWT6B!_inQ;7-3Tu<3_Z9lD^QH=EM$byGj7d z^yWoIxs__DWH20vcrtN|A|6!syWScZxXBSuiQEiJ2FgnkISvvIes%pmEAG(Bz zQ-OeLme8HPgw0mRB^*%AuA2JLC1Upr1qPRF_t(hfN*9WCmqo#LAFyoqQL?FvTjFA$ z0xI>0i|vPm?T4Ts+8Rz<>eNZ0{DO<}OI1?_AUmz|0)tCt{(HIn#)aa?%c5ZB0ZYu+ zdKRVd>~+boTV$ZZNz@z>`iETfAFV`ReskRx z;k3Z)wng|rF5h>d`0%nQSOmbb2q-xy0!t*qt!@!0ATpZidZRt^$k3jYjj!XbXnb9; zEAZVZZbi{LK2Jsf6mHtHr2BPMb_o!0^g1E{t58MVqVF4xpn;H0u7qsHWs$LDz`&BB z$4MpEahzn7GQyn?SzYmqn!@O;dXmD~be+UXw&99~;Q}(=Z2vL{K$~?QtKBpv3xBH;aQ2lvykhRP#Vbs_73=AS0U_-?#5G4sPZdj~gli5U0QbOC))@7U4fm!E_o)qsYYujPu;Fm+z;WPF zcbTveu}tW-SSIvv9Sl4=FB77uw1GK|;+E=?G|JLqjyh8DZEf24F}K(qbu6Whw4;ti zq#cPm<`wG;tvv%H=eZQ-tLy3AIBl8J9qDw&4<6H(+1;U^K5os9{F-)TNkG~*O*TN) zO*=gFuhw;jDuE2NovC=4{^oh>Z&W@I<*CrWoWwWEvtP8%QVs7ZT6)4dpdMVtiMf5j zR>Rb>Wzw7bK;I^F6jk^karLIH##fhdZ@#pQe??qea1?ly7!n2Ew@egxpNmIUVBB&?frIIc z6*zb~E3kMOKe_$t6>Ar%Vf(ArIXnltkcP#;)a82rYu2lVpDyjde=6W@1!OGe-?CFH zmg{rZsV;^{w0JrHj-9%}7PKNz%|Bwd->$wtP|ZJL_gB06nLsuFh}~Jc`rSY^EAdCW z`dg=($E2GzjLA(Zb2%#zg0KQ7E*Ax6LI_8J86=P>aME&7;3SuftiT>6jsknq87r`7 z$t4P`uAiHNh-^?jPplsFsx?5Dy=&F8mD)|e^e)EM*b*LFBTGoLH|nYHS$)*Z5*b?~ zf!S8=j1p08UWtsBtP&nA86~3I4R-aKKsD=jk6nFhpqh1i#IAlgP|dnMZC9TRRI_eB zwX4rN)jU8$dDEqfodKFu!uo|ctluFeqTeJ);^=nTZizA46TYWN_(%+?|o!_fv?cw(jKL~fy7qjm=)Uh4xHTV zd7d$9-U`v@>=mrf87uTlpWr7Hm)g|}1J$gG9d`8=r<#YE8#fIz15fMLc%q0CRzt59 zqK0^I;izE@aU^QEdWERrYB!T)=uV(Y{LV=_V>O&~sbS$~SZYHJtv%>fM5tP&qJbKvtO1h>`%_=;)-2c$A{Yp;U}tQ>egIxa zuy2Wm2==}$*!wQB+`q$09l?gv84EVNG_-$9zqW2C!A{q~M;hv6VG5n??%AN`l=6g` zSt=$w;v1`$UX<AAoz<5=S*P*K7^kntBnV$gL*N4=hA!<% z5WX^?ndp@|XQ5|->bjD8y-_UgVCfS4{${;#`2Oa;-S{eXKW?F7SIWmXM+GkVw;iTB zm#hyr0oF&0m8=i2!vn@zDKnu35wu-?G_fVRsJ>EkQQgHU>tYkCL>Far#=0n589HD} z49}lwz_2c+DbEwwQ%$;v*b~53c7s09!?RJ>QJ%W`i(=1E`iOHtKf!kXgDc72Hqp0O zo(bxOl`=)16JeiPDMsQ0yZXIA^;wT+tomuCKK78O3tkN7@f5a7#?$vJ^<&l1F^yNr zP{K72rKeW1>fE$hb%#WCdu-L+Vyo^Dq;XVtkR%h;-Da!qwt%oC1gkD#m7}_ebjGTi zxGGe2?}dBzkm^cndfreAR`Co=Tcw+IMo`o-!ZTPcT_uw%4G8SXwPcmvwZSt@ZCfSU zDO<$`Wc@1nuKeA0^J-nXh7|DKYInum;+2ZLYcBbKK)P zubPJl{bUQzU8?yS?(nEJ;#jV0;i;vEc;Q$&uaTaQ0xEm#cUq&5-GX1biCZIMzyBH@ z`+caeo#!R3NyM&eSVU0ZG2L*DjOnhx>xkEhXh@$nUL$?l*hQB6^g2{YpBB>@i&yLt z?|d82tpQuOuPwy8Uc}qJMn?_Atj=od3DZ}$_w-VGZSl4T>D<0Xelp@Ows?m`ya(6F z)PBXTKIK&NKz74(pWZ0~gC+}HW(#~LC^-V(PEX!*N-wqs0=pM`traoHtd(9Iv6g#r$XXFI*{+`B zRI`|Fcoy@TwcM|u#9}sIEB$&6C^=&GBB~;0%eB(4EnSSTn75)z`t>$CV=-@YiTO>p zkYPL7FzvZ|SY$o8R>tn~?ijnvl-Wl;CsMaM7$o*AonA6)D@Mt8U*EAsep5t#jS71_ zyD-R<{j10gsx0?DTkgMtfFrjM7Xht#jmVOlK^2<8`6k`^nXw(}D4P z{udJN0y%C)NAl+GZFZuROdeb$YR{DnCo^&-}opgDyjlcgoiFzkt_aVL1Ibj`l z`B*A!LEWvng9okS4(_y01Z}@gI(QIhJA(EnKBR*?tdkDz5MYhSatCigm2~h{I%7e% zx&&R&KV;%AY~ytB-7KxzyN+h>%j&Q#+1?VoebvlcJ(=@ng_ER zo@G5EvVszK>MmQ>BcSBSdWfistb1%(_qZ5gSzE4mWcAV+%j#WEo!YFJ5)v`;GkK}$ zEtXYuNcSdHbnxG_?C`N6p7n;ha=j#IIAGs?8|RFqOAnI)65Z+w(maRg|Dvw_ZAb)q3vylJ%0H*=1MX9H{0+-(kD@ zpi|A7bE7qG@RX{8^*lwPl1TJuZ4fn{Q-gMr{;j_O`393RJT)6YT17 zfofJ}hFzT+sAgrB+SSVf)vU}L?dt2DYF4J3H&$lkMph<7VP$?}D>HIqP? zx>Z6S^s6KKtNg0{qO^zOx(HZMu*``oE+%VsBJ?Q}%nX#gRotsz%J2vUA z>7G;Sj!iO@b^w8`g1wu>FFk6@b6Dg#NQLc}+^t!tyEbv>0xt`-d6Nir7w|el?Iju_ z)YeTR)K(W+?%eNCC7t^{ov~2gyM#*2^lT-eoNUZ4Ri})Fs#PYYIWrqZw?&yqR;w%^ zS@SZ@!y0+W*2pO$XlG$wB(_8&hir`;a&gKU>ATs{NIyDbjr7|bs*$^w zdTykF!WzlC&T~*r-pm6fVY8lH?0HS4Y?d*S00j2vPu?uQ2D6amXq1mTE>>m*wVifB=PxXnfuljnIYA-aBu!hve|Rr z-I^u)N+bhvmh25%vaf*Gk?eD#A(FjiOZJwFEK4?EizC@UI%CNOZVByD&pOX$>QZEf z#>x1ewuQTN(iXkpE`(**t@G5>EjD7a78L&V&&l`Q?UYLAH7Y6}q*55Aje<43R4gTEzWXM)rrTgC^s z^00n@3VT?)FlZL^b`cb`SkMhyr9W>60jEEO7_TWeZI%Aq6v~P$wZ1}?2zr6eSkMbD zL9e*UQyO*s;-1y`FTHwvVTF%qXl#ja3>Y#)Eo8{o+{S%feVe$u+it=Vd%!IS45PM5 zuUFp|P)E~kk{;^1O?o|c8;gDg6}IT^)+~DTHtu6kVA1j8&%Az!23|+>rbI)AGJdp} zpN^X@vfRh%s1ngL=!`|r*cRHyCwAdUiX$V^L$-`7S;mOv0hDFfv(UrSOxMl!%+iB) zBW3jV9;A%6+ad=W`nPS9<>y_t$hV8gySB+Jf6}gg)T!oy?uKV2l#3FU+e%nsE1?`L zIZD_@?1>Vtvz2gNfMa5WmGCcAi4uOIGgiV+E+zbOhnO`^8Sy%@!FKNBI@`&tJ*8T2 zm!7S&JFq`VW{DB* z?Q&E}Z&%P6%U{9rH|tD!ger0>qu1;6hdrN!-$B@-{kQLs%*yT^I_!w2M4{Yp1iuda z_>RM8k9eL|>YE*h?|96!-n^zqMN);oY(TYYzA~dbohOdLc~M#Q{M7jwDFqeFD#~hP z`?6Byyo3KFarD9tz4NH2XUTyXxY$RBd+2Z{9rAnQkVA*R(&1S;MD)QSoDRe2FqjTs z&d1?1I($rr59zQ386*EfI^@wIn+_c*IntgEw-V`_=6oYW6k;u&*@|bD)-yZnnU(d-#(HKUp2^2E>3Aj^&m`lSTs)JCFG}|k?iY3( ze*Ktdx>EaQ=x)z=j+L;$y#oSg1O(neXDsk3I%9$BlGKyvFr5xd`{7VRhp+qN@BKUrZO-r?8jZuFba;^{QSbTR9E-!dbeJ|C z2l|y~{J<&>^kc04?6X`&>Btv7Eftl=J)07ptzJOO1EWD28s*Y~KKtZv zpNRu~xWHeKhXcLv=Rb^{etz00@1I+Y1MOaup+u=kYt%Rdo2gZ#A5$UlU1&Wi7~0%yJHaE1;a&|wNazo+d2ehbfM{q&&Re+wPxxvziT zDjet`sJ|ATW%=omp#K&+&~q|>Lp%@hM-8T986D_Bi@*6=9Ozkue;*wvJMVA54hKqK z`*+cSlDjf1$Ts+42v}=Wl2E6f_be}At06*7r$&_4^ef7iQDV;j4IL<}f20G2xBd+jlx-PI zM}zz`#Y6rjbfBQXUvoPSBe*obN4glBnmo`7}rniHO^o(ArY@f577m{?I(8$YyGkUA$TwV=S4 z2IG-bkse-FyI=`^c`M7QtthY12d>3$9`+fQU6hrI-zrZ{@$&Cj78K%FCFK{r@OzSl z-kkgt{3KUqm+pPMtp~UEcCfF#Jvur+`!V1Tp!i_LErUN;acclgL&D?!(U61X{AHK@ z<>h!qYLF*0bzy2|#TcW^B2!pVW{rv;KE9%?dO=Q6esXGs{|M%BnMJ>0UNI&d-pIcX Ss=!<&?G(-P>AEL9eg7Zt__{^_ diff --git a/docs/build/.doctrees/drawing/drawing.doctree b/docs/build/.doctrees/drawing/drawing.doctree index 77265ab86771f750fde97abfa630d34220f84f5f..cb67592cfb1862c19a851386ae0c9ad77ecc0609 100644 GIT binary patch delta 4567 zcmb7{dvH|M8Nl;>3+ygQNJ1VYZXO%LBgwvQb~jdm65r@d0G=f3EDp*XUVn-3$(i!5T(C<4Z*-eCTru)Y| z_n!N`&-vZ&?8#$}uTMFKtsb_~-Z3l~bS}AfaZ|K*X~RNFT>xV*DZ*+o1zhS zeck*>z58bk%dIuxEE~^Qd&+&gejnTi7hagPD(n%;!%j&qPb=teYqc&VT#vkJrux1uhmhKZ}l8X zLc5m^9)KU>ME$17Yi)hlhvh7AIDkc7dhQ^UwX0ONU zSUa9hb%87UYtCpzv^2U(Qj+ZQW&j&HO=)k#ht5BGdB$aKXTL2WDmetz_>9-O=kX=@ zDi7yNoUe$0Xwuf7Lp7O)VIH3FT9tb}z>&A5<9lAKZc2^}aNBsA@eY*Wd9Sr5RDx%{ zbWaV;9sMshVUDvUBs^x0=c~DmEl)~^_o%QNM3JB!a`|yiH_YbY$WaL6QLi=Y{u1o= zT7};6RDJ{=f?|T>TuP%V3E6_U9_Lf8dFXAMU~Q;z!}mp|$f~~G4wJO_7GE+B^I5ZQ z^^LCZT`dl=(z-932Nj27982-0&7$>3VJbax6mA!ZJ)RI0YU3MRcIcK>DaAuvw&oIe zMV^YRo$Yqq?WNR9FoEU)OoWF-q13sWEyhZiOV!7r1KWJmn}H!{)izV>?6}%TdmiQL zX%n;I1+D%3Q0==WLnR!LR|S^Njn-sgg_J~!x>wtC|HKZj#Z5o2O~Y;#C|%wIc6de# z?IU9u*pp8W+VvhvI}2mDW>oh&xU+^y(G*Ev$(A(k&N^lZx!m)nERC0=+^Vduj`f1%sSP0iq*~gGhTO5!FVY%0c7;Ni9n9KVt_8MpU z1uvMVrmWX?V#-KEge;(id3R?JC0>F%b0(PHYHt|?%%RxKr(J{&1-l?Wb@Hu}R4_p~gUzkRA z7b{qF ziy(!3S&|-WF(bH3+is8BmgqGgPg3{i5Kc~#^^EOa#l}UyX5*qPlw#uxDVbv9i-77f zwEHZa!x4sN0}Z;X5^|~H0+iFW=U_T!8k!9>V%9SZ#Rhr|)o>;nv4&H`W*7^+QNO7$ zRKuCY0^D%;X@CZ-HKghAlm?-Q1sQ}PKH?yZF$4raD(izBoB7{B9E33()c^J#ynXU(Gagc9U?OL2e&pb+%rB?}4LTOKqX<|8OZcYg0DX zTW6>y^CA!2WIE(UuE`Y;X7C-3Y;P$(&IvLzU547VJPa^{rwz4jdko!RK4JC2z_6mM zGi%6(?K1R+nd#1rxAA_FAANBKZiZ}v;h19?Q(cg$EpNCk5#KOk zDBH-FZzb-uda=o&+(elu-qBD@&4{*<0 zDM<)2C9e3fG&x~NXq;@8I;3X)g6DOn&2pzQZSG7RffWG1RY^h{wb;?(UM)E$wZ#o> zqeTu}N{=LChU%iYJC>#6B^KioOb2aV$@}RCP7L93U5X|YrHlAI5kL-GCzr?iS3I!+F*q$dB< zbnJuk)RTc>IIFFk9AafQCWqD6cVu|bGUO`aaco$f2x>!`_Wn}lhhv@7#RB|A8X1lS z7VDh6(sbl=2qm`;+OI)PIUAH?C0B|j4_|bCB#JKcZ(Oz(4{e?b!{G$o znvV}SPV}3k?qZyrvSNNDnlJyB-FLPXn-@DX3h*z8zVgmvg}BLPx0kc^VdZrHcy!(I zS8;VrE|=x<4PREYIG5Jl9cj9MS)@_^bep136}_R#%*^K-^Ljq%SDB(%rIe~xMBO6B zCVR)@Y)1o&M{`>$EyG?(%@c4jE-R-s+?~ia5sL<1S5RdIRM6cMadMjSKZVVjbL280 z{_Y_05N+;(5E;docSo-X$-h{0q@14QzX2aT_#&qi^OAh>b%7 delta 3923 zcmZu!dvH|c73Z8yvI$`^LY^#;7YSx}?>_D>B9DOtS`b5nVGM$nlu)5@X(<_qqn#3x zLBW7x(hD3%(gd(NN;*IqFr``^#X@9_DAu5dp|y=HfEcnEygsd_j~YK`*pJsD7X~r+XEA>oVJ!68fxA6I&PaA z%!F+)1cu0;RpkvGY=oP~VKx=nF+_Q%A%%`lhiM(xP5}bl_&v-XwD-&zD50|_;Bm@1 z3ybN=lkk1|`&lT#y>6>#xexcaw_9M~J3Q-@8ND?JCg5qewd!+l3V{i`vdYrgd~xV*E@wG&;PFd|j{)D)dIfjA3Z7S@}p@?6FGAGGyp1 z&!dX>p#YxJhx~IAVYU8w*)?^<)9ffb&NgJo^Wi*xhS|QYTG_OujEr!Ib^}R11OH^pF0f)ZuVGp zJB$%0?f^qQ;~*J4mVjwhV6fFg3qOD@JSsGA44{EWspuRRD5*9P9?{D;HbNrI)XSIC zVyGb->{#d_pAGY=7H~WcHLTKI1qpUT7t~D+HYlKvKZF4N;zL-CPJ^<$A&BV)wILLs z-JrT|9<`G)oTTmV@3*g=0*kT4u!<(TbzUA?6@w$?%}8Y*lJhxM?KC^ij<~#EyM)3h zoTJgsubr}EAxME!kZTKX_E2FDWTkGFBGt$9qmE;SH|bwgln*0m=P4%1s~^E9_=>^w z8BK+D7#qJ`utBAb+OEPJO1}&<@lCaD?Oi|C(YMQVdc&ZLH{l69Vkp|bZCF*yUD#nz z%NOt%{#CDb8A=RSdB$SsGH8j#<+;fYSqxo<5<>?M`&|F$`kRxm1Y0@%{Z8C&Q0GC& zz(#|PIv|e@{{fsCt&Fp}(p9-uL1D~eih-428C3N$xH?KdhVc+T-pd=#a30lOg>{(Y z6=mRLuX>3SWlz$u{2i}jnb+zuU6}1viYVnHtcX%CUHK`@$0xm3_PYhR+{^FDMgC;) zk66Lh(NbVTd0}kG?31CbaJ1Oh1>jpyO4*-62us-AA{JLM)qM(WxO-5@W*w4;KKf&a z4tqety0ONqXn*MLf!R(1^B{qnz4Xp!@B%dJz&mtcEM^xw3-Cd&X6HnPC+rM)qbT01 ztMgS~bu`7#{~I2H;gr?QX(alz`B#I?%h2OfbuI8k>Z&cvuftp{@(rZmq1YRcDV1)k zP6n~j1fQ(kvYu+Y!Ns5$YYsIqw;@N;Flg29!0xaj{L~Drh=nw$q3J!4omwN6QCwfc z5H#1<_@cP}ryl6W7k&L)$9f+%6~SmaG7NJl@pBkK#W&$WY*I%Q-|HnARs%=d;nRHo zl~3_~uTSxPk5BXcv|c^oQ+)q`XUz8#KF#-Q?2!3>!l(GYhX>~SVAqQ8&0G!UJHt|Z zf7Tbp_hv@Y$M+qAO7ne#Pw}1VZ^7DxQGQ-`hSUA38@NkZM>6pxMxP8p`E3wS? zE3rUW9r)RzH28r&S~xLXYvzaSC|WaYFPdR{p)LAEu@II`--Sl5rf!__XKpe7#7fix*hFe0U80NCs!a+ zep1b1MnFkPHXkvdM3^IA(&A|71rGQv9lEI5rHTxrmAj zr8~$vsgzPxH3QMAst!b{${UAga7$o7Rht4d(T;2J=W0#KXp`iiWz-a)UOQG|dqBx( zUqECO*UAT~my-7+Ou;jH=cL-f)__vWU=K&G%Q!PC+1f}ZUDp{wg zlT9@Tl1=wBhQ4GC!K0H+r>rD=IpHN)afV+s=~xoh;3iWw!QJd{S3!J>YznYT_u_Bq zGY2MHqU?Hx&&oc;hqw*$AQ!{Tk(h~{rfP-f%z;+e#Xh2C&Bh{Ywh}GzAX++P(zs-N zTBfWee9Kg=5Zl=uUkP(la0LvdUFn!<$;C7hKQ5@^^8kluL8k2IT1kuS{K-~F}Ngp1nKBlY{!_8tSOrJNFq9bjo!}0DjXG3)J_elMma);{G>fh z#X(1pWnl%zg@oPEBQGYy7XBBz@4x89g$CGdcLxWgJghq^`1Tx}jE{sUc?|zI zw<-q@BrF#zXZQ&L>!$0+A(sCMD$m8A@Ngj)ABLI^*El?kcp%jA&wOmQ*|)Gocyowe za-s7()NgNg~gzxaz;rwWi0;)Pjbss3`>+z zNp)qG9AlTaTv&$x4N-|3_v5V)Rm_77%J5*WEKc)a?Y%10RQ3>FB#*u`pG3|$3|ug? zQEu~c84o|ppYE{SCBo0~_be>m$#AN?QTd2)CHW!sRc)B zF(1S$C((Xs$IZ?e6q%F<31v zc3W<8Ef=*qs0taWsRl9AjYi<3+(i-ZP70=n(H03n^Tj$y%?Xq z#b{q9mZe68sFxUP>kW3Oh0$!Y>zkUoJLAODGRlcD#Wjk|ozq{X*;LOd>eaF<_m3yk(6zj^}eAbkqh?HtS zvG(QrOes#Jp=Y3YHFKCJT4!T-T4$zsGCNwlCp(Cw;FDifmC2S+D3<3mh#zG~0o6dUJ3Gy@ ziPO| zi}KTDa$n3Z6vq@qi}x19i}MRo#TWCZK-H=WBE;(ac=1MFKXGripQ6hYB|cp+ilmEQ z7Q|$s%~69mW3#>Fh|7vVY)_C_IYKAi0Kucqg^B)82Z}dF6pMvW z^gAC%iA^JCh);lwKV1qHe;QdXo*9`aHjQcpo1v1*;`>=gGHr*du+}v7;uCft(^>q% z&SQE9f3V}2uHp}N8q*E@!8S78#vkk~CT}JDU`H`U;!_kprQ#Fz57R(=!oFb|gHLEZ z(^TaQ_{9b?RUr^NgsB;yurrwM#wY9urVf0B{NbfZ_wa|x z$yNN}RoyYp&^SGmu27QDo|ENMI4XA92!`ruoc=L~%$#X<5qJ6m@tsPEc)=ViC%!!I zHYfge!DIYk*TNPe8a^H*=8Y)=zsV4HS67I|pCt7p)}#`62ozD-<+ba0=JWcvp1itU zEYtIOeK&{hY4sAnn&;i~h_RlC87nN{coE_QjTNHL$|R9A?H8}C4Cp!2bcTq(JYoTF zj1~PX72?7jfnq>SaL-x`l(cY1kZ4`gzvq&*j%9kLw^06Y+qQ#0>}~Df4-=P|3HbAc zx6NY8vIenhbdvb|vi;&4V}p9^%MTNA`(+C_xJEp>qC#A81>(rc{UomE>dFd1Tw}I^ z&&Pjr z0~WD(XsnpuRUr;rpVaeoR~mtbK=FYsnjW(GXD_j=#{xYsLG0XKAzBXw_T1cVCGZd= z?mSY|^W2X6S*B;)uBZ9Koj)%l;(&Hu;@9toioZNwvH4zJV9OIPi%)E{K&MR=-+r<} zJim$8dfZcq1TsP5+h4}?Y<+q<&y;_y>4|xEHqU%=EkS(dxr14LI9OS84r&W7dg zwcV?kr|$sw@9cvVm+dY?u|EEZRwtLZ?|PW{{q9eRm$-3iM6j1D2zHyE!9J8CuHHQX z*;L9zV{N`zwfDx*!0AgY*)~HB&IB~|EqZH%d%=Hz=fWmAgarpB+FdfTnGEsrz8giM zW13oRc0&uyCNw2Y^%j^vG&I}X#s49V_|yJ!R=cpo8m9>H*@dy<$^)HZ`<|)d+xrX2 zW`_dezFP@oqj=`P5V3ul7wLqjQl!fAnr5gov>41aFy(QxrX0a*bJ!uw+dHwv!6oiG z6HI!T;JnYh#nOXAMC+b_9?$2;6Y+xwd1pS^6H9(!%*Qq)kslpe2tW9Vf#)sa+)dde zN<45XUtDMm6OV0R-Y1SfQXrmQ6DWRkq)5DSx0ev;FD@D%%YsAO;elkN8-s6elIZaQ zV-SY;GpW6g>PeLw(T*cS#U;nrk(uJcqfe20+;9tl+2`asVJ4O`L$n;Tkf+=T1|KgL z_n&eSOg~XX-f$!M+ldtNDnLe)v+z5F=`2b6>f{)5!;N_DsZ8<>BmPQGeBj{{RG*kj zuLzg!^hdd)nY1ZSB#}8>!kR!b zOAI)Ff~=4*;7b={#W5Fx#fRQqC)fg+ecI1Qi&HKZh!4G2Do%L7Vl_s0kyE6&GF_Dqed(iTH@S zu5f3^5*t2YUOyEK4FaD48r|f}k6LnNZ~Zr5@t>w=FOq(vVF~`YZ48Kt1Dc zMHnT7^-g`n0Urj4UwoP-91aq1H7yhG|Evu9heJ7_!#26!DLUhlMfACnMlL%=lTVY# zCCH5yK0=`u@!IN4@|y!qGQ_Gix#Gkx)(JlZv1WPq^Vwj7MPD;B0ebO&jU#c8A5CK5 zm-j9p-So~(@z~-4WW0Um2I%$*mQ*jyq4ub_P z6HC6aI%kusua$1DV?&g9``Sct@z;ge!^swL(N}B9ixPX92&0zx?l(Mgm43@27o+LV z0@svlh2(EeS{T&Gf3c#jKvAOL5rTajOUd~*N*s4{9T;fQS2M-r>*?Z*Tdeq}ug8iP zZaENB_GJoXPNvkG{e{UPtglTB!M-N3!#E(=oK!Fl2v$zj?4XMOjyV~*BF29=8q{^< zm$Cd}P!T;wE&lnta5hiY2Czx8R>fw>TEeEsS~Z&+Lk=g#kl?HsfH)_HVv}De0cXCzj?-Si;hYze3=d3|FiMa5eoPv^-p%he#i{PP zOV3n+Ol?2R;4B?7+dMH7CqDaq47^*t^8EyVlnMwG2gzYE_J<@^r7p$@uQ5zc7vsbn z3hFX_1#&QlHS02cKC61hU`iEA}!$BMu+cWZ? zb4b#q5SWS-0G;VmcoMtQ`7{S-C{s*V0W`(*xp@3v12lhS&tI_L9I(R_)8F8^!}NFf zg$jNp2K<--FJ!}h{KDHS6y`|7YHMuO&Emcvhln*l4N@~$3Egyugo!Wwm@R((({Mo@ zDr$b7B6x+0)}M`-eDZS*CdI$Z#^m8&7*PFd73S)Htqt0uTS)H0e+|p0*uhEwe)m^K zH0pLE679ad8k5}L^Z~5Eg2tK#z0q8)w;3t%fwBL$UjxL~f6GRqpMEo7vhWV$eCtlF zr&H>dF{B=FTXvHUuJ{OgHC+g&(+F7zaC(q1JdG8I9%@0`1+st>4xzikNCG{rCPV4D zHN?v$ljf=ks!F%1$s$aBJqVUemwONkrq?`(4bz4F$RaLmEFI-ZLg_dCh!qhFJUPP~ zo}^VOHJq+IE`(8w7s>Zw6&?sHK~+|xxccWvM1|6iy+|Q4`+M_J+P!%xKX`){T=j6; z=0hg%A}7$|Hw0kyBY8B`myG7naWwya5=K|}k$n2NFDXWWfAb}GJJiho7)k;xQ}enX z*^ZJQ^e3H27#%=%V0t`&JdEk=K=LqWoJmV!NeIMsBzF^*($s%s%+8d8l4g_0rtER>wUbbA=*8X3;h%i-it2wxsSwsR?E zG%g7WXBu>cli~EKND|0Vb+rA45Kk{hlBbcUBZ~YgqGc6ztdrGRk^ogXN}k3{Yfv0SAOW635iWgIOJ29=nUp$R0H zUfN8&=#BowgX2r-tza+@!;g<6V^GTaII;!P1PxD*Y8;L`ovw~hL{hT?G`M)uHdXP9LX&G%#Bz{QJl}I`;jZSirJ{U!kP~d!Q?%ZTj;E&6K z^Sjg1Ow3X#XsKYDMM(&y$s`x~Uri>fxg90!al|f8;n=5Az_o#@e=2F^)h(f26BVI! zZz?e%A}Wo$??y-+1Oew1Av7kPcaDy9CShv^X-2Iq8DxAAuC73c!le~SIr>Tl8H(~0 znPjXd>p4mK+FE)mha^&4CQ0D1EQH-GAer==LK04|WRhTH`XQ4vU|N+$W^yG`=-z>( zKegsTQ|-tG(x0=4FOqm>lLj6SQ)&%Xdjl^4NRoV6sp+ur+ziWb^gtfkG8b6Ab4UQP z#^#U(n6Atr)tFwD@+G+(zAKl^MEFm+ypL$}c>b|G?wBN>7`b+X=%TN|hHLZ5EM)jB zpUlB@XaSkYqeg0rq1m$7z?C~yK#~#tw*pel`3KSivy>s!D+`Qco=ZIGhC-5oBrg<_ zd0wm@$u{G1iGR!hk|>$9)mTd}WTQ!Uk!ZSS0P&E@1BA_B=eYkGz{~R*NG5T$Skhm0D5^gi9tXVJw1pNaA+<)SqN5*84PU= zzEC-sj7F^=8BC`0g7UGTPlrIPJ5>x`UpjKCJlyj8OT~Vqix^yy0 zrz3`wQCR4z;baA-zYZrRPBoq`nx_h;r=J2R+fxQ592`N$a?}V!g;AdoC}1QRhK$Qc zlBIsEaSCenc70uozL{Pff-w_y+WoZK;Iv?rqp|bpCncm14H8eIih1YHC?@lGLHYE| zXpUvQ`DQWq#qtu;j%8jf;bXwq(Y%UBM)Q2e7@mI=Qr;bM46tcoqf@7mNp$CEqV%@8 zVkNbYB}-AwmsioS{WCq>2kNDFQ6N!hU zJ|LXx?;}bYJBfHAZ`vec$MjK1xr`i3OD(-xLb7S8mSiDrv6frwM=fc>eBEST{`-@8 zeyon?59+uXa;EV5?VQ5%QDtN)ujfd*s8azdJkFI)BMZ44@CzMC z2LcWK^bq)=Zw2w-)y#$nz}Y8OkW5z13U1ENDnJbMztZVsJi@!C^8j^yI_ZxHX4@EsySo;{S06{ z&gBEl##Nm(kB?b*&m$%Y*`%+wp_FNU_#|`aviaPSN9S|z$X!70MYRqsAZsu!s^Zn# zRmH0pypXr_gA2(ch|koMT1+?T`7n4}&x6C=)qHHYTurv|>W!sVt2%^=HN?&V#pwA1 z>5&@ZOUr5rn{{lg1?xbIcpFGor1S=ZZyYd4;=7b%kSsTlvAkv}xJ5`88DSE*v4Qx} zniolwuY7JZgr3>04x@Jrj)+`9^+pKbK%KbtFCR{Sj2P4L7^)T~|dx-?gI1LTJ0cyY5zyo=Jk!VrEd?Ug42Ku6rOH4O$x-JtL6u~?^ z8^cD8rKw48wHa!w*kHwKQ`Sg4a^j>7(41Dgu_@>Gq^^d9SzHc1a1oTeY~s$G+DIy} z>=lh<79Y|RjOMx~z1_ggg$2;tjU*O{VwyPdq9%A1fQnpdB3VKB$_9qV*H&hDpo?ne zW5C>I66GBl0WVwpZHKlulQ1{HuD%p6HU~|7Lw!!Sl<`$0s!*ds?@2r zWiQb;HFkG;!yf;)YX;1UNU zxWoYo?(ab&Ek@uH2jrwR3Eb0jboH-nd=fd#&R=>5J3XucyA1{S&gZq&4yIWg31ry(cyhbCI zk@&W2upY6pyVJKp4`F>#t8}PhC9D?UBF_D6J*k7Oy>zfQf)2Lw(!o|-I#~GYU@J)- ztVeb*BX=+xcQ6xoFbj7u1K-c=%MTKCumz-!IA+}rwqe@AHcLBN8`89QwKg0^9j?(2 zmXR_KRK?UUlx8m{V||{74$ieR`OuPfcpZAU3s!C|KPC1vNjSQ9HHoBm{|Ug?e@gdt zfKf^iXBm7qi4KuT3WZlyQx8a{zAIs{K6f{Sp$G3K{p6)ZaMnY2lN1j}L08v6LHl8` zL|pYth+9GZId$1RFpy|g5?}GgFMcis+wK9rwJS)HQ_`FFz^3=UUt`>aq`C`h+_k!r3*FcGPZl}6Ryp7=P?XalR_M4YmA%(n<;#DNoDP-qb zpaw{ksJ-Lk5^@8+Q|0P2KVq7=^{<&=qaO zPtH7=YjI->@u8Df!JrvHNPkz!0rx@4#`^*3-!8NNd<4&ECuwp~S}qDIn=A)U;oyGj zK*;rW5*{sU|EZ4l@36zpWoHImqy#s3dkyiCOPJ0jK%1t^!LvAc&l*_Ddf^`8?X=>8 z4sNc5kS3$qpl@-r!+c8CkU?S1dJ}FgE_1^zCI-rk*TRfP zhp!`r&Z=KQ!j0=lmfTQFWz~Aao5Z{6bhoogK6I21(YPU3aU^TI+dd>T*iGrxT!Clr zBjcP3T;2c*_}@>4$`!beivd3>kb@uKU}i8p7D3eO*hfuS@hI;GAev54iARu`lW6}Te+Ov2gqn=1y*f>QUNkDVB8ot z;zu~~cMlM)T$ddjykI?uF2B|;<)QXQsJZf$`KvGFM*KU;32g-=_=EE64={N}halc+J^{i#lM* zseBXBg#J-8zR9JJ+C+xP3q9uq59r&L7oGZ)$T{BQ;PTC6gdF@K2Rqy?5f?k%O!x_{sy_9Uf=GDanuq4_v`DDZ-@510OsJXgmcQWT%i&KWX4& zWbndAI$(hprVH_scQLr9gPp$dCwK>3l}da9raA1(D10PNh|0OXJn%BOzXXSwj)wl4 z+NZoA2@9i0j+`sh1Sbqtt*+w$uT=5dTaT^IA zE>k3DvOJ}6?R(+`t)$%B}vA++RCk_l>A%+8LGMK2?1 z*I`(t1X&u#=#WL@cL99uBc#7vL=fpSrkoGgyn45QZ36Wg8rfkDbS;g>r=cIKnb&d+cah;xj23^J2z*ad6wuDVzr z1K#tGk|enT^SInPtbgVX=ai30siKx=!RoVfz`9l_Sk6~X#T{gz9Aa>4=w|N*4&H}` zEZ*sybh82)xlu;#gbDe!SxQGlV>mNidB9OP@TuvmJKeQg#C_tzYs-aYJsz51AQ#={^F(Ou8MHe}`F z?v{C&zP%g3Sw$q!5%?JSqjb|VBtHxm`m5k5yK_>?aJx9quqQ~6T;0bx_yGXREB6%Z zczL**@0buncY6-j5tzXXE;oO9(wgjGG1QD;V}B% zL3q=?Je+v?+uS0@QM%$OQZ85YIQ{S`GQnBs{5@b-%k!}CJMU={ESLJCq&h76yHvl4 zvqeszUYuOB1t z{-5p#eaoH%lfWK>iY`1LYr32RBz)^`k|HniE8ZJC_5w%wINF>nW9w zoGUY&TAzh_o}LVGT60?ESU6!Wn8O*rnnKdt%$)CpCxoyKB4^LRJ~bem#-K>pmVv#? zIVvwdH}qioZ4VhG*I{Tl-SlS|>R%fU%?4abdc7PvwloD}nv9S&?82$U(+ycn-#zG@ zZ!_{Sbk$+-zA1-@x4fY7yitQ~4SK749GJ-4^u!_OM2N|m98OPe2hE^!JC<-5yiBIf zn-#>*h(&fWIWjM|}xAwZX?p#{b>mC~x(6znpRnv1#Kks$# zi?tm5{xR57*fFz@f$9-CI0&~#-I~J0nO-~MK7=-NaQIR8vDV7Lbw}OjZ#E8QCBISG zr{u*PdFLn@BzI%jgL=XF%AZAlI|c7_8I|1(_vgs~mF(HcTPLNc&lx@7MhBL766jKEYjky-Jr<`%S)Fqyo4OQ|z zNkZh@J5l$6^lhWsEdV^mx^fiVc+$Ci#uR)Kd#hvll|i1N0jJf~}{0;Hn?l?*@p4IJVE_3V4?sd2= z74_rG#4iRnEpbV(G9yUnUyKftXYU2cLP_`HsWDY7PdWj@Lg1_T=C} zuQ;dY4D7?fZLbh7xz+ulqNX0Y?G=(Nf!Y1B6&!s26=%?6;5IJgzSl_f)>hYai{aLA z+*^R7_q|Gdr5Z5YT8=yTsxyW#@ckV8%c~?)PQ9Lk$G_%$HD=TsI2a0dOavKt69+@N zj)iyz?ndx9xmb#jd^%&PYhY%S+nfTTBkdMzlVNF9tg`vora@IGi<5O-nA`e1Rj+J&v_gT?h?7j{n{EUrWXwoTId`#xw~pmH() z%Y|jjMYvAo!v59=i_1_hYld zj0-yv+CVZj!(u>oVJCBJ8@3bcQW%(BXp4)PVKJV$uxt8YG0M2GyZT_USGur|yRhJO zBkwX{?{uNxaYNr_LZ@_M`Hp6%q6Hk9?`ppI7DA0&e} z%HT~dc$+C)hKO(>OhaVwA{l&`3_e^2uaLo~%itqr@KG|jnLW4htH2f+B1*;(ErVCf z;59P%JQ;kx3~rFY>tyf}8GLk`3^7}Vm?MLamBGi!;4@|LSu(gz2A?8>Pm;m461)wa zYk~|>Dq|>@!KcdLRWkTO89Z1950SweWN;&UZtYaVX)i;QEfD(xeDT3x=3ja+O{TZb zlWf>Hin{=>#8W}NhT*1ZsAUD6cY&n%mBDkG$$_9<7fASQM-Iv6!q0u(>$xT0q zu-{tK&~4HKuwl8$oflx8-W7~OQx2EzyK&doDH&x|Z@ow&Ndt|#NRnZ7qxd3d7ER3; zNghl}cU&ataCG#wizF2e%-*_42ElQ~w0D3qn@)L$WWaQM**hc`HUXY_2l!Ix2k(%= zsgqGI{xmFyt~Z2XPU|>{N(iw;vFo!>g>-1@`m@O?D+cLPA3IQXF&V84} z!wJ=u?~*h~o_v>NLUQq4lG=u$vlBMA;5-L>&;mbO00)S{G^R$Bh=TJ2_w@0@!u=E_(Pq64$_#;*qe^pI0JBI{ytzeGJRvp%YuvU~H;|JF^m&M@1*L z#ZV7Z3YY?LULG4egADZQqa?VE$-}q^hIW+JWLON23WnnxJUDjxNV>2S>Jo~_$4*a| ztvL&ikDXo(_;nO%vdK!~0kYHA)>_SQ^xXTzV< z;AlmQ5w?*Tx`+mRK%&*`c{t7g0FIflXC4me6$=T?ZgY~O4{*A#u1JsR;CsYWu5(igKeuz^gNKEPN=N}LBG&a&%AHi8E=~^4khu~P4+cgQt#WuHNq3mE3 zP@=AoV4s5I|M~nGUa=03k&CLncK?m;^Cm@?Yd_NwygwXYe#GAVASeY z-KdkgHG>+pYj7;9YOyWR!$A!OTdSoERwKcvRWF1^Gt?gKX+ZfP0*~*uHpBOkOqFz( zUI-t=2tLwY0|H;%-mzOG5=rcJ-CYE5-j0)7wH63_mGrt^z`FTV3(=6IRSU6AC`ngG zbe%}AsItL^2Lp*j*DZl+BoHOKhd2liKQXF@iS7{zv!l$C(9p2I^8Amrr(CH8xN!y1CkqWGVwnm65 zWUTdqt_zXA?3N=tWC<^t4($cqK}4#m4R*T`86T<~? zO{e>ppc|@SWu9J1&`Q772oaEk)(V=r=n0)(lEGUnOS;*)N@pny?aPX+GPgEY8(Kgr z1Ik>1<$qV-WZG3Lgv4-UU&R)(2=H*J7w`#n8=Qv2YePh>#c$o(;o`p47(UgQ7%|!E1&1jf~+AW%CIXYk*(*WQ7 zs55HD$ev5#S!ZdnXvV?AGz)l;W-=qL&_jUCF4wozS~NOZ(jY|Uq-dr$7>q5N606nu zoifeD${fv@GF|!Pag~}Wy} zgYL%Fo6aOesuB_syu7x4Bec@bO~MazCKwkR%o@E0&s4$JRiK~2gu`mZ&!N~G3>rLB zqp5?*s6k)Tpjm8au^X0VX~4T$TaXLUM)p+}y$!NWFrI5HbsCsT*@n@7H3~n657WTm z3Iw>MAQQdSBz)^36;8ih1($2jf2arQ!eR(ak~(UEMBd=uWfX z8LV-y%@X*a3%%JaMA661!kcuFMHtuSnx$zH<<*%}Wf%`Ubn9HcPWOhd(16Z3t!My9~*jL$g=|Zc;kIshL6J z5TN1Hr9a{Td3I2?7I@pB7?>{`%=Pw$9I18&C<;EBr@@I86K8L*xx_GN4Ri&gS>FWj z985ijI8>#!imPDb@Wno;m_9SZ*{oo~w(M4`%t}%tuoLPUJ=oK! zC`!h2X()vYo@le+oswGSneYaLI>KlHKGy_FnVI@&HZVK*B;3RVYT+h3XP@$rlWFyK&R3`oP?6~c9@=5x7wj>E)P2x zdSbA6M+4qq~aR*wi)j1J6UTcJax5`dlt-V%aod$1yiYHkWCH2+7q zK7KA((M(5#D6%5?)xf!i`ROhmRu1xM>G=@o;Zs|LG3lJpMO0&NncHMAr(GrYYiU_4!8-?lAY7tbl z(k?`eXNH9S&S&$?f&Q>pY33HpuVUz0Hka2h1J_8+N=LQ|5n6|9LRWzQ7@TSib^6vO zyQZeW0&UOO-8vf%Bk<)$M`zR2wVGLwgDuEZA(`qH3sGw9L&h5F-zsF$>3#nvc+JtTFgAWU|k)@ z$05)(8F09$qKh(wr2nB3aYHrL7E6=lU-^A2;RUsbrq>q>MgPfK!o7#lgbmilMe+qL&8+^7@<2p9$*6kM?A!M*` zQiP|KPDp14FVPH@jfT{-9fqj4HVd9Kvt3Bd`QLR5dL~ndXd4ehN9vN=n$#?qUYO`w60RqpwPuetjnk5Y|#@A?ukIdVG01zHPzv& zsjQ?_-sY(^|A-LBI@WmJv8JO}JI7U5g|OXR^NJFP+3ln+Jb8bF_nF8mh`b5h5_ zIi8G#b5yq)A@0<*SeijMx?qj4!53l#2HA1xBlCxP(VP1e@$~i@A%~{?0ypr7^jE~v zsr?l2BuK3c%;#`HiL=5mX5E{F|Fx=dxb6ja;JhB5M9&(H{W00 zECj$;bU~BeNo$2mN-vzt6`%>z=*>h$5=nPPWzVWwLz7)!WvHQ}1c>Zy8wCH}_Z|?& z5Y{ri`JKWH>_xg1ML6C5px_+>vo}~)grOVdNzO(OJSfk=^pD!`1x0dR6c>6oFxtX2AIE@4NK^;%|diC ztCqtuc#|O;uQJGO0|N)^AQ@~*22QHSQDYg5_sy`qD5GS30s;ZtPwy`Z$@ze9fv-Er z8^Ps{-1jG9CtB2MgkTK5OfPsS`ZqymGQw0?>L-?JJA6n7Jc~t}rFvE-1O)z-LXGRo zfau21XJnoP9k+Bs7V|Rjc;xwA%hNMQ)G0VK@u%|>RUT=Kc(Pk)an6I~W@WZhGuwI5 zymlcW_kS00yl8m05KG_ui_niQd{C*RUTcN^3h1`9yPLDGmKjWzgAB;kM2h2o-z?EBOlp^G890f_O2 zFH=@YU$>M{MMO6ZQL4t)Hi!libQaOAN2se8ONcI_+s_fMYa47Oq=)Fv$~kIrtp}HN zUM0F)4B?y~Nr>->E)AAIT5hM>187~7k?agvBOS8P20k{BJ zOD5CP+k}_|F2~UtEaz^XjxVs%~f!( zU4nMACiR5(jhZU#lmMpj$A$0#tOyx3%X>j&sIIcZ;)7$AM$Vbs+jLwwmkldF!@m}i zeORe?YY#)IVfaBPToPLMwUEUTH?{u+L@32?L1`pqe$tIZ+xx*y+#1}qfy;U9Vqhix z?rR~FB-6NSLaeml)0c+n$Hl>_S2DF;6H?R&Z0)4Cd`NuniEBchh88^}1Vk{+e6_() zmRqNx{e2W+pyfaxMRq77O4XJCk+c0OX?u<$l%vYEQxWB85?E#QQQ%75`5Y$WGap54 zFq5%NyBY|URWL_E>-zaBq9-tfsND((pVBFnRRwNtuHCME8Ze%SVX;rL&u;An0DFwZ zp$hNBRZ^?3A~cK%`$qe%69*rX0^GyCim2g;(|HkHA{dtY?pW6aJZ3X44q0Sbl%7m) z`6|K<4BAIY29vAjBEqoVe6V6R$At-$m>nXSDf&J z&i&U>ibNIef7xgp++yp^E>;}h;J4+Y4)&Qr*d0mh48bpug+L6dq^fg@cMiiu#Q|wN1jtefymkC6dFjDpHsv^vh5tqODpNAbBYW|zCNeu4~ge_mZY6$ zB&FvSsQ|Q|SENDmr}K&wNKT(uVBo!eUNL~Vnr;Ts{S58e=>vO-@D+W#p{inH1r|H% zf+7Ye=ka9O1%)OIE7a*b!Po>VxmKe@nMU_rP()`joLtB>zVXyoFw#-KbBc&eF3mBW zsDgbASPW-CdHoU4O821==Uh~*T^WwH^B>2y>T?{Q^hbT~lqt~${mYf%>C9p8*RF?p zIY?a_;tcs9o6NB9k%aVTkZ$dtW#q0M@=DrOu0%`hDOX}Uyjrfry{u~-*l(&*Q^fc$ zYCi^kU#DhAdhastcsuFjN+NLtyADzlXpP8zRABBv%6rvl9 zoQ_IBePNZ=Ay)^poMG;0Tsq~Fi+_4$TbV$CK!PvcKDhq5naVhKlw^!7dSRwAiu3QRP=-%*)~M4a0zv;s|uu1Ff|Q&6`E5Dxn3k=;}&nfvo!;tyK0tTB#Cd61Z-) zSCue=b%eXwKor1Urr1INUs091$%op{!6D>W>9R}Z)_p2$js zJNButKknwaw|VXsr;OOI!l~BC{fxb8Kcif;UxnWK?0!`uByaCmCHXNgf1T()L@$q} z7qq0m03k(lfH90YphCZHI-o+oe&_&WKYf6)|CN(?JkLrrnRo;-M%>9+qcEB1rKD(sQJa$H)kDi3nAds(p^y^Lgk zuPO(CmwQ!%AqhXi7=|BF;V5J};wbj8hwcLpC^nK_g=?~aI(~$ebNL7}w#QMH%Q&h^ zK&B_}eWp)wtD62lgdFb+Up>rA6lN5W}2_*SD2mluZsjR(iZ(5X+8{?G}_j;o>v zGKN5zG{@N=hLBgQ*627b1S|K)RiQC|K;bxOlts@USB3GyHMHbek^tqEpHPL<-;SvI z!SkjQ@Z~_fT*=}1HC+b68GstMTnN}ZF#ip{NB4t|QqF|Nd%aV=&6;02KBQepu zn({i~tE&;FNm(@SBy=-S<)kVKlE#zZFVY% zHL3#{3|l#;RN=f*y!)$IQ8d3t6~a23qs78${6STihtwX3kK%0M^!fo3rep#8i&N0m zvZ&{2RUEHr@nKf5?lglhI;~1|FrDflVV;iGh0g+)lLXZ2>S@pv7|V?2FEg4fmIu=_6(poTH&uv?hP{sCZXZ~ycjp<^mybi==%`a;EPc98jRE$x zI<*uBN~Hx9*iSo7LID+8ug2Iryk3n_b74KBUtO=p{{LjX8YA4x^=gd7UvZ#cgBru^ zpaw=Vw?WN@@CG#oPQW1_z*cQEQ7-;6PqaDU<@C1jRfK|W;Hs_XJ&N-7xG!7It&1T7PaJh+#*?Yp+zml zD!2;@L~0&ssJT(ypR-;vsq=vKTMO6)Bv_fCG^@G*a&xWfOh+tO1-I(}w9l$e@IXCV zF*?u2D_&N0wD90Cy6B>!j8-mEPxXT{SF%qg_U>4u?n>&-+p5m>423@<*QAHdNINWk zvh&ZlGEDoQhL3`1zEgztcJ5HmRM9Jss~3b|-*=akwz#+U33a|lKeVqlNn1p>?^Z|f z+;Hs-dSbUahUY4^_tC3P+ymOPlm>Kr4{>TIP{vqEY)38ljIMHBds+ct+>CRulBTw&C zdeE{r)GA`8bKX#opl`M-`}O|#hT5d&3ozc=Ah4RB^vTVt#wGfedT0=MQ@f~6Nn_rX zuFU|Fpx>(?&1SQ;1L&TM>d0zFJxE)Q0+AXQ)gAZg7_LIQ2yg+2gEK0y=(>#kIVDMs zUE3u!*B_R}d}QB6b@*)NN53n9LH|HDgTtS8WZx9!uJ-%-GNMR&Zjl;~2xj(fdq*we zY%WLeh^}F}=WEAoq~~95lq1$?pF)J&oi-RK z1P_eKZ$mPU@qeiO3dofBSxCt~-|+1LIJZ}40fG3LHl)-+Pc6ot3cGpi1cTnr8gPKZ zBe4zd!@23k0PeM5AU9?P@naj`INnOyGpM8xdzNd$0PeK~km4=~jd`!b#5IH7RCq*( Pa5A^LXV5sMN6P;JxR`O5 delta 27582 zcmc(Id3;pG@-KB}$-b{-oh)SECSl*h4#Tj^4#SWP$qY#*WD+)k03sTV5Ili)L*Sy< z6-1*%_8$uYqeW5UeADTF3MXjxFf~|h0 zfur(ML0NwQ+b#ud3JeWVSo4K|rE&8DdxVtNS(n*sDhjKesMf1 z_L_3ra=CwI5BWr9cz11?vko=TFSD0d*y>TKDtno&29=VJR>dYYYb%}ANDjAFRXDMH zWwonQY_i-@6(wIA5nyT7)-0<;*@3kU3$eVl-tMfydKFFF8X;0=t991ZtAvDjV}FkZp86%i|=t{D7ksH8IB&E zxxe{3a+TckpS3uWl-!FC9dH;rb6-5=$X0T1zYyt&Q*!SNTH(l7a%Wb~aHKaX+4ln4 z90{Gdqpvvfl-&FCzjmZ5xeo?zbi^yUvuXD_GL_uN6Gk`^mE0#C&pEP`-1#YCj-1Zi zQ>l(*O?OZ0zgT(Mk)jZ7>uhe#l6Msjkt2(SKAhhRhRP`g@$%+8o!p$C zD!cN`kjs(2dExTX;!L?`exiJ^AWnw-3|U_k3>ifAwOH&m_Iis&HWyUOlk?+bLqW0J zD?dTLUeMjIh1*@!W0N!UgQ+g5xk&!9u!nple`r7pH$E~nXUdkMSb2L<7_VzyQKr1N zC=CUW$vujii~Gy-i+$w;V+e6c%_jMHUa?$U91r5@31d8@$rFlWgFR{$)S{my6C|HC z`f6Gn>GICvA&@FxElz-V`IV9$k|R;hD#`Upc2~5vq)?t+l7Xx#NFM4(-9rjyZJC@R zC-gL71(iLoVe5G#{9{dme4+PDId^cHJf)9K z_UoM{U+QzW{QZD5dB@sBd28R9a`*mea%exBoIEH^ex%>p7_ll#Po@gXEPSPJ51&)vC4~+oFm-L6c-*Jup zP&YVk(I4`9M}QXpkjFc^(^EP}zvxCtSb&KO^dLk!wwBb`jv$oz@=4xHtn&n7qn_F50@&hZ<+x891 zp@%T}`{AF<^H+wqT^uo2;y}{Z$!)gL8#uM?w=u7YhmLXgh=*z8E#l!#^C4cKt!2`3 zfpniTNQh{e6vh7T57F{t)5F?!PkU03r%bQuBtIAgQEfM8P7tKovulN{&*t6|$hY%% zi-+wC>OlVNTPJ$YYB}Uum~2=V(Ux463783&lfO=CTUx$RWUf>svE&T!mwlcOYa6{t zplw$wWKM3|V80vC_l#?6 zSL}nNHp|M>Am4E~(Yp=uYpbTp7Z<0?d8_xyd#Xa)epo$1lIx%9EjP;j<(+Gt^0=qd z+e+4<2IHO%lc`@GSdYfQOk2=~9pd50y)EKl{(b8OU)4AMZ8z^n4R&pI;(&>gzj|P* zytXA>p7Y>7h-(XMMh&*^?=81%?2F?lO)lRwRjxWFZ1LHqQ*zHgW3iT6`N~Ve1W&bO z0cOJ6zSz<|Kwg8!sTzy)1w8Q?ATj5?Iwg zlI7@qm*vWTmdHI{7MP(eO>Td=xozTWqd@-o2`9#hMES(4#q!W;(Q@G~VH5pp{p61E z{;)?r*S;M-!F8AX`{Bv*vV)zB&m1vAUu5XZ z_3EW&eeI|dEC?9gO$e9Q92+7hAKvJZ^;kzgxW|hj|9B7i(<2H);Tyx{^kZYN`Qh@J zH*(}7$2J1C=-e>rda(TRgi=rYX1|#Y7rX>relrn1Mue*+oePn-wARWmoy>z@yx4l3 zN``-V*x)C88YW#64V`j2QXY9aQ%VSxcezG!6;7Sbkh+J;DP^Tnbf~;;L#c=N?YAaN zLp%BE-!{tAPHps1*S}+u$~&o_Xale+l* zLiw(D73#7NM$7gy!alKQ^W?vs*$CIjK5!2I_I-GcKUwJ9Yo*d2ZZCMAcYAG3uv~k^ zDc}2Hiag@1=*!Vp)8+fl@(!0m!q8k!dBrF3@|PbKOFhv6uFjB?&nL=FA4f>WFm8c@ zFm8cdB%)ia_{1z>3=@1s(p`dQio#=EI$h4c&{K~1c#J&yYM6ZRLc09K#~USg@k5`c z$)`RMq&^qp<%#DxiF}u)Q0oOj+IuNM{^G(|xnXsM9RFpg{J>|QNS}wv*3V0&qhZ`3 zP#!fGTR`p+h&gLLk1i7i#|1iCXf}F|W}_t8c*Q6em1j~2$K{0aig4gqdEM8;APVzC zeA!2;375l0y5t4bdinh? zC*m+LUKuENJgkS|vh^>+aK%OyWUO-HET1Sl{~7>Hk?gxV9a{qey z5DaY|HrPjcOMAk3XT<&2EV$&weCEG;;Q;Y8@?#IPb?F20$*;!9zN^FJs&CTdL0@mg z&|>{dU-|kITCVOEuI?r*Iz(RZfb-#@Nr8@oiaGko#vjD!?(<+e^u*jS$r!;E#+40B zSCPZyhG!FDDzU&M{L7uw9KrQGoFM%h$cqtSY{V-PE z+Pav+vAqB9!lU~4lP_Pl zVGXos!{W4JLmDmDFiG~IRErDedw(9DMXwn1S{=!{Zr!Y070i_TQic|on0d^PG6wnj z&j}DG2mLZSgrp*Y#9>k+m;I7nMq*lcL0pA%IW4>-XU8STmS4Y}$z|QP zQ!@UJg_1pY%Ka)=PN+wFyb-xI4T!dN6D-zq`9qnpOzO^J0m>0)YXGd2ehFZsbc`g@ zmn2wB`2-D=hi~=k2HohNB9=92ph5^7!CIzCN$e3VWU+x-AZ6KVE!<7%Z(67b=G7Lt zmRV~pbZnqNvYr?s_j>9tEjWt(4Z}$5HG~G`a){52e&Tw z65OZUR#k7cl(TbwP|OubawGNsiO*KX!hFCeG!=}tGB3uyY+fp)QpNq*Mm>ZDicYZM z-if7*?MsAo(STy!fRqA3aMsk<*%vm@u85bko{QPB2uKv|_(%_hLR??m5vjMhm@^p? zL{(3&2Y*)M13vz|zFt-K8XG%~6O<^b@rTq%!mM>(^82vcX)5dI2C0qSkKIa!G$8>`C*{8j02if}Kv<$G z!yY;zMYB7BU=*G)n(Ycgrak;(B+mRQsjwp?RjfsQj{CI=MyXx!M=5+J|uHoacIJVZrL$Gu-l5L8I<)o8u z0z4>m8P0OTpa-i>0t5RZ0R|H_DG~0Y^qE9>RE)wgV(szrqkTvxXws^D$ z6o=5B;=Iq&5NzCuXFon`dC9qJ4>BT0`(MGZ}B`{q8dF)m> zHg8BzC>0Iw;j*v9_)1pX(-YF%mTj&Wi4ZUEB5R=BPEyRG`+6!{1!VV;e zR%e%0IVoNj;q-R37upb;7|n+E0WF)?8-hgXA?)Q~T>@+F4GXFC_1;iL>HI#J$FcfG z9~ee?eP5VH>D<1sh|)LvLLH@j`@vF5_w^IylKPAMHg_64K=ktR0fPSrce-F8Y!nI% zWX3a6E2lBlqjDuT9|dyXs8pl_hVycW3OH*McwB0$3F|kKr9tqIRFtTImddZ4B>1mSg7sqj8C@2> zeqzSaU}En~79H^UWN?Ts!%$z(uA6W{Xc&mW5ZhNI9J|0(X|1&}7=+VB$7qZI`$j-= z1P8q)uM(C$LHJTK>v$CcREAkQRm6&`Q#-8~vH+sk7emmBYo|d`Fb~EF;xKD~f!&x3 zk?g~1kU@?4ZJG!YGw%`s^}V~qI=E!IaQ_#ki^j&!5dC`33}Kvq%@F-zpDB!ZVW!|4 zJxdtr?OCvrH=icp*-%8(!a1V!_Bo<-&|HzOo(qdasgf$|LJWAu z`M6w3nkN=aljjLt4$Ol};W&L+m=T*ia6Zftz)-epx;~PGD0v3qItzH1jg`Dwh@IbwR#am&y=*l7rVl6YU4ewf^fC}BTf`ihE zgW5}rV8b7%vYm*?y83IBw%+n;B#FWy&Ln*-`?b=CRqoa6HteA^T+dbs4%mN!`O5Y$hw&~q0C z{m7bnexxg;xN}{YLIhk;R%x#)ud}foH4qn|s))T=0~ubxziJ@Q3&?XqUWlp)>K4w{ zIU$0rbV9rr>rQ0#0xmeg-~~vvkm>~#)ItE=nMnC*34bU$AQ!@Md>b3wWn zFr_Q7t}C$5)uo3%abdt!sl!6+A*)dZV29}IowZfAQfI;x?rDBcv{8FeUrtZsNmRQ6Y?KO*>7JQ>&>l$DbJGBhLh~eTg zsGxM%a#%s>5qH{m1>!i^cCEm{Mkk5$S{oXgC5Jdg+|916fE3uxU?p_tn`rm*J+vmi z-_*nx(M^1}sfq70HSyTg#COk{_@Hg#CTrprYvKlL;`VCd=4#^Bx}O`XiEo-UaZ@$% zXGeh9{n=yyMMCHhCS6 zsK(6@D8F+vOg{B{c(6moaN5mac|N)ah8#71 zrBhh88vjcte;gaM213=uuieDzHPB0qf5Y*7_CA2Wm&v}5WY#xvu`sa_y2mXptF+o{ z7Fu0)SF75tKXx*C+2Mv-m6sa5RrzNZVnBFx1BPc+%YSxrB&hb8@DCeM6zc*FJ^7}evgf8| zi24Hq?zO2IVg?Bb>yqzA^~$}7_Y~L zVEhJ%RV@AE8ms^ZP!Llt=@nmQN^|Q8P7+)sFppD?A{20N=J&d;a!abHDCoqZXHf@BFijU?LTw|OQDQRw%@>Sj(8Fq)^I8lEBG%MYTY7w*ILUe!-hlv2yS^`e$A+V{$g_Hvtf zirexOJ>neF3vJ!IaPu)4b3!LZ>UWe0dMna-j)KYQ^kAv7iTG5Nmt@ z{MEE7K^w9OhtzK%Aum>Sr%zqHLGYnjhO3J& z7x2(#n5u^F7BJTb_bCIEmX&VAmT`E}L*9d7ohXvD8TxtloYnPjQ0iChWU3kNQ>q-H z*5m;J@7@G+6eX_Qhe}{YrE10&!H5%Ok=nCm;c6Y5z1zJ_aNpemJ-hbhqYA07QsdDk zQDYdZl_7~`B|(^1^xYxKe7^-s!e|GSZ|Qql$66l1(&*ZStYeF}(VkYy4N|w|SpkoE z7>20f7ZkXJg`_}$mkN6Ykw=Rwt+++?L<;zMal9-z&{f7N4ZGd!R_(tDrR|~&R~|Q6 z{k;Sp6vS1n&_}K25pPYr!a|24ufMv;2}M$2ByQNz_O-`)6faH*^5v}%r0(0dMc>}s z3OVs&0-~K-$9tY}+*IGXuC=+C9nZkviMOl#*!*n}?XH)zo)v;yw?RszO2S7T3GAgu z@SXG8HV9FdIxk9LfGt$R7X^$fF#~&uc~|tgn|2RIr4JaSs>@wb)3P8rWU8m}T*tpC zVtcDgeJx;A!oWUx#9P5{1Wo%Wp6RSSoHjbeU}DGM_W{T{_aAIDvzy?GEGtCoHTr8n!V zZS|G-9&zId9B3i=oj%OVO6=TY$~3?c9SeCJ2D+Qc5nndraVUxrE#yc*bXUQLA6Eh} z=Ljabk*xL!v7a5RmKVn0MLbd~_!G{xL!Q9HJ(c80rJQGd#l^+2T~8>3f+KN)@ywH0 zLCALR$`gr}%O-6HAGOwoKWL2{x@|_#dPX6aoW^_~gKYNhb_h~ymLV$qWjkc5;cWK9 zbJ&>pVqB4{ZJ)=MJqf+koP~)&*x_xYzHIdl zC6aNS2e7wxfXAIVGMM@AghEB)rpHm@jGd66An!enkqT>#iNw7s+rwrpSvJJ zQE>itl@XE@css$4KS7i`a&dj6B4}62Y^jgSL zp2m|^lU-Gxh- zg`^yLN}0bncRgv-pC!Mc^HCZ+bSE}AaW}pz?syuyD6^8)JguyXxP;a0_8iC2^<{+0lpp=WktJCYTchE{UY+zf4gYv{cq&AP^W8d6 zGiZ8xlHGY0#;8T_qA}T*T|K7rRl`q-u0H!5gsS0Zh!UbHRpYeSS5UfvOHVhG)AM`Nvh0kxm-g152u3w4H$1RXmzvU4fG zOCKP&F)~hkvP0(;i4K#MJ!^E%eTu z3srW|YMkZx9gSLLLzw8xus0W-cfseXpu8N)scGi{Kl!bpT zTl+lL_32B>YgsOPkC3DLFgQ%y2YR)x-%^;3rxvFxx-NJ@aOWw=9zr~(Nn|_CfAEB# zXbc(I>Io3u=~JaIqSX-c4X($}c$7DCjQua7ylXFePs%rii21KTgj&Pfa&fl#({FBsiNPwIY( zV3XQ$zTbBgUF7*!AypB)`XvoO#g z+4HYKwtHd9^-INle7>H_fdcAi>RO4MUVAX_cQ#r}B&rmK;qZ2D2~ynylUT%j~JSJ86G0o3y5QRt~I z+Qb24q zK5AI-aY%N!E(%m(2_%XKS4ICWnk1L~q!`lV?!Et3Z%;B8^j($VgHRbSb zh1ag_mjrFi35aqB2rlUr?*I|Q z?${wl>0{3Fnkf0V6Of@6__|UVE_L3-Blnvp@Xz=+!Q-~P@F8!mB(;R2f_CUl2ywp( z^CHJRDlx-J#jQByO;I4}6duUtR&_2%Iq|e0K64Tb>Pp@b@ZU}<69DIaPZ8y%(piEN z+1=F`josmbvwy_d<9$4zkE&_sgH8;;xAO6X;S8*owYF2%`siRBb} z&7QX)KBmlBU5n>UF69k_bBq-nm*1wSvlIfoijNn>OQ#j@;$ehHgG%ri5jZSJ7_JP}$oJvu(lbS22jnq$kh*5OyBv0rp%&8QoI;qw!R0M|E@LMXp7;WNdSc!P1dK(>Is0v(B6}Y3qyLA?DNGd@Z6&|O;<5jp$h3i##FaF#p zJ`C=yB7CW;_%AAjvQ>g|RQzcwJY9w7sqlOi?yJK68dU^?ijbnh15|jR3QttwNh&;2 zg-5CIFclu|#<}kXs|X>g0x>E)R)uG%@JtncNoBduRF*4L@fY#uhGsp#J?3Kivw(+K zEaoia<96!EvrrV2mx#k>pd%j>M*&-Z7BU)h@Hxw&A~Xkzz-<^yzxdEfo8)S;qc;|~ zuXSVV!u#;|A6)qHKv`w0V;~7$>G-3tfT^6rFv(hB`=hKui5Lz&!UAenCgC4khz33e3pU+y)0bc!OC(l7PE)1`ogG`)iV?Ko5*u)7RB4;M6{}6I;s@w4) z6k>xv_z=sbuv;HONk}F&nEq6*#F5gI4fzP7^GIj6;s_3>9Q`4d=DpsGZA-Qb#N05cdnU5eBlV3hUEpnORV~A}WMdj(nnqxFRp}=tj@NeTy0+>Jm z|3rSG0HzSYzlxtKfawJAZ{ueOU^W5#6Zts;n6Cg90W2f{FW>a&<|q@$B0~5l^c4bd z5Wv5hU(7!L7?OK%r9=p94iVe<1^AmH)%b|-E1ddc3^RNJF+M!=O$-~XWPXleb3cJN zLHTD4TaA=P&L(18bD&#dxvjPyJ%f`(cx(0(B)&b%aS`5{{VVCmODgKDwVnbZzBLEA z8W#S6CSqK(=QbSHfS+?YM1W()T8PLJm5R{T9PY}umOHHY^CV>}4AoXVKEfkgUb}|P zI1dIre~xFX&qMqe{w!jdayd#ci#X=KUMC-*eyAST|Ezx@m!h+ zTsTzJ4EZeN0`wPE`>}}^AT5A1i{QlOF9Cx)eu}Wv9O}xauY&T+oHaFgfM_ds*Ce7- zv%f2!)}&&qkxLTcNf4}fXhPdYoFL*;voDt5N7!yyL?=>{>OoLPz8uR^K7~l3XC@o) zDeky)A)m#t`JX~}K`3IYKZO*2UkdMj(ZU52ukQ{#pZa;-=;YU?kdp*rLL&o{|Kq!6 z^wA-fPhsg7p%f#hQ`)mXhkvBv4rSsUNW^5o9Z1I=kOg-jMoSBPcJ(%N58JvcKzu967yS**3G%U< ziR}mOz}^LuX-O8~b+4)@2|pJdW?F<3itob3X7>=~sNJ-lrWs2eeL2|b;Mlz;#<4Ot z#*QzLqDwewkEsJmfkd#BIjb6~@%My8p%bR_Nb%?2RXXVtM8uiDRf>z}1%EVm1EdF3 zIV`2{+{7+5zUB1cg}emy|3uw&nGfTKiI7KUK9G%qNH(?W)EfQnIl7v=FKx z-Le)+dC6R;Rx+1J$P&EJ*-%q%ty^KKsB<=uxtMFAl!clt7V(DeP5_6dpi*+q3 zlMHjn6}+v@5szS=3F5U_Y8t8++Ul^MIH+;}p8rFe(%6?}Qe>PUcGZPYR#=AJ#OZ$a z8jO3jb#xra0dKz(kXM&;DA)txY=I8rE~`fQy*#V!L?nP-?hk=c?Ra=kkS|B8HPD48mjFl*K;*aZs2-Mw^p5a zuFh^VOma5hH8@nvy3B?wsNBi*s2u`zD{a*)T$o#Cn7XoIk*zAvFbW%a;u)virB=JR z$k;EBg?)whH6}Zx?s|VRkU#7AT;G!={X-hYzH&(aV%O(NzOi%;gdfi0V4z0Q9Y8zf z^H@oh^cx#dE&ZMI`FHZMAxP#nC40w;HLK&38(tSy6Y5 zj+Of13DS^SDVH7WuE`ulB}&W7U4~^gdqrh^o&oQx*lH^3D|s*AWl7Sg%xPP+$X;g0 z<&eQzFTV1%xlmDFBlZI;{kN3HCLh-0G0r+EQiJVmZ~3=m(y+x#r34oCn`9TYfAX6Y zUPe}+dS~z|4T~CTc-sw&a0Sa(Pc)+!7)INdQYF~S*0Os1Fu+hpzqn?wx(xWCSp}DX z&-Qu)8Xdovw&Ku1ZuGUH0{r;xu)YVIc()X%!L<%^-Yw;^$Cm4Y*bDd+w5ZByt>*$z z6*fLeGkz{UkV^kMdQGt{X|UJXgt^NM3vFCS&-bk4hUEo@DYp8Cx*C^Zq0?E#b_Gc3 z|D7@{ccR9ROq5OZ^L`7;e3t1%%Vr9vi9&Aju6v8r+HGSI; ztdgpMWv-DHhv7i7qGfB!oz-YXT+y;EYb3^Y-jNLL^BbiFz>FKEL{@WK(z8{ql5hKW zYo%=xShuZ{V%cN&NaNXSqovS-QbT_OIUzOQ+ZiW$e3?kpo75rp4N?w^ZItjt>yAX< z;T})I&+(I5r6@K%O;bd58hkx>ZrO`znlj-(Z)}uG0*mwAchU^@3Ko8!^my!OtliGF zFBna2@bqnaX}abwAd22 z(q~#W!B?}KmquOa6MOs^*xD-Pw13ni$qBO93oA zA3yXzC`;vRWup|qM&)Y^I{$LpB18MER%x7uH3extWKLPqu{DoKz1XB|$%kb;fM$kA zrMSR?ImM_9Z^KkJKUfp5LC<6>gEhVQ!@gim9&uPfCAHqe`k_~4pP zv8G<8Q3yv-*oYb@76m4g$%&ZWb4QpLuQzQ#%-1st+?-9OM~TyO2aa<-Y1(sND>FxF z68PH0^or>;@}PCZZywMERnGU0>Aw&Uqga5`zUsFH1o>ODhL#!XAAJ;y0)H^$x7_rJ z$TFuzxx7ds&8cASM-2$g`>*Ao^0?nqUJD4~u)UNSAEea#8!*?e3LX5Nr`U}C-@rD3w$k(jI|sRipQ<-`!-Gr_)Nu81+!mpU#_D4tH2Fg~Po}Aq?B+EN z$C*rssd|xfPBKj9Yws&(lVPT6950x9ag;qXq=NyRg$w>kJ8e=k<`1xG(KWq+>laxbDO?W*Y~PmP^j zT*Nl@*2Mq+b}S#aW3j?Mny4`07@gCM@UL>=AODyAt1}9a_y%T-))ck-_SGB-#xZwl zlqO9_8)7aNHX0W*^c$Y-A)_^iH__L9&5>-2NfW7|L3;fYO^VpVC^5~U-m9;~C2^&* z3SU2Ym&`Zak8qG@L14ih0&+w);=IOyHQCNpk);4%4{h{C6es^+y4i)?P+N{Ge>eG-nEKVl#SaWn%_SE!nON7H3mV#D*lo`%{n+FpfwY_)cqW$M_eB(3~YjUk>) z^#>XUg{gAI23T_-Zg!U8Ydw{o@*gmin8Cil$}@$;E>`29+QJ#_hKri#R~xqe6@QqF z{!#CEzJd1qhA?UQ<#cTl6m6TKjltH%&d??Vax*?;dKeqUKW~m4z>!wdpAhkgaDPT! z<2LJ4v}99=o}VEh`I~2GDONu}LrW9ndsqP`-_6ic7YEGL#tr1cKQ{v{jDknt0ViCe|8E4jcQ!(suDznZc?b+IyT>9>r+7v$VnKQuLhl+UA#TMhspCYjzm1 zaf_@`>>#h?VP1e z4d$-$HkiL4mRM$d9LOl&&(e~kMb6gdVls3#x73{3TAD$Z&E`#dY&O^C@N8{&1kcab zl7rqBpkWR-=Ab#+90V)pXlbB4B$8L=XnSMs+d16O{&TsMyt%x<*ty(jm2?2_&eJ9rd6C>^ z;8=uPBK@3Br;FsW_vdNj#(I_FwiTPfN*8w8o3lo_g;mr3kHtoQW2Y@{)O5Z!Et>NL zxp_PzJeN7;t-NKR&`POXeHQct98D>oE#jeLzRk$o2y|+V?(mfQz|ufLGyr zkn3A;kZXU}K^+Z*#)CTdc-^BhU!u_7of&rnjmdWp>T>*fMb|XuP%R5PsEg*el985h zNSDDwr`f1Amum3^cG5{+bIl>05$QV*>1dR`dq`J^$xV^VILy@ z&(QIjZyeS|^3M&h&>^H_^3`EoF(xrbbh)C{@p^MFmV5+%HR;+!(31(BN4S|DKEfMz z;0U+*7gzwx`5)C4VKU$-S7h-~&hqe4T>*l}kLvnh^6ODvAnBNnX1?*qc(n~8dGZ(+ zef${jmTyHau!HA%cJSuR?BE5~b?_FyC~}{4=qMls9@ixbKaU`*M&OPzA4I~&o}syB zHz-`>7CwS0o-OGQ2{c?zAJ?UdXkoPbo@h6sy5+90W%c|K7pV z*unS_gXj6Bkr3x@M{|cRIg!^IrW*M4Ds9J=7!jDW-D8Fbwq!JoLTu?t>}ULV;3T?c zF57&PH~)o`T&o{Xc8;)IhB>-O5PC`%Bf8{jyDmjM!6987k`|xRB`H88UBTD6dlYZx zpVCEZdE7pIid*{oQ``WBr*#RQQJl*#mnPa~U~5m~sGEOO7l@MYKF!s8?6fXRRB-Nf z9mY=f1(wF-=4qYVBvH(YAEzV1z-r)+CS^Ax)6;%Ys` zzz3@J6xm*=*3&@zxLQx4@aJkh1;3;keOe(84%L7=LpXZFrt;Y8o-vAArfVC3=ci z$#uNik#%|skm1Xna$dj#BNC)MfQj(v2Az6h)RV5vS$47Lz;=XWj^a@oEz{8<qA+_Y5n^4+ke*I(6SpZ=;vl+;qp0aX|}0eX_{RrZiJuG;%nF1>A+UHdNJH>a687)77XgY2B8YjPVuj(U%IPW*+NIn=?>6;Ld&-*k6%%cd?(uHRC zB7ZEHe?p9Jfx7W**7&MEQA?-)+fbaI9-70r9K3%1{;T?!cn7nA+ zuq?C`vfy4A-f1Dcft_21W*m=v(b6YFSjD^gB+;NiQ*YiN@ha5MG!JzZ_2%&=Eii5L z*51@a+gV<^{|BXcOIYo@`Y2St*BO1D=KDa=x9xk*=pWOxe{)uU6#SKL>d(%8sE_k? z_vK)A<0eFI`&Hu;uqbu8;cfx5;E(i!Sk53_N?~fAA+^BawAWGHXk3LFST>9~2oI$X`9Qi}rIO?vQKdYfE0z;~C?3m_MX|g)QgSSru7b$=jwH&JWSfAioR+sk?ymNB zPy0cN0aCa{liI)nDVj^sAW4B50g4tW8lXYz*8jAPUG$H(|MZ^%1rpdnfTI5uNI%dP zXn${JcV_lT9*<|)Py!hi$(x&b^XAR_n)lw!>koYW>G}J_f7#Bs?YP11nq}KTX!E#} zjo4u`ZSf%Ke64fnS36(ojAx^fwH$|O)a0G)KJ;ihu5U*?=v>E_W7I!!{kWC`7*4Ec zk?Y~kP`0yHV~tj5r@j@r$;wQ}*>=5?Rjee5-1#)Yv^33D*Ny=)+t&20I7TD>9ty0M z{A#}=Y&r^%yq?67&quI8YHxoAu z6xEy|XPA~bjxUE2H}SdgRrcs5MGa;JHaqF3al#`vSQNCumjO zwyebxnwPL~qH$(M{*LP!@vF^<^WdrzCbf33SYeFKEW38%%&;RQ(8xo_j=VCa1i^2FUMt8EUY2}XB$k0mjhzqJA`f_E`IF4S*7C>zi|)4D zBg+cPD{@1?Ei_fbb5- zNfbhs3l$0-_~^R3Q1I8DHFrtl41L0!)<&Xzlh6TM)w6Hk-P^18;x0$Ddw+RP)q2Bu zN~qK^3=^V`y|9#0$*wJjWg(S<1SyTvCWa_^*D?b+ig+R7_6)@NLfW#TgE0iy@)tvx zRHrp5%m|5X?6%rI3H?PYPO4WE-bOz%kpU;Ou7_v|J9!7N(tMiT5pbZzS4vXlDrGhOxF2n>Do=j8#`@+(g+XNE~Zdb@=@!Q&##cav7B+Tl(MLm_o7#8N}fSBtY z%LWDkQogeepYK_dG6my2w2EpP9l=2F_QOE$2+p^qsrZP=t=aM8nXj9){W13*&D?rG zHWC)zpj9~3r8dL4ei{Hw=bD_n9)9Nb^Ff?gzHf=+105M)yUUG=aL&I1_P?0yvaz54 z6WLuDrN>IfXyK|GG~q)g%#DS}kZT5aa*_oh!|5Zk;LB;;s8_^qrEbLSTBR&9B#@9r zn4S;<2q}Xr8v1^?9M8zp9@fM^mg%$*d|VS^Oa7z(Eh+qi$1zN74Yp)7+u^(LlP$k7 z?}yEsrr-Fyq^a}_0-oTkUfBG&rWK=kJOJ?>xcP;m_z(~@=oqIi}MGS)6NO z=oi%KZdOb7_7l#`+eg1PbF%iHR3n7YJ+%Q|FGXCQ)Jhs)tgBh$^%;rR5j9JnCw^~A zjSzSpC@F*A#Gx*XY#ilaOIy}WE+}OnM#@3+n|a`Ch44@L!h+j$;b2a(IBaotE42bT zPvRIDaA~2TAixyW{8$Pda#kEX4WQ>HVcLB}YZkTI`08DTn4w_qI@G|fC)6hQ!xn`$ zI*5@4d!+N;Fn}m`d*8XU?6}Y? zphp zGKl&G`qj6-D;Nj?NP~e4#XhEZMyfZh+E;&5R_@(;l=M{({a(?L$$}ltCcVxTxz6QH ztrHPbFHs|e{;Zq&bMEi)ZrkGaz(Vs^F46$CxQmYxU^z?C-QaK697R$T7eChUM)5&I&KLN*TXFL&Mo zzKAVa;)m#vg$P1Bi~)Ozn*%Xt9^Ruba;&h!F^4Bv!RJ+Ryh&IU2>~%Z2CosTp?F5j zK>!HK5>f(!Gst6v@qu}^DOe$vMKi2P9eqA;SqM+s?!p29lQ-*=#X%DQRdSh?he!%C z=G_t@y+B8^3^})jVWKmmvOjZwcW+~Vt~OTN?|WPOb0)JLDCsXNCA}H{XWCHj-Tu!r zw+i=1=T64YotW+HC$)KdN_-?|Us0v?of=^5`pC@dh zIYF@++NvOJ&0`+SAd>f$#IX{5=OcGT$3VH8ae}aqwDII36PJ*I1-uuSy8v+Pv&^m^ z26Pj5@CrDGoMZsPbUF7BNPkuf#IJud9&<13a ztZ2a+1TH@v0GCsDLf|zX*`0yc2OWCs$+r79F9AbBV;Kv{8#csbRB5?_ zU$^CIPXiOX^Vk4beeT0*PK`tM;bCn^i`}agbwQL}-6~vTCa6;#fI2rmax0(=efH7M zwFY%{u%yT=nWb7qfaL{@ECLYF=A+q8eN1lnF6z*9u?c9MSDroG)v19rL(AxyO;1cn z|8-)bvOl-I$62#(?7KWFm&lZ^YQ&)Gk(!b!?ce@#k45qg>o_V>;c*z1E~D0dUy#s7KT<~v|4GQ+ETi2bar}U06ezk ztYM&{nl`{@%akz)AJF}q17NVFW`Y5K7F6m_uuh=A-^a8b`k<@irkNb7@L}Cz$uQ0<+#ZWRkjf zH|}Rj-HRCVmtu%_6TgZ@uadO=TGBRL_nQ1%Q4%(-SC*Nrz~qBIkk6&-hoUQ7<#nY? z_83UI{(S&ElqhK%rJAs`Uf7t@<-LWu(SKy?#y(RwDNOP5{z$@+^Y#=1r!ZV6G4DV3 zvGiQNB;WJ|ZORw18p#)BED*RU$yj}20x2H1nXD~g$B#4HG;9?iK;vZzgH0=7bj#N? zh*{V)*+=uBkgg*eQ(M@y`o{fC={k)i|AH9OTe_ZJL%O=xoG*EB;kwiZ?m0)D7hQV^ zm+Y~&a1A#ZA+JfC6)hmgjRCMwa(TWE^VJ*>;^Y-L_C0fJ@XZn~o7cI#& zJwcnIMbNcq^&WOc>V?8VmBx<>>~-nuXRjgjb5J^e2UV2`v7UL-Idy#6~95DGpA0Ht=^`(02E%lsDo+H}tIEJhY9PUkg& z=qAgwu3;f>zH~XoDz0ImQmi}4S7~6rDp-9hrFw z5MB(^B}(h5o+6xi`s7)JrA2fUmjzPf>(r$g-LqD?93a?etx(u8oCgb0#9BwTDaUAJ z6j9m$2BZjOiaSjrFWU?wlH4#><^#FI1%kT3|CDF*xaWskm<$WExD)55~6 z^R1@A|4JJC_oiq-R^~s|2w`PjDOs7bq$F{P#!&v_-T5ylZj@swjAgw^o(>W(-e%|=jsb;kr^ymxk;ddmEwP@2*T1VU6?(0<{nA!5J?S7bdRWaiccw^H}b$B4*Hy-OuB8; z?h8d#$4$CTXIiDTzEWyU*rQJZoq_DpFPAXV_Gs>u^!-=}g%H6R&U4pFiAg<`-|frw z6x>!^4#3r4^PaF#et~*7)axe5bQd~-Dgr8^whond+9^iQ|J6vNMx}>NJ9&?u2rYkTdKwDt z9z`iw|KTYL4RBx}ZT5wd?kLyjAGla(GrXOVH1L*0yGMyypS{aE@7Y0h-h2sft@E-| z%OYAps^=gI=?;=clEpxz?Wj4%M+yqy5KkXQ;l28QSmz)@&uAb@Jw-YMEA z=Dj%3cG;z68(#b#?YdT#evb`L-soU>u^W2xsf-<_RcwBr`46v;zJ?Hf-}7DsNg6-eud zsxUlQfvQW~W>=MIqK2IKhU}FE(i_5&wx%pN0Tb z8C+2)#%`Q|coE3MWYES>9l9Zm(v4GSrk2C>-S~+^HzYe!N!j;*7T2lO{m&@=;67Jz zpA2JAsf2icr=bF?M)p73y?Sp8(b(0!>zX{m3rsLmFE5a4@FK!SA_Xz1XG4NkMjraR%gwS|_sqyCChAWDoK^{uD zR-0t6SgyTd1t=Rq-03PBP6W6t=~!*cZlM`j6FlpK=9i#aP#OxWm^drwD0t_JwS+4; z5xTrsLn7o`C)g}sW|#CuA@foT;Hb+*@eI4>aHLl7st??MTsx;rZxs8o*?GA7^k@%a z4e}n&X3|nBe-1mMxzUe}VL(fOnd%uR87cWT-f6e*9 zAK+RBu1=g08Xj6syo$EMPT{76@N0EEp2=qow3;_AH7bSuBHU$Y=_|EMn61#>Cyl?! z+N@IA)cLCA^SBB_WQXk1x*Fh_wUqezxiwYz;i*lj@aLaBGKdO)?lVW8Jz725m_DLZ zI0Z27(S9@Lb4}VCn)e<>r|twdlfwL`E`{m+H5!!j--p8MV)*{Fq%o+hCEq9A*W(dU zttEd9I35&x(r^z@5y(h%Q> z;8Olg8E)wzCp_e$fsSX7>TpyQer#2Tbvn*oUYLhcKjH5h{Yqs)ylYMJk=neDcsP5M z-)=+s>r<@Vm|tnoO*Xuq>un!`k=h<-pIoz-Yom{|7ahzc6et$+s`qf6eb`ZFOE%U_ zQ413wo+w}coz1ovEmVHhnaxHJok>7jJRJOxdI%VapCG|uppy;1V)-dzP3mgG_-rb9Qbh6zGVVEE= z#EmcU((ewcHo*19#%<&>TJ-ks7}aRD8@S|8&mhJC`M7N1zIYfhZqOCQM)CMCX3Pip z@-<>C9?>B@Z`JVebb;TwaDP^DEMI*b${ymkU5dRTz!n#shO!}>&!>x+Zfx0#5VcxF z58Re*p=A_LY&AJ8s`ExR<_0*+G;nwcDGS`mM$=@WdaR05S|@v={AGhjQ5ZFFPPBa< zfzE_#GwEbI!!#*F84^%B+1{>ix`X2^+d8yC004?Xc5U8`UH^!x z8o0v)DktKVE#9skn0gju8H*O0UDHsDp5}?d)h10B*Ib?~h-sXpA!vZZ|YhJ4=Z&Fm3@4PNb{XH3EXd`mR4!N?Edxp4Ing10MP2@>megI)^8 z>D&REK0r5&JK6Ranb)LoD~0&R82JhDS$=HbAvE$m=@sz32qHjIu)k8Quw$fu8W?>u zZFk-jkC4k4pA`2u!6aeR4HmG~h5YX(jR5^u?zfBM2DA;4T)|up$GhckW{AIs*3AaT z(;|KYFQX$`PS26Qk!|I{QU_J7*@)Bx5It}aJ%~bi)`ooeTwweVCIVL^qIv@yf|ts_ z1YK2XCDkpO0c}BjpyLNUgiiVzPXTVX;%pmzDci=SqTlO12yyjBNa=sz{K$I(uC4bd z{n$$F_tB4A^f90xKi!HS|3*JvhIRI?(2wuXkKdyo|3g3i3qR6qlqSNnF#;_))f-}_ zS?7aUG0Ci$U{(O16@X_2&{+X+Rsfq#o!#{I_+ICL#PERgBj>U}&-sbOjb2ETxY46i z-ggN%s-%@zQkAL1k}82DmU9J`BG#5>J51ITRNT>YWh97%K(>(jDEi55F?7=3UOavoA zoM7O#oy^<6M)v&_w)Dc5_{*IukaVORpgX`lG4v}t7z3GH-CrT$h%cf zr83qg9&AL^4Q~KR-W3<)XV^aN+3ZsZw&{&VAr`5}(a}?%6vWB<8(2awpOSoz8*mAK zdxL(0U94;Jd%7uVX~vfnx!5lS);y=b!7@lsI^kPg0Q3Bp&6X;F}<=RFrgg6Xnk~SZqO*?{Cm= j6*;^=#)vhDv85met^7f?a-qs_K@#RUw`B50P3`{y?-d?- literal 15732 zcmeHO&5s<%btgscE@wzCKlEYIGNlq7CCl~hjEoqdyp{-ClxR|lMuJJ%3}U&}GhH*) zvpwDI{@4!*MC=$2D5wlLQQLs?aZ64He8?e(BoH7V@E;%`Ctm_22Ooo+a?J0&>gw*9 zT`q?}Y#9*+XqHn`AFp1$_j@1p>U&3j;=gc+|I??^Soz^j$Mw80@?=`*V_r1KCo;^6 z4~mQ5FK!nrx|O)wX_O}eS?ELPF;ITsB{D4D!jH?;Kl6jMQ%o6Z5ZQULMz9`tD#e zkweo5|L{G`SFcdydMR^9)W1+|eKhbxS-g5!FUBtD&I)xz9acy5qTSV3x^Jd3NxO04 zjxK%iQun2j2WdC+CS4Wobg|2{Yr1#qbvj9!6?tE^^inDZnIDA}*VKYqq;0O?$A!$# z0@?UUpL?z1hHyhqTnX|tlZhXWm^T=6^rqY)CLYxFhXo%HmReInt?W6FYmkR zGlosPo8W`+C+gwM;!s_$53mCZSYUQE&*C~1i5oXI%-^*8E$04>K_X>%Lq%C94o7Vv#0HKfQybzB zpM5so;RZMGN8yG@{E^DqhKYBR5xu~pp?^Fae|a$e^5{Yz$rHcOPcSX}(*S)OK(c+$ zHTta&uNLZ0Qj)$`>@Ll&A+7574Aj(0bI~^FW3O+?WXqS^_T1}+n%^F#?9b0{l7FWm z|EEF1SL}+nE+0O0=q5z`+HNe9p3H6;9yoe^|7?|1yD%WH))+gUfgyO-Z~_^4J? z7bu2Vit3!o@8EviJS@On3m^^v31MTb-GSjaJ$nRJ22RcV&{$&6Pgw2Ug8)$A4s-~0a#+CG>=;_ouMdS6%ae=l9Hj0k; zPS(kdN-90tG^>t)7I?@SV%GH)bhSO&^fZk_zXI;k!TLk$0BQvYMPpD z4KM5Sn4sxpk(D{X(4Dv*rkNWAY%MI%O*d9Rs(y*N|Fwys_jdR`Y8brFG!32}5K1$m z5{@^m-InVIZa*N$OT7JcKf9Lq>!Yo~5s=TOg{((kwtce^Sh75YODkeOHa`D>CgJ1e zc2uoS#t1Yhv%E1#U;oh1i#(Etvbi=Gn^sK#|YxHDr~_emteJkM4V}Gs?MWj=Z6cbxSGu zl>&|r!Hr@aRR6$=N9%R9xTm00LOw^|Sw7FF*^grwYXc~1>63`<;Gug#bU=@48;AqN_z7ahOjM?q%9X*-Mu4VUJCk4|jp5EA*pA^= z!2gwWY~yp7#nNsgy?V>~L`kPXkQ$$6+Yi7f0)_)HYWYo8Xz~qj!V9%*m~F21KK?RO zJibJK8v1Zgv>Jb@8o`m#lLyna_(v{6P6YOdxFKZrYfZ;1hK_61taFCR7$%XuIZ;rSF?PfUQEs%Sec-EesF13wuA@Wd$>z>}4pIex;n zD1DK(DRPFhPk>7NXBWO}2cl({V{7yDQZD1?)eF_6@cd`&iqLkpHM7< zr~y4D976=mQJRZ35sV_FZq$TuFpM(pi8+|yG@3}vg9S#2SW<-iX&z)03n6+ZQn7d< zCKPuH3i)Xze-bCKq#}=_u+zSYPSfDzt~z#UkxMp8!;2{Eb24e{R6SPuN55B~HqQAvMCT*;C^tbEH4Jy3R0 zApW}&O(t|=E3dhOi((M?gH1XemnUu0a^OWE5!0b9_QMcU(t4B(!G@6GXi!rxS5a(J z9k(9H8L|0+oc+e+tOm+k1}MK-!QMPTXnapd6{f(A-SKl?eitcMy$P$f%Y6Qdo>J`Pq!%f6%48Xx zu7H7%sw3_-0stdpB$JRO1)d>MBwZw!A`DK}+I~lN)(AgUG-Jd}gq*AY@|bFboIC>JD6( zQrMD#aznHu^G9m%!c?^)U1@dPA{&BJhWm~Q``8xjjoAAR1ek?JEsW{b>KWar{h98b9m5U0uDwz}tXpGA97((qUL#g?-;Bq0ct1Ro^REWs8Fl zi;#Pb&@wy-I%{Aicr~#Wq=Ogch>NBvgPba;9v8%`^5%Ro&5{V0;`Ltj=jYgr%>z7j zk8Ri4HBQp}HCCQY(lj$Ol!E!0InbYSSEL%=`2-nPiP{9pjFG7n zTQ|TRYY2Z2zKF5-tQ>n>k{NGcuM=Y&!+m`oBZjqP{|oJfBzyc<%{@G}{~oLmpWVB` z8M*xkv&|{DA97d7?VmT~_LF2$EyKSn6hS@>m*$h_=g|M3<~V@)3 zOG7HD)!+ZbNn%ZyQJS-Rsj>>QJk7xwW%>0SXH|mye_CZhd<5(G+dG4rWc^!T8scpB zJ)ii08U6o#4wl|c{O`VsUNqiLp}s?g_kAomdaseQvo^7aj?ma($dY^tDa`~>a=K;t?3b6>X}k5wZ$-Sqray6M~Cz)KQ$00C|V z!r3HAt;sKS#R z1*gbpFj%mWL}2YB$zO3?z{F-4V6z?NS2CD`D2Niy#v{Z=-XGTtTm;q3K2W%jMDT9C zI+tH&NhsCD6-r$F#FJ54%otvpzckwLWjSNT{v~8*)6^s)C9*ApV9k)e<5Ci4&Co>X zhrE{AW1Ve(z-P$7Z;;QmqFgkJ@&}dJ-%oh-0#wW@%oGf?N$%HKW*Ruatwxg2dN2@> z;vE14%dbOA5^)vfsX1=DEEvo-*ZX|Y?DMPlwNJ`Fzfg@}K=^7C5cGfdIQh(Vwb*f( z`2#mFDZ`1J^x+gMTwa$sOPpum#$|aSl^v!z^pLg47BSlFrVQd?9;ARZ`up+i#UEdUjkpaxT&ymz=?ia@4%9Sf())@;CfkWm{Op8 zwvBRU?oty%mVo+9-L|I|)%E|=-PJ9D|neWNg zOOZ+&@Na&x!bZOwYytqawQs7Ixsr|R66rFcIj^a4Bgrd&nVxs51Q2B`^`tBMT>X?} z9pbw_XgmWsFKrfOsvnYbaH#5jQ_o)H%%03Um(puq)aP);gUeE0_zn z&=zlx&Y&L3yd{F?N6$p?s%P<=Zji&oRvEyRo^Fj~h^wwFN-(~~FAOrcb@+1JEQ17T zxQVar@@t+1_;xaj1`b^;(yv%<0dyt1g+4osq6}$C+4vEUg-+6wo-m&{J5&=x&xe+g zjr3y&_jq(Um}1H&Obe2$+~SZacI^8ltXK{xA2ea}cn%XKNZ=wN8WgWSq}vLKDEoCm zpOHI0cm+>7PwUnNPxkW>mRsI-6AIbU1DC23w6p-V14)}I?&xJdjPuMv6@5h4h=p$D z*>L^2_0-3|KR5lOBa=u@~io}OiaXDRggnO|n682=*#NH3dt zYZ?~bym@u~IjleZ!^~iPn)Z-_QL20~ROpidZaH(j2m;4F)WSW4CujO$lrBYy=h!>) z0bWyp4~vK&hS(-J;(HE^$k5*@)S_N?^DJ`eCjAgS6+7wWlh~;Q4~*poent`EJ`~fO z;udf>OzHURCb&?!7dfcWL$A}&l_-@?b0kX%w2WdgBci7q&rd1D!bVCem#jjl3;F~$ zlOnrg%Jv{NWjAMtyLEozwj9g2)>@+F1hvp>5r<@3SAe{|z1@kCIYL!cl#IHb-0Grd z+AZ{>G=gTN5e{w$aLb)>g`tespLzC6V9Rnc9Ly|3E%h>=v$#)4mv(%a4S5-rO#&>@ zLL3Pdz`LX3)nocR1Zv_`x>bRWV@ey}(~mJVs9B_NFbW~J#3K)ad>k(htFKVJ)9Eg1 z-*y~@OcrOe3cTr~djWkSoxl%qzMDgRQ;d9=zniZPxep}=&*RFRp47<1s1;6(@W;XE z%{(sN<9CewOs`b;_qZ<94~IBvtN&*S5rBWw{Z5ms?Lj4Xv6hl}`uLuHbPz=DrX%q> zCvfm!2tfk9XuPA3$Z)Gb7Mq@n5E4N3AVnzo;F3JYyq)ATKZAuJ6&U~#yz%I<<85Ho zR4YL@H)FP8^<|GKrB{gWs$a>Ji*xCjB|$kVB~Q4iUZQcPZN=Dt^W*oS$>Td@4?a}? ztbU<>KK>$N;&F#Q+VrtRA3va8-=hzoz7FZ*VfuQSJ~rs%5`DZ&A3OAs(npMsT(@Z1 z41NVa{^W)4@VY(qVNdg+2Ott#RkbIbEWRj`A)R0UCONM01DH)O}tTZH30LCPb$4vLhra&|APY!(GH60QT z!L$(Q*nZ|Pn=PgvUbjN5C;cd>bcDM;GSgV~ZcWYDva9l-vs3f)Wd|9oM`!0Me>tSv zfJ8jdq=#mOK2EVAL>qg6SzNkOwtoOI`C;)5-~xPoAPr4nlPAJ#`nG66Ezdskm4+h|@t1FP**V#-K9Zsya+D_*cRw>`2@C76>Zj(2kZ E3x-qtIRF3v diff --git a/docs/build/_modules/algorithms/contagion/animation.html b/docs/build/_modules/algorithms/contagion/animation.html index 546dedf2..9236cbe3 100644 --- a/docs/build/_modules/algorithms/contagion/animation.html +++ b/docs/build/_modules/algorithms/contagion/animation.html @@ -7,7 +7,7 @@ - algorithms.contagion.animation — HyperNetX 1.1.4 documentation + algorithms.contagion.animation — HyperNetX 1.2 documentation @@ -68,7 +68,7 @@

    - 1.1 + 1.2
    diff --git a/docs/build/_modules/algorithms/contagion/epidemics.html b/docs/build/_modules/algorithms/contagion/epidemics.html index 4971bcc4..1ac8e451 100644 --- a/docs/build/_modules/algorithms/contagion/epidemics.html +++ b/docs/build/_modules/algorithms/contagion/epidemics.html @@ -7,7 +7,7 @@ - algorithms.contagion.epidemics — HyperNetX 1.1.4 documentation + algorithms.contagion.epidemics — HyperNetX 1.2 documentation @@ -68,7 +68,7 @@
    - 1.1 + 1.2
    diff --git a/docs/build/_modules/algorithms/generative_models.html b/docs/build/_modules/algorithms/generative_models.html index 99ca0deb..9a4e5f00 100644 --- a/docs/build/_modules/algorithms/generative_models.html +++ b/docs/build/_modules/algorithms/generative_models.html @@ -7,7 +7,7 @@ - algorithms.generative_models — HyperNetX 1.1.4 documentation + algorithms.generative_models — HyperNetX 1.2 documentation @@ -68,7 +68,7 @@
    - 1.1 + 1.2
    diff --git a/docs/build/_modules/algorithms/homology_mod2.html b/docs/build/_modules/algorithms/homology_mod2.html index 8d12279f..165c3db3 100644 --- a/docs/build/_modules/algorithms/homology_mod2.html +++ b/docs/build/_modules/algorithms/homology_mod2.html @@ -7,7 +7,7 @@ - algorithms.homology_mod2 — HyperNetX 1.1.4 documentation + algorithms.homology_mod2 — HyperNetX 1.2 documentation @@ -68,7 +68,7 @@
    - 1.1 + 1.2
    diff --git a/docs/build/_modules/algorithms/hypergraph_modularity.html b/docs/build/_modules/algorithms/hypergraph_modularity.html index e10db50b..c6e00bc6 100644 --- a/docs/build/_modules/algorithms/hypergraph_modularity.html +++ b/docs/build/_modules/algorithms/hypergraph_modularity.html @@ -7,7 +7,7 @@ - algorithms.hypergraph_modularity — HyperNetX 1.1.4 documentation + algorithms.hypergraph_modularity — HyperNetX 1.2 documentation @@ -68,7 +68,7 @@
    - 1.1 + 1.2
    @@ -179,13 +179,11 @@

    Source code for algorithms.hypergraph_modularity

    References ---------- -.. [1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S., Ravindran B. (2020) A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24 -.. [2] B. Kaminski, P. Pralat and F. Théberge, Community Detection Algorithm Using Hypergraph Modularity, to appear in the proceedings of Complex Networks 2020, Springer. -.. [3] Clustering via hypergraph modularity, Bogumił Kamiński, Valérie Poulin, Paweł Prałat , Przemysław Szufel, François Théberge, 2019, https://doi.org/10.1371/journal.pone.0224307 - +.. [1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. "A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering". In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24 +.. [2] Kamiński B., Prałat P. and Théberge F. "Community Detection Algorithm Using Hypergraph Modularity". In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13 +.. [3] Kamiński B., Poulin V., Prałat P., Szufel P. and Théberge F. "Clustering via hypergraph modularity", Plos ONE 2019, https://doi.org/10.1371/journal.pone.0224307 """ - from collections import Counter import numpy as np from functools import reduce @@ -201,7 +199,7 @@

    Source code for algorithms.hypergraph_modularity

    [docs]def dict2part(D): """ - Returns dictionary to partition, inverse function to part2dict + Given a dictionary mapping the part for each vertex, return a partition as a list of sets; inverse function to part2dict Parameters ---------- @@ -212,7 +210,7 @@

    Source code for algorithms.hypergraph_modularity

    Returns ------- : list - List of sets in the partition + List of sets; one set for each part in the partition """ P = [] k = list(D.keys()) @@ -224,17 +222,18 @@

    Source code for algorithms.hypergraph_modularity

    [docs]def part2dict(A): """ - Returns dictionary {vertex: partition index}, inverse function + Given a partition (list of sets), returns a dictionary mapping the part for each vertex; inverse function to dict2part Parameters ---------- - A : list of lists - partition of vertices + A : list of sets + a partition of the vertices Returns ------- : dict + a dictionary with {vertex: partition index} """ x = [] for i in range(len(A)): @@ -243,63 +242,78 @@

    Source code for algorithms.hypergraph_modularity

    ################################################################################ -
    [docs]def precompute_attributes(HG): """ - Precompute some values on HNX hypergraph for computing qH faster - Adds weight, strength and binary coefficient attributes to - the hypergraph for computing qH faster. + Precompute some values on hypergraph HG for faster computing of hypergraph modularity. + This needs to be run before calling either modularity() or last_step(). + + Note + ---- + + If HG is unweighted, v.weight is set to 1 for each vertex v in HG. + The weighted degree for each vertex v is stored in v.strength. + The total edge weigths for each edge cardinality is stored in HG.d_weights. + Binomial coefficients to speed-up modularity computation are stored in HG.bin_coef. + Isolated vertices found only in edge(s) of size 1 are dropped. Parameters ---------- HG : Hypergraph + Returns + ------- + : Hypergraph + Same hypergraph with added attributes + """ + H = HG.remove_singletons() # 1. compute node strenghts (weighted degrees) - for v in HG.nodes: - HG.nodes[v].strength = 0 - for e in HG.edges: + for v in H.nodes: + H.nodes[v].strength = 0 + for e in H.edges: try: - w = HG.edges[e].weight + w = H.edges[e].weight except: w = 1 # add unit weight if none to simplify other functions - HG.edges[e].weight = 1 - for v in list(HG.edges[e]): - HG.nodes[v].strength += w + H.edges[e].weight = 1 + for v in list(H.edges[e]): + H.nodes[v].strength += w # 2. compute d-weights - ctr = Counter([len(HG.edges[e]) for e in HG.edges]) + ctr = Counter([len(H.edges[e]) for e in H.edges]) for k in ctr.keys(): ctr[k] = 0 - for e in HG.edges: - ctr[len(HG.edges[e])] += HG.edges[e].weight - HG.d_weights = ctr - HG.total_weight = sum(ctr.values()) + for e in H.edges: + ctr[len(H.edges[e])] += H.edges[e].weight + H.d_weights = ctr + H.total_weight = sum(ctr.values()) # 3. compute binomial coeffcients (modularity speed-up) bin_coef = {} - for n in HG.d_weights.keys(): + for n in H.d_weights.keys(): for k in np.arange(n // 2 + 1, n + 1): bin_coef[(n, k)] = comb(n, k, exact=True) - HG.bin_coef = bin_coef
    + H.bin_coef = bin_coef + return H
    ################################################################################
    [docs]def linear(d, c): """ - Weight function for hyperedge. Gives the actual ratio as long - as it is greater than 0.5. + Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class. + This is the default choice for modularity() and last_step() functions. Parameters ---------- d : int - Number of nodes in an edge + Number of vertices in an edge c : int - Number of nodes in the majority class + Number of vertices in the majority class Returns ------- - float + : float + c/d if c>d/2 else 0 """ return c / d if c > d / 2 else 0
    @@ -308,19 +322,21 @@

    Source code for algorithms.hypergraph_modularity

    [docs]def majority(d, c): """ - Weight function for hyperedge. Requires - c be the majority of d. Returns bool + Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class. + This corresponds to the majority rule [3]_ Parameters ---------- d : int - Number of nodes in an edge + Number of vertices in an edge c : int - Number of nodes in the majority class + Number of vertices in the majority class Returns ------- - bool + : bool + 1 if c>d/2 else 0 + """ return 1 if c > d / 2 else 0
    @@ -329,25 +345,27 @@

    Source code for algorithms.hypergraph_modularity

    [docs]def strict(d, c): """ - Weight function for hyperedge. Requires c == d. + Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class. + This corresponds to the strict rule [3]_ Parameters ---------- d : int - Number of nodes in an edge + Number of vertices in an edge c : int - Number of nodes in the majority class + Number of vertices in the majority class Returns ------- - bool + : bool + 1 if c==d else 0 """ return 1 if c == d else 0
    ######################################### -
    [docs]def compute_partition_probas(HG, A): +def _compute_partition_probas(HG, A): """ Compute vol(A_i)/vol(V) for each part A_i in A (list of sets) @@ -368,13 +386,13 @@

    Source code for algorithms.hypergraph_modularity

    vol += HG.nodes[v].strength p.append(vol) s = sum(p) - return [i / s for i in p]
    + return [i / s for i in p] -
    [docs]def degree_tax(HG, Pr, wdc): +def _degree_tax(HG, Pr, wdc): """ Computes the expected fraction of edges falling in - the partition in a random graph as per [2]_ + the partition as per [2]_ Parameters ---------- @@ -383,7 +401,7 @@

    Source code for algorithms.hypergraph_modularity

    Pr : list Probability distribution wdc : func - weight function (ex: strict, majority, linear) + weight function for edge contribution (ex: strict, majority, linear) Returns ------- @@ -399,10 +417,10 @@

    Source code for algorithms.hypergraph_modularity

    tax *= HG.d_weights[d] DT += tax DT /= HG.total_weight - return DT
    + return DT -
    [docs]def edge_contribution(HG, A, wdc): +def _edge_contribution(HG, A, wdc): """ Edge contribution from hypergraph with respect to partion A. @@ -428,7 +446,7 @@

    Source code for algorithms.hypergraph_modularity

    if HG.size(e, part) > d / 2: EC += wdc(d, HG.size(e, part)) * HG.edges[e].weight EC /= HG.total_weight - return EC
    + return EC # HG: HNX hypergraph # A: partition (list of sets) @@ -437,31 +455,36 @@

    Source code for algorithms.hypergraph_modularity

    [docs]def modularity(HG, A, wdc=linear): """ - Computes modularity of a hypergraph with respect to partition A. + Computes modularity of hypergraph HG with respect to partition A. Parameters ---------- HG : Hypergraph - Description - A : list of lists - Partition of the nodes in HG + The hypergraph with some precomputed attributes via: precompute_attributes(HG) + A : list of sets + Partition of the vertices in HG wdc : func, optional - weight function (ex: strict, majority, linear) + Hyperparameter for hypergraph modularity [2]_ + + Note + ---- + For 'wdc', any function of the format w(d,c) that returns 0 when c <= d/2 and value in [0,1] otherwise can be used. + Default is 'linear'; other supplied choices are 'majority' and 'strict'. Returns ------- : float - + The modularity function for partition A on HG """ - Pr = compute_partition_probas(HG, A) - return edge_contribution(HG, A, wdc) - degree_tax(HG, Pr, wdc)
    + Pr = _compute_partition_probas(HG, A) + return _edge_contribution(HG, A, wdc) - _degree_tax(HG, Pr, wdc)
    ################################################################################
    [docs]def two_section(HG): """ - Creates a random walk 2-section igraph with transition weights defined by the + Creates a random walk based [1]_ 2-section igraph Graph with transition weights defined by the weights of the hyperedges. Parameters @@ -470,17 +493,19 @@

    Source code for algorithms.hypergraph_modularity

    Returns ------- - G : igraph.Graph + : igraph.Graph + The 2-section graph built from HG """ s = [] for e in HG.edges: E = HG.edges[e] # random-walk 2-section (preserve nodes' weighted degrees) - try: - w = HG.edges[e].weight / (len(E) - 1) - except: - w = 1 / (len(E) - 1) - s.extend([(k[0], k[1], w) for k in itertools.combinations(E, 2)]) + if len(E)>1: + try: + w = HG.edges[e].weight / (len(E) - 1) + except: + w = 1 / (len(E) - 1) + s.extend([(k[0], k[1], w) for k in itertools.combinations(E, 2)]) G = ig.Graph.TupleList(s, weights=True).simplify(combine_edges='sum') return G
    @@ -489,8 +514,7 @@

    Source code for algorithms.hypergraph_modularity

    [docs]def kumar(HG, delta=.01): """ - Compute a partition of the vertices as per Kumar's algorithm [1]_ - + Compute a partition of the vertices in hypergraph HG as per Kumar's algorithm [1]_ Parameters ---------- @@ -501,7 +525,8 @@

    Source code for algorithms.hypergraph_modularity

    Returns ------- - dict + : list of sets + A partition of the vertices in HG """ # weights will be modified -- store initial weights @@ -539,12 +564,12 @@

    Source code for algorithms.hypergraph_modularity

    G.vs['part'] = CG.membership for e in HG.edges: HG.edges[e].weight = W[e] - return {v['name']: v['part'] for v in G.vs}
    + return dict2part({v['name']: v['part'] for v in G.vs})
    ################################################################################ -
    [docs]def delta_ec(HG, P, v, a, b, wdc): +def _delta_ec(HG, P, v, a, b, wdc): """ Computes change in edge contribution -- partition P, node v going from P[a] to P[b] @@ -566,8 +591,7 @@

    Source code for algorithms.hypergraph_modularity

    Returns ------- - TYPE - Description + : float """ Pm = P[a] - {v} Pn = P[b].union({v}) @@ -577,12 +601,12 @@

    Source code for algorithms.hypergraph_modularity

    w = HG.edges[e].weight ec += w * (wdc(d, HG.size(e, Pm)) + wdc(d, HG.size(e, Pn)) - wdc(d, HG.size(e, P[a])) - wdc(d, HG.size(e, P[b]))) - return ec / HG.total_weight
    + return ec / HG.total_weight -
    [docs]def bin_ppmf(d, c, p): +def _bin_ppmf(d, c, p): """ - exp. part of binomial pmf + exponential part of the binomial pmf Parameters ---------- @@ -595,13 +619,13 @@

    Source code for algorithms.hypergraph_modularity

    Returns ------- - float + : float """ - return p**c * (1 - p)**(d - c)
    + return p**c * (1 - p)**(d - c) -
    [docs]def delta_dt(HG, P, v, a, b, wdc): +def _delta_dt(HG, P, v, a, b, wdc): """ Compute change in degree tax -- partition P (list), node v going from P[a] to P[b] @@ -639,35 +663,39 @@

    Source code for algorithms.hypergraph_modularity

    for d in HG.d_weights.keys(): x = 0 for c in np.arange(int(np.floor(d / 2)) + 1, d + 1): - x += HG.bin_coef[(d, c)] * wdc(d, c) * (bin_ppmf(d, c, voln) + bin_ppmf(d, c, volm) - - bin_ppmf(d, c, vola) - bin_ppmf(d, c, volb)) + x += HG.bin_coef[(d, c)] * wdc(d, c) * (_bin_ppmf(d, c, voln) + _bin_ppmf(d, c, volm) + - _bin_ppmf(d, c, vola) - _bin_ppmf(d, c, volb)) DT += x * HG.d_weights[d] - return DT / HG.total_weight
    + return DT / HG.total_weight
    [docs]def last_step(HG, L, wdc=linear, delta=.01): """ - Compute a partition of the vertices as per Last-Step algorithm.[2]_ + Given some initial partition L, compute a new partition of the vertices in HG as per Last-Step algorithm [2]_ - Simple H-based algorithm -- - try moving nodes between communities to optimize qH - requires L: initial non-trivial partition + Note + ---- + This is a very simple algorithm that tries moving nodes between communities to improve hypergraph modularity. + It requires an initial non-trivial partition which can be obtained for example via graph clustering on the 2-section of HG, + or via Kumar's algorithm. Parameters ---------- HG : Hypergraph - - L : list of sets + + L : list of sets + some initial partition of the vertices in HG wdc : func, optional - weight function (ex: strict, majority, linear) - delta : float, optional + Hyperparameter for hypergraph modularity [2]_ + delta : float, optional + convergence stopping criterion Returns ------- : list of sets - + A new partition for the vertices in HG """ A = L[:] # we will modify this, copy D = part2dict(A) @@ -682,14 +710,14 @@

    Source code for algorithms.hypergraph_modularity

    if c == i: M.append(0) else: - M.append(delta_ec(HG, A, v, c, i, wdc) - delta_dt(HG, A, v, c, i, wdc)) + M.append(_delta_ec(HG, A, v, c, i, wdc) - _delta_dt(HG, A, v, c, i, wdc)) i = s[np.argmax(M)] if c != i: A[c] = A[c] - {v} A[i] = A[i].union({v}) D[v] = i - Pr = compute_partition_probas(HG, A) - q2 = edge_contribution(HG, A, wdc) - degree_tax(HG, Pr, wdc) + Pr = _compute_partition_probas(HG, A) + q2 = _edge_contribution(HG, A, wdc) - _degree_tax(HG, Pr, wdc) if (q2 - qH) < delta: break qH = q2 diff --git a/docs/build/_modules/algorithms/laplacians_clustering.html b/docs/build/_modules/algorithms/laplacians_clustering.html index 3159750c..5341e06a 100644 --- a/docs/build/_modules/algorithms/laplacians_clustering.html +++ b/docs/build/_modules/algorithms/laplacians_clustering.html @@ -7,7 +7,7 @@ - algorithms.laplacians_clustering — HyperNetX 1.1.4 documentation + algorithms.laplacians_clustering — HyperNetX 1.2 documentation @@ -68,7 +68,7 @@
    - 1.1 + 1.2
    diff --git a/docs/build/_modules/algorithms/s_centrality_measures.html b/docs/build/_modules/algorithms/s_centrality_measures.html index 09e3bf33..ae8081ea 100644 --- a/docs/build/_modules/algorithms/s_centrality_measures.html +++ b/docs/build/_modules/algorithms/s_centrality_measures.html @@ -7,7 +7,7 @@ - algorithms.s_centrality_measures — HyperNetX 1.1.4 documentation + algorithms.s_centrality_measures — HyperNetX 1.2 documentation @@ -68,7 +68,7 @@
    - 1.1 + 1.2
    diff --git a/docs/build/_modules/classes/entity.html b/docs/build/_modules/classes/entity.html index 6380bf6b..4c494f2e 100644 --- a/docs/build/_modules/classes/entity.html +++ b/docs/build/_modules/classes/entity.html @@ -7,7 +7,7 @@ - classes.entity — HyperNetX 1.1.4 documentation + classes.entity — HyperNetX 1.2 documentation @@ -68,7 +68,7 @@
    - 1.1 + 1.2
    diff --git a/docs/build/_modules/classes/hypergraph.html b/docs/build/_modules/classes/hypergraph.html index 99f91f7a..780786a4 100644 --- a/docs/build/_modules/classes/hypergraph.html +++ b/docs/build/_modules/classes/hypergraph.html @@ -7,7 +7,7 @@ - classes.hypergraph — HyperNetX 1.1.4 documentation + classes.hypergraph — HyperNetX 1.2 documentation @@ -68,7 +68,7 @@
    - 1.1 + 1.2
    diff --git a/docs/build/_modules/classes/staticentity.html b/docs/build/_modules/classes/staticentity.html index 8a4decbc..0fe3a4c8 100644 --- a/docs/build/_modules/classes/staticentity.html +++ b/docs/build/_modules/classes/staticentity.html @@ -7,7 +7,7 @@ - classes.staticentity — HyperNetX 1.1.4 documentation + classes.staticentity — HyperNetX 1.2 documentation @@ -68,7 +68,7 @@
    - 1.1 + 1.2
    diff --git a/docs/build/_modules/drawing/rubber_band.html b/docs/build/_modules/drawing/rubber_band.html index 040f5515..09543511 100644 --- a/docs/build/_modules/drawing/rubber_band.html +++ b/docs/build/_modules/drawing/rubber_band.html @@ -7,7 +7,7 @@ - drawing.rubber_band — HyperNetX 1.1.4 documentation + drawing.rubber_band — HyperNetX 1.2 documentation @@ -68,7 +68,7 @@
    - 1.1 + 1.2
    @@ -176,6 +176,7 @@

    Source code for drawing.rubber_band

     from hypernetx import Hypergraph
 from .util import (
     get_frozenset_label,
+    get_collapsed_size,
     get_set_layering,
     inflate_kwargs,
     transpose_inflated_kwargs,
@@ -497,7 +498,6 @@ 
    Source code for drawing.rubber_band
                 }
         )
    
 
-
 
    [docs]def draw(
     H,
     pos=None,
@@ -569,7 +569,7 @@ 
    Source code for drawing.rubber_band
         with_color: bool
         set to False to disable color cycling of edges
     with_node_counts: bool
-        set to True to label collapsed nodes with number of elements
+        set to True to replace the label for collapsed nodes with the number of elements
     with_edge_counts: bool
         set to True to label collapsed edges with number of elements
     layout: function
@@ -604,9 +604,11 @@ 
    Source code for drawing.rubber_band
         r0 = get_default_radius(H, pos)
     a0 = np.pi * r0 ** 2
 
+
+
     def get_node_radius(v):
         if node_radius is None:
-            return np.sqrt(a0 * (len(v) if type(v) == frozenset else 1) / np.pi)
+            return np.sqrt(a0 * get_collapsed_size(v) / np.pi)
         elif hasattr(node_radius, "get"):
             return node_radius.get(v, 1) * r0
         return node_radius * r0
diff --git a/docs/build/_modules/drawing/two_column.html b/docs/build/_modules/drawing/two_column.html
index 52d47bf7..fb163bdc 100644
--- a/docs/build/_modules/drawing/two_column.html
+++ b/docs/build/_modules/drawing/two_column.html
@@ -7,7 +7,7 @@
   
   
   
-  drawing.two_column — HyperNetX 1.1.4 documentation
+  drawing.two_column — HyperNetX 1.2 documentation
   
 
   
@@ -68,7 +68,7 @@
             
             
               
    
-                1.1
+                1.2
               
    
             
           
diff --git a/docs/build/_modules/drawing/util.html b/docs/build/_modules/drawing/util.html
index f0d22823..2e179d57 100644
--- a/docs/build/_modules/drawing/util.html
+++ b/docs/build/_modules/drawing/util.html
@@ -7,7 +7,7 @@
   
   
   
-  drawing.util — HyperNetX 1.1.4 documentation
+  drawing.util — HyperNetX 1.2 documentation
   
 
   
@@ -68,7 +68,7 @@
             
             
               
    
-                1.1
+                1.2
               
    
             
           
@@ -214,6 +214,15 @@ 
    Source code for drawing.util
         return [dict(zip(inflated, v)) for v in zip(*inflated.values())]
    
 
 
+
    [docs]def get_collapsed_size(v):
+    try:
+        if type(v) == str and ':' in v:
+            return int(v.split(':')[-1])
+    except:
+        pass
+    
+    return 1
    
+
 
    [docs]def get_frozenset_label(S, count=False, override={}):
     """
     Helper function for rendering the labels of possibly collapsed nodes and edges
@@ -232,13 +241,12 @@ 
    Source code for drawing.util
         """
 
     def helper(v):
-        if type(v) == frozenset:
-            if count and len(v) > 1:
-                return f"x {len(v)}"
+        if type(v) == str:
+            n = get_collapsed_size(v)
+            if count and n > 1:
+                return f"x {n}"
             elif count:
                 return ""
-            else:
-                return ", ".join([str(override.get(s, s)) for s in v])
         return str(v)
 
     return {v: override.get(v, helper(v)) for v in S}
    
diff --git a/docs/build/_modules/index.html b/docs/build/_modules/index.html
index 0b32af12..fb92b41a 100644
--- a/docs/build/_modules/index.html
+++ b/docs/build/_modules/index.html
@@ -7,7 +7,7 @@
   
   
   
-  Overview: module code — HyperNetX 1.1.4 documentation
+  Overview: module code — HyperNetX 1.2 documentation
   
 
   
@@ -68,7 +68,7 @@
             
             
               
    
-                1.1
+                1.2
               
    
             
           
diff --git a/docs/build/_modules/reports/descriptive_stats.html b/docs/build/_modules/reports/descriptive_stats.html
index 241dc8d9..f7d4d96b 100644
--- a/docs/build/_modules/reports/descriptive_stats.html
+++ b/docs/build/_modules/reports/descriptive_stats.html
@@ -7,7 +7,7 @@
   
   
   
-  reports.descriptive_stats — HyperNetX 1.1.4 documentation
+  reports.descriptive_stats — HyperNetX 1.2 documentation
   
 
   
@@ -68,7 +68,7 @@
             
             
               
    
-                1.1
+                1.2
               
    
             
           
diff --git a/docs/build/_sources/modularity.rst.txt b/docs/build/_sources/modularity.rst.txt
index d2eb603c..8738ced1 100644
--- a/docs/build/_sources/modularity.rst.txt
+++ b/docs/build/_sources/modularity.rst.txt
@@ -5,65 +5,110 @@
 Modularity and Clustering
 =========================
 
-Francois - I left the code from widget here so that you could replace it with content you want.
-I think an image would be great if you have one.
-
-.. image:: images/WidgetScreenShot.png
+.. image:: images/ModularityScreenShot.png
    :width: 300px
    :align: right
 
 Overview
 --------
-The HyperNetXWidget_ is an addon for HNX, which extends the built in visualization 
-capabilities of HNX to a JavaScript based interactive visualization. The tool has two main interfaces, 
-the hypergraph visualization and the nodes & edges panel.
-You may `demo the widget here `_
+The hypergraph_modularity submodule in HNX provides functions to compute **hypergraph modularity** for a
+given partition of the vertices in a hypergraph. In general, higher modularity indicates a better
+partitioning of the vertices into dense communities.
+
+Two functions to generate such hypergraph
+partitions are provided: **Kumar's** algorithm, and the simple **Last-Step** refinement algorithm.
+
+The submodule also provides a function to generate the **two-section graph** for a given hypergraph which can then be used to find
+vertex partitions via graph-based algorithms.
+
 
 Installation
 ------------
-The HypernetxWidget_ is available on `GitHub `_ and may be
-installed using pip:
+Since it is part of HNX, no extra installation is required.
+The submodule can be imported as follows::
 
-    >>> pip install hnxwidget
+   import hypernetx.algorithms.hypergraph_modularity as hmod
 
 Using the Tool
 --------------
 
-Layout
-^^^^^^
-The hypergraph visualization is an Euler diagram that shows nodes as circles and hyper edges as outlines 
-containing the nodes/circles they contain. The visualization uses a force directed optimization to perform 
-the layout. This algorithm is not perfect and sometimes gives results that the user might want to improve upon. 
-The visualization allows the user to drag nodes and position them directly at any time. The algorithm will 
-re-position any nodes that are not specified by the user. Ctrl (Windows) or Command (Mac) clicking a node 
-will release a pinned node it to be re-positioned by the algorithm.
-
-Selection
-^^^^^^^^^
-Nodes and edges can be selected by clicking them. Nodes and edges can be selected independently of each other, 
-i.e., it is possible to select an edge without selecting the nodes it contains. Multiple nodes and edges can 
-be selected, by holding down Shift while clicking. Shift clicking an already selected node will de-select it. 
-Clicking the background will de-select all nodes and edges. Dragging a selected node will drag all selected 
-nodes, keeping their relative placement.
-Selected nodes can be hidden (having their appearance minimized) or removed completely from the visualization. 
-Hiding a node or edge will not cause a change in the layout, wheras removing a node or edge will. 
-The selection can also be expanded. Buttons in the toolbar allow for selecting all nodes contained within selected edges, 
-and selecting all edges containing any selected nodes.
-The toolbar also contains buttons to select all nodes (or edges), un-select all nodes (or edges), 
-or reverse the selected nodes (or edges). An advanced user might:
-
-* **Select all nodes not in an edge** by: select an edge, select all nodes in that edge, then reverse the selected nodes to select every node not in that edge.
-* **Traverse the graph** by: selecting a start node, then alternating select all edges containing selected nodes and selecting all nodes within selected edges
-* **Pin Everything** by: hitting the button to select all nodes, then drag any node slightly to activate the pinning for all nodes.
-  
-Side Panel
+
+Precomputation
+^^^^^^^^^^^^^^
+
+In order to make the computation of hypergraph modularity more efficient, some quantities need to be pre-computed.
+Given hypergraph H, calling::
+
+   HG = hmod.precompute_attributes(H)
+
+will pre-compute quantities such as node strength (weighted degree), d-weights (total weight for each edge cardinality) and binomial coefficients.
+
+Modularity
 ^^^^^^^^^^
-Details on nodes and edges are visible in the side panel. For both nodes and edges, a table shows the node name, degree (or size for edges), its selection state, removed state, and color. These properties can also be controlled directly from this panel. The color of nodes and edges can be set in bulk here as well, for example, coloring by degree.
+
+Given hypergraph HG and a partition A of its vertices, hypergraph modularity is a measure of the quality of this partition.
+Random partitions typically yield modularity near zero (it can be negative) while positive modularity is indicative of the presence
+of dense communities, or modules. There are several variations for the definition of hypergraph modularity, and the main difference lies in the
+weight given to different edges. Modularity is computed via::
+
+   q = hmod.modularity(HG, A, wdc=linear)
+
+In a graph, an edge only links 2 nodes, so given partition A, an edge is either within a community (which increases the modularity)
+or between communities.
+
+With hypergraphs, we consider edges of size *d=2* or more. Given some vertex partition A and some *d*-edge *e*, let *c* be the number of nodes
+that belong to the most represented part in *e*; if *c > d/2*, we consider this edge to be within the part.
+Hyper-parameters *0 <= w(d,c) <= 1* control the weight
+given to such edges. Three functions are supplied in this submodule, namely:
+
+**linear**
+  $w(d,c) = c/d$ if $c > d/2$, else $0$.
+**majority**
+  $w(d,c) = 1$ if $c > d/2$, else $0$.
+**strict**
+  $w(d,c) = 1$ if $c == d$, else $0$.
+
+The 'linear' function is used by default. More details in [2].
+
+Two-section graph
+^^^^^^^^^^^^^^^^^
+
+There are several good partitioning algorithms for graphs such as the Louvain algorithm and ECG, a consensus clustering algorithm.
+One way to obtain a partition for hypergraph HG is to build its corresponding two-section graph G and run a graph clustering algorithm.
+Code is provided to build such graph via::
+
+   G = hmod.two_section(HG)
+
+which returns an igraph.Graph object. 
+
+   
+Clustering Algorithms
+^^^^^^^^^^^^^^^^^^^^^
+
+Two clustering (vertex partitioning) algorithms are supplied. The first one is a hybrid method proposed by Kumar et al. (see [1])
+that uses the Louvain algorithm on the two-section graph, but re-weights the edges according to the distibution of vertices
+from each part inside each edge. Given hypergraph HG, this is called as::
+
+   K = hmod.kumar(HG)
+
+The other supplied algorithm is a simple method to improve hypergraph modularity directely. Given some
+initial partition of the vertices (for example via Louvain on the two-section graph), move vertices between parts in order
+to improve hypergraph modularity. Given hypergraph HG and initial partition A, this is called as::
+
+   L = hmod.last_step(HG, A, wdc=linear)
+
+where the 'wdc' parameter is the same as in the modularity function.
+
 
 Other Features
 ^^^^^^^^^^^^^^
-Nodes with identical edge membership can be collapsed into a super node, which can be helpful for larger hypergraphs. Dragging any node in a super node will drag the entire super node. This feature is available as a toggle in the nodes panel.
 
-The hypergraph can also be visualized as a bipartite graph (similar to a traditional node-link diagram). Toggling this feature will preserve the locations of the nodes between the bipartite and the Euler diagrams.
+We represent a vertex partition A  as a list of sets, but another conveninent representation is via a dictionary.
+We provide two utility functions to switch representation, namely `A = dict2part(D)` and `D = part2dict(A)`.
+
+References
+^^^^^^^^^^
+[1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. “A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering”. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24
+
+[2] Kamiński B., Prałat P. and Théberge F. “Community Detection Algorithm Using Hypergraph Modularity”. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13
 
-.. _HypernetxWidget: https://github.com/pnnl/hypernetx-widget
diff --git a/docs/build/_static/documentation_options.js b/docs/build/_static/documentation_options.js
index 97abb98a..12dbdc97 100644
--- a/docs/build/_static/documentation_options.js
+++ b/docs/build/_static/documentation_options.js
@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '1.1.4',
+    VERSION: '1.2',
     LANGUAGE: 'None',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/docs/build/algorithms/algorithms.contagion.html b/docs/build/algorithms/algorithms.contagion.html
index 4e4acd56..96fac25d 100644
--- a/docs/build/algorithms/algorithms.contagion.html
+++ b/docs/build/algorithms/algorithms.contagion.html
@@ -7,7 +7,7 @@
   
   
   
-  algorithms.contagion package — HyperNetX 1.1.4 documentation
+  algorithms.contagion package — HyperNetX 1.2 documentation
   
 
   
@@ -70,7 +70,7 @@
             
             
               
    
-                1.1
+                1.2
               
    
             
           
diff --git a/docs/build/algorithms/algorithms.html b/docs/build/algorithms/algorithms.html
index 4d271d62..85a9687e 100644
--- a/docs/build/algorithms/algorithms.html
+++ b/docs/build/algorithms/algorithms.html
@@ -7,7 +7,7 @@
   
   
   
-  algorithms package — HyperNetX 1.1.4 documentation
+  algorithms package — HyperNetX 1.2 documentation
   
 
   
@@ -71,7 +71,7 @@
             
             
               
    
-                1.1
+                1.2
               
    
             
           
@@ -819,139 +819,27 @@ 
    Hypergraph_Modularity
 
    References
    
 
    
-
    1
    
-
    Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S., Ravindran B. (2020) A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24
    
+
    1(1,2)
    
+
    Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. “A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering”. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24
    
 
    
-
    2
    
-
      
-
    1. Kaminski, P. Pralat and F. Théberge, Community Detection Algorithm Using Hypergraph Modularity, to appear in the proceedings of Complex Networks 2020, Springer.
      2. 
-
    
-
    
-
    3
    
-
    Clustering via hypergraph modularity, Bogumił Kamiński, Valérie Poulin, Paweł Prałat , Przemysław Szufel, François Théberge, 2019, https://doi.org/10.1371/journal.pone.0224307
    
-
    
-
    
-
    
-
    
-algorithms.hypergraph_modularity.bin_ppmf(d, c, p)[source]
    
-
    exp. part of binomial pmf
    
-
    
-
    Parameters
    
-
      
-
    • d (int) – 
      • 
-
    • c (int) – 
      • 
-
    • p (float) – 
      • 
-
    
-
    
-
    Returns
    
-
    
-
    
-
    Return type
    
-
    float
    
-
    
-
    
-
    
-
-
    
-
    
-algorithms.hypergraph_modularity.compute_partition_probas(HG, A)[source]
    
-
    Compute vol(A_i)/vol(V) for each part A_i in A (list of sets)
    
-
    
-
    Parameters
    
-
    HG (Hypergraph) – A : list of sets
    
-
    
-
    Returns
    
-
    normalized distribution of strengths in partition elements
    
-
    
-
    Return type
    
-
    list
    
-
    
-
    
-
    
-
-
    
-
    
-algorithms.hypergraph_modularity.degree_tax(HG, Pr, wdc)[source]
    
-
    Computes the expected fraction of edges falling in
-the partition in a random graph as per 2
    
-
    
-
    Parameters
    
-
      
-
    • HG (Hypergraph) – 
      • 
-
    • Pr (list) – Probability distribution
      • 
-
    • wdc (func) – weight function (ex: strict, majority, linear)
      • 
-
    
-
    
-
    Returns
    
-
    
-
    
-
    Return type
    
-
    float
    
-
    
-
    
-
    
-
-
    
-
    
-algorithms.hypergraph_modularity.delta_dt(HG, P, v, a, b, wdc)[source]
    
-
    Compute change in degree tax –
-partition P (list), node v going from P[a] to P[b]
    
-
    
-
    Parameters
    
-
      
-
    • HG (Hypergraph) – 
      • 
-
    • P (list of sets) – 
      • 
-
    • v (int or str) – node identifier
      • 
-
    • a (int) – 
      • 
-
    • b (int) – 
      • 
-
    • wdc (func) – weight function (ex: strict, majority, linear)
      • 
-
    
+
    2(1,2,3,4,5,6)
    
+
    Kamiński  B., Prałat  P. and Théberge  F. “Community Detection Algorithm Using Hypergraph Modularity”. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13
    
 
    
-
    Returns
    
-
    
-
    
-
    Return type
    
-
    float
    
+
    3(1,2)
    
+
    Kamiński  B., Poulin V., Prałat  P., Szufel P. and Théberge  F. “Clustering via hypergraph modularity”, Plos ONE 2019, https://doi.org/10.1371/journal.pone.0224307
    
 
    
 
    
-
    
-
-
    
-
    
-algorithms.hypergraph_modularity.delta_ec(HG, P, v, a, b, wdc)[source]
    
-
    Computes change in edge contribution –
-partition P, node v going from P[a] to P[b]
    
-
    
-
    Parameters
    
-
      
-
    • HG (Hypergraph) – 
      • 
-
    • P (list of sets) – 
      • 
-
    • v (int or str) – node identifier
      • 
-
    • a (int) – 
      • 
-
    • b (int) – 
      • 
-
    • wdc (func) – weight function (ex: strict, majority, linear)
      • 
-
    
-
    
-
    Returns
    
-
    Description
    
-
    
-
    Return type
    
-
    TYPE
    
-
    
-
    
-
    
-
 
    
 
    
 algorithms.hypergraph_modularity.dict2part(D)[source]
    
-
    Returns dictionary to partition, inverse function to part2dict
    
+
    Given a dictionary mapping the part for each vertex, return a partition as a list of sets; inverse function to part2dict
    
 
    
 
    Parameters
    
 
    D (dict) – Dictionary keyed by vertices with values equal to integer
 index of the partition the vertex belongs to
    
 
    
 
    Returns
    
-
    List of sets in the partition
    
+
    List of sets; one set for each part in the partition
    
 
    
 
    Return type
    
 
    list
    
@@ -959,32 +847,10 @@ 
    Hypergraph_Modularity
 
    
 
-
    
-
    
-algorithms.hypergraph_modularity.edge_contribution(HG, A, wdc)[source]
    
-
    Edge contribution from hypergraph with respect
-to partion A.
    
-
    
-
    Parameters
    
-
      
-
    • HG (Hypergraph) – 
      • 
-
    • A (list of sets) – 
      • 
-
    • wdc (func) – weight function (ex: strict, majority, linear)
      • 
-
    
-
    
-
    Returns
    
-
    
-
    
-
    Return type
    
-
    float
    
-
    
-
    
-
    
-
 
    
 
    
 algorithms.hypergraph_modularity.kumar(HG, delta=0.01)[source]
    
-
    Compute a partition of the vertices as per Kumar’s algorithm 1
    
+
    Compute a partition of the vertices in hypergraph HG as per Kumar’s algorithm 1
    
 
    
 
    Parameters
    
 
      
@@ -993,10 +859,10 @@ 
      Hypergraph_Modularity
 
    
 
    Returns
    
-
    
+
    A partition of the vertices in HG
    
 
    
 
    Return type
    
-
    dict
    
+
    list of sets
    
 
    
 
    
 
    
@@ -1004,20 +870,24 @@ 
    Hypergraph_Modularity
 
    
 algorithms.hypergraph_modularity.last_step(HG, L, wdc=<function linear>, delta=0.01)[source]
    
-
    Compute a partition of the vertices as per Last-Step algorithm.[2]_
    
-
    Simple H-based algorithm –
-try moving nodes between communities to optimize qH
-requires L: initial non-trivial partition
    
+
    Given some initial partition L, compute a new partition of the vertices in HG as per Last-Step algorithm 2
    
+
    
+
    Note
    
+
    This is a very simple algorithm that tries moving nodes between communities to improve hypergraph modularity.
+It requires an initial non-trivial partition which can be obtained for example via graph clustering on the 2-section of HG,
+or via Kumar’s algorithm.
    
+
    
 
    
 
    Parameters
    
 
      
-
    • HG (Hypergraph) – L : list of sets
      • 
-
    • wdc (func, optional) – weight function (ex: strict, majority, linear)
      • 
-
    • delta (float, optional) – 
      • 
+
    • HG (Hypergraph) – 
      • 
+
    • L (list of sets) – some initial partition of the vertices in HG
      • 
+
    • wdc (func, optional) – Hyperparameter for hypergraph modularity 2
      • 
+
    • delta (float, optional) – convergence stopping criterion
      • 
 
    
 
    
 
    Returns
    
-
    
+
    A new partition for the vertices in HG
    
 
    
 
    Return type
    
 
    list of sets
    
@@ -1028,17 +898,17 @@ 
    Hypergraph_Modularity
 
    
 algorithms.hypergraph_modularity.linear(d, c)[source]
    
-
    Weight function for hyperedge. Gives the actual ratio as long
-as it is greater than 0.5.
    
+
    Hyperparameter for hypergraph modularity 2 for d-edge with c vertices in the majority class.
+This is the default choice for modularity() and last_step() functions.
    
 
    
 
    Parameters
    
 
      
-
    • d (int) – Number of nodes in an edge
      • 
-
    • c (int) – Number of nodes in the majority class
      • 
+
    • d (int) – Number of vertices in an edge
      • 
+
    • c (int) – Number of vertices in the majority class
      • 
 
    
 
    
 
    Returns
    
-
    
+
    c/d if c>d/2 else 0
    
 
    
 
    Return type
    
 
    float
    
@@ -1049,17 +919,17 @@ 
    Hypergraph_Modularity
 
    
 algorithms.hypergraph_modularity.majority(d, c)[source]
    
-
    Weight function for hyperedge. Requires
-c be the majority of d. Returns bool
    
+
    Hyperparameter for hypergraph modularity 2 for d-edge with c vertices in the majority class.
+This corresponds to the majority rule 3
    
 
    
 
    Parameters
    
 
      
-
    • d (int) – Number of nodes in an edge
      • 
-
    • c (int) – Number of nodes in the majority class
      • 
+
    • d (int) – Number of vertices in an edge
      • 
+
    • c (int) – Number of vertices in the majority class
      • 
 
    
 
    
 
    Returns
    
-
    
+
    1 if c>d/2 else 0
    
 
    
 
    Return type
    
 
    bool
    
@@ -1070,20 +940,27 @@ 
    Hypergraph_Modularity
 
    
 algorithms.hypergraph_modularity.modularity(HG, A, wdc=<function linear>)[source]
    
-
    Computes modularity of a hypergraph with respect to partition A.
    
+
    Computes modularity of hypergraph HG with respect to partition A.
    
 
    
 
    Parameters
    
 
      
-
    • HG (Hypergraph) – Description
      • 
-
    • A (list of lists) – Partition of the nodes in HG
      • 
-
    • wdc (func, optional) – weight function (ex: strict, majority, linear)
      • 
+
    • HG (Hypergraph) – The hypergraph with some precomputed attributes via: precompute_attributes(HG)
      • 
+
    • A (list of sets) – Partition of the vertices in HG
      • 
+
    • wdc (func, optional) – Hyperparameter for hypergraph modularity 2
      • 
 
    
 
    
-
    Returns
    
-
    
+
    
+
    
+
    Note
    
+
    For ‘wdc’, any function of the format w(d,c) that returns 0 when c <= d/2 and value in [0,1] otherwise can be used.
+Default is ‘linear’; other supplied choices are ‘majority’ and ‘strict’.
    
+
    
+
    
+
    Returns
    
+
    The modularity function for partition A on HG
    
 
    
-
    Return type
    
-
    float
    
+
    Return type
    
+
    float
    
 
    
 
    
 
    
@@ -1091,14 +968,14 @@ 
    Hypergraph_Modularity
 
    
 algorithms.hypergraph_modularity.part2dict(A)[source]
    
-
    Returns dictionary {vertex: partition index}, inverse function
+
    Given a partition (list of sets), returns a dictionary mapping the part for each vertex; inverse function
 to dict2part
    
 
    
 
    Parameters
    
-
    A (list of lists) – partition of vertices
    
+
    A (list of sets) – a partition of the vertices
    
 
    
 
    Returns
    
-
    
+
    a dictionary with {vertex: partition index}
    
 
    
 
    Return type
    
 
    dict
    
@@ -1109,29 +986,43 @@ 
    Hypergraph_Modularity
 
    
 algorithms.hypergraph_modularity.precompute_attributes(HG)[source]
    
-
    Precompute some values on HNX hypergraph for computing qH faster
-Adds weight, strength and binary coefficient attributes to
-the hypergraph for computing qH faster.
    
+
    Precompute some values on hypergraph HG for faster computing of hypergraph modularity.
+This needs to be run before calling either modularity() or last_step().
    
+
    
+
    Note
    
+
    If HG is unweighted, v.weight is set to 1 for each vertex v in HG.
+The weighted degree for each vertex v is stored in v.strength.
+The total edge weigths for each edge cardinality is stored in HG.d_weights.
+Binomial coefficients to speed-up modularity computation are stored in HG.bin_coef.
+Isolated vertices found only in edge(s) of size 1 are dropped.
    
+
    
 
    
 
    Parameters
    
 
    HG (Hypergraph) – 
    
 
    
+
    Returns
    
+
    Same hypergraph with added attributes
    
+
    
+
    Return type
    
+
    Hypergraph
    
+
    
 
    
 
    
 
 
    
 
    
 algorithms.hypergraph_modularity.strict(d, c)[source]
    
-
    Weight function for hyperedge. Requires c == d.
    
+
    Hyperparameter for hypergraph modularity 2 for d-edge with c vertices in the majority class.
+This corresponds to the strict rule 3
    
 
    
 
    Parameters
    
 
      
-
    • d (int) – Number of nodes in an edge
      • 
-
    • c (int) – Number of nodes in the majority class
      • 
+
    • d (int) – Number of vertices in an edge
      • 
+
    • c (int) – Number of vertices in the majority class
      • 
 
    
 
    
 
    Returns
    
-
    
+
    1 if c==d else 0
    
 
    
 
    Return type
    
 
    bool
    
@@ -1142,14 +1033,14 @@ 
    Hypergraph_Modularity
 
    
 algorithms.hypergraph_modularity.two_section(HG)[source]
    
-
    Creates a random walk 2-section igraph with transition weights defined by the
+
    Creates a random walk based 1 2-section igraph Graph with transition weights defined by the
 weights of the hyperedges.
    
 
    
 
    Parameters
    
 
    HG (Hypergraph) – 
    
 
    
 
    Returns
    
-
    G
    
+
    The 2-section graph built from HG
    
 
    
 
    Return type
    
 
    igraph.Graph
    
diff --git a/docs/build/algorithms/modules.html b/docs/build/algorithms/modules.html
index 02a2ef53..1ffeb110 100644
--- a/docs/build/algorithms/modules.html
+++ b/docs/build/algorithms/modules.html
@@ -7,7 +7,7 @@
   
   
   
-  algorithms — HyperNetX 1.1.4 documentation
+  algorithms — HyperNetX 1.2 documentation
   
 
   
@@ -70,7 +70,7 @@
             
             
               
    
-                1.1
+                1.2
               
    
             
           
diff --git a/docs/build/classes/classes.html b/docs/build/classes/classes.html
index 1506ffbb..749e2fcc 100644
--- a/docs/build/classes/classes.html
+++ b/docs/build/classes/classes.html
@@ -7,7 +7,7 @@
   
   
   
-  classes package — HyperNetX 1.1.4 documentation
+  classes package — HyperNetX 1.2 documentation
   
 
   
@@ -70,7 +70,7 @@
             
             
               
    
-                1.1
+                1.2
               
    
             
           
diff --git a/docs/build/classes/modules.html b/docs/build/classes/modules.html
index b881007c..0397cea2 100644
--- a/docs/build/classes/modules.html
+++ b/docs/build/classes/modules.html
@@ -7,7 +7,7 @@
   
   
   
-  classes — HyperNetX 1.1.4 documentation
+  classes — HyperNetX 1.2 documentation
   
 
   
@@ -70,7 +70,7 @@
             
             
               
    
-                1.1
+                1.2
               
    
             
           
diff --git a/docs/build/core.html b/docs/build/core.html
index d96f9e26..d2e4aed2 100644
--- a/docs/build/core.html
+++ b/docs/build/core.html
@@ -7,7 +7,7 @@
   
   
   
-  HyperNetX Packages — HyperNetX 1.1.4 documentation
+  HyperNetX Packages — HyperNetX 1.2 documentation
   
 
   
@@ -70,7 +70,7 @@
             
             
               
    
-                1.1
+                1.2
               
    
             
           
diff --git a/docs/build/drawing/drawing.html b/docs/build/drawing/drawing.html
index daa7acc0..a632acf4 100644
--- a/docs/build/drawing/drawing.html
+++ b/docs/build/drawing/drawing.html
@@ -7,7 +7,7 @@
   
   
   
-  drawing package — HyperNetX 1.1.4 documentation
+  drawing package — HyperNetX 1.2 documentation
   
 
   
@@ -70,7 +70,7 @@
             
             
               
    
-                1.1
+                1.2
               
    
             
           
@@ -243,7 +243,7 @@ 
    SubmodulesHypergraph) – the entity to be drawn
    
 
  • pos (dict) – mapping of node and edge positions to R^2
    • 
 
  • with_color (bool) – set to False to disable color cycling of edges
    • 
-
  • with_node_counts (bool) – set to True to label collapsed nodes with number of elements
    • 
+
  • with_node_counts (bool) – set to True to replace the label for collapsed nodes with the number of elements
    • 
 
  • with_edge_counts (bool) – set to True to label collapsed edges with number of elements
    • 
 
  • layout (function) – layout algorithm to compute
    • 
 
  • layout_kwargs (dict) – keyword arguments passed to layout function
    • 
@@ -530,6 +530,11 @@ 
    Submodules
 
    drawing.util module
    
+
    
+
    
+drawing.util.get_collapsed_size(v)[source]
    
+
    
+
 
    
 
    
 drawing.util.get_frozenset_label(S, count=False, override={})[source]
    
diff --git a/docs/build/drawing/modules.html b/docs/build/drawing/modules.html
index 9e784477..c038e661 100644
--- a/docs/build/drawing/modules.html
+++ b/docs/build/drawing/modules.html
@@ -7,7 +7,7 @@
   
   
   
-  drawing — HyperNetX 1.1.4 documentation
+  drawing — HyperNetX 1.2 documentation
   
 
   
@@ -70,7 +70,7 @@
             
             
               
    
-                1.1
+                1.2
               
    
             
           
diff --git a/docs/build/genindex.html b/docs/build/genindex.html
index b3a27000..3b0f23ee 100644
--- a/docs/build/genindex.html
+++ b/docs/build/genindex.html
@@ -7,7 +7,7 @@
   
   
   
-  Index — HyperNetX 1.1.4 documentation
+  Index — HyperNetX 1.2 documentation
   
 
   
@@ -68,7 +68,7 @@
             
             
               
    
-                1.1
+                1.2
               
    
             
           
@@ -314,11 +314,9 @@ 
    B
    
 
       
  • betti_numbers() (in module algorithms.homology_mod2)
 
    • 
-      
  • bin_ppmf() (in module algorithms.hypergraph_modularity)
+      
  • bin_ppmf() (in module algorithms.untitiled_modularity_and_clustering_original)
 
       
    • 
@@ -386,10 +384,10 @@ 
    C
    
         
  • (classes.entity.EntitySet method)
 
    • 
       
-      
  • collapse_edges() (classes.hypergraph.Hypergraph method)
-
    •